neural_net_model_diabtetes_prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JislordCodes/FashionPredictionModel/blob/main/neural_net_model_diabtetes_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "DIA_YyhYmjX9"
      },
      "outputs": [],
      "source": [
        "# Required libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from imblearn.over_sampling import SMOTE\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "i4-ceOohYfEp"
      },
      "outputs": [],
      "source": [
        "# Load your data (replace with your actual dataset)\n",
        "data = pd.read_csv(\"diabetes.csv\")\n",
        "\n",
        "# Preprocessing Steps\n",
        "\n",
        "# Handling missing data (example using mean imputation)\n",
        "data.fillna(data.mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "e6AeSqI-Lc6c"
      },
      "outputs": [],
      "source": [
        "# Separating features and labels\n",
        "X = data.drop('Outcome', axis=1)\n",
        "y = data['Outcome']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "iV9YGjwtN-_K"
      },
      "outputs": [],
      "source": [
        "# Scaling\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "XUebU3x5YjrQ"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Splitting data with stratification\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X_scaled, y, test_size=0.3, stratify=y, random_state=42)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, stratify=y_temp, random_state=42)\n",
        "\n",
        "# Balancing the training data using SMOTE\n",
        "smote = SMOTE(random_state=42)\n",
        "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "xFAnqFbYYnBK",
        "outputId": "511d12f3-7fc5-4a59-9cd1-21c64c96ec47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 249ms/step - accuracy: 0.5113 - loss: 0.7409 - val_accuracy: 0.5130 - val_loss: 0.6912\n",
            "Epoch 2/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.5679 - loss: 0.6840 - val_accuracy: 0.4957 - val_loss: 0.6948\n",
            "Epoch 3/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6439 - loss: 0.6512 - val_accuracy: 0.6000 - val_loss: 0.6773\n",
            "Epoch 4/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.6237 - loss: 0.6440 - val_accuracy: 0.6348 - val_loss: 0.6551\n",
            "Epoch 5/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.6963 - loss: 0.6320 - val_accuracy: 0.6696 - val_loss: 0.6353\n",
            "Epoch 6/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.7266 - loss: 0.5994 - val_accuracy: 0.7043 - val_loss: 0.6155\n",
            "Epoch 7/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7125 - loss: 0.5983 - val_accuracy: 0.7217 - val_loss: 0.5928\n",
            "Epoch 8/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7422 - loss: 0.5705 - val_accuracy: 0.7217 - val_loss: 0.5729\n",
            "Epoch 9/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7043 - loss: 0.5904 - val_accuracy: 0.7304 - val_loss: 0.5659\n",
            "Epoch 10/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7452 - loss: 0.5365 - val_accuracy: 0.7478 - val_loss: 0.5535\n",
            "Epoch 11/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7607 - loss: 0.5115 - val_accuracy: 0.7391 - val_loss: 0.5498\n",
            "Epoch 12/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7607 - loss: 0.5163 - val_accuracy: 0.7304 - val_loss: 0.5523\n",
            "Epoch 13/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7380 - loss: 0.5249 - val_accuracy: 0.7304 - val_loss: 0.5444\n",
            "Epoch 14/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7329 - loss: 0.5330 - val_accuracy: 0.7304 - val_loss: 0.5352\n",
            "Epoch 15/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7400 - loss: 0.4980 - val_accuracy: 0.7304 - val_loss: 0.5396\n",
            "Epoch 16/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7663 - loss: 0.4895 - val_accuracy: 0.7304 - val_loss: 0.5456\n",
            "Epoch 17/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7250 - loss: 0.5358 - val_accuracy: 0.7391 - val_loss: 0.5431\n",
            "Epoch 18/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7697 - loss: 0.4941 - val_accuracy: 0.7478 - val_loss: 0.5372\n",
            "Epoch 19/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7659 - loss: 0.5059 - val_accuracy: 0.7391 - val_loss: 0.5346\n",
            "Epoch 20/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7793 - loss: 0.4727 - val_accuracy: 0.7391 - val_loss: 0.5343\n",
            "Epoch 21/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7759 - loss: 0.4906 - val_accuracy: 0.7478 - val_loss: 0.5404\n",
            "Epoch 22/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7543 - loss: 0.5136 - val_accuracy: 0.7478 - val_loss: 0.5430\n",
            "Epoch 23/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7793 - loss: 0.4985 - val_accuracy: 0.7478 - val_loss: 0.5436\n",
            "Epoch 24/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7465 - loss: 0.5140 - val_accuracy: 0.7478 - val_loss: 0.5450\n",
            "Epoch 25/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7570 - loss: 0.5273 - val_accuracy: 0.7391 - val_loss: 0.5375\n",
            "Epoch 26/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7842 - loss: 0.4695 - val_accuracy: 0.7304 - val_loss: 0.5377\n",
            "Epoch 27/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7866 - loss: 0.4782 - val_accuracy: 0.7391 - val_loss: 0.5432\n",
            "Epoch 28/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7838 - loss: 0.4712 - val_accuracy: 0.7391 - val_loss: 0.5465\n",
            "Epoch 29/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.7895 - loss: 0.4829 - val_accuracy: 0.7391 - val_loss: 0.5454\n",
            "Epoch 30/100\n",
            "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7826 - loss: 0.4883 - val_accuracy: 0.7391 - val_loss: 0.5484\n"
          ]
        }
      ],
      "source": [
        "# Neural Network Model\n",
        "def build_nn_model(input_shape):\n",
        "    model = Sequential()\n",
        "    model.add(Dense(64, input_dim=input_shape, activation='relu'))\n",
        "    model.add(Dropout(0.5))\n",
        "    model.add(Dense(32, activation='relu'))\n",
        "    model.add(Dropout(0.3))\n",
        "    model.add(Dense(16, activation='relu'))\n",
        "    model.add(Dense(1, activation='sigmoid'))  # Binary classification\n",
        "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "    return model\n",
        "\n",
        "# Build and compile the model\n",
        "input_shape = X_train_balanced.shape[1]\n",
        "model = build_nn_model(input_shape)\n",
        "\n",
        "# Early stopping to avoid overfitting\n",
        "early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)\n",
        "\n",
        "# Training the model\n",
        "history = model.fit(X_train_balanced, y_train_balanced,\n",
        "                    validation_data=(X_val, y_val),\n",
        "                    epochs=100,\n",
        "                    batch_size=64,\n",
        "                    callbacks=[early_stopping],\n",
        "                    verbose=1)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "YWqNESeQYrNx",
        "outputId": "93f97240-ac1c-4f2c-f8dd-2773e26496fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step\n",
            "\n",
            "Validation Set Evaluation:\n",
            "Accuracy: 0.7391304347826086\n",
            "F1 Score: 0.6938775510204082\n",
            "Confusion Matrix:\n",
            " [[51 24]\n",
            " [ 6 34]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.68      0.77        75\n",
            "           1       0.59      0.85      0.69        40\n",
            "\n",
            "    accuracy                           0.74       115\n",
            "   macro avg       0.74      0.77      0.73       115\n",
            "weighted avg       0.79      0.74      0.75       115\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Model Evaluation on Validation Set\n",
        "y_val_pred = (model.predict(X_val) > 0.5).astype(int)\n",
        "print(\"\\nValidation Set Evaluation:\")\n",
        "print(\"Accuracy:\", accuracy_score(y_val, y_val_pred))\n",
        "print(\"F1 Score:\", f1_score(y_val, y_val_pred))\n",
        "print(\"Confusion Matrix:\\n\", confusion_matrix(y_val, y_val_pred))\n",
        "print(\"Classification Report:\\n\", classification_report(y_val, y_val_pred))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "zfBc9tilYuio",
        "outputId": "4f619cf9-b981-4f8e-fbb0-6055a80c514b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n",
            "\n",
            "Test Set Evaluation:\n",
            "Accuracy: 0.8017241379310345\n",
            "F1 Score: 0.7228915662650602\n",
            "Confusion Matrix:\n",
            " [[63 12]\n",
            " [11 30]]\n",
            "Classification Report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.84      0.85        75\n",
            "           1       0.71      0.73      0.72        41\n",
            "\n",
            "    accuracy                           0.80       116\n",
            "   macro avg       0.78      0.79      0.78       116\n",
            "weighted avg       0.80      0.80      0.80       116\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Model Evaluation on Test Set\n",
        "y_test_pred = (model.predict(X_test) > 0.5).astype(int)\n",
        "print(\"\\nTest Set Evaluation:\")\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_test_pred))\n",
        "print(\"F1 Score:\", f1_score(y_test, y_test_pred))\n",
        "print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_test_pred))\n",
        "print(\"Classification Report:\\n\", classification_report(y_test, y_test_pred))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.inspection import permutation_importance\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# Define a scoring function compatible with permutation_importance\n",
        "def keras_score(model, X, y):\n",
        "    y_pred = (model.predict(X) > 0.5).astype(int)  # Binary classification threshold\n",
        "    return accuracy_score(y, y_pred)\n",
        "\n",
        "# Calculate permutation importance\n",
        "results = permutation_importance(\n",
        "    model,\n",
        "    X_val,\n",
        "    y_val,\n",
        "    n_repeats=30,\n",
        "    random_state=42,\n",
        "    scoring=keras_score  # Use the custom scoring function\n",
        ")\n",
        "\n",
        "# Create a DataFrame for visualization\n",
        "importance_df = pd.DataFrame({\n",
        "    'Feature': X.columns,\n",
        "    'Importance': results.importances_mean\n",
        "})\n",
        "\n",
        "# Sort the DataFrame by importance\n",
        "importance_df = importance_df.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "# Plotting the feature importance\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Importance', y='Feature', data=importance_df, palette='viridis')\n",
        "plt.title('Feature Importance - Neural Network Model')\n",
        "plt.xlabel('Importance Value')\n",
        "plt.ylabel('Features')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "OLOY9P7vDgR1",
        "outputId": "744919bf-7ac1-49c3-d9d4-fac698b5fac5"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step  \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n",
            "\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-33-475bff648736>:34: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x='Importance', y='Feature', data=importance_df, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation heatmap\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(data.corr(), annot=True, cmap='coolwarm', linewidths=0.5)\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 866
        },
        "id": "cyK1xGwdEPN_",
        "outputId": "7b1e6a3d-4258-487d-9dbd-4df53569468c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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0L1RYWIisrCyDT2Fh4TNZ9okTJ9C+fXuDaZ06dcKJEyeeaDlmz6Q2REREREREVMJ2WZDJ1n3mi9cwZcoUg2mTJk3C5MmTn3rZCQkJcHV1NZjm6uqKrKws5Ofnw8LColzLYUBKVA6mPJBURt2KIvHVGrWpq1GpTBxohqnrNaauRqXyeX8p2r921tTVqFT2/hqKjm9cMHU1Ko3dv9THkMmJpq5GpbJysiteGnnL1NWoVP6YVxVZ5/eYuhqVik2DDlh50NS1qFyGtDZ1DSqn8ePHY/To0QbT5HK5iWpjHANSIiIiIiKiCiLIBJOtWy6XV1gA6ubmhsREw5uKiYmJsLGxKXd2FOAzpERERERERPSEwsLCsG/fPoNpe/bsQVhY2BMthwEpERERERHRf1xOTg4uXryIixcvAtC91uXixYuIiYkBoOv+O2jQIH359957D3fv3sXYsWNx48YNzJ8/Hxs2bMDHH3/8ROtll10iIiIiIqIKIjEzXZfdJ3H27Fm0adNG//3hs6eDBw/GypUrER8frw9OAcDPzw/bt2/Hxx9/jB9++AFVqlTB0qVL0alTpydaLwNSIiIiIiKi/7jWrVtDFMVSf1+5cqXReS5ceLrB+hiQEhERERERVRBBxqcky8KtQ0RERERERCbBgJSIiIiIiIhMgl12iYiIiIiIKsiLMqiRqTBDSkRERERERCbBDCkREREREVEFEWTMkJaFGVIiIiIiIiIyCWZIiYiIiIiIKgifIS0bM6RERERERERkEgxIiYiIiIiIyCTYZZeIiIiIiKiCcFCjsjFDSkRERERERCbBDCkREREREVEF4aBGZWOGlIiIiIiIiEyCASkRERERERGZBLvsEhERERERVRBByi67ZWGGlIiIiIiIiEyCGVIiIiIiIqIKImGGtEzMkBIREREREZFJMENKRERERERUQQQJM6RlYYaUiIiIiIiITIIBKb1QBEHA5s2bTV0NIiIiIiJ6Bthl9wUzZMgQrFq1CgAgk8ng7e2NQYMG4fPPP4eZ2b//vzM+Ph729vamrsZz5dA8FP6fDIVtg1pQeLjgbN/3kbhln6mrVSFCqwloGiyBlQWQmA7sOKtBXKrxss62QOs6Erg7CLCzErDrrAanIkWDMiFVBYRWlcDOSvc9OQM4fFWL23FiyQVWUiGBAhpXF2ClABIzgN3ntYhPM17WyQZoWUsCNwfATilgzwUtztwsva1h1QW0qSvB6Zta7L1QubfJ4Jc90LWtE6yUZrgWmYMflkfjQUJhmfP07OCMfj3c4GArw52YPMxbGYvIO7n63+1tzfDOQC+E1LaBhUKC+/EFWLs5HkdOZwAAXJ3M8Xofd9SraQMHOxlS01XYezQNa/+Ih1pj2u01qI8burRxgpWlFNdu5mLuyljEJZa9PXq0d8IrXV3gYCvD3dh8/PTzfUTezQOga+sv39c0Ot//fryn3yYPWVtJsfCb6nB2MMdL715Gbp7mmbTrn2rX0AJdmilhayVBTIIaq3dk4d4DdanlG9aQo09bKzjZSZGQqsbGvTm4fEul/z0kWI42oRbwdZfBylKCiQtTEZNguDxbKwn6d7BCzQBzKMwliE9VY9vhXJyNKPv/4Xl6rZsD2je1hdJCght3C7BofRLik4vKnKdLS1v0bmcPOxspoh6osHRjEm5FF7fpvVddUDfIAva2Zigo1CLyXgF+/jMFDxJLLtdaKcHsz7zhZC/DwDF3kJevfeZtfBobdh/C6q37kJqZharenhgz5BXUDPQ1WnbroZP4auFqg2nmMjMc+3mO0fLTlv6K3/cdw8dv9MWArm2ecc0rzrkDa3BqzzLkZCbDpUp1dHx1Ajz86hgte/HIBlw5uRkpcbcAAG7eNdGq92h9eY2mCIc3z8Gdq4eRkRILuYUVfIObovVLn8DazvW5takyEKTMAZaFW+cF1LlzZ8THx+PWrVv45JNPMHnyZMyYMaNEOZVKZWTuF5ubmxvkcrmpq/FcSZWWyLociaujppi6KhWqho+Ajg0kOHRFi8XhGiSkixjYRgrLUv67ZVIgPQfYd1GL7HzjwUF2nu73JTs0WLJDg3uJIvq3lMDZtgIb8gwFewloV0/A0Wsilu/WIilDxKutJKVvEzMgI1fEwUsickrZJg+5OwD1AwQkZlTuQBQA+vdww0udXfDDshiMnBCBgkINvv2sGmSy0p/Jad3EHu+94YVfNsXhvc+v4250Pr79rCrsbIpv3I173w9e7gpMmHkb74y7hqNnMvDlhwEI9LUAAHh7KiAIAuYsjcawMVex4JdY9GjvjLde9azwNpelXzcX9O7ojLkrYjFqciQKCjWYNjagzO3RqrEd3h3gidV/JOD9CZG4G5OPqWMD9NsjOVWF/iOvGHxWbYpHXr4GZy5llVjeJ8O8cS82v8La+CQa1ZTj1U7W2HwwB5MWpSI2sQifvm4Pa6Xx7RHoJcN7L9vi8Pl8TFyYigs3CjHqVTt4ukj1ZeQyATdjirBhb06p6337JRu4OZlhzq8Z+HJBKs5FFOL9V2zh7VY5bg6/1N4e3VrZYdG6JIybGYtClRYTR3hCZlb6ftKsgRXefMkJ63ek4ZPvYhH1oBATR3jC1qp429yJLcCPqxPxwdfR+OqnOEAAJo3whLFH5EYMcEV0XOW8Ftl94hzm/PIHhvXtgl+mjkNVH0988O1PSMvMLnUepYUCOxZM1X+2zP3KaLkDZy7hyu0oONu/ICebv1w/E459v01D824j8NYXf8C1SnWsnzsUuVnG7wxH3zyFGg27YcDonzFo3DpY27tj3Q9vITs9EQBQpCpAQux1NOs2HG9+8Tv6vDcPqQn38NtPw59ns+gFwID0BSSXy+Hm5gYfHx8MHz4c7du3x5YtWzBkyBD07t0b33zzDTw8PBAUFAQAiI2NRb9+/WBnZwcHBwf06tULUVFR+uWp1WqMGjUKdnZ2cHR0xLhx4zB48GD07t1bX6Z169YYNWoUxo4dCwcHB7i5uWHy5MkG9Zo9ezZq164NpVIJLy8vvP/++8jJKT6Zr1y5EnZ2dti1axeCg4NhZWWlD64ftXz5ctSsWRNyuRzu7u4YOXKk/rfHu+z+XdsOHjyIRo0aQalUws7ODs2aNUN0dPQ/3/gmkLzrMG5OmoPEP/eauioVKqy6BOdvi7h0V0RKFrD9tBZFGl3QZExcGrD3ghbXokVoSknO3Hwg4naciLRsIC0bOHBJC5Ua8HR6MQYXaBQk4OJdEZfv6bbJjrMi1Gqgrp/x+senAfsvibgeK0JdRiJCZgb0bCJB+FktCirntaKBPl1csOaPeBw/l4F7Mfn4bn4UHO1laBZqV+o8fbu5Inx/CnYdSkXMgwLMWRaNQpUWnVs76cvUrGaFzbsSEXknF/FJKqz5Ix65uRpU9VMCAM5cysLMRVE4dyUL8UkqnDiXiY3bEtCioWl7abzU2QVrtyTixPlM3IstwPRF0XC0k6FZSOkXv327uGDHwVTsPpKGmLgC/LAiFoWFWnRq6QgA0IpAeqba4NMsxBaHT2egoNBwZ+rezglKSyl+C0+q0HaWV6cwJQ6dz8fRiwWIS9Zg1bZsqIpEtKxvYbR8h8aWuHJbhR3H8xCfosHvB3IRHV+E9o0s9WWOXy7AlkO5uH639GxnoJcMe0/l4d4DNZLTNdh6OBd5BSJ8PWTPvI3/RPc2dti4Kw2nr+QiOk6FH35OhIOtFI3rKkudp2dbe+w5noX9J7NwP0GFheuSUKgS0S7MRl9mz7EsXL9TgOQ0Ne7eL8TaralwdpDBxdGw3Z2a20JpKcGf+9IrrI1PY+32/ejdtil6tg6DfxV3jB/6KhTm5thy8ESp8wiCACc7G/3H0c6mRJmktAzMXLkR/xsxBGZSqZGlVF6n965A3eb9UKdZXzh5BKLzwCkwM1fg8vFNRsv3GjoLIa0HwtUrGI5uAeg66GuIohZRN3TbUGFhjdc+WoHg0K5wdPOHp389dHxtAhJiriEzLe55Ns3kJFLBZJ8XAQPSfwELCwt9NnTfvn2IjIzEnj17sG3bNhQVFaFTp06wtrbGkSNHcOzYMX0g+HCe7777DmvWrMGKFStw7NgxZGVlGX1Oc9WqVVAqlTh16hSmT5+Or776Cnv27NH/LpFIMHfuXFy7dg2rVq3C/v37MXbsWINl5OXlYebMmfjll19w+PBhxMTE4NNPP9X/vmDBAowYMQLvvPMOrly5gi1btiAwMNBou/+ubWq1Gr1790arVq1w+fJlnDhxAu+88w4E4cX44/wvkUh0Gbt7CYbZunsJIqo8o+BREICaPgJkZsD95MqfFZRIAHd7ICrxsW2SKD51QN2pgYA7cSKiEp9qMc+Fu4s5HO3Ncf5qcZYuN1+DiDu5qFHVyug8ZlIB1fyUBvOIInD+ahZqVC2+GL92MwetwxxgrZRCEIDWYfaQyQRcul5GhsRSiqzc0ruCVjQ3Z3M42slw/mpxHfPytbhxNxfBgcYDDTOpgKq+lrhwrXgeUQQuXMtGcKCl0Xmq+log0NcSOw8ZZka8PRQY2NsN0xdFQ1sJel9KpYCvhxmu3y2+syKKwLW7KgRUMR4YBnrJDMoDwJXbpZcvze3YIjSqpYDSQoAgAI1rySEzE3AjyvR3eVwdzeBga4ZLN/L00/IKtLgVVYAgX4XRecykQICXHJcii+cRReByZB6C/IzPIzcX0LaJDRJSipCSXtxlt4qbOfp1ccAPPydCWwkPt0VqNW7ci0WjWkH6aRKJBI1qBeHKrXulzpdfUIgeH0xAtxFf4pOZi3An1vCGularxaSffsbr3dshwMu9wupfETRqFRJirsEvuKl+miCRwLd6Uzy4e6FcyyhS5UOrUUOhLP3mWGF+DiAIUFiUDObpv6ty9Cuhf0QURezbtw+7du3CBx98gOTkZCiVSixduhTm5uYAgNWrV0Or1WLp0qX6QGzFihWws7PDwYMH0bFjR/z4448YP348XnrpJQDAvHnzEB4eXmJ9derUwaRJkwAAVatWxbx587Bv3z506NABAPDRRx/py/r6+uLrr7/Ge++9h/nz5+unFxUVYeHChQgICAAAjBw5El99Vdzl5euvv8Ynn3yCDz/8UD+tYcOGRtu/fv36MtsWGhqKzMxMdO/eXb++4ODgMrdpYWEhCgsN74j/17oIm4KlHJBIBOQWGF655BYATjZPF3y52AFvdZTCTAqo1MCGw1qklOyBWOlYmj/cJobTcwsAx6c4j9fwEuBmL2DFnkoQTZSDva0uSEjPNAwCMzKL4GBnPICwtTGDVCogPdPwmbb0TDW8PIovrP/3w11MGOWPP5bWh1qtRaFKi8mz75T6LKaHqxy9O7lg0Zr7T9Okp/KwzRlG2vZwWz3OxlpqfHtkGW6PR3Vu5YjoB/m4fqv4mVuZmYDxI3yx9NcHSE4tgruz6Y+N1pYSSCUCMnMM9+esXC3cncyNzmNrJTFa3tbqye7Rz9+YieEv2+KncS5Qa0SoikTMXZ+BpDTTPk8LQN8VOzPbsC4Z2RqDbuuPsrbS7Scl5slSw9PV8MZF5xa2GNTbCRZyCe4nqDBl3gOo/5rNzEzA6CFu+HlzClLS1XB1qhwZ40dlZOVAo9XCwdbaYLqDrQ2i4ozfqfNxd8GEdwci0NsTOXn5WL19H4ZOmoX1M76Aq6Ou18SqLXsglUrwaufWFd2EZy4vJx2iVgNLa0eD6UobR6Qm3C3XMg78PhNWti4GQe2j1EWFOPj7TNRo2A1yC+M3FOm/iQHpC2jbtm2wsrJCUVERtFotBgwYgMmTJ2PEiBGoXbu2PhgFgEuXLuH27duwtjY86BYUFODOnTvIzMxEYmIiGjVqpP9NKpUiJCQE2sduf9epY/hQu7u7O5KSirts7d27F9OmTcONGzeQlZUFtVqNgoIC5OXlwdJSdzKztLTUB4ePLyMpKQlxcXFo165dubbD37WtY8eOGDJkCDp16oQOHTqgffv26NevH9zdS79rOW3aNEyZYvis5qRJk2A8JKYXQUoWsChcA4U5EOwtQa8wCVbt0bwQQemzZm0BdGggYO1BLTSVNB5t28wBHw/z0X//YvqtClvXm/08oFRKMebrSGRmq9GsoR0mfOiPj6dElng+0tFehmmfVcWhk+kI359SYXV6XNum9vjwTS/99y9nle/C8GmYywS0CbPHmj8NL8zf6ueB2LgC7DteObtgPm992ljBUiHBd6vSkZOnRYPqcox4xRZTl6fjftLzzaK3DLXGe6+56L9/s6Biu0MePpONSzfyYG8jRa/29vj0LTeMn30fRWoRb/R0xP1EFQ6dKb2nwYuoTjV/1Knmr/9et5o/Xvn0f/h93zEM79cdEXdjsG7nQayeOu4/2RPrxM7FiDgTjoGf/AwzWcmbVRpNEf5Y/CFEUUTnAf/uMTGM4XtIy8aA9AXUpk0bLFiwAObm5vDw8DAYXVepNOyylZOTg5CQEKxZs6bEcpydnZ9ovTKZ4V1OQRD0QWtUVBS6d++O4cOH45tvvoGDgwOOHj2KoUOHQqVS6QNSY8sQRV1WzMLC+PM+pSlP21asWIFRo0Zh586dWL9+Pb788kvs2bMHTZo0MbrM8ePHY/To0QbT5HI59n7z6xPVjZ5MXiGg1YpQKgQAxVlSpQJ/OzjP39FqdYMfAUB8mhYeDhI0ri7B9tOVNCL7S57q4TYxnK5UoETWtLzcHQClQsDQjsWZIIlEgLczEBoo4LvftBBN3L3uxLkM3Lj9SFbur4F67G3NkJZRnOGzs5XhTlReifkBIDNLDY1GLJExtLc1Q/pfy3B3kaN3J1cMHXMV0fd1G/RuTD5qB1mjZ0dn/LAsRj+fo70MsyYE4frNHHy/9Pk+g37ifOZj20P3f2dnK0PaI1lje1sz3Ik2PshQVrbG+PawMdymD7VoZAe5XIK9Rw2Hc65Xwwq+XhbY0bCebsJf11e/za+NtVsS8MvvCU/avKeWnaeFRiuWyG7aKCXIzDGeqczMKZkN1ZUv/zHB2V6K9o0t8flPKYhL1q0nNlGNaj4ytGtkgVXbnm8wdvpKDm5GFR8YHg5cZGstRXpW8Xaws5bi3n3jPQCyc3T7ia214XOPdjZmyMgyDLDzCrTIK9AiPrkIN6Pi8cv0ADSuq8TRczmoXc0S3h7maFrvr8dt/tpPfv7WH7/tSsO68FKGCX+O7GysIJVISgxglJaZZfS5UGPMzKQI8vXC/YRkAMCFG3eQnpWDHh9M1JfRaLX4YfXvWLfjALb8aHwApMrC0soegkSKvGzDbvq5WamwsnUqZS6dU7uX4cTOxXjtoxVwqVK9xO8aTRE2L/4IWWlxeO3jVcyOUgkMSF9ASqWy1OcqH9egQQOsX78eLi4usLExfpB1dXXFmTNn0LJlSwCARqPB+fPnUa9evXLX6dy5c9BqtZg1axYkEt2JfsOGDeWeHwCsra3h6+uLffv2oU2bvx8ivTxtA4D69eujfv36GD9+PMLCwrB27dpSA1K5XM4uuiag1eoG5PFzExB5vzgi8nMTcCby2QaOggC8CKOva7VAfDrg6yrg5oPibeLrKuDcrX8WNUYlAkt2Gl6kd2skQWqWiJM3RJMHowCQX6BFfoHhBXNqugr1a9noAy5LCwmCA5TYusf4oDpqjYib93LRoJY1jp/NAKD7f69f0wZ/7tbNo5DrdgLxsd1LqxUheSS78TAYvXkvFzMWRj33baTbHobPJKZmFKF+TWvcjflreygkqO6vxLZ9xjO3ao2IW1F5qFfDGsfPZQLQbY96Na2xZU/JeTq3csTJ85nIzDYMQr6aew9y8+I/nmp+lvj0HR+M/vom4pNM89ykRgNExalRw88c52/o9htBAGr4m2PfaeM3LG7HFqGGnzl2nyz+vWaAOe7cL/t1KI+S/3Wj5PH9QavVrf95KygUkVBoWP+0TDXqBFki6oHu/8ZCIUFVXwV2Hs00ugy1BrgTW4g6QZY4fVl3E0QQgNrVLLDjsPF5HhYShOIgePrSeJg/MuJzoI8CH7zuii/m3EfC37xy5nmRmZmhup8XzlyNROuGdQHonv88c+0mXunYslzL0Gi1uB0bh2b1agAAurZoiEa1gwzKjJr2E7q0aIQerYxfc1QmUjNzuHnXRFTECVSr1x4AIGq1iL5xAiFtXi91vpO7luB4+EL0/3AZ3H1rl/j9YTCalhSNgaN/hqXVf+vVfQ+9KIMLmcoLcFlGT2PgwIFwcnJCr169cOTIEdy7dw8HDx7EqFGjcP++7jmoDz74ANOmTcOff/6JyMhIfPjhh0hPT3+iLieBgYEoKirCjz/+iLt37+KXX37BwoULn7i+kydPxqxZszB37lzcunUL58+fx48//viP2nbv3j2MHz8eJ06cQHR0NHbv3o1bt2797XOklY1UaQmbutVhU1d319HSrwps6laH4gUbMOHvnLihRYNAAXX8BDjZ6AIlmRS4eFd3xdcrTIK29R7N7AGu9rqPVAJYWwpwtQfsH7nx2raeBN4ugK1S9yxp23oS+LoKuBpVCSKvcjgdKaKev4DavgIcrYEuobpBmS7f09W/R2MBrWsX/51KJLp2utj9tU0sdP9+uE1UaiA50/BTpAbyVbp/V1a/70jCwN7uCAuxhZ+XBcYN90NqehGO/RVsAsD0L6qhV8fiXh+btieiaxtndGjpCG8PBT58ywcKuQQ7D+kCsJi4AtyPL8BHw3wQFKCEu4scL3dzRYPaNvrlPgxGk1JUWLT6PmxtzGBvq/uY0h87kzCglyua1LeBbxUFxr7ng9SMIhw7V/yf+N1ngejZvjirsWlHErq2dkSH5g7w8pBj1BAvKOQS7DpsmA3xcDFH7SAr7DhY8jUP8UkqRN0v0H8SknWBTkxcYYkM2vO060QuWoVYoFldBdydpBjUzRpymYAjF3QZw7dfssHL7YoPDHtO5aFWoDk6h1nC3UmK3q2V8POQYe8jAazSQoC3mxk8nHX/126OUni7mekzq/EpaiSkqjGkhw38PM3gbC9F5zBL1AwoDoxNbduBDLzS2QENayvh7WGOD99wRVqmBqcuFWfcp3zgiS4tiweg2bI/HR2a2qBNY2tUcZXh3f4uUMgl2HdS94yDq6MZ+nS0h7+XHE72ZgjyU2DMUDeoikScv6bbfgkpRYiJV+k/iam6IDQ2QVVq1toUBnRri80HjmPboZO49yAB3y5fj/zCQn3wOGn+z5j365/68ks27cDJyxG4n5iCG/diMXHeKiQkp6FXG93zknbWVgj08jD4mEmlcLS1ga/Hi/HOzUbt38TFoxtw+cQfSIm/g51rJ6NIlY86TfsAALauGIuDf8zSlz+xczEOb/kBXQdPha2jJ3Iyk5GTmQxVgW4f02iK8MeiUYiPvoqeb82EVqvRl9GoTT/4F1UezJD+y1laWuLw4cMYN24c+vTpg+zsbHh6eqJdu3b6rOK4ceOQkJCAQYMGQSqV4p133kGnTp0gfYLhyuvWrYvZs2fju+++w/jx49GyZUtMmzYNgwYNeqL6Dh48GAUFBfj+++/x6aefwsnJCS+//PI/alt+fj5u3LiBVatWITU1Fe7u7hgxYgTefffdJ6qTqdmG1ELYvl/032vM/BwAEPvz77g8dLypqvXMXY8WoZRr0bquBFYKIDEdWHtAo++eaqss7t4N6IKtd7sWH8Ka1hDQtIYEUYkift6ru+hRyoHeYVJYWQCFRUBiuog1+7W4m/BiBKQRsSIs5UDLWgKUCgGJGcD6Q1rk/nW9a2P52DZRAMM6Ff/dNqkuoEl1IDpJxJoDlbuLclnWb02AQi7Bx8N8YWUpxdXIHHz27U0UFRW33cNVDlvr4i6pB0+mw9bGDENe9oC9nQx3ovMw/ttbyPirm6tGI+KL6bcw7NUq+HpMIBRyCeISCzF9wT2cvqgL7EJq26CKuwJV3BVYP7+uQZ3av3b2ObTcuA3bk6CQS/DRW9667XEzF5/PuGOwPdxdzGFrXfz3cehUBmytzTCorzvsbc1wNyYfX8y4UyKQ7NTKESlpRTh39cV5/u/0tUJYK7PxUhsr2FpJEJOgxqzV6cjK1e3zjrZSg0zm7dgiLNqUiT5trdC3nRUS0zSYuy4DD5KKg6X6QXIM610cqL3/ih0AYPPBHGw+mAuNFvh+TQZeaW+Fj16zg8JcgsQ0NZb+kYXLtyrHhfYfe9OhkAsY/poLlBYSRNwpwP/mP0CRunhjuDnJYPPIO0aPnc+BjZUUr3ZzhL21FPceqPDVTw/0Ax2p1CJqBFigR2s7KC2lyMxW49rtfHw2K7ZSBZvl0TEsBBlZOVj023akZmSjmo8n5n42Qt9lNyElzeDGfHZuHr5ZshapGdmwVlog2M8by6aMhn+Vf8/N4RoNuyIvJw1HtsxFblYyXKoEo9+opVDa6G5uZaXFQxCKbwxfOLwOGrUu6HxU8+4j0aLHB8hOT8StS/sBAMu/7mVQZsDon+ET1LiCW0QvCkEUK0MnLapMtFotgoOD0a9fP/zvf/8zdXUqhe2yoL8v9B/SrSgSX60xXUakMpo40AxT179YF2QV7fP+UpMGbpXR3l9D0fGN8r1C4b9g9y/1MWTyC/D+oedo5WRXvDSy4gbzehH9Ma8qss7v+fuC/yE2DTpg5UFT16JyGdLa1DUo3dlWYSZbd+ih0t+tW1kwQ0r67qytWrVCYWEh5s2bh3v37mHAgAGmrhoREREREf2LMSAlSCQSrFy5Ep9++ilEUUStWrWwd+/eF+5ZSyIiIiKiykaQcNiesjAgJXh5eeHYsWOmrgYREREREf3HMCAlIiIiIiKqIIKEr30pC/PHREREREREZBIMSImIiIiIiMgk2GWXiIiIiIiogkik7LJbFmZIiYiIiIiIyCSYISUiIiIiIqogHNSobMyQEhERERERkUkwICUiIiIiIiKTYJddIiIiIiKiCiJImAMsC7cOERERERERmQQzpERERERERBWEgxqVjRlSIiIiIiIiMglmSImIiIiIiCqIRMoMaVmYISUiIiIiIiKTYEBKREREREREJsEuu0RERERERBWEgxqVjRlSIiIiIiIiMglmSImIiIiIiCqIIGEOsCzcOkRERERERGQSDEiJiIiIiIjIJNhll4iIiIiIqIJwUKOyMUNKREREREREJsEMKRERERERUQVhhrRszJASERERERGRSTBDSkREREREVEGYIS2bIIqiaOpKEBERERER/RvdfK2zydZd7dedJlt3eTFDSlQOX61Rm7oKlcrEgWbYLgsydTUqlW5FkZi+SWvqalQqY/tKMHJ2pqmrUanMG22LQ9fyTF2NSqNVTUusOGDqWlQub7YBvlmnMXU1KpUvXpXit1M8vj7q5cYSxN66bupqVCpeVWuYugr0DzEgJSIiIiIiqiCChMP2lIVbh4iIiIiIiEyCGVIiIiIiIqIKIpFyUKOyMENKREREREREJsGAlIiIiIiIiEyCXXaJiIiIiIgqCN9DWjZmSImIiIiIiMgkmCElIiIiIiKqIHztS9m4dYiIiIiIiMgkmCElIiIiIiKqIHyGtGzMkBIREREREZFJMCAlIiIiIiIik2CXXSIiIiIiogrCLrtlY4aUiIiIiIiITIIZUiIiIiIiogrC176UjVuHiIiIiIiITIIBKREREREREZkEu+wSERERERFVEA5qVDZmSImIiIiIiMgkmCElIiIiIiKqIBzUqGzcOkRERERERGQSzJASERERERFVFIHPkJaFGVIiIiIiIiIyCQakREREREREZBLssktERERERFRB+NqXsjFDSkRERERERCbBgJSeKUEQsHnzZlNXg4iIiIioUhAkEpN9XgTsskvllpCQgGnTpmH79u24f/8+bG1tERgYiNdffx2DBw+GpaWlqav4QgmtJqBpsARWFkBiOrDjrAZxqcbLOtsCretI4O4gwM5KwK6zGpyKFA3KhFQVEFpVAjsr3ffkDODwVS1ux4klF/gCc2geCv9PhsK2QS0oPFxwtu/7SNyyz9TVqjDXT6zBlSPLkZ+TAge36gjr8QWcveoYLZueeAvn9/6IlAfXkJMRh8bdPkOtZoMNypzfOw8X9v9kMM3WyQ8vjw6vsDY8jZZ1zdEuVA4bpYAHyRpsPFCA6ARNqeXrVzVDt2YKONpIkJyhxeYjBbh+T63//fVOFmhS09xgnutRRZj/ex4AoGoVKT7sZ2V02dPX5CAmsfR1m9KBHeuxe/MqZGakoopvNbw2bBz8qtYyWvb8yX3YsWkZkuJjodGo4eLujQ4930BY6+76MlvWLcSZY7uQnpIAMzMZvAOC0XvASPhXq/28mvRUzh1cg1O7lyE3KxkuVaqjQ/8J8PAz/ndz8cgGXD21GclxtwAAbt410arXaH15jaYIh/+cg7tXDyMjJRZyCyv4VG+K1i99Ams71+fWpqcVEiigSbAAKwWQmAHsPqdFXJrxsk42QKvaErg5AHZKAbvPa3HmZunnkrBgAW3rSnA6Uos9F16cc87JvWtwJHw5cjJT4OZVHd3f+AJeAcb3kzMHNuDCsS1IvK/bTzx9a6DDKx+XKJ/04A52bZiFezfOQKvRwMUzAAM++AF2Th4V3p5n4c9t4djw+2akpWcgwM8XI98dhupB1f52vgOHjuCbGbPRtEkjfPXleP309t1fMlr+7TcHoX9f47/Rfw8DUiqXu3fvolmzZrCzs8PUqVNRu3ZtyOVyXLlyBYsXL4anpyd69uxp6mq+MGr4COjYQILtp7V4kCKicXUJBraR4qetGuQVliwvkwLpOcD1GC06hhi/25WdB+y7qEVatu5ioK6/BP1bSrB4hwbJmRXZmudLqrRE1uVIxK7chNDffvr7GV5gdy+H41T4d2jWezKcq9TBteM/Y+eKt/Hy6HBYWDmWKK8uKoC1gxd8a3XCqfBvS12unUsgugxdrv8ukVTOU0GDajK81EqB9fvyERWvQZsGcozoo8RXK7KRk1/yotfPXYoh3Syx5WgBrt5VI7S6DO/0tMR3q3MQn6rVl7t2rwird+Xrv6s1xcu6G6fB+IVZBsvt3kyBIC+zShuMnjm6CxtXzMLAd7+AX7Va2LdtLX746n189eNm2Ng5lCivtLJF177D4FbFF1IzGa6cPYJV8ybDxtYBNes3BQC4evjgtWHj4OxaBUWqQuzduhpzvnof3/z0J6xtSy6zMok4G479v01DpwFT4OFbF2f2r8L6H4finck7obQp+XcTc/MUaoR2g2dAA5jJzHFy11Ksn/sWhk3cDmt7VxSpCpAYcx1Nuw6HS5XqKMjLwt4N32DT/OEY8vnvJmjhkwv2EtC+voAdZ0XEpYpoFCTg1dYSLNyuNX7OMQPSc0RExAId6pe9bHcHoEGAgMT0FycQBYDLJ8MRvvY79BoyGV4BdXBs189YOeNtfDw9HFZG9pN7N86gTpOu8K5aHzKZHIe3L8XKGcMwaupW2DrobkykJsZg8dcDEdqqL9q9NBJyCyskPbgNM3P5827eP3Lg8FEsXLoCH454D8FB1bDpz634bOJXWLFoHuzt7EqdLyExCYuWr0LtmjVK/Lbhl+UG30+fPY9Zc39Ci2Zhz7r69AJ7MfK4ZHLvv/8+zMzMcPbsWfTr1w/BwcHw9/dHr169sH37dvTo0aPEPAcPHoQgCMjIyNBPu3jxIgRBQFRUlH7asWPH0Lp1a1haWsLe3h6dOnVCeno6AKCwsBCjRo2Ci4sLFAoFmjdvjjNnzujnTU9Px8CBA+Hs7AwLCwtUrVoVK1as0P8eGxuLfv36wc7ODg4ODujVq5fBuk0lrLoE52+LuHRXREoWsP20FkUaoH6A8Yfe49KAvRe0uBYtQlPKNfHNByJux4lIywbSsoEDl7RQqQFPp3/Xg/TJuw7j5qQ5SPxzr6mrUuGuHl2FoIavoFpIH9i7BqJZr8kwM1fg5jnjF8HOVWqjUZcxCKjbDVKpudEyACCRmsHS2ln/USjtK6oJT6VtiDmOX1Xh5LUiJKRpsW5vPlRqEWG1jLetdQNzRESpse+sColpWmw/XojYJA1a1TMsr9YA2Xmi/pP/yAW5Rmv4W26BiDoBZjh5TVWRTX0qe7auRvMOfdCsXS94eAVg4LtfwFyuwLH9m42WD6oVivpN2sK9ij9c3LzQrvsAePpUxe2IC/oyjVt2QY26TeDsVgUe3gF45c1PUJCXg/vRt55Tq/6503tXoG6zfqjTtC+cPALRecAUyGQKXD6+yWj5nkNnoUHrgXD1CoajWwC6vPE1RFGLqMgTAACFhTVe/WgFgkO7wtHNH57+9dDx1QlIiLmGzLS459m0f6xxdQEX74i4fE93zgk/I0KtBur6Gz8/xKcB+y+JuB4jQq01WgSALnDt1USC7We0KCiqoMpXkGM7VyG09SsIadkHLp6B6DVkMmRyBc4dMn587Td8Bpq0HwAPn2A4e/jjpaH/g6jV4u71E/oye36bg6C6LdH51THw8K0BR1dvBDdoazTArYw2bd6Crp06oHOHdvDx9sJHI96DXC7Hzj2l90LSaDSYNvN7DB74KtzdSvYYcLC3N/gcP3Ua9WrXgoebW0U2pdIRJILJPi8CBqT0t1JTU7F7926MGDECSqXSaBnhH77w9+LFi2jXrh1q1KiBEydO4OjRo+jRowc0f0VdY8eOxaZNm7Bq1SqcP38egYGB6NSpE9LSdP2MJkyYgOvXr2PHjh2IiIjAggUL4OTkBAAoKipCp06dYG1tjSNHjuDYsWOwsrJC586doVKZ7uJSItHdUb6XYHg3+V6CiCrPKHgUBKCmjwCZGXA/+cW6a006GrUKKXHX4BFYfBdZkEjgERCGpJiLT7XsrJRo/DqtJTbM6ICD68cgJ6PyXVRLJYCXqxSR0cXdbUUAkdFq+LlLjc7j526GG4+UB4CIKDV8PQwzwFWrmGHae9aYMMQK/dspoFSU/ndXJ8AMSoVQaQNSdVERYu5EILhOY/00iUSC4DqNcTfy8t/OL4oiIi6fQmJcFKrWCCl1HUd2/w4LSytU8f37rnumpFGrkBBzDb7BTfXTBIkEvsFN8eDuhTLmLFakyodWo4aFpW2pZQrzcwBBgMLC5qnrXNEkEsDdHriX+Ng5J1FEFcenO+d0DhFwO15EVOJTLea5U6tViIu6hsCaxcdXiUSCwBphiLl9sVzLKCosgEajhoVSt59otVpEXjoERzdfrJg+DFNHNMOCyf1x/dyLcfO0qKgIN2/fQYN6dfXTJBIJGtSrg+s3Ikudb/W6DbCztUWXju3/dh3p6Rk4deYcOpejLP23VM5+WlSp3L59G6IoIigoyGC6k5MTCgoKAAAjRozAd99998TLnj59OkJDQzF//nz9tJo1awIAcnNzsWDBAqxcuRJdunQBACxZsgR79uzBsmXLMGbMGMTExKB+/foIDQ0FAPj6+uqXs379emi1WixdulQfMK9YsQJ2dnY4ePAgOnbs+MT1fRYs5YBEIiC3wPDiILcAcLJ5uosDFzvgrY5SmEkBlRrYcFiLlKy/nY0qoYK8DIhaTYmuuRZWjshMvvePl+vsVQctX54KWyc/5GUn48L+n7Bt8evo8+FWmMuN33AyBSsLAVKJgOw8w7+TrDwRrg7G76XaKEuWz84TYWNZ/HcVEaXGpVtFSM3SwslWgh7NFRjeR4pZv+ZCNHLvJqyWOSKi1cjIqZw3dnKy06HVakp0zbW2c0T8g6hS58vLzca4tzuhqKgIEokEA94Zjxr1mhiUuXz2MJbM/gyqwgLY2jvh40kLYW1TObPpD+XlpEPUakp0zVVaOyI14W65lnHw95mwsnUxCGofpS4qxIE/ZqJGaDfILYw/b1yZWJo/POcYTs8tAByfIp6u4S3AzV7A8t1lpFArqbzsDGi1mhKZSytbRyTHl+/4unP9TNjYuyCgpm4/yc1KhaogD4e3LUWHl0ehU/9PcOvyUaydOwpDx6+EX/VGz7wdz1JmVja0Wi3s7QxvxNjb2SH2/gOj81y5dh07du/Dormzy7WO3fsOwNLCAi2aNvn7wv8yL8rgQqbCgJT+sdOnT0Or1WLgwIEoLDTyEEo5XLx4Ea+88orR3+7cuYOioiI0a9ZMP00mk6FRo0aIiIgAAAwfPhx9+/bF+fPn0bFjR/Tu3RtNm+pODpcuXcLt27dhbW1tsNyCggLcuXPH6DoLCwtLtEUulwMwnpGpbFKygEXhGijMgWBvCXqFSbBqj4ZBKel5BbXU/9vBPQjOXnWwfno73LuyA0GhL5uwZs/HucjifoVxKVo8SMnFlKE2qFpFipuxhv3h7awEBPuYYfn2vOddzQqnsFBiwqx1KCzIR8TlU9i4YhacXasgqFaovkxQrYaYMGsdcrIycGTv71g0ayzGf/uL0edS/y1O7FyMiLPhGDD6Z5jJSj73p9EUYfOSDwFRRKcBU0xQw8rB2hLo0EDArwe00Lx48ehTO7R1Ca6c2oFh41dB9tfzoeJfd7SCG7RFs85DAAAePsGIuX0Bp/evr/QB6ZPKy8vHd7N/wOgPhsPWtnx3Nnbu3Ye2rVvC3Lz0R0rov4kBKf2twMBACIKAyEjDLhv+/v4AAAsLC6PzSf66GyQ+knYoKjJ8yKS0ecurS5cuiI6ORnh4OPbs2YN27dphxIgRmDlzJnJychASEoI1a9aUmM/Z2dno8qZNm4YpUwwvMiZNmgRJ1S+fqp6PyisEtFrxr26CxdtGqYDRgVqehFarG/wIAOLTtPBwkKBxdd3gSfRiUVjaQZBIkZ9jOPRyfk4qLKydntl65BY2sHXyRVZqzDNb5rOQky9CoxVhbWnYa8DGUkBWrvG/k6zckuWtLQVk5ZX+d5WaKSI7Twtnu5IBaZOa5sgtEHH5jrqUuU3PytoeEokUWRmGw6VmZ6TC1q7059YkEglc3L0BAF5+QUi4fw87fl9uEJDKFRZwcfeGi7s3/IPq4MsRPXFs3x/o0ndoxTTmGbC0socgkSI3y/DvJjc7FUqbsv9uTu1ehpO7FuPVj1bApUr1Er9rNEXYvPgjZKbGYcDHq16I7CgA5KkennMMpysVQG6+8Xn+jrs9YKUQMLRTcdZHIhHg7QyEVhXw7Uat0R4HlYWltR0kEilyHttPcjJTYWVb9n5yJHw5Dm9fgjfHLoebd3HPMUtrO0ikZnDxDDAo7+zhj+ib559d5SuIrY01JBIJ0jMMR0FMz8iAvb1difJxCQlISEzCl19N1U97eL3XsWdfrFw0Dx7u7vrfrly9jtj7D/Dl2E8qpgGV3IvyLKepMH9Mf8vR0REdOnTAvHnzkJubW+75HgZ98fHx+mkXL140KFOnTh3s22f8YfmAgACYm5vj2LFj+mlFRUU4c+YMatQoHsnN2dkZgwcPxurVqzFnzhwsXrwYANCgQQPcunULLi4uCAwMNPjY2hp/Nmj8+PHIzMw0+IwfP95o2X9Kq9UNGOHnZnhw8nMTcD/l2Z7BBUH3LB69eKRm5nDyqIn42yf100StFnF3TsLFu94zW09RYS6y0mJhaW38Jo2paLRAbKIGQd7F900FANW8zXAv3vjIXvfi1QblAaC6jxmi4koPKO2sBCgtBGTllrxp06SmOU5fL4K2Et/PMZPpXsly4/Ip/TStVouIy6fhH2T89RXGaEUR6qKyn5PVasUSNxUrG6mZOdy8ayLqRvFAM6JWi+gbJ+DpX/pwsSd3LcHx8Pno98FSuPuUfLXNw2A0PTkar320EhZWlbvr8qO0WiA+HfB1NTzn+LoKuJ/6z845UYnA4h0aLN2l1X/iUkVcjRaxdFflDkYBwMzMHB6+NXHnWvHxVavV4s71k/AOrFfqfIe3L8WBPxdg8KeLUcXf8LVKZmbmqOJXCymPdflNSYiCnWPlf+WLTCZDtcAAnL9U/Oy5VqvFhUtXUKN6UIny3lU8sWTeHCyaO1v/CWvcEPVq18KiubPh7GQY2O/YsxfVAgMQ4O9X4W2hFw8vValc5s+fD7VajdDQUKxfvx4RERGIjIzE6tWrcePGDUilJbu0BgYGwsvLC5MnT8atW7ewfft2zJo1y6DM+PHjcebMGbz//vu4fPkybty4gQULFiAlJQVKpRLDhw/HmDFjsHPnTly/fh1vv/028vLyMHSo7g79xIkT8eeff+L27du4du0atm3bhuDgYADAwIED4eTkhF69euHIkSO4d+8eDh48iFGjRuH+/ftG2ymXy2FjY2Pw0XXZfbZO3NCiQaCAOn4CnGyAbo0kkEmBi3d1Z/FeYRK0rffonWfA1V73kUp0WR9Xe8D+kRv0betJ4O0C2Cp1z5K2rSeBr6uAq1GV/MrgCUmVlrCpWx02dXUZDEu/KrCpWx0KL/e/mfPFU6v5YESe3Yhb5zcjI+kOjv05BWpVPqo10L277dDGcTizq/jZHY1ahdS4CKTGRUCrKUJeVhJS4yKQlRqtL3MqfDri755GdvoDJEZfwN41H0AiSOBfp9tzb9/f2X9Ohaa1zdG4hgyuDhL0b6+AXFY8wNAbnS3Qs3nx3+fB8yrU8DVD2xBzuNpL0DVMDm9XKQ5d1JU3lwG9Wyrg6y6Fg42Aal5SvNNLiZQMLSIeGwypmpcUTnYSHL9SOQczelSHHq/jyN4/cPzAFsTfv4s1i6ZCVZiPZm17AQCW//Alfl89V19+x6ZluH7xJJIT7iP+/l3s/vNnnDy0HY1bdgUAFBbk44/VP+Ju5GWkJsUh+s51rJw3GRlpSQht2sEkbXwSjdq/iUtHN+DKiT+QEn8Hu36dDJUqH3Wa9gEAbF0xFgf/KD4Xndy1GEe2/oAug6bC1tETOZnJyMlMhqpAdwNWoynCH4tGISHmKnq8NRNarUZfRqOu/PsHAJy6IaJ+gIDavgIcbYAuobpB7y7/dc7p0VhA6zrFAatEArja6T5SCWBtofv3w3OOSg0kZxp+ijRAfiFemNeMNes8GGcPbcT5I5uR9OAOtqyaAlVhPkJa6o6vGxeNw64NxcfXw9uWYO+muegz7BvYO3kiOyMZ2RnJKCwovlHfvOtbuHJqJ84c2IDUxGic2LMGkRcOonG71557+/6Jvr17InzXHuzetx/RsbH4Yf4iFBQUoHP7dgCAb2f9gKUrfwEAmJubw8/Xx+BjpVTCwtICfr4+kMlk+uXm5uXh8NHj5Rr4iP6b2GWXyiUgIAAXLlzA1KlTMX78eNy/fx9yuRw1atTAp59+ivfff7/EPDKZDL/++iuGDx+OOnXqoGHDhvj6668NnhmtVq0adu/ejc8//xyNGjWChYUFGjdujNde0x28v/32W2i1WrzxxhvIzs5GaGgodu3aBXt73d1pc3NzjB8/HlFRUbCwsECLFi2wbt06AIClpSUOHz6McePGoU+fPsjOzoanpyfatWsHGxvTjox4PVqEUq5F67oS3UvK04G1BzT6QSdslYJBV2drC+DdrsV/rk1rCGhaQ4KoRBE/79Vli5RyoHeYFFYWQGERkJguYs1+Le4m/LsCUtuQWgjb94v+e42ZnwMAYn/+HZeHPttstqn51+mKgtx0nNs7F/nZKXB0D0anNxfru+zmZMRDEIpvXORlJ2PzvD7671eOLMeVI8vh5tcQ3d7+GQCQm5mAg+s/RUFeBhRKB7j6NECP4etgYVX5ngs8f7MIVpYCujVVwNpSwINkDX76PVc/cJGDtcQgE3MvXoOV4Xno3kyBHs0USM7QYvGWPP07SEUR8HSSoHENS1jIBWTmiLgRrca24wVQP5Z0bVrbHHceqJGYXonTo39p2LwTsrPSseXXBcjKSEUVvyCMmvATbP7qspuWkmAwoEZhYQHWLpmK9NQkyMzlcPP0xdAPv0bD5p0A6LrzJjyIwomDW5GTlQGltS18A2ti7NfL4eEdYLQOlUlwaFfkZafhyNa5yM1KhkuVYPT/YKm+y25WmuHfzflD66BRF2Hz4lEGy2nWbSRa9PgA2emJuH15PwBgxde9DMq89vHP8AlqjMouIlbXZbdVbQFKhYDEDGDdQS1y/xoywVYpQIThOWdY5+IbzWHBAsKCgegkEav3V/6/ifKo06QrcrPTse/3ucjOTIG7dzCGjFms77KbmWq4n5zar9tPfv3xQ4PltO09Au36jAQA1AztgJ5DJuHwtsXYtnoqnNz98NoHP8A3yPgI1pVNm5bNkZmZhZWr1yE9PR0B/n6Y9tVEfZfdpORkSP5B19MDh49ChIg2rVo84xq/OF6kLrs//fQTZsyYgYSEBNStWxc//vgjGjUq/RnoOXPmYMGCBYiJiYGTkxNefvllTJs2DQqFotR5HieIYmXvWEFkel+tqbzPkJnCxIFm2C4r2YXnv6xbUSSmb/p3XKg9K2P7SjBy9guSLnlO5o22xaFr/75Bkv6pVjUtseKAqWtRubzZBvhmXSkvnP6P+uJVKX47xePro15uLEHsreumrkal4lW1xt8XMpGk8YNMtm6XaT+Xu+z69esxaNAgLFy4EI0bN8acOXOwceNGREZGwsXFpUT5tWvX4q233sLy5cvRtGlT3Lx5E0OGDMGrr76K2bPLN/oywC67REREREREFUciMd3nCcyePRtvv/023nzzTdSoUQMLFy6EpaUlli9fbrT88ePH0axZMwwYMAC+vr7o2LEjXnvtNZw+ffrJNs8TlSYiIiIiIqJ/FZVKhXPnzqF9++JnfSUSCdq3b48TJ04Ynadp06Y4d+6cPgC9e/cuwsPD0bVr1ydaN58hJSIiIiIi+hcqLCxEYWGhwTS5XF5i0M6UlBRoNBq4uroaTHd1dcWNGzeMLnvAgAFISUlB8+bNIYoi1Go13nvvPXz++edPVEdmSImIiIiIiCqIIAgm+0ybNg22trYGn2nTpj2Tdh08eBBTp07F/Pnzcf78efz+++/Yvn07/ve//z3RcpghJSIiIiIi+hcaP348Ro8ebTDN2CsNnZycIJVKkZiYaDA9MTERbm5uRpc9YcIEvPHGGxg2bBgAoHbt2sjNzcU777yDL774ApJyPsPKDCkREREREVEFESQSk33kcjlsbGwMPsYCUnNzc4SEhGDfvn36aVqtFvv27UNYWJjRduXl5ZUIOqVS3SujnuRFLsyQEhERERER/ceNHj0agwcPRmhoKBo1aoQ5c+YgNzcXb775JgBg0KBB8PT01Hf57dGjB2bPno369eujcePGuH37NiZMmIAePXroA9PyYEBKRERERET0H9e/f38kJydj4sSJSEhIQL169bBz5079QEcxMTEGGdEvv/wSgiDgyy+/xIMHD+Ds7IwePXrgm2++eaL1MiAlIiIiIiKqIIJEMHUVym3kyJEYOXKk0d8OHjxo8N3MzAyTJk3CpEmTnmqdfIaUiIiIiIiITIIZUiIiIiIioopSztFm/6u4dYiIiIiIiMgkmCElIiIiIiKqIC/SM6SmwAwpERERERERmQQDUiIiIiIiIjIJdtklIiIiIiKqIILAHGBZuHWIiIiIiIjIJJghJSIiIiIiqigc1KhMzJASERERERGRSTAgJSIiIiIiIpNgl10iIiIiIqIKIkiYAywLtw4RERERERGZBDOkREREREREFUTgoEZlYoaUiIiIiIiITIIZUiIiIiIioooiMAdYFm4dIiIiIiIiMgkGpERERERERGQS7LJLRERERERUQTioUdmYISUiIiIiIiKTEERRFE1dCSIiIiIion+jrDmjTbZum49mm2zd5cUuu0TlMHW9xtRVqFQ+7y/F9E1aU1ejUhnbV4LtsiBTV6NS6VYUiSV7TV2LyuXt9sDYhfmmrkalMf09C2w5y+Pro3qGSvHut2mmrkalsugzB6w9yvzJowY0F/DhD9mmrkal8sOH1qauAv1D7LJLREREREREJsEMKRERERERUQURBA5qVBZmSImIiIiIiMgkmCElIiIiIiKqKBLmAMvCrUNEREREREQmwQwpERERERFRBREkfIa0LMyQEhERERERkUkwICUiIiIiIiKTYJddIiIiIiKiiiIwB1gWbh0iIiIiIiIyCWZIiYiIiIiIKgoHNSoTM6RERERERERkEgxIiYiIiIiIyCTYZZeIiIiIiKiCCBzUqEzcOkRERERERGQSzJASERERERFVFA5qVCZmSImIiIiIiMgkmCElIiIiIiKqIIKEOcCycOsQERERERGRSTAgJSIiIiIiIpNgl10iIiIiIqKKInBQo7IwQ0pEREREREQmwQwpERERERFRReGgRmXi1iEiIiIiIiKTYEBKREREREREJsGAtJKKioqCIAi4ePFiha7n4MGDEAQBGRkZFboeIiIiIqL/JEEw3ecFwGdITWTIkCFYtWqV/ruDgwMaNmyI6dOno06dOiar18GDB9GmTRv9dxcXFzRv3hwzZsyAv7+/yer1bxQSKKBxdQFWCiAxA9h9Xov4NONlnWyAlrUkcHMA7JQC9lzQ4sxNsdRlh1UX0KauBKdvarH3QunlKpvrJ9bgypHlyM9JgYNbdYT1+ALOXsb/HtITb+H83h+R8uAacjLi0LjbZ6jVbLBBmfN75+HC/p8Mptk6+eHl0eEV1gZTcGgeCv9PhsK2QS0oPFxwtu/7SNyyz9TVqjAXDq3Bmb3LkJuVDGfP6mjXbwLcfY3vJ5ePbcC1U5uREncLAODqXRMteo42KH/z4m5cOrIOibHXUJCbgUGfbYaLV/Bzacs/EVZTilb1zGBtISA+VcSfx1SITSr977y2vwSdGspgby0gJVPEjlNFuBGj1f9ubgZ0aSJDTV8plAogLUvEsatqnLyu0ZdxsBHQPUwGXzcJzKRAZKwGfx4tQk5+hTb1Hzu2ey0ObV+O7MwUuHsHoffgL+AdYHwfObV/I84d/RMJsbcBAJ5+NdCl/0cG5QsLchG+7ntcO7sPuTkZcHD2RPNOryOs/avPpT3/ROsGcnRorICtUoL7SRqs25OLqHhNqeUbBMnQq6UlHG0lSErT4PeD+bh6t8ho2QGdLNGqvgIb9uZi39lC/fQuYQrUDpDBy9UMao2Ij+dkPOtmPVOn96/B8Z3LkJOZAjev6ugy4Et4+hvfT84d2oDLJ/5E0gPdscTdpyba9fm41PLbfp6Ec4fWo9Or49Gkw2CjZSqD5nVkaBtiDhtLAQ9StNh0sAAxidpSy9cLNEPXMHM42EiQnKHF1mOFuB5luF+52kvQo7kcgZ5SSCRAYpoWy7fnIz1bd5wa2dcCVasYhiDHrqiwYX8h6L+JGVIT6ty5M+Lj4xEfH499+/bBzMwM3bt3N3W1AACRkZGIi4vDxo0bce3aNfTo0QMaTckTmSiKUKvVJqhh6SpjnR4X7CWgXT0BR6+JWL5bi6QMEa+2ksBSbry8zAzIyBVx8JKInPyyA0x3B6B+gIDEjBcnEAWAu5fDcSr8O9RvNwK9RmyCg3sQdq54G/k5qUbLq4sKYO3ghdBOo2Fh7VTqcu1cAvHa+MP6T/d311RUE0xGqrRE1uVIXB01xdRVqXA3zoXj4O/TENZ1BN747A+4VKmO3+YNRW628f0k9uYpVA/thv4f/owBn66Dtb07fpv3FrIzEvVligrz4BnQAC17ffq8mvGP1Q2QokdTGfaeVeOHTYWIT9ViaDc5lArj5X1cJRjQ3hxnbmjww2+FuBalwaBO5nC1L75r3qOpDEFeEqzbr8LM9YU4ekWNXs1lqOGju0SQmQFvdzOHKAKLtxZi/uZCSCUChnSRozLee794Yge2rvkOHfq8j4++/g0e3tWx9Nt3kJNpfB+5E3Ea9cK64d0vVmDklLWwc3TDkm/fRmZa8T6ydfV0RF4+gtfe/w5jZmxDiy6DsHnVN7h2bv/zatYTCa1ujpfbWmL70Xx8syIT95PUGNXfGtaWxv/H/D3NMKyXFY5dKsTXKzJx8VYRhve1goeTtETZetVk8PcwQ3p2yaDFTCrgXKQKhy4UPPM2PWtXT4dj9/pv0arnCLw76Xe4egVh9ffDkJtlfD+JjjyNWo26YfCYVRj6+TrYOrjhl9lDkZWeWKJsxPk9uH/3EqztXCq6GU+lflUzvNRCjl2nCjHj1zzEJWswvLclrCyM7ye+7hIM6qLAyWtFmLE2D1fuqDG0uwXcHYvDCUdbAR++YomkNC1+3JSH79bkYtepQhQ9dll2/IoKXy7J0X/+PPrvDkYFicRknxfBi1HLfym5XA43Nze4ubmhXr16+OyzzxAbG4vk5GSj5Q8dOoRGjRpBLpfD3d0dn332mUHgVVhYiFGjRsHFxQUKhQLNmzfHmTNnDJYRHh6OatWqwcLCAm3atEFUVJTRdbm4uMDd3R0tW7bExIkTcf36ddy+fVvfxXfHjh0ICQmBXC7H0aNHodVqMW3aNPj5+cHCwgJ169bFb7/9pl9eeno6Bg4cCGdnZ1hYWKBq1apYsWIFAEClUmHkyJFwd3eHQqGAj48Ppk2bBsB41+WMjAwIgoCDBw8CwD+ukyk1ChJw8a6Iy/dEpGQBO86KUKuBun7GTwLxacD+SyKux4pQl37jEjIzoGcTCcLPalGgqqDKV5CrR1chqOErqBbSB/augWjWazLMzBW4ee53o+Wdq9RGoy5jEFC3G6RS81KXK5GawdLaWf9RKO0rqgkmk7zrMG5OmoPEP/eauioV7uy+FajdtB9qh/WFk3sgOrw6BTJzBa6e2GS0fLc3Z6F+y4Fw8QqGo1sAOg38GqKoRUzkCX2Zmo17o2nXkfCpHva8mvGPtahjhlMRGpyN1CApXcTvh4tQpAYaVjfe4al5bSluxmpx6JIaSRkidp9R40GKiGa1isv7uElwLlKDu3FapGeLOBWhQXyqCC8X3SWCr5sE9tYCNhxQISFNREKaiA0HVKjiLCDAs/JdRhzesRKN27yChq36wLVKIPq8NQkyuQKnDxk/lgwYMQNNO7wGT99guHj445W3/wdRq8Wtayf1ZaJuXUBIi94IqNEIDs6eaNK2H9y9gxB758rzatYTad9IgaOXCnH8igrxqVqs2ZkHVRHQtI7xu57tQuW4drcIu08XICFViy1H8hGToEHrEMPydlYCXm2vxLKtudBoS9703Ho0H/vOFOJBcumZ2Mri5O6VaNDyFdRv3hfOHoHo/obuWHLhqPFjSZ93ZqJh2wFw8w6Gk7s/egzRHUvuRZwwKJeVnogda79Gn7dnQCKt3B0RWzcwx/FrRTh1XY3ENC027C+ESi2iSU2Z0fKt6pnjRrQG+88XITFdi/CTKtxP0qJF3eLy3cPkuB6lxpZjhXiQrEVqpoir9zQlbqar1EB2nqj/FL5g1yz0bFW+M8l/VE5ODlavXo3AwEA4OjqW+P3Bgwfo2rUrGjZsiEuXLmHBggVYtmwZvv76a32ZsWPHYtOmTVi1ahXOnz+PwMBAdOrUCWlpun6gsbGx6NOnD3r06IGLFy9i2LBh+Oyzz/62bhYWFgB0geNDn332Gb799ltERESgTp06mDZtGn7++WcsXLgQ165dw8cff4zXX38dhw4dAgBMmDAB169fx44dOxAREYEFCxbAyUmX1Zo7dy62bNmCDRs2IDIyEmvWrIGvr+8Tb8MnrZOpSCSAuz0QlWh4cL6XKMLT6enyDZ0aCLgTJyKq5A3bSk2jViEl7ho8AosDAkEigUdAGJJiLj7VsrNSovHrtJbYMKMDDq4fg5yMuKesLZmKRq1CYuw1+FRvqp8mSCTwrt4UcXcvlGsZalU+tBo1FJa2FVXNCiOVAJ7OAm7fL77YFwHcuq+Bj6vx07m3qwS37hsGBzdjNfB+pHx0ghY1fKWwUeq+B3hI4Gwr4OZ93d0vM6luPepHFlOkBkQR8HOvXJcRarUKD+5dR9VaTfTTJBIJqtYKQ/Sti+VahqqwABqNGpbK4n3Et2p9XD9/AJlpiRBFEbevnUJKQhSq1W72rJvw1KQSwNtNioio4u62IoAbUUXw9zQeIPl7mOFGlGH33Ov3DMsLAN7sYYXdp/MRn1L5A86yaNQqxEVfg3+w4bHEv0YY7t+5WK5lFBXqjiUWj+wnolaLP5aORdNOQ+HiWfVZV/uZkkoALxcJbsYYHk9uxmjg62b879rPXYrIGMNU540YNXzddJl0AUANPzMkZWjxXm8LfP22Eh/3t0Rt/5L7XWiQGb55R4nPBlqie1NzyCp37P70BInpPi+Af/t/f6W2bds2WFlZAQByc3Ph7u6Obdu2QWIkvT5//nx4eXlh3rx5EAQB1atXR1xcHMaNG4eJEyciPz8fCxYswMqVK9GlSxcAwJIlS7Bnzx4sW7YMY8aMwYIFCxAQEIBZs2YBAIKCgnDlyhV89913pdYxPj4eM2fOhKenJ4KCgnD8+HEAwFdffYUOHToA0GVmp06dir179yIsTBdQ+Pv74+jRo1i0aBFatWqFmJgY1K9fH6GhoQBgEHDGxMSgatWqaN68OQRBgI+Pzz/ank9aJ1OxNAckEgG5j/Voyi0AHG3++XJreAlwsxewYk8ZKdRKqiAvA6JWAwsrw5sxFlaOyEy+94+X6+xVBy1fngpbJz/kZSfjwv6fsG3x6+jz4VaYy5VPW216zvJz0iFqNVBaG+4nSmtHpCXcLdcyDm2eCaWti0FQ+6JQKgCpRED2Y89t5uSLcLEzftFhbSmUeM4zJ1806Lq5+WgR+raS4cs3LKDRiBAB/HaoCPfidceSmEQtVEVA1yYy7DytC1q6NpZBKhFK7QJqKrnZGdBqNbCyNezGb2XjiKS48u0j4etmwcbeBVVrFd8g6z34C/y2bBK+/qANJFIzCIKAl4d9Bf/g0Gda/2fBylLQ7Se5hjc9s3K1cHM0nvmysZIgy0h5W2XxftWpiQJaLbD/7IvftTIv+69jic1jxxIbJ6TEl++cs/e3WbC2c4F/jeJjydEdSyCRSNG4/RvPtL4VQWnx136SZ3jNkJ0nwsWhZFdtQHc8yc4TS5S3UeqOA1aWAhTmAtqHmiP8RCG2HtUg2NcMb3VXYN6mfNx5oAt+z0WqkZ6lRWauCA8nCXo2k8PFXoLl2yt/V2+qGAxITahNmzZYsGABAF2X1vnz56NLly44ffp0ibIREREICwuD8MhoWc2aNUNOTg7u37+PjIwMFBUVoVmz4ru1MpkMjRo1QkREhH4ZjRs3Nljuw2DtcVWqVIEoisjLy0PdunWxadMmmJsXd4t8GFgCwO3bt5GXl6cPBh9SqVSoX78+AGD48OHo27cvzp8/j44dO6J3795o2lR3EB8yZAg6dOiAoKAgdO7cGd27d0fHjh3/fgM+5knrZExhYSEKCw1PtnK5HJX9T8XaAujQQMDag1poXrx4tMJ4BbXU/9vBPQjOXnWwfno73LuyA0GhL5uwZmQKp3YvRuS5cPT/6GeYyUp5YPs/qFltM/i4SrBiRyHSs0X4u0vwUnMZsnJF3H6gRW4BsHqPCn1ayNCstgKiCFy8rcH9ZC3EF+tR9b+1f8sSXDwRjve+XAWZefE+cnT3asTcvoQ3P/kJdk4euHfjLDav/B9s7J1RrdaLd3PjSXm7StE2VIFvVmaauiqVwtHwxbh6OhxDxhYfS+KiruLU3l/w7sRNBtdq/yUPm331rhoHL+huXj1IUcHXXYpmtWX6gPTE1eJsfHyqFlm5Ikb2tYSjbSFSM/9lBxUql8p9lf0vp1QqERgYqP++dOlS2NraYsmSJRg2bJgJawYcOXIENjY2cHFxgbW1dYnflcri7FJOTg4AYPv27fD09DQopwvmgC5duiA6Ohrh4eHYs2cP2rVrhxEjRmDmzJlo0KAB7t27hx07dmDv3r3o168f2rdvj99++02fLRYfueopKjI+6t+T1smYadOmYcoUw4FhJk2aBPPgCaXO86TyVIBWK5YYhESpQImsaXm5OwBKhYChHYvvZkskArydgdBAAd/9VrkvHBWWdhAk0hIDGOXnpJY5YNGTklvYwNbJF1mpMc9smfT8WFjZQ5BISwxglJudCqVN2fvJmb3LcHr3YrzywQo4e1avyGpWmNwCQKMVYW1hON3KomTW4qHsPBFWZZQ3kwKdG5nh510q/ci7CWkaeDhJ0KquGW4/0D2qceu+Ft/9WghLBaDVAgUqYMIgBS5lVa4Di9LaDhKJFDmZKQbTc7JSYW1b9j5ycPtyHNi6FO+MXwYP7yD99CJVAXaun4PBH/+I4Pq63jUe3kGIi76BQ9tXVrqANCdP1O0nSsOgyEYpQWau8TuWWTlafZbLWPmqXmawVgqY9r6d/nepRMDLbS3RtqECXyx4sQJVS+u/jiWPDWCUm5VSIrv+uOM7l+Fo+BIM+nQ5XL2K95OYW+eQm52K78e21U8TtRrsXv8dTu5ZhY+mV64BsHLz/9pPLCUAivcLa0sB2aXsJ9l5YoleEdaWgj67npsvQqMRkZBqOH9imgb+HqWHHNEJukDV2VaC1MwXuzt4qST/zZsU5cWAtBIRBAESiQT5+SXH0Q8ODsamTZsgiqL+ztuxY8dgbW2NKlWqwNHREebm5jh27Ji+y2tRURHOnDmDjz76SL+MLVu2GCz35MmTMMbPzw92dnblqneNGjUgl8sRExNTZldYZ2dnDB48GIMHD0aLFi0wZswYzJw5EwBgY2OD/v37o3///nj55ZfRuXNnpKWlwdnZGYCu6/DDzGZ53s1a3jo9bvz48Rg9erTBNLlcjlmby72Iv6XVAvHpgK+rgJsPii/mfF0FnLv1zy7uohKBJTsND+LdGkmQmiXi5A2xUgejACA1M4eTR03E3z4J3xrtAeiexYm7cxI1wgY+s/UUFeYiKy0WgdY9n9ky6fmRmpnD1asmYiJPoGrd4v0kJvIE6rd6vdT5Tu9ZgpM7F+Llkcvg5lP7eVX3mdNogQfJIgI9pbgWpbvgEwAEekpx/KrxkcVjErUI9JTi6JXi40PVKhL9ax2kEt3IqI8fI7Si8dfX5f110yzAQwKlBUq87sHUzMzM4elXA7evnUStUN0+otVqcfvqSTTtOKDU+Q5sXYb9fy7CsHFL4OVfy+A3jVoNjUZdIuslSCQQtZWvS4pGC8QkaBDsK8OlW7obuAKA6j4yHDhv/K7n3Tg1qvvKDF7hEuxrhrsPdPvVyasqREQZ7mOj+lvj1NVCHL/y4nXhlZqZw8OnJu5GnED1BsXHkrsRJ9GobennnGM7luLI9oV4/eOl8PA1PJbUCesJ/2DDXmervx+GOmG9UK/5S8++EU9JowVik7So5iXFlbu6/1sBQDUvKY5cNn7j/168BtW8zHDoYvHvQV5miPoroNRodcccF3vDRwhc7CRGR2V+yNNZ10U4q5Qba/Tvx4DUhAoLC5GQkABA12V33rx5yMnJQY8ePUqUff/99zFnzhx88MEHGDlyJCIjIzFp0iSMHj0aEokESqUSw4cPx5gxY+Dg4ABvb29Mnz4deXl5GDp0KADgvffew6xZszBmzBgMGzYM586dw8qVK5+6HdbW1vj000/x8ccfQ6vVonnz5sjMzMSxY8dgY2ODwYMHY+LEiQgJCUHNmjVRWFiIbdu2IThY956/2bNnw93dHfXr14dEIsHGjRvh5uYGOzs7SCQSNGnSBN9++y38/PyQlJSEL7/88pnUyRi5XF5KBvXZXnSdjhTRo7GA+DQgLlVEoyABMjPg8j3dwbhHYwHZecDBK7rvEonuXaSA7gLS2gJwsdMNLJKeoxutLvmxG9RFaiBfVXJ6ZVWr+WAc/m08nKrUgnOV2rh67GeoVfmo1kB3Ij+0cRwsbVzRsJPuhoFGrUJG0h0AgFZThLysJKTGRUAmt4SNo+6mzKnw6fCu3hpW9p7Iy0rC+X0/QiJI4F+nm2kaWUGkSksoA7313y39qsCmbnWo0jJREBtvwpo9e6Ht3sSOn8fB1bsW3H3r4Nz+VSgqzEetJn0AAOGrxsLKzhUte30CQNdN9/j2ueg2ZBZsHTyRm6kbxVwmt4S5QterIj83A9lp8cjJTAIApCXpniFT2jhBaev8vJtYpiOX1ejXRob7yVrEJmnRvI4ZzGXA2UjdBWX/NjJk5orYeVr3/egVDd7raY6WdcwQEaNBvUApqjhLsOmQLogoLALuxGnQLUyGIk2RrsuuhwQh1aTYerz4ojM0SIqkdC1yCnSvkunZTIajl9VIroTd61p2GYL1i8ajil8teAXUxpGdP0NVmI+GrXTHkl8XfAZbexd0fVV3LDmwdSl2/fYjBoyYAXtnD2Rl6PYRucIScoUSCksr+Ac3xLZfZ0JmroC9kwfuRJzBuSNb0OP1cSZrZ1n2ni7AkO5KRMWrERWvRrtQBczNgeOXdf/vQ7orkZGtxeZDuhvg+84W4tMB1mjfSIErt1VoWEMOH3czrN6ZBwDILRCRW2B4HtRoRWTlapGYVhxo2NtIoFQIcLCRQCIIqOKiCzSS0zUoNB7jmEyTjkOwedln8PCtBU+/Oji5V3csqddMdyz5Y+k4WNu7oH1f3bHkaPgSHPxzLvq8PRN2Tp7I+etYYv7XscTSyh6WVoajuEukZrCydYKTW+V8j/vB8yoM7KhATJIGMQlatKovg7lMwKnruv+sgR0VyMzRYttxXU+JQxdVGNXXEm3qy3AtSoMG1czg5SrB+v3FNzr2n1dhcBcF7jyQ4dZ9NYJ9zFDT3wzzNun2NUdbASFBMlyPUiMvX/cM6UstFbh9X424lMp3g+dZEV6QwYVMhQGpCe3cuRPu7u4AdAFU9erVsXHjRrRu3brE61g8PT0RHh6OMWPGoG7dunBwcMDQoUMNgrNvv/0WWq0Wb7zxBrKzsxEaGopdu3bB3l53gPT29samTZvw8ccf48cff0SjRo0wdepUvPXWW0/dlv/9739wdnbGtGnTcPfuXdjZ2aFBgwb4/PPPAQDm5uYYP348oqKiYGFhgRYtWmDdunX6tk+fPh23bt2CVCpFw4YNER4eru+uu3z5cgwdOhQhISEICgrC9OnTy/WM6d/VyZQiYkVYyoGWtQQoFQISM4D1h7TI/etGs42lYNBN2VoBDOtUPMhAk+oCmlQHopNErDnw7ziA+9fpioLcdJzbOxf52SlwdA9GpzcX67vs5mTEGxzQ87KTsXleH/33K0eW48qR5XDza4hub/8MAMjNTMDB9Z+iIC8DCqUDXH0aoMfwdbCwcni+jatgtiG1ELbvF/33GjN1+3jsz7/j8tDxpqpWhage0hV52Wk4tm0u8rKT4ewZjJdHLNV32c1KN9xPLh1ZB426CFuWjjJYTljXkWjW7QMAwJ3L+7FzdfF22rb84xJlKotLdzRQKoCODc1gbSkgLkXEsu2F+oGL7KwFPBoiRidqsXafCp0bydC5sRlSMkX8vEuFxPTiUmv2qNClsQyvtTOHpRxIz9YFtCevFwcgznYCujSWw+Kv3/efV+PI5cr5vud6YV2Qm52GXb/9iOzMFHj4VMewcYv0XXYzUg33kRN7dfvILz98ZLCcDn3eR8e+IwEAA0fOxI7132Pt/LHIy8mEvZMHOvf7EGHt+j+3dj2JszdUsLIU0LOFBWyUEtxP0mDu+mx9V20HG4lBVvzuAzWWbslBr5aW6N3SAknpGizYlIO4JxxNt2cLCzStXXxTd8JbuhFoZ63Nws2YyrW/1GqkO5Yc3PwjcrKS4eYVjIEfL9F32c1MizPIip89+Cs06iJsXPChwXJa9RyB1r0q13GivC7cUsPKohBdm8hhYyngfooWCzfn6fcTe2sBolj8txIVr8XPOwvQtakc3ZsKSM7QYtm2fMQ/0kX38h01NuwvQIeGcvRpLUdSuhbLtxfgbtxfWVQNEOQlRet65jCXARk5Ii7dVmPXmRcv007PjiCKlb0zH5HpTV1fubqlmdrn/aWYvunfEQg/K2P7SrBdFvT3Bf9DuhVFYsm//9WoT+Tt9sDYhSUfy/ivmv6eBbac5fH1UT1DpXj32zRTV6NSWfSZA9Ye5eXqowY0F/DhD9mmrkal8sOHJcc8qSwKfi39jRYVTfFa5ezJ8ShmSImIiIiIiCoKBzUqEzs0ExERERERkUkwQ0pERERERFRROKhRmbh1iIiIiIiIyCSYISUiIiIiIqooxl7sTHrMkBIREREREZFJMCAlIiIiIiIik2CXXSIiIiIioooiYQ6wLNw6REREREREZBLMkBIREREREVUUvvalTNw6REREREREZBIMSImIiIiIiMgk2GWXiIiIiIiookj4HtKyMENKREREREREJsEMKRERERERUUXhoEZl4tYhIiIiIiIik2BASkRERERERCbBLrtEREREREQVReCgRmVhhpSIiIiIiIhMghlSIiIiIiKiiiJhDrAs3DpERERERERkEsyQEhERERERVRQ+Q1omZkiJiIiIiIjIJBiQEhERERERkUmwyy4REREREVFFEZgDLAu3DhEREREREZkEM6REREREREQVha99KRO3DhEREREREZkEA1IiIiIiIiIyCUEURdHUlSAiIiIiIvo3Kti1zGTrVnQaarJ1lxefISUqh/avnTV1FSqVvb+GYuTsTFNXo1KZN9oWS/aauhaVy9vtge2yIFNXo1LpVhSJ23fumboalUZggB8u30oydTUqlTpVXTAvnLmCR43sKqB5j0OmrkalcnRrK3w8L8fU1ahUvh9pZeoq0D/EgJSIiIiIiKii8LUvZeLWISIiIiIiIpNghpSIiIiIiKiiCIKpa1CpMUNKREREREREJsGAlIiIiIiIiEyCXXaJiIiIiIgqioQ5wLJw6xAREREREZFJMENKRERERERUQUQOalQmZkiJiIiIiIjIJBiQEhERERERkUkwICUiIiIiIqoogsR0nyf0008/wdfXFwqFAo0bN8bp06fLLJ+RkYERI0bA3d0dcrkc1apVQ3h4+BOtk8+QEhERERER/cetX78eo0ePxsKFC9G4cWPMmTMHnTp1QmRkJFxcXEqUV6lU6NChA1xcXPDbb7/B09MT0dHRsLOze6L1MiAlIiIiIiKqKP8gU2kKs2fPxttvv40333wTALBw4UJs374dy5cvx2effVai/PLly5GWlobjx49DJpMBAHx9fZ94vS/G1iEiIiIiIqInUlhYiKysLINPYWFhiXIqlQrnzp1D+/bt9dMkEgnat2+PEydOGF32li1bEBYWhhEjRsDV1RW1atXC1KlTodFonqiODEiJiIiIiIgqiCgIJvtMmzYNtra2Bp9p06aVqGNKSgo0Gg1cXV0Npru6uiIhIcFou+7evYvffvsNGo0G4eHhmDBhAmbNmoWvv/76ibYPu+wSERERERH9C40fPx6jR482mCaXy5/JsrVaLVxcXLB48WJIpVKEhITgwYMHmDFjBiZNmlTu5TAgJSIiIiIi+heSy+XlCkCdnJwglUqRmJhoMD0xMRFubm5G53F3d4dMJoNUKtVPCw4ORkJCAlQqFczNzctVR3bZJSIiIiIiqigvwGtfzM3NERISgn379umnabVa7Nu3D2FhYUbnadasGW7fvg2tVqufdvPmTbi7u5c7GAUYkBIREREREf3njR49GkuWLMGqVasQERGB4cOHIzc3Vz/q7qBBgzB+/Hh9+eHDhyMtLQ0ffvghbt68ie3bt2Pq1KkYMWLEE62XXXaJiIiIiIgqiiCYugbl0r9/fyQnJ2PixIlISEhAvXr1sHPnTv1ARzExMZBIivOZXl5e2LVrFz7++GPUqVMHnp6e+PDDDzFu3LgnWi8DUiIiIiIiIsLIkSMxcuRIo78dPHiwxLSwsDCcPHnyqdbJLrtERERERERkEsyQEhERERERVRQJc4Bl4dYhIiIiIiIik2CGlIiIiIiIqIKIL8igRqbCDCkRERERERGZBAPSSkwQBGzevLnU3319fTFnzpxnus7WrVvjo48+eqp6PWry5MmoV6/eU9eLiIiIiOiFJEhM93kBsMuuCT18z8/27duRmJgIe3t71K1bFxMnTkSzZs3+dv4zZ85AqVSWa12TJ0/GlClTyiwjimK5lhUfHw97e/tylSVDg1/2QNe2TrBSmuFaZA5+WB6NBwmFZc7Ts4Mz+vVwg4OtDHdi8jBvZSwi7+Tqf7e3NcM7A70QUtsGFgoJ7scXYO3meBw5nQEAcHUyx+t93FGvpg0c7GRITVdh79E0rP0jHmpN+f7PK0LLuuZoFyqHjVLAg2QNNh4oQHSCptTy9auaoVszBRxtJEjO0GLzkQJcv6fW//56Jws0qWluMM/1qCLM/z0PAFC1ihQf9rMyuuzpa3IQk1j6uk3pwqE1OLN3GXKzkuHsWR3t+k2Au28do2UvH9uAa6c2IyXuFgDA1bsmWvQcbVD+5sXduHRkHRJjr6EgNwODPtsMF6/g59KW58mheSj8PxkK2wa1oPBwwdm+7yNxyz5TV6vCbNu6BZs2/Yb09HT4+fnjveHvIygo6G/nO3ToIKZ/9y2aNAnDhImTjJaZ9+Nc7NgRjrffeRe9e7/0rKteIXZu+x1bfv8VGelp8PELwFvvfoSqQTWMlj11/BB+3/ALEuIfQKNWw82jCnq81B+t2nbWl8lIT8PqlQtw+cIZ5ObmILhmXQx99yO4e3o9ryY9tctH1+D8/mXIy06Bk0d1tOzzJdx8jB9LUuNv4dTOuUiKvYbs9Di06D0e9VoNNiij1Wpweuc8RJ7bgtzsFChtXBDc6CU07DAcwgvUPXHoQF/06OgGa6UZrkRkYeb8W7gfn19q+bo1bTGgjxeCAqzg5CjH+G+u4sjJVIMyLcOc0LuLO4ICrGFrI8OQUWdx+15uKUs0rWa1ZWhbXwZrSwFxKVr8frgQMUnaUsvXDZCiSxM5HKwFJGdqse24ChHRxefP70caP89uOVaIAxeKAAATBlnCwcYwUNp2vBD7zhc9gxbRi4gBqQn17dsXKpUKq1atgr+/PxITE7Fv3z6kpqb+/cwAnJ2dy72uTz/9FO+9957+e8OGDfHOO+/g7bfffuJ6u7m5PfE8BPTv4YaXOrtg+oIoxCcX4s1XPPDtZ9Xw1pirKCoyHhi2bmKP997wwg/LohFxOxd9u7ji28+q4s1PriIjSxeMjXvfD1aWZpgw8zaysovQtpkjvvwwACO+uI7bUfnw9lRAEATMWRqNuMQC+HpZYPTbvlDIJVi85v7z3AR6DarJ8FIrBdbvy0dUvAZtGsgxoo8SX63IRk5+yW3h5y7FkG6W2HK0AFfvqhFaXYZ3elriu9U5iE8tPnFeu1eE1buKLyQeDbjvxmkwfmGWwXK7N1MgyMus0gajN86F4+Dv09D+1Slw962L8wdW4bd5Q/HWpJ1QWjuWKB978xSqh3aDp18DSGXmOL1nKX6b9xaGfLkd1na6l1oXFebBM6ABghp0we61Xz7vJj03UqUlsi5HInblJoT+9pOpq1OhDh86hCVLlmDkyA8QVD0ImzdvxoQJX2Dx4qWws7Mrdb7ExAQsW7oUNWvWKrXM8ePHcCPyBhwdS+5vldWxw/uwauk8vDPiEwQG1cD2Pzfim4mf4IdFa2FrV/JmqpWVDfr0GwRPL2+Ymclw7vRxzJ/zLWxt7VEvpDFEUcT0rz+HmZkZxn45DRaWSmzbvB5fffkxvl/wCxQKCxO08sncvBCOI5u/RZtXJsPNpy4uHlqFLYuG4fXxO2Bp5FiiLiqAjaMXAut2xpHN3xpd5rl9S3Dl+K9o/9q3cHQPRFLMVexd9znkCivUbTmoopv0TAzs64WXu3vimzk3EJ9YgGEDfTH7q9p4/f0zUJVyXrZQSHH7Xg6274nH1C+M/+1YKCS4fD0L+48m47MP/v7GkKnUCzRD7+bm2HiwENEJGrSqZ453e1pg2po8o+diXzcJ3uikwPYTKlyLUiOkmgxvdVVg1vp8JKTpzsUTlxsG3sE+UvRvK8flO2qD6eEnC3HyevG0QpXpbpCT6b0Yedx/oYyMDBw5cgTfffcd2rRpAx8fHzRq1Ajjx49Hz549jc4zadIkuLu74/LlywBKdtkVBAFLly7FSy+9BEtLS1StWhVbtmwBAFhZWcHNzU3/kUqlsLa2Npj2kFarxdixY+Hg4AA3NzdMnjzZoB6Pd9m9f/8+XnvtNTg4OECpVCI0NBSnTp0y2oY7d+7A398fI0eOhCiKWLlyJezs7LBr1y4EBwfDysoKnTt3Rnx8vMF8S5cuRXBwMBQKBapXr4758+frf1OpVBg5ciTc3d2hUCjg4+ODadOmAdBlfSdPngxvb2/I5XJ4eHhg1KhRZf/nVJA+XVyw5o94HD+XgXsx+fhufhQc7WVoFmpX6jx9u7kifH8Kdh1KRcyDAsxZFo1ClRadWzvpy9SsZoXNuxIReScX8UkqrPkjHrm5GlT102XPz1zKwsxFUTh3JQvxSSqcOJeJjdsS0KKh6bLcbUPMcfyqCievFSEhTYt1e/OhUosIq2VutHzrBuaIiFJj31kVEtO02H68ELFJupPno9QaIDtP1H/yH0k+a7SGv+UWiKgTYIaT11QV2dSncnbfCtRu2g+1w/rCyT0QHV6dApm5AldPbDJavtubs1C/5UC4eAXD0S0AnQZ+DVHUIibyhL5Mzca90bTrSPhUD3tezTCJ5F2HcXPSHCT+udfUValwf/zxOzp37owOHTvC29sHI0d+AIVcjt27d5U6j0ajwYzp0zHw9dfh5m78JmNKSgoWLliAMWPGQiqVVlT1n7ltm9ejXaceaNOhG7y8/fDOiE9hLldg/57tRsvXrFMfjZu2RBUvX7i5e6Jbr1fg4+ePG9evAADi42JxK/Ia3n7/EwRWC4ZnFW+8/f4nUKkKcezQi7F/XTy4EjXDXkGNxn3h4BaINq9MgZm5AtdPGT+WuHrXRvOeY1GtQTdIzWRGyyREXYB/rXbwq9kaNg5VEFivM7yDmiEx5kpFNuWZeqWnJ37eEI2jp1JxJyoXX39/A44OcrRo4lTqPCfPpWHJ6igcPll68mDXgSSsXBeNsxfTK6Laz0zrejKcuFaE0xFqJKaL2HigECq1iMbBxvNVLevKcCNGgwMXipCULmLHKRXuJ2vRok7xPvLoeTY7T0QtPzPcvq9BapZhwFlYZFhWpX58bf8uoiAx2edF8GLU8l/IysoKVlZW2Lx5MwoLy+6yKYoiPvjgA/z88884cuQI6tQx3sUGAKZMmYJ+/frh8uXL6Nq1KwYOHIi0tLQnqtuqVaugVCpx6tQpTJ8+HV999RX27NljtGxOTg5atWqFBw8eYMuWLbh06RLGjh0LrbZkd4/Lly+jefPmGDBgAObNm6fv0pOXl4eZM2fil19+weHDhxETE4NPP/1UP9+aNWswceJEfPPNN4iIiMDUqVMxYcIErFq1CgAwd+5cbNmyBRs2bEBkZCTWrFkDX19fAMCmTZvw/fffY9GiRbh16xY2b96M2rVrP9H2eBbcXczhaG+O81eLM3S5+RpE3MlFjarGu7eYSQVU81MazCOKwPmrWahRtbir9rWbOWgd5gBrpRSCALQOs4dMJuDS9exS66O0lCIr1zRHf6kE8HKVIjK6eP0igMhoNfzcjV/0+rmb4Ua0YX0jotTw9TA8aVatYoZp71ljwhAr9G+ngFJRerexOgFmUCqEShuQatQqJMZeg0/1pvppgkQC7+pNEXf3QrmWoVblQ6tRQ2FpW1HVJBMrKirC7du3UK9eff00iUSCevXq48aNiFLn+/XXtbCzs0OnTp2N/q7VajFr5gz07fsyfHx8n3W1K0xRURHu3r6JOvVC9NMkEgnq1AvFzRvX/nZ+URRx5eJZxN2PRXCtuvplAoDMvPgGmEQigUxmjojrl59xC549jVqFpPvX4FXN8FjiVTUMCdEX//Fy3XzrI/bmCaQn3QMAJD+4gbi75+ET3PJpq/xceLgq4OQgx5lHgsbcPA2u38xCreo2JqzZ8yGVAFVcJLgZW9xDSARw674GPm7Gz8W+blKD8gAQGaOBj5vxcMLKQkANHylORZS83mjXQIavhynxSX8LtKkvg+TF6eVNFYBddk3EzMwMK1euxNtvv42FCxeiQYMGaNWqFV599VWDgFOtVuP111/HhQsXcPToUXh6epa53CFDhuC1114DAEydOhVz587F6dOn0bmz8YsOY+rUqYNJk3TPElWtWhXz5s3Dvn370KFDhxJl165di+TkZJw5cwYODg4AgMDAwBLljh8/ju7du+OLL77AJ598YvBbUVERFi5ciICAAADAyJEj8dVXX+l/nzRpEmbNmoU+ffoAAPz8/HD9+nUsWrQIgwcPRkxMDKpWrYrmzZtDEAT4+Pjo542JiYGbmxvat28PmUwGb29vNGrUqNzb4lmxt9XdPUzPNDwoZ2QWwcHO+N1nWxszSKUC0jMNn6lIz1TDy0Oh//6/H+5iwih//LG0PtRqLQpVWkyefQdxicZvdHi4ytG7kwsWmai7rpWFAKlEQHae4d3SrDwRrg7GT2o2ypLls/NE2FgWn8EiotS4dKsIqVlaONlK0KO5AsP7SDHr11wYezw6rJY5IqLVyMipnN2E8nPSIWo1JbrmKq0dkZZwt1zLOLR5JpS2LgZBLf27ZGVlQavVws7ezmC6nZ0dYmNjjc5z7dpV7N61Cz/OK70r828bN0AqlaJnr17PsroVLjsrE1qtBrZ2DgbTbe3s8eB+dKnz5ebm4N3BfaAuUkEikWLY8NGoW78hAMCzig+cnF2xdtUivDNyDORyBbb/uQGpKUnISCvfIzamlJ+rO5Y83jXX0tpJH0z+E6Ht3oGqIBerv+0KiSCFVtQgrOtHCArp8bRVfi4c7HU3GNIzHjvHZqj0v/2bKR+ei/NLnltd7Iyfi60tjZ2LtbCxNB5ONKpuhoIilOiue/hyEe4na5FXIMLPTYJuYbrxJP48WjlvED8TL9Bz1abAgNSE+vbti27duuHIkSM4efIkduzYgenTp2Pp0qUYMmQIAODjjz+GXC7HyZMn4eRUeheShx4NZpVKJWxsbJCUlPRE9Xo8A+vu7l7qMi5evIj69evrg1FjYmJi0KFDB3zzzTdGR/C1tLTUB6OPry83Nxd37tzB0KFDDZ53VavVsLXVZX2GDBmCDh06ICgoCJ07d0b37t3RsWNHAMArr7yCOXPmwN/fH507d0bXrl3Ro0cPmJkZ3/ULCwtLZKzlcnmpbStN22YO+HhYcWD8xfRbT7yM8nqznweUSinGfB2JzGw1mjW0w4QP/fHxlEjcizUcmMHRXoZpn1XFoZPpCN+fUmF1MoVzkcUXFXEpWjxIycWUoTaoWqXkHV07KwHBPmZYvj3veVfzuTm1ezEiz4Wj/0c/w0z25Psw/Tvl5eVh1swZGDXqQ/0x9HG3bt3Cn1v+xNy5816owWmehoWFJWbMXY6CgnxcvXgOq5bNg6ubB2rWqQ8zMzN8+sU3WPDDt3jz1a6QSKSoXS8E9UOaQETlvKH1PNy6uAM3z29Fp9dnwsEtECkPbuDI5qn6wY0qmw6tXDBmRDX997FfvThdi19UjWrIcP5mEdSPDdNw6GLx+To+VQu1FujXWo5tx1XQlD6eEv2LMSA1MYVCgQ4dOqBDhw6YMGEChg0bhkmTJukD0g4dOuDXX3/Frl27MHDgwL9dnkxmmG0TBMFo99lntQwLi78fzMHZ2RkeHh749ddf8dZbb+H/7N13eFNVHwfwb1abphndu6W0hZa9KRtZsjeI4gABN6KCgqio8KoggiKiogxZsvfee8+yKVAoFLr3bub7RyAlNK0FGhL0+3mePNqbc2/OuSTn3HN/55yrVJoPhbH0efdX/M3NzQUAzJo1C5GRkWbp7s9pql+/Pm7evIktW7Zg586deOGFF9C+fXusXLkSgYGBiI6Oxs6dO7Fjxw68++67+OGHH7Bv374SnwsAEydOLLEasTFa3O0fy/mgI6cyceV68cR+icR4UeeqEiP9gbuxLioJYmItd4qysrXQ6Qym6Op9riqx6Y6ur5cjenX0xtBPLuDWnUIAwI3bBagVrkCP5z3x85zbpv3cXSWYOi4cl67m4qfZpUcKrC23wACd3gCFzPxCVykTIDvP8sVddl7J9AqZANn5pV8MpmUZkJOvh6dLyQ5pkxoOyCs0lLhra0+c5K4QCEXIyzGPwOTlpMFZWfbNqRM75+D49j/R//2/4OkfYc1sko0plUoIhUJkZmSabc/MzISrW8l54gkJCUhKSsL48cUr6t6vb7t364I/Z83GxYsXkJWZicGDXjWl0ev1mDN7FtatXYO/5i2wTmEqgEKpglAoQlam+VSVrMwMuLiWvjCTUCiEr18AAKBySBXcuROLNSsWokZt41Do0LBwTPnlL+Tl5UKr1UClcsXYkW8itIr9/76cnI11Sf5DdUl+Tipk/1CXlOXQhh/QoN0bqFq/KwDAwy8cORnxOLnrT7vskB48noZLV0+a/naQGKOArvdWn7/P1cUB12/kPvX8PW1599tip/K3rTn5ltpiocX0Ib5CeLsKsWDrP7ezt5P0EIkEcFMKkJL5373J81/GOaR2pnr16sjLK+7I9OjRA4sXL8awYcOwdOlSG+bMstq1ayMqKqrMeapOTk7YuHEjpFIpOnbsiJyc0uc2Pszb2xt+fn64ceMGwsLCzF6VK1c2pVMqlRgwYABmzZqFZcuWYdWqVaY8OTk5oXv37pg+fTr27t2LI0eO4Px5y3dGx44di6ysLLPX2LFjy53f+woK9YhPKjK9bt0pRFqGGvVqFnfGZU5CVAt1xqVrlhs+rc6AqzfzUL+mwrRNIADq1VDi0jXjd0TqaPwJGx66X6DXGyB8ILJxvzN69WYefpgZa3EI69Oi0wNxSTqEBxXfDxMAqBokxs0Ey6vd3kzQmqUHgIhKYsTGl97QucgFcHYSIDuv5M2UJjUccPySBo94r+apEokd4B1Yw2xBIoPeuECRX0i9Uvc7vmMWjmz5DX3fmw2fSk9/vjQ9XRKJBGFhVRB1Nsq0Ta/XIyoqChERJR/nExgYiF9/m4lfZvxmekVGNkHt2nXwy4zf4OHhibZt22HGr7+bpXF3d0efvv3wv2++fYqle3QSiQQhYVVx/uwp0za9Xo/zZ0+hakSNch/HoDeY5o4+yNlZDpXKFQl34xBzPRqNIltUSL6tSSR2gFdADdy5al6XxF07Cp9KdR/7uFp1AQQPLZgiEApLNkh2oqBAh7sJhabXzdv5SE0vQsM6xTduZE4iVK+qxIUr2WUc6d9BpwfuJOtRNbB4vqgAxkeklfYItthEHaoGmM8vrRoowq3Ekv/mkdUliEvWIT7tn78Pfh5C6PUGiyv7/ltwUaOyMUJqI2lpaejfvz+GDBmC2rVrQ6FQ4OTJk5g8eTJ6PjRnp3fv3li4cCFeffVViMVi9OvXz0a5Lumll17Cd999h169emHixInw9fXFmTNn4Ofnh6ZNi1fxdHZ2xqZNm9C5c2d07twZW7duhVxueTGfh40fPx4jRoyASqVCp06dUFRUhJMnTyIjIwMjR47Ejz/+CF9fX9SrVw9CoRArVqyAj48PXFxcMG/ePOh0OkRGRkImk2HRokVwcnIym2f6IEdHx8caolseq7ck4+VevribWIjEZDUG9/dDWoYGh05mmtJM/rwqDp3IwLrtKQCAVZuSMPqdyoi+kY/o63no09kbUkchtu4zDre9HV+IOwmF+HBYJfzx9x1k3xuyW7+WEl/8cB1AcWc0OVWNPxbdgUpZ/LN/eE7r07L7lBqvdnLC7SQdYhN1aFPfAY6S4gWGXu3khKxcPdYfNA6f3ntajQ9fcEbbBg64eEOLBhESBHmLsGSHcUiygwTo0lSKqGsaZOcZ55D2auWE1Ew9Lj+0GFLVQBE8XIQ4fN7+56o0bPc6tiwYA++gmvANro1Tu+dDU1SAmk2M86k3zx8NuYs3WvU0zss+tv1PHN40HV0HT4XKzR95WcbvkcRRBgepcSGsgrxM5KQnIDfLOCw+/d4cMmelB5xV5X+UlL0TOcvgHBZk+ltWOQDKOhFQp2ehMC6hjD2fPb1798GPP05BlSpVULVqONatW4PCokJ06GCcujB1yg9wd3fH4NeHwMHBwbTo233OcuN34/52iURSYiSLSCSCq6srAgLs/7mb3XoNwK8/fYfQKhEIq1oNm9atQFFhAdq07wIA+GXqN3Bz98DLg42PQluzfCFCqkTAx9cfGo0aZ04cxf492/DGu8XrHRw5uAdKpQs8vLxxOzYGf/05HY2btESd+k9/TYLHUfe5wdi5+FN4BdaEd6XaiNo3H1p1AapHGuuS7X+PgVzlhWbdjGXWadVIT4oBAOh1GuRmJSHl7mVIHGRw8TS2n8E12uDEjpmQu/jC3TcMKXcu48zeeage2dc2hXwMK9bfxaABQYiLLzA+9uWVYKSlF+HA0eIpLdO+qY39R1KxelM8AOMjXfx9i0eH+XpLEVbZGTm5WiSlGNsshVwMb09HeLgZryeC/GUAgPQMtdkoKVvbG6XBwPaOiEvW41aSDq3rOMBBLDAtQjSwvSOy8gzYdMTYXu4/q8Hw3k54rq4El2K1qFdVgkAvIZbveWiqkwSoEyY2teEPquQjRCVvEa7f0aFIY0AlHxF6tXDAqatas5Xx6b+FHVIbkcvliIyMxE8//YSYmBhoNBoEBgbijTfewGeffVYifb9+/aDX6/Hqq69CKBSaFvixNQcHB2zfvh2jRo1Cly5doNVqUb16dfz6a8nFMuRyObZs2YKOHTuia9eu2Lx5c7k+Y9iwYZDJZPjhhx/wySefwNnZGbVq1TLNR1UoFJg8eTKuXbsGkUiERo0aYfPmzRAKhXBxccGkSZMwcuRI6HQ61KpVCxs2bLDJM/WWbUiE1FGIj4YFQy4T4UJ0Lj6ddNXsGaR+3o5QKYqH6O49mgGVUozB/fzg6iJBzK18jJ10DZn3OpI6nQGfT76GYS8G4JtPwiB1FCI+qQiTf7+J41FZAIAGtZQI8JUiwFeKZb/VMctT+5dOwhZOX9VALhOgazMpFDIB7qbo8OvqPNNiCW4KoVkU92aCDvM256Nbcym6N5ciJVOPP9fnm55BajAA/h5CRFaXwclRgKxcA67c0mLj4cISc1ea1XJAzF0tkjLs8y7+gyIadEF+TjoObZyO/JwUePpXQ7/3ZpuG7GZnJJhFKM4eWAqdVoP1s80fbdS0y3A07/o+ACDm3G5sXVQc9d8496MSaf4NVA1qoumuhaa/q08x1qtxC1bj3NBHH/Vgz1q1bo2s7CwsWrgQGRkZCAkJwYQJ38DV1Rj5SUlJhuA/tIRl81btkJ2ViWWL5iAzIx3BIWH4fMIUuLga1zpITUkyOx+FRYWY/duPSEtLhoODI/wDKuH9UePQvFU7U5qM9DTMnz0DmZnpcHV1R+u2ndD3xUFPvWyPq2q9LijITcexrb8gL9tYl/R4axZkCmNdkpsRbzZfOC87GUunFA+7PbNnLs7smQv/0EboM9z4u2rd5wsc3TId+1ZNQH5uGpyVXqjZbAAaP//u0y3cE/h7VRykUhFGD68KubMY5y9lYdRX582eQerv4wQXZXG7HBGmwC8T65r+HjHMuJDj5l2J+G5aNACgRaQ7Pv+weDj3hDHVAQBzF8di7hLbTZl5WNR1LeROAnRq7AClswB3U/T4Y0OBKVLpqhDC8EDEOzZRj4XbC9GliSO6NnVASqYeczcXmp5Bel/9qmIIAJy+VvKmt04H1KsiRqfGDhCJgPRsA/ad1WDvGfvpqFvFf2Q+/uMSGAy2HLxH9GywVcfNXu1c0hDDf8yydTbsyoyRKsx6Nh5J+NS80R7YJLHfh8LbQldNNK7HPP7Kpv82YaGVce7aoy28929Xu4oXZmzmpdmDhncRoEX3fbbOhl05uKE1Pprx75/r+ih+Gl6+kXe2kHOifEEYa1A06mKzzy4vRkiJiIiIiIis5RmZy2krPDtERERERERkE+yQEhERERERkU1wyC4REREREZGVGLioUZkYISUiIiIiIiKbYISUiIiIiIjIWrioUZl4doiIiIiIiMgm2CElIiIiIiIim+CQXSIiIiIiIisxgIsalYURUiIiIiIiIrIJRkiJiIiIiIisxMBFjcrEs0NEREREREQ2wQgpERERERGRtTBCWiaeHSIiIiIiIrIJdkiJiIiIiIjIJjhkl4iIiIiIyEoMAj72pSyMkBIREREREZFNMEJKRERERERkJXzsS9l4doiIiIiIiMgm2CElIiIiIiIim+CQXSIiIiIiImvhokZlYoSUiIiIiIiIbIIRUiIiIiIiIivhokZl49khIiIiIiIim2CHlIiIiIiIiGyCQ3aJiIiIiIisxAAualQWRkiJiIiIiIjIJhghJSIiIiIishIualQ2nh0iIiIiIiKyCYHBYDDYOhNERERERET/RimXjtvssz2rN7bZZ5cXh+wSlcPzr56xdRbsyvaF9bDvYr6ts2FXWteQYfTMAltnw65MftsJ12Nu2jobdiUstDI2ScJtnQ270VUTjZnbbJ0L+/J2R2DGZsYKHjS8iwA934m2dTbsyrrfw/G/JVpbZ8OujHuJ3ZpnFYfsEhERERERkU3wVgIREREREZGVGBgDLBPPDhEREREREdkEI6RERERERERWYhAIbJ0Fu8YIKREREREREdkEO6RERERERERkExyyS0REREREZCUGAWOAZeHZISIiIiIiIptghJSIiIiIiMhKDOCiRmVhhJSIiIiIiIhsghFSIiIiIiIiK+Ec0rLx7BAREREREZFNsENKRERERERENsEhu0RERERERFZiEHBRo7IwQkpEREREREQ2wQgpERERERGRlfCxL2VjhJSIiIiIiIhsgh1SIiIiIiIisgkO2SUiIiIiIrISPoe0bDw7REREREREZBOMkBIREREREVkJFzUqGyOkREREREREZBOMkBIREREREVkJ55CWjWeHiIiIiIiIbIIdUiIiIiIiIrIJdkjpmRUbGwuBQICoqCgAwN69eyEQCJCZmWnTfBERERER3WeAwGavZwHnkFKFGjx4MDIzM7F27dqn/tnNmjVDQkICVCrVU//s8nqtjw86t/GAXCbCxat5mD4vDvFJRWXu0729B/p38YKbSoIbcQX4dcEdRN/IBwB4ezhg4U81LO73v19u4sDxTLNtCrkIM7+NgKebA3q/dQ55+boKKVdF2bNlGbavnY+szDQEBFfFS8PGoHKVmhbTnj66C1tWzUFyQhx0Oi28fIPQoceraPpcN1Oa9Utn4sShbchITYRYLEFQaDX0GjgcIVVrPa0iPZKmNURoXVcMhZMACWkGrDukRlyyodT0tUKE6NhIAleFAKlZBmw5psGV23rT+w5ioHMTCWoEi+AsBdKzDTh0QYujl4r/3d2UAnRrKkGwjxBiERAdp8O6gxrkFli1qE9k44b1WLVqJTIyMlC5cgjefuddhIeH/+N++/btxeTvJ6FJk6YY9+VXFtPM+GU6tmzZjDfefAu9evWu6KzblFuLhggZNRSq+jUh9fPCyb7vImn9Lltn66mJ2v83Tu2eg7zsFHj6R6BNv3HwqVTbYtrzh5fj0vG1SEu4BgDwCqyBFt1Hlpr+WXDu4N84vXsO8nNS4eEXgVZ9vii1PGkJ13Bs63Qkx11ETkY8WvYai7qtB5mlURfm4uiW6bhxfifyc9Pg6V8NrXp/Du8g+6xf7xvYzR0dWrjA2UmIKzcK8PviJCSkaMrcp0trF/Tq4AZXpQixd4rw57JkXLtVaDHtl8P90aCGHN/NvItjZ3MBAMH+jujb0Q3VQ52gkIuQnKbB1gOZ2Lgns6KL98QaVhGgaYQQcicgKQPYekqH+HTLaT2VQOvaQvi6CuAiF2DbaR2OR5u3WQ3CBGhQRQgXZ+PfKVnA/gt6xCSU3rbRfw8jpPSv4eDgAB8fHwgE9nk36IWuXuj1vCem/xWHEV9Ho7BIh4mjQyGRlJ7f1pEueGugPxatScS746Jx43YBvhsdChel8V5SSpoaA4afN3vNX5WA/AIdTpzNLnG8UcOCcDPOPnsaJw5uw4q/pqLbC2/hiymLERhcFT9PeBfZmZZbQme5Cl36DsOnk+bjy5+Wo3nbnpg/42tcPHPYlMbbrxJeGjYGX/20AqO//Qsenn6YNuFd5GSV0rraUJ1QEbo3k2DnSS1+XlWEhDQ9hnZ1hLPUcvpK3kIMbO+AE1d0+HllES7G6vBaRwd4uxZ/n7o3kyA8UIilu9WYsqwIB89r0bOFBNUrGat+iRh4o6sDDAbgzw1F+G1tEURCAQZ3drTbe6r79+3DrFmzMHDgK5j+ywxUDgnBuHGf/+PIiKSkRMyZPRs1ali+wQEAhw8fwpXoK3B3d6/gXNsHkbMM2eeicWHEeFtn5amLPr0Z+9dMRJNO7+HlT9bAwz8Cq38bivycNIvp71w7hogGXdHv/QV4ceRSKFx9sfq3IcjNTHrKOa8YV89sxoG1k9C443t4cdRqePiFY/0fw0otv1ZTCKV7IJp1GwWZwtNiml3LxiEu+jA6vPw9Bn6yHkHhzbH299ft+hz1ed4NXdu44vfFSfhk8m0UFunx9YgASMSl13gtGigwpK8nlm1KxcjvbuHmnSJ8PSIAKoWoRNoebV1hsNDPCg1yRFaODj/OS8D7/4vFiq3peK2XJ7q0dqnA0j256kECdKgnxP4LeszaqkNSpgED24ggc7ScXiwGMnKB3Wf1yCmw3MHMzgd2R+kxe6sOs7fpEJtkwICWQngqrVgQO2QQCG32ehY8G7mkZ9Jzzz2HESNGYPTo0XBzc4OPjw++/vpr0/sGgwFff/01goKC4OjoCD8/P4wYMcL0vkAgKBFpdXFxwbx58yx+3sNDdufNmwcXFxds27YN1apVg1wuR6dOnZCQkFDBJS2f3p28sHh9Eo6czsLNuEJM/uMW3F0kaN6g9Ihu385e2LI3DdsPpON2fCF+/isORUV6dGxlvGDWG4CMLK3Zq3kDFfYfz0Rhkd7sWN3aecBZJsLKzclWLefj2rFhEVp06IPm7XrCLzAUL7/1ORwcpTi0e63F9OE1G6Jek7bwDQiBl08g2nUbCP9KVXD98hlTmshWnVG9ThN4+gTALygU/V8fhcL8XNy5de0plar8WtYW49hlHU5G65CcYcDq/RpotECjCMsDWVrUEuFqnB77zmqRnGnA9hNa3E01oHnN4vSVfIQ4Fa3DjXg9MnIMOHZZh4Q0AwK9jFV/sI8QrgoBlu9RIzHdgMR0A5bvUSPAU4BQf/tsHtasWY1OnTqhw/PPIyioEoYPfx9SR0ds376t1H10Oh1+mDwZL7/yCnx8fSymSU1Nxczff8cnn4yGSFTyQvPfIGXbflz9ahqS1u20dVaeutN7/kLNZi+gRpO+cPcNQ/sXxkPsIMWFo6sspu88aCrqtHwZXgHV4OYdig4vfQODXo/bV4885ZxXjKi981CjaX9Uj+wLN58wtOlvLP+lY5bL7x1UCy16jEbV+l0hEktKvK9VFyLm3HY06/4x/EMbwcWzEiI7vQ+VRxDOH15i7eI8tu5tXbFiSxqOn8vFrbtFmDYvEW4qMZrUlZe6T892rth+KAu7jmQjLlGN35ckoUitR/um5m135QBH9Gzvil8WJpY4xq4j2Zi9IhkXrxUgKVWDfcezsetIFprWK/1zbaFJuBBnYgw4e9OA1Gxg0wk9NFqgbojlDntCOrArSo+Ltw3QlTLg6lq8AdcTDEjPBdJzgD3n9FBrAX8Pe73tSbZgn1cc9K8xf/58ODs749ixY5g8eTImTJiAHTt2AABWrVqFn376CX/88QeuXbuGtWvXolatih3qk5+fjylTpmDhwoXYv38/bt++jY8//rhCP6M8fDwd4O4iwekLOcV5K9Djyo08VAtztriPWCRAlWAZzlws3sdgAM5czEG1MJnFfaoEOyEsWIat+8zvegf5SfFyLx9M/uMW9HqLu9qUVqPB7ZjLqFY70rRNKBSiWu1I3Ig+94/7GwwGXD53DEnxsahSvUGpn3Fg+2o4yeQICK5aYXmvCCIh4O8pwPU7xS26AcC1OzpU8rZcTQd5C3HtjvkVwNU4HYIeSH8rUY/qwSIo733FQv2E8FQJcPWO8UsgFhk/R/vAYTRa4/essq/9NQ8ajQbXr19D3br1TNuEQiHq1q2HK1cul7rfkiWL4eLigo4dO1l8X6/XY+qUH9C3bz9UqhRc0dkmG9Np1UiKu4ig8GambQKhEEHhzZBw80wZexbTqgug02shldnvlJDS6LRqJN+5iMCq5uUPrNIUibeiHuuYer0WBr0OYol56EwskSLhxqknya7VeHtI4KYS4+yVfNO2/EI9rt4sRHhlJ4v7iEVAaJDUbB+DATh7JR/hIcXDVxwkAowa4os/liYjM7t8U2FkUhFy8+ynQRYKAV834GaieaTzZpIBARXUeRQIgBpBAkjEwJ1UDtmlYpxDSlZVu3ZtfPWVca5WlSpVMGPGDOzatQsdOnTA7du34ePjg/bt20MikSAoKAiNGzeu0M/XaDSYOXMmQkNDAQDDhw/HhAkTKvQzysPNxXiHOTPLfJ5KRpYWrqqSd58BQKkQQSQSIOPhfbK1CPSzPI6zU2t33LpbgEvX8kzbJGIBxr4XjNlL7iIlTQNfz1LG3thQbk4G9HodlC5uZtsVLu5IuBtb6n75eTkY80ZHaDQaCIVCDHxzLKrXbWKW5tzJ/Zj146dQFxVC5eqBj76aCYXS1RrFeGzOUkAkFCDnodHUuQUGeLlY7hgqZIIS8zxzCwxQyIovHNYe1KBvawm+eNUJOp0BBgAr92lwM8F4EXQ7SQ+1BujSRIKtx43fsy6REoiEArPj2Ivs7Gzo9Xq4uLqYbXdxcUFcXJzFfS5evIDt27bhlxm/lnrclSuWQyQSoUfPnhWZXbITBXkZMOh1kCnMh2LLFO7ISLpRrmMcWD8FcqWXWaf2WVF6+T2QkXzzsY7pIJXDJ7guTmz/Da7eIZApPHD19CYkxkZB5RFUEdmucK5K48iHzGyt2fbMHK3pvYcp5cZ2uMQ+2ToEeDuY/h7a3wtXbhTg+LnccuUlIkSKFg0V+N+vdx6lCFYlcwSEQgFyC807inmFgIfiydoDLxXwegcRxCJArQVWHNAjteSson+1Z2VxIVthh5SsqnZt8wUTfH19kZxsHDLav39/TJs2DSEhIejUqRO6dOmC7t27QyyuuK+lTCYzdUYf/nxLioqKUFRkvsiQo+Ojd+DaNnPFB68Hmv7+Ymr5LnqehINEgDZNXfH3OvP5O0Ne8ENcfCF2Hc6weh6eNqmTM8ZNXYqiwgJcPncMK/6aCk/vAITXbGhKE16zEcZNXYrc7Ewc2Lkaf0wdjbGTFpbo/P4bNa8lRiVvIf7aUoSMHANCfIXo3UKC7DwDrt/VI68QWLRDjT4tJWheSwqDAYi6rsOdFL3FeVDPmvz8fEyd8gNGjPig1MXOrl27hnXr12H69Bl2O/+cbOv4jj8RfXoz+r+/oERE8L/s+ZcnY+fSz/DX160hEIrgGVAdVet3RXLcRVtnDQDQupEC7wwsHqL/v9+s0/lrXNsZtcNl+Oi72HKlD/JzwGdv+2PpplREXc7/5x3+BVJzgD+36uAoAaoHCdGjiRALdun+c51SKh07pGRVEol59E8gEEB/b8xoYGAgoqOjsXPnTuzYsQPvvvsufvjhB+zbtw8SiQQCgQCGh66KNZqyV8Irz+c/fMwHTZw4EePHmy/4YYzwPlrk5MjpLFy5/kCUUmKMcrmoJEjPKr7T6qoSI+aW5UWGsnN00OkMJSKorkox0jNLnoeWjV3g6CjEzoPmC/bUrS5HcKATtjSqa9xw75p75W+1sHh9IhauLjnf5WmTK1whFIpKLGCUk5kGlUvpC8wIhUJ4+RrvxgdWDkfinZvYsnquWYfUUeoEL98gePkGISS8Nr54rwcO7VqDzn2HWqcwjyGvENDpDVA8NGpM7iRATr7l72tOvgHyMtKLRUCnxmIs2KY2rbybmK6Dn4cQreuIcf2uGgBw7Y4e3y8pgkwK6PVAoRoY95oUZ7Ptr0eqVCohFAqRmZFptj0zMxOubiWj3gkJCUhKSsL48cUr6t7//Xfv1gV/zpqNixcvICszE4MHvWpKo9frMWf2LKxbuwZ/zVtgncLQU+Pk7AqBUFRiAZ/8nDTIFB5l7nty1xyc3Pkn+rz3Fzz9I6yZTaspvfypkCnLLn9ZVB5B6Dt8ETRF+VAX5sJZ5YUt8z+C0j3wn3d+Co6fy0V0bKzp7/sLF7koxch4YFiti0KMm3csr3afnWtsh+8vJGjaRylCxr2oaa1wGXw8JFg8tYpZmjFv+uHS9QJ88VPx6I1AHwf874NAbD+YhRVb7GtxvfwiQK83QC4VwDiZw8hZihJR00el1xsXPwKAxAw9fN2EaBwuxOYT9jNk2doMvOFZJnZIyaacnJzQvXt3dO/eHe+99x4iIiJw/vx51K9fH56enmYLEF27dg35+da9mzh27FiMHDnSbJujoyO6D7v0SMcpKNSjoFBtti0tU4N6NRS4cdvYAZVJhYgIccbGXakWj6HVGXAtNh91qytw+FQWAOP8i7o1FFi/o+Q+nVq74+jpLGTlmA8tmjD9Jhwdiod9Vq0sw8dvVsLIb64iIVn98GFsQiwxPpLlyrljqBfZBoCxU3D53HG06TKg3MfRGwzQasouk15veOQbG9am0wN3UwwI8xfhYqyxgRYACPMX4fAFrcV9bifpEeYvwsHzxRdWVQKEuJ1k3F8kNM5Dfvj+i95g/B49LP/eEwxC/YRwdgIuxdrXI4EA4w2msLAqiDobhabNjEMn9Xo9oqKi0K179xLpAwMD8etvM822LVwwHwUFBXjzrbfh4eGJtm3bmc1JBYAvx32ONm3boUOHDtYrDD01IrEDvANrIO7qEYTVbg8AMOj1iIs+gjqtXil1vxM7Z+H49pno884c+Nj5o0zKIhI7wCugBu5cPYLQWg+U/9pR1G7x8hMfX+Iog8RRhsL8LNy+chDNuz/9dRosKSgyoOChx7mkZ2lRO1xm6oA6SYWoWlmKrQcyLR5DqwNibheidrjM9AgXgQCoHS7D5r3GfVZtS8eOQ1lm+/0yrjLmrkzG8XPFN6YDfR3wzYeB2H00G4vWW273bUmvNy5SFOwjQPTd4oajsrcAJ65WbMdRIADE9rdMAdkQO6RkM/PmzYNOp0NkZCRkMhkWLVoEJycnVKpUCQDQtm1bzJgxA02bNoVOp8OYMWNKRDwrmqOj42MN0S2PNVuTMbCnN+4mFiIxRY3B/XyRlqnBoVPFDdn3n4bh0MlMrN9pbKxWbUnGJ29WwrWb+bhyIw99OnpB6ijEtv3md7r9vBxQK1yOL6bElPjchzudSrnxZ387vsiunkPaofsr+OuXL1EprDoqV6mJnRsWQ11UgOZtjdHpuT9/ARd3L/R5xbgS85ZVc1AptAY8fQKg1apx/tRBHN23CS+/ORYAUFRYgM0rZ6NOo9ZQuXogNycTe7YsR2Z6Mho2s7+OxoFzWrzQRoI7KXrEJevRorYYDhLgZLSxQzqgjQRZeQZsPW78++B5Hd7u4YBWtcW4fFuHumEiBHgKsWqf8UKrSAPExOvQtakEGp3GOGTXT4gGVUXYcLj4Iq1huAjJGXrkFhofJdOjuQQHz2mRkmV/EVIA6N27D378cQqqVKmCqlXDsW7dGhQWFaJDh+cBAFOn/AB3d3cMfn0IHBwcEBwcbLa/s9y4wtP97RKJBEql+fMHRCIRXF1dERBgH5GeiiJylsE5rHh+n6xyAJR1IqBOz0JhnG1WH39a6rd5HdsWjYFXYE34VKqNM3vnQ6MuQI3IPgCArQtHQ67yRoseowAAJ3b8iSObp6PzoKlQuvsjLzsFgLHz5eBoeSE6e1b3ucHYufhTeAXWhHel2ojaNx9adQGq3yv/9r/HQK7yQrNuxvLrtGqkJxnbE71Og9ysJKTcvQyJgwwunsY2+taVA4ABcPGqjKzUWzi0/ge4eoeg2r1j2qMNuzPwQhd3JKSokZSqwcDuHkjP0uJoVPHczwkfBOBoVC4278sEAKzblYEPBvng+u1CXIstRPe2rpA6CrHziLHtzszWWVzIKCVdi+Q0Y10b5OeA/30YiDOX8rBuVzpc7s1Z1euNUVh7cTRaj55NhEhIFyA+zYDG4UJIxMDZm8b2oGcTIXIKjI95AYwLId1/fItICCicBPB2MUCtLY6Itq0jxPV4PbLyAUcxUDNYiGAvAf7e+9+JjgKAwcAIaVnYISWbcXFxwaRJkzBy5EjodDrUqlULGzZsMD0DcOrUqXj99dfRsmVL+Pn54eeff8apU/a5el95LN+UDKmjEB8OCYJcJsKFq3n47IcYaDTFF/6+Xg5QKYp/lvuOZUKlEOO1vr5wVYlx43YBPv8hpsQCCx1buyM1XYNTD6zi+6xp1KIjcrIzsH7J78jOTENA5XCMGPcrlPeG7KanJkIgLL6lWlRUiMWzvkNGWjIkDo7w8Q/G0A++QaMWHQEYh/Mm3o3Fkb0bkJudCWeFCsFhNTD6m7nwCwq1mAdbOhujg7MUeL6RGAqZAPGpBszZVGRauMhFIcCDXcRbSXos3qVGp8YSdIoUIzXLgAXb1EjKKE719w41OkdK8FI7B8gcgYwcY4f26KXiCyBPFwE6RzrC6d77u09rceCc5aisPWjVujWysrOwaOFCZGRkICQkBBMmfANXV+OQ3ZSUZAiEbPgtUTWoiaa7Fpr+rj7lMwBA3ILVODd0rK2y9VSE1++Cgtx0HNk8HfnZKfAMqIbe78yG870hqzkZCRA88Ly+c4eWQqfTYOPcEWbHadJpOJp2ef+p5r0iVK1nLP+xrb8gLzsFnv7V0OOtWaYhy7kZ8WZzqPOyk7F0Sm/T32f2zMWZPXPhH9oIfYYbv0Pqglwc3vQjcjMTIZW5ILROBzTt8hFEIuveOH4Sq7enQ+ogwLsDfeAsE+JyTAHG/3IHGm1xvenj6QClvHiRo4OncqCUizCwmwdclSLcvFOE8b/cQVZO+TuSzeop4KIQo02kCm0ii+ezJ6Vp8OYX1l9jorwu3TZA5qhH61pCyKVAUgaweK8OefdG0Chl5tOeFE7Am52Lr1maVROgWTUhYpMMWLjbeH5kjkDPJiLInYw3SpMyDfh7r77Ear703yYwlDWhjogAAM+/Wr5HA/xXbF9YD/su/jcWYyiv1jVkGD3T8nzg/6rJbzvheszjreL5bxUWWhmbJOG2zobd6KqJxszSHyH7n/R2R2DGZl6aPWh4FwF6vhNt62zYlXW/h+N/S+z35qEtjHvJfuNstmwLw0Ir2+yzy8t+/+WIiIiIiIiecQZw0mxZeHaIiIiIiIjIJhghJSIiIiIishIDuLZBWRghJSIiIiIiIptgh5SIiIiIiIhsgkN2iYiIiIiIrIRDdsvGCCkRERERERHZBCOkREREREREVsIIadkYISUiIiIiIiKbYISUiIiIiIjIShghLRsjpERERERERGQT7JASERERERGRTXDILhERERERkZUYDByyWxZGSImIiIiIiMgmGCElIiIiIiKyEi5qVDZGSImIiIiIiAi//vorgoODIZVKERkZiePHj5drv6VLl0IgEKBXr16P/JnskBIREREREf3HLVu2DCNHjsRXX32F06dPo06dOujYsSOSk5PL3C82NhYff/wxWrZs+Vifyw4pERERERGRlRggsNnrUfz4449444038Prrr6N69eqYOXMmZDIZ5s6dW+o+Op0OL7/8MsaPH4+QkJDHOj/skBIREREREf0LFRUVITs72+xVVFRUIp1arcapU6fQvn170zahUIj27dvjyJEjpR5/woQJ8PLywtChQx87j+yQEhERERERWYktI6QTJ06ESqUye02cOLFEHlNTU6HT6eDt7W223dvbG4mJiRbLdfDgQcyZMwezZs16ovPDVXaJiIiIiIj+hcaOHYuRI0eabXN0dHzi4+bk5ODVV1/FrFmz4OHh8UTHYoeUiIiIiIjISgwG2z32xdHRsVwdUA8PD4hEIiQlJZltT0pKgo+PT4n0MTExiI2NRffu3U3b9Ho9AEAsFiM6OhqhoaHlyiOH7BIREREREf2HOTg4oEGDBti1a5dpm16vx65du9C0adMS6SMiInD+/HlERUWZXj169ECbNm0QFRWFwMDAcn82I6RERERERET/cSNHjsSgQYPQsGFDNG7cGNOmTUNeXh5ef/11AMBrr70Gf39/TJw4EVKpFDVr1jTb38XFBQBKbP8n7JASERERERFZif4RH79iKwMGDEBKSgq+/PJLJCYmom7duti6datpoaPbt29DKKz4AbbskBIRERERERGGDx+O4cOHW3xv7969Ze47b968x/pMdkiJiIiIiIisxPCMREhthYsaERERERERkU2wQ0pEREREREQ2ITAYDAZbZ4KIiIiIiOjf6PTVNJt9dv2q7jb77PLiHFKichj8ddI/J/oPmfe1N/7aY+tc2JfX2wDrT+psnQ270qOhCOeuJds6G3aldhUvzNxm61zYj7c7Apsk4bbOhl3pqonGd8tYlzzoswEivPGd7S7o7dGsz9yx+bTG1tmwK13qS2ydBXpM7JASERERERFZCRc1KhvnkBIREREREZFNsENKRERERERENsEhu0RERERERFZiMHDIblkYISUiIiIiIiKbYISUiIiIiIjISrioUdkYISUiIiIiIiKbYISUiIiIiIjISjiHtGyMkBIREREREZFNsENKRERERERENsEhu0RERERERFait3UG7BwjpERERERERGQTjJASERERERFZCRc1KhsjpERERERERGQT7JASERERERGRTXDILhERERERkZUYwCG7ZWGElIiIiIiIiGyCEVIiIiIiIiIr4aJGZWOElIiIiIiIiGyCEVIiIiIiIiIr4RzSsjFCSkRERERERDbBDikRERERERHZBIfsEhERERERWYneYOsc2DdGSImIiIiIiMgmGCElIiIiIiKyEi5qVDZGSImIiIiIiMgm2CElIiIiIiIim+CQXbJbgwcPxvz5801/u7m5oVGjRpg8eTJq164NABAIjEMgjhw5giZNmpjSFhUVwc/PD+np6dizZw+ee+45U/o1a9agV69eT60cANCukRM6N3eGSi7E7UQtFm3Jxs272lLTN6ruiD5t5fBwESExTYsVO3Nx7pra9H6Dao5o09AJwb4SyGVCfDkzDbcTzY+nkgsxoIMcNUIdIHUQIiFNi43783DycpHVyvmkTu39G8e2z0Fedgq8AiLQYcA4+FWubTFt1IHluHBsLVLirwEAfIJqoHXPkab0Op0G+9dNw40L+5GZGgdHJzkqRTTDc71HQeHi/dTK9KQObV+MfZvmIicrFb5B4eg16HMEhVo+J8d2r8Cpg+uQGHcdAOBfuTo6D/jQLH1RYR42L/0JF0/uQl5uJtw8/dGi4yto2v7Fp1KeirB142qsX70EmRnpqFQ5FEPe+hBVwqtbTHvs8D6sXr4QiQl3odNq4eMXgO69B6B1206mNJkZ6Vg073ecO3MCeXm5qFajDoa+9SF8/QOfVpEqXNT+v3Fqt/G35OkfgTb9xsGnkuXvzfnDy3Hp+FqkJRh/S16BNdCi+8hS0/+buLVoiJBRQ6GqXxNSPy+c7PsuktbvsnW2rKJBmACREQLIpUBSJrD9tB4J6ZbTeiiBVjWF8HEDXJwF2HFGjxNXS1+VpWmEAG3qCHH8qh47z9jv6i3PNXBEx0gnqORCxCVpsWR7PmITSm+LG0Q4oGdrGTxUQiSl67BqTz4uxGhM73dv6YRG1R3hphBCqzPgVqIWa/cV4Ga8+TFrhUrQrYUTArzE0GgNuHpbi99W5VitnE/i4PYl2L3hL+RkpcIvKBx9Bn+GSmG1LKY9smslThxYj8Q7xjYnoHJ1dB3wgVn6nMxUbFjyE6LPHUZBfg5CIxqgz+DP4Olb6amUx14YDByyWxZGSMmuderUCQkJCUhISMCuXbsgFovRrVs3szSBgYH466+/zLatWbMGcrn8aWa1VI1rOOLFjgqs3ZuLr/5IQ1ySBh+/4gqFs+XKKSxQgrf7qbD/dAG+nJmGM1eKMOJFF/h7iUxpHCUCXL2twfKduaV+7hu9lfDxEGPakkx88XsaTl0uwrv9VQjysc/7UJdPbsbulRPRott7eP2zNfAKiMCyX4YiLzvNYvrbV4+hesOuGPjRArw2eimUrr5YNn0IcjKSAAAadSGSbl9Csy7vYPBnq9H7rRlIT7qJVb+98zSL9USijmzBhr+/R4c+7+LDb1bCLygCsye9idwsy+ck5vJx1G3aFW99/heGj18MF3cfzJr0BrLSk0xpNiyajOhzB/DSu9/jkx82omXn17B2/re4eGr30yrWEzm0fxfmz56B/i8Nxvc/z0alymH49stRyMrMsJheLleizwuv4dspv2PKjHlo074Lfps2CVGnjgEADAYDJn/zGZITEzD6i4mY/PNceHr5YMIXH6GwsOBpFq3CRJ/ejP1rJqJJp/fw8idr4OEfgdW/DUV+juXvzZ1rxxDRoCv6vb8AL45cCoWrL1b/NgS5mUkW0/+biJxlyD4XjQsjxts6K1ZVLVCAdnUFOHjRgLnb9UjONODF1kLIHC2nl4iBzDwD9p41ILeg7A6mrxtQL1SApEz77YgCQMNqDnihnTM2HCzA/+Zm4U6yDh++qIBCZrktDvUX441echyMKsSEOVmIuqrGe/0U8PMsbouT0nRYsi0PX8/OxOSF2UjL0uPDFxWQP3DM+uEOGNpDjsPnijBhTia+X5iN45fs88bwmSNbsHbhZHTs+w5GfbcCfpXC8cekt5BTSptz/fIJ1G/WBe99MRcfjF8EV3cfzJz4JjLvtTkGgwFzfvwAacl3MPTj6fh44gq4evrh9++Goagw/2kWjewcO6Rk1xwdHeHj4wMfHx/UrVsXn376KeLi4pCSkmJKM2jQICxduhQFBcUXj3PnzsWgQYNskeUSOjZ1xr7TBTgYVYj4FB3mb8yBWmNAq3pOFtN3iJTh/HU1thzOR0KqDqv35OFWggbtG8tMaQ6fK8T6fXm4dKP0Ri0sUIKdx/Jx864WKRk6bNifh/xCA4L9JBVexopwfOdfqNP8BdRu1hcefmHoNHA8JBIpzh1eZTF9j6FTUf+5l+EdWA3uPqHo/Oo3MBj0iI0+AgCQOinw4od/oVrDLnD3CYF/SF08/+I4JN6+iKz0+KdZtMe2f8s8RLbpj0at+8A7IAx9hnwFiaMUx/ettph+4Hs/oFmHl+AfXA1efiHo/8b/YNDrce3iUVOa2Gtn0KBlL4RWbww3T380afsCfIPCERdz/mkV64lsXLsM7Tp2R5sOXREYVBlvvvcxHByl2L1jk8X0NWrXQ2SzVggIDIaPrz+69uyPSpVDcOWSsbwJ8XG4Fn0Rb7w7CmFVq8E/IAhvvDsKanURDu3b+TSLVmFO7/kLNZu9gBpN+sLdNwztXxgPsYMUF45a/i11HjQVdVq+DK+AanDzDkWHl76BQa/H7atHnnLOn76Ubftx9atpSFr3bP5bl1fjcAGibhhw7qYBqdnAlpMGaLVAncqWO2MJ6cDuswZcijNAqy/9uBIx0KOJEJtP6lGoLj2dPejQWIoDUUU4fK4ICak6LNqSB7UWaF7Hcq+8XSMpLsZosP1YIRLTdFi3vwC3E7Vo20BqSnP8khqXYzVIzdQjPlWH5TvzIZMKEXDvBrJQALzYQYaVu/Ox70wRktL1SEjV4eRl+zxZezctQNO2/RD5XG/4BISi/9Av4eAgxbG9ayymf3X492jx/IvwD46At38IBrw5HgaDHtcuGNuclMRbuHXtLPoNGYeg0Frw8quMfkPGQaMuwpnDm59m0WzOYLDd61nADik9M3Jzc7Fo0SKEhYXB3d3dtL1BgwYIDg7GqlXGi63bt29j//79ePXVV22VVRORCAj2E+PSjeLGx2AALt5QIzTAcscwLFBilh4Azl8vPX1prsdp0LimFM5OAggEQGRNR0jEAlyJtb+GUKdVI/H2RQRXa2baJhAKEVytGe7eOFOuY2jUBdDrtHCSqUpNU1SQCwgEkDopnzjP1qbVqnH35iVUqVk8FF0oFKJKzaa4dS2qXMdQFxVCp9NC5lx8ToKr1MOl03uQlZ4Eg8GA6xePITUxFlVrNa/oIlQ4jUaDG9evonbdBqZtQqEQtes2xNUrF/9xf4PBgPNRJxF/Jw7VatYxHRMAJA4OZseUSBxw+dK5Ci6B9em0aiTFXURQuPlvKSi8GRJulu+3pFUXQKfXQlrGb4meHUIh4OsKxCaZX5neTDLA3+PJhhF2rC9ATLwBsXYeTBcJgUq+Ylx+oP0zALh8U41Qf8tta4i/GJdiNWbbLt7QIMTf8igjkRBoVc8R+YV63EnSAQCCfMRwVYqgNwDjhqjwwwhXjBhgHmW1F1qtBnduXkLVEm1OE9y6drZcx1AXFUKv1UImN9YdWo3xfD9cv4rFEtyILl99RP8N9jl2j+iejRs3mobe5uXlwdfXFxs3boRQaH4vZciQIZg7dy5eeeUVzJs3D126dIGnp+cjf15RURGKisyjjo6OpYxpKgeFTAiRUICsXPNbzNl5evh6OFjcRyUXWkyvkj/a/aPfVmThnX4q/DrGC1qdAWqNAdOXZSI5XfdohXgK8nMzYNDr4Kx0N9vurHBHWuKNch1j7+opkKu8zDq1D9JqirBnzRRUb9gVjk72MZy7LHk5mdDrdZCrPMy2y5XuSI4v3znZvHQqlK5eqFKzqWlbr0GfY+Wcr/DN+20gFIkhEAjQb9gEhFRrWKH5t4ac7Czo9TqoXNzMtqtcXHH3zq1S98vLy8Vbg/pAq1FDKBRh2DsjUadeIwCAf0AleHh6Y/H8P/Dm8E/g6CjFpnXLkZaajMx0y8PU7FlBnvG3JFOY/5ZkCndkJJXve3Ng/RTIlV5mnVp6dskcAKFQgLxC8+15hYD7E9ybqx4ogI+rAH/tKCOEaifkMgFEQgGy88w75dl5Bvi4W+6Uq+RC5OT9c1tcO0yCN3op4CABsnIN+GlJtmmYs6erMW2Plk5YvjMfqVk6PB/phI9fVuKLmZnIL7Sf8FVedgb0eh0UKvO6Q6FyR3L8zXIdY+PiH6F09UTVe22Ot19luHr4YuOSn/HCsC/hIJVh3+YFyExPQnZmyj8c7d9Fz8e+lIkRUrJrbdq0QVRUFKKionD8+HF07NgRnTt3xq1b5hefr7zyCo4cOYIbN25g3rx5GDJkyGN93sSJE6FSqcxeEydOrIiiPHV92sghkwrx/fwMjP8zHduO5OO9/ioEeP377kMd2fonLp/cjD5vz4BYUvIGgk6nwdpZHwAGAzoO/HfPFbtv9/pZiDqyGYM+mg6JQ/E5Obh9EW5fP4vXR/2KD75Zge4vj8baef/D1QuHbZhb63JykuGH6XMx8adZeOnVNzB/zgxcPGe8Oy8Wi/Hx598i/m4cXn+xC17p2wEXzp1GvQZNIBD+95rI4zv+RPTpzeg+zPJviQgAFE5Ah/oCrDuqh87++6NWdeWWxjg3dH42Lsao8Vbv4nmp97sgmw4V4HS0GrcTdZi30bj2Q8Nqlm9KP6t2rpuNM0e2YMjIn01tjkgswesfTUNKYiw+f6M5xgxqiOsXj6Na3ZYQCP579SuV7t93ZUr/Ks7OzggLCzP9PXv2bKhUKsyaNQvffPONabu7uzu6deuGoUOHorCwEJ07d0ZOzqOvYDd27FiMHDnSbJujoyPempj5WPnPyddDpzeUuKOqdBYiK9dypDIrt+QdWGP68rf6nq4itI+U4bNfUxGfYvycuCQtqlaSoF1jJ8zfaF+r+8nkrhAIRSUWMMrLSYOz0qOUvYyObZ+Do9v+xIsf/gWvgIgS7+t0Gqz980NkpcVj4Efzn4noKAA4K1wgFIqQm5Vqtj03Ow0KVdnnZO+mudizYTbeHDsHfkHhpu0adSG2LpuGQR/9gmr1WgMA/ILCEX/rCvZtmoeqNe07IqZQqiAUipCVab40aFZmBlxc3UvZyzhEzNcvAABQOaQK7tyJxZoVC1Gjdj0AQGhYOKb88hfy8nKh1WqgUrli7Mg3EVql5PfJ3jk5G39LDy9glJ+TBpmi7O/NyV1zcHLnn+jz3l/w9H/2yk6W5asBvd4AZ6n5dmcpSkRNy8vXDXCWCjD0+eK2SigUIMgTaBgmwPcr9XY1dy033wCd3gDlQ4sJKp1LRk3vy8rVQ+H8z22xWgOkZOiRkqHHjXgtvnnbBS3qOGLLkUJk3YuwJqQWt/daHZCSoYOb0r46ZM5KVwiFohILGOVkpUHpUnbdsWfjX9i1fg7e+WwW/CqFm70XGFIDn0xahYL8HOi0GsiVbvjpi5cQGFKjwstAzy77+jUQ/QOBQAChUGi2gNF9Q4YMwd69e/Haa69BJHq8+RmOjo5QKpVmrycZsqvTAbHxWlSvXHwnVCAAqoc4IOaOxuI+1+M0ZukBoEZo6ektcZQYG92HLwj0euPn2xuR2AE+QTUQe6V4ERWDXo9bV47AP6Reqfsd3TYLhzf/hhfenw3fSiWXpb/fGc1IuYWXPpwHJ7mrVfJvDWKxA/wrV8f1BxYk0uv1uH7hKCpVqVvqfns2zMGuNTMxbPSfCAypafaeTquFTqc1PS7pPoFQCIPe/sMcEokEIWFVcf7sKdM2vV6P82dPoWpE+S9uDHqDae7og5yd5VCpXJFwNw4x16PRKLJFheT7aRKJHeAdWANxV81/S3HRR+BbufTf0omds3Bs22/o/fZs+ARZfsQDPZv0eiAhAwj2Nv/dB3sLcDf18XqNsUnArK06zNmuN73i0w24cMuAOdvtqzMKADo9cCtBi2rBxfNFBQCqBUsQc9dy23rjrnl6AKhWWYIbZTyyDTC2sWKx8VzfStBBozXAx734mkQkBDxcREjLsq86VyyWIKBydVy9cMy0Ta/X49rFY6hUpU6p++1aPxfbV/+Btz6diaDQmqWmc5IpIFe6ISXhFuJuXETNhm0qNP/2zmAQ2Oz1LGCElOxaUVEREhMTAQAZGRmYMWMGcnNz0b179xJpO3XqhJSUFCiV9rVgzbYjeXijtwo34zW4cVeD55vI4CgR4MAZ463pN3orkZGtx8pdxmE8O47l49PBrujUVIaz14oQWVOKyn4SzNuQbTqms5MA7ioRXBTGe0r3G7usXD2ycvVISNUiMU2Lwd2VWLo9B7n5BjSIcESNUAdMW5z5dE9AOTVu/zo2zhsD30o14RtcGyd3z4daXYDazfoAADb8NRoKF28813sUAODotj9xYMN0dB8yFSp3f+RmGeejODjK4CB1hk6nwZo/RiAp7hL6vfcH9HqdKY2Tswoisf0Pl2rVeTCW/TEWAZVrIjC0Fg5sXQB1UQEate4NAFjy+6dQuXqhy4vGqP6eDbOxbeUvGPjeD3D19DPN0XGUyuAodYZUJkdItUbYuGQKJA5SuHr4IebyCZw6sB7dXxljs3I+im69BuDXn75DaJUIhFWthk3rVqCosABt2ncBAPwy9Ru4uXvg5cFvAwDWLF+IkCoR8PH1h0ajxpkTR7F/zza88e4o0zGPHNwDpdIFHl7euB0bg7/+nI7GTVqiTv3GNinjk6rf5nVsWzQGXoE14VOpNs7snQ+NugA1Io2/pa0LR0Ou8kaLHsZzcGLHnziyeTo6D5oKpbs/8rKN3xuJowwOjs42K8fTIHKWwTksyPS3rHIAlHUioE7PQmFcgg1zVrGORxvQPVKAhHQgPs2AxuECSMTAuZvGnmP3SAFy8oG9541/C4XGZ5ECxg6UwgnwcgE0WiAjF1BrgZQs88/QaIECdcnt9mLH8UIM6S5HbIION+O1aN9YCgeJAIfOGdeNGNJdjowcPdbsNT6OZNeJQnz8ihIdGktxPkaNRtUdEewrxsIteQAABwnQtZkTzl7TIDNXD7lMgDYNpHBVCHHq3iq6hWoD9p0uRI+WTkjP1iMtS4eOTYwr7J+6Yn8LDD7X9TUs/v1zBIbUQKWwmti3ZRHURQWIbN0LAPD3b2OhcvVCt5c+AgDsWj8HW1bMwKvDJ8PN0x/ZmcYRPcY2x/hkgKij2yBXusLF3RcJcdewZv4k1GrUFhG17X8hPXp62CElu7Z161b4+voCABQKBSIiIrBixQo899xzJdIKBAJ4eJQ9rMQWjl8sgsI5B73byKGSC3E7UYupizKQfW8oj7tKZHY3+XqcBn+sykKftnL0bSdHUroO05dm4m5y8ZCfeuGOGNareAXMd/u7AADW7s3F2r150OmBn/7ORP/2cnz4kgukDkIkpWsxe002zl2zv0YQAKo17IL8nHQc2DAdedkp8AqohgHvzzYN2c1OTzCbc3J631LotBqs/XOE2XGadx2Olt3fR05GEq6fMz5b869vepqleemjBagUHmnlEj25uk07Iy8nHdtW/mJ8SHmlCAwb84dpyG5mmvk5ObLTeE4W/vyh2XE69HkXz/cdDgB4efgUbFn2Exb/Nhr5uVlw9fBDpxc+QNN2A55auZ5E81btkJ2ViWWL5iAzIx3BIWH4fMIUuLgaFzpKTUmCQFh8R7iwqBCzf/sRaWnJcHBwhH9AJbw/ahyat2pnSpORnob5s2cgMzMdrq7uaN22E/q+aB+PjXoc4fW7oCA3HUc2T0d+dgo8A6qh9zvFv6WcDPPvzblDS6HTabBxrvlvqUmn4Wja5f2nmvenTdWgJpruWmj6u/qUzwAAcQtW49zQsbbKVoW7HGeAzBFoVVMAZ6kASZnAsn165N1bw08pE8DwQEOkkALDOhZH9ZpECNAkAriVbMDfe+wrsldeJy+roZDlo2crJyidhYhL0uLnZTnIuTdk100pNDsHMXe1mL0uF71ay9D7ORmSM3T4dWWOaRqMXg/4eIjQtLYUcicB8goMiE3QYvLCLMQ/MER35e586PTA0B5ySMTAzXgtpv6dbVcLGt1Xr2ln5GZnYOvKGcjOTIV/pQi89elMKO4N2c1INa87Du1YBp1Wg3nTPjI7Tse+76BTv/cAANmZKVi3cLJx6K+rJxq27IHn+7z99AplJ+xt1IC9ERgMPEVE/2Tw13a+pv1TNu9rb/y1x9a5sC+vtwHWn7S/FYxtqUdDEc5dS7Z1NuxK7SpemLnN1rmwH293BDZJwv854X9IV000vlvGuuRBnw0Q4Y3vnr1Vr61p1mfu2Hy6/FN5/gu61LfP56wDwI6zpT833to6lPKsXXvCOaRERERERERkExyyS0REREREZCUGPoe0TIyQEhERERERkU0wQkpERERERGQleq7YUyZGSImIiIiIiMgmGCElIiIiIiKyEoOBc0jLwggpERERERER2QQ7pERERERERGQTHLJLRERERERkJQYualQmRkiJiIiIiIjIJhghJSIiIiIishI9uKhRWRghJSIiIiIiIptgh5SIiIiIiIhsgkN2iYiIiIiIrISLGpWNEVIiIiIiIiKyCUZIiYiIiIiIrMRg4KJGZWGElIiIiIiIiGyCEVIiIiIiIiIr0XMOaZkYISUiIiIiIiKbYIeUiIiIiIiIbIJDdomIiIiIiKyEj30pGyOkREREREREZBOMkBIREREREVmJAXzsS1kYISUiIiIiIiKbYIeUiIiIiIiIbIJDdomIiIiIiKyEzyEtGyOkREREREREZBOMkBIREREREVkJH/tSNkZIiYiIiIiIyCYEBgP77ERERERERNaw/IjeZp/9QlP7jz9yyC5ROfQefs3WWbAra2ZUwbdLdbbOhl35/EUR3pqUbuts2JU/PnXDjM285/mg4V0EPCcPGN5FgO+WsS550GcDRNgkCbd1NuxKV000hn2bauts2JXZn3vg5w2sSx70QXc+6/NZZf9dZiIiIiIiIvpXYoSUiIiIiIjISvQGRm/LwggpERERERER2QQjpERERERERFbCJWTLxggpERERERER2QQ7pERERERERGQTHLJLRERERERkJRyyWzZGSImIiIiIiMgmGCElIiIiIiKyEj0jpGVihJSIiIiIiIhsgh1SIiIiIiIisgkO2SUiIiIiIrISg0Fg6yzYNUZIiYiIiIiIyCYYISUiIiIiIrISPvalbIyQEhERERERkU0wQkpERERERGQlfOxL2RghJSIiIiIiIptgh5SIiIiIiIhsgkN2iYiIiIiIrISLGpWNEVIiIiIiIiKyCUZIiYiIiIiIrIQR0rIxQkpEREREREQ2wQ4pERERERER2QSH7BIREREREVkJn0NaNkZIiYiIiIiIyCas0iEVCARYu3ZtudN//fXXqFu3rjWyYpcGDx6MXr16mf5+7rnn8OGHH9osP8+Ch88ZEREREdGzwGCw3etR/frrrwgODoZUKkVkZCSOHz9eatpZs2ahZcuWcHV1haurK9q3b19m+tI80pDdwYMHY/78+cYdxWK4ubmhdu3aeOmllzB48GAIhcb+bUJCAlxdXR85M08iNjYWlStXxpkzZyq0cxscHIxbt24BAGQyGcLDwzF27Fj079+/wj5j9erVkEgkFXa8JzFv3jy8/vrrJbbPmjULw4YNs/rnl/bv+PPPP8PwL1ii7KWubmjfTAVnJyGu3CjEH8uSkZCiKXOfzq1U6NXOFS5KEWLvqjF7RTKu3Soyvf/2i16oE+4EV5UYhUV6RN8sxIJ1qbibVPK4Cmchfvw0CB6uErz8SQzyC/QVXsYn0SBMgCbVBJBLgaRMYPspPeLTLaf1UAKtawnh4wa4OAuw/bQeJ66W/h1pWk2AtnWEOB6tx44z9vldeq6+IzpESqFyFuJOsg5Ld+QhNkFXavr64RL0bCWDu0qI5HQdVu8twIUblr9PAzvK0LqeFMt35mHXyeLvT+emUtQKlSDQWwytzoCPpmVWdLEq3LmDf+P07jnIz0mFh18EWvX5Aj6ValtMm5ZwDce2Tkdy3EXkZMSjZa+xqNt6kFkavV6H41tnIPrUeuTlpMJZ6YVqjXujUYd3IBAInkaRnkhFnw91YS6ObpmOG+d3Ij83DZ7+1dCq9+fwDqr1NIpTIRqECRAZ8UBdclqPhDLqklY1i+uSHWf+oS6JEKBNHSGOX9Vjp53WJU/CrUVDhIwaClX9mpD6eeFk33eRtH6XrbNVIdo0kKJjEyeo5ELEJWmxZHsebsZrS03fIMIBvVrL4OEiQlK6Dqt25+F8THEd26OlDI2qO8BNKYJWZ8CtRC3W7M23eEyxCPjsdRcEeYsxfnYG4pJKr9tt6fyhvxG111ifuPtGoGXvL+AdZLk+SU+8huPbpiPljrE+ad5jLOq0Mq9PFn7bFjkZ8SX2rdlsIFr1+dIqZaDHt2zZMowcORIzZ85EZGQkpk2bho4dOyI6OhpeXl4l0u/duxcvvfQSmjVrBqlUiu+//x7PP/88Ll68CH9//3J/7iNHSDt16oSEhATExsZiy5YtaNOmDT744AN069YNWq3xB+jj4wNHR8dHPbTdmjBhAhISEnDmzBk0atQIAwYMwOHDhyvs+G5ublAoFE90DI2m7E7No1AqlUhISDB7vfzyyxV2/MehUqng4uJi0zw8qd7tXdG1tQv+WJqMMVPiUKTW48v3/CERl37B27y+HK/39sCyLekY9X0cYu8W4cv3/KGSi0xpYuIK8cuiJLz/zS1M+DUeEABfvecPoYXDvjfQG7fi1dYo3hOrFihA+3oCHLhgwJxteiRnGvDic0LISqlKJGIgI9eAPWcNyC0o+6LQ1w2oHypAUob9Xjw2jHBAv7YybDpYgG//ysKdZC1GDFBAIbP8/QjxF2NYTzkOnS3CN39lIeqaBu/0lcPPQ1Qibd2qEoT4iZGRU/IGhFgkwKloNfadKazwMlnD1TObcWDtJDTu+B5eHLUaHn7hWP/HMOTnpFlMr9UUQukeiGbdRkGm8LSY5tSuWTh/eAla9RmHVz7dhObdRuH07tk4d2ChNYtSIaxxPnYtG4e46MPo8PL3GPjJegSFN8fa319HbmaSNYtSYaoFCtCurgAHLxowd/u9uqR12XVJZp4Be8tZl9QLFSAp037rkiclcpYh+1w0LowYb+usVKhG1RzwQntnbDiQjwlzMhGXrMOHLypLrWND/cV4s7cCB88WYcLsTJy5qsZ7/ZXw8yyuYxPTdVi8LQ9fzcrA9wuykJalx0cvKSG3cMx+bZ2RZaEOtifXojbj0PpJaNjhPfT/0FifbJxVen2iURdC6RaIJl1Kr0/6fbASg788YHp1f3MuACC0dkerlcMe6fW2ez2KH3/8EW+88QZef/11VK9eHTNnzoRMJsPcuXMtpv/777/x7rvvom7duoiIiMDs2bOh1+uxa9ej3cR65A6po6MjfHx84O/vj/r16+Ozzz7DunXrsGXLFsybNw9AySG7Y8aMQdWqVSGTyRASEoJx48ZZ7ED98ccfCAwMhEwmwwsvvICsrCyz92fPno1q1apBKpUiIiICv/32m+m9ypUrAwDq1asHgUCA5557rlz7qdVqDB8+HL6+vpBKpahUqRImTpxo9rkKhQI+Pj6oWrUqfv31Vzg5OWHDhg0AgLi4OLzwwgtwcXGBm5sbevbsidjYWNO+Op0OI0eOhIuLC9zd3TF69OgSkb6Hh+wmJCSga9eucHJyQuXKlbF48WIEBwdj2rRppjQCgQC///47evToAWdnZ3z77bcAgHXr1qF+/fqQSqUICQnB+PHjTTcKACAzMxPDhg2Dp6cnlEol2rZti7Nnz5rlRyAQwMfHx+zl5OSEefPmlegUrl271iyCcH/49cKFCxEcHAyVSoUXX3wROTk5pjR6vR6TJ09GWFgYHB0dERQUZMp/af+ODw/ZLSoqwogRI+Dl5QWpVIoWLVrgxIkTpvf37t0LgUCAXbt2oWHDhpDJZGjWrBmio6NhK93auGDFtnQcP5+HW/Fq/LwgCW4qESLrOJe6T4+2rthxOBu7j2bjTqIaM5cmo0htQLumSlOaHYeycSmmECnpWty4U4TFG9Lg6SaBl7t51L1jCxWcZUKs25VhtTI+icgIAaJiDDh304DUbGDzCQO0WqBOiOWLhYR0YPdZAy7dNkBbRoUrEQM9mwix6YQehRV336bCtW8sxcGzRTh8Xo2END3+3poPtQZoVtvyVXS7ho64eEOD7ccLkZimx/oDBbidqMNzDczTu8gFeLG9M+ZsyIPOwqoKGw4WYNeJItxNsc+79Q+L2jsPNZr2R/XIvnDzCUOb/uMhdpDi0rFVFtN7B9VCix6jUbV+V4jElkeiJMaeQUjNdqhc4zko3QIQVrcTgsKbI+n2eWsWpUJU9PnQqgsRc247mnX/GP6hjeDiWQmRnd6HyiMI5w8vsXZxKkTjcAGibhTXJVtO3qtLKv9DXRL3z3VJjyZCbD6pR6F93terECnb9uPqV9OQtG6nrbNSoTpEOuFAVCEOnStCQqoOizbnQq01oEUdqcX07Rs74UKMBtuOFiAhTYd1+/JxK1GLtg2L0x+/WITLsRqkZuoRn6rDsh15kEmFCPAyH4BYM1SCGiESLN+VZ9UyPqmz++ahemR/VGtsrE9a9x0PsUSKKydKr0+adR+NKvVKr1+d5G6QKT1Nr1uX90LpHgS/0MbWLAo9oKioCNnZ2WavoqKiEunUajVOnTqF9u3bm7YJhUK0b98eR44cKddn5efnQ6PRwM3N7ZHyWCFzSNu2bYs6depg9erVFt9XKBSYN28eLl26hJ9//hmzZs3CTz/9ZJbm+vXrWL58OTZs2ICtW7fizJkzePfdd03v//333/jyyy/x7bff4vLly/juu+8wbtw40xDi++OVd+7ciYSEBFNe/mm/6dOnY/369Vi+fDmio6Px999/Izg4uNSyisViSCQSqNVqaDQadOzYEQqFAgcOHMChQ4cgl8vRqVMnqNXG1mrq1KmYN28e5s6di4MHDyI9PR1r1qwp83y+9tpriI+Px969e7Fq1Sr8+eefSE5OLpHu66+/Ru/evXH+/HkMGTIEBw4cwGuvvYYPPvgAly5dwh9//IF58+aZOnsA0L9/fyQnJ2PLli04deoU6tevj3bt2iE9vZSxTI8hJiYGa9euxcaNG7Fx40bs27cPkyZNMr0/duxYTJo0CePGjcOlS5ewePFieHt7Ayj93/Fho0ePxqpVqzB//nycPn0aYWFh6NixY4lyfP7555g6dSpOnjwJsViMIUOGVFg5H4W3uxhuKjHOXsk3bcsv1ONabCHCgy03hmIREBroiLPRxfsYDMC56HyEV7a8j6ODAG2bKJGYqkFqRnHvK8DHAS90dsPPC5LscqU3oRDwdQVuJpln7maSAQHuTzZkslMDAa4nGBBrx8EdkRAI8hHhcmzxv5kBwJVYDUL8Lc+sCPET40qseQ/70k3z9AIAr3eXY/vxAiSkPhsdzrLotGok37mIwKrNTNsEQiECqzRF4q2oxz6uT3A9xF09gozkmwCAlLtXEH/jNCpVa/WkWbYqa5wPvV4Lg14HscT8xoZYIkXCjVNPkt2n4n5dEmuhLvH3eLK6pGN9AWLi7bsuIctEQqCSrxiXbprXsZdvahASUEod6y/G5Zvmdx4u3tAg1N9yx0skBFrVkyK/UI87ScWBAKWzAK91kWP2+lyoNXbYAN+j06qRcvciAh6qTwKesH59+DOunlqPao37PBPTIf4tJk6cCJVKZfZ6OPgGAKmpqdDpdKZr8vu8vb2RmJhYrs8aM2YM/Pz8zDq15VFhj32JiIjAuXPnLL73xRdfmP4/ODgYH3/8MZYuXYrRo0ebthcWFmLBggWm8ca//PILunbtiqlTp8LHxwdfffUVpk6dij59+gAwRtLud7oGDRoET0/jUAF3d3f4+PiYjvtP+92+fRtVqlRBixYtIBAIUKlSpVLLqFarMXXqVGRlZaFt27ZYtmwZ9Ho9Zs+ebfph/fXXX3BxccHevXvx/PPPY9q0aRg7dqzp82fOnIlt27aV+hlXrlzBzp07ceLECTRs2BCAMcJbpUqVEmkHDhxoNt9zyJAh+PTTTzFokHH8fkhICP73v/9h9OjR+Oqrr3Dw4EEcP34cycnJpiHVU6ZMwdq1a7Fy5Uq8+eabAICsrCzI5XLTceVyebm/iIAxAjpv3jzTMORXX30Vu3btwrfffoucnBz8/PPPmDFjhimfoaGhaNGiBQCU+u/4oLy8PPz++++YN28eOnfuDMA4x3XHjh2YM2cOPvnkE1Pab7/9Fq1btwYAfPrpp+jatSsKCwshlVru0FmLi9L4U8vKMe8UZOboTO89TCEXQSQSlNwnWwt/b5nZtk4tVXitlwecHIW4k6jG+Bl3ob23m1gswMjBPliwNhWpGVp4e9jHfOUHyRwAoVCAvIdGjeYVAu5Ky/uUR/UgAXxcBZi73b6HScllAoiEAuTkmV+sZOfp4eNu+d9LKRci20J6lXPxfcaOTaTQ64HdJ0veCX0WFeRlwKDXQaZwN9suU3iYOpOPo2G7N6EuzMOiSV0gFIigN+jQtMuHCG/Q/UmzbFXWOB8OUjl8guvixPbf4OodApnCA1dPb0JibBRUHkEVkW2rslpdEmisS/7aYd91CVkmlwkhEgqQnWf+71dWHauSCy2mf7COBYDaYRK82VsJBwmQlavHj4uzzYZ+v95dgX2nC3ErQQt3lf0+3KLwfn0iN69PnJ6wfn3QzQu7UFSYg4iGvSvkeM8SWy6DMnbsWIwcOdJsmzWmVk6aNAlLly7F3r17H/k6u8I6pAaDodS7HcuWLcP06dMRExOD3NxcaLVaKJXmLUNQUJDZ5NemTZtCr9cjOjoaCoUCMTExGDp0KN544w1TGq1WC5VKVWqe8vLy/nG/wYMHo0OHDggPD0enTp3QrVs3PP/882bHGTNmDL744gsUFhZCLpdj0qRJ6Nq1Kz755BNcv369xPzPwsJCxMTEICsrCwkJCYiMjDS9JxaL0bBhw1IX6ImOjoZYLEb9+vVN28LCwiwuEnW/w3rf2bNncejQIbOIqE6nQ2FhIfLz83H27Fnk5ubC3d28sikoKEBMTIzpb4VCgdOnT5v+vr9YVXkFBwebnRNfX19ThPfy5csoKipCu3btHumYD4qJiYFGo0Hz5s1N2yQSCRo3bozLly+bpa1du3givq+vLwAgOTkZQUGWL6yKiopKDGN4nB9tq4YKvP1S8eTvb38vOaG/Iu0/kYOzV/LhqhShZ3tXfDzEB2N/vAON1oBXe7jjTpIa+07k/POB/kUUMqBDfQGW7NFD9x+8hgzyFqFtQym+nZf1z4n/465FbcHV0xvQ8ZUpcPMJQ+rdKziw9jvT4kb/Nc+/PBk7l36Gv75uDYFQBM+A6qhavyuS4y7aOms2oXAy1iWL9/436xIq25VbGkyYnQG5kxAt60nxVh8FvvsrEzn5BrRrKIXUQYDNhwtsnU27cPn4SgSFt4SzyvufE1OFcXR0LNe1rIeHB0QiEZKSzIeBJCUllRokum/KlCmYNGkSdu7caXbtXV4V1iG9fPmyaf7fg44cOYKXX34Z48ePR8eOHaFSqbB06VJMnTq13MfOzc0FYIyCPdi5AwCRqOQCHo+yX/369XHz5k1s2bIFO3fuxAsvvID27dtj5cqVprSffPIJBg8eDLlcDm9vb1PHOzc3Fw0aNMDff/9d4rPvR/qsydnZfO5hbm4uxo8fb4rGPkgqlSI3Nxe+vr7Yu3dvifcfnBsqFAoRFhZWIo1QKCzRkbY0F/jhFYMFAgH092ZVOzk5lVoea3gwL/f/3fRlzPCeOHEixo83X8jhq6++AvBoizodP5+Lq7HFt+jvL1ykUoiQkV0c8XRRiHDzjuXoVU6uDjqdASqF+XfcRSlGZrb5Cn75hXrkF+qRkKLB1dgELJwcisg6zjh4Khe1qsoQ5OeAZnXv/Zveu2+0YFIIVm5Lx9LNFTdc+3HlqwG93gDnh26oOUuBvMdsx31dAblUgKEdi2+oCIUCBHkCDasIMGmF3qZ3LB+Um2+ATm+Awtn8pp7SWYisPMvf1+xcPZRlpK8SKIbCWYCJ77qY3hcJBejXVoa2jaT4/Pdnr6Pq5OwKgVBUYoGN/JxUyJQej33cQxt+QIN2b6Bq/a4AAA+/cORkxOPkrj/tukNqrfOh8ghC3+GLoCnKh7owF84qL2yZ/xGU7oFPmmWrK7Muecx1u3zdAGepAEOft1CXhAnw/Ur7qUvIstx8PXR6A5QPRTfLqmOzcvXlSq/WAMkZeiRn6HEjPhffvuOKFnWl2HK4ABHBEoT6izHzU/NAwBdDXHDsQhHmbsitgNJVDOn9+iTXvD4peML65L6c9Lu4c+0IOg365YmP9Sx6FuoIBwcHNGjQALt27TKt23J/gaLhw4eXut/kyZPx7bffYtu2bSWCZeVVIR3S3bt34/z58/joo49KvHf48GFUqlQJn3/+uWnb/ceoPOj27duIj4+Hn58fAODo0aMQCoUIDw+Ht7c3/Pz8cOPGjVJXe3VwcABgjAjeV579AOOqsgMGDMCAAQPQr18/dOrUCenp6aYJuR4eHhY7aPXr18eyZcvg5eVVIuJ7n6+vL44dO4ZWrYxzkbRarWnupiXh4eHQarU4c+YMGjRoAMA4vzYj458Xoqlfvz6io6Mt5vX++4mJiRCLxWXOky2Np6cncnJykJeXZ+oMR0VFPdIxqlSpAicnJ+zatcviY2Qs/Ts+LDQ0FA4ODjh06JBpiLVGo8GJEyee+HmupQ1reHHU7Uc6TmGRAYlF5p319CwtaofLEHvXOCfFSSpElWApth603DHQ6oCYuCLUDpfh+DnjQggCAVCrqhO27C+jMyEQQCAo7gRPnp0AB0lxxyWskhTvv+KNz6fdQeI/PHLmadHrgYQMINhbgKt3i2vtYG8BTl57vFo8Ngn4c4v596hbYyHScgw4ctlgV42DTg/cTtShWrAEZ68Z/00EACIqSbDntOWr6BvxWkQES8we4VItWIwbd403K45eUONyrPmNixEDFDh2oQiHzz+bQ3hFYgd4BdTAnatHEFrLOD/FoNcj7tpR1G7x+CuBa9UFEAjMLzwFQiFgsO9wmLXOx30SRxkkjjIU5mfh9pWDaN794yc+prWVVZeceoK6ZNZW87qka2Mh0rINOHrFvuoSskynB24laFEtWIKoq8Y2WAAgIliCPSdLqWPvalGtsgN2nih+v3plCWLult1uCgSARGRsc5dsz8OafcXrQLjIhRg5UIU/VueU+bgZWxCJHeDpXwN3rx1BSM3i+uTO9aOo1fzJ65PLJ1bDSe6OStVaP/GxyHpGjhyJQYMGoWHDhmjcuDGmTZuGvLw80xTB1157Df7+/qY5qN9//z2+/PJL0wKs96f4yeVys+l//+SRO6RFRUVITEyETqdDUlIStm7diokTJ6Jbt2547bXXSqSvUqUKbt++jaVLl6JRo0bYtGmTxUV9pFIpBg0ahClTpiA7OxsjRozACy+8YAoRjx8/HiNGjIBKpUKnTp1QVFSEkydPIiMjAyNHjoSXlxecnJywdetWBAQEQCqVQqVS/eN+P/74I3x9fVGvXj0IhUKsWLECPj4+5XrEyMsvv4wffvgBPXv2xIQJExAQEIBbt25h9erVGD16NAICAvDBBx9g0qRJqFKlCiIiIvDjjz8iMzOz1GNGRESgffv2ePPNN/H7779DIpFg1KhRcHJy+scJ4F9++SW6deuGoKAg9OvXD0KhEGfPnsWFCxfwzTffoH379mjatCl69eqFyZMno2rVqoiPj8emTZvQu3fvf7yrERkZCZlMhs8++wwjRozAsWPHTCsrl5dUKsWYMWMwevRoODg4oHnz5khJScHFixcxdOjQUv8dH+Ts7Ix33nkHn3zyCdzc3BAUFITJkycjPz8fQ4cOfaT8PKy8wxoex8Y9mejfyQ0JKRokpWkwsKs70rN0OHa2eNW98e/74+jZXFOHc/3uDIx41RsxtwtxLbYQ3dq4QuooxK6j2QCMiyU1b6BA1OV8ZOfq4O4iRp/nXaHWGHD6orERTEw1bzwV9x4ZE5eotqvnkB67YkCPJgIkpAPx6QY0riqARAycu2G82useKUBOAbD3nPFvoRDwvHcfSCQ0DqvzdgHUWiAj1/jflIf67RodUFBUcrs92Hm8EIO7OSM2QYvYBC3aNZTCwQE4fM7YeRzczRmZOXqs3WcMGe86WYSPByrQvrEU56+r0ai6Iyr5irFoq/HfPa/QgLxC84tond6A7Dw9ktKL/91dlUI4SwVwUwohFAgQ4GX8fqRk6FBkH/crzNR9bjB2Lv4UXoE14V2pNqL2zYdWXYDqkcaRIdv/HgO5ygvNuo0CYFxEIz3JOCVBr9MgNysJKXcvQ+Igg4un8YZWcI02OLFjJuQuvnD3DUPKncs4s3ceqkf2tU0hH4E1zsetKwcAA+DiVRlZqbdwaP0PcPUOQbXIkqNv7NHxaAO6R96rS9IMaBx+ry65+UBdkg/sPV9cl3g8VJd4uQCasuoSLVCgts+65EmJnGVwDiue1iKrHABlnQio07NQGJdgw5w9mR3HCjCkhwK3ErS4Ga9F+8ZSOEoEOHTO2OEc0l2OzBw9Vu811qE7jxfgk1dVeD7SCeeuq9G4uiOCfcVYsNkY1XSQAF2by3D2qhqZuXooZAK0aegEV4UQJy8b6+30bPM2tkht/M6lZOosPobL1uq0HozdSz+FZ0BNeAXVxrkDxvokopHxt79zyRg4q7zQtEtxfZJxrz7R6TTIy0pC6t3LkDjKoPIoXpPFoNfjyok1CG/YC0JRhQ3OJCsYMGAAUlJS8OWXXyIxMRF169bF1q1bTQsd3b5922wq3++//w61Wo1+/fqZHeerr77C119/Xe7PfeRvxdatW+Hr6wuxWAxXV1fUqVMH06dPx6BBgyzONezRowc++ugjDB8+HEVFRejatSvGjRtXIpNhYWHo06cPunTpgvT0dHTr1s3s8SzDhg2DTCbDDz/8gE8++QTOzs6oVauWKSImFosxffp0TJgwAV9++SVatmyJvXv3/uN+CoUCkydPxrVr1yASidCoUSNs3ry5XPMmZTIZ9u/fjzFjxqBPnz7IycmBv78/2rVrZ4qYjho1CgkJCabzM2TIEPTu3bvEI20etGDBAgwdOhStWrWCj48PJk6ciIsXL/7jBOGOHTti48aNmDBhAr7//ntIJBJERESYIpECgQCbN2/G559/jtdffx0pKSnw8fFBq1atSqyoZYmbmxsWLVqETz75BLNmzUK7du3w9ddfmxZDKq9x48ZBLBbjyy+/RHx8PHx9ffH2228DKP3f8WGTJk2CXq/Hq6++ipycHDRs2BDbtm2zONfWXqzZmQGpowDvvOQFZychLscU4n+/3YVGW3x73cdDAuUDzxg9dDoXSrkIL3Z1h6tChJt31Zjw613TQkdqrQHVQ53Q/TkXOMtEyMrR4uL1Anw6NQ5Zuc/WqqqX44zD7FrXEsBZKkBSJrB0rx5594J5KmcBDCg+VwonYFin4nPVtJoATasBt5INWLTb/hr6f3LyihpymQA9WjpB6SzEnWQdpi/LQU6+scxuSqFZJObGXS1mr89Fz1Yy9GrlhOQMHX5flYv4R1xNt0dLJzSrVXwTZtwQ4w2gqYuzcfW2fd3BB4Cq9bqgIDcdx7b+grzsFHj6V0OPt2ZBpjAOKcvNiDe7eZeXnYylU4qH3Z7ZMxdn9syFf2gj9BlufM5o6z5f4OiW6di3agLyc9PgrPRCzWYD0Pj5d2HvrHE+1AW5OLzpR+RmJkIqc0FonQ5o2uUjiET2tyCaJZfjDJA5Aq1qFtcly/YV1yVKmcBs+olCCgzrWFyXNIkQoEmEsS75e8+zV5c8KVWDmmi6q/gZvNWnfAYAiFuwGueGjrVVtp7YictqyJ3z0LO1DEpnIeKStJi2NNu0OJy7SmRWx8bc1WLW2hz0fk6G3s/JkJyuw68rshF/7xFZej3g6y5Cs34KyJ2EyCvQ42aCFt8vyHrketheVKnbBYW56Ti+7Rfk56TAw68aug0ruz5Z/lNxfRK1by6i9s2FX0gj9Hq3+DsUd+0wcjPjUa3xs3FTyxrs8QkHpRk+fHipQ3QfviZ/8FGXT0JgKG11HbIbd+7cQWBgIHbu3PlEiwHR4+s9/Jqts2BX1syogm+XPpsNrrV8/qIIb02y/Xxce/LHp26YsZlNzIOGdxHwnDxgeBcBvlvGuuRBnw0QYZMk3NbZsCtdNdEY9m2qrbNhV2Z/7oGfN7AuedAH3e33UTK/brHdZ7/X2XafXV6Mm9uh3bt3Izc3F7Vq1UJCQgJGjx6N4OBg0zxUIiIiIiJ6Ntg2/me/HfX72CG1QxqNBp999hlu3LgBhUKBZs2a4e+//y6xei0REREREdGzjB1SO9SxY0d07NjR1tkgIiIiIqInxAmSZfvnlXuIiIiIiIiIrIAdUiIiIiIiIrIJDtklIiIiIiKyEv1/7wlSj4QRUiIiIiIiIrIJRkiJiIiIiIishIsalY0RUiIiIiIiIrIJdkiJiIiIiIjIJjhkl4iIiIiIyEr0HLJbJkZIiYiIiIiIyCYYISUiIiIiIrISLmpUNkZIiYiIiIiIyCYYISUiIiIiIrISg00nkQps+NnlwwgpERERERER2QQ7pERERERERGQTHLJLRERERERkJXzsS9kYISUiIiIiIiKbYISUiIiIiIjISvjYl7IxQkpEREREREQ2wQ4pERERERER2QSH7BIREREREVmJnqsalYkRUiIiIiIiIrIJRkiJiIiIiIishIsalY0RUiIiIiIiIrIJRkiJiIiIiIishBHSsjFCSkRERERERDbBDikRERERERHZhMBgYBCZiIiIiIjIGv63RGuzzx73kv3P0LT/HBLZgezTO2ydBbuirN8BK4/pbZ0Nu9IvUojFB3l/70EDWwjQovs+W2fDrhzc0Bo934m2dTbsxrrfw/HGd2m2zoZdmfWZO4Z9m2rrbNiV2Z97YJMk3NbZsCtdNdFYdIBtzoNeaSmwdRboMbFDSkREREREZCUG3sMvE+eQEhERERERkU2wQ0pEREREREQ2wSG7REREREREVsI1ZMvGCCkRERERERHZBCOkREREREREVqLnokZlYoSUiIiIiIiIbIIRUiIiIiIiIivhHNKyMUJKRERERERENsEOKREREREREdkEh+wSERERERFZiZ4jdsvECCkRERERERHZBCOkREREREREVmJgiLRMjJASERERERGRTbBDSkRERERERDbBIbtERERERERWwseQlo0RUiIiIiIiIrIJRkiJiIiIiIisRM9FjcrECCkRERERERHZBDukREREREREZBMcsktERERERGQlBq5qVCZGSImIiIiIiMgmGCElIiIiIiKyEoPe1jmwb4yQEhERERERkU0wQkpERERERGQles4hLRMjpERERERERGQT7JASERERERGRTXDILtmtI0eOoEWLFujUqRM2bdpk6+xUuOXb92HRhl1Iy8pGlSB/fDK4P2qEBVtMu2HfUUyYuchsm4NEjEMLpllMP3H2EqzedQgfvdoXA7u0qeCcW8/RnX/jwOa5yM1KhU9gBLq9+jkCQ2tbTHtiz3KcObQeSXeuAQD8g6ujQ/+PSqRPvhuDbcun4uaVE9DrdPDyD8XA93+Gi4ef1ctTEY7v/huHt84xnZPOA7+Af4jlc3Jq33KcO7IOyXeN58S3Ug206/NRqek3LvgKp/YtQ8cXx6JJh0FWK4M1DH05GN2f94HCWYzzl7Mx5bdruJNQUGr6OjVUGNgnEOGhcni4O2Lstxdw4GiaWZpWTT3Qq7MvwkMVUCklGDziJK7fzLN2UR7LwG7u6NDCBc5OQly5UYDfFychIUVT5j5dWrugVwc3uCpFiL1ThD+XJeParUKLab8c7o8GNeT4buZdHDubCwAI9ndE345uqB7qBIVchOQ0DbYeyMTGPZkVXbxH9lwDR3SMdIJKLkRckhZLtucjNkFbavoGEQ7o2VoGD5UQSek6rNqTjwsxxeeve0snNKruCDeFEFqdAbcStVi7rwA3482PWStUgm4tnBDgJYZGa8DV21r8tirHauV8FG0aSNGxyYPnJK9E/h/UIMIBvVrL4OEiMp6T3Xk4/8A56dFShkbVHeCmFJnOyZq9+RaPKRYBn73ugiBvMcbPzkBcks4qZXxa3Fo0RMiooVDVrwmpnxdO9n0XSet32TpbVnFi9984ss3Y5ngHRqDTS6W3Oaf3G9uclAfanDa9S29zNi38Cqf3LcPzA8Yi8hlrc54UH/tSNkZIyW7NmTMH77//Pvbv34/4+HhbZ6dCbT9yCtMWrsGwvp2x8LsxqFLJH+9P+hXpWaVfyDg7SbHl9+9Mr/XTJ1hMt+fEWZy/HgtPV5W1sm8V545uxubF36Ntr/fw3oRV8AkKx7wf3kBudprF9DevnEDtJl0wdOw8vP3lEqjcfTHvh2HISk8ypUlLuo0/v3kZnr6VMWzsfLz/7Vq06fkOxA6OT6tYT+TC8c3YvmwSWvd4D299tRregeFY9NMw5JVyTm5FH0fNxl0x6JP5GPrZUqjcfLDwx6HIzkgqkfby6R24c+MsFC5e1i5GhXu5byD6dfPHlN+u4c2Pz6CgUIcfJ9SCg0RQ6j5OUhGu38zFjzOvlZFGiHOXsvH7/BvWyHaF6fO8G7q2ccXvi5PwyeTbKCzS4+sRAZCISy9/iwYKDOnriWWbUjHyu1u4eacIX48IgEohKpG2R1tXWLp2Cg1yRFaODj/OS8D7/4vFiq3peK2XJ7q0dqnA0j26htUc8EI7Z2w4WID/zc3CnWQdPnxRAYXM8vkI9RfjjV5yHIwqxIQ5WYi6qsZ7/RTw8yw+F0lpOizZloevZ2di8sJspGXp8eGLCsgfOGb9cAcM7SHH4XNFmDAnE98vzMbxS0VWL295NKrmgBfaO2PDgXxMmJOJuGQdPnxRWeY5ebO3AgfPFmHC7EycuarGe/2VZuckMV2Hxdvy8NWsDHy/IAtpWXp89JLS7Jzc16+tM7Jy/j3LioqcZcg+F40LI8bbOitWdfH4ZuxYPgmtur+HN740tjmLp/1zm/Pqx/Px+tilULr64O+fLLc5V07vwN1ntM0h62OHlOxSbm4uli1bhnfeeQddu3bFvHnzzN5fv349qlSpAqlUijZt2mD+/PkQCATIzMw0pTl48CBatmwJJycnBAYGYsSIEcjLs49ox+JNu9GrbTP0eK4pQgJ8MXboi5A6OGD93iOl7iMQCODhojS93F2UJdIkp2diyrwV+N97gyEWlbzQtGeHts5Hw+f6o0GrPvDyD0PPwV9D4ijFqX2rLaZ/4Z0f0KT9QPhVqgZPvxD0Hvo/GPR63LhUfA53rJyG8Dqt0OnFT+AXXB3u3kGoVr8t5Er3p1WsJ3J0+zzUb9Uf9Vr0hadfGLq9Oh4SBynOHFxlMX2fN6egUduB8AmqBg/fEHQf/A0MBj1uXjb/XmVnJGHL4m/Q540fIBQ9ewNl+vfwx4Llt3DwWBpiYvPwzU9X4O7miJZNPErd5+ipdMxaFIv9Ry1fWAHAtj3JmLf0Fk5GZVgj2xWme1tXrNiShuPncnHrbhGmzUuEm0qMJnXlpe7Ts50rth/Kwq4j2YhLVOP3JUkoUuvRvqn5javKAY7o2d4VvyxMLHGMXUeyMXtFMi5eK0BSqgb7jmdj15EsNK1X+uc+DR0aS3EgqgiHzxUhIVWHRVvyoNYCzetYvvHUrpEUF2M02H6sEIlpOqzbX4DbiVq0bSA1pTl+SY3LsRqkZuoRn6rD8p35kEmFCPAy1qtCAfBiBxlW7s7HvjNFSErXIyFVh5OX1U+lzP+kQ6QTDkQV4tD9c7I5F2qtAS3qSC2mb9/YCRdiNNh2tAAJaTqs25ePW4latG34wDm5WGR2TpbtyLt3TszrkJqhEtQIkWD5LvtobytCyrb9uPrVNCSt22nrrFjV0R3zUK9lf9S91+Z0fcXY5kSV0ub0fmMKGrYpbnO6ldHmbF3yDXoNezbbnIqg1xts9noWsENKdmn58uWIiIhAeHg4XnnlFcydO9c03OHmzZvo168fevXqhbNnz+Ktt97C559/brZ/TEwMOnXqhL59++LcuXNYtmwZDh48iOHDh9uiOGY0Wi2u3IxD45rhpm1CoRCNa4bj/LWbpe5XUFiE7u+PQ9f3vsCoKX8gJi7B7H29Xo+vfl2AV7q1Q2igr9Xybw1arRrxsRcRVqOpaZtQKERY9aa4fT2qXMfQFBVCp9PCydl4ga3X6xF9dh/cfYLx1+Rh+O695vj96wG4dOrZuKDQadWIv3URIdWambYJhEKEVG+KOzFR5TqGpqgA+gfOCQAY9HqsmT0azToOhZd/lYrOttX5eUvh4eaIEw90GvPydbh0NRs1I0repPm38faQwE0lxtkr+aZt+YV6XL1ZiPDKThb3EYuA0CCp2T4GA3D2Sj7CQ4o7HA4SAUYN8cUfS5ORmV2+IZYyqQi5ebaLhImEQCVfMS7HFncEDQAu31Qj1F9icZ8QfzEuxZoPb754Q4MQf8sXyiIh0KqeI/IL9bhzb+hpkI8YrkoR9AZg3BAVfhjhihEDzKOstnL/nFy6WVxG4znRICTAchlD/MW4fNO8M33xhqbUc2g8J9J756R4yK7SWYDXusgxe30u1Jpn40KYjHRaNRJuXUTl6uZtTuVqTXHnRlS5jqFRW25z1s0ZjabPaJtDTwc7pGSX5syZg1deeQUA0KlTJ2RlZWHfvn0AgD/++APh4eH44YcfEB4ejhdffBGDBw8223/ixIl4+eWX8eGHH6JKlSpo1qwZpk+fjgULFqCw0PKcKQAoKipCdna22auoqGKHYGVm50Kn18NNpTDb7qZSIi0z2+I+lXy9MO6tlzFl1FuY8N4g6A0GDP1qKpLSii/K56/fAZFIiBc7PVeh+X0a8nMyodfrSkQu5Sp35GallusYW5dNgdLVC6E1jI1pXnYa1IX52L9xNqrWboHBo2ejeoP2WDx9BG5eOV7hZaho+TkZMOh1cH7onDgrPcp9TnaunAqFixdCHrjAOLhlFoRCESLbv1qh+X1a3FwdAAAZmeYdioxMtem9fzNXpbHDk5ltPm8vM0dreu9hSrkIIpGg5D7ZOrgqizsoQ/t74cqNAhw/l1uuvESESNGioQLbDmY+QgkqllwmgEgoQHaeeecnO88ApbPl4akquRA5D3Wis/P0UMnNL4lqh0nwy8du+G2MG9o3dsJPS7KRW2D8HE9XY9oeLZ2w6VABflmejfxCAz5+WQmZtPSh00+DXCa8d04slNHZ8mWfSi4sV/raYRLM+MQdv3/qjg6RUvy4uPicAMDr3RXYd7oQt8qYv0v2KT/X2OY83A4/Spuzy0Kbc2irsc1p3O7ZbHPo6WCHlOxOdHQ0jh8/jpdeegkAIBaLMWDAAMyZM8f0fqNGjcz2ady4sdnfZ8+exbx58yCXy02vjh07Qq/X4+bN0qOQEydOhEqlMntNnDixgkv46GpXDUHXVpEIDw5Ag+pV8MNHb8BVKcfqXYcAAJdv3MbSrXvx1duvQCCw7cWQLezbMAvnj23ByyN+geTe/ND7EfVq9duieafB8KtUDa27v4Hwus/h+O5ltszuU3Fw85+4cHwzBrw3A2KJ8ZzEx17AsZ0L0WvIxGfme9KhtRe2L29heonLmCf5b9S6kQJLf6pieolE1il/49rOqB0uw+wVyeVKH+TngM/e9sfSTamIupz/zzs8g67c0hjnhs7PxsUYNd7qXTwv9f6/wqZDBTgdrcbtRB3mbTR25BtW+/feGLlyS4MJszMwaV4WLsRo8Faf4nPSrqEUUgcBNh8ufXEx+vc6tPlPXDy+Gf3fLW5zEmIv4PjOhejxDLU51mIw2O71LPhvDuQmuzZnzhxotVr4+RWvgmowGODo6IgZM2aU6xi5ubl46623MGLEiBLvBQUFlbrf2LFjMXLkSLNtjo6OKLq4v5y5/2cuSjlEQmGJBYzSs7Itzgu1RCwWITw4EHcSUwAAZ67EICM7F93f/9KURqfX4+dFq7F0yx6s/8XyAkj2QqZwgVAoKrGAUW5WGuSq0ucFAsCBzXOxf9MsvD56LnyCiodByxQuEIrE8PIPNUvv6ReCW1dPV1zmrUSmcIVAKCqxmEReduo/npPDW+fg4OZZeO3jufAOLD4nt6+dQl5OGn4a3da0zaDXYfuy73F0x3x8OHl3xRaiAhw8noZLV0+a/naQGO+jurpIkJZRPMTQ1cUB12+UL7L3LDl+LhfRsbGmv+8vXOSiFCPjgWG1Lgoxbt6xPJojO1cHnc4AF6V5k++iFCHjXtS0VrgMPh4SLJ5qPqRuzJt+uHS9AF/8FGfaFujjgP99EIjtB7OwYkv6E5XvSeXmG6DTl4yGKp1LRk3vy8rVQ/FQ5E/pLERWrnmEUK0BUjL0SMnQ40a8Ft+87YIWdRyx5Ughsu5FExNSi/8NtDogJUMHN6Vt7/Xn5uvvnRMLZSxleHVWrr5c6dUaIDlDj+QMPW7E5+Lbd1zRoq4UWw4XICJYglB/MWZ+ah5h+2KIC45dKMLcDf++3+e/iUxubHMebofL0+Yc2TYHh7bMwiujLLc5Pz/U5uxY/j2O7ZyPEd/bX5tDtsEOKdkVrVaLBQsWYOrUqXj++efN3uvVqxeWLFmC8PBwbN682ey9EydOmP1dv359XLp0CWFhYY/0+Y6OjnB0LLkQRkUO2pWIxYioHIgTF6LxXKM6AIzzHU9cvIr+z7cq1zF0ej2ux8Wjed3qAIAuLRuhca1wszQjJv6Kzi0bo3vrJhWYe+sQix3gF1wDMRePonqD9gCM5yTm0lE0af9yqfvt3zQbe9f/gcGfzEJASM0SxwyoXBOpCeYR8dTEWLi42/8jX0RiB/hVqoEbl48gor7xnBj0ety4fBSN25Z+Tg5tmY0Dm2bilY9mwy+4ltl7tZv2QEi1pmbbFv00DLWb9kTdFr0rvhAVoKBAh7sF5vMZU9OL0LCOq+mRLDInEapXVWLt5n/XatwAUFBkQMFDj3NJz9KidrjM1AF1kgpRtbIUWw9kWjyGVgfE3C5E7XCZ6REuAgFQO1yGzXuN+6zalo4dh7LM9vtlXGXMXZmM4+eKF6cJ9HXANx8GYvfRbCxaX75hfNak0wO3ErSoFixB1FXjeRIAqBYswe5Tlqdn3LhrTL/rRPH71SpLcONu2cNMBQKYIvS3EnTQaA3wcRfh+h3jfiIh4OEiQlqWbVfaNT8nxps2AgARwRLsOVnGOansgJ0PnJPqlSWIuVv2o4QEAkByL2q/ZHse1uwrjpa7yIUYOVCFP1bnlPm4GbIPIrEDfCvVQOzlI4ioV9zm3LxyFI3alN7mHN4yGwc3z8TAD0u2ObWa9kDl6uZtzuKfhqFWk56oY6dtjrUYnpHFhWyFHVKyKxs3bkRGRgaGDh0Klcp89ce+fftizpw5WL58OX788UeMGTMGQ4cORVRUlGkV3vtDQsaMGYMmTZpg+PDhGDZsGJydnXHp0iXs2LGj3FFWaxrYtS3G/74Q1UKCUCMsGEu27EFBUZGp8/jVbwvg6arC8Jd6AgBmrdqCWlWCEeDtidz8AizcsBOJKeno2cY4T8NFIYeLwnylS7FIBHeVEsF+3k+3cI+peadBWDVrLPwr10RASC0c3r4A6qICNGhlbLRW/DEGSldvdHzBGMHev3EWdq7+BS+8MwWuHv7IyTRGix2kMjhKnQEALboMwbJfRyE4vCFCqkfi6rmDiD6zF0PHzrdNIR9Rk+cHY+2cT+EXXBP+lWvj6M750BQVoG7zPgCANbPHQOHqhfZ9RwEADm6ehb3rpqPPG1Pg4uGP3Kx758RRBgepM2RyV8jkrmafIRSJIVd5wMMn5OkW7gmsWH8XgwYEIS6+AAlJhRj2SjDS0otw4GhxB2naN7Wx/0gqVm8ydlKdpEL4+xYv+uPrLUVYZWfk5GqRlGLsQCjkYnh7OsLDzXhTKshfBgBIz1AjPbPsURajjgAAYqlJREFUC/OnacPuDLzQxR0JKWokpWowsLsH0rO0OBpVHIGa8EEAjkblYvO+TADAul0Z+GCQD67fLsS12EJ0b+sKqaMQO48YO6GZ2TqLCxmlpGuRnGYse5CfA/73YSDOXMrDul3pcLk3Z1WvN0ZhbWXH8UIM6S5HbIION+O1aN9YCgeJAIfOGf9dh3SXIyNHjzV7jZ2lXScK8fErSnRoLMX5GDUaVXdEsK8YC7cYO94OEqBrMyecvaZBZq4ecpkAbRpI4aoQ4tS9VXQL1QbsO12IHi2dkJ6tR1qWDh2bGL9fp67YfqXdHccKMKSHArcStKZz4igR4NA5Y4dzSHc5MnP0WH3vnOw8XoBPXlXh+UgnnLuuRuN752TBZuN3ykECdG0uw9mramTm6qGQCdCmoRNcFUKcvGw8z+nZ5tHUIrXxAjwlU4eMZ/wRMCJnGZzDikdXySoHQFknAur0LBQ+tMDgs6xJh8FYN/dT+FaqCb/KtXH8XptT516bs3bOGChcvNDuXptzaMss7Fs3Hb3/5W0OWR87pGRX5syZg/bt25fojALGDunkyZORk5ODlStXYtSoUfj555/RtGlTfP7553jnnXdM0c3atWtj3759+Pzzz9GyZUsYDAaEhoZiwIABT7tIFj3ftAEys3Pxx8pNSMvMQdVK/pj+6XumIbuJqelm8y1y8vLx7azFSMvMgcLZCdUqB2HO+JEICXi2VtMtS+0mXZCXk4Fdq6cjJysVvkHVMPiTP01DhbLSEiAQFA8pO7Z7KXRaDZb88oHZcdr2eg/t+hhXU67RsAN6DP4K+zf+iY2LvoOHb2W89P7PCA5v8PQK9gRqNu6C/Jx07F37C3KzU+ATWA0vfzSr+Jykx5t9T07uXQKdVoMVv5ufk9Y93sNzPd9/qnm3pr9XxUEqFWH08KqQO4tx/lIWRn113mxVT38fJ7goi1cIjQhT4JeJdU1/jxhmHD2xeVcivpsWDQBoEemOzz+MMKWZMMY4AmHu4ljMXXLLmkV6JKu3p0PqIMC7A33gLBPickwBxv9yBxptcfl9PB2glBcvcnTwVA6UchEGdvOAq1KEm3eKMP6XO8jKKX9Hslk9BVwUYrSJVKFNZHEdnZSmwZtf2O7ZrScvq6GQ5aNnKyconYWIS9Li52U5yLk3ZNdNKTR7KH3MXS1mr8tFr9Yy9H5OhuQMHX5dmYP4FOO50OsBHw8RmtaWQu4kQF6BAbEJWkxemIX4B4bortydD50eGNpDDokYuBmvxdS/jYsb2dqJy2rInfPQs7XMdE6mLc02DWN2V4nM5pbF3NVi1toc9H7u3jlJ1+HXFdlm58TXXYRm/RSQOwmRV6DHzQQtvl9gfk7+rVQNaqLproWmv6tP+QwAELdgNc4NHWurbFW4Go27ID83HfvWGdsc78BqGPhhcZuTnWbe5py61+asfKjNadX9PbT+F7U5FUH/rEzmtBGBwcAzRM++b7/9FjNnzkRcXNw/J34M2ad3WOW4zypl/Q5Yeez/7d15VM35/wfw573taZG0UiqFskb2IbLGIAzGvs+MfRkGYzf2ZWSYkS3bGMsXY9cgO43sMYmQCqW0SKXt3vv7o587rptlZtT71n0+zukc9/35VM/7wf3c131vxfsT70/ti/pS/HaeL6dv6vWZBJ91OCM6hkY5f9AbnYbdFR1DY+xfXRlD5797b1httO57SwyZJ344tCZZP7UsDutV/vCJWqR97l38eo73nDf1aaK5CyeN8i94F4WisHKs5m+Jxh5SKpZ++eUX1K1bF5aWlrhw4QKWLFmiEXuMEhERERHRx2NBSsVSZGQk5s6di+TkZDg6OuLbb7/FlCklZ9gMEREREZUMXNTo/ViQUrG0fPlyLF++XHQMIiIiIiL6D1iQEhERERERFRL2kL6f2N2biYiIiIiISGuxICUiIiIiIiIhOGSXiIiIiIiokHDE7vuxh5SIiIiIiIiEYA8pERERERFRIeGiRu/HHlIiIiIiIiISgj2kREREREREhUShYA/p+7CHlIiIiIiIiIRgQUpERERERERCcMguERERERFRIZFzUaP3Yg8pERERERERCcEeUiIiIiIiokLCRY3ejz2kREREREREJAQLUiIiIiIiIhKCQ3aJiIiIiIgKiYKLGr0Xe0iJiIiIiIhICPaQEhERERERFRL2kL4fe0iJiIiIiIhICPaQEhERERERFRI5t315L/aQEhERERERkRAsSImIiIiIiEgIDtklIiIiIiIqJFzU6P3YQ0pERERERERCsIeUiIiIiIiokCi4qNF7sYeUiIiIiIiIhJAoWLITEREREREVin7T44T97i0/2An73R+LQ3aJPsKm06ITaJYBzYDYyHDRMTSKg5sHxqx4KTqGRlkxxhTjVqWLjqFRlo80wQ/b80TH0BjTe+riyLVc0TE0SrvaelhxkH0FbxrTQYJfz/GavKlPEwkO61UWHUOjtM+9KzrCO8mL0aJGP//8M5YsWYL4+HjUrFkTK1euRL169d55/v/+9z9Mnz4djx49gpubGxYtWoR27dr9o9/JIbtERERERERabufOnRg/fjxmzpyJa9euoWbNmmjTpg0SEhIKPP/ixYvo2bMnBg8ejOvXr8PPzw9+fn64ffv2P/q9LEiJiIiIiIgKiUKuEPb1T/z4448YOnQoBg4cCA8PDwQEBMDY2BiBgYEFnr9ixQq0bdsWEydOhLu7O3744QfUrl0bq1at+ke/lwUpERERERFRCZSdnY20tDSVr+zsbLXzcnJycPXqVbRs2VLZJpVK0bJlS4SEhBT4s0NCQlTOB4A2bdq88/x3YUFKRERERERUSBQKhbCvBQsWwNzcXOVrwYIFahmfP38OmUwGGxsblXYbGxvEx8cX+Lzi4+P/0fnvwkWNiIiIiIiISqApU6Zg/PjxKm0GBgaC0hSMBSkREREREVEJZGBg8FEFaNmyZaGjo4Nnz56ptD979gy2trYFfo+tre0/Ov9dOGSXiIiIiIiokCjkcmFfH0tfXx916tRBcHCwsk0ulyM4OBgNGzYs8HsaNmyocj4AHD9+/J3nvwt7SImIiIiIiLTc+PHj0b9/f3h5eaFevXrw9/dHRkYGBg4cCADo168fypUrp5yDOmbMGHh7e2PZsmVo3749duzYgStXrmDt2rX/6PeyICUiIiIiIiok8n+4/YooPXr0QGJiImbMmIH4+HjUqlULQUFByoWLYmJiIJX+PcC2UaNG+O233zBt2jR8//33cHNzw759+1CtWrV/9HtZkBIRERERERFGjhyJkSNHFnjs9OnTam3dunVDt27d/tPv5BxSIiIiIiIiEoI9pERERERERIVEoSgeQ3ZFYQ8pERERERERCcEeUiIiIiIiokKiKCaLGonCHlIiIiIiIiISgj2kREREREREhYQ9pO/HHlIiIiIiIiISggUpERERERERCcEhu0RERERERIVErpCLjqDR2ENKREREREREQrCHlIiIiIiIqJBwUaP3Yw8pERERERERCcGClIiIiIiIiITgkF0iIiIiIqJCwiG778ceUiIiIiIiIhKCPaRERERERESFRKFgD+n7sCAlpdjYWMycORNBQUF4/vw57Ozs4OfnhxkzZsDS0vKjfsajR4/g7OyM69evo1atWoUbuJi7emobLh3fgPQXibAuXwWtv5wOe+caBZ5749wu3PpzH54/jQQA2DpWhbffeOX5Mlkuzu7zx4PbZ5H6PBYGRiZwcm+EZp2/hWlpmyJ7Tv/V/kNHsGvvPiSnpKKisxNGfj0EVSpX+uD3nTpzDvOW/IhGDephzrQpyvaWn3cu8PyhA/uhR9eCj4n0WQ09+NTRh5mxBE+ey7HndBZinr1777Jarrpo11AfZcykSEyV4+CFbIQ/kqmcY2MhRYfPDOBaTgdSKfAsWY7Aw6+Q8jL/5jiyqxHcyqveCi7cysGuk9mf/gn+C42r68HHUw+mxhI8fS7H3rPZiEl49zWpWVEHvg0MUMZUgsQXchy6mIM70X9fk+UjTQr8vgMXsnHqei4AYHo/Y5QxUx1AdOhiNoKv5X6CZ/TpeblJ0LCKFCZGwLMUIOiqDE+TCz7XygzwriGFnYUEpU0k+OOaDKF3Vd8o1XGVoI6bFKVL5T9OfAGcvS3Hg7ji84bq/LHtOHlwI16+eA57x8roMuB7VHCtXuC5IcG7cfncAcQ/vg8AKO/sgfY9xqic/zL1OQ5uX467YRfxKvMlKlapgy4DvoeVXYUieT6fwq0L23Dj9AZkvnwOS7sqaNJ5GmwcC77nJMdHIvSPn5D4+C+8THmKxh2noGbT/irnbJ3ng5cpT9W+t1qjXmjaZUahPIdP7fLJbQj5YwPSXzyHjUMVtO05DeVcCr4m187uQljIfiQ+yb8P21Woiuadx73z/MNbZ+LamZ1o3WMK6rfqX+A5xVmZz7zg8u1gmNeuBkN7a1zpOhzPDgSLjkXFFAtSAgA8fPgQDRs2RKVKlbB9+3Y4Ozvjr7/+wsSJE3H06FH8+eefKFOmjOiYJUb45SMI3r0AbXvNhr1zTVwO3oydPw3GV7ODUMpMvfiPvncJHnXbo3zF2tDV00dI0HrsWDEIQ2cehqmFDXJzshAfG47G7YfBunwVZGWm4fjOedj98zAMnLpXwDP8506dPY+A9RsxZsQ3cK9cCXv2H8TkGXOwcc0qWJQu/c7vi3+WgDWBm1G9qofasV1bA1Ueh165hmU//YwmjRt+6vj/maebLjo3McCuU1l4FC9Hs1p6GOZnjHlbMpD+Sr0QcLKTop+vIQ5dyMZfUTLUqayLwZ8bYen2TMQl5RdsluYSjOlmjD//ysXRP7ORlaOAXRkpcvNUf9bFWzk48meO8nFOnmYUHrVcdeH3mT7+dzob0fEyeNfSx9cdjbBgW2bB18RWir5tDHE4JAd/PcpDnUp6GNTOEMt2vkJ8cv41mRGYofI97hV00MPHAGEPVC/KkT+z8Wf4323ZOZpxTd7m4ShBK08pjlyW40mSAvUrS9GruQ5+OSRDZgGfKejqAinpwJ0YOVrVLnjWTlomcPKGHMkvFYAEqOksRY8mUqwLkiExrZCf0CdwPeQo9m1djG6DZ6CCaw2cOboVaxZ+jSnLDsLUXP319f6dy6jdqB2cK9WCrp4+Th4MRMCCrzBpyT6ULmMDhUKBDT+OgY6OLgZP+AmGRiY4fWQLVs8fgklL9sPA0FjAs/xnIm8cwYUDC+HddRZsHGsi7NxmHFo3BD2/OwpjU/VrkpuTBbMyDqhYoy0uHFhY4M/8YsxuKOR/f9iTFB+Jg2sHoWKNNoX2PD6lv0KP4PiuhWjXZxbKudTEpROb8Zv/EAyfe7Tg+/DdUFSr1x7lK3pCV88AF4+uw7blg/HNnEMws1D94Dfi2nE8eXgTpqWti+rpFDmdUsZIC7uL2E174LX7Z9FxqJjjHFICAIwYMQL6+vo4duwYvL294ejoCF9fX5w4cQJPnjzB1KlTAQASiQT79u1T+d7SpUtj06ZNAABnZ2cAgKenJyQSCZo1a6Y8LzAwEFWrVoWBgQHs7OwwcuRI5bGYmBh06tQJJiYmMDMzQ/fu3fHs2TPl8VmzZqFWrVoIDAyEo6MjTExMMHz4cMhkMixevBi2trawtrbGvHnzVLKlpqZiyJAhsLKygpmZGXx8fHDz5s1PeOX+ndATG1Hzs+6o0bgrytq7om3v2dDVN0TYxT0Fnt9p8DLUadYbNg7usLStiHb95kKhkONRRAgAwNDIFD3HboS7VztY2rqgnEsttO45HfExf+FFsvon2Jpoz74DaNemFdq2aoEKjg4YO+IbGBgYIOj4uz9xlclkWLB0Ofr3/hJ2tuo9wWUsLFS+Ll4KRa3q1WBva1uYT+VfaVZbHxf/ysWl8Dw8S5Zj18ls5OQp0KCqXoHne9fSR0S0DCev5eJZihxH/szB4wQ5mtT8+/zPGxog/FEeDlzIxpNEOZJeKHA7SqZWzOXkAS8zFcqv7Jy3f5sYzWrpIeSvXITeycOzFAX+dyr/mtR3L/iz1KY19RARI8Op67lISFHg6KUcPE6Uo0mNv6/Jm8/zZaYC1Zx1cf+xDElpqtckO1f13Jy8t3+bZmhQWYrrDxS4GaXA8zTg8GU5cvOAWi6SAs+PSwaCb8jxV4wCMlmBpyDyqQL34xRITgeSXwKnwuTIyQPKlS34Z2qa04e3oKHPF6jfrDNsy1dEt8EzoK9viEunfy/w/L4jF+Gz1l+inFMV2JRzQY+vZkOhkCPy9p8AgMT4aERH3sQXg6bDsWJ1WNs744tB05Gbk43rF48U5VP7126e2QSP+t3gXq8ryti6wrvrbOjqGSLicsH3HBvH6mjU4Tu4ebaHjm7Br0FGJmVgbGal/Iq+cxpmlo6wr1ivMJ/KJ/Pn8U3wbNINtT7rCit7V7TvMxt6+oa4cb7ga9J56FJ4Ne8FW0d3lLVzwecD8u/DUXdCVM5LS3mGoO1z4TdkCaQ6JbffJ/GPs7g30x/P9p8QHaVYkMvlwr6KAxakhOTkZPzxxx8YPnw4jIyMVI7Z2tqid+/e2Llz50eNfw8NDQUAnDhxAnFxcdi7N793bvXq1RgxYgS++uor3Lp1CwcOHICrqyuA/P+knTp1QnJyMs6cOYPjx4/j4cOH6NGjh8rPfvDgAY4ePYqgoCBs374dGzZsQPv27fH48WOcOXMGixYtwrRp03Dp0iXl93Tr1g0JCQk4evQorl69itq1a6NFixZITn7HeLYiIMvLQXzMX3B2b6Rsk0ilcKrSCE8eXv+on5Gb8wpyWR4MS5m/85zsV+mARAJDI7P/nLmw5ebm4t79B6hdq6ayTSqVonatGgiPuPvO7/t1xy6UNjeHb+uWH/wdKSmpuHT5Ktp+xLlFTUcKOFhLcS/m7wpBAeBejAxOtgW/TDvb6eBujGqVFBGTBydbHQCABICHsy4SUuX4xs8Ic4eWwrgexqjuov4GyauyLuZ9VQqTexvj80b60NOA91A6UqC8tRT3YlWvSeRjGSr8/3N8m5Otjsr5AHA3RoYK77iGJkYSeFTQwaU76tVmi9p6mDukFL7tYYTmnnqQamAtJpUCdmWAqHjV1+aoZwqU/0TFo0QCVHWUQE8XePxcM3uJ35SXl4vHUeGoVK2Bsk0qlcKtWgNER37ch5E52VmQ5+XB2CT/9TUvN/8TGj19fZWfqaurh4d3P+41WyRZXg4Sn/yF8pVU7znl3RoiPvrGJ/sd964egHu9LpBINPA/y1tkeTmIi/4Lzh6q18TZvSEeP7zxUT/j9X3Y6I37sEIux/4N36Fhm8GwLuf2qWMTlVga8LaDRIuMjIRCoYC7u3uBx93d3ZGSkoLExMQP/iwrKysAgKWlJWzf6IWaO3cuvv32W4wZM0bZVrduXQBAcHAwbt26haioKDg4OAAAtmzZgqpVq+Ly5cvK8+RyOQIDA2FqagoPDw80b94cd+/exZEjRyCVSlG5cmUsWrQIp06dQv369XH+/HmEhoYiISEBBgYGAIClS5di37592L17N7766qt/cbX+u8z0FCjkMrVhUqXMLJEU//CjfsapvUthYm6tUtS+KS83G6f3LoVH3fYwMCp4zpwmeZH2EnK5HBalVQtsi9KlEfv4SYHfc+uvcBw9Fow1P/34Ub/jWPApGBsZoUmjBh8+uYiVMpJARyrBy0zVTzJfZipgXabg4svUWIKXmQq1881K5b8ZNDGWwFBfgpZe+jgSko2D52Vwd9LFoM8NsWrPKzx4kl+4Xb2bh5Q0OV5kKGBfVoqOjQ1gbSFF4OGsQnimH095TV6pP0fr0gUXmAVfEznMjAu+1dWroousXKgN1z0blovHiXJkZingbCtF+4YGMCslwf7zGtJ1/P+MDQCpVIL0LNXnnJEFlDX9b0WBtTkwsJUOdHXye9D/d06O58VguG5GWgrkcpna0FxTc0skPI36qJ9x6LcfYWZhhUrV8of229g7w6KsHQ5tX4HuQ2ZA39AYZ45sQWryM6Slfvi+KFpWxv/fc0xUr4mRaVmkJHzcNfmQqNvByM56iSpemjc3vyCv78MmZm/fh8viefzHXZPg3ctgWtoaLm8UtReC1kEq1UG9Fn0/aV4q/rjty/uxICWlwloBLCEhAU+fPkWLFi0KPH7nzh04ODgoi1EA8PDwQOnSpXHnzh1lQerk5ARTU1PlOTY2NtDR0YFUKlVpS0hIAADcvHkT6enpagsyvXr1Cg8ePCgwS3Z2NrKzVSde5RezBh//hAtZSNBa3Ll8BL2/3QJdPfVcMlkufl87BgqFAm17zRaQsPBlZr7Coh9XYPyoYTA3/7ge4KATwfBp1hT6b/RylGSvOyluP8zD6f9frOfJ8xw42emgcXU9ZUEacvvvhXrikuRIy1BgZFdjWJpnI+lFyb6B1vPQw7V7uch7a+jqmRuq1yRPDnRvZoBDF3MgKx6jn/6z5y+BtUEyGOgBHo5SdGwgxZZgWbEoSv+LE/vX43rIUYyYvhF6+vmvrzq6ehg4zh871s7A1KGNIZXqoFK1BnCv1YQrZ/6/O6G74Vi5CUqZF59F9P6LC0fW4q/QI+g38e/7cNyj2wg9sRVDZ+wpFr3ERJqEBSnB1dUVEokEd+7cQefO6p9u3rlzBxYWFrCysoJEIlG7Aefmvn/lybeHAf9benqq81gkEkmBba/Hy6enp8POzg6nT59W+1ml37FIzoIFCzB7tmoRN3PmTDg1m/Wvc7/N2MQCEqkOMl8mqbRnpCXBxLzse7/30rENCAlai55jN8K6fBW14zJZLvatHYu05KfoOW5zsegdBQBzM1NIpVKkpL5QaU9JTYWFRWm185/GxyP+WQKmzZmvbHv977J1x67YtGYV7O3slMdu3Q5H7OMnmPbdt4XzBP6jjFcKyOQKmBpLAfxd8ZgaS/Ayo+AK6GWmAqbGqm96TI0lSMtQ/P0zZQrEJ6l+/7NkGVzs3/3SHx2fX51ZmUuR9OIdkwyLgPKaGBXwHDMLLgIKvibSAs93sZPCxkKKLUEfnhwa80wOHR0JyphJkJiqOQVIZjYglytgYihB/oDmfKUModZr+k/J5fmLHwFAfIocdmWkqFc5f/EkTVbKzAJSqQ5evlB9fX35Iglmpd//+nrq0EYEH9iAYd+vg32FyirHHFyqYuLCPXiV+RKyvFyYmJXB8mk94eBS9ZM/h0/NsNT/33PSVa/Jq5fPYWz2/mvyMV4mP8HjyBC07b/yP/+sovL6Ppye9vZ9+PkH78Mhf2zAhaPr0OfbQNg4/P3vJCbyKjJeJmHFdz7KNoVchuO7FuHSic0Yvejkp30SVKwoFJr92ika55ASLC0t0apVK/zyyy949eqVyrH4+Hhs27YNPXr0gEQigZWVFeLi4pTHIyMjkZmZqXz8uvdJ9sZqGaampnByckJwcMGL07i7uyM2NhaxsbHKtvDwcKSmpsLDQ33l1I9Vu3ZtxMfHQ1dXF66uripfZcsWfMOZMmUKXrx4ofI1ZcqUAs/9t3R09WHrWBWP3lgIQSGXIzoiBOVcPN/5fX/+sQ4XDv+CHqPXw85JffuC18VockI0eo7dBGMTi0+auzDp6emhkmtFXLsZpmyTy+W4fvMWPKpUVjvfsXw5rFvljzU//aj8ali/LmpVr4Y1P/0Iq7f+fo8eP4FKrhVR0cW50J/LvyGTA7EJclRy+Ht4rgRAJQcdPIov+CYWFSdDJQfVwrKygy4e/X9BKZPnF1LWFqov89alpUh5+e4bYzmr/AzvKvqKikwOPC7gmriV11EWzW97FC9DpfKqQ5wrOegguoBrWN9DD7EJMjxN+vCbBPuyUsjligJX9hVJLs9fpMjJVrUId7aRfPL5nhIJoFsM3jHo6uqhvLMH7t3+ey0BuVyOyL8uoYJbzXd+X/CBQBzbuwZfTw6AY8Vq7zzPyNgUJmZlkBgXjdiHf6GaV/NPmr8w6Ojqw6pcVTyJVL3nPL7/J2wr1PrPP//O5b0wMrFEBXfv//yzioqOrj7sKqjfh6Mi/kR5l1rv/L6LR9fj3KHV6DV2Hezfug9Xb9gRX8/aj69m/q78Mi1tjYZtBqPXuPWF9VSISgT2kBIAYNWqVWjUqBHatGmDuXPnqmz7Uq5cOeXqtT4+Pli1ahUaNmwImUyGSZMmqfRSWltbw8jICEFBQShfvjwMDQ1hbm6OWbNm4ZtvvoG1tTV8fX3x8uVLXLhwAaNGjULLli1RvXp19O7dG/7+/sjLy8Pw4cPh7e0NLy+vf/2cWrZsiYYNG8LPzw+LFy9GpUqV8PTpUxw+fBidO3cu8GcbGBgo55sWpnotB+LQpkmwdaoGe6cauBy8Gbk5r1CjURcAwMGN38G0tA2adc7v0QsJWotzB39Cx8HLYG5ZDukv8uct6RsYQ9+wVP4w3TWjER8Tjm4j1kAulynPMSplDh1dzR+m2tWvIxYv/wmV3SqiciU37N1/CFlZWWjbMn+o98JlK1DWsgyGDOgLfX19ODup7v9nUip/08S32zMyM3H2/EV8PXhAkTyPf+v0tRz0bm2ImAQZYuLl8PbUg76eBJfC80cg9G5tiBfp+ftqAsCZGzkY3dUYzT318NcjGWpX0oWDjRQ7T/499/PktRz09zXEgyd6iHycB/cKuqjqootVe/I/eLI0l6BOZT2EP8pD5qv8OaSdmxri/uM8PH0u/tPc0zdy0aulAWIT5Ih+JoN3TX3o60qUixD1ammAFxkKHA7JvyZnb+ZiZGcjNKuV/5w8K+nBwVqKXafeGoavB9R01cWB8+r7olSwlaKCjQ7uP5YhO1eBCrY68PtMH1fv5eGVZmzNquLPu3J0aiBFXLIET5MUqFdZCj1d4GZUfkHaqYEUL18BJ2/m/31Kpfl7kQL5C0eZGklgUzp/FeHXPaI+NaW4/1SOF5mAgS5QzUkKJ2sJtp0W/2/iYzRr3w+/rZ4KB5eqqOBaDWeO/oqc7Feo7+0HANj2yxSYW1jj857jAADBBzbg6P9Woe/IxShjVQ5pqc8BAAaGxsotXW78+QdMzCxQ2tIOcbGR+H3zQlSv64MqNRoLeY7/VE3vATi5YzKsyleDtWMNhJ3bjLycV6hSN/+ec2L7JJQyt0bDdvn3HFleDlKe5U9tkclykfHiGZ4/uQM9A2OYl/37NVYhlyPi8u+o7OVX7FaUbdBqAPYHToZdhWqwd66B0BObkZv9CjUb51+TfRsmwbS0NVp0zb8mF46uw5n9P6Hz0KUoXVb9PmxsYqH2QbBURxcm5mVR1talaJ9cEdApZYxSro7Kx8bO5WFWswpykl8gKzbuPd9JpK54vXpQoXFzc8OVK1cwc+ZMdO/eHcnJybC1tYWfnx9mzpyp3IN02bJlGDhwIJo0aQJ7e3usWLECV69eVf4cXV1d/PTTT5gzZw5mzJiBJk2a4PTp0+jfvz+ysrKwfPlyTJgwAWXLlsUXX3wBIH+Y7f79+zFq1Cg0bdoUUqkUbdu2xcqV/234j0QiwZEjRzB16lQMHDgQiYmJsLW1RdOmTWFjI3aei0fddshMT8a5Az8hIy0R1uXd0X30epT6/+FTaclxkEj+7o64fnYHZHn5ReebPvt8JJp0GIWXKc8QeTN/OFDg3E4q5/QavwUVKtcv5Gf03zVv+hlevEjDpl93ICUlBRVdnLFgzgzlkN2ExERI/8VSp6fOnocCCjT3bvKJE39a1yPzYGKUjXYNDGBmLMHj53IE7MtULtJjYSqBQvH3v4lHcXJsCcpCu0YG+LyRBImpcmw49Eq5BymQv1jPrpNZaFXXAF2aGSAhRY7Aw1l4+PT/e1FlQGUHHTSrpQ99PSA1XYGb9/Pwx2XNqLxu3M+DiZEEbevpw6yUBE8S5Vhz8JWyp9LCVKoyDOpRvBxbj2WhXQMDtG+oj8RUOQKPZCn3IH2tdiVdSABci1QfriuT5e8J27aePnR0gOQ0Bc7czFXOw9U04TEKGBvI4V1dChND4FkK8NtpGTL+/3MJM2PVaRamRsBXvn/f+hu5S9DIXYpHzxTYejL/34WxAdCpgQ5MjPK3v3mWqsC203K11Xw1lWdDX6SnpSBo9yqkpT5HuQpV8PXkAJj+/5DdlOeqr68Xju+ELC8Xm/zHqfycNl2Hoe0XIwAAaamJ2L91cf7QXwsreDXpiNZdvim6J/UfudVqh6z0ZIT+sRKZLxNR1t4dnw9ZB2PT/GuSnvJUZd5jRloCdi3/ewrPjTOBuHEmEPYudeE3fKuyPTbyItJTn8K9XpeiezKfSNV6+ffhM/tXIj0tETYO7ug1dp1yyG5akuo1uXp6O2R5udi9eozKz2naYQS8O40q0uyawLxONTQM/vvfgsfS7wEAsVv2Imzwpx1ZVhJwUaP3kyg4I5/ogzadFp1AswxoBsRGhouOoVEc3DwwZsVL0TE0yooxphi3Kl10DI2yfKQJftiuoZuaCjC9py6OXNPMYl+UdrX1sOIg35q9aUwHCX49x2vypj5NJDispz6lRZu1z333NnGitRt0S9jvPhKoPs1L07CHlIiIiIiIqJCwh/T9isESBURERERERFQSsSAlIiIiIiIiIThkl4iIiIiIqJDIuQ/pe7GHlIiIiIiIiIRgDykREREREVEh4aJG78ceUiIiIiIiIhKCPaRERERERESFRCHnHNL3YQ8pERERERERCcGClIiIiIiIiITgkF0iIiIiIqJCwkWN3o89pERERERERCQEe0iJiIiIiIgKiULBRY3ehz2kREREREREJAQLUiIiIiIiIhKCQ3aJiIiIiIgKiZyLGr0Xe0iJiIiIiIhICPaQEhERERERFRKFnIsavQ97SImIiIiIiEgI9pASEREREREVEgXnkL4Xe0iJiIiIiIhICBakREREREREJASH7BIRERERERUShYKLGr0Pe0iJiIiIiIhICPaQEhERERERFRIuavR+7CElIiIiIiIiIViQEhERERERkRAcsktERERERFRIFHIuavQ+7CElIiIiIiIiISQKhYKzbIk0XHZ2NhYsWIApU6bAwMBAdByNwGuijtdEHa+JOl4Tdbwm6nhN1PGaqOM1oU+BBSlRMZCWlgZzc3O8ePECZmZmouNoBF4Tdbwm6nhN1PGaqOM1Ucdroo7XRB2vCX0KHLJLREREREREQrAgJSIiIiIiIiFYkBIREREREZEQLEiJigEDAwPMnDmTCwa8gddEHa+JOl4Tdbwm6nhN1PGaqOM1UcdrQp8CFzUiIiIiIiIiIdhDSkREREREREKwICUiIiIiIiIhWJASERERERGRECxIiYiIiIiISAgWpERERERERCQEC1IiKlby8vJw4sQJrFmzBi9fvgQAPH36FOnp6YKTkaaSyWS4ceMGUlJSREchomIoJycHd+/eRV5enugoGiE1NRXr16/HlClTkJycDAC4du0anjx5IjgZFVcsSIk0WGxsLB4/fqx8HBoairFjx2Lt2rUCU4kTHR2N6tWro1OnThgxYgQSExMBAIsWLcKECRMEpyNNMXbsWGzYsAFAfjHq7e2N2rVrw8HBAadPnxYbjqgYyMnJwePHjxETE6PypW0yMzMxePBgGBsbo2rVqsprMGrUKCxcuFBwOjHCwsJQqVIlLFq0CEuXLkVqaioAYO/evZgyZYrYcFRssSAl0mC9evXCqVOnAADx8fFo1aoVQkNDMXXqVMyZM0dwuqI3ZswYeHl5ISUlBUZGRsr2zp07Izg4WGAy8c6dO4c+ffqgYcOGyk+pt27divPnzwtOVvR2796NmjVrAgAOHjyIqKgoREREYNy4cZg6dargdGI8e/YMffv2hb29PXR1daGjo6PypU0OHDjwUV/aKDIyEk2aNIGRkREqVKgAZ2dnODs7w8nJCc7OzqLjFbkpU6bg5s2bOH36NAwNDZXtLVu2xM6dOwUmE2f8+PEYMGAAIiMjVa5Ju3btcPbsWYHJqDjTFR2AiN7t9u3bqFevHgBg165dqFatGi5cuIBjx47hm2++wYwZMwQnLFrnzp3DxYsXoa+vr9Lu5OSk1UOF9uzZg759+6J37964fv06srOzAQAvXrzA/PnzceTIEcEJi9bz589ha2sLADhy5Ai6deuGSpUqYdCgQVixYoXgdGIMGDAAMTExmD59Ouzs7CCRSERHEsbPz++D50gkEshkssIPo2EGDBgAXV1dHDp0SOv/nQDAvn37sHPnTjRo0EDlWlStWhUPHjwQmEycy5cvY82aNWrt5cqVQ3x8vIBEVBKwICXSYLm5uTAwMAAAnDhxAh07dgQAVKlSBXFxcSKjCSGXywt8k/j48WOYmpoKSKQZ5s6di4CAAPTr1w87duxQtjdu3Bhz584VmEwMGxsbhIeHw87ODkFBQVi9ejWA/OF32tYb+Nr58+dx7tw51KpVS3QU4eRyuegIGuvGjRu4evUqqlSpIjqKRkhMTIS1tbVae0ZGhtYW6wYGBkhLS1Nrv3fvHqysrAQkopKAQ3aJNFjVqlUREBCAc+fO4fjx42jbti2A/EV8LC0tBacreq1bt4a/v7/ysUQiQXp6OmbOnIl27dqJCybY3bt30bRpU7V2c3Nz5fwebTJw4EB0794d1apVg0QiQcuWLQEAly5d0to32g4ODlAoFKJjkIbz8PDA8+fPRcfQGF5eXjh8+LDy8esidP369WjYsKGoWEJ17NgRc+bMQW5uLoD8axITE4NJkyaha9eugtNRccUeUiINtmjRInTu3BlLlixB//79lfPiDhw4oBzKq02WLVuGNm3awMPDA1lZWejVqxciIyNRtmxZbN++XXQ8YWxtbXH//n04OTmptJ8/fx4uLi5iQgk0a9YsVKtWDbGxsejWrZtylIGOjg4mT54sOJ0Y/v7+mDx5MtasWaP270TbfOw8t4I+5CnpFi1ahO+++w7z589H9erVoaenp3LczMxMUDIx5s+fD19fX4SHhyMvLw8rVqxAeHg4Ll68iDNnzoiOJ8SyZcvwxRdfwNraGq9evYK3tzfi4+PRsGFDzJs3T3Q8KqYkCn5kSqTRZDIZ0tLSYGFhoWx79OgRjI2NCxxKVNLl5eVh586duHnzJtLT01G7dm307t1bZZEjbbNgwQL8+uuvCAwMRKtWrXDkyBFER0dj3LhxmD59OkaNGiU6onCpqakoXbq06BjCWFhYIDMzE3l5eTA2NlYrNF5v3aANpFKpsqfrXW+BtHUOqVSaP3Du7eGoCoVCa6/JgwcPsHDhQpV7zqRJk1C9enXR0YQ6f/48wsLClNfk9UgUon+DBSmRhsvLy8Pp06fx4MED9OrVC6ampnj69CnMzMxgYmIiOh5pAIVCgfnz52PBggXIzMwEkD/PZ8KECfjhhx8Epyt6ixYtgpOTE3r06AEA6N69O/bs2QM7OzscOXIENWrUEJyw6G3evPm9x/v3719EScSztLSEqakpBgwYgL59+6Js2bIFnmdubl7EycT7UK+ft7d3ESUhIm3CgpRIg0VHR6Nt27aIiYlBdnY27t27BxcXF4wZMwbZ2dkICAgQHbFIbd68GWXLlkX79u0BAN999x3Wrl0LDw8PbN++HRUqVBCcsOjJZDJcuHABNWrUgLGxMe7fv4/09HR4eHho7QcWzs7O2LZtGxo1aoTjx4+je/fu2LlzJ3bt2oWYmBgcO3ZMdEQSKCcnB7///jsCAwNx7tw5tGvXDoMHD0bbtm21dqEaKlhBi/cA+T3IBgYGaiu+a4vLly/j1KlTSEhIUFsk7McffxSUioozFqREGszPzw+mpqbYsGEDLC0tcfPmTbi4uOD06dMYOnQoIiMjRUcsUpUrV8bq1avh4+ODkJAQtGjRAv7+/jh06BB0dXWxd+9e0RGFMDQ0xJ07d7Ryn8CCGBkZ4d69e3BwcMCYMWOQlZWFNWvW4N69e6hfvz5SUlJERywSaWlpyjl/73pj/Zq2zQ18LSYmBps2bcLmzZuRnZ2N/v37Y/bs2dDV1d4lNlJTU7FhwwbcuXMHQP7ieoMGDdLKHuM3h3cXpHz58hgwYABmzpypHO5c0s2fPx/Tpk1D5cqVYWNjo3J9JBIJTp48KTAdFVcsSIk0mKWlJS5evIjKlSvD1NRUWZA+evQIHh4eyuGZ2sLY2BgRERFwdHTEpEmTEBcXhy1btuCvv/5Cs2bNkJiYKDqiEF5eXli0aBFatGghOopGsLe3x+7du9GoUSNUrlwZc+fORbdu3XD37l3UrVv3g8VZSaGjo4O4uDhYW1u/8421Ns8NfFNUVBQGDx6MM2fOIDExEWXKlBEdSYgrV66gTZs2MDIyUi6cd/nyZbx69QrHjh1D7dq1BScsWlu2bMHUqVMxYMAA5fUIDQ3F5s2bMW3aNCQmJmLp0qWYOHEivv/+e8Fpi4aNjQ0WLVqEAQMGiI5CJYj2fgRIVAxw301VJiYmSEpKgqOjI44dO4bx48cDyO8hfPXqleB04sydO1c5X7ROnTooVaqUynFt6/3q0qULevXqBTc3NyQlJcHX1xcAcP36dbi6ugpOV3ROnjypLKxOnTolOI3myc7Oxp49exAYGIiQkBC0b98ehw8f1tpiFADGjRuHjh07Yt26dcpe4ry8PAwZMgRjx4796BWKS4rNmzdj2bJl6N69u7KtQ4cOqF69OtasWYPg4GA4Ojpi3rx5WlOQSqVSNG7cWHQMKmHYQ0qkwXr06AFzc3OsXbsWpqamCAsLg5WVFTp16gRHR0ds3LhRdMQi1bt3b0RERMDT0xPbt29HTEwMLC0tceDAAXz//fe4ffu26IhCvDlU7M1eMG3t/crNzcWKFSsQGxuLAQMGwNPTEwCwfPlymJqaYsiQIYITkkihoaHYuHEjduzYAScnJwwcOBB9+vTR6kL0NSMjI1y/fl1tv97w8HB4eXlp3agcIyMjhIWFwc3NTaU9MjISNWvWRGZmJqKiolC1alWtuTaLFy/G06dPVfYEJ/qv2ENKpMG476aqn3/+GdOmTUNsbCz27NkDS0tLAMDVq1fRs2dPwenEYe+XKj09PUyYMEGtfdy4cQLSiBMWFvbR52rTysMNGjSAo6MjRo8ejTp16gDI38LibR07dizqaMKZmZkhJiZGrSCNjY3VylE5Dg4O2LBhAxYuXKjSvmHDBjg4OAAAkpKSVLZlK+kmTJiA9u3bo2LFivDw8FDbQkpb13Kg/4Y9pEQaLi8vDzt27FDZ70vb990k+pCtW7dizZo1ePjwIUJCQlChQgX4+/vD2dkZnTp1Eh2vSLyeN/qh27y29aJ/zOIz2nZNXhs9ejR+//13LF26FI0aNQIAXLhwARMnTkTXrl21rlfswIED6NatG6pUqYK6desCyJ9ne+fOHezZsweff/45Vq9ejcjISK1ZXXbkyJFYv349mjdvrraoEQCtG7lFnwYLUiIqVrgCpLoPzetq2rRpESXRDKtXr8aMGTMwduxYzJs3D7dv34aLi4tyNVVt6VGOjo7+6HO1ccskUpeTk4OJEyciICAAeXl5APJHHAwbNgwLFy6EgYGB4IRF79GjRwgICMC9e/cA5K/2/vXXXyM9PR3VqlUTnK7omZqaYseOHcrt14g+BRakRBrmwIED8PX1hZ6eHg4cOPDec7VtSBlXgCxYQT0+b35qrW09PR4eHpg/f75y26TXq1Pfvn0bzZo1w/Pnz0VHJA2QlJSkHPYfGxuLdevWISsrCx06dECTJk0EpxMrMzMTDx48AABUrFgRxsbGghNphrS0NGzfvh2BgYG4cuWK1r22AvkfXv3xxx9qw7qJ/gsWpEQaRiqVIj4+XrlVw7to45CyJk2awNXVtcAVIB8+fKh1K0C+9uLFC5XHubm5uH79OqZPn4558+Zp3XYwRkZGiIiIQIUKFVQK0sjISNSoUUMrV2TesmXLe4/369eviJKId+vWLXTo0AGxsbFwc3PDjh070LZtW2RkZEAqlSIjIwO7d++Gn5+f6KikIc6ePYsNGzZgz549sLe3R5cuXdC1a1flMF5tsnHjRgQFBWHjxo38oII+GRakRFRscAXIf+bMmTMYP348rl69KjpKkfLw8MCCBQvQqVMnlYJ05cqV2LhxI65duyY6YpF7e9GV3NxcZGZmQl9fH8bGxkhOThaUrOj5+vpCV1cXkydPxtatW3Ho0CG0adMG69atAwCMGjUKV69exZ9//ik4adHo0qULNm3aBDMzM3Tp0uW952rTgjXx8fHYtGkTNmzYgLS0NHTv3h0BAQG4efMmPDw8RMcTxtPTEw8ePIBCoYCTk5Paokba+PpK/x1X2SWiYoMrQP4zNjY2uHv3rugYRW78+PEYMWIEsrKyoFAoEBoaiu3bt2PBggVYv3696HhCpKSkqLVFRkZi2LBhmDhxooBE4ly+fBknT55EjRo1ULNmTaxduxbDhw9XjkgZNWoUGjRoIDhl0TE3N1cO8TczM1NbpEYbdejQAWfPnkX79u3h7++Ptm3bQkdHBwEBAaKjCceRA1QY2ENKpMFGjx4NV1dXjB49WqV91apVuH//vtateMgVIAv29vYeCoUCcXFxWLhwIfLy8grc0qKk27ZtG2bNmqWcB2dvb4/Zs2dj8ODBgpNplitXrqBPnz6IiIgQHaXIvDktAoBKLzoAPHv2DPb29lo3JYL+pquri9GjR2PYsGEqe5Dq6elpfQ8pUWFgDymRBtuzZ0+BCxs1atQICxcu1LoCbOnSpZBIJOjXr1+BK0Bqq1q1ahW4vUeDBg0QGBgoKJVYvXv3Ru/evZGZmYn09HRl8UGqdHV18fTpU9ExitzbvYDsFczn4+ODvXv3onTp0irtaWlp8PPzw8mTJ8UEK2Lnz5/Hhg0bUKdOHbi7u6Nv37748ssvRcfSKFevXlVZ7d7T01NwIirO2ENKpMEMDQ1x+/ZtuLq6qrTfv38f1apVQ1ZWlqBkYnEFSFVvb+8hlUphZWUFQ0NDQYlI07z9wdbrXvRVq1bBwcEBR48eFZSs6EmlUvj6+iq3MDl48CB8fHxQqlQpAEB2djaCgoK0sof07d7j1xISElCuXDnk5uYKSiZGRkYGdu7cicDAQISGhkImk+HHH3/EoEGDtHaaSEJCAr788kucPn1a+cFFamoqmjdvjh07dsDKykpsQCqWWJASabBq1arhm2++wciRI1XaV65cidWrVyM8PFxQMjFevHgBmUyGMmXKqLQnJydDV1cXZmZmgpJpntTUVLVeDm3x7NkzTJgwAcHBwUhISFDrOdbWQuNNEokEVlZW8PHxwbJly2BnZycoWdEbOHDgR523cePGQk6iOV4P+69VqxZOnjyp8hork8kQFBSENWvW4NGjR4ISinf37l1s2LABW7duRWpqKlq1avXBrdlKoh49euDhw4fYsmUL3N3dAeQvLNi/f3+4urpi+/btghNSccSClEiDBQYGYuTIkZg4cSJ8fHwAAMHBwVi2bBn8/f0xdOhQwQmLlq+vLzp06IDhw4ertAcEBODAgQM4cuSIoGRiLVq0CE5OTujRowcAoHv37ti9ezfs7Oxw5MgR1KxZU3DCouXr64uYmBiMHDkSdnZ2asMxO3XqJCgZkWaSSqXK/ycFvS00MjLCypUrMWjQoKKOpnFkMhkOHjyIwMBArSxIzc3NceLECbUtb0JDQ9G6dWukpqaKCUbFGgtSIg23evVqzJs3TznPy8nJCbNmzdKqfQNfK1OmDC5cuKD8VPa1iIgING7cGElJSYKSieXs7Ixt27ahUaNGOH78OLp3746dO3di165diImJwbFjx0RHLFKmpqY4d+4catWqJTqKxpLJZLh16xYqVKigtiUMaZ/o6GgoFAq4uLggNDRUZdilvr4+rK2toaOjIzAhaYp3vb5ev34d3t7eSEtLExOMijUuakSk4YYNG4Zhw4YhMTERRkZGMDExER1JmOzsbOViRm/Kzc3Fq1evBCTSDPHx8XBwcAAAHDp0CN27d0fr1q3h5OSE+vXrC05X9BwcHArs5dFmY8eORfXq1TF48GDIZDI0bdoUISEhMDY2xqFDh9CsWTPREUmgChUqAADkcrngJKTpfHx8MGbMGGzfvh329vYAgCdPnmDcuHFo0aKF4HRUXEk/fAoRaQIrKyutLkYBoF69eli7dq1ae0BAAOrUqSMgkWawsLBAbGwsACAoKAgtW7YEkD/0ThvnS/r7+2Py5MlaPd/tbbt371YO3T548CAePXqEiIgIjBs3DlOnThWcjjTFggULClyZOzAwEIsWLRKQiDTNqlWrkJaWBicnJ1SsWBEVK1aEs7Mz0tLSsHLlStHxqJjikF0iDcbFWVRduHABLVu2RN26dZWfxAYHB+Py5cs4duwYmjRpIjihGCNHjsShQ4fg5uaG69ev49GjRzAxMcGOHTuwePFiXLt2TXTEImVhYYHMzEzk5eXB2NgYenp6KseTk5MFJRPH0NAQ9+/fR/ny5fHVV1/B2NgY/v7+iIqKQs2aNTnMjgDkTwn57bfflPs8v3bp0iV8+eWXiIqKEpSMNIlCocCJEyeU+xe7u7srPwgl+jc4ZJdIgw0YMAAxMTGYPn16gYuzaJvGjRsjJCQES5Yswa5du2BkZIQaNWpgw4YNKpuXa5vly5fDyckJsbGxWLx4sbInPS4uTm0BKG2wfPlyrf+/8jYbGxuEh4fDzs4OQUFBWL16NYD8LZQ4N5Bei4+PL3DFZSsrK8TFxQlIRJpIIpGgVatWaNWqlegoVEKwICXSYOfPn+fiLG+pVasWtm3bJjqGRtHT08OECRPU2seNGycgjXgDBgx45zFtnWs8cOBAdO/eXfnB1uvejEuXLqFKlSqC05GmcHBwwIULF+Ds7KzSfuHCBeV8QdJuo0ePhqurK0aPHq3SvmrVKty/fx/+/v5iglGxxjmkRBqMi7OoiomJee+Xttq8eTMOHz6sfPzdd9+hdOnSaNSoEaKjowUmE+PtN0qvZWRkoF27dkWcRjPMmjUL69evx1dffYULFy7AwMAAAKCjo4PJkycLTkeaYujQoRg7diw2btyI6OhoREdHIzAwEOPGjdO6bcaoYHv27EHjxo3V2hs1aoTdu3cLSEQlAeeQEmmwY8eOYdmyZVizZg2cnJxExxHuzb3yCqJtc2pfq1y5MlavXg0fHx+EhISgZcuWWL58OQ4dOgRdXV3s3btXdMQiVbFiRfTp0wezZ89WtmVkZKBt27YAgHPnzomKRqTRFAoFJk+ejJ9++gk5OTkA8ucfT5o0CTNmzBCcjjSBoaEhbt++DVdXV5X2+/fvo1q1asjKyhKUjIozDtkl0mA9evRAZmYmKlasyMVZkL/P2Ztyc3Nx/fp1/Pjjj5g3b56gVOLFxsYq3xzs27cPXbt2xVdffYXGjRtr5XYerxe4srCwwNixY/Hy5Uu0adMGurq6OHr0qOh4wgQHBysXSHt7e4+CVlYl7SORSLBo0SJMnz4dd+7cgZGREdzc3JQ96kSurq4ICgrCyJEjVdqPHj0KFxcXQamouGNBSqTBOBdD1ettK97k5eUFe3t7LFmyBF26dBGQSjwTExMkJSXB0dERx44dw/jx4wHkf5KtjXMmK1asiKCgIDRv3hxSqRTbt2+HgYEBDh8+jFKlSomOJ8Ts2bMxZ84ceHl5cYE0+iATExPUrVtXdAzSQOPHj8fIkSORmJgIHx8fAPkfdi1btozvWehf45BdIir27t+/j5o1ayIjI0N0FCF69+6NiIgIeHp6Yvv27YiJiYGlpSUOHDiA77//Hrdv3xYdUYiQkBC0atUK9evXx6FDh2BkZCQ6kjB2dnZYvHgx+vbtKzoKabCMjAwsXLjwnT3pDx8+FJSMNMnq1asxb948PH36FED+dkGzZs1Cv379BCej4oo9pETFRFZWlnJOz2tmZmaC0ojx9l6JCoUCcXFxmDVrllZv+/Lzzz9j2rRpiI2NxZ49e2BpaQkAuHr1Knr27Ck4XdHw9PQssNfPwMAAT58+VVmEQ9v2ZQWAnJwctb0lid42ZMgQnDlzBn379mVPOr3TsGHDMGzYMCQmJsLIyEi51RjRv8UeUiINlpGRgUmTJmHXrl1ISkpSO65ti/gUtKiRQqGAg4MDduzYgYYNGwpKRqK9uYDRh8ycObMQk2imSZMmwcTEBNOnTxcdhTRY6dKlcfjw4QJXUSUCAB8fH+zduxelS5dWaU9LS4Ofnx9OnjwpJhgVa+whJdJg3333HU6dOoXVq1ejb9+++Pnnn/HkyROsWbMGCxcuFB2vyJ06dUrlsVQqhZWVFVxdXaGrq90vZ+fOncOaNWvw8OFD/O9//0O5cuWwdetWODs747PPPhMdr9BpY5H5T2RlZWHt2rU4ceIEatSoobZA2o8//igoGWkSCwsLlClTRnQM0mCnT59WG60F5L/GcAVz+re0+x0ckYY7ePAgtmzZgmbNmmHgwIFo0qQJXF1dUaFCBWzbtg29e/cWHbFIeXt7i46gkfbs2YO+ffuid+/euHbtGrKzswEAL168wPz583HkyBHBCYvW5cuXIZfLUb9+fZX2S5cuQUdHB15eXoKSiRMWFoZatWoBgNbOKaYP++GHHzBjxgxs3rwZxsbGouOQBgkLC1P+OTw8HPHx8crHMpkMQUFBKFeunIhoVAJwyC6RBjMxMUF4eDgcHR1Rvnx57N27F/Xq1UNUVBSqV6+O9PR00REL3YEDBz763I4dOxZiEs3l6emJcePGoV+/fjA1NcXNmzfh4uKC69evw9fXV+WNgzaoV68evvvuO3zxxRcq7Xv37sWiRYtw6dIlQcmINJunpycePHgAhUIBJycntZ50bZx/TfnenDJTUOlgZGSElStXYtCgQUUdjUoA9pASaTAXFxdERUXB0dERVapUwa5du1CvXj0cPHhQbf5GSeXn5/dR50kkEq2bU/va3bt30bRpU7V2c3NzpKamFn0gwcLDw1G7dm21dk9PT4SHhwtIJM7HbIUkkUiwZ8+eIkhDmu5jX29J+0RFRUGhUMDFxQWhoaGwsrJSHtPX14e1tTV0dHQEJqTijAUpkQYbOHAgbt68CW9vb0yePBkdOnTAqlWrkJubqzVzvt7edoDU2dra4v79+3ByclJpP3/+vFZuVG5gYIBnz56pPfe4uDitm2tsbm4uOgIVI5yLTe9SoUIFALwnU+HgkF2iYiQ6OhpXr16Fq6sratSoITpOkcnKysKJEyfw+eefAwCmTJminCcJALq6upgzZw4MDQ1FRRRqwYIF+PXXXxEYGIhWrVrhyJEjiI6Oxrhx4zB9+nSMGjVKdMQi1bNnT8TFxWH//v3Kgiw1NRV+fn6wtrbGrl27BCckIiqetmzZ8t7j3IuU/g0WpESk8QICAnD48GEcPHgQAGBqaoqqVavCyMgIABAREYGJEydi/PjxImMKo1AoMH/+fCxYsACZmZkA8nsJJ0yYgB9++EFwuqL35MkTNG3aFElJSfD09AQA3LhxAzY2Njh+/DgcHBwEJyTSTAVtrfUmbZ0WQX+zsLBQeZybm4vMzEzo6+vD2NgYycnJgpJRccaClEjDBQcHIzg4GAkJCWpDZQIDAwWlKlpNmjTBd999hw4dOgCAysI9APDrr7/i559/RkhIiMiYQshkMly4cAE1atSAsbEx7t+/j/T0dHh4eGj1ZuUZGRnYtm0bbt68CSMjI9SoUQM9e/ZUW6SFiP62f/9+lce5ubm4fv06Nm/ejNmzZ2Pw4MGCkpEmi4yMxLBhwzBx4kS0adNGdBwqhliQEmmw2bNnY86cOfDy8oKdnZ3aJ9e///67oGRFy87ODiEhIco5klZWVrh8+bLy8b1791C3bl28ePFCXEiBDA0NcefOHTg7O4uOQkQl0G+//YadO3eqFaxEr125cgV9+vRBRESE6ChUDGnX6g5ExUxAQAA2bdqEvn37io4iVGpqqsqc0cTERJXjcrlc5bi2qVatGh4+fKjVBemBAwfg6+sLPT29D24VpK3bAxH9Ww0aNMBXX30lOgZpMF1dXTx9+lR0DCqmWJASabCcnBw0atRIdAzhypcvj9u3b6Ny5coFHg8LC0P58uWLOJXmmDt3rnK+aJ06dVCqVCmV42ZmZoKSFR0/Pz/Ex8fD2tr6vVtXaPP2QET/xqtXr/DTTz+hXLlyoqOQBnj7Az+FQoG4uDisWrUKjRs3FpSKijsO2SXSYJMmTYKJiQmmT58uOopQY8aMwYkTJ3D16lW1lXRfvXoFLy8vtGzZEitWrBCUUCypVKr885vDuhUKBQswIvpoFhYWaq8hL1++hLGxMX799VeOLiCV+w2Qf8+xsrKCj48Pli1bBjs7O0HJqDhjQUqkwcaMGYMtW7agRo0aqFGjhtqCLNqyF+mzZ89Qq1Yt6OvrY+TIkahUqRIA4O7du1i1ahXy8vJw/fp12NjYCE4qxpkzZ9573Nvbu4iSEFFxtnnzZpXHUqkUVlZWqF+/vtrqqqTdXk+dsbKyEpyESgIWpEQarHnz5u88JpFIcPLkySJMI1ZUVBSGDRuG48eP4/XLlkQiQatWrfDLL78oV9zVNgqFAvfv30dOTg4qV64MXV3OxAC4OjXRPxEYGIjevXvDwMBAdBTSYKmpqZg6dSp27tyJlJQUAPm96l9++SXmzp2L0qVLiw1IxRYLUiIqVpKTk3H//n0AgKurK8qUKSM4kThRUVHo2LEjwsPDAeTPtd2zZw+8vLwEJxOLq1MT/TM6OjqIi4uDtbU1AMDe3h4XL15UrmROlJycjIYNG+LJkyfo3bs33N3dAQDh4eH47bff4ODggIsXL7Innf4VFqRERMXUF198gb/++gszZsyAoaEhli5diqysLFy9elV0NKHs7OywePFirV+dmuhjSaVS5aJggPpez0Rjx45FcHAwTpw4oTY9Jj4+Hq1bt0aLFi2wfPlyQQmpOGNBSqTBOnfurNa7A+QPVTU0NISrqyt69er1ztVnqWSztbXF7t278dlnnwEA4uLiUL58eaSlpamttKtNLC0tERoaiooVK4qOQlQssCClD3FycsKaNWvQpk2bAo8HBQXhm2++waNHj4o2GJUI0g+fQkSimJub4+TJk7h27RokEgkkEgmuX7+OkydPIi8vDzt37kTNmjVx4cIF0VFJgISEBLi5uSkf29nZwcjICAkJCQJTiTdkyBD89ttvomMQFRuv7y/vekwUFxeHqlWrvvN4tWrVEB8fX4SJqCTh6hdEGszW1ha9evXCqlWrlEuty+VyjBkzBqamptixYwe++eYbTJo0CefPnxecloqaRCJBeno6jIyMlG1SqRQvX75EWlqask0b9iEdP3688s9yuRxr167FiRMntHp1aqKPpVAoUKlSJWURmp6eDk9PT7UtPpKTk0XEIw1QtmxZPHr06J17fkdFRWn1mg7033DILpEGs7KywoULF5TbnLx27949NGrUCM+fP8etW7fQpEkTpKamiglJwkilUrVejNd7j775Z23Yh/R9K1K/SdtWpyb6GG9v9/Iu/fv3L+QkpKkGDRqEBw8e4Pjx49DX11c5lp2djTZt2sDFxYWrmNO/wh5SIg2Wl5eHiIgItYI0IiJCWWQYGhpyaJWWOnXqlOgIGoPXgujfY6FJH/J65XI3NzeMGDECVapUgUKhwJ07d/DLL78gOzsbW7duFR2TiikWpEQarG/fvhg8eDC+//571K1bFwBw+fJlzJ8/H/369QMAnDlz5r3zOqjk8vb2Fh1Bo7y9dQUR/XOpqanYvXs3Hjx4gIkTJ6JMmTK4du0abGxsUK5cOdHxSJDy5csjJCQEw4cPx5QpU9T2A1+1ahUcHBwEp6TiikN2iTSYTCbDwoULsWrVKjx79gwAYGNjg1GjRmHSpEnQ0dFBTEwMpFLpO+d1UMn05hzRD9GGOaSA+kqhRPTPhIWFoWXLljA3N8ejR49w9+5duLi4YNq0aYiJicGWLVtERyQNkJKSgsjISADcD5w+DRakRMXE6wJEW4oLer+C5o++izbMIQVYkBL9Vy1btkTt2rWxePFila1fLl68iF69enFLDyIqFByyS6Th8vLycPr0aTx48AC9evUCADx9+hRmZmYwMTERnI5EeXPO5KNHjzB58mQMGDAADRs2BACEhIRg8+bNWLBggaiIQqxfv/6D/y9Gjx5dRGmIipfLly9jzZo1au3lypXjlh5EVGjYQ0qkwaKjo9G2bVvExMQgOzsb9+7dg4uLC8aMGYPs7GwEBASIjkgaoEWLFhgyZAh69uyp0v7bb79h7dq1OH36tJhgRez10HUdHZ13niORSPDw4cMiTEVUfFhbW+OPP/6Ap6enSg/p8ePHMWjQIMTGxoqOSEQlEAtSIg3m5+cHU1NTbNiwAZaWlso3B6dPn8bQoUOVczhIuxkbG+PmzZtwc3NTab937x5q1aqFzMxMQcmKFofsEv03Q4YMQVJSEnbt2oUyZcogLCwMOjo68PPzQ9OmTeHv7y86IhGVQNIPn0JEopw7dw7Tpk1T2/PLyckJT548EZSKNI2DgwPWrVun1r5+/XqtWvWQ2x8R/TfLli1Deno6rK2t8erVK3h7e8PV1RWmpqaYN2+e6HhEVEJxDimRBpPL5QUuSPP48WOYmpoKSESaaPny5ejatSuOHj2K+vXrAwBCQ0MRGRmJPXv2CE5XdDjgh+i/MTc3x/Hjx3H+/HmEhYUhPT0dtWvXRsuWLUVHI6ISjEN2iTRYjx49YG5ujrVr18LU1BRhYWGwsrJCp06d4OjoiI0bN4qOSBri8ePH+OWXXxAREQEAcHd3xzfffKNVPaSzZ8/GxIkTYWxsLDoKUbGXlZUFAwMDjjwgokLHgpRIg8XGxqJt27ZQKBSIjIyEl5cXIiMjUbZsWZw9e5Zz5YjeITU1FaGhoUhISIBcLlc51q9fP0GpiDSbXC7HvHnzEBAQgGfPnikX0ps+fTqcnJwwePBg0RGJqARiQUqk4fLy8rBz507cvHlTOXyqd+/eMDIyEh2NNEhqaio2bNiAO3fuAACqVq2KQYMGwdzcXHCyonfw4EH07t0b6enpMDMzU+nhkUgkSE5OFpiOSHPNmTMHmzdvxpw5czB06FDcvn0bLi4u2LlzJ/z9/RESEiI6IhGVQCxIiTRUbm4uqlSpgkOHDsHd3V10HNJgV65cQZs2bWBkZIR69eoByN9P8NWrVzh27Bhq164tOGHRqlSpEtq1a4f58+dz+C7RP+Dq6oo1a9agRYsWKtu+REREoGHDhkhJSREdkYhKIC5qRKSh9PT0kJWVJToGFQPjxo1Dx44dsW7dOujq5r+s5+XlYciQIRg7dizOnj0rOGHRevLkCUaPHs1ilOgfevLkCVxdXdXa5XI5cnNzBSQiIm3AbV+INNiIESOwaNEi5OXliY5CGuzKlSuYNGmSshgFAF1dXXz33Xe4cuWKwGRitGnTRiufN9F/5eHhgXPnzqm17969G56engISEZE2YA8pkQa7fPkygoODcezYMVSvXh2lSpVSOb53715ByUiTmJmZISYmBlWqVFFpj42N1crtgdq3b4+JEyciPDwc1atXh56ensrxjh07CkpGpNlmzJiB/v3748mTJ5DL5di7dy/u3r2LLVu24NChQ6LjEVEJxTmkRBps4MCB7z3ObV8IAEaPHo3ff/8dS5cuRaNGjQAAFy5cwMSJE9G1a1f4+/uLDVjEpNJ3D/6RSCQF7u1LRPnOnTuHOXPmqCykN2PGDLRu3Vp0NCIqodhDSqSB5HI5lixZgnv37iEnJwc+Pj6YNWsWV9alAi1duhQSiQT9+vVTDu/W09PDsGHDsHDhQsHpit7b27wQ0Yfl5eVh/vz5GDRoEI4fPy46DhFpEfaQEmmgH374AbNmzULLli1hZGSEP/74Az179kRgYKDoaKTBMjMz8eDBAwBAxYoVuagPEf0jJiYmuH37NpycnERHISItwoKUSAO5ublhwoQJ+PrrrwEAJ06cQPv27fHq1av3Dkckevz4MQCgfPnygpMUrZ9++glfffUVDA0N8dNPP7333NGjRxdRKqLipVOnTujSpQv69+8vOgoRaREWpEQayMDAAPfv34eDg4OyzdDQEPfv39e6QoM+TC6XY+7cuVi2bBnS09MBAKampvj2228xdepUrfgQw9nZGVeuXIGlpSWcnZ3feZ5EIsHDhw+LMBlR8REQEIDZs2ejd+/eqFOnjtpCelwQjIgKAwtSIg2ko6OD+Ph4WFlZKdtMTU0RFhb23jfbpJ2mTJmCDRs2YPbs2WjcuDEA4Pz585g1axaGDh2KefPmCU5IRMUBFwQjIhFYkBJpIKlUCl9fXxgYGCjbDh48CB8fH5VPrLntCwGAvb09AgIC1Hov9u/fj+HDh+PJkyeCkolx+/ZtVKtWrcBj+/btg5+fX9EGIiIionfiKrtEGqig+Tt9+vQRkISKg+TkZLU9SAGgSpUqSE5OFpBIrDZt2uD8+fNqown27NmDfv36ISMjQ1AyIiIiehsLUiINxP1F6Z+oWbMmVq1apbaYz6pVq1CzZk1BqcQZMmQIWrZsiQsXLsDW1hYAsHPnTgwaNAibNm0SG45Ig71rQTCJRAJDQ0O4urqiadOm0NHRKeJkRFSSccguEVExd+bMGbRv3x6Ojo5o2LAhACAkJASxsbE4cuQImjRpIjhh0Rs1ahROnTqFs2fPIigoCEOGDMHWrVvRtWtX0dGINJazszMSExORmZkJCwsLAEBKSgqMjY1hYmKChIQEuLi44NSpUyqL7hER/Rclf+lFIqISztvbG/fu3UPnzp2RmpqK1NRUdOnSBXfv3tXKYhQAVq5ciZo1a6JBgwYYOnQotm/fzmKU6APmz5+PunXrIjIyEklJSUhKSsK9e/dQv359rFixAjExMbC1tcW4ceNERyWiEoQ9pEREVOwdOHBArS03Nxfjxo1D69atVRZ84tYVRAWrWLEi9uzZg1q1aqm0X79+HV27dsXDhw9x8eJFdO3aFXFxcWJCElGJw4KUiKgYCgsL++hza9SoUYhJNMPH7rXKrSuI3s3Y2Bhnz56Fl5eXSvvly5fh7e2NzMxMPHr0CNWqVVPueUxE9F9xUSMiomKoVq1akEgk+NBnitpSgMnlctERiIq95s2b4+uvv8b69evh6ekJIL93dNiwYfDx8QEA3Lp1i/thE9EnxYKUiKgYioqKEh1B44SEhCApKQmff/65sm3Lli2YOXMmMjIy4Ofnh5UrV6rs70tEf9uwYQP69u2LOnXqQE9PDwCQl5eHFi1aYMOGDQAAExMTLFu2TGRMIiphOGSXiKiYS0pKgqWlJQAgNjYW69atw6tXr9CxY0etWtSobdu2aN68OSZNmgQgvyendu3aGDBgANzd3bFkyRJ8/fXXmDVrltigRBouIiIC9+7dAwBUrlwZlStXFpyIiEoyFqRERMXUrVu30KFDB8TGxsLNzQ07duxA27ZtkZGRAalUioyMDOzevRt+fn6ioxYJOzs7HDx4UDn/berUqThz5gzOnz8PAPjf//6HmTNnIjw8XGRMIiIiegOH7BIRFVPfffcdqlevjm3btmHr1q34/PPP0b59e6xbtw5A/l6cCxcu1JqCNCUlBTY2NsrHZ86cga+vr/Jx3bp1ERsbKyIakcYaP348fvjhB5QqVQrjx49/77k//vhjEaUiIm3CgpSIqJi6fPkyTp48iRo1aqBmzZpYu3Ythg8frlxxdtSoUWjQoIHglEXHxsYGUVFRcHBwQE5ODq5du4bZs2crj798+VI5L46I8l2/fh25ubnKP7+LRCIpqkhEpGVYkBIRFVPJycmwtbUFkL/QSKlSpWBhYaE8bmFhgZcvX4qKV+TatWuHyZMnY9GiRdi3bx+MjY1V5tCGhYWhYsWKAhMSaZ5Tp04V+GcioqLCgpSIqBh7u9dCm3sxfvjhB3Tp0gXe3t4wMTHB5s2boa+vrzweGBiI1q1bC0xIREREb+OiRkRExZRUKoWvr69yG5ODBw/Cx8cHpUqVAgBkZ2cjKChIK/YhfdOLFy9gYmICHR0dlfbk5GSYmJioFKlE2q5Lly4ffe7evXsLMQkRaSv2kBIRFVP9+/dXedynTx+1c/r161dUcTSGubl5ge1lypQp4iREmu/N/y8KhQK///47zM3NlatVX716Fampqf+ocCUi+ifYQ0pEREREmDRpEpKTkxEQEKAcYSCTyTB8+HCYmZlhyZIlghMSUUnEgpSIiIiIYGVlhfPnz6Ny5coq7Xfv3kWjRo2QlJQkKBkRlWRS0QGIiIiISLy8vDxERESotUdEREAulwtIRETagHNIiYiIiAgDBw7E4MGD8eDBA9SrVw8AcOnSJSxcuBADBw4UnI6ISioO2SUiIiIiyOVyLF26FCtWrEBcXBwAwM7ODmPGjMG3336rtnI1EdGnwIKUiIiIiFSkpaUBAMzMzAQnIaKSjnNIiYiIiAhA/jzSEydOYPv27ZBIJACAp0+fIj09XXAyIiqp2ENKRERERIiOjkbbtm0RExOD7Oxs3Lt3Dy4uLhgzZgyys7MREBAgOiIRlUDsISUiIiIijBkzBl5eXkhJSYGRkZGyvXPnzggODhaYjIhKMq6yS0REREQ4d+4cLl68CH19fZV2JycnPHnyRFAqIirp2ENKRERERJDL5ZDJZGrtjx8/hqmpqYBERKQNWJASEREREVq3bg1/f3/lY4lEgvT0dMycORPt2rUTF4yISjQuakREREREePz4Mdq0aQOFQoHIyEh4eXkhMjISZcuWxdmzZ2FtbS06IhGVQCxIiYiIiAhA/rYvO3fuxM2bN5Geno7atWujd+/eKoscERF9SixIiYiIiLTcn3/+iYMHDyInJwc+Pj7w9fUVHYmItAQLUiIiIiIttnv3bvTo0QNGRkbQ09NDWloaFi1ahAkTJoiORkRagAUpERERkRarU6cO6tati59//hk6OjpYsGABlixZguTkZNHRiEgLsCAlIiIi0mImJia4ceMGXF1dAQA5OTkoVaoUnjx5woWMiKjQcdsXIiIiIi2WmZkJMzMz5WN9fX0YGhoiPT1dYCoi0ha6ogMQERERkVjr16+HiYmJ8nFeXh42bdqEsmXLKttGjx4tIhoRlXAcsktERESkxZycnCCRSN57jkQiwcOHD4soERFpExakREREREREJATnkBIRERFRgVJTU0VHIKISjgUpEREREWHRokXYuXOn8nG3bt1QpkwZlCtXDjdv3hSYjIhKMhakRERERISAgAA4ODgAAI4fP44TJ04gKCgIvr6+mDhxouB0RFRScZVdIiIiIkJ8fLyyID106BC6d++O1q1bw8nJCfXr1xecjohKKvaQEhEREREsLCwQGxsLAAgKCkLLli0BAAqFAjKZTGQ0IirB2ENKREREROjSpQt69eoFNzc3JCUlwdfXFwBw/fp1uLq6Ck5HRCUVC1IiIiIiwvLly+Hk5ITY2FgsXrwYJiYmAIC4uDgMHz5ccDoiKqm4DykREREREREJwTmkRERERAQA2Lp1Kz777DPY29sjOjoaAODv74/9+/cLTkZEJRULUiIiIiLC6tWrMX78ePj6+iI1NVW5kFHp0qXh7+8vNhwRlVgsSImIiIgIK1euxLp16zB16lTo6Ogo2728vHDr1i2ByYioJGNBSkRERESIioqCp6enWruBgQEyMjIEJCIibcCClIiIiIjg7OyMGzduqLUHBQXB3d296AMRkVbgti9EREREhPHjx2PEiBHIysqCQqFAaGgotm/fjgULFmD9+vWi4xFRCcVtX4iIiIgIALBt2zbMmjULDx48AADY29tj9uzZGDx4sOBkRFRSsSAlIiIiIhWZmZlIT0+HtbW16ChEVMJxDikRERERwcfHB6mpqQAAY2NjZTGalpYGHx8fgcmIqCRjDykRERERQSqVIj4+Xq1XNCEhAeXKlUNubq6gZERUknFRIyIiIiItFhYWpvxzeHg44uPjlY9lMhmCgoJQrlw5EdGISAuwh5SIiIhIi0mlUkgkEgBAQW8LjYyMsHLlSgwaNKiooxGRFmBBSkRERKTFoqOjoVAo4OLigtDQUFhZWSmP6evrw9raGjo6OgITElFJxoKUiIiIiIiIhOAqu0REREQEANi6dSsaN24Me3t7REdHAwCWL1+O/fv3C05GRCUVC1IiIiIiwurVqzF+/Hi0a9cOqampkMlkAAALCwv4+/uLDUdEJRYLUiIiIiLCypUrsW7dOkydOlVlzqiXlxdu3bolMBkRlWQsSImIiIgIUVFR8PT0VGs3MDBARkaGgEREpA1YkBIRERERnJ2dcePGDbX2oKAguLu7F30gItIKuqIDEBEREZF448ePx4gRI5CVlQWFQoHQ0FBs374dCxYswPr160XHI6ISitu+EBEREREAYNu2bZg1axYePHgAALC3t8fs2bMxePBgwcmIqKRiQUpEREREKjIzM5Geng5ra2vRUYiohOOQXSIiIiJSSkhIwN27dwEAEokEVlZWghMRUUnGRY2IiIiICC9fvkTfvn1hb28Pb29veHt7w97eHn369MGLFy9ExyOiEooFKRERERFhyJAhuHTpEg4fPozU1FSkpqbi0KFDuHLlCr7++mvR8YiohOIcUiIiIiJCqVKl8Mcff+Czzz5TaT937hzatm3LvUiJqFCwh5SIiIiIYGlpCXNzc7V2c3NzWFhYCEhERNqABSkRERERYdq0aRg/fjzi4+OVbfHx8Zg4cSKmT58uMBkRlWQcsktERESkpTw9PSGRSJSPIyMjkZ2dDUdHRwBATEwMDAwM4ObmhmvXromKSUQlGLd9ISIiItJSfn5+oiMQkZZjDykREREREREJwTmkREREREREJASH7BIRERERZDIZli9fjl27diEmJgY5OTkqx5OTkwUlI6KSjD2kRERERITZs2fjxx9/RI8ePfDixQuMHz8eXbp0gVQqxaxZs0THI6ISinNIiYiIiAgVK1bETz/9hPbt28PU1BQ3btxQtv3555/47bffREckohKIPaREREREhPj4eFSvXh0AYGJighcvXgAAPv/8cxw+fFhkNCIqwViQEhERERHKly+PuLg4APm9pceOHQMAXL58GQYGBiKjEVEJxoKUiIiIiNC5c2cEBwcDAEaNGoXp06fDzc0N/fr1w6BBgwSnI6KSinNIiYiIiEhNSEgIQkJC4Obmhg4dOoiOQ0QlFAtSIiIiIiIiEoL7kBIRERFpqQMHDsDX1xd6eno4cODAe8/t2LFjEaUiIm3CHlIiIiIiLSWVShEfHw9ra2tIpe9eWkQikUAmkxVhMiLSFixIiYiIiIiISAgO2SUiIiLScnK5HJs2bcLevXvx6NEjSCQSuLi4oGvXrujbty8kEonoiERUQrGHlIiIiEiLKRQKdOjQAUeOHEHNmjVRpUoVKBQK3LlzB7du3ULHjh2xb98+0TGJqIRiDykRERGRFtu0aRPOnj2L4OBgNG/eXOXYyZMn4efnhy1btqBfv36CEhJRScYeUiIiIiIt1rp1a/j4+GDy5MkFHp8/fz7OnDmDP/74o4iTEZE2ePdyakRERERU4oWFhaFt27bvPO7r64ubN28WYSIi0iYsSImIiIi0WHJyMmxsbN553MbGBikpKUWYiIi0CQtSIiIiIi0mk8mgq/vuZUV0dHSQl5dXhImISJtwUSMiIiIiLaZQKDBgwAAYGBgUeDw7O7uIExGRNmFBSkRERKTF+vfv/8FzuMIuERUWrrJLREREREREQnAOKREREREREQnBgpSIiIiIiIiEYEFKREREREREQrAgJSIiIiIiIiFYkBIREREREZEQLEiJiIiIiIhICBakREREREREJAQLUiIiIiIiIhLi/wDiFspo8SGGSAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
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