{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8eKA0JEWYzq6"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix, classification_report, roc_curve, roc_auc_score, f1_score\n",
        "from sklearn.metrics import confusion_matrix, classification_report, f1_score, roc_curve, roc_auc_score, accuracy_score\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "data_folder_path = '/content/drive/'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjTMJepKY6U4",
        "outputId": "c28541d9-9b70-4754-d814-4e80d473ded2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/dataset/GNN_Dataset/patientdata.csv\")  # Replace with the actual file path"
      ],
      "metadata": {
        "id": "ZyEyN9NUY9sk"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the label column\n",
        "label_counts = data['label_class'].value_counts()\n",
        "label_counts.plot(kind='bar')\n",
        "plt.xlabel('Labels')\n",
        "plt.ylabel('Counts')\n",
        "plt.title('Label Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "9SL3mHSzY_ga",
        "outputId": "22e008c4-c838-471e-ca54-24d223948ec8"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate features and labels\n",
        "X = data.drop(\"label_class\", axis=1)\n",
        "y = data[\"label_class\"]"
      ],
      "metadata": {
        "id": "jnh90eNjZBdZ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert labels to numerical values\n",
        "label_encoder = LabelEncoder()\n",
        "y_encoded = label_encoder.fit_transform(y)"
      ],
      "metadata": {
        "id": "t2AyEyPqZDOX"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the dataset into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "2Jvv9miwZE9F"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize the feature data\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "N_ueeOgzZGql"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the data to TensorFlow tensors\n",
        "X_train_tensor = tf.constant(X_train_scaled, dtype=tf.float32)\n",
        "y_train_tensor = tf.constant(y_train, dtype=tf.int32)\n",
        "X_test_tensor = tf.constant(X_test_scaled, dtype=tf.float32)\n",
        "y_test_tensor = tf.constant(y_test, dtype=tf.int32)"
      ],
      "metadata": {
        "id": "8b52-GJVZIuv"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class LSTMModel(tf.keras.Model):\n",
        "    def __init__(self, num_classes):\n",
        "        super(LSTMModel, self).__init__()\n",
        "        self.lstm_layer = tf.keras.layers.LSTM(16, activation='relu')\n",
        "        self.dense_output = tf.keras.layers.Dense(num_classes, activation='softmax')\n",
        "\n",
        "    def call(self, inputs):\n",
        "        # Assuming inputs has shape (batch_size, sequence_length)\n",
        "        # Add a dimension for the number of features\n",
        "        inputs = tf.expand_dims(inputs, axis=-1)\n",
        "\n",
        "        x = self.lstm_layer(inputs)\n",
        "        output = self.dense_output(x)\n",
        "        return output"
      ],
      "metadata": {
        "id": "5VtXOJQqZLDJ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the LSTM model\n",
        "num_classes = len(label_encoder.classes_)\n",
        "model = LSTMModel(num_classes)  # Replace 'GNNModel' with the appropriate LSTM model class\n",
        "loss_object = tf.keras.losses.SparseCategoricalCrossentropy()\n",
        "optimizer = tf.keras.optimizers.Adam()\n",
        "epochs = 10\n",
        "batch_size = 32\n",
        "steps_per_epoch = len(X_train_scaled) // batch_size\n",
        "train_loss_history = []\n",
        "test_loss_history = []\n",
        "train_accuracy_history = []\n",
        "test_accuracy_history = []\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    for step in range(steps_per_epoch):\n",
        "        start = step * batch_size\n",
        "        end = (step + 1) * batch_size\n",
        "        with tf.GradientTape() as tape:\n",
        "            predictions = model(X_train_tensor[start:end])\n",
        "            loss = loss_object(y_train_tensor[start:end], predictions)\n",
        "\n",
        "        gradients = tape.gradient(loss, model.trainable_variables)\n",
        "        optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
        "\n",
        "    train_loss = loss_object(y_train_tensor, model(X_train_tensor))\n",
        "    test_loss = loss_object(y_test_tensor, model(X_test_tensor))\n",
        "    train_loss_history.append(train_loss.numpy())\n",
        "    test_loss_history.append(test_loss.numpy())\n",
        "\n",
        "    y_pred_train = np.argmax(model(X_train_tensor).numpy(), axis=1)\n",
        "    y_pred_test = np.argmax(model(X_test_tensor).numpy(), axis=1)\n",
        "\n",
        "    train_accuracy = accuracy_score(y_train, y_pred_train)\n",
        "    test_accuracy = accuracy_score(y_test, y_pred_test)\n",
        "\n",
        "    train_accuracy_history.append(train_accuracy)\n",
        "    test_accuracy_history.append(test_accuracy)\n",
        "\n",
        "    print(f\"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.4f}, Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUnEjkGEZNSg",
        "outputId": "d2c95a4c-7411-4df5-bcd2-f7b41e20cb05"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10, Train Loss: 0.3771, Train Accuracy: 0.8359, Test Loss: 0.3811, Test Accuracy: 0.8337\n",
            "Epoch 2/10, Train Loss: 0.3568, Train Accuracy: 0.8459, Test Loss: 0.3601, Test Accuracy: 0.8438\n",
            "Epoch 3/10, Train Loss: 0.3420, Train Accuracy: 0.8550, Test Loss: 0.3445, Test Accuracy: 0.8544\n",
            "Epoch 4/10, Train Loss: 0.3323, Train Accuracy: 0.8603, Test Loss: 0.3349, Test Accuracy: 0.8606\n",
            "Epoch 5/10, Train Loss: 0.3318, Train Accuracy: 0.8615, Test Loss: 0.3347, Test Accuracy: 0.8607\n",
            "Epoch 6/10, Train Loss: 0.3244, Train Accuracy: 0.8651, Test Loss: 0.3278, Test Accuracy: 0.8631\n",
            "Epoch 7/10, Train Loss: 0.3196, Train Accuracy: 0.8675, Test Loss: 0.3228, Test Accuracy: 0.8650\n",
            "Epoch 8/10, Train Loss: 0.3179, Train Accuracy: 0.8681, Test Loss: 0.3211, Test Accuracy: 0.8651\n",
            "Epoch 9/10, Train Loss: 0.3185, Train Accuracy: 0.8669, Test Loss: 0.3216, Test Accuracy: 0.8654\n",
            "Epoch 10/10, Train Loss: 0.3149, Train Accuracy: 0.8690, Test Loss: 0.3185, Test Accuracy: 0.8664\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluation\n",
        "y_pred_train_prob = model(X_train_tensor).numpy()\n",
        "y_pred_test_prob = model(X_test_tensor).numpy()\n",
        "\n",
        "# Convert probabilities to class indices\n",
        "y_pred_train = np.argmax(y_pred_train_prob, axis=1)\n",
        "y_pred_test = np.argmax(y_pred_test_prob, axis=1)\n"
      ],
      "metadata": {
        "id": "j2BhA8ZoZPgH"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# F1 Score\n",
        "f1_train = f1_score(y_train, y_pred_train, average='weighted')\n",
        "f1_test = f1_score(y_test, y_pred_test, average='weighted')\n",
        "\n",
        "# Accuracy\n",
        "train_accuracy = np.mean(np.equal(y_train, y_pred_train))\n",
        "test_accuracy = np.mean(np.equal(y_test, y_pred_test))\n",
        "\n",
        "# Other metrics\n",
        "print(\"Train F1 Score:\", f1_train)\n",
        "print(\"Test F1 Score:\", f1_test)\n",
        "\n",
        "# Accuracy\n",
        "print(\"Train Accuracy:\", train_accuracy)\n",
        "print(\"Test Accuracy:\", test_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FhYx6g9JZSI3",
        "outputId": "0b400b7b-6ac1-4338-efd9-a7d60a9a39cd"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train F1 Score: 0.8687526646481393\n",
            "Test F1 Score: 0.8662175084354155\n",
            "Train Accuracy: 0.8689919420583364\n",
            "Test Accuracy: 0.8664229567844026\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "cm_train = confusion_matrix(y_train, y_pred_train)\n",
        "cm_test = confusion_matrix(y_test, y_pred_test)\n",
        "\n",
        "# Plot Accuracy Curve\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(range(epochs), train_accuracy_history, label='Train Accuracy')\n",
        "plt.plot(range(epochs), test_accuracy_history, label='Test Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy Curve')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "odb4C2YWZUcC",
        "outputId": "6a50362a-fe7c-4cc2-a2f8-2c858e55b635"
      },
      "execution_count": 14,
      "outputs": [
        {
          "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": [
        "# Loss Curve\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(train_loss_history, label='Train Loss')\n",
        "plt.plot(test_loss_history, label='Test Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Loss Curve')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "IBUv-mfeZW16",
        "outputId": "d304a8ba-19b5-45a2-ad2e-109162534f7b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "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": [
        "# Classification Report\n",
        "report_train = classification_report(y_train, y_pred_train, target_names=label_encoder.classes_)\n",
        "report_test = classification_report(y_test, y_pred_test, target_names=label_encoder.classes_)"
      ],
      "metadata": {
        "id": "QjvF0qWfZZP-"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print Confusion Matrix and Classification Report\n",
        "print(\"Train Classification Report:\")\n",
        "print(report_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65Pjo0luZZFK",
        "outputId": "35783385-7837-4a9c-803e-b114c0795bb4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "  noseizures       0.84      0.91      0.87    116674\n",
            "    seizures       0.90      0.83      0.86    116388\n",
            "\n",
            "    accuracy                           0.87    233062\n",
            "   macro avg       0.87      0.87      0.87    233062\n",
            "weighted avg       0.87      0.87      0.87    233062\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Test Classification Report:\")\n",
        "print(report_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySiBRP1UZd8f",
        "outputId": "60db277d-5a0c-4410-9d36-29f4bd6f89cd"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "  noseizures       0.84      0.91      0.87     28990\n",
            "    seizures       0.90      0.83      0.86     29276\n",
            "\n",
            "    accuracy                           0.87     58266\n",
            "   macro avg       0.87      0.87      0.87     58266\n",
            "weighted avg       0.87      0.87      0.87     58266\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Confusion Matrix Heatmap for Train Set\n",
        "plt.figure(figsize=(15, 10))\n",
        "sns.heatmap(cm_train, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix (Train Set)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "id": "ACPd67uAZhDS",
        "outputId": "e3de6680-21f5-4f34-9e5c-135e47d5873e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ],
            "image/png": 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nJEm22267RVZXt27dMnny5Dz11FPFY++8805uvPHGBus++OCD+T7bs2fPJJlva/p5lltuufTs2TNXXXVVg5DpmWeeyV133VX8novDFltskdNOOy0XXXRROnbs+LnrmjRpMl+nynXXXZf//Oc/DY7NC6oWFJYtrGOOOSZvvPFGrrrqqpxzzjnp0qVLBg4c+Lk/x3maNWuW3r1759FHH/3aNczTpEmT1NTUNOj4eu2113LTTTctsnt8nrvvvnuBx+c9G2re6ODuu++eOXPm5LTTTptv7ezZsxv8Tlq2bPmFv6OxY8d+aQALAHwz6QACYJHo1q1bRo4cmT322CPdu3fPj370o6y55pqZOXNmHnzwwVx33XXZZ599kiTrrLNOBg4cmMsuuyyTJk3KZpttlocffjhXXXVVdtppp8/dYvyr2HPPPXPMMcdk5513ziGHHJLp06fnkksuybe+9a0GDwEeNmxY7r///my33Xbp3LlzJk6cmIsvvjgrrLBCNt5448+9/i9/+ctsu+226du3b/bff/98/PHHufDCC9O6desvHM36umpra3PiiSd+6brtt98+w4YNy7777psNN9wwTz/9dK655pqsvPLKDdZ169Ytbdq0yaWXXpqllloqLVu2TJ8+fT73uTqf55577snFF1+ck08+ubid+5VXXpnNN988J510Us4666wv/PyOO+6YE044IVOmTCk+W+rr2G677XLOOedkm222yV577ZWJEyfm17/+dVZZZZUGoeDisOOOO6Zr16757ne/m27dumXatGn529/+lltuuSXrr79+vvvd7yb59OHVBx10UIYPH54nnngi/fv3T7NmzfLSSy/luuuuy/nnn5/ddtstyafbvF9yySU5/fTTs8oqq6R9+/bZcsstkyQTJ07MU089lUGDBi3W7wUAVCYBEACLzA477JCnnnoqv/zlL3PzzTfnkksuSV1dXdZee+2cffbZ+fGPf1xce8UVV2TllVfOiBEjcuONN6Zjx4457rjjcvLJJy/SmpZddtnceOONGTJkSI4++uh07do1w4cPz0svvdQgANphhx3y2muv5Xe/+13ee++9tG3bNptttllOPfXUtG7d+nOv369fv4waNSonn3xyhg4dmmbNmmWzzTbLL37xi4UOTxaH448/PtOmTcvIkSNz7bXXZr311sttt92WY489tsG6Zs2a5aqrrspxxx2Xn/zkJ5k9e3auvPLKhfoOH330Ufbbb7+su+66OeGEE4rHN9lkkxx66KE5++yzs8suu2SDDTb43Gv88Ic/zLHHHpu//vWv+cEPfrDwX/i/bLnllvntb3+bn//85znssMPStWvX/OIXv8hrr7222AOgK664IjfffHP+/Oc/5+23306hUMjKK6+cE044Icccc0yaNv2//xp26aWXplevXvnNb36T448/Pk2bNk2XLl3ygx/8IBtttFFx3dChQ/P666/nrLPOykcffZTNNtusGAD95S9/SV1dXXbffffF+r0AgMpUU/iqW20AACwG+++/f/7973/nH//4R7lLqSjrrrtuNt9885x77rnlLgUAaIQEQABAo/LGG2/kW9/6Vu6+++4G3S98vlGjRmW33XbLq6++mvbt25e7HACgERIAAQAAAFQ5u4ABAAAAVDkBEAAAAECVEwABAAAAVDkBEAAAAECVEwABAAAAVLmm5S5gcWix7uBylwAAFenDRy4qdwkAUJHqq/Jv1/NrzH/f/vhx/z3mi+gAAgAAAKhyAiAAAACAKvcNaVIDAAAAvrYafSSVym8OAAAAoMoJgAAAAACqnBEwAAAAoDQ1NeWugK9IBxAAAABAlRMAAQAAAFQ5I2AAAABAaewCVrH85gAAAACqnAAIAAAAoMoZAQMAAABKYxewiqUDCAAAAKDKCYAAAAAAqpwRMAAAAKA0dgGrWH5zAAAAAFVOAAQAAABQ5YyAAQAAAKWxC1jF0gEEAAAAUOUEQAAAAABVzggYAAAAUBq7gFUsvzkAAACAKicAAgAAAKhyRsAAAACA0tgFrGLpAAIAAACocgIgAAAAgCpnBAwAAAAojV3AKpbfHAAAAECVEwABAAAAVDkjYAAAAEBp7AJWsXQAAQAAAFQ5ARAAAABAlTMCBgAAAJTGLmAVy28OAAAAoMoJgAAAAACqnBEwAAAAoDR2AatYOoAAAAAAqpwACAAAAKDKGQEDAAAASmMXsIrlNwcAAABQ5QRAAAAAAFXOCBgAAABQGiNgFctvDgAAAKDKCYAAAAAAqpwRMAAAAKA0tTXlroCvSAcQAAAA8I1y//3357vf/W46deqUmpqa3HTTTQ3OFwqFDB06NMstt1xatGiRfv365aWXXmqw5oMPPsjee++dVq1apU2bNtl///0zderUBmueeuqpbLLJJqmvr8+KK66Ys846a75arrvuuqy++uqpr6/PWmutldtvv32haymFAAgAAAD4Rpk2bVrWWWed/PrXv17g+bPOOisXXHBBLr300jz00ENp2bJlBgwYkE8++aS4Zu+9986zzz6b0aNH59Zbb83999+fAw88sHh+ypQp6d+/fzp37pyxY8fml7/8ZU455ZRcdtllxTUPPvhgvv/972f//ffP448/np122ik77bRTnnnmmYWqpRQ1hUKhsFCfqAAt1h1c7hIAoCJ9+MhF5S4BACpS/TfkASsttjyj3CV8ro/vOeErfa6mpiY33nhjdtpppySfdtx06tQpRxxxRI488sgkyeTJk9OhQ4eMGDEie+65Z55//vn06NEjjzzySHr37p0kGTVqVL7zne/krbfeSqdOnXLJJZfkhBNOyPjx49O8efMkybHHHpubbropL7zwQpJkjz32yLRp03LrrbcW69lggw3Ss2fPXHrppSXVUiodQAAAAEDFmzFjRqZMmdLgNWPGjIW+zrhx4zJ+/Pj069eveKx169bp06dPxowZkyQZM2ZM2rRpUwx/kqRfv36pra3NQw89VFyz6aabFsOfJBkwYEBefPHFfPjhh8U1n73PvDXz7lNKLaUSAAEAAAAVb/jw4WndunWD1/Dhwxf6OuPHj0+SdOjQocHxDh06FM+NHz8+7du3b3C+adOmWWaZZRqsWdA1PnuPz1vz2fNfVkupviFNagAAAMDXVtN4dwE77rjjMmTIkAbH6urqylRN46MDCAAAAKh4dXV1adWqVYPXVwmAOnbsmCSZMGFCg+MTJkwonuvYsWMmTpzY4Pzs2bPzwQcfNFizoGt89h6ft+az57+sllIJgAAAAAD+v65du6Zjx465++67i8emTJmShx56KH379k2S9O3bN5MmTcrYsWOLa+65557MnTs3ffr0Ka65//77M2vWrOKa0aNHZ7XVVsvSSy9dXPPZ+8xbM+8+pdRSKgEQAAAAUJqa2sb7WghTp07NE088kSeeeCLJpw9bfuKJJ/LGG2+kpqYmhx12WE4//fT89a9/zdNPP50f/ehH6dSpU3GnsO7du2ebbbbJj3/84zz88MP55z//mcGDB2fPPfdMp06dkiR77bVXmjdvnv333z/PPvtsrr322px//vkNxtQOPfTQjBo1KmeffXZeeOGFnHLKKXn00UczePCnu5uXUkupPAMIAAAA+EZ59NFHs8UWWxTfzwtlBg4cmBEjRuToo4/OtGnTcuCBB2bSpEnZeOONM2rUqNTX1xc/c80112Tw4MHZaqutUltbm1133TUXXHBB8Xzr1q1z1113ZdCgQenVq1fatm2boUOH5sADDyyu2XDDDTNy5MiceOKJOf7447PqqqvmpptuypprrllcU0otpagpFAqFhf5JNXIt1h1c7hIAoCJ9+MhF5S4BACpS/TekvaJFv5+Xu4TP9fHfji13CY3aN+Q/ogAAAMDX1oh3AeOLeQYQAAAAQJUTAAEAAABUOSNgAAAAQGkWcrctGg+/OQAAAIAqJwACAAAAqHJGwAAAAIDS2AWsYukAAgAAAKhyAiAAAACAKmcEDAAAACiNXcAqlt8cAAAAQJUTAAEAAABUOSNgAAAAQGnsAlaxdAABAAAAVDkBEAAAAECVMwIGAAAAlMYuYBXLbw4AAACgygmAAAAAAKqcETAAAACgNHYBq1g6gAAAAACqnAAIAAAAoMoZAQMAAABKYxewiuU3BwAAAFDlBEAAAAAAVc4IGAAAAFAaI2AVy28OAAAAoMoJgAAAAACqnBEwAAAAoDQ1NeWugK9IBxAAAABAlRMAAQAAAFQ5I2AAAABAaewCVrH85gAAAACqnAAIAAAAoMoZAQMAAABKYxewiqUDCAAAAKDKCYAAAAAAqpwRMAAAAKA0dgGrWH5zAAAAAFVOAAQAAABQ5YyAAQAAAKWxC1jF0gEEAAAAUOUEQAAAAABVzggYAAAAUJIaI2AVSwcQAAAAQJUTAAEAAABUOSNgAAAAQEmMgFUuHUAAAAAAVU4ABAAAAFDljIABAAAApTEBVrF0AAEAAABUOQEQAAAAQJUzAgYAAACUxC5glUsHEAAAAECVEwABAAAAVDkjYAAAAEBJjIBVLh1AAAAAAFVOAAQAAABQ5YyAAQAAACUxAla5dAABAAAAVDkBEAAAAECVMwIGAAAAlMQIWOXSAQQAAABQ5QRAAAAAAFXOCBgAAABQGhNgFUsHEAAAAECVEwABAAAAVDkjYAAAAEBJ7AJWuXQAAQAAAFQ5ARAAAABAlTMCBgAAAJTECFjl0gEEAAAAUOUEQAAAAABVzggYAAAAUBIjYJVLBxAAAABAlRMAAQAAAFQ5I2AAAABASYyAVS4dQAAAAABVTgAEAAAAUOWMgAEAAAClMQFWsXQAAQAAAFQ5ARAAAABAlTMCBgAAAJTELmCVSwcQAAAAQJUTAAEAAABUOSNgAAAAQEmMgFUuHUAAAAAAVU4ABAAAAFDljIABAAAAJTECVrl0AAEAAABUOQEQAAAAQJUzAgYAAACUxgRYxdIBBAAAAFDlBEAAAAAAVc4IGAAAAFASu4BVLh1AAAAAAFVOAAQAAABQ5YyAAQAAACUxAla5dAABAAAAVDkBEAAAAECVMwIGAAAAlMQIWOXSAQQAAABQ5QRAAAAAAFWuUY6ATZkyJffcc09WW221dO/evdzlAAAAADECVskaRQfQ7rvvnosuuihJ8vHHH6d3797Zfffds/baa+eGG24oc3UAAAAAla1RBED3339/NtlkkyTJjTfemEKhkEmTJuWCCy7I6aefXubqAAAAACpbowiAJk+enGWWWSZJMmrUqOy6665ZYoklst122+Wll14qc3UAAABAkqSmEb/4Qo0iAFpxxRUzZsyYTJs2LaNGjUr//v2TJB9++GHq6+vLXB0AAABAZWsUD4E+7LDDsvfee2fJJZfMSiutlM033zzJp6Nha621VnmLAwAAAKhwjSIA+tnPfpZvf/vbefPNN7P11luntvbTxqSVV17ZM4AAAACgkbALWOVqFAFQkvTu3Ttrr712xo0bl27duqVp06bZbrvtyl0WAAAAQMVrFM8Amj59evbff/8sscQSWWONNfLGG28kSQ4++OD8/Oc/L3N1AAAAAJWtUQRAxx13XJ588snce++9DR763K9fv1x77bVlrAwAAACYp6amptG++GKNYgTspptuyrXXXpsNNtigwS9tjTXWyCuvvFLGygAAAAAqX6PoAHr33XfTvn37+Y5PmzZNigcAAADwNTWKAKh379657bbbiu/nhT5XXHFF+vbtW66yAAAAgM8o95iXEbCvrlGMgJ155pnZdttt89xzz2X27Nk5//zz89xzz+XBBx/MfffdV+7yAAAAACpao+gA2njjjfPkk09m9uzZWWuttXLXXXelffv2GTNmTHr16lXu8gAAAAAqWtk7gGbNmpWDDjooJ510Ui6//PJylwMAAAB8HpNWFavsHUDNmjXLDTfcUO4yAAAAAKpW2QOgJNlpp51y0003lbsMAAAAgKpU9hGwJFl11VUzbNiw/POf/0yvXr3SsmXLBucPOeSQMlUGAAAAzGO3rcrVKAKg3/72t2nTpk3Gjh2bsWPHNjhXU1MjAAIAAAD4GhpFADRu3LhylwAAAABQtRpFAAQAAAA0fkbAKlejCID222+/Lzz/u9/97n9UCQAAAED1aRQB0Icfftjg/axZs/LMM89k0qRJ2XLLLctUFQAAAEB1aBQB0I033jjfsblz5+anP/1punXrVoaKAAAAgP9mBKxy1Za7gM9TW1ubIUOG5Nxzzy13KQAAAAAVrdEGQEnyyiuvZPbs2eUuAwAAAKCiNYoRsCFDhjR4XygU8s477+S2227LwIEDy1QVAAAA8FlGwCpXowiAHn/88Qbva2tr065du5x99tlfukMYsGhttF63HP6jflmvx0pZrl3r7H74Zbnl3qcarDnpp9tl3503TJulWmTMk6/mkDOvzStvvNtgzTYbr5HjD9w2a67aKZ/MnJ0Hxr6U3YdcniRZpnXLXHnGwKz1reWzTOsl8u4HU3PrvU9l6EW35KNpnyRJOrZtlZ8P2SXr9Vgp3VZsm4v/eF+O+tUNDe6x784bZu/tv50eq3RKkjz+/Bs5+cJb8uizry+uHw8AlGzso49kxO9+m+efeybvvvtuzr3g19lyq37F84VCIRdfdEH+cv11+eijKem57no5Yegp6dy5S3HNIYN+khdfeCEffPB+WrVqnT59++awIUemffsODa7z+xG/y/XX/TnvvP2ftFl66eyx51758UE/TZL8bfRdue7aP+bFF57PzJkz022VVfOTnw3ORhtv8j/7WQBQfo0iAPr73/9e7hKA/69li7o8/e//5Pc3j8m15xw43/kj9umXn31/s/x46NV57T/vZ+jPts8tvx6UdXc9PTNmfjqyudNWPfPrk76fky+6Jfc+/O80bVqbNbotV7zG3Llzc+t9T+XUi2/Nex9+lJVXbJfzjt09F7ZumX2OH5Ekad6sad778KP8/IpROXjvLRZY66a9V82fR43Nv568Lp/MnJ0j9tk6t1wyKL12PSNvvzt50f9wAGAhfPzx9Ky22mrZaZddM+TQwfOdv/K3l+eP11yd0878eZZffoX8+sLz89MD98+Nf709dXV1SZL1v71BDjjwJ2nbrl0mTpiQc351Vo48/ND8/po/Fa/zi+FnZMyDD+SII4/OKt/6VqZMnpzJk//vz8HHHn0kG/TdMAcfeniWatUqN9/4lxwy6Kf5w5/+nO7deyz+HwQAjUJNoVAolLuIYcOGZeONN55vy/dp06bl7LPPztChQxfqei3Wnf8PWGDhffz4RfN1AL161xm54Op7ct7VdydJWi1Zn9f/NjwHnvyHXHfn2DRpUpsXbzs1p116e666aUzJ9/rZ9zfL4T/ql1W3PWm+c3defmieevGt+TqA/lttbU3eue+sHP6L6zLy1odLvjfwfz585KJylwBVaZ01VmvQAVQoFNJv803yo332zcB990+SfPTRR9ly0w0z7IyfZ9vvbLfA69x7z9057JBBeeTxp9OsWbO8+sor+d4uO+SGm25Jl64rl1zPzjtslwHbbJuf/Mx/b4ZFpb5RtFcsfl0Pv63cJXyucecu+P938qlG8RDoU045Jdtuu23OOeecBsenTp2aU089tUxVAf+ty/LLZrl2rXPPQy8Uj02Z+kkeeea19Fm7S5Jk3dVXzPIdls7cuYWM+eMxefWuM3LTRT9Nj890AP235dq1zo5b9sw/xr70tepbor55mjVtkg8nT/9a1wGAxe0/b72V9957N3022LB4bKmllspaa6+Tp558fIGfmTxpUm677Zas03PdNGvWLEly3733ZPkVVsh9992bbftvmW233jKnDD0hkydN+tx7z507N9OnTUvr1m0W5VcCoJFrFAFQkvz+97/PmWeemX333TczZ84s+XMzZszIlClTGrwKc+csxkrhm6tj21ZJkokffNTg+MT3P0qHZT8913WFtkmSE3/ynfziijuz66GXZtKUj3Pn5Ydm6VZLNPjcVcP3yfsPnpNX7zojU6Z9kp8OG/m16jv90B3zzruTGwRUANAYvffep8/OW7btsg2OL7vssnnvvfcaHDv37F+mT++e2XSjPhn/zjs5/6KLi+feeuvNvPP22xl956icMfysDDtjeJ579tkccfghn3vvq678baZPn57+22y7CL8RAI1dowmAtthiizz00EN56KGHsvnmm2fixIklfW748OFp3bp1g9fsCWMXc7XA56n9/7sC/OKKO3PT3U/k8effzIEn/yGFFLLL1us2WHv0r25I371+kd0O+01WXqFtfnHELl/5vkfuu3W+N6BX9jji8uKziACgGuyz3/659vobc+nlv0ttbW1OPO6YzHuKQ2FuITNnzszpw3+R9Xr1zvrf7pNTTzsjjzz8UF4b9+p817r91lty6SW/zi/POS/LLrvsfOcBvkxNTU2jffHFGkUANO8X1a1bt/zrX/9Kq1at0qtXrzz66KNf+tnjjjsuk///g+7mvZp26LW4S4ZvpPHvTUmStF9mqQbH2y+7VCa8/+m5d9779KGTL7z6TvH8zFmz89pb72fFjss0+NyE9z/Kv1+bkNvuezoHn/7HHLT7psUuo4Vx2A+3yhH7bp3v/uzXeealtxf68wDwv9a2bbskyfvvvd/g+Pvvv5+2bds2OLb00sukS5eu6bvhRjnrV+fmH/ffl6eefOLT67Rrl6ZNm6ZLl67F9V1X7pYkeeeddxpc547bb8upJ5+YX559Xjbou2EA+GZpFAHQZ59D3apVq9x+++3Zeeeds9NOO33pZ+vq6tKqVasGr5raJouxWvjmeu0/7+eddydniz6rFY8t1bI+66/ZJQ899VqS5PHn38wnM2Zl1S7/tz1t06a1WanTMnnjnQ8+99o1tZ8Gwc2bLdzT84YM7Jdjf7xNdhx0cR577o2F+iwAlMvyK6yQtm3b5aGH/m/DhKlTp+bpp57M2uus+7mfmzt3bpIUH5nQc931Mnv27Lz5xv/9Gfj6a68lSZbr1Kl47I7bbs3JJx6Xn//y7Gy62eaL8JsAUCkaxXPKr7zyyrRu3br4vra2NhdccEHWXXfd3H///WWsDL55WrZonm4rtiu+77L8sln7W8vnwynT8+b4D/PrkX/PMQdsk5ffeDev/ef9nPyz7fLOu5Pz178/mST5aNonueL6B3LST76Tt8Z/mDfe+SCHD/x0x5O/jH4sSTJg4x5pv0yrjH329UydPiM9ui2XMw/fKQ8+/kqDkGjtby3/aU1L1KXt0ktm7W8tn5mz5+SFV8cn+XRL+pN+ul32Of6qvP72++mw7KedSVOnz8i0j0t/lhgALA7Tp03LG58JZv7z1lt54fnn07p16yzXqVP2/uGPcvlvLknnlTpn+RU+3Qa+Xfv2xZ3CnnrqyTz79NNZd71eadW6Vd58441cfOH5WXHFlbJOz09Dog36bpjuPdbIyScdn6OOPT6FuXNz5unDssGGGxW7gm6/9ZacdMKxOfrY47PWWuvkvXc/ff5QXX19llpqqQAsDKNWlatRbAO/qNkGHr66TXqtmruuOHS+41f/9V858OQ/JElO+ul22W+XjdJmqRZ58IlXcuiZf87Lb/zfc7uaNq3NaQfvmO9vt35a1DXLI8+8nqN+eX2e///Bzaa9V82pg7+b1VfumLpmTfPWhEm5+Z4n8qvfjc7kqR8Xr/Px4/NvR/362+9n9e1OTpK8cNup6dxp/ucXnH7p7TnjN7d/vR8EfEPZBh4WnUcefigH7Puj+Y7vsOPOOe3Mn6dQKOTiiy7IDdf9OR99NCXrrtcrx590cjG4eenfL+YXw8/Iv198MR9/PD1t27XLRhtvkh8f9LN06PB/nbYTJ07Iz884PWMefCAtWiyRjTbZNEcedUxat2mTJNl/nx/m0Uce/tw6gEXjm7INfLcj7ih3CZ/rlbM93P6LlC0AuuCCC3LggQemvr4+F1xwweeuq6mpycEHH7xQ1xYAAcBXIwACgK9GAFR+AqAvVrb/iJ577rnZe++9U19fn3PPPfdz132VAAgAAABY9EyAVa6yBUDjxo1b4L8DAAAAsGg1il3A5pk5c2ZefPHFzJ49u9ylAAAAAFSNRhEATZ8+Pfvvv3+WWGKJrLHGGsXdEg4++OD8/OceTAcAAACNQU1NTaN98cUaRQB03HHH5cknn8y9996b+vr64vF+/frl2muvLWNlAAAAAJWvUTyn/Kabbsq1116bDTbYoEFqt8Yaa+SVV14pY2UAAAAAla9RBEDvvvtu2rdvP9/xadOmaeMCAACARsJf0StXoxgB6927d2677bbi+3mhzxVXXJG+ffuWqywAAACAqtAoOoDOPPPMbLvttnnuuecye/bsnH/++Xnuuefy4IMP5r777it3eQAAAAAVrVF0AG288cZ54oknMnv27Ky11lq566670r59+4wZMya9evUqd3kAAABAqmcXsDlz5uSkk05K165d06JFi3Tr1i2nnXZaCoVCcU2hUMjQoUOz3HLLpUWLFunXr19eeumlBtf54IMPsvfee6dVq1Zp06ZN9t9//0ydOrXBmqeeeiqbbLJJ6uvrs+KKK+ass86ar57rrrsuq6++eurr67PWWmvl9ttvX6jvU4pG0QGUJN26dcvll19e7jIAAACAKveLX/wil1xySa666qqsscYaefTRR7PvvvumdevWOeSQQ5IkZ511Vi644IJcddVV6dq1a0466aQMGDAgzz33XHEH87333jvvvPNORo8enVmzZmXffffNgQcemJEjRyZJpkyZkv79+6dfv3659NJL8/TTT2e//fZLmzZtcuCBByZJHnzwwXz/+9/P8OHDs/3222fkyJHZaaed8thjj2XNNddcZN+5pvDZeKtMHnvssTRr1ixrrbVWkuTmm2/OlVdemR49euSUU05J8+bNF+p6LdYdvDjKBICq9+EjF5W7BACoSPWNpr1i8VrtmDvLXcLnevEXA0peu/3226dDhw757W9/Wzy26667pkWLFvnDH/6QQqGQTp065YgjjsiRRx6ZJJk8eXI6dOiQESNGZM8998zzzz+fHj165JFHHknv3r2TJKNGjcp3vvOdvPXWW+nUqVMuueSSnHDCCRk/fnwx2zj22GNz00035YUXXkiS7LHHHpk2bVpuvfXWYi0bbLBBevbsmUsvvfRr/1zmaRQjYAcddFD+/e9/J0leffXV7LHHHlliiSVy3XXX5eijjy5zdQAAAEDy6S5gjfU1Y8aMTJkypcFrxowZC/weG264Ye6+++5iFvHkk0/mgQceyLbbbpskGTduXMaPH59+/foVP9O6dev06dMnY8aMSZKMGTMmbdq0KYY/SdKvX7/U1tbmoYceKq7ZdNNNGzS2DBgwIC+++GI+/PDD4prP3mfemnn3WVQaRQD073//Oz179kzy6dzbZpttlpEjR2bEiBG54YYbylscAAAA0OgNHz48rVu3bvAaPnz4Atcee+yx2XPPPbP66qunWbNmWXfddXPYYYdl7733TpKMHz8+SdKhQ4cGn+vQoUPx3Pjx49O+ffsG55s2bZplllmmwZoFXeOz9/i8NfPOLyqNokmtUChk7ty5SZK//e1v2X777ZMkK664Yt57771ylgYAAABUgOOOOy5DhgxpcKyurm6Ba//85z/nmmuuyciRI7PGGmvkiSeeyGGHHZZOnTpl4MCB/4ty/+caRQDUu3fvnH766enXr1/uu+++XHLJJUk+bbn67xQMAAAAKI/a2oXbbet/qa6u7nMDn/921FFHFbuAkmSttdbK66+/nuHDh2fgwIHp2LFjkmTChAlZbrnlip+bMGFCcYKpY8eOmThxYoPrzp49Ox988EHx8x07dsyECRMarJn3/svWzDu/qDSKEbDzzjsvjz32WAYPHpwTTjghq6yySpLk+uuvz4Ybbljm6gAAAIBqMn369NTWNoxEmjRpUpxO6tq1azp27Ji77767eH7KlCl56KGH0rdv3yRJ3759M2nSpIwdO7a45p577sncuXPTp0+f4pr7778/s2bNKq4ZPXp0VltttSy99NLFNZ+9z7w18+6zqDSKDqC11147Tz/99HzHf/nLX6ZJkyZlqAgAAACoVt/97ndzxhlnZKWVVsoaa6yRxx9/POecc07222+/JElNTU0OO+ywnH766Vl11VWL28B36tQpO+20U5Kke/fu2WabbfLjH/84l156aWbNmpXBgwdnzz33TKdOnZIke+21V0499dTsv//+OeaYY/LMM8/k/PPPz7nnnlus5dBDD81mm22Ws88+O9ttt13+9Kc/5dFHH81ll122SL9zowiA5hk7dmyef/75JEmPHj2y3nrrlbkiAAAAYJ6axjsBtlAuvPDCnHTSSfnZz36WiRMnplOnTjnooIMydOjQ4pqjjz4606ZNy4EHHphJkyZl4403zqhRo1JfX19cc80112Tw4MHZaqutUltbm1133TUXXHBB8Xzr1q1z1113ZdCgQenVq1fatm2boUOH5sADDyyu2XDDDTNy5MiceOKJOf7447PqqqvmpptuypprrrlIv3NNoVAoLNIrfgUTJ07MHnvskfvuuy9t2rRJkkyaNClbbLFF/vSnP6Vdu3YLdb0W6w5eDFUCQPX78JGLyl0CAFSk+kbVXrH4rHHCXeUu4XM9e0b/cpfQqDWKZwAdfPDBmTp1ap599tl88MEH+eCDD/LMM89kypQpOeSQQ8pdHgAAAEBFaxQZ5ahRo/K3v/0t3bt3Lx7r0aNHfv3rX6d/fwkeAAAANAY11TID9g3UKDqA5s6dm2bNms13vFmzZsUncAMAAADw1TSKAGjLLbfMoYcemrfffrt47D//+U8OP/zwbLXVVmWsDAAAAKDyNYoA6KKLLsqUKVPSpUuXdOvWLd26dUuXLl0yZcqUXHjhheUuDwAAAMinu4A11hdfrFE8A2jFFVfMY489lrvvvru4DXz37t3Tr1+/MlcGAAAAUPkaRQCUJPfcc0/uueeeTJw4MXPnzs3jjz+ekSNHJkl+97vflbk6AAAAgMrVKAKgU089NcOGDUvv3r2z3HLLeao4AAAANEL+vl65GkUAdOmll2bEiBH54Q9/WO5SAAAAAKpOo3gI9MyZM7PhhhuWuwwAAACAqtQoAqADDjig+LwfAAAAoHGqqalptC++WKMYAfvkk09y2WWX5W9/+1vWXnvtNGvWrMH5c845p0yVAQAAAFS+RhEAPfXUU+nZs2eS5JlnnmlwTooHAAAA8PU0igDo73//e7lLAAAAAL6EHo3K1SieAQQAAADA4iMAAgAAAKhyjWIEDAAAAGj8PKe3cukAAgAAAKhyAiAAAACAKmcEDAAAACiJCbDKpQMIAAAAoMoJgAAAAACqnBEwAAAAoCR2AatcOoAAAAAAqpwACAAAAKDKGQEDAAAASmICrHLpAAIAAACocgIgAAAAgCpnBAwAAAAoiV3AKpcOIAAAAIAqJwACAAAAqHJGwAAAAICSmACrXDqAAAAAAKqcAAgAAACgyhkBAwAAAEpiF7DKpQMIAAAAoMoJgAAAAACqnBEwAAAAoCQmwCqXDiAAAACAKicAAgAAAKhyRsAAAACAktgFrHLpAAIAAACocgIgAAAAgCpnBAwAAAAoiQmwyqUDCAAAAKDKCYAAAAAAqpwRMAAAAKAkdgGrXDqAAAAAAKqcAAgAAACgyhkBAwAAAEpiAqxy6QACAAAAqHICIAAAAIAqZwQMAAAAKIldwCqXDiAAAACAKicAAgAAAKhyRsAAAACAkhgBq1w6gAAAAACqnAAIAAAAoMoZAQMAAABKYgKscukAAgAAAKhyAiAAAACAKmcEDAAAACiJXcAqlw4gAAAAgConAAIAAACockbAAAAAgJKYAKtcOoAAAAAAqpwACAAAAKDKGQEDAAAASmIXsMqlAwgAAACgygmAAAAAAKqcETAAAACgJCbAKpcOIAAAAIAqJwACAAAAqHJGwAAAAICS1JoBq1g6gAAAAACqnAAIAAAAoMoZAQMAAABKYgKscukAAgAAAKhyAiAAAACAKmcEDAAAAChJjRmwiqUDCAAAAKDKCYAAAAAAqpwACAAAAKDKeQYQAAAAUJJajwCqWDqAAAAAAKqcAAgAAACgyhkBAwAAAEpiG/jKpQMIAAAAoMoJgAAAAACqnBEwAAAAoCQmwCqXDiAAAACAKicAAgAAAKhyRsAAAACAktTEDFil0gEEAAAAUOUEQAAAAABVzggYAAAAUJJaE2AVSwcQAAAAQJUTAAEAAABUOSNgAAAAQElqasyAVSodQAAAAABVTgAEAAAAUOWMgAEAAAAlMQFWuXQAAQAAAFQ5ARAAAABAlTMCBgAAAJSk1gxYxdIBBAAAAFDlBEAAAAAAVc4IGAAAAFASE2CVSwcQAAAAQJUTAAEAAABUOSNgAAAAQElqzIBVLB1AAAAAAFVOAAQAAABQ5YyAAQAAACUxAVa5dAABAAAAVDkBEAAAAECVMwIGAAAAlKTWDFjF0gEEAAAAUOUEQAAAAABVzggYAAAAUBIDYJVLBxAAAABAlRMAAQAAAFQ5I2AAAABASWrsAlaxdAABAAAAVDkBEAAAAECVMwIGAAAAlKTWBFjF0gEEAAAAUOUEQAAAAABVzggYAAAAUBK7gFUuHUAAAAAAVU4ABAAAAFDljIABAAAAJTEBVrl0AAEAAABUOQEQAAAAQJUzAgYAAACUxC5glUsHEAAAAECVEwABAAAAVDkjYAAAAEBJak2AVSwdQAAAAABVTgAEAAAAUOWMgAEAAAAlsQtY5dIBBAAAAFDlBEAAAAAAVc4IGAAAAFASA2CVSwcQAAAAQJUTAAEAAABUOSNgAAAAQElq7QJWsXQAAQAAAFQ5ARAAAABAlRMAAQAAACWpqWm8r4X1n//8Jz/4wQ+y7LLLpkWLFllrrbXy6KOPFs8XCoUMHTo0yy23XFq0aJF+/frlpZdeanCNDz74IHvvvXdatWqVNm3aZP/998/UqVMbrHnqqaeyySabpL6+PiuuuGLOOuus+Wq57rrrsvrqq6e+vj5rrbVWbr/99oX/Ql9CAAQAAAB8o3z44YfZaKON0qxZs9xxxx157rnncvbZZ2fppZcurjnrrLNywQUX5NJLL81DDz2Uli1bZsCAAfnkk0+Ka/bee+88++yzGT16dG699dbcf//9OfDAA4vnp0yZkv79+6dz584ZO3ZsfvnLX+aUU07JZZddVlzz4IMP5vvf/37233//PP7449lpp52y00475Zlnnlmk37mmUCgUFukVG4EW6w4udwkAUJE+fOSicpcAABWp/huyxdKP/7xoQ4lF6fLd1yx57bHHHpt//vOf+cc//rHA84VCIZ06dcoRRxyRI488MkkyefLkdOjQISNGjMiee+6Z559/Pj169MgjjzyS3r17J0lGjRqV73znO3nrrbfSqVOnXHLJJTnhhBMyfvz4NG/evHjvm266KS+88EKSZI899si0adNy6623Fu+/wQYbpGfPnrn00ku/0s9iQXQAAQAAACWpqalptK8ZM2ZkypQpDV4zZsxY4Pf461//mt69e+d73/te2rdvn3XXXTeXX3558fy4ceMyfvz49OvXr3isdevW6dOnT8aMGZMkGTNmTNq0aVMMf5KkX79+qa2tzUMPPVRcs+mmmxbDnyQZMGBAXnzxxXz44YfFNZ+9z7w18+6zqAiAAAAAgIo3fPjwtG7dusFr+PDhC1z76quv5pJLLsmqq66aO++8Mz/96U9zyCGH5KqrrkqSjB8/PknSoUOHBp/r0KFD8dz48ePTvn37BuebNm2aZZZZpsGaBV3js/f4vDXzzi8qX6lJ7R//+Ed+85vf5JVXXsn111+f5ZdfPldffXW6du2ajTfeeJEWCAAAAPBljjvuuAwZMqTBsbq6ugWunTt3bnr37p0zzzwzSbLuuuvmmWeeyaWXXpqBAwcu9lrLYaE7gG644YYMGDAgLVq0yOOPP15sp5o8eXLxBwcAAABUn3Lv9PVFr7q6urRq1arB6/MCoOWWWy49evRocKx79+554403kiQdO3ZMkkyYMKHBmgkTJhTPdezYMRMnTmxwfvbs2fnggw8arFnQNT57j89bM+/8orLQAdDpp5+eSy+9NJdffnmaNWtWPL7RRhvlscceW6TFAQAAACxqG220UV588cUGx/7973+nc+fOSZKuXbumY8eOufvuu4vnp0yZkoceeih9+/ZNkvTt2zeTJk3K2LFji2vuueeezJ07N3369Cmuuf/++zNr1qzimtGjR2e11VYr7jjWt2/fBveZt2befRaVhQ6AXnzxxWy66abzHW/dunUmTZq0KGoCAAAAWGwOP/zw/Otf/8qZZ56Zl19+OSNHjsxll12WQYMGJfn0YdeHHXZYTj/99Pz1r3/N008/nR/96Efp1KlTdtpppySfdgxts802+fGPf5yHH344//znPzN48ODsueee6dSpU5Jkr732SvPmzbP//vvn2WefzbXXXpvzzz+/wajaoYcemlGjRuXss8/OCy+8kFNOOSWPPvpoBg9etDucL/QzgDp27JiXX345Xbp0aXD8gQceyMorr7yo6gIAAAAamdqamnKXsEisv/76ufHGG3Pcccdl2LBh6dq1a84777zsvffexTVHH310pk2blgMPPDCTJk3KxhtvnFGjRqW+vr645pprrsngwYOz1VZbpba2NrvuumsuuOCC4vnWrVvnrrvuyqBBg9KrV6+0bds2Q4cOzYEHHlhcs+GGG2bkyJE58cQTc/zxx2fVVVfNTTfdlDXXLH1b+1LUFAqFwsJ8YPjw4fnDH/6Q3/3ud9l6661z++235/XXX8/hhx+ek046KQcffPAiLfCraLHuok3JAOCb4sNHLip3CQBQkeq/0hZLleenNzxX7hI+1yW79vjyRd9gC/0f0WOPPTZz587NVlttlenTp2fTTTdNXV1djjzyyEYR/gAAAADQ0EIHQDU1NTnhhBNy1FFH5eWXX87UqVPTo0ePLLnkkoujPgAAAKCRqJIJsG+kr9yk1rx58/m2TAMAAACg8VnoAGiLLbZIzRdEfvfcc8/XKggAAACARWuhA6CePXs2eD9r1qw88cQTeeaZZzJw4MBFVRcAAADQyHxRQwiN20IHQOeee+4Cj59yyimZOnXq1y4IAAAAgEWrdlFd6Ac/+EF+97vfLarLAQAAALCIfOWHQP+3MWPGpL6+flFd7mt56Z6zy10CAFSkpXe6qNwlAEBF+vjWweUu4X9ikXWR8D+30AHQLrvs0uB9oVDIO++8k0cffTQnnXTSIisMAAAAgEVjoQOg1q1bN3hfW1ub1VZbLcOGDUv//v0XWWEAAAAALBoLFQDNmTMn++67b9Zaa60svfTSi6smAAAAoBGyC1jlWqjxvSZNmqR///6ZNGnSYioHAAAAgEVtoZ/ftOaaa+bVV19dHLUAAAAAsBgsdAB0+umn58gjj8ytt96ad955J1OmTGnwAgAAAKpTbU3jffHFSn4G0LBhw3LEEUfkO9/5TpJkhx12aDD7VygUUlNTkzlz5iz6KgEAAAD4ykoOgE499dT85Cc/yd///vfFWQ8AAAAAi1jJAVChUEiSbLbZZoutGAAAAKDxMmpVuRbqGUC2ewMAAACoPCV3ACXJt771rS8NgT744IOvVRAAAAAAi9ZCBUCnnnpqWrduvbhqAQAAABoxk0GVa6ECoD333DPt27dfXLUAAAAAsBiU/AwgKR8AAABAZVroXcAAAACAbya7gFWukgOguXPnLs46AAAAAFhMFmobeAAAAAAqz0I9BBoAAAD45vJ44MqlAwgAAACgygmAAAAAAKqcETAAAACgJLVmwCqWDiAAAACAKicAAgAAAKhyRsAAAACAkugiqVx+dwAAAABVTgAEAAAAUOWMgAEAAAAlsQlY5dIBBAAAAFDlBEAAAAAAVc4IGAAAAFCSWjNgFUsHEAAAAECVEwABAAAAVDkjYAAAAEBJTIBVLh1AAAAAAFVOAAQAAABQ5YyAAQAAACWpNQJWsXQAAQAAAFQ5ARAAAABAlTMCBgAAAJSk1jZgFUsHEAAAAECVEwABAAAAVDkjYAAAAEBJTIBVLh1AAAAAAFVOAAQAAABQ5YyAAQAAACWpNQJWsXQAAQAAAFQ5ARAAAABAlTMCBgAAAJSkJmbAKpUOIAAAAIAqJwACAAAAqHJGwAAAAICS2AWscukAAgAAAKhyAiAAAACAKmcEDAAAACiJEbDKpQMIAAAAoMoJgAAAAACqnBEwAAAAoCQ1NWbAKpUOIAAAAIAqJwACAAAAqHJGwAAAAICS2AWscukAAgAAAKhyAiAAAACAKmcEDAAAACiJTcAqlw4gAAAAgConAAIAAACockbAAAAAgJLUmgGrWDqAAAAAAKqcAAgAAACgyhkBAwAAAEpSawKsYukAAgAAAKhyAiAAAACAKmcEDAAAACiJTcAqlw4gAAAAgConAAIAAACockbAAAAAgJLUxgxYpdIBBAAAAFDlBEAAAAAAVc4IGAAAAFASu4BVLh1AAAAAAFVOAAQAAABQ5YyAAQAAACWpNQJWsXQAAQAAAFQ5ARAAAABAlTMCBgAAAJSk1jZgFUsHEAAAAECVEwABAAAAVDkjYAAAAEBJTIBVLh1AAAAAAFVOAAQAAABQ5YyAAQAAACWxC1jl0gEEAAAAUOUEQAAAAABVzggYAAAAUBITYJVLBxAAAABAlRMAAQAAAFQ5I2AAAABASXSRVC6/OwAAAIAqJwACAAAAqHJGwAAAAICS1NgGrGLpAAIAAACocgIgAAAAgCpnBAwAAAAoiQGwyqUDCAAAAKDKCYAAAAAAqpwRMAAAAKAktXYBq1g6gAAAAACqnAAIAAAAoMoZAQMAAABKYgCscukAAgAAAKhyAiAAAACAKmcEDAAAACiJTcAqlw4gAAAAgConAAIAAACockbAAAAAgJLUmAGrWDqAAAAAAKqcAAgAAACgyhkBAwAAAEqii6Ry+d0BAAAAVDkBEAAAAECVMwIGAAAAlMQuYJVLBxAAAABAlRMAAQAAAFQ5I2AAAABASQyAVS4dQAAAAABVTgAEAAAAUOWMgAEAAAAlsQtY5dIBBAAAAFDlBEAAAAAAVc4IGAAAAFASXSSVy+8OAAAAoMoJgAAAAACqnBEwAAAAoCR2AatcOoAAAAAAqpwACAAAAKDKGQEDAAAASmIArHLpAAIAAACocgIgAAAAgCpnBAwAAAAoiU3AKpcOIAAAAIAqJwACAAAAqHJGwAAAAICS1NoHrGLpAAIAAACocgIgAAAA4Bvr5z//eWpqanLYYYcVj33yyScZNGhQll122Sy55JLZddddM2HChAafe+ONN7LddttliSWWSPv27XPUUUdl9uzZDdbce++9WW+99VJXV5dVVlklI0aMmO/+v/71r9OlS5fU19enT58+efjhhxfH1xQAAQAAAKWpqWm8r6/ikUceyW9+85usvfbaDY4ffvjhueWWW3Ldddflvvvuy9tvv51ddtmleH7OnDnZbrvtMnPmzDz44IO56qqrMmLEiAwdOrS4Zty4cdluu+2yxRZb5Iknnshhhx2WAw44IHfeeWdxzbXXXpshQ4bk5JNPzmOPPZZ11lknAwYMyMSJE7/aF/oCNYVCobDIr1pmb304o9wlAEBFWvWHl5e7BACoSB/fOrjcJfxP3PrMhC9fVCbbr9lhodZPnTo16623Xi6++OKcfvrp6dmzZ84777xMnjw57dq1y8iRI7PbbrslSV544YV07949Y8aMyQYbbJA77rgj22+/fd5+++106PDpfS+99NIcc8wxeffdd9O8efMcc8wxue222/LMM88U77nnnntm0qRJGTVqVJKkT58+WX/99XPRRRclSebOnZsVV1wxBx98cI499thF8WMp0gEEAAAAVLwZM2ZkypQpDV4zZnx+g8igQYOy3XbbpV+/fg2Ojx07NrNmzWpwfPXVV89KK62UMWPGJEnGjBmTtdZaqxj+JMmAAQMyZcqUPPvss8U1/33tAQMGFK8xc+bMjB07tsGa2tra9OvXr7hmURIAAQAAACWpacT/DB8+PK1bt27wGj58+AK/x5/+9Kc89thjCzw/fvz4NG/ePG3atGlwvEOHDhk/fnxxzWfDn3nn5537ojVTpkzJxx9/nPfeey9z5sxZ4Jp511iUbAMPAAAAVLzjjjsuQ4YMaXCsrq5uvnVvvvlmDj300IwePTr19fX/q/LKTgcQAAAAUPHq6urSqlWrBq8FBUBjx47NxIkTs95666Vp06Zp2rRp7rvvvlxwwQVp2rRpOnTokJkzZ2bSpEkNPjdhwoR07NgxSdKxY8f5dgWb9/7L1rRq1SotWrRI27Zt06RJkwWumXeNRUkABAAAAJSk3Dt9LYpdwLbaaqs8/fTTeeKJJ4qv3r17Z++99y7+e7NmzXL33XcXP/Piiy/mjTfeSN++fZMkffv2zdNPP91gt67Ro0enVatW6dGjR3HNZ68xb828azRv3jy9evVqsGbu3Lm5++67i2sWJSNgAAAAwDfGUkstlTXXXLPBsZYtW2bZZZctHt9///0zZMiQLLPMMmnVqlUOPvjg9O3bNxtssEGSpH///unRo0d++MMf5qyzzsr48eNz4oknZtCgQcWuo5/85Ce56KKLcvTRR2e//fbLPffckz//+c+57bbbivcdMmRIBg4cmN69e+fb3/52zjvvvEybNi377rvvIv/eAiAAAACAzzj33HNTW1ubXXfdNTNmzMiAAQNy8cUXF883adIkt956a37605+mb9++admyZQYOHJhhw4YV13Tt2jW33XZbDj/88Jx//vlZYYUVcsUVV2TAgAHFNXvssUfefffdDB06NOPHj0/Pnj0zatSo+R4MvSjUFAqFwiK/apm99eHnb/MGAHy+VX94eblLAICK9PGtg8tdwv/EqGffLXcJn2ubNdqVu4RGzTOAAAAAAKqcAAgAAACgynkGEAAAAFCShdlti8ZFBxAAAABAlRMAAQAAAFQ5I2AAAABASYyAVS4dQAAAAABVTgAEAAAAUOWMgAEAAAAlqYkZsEqlAwgAAACgygmAAAAAAKqcETAAAACgJLUmwCpWo+wAmjRpUrlLAAAAAKgaZQ+AfvGLX+Taa68tvt99992z7LLLZvnll8+TTz5ZxsoAAAAAqkPZA6BLL700K664YpJk9OjRGT16dO64445su+22Oeqoo8pcHQAAADBPTSP+hy9W9mcAjR8/vhgA3Xrrrdl9993Tv3//dOnSJX369ClzdQAAAACVr+wdQEsvvXTefPPNJMmoUaPSr1+/JEmhUMicOXPKWRoAAABAVSh7B9Auu+ySvfbaK6uuumref//9bLvttkmSxx9/PKusskqZqwMAAADmqTFpVbHKHgCde+656dKlS958882cddZZWXLJJZMk77zzTn72s5+VuToAAACAylf2AKhZs2Y58sgj5zt++OGHl6EaAAAAgOpT9mcAJcnVV1+djTfeOJ06dcrrr7+eJDnvvPNy8803l7kyAAAAYJ5y7/RlF7CvruwB0CWXXJIhQ4Zk2223zaRJk4oPfm7Tpk3OO++88hYHAAAAUAXKHgBdeOGFufzyy3PCCSekSZMmxeO9e/fO008/XcbKAAAAAKpD2Z8BNG7cuKy77rrzHa+rq8u0adPKUBEAAACwILUmrSpW2TuAunbtmieeeGK+46NGjUr37t3/9wUBAAAAVJmydwANGTIkgwYNyieffJJCoZCHH344f/zjHzN8+PBcccUV5S4PAAAAoOKVPQA64IAD0qJFi5x44omZPn169tprr3Tq1Cnnn39+9txzz3KXBwAAAPx/dtuqXGUNgGbPnp2RI0dmwIAB2XvvvTN9+vRMnTo17du3L2dZAAAAAFWlrM8Aatq0aX7yk5/kk08+SZIsscQSwh8AAACARazsD4H+9re/nccff7zcZQAAAABfoqam8b74YmV/BtDPfvazHHHEEXnrrbfSq1evtGzZssH5tddeu0yVAQAAAFSHsgdA8x70fMghhxSP1dTUpFAopKamJnPmzClXaQAAAABVoewB0Lhx48pdAgAAAFACk1aVq+wBUOfOnctdAvAFRl51RR649+688fq41NXVpcdaPXPgoMOyYueuxTUzZ8zIJRf8Kn8fPSqzZs3M+n02zCFHnZhlll22uOais3+eZ556PK+9+nJW6rJyLrv6ugb3ueryi/P731463/3r6+tz270PJ0lee/XljLjs1/n3C89nwvi387PDjsque/5wMX1zAPj6lmzRLCf/oE926Lty2rVeIk+++m6OvOwfGfvSxOKa1VZYOqfvu2E2WbNTmjapzQtvfJDvD78jb747NUsvWZeT9u6TrdZdMSu2WyrvTf44t/zr1Zz6h4cyZfrM4jXOPnCTbNBjuazRedm88OYH2eCQaxvUccJe386Je317vvqmfTIrbXf7zeL7AQDQaJQ9APr973//hed/9KMf/Y8qARbkqccfzQ677pnVe6yROXPm5LeXXJCjD/1JfvfHG9OixRJJkovPOysPPfiPnHzmr9JyyaVywa/OzCnHHp4LLm/4f9/bfHfnvPDsU3n15Zfmu8/ue++T7+6ye4NjRw7+cVbrvkbx/SeffJLlll8hm27VP5ec98vF8G0BYNG65OAt06PzMtnv7L/lnQ+m5ftbrJbbTt8x6/1sZN5+f1q6dmyVu8/aNVeNfi6nX/NpqNNjpWXyycxPH4Ow3LIts9wyLXPc7/6Z59/4ICu1XyoXDtoiyy3bMnsNH9XgXr8f/XzWX61D1uyy7Hx1nPeXx3PF7c80OHb7GTs2CKIAqG5lD4AOPfTQBu9nzZqV6dOnp3nz5lliiSUEQFBmPz+vYVfO0Sedll233TwvvfBc1l63d6ZO/Sh33HJjjh/286zbu8+na048LfvuuWOee+bJ9FhznSTJ4COOTZJc9eEHCwyAWiyxRFossUTx/SsvvZjXx72Sw445sXhs9R5rZvUeayZJrvj1+Yv2iwLAIlbfvEl22qhbvnfabfnns28nSc4Y+XC+8+0u+fG2a+bUPzyUU3+0Qe589LWccOWDxc+NGz+l+O/Pvf5pN9Bnz53y+zH53ZH906S2JnPmFpIkR1z2jyRJ29YtFhgATftkVqZ9Mqv4fq2uy6ZH52VzyMX3LtLvDFS/WtttVayybwP/4YcfNnhNnTo1L774YjbeeOP88Y9/LHd5wH+ZNnVqkmSpVq2TJC+98Fxmz56dXutvUFyzUpeuad9xuTz39FNf+T633/yXrLBS56zds9fXKxgAyqRpk9o0bVKbT2Y13NTkkxmzs+EanVJTk2zTu0teentS/jpsh7z+h/1y/9m75bsbdP2cK36qVcu6TJk+sxj+fBX79l8j/37rw/zz2Xe+8jUAqCxlD4AWZNVVV83Pf/7z+bqDFmTGjBmZMmVKg9eMGTP+B1XCN8/cuXPz6/POypprr5uu3VZNknzw/ntp1qxZllyqVYO1Sy+zbD54/72vdJ+ZM2bk7rtuy7bf3flr1wwA5TL141n51/Pv5Lg9189yy7RMbW1N9tz8W+mzesd0XHqJtG+9RJZaonmO3K1XRo99Pd896a/565hX86fjv5ON1+y0wGsu26o+x+3ZO78b9exXrquuWZPssfm3ctXo577yNQCoPI0yAEqSpk2b5u233/7SdcOHD0/r1q0bvH597ln/gwrhm+eCX56R1155OSee/ovFep8H7rs706dNT//v7LhY7wMAi9t+Z49OTZJXf79vJt/40wzaYZ38+f6XMrdQSG3tp2MUt/5rXC68+ck8Ne69/Or6x3L7I6/lx9uuOd+1lmrRLDeevH2ef+PDnD7y4a9c0459V85SLZrlD3e/8JWvAXxz1TTiF1+s7M8A+utf/9rgfaFQyDvvvJOLLrooG2200Zd+/rjjjsuQIUMaHHt3+iItEUhywa/OzL/+eX/OvfTKtGvfsXh8mWXbZtasWZn60ZQGXUAffvB+llm27Ve61+1//Us22HjTBruIAUAlGjd+Svofd2OWqGuaVks0z/gPp+fqowdk3PgpeW/Kx5k1e06ef/ODBp958c0PsmGPhh1AS7Zolr8O2yEffTwre5xxe2bPmfuVa9pnQI/c8chrmTjp4698DQAqT9kDoJ122qnB+5qamrRr1y5bbrllzj777C/9fF1dXerq6hocmzLHCBgsKoVCIReePTwP3HdPzvn1b7NcpxUanF919R5p2rRpHnvkoWy65dZJkjdfH5eJ499Jj7XWXuj7vfP2W3li7CM57ZcXLJL6AaAxmD5jdqbPmJ02LevSb72VcsKVD2bW7LkZ+9LEfGv5Ng3Wrrp8m7wx8aPi+6VaNMstp+2YGbPmZLfTbsuM/3qm0MLo3GGpbLbWCtnttNu+8jUAqExlD4Dmzv3q/+sFsPhd8Mszcvddd+S0s87PEi1bFp/r07Llkqmrr8+SSy6Vbb+7cy654FdZqnXrtGy5ZC48e3h6rLVOcQewJPnPm2/k44+n54MP3s+MGZ/k5X9/2nbeuWu3NGvWrLhu1C03ZZm27fLtvhvPV8usWbPy+rhXkiSzZ8/Ke+9OzMv/fiEtWiyR5VdcaXH+GADgK+m33kqpSfLv/3yYbsu1yZn7bZh/v/Vhfv+355Mk5/7l8Vx99IA88Ozbue+p/6R/r5XynW93zYDjbkzyafhz62k7pkVd0+z7q7vSqkXztGrRPEny7pSPM/f/Pwh65eVaZ8n6Zumw9BJp0bxp1u76aRfu829+kFmz/++/bw/cukfGfzgtd459/X/4UwCqilmrilVTKBS++vYBi8D999+f1VdfPe3bt29wfNasWRkzZkw23XTThb7mWx/qAIJFZasNFtzFc9SJp2Wb7T99Rs/MGTNyyQW/yt9H35FZM2emd5+NcujRJzQYARvy0/3y5OOPzneda/5yRzp2Wj7Jp4HwXjsNyNbbfjf7//SQ+daOf/s/2XuXbec7vs66vXPOJb/7St8PaGjVH15e7hKgquy68SoZNrBvlm+7ZD746JPc/OArOfn3/8qU6TOLa360dfcc9b1eWX7ZJfPv/3yY0695OLc+NC5Jsslay+eu4QveFGG1/a4qdgrdOXznbLrW8l+4pqYm+ffvBuaae17MKVf/a1F/VfjG+/jWweUu4X/iX69MKncJn2uDbm3KXUKjVvYAqLa2Nh06dMiNN96YDTb4v22kJ0yYkE6dOmXOnIVvcRUAAcBXIwACgK9GAFR+AqAv1ih2Adtzzz2z1VZbZcSIEQ2OlzmbAgAAAD6jphH/wxcrewBUU1OT4447LldffXUGDx6cIUOGFIOfmhq/QAAAAICvq+wB0LywZ5dddsk//vGPXH/99dl2220zadKk8hYGAAAAUCXKHgB91rrrrpuHH344kyZNylZbbVXucgAAAIDPqKlpvC++WNkDoIEDB6ZFixbF9x07dsx9992XrbbaKiutZFtnAAAAgK+rabkLuPLKK+c7VldXl6uuuqoM1QAAAABUn7IEQE899VTWXHPN1NbW5qmnnvrCtWuvvfb/qCoAAADgi5i0qlxlCYB69uyZ8ePHp3379unZs2dqamoabPk+731NTU3mzJlTjhIBAAAAqkZZAqBx48alXbt2xX8HAAAAYPEpSwDUuXPnBf47AAAA0IiZAatYZd8FLEmuvvrqbLTRRunUqVNef/31JMl5552Xm2++ucyVAQAAAFS+sgdAl1xySYYMGZLvfOc7mTRpUvGZP23atMl5551X3uIAAAAAqkDZA6ALL7wwl19+eU444YQ0adKkeLx37955+umny1gZAAAA8Fk1jfgfvljZA6Bx48Zl3XXXne94XV1dpk2bVoaKAAAAAKpL2QOgrl275oknnpjv+KhRo9K9e/f/fUEAAAAAVaYsu4B91pAhQzJo0KB88sknKRQKefjhh/PHP/4xw4cPzxVXXFHu8gAAAID/r8akVcUqewB0wAEHpEWLFjnxxBMzffr07LXXXll++eVz/vnnZ8899yx3eQAAAAAVr+wB0Mcff5ydd945e++9d6ZPn55nnnkm//znP7PCCiuUuzQAAACAqlD2ZwDtuOOO+f3vf58kmTlzZnbYYYecc8452WmnnXLJJZeUuToAAABgnppG/OKLlT0Aeuyxx7LJJpskSa6//vp06NAhr7/+en7/+9/nggsuKHN1AAAAAJWv7AHQ9OnTs9RSSyVJ7rrrruyyyy6pra3NBhtskNdff73M1QEAAABUvrIHQKusskpuuummvPnmm7nzzjvTv3//JMnEiRPTqlWrMlcHAAAAFJV7zssM2FdW9gBo6NChOfLII9OlS5f06dMnffv2TfJpN9C6665b5uoAAAAAKl/ZdwHbbbfdsvHGG+edd97JOuusUzy+1VZbZeeddy5jZQAAAADVoewBUJJ07NgxHTt2bHDs29/+dpmqAQAAABakxqxVxSr7CBgAAAAAi5cACAAAAKDKNYoRMAAAAKDxqzEBVrF0AAEAAABUOQEQAAAAQJUzAgYAAACUxARY5dIBBAAAAFDlBEAAAAAAVc4IGAAAAFAaM2AVSwcQAAAAQJUTAAEAAABUOSNgAAAAQElqzIBVLB1AAAAAAFVOAAQAAABQ5YyAAQAAACWpMQFWsXQAAQAAAFQ5ARAAAABAlTMCBgAAAJTEBFjl0gEEAAAAUOUEQAAAAABVzggYAAAAUBozYBVLBxAAAABAlRMAAQAAAFQ5I2AAAABASWrMgFUsHUAAAAAAVU4ABAAAAFDljIABAAAAJakxAVaxdAABAAAAVDkBEAAAAECVMwIGAAAAlMQEWOXSAQQAAABQ5QRAAAAAAFXOCBgAAABQGjNgFUsHEAAAAECVEwABAAAAVDkjYAAAAEBJasyAVSwdQAAAAABVTgAEAAAAUOWMgAEAAAAlqTEBVrF0AAEAAABUOQEQAAAAQJUzAgYAAACUxARY5dIBBAAAAFDlBEAAAAAAVc4IGAAAAFAaM2AVSwcQAAAAQJUTAAEAAABUOSNgAAAAQElqzIBVLB1AAAAAAFVOAAQAAABQ5YyAAQAAACWpMQFWsXQAAQAAAFQ5ARAAAABAlTMCBgAAAJTEBFjl0gEEAAAAUOUEQAAAAABVzggYAAAAUBozYBVLBxAAAABAlRMAAQAAAFQ5I2AAAABASWrMgFUsHUAAAAAAVU4ABAAAAFDljIABAAAAJakxAVaxdAABAAAAVDkBEAAAAECVMwIGAAAAlMQEWOXSAQQAAABQ5QRAAAAAAFXOCBgAAABQGjNgFUsHEAAAAECVEwABAAAAVDkjYAAAAEBJasyAVSwdQAAAAABVTgAEAAAAUOWMgAEAAAAlqTEBVrF0AAEAAABUOQEQAAAAQJUTAAEAAABUOc8AAgAAAEriEUCVSwcQAAAAQJUTAAEAAADfKMOHD8/666+fpZZaKu3bt89OO+2UF198scGaTz75JIMGDcqyyy6bJZdcMrvuumsmTJjQYM0bb7yR7bbbLksssUTat2+fo446KrNnz26w5t577816662Xurq6rLLKKhkxYsR89fz6179Oly5dUl9fnz59+uThhx9e5N9ZAAQAAACUpKam8b4Wxn333ZdBgwblX//6V0aPHp1Zs2alf//+mTZtWnHN4YcfnltuuSXXXXdd7rvvvrz99tvZZZddiufnzJmT7bbbLjNnzsyDDz6Yq666KiNGjMjQoUOLa8aNG5ftttsuW2yxRZ544okcdthhOeCAA3LnnXcW11x77bUZMmRITj755Dz22GNZZ511MmDAgEycOPGr/6IWoKZQKBQW6RUbgbc+nFHuEgCgIq36w8vLXQIAVKSPbx1c7hL+Jxrz37dXWLruK3/23XffTfv27XPfffdl0003zeTJk9OuXbuMHDkyu+22W5LkhRdeSPfu3TNmzJhssMEGueOOO7L99tvn7bffTocOHZIkl156aY455pi8++67ad68eY455pjcdttteeaZZ4r32nPPPTNp0qSMGjUqSdKnT5+sv/76ueiii5Ikc+fOzYorrpiDDz44xx577Ff+Tv9NBxAAAABQ8WbMmJEpU6Y0eM2YUVpgNXny5CTJMssskyQZO3ZsZs2alX79+hXXrL766llppZUyZsyYJMmYMWOy1lprFcOfJBkwYECmTJmSZ599trjms9eYt2beNWbOnJmxY8c2WFNbW5t+/foV1ywqAiAAAACgRDWN9jV8+PC0bt26wWv48OFf+o3mzp2bww47LBtttFHWXHPNJMn48ePTvHnztGnTpsHaDh06ZPz48cU1nw1/5p2fd+6L1kyZMiUff/xx3nvvvcyZM2eBa+ZdY1GxDTwAAABQ8Y477rgMGTKkwbG6ui8fCxs0aFCeeeaZPPDAA4urtEZBAAQAAABUvLq6upICn88aPHhwbr311tx///1ZYYUVisc7duyYmTNnZtKkSQ26gCZMmJCOHTsW1/z3bl3zdgn77Jr/3jlswoQJadWqVVq0aJEmTZqkSZMmC1wz7xqLihEwAAAAoCTl3ulrUe0CVigUMnjw4Nx4442555570rVr1wbne/XqlWbNmuXuu+8uHnvxxRfzxhtvpG/fvkmSvn375umnn26wW9fo0aPTqlWr9OjRo7jms9eYt2beNZo3b55evXo1WDN37tzcfffdxTWLig4gAAAA4Btl0KBBGTlyZG6++eYstdRSxefttG7dOi1atEjr1q2z//77Z8iQIVlmmWXSqlWrHHzwwenbt2822GCDJEn//v3To0eP/PCHP8xZZ52V8ePH58QTT8ygQYOKnUg/+clPctFFF+Xoo4/Ofvvtl3vuuSd//vOfc9tttxVrGTJkSAYOHJjevXvn29/+ds4777xMmzYt++677yL9zgIgAAAA4BvlkksuSZJsvvnmDY5feeWV2WeffZIk5557bmpra7PrrrtmxowZGTBgQC6++OLi2iZNmuTWW2/NT3/60/Tt2zctW7bMwIEDM2zYsOKarl275rbbbsvhhx+e888/PyussEKuuOKKDBgwoLhmjz32yLvvvpuhQ4dm/Pjx6dmzZ0aNGjXfg6G/rppCoVBYpFdsBN76sLRt3gCAhlb94eXlLgEAKtLHtw4udwn/E29PmlnuEj5XpzbNy11Co+YZQAAAAABVTgAEAAAAUOU8AwgAAAAoycLutkXjoQMIAAAAoMoJgAAAAACqnBEwAAAAoCQ1MQNWqXQAAQAAAFQ5ARAAAABAlTMCBgAAAJTGBFjF0gEEAAAAUOUEQAAAAABVzggYAAAAUBITYJVLBxAAAABAlRMAAQAAAFQ5I2AAAABASWrMgFUsHUAAAAAAVU4ABAAAAFDljIABAAAAJamxD1jF0gEEAAAAUOUEQAAAAABVzggYAAAAUBoTYBVLBxAAAABAlRMAAQAAAFQ5I2AAAABASUyAVS4dQAAAAABVTgAEAAAAUOWMgAEAAAAlqTEDVrF0AAEAAABUOQEQAAAAQJUzAgYAAACUpMY+YBVLBxAAAABAlRMAAQAAAFQ5I2AAAABASewCVrl0AAEAAABUOQEQAAAAQJUTAAEAAABUOQEQAAAAQJUTAAEAAABUObuAAQAAACWxC1jl0gEEAAAAUOUEQAAAAABVzggYAAAAUJKamAGrVDqAAAAAAKqcAAgAAACgyhkBAwAAAEpiF7DKpQMIAAAAoMoJgAAAAACqnBEwAAAAoCQmwCqXDiAAAACAKicAAgAAAKhyRsAAAACA0pgBq1g6gAAAAACqnAAIAAAAoMoZAQMAAABKUmMGrGLpAAIAAACocgIgAAAAgCpnBAwAAAAoSY0JsIqlAwgAAACgygmAAAAAAKqcETAAAACgJCbAKpcOIAAAAIAqJwACAAAAqHJGwAAAAIDSmAGrWDqAAAAAAKqcAAgAAACgyhkBAwAAAEpSYwasYukAAgAAAKhyAiAAAACAKmcEDAAAAChJjQmwiqUDCAAAAKDKCYAAAAAAqlxNoVAolLsI4JtjxowZGT58eI477rjU1dWVuxwAqBj+DAXg6xAAAf9TU6ZMSevWrTN58uS0atWq3OUAQMXwZygAX4cRMAAAAIAqJwACAAAAqHICIAAAAIAqJwAC/qfq6upy8skne3glACwkf4YC8HV4CDQAAABAldMBBAAAAFDlBEAAAAAAVU4ABAAAAFDlBEDAYtelS5ecd9555S4DABqV1157LTU1NXniiSfKXQoA3wAeAg0sdu+++25atmyZJZZYotylAECjMWfOnLz77rtp27ZtmjZtWu5yAKhyAiCg4s2cOTPNmzcvdxkA0OjMmjUrzZo1K3cZADQCRsDgG2DzzTfPIYcckqOPPjrLLLNMOnbsmFNOOaV4/o033siOO+6YJZdcMq1atcruu++eCRMmFM8/+eST2WKLLbLUUkulVatW6dWrVx599NHi+QceeCCbbLJJWrRokRVXXDGHHHJIpk2bVjz/2RGwESNGpKamZr7XvHo233zzHHbYYQ3q32mnnbLPPvs0uN5pp52WH/3oR2nVqlUOPPDAkuq4+OKLs+qqq6a+vj4dOnTIbrvt9jV/sgCQXH/99VlrrbXSokWLLLvssunXr1/xz58rrrgi3bt3T319fVZfffVcfPHFxc/99wjYPvvss8A/I++9994kSU1NTW666aYG927Tpk1GjBjR4HrXXnttNttss9TX1+eaa6750jpmzpyZwYMHZ7nllkt9fX06d+6c4cOHL54fFgBlIwCCb4irrroqLVu2zEMPPZSzzjorw4YNy+jRozN37tzsuOOO+eCDD3Lfffdl9OjRefXVV7PHHnsUP7v33ntnhRVWyCOPPJKxY8fm2GOPLf6via+88kq22Wab7Lrrrnnqqady7bXX5oEHHsjgwYMXWMcee+yRd955p/j64x//mKZNm2ajjTZaqO/zq1/9Kuuss04ef/zxnHTSSV9ax6OPPppDDjkkw4YNy4svvphRo0Zl0003/Yo/TQD41DvvvJPvf//72W+//fL888/n3nvvzS677JJCoZBrrrkmQ4cOzRlnnJHnn38+Z555Zk466aRcddVVC7zW+eef3+DPyEMPPTTt27fP6quvvlA1HXvssTn00EPz/PPPZ8CAAV9axwUXXJC//vWv+fOf/5wXX3wx11xzTbp06fJ1fzQANDYFoOptttlmhY033rjBsfXXX79wzDHHFO66665CkyZNCm+88Ubx3LPPPltIUnj44YcLhUKhsNRSSxVGjBixwGvvv//+hQMPPLDBsX/84x+F2trawscff1woFAqFzp07F84999z5Pvvyyy8XlllmmcJZZ53VoNZDDz20wbodd9yxMHDgwOL7zp07F3baaaeFquOGG24otGrVqjBlypQFfg8A+CrGjh1bSFJ47bXX5jvXrVu3wsiRIxscO+200wp9+/YtFAqFwrhx4wpJCo8//vh8n73hhhsK9fX1hQceeKB4LEnhxhtvbLCudevWhSuvvLLB9c4777yFquPggw8ubLnlloW5c+eW9J0BqEyeNgffEGuvvXaD98stt1wmTpyY559/PiuuuGJWXHHF4rkePXqkTZs2ef7557P++utnyJAhOeCAA3L11VenX79++d73vpdu3bol+XQ87Kmnniq2mCdJoVDI3LlzM27cuHTv3n2B9UyePDnbb799tttuuxx11FEL/X169+7d4P2X1bH11lunc+fOWXnllbPNNttkm222yc477+zB1AB8Leuss0622mqrrLXWWhkwYED69++f3XbbLc2bN88rr7yS/fffPz/+8Y+L62fPnp3WrVt/4TUff/zx/PCHP8xFF1200B2yScM/I6dNm/aldeyzzz7Zeuuts9pqq2WbbbbJ9ttvn/79+y/0fQFo3ARA8A3x3w+ArKmpydy5c0v67CmnnJK99tort912W+64446cfPLJ+dOf/pSdd945U6dOzUEHHZRDDjlkvs+ttNJKC7zenDlzsscee6RVq1a57LLLGpyrra1N4b+eTT9r1qz5rtGyZcsG77+sjubNm+exxx7Lvffem7vuuitDhw7NKaeckkceeSRt2rT5sh8BACxQkyZNMnr06Dz44IO56667cuGFF+aEE07ILbfckiS5/PLL06dPn/k+83nGjx+fHXbYIQcccED233//BudqamoW+s/IqVOnfmkd6623XsaNG5c77rgjf/vb37L77runX79+uf7667/s6wNQQQRA8A3XvXv3vPnmm3nzzTeLXUDPPfdcJk2alB49ehTXfetb38q3vvWtHH744fn+97+fK6+8MjvvvHPWW2+9PPfcc1lllVVKvufhhx+ep59+Oo8++v/au/uYqss+juPvowQhDx3JR5wSSSE15jRbOaeIIzv9URax3KKQzFb5lFKkrpWYS3oYTtOFbhbaKpelkkM2RxT4sMXKpuvBDDWXbtRYZo0MkYf7j+64Q0u571vv247v13/n97vO9/qe65+zfXZdv98nXH755V3u9e3bl4aGhs7PbW1tfP7552RmZp61Znf6iIiIICsri6ysLBYuXEgwGOSDDz4gOzu7271LknS6QCDAmDFjGDNmDM888wxJSUns2rWLxMREDh06RG5ubrfqNDc3M2nSJIYNG8bSpUvPuH/6f2R9fT0nTpw4a83+/ft3q4/4+HgmT57M5MmTycnJIRQKcezYMRISErrVuyTp4mcAJF3isrKySE9PJzc3l2XLltHa2sr06dPJyMhg1KhR/PrrrxQWFpKTk0NycjJHjx7l448/5u677wZg3rx53HzzzcycOZNp06YRExPDl19+SVVVFStXrjxjvrKyMl555RU2b95MIBDgu+++AyA2NpbY2FgmTJhAQUEBW7duZejQoSxdupTjx4+f83ecq4+KigoOHTrEuHHj6N27N5WVlbS3t5Oamnpe11OSdGmpq6ujurqaiRMn0q9fP+rq6mhsbCQtLY1FixYxe/ZsrrjiCkKhECdPnuSTTz7hxx9/pKCg4IxaDz/8MEeOHKG6uprGxsbO6wkJCURGRjJhwgRWrlzJ6NGjaWtrY968ed16xfu5+li6dCkDBw5kxIgR9OjRg3feeYcBAwa4Q1aSwowBkHSJCwQCvPfee8yaNYtx48bRo0cPQqEQK1asAH7bHv7DDz+Ql5fH999/T58+fcjOzmbRokXAb88Wqq2t5amnnmLs2LF0dHQwdOjQLm8R+6Pa2lra2tq44447ulxfuHAhRUVFTJ06lb1795KXl0dERARz58495+6f7vQRDAbZtGkTRUVFNDc3c80117B+/Xquv/76/2b5JEmXuPj4eLZv386yZcv4+eefSUpKoqSkhNtuuw2AXr168dJLL1FYWEhMTAzp6enMmTPnT2vV1tbS0NDQZQcuwIcffsj48eMpKSnhgQceYOzYsSQmJrJ8+XJ27959zh6nTZt21j7i4uJ48cUXqa+vp2fPntx4441UVlbSo4cvDJakcBLoOP0gsSRJkiRJksKKsb4kSZIkSVKYMwCSJEmSJEkKcwZAkiRJkiRJYc4ASJIkSZIkKcwZAEmSJEmSJIU5AyBJkiRJkqQwZwAkSZIkSZIU5gyAJEmSJEmSwpwBkCRJl6j8/HzuvPPOzs/jx49nzpw5//M+ampqCAQCHD9+/H8+tyRJ0qXCAEiSpItMfn4+gUCAQCBAZGQkKSkpPPvss7S2tl7QeTdt2sTixYu7NdbQRpIk6e8l4v/dgCRJOlMoFKKsrIyTJ09SWVnJjBkzuOyyy1iwYEGXcS0tLURGRp6XORMSEs5LHUmSJF183AEkSdJFKCoqigEDBpCUlMSjjz5KVlYWW7Zs6Ty29dxzz5GYmEhqaioAR44c4Z577iEYDJKQkMCkSZM4fPhwZ722tjYKCgoIBoNceeWVPPnkk3R0dHSZ8/QjYCdPnmTevHkMHjyYqKgoUlJSePXVVzl8+DCZmZkA9O7dm0AgQH5+PgDt7e0UFxeTnJxMdHQ0w4cP59133+0yT2VlJddeey3R0dFkZmZ26VOSJEkXhgGQJEl/A9HR0bS0tABQXV3N/v37qaqqoqKiglOnTnHrrbcSFxfHjh072LVrF7GxsYRCoc7vlJSUsHbtWl577TV27tzJsWPH2Lx581nnzMvLY/369bz88svs27eP1atXExsby+DBg9m4cSMA+/fvp6GhgeXLlwNQXFzM66+/zqpVq/jiiy+YO3cu9913H7W1tcBvQVV2dja33347e/bsYdq0acyfP/9CLZskSZL+ySNgkiRdxDo6Oqiurmbbtm3MmjWLxsZGYmJiWLNmTefRrzfeeIP29nbWrFlDIBAAoKysjGAwSE1NDRMnTmTZsmUsWLCA7OxsAFatWsW2bdv+ct6vv/6aDRs2UFVVRVZWFgBXX3115/3fj4v169ePYDAI/LZjaMmSJbz//vuMHj268zs7d+5k9erVZGRkUFpaytChQykpKQEgNTWVzz77jBdeeOE8rpokSZJOZwAkSdJFqKKigtjYWE6dOkV7ezv33nsvRUVFzJgxg/T09C7P/dm7dy8HDhwgLi6uS43m5mYOHjzITz/9RENDAzfddFPnvYiICEaNGnXGMbDf7dmzh549e5KRkdHtng8cOMCJEye45ZZbulxvaWlhxIgRAOzbt69LH0BnWCRJkqQLxwBIkqSLUGZmJqWlpURGRpKYmEhExL/+smNiYrqMbWpq4oYbbuDNN988o07fvn3/o/mjo6P/7e80NTUBsHXrVgYNGtTlXlRU1H/UhyRJks4PAyBJki5CMTExpKSkdGvsyJEjefvtt+nXrx/x8fF/OmbgwIHU1dUxbtw4AFpbW9m9ezcjR4780/Hp6em0t7dTW1vbeQTsj37fgdTW1tZ57brrriMqKopvv/32L3cOpaWlsWXLli7XPvroo3P/SEmSJP1XfAi0JEl/c7m5ufTp04dJkyaxY8cOvvnmG2pqapg9ezZHjx4F4LHHHuP555+nvLycr776iunTp3P8+PG/rHnVVVcxZcoUpk6dSnl5eWfNDRs2AJCUlEQgEKCiooLGxkaampqIi4vjiSeeYO7cuaxbt46DBw/y6aefsmLFCtatWwfAI488Qn19PYWFhezfv5+33nqLtWvXXuglkiRJuuQZAEmS9DfXq1cvtm/fzpAhQ8jOziYtLY0HH3yQ5ubmzh1Bjz/+OPfffz9Tpkxh9OjRxMXFcdddd521bmlpKTk5OUyfPp1hw4bx0EMP8csvvwAwaNAgFi1axPz58+nfvz8zZ84EYPHixTz99NMUFxeTlpZGKBRi69atJCcnAzBkyBA2btxIeXk5w4cPZ9WqVSxZsuQCro4kSZIAAh1/9fRHSZIkSZIkhQV3AEmSJEmSJIU5AyBJkiRJkqQwZwAkSZIkSZIU5gyAJEmSJEmSwpwBkCRJkiRJUpgzAJIkSZIkSQpzBkCSJEmSJElhzgBIkiRJkiQpzBkASZIkSZIkhTkDIEmSJEmSpDBnACRJkiRJkhTm/gFIp0+CXmYYgwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Confusion Matrix Heatmap for Test Set\n",
        "plt.figure(figsize=(15, 10))\n",
        "sns.heatmap(cm_test, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix (Test Set)')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "id": "MJkWzrDWZjZx",
        "outputId": "550f595b-cc4d-4141-c8fd-ede63063c0ee"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
        "import numpy as np\n",
        "\n",
        "# Assuming y_pred_train_prob and y_pred_test_prob are the predicted probabilities for each class\n",
        "# Extract the probabilities for each class\n",
        "y_pred_train_prob = model(X_train_tensor).numpy()\n",
        "y_pred_test_prob = model(X_test_tensor).numpy()\n",
        "\n",
        "# Convert probabilities to class indices\n",
        "y_pred_train = np.argmax(y_pred_train_prob, axis=1)\n",
        "y_pred_test = np.argmax(y_pred_test_prob, axis=1)\n",
        "\n",
        "# Calculate ROC AUC score for each class\n",
        "roc_auc_train = []\n",
        "roc_auc_test = []\n",
        "\n",
        "for i in range(num_classes):  # Replace num_classes with the actual number of classes\n",
        "    roc_auc_train.append(roc_auc_score(y_train == i, y_pred_train_prob[:, i]))\n",
        "    roc_auc_test.append(roc_auc_score(y_test == i, y_pred_test_prob[:, i]))\n",
        "\n",
        "# Print or use roc_auc_train and roc_auc_test as needed\n",
        "print(\"ROC AUC Train:\", np.mean(roc_auc_train))\n",
        "print(\"ROC AUC Test:\", np.mean(roc_auc_test))\n",
        "\n",
        "# Plot ROC curves\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "for i in range(num_classes):\n",
        "    fpr_train, tpr_train, _ = roc_curve(y_train == i, y_pred_train_prob[:, i])\n",
        "    roc_auc_train_class = auc(fpr_train, tpr_train)\n",
        "    plt.plot(fpr_train, tpr_train, label=f'Train ROC Curve (Class {i + 1}, AUC = {roc_auc_train_class:.2f})')\n",
        "\n",
        "    fpr_test, tpr_test, _ = roc_curve(y_test == i, y_pred_test_prob[:, i])\n",
        "    roc_auc_test_class = auc(fpr_test, tpr_test)\n",
        "    plt.plot(fpr_test, tpr_test, label=f'Test ROC Curve (Class {i + 1}, AUC = {roc_auc_test_class:.2f})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve for each class')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "6ijUpdJe8QZP",
        "outputId": "30f675f2-1043-42ba-8b45-f2a9b651f46b"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROC AUC Train: 0.9358273355234856\n",
            "ROC AUC Test: 0.9346754079750375\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}