{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "he3uDmCutRvS"
      },
      "source": [
        "# **Image Classification on CIFAR-10 Dataset Using Convolutional Neural Networks (CNN)**\n",
        "**Name: Tareque Hasnat Tuhin**<br>\n",
        "Mail: 2513674@student.uwtsd.ac.uk <br>\n",
        "ID: 2513674"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "TdBatiroZWYz"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers, models\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "import random"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmp78Vl6j7ZU"
      },
      "source": [
        "*Imports TensorFlow/Keras for building the CNN, NumPy for data handling, Matplotlib & Seaborn for visualization, and sklearn for performance metrics.* EarlyStopping: stops training when no improvement — avoids overfitting.\n",
        " ModelCheckpoint: saves the best model automatically."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "r-mgBwroaTRP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c4c1acd-e55d-487d-edac-cb5bb6aff727"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 0us/step\n"
          ]
        }
      ],
      "source": [
        "# --------- 2. Load and Prepare CIFAR-10 Dataset ----------\n",
        "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "eMHXGR1Iaenk"
      },
      "outputs": [],
      "source": [
        "# Normalize pixel values (0–1)\n",
        "x_train = x_train.astype('float32') / 255.0\n",
        "x_test = x_test.astype('float32') / 255.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Yg8CkpPyaiuR"
      },
      "outputs": [],
      "source": [
        "# One-hot encode labels\n",
        "y_train = keras.utils.to_categorical(y_train, 10)\n",
        "y_test = keras.utils.to_categorical(y_test, 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F55drijGarRw",
        "outputId": "6fe2b7af-c9ef-4eda-e204-b43d60e963f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape: (50000, 32, 32, 3)\n",
            "Testing data shape: (10000, 32, 32, 3)\n"
          ]
        }
      ],
      "source": [
        "# Define class names for 10 object\n",
        "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
        "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
        "\n",
        "print(\"Training data shape:\", x_train.shape)\n",
        "print(\"Testing data shape:\", x_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "id": "YVzX027gbKVn",
        "outputId": "35a1f435-341e-4629-f779-73f608fd96b8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ---------- 3. Visualize Sample Images ----------\n",
        "plt.figure(figsize=(8,8))\n",
        "for i in range(16):\n",
        "    plt.subplot(4,4,i+1)\n",
        "    plt.imshow(x_train[i])\n",
        "    plt.title(class_names[np.argmax(y_train[i])])\n",
        "    plt.axis('off')\n",
        "plt.suptitle(\"Sample CIFAR-10 Images\", fontsize=16)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78t07wfih7rR",
        "outputId": "00202332-1c96-488b-81b7-27e226671515"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: 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"
          ]
        }
      ],
      "source": [
        "# ---------- 4. Build CNN Model ----------\n",
        "model = models.Sequential([\n",
        "    layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),  #ReLU(x) = max(0, x)\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "\n",
        "    layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "\n",
        "    layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    layers.Flatten(),\n",
        "\n",
        "    layers.Dense(128, activation='relu'),\n",
        "    layers.Dropout(0.5),\n",
        "\n",
        "    layers.Dense(10, activation='softmax') #Softmax(zi​)\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "BhQxSQPHiM9U",
        "outputId": "9760e3aa-96d4-4a2d-c390-e458972a51d6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │       \u001b[38;5;34m262,272\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,272</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m356,810\u001b[0m (1.36 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">356,810</span> (1.36 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m356,810\u001b[0m (1.36 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">356,810</span> (1.36 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Model summary\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "nIBT15HhiP-y"
      },
      "outputs": [],
      "source": [
        "# ---------- 5. Compile the Model ----------\n",
        "#Uses Adam optimizer, categorical cross-entropy loss, and accuracy metric to prepare the model for training.\n",
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m7lghpQZiVwo",
        "outputId": "61508a74-f471-4b23-ddde-d908662912ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 95ms/step - accuracy: 0.2937 - loss: 1.8782 - val_accuracy: 0.5283 - val_loss: 1.3402\n",
            "Epoch 2/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 95ms/step - accuracy: 0.5103 - loss: 1.3724 - val_accuracy: 0.5928 - val_loss: 1.1431\n",
            "Epoch 3/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 94ms/step - accuracy: 0.5756 - loss: 1.2010 - val_accuracy: 0.6056 - val_loss: 1.1247\n",
            "Epoch 4/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 91ms/step - accuracy: 0.6264 - loss: 1.0751 - val_accuracy: 0.6646 - val_loss: 0.9495\n",
            "Epoch 5/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 95ms/step - accuracy: 0.6612 - loss: 0.9683 - val_accuracy: 0.6776 - val_loss: 0.9210\n",
            "Epoch 6/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 93ms/step - accuracy: 0.6889 - loss: 0.8872 - val_accuracy: 0.6869 - val_loss: 0.8976\n",
            "Epoch 7/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 93ms/step - accuracy: 0.7044 - loss: 0.8376 - val_accuracy: 0.7142 - val_loss: 0.8365\n",
            "Epoch 8/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 93ms/step - accuracy: 0.7219 - loss: 0.7840 - val_accuracy: 0.7261 - val_loss: 0.8059\n",
            "Epoch 9/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 91ms/step - accuracy: 0.7426 - loss: 0.7282 - val_accuracy: 0.7171 - val_loss: 0.8191\n",
            "Epoch 10/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m73s\u001b[0m 93ms/step - accuracy: 0.7588 - loss: 0.6867 - val_accuracy: 0.7273 - val_loss: 0.8162\n",
            "Epoch 11/20\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 91ms/step - accuracy: 0.7653 - loss: 0.6631 - val_accuracy: 0.7330 - val_loss: 0.8078\n"
          ]
        }
      ],
      "source": [
        "# ---------- 6. Train the Model ----------\n",
        "early_stop = EarlyStopping(\n",
        "    monitor='val_loss',       # Watch validation loss\n",
        "    patience=3,               # Stop if no improvement for 3 epochs\n",
        "    restore_best_weights=True # Revert to best model weights\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    x_train, y_train,\n",
        "    epochs=20,\n",
        "    batch_size=64,\n",
        "    validation_data=(x_test, y_test),\n",
        "    callbacks=[early_stop]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "tFxMYAz8icUt",
        "outputId": "52b14666-dbe1-4c57-9884-c4a58cfcda18"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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KYt3hea5fv06dOnUyrX/8e1SqVClbf8+/++47+vXrh5eXFzVq1KBNmzb07dvXIEF/0t27d0lKSsp0Ph7GoNVquXHjBgEBAQQGBuLv78/SpUsZOHAgoOtW7+Lioq8DvMh5zOn1uWTJktm6PhcvXjzTo3APR7a/du0adevWzXbdJCf1tlKlSmVa5+TkZNDo8yI/K1H0SCIvCqw+ffrw9ttvExkZSevWrXF0dMyX4z6c2/2NN97I9BzXQ1WqVMnRPi9duqS/C/5k0ga6ZPZhIp9bntYyr9FonrpNVne0e/Towb59+/j444+pWrUqtra2aLVaWrVqpT9XOdG3b1+WL1/Ovn37qFy5MmvWrGHw4MHPHGE2p3r16sVvv/3Ghg0b6NSpE8uWLcPf35/AwEB9Ga1WS+XKlfnhhx+y3IeXl5fB+5zc7X9ZT/vZvUxviyeZmJhkuV55bJCnChUqcOHCBdauXUtISAh///03M2fOZOzYsYwfPz7XYhFCiNxSlOoOuSk7f8979OhBo0aNWLVqFZs2bWLKlCn873//Y+XKlfpxB15Wz549+frrr4mOjsbOzo41a9bQu3dvfePFi5zH/Lw+54fsXJ/z42clCj5J5EWB1blzZ959910OHDhg0E36Sd7e3mzZsoX4+HiDO+vnz5/Xf/7wX61Wq2/xfujChQsG+3s4Kq1Go8m1qeEWLlyImZkZf/75Z6Y/0Hv27GH69OmEh4dTqlQpypQpw8GDB0lPT8fMzCzL/ZUpU4aNGzdy//79p97ddXJyAiAmJsZg/cO7w9nx4MEDtm7dyvjx4xk7dqx+/aVLlwzKubq6Ym9vz+nTp5+7z1atWuHq6srChQupU6cOSUlJvPnmm8/dztvbm5MnT6LVag2S/id/zgCNGzfG09OTpUuX0rBhQ7Zt28aYMWMM9lemTBlOnDhB8+bNczVBfhGXLl0yaFG4fPkyWq1WP6Dew9/dS5cu6e/yg25AoJiYGP13f9hidPr06Uyt9y/KxsaGnj170rNnT9LS0ujSpQtff/01o0ePfub0e0IIYQxFqe6QHd7e3pligayvjdn5e+7p6cngwYMZPHgwd+7coXr16nz99ddPTQ5dXV2xtrZ+agxqtdrgxnjPnj0ZP348f//9N+7u7sTFxekHoX24P2Ocx6zcvn0701S8Fy9eBDC4PmenbpKdeltO5fRnJYoeeUZeFFi2trb8+uuvjBs3jvbt2z+1XJs2bdBoNMyYMcNg/Y8//ohKpdL/QXv475Mj106bNs3gvYmJCV27duXvv//OMjG9e/dujr/LwoULadSoET179qRbt24Gr48//hhAP/Va165diY6OzvR94NHd2K5du6IoSpatog/L2Nvb4+Liwq5duww+nzlzZrbjfnjT4fG7wJD5nKnVajp16sS///6b5TQqj29vamqqH/11/vz5VK5cOVutFG3atCEyMtKgYpaRkcHPP/+Mra0tTZo0MYinW7du/Pvvv/z5559kZGQYdKsH3d3sW7du8fvvv2c6VnJyMomJic+NKbf88ssvBu9//vln4NHvbJs2bYDM5/1hb4KHoye3bNkSOzs7Jk+enGn6uid/htnx5BSD5ubmVKxYEUVRSE9Pz/H+hBAirxWlukN2tGnThkOHDrF//379usTERGbPno2Pj49+bJfn/T3XaDT6x+UecnNzo3jx4llOc/qQiYkJLVu25J9//tE/Dga6G82LFi2iYcOG2Nvb69dXqFCBypUrs3TpUpYuXYqnpyeNGzc22J8xzmNWMjIy+O233/Tv09LS+O2333B1daVGjRpA9usm2am3ZdeL/qxE0SMt8qJAe1q3qse1b9+eZs2aMWbMGK5du0ZgYCCbNm3in3/+YcSIEfpWyqpVq9K7d29mzpxJbGws9evXZ+vWrVy+fDnTPr/99lu2b99OnTp1ePvtt6lYsSL379/n2LFjbNmyhfv372f7Oxw8eFA/LUlWSpQoQfXq1Vm4cCGffvopffv25Y8//mDkyJEcOnSIRo0akZiYyJYtWxg8eDAdO3akWbNmvPnmm0yfPp1Lly7pu7nv3r2bZs2a6Y81aNAgvv32WwYNGkTNmjXZtWuX/m5ydtjb29O4cWO+++470tPTKVGiBJs2bSIsLCxT2W+++YZNmzbRpEkT/XRuERERLF++nD179hh0b+zbty/Tp09n+/bt/O9//8tWLO+88w6//fYb/fv35+jRo/j4+LBixQr27t3LtGnTMj3n2LNnT37++We++uorKleubNCSDfDmm2+ybNky3nvvPbZv306DBg3QaDScP3+eZcuWsXHjxheexg904wf89ddfWX725MB+YWFhdOjQgVatWrF//379NEcPHwUIDAykX79+zJ49m5iYGJo0acKhQ4dYsGABnTp10g/QY29vz48//sigQYOoVasWffr0wcnJiRMnTpCUlJTlvLbP0rJlSzw8PGjQoAHu7u6cO3eOGTNm0LZt20znWwghCoqiUHd43N9//61v4X3ye3722Wf6KfeGDRtGsWLFWLBgAWFhYfz999/6VuLn/T2PiYmhZMmSdOvWjcDAQGxtbdmyZQuHDx9m6tSpz4xv0qRJbN68mYYNGzJ48GBMTU357bffSE1N5bvvvstUvmfPnowdOxZLS0sGDhyY6dG6vDqPD128eDHL67O7uzstWrTQvy9evDj/+9//uHbtGuXKlWPp0qWEhoYye/ZsfY/J7NZNsltvy474+PgX/lmJIiZ/B8kX4uken0LmWZ6cQkZRdFOtfPjhh0rx4sUVMzMzxc/PT5kyZYrBlB6KoijJycnKsGHDFGdnZ8XGxkZp3769cuPGjUxTyCiKbrq4IUOGKF5eXoqZmZni4eGhNG/eXJk9e7a+THamkPnggw8UwGBalieNGzfOYLqxpKQkZcyYMUrp0qX1x+7WrZvBPjIyMpQpU6Yo/v7+irm5ueLq6qq0bt1aOXr0qL5MUlKSMnDgQMXBwUGxs7NTevToody5c+ep08/dvXs3U2w3b95UOnfurDg6OioODg5K9+7dldu3b2d5zq5fv6707dtXcXV1VSwsLBRfX19lyJAhSmpqaqb9BgQEKGq1Wrl58+ZTz8uToqKilLfeektxcXFRzM3NlcqVKz/13Gu1WsXLyyvL6YUeSktLU/73v/8pAQEBioWFheLk5KTUqFFDGT9+vBIbG6svB+RompdnTT/3+J/dh+f97NmzSrdu3RQ7OzvFyclJGTp0aKbp49LT05Xx48frfye8vLyU0aNHKykpKZmOv2bNGqV+/fqKlZWVYm9vr9SuXTvTtDpZTUPUr18/xdvbW//+t99+Uxo3bqw4OzsrFhYWSpkyZZSPP/7Y4NwIIYQxFdW6g6I8ml7taa+HU85duXJF6datm+Lo6KhYWloqtWvXVtauXWuwr+f9PU9NTVU+/vhjJTAwULGzs1NsbGyUwMBAZebMmc+M8aFjx44pwcHBiq2trWJtba00a9ZM2bdvX5ZlL126pP8Oe/bsybJMds5jdqbne9KzzmeTJk305R5eJ48cOaLUq1dPsbS0VLy9vZUZM2ZkGWt26ibZqbc9rb7h7e2tn6b3ZX9WouhQKcoL9LcUQoiXVK1aNYoVK8bWrVuNHYrRjBs3jvHjx3P37l1cXFyMHY4QQgghgKZNmxIdHZ2tsX+EMBZ5Rl4Ike+OHDlCaGgoffv2NXYoQgghhBBCFDryjLwQIt+cPn2ao0ePMnXqVDw9PTMNQCeEEEIIIYR4PmmRF0LkmxUrVvDWW2+Rnp7O4sWLZfoyIYQQQgghXoA8Iy+EEEIIIYQQQhQi0iIvhBBCCCGEEEIUIpLICyGEEEIIIYQQhYgMdpcFrVbL7du3sbOzQ6VSGTscIYQQAkVRiI+Pp3jx4qjVch/+Zcm1XgghREGTk2u9JPJZuH37Nl5eXsYOQwghhMjkxo0blCxZ0thhFHpyrRdCCFFQZedaL4l8Fuzs7ADdCbS3tzdyNEIIIQTExcXh5eWlv0aJlyPXeiGEEAVNTq71kshn4WEXO3t7e7m4CyGEKFCkG3jukGu9EEKIgio713p5yE4IIYQQQgghhChEJJEXQgghhBBCCCEKEUnkhRBCCCGEEEKIQkSekX9BiqKQkZGBRqMxdihC5DoTExNMTU3lWVwhhBBCvJI0Gg3p6enGDkMUMblZx5ZE/gWkpaURERFBUlKSsUMRIs9YW1vj6emJubm5sUMRQgghhMg3CQkJ3Lx5E0VRjB2KKIJyq44tiXwOabVawsLCMDExoXjx4pibm0urpShSFEUhLS2Nu3fvEhYWhp+fH2q1PIUjhBBCiKJPo9Fw8+ZNrK2tcXV1lXq+yDW5XceWRD6H0tLS0Gq1eHl5YW1tbexwhMgTVlZWmJmZcf36ddLS0rC0tDR2SEIIIYQQeS49PR1FUXB1dcXKysrY4YgiJjfr2NLM9oKkhVIUdfI7LoQQQohXlbTEi7ySW3VsqakLIYQQQgghhBCFiCTyQgghhBBCCCFEISKJvHhhPj4+TJs2zdhhCCGEEEIIIXKZ1PULNknkXwEqleqZr3Hjxr3Qfg8fPsw777yTKzEuXrwYExMThgwZkiv7E0IIIYQQ4lVQkOv6TZs2ZcSIES+1D5E1GbX+FRAREaFfXrp0KWPHjuXChQv6dba2tvplRVHQaDSYmj7/V8PV1TXXYpwzZw6ffPIJv/32G1OnTjXqKOlpaWkyd7oQItcoiiKDJr0CFEVBo1UwNZE2EiFE/ioMdX2R++RqkwsURSEpLSPfX4qiZCs+Dw8P/cvBwQGVSqV/f/78eezs7NiwYQM1atTAwsKCPXv2cOXKFTp27Ii7uzu2trbUqlWLLVu2GOz3ye42KpWK//u//6Nz585YW1vj5+fHmjVrnhtfWFgY+/bt47PPPqNcuXKsXLkyU5m5c+cSEBCAhYUFnp6eDB06VP9ZTEwM7777Lu7u7lhaWlKpUiXWrl0LwLhx46hatarBvqZNm4aPj4/+ff/+/enUqRNff/01xYsXp3z58gD8+eef1KxZEzs7Ozw8POjTpw937twx2NeZM2do164d9vb22NnZ0ahRI65cucKuXbswMzMjMjLSoPyIESNo1KjRc8+JEKJw0moVLkTGs/hQOB8vP0HzqTuYsyfM2GGJPDZvbxiNp2xn1fFbxg5FCJHLjFXPL0p1/Wf5+++/9XV8Hx8fpk6davD5zJkz8fPzw9LSEnd3d7p166b/bMWKFVSuXBkrKyucnZ0JCgoiMTHxpeIpTKRFPhckp2uoOHZjvh/37IRgrM1z50f42Wef8f333+Pr64uTkxM3btygTZs2fP3111hYWPDHH3/Qvn17Lly4QKlSpZ66n/Hjx/Pdd98xZcoUfv75Z15//XWuX79OsWLFnrrNvHnzaNu2LQ4ODrzxxhvMmTOHPn366D//9ddfGTlyJN9++y2tW7cmNjaWvXv3AqDVamndujXx8fH89ddflClThrNnz2JiYpKj779161bs7e3ZvHmzfl16ejoTJ06kfPny3Llzh5EjR9K/f3/Wr18PwK1bt2jcuDFNmzZl27Zt2Nvbs3fvXjIyMmjcuDG+vr78+eeffPzxx/r9LVy4kO+++y5HsQkhCq7Y5HSOhz/gWHgMx8MfEBoeQ3xqhkGZo9cfMEju3xVp8SkZ3LifzMYzkXSv6WXscIQQuchY9XwoOnX9pzl69Cg9evRg3Lhx9OzZk3379jF48GCcnZ3p378/R44cYdiwYfz555/Ur1+f+/fvs3v3bkDXC6F379589913dO7cmfj4eHbv3p3tmx9FgSTyAoAJEybQokUL/ftixYoRGBiofz9x4kRWrVrFmjVrDFrDn9S/f3969+4NwDfffMP06dM5dOgQrVq1yrK8Vqtl/vz5/PzzzwD06tWLUaNGERYWRunSpQGYNGkSo0aNYvjw4frtatWqBcCWLVs4dOgQ586do1y5cgD4+vrm+Pvb2Njwf//3fwZd6gcMGKBf9vX1Zfr06dSqVYuEhARsbW355ZdfcHBwYMmSJZiZmQHoYwAYOHAg8+bN0yfy//77LykpKfTo0SPH8QkhjE+rVbh8N4Fj1x9w7L/k/fKdhEzlrM1NCCzpSHVvR6qXcqJaKScjRCvyU6tKHvyw+SK7LkWTkJqBrYVUr4QQBYux6vrP8sMPP9C8eXO+/PJLQFePPnv2LFOmTKF///6Eh4djY2NDu3btsLOzw9vbm2rVqgG6RD4jI4MuXbrg7e0NQOXKlXMcQ2EmV5pcYGVmwtkJwUY5bm6pWbOmwfuEhATGjRvHunXr9P9RkpOTCQ8Pf+Z+qlSpol+2sbHB3t4+U3f0x23evJnExETatGkDgIuLCy1atGDu3LlMnDiRO3fucPv2bZo3b57l9qGhoZQsWdIggX4RlStXzvRc/NGjRxk3bhwnTpzgwYMHaLVaAMLDw6lYsSKhoaE0atRIn8Q/qX///nzxxRccOHCAunXrMn/+fHr06IGNjc1LxSqEyB+xyemE3ojRJ+6hN2KIT8nIVM7H2VqXsHs7Ub2UI+Xd7eQ56VeMn5stvi42XI1OZMeFO7SrUtzYIQkhcomx6vkPj51bjFXXf5Zz587RsWNHg3UNGjRg2rRpaDQaWrRogbe3N76+vrRq1YpWrVrpu/UHBgbSvHlzKleuTHBwMC1btqRbt244Ob06N88lkc8FKpUq17q9GMuTyeVHH33E5s2b+f777ylbtixWVlZ069aNtLS0Z+7nyaRWpVLpE+CszJkzh/v372NlZaVfp9VqOXnyJOPHjzdYn5Xnfa5WqzN1sUlPT89U7snvn5iYSHBwMMHBwSxcuBBXV1fCw8MJDg7Wn4PnHdvNzY327dszb948SpcuzYYNG9ixY8cztxFCGIdWq3DlboKupf16DMfCH3D5bgJP9tCzMjMh0MuB6qWc/mttd8TZ1sI4QYsCQ6VSEVzJg193XCHkdKQk8kIUIUWhng/Gq+u/DDs7O44dO8aOHTvYtGkTY8eOZdy4cRw+fBhHR0c2b97Mvn372LRpEz///DNjxozh4MGD+l69RV3h/60UeWLv3r3079+fzp07A7q7dteuXcvVY9y7d49//vmHJUuWEBAQoF+v0Who2LAhmzZtolWrVvj4+LB161aaNWuWaR9VqlTh5s2bXLx4MctWeVdXVyIjIw1GjQ4NDX1ubOfPn+fevXt8++23eHnpnnc8cuRIpmMvWLCA9PT0p7bKDxo0iN69e1OyZEnKlClDgwYNnntsIUTei0tJJzQ8Rt9FPjT8AXFZtLZ7/9faXr2UI9VKOeHvIa3tImutAnSJ/Pbzd0hJ12CZiy1pQgiR2/Kjrv88FSpU0I979Xhc5cqV0493ZWpqSlBQEEFBQXz11Vc4Ojqybds2unTpgkqlokGDBjRo0ICxY8fi7e3NqlWrGDlyZL5+D2ORRF5kyc/Pj5UrV9K+fXtUKhVffvllrt9t+/PPP3F2dqZHjx6ZpmZq06YNc+bMoVWrVowbN4733nsPNzc3/cB2e/fu5YMPPqBJkyY0btyYrl278sMPP1C2bFnOnz+PSqWiVatWNG3alLt37/Ldd9/RrVs3QkJC2LBhA/b29s+MrVSpUpibm/Pzzz/z3nvvcfr0aSZOnGhQZujQofz888/06tWL0aNH4+DgwIEDB6hdu7Z+5Pvg4GDs7e2ZNGkSEyZMyNXzJ4TIHq1W4Wp0gr6l/Vj4Ay7dybq1vUpJB6p7P2ptd5HWdpFNVUo64OlgSURsCnsvR9O8gruxQxJCiKfKj7r+Q3fv3s3UkObp6cmoUaOoVasWEydOpGfPnuzfv58ZM2Ywc+ZMANauXcvVq1dp3LgxTk5OrF+/Hq1WS/ny5Tl48CBbt26lZcuWuLm5cfDgQe7evUuFChXy5DsURJLIiyz98MMPDBgwgPr16+Pi4sKnn35KXFxcrh5j7ty5dO7cOcv5lbt27cqbb75JdHQ0/fr1IyUlhR9//JGPPvoIFxcXg6kn/v77bz766CN69+5NYmIiZcuW5dtvvwV0d/pmzpzJN998w8SJE+natSsfffQRs2fPfmZsrq6uzJ8/n88//5zp06dTvXp1vv/+ezp06KAv4+zszLZt2/j4449p0qQJJiYmVK1a1aDVXa1W079/f7755hv69u37sqdMCJEN8SkPn23XJe7Hn9LaXqqYNdVLOeoTd2ltFy9DpVIRHODB/H3XCDkdKYm8EKJAy4+6/kOLFi1i0aJFBusmTpzIF198wbJlyxg7diwTJ07E09OTCRMm0L9/fwAcHR1ZuXIl48aNIyUlBT8/PxYvXkxAQADnzp1j165dTJs2jbi4OLy9vZk6dSqtW7fOk+9QEKmUV2mM/myKi4vDwcGB2NjYTC23KSkp+hHVLS0tjRShKEwGDhzI3bt3X3qezfwmv+uisLj5IIl9V+7ppoG7HsPFO/GZWtstzdRUKemo7yZf3dup0LW2P+vaJHIuL87ngav36DX7AI7WZhwZEyQ3hoQohKT+I/Las37HcnJtKhAt8r/88gtTpkwhMjKSwMBAfv75Z2rXrp1l2aZNm7Jz585M69u0acO6desA3WjhCxYsMPg8ODiYkJCQ3A9eiKeIjY3l1KlTLFq0qNAl8UIUdOH3klh/OoINpyI4cTM20+elillTrZSjflA6f087zCSpEnmslk8xnG3MuZeYxqGw+9Qv62LskIQQQhRRRk/kly5dysiRI5k1axZ16tRh2rRpBAcHc+HCBdzc3DKVX7lypcFoivfu3SMwMJDu3bsblGvVqhXz5s3Tv7ewKFwtL6Lw69ixI4cOHeK9994zmLdTCPFiwqITWX8qgg2nIzh961H3P7UKqpdyooaPkz5xd7WTv/ki/5moVbSo6M6SwzcIORMpibwQQog8Y/RE/ocffuDtt9/mrbfeAmDWrFmsW7eOuXPn8tlnn2UqX6xYMYP3S5YswdraOlMib2FhgYeHR94FLsRzyFRzQry8y3cS2HAqgvWnIzkX8Sh5N1GrqOfrTOvKHgQHeBS6bvKi6Aqu5MGSwzfYeCaSce0DUKszjwMjhBBCvCyjJvJpaWkcPXqU0aNH69ep1WqCgoLYv39/tvYxZ84cevXqlWluxB07duDm5oaTkxOvvfYakyZNwtnZOct9pKamkpqaqn+fVwM9CCGEeL6LUfG6lvdTkVyIitevN1WrqF/WhTaVPGgZ4EExG3MjRilE1uqXccbOwpSouFRCb8ZQvZSTsUMSQghRBBk1kY+Ojkaj0eDubjiyq7u7O+fPn3/u9ocOHeL06dPMmTPHYH2rVq3o0qULpUuX5sqVK3z++ee0bt2a/fv36+ckfNzkyZMZP378y30ZIYQQL0RRFC5ExbP+pK7l/fKdBP1nZiYqGpR1oU1lT1pWdMfRWpJ3UbBZmJrwWgU3/gm9zcbTkZLICyGEyBNG71r/MubMmUPlypUzDYzXq1cv/XLlypWpUqUKZcqUYceOHTRv3jzTfkaPHs3IkSP17+Pi4vDy8sq7wIUQ4hWnKApnI+L0Le9XoxP1n5mbqGnkp0vegyq442BtZsRIhci5VgEe/BN6m5AzkXzW2j/LaVaFEEKIl2HURN7FxQUTExOioqIM1kdFRT33+fbExESWLFnChAkTnnscX19fXFxcuHz5cpaJvIWFhQyGJ4QQeUxRFE7fimPdfwPWXb+XpP/M3FRNk3KutK3syWsV3LC3lORdFF5NyrtiYarm+r0kzkfGU8FTpgsUQgiRu4yayJubm1OjRg22bt1Kp06dANBqtWzdupWhQ4c+c9vly5eTmprKG2+88dzj3Lx5k3v37uHp6ZkbYQshhMgmRVE4cTOW9aciWH8qgpsPkvWfWZiqaVbejdaVPWhewR1bi0LdSUwIPWtzU5qUc2XT2ShCTkdKIi+EECLXGb3WNHLkSPr160fNmjWpXbs206ZNIzExUT+Kfd++fSlRogSTJ0822G7OnDl06tQp0wB2CQkJjB8/nq5du+Lh4cGVK1f45JNPKFu2LMHBwfn2vYQQ4lWl1SocvxHD+lMRhJyO5FbMo+TdysyE1/x1yXuz8m7YSPIuiqhWlTzYdDaKjWci+bBFOWOHI4QQoohRGzuAnj178v333zN27FiqVq1KaGgoISEh+gHwwsPDiYiIMNjmwoUL7Nmzh4EDB2ban4mJCSdPnqRDhw6UK1eOgQMHUqNGDXbv3i3d519S06ZNGTFihP69j48P06ZNe+Y2KpWK1atXv/Sxc2s/Qoi8odUqHL52n3FrzlD/2210/XUfc/aEcSsmGWtzE9oHFufX16tz7MsW/PJ6ddpVKS5JvCjSmvu7Y6pWcT4ynrDHxoAQQoiCSur6hUuBqEUNHTr0qV3ps5qLu3z58iiKkmV5KysrNm7cmJvhFXrt27cnPT2dkJCQTJ/t3r2bxo0bc+LECapUqZKj/R4+fDjTtH8va9y4caxevZrQ0FCD9RERETg55c/Iv8nJyZQoUQK1Ws2tW7fkBpAQT6H5L3l/2PJ+J/7RNJ62FqYEVXCjdWVPmpRzxdIs84whQjy0a9cupkyZwtGjR4mIiGDVqlX6R+6eJjU1lQkTJvDXX38RGRmJp6cnY8eOZcCAAfkT9HM4WJtRr4wzuy9Fs/FMJO81KWPskIQQRZTU9bNn/vz5jBgxgpiYmDw9Tn4pEIm8yFsDBw6ka9eu3Lx5k5IlSxp8Nm/ePGrWrJnj/9gArq6uuRXicz1v8MPc9PfffxMQEICiKKxevZqePXvm27GfpCgKGo0GU1P5ryoKhgyNlkNh91l3KoKNZyKJTkjTf2ZnaUqLiu60qeRJQz8XSd5FtiUmJhIYGMiAAQPo0qVLtrbp0aMHUVFRzJkzh7JlyxIREYFWq83jSHOmVSUPdl+KJuS0JPJCiLwjdf1Xk9G71hcJigJpifn/ekqvhCe1a9cOV1dX5s+fb7A+ISGB5cuXM3DgQO7du0fv3r0pUaIE1tbWVK5cmcWLFz9zv092t7l06RKNGzfG0tKSihUrsnnz5kzbfPrpp5QrVw5ra2t8fX358ssvSU9PB3R3ycaPH8+JEydQqVSoVCp9zE92tzl16hSvvfYaVlZWODs7884775CQ8Gju6f79+9OpUye+//57PD09cXZ2ZsiQIfpjPcucOXN44403eOONN5gzZ06mz8+cOUO7du2wt7fHzs6ORo0aceXKFf3nc+fOJSAgAAsLCzw9PfW9Ta5du4ZKpTK4AxkTE4NKpdL3PNmxYwcqlYoNGzZQo0YNLCws2LNnD1euXKFjx464u7tja2tLrVq12LJli0FcqampfPrpp3h5eWFhYUHZsmWZM2cOiqJQtmxZvv/+e4PyoaGhqFQqLl++/NxzIl5tGq3CvsvRjF55ktrfbKXP/x1k4cFwohPSsLc0pVuNkszrX4sjXwTxQ4+qBFV0lyRe5Ejr1q2ZNGkSnTt3zlb5kJAQdu7cyfr16wkKCsLHx4d69erRoEGDPI40Z1pUdEelgtAbMUTEJj9/AyFEwWOser7U9fOsrv804eHhdOzYEVtbW+zt7fU3jB86ceIEzZo1w87ODnt7e2rUqMGRI0cAuH79Ou3bt8fJyQkbGxsCAgJYv379C8eSHdLMlxvSk+Cb4vl/3M9vg/nzu7uYmprSt29f5s+fz5gxY/Tz2S5fvhyNRkPv3r1JSEigRo0afPrpp9jb27Nu3TrefPNNypQpQ+3atZ97DK1WS5cuXXB3d+fgwYPExsYaPGPzkJ2dHfPnz6d48eKcOnWKt99+Gzs7Oz755BN69uzJ6dOnCQkJ0SepDg4OmfaRmJhIcHAw9erV4/Dhw9y5c4dBgwYxdOhQgz9g27dvx9PTk+3bt3P58mV69uxJ1apVefvtt5/6Pa5cucL+/ftZuXIliqLw4Ycfcv36dby9vQG4desWjRs3pmnTpmzbtg17e3v27t1LRkYGAL/++isjR47k22+/pXXr1sTGxrJ3797nnr8nffbZZ3z//ff4+vri5OTEjRs3aNOmDV9//TUWFhb88ccftG/fngsXLlCqVClANzDk/v37mT59OoGBgYSFhREdHY1KpWLAgAHMmzePjz76SH+MefPm0bhxY8qWLZvj+ETRpygKoTdiWHPiNutORhh0m3e0NiO4ogdtqnhSz9cZc1O5Jyzy15o1a6hZsybfffcdf/75JzY2NnTo0IGJEydiZWWV5Tapqamkpj76PY6Li8vzON3sLKnp7cThaw/YeDqS/g1K5/kxhRC5zFj1fJC6fh7U9Z/1/R4m8Tt37iQjI4MhQ4bQs2dPfYPb66+/TrVq1fj1118xMTEhNDQUMzPddLlDhgwhLS2NXbt2YWNjw9mzZ7G1tc1xHDkhifwrYsCAAUyZMoWdO3fStGlTQJfIde3aFQcHBxwcHAySvA8++ICNGzeybNmybP3n3rJlC+fPn2fjxo0UL677Y/fNN9/QunVrg3JffPGFftnHx4ePPvqIJUuW8Mknn2BlZYWtrS2mpqbP7F6zaNEiUlJS+OOPP/TP7cyYMYP27dvzv//9Tz9QopOTEzNmzMDExAR/f3/atm3L1q1bn/mfe+7cubRu3Vr/jE5wcDDz5s1j3LhxAPzyyy84ODiwZMkS/X/ccuUejUY8adIkRo0axfDhw/XratWq9dzz96QJEybQokUL/ftixYoRGBiofz9x4kRWrVrFmjVrGDp0KBcvXmTZsmVs3ryZoKAgAHx9ffXl+/fvz9ixYzl06BC1a9cmPT2dRYsWZWqlF+JCZDxrTtzi3xMRhN9/NM+7vaUprSt50i7Qk7q+zpiZSPIujOfq1avs2bMHS0tLVq1aRXR0NIMHD+bevXvMmzcvy20mT57M+PHj8zlSCA7w4PC1B4SckUReCJF3pK6fvbr+02zdupVTp04RFhaGl5cXAH/88QcBAQEcPnyYWrVqER4ezscff4y/vz8Afn5++u3Dw8Pp2rUrlStXBgzr4XlFEvncYGatu2NmjONmk7+/P/Xr12fu3Lk0bdqUy5cvs3v3biZMmACARqPhm2++YdmyZdy6dYu0tDRSU1Oxts7eMc6dO4eXl5f+PzZAvXr1MpVbunQp06dP58qVKyQkJJCRkYG9fc7m1z137hyBgYEGg280aNAArVbLhQsX9P+5AwICMDF51L3X09OTU6dOPXW/Go2GBQsW8NNPP+nXvfHGG3z00UeMHTsWtVpNaGgojRo10ifxj7tz5w63b9+mefPmOfo+WalZs6bB+4SEBMaNG8e6deuIiIggIyOD5ORkwsPDAV03eRMTE5o0aZLl/ooXL07btm2ZO3cutWvX5t9//yU1NZXu3bu/dKyi8Au/l8S/J2+zJvQ2F6Li9eutzEwIquhOh8DiNC7ngoWpdJcXBYNWq0WlUrFw4UJ9a84PP/xAt27dmDlzZpat8qNHj2bkyJH693FxcfrKWl5qVcmDSevOcSjsPvcSUnG2lQFUhShUjFXPf3jsbJK6/vPr+s87ppeXl8F1oWLFijg6OnLu3Dlq1arFyJEjGTRoEH/++SdBQUF0796dMmV0458MGzaM999/n02bNhEUFETXrl1faFyCnJAmldygUum6veT3679uM9k1cOBA/v77b+Lj45k3bx5lypTRJ35Tpkzhp59+4tNPP2X79u2EhoYSHBxMWlrac/aaffv37+f111+nTZs2rF27luPHjzNmzJhcPcbjnky2VSrVMwdC2rhxI7du3aJnz56YmppiampKr169uH79Olu3bgV4apfN530GoFbr/rs9PuPC057jeXKE0I8++ohVq1bxzTffsHv3bkJDQ6lcubL+3D3v2ACDBg1iyZIlJCcnM2/ePHr27JntP96i6LkTl8LcPWF0+mUvjadsZ8rGC1yIisfMREVQBXd+6lWVo18G8XPvarSo6C5JvChQPD09KVGihEGXzAoVKqAoCjdv3sxyGwsLC+zt7Q1e+aGkkzWVSzigVWDLuajnbyCEKFiMVc+Xuv5z5bSu/7LGjRvHmTNnaNu2Ldu2baNixYqsWrUK0NWzr169yptvvsmpU6eoWbMmP//8c57FApLIv1J69OiBWq1m0aJF/PHHHwwYMED/DM3evXvp2LEjb7zxBoGBgfj6+nLx4sVs77tChQrcuHGDiIgI/boDBw4YlNm3bx/e3t6MGTOGmjVr4ufnx/Xr1w3KmJubo9FonnusEydOkJj4aF7evXv3olarKV++fLZjftKcOXPo1asXoaGhBq9evXrpB72rUqUKu3fvzjIBt7Ozw8fHR5/0P+nhyJ+Pn6Mnp954mr1799K/f386d+5M5cqV8fDw4Nq1a/rPK1eujFarZefOnU/dR5s2bbCxseHXX38lJCSkwEzRJPJPbFI6Sw6F0+f3A9SZvJUJa88SeiMGtQoalHXmf10rc2RMC/6vX006Vi2Btbl02hIFU4MGDbh9+7bBwEcXL15ErVZnGrG5IGhVSdeFNOR0pJEjEUIUZVLXf3EPv9+NGzf0686ePUtMTAwVK1bUrytXrhwffvghmzZtokuXLgaPc3l5efHee++xcuVKRo0axe+//54nsT4kifwrxNbWlp49ezJ69GgiIiLo37+//jM/Pz82b97Mvn37OHfuHO+++67BKI3PExQURLly5ejXrx8nTpxg9+7djBkzxqCMn58f4eHhLFmyhCtXrjB9+nT9XayHfHx8CAsLIzQ0lOjoaIOBiR56/fXXsbS0pF+/fpw+fZrt27fzwQcf8Oabb+q72uTU3bt3+ffff+nXrx+VKlUyePXt25fVq1dz//59hg4dSlxcHL169eLIkSNcunSJP//8kwsXLgC6O3VTp05l+vTpXLp0iWPHjunvxllZWVG3bl2+/fZbzp07x86dOw2eI3oWPz8/Vq5cSWhoKCdOnKBPnz4Gdxx9fHzo168fAwYMYPXq1YSFhbFjxw6WLVumL2NiYkL//v0ZPXo0fn5+WXaHEkVPYmoG/4TeYtCCw9T8ejOfrTzFviv3UBSoVsqRr9pX5MDnzVk4qC49a5XCwTrzYyPiBaQmQOQpOLsGbocaO5oCLyEhQX/zFNBfBx4+PjR69Gj69u2rL9+nTx+cnZ156623OHv2LLt27eLjjz9mwIAB2eqhlN+CA3SJ/N7L94hLefERlYUQ4lmkrv98Go0mU6PduXPnCAoKonLlyrz++uscO3aMQ4cO0bdvX5o0aULNmjVJTk5m6NCh7Nixg+vXr7N3714OHz5MhQoVABgxYgQbN24kLCyMY8eOsX37dv1neUUS+VfMwIEDefDgAcHBwQbPuHzxxRdUr16d4OBgmjZtioeHB506dcr2ftVqNatWrSI5OZnatWszaNAgvv76a4MyHTp04MMPP2To0KFUrVqVffv28eWXXxqU6dq1K61ataJZs2a4urpmOS2GtbU1Gzdu5P79+9SqVYtu3brRvHlzZsyYkbOT8ZiHg2lk9Xx78+bNsbKy4q+//sLZ2Zlt27aRkJBAkyZNqFGjBr///ru+a0+/fv2YNm0aM2fOJCAggHbt2nHp0iX9vubOnUtGRgY1atRgxIgRTJo0KVvx/fDDDzg5OVG/fn3at29PcHAw1atXNyjz66+/0q1bNwYPHoy/vz9vv/22wZ1M0P3809LSeOutt3J6ikQhkpqhYdOZSIYuOkbNSVsYviSULefukK5R8Pew4+Pg8uz+pBmrBjfgrQalcbOzNHbIhY+iQOI9uHEYTi6DHf+Dle/CnJYwxQ8ml4BZDWHZm3ByqbGjLfCOHDlCtWrVqFatGgAjR46kWrVqjB07FtD1ZHqY1IOusrp582ZiYmKoWbMmr7/+Ou3bt2f69OlGif95yrrZUtbNljSNlu3n7xg7HCFEESZ1/WdLSEjQX28evtq3b49KpeKff/7BycmJxo0bExQUhK+vL0uX6q7hJiYm3Lt3j759+1KuXDl69OhB69at9YOoajQahgwZQoUKFWjVqhXlypVj5syZLx3vs6gUJZsTFL5C4uLicHBwIDY2NtMzdCkpKYSFhVG6dGksLaXyKwqX3bt307x5c27cuPHcO5ryu164aLQK+6/cY82JW4ScjiQuJUP/Wali1nQILE6HqsUp525nxCgLGa0W4m/D/atwPwwehOn+vX8VHlyD1OdMX2ZVDIqVhoqdoMGwlw7nWdcmkXP5fT6/33iBGdsv06ayBzNfr5HnxxNCvBip/4i89qzfsZxcm+QBSCFeAampqdy9e5dx48bRvXv3l+6WJAoGRVE4Fh7Dvydus/ZkBNEJj7qnudlZ0K6KLnkPLOmgf0ZOPCEjDWLC/0vOwx5L2K/Cg+ugydzlz4B9CXAqrUvYi5V+tOxUGqwc8+UriMKhVSUPZmy/zPbzd0lJ12BpJgNICiGEeHGSyAvxCli8eDEDBw6katWq/PHHH8YOR7wERVE4HxnPmhO3+ffEbW4+SNZ/5mhtRutKnrQP9KROaWdM1JK8A7rn1Q1a0x8uh0HcTVCeMcKt2hQcvR9L0n0fLTt5g1nBex5bFEwBxe0p4WjFrZhkdl28S8uAp8+hLIQQQjyPJPJCvAL69+9vMOCJKHyuRSfy74nbrDlxm0t3Ho3UbW1uQsuK7nSoWpyGZV0xN30Fhz5RFEi6Z9ia/nhX+MTnPJNsZq1L0J18/mtZ933Usm5fEkzkUilenkqlolUlD+bsCSPkTKQk8kIIIV6K1E6EEKKAioxNYe1JXcv7iZux+vXmJmqalnelQ9XiNPd3x8r8FeuiqygQfRGu7YHre+HaXkh4zrReVsUMW9MfX7Z1y/FcvUK8iIeJ/JazUaRrtJiZvII33oQQQuQKSeRfkIwRKIo6+R3PR4oC9y7DpU2kn99IUvR1Tmu82ZpQiuOaspxTfFCrzGhQ1oX2gcUJDvDAweoVmiZOq4W75/9L2v9L3hPvZi5nX8KwZf3xhN3SId/DFuJJ1Us54WJrQXRCKgeu3qORn6uxQxJCPIXUg0Reya3fLUnkc+jhNGNJSUkFcq5aIXJLUlIS8Oh3XuSytCRdUnppk+4Vcx0AM8ABaMA1GpgCpqBRmaH1qIJZ8VpgUQtSaoKld9FtRdZq4c4Z3fm5tgeu74Pk+4ZlTC2hZC3waQjeDaBEDTC3Nk68QmSTiVpFywB3Fh0MJ+R0pCTyQhRAJia6Xm5paWlS1xd5Irfq2JLI55CJiQmOjo7cuaN75tLa2lpGgxZFiqIoJCUlcefOHRwdHfUXNJEL7l+FS5t1ifu1PZCRov8oHRMOaCqwQxtIkn0ZOnlEU4VLWEUdwyTpHiYRRyHiKBycpdvAxhVK1ISSNXUJbYnqYFFIp5bTaiDypK6L/PW9usQ9JcawjJk1eNUG74bg81/ibmphlHCFeBmtAjxYdDCcjWeimNCxkgxKKUQBY2pqirW1NXfv3sXMzAy1Wh6BEbkjt+vYksi/AA8P3QA1D5N5IYoiR0dH/e+6eEHpKbrE9NJmuLxZ133+MfEW7oSkVmFjWhX2aQMo4ebC8CA/2lTyRP2wcq8oukHbbh6Fm4d1r8hTuq7lFzfoXgCowK0ilKyhS+xL1gKX8lAQKyCaDIg4Adf36JL38P2Z52Q3twWvOrqk3acReFYFU3OjhCtEbqrr64y9pSnRCakcD39ATZ9ixg5JCPEYlUqFp6cnYWFhXL9+3djhiCIot+rYksi/gIf/wd3c3EhPTzd2OELkOjMzM2mJf1EPruuS9kubIWwXpCc9+kxtSkbJOhw0qcHUMB+OxboDKsq42vBtUDnaVvbM3DqnUv33nLcvVOmuW5eeomvBfpjY3zwCsTd03dHvnIFj/00xaG6na6l/mNiXrAk2LvlyGgxo0uH28Udd5W8chLQEwzIW9lCqni5x924InoEyWrwoksxN1QRVcGfl8VuEnI6URF6IAsjc3Bw/Pz/S0tKMHYooYnKzji21pJdgYmIiyY4Qr7qMNF2L8qVNcHmLblC2x9l6gF8Lkks358+o0szcf4eYJN0NQF9XG4Y396NdleI5615rZqnrZu5V+9G6+EhdQv8wsb99DNLiIWyn7vWQk8+jxL5ETfConPst3RmpcOvof13l98CNQ4Y3NEA3+Jx3A93LpwF4VAG1/D0Vr4bgSh66RP5MJGPaVpBH9IQogNRqNZaWlsYOQ4inkkReCCFyKvbWo1b3qzsMW5dVJroE268F+LUk0dGfBQeu8/vqqzxIugWAr4sNw5r70T4whwn8s9h5QIV2uhfouq/fPfcosb95BKIvwINrutep5bpyJha61u+StR51y3fwytlAeukpuuM8HFX+5mGD5/8B3fRv3vUfDU7nHiCJu3hlNfZzxcrMhJsPkjlzO45KJWRWBSGEEDkjibwQQjyPJl3Xqvyw1T3qtOHnNm5QNkiXvJdpBlZOJKZm8Mf+68zetZ0H/7XAl3axYVjzsrSvUhzTvJ4/2sRU19ruURlqDtCtS47RtZTfeux5++QHcPOQ7vWQrft/Lfb/JfbFq4GF7aPP05J05a/9l7jfOgKaJ7ofWrs86ibv0xBc/Qvm8/pCGIGVuQlNy7uy4XQkG89ESiIvhBAixySRF0KIrMRH6pL2S5vgyg5IjX3sQ5XueXO/lrrk3SNQn6QmpWXw584r/LbrKvcTdcmtj7M1w5r70SEwHxL4Z7FyhLLNdS/QDaR3/+pjXfIP625SJETB+bW6F4BKrRtIz6MK3L8Ct46B9onxQWzd/+sm/1/i7lKu6E6PJ0QuaFXJgw2nIwk5HcmoluWNHY4QQohCRhJ5IYQA3RRoN4/81+q+WTeq+uOsnaFMc13yXuY1sHE2+Dg5TcOfB67x286r3Psvgfd2tuaD1/zoVNXICfzTqFTgXEb3CuypW5eWpPvutx573j7uli7Bf7wngn2JR8+3ezfU7UMSdyGyrZm/G2YmKi7dSeDynQTKutk+fyMhhBDiP5LICyFeXYnRj7W6b9N1M39c8er6Z90pXi3LZ7qT0zQsPHidWTuvEJ2gS+BLFbPmg9fK0rlaiYKZwD+LuTV419O9Hoq7rUvoI0+BYyldi7uTjyTuQrwEe0szGpR1YceFu2w8E0lZt7LGDkkIIUQhIom8EOLVE3ECQkbD9X2A8mi9paOutd2vpa77ua3bU3eRkq7hrwPXmbXzKtEJqQB4FbPig2Z+dK5eArPClsA/i31xqNhB9xJC5JpWAR7suHCXkNORDGkmibwQQojsk0ReCPHqyEiFXVNgz4+gzdCt86jyqNW9RM3nzl2ekq5h4cFwZu28wt14XQJf0smKD14rS5fqJYtWAi+EyFNBFd1RrzrFqVux3HyQREkna2OHJIQQopCQRF4I8Wq4dQxWD9ZNyQZQsSMEfwMOJbO1eUq6hsWHwvl1xxXu/JfAl3C0YuhrZelavSTmppLACyFyxsXWglo+xTgYdp+NZ6IY2LC0sUMSQghRSEgiL4Qo2tJTYMdk2DcdFK1uWrS2UyGgU7Y2T0nXsORQOL/uvEJU3KMEfkizsnSrIQm8EOLltKrkoUvkT0dKIi+EECLbJJEXQhRdNw7DP4Mh+qLufaVu0Pq7TCPOZyU1Q8PSwzeYuf0KkXEpABR3sGTIa2XpXsNLEnghRK4IDvBg/L9nOXz9PnfjU3G1szB2SEIIIQoBSeSFEEVPejJsmwQHZupa4W3dod2P4N/2uZumZmhYdvgGM3dcISJWl8B7OlgyuFlZetQsiYVp5pHrhRDiRRV3tCKwpAMnbsay+WwUfeqUMnZIQgghCgFJ5IUQRcv1/fDPELh/Rfc+sLfuWXjrYs/cLDVDw/IjN5m5/TK3/0vgPewtGdysDD1reUkCL4TIM8GVPDhxM5aQM5GSyAshhMgWSeSFEEVDWiJsnQAHfwMUsPOE9j9BueBnb5ahZfnRG/yy7VEC725vweCmZelZywtLM0nghRB5q1WAB9+FXGDf5Whik9NxsDIzdkhCCCEKOEnkhRCFX9huWDMUHlzTva/2BrT8Gqwcn7pJWoaWv4/dZMa2y9yKSQbAzc6C95uWoXftUpLACyHyja+rLeXd7bgQFc+281F0rpa92TSEEEK8uiSRF0IUXqnxsGUcHP4/3Xv7ktDhJygb9NRN7iWksuzITf46cF2fwLvaWfB+kzL0qSMJvBDCOIIreXAhKp6Q05GSyAshhHguSeSFEIXTle2wZhjEhuve13gLWkwAS/tMRRVF4fiNGP7cf511JyNI02gB3RzO7zctw+uSwAshjKxVgAfTt15i58W7JKVlYG0uVTQhhBBPVyDmT/rll1/w8fHB0tKSOnXqcOjQoaeWbdq0KSqVKtOrbdtHo1ErisLYsWPx9PTEysqKoKAgLl26lB9fRQiR11JidQn8n510SbxjKej7D7SflimJT07TzQHf7uc9dJm5j1XHb5Gm0VK5hAPfdavCnk+bMbBhaUnihRBGV8HTjlLFrElJ17Lr4l1jhyOEEKKAM/rt3qVLlzJy5EhmzZpFnTp1mDZtGsHBwVy4cAE3N7dM5VeuXElaWpr+/b179wgMDKR79+76dd999x3Tp09nwYIFlC5dmi+//JLg4GDOnj2LpaVlvnwvIUQeuLQF/h0Gcbd072u/A82/Agtbg2JX7ybw14FwVhy9QVxKBgDmpmraVylO33reBHo55nPgQgjxbCqVilaVPJi96yohpyNpVcnT2CEJIYQowFSKoijGDKBOnTrUqlWLGTNmAKDVavHy8uKDDz7gs88+e+7206ZNY+zYsURERGBjY4OiKBQvXpxRo0bx0UcfARAbG4u7uzvz58+nV69emfaRmppKamqq/n1cXBxeXl7ExsZib5+5m64QIp8lP4CNYyB0oe69U2noOAN8GuqLZGi0bDt/hz8PXGf3pWj9eq9iVrxRx5vuNb0oZmOe35ELkWvi4uJwcHCQa1MuKYjn8+j1B3T9dR92FqYc/bIF5qYFouOkEEKIfJKTa5NRW+TT0tI4evQoo0eP1q9Tq9UEBQWxf//+bO1jzpw59OrVCxsbGwDCwsKIjIwkKOjRYFcODg7UqVOH/fv3Z5nIT548mfHjx7/ktxFC5IkLG+DfEZAQCaig7vvw2pdgbg3A3fhUlh25wcID1/XTx6lU0Ky8G2/W9aZJOVfUapXx4hdCiGyq5uWIm50Fd+JT2XclmqblM/dMFEIIIcDIiXx0dDQajQZ3d3eD9e7u7pw/f/652x86dIjTp08zZ84c/brIyEj9Pp7c58PPnjR69GhGjhypf/+wRV4IYURJ92HDp3Bqme69c1no+AuUqouiKBy9dp8/9l9nw+kI0jW6jkVO1mb0qOXFG3W88SpmbcTghRAi59RqFcEBHvx54Dobz0RKIi+EEOKpjP6M/MuYM2cOlStXpnbt2i+1HwsLCywsLHIpKiHESzv3L6wdCYl3QKWGekOh2eckas1YffA6f+6/zvnIeH3xql6OvFnXm7ZVPGXgOiFEodaqki6R33QmikmdFEykR5EQQogsGDWRd3FxwcTEhKioKIP1UVFReHh4PHPbxMRElixZwoQJEwzWP9wuKioKT89HA8VERUVRtWrV3AlcCJE3EqNh/cdwZqXuvas/dPyFy+b+/LXhKn8fvUl8qm7wOgtTNR2rFufNuj5ULulgxKCFECL31C5dDEdrM+4lpnHk2n3q+DobOyQhhBAFkFFHUTE3N6dGjRps3bpVv06r1bJ161bq1av3zG2XL19Oamoqb7zxhsH60qVL4+HhYbDPuLg4Dh48+Nx9CiGMRFHg9Er4pY4uiVeZoG0wko0Nl9FnQwZBP+xk/r5rxKdm4ONszRdtK3Dw8+Z81y1QknghRJFiZqImqILu8cCQM1k/EiiEEEIYvWv9yJEj6devHzVr1qR27dpMmzaNxMRE3nrrLQD69u1LiRIlmDx5ssF2c+bMoVOnTjg7G96pVqlUjBgxgkmTJuHn56effq548eJ06tQpv76WECK7Eu7AupG67vRAhksFlpf8nJ8O2xAZdxoAtQpe83enbz1vGpZ1kcHrhBBFWqsAD1YcvcnG05GMbVcRlUr+5gkhhDBk9ES+Z8+e3L17l7FjxxIZGUnVqlUJCQnRD1YXHh6OWm3YceDChQvs2bOHTZs2ZbnPTz75hMTERN555x1iYmJo2LAhISEhMoe8EAWJosCp5bDhE0h+gFZlSohTH0bdbkHyTRMgBWcbc3rW8qJPnVKUdJLB64QQr4aGfi5Ym5twOzaFU7diqVLS0dghCSGEKGCMPo98QVQQ55YVokiJj4S1H8KF9QBcVvsyLHkQZxUfAGp4O9G3njetKnlgYSqD1wkBcm3KbQX9fA5ZdIx1JyMY3LQMn7TyN3Y4Qggh8kGhmUdeCPGKURQ4sRjN+s8wSYslXTHhp4wuzNK0x8zMgt7VSvBG3VIEFJfn3oUQr7ZWAR6sOxlByOlIPg4uL93rhRBCGJBEXgiRL9If3ODBksG4Re3CBDih9eWT9HdId6nAmLredKleEgcrM2OHKYQQBUIzfzfMTdRcjU7k0p0EyrnbGTskIYQQBYgk8kKIPBUZk8ypf3+m3pUfcSOJVMWUnzTduFZuIGPr+1K/jLO0NAkhxBNsLUxp5OfC1vN3CDkdKYm8EEIIA5LICyHyRFzMPVb9uxrfS/NooT4FwEnKcazaRN5s1gRPBysjRyiEEAVbcCUPfSI/rLmfscMRQghRgEgiL4R4eYoC967AjYMoNw6ScHkftnGX6YcCakjDnEuVRuDf4ROqmEv3eSGEyI6gCu6YqFWcjYgj/F4SpZxl9g4hhBA6ksgLIXIuLRFuHYMbB+HmYbhxCJLvA6ACHnYAvaXywKx0fdzajCHApazRwhVCiMKomI05dUoXY9+Ve2w8E8nbjX2NHZIQQogCQhJ5IcSzKQrEhOuS9ZuHdMl75GlQNAbFMtQWnNCU5rDGj1DKUbVeC/q3rI2lmUwfJ4QQL6pVJQ/2XblHiCTyQgghHiOJvBDCUHoKRJx4lLTfOAwJkZnL2ZcAr9rctqvMlLMOrL3jSjqm1PYpxjddKlHWTQZmEkKIl9Wyogdj/znD0esPuBOXgpu9pbFDEkIIUQBIIi/Eqy4u4r+k/b9XRCho0gzLqE3BMxBK1gYv3SvB0oOpmy6wYOc1tAo4WJnxeRt/utfwQq2WUeiFECI3eDhYUq2UI8fDY9h4Noo363obOyQhhBAFgCTyQrxKNOkQdfpR0n7jEMSGZy5n4/pY0l4HilcFs0ejzG86E8lXa3YSEZsCQKeqxfmiXUVcbC3y6YsIIcSro1WAhy6RPx0pibwQQghAEnkhirbEe4Zd5G8dhYxkwzIqNbgF6Fva8aoNTqUhi7ndI2KT+eqfM2w6GwWAt7M1kzpVopGfa358GyGEeCUFB3gwecN59l+9R0xSGo7W5sYOSQghhJFJIi9EUaHVwN3zj5L2Gwfh/pXM5SwdDLrIU6IGWDz7eXaNVuGP/df4fuMFEtM0mKpVvNvElw9e85PB7IQQIo/5uNjg72HH+ch4tpy7Q7caJY0dkhBCCCOTRF6IwizhDlxYD+fXQ/h+SI3LXMalPHjV0nWRL1kbXMqBWp3tQ5y+Fcvnq05x8mYsADW8nfimc2XKe8hgdkIIkV9aVfLgfGQ8IacjJZEXQgghibwQhc79q3B+HZxbq2t1R3n0mZkNlKzxKGkvWROsi73QYZLSMvhx80Xm7r2GRqtgZ2nKZ6396V2rlAxmJ4QQ+ax1JU+mbbnErkt3SUzNwMZCqnBCCPEqk6uAEAWdokDkSV3ifn4d3Dlj+HnxauDfFvxa6p51N3n5/9bbzkfx5eoz3IrRPU/ftoonX7WrKNMeCSGEkZRzt6W0iw1h0YnsuHCXtlU8jR2SEEIII5JEXoiCSJOh6yp/fp3u9fjI8ioT8GkA/u3Bvw045F4Xy6i4FCb8e5Z1pyIAKOFoxaROlWjm75ZrxxBCCJFzKpWK4AAPZu28QsiZSEnkhRDiFSeJvBAFRXoyXNkO59fChQ2QfP/RZ6ZWULY5+LeDcsEv3F3+abRahYUHr/NdyAXiUzMwUasY1LA0w4P8sDaXPxNCCFEQtKqkS+S3nYsiJV0jg40KIcQrTGroQhhT8gO4uFGXvF/eCulJjz6zcoJyraFCO/BtBubWeRLC+cg4Rq88xfHwGAACvRyZ3LkyFYvb58nxhBBCvJgqJRzwdLAkIjaFfVeiec3f3dghCSGEMBJJ5IXIb7G3dCPNn/sXru8Fbcajz+xL6p53r9AOStXPlefdnyY5TcNPWy/xf7uvkqFVsLUw5ePg8rxR1xsTGcxOCCEKHLVa171+/r5rhJyOlEReCCFeYZLIC5Ef7l7QJe7n18HtY4afuVbQJe7+bcGzKqjyPoneefEuX6w+xY37usHsWgV4MK5DAB4OMpidEEIUZA8T+c1no8jQaDE1yf50okIIIYoOSeSFyAtarS5hf5i837v02Icq8KqtS9z924FzmXwL6258KhPXnmXNidsAeDpYMqFjJVpUlFYdIYQoDGr5OFHMxpz7iWkcunaf+mVcjB2SEEIII5BEXojckpEG13brEvcL6yE+4tFnajPwbaJL3Mu3Abv8TZy1WoWlR24wef054lIyUKugf/3SjGxZDluZi1gIIQoNUxM1LSq4s/TIDTaejpREXgghXlFSgxfiZaQmwOUtusHqLm6C1NhHn5nbgV+LR3O8Wxpn8LhLUfF8vuoUh689AKBSCXsmd65C5ZIORolHCCHEy2lVyUOXyJ+J4qv2AahlXBMhhHjlSCIvRE4lRuta3M+v000Xp0l99JmNq67FvUJ7KN0YTC2MFmZKuoYZ2y7z264rpGsUrM1NGNWyPP3qecszlUKIAmXXrl1MmTKFo0ePEhERwapVq+jUqVO2tt27dy9NmjShUqVKhIaG5mmcBUX9ss7YWpgSGZfCiZsxVCvlZOyQhBBC5DNJ5IXIrujLsHaEbqR5RftovVPp/warawcla4Ha+PP67r0czZhVp7h2TzedXVAFN8Z3rEQJRysjRyaEEJklJiYSGBjIgAED6NKlS7a3i4mJoW/fvjRv3pyoqKg8jLBgsTA14TV/N9acuE3ImUhJ5IUQ4hUkibwQ2ZGeAkvfgLvndO89quha3f3bglvFfBlpPjvuJaTy9bpzrDx+CwB3ewvGdwggOMADVQGJUQghntS6dWtat26d4+3ee+89+vTpg4mJCatXr879wAqwVpU8dIn86Ug+a+Uvf+OFEOIVI4m8ENmxdbwuibdxgwEh+TrSfHYoisLyozf5Zv05YpLSUamgb11vRgWXx97SzNjhCSFErps3bx5Xr17lr7/+YtKkSc8tn5qaSmrqo0eh4uLi8jK8PNeknCsWpmqu30vifGQ8FTyNMw6LEEII45BEXojnubINDszULXf8pcAl8bdjkhm17AT7r94DwN/DjsldKktXSyFEkXXp0iU+++wzdu/ejalp9qoykydPZvz48XkcWf6xsTClcTlXNp+NIuR0pCTyQgjxipERr4R4lqT7sHqwbrnmQCjX0rjxPCHkdAStf9rN/qv3sDRTM7q1P/9+0FCSeCFEkaXRaOjTpw/jx4+nXLly2d5u9OjRxMbG6l83btzIwyjzR6sADwA2nok0ciRCCCHym7TIC/E0igJrP9TNB+/sBy2f33UzvySnaZiw9gyLD+kqolVKOjC9VzV8XGyMHJkQQuSt+Ph4jhw5wvHjxxk6dCgAWq0WRVEwNTVl06ZNvPbaa5m2s7CwwMLCeDOJ5IXmFdwwVas4HxlPWHQipeUaIIQQrwxJ5IV4mpNL4exqUJtCl9lgbm3siAA4ezuODxYf48rdRFQqeLdxGUa2KIe5qXSwEUIUffb29pw6dcpg3cyZM9m2bRsrVqygdOnSRoos/zlam1OvjDO7L0Wz8Uwk7zUpWI9+CSGEyDuSyAuRlQfXYf3HuuUmn0GJ6saNB92AdvP2XuPbDedJ02hxs7Pgx55VaVDWxdihCSHES0lISODy5cv692FhYYSGhlKsWDFKlSrF6NGjuXXrFn/88QdqtZpKlSoZbO/m5oalpWWm9a+C4AAPdl+KJuS0JPJCCPEqkSY8IZ6k1cCq9yA1DrzqQMMPjR0R0QmpvDX/MBPWniVNoyWoghshIxpLEi+EKBKOHDlCtWrVqFatGgAjR46kWrVqjB07FoCIiAjCw8ONGWKB1bKiOyoVhN6IISI22djhCCGEyCcqRVEUYwdR0MTFxeHg4EBsbCz29jIK7Ctnz4+wZRyY28J7e6CYcbtp7rx4l1HLThCdkIq5qZov2lbgzbreMmewEK8YuTblrqJ0Prv9uo8j1x8wvkMA/er7GDscIYQQLygn1yZpkRficREnYNvXuuVW3xo1iU/N0DBp7Vn6zT1EdEIq5dxtWTO0AX3r+UgSL4QQQq9VJd3o9SGnZfR6IYR4VRg9kf/ll1/w8fHB0tKSOnXqcOjQoWeWj4mJYciQIXh6emJhYUG5cuVYv369/vNx48ahUqkMXv7+/nn9NURRkJ4Mf78N2nTwbwfV3jBaKFfuJtD11338354wAN6s682aoQ3x9yjcrUZCCCFyX/B/09AdDLvH/cQ0I0cjhBAiPxh1sLulS5cycuRIZs2aRZ06dZg2bRrBwcFcuHABNze3TOXT0tJo0aIFbm5urFixghIlSnD9+nUcHR0NygUEBLBlyxb9e1NTGdNPZMOWcRB9AWzdof10MEKrt6IoLD9yk6/WnCE5XYOjtRnfda1Cy/8qaUIIIcSTvIpZE1DcnjO349hyNooetbyMHZIQQog8ZtQM94cffuDtt9/mrbfeAmDWrFmsW7eOuXPn8tlnn2UqP3fuXO7fv8++ffswMzMDwMfHJ1M5U1NTPDwk8RE5cHkrHJylW+74C9g453sIscnpfL7qFOtORgBQv4wzP/SoioeDZb7HIoQQonBpFeDBmdtxhJyJlEReCCFeAUbrWp+WlsbRo0cJCgp6FIxaTVBQEPv3789ymzVr1lCvXj2GDBmCu7s7lSpV4ptvvkGj0RiUu3TpEsWLF8fX15fXX3/9uSPdpqamEhcXZ/ASr5Ck+7B6sG651tvg1yLfQzhy7T5tftrNupMRmKpVfNKqPH8OrCNJvBBCiGx5+Jz8nkvRxKekGzkaIYQQec1oiXx0dDQajQZ3d3eD9e7u7kRGZj1Yy9WrV1mxYgUajYb169fz5ZdfMnXqVCZNmqQvU6dOHebPn09ISAi//vorYWFhNGrUiPj4+KfGMnnyZBwcHPQvLy+5k/3KUBRYOwISIsGlHLSYkK+Hz9BombblIj1+28+tmGRKFbNmxfv1Gdy0LCZqGdBOCCFE9pR1s8XX1YY0jZbtF+4aOxwhhBB5zOiD3eWEVqvFzc2N2bNnU6NGDXr27MmYMWOYNWuWvkzr1q3p3r07VapUITg4mPXr1xMTE8OyZcueut/Ro0cTGxurf924cSM/vo4oCE4sgbP/gNoUuswGc+t8O/TNB0n0/v0A07ZcQqtAl2olWDesIVW9HPMtBiGEEEWDSqWi9X+t8htl9HohhCjyjPaMvIuLCyYmJkRFRRmsj4qKeurz7Z6enpiZmWFiYqJfV6FCBSIjI0lLS8Pc3DzTNo6OjpQrV47Lly8/NRYLCwssLCxe8JuIQuvBNVj/sW656WgoXi3fDr3uZASfrTxJfEoGthamTOpUiU7VSuTb8YUQQhQ9rQI8+WX7FbZfuENKugZLM5PnbySEEKJQMlqLvLm5OTVq1GDr1q36dVqtlq1bt1KvXr0st2nQoAGXL19Gq9Xq1128eBFPT88sk3iAhIQErly5gqenZ+5+AVG4aTWw6j1IiwevutDww3w5bFJaBp+uOMmQRceIT8mgqpcj64c1kiReCCHES6tUwp4SjlYkpWnYfSna2OEIIYTIQ0btWj9y5Eh+//13FixYwLlz53j//fdJTEzUj2Lft29fRo8erS///vvvc//+fYYPH87FixdZt24d33zzDUOGDNGX+eijj9i5cyfXrl1j3759dO7cGRMTE3r37p3v308UYHt/gvD9YG4LXX4Ddd63Wpy+FUu76XtYeuQGKhUMaVaG5e/Vo5Rz/nXnF0IIUXSpVCr9nPIh0r1eCCGKNKNOP9ezZ0/u3r3L2LFjiYyMpGrVqoSEhOgHwAsPD0etfnSvwcvLi40bN/Lhhx9SpUoVSpQowfDhw/n000/1ZW7evEnv3r25d+8erq6uNGzYkAMHDuDq6prv308UULdDYfvXuuXW34GTT54eTqtVmLs3jP+FnCddo+Bhb8kPPQOpX8YlT48rhBDi1dOqkgdz94ax5VwU6RotZiaFajgkIYQQ2aRSFEUxdhAFTVxcHA4ODsTGxmJvb2/scERuSk+G35pA9AWo0B56/AmqvBsd/k58Ch8tP8mui7oRhFtWdOd/XavgZJP1oyBCCPE0cm3KXUX1fGq0CnW+2UJ0Qhp/DaxDQz+5aSyEEIVFTq5NcptWvFo2f6VL4m3dod1PeZrEb79whzY/7WbXxbtYmKqZ1KkSv71ZQ5J4IYQoatJTYN/PkGD8ad9M1CpaVPyve/2ZCCNHI4QQIq9IIi9eHZe3wKHfdMsdZ4KNc54cJjVDw4R/z/LWvMNEJ6Th72HH2g8a8kZdb1R5eONACCGEkfw9EDZ9ATv/Z+xIAF33eoCNZ6LQaqXjpRBCFEWSyItXQ9J9WP3foIi13wG/oDw5zOU78XT6ZR9z94YB0L++D6uHNMDP3S5PjieEEKIAqP2O7t8jcyH6knFjAer5OmNnacrd+FSOhT8wdjhCCCHygCTyouhTFPh3GCREgks5CBqfB4dQWHwonHY/7+FcRBzFbMyZ068m4zoEyDy+QghR1Pk2gXKtQNHAlnHGjgZzUzVBFXQDB8vo9UIIUTRJIi+KvtBFcO5fUJtCl9/BPHene4tJSmPwwmOMXnmKlHQtDcu6EDK8Ec3/q0QJIYR4BQSNB5Uazq+Fa3uNHc2jaejORCLjGgshRNEjibwo2h5cgw3/TU/Y7HMoXjVXd3/w6j1a/7SbDacjMVWrGN3anz8G1MbN3jJXjyOEEKKAc/OH6v10y5u+AK3WqOE0KeeKpZmamw+SOXM7zqixCCGEyH2SyIuiS6uBle9CWjyUqgcNRuTarjM0Wn7YdIHevx8gIjaF0i42rBxcn3eblEGtlgHthBDildR0NJjbwu1jcGalUUOxMjehaTk3ADaeke71QghR1EgiL4quPT/CjQNgbgedZ4E6d55Vv3E/iR6/7Wf6tstoFehWoyRrP2hIlZKOubJ/IYQQhZSd+6ObxlvG66alM6KHo9evOxWBRkavF0KIIkUSeVE03T4OOybrltt8B04+ubLbNSdu0+an3RwLj8HOwpTpvavxffdAbCxMc2X/QgghCrl6Q8DOE2LD4dBso4byWgU3bC1MuXo3kVk7rxg1FiGEELlLEnlR9KQlwcp3QJsBFTpAYO+X3qWiKEzddIFhi48Tn5pB9VKOrB/eiA6BxXMhYCGEEEWGuTW89oVueff3uulPjcTe0oxxHQIA+HHzRU7djDVaLEIIIXKXJPKi6Nk8FqIvgq0HtP8JVC//zPr0rZf5edtlAIY2K8uyd+vhVSx3R78XQghRRAT2BvdKkBILu6YYNZSu1UvQprIHGVqF4UuPk5ymMWo8Qgghcock8qJoubQFDv+uW+70C1gXe+ldztxxmR+3XATgi7YV+Ci4PKYm8l9HCCHEU6hNoMUE3fKh3+Ge8bq1q1Qqvu5UGXd7C67eTeTr9WeNFosQQojcI9mIKDoS78E/g3XLtd+FskEvvcv/232V70IuAPBJq/IMauT70vsUQgjxCijbHMo0B206bB1v1FCcbMyZ2r0qAH8dCGfb+SijxiOEEOLlSSIvigZFgX+HQUIUuJSHFi9faVqw7xqT1p0DYESQH4Obln3pfQohhHiFtJwIKjWc/QduHDJqKA39XBjYsDQAn6w4SXRCqlHjEUII8XIkkRdFQ+hCOL8W1GbQZTaYWb3U7hYevM5Xa84AMKRZGYY398uNKIUQQrxK3AOg6uu65Y1jdDedjejj4PL4e9gRnZDGpytOohg5HiGEEC9OEnlR+N0Pgw2f6pabfQ7Fq77U7pYdvsGYVacBeKexLx+1LI8qFwbME0II8QpqNgbMrOHmIV3LvBFZmpkwrVdVzE3UbD1/h0WHwo0ajxBCiBcnibwo3DQZsOpdSEuAUvWhwfCX2t2q4zf5dOVJAPrX92F0a39J4oUQQrw4e0+o/4Fuecs4yEgzajj+HvZ80qo8ABPXnuXK3QSjxiOEEOLFSCIvCre9P8KNg2BuB51n6UYKfkH/nrjNqGUnUBR4o24pvmpfUZJ4IYQQL6/+MLBxgwdhcGSOsaNhQIPSNCzrQkq6lg+XhpKu0Ro7JCGEEDkkibwovG4dgx3f6pbbTAEn7xfeVcjpCEYsDUWrQM+aXkzoUEmSeCGEELnDwlb36BfAzv9BcoxRw1GrVXzfPRAHKzNO3ozlpy2XjBqPEEKInJNEXhROaUmw8h3QZkDFjhDY64V3teVsFB8sPo5Gq9Clegkmd6mMWi1JvBBCiFxU7U1w9YfkB7B7qrGjwcPBksldKgMwc8dlDl+7b+SIhBBC5IQk8qJw2vwl3LsEdp7Qbhq8YOv5jgt3GLzwGOkahQ6BxZnSLVCSeCGEELnPxBRaTNQtH5wFD64bNx6gTWVPulYviVaBD5eGEpeSbuyQhBBCZJMk8qLwubgJDv+fbrnTTLAu9kK72XMpmnf+PEqaRkvrSh780CMQE0nihRBC5BW/FlC6CWjSYOsEY0cDwLgOFfEqZsXNB8mM+2/aVSGEEAWfJPKicEmMhn+G6JbrvAdlXnuh3Ry4eo9BfxwmLUNLUAV3fupVDVMT+e8ghBAiD6lU0HIioILTK+DWUWNHhJ2lGT/2qIpaBSuP3WLtydvGDkkIIUQ2SOYiCg9FgX+HQ+Id3XOGQeNeaDdHrt1nwPzDpKRraVrelV9er4a5qfxXEEIIkQ88Ax+N67LpS921zchq+hRjSLOyAIxZdZqI2GQjRySEEOJ5JHsRhcfxv+D8WlCbQZffwcwq57sIf0D/eYdJStPQyM+FWW/UwML0xaesE0IIIXLstS/A1BKu74UL640dDQDDmvsRWNKB2OR0Ri07gVZr/BsMQgghnk4SeVE43L8KGz7VLb82Bjyr5HgXp27G0nfuIRJSM6jrW4zZb9bE0kySeCGEEPnMoSTUHaxb3jwWNMYfZM7MRM2PPatiZWbCviv3mLs3zNghCSGEeAZJ5EXBp8mAle9CeiJ4N4D6w3K8i7O343hjzkHiUzKo5ePEnH61sDKXJF4IIYSRNPwQrF3g3mU4Ot/Y0QDg62rLl+0qAvBdyAXO3o4zckRCCCGeRhJ5UfDt+QFuHgILe+g8C9Q5S8AvRMbzxpyDxCanU62UI/Peqo2NhWkeBSuEEEJkg6U9NP1Mt7xjMqTEGjee//Su7UVQBXfSNFpGLD1OSrrG2CEJIYTIgiTyomC7dRR2fKtbbjMFHEvlaPPLdxJ4/f8Ocj8xjSolHZj/Vm1sJYkXQghRENToD85lIeke7Jlm7GgAUKlU/K9rZVxsLbgYlcB3IReMHZIQQogsSCIvCq60RFj5DigaCOgMVXrmaPOw6ET6/H6A6IRUKnra88eA2jhYmeVRsEIIIUQOmZhBi//mkz8wE2JvGjee/zjbWjClm24smrl7w9h96a6RIxJCCPEkSeRFwbXpC92zg3bFoe0Puvl3s+nG/ST6/H6AO/GplHe3469BdXC0Ns/DYIUQQogXUL6NbvyXjBTYNsnY0eg183ejbz1vAD5afoIHiWlGjkgIIcTjJJEXBdPFjXBkrm6500ywLpbtTW/FJNNr9gEiYlMo42rDX4PqUMxGknghhBAFkEoFLSfqlk8sgduhRg3ncaNbV6CMqw1RcamMXnkKpQDMeS+EEEJHEnlR8CTchX+G6JbrDoYyzbK9aWRsCr1nH+BWTDKlXWxY/HZdXO0s8ihQIYQQIheUqAGVugEKbP4SCkjCbGVuwk+9qmFmoiLkTCQrjhaMrv9CCCEkkRcFSVwEHJ4Df3WBxLvgWgGaf5Xtze/EpdDn9wOE30+iVDFrFr1dBzd7yzwMWAghhMglzceCiTmE7YJLm40djV6lEg6MbFEegHFrznD9XqKRIxJCCAGSyAtjUhS4cw52TYHZzeAHf1g3EiJPgqkVdJkNZtlLxKMTUunzfwe5Gp1ICUcrFr1dB08Hqzz+AkIIIUQucfKGOu/pljd/CZoM48bzmHca+1K7dDES0zR8uDSUDI3W2CEJIcQrL8eJvI+PDxMmTCA8PDwv4hFFnVYD1/bCxjEwvRrMrKsb3Of2Md3nJWvpWuGHHADPKtna5YPENN74v4NcvpOAh70li9+uS0kn6zz8EkIIIUQeaDQKrJzg7nk4/qexo9EzUav4oUcgdpamHAuPYeaOK8YOSQghXnk5TuRHjBjBypUr8fX1pUWLFixZsoTU1NQXDuCXX37Bx8cHS0tL6tSpw6FDh55ZPiYmhiFDhuDp6YmFhQXlypVj/fr1L7VPkcfSkuDcWlg9GL73g/ltYP8MeBAGJhbg1xLaTYNRF2DQFmg0Epx8srXr2KR03phzkPOR8bjZWbD4nbqUcpYkXgghRCFk5QhNPtUtb/8GUhOMGs7jSjpZM6lTJQB+2nqJ4+EPjByREEK82l4okQ8NDeXQoUNUqFCBDz74AE9PT4YOHcqxY8dytK+lS5cycuRIvvrqK44dO0ZgYCDBwcHcuXMny/JpaWm0aNGCa9eusWLFCi5cuMDvv/9OiRIlXnifIo8kRsOxP2Fxb/jOF5a+DqELIekeWDrq5oTvvgA+uQKvL4eab4GdR44OEZeSTt+5BzlzOw4XW3MWvV2H0i42efN9hBBCiPxQcyA4lYbEO7BvurGjMdCxagk6BBZHo1X4cGkoiakFp/u/EEK8alTKS84lkp6ezsyZM/n0009JT0+ncuXKDBs2jLfeegvVc+b9rlOnDrVq1WLGjBkAaLVavLy8+OCDD/jss88ylZ81axZTpkzh/PnzmJmZ5co+AVJTUw16FcTFxeHl5UVsbCz29vbZOg8CuHcFzq+DC+vhxkFQHnuGzqEU+Lf5b77c+mCS9c8vuxJSM+g75yDHwmNwsjZjyTv1KO9h95JfQAghCq64uDgcHBzk2pRLCvT5PPsPLOsLZtbwwTGw9zR2RHqxyem0nraL27Ep9K7txeQu2XsMTgghxPPl5Nr0woPdpaens2zZMjp06MCoUaOoWbMm//d//0fXrl35/PPPef3115+5fVpaGkePHiUoKOhRMGo1QUFB7N+/P8tt1qxZQ7169RgyZAju7u5UqlSJb775Bo1G88L7BJg8eTIODg76l5eXV05OxatLq4WbR2DLePilDvxcXTdAT/h+XRLvUQWafg7v7YERJ6H1/8C3yUsn8UlpGQyYd5hj4TE4WJnx16A6ksQLIYQoOip0AK86kJ4E2782djQGHKzMmNqjKioVLD50g41nIo0dkhBCvJJMc7rBsWPHmDdvHosXL0atVtO3b19+/PFH/P399WU6d+5MrVq1nrmf6OhoNBoN7u7uBuvd3d05f/58lttcvXqVbdu28frrr7N+/XouX77M4MGDSU9P56uvvnqhfQKMHj2akSNH6t8/bJEXWchI1U2Nc34dXNgACY9dwNWm4N0A/NtB+dbgmPvnMDlNw8D5Rzh07T52Fqb8ObA2AcUdcv04QgghhNGoVNByEsxpAcf/0o1m71HJ2FHp1SvjzDuNfflt51U++/sk1bwcZbpXIYTIZzlO5GvVqkWLFi349ddf6dSpU5Zd3EuXLk2vXr1yJcDHabVa3NzcmD17NiYmJtSoUYNbt24xZcoUvvoq+/ONP8nCwgILC4tcjLSISX6gm9P2/Dq4vAXSHht8x9wWygbpkne/IN1ou3kkJV3DO38eYf/Ve9iYm7BgYG2qlHTMs+MJIYQQRuNVGyp2grOrYfNYeHOlsSMyMLJFOXZfjOZsRBwfrzjJ/LdqPfeRSiGEELknx4n81atX8fb2fmYZGxsb5s2b98wyLi4umJiYEBUVZbA+KioKD4+sBz3z9PTEzMwMExMT/boKFSoQGRlJWlraC+1TPEXMDd2z7ufXwfW9oH1sQBtbj/+ed28LpRuBad7fBEnN0PD+X0fZfSkaKzMT5g+oTfVSeXfTQAghhDC6oK901+ErW+HyVijb3NgR6VmYmvBTr6q0+3kPOy/e5Y/91+lX38fYYQkhxCsjx8/I37lzh4MHD2Zaf/DgQY4cOZLt/Zibm1OjRg22bt2qX6fVatm6dSv16tXLcpsGDRpw+fJltNpHg6hdvHgRT09PzM3NX2if4j+KAhEnYce3MKshTKsEGz6BsJ26JN7VXze/7aBtMPIctPtR1wKfD0l8ukbL0EXH2X7hLpZmaub2r0Utn2J5flwhhBDCqIr5Qu23dcubx4JWY9x4nuDnbsfnbSoA8M36c1yKijdyREII8erIcSI/ZMgQbty4kWn9rVu3GDJkSI72NXLkSH7//XcWLFjAuXPneP/990lMTOStt94CoG/fvowePVpf/v333+f+/fsMHz6cixcvsm7dOr755huD4z5vn+IxmnS4uhM2fArTqsBvjWDHZIg8BSo1lKqve0bvg2Mw5CA0Hwsla4D6hcdIzLEMjZYRS0LZfDYKc1M1v/etSb0yzvl2fCGEEMKoGn8Mlg4QdRpOLDZ2NJn0redNk3KupGZoGb4klLQM7fM3EkII8dJy3LX+7NmzVK9ePdP6atWqcfbs2Rztq2fPnty9e5exY8cSGRlJ1apVCQkJ0Q9WFx4ejvqxpNHLy4uNGzfy4YcfUqVKFUqUKMHw4cP59NNPs71P8Z/IU/BXV0h47DEEUyso85qu23y5VmDjYrz4AI1WYdTyE6w7FYG5iZrf3qxBIz9Xo8YkhBBC5CvrYrpkftMXsG0SBHQGcxtjR6WnUqmY0r0Krabt5mxEHFM3X2B06wrGDksIIYq8HM8j7+zszNq1azN1Vd+3bx9t27blwYMHuRqgMRTouWVzgyYdfm+mS+atnaFca13y7tsMzK2NHR0AWq3CxytO8vexm5iqVfz6Rg1aVJSbMUKIV1eRvzbls0J1PjNSYUZNiAmHZl9Ak4+NHVEmG89E8u6fR1GpYNGgutJ7TgghXkCeziPfsmVLRo8eTWxsrH5dTEwMn3/+OS1atMh5tCL/7Z+hS+KtnGDwQej0C/i3LVBJ/OerTvH3sZuYqFX83LuaJPFCCFGE7dq1i/bt21O8eHFUKhWrV69+ZvmVK1fSokULXF1dsbe3p169emzcuDF/gjUGUwto/t/sPHt+hPioZ5c3guAAD3rV8kJRYNSyUGKT040dkhBCFGk5TuS///57bty4gbe3N82aNaNZs2aULl2ayMhIpk6dmhcxitx074puQDuA4MlgW/C6qk9cd5Ylh2+gVsEPPQJpXdnT2CEJIYTIQ4mJiQQGBvLLL79kq/yuXbto0aIF69ev5+jRozRr1oz27dtz/PjxPI7UiCp1hRI1ID1RN55NAfRlu4r4OFtzOzaFL1efNnY4QghRpOW4az3oLrgLFy7kxIkTWFlZUaVKFXr37p3lnPKFUaHqbpcTigIL2sO13bpu9G+uggI252vI6Qje++sYKhV83y2QrjVKGjskIYQoEIrstekJKpWKVatW0alTpxxtFxAQQM+ePRk7dmy2yhfK83l9H8xrDSoTeH8fuPkbO6JMjoc/oNus/Wi0Cj/1qkrHqiWMHZIQQhQaObk25XiwO9DNE//OO++8UHDCiI79oUvizayh/bQCl8RHxaXw2cpTALzfpIwk8UIIIbJFq9USHx9PsWJPn5o0NTWV1NRU/fu4uLj8CC13edcH/3Zwfi1s+Qr6LDV2RJlUK+XEsNf8+HHLRb5YfZoa3k6UdCoYj+4JIURR8kKJPOhGrw8PDyctLc1gfYcOHV46KJEH4iNh05e65WZjwMnHqOE8SatV+Gj5CWKS0qlUwp4RQeWMHZIQQohC4vvvvychIYEePXo8tczkyZMZP358PkaVR4LGw8UQ3StsF5RubOyIMhnSrAw7L97hWHgMI5edYPHbdTFRF6zGAyGEKOxynMhfvXqVzp07c+rUKVQqFQ975qv+a93VaDS5G6HIHes/htRYKF4N6rxn7GgyWbD/GrsvRWNppmZaz2qYm+bfXPVCCCFezI0bN1CpVJQsqetBdejQIRYtWkTFihXzrefeokWLGD9+PP/88w9ubm5PLTd69GhGjhypfx8XF4eXl1d+hJi7XMpCzQFwaLZuSrq3d4C6YF0zTU3U/NizKm1+2s2hsPvM3nWV95uWMXZYQghRpOT4L//w4cMpXbo0d+7cwdramjNnzrBr1y5q1qzJjh078iBE8dLO/Qvn1oDaFDr8DCYv3BEjT1yMimfyhvMAjGlTgbJutkaOSAghRHb06dOH7du3AxAZGUmLFi04dOgQY8aMYcKECXl+/CVLljBo0CCWLVtGUFDQM8taWFhgb29v8Cq0mnwKFvYQcQJOLTd2NFnydrbhqw4BAPyw+QKnb8U+ZwshhBA5keNEfv/+/UyYMAEXFxfUajVqtZqGDRsyefJkhg0blhcxipeRHAPrPtItNxgOHpWNGs6TUjM0DF8SSlqGlqblXXmjrrexQxJCCJFNp0+fpnbt2gAsW7aMSpUqsW/fPhYuXMj8+fPz9NiLFy/mrbfeYvHixbRt2zZPj1Xg2LhAww91y1snQHqyceN5iu41StK6kgfpGoXhS46TnCa9NoUQIrfkOJHXaDTY2dkB4OLiwu3btwHw9vbmwoULuRudeHlbxkFCJDiXhcafGDuaTH7YdJFzEXEUszHnu25V9I9oCCGEKPjS09OxsLAAYMuWLfpxcvz9/YmIiMj2fhISEggNDSU0NBSAsLAwQkNDCQ8PB3Td4vv27asvv2jRIvr27cvUqVOpU6cOkZGRREZGEhv7CrX61n0f7EtC3E048Kuxo8mSSqXim86VcbOz4MrdRCZvOGfskIQQosjIcSJfqVIlTpw4AUCdOnX47rvv2Lt3LxMmTMDX1zfXAxQv4dpeODpPt9z+JzCzNG48T9h3JZrZu68C8G2XyrjZFaz4hBBCPFtAQACzZs1i9+7dbN68mVatWgFw+/ZtnJ2ds72fI0eOUK1aNapVqwbAyJEjqVatmn4quYiICH1SDzB79mwyMjIYMmQInp6e+tfw4cNz8dsVcGZW0Py/QWz3/AiJ0caN5ymcbMyZ2iMQgD/2X2f7+TtGjkgIIYqGHM8jv3HjRhITE+nSpQuXL1+mXbt2XLx4EWdnZ5YuXcprr72WV7Hmm0I5t+yT0lNgVgO4dxlq9Ncl8gVIbFI6rX7aRURsCr1rezG5SxVjhySEEAVaQbw27dixg86dOxMXF0e/fv2YO3cuAJ9//jnnz59n5cqVRo7w6Qri+cwxrRZmN4HIk1D7HWgzxdgRPdWEf88yd28YLrYWhIxohIuthbFDEkKIAicn16YcJ/JZuX//Pk5OTkWmW3SRuLhvnQi7vwdbDxhyEKwcjR2RgWGLj7PmxG18nK1ZN6wRNhYFawA+IYQoaArqtUmj0RAXF4eTk5N+3bVr17C2tn7mKPLGVlDPZ45d3Ql/dNANaDv4oG5U+wIoJV1Dhxl7uBiVQFAFd37vW6PI1BuFECK35OTalKOu9enp6ZiamnL69GmD9cWKFZM/xgVJ5GnYO0233Pb7ApfE/xN6izUnbmOiVvFjz6qSxAshRCGVnJxMamqqPom/fv0606ZN48KFCwU6iS9SfJtAuVagzYAtXxk7mqeyNDPhp17VMDdRs+VcFEsO3zB2SEIIUajlKJE3MzOjVKlSMld8QabVwJoPdBf0Cu11rwLk5oMkvlituxE07DU/qpVyes4WQgghCqqOHTvyxx9/ABATE0OdOnWYOnUqnTp14tdfC+YAbEVS0HhQqeH8Wri+z9jRPFUFT3s+aVUe0HW1v3o3wcgRCSFE4ZXjwe7GjBnD559/zv379/MiHvGyDv4Gt4+BhQO0LljPymm0CqOWnSA+JYNqpRwZ0qyMsUMSQgjxEo4dO0ajRo0AWLFiBe7u7ly/fp0//viD6dOnGzm6V4ibP1Tvp1ve9AW8/FOTeWZAg9LUL+NMcrqGD5eGkq7RGjskIYQolHKcyM+YMYNdu3ZRvHhxypcvT/Xq1Q1ewogeXIdtE3XLLSeAvadx43nC77uvcjDsPtbmJkzrWRVTkxz/+gkhhChAkpKS9FPSbtq0iS5duqBWq6lbty7Xr183cnSvmKajwdwWbh2FMwV3kEG1WsXUHoE4WJlx4mYsn/19Cq224N54EEKIgirHDyd36tQpD8IQL01RYO2HkJ4E3g2hWt/nb5OPTt+KZeqmCwCMax+At7ONkSMSQgjxssqWLcvq1avp3LkzGzdu5MMPPwTgzp07hXsAucLIzh0aDIftX8OWceDfDkwL5sjwng5W/NAjkHf+PMrfx25iZa5mYsdKMt6SEELkQI4T+a++KrgDqbzSTi6DK1vBxEI31Zy64LR2p6RrGLE0lHSNQnCAO91rljR2SEIIIXLB2LFj6dOnDx9++CGvvfYa9erVA3St8w/nhBf5qN4QODIXYsLh0Gyo/4GxI3qq5hXc+aFHICOWhvLXgXCszU0Z3dpfknkhhMimgpPtiReXGA0hn+mWm35a4Kae+XbDeS7fScDNzoLJXarIRVoIIYqIbt26ER4ezpEjR9i4caN+ffPmzfnxxx+NGNkrytwGXvtCt7xrCiQV7PGMOlYtweTOlQGYvesq07ZcMnJEQghReOQ4kVer1ZiYmDz1JYwgZDQk3wf3SlB/mLGjMbDjwh3m77sGwJTugRSzMTduQEIIIXKVh4cH1apV4/bt29y8eROA2rVr4+/vb+TIXlGBvXX1gZRY2PW9saN5rl61S/FV+4oA/LT1ErN2XjFyREIIUTjkuGv9qlWrDN6np6dz/PhxFixYwPjx43MtMJFNlzbDqWW6aWc6TAcTM2NHpHcvIZWPV5wEoH99H5qUczVyREIIIXKTVqtl0qRJTJ06lYQE3VRidnZ2jBo1ijFjxqAuQI95vTLUJtBiAvzVRde9vvYgKOZr7Kie6a0GpUlK0zBl4wW+3XAeKzMT+tX3MXZYQghRoOU4ke/YsWOmdd26dSMgIIClS5cycODAXAlMZENqgm6AO4C6g6FEDePG8xhFURi98hR341Pxc7Pls9bSMiOEEEXNmDFjmDNnDt9++y0NGjQAYM+ePYwbN46UlBS+/vprI0f4iirbHMo0142ds2U89Fhg7Iiea0izsqSka/h522W+WnMGKzMTetTyMnZYQghRYOXarfK6deuydevW3NqdyI5tkyD2BjiWgmafGzsaA8uO3GDT2SjMTFRM61UVSzN57EIIIYqaBQsW8H//93+8//77VKlShSpVqjB48GB+//135s+fb+zwXm0tJ+p6651dDTcOGTuabBnZohwDG5YG4NOVJ1lz4raRIxJCiIIrxy3yWUlOTmb69OmUKFEiN3YnsuPmETg4S7fcbppugJsC4lp0IuP/PQvARy3LE1DcwcgRCSGEyAv379/P8ll4f39/7t8v2AOtFXnuAVC1Dxz/C5b1BZ9G4FZB93L1B0fvAjXDDYBKpeKLthVITtew6GA4Hy4NxdJUTcsAD2OHJoQQBU6OE3knJyeDUccVRSE+Ph5ra2v++uuvXA1OPEVGGqz5AFB0g9qUbW7siPQyNFpGLA0lKU1DXd9iDGpUsJ/LE0II8eICAwOZMWMG06dPN1g/Y8YMqlSpYqSohF6zL+DiRoiP0I2n8zgza3AtD64VwM3/0b8OXmDE2WVUKhWTOlYiJU3DyuO3GLroOL/3qynj7AghxBNynMj/+OOPBom8Wq3G1dWVOnXq4OTklKvBiafY+xPcOQvWLhD8jbGjMTBj+2VCb8RgZ2nK1B5V/7+9+46Polr/OP7Z3fSQhEBIgwChBUJJkGYAKRIFRISLClwREEVsqIjoT1TAjhVRQVEU9dpAvQpcURQivbfQe0sgBQKkAgnJ7u+PhUAElECS2U2+79frvJjMzsw+M6LHJ3POebCYVWpORKS8evPNN+nRowfz588vrCG/YsUKEhMT+fXXXw2OTvANgUdWQ8JK+/83HN0BR3ZA2i44cxKSNtjbhdx87An+hcl9YCT4hJRZgm82m3jzjmaczi/g180pPPDVWr4c0po2daqWyfeLiDgDk81msxkdhKPJzMzEz8+PjIwMfH19jQ6nqKO7YEo7KMiD2z+DpncYHVGh9QknuHPKCgqsNt7rH02vaE21EBEpKY7aNyUlJTF58mR27NgBQKNGjRg2bBivvPIKn3zyicHRXZ6jPs8yUZAPJ/bDke1nk/uzf6btBuuZS5/j7nc2uW94wRD9RlApsNQS/Lx8Kw9+vY4/dxzB283C10Pb0LymXhqJSPlVnL6p2In8559/TqVKlbjzzjuL7P/hhx84efIkgwcPLn7EDsZhO3erFb64BRJWQP2b4a7vDR3+dqGc3HxueX8JB4+dpFd0KO/1b250SCIi5YrD9k2XsHHjRq677joKCgqMDuWynOl5lpmCM3BsLxzdbn9zf3S7Pck/thdsl/ln6elf9M39uUTfO6BEQjp9poB7v1jD8r3H8PVw4bth12vtHREpt4rTNxV7aP348eP5+OOPL9ofGBjIsGHDykUi77DWfW5P4l29occEh0niAV7+ZRsHj50k1M+Dl3o1MTocERERKS6L69mEvCE0vmB/fi4c22NP6i98i39iP5w6AQnL7e1CXgFFF9c796dXlWKF5OFqYeqglgyatpp1B08w8LPVzBh2PfWDfK79fkVEnFixE/mEhATCw8Mv2l+rVi0SEhJKJCi5hMwkmDfOvh07Dio7Tm3V37emMH1NIiYTvNM3Gj9PV6NDEhERkZLi4m5fBT+ocdH9Z07Z59sXvr0/++eJA3AyDQ4ssbcLVQo+P/++Rkto3OcfV8/3dnfh8yGtGDB1FZsPZzDg01V8/0AMtQMcp2KPiEhZK3YiHxgYyKZNm6hdu3aR/Rs3bqRqVS1CUipsNpjzJORlQY1W0Gqo0REVOpJ5mmf+uwmAYR3qEFNXfwdEREQqBFdPCImytwvl5cDRneff3J97i5+RCNkp9rZvIazCftyNz/3jV/l6uPKfe1vT/5OV7EzNsifzD8ZQvbJnqdyaiIijK3Yi/+9//5vHHnsMHx8fOnToAMCiRYt4/PHH6d+/f4kHKMC2WbDzVzC7wm0fgNlidESAvfTgUz9u4sTJM0SG+DLypgZGhyQiImWgT58+f/t5enp62QQijsnNG6pfZ28Xys2yJ+5HtsOh1bD+P7D0XfvCvdUi/vGy/t5ufDW0Nf0/Xsm+tBwGTF3J9w/EEOjrUUo3IiLiuIqdyL/88sscOHCALl264OJiP91qtTJo0CBee82xSqGVC6dOwK9P2bdvGGmfY+Ygvlp5kEW7juLuYua9/tG4uzjGLxhERKR0+fn9/WJjfn5+DBo0qIyiEafh7mMfTl+jJTS/G7KPwK658MtIuOeXK1r7J9DHg6+HtqHvxys4cOwkAz5dxYwHYqji7VYGNyAi4jiuuvzc7t27iY+Px9PTk6ZNm1KrVq2Sjs0wDrWS7azhsOErCGgADy61z1NzAHuOZNHj/aXk5lt5oWck97S7eN0EEREpOQ7VN5UDep4OID0BJrex17Tv9SE0H3DFpyYcO0nfj1eQknmaxqG+fHv/9VqjR0ScXnH6pr9fXeRv1K9fnzvvvJNbb721XCXxDmXfInsSD/Yh9Q6SxOflW3l8ejy5+VY6NKjG4La1jQ5JREREnE3lmtDpGfv2H89DzrErPrVmVS++HtqGqt5ubE3KZMjnq8nJzS+lQEVEHE+xE/nbb7+dN95446L9b7755kW15a/U5MmTqV27Nh4eHrRp04bVq1df9tgvvvgCk8lUpHl4FJ0bdc8991x0TLdu3a4qNsOcOQX/e9y+3Woo1Lze2Hgu8O78XWxNysTfy5W372iGyYHK4ImIiIgTuf5hCGwMp47DvDHFOrVeYCW+HtoGP09X1iekc9+Xazh95jL17kVEypliJ/KLFy/mlltuuWh/9+7dWbx4cbEDmDFjBiNHjmTcuHGsX7+eqKgounbtypEjRy57jq+vL8nJyYXt4MGDFx3TrVu3Isd89913xY7NUAtft9dn9QmFLuOMjqbQyn3HmLJoLwDj+zTTAjMiIiJy9Syu0HMiYIL4b2D/kn86o4hGIb78597WVHJ3YeW+4zzw1Tpy85XMi0j5V+xEPjs7Gze3ixcUcXV1JTMzs9gBTJgwgfvvv58hQ4YQGRnJlClT8PLyYtq0aZc9x2QyERwcXNiCgoIuOsbd3b3IMf7+/sWOzTDJG2H5B/btWyeAh2PM3cs4dYYnv9+IzQb9WobRrUmw0SGJiIiIswtrDS2H2Ld/eQLyc4t1elRYZabd0woPVzOLdh3lse82kF9gLYVARUQcR7ET+aZNmzJjxoyL9k+fPp3IyMhiXSsvL49169YRGxt7PiCzmdjYWFasWHHZ87Kzs6lVqxZhYWH06tWLrVu3XnTMwoULCQwMJCIigoceeohjxy4/7yo3N5fMzMwizTAF+TD7UbAVQOM+ENHduFj+YtysLRxOP0Wtql6M7Vm8f9YiIiIil9VlHHgHwrHdsOy9Yp/eOrwKUwe1xM1i5vetqTz5w0YKrFe1nrOIiFModvm5MWPG0KdPH/bu3cuNN94IQFxcHN9++y0//vhjsa6VlpZGQUHBRW/Ug4KC2LFjxyXPiYiIYNq0aTRr1oyMjAzefvtt2rZty9atW6lRowZgH1bfp08fwsPD2bt3L88++yzdu3dnxYoVWCwXl0gbP348L774YrFiLzUrJ9vfyHtUhu4Xr0VglFnxh5kZn4TFbOLdftF4uxf7r46IiIjIpXlWhm7j4b/3weK3ocntULVusS5xQ/1qfDjgOh78eh2z4pPwdLUwvk9TreUjIuVSsd/I9+zZk5kzZ7Jnzx4efvhhnnzySQ4fPsyff/5JvXr1SiPGImJiYhg0aBDR0dF07NiRn376iWrVqvHxxx8XHtO/f39uu+02mjZtSu/evfnll19Ys2YNCxcuvOQ1R48eTUZGRmFLTEws9fu4pGN7YcFr9u2ur0GlQGPi+IvD6ad4fuYWAIZ3rsd1NZ1omoKIiIg4hya3Q90boSDXPsT+Kiokx0YGMbF/NGYTTF+TyIv/28ZVVloWEXFoV1V+rkePHixbtoycnBz27dtH3759GTVqFFFRUcW6TkBAABaLhdTU1CL7U1NTCQ6+svnXrq6uNG/enD179lz2mDp16hAQEHDZY9zd3fH19S3SypzNBr+MgPzTEN4Rou8q+xguwWq18eT38WSdzic6rDLDbyz9X9aIiIhIBWQyQY93wMUD9i+CzT9c1WVubRbKm3fY/5/0i+UHeOv3nSUZpYiIQ7jqOvKLFy9m8ODBhIaG8s4773DjjTeycuXKYl3Dzc2NFi1aEBcXV7jParUSFxdHTEzMFV2joKCAzZs3ExISctljDh06xLFjx/72GMPFfwP7F4OLp331VgcZBvbp0n2s3HccLzcL7/aLxtVy1X9lRERERP5elTrQ4Sn79u/PwsnjV3WZO1rU4OVejQH4cOFeJv25u6QiFBFxCMXKylJSUnj99depX78+d955J76+vuTm5jJz5kxef/11WrVqVewARo4cydSpU/nyyy/Zvn07Dz30EDk5OQwZYl+9dNCgQYwePbrw+Jdeeok//viDffv2sX79eu6++24OHjzI0KFDAftCeE899RQrV67kwIEDxMXF0atXL+rVq0fXrl2LHV+ZyEqF35+zb3d+1t6JOYBtSZmFv8Uee2sk4QHeBkckIiIi5V7bx6BaQ8g5CvNfuOrLDIypzbO3NATg7T928emSfSUUoIiI8a44ke/ZsycRERFs2rSJiRMnkpSUxAcffHDNAfTr14+3336bsWPHEh0dTXx8PHPnzi1cAC8hIYHk5OTC40+cOMH9999Po0aNuOWWW8jMzGT58uWFK+ZbLBY2bdrEbbfdRoMGDbjvvvto0aIFS5Yswd3d/ZrjLRVz/w9Op0NIFFz/sNHRAHD6TAEjZmzgTIGNmyKD6NcqzOiQREREpCJwcYNb37Vvr/8SEoo34vNCwzrU5YnYBgC8Mmc736w6WBIRiogYzmS7whVAXFxceOyxx3jooYeoX79+4X5XV1c2btxY7NJzjiwzMxM/Pz8yMjJKf778jl9h+r/BZIFhC+zJvAN48X9b+XzZAQIqufP7iBuoWslBfwkiIlJBlGnfVAHoeTqBWcNhw1cQGAkPLAaL61Vdxmaz8frcHXy8aB8mE7xzZxR9rqtRwsGKiFy74vRNV/xGfunSpWRlZdGiRQvatGnDpEmTSEtLu+ZgK7TTmTDnSft220cdJolfvOsony87AMBbdzZTEi8iIiJl76aXwKsqHNkGy69+FKjJZOKZbg0ZHFMLmw1G/bCRXzcn//OJIiIO7IoT+euvv56pU6eSnJzMAw88wPTp0wkNDcVqtTJv3jyysrJKM87yKe5FyEoC/3Do9IzR0QBwIiePUT9sBGBQTC06RzhGCTwRERGpYLyqwM2v2rcXvQknDlz1pUwmE+N6NubOFjWw2uCx7zbw547Ufz5RRMRBFXsJcm9vb+69916WLl3K5s2befLJJ3n99dcJDAzktttuK40Yy6eElbDmU/v2be+Dq6ex8WAfejb6p80cycqlbjVvRndvZHRIIiIiUpFF9YfaN0D+KZgz6qpqy59jNpt4/fZm9IwKJd9q48Gv17Nsj0aXiohzuqZaYhEREbz55pscOnSI7777rqRiKv/yc2H2o/bt5gMhvIOx8Zz147pDzN2agovZxHv9m+PpZjE6JBEREanITCb7wncWN9gzD7bNvKbLWcwmJvSN4qbIIPLyrQz9ci1rD1xdiTsRESOVSFFwi8VC7969mT17dklcrvxb8g6k7QLvQLj5ZaOjASDh2ElemL0VgJE3N6BJdT+DIxIREREBAupD+5H27d/+D05nXNPlXC1mJt3VnBvqB3DqTAFDPl/DpkPp1x6niEgZKpFEXoohdRssmWDfvuUt8PQ3Nh4gv8DKE9/Hk5NXQOvwKjzQoa7RIYmIiIic1/4JqFoPslMh7tpfgri7WPhkYEtah1chKzefQdNWsyMlswQCFREpG0rky5K1wD6k3noGInpAZC+jIwLgw4V7WXfwBD7uLkzoG4XFbDI6JBEREZHzXD3O15Zf8ykcWnfNl/R0szDtnlZEhVUm/eQZ7v50FXuPZl/zdUVEyoIS+bK05lM4vBbcfaHH2/Z5XwaLT0znvbjdALzcuwk1/L0MjkhERETkEsI7QLP+gA1+eRwK8q/5kpXcXfjPkNY0CvElLTuPAVNXkXj85LXHKiJSypTIl5X0RJj/on079gXwDTU0HICc3HxGTN9AgdVGz6hQekUbH5OIiIjIZXV91T4tMWUzrJpSIpf083Llq/taU7eaNymZp7nr05UkZ5wqkWuLiJQWJfJlwWaDOSPhTA7UjIEWQ4yOCIBX5mznwLGThPh58EqvJpgcYISAiIiIyGV5B8BNL9m3F7xmf1FSAgIqufPt/ddTq6oXicdPccdHK9hy+NoW1RMRKU1K5MvClv/C7j/spVN6vg9m4x/76v3H+W51AiYTvNM3Cj8vV6NDEhEREfln0XfbX4ycyYHfni6xywb5evDN0DbUrurF4fRT3P7Rcv677lCJXV9EpCQZn1GWdznHzncyHZ6Gag2MjeesXzYlAdCneQ3a1g0wOBoRERGRK2Q22xe+M7vAzl9h+y8lduka/l7MGt6eGxsGkptv5ckfNjJ21hby8q0l9h0iIiVBiXxp++M5OHkMAiOh3eNGRwOAzWZj4c6jAHRrEmxwNCIiIiLFFNjo/P9X/fY05GaV2KX9PF35dFBLHu9SH4D/rDjIgE9XciTrdIl9h4jItVIiX5ryciBlC2CC2z4AFzejIwJgf1oOCcdP4mYx07ZuVaPDERERESm+Dk+Bf23IPGyfL1+CzGYTT9zUgE8HtcTH3YU1B05w6/tLWXfwRIl+j4jI1VIiX5rcvGHYAhj4M9RoaXQ0hc69jW8V7o+3u4vB0YiIiIhcBVdP6PGOfXvVFEiKL/GviI0MYvaj7akfWIkjWbn0/2QFX608iM1mK/HvEhEpDiXypc3iCnU7Gx1FEQt2HgGgU4NAgyMRERERuQb1YqHJ7WCzwi8jwFpQ4l8RHuDNzEfa0aNpCGcKbIyZuYWnf9zE6TMl/10iIldKiXwFcyqvgFX7jwPQuWE1g6MRERERuUZdx4O7HyRtgDWflcpXeLu7MOmu5ozu3hCzCX5Yd4g7p6zgcLrqzYuIMZTIVzAr9qWRl2+lemVP6larZHQ4IiIiItfGJwhix9q3416CzKRS+RqTycQDHevyn3vb4O/lyubDGfT8YCnL96SVyveJiPwdJfIVzLn58Z0iqmEymQyORkRERKQEtLgXqreEvCyY+0ypflX7+gHMHt6eJtV9OZ6Tx92freKTxXs1b15EypQS+QrEZrOdnx8fofnxIiIiUk6YzdDzPTBZYNss2PV7qX5dWBUvfnywLbdfVwOrDV77dQfDv9tATm5+qX6viMg5SuQrkH1pOSQeP6WycyIiIlL+BDeBmIft23NG2csAlyIPVwtv39mMl3s1xsVsYs6mZPp8uJwDaaX7vSIioES+Qjk3rL51eBWVnRMREZHyp9No8AuDjARY9Eapf53JZGJgTG2mD7ueaj7u7EzNouekpfy5I7XUv1tEKjYl8hXIwsJh9VqtXkRERMohN2+45W379orJkLq1TL62Ze0q/PJoe1rU8ifrdD73frGWifN3YbVq3ryIlA4l8hXEybx8Vu2zl53T/HgREREptyK6QaOeYM2H/z0OVmuZfG2Qrwff3X89A6+vBcDE+bu5/z9ryTh1pky+X0QqFiXyFcSKvcfIK7BSw9+TutW8jQ5HREREpPR0fxPcfODQGlj/RZl9rZuLmZd7N+GtO5rh5mImbscRek9exq7UrDKLQUQqBiXyFYTKzomIiKNavHgxPXv2JDQ0FJPJxMyZM//xnIULF3Ldddfh7u5OvXr1+OKLL0o9TnEivqFw4/P27fkvQFbZzlm/s2UY/32wLdUre7I/LYfek5cxZ1NymcYgIuWbEvkKoEjZuQYaVi8iIo4lJyeHqKgoJk+efEXH79+/nx49etC5c2fi4+MZMWIEQ4cO5fffS7fkmDiZ1vdDSDSczoDfny3zr29aw4/Zw9vRrl5VTuYV8Mi36xn/63byC8pmqL+IlG9K5CuAvUdzOHTibNm5eio7JyIijqV79+688sor/Otf/7qi46dMmUJ4eDjvvPMOjRo1Yvjw4dxxxx28++67pRypOBWzBXpOBJMZtvwIe+LKPISqldz5ckhrHuhQB4CPF+9j8OerOZ6TV+axiEj5okS+Aji3Wn2bOlXwclPZORERcW4rVqwgNja2yL6uXbuyYsWKy56Tm5tLZmZmkSYVQGhzaP2AfXvOk3DmVJmH4GIxM/qWRky6qzlebhaW7TlGzw+WsvlQRpnHIiLlhxL5CmDRLvv8+I4NVHZOREScX0pKCkFBQUX2BQUFkZmZyalTl07Uxo8fj5+fX2ELCwsri1DFEdz4HPiEwon9sPhtw8K4tVkoPz/cjtpVvTicforbpyznx3WHDItHRJybEvlyLidXZedERERGjx5NRkZGYUtMTDQ6JCkr7j5wy5v27WXvwdGdhoUSEezDrOHt6dIwkLx8K6N+2MiYmVvIy9e8eREpHiXy5dy5snNhVVR2TkREyofg4GBSU4uuQp6amoqvry+enp6XPMfd3R1fX98iTSqQhrdCg+5gPQO/PAE2m2Gh+Hm6MnVQS0bE1gfgq5UH+ffUlaRmnjYsJhFxPkrky7mFu86vVq+ycyIiUh7ExMQQF1d04bJ58+YRExNjUETi8Ewm+1t5Vy84uAzivzE0HLPZxIjYBnw2uCU+Hi6sO3iCWz9YytoDxw2NS0SchxL5csxmsxWpHy8iIuKIsrOziY+PJz4+HrCXl4uPjychIQGwD4sfNGhQ4fEPPvgg+/bt4+mnn2bHjh18+OGHfP/99zzxxBNGhC/OonJN6DTavv3H85CTZmw8QJdGQcwe3p4GQZU4mpVL/09W8p8VB7AZOGJARJyDEvlybO/R7MKyczF1VXZOREQc09q1a2nevDnNmzcHYOTIkTRv3pyxY8cCkJycXJjUA4SHhzNnzhzmzZtHVFQU77zzDp9++ildu3Y1JH5xItc/BEFN4dQJ+GOM0dEAEB7gzc8Pt6NHsxDyrTbGztrKqB82cfpMgdGhiYgDc4hEfvLkydSuXRsPDw/atGnD6tWrL3vsF198gclkKtI8PDyKHGOz2Rg7diwhISF4enoSGxvL7t27S/s2HM65t/EqOyciIo6sU6dO2Gy2i9oXX3wB2Pv+hQsXXnTOhg0byM3NZe/evdxzzz1lHrc4IYurvbY8Jtj4LexfYnREAHi7uzDp38159paGmE3w3/WHuGPKcg6dOGl0aCLioAxP5GfMmMHIkSMZN24c69evJyoqiq5du3LkyJHLnuPr60tycnJhO3jwYJHP33zzTd5//32mTJnCqlWr8Pb2pmvXrpw+XbEWETk/rF6r1YuIiIgAUKMltLzXvv3LCMjPNTScc0wmE8M61OWr+9rg7+XKlsOZ9PxgKUt3Gz8FQEQcj+GJ/IQJE7j//vsZMmQIkZGRTJkyBS8vL6ZNm3bZc0wmE8HBwYXtwlqyNpuNiRMn8vzzz9OrVy+aNWvGf/7zH5KSkpg5c2YZ3JFjyMnNZ/X+c2XnND9eREREpFCXsVApCI7tgaUTjY6miHb1Avjfo+1pWt2PEyfPMGjaKqYs2qt58yJShKGJfF5eHuvWrSM2NrZwn9lsJjY2lhUrVlz2vOzsbGrVqkVYWBi9evVi69athZ/t37+flJSUItf08/OjTZs2l71mbm4umZmZRZqzW35B2bk6ASo7JyIiIlLIszJ0G2/fXvIOpO0xNJy/quHvxQ8PxnBHixpYbfD6bzsY/u0GcnLzjQ5NRByEoYl8WloaBQUFRd6oAwQFBZGSknLJcyIiIpg2bRqzZs3i66+/xmq10rZtWw4dOgRQeF5xrjl+/Hj8/PwKW1hY2LXemuEW7rRPTegcobJzIiIiIhdp3AfqdoGCXJhjbG35S/FwtfDWHc14uXcTXC0m5mxOpvfkZWxPdv4XTiJy7QwfWl9cMTExDBo0iOjoaDp27MhPP/1EtWrV+Pjjj6/6mqNHjyYjI6OwJSYmlmDEZU9l50RERET+gckEPd4BFw/Yvxg2fW90RBcxmUwMvL4W04ddT6CPO7uPZNNr0jI+XrSXAqtj/eJBRMqWoYl8QEAAFouF1NTUIvtTU1MJDg6+omu4urrSvHlz9uyxD4k6d15xrunu7o6vr2+R5sz2Hs3mcPop3FzMxNQJMDocEREREcdUJRw6Pm3f/v1ZOHnc2Hguo0WtKsx57AZiGwWSV2Bl/G87+PfUlSQe16r2IhWVoYm8m5sbLVq0IC4urnCf1WolLi6OmJiYK7pGQUEBmzdvJiQkBLDXlg0ODi5yzczMTFatWnXF13R2C3acLTsXXgVPN4vB0YiIiIg4sJhHoVpDOJkG88cZHc1lVfNxZ+qglrzepylebhZW7z9O9/eW8MPaRC2EJ1IBGT60fuTIkUydOpUvv/yS7du389BDD5GTk8OQIUMAGDRoEKNHjy48/qWXXuKPP/5g3759rF+/nrvvvpuDBw8ydOhQwD4EacSIEbzyyivMnj2bzZs3M2jQIEJDQ+ndu7cRt1jmFu46Pz9eRERERP6GixvcOtG+vf4/cPDyCy4bzWQy0b91TX57/AZa1PInOzefp37cxANfreNYtmOU0RORsuFidAD9+vXj6NGjjB07lpSUFKKjo5k7d27hYnUJCQmYzed/33DixAnuv/9+UlJS8Pf3p0WLFixfvpzIyMjCY55++mlycnIYNmwY6enptG/fnrlz5+Lh4VHm91fWcnLzWbP/BKD58SIiIiJXpFYMXDfInsj/8gQ8sNie4DuoWlW9+f6BGD5evJd35+3ij22prE84wRu3N6NLo6B/voCIOD2TTWNxLpKZmYmfnx8ZGRlON19+3rZU7v/PWmpW8WLRU520Yr2ISDnhzH2TI9LzlIucPA6TWtmH2EfdBdcNhOotHTqhB9ialMETM+LZlZoNQP9WYTx/aySV3A1/XycixVScvsnwofVSshacLTvXKaKakngRERGRK+VVBbq+Zt/e+C183h3eqA1f3w7L3ofkjWC1GhripTQO9WP28PYMbR+OyQTT1yRyy3tLWHvAMRfuE5GSoV/VlSM2m41FZ8vOaX68iIiISDE16wsu7rBtlr0k3ck02DPf3gA8/aH2DVCnI4R3gqp17WXsDObhauH5WyPp0iiIUT9sJOH4Sfp+vIIHO9ZlRGwD3Fz07k6kvNHQ+ktw1uF2u1OzuOndxbi5mNk49matWC8iUo44a9/kqPQ85R9ZrXBkG+xfBPsWwcFlkJdd9Bjf6hDe8Wxi3xF8Q4yJ9QKZp8/wwuyt/LT+MACRIb682y+aiGAfgyMTkX9SnL5Jb+TLkXPD6q+vU1VJvIiIiMi1MJshuIm9xTwCBWcgaYM9qd+/CBJXQeZh+zD8jd/azwloAOEd7El9+A32N/hlzNfDlQl9o7mpURDP/ryZbcmZ9Jy0lKe7RnBvu3DMZuNHEIjItVMiX44sPDusvlMDrVYvIiIiUqIsrhDW2t46PgV5JyFx5fnEPike0nbZ25pPAROERJ1/W18zBty8yizc7k1DaFHLn//77yYW7DzKK3O2E7f9CG/3jaJ6Zc8yi0NESoeG1l+CMw63y87Np/lLf3CmwMaCUZ0ID/A2OiQRESlBztg3OTI9Tylxp07AgaVnE/vFkLaz6OcWN6jR2v7Gvk5HqN7C/suBUmaz2fhudSIv/7KNU2cK8HF34YXbGtPnuupaGFnEwWhofQW0fE8aZwps1KrqpSReREREpKx5+kOjnvYGkJlsT+jPzbHPPAQHl9rbwtfArRLUant+jn1gY/tw/hJmMpm4q01N2tatyhPfx7MhIZ0nf9jI/O2pvPqvplTxduzyeiJyaUrky4kFGlYvIiIi4jh8QyCqn73ZbHB8H+xbaE/s9y+BU8dh9x/2BuBV9fz8+jodwT+8RFfErx3gzQ8PxDBl0V4mzt/Nb1tSWHvwBG/e0UzVjkSckBL5csBedu5s/fiG+g+xiIiIiEMxmeyl6qrWhVb32VfET91sf2O/bxEcXA4nj8HWn+0NwC/sghXxO4BP8DWH4WIxM/zG+nSKCGTEjHj2HMlmyOdruKtNTZ67pRHe7koNRJyF5shfgrPNm9uVmsXN7y7G3cXMxnE34+GqFetFRMobZ+ubHJ2epziU/Dw4vO78MPxDa8B6pugx1Rraa9jXaAU1WkKVOtf0xv70mQLenLuTacv2A1C7qhcT+kVzXc2yX2lfROw0R76CWXhB2Tkl8SIiIiJOxsUNasXYW6dnIC8HDq44Owx/ESRvgqM77G3NVPs5nv72BfOqtzz7ZwvwrnrFX+nhamFsz0i6NApk1A8bOXDsJHd8tJxHOtfjsS71cbWU/Hx9ESk5SuTLgQU7zs6Pj9D8eBERERGn5+YN9WPtDeDkcTiwxJ7cH15rT+xPnYA98+3tHP9w+9v6c8l9SDNwcf/br2pXL4C5Izrwwuyt/LzhMB/8uYcFO4/wbt9o6gf5lOJNisi10ND6S3Cm4XYXlp1bOKoTtbVivYhIueRMfZMz0PMUp5afB6lb7MPxD621J/fH9lx8nNkVgpsWTe6r1r3skPxfNiXx3M9byDh1BjcXM890a8g9bWtjNqtMnUhZ0ND6CmTZ2bJztat6KYkXERERqQhc3KD6dfbW+n77vlMn4PD6osn9yWOQtN7e+MR+nEdle0J/YXJ/dkj+rc1CaVW7Ck/9uInFu47y0i/biNuRylt3RBFa2dOQWxWRS1Mi7+QWnis7p7IhIiIiIhWXpz/U62JvYC95d+KAPbE/l9wnb4TT6bA3zt7O8a9tT+prtCSoeku+HNiUr9cd4dU521i25xhdJy7m5V5N6BUdiqkES+KJyNVTIu/EbDZb4UJ3HTU/XkRERETOMZmgSri9Nb3Dvi8/D45sPfvG/mxyf2y3PeE/cQC2/Gg/1ezKwOAm3BYdxbQDVZmdFsqIGWeYtz2VV3s3obKXm2G3JSJ2SuSd2K7UbJIzTuPuYiamzpWvUioiIiIiFZCLG4Q2tzcuGJKftAEOrbMPxz+0Fk6mQdIG/JI28ATwhDuk27zZtL0OP+6JoM0NXWna+kbwDjDybkQqNCXyTuzc2/iYuio7JyIiIiJXwdMf6t5ob2Afkp9+8Owb+7PJffJGKufn0MGymQ7WzbDoR1gE1sq1MddoYa9tH9kbfEMMvRWRikSJvBMrnB/fQMPqRURERKQEmEz2OfP+taHJ7fZ9BWcgdQt5B9ewbc0CKqXFU8+chDn9AKQfgC3/hbiXoO1j0O4xe/k8ESlVSuSdVNbpM6w5cBzQQnciIiIiUoosrhDaHLfQ5kTHDGPJ7qM88P1yQnK209y8l7sqbyMkewsseh3WfQFdxkDUv8GsEaMipcVsdABydZbtOUa+1UZ4gLfKzomIiIhImbmhfjX++0R3/Jt25YP83sSkjWaM29NkeVaH7BSY9Qh80hH2LTI6VJFyS4m8k1q06+xq9RpWLyIiIiJlrLKXGx/8uznv/7s5Vbzd+SozmhYnXmOieTC5LpUgZTP85zb4tj+k7TY6XJFyR4m8E7LZbCzYca5+vBJ5ERERETHGbVGhLP2/zozrGUmAnw8TT3bl+uy3+ZZuWLHArt/gw+vh16cg55jR4YqUG0rkndDO1CxSMu1l565X2TkRERERMZCXmwtD2oWz8KnOvHlHM/yrhfDs6UHE5r5BnK0FWPNh9SfwfnNY9j7k5xodsojTUyLvhM6tVt9WZedERERExEG4uZjp2zKMeU905KMB1+Ed2oj7cp/k33nPsc1aC3IzYN4YmNQKtv5sL3UnIldFibwTOlc/XqvVi4iIiIijsZhNdG8awuzh7fjqvtbYat/ArXmvMurMA6TaKtvr1P9wD0zrCofWGh2uiFNSIu9ksk6fYe2BE4Dmx4uIiIiI4zKZTNxQvxrTh8Xw48PtSW/Ql065E3j3zO2ctLlD4ir4tAu2H++D9ASjwxVxKkrkncyyPWmFZedqVVXZORERERFxfNfV9OfTwS2ZOeJmEpo9RpczE/g+vyNWmwnTlh8peL8FtnkvwOlMo0MVcQpK5J3MufnxehsvIiIiIs4mItiHd/tF8/2oPmxu+Rr/KhjPsoLGWKx5mJa9S+6EKApWfwoF+UaHKuLQlMg7EZvNdkEir/nxIiIiIuKcwqp48XLvJkz9vyEsifmMR2xPs9cagnvecSy/Pkn6hFbkbZ+rBfFELkOJvBPZkWIvO+fhaqZNeBWjwxERERERuSaBPh48c0sjXnvmaf7o+DNvmO7juK0SlXP24TajH4nvdyMnYaPRYYo4HCXyTuTc2/iYOio7JyIiIiLlh5+nKw91acRjo99i7o2/8a2lF7k2F8JOrMRjWkc2fjiIY6laEE/kHCXyTuRc2bnODTWsXkRERETKH083C3d1bMadz37Bn11+YZFLOyzYiDoyC48PWxH38VMkHT1mdJgihlMi7yQyT59h3cGzZecaKJEXERERkfLL1WKme4cYbnh2Dis7fctOlwi8TafpkvwJpkkt+eaTt9iTmmF0mCKGUSLvJJbttpedqxPgTc2qXkaHIyIiIiJS6sxmE9d36kGDZ1eys/1EjlqCCDEdZ0DSK+RM7sTbn0xjY2K60WGKlDkl8k7i3Pz4jio7JyIiIiIVjMlsJiJ2CNWe2URSy2c4ZfIiyryPUUlPkPzJHTw55SeW70nDplXupYJwiER+8uTJ1K5dGw8PD9q0acPq1auv6Lzp06djMpno3bt3kf333HMPJpOpSOvWrVspRF42bDYbi3bZE/nOKjsnIiIiIhWVqweht47G88lNpDcehBUz3SxrGJ88lO1fDOfuSb/z+9YUrFYl9FK+GZ7Iz5gxg5EjRzJu3DjWr19PVFQUXbt25ciRI3973oEDBxg1ahQ33HDDJT/v1q0bycnJhe27774rjfDLxLmyc56uFlqr7JyIiIiIVHSVqlH5zg8wP7ycU7VuxM1UwH0uvzE57V5WffsKPd6N47/rDnGmwGp0pCKlwvBEfsKECdx///0MGTKEyMhIpkyZgpeXF9OmTbvsOQUFBQwYMIAXX3yROnXqXPIYd3d3goODC5u/v39p3UKpW3B2tfqYuio7JyIiIiJSKLARnkN+hrt/Ij+gEZVNOYx1/YoPMx7m9/9+Sue3FvD92kTyldBLOWNoIp+Xl8e6deuIjY0t3Gc2m4mNjWXFihWXPe+ll14iMDCQ++6777LHLFy4kMDAQCIiInjooYc4duzyZSpyc3PJzMws0hzJufnxnTU/XkRERETkYvW64PLwMuj5PlbvQMLNqXzi9i6TTo7iwM8v8/DbnzNrQyIFGnIv5YSLkV+elpZGQUEBQUFBRfYHBQWxY8eOS56zdOlSPvvsM+Lj4y973W7dutGnTx/Cw8PZu3cvzz77LN27d2fFihVYLBe/0R4/fjwvvvjiNd1LaSlSdk7z40VERERELs1sgRaDMTfpA8vew7b8A6Lz9xFt3genZnB0pi9//tqCwOhbaNrhX5h99JJMnJehiXxxZWVlMXDgQKZOnUpAQMBlj+vfv3/hdtOmTWnWrBl169Zl4cKFdOnS5aLjR48ezciRIwt/zszMJCwsrGSDv0pLd6dRYLVRp5o3YVVUdk5ERERE5G+5+8CNz2NqNRR2/EL+rnnY9i6imjWTm84sgDULsK55mkz/xvg06Yap/k1QvSVYnCo1kgrO0L+tAQEBWCwWUlNTi+xPTU0lODj4ouP37t3LgQMH6NmzZ+E+q9U+38XFxYWdO3dSt27di86rU6cOAQEB7Nmz55KJvLu7O+7u7td6O6Vi4dn58Z0a6G28iIiIiMgV8wmGVkNxaTUU8vPI3ruMbYt/wvfQIhqaDuJ7Ygss2QJL3sbm7oupTieoFwv1uoBfDaOjF/lbhibybm5utGjRgri4uMISclarlbi4OIYPH37R8Q0bNmTz5s1F9j3//PNkZWXx3nvvXfYt+qFDhzh27BghISElfg+lqUjZuYYa+iMiIiIiclVc3KgU0ZnWEZ05kZPHB/NXc3jdHNraNnCDeTP+uZmwfba9AVRreD6pr9kWXD2MjV/kLwwfPzJy5EgGDx5My5Ytad26NRMnTiQnJ4chQ4YAMGjQIKpXr8748ePx8PCgSZMmRc6vXLkyQOH+7OxsXnzxRW6//XaCg4PZu3cvTz/9NPXq1aNr165lem/XantyFqmZuSo7JyIiIiJSQvy93Xi0V3uO3tiKjxbu5elV+2lYsIcO5k3c6r2N+md2YDq6A47ugBWTwMUTarc/m9jHQtW6YDIZfRtSwRmeyPfr14+jR48yduxYUlJSiI6OZu7cuYUL4CUkJGA2X/ni+haLhU2bNvHll1+Snp5OaGgoN998My+//LLDDp+/nHNl59rWrYq7i8rOiYiIiIiUlGo+7oztGcmwDnWYtKAmH62pz/uZffAjmwfCEvm3/y78kxdDVjLsmWdvAJVrnk/qwzvY5+Q7G5sNTqdDeiJkJJ7/MyMRzpwGiyu4uIPFHVzcwHK2ndtX+Pml9l3pOW5gdtEvRa6SyWazqQbDX2RmZuLn50dGRga+vr6GxdF3ygpWHzjOy70aMzCmtmFxiIiI8Rylbyov9DxF5K8Sj5/kgz9389/1hwvL1HWLDOL/WlgJT18Je+ZDwgooyDt/ktkFwq63D8GvFwvBTR0jMbVaIecopCdARsLFCXt6IuRlGR0lYPr7RP/CbVdP8KwCXuda1YubR2UoxktgR1OcvkmJ/CU4QueeceoM1708jwKrjSVPd9aK9SIiFZwj9E3liZ6niFzO/rQc3pu/i1kbk7DZ7Hl5z2ahjIitTx1f4MBS2BtnT+yP7yt6cqUgqHujPamv0xm8q5ZOkAVnIDPpL8l5wgU/H4KC3H++jldV8AuDymHgV9P+p1sl+7n5efY/C85Afu4F+862c/sKP887/+dF22evlZ8LlGL6aTKDp//5xN7zb5L+c/s9/Bzjly8okb9mjtC5/7o5mYe/WU/dat7EPdnJkBhERMRxOELfVJ7oeYrIP9mVmsXE+bv4dXMKAGYT9LmuBo93qX/+JduxvbD3T9gTB/sXw5mcC65ggurXQd2zb+urt7jyEnd5J+3J+OXepmclgc3699cwmcEn5IJE/S8Ju18NcPMu/oO5FjYbWAvOJ/WXSvQLzhT9RcK5fWdOwqnjcPI4nDx2th0//2duxtXFZLL8Jdn/m8Tf8+xn7j6lkvwrkb9GjtC5P/XDRn5Yd4j72ocz5tZIQ2IQERHH4Qh9U2maPHkyb731FikpKURFRfHBBx/QunXryx4/ceJEPvroIxISEggICOCOO+4oXBj3SpT35ykiJWdrUgbvztvF/O329atczCb6tgrj0RvrEeLnef7A/FxIODsEf++fkLql6IU8/OBcibva7SE3+/Jv1E+m/XNgFjd7Mn5Rgn72Z9/q9uHqFUV+Hpw6cUGS/5dk/9Txi/fnZV/dd5ldiyb4/rWh16RrvoXi9E2GL3YnF7uw7FynCJWdExGR8m3GjBmMHDmSKVOm0KZNGyZOnEjXrl3ZuXMngYGBFx3/7bff8swzzzBt2jTatm3Lrl27uOeeezCZTEyYMMGAOxCR8qxxqB+fDm5FfGI67/yxkyW70/h2VQI/rjvEXa1r8nDnugT6eNjnctfpaG+8bB/6vvfPs4n9Avvicttm2duVcPP5y5v0vyTs3oFOPR+8xLm4gU+QvV2pM6cvneCf/Ou+c/vTIP80WM9Adoq9AWSllM49/Q29kb8Eo39LvzUpgx7vL8XT1UL8uJu0Yr2IiBjeN5WmNm3a0KpVKyZNsr/NsFqthIWF8eijj/LMM89cdPzw4cPZvn07cXFxhfuefPJJVq1axdKlS6/oO8vz8xSR0rV6/3He+WMnq/YfB8DD1czgmNo80LEuVbzdLn2StQAOrz+b1MfB4XX2udx+YfZV8CvXvDhh96jsMHO35QJ5J88n9+eG+pst0Phf13xpvZF3cgt32t/Gt6unsnMiIlK+5eXlsW7dOkaPHl24z2w2Exsby4oVKy55Ttu2bfn6669ZvXo1rVu3Zt++ffz6668MHDjwst+Tm5tLbu75hZ8yMzNL7iZEpEJpHV6F6cOuZ/neY7z9x042JKTz8eJ9fL3yIPe2D2foDXXw8/zLkHazBcJa2Vvn0fZV5fU23Tm5edlb5TBDw1Ai74AWnq0f3zHi4uGEIiIi5UlaWhoFBQUEBRUdChkUFMSOHTsuec5dd91FWloa7du3x2azkZ+fz4MPPsizzz572e8ZP348L774YonGLiIVl8lkol29ANrWrcrCnUd5+4+dbE3K5IM/9/DF8gMMu6EOQ9qHU8n9MumWkni5Rvob5GAyTp1hfUI6AJ0aaH68iIjIXy1cuJDXXnuNDz/8kPXr1/PTTz8xZ84cXn755cueM3r0aDIyMgpbYmJiGUYsIuWVyWSic8NAfnm0PVPubkFEkA9Zp/N5Z94ubnjjTz5etJdTeQVGhynlkN7IO5ilu9MosNqoF1hJteNFRKTcCwgIwGKxkJqaWmR/amoqwcHBlzxnzJgxDBw4kKFDhwLQtGlTcnJyGDZsGM899xzmS7zpcnd3x93dveRvQEQEe0LfrUkwN0cG8cvmZCbO28W+tBzG/7aDqUv280jnuvy7dU08XDVtVkqG3sg7mHPD6vU2XkREKgI3NzdatGhRZOE6q9VKXFwcMTExlzzn5MmTFyXrFov9f461hq+IGMlsNnFbVCh/PNGBt++MIqyKJ2nZubz4v210fnsh36w6SF7+P9R/F7kCSuQdiNVqY2Fh2TnNjxcRkYph5MiRTJ06lS+//JLt27fz0EMPkZOTw5AhQwAYNGhQkcXwevbsyUcffcT06dPZv38/8+bNY8yYMfTs2bMwoRcRMZKLxcwdLWrw55OdeO1fTQnx8yA54zTP/byFG99ZyA9rEzlToIRerp6G1juQbcmZHM3KxcvNQqtwf6PDERERKRP9+vXj6NGjjB07lpSUFKKjo5k7d27hAngJCQlF3sA///zzmEwmnn/+eQ4fPky1atXo2bMnr776qlG3ICJySa4WM3e1qUmf66ozfXUCkxbs5dCJUzz14ybemLuD26+rQd9WYdStVsnoUMXJqI78JRhVW3bygj289ftOYhsF8englmX2vSIi4vhU97xk6XmKiBFO5RXw1coDTF2yn6NZ50titq5dhX6twrilaQiebhpZVFGpjryTWrDj7Pz4CM2PFxEREREpbzzdLAzrUJd724WzYOdRZqxJ4M8dR1h94DirDxznhdlb6dU8lP6tatKkup/R4YoDUyLvIDJOnmF9wglAibyIiIiISHnmYjFzU2QQN0UGkZJxmv+uP8SMNYkkHD/J1ysT+HplApEhvvRvHUavqOr4ebkaHbI4GC125yCW7DmK1Qb1AitRw19l50REREREKoJgPw8e6VyPhaM68e3QNtwWFYqbxcy25EzGztpK69fm88SMeFbuO6bKHFJIb+QdxMKd9tXqO+ttvIiIiIhIhWM2m2hbL4C29QI4kZPHzPjDzFiTyI6ULH7ecJifNxwmPMCbvi3DuL1FdQJ9PIwOWQykRN4BWK22wkReZedERERERCo2f283hrQL5562tdl0KIPpaxKZHX+Y/Wk5vDF3B2//sZMuDQPp3zqMDvWr4WLRQOuKRom8A9iWnElatr3sXMvaKjsnIiIiIiJgMpmICqtMVFhlnu/RiDmbk5mxJpF1B0/wx7ZU/tiWSpCvO3e2CKNvyzBqVtUU3YpCibwDWLjTvlp927oBuLuo3ISIiIiIiBTl7e5C35b2hH13ahYz1iTy04bDpGbmMmnBHiYt2EO7elXp16omN0cG4eGqvKI8UyLvAArnxzfU/HgREREREfl79YN8eP7WSJ7qFsH8bUeYviaBpXvSWLbnGMv2HKOylyu9o6vTv3UYDYP/vh65OCcl8gZLP5l3Qdk5zY8XEREREZEr4+5ioUezEHo0C+HQiZP8sPYQP6xNJCnjNF8sP8AXyw8QFVaZ/q3C6BkVSiV3pX/lhf5JGmzJ7jSsNqgfWInqlT2NDkdERERERJxQDX8vnripAY91qc+S3UeZsSaRedtS2ZiYzsbEdF7+ZRu3NguhX6uaXFezMiaTyeiQ5RookTfY+WH1ehsvIiIiIiLXxmI20SkikE4RgaRl5/LT+kNMX5PIvqM5fL/2EN+vPUT9wEr0axVGn+tqUMXbzeiQ5SookTeQ1Wpj0a6zZecaaH68iIiIiIiUnIBK7gzrUJf7b6jD2oMnmL46kTmbk9h9JJtX5mznjbk7uDkymH6twmhfLwCzWW/pnYUSeQNtTbKXnfN2s9CydhWjwxERERERkXLIZDLRqnYVWtWuwrjbIvnfxiRmrElk06EM5mxOZs7mZKpX9qRvyzDuaFlDU36dgBJ5AxWWnasXgJuL2eBoRERERESkvPP1cGVAm1oMaFOLrUkZfL8mkZ83HOZw+inenb+Ld+fvonV4FXpFh9KjaQiVvTT03hEpkTfQwrPD6jtrtXoRERERESljjUP9eLGXH6NvacTcLSlMX5PAyn3HWb3f3l6YvZWODapxW3R1bmoUhKebatM7CiXyBkk/mceGwrJzmh8vIiIiIiLG8HC10Lt5dXo3r05S+in+tzGJWfFJbEvOZP72I8zffgQvNwtdGwfTKzqU9vUCcLFoRLGRlMgbZPHZsnMNgioRqjkoIiIiIiLiAEIre/JAx7o80LEuu1OzmBWfxKyNh0k8foqfNxzm5w2Hqertxq3NQrgturpK2RlEibxBzs2P76Rh9SIiIiIi4oDqB/kwqmsET97cgPUJ6cyKP8wvm5I5lpPHlysO8uWKg4RV8aRXVHV6Nw+lXqCP0SFXGErkDWC12lh8ruychtWLiIiIiIgDM5lMtKjlT4ta/oy5NZKle9KYHZ/E71tTSDx+ikkL9jBpwR4iQ3zp3TyUnlGhhPhp1HFpUiJvgC1JGaRl59nLztVS2TkREREREXEOrhYznSMC6RwRyMm8fOZvP8KsDYdZtOso25Iz2ZacyfjfdtAmvAq9oqtzS5MQ/LxcjQ673FEib4CFO+1v49up7JyIiIiIiDgpLzcXbosK5baoUE7k5DFnczKz45NYfeA4K/fZ29hZW+gUEUjv6Op0aRSIh6tWvi8JSuQNoPnxIiIiIiJSnvh7u3H39bW4+/paHDpxkv9tTGZW/GF2pGQxb1sq87alUsndpXDl+7Z1q2rl+2vgEE9u8uTJ1K5dGw8PD9q0acPq1auv6Lzp06djMpno3bt3kf02m42xY8cSEhKCp6cnsbGx7N69uxQiL74TOXnEJ6YDmh8vIiIiIiLlTw1/Lx7qVJe5Izowd8QNPNSpLtUre5Kdm89/1x9i0LTVXD/+T16YvZUNCSew2WxGh+x0DE/kZ8yYwciRIxk3bhzr168nKiqKrl27cuTIkb8978CBA4waNYobbrjhos/efPNN3n//faZMmcKqVavw9vama9eunD59urRu44ot3n0Uqw0ignxUdk5ERERERMq1hsG+/F+3hix5ujM/PBjD3dfXxN/LlbTsXL5YfoB/fbiczm8vZMK8Xew9mm10uE7DZDP41x9t2rShVatWTJo0CQCr1UpYWBiPPvoozzzzzCXPKSgooEOHDtx7770sWbKE9PR0Zs6cCdjfxoeGhvLkk08yatQoADIyMggKCuKLL76gf//+/xhTZmYmfn5+ZGRk4OvrWzI3etbIGfH8tOEwD3Sow+hbGpXotUVEpPwqzb6pItLzFBExTl6+laV7jjJzQxLztqVy6kxB4WdNq/vRKzqUW5uFEuznYWCUZa84fZOhb+Tz8vJYt24dsbGxhfvMZjOxsbGsWLHisue99NJLBAYGct9991302f79+0lJSSlyTT8/P9q0aXPZa+bm5pKZmVmklQar1cais2XnOmpYvYiIiIiIVEBuLmZubBjE+/9uztrnY5nYL5rOEdWwmE1sPpzBK3O2E/N6HHdNXcmMNQlknDpjdMgOx9DF7tLS0igoKCAoKKjI/qCgIHbs2HHJc5YuXcpnn31GfHz8JT9PSUkpvMZfr3nus78aP348L774YjGjL74tSRkcy8mjkruLys6JiIiIiEiF5+3uQu/m1endvDrHsnP5dXMyM+OTWHfwBMv3HmP53mOMmbmVzg2r0bVxMDc2DKSyl5vRYRvOqVatz8rKYuDAgUydOpWAgIASu+7o0aMZOXJk4c+ZmZmEhYWV2PXPWbDjXNm5qio7JyIiIiIicoGqldwZGFObgTG1STx+ktkbk5i54TC7j2Tz+9ZUft+aisVsolVtf26KDObmyCDCqngZHbYhDE3kAwICsFgspKamFtmfmppKcHDwRcfv3buXAwcO0LNnz8J9VqsVABcXF3bu3Fl4XmpqKiEhIUWuGR0dfck43N3dcXd3v9bb+UcLd6nsnIiIiIiIyD8Jq+LFI53r8XCnumxPzuLXzcnM25bKztSswhr1L/+yjYbBPsQ2CuKmyCCaVvfDbDYZHXqZMDSRd3Nzo0WLFsTFxRWWkLNarcTFxTF8+PCLjm/YsCGbN28usu/5558nKyuL9957j7CwMFxdXQkODiYuLq4wcc/MzGTVqlU89NBDpX1Ll6WycyIiIiIiIsVjMpmIDPUlMtSXUV0jSDh2kj+2pTBvWyprDhxnR0oWO1KymLRgD0G+7oVJfUzdqri7WIwOv9QYPrR+5MiRDB48mJYtW9K6dWsmTpxITk4OQ4YMAWDQoEFUr16d8ePH4+HhQZMmTYqcX7lyZYAi+0eMGMErr7xC/fr1CQ8PZ8yYMYSGhl5Ub74sLd59FJsNGgb7EOKnsnMiIiIiIiLFVbOqF0NvqMPQG+pwIiePBTuPMG9bKot2HSU1M5dvViXwzaoEKrm70LFBNWIjA7kxIgg/L1ejQy9Rhify/fr14+jRo4wdO5aUlBSio6OZO3du4WJ1CQkJmM3Fm0/+9NNPk5OTw7Bhw0hPT6d9+/bMnTsXDw/jyhcs3KnV6kVEREREREqKv7cbfa6rQZ/ranD6TAEr9h5j3vZU5m9L5UhWLnM2JzNnczIWs4nWtatwU6T9bX15mFdveB15R1TStWWtVhutXp3PsZw8vrv/emLqVi2BKEVEpCJR3fOSpecpIlJ+Wa02Nh3OYN7ZIfi7UrOLfN4w2IebI4O4KTKYJtV9MZkcY159cfomw9/IVwSbD19Qdq62v9HhiIiIiIiIlFtms4nosMpEh1Xmqa4NOXgsh3nbUi+aV//+n3sI9vUgNjKQmyKDianjPNXFlMiXgXPD6tvXC8DV4hx/MURERERERMqDWlW9i8yr/3OHfV794t1HSck8zdcrE/h65dl59RHVuDkyiE4Rgfh5Ou68eiXyZWDBznNl5zQ/XkRERERExCj+3m7c3qIGt7c4P6/+j22pzN+eytGsXOZsSmbOpmRczCZah5+fV1/D37Hm1SuRL2XHc/LYeCgd0EJ3IiIiIiIijsLD1ULnhoF0bhjIq9YmbDyUzvztqYXz6pfvPcbyvcd48X/baBTiy02RQdwcGUTjUOPn1SuRL2VLVHZORERERETEoZnNJprX9Kd5Tf8i8+r/2JbK2gPH2Z6cyfbkTN6P202In0dhvfrrDZpXr0S+lC3YcW5YfaDBkYiIiIiIiMiVuHBe/fGcPBZcMK8+OeM0X608yFcrD+Jzdl79hL7RZZrQK5EvRVarjSW70wDNjxcREREREXFGVf4yr3753jTmbUtl/vYjHM3KZc+R7DJ/K69EvhSZzSZ+e/wGFu46SotaKjsnIiIiIiLizDxcLdzYMIgbGwbxqtXGxkPp5OQWlHkcSuRLWaCvB31bhhkdhoiIiIiIiJSgc/PqDfluQ75VRERERERERK6KEnkRERERERERJ6JEXkRERERERMSJKJEXERERERERcSJK5EVERERERESciBJ5ERERERERESeiRF5ERERERETEiSiRFxEREREREXEiSuRFREREREREnIgSeREREREREREnokReRERERERExIkokRcRERERERFxIkrkRURERERERJyIEnkRERERERERJ+JidACOyGazAZCZmWlwJCIiInbn+qRzfZRcG/X1IiLiaIrT1yuRv4SsrCwAwsLCDI5ERESkqKysLPz8/IwOw+mprxcREUd1JX29yaZf7V/EarWSlJSEj48PJpPpmq6VmZlJWFgYiYmJ+Pr6llCE5ZueWfHpmRWfnlnx6ZkVX0k+M5vNRlZWFqGhoZjNmhl3rUqyrwf9+1Fcel7Fp2dWfHpmxadnVnxG9fV6I38JZrOZGjVqlOg1fX199S9DMemZFZ+eWfHpmRWfnlnxldQz05v4klMafT3o34/i0vMqPj2z4tMzKz49s+Ir675ev9IXERERERERcSJK5EVERERERESciBL5Uubu7s64ceNwd3c3OhSnoWdWfHpmxadnVnx6ZsWnZ1Zx6J918eh5FZ+eWfHpmRWfnlnxGfXMtNidiIiIiIiIiBPRG3kRERERERERJ6JEXkRERERERMSJKJEXERERERERcSJK5EVERERERESciBL5UjZ58mRq166Nh4cHbdq0YfXq1UaH5LDGjx9Pq1at8PHxITAwkN69e7Nz506jw3Iar7/+OiaTiREjRhgdikM7fPgwd999N1WrVsXT05OmTZuydu1ao8NyWAUFBYwZM4bw8HA8PT2pW7cuL7/8Mlon9bzFixfTs2dPQkNDMZlMzJw5s8jnNpuNsWPHEhISgqenJ7GxsezevduYYKVUqK+/currr536+yuj/r541N//M0fr75XIl6IZM2YwcuRIxo0bx/r164mKiqJr164cOXLE6NAc0qJFi3jkkUdYuXIl8+bN48yZM9x8883k5OQYHZrDW7NmDR9//DHNmjUzOhSHduLECdq1a4erqyu//fYb27Zt45133sHf39/o0BzWG2+8wUcffcSkSZPYvn07b7zxBm+++SYffPCB0aE5jJycHKKiopg8efIlP3/zzTd5//33mTJlCqtWrcLb25uuXbty+vTpMo5USoP6+uJRX39t1N9fGfX3xaf+/p85XH9vk1LTunVr2yOPPFL4c0FBgS00NNQ2fvx4A6NyHkeOHLEBtkWLFhkdikPLysqy1a9f3zZv3jxbx44dbY8//rjRITms//u//7O1b9/e6DCcSo8ePWz33ntvkX19+vSxDRgwwKCIHBtg+/nnnwt/tlqttuDgYNtbb71VuC89Pd3m7u5u++677wyIUEqa+vpro77+yqm/v3Lq74tP/X3xOEJ/rzfypSQvL49169YRGxtbuM9sNhMbG8uKFSsMjMx5ZGRkAFClShWDI3FsjzzyCD169Cjyd00ubfbs2bRs2ZI777yTwMBAmjdvztSpU40Oy6G1bduWuLg4du3aBcDGjRtZunQp3bt3Nzgy57B//35SUlKK/Pvp5+dHmzZt1BeUA+rrr536+iun/v7Kqb8vPvX318aI/t6lVK4qpKWlUVBQQFBQUJH9QUFB7Nixw6ConIfVamXEiBG0a9eOJk2aGB2Ow5o+fTrr169nzZo1RofiFPbt28dHH33EyJEjefbZZ1mzZg2PPfYYbm5uDB482OjwHNIzzzxDZmYmDRs2xGKxUFBQwKuvvsqAAQOMDs0ppKSkAFyyLzj3mTgv9fXXRn39lVN/Xzzq74tP/f21MaK/VyIvDumRRx5hy5YtLF261OhQHFZiYiKPP/448+bNw8PDw+hwnILVaqVly5a89tprADRv3pwtW7YwZcoUdeyX8f333/PNN9/w7bff0rhxY+Lj4xkxYgShoaF6ZiJyTdTXXxn198Wn/r741N87Hw2tLyUBAQFYLBZSU1OL7E9NTSU4ONigqJzD8OHD+eWXX1iwYAE1atQwOhyHtW7dOo4cOcJ1112Hi4sLLi4uLFq0iPfffx8XFxcKCgqMDtHhhISEEBkZWWRfo0aNSEhIMCgix/fUU0/xzDPP0L9/f5o2bcrAgQN54oknGD9+vNGhOYVz/71XX1A+qa+/eurrr5z6++JTf1986u+vjRH9vRL5UuLm5kaLFi2Ii4sr3Ge1WomLiyMmJsbAyByXzWZj+PDh/Pzzz/z555+Eh4cbHZJD69KlC5s3byY+Pr6wtWzZkgEDBhAfH4/FYjE6RIfTrl27i8oc7dq1i1q1ahkUkeM7efIkZnPRrsJisWC1Wg2KyLmEh4cTHBxcpC/IzMxk1apV6gvKAfX1xae+vvjU3xef+vviU39/bYzo7zW0vhSNHDmSwYMH07JlS1q3bs3EiRPJyclhyJAhRofmkB555BG+/fZbZs2ahY+PT+F8Ej8/Pzw9PQ2OzvH4+PhcNKfQ29ubqlWraq7hZTzxxBO0bduW1157jb59+7J69Wo++eQTPvnkE6NDc1g9e/bk1VdfpWbNmjRu3JgNGzYwYcIE7r33XqNDcxjZ2dns2bOn8Of9+/cTHx9PlSpVqFmzJiNGjOCVV16hfv36hIeHM2bMGEJDQ+ndu7dxQUuJUV9fPOrri0/9ffGpvy8+9ff/zOH6+1JZC18KffDBB7aaNWva3NzcbK1bt7atXLnS6JAcFnDJ9vnnnxsdmtNQOZp/9r///c/WpEkTm7u7u61hw4a2Tz75xOiQHFpmZqbt8ccft9WsWdPm4eFhq1Onju25556z5ebmGh2aw1iwYMEl/9s1ePBgm81mL0kzZswYW1BQkM3d3d3WpUsX286dO40NWkqU+vorp76+ZKi//2fq74tH/f0/c7T+3mSz2Wyl8ysCERERERERESlpmiMvIiIiIiIi4kSUyIuIiIiIiIg4ESXyIiIiIiIiIk5EibyIiIiIiIiIE1EiLyIiIiIiIuJElMiLiIiIiIiIOBEl8iIiIiIiIiJORIm8iIiIiIiIiBNRIi8iDslkMjFz5kyjwxAREZFSor5e5OopkReRi9xzzz2YTKaLWrdu3YwOTUREREqA+noR5+ZidAAi4pi6devG559/XmSfu7u7QdGIiIhISVNfL+K89EZeRC7J3d2d4ODgIs3f3x+wD4X76KOP6N69O56entSpU4cff/yxyPmbN2/mxhtvxNPTk6pVqzJs2DCys7OLHDNt2jQaN26Mu7s7ISEhDB8+vMjnaWlp/Otf/8LLy4v69esze/bs0r1pERGRCkR9vYjzUiIvIldlzJgx3H777WzcuJEBAwbQv39/tm/fDkBOTg5du3bF39+fNWvW8MMPPzB//vwinfdHH33EI488wrBhw9i8eTOzZ8+mXr16Rb7jxRdfpG/fvmzatIlbbrmFAQMGcPz48TK9TxERkYpKfb2IA7OJiPzF4MGDbRaLxebt7V2kvfrqqzabzWYDbA8++GCRc9q0aWN76KGHbDabzfbJJ5/Y/P39bdnZ2YWfz5kzx2Y2m20pKSk2m81mCw0NtT333HOXjQGwPf/884U/Z2dn2wDbb7/9VmL3KSIiUlGprxdxbpojLyKX1LlzZz766KMi+6pUqVK4HRMTU+SzmJgY4uPjAdi+fTtRUVF4e3sXft6uXTusVis7d+7EZDKRlJREly5d/jaGZs2aFW57e3vj6+vLkSNHrvaWRERE5ALq60WclxJ5Ebkkb2/vi4a/lRRPT88rOs7V1bXIzyaTCavVWhohiYiIVDjq60Wcl+bIi8hVWbly5UU/N2rUCIBGjRqxceNGcnJyCj9ftmwZZrOZiIgIfHx8qF27NnFxcWUas4iIiFw59fUijktv5EXkknJzc0lJSSmyz8XFhYCAAAB++OEHWrZsSfv27fnmm29YvXo1n332GQADBgxg3LhxDB48mBdeeIGjR4/y6KOPMnDgQIKCggB44YUXePDBBwkMDKR79+5kZWWxbNkyHn300bK9URERkQpKfb2I81IiLyKXNHfuXEJCQorsi4iIYMeOHYB9ldnp06fz8MMPExISwnfffUdkZCQAXl5e/P777zz++OO0atUKLy8vbr/9diZMmFB4rcGDB3P69GneffddRo0aRUBAAHfccUfZ3aCIiEgFp75exHmZbDabzeggRMS5mEwmfv75Z3r37m10KCIiIlIK1NeLODbNkRcRERERERFxIkrkRURERERERJyIhtaLiIiIiIiIOBG9kRcRERERERFxIkrkRURERERERJyIEnkRERERERERJ6JEXkRERERERMSJKJEXERERERERcSJK5EVERERERESciBJ5ERERERERESeiRF5ERERERETEifw/CdllUwGV8GsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ---------- 7. Plot Training Accuracy & Loss ----------\n",
        "plt.figure(figsize=(12,5))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy over Epochs')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss over Epochs')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyx-UMOyidd7",
        "outputId": "6e0c5484-99f8-436f-a119-6adbc721c77e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 23ms/step - accuracy: 0.7282 - loss: 0.7990\n",
            "\n",
            "Test Accuracy: 72.61%\n"
          ]
        }
      ],
      "source": [
        "# ---------- 8. Evaluate Model on Test Set ----------\n",
        "#Evaluates model performance on unseen test data and prints the test accuracy.\n",
        "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
        "print(f\"\\nTest Accuracy: {test_acc * 100:.2f}%\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XzJSejmzigM4",
        "outputId": "ae275be9-b1ae-4695-a8f9-8962c44f4b71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 19ms/step\n"
          ]
        }
      ],
      "source": [
        "# ---------- 9. Generate Predictions ----------\n",
        "#Predicts labels for test images and converts probabilities into class indices.\n",
        "y_pred = model.predict(x_test)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_true = np.argmax(y_test, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 783
        },
        "id": "Ae2oP4_2iimz",
        "outputId": "c079267a-760e-43c0-d0f5-cfcb2c33ec53"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ---------- 10. Confusion Matrix ----------\n",
        "#Displays a heatmap showing how often each class was correctly or incorrectly predicted.\n",
        "cm = confusion_matrix(y_true, y_pred_classes)\n",
        "\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=class_names, yticklabels=class_names)\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix for CIFAR-10 CNN')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-IgfrFpnil_-",
        "outputId": "c36dd8b5-875a-4fa7-f80f-0eea61393f3d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.76      0.77      0.76      1000\n",
            "  automobile       0.85      0.85      0.85      1000\n",
            "        bird       0.64      0.57      0.60      1000\n",
            "         cat       0.54      0.55      0.55      1000\n",
            "        deer       0.67      0.68      0.68      1000\n",
            "         dog       0.67      0.57      0.62      1000\n",
            "        frog       0.80      0.79      0.79      1000\n",
            "       horse       0.70      0.81      0.75      1000\n",
            "        ship       0.85      0.85      0.85      1000\n",
            "       truck       0.78      0.82      0.80      1000\n",
            "\n",
            "    accuracy                           0.73     10000\n",
            "   macro avg       0.72      0.73      0.72     10000\n",
            "weighted avg       0.72      0.73      0.72     10000\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# ---------- 11. Classification Report ----------\n",
        "#Prints precision, recall, and F1-score for each class to show detailed performance.\n",
        "print(\"\\nClassification Report:\\n\")\n",
        "print(classification_report(y_true, y_pred_classes, target_names=class_names))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "a4w1_pfd38Dj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa822b49-6c52-415d-c030-c62c7093ae17"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - accuracy: 0.6367 - loss: 1.0550\n",
            "Epoch 1: val_accuracy improved from -inf to 0.68440, saving model to best_cifar10_model.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 135ms/step - accuracy: 0.6367 - loss: 1.0550 - val_accuracy: 0.6844 - val_loss: 0.9138\n",
            "Epoch 2/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - accuracy: 0.6610 - loss: 0.9861\n",
            "Epoch 2: val_accuracy improved from 0.68440 to 0.70010, saving model to best_cifar10_model.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 132ms/step - accuracy: 0.6610 - loss: 0.9861 - val_accuracy: 0.7001 - val_loss: 0.8868\n",
            "Epoch 3/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - accuracy: 0.6596 - loss: 0.9778\n",
            "Epoch 3: val_accuracy improved from 0.70010 to 0.71460, saving model to best_cifar10_model.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 130ms/step - accuracy: 0.6596 - loss: 0.9778 - val_accuracy: 0.7146 - val_loss: 0.8396\n",
            "Epoch 4/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - accuracy: 0.6708 - loss: 0.9450\n",
            "Epoch 4: val_accuracy did not improve from 0.71460\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m101s\u001b[0m 129ms/step - accuracy: 0.6708 - loss: 0.9451 - val_accuracy: 0.7077 - val_loss: 0.8784\n",
            "Epoch 5/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - accuracy: 0.6758 - loss: 0.9411\n",
            "Epoch 5: val_accuracy improved from 0.71460 to 0.74210, saving model to best_cifar10_model.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 130ms/step - accuracy: 0.6758 - loss: 0.9411 - val_accuracy: 0.7421 - val_loss: 0.7533\n",
            "Epoch 6/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - accuracy: 0.6915 - loss: 0.9020\n",
            "Epoch 6: val_accuracy did not improve from 0.74210\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 131ms/step - accuracy: 0.6914 - loss: 0.9020 - val_accuracy: 0.7359 - val_loss: 0.7817\n",
            "Epoch 7/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - accuracy: 0.6936 - loss: 0.8895\n",
            "Epoch 7: val_accuracy did not improve from 0.74210\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m103s\u001b[0m 131ms/step - accuracy: 0.6936 - loss: 0.8895 - val_accuracy: 0.7397 - val_loss: 0.7522\n",
            "Epoch 8/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - accuracy: 0.6949 - loss: 0.8812\n",
            "Epoch 8: val_accuracy improved from 0.74210 to 0.74530, saving model to best_cifar10_model.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m107s\u001b[0m 136ms/step - accuracy: 0.6949 - loss: 0.8812 - val_accuracy: 0.7453 - val_loss: 0.7364\n",
            "Epoch 9/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - accuracy: 0.7085 - loss: 0.8595\n",
            "Epoch 9: val_accuracy improved from 0.74530 to 0.75300, saving model to best_cifar10_model.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m106s\u001b[0m 136ms/step - accuracy: 0.7085 - loss: 0.8595 - val_accuracy: 0.7530 - val_loss: 0.7140\n",
            "Epoch 10/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - accuracy: 0.7045 - loss: 0.8581\n",
            "Epoch 10: val_accuracy did not improve from 0.75300\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m137s\u001b[0m 130ms/step - accuracy: 0.7045 - loss: 0.8581 - val_accuracy: 0.7430 - val_loss: 0.7584\n",
            "Epoch 11/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - accuracy: 0.7124 - loss: 0.8440\n",
            "Epoch 11: val_accuracy did not improve from 0.75300\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m99s\u001b[0m 127ms/step - accuracy: 0.7124 - loss: 0.8440 - val_accuracy: 0.7478 - val_loss: 0.7451\n",
            "Epoch 12/25\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - accuracy: 0.7150 - loss: 0.8343\n",
            "Epoch 12: val_accuracy improved from 0.75300 to 0.76110, saving model to best_cifar10_model.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m100s\u001b[0m 128ms/step - accuracy: 0.7150 - loss: 0.8344 - val_accuracy: 0.7611 - val_loss: 0.7178\n"
          ]
        }
      ],
      "source": [
        "#This code trains the CNN with data augmentation, early stopping, and model checkpointing.\n",
        "\n",
        "# --- Data Augmentation ---\n",
        "datagen = keras.preprocessing.image.ImageDataGenerator(\n",
        "    rotation_range=15,\n",
        "    horizontal_flip=True,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1\n",
        ")\n",
        "datagen.fit(x_train)\n",
        "\n",
        "# --- Callbacks ---\n",
        "early_stop = EarlyStopping(\n",
        "    monitor='val_loss',       # Watch validation loss\n",
        "    patience=3,               # Stop if no improvement for 3 epochs\n",
        "    restore_best_weights=True # Revert to best model weights\n",
        ")\n",
        "\n",
        "checkpoint = ModelCheckpoint(\n",
        "    'best_cifar10_model.h5',  # Save best model file\n",
        "    monitor='val_accuracy',   # Track validation accuracy\n",
        "    save_best_only=True,      # Save only the best model\n",
        "    mode='max',\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "# --- Train Model with Augmentation and Callbacks ---\n",
        "history_aug = model.fit(\n",
        "    datagen.flow(x_train, y_train, batch_size=64),\n",
        "    epochs=25,\n",
        "    validation_data=(x_test, y_test),\n",
        "    callbacks=[early_stop, checkpoint]\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "p36YYWKyioC8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "outputId": "0e4e6f8f-aa18-40bd-9144-5407f470e36a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ---------- 12. Show Random Image Predictions ----------\n",
        "#Shows random test images with predicted vs. true labels (green for correct, red for wrong).\n",
        "num_images = 15\n",
        "indices = random.sample(range(len(x_test)), num_images)\n",
        "sample_images = x_test[indices]\n",
        "sample_labels = y_true[indices]\n",
        "sample_preds = y_pred_classes[indices]\n",
        "\n",
        "plt.figure(figsize=(15,6))\n",
        "for i in range(num_images):\n",
        "    plt.subplot(3,5,i+1)\n",
        "    plt.imshow(sample_images[i])\n",
        "    true_label = class_names[sample_labels[i]]\n",
        "    pred_label = class_names[sample_preds[i]]\n",
        "    color = 'green' if true_label == pred_label else 'red'\n",
        "    plt.title(f\"P: {pred_label}\\nT: {true_label}\", color=color, fontsize=9)\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.suptitle(\"Random CIFAR-10 Predictions\", fontsize=16)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "j8HnAYTFirg1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "dbe92a59-3774-4013-aa47-55be58ca2797"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ---------- 13. Test a Single Image ----------\n",
        "#Tests one image to display its predicted class label.\n",
        "img = x_test[25]\n",
        "plt.imshow(img)\n",
        "plt.title(\"Sample Test Image\")\n",
        "plt.axis('off')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ZXD6lJmqiuEp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23f4f0eb-0638-4a15-dc93-5fdc6736dd52"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
            "Predicted Class: bird\n"
          ]
        }
      ],
      "source": [
        "# Predict single image\n",
        "pred = model.predict(np.expand_dims(img, axis=0))\n",
        "pred_class = class_names[np.argmax(pred)]\n",
        "print(\"Predicted Class:\", pred_class)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "F7WhRlUSi13R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c0f4ee3-4b7d-4099-db09-eb81698aff07"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Model saved successfully as 'cifar10_cnn_model.h5'\n"
          ]
        }
      ],
      "source": [
        "# ---------- 14. Save the Model ----------\n",
        "#Saves the trained CNN model as cifar10_cnn_model.h5 for future use.\n",
        "model.save('cifar10_cnn_model.h5')\n",
        "print(\"\\nModel saved successfully as 'cifar10_cnn_model.h5'\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}