{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "a77emryM3ea6"
      },
      "outputs": [],
      "source": [
        "!pip install tensorflow scikit-learn matplotlib -q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "HP5UL_ZO4LZx"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = keras.datasets.cifar10.load_data()\n",
        "\n",
        "print(\"Train shape:\", X_train.shape)\n",
        "print(\"Test shape:\", X_test.shape)"
      ],
      "metadata": {
        "id": "Cgtn-9ie3nty",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "baacb14a-220e-4f53-acfb-e4e1bbab5939"
      },
      "execution_count": 3,
      "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[1m2s\u001b[0m 0us/step\n",
            "Train shape: (50000, 32, 32, 3)\n",
            "Test shape: (10000, 32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train / 255.0\n",
        "X_test = X_test / 255.0"
      ],
      "metadata": {
        "id": "IdlYrMTE3pVV"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['airplane','automobile','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','truck']\n",
        "\n",
        "plt.imshow(X_train[0])\n",
        "plt.title(class_names[y_train[0][0]])\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "an60P9WV3qLi",
        "outputId": "a6879d2d-47b5-4558-e315-9177b58151e0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential([\n",
        "    keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),\n",
        "    keras.layers.MaxPooling2D((2,2)),\n",
        "\n",
        "    keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    keras.layers.MaxPooling2D((2,2)),\n",
        "\n",
        "    keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    keras.layers.MaxPooling2D((2,2)),\n",
        "\n",
        "    keras.layers.Flatten(),\n",
        "\n",
        "    keras.layers.Dense(128, activation='relu'),\n",
        "    keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "DYPwc3p_3r7O",
        "outputId": "de19e8dc-a93a-4938-d468-9d91c78d1f8e"
      },
      "execution_count": 6,
      "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"
          ]
        },
        {
          "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",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\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;34m65,664\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",
              "│ max_pooling2d_2 (<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\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">512</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\">65,664</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;34m160,202\u001b[0m (625.79 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">160,202</span> (625.79 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m160,202\u001b[0m (625.79 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">160,202</span> (625.79 KB)\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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    X_train, y_train,\n",
        "    epochs=10,\n",
        "    batch_size=64,\n",
        "    validation_split=0.1\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kUscc4M_3tV6",
        "outputId": "40c20958-7584-4a8d-d784-862453944a91"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m69s\u001b[0m 94ms/step - accuracy: 0.4251 - loss: 1.5800 - val_accuracy: 0.4982 - val_loss: 1.3890\n",
            "Epoch 2/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 88ms/step - accuracy: 0.5744 - loss: 1.2077 - val_accuracy: 0.5802 - val_loss: 1.1777\n",
            "Epoch 3/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 87ms/step - accuracy: 0.6295 - loss: 1.0557 - val_accuracy: 0.6332 - val_loss: 1.0714\n",
            "Epoch 4/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 86ms/step - accuracy: 0.6689 - loss: 0.9499 - val_accuracy: 0.6652 - val_loss: 0.9750\n",
            "Epoch 5/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 88ms/step - accuracy: 0.7000 - loss: 0.8628 - val_accuracy: 0.6906 - val_loss: 0.9051\n",
            "Epoch 6/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 85ms/step - accuracy: 0.7259 - loss: 0.7932 - val_accuracy: 0.7108 - val_loss: 0.8680\n",
            "Epoch 7/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 85ms/step - accuracy: 0.7433 - loss: 0.7388 - val_accuracy: 0.7014 - val_loss: 0.8784\n",
            "Epoch 8/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 88ms/step - accuracy: 0.7618 - loss: 0.6856 - val_accuracy: 0.7040 - val_loss: 0.8817\n",
            "Epoch 9/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 86ms/step - accuracy: 0.7775 - loss: 0.6372 - val_accuracy: 0.7210 - val_loss: 0.8439\n",
            "Epoch 10/10\n",
            "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 86ms/step - accuracy: 0.7929 - loss: 0.5954 - val_accuracy: 0.7236 - val_loss: 0.8382\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, accuracy = model.evaluate(X_test, y_test)\n",
        "print(\"Test Accuracy:\", accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lQOzuITx3uO6",
        "outputId": "1f8f7c50-33e8-41b6-8866-d50ebf0d1eee"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 13ms/step - accuracy: 0.7129 - loss: 0.8791\n",
            "Test Accuracy: 0.7128999829292297\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history['accuracy'], label='train acc')\n",
        "plt.plot(history.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.title(\"Accuracy\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "AXMwCjMS3vWi",
        "outputId": "b6239952-81ed-4ea0-b0c3-6a073fe8c8e3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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yC+1vU8ePvs0C6dssiI71auBhdv26WFUysHh7mNn7SqTLXrskdOnSpdDPGRkZvPTSSyxbtowzZ86Qn5/P+fPnOX78+BWv065dO8dzX19f/Pz8SEhIuOzxW7du5aWXXmLHjh2cO3cOm80G2McutWrViu3bt9OnTx9HWLlUQkICp0+f5tZbb3XmrYqIyA3KzrOy6chZ1sYksu5gIrEJGYX21/L1pE/TAPo2C6RP00ACq5e/pUOqZGAxmUzX1C1Tnv1xts9TTz3FqlWreOutt2jSpAne3t7cc8895ObmXvE6fwwWJpPJEUL+KDMzk8jISCIjI1mwYAGBgYEcP36cyMhIx+t4e3tf9rWutE9EREqOYRgcTspkbYy9m+fXw8nk5F/8u93NBJ3q1bS3ojQPpE2YP25u5fs+exX7W7uS8/T0xGq1XtOx69evZ8yYMdx9992AvcXlwniXkrJ//36Sk5P5xz/+QXh4OAC//fZboWPatWvHvHnzyMvLKxKGqlevToMGDYiOjubmm28u0dpERKq6jJx8NsQmOcainDx3vtD+ED8vR0Dp1TgAf5+iLeHlmQJLOdagQQM2bdrE0aNHqVat2mUHwgI0bdqUxYsXM2TIEEwmE1OnTr1sS8n1qlevHp6enrzzzjs8+uij7N69u8gtFh577DHeeecdRo4cyZQpU/D39+fXX3+lW7duNG/enJdeeolHH32UoKAgBg4cSHp6OuvXr+fxxx8v0VpFRCo7wzDYeybNHlBiEtl67Bz5NsOx39PsRreGtRwhpWlQNUym8t2KciUKLOXYU089xejRo2nVqhXnz5/nyJEjlz125syZjBs3jp49exIQEMCzzz57xbtYX4/AwEA++eQTnnvuOd5++206derEW2+9xZ133uk4pnbt2vz00088/fTT9O3bF7PZTIcOHejVqxcAo0ePJjs7m3//+9889dRTBAQEcM8995RonSIildW5zFx+jk1yjEVJTM8ptL9BbR9HQOneqHaFH/5wKZNhGMbVDyvf0tLS8Pf3JzU1FT8/v0L7srOzOXLkCA0bNsTLy8tFFcr10GcnIlWd1Waw/UQKaw8ksu5AIjtOpnDpt7aPp5mejWtzU7NAbmoaSIOAirWa+ZW+v/+o8kQvERGRSiA+LdsxDuWXg0mkns8rtL9FSPWCKceBdG5QE4t7ycw+Le8UWERERFwoN9/Gb8fOOsai7I9LL7Tfz8udPs0C6ds0kJuaBRLiXzVbnBVYREREytjx5CzWHkhg7YFENhxKJiv34oxQkwna1a3haEVpX9cf93KwcJurKbCIiIiUsqzcfDYdPuvo6jmSlFlof0A1Czc1u7hwWy1fTxdVWn4psIiIiJQwwzCITchwBJRNR86Se8nCbe5uJjrXr0nf5vZWlJYhfuV+4TZXU2AREREpAZk5+Ww4lMyamATWxCRyKqXwwm11ang7AkrPxrWp7lWxFm5zNQUWERGR62AYBocSM1gTk8iamEQ2HzlLrvViK4qnuxsRDWvRr3kQ/ZoH0ijAt0Iv3OZqCiwiIiLXKCs3nw2xyaw5kMDq/UVbUerV8qFf80D6NQ+kR6MAvD2rxpTjsqDAIiIichn2VpRMRzePWlFcR4GlkmvQoAFPPPEETzzxhKtLERGpEC5tRVkTU/QmguG1vOnXzB5QejSuXMvfl2f6LYuISJV2aSvK2gOJbDr8h1YUsxsRjew3Eby5RZBaUVxEgUVERKqcrNx8Nh5KZk1MIqtjEoq0otSt6U2/5oHc3DxIrSjlRNVcOs8wIDfTNY9rvNfkhx9+SFhYGDabrdD2u+66i3HjxgFw6NAh7rrrLoKDg6lWrRpdu3blxx9/dOpXsWXLFm677TYCAgLw9/enb9++bNu2rdAxKSkp/OUvfyE4OBgvLy/atGnD0qVLHfvXr19Pv3798PHxoWbNmkRGRnLu3Dmn6hARKU0XZvTM+eUID87ZRIdXVvHQvN/4v1+PcfLceTzNbvRuEsALd7Tkx6i+/PzMzbw6tC23tgxWWCknquankJcFr4e55rWfOw2eV7+b5r333svjjz/O6tWrufXWWwE4e/YsK1asYPny5QBkZGQwaNAgXnvtNSwWC/Pnz2fIkCHExMRQr169ayonPT2d0aNH884772AYBv/6178YNGgQBw8epHr16thsNgYOHEh6ejqffvopjRs3Zu/evZjN9pHv27dv59Zbb2XcuHH85z//wd3dndWrV2O1Wq/yyiIipevSVpQ1BxI4cbbouig3twikXzN7K4qvpWp+JVYU+nTKqZo1azJw4EA+++wzR2D58ssvCQgI4Oabbwagffv2tG/f3nHO9OnT+frrr1myZAmPPfbYNb3OLbfcUujnDz/8kBo1arB27VoGDx7Mjz/+yObNm9m3bx/NmjUDoFGjRo7j33jjDbp06cL777/v2Na6devre9MiIjfAMAwOJ2UWrIuSUGR1WQ+ziYiGtR3TjhsHVtNYlAqkagYWDx97S4erXvsa3X///YwfP573338fi8XCggULGDlyJG5u9p68jIwMXnrpJZYtW8aZM2fIz8/n/PnzHD9+/JpfIz4+nhdeeIE1a9aQkJCA1WolKyvLcY3t27dTt25dR1j5o+3bt3Pvvfde8+uJiJSk87lWNh5OcizedvxsVqH9dWp4FwSUIHqqFaVCq5qfnMl0Td0yrjZkyBAMw2DZsmV07dqVn3/+mX//+9+O/U899RSrVq3irbfeokmTJnh7e3PPPfeQm5t7za8xevRokpOT+c9//kP9+vWxWCz06NHDcQ1vb+8rnn+1/SIiJckwDI4UtKKsvkwrSreGtRzTjpsEqRWlsqiagaWC8PLyYtiwYSxYsIDY2FiaN29Op06dHPvXr1/PmDFjuPvuuwF7i8vRo0edeo3169fz/vvvM2jQIABOnDhBUlKSY3+7du04efIkBw4cKLaVpV27dkRHR/Pyyy9fxzsUEbm687lWfj1sv0fP6su0ovRtHki/ZoH0bBJANbWiVEr6VMu5+++/n8GDB7Nnzx4eeOCBQvuaNm3K4sWLGTJkCCaTialTpxaZVXQ1TZs25f/+7//o0qULaWlpPP3004VaTfr27ctNN93E8OHDmTlzJk2aNGH//v2YTCYGDBjAlClTaNu2LX/961959NFH8fT0ZPXq1dx7770EBASUyO9ARKqeo0mZrC4IKJsOJ5Pzh1aUrg1qObp6mqoVpUpQYCnnbrnlFmrVqkVMTAx//vOfC+2bOXMm48aNo2fPngQEBPDss8+Slpbm1PXnzJnDI488QqdOnQgPD+f111/nqaeeKnTMV199xVNPPcV9991HZmYmTZo04R//+AcAzZo1Y+XKlTz33HN069YNb29vIiIiuO+++27sjYtIlXMsOZOlO8+wdOcZ9p0p/HdZmL8XfZsHcXNztaJUVSbDuMaFQcqxtLQ0/P39SU1Nxc/Pr9C+7Oxsjhw5QsOGDfHy8nJRhXI99NmJVH4nzmaxfJc9pOw6lerYbnYz0a1BLfu0Y7WiVFpX+v7+I0VUEREpU6dTzjtCyvYTKY7tbibo2TiAO9qFMqB1CDV9PV1XpJQ7CiwiIlLqEtKyWbbrDMt2nuG3YxdXwjaZIKJhLQa3C2NAmxACqllcWKWUZwosIiJSKhLTc1ix296Ssvno2UJ3JunaoCaD24UxsE0IQX7q8pWrU2AREZESczYzlxW741i26zQbDyVjuySkdKxXg8HtwhjUNoRQf63hJM6pMoGlEowtrnL0mYlUDKlZefywJ47vdp5mw6FkrJeklHZ1/RncLpRBbUOpW/PaV/oW+aNKH1g8PDwAyMrK0qqsFUxWln1xqAufoYiUH2nZeazaE8/Snaf5JTaJPOvFkNI6zI872oUyuG0Y9WorpEjJuK7A8t577/Hmm28SFxdH+/bteeedd+jWrVuxx/br14+1a9cW2T5o0CCWLVsGwJgxY5g3b16h/ZGRkaxYseJ6yivEbDZTo0YNEhISAPDx8dHUuHLOMAyysrJISEigRo0ajjtDi4hrZeTkE70vnu92nGHdgURyrRcXc2sRUp072oZyR7tQGgVWc2GVUlk5HVgWLVpEVFQUs2fPJiIiglmzZhEZGUlMTAxBQUFFjl+8eHGhe9skJyfTvn37IjfMGzBgAB9//LHjZ4ul5EaKh4SEADhCi1QMNWrUcHx2IuIaWbn5/LQ/gaU7zrA6JqHQirONA30Z3C6MIe1DaRJU3YVVSlXgdGCZOXMm48ePZ+zYsQDMnj2bZcuWMXfuXCZPnlzk+Fq1ahX6eeHChfj4+BQJLBaLpdS+nEwmE6GhoQQFBZGXl1cqryEly8PDQy0rIi6SnWdlTUwC3+08w0/7EjifZ3Xsaxjgy+B29paU5sHV1WItZcapwJKbm8vWrVuZMmWKY5ubmxv9+/dn48aN13SNOXPmMHLkSHx9C98tec2aNQQFBVGzZk1uueUWXn31VWrXrl3sNXJycsjJyXH8fK3L0ZvNZn0JiogUIyffytqYRJbtOsOPe+PJzL0YUsJreTO4XRiD24XSKtRPIUVcwqnAkpSUhNVqJTg4uND24OBg9u/ff9XzN2/ezO7du5kzZ06h7QMGDGDYsGE0bNiQQ4cO8dxzzzFw4EA2btxYbMCYMWOG7g4sInKDcvNt/BKbyNKdZ1i1J570nHzHvjo1vO0DZ9uF0raOv0KKuFyZzhKaM2cObdu2LTJAd+TIkY7nbdu2pV27djRu3Jg1a9Zw6623FrnOlClTiIqKcvyclpZGeHh46RUuIlJJ5FltbDiUzLKdp/lhTzyp5y92k4f4eTGobSiD24fSMbyGQoqUK04FloCAAMxmM/Hx8YW2x8fHX3X8SWZmJgsXLuSVV1656us0atSIgIAAYmNjiw0sFoulRAfliohUZvlWG5uOnGXpztOs2B3HuayLISWwuoVBbUIY3D6MzvVq4uamkCLlk1OBxdPTk86dOxMdHc3QoUMBsNlsREdH89hjj13x3C+++IKcnBweeOCBq77OyZMnSU5OJjQ01JnyRESkgNVmsOXoxZCSlHFxtmZtX08GtAlhcLswujWshVkhRSoAp7uEoqKiGD16NF26dKFbt27MmjWLzMxMx6yhUaNGUadOHWbMmFHovDlz5jB06NAiA2kzMjJ4+eWXGT58OCEhIRw6dIhnnnmGJk2aEBkZeQNvTUSkarHZDLYdP8fSnWdYvusMCekXJyfU8PFgYJsQ7mgbRvdGtXA3u7mwUhHnOR1YRowYQWJiItOmTSMuLo4OHTqwYsUKx0Dc48eP4+ZW+A9CTEwMv/zyCytXrixyPbPZzM6dO5k3bx4pKSmEhYVx++23M336dHX7iIhcRW6+jY2Hk/lhTxyr9saTeElI8fNyJ7K1vbunZ+PaeCikSAVmMirBDVvS0tLw9/cnNTUVPz8/V5cjIlKqMnPyWXsgkR/2xPHT/gTSsy/O7qlucee2VsEMbh9K7yaBeLorpEj55cz3d6W/l5CISGVwNjOXH/fFs3JPHOsOJpF7yYqzgdUt3NYqmMjWIfRoVFshRSolBRYRkXLqVMp5Vu6J44c9cWw+cpZLboJM/do+RLYOIbJ1MB3DNbtHKj8FFhGRcsIwDGITMvhhTxw/7Iln16nUQvtbhfrZQ0qbYC2LL1WOAouIiAvZbAY7Tqbwwx57d8/hpEzHPpMJutavxe2t7d094bV8XFipiGspsIiIlLE8q41Nh8/yw544Vu6NIz7t4sweT7MbvZrUJrJ1CP1bBRNQTbMlRUCBRUSkTJzPtbL2QCIr98QRvT+h0JL4vp5mbm4RRGTrEPo1D6S6l4cLKxUpnxRYRERKSUpWLtH7EvhhTxzrDiaSnXdxZk9tX0/HzJ6eTWpjcded5EWuRIFFRKQExaVms3KvfWbPr4fPYr1kak/dmt4FM3tC6Fy/ppbEF3GCAouIyA06lHhxZs+OEymF9rUIqc7tBdOPW4X6aWaPyHVSYBERcZJhGOw6leoIKbEJGY59JhN0qleTyNbB3N4qhAYBvi6sVKTyUGAREbkG+VYbm4+eZWXB9OPTqdmOfR5mEz0aBxDZOpjbWgYT5OflwkpFKicFFhGRy8jOs/LzwSR+2BNH9L54zmVdnNnj7WGmX/NABrQJoV/zIPy9NbNHpDQpsIiIXCL1fB6r99tn9qw9kEhWrtWxr6aPB7e2tM/s6dM0AC8PzewRKSsKLCJS5aVn57Fkx2lW7I7j18PJ5FkvzuwJ8/cqGDQbQtcGNXE368aCIq6gwCIiVVZyRg4frz/K/I1HScvOd2xvGlTNMf24TR3N7BEpDxRYRKTKOZVyno/WHWbhluOOxdwaB/pyT+dwIlsH0yiwmosrFHExax7kZkBuZsEjA/JzoX4Pl5WkwCIiVUZsQjofrDnMt9tPkV+woFv7uv5M6NeE21sF46aF3KQiys/9Q7jIhNz0wmHj0uc5GZfZd8l51tyir+PhC8+fLvv3V0CBRUQqvR0nUnh/TSwr98ZjFAxP6dm4Nn/t14ReTWqry0fKhmHYg8Afg0LOFcJFkZ8zi55ny7v6a18vsyd4+oJnNfvDMOyLDbmAAouIVEqGYbDhUDLvr4llfWyyY/vtrYL5681N6BBew3XFSeVmGBC/G/Yvh4MrIT3uYviw5V/9/OtltlwSLnzBUq3wz45HtcIhpMj2gnM9fMHds/TqdZICi4hUKjabwap98by/5pBjmXyzm4m7OoQxoW9jmgZXd22BUjlZ8+H4BntIiVkGKcevfLy71yVBofplAkXB8ysGj0u2mSv3WkAKLCJSKeRZbSzZfprZaw9xsGCpfIu7GyO7hjP+pkbUrenj4gql0snJgEPR9pByYAVkp1zc5+4FjW+B5oMguDVYLgklHr5g1tevs/QbE5EK7Xyulf/9doIP1x3mVMp5AKp7uTOqR33G9mpIQDWLiyuUSiU9Hg58bw8ph9eANefiPu9a0HygPaQ0vtkeTqTEKLCISIWUej6PT389xtxfjpCcaZ/REFDNwkO9G3J/93r4eVXu5nEpQ0kHYf9Se0g5uQW4uLAgNRtAi8H2kBIeoZaTUqTfrIhUKAnp2cz95Sif/nqMjBz7AMa6Nb35S9/G3Nu5rpbLlxtns8Gp3y6GlOSDhfeHdYQWd0DzOyCopctmzVQ1CiwiUiGcOJvFf9cd4n+/nSQ3377YW7Pgakzo15gh7cK0ZL7cmLxsOLLWHlJiVkBmwsV9bh7QsI+9FaX5IPCv47o6qzAFFhEp12Li0vlgTSzf7TyDtWCxt471avDXfk24tUWQFnuT65d11j7teP8yiI2GvMyL+yx+0PQ2e0Bpeht4+buuTgEUWESknNp67BwfrInlx30X/6Xbp2kAf+3XhO6NammxN7k+545BzHJ7SDm2AYyLd+Omehi0KGhFadCnXK1BIgosIlKOGIbBuoNJvL86lk1HzgL24QED24QwoW8T2tYthX/lGgac2AxH1oFfKAS2hMDm9rUvpOIzDIjbaQ8o+5dD/K7C+4NaFYxHGWQfm6IgXG4psIiIy1ltBit2x/HB2lh2n0oDwMNs4u6OdfhL38Y0Lo2bEZ47CjsWwY7P4dyRovtr1Ld/mQW1vPjfgKbgrmnS5Z41D46tt4eUmO8h9cTFfSY3qNfjYkip1dB1dYpTFFhExGVy82188/spZq89xOEk+/gBbw8z93Wrx8N9GhJWw7tkXzA7DfZ+AzsW2r/QLvDwhab9ITsVEvZBRjykHLM/Dnx/8TiTGWo3LhxiglpBzYaazupqOekQ+6M9pBxcaf8sL3D3hia32kNK00jwre26OuW66U+YiJS5rNx8Pt98gv/382HOpGYD4O/tweieDRjTswG1fEtw7IA1377A147P7TNA8rMLdpigUV9o/2doObjwIl+ZyZC4zx5eEvZe/G92KiQdsD/2fnvxeLMFAptdDDGBLe3/9Q8HN81eKjXpcQXjUZbbZ/hceodhnwBoPsC+RkqjfuBRwuFXypwCi4iUmZSsXOZtOMYnG45wLst+h9mg6hbG92nEfRH1qGYpwb+S4vfYQ8rOLyAj7uL2gObQ4T5o+6fLT0/1rQ2+vaFB74vbDMP+BekIMAUhJnE/5GVB3C7741Ke1SCwxR9aZFpCtWCNlbgehgGJMfZ79exfbl8r5VK1GtlbUVoMhrpdwU1r8lQmJsMwjKsfVr6lpaXh7+9Pamoqfn5+ri5HRP4gPi2b//fzYT7bdJzMXPusjPq1ffjLTY0Z3rkOFvcS+mLJSIBdX8KOzwqHB+9a0PZeaD+y5AdW2mz2rqOEfZe0yuyzf7Ha8oo/x7tm4QAT1MoebHxqlVxdlYXNah8UfSGknD1UeH+dLgUze+6wD5ZWEKxQnPn+VmARkVJzNCmT/647xFdbT5FrtS/21jLUj7/2a8ygtqGYS2INlbxs+ziT7Z/bxzBcmKbq5mHvEmh/HzS5reynqFrz4Ozhwl1KCfvs2wxb8edUDy1okblkfExlnrFkGPZunNxM+xiU3MyCRwacPwuHfrIv4paVdPEcsyc07GsPKc0G2md2SYWlwCIiLrXndCofrDnE8l1nKFjrja4NavLXfk3o1zzwxtdQuTAVecdnsPtryLlkgGWdLvaWlDbDy2eLRV62fQxMofEx+yD1+OXPccxYuiTMBDQr2xlLhgH5OfYwkZtROFxceF4odGQWc1wx59nyr/7aFn9oFmkPKU362+98LJVCqQeW9957jzfffJO4uDjat2/PO++8Q7du3Yo9tl+/fqxdu7bI9kGDBrFs2TLAvvbCiy++yEcffURKSgq9evXigw8+oGnTptdUjwKLSPmw+chZ3l8Ty5qYRMe2m5sH8tebm9C1QQmEh8tNRfarC+1H2FtTAq7t741yJzvN3o10IcRc6F7KiC/++D/OWLrQMlOrkX3sRt75y4SG4v77x+Dxx3BRsP/SRdZKmru3feCzp6997I+nL4R1sI9Jqd8LzLqZZWXkzPe30yPcFi1aRFRUFLNnzyYiIoJZs2YRGRlJTEwMQUFBRY5fvHgxubkXR24nJyfTvn177r33Xse2N954g7fffpt58+bRsGFDpk6dSmRkJHv37sXLy8vZEkWkDBmGweqYBN5ffYjfjp0DwM0Ed7QLY0LfxrQKu8F/RGSn2mfkFDcVudVd9gG09XtX/Nk4Xn4Q3tX+uJSzM5bc3O1dTpfrdioJHj5Fw4Xj+R9/LnhuucI+D19NC5ercrqFJSIigq5du/Luu+8CYLPZCA8P5/HHH2fy5MlXPX/WrFlMmzaNM2fO4Ovri2EYhIWF8fe//52nnnoKgNTUVIKDg/nkk08YOXJkkWvk5OSQk5Pj+DktLY3w8HC1sIiUIcMw+HFfAv9aGcP+uHQAPM1uDO9cl7/c1IgGAb5XucIVXHEqcj97S8ofpyJXJVebsXQpD9+iIcFSrZiw8cfAcZlw4emr2TdSYkqthSU3N5etW7cyZcoUxzY3Nzf69+/Pxo0br+kac+bMYeTIkfj62v+iOXLkCHFxcfTv399xjL+/PxEREWzcuLHYwDJjxgxefvllZ0oXkRK070wa05fuZcOhZAB8Pc3c370+D/VuSLDfDbSKxu+B7Z/Bri8Kd4Vcy1TkqsRksg829Qu1L4h2gc0G6aftrSye1ewtIRW95UmkgFOBJSkpCavVSnBwcKHtwcHB7N+//6rnb968md27dzNnzhzHtri4OMc1/njNC/v+aMqUKURFRTl+vtDCIiKlKzE9h5mrYli05QQ2Azzd3RjXqyGP9m1EDZ/rnIWTkWAPKDs+L7upyJWVmxv413V1FSKlokw7DefMmUPbtm0vO0D3WlksFiwW3c9DpKxk51mZu/4I768+REaOfVbHHW1DmTywBeG1fJy/YF62fYXSHQsvMxX5z/bZILpbrogUcCqwBAQEYDabiY8vPGo9Pj6ekJCQK56bmZnJwoULeeWVVwptv3BefHw8oaEX59PHx8fToUMHZ8oTkRJmGAbLd8Ux4/t9nDx3HoB2df2ZOriV87N+DANObLK3pBQ3FbnDfdB6WPmciiwiLudUYPH09KRz585ER0czdOhQwD7oNjo6mscee+yK537xxRfk5OTwwAMPFNresGFDQkJCiI6OdgSUtLQ0Nm3axIQJE5wpT0RK0M6TKUxfupctR+0zf0L8vHhmQHOGdqiDmzMLvl1uKrJ/OLQbYe/yqahTkUWkzDjdJRQVFcXo0aPp0qUL3bp1Y9asWWRmZjJ27FgARo0aRZ06dZgxY0ah8+bMmcPQoUOpXbvwXTJNJhNPPPEEr776Kk2bNnVMaw4LC3OEIhEpO3Gp2bzxw34WbzsFgJeHG3+5qTF/6dsIH89r/CvjwlTk7Z/D8Q0Xt3tWs09Fbj+yckxFFpEy43RgGTFiBImJiUybNo24uDg6dOjAihUrHINmjx8/jtsf/hKKiYnhl19+YeXKlcVe85lnniEzM5NHHnmElJQUevfuzYoVK7QGi0gZysrN58N1h/nv2sOcz7OPKRnWsQ5PD2hOqP813OnWMRX5M9i/TFORRaREaWl+kSrOZjP4Zvsp3lgRQ1yaPWR0qV+TFwa3okN4jatfIG63vbtHU5FFxEmlutKtiFQevx09y/Sle9lx0j4Atk4Nb6YMasEdbUOvfL8fmw0OroT1s+D4JWsw+dSGNvfYg0poB01FFpESo8AiUgWdOJvFP1bsZ9nOM4B94beJtzRhXK+GeHlcYRXT/FzY/SWsf9u+XDxoKrKIlAkFFpEqJD07j/fXHGLOL0fIzbdhMsGILuFE3d6MoOpXGDOWkw5b58Gv70OafTAuFj/oMhYiJthXXBURKUUKLCJVgNVm8MVvJ3hr5QGSMuz34erZuDYv3NHqyjcnzEiATbNhy/+zz/wBqBYC3SfYw4qXfxlULyKiwCJS6W2ITWL6sn3sO5MGQMMAX54b1JL+LYMuP04l+RBseMd+Xx9rwY1GazeBnn+zT0l210rTIlK2FFhEKqkjSZm8vnwfq/baZ+74ebnzt1ubMqpHAzzdL7P+yalt9oG0e5cABRMI63SB3k9A8zu0boqIuIwCi0glk5qVx9s/HWT+xqPkWQ3MbiYeiKjHpP7NqOVbzIBYw4BD0fDLLDj688XtTSOh1ySo31OzfUTE5RRYRCqJPKuNzzYdZ9aPBziXlQfAzc0Def6OljQJql70BGs+7Pka1v8H4gvukuzmbp+W3OtvENy6DKsXEbkyBRaRSmB1TAKvLdtHbEIGAE2DqvHC4Fb0bRZY9ODcTPj9U9j4LqQct2/z8IXOo6H7X6FGeBlWLiJybRRYRCqwA/HpvLpsH+sOJAJQ08eDqNubc1/XcNzNfxhvkpkMmz+0P86ftW/zCYCIR6HrQ7pLsoiUawosIhXQ2cxc/r3qAJ9tPo7VZuBhNjGmZwMeu6Up/t4ehQ8+d8zemrLt/yD/vH1bzQbQ83HocD94XMN9gkREXEyBRaQCyc23MW/DUd7+6SDp2fkARLYOZsrAljQI+MNNBeN22cen7F4Mhv1mhoS2h15PQMs7waw//iJScehvLJEKwDAMVu6N5/Xl+ziWnAVAq1A/pg5uRY/GtS89EI6ssweVQ9EXtze62T41uWFfzfgRkQpJgUWknNtzOpXpS/fy62H7uJPA6haevr05wzvXxexWED5sVti3xB5UTv9u32Zyg9Z32xd7C+vgmuJFREqIAotIOZWQls1bK2P4YutJDAMs7m6M79OIR/s1ppql4I9u3nn7arQb3oFzR+zb3L2g44PQYyLUaui6NyAiUoIUWETKmew8K3N+OcJ7q2PJyrWPPRnSPoxnBzSnbk0f+0Hnz8GWOfb7/GTaZwjhXRO6PWJ/+Aa4qHoRkdKhwCJSThiGwXc7z/DP7/dzKsU+m6dDeA2mDm5F5/o17QelnoRfP4Ctn0Cufc0V/MPtrSkdHwRLNdcULyJSyhRYRMqB7SdSmL50L1uPnQMg1N+LZwe04M72Ybi5mSBhH6x/G3b9D2z22UEEtbYvnd9mGJg9rnB1EZGKT4FFxIVOp5znjRX7+Wb7aQC8PcxM6NeY8X0a4e1phmMb7TcjPLDi4kkN+tiDSpP+mvEjIlWGAouIC2Tl5jN77WE+XHeI7DwbAPd0rsvTkc0JruYJB763z/g5sangDBO0HGxfQ6VuF5fVLSLiKgosImVs58kUJi3czpGkTAC6NajF1MGtaBviBTv/BxvehqQD9oPNntD+PvvU5IAmLqxaRMS1FFhEyojNZvDhz4d564cY8m0Gof5eTBvcigFNfTBt/QQWfQDpZ+wHW/yh6zj7fX6qh7i0bhGR8kCBRaQMxKVm8/cvtrM+NhmAQW1D+MdtQfjtmA1L50JOmv3A6qH2OyZ3HgNefq4rWESknFFgESllK/fE8exXOzmXlYe3h5nXBtbl7tRPMf13Dlhz7QcFNLMPpG17L7hbXFuwiEg5pMAiUkrO51p5bflePv31OABtQ32Z0z6GoF/+Cln2lhbCI+wDaZsNADc31xUrIlLOKbCIlIJ9Z9L42+e/czDBvrjbKx3TeeDcP3Bbs9N+QEBzGDADmtzqwipFRCoOBRaREmQYBp9sOMqM7/eTm2+jVbUMPqm7lKB9S+wHWPyh32ToNl6LvYmIOEGBRaSEJGXk8PQXO1gdk4iFXP4Vso5hmQsxHc0CTNBpFNwyFaoFurpUEZEKR4FFpASsPZDI3/+3g6SMbAZ5bOOf1RZRPeWkfWd4BAz8J4R1dG2RIiIVmAKLyA3IybfyxooY5vxyhCamk/y32md0zt8O57FPUb7tFfvMHy2hLyJyQxRYRK5TbEI6j3++nVNnzjDN/StGu6/CnG+1r07b83HoHaW7J4uIlBAFFhEnGYbB55tP8OrSXdxp+4nPvP5HTQoWfmt+B0S+CrUaubZIEZFKRoFFxAnnMnOZvHgnSXvXschjHm09jtp3aJqyiEipUmARuUYbDiUxY+FPPJT9MUMtGwAwLH6Y+k3RNGURkVJ2XUtrvvfeezRo0AAvLy8iIiLYvHnzFY9PSUlh4sSJhIaGYrFYaNasGcuXL3fsf+mllzCZTIUeLVq0uJ7SREpcntXGv5bvYP3HU1iY+xhDzRswCqYpmx7fBj3+qrAiIlLKnG5hWbRoEVFRUcyePZuIiAhmzZpFZGQkMTExBAUFFTk+NzeX2267jaCgIL788kvq1KnDsWPHqFGjRqHjWrduzY8//nixMHc1/ojrHUvK4NP5s3kg5b/Ud08AwFq3G+ZBb2iasohIGXI6FcycOZPx48czduxYAGbPns2yZcuYO3cukydPLnL83LlzOXv2LBs2bMDDw/6v0AYNGhQtxN2dkJAQZ8sRKRWGYfDjunX4/PQCz5t2ghtkewXhNeg1zJqmLCJS5pzqEsrNzWXr1q3079//4gXc3Ojfvz8bN24s9pwlS5bQo0cPJk6cSHBwMG3atOH111/HarUWOu7gwYOEhYXRqFEj7r//fo4fP37ZOnJyckhLSyv0ECkpaSlJrHn7Yfr9dDe9TDvJw4P0rn/D68nfod2fFFZERFzAqcCSlJSE1WolODi40Pbg4GDi4uKKPefw4cN8+eWXWK1Wli9fztSpU/nXv/7Fq6++6jgmIiKCTz75hBUrVvDBBx9w5MgR+vTpQ3p6erHXnDFjBv7+/o5HeHi4M29DpHg2K8dWvod1VkduPvclHiYrh2vfhNtjm6l+x3StqSIi4kKlPlDEZrMRFBTEhx9+iNlspnPnzpw6dYo333yTF198EYCBAwc6jm/Xrh0RERHUr1+f//3vfzz00ENFrjllyhSioqIcP6elpSm0yA3JP7qB5C+epH7mfgCOmuqQd/sMmva4y8WViYgIOBlYAgICMJvNxMfHF9oeHx9/2fEnoaGheHh4YDabHdtatmxJXFwcubm5eHp6FjmnRo0aNGvWjNjY2GKvabFYsFgszpQuUry002QtfwGf/V8RDKQZ3kSHPET/Uc9T3dfH1dWJiEgBp7qEPD096dy5M9HR0Y5tNpuN6OhoevToUew5vXr1IjY2FpvN5th24MABQkNDiw0rABkZGRw6dIjQ0FBnyhO5dnnZsO4t8v/TGZ/9X2EzTHxp3MLPA3/g7gmvKayIiJQzTq/DEhUVxUcffcS8efPYt28fEyZMIDMz0zFraNSoUUyZMsVx/IQJEzh79iyTJk3iwIEDLFu2jNdff52JEyc6jnnqqadYu3YtR48eZcOGDdx9992YzWbuu+++EniLIpcwDNi/DNt7EfDTdNytWWy1NeXpmrPo9rcF3NG9vasrFBGRYjg9hmXEiBEkJiYybdo04uLi6NChAytWrHAMxD1+/DhubhdzUHh4OD/88ANPPvkk7dq1o06dOkyaNIlnn33WcczJkye57777SE5OJjAwkN69e/Prr78SGBhYAm9RpEBiDHz/LBxejRsQZ9TkH/n3EX7TaP7Rvxke5utaR1FERMqAyTAMw9VF3Ki0tDT8/f1JTU3Fz8/P1eVIeXM+Bdb+E2Pzh5hs+eQa7nxkHcRXPiN4fWQPujeq7eoKRUSqJGe+v7WcrFReNiv8/ilEvwJZSZiAVdbOTM9/gNat27N4WFtq+BQ/jkpERMoXBRapnI7/Ct8/A2d2AHCYOryY+yC/mTvy4t2tGNE1HJMWgBMRqTAUWKRySTsNq16EXf8DINvNlzdz7mae9Xaah9Vi6X0daRyoBeBERCoaBRapHPKyYeO78PNMyMvEwMQKj/68kD6MZPx55KZG/P32ZljczVe/loiIlDsKLFKxGQbELIcfnoNzRwFI8G/HX5JH8nt2AwKrW/i/P7WnT1PNOBMRqcgUWKTiSoyBFZPh0E8AWKuF8P8sY5hxqi1g4tYWQbxxTztqV9OqyCIiFZ0Ci1Q8BdOU2fwh2PLB7Mnx5uO4P6Y3J5Lc8HR344U7WvJg9/oaWCsiUkkosEjFsucbWPZ3yEoCwNpsILM9x/Lmb/kANAuuxtv3daRFiNbjERGpTBRYpOLY+gl89wRgQEAzTnd/kYfX+7P3TBoAo3rU57lBLfHy0MBaEZHKRoFFKoZNH8L3TwNgdHmYRQF/5aVvD5Cdl0YtX0/eGN6O/q2CXVykiIiUFgUWKf/W/wdWTQMgu+tEnkgexopf9gPQp2kA/7q3PUF+Xq6sUERESpkCi5RfhgHr3oTVrwGQERHFnbtv4nByPB5mE89EtuCh3g1xc9PAWhGRyk6BRconw7DfA+iXmQBk9JrCnTu6czg5kzo1vJn9QGfa1vV3cZEiIlJWFFik/DEM+0Jwv74PQHrfl7lrWwcOJ9nDysJHuhNey8fFRYqISFlSYJHyxWaD5X+H3+YCkH7rPxm6uQWHEzMJ8/fi8/EKKyIiVZECi5QfNisseRy2LwBMpN0+k7s3NuJQQVhZ+EgP6tVWWBERqYoUWKR8sObB14/C7i/BZCZt4LsM+6UOhxIzCPX34vNHuiusiIhUYQos4nr5ufDlWNi/FNw8SL1jNsPXBhKbYA8rCx/pTv3avq6uUkREXEiBRVwrLxv+NwoO/gBmT1KHzOWen/yITcggxM8+ZkVhRUREFFjEdXKzYOF9cHgNuHuTOvQT7lnpzcGCsLLwke40CFBYERERBRZxlZx0+GwEHFsPHr6kDFvAn1a4cTAhg2A/C58rrIiIyCUUWKTsnU+BBffAyS1g8SN12GeMWG5wID6dYD8LCx/pQUOFFRERuYSbqwuQKibrLMy/0x5WvGqQcu+X/Gm5QUx8OkHVLXw+vrvCioiIFKHAImUnIwE+GQxndoBPACkjvmbk0pyLYeWR7jQKrObqKkVEpBxSYJGykXYaPrkDEvZAtRBSRn7LyG8z2B+XTmBBWGmssCIiIpehwCKlL+U4fDwIkg6AX117WFl89mJYGa+wIiIiV6bAIqXr7GF7WDl3BGo2IHXkt9z3VSL749IJqGYPK02CFFZEROTKFFik9CQesIeV1BNQuwkpI5Yw8osz7DuTRkA1CwsfiVBYERGRa6LAIqUjfg98MgjSz0BgS1L+9A1/XnS8IKx48vn4CJoEVXd1lSIiUkEosEjJO73dPsA2MxFC2pE64mvuX3SUvY6w0p2mwQorIiJy7RRYpGSd/A3m3Qnnz0GdzqT+6Sv+/Fkse06nUdvXk88UVkRE5DoosEjJObYB5t8FOalQrwep93zJ/QtiHGHl80e600xhRUREroMCi5SMw2vg0+GQmwENbyJ12EIe+HQvu09dbFlRWBERkeulwCI37sBKWPAnyMuCJreRevcCHvx0N7tOpVKrIKw0D1FYERGR66fAIjdm33ew8M9gzYHmd5B61yc8OH8nO09eCCsRCisiInLDriuwvPfeezRo0AAvLy8iIiLYvHnzFY9PSUlh4sSJhIaGYrFYaNasGcuXL7+ha0o5sOtL+N9osOVB62GkDvl/jJq3nZ0nU6np48GChyNoEeLn6ipFRKQScDqwLFq0iKioKF588UW2bdtG+/btiYyMJCEhodjjc3Nzue222zh69ChffvklMTExfPTRR9SpU+e6rynlwPbPYPF4MKzQ/j7S7viAUfN+Z0dBWPlsfHdahiqsiIhIyTAZhmE4c0JERARdu3bl3XffBcBmsxEeHs7jjz/O5MmTixw/e/Zs3nzzTfbv34+Hh0eJXPOP0tLS8Pf3JzU1FT8/fUmWut/mwtIn7c87jyGt/xs8OPc3dpxIKWhZ6U6rMH0OIiJyZc58fzvVwpKbm8vWrVvp37//xQu4udG/f382btxY7DlLliyhR48eTJw4keDgYNq0acPrr7+O1Wq97mvm5OSQlpZW6CFl5NcPLoaViEdJ6/8mowrCSg2FFRERKSVOBZakpCSsVivBwcGFtgcHBxMXF1fsOYcPH+bLL7/EarWyfPlypk6dyr/+9S9effXV677mjBkz8Pf3dzzCw8OdeRtyvX75N6woaPHq9QTp/aYz+uMtbHeElQiFFRERKRWlPkvIZrMRFBTEhx9+SOfOnRkxYgTPP/88s2fPvu5rTpkyhdTUVMfjxIkTJVixFGEYsOYf8ONL9p/7TSG99/OM+ngLvx9Pwd/bg08fiqB1mL9LyxQRkcrL3ZmDAwICMJvNxMfHF9oeHx9PSEhIseeEhobi4eGB2Wx2bGvZsiVxcXHk5uZe1zUtFgsWi8WZ0uV6GYY9qKyfZf/51hdJ7/o4o+dudoSVBQ9H0KaOwoqIiJQep1pYPD096dy5M9HR0Y5tNpuN6OhoevToUew5vXr1IjY2FpvN5th24MABQkND8fT0vK5rShkxDHsX0IWwEjmDjG5/Y8zHW9h2PAU/L3eFFRERKRNOdwlFRUXx0UcfMW/ePPbt28eECRPIzMxk7NixAIwaNYopU6Y4jp8wYQJnz55l0qRJHDhwgGXLlvH6668zceLEa76muIDNZh9cu6mg6+6OmWR0eoTRczez9di5grDSXWFFRETKhFNdQgAjRowgMTGRadOmERcXR4cOHVixYoVj0Ozx48dxc7uYg8LDw/nhhx948sknadeuHXXq1GHSpEk8++yz13xNKWM2K3z7GOz4DExucOe7ZLQawZhLwsqnD0fQtq7CioiIlA2n12Epj7QOSwmy5sHiR2DPYjCZYdiHZDQbytiPN7Pl6DmqF3QDtatbw9WViohIBefM97fTLSxSieXnwJfjYP9ScPOAez8ms9HAQmHl04cUVkREpOwpsIhd3nlY9CDErgKzBUZ8Smb9Wxj78RZ7WLG4838PRdA+vIarKxURkSpIgUUgNxM+HwlH1oG7N9z3OZl1+zD2ky1sPnrWHlYejqCDwoqIiLiIAktVl50Gn/0Jjm8Ez2pw/xdkhXZj3Mdb2HzEHlbmP9RNYUVERFxKgaUqO58Cnw6HU7+BxR8e+Iqs4I6M+2QLm46cpZrFnXkPdaNjvZqurlRERKo4BZaqKjMZ/m8oxO0E71rw4NecD2jLQ59s4dfD9rAy/6FudFJYERGRckCBpSrKSID5d0HCXvANhFFLOF+zOeM+2cLGw8n2lpVxCisiIlJ+KLBUNWmnYd6dkHwQqofaw4p/Yx6aZw8rvp5m5o3rSuf6CisiIlJ+KLBUJSnHYd4QOHcU/MNh9BLOV6vPw/O3sOGQPazMf6gbnevXcnWlIiIihSiwVBXJh+wtK2knoWZDGL2EbN86jJ/3G+tjL7SsKKyIiEj5pMBSFeSk2wfYpp2EgGYw6luyvYN5eN5v/BKbhI+nmU/GdaNLA4UVEREpnxRYqoKVU+3dQTXqwZhlZFtqM37+JWFlbDe6KqyIiEg55nb1Q6RCO7Qatn5sf37X+46w8vNBe1j5eExXujVUWBERkfJNgaUyy06DJY/bn3d7hOy6PXnk/7by88EkvD3sYSWiUW3X1igiInINFFgqs5UvQOoJqNmA7L5TeeT/trLuQKI9rIxVWBERkYpDgaWyio2GbfPsz+96j79/E+sIK3PHdKW7woqIiFQgCiyVUXYqLPmb/Xm3v3CsekeW7TqDmwnmjOlCj8YKKyIiUrEosFRGK1+4uN5K/xdZvO0UAL2bBtKzcYCLixMREXGeAktlE/sjbJsPmGDo+xgePiz+/SQAwzvVcW1tIiIi10mBpTK5tCso4lGo35MtR89x4ux5fD3N3N4qxLX1iYiIXCcFlsrkh+cg7RTUagS3TgNg8TZ768qgtqF4e5pdWZ2IiMh1U2CpLA6ugt8/BUxw1/vg6UN2npVlO88AMKxTXdfWJyIicgMUWCqD8ykXu4K6T4D6PQBYtTee9Jx86tTwJkKr2YqISAWmwFIZ/PA8pJ+GWo3hlqmOzRe6g+7uWAc3N5OrqhMREblhCiwV3YEfYHtBV9BQe1cQQEJ6NusOJgFwt2YHiYhIBafAUpGdPwffTbI/7zER6nV37Fqy/TRWm0GH8Bo0DqzmogJFRERKhgJLRbbiOUg/A7WbwC0vFNp1YbE4rb0iIiKVgQJLRRWzAnZ8BiY3GPoBeHg7du07k8beM2l4mE0MbhfmwiJFRERKhgJLRfTHrqDwboV2f/27vXXllhZB1PT1LOvqRERESpwCS0X0/WTIiIPaTeHm5wvtyrfaHIFFa6+IiEhlocBS0cR8DzsXFtsVBLD+UDKJ6TnU8PHg5uZBLipSRESkZCmwVCRZZy/pCnoMwrsWOeTC2it3tg/D010fr4iIVA76RqtIVkyGjHgIaFakKwggPTuPH/bEAeoOEhGRykWBpaLYvwx2LiroCpoNHl5FDvl+dxzZeTYaBfrSvq6/C4oUEREpHQosFUHWWfjuCfvznn+Dup2LPexCd9DwTnUxmbQUv4iIVB7XFVjee+89GjRogJeXFxEREWzevPmyx37yySeYTKZCDy+vwq0DY8aMKXLMgAEDrqe0yun7ZyAzAQJbQL8pxR5y8lwWvx4+i8kEQztqsTgREalc3J09YdGiRURFRTF79mwiIiKYNWsWkZGRxMTEEBRU/KwUPz8/YmJiHD8X96//AQMG8PHHHzt+tlgszpZWOe1bCru+sHcF3fV+sV1BAF8XrGzbo1Ft6tTwLvYYERGRisrpwDJz5kzGjx/P2LFjAZg9ezbLli1j7ty5TJ48udhzTCYTISEhV7yuxWK56jEX5OTkkJOT4/g5LS3tGquvYDKTYekT9ue9nrhsV5BhGCzW2isiIlKJOdUllJuby9atW+nfv//FC7i50b9/fzZu3HjZ8zIyMqhfvz7h4eHcdddd7Nmzp8gxa9asISgoiObNmzNhwgSSk5Mve70ZM2bg7+/veISHhzvzNiqO75+GzEQIbAn9ig+DAL+fSOFIUibeHmYGtLm20CciIlKROBVYkpKSsFqtBAcHF9oeHBxMXFxcsec0b96cuXPn8u233/Lpp59is9no2bMnJ0+edBwzYMAA5s+fT3R0NP/85z9Zu3YtAwcOxGq1FnvNKVOmkJqa6nicOHHCmbdRMexdAru/ApMZhr4P7pfvIrsw2HZAmxCqWZxuNBMRESn3Sv3brUePHvTo0cPxc8+ePWnZsiX//e9/mT59OgAjR4507G/bti3t2rWjcePGrFmzhltvvbXINS0WS+Ue45KZDMui7M97PwF1Ol320Jx8K9/tOAPAMN2ZWUREKimnWlgCAgIwm83Ex8cX2h4fH3/N4088PDzo2LEjsbGxlz2mUaNGBAQEXPGYSm35U/auoKBW0PfZKx66en8CqefzCPaz0LNxQBkVKCIiUracCiyenp507tyZ6OhoxzabzUZ0dHShVpQrsVqt7Nq1i9DQ0Msec/LkSZKTk694TKW15xvYs/iauoIAviqYHTS0Yx3Mblp7RUREKien12GJiorio48+Yt68eezbt48JEyaQmZnpmDU0atQopky5uFbIK6+8wsqVKzl8+DDbtm3jgQce4NixYzz88MOAfUDu008/za+//srRo0eJjo7mrrvuokmTJkRGRpbQ26wgMpNg2d/tz/tEQVjHKx5+NjOX1fsTABjWUbODRESk8nJ6DMuIESNITExk2rRpxMXF0aFDB1asWOEYiHv8+HHc3C7moHPnzjF+/Hji4uKoWbMmnTt3ZsOGDbRq1QoAs9nMzp07mTdvHikpKYSFhXH77bczffr0yj1OpTjL/g5ZSRDUGm565qqHf7fjNPk2gzZ1/GgeUr0MChQREXENk2EYhquLuFFpaWn4+/uTmpqKn5+fq8u5Pnu+hi/G2LuCxv8EYR2uespd7/7CjpOpTBvcinG9G5Z6iSIiIiXJme9v3UuoPMhIvNgVdNNT1xRWYhPS2XEyFbObiTs7hJVufSIiIi6mwOJqhmGfwpyVDMFtoc9T13Ta4oLBtv2aBRJQrYp1nYmISJWjwOJqexbDviXg5l4wK8jzqqfYbAZfayl+ERGpQhRYXCkjAZYVtKj0eQpC213Tab8eTuZMajZ+Xu7c2rL4G06KiIhUJgosrnKhK+j82YKuoL9f86kX1l4Z3D4MLw9zaVUoIiJSbiiwuMrur2Dfd/auoLs/uKauIICs3Hy+321fin+4luIXEZEqQoHFFdLj7cvvg329lZC213zqD3viyMq1Ur+2D53q1SylAkVERMoXBZay5ugKOgch7ewr2jrhq60Fg2071sVk0lL8IiJSNSiwlLVdX8L+peDmAUM/ALPHNZ96JvU86w8lAXB3R3UHiYhI1aHAUpbS4+H7p+3P+z4DIW2cOv2b309jGNCtQS3q1fYphQJFRETKJwWWsmIYsPRJe1dQaHvo/aSTpxss3nYSgGEabCsiIlWMAktZ2fk/iFl2XV1BALtPpXEwIQNPdzcGtQstpSJFRETKJwWWspAeB98X3H2537MQ3NrpS3xV0Lpye6tg/LycCzsiIiIVnQJLaTMM+O4JyE6B0A7Qy7muIIA8q40lO04DMFxL8YuISBWkwFLadi6CA9+D2bOgK8jd6UusjUnkbGYuAdU86dM0oBSKFBERKd8UWEpT2plLuoImQ3Cr67rM4t/t3UF3daiDu1kfmYiIVD369isthgHfTYLsVAjrBD0nXddlUrPy+HFvAqDZQSIiUnUpsJSWHZ/DwR9uqCsIYOmu0+RabbQIqU6rUL8SLlJERKRiUGApDWmn4fvJ9uf9pkBQi+u+1OKCOzMP61RHS/GLiEiVpcBS0i50BeWkQp3O0PNv132po0mZbD12DjeTffyKiIhIVaXAUtK2L4CDK8FsuaGuIIDFv9tbV3o3DSTYz6ukKhQREalwFFhKUuopWDHF/vzm5yCw+XVfyma7uBT/cA22FRGRKk6BpaQYBnz3N8hJgzpdoOfjN3S5346d4+S581SzuHN7q5ASKlJERKRiUmApKb9/CrE/XuwKcjPf0OUutK4MahuCt+eNXUtERKSiU2ApCakn4Yfn7M9veQECm93Q5bLzrCzbeQaAYVqKX0RERIHlhhkGLCnoCqrbDXpMvOFLrtwbT3pOPnVqeNOtQa0SKFJERKRiU2C5Udvmw6FocPeCoe/fcFcQXOwOGtapDm5uWntFREREgeVGpJyAH563P7/lBQhoesOXTEjPZt2BRADu7qjZQSIiIqDAcv0MA5Y8DrnpEB4B3f9aIpddsv00NgM61qtBo8BqJXJNERGRik6B5XptmweHV9u7gu4qma4ggK8cS/FrsK2IiMgFCizXI+X4xa6gW6dBQJMSueze02nsO5OGh9nEkHahJXJNERGRykCBxVmOrqAMCO8OEY+W2KW//t0+2PbWFsHU8PEsseuKiIhUdAosztr6MRxeA+7eJTYrCCDfauOb7acB++wgERERuUiBxRnnjsHKqfbn/V+E2o1L7NK/xCaRmJ5DTR8P+jUPKrHrioiIVAbXFVjee+89GjRogJeXFxEREWzevPmyx37yySeYTKZCDy+vwnceNgyDadOmERoaire3N/379+fgwYPXU1rpsdlgyWP2rqB6PaHbX0r08osLBtve2T4MT3flSBERkUs5/c24aNEioqKiePHFF9m2bRvt27cnMjKShISEy57j5+fHmTNnHI9jx44V2v/GG2/w9ttvM3v2bDZt2oSvry+RkZFkZ2c7/45Ky9a5cGSdvSvornfBreRCRXp2Hj/siQM0O0hERKQ4Tn/rzpw5k/HjxzN27FhatWrF7Nmz8fHxYe7cuZc9x2QyERIS4ngEBwc79hmGwaxZs3jhhRe46667aNeuHfPnz+f06dN888031/WmSty5o7Bymv15/5dKtCsI4PtdceTk22gc6Eu7uv4lem0REZHKwKnAkpuby9atW+nfv//FC7i50b9/fzZu3HjZ8zIyMqhfvz7h4eHcdddd7Nmzx7HvyJEjxMXFFbqmv78/ERERl71mTk4OaWlphR6lxmaDbx+DvEyo3wu6PVLiL/GVYyn+uphMWopfRETkj5wKLElJSVit1kItJADBwcHExcUVe07z5s2ZO3cu3377LZ9++ik2m42ePXty8qT9S/rCec5cc8aMGfj7+zse4eHhzrwN5/w2B47+DB4+Jd4VBHDibBabjpzFZNJS/CIiIpdT6qM7e/TowahRo+jQoQN9+/Zl8eLFBAYG8t///ve6rzllyhRSU1MdjxMnTpRgxZc4ewRWXegKehlqNSrxl/jmd/tg256NaxNWw7vEry8iIlIZuDtzcEBAAGazmfj4+ELb4+PjCQkJuaZreHh40LFjR2JjYwEc58XHxxMaenF11/j4eDp06FDsNSwWCxaLxZnSr4+XP7S8E9JOQdeHS/zyhmGwuCCwDOuowbYiIiKX41QLi6enJ507dyY6OtqxzWazER0dTY8ePa7pGlarlV27djnCScOGDQkJCSl0zbS0NDZt2nTN1yw1PrVg2H/h/i9KvCsIYNvxFI4kZeLtYWZAm2sLfCIiIlWRUy0sAFFRUYwePZouXbrQrVs3Zs2aRWZmJmPHjgVg1KhR1KlThxkzZgDwyiuv0L17d5o0aUJKSgpvvvkmx44d4+GH7S0WJpOJJ554gldffZWmTZvSsGFDpk6dSlhYGEOHDi25d3ojPEqnq2ZxwWDbgW1C8LU4/VGIiIhUGU5/S44YMYLExESmTZtGXFwcHTp0YMWKFY5Bs8ePH8ftktaIc+fOMX78eOLi4qhZsyadO3dmw4YNtGrVynHMM888Q2ZmJo888ggpKSn07t2bFStWFFlgrjLJybfy3Y4LS/GrO0hERORKTIZhGK4u4kalpaXh7+9Pamoqfn5+ri7nmny/6wwTFmwjxM+L9ZNvweym6cwiIlK1OPP9rTXgXeSrgqX4h3aso7AiIiJyFQosLpCckcOaGPutDHRnZhERkatTYHGB73acJt9m0LaOP82Cq7u6HBERkXJPgcUFHGuvqHVFRETkmiiwlLGD8ensPJmKu5uJIe3DXF2OiIhIhaDAUsYutK70ax5IQLUyWK1XRESkElBgKUNWm+G4d5DWXhEREbl2Cixl6NfDyZxJzcbPy51bWgS5uhwREZEKQ4GlDH1VsBT/4PZheHmYXVyNiIhIxaHAUkYyc/JZsTsOgOHqDhIREXGKAksZ+WFPHFm5VhrU9qFTvRquLkdERKRCUWApI4u3XRxsazJpKX4RERFnKLCUgTOp51l/KAmAuztqsTgRERFnKbCUgW9+P41hQLeGtQiv5ePqckRERCocBZZSZhiGY3bQcC3FLyIicl0UWErZrlOpxCZkYHF3Y2DbUFeXIyIiUiEpsJSyC4Ntb28dgp+Xh4urERERqZgUWEpRbr6NJTtOA7ozs4iIyI1QYClFaw8kcjYzl4BqFvo0CXB1OSIiIhWWAkspWlww2HZohzDczfpVi4iIXC99i5aSlKxcovclALozs4iIyI1SYCklS3eeIddqo0VIdVqF+bm6HBERkQpNgaWULHasvaLWFRERkRulwFIKjiRlsu14Cm4muKtDmKvLERERqfAUWErB1wWtK32aBhLk5+XiakRERCo+BZYSZrMZLP79wp2ZtfaKiIhISVBgKWFbjp7l5LnzVLe4E9k6xNXliIiIVAoKLCXswlL8g9qG4uVhdnE1IiIilYMCSwnKzrOybNcZQN1BIiIiJUmBpQSt3BtPRk4+dWt607VBLVeXIyIiUmkosJSgC2uvDOtYBzc3k4urERERqTwUWEpIQlo26w4kAnC3FosTEREpUQosJeTb7aexGdCpXg0aBvi6uhwREZFKRYGlhHx1oTtIrSsiIiIlToGlBOw9ncb+uHQ8zW4Mbhfq6nJEREQqnesKLO+99x4NGjTAy8uLiIgINm/efE3nLVy4EJPJxNChQwttHzNmDCaTqdBjwIAB11OaS1wYbHtryyBq+Hi6uBoREZHKx+nAsmjRIqKionjxxRfZtm0b7du3JzIykoSEhCued/ToUZ566in69OlT7P4BAwZw5swZx+Pzzz93tjSXyLfa+Gb7aUDdQSIiIqXF6cAyc+ZMxo8fz9ixY2nVqhWzZ8/Gx8eHuXPnXvYcq9XK/fffz8svv0yjRo2KPcZisRASEuJ41KxZ09nSXOLn2CSSMnKo5etJ32aBri5HRESkUnIqsOTm5rJ161b69+9/8QJubvTv35+NGzde9rxXXnmFoKAgHnroocses2bNGoKCgmjevDkTJkwgOTn5ssfm5OSQlpZW6OEqF5biv7N9GJ7uGhIkIiJSGpz6hk1KSsJqtRIcHFxoe3BwMHFxccWe88svvzBnzhw++uijy153wIABzJ8/n+joaP75z3+ydu1aBg4ciNVqLfb4GTNm4O/v73iEh4c78zZKTFp2Hiv32N+3luIXEREpPe6lefH09HQefPBBPvroIwICAi573MiRIx3P27ZtS7t27WjcuDFr1qzh1ltvLXL8lClTiIqKcvyclpbmktDy/a4z5OTbaBJUjbZ1/Mv89UVERKoKpwJLQEAAZrOZ+Pj4Qtvj4+MJCQkpcvyhQ4c4evQoQ4YMcWyz2Wz2F3Z3JyYmhsaNGxc5r1GjRgQEBBAbG1tsYLFYLFgsFmdKLxVfFXQHDetUB5NJS/GLiIiUFqe6hDw9PencuTPR0dGObTabjejoaHr06FHk+BYtWrBr1y62b9/ueNx5553cfPPNbN++/bKtIidPniQ5OZnQ0PK7psmJs1lsPnIWkwnu7qjuIBERkdLkdJdQVFQUo0ePpkuXLnTr1o1Zs2aRmZnJ2LFjARg1ahR16tRhxowZeHl50aZNm0Ln16hRA8CxPSMjg5dffpnhw4cTEhLCoUOHeOaZZ2jSpAmRkZE3+PZKz9e/21tXejUOINTf28XViIiIVG5OB5YRI0aQmJjItGnTiIuLo0OHDqxYscIxEPf48eO4uV17w43ZbGbnzp3MmzePlJQUwsLCuP3225k+fXq56PYpjmEYF+/MrMG2IiIipc5kGIbh6iJuVFpaGv7+/qSmpuLn51fqr7f12DmGf7ABH08zW57vj6+lVMcui4iIVErOfH9r4ZDrcKF1ZUCbEIUVERGRMqDA4qScfCvf7bAvxT9cS/GLiIiUCQUWJ0XvSyAtO59Qfy+6N6rt6nJERESqBAUWJ13oDhrasQ5mN629IiIiUhYUWJyQlJHDmphEAIZp7RUREZEyo8DihO92nCbfZtCurj9Ng6u7uhwREZEqQ4HFCRfuzKzWFRERkbKlwHKNDsSns+tUKu5uJoa0D3N1OSIiIlWKAss1utC60q95ELWrlc8VeEVERCorBZZrYLUZfFNw76DhWopfRESkzCmwXIONh5KJS8vGz8udW1oGubocERGRKkeB5RpcWHtlSPswLO5mF1cjIiJS9SiwXEVmTj7f744DYJiW4hcREXEJBZarWLE7jvN5VhoG+NKpXg1XlyMiIlIlKbBcxeLf7d1BwzrWwWTSUvwiIiKuoMByBadTzrPhUDJgv3eQiIiIuIa7qwsoz/y8PXhtaFsOxKcTXsvH1eWIiIhUWQosV1DN4s6fI+q5ugwREZEqT11CIiIiUu4psIiIiEi5p8AiIiIi5Z4Ci4iIiJR7CiwiIiJS7imwiIiISLmnwCIiIiLlngKLiIiIlHsKLCIiIlLuKbCIiIhIuafAIiIiIuWeAouIiIiUewosIiIiUu5Virs1G4YBQFpamosrERERkWt14Xv7wvf4lVSKwJKeng5AeHi4iysRERERZ6Wnp+Pv73/FY0zGtcSacs5ms3H69GmqV6+OyWQq0WunpaURHh7OiRMn8PPzK9Fri/P0eZQv+jzKH30m5Ys+jyszDIP09HTCwsJwc7vyKJVK0cLi5uZG3bp1S/U1/Pz89D9bOaLPo3zR51H+6DMpX/R5XN7VWlYu0KBbERERKfcUWERERKTcU2C5CovFwosvvojFYnF1KYI+j/JGn0f5o8+kfNHnUXIqxaBbERERqdzUwiIiIiLlngKLiIiIlHsKLCIiIlLuKbCIiIhIuafAIiIiIuWeAstVvPfeezRo0AAvLy8iIiLYvHmzq0uqkmbMmEHXrl2pXr06QUFBDB06lJiYGFeXJQX+8Y9/YDKZeOKJJ1xdSpV16tQpHnjgAWrXro23tzdt27blt99+c3VZVZLVamXq1Kk0bNgQb29vGjduzPTp06/pBn9yeQosV7Bo0SKioqJ48cUX2bZtG+3btycyMpKEhARXl1blrF27lokTJ/Lrr7+yatUq8vLyuP3228nMzHR1aVXeli1b+O9//0u7du1cXUqVde7cOXr16oWHhwfff/89e/fu5V//+hc1a9Z0dWlV0j//+U8++OAD3n33Xfbt28c///lP3njjDd555x1Xl1ahaR2WK4iIiKBr1668++67gP0mi+Hh4Tz++ONMnjzZxdVVbYmJiQQFBbF27VpuuukmV5dTZWVkZNCpUyfef/99Xn31VTp06MCsWbNcXVaVM3nyZNavX8/PP//s6lIEGDx4MMHBwcyZM8exbfjw4Xh7e/Ppp5+6sLKKTS0sl5Gbm8vWrVvp37+/Y5ubmxv9+/dn48aNLqxMAFJTUwGoVauWiyup2iZOnMgdd9xR6M+JlL0lS5bQpUsX7r33XoKCgujYsSMfffSRq8uqsnr27El0dDQHDhwAYMeOHfzyyy8MHDjQxZVVbJXibs2lISkpCavVSnBwcKHtwcHB7N+/30VVCdhbup544gl69epFmzZtXF1OlbVw4UK2bdvGli1bXF1KlXf48GE++OADoqKieO6559iyZQt/+9vf8PT0ZPTo0a4ur8qZPHkyaWlptGjRArPZjNVq5bXXXuP+++93dWkVmgKLVDgTJ05k9+7d/PLLL64upco6ceIEkyZNYtWqVXh5ebm6nCrPZrPRpUsXXn/9dQA6duzI7t27mT17tgKLC/zvf/9jwYIFfPbZZ7Ru3Zrt27fzxBNPEBYWps/jBiiwXEZAQABms5n4+PhC2+Pj4wkJCXFRVfLYY4+xdOlS1q1bR926dV1dTpW1detWEhIS6NSpk2Ob1Wpl3bp1vPvuu+Tk5GA2m11YYdUSGhpKq1atCm1r2bIlX331lYsqqtqefvppJk+ezMiRIwFo27Ytx44dY8aMGQosN0BjWC7D09OTzp07Ex0d7dhms9mIjo6mR48eLqysajIMg8cee4yvv/6an376iYYNG7q6pCrt1ltvZdeuXWzfvt3x6NKlC/fffz/bt29XWCljvXr1KjLN/8CBA9SvX99FFVVtWVlZuLkV/no1m83YbDYXVVQ5qIXlCqKiohg9ejRdunShW7duzJo1i8zMTMaOHevq0qqciRMn8tlnn/Htt99SvXp14uLiAPD398fb29vF1VU91atXLzJ+yNfXl9q1a2tckQs8+eST9OzZk9dff50//elPbN68mQ8//JAPP/zQ1aVVSUOGDOG1116jXr16tG7dmt9//52ZM2cybtw4V5dWsRlyRe+8845Rr149w9PT0+jWrZvx66+/urqkKgko9vHxxx+7ujQp0LdvX2PSpEmuLqPK+u6774w2bdoYFovFaNGihfHhhx+6uqQqKy0tzZg0aZJRr149w8vLy2jUqJHx/PPPGzk5Oa4urULTOiwiIiJS7mkMi4iIiJR7CiwiIiJS7imwiIiISLmnwCIiIiLlngKLiIiIlHsKLCIiIlLuKbCIiIhIuafAIiIiIuWeAouIiIiUewosIiIiUu4psIiIiEi59/8BlCfSmEDovt8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "img_path = \"horse.jpg\"\n",
        "\n",
        "# Load image\n",
        "img = Image.open(img_path).resize((32,32))\n",
        "\n",
        "# Convert to array\n",
        "img_array = np.array(img) / 255.0\n",
        "\n",
        "# Reshape for model\n",
        "img_array = img_array.reshape(1,32,32,3)\n",
        "\n",
        "# Predict\n",
        "prediction = model.predict(img_array)\n",
        "class_index = np.argmax(prediction)\n",
        "\n",
        "print(\"Predicted class:\", class_names[class_index])\n",
        "\n",
        "# Show image\n",
        "plt.imshow(img)\n",
        "plt.title(\"Input Image\")\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "zuY8qe3u3-D2",
        "outputId": "bb718e70-4444-4aee-8742-1b3070ec7ce6"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n",
            "Predicted class: horse\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}