{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "565162b6-9432-46c2-8fa9-af3b856dae43",
   "metadata": {},
   "source": [
    "# CFAR10 **Convolutional Neural Network (CNN)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e40e38da-f884-442e-9b7f-ff2ef51783ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, models\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Load CIFAR-10 dataset\n",
    "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
    "\n",
    "# Normalize the pixel values to range [0, 1]\n",
    "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
    "\n",
    "# Define class names for CIFAR-10\n",
    "class_names = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \n",
    "               \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]\n",
    "\n",
    "\n",
    "# Split training data into training & validation sets\n",
    "x_train, x_val = x_train[:45000], x_train[45000:]\n",
    "y_train, y_val = y_train[:45000], y_train[45000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da8cb35a-e9f3-4b96-bbff-071ac65634d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "\n",
      "Dimensions of the TRAINING dataset (x_train): (45000, 32, 32, 3)\n",
      "\n",
      "Dimensions of the VALIDATION dataset (x_val): (5000, 32, 32, 3)\n",
      "\n",
      " =============== View y_train:\n",
      " [[6]\n",
      " [9]\n",
      " [9]\n",
      " ...\n",
      " [1]\n",
      " [5]\n",
      " [6]]\n",
      "\n",
      " =============== View x_train:\n",
      " [[[0.60392157 0.69411765 0.73333333]\n",
      "  [0.49411765 0.5372549  0.53333333]\n",
      "  [0.41176471 0.40784314 0.37254902]\n",
      "  ...\n",
      "  [0.35686275 0.37254902 0.27843137]\n",
      "  [0.34117647 0.35294118 0.27843137]\n",
      "  [0.30980392 0.31764706 0.2745098 ]]\n",
      "\n",
      " [[0.54901961 0.62745098 0.6627451 ]\n",
      "  [0.56862745 0.6        0.60392157]\n",
      "  [0.49019608 0.49019608 0.4627451 ]\n",
      "  ...\n",
      "  [0.37647059 0.38823529 0.30588235]\n",
      "  [0.30196078 0.31372549 0.24313725]\n",
      "  [0.27843137 0.28627451 0.23921569]]\n",
      "\n",
      " [[0.54901961 0.60784314 0.64313725]\n",
      "  [0.54509804 0.57254902 0.58431373]\n",
      "  [0.45098039 0.45098039 0.43921569]\n",
      "  ...\n",
      "  [0.30980392 0.32156863 0.25098039]\n",
      "  [0.26666667 0.2745098  0.21568627]\n",
      "  [0.2627451  0.27058824 0.21568627]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.68627451 0.65490196 0.65098039]\n",
      "  [0.61176471 0.60392157 0.62745098]\n",
      "  [0.60392157 0.62745098 0.66666667]\n",
      "  ...\n",
      "  [0.16470588 0.13333333 0.14117647]\n",
      "  [0.23921569 0.20784314 0.22352941]\n",
      "  [0.36470588 0.3254902  0.35686275]]\n",
      "\n",
      " [[0.64705882 0.60392157 0.50196078]\n",
      "  [0.61176471 0.59607843 0.50980392]\n",
      "  [0.62352941 0.63137255 0.55686275]\n",
      "  ...\n",
      "  [0.40392157 0.36470588 0.37647059]\n",
      "  [0.48235294 0.44705882 0.47058824]\n",
      "  [0.51372549 0.4745098  0.51372549]]\n",
      "\n",
      " [[0.63921569 0.58039216 0.47058824]\n",
      "  [0.61960784 0.58039216 0.47843137]\n",
      "  [0.63921569 0.61176471 0.52156863]\n",
      "  ...\n",
      "  [0.56078431 0.52156863 0.54509804]\n",
      "  [0.56078431 0.5254902  0.55686275]\n",
      "  [0.56078431 0.52156863 0.56470588]]]\n"
     ]
    }
   ],
   "source": [
    "print(type(x_train))\n",
    "\n",
    "print(\"\\nDimensions of the TRAINING dataset (x_train):\", x_train.shape)\n",
    "print(\"\\nDimensions of the VALIDATION dataset (x_val):\", x_val.shape)\n",
    "\n",
    "print(\"\\n =============== View y_train:\\n\", y_train)\n",
    "print(\"\\n =============== View x_train:\\n\", x_train[1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d06ef9cf-650c-4595-a29e-224b7550810a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define batch size\n",
    "BATCH_SIZE = 64\n",
    "\n",
    "# Data Augmentation\n",
    "data_augmentation = keras.Sequential([\n",
    "    layers.RandomFlip(\"horizontal\"),  # Randomly flip images horizontally\n",
    "    layers.RandomRotation(0.1),       # Slight rotation\n",
    "    layers.RandomZoom(0.1)            # Slight zoom\n",
    "])\n",
    "\n",
    "# Create ImageDataGenerator for training\n",
    "train_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1,\n",
    "    horizontal_flip=True\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow(x_train, y_train, batch_size=BATCH_SIZE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cc55e102-ec19-417c-85c0-bd85a49726f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the CNN Model\n",
    "def create_cnn_model():\n",
    "    model = models.Sequential([\n",
    "\n",
    "        # define the input size before adding layers.\n",
    "        keras.Input(shape=(32, 32, 3)), \n",
    "        \n",
    "        # ===================================================================\n",
    "        # First stage is to find features at different levels\n",
    "        # ===================================================================\n",
    "        # Define Convolutional (creates feature maps) and Pooling layers (reduces dimension while retain feature)\n",
    "        layers.Conv2D(32, (3, 3), activation='relu'), # 32 nodes, 3x3 filter\n",
    "        layers.MaxPooling2D((2, 2)),\n",
    "        \n",
    "        layers.Conv2D(64, (3, 3), activation='relu'), # 64 nodes, 3x3 filter\n",
    "        layers.MaxPooling2D((2, 2)),\n",
    "        \n",
    "        layers.Conv2D(128, (3, 3), activation='relu'), # 128 nodes, 3x3 filter\n",
    "        layers.MaxPooling2D((2, 2)),\n",
    "        \n",
    "        # ===================================================================\n",
    "        # The Following is like MLP - Input layer, Hidden layer, Output Layer\n",
    "        # ===================================================================\n",
    "        layers.Flatten(),\n",
    "        layers.Dense(128, activation='relu'),\n",
    "        \n",
    "        layers.Dropout(0.5),  # Dropout (deactivates some nodes) to prevent overfitting\n",
    "        \n",
    "        layers.Dense(10, activation='softmax')  # Output layer with 10 classes\n",
    "    ])\n",
    "    return model\n",
    "\n",
    "# Compile the model\n",
    "model = create_cnn_model()\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "766f069b-7f45-44fe-8bd6-2359f18e21b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Callbacks\n",
    "checkpoint_callback = keras.callbacks.ModelCheckpoint(\n",
    "    \"best_model.keras\", monitor=\"val_accuracy\", save_best_only=True)  # Save as .keras format\n",
    "\n",
    "early_stopping_callback = keras.callbacks.EarlyStopping(\n",
    "    patience=5, restore_best_weights=True)\n",
    "\n",
    "log_dir = \"logs\"\n",
    "tensorboard_callback = keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n",
    "\n",
    "\n",
    "# Define CSV Logger: logs training details into 'training_log.csv'\n",
    "csv_logger = keras.callbacks.CSVLogger(\"training_log.csv\", append=True)  # append=True to continue logging if file exists\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1683bfa6-2511-4525-b510-7bb96886c7b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aliyu\\anaconda3\\Lib\\site-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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 30ms/step - accuracy: 0.2434 - loss: 2.0054 - val_accuracy: 0.4344 - val_loss: 1.5166\n",
      "Epoch 2/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 30ms/step - accuracy: 0.4296 - loss: 1.5622 - val_accuracy: 0.5300 - val_loss: 1.3054\n",
      "Epoch 3/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 30ms/step - accuracy: 0.4859 - loss: 1.4217 - val_accuracy: 0.5370 - val_loss: 1.2680\n",
      "Epoch 4/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 30ms/step - accuracy: 0.5373 - loss: 1.3069 - val_accuracy: 0.5608 - val_loss: 1.1974\n",
      "Epoch 5/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 30ms/step - accuracy: 0.5642 - loss: 1.2365 - val_accuracy: 0.6266 - val_loss: 1.0513\n",
      "Epoch 6/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 30ms/step - accuracy: 0.5801 - loss: 1.1834 - val_accuracy: 0.6252 - val_loss: 1.0501\n",
      "Epoch 7/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 31ms/step - accuracy: 0.5994 - loss: 1.1412 - val_accuracy: 0.6342 - val_loss: 1.0309\n",
      "Epoch 8/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 30ms/step - accuracy: 0.6114 - loss: 1.1043 - val_accuracy: 0.6850 - val_loss: 0.9017\n",
      "Epoch 9/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 29ms/step - accuracy: 0.6286 - loss: 1.0603 - val_accuracy: 0.6800 - val_loss: 0.8975\n",
      "Epoch 10/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 31ms/step - accuracy: 0.6350 - loss: 1.0438 - val_accuracy: 0.7058 - val_loss: 0.8286\n",
      "Epoch 11/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 30ms/step - accuracy: 0.6513 - loss: 1.0048 - val_accuracy: 0.6866 - val_loss: 0.8889\n",
      "Epoch 12/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 31ms/step - accuracy: 0.6578 - loss: 0.9810 - val_accuracy: 0.7088 - val_loss: 0.8232\n",
      "Epoch 13/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 31ms/step - accuracy: 0.6676 - loss: 0.9534 - val_accuracy: 0.7084 - val_loss: 0.8495\n",
      "Epoch 14/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 31ms/step - accuracy: 0.6752 - loss: 0.9371 - val_accuracy: 0.7128 - val_loss: 0.8073\n",
      "Epoch 15/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 30ms/step - accuracy: 0.6798 - loss: 0.9310 - val_accuracy: 0.7212 - val_loss: 0.8090\n",
      "Epoch 16/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 30ms/step - accuracy: 0.6830 - loss: 0.9196 - val_accuracy: 0.7414 - val_loss: 0.7487\n",
      "Epoch 17/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 30ms/step - accuracy: 0.6926 - loss: 0.8927 - val_accuracy: 0.7360 - val_loss: 0.7760\n",
      "Epoch 18/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 31ms/step - accuracy: 0.6932 - loss: 0.8832 - val_accuracy: 0.7298 - val_loss: 0.7976\n",
      "Epoch 19/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 31ms/step - accuracy: 0.6962 - loss: 0.8850 - val_accuracy: 0.7486 - val_loss: 0.7193\n",
      "Epoch 20/20\n",
      "\u001b[1m704/704\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 29ms/step - accuracy: 0.7032 - loss: 0.8679 - val_accuracy: 0.7398 - val_loss: 0.7620\n",
      "\n",
      "Evaluating Final Model (from last epoch):\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.7413 - loss: 0.7544\n",
      "\n",
      "Final Model Test Accuracy: 0.7387\n"
     ]
    }
   ],
   "source": [
    "# Train the model with data augmentation\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    epochs=20,\n",
    "    validation_data=(x_val, y_val),\n",
    "    callbacks=[checkpoint_callback, early_stopping_callback, tensorboard_callback, csv_logger]\n",
    ")\n",
    "\n",
    "# First, evaluate with the final model (from the last epoch)\n",
    "print(\"\\nEvaluating Final Model (from last epoch):\")\n",
    "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
    "print(f\"\\nFinal Model Test Accuracy: {test_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9743939c-c92d-44ed-aa80-05f5afce4b2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluating Best Model (from checkpoint):\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - accuracy: 0.7413 - loss: 0.7544\n",
      "\n",
      "Best Model Test Accuracy: 0.7387\n"
     ]
    }
   ],
   "source": [
    "# Then, evaluate with the best model (from checkpoints)\n",
    "print(\"\\nEvaluating Best Model (from checkpoint):\")\n",
    "best_model = keras.models.load_model(\"best_model.keras\")  # Load the best model\n",
    "test_loss_best, test_acc_best = best_model.evaluate(x_test, y_test)\n",
    "print(f\"\\nBest Model Test Accuracy: {test_acc_best:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "439df713-288f-4638-b2f5-dbdc81c0f82f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on test images using the best model\n",
    "predictions = best_model.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "39e2ae8a-000f-4c4e-b3d5-c725f380b04b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7YAAADYCAYAAADf7H8vAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB3CElEQVR4nO29d5xldX3//z7n3F5m7pTdKdsru3SQ4iIBrCB+VTTGglGJJsZEoxhNiD8bRMESiUSjxq+GEjVfxUYUu7RQXJCusLuwvZfpM7ef8vtjZO7Mvl6Lsyg7e3dfz8fDh8x7zz3ncz7n/Wn33s/zOlEURSaEEEIIIYQQQjQp7kwXQAghhBBCCCGE+EPQwlYIIYQQQgghRFOjha0QQgghhBBCiKZGC1shhBBCCCGEEE2NFrZCCCGEEEIIIZoaLWyFEEIIIYQQQjQ1WtgKIYQQQgghhGhqtLAVQgghhBBCCNHUxKZzUBiGtnPnTsvn8+Y4zrNdJiGmEEWRjY6OWm9vr7nuoX8vRvkvZhq1AXE0o/wXRztqA+Jo5mDyf1oL2507d9q8efP+KIUT4pmybds2mzt37iG/rvJfHC6oDYijGeW/ONpRGxBHM9PJ/2ktbPP5vJmZfeZtx1k64U3EnSiEY+NxPKVDVtf1WhVifliHWCKegFgQ4nWjMCLXDSDmehCyqJ7F1xq+NpaoQMwjVei4WJYg9PHCZub7eC9hSN4Nc/A6PjmuSmLsvbWQPDv2Lly9hs8kCMg9k/O5pA5r5DmV8DAr1RrBWj20L/9o20QeHmqeuu4jjzwypQy+j8/0cH8n85CUDx8xjx3oUPJmXESOdPmBiMNynfQhpKU4ZLdGFB3gZqbBdOt//2uMjo7aqaeeOuNt4DPfutvSmVzjHwJsvAN9eyBWrWLfuXDRYogVWlogFvOwzhJx7MgTZJyJk1jMIf1zgOXLZbCfY2VhMc/B8g0NDULMzCyXz0EsHovjdcg5HRev7Yc1iE33Ax7XwQNLpTKWJYZlSSZTEKvXsCx+HWMp8lpn0mA9OjpqJxy7bMbz/1Of/ril042yZjuWwrFpD+cs+RzOMcaq2HaKowMQc8l8IiQdXYw85HQMy5L0yLSPtAk6cSCHBREZwMlxUUiOM34vHimjSyZv0+1PHXJ/jsvmXryM07luMpmEWNzF+rcIj3NIf1YeWDfl71KpbH/25nfMeBtYdfbxU9p/PIfl2Uf6usHBYYhVx7DfLcwmY0BbG8ScOOnUPLLWGMN52vaH10Is3oLPas6SLoilSX8f+thfBz4eV+jEZ29m1r2gHWJuDNtAFOC9eGSNNDqI8/a+PfsgVifrhTOesxyvW8Xr3nrrPRDrXdADsVQM73n3DpwjeCkcB3PZRsyv+3b3D++ZVv5Pa2H7VCNOJzxLJycvbKc34WAL2xoZpP0AO5pEAo8LyMPgC1sI8YUt6zDxMIuTsnjG7pctbPlkuM4aCV3YsvrC49xpL2zJcaSz9gyfSRCQeybnY3Mpl7wpQdqqBWTxMFOLxqeum8/ntbCdDlrYHpBnurA92Nf/sZkYAzI5S2cnDSyk8aZKY/h60sdmsjhAZcgkKU4mK3Rhy46b7sKWTExyWYzFp7uwJffrs47OjA7U8fjhs7Bli4wYmXSlUrg4rbGFLXlTO51KQ4zlzIznfzpl6XSjrJkMLljZwjabxUlbGMNFVBRg3bhsATbthS1OKlNsYUvmLNNe2LLFIDnuQItGei8kv5pxYZtwyUKGLWzJ3NKtZqZ97UPBU9eNxbwpC9sY+TDLI298uaR/Zl8p9WIkxj4wm+bCNoyxZ0/GehJj9+HF2DyBLCxIA2J1ZWYWT2J/75E2EJI5f4yMFWStS68dkfVCMoUvjti4QJ4Tuwb7sJPWKxnT2fmmk/+SRwkhhBBCCCGEaGqm9YntU9TMNW/SWjiK8CtKRj6RSxq+q+mSdzhi5B1M+k4ze3ORvHtTZe8Wh+S65JMfj7wBQ96gMId8fdp88q4r+VqumVlIylNz8J3vwMN3+WrstQEW0iHvQjrkq9EpUocx8k6NS94BC+qkHhy8RkTqgX1S5k16581jn2DPAK7rmscSo4mYsXd7D/BOOP1ckrxzGrKPDyL29Qv2Tix5x5Z8E4GX5vD4xPZwybtsJmmZTKMvciMcQqpF7KvCWgliqQTWRTZNPh0kVcb60yTpoNMJ0n+RZ18lX6lOxrAfTpA+kqQr/aou+5R5/PXTy89kAt9JJx9QWLGEfTEbRhPkfBEZl11yg3HyaQL7lLlexbGQjSlp8mmXTWortQSeeyYIo5SFUSMvfA+/IlmP43wn8PATWzdOvopcxm87REERYqSqrUq+Elwnn0pW2CctpPprdfyaqEv6oTL5qjrrr1h+mJnVyHYn18VYxL6JwL6lQfLa98m8gwwBDvlWBPv0uI18NTaZxm9e0K+Rs/GIfGIXjE3NGfbV1pkglk1O+SQtPQvrIkfa/cAgfj25vQvrrHsJfp11qMLGa9rxQqhUwTYVkHl7a0srxGbNxvLFIsyvkWHMr9DD6+Y6+afwdTL+VMsYC8g2jmSW5QXmcb2K9xxL4DdlOlrxq+ClMfwaeWkEx/R9O/shlmbjFpm7ZVsKEKtNqgOfLDEOhD6xFUIIIYQQQgjR1GhhK4QQQgghhBCiqdHCVgghhBBCCCFEU6OFrRBCCCGEEEKIpuag5FFR6E/9WZ0IN4iz31lyyM/DhGQTtJcm4iMi0WAeFaZpTxBZgR9hLKyT8pHzMQGBQ0Qy7GdIHPITAGZmkYeCknJAfvepH3dOF2t47bExPM4jUol8ishNyE+gtGRwc3k6ic84dInYgUqhiFQCImb1SXnGVP0zQRRFU6Q+f4hEaKb4Y5eZypDYNZipw4z6HyImhSLvwVXrmIdMfW/kZ8S8aefUAcr9LLP/czpcci1mvsUmSeGYxCnhkd83J78pnnSJwI69lvycTrWM4gqPCPZSMey/6uQ3dV0jojsfj4vI74kHRDqWiON1mSRq/KRkXCH5zoQnpRLWQ/8+/L3Crk7yO5Ds5y0S5KcZ2O+Mknthv74RI9eokjkC+8mk+qT2zeYVM4Eb+eZGjbKw33ANyDgaOJhLqTzWdccC/N1MdxilOznyk1q1Cs7HghzOL8LWAsTyROQ2+T4nYuynG6s49gdEIppK8d/wZM2C9XdsrGExVkafjBWkiHQ8SpDflJ78k0+NlzIJHLbZkPSZIfuMaf97O0x+StBrabHYpH4insS5ba4FpUvZATyuay7+fms6j/K14Rrme4w8F3NJ/1wmv8NNqjtL2kqd/KSjG+GzrxRHMFbDWOh34oXNrDKM/cPA7iGIeUSiN2s+lidG2nO1SH4/PI11nWIiswrOySpEUlgrYRvo6sBnnGpBmV6dtIFdW3Y2ykDWXwdCn9gKIYQQQgghhGhqtLAVQgghhBBCCNHUaGErhBBCCCGEEKKp0cJWCCGEEEIIIURTc1DyqFhYtdhkEZRHxElEcJH0iPghRjbCk03/rkfW3kQ24IckSMQV8QRutO5euBxiI0N9EOvrR1FHPIYb4l1DSULN51VdJhvR12zBa0dJ3IBd93Djd41sgB8bHoDYjr1DEMslycZ7soF9fhfec0eeiVvwfA4RUpB97lOlHNHhIU1wHGeKrIKKk2aIw0UuZGZUwBEcoHxRiAf7xOpRJ+KAJzduhFhX92yIhTWUJsxqR5kOkyaEM1Sv++fW4ZJrCTewxCQRVOgTCSARpsRdIoUix7kB9rGJOPYtjof5ECcCuziRiYQOEd2FKN7xK0R4RfrcCsmvDJHueWQ8Gi8QsdiQvCtWUDDywAMPQqxOxFptLadDLJnEsZU4nMxh4jciV3SZPIfIlcKQiLrI+aJJx0XkNTOBbznzLTPxt2s4FoZEgFYlQjyPxLIxzPWWDOmXHvw1xGp9KNjpOf4YiDn7cI5QdTCvcyQZRstFiKXIc08SSafbgcIYMzO3RoSXZNpXzWC5Y3UiNquTcmexjSaHh/F8846FWKnQCrGQiOUC0r5TIeYHFY4GeJwXuE/790zR2tk5RRg1OoTzy1QuA7F8Gz7/Qg/Oa8ewK7a4i/mUInP5OulLfdJvJoiEyfHxuQzuxjaVYrk5NopBB/u0DJHFmpnls1g3YR0vVCfzAI/Ms0OfjK2kPceJbNNzsV9KJ7Hc3fN6ITZ33gKI9czBOVmVTPq3b94OsVK5Ic4LiAT0QBweLUUIIYQQQgghhHiGaGErhBBCCCGEEKKp0cJWCCGEEEIIIURTo4WtEEIIIYQQQoim5qDkUeNWmEnynFgBjyCbm30in3BdlEHUiIgk4aFMIQiYaAJjRsqSiONa/swXvRhiD9zzK4jtHOqHWJFIofwARQxbtu/D8pnZph07IJYs9EBsbtciiEXJPMRqRD4Rz83CMlZwU3z/3p0QyxRwc//2sT0Qq5BN+1153JieiePG9KCOshN30j5+5zDxIkVRNEXSxIRNh4vk50BMt3x/mIyKCA7iKMcwMwuIGKxM7BFDwygt2dOH0op0HtteRx7bietgP+CQ9/kcZ/rCAvJico1neqrDI6/iMccSk8R/ESlX3CW5E+Az9QzHAIccFzfsM+pM3kJEZF4Lk7egWMNCHHtCnzz7APvXsZEhiOWI6MY9QJvya3jPsTiOK0Ml7CcHRjCWjmEe14h7qVbH+4slsIwRGb+DAOvQJ+N3jdxbgshOIjJ+hJPG+ZCM+TPDfnMgIkN0SX4FPuYNMyQ5RLpUcbD/iofYzzmdKGkpjeIzqW96AmK+gyKekHhuinHyHMizS9SJQHMbtuPxAuHrHcNYhYgxvQrJYbxlq3ZjvZZ34/iRd3Cu5LR2QixggkPS78WJUCok7clzsV5j+51v/79nikTMs0Ss8SydGD7X2d0oFhqpohTVIf1cdZj0GS6Z14bYfti8pUbkfmxUH+4bhFg6S2SBKXxWhY4CxHJ5zNdRItMzMyux8SxDpImkIy8PE+FigvQtcaybDJF8JV3sC1pm43ErTl4BMSO5EKWJLI30fZk0ttFTzzpx4r/rtbpteGQzXpOgT2yFEEIIIYQQQjQ1WtgKIYQQQgghhGhqtLAVQgghhBBCCNHUaGErhBBCCCGEEKKpOSh5VNXNm+s2NgcPl3BDceDjxu+2HG54bvHIZnmy8TskQgomE4pCInEgG5RLJdwgfuvN/wOxPUN4H3vG8HxbduD5tuzaBjEvlYOYmVngtUAs24KygngGXx9L4SbvJJHipFzcAN9XK0OsZ+58iFXKKO3ZtAnlUQPDuPndc7DMC2dhLB4QeUTQeJ4BESvMBK7rmDtJBhERWc0fG+JWMpumQ4IJh9xpSogCojkKiTDDI22sVkN5yr7+EXqdkSLmTbmKz7tYwvboJrH/KZaxv8hlsMJ8UodMb/XHdjYdLhKoZ0rSCSzpNJ5P4GC/G3eJWKWKz9kl8qgoJMc5OEzFiHww5hF5lIO5GBFBFWtUfkgkQIaxsVHM7a3sfonUyYzLmea1YG7370MB4SOPPgqxE487DmIhqZtqgG0lReRFIRFrlUtE9BjD+/CJGNCL4b3VfXye1WrjtTUyXs0EQRBMkVeGZOyK2OcFIZHaEPFUQOqwdZTk8KwuiKVnL4CYHw1jWRJE3tXZDbEykc3EdqNA0zwUxhTJ3CTq6sDXGpcAVYgMNEvEgLVRzK8qyfVYGnt3j4w9sQ4UcDlxIk+LUOyTJ127RzRFvoNtzHExZiDNO4B86xAzNjJi8USjvA6ZF2zbugVi2TjWWYnMC4I6SpcSpH6KQ0MQczP4nEMf2w+bByWSeI2O+QWIZQutEMvkyfzexbwO6nzyVifCTIdM/sb2ovBseB+2yWNPPwZiHd1teGFSnGQc67/Qgm0v245rlzKRCtZJG2jLFTA2D/NjdKwhua1ViRXuAOgTWyGEEEIIIYQQTY0WtkIIIYQQQgghmhotbIUQQgghhBBCNDVa2AohhBBCCCGEaGoOSh7VX3YtGTQ2sA/UC3DM/95zB8RWLsONx88/DgVJbR6RRwUoEXCJrMAlm8uDCDcyE7eSbdqyCWIDZdzIHGVw87WXw03jbtsoxNJkw7mZWa2CAoOag5utW9qwDltyGNu7ezfERgZxw3meCCRSaRQ+bB3sg1g8j4KFfbu3Qiy3B+uhuwWvkSZymCniFiLemAlK5Yp5sUl5RsoVI7kZkeO8GB7HYg4xpTGhlEsEHAyXSKGYIWmMyG8iIndLx/DZVeooRdl1AHnU3kGMh6SMdWJ7Ko2OQWxvH+b69h27IHbsssUQW7JwLsS8CPsfVg8WkfpnnihW/eR0+z8n+txmAM+vmuc32kBI5EAuEf6Vh8nzr+JrIxf7bC+NOZYgYqcEaz91lN8F5LoWkNfGsM4jB++tWERBz549eI1sCxcIRkQyEpF2VRvDc6aIkGUfkao8+FuUTGWTeM9LF2O7iBHDSLVExrgYGb+rKH0KfCLjYe6cyqScqWBbnxEcm9KGAyI5Ctl4RbqHgMxt4g7GkuufhFjlgTsh5p9OpGgumcdEKO9KEEFVxTDfcruGIOYl8RphFu/DiZiezyyo47XzHQWIxXcQcdUY5kW8K4/HbcPXxkh7rOzDduIRcWe4/Fh8bQLvzyVzuYRP5FZkfNvfKUccczPCWLFssUnPrE5kgZsf/g3E5izohVg+i3PYQhbzMyKpPTyMfbsRUVRYw/lIjlx30UkoT+1cisIzJsx0yMJizxYcF7at2Q4xM7P2PK4tjjv+BIjd/xhKuYb6sA1k87jecMn6qkpEnZkCtp9UEttANouSqXSExzkBXrezMAtiv3nsQYite/yJif8O/OlLZPWJrRBCCCGEEEKIpkYLWyGEEEIIIYQQTY0WtkIIIYQQQgghmhotbIUQQgghhBBCNDUHJY/yWhZaLNmwPJT6cV1cT+Cm4IESSipKNdx43JJAMUcY4cZvJu3xPNxwXqmhqGgf2YTeN8o2ULdDrG0Wbi4vhihF6TS8rpfCmJlZLY73XCmimKMyhtdZ0IUb20tECrW3hgIPh0hHhgeIVIXIMcpF3LTvJbD+944MQmzXMEqJFnQSGVjI/3smGS5XLYg1BBG5DAoI3BiRmIWYw9T1RPxAHhNFEXuUQwQ0FCI+cog8aveuHRBrb8c2kU6hMKNawTzKJLk4pHsWSuQiUhHFEuZNlsg6ahXMdY8k0FgVOwKf1INDxGZUHkXKTE7HFVAkuP8lmDBsJkg6kaUm2a4cUhdMHpUkEq5ciDfVaqQvIJKQJOmXUszZU8J8cEl+JohkxwIsX20E7y2fxde2kbayaTuK/czMNm7D+BPrb4HYYN8QxMYqRNRVfwxinuFxdSK9Ov6Y5RB7xcsugNgcMvZUU/hMKmSsqBXxflsinDc45cY46JDzzARxL25xr9HHuyRfwwD7m9AlokHyuUJuEO/T374TYi1k/B7difVaS6FEJjKcezm790Is24vjW62FSI4M++b0GPaviSGc15iZVQyFP34fCv8SJNf9Eczh5EALxOplIoJLoyhtaNM2vG4ahTj5ngUQ87BaLXKxTVSJjM0n40wtDJ/275miVC1bLGyUt0bmN1Uyb8/2Yp+RDjGPgxrmjutgO8ulsML3DeCcs1LG8y05fiHEFp4yB2LVCHOOCWhHd2IePnHPbyE2NswleNljyLzRsNwts1HcmiTlSbo4N6qTIS4/B9cle6tENptDoVQ2jXP+WEjmeUwWWMdCb3wC296eDY1+KTyI/NcntkIIIYQQQgghmhotbIUQQgghhBBCNDVa2AohhBBCCCGEaGq0sBVCCCGEEEII0dQclDxq2fHPsUy6sWF7++p1cEyuFSUQZ6w6A2IZbwvEakSaxGQ8Thw3PAdRAWL52fMg9vCj6yGWK+Cm9jkLjoNYRAQjcSJ/Cqv9EKvV+MZndn8eEQk89sijEGtJ4mszWRQ+ZDMoP9i5ew/EfCblIpKKtjzW/3CAAojBAYxt2o2b7Hu7uiEWmyQScwwFDDNBrKXdYvmGmCIgwqa6i5IDc0j5SSwgQhyXyZ5ILCJCCgaTELkk5hOBg0MEQEbEEYU85mC9foDyeSSHiaiAyaMcD3PTIbatZJr0IeSmfWKFiFiznWYdGnkmWJIDCKWooGrm2bF1q2UyDWlEvY793+gI9uNBHfNpxw4UlA2SPq1IxHmzO1DOlMuiTMSLYc7W6pizsQT2aW4MRRhFIp6qsIcfYR++dWcfHmdmm7ajrKNYw2unWlEc4mQxQbG3N8smMLd3bXkCYjt34rhw5513Q2zlMhTvzCqgtKc8NgSx4giOj/WVx0BsbLghgikSCdhMkEykLJlo5FlE+i8LiaGSiNJcEhuL43MaO+0kiLXEngOx0ii2u7pHxo8kmfbVsJ3E09ieigG2d5dY8uoB3kecjY1mVia5yY4sB1jG0hjec5aUu0KukcxhS2nPt0EsIHO0MTKmWBzrOk0kOUxSSFLB6vuNAfv/PVOks2mLJRr3P9aH/Vr3nLkQW7gE+4y2NNb31g2bILZzI64X2mfhPCFOhEu1bhSozV2Bc06XtD23gpno+PiwNj6wHWLFARTBHXMi1oGZ2YozV0Js11aUKbUQU9SK01H457ZgfqbJOieewfNVakMQ2zOAoijHcIzyyFgYkHY/Oor9+b69OC6Ek9YkIVmfHAh9YiuEEEIIIYQQoqnRwlYIIYQQQgghRFOjha0QQgghhBBCiKZGC1shhBBCCCGEEE3NQcmjMi3tlsk0JBsLFuOm5TL6gmz+oqUQ6yQymaFNuEG8HqHoI/BxI/MZ51yE1118GsQWnbAZYg889AjE2nK4uXznXtwkH4twA3UyTsQCB9j3PFbEDebDgygTacviOdkpA7LBunMWCr2qRKDSN4hiJ8fD9z7yOZQDxTxMpRoRrWzchpvsZxVQ3LJsbkMMUDcs60zwtf/3LUumGmV1SF3HiWgil0eZxdJF8yF2+onHQixG3nqKyHUjJpRiUhsirvCJAKqtHeU8iSTeR0TUR4kESp062rg4JCKakFgC21QiRrqqOJGE+HgvQyODGBvGXB8dHoJYnUlrHKzrjo4CxJYtRVFEPIH3wZwgILfidqpDzj33/tqSycbzdRx8fiGRoJXL2M9t3r0TYuw2WRtoa0VRUTZF+mJyvniM5FwSc9aNYX6VKijPiZGyRERstntgDAtjZvUQbzCTL5AjMbdrY9jHuqRNVipY/y15LPdzn3MCxIrDOB5VKihz27oV29mGDRsgVvYx4bf0YzsrlxplrlaJkGkGyGRSU+ZAPsm5esD6DGwTfojiL4dIzNJdKL8ZKeJz3zeM+eV4mOu1Ek7SEkRYWRvCa/jEppdM4Jg3QsaoVPwA000X46wPqZaYlAvvb7hM2gl5aSaG95Kfi8JRjwkEXSLlYp8TkZDDZm5kEAj3q+uImgwPPam2vMUnSf4SZN7oknE9l8L+Jt2Cc/nFRCS3e+tujO1B2VB3Dvvsk09EMdO87l6IRaQf9l1sK08+hgLafVv3QaxrEc67V5yJUlozs3wH1kO5jH1sSx7HlWQXztXcOOmXyPixZz2We97yLiyLj31ajLQBc8l1iRmtbx+O/YP9uL5Ku416cbhmk6JPbIUQQgghhBBCNDVa2AohhBBCCCGEaGq0sBVCCCGEEEII0dRoYSuEEEIIIYQQoqk5KHmUm8ial2xs5t25Zw0cc/JzTodYthU3RnujOyAWEKlEjMhWNm4bhdjZbYuwwJm5EMpnUYiQiuUglk5gmVNEimNEcjCntwdijxOJhplZIoGb3UdG8f4Wzl0GseUrUDY0MIACj1xLAWI7d++FmOPihv9CG25MHyYyHo9IptIZvG55FOt/PXme6UTjfLU61vFMUClVLZy0Eb5GNvfHieRoFN0KliHHBStX4DUjlNW4RMyRJNIRJiUKmGSKCKVa21F84JLjzMXnXiNSFI8IoczMzMHXM0VGSIQbm7dshNiOvZjXA/0omSiXUYYQVIl0pIz1X61iDs+dh8KF+fOw/8mS/oxp4PaXch3APXfI+c2GzRabJMfLpPNwTESEf1Uf66y1rQNiSdIf1oj4aN8Ytj2P5Gc+haI7P0AhiBPHPPQ8LIsTw/MliyjPqdVHIDYwgBKmcYiIhjS1WoAGnNEi5nGtjMfNm4X9eEcbChKLReysBgZRMNJRwLo57SQUo2zfheP8cBnHmbXbsY26k8ajWo1YKWeAWNy12KRcSedxnjBWQolTjBjQAiJfiTnY+7lkDAgNY46H7S5GxnSm8avXsH2miQQzRkRPTMYWJ9cNiNjPzKxWwXz1ySgQT2OjCAMiLyRtOU7EQHGfiLUiPJ9DypIK2OBK7o+045AE2SdMKMs5PASCqVh8iiQzTuRAPpmzhQGRpRFbYDqLfcuS41Ao9cD/3guxtTuwvznhbOyXqnEi/hzG8nVEWJZRK0DsuOU4P+9chnOCeJasIcysWMIxbtYCvE6iFcvDhL3tacztDQ+jgGv7Vpwvnb0CBYKhi+MtmYZa5OJaqh7gmBLWsb8JA5Izk6R7+8vUng59YiuEEEIIIYQQoqnRwlYIIYQQQgghRFOjha0QQgghhBBCiKZGC1shhBBCCCGEEE3NQcmj4qm8xSfJOCoVJlbBncxxImLKZFsglk2hACdJhAi5GMoGrv+//wmxl7/uXViWIm6gTiRxfe+6eN1Fi+dAbO/ATohVxnAjePfsToiZmQ2M4Cbqag3rdfHSpRBbsnQ5xIYfehBixVGUWYwU8bo+2dxfJoKkQqEVYkGEAqiWAson/BrWq+fi89y+q7Gpve5Pf9P4s8mrXvEKy+YaspxqCcUt2TTmsEPkMGkiESLeEBsZQQlN6JM2FkOpQCyNsYiIPsp1zLcoxPK5RBQ1WSIxcV0mE4lz8YVDBCpMZlUn0qtKiPWQbUF5QVuhALGAyGhSHj67oX4UH2zfsRliSxdh+/SIaIXJu5j0iIm/DgfG/Mg8p1G4iEhZMhki4yMiprnzlkCsTp7Lvt3YZ/cRIVhX12yIJTtR4FUcwteGLja+1jaUfySTbRCrEHlHycd2myJjnplZUMf+2XNQpJHwUDwST2Bbq6cwdsapKFBZvqAXYpUajl2bNuCz27DucYitOh2lI/Pm4TW2ProFYnUi4wknyXjqh4lAMBH3LDGpzhMpbONhhM8pHcc69B0cC0dHsC8OPHyeqVaUgXVlUeRmRLjCxiMUFZl55HMPj8j+EkSEeDBEZN7B5FGBR8YKcn8uiSWYMovcS5XM+8hhFiOCxMAwRx3StztkbPXI8Li/kJMJOmeC2V7aEl5DBrm5hHPEgIi06lWS2z7WmZvEZzV3+UKI7dqM/cjuPnwuyV4c1/tJ/zx7GMuSD3Cu25bG8W3p818IsfZeIl4tE5OomY05KBasBji/TOwkgqUi3vNYGuf3cQfrdekpKOVKdWI/0t+PwthSHc+XI+MRW8ORIYrKScfGGuuK6CAmRYdHSxFCCCGEEEIIIZ4hWtgKIYQQQgghhGhqtLAVQgghhBBCCNHUaGErhBBCCCGEEKKpOahd/44XN8dryGJKRJJUIUKdeBxlCqP9RAZB5C1xw83WPQXcefzkmvUQ27kdY1ZC2dOW7Zshdkr3GRCbs6AbYr17UTBSXI+b2tuTBSyLmeULKJXauBHL09OL4qohIhaqExHDnn1ElhIRqYGH6VAi8ijHJZIEiJhlc1kMhrihPuFgztT6G8KYgMggZoKwHlpYb5SFyjXI63IJrId0CttEuYLPs0SkKZtJfiQS2HbmL1oAsU3bMP9v/uktEKu7KIVKJRMQy5D7yBJpVWsLF+cUWlFUcMopJ0JsVidKe5bMxTbhEkECE57UKigsixHZU3k25mtvTwFjc3ogFgT47EolIrxiwrH9ihyR+5oJYsmsxeKNPJg1G+VAqQTWd1/fdogViyicsxB7kkod5ROts7AvnkMEXvlWzJuWTpRM9Q+gHCMgkhfmMCqXcRwslVAIVatjP/e7s0IkQeRyqST2I/EIhSyzSVub1YaxVByf0ywizGpJYF/Qv3UrxLZs2Ayx7nYc34b3rIZYvH0WxGqTxqO6y+VzhxrPDSw2afzzHKz/lIf1NbQX5TADY7sgtm8XtpO2fAfEjj8WRV1xIt+sElFUnYh93JBI7cj45pLnwKSCTJp0IPlLQKyJLpmfGCkjm3m4ZOwyWh68boxcg40p7BpxJndjaUtuwyWCsGC/uvYPkzYwNjRmiUl9QpGsA9hwNTyI85uIjJGz52Hf7pI5xfGrToLYCRUUEnoe9q/lPlxXdCXw+WUCUueD2Lfv3ohrDc/D+UmLiyJdMzMvwPur1okEbRDnLYkYnrNvJ45nS3M416oa3nNlFOf8MSIJHSniuqIaYV13F7B8Ibm3GBnzersa40IQhPbkWhx3GPrEVgghhBBCCCFEU6OFrRBCCCGEEEKIpkYLWyGEEEIIIYQQTY0WtkIIIYQQQgghmpqDkkdZGE3ZwO+Rzfc9nSg6YIKZWx/dALE2H8+3rJ1JbHDDeSKGG5737d0MsbCKm6rnL1kEMY+UOdOCIpLOrrkQ6x/AzeXDIyWImZmRvfM2axaKNGJEwFWpoQSiRkQrZSLK8cmFWaxSRTmG7+P7IR1EyOI4+OwSDj6npINlDqLGhvMa2Wg+E9z8k1stOUnQEdZxo7xrWF+5BG6ezxPBy8JlmEuzOnIQ6+iZD7F2Uv+pLAoJhtag2Oy3a7ZBrExEHzEihIgRE0aeXHfpfBRZmZmtOuNUiHVkUXKQJWIz5hepkTbhB5j/peEhiNUDfJ7pDN5LoYASnz2790Csrw+FMeksCl66uvHZZTJT2/toGe9hJmht7bD4JMmGR55LtUqEc+Q91IH+IYiNjGDf6ZG+zwsxGbfswGfQMoLCptbWAp7PI/KOCrZlh/RVyTgZRrPY5tMR3oeZmRsjiUzG1mwazxknso65HZifmQTWV3FkCGI+kV45RHaziIi61qzdCLHly4/BFxN50a6dOyCWbGuI2/w6PouZwHGcKWKkGJH+hESmNDqKorR9+3ZDbGgQ6+GJR++D2NpHfgWxpUuPhdjCpSsh1taJgjAjYqIgxOdkERFF4VHmuUx2x+VHMTKwMPlUGGKbCNkEilzHI9dgKiomuDqQ9AqOI+JOn52PvJb1K/vP79h8byZw0glzJokku+diPrExICBzUyZxHNy9D2KzF86DWFsHih2zA2Q8IsLMOQmcf9VdIjF1sH/t7SWvJXPU+ra9ENtX57kUkn4kn8W5XzbdCrFYAqWerouxliSROvajRKu2GWNRO46PGXJdL00+K43jOqBKJG0Lj1kMsUXzGwKueq0ueZQQQgghhBBCiKMDLWyFEEIIIYQQQjQ1WtgKIYQQQgghhGhqtLAVQgghhBBCCNHUHJQ8Kh7zLD5pE35rDkUohTzGHCIhGIlQcNE3iJv+O/NYxGwCNyMHLm7y3rxzM8S62nDz9QIiXajg6ey+B9ZAbMculFHlcyiZisdx87WZ2WPr2WZofL8hJLEqkQmMFXEDfKEdN9n7xLyzaw9uds/myWZ1Dzd+ZzIoNkkkiCyl3g+hoDgEsa7ZDYFQtcYEEYeehx5dY7F4Y8N8Ko6b52vVEYjFE/jsznzu6RDbsgMlTv27sBzHH3ccxBJpzK8SEX/FiRTtlFNPhFiFyIoSRJKzbDGK145bicKY3s4CxMzMWjLYX4RE2rONCCX2DmLb29WHxxXHihAbGhqCWK2O9xxP4D0nkljXgY9tok5kGZkCirGON3yera1TjyuOodRnJvDiySkyp1IZn5VHbENejNRZgO0iFkNhRkiENYkk1mNnZw/EcmSMSpG20kqeaYy074hIbaIA79f3cQBpbcF7MzNzXXx9GGC9xiKMhVXMi9YkKaOPuR0QqVrNR4lJmbTHDBkXtuzGvv3xDT+HWLWKY1S9im0lmiRU8Ymo73CASYlSKcylFcesgNjSlXMgVhpFodRjDz4IsYfuXw2xO/8XxYBrHv8txJavPBliy45ByVShrQCxBOkPPSK+4aKoA0kg2bGkPw1xHhCSdsYIAyLHInOgkFyXK6+mh8PkUQ7Wl+tivfr7CXZINzMjpFqzlkg1+sZEH/aT6RZsA4kY3mOMyAcHd2IbmN3TDbHAwyfjj2CfVh9Ecete0r+yuVFLDu8jhcsPy+RRKFUpYZ9WLaFUy8wsIhK0sTEUzo0RSa4XIwXyyHy8A9cl81pxbRCGWDfr122HWFsXSi+rccztMTZHIEvPdBJjtUljXj2avjxNn9gKIYQQQgghhGhqtLAVQgghhBBCCNHUaGErhBBCCCGEEKKp0cJWCCGEEEIIIURTc1DyKM9xzJskz+iejRu6Y0x8VMEN3T1zUTpzP5E9DTkomYo8lMG0duLm69YW3FQdT6F0ZCGRR+VaOyB23bVfg1iJ3NtIeQCPK2OZzcyIj8e627DclQEUQxST7J6xvtauexJie/agZGdkFEUkhQIWsCWLEhQvQolDvIb37JV2QmxWlohWUo08qxBJwEzQt3OreV7j2bS34Wb8OXNxQ/2xJy6DWJwIXh57+D6IdRERSc7B5763Dy1T2RYUvHQQqcMrLjgHYq6D7bi1Fc/X2YHtZGAAJTKbtmAOmpkND6Fsa2QYpQmjIyiAGCpifg2MDEOMiWficWxjiSTGXI/UQws+u0KhALG22djXJJlkLY2xsfJUSUSxzKUTh5r2ztmWSDaETGEdhTC5NNZjGKAwKO5iLs6e3Qsxh8gxEimUQjGpVypFZDcxfKZMCuWwfocc55G2UipiX+pGXJ6TJINARIRSpWFsVzs2Y7saiJP8TOM1ujoKEEulMBcrRFIYxVC0EsugQGXfduzv5/XMgli+hnUzMkko5R8mb8GHYWhh2Cir66IsJXLxXlyX5I2HeV3omAexs8/DMWXpUpw/3XXH7RDbtGkHxIoPkTnLyBDETjjxJIjNm4flYwKgwMcxKgh5/odELhoRiZMREZNDRHWkiZrjYgI5bK5KLuuS10akLPT+mDyKXvf3y60OF3lUsVSyejCpbdZwfCUOOvPJcw7ITcWIULI0gnOCVCvOQ2MtOOaedd65ELuXCNnuvv8hiJ2wHOduXW14jdF+IvEr4HxpbhcKDs3MymS86B/CdUSFiJiMyFz39KOAK5NHydeCpSj6dCr4nBaR3N48gLLZWAuO30UiH9z85AaIbXpiLcR6Fj5v4r9dMm4fiMNkuBBCCCGEEEIIIZ4ZWtgKIYQQQgghhGhqtLAVQgghhBBCCNHUaGErhBBCCCGEEKKpOSh5VDyesESiIY1oaUN5lB/gKZNENLF80XyI3f8AbsoeiS+FWOjgRvKuOShieHzNaoidde4lEPvVPXhcsYhSm3qtD2J7d2+DGHu/YKzO30OIGW68b3MHITYnjeUZ3ofiEN9DoVHXbIwFAW4QLxNBTaWM0p5iHJ+nH+Lm93oFxRWz4yiR6c2hsKTqTz6OSycONbvWrzNnkihmpAXlBf/nJe+A2AUXvBBiv7z15xCbXcD8n51BGVg6hqKJlIN11NWKMpc8iaUyKN3xibyDyXn8AK+7ex0+961790DMzKxWJ/KIFN5zPt8OsdlEdFMnIgtGPIH9hUdEUSyWz+NzaiHSCo/Ih8aK2J727MF+pVKZely5xOVzh5pMOm+JSfVeJ2KIdBbzpNCCApzQJ88+gYKLdA7rNnLQTuISiU0YkePY+7kkFLEY6Yt8H/s0P8DnPNKPz9mMD8JxIo8aG0bh366dKGfqasc2Xsh2QqxEhE0hEXT4pIQRkb7MmYtioWOWLYbYycdi7ImNOI4+9Js1E/9dq6HwaCZwXM+cScIo18G6cWNY1jgRvAQkhx2SX24c28Sy5SdCLCSGrV27vguxwT7MmSerKN3bs2MdxJYsWwGxlcdhWWYTSU6MzAPNzPw63l/dJ6KhCIVUrB9wiKiLQmRujk3vtRE7jojEWFEiZqgixivXjT/t3zNFvVwxCxvPIpvBeVCdzGvDFNZ3msyhMlmUy7H5ahhgPuwggr1lGRw/zjjhVIg98ODjECtV8brpNEqhUgny7MnD37mTz4OSRFy5YOFCiEUhnjMex2vPG8P5wi5y7fVr8J6XH3cKxJa0HwexgXtxPBoYxHGvbli+fiL5bG3DMWrxkiUT/10lc40DoU9shRBCCCGEEEI0NVrYCiGEEEIIIYRoarSwFUIIIYQQQgjR1GhhK4QQQgghhBCiqdHCVgghhBBCCCFEU3NQVuRsLmvZXMNY2taJFiufWAIrLlrvUjlibiygbWzrtt0QO/t0NHRVxtC4lskTi+SO7RBb/8QTEPMDNHAR6Z0Vid0r34FGwOFhtIWZmbXm0CB6zPLjIfbrR9ZC7MG1myF29nkvhVg8gfbYjevXYxlHsYwhee+jUkYD8oIuNM+ls2mItbcTw2kMzXN+rWEO9IkNcSaolIpTrMgnnITP6QUvfAHEOgodEHvemedAzCUm1DwxULfk0BrsJTCPYgms/4hcIzTM9eFBtAu2EKtlSIx3i4/Bepk9dznEzMwGBtH2nS8UIFYnFlaHaGvjpJGGIfYNlQoawMeKmNdRiLk3VsLjtu3ahdcgRvF6Ca8bELtjJju1rtm5ZoJipWr1SfWeTzMbNI4Be/dhPo0MD0EsDPGZLl1+DMQK7Tj2eMQO6ZD8ZCZvZt0t1dAsWanic/BrmMNOgFbQqMrNvlli6C4U0AKeTqAtNOZguygQy3xrHmM1Up4Sqf9aFe/FdbDPbiPG9UwSz7d92xaIEWmwHXfMson/LpfRPD0TuI5j7iSDrUdsth55Jglixw3ZhIIYcyMyBteI/X3uvIUQW0jMqr/eg32VTwzl+/YOYYwYldeseRRiixbhr1ksWbIMYmZmXV1zIJbP41zQHGwnlRr2nUEN7yVObOtRxMZCUv8kNyPyKwQcNm5hMjAXs7df1J2msfnZxrPIvEn3lcmh2bilA2PVEPubRAJzu2875me2E/vDkZ14XIr0pasfx7nz8046HWKvevWrILZ9y2aIBaTtpcgvJbDHlc/xJVcQ4jl3bse1T4LM6UIfXxtLYz10zcXxY7gfx7i+3WSNNIxjXE/3Qoht370ZYlEO2978Y/BXcTY/vgliu7c3fkmAjUMHQp/YCiGEEEIIIYRoarSwFUIIIYQQQgjR1GhhK4QQQgghhBCiqdHCVgghhBBCCCFEU3NQ8qjQL1noN9bCre24QbxYxs38JSJ+8TxcU8+fNxdiTzz2JMSGS7hxP5fFzcjzlkDItjyB4oodZBP6qlW4ubxEpDH5XhQftPcugtjWAdzAbmZWruK9JLK4Ub5l1jyInZLH+tpHJC2btzwCsWIZhUFDw3h/s2bhhvPWCOtrQQ7PN7sF5RhxBzeh1+ooBslOknK4zuEhj1q4/IQpYpzXvekv4ZhSgJv2163fA7GQiDBSLdie6kQ0MTBE6iNEqU0QYL0St5uFhlKH0ZFRiHl7iOBg716IVckm/7CCshkzs2wGRVgbn0R5waatWyHmxLAO2ztR1MUkOcPDKH3r7+uDWETETq6LbdYhsWwaRQ+FFN5vKoVSrvLY1GfHZFczQTIWt0S8Ue/9ffj8NwxiPQYBPoNCWxvEenq6IFYjcox6DesjJJK5kRLKMcpExBX4WD6PiNYScRy3mPwpRcR56TgfbitkXAmN5BORtDB5UcLDfpeNt3FS7oqP7dQh53NI+ep1HAO29w9CrFTEthcjYrrunsb45ni8/zjUeE5o3iRxkMckQqQOzSHiE2Iliki9UgsNeW0qhQLBfB6FXo5LzkfyiMmVnAjvbXQQ+4CH+lB889gjv8brmll7B/YD3d043+nuWQixVAolUx1E3jmrqxtijof3zPoQnwgE/QifU0AkhezROUTQFhGhXbT/+dj5Z4B0OmWJZEMI5Ad4k21E7ueSeUGF9ON7ieC1jQi8/DrOUdI9syE2EMfnd88jD0HsZS94CcSiCs6htm5A8WoyTWRZNewPe7uxXszMkkkcG4ZGcVxIJbCfZKLCPWwMJiK/dBb7jHIR5+h1Ik284yFcm20u4TPJFXCcae3A8XHuMbie6exqzAeqFS5fZOgTWyGEEEIIIYQQTY0WtkIIIYQQQgghmhotbIUQQgghhBBCNDVa2AohhBBCCCGEaGoOSh41NrDHompjc3A6jhuZqxXcMO2EeBnHwd3gne0ofnnC3QixvQMoBOn3cGN9aw6FASuOR9nAxi3bIFYnfp6hEdxAvWzZMowtQmvVll0ozDAze+yx30Csvy8DsUQSN6e35fIQ2/4YSqp29+NmcMdNQMxL4fl65qIIawERIszP4yb0lIuiiWoFn1MY4uby+iQBB3E3zAiv/LM/s2Sqsem9rRs3uz/yWxQf1Gq4ub9GRBCBoaQlIqIJjxgpHMP2FDAhBTnOpW9v4XF1H8/X149iLN9H4QJxK5mZWaGlALFaDSUBA/3Y5o1Ibfr6UEZRJXIyv4zHBUT24CWw78qksO0kiZzH87F8tQqRyBgm+P5SB+cweQtyeGjA4slG2Xbt2AnHZLLYf6049gSItXei6COTQalEpYzPfnBwAGL1OuZNKcJnmslgX9XagmNZNomxNBEuxYh4JyBCD9/HspiZ1clgUyF9p0PavetijgVE1lgn8pWYh3kchdguKlWM9e9DOUlfP8ZGR1EmMjg0BDEmkUvmG/OBw0We5kShOdFkeRQeExGhlEOkRA6RMzGJE4vFE/jsymNY17t3o+hx1y4UO40M4/nipH/Nk7adJdKqTAzPFxARn5nZjl04Zj65Ged9lcqtEPMD7Bg7OnshdsIJx0Js2VIUVM2ahX1SSysKf5JplHJFhvXAhE8+qwbSwdf2a+8uM1HNAKmWFktOGgODCPPEdbGf3LllE8RqWSLwimFsz1bMkbkLiWiQjOvtc/CZPv6rhyGW/d87IXbK8Ti/r5RR6pTI4Py8sxvn07USXwewOQ9bD4WkL9i5E9tzUCMThhq+1mdjF5lwp5PYnrcRcajbgW1loA8Fgj4ZA04953kQ6+5sPONKafpjwGEyXRJCCCGEEEIIIZ4ZWtgKIYQQQgghhGhqtLAVQgghhBBCCNHUaGErhBBCCCGEEKKpOSh51KaNmyyTbsg95i9bCcekXBRkhDWUt8SIcCBFYvk8bsrOteDG/RUrjoHYL3/+Y4iVhnGjdaYdN5ev344bo+fNnQ+xRcecCrEkEc4sno+vNTMbGsCN1Y+veRJiIZFP7BjCuh4pExFJgBKUkSEUYc0mMqSt/Xhc+zwUcPUT0YqFWL4hYk6IYvjcq5NeWw1RpDITPPKbhyweb2yif/Q3D8MxjqH8xvOIcIaI1zxSD2b4Wo9IPWIJfI+Ktad4HM+XIM/OTZD7iPC1LYk2fC0RndU9Lg6pBPhsfeJUSWRQWlIvEVlQEUVpNR+Pc+pE4kQsWjUi4gmK2CaKo3iNDOkHZrVi3cSIzGh/R9Fh0gSsrXOWJVKZSX9j3xlj+UlycXQMJRxjY/j8kkkil6uTccbHZ9rbNQvPR+RfnovPOSKVXqzgWFYZQWnPEJFb9Q/sg5iZWZnIsVauxPEsXihAjOlkPBejFZ+I/IpY7u27UaS4rw/LXSOitVIR72N4CGUpCQ/bBcuFW25tyIJ88mxnBMcf/9/vCEOSNz72p35EpInkYwWHCr3wtZ5h7JEHH4DY2CA+u/Y89qXbd+FxLa04z4qTMSokssCWHOagF8d+wcwsEcPyxJMoE/NczK8Bkl9bNj8OseEhlA89eD/mYSKB9zdv3mKI9fbgfK6nF2VUvV14XDaHY6aTxmRw3OR+fx8eg0A6m7ZkulG20QqO7ZvWrYdYcRDlctkM9s91kiZF0kd6cXxWGzdvhdjIAPZzc05YCrEf33IXxEarOB6dcQKKEKtECskkhYk4X3INE5kSE2GliaTKjeNcLZnG/iFN+t0aEUVVydyoSuZp8xajJHcshmP1MBlb28i4bGQeuqfS3yhDFedYB0Kf2AohhBBCCCGEaGq0sBVCCCGEEEII0dRoYSuEEEIIIYQQoqnRwlYIIYQQQgghRFNzUPKo32zss+SkDb7zjz8DjgkNN3k7RFxhRLowMkokHEO44byj/WSIXXjB8yF28kkrIHbj976P5XNwt3prK27wn9OLcqVcSwFino910N7Nq7pnEW7UHk7jpvOHHnkEYrvGUNAQxVH40NrdAbHOJSiAYvKiIMJrrItQ7LB+N25CT3j42nIFN8SXSHr4YeOZ+PWqmd2NBx1iVt99mzluo1ylkSE4JhFHEUY6kydnw3zwIoxF5L0nl0g4Ygms61SSCdpwg/5kGdDE+TKYM6kE5kzCJWIsJkVJMc2NmeNgP1CvopimSkQKVCDkoDTByDViRgxVLrFWEHFRa5bF8Nnl0iiCScaxfHEH+wAnqD7t3zNFPYrMiRp1x/IpRgQSAZHneOy5eCTfSeqkiACqXMR8KA/jmFLGEJWvuXGMRUSisW4Nymq2bt4MMT/A8pmZRUQM2NvTDbH2Vmx/5RKKzFhsaHAIYv2D/RArE9FjQO65RK4xPIKiFZe0s0wM28ruXbswtrshegyJ5GQmqPs1q/uN8tdqpO36eH+ug3VIeiqLDI8jw6iNjWESV8rYRxyzHAWfp558GsQeePS3ELv3/l9DbHgMn3vgY17P7umF2Nlnnw0xM7MY6UM2b9kCsdWrfwWx41YeC7EW0k727EZp6J49eyDGxpTurh6ILVq0EGJBgE+0OIpyq4i0iXgM51SV/XKrSnJtJkjGkpaMNZ7Zrn0onNuydh3ETjj9OIh5MRxzR0k95sgzrZTxWXW0t0Ns6zbMpZ7lCyC26DmYS+s3o3Rs8UIUgi1ZgOerjOE6wCcySjOz2d1zILZzO5Z7kIgKE6Qn8UPMlUEi0UpmsO0xaWJEpK8JMqcrDuOYMncR1teCY1E8tWMQxV9jlcYzrlX4+MnQJ7ZCCCGEEEIIIZoaLWyFEEIIIYQQQjQ1WtgKIYQQQgghhGhqtLAVQgghhBBCCNHUHJQ8av1IyuKJhpCmL0ApThRHyYtbIxvoQ9w07hJ5S2/PbIj9yVmnQiwVx83NixbghuyXveb1EPvO938Esb7dWOZdw7hJu1JZD7EEEUAMlIkhyczWb0GpgRFJQNR5DMTaZqP0JyRiAsdBmUtIhEGhg0KWOtnsPhzg+VJxfG0qRjaXOyifqMfxfNGkze9BhDk1E8zuzJvrNZrMrvI+OCYIhiDWQoQGMfJMRvoGITY6ggKCOpHQhD6KQ6KQ6UkIRACVSGO7Y2Iy3yGiFGKPyiTS9NLZNOZhUJ+ebM6SeB2HSbQSWMY0EZa051DgMTeHfdzcnk6IZdDTZdUKyhpckssxYocptEytrzI+ohlhw/onLJZo1N2xx6FwI03ETiwVXcP7ZpKgPXv3Qqw4gv1ztUzER0RcyGRIi5cuhNis2ficA3IjcSLLam3FtpIk9WJm5hFnWaWKebJ2HQpZxopj03ptndRDGGGbKhKBY5nUa6mE/VKthv1SkoiiRvaiEHJoaAhiwaQ2H7L2PwNEUWTRpHpjIiAWcogBjXjSLCSSKdJMLJ3BfvNPznsheSleJObhM1l+MopAj3/O6RBzmXOPFLCzA+WDixejMMbMLEbaxcJlJ0Ksdz7OgdJpHFdaiWgoIrk+MICiGyaAmj0LRW75PJNvkrEwxPoPQhyr6yQ/wv3kevv/PVMMD49YstoYA8aGh+CYXAb7RIdIiZJJvKf2NhxMd/VhH1SsYT0uXIKiotZZKILd8OQGiK1YgPnpxrCd1SLs50oV7A9bSB2M+nwuW6tjPEPEtH1DOBaWB3He2ELyM0NkiK6D421bFtvUaIDjTLaIc/lCEudVrV04l9xXxbnzmE+sjlGjbwj86ee/PrEVQgghhBBCCNHUaGErhBBCCCGEEKKp0cJWCCGEEEIIIURTo4WtEEIIIYQQQoim5uDkUcOueZM2IP/PXb+BY05egMKN7gRKWTJxvHRPN27S7+lECceSxXOxcGRD9659KAe49psoinrw4cchVq3g+Yh/wyzC9wYiIvcJkngfZmYBEffEDDdv+w4aRnwXj0uxJxqhmKBSI+UmAoNYDDfye0SgElWwcnwjohUiU/AcjNXqk8riE3vGDBDVy1OkZ61ZlF6MVoi4hWy8P2bFcXj+HpRM7evDHN7bj/KVsSGUAJRKuLmfiXNCIjTIxlA+sOJElCvsHMEN//tGhiBWrqFcwcysXEEphEdkJEkiJ8sS6ViBiA9mFQoQ6+7FvmbpnC6IzU5iuxsrjkBsYABlCF6CSLSyKLLI5bHMHR1TjyuVUMowE9SroxaFjf6tMjYEx7ik/2OSHZdIbAIfxXlPPvkExJiwJEHGlHgS+68YsTWFPrYf1yfGKyLT6yByONKVWqmM/YCZWZnEt23bPq1zkq7TIheDpRq28WEibCr2o5QrTqQ4PnlOfoB1WBzCtuKXsS8IyGunWpgOD3FOpVI2d9KD8Ej/F4swv5hwxje8Z5/kHKubkI3BpIp80t87JD9qRNrWO38RnjDEJHRIzCXzok1bB/B8Zlau4b2wMuZbsTysHgaH8Z5jJIezLQuxMGSuNDCMY9TOPXgvTHCWdHHcShCHnJPD8lUGp7bZchllSTNBuTRqQdAoSyaJ4/BZL3o+xFasXAyxbf0ocdo+gu2n/CQ+gzIR2I0S8eSsHIrM+kOcQ615bC3EzjnuJIh15nAuP9qP8zQmDXWI5NPMbLiE/YMxMScZkrJZFFxmUjinKJN5SzJJRI8OjhWlJL42U8LCLO5BYW9/DM83OIz1H0/jHMcvN/qliKwnDoQ+sRVCCCGEEEII0dRoYSuEEEIIIYQQoqnRwlYIIYQQQgghRFOjha0QQgghhBBCiKbmoORRRTdh7qTN8Lc8iFKPJzdshNgFzzkWYkt6UU6zaeOTEDvn9OMhliLSmNEabji/8ae/hthDj++EWMknYhYiTXLj+D4AEwa4Dm5gZ2ImM7OASBuqRLBUJwIJx0GBR9WwbiJilYjFiMTJI7KbDJEfEOlFwDwrZPN7QA70yYb/RL7QeE0NxQEzwcDuneY4jecY1HFTfJlITkrbtkKs3cPn1JlCyVq8igKoNDEIlD28bhQx2xmRtDikzGXc3P8np6Pw6riVJ0Bs69YtEOsfGiRlMatWiTSBtKmYi+077eJxnSlsy4Us1mtA6mF3Hz6ndX27IOaksE20zEZBRbqFSB3yWJb2TnxtrnVq/+gQ+clMkIq5U/qOGhEfpWJEMEOen0v6G5cIoFpacniNOJ4vl81AzCP5kElh3+7XibRqLcpEhgdQGjNcRIFQEGF+xRNYZjOzGKmHJDHMOCTfS0S+tm8ARSalKvZVHnkmbS0FiNWIEI+JsPw6kRxRKRSzYJGcmWTGckgfNRPcffedU4Qrw/6jcEw2hnkYkH68TsRHdSaeDDA32ZheJ0IvNr/wSF9SqZIxnYjSHCLGisewjbUXUCKayxUgZmZWD8i8ijxuh+YIEVcR8ZRDLGsuETvFYhhzyWvZ+Zi8yyHzIschc7kMKV9lqpCwWj085kFts9ssOUn007NsORxz8vIF+LpOnPO3tOM8KIFTD4vl8Dn37yEizBD74q1bcAwvZLAs8VkolNxbxvPNI/MJz8eHH1RQFOXXuDwqMByTEkSumCB5VyZW257Z5F724nXHyNg1RO65QqRq5SG87r4ySg+jTpRyOjXsq5JZHOfd5KTjWKdwAPSJrRBCCCGEEEKIpkYLWyGEEEIIIYQQTY0WtkIIIYQQQgghmhotbIUQQgghhBBCNDUHZSRpb+80L5me+HtgEDfz7hocgtg9j6CEI6jj5nIz3Lg/q3suxBwPZQX33f9biP3o1l9BrBqi2MGI/IAJCBgBkd9EZJNzSCQOZlwCEZCN2nEifHA8IiPxiBCBHOeRjen5PG7e9kg9uBGRVEREAEFEVswy1d2NG/nzLY1YvVKyh/FMh5zZXW1TBFvbt+JGeb9KhE1EJrbpiXUQG05gbrIsLIZY/0UiDgkDJo/CfPOIgKNaQYHAg3f/HGLnkQ3/x5OcKbeiSMnMLPSJSIPIECo1FNgMByhi2NuP5okta/dArK88gteIYz2kZ7dDrK27ALFkCxEXpbEtZlpb8LUZlFE4+7XP/f+eKVzHM3eSdCjwsT07DvY37DlXq0S6RPI4Tfo+lwgEy8UiXmMAZYHbSig+CknOOaRvjpPrekQ0GE8RWdYBHmGthtceG0RRTKWC5a5UUErENIUp0ibrFRy76oblLhNBVbmMsZDIkBwiTfSJtCoiAqHEpPbIJVSHnlQ8bcl4Y75Q90i7D/FBJ5PY7kMmVyR16JI6jEg/HoYkh6nkiEi+yJjukEyKyNyEtnfyuFwjokAzi3lY7moV+3aHzclIsvtE5FMngkomy3RJbk5XUMWojWEfF5GyVMhULulNlcDVarz+DjXlctXCSX3j9rEdcEytjmPugkWLIDa3CyVjx/QeAzGPdJ7pBIr8qkSCVh3FucPIMOb7ictRgpXKYH8/tBflfLPIGLB9H85FdvTja83MojjOARZ3o3Qpn0lDjK0DyiRXYkSWNkbyk4kUu3KzIfZ4EWW/j23aBLFFC4hEM4H1Wi/jc9q2pSH0rFexXAdCn9gKIYQQQgghhGhqtLAVQgghhBBCCNHUaGErhBBCCCGEEKKp0cJWCCGEEEIIIURTc1BGkpjnmjdpo3I8jtIlv4IblDfvQVFLtbgGYuecipu304UeiA1XULBwx733Q6wS4Sb9OpGTJJO48ZuJMEolFHUwPCKFIP6BcdBzYEkiinGYeYTEnCTKLNJp3HAeI0IWJlgYJUKWgMixqkQi09qGYoCuHozlUliW8mhjU3u9Or16f7aZu2SOxeKNso4UMa+L21EYwAwXFSJ2GiB1mCC5VCN5HRAhiEV4PoZDhSB43PpHfw2xbaPYnma5mG9MkmZmFhAJx5iL5d4doVhgPcmL7T5KR0oZIkqbh/1K1yIU2qUKKH2hFiAiIsnlUKyVaUGRgkv60Wg/6cv+f88UY8P95sUbfXx5dAiO2bsTx4BqBZ9LQJ5VvU6ERqRfYvnEJDvxOLaLWAzr0iMCjliciWQgZH6AbaBSxPuoVrEvNTMbHUERE2nils3jOMXkfhHpR6pFbCs+GQuHiQyRiaICYgdisqFwmn1QLIYyEWeSDOlAw+ehJvRrFk5Ki7HiIByTIXJL0sVaQD5XqBOJWa3Onh32h+aydoLPmLWx0Mc+zQ+I2JLJ/kjfFNL2CaHflRH7gSoRlgVEIMauw+SdkbE8xPMxKReTR7F8ZGXxiIjHJ/1ZqYDjQve8qeNHnTzLmWBwT7/Fk4326pOceHztVogt2oOSqbNWnQ6xzgKOmws6USLrEdHXtqG9EJu3EsVHe7dju12/Huc3hbZuiLWQ5zyK6WpbiVx03ZZteKCZze7AMnZmiEy30AGxNjJH2bYL67+FiKcK7QWIFYu4htg3gqKugSLKDIdHUEbFBs0yyZndG9dDLD2pLUdEsnggDo/ZkhBCCCGEEEII8QzRwlYIIYQQQgghRFOjha0QQgghhBBCiKZGC1shhBBCCCGEEE3NQcmjQj80x5u06Tci0gAPBRc1w03ee8dQGPDgup0Qu7BENmpHuEF5xyDGkkTe4pewLJUqliVDNlpPFgc93WsdsqnddTBmZhYnEqeIyGki8h5EnEivxuq4Kbvmo7SECaWYkIVJoYoVlE/kCiiFKszCjfc1H1+7bu1aiMUnyUmCGhFlzAD5QpvFEw1pwqwu3PC/i8ijuGgCY1Uis6iT45goKqByjOnBhBms0HUikSn27YOYmyxAzKvyZ7iT3PPDhm1qfYzkYQ6FM9m5bRCb1dsLsY5ZXRBLZlGaUCN1ExEhTjKG7dtjMSIp8kgf4O53nEv6lJlgz7YN5k6S20VEssckLw4RO8WSRBjkMZEZxhJxFGtkMvj82GuZGNAn0p6xMZS11IjAIiRmINchohsimTIzSxDh32ySs8WxYYiNDKEExa/hdSJyf0z2VKoxUdH05F2sz2DXiJNc8Ej/VSo1xnT2zGaCHTvWWHzSPGD9bhzPMiQ3Y0z4R0cGbBNBSHIuxGccT5D5GDnOJ+JCmpqk7XhEkuc40xO5Hcig6RFZJnvetRqRXgV4HOtrXCr0xLoOmXiK9PfTTH+rG6nrNmzvvSeshFhrdurfNTLvmglKlZrFJ9VTSwrv58nNOC/YumkPxMZGcG56+lnHQqy9Dcf17s75EMumW/G6g5shFs7NQmwshWUZKaLsyU/hvHs0JIKkWSgEi8XmQczMbHAMRUw+G/JJ4o0MDkGsowvnN2UyfgwOY8yNYf+1ox/ntQ+u3wSxzpMXQyxB5HLbn0CxVo7IshKThGmxgxBo6hNbIYQQQgghhBBNjRa2QgghhBBCCCGaGi1shRBCCCGEEEI0NVrYCiGEEEIIIYRoag5KHmVRNNV6QzbVex7ZkB/hLujAxeM270UB1LU3/hhiLzjvNIht2omb1UsBkSkwCVMKNy17CSInIeKERBo3kpdHcRN6vY4SATOziMiZ4il8LExEw87J5DRMiFAu4WZ1dhw7X6GtHWIdXT0Q6+sfgNhQ326MbX0SYksXLWr8QWQ0M0EqlbHEpLxIppJwDBN4BHUinyCmCZ9IOIxJodhh7ITMcMGuQKQeEYmNEaHHWiKbaU2gmGxtBcURZmaPEbHZQAvKKNrnLYJYz0IU7BR6MDeTWZTIuUT2UGf9GREpeHF87jHSXzCJCRUrkbp29xMluAcQrxxqvLAyRYTH5C0hExWxuiCSPDdikhcsRzVAwZhfx1xkYif2DBgxIvWKk+fsxXAsi5G2F5C+3swslcDrJNOYY4P9eM/FUezH40Q05hHxRo2ID30myiEdDs1Zl4iFSD2kyFg2NjIEsVKxITZhAp+ZwImSU3I0ziRCIZE/knpwmAzFJTIkIp6KMQkdESSxamNtLGJySzYuhKTtkNtg8ic2hzEzC0g91Mk9h2RuGblM9oTXiJjMikgYHZrrWL4ohjGfxPK9KPGZe8JyiMUcbO9DT/xmyt91IoWbCdLplMUni/+IENQNsL737O6H2C3/cxfEWloxT5adsBRimVgLxObmZ0EsSdrUuhDlRQ5OYS1RJflVxfutp/DZdHWiXHS2Ty5iZsWBEYiNkuvkiDi3ROSqsTSOU9kkGVNIY9m0fSPE1m5eDzEjgt3Zc+ZC7NE77oXYuafhGu70P1kFsTtv/fnEf9cq089/fWIrhBBCCCGEEKKp0cJWCCGEEEIIIURTo4WtEEIIIYQQQoimRgtbIYQQQgghhBBNzUHJo9paWy2WbIhdKhXcyFws44bnhIebjH0i0nCJlOV/73sUYpt27oTYcBE3Fg+MlfG6WDzLErmMT+QHSbL5mkljUmmUEnhE6GFmFovj6wPyfoNPxE4OiUVEiBDUsW5qdayIdApFWJ0dHRBr68QN8LUIy1wlUpRyEu83JPKVYqXx7II6Sk5mAj8IzAkaUotiGfM/X8A6rBSx/AHJLybRCJj/iQQd6sOZnnAoIoKdyMNnV3RR6HFXbRhiW0p43ECGv4cW65oHse45KIBYNKsTYh2tmJsuactFIgSpEFFXjMhNUkQQlspk8bUJfO6pNEqwkqSNxeOY/4crYVC3yUIzJsWJiPglIrKuqE4ES0yuRcrhEFFRwIQ6pH9l/bhHzueS81FvGxHqsD4rKON4ZGZWI+NeuYxSteIYEf4xUVcCy10poViLPjvSTNk9M3kUOy5G6jWqYd0M9qNcrl5r1Bcr60wQ+DXznUb+BzV8pnUXn6fPJh5EMkV8ahaSMd0l9VEneRgyCRMTvoVEjEnykvmu2DWYLI691ozPT4yUkYnImESLSa8cIhCyCK8bJ4Vkc696BvuVtmMWQ2zOQhzfKnsw1zeufQBiqfrU9u7XDw+JZizrWDzZqCfSBVm8Dce+BYVuiG1fgzLRu37xCMQyLbiGyGRxLM2m8fnNbsXnEs/g3GFLHwqSRkqYIxUyvx8cRnntaA1jlb04XzIzy5TwXuohijCHUpjHiWQeYrUaHjc4hjLXHWNYngFixAvyWL7uDnwm+zZtgViMlGX+UpyneTGUixVyrRP/XY2RPvQA6BNbIYQQQgghhBBNjRa2QgghhBBCCCGaGi1shRBCCCGEEEI0NVrYCiGEEEIIIYRoag5KHlWtlC2YJJpJkmVxNSAb8j3caO+TPf8RE3ikcZPxlp24Kdsl4hefyEmYtKpSqUCsWER5h0vKx0Qk2QTKYNJp3Hw9fk6yGZwIa9IZrIdaDXft9w3gBvHQ8LhYHO+lrQWlOF3tBYh1d5NN7USQNDo0CLGx4SGIFdrxfH37+ib+O2TijRmgHlSnSC28BOZX2yysw3qO5H8dnzsJWZ1IpiIij2JuDIdod6j0hcSMCL1iMTyunsZ7q7bi81zcOhuvYWZt7S0Qy7Vgt5TLYPtOpvC4io9ih5phLCLCJi9OukNWNyQWJxI5j/RJcXINj0qKov3+Pjyo1GvmBo3yxmJ4PyyfPHKcS3LMJdIy1u8yGR+TPZmHr2XiqYi0M59YUQIi6KmTnPMqRCo0hrI5M7OA3Eu2imMSE0W5pK6rZXytEQEOI5ympInVTYy1KfJMBvbshVi9iuPt1Fs7TFqA97v/PfVnHOvfJfKVeIxZl5ipi+S6YR0yoVpEDIIOEQMm43i+tpY2iLnkKkHA2gTGPI9cl4gjzcx8n8gQybVDJlwk7XF0FCVrERkfQw/zdcTBA2OdWDfzly+HWFsbCg53rEUhUf/6TXgNUoep/XKr7k9PBvlsE4VliyaVd6gf2+6uHThHX3nmQojVivjsh/qxn7ztZ/dDzHfJWL8c67G3jrGOFpRHHdN9HMQGR1GutLfUBzGPzDEyLgq0qokCxMzMnnjocYjt2ov9ZM/cJRAb2LgBYjUy/rA2lZ6N5Zl/7DEQa5s/H2LFCrYzl/RzHT0494vS+EyGRjGPhkYa91GrEMncAdAntkIIIYQQQgghmhotbIUQQgghhBBCNDVa2AohhBBCCCGEaGq0sBVCCCGEEEII0dQclDyqVqlaMEl4kCSCgAw5Y1gnG5mJ5yM03Lgfkl3/IZEp+DXchB4FRJRD5BgsxkQFTGIyOIiCpAFyvy05lAqZmbW2oWinhQhPUobyqSBEYVOMCCS8JNZXtYKvTRI5EDufX8IN9X4Jzzc21A+xsI4iqFQSJQ6VSdIRhwg1ZgIv7kyRhRTaUeiVy2BZA5KbTB7lB0QURTb8uy42Moe8R8XEMi6T7pAN/zEiQEkTGVI+T4RjuVaI5ZJpiJmZZRMYT5B8qGHIxhJY7jKTmzikPRFxUYKIi5gUikmKqJCI9Cu1GgoQEgkS20/wws41E8QTqSn3z/IpziSArH7Ic2F6FIfdOpEhRRE+ewuw/wpI3x4SAZRfx+dSq2H/VSaijqBcwvOV8Tgzsyy5droV5SY+yZ16BcvD2j2DieSMCXpI/e8vNzMzy5JxqziC4+PIyBA7ITC5nxu/3sxLBF3fNW9y3tbY/ATHwsjw2XlGZFskxp5TSGRDDmkoLBb6WJZSCYU9rE8zMkeL2BytjjldqZNJn/GxyyF9AzdmYSggdc06kZD04/nZKIqatXwRxFxSD+t+fS/EqntxDuSRPskjdb2/yG26Yrdnm+G9QxZPNNrm2geegGMqRCbqpXAO2zGvALFaGV+740kUNq22RyAWTxMh2CwUqrYM4HV7Zy+GWCGPQrAEEa9mHJwnzMrga2ctRKGUmdmC1jzE7liNwqxNxd0Q6yvugFhHoRtic+YvgNjcuT0Qm9c7D6/RT0SwRiSFpEHm89imqiGKoizAupk9p9HnV0heHIjDY8UghBBCCCGEEEI8Q7SwFUIIIYQQQgjR1GhhK4QQQgghhBCiqZnWHtun9ncFtal7hMKQ7C2qkx+WD8i+Q/KD2TTo4/eqwzrZw0L2XIU+7skJyR680CV7WNhxbC8JuYaRPSz0ODMLyL34NazDehW/w+9X8ZwBeS3bnxeQva70uhXcK1ZL4D6GOtmzy8rCnl1Ifmg7nPTcn6q7mdpn+NR16/vtcfPJPiKf7JcLyA/Q0xjZ38b22EbkR+kdsrchInuz2HblkG1UInuSyA5Gq5MfP2f7SKsO72piZL88Kw/ZkmwW4TmrZEOgz/ZyhmTPJ3kt2wfKuqmISQMisinMwbbjkmvU41Ofcak4vidlpttAuP/+MFYcVkbmNnCwIukeW3YJ9kzpi7Gt0LGC7HujMdL2ItZuyVhxoGfHfA6BT8afaZaHVwQ7jIx71HOBr2V7bKddX9P0XEyOPfXfM53/+/d3Prlnh3SUkZF6IJ8rsP6e77El153uHltShx7JN5vmHltj86KI5MIBnh3dY8s+c5nmHls2BrMDQ1I3ERnP6D52Uj6fvJaVJSI5Yy4Z8/ar16fONeNtoDb1PkMyIIZkLK1XSf3U2Hxpeudj869qGecelRI+v7iH89DSGK5n4hHOf5lToVTEuW6RrI/SbBJlZiXiqKmR+iLTZ1oP+z8jM7MqyWO2b7VUxHKXS3h/lWnusY15eL5ahK9169jAJ5ev+rv/nk7+O9E0jtq+fbvNm4cbioU4lGzbts3mzp17yK+r/BeHC2oD4mhG+S+OdtQGxNHMdPJ/WgvbMAxt586dls/nuUlRiGeRKIpsdHTUent7qV312Ub5L2YatQFxNKP8F0c7agPiaOZg8n9aC1shhBBCCCGEEOJwRfIoIYQQQgghhBBNjRa2QgghhBBCCCGaGi1shRBCCCGEEEI0NVrYPotcfvvldvJ/nDzTxRDikPHHzvnpnO+868+zS3966R/tmkL8IagNiCOBZ2v+4lzh2E1rb/qDz6P5lTjsuPxys5NP/sPPc8klZhdd9Ief5yhlWr9jeyRxyU2X2A2P3GBmZjE3ZvNa5tmrV77arjjvCssmsjNcuj+c6x++3i796aU29E9DM10UcZhwpOf89173PYu7+PuwQjyF2oA4EjgS8njX+3ZZW6ptposhjhYuucTshvE2Y7GY2bx5Zq9+tdkVV5hlm6PNiIPjqFvYmpldsPQCu+6V11k9qNudW++0v/zBX1qxVrQv/Z8vwbH1oG5xTxMG0dwcyTnfnm6f6SKIJkBtQBwJNHsed+e6n/bfD8cyiybnggvMrrvOrF43u/NOs7/8S7Ni0exL2GasXjeLHwX5V6uZJRIzXYpnhaPyq8hJL2nduW6b1zrPLj7hYnvjCW+0m9bdZGaNr7dc+9C1tvjfFlvy40mLosiGK8P29h++3Wb/y2xr+USLveCGF9gjux+Zct5P3vVJ6/pMl+U/kbe3/c/brOJXnlH5rn3oWjvui8dZ8uNJ67m6x97143dN/Nu//upf7YQvnWDZq7I277Pz7G9/9Lc2VhszM7PbN99uf/E/f2HD1WFzrnDMucKxy2+//BmVQRxZHM45f/vm2+2Mr5xh2auyVvhkwZ537fNsy9CWKcd87ZGv2cJrFlrrJ1vt9d95vY1WRyf+bf+vYS68ZqF97I6P2cXfvdhyV+Ws9+pe+/y9nz/ocokjC7UBtYEjgcM5j3+949f24q+92Do/3Wmtn2y1c68/1x7c9eCUYyZ/FXnz0GZzrnDsxsdutPOuP89SH0/Z1x/9ul3/8PVW+GTBblp7ky3//HJLfTxlL/7ai23b8LY/+NpfffCr9qpvvcoyV2Zs2eeX2Q/W/WDKMY/ve9wu/MaFlrsqZ12f6bI3ff9N1lfqO+i6EIcRyaRZd/f4p7UXX2z2xjea3XTT+L899fXha681W7x4/NgoMhseNnv7281mzzZraTF7wQvMHpnaZuyTnzTr6jLL583e9jazyjOY8weB2d//vVmhYNbRYfaP/zh+/clEkdmnPz1evnTa7KSTzL7znanHPP642YUXmuVy42V605vM+ibl7Xnnmb3rXePX6uw0e/GLD76sTcJRubDdn3Q8bfWgPvH3+oH1duNjN9p3X/tde/gdD5uZ2cv++2W2e2y3/fiNP7YH3v6Andpzqr3wv15oA+UBMzO78bEb7aO3f9SufMGVdv9f3W89+R774q+/OOU6t2++3ZwrHNs8tPmAZfnSr79k7/zxO+3tp77dfvM3v7EfvP4HtrR96cS/u45rn7vgc/bbv/mt3XDRDXbrplvtH3/xj2Zmdta8s+ya86+xlmSL7XrfLtv1vl32/rPe/0eqJXEkcbjkvB/6dtE3L7JzF5xrj77jUfvV235lbz/17VN+AH7D4Aa7ad1NdvPFN9vNb7jZ7thyh33yrk8+7f39yz3/Yid2nWgP/vWD9oGzP2Dv/dl77RcbfvEMakocqagNiCOBwyWPzcxGa6P2lpPeYnf+xZ22+m2rbVn7MrvwGxdOeROGcdkvL7N3n/luW/PONXb+0vPNzKxUL9mVd15pN1x0g9391rttpDpir//u6//ga19xxxX22mNfa4/+zaN24dIL7Y3fe+NEPewa3WXnXn+undx9st3/9vvtp2/8qe0Z22Ov/fZrn7b8oslIp8c/mX2K9evNbrzR7LvfNXv44fHYy15mtnu32Y9/bPbAA2annmr2wheaDYznit14o9lHP2p25ZVm999v1tNj9sWpbcZuv93Mccw2bz5wWa6+enxR/Z//aXbXXePn//73px7zoQ+Nf+L8pS+ZPfaY2Xvfa/bnf252xx3j/75rl9m5544v0O+/3+ynPzXbs8fstfvl7Q03jH8d++67zb785YOrs2YiOsp4y/ffEr3y/71y4u97t98bdXyqI3rtt18bRVEUffS2j0bxf45He8f2Thxzy8ZbopZPtESVemXKuZb825Loy/d/OYqiKFr11VXRO374jin/fuZXzoxO+tJJU651zOePibYPbz9g+Xqv7o0+eMsHp30/N/72xqjjUx0Tf1/30HVR6ydap/16ceRzOOd8f6k/ssstun3T7fTfP3rbR6PMlZlopDIyEfuHn/9DdOZXzpz4+9zrzo3e85P3TPy94LMLogu+fsGU87zu26+LXvr1l9JriCMftQG1gSOBwzmPGX7gR/mr8tEP1/1wImaXW/T9Nd+PoiiKNg1uiuxyi6751TVTXnfdQ9dFdrlFq7etnoit2bcmssstunf7vRP3Orl80732h2750MTfY9WxyLnciX7y5E+iKIqiD9/64eglX3vJlPNsG94W2eUWretbN+37FocRb3lLFL3ylY2/7703ijo6oui1420m+uhHoygej6K9jTYT3XJLFLW0RFFlapuJliyJoi+Pt5lo1aooesfUNhOdeWYUnXTS1Gsdc0wUbX+aNtPTE0Wf/GTj73o9iubObZR5bCyKUqkouueeqa9729ui6A1vGP/vD384il4yNW+jbduiyCyK1v0ub889N4pOPvnA5TiCOCr32N78xM2WuypnfuhbPazbK495pX3+pY2vaS0oLLBZ2VkTfz+w8wEbq41Zx6c7ppyn7Jdtw8AGMzNb07fG3nHaO6b8+6q5q+y2zbdN/H3GnDNs7bvWHrBce4t7befoTnvhohce8JjbNt1mV911lT2+73EbqY6YH/pW8StWrBWbRh4hDj2Ha863p9vtkpMvsfO/fr69eMmL7UWLXmSvPe611pPvmThmYWGh5ZP5ib97cj22t7j3ae931dxV8Pc1917ztK8RRzZqA2oDRwKHax6bjc9hPnLbR+zWTbfanuIeC8LASvWSbR3e+rSvO633NIjF3NiU+IrOFVZIFWzNvjV2xpwznvG1T+w6ceK/s4ms5ZP5ibb0wK4H7LZNt1nuqhycf8PABlvesfxp70Mcptx88/hXdH1//JPaV77S7POTtmYsWGA2q9Fm7IEHzMbGxr8aPJly2WzDeJuxNWvM3jG1zdiqVWa3NdqMnXGG2dqnaTPDw+Oftq6a1FfHYmanndb4OvLjj49/xXn/rw7XamannNIo7223jd/j/mzYYLb8d3l7GrazI5GjcmH7/EXPty+97EsWd+PWm+8FUUE2PnWBGEah9eR67PZLbodzFVKFP1q50rH00/77lqEtduF/X2jveM477GPP/5i1p9vtrq132dt+8Darh/Wnfa04ujlcc97M7LpXXmfvPuPd9tP1P7VvPfYt+9BtH7JfvOkX9ty5zzUzA9ur4zgWRuFBX8cx5/cfJI5Y1AbUBo4EDuc8vuSmS2xfaZ9dc8E1tqB1gSVjSVv1n6usFtSe9nUHelN+8tfxny52MNfev74ca7SlMArt5ce83D71ok/B+XtyPRATTcLznz/+Nd543Ky3F+VQ+9uRw3D8q8W3347nKhSerVJywt/18z/6kdmcOVP/LZlsHPPyl5t9CvPWeibl7VFigT4qF7bZeHbKvtXfx6k9p9rusd0Wc2O2sLCQHrOyc6Wt3r7a3nzSmydiq3esPqhy5ZN5W1hYaLdsusWev+j58O/377zf/NC3q8+/2lxnfHv0jY/dOOWYhJewIAoO6rriyOdwzfmnOKXnFDul5xT7wJ98wFb95yr779/898Sk/pmwevvUcqzesdpWdK54xucTzY/agNrAkcDhnMd3br3TvnjhF+3CZReamdm24W3PWLzkh77dv/P+iU9n1/Wts6HK0AFz+I9x7VO7T7XvrvmuLSwstJh7VE6Pj0yyWbOl028zduqp4/trYzGzhQv5MStXmq1ebfbmRpux1QfZZlpbxxeeq1ebnXPOeMz3G3t6zcyOPXZ8Abt16/g+2gOV97vfHS9rTHkredQ0eNHiF9mqeavsom9eZD9b/zPbPLTZ7tl2j33o1g/Z/TvvNzOz95z5Hrv2oWvt2oeutSf6n7CP3vZRe2zvY1POc9+O+2zFv6+wHSM7Dnity8+93K7+1dX2uXs/Z0/2P2kP7npwwma5pH2J+aFvn7/387ZxcKN97ZGv2X/c/x9TXr+wsNDGamN2y8ZbrK/UZ6V66Y9cG+Jo4FDl/KbBTfaBX37AfrXtV7ZlaIv9fMPP7Yn+J2xl58o/qPx3b7vbPn33p+2J/ifsC/d9wb792LftPWe+5w86pzi6UBsQRwKHcv6ytH2pfe3Rr9mafWvs3u332hu/98bf+020AxF34/Z3P/k7u3f7vfbgrgftL/7nL+y5c59Lv4b8x7r2O894pw2UB+wN332D3bfjPts4uNF+vuHn9tb/easFoT4wOGp40YvGvx580UVmP/vZuPzpnnvGJU73j7cZe897xqVP115r9sQT4yKpx6a2GbvvPrMVK8x2HLjN2HveM25X/v73x7+2/Ld/azY01Pj3fN7s/e8fF0bdcMP4V4sfesjsC19o/D7vO985Lp16wxvGr7lxo9nPf2721reOW5ePMrS0nwaO49iPL/6xffDWD9pbf/BW21fcZ925bjtnwTnWle0yM7PXHf862zC4wS775WVW8Sv2pyv/1P7mtL+xn2342cR5SvWSretf97RfG37LyW+xil+xz67+rL3/5++3zkynvebY15iZ2cndJ9u/vuRf7VN3f8o+cMsH7JwF59gnXvgJe/NNjXeMzpp3lr3jOe+w133nddZf7rePnvtRu/y8y5+dihFHLIcq5zPxjK3tX2s33HiD9Zf7rSfXY+86/V3216f99R9U/vetep89sOsBu+KOKyyfyNvVL7l6wrYpxHRQGxBHAody/nLtK661t9/8djvly6fY/Nb5dtULr7L3//yZ/TJDJp6xy553mV38vYtt+8h2O3v+2XbtK659Vq/dm++1u996t132y8vs/K+fb1W/agsKC+yCJRdMfEtOHAU4zrgN+YMfHF8c7ts3/nNB55wz/lM6Zmave934IvOyy8b3wP7pn5r9zd+ML4SfolQyW7duqoF5f973vvF9tpdcYua649d71avG998+xcc+Nv6zQ5/4xPiitVAY/5T2//v/xv+9t3fcdHzZZWbnn29WrY7vG77ggvFzHmU4UbT/DyYJIUTzsvCahXbpcy+1S5976UwXRYgZQW1ANDPXP3y9XfrTS23on4ZmuihCiCbj6FvKCyGEEEIIIYQ4otDCVgghhBBCCCFEU6OvIgshhBBCCCGEaGr0ia0QQgghhBBCiKZGC1shxGGLc4VjN6296YD/fvvm2825wrGhytAhK5MQhwrlvziSuPz2y+3k/zj5j3Ku39c2hGgq1q41e+5zzVIps5NPnunSNDVH3c/9OFc4T/vvbznpLXb9RdcfmsI8C8iGKfbnSM75s+adZbvet8tak60zXRRxmKL8F0cKzZ7L7z/r/fZ3Z/zdTBdDHI04T9927C1vMbv++kNSFMpHP2qWzY7/PFAuN3PlOAI46ha2u963a+K/v/Xbb9lHbv+IrXvXuonY/j/oXQ/qFvfih6x8QvyxOZJzPuElrDvXPdPFEIcxyn9xpNDsuZxL5CyXOPCkvRbULOElDmGJxFHDrkbbsW99y+wjHxlfRD5FemrbsXrdLH4I286GDWYve9n4788eiENdpiblqPsqcneue+J/ralWc8yZ+LviV6zwqYLd+NiNdt7151nq4yn7+qNfp1+fuWb1NbbwmoVTYtc9dJ2t/MJKS308ZSv+fYV98ddfPOjybR/Zbq//zuut/VPtlr0qa6f939Ps3u33mpnZhoEN9spvvtK6PtNluatydvpXTrdfbvzlxGvPu/482zK8xd77s/eac4Xze9/dFUcHh3PO14KavevH77Keq3ss9fGULbxmoX3izk9MOaav1Gev+tarLHNlxpZ9fpn9YN0PJv5t/69iXv/w9Vb4ZMFuWnuTLf/8ckt9PGUv/tqLbdvwtoMqlzhyUP4r/48UDudcNjO77BeX2fLPL7fMlRlb/G+L7cO3ftjqQX3i3/cvyyU3XWIXffMi+8Sdn7Deq3tt+eeXm9n4N88+dsfH7OLvXmy5q3LWe3Wvff7ez/9Rrv21R75mC69ZaK2fbLXXf+f1NlodnTgmiiL79N2ftsX/ttjSV6btpP84yb7z+HcOuh7EYUh3d+N/ra3jn+A+9XelYlYomN14o9l5541/HfjrXze7/HL8WvA115gtXDg1dt11ZitXjr9uxQqzLx5k23EcswceMPvnfx7/78svN9u8efy/9y9TGI4fN3euWTI5Xr6f/nTq+e65ZzyeSpmddprZTTeNn+vhhw+uXE3KUbewnQ6X/fIye/eZ77Y171xj5y89f1qv+coDX7EP3vpBu/IFV9qad66xq154lX34tg/bDQ/fMHHMedefZ5fcdMkBzzFWG7Nzrz/Xdo7utB+84Qf2yDsesX983j9aGIUT/37h0gvtl2/6pT301w/Z+UvOt5f/v5fb1uGtZmb2vdd9z+a2zLV/Pu+fbdf7dk15d1eIp2Omcv5z937OfrDuB3bja260de9aZ19/9ddtYWHhlGOuuOMKe+2xr7VH/+ZRu3DphfbG773RBsoDBzxnqV6yK++80m646Aa7+61320h1xF7/3ddP657E0YnyXxwpzFQum5nlk3m7/qLr7fF3Pm7/dsG/2Vce/Ip9dvVnn/Y1t2y6xdb0rbFfvOkXdvPFN0/E/+Wef7ETu060B//6QfvA2R+w9/7svfaLDb/4g669YXCD3bTuJrv54pvt5jfcbHdsucM+edcnJ/79Q7d+yK57+Dr70su+ZI/97WP23ue+1/78e39ud2y+42nvQRwhXHaZ2bvfbbZmjdn502s79pWvmH3wg2ZXXjn+uquuMvvwh81uaLQdO+88s0suOfA5du0yO+44s/e9b/y/3//+A5fp3/7N7OqrzT7zGbNHHx2PveIVZk8+OX786KjZy19udsIJZg8+aPaxj42f4yjiqPsq8nS49MxL7dUrX31Qr/nY/37Mrn7J1ROvW9S2yB7f97h9+YEv21tOfouZmc1vnW89uZ4DnuO/f/Pftq+4z379V7+29nS7mZktbV868e8ndZ9kJ3WfNPH3x1/wcfv+2u/bD9b9wN51xrusPd1unuNZPpnX19PEQTFTOb91eKst61hmZ88/2xzHsQUF/BrOJSddYm844Q1mZnbVC6+yz9/3ebtvx312wdIL6DnrYd3+/aX/bmfOPdPMzG646AZb+YWVdt+O++yMOWcc1D2KowPlvzhSmKlcNjP70DkfmvjvhYWF9r6+99m3HvuW/ePz/vGAr8nGs/bVV3wVvoL8vPnPs386+5/MzGx5x3K7e9vd9tnVn7UXL3nxM752GIV2/Suvt3wyb2ZmbzrxTXbLplvsSrvSirWi/evqf7Vb33yrrZq3yszMFrcttru23mVffuDLdu7Cc5/23sURwKWXmr364NqOfexj4wvNp163aJHZ44+bffnL4/t2zczmzzfreZq2091tFouN763t/t3cva+Pl+kznxlfqL7+d29WfupTZrfdNv5J8he+YPaNb4x/OvuVr4x/YnvssWY7dpj91V8d3H01MVrYEk7rPe2gjt9X3GfbRrbZ237wNvurHzaSxw99a001pB7/9ar/etrzPLz7YTul55SJRe3+FGtFu+KOK+zmJ262naM7zQ99K/vliU9shXimzFTOX3LyJfbir73Yjvn3Y+yCpRfY/1n+f+wlS14y5ZgTu06c+O9sImv5ZN72Fvce8JwxNzblflZ0rrBCqmBr9q3RxF5QlP/iSGGmctnM7DuPf8euWX2NrR9Yb2O1MfND31qSLU/7mhO6TqD7alfNXQV/X3PvNX/QtRcWFk4sas3MenI9E23p8X2PW8Wv2Iu/NnXhXAtqdkrPKU97D+II4bSDazu2b5/Ztm1mb3vb1IWj749/3fkp/uv3t51plWlkxGznTrPnPW/qMc97ntkjj4z/97p1ZieeOL6ofYozjq5+XwtbQjaRnfK367gWWTQlNnnvxlNfFf7Ky78y8S75U3iON+3r7i9+2J9/+MU/2M82/Mw+8+LP2NL2pZaOp+01N77GakFt2tcQgjFTOX9qz6m26T2b7CdP/sR+ufGX9tpvv9ZetPhF9p3XNvY17S8/ccyZuP6BcIgBkcWEMFP+iyOHmcrl1dtX2+u/83q74rwr7Pyl51trstW++dtv2tW/uvrpyxvPPu2/T8YxnsPTvXbc3a8tOY229NT//+jiH9mcljlTjkt6yWmXUTQx2f1y0XXNoqltx+qNtmPh7/rhr3zF7Mypbce86bedgyqTGRqeo6gRm/zfk//9KEIL22kwKzPLdo/ttiiKJiYHD+95eOLfu3JdNic/xzYObrQ3nvjGZ3ydE7tOtK8+9FUbKA/QT23v3HqnXXLSJfaqla8ys/E9t5uHNk85JuElLAiDZ1wGIcwOXc6bmbUkW+x1x7/OXnf86+w1x77GLvjGBQdsA9PBD327f+f9E59OretbZ0OVIVvRueIPKqc4elD+iyOFQ5XLd2+92xYUFtgHz/ngRGzL8JZnfL7V21dP/XvH6gPm8B/j2sfOOtaSXtK2Dm/V147FOLNmme3ePXWxOFnA1NVlNmeO2caNZm/8w8aBadHSYtbba3bXXWbnnNOI33NP41PZFSvGv45crY7LpczM7r//2S/bYYQWttPgvIXn2b4f77NP3/1pe82xr7Gfrv+p/eTJn0z5msvl511u7/7Ju60l2WIvXfZSq/pVu3/n/TZYGbS/X/X3Zmb25u+/2ebk59gnXvQJep03nPAGu+quq8YtgS/8hPXke+yhXQ9Zb77XVs1bZUvbl9r31n7PXn7My80xxz5824fhnfuFhYX2v1v/115//OstGUtaZ6bz2asYccRyqHL+s7/6rPXke+zk7pPNdVz79uPftu5ctxVShWdc9rgbt7/7yd/Z5y74nMW9uL3rx++y5859rr6GKaaN8l8cKRyqXF7avtS2Dm+1b/72m3Z67+n2oyd/ZN9f+/1nXO67t91tn77703bRiovsFxt+Yd9+7Nv2o4t/9KxdO5/M2/vPer+992fvtTAK7ez5Z9tIdcTu2XaP5RK5ib3G4ijivPPGv2786U+bveY14/bhn/xkfIH5FJdfPi53amkxe+lLxxeU999vNjho9vfjbcfe/ObxBfAneNs5KP7hH8Z/83bJknHz8XXXjS+2v/GN8X+/+OJxmdXb3272T/9ktnXr+L5cs9//W75HCLIiT4OVs1baF1/2RfvCr79gJ/3HSXbfzvvs/We9f8oxf3nqX9pXX/FVu/6R6+2EL51g515/rl3/yPW2qLBo4pitw1tt19iBTcUJL2E///Of2+zsbLvwvy+0E750gn3y7k+a545/peGz53/W2lJtdtZ/nmUv/38vt/OXnG+n9pw65Rz//Px/ts1Dm23J55bYrH+Z9UesBXE0cahyPpfI2afu/pSd9n9Ps9O/crptHtpsP774x+Y6z7xrysQzdtnzLrOLv3exrfrPVZaOp+2bf/rNZ3w+cfSh/BdHCocql1+54pX23ue+197143fZyV8+2e7Zdo99+JwPP+Nyv2/V++yBXQ/YKV8+ZUJudSDL8x/r2h97/sfsI+d+xD5x1yds5RdW2vlfP99++MQPbVHbot//YnHksXLl+E/3fOELZiedZHbffVONxWZmf/mXZl/9qtn114+biM89d/y/F03Kma1bp/6O7h/Cu989bk9+3/vGr/fTn5r94Admy5aN/3tLi9kPfzi+2D355PFF7kc+Mv5vk/fdHsE4UXSUfflaCHHEcv3D19ulP73Uhv5paKaLIsQhR/kvjgQWXrPQLn3upXbpcy+d6aII0fx84xtmf/EXZsPDZumnd/kcCeiryEIIIYQQQgjR7PzXf5ktXjz+9edHHhn/eaDXvvaoWNSaaWErhBBCCCGEEM3P7t3jXz/evXv893P/7M/Mrrxypkt1yNBXkYUQQgghhBBCNDWSRwkhhBBCCCGEaGq0sBVCCCGEEEII0dRoYSuEEEIIIYQQoqnRwlYIIYQQQgghRFOjha0QQgghhBBCiKZGC1shhBBCCCGEEE2NFrZCCCGEEEIIIZoaLWyFEEIIIYQQQjQ1WtgKIYQQQgghhGhq/n/nl55jr5XvqgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to plot sample predictions\n",
    "def plot_sample_predictions(images, labels, predictions, num_samples=5):\n",
    "    plt.figure(figsize=(12, 6))\n",
    "    for i in range(num_samples):\n",
    "        plt.subplot(1, num_samples, i + 1)\n",
    "        plt.xticks([])\n",
    "        plt.yticks([])\n",
    "        plt.grid(False)\n",
    "        plt.imshow(images[i])\n",
    "        \n",
    "        predicted_label = np.argmax(predictions[i])\n",
    "        true_label = labels[i][0]\n",
    "        \n",
    "        color = \"green\" if predicted_label == true_label else \"red\"\n",
    "        plt.xlabel(f\"Pred: {class_names[predicted_label]}\\nTrue: {class_names[true_label]}\", color=color)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Plot predictions on a few test images\n",
    "plot_sample_predictions(x_test, y_test, predictions, num_samples=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "875e47d0-757f-40db-b72a-fd62feb87d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot Training History (Loss & Accuracy)\n",
    "def plot_history(history):\n",
    "    plt.figure(figsize=(12, 4))\n",
    "    \n",
    "    # Plot Loss\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['loss'], label='Train Loss')\n",
    "    plt.plot(history.history['val_loss'], label='Val Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "    plt.title('Loss Over Epochs')\n",
    "    \n",
    "    # Plot Accuracy\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='Val Accuracy')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "    plt.title('Accuracy Over Epochs')\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "# Show training curves\n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9874aded-4041-4682-80e0-c6b51b142769",
   "metadata": {},
   "outputs": [],
   "source": [
    "### C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a7c954c-a428-4324-b6b0-de233724e9e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be13c74a-796a-4a93-ad79-e85acfeeb5b0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}