{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cc8e9454",
   "metadata": {},
   "source": [
    "# PyTorch CNN Activity: Experimenting with Model Architecture and Data\n",
    "\n",
    "In this activity, you will experiment with different aspects of the CNN model from the Lesson 31 demo to understand how architectural choices and data preprocessing affect model performance.\n",
    "\n",
    "## Activity Overview\n",
    "\n",
    "You will conduct **three experiments** using the code provided in the demo notebook:\n",
    "\n",
    "1. **Experiment 1**: Add more convolutional blocks and/or modify filters and filter sizes\n",
    "2. **Experiment 2**: Use RGB images instead of grayscale\n",
    "3. **Experiment 3**: Add image augmentation using PyTorch transforms\n",
    "\n",
    "For each experiment, you will:\n",
    "- Modify the relevant code sections\n",
    "- Train the model\n",
    "- Compare results with the baseline model\n",
    "- Document your observations\n",
    "\n",
    "**Note**: You may want to reduce the number of epochs (e.g., to 20-30) to speed up experimentation.\n",
    "\n",
    "## Notebook Setup\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fdd302c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda:1\n"
     ]
    }
   ],
   "source": [
    "# Standard library imports\n",
    "from pathlib import Path\n",
    "\n",
    "# Third party imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "from sklearn.metrics import (\n",
    "    confusion_matrix, roc_curve, auc, \n",
    "    precision_recall_curve, average_precision_score\n",
    ")\n",
    "\n",
    "from sklearn.preprocessing import label_binarize\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# Set random seeds for reproducibility\n",
    "torch.manual_seed(315)\n",
    "np.random.seed(315)\n",
    "\n",
    "# Check for GPU availability\n",
    "device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')\n",
    "print(f'Using device: {device}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "665bb71a",
   "metadata": {},
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df8b361b",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 1000 # Training images come in 5 batches of 10,000\n",
    "learning_rate = 1e-3\n",
    "epochs = 30\n",
    "print_every = 5 # Print training progress every n epochs\n",
    "\n",
    "# CIFAR-10 class names in class order\n",
    "class_names = [\n",
    "    'airplane', 'automobile', 'bird', 'cat', 'deer',\n",
    "    'dog', 'frog', 'horse', 'ship', 'truck'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc030588",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Experiment 1: Modify Convolutional Architecture\n",
    "\n",
    "**Objective**: Explore how adding more convolutional blocks or changing filter counts and kernel sizes affects model performance.\n",
    "\n",
    "### 1.1. Load and Preprocess Data (Baseline - Grayscale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4da77825",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training samples: 50000\n",
      "Test samples: 10000\n",
      "Image shape: torch.Size([1, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "# Make sure data directory exists\n",
    "data_dir = Path('../data')\n",
    "data_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Data preprocessing: convert to grayscale, tensor, and normalize\n",
    "transform_exp1 = transforms.Compose([\n",
    "    transforms.Grayscale(num_output_channels=1),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "# Load training and test datasets\n",
    "train_dataset_exp1 = datasets.CIFAR10(\n",
    "    root=data_dir,\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform_exp1\n",
    ")\n",
    "\n",
    "test_dataset_exp1 = datasets.CIFAR10(\n",
    "    root=data_dir,\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform_exp1\n",
    ")\n",
    "\n",
    "print(f'Training samples: {len(train_dataset_exp1)}')\n",
    "print(f'Test samples: {len(test_dataset_exp1)}')\n",
    "print(f'Image shape: {train_dataset_exp1[0][0].shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb6f135",
   "metadata": {},
   "source": [
    "### 1.2. Create Data Loaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "65ff48e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training batches: 40\n"
     ]
    }
   ],
   "source": [
    "# Create training, validation and testing tensors\n",
    "X_train_full = torch.stack([img for img, _ in train_dataset_exp1]).to(device)\n",
    "y_train_full = torch.tensor([label for _, label in train_dataset_exp1]).to(device)\n",
    "\n",
    "X_test_exp1 = torch.stack([img for img, _ in test_dataset_exp1]).to(device)\n",
    "y_test_exp1 = torch.tensor([label for _, label in test_dataset_exp1]).to(device)\n",
    "\n",
    "# Split training data into train and validation sets (80/20 split)\n",
    "n_train = int(0.8 * len(X_train_full))\n",
    "indices = torch.randperm(len(X_train_full))\n",
    "\n",
    "X_train_exp1 = X_train_full[indices[:n_train]]\n",
    "y_train_exp1 = y_train_full[indices[:n_train]]\n",
    "X_val_exp1 = X_train_full[indices[n_train:]]\n",
    "y_val_exp1 = y_train_full[indices[n_train:]]\n",
    "\n",
    "# Create TensorDatasets and DataLoaders\n",
    "train_tensor_dataset_exp1 = torch.utils.data.TensorDataset(X_train_exp1, y_train_exp1)\n",
    "val_tensor_dataset_exp1 = torch.utils.data.TensorDataset(X_val_exp1, y_val_exp1)\n",
    "test_tensor_dataset_exp1 = torch.utils.data.TensorDataset(X_test_exp1, y_test_exp1)\n",
    "\n",
    "train_loader_exp1 = DataLoader(train_tensor_dataset_exp1, batch_size=batch_size, shuffle=True)\n",
    "val_loader_exp1 = DataLoader(val_tensor_dataset_exp1, batch_size=batch_size, shuffle=False)\n",
    "test_loader_exp1 = DataLoader(test_tensor_dataset_exp1, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "print(f'Training batches: {len(train_loader_exp1)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e926ebcb",
   "metadata": {},
   "source": [
    "### 1.3. Define Modified CNN Architecture\n",
    "\n",
    "**TODO**: Modify the model architecture below. Try one or more of these changes:\n",
    "\n",
    "- Add another convolutional block (Conv2d -> BatchNorm2d -> ReLU -> Conv2d -> BatchNorm2d -> ReLU -> MaxPool2d -> Dropout)\n",
    "- Increase the number of filters (e.g., change 32 to 64 or 128)\n",
    "- Experiment with different kernel sizes (e.g., 5x5 instead of 3x3)\n",
    "- Try different pooling strategies\n",
    "\n",
    "**Important**: Remember to update the input size to the first Linear layer if you change the architecture! The size depends on:\n",
    "- Number of filters in the last conv layer\n",
    "- Final spatial dimensions after pooling\n",
    "\n",
    "*Hint*: For a 32x32 image, each MaxPool2d(2,2) layer divides dimensions by 2. So:\n",
    "- After 1 pooling: 32 → 16\n",
    "- After 2 poolings: 32 → 16 → 8\n",
    "- After 3 poolings: 32 → 16 → 8 → 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e834bc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): ReLU()\n",
      "  (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (5): ReLU()\n",
      "  (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (7): Dropout(p=0.25, inplace=False)\n",
      "  (8): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (9): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (10): ReLU()\n",
      "  (11): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (12): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (13): ReLU()\n",
      "  (14): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (15): Dropout(p=0.25, inplace=False)\n",
      "  (16): Flatten(start_dim=1, end_dim=-1)\n",
      "  (17): Linear(in_features=8192, out_features=256, bias=True)\n",
      "  (18): ReLU()\n",
      "  (19): Dropout(p=0.5, inplace=False)\n",
      "  (20): Linear(in_features=256, out_features=10, bias=True)\n",
      ")\n",
      "\n",
      "Total parameters: 2359754\n"
     ]
    }
   ],
   "source": [
    "num_classes = 10\n",
    "\n",
    "model_exp1 = nn.Sequential(\n",
    "\n",
    "    # Conv block 1: grayscale input -> 64 filters\n",
    "    nn.Conv2d(1, 64, kernel_size=3, padding=1),\n",
    "    nn.BatchNorm2d(64),\n",
    "    nn.ReLU(),\n",
    "    nn.Conv2d(64, 64, kernel_size=3, padding=1),\n",
    "    nn.BatchNorm2d(64),\n",
    "    nn.ReLU(),\n",
    "    nn.MaxPool2d(2, 2),\n",
    "    nn.Dropout(0.25),\n",
    "    \n",
    "    # Conv block 2: 64 -> 128 filters\n",
    "    nn.Conv2d(64, 128, kernel_size=3, padding=1),\n",
    "    nn.BatchNorm2d(128),\n",
    "    nn.ReLU(),\n",
    "    nn.Conv2d(128, 128, kernel_size=3, padding=1),\n",
    "    nn.BatchNorm2d(128),\n",
    "    nn.ReLU(),\n",
    "    nn.MaxPool2d(2, 2),\n",
    "    nn.Dropout(0.25),\n",
    "    \n",
    "    # Classifier\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(8192, 256),\n",
    "    nn.ReLU(),\n",
    "    nn.Dropout(0.5),\n",
    "    nn.Linear(256, num_classes)\n",
    "\n",
    ").to(device)\n",
    "\n",
    "trainable_params = sum(p.numel() for p in model_exp1.parameters() if p.requires_grad)\n",
    "print(model_exp1)\n",
    "print(f'\\nTotal parameters: {trainable_params}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0c435f3",
   "metadata": {},
   "source": [
    "### 1.4. Train Modified Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8e4102e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(\n",
    "    model: nn.Module,\n",
    "    train_loader: DataLoader,\n",
    "    val_loader: DataLoader,\n",
    "    criterion: nn.Module,\n",
    "    optimizer: optim.Optimizer,\n",
    "    epochs: int = 10,\n",
    "    print_every: int = 1,\n",
    "    device: torch.device = None\n",
    ") -> dict[str, list[float]]:\n",
    "    '''Training loop for PyTorch classification model.\n",
    "    \n",
    "    Args:\n",
    "        device: If provided, moves batches to this device on-the-fly.\n",
    "                If None, assumes data is already on the correct device.\n",
    "    '''\n",
    "\n",
    "    history = {'train_loss': [], 'val_loss': [], 'train_accuracy': [], 'val_accuracy': []}\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "\n",
    "        # Training phase\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "        correct = 0\n",
    "        total = 0\n",
    "\n",
    "        for images, labels in train_loader:\n",
    "            \n",
    "            # Move batch to device if specified\n",
    "            if device is not None:\n",
    "                images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "            # Forward pass\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "\n",
    "            # Backward pass\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            # Track metrics\n",
    "            running_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "        # Calculate training metrics\n",
    "        train_loss = running_loss / len(train_loader)\n",
    "        train_accuracy = 100 * correct / total\n",
    "\n",
    "        # Validation phase\n",
    "        model.eval()\n",
    "        val_running_loss = 0.0\n",
    "        val_correct = 0\n",
    "        val_total = 0\n",
    "\n",
    "        with torch.no_grad():\n",
    "\n",
    "            for images, labels in val_loader:\n",
    "                \n",
    "                # Move batch to device if specified\n",
    "                if device is not None:\n",
    "                    images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "                outputs = model(images)\n",
    "                loss = criterion(outputs, labels)\n",
    "\n",
    "                val_running_loss += loss.item()\n",
    "                _, predicted = torch.max(outputs.data, 1)\n",
    "                val_total += labels.size(0)\n",
    "                val_correct += (predicted == labels).sum().item()\n",
    "\n",
    "        val_loss = val_running_loss / len(val_loader)\n",
    "        val_accuracy = 100 * val_correct / val_total\n",
    "\n",
    "        # Record metrics\n",
    "        history['train_loss'].append(train_loss)\n",
    "        history['val_loss'].append(val_loss)\n",
    "        history['train_accuracy'].append(train_accuracy)\n",
    "        history['val_accuracy'].append(val_accuracy)\n",
    "\n",
    "        # Print progress\n",
    "        if (epoch + 1) % print_every == 0 or epoch == 0:\n",
    "\n",
    "            print(\n",
    "                f'Epoch {epoch+1}/{epochs} - ' +\n",
    "                f'loss: {train_loss:.4f} - ' +\n",
    "                f'accuracy: {train_accuracy:.2f}% - ' +\n",
    "                f'val_loss: {val_loss:.4f} - ' +\n",
    "                f'val_accuracy: {val_accuracy:.2f}%'\n",
    "            )\n",
    "\n",
    "    print('\\nTraining complete.')\n",
    "\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "865041b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30 - loss: 2.5161 - accuracy: 14.88% - val_loss: 2.1568 - val_accuracy: 19.97%\n",
      "Epoch 5/30 - loss: 1.7333 - accuracy: 33.09% - val_loss: 1.5394 - val_accuracy: 45.78%\n",
      "Epoch 10/30 - loss: 1.4851 - accuracy: 42.22% - val_loss: 1.3984 - val_accuracy: 49.14%\n",
      "Epoch 15/30 - loss: 1.3611 - accuracy: 46.81% - val_loss: 1.0783 - val_accuracy: 61.21%\n",
      "Epoch 20/30 - loss: 1.2786 - accuracy: 49.56% - val_loss: 0.9796 - val_accuracy: 67.03%\n",
      "Epoch 25/30 - loss: 1.2156 - accuracy: 51.73% - val_loss: 0.9061 - val_accuracy: 69.78%\n",
      "Epoch 30/30 - loss: 1.1683 - accuracy: 53.66% - val_loss: 0.8765 - val_accuracy: 70.28%\n",
      "\n",
      "Training complete.\n",
      "CPU times: user 6min 20s, sys: 1.03 s, total: 6min 21s\n",
      "Wall time: 6min 18s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "criterion_exp1 = nn.CrossEntropyLoss()\n",
    "optimizer_exp1 = optim.Adam(model_exp1.parameters(), lr=learning_rate)\n",
    "\n",
    "history_exp1 = train_model(\n",
    "    model=model_exp1,\n",
    "    train_loader=train_loader_exp1,\n",
    "    val_loader=val_loader_exp1,\n",
    "    criterion=criterion_exp1,\n",
    "    optimizer=optimizer_exp1,\n",
    "    epochs=epochs,\n",
    "    print_every=print_every\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73fdf140",
   "metadata": {},
   "source": [
    "### 1.5. Evaluate and Visualize Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08942db9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_model(\n",
    "    model: nn.Module,\n",
    "    test_loader: DataLoader,\n",
    "    device: torch.device = None\n",
    ") -> tuple[float, np.ndarray, np.ndarray]:\n",
    "    '''Evaluate model on test set.\n",
    "    \n",
    "    Args:\n",
    "        device: If provided, moves batches to this device on-the-fly.\n",
    "                If None, assumes data is already on the correct device.\n",
    "    '''\n",
    "\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    all_predictions = []\n",
    "    all_labels = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "\n",
    "        for images, labels in test_loader:\n",
    "            \n",
    "            # Move batch to device if specified\n",
    "            if device is not None:\n",
    "                images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "            outputs = model(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "\n",
    "            all_predictions.extend(predicted.cpu().numpy())\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "    accuracy = 100 * correct / total\n",
    "    return accuracy, np.array(all_predictions), np.array(all_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c0afcf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Experiment 1 Test Accuracy: 69.80%\n"
     ]
    }
   ],
   "source": [
    "# Learning curves\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "axes[0].set_title('Loss - Experiment 1')\n",
    "axes[0].plot(history_exp1['train_loss'], label='Train')\n",
    "axes[0].plot(history_exp1['val_loss'], label='Validation')\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('Loss (cross-entropy)')\n",
    "axes[0].legend(loc='best')\n",
    "\n",
    "axes[1].set_title('Accuracy - Experiment 1')\n",
    "axes[1].plot(history_exp1['train_accuracy'], label='Train')\n",
    "axes[1].plot(history_exp1['val_accuracy'], label='Validation')\n",
    "axes[1].set_xlabel('Epoch')\n",
    "axes[1].set_ylabel('Accuracy (%)')\n",
    "axes[1].legend(loc='best')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Test accuracy\n",
    "test_accuracy_exp1, predictions_exp1, true_labels_exp1 = evaluate_model(model_exp1, test_loader_exp1)\n",
    "print(f'\\nExperiment 1 Test Accuracy: {test_accuracy_exp1:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d510de",
   "metadata": {},
   "source": [
    "### 1.6. Experiment 1 Observations\n",
    "\n",
    "**TODO**: Document your findings:\n",
    "- What architectural changes did you make?\n",
    "- How did these changes affect the number of parameters?\n",
    "- Did the model perform better or worse than the baseline?\n",
    "- What did you observe about training time and convergence?\n",
    "- Did you notice any overfitting or underfitting?\n",
    "\n",
    "*Your notes here:*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc07230",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Experiment 2: Use RGB Images Instead of Grayscale\n",
    "\n",
    "**Objective**: Compare model performance using full RGB color information versus grayscale.\n",
    "\n",
    "### 2.1. Load and Preprocess RGB Data\n",
    "\n",
    "**TODO**: Modify the transform to keep RGB channels (3 channels) instead of converting to grayscale. Update the normalization to use 3 mean and std values.\n",
    "\n",
    "*Hint*: For RGB, use `transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9cf37587",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image shape: torch.Size([3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "# TODO: Modify this transform to use RGB instead of grayscale\n",
    "transform_exp2 = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "])\n",
    "\n",
    "# Load training and test datasets with RGB\n",
    "train_dataset_exp2 = datasets.CIFAR10(\n",
    "    root=data_dir,\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform_exp2\n",
    ")\n",
    "\n",
    "test_dataset_exp2 = datasets.CIFAR10(\n",
    "    root=data_dir,\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform_exp2\n",
    ")\n",
    "\n",
    "print(f'Image shape: {train_dataset_exp2[0][0].shape}')  # Should be [3, 32, 32]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "742ac7b8",
   "metadata": {},
   "source": [
    "### 2.2. Visualize RGB Sample Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72836e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x300 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot first 10 RGB images from the training dataset\n",
    "ncols = 5\n",
    "nrows = 2\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*1.5, nrows*1.5))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, ax in enumerate(axes):\n",
    "    img, label = train_dataset_exp2[i]\n",
    "    \n",
    "    # Unnormalize and transpose for plotting\n",
    "    img = img * 0.5 + 0.5\n",
    "    img = img.numpy().transpose(1, 2, 0)  # Change from CxHxW to HxWxC\n",
    "    ax.set_title(class_names[label])\n",
    "    ax.imshow(img)\n",
    "    ax.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da306112",
   "metadata": {},
   "source": [
    "### 2.3. Create Data Loaders for RGB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0a11adc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: torch.Size([40000, 3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "# Create training, validation and testing tensors\n",
    "X_train_full_exp2 = torch.stack([img for img, _ in train_dataset_exp2]).to(device)\n",
    "y_train_full_exp2 = torch.tensor([label for _, label in train_dataset_exp2]).to(device)\n",
    "\n",
    "X_test_exp2 = torch.stack([img for img, _ in test_dataset_exp2]).to(device)\n",
    "y_test_exp2 = torch.tensor([label for _, label in test_dataset_exp2]).to(device)\n",
    "\n",
    "# Split training data\n",
    "n_train = int(0.8 * len(X_train_full_exp2))\n",
    "indices = torch.randperm(len(X_train_full_exp2))\n",
    "\n",
    "X_train_exp2 = X_train_full_exp2[indices[:n_train]]\n",
    "y_train_exp2 = y_train_full_exp2[indices[:n_train]]\n",
    "X_val_exp2 = X_train_full_exp2[indices[n_train:]]\n",
    "y_val_exp2 = y_train_full_exp2[indices[n_train:]]\n",
    "\n",
    "# Create DataLoaders\n",
    "train_tensor_dataset_exp2 = torch.utils.data.TensorDataset(X_train_exp2, y_train_exp2)\n",
    "val_tensor_dataset_exp2 = torch.utils.data.TensorDataset(X_val_exp2, y_val_exp2)\n",
    "test_tensor_dataset_exp2 = torch.utils.data.TensorDataset(X_test_exp2, y_test_exp2)\n",
    "\n",
    "train_loader_exp2 = DataLoader(train_tensor_dataset_exp2, batch_size=batch_size, shuffle=True)\n",
    "val_loader_exp2 = DataLoader(val_tensor_dataset_exp2, batch_size=batch_size, shuffle=False)\n",
    "test_loader_exp2 = DataLoader(test_tensor_dataset_exp2, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "print(f'X_train shape: {X_train_exp2.shape}')  # Should show 3 channels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee7a5f59",
   "metadata": {},
   "source": [
    "### 2.4. Define CNN for RGB Images\n",
    "\n",
    "**TODO**: Modify the first Conv2d layer to accept 3 input channels instead of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "aadebd5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): ReLU()\n",
      "  (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (5): ReLU()\n",
      "  (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (7): Dropout(p=0.5, inplace=False)\n",
      "  (8): Flatten(start_dim=1, end_dim=-1)\n",
      "  (9): Linear(in_features=8192, out_features=128, bias=True)\n",
      "  (10): ReLU()\n",
      "  (11): Dropout(p=0.5, inplace=False)\n",
      "  (12): Linear(in_features=128, out_features=10, bias=True)\n",
      ")\n",
      "\n",
      "Total parameters: 1060266\n"
     ]
    }
   ],
   "source": [
    "# Updated first conv layer to accept 3 channels (RGB)\n",
    "model_exp2 = nn.Sequential(\n",
    "\n",
    "    # Conv block: RGB input (3 channels)\n",
    "    nn.Conv2d(3, 32, kernel_size=3, padding=1),\n",
    "    nn.BatchNorm2d(32),\n",
    "    nn.ReLU(),\n",
    "    nn.Conv2d(32, 32, kernel_size=3, padding=1),\n",
    "    nn.BatchNorm2d(32),\n",
    "    nn.ReLU(),\n",
    "    nn.MaxPool2d(2, 2),\n",
    "    nn.Dropout(0.5),\n",
    "    \n",
    "    # Classifier\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(32 * 16 * 16, 128),\n",
    "    nn.ReLU(),\n",
    "    nn.Dropout(0.5),\n",
    "    nn.Linear(128, num_classes)\n",
    "\n",
    ").to(device)\n",
    "\n",
    "trainable_params = sum(p.numel() for p in model_exp2.parameters() if p.requires_grad)\n",
    "print(model_exp2)\n",
    "print(f'\\nTotal parameters: {trainable_params}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c4f01b3",
   "metadata": {},
   "source": [
    "### 2.5. Train RGB Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d6104f3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30 - loss: 2.0274 - accuracy: 26.66% - val_loss: 1.6970 - val_accuracy: 41.92%\n",
      "Epoch 5/30 - loss: 1.4305 - accuracy: 47.35% - val_loss: 1.2271 - val_accuracy: 57.10%\n",
      "Epoch 10/30 - loss: 1.2704 - accuracy: 53.47% - val_loss: 1.0813 - val_accuracy: 62.64%\n",
      "Epoch 15/30 - loss: 1.2068 - accuracy: 55.47% - val_loss: 1.0268 - val_accuracy: 64.21%\n",
      "Epoch 20/30 - loss: 1.1688 - accuracy: 56.88% - val_loss: 1.0073 - val_accuracy: 65.22%\n",
      "Epoch 25/30 - loss: 1.1404 - accuracy: 58.26% - val_loss: 0.9879 - val_accuracy: 65.55%\n",
      "Epoch 30/30 - loss: 1.1112 - accuracy: 59.17% - val_loss: 0.9871 - val_accuracy: 66.01%\n",
      "\n",
      "Training complete.\n",
      "CPU times: user 2min 4s, sys: 392 ms, total: 2min 5s\n",
      "Wall time: 2min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "criterion_exp2 = nn.CrossEntropyLoss()\n",
    "optimizer_exp2 = optim.Adam(model_exp2.parameters(), lr=learning_rate)\n",
    "\n",
    "history_exp2 = train_model(\n",
    "    model=model_exp2,\n",
    "    train_loader=train_loader_exp2,\n",
    "    val_loader=val_loader_exp2,\n",
    "    criterion=criterion_exp2,\n",
    "    optimizer=optimizer_exp2,\n",
    "    epochs=epochs,\n",
    "    print_every=print_every\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8ea58e7",
   "metadata": {},
   "source": [
    "### 2.6. Evaluate RGB Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5ed85bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Experiment 2 Test Accuracy: 67.03%\n"
     ]
    }
   ],
   "source": [
    "# Learning curves\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "axes[0].set_title('Loss - Experiment 2 (RGB)')\n",
    "axes[0].plot(history_exp2['train_loss'], label='Train')\n",
    "axes[0].plot(history_exp2['val_loss'], label='Validation')\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('Loss (cross-entropy)')\n",
    "axes[0].legend(loc='best')\n",
    "\n",
    "axes[1].set_title('Accuracy - Experiment 2 (RGB)')\n",
    "axes[1].plot(history_exp2['train_accuracy'], label='Train')\n",
    "axes[1].plot(history_exp2['val_accuracy'], label='Validation')\n",
    "axes[1].set_xlabel('Epoch')\n",
    "axes[1].set_ylabel('Accuracy (%)')\n",
    "axes[1].legend(loc='best')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Test accuracy\n",
    "test_accuracy_exp2, predictions_exp2, true_labels_exp2 = evaluate_model(model_exp2, test_loader_exp2)\n",
    "print(f'\\nExperiment 2 Test Accuracy: {test_accuracy_exp2:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "629a099e",
   "metadata": {},
   "source": [
    "### 2.7. Experiment 2 Observations\n",
    "\n",
    "**TODO**: Document your findings:\n",
    "- Did RGB improve accuracy compared to grayscale?\n",
    "- How did the number of parameters change?\n",
    "- Which classes benefited most from color information?\n",
    "- Were there any classes that performed similarly with both grayscale and RGB?\n",
    "\n",
    "*Your notes here:*\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed2ee0bd",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Experiment 3: Add Image Augmentation\n",
    "\n",
    "**Objective**: Use PyTorch transforms to augment training data and improve model generalization.\n",
    "\n",
    "### 3.1. Define Augmented Transforms\n",
    "\n",
    "**TODO**: Add image augmentation transforms to the training data. Consider:\n",
    "- `transforms.RandomHorizontalFlip(p=0.5)` - Randomly flip images horizontally\n",
    "- `transforms.RandomCrop(32, padding=4)` - Random crop with padding\n",
    "- `transforms.RandomRotation(degrees=15)` - Small random rotations\n",
    "- `transforms.ColorJitter(brightness=0.2, contrast=0.2)` - Random brightness/contrast adjustments\n",
    "\n",
    "**Note**: Apply augmentation only to training data, not validation or test data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26c96439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Datasets loaded with augmentation\n"
     ]
    }
   ],
   "source": [
    "# Training transform with augmentation\n",
    "transform_train_exp3 = transforms.Compose([\n",
    "    transforms.RandomHorizontalFlip(p=0.5),\n",
    "    transforms.RandomCrop(32, padding=4),\n",
    "    transforms.RandomRotation(degrees=15),\n",
    "    transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "])\n",
    "\n",
    "# Validation and test transforms (no augmentation)\n",
    "transform_test_exp3 = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "])\n",
    "\n",
    "# Load datasets with augmentation\n",
    "train_dataset_exp3 = datasets.CIFAR10(\n",
    "    root=data_dir,\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform_train_exp3  # Augmented transform\n",
    ")\n",
    "\n",
    "test_dataset_exp3 = datasets.CIFAR10(\n",
    "    root=data_dir,\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform_test_exp3  # No augmentation\n",
    ")\n",
    "\n",
    "print('Datasets loaded with augmentation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce43da7e",
   "metadata": {},
   "source": [
    "### 3.2. Visualize Augmented Images\n",
    "\n",
    "Run this cell multiple times to see different augmentations of the same images!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cafa7a78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x300 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize augmented versions of the same images\n",
    "ncols = 5\n",
    "nrows = 2\n",
    "\n",
    "fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*1.5, nrows*1.5))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, ax in enumerate(axes):\n",
    "    # Get augmented version each time this is run\n",
    "    img, label = train_dataset_exp3[i]\n",
    "    \n",
    "    # Unnormalize and transpose for plotting\n",
    "    img = img * 0.5 + 0.5\n",
    "    img = img.numpy().transpose(1, 2, 0)\n",
    "    ax.set_title(class_names[label])\n",
    "    ax.imshow(img)\n",
    "    ax.axis('off')\n",
    "\n",
    "plt.suptitle('Augmented Training Images (run cell again to see different augmentations)', y=1.02)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0683e5",
   "metadata": {},
   "source": [
    "### 3.3. Create Data Loaders with Augmentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "842ee568",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training samples: 40000\n",
      "Validation samples: 10000\n",
      "Test samples: 10000\n"
     ]
    }
   ],
   "source": [
    "# For data augmentation, we must NOT preload data to GPU as tensors.\n",
    "# Transforms need to be applied on-the-fly during each epoch so each \n",
    "# batch sees different augmented versions of the images.\n",
    "\n",
    "# Split training data into train and validation sets using Subset\n",
    "n_train = int(0.8 * len(train_dataset_exp3))\n",
    "n_val = len(train_dataset_exp3) - n_train\n",
    "indices = torch.randperm(len(train_dataset_exp3)).tolist()\n",
    "\n",
    "train_subset_exp3 = torch.utils.data.Subset(train_dataset_exp3, indices[:n_train])\n",
    "val_subset_exp3 = torch.utils.data.Subset(train_dataset_exp3, indices[n_train:])\n",
    "\n",
    "print(f'Training samples: {len(train_subset_exp3)}')\n",
    "print(f'Validation samples: {len(val_subset_exp3)}')\n",
    "print(f'Test samples: {len(test_dataset_exp3)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "70daf63e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training batches: 40\n",
      "Validation batches: 10\n",
      "Test batches: 10\n"
     ]
    }
   ],
   "source": [
    "# Create DataLoaders directly from Dataset/Subset objects\n",
    "# Transforms are applied on-the-fly when batches are loaded\n",
    "train_loader_exp3 = DataLoader(\n",
    "    train_subset_exp3,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "val_loader_exp3 = DataLoader(\n",
    "    val_subset_exp3,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "test_loader_exp3 = DataLoader(\n",
    "    test_dataset_exp3,\n",
    "    batch_size=batch_size,\n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "print(f'Training batches: {len(train_loader_exp3)}')\n",
    "print(f'Validation batches: {len(val_loader_exp3)}')\n",
    "print(f'Test batches: {len(test_loader_exp3)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1a28d8",
   "metadata": {},
   "source": [
    "### 3.4. Define Model for Augmented Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ac5365b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total parameters: 1060266\n"
     ]
    }
   ],
   "source": [
    "# Same architecture as Experiment 2 (RGB)\n",
    "model_exp3 = nn.Sequential(\n",
    "\n",
    "    # Conv block: RGB input\n",
    "    nn.Conv2d(3, 32, kernel_size=3, padding=1),\n",
    "    nn.BatchNorm2d(32),\n",
    "    nn.ReLU(),\n",
    "    nn.Conv2d(32, 32, kernel_size=3, padding=1),\n",
    "    nn.BatchNorm2d(32),\n",
    "    nn.ReLU(),\n",
    "    nn.MaxPool2d(2, 2),\n",
    "    nn.Dropout(0.5),\n",
    "    \n",
    "    # Classifier\n",
    "    nn.Flatten(),\n",
    "    nn.Linear(32 * 16 * 16, 128),\n",
    "    nn.ReLU(),\n",
    "    nn.Dropout(0.5),\n",
    "    nn.Linear(128, num_classes)\n",
    "\n",
    ").to(device)\n",
    "\n",
    "trainable_params = sum(p.numel() for p in model_exp3.parameters() if p.requires_grad)\n",
    "print(f'Total parameters: {trainable_params}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b46d131b",
   "metadata": {},
   "source": [
    "### 3.5. Train Model with Augmented Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "00a6fc79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30 - loss: 2.2027 - accuracy: 19.19% - val_loss: 1.9447 - val_accuracy: 30.73%\n",
      "Epoch 5/30 - loss: 1.7946 - accuracy: 30.69% - val_loss: 1.6295 - val_accuracy: 41.46%\n",
      "Epoch 10/30 - loss: 1.7316 - accuracy: 33.27% - val_loss: 1.5341 - val_accuracy: 44.27%\n",
      "Epoch 15/30 - loss: 1.6964 - accuracy: 35.01% - val_loss: 1.5118 - val_accuracy: 45.42%\n",
      "Epoch 20/30 - loss: 1.6718 - accuracy: 36.13% - val_loss: 1.4590 - val_accuracy: 48.64%\n",
      "Epoch 25/30 - loss: 1.6460 - accuracy: 36.90% - val_loss: 1.3864 - val_accuracy: 51.16%\n",
      "Epoch 30/30 - loss: 1.6270 - accuracy: 37.69% - val_loss: 1.3970 - val_accuracy: 51.44%\n",
      "\n",
      "Training complete.\n",
      "CPU times: user 32min 42s, sys: 14.4 s, total: 32min 57s\n",
      "Wall time: 32min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "criterion_exp3 = nn.CrossEntropyLoss()\n",
    "optimizer_exp3 = optim.Adam(model_exp3.parameters(), lr=learning_rate)\n",
    "\n",
    "# Pass device to move batches on-the-fly (required for on-the-fly augmentation)\n",
    "history_exp3 = train_model(\n",
    "    model=model_exp3,\n",
    "    train_loader=train_loader_exp3,\n",
    "    val_loader=val_loader_exp3,\n",
    "    criterion=criterion_exp3,\n",
    "    optimizer=optimizer_exp3,\n",
    "    epochs=epochs,\n",
    "    print_every=print_every,\n",
    "    device=device\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ad8659",
   "metadata": {},
   "source": [
    "### 3.6. Evaluate Augmented Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc7e3481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KyspKnTt3Tr8sPDxcOTo6qmbNmumX1ahRQ3Xs2DHLcm7duqUA9emnnxon+CykP6fMbtbW1gbbHj16VFlZWakXX3xR3bp1S5UuXVrVqVPH4ASdfkIqXbq0wQntl19+UYD64osvlFLaiSUwMFCFhIQYnLjv3r2rypYtq9q2batfln4S6NOnT4b4s0q4ra2tDb4cv/32WwWoUqVKGcQ1YcIEgxNJbuIaPHiwwfG7deum3NzcDJbZ29tnmng9zk8//WTwftSpU0cdOXLkifvNnz//sV/snTp1Uo0bN9Y/njt3rrKwsFBRUVEG22U34f71118VoD7//HP9stTUVNWqVatMf7wCasqUKfplt27dUra2tkqn06nQ0FD98pMnTypAvf/++/plH3zwgbK3t1enT582iOnNN99U5ubm+osI6T8q3dzc1M2bN/XbrV69WgHq999/1y8bMWJEpj8ytm7dqgC1ZMkSg+XpF5rSl0dFRSkrKyvVsWNHg/+Zt956SwGZvu9TpkxRgIqMjMywTojiZt++fQpQ69atU0pp38M+Pj4GF9GUUuq9995TgFqxYkWGMtI/e99//70C1MyZM7PcJqcJN6DefPPNDOU9fAE53dSpU5VOp1OXLl3SL2vWrJlydHQ0WPZwPEpp5yJra2t1+/Zt/bKoqChlYWFh8B34tNKfY1a3nTt36reNj49X5cuXV1WrVlUJCQmqY8eOysnJKcPzSN933759+mWXLl1SNjY2qlu3bvplQ4YMUV5eXgYX85VSqnfv3srZ2Vn/eqa/P+XKlcvwGmeVcANq6dKl+mXp5w8zMzODyo61a9dmeI9zGlflypUNktUvvvgiwzm3Y8eOmSZejxMREWHwXvj4+Kiff/75ifudPXtWAeqrr77KdP306dOVra2t/rfP6dOnFZDhIkB2E+5r164pCwsL1bVrV4PtJk6cmOGcl77vo7+pGjZsqHQ6nXrppZf0y1JSUpSPj4/B747snoeV0hJuQG3ZskW/LCoqSllbW6vXXntNv2zZsmWZfv7v3LmjXFxc1NChQw2WX7t2TTk7Oxssr1mzpvLy8jL4vP7zzz8KyPR937FjhwKy9X4WF9KkXORaamoq//zzD127dqVcuXL65V5eXvTt25dt27YRGxsLgIuLC//99x9nzpzJtCxbW1usrKzYtGlThmYzeeHrr79m3bp1BrdHR68OCgpi0qRJzJ8/n5CQEK5fv84PP/yQaR/q/v37GwwI1aNHD7y8vPRTIxw6dIgzZ87Qt29fbty4wfXr17l+/Trx8fG0bt2aLVu2GDQRBq0fc3a1bt3aoAlX+qi33bt3N4grffn58+eNFlfTpk25ceOG/r3OrZYtW7Ju3TqWLVvGSy+9hKWlJfHx8U/c78aNGwC4urpmum7t2rUGg6p0795d30wuN9asWYOlpSVDhw7VLzMzM2PEiBFZ7vPiiy/q77u4uFCxYkXs7e3p2bOnfnnFihVxcXHRvzcAy5Yto2nTpri6uurfm+vXr9OmTRtSU1MzNGHs1auXwevQtGlTAIMys7Js2TKcnZ1p27atwbGCg4NxcHBg48aNAKxfv56kpCRGjRpl0Bxv9OjRWZadHtPDzWCFKK6WLFmCp6cnLVu2BLQmyr169SI0NJTU1FT9dr/++is1atSgW7duGcpI/+z9+uuvlCxZklGjRmW5TW783//9X4Zltra2+vvx8fFcv36dRo0aoZTi4MGDgDaY1ZYtWxg8eDB+fn5ZxtO/f38SExP1zaABfv75Z1JSUvKkr/WwYcMynPPXrVtHlSpV9NvY2dmxcOFCTpw4QbNmzfjzzz/57LPPMjwP0PobBwcH6x/7+fnRpUsX1q5dS2pqKkopfv31Vzp37oxSyuA7NSQkhJiYGA4cOGBQ5oABAwxe48dxcHAw6Oecfv6oXLmywaj3j57zcxPXoEGDsLKy0j/OyXnlcUqUKMG6dev4/fffmTx5MiVLlszWtJ6PO+eD9vnq2LGj/rdPYGAgwcHBWXbbeJINGzaQkpLCyy+/bLA8s89cuiFDhhj8v9evXx+lFEOGDNEvMzc3p06dOhnO+dk5D6erUqWK/v0AcHd3p2LFitl6b9atW8ft27fp06ePwbHMzc2pX7++/lgREREcOnSIAQMG4OzsrN+/bdu2Bp+fh8k5P6OiNfqSyFfR0dHcvXuXihUrZlhXuXJl0tLSuHz5MlWrVmXy5Ml06dKFChUqEBQURLt27XjhhReoXr06ANbW1nz88ce89tpreHp60qBBAzp16kT//v0fO2DWvXv3iImJMVj2pAG2AOrVq5etQdPeeOMNQkND2bNnD1OmTMnyyyUwMNDgsU6no3z58vo+QOkXGgYMGJDlsWJiYgxOIJmNop6VR38QpH8p+vr6Zro8/aJGbuJ69Fjp627duoWTk1O2Y36Up6cnnp6egHbBYsqUKbRt25YzZ85k6z1VmfQV+vnnn0lOTqZWrVoGfc7q16/PkiVLHpskZ+XSpUt4eXlhZ2dnsDyrEYZtbGxwd3c3WObs7IyPj0+GH8TOzs4GF5zOnDnDkSNHMuyfLioqyuDx496bJzlz5gwxMTF4eHg89liXLl0CMv7Pu7u7Z/kDKP29Ka6jxAqRLjU1ldDQUFq2bMmFCxf0y+vXr8+MGTPYsGEDzzzzDADnzp2je/fujy3v3LlzVKxY0aiDaVpYWODj45NheVhYGO+99x6//fZbhu+U9PNw+g/9oKCgxx6jUqVK1K1blyVLluiTkCVLltCgQYPHjtaempqaYeqoEiVKGCSEmQkMDMxynI+HNW7cmP/7v//j66+/JiQkJMuBHh/9/gOoUKECd+/eJTo6GjMzM27fvs3cuXOZO3dupmU8+v2dk3N+VuePJ53zo6OjcxzX05xXHsfKykr/nnTq1InWrVvTuHFjPDw86NSp0xP3z+ycf+LECQ4ePEj//v0z9DP/+uuviY2NzfHvlPRz3qP/lyVKlMjynJeT32SPnvOzcx7O6jigvT/ZPecDWQ5slv46ZXXOB+1Cz6MXaEDO+ZmRhFvki2bNmnHu3DlWr17NP//8w/z58/nss8+YM2eOvvZv9OjRdO7cmVWrVrF27Vreffddpk6dyr///kutWrUyLffnn39m0KBBBssy+xLOrfPnz+u/lNIHIcuN9FriTz/9lJo1a2a6zaODf2T3SjdoV0pzsjz9NcpNXE8q01h69OjB22+/zerVqxk+fHiW27m5uQHayf/RH4npV7Szmqfz/Pnz+tYZOp0u0+fwcI1TbuT2vQHt/Wnbti3jxo3LdNsKFSrkuMyspKWl4eHhkWUtQFZJf3akn/wL8pR7QuSHf//9l4iICEJDQwkNDc2wfsmSJfqE21iy+tGb1XebtbV1himoUlNTadu2LTdv3mT8+PFUqlQJe3t7rl69ysCBAzO0hMqO/v378+qrr3LlyhUSExPZtWsXs2bNeuw+ly9fzpCYGnO6o8TERP2AUufOnePu3bsZLq5mR/rr8b///S/LC9rpFQ7p8vOcn5O48uuc36hRI7y8vFiyZMljE+6Hz/mPSp/yc8yYMYwZMybD+l9//VX/mzGnn4ucyMn78+g5Pyfn4ac95wP8+OOPmVZqPM1FPDnnZyQJt8g1d3d37OzsOHXqVIZ1J0+exMzMzOBqXokSJRg0aBCDBg0iLi6OZs2aMXHiRIPmtgEBAbz22mu89tprnDlzhpo1azJjxows500OCQkxGOncmNLS0hg4cCBOTk6MHj2aKVOm0KNHD5577rkM2z7aVF4pxdmzZ/UnroCAAEC7Ypidq+z5Ja/iMsZVzfSRbx9twfCoSpUqAdo0O9WqVdMvT592Z+TIkRmmA0lLS+OFF15g6dKlvPPOO4B2VTizZljpV3fT+fv7s3Hjxgw/xB6+mm4sAQEBxMXF5ct7ExAQwPr162ncuPFjf/j5+/sD2v/8w11JoqOjs7yqfuHCBUqWLPlUSbsQRcGSJUvw8PDQzxbwsBUrVrBy5UrmzJmDra0tAQEBHDt27LHlBQQEsHv3bpKTk7G0tMx0m/RauEdHf370u+1xjh49yunTp/nhhx/o37+/fvmj59/074QnxQ3Qu3dvxo4dy08//cS9e/ewtLSkV69ej92nVKlSGY5Zo0aN7D6NJ3r//fc5ceIE06dPZ/z48bz55pt8+eWXGbbLrHvc6dOnsbOz03/POTo6kpqaWqDO+e7u7nkSl7FqMhMSEp54zvfz88PW1taghQhov7uWLl1Ky5YtMzT/Bvjggw9YsmSJPuF++HPh4uKi3y6zcz5o5/iHL/bcuHHD6F0gs3sezonHnfNBm0nkcf8LD5/zH5XZ739A/95Urlw5R7EWZdKHW+Saubk5zzzzDKtXrzaYPiEyMpKlS5fSpEkTfZOU9D436RwcHChfvjyJiYkA3L17l4SEBINtAgICcHR01G+TGS8vL9q0aWNwM5aZM2eyY8cO5s6dywcffECjRo34v//7v0z7pCxatIg7d+7oHy9fvpyIiAjat28PQHBwMAEBAUyfPj3TPkqPNpHLL3kVl729fbam9gCtj09mV2Pnz58P8MSm/8HBwVhZWbFv3z6D5elXiMeNG0ePHj0Mbj179qR58+YGV5EDAgI4efKkwXM+fPhwhunRQkJCSE5OZt68efplaWlpmf6Aflo9e/Zk586drF27NsO627dvk5KSkuMy0+fTffT96dmzJ6mpqXzwwQcZ9klJSdFv36ZNGywtLfnqq68M3rfPP/88y2Pu37+fhg0b5jhWIYqSe/fusWLFCjp16pThO6lHjx6MHDmSO3fu8NtvvwHaeBOHDx/OdPqd9M9e9+7duX79eqY1w+nb+Pv7Y25unmHMh2+++SbbsafXpD38mVdK8cUXXxhs5+7uTrNmzfj+++8JCwvLNJ50JUuWpH379ixevJglS5bQrl27J9aI2djYZDjnZ9WsN6d2797N9OnTGT16NK+99hpvvPEGs2bNYvPmzRm23blzp0FT2suXL7N69WqeeeYZzM3NMTc3p3v37vz666+ZXnww1Tk/r+Kyt7d/YqKcLj4+3mAq0XS//vort27deuI539LSkjp16mQ452/fvp2LFy8yaNCgTD9fvXr1YuPGjYSHhwMPEs6HPxfpU5o+rHXr1lhYWBhMDQc8sTVGbmT3PJwTWZ3zQ0JCcHJyYsqUKSQnJ2fYL/1/wcvLi5o1a/LDDz8YvMfr1q3j+PHjmR5z//79ODs7U7Vq1RzHW1RJDbd4ou+//541a9ZkWP7qq6/y4Ycfsm7dOpo0acLLL7+MhYUF3377LYmJiXzyySf6batUqUKLFi0IDg6mRIkS7Nu3j+XLlzNy5EhAuzLcunVrevbsSZUqVbCwsGDlypVERkYaDAxiLH///TcnT57MsLxRo0aUK1eOEydO8O677zJw4EA6d+4MaPMZ1qxZk5dffjnDgFslSpSgSZMmDBo0iMjISD7//HPKly+vH1jLzMyM+fPn0759e6pWrcqgQYMoXbo0V69eZePGjTg5OfH7778b/Xk+SV7FFRwczPr165k5cybe3t6ULVvWYCCXhy1evJg5c+boB9+7c+cOa9euZd26dXTu3DnL/kXpbGxseOaZZ1i/fj2TJ0/WL1+yZAk1a9bM0Gcq3bPPPsuoUaM4cOAAtWvXZvDgwcycOZOQkBCGDBlCVFQUc+bMoWrVqgYDwnXt2pV69erx2muvcfbsWSpVqsRvv/3GzZs3AeP2WXrjjTf47bff6NSpEwMHDiQ4OJj4+HiOHj3K8uXLuXjxYo6bbKUP9PPKK68QEhKCubk5vXv3pnnz5gwfPpypU6dy6NAhnnnmGSwtLTlz5gzLli3jiy++oEePHri7u/P6668zdepUOnXqRIcOHTh48CB///13prFERUVx5MiRXPWXF6Io+e2337hz5w7PPvtspusbNGiAu7s7S5YsoVevXrzxxhssX76c559/nsGDBxMcHMzNmzf57bffmDNnDjVq1KB///4sWrSIsWPHsmfPHpo2bUp8fDzr16/n5ZdfpkuXLjg7O/P888/z1VdfodPpCAgI4I8//sjQH/RxKlWqREBAAK+//jpXr17FyclJnyA96ssvv6RJkybUrl2bYcOGUbZsWS5evMiff/7JoUOHDLbt378/PXr0AMg0yTCWAwcOZNpSLiAggIYNG5KQkMCAAQMIDAzko48+AmDSpEn8/vvvDBo0iKNHj+oTF9D6qIeEhPDKK69gbW2tv3gxadIk/TbTpk1j48aN1K9fn6FDh1KlShVu3rzJgQMHWL9+vf6ckd/yIq7g4GB+/vlnxo4dS926dXFwcND/dnrUmTNnaNOmDb169aJSpUqYmZmxb98+Fi9eTJkyZXj11VefeLwuXbrw9ttvG/TJXrJkCebm5nTs2DHTfZ599lnefvttQkNDGTt2LM888wx+fn4MGTKEN954A3Nzc77//nvc3d0NLhZ5enry6quvMmPGDJ599lnatWvH4cOH9ec8Y57zs3sezomaNWtibm7Oxx9/TExMDNbW1rRq1QoPDw9mz57NCy+8QO3atendu7f+uf/55580btxYf1Fh6tSpdOzYkSZNmjB48GBu3rzJV199RdWqVTOtrEn//SZ9uB+SL2Ohi0LpcVNoAery5ctKKaUOHDigQkJClIODg7Kzs1MtW7bMMO/yhx9+qOrVq6dcXFyUra2tqlSpkvroo4/0cwpev35djRgxQlWqVEnZ29srZ2dnVb9+ffXLL7/k63NasGCBSklJUXXr1lU+Pj4GUyAo9WA6jPSpDtKnzfjpp5/UhAkTlIeHh7K1tVUdO3bMMJWIUkodPHhQPffcc8rNzU1ZW1srf39/1bNnT7Vhwwb9NulTVURHR2fYP6tpwUaMGGGwLH0qlEenWUuPd9myZUaLK7M5K0+ePKmaNWumbG1ts5wqKt3evXvV888/r/z8/JS1tbWyt7dXtWvXVjNnznziHNzpVqxYYTDX9v79+xWg3n333Sz3uXjxogLUmDFj9MsWL16sypUrp6ysrFTNmjXV2rVrM0wLppRS0dHRqm/fvsrR0VE5OzurgQMHqu3btyvAYKqvAQMGKHt7+wzHbt68uapatWqG5f7+/hmmz7tz546aMGGCKl++vLKyslIlS5ZUjRo1UtOnT9d/frJ6v5XS/j8enmYnJSVFjRo1Srm7uyudTpfh/2nu3LkqODhY2draKkdHR1WtWjU1btw4FR4ert8mNTVVTZo0SXl5eSlbW1vVokULdezYMeXv75/hvZ49e7ays7MzmJ5OiOKoc+fOysbGRsXHx2e5zcCBA5WlpaV+yqYbN26okSNHqtKlSysrKyvl4+OjBgwYYDCl0927d9Xbb7+typYtqywtLVWpUqVUjx49DKbrjI6OVt27d1d2dnbK1dVVDR8+XB07dizTacEy+85SSqnjx4+rNm3aKAcHB1WyZEk1dOhQdfjw4QxlKKXUsWPHVLdu3ZSLi4uysbFRFStWzPT7ODExUbm6uipnZ2d179697LyMOfKkacHSv6/GjBmjzM3N1e7duw3237dvn7KwsFD/93//p1+Wfs5dvHixCgwMVNbW1qpWrVoZpl1SSqnIyEg1YsQI5evrq39vWrdurebOnavfJqvz8sPrHp0WLLvnj4fjNVZcmU0nFxcXp/r27atcXFyynCoqXXR0tBo2bJj+956VlZUKDAxUo0ePzvR3T2YiIyOVhYWFfq7tpKQk5ebmppo2bfrY/cqWLatq1aqlf7x//35Vv359ZWVlpfz8/NTMmTMz/U2TkpKi3n33XVWqVClla2urWrVqpU6cOKHc3NwMpvrKajrdrH4/ZfV5y855OKv3O7MpTufNm6fKlSunzM3NM/w/bdy4UYWEhChnZ2dlY2OjAgIC1MCBAw2mvVNKmxK1cuXKytraWlWpUkWtWLEi099HJ06cUIBav359htiKM51SRh71QIhiZNOmTbRs2ZJly5bl+KqjMJ7U1FSqVKlCz54987SW5HFWrVpFt27d2LZtW5aDtBVHtWrVokWLFnz22WemDkUIUcCkpKTg7e1N586d+e6770wdTrbodDpGjBiRJ02KRfYNGTKE06dPs3XrVpMc//bt27i6uvLhhx/y9ttvmySGgmj06NFs2bKF/fv3Sw33Q6QPtxCi0DM3N2fy5Ml8/fXX2ZrH82mlD+iWLjU1la+++gonJydq166d58cvLNasWcOZM2eYMGGCqUMRQhRAq1atIjo62mAgNiGy4/3332fv3r0ZxlnJC4+e8+HBuCXGGiG/KLhx4wbz58/nww8/lGT7EdKHWwhRJPTq1euJI9way6hRo7h37x4NGzYkMTGRFStWsGPHDqZMmWK0kUWLgnbt2uXLBRAhROGye/dujhw5wgcffECtWrUyzCQhxJP4+fllGGw3r/z8888sXLiQDh064ODgwLZt2/jpp5945plnpEXbQ9zc3OScnwVJuIUQIodatWrFjBkz+OOPP0hISKB8+fJ89dVX+kEAhRBCZG327NksXryYmjVrsnDhQlOHI8RjVa9eHQsLCz755BNiY2P1A6l9+OGHpg5NFBLSh1sIIYQQQgghhMgD0odbCCGEEEIIIYTIA5JwCyGEEEIIIYQQeaDY9eFOS0sjPDwcR0dHGUFPCCFEgaKU4s6dO3h7e2NmVnyvicu5WgghREGUm/N0sUu4w8PD8fX1NXUYQgghRJYuX76Mj4+PqcMwGTlXCyGEKMhycp4udgm3o6MjoL1ITk5OJo5GCCGEeCA2NhZfX1/9uaq4knO1EEKIgig35+lil3CnN01zcnKSk7gQQogCqbg3o5ZztRBCiIIsJ+fp4ttBTAghhBBCCCGEyEOScAshhBBCCCGEEHlAEm4hhBBCCCGEECIPFLs+3EIIUdikpqaSnJxs6jCEEVhaWmJubm7qMIqEtLQ0kpKSTB2GMBIrK6tiPRWeEKLokoRbCCEKKKUU165d4/bt26YORRiRi4sLpUqVKvYDoz2NpKQkLly4QFpamqlDEUZiZmZG2bJlsbKyMnUoQghhVJJwCyFEAZWebHt4eGBnZycJWiGnlOLu3btERUUB4OXlZeKICielFBEREZibm+Pr6yu1okVAWloa4eHhRERE4OfnJ991QogiRRJuIYQogFJTU/XJtpubm6nDEUZia2sLQFRUFB4eHtK8PBdSUlK4e/cu3t7e2NnZmTocYSTu7u6Eh4eTkpKCpaWlqcMRQgijkcvCQghRAKX32ZaEouhJf0+lX37upKamAkjT4yIm/f1Mf3+FEKKoMGnCPXXqVOrWrYujoyMeHh507dqVU6dOPXafefPm0bRpU1xdXXF1daVNmzbs2bMnnyIWQoj8JU0rix55T41DXseiRd5PIURRZdKEe/PmzYwYMYJdu3axbt06kpOTeeaZZ4iPj89yn02bNtGnTx82btzIzp078fX15ZlnnuHq1av5GLkhpZTJji2EEEIIIYQQIgspSXDvlskOb9KEe82aNQwcOJCqVatSo0YNFi5cSFhYGPv3789ynyVLlvDyyy9Ts2ZNKlWqxPz580lLS2PDhg35GLlmzbEIWny6kbG/HM73YwshRHFSpkwZPv/8c1OHIUSBIp8LIYR4ghvn4Lu28OuLYKKZLQrUoGkxMTEAlChRItv73L17l+Tk5Cz3SUxMJDExUf84Njb26YJ8iKW5GRdv3MXWqkC9jEIIYTJPahb6/vvvM3HixByXu3fvXuzt7XMZlRCmJZ8LIYQwgcM/w59jISkObF3h1gVwC8j3MApMppiWlsbo0aNp3LgxQUFB2d5v/PjxeHt706ZNm0zXT506lUmTJhkrTAOBHo4AnIuOIzVNYW4m/Y+EEMVbRESE/v7PP//Me++9ZzA2h4ODg/6+UorU1FQsLJ58KnJ3dzduoELkI/lcCCHyVVoaXNmj1e5W7wXmBSblyx+Jd+DP1+FIqPbYvwk8NxecS5sknAIzSvmIESM4duwYoaGh2d5n2rRphIaGsnLlSmxsbDLdZsKECcTExOhvly9fNlbI+LjaYmNpRlJKGmE37xqtXCGEKKxKlSqlvzk7O6PT6fSPT548iaOjI3///TfBwcFYW1uzbds2zp07R5cuXfD09MTBwYG6deuyfv16g3IfbTqr0+mYP38+3bp1w87OjsDAQH777bd8frZCZI98LoQQ+SLqBKyfBF/UgO9DYPXLsHWGqaPKX+EH4dtmWrKtM4OWb8OA30yWbEMBqeEeOXIkf/zxB1u2bMHHxydb+0yfPp1p06axfv16qlevnuV21tbWWFtbGytUA2ZmOsp7OHDsaixnIu9QtqQ06xJC5B2lFPeSTTNljq2ludFGEX7zzTeZPn065cqVw9XVlcuXL9OhQwc++ugjrK2tWbRoEZ07d+bUqVP4+fllWc6kSZP45JNP+PTTT/nqq6/o168fly5dylG3JFH4yefCkHwuhChmYq7A0eXaLfLog+Xm1pCaCDtnQb2hYJeH3wHJ98DM0rQ16WlpsOtr7YJDWjI4+UD3+eDf0HQx3WfShFspxahRo1i5ciWbNm2ibNmy2drvk08+4aOPPmLt2rXUqVMnj6N8vEAPRy3hjorjmaomDUUIUcTdS06lyntrTXLs45NDsDPSeBWTJ0+mbdu2+sclSpSgRo0a+scffPABK1eu5LfffmPkyJFZljNw4ED69OkDwJQpU/jyyy/Zs2cP7dq1M0qconCQz4Uh+VwIUQzcuwXHV8ORZXBpO3B/xiQzSwh8Bqr1gAohWi33taOw40toMzFvYrl7E+a2gJQE6LUYfOvlzXEeJy4KVv0fnL3fCqhyZ3j2K63fdgFg0oR7xIgRLF26lNWrV+Po6Mi1a9cAcHZ2xtbWFoD+/ftTunRppk6dCsDHH3/Me++9x9KlSylTpox+HwcHB4M+UPmlvId2zDORd/L92EIIURg9eqE0Li6OiRMn8ueffxIREUFKSgr37t0jLCzsseU83LrJ3t4eJycnoqKi8iRmIfKafC6EEI+VfA9Or9GS7DP/aLW46fwbQ7XnoUoXw5rslu/AT71g97dQ///A0dP4cW3+BG5f0u4v7ATd5kDQc8Y/TlbO/QsrhkN8FFjYQLupEDwIjNT6yBhMmnDPnj0bgBYtWhgsX7BgAQMHDgQgLCwMMzMzg32SkpLo0aOHwT65HeHzaVXw1AZOOxMVl+/HFkIUL7aW5hyfHGKyYxvLo6Mqv/7666xbt47p06dTvnx5bG1t6dGjB0lJSY8tx9LS0uCxTqcjzURTfgjTkc+FIflcCFHExEXD5mnaiNtJD1XweQZpNdlBPcDFN/N9K4RA6TpwdR9s+wzaTzNubNfPwN552v3SwXB1PywfpI0G3mRs3ia9qcnw7wew/QvtsXtleH4BeFTOu2PmksmblD/Jpk2bDB5fvHgxb4LJpcD7Ndxno2SkciFE3tLpdEZrvlqQbN++nYEDB9KtWzdAq9kraN/1ouCSz4UQokhKSYLdc2DLp5B4f1pjZ18tya7WEzyrPLkMnQ5avwuLusC+76DRSHDO3nhZ2fLPO5CWAhXaQ+8l2uNd38CGyXDzPHT8DCysjHe8dDcvwK9DtAQfoM4QCPkILG2NfywjKDCjlBdWviXssLYwIzEljSu3ZKRyIYTIqcDAQFasWMGhQ4c4fPgwffv2lRq5AmrixInodDqDW6VKlfTrExISGDFiBG5ubjg4ONC9e3ciIyNNGHHhJZ8LIYoppeDkX/BNfVj3rpZse9WAF1bBq0e0vtjZSbbTlW0OZZpCapKWvBvLuX+1Ju5mFvDMh2BmrjXn7jBdGx384GJY0h3u3TbeMUEbHG5OUy3ZtnGGnj9Cp5kFNtkGSbifmrmZjgD39H7c0qxcCCFyaubMmbi6utKoUSM6d+5MSEgItWvXNnVYIgtVq1YlIiJCf9u2bZt+3ZgxY/j9999ZtmwZmzdvJjw8nOeey8e+fEWIfC6EKIYij8OPXSG0j1ZDbO8Bz86CoZsgoCWY5SJ10+mg1Tva/YOLtXKfVmoKrH1bu19vGJQs/2BdvaHQ52ewcoALW+C7Z+DWxac7nlLagGjft9dqtpPugF9DeGk7VHn26crOBzqVnXbdRUhsbCzOzs7ExMTg5ORklDJfDT3I6kPhjGtXkZdblH/yDkII8QQJCQlcuHCBsmXLYmNjY+pwhBE97r3Ni3OUMU2cOJFVq1Zx6NChDOtiYmJwd3dn6dKl+nFWTp48SeXKldm5cycNGjTI9nEe9zrIZ6NokvdVFGvxN2DjR7B/Aag0MLeChiOg6Wtg7WicYyzuAWfXQfXe8Ny3T1fW3u/gz7HaKOCvHMx8NPBrR2FJT7gTDnYloU8o+NbN2XHS0uD031rNfPhBbZm5ldY/vNkbJpmGLDfnaanhNgJ9P26p4RZCCFHEnTlzBm9vb8qVK0e/fv30o2bv37+f5ORk2rRpo9+2UqVK+Pn5sXPnTlOFK4QQBVdqMuz8Br6qpfWxVmnalFYj9mhNx42VbAO0ul8jfeRniDqZ+3ISYrSLAwAt3sp66q1S1WDoBihVHe5ehx86wX8rs3eMtNT7TcebQGhfLdm2sIUGI+DVw9Bygmnn/M6hwhNpARYoI5ULIYQoBurXr8/ChQupWLEiERERTJo0iaZNm3Ls2DGuXbuGlZUVLi4uBvt4enrqp/DMSmJiIomJifrHsbGxeRG+EEIUHKf/gbVvwY0z2mPPIK0PdNlmeXM871pQqROc/AM2TYWeP+SunC2fwt0bULIC1Bn0+G2dvGHQ3/Dri1pN9bKBWpP2rEYwT02GI7/Atplw46y2zMpRa6be4GVwcM9dzCYmCbcRPDxSeVqawkxGKhdCCFEEtW/fXn+/evXq1K9fH39/f3755RdsbXM/YM3UqVOZNGmSMUIUQoiCLfqUlmifXa89tiup9bGu3V8beCwvtXwbTv4Jx1dBxGFtMLacuHEOds3R7odMAXPLx28PYO2gjWC+9m3YPfvBCOadPn+wf3ICHFoM276AGK3VFLauWpJdb2jWteiFhDQpNwK/EnZYmZtxLzmVq7fvmTocIYQQIl+4uLhQoUIFzp49S6lSpUhKSuL27dsG20RGRlKqVKnHljNhwgRiYmL0t8uXL+dh1EIIYSIHfoTZjbRk28wSGo2CVw5oNcV5nWyDNrp5NW2MDTZOyfn+696DtGQIaA2BbbO/n5m5Ngd4+08fjGC++DmIDYedX8MXNeDP17Rk294d2k6G0Ueh+bhCn2yDJNxGYWFuRjl3ewDORN15wtZCCCFE0RAXF8e5c+fw8vIiODgYS0tLNmzYoF9/6tQpwsLCaNiw4WPLsba2xsnJyeAmhBBFhlLw74fw20ht3urybWHEbm06LRvn/I2lxQTQmWtTel3em/39LmzVmqPrzLU5r3Oj/jBt8LT0EcxnVtZq++OugVNpLSEffRQav2rc/usmJgm3kej7ccvAaUIIIYqo119/nc2bN3Px4kV27NhBt27dMDc3p0+fPjg7OzNkyBDGjh3Lxo0b2b9/P4MGDaJhw4Y5GqFcCCGKlJREWDn8wRzYTV+HfsvALcA08bgFQM2+2v1/P8jePmmpsHaCdr/OIPConPvjVwiBwWvA0Vt77FoGOn8JrxzSEvICPJ92bkkfbiNJ78d9WhJuIYQQRdSVK1fo06cPN27cwN3dnSZNmrBr1y7c3bWBbD777DPMzMzo3r07iYmJhISE8M0335g4aiGEMJF7t+DnF+DiVq1muNNnEDzA1FFpTbUPh8KFzVpN85MGaju0VJvmy9pZG5n8aZWqBv+3Ha4dAf8mhWrE8dwo2s8uHz0YOE2alAshhCiaQkNDH7vexsaGr7/+mq+//jqfIhJCiALq1iVY8jxcP6U1oe75A5Rv8+T98oOLHwQPhL3ztKbug9dmPmo4QOIdbaAz0BJ1ezfjxGBXAsq1ME5ZBZw0KTeSh6cGU0qZOBohhBBCCCEeI/46/POuNmK0MK6rB2B+Gy3ZdvTWmlAXlGQ7XbPXwcIGLu9+MGJ6ZrZ9BvFRUKIc1BuWf/EVIZJwG4m/mx2W5jruJslI5UII8TRatGjB6NGj9Y/LlCnD559//th9dDodq1ateupjG6scIYxNPhfC6NZPhB1fanMjp6WZOhrTSU2B6NNw/Yw2uNnTOvU3LOyoJameQfDieq0JdUHjWEqbcgu0vtyZPfdbl2DHLO3+Mx+ChVX+xVeESMJtJJbmZpQtmT5SufTjFkIUT507d6Zdu3aZrtu6dSs6nY4jR47kqMy9e/cybJhxr6pPnDiRmjVrZlgeERFhMNe0EMYgnwtR4Ny9CUeXafcjDsOhJaaNJ7/E34Dzm2HnN7BqBHzbHKaWhq/rwqw68E1D2DoDboflrvw98yC0LyTfhYBWMOhvcC5t3OdgTI3HaM3dIw7Did8zrl8/EVIToUxTqNgh38MrKqQPtxEFejpyOjKOs5FxtKzoYepwhBAi3w0ZMoTu3btz5coVfHx8DNYtWLCAOnXqUL169RyVmT4gV3540nzRQuSGfC5EgXPwR0hJAHNrLaHaMAmqdAGbIjIlX2oyXD8Nkf9B5LH7f/+DOxGZb29pp03XFX1C66+8YTL4NoDqz0OVbk/ut5yWBuvehZ33a4NrvaANkGZuadznZWz2btDgZdjyiTYvd6WOD+YDD9sF/60AdNBuatZ9vMUTSQ23ET0YqVwGThNCFE+dOnXC3d2dhQsXGiyPi4tj2bJldO3alT59+lC6dGns7OyoVq0aP/3002PLfLTp7JkzZ2jWrBk2NjZUqVKFdevWZdhn/PjxVKhQATs7O8qVK8e7775LcnIyAAsXLmTSpEkcPnwYnU6HTqfTx/to09mjR4/SqlUrbG1tcXNzY9iwYcTFPWjFNHDgQLp27cr06dPx8vLCzc2NESNG6I8lBMjnQj4XBUxaKuydr91vNxXcykN89INpqwoKpbSa+JvnIfyQNpr2iT+0EbN3zYHNn8Dat+G3UfDLAPjxOa3f9Ky68JEXzG4EK4bC9i+0PsrpybZrGajUCZq/CT1/hFEHYMJVeP00PPuVVpuLDi7vgj9fgxkVYGkvOLocku5mjDP5Hiwb8CDZbvWuVk5BT7bTNRyhzQUefQKO/aotS0uDNfenAav9QsFsEl+ISA23EQV6PBg4TQghjE4prZmaKVjaZevqtoWFBf3792fhwoW8/fbb6O7vs2zZMlJTU/nf//7HsmXLGD9+PE5OTvz555+88MILBAQEUK9evSeWn5aWxnPPPYenpye7d+8mJibGoF9rOkdHRxYuXIi3tzdHjx5l6NChODo6Mm7cOHr16sWxY8dYs2YN69drA8U4OztnKCM+Pp6QkBAaNmzI3r17iYqK4sUXX2TkyJEGidPGjRvx8vJi48aNnD17ll69elGzZk2GDh36xOcjjEA+F/K5EDlzeq3WZNrWVZuP2dkXlj4Pu2ZrI1fn5/zQSkFcFNw8pyXWN8/DjfT7FyDpKSqxrBzBs6p2KxWk9af2qAzWjplvb+sKtftrt5irWu3ukV+0qatOr9FulvZQuRNU66mNsJ1wG37qA1f2gLkVdPlGqxUvTGxdoNErWj/uTVOhajct8Q4/oL2Grd41dYSFniTcRlTBM31qMG2kcp00vRBCGFPyXZjibZpjvxUOVvbZ2nTw4MF8+umnbN68mRYtWgBas9nu3bvj7+/P66+/rt921KhRrF27ll9++SVbicX69es5efIka9euxdtbey2mTJmSoX/pO++8o79fpkwZXn/9dUJDQxk3bhy2trY4ODhgYWHx2KayS5cuJSEhgUWLFmFvrz33WbNm0blzZz7++GM8PT0BcHV1ZdasWZibm1OpUiU6duzIhg0bJLHIL/K5kM+FyJk9c7W/tfuDpS1UeEYbQfvsevjnHejz+NYVuZKSpCWlBkn1Be1+cvzj97W015q6Wztl/GvtqNXOPrrOxV+b+iq3v8WdS0OjUdot+pSWeB9dBrcvwZGftZtdSW2U79grWgy9l0KZJrk7nqnVf0m74HLzPOz9TmsVANB0LDhIN9mnJQm3Efm72WNhpiMuMYWImAS8XWxNHZIQQuS7SpUq0ahRI77//ntatGjB2bNn2bp1K5MnTyY1NZUpU6bwyy+/cPXqVZKSkkhMTMTOzi5bZZ84cQJfX199UgHQsGHDDNv9/PPPfPnll5w7d464uDhSUlJwcspZ38QTJ05Qo0YNfVIB0LhxY9LS0jh16pQ+sahatSrm5ub6bby8vDh69GiOjiWKPvlcyOeiQIg+Dec3gs4M6gx5sDxkCpzfBKf+gnP/agN+GUtiHCxor9UUZ0ZnptWylyin1a6XKPfg5loGLKyNF0tuuFeE1u9Cq3fgyl4t+f5vBdy9rq138YN+y7XtCitrBy25XvsWrHkTUNrzavCyqSMrEiThNiIrCzPKlLTnbFQcZ6LiJOEWQhiXpZ1Wo2aqY+fAkCFDGDVqFF9//TULFiwgICCA5s2b8/HHH/PFF1/w+eefU61aNezt7Rk9ejRJSUlGC3Xnzp3069ePSZMmERISgrOzM6GhocyYMcNox3iYpaVhPz2dTkdacZ5iJ7/J5yJb5HMhANg7T/tboT24+j9Y7l5Rm2N51zda392XtoO5EdIEpWD1y1qybe0EPnUzJtYu/oVjuimdDnzrabd2U7ULFOGHIHhA0agFrjMYdnz1oK9728lgaWPamIoISbiNrIKng5ZwR96heYX8G0FUCFEM6HTZbr5qaj179uTVV19l6dKlLFq0iP/7v/9Dp9Oxfft2unTpwv/+9z9A63t6+vRpqlSpkq1yK1euzOXLl4mIiMDLywuAXbt2GWyzY8cO/P39efvtt/XLLl26ZLCNlZUVqampTzzWwoULiY+P19fmbd++HTMzMypWLMQ1GUWNfC7kcyGyJyFWG3AMHsy//LDm4+BwKESfhH3fQ30jTDu37TM4vhrMLLVaYL/6T19mQWBuCYFttVtRYWkLLd6E318F/8ZQpaupIyoyZJRyIyufPnBapAycJoQovhwcHOjVqxcTJkwgIiKCgQMHAhAYGMi6devYsWMHJ06cYPjw4URGRma73DZt2lChQgUGDBjA4cOH2bp1q0ECkX6MsLAwQkNDOXfuHF9++SUrV6402KZMmTJcuHCBQ4cOcf36dRITEzMcq1+/ftjY2DBgwACOHTvGxo0bGTVqFC+88IK+2awQOSGfC2FSh0MhKQ5KVtAG/HqUravWbBpg40faCOFP48x6bXotgA6fFJ1kuyirPQAG/KH145exqIzGpAn31KlTqVu3Lo6Ojnh4eNC1a1dOnTr12H3+++8/unfvTpkyZdDpdAZTYhQE6VODnYmSqcGEEMXbkCFDuHXrFiEhIfq+pe+88w61a9cmJCSEFi1aUKpUKbp27ZrtMs3MzFi5ciX37t2jXr16vPjii3z00UcG2zz77LOMGTOGkSNHUrNmTXbs2MG77xqOstq9e3fatWtHy5YtcXd3z3QKJjs7O9auXcvNmzepW7cuPXr0oHXr1syaNSvnL4YQ98nnQphEWtqDwdLqDcs6mao9ADyqaqNvb5qa++PdOAe/DgaUVmadwbkvS+QfnQ7KNtUGgRNGo1NKKVMdvF27dvTu3Zu6deuSkpLCW2+9xbFjxzh+/LjBYBwP27t3L7/88gvBwcGMGTOG8ePHZzr1RVZiY2NxdnYmJiYmxwOFZMepa3cI+XwLjjYWHHn/GRmpXAiRKwkJCVy4cIGyZctiYyN9qIqSx723eX2OKiwe9zrIZ6Nokvc1j537F37spk3z9NqJrKfGAji/GRY9CzpzeGkbeGava4NeYhx81xaijoNPPRj4h+kHPhPCSHJznjZpH+41a9YYPF64cCEeHh7s37+fZs2aZbpP3bp1qVu3LgBvvvlmnseYU2VK2mFupuNOQgqRsYmUcpaThhBCCCGEMKHd92u3a/Z9fLINUK45VO4MJ36HtRPghVXZb16cPkha1HFw8ISeiyTZFsVegerDHRMTA0CJEiVMHEnuWVuY4++mjVoqzcqFEEIIIYRJ3boIp+9XcmU2WFpm2n4A5lb3pwr7O/vHeniQtJ4/gpNXTqMVosgpMAl3Wloao0ePpnHjxgQFBRmt3MTERGJjYw1uea2CDJwmhBBCCCEKgr3zAaXNrV0yMHv7lCgLDUdq99e+BSkZB9DLQAZJEyJTBSbhHjFiBMeOHSM0NNSo5U6dOhVnZ2f9zdfX16jlZybQUwZOE0IIIYQQJpZ0Fw78qN2vNzxn+zYdCw6l4NYF2DX78dvePC+DpAmRhQKRcI8cOZI//viDjRs34uPjY9SyJ0yYQExMjP52+fJlo5afmfLpI5VLDbcQQgghROGXfA+unzF1FDl3dJk24riLf87njLZ2hDbva/e3TIc7WUxVlxgHof0gIQZ86kKHT58qZCGKGpMm3EopRo4cycqVK/n3338pW7as0Y9hbW2Nk5OTwS2vVfC836Q8Kg4TDgIvhCgC0tLSTB2CMDJ5T41Dzq9FS4F+P6NOwjcNYVYdOPmnqaPJPqVgzzztfr2hYGae8zKq9wbv2pB0B/6dnPkxDAZJ+1EGSRPiESYdpXzEiBEsXbqU1atX4+joyLVr1wBwdnbG1tYWgP79+1O6dGmmTtXmAkxKSuL48eP6+1evXuXQoUM4ODhQvnx50zyRR5QtaY+ZDmLuJRN9JxEPJxmpXAiRM1ZWVpiZmREeHo67uztWVlYyzWAhp5QiKSmJ6OhozMzMsLKyMnVIhZKlpSU6nY7o6Gjc3d3lc1EEKKWIjo5Gp9NhaWlp6nAMnf4Hlg/WEk6A7V9ApY6mjSm7wnZC5FGwsIWa/XJXhpkZtP9Ym+br4BKo+yJ413qwXgZJE+KJTJpwz56t9Qdp0aKFwfIFCxYwcOBAAMLCwjAze1ARHx4eTq1aDz7o06dPZ/r06TRv3pxNmzbldcjZYmNpjr+bPReux3MmKk4SbiFEjpmZmVG2bFkiIiIIDw83dTjCiOzs7PDz8zM4t4nsMzc3x8fHhytXrnDx4kVThyOMRKfT4ePjg7l5Lmph84JSsOMrWPceoMC3AVzdD5d3w9UDULq2qSN8sj33pwKr3hPsnmIGIN96UK0nHP0F/n4TBq/RpgmTQdKEyBaTJtzZaT70aBJdpkyZgt3s6L5ADwct4Y68Q+PyJU0djhCiELKyssLPz4+UlBRSU1NNHY4wAnNzcywsLKRW9ik5ODgQGBhIcnKyqUMRRmJpaVlwku3kBPhjNBz+SXscPBDafwqrR2hJ55650G2OKSN8sthwOP6bdr/esKcvr81EOPkHXN4Fx37VLjjoB0nrD8GDnv4YQhRRJk24i7JATwf+OR7J6SgZOE0IkXvpTSwLXDNLIUzM3Ny84CRooui4Ewk/94Mre0FnDu2maf2fdTqo/5KWcB/7FdpOBgcP4x8/NQX+HKv1t279Hti65q6cfQtApYJ/YyhlhOl2nUtDkzGw8SOt1t/G+aFB0qZrr48QIlPSni2PBN6fi/usjFQuhBBCCFHwhR+CeS21ZNvGBf73K9Qf9iCZ9AmG0nUgNQn2L8ybGI78DAd+gH3fw+wmcGlnzstISYT9C7T79YYaL7ZGo8DZF2KvyiBpQuSAJNx5JH0u7tNRdwpFE3ghhBBCiGLr2Ar4vp2WTJasAEP/hYCWGber/5L2d+93kJJk3BhSU2DL/Sm1LO0h9gos7AAbp2rrsuv4aoiPBkdvqNTJePFZ2sIzH2j3zSyh5yIZJE2IbJCEO48EuDug08Htu8lcjzPyF7IQQgghhHh6aWnw70ewfBCk3IPybeDF9eAWkPn2VbpoNbtx1+DEb8aN5Ugo3LoAdiXh1UNQoy+oNNg8DRZ2hNth2Stn97fa3zqDwdzI3ZGqdIUuX8P/loNfA+OWLUQRJQl3HrGxNMevhB0AZ6LumDgaIYQQQghhICkelvWHLZ9ojxuOhL6/aP2Ts2JhBXWGaPd3G3HgtNTkB7XbjV/V+od3mw3PzQdrJ22wstlNtJr4x7m6H67uA3MrbbA3Y9PpoNb/oFwL45ctRBElCXce0vfjloHThBBCCCEKjtth8F0InPhdS067fAMhH2mDlT1JnUFak+ore+HKfuPEczgUbl0Ee3eoO+TB8urPw0tbtcHJEmO0mvjVIyAxi9+We+Zpf6t2Awd348QmhHgqknDnofR+3Gdk4DQhhBBCiIIhbBfMbQmRR7UEd8AfUKtf9vd38ICg7tr9Pd8+fTyP1m5b2Ruudy0Dg/6Gpq8DOji4GOY21wZ5e1j8dW0EdYB6w58+LiGEUUjCnYcCPe4PnBYpTcqFEEIIIUzu9D/ww7Nw9zqUqgZDN4Jf/ZyXU//+3NbHVmhTiT2Nwz/B7Uta8l9nSObbmFtC63dh4B/gVBpunIX5bWDHV1o/dNBGTk9NAu/a2ojqQogCQRLuPFTBU5qUCyGEEEIUCCf/hNC+kJoIFdrD4LXg4pu7skoHg089SEt+MAVXbqQkPVS7PRqs7B6/fZkm8NI2bfTxtGT45x1Y0h1iw7WpxADqS+22EAWJJNx5KH2k8hvxSdyISzR1OEIIIYQQxdN/K+GX/lqSWqUr9PoxY9PtnEpPbJ9mirDDP2n9ye09tFHFs8OuBPRaDJ0+AwtbOPcvfFlbm9LMrqTWf1sIUWBIwp2HbK3M8XG1BeCM1HILIYQoYqZNm4ZOp2P06NH6ZS1atECn0xncXnrpJdMFKcSRZbB8MKSlQLWe0P0740yXVaULOHpBfBQcX5Xz/VOSYMt07X6T0U+u3X6YTqcl6MM2gWeQNqUZaCOTW1jnPBYhRJ6RhDuPVbg/Urkk3EIIIYqSvXv38u2331K9evUM64YOHUpERIT+9sknn5ggQiGAg0tgxVBtPuua/aDbHDC3ME7Z5pZPN0XY4aUQE6bN653d2u1HeVSCFzdozdEDn4EGL+euHCFEnpGEO4+Vvz9S+VkZOE0IIUQRERcXR79+/Zg3bx6urq4Z1tvZ2VGqVCn9zcnJyQRRimJv3wJY/TKgIHgQPDsre9N+5UTwQG1asav74cq+7O/3cO1249FgaZv7GCxtoO0k6LcM7N1yX44QIk9Iwp3H0ufiPi1TgwkhhCgiRowYQceOHWnTpk2m65csWULJkiUJCgpiwoQJ3L1797HlJSYmEhsba3AT4qnsngt/jNbu1xuu9Xc2y4OfvQ7uENTj/jFzUMt9aAnEXL5fuz3I+HEJIQoMI7WpEVlJnxpMmpQLIYQoCkJDQzlw4AB79+7NdH3fvn3x9/fH29ubI0eOMH78eE6dOsWKFSuyLHPq1KlMmjQpr0IWxc2Or7TRuwEajYK2H2h9nvNK/WFa8/D/VmrHcvJ6/PYpSbB1hna/yZinq90WQhR4knDnsfL3E+7rcYncik/C1d7KxBEJIYQQuXP58mVeffVV1q1bh42NTabbDBs2TH+/WrVqeHl50bp1a86dO0dAQECm+0yYMIGxY8fqH8fGxuLrm8vpmkTxtnUGbJis3W/6OrR6J2+TbQDvWuDbAC7v0qYIa/nW47c/tPh+7XYprUm6EKJIkyblecze2oLSLjJSuRBCiMJv//79REVFUbt2bSwsLLCwsGDz5s18+eWXWFhYkJqammGf+vXrA3D27Nksy7W2tsbJycngJkSOKAWbpj1Itlu+Da3fzftkO136FGH7voeUx0wFm5IIW6R2W4jiRBLufBDomd6sXAZOE0IIUXi1bt2ao0ePcujQIf2tTp069OvXj0OHDmFunnFAqkOHDgHg5fWEZrZC5HYua6W0RHvTVO1xm4nQfJzRwsqWyp3B0Rvio7Wm5Vk5+CPEXpHabSGKEWlSng8qeDqy6VQ0Z2TgNCGEEIWYo6MjQUFBBsvs7e1xc3MjKCiIc+fOsXTpUjp06ICbmxtHjhxhzJgxNGvWLNPpw4QAIPEO/DEGjv0K1o7g4g+u/vf/lnnosV/GGmGltP7aO2dpj0OmQMMR+f4UMLeEukPg3w9g12yo3itj7XpKImydqd1vOlYbXVwIUeRJwp0PyntIDbcQQoiiz8rKivXr1/P5558THx+Pr68v3bt355133jF1aKKgijoJv7wA109rjxNi4NoR7ZYZB0/DhDzmChwJ1dZ1mA71huZP3JkJHgibP4GIQ3BlL/jWM1x/YBHEXgVHL6g9wBQRCiFMQBLufKAfqVxquIUQQhQxmzZt0t/39fVl8+bNpgtGFC5Hl8Nvr0ByvNYcu/s8sHODW5fg9qWMfxNjIS5Su13Z81BBOuj8BQSbOIm1LwnVntcGRds9xzDhfrh2u4nUbgtRnEjCnQ8CPbW5uKPuJBJzNxlnO0sTRySEEEIIYSIpSbDu3QfzVpdtDj2+1xJWAI/KGfdRCu7dypiIx0drzbcrd8q/+B+n/jAt4T6+GmLDwclbW35gEdwJ1y4s1O5v2hiFEPlKEu584GBtgbezDeExCZyJukOdMiVMHZIQQgghRP6LuQrLBj6ooW76ujaNllnGAfcM6HRgV0K7edfK8zBzzasG+DWCsB3aiOWt3oHkBOm7LUQxZtJRyqdOnUrdunVxdHTEw8ODrl27curUqSfut2zZMipVqoSNjQ3VqlXjr7/+yodon075+7XcMjWYEEIIIYql85vg22Zasm3jDH1+1qbuelKyXdjopwhboCXbUrstRLFm0oR78+bNjBgxgl27drFu3TqSk5N55plniI+Pz3KfHTt20KdPH4YMGcLBgwfp2rUrXbt25dixY/kYec5VkH7cQgghhCiO0tJgy3T4sRvcvQ6lqsOwzVCxnakjyxuVOoFTae25Hl4K2x6q3bawNm1sQoh8p1NKKVMHkS46OhoPDw82b95Ms2bNMt2mV69exMfH88cff+iXNWjQgJo1azJnzpwnHiM2NhZnZ2diYmJwcnIyWuxP8vPeMMb/epSmgSX5cUj9fDuuEEKIwsNU56iCRl6HIuTeLVj5Epxeoz2u9QJ0+DTj9F5FzdaZsGESmFtBapKWgL9yUBJuIQq53JyfTFrD/aiYmBgASpTIuo/zzp07adOmjcGykJAQdu7cmaexPa3yHveblEsNtxBCCCGKg4jD8G1zLdk2t4Znv4Ius4p+sg3atF8WNlqyDVK7LUQxVmAGTUtLS2P06NE0btyYoKCgLLe7du0anp6eBss8PT25du1aptsnJiaSmJiofxwbG2ucgHMo0FNrUn4tNoHYhGScbGSkciGEEEIUUQd+hD9fg9REbb7sXj9qA4oVF/Zu2hRhB38EJx+tZl8IUSwVmBruESNGcOzYMUJDQ41a7tSpU3F2dtbffH19jVp+djnZWFLKSRuVUmq5hRBCCFEkxd/QmpD/NlJLtiu0g+Gbi1eyna7lW1D5Wej6jdRuC1GMFYiEe+TIkfzxxx9s3LgRHx+fx25bqlQpIiMjDZZFRkZSqlSpTLefMGECMTEx+tvly5eNFndOpddyn426Y7IYhBBCCCGMLi0N9i+EWcFw+CfQmUGrd6H3T2DrauroTMPJW6vZL9fc1JEIIUwoR03KT5w4QWhoKFu3buXSpUvcvXsXd3d3atWqRUhICN27d8faOvtX8JRSjBo1ipUrV7Jp0ybKli37xH0aNmzIhg0bGD16tH7ZunXraNiwYabbW1tb5yimvBTo4cjWM9elhlsIIYQQRUf4Ia35+NV92mPPIOg4A/wamDQsIYQoCLKVcB84cIBx48axbds2GjduTP369enWrRu2trbcvHmTY8eO8fbbbzNq1CjGjRvH6NGjs5XkjhgxgqVLl7J69WocHR31/bCdnZ2xtdUG1Ojfvz+lS5dm6tSpALz66qs0b96cGTNm0LFjR0JDQ9m3bx9z587N7WuQb9JruE/LXNxCCCGEKOzu3YZ/P4R934FKAytHrRl1vWFgXmCGCRJCCJPK1rdh9+7deeONN1i+fDkuLi5Zbrdz506++OILZsyYwVtvvfXEcmfPng1AixYtDJYvWLCAgQMHAhAWFoaZ2YOW740aNWLp0qW88847vPXWWwQGBrJq1arHDrRWUATen4v7bKQ0KRdCCCFEIaUUHA6Fde9CfLS2LKgHPPMhOHmZNjYhhChgsjUPd3JyMpaW2R9VO6fb5ydTzu0ZczeZGpP/AeDoxGdwlJHKhRBCPETmn9bI61CARR6Hv16HS9u1xyUrQIfp0k9ZCFEs5Nk83A8nz+fPn8/R9uIBZztLPBy1pvZnpVm5EEIIIQqLxDuw9m2Y00RLti3toM1EeGm7JNtCCPEYOR6lvHz58rRs2ZLFixeTkJCQFzEVaen9uM9Iwi2EEEKIgk4pOLYCZtWFnbNApUKlTjBiDzQZAxZWpo5QCCEKtBwn3AcOHKB69eqMHTuWUqVKMXz4cPbs2ZMXsRVJgR6OgNRwCyGEEKKAiw2Hxc/B8kFwJwJcy0K/5dB7Cbj4mjo6IYQoFHKccNesWZMvvviC8PBwvv/+eyIiImjSpAlBQUHMnDmT6OjovIizyNDXcMvAaUIIIYQoqM6s15qPn/sXzK2hxQR4eRcEtjV1ZEIIUajkOOFOZ2FhwXPPPceyZcv4+OOPOXv2LK+//jq+vr7079+fiIgIY8ZZZKTXcJ+WubiFEEIIUdCkpsD6ibCkO9y9AV414P92QIs3wdLG1NEJIUShk+uEe9++fbz88st4eXkxc+ZMXn/9dc6dO8e6desIDw+nS5cuxoyzyKhwv4b76u17RN9JNHE0QgghhBD3xVyBhR1h22fa43rDYMg6KFnetHEJIUQhlq15uB82c+ZMFixYwKlTp+jQoQOLFi2iQ4cO+rmyy5Yty8KFCylTpoyxYy0SXOysqOXnwsGw2yzbf5mXW8hJTAghhBAmdvofWDkc7t0Eayd49iuo2tXUUQkhRKGX4xru2bNn07dvXy5dusSqVavo1KmTPtlO5+HhwXfffWe0IIuafvX9AVi6O4y0tCdOgy6EEEIIkTdSk+Gfd2Hp81qy7VUThm+WZFsIIYwkxzXcZ86ceeI2VlZWDBgwIFcBFQedqnsx+ff/uHLrHlvORNOiooepQxJCCCFEcXP7MiwfDFfuzzZT/yVoOxksrE0blxBCFCE5TrgBbt26xXfffceJEycAqFy5MoMHD6ZEiRJGDa6osrE0p0ewL99vv8CS3WGScAshhBAi++5cg/jrUDIw98nxqb9h5UuQcBusnaHLLKjyrFHDFEIIkYuEe8uWLXTu3BlnZ2fq1KkDwFdffcUHH3zA77//TrNmzYweZFHUt74f32+/wIYTkUTE3MPL2dbUIQkhhBCiIFIKrh3VkuTTf0P4QW25mQW4VwLPIChV7cHN7jEVIKnJ2ijkO2dpj71rw/MLwLVMXj8LIYQolnKccI8YMYJevXoxe/ZszM3NAUhNTeXll19mxIgRHD161OhBFlix4dr8lLauUKljjnYt7+FA/bIl2H3hJqF7LjOmbYU8ClIIIYQQhU5KIlzYqiXYp9ZA7JWHVuq0gc0SYyDymHY7EvpgtZOPYQJeqhq4+GtlLBsEV/dp2zV4GdpMAgurfH1qQghRnOQ44T579izLly/XJ9sA5ubmjB07lkWLFhk1uALv9Br4YwyUbZbjhBugXwN/LeHeG8aoVuWxMM/1LG1CCCGEKOzir8OZf+DUX3BuIyTFPVhnaQflWkLF9lAhBOzdIfaqVvN97ShcOwLXjsGtC1piHXtFS9bTWTkCSivTxhm6fAOVO+X7UxRCiOImxwl37dq1OXHiBBUrVjRYfuLECWrUqGG0wAoFv4ba3yv7tCZa5pY52j2kqidu9lZExiby78konqlaKg+CFEIIIUSBdfMCHF+tNRe/vBt4aPYSRy+o0E5Lsss2A8tHup85+2i3iu0fLEuIhcj/HkrCj0LUCUi6o60vXQd6fA+u/nn+1IQQQuQi4X7llVd49dVXOXv2LA0aNABg165dfP3110ybNo0jR47ot61evbrxIi2ISlbUmpPfuwURR8AnOEe7W1uY83wdX+ZsPseS3WGScAshhBDFycXt8GNXSE16sKxUNajYQUu0vWqCWQ5bv9k4gX9D7ZYuNQVunIG7N8GnrjQhF0KIfJTjhLtPnz4AjBs3LtN1Op0OpRQ6nY7U1NSnj7AgMzMD3wZak62wnTlOuAH61vNjzuZzbDkTTdiNu/i52eVBoEIIIYQoUOKvw69DtGS7dB2o0VtLsl18jX8scwvwqGz8coUQQjxRjhPuCxcu5EUchZffQwl3o5E5393NjmYV3NlyOpqf9oYxvl2lPAhSCCGEEAVGWpo2JdedCChZAfqvBmsHU0clhBAiD+Q44fb3lz4/BtL7cYft0qbt0OlyXES/+n5sOR3NL3svM6ZNBawsZPA0IYQQosjaOQvOrgMLG+ixQJJtIYQownKV2Z07d45Ro0bRpk0b2rRpwyuvvMK5c+eMHVvh4F1TO2HevQ43zuaqiNaVPPB0suZGfBJr/7tm3PiEEEIIUXBc3gsbJmn3202DUkGmjUcIIUSeynHCvXbtWqpUqcKePXuoXr061atXZ/fu3VStWpV169blRYwFm4U1lL7fdztsZ+6KMDejV10/AJbsvmSsyIQQQgjS0tLYuHEjkydPZsiQIfTp04dXXnmFBQsWcPny5acqe9q0aeh0OkaPHq1flpCQwIgRI3Bzc8PBwYHu3bsTGRn5lM+iiLh3C5YPhrQUqPocBA80dURCCCHyWI4T7jfffJMxY8awe/duZs6cycyZM9m9ezejR49m/PjxeRFjweenjdZO2K5cF9G7ri9mOth1/iZno+KevIMQQgjxGPfu3ePDDz/E19eXDh068Pfff3P79m3Mzc05e/Ys77//PmXLlqVDhw7s2pXz89fevXv59ttvM8xIMmbMGH7//XeWLVvG5s2bCQ8P57nnnjPW0yq8lILVIyEmDFzLQucvctUNTQghROGS44T7xIkTDBkyJMPywYMHc/z4caMEVejo+3HnroYbwNvFllaVPAH4aU+YMaISQghRjFWoUIEjR44wb948YmNj2blzJ7/++iuLFy/mr7/+IiwsjHPnztG0aVN69+7NvHnzsl12XFwc/fr1Y968ebi6uuqXx8TE8N133zFz5kxatWpFcHAwCxYsYMeOHblK6ouUPfPg5B9gZgnPL9Cm7xJCCFHk5Tjhdnd359ChQxmWHzp0CA8PD2PEVPj41AV0cPM83Ml9s7l+DbRm5cv3XyEhuYhPqSaEECJP/fPPP/zyyy906NABS0vLTLfx9/dnwoQJnDlzhlatWmW77BEjRtCxY0fatGljsHz//v0kJycbLK9UqRJ+fn7s3Jn1RenExERiY2MNbkVK+CH4523t/jMfgnctk4YjhBAi/+Q44R46dCjDhg3j448/ZuvWrWzdupVp06YxfPhwhg4dmqOytmzZQufOnfH29kan07Fq1aon7vP1119TuXJlbG1tqVixIosWLcrpUzA+WxfwvD/oyVPUcjcLdMfH1ZaYe8n8eSTCOLEJIYQolipXzv68y5aWlgQEBGRr29DQUA4cOMDUqVMzrLt27RpWVla4uLgYLPf09OTatawHBZ06dSrOzs76m69vHsxFbSoJsbB8kDbfdsWOUH+4qSMSQgiRj3I8Ldi7776Lo6MjM2bMYMKECQB4e3szceJEXnnllRyVFR8fT40aNRg8eHC2+nfNnj2bCRMmMG/ePOrWrcuePXsYOnQorq6udO7cOadPxbj8GkDkUa0fd9WuuSrC3ExHn3p+fLr2FEt2X6J7sI9xYxRCCFGspaSk8O2337Jp0yZSU1Np3LgxI0aMwMbGJlv7X758mVdffZV169Zle5/smDBhAmPHjtU/jo2NLRpJt1Lwx2itBZyzL3SZJf22hRCimMlRwp2SksLSpUvp27cvY8aM4c6dOwA4Ojrm6uDt27enffv22d7+xx9/ZPjw4fTq1QuAcuXKsXfvXj7++OOCkXDvnfdUNdwAz9fx4bN1pzkQdpvj4bFU8ZY+XkIIIYzjlVde4fTp0zz33HMkJyezaNEi9u3bx08//ZSt/ffv309UVBS1a9fWL0tNTWXLli3MmjWLtWvXkpSUxO3btw1quSMjIylVqlSW5VpbW2NtbZ3r51VgHVgEx34FnTn0+B7sSpg6IiGEEPksRwm3hYUFL730EidOnAByn2jnVmJiYoYr6ra2tuzZs4fk5OQs+6jli/SB064dgcQ7YJ2718bD0YaQqqX482gES/dc4sOu1YwYpBBCiOJk5cqVdOvWTf/4n3/+4dSpU5ibmwMQEhJCgwYNsl1e69atOXr0qMGyQYMGUalSJcaPH4+vry+WlpZs2LCB7t27A3Dq1CnCwsJo2LChEZ5RIRJ5HP4ep91v/R741jNtPEIIIUwix32469Wrx8GDB/MilicKCQlh/vz57N+/H6UU+/btY/78+SQnJ3P9+vVM98m3gVicS4OzH6g0uLL3qYrqV18bPG3VwXDiE1OMEZ0QQohi6Pvvv6dr166Eh4cDULt2bV566SXWrFnD77//zrhx46hbt262y3N0dCQoKMjgZm9vj5ubG0FBQTg7OzNkyBDGjh3Lxo0b2b9/P4MGDaJhw4Y5SuwLvaR4WDYQUhKgfBtolLMud0IIIYqOHPfhfvnll3nttde4cuUKwcHB2NvbG6x/dD5OY3r33Xe5du0aDRo0QCmFp6cnAwYM4JNPPsHMLPNrB1OnTmXSpEl5FpMB/4ZwJEzrxx2Q/dFeH9UwwI1yJe05fz2e3w6H06eenxGDFEIIUVz8/vvv/Pzzz7Ro0YJRo0Yxd+5cPvjgA95++219H+6JEyca9ZifffYZZmZmdO/encTEREJCQvjmm2+MeowC769xcP0UOHpBt28hi98oQgghij6dUkrlZIfMEludTodSCp1OR2pq7qaz0ul0rFy5kq5duz5x2+TkZCIjI/Hy8mLu3LmMHz+e27dvZxpbYmIiiYmJ+sfpA7HExMTg5GTk/tH7voc/xkDZZjDg96cqav7W83z45wmqejvxx6gm6GSQFSGEKPJiY2NxdnY2+jnq9u3bjBs3jsOHDzNnzhxq1SrY01Ll1euQLw6HwsrhoDPTfguUaWLqiIQQQhhJbs5POa7hvnDhQo4DMzZLS0t8fLQRvENDQ+nUqVOWNdz5OhBLej/uK/sgNRnMc9+nvHttHz5Ze4r/wmM5ciWGGr4uxolRCCFEsePi4sLcuXPZsmUL/fv3p127dnzwwQdGHWlcANfPwB/3R1tv/qYk20IIIXLeh/vSpUuULl0af39/g1vp0qW5dOlSjsqKi4vj0KFDHDp0CNCS+UOHDhEWFgZo04T0799fv/3p06dZvHgxZ86cYc+ePfTu3Ztjx44xZcqUnD6NvFGyIti4QPJdbfC0p+Bqb0Wnal4ALNmds9dVCCGEAAgLC6Nnz55Uq1aNfv36ERgYyP79+7Gzs6NGjRr8/fffpg6x6Ei+p/XbTo6HMk2h2eumjkgIIUQBkOOEu2XLlty8eTPD8piYGFq2bJmjsvbt20etWrX0TdvGjh1LrVq1eO+99wCIiIjQJ9+gTT0yY8YMatSoQdu2bUlISGDHjh2UKVMmp08jb5iZadODAVx6uunBAPreHzztt8PhxNxLfuryhBBCFC/9+/fHzMyMTz/9FA8PD4YPH46VlRWTJk1i1apVTJ06lZ49e5o6zMLv7k1Y2hMij4FdSeg+H8zMTR2VEEKIAiDHTcrT+2o/6saNGxkGUHuSFi1a8Lgu5AsXLjR4XLlyZZONkJ5tfg3h9BptPu5GI5+qqGB/Vyp6OnIq8g4rD1xhYOOyRgpSCCFEcbBv3z4OHz5MQEAAISEhlC374DxSuXJltmzZwty5c00YYREQdQJ+6g23LoKlPTy/AByznnNcCCFE8ZLthPu5554DtMHNBg4caNAvOjU1lSNHjtCoUSPjR1jYpPfjDtsFSsFTDHam0+no18CP91b/x9I9YQxoVEYGTxNCCJFtwcHBvPfeewwYMID169dTrVq1DNsMGzbMBJEVESf/ghVDISkOXPygTyh4VjV1VEIIIQqQbDcpd3Z2xtnZGaUUjo6O+sfOzs6UKlWKYcOGsXjx4ryMtXDwrgnm1nD3Otw499TFda1VGltLc05HxrHv0q2nj08IIUSxsWjRIhITExkzZgxXr17l22+/NXVIRYNSsOVTCO2rJdtlmsLQTZJsCyGEyCDbNdwLFiwAoEyZMrz++us5bj5ebFhYQ+lgCNuh3UqWf6rinGws6VLTm9C9l1my6xJ1y5QwUqBCCCGKOn9/f5YvX27qMIqWpLuwegT8t0J7XPdFaDftqWYmEUIIUXTleNC0999/X5LtJ0kfOC1sl1GK61ffH4C/jl7jRlziE7YWQgghID4+Pk+3L5ZirsCCdlqybWYBnT6HjjMk2RZCCJGlHCfckZGRvPDCC3h7e2NhYYG5ubnBTQD+9/uyhz39SOUA1Xycqe7jTFJqGmN+OUxSSppRyhVCCFF0lS9fnmnTphEREZHlNkop1q1bR/v27fnyyy/zMbpCKGwXzG0BEYfBzg36/wZ1Bpk6KiGEEAVcjkcpHzhwIGFhYbz77rt4eXnJIF6Z8akL6ODmebgTCY6eT13kB12C6D13F1tORzP+1yPMeL4GZmby2gshhMjcpk2beOutt5g4cSI1atSgTp06eHt7Y2Njw61btzh+/Dg7d+7EwsKCCRMmMHz4cFOHXHAd+BH+GANpyeBZDfos1QZJE0IIIZ5Apx43L1cmHB0d2bp1KzVr1syjkPJWbGwszs7OxMTE4OTklHcHmt1Ym4+z5yKo0sUoRW48FcXQH/aRkqYY2rQsb3esYpRyhRBCFAx5cY4KCwtj2bJlbN26lUuXLnHv3j1KlixJrVq1CAkJoX379gWuhVq+naufJDUF/nkbds/RHld+FrrNASvpWieEEMVRbs5POa7h9vX1fezc2eI+vwZawn1pp9ES7pYVPfi4e3VeW3aYeVsv4O5ozbBmAUYpWwghRNHk5+fHa6+9xmuvvWbqUAqXuzdh+SA4v0l73OItaPYGmOW4N54QQggTS0hOxcbSNBeXc3zW+Pzzz3nzzTe5ePFiHoRThOjn4zZOP+503YN9eKtDJQCm/HWSFQeuGLV8IYQQotiLPgXzWmnJtqU99FoMLcZLsi2EEIXIhevxfLPpLM/O2kbPb42bk+VEjmu4e/Xqxd27dwkICMDOzg5LS8OROW/evGm04Aq19IT72hFIvAPWjkYrelizAKJiE5m/7QLjlh+hhL0VLSp6GK18IYQQolhbORxuXdD6afcJlfm1hRCiEFBKcToyjr+PRbDm2DVOXrujX2emg6g7CXg42uR7XDlOuD///PM8CKMIci4Nzn4QEwZX9kFAS6MW/1aHylyPS2TVoXD+b/EBlg6tTy0/V6MeQwghhCh27t2G8IPa/UF/g7OPScMRQgiRNaUUx67G6pPs89cfTHFpYaajYYAb7YO8aFvFE3dHa5PEmOOEe8CAAXkRR9Hk1wCOhmlTiRg54TYz0/FJjxrciE9i65nrDF64l+X/14gAdwejHkcIIYQoVq7u0/6WKCfJthBCFEBpaYoDYbdYc+waa/67xpVb9/TrrCzMaBZYknZBXrSp7IGLnZUJI9XkOOEGOHfuHAsWLODcuXN88cUXeHh48Pfff+Pn50fVqtLsSs+vARz9BcJ25EnxVhZmzPlfMH3n7eLwlRj6f7eHFS83wtMp/5tKCCGEEEXC5T3aX596po1DCCGKuJTUNO4lp3IvKZW7SancS77/V38/xWDdvaRUrscl8u/JKKLuJOrLsbU0p2Uld9oFedGqkgcO1rlKcfNMjqPZvHkz7du3p3HjxmzZsoWPPvoIDw8PDh8+zHfffcfy5cvzIs7CKb0f95V9kJoM5paP3z4X7K0t+H5gXXrM2cmF6/EM+H4PPw9viLOt8Y8lhBCicCpTpgyDBw9m4MCB+PnJ/NGPdXm39tdXEm4hhHgSpRQRMQlExCRwJyGZ2IQUYu8lcychhdiEZGLvacvuZLifwr3k1Fwf19HagtaVPWgX5EXzCu7YWhWs6S0fluOE+8033+TDDz9k7NixODo+GAisVatWzJo1y6jBFXrulcDGBRJua4OnlQ7Ok8O4OVizaHA9npu9g5PX7jB00T4WDa5nsqHvhRBCFCyjR49m4cKFTJ48mZYtWzJkyBC6deuGtbVp+rMVWGmpcGW/dl8SbiFEIZaUkkZiSioO1hbodDqjlJmQnMqZyDhORMRyPCKWExGxnLx2h5h7yU9Vrk4Hdpbm2FpZYGdljq2lObZW5hnu291fX7dMCRqVd8PaonDkOjlOuI8ePcrSpUszLPfw8OD69etGCarIMDPTmpWfXqP1486jhBvAt4QdPwyqR69vd7Lnwk1Ghx7i6361MTczzgdMCCFE4TV69GhGjx7NgQMHWLhwIaNGjeLll1+mb9++DB48mNq1a5s6xIIh6gQk3QErB/CoYupohBACpRRhN+9y+ea9h2qMk4m5p9USP1yL/PC6hOQ0AKwtzPBwssbT0QZPJxvcHa3xdLLB4/5fTydrPBxtcLJ9kJgrpYi+k3g/qb7DifvJ9fnr8aSmqQwxWpjpKOVsg7OtJU42ljjaWOCU4b4FjjaWONla4GSjrXOw0RJoawszo10UKIhynHC7uLgQERFB2bJlDZYfPHiQ0qVLGy2wIkOfcO+EhiPy9FBVvJ2Y278OA77fw5r/rvHu6mN81DWoSP8DCyGEyL7atWtTu3ZtZsyYwTfffMP48eOZPXs21apV45VXXmHQoEHF+5xx5X7/7dLBYFY4ak6EEEVLUkoa/4XHsP/SLfZdvMW+S7e4Hpf45B2zkJiSxuWb97h8895jt0tPzEvYW3Pl5l1uxCdlup2rnSWVvZweujlS3sOh0NQ2m0KOE+7evXszfvx4li1bhk6nIy0tje3bt/P666/Tv3//vIixcEvvx31pJyiltZnIQw0D3Piid01eXnqApbvD8HC0ZnSbCnl6TCGEEIVDcnIyK1euZMGCBaxbt44GDRowZMgQrly5wltvvcX69eszbcVWbKQPmOZb37RxCCGKjdt3kzgQ9iC5Pnz5NokpaQbbWJmbUbakvVaDnF5DbGuprzlOr01OX5de02xpoeNGXBKRsQlE3UkkMjaByNhEou4kEHX/b2RsIjH3kjMk5mY6KFvSXp9YV7n/19PJunhfmM2FHCfcU6ZMYcSIEfj6+pKamkqVKlVITU2lb9++vPPOO3kRY+HmXQvMreHudbhxDkqWz/NDtq/mxQddgnhn1TE+X38GG0tzhjcrJx8OIYQopg4cOMCCBQv46aefMDMzo3///nz22WdUqlRJv023bt2oW7euCaMsACThFkLkgFKKNAUpaWmkpUGqUqSmPbilPfI4VSkSk9M4HhHL/ks32XfxFmei4jKU62pnSbB/CeqUcaWOvytBpZ1zPTaTXQkLfEvYPXabhORUou9oCXj0nSS8nG2o4OlYoAciK0xynHBbWVkxb9483nvvPY4ePUpcXBy1atUiMDAwL+Ir/CystaZpYTu0ZuX5kHAD/K+BP9F3Evliwxmm/X2SI1du83H36jjayOjlQghR3NStW5e2bdsye/ZsunbtiqVlxnNB2bJl6d27twmiKyDir8PNc9p9n7wbc0UIUbjF3E3m72MR/HY4nN0XbmbapzmnypW0J9jflbplShBcxpVyJe3ztaLMxtIc3xJ2T0zMRe7kepIyX19fwsLCaN26tYxy+iR+De4n3Lug9gv5dtjRbQJxc7Digz+O89fRa5y8doc5/wumgqfjk3cWQghRZJw/fx5/f//HbmNvb8+CBQvyKaIC6Mpe7a97JbB1NW0sQogC5W5SCutPRPHboatsPh1Ncmr2kmxzMx3mOh1mZmBhZoaZDizuNw+v4+9K8P2bm4PkUkXZU80K3r59ew4dOkS5cuWMFU/RlN6PO2xHvh5Wp9PRv2EZgko7M2LJAc5Hx9Nl1namda9Gl5oywJ0QQhQXUVFRXLt2jfr1DZtK7969G3Nzc+rUqWOiyAqQ9Pm3fYp5s3ohCrGb8UmsOHAFpaC8pwOBHg54O9tilotZe5JS0thyOprfDoez7nikwZzRlUo50rmGN+2CSlHCzgozMx0WZjrMzXSY6dL/It05BfCUCbdST9+EoljwrQfo4OZ5uBMJjp75evjafq78MaoJr4YeYtvZ67waeogDl27xdscqWFmY5WssQggh8t+IESMYN25choT76tWrfPzxx+zevdtEkRUg0n9biELrZnwS87ae54cdF7mblGqwzs7KnPIeDpT3cCDQw5FADwcCPR3wcbXLMH1uappi9/kb/HY4nL+PXTOYX9qvhB3P1vDm2Zre0lpU5MhTJdxPa8uWLXz66afs37+fiIgIVq5cSdeuXR+7z5IlS/jkk084c+YMzs7OtG/fnk8//RQ3N7f8CTo3bF3AsypEHoPLu6BKl3wPwc3Bmh8G1+Pz9af56t+z/LDzEoevxPBNv9p4u9jmezxCCCHyz/HjxzOda7tWrVocP37cBBEVMKnJcPWAdt+3nmljEaKIibmbzO9Hwom+k0ibyp4ElXYyWs3vjbhE5m29wKKdDxLtqt5OlClpz9nIOM5fj+NuUipHrsRw5EqMwb7WFmYEuGvJd6CHAzfjk/njSDhRdx5MweXuaE3n6lqSXcPHWWqsRa48VcL97bff4umZ+9ra+Ph4atSoweDBg3nuueeeuP327dv1I6t27tyZq1ev8tJLLzF06FBWrFiR6zjyhV8DLeEOM03CDVo/kteeqUgtPxfG/HyYQ5dv0+mrbXzRuyZNA91NEpMQQoi8Z21tTWRkZIYuYBEREVhYmPTae8Fw7Sik3AMbF3CTQWCFeFqpaYrtZ6/zy77L/HM8kqT701x9seEM5Ura82xNb56t4U05d4dclX8jLpG5W8/z485L+kQ7qLQTr7auQJvKHvrEOCU1jUs373ImMo6zUXc4ExXHmcg4zkXHkZiijRZ+PCLWoGxnW0vaB5Xi2Rre1C/nlqEWXIic0qmnbBceGxvLv//+S8WKFalcuXLuA9HpnljDPX36dGbPns25c+f0y7766is+/vhjrly5ku14nZ2diYmJwcnJKdfx5tjR5fDrEG2asGGb8u+4Wbh88y7/t2Q/x67GotPB2DYVGNGyfK76uAghhDCOvDpH9enTh4iICFavXo2zszMAt2/fpmvXrnh4ePDLL78Y7VjGkO/n6t3fwt/joHxb+N/yvD+eEEXUxevxLN9/hV8PXCEiJkG/vFIpR8q42bPxVJTBHNPVfZx5toY3nWt44+lk88Tyr8clMm/LeRbtvKTvU12ttDOj2wTSqpJHtmugU9MUV27d5XRkHGei7nA2Mg4zMx3tqpaiWQV36XIpspSb81OOL2v37NmTZs2aMXLkSO7du0edOnW4ePEiSilCQ0Pp3r17jgPProYNG/LWW2/x119/0b59e6Kioli+fDkdOnTIs2MajV8D7W/EEUiMA+vcXdEzFt8Sdix/qRGTfv+Pn/ZcZsa60xwIu8VnvWriYmdl0tiEEEIY1/Tp02nWrBn+/v7UqlULgEOHDuHp6cmPP/5o4ugKgPQB06T/thA5Fp+Ywl9HI1i2/wp7LtzUL3eysaBrrdI8H+yrb0Yel5jCP/9dY/WhcLadva5v6v3RXydoWM6NLjW9aVfVC2c7w6kLr8clMneLVqOdnmhX99ES7ZYVs59opzM30+HvZo+/mz1tq+Tv2Eqi+MlxDXepUqVYu3YtNWrUYOnSpbz//vscPnyYH374gblz53Lw4MHcBZKNGm6AZcuWMXjwYBISEkhJSaFz5878+uuvmc4pCpCYmEhi4oO+GLGxsfj6+uZ/DTfAZ0EQcxleWAUBLfP32I/xy77LvLvqGIkpafi42jK7XzDVfJxNHZYQQhQ7eVmzGx8fz5IlSzh8+DC2trZUr16dPn36ZHn+NKV8r+H+rBrEhEH/36Bc87w/nhCFnFKKfZdusWzfZf48EkH8/WbdOh00DXTn+WAf2lbxxMbSPMsybsQl8tfRCFYfCmffpVv65VbmZjSv6E6Xmt7U8HFh0c6L/LjrEgnJWs14DR9nRrepQIuK7tKnWuS73Jyfcpxw29racvr0aXx9fenfvz/e3t5MmzaNsLAwqlSpQlxcXK6Cz07Cffz4cdq0acOYMWMICQkhIiKCN954g7p16/Ldd99lus/EiROZNGlShuUmSbh/HQpHf4Hmb0LLCfl77Cf4LzyG/1t8gLCbd7GyMGN8u0r0q+/32C9KIYQQxmWybk/ZNHv2bGbPns3FixcBqFq1Ku+99x7t27cHoEWLFmzevNlgn+HDhzNnzpwcHSdfX4fYCJhZCXRm8OZlk7dAEyK/pKUprsUmcDcphYTkNBJT0khMSSUxJY2klPuPk1PvL9fWJaWkEZ+ozUl94Xq8vix/NzueD/bhudo+uRqM9/LNu/x+JJzfDoVz8tqdTLep6evCq20CaVFBEm1hOvnSpNzX15edO3dSokQJ1qxZQ2hoKAC3bt3CxubJfS+extSpU2ncuDFvvPEGANWrV8fe3p6mTZvy4Ycf4uXllWGfCRMmMHbsWP3j9Bpuk/BroCXcYTtNc/zHqOrtzO+jmvDaL4dYfyKKD/44zuxN5xjUuAz/q++foWmPEEKIwuf48eOEhYWRlJRksPzZZ5/N1v4+Pj5MmzaNwMBAlFL88MMPdOnShYMHD1K1alUAhg4dyuTJk/X72NnZGe8J5IUr96cD86wqybYokh7tr3wmvd9yVJy+1jg37KzM6VDNi551fKlbxvWpkmDfEna83KI8L7coz8lrsfx2KJzVh8K5evseNX1dGN0mkOaSaItCKscJ9+jRo+nXrx8ODg74+/vTokULQJviq1q1asaOz8Ddu3czjKZqbq7VwGZVUW9tbY21tXWexpVtfg21v1f2aVOQmBesJNbZ1pK5L9Rhye5LzNl8nqu37/Hp2lN8vfEsvev6MaRpWUrLFGJCCFHonD9/nm7dunH06FF0Op3+nJn+4zU1NfVxu+t17tzZ4PFHH33E7Nmz2bVrlz7htrOzo1SpUkaMPo+lz7/tI9OBicItNU1x+eZdTkemj8at/T0bFWcwUNnDLM112FtbYG1hhrWFOdYWZlhZmD14bGmWyTpzKnk50qGaFw7Wxp/loFIpJyq1c+KNkIrcupuMq52lJNqiUMvxp+Tll1+mXr16XL58mbZt22Jmpo3iV65cOT788MMclRUXF8fZs2f1jy9cuMChQ4coUaIEfn5+TJgwgatXr7Jo0SJAO9EPHTqU2bNn65uUjx49mnr16uHt7Z3Tp5L/3CuBjTMkxMC1I1A62NQRZWBmpuOFhmXoXc+PP49EMGfzOU5eu8P32y/ww86LdK7uxbBmAVTxLnhNHYUQQmTu1VdfpWzZsmzYsIGyZcuyZ88ebty4wWuvvcb06dNzVWZqairLli0jPj6ehg0b6pcvWbKExYsXU6pUKTp37sy77777xFruzMZbyTcyYJooxMJu3GXdiUjWH4/kQNitLBPrh+ecruDpSHkP7a+vqy0W5gVzRG6dTkcJexnIVxR+ubosVadOHerUqQNoJ9yjR4/SqFEjXF1dc1TOvn37aNnyweBh6U2/BwwYwMKFC4mIiCAsLEy/fuDAgdy5c4dZs2bx2muv4eLiQqtWrfj4449z8zTyn5kZ+DaAM2u1+bgLYMKdztLcjK61StOlpjdbz1zn2y3n2H72BqsOhbPqUDhNA0vyUvMAGgW4yVVHIYQo4Hbu3Mm///5LyZIlMTMzw8zMjCZNmjB16lReeeWVHA14evToURo2bEhCQgIODg6sXLmSKlWqANC3b1/8/f3x9vbmyJEjjB8/nlOnTrFixYrHljl16tRMx1vJc8kJEHFYu+9bN/+PL0QOpaUpDl25zfrjkaw/EcnpSMOxk6wtzCjv4UCghwOBno4EpifWJexkPmkhTCTHg6aNHj2aatWqMWTIEFJTU2nevDk7duzAzs6OP/74Q9/EvKAy+YA02z6D9ROhcmfotTj/j/8Ujl2N4dst5/nzSDhp9/9rqno7Mbx5AB2CShXYK6RCCFFY5NU5ytXVlQMHDlC2bFkCAgKYP38+LVu25Ny5c1SrVo27d+9mu6ykpCTCwsKIiYlh+fLlzJ8/n82bN+uT7of9+++/tG7dmrNnzxIQEJBlmSabUSRsN3z/DNi7w+tntCGWhShg7iWlsu3sddYfj2TDySiuxz34rJib6ahXpgStK3vQoqIHZUvaS2ItRB7Kl0HTli9fzv/+9z8Afv/9dy5cuMDJkyf58ccfefvtt9m+fXtOiyxe0vtxX9oJShWqk3tQaWe+6lOLcSEV+W7bBUL3hvFfeCyv/HSQT1xtGdKkLM/X8c2T/jxCCCFyLygoiMOHD1O2bFnq16/PJ598gpWVFXPnzqVcuXI5KsvKyory5csDEBwczN69e/niiy/49ttvM2xbv77WTPtJCbfJxlu58lD/7UJ0PhZFX9SdBP49EcX6E5FsPXPdoKm4o7UFzSu607aKJy0qeMjAtkIUcDnOjK5fv64fDOWvv/7i+eefp0KFCgwePJgvvvjC6AEWOd61wMoR7l6H02uhYjtTR5RjviXsmPhsVV5tHciPuy6xcMdFrty6x6TfjzPzn9P0quvLgEZl8C1RwEemFUKIYuKdd94hPl6bwmfy5Ml06tSJpk2b4ubmxs8///xUZaelpRnUTj/s0KFDAJnOIlIg6Ptvy4BpIn8lJKdyLSaB8Jh7RNxOICLmHlfT/966x5kow6bipV1saVvFkzaVPalXtgRWFtKqUIjCIscJt6enJ8ePH8fLy4s1a9Ywe/ZsQBtBPH3EcPEYFtZQZxDs+BK2TocKIYX2qrqrvRWvtA5kWLNyLNt/hQXbLnD+ejzzt13g++0XaFPZk8FNylK/bAnp5y2EECYUEhKiv1++fHlOnjzJzZs3cXXN2VQ+EyZMoH379vj5+XHnzh2WLl3Kpk2bWLt2LefOnWPp0qV06NABNzc3jhw5wpgxY2jWrBnVq1fPi6f1dJR6MEK5DJgmjEwpxeWb9zgWHsOVW3cJv59Mp/+9Hpf0xDJq+DjTprInbap4UqmUo/yWEqKQynHCPWjQIHr27ImXlxc6nY42bdoAsHv3bipVqmT0AIukhiNh97dwZS9c3Aplm5k6oqdiY2nOCw386VfPj81novl+2wW2nrnOP8cj+ed4JJW9nBjUuAzP1vDGxlIuygghRH5KTk7G1taWQ4cOERQUpF9eokSJHJcVFRVF//79iYiIwNnZmerVq7N27Vratm3L5cuXWb9+PZ9//jnx8fH4+vrSvXt33nnnHWM+HeO5HQZxkWBmAd41TR2NKOQiYxM4fPk2R67EcPjKbY5ejeH23eTH7mNjaYa3sy3eLrZ4Odvg5WJLaRcbvJxtqVjKEU8nm3yKXgiRl3KccE+cOJGgoCAuX77M888/r+9zZW5uzptvvmn0AIskR0+o/QLsnQ9bZxT6hDudmZmOlhU9aFnRg7NRd1iw/SK/HrjCiYhYxi0/wsd/n6RffT/+18AfDzmJCCFEvrC0tMTPzy/bc20/znfffZflOl9fXzZv3vzUx8g3V/Zqf71qgKWtaWMRhcqt+CSOXI3hyOXbHL4Sw9Grt4mMzditwsrcjEpejpRxs8fLxYbSLrZ4OWvJdWkXW1xkfmkhioUcj1Je2Jl8lPJ0t8Pgy1qQlgIv/gs+BXeKsKdx+24SoXsvs2jHRcJjEgCwNNfRqbo3gxqXobqPi2kDFEKIAiSvzlHfffcdK1as4Mcff8xVzXZ+y5dz9V9vwJ65UP//oP20vDmGKPSSUtI4HhHL/ku3OBh2iyNXYgi7mXFUfzMdVPB0pFppZ6r7ulDDx5mKpRyxtpCWfUIUJfkySjnA5s2bmT59OidOnACgSpUqvPHGGzRt2jQ3xRVPLn5QrSccXqrVcvdZauqI8oSLnRUvNQ/gxSZlWftfJAu2X2DfpVusPHiVlQevEuzvSsdqXjQNLEl5Dwe50iuEEHlg1qxZnD17Fm9vb/z9/bG3tzdYf+DAARNFZkIyYJrIxM34JA5cusW+S7c4cOkWh6/cNhghPF3ZkvZU93GmWmlnavi6UNXbCTsrmaVFCJFRjr8ZFi9ezKBBg3juued45ZVXANi+fTutW7dm4cKF9O3b1+hBFllNxsDhn+DUnxD5H3hWNXVEecbC3IyO1b3oWN2LI1dus2D7Rf44Es7+S7fYf+kWAJ5O1jQuX5KmgSVpHFBSmp0LIYSRdO3a1dQhFCxJ8XDtmHZfEu5iKy1NcTY6Tv9b5MClW5y/Hp9hOxc7S4L9XKnt70oNHxeqlXaWqbiEENmW4ybllStXZtiwYYwZM8Zg+cyZM5k3b56+1rugKjBNytP90h+Or4Zqz0P3+aaOJl9FxSaw8uBVtp29zp4LNzNcQa7g6UCT8u40CXSjflk37GV+byFEEVfgzlEmkuevw4Wt8EMncCoNY48bv3xRIKWlKf4Lj2XLmWj2XrzJgUu3iE1IybBdoIcDwf5agh3s70q5kvbSAk8IAeTu/JTjhNva2pr//vuP8uXLGyw/e/YsQUFBJCQk5KS4fFfgfsxEHIZvm4HODEbuA7cAU0dkEgnJqey/dIttZ6+z7cx1joXH8PB/poWZjtp+rjQJLEnj8iWp4eOMhbnMQSmEKFoK3DnKRPL8ddg6AzZMhipdoecPxi9fFBjXYhLYciaarWeus/3sdW7GG07HZWtpTk1fF4LvJ9e1/FxwsbMyUbRCiIIuX/pw+/r6smHDhgwJ9/r16/H19c1pccKrBpRvC2fXwfYv4NkvTR2RSdhYmtO4vJZMj2+njQC649wNLQE/G83lm/fYc/Emey7eZOa609hamlPdx5lafq7U9nOhlp8r7o7Wpn4aQghRIJmZmT22hs4YI5gXKjL/dpF1LymV3RdusPXMdbaeieZ0ZJzBegdrCxoGuNE4wI06ZUpQqZSjXMAXQuSpHCfcr732Gq+88gqHDh2iUaNGgNaHe+HChXzxxRdGD7BYaPa6lnAfWgrNx4NzaVNHZHKu9lb6Pt8AYTfusvVsNNvPXmf72RvE3Etm94Wb7L5wU7+Pbwlbavk+SMArezlhZSEnUSGEWLlypcHj5ORkDh48yA8//MCkSZNMFJWJKCUJdxGSlqY4ee3O/VrsaPZeuEVS6oMuajod1PBxoVlgSZpWcKemrwuWkmALIfJRrqYFW7lyJTNmzND3165cuTJvvPEGXbp0MXqAxlZgm+st6ACXtkODl6HdVFNHU6ClpSnORcdxMOw2B8JucTDsNqej7vDof7K1hRnVSjtT29+VWr4u1PZ3xVMGYhNCFGD5fY5aunQpP//8M6tXr87zY+VEnr4O18/CrGCwsIE3L4OFNB8ubOISU9h2JpoNJ6LYeCqa63GGc2B7O9vQrII7TQPdaVzeTZqICyGMJs+blKekpDBlyhQGDx7Mtm3bchWkyELT17SEe/9C7b59SVNHVGCZmekI9HQk0NORnnW1bgx3EpI5fDmGg2G3tCT88m1u301m3/2pPdKVdrGlpp8Lte83Ra/q7Sy14EKIYqtBgwYMGzbM1GHkryv3a7e9a0myXYhcuhHPvyej+PdkFLvO3yA59cFVdltLcxoGuNE0sCTNKrjLIGdCiAIlRwm3hYUFn3zyCf3798+reIqvgFbgVRMiDsGu2dD6XVNHVKg42ljSJLAkTQK1CxXq/9u77/ioqvSP459Jm/ReSCCE0EKPioIBpUuxIlhxFYWVBcGG666sq2DFld/aVsV1pegqZUFBLKjUIEiXKtIDoSSEENJ75v7+uCExUgMzGZJ836/Xfc3MnTt3nrmOnDxzznmOYZCUnlfRC/5zcia7UrM5klnAkcwCvtmaAoCHmwvtovzNBDwmiKsaB9EgQL3gIlL3FRQU8M4779CwYT2bxnRq/e1G1zg3DjmnkjIbGw+eZOnONJb8eox9x6su19UkxJterSLo1Sqca2KDsLq5OilSEZFzq/Yc7t69e5OYmEiTJk0cEE49ZrGYc7ln/wHW/Qe6PgaeAc6OqtayWCw0DfOlaZgvgzs2AiCvqJQthzPZlJxZ3hOeSUZeMT8nZ/JzciasTAIgMsCTqxqblUqvbBxIw0BvQnw9NOdLRGqtoKCgKj1+hmGQk5ODt7c3n376qRMjcwLN375sncwrJnH3cZbsTCNxV1qVJbvcXCxc0ySY3q3D6dUqnKZhvk6MVETkwlU74R4wYADPPPMM27Zto2PHjvj4+FR5/tZbb7VbcPVO3E0Q1gqO74T1H5lDy8VufKxudGkWSpdmlb3gB0/kl/eAm3PBd6bmkJJVyDfbUvhmW0qV1wd5uxPqayXMz3qGWw/C/KyE+VoJ9vFQxVMRuay8+eabVRJuFxcXwsLC6Ny5M0FBQU6MrIYVZkGaWX+G6E7OjaUeKyotIyk9j93HctlzLIfdx3LYcyyXAyfysP2mHkuQtzs948Lp1Tqc61uEEeDl7rygRUQuUrWLprm4nD2RsFgsl/3SIpdt0bRTtsyGeSPAOxSe2AYe3s6OqF7JKypl6+Gs8gT8JL8czeZ4ThGltgv/38RigZbhfnRtHsr1LULpFBuMj7Xav22JSD102bdRNcRh12HvEvh0EAQ1gce32O+8ckbFpbbyxDqnPLHOZXdaDgdP5FN2lna1VQO/8l7sCK6IDsTVRXOxReTyUSPrcNtstvMfJBev3WBY9jJkJsPPn8C1I50dUb3iU74+Z0KzkIp9NptBZkEJ6blFHM8pqrg9nlPE8Yp9xRzPKSIjrwibAbuO5bDrWA5TVyXh7mrhysZBXNfcnGPeoWGAesBFpEZNmzYNX19f7rzzzir758yZQ35+PkOHDnVSZDXs8HrzVsPJHSIjr5gf9xwncfdxth7O4kB63ll/sPazutEiwpeW5UVQW0b4EtfAj3A/1VERkbpF3W6XG1c36PoEfDMWfnoHrh6mKqpO5uJiIdjHg2AfD1pG+J3z2DKbQXpuERsOnGTl3uP8uCedwycLWJeUwbqkDN5YtBs/TzcSmoaYRd6ahxKraqoi4mATJ07k3//+92n7w8PDGTFiRP1JuFUwza5Ky2xsOZxJ4q7yJPtI1mlLdPpZ3Wge4UvLcL+KBLtlhB8R/la1fSJSL1Q74X7sscdo3rw5jz32WJX97777Lnv37uWtt96yV2z11xX3QeLrkH0Ets6Cq1QVvrZwdbEQ4e/JTR0iualDJGAuZbJybzor96Tz074TZBWU8MOOY/yw4xhgrhfatbz3+/oWYQT76AcWEbGv5ORkYmNjT9sfExNDcnKyEyJyApsNDm8w76uH+6KlZBWwYreZYK/ck16lsBmYQ8K7x4VxbdMQWjXwo4G/pxJrEanXqp1wf/755yxYsOC0/V26dOG1115Twm0P7p7QZQz88HdY+aaZgLtouYvaKibEh5gQH+7rHEOZzWD7kayKBHzjwZMczSpkzsbDzNl4GIsFOjQMoHvLMLq1DOOK6EANPxeRSxYeHs7WrVtPW2Fky5YthISEnPlFdc3xnVCUDe4+EN7G2dHUGoUlZWw4cJLE3Wkk7j7O7mO5VZ4P8HLn+hahFe1WhL+GhIuI/Fa1E+4TJ04QEHD6clX+/v6kp6fbJSgBOj4EP/4TMvbDL/Og/R3OjkjswNXFQnx0IPHRgYzu2Zz84lLWHzjJyj3m8POdqTlsOZzFlsNZvLN0L36eblzXvPIPmahAL2d/BBGphe69914ee+wx/Pz86NatGwCJiYk8/vjj3HPPPU6OroYcLl8OrFFHc/qWnNc3W1P427xtZBWUVOxzsUB8dCDdW4bRvWUYHRqpsJmIyLlUu8Vp3rw53333HWPGjKmyf+HChTRt2rRa51qxYgWTJk1i48aNpKSkMG/ePAYOHHjW4x988EE+/vjj0/a3adOGX375pVrvfdmz+kLnUbD8VfjxDbOYmoZk1TneHm4Vf7QApGYVsmLPcVbsNhPwrIISFm5PZeH2VABahPvSrTz57hwbjKe7Rj6IyPm99NJLHDhwgN69e+PmZjb9NpuNBx54gFdffdXJ0dWQU+tvN9JyYOdTVFrGxG93Mv2nAwCE+1krfvi9rnkoQZr6JCJywaqdcI8dO5YxY8Zw/PhxevXqBcCSJUv45z//We3h5Hl5ecTHxzNs2DAGDRp03uPffvttXnvttYrHpaWlxMfHn1Z1tc7oPMIsnJb2C+z+DuIGODsicbAGAZ7cdXU0d10dTZnNYOvhTFbsTidxdxqbD2WyJy2XPWm5TFmZhNXNhU6xwbSM8KNJiDcxIT40CfEhKtBTw9BFpAoPDw9mz57Nyy+/zObNm/Hy8qJ9+/bExMQ4O7Sac6pgmuZvn9OhjHzGzPiZLYezABjVoxlP3dBS7YqIyEWq9jrcAJMnT+aVV17h6NGjADRp0oQJEybwwAMXX9zLYrGct4f79+bPn8+gQYNISkq64D8aat0ap4ueh1VvQ8Or4Y+L1ctdj2Xll7Bybzordh9nxZ7jpGQVnvE4NxcL0cHexIR40yTEp8ptoyBvPNz0R5PI5arWtVEOYvfrkHcCJpWPwvtLEngHX/o566BFO47x1P82k11YSoCXO2/eHU+vVhHODktE5LJRI+twA4waNYpRo0Zx/PhxvLy88PX1vZjTXLIpU6bQp0+fuv0LfcIYWPtvOLIBklZA0+7OjkicJMDbvaL6uWEY7EnLZW1SBgfT8zhwIp+DJ/I4mJFPcamNpPQ8ktLzgONVzuFigYZBXjQK9CYq0IuGgZ5EBXr9ZvPE20NzG0XqmsGDB9OpUyf++te/Vtn/+uuvs379eubMmeOkyGrIqfW3Q1sq2T6DkjIb//f9Lv69Yj8AV0QH8u6QK2kU5O3kyEREar9L+ss6LCzMXnFU29GjR1m4cCEzZsw453FFRUUUFRVVPM7OznZ0aPblGw5X3g/r/2MWUVPCLZgjQk6tZfpbNptBanYhB07kcfBEvnmbns/BDDMhzy8u41BGAYcyCs567iBv94oEvGF5Eh4V6EVchB/Nw321vItILbRixQomTJhw2v4BAwbwz3/+s+YDqmmnCqZFa/7276VkFTBmxiY2HjwJwLCusTwzoJVGQ4mI2MkFJdz9+/dnwoQJXHvttec8Licnh/fffx9fX19Gjx5tlwDP5uOPPyYwMPC8Q9AnTpzICy+84NBYHK7rY7BxGiQlwrr/wDV/1NByOSMXF0tFstylWdXnDMPgeG4RB9LzOZpZwJHMAo5WbIUcySwgt6iUk/klnMwv4Zejp/84FRvqQ7+2DejfrgHxjQKUfIvUErm5uXh4nF7oyt3dvfb9EH0xVDDtjBJ3H+fJ2ZvJyCvGz+rGpDs70L9dpLPDEhGpUy4o4b7zzjsZPHgwAQEB3HLLLVx99dVERUXh6enJyZMn2bFjBytXruTbb7/lpptuYtKkSQ4N2jAMpk6dyv3333/GPyB+a9y4cYwdO7bicXZ2NtHR0Q6Nz+4CG8PVw2Hdv+HbP8OuhXDbu+Af5ezIpBaxWCyE+3kS7nf2NVKzC0sqkvAjmYUV9w+fLGDbkSyS0vP4IHEfHyTuIzLAk35tG9CvbQOuaRKkgjoil7H27dsze/Zsnn/++Sr7Z82aRZs2dXxN6rJSOLLRvK+CaQCU2QzeWrybd5ftxTCgbZQ/7993FTEhPs4OTUSkzrmghHv48OH84Q9/YM6cOcyePZsPP/yQrCyzeqXFYqFNmzb069eP9evX07p1a4cGDObaoXv37mX48OHnPdZqtWK1Wh0ek8P1fw2CmsCSF2DfEnj/WhgwCTrcpd5usRt/T3f8G7jTqsHpRSByi0pZviuN77ansmxnGilZhUz/6QDTfzpAsI8HfdtE0K9dA7o0C8HqpuXKRC4nzz33HIMGDWLfvn1VVhiZOXNm3Z+/fWw7lOSDZ4A5h7ueS8sp5PGZm1m9/wQAf7i2MX+/qY2WmRQRcZCLqlIOkJWVRUFBASEhIbi7u1/Um+fm5rJ3714ArrzySt544w169uxJcHAwjRs3Zty4cRw5coRPPvmkyuvuv/9+9uzZw5o1a6r9nrW+Auzx3TDvT3D0Z/Nx61vg5rfAJ9SpYUn9UlhSxso96Xz3SyqLdhwjq6Ck4jk/qxu9WofTv20DuseFYXVzpdRmo7TMoNRmUFpmo8x26r5hPveb+2U2g6hAL8L9rBqyLvWOI9uob775hldffbViWbAOHTowfvx4une//GqD2PU6rP0QFj4NzfvAHz63T4C11E/70nls5mbSc4vw9nBl4qD23HZFQ2eHJSJSa1xM+3TRCbc9LF++nJ49e562f+jQoUyfPp0HH3yQAwcOsHz58ornsrKyiIyM5O233+bhhx+u9nvW+oQbzOFxK9+ExNfAVgreoXDL29D6ZmdHJvVQSZmNdUkZfLc9le9/SSUtp+j8L7oA/p5utIjwo2WELy3C/cqLxPkSpkRc6jBntFHbt2+nXbt2NfJeF8qu1+HzP8K2OdDjb9Djr+c/vg4oKbNx8EQee9NyK7Z9x/P45WgWNgPiIvx4776raB7unFVmRERqq1qXcDtDnUi4T0nZavZ2p+0wH3e4Bwb8A7wCnRqW1F82m8GmQyf5bnsqC7encvjkmauhu7pYcHWx4HZqc3WpuG+xWEjNLqTMduZ/mgK83GkR7luRjLeM8KNFhC9hvkrEpfarqTYqJyeHmTNn8tFHH7Fx40bKysoc9l4Xw67X4a32kJkM98+HZqf/yF+b5RWVsu947u8S61wOnsin9Cz/ht7RsREv3dYOLw8NIRcRqS4l3BegTiXcAKVFsHwirHobDBv4RZkF1Zr3dnZkUs8ZhkFGXjEuFgturhbcXFxwc7XgarHg4nLuxLiotIz9x/PYfSyHPcdy2X0sh71puRw4kcdZ/obExUKVxN3d1aXK+5r7y++XH+ft4UqjIG+ahHgTE+JNTIgPMSHeWotcnMbRbdSKFSv46KOP+OKLL4iKimLQoEEMHjyYa665xu7vdSnsdh1yUuGfcYAFnkkGz9rf7heWlDHp+10s3JbC0azCsx7n4+FKs3Bfmof5mrfhvrRq4KfCaCIil+Bi2if9VVnbuVmhzwRoOQDmj4SM/fDpILOq+Q0vglXDxcQ5LBYLIb4XV7DQ6uZK60h/WkdW/YessMRMxPek5VQk43vScjlYnogXl9ootkPsYX5WYoIrE/BTyXiTEG8CvT0wDIPCEhuZBcVk5peQVVBSflv8m/slZBaUkFV+P9TXg2ubhtClWShtovxxPc+PDiL2kpqayvTp05kyZQrZ2dncddddFBUVMX/+/LpfoTwnBUKag5tnnUi296blMmbGz+xMzanYF+rrQbMwM6E+dds83JfIAE+N+hERuQyoh7suKc6DxRNg3Yfm46BYGDgZYhKcGpaIoxWWlJFdUEJJeVG2U0XYSioKtNkoKTMos5n7ThVwyyks4VBGPgcz8jlwIp/kE3mczC8553v5Wt3MxL7MdtHx+nu60blpCAlNQ+jSPISW4X7n7fWX+sHebdQtt9zCihUruOmmm7jvvvvo378/rq6uuLu7s2XLlss24bZ7W11SCO5nXxKxNpi78TDPzd9OQUkZob4evDywHdc2DSHQ+9zLo4qIiP3USA/3oUOHsFgsNGrUCIB169YxY8YM2rRpw4gRI6p7OrEnDx+4cRK0ugnmj4aTSTBtgNkDft0Tzo5OxGE83V3ttqRNVkEJySfyOXAij+SMfA6k53EwI5+DJ/I4ll1EblFpxbFuLhYCvd3x93In0MudQG8PArzcCfByJ9C78tbf052k9DzW7D/B2v0ZZBeWsmjHMRbtOAZAsI8HCU1DuLZZCF2ahdA01Ec9U2IXCxcu5LHHHmPUqFG0aNHiks83efJkJk+ezIEDBwBo27Ytzz//PAMGDACgsLCQp556ilmzZlFUVES/fv14//33iYiIuOT3viS1ONnOKyrluS+388XPRwDo2jyEN+++gnC/2vuZRETqk2r3cF9//fWMGDGC+++/n9TUVOLi4mjbti179uzh0Ucf5fnnn3dUrHZRp3u4f6swC74bB5s/Mx/3eg66/dm5MYnUcgXFZRzNKsDT3ZUAL3d8PFyrnRiXltn45Wg2P+07wer9J1iflEFBSdWCVeF+VhKahXBNk2Cah/vSNNRH1dnrCXu3UWvWrGHKlCnMnj2b1q1bc//993PPPfcQGRl5UT3cX331Fa6urrRo0QLDMPj444+ZNGkSmzZtom3btowaNYpvvvmG6dOnExAQwJgxY3BxcWHVqlXVep9601afx68p2Yye8TP7j+fhYoGxN7RkVI/mmpIiIuIkNVI0LSgoiDVr1hAXF8c777zD7NmzWbVqFT/88AMjR45k//79FxV8Tal3jfiK/4OlL5n3lXSLXHaKS21sOZzJ6n0n+GlfOj8nZ1JcevpwdR8PV5qE+tAk1IemoT7E/ua+hpTWHY5qo/Ly8pg9ezZTp05l3bp1lJWV8cYbbzBs2DD8/Pwu6dzBwcFMmjSJO+64g7CwMGbMmMEdd9wBwM6dO2ndujWrV6/m2muvveBz1ru2+ncMw+Cztcm8+PUOikttNPD35J17r6RTbLCzQxMRqddqZEh5SUkJVqtZCGnx4sXceuutALRq1YqUlJTqnk4c7VSCvfQlc7NY4PqnnBuTiFTwcHPhmibBXNMkmMd6t6CwpIyfD57kp30n2HokiwPpeRw+mU9ecRm/HM3ml6PZp50j0Nud2FAfYkPMRLxxiDeNy4u+BXm7q2dc8PHxYdiwYQwbNoxdu3YxZcoUXnvtNZ555hluuOEGFixYUO1zlpWVMWfOHPLy8khISGDjxo2UlJTQp0+fimNatWpF48aNz5twFxUVUVRUVPE4O/v073l9kV1YwrjPt/HNNvNvql6twvm/O+MJ9tEPayIitVG1E+62bdvywQcfcNNNN7Fo0SJeesnsPT169CghISF2D1Ds4LdJ95IXzftKukUuS57urnRpHkqX5qEV+4pKyziUUUBSeh4H0vPYn55HUnouB9LzSc0uJDO/hE3JmWxKzjztfH5WN6KDTyXg3lXuRwV64e7qUoOfTi4HcXFxvP7660ycOJGvvvqKqVOnVuv127ZtIyEhgcLCQnx9fZk3bx5t2rRh8+bNeHh4EBgYWOX4iIgIUlNTz3nOiRMn8sILL1T3o9Q5Ww5lMmbmzxzKKMDNxcIzA1oxrGusiiqKiNRi1U64//GPf3D77bczadIkhg4dSnx8PAALFiygU6dOdg9Q7KTbnwEDlr5cnnRb4Pqxzo5KRC6A1c21Yqmf38svLuVAej5J5Ul4Unp+eeV1s8hbTlEpO1Ky2ZFyeo+hq4uFqEBPGgd70zzMlzZR/rSNCqBFhC9WN/sUoZPLl6urKwMHDmTgwIHVel1cXBybN28mKyuLuXPnMnToUBITEy8plnHjxjF2bGWblJ2dTXR09CWdszYxDIMpK5P4x3c7KSkzaBTkxbtDruKK6EBnhyYiIpeo2gl3jx49SE9PJzs7m6CgoIr9I0aMwNvb267BiZ11e9q8XfoyLCnvSVDSLVKreXu40SbKnzZRp88jKiwp41BGPsnl28ET+RXLoB3KyKeo1MahjAIOZRSwau+Jite5uVhoHm4m4G0izXO3jQwgwNu92vEVlZaRnltMek4Rx3PMKu/tGgbQLEyV2GsrDw8PmjdvDkDHjh1Zv349b7/9NnfffTfFxcVkZmZW6eU+duwYDRo0OOc5rVZrxXS1+uZkXjFPz93C4l/TABjQrgGvDe5AgFf1/38TEZHLT7UT7oKCAgzDqEi2Dx48yLx582jdujX9+vWze4BiZ0q6ReoNT3dXWkT40SLi9KJYNpvB8dwiDp4wlzzbfSyHX46aPeGZ+SXsTM1hZ2oOX3Ck4jUNA70qkvC2Uf5E+HtyIq+I9JxijueaCfXx3CIzuS6/zS4sPe29ARr4e9KleQjXNQ+la/NQIvy1xFFtZbPZKCoqomPHjri7u7NkyRIGDx4MwK5du0hOTiYhIcHJUV6edqXm8NC0dRzNKsTDzYXnbmrNH66N0Y9RIiJ1SLUT7ttuu41BgwYxcuRIMjMz6dy5M+7u7qSnp/PGG28watQoR8Qp9tTtaTCAZeVJt8UC1z3p7KhEpAa5uFiI8Pckwt+zSuVjwzBIySpkR3mBth0pWexIyeZQRgFHMs3t1PrhF8rd1UKYr5VQPyseri5sPZJFanYhX/x8pGJt4ebhvlzXPJQuzcz1yP09q9e7V2YzSMsp5PDJAg5l5HP4ZAGZ+SXYyhfisBkGNsPAMMBmABjYbOZ+o/x5DLC6u3JLfCQJTUOU9JzBuHHjGDBgAI0bNyYnJ4cZM2awfPlyvv/+ewICAhg+fDhjx44lODgYf39/Hn30URISEqpVoby+WH8gg+HT15NdWEpsqA/vDrmStlEBzg5LRETsrNoJ988//8ybb74JwNy5c4mIiGDTpk18/vnnPP/880q4a4vu5T3dy16GxRPM+0q6Reo9i8VCVKAXUYFe9GkTUbE/q6CEX1Oy2VHeC/7L0WxO5hUT4utBmJ+VUF/r7249CPezEubrib+XW5XktbCkjA0HTrJqXzqr9qaz7UgWe9Ny2ZuWy/SfDuBigfjoQLo2M3u/r4oJxMPVheM5RRw6WcDhk2ZCffhkPocyzNsjmQWUlFVrlcuzmrkumdaR/vzxulhuiY/Cw02F5U5JS0vjgQceICUlhYCAADp06MD333/PDTfcAMCbb76Ji4sLgwcPpqioiH79+vH+++87OerLz+Idxxg942eKSm10jAliytCrtbyfiEgdVe11uL29vdm5cyeNGzfmrrvuom3btowfP55Dhw4RFxdHfn6+o2K1i/q+tudpEl+HZa+Y9/u8ANc94dRwRKT+ycwvZs3+E6zcm86qvSdISs+r8ry1POEtOsP65L91qghcdJA3jYK8CPG14mIBF4sFC+aPCZbyxy6WyscWTj2Ggyfy+eLnIxSUlAEQ5mdlaEIMQzrH1MiyTGqjTHX5OvxvwyHGfbGNMptB71bhvDvkKrw8VKRQRKQ2qJF1uJs3b878+fO5/fbb+f7773nySbNXNC0trc41ivVC97+Yt8tegcXjzftKukWkBgV6e9C/XST920UCcCSzgFV708u3E6Tnmuszu1ggMsCLhkFeFUl1dHDlbYSfFTc7LHP2dL84ZqxL5uOfDnAsu4j/+2E3/1q6l8EdGzGsa+wZq8WLnI9hGPx7xX5eW7gTgMFXNeK1we21NJ+ISB1X7R7uuXPnMmTIEMrKyujVqxeLFi0CzDU0V6xYwcKFCx0SqL3U5V/NL4l6ukXkMmQYBvvT83B3caFBgGeNDu8uLrXx7bYUPlq5n+1HKpdV6xkXxvDrmtK1uf3neauNMtW162CzGbz67a98tDIJgD91b8oz/VupToCISC1zMe1TtRNugNTUVFJSUoiPj8fFxfzjZ926dfj7+9OqVavqnq5G1bVG3K6W/wOWv2re7/a0Oafbw8e5MYmIOJlhGKxLymDKyiQW/XqMU61mqwZ+DLsultuuiLLbuuVqo0x16TqUlNn4y9ytzNtkFgh89sbWPNytqZOjEhGRi1FjCfcphw8fBqBRo0YXe4oaV5cacYf4bdLtGQidHoZOI8A33KlhiYhcDg6k5zH9pwP8b8Mh8ovNed6hvlYeSIjhkR7NLnlIu9ooU125DvnFpYz69GcSdx/HzcXC63d0YNBVtedvJhERqepi2qdq/2Vgs9l48cUXCQgIICYmhpiYGAIDA3nppZew2c5d0EZqgR5/hYEfQHAzKMyEFZPgzXbw1eOQvtfZ0YmIOFWTUB8m3NqW1c/0ZtyAVkQGeJKeW8SSnWm4umh4sFQ6mVfMkP+sJXH3cTzdXfjP0KuVbIuI1EPVLpr27LPPMmXKFF577TW6du0KwMqVK5kwYQKFhYW88sordg9SatgV90KHu2DnN7DqbTiyATZOh40fQ6uboOvjEN3J2VGKiDhNgLc7f+rejGHXxbJweypB3u6ajysVjmQW8MCUtew7nkeAlztTH7yGjjFBzg5LREScoNpDyqOiovjggw+49dZbq+z/8ssveeSRRzhy5IhdA7S3ujJMrcYYBiSvhlXvwO7fFMRrnABdHoOW/cFFFVZFROxBbZSpNl+HPcdyeGDqOlKyCokM8OSTYZ1oEeHn7LBERMQOamRZsIyMjDMWRmvVqhUZGRnVPZ1c7iwWiOlibmk74ad/wdbZZhKevBpCW0KXR6HD3eBmdXa0IiIiTrPx4EmGTV9PVkEJzcJ8+O/wzkQFejk7LBERcaJqd03Gx8fz7rvvnrb/3XffJT4+3i5ByWUqvBUMfA+e2AZdnwCrP6TvhgWPwlvtYeVbUFbi7ChFRERq3PoDGdz30RqyCkq4IjqQuSO7KNkWEZHqJ9yvv/46U6dOpU2bNgwfPpzhw4fTpk0bpk+fzqRJk6p1rhUrVnDLLbcQFRWFxWJh/vz5531NUVERzz77LDExMVitVpo0acLUqVOr+zHkUvhHwg0vwJO/QN+XwS8Kco/B4vHw39sh74SzIxQREakxxaU2/vr5VgpLbHRrGcaMhzsT5OPh7LBEROQyUO2Eu3v37uzevZvbb7+dzMxMMjMzGTRoELt27eL666+v1rny8vKIj4/nvffeu+DX3HXXXSxZsoQpU6awa9cuZs6cSVxcXHU/htiDp785nPzxLXDrv8DDFw78CP/pCcd2ODs6ERGRGjFtVRL7j+cR6uvBu0OuxNuj2jP2RESkjrqoFiEqKuq0auSHDx9mxIgRfPjhhxd8ngEDBjBgwIALPv67774jMTGR/fv3ExwcDECTJk0u+PXiIG4ecNUD0OgamHkPnDwAU26AQR+aVc1FRETqqGPZhbyzZA8Af+3fCn9PdydHJCIilxO7lZc+ceIEU6ZMsdfpzmjBggVcffXVvP766zRs2JCWLVvy5z//mYKCgrO+pqioiOzs7CqbOEh4a3h4GTS5HopzYdYQcx3v6hXCFxERqTUmfvsrecVlXNk4kMFaZ1tERH6nVq3ntH//flauXMn27duZN28eb731FnPnzuWRRx4562smTpxIQEBAxRYdHV2DEddD3sFw/zzoNMJ8vPRlmDsMivOdG5eIiIidrUvKYP7mo1gs8MKtbXFx0VrsIiJSVa1KuG02GxaLhc8++4xOnTpx44038sYbb/Dxxx+ftZd73LhxZGVlVWyHDh2q4ajrIVd3uHES3PwWuLjBL1/AtP6QddjZkYmIiNhFmc1g/IJfALjnmmg6NAp0bkAiInJZqlUJd2RkJA0bNiQgIKBiX+vWrTEMg8OHz5zMWa1W/P39q2xSQ65+CB5YAN4hkLIFPuwByWudHZWIiMglm7H2IL+mZBPg5c7T/Vo5OxwREblMXXDRtEGDBp3z+czMzEuN5by6du3KnDlzyM3NxdfXF4Ddu3fj4uJCo0aaN3VZatLVnNc9awgc2w7Tb4Kb34Sr7nd2ZCIiIhclI6+Y//thNwBP9W1JsJYAExGRs7jgHu7fzoM+0xYTE8MDDzxQrTfPzc1l8+bNbN68GYCkpCQ2b95McnIyYA4H/+05hwwZQkhICA899BA7duxgxYoVPP300wwbNgwvL69qvbfUoKAYGPY9tL4VbCWwYAx8Nw7KSp0dmYiISLVN+n4XWQUltGrgx5BOjZ0djoiIXMYuuId72rRpdn/zDRs20LNnz4rHY8eOBWDo0KFMnz6dlJSUiuQbwNfXl0WLFvHoo49y9dVXExISwl133cXLL79s99jEzqy+cOfHsOJ1WD4R1rwPab/CndPAK8jZ0YmIiFyQbYezmLXe/Nvkxdva4eZaq2bniYhIDbMYRv1asyk7O5uAgACysrI0n9tZdnwJ80ZCST4EN4X2d4FnAHgFmreep27L93n4gkWVX0Wk7lMbZbpcr4PNZjD4g5/YlJzJbVdE8fY9Vzo7JBERqUEX0z5dcA+3iN20uc1MtGfeCxn7IfG1cx9vca2agHsGQGgcNOsFTa4ze89FREQc7ItNR9iUnImPhyt/u7G1s8MREZFaQAm3OEeD9jBiOWyYCjkpUJAJhVnlW/n9gkxzzrdRBgUZ5nay/PX7l8O6f4OLOzS+1ky+m/eGiPbgouF9IiJiX9mFJby28FcAHu3dggh/TydHJCIitYESbnEen1Do/pezP28YUFJQmYCfSsILMuDQOti3BDKT4cCP5rbkBfAJg6Y9zeS7WS/wDa+pTyMiInXY24v3kJ5bTNNQH4Z1jXV2OCIiUkso4ZbLl8UCHt7m5h9V9bkrhpgJecZ+2LsE9i2FpBWQdxy2/c/cwOzxbt4LmvU2e8LdrDX/OUREpFbbfSyH6T8dAGD8rW3xcNNIKhERuTBKuKX2slggpJm5dR4BpcVwaK2ZfO9bAilb4Ng2c1v1NngFw6D/QIs+zo5cRERqCcMwmLDgF8psBn3bRNC9ZZizQxIRkVpECbfUHW4eEHu9ufUZD3npsG+ZmXzvXQJ5aTDjTugzAbo8psrnIiJyXt9uS+WnfSewurnw3M1tnB2OiIjUMhoTJXWXTyh0uBNu/wCe3A5X3g+GDRY9D188bM4PFxEROYv84lJe+WYHACO7NyM62NvJEYmISG2jhFvqBzcr3PovuPH/zGXGts2Bqf0h67CzIxMRkcvU+8v2cTSrkIaBXozq0czZ4YiISC2khFvqD4sFOj0MD3xpzudO2Qwf9oCDq50dmYiIXGYOpOfx4Yr9ADx3cxs83V2dHJGIiNRGSril/om93lwDPKKdWdX841tg43RnRyUiIpeRl77eQXGZjetbhNKvbYSzwxERkVpKCbfUT0ExMPwHaHMb2Ergq8fhm6egrMTZkYmIiJMt3XmMJTvTcHOxMP6WtlhUZFNERC6SEm6pvzx84M6PodffzcfrP4JPbjOrm4uISL315qI9AAy7Lpbm4b5OjkZERGozJdxSv1ks0O1puGcmePjBwVXmvO6Urc6OTETksjNx4kSuueYa/Pz8CA8PZ+DAgezatavKMT169MBisVTZRo4c6aSIq+9kXjHbjmQB8PD1TZ0cjYiI1HZKuEUAWt0IDy+B4KaQdQim9IXtnzs7KhGRy0piYiKjR49mzZo1LFq0iJKSEvr27UteXl6V4x5++GFSUlIqttdff91JEVff+gMZADQP9yXMz+rkaEREpLZzc3YAIpeNsDh4eCnMHQ77lsDcYZC63Rxy7nKZVac1DNj0qfmjQO/noeFVzo5IROqB7777rsrj6dOnEx4ezsaNG+nWrVvFfm9vbxo0aFDT4dnF2iQz4e4cG+zkSEREpC5QD7fIb3kFwX1zoMtj5uOVb8C/u8PeJc6N67cKMmHOg7BgDOxfBjPvhZxjzo5KROqhrCxz6HVwcNXk9LPPPiM0NJR27doxbtw48vPzz3meoqIisrOzq2zOsjbpBACdm4Y4LQYREak7lHCL/J6LK/R9CQb9B6wBcGwbfDoI/ns7pG5zbmzJa+GD62HHfHBxA78oyE2FOUOhtNi5sYlIvWKz2XjiiSfo2rUr7dq1q9g/ZMgQPv30U5YtW8a4ceP473//yx/+8IdznmvixIkEBARUbNHR0Y4O/4yyC0vYcdRM9tXDLSIi9mAxDMNwdhA1KTs7m4CAALKysvD393d2OHK5y8+AFZNg3X/M5cOwQPw90PNZCKzBPwhtZWZv+7KJYJRBUBMYPBU8A+A/PaEoGzr9CW6sPfMkReR0tamNGjVqFAsXLmTlypU0atTorMctXbqU3r17s3fvXpo1a3bGY4qKiigqKqp4nJ2dTXR0dI1fh6U7jzFs+gaahHiz/OmeNfa+IiJSO1xMO60ebpFz8Q6G/hNhzHpoNxgwYMtM+FdHWPS8Obzb0bKPmsuVLX3ZTLbb3wl/+hEadYTQ5nD7v83j1v0btsxyfDwiUu+NGTOGr7/+mmXLlp0z2Qbo3LkzAHv37j3rMVarFX9//yqbM6zdf2r+toaTi4iIfSjhFrkQwbFwx1SzqFrMdVBWBKvehneugNXvQ2nReU9xUXZ+C5O7wIEfwd0HBn5gDnX3/M0fo61uhG5/Me9/9TikbHFMLCJS7xmGwZgxY5g3bx5Lly4lNjb2vK/ZvHkzAJGRkQ6O7tKtOVUwramGk4uIiH0o4RapjoYd4cGv4d7ZEBoHBSfh+3Hw7jWwbS7YbPZ5n5JC+PZpmHWv+R6R8fCnFXDFveba4b/X4xlofgOUFsLsP5hD4UVE7Gz06NF8+umnzJgxAz8/P1JTU0lNTaWgoACAffv28dJLL7Fx40YOHDjAggULeOCBB+jWrRsdOnRwcvTnlltUyvby9bdVME1EROxFCbdIdVksENcfRv0Et7wNvhGQeRA+Hw4f9YYDKy/t/Gk7zfOs+9B8nDAGhi8yh4+fjYsrDPrQnNudmQyf/9Gc9y0iYkeTJ08mKyuLHj16EBkZWbHNnj0bAA8PDxYvXkzfvn1p1aoVTz31FIMHD+arr75ycuTnt/HgScpsBo2CvGgY6OXscEREpI7QOtwiF8vVDTo+aM6pXv2eOcT86M8w/SYIbgbBTc0E+NQWHAuBMWD1PfP5DAN+/hgWPgOlBeATZg4hb9HnwuLxDoa7P4WPbjDXEV/2KvR+7tI/Z2kRrP0AyorhuqfARb/TidRX56uzGh0dTWJiYg1FY19r95cvB6b52yIiYkdOTbhXrFjBpEmT2LhxIykpKcybN4+BAwee9fjly5fTs+fpVUNTUlJo0KCBAyMVOQcPH+j+FzP5TvwHbJgGGfvM7Ux8wsqT8NjKZDywMaz/yFzuC6BpT7MYml9E9WJp0B5u/Rd88Uf48f8g6kpoffNFfzQOrYcvR0P6LvOxrcwcvi4iUses1fxtERFxAKcm3Hl5ecTHxzNs2DAGDRp0wa/btWtXlQqm4eHhjghPpHp8w+Gmf0L3v0Lar3DyQPmWVHm/4CTkHTe3w+tPP4eLG/R+HhIevfie5A53mj3ta96HeSMhdCmEtazeOYrzzaroa94HDHP5scIsWD4RIq8wh9SLiNQRBcVlbD2cCcC16uEWERE7cmrCPWDAAAYMGFDt14WHhxMYGGj/gETswTfc3Oh++nMFmb9JxA9UJuQZSeATCjdOMguzXaobXjSrlR9cBbPvM6urW/0u7LVJK2DBo2ZsAPH3Qr9XzSHq6/8DX4yAEcsg5Mzr6YqI1Dabkk9SUmYQGeBJdLDmb4uIiP3UyjncV1xxBUVFRbRr144JEybQtWvXsx5bVFREUVHlkk3Z2dk1EaLImXkFgtcVEHWFY9/H1R3unA7/7gbpu2H+KLjrv2eucH5KYba5tvjGaeZj/4ZmUbgWN5iP+70KqVvh0FqzEvrwRWefjy4iUoucWg6sU2wwlnP9OykiIlJNtar6UWRkJB988AGff/45n3/+OdHR0fTo0YOff/75rK+ZOHEiAQEBFVt0dHQNRiziRL7hZpLt4g6/fgWr3jr7sXsWwfvXVibbVw+DR9ZUJtsAbh5w58dmVfa0HWYv+HkKKImI1AYqmCYiIo5SqxLuuLg4/vSnP9GxY0e6dOnC1KlT6dKlC2+++eZZXzNu3DiysrIqtkOHDtVgxCJOFn0N3Pi6eX/Ji7BvadXn8zPMed6f3QHZR8wCbkO/hpvfBE//006HfyTc9Yk51/yXL2D1uw7/CCIijlRYUsamQ5mACqaJiIj91aqE+0w6derE3r17z/q81WrF39+/yiZSr3R8CK68HwwbzB0OJw+a+3d8Ce91hi0zAQtcO9pcWzz2+nOfr/G10P818/6i52F/7VwCSEQEYMuhTIpLbYT6Wmka6uPscEREpI6p9Qn35s2biYyMdHYYIpcviwVuLF8irCDDnH/9vwfMLS8NQuPM+dj9XzWXOLsQ1/wR4oeUJ/EPQaadRo5kHYHFEyB5jX3OJyJyHr9dDkzzt0VExN6cWjQtNze3Su90UlISmzdvJjg4mMaNGzNu3DiOHDnCJ598AsBbb71FbGwsbdu2pbCwkI8++oilS5fyww8/OOsjiNQO7p7mfO4Pu5uFz1K3gsUVrnvSXEPczVq981kscPMbcGy7ea7/3Q8PfWe+z8UwDNj0X/j+WSjKhp/ehcEfQduBF3c+EZELtDbJnL99bayGk4uIiP05tYd7w4YNXHnllVx55ZUAjB07liuvvJLnn38egJSUFJKTkyuOLy4u5qmnnqJ9+/Z0796dLVu2sHjxYnr37u2U+EVqlcBos3K5hy9ExptLe/V+rvrJ9inuXnD3p+AVDEc3wbdPXVwRtcxk+O/tZhG2omzwDgVbCcx5EDZOv7jYREQuQHGpjY0HTwLQuakKpomIiP1ZDKN+lRnOzs4mICCArKwszeeW+qmk0Eyy7TV0ct9S+HSwObz85jfNCucXwmaDjVNh0XgozgU3T+j1d+j0J1j4l8qK6b3Hmz3xGuop9YDaKFNNXYeNBzMYPHk1wT4ebPx7Hw0pFxGRc7qY9qnWz+EWkWpy97Rv8tqsF/Q2R6Xw7V/g0PrzvyYjCT65Fb55yky2GyfAyFXQ5VFz+bGb34Tr/2weu+QF+OHvWoJMROxuzf7y9bebaP62iIg4hhJuEbl0XZ+A1reaQ8H/dz/kHDvzcTYbrJkMk7vAgR/B3Rv6/wMe/BZCm1ceZ7GYw937vWo+Xv0ufDkaykod/lFEpP5YV14wrZPmb4uIiIMo4RaRS2exwMD3zYrnOSnm/OuykqrHpO+FaQPgu2egJB+aXG8uQ3btSHA5yz9FCaNh4GSzwNvmz8zK6iWFDv84IlL3lZbZ2HCgskK5iIiIIyjhFhH7sPrBPZ+B1R+SfzKHgQPYymDVO/BBVzi0xizadtMb8MACCI49/3mvGGIWZ3O1wq5vzPnihdmXFmteuhnTFyNg5VtwYBUU513aOUWkVvnlaDZ5xWX4e7rRqkH9nS8vIiKO5dRlwUSkjgltAbd/ALOGwNoPwCcMdn0LRzaazzfrBbe8DYGNq3feVjfC/V/AzHvh4Er4+Ga473PwDbvwcxgGHFwFG6bCr19BWXHV5y2u0KAdNLqmcgtuqmJtInXUqeXAOsUG4+qi/89FRMQxlHCLiH21ugm6PQ0rJsHSl8x91gDo9wpc+YeLT2CbXAcPfg3/HQQpW2BqP3hg/vmT9/wM2DLTXGIsfXfl/qgroUVfSPsVDq83h8KnbDG39R+Zx3gF/yYBvxoadgTPGugJy88wC8qlbIY7ppqxiohdrS0vmNY5VsuBiYiI4yjhFhH76zEOUrfB7u+gRT+45S3wj7r080bGw7Dv4b8DIWMfTOkH98+D8FZVjzMMOLQWNkyDX+ZBWZG5390HOtwJHR+CqCuqvibrCBxeB4c3mAn40c1QkAF7vjc3ACwQ3ho6PQxXPXj2ueeX4uBP8PkfIfuI+fi/g+Chb833rQvKSsDV3dlRSD1XZjNYp/nbIiJSA7QOt4g4hq3MXP4rpJn9h2VnHYFPB8HxneAVBPfNNXugCzJh62yzNzttR+XxDdqbSXb7Oy+8h7q02PzR4PD6yi3zYOXzMV3N4fGhLezzmWxl8OM/YflEc03z4GbmvPiUzeDbAIYtNIe412YbpsLXY83r1nGos6O5LKmNMjn6Omw/ksXN/1qJr9WNzc/fgJurStqIiMj5XUz7pB5uEXEMF9eqS33ZU0BDeGghfHYnHNkAH98KcQNg5zdQWmAe4+YF7QbD1cOg4VXVT/rdPKBRR3NjpLkv5xhsnwtLXzHng0/uCt3/Al0fv7Re2+yjZgG3Az+aj+PvhRsnmb3BH98Cx7bDx7fBsO/Mz14b5Z2ARRMAA77/GzTvDQGNnB2V1FNry5cDu7pJkJJtERFxKLUyIlI7eQfDA19C055QkmcmwqUFEN4GBkyCp3bCwPfMhNlePex+EeZSZY+shma9zaHqS1+CD3vAkZ8v7py7vzcT9wM/mkPeB35gFp6z+pmf8f55Zm93VjJ8chvkHrfPZ6lpif+AoizzfnEufPNnc+i/iBOs3W8WTNP8bRERcTQl3CJSe1l9YchsuHa0WZBt2Pfm2t6dR4BXoOPeNygG/vA53P6hWVjt2Hb4qDd8/+yFLy9WWgTf/Q1m3GXOFW/QAf60Aq64t+pxvuHmDwsB0XBiD/z3dig4af/P5Ejpe2HDFPP+gEng4g67F8KvC5wbl9RLNs3fFhGRGqSEW0RqNzcr9H8VbnsPGl9bc8t4WSwQfzeMXgft7jDnXa9+F95PgH3Lzv3aE/tgSl9Y8575uPMo+OPisw/BD4w2k27fCDi2zRxKX5Rj38/jSIvHg63ULKDXeQRc96S5/9u/QGGWc2OTemd3Wg6Z+SV4ubvSvmGAs8MREZE6Tgm3iMil8A2DO6bAkP+BfyOzsNp/B8L80ebyXr+39X/w725mMTSvILhnJgx4zfzh4FxCmsH9883XHF5vrkleUuCAD2RnB1bBzq/Ndc5veNHcd/1TENIcclNh8QvOjU/qnXXl87c7xgThrvnbIiLiYGppRETsoWU/GL0GOo0ALLD5U3ivs7ksmWFAUS7MfwS+eNicwxzTFUauglY3Xvh7RLQxh7J7+Jlzvv831Cysdrmy2eCHZ837HYdWLt/m7gk3v2Xe3zAVktc6JTypnyrX39ZwchERcTwl3CIi9mL1M6uLD/seQuMgLw3mPAgz7zELq23+DCwu5jrlQ7+6uIrjDTua89bdvMz1wb8YYS4pdjna/jkc3QQevuZn/q3Y6+GKPwAGfPW4uQybiIMZhsHapPKCaU1VME1ERBxPCbeIiL017gwjf4Tufy0vEPadWfDML9JMtHs8Yy6bdrGadIW7PzXP/csX8NVjZm/y5aSkEJaUDxe/7gmz+Nvv9X0JvEPh+K/w0zs1Gp7UT/uO55GeW4zVzYX4aM3fFhERx1PCLSLiCG5W6Pk3s/J48z7Q/k5zCHmT6+xz/hZ9zLnjFhfY9Km5tvXltMzW2smQdQj8G5pV5M/EOxj6TzTvJ75uFpMTcaBTvdtXNg7E6nYJP3qJiIhcICXcIiKOdGre9eCPwMfOQ1jb3Aa3vW/eXzsZlr1q3/NfrLx0+PEN836v58DD++zHtr8TmvUy1zT/+gn7/WiQtAK2zb28foQQp6ucv63h5CIiUjOUcIuI1GZX3As3/p95f8XrsOpt58YDkPgPKMo21xbvcPe5j7VY4KY3zDnpSStgy8xLe29bGSyeAB/fAp8PhwVjLu/CclJjqs7fVsE0ERGpGW7ODkBERC5Rp/LK54snwKLnzcrfXsHmkO2K26Cq93/7nIev/dYvT99jvj9A35fB5QJ+1w2ONee1Lx5vDo1v0Rd8Qqv/3vkZMHcY7D+1DrrFHG6fmwZ3TgcPn+qfU+qMgyfyOZZdhIerC1c1DnJ2OCIiUk8o4RYRqQuue9JclzvxH3DygLldKFcPc1mzW/9lJuOXYtF4sJVCy/7QtPuFvy5hNGybA8e2w/fPwqB/V+99j26G2fdDVjK4e5ufxcMH5jwEe36A6Teba6X7hlXvvFLFxIkT+eKLL9i5cydeXl506dKFf/zjH8TFxVUcU1hYyFNPPcWsWbMoKiqiX79+vP/++0RERDgx8sr52/HRAXi6a/62iIjUDA0pFxGpK3r+DZ7YDsN+gHtnmfO7+74M142Fjg9C61uhyfUQ3tasmO5qNV9XVgy/fgX/6QVpOy/+/Q+shF3fgMUVbnixeq91dYdb3gEssHUW7Ft64a/dPAOm9jOT7aBYGL4I2t8BcQPMqvBewXD0Z5hygwqzXaLExERGjx7NmjVrWLRoESUlJfTt25e8vLyKY5588km++uor5syZQ2JiIkePHmXQoEFOjNp0av52J62/LSIiNchiGPWrokx2djYBAQFkZWXh7+/v7HBERJzHMKAkH479AnOHmwmrhx8M+hBa3Vi9c9ls8J+ekLIZrh4ON79xcTF9+xdY928IagKjVp+74FppsTkEff1/zMct+pqx/76XPn0PfDoIMpPNZcju+5+5nvllqLa1UcePHyc8PJzExES6detGVlYWYWFhzJgxgzvuuAOAnTt30rp1a1avXs211157Qed1xHXo+tpSjmQW8MmwTnRrqZEOIiJSfRfTPjm1h3vFihXccsstREVFYbFYmD9//gW/dtWqVbi5uXHFFVc4LD4RkTrNYjGHXUd3ghHLIOY6KM6BWfeay3RVZ23v7XPNZNvDD3qMu/iYej9nLiV28oBZBO5sslPg45srk+3uz8C9s888JD60BQxfbBZxy083h5fvWXTxMUqFrKwsAIKDzV7jjRs3UlJSQp8+fSqOadWqFY0bN2b16tVOiRHg8Ml8jmQW4OpioWOM5m+LiEjNcWrCnZeXR3x8PO+99161XpeZmckDDzxA7969HRSZiEg94xMKD8yHTiPMx8tegTlDoSj3/K8tKYAl5UPIr3/y0uZJW/3gxknm/Z/+BanbTz/m4Gr4sDscWgvWADPR7jnu3AXa/CLgoW/NJchK8mHG3WZBNbloNpuNJ554gq5du9KuXTsAUlNT8fDwIDAwsMqxERERpKamnvVcRUVFZGdnV9ns6dRw8vYNA/CxqnyNiIjUHKcm3AMGDODll1/m9ttvr9brRo4cyZAhQ0hISHBQZCIi9ZCru5ns3vIOuLjDrwtgSt/zF2BbMxmyDpk909c+culxtLoJWt9iFl/76nFzqS8wh8Cv+4/Zs517DMJamz3zcf0v7LxWPzM573APGGXw5WhInKS1ui/S6NGj2b59O7Nmzbrkc02cOJGAgICKLTo62g4RVtJyYCIi4iy1rmjatGnT2L9/P+PHj7+g4x39q7mISJ3TcSg8+A34hEPaL/BhD9i//MzH5qXDj+XztXs/D+5e9olhwOvm8PQjG8xlxkoKYP4o+PbPZiLedhD8cTGENKveed084PYPzEJyAMtehq+fhLJS+8RdT4wZM4avv/6aZcuW0ahRo4r9DRo0oLi4mMzMzCrHHzt2jAYNGpz1fOPGjSMrK6tiO3TokF3jXZtk9nBfGxti1/OKiIicT61KuPfs2cMzzzzDp59+ipvbhQ0Jc/Sv5iIidVLjzjBiOURdBQUn4b+DzJ7s3/cGL59ozvuOjIf2d9nv/f2joE/5D6uLXzArjG+ZaVZA7/sK3DEVrL4Xd26LxTz3gEmABTZOg//dD8X553+tYUDWYdi7xLweXz0B0240b+sBwzAYM2YM8+bNY+nSpcTGxlZ5vmPHjri7u7NkyZKKfbt27SI5Ofmco9KsViv+/v5VNntJzSrk4Il8XCxwdRPN3xYRkZpVayYylZWVMWTIEF544QVatmx5wa8bN24cY8eOrXicnZ2tpFtE5EIENISHFsLXT5jJ7nfPQMpWuPlNcPeE47thwzTz2L4vn3sO9cW4ejhsnQ2H10PqNrPC+J3TILabfc7feQT4NYDP/wi7voVPboMhs8E72OzxPnkA0nfB8fItfZdZ8bz4DPPaC07aJ6bL3OjRo5kxYwZffvklfn5+FfOyAwIC8PLyIiAggOHDhzN27FiCg4Px9/fn0UcfJSEh4YIrlNvbqeHkbaMC8PN0d0oMIiJSf9WahDsnJ4cNGzawadMmxowZA5gFWwzDwM3NjR9++IFevXqd9jqr1YrVaq3pcEVE6gZ3Txg42azw/cOzsGWGmXje/RksHm/OhW45wH5J8G+5uJjzyT++BUKawx1TIKDR+V9XHW1uBZ8vYeY9cHgdfHC9Odc7Y5+5PvkZ43KD4KYQ2hLC4iA0DsJb2zeuy9TkyZMB6NGjR5X906ZN48EHHwTgzTffxMXFhcGDB1NUVES/fv14//33azjSSmvKC6Z11vrbIiLiBLUm4fb392fbtm1V9r3//vssXbqUuXPnnjasTURE7MRigYRHzKRyzoNwZCNMTjB7dS2ucMOLjnvviDbw1C5wdWBzFZMAw76HTwdD9uHK/W5e5pJip5LqsPItKNacC14PGRdQYM7T05P33nuv2iuQOMqpHu5OSrhFRMQJnJpw5+bmsnfv3orHSUlJbN68meDgYBo3bsy4ceM4cuQIn3zyCS4uLhXLjpwSHh6Op6fnaftFRMQBmvU0q4LPug/Sdpj7rn4Iwi58ms9FcWSyfUp4K/hTIuz5AXzCzN7rgGj7D5OXGpWWU8j+43lYLEq4RUTEOZyacG/YsIGePXtWPD4113ro0KFMnz6dlJQUkpOTnRWeiIj8XnBTGL4IFv7VnOPcY5yzI7Ifn1C4YoizoxA7SskspHGwN94ergR6189RCSIi4lwW40LGh9Uh2dnZBAQEkJWVZdcqqCIiIpdKbZTJ3tcht6gUX2utmUUnIiKXqYtpnzRWTkREROo0JdsiIuIsSrhFREREREREHEAJt4iIiIiIiIgDKOEWERERERERcQAl3CIiIiIiIiIOoIRbRERERERExAGUcIuIiIiIiIg4gBJuEREREREREQdQwi0iIiIiIiLiAEq4RURERERERBxACbeIiIiIiIiIA7g5O4CaZhgGANnZ2U6OREREpKpTbdOptqq+UlstIiKXo4tpp+tdwp2TkwNAdHS0kyMRERE5s5ycHAICApwdhtOorRYRkctZddppi1HPfka32WwcPXoUPz8/LBbLJZ8vOzub6OhoDh06hL+/vx0irJ10HSrpWph0HSrpWph0HSqd7VoYhkFOTg5RUVG4uNTfWV/2bKv1vTPpOlTStaika2HSdaika2GyZztd73q4XVxcaNSokd3P6+/vX6+/lKfoOlTStTDpOlTStTDpOlQ607Wozz3bpziirdb3zqTrUEnXopKuhUnXoZKuhcke7XT9/flcRERERERExIGUcIuIiIiIiIg4gBLuS2S1Whk/fjxWq9XZoTiVrkMlXQuTrkMlXQuTrkMlXYuao2tt0nWopGtRSdfCpOtQSdfCZM/rUO+KpomIiIiIiIjUBPVwi4iIiIiIiDiAEm4RERERERERB1DCLSIiIiIiIuIASrgvwXvvvUeTJk3w9PSkc+fOrFu3ztkh1bgJEyZgsViqbK1atXJ2WDVixYoV3HLLLURFRWGxWJg/f36V5w3D4PnnnycyMhIvLy/69OnDnj17nBOsA53vOjz44IOnfUf69+/vnGAdaOLEiVxzzTX4+fkRHh7OwIED2bVrV5VjCgsLGT16NCEhIfj6+jJ48GCOHTvmpIgd50KuRY8ePU77XowcOdJJETvG5MmT6dChQ8UangkJCSxcuLDi+fryfXAmtdNqp9VOq50+Re10JbXTlWqirVbCfZFmz57N2LFjGT9+PD///DPx8fH069ePtLQ0Z4dW49q2bUtKSkrFtnLlSmeHVCPy8vKIj4/nvffeO+Pzr7/+Ou+88w4ffPABa9euxcfHh379+lFYWFjDkTrW+a4DQP/+/at8R2bOnFmDEdaMxMRERo8ezZo1a1i0aBElJSX07duXvLy8imOefPJJvvrqK+bMmUNiYiJHjx5l0KBBTozaMS7kWgA8/PDDVb4Xr7/+upMidoxGjRrx2muvsXHjRjZs2ECvXr247bbb+OWXX4D6831wFrXTldROq51WO612+rfUTleqkbbakIvSqVMnY/To0RWPy8rKjKioKGPixIlOjKrmjR8/3oiPj3d2GE4HGPPmzat4bLPZjAYNGhiTJk2q2JeZmWlYrVZj5syZToiwZvz+OhiGYQwdOtS47bbbnBKPM6WlpRmAkZiYaBiG+d/f3d3dmDNnTsUxv/76qwEYq1evdlaYNeL318IwDKN79+7G448/7rygnCQoKMj46KOP6vX3oaaonTapnTapnTapna6kdrqS2umq7N1Wq4f7IhQXF7Nx40b69OlTsc/FxYU+ffqwevVqJ0bmHHv27CEqKoqmTZty3333kZyc7OyQnC4pKYnU1NQq35GAgAA6d+5cL78jy5cvJzw8nLi4OEaNGsWJEyecHZLDZWVlARAcHAzAxo0bKSkpqfKdaNWqFY0bN67z34nfX4tTPvvsM0JDQ2nXrh3jxo0jPz/fGeHViLKyMmbNmkVeXh4JCQn1+vtQE9ROV6V2+nRqp6tSO612Gup3Ow2Oa6vdHBFsXZeenk5ZWRkRERFV9kdERLBz504nReUcnTt3Zvr06cTFxZGSksILL7zA9ddfz/bt2/Hz83N2eE6TmpoKcMbvyKnn6ov+/fszaNAgYmNj2bdvH3/7298YMGAAq1evxtXV1dnhOYTNZuOJJ56ga9eutGvXDjC/Ex4eHgQGBlY5tq5/J850LQCGDBlCTEwMUVFRbN26lb/+9a/s2rWLL774wonR2t+2bdtISEigsLAQX19f5s2bR5s2bdi8eXO9/D7UFLXTldROn5na6Upqp9VO1+d2GhzfVivhlksyYMCAivsdOnSgc+fOxMTE8L///Y/hw4c7MTK5XNxzzz0V99u3b0+HDh1o1qwZy5cvp3fv3k6MzHFGjx7N9u3b6808yXM527UYMWJExf327dsTGRlJ79692bdvH82aNavpMB0mLi6OzZs3k5WVxdy5cxk6dCiJiYnODkvqEbXTcj5qp+u3+t5Og+Pbag0pvwihoaG4urqeVqHu2LFjNGjQwElRXR4CAwNp2bIle/fudXYoTnXqe6DvyOmaNm1KaGhonf2OjBkzhq+//pply5bRqFGjiv0NGjSguLiYzMzMKsfX5e/E2a7FmXTu3Bmgzn0vPDw8aN68OR07dmTixInEx8fz9ttv18vvQ01SO312aqdNaqfPTu10ZpXj6/J3Qu20ydFttRLui+Dh4UHHjh1ZsmRJxT6bzcaSJUtISEhwYmTOl5uby759+4iMjHR2KE4VGxtLgwYNqnxHsrOzWbt2bb3/jhw+fJgTJ07Uue+IYRiMGTOGefPmsXTpUmJjY6s837FjR9zd3at8J3bt2kVycnKd+06c71qcyebNmwHq3Pfi92w2G0VFRfXq++AMaqfPTu20Se302amdrvv/LqudPje7t9X2rupWX8yaNcuwWq3G9OnTjR07dhgjRowwAgMDjdTUVGeHVqOeeuopY/ny5UZSUpKxatUqo0+fPkZoaKiRlpbm7NAcLicnx9i0aZOxadMmAzDeeOMNY9OmTcbBgwcNwzCM1157zQgMDDS+/PJLY+vWrcZtt91mxMbGGgUFBU6O3L7OdR1ycnKMP//5z8bq1auNpKQkY/HixcZVV11ltGjRwigsLHR26HY1atQoIyAgwFi+fLmRkpJSseXn51ccM3LkSKNx48bG0qVLjQ0bNhgJCQlGQkKCE6N2jPNdi7179xovvviisWHDBiMpKcn48ssvjaZNmxrdunVzcuT29cwzzxiJiYlGUlKSsXXrVuOZZ54xLBaL8cMPPxiGUX++D86idtqkdlrttNppk9rpSmqnK9VEW62E+xL861//Mho3bmx4eHgYnTp1MtasWePskGrc3XffbURGRhoeHh5Gw4YNjbvvvtvYu3evs8OqEcuWLTOA07ahQ4cahmEuOfLcc88ZERERhtVqNXr37m3s2rXLuUE7wLmuQ35+vtG3b18jLCzMcHd3N2JiYoyHH364Tv7Be6ZrABjTpk2rOKagoMB45JFHjKCgIMPb29u4/fbbjZSUFOcF7SDnuxbJyclGt27djODgYMNqtRrNmzc3nn76aSMrK8u5gdvZsGHDjJiYGMPDw8MICwszevfuXdGAG0b9+T44k9pptdNqp9VOn6J2upLa6Uo10VZbDMMwLrw/XEREREREREQuhOZwi4iIiIiIiDiAEm4RERERERERB1DCLSIiIiIiIuIASrhFREREREREHEAJt4iIiIiIiIgDKOEWERERERERcQAl3CIiIiIiIiIOoIRbRERERERExAGUcIuIU1gsFubPn+/sMEREROQM1E6L2IcSbpF66MEHH8RisZy29e/f39mhiYiI1Htqp0XqDjdnByAiztG/f3+mTZtWZZ/VanVSNCIiIvJbaqdF6gb1cIvUU1arlQYNGlTZgoKCAHMY2eTJkxkwYABeXl40bdqUuXPnVnn9tm3b6NWrF15eXoSEhDBixAhyc3OrHDN16lTatm2L1WolMjKSMWPGVHk+PT2d22+/HW9vb1q0aMGCBQsc+6FFRERqCbXTInWDEm4ROaPnnnuOwYMHs2XLFu677z7uuecefv31VwDy8vLo168fQUFBrF+/njlz5rB48eIqDfXkyZMZPXo0I0aMYNu2bSxYsIDmzZtXeY8XXniBu+66i61bt3LjjTdy3333kZGRUaOfU0REpDZSOy1SSxgiUu8MHTrUcHV1NXx8fKpsr7zyimEYhgEYI0eOrPKazp07G6NGjTIMwzA+/PBDIygoyMjNza14/ptvvjFcXFyM1NRUwzAMIyoqynj22WfPGgNg/P3vf694nJubawDGwoUL7fY5RUREaiO10yJ1h+Zwi9RTPXv2ZPLkyVX2BQcHV9xPSEio8lxCQgKbN28G4NdffyU+Ph4fH5+K57t27YrNZmPXrl1YLBaOHj1K7969zxlDhw4dKu77+Pjg7+9PWlraxX4kERGROkPttEjdoIRbpJ7y8fE5beiYvXh5eV3Qce7u7lUeWywWbDabI0ISERGpVdROi9QNmsMtIme0Zs2a0x63bt0agNatW7Nlyxby8vIqnl+1ahUuLi7ExcXh5+dHkyZNWLJkSY3GLCIiUl+onRapHdTDLVJPFRUVkZqaWmWfm5sboaGhAMyZM4err76a6667js8++4x169YxZcoUAO677z7Gjx/P0KFDmTBhAsePH+fRRx/l/vvvJyIiAoAJEyYwcuRIwsPDGTBgADk5OaxatYpHH320Zj+oiIhILaR2WqRuUMItUk999913REZGVtkXFxfHzp07AbMy6axZs3jkkUeIjIxk5syZtGnTBgBvb2++//57Hn/8ca655hq8vb0ZPHgwb7zxRsW5hg4dSmFhIW+++SZ//vOfCQ0N5Y477qi5DygiIlKLqZ0WqRsshmEYzg5CRC4vFouFefPmMXDgQGeHIiIiIr+jdlqk9tAcbhEREREREREHUMItIiIiIiIi4gAaUi4iIiIiIiLiAOrhFhEREREREXEAJdwiIiIiIiIiDqCEW0RERERERMQBlHCLiIiIiIiIOIASbhEREREREREHUMItIiIiIiIi4gBKuEVEREREREQcQAm3iIiIiIiIiAMo4RYRERERERFxgP8H0j1Y++D2lZwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Experiment 3 Test Accuracy: 55.37%\n"
     ]
    }
   ],
   "source": [
    "# Learning curves\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "axes[0].set_title('Loss - Experiment 3 (Augmented)')\n",
    "axes[0].plot(history_exp3['train_loss'], label='Train')\n",
    "axes[0].plot(history_exp3['val_loss'], label='Validation')\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('Loss (cross-entropy)')\n",
    "axes[0].legend(loc='best')\n",
    "\n",
    "axes[1].set_title('Accuracy - Experiment 3 (Augmented)')\n",
    "axes[1].plot(history_exp3['train_accuracy'], label='Train')\n",
    "axes[1].plot(history_exp3['val_accuracy'], label='Validation')\n",
    "axes[1].set_xlabel('Epoch')\n",
    "axes[1].set_ylabel('Accuracy (%)')\n",
    "axes[1].legend(loc='best')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Test accuracy (pass device for on-the-fly batch loading)\n",
    "test_accuracy_exp3, predictions_exp3, true_labels_exp3 = evaluate_model(\n",
    "    model_exp3, test_loader_exp3, device=device\n",
    ")\n",
    "print(f'\\nExperiment 3 Test Accuracy: {test_accuracy_exp3:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcbd1142",
   "metadata": {},
   "source": [
    "### 3.7. Experiment 3 Observations\n",
    "\n",
    "**TODO**: Document your findings:\n",
    "- Which augmentation techniques did you use?\n",
    "- Did augmentation improve test accuracy?\n",
    "- Did you notice any effect on the gap between training and validation accuracy (overfitting)?\n",
    "- How did training time compare to non-augmented training?\n",
    "- Would you recommend augmentation for this dataset?\n",
    "\n",
    "*Your notes here:*\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
