{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Classification with CNNs\n",
    "\n",
    "In this tutorial, we'll build a complete image classification system using Convolutional Neural Networks (CNNs) in BrainState.\n",
    "\n",
    "## Learning Objectives\n",
    "\n",
    "By the end of this tutorial, you will be able to:\n",
    "- Build CNN architectures for image classification\n",
    "- Load and preprocess image datasets (MNIST-like)\n",
    "- Implement complete training loops\n",
    "- Evaluate model performance\n",
    "- Visualize results and predictions\n",
    "- Apply data augmentation\n",
    "- Monitor training progress\n",
    "\n",
    "## What We'll Build\n",
    "\n",
    "We'll create:\n",
    "- A CNN architecture from scratch\n",
    "- A training pipeline with validation\n",
    "- Evaluation metrics and visualization\n",
    "- A complete end-to-end workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:06:48.994047Z",
     "start_time": "2025-10-11T10:06:47.316860Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JAX devices: [CpuDevice(id=0)]\n"
     ]
    }
   ],
   "source": [
    "import brainstate\n",
    "import braintools\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "brainstate.random.seed(42)\n",
    "\n",
    "print(f\"JAX devices: {jax.devices()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Dataset Preparation\n",
    "\n",
    "We'll create synthetic MNIST-like data for demonstration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:07:15.863644Z",
     "start_time": "2025-10-11T10:06:51.731643Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating synthetic dataset...\n",
      "Training set: (5000, 28, 28, 1), (5000,)\n",
      "Validation set: (500, 28, 28, 1), (500,)\n",
      "Test set: (1000, 28, 28, 1), (1000,)\n"
     ]
    }
   ],
   "source": [
    "def generate_synthetic_mnist(n_samples: int = 1000, img_size: int = 28, n_classes: int = 10):\n",
    "    \"\"\"Generate synthetic MNIST-like dataset.\n",
    "    \n",
    "    Args:\n",
    "        n_samples: Number of samples to generate\n",
    "        img_size: Image size (height and width)\n",
    "        n_classes: Number of classes\n",
    "        \n",
    "    Returns:\n",
    "        images: Array of shape (n_samples, img_size, img_size, 1)\n",
    "        labels: Array of shape (n_samples,)\n",
    "    \"\"\"\n",
    "    # Generate random images with patterns\n",
    "    images = brainstate.random.randn(n_samples, img_size, img_size, 1) * 0.4\n",
    "    labels = brainstate.random.randint(0, n_classes, (n_samples,))\n",
    "    \n",
    "    # Add class-specific patterns\n",
    "    for i in range(n_samples):\n",
    "        label = int(labels[i])\n",
    "        \n",
    "        \n",
    "        if label == 0:  # circle\n",
    "            center_x, center_y = img_size//2, img_size//2\n",
    "            radius = 8\n",
    "            for x in range(img_size):\n",
    "                for y in range(img_size):\n",
    "                    dist = (x-center_x)**2 + (y-center_y)**2\n",
    "                    if radius**2 - 3 <= dist <= radius**2 + 3:\n",
    "                        images = images.at[i, x, y, 0].set(0.9)\n",
    "        \n",
    "        elif label == 1:  # Vertical line + bottom small horizontal line (similar to 7)\n",
    "            center = img_size//2\n",
    "            images = images.at[i, center-1:center+1, 5:img_size-5, 0].set(0.9)\n",
    "            images = images.at[i, img_size-6:img_size-4, center-3:center+3, 0].set(0.9)\n",
    "        \n",
    "        elif label == 2:  \n",
    "            images = images.at[i, 5, 5:img_size-5, 0].set(0.9)\n",
    "            images = images.at[i, 6, 5:img_size-5, 0].set(0.9)\n",
    "            for j in range(10):\n",
    "                images = images.at[i, 6+j, img_size-6-j, 0].set(0.9)\n",
    "                images = images.at[i, 7+j, img_size-6-j, 0].set(0.9)\n",
    "            images = images.at[i, img_size-6, 5:img_size-5, 0].set(0.9)\n",
    "            images = images.at[i, img_size-7, 5:img_size-5, 0].set(0.9)\n",
    "        \n",
    "        elif label == 3:   \n",
    "            images = images.at[i, 5, img_size-10:img_size-5, 0].set(0.9)\n",
    "            images = images.at[i, img_size//2, img_size-10:img_size-5, 0].set(0.9)\n",
    "            images = images.at[i, img_size-6, img_size-10:img_size-5, 0].set(0.9)\n",
    "            images = images.at[i, 5:img_size-5, img_size-6, 0].set(0.9)\n",
    "        \n",
    "        elif label == 4:  \n",
    "            images = images.at[i, 5:img_size//2+3, 6, 0].set(0.9)\n",
    "            images = images.at[i, img_size//2, 5:img_size-5, 0].set(0.9)\n",
    "            images = images.at[i, 5:img_size-5, img_size-6, 0].set(0.9)\n",
    "        \n",
    "        elif label == 5: \n",
    "            images = images.at[i, 5, 5:img_size-5, 0].set(0.9)\n",
    "            images = images.at[i, 5:img_size//2+1, 6, 0].set(0.9)\n",
    "            images = images.at[i, img_size//2, 5:img_size-5, 0].set(0.9)\n",
    "            images = images.at[i, img_size//2:img_size-5, img_size-6, 0].set(0.9)\n",
    "            images = images.at[i, img_size-6, 5:img_size-5, 0].set(0.9)\n",
    "        \n",
    "        elif label == 6: \n",
    "            center_x, center_y = img_size//2+3, img_size//2+3\n",
    "            radius = 7\n",
    "            for x in range(img_size):\n",
    "                for y in range(img_size):\n",
    "                    dist = (x-center_x)**2 + (y-center_y)**2\n",
    "                    if radius**2 - 3 <= dist <= radius**2 + 3 and x >= center_x-2:\n",
    "                        images = images.at[i, x, y, 0].set(0.9)\n",
    "            images = images.at[i, 5:img_size-5, 6, 0].set(0.9)\n",
    "        \n",
    "        elif label == 7: \n",
    "            images = images.at[i, 5, 5:img_size-5, 0].set(0.9)\n",
    "            for j in range(15):\n",
    "                images = images.at[i, 6+j, img_size-6-j, 0].set(0.9)\n",
    "        \n",
    "        elif label == 8: \n",
    "            center_x1, center_y1 = img_size//3+1, img_size//2\n",
    "            radius1 = 4\n",
    "            for x in range(img_size):\n",
    "                for y in range(img_size):\n",
    "                    dist = (x-center_x1)**2 + (y-center_y1)**2\n",
    "                    if radius1**2 - 2 <= dist <= radius1**2 + 2:\n",
    "                        images = images.at[i, x, y, 0].set(0.9)\n",
    "            \n",
    "            center_x2, center_y2 = 2*img_size//3-1, img_size//2\n",
    "            radius2 = 4\n",
    "            for x in range(img_size):\n",
    "                for y in range(img_size):\n",
    "                    dist = (x-center_x2)**2 + (y-center_y2)**2\n",
    "                    if radius2**2 - 2 <= dist <= radius2**2 + 2:\n",
    "                        images = images.at[i, x, y, 0].set(0.9)\n",
    "        \n",
    "        elif label == 9:\n",
    "            center_x, center_y = img_size//2-3, img_size//2-3\n",
    "            radius = 7\n",
    "            for x in range(img_size):\n",
    "                for y in range(img_size):\n",
    "                    dist = (x-center_x)**2 + (y-center_y)**2\n",
    "                    if radius**2 - 3 <= dist <= radius**2 + 3 and x <= center_x+2:\n",
    "                        images = images.at[i, x, y, 0].set(0.9)\n",
    "            images = images.at[i, 5:img_size-5, img_size-6, 0].set(0.9)  \n",
    "    \n",
    "    # Normalize to [0, 1]\n",
    "    images = (images - images.min()) / (images.max() - images.min())\n",
    "    \n",
    "    return images, labels\n",
    "\n",
    "# Generate datasets\n",
    "print(\"Generating synthetic dataset...\")\n",
    "X_train, y_train = generate_synthetic_mnist(n_samples=5000)\n",
    "X_val, y_val = generate_synthetic_mnist(n_samples=500)\n",
    "X_test, y_test = generate_synthetic_mnist(n_samples=1000)\n",
    "\n",
    "print(f\"Training set: {X_train.shape}, {y_train.shape}\")\n",
    "print(f\"Validation set: {X_val.shape}, {y_val.shape}\")\n",
    "print(f\"Test set: {X_test.shape}, {y_test.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:07:16.196062Z",
     "start_time": "2025-10-11T10:07:15.864654Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize sample images\n",
    "fig, axes = plt.subplots(2, 5, figsize=(12, 5))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i in range(10):\n",
    "    axes[i].imshow(X_train[i, :, :, 0], cmap='gray')\n",
    "    axes[i].set_title(f'Label: {y_train[i]}')\n",
    "    axes[i].axis('off')\n",
    "\n",
    "plt.suptitle('Sample Images from Training Set')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Build CNN Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-11T10:20:06.325585Z",
     "start_time": "2025-10-11T10:20:05.778900Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (4, 28, 28, 1)\n",
      "Output shape: (4, 10)\n",
      "+-----------------------------+------------+\n",
      "|           Modules           | Parameters |\n",
      "+-----------------------------+------------+\n",
      "|  ('conv1', 'bn', 'weight')  |     64     |\n",
      "| ('conv1', 'conv', 'weight') |    288     |\n",
      "|  ('conv2', 'bn', 'weight')  |    128     |\n",
      "| ('conv2', 'conv', 'weight') |   18.43K   |\n",
      "|      ('fc1', 'weight')      |  401.54K   |\n",
      "|      ('fc2', 'weight')      |   1.29K    |\n",
      "|            Total            |  421.74K   |\n",
      "+-----------------------------+------------+\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "421738"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "class ConvBlock(brainstate.nn.Module):\n",
    "    \"\"\"Convolutional block with Conv -> BatchNorm -> ReLU -> MaxPool.\"\"\"\n",
    "\n",
    "    def __init__(self, in_size, out_channels, kernel_size=3, pool_size=2):\n",
    "        super().__init__()\n",
    "        if isinstance(kernel_size, int):\n",
    "            kernel_size = (kernel_size, kernel_size)\n",
    "        if isinstance(pool_size, int):\n",
    "            pool_size = (pool_size, pool_size)\n",
    "\n",
    "        self.conv = brainstate.nn.Conv2d(\n",
    "            in_size=in_size,\n",
    "            out_channels=out_channels,\n",
    "            kernel_size=kernel_size,\n",
    "            padding='SAME'\n",
    "        )\n",
    "        self.bn = brainstate.nn.BatchNorm2d(in_size=self.conv.out_size)\n",
    "        self.activation = brainstate.nn.ReLU()\n",
    "        self.pool = brainstate.nn.MaxPool2d(\n",
    "            kernel_size=pool_size,\n",
    "            stride=pool_size,\n",
    "            channel_axis=-1,\n",
    "            in_size=self.conv.out_size\n",
    "        )\n",
    "\n",
    "        self.in_size = self.conv.in_size\n",
    "        self.out_size = self.pool.out_size\n",
    "\n",
    "    def update(self, x):\n",
    "        x = self.conv(x)\n",
    "        x = self.bn(x)\n",
    "        x = self.activation(x)\n",
    "        x = self.pool(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class ImageClassifier(brainstate.nn.Module):\n",
    "    \"\"\"CNN for image classification.\"\"\"\n",
    "\n",
    "    def __init__(self, input_shape=(28, 28, 1), num_classes=10, dropout_prob=0.5):\n",
    "        super().__init__()\n",
    "        self.conv1 = ConvBlock(in_size=input_shape, out_channels=32, kernel_size=3)\n",
    "        self.conv2 = ConvBlock(in_size=self.conv1.out_size, out_channels=64, kernel_size=3)\n",
    "\n",
    "        self.flatten = brainstate.nn.Flatten(in_size=self.conv2.out_size)\n",
    "        self.fc1 = brainstate.nn.Linear(in_size=self.flatten.out_size, out_size=(128,))\n",
    "        self.activation = brainstate.nn.ReLU()\n",
    "        self.dropout = brainstate.nn.Dropout(prob=dropout_prob)\n",
    "        self.fc2 = brainstate.nn.Linear(in_size=self.fc1.out_size, out_size=(num_classes,))\n",
    "\n",
    "        self.in_size = self.conv1.in_size\n",
    "        self.out_size = (num_classes,)\n",
    "\n",
    "    def update(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.conv2(x)\n",
    "        x = self.flatten(x)\n",
    "        x = self.fc1(x)\n",
    "        x = self.activation(x)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "# Create model\n",
    "model = ImageClassifier(num_classes=10)\n",
    "\n",
    "# Test forward pass\n",
    "test_batch = X_train[:4]\n",
    "with brainstate.environ.context(fit=True):\n",
    "    test_output = model(test_batch)\n",
    "\n",
    "print(f\"Input shape: {test_batch.shape}\")\n",
    "print(f\"Output shape: {test_output.shape}\")\n",
    "\n",
    "brainstate.nn.count_parameters(model)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Training Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 5.3273\n",
      "Test accuracy: 0.0000\n"
     ]
    }
   ],
   "source": [
    "def cross_entropy_loss(logits, labels):\n",
    "    \"\"\"Compute cross-entropy loss.\n",
    "    \n",
    "    Args:\n",
    "        logits: Model outputs of shape (batch_size, num_classes)\n",
    "        labels: True labels of shape (batch_size,)\n",
    "        \n",
    "    Returns:\n",
    "        Scalar loss value\n",
    "    \"\"\"\n",
    "    # Convert labels to one-hot\n",
    "    num_classes = logits.shape[-1]\n",
    "    one_hot_labels = jax.nn.one_hot(labels, num_classes)\n",
    "    \n",
    "    # Compute log softmax\n",
    "    log_probs = jax.nn.log_softmax(logits, axis=-1)\n",
    "    \n",
    "    # Compute loss\n",
    "    loss = -jnp.mean(jnp.sum(one_hot_labels * log_probs, axis=-1))\n",
    "    \n",
    "    return loss\n",
    "\n",
    "def accuracy(logits, labels):\n",
    "    \"\"\"Compute classification accuracy.\n",
    "    \n",
    "    Args:\n",
    "        logits: Model outputs of shape (batch_size, num_classes)\n",
    "        labels: True labels of shape (batch_size,)\n",
    "        \n",
    "    Returns:\n",
    "        Accuracy as a float\n",
    "    \"\"\"\n",
    "    predictions = jnp.argmax(logits, axis=-1)\n",
    "    return jnp.mean(predictions == labels)\n",
    "\n",
    "# Test loss and accuracy\n",
    "test_loss = cross_entropy_loss(test_output, y_train[:4])\n",
    "test_acc = accuracy(test_output, y_train[:4])\n",
    "\n",
    "print(f\"Test loss: {test_loss:.4f}\")\n",
    "print(f\"Test accuracy: {test_acc:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training step metrics: loss=3.5152, accuracy=0.2812\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def make_train_step(model, optimizer):\n",
    "    \"\"\"Create a training step function for the model.\"\"\"\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    def train_step(x_batch, y_batch):\n",
    "        \"\"\"Perform one training step.\n",
    "\n",
    "        Args:\n",
    "            x_batch: Input batch\n",
    "            y_batch: Label batch\n",
    "\n",
    "        Returns:\n",
    "            Dictionary with loss and accuracy\n",
    "        \"\"\"\n",
    "        with brainstate.environ.context(fit=True):\n",
    "            # Define loss function\n",
    "            def loss_fn():\n",
    "                logits = model(x_batch)\n",
    "                return cross_entropy_loss(logits, y_batch)\n",
    "\n",
    "            # Compute loss and gradients\n",
    "            grads, loss = brainstate.transform.grad(\n",
    "                loss_fn,\n",
    "                model.states(brainstate.ParamState),\n",
    "                return_value=True\n",
    "            )()\n",
    "\n",
    "            # Update parameters using optimizer\n",
    "            optimizer.update(grads)\n",
    "\n",
    "            # Compute accuracy\n",
    "            logits = model(x_batch)\n",
    "            acc = accuracy(logits, y_batch)\n",
    "\n",
    "            return loss, acc\n",
    "\n",
    "    return train_step\n",
    "\n",
    "def make_eval_step(model):\n",
    "    \"\"\"Create an evaluation step function for the model.\"\"\"\n",
    "\n",
    "    @brainstate.transform.jit\n",
    "    def eval_step(x_batch, y_batch):\n",
    "        \"\"\"Perform one evaluation step.\n",
    "\n",
    "        Args:\n",
    "            x_batch: Input batch\n",
    "            y_batch: Label batch\n",
    "\n",
    "        Returns:\n",
    "            Dictionary with loss and accuracy\n",
    "        \"\"\"\n",
    "        with brainstate.environ.context(fit=False):\n",
    "            logits = model(x_batch)\n",
    "            loss = cross_entropy_loss(logits, y_batch)\n",
    "            acc = accuracy(logits, y_batch)\n",
    "\n",
    "            return loss, acc\n",
    "\n",
    "    return eval_step\n",
    "\n",
    "# Test training step\n",
    "batch_size = 32\n",
    "x_batch = X_train[:batch_size]\n",
    "y_batch = y_train[:batch_size]\n",
    "\n",
    "# Create optimizer\n",
    "optimizer = braintools.optim.Adam(lr=0.001)\n",
    "optimizer.register_trainable_weights(model.states(brainstate.ParamState))\n",
    "train_step = make_train_step(model, optimizer)\n",
    "loss, acc = train_step(x_batch, y_batch)\n",
    "print(f\"Training step metrics: loss={loss:.4f}, accuracy={acc:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Complete Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training...\n",
      "======================================================================\n",
      "Epoch  0/20: train_loss=2.0550, train_acc=0.3200, val_loss=2.2914, val_acc=0.1121\n",
      "Epoch  5/20: train_loss=0.4747, train_acc=0.7969, val_loss=0.0065, val_acc=1.0000\n",
      "Epoch 10/20: train_loss=0.4055, train_acc=0.8105, val_loss=0.0008, val_acc=1.0000\n",
      "Epoch 15/20: train_loss=0.3754, train_acc=0.8068, val_loss=0.0009, val_acc=1.0000\n",
      "Epoch 19/20: train_loss=0.3752, train_acc=0.8129, val_loss=0.0002, val_acc=1.0000\n",
      "======================================================================\n",
      "Training completed in 70.75s\n",
      "Best validation accuracy: 1.0000 at epoch 3\n"
     ]
    }
   ],
   "source": [
    "def create_batches(X, y, batch_size, shuffle=True):\n",
    "    \"\"\"Create batches from dataset.\n",
    "    \n",
    "    Args:\n",
    "        X: Input data\n",
    "        y: Labels\n",
    "        batch_size: Batch size\n",
    "        shuffle: Whether to shuffle data\n",
    "        \n",
    "    Yields:\n",
    "        Tuples of (x_batch, y_batch)\n",
    "    \"\"\"\n",
    "    n_samples = X.shape[0]\n",
    "    indices = np.arange(n_samples)\n",
    "    \n",
    "    if shuffle:\n",
    "        np.random.shuffle(indices)\n",
    "    \n",
    "    for start_idx in range(0, n_samples, batch_size):\n",
    "        end_idx = min(start_idx + batch_size, n_samples)\n",
    "        batch_indices = indices[start_idx:end_idx]\n",
    "        yield X[batch_indices], y[batch_indices]\n",
    "\n",
    "def train_epoch(train_step, X_train, y_train, batch_size):\n",
    "    \"\"\"Train for one epoch.\n",
    "\n",
    "    Returns:\n",
    "        Dictionary with average metrics\n",
    "    \"\"\"\n",
    "    losses = []\n",
    "    accuracies = []\n",
    "\n",
    "    for x_batch, y_batch in create_batches(X_train, y_train, batch_size, shuffle=True):\n",
    "        loss, acc = train_step(x_batch, y_batch)\n",
    "        losses.append(float(loss))\n",
    "        accuracies.append(float(acc))\n",
    "\n",
    "    return {\n",
    "        'loss': np.mean(losses),\n",
    "        'accuracy': np.mean(accuracies)\n",
    "    }\n",
    "\n",
    "def evaluate(eval_step, X, y, batch_size):\n",
    "    \"\"\"Evaluate model on dataset.\n",
    "\n",
    "    Returns:\n",
    "        Dictionary with average metrics\n",
    "    \"\"\"\n",
    "    losses = []\n",
    "    accuracies = []\n",
    "\n",
    "    for x_batch, y_batch in create_batches(X, y, batch_size, shuffle=False):\n",
    "        loss, acc = eval_step(x_batch, y_batch)\n",
    "        losses.append(float(loss))\n",
    "        accuracies.append(float(acc))\n",
    "\n",
    "    return {\n",
    "        'loss': np.mean(losses),\n",
    "        'accuracy': np.mean(accuracies)\n",
    "    }\n",
    "\n",
    "# Training configuration\n",
    "config = {\n",
    "    'num_epochs': 20,\n",
    "    'batch_size': 64,\n",
    "    'learning_rate': 0.001,\n",
    "}\n",
    "\n",
    "# Training history\n",
    "history = {\n",
    "    'train_loss': [],\n",
    "    'train_acc': [],\n",
    "    'val_loss': [],\n",
    "    'val_acc': [],\n",
    "}\n",
    "\n",
    "# Recreate model for fresh training\n",
    "model = ImageClassifier(num_classes=10)\n",
    "\n",
    "# Create optimizer\n",
    "optimizer = braintools.optim.Adam(lr=config['learning_rate'])\n",
    "optimizer.register_trainable_weights(model.states(brainstate.ParamState))\n",
    "\n",
    "# Create train and eval step functions\n",
    "train_step = make_train_step(model, optimizer)\n",
    "eval_step = make_eval_step(model)\n",
    "\n",
    "print(\"Starting training...\")\n",
    "print(\"=\" * 70)\n",
    "\n",
    "best_val_acc = 0.0\n",
    "start_time = time.time()\n",
    "\n",
    "for epoch in range(config['num_epochs']):\n",
    "    # Train\n",
    "    train_metrics = train_epoch(\n",
    "        train_step, X_train, y_train,\n",
    "        config['batch_size']\n",
    "    )\n",
    "\n",
    "    # Validate\n",
    "    val_metrics = evaluate(eval_step, X_val, y_val, config['batch_size'])\n",
    "    \n",
    "    # Record history\n",
    "    history['train_loss'].append(train_metrics['loss'])\n",
    "    history['train_acc'].append(train_metrics['accuracy'])\n",
    "    history['val_loss'].append(val_metrics['loss'])\n",
    "    history['val_acc'].append(val_metrics['accuracy'])\n",
    "    \n",
    "    # Track best model\n",
    "    if val_metrics['accuracy'] > best_val_acc:\n",
    "        best_val_acc = val_metrics['accuracy']\n",
    "        best_epoch = epoch\n",
    "    \n",
    "    # Print progress\n",
    "    if epoch % 5 == 0 or epoch == config['num_epochs'] - 1:\n",
    "        print(f\"Epoch {epoch:2d}/{config['num_epochs']}: \"\n",
    "              f\"train_loss={train_metrics['loss']:.4f}, \"\n",
    "              f\"train_acc={train_metrics['accuracy']:.4f}, \"\n",
    "              f\"val_loss={val_metrics['loss']:.4f}, \"\n",
    "              f\"val_acc={val_metrics['accuracy']:.4f}\")\n",
    "\n",
    "training_time = time.time() - start_time\n",
    "print(\"=\" * 70)\n",
    "print(f\"Training completed in {training_time:.2f}s\")\n",
    "print(f\"Best validation accuracy: {best_val_acc:.4f} at epoch {best_epoch}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Visualize Training Progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training curves\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Loss curves\n",
    "epochs = range(len(history['train_loss']))\n",
    "ax1.plot(epochs, history['train_loss'], 'b-', label='Train Loss', linewidth=2)\n",
    "ax1.plot(epochs, history['val_loss'], 'r-', label='Val Loss', linewidth=2)\n",
    "ax1.set_xlabel('Epoch')\n",
    "ax1.set_ylabel('Loss')\n",
    "ax1.set_title('Training and Validation Loss')\n",
    "ax1.legend()\n",
    "ax1.grid(True, alpha=0.3)\n",
    "\n",
    "# Accuracy curves\n",
    "ax2.plot(epochs, history['train_acc'], 'b-', label='Train Accuracy', linewidth=2)\n",
    "ax2.plot(epochs, history['val_acc'], 'r-', label='Val Accuracy', linewidth=2)\n",
    "ax2.set_xlabel('Epoch')\n",
    "ax2.set_ylabel('Accuracy')\n",
    "ax2.set_title('Training and Validation Accuracy')\n",
    "ax2.legend()\n",
    "ax2.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Performance:\n",
      "==================================================\n",
      "Loss: 0.0005\n",
      "Accuracy: 1.0000\n",
      "Error Rate: 0.00%\n"
     ]
    }
   ],
   "source": [
    "# Evaluate on test set\n",
    "test_metrics = evaluate(eval_step, X_test, y_test, batch_size=64)\n",
    "\n",
    "print(\"Test Set Performance:\")\n",
    "print(\"=\" * 50)\n",
    "print(f\"Loss: {test_metrics['loss']:.4f}\")\n",
    "print(f\"Accuracy: {test_metrics['accuracy']:.4f}\")\n",
    "print(f\"Error Rate: {(1 - test_metrics['accuracy']) * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Per-Class Accuracy:\n",
      "==================================================\n",
      "Class 0: 1.0000 (109/109)\n",
      "Class 1: 1.0000 (90/90)\n",
      "Class 2: 1.0000 (103/103)\n",
      "Class 3: 1.0000 (90/90)\n",
      "Class 4: 1.0000 (100/100)\n",
      "Class 5: 1.0000 (93/93)\n",
      "Class 6: 1.0000 (107/107)\n",
      "Class 7: 1.0000 (98/98)\n",
      "Class 8: 1.0000 (91/91)\n",
      "Class 9: 1.0000 (119/119)\n"
     ]
    }
   ],
   "source": [
    "def compute_confusion_matrix(model, X, y, num_classes=10, batch_size=64):\n",
    "    \"\"\"Compute confusion matrix.\n",
    "    \n",
    "    Returns:\n",
    "        Confusion matrix of shape (num_classes, num_classes)\n",
    "    \"\"\"\n",
    "    confusion_matrix = np.zeros((num_classes, num_classes), dtype=int)\n",
    "    \n",
    "    with brainstate.environ.context(fit=False):\n",
    "        for x_batch, y_batch in create_batches(X, y, batch_size, shuffle=False):\n",
    "            logits = model(x_batch)\n",
    "            predictions = jnp.argmax(logits, axis=-1)\n",
    "            \n",
    "            for true_label, pred_label in zip(y_batch, predictions):\n",
    "                confusion_matrix[int(true_label), int(pred_label)] += 1\n",
    "    \n",
    "    return confusion_matrix\n",
    "\n",
    "# Compute confusion matrix\n",
    "cm = compute_confusion_matrix(model, X_test, y_test)\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.imshow(cm, cmap='Blues', interpolation='nearest')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.colorbar()\n",
    "plt.xlabel('Predicted Label')\n",
    "plt.ylabel('True Label')\n",
    "\n",
    "# Add text annotations\n",
    "for i in range(10):\n",
    "    for j in range(10):\n",
    "        plt.text(j, i, str(cm[i, j]), \n",
    "                ha='center', va='center',\n",
    "                color='white' if cm[i, j] > cm.max() / 2 else 'black')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Per-class accuracy\n",
    "print(\"\\nPer-Class Accuracy:\")\n",
    "print(\"=\" * 50)\n",
    "for i in range(10):\n",
    "    class_total = cm[i].sum()\n",
    "    class_correct = cm[i, i]\n",
    "    class_acc = class_correct / class_total if class_total > 0 else 0\n",
    "    print(f\"Class {i}: {class_acc:.4f} ({class_correct}/{class_total})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Visualize Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaUAAASdCAYAAABQGjBLAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjYsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvq6yFwwAAAAlwSFlzAAAPYQAAD2EBqD+naQABAABJREFUeJzs3Qd4FNX79vET0ghJIBAgoXcIvffeQQQEpCgdFGkWUFEsVEVsKKggNhClCCqI9N57Cb33EkoogYSasO915vcm/9DOszCwrOT7uS6U5Bnu3Z2dOTvz7OxZD4fD4VAAAAAAAAAAALhAMlfcCAAAAAAAAAAAGk1pAAAAAAAAAIDL0JQGAAAAAAAAALgMTWkAAAAAAAAAgMvQlAYAAAAAAAAAuAxNaQAAAAAAAACAy9CUBgAAAAAAAAC4DE1pAAAAAAAAAIDL0JQGAAAAAAAAALgMTWkAAOCUsWPHKg8PD+v/duiMatWqqf+yJUuWWI9jwIABt/0+e/bs1p/H5b++7t544w2VNm1adfny5Sd9VwC3HUfw8Nq0aaOyZcumrl279qTvCgAAENCUBgDATR0+fNhqWOg/oaGhKjY29p7L7dq1K2G5x9kQfZJNm8R/kidPrnLmzKlefvllax09TXTDWT/Gp9G+ffvUyJEj1VtvvaUCAwPvqjscDjV9+nTVokULaztOkSKF8vPzUzly5FDPP/+8+u2339SNGzeeyH13RxcuXFAfffSRKl++vAoODlbe3t4qXbp0qlatWuqbb75R0dHR6mn0qN+Y0dta4vHF09PTWp81a9ZUU6ZMUf81Q4cOtR6H/v+9FCtWzKp37dr1nvXnnnvOqi9cuFD9F/Xr10+dOHFCff3110/6rgAAAIGXtAAAAHiyvLy81OnTp9WsWbNUo0aN7qr//PPPKlmyp/t95pIlS6pnn33W+vvFixetZvVPP/2k/vrrL7V27VqVJ08e5Q4edyNHvwGhm7X/RYMHD7Yapz169Lirdv78edWyZUu1YMEClTJlSqshmCtXLqtBeOzYMbV06VLruR42bJjavHmzSur0dqab93q95c+fXzVv3txqpJ47d04tW7ZMvfbaa1ZT7sCBA0/6rv4n6O3sgw8+sP5+8+ZNtX//fjV16lS1aNEiNWTIENW3b1/1X1G9enXr/3qMfPfdd2+r6e1j69atVtNZ1+9069Yta/vx9fVVFStWVP9FefPmVY0bN7aa8q+++qry9/d/0ncJAADcB01pAADcXIUKFdSWLVvUL7/8cldTWl89/fvvv1tXR+rG3dOqVKlSt33EXV9V2759e+vq2Y8//tj2lCKPim6kPk5hYWHqv0g3wyZPnmxd8XznVdJ6G9ZXZy5fvly1bdtWjRgxQgUFBd3VLPv333/VV199pZI6PRY0bNjQ+rve91u3bn3XMrrh+F9qpLrDG393TqGxcuVKVaVKFevNlNdff/0/82aQfgNP72MrVqyw9i392OLp1wg9djZt2lT9/fffKiIiQmXIkOG2bUtfgV+1alXrEyn/5Sk89OObNGmS6ty585O+OwAA4D6e7suqAAB4CugpDFq1aqVmzpypzpw5c1ttxowZ1lXUnTp1uu+/j4mJUf3797camrrRkCZNGtWgQQOr6XIv+upL/dHukJAQqxFTunRp66pBE331nb6PusHh4+Njzempr1LTzcjHQV/pF3/F7fr162+b7qRDhw7WFcVNmjSxrh7Vv0s8zcc///xjXYmbOnVqa30UKlRIffHFFyouLu6u27l69ap1tWGWLFkSlv3xxx/ve7/uN6e0bgSNGTNGVa5c2Wq46vWqr+5+5ZVX1NGjRxMeU/wbC4mnE9CPR5q6IDIy0pqvWU91oa9yTJ8+vXUl7fbt2+9aVufpnEOHDlkNYL1d6H+jn7OBAwdaDeDE9M/6qvQyZcpY247eHjNnzmw1Ru91teW9TJw4UV2/ft26ovdOv/76q9WQ1ld46r/f2ZDW9CcB9NWP+krq+81zrpvW+upO3ZBL/BzoKT/0FdYlSpSwrprUdf086KlC7uVBln/Qdfko6Kug9Xapp+i4V0Na09vIvZ4bvQ2WLVtWBQQEWH/03+/1hk7iuY5XrVql6tSpYz0v8VPL6N/HX22r/71eV3qbTrxt6nnD9bhTsGBBa5vR/75u3bpWs/Re9PJ6nRUpUsTKSpUqlSpevLj68MMPrauX4++TpveTxPvI43hTSm9L+vnU63rnzp131R/nOGKHbkLr7VWP+/FjYzy9DvVz8c4771g/L168+K564qutnRlTdeNb7y9Fixa1svXzpv+93h/vlHh/nTdvnvWGq36udaZ+k/F+rxejR4+2tiO97vQ67NOnjzVn9P3GQ/36pnPd5c1KAABwb1wpDQDAf4BuOusTc31l8Jtvvpnwe331tG4U6itN70WfuNeoUUOtW7fOahzpxqVuYv/xxx9q7ty5VrMwcaPwypUr1kn+tm3brLlq9RVzevoEPbWCbkzdS/w8wPGNQ9000E2cb7/91roNPb2Gbtw8LnfOwaw/el+uXDlVuHBhq5miGx26Ua7pq0f1x7ozZcpkXS2oGyi6Ifr2229b9zPxHLK6oaivTNeNUJ314osvWlm9evVKaNo4Q+fo9ffnn39at/vCCy9YU1Topo6+erh+/foqa9asVgNPN1GOHDli/T3xHLAmZ8+etZ4rPVWDfu70mwO6SapvT7+RoZ+DSpUq3fXv9GPWzT09LYpuFk6bNs1qNuqmrL76PJ5eZ5999pl1FbheB7pJq+ds1c1FvW6cmd83floT/bzcSW/D2vvvvy/Op534qs/E9POmm1z6sXTv3l1dunTJ+r1uhNerV89qtun1qK+a1A1OvV70tqobuz179kzIedDlH3Rd2qW3bT29gt7HOnbsaFxWN8fvbGbr+6+3wfirR/WUKDpHT4kyfPjwuzJ0Q1pPX6G39y5duiS8gRLv888/txqbet3o8UFPgxH/xpa+ynjHjh1Wc1e/yaWfE93I1Vn6+Uo8Zuk32/RYs3v3bmu9d+vWzdpv9M+ffvqpNebpNxr0fqEb17rpn/jNGmkfsevO7e5JjCMPQufq6Z70c6PHhnj6Z70P6k+e6DFZ/6zvT+J6/L93ZkzVb7bpTz/o51VPm6HfKNTNcP36oh+zblbrx3mv1wy9T+k3tnRjWm/T48aNs8awO9+00HNE66vV9Zuk+nsE9BRAetzU28b96PumrxhfvXq1dX+YwgMAADflAAAAbunQoUMO/VJdt25d6+dChQo5ChYsmFCPiIhweHl5OV599VXrZ19fX0e2bNluyxg4cKCV0bp1a8etW7cSfr9p0yaHj4+PIygoyHHp0qWE3/fv399a/uWXX74tZ86cOdbv9Z8xY8Yk/D4yMtKRMmVKR6ZMmRyHDx++7d9MnDjRWr5nz563/V7/rmrVqk6tg8WLF1vLv/LKK7f9Xj+W9u3bW7WOHTvetr70n379+t2VNW/evIT1GR0dfVtW165drdqff/6Z8Hv9OPXv6tWr54iNjU34/datW611p2t6fSWm1/+dz8E333xjLVuzZk3HlStXbqvpn8+dO5fws14vpsOze607/fj17/v27Xvb72fOnGn9Pnfu3I64uLiE38evtxw5cjhOnjyZ8PuzZ89a20NgYKDj+vXrCb9PkyaNI2PGjI6YmJi77k/i+26SLl06axu5082bNx3e3t7Wdnzt2jXHg4p/jpIlS+aYP3/+XfX33nvPqn/44Ye3bf96my9VqpT1PJ44ceKhl3/QdanpbeZB/ujtOt7YsWOt22vTps0DraelS5da/y5//vyOixcvJvz+/Pnzjrx581q1ZcuW3bXf6T+//PLLXXnx44S/v7+1P9zpxRdftOo//vjjbb8/ffq0I0uWLNb2cPXq1YTfN2vWzFper/87nTp1ytpOHmb8cIbeX/XYeacVK1ZY21VwcPBt99UV48jUqVMfaBvRyye2YcMGK7d27dq3bZMeHh7Wa4LWqFEjR65cuRLqeozQ26yfn1/CNiuNqb/++mvC85F4Oz9y5Igjbdq01n594MCBu9aF/r1ev/H0eqlWrZpVW716dcLv9+zZ4/D09LTGDr3tJN4fCxQoYNwWevXqZdUXLVp0zzoAAHjyaEoDAPAfaUoPGzbM+nnNmjXWz0OHDrV+3rx5832b0jlz5rSafseOHbsrXzee9b8fN25cwu90c003SnTD+066qXpnUzr+PiXOSKxEiRJWc8JuU7pkyZIJDZg33njDUaxYMev3umG6f//+29ZXaGjoXY3A+CaMruuGyZ10o043bHRzLF716tWt5Tdu3HjX8p07d3a6Ka0bgbqxsnfvXvHxPmhTWj/O5MmTW42zezWNdVPqzoZjfCP1Xs3G+FriRqNex9mzZ3+opnH8fdSZelu4k2406VpISMg9/63e1kxN2vgmV5MmTe76t7rJljp1aqvxlrjBHG/69OnWv9VvGjzM8g+zLrX4Jp+zf/Q+EC9+n3/33XcdD6JTp07Wv/vjjz/uqo0fP96q6WXu3O/u9Zwlbkrrxt+ddPNTb+81atS4578dMWKE9W///fdf62c91uh9T6/3GzduiI/lcTSl9f2N3750Y7xFixYJb5bcuc5cMY7EbzvO/tHLJxbfYE6RIkXCWDhlyhRrWf0Ghfbll19aPx89etT6Wd+/+DfP4kljqn6OdX3t2rV31T7++GOrNmjQoLv213bt2t21fHxNbx/xBgwYYP1Ov87cacKECcZtIX5fud9rEwAAePKYvgMAgP/QlzfpuUD1dAd6Llg9P6yec/V+H13XH5c/ePCgyp8/vzUH8J30R7T1vKbh4eHWF8zp5fW0DwUKFFChoaF3La/nKY2fhiHemjVrrP/rj6zrj17fa/oQPd+x/pM2bdqHfuwbN260/sR/NFt/bF5/lFtP+aA/yp+Ynts0frqOO++r/hh3/HQRd9LzoSb+SLj+0i+9vJ725F7r4ueffxbvd3R0tDUXa+7cua05pB81fX/1OtbP5b2+iE3/fv78+dZzrO9zYvrj7XeK304uXryY8Ds9HcjIkSOteXD133WmnhJAry9nxM8Te6+5oiV6OpM7v8BTTxdy57zder7rO+3Zs8f60raMGTNaUz7ca9oTLf45f9DlH2Zdav/rq7qWnp5Du9dUK/FTNeht5E56PnmTe613PY+xnldZT4Vy55cHavv27UtYj3q6kw0bNljrRN8PPTXDk6Dv753PuZ6y485pRlw1jujt3s58yHoqJZ2t53XWUzfp6Xv01Bx6Tmb92qHp6VI0/ft27drdNZ+0M2Oq3q70uHOv7cC0XTm7v+h1p91r+iE9LYyJntZK0689AADAPdGUBgDgPyJdunTWHJyTJk2y5oHWTTQ9R+z9xM+rq+fivBf9pYSJl4v/v/6SvHu5V46eO1b77rvvjPddz+tppymtvxDw+++/d2rZ+z1efV/1l3Ldq+GY+H7Gi4qKsubufZDbuJPO0HQT/XF40Oc4MT2v9f3mzk38ZW16rmH9BYr6TZCPPvrI+qObW3oe8S+//FJ8XuOb17p5fq/GkW5E6sa1bmLeOQ9y4i/r0/MS63nVH3Tb1PMa6z/Sc/6gyz/MurQr/s0iPaf3g9DPv25U6jHkXutOz+V9r21E2s5N611/ker9vkw18Xp83PuIM/R2F7996jeSFi1aZM3jr9+s03Mc66bskxxHHkb8lw3qfUg3dfX/9bzQ8fuYfjNTz4Ud35SOn09afweBs/dTbzP3e2yPYuwxvSZJ605/uaR2rzfrAACAe6ApDQDAf4j+grK///7b+rIp3Rhs3br1fZeNP/HXX2x4L6dOnbptufj/6y8du5d75cT/G/3FiPpKWndwvy/L0/dV15y9ck43bOKvjr3T/dbpvTIeponorAd9jh+Gbha99dZb1p+TJ09aVy7rBrX+YjKdr79I0URfIa0bz/HNyjuz9dW4+gv1dPOvZs2aj+w5j3/MzZo1s770UfKgyz+se109bKL39fgrw+OvDtUNRv0FerrR7Az92PTyenu+s8Gn93d9pfK9thHpiydN611/OeEXX3wh3rf4K+gf1z7yoAICAqwv6dNf1lerVi3riyD1pzTiH6srxhH9RZn3usL4fnSD+c4ruuOvVNbNZv0llfrLZ/UbSfH0l1LGX0Gttw39RY36sd/r6njTmHq/14tHMfYkfk268xMx0hgcP97c640YAADgHmhKAwDwH1K3bl3rikLdwNFTKaROndp4Qp8zZ061f/9+a/k7r0SMvwo1fvoPvby+IlYvrxsKd07hoZsWd9IfBddN8tWrV7tNU/p+9H2dPXu2NX2AM1Np6KsjdcNm06ZNd330/l7r4l50k0dPh6KvanfmdnWjKP5qwfi/m4SFhVlvTugpE65cuXLXVYF3Psd26aktXnjhBdWyZUuVL18+tWDBAuuKRGkqD71t6KbYjRs37poGQF+RqpvSn3zyiXWVptQIdZaetkZv03p6iJs3b4pTQzzo8g/LdIXtvSSerkRPA1OlShW1bNky9euvv1oN0/tJfOW5nuZHT7Wgt4fEjcnHsY3opqZ+DvWY4IxSpUpZzXW9rzmz3vWyj/Lq8/vRb5DoRq9uEE+cOFG9+OKLLhtH9G3q59dZ7du3v6spXaRIEeuTCPp5iH/j6M7pW/QUHjNnzrTGcH1Fd7169RKuWHaG3q70VeV6ipA7p/B4FNuVXndTp061rri/s1muxwwTPeZqhQsXfujbBwAAj5dzl1cAAAC3oBuVumGhT9R1E8+ZZoVu9PTt2/e2uWy3bt1qzVmqr+JL3MzQH1fXjcN+/frdljNv3ry75pPWdFMsMDDQmtv5XlMe6EZp/LzTT9prr72W0ASNn+c4Md2I1/M/J14Xmn5siZtg+qrw3377zenb7dGjh/Xvu3fvnvCR8nh6yoDEVxDHz4N67Ngxp7J1g1c3ifVVm3duD3PmzLGaUbqRKc2/amps3qv5o6cn0NMc6AaiM1fr6uaXzoqfI/bObVRfsam3L709xU/nkJjedu81DYCJbq5169ZNHTlyxLrKW+8Hd9q+fXvClZ4PuvzD+v9fNO70nzsbiXo6Ff0mQM+ePa2ree9FNzsTT8Og13F8QzzxetTrOr5JHr+MXfrNLN341tvN559/fs85tPUc9HpsiJ+GQV+druekv1fDXq9vPV1G4n3k+PHj97193cDXTfHDhw8/kqvadZa+X/FjgCvGET02P8g2cq/5p/V+qfc7PeZ89tln1ptXevqOxOLnlY5f7/eaT9okfpvRry+J9xc9fg0bNszap0yf5pHoN17149DTBCW+Ml2PPx9//LHx3+ptTE8h8jjm8gcAAI8GV0oDAPAfo68s1H+c0adPH+tKON380I0SffWfbvLoZpZu9OgvOtRN5cTL66vm9O91k1lflakbDJMnT1YNGjSwshLTH43WVxHqOa71VW36Sjt99a5uQOqmkJ7qoUKFClaD9EnT9+3DDz9UgwcPthq1+mf9kXDdWNJXh+tGnp4vWV8xG99wmTBhgnXf9RWB9evXtxrI+vHWqVNHzZgxw6nb1Y1OvR70OtQNEj01gL4i9+jRo1bTWH/RWfwbA7qRqKeO0E06fXu6kaTXq55L/H4+/fRTK1/fd90I1Fdy6nWvv6RNXzmtp9pwdpqHO+mGlm5o582b1/pysqxZs1rNaP3YdfNNN2/vnAf6Xpo0aaK+/vpr60sX77ziUTeu/vnnH6uRqa8O1W+46O00V65c1v3Wt6OvDNaPST9f95vD9l50s01foTpixAhr29Xbs56+Qn9yQDcFdZNcX0kaP6XFgy7/JOgrT/VcwXp96abdoEGDrPupm7V6+9RXler7qrfxeLr+6quvWnPQ66vW9falm5l//fWX1eDVjVa9zKOivxhTX6mqxxM99ugvxtTTdOixRF+Jrq8yjoiISLiyXy+vG/660Thr1ixrP9D3b+/evdYbYnqqhvhpPnRN70t6n9H7pX6jTu9T+spgTU9FoT3IFb/3o/c9ve3qMfH333+3xoQnNY48DN1k1vuTXrf6zY0791V95bb+NIeuxy//IHTDXa8bvf/q9a+/uFI3jPXri36MupmsP63zsPSnMd599101ZMgQ64pnvc3r51Xfpv5Z3+97jW36DQ79pb167AUAAG7MAQAA3NKhQ4f0JYaOunXrOrW8r6+vI1u2bHf9Pjo62vHhhx868ubN6/Dx8XEEBQU56tev71i+fPk9c86dO+fo0qWLI126dI7kyZM7SpYs6fj7778dY8aMse6P/v+ddu/e7ejcubN1+/o2UqdO7ShcuLDjtddec6xbt+62ZXVG1apVnXpMixcvtpZ/5ZVXnF5f7du3Ny43f/58R8OGDa3H5+3t7QgNDXWUL1/eMXjwYMfRo0dvWzYmJsbRp08fR6ZMmaz1W6BAAccPP/yQcL/69+9/2/L68d/rObh165bjp59+cpQrV87h7+/vSJEihSNPnjyOrl273nabN2/etG4va9asDi8vr7sez/3W3dmzZ611rW9bP6a0adM6nn/+ece2bdvuWlbn6Ry9vu6kH4+u6cen3bhxw/Hpp5866tSp48icObP13IaEhDiqVKnimDBhgvW4nKXXnf5zPzpr2rRp1v3OkiWLte3pP/oxNWnSxDFu3DjH1atXb/s3pm0yXmxsrGP06NGOihUrOlKmTGk9j3r91qtXzzFq1Chr/3jY5R9kXT5qej/V26zepvT+preX4OBgR7Vq1RwjRoy463Fpv/zyi6N06dLW9qf/6L/r393pftv3gzy2K1euOD777DNr/NDbvJ+fnyNHjhyO5557znou9baeWFRUlDVOhYWFWes8VapUjmLFijn69etnbYfxIiIiHC1atLC28WTJkt32/F+4cMH6nX7unKW3L31797NlyxaHh4eHI2fOnLfd58c5jjwqev/X+frPgAED7rmMfn3Rdb2t623/QcdUvU6++OILa7zXjy0wMNAao/7555+7ljXtr6Z1MXLkSEf+/Pmt8UePQ2+99Zbj2LFj1vKNGze+a3n9WHUtPDz8vvcbAAA8eR76P0+6MQ4AAICnm74i/KWXXrK+0PBhpxMBTPRVx/pTBfoq92eeeeZJ3x08Rno++9q1a1tX4+tPi8TTnwDSn0jR34+g57sGAADuizmlAQAA8Nh16NBBFSxY8IG/6A9wlp46Q09vQkP66XH27Nm7vtjy4sWL1jzW2p1f8KinANLzwn/xxRcuvZ8AAODBcaU0AAAAXGLdunVq9uzZqnfv3rfNZQ4A96LnotcNZj2XeMaMGa25yPX83Pq7EfQbXXrO/MR++eUXaz7yzp07P7H7DAAAnENTGgAAAADglm9k6S/AXL9+vfXlifqLLfWXSOqGdPfu3R/6S1wBAMCTR1MaAAAAAAAAAOAyvLUMAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBma0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSeKIGLBmgin1f7EnfDQC4J8YoAO6sw7QO6rlJzz3puwEA98QYBcCdMUY9eV5P+g7813gM9DDW+1ftrwZUG6CepLHhY9Ww1cPU3nN7VUrflKp5gebquwbfPVATZuDSgdbfPT08VeaUmVWTsCZqcI3BKsAnQLmDi9cuqvcXvq/+3v23On/1vMqWKpv6ut7X6pk8zzzpuwY8Ue4+Rq0/sV69u/BdtfHkRuXh4aHKZCqjPqv1mSoaWvSpGqO+XvO1GrVhlDoadVSlTZFWPZ//efVJrU9Ucq/kT/quAU+Uu49Rep/tNrObWnxosTWetC/a3tp3vZJ5PdAJzq9bfrX+7p3MW2VNlVW1K9pOvVf5vQfKeZy+W/ed+nzV5+pU9Clr/P2m/jfWeAwkde48Rm05tUUNXTlUrTi6QkVeiVTZg7KrriW7qtfLvf5AOe4+Rn2y/BPrHG935G7l5+WnKmSpoD6t9anKlzbfk75rwBPnzmOUxnEUHpR7PKP/IRFvRiT8/Y/tf6h+S/qpPT33JPwucUPE4XCoOEecS3cc3Yz+cvWX6vPan6uymcqqmJsx6vDFww+cUzBdQbWg3QIVeytWrTy6UnWa3klduXlFjW44+q5lb8TdUD6ePspV9O3V/q22Su+fXv3Z/E+VKWUmdeTiERWUPMhl9wFwV+48RkXfiFb1xtdTjfI1UiOfGWmNL/2X9Fd1f6+rjvU6prw9vZ+KMWrCtgnq3QXvql8a/2KdSOk3CPXBlW7CD6s7zGX3A3BH7jxGxd2KUw0mNFChAaFqVedVKuJyhGo3rZ01Ng2pOeSBsurlrqfGNB6jrsdeV7P2zVI9ZvWwTqz6Vu77xMcovd57z+utvm/wvSqbuaz1Jpoeh/XzoI+tgKTMnceojREbVfoU6dXvTX5XWVJlUauOrVJd/u2iPJN5qp5lej41Y9TSI0tVj9I9VOmMpa3jvPcWvafq/F5H7ey+U/n7+LvsfgDuyJ3HKI6jOI56GEzf8YD0Dhb/J1XyVMpDeST8rN/NDfwkUM3eN1uV/KGk8v3I13on+14fCXhjzhuq2thqCT/fctyy3hXOMTyH8vvYTxX9vqj6c+efD3TfLly9oD5Y9IEa99w49WLhF1WuNLlUkZAiVgPoQemBSz8mfQViy0ItVevCrdX0vdNv+zj7T5t+su5v8o+SJ1y9/NL0l1S6z9OplJ+kVDV+rWG9o5/Y0BVDVcgXIdZ66vxPZ3Ut9toD37dfNv9iXR09reU0VTFrResqgarZqz7QlZbA08qdxyh9+3rfHVRtkHW1S8H0Ba1380/HnFZHoo48NWOUPknUY5Meh/X4VCdXHfVCoRfUuhPrHjgLeNq48xg178A8tfPsTqvhUyy0mKqfp74aXH2w+m79d9YJz4Pw9fS1HlO2oGyqW+luqlbOWgljVPzj+XjZxyrjlxlVvm//d/XfsahjqsWUFipoaJBK82ka1XhS49suLNAne73n9rbqwZ8Fqz7z+yiHcqgHNWzNMPVyiZdVx+IdVYF0BdT3z36vUninsI6vgKTOnceoTsU7qeH1h1vnPTlT51RtirRRHYt1VH/v+vuBH6c7j1Fz2sxRHYp1sI4T9fnd2MZjrasvdVMeSOrceYziOIrjqIdBU/ox0B9NH1pzqNrVY5fVFHaGHgDGbR1nvduyo/sO1atcL9Xm7zZq6eGlCctk/zq71Wy5n/kH51uDyYnLJ1T+7/KrzMMyWzul3jnt0h+dSjyQ7D+/X/216y/1d4u/VXjXcOt3zac0V2dizqjZrWerjV02qhIZSqia42paTSht8o7J1v0fUmOI2vDyBpUhMIMauX7kbbez5PAS6yMppqu7p++ZrspnLm+9W6abR4VGFlJDlg+xBhkA7jtG5QvOp4L9gtXPm3+2xpOrN69af8+fNr/VvH1axih9dbSeniS+CX3wwkE1a/8sphcC3HyMWn18tSqcvrAKCQhJ+F3dXHXVpeuX1I4zO2w9Jj/v28eohYcWqj3n9qj5beerGS/MUDfjblpX2QT6BKrlHZerlZ1WWlc71fu9XsK/05+E01O06U9hrOi4whq7pu6aetvt6Lrpo706S49P+uQuXjKPZNbP+vEDcN8x6l6irkepNH5plF3uMkbd7zFqj+JxAkkBx1EcR/2XMH3HY6CvAqydq7bTy+uPJAxZMUQtaLtAlc9S3vqdfvdbv6s1euNo691wTV/5rOcmvR/d+NBNad2gHV5vuPXOmb5yWk91sbXb1of+SIPe6SZsn6Bq5Khx286or8hO55/O+lnfV92AOfPWGeXr5Wv97os6X6hpu6dZ77B1KdnF+lhD5+KdVecSna36RzU+UgsOLrjtSkT9DpNuXOmPZpge56JDi6wrI2e9OMtqPnWf1d0aiPpX6/9QjxFISp7UGBXoG6iWdFhivbM9eNlg63d50uRRc9vMtfWxMncbo/QV0nqux0q/VLLefdcfPdVzPup50AC47xil5wVMfCKlxf+saw9Df3RWnzjN3T9XvVrm1YTf+3v7q58a/ZRwbPb71t+tYzj9Oz3Vj6Y/tqqv5tFvhulPXOgxqm+lvqpp/qZWXV+ZM/fA3NtuL5VvKmuMuh89NumP8ob43/E4/UOsK6wAuO8Yda9PZv2x4w8188WZ6mG52xh1J32b+orOilkqqkLpCz304wSSEo6jOI76L6Ep/RiUyljqgZbXTVU9F6puHiemmyrFMxRP+Hlhu4XGHL0T3rx1U42oP8La6bSJzSaq0C9DrYnm6+au6/R92nZmmwoYEmDtcPp+NMjTQH1b/9uEuv4YRXyzR9MfgdfzxeqPQSR2NfaqOnD+gPX3XZG7VNdSXW+r6yueFx9enPCznhx+d8/d4uPUc/X80PAHaw61khlLWleH64nmaUoD7jtG6SujO0/vbE1toccmPb58seoLa+6x9S+vt94BfxrGKH3go98cHNlgpDW3v15/r895XQ1eOlh9WPVDpx8jkFQ9qTHqUZqxd4Y1RunjMn3cot+sSvzFQ4VDCt92sYAeo/Tj0B+7TUy/KabHqKhMUSoiOsKauzCefjNPryt9whavSf4m1h8AT/cYtf3Mduuj6XoatPjzvqdxjOoxs4f1WFd0WvHAjxFIqtxhjLLrvzJGwT6a0o/BnV/AoC/nv3OuGn1VbzzdKNH0u9z6S/vunEvHWRkCMlj/1/PaxNNNGf1ulp6H60Hod4emvzDd2lEzBma86ypr/c5UYvox6NvXV0He6VF/AaH+SL2+SlE3pOPpj//rd99cPck98F/0pMYo/QWAetqL1Z1XW7dp/a7ZBJX609Tqnz3/qFaFWj0VY9SHiz9UbYu0VS+VeCnhoEl/6az+MqL3q7yf8NgBuNcYpecuvHPu99PRpxNqD6J6jupqVINR1tikx6g7Pw1yrzFKv8k+vun4u7LSpfi/N9js0seEnh6e1lz+iemfH/QxAknVkxqj4uk5W/X0Y11KdFEfVPlAPQx3HaMS6zmrp5qxb4Za1mGZ9R0iAJzDcdTtOI5ybzSlXUDvBPod3sTCT4cnfPxbN5H1zq4bx/EfjXgY+upDbU/knoQXbj1Pjv6Igb5q8EHonT93mtxOL6/nZtVNYT1Y3G9uWN04Xnt8rWpXtF3C79acWKMelP74lm5u6XfM4ps7e8/ttRpONKQB9x2j9Dvwep/VX8gRL/5nvT8/LWNU/ONMTB+8aNY78Q82lSKQ5LlqjNKfjPh4+cfW3PPx356uv68jpW/K297wd4Y+WXrQMUp/DF/frr69e9HHOXqMqpKtivWznhpIT1+k/+2DjJ36pG3hwYXqubD/femRHn/1zz3L9HQ6B4DrxyhNz8taY1wN1b5oe/VxzY8fOsddx6j4Y6VXZ7+qpu6eqpa0X6JypM7xQP8ewO04jvofjqPcE5druYCe53TDyQ1q3JZxat+5far/4v63DQp6ntW3Kryles3tpX4N/9X6eMGmiE3qm7XfWD/H0++If7vu/z6efqe8wXlV43yNrY+J6znG9G20n9ZehaUNU9WzV3+sj1FP7K7nH9JzxepvXdVXQ+r78P7C963Hrr1e9nX1S/gvaszmMVYTWa+HOye81++shX0bpk5cOnHf2+pWqpvVbH999utWzsy9M605kHqU7vFYHyPwtHLVGKXnNrtw9YL1JaW7zu6y9v+O/3S0GsVP0xjVMG9DNWrDKDVp+yR16MIhNf/AfOvq6Yb5Gt72CQ8A7jVG6Y/A65OmtlPbWh8D1fMX6u/m0McX8XPRPy6ti7S2rr7RH8dffmS5NXboqYBem/2aOn7peMIYNXTlUGsufD1vYfeZ3dXFaxdvy9Ff2KPHKJPe5XqrHzf9aK0bPRZ3m9HN+jRHx2IdH+tjBJ5WrhqjdGb1X6tbY1Xv8r2tN9v1n7MxZx/7Y3TlGKWPE/X8sBOaTrDWXfzj1NPAAXhwHEdxHOXOuFLaBfRczh9W+VD1md/HmtOmU/FOql2RdtacqPEGVx9svYP1yYpPrC/y0x8n1+/YJP5iLD046KueTcY1Gad6zellzdGqr9Srmq2qmtN6jvL2/L8v5dLfJqonfe9QrMMje4x6Mnn9pYPvL3rfajLpgyP98QX9LlT8JPAtC7VUBy4cUH0W/G89NMvfzGowJ55cXl9hqL9FVc8ddD9ZUmWxvhhND5pFRhWxPmKiB5h3Kr7zyB4PkJS4aozSb5D9+8K/auDSgar8z+WtMUrPUzanzRxrWp6nZYzSH6XVV3/rgzA9371eb7pRbeeKJiApc9UYpd800t/g3m1mN2uM0h9/1VcjDqo+KGEZ/YZWjuE51OL2i1W17NUe2WPUX6K6rOMy9c6Cd1TTyU3V5euXreObmjlqJlzx82aFN635EPUFB3r87FSskzXvYdS1qIScqOtR1hhlose6s1fOqn5L+lmNnmKhxaxjxTu/nAiAe41R+ouZ9b6rG7b6T7xsqbKpw28cfmrGKP3Gvlbt19vv/6M+NgSSCo6jOI5yZx6OxLN646mn3zHK+21etbP7TpUnOM+TvjsAcBvGKADuTH9xtD7ZOfjaQZXaL/WTvjsAcBvGKADujDEKd2L6jiRm1r5Z1pdi0OwB4I4YowC4+xj1XqX3OJEC4JYYowC4M8Yo3IkrpQEAAAAAAAAALsOV0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSSYCeoaXLv11Umk/TKI+BHir8VPiTvksAkIAxCoA7Y4wC4M4YowC4M8YomNCUfoJORZ9Sr856VeUcnlP5fuSrsnyVRTWc2FAtPLjwkd7OnP1z1NjwsWrGizNUxJsRqlD6Qk79u9dmv6ZK/lDSum/Fvi/m1L+5FntN9ZjZQwV/FqwChgSoZpObqdPRp29b5mjUUdVgQgOV4uMUKv3n6dXb895WsbdiH+qxAXh8GKMYowB3xhjFGAW4M8YoxijAnTFGMUa5A68nfQeSqsMXD6uKv1RUQcmD1Oe1P1eFQwqrm3E31dwDc1WPWT3U7p67H9ltHbhwQGUIzKAqZKnwwP+2U7FOau2JtWrr6a1OLd9rTi81c99MNaX5FJXKN5XqObunajq5qVrZaaVVj7sVZw0AoQGhalXnVSricoRqN62d8vb0VkNqDnng+wfg8WCMYowC3BljFGMU4M4YoxijAHfGGMUY5TYceCLq/17fkenLTI7o69F31S5cvZDw9yMXjzgaTWzk8P/Y3xE4JNDRfHJzx6nLpxLq/Rf3dxQdVdQxLnycI9tX2RwpP0npaDmlpePStUtWvf3U9g41QCX80cs8qPjbkFy8etHhPcjbMWXHlITf7Tq7y7rd1cdWWz/P2jvLkWxgstsew6j1o6z7fT32+gPfNwCPB2MUYxTgzhijGKMAd8YYxRgFuDPGKMYod8H0HU/A+avnrY8w9CjdQ/n7+N9V1+9Wabcct1TjSY2t5Zd2WKrmt52vDl44qFr+2fKud56m7ZlmfRxixgsz1NIjS9XQFUOt2vB6w9WgaoNU5pSZrY9KrH95vfX77F9nVwOWDHikj2tjxEZ189ZNVStnrYTfhaUNU1lTZVWrj622fl59fLUqnL6wCgkISVimbq666tL1S2rHmR2P9P4AeDiMUYxRgDtjjGKMAtwZYxRjFODOGKMYo9wJ03c8AfvP71cO5bB2EBM9l8+209vUodcPqSypsli/G9dknCo4sqBaf2K9Kp2pdMJgMbbxWBXoG2j93LZIW7Xw0EL1sfpYpUqeyvq9p4en9RGFeLnS5FJpU6R95HMS+Xj6JAxi8UL8Q6xa/DKJBwCr/v9/jl8GwJPFGMUYBbgzxijGKMCdMUYxRgHujDGKMcqd0JR+Qt8+6oxdkbusnT9+ANAKpCtg7WS6Fj8IZA/KnjAAaBkCMqgzMWeM2QvbPdrJ6wE8PRijALgzxigA7owxCoA7Y4yCO2H6jicgT3Ae5aE81O7IRzN5vHcy79t+9vDwsN6tcjX9zteNuBvq4rWLt/3+dMzphHfF9P/v/PbT+J8Tv3MG4MlhjGKMAtwZYxRjFODOGKMYowB3xhjFGOVOaEo/AWn80qi6ueuq79Z/p2JuxNxVj9+J8qfNr45FHbP+xNt5dqdV1+9QuZuSGUpaA5L+mEe8PZF71NGoo6p8lvLWz+Uzl1fbzmy77Z2z+Qfnq5S+Kd3yMQFJEWMUYxTgzhijGKMAd8YYxRgFuDPGKMYod0JT+gn57pnvVJwjTpX5qYz6a+dfat+5fWrX2V1qxNoRqvzP/9th9ATthUMKq9Z/t1abIjapdSfWqXZT26mq2aqqUhlL2br9muNqqm/XfSvONRR+KtyaW+dq7FXr7/qPfvdJO3HphAr7Nsy6X5qeL6hz8c6q97zeavGhxWrjyY2q4z8drR2/XOZy1jJ1ctWxdva2U9uqLae2qLn756oPFn1gTbLv6+Vr6zEBeHQYoxijAHfGGMUYBbgzxijGKMCdMUYxRrkL5pR+QnKmzqk2ddmkPl7+sXpz3psqIjpCpUuRTpXMWFKNajAq4WMP/7T6R706+1VVZUwVlcwjmaqXu576pv43tm//wPkDKvJKpHGZl6a/ZH1zarzio4tb/9cT3et5g/Q3m+45t0dduXklYZmv6n2lks1NpppNbqaux123vsl0ZIORCXXPZJ7WN7J2m9nNGuz0t722L9peDao+yPZjAvDoMEYxRgHujDGKMQpwZ4xRjFGAO2OMYoxyFx4OZ2c5BwAAAAAAAADAJqbvAAAAAAAAAAC4DE1pAAAAAAAAAIDL0JQGAAAAAAAAALgMTWkAAAAAAAAAgMvQlAYAAAAAAAAAuAxNaQAAAAAAAACAy9CUBgAAAAAAAAC4jJezC86ePdtY/+OPP8SMkiVLGuvz588XM+rXr2+sX7lyRcy4fv26sX78+HExI0WKFMZ6sWLFxIxjx44Z6z4+PmJGQECAsV6+fHkxY8eOHbaf25CQEGO9bdu2Yoafn5+xvnz5cjFDWmehoaFihvT8p0mTRsyIiIgw1nPmzClm7N+/31hv3bq1mJE5c2aVVGTJksVYf+utt8SMAwcOGOvnz58XM3r16mWs9+zZU8y4ceOGse7lJQ/d+fPnN9ZbtmwpZkycONH2OHfw4EFj/dKlS2JGly5djPWtW7eKGePHjzfW69WrJ2Zs2LDBWM+UKZOYMWrUKGP9r7/+EjOk7TA8PFzMqFu3rrHu4eEhZkRHRxvrp06dEjPeeOMNlVR07drV9n69ceNGYz116tRiRo0aNYz1TZs22d4GfX19xYzTp08b64UKFRIzpDEoJibG9jFQ8uTJxYzg4GBbx0iav7+/sf7qq6/aPr51ZozKkCGDrddarXr16sZ6VFSUmNGoUSNjfdy4cWJGypQpjfUJEyaIGbt371ZJxTfffGP7fKBMmTLGelxcnJhRokQJY71gwYJixi+//GKsX758WczImDGjsV6zZk0xY+bMmcZ6YGCg7fOB5s2b2z63uXjxopgRFBRkrEdGRooZ0rFF2rRpbZ9jXbhwQcxIliyZrTHdmb6FM69hJ0+etH3eKm3rT5O3337b9j65b98+28dA0jmUdO6jrVmzxtZY6syxxZ49e8SMFStWGOtt2rQRM1atWmWsV61a1XZfbOfOnWKGtM9t3rzZ9nFUvnz5xAxpGemc1JnH4szxfseOHY31vn372u555siRw/Z+q3GlNAAAAAAAAADAZWhKAwAAAAAAAABchqY0AAAAAAAAAMBlaEoDAAAAAAAAAFyGpjQAAAAAAAAAwGVoSgMAAAAAAAAAXIamNAAAAAAAAADAZbycXXDx4sXGesmSJcWMiIgIY93T01PMyJIli7G+atUqMSN37tzG+tGjR8WMDBkyGOs7d+4UM3x9fY3106dPixnR0dG2M/bt22esp06dWsw4dOiQsd6rVy8xo3///sb6woULxYw+ffoY63PnzrW9TqVt0JltKE+ePGKGtD8MGzZMzHBmmafF+PHjjfUjR46IGZ999pmx3qlTJzGjWbNmxvrXX38tZixbtsz2GLV//35jff78+WJGwYIFjXWHwyFm1KtXz1hfuXKl7defoKAgMSMuLs5YT548uZjRpEkTW+tcGzJkiLH+ww8/iBnPP/+8sX7lyhUxQxoLGzZsKGZIz93s2bPFjDfeeEMlFXnz5jXWN2zYIGbUr1/fWE+XLp3t8SV9+vRixrVr14z1RYsWiRk9e/Y01lu2bClm9O3b19Y618qVK2esL1iwQMwoUaKEsT5r1iwx4+TJk8Z60aJFxYzChQsb6xcvXhQzKlSoYKyfP39ezEiZMqXtjI8++sjW/dQuXLhg+zU9KfHz8zPW06ZNK2ZIr6VhYWG2z0vSpEkjZqxbt872a8+mTZuM9e+//17ZJZ2TOnNcGRwcLGaULVvW9nnrL7/8Yvv1RzoWi42Ntb2d3rx5U8yQbseZc73KlSsb697e3mLGmTNnbPVOkhrpOFvazrXNmzfbOvdx5nhNGju0GjVqGOs3btwQM6RzuYCAADHj6tWrxnquXLnEjFGjRtkeo6S+mLSvaD///LOx/sknn4gZUr8pX758YsaxY8eM9WLFiokZp4UeXkxMjO1j5JCQEDFD6gNGRkaqR4ErpQEAAAAAAAAALkNTGgAAAAAAAADgMjSlAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALkNTGgAAAAAAAADgMl7OLti2bVtj/d9//xUzIiMjjfWCBQuKGc8884yxvnz5cjFj8eLFxnpgYKCYERISYqynSZNGzIiKijLWy5QpI2ZMmDDBWM+ZM6eYkSNHDmN9165dYkbatGmN9QoVKogZw4cPN9bbtWsnZowZM8ZYj4uLEzPy589v635q77zzjrH+/fffixmZMmUy1qtVqyZmJCVr16411vfs2SNmNGnSxFgPDw8XM7Jnz25rv3dmHOvdu7eY4ePjY6yPGzdOzAgICLA9Vq5fv95Yv3z5sphRv359289LgQIFjPWOHTuKGSNHjjTWN27cKGY0bdrUWG/WrJmYIY0fb731lpghjWPPP/+8mNGhQwdjPSYmRsxISubNm2d7nU+fPt3Wa7Ezrx1Xr14VM7Zt22asv/zyy2KGtH306tVLzChfvryxfuPGDTFDOsapVauWmCGN6848L9Lzf+nSJTFDOhavWLGimLFgwQJjPSwszPYxkDPbR5UqVYz148ePixlbt2411oOCgsSMpEQao44cOWL7GEh6Tpx5Tc+aNauY8dxzzxnr69atEzPSp09vrB86dEjMSJkypbJLWme5cuUSM/bt22esJ0+eXMwIDQ21fY4lHTeeOHFCzChevLit1yetZcuWth/LgQMHbJ+n+fr62t7Wk5Lt27fbPqeQXn+cGRuk500aBzVPT09b/QhntlMPDw8xo1ixYsa6v7+/7fO08+fPixn58uUz1hs2bChmSM/dDz/8IGZ07drVWD98+LDt3tqzzz4rZnzwwQe2j8WkfqQ0hmkXL1401k+ePKkeBa6UBgAAAAAAAAC4DE1pAAAAAAAAAIDL0JQGAAAAAAAAALgMTWkAAAAAAAAAgMvQlAYAAAAAAAAAuAxNaQAAAAAAAACAy9CUBgAAAAAAAAC4jJezCz7//PPGeuHChcWMChUqGOtp06YVM3766SdjPTAwUMxo0aKFse7n5ydmbN682VjfsWOHmFGsWDFjffbs2WLG/v37jfXq1auLGalSpbK9PqT76u3tLWa89NJLyq7cuXMb61euXBEzgoODba+PEydOGOvdu3e3nfHbb7/Z3tafJps2bTLWIyIibI9Rbdu2FTPOnz9ve/uJjIw01vfu3StmpE+f3lgPCQmxfT+cWafR0dHG+sWLF5Vdt27dEpfJmTOn7TH76NGjxnrx4sXFjO3bt9u6n1qHDh2M9cuXL4sZderUMdYPHjwoZqROndpYr1SpkpiRlEjHOM68Tp49e9ZYr1y5spghjUFTp061fUwYHh4uZhw5csRYb9asmZghjUHS66gzxw2enp5ixrlz54z1oKAgMUMaC8eMGSNmvPrqq8b6qFGjxIyWLVsa63379hUzhg8fbvtY3cvL6VOU+/rrr79sn3ckJaGhocZ6xowZxYyCBQsa62PHjhUzBg0aZOvcR5s/f76x3rhxY9uv19IxoxYbG2trTNd8fHxsHZs4MxbevHlTzBg2bJix/tFHH9k+RpbOjbWqVasa6xkyZLD9+iON6Vq9evWM9TVr1ogZpUqVMtaHDh0qZnTs2FElFdL6OHz4sJgxZcoUW+dPWuvWrW2NP9qePXuM9SxZstg+fmnatKmY8fPPPxvrK1euFDPmzp1r67xFczgcxvr48ePFjOPHjxvrUVFRYsYXX3xhrNesWVPMOHnypK3zJ2fGhhs3bijJ6tWrbffF/P39jfUPPvhAPQpcKQ0AAAAAAAAAcBma0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSAAAAAAAAAACXoSkNAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBma0gAAAAAAAAAAl/FwOBwOZxZs3ry5sV67dm0xIzY21lg/cuSImJEsmbmPvmDBAjGjcOHCxnqaNGnEjBQpUhjrGTNmFDOOHj1qrPv4+NjOmDdvnpjRpUsXY93b21vMiIuLM9YvXrxoOyNlypRiRp48eYx1Ly8vMWPz5s3GepYsWcSMGTNmGOu1atUSMwICAoz1kJAQMaNJkyYqqejTp4+xXrNmTTFj3bp1xnr58uXFjLNnzxrrhw4dEjOuXLlirEdHR4sZvr6+xnr//v3FjLp169pep9IYFRoaKmZ07NjRWJ8/f76YIe0vMTExYsalS5eM9cyZM4sZx44dM9YPHjxoe4x67rnnxAxpLJS2Y83Dw8NY9/PzEzPeeecdlVRIx0n58+cXM6Tjk+PHj4sZ0nFSpUqVbN8PZ17jfvnlF9vHUQcOHDDWW7VqJWYsXbrUWM+dO7eYsWHDBmN948aNYsbEiRNtH89VqFDB9mvHpEmTjHV/f3/brx3bt28XM1avXm2s16hRw/aYvWLFCtv342ny008/Gevnz58XMw4fPmz7tUXaxpw5lnfm3EUinZd26NBBzNi2bZuxni9fPtv7ZFRUlO3zI2eOxYKCgmw9984cm0ZERIgZLVu2NNb37t0rZmTIkMFYT5Uqle3XjrJly4oZ0picLl06MaNRo0Yqqfj222+N9Xr16okZ06dPt3XeohUtWtRYL126tJhx6tQp272TM2fO2H4s0njrzLnNjh07bI0/zvROOnXqZPu4Ulrn2pAhQ4z1rVu3ihnBwcG2n9vVwrFH9erVxYyrV68a64sXLxYz0qdPb6xXrlxZzGjfvr24DFdKAwAAAAAAAABchqY0AAAAAAAAAMBlaEoDAAAAAAAAAFyGpjQAAAAAAAAAwGVoSgMAAAAAAAAAXIamNAAAAAAAAADAZWhKAwAAAAAAAABcxsvZBbNkyWKs7969W8zo2bOnsb5mzRox4+DBg8Z6gQIFxIxBgwYZ60OGDBEzypYta6zv2LFDzFi/fr2xnjFjRjEjTZo0xnqbNm3EjCVLlhjrmTNnFjOk29m/f7+Y8dlnnxnrs2bNEjOcWe+SzZs3G+u1atUSM06fPm2sx8XFiRnBwcG212lS4uvra6zv27dPzPjyyy+N9a5du4oZN27cMNYPHz4sZvTu3dtYv3z5spgRFBRkrFetWlXM6NWrl7F+4MABMePWrVvKrs8//9xYT5ZMfn/11KlTtjOkbax06dJixoYNG4z1okWLihm//fabsb5o0SIxI3Xq1Mb6hQsXxIxUqVIZ61mzZhUzkpLatWsb68eOHRMz0qZNa6x7ecmHdSVKlDDWz507J2Y8++yzxvqZM2fEjEqVKtnaV7T69esb6zdv3hQzpOPG2bNnixmtWrUy1mNjY8WMCRMmGOsBAQFihrTv58qVS8yIiYmxtQ1qkydPtj3OlS9f3li/evWqmOFwOIz1999/X8xISjZt2mSst2vXTsyQxqDkyZOLGY0bNzbW06dPr/4rChcubDujZMmSj+S+JBVhYWG2M/r06SMuI23LgYGBYkaNGjWM9XXr1okZScmePXuM9a1bt4oZVapUMdaPHDkiZkjHyGPHjhUzUqZMaaynSJFCzGjZsqWxPmfOHDEjMjLSWF++fLmY8cILL9g+p2jUqJGx3qRJEzFj6NChturOHL8402vcuXOnsV6kSBHbx8iphHMwbe/evcZ606ZNbY9RkyZNUo8CV0oDAAAAAAAAAFyGpjQAAAAAAAAAwGVoSgMAAAAAAAAAXIamNAAAAAAAAADAZWhKAwAAAAAAAABchqY0AAAAAAAAAMBlaEoDAAAAAAAAAFyGpjQAAAAAAAAAwGW8nF3w2WefNdbPnTsnZnz00UfGekxMjJjRsGFDYz179uzKrnTp0onLSPf1hRdeEDMqVapkrO/atUvMuHbtmrHu4+MjZpQrV85YL1KkiJixdetW2/fjvffeM9Z//PFHMePmzZvG+htvvCFmvPjii8b6kSNHxAxPT09jPTg4WMzYs2ePsZ4hQwYxIympXLmysX706FExI3Xq1MZ6+vTpxYzIyEhjvUuXLmKGw+Ew1leuXClmnD9/3lgvXbq0mPHpp58a602bNhUzzpw5Y6x/9dVXyhUGDx5srJctW1bM+Oeff4x1Ly/5JTUuLs7WeKz9/vvvtl9LkyVLZmsb1A4cOGCsh4aGihlJycaNG431TZs2iRlt2rQx1tu1aydm5MiRw1g/ceKEmHHjxg1j/ezZs2LGtm3bjPWIiAgxY9GiRca6v7+/mBEWFmasDxs2TNlVpkwZcZnDhw8b68uWLRMzatSoYazPmjVLzGjdurWxvn37dtvbh/S6oF2+fNlY37dvn5jx8ssvG+szZsywff7zNJGOcZw5L1m8eLGx3rlzZzFj3bp1xnpSek7wZKRNm1Zcpn379sb6xx9/LGaEh4cb6xUrVhQzkpJcuXLZOr7R1q5da6yfPHlSzPD19bV1fKyVKFHCWL9165aYsWrVKtvnvt9++63tbVA6fpWOK5xx/Phx273GL7/80vbxmnRsogUEBBjrQUFBto+RFyxYIGbUrl3bWD916pSYsWLFCmPdw8NDPQpcKQ0AAAAAAAAAcBma0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSAAAAAAAAAACXoSkNAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBkvZxdctWqVsZ45c2bbd+by5cviMnFxccb67t27xYxq1aoZ6zdv3hQzNm/ebKxfu3ZNzChbtqyxHhUVpexyJiM0NNRYnz17tpiROnVqYz02NlbM2LFjh7F+4cIFMaNr167G+vDhw8WM6tWrG+ujR48WM0JCQoz1ihUrihnffvutsV66dGkxo3PnziqpOHPmjLGeLJn8HlzevHltj1H58uUz1s+fPy9m3Lp1y/Z+/ffffxvrU6ZMETM6depkrOfKlUvMaNasmbE+YsQIMWPgwIHG+pdffqnsqlOnju2MgwcPiss4HA5jfd26dbZvx9/fX8yYOXOmsd63b18xY8OGDcZ6eHi4mNGiRQuVVLRv395YP3nypJjx3XffGevly5cXM3777TdjvV+/fmLGiRMnbB8DRUREGOtbt24VMxYtWmSsv//++2KGn5+fsT5p0iQxI0uWLMquixcvGuuRkZFihnQsLh13OnNMeOrUKTFDOl6Tjhm169evG+svvviimDFnzhzbY2VScuXKFWP90qVLYkaePHlsnU868xonHWM/quMoT09P28dzHTt2NNYXLFggZoSFhRnrX3/9tZhRoEABW/u99vvvvxvrb731lpghbUPSMaPWqFEjY/2bb75RduXMmVNcZvr06bbHKGl/8PHxETOSkujoaGP90KFDYkbJkiWN9RQpUogZR44csT2+FCxY0FifOHGi7b6YVNe2bdtmrI8ZM8b2OnXmfOD48eO2zzml4zln+pUvvfSSsb5p0yYx45NPPjHW06dPL2ZkFu6rM/3KGzduGOt79uwRM8qUKWPrtdZZXCkNAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBma0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSAAAAAAAAAACXoSkNAAAAAAAAAHAZmtIAAAAAAAAAAJfxcnbBK1euGOuhoaFiRtGiRY316OhoMcPDw8NYv3DhgrLr7Nmz4jK5c+c21qdOnWr7fhQpUkRcxsfHx1ifOXOmmLF06VJj/auvvhIzRowYYaxXq1ZNzMiSJYux/swzz4gZEydONNaPHTsmZqxevdpYL1y4sJhRsmRJY7179+5iRunSpY31559/XsxISr777jtj/YUXXhAzcubMaazHxsaKGf369TPW8+XLJ2YEBwcb67du3RIzfvjhB2M9ZcqUYsZff/1lrGfNmlXMaNGihbF+9epVMePll1+2NXZo69evN9Zr1KghZnTu3NlYz5Ytm5jh6elprIeEhNje1jt06CBmlCtXzlhfvHixmHHixAljfe/evWLGkCFDVFLx559/GusvvfSSmHHp0iVj3c/PT9l14MABcZlVq1YZ6+3btxczpG190aJFYsbkyZON9fTp04sZf/zxh7Hu5SUfKtevX9/WsYmWI0cOY/3ZZ58VMz777DNjvU+fPmLG+++/b6x7e3uLGdLr7ZgxY8SMDBkyGOtRUVG2j5GvX78uZiQlp06dsn1eMnv2bGM9KChIzChWrJixvnnzZjHjyJEjxnrDhg3FjH379hnrO3bssL0NOnNcGRkZaazXqlXL9muHv7+/mCGdd0jrS5s0aZKxfubMGTHjm2++UXZ9/vnnxnpgYKDtnoQzY6U0jkm3kdSkTZvWWG/durWYIR2fxMTEiBkOh8NYb9KkiZghnUOlS5dOzJD227Zt24oZs2bNst33kI49pPMn7caNG7Z7a9L4kTx5cjFj+vTpxnqjRo3EDKln5cz50TNChjPH+2FhYbZfj+Pi4oz1nTt3qkeBK6UBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALkNTGgAAAAAAAADgMjSlAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC7j5eyCRYsWNdbTpk0rZkRHRxvrV69eFTNiY2ON9ddee03ZVahQIXGZ119/3VhftGiRmOFwOIz1adOmiRm9e/c21uPi4sSMS5cuGetffPGFmFG/fn1j/cCBA2LG/v37jfWTJ0/afizJksnvwwQHBxvrX3/9tZjRoUMHW8+bM493/fr1YkbdunVVUpE7d25j3cPDQ8yoVauWsT5//nwxI3ny5Lbvx0svvWSsL1iwQMyYPHmysZ4jRw4xQ9pON27cKGZ8+eWXxnrhwoXFDGm/PXr0qJgRERFhrAcEBIgZrVu3NtbXrFlje3yRXhe0gQMHGuve3t5ixvjx4431UaNGiRn//PPPY389fprExMQY67NmzRIzpPGjf//+trfBI0eOiBnS68/YsWPFjD///NNY79Onj5iRPXt2W3Vt27ZtxnpYWJiYsXfvXltjh1akSBFjfdmyZba3jwEDBogZH3/8sbHepk0bMWP16tXGuqenp5hRqlQpY/348eNihjSe+vn5iRlJibStV61aVcxYsmSJreN0LTw83Naxmnbs2DFbxwRaqlSpbB9H3bhxw1jPkyePmCFt60OGDBEzOnfubPu4ISoqytZ5vvbCCy8Y688884yya8qUKeIyTZo0MdYzZswoZsyePdt2L6BEiRK2jhuSGml/Gj16tO2eltSP0M6dO2es79ixQ8xInTq1sV69enUxo3z58sb6sGHDbG+DkyZNEjN69epl+3xAOl6rXbu2mNG2bVtj/ezZs7af299++03M6Nixo7E+c+ZMMWP37t3G+p49e8SMgwcPGutp0qQRM3bt2mWslylTRj0KXCkNAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBma0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSAAAAAAAAAACXoSkNAAAAAAAAAHAZmtIAAAAAAAAAAJfxcnbBNWvWGOsXL14UM37//Xdj/euvvxYz2rdvb6wXKlRIzPjiiy+M9Xz58okZc+fONdYbNmwoZowbN85YL1eunJhx48YNYz0gIMD28zJnzhwxY/Xq1cZ6mTJlxIw9e/YY66lSpRIzpOdu1qxZYka2bNlsrS/t8uXLxnrOnDnFjIMHDxrrFSpUEDOSkuLFixvrKVKkEDNSp05trIeGhooZzz33nLGeK1cu22NUo0aNbI8vzozZkr///tv2vlCkSBExQ7qv3t7eYka9evVsj/uSkydPisvMnj3bWI+KirK9fTjz3FaqVMlYHzZsmJiRMmVKY3379u2295enSefOnY318ePHixnSvv/++++LGQUKFDDW9+3bJ2b07dvXWC9cuLCYIW0fyZLJ102sWLHCWB8wYICYkTZtWmN95syZYoY0fqRPn17MOHDggK39TWvSpImx/tFHH4kZbdu2tX0MtHjxYluvC9rSpUtt7U/a1KlTbb8eJyXSOUO/fv3EjJIlSxrrsbGxYkZgYKCxPmPGDDHjm2++Mda//PJLMaNDhw62z1tz585trB8/flzMKFiwoLH+2WefiRnSel+yZIntjCxZsogZzzzzjLLrq6++MtazZ88uZkyfPt1YT548uZhx9epV2+fx//77r7Fev359MSMp2b17t7FepUoVMUM675g8ebKY4eHhYft8cdu2bcb6q6++Kmb8+OOPtvti0utxqVKlbD8vzhyb7ty501gPCQkRM6TjtaCgIDEjJibG9nnaoEGDbL+WNmjQwFh/4403xIybN2/aOkZypr8iveY7iyulAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALkNTGgAAAAAAAADgMjSlAQAAAAAAAAAu4+FwOBzOLNipUydjvV69emLGjBkzjPX8+fOLGYsXLzbWY2NjxYxx48YZ61OnThUz9u/fb6wHBQWJGSVLljTWb9y4IWYsX77cWC9VqpSY8dtvvxnrxYsXFzMGDBhgrPfp00fMSJMmjbGeI0cOMSM8PNxYb9++vZjh5+dnrP/www+2H0v27NnFjFGjRhnrbdu2FTO6du2qkoqePXsa6y1atBAz3nnnHWP90KFDYsbXX39trP/9999iRmhoqLG+ceNGMWPw4MHG+uTJk8WMTz75xFgfMWKEmCG9NixbtkzMmD59urGeOXNmMaNw4cK29nutQ4cOxnqjRo3EjEKFChnrTZo0sb0+QkJCxAxvb29jPU+ePGLGTz/9ZKx3795dzGjYsKFKKtq1a2es582bV8zYvXu3sV6pUiUxY9euXcZ6rVq1xIyRI0ca68HBwbbHuZs3b4oZ0na6fv16MWPTpk3G+ttvvy1mSGPywYMHxYy6devazoiJiTHWlyxZImZUqFDB9tggjUERERFiRrJk5utmJk6cKGYUKFDAWL9w4YKYMW/ePJVUSMeMmTJlEjM8PDyM9R9//FHM6NWrl+3jBh8fH2O9bNmyYkbKlCmN9cOHD9u+H9L6cmYcy5Ahg+39qVixYmKGNH58+OGHyq6//vpLXEY6XvPy8hIzjhw5YqyfO3dOzDh79qyxXrNmTTFD2qeOHTsmZjjzGvW0+P777431K1eu2D4vkW7Dmb7YwIEDxYx8+fIZ69euXRMzoqOjbfWatMDAQGP9xIkTYkbBggVtPy/S+dG3334rZkjHJydPnhQzpMf72muviRlHjx411jds2CBmRAjHSc5kSP259OnT214fzvQrpf1F40ppAAAAAAAAAIDL0JQGAAAAAAAAALgMTWkAAAAAAAAAgMvQlAYAAAAAAAAAuAxNaQAAAAAAAACAy9CUBgAAAAAAAAC4DE1pAAAAAAAAAIDL0JQGAAAAAAAAALiMl7MLZsyY0VgfOXKkmPHJJ58Y66NGjRIzSpUqZaxv27ZN2bVx40ZxmWLFihnrtWrVEjNiYmKM9eXLl9teHxMmTBAzzp49a6yHhoaKGZMnTzbWr169KmY4HA5jfdmyZWLGK6+8Yvu5TZ48ue31sX37dmM9KipKzOjWrZuxvmnTJjEjKTl//ryxniZNGjGjYsWKxnrjxo3FDGmfc+a5v3nzprFetmxZMUMaP3LmzGl7G3Rmnf7000/G+rVr18SMt956y1ifNGmSmDFx4kRjvVy5cmJGq1atjPVGjRqJGbNnzzbWV6xYIWacO3fOWJ8xY4aY0aVLF9uvpdKYXa1aNTEjKbl06ZKxvmbNGjEjQ4YMxnqKFClsH7/s3btXzOjYsaPt/TooKMhY37p1q5gRGxtrrCdLJl97IY2n69evFzPCw8ON9Zo1a9rer50ZoxYtWmSsV65cWczYvXu3sb5//34x44033rA9ZpcuXdpYDwsLEzPq1KljextLStq3b2+snzlzRsyQxv1cuXKJGb/99puxXrRoUTFD2sacOX6Jjo421gsVKmR7fypYsKCYkS5dOtvrVBobnHnN/+GHH5RdU6ZMMdazZ89u+3hfOobWsmXLZuv4xpkxWXqN0xYsWGCst27dWsxISg4dOmR7nX/66afGenBwsJjxxx9/GOtZs2YVM8qUKWOsz5kzR8yYP3++sZ46dWoxIyAgwFhPlSqVmCH1eQ4cOGD7/PnVV18VM3bs2GFrnTszvkh9My0yMtJY9/Pzs33uO378eNvn4L169VJ2eXk53U424kppAAAAAAAAAIDL0JQGAAAAAAAAALgMTWkAAAAAAAAAgMvQlAYAAAAAAAAAuAxNaQAAAAAAAACAy9CUBgAAAAAAAAC4DE1pAAAAAAAAAIDLeDm74LFjx4z1559/XswYOHCgsZ4iRQoxo2TJksb64cOHlV3lypUTl9mwYYOxXrhwYTFDuq8LFiwQM3r06GGsN2rUSMxInTq1rede27Jli7GeJ08eMePWrVvG+r59+8SM8PBwYz1lypRixqZNm4x1X19fMaNBgwbG+tq1a8WMTJkyGesnT54UM5KSzp07G+tXr161vc6dee5jY2ON9e7du4sZFy9eNNY9PDzEjMmTJxvrr7/+upixc+dOY/3jjz8WMz755BNjvW/fvsquKlWqiMsMGDDAVt2Z+9q+fXsx48SJE8Z6+vTpxYyCBQsa6++//76YsX37dmO9f//+YkarVq2M9VdffVXMGDt2rEoqrly5Yus1UPP29jbW58yZI2aEhYXZGn+0GjVqGOtjxowRM/z8/GwfewQEBNgaj7WtW7fafl6k15/Ro0eLGWXLljXWly9fLma0aNHCWF+1apXtY8KXX35ZzMiSJYutMUzLnTu37ddj6Zhwz549YkZSMm3aNFuvX9rmzZuN9Z9//lnMeO2114z1CxcuiBn169e3fc4ZHR392M8HqlWrJmYMHTrUWM+fP7+YIR0DN2/eXNn12Wef2R7nnDlfPHDggLG+YsUK26+lqVKlEjPOnDljuydRrFgxY33x4sViRvXq1VVS4enpaescXkuePLmx7u/vL2ZERkYa63Xq1BEzSpcubfs8TRpfpNvQ4uLijPXZs2eLGcmSJbP92iH1+Jw5jpIe79mzZ8WMdOnS2T5Gzpo1q63jcC04OFiZ9OzZU0neeOMNYz0mJsZ2j0/qnTiLK6UBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALkNTGgAAAAAAAADgMjSlAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC5DUxoAAAAAAAAA4DJezi6YP39+Yz0gIEDMaNOmjbGeN29eMWPmzJnqcZPup5YzZ05j/ejRo2LG0qVLba+PUaNGGeshISFiRoYMGYz1lClTihnh4eHGerVq1cSMY8eOGevPPvusmJEpUyZj/ZdffhEzXnzxRWO9XLlyYkZgYKCxvnPnTjFj/fr1xvrEiRPFjD59+qik4sKFC8b6uXPnxIxTp04Z682bNxczypYta6yPHj3a9vM2adIk2xnr1q0TM+rXr6/sOn78uHIHQ4cONdavXbsmZly/ft1Y37Nnj5hRpkwZYz0uLk7MaNeunbHesWNHMWPv3r3GesWKFcWMokWL2qonNb6+vsZ6nTp1xAzp2OLw4cNixh9//GGst2rVStmVPn16cRlpDDpx4oSYkTVrVlv7rFa6dGljfffu3bZf8z09PcWMQoUKGevnz58XM/r27WusN2vWTMxo0KCBsT5w4EAx46effjLWw8LCxIxkyczXzXh4eIgZKVKkMNZLlCghZiQlMTExtvY3Z57b1157TcxYtWqVsV6zZk0x49KlS7bOa7VixYrZOm/R1qxZY3tsOHTokO3jl+7duyu7pOPXggUL2r6N+fPni8tI56XJkycXM3r27GmsL1++3PZ5WpYsWWyfu4SGhooZSUnDhg2N9Z9//lnMKFy4sLGeK1cuMWPr1q3G+qZNm2wfezizz/r5+dnqz2jBwcG2t0EfHx9j/eTJk7Zf86V+lTP31dvbW8yQlnHmPF461tq3b5+Y0bt3b2P93Xfftf3cBgUF2R6Ta9WqpR4FrpQGAAAAAAAAALgMTWkAAAAAAAAAgMvQlAYAAAAAAAAAuAxNaQAAAAAAAACAy9CUBgAAAAAAAAC4DE1pAAAAAAAAAIDL0JQGAAAAAAAAALiMl7MLnj9/3lhPlSqVmBETE2Os//nnn2JGxYoVjfVMmTIpu55//nlxmQIFChjr5cqVEzMOHTpkrAcGBooZxYoVM9azZMkiZmzbts1YL1KkiJhx6dIlY7169epixuLFi431W7duiRnjx4831gsXLmz7sUycONH2dpohQwYx48UXXzTWixcvLmYkJXv37jXWvbzk4S5t2rTG+nvvvSdmfPzxx8Z6SEiImOHh4WGsx8XFiRmrV6821rdv3y5mlC9f3lgfMGCAmOHp6Wmsjx49WszIlSuXse7j4yNm7N692/Z+nSdPHltjmJYzZ05br7Vau3btjPULFy6IGYUKFTLWixYtKmZkz57dWN+8ebOYkZRIrz/z588XMwYPHmysP/vss7a34+vXryu7Vq1aJS4jHa85c1wZHh5urJcoUcL2vnDx4kUxY8uWLcZ6nTp1xIwDBw7Yfm7DwsJs3Yb2+++/G+tt2rRRdpUsWVJcRnptcOY4Sjqed+a1Iyk5fPiwsR4UFGT7NW7fvn1ihnQO5XA4xAzpNWzlypXKroCAAHGZ0NBQY33RokVihjSONWnSRNk1adIkcRln1rvd1wZntrH06dPbPl+UHu/ly5fFjNjYWGO9cuXKYsaQIUOM9eHDh4sZScm8efOM9TJlyogZfn5+to+hpfOwfPnyKbucOc9Plsx8femNGzfEDF9fX2M9efLkYsaKFStsnQtqmTNntr1PSmPhF198IWZERkYa61WrVrW9TrNmzarsaty4sbiMtM6uXbtm+9zFmT6hM7hSGgAAAAAAAADgMjSlAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALkNTGgAAAAAAAADgMjSlAQAAAAAAAAAu4+XsgpGRkcb6oUOHxIyaNWsa61mzZhUzJkyYYKy//fbbyq6qVauKy/zwww/G+oEDB8SMiRMnGuvdunUTM3bu3GmsOxwOZdeSJUvEZdKlS2esz58/X8y4du2asR4VFSVmPP/888Z6RESEmLFv3z5j3d/fX8yQtuU9e/aIGa+88oqxXqZMGTGjdu3aKqnYtWuXsV6lShUxIzo62livX7++mHHkyBFjfdu2bba3wePHj4sZ+fLlM9Zr1KghZpQoUcJYX7ZsmZjh7e1trB89elTMkG4nTZo0Ysb27dttjWHOjPurV68WM4YMGWL7dbBjx47G+r///itm5MyZ01jPnTu3mDF8+HBjvXz58mJGUuLp6WmslypVSsyQXkvz5MkjZkjHBc68PknbcbZs2cSMmJgYW+OxVrFiRWO9ePHiYsaxY8eM9SxZsogZ0jrLnj27mPHNN98Y602bNhUzvLzMh/WXLl0SM4KCgoz1yZMnixnSeCodu2opU6Y01j08PMSMCxcuGOsnTpwQM4oWLaqSiowZM9qqa+vWrbN9nvbHH38Y6ydPnhQztmzZYqyHhYW5ZH+Szp/z5s0rZmzdulXZNX36dGM9bdq0Yoa0vzhznpYpUyZb+70z58etWrUSM1KlSmX7GGj58uXG+rhx48SMRo0a2eoVaP369VNJxdKlS20dHzvzeu1MhnQO/umnn4oZFy9etPX6pfn6+hrrhQoVEjM2bNhgrAcHB4sZ0v7izPnAmDFjjPX9+/eLGQsXLjTWz5w5I2ZIx9HS+ZN26tQpY/306dNiRocOHWyft+bIkcN2fy5//vzG+qRJk8SMdu3aictwpTQAAAAAAAAAwGVoSgMAAAAAAAAAXIamNAAAAAAAAADAZWhKAwAAAAAAAABchqY0AAAAAAAAAMBlaEoDAAAAAAAAAFyGpjQAAAAAAAAAwGW8nF2wQYMGxnp4eLiYUbZsWWN9yZIlYka3bt2M9e3bt4sZVapUMdYrV64sZhw+fNhYr1u3rrIrbdq04jLSOqtQoYKYcfPmTWO9WrVqYsakSZOM9YIFC4oZERERxnqWLFnEjFSpUhnrmTJlEjPeffddY71GjRpiRocOHYz1oKAg24/Fy8vp3TdJ+Oijj4z1wYMHixnnz5831gsVKiRm/PXXX8Z63rx5xYw5c+YY68HBwWKGt7e3sd6kSRNlV5EiRcRl9u7da6x36dJFzHj99deN9V9//VXMkMbkHDlyKLtGjRolLvPOO+8Y68uWLRMzVq5caawnSya/33zw4EFj/erVq2LGxYsXjfXY2FgxIylZt26dsV67dm0x4+TJk8Z669atxYxPPvnEWK9UqZLt8UW6n9rx48eN9TJlyogZvr6+xnrfvn3FjN69exvrKVOmFDNOnTplrJ89e1bMaN68ue3jhpCQEGN906ZNtsd1aUx35pjQmeOoRYsW2d4+Ro4caay3b99ezEhKpOP9efPmiRnFihUz1v/9918xIzo62liPiYkRM3LlymXrNVBr166dsf7555+LGR07djTWx4wZI2a0bNnSWP/555/FDH9/f2Pdz8/P9vlivnz5xIxjx47ZPhb7+uuvjfU1a9aIGRs2bDDW9+zZI2ZIr9mLFy8WM27dumWs586dW8xISqRjD2fWl9TDmTt3rpjh4eFh6xxeK1y4sLH+6aefihnSudy5c+dsnx/5+PjYPr6Vzn2cOQYqWbKkmLF06VJjvV+/fmLGBx98YKzv3LlTzPD09DTWW7VqJWZ4CX2eKVOmiBnPPfec7cdSoEABl4xRXCkNAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBma0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSAAAAAAAAAACXoSkNAAAAAAAAAHAZmtIAAAAAAAAAAJfxcDgcDmcWfOaZZ4z1Jk2aiBmenp7GesWKFcWMCRMmGOs5cuQQM2rVqmWsv/XWW2JGoUKFjPWFCxeKGb/99pux3rJlSzEjV65cxnpkZKSYkSlTJmM9Xbp0YkZcXJyxniJFCjEja9astu/H2LFjjfVWrVqJGdu2bTPWly1bZvu5XbBggZiRO3duY/3TTz8VM/7991+VVEjjR506dcSM8uXLG+tDhw4VM27evGmsHzx4UMzImDGjrf1e+/777431v//+W8yQxvVFixaJGdOnT1d2BQcHG+v169e3PUY5o2zZssb6l19+KWaEhITYep3U/P39jfVdu3aJGdJLf2BgoJiRL18+Y33z5s1ixpAhQ1RSMXz4cGN97dq1trefsLAwMSMiIsLWMYHWp08f22PDDz/8YHufbdq0qe3jubx58xrrhQsXFjOkZX755Rfb4+3q1avFjDJlyhjr48ePFzMyZMhgrPft29f2NiY991poaKix7uHhIWYULFjQWB89erTtY8Knyeuvv2779WnVqlXG+rPPPitmSOPYunXrbG/Hhw4dEjOkc6hKlSrZXh/79u0TM7p37277uPLixYu2X/Olx+vM/lS7dm1jfcuWLWJGVFSUsf7cc8+JGV26dDHWly5dKmZ07drVWE+fPr2YUbp0advn8SNGjFBJhbQvZMuWTcxIlsx8Tea1a9fEjAMHDtgaf7Rz584Z6/369bN9jrVnzx4xQ9r3pZ6XdvXqVduvo9I+OWXKFDHjwoULxnq7du3EjOTJkxvrv//+u5hRr149Yz179uxixsyZM431w4cP2z5PO378uJgRGxtrrF+6dEnMGDZsmLgMV0oDAAAAAAAAAFyGpjQAAAAAAAAAwGVoSgMAAAAAAAAAXIamNAAAAAAAAADAZWhKAwAAAAAAAABchqY0AAAAAAAAAMBlaEoDAAAAAAAAAFzGy9kFixYtaqyHhoaKGVmzZjXWO3ToIGa8+uqrxvr27dvFjFq1ahnrZcuWFTPmz59vrDdr1kzMGDlypLGeLl06MaNgwYLG+vXr18WMy5cvG+uVKlUSMzZs2GCsp06dWsyYNm2asd6gQQMxIzY21ljv27evmNG8eXNjvUWLFmJG165djfXixYuLGatWrbK9fSQl0thw+vRpMWPdunXGepcuXcSM0aNHG+vdunUTM3bu3Gmsp0yZUsxo1aqVsT5u3Dgx46effrL1WLW33nrLWF+5cqXt50V6bdHKlStnrDdt2lTMCAkJMdYDAgJsP5Yff/xR2bVixQpxmTfffNP27UyZMsVYb9mype3beJp4enoa676+vmJGVFSUsb5lyxYx44UXXjDWf//9dzGjdevWxvpLL71ke3xxZhvdtm2bsd6jRw/bx3OHDh0SMzZv3mysh4WFiRnr16831uvXry9mSM+ddPyrNWrUyFjft2+fmPHll18a69WqVRMzDhw4YKwXLlxYzBg8ePBjHwefJvny5TPWAwMDxYwbN24Y6zlz5hQzNm3aZKx7ecmnr6tXr7Z1rO/M471586bt8+eSJUvaPk/LnDmzmCGdMwQFBYkZy5YtM9bTpk0rZqRIkcL22DBo0CDb63TChAnGepMmTcQM6fXl1q1bYoa0DTlzbArnty8tMjLSVm9F8/HxsXUOr3Xq1MlYX7x4se3X659//lnMOHLkiK31pWXPnt12L+nq1avG+tKlS8UMafwYM2aMmHHs2DFj/cqVK7b7QBMnThQz9u7da6xfuHBBzEiePLmxvnv3bjFjyJAhth+LM7hSGgAAAAAAAADgMjSlAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALkNTGgAAAAAAAADgMjSlAQAAAAAAAAAu4+FwOBzOLLh8+XJjferUqWJG5syZjfXq1auLGStXrjTWIyMjxYyXXnrJWJ8wYYKY4efnZ6x7eHgou4oUKSIus3XrVmPdy8tLzIiNjTXWb926JWYUK1bMWP/555/FjFSpUhnr//zzj5jRv39/Y/3GjRtixvXr1431vHnzihnbt2831kuXLi1m/Pnnn8Z6aGiomPHBBx+opGLUqFHG+tWrV8WM0aNHG+s1a9YUM+rXr2+sL1q0SMxo1qyZsf7999+LGb///ruxXrlyZdv3o3379rbvR6NGjcSM2bNnG+s7duwQM0JCQoz1Hj16iBmdOnUy1gMDA8WM8uXLG+sBAQFiRrZs2Yx1b29vMUPaH6TnTUufPr2xnjt3btuvx0+TZ555xlh/8cUXxYz9+/cb67Vq1RIzpNfjbdu2iRnvvPOOsb569WoxQ3oNq127tpgxcuRIY33OnDliRps2bYz1SZMm2V4fUl37/PPPjXUfHx8xY+PGjbbGMG3cuHG21rnWoEEDY/3s2bNihvQadezYMTFj6dKlto+zZ8yYoZKKfv36GeuXL18WM6Kjo22/Tv7yyy+2xyjp+KRDhw62x1tntp+dO3ca65s2bRIzfH19bR13aleuXDHWkyVLZvs4ypnj24IFCxrrK1asEDOaNGlirG/YsEHMkLZDZ87jpWOc8+fPixnp0qVTdjVt2lQlFdJxQ9u2bW0/985sgy1btjTWixcvbvu15dSpU2JGcHCwsf7KK6+IGdOnT3/s++Tx48fFDGl/cWack3pWzhw3zJs3z1gfPHiwmCGdQ+XIkUPMKFCggK1ekzNjlDO9kZkzZxrrzz33nO1zX40rpQEAAAAAAAAALkNTGgAAAAAAAADgMjSlAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALuPhcDgczizYpk0bYz137txixubNm431559/Xsz4888/jfXQ0FAx48MPPzTW//rrLzFj3759xvqtW7fEjKxZs9pa59qiRYuM9Tp16ogZI0aMMNbXrFkjZrRq1cpYX7JkiZiRJ08eY/3mzZtiRrFixYz1o0ePihkXL1401rt27SpmjBs3zlg/fPiwmFGpUiVjPTw8XMwYNmyYSip69+5trM+ePVvMKF26tLHu4+MjZlSpUsVYv3Dhgpixbds2Yz1jxoxixsmTJ431+fPnixnjx4+3Pc5JY9Trr7+uXOHNN9801sPCwsSMY8eOGevdu3cXM/r06WOsZ8qUScy4fv26sV6zZk0xQ3rp9/LyEjNOnDhhrEdGRooZQ4cOVUmFNEatW7fO9jqvWLGimBEQEGCsx8TEiBkHDx401oODg20fR7Vv317MqFu3rrHet29fMWPjxo2216m0jDQeO/P89+jRQ8zInz+/sb5+/XoxY9KkScb61atXxYzUqVPbPmfInj27sX758mUxIyoqyvYYJR3PPU1GjRplrE+ePFnMkM47nDn+lbZjab8H7Jo+fbq4zOnTp431nDlzihmrVq0y1pcuXWr7uOBpkiNHDmO9SZMmYkbBggVtHwNJxy9xcXFiRqlSpYz12NhY270k6VhNa9SokbH+7bff2u4D9ezZU8yQnrsbN26IGR4eHsb6/v37xYwBAwYY67169bL9GuZMv/L8+fO2juU1T09PW+tLCwwMNNanTZsmZkydOlVchiulAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALkNTGgAAAAAAAADgMjSlAQAAAAAAAAAuQ1MaAAAAAAAAAOAyXs4umDt3bmO9Tp06Ysa6deuM9X/++UfM8PDwMNZr1aql7CpYsKC4zPXr1431bNmyiRmXL1821j/88EMxY/Dgwcb6n3/+KWaUK1fOWD916pSYsWzZMmM9WTL5/Y9du3YZ67GxsWJGly5djPXz58+LGa1atTLWt27dKmZs377dWK9cubKYERERYazny5dPzEhKpP02JiZGzJg9e7ax7uvrK2ZUqVLFWB85cqSYIY2nx48fFzM8PT2N9ZdfflnMSJ48ubHesmVLMaNbt27GeosWLcQMab1Xr17d9mNZsmSJmFG2bFlb+702YsQIY/3XX38VMxYuXGis58iRQ8yYOXOmsV60aFExo0iRIrafl6TE29vb9vMmjXPS64aWIUMG28+btK3nyZNHzPDx8THW165dK2b89ttvxvqzzz4rZqxfv95Y79+/v3KFTJkyGetHjhwRM9KkSWOsX7t2Tcx47bXXjPUJEyaIGdJ9PXjwoJgREhJirJcsWdL2uN+3b18xIymJi4sz1t98800xY9u2bcZ6VFSUmHHgwAFxGeBxcmY7lcbTFClSiBmBgYHGes6cOcWMpEQ6z5d6CVpQUJCx/u+//4oZZcqUMdYXLFggZtStW9dYf++998SMOXPmGOurV68WM7777jtbrwvagAEDjPVJkyYpV/j5559t9wLefvttY71x48a2j8X27NkjZuzfv99YDw0NFTOk/ltYWJjtjJ49e6pHgSulAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALkNTGgAAAAAAAADgMjSlAQAAAAAAAAAu4+FwOBzOLFi9enVjPSAgQMx45plnjPVt27aJGXXq1DHWx40bJ2ZI99XDw0PMKFWqlLEeFxcnZpQsWdJY37Jli5hx69YtY71p06ZiRrdu3Yz1tGnTihlZs2Y11lOlSqXsCgsLE5dZsGCBsd67d28xY+vWrcb69u3bxYw8efIY66lTpxYzVq9ebazv3r1bzPj1119VUlGuXDljfcKECWLGRx99ZKw/99xzYsb169eN9U2bNokZRYoUMdYPHTokZuzfv99Yb9u2rZgxe/ZsYz1lypRixr59+4z1Zs2aiRnSWJgiRQoxo27dusZ6586dxQwfHx9jPTY21vbrYPLkycWMs2fP2rqf2tq1a431kydPihm1atUy1s+fPy9m/PbbbyqpmDhxorF+7NgxMWPx4sW2xh/tnXfeMdY//fRTMePy5cvGerZs2cQMaVsvVqyYmCFtY868TkqHwX5+fmKGNK7Xr19fzPDy8rL1Gqf9+OOPxnr+/PnFjHTp0hnroaGhYsbvv/9urOfOnVvMCAoKsnX868yxZ8aMGcUMZ14bnhbdu3e3vb6k1+PJkyfbfm1x5rX2xIkTxnq1atXEjKVLlxrrq1atEjOkYxxnTsOjo6ON9RIlSogZU6ZMMdavXLkiZmTPnt1Yv3Hjhpjx4YcfGuvTpk0TM6TXuZs3b4oZc+fONdZHjRolZnz22We2j8XKly9vrM+YMUPMmD9/vkoq/vnnH9vHlNKxhTPHrp6ensb6pUuXbN+PHDlyiBlvvfWWsV6mTBnb4/7y5cvFjAYNGth63pw5pzx37pyYceHCBWP9xRdftD3uO3N+JB3PSeeCzmyHY8eOVZIaNWoY6wcOHBAzpNd9aX05O45xpTQAAAAAAAAAwGVoSgMAAAAAAAAAXIamNAAAAAAAAADAZWhKAwAAAAAAAABchqY0AAAAAAAAAMBlaEoDAAAAAAAAAFyGpjQAAAAAAAAAwGVoSgMAAAAAAAAAXMbL2QXTpUtnrF+6dEnM2Lhxo7H++uuvixmDBg0y1jt27ChmHDp0yFgPCgoSM9asWWOsDxkyRMz4999/jfX69euLGe+//76x3qZNGzHD29vbWC9TpoyY0bBhQ/W4jRw5UlymTp06xvqMGTPEjK1btxrrqVKlEjPWrVtnextLkSKFsb548WIxIyn54YcfjPUBAwaIGTt37jTWkyWT38crUqSIse7r6ytm5M6d21hPnjy5mLF9+3ZjfdiwYWJGmjRpjPUbN26IGTlz5rT92lGoUCFj/cyZM2LGd999Z6zXrFlTzAgMDDTWjxw5ImaULl3aWP/ss8/EjJIlS9oeXwoXLmx7fRQoUMBYX79+vZiRlGTPnt1Yv3r1qpjh4eFhrNerV0/MOH78uK1jAme2n1u3bokZ0ljozHgbERFhrF+5ckXMaNKkibE+a9Ys2+N+rly5xIyUKVMa63PmzBEzihcvbqw7HA4xo0SJEsb63r17bR+LhYeHixkZM2Y01v39/cUMPz8/Y33u3LliRufOnVVS4enpaWu/1/bv32/72OPkyZPG+qRJk8SM4OBgY338+PFihnRsIe1vWuPGjY311atXixlp06Y11vPlyydm1KpVy1gPCAgQM27evGmsnzhxQszo1auXrfXlzLh+7tw5MeOVV14x1t977z0x49lnnzXWo6KixIwDBw7Yer1OasqVK2d7nUvnJQsXLrT9mr9nzx4xQ3pNr1KlipjRqlUrYz02NlbM+PXXX431atWqiRn9+/e3PUZJ54PSMZIzxw2RkZFiho+Pj7GeIUMGMUPq4U2YMMH2cVQZJ/pz0n115nxxw4YNxnrZsmXVo8CV0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSAAAAAAAAAACXoSkNAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBma0gAAAAAAAAAAl/FwOBwOZxZs2bKlsV6yZEkxY+PGjcZ6rly5xIwOHToY62vXrhUzrl27ZqwfOHBAzKhSpYqxfuvWLTHj+++/N9a9vb3FjKJFixrrUVFRYsapU6eM9bNnz4oZuXPnNtazZs0qZhw6dMhYz5Ytm+3tVNp+tKpVqxrrNWrUEDOkbcjf31/MKFasmLE+fPhwMePHH39USUXz5s2N9TNnzogZo0aNMtZHjBghZrRu3dpYnz59upiRLFky2+Pt3r17be/XmTJlsjWma/nz51d2SfvLwoULxYzy5csb6+Hh4WJGypQpbdW1JUuWGOt9+vQRM2bMmGGsx8XF2R5fzp8/L2Y0bNjQWG/VqpWYcfr0aZVUfPjhh8Z6jhw5xIx9+/YZ6ylSpBAzKlasaKxv2bJFzJAOHdevX297n3Tm9atChQrGup+fn5hRuHBhYz1VqlRixrx584z1mzdvihlXrlwx1i9fvixmZM6c2dZrixYQEGCsHzx4UMx46623jPVNmzaJGX///bexnidPHjHj6tWrxnqpUqXEjNdee00lFd26dbO9vi5evGisHzlyRMzo3r27sb5r1y4xY+7cubbPKaTXOOk4S9uzZ4+xfvLkSTFDOn515vy5XLlyxvqwYcPEjLx589p+7ShYsKCxvmrVKjGjY8eOts+x9u/fb+v12plzhuzZs4sZyZMnt/Uap2XMmFElFY0aNbI9XkvHJyVKlBAzpDFIOn/SBg0aZGufdaZnIR0TaLNnz7adcf36ddvHQNLrtY+Pj5gRHBxsrHt5eYkZW7dutX1eGxMTY/t4rpzw/O/YscN2f2779u1ihnRfQ0NDxYzevXuLy3ClNAAAAAAAAADAZWhKAwAAAAAAAABchqY0AAAAAAAAAMBlaEoDAAAAAAAAAFyGpjQAAAAAAAAAwGVoSgMAAAAAAAAAXIamNAAAAAAAAADAZWhKAwAAAAAAAABcxsvZBePi4oz19evXixn+/v7G+qFDh8SMoUOHGus9e/YUM7y9vW3fj379+tm6n1qfPn2M9bNnz4oZ0dHRxnpMTIyyq0mTJuIya9asMdbz5s0rZowfP95Yf+mll8SMhg0bGutVqlQRM2rUqGGs165dW8yYPn26sX7w4EExY8SIEcZ6+fLlxYykJFWqVMZ69uzZxYyBAwca6x06dBAzvvrqK2M9TZo0YsaePXuM9Rw5cogZO3bsMNb9/PzEjBs3bhjrPj4+YkbBggWN9f3794sZzZs3N9azZs0qZly8eNFYz5Ahg5hRqFAhY93T01PMyJMnj7G+detWMSMoKMhYT5Eihe3x5Y033hAzdu3aZaw3a9ZMzEhKAgICjPXevXuLGW+99ZaxHhUVJWZs2LDBWN+8ebOYsXv3bmP9yJEjYkZ4eLixHhYWJmZcu3bNWK9cubKY8emnnxrrr7/+upiRNm1a2+O+NJ5GRkaKGdIyly5dEjPSpUtn67E68/w7M+5Lx2teXvIpTJYsWWyP2UmJdJzkzPqSji2k1x6tb9++to89pG3dw8NDzFi+fLntc6zr16/bPvbYtGmTsV6xYkUx4/jx48Z67ty5xYyXX37ZWB80aJDt/dqZ8zTpNaxo0aK2j2+dWafSOeeWLVts91ek1zitTZs2Kqm4cuWKsX7r1i0x4/Lly7bOwZy5neTJk4sZo0ePNtYnT54sZowaNcp27yR9+vTG+s6dO8UM6bxUug1neonOjA1215cz48fRo0fFjFy5ctk+Flu3bp2xfubMGTGjSJEitsZBLTY21lgPDg5WjwJXSgMAAAAAAAAAXIamNAAAAAAAAADAZWhKAwAAAAAAAABchqY0AAAAAAAAAMBlaEoDAAAAAAAAAFyGpjQAAAAAAAAAwGVoSgMAAAAAAAAAXMbD4XA4nFlw4MCBxnrZsmXFjDNnzhjre/fuFTN2795trBcuXFjMaNmypbF+4cIFMeObb76xfT8KFSpkrP/2229ihrTenXksOXLkMNZPnDih7Jo6daq4TO3atY3106dPixkZM2Y01ps3by5m/Pjjj8Z6RESEmHH16lVjPSgoyPb+EhsbK2YsX75cJRU1atQw1t9//30xw8vLy9a2oV28eNFYf+mll8SMxYsXG+vXr18XM4oUKWKsJ0+eXMwIDAw01j/66CMxY/78+cb6nj17xIz+/fvbXqfbt2+39bxp9evXN9Z79+4tZvz888/GeooUKcSMy5cvG+v79u0TM6Tb+fXXX8WMDBkyGOu5c+cWM/r166eSCunYw9PTU8yoUKGCsX7y5EnbrwvPPPOMmFG0aFFj/aeffhIzbt68aev13JljQm9vbzFD2k59fHzEjFq1ahnrzhxqnzt3zlhfunSpmCEdRztzP5599lljfebMmWJG6dKljfXixYsru6Kjo8VlUqdObft47u2331ZJhXTu4sy6uHTpkrG+Y8cOMcPf39/2c58nTx5jff369bZfJ6VjRu38+fPGer169cSMVKlSGetRUVFixq1bt2wf3+bLl89YT5MmjZiRPXt2Yz1TpkxixurVq411X19fMWPlypW2xyhpfdy4cUPM8PDwsH0eP2jQIJVUHD582Fg/duyY7WOgU6dOiRlST0Lq8WgHDx401itVqiRmhIeH29pGnbkfcXFxYoafn5/t19qaNWsa62vWrBEz+vbta6wPHz5czAgLC7N17Kpt2rTJWL927ZqYERISYvv1R9qGpPvpzOtggQIFxAxnziu4UhoAAAAAAAAA4DI0pQEAAAAAAAAALkNTGgAAAAAAAADgMjSlAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALuPhcDgcrrs5AAAAAAAAAEBSxpXSAAAAAAAAAACXoSkNAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBma0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSAAAAAAAAAACXoSkNAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBma0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSAAAAAAAAAACXoSmNJ6rDtA7quUnPPem7AQD3xBgFwJ0NWDJAFfu+2JO+GwBwT4xRANwZ53pPnteTvgP/NR4DPYz1/lX7qwHVBih3un8Tm01UrQq1eqAd89ctv1p/907mrbKmyqraFW2n3qv8nvJK5uUWBzcDlw687Xf5gvOp3T13P7H7BLgLdx+jjkYdVd1mdlOLDy1WAT4Bqn3R9uqTWp880Nji7mOU9t2679Tnqz5Xp6JPqaKhRdU39b9RZTKVedJ3C3ji3H2Mem32a2rlsZVq+5ntKn/a/Cq8a7it4xRPD0+VOWVm1SSsiRpcY7A17j1pO87sUP2W9FMbT25UR6KOqK/qfqXeKPfGk75bgFtgjGKMAtyZu49R8c5dOaeKfl9Unbh8Ql1454IKSh70VJ3rxRu6Yqjqu7Cver3s6+rrel8/6bvzn+Rez+h/QMSbEQl//2P7H9YL5p6eexJ+l/iF3OFwqDhHnMt3nDGNx6h6uesl/PwgA0A8/e91zvXY62rWvlmqx6we1oDQt3Lfu5a9EXdD+Xj6KFcqmK6gWtBuQcLP7jY4AU+KO49RcbfiVIMJDVRoQKha1XmVirgcodpNa6e8Pb3VkJpDnpoxSq/33vN6q+8bfK/KZi6rvl7ztar7e13reUjvn95l9wNwR+48RsXrVKyTWntirdp6eqvt45TYW7Fq5dGVqtP0TurKzStqdMPRT3yM0vcjZ1BO1bxAc9Vrbi+X3S7wX8AYxRgFuLP/whildZ7eWRUJKWI1pR+GO5/rxVt/Yr0avXG09Tjx8Ji+4wHpZkr8n1TJUykP5ZHw8+7I3Srwk0A1e99sVfKHksr3I1+14uiKe34k4I05b6hqY6sl/HzLcUt9svwTlWN4DuX3sZ/1rtKfO/98qPuom9CJ72dyr+QPnOHr6Wv922xB2VS30t1UrZy11PS9061a/OP5eNnHKuOXGVW+b/NZvz8WdUy1mNJCBQ0NUmk+TaMaT2qsDl88fFtDqvfc3lY9+LNg1Wd+H+VQjod6jHpgTfwY06ZI+1A5wNPGnceoeQfmqZ1nd6rfm/yuioUWU/Xz1FeDqw9W363/zjqYeFrGqGFrhqmXS7ysOhbvqAqkK6C+f/Z7lcI7hfpl8y8PnAU8bdx5jNJG1B+hepTpoXKmzmnrccYfp+grEFsWaqlaF26dMEbFf5z9p00/Wfc3+Uf/O067eO2iemn6Syrd5+lUyk9Sqhq/1lBbTm2564qckC9CrPXU+Z/O6lrstQe+b6UzlVaf1/nc+hSdHksB/B/GKMYowJ25+xiljVo/yhov3qrw1kM/Tnc+19Oib0Sr1n+3Vj82/FGlTp76oR8naEo/Fu8ufFcNrTlU7eqxy+l3TfQAMG7rOOvKuh3dd6he5XqpNn+3UUsPL01YJvvX2a2DBIl+FyntZ2lVmR/LWE0Q/Q6ZXX7efrc1jRYeWqj2nNuj5redr2a8MEPdjLtpXQkY6BOolndcrlZ2Wmm9S1fv93oJ/+7L1V+qseFj1S+Nf1ErOq5Q56+eV1N3Tb3tdnRd+kiKtu/8PmsAyjk8pzUY6CkBALj3GLX6+GpVOH1hFRIQkvC7urnqqkvXL1kf1XwaxiidpT9uqg+c4iXzSGb9rB8/APc/jnoc/LxuH6P2n9+v/tr1l/q7xd8JH79vPqW5OhNzRs1uPVtt7LJRlchQQtUcV9Mai7TJOyZb939IjSFqw8sbVIbADGrk+pG33c6Sw0usMSrxSRiAR4sxijEKcGdPcozSFyANWjZIjWsyzjoHelTc5Vwvcc+tQZ4Gt53z4eEw58FjMKjaIFU7V22nl9cfSRiyYoha0HaBKp+lvPU7/e63fldLfxygavaq1u9ypcklXhGsb7tGjhrWVXn6qsTuM7tb7+K8Vva1h3osuqGtd/i5++eqV8u8mvB7f29/9VOjnxI+JvH71t+td9f07zw8/rcT649b6Heh9MFHnVx1rI+w963UVzXN39Sq66sH5x6Ye9vtpfJNZc0PbVI2U1k1tvFYlS9tPuvj/3pOtMpjKqvt3barQN/Ah3qcQFLypMYoPb9y4oa0Fv+zrj0NY1TklUjrY3Ih/nc8Tv8Q6+oFAO59HPU46DeqJmyfYB2fxdMnSOOeG6fS+aezftb3dd2JderMW2eUr9f/rgz8os4XatruadaVSl1KdrHGqM7FO6vOJTpb9Y9qfKQWHFxw25WI+vhPj1H6I64AHg/GKMYowJ09qTFK57zw1wvq89qfW/NAH7xw0PZjcbdzPW3S9klqU8Qmtf7l9bYfH2hKPxalMpZ6oOX1O9F67qzav90+cOiDgeIZiif8vLDdQjHrw6ofJvxd/9uYmzHWl209aFN6xt4ZKmBIgLp566a1c79Y+MXbJswvHFL4tnl79Ee39OPQHxdJTB+EHDh/QEVlilIR0RHW/KqJPzam11XiK7mb5G9i/THRH/mPp9/505nZvs5mvTsffxAEwD3HqEfFnccoAPY8DWPUtjPbrDFKv0ml74e+mubb+t8m1PXHUeObPfFjlL6IQH+cNLGrsVetMUrbFblLdS3V9bZ6+czl1eLDixN+1l+oyhc/A48XY9T/YYwC3M+TGqP0F/7pL2BtU6SNelrP9fQUIa/Ped26QvthpsnF3WhKPwb+Pv63/aw/tnDnXDX64wXx9Au8NvPFmSpTyky3LWd3Hi19VfHgZYOtd63i39V2RvUc1dWoBqOsHT1jYMa7JsfX70wlph9DyYwl1fim4+/KSpfi/w5oHgc9h3be4LzWIATAfccoPS+YvsomsdPRpxNqD8Jdxyh99YD+JvvTMf97XPH0zw/6GIGkyp2Oox6Wvspm+gvTrbFJj1F3fgHPvcaoDAEZ1JIOS+7KepgvrAbw+DBG3Y4xCnAvT2qMWnRokfWG15+D/jcXdfxt6qll36/8vhpYfeB//lxvY8RGaxqjEqNLJPxOv7m37Mgy9e26b9X1D64rz2Sej+z2kgKa0i6gd4LtZ7bf9rvw0+EJH1vSX4Sld3Y9L3L8RyMelfBT4dbE6w/SkI7fyXOnye308npOsT92/KHS+6dXKX1T3nMZfSCz9vhaVSVbFetn/W3P+uNi+t/aoQcg/e5X2yJtbeUASZWrxih9tczHyz+2Xsj1WKHNPzjfGjP0bTwNY5Q+cNIHRAsPLlTPhf3vC0X0u/v6555lejqdA8A9jqMelh4LHnSM0tMY6ZOu7EHZ77mMvvpIj1HtirZL+N2aE2seyf0F8PAYo/6HMQpI2mPUXy3+sj49EW/9ifWq0/RO1hzPeuqPp+Fcr2aOmmpbt223/a7jPx1VWNow9U7Fd2hIPwS+6NAF9PxcG05uUOO2jFP7zu1T/Rf3v21Q0PMg628m7TW3l/o1/FerwarnqPlm7TfWz/H0F0nod1/u5989/1rfkqyz9VXD+ltP9dxAiefeeVxaF2ltXSGov+F0+ZHl6tCFQ9bcPa/Nfk0dv3TcWub1sq+roSuHWnOP6blV9XzX+ltZE9MTzYd9G2a8rbfmvWVNuK+/IGPVsVWqyR9NrJ3/hUIvPNbHCDytXDVG6bm89EFP26ltrY9Y6bnBPlj0gepRuscDv3HmzmNU73K91Y+bfrTWza6zu1S3Gd2sqZQ6Fuv4WB8j8LRy1Ril6eMn/Ya+br7oEyv9d/0n8ZfrPA76i3L0PI762+T1d4LEH+O8v/B967HHj1G/hP+ixmweo/ae22uthzu/JFZ/GkWPUScunbjvbenHkvhx6WX13/nEGfBwGKMYowB35qoxSjeeC6UvlPAnR+oc1u/zp8ufcEHSf/1cT6+rxI9R/9EN9GC/YOvveHBcKe0CdXPXVR9W+VD1md/HmtOmU/FOql2RdtZHG+INrj7YegfrkxWfWBPC649B6Xds3qv8XsIyenDQX6J1P96e3uq79d9Zg4meF0e/szSszjD1csmXE5bRBxA5hudQi9svVtWyV3tkj1F/acWyjsvUOwveUU0nN1WXr1+2Pvqh30mKf6fqzQpvWvP4tJ/W3voISadinaz5eqKuRSXkRF2Psr5F1UQPKnoC/XNXz1nrrFLWSmpN5zW3zXsGwP3GKP3mkf525G4zu6nyP5e3PlrWvmh7Naj6oKdqjGpZqKU6e+Ws6rekn3XSWCy0mJrTes5dX/IIwL3GKO2l6S+ppUf+75vmi4/+31yKh14/lHB1oP5Wdv3lOR2KdXhkj1F/Kc+sF2ep9xe9b11xczbmrDXlj76aJ/6LU/XYcuDCAdVnwf/WQ7P8zVS3Ut1u+5IePSekHqP0HIz3c/LyyYTHpX2x+gvrT9VsVe/50XwAZoxRjFGAO3PlGCV5Gs718Gh5OBLP6o2n3uJDi62d9OBrB1Vqv9RP+u4AwG0YowC4M33lTd5v86qd3XeqPMF5nvTdAYDbMEYBcGec6+FOTN+RxMzaN0u9V+k9BgAAbokxCoC7j1FdSnSh2QPALTFGAXBnnOvhTlwpDQAAAAAAAABwGa6UBgAAAAAAAAC4DE1pAAAAAAAAAIDL0JQGAAAAAAAAALgMTekkQE8b3uXfLirNp2mUx0APFX4q/EnfJQBIwBgFwJ0xRgFwZ4xRANwZYxRMaEo/QaeiT6lXZ72qcg7PqXw/8lVZvsqiGk5sqBYeXPhIb2fO/jlqbPhYNePFGSrizQhVKH0hp/7da7NfUyV/KGndt2LfF3Pq31yLvaZ6zOyhgj8LVgFDAlSzyc3U6ejTty1zNOqoajChgUrxcQqV/vP06u15b6vYW7EP9dgAPD6MUYxRgDtjjGKMAtwZYxRjFODOGKMYo9yB15O+A0nV4YuHVcVfKqqg5EHq89qfq8IhhdXNuJtq7oG5qsesHmp3z92P7LYOXDigMgRmUBWyVHjgf9upWCe19sRatfX0VqeW7zWnl5q5b6aa0nyKSuWbSvWc3VM1ndxUrey00qrH3YqzBoDQgFC1qvMqFXE5QrWb1k55e3qrITWHPPD9A/B4MEYxRgHujDGKMQpwZ4xRjFGAO2OMYoxyGw48EfV/r+/I9GUmR/T16LtqF65eSPj7kYtHHI0mNnL4f+zvCBwS6Gg+ubnj1OVTCfX+i/s7io4q6hgXPs6R7atsjpSfpHS0nNLScenaJavefmp7hxqgEv7oZR5U/G1ILl696PAe5O2YsmNKwu92nd1l3e7qY6utn2ftneVINjDZbY9h1PpR1v2+Hnv9ge8bgMeDMYoxCnBnjFGMUYA7Y4xijALcGWMUY5S7YPqOJ+D81fPWRxh6lO6h/H3876rrd6u0W45bqvGkxtbySzssVfPbzlcHLxxULf9sedc7T9P2TLM+DjHjhRlq6ZGlauiKoVZteL3halC1QSpzyszWRyXWv7ze+n32r7OrAUsGPNLHtTFio7p566aqlbNWwu/C0oaprKmyqtXHVls/rz6+WhVOX1iFBIQkLFM3V1116foltePMjkd6fwA8HMYoxijAnTFGMUYB7owxijEKcGeMUYxR7oTpO56A/ef3K4dyWDuIiZ7LZ9vpberQ64dUllRZrN+NazJOFRxZUK0/sV6VzlQ6YbAY23isCvQNtH5uW6StWnhoofpYfaxSJU9l/d7Tw9P6iEK8XGlyqbQp0j7yOYl8PH0SBrF4If4hVi1+mcQDgFX//z/HLwPgyWKMYowC3BljFGMU4M4YoxijAHfGGMUY5U5oSj+hbx91xq7IXdbOHz8AaAXSFbB2Ml2LHwSyB2VPGAC0DAEZ1JmYM8bshe0e7eT1AJ4ejFEA3BljFAB3xhgFwJ0xRsGdMH3HE5AnOI/yUB5qd+SjmTzeO5n3bT97eHhY71a5mn7n60bcDXXx2sXbfn865nTCu2L6/3d++2n8z4nfOQPw5DBGMUYB7owxijEKcGeMUYxRgDtjjGKMcic0pZ+ANH5pVN3cddV3679TMTdi7qrH70T50+ZXx6KOWX/i7Ty706rrd6jcTckMJa0BSX/MI96eyD3qaNRRVT5Leevn8pnLq21ntt32ztn8g/NVSt+UbvmYgKSIMYoxCnBnjFGMUYA7Y4xijALcGWMUY5Q7oSn9hHz3zHcqzhGnyvxURv218y+179w+tevsLjVi7QhV/uf/7TB6gvbCIYVV679bq00Rm9S6E+tUu6ntVNVsVVWpjKVs3X7NcTXVt+u+FecaCj8Vbs2tczX2qvV3/Ue/+6SduHRChX0bZt0vTc8X1Ll4Z9V7Xm+1+NBitfHkRtXxn47Wjl8uczlrmTq56lg7e9upbdWWU1vU3P1z1QeLPrAm2ff18rX1mAA8OoxRjFGAO2OMYowC3BljFGMU4M4Yoxij3AVzSj8hOVPnVJu6bFIfL/9YvTnvTRURHaHSpUinSmYsqUY1GJXwsYd/Wv2jXp39qqoypopK5pFM1ctdT31T/xvbt3/g/AEVeSXSuMxL01+yvjk1XvHRxa3/64nu9bxB+ptN95zbo67cvJKwzFf1vlLJ5iZTzSY3U9fjrlvfZDqywciEumcyT+sbWbvN7GYNdvrbXtsXba8GVR9k+zEBeHQYoxijAHfGGMUYBbgzxijGKMCdMUYxRrkLD4ezs5wDAAAAAAAAAGAT03cAAAAAAAAAAFyGpjQAAAAAAAAAwGVoSgMAAAAAAAAAXIamNAAAAAAAAADAZWhKAwAAAAAAAABchqY0AAAAAAAAAMBlaEoDAAAAAAAAAFzGy9kFJ02aZKxHRkaKGbNmzTLW8+bNK2ZcunTJWL9586aYUbduXWP95MmTYsacOXOM9VKlSokZvr6+xnrWrFnFjNWrVxvr3t7eYsaFCxeM9WPHjokZzz33nLEeFBQkZpw/f95Yj42NFTNiYmKM9WTJ5Pdhtm/fbqynT59ezPDw8DDWr1+/LmZIt5M5c2Yx44033lBJRaNGjYz1Zs2aiRkjR4401s+dOydm9O3b11hfuXKlmJEzZ05jPW3atGLGqlWrjPV27dqJGWvWrLE9Vh4/ftz2OOfM64vd147mzZuLGYcPHzbWr1y5ImZIY3JYWJiYERoaauv1SQsPD7c9vkjbYapUqcSM/v37q6Ri9uzZtrYvZ17DNm3aJGYEBgbaGn+cOV7r1auXmNGyZUtj3ctLPkSVttP27duLGVWqVDHWX3jhBTHjt99+s70+9u/fb6zHxcWJGVevXjXWIyIibB9HXbx4UcyQjoFHjx4tZnTu3NlY9/T0tD1GHTx4UMwYPny4Sipq165trL/00ktixh9//GGsV6hQQczYuXOnse7v7y9mpEmTxlj38/OzfV5SvXp1MePEiRPG+qJFi8SMhg0b2h5ffHx8jPWOHTuKGdI+98wzz9g+rly+fLmYceTIEdvbWKZMmYz1kJAQMWPKlCm218e1a9eM9cWLF4sZ06ZNU0nFiBEjbK/z9evX2z5Pk4498uTJI2bMnTvX9nmalHH27Fkx48yZM8qu0qVL2zqu0KKjo231Z7Ry5coZ6+nSpbO9Tlu0aCFmbN682fb5c4MGDWydXztzrNW0aVMxQ+qtOhwOMWPUqFHiMlwpDQAAAAAAAABwGZrSAAAAAAAAAACXoSkNAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBma0gAAAAAAAAAAl6EpDQAAAAAAAABwGS9nF1y7dq2xXrNmTTEje/bsxrqHh4eYERgYaKwHBQWJGatWrTLWDx8+LGbkzJnTWM+UKZOYsXnzZmN9x44dYkbhwoWVXdLzkjJlSjHDz8/PWP/jjz/EjMqVKxvru3btEjPq1atnrJ84cULMyJgxo+378e677xrrJ0+eFDO+/fZbY71Tp05iRlIiPfd79uwRMxo2bGisT506Vcw4ffq0se7v7y9mHDx40Fi/fv26mOHr62us79u3T8xwOBzGekxMjJjx/vvvG+vjx48XM4oXL26sz5kzR8yQXl9u3bpl+3nJkyePmCG9vqRJk0bMOHr0qLJLGueWL18uZrRp08ZYX7ly5QPfr6eZNH6kSJFCzJD2/Xz58okZhw4dMtaXLVsmZkjjxzvvvCNmSK/HzqwPabwdO3asmCGNyf379xcznn/+eWPd29tbzJg/f76x3r59ezFDGgudOWZs0aKFrfMBbfXq1baPXyIiImy9Pmlnz5411q9cuSJmJCWlSpWyffybN29eY71EiRJiRmhoqLG+aNEiMaNRo0bG+vTp08WMgIAA2xleXuZT7UKFCokZ4eHhxvqAAQPEjM8//9xY37hxo+3to1+/fmLGkSNHjPUPPvhAzEiXLp3t47nevXvbPr7t0aOHrfHHmdvp06ePmJGUbNq0yVhPlSqVmCFtH1mzZrV9TuHM/iSNldOmTRMzpGOLxo0bixlfffWVsf7CCy/Y7s9lyZJFzPjoo49snU9qEydOtH2e9uyzz9o+b61ataqt43Bt9+7dym7Ps2nTprbGY61ChQq2exLO4EppAAAAAAAAAIDL0JQGAAAAAAAAALgMTWkAAAAAAAAAgMvQlAYAAAAAAAAAuAxNaQAAAAAAAACAy9CUBgAAAAAAAAC4DE1pAAAAAAAAAIDL0JQGAAAAAAAAALiMl7ML1qlTx1jftWuXmFG5cmVjPUuWLGLGunXrjPUtW7aIGbdu3TLWn3/+eTFjxowZxvqGDRvEjPz58xvre/fuFTNKlChhrP/1119ihvTc1a9fX8woWrSosb5mzRoxw8PDw1iPjY0VMyIjI43169evixmFChUy1pMnTy5mfP7558b66NGjxYxcuXLZ2gaTGmnfL1y4sJhx/vx52/tCmjRpjPXo6Ggx4+jRo7Yfy40bN4z1ffv2iRkxMTHG+ssvvyxmDBw40FivXbu2mHHgwAFj3c/PT8yoWrWqsd63b18xo3Xr1sZ6zpw5xYzZs2fbfi2VxjkvL/mlvXPnzsb65cuXxYxDhw4Z6wEBAWJGUpI7d25b27kz+/6mTZvEjKtXrxrrZ86cETN27Nhh+3hu9+7dxnq+fPnEjK1btxrrzz33nJghHWtVrFhRzJBej0uWLGl7jJo+fbqYUa1aNWN90aJFYoY0fhw5csT2GNWuXTsxQzpOypo1q5hRunRpY33evHliRlJSoEAB29vxP//8Y6wvWLBAzEiRIoWx7uPjI2bMnTvX9mORxlPp3NiZ++HMOUWGDBlsj9nS402fPr2Y4e/vb+s2nDnmc+a5lcaxKlWqiBk///yzsb548WIx49KlS8Z6mTJlbB9n//rrr2JGhQoVxGWSigEDBojL1KpVy1hftmyZmFGsWDFjvVKlSrbHF2fOsQ4ePGis79+/X8zo2rWr7d7axo0bjfWoqCgxY/jw4cb6m2++afsY+YcffrC9PwUFBYkZ48ePN9Zr1qxpuw+U04lzzuXLl9saw7R///3XWG/ZsqV6FLhSGgAAAAAAAADgMjSlAQAAAAAAAAAuQ1MaAAAAAAAAAOAyNKUBAAAAAAAAAC5DUxoAAAAAAAAA4DI0pQEAAAAAAAAALkNTGgAAAAAAAADgMl7OLrhr1y5j3cfHR8yYNm2asR4YGChmlChRwlgPDg4WMy5evGisL1myRMyQ7muVKlXEjLi4OGP96NGjYsaMGTOM9Ro1aogZ+fPnN9aLFy8uZrz++uvGeqlSpcSM69evG+u+vr5iRkBAgLHu6ekpZqxbt85Yj4yMFDNatmxprP/zzz9ixoYNG2yv06RE2idXrFghZmTIkMFYz5Qpk5ixfft2Yz1btmxihnQ70dHRYsapU6eM9U2bNtm+HwMHDhQzateubawvWrRIzJDGMWfWR758+Yz1Tz75xPZ4mz17djGjSZMmtvZ77cUXXzTWp06dKmacPn3aWM+YMaOYcezYMWPd399fzEhKli1bZqynSJFCzDh58qSxvm3bNjGja9euxvr8+fPFjKioKGM9Z86cYkahQoVsHTM6s52Gh4eLGdJ6r169upgxefJkY33t2rVixqVLl2ztb9rBgweN9apVq4oZhw4dMta3bNkiZtSvX99Y/+OPP8QM6Xj+22+/FTOGDx9urFeqVEnMSEpCQ0ON9VWrVokZixcvNta9vORTz2bNmtk6vnHmfEAaw7Q6derYOjd2ZszOkSOH7dfr1KlTixnSeakz50fScfbNmzfFjKtXr9o6hnbm+PXZZ58VM/bt22f7XE96ndu6dauYERERYWs8TmqSJUtma5/VLl++bKy/+uqrtnsnzhzLS/uLM8cN0nFlmTJlxIy///7bWE+fPr2Y8fzzzxvr48aNEzN++uknY7106dJihjR+vPvuu2KG9DrnzFjZunVrZdd1oS+2ceNGMUO6r1Jf1Zn+2/Hjx9WjwJXSAAAAAAAAAACXoSkNAAAAAAAAAHAZmtIAAAAAAAAAAJehKQ0AAAAAAAAAcBma0gAAAAAAAAAAl6EpDQAAAAAAAABwGZrSAAAAAAAAAACXoSkNAAAAAAAAAHAZD4fD4XBmwS5duhjrpUuXFjMuXLhgrKdNm1bMmDp1qrH+wQcfiBlr16411j08PMSMdOnSGetz584VM/LmzWusBwYGihnnzp0z1qtUqSJmLF261FgPCwsTMzZs2GCsBwQEiBm3bt0y1tOnTy9m+Pv7G+uzZs0SM5InT26sR0dH235undnGpPW+d+9eMWPIkCEqqciRI4ex/vLLL4sZ+/fvN9YzZcqk7IqJiRGXiY2NNdbTpEkjZpw/f95Yj4yMFDMOHTpkrNetW9f2Oi1WrJiYsXPnTmO9XLlyYsaYMWOM9erVq4sZtWrVMtY/+ugjMUNaZ8ePHxczpPH09OnTYkZQUJDt17AaNWoY6zdu3BAzRo8erZKKUqVKGeu9e/e2/Top1Z3Zny5evChmpEiRwlj/888/bR9XxsXFiRlXrlwx1o8ePSpmSPtcs2bNbB97zJgxQ8yoWbOm7bEhT548xvq4cePEjJYtWxrryZLJ17NI2+HAgQPFjIoVKxrr+fPnt/28SMdqWosWLVRS8e+//9o+hj5w4ICxXq1aNdvbjzP7wpkzZ4z1EiVKiBmpUqWydb6g5cyZ0/b50SuvvGKsd+zYUczYvn277eO5qP/H3n2AZ1Gt6/+fkEoSCAmQQELvLYTeO1JVqkhHxAKIvW3L3tZt162wZVuwoIiISm/Se++E3kISSEJJAiSBkPq/Juevv+0pzzOehWOO7/dzXV4K93jnLTNr1qy35MoV42Ny3LhxYt6jRw+1Q7sOK1mypNpx4sQJMX/jjTfUDm0eHRsba7yPbdq0Se1wMq7/WTz66KNiXqtWLbVDO+ZWr16tdnh7e4v5vffeq3asWLHCaK5m27lzp5j7+vqqHdoxp6152XJzc42uOZxcD86aNUvt0Mbkdu3aqR1Hjx417nj11VfF/Mknn1Q7DivPvzaGORmztetaJ8+dk7URJz+Hd0oDAAAAAAAAAFzDojQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABcw6I0AAAAAAAAAMA1LEoDAAAAAAAAAFzj43TD3bt3i3lQUJDakZubK+bbtm1TOx544AEx9/PzUzs6duxo/d7q1q1r/V/Rv39/j7m/TZo0Me5YsmSJus2xY8fEfOzYsWrH22+/LeYNGzZUOzzJXXfdJeZLly5VO3r37i3mSUlJakdcXJyYZ2Zmqh0BAQHGx2xeXp6YX716Ve0oU6aMmPv46KeQQ4cOifmtt96qdqSlpYl5RkaG2qH9nDNnzqgd77zzjph7e3urHfHx8cbPS7NmzYzPxzNnzhTzRo0aqR3JycliXqIEr3v/u8cff1zMf/zxR+N51KhRo9SOS5cuiXm5cuXUjvr164v5hAkT1I4RI0YY3VfbmDFjxLxVq1bGY3aHDh3UDu25q1mzptpx5coVMS9btqzakZ6eLubVq1dXO/bt22c0pjt53LXzte3GjRtiXrVqVbVj6tSpYt66dWu1484777Q8xfr168W8U6dOasfBgwfFPDAwUO1YtWqV8bVefn6+Ue5kX2/btq3ace3aNTFfvXq12tGmTRsx37Vrl9oxY8YMMd+6dava8dVXX4n52bNn1Y5nn31WzHfu3Kl29OnTR8wjIyONx8r33ntP7UhISBDz9u3bqx2hoaHG53RPoh1z27dvVztycnLEvHz58sbrHk72n9dff13M582bp3bccsstRut3To6nH374Qe0YPny40fjjZF6ZnZ2tdmjXUMHBwWqHdi3nZD2hZ8+exs/t3/72NzG///77jc8dy5cvVzucrFndDFwxAgAAAAAAAABcw6I0AAAAAAAAAMA1LEoDAAAAAAAAAFzDojQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABcw6I0AAAAAAAAAMA1Pk43fOaZZ8R869atakdiYqKYe3t7qx3r168X859++knt+PLLL8V89uzZaseBAwfEvFatWmpHo0aNxPydd95ROwYPHizmdevWVTsOHTok5mvXrlU7xo8fb/SY20qXLi3mPXr0UDvWrFkj5sHBwWpH27ZtxTw7O1vt6Natm5j/9a9/VTvuvvtuMY+NjVU7PElqaqqY9+rVS+2oWrWqmGdmZqodhYWFYu7r66t25Ofni/nq1avVjr1794p5mTJl1I4+ffqI+b59+4w7wsPD1Y7u3btbplasWCHmoaGhasfu3bvF/JFHHjE+h125ckXtOHHihNF9tQ0ZMkTM4+Li1I6ePXuKeXJystrhSX744Qcxz8vLUzsqVqxo/Lxpz72T502bF2jjsa1p06Zift9996kdp0+fFvOLFy+qHVevXjWau9quXbsm5pMmTVI7PvvsMzEPCQlROwoKCozmN07ub+vWrdWOXbt2Gc9NtefFyVjZokULo9zTaOPLtGnT1I7+/fsbj1FBQUFiHhMTo3Y0aNBAzOPj49UObV6gnYudzHG0OZJtwYIFYt6yZUvj5+Xee+9VO8qXLy/mixcvVju0Y+7MmTNqR0REhJgfP35c7QgMDDRek9DGKCfXDHPnzhXzgIAA47nYn4l2DZWRkaF23HLLLUbjj23nzp1iXqFCBcvUe++9Z3x91KxZM7VDW6Nxckxqx76Tce7GjRti3rBhQ+N55aVLl9SOUqVKiXmrVq3UDu067KmnnrJM3Xrrrcbnwdq1a6sd9erVE/MPP/zQ+Jiz8U5pAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrfJxuePLkSbnIR69q3LixmF+5ckXtSElJEfOJEydapnr06KFuk5eXJ+aRkZFqR2xsrJh/8sknasc777wj5n5+fmrHqlWrxHzy5Mlqx5w5c8S8U6dOasf169fFfN26dWrHgAEDxPzIkSNqh7bN1atXjffTypUrqx1VqlQR8/Pnz6sdniQoKMhoH7WVL19ezPv166d2BAcHW7+3GTNmqNtMmTLFeLzVxjFt7LC1bdtWzLdu3ap27N+/X8xDQkLUjlKlSon54sWL1Y6PP/5YzF966SW1o0SJEkZjmJN92UnHZ599JuajRo1SOy5cuCDmM2fOVDuefvppy1O8/PLLYj516lS1Izo62mhuYluwYIGYHzhwQO3o37+/mCcmJhqP2dr8xtasWTOjeZatTJkyYn7ixAm1Y/fu3WL+ww8/qB3e3t5ifvbsWeMx28ncY/ny5WJeWFiodpw7d874Me3Zs6eY79mzR+1Yu3at8XlwwoQJlqfw9fUV886dOxufa7V5lq106dJinp+fbzyX1+YmTq47Bg0apHZoc/Xvv/9e7Wjfvr2Yp6enG48NlSpVUjuqVq1qtP84eTyGDx+uduzYscPoPOlk/uLkHNa9e3cxb9eundqh/Rxt/utptHNLhQoV1I7Zs2cbHW9O1r2SkpIsU06OhRo1ahjfjnLlyol5xYoV1Y5GjRqJ+ZIlS4zXPVJTU9WOixcvGq9XanOc48ePqx25ubnG18+33XabmCckJKgd2m11cg7bvHmzK+sevFMaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK7xcbrh5cuXxbxOnTpqR0ZGhpiHhISoHceOHRPz+Ph4taNVq1ZiPmPGDLWjoKBAzLOzs9WObt26ifnHH3+sdlSuXFnMAwMD1Y477rhDzH/66SfjxyM5OVnt6Ny5s5iXL19e7UhJSRHz4OBgtcPb21vML168qHbcf//9Yr537161Iy8vT8zPnz+vdniS/Px8Ma9ataraUbduXTG/ceOG2pGbmyvmhw4dUjuaNWtmPL5o+1iHDh3UjkWLFhnflw8//FDM27dvr3acOXPGaPxx4sEHH1S3eeKJJ8T8vffeUzveeecdMc/KylI7GjduLOaZmZlqx5gxY8Q8LCxM7bh27ZqYx8TEqB2eZOnSpWJerVo1teP69eti7uvrq3Z07NhRzFu3bq12aOfjq1evqh1RUVFi7u/vr3Zs3LhRzMuWLat2PProo2L+6quvqh3a/HXfvn1qx7hx48R8+vTpakft2rXFfObMmWrHyJEjxXzWrFlqR82aNcV8yJAhaoc2F3cyv9XO6fi1cuXKGc9/J0yYYHxeKCwsFPPJkyerHU2aNBHzNWvWGM8r+/btq3a8++67xtdHX3/9tZiXKlXK+PFwMn9p3ry5mJ8+fdr4/ONkH0tNTTUaw2zz5883Oi/Y/vGPfxifB++66y4xP3LkiNrhSbR90Mn+o23j5Hx98OBBo7UmJ5xc6x04cEDMa9WqpXYEBQWJeWxsrPHt0I57m5eXl9G6mW3Xrl3G6y/avLFChQpqR1JSkpj7+DhegjWa72s/58cff1Q7BgwYYHzN6QTvlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuMbH6YalSpUS8zNnzqgdwcHBYh4REaF29OvXT8x/+OEHy1Tnzp3VbUqWLCnmW7ZsUTtyc3PFPCcnR+0oV66cmCckJKgdVatWNb4vffv2FfOwsDC1Y/fu3WJ+7do1taNFixZivn79erWjffv2Yh4dHa12TJo0Scx79eqldmzYsEHMw8PD1Q5PcvDgQTGvVauW2nH77beL+ebNm9WOxMRE42Nh3759Yj5o0CC1o0OHDmL++eefG4/ZnTp1UjsCAwONxkHbAw88YPy8nDt3TsznzZundtSuXVvMZ86cqXY89dRTYv7aa6+pHb179xbzrKwstWPu3Lli/vjjjxs/pi1btlQ7PIn2eCUnJ6sdNWvWNPoZtp07d4r5I488onacOnXKMqWd45zQ9jEnc6B3331XzHv27Gk8J3Qyn1u1apXRc2974YUXxHzx4sVqx+jRo8X8yy+/VDtWr14t5ikpKWpHmzZtjPefhx56yLjDk+zfv1/MCwoK1I6HH35YzMuXL692DB06VMw//fRTteP48eNinpGRoXbcuHFDzGfNmqV2aGNyw4YN1Q5tP3UyJzx9+rSYb9++3fh5+eabb9SOpk2bGo2lTh6zTz75RO3Q9uVmzZqpHT169DB6zG0PPvig8VzMk2RnZ4v5gQMH1A7tMX366afVjtatWxutizjh7e1tPAdycq7VxkrtmsM2ZcoUMffy8lI7tNvqZN6pPf9VqlQxHudKlNDf05uXl2d8/jEdf5xwsn67cOFCMY+KirJuBt4pDQAAAAAAAABwDYvSAAAAAAAAAADXsCgNAAAAAAAAAHANi9IAAAAAAAAAANewKA0AAAAAAAAAcA2L0gAAAAAAAAAA17AoDQAAAAAAAABwDYvSAAAAAAAAAADX+DjdcMeOHXKRj151++23G/0MW8OGDcW8TZs2lqnZs2er2/To0UPMO3TooHZMnz5dzKOjo9WOOXPmiPk999yjdlSoUEHMhwwZonYcO3ZMzKOiotSO8uXLi3lOTo5lavz48eo2a9asEfOEhAS146mnnhLz5s2bqx1ly5YV840bN6odnkQ7Xnbu3Kl2HDhwQMwHDx5svB+vW7dO7ahdu7aY33bbbWrH6tWrxdzf31/tKFOmjJi3bNlS7UhJSRHzzMxMtWP79u1ifvToUbWjS5cuYr5+/Xq1w9fX12gstS1atEjMn3/+ebVj9+7dYj537ly14y9/+YuYL168WO0IDg4W88LCQrXDk0RERIj5lStX1I6YmBgxr1WrltrRqlUrMZ88ebLa8d5774n5hx9+qHaUKCG/L+Khhx5SO7TzYEhIiNpRpUoVMd+0aZPaof2ckiVLqh3a+KHN92zLli0T8z179qgd3377rZhPnTpV7dDOUVWrVlU7Vq5cKebh4eHG58EVK1aoHU725T8LbR88fvy42tGiRQsxv3TpkvFzX6dOHbUjLi7O+HoxMTHReP5iOg7amjZtajzO1a1bV8xHjhypdpw+fVrM77jjDrXj8uXLYp6dna12JCUlGT1etqysLOPxRRvXGzVqpHbs37//d9/H/ky0a/QzZ86oHQ888IDRepUtLS3td5//du7c2XiMcvJ4aGND6dKl1Y5hw4YZH0/avq495rYjR46I+SOPPGI8r8zNzVU7qlWrJuZ79+5VO4Yo629NmjQxXtcICgpSOwYOHCjmJ0+etG4G3ikNAAAAAAAAAHANi9IAAAAAAAAAANewKA0AAAAAAAAAcA2L0gAAAAAAAAAA17AoDQAAAAAAAABwDYvSAAAAAAAAAADXsCgNAAAAAAAAAHCNj9MNq1WrJuZeXl5qR3BwsJivWLFC7Rg4cKCYb9iwwTLVq1cvdZucnBzj2/Hggw+KeWxsrNrx0EMPiXlCQoLacerUKTHv06eP2pGSkiLmly5dUjtSU1PF/Nq1a2pHw4YNxXzbtm1qR/Xq1cU8NDRU7Vi3bp2Yx8fHqx2lSpUyerxsw4cPtzzF/v37xbxMmTJqx4gRI8R87ty5xvvPyJEj1Y5Vq1aJ+dmzZ9UObV/39vZWOypWrCjm6enpasexY8fEvEqVKmpH8+bNjc5PttzcXKP7asvOzhbzM2fOqB3ly5cX8xMnTqgdN27cEPOWLVuqHe+++66YN2jQQO3w8/MT86ioKLXDk4SHh4v57t27jceG69evqx3aOWzixIlqx4cffijmJUuWVDtuvfVWMd+4caPa4e/vb7SPOhlPncwJT548KeaFhYVqR2RkpJi/+uqrasebb75pPCeYNWvW776POTmXrl27VszfeecdtUMbk52Mt55EmydVrVpV7QgJCTG65nAyfhw8eND4vhw/ftz4uH7ppZfUjk2bNhnPTbV5kjbPskVERBhfl7Ro0ULMZ8+ebdxRUFCgduzYsUPMa9WqZTxXd7KfardD+xm2H3/80Xic8yTasV+7dm21Iy0tzWheYbt48aKYHzp0SO2oU6eO0XzPNmTIEDEvV66c2qHN1bOystSOevXqifnUqVPVjiZNmoh5t27d1I5BgwYZXYM5ubZ18tw2btzY6DzpxBNPPGH8mJ4+fVrtyMvLE/Px48dbNwPvlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa3ycbtiyZUsxv3btmtqxaNEiMW/btq3a0blzZzHfvHmzZSolJUXdZvfu3WJepUoVtWPDhg1iXq1aNbVj+fLlYh4cHKx2eHt7i/mECRPUjgEDBoh5QECA2hEZGWn0eNm+/fZbMe/bt6/asWPHDjFPTExUOxo3bizmISEhasfRo0fFPCgoSO3wJA0aNBDz3NxctePGjRtiXrJkSbUjNTVVzI8cOaJ2VKpUSczXr1+vdnTs2FHMc3Jy1I5y5coZH5NhYWFinp2drXZcuXJFzEuU0F9f/f7778W8Xr16akd4eLhlSts/zpw5o3bs2bPH6HlzMt46Gedq1Kgh5gkJCWqHJ9GOuWHDhqkd2vPi5DHX9uPMzEy1Q5tbaGOprXr16mL+1VdfqR1nz541mjPaWrRoIeanTp1SO2bMmCHm48ePVzvee+89MR88eLDaMWrUKOP9o2HDhmK+ZMkStSMwMFDM3377bbXj1VdfFfOVK1ca7+va4+VpYmNjxdzX19d4Du3kekA7Tzpx+fJl43HujjvuEPOFCxeqHdOmTRPz6OhotePEiRNi3r9/f7Xj/PnzYh4TE6N2aPe3bt26akd+fr6YN2rUSO3IysoymjM6GYMef/xxtaNdu3ZinpSUpHZ89tlnYj5u3Di1w5Noc3VtTmDr3bu3mHt5eakd2jpPfHy8ZUq7jnMyVmrHm23r1q1i7u/vr3YcPHhQzEuVKqV2VK5cWcxPnjypdjzwwANifunSJeNzWHp6utqh7Ye33367Zap9+/bqNtu2bTNav7OlpaUZPV62Nm3aqNvwTmkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGu8CgsLC51s+OWXX4r5kiVL1I7+/fuLeXp6utqRnZ0t5gcPHlQ7HnvsMTGfOnWq2tG+fXsxP3r0qNpRUFAg5mXLllU7wsLCjB+P4OBgMY+MjFQ7UlJSxLxOnTpqR5UqVcR8ypQpake3bt3EfPny5WqHv7+/mA8YMEDtyMzMFPNq1aqpHbt27RLza9euqR2vv/665SkmTpwo5l5eXsb7cYMGDdSO9evXi3lERITxfdm8ebPa4evrK+YrVqxQO7T91MljGhMTY3S82U6ePCnmQUFBakdOTo6YN2/eXO345z//KeZ5eXnG58Ft27apHZMmTRLzZ555Ru1o27atmNerV0/tmDZtmphXrVpV7Zg/f77lKV544QUx//zzz9WOCRMmGB33tlWrVhnPgQATTz/9tLqNn5+fmFesWFHtiIqKEvO4uDi14+OPP7Y8hXatl5WVpXYcPnzYaK7v5Jrys88+UzuA33uMKlWqlPE8KjExUcxr1KihdvzlL3+xPIU2h+7Tp4/asWbNGqN1EdvZs2fFPDQ0VO146aWXxPzEiRNqx8yZM8V84MCBasd3330n5j179rRMBQQEqNusXLnSeH57/Phxo3UiJ3OPWrVqGe9jycnJasc//vEP4+P+4sWLYj527Fi1Y//+/WJ+/fp147mFjXdKAwAAAAAAAABcw6I0AAAAAAAAAMA1LEoDAAAAAAAAAFzDojQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABcw6I0AAAAAAAAAMA1Pk43XLt2rZi3adNG7QgODhbzuXPnqh39+/cX88cee8wyNWnSJOOOJk2aWMVBy5YtrT+Lv/3tb8Yd7du3N+7YuXOnus2BAwfEfMeOHWrHwIEDxXz16tVqhyc5e/asmCckJKgdqampYp6Xl6d2dOzYUczr1q1rmapXr566zbx588R8woQJasfWrVvFPCoqSu3YtGmTmIeFhakdfn5+Yr5+/Xq1o3Xr1mK+a9cutWPs2LFiHhsbq3YcPnxYzCtVqmSZGjNmjLrN9u3bjc/H2n6oPW+e5uDBg2L+5Zdfqh2XL18W8/nz5xs/98DvLS0tTd2mQoUKYr5nzx61o127dmKenp6udniSK1euGM9dtTnO+PHj1Y7r16+r2wC/p6ZNm6rbLFiwQMxPnDihdowbN07Mk5KS1A5PEh4ebjyXr1Gjhpg3bNhQ7bjtttvEPCUlxTJVu3ZtdZuXXnrJ+OfExMRYxUHbtm2tP4vmzZv/7j/jsHI9aevXr5+YL126VO346aefxLxUqVLWzcA7pQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArvFxuuH169fF/Nq1a2pHiRLyGnjPnj3VjkOHDon5mjVr1I6yZcuKeXx8vNpRuXJlMV++fLnaUbduXTF/5pln1I5FixaJ+dGjR9WOrl27ivn58+fVjrNnz4p5QUGB2qE9/zExMWrHiy++KOZt2rRRO3r06CHmgYGBxh3vvfee2rFjxw4xb9CggdrhScqXLy/mYWFhakdISIiYV6hQQe1Yv369mKekpKgdI0eOFPN58+apHSVLljQaO5yM6ytXrlQ7+vfvL+bLli1TO/bt2yfmDz/8sNqRnJws5ufOnVM7tHEsLS1N7ahRo4ZxhyYjI8P4POjj43h68D/avn27ccefSXh4uJj/4x//UDu082Dt2rXVjnbt2hnPX2bMmCHm48ePVzu+/fZbMa9ataraUbFiRTE/deqU8ePhZA6kdTi5HdrPadasmdqxceNGMd+yZYvacd999xnN92x+fn5ifvHiReN5dsOGDdWOnJwcMa9Xr57a4Un27t1rPO8MCAgQ8927d6sdq1evNpqrObmWczJWauewUqVKqR3VqlUT88OHD6sdhYWFRtcctu+++07Mmzdvrnbk5uaKeX5+vtqh7UNXr15VO7THPTQ0VO3QxqALFy6oHePGjRNzb29vteOLL74Q8969e6sdnsTX19d4Heiuu+4S8w8++EDtSE1NNZ7LDx8+XMw/+eQTtaNx48ZiHhwcrHYcP37ceI1POya187mT2zFp0iS1Y/78+UbX6DdrXWzDhg1iPmTIELWjQ4cORuOPkzE5KSlJ7XjuuefEfNWqVdbNwDulAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaH6cbent7i/nly5fVjgEDBoj5+++/r3ZcuHBBzJOTk9WO4cOHi/nbb7+tdjRq1EjM+/fvr3a0a9dOzKdMmaJ2jBkzRsyzs7PVjqSkJDEPDw9XO/z9/cU8MTFR7ZgzZ46Y79mzR+1o3bq1mO/bt0/t6NGjh5h36NBB7Zg9e7aY33fffWrHkiVLxDw2NlbtuP/++y1P0aRJEzE/c+aM2lGjRg2j59X2+OOPi/nTTz+tdpQvX17MDx8+rHaMGzdOzOPj49WOo0ePinmrVq3UjqysLDHv1KmT2tGmTRsxj46OVjsqVaok5lu2bFE72rdvL+Yff/yxZapFixbqNosWLRJzHx/91F6lShXLVFBQkJiHhoYa/4w/k7CwMDH39fVVO8qUKWO8H2tj5caNG9WOO+64Q8xPnjypdgwaNMh4jFqwYIGYt2zZUu0YNmyYmI8dO1bt6Nu3r5hv2rTJeLy9evWq2qHNxZ08HtevXzc6PzmZi5cqVUrt0OaecXFxasc333wj5o0bN1Y7PMnBgwfF/IknnlA7vvzySzE/ceKE2lFYWCjmISEhaoe2r2dkZKgd2n6qHbNO9mMnY8M999wj5mfPnlU7Hn74YTEPDAxUO44fP2487gcEBBhf29SvX1/MZ86cqXZo89eaNWuqHdo5+/vvv1c7qlatKuZRUVFqhyfRxn3tGt6WlpZmfF7Qxign5yeNk3P+xYsXxXzXrl1qh3YNdeedd6od2s+5du2a2hEZGSnmH3zwgdrh5+dnvKZVsWJF4zXPd955R8y3b99umVq2bJm6Td26dY3nc9o6oTamO8U7pQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArvFxuuHVq1fFvH///mrHTz/9JObvvvuu2vHQQw+J+bJly9SOY8eOiXm9evXUju3bt4t55cqV1Y4tW7aIua+vr9rxr3/9S8wDAwPVjgoVKoj5zp071Y6tW7eKec+ePdWOMmXKiHnp0qXVjmrVqom5t7e3Zeqll15St7n99tvF/PTp02qHj498eLZp00bt8CTHjx8X84CAALVD2z9atGihdnz//fdifvfdd6sdc+bMEfPGjRsbj3PamO5kbHBix44dYt6+fXu1IzIyUszT0tLUjsLCQjHv3r272rF+/Xox79evn9qRkpIi5pmZmWrHhQsXxDw6Olrt2Ldvn5jXqFHDeB/Ly8tTOzxJUlKSmJ89e1btqFixophXrVpV7bh8+bKY9+nTR+04evSomL/zzjtqx9NPPy3mycnJasfrr78u5nv27FE7pk+fbnxMavOX7OxstUO7v+XKlVM7li9fLuZPPvmk8dx0w4YNakd4eLiYv/3222rHG2+8Yfx4hIaGivm9996rdniSmJgYMV+4cKHaER8fL+a5ublqh3YOczKX164Z3nvvPbXjxRdfFPOQkBC1o23btsb3RZs3REREGI/7Ts4/2rxBGzuczMXuvPNOtePHH380fjz8/f3F3M/PT+1IT08X89tuu03t0B73r7/+Wu24//77LU+hrQOtWbNG7dDOHU7Wga5cuSLm9evXVzu046VOnTrGHV27djW+fk5MTFQ7ypYtK+bbtm1TOzp06GA8Vt64cUPMvby8jMco7brWtnTpUjGvVauW2tGxY0cx//zzz9WODz74QMxr1qypdmjXcto1ulO8UxoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArvFxumG7du3EfNmyZWpHQUGBmL/22mtqR2ZmppgHBwerHWvWrBHzWrVqqR1eXl5iXrlyZbUjNTVVzHv06KF2eHt7i/mMGTPUjooVK4p5q1at1I4KFSqIeW5urtoRFhYm5teuXVM7Vq1aJebdunWzTPXs2VPd5sqVK2KelJSkdlSrVk3Mz5w5o3Z4kkOHDol53bp11Q7tMR02bJjaMWvWLDFPSEhQOyIiIsR87NixasfMmTPFvH///mrH4sWLxXzXrl3G5w5t7LCdPn3a+HjauHGj8TiXn58v5iVK6K/z3rhxQ8wHDBigdly+fNnodtruvvtuMT98+LDxMaflnqZRo0ZiXqdOHbVDm2u9+uqrakdhYaGYL1q0SO24fv268XkyJSVFzEuVKqV2jBkzxvjxWLlypdEcyXbPPfeIeceOHdWOgQMHinl2drbxPDstLU3tuHr1qpi3bdvWeB/76KOP1I5+/fqJ+aZNm4zvy8SJE9WOtWvXWp4iJydHzCtVqmR8XGvjoO3cuXNivnDhQuOxwck5f/369UbXcbYPPvhAzAcPHqx2xMXFiXnNmjXVjnfeeUfM69evr3YMGTJEzOPj49WO7du3i3lkZKTaoY3JoaGhaoc2x1m+fLna0alTJzHPyMhQOzp37izm9erVUzs8iXaO09YSnIwvTtYsjh8/bryWpN2OMmXKqB3h4eFifunSJbVDG5N37NihdsTExBg/HoGBgUbXcU6OJydrAdoaXocOHdQO7frHyZxQ8/rrr6vbaM+/k/Oxtq8nJydbNwPvlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuMbH6YZfffWVmL/44otqx7Vr18R8y5Ytase8efPEfPz48WpHhQoVxDw+Pt64Izk5We0ICwsT88zMTLUjOjpazFu3bq12XL9+XczT09PVjvz8fOPbcebMGTF/++231Y42bdqI+alTp9SOevXqiXmTJk3Ujscee0zMhw4dqnZcvHhRzMuXL692eJKgoCAxr169utpx5coVMZ8+fbrakZiYKOYjRoxQO5YtWybmmzdvVjtGjhwp5gcOHFA7du/eLeajRo1SO1q1aiXm7733ntpRs2ZNo/HHNnHiRKPjzbZ9+3aj84KtadOmxvuYdn+dnH+057Z+/fpqR7t27YzGY09z4sQJMS9btqzaERMTI+YzZ85UOxo0aGD0vNri4uLE/MiRI2pHly5dLFPaOX3OnDlqx6233mr8eHzxxRdifvLkSbUjICBAzA8fPqx2DBkyRMy//fZbtUObz2tzNdu5c+fEPCMjQ+3Ytm2bmO/atUvtaNy4sZhXqlRJ7fAkkZGRRse9k/NgQkKC2pGbmyvmderUUTu0bVavXq12NGrUyOh22kqUkN//NXfuXLWjb9++RvMs29GjR8V8x44dasf+/fvFPDw8XO3Q5o1r165VO9q2bSvmXl5eakdERITxXL2goEDMt27dqnbk5OSI+Z49e9QOT/LTTz8ZrYvYzp8/L+ZRUVFqx6pVq4yuSZ1chzlZs/j+++/F/MKFC8bnWm3O6OTcoJ2LbampqWJ+9913qx3BwcFi3rlzZ7XjlVdeEfMePXqoHVWrVjU+rlu0aGF0ne9kTUJ7vGwhISGuzKN4pzQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABcw6I0AAAAAAAAAMA1LEoDAAAAAAAAAFzDojQAAAAAAAAAwDUsSgMAAAAAAAAAXOPjdMO77rpLzHfs2KF2dOrUScyTk5ONO7788ku1o3r16mJeqVIltaN8+fJiHhQUpHbs37/f6GfYvvnmGzGvVauW2jFx4kQxnzx5stpx9epVMT958qTaERwcLObDhw9XO7y9vcX80qVLlqkpU6ao25QoIb/es3jxYrUjKipKzMuVK6d2eJJJkyaJ+fvvv692NGrUSMwPHjyodtStW1fMz549q3ZkZWWJeUpKitqRlJQk5o0bN1Y7IiMjxfzo0aNqx7lz54zHSu14Cg0NVTu05y4nJ0ftaNOmjZg3bdpU7Zg9e7aYr169Wu1o2bKlmK9du1bt+Pvf/258Tt+zZ4/xPvbkk09ankKb40RERKgd2r4eExOjdmjjx5EjR9SO8+fPG+VObod23NuuXLki5o888ojasWrVKjF/44031I4TJ06I+QsvvKB2bNq0SczbtWundqSnp4t5QECA2vHpp58aj7fauF66dGm1IzMz02iOZBs1apSYf/LJJ2qHJ6lWrZrxXL5hw4ZifuDAAbUjPj5ezCtWrKh2TJs2zXiMiouLE/OQkBC1o3LlymLes2dPtaNkyZLG85fLly+L+QMPPKB2LFiwwOga3cn5JTo6Wu1YsWKF8f6hzZGHDBmidmjztf79+6sdtWvXNjomPU3NmjWNrjlsx48fF3N/f3+1Q3tetOsnJ+f0ChUqqB2lSpUS8zlz5hifr7Ozs9WOzz//XMzvuece4/nc/Pnz1Y7u3buL+f333298nbZw4UK1QxuTnewfGidrntp1x+OPP258DtPOC7bRo0er2/BOaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa3ycbhgfHy/mNWvWVDtWrFgh5gEBAWpH//79xbxcuXJqx8mTJ8Xcy8tL7UhOThbzvn37qh2FhYVinpGRoXaEh4cb35d7771XzDt27Kh2HD16VMyrVq2qdmzfvl3MS5curXaEhISIeeXKlS1TjzzyiLrNlClTxLxMmTJqR6tWrcT84MGDaocniYuLE/MbN26oHXv37hXzJk2aqB2HDh0S84sXL6od2v7hZLzV9o8rV66oHX/5y1/EfPHixWpHpUqVxPz8+fNqR0FBgZjv2bNH7RgwYICYb9myRe0YMmSImC9cuFDtiI2NFfNXXnlF7VizZo2Yd+nSRe1o1qyZ8bn0mWeeEfO8vDy1w5No52vtPGrz8/MzPk9q51ofH31qWK1aNTEPCwtTO7T5SW5urtoxcuRIMf/xxx/VjsDAQDEfM2aM2vHSSy8ZzRlt999/v5g3bdrUeO7x6aefqh3Tpk0znpvm5OSIeXZ2tvG+3rVrV7Xjs88+E/OYmBi1w5OULFnSaH5j8/b2Nj4WypcvL+b16tVTO7Tn9rvvvlM7atWqJeaZmZlqx7p168R81KhRasfp06fF/Pjx42pHgwYNjOcejz76qPH1ojYvmDx5stpRp04dMY+OjlY7jh07JuapqanG45yTDu08FxERoXZ4Eu1Y0MYfJ/vp+++/r3aMHj1azENDQ9UO7fpn48aNakdwcLCYHzhwwHi9QdvPncxvjxw5ona0adNGzOvXr692HD582Oi61jZ79mzjuak23mprgE44WWvcv3+/0X21tW/f3ih3indKAwAAAAAAAABcw6I0AAAAAAAAAMA1LEoDAAAAAAAAAFzDojQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABcw6I0AAAAAAAAAMA1XoWFhYVONnznnXfEPDc3V+04cuSImJ85c0btaNKkiZiPGzdO7QBMvPbaa+o23t7eRvuxLTY2VsyvXr2qdixevNjyFCNGjBDzGjVqqB1z5swR8+joaLVj0KBBRj/DVrt2bTFPSEhQO7T7u3z5crUjJiZGzLOzs9UOPz8/MR8+fLjlhs8//1zMo6Ki1A7tcR81apTasX//fjHfsGGD2tG+fXsxT05OVjtq1aol5vHx8WqHj4+PmOfn56sdU6ZMsTzF9OnTxfz06dPGx9OpU6eM95/q1asbH0+VK1dWO9avXy/mdevWVTueeuopMff19bX+LJYuXapuU6lSJTFfsmSJ2tGuXTsx37Jli9rxwAMPiPmqVavUjl69eon5pk2b1I7t27eLeWJiotqxceNGy1P07dtXzHv06KF2rFy5UsydXHYGBASIeWhoqNrx008/GV8vpqWlGc+BwsPDxfzuu++2/izuuecedZuwsDCj6yfbsGHDxHzPnj1qx8WLF8W8W7duaoc2xzl27JhlShvTnYy3fyYvvviimP/4449qR+vWrcW8UaNGakepUqWMxg4n29xyyy1qhzZv/Ne//qV2TJo0ScwLCgrUjry8POO5WHp6uvFaY0hIiPHx9Mknn4h5Tk6O8bnUydxj4sSJYv7FF1+oHdo8etGiRWpHUFCQmB8+fFjt2Ldvn7oN75QGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALjGx+mGSUlJYt6gQQO14+DBg2I+ZswYtWP//v1iPnv2bLWjsLBQzIODg9WOjIwMMc/OzlY7IiMjjR4vW8+ePcW8RAn9dYdt27aJeXR0tNqRk5NjtP/YwsPDxTwzM1Pt2L17t5j7+fmpHT4+8mExcuRItWPLli1iPn/+fLVDu61t2rRROzxJQECAmIeFhakdFStWFPNy5cqpHfPmzRPz0NBQ47Fh7ty5akdWVpaY+/r6qh3avr548WK1IyIiQszXr1+vdtSvX1/Mf/jhB7WjZs2aYp6QkKB2REVFifm7776rdnTt2lXMjx8/rnb06NFDzI8dO6Z2bN68WcyHDh2qdpQvX17MZ8yYoXZ4kqCgIKPj3hYYGCjmp0+fVjs6duwo5nXr1lU7vvvuO6M5kpP53NSpU9WO7du3i3mHDh2sP4sdO3ao29x1111ifvToUbXjyJEjxnPCZ555RszLli2rdqxdu1bMO3XqpHa0bt3a6PzkaZo0aSLmP/30k9oxfvx4MT916pTaUadOHeP9WLsubdy4sfF4u3HjRrWjevXqlqe4/fbb1W3i4+PF/Nq1a2qHNsfZuXOnK9fg2j7m5Lq1oKDAaN7gaVq2bPm7X6dpc33b119/LeZ33HGH2jF58mTjNRzteHJyOxo1amQ8Zq9cuVLMW7VqpXakpaUZj7fa/FZbn7H16dNHzCtVqqR2XLlyRcxjY2PVjueff17MmzZtqnacPHnS6FzrZAy6ePGidTPwTmkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuMarsLCw0MmGs2fPFvNy5cqpHdevXxfzjRs3qh2JiYliHh0drXb4+fmJ+e7du9WOyMhIMe/bt6/asWzZMjFPTk5WO/Ly8sS8TZs2xo/HihUr1I6ePXsaPy9z584V86ioKLXjzJkzxvtpTk6OmNevX1/tOHLkiJhfvXpV7ahevbqYR0REqB2jRo2yPEXnzp2NHk9bXFycmIeEhKgd2v5x/PhxtaNp06ZiXqKE/npiUlKSmAcGBqodP/30k5g/9NBDasf8+fONjyftuA4ODlY7cnNzxbxKlSpqR0pKipj37t3b+PFIT083Hm+PHj2qdvj4+BjvH5cuXRLzHj16qB2jR4+2PMWdd94p5pMmTVI7nn/+eTEfNGiQ2nH27FkxDwgIUDu6desm5qtXr1Y7Ll++LOb16tVTO7Zu3Wo0V7M1a9ZMzA8dOqR2pKamivnOnTvVjrfeekvMd+3apXYEBQUZzz1iY2PFPDMzU+3o0KGDmB8+fFjt2L59u/Fze99994n5qVOn1I4XX3zR8hQxMTFifvfdd6sda9asMZ7La9cM+/fvVztq1Kgh5hUqVFA7tGuof/3rX2qHNiZrY7qT8/XgwYPVjpMnT4p57dq11Y5NmzaJ+csvv6x2LF26VMy3bdumdoSGhop5fn6+2vHII4+I+Xfffad2zJgxQ8yHDBmidmjjmJNrho8//tjyFNrzVrlyZbVDWwv4/vvv1Y4xY8YYryV1795dzE+cOGE8j9LmBE7O6Vu2bFE7li9fLuaPPfaYK+d87ZrijTfeUDsaNWpkPL60bdtWzLOystSOZGUd0MnYoF1jO3lMNU7G7G+++UbdhndKAwAAAAAAAABcw6I0AAAAAAAAAMA1LEoDAAAAAAAAAFzDojQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABc4+N0w1OnTon5zJkz1Y66deuKua+vr9oxfPhwMV+7dq3akZGRIebt2rVTO7KyssR8+/btasftt99u9Jjb7r77bjG/66671I5t27YZ3U5bVFSUmP/444/GHSdPnlQ75syZI+b33Xef2pGamirmKSkpasfx48fFPCcnR+0IDw8X88LCQrXDk4SGhop5/fr11Y4SJeTX6SIiItSOpUuXivkdd9xhPL6cO3dO7dDub+PGjdWORx55RMxXrFihdpQrV854fPnqq6/EvFu3bmrHTz/9JObly5dXO3bu3Gk8RrVv39543Nf0799f3Wbr1q1iHhkZqXasXLlSzGvUqKF2eJLY2Fgx379/v9qhHbfafm6rVauWmAcGBho/92lpaWrH4MGDxfzatWvG8zknx2TVqlXF/OzZs2pHTEyMmLdp00btOH/+vJj7+/urHdpz5+Tx0J676tWrG89flixZYjzf154329NPPy3mY8eOVTs8iXbN4OXlpXaMGzfOaJ5uCwgIEPMuXbqoHdr1oJN5+N69e8X8xo0bxmNly5Ytja+PnIwN2viSm5urdmRnZxtdTzq5Pho/frzaMWnSJDFv2rSp2jF58mTjMfv5558X87i4OON5wb59+9QOT+Ln52e8/rJhwwYxf+CBB9QObV2jQoUKaseTTz4p5i1atFA7+vXrZzSWOrmtTsZKbb6fn5+vdpQuXVrMy5Qpo3ZMnz5dzP/yl7+oHadPnxbzzz//3PgxbdWqldrh4yMv016/fl3tqFKlivHxcjPm6k7wTmkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuMbH6YYRERFiHhISonZUrlxZzBMSEtSO2bNni3np0qXVjt69e4v5ihUr1I7c3Fwxv/vuu9WO77//XszLlCmjdrz99ttiXqpUKbXjoYceEvOSJUuqHatWrTLuyM/PN+5o3bq1mKelpakd0dHRYh4YGKh25OXliXlQUJDasXr1ajEfOnSo2uFJzpw5Y/y8NW/eXMwzMjLUjqioKDHPyspSO65fvy7m3bp1Uzv2798v5klJSWrH9OnTxbxx48Zqh/aYjR49Wu3o27evmG/YsEHt0M5RM2bMUDsmTZok5rt37zZ+PJzsHz/88IOY33PPPWrH5cuXxfzcuXNqx5133inmwcHBaocn6devn5jHxcWpHe3atTM6B9q+/fZbMd+5c6faERAQIOY9evRQO06ePGk8Ru3bt894jNIeDyfHgnb+cTK/rVatmpgnJyerHf7+/mLetm1by1S5cuXUbbTHrEOHDmqHl5eX0X110uFkjuxJ9uzZI+Zly5Y1nkNruW3p0qVG15O26tWri7mvr6/xvr5kyRK1Q9tPR44caTyv3Lp1q/F9uXr1qtqhXWN7e3urHS1btjQaj21Tp041nhPWqFFDzI8ePap2hIWFiXnHjh2N1xOcnAc9iXb9rD2vTuYFFStWdOXaRjuennvuObXj3XffNZqrOdnHzp8/r3Zo51I/Pz+1o2fPnsZrjdpcS5sz2ho0aCDmr732mvF65fDhw9WOJcr5xckcSFv30s6TtuPHj4t5Tk6OdTPwTmkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGu8CgsLC51s2KVLFzFv0qSJ2pGZmSnmFStWVDtOnDgh5k2bNlU7SpUqJeaXL19WO7RtSpcurXZs375dzGvXrq12BAUFiXnDhg3Vjjp16oj5c889p3akpqaKedu2bdWOrl27ivnWrVvVjjNnzoh5yZIl1Q4/Pz8xHzJkiNoRHx8v5v7+/mrHP//5T6Pbadu8ebPlKd5++20xT0hIUDu0/cPX11ftyM7OFvO8vDy1o6CgQMyTkpLUjnbt2on50aNH1Y5q1aqJ+cmTJ9WOtLQ0MU9OTlY7tOPFyXE9YcIEMd+4caPaceXKFTE/ffq0ZSoqKsr4eUlPT1c7vLy8xDwxMVHt0M632vnaNmvWLMtTDBo0SMyHDRumdmjHbX5+vtpx9epVo/3c1rFjR6OfYfvoo4/EvHPnzmpHXFycmFeqVEntCAgIEPM777xT7RgwYICYf/jhh2rHnDlzxHzt2rVqx+TJk8X80qVLaoc2f92xY4fxnHDMmDFqx9SpU43mjLZatWqJ+d69e9WON954w/IULVq0MH7etMe0fv36ase6devEPDAw0Phc6uT81LhxYzEfMWKE2rFt2zbjeUO5cuXEPDIyUu2IjY01fjxGjhwp5ufOnTM+h3Xr1k3t0ObAPj4+aod2TamNYU72Q238cXJfqlatqna8/PLLlqfYv3+/8XjdqFEjo+sWW25urphnZWUZr60dOnTIeJxzcm3zySefiPkzzzyjdnTq1Mn4MdWufe+99161Q5sXPPbYY8bnMG0908laQGhoqPFcLNnB9bM2rmtzeVuZMmXEfOHChWqHk/kr75QGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGu8CgsLC937cQAAAAAAAAAAT8Y7pQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGn+osfPHWgO+G/BH3wwA+G8xRgEozl5a95LV5OMmf/TNAID/FmMUgOKMa70/ns8ffQP+r/F62UvMX+z8ovVSl5esP0LqtVRr5NyR1oHzB6zU66lWeFC41b9uf+v17q9bpf1L/6YD86v9XxX9t28JX6tKSBVrTMwY67mOz1k+Jf74XeajnR9ZH+36yDpz+UzRnxuGN7Re6PSC1ad2nz/6pgF/uOI8Rv3n8Srm4xjrXMY5K/0v6VaZgDJ/mjHq37256U3r2dXPWo+0fsT6oPcHf/TNAf5wxX2MWn16tfW3tX+zYi/EWkG+QdZdMXdZr3V/7TeNLfYizMvrXy76b28vb6tS6UrWwHoDrVe7vWoF+wVbf7Tc/FzrjU1vFI2j566es+qWq2u9dctbVu9avf/omwb84RijGKOA4qw4j1GsR7Ee9b/xxz+j/8ckP5H8y3/PPjjbemHdC9axB4/98nf/fiIvLCy08gvzXTtwSniVKDro/97t71b5wPLWybST1qSlk6y062nWt4O//U1d9kn/y/5fWjfyblhLTywt6rEHhGc7Pvtfts3Jz7H8vP0st9gTpzdvedOqHVbbKrQKra/2fWX1/66/tXf83qIBAfBkxXmM+nf3LLzHahzRuGhR+n+jOI9RP9t5bqf1ye5Piu4ngOI/Ru1P2W/1/bav9XzH562vB35dtBgyYcmEotvwbs93f1NXw/INrVVjVll5BXnW5oTN1riF46xrudesT27/5A8fo/665q/WN7HfWNNun2bVK1fPWn5yuTVw9kBry7gtVtOKTV27HUBxxBjFGAUUZ8V5jGI9ivWo/w2+vuM3qhBc4Zd/QgJCLC/L65c/H7101Cr1Rilr2YllVvNPm1v+f/e3NiVs+m8/EvDoT49aXaZ3+eXPBYUF1hsb37CqT65ulXytZNE7CH88/ONvum2hJUOtiS0nWi0iW1hVy1S1utfobj3Q8gFrY8LG33w//b39i+6T3WN33lLjFmvh8YVF2c/357UNr1mR70VadT+sW/T3iVcSrTt/uNMq82YZK+ytsKID8+dXj2z5BfnW48sfL8rLvl3Wenrl00UH8W91e93brb61+1q1y9a26pStU/TuAHvw3XZ222/uAv5sivMY9e+vLl/Ovmw92e7J//X9LM5jlC0zJ7PonQL2BVVoQOj/+n4CfzbFeYyafWh20YtIL3R+waoVVsvqXK2z9fYtb1tTd061Mm5k/KYu+wLQvk/2hcvQRkOtkdEjfxmjfv44+2d7Piu6vQF/Dyj6e3tcvHfhvVb5d8pbpd8obXX7qlvRItR//vRFxLsRRY/TPQvusbLzsq3fasaBGdZzHZ4rmkvVCK1RNIba//3e1vd+cxfwZ8MYxRgFFGfFeYxiPYr1qP8NFqV/B8+sfsZ6s/ub1pFJRxy/Q84eAL4+8LX18a0fW4ceOGQ91uYxa9TcUdb6M+t/2abaB9WKJglOJWUkWXOPzLU6V+1smSrpW7LoFaifrY5bbR1LPWatHL3SWjx8cdHHrHp908sq5VfK2nj3RmvzuM1FB2bvb3r/8v/ZE4np+6ZbX/T/wtp096aiV8zmHZn3q59j59pHUv6dPbB8d/A7Kys3y2pbua3x/QQ8wR85Rh2+eNh6ZcMrRe/wsV9Nv1mK2xhlv5p/a+1biyZQAP5vjFH2u3ECfP5j8eXfxxZ7UWV38m6De2RZJX1+PUbZ7x6ac2SONffOuda+CfuK/m7ID0OsC1kXrGUjl1m7799tNavYzOr+dfeiscj2/aHvi27/691et3bdt8uqWKqi9a+d//rVz1l3Zl3RGPXvF2H/5X7m/zf306dk0YUrAB1jFGMUUJyxHsV61P8lfH3H7+CVLq9YPWr2cLy9PcF4fdPr1qrRq37Zke1Xhe0Tr/3Rb/tVcFvNsJpWucByat/wOcOtBUcXWNfzrlu317nd+qzfZ//r+2J/5MM+4O2PTT3U6qFf/t7+DjO79+ePSXxz4JuiV9fsv/Py+o+D2P64hf0qlD356Fmzp/XBtg+sZzs8aw2qP6go//i2j63lp5b/6ueF+IdYdcv+xytdktjzsVbbz9sWTcLswWbe0HlWg/IN/tf3E/Akf9QYZffY49M7Pd4p+m6w0+mnje9LcRyj7InJnuQ91s77dhrfP8AT/VFjVK9avawPtn9gzYqdZd3Z8E4rJTPFemX9K0VZcsb/+7jsb7U7abf17cFvrW7Vu/3yd/YF0tcDvrbKB5Uv+rN9W3ec22FdePKC5e/jX/R39sfx5x+dX/ROpfub3180Rt3T9B7rnmb3FOX2x2NXnV71q3ciBvoGFo1R9kdc/8f7WbOX9Y9t/7A6Ve1U9JjY31FrXzTaH/EFoGOMYowCijPWo1iP+r+ERenfgf1xhd/CfiXa/g6vHjN+PXDYk4F//96s1WNWO+p7v9f7RV9wfzz1eNEv2LI/ovCvW3/9KrVm8fHFVvDrwVZuQW7RwT0iesSvvjA/OiL6V9/bY390y74f9sdF/p19kJ5KO2VdibpiJWcmW60rtf7Vx8bsx8oeaH42sP7Aon809i+8sF+1v5J9pWgidNf8u6z1Y9czEADFeIyyx6P65epboxqPskwV1zHK/tjYIz89UvSq/X9+lw+A4j1G2Rcs9otm9ne0jp43umjh5W+d/lb0sdPf+skO+5eQ2WOUvYhi3w77kxMf9vnwl9z+OOrPiz0/j1H21/7YHyf9d/YFnT1G2Y5cOmJNaDHhV3nbSm2ttWfW/vLnVlGtrKMPHhVv2+Tek637Ft1n1Ztar+hjv/ZF5t1N7ra+2PfFb7qPgKdijPp/GKOA4of1qP+H9ajij0Xp30GQX9Cv/mxPEv7zd9XYHy/4mX2Cty0ZscSKKh31X75L57f6+TuF7F8MEVYyzOr4ZceiCYv9ESqnulbvan1060dFB3pkqcj/8uX49itT/86+D80jm1szB838L132l9zfbPbtsr9LzWb/3J1JO63J2yb/t7+cA0DxGKPWxK0pugj68ZX/+H6yn39mubfLFf3Snpe7/sdvgv+/PEbZH5+1P9ra7JNmv/ydfcG3IX6D9eGOD60bf71heZfwvmk/D/gz+iPnUY+3fbzoI6v2hYv9ffD2R8ztCyr7HUO/hf0um4XDFxaNTfYY9Z9/Ac9/N0ZVDK5orRu77r90lQkoY91M9kLT/GHziy7UUq+lFt2+Z1Y985vvI+CpGKN+jTEKKF5Yj/o11qOKNxalXWAfBAcvHPzV3+07v++Xjy3Zr6bYB3vClYRfPhpxs9ivKv383Vy/hX2Q/3yQOWF/p5j9yzfCg8Kt0v6l/9tt7InM9rPbiz6KZbN/27P9cTH7/70Z9/O33kcA7o5Rc+6cU/SOmp/tPLez6Le929/7Zb8L5s8wRnWv3t2KnRj7q7+7e8HdRZOyv7T/CwvSwP+BeZT9sU/7Asg26+Asq3Lpyr95rvLvFytO2P32R/Hti65qZar9t9vYnzSxx6gxMWN++btt5/73v1TH/jSHffFpX5ja3x1rfx0AgN+OMeo/MEYBxRPrUf+B9ajiiV906AL7+7l2Je2yvt7/tXUi9YT14toXfzUolPIvZT3Z7knrseWPWV/t+6ro4wX295H+c/s/i/78M/sXSdjvtPufLD2x1Ppy75dF3far5kuOL7EmLJ5gta/c/n+cPNwsIxuPLPp+Ifs3nG6M32jFpccVfXfPw8sets5ePVu0zSOtH7He3Pxm0XeP2b8Z9oElDxT9Fud/Z3/RfL0P64k/69lVzxa969C+j/Z3+dh/tn+W/VujARTfMcpeeG4U3uiXf6qHVi/6+/rl6xdNIP4MY5T9WP37fbT/sSdVZUuWLfpvAMV3jLK9s/mdornFoQuHrFfXv2q9uelNa0qfKb/7C0r2L0W1v8fR/m3yK06tKJrjbEncYj2/+vmi+/7zGGV/fN2e69kfibUfB/t2/jv7O1/tMerc1XP/48+yL8js72e1v9ffHg97z+xddDH1dPunf9f7CPxZMUYxRgHFGetRrEcVZ7xT2gX2L6WwP67w9Mqniz6GNK7pOGtM4zFFH2P/2atdXy16BeuNTW8UnYDtj0HZr9g81/G5X7axB4dL1y79jz/H/q3E0/ZMKxpM7Fdp7FfN7S9xf6bDM79sYx841SdXt9betdbqUq3LTbuP9i+t2HD3Busvq/5iDfp+kJVxI6PolW37XYM/v1L1RLsnij5qZn/fjv0RknFNxhV9X4/9PTw/u3LjStFvUZXYH40fM29MUZf9RfT2b5RdPmr5b/oyfwDuj1FO/BnGKAD/d8eoZSeXWa9tfK1oHhUTEWMtGLbA6lO7z6+2sX8ru/3Lc8Y2GXvT7qP9zselI5Zaz695vujTFRezLhZ99NV+N09EUETRNkMbDbVOpZ+ynl71H4/D4PqDrYktJv7ql/TY3wlpj1H2dzD+T+z/969r/lr0ONm/nKdv7b7WjIEzbvpH8AFPwRjFGAUUZ6xHsR5VnHkV/vu3euNPb23c2qKD9PTDp63QkqF/9M0BgF9hjAJQnNnvvKnzYR3r8AOHrdpla//RNwcAfoUxCkBxxrUe/jO+vsPD2B+peK7DcwwAAIolxigAxX2Mur/Z/Sz2ACiWGKMAFGdc6+E/453SAAAAAAAAAADX8E5pAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkPYH9Dy/2L7rfC3gor+m3M+1L2/dE3CQB+wRgFoDhjjAJQnDFGASjOGKMgYVH6D5SSmWI9tPQhq8bkGpb/3/2tyu9Xtm6fdbu1+vTqm/pzfjr5kzV933Rr8YjFVvITyVaj8EaO/r+Hlz1sNf+0edFta/JxE0f/T3ZetjVpySSr7NtlreDXg63B3w+2zmee/9U2CVcSrFu/vdUKfC3QCn8n3HpqxVNWXkHe/+q+Afj9MEYxRgHFGWMUYxRQnDFGMUYBxRljFGNUceDzR98AT3Xm8hmr/RftrTIBZax3erxjRUdEW7n5udbyU8utSUsnWUcfPHrTftap9FNWxVIVrXaV2/3m/3dck3HW9nPbrQPnDzja/rGfHrOWnFhi/TDkByvEP8R6cNmD1qDvB1mbx20uyvML8osGgArBFawt92yxkjOSrTHzx1i+3r7W691f/823D8DvgzGKMQoozhijGKOA4owxijEKKM4Yoxijio1C/CH6fNOnMOq9qMLMG5n/JUu/nv7Lf8dfji/sN6tfYdBrQYWlXi9VOOT7IYUpGSm/5C+ufbEw5qOYwq/3fV1Y9f2qhaXfKF049IehhVezrxbld827q9B6yfrlH3ub3+rnn6G5fP1yoe8rvoU/HPrhl787cvFI0c/dmri16M9Ljy8tLPFyiV/dh492flR0u2/k3fjNtw3A74MxijEKKM4YoxijgOKMMYoxCijOGKMYo4oLvr7jD5B2Pa3oIwyTWk6ygvyC/ktuv1plKygssPp/179o+/Vj11srR6+0Tqeftob+OPS/vPI0/9j8oo9DLB6+2Fofv956c9ObRdnk3pOtV7q8YlUqXanooxI779tZ9PfVPqhmvbTupZt6v3Yn77ZyC3KtW2rc8svf1StXz6oSUsXamri16M9bz261osOjrYjgiF+26VWzl3X1xlXr0IVDN/X2APjfYYxijAKKM8YoxiigOGOMYowCijPGKMao4oSv7/gDnEw7aRVahUUHiMT+Lp/Y87FW3CNxVuWQykV/9/XAr62G/2po7Ty302oZ1fKXwWJ6/+lWKf9SRX8e3Xi0tTputfWa9ZoVEhBS9PfeXt5FH1H4Wc2wmla5wHI3/TuJ/Lz9fhnEfhYRFFGU/bzNvw8ARfn//+eftwHwx2KMYowCijPGKMYooDhjjGKMAoozxijGqOKERek/6LePOnHk0pGig//nAcDWoHyDooPMzn4eBKqVqfbLAGCrGFzRupB1QexePebmfnk9gD8PxigAxRljFIDijDEKQHHGGIXihK/v+APULlvb8rK8rKOXbs6Xx/uW8P3Vn728vIperXKb/cpXTn6OdTn78q/+/nzW+V9eFbP//Z9/++nPf/73V84A/HEYoxijgOKMMYoxCijOGKMYo4DijDGKMao4YVH6DxBWMszqVauXNXXnVCsrJ+u/5D8fRPXL1bcSryQW/fOzwxcPF+X2K1TFTfOKzYsGJPtjHj87dumYlXAlwWpbuW3Rn9tWamvFXoj91StnK0+vtEr7ly6W9wnwRIxRjFFAccYYxRgFFGeMUYxRQHHGGMUYVZywKP0Hmdp3qpVfmG+1+qyVNefwHOtE6gnryMUj1pTtU6y2n//HAWN/QXt0RLQ1cu5Ia0/yHmvHuR3WmHljrM5VO1stIlsY/fzuX3e3PtzxofpdQ/tS9hV9t871vOtF/23/Y7/6ZDt39ZxV78N6RbfLZn9f0D1N77EeX/G4tTZurbU7abd194K7iw78NpXaFG3Ts2bPooN99LzR1v6U/dbyk8utv675a9GX7Pv7+BvdJwA3D2MUYxRQnDFGMUYBxRljFGMUUJwxRjFGFRd8p/QfpEZoDWvP/Xus1za+Zj2x4gkrOTPZKh9Y3moe2dz66NaPfvnYw4JhC6yHlj1kdfqyk1XCq4TVu1Zv6599/mn880+lnbIuXbskbnPvwnuLfnPqz5p+0rTo3/YX3dvfG2T/ZtNjqcesa7nXftnm/d7vWyWWl7AGfz/YupF/o+g3mf7r1n/9knuX8C76jawTl0wsGuzs3/Z6V8xd1itdXzG+TwBuHsYoxiigOGOMYowCijPGKMYooDhjjGKMKi68Cp1+yzkAAAAAAAAAAIb4+g4AAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuMbH6YazZs0S81KlSqkdH374oZi3adNG7UhJSRHzkiVLqh35+fliXqKEvlZ/4cIFMe/bt6/aUbVqVTH/6KOP1I5y5cqJuY+P46f4f1SjRg11m1OnTon5uXPn1I5atWoZPfe2y5cvi3nXrl3VjvDwcDFfvXq12hEcHCzmUVFRasfJkyfFvEuXLmrH0KFDLU/x3HPPGe/H2mP6zjvvqB3aPhYREaF2bNu2TczT0tLUjgoVKoh5UFCQ2pGTkyPm2dnZakeDBg3EfNeuXWpH2bJlxTw9PV3tqFu3rpgfO3bMMqWNYbY1a9aI+aVLl9SOO++8U8wTExPVjtzcXDEvU6aM2rF06VIxHzFihNoxatQoy1P85S9/MT5Pas9LVlaW2hEfHy/mkZGRxsf1tWvX1I6EhATj+Yv2mPn5+akd2mPm5Hhq3bq18XOrjaf169dXO7RzQ5UqVdSOypUrG81/bd99952Yt2jRQu0YOXKkmK9fv17t0K5NSpcurXY888wzlqdo0qSJmPfu3VvtWLdunZi/8cYbasehQ4fE/M0331Q7evbsaXRNapswYYKYT5w4Ue24ePGimMfFxakdeXl5Yu7v7692aHNPJ2OlNhdz8ry0a9fO+JjU5i/aNZiTOfCZM2eM1xOc3JfU1FSja1Lbgw8+aHmKxx57zPg8qc1fFixYoHZo1/HXr19XO5zMLUzHbG3/cjKv1PZzJ/OCmjVrqh3aGp6T65IZM2aIeb9+/dSO5ORk4/Uo7TGLiYkxnpv26tVL7Vi4cKGYd+zYUe3IyMgQ82+++Ubt+Pjjj9VteKc0AAAAAAAAAMA1LEoDAAAAAAAAAFzDojQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABc41VYWFjoZMOHH35YzJOSktSO8PBwMT906JDa0bBhQzEPCQlRO2rUqCHmZ8+eVTvCwsLE3NfXV+3QtsnMzFQ7UlJSxLx69epqR0JCgpifOHFC7ahXr56YDxo0SO1Yt26dmGdlZakdfn5+lql9+/aJube3t9pRq1YtMb9y5Yrace3aNTGvWLGi2vHiiy9anuL7778X83Pnzqkd5cqVE3MvLy+1Y8uWLcbH5JEjR8T8zJkzakezZs3EvE6dOmpH8+bNxXzatGlqR7t27cQ8Pz9f7bh06ZLxWJmWlibmY8aMUTu2bt1q/Jhqt/Xq1atqx44dO8S8QoUKakeXLl3EfOPGjcbjbdmyZdWOe++91/IUH330kZh/9913aoc27l+4cEHt2Lt3r/F5Y+nSpWLetWtXtWPlypViHhkZqXZ06NBBzN9880214+9//7uYlyxZUu2YM2eOmO/Zs0ftuP/++8W8ffv2ascbb7wh5vXr1zce5/z9/dWOnj17Gs8rS5cubbx/XL9+3WgftK1Zs8byFK+++qrRXN/21ltvifmAAQPUDm1eUKVKFbVDe25bt26tduTk5Ih5fHy82tGoUSMxT09PVztKlChhNEdyMi/Iy8tTO9q0aWN0jnOyFqCN6U6uF318fNSOwMBAo7mrbfr06cbzm927d4v5sWPH1I5vvvnG8hSrV682Ot5sjz32mJhXrlxZ7UhNTTU6f9l69+4t5omJiWpH7dq1xXzhwoXGt+PTTz9VO4KCgoyvS5577jkxf/fdd9UO7efExMSoHdpj1qNHD7Xj9OnTxnOxbdu2ibmTJVzt3FCzZk214/Lly2K+atUqtWPFihXqNrxTGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACu8SosLCx0suH8+fPFfOnSpWrH2bNnxbxy5cpqR/ny5cV83bp1akfDhg3FvGbNmmpHbGysmHfu3FntaN68uZgfPXpU7fD19RXzlJQUtSMjI0PMt2/frnbUqFFDzC9fvqx29OvXT8xLlNBfQ9m2bZuYv/HGG2rHrbfeKua33HKL2pGWlibmBQUFakdAQIBl6q9//avlKYYOHSrmjRo1UjtKly4t5qdPn1Y7cnNzxbxdu3ZqR05Ojpj7+fmpHXl5eUaPl5Nx//z582qHNhYuW7ZM7cjOzhbzuLg4taNWrVpifuPGDbXDyT6kqVKlipjv3btX7cjMzBTza9euqR2NGzcW82PHjqkd9evXF/PDhw+rHX/7298sT/Hpp5+K+YwZM4zPLbfddpvaERMTI+ZbtmxRO7TzcaVKldSOCxcuGM9ftOPp6tWrasdnn30m5k8//bTacfLkSTEPDg5WO7THzMmcQNvGyTGpzSu1c4stKytLzK9cuWJ8O5ycB7XH48iRI2rH1q1bLU9x//33i3nfvn3VDu2Y27hxo9qhXZpq1ws2Ly8vozmBk+ujadOmqR3avt6jRw+1IyQkxPj6SBtv169fr3ZERkaK+fXr19WODh06iPlbb72ldjz66KNGz5uT61InY7b2c5ysSbz//vti/uOPP6odr7/+uuUpPvzwQzG/dOmS8ZqFk2PynXfeEfNmzZqpHf7+/sbn2sTERKPj3jZ27Fgx37x5s9oRHx9vfDwlJCQYXbfYmjZtKubLly9XO7R9SFvPtI0ePVrMq1WrpnYsWrTI6BznZE2zZMmSasfOnTvFvFWrVmrHoEGD1G14pzQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABcw6I0AAAAAAAAAMA1LEoDAAAAAAAAAFzDojQAAAAAAAAAwDU+TjdMSkoS87y8PLXjvvvuE/OcnBy144MPPhDz2rVrqx0XL14Uc29vb7Wjc+fORo+X7cCBA2K+d+9etaNr165iHhcXp3a0aNFCzLOystSO8PBwMY+KilI7lixZIuZeXl5qx8MPPyzmZ8+eVTv69Okj5idOnFA7cnNzjfZBJ8eDk2POk0RHR4u5r6+v2nHw4EExf+ihh9SO+Ph4MT927Jja8cknn4j5tGnT1A5tHPvpp5/UjtTUVDFv0qSJ2rFt2zaj583Wrl07MX/iiSfUjqCgIKPnzcn5xck+tmPHDjGPjIxUO65duybmLVu2VDuys7PFPCMjw/gxHTt2rNrhSY4cOSLmQ4YMUTsuXbok5idPnlQ7UlJSxLxv375qx6pVq8Q8IiLC+HbUq1dP7dizZ4+Y16hRQ+0YNWqUmF+5ckXt0MbCq1evqh1Hjx4V85CQEOOxslatWsZz02bNmhl3NGzYUO3Q9iHtWLDt379fzLt37652eBLtMV+2bJnaoc33W7durXYsWrTIeJxbsGCBmPfo0UPtOHz4sJh37NhR7Th16pSYJyYmqh0VKlQQ85UrV6odYWFhYl6zZk3j/SMtLU3tWL9+vfE8WxtPjx8/bnzd6uTa18dHXkaZNGmS2jFv3jwxv3z5strhSbRzrZO1E22N5s033zSevwwYMEDt2L59u5hv3bpV7fjoo4/E/K233rJMORmjtPHj0KFDakejRo3EfMaMGWrH9evXjedR6enpxs/t559/LuYdOnRQO1q1aiXmZcqUUTvOnDljPL996qmnxHz69OnWzcA7pQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGh+nG16/fl3M69evr3bEx8eL+YoVK9QOHx/5Jvv5+akdY8aMEfPAwEC1Y+PGjWKen5+vdpw/f17Mx48fr3ZMnTpVzCtXrqx2REVFiXn58uXVjq+++krMBw4cqHZUr15dzOvVq6d2DBkyRMwrVqyodqxcuVLM/f391Y6CggIxr1atmtpRu3ZtMZ89e7ba4UmqVq0q5gsXLlQ7HnroITFfu3at2pGamirm0dHRasfgwYPFfOfOnWrHgAEDxHz58uVqh7avHz9+XO04duyYmOfk5Kgd2s9p166d2nHu3Dkxf/zxx9WOKVOmiHnZsmXVjqCgIKPcyTimnVucPKa33HKL2hEeHi7mkydPVjveffddy1No+6C3t7fxHKhBgwaWKSfnFm1eoM33bLt37zbav5zOk0y9+eab6jYlS5YU89DQULVj69atYt6mTRu1Y9OmTWKekZFhPO6vXr1a7fD19bVMnT59Wszbt2+vdmjH1M04Xv5MLl26JObXrl1TOyIiIsT84MGDaoc2R05OTjaeQx8+fFjt0K47CgsLja85b4auXbuq28ycOdP42vfs2bNiXqNGDbVD20YbS2179+4V8xMnTqgdAQEBRnN5W8OGDY3nyJcvXza+BvckCQkJYt60aVO149tvvxXzVq1aqR0XLlwwvqbo3r27mI8aNUrtSEpKEvO4uDi1Y9WqVcbXWPPmzRPzWrVqGR8LTsa5vLw8Md+/f7/a0axZMzHv0qWL2rFt2zbj809F5VpPW2uy9ejRQ8w//PBDteO9994zPnc4wTulAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACu8XG6YatWrcQ8JSVF7Th79qyYP/roo2rHrFmzxPzZZ59VO+bMmSPmR44cUTtu3Lgh5nfccYfaof2cCxcuqB2dOnUS85ycHLVjw4YNYn7PPfeoHRcvXjTeP/r06WOZmj9/vvV7++qrr9RtYmJixLxixYpqx/bt28W8Xr16aocnOXHihJiXK1dO7bh06ZKYlypVSu3o16+fmP/www9qx6RJk8T822+/VTsOHjwo5lWrVlU7fvzxRzFv3ry52pGenm40htnCw8PFfOPGjWqH9txNnz5d7WjcuLGYL1q0SO0YNmyYmC9YsEDtePHFF43HqLy8POPzz/79+8W8Ro0aaocn0Y79Hj16qB0lS5YU87p166od165dE/PKlSurHfXr1xfz6tWrG88bMjIy1A5tjrN06VK149SpU2I+ZswYtWPlypVi3rVrV7XjtttuE/PLly+rHXXq1BHzV155Re147LHHjOcvnTt3NprLO7F69Wp1m1tuuUXMly9frnaMGDHC8hTly5cX8ypVqhgf1yVKmL8fSjv32AYMGCDmHTp0MJ4DOZl7aGNUQUGB2lFYWGh8jdWkSROjuatt7ty5Yh4cHKx2HD9+3HjeoM1fnNyOu+++W8xfeukl4+clKSnJ+HysnRc8zZkzZ8T8/fffVzsGDx4s5ps3b1Y7tDmOk/NkVFSU0bqZk3OpNldzMt93cj1w1113GY9zO3bsEPPU1FS1Iz8/3/ja97777rNMvfXWW9bv7W5lDHOyD/n46EvB1apVE/MDBw5YNwPvlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa3ycbhgcHCzmycnJasfy5cvFvFmzZmpHz549xXzJkiVqR0xMjJgPHTpU7Vi2bJmYx8bGqh1nz54V88mTJ6sdjz76qJjv27dP7ahRo4aYHzlyRO24dOmSmFeoUEHt0H7OlClT1I6RI0eK+Y0bN9SO7t27i3l+fr7aMWfOHDHv1KmT2uHv7y/mYWFhaocniY6OFvOuXbuqHd99952YJyUlqR15eXlivnr1arVj+PDhxh3afurl5aV2dOjQQcxDQkLUjurVq4v5uXPnjMfbWrVqqR0ZGRliXr9+fbXj1KlTRvugk3H//PnzlqnSpUur25QrV07MmzZtanw7EhMTjTX33FsAAJiTSURBVDv+TO655x4xHzt2rNrxwgsvGJ17nMxxbr/9drXj6tWrYr5gwQLjfXDDhg1qhzbX0o43W+PGjcX88uXLakepUqXE/P3331c7HnvsMTFfuXKl2vHPf/5TzDt27Kh2TJ8+Xcx79eplmVq/fr26TUpKipg///zzaoc2jy5btqza4Ul8fOTLwoEDB6odH3/8sZj37dvX+LkvLCw0nr9o16S2SpUqiXnNmjXVjtDQUDFPSEgwHm8DAgLUjrS0NONjctCgQWK+du1atUN77pxcY3l7e4v5nXfeaZm64447jOemTs7pWsexY8fUjpsxJv9ZvPzyy+o22jn9gQceUDumTp0q5tWqVVM7GjRoIOabNm0y3k+drM/Fx8eL+XPPPWf8eDhZ98jJyTF+TLXx9sCBA8Zra61atTIe5woKCtSO0aNHi/ltt91mfD3o5+endhw6dEjMu3XrZt0MvFMaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaH6cbnj17VswLCwvVjrCwMDH/17/+pXZkZGSIeeXKldWOihUrivmKFSvUjvj4eDEPCgpSO1555RUxnzdvntqxefNmMe/bt6/aMXv2bDGvVq2a2hETEyPmWVlZaseBAwfEvEmTJsb72Ndff612dO/eXcwPHz6sdkRGRop5Tk6O2rF161YxDw0NVTs8SWpqqpiPHDlS7bj11lvF/Pr162rH+PHjxfyrr76yTI0YMULdxtvbW8yzs7PVjoCAADHPzMxUO+rWrSvmS5cuNT6enIwv2rkhMTFR7fD19RXzMmXKqB3r1q0zHuc0ly5dUrdp0aKFmB8/flztOHLkiJh369ZN7fAk2mO+atUqtaN3795G46CTff3q1atqR25urpgvX75c7Rg0aJCY16lTR+2YM2eOmE+ZMkXtOH36tJifOHHCeE6ojYO2goICMe/Ro4faUbJkSTFv3bq12vHxxx+L+YYNG4yf2169eqkd6enpYv7Pf/5T7XjxxReN5p2eZtu2bcbz3/bt24v5Z599ZnxumTlzptqxfft2o2PWVqFCBeMxSruWa9q0qfF1iXYd52Sci4qKUju06+OePXsaP6badb6Ta73SpUtbpmbMmKFuo12Hff7552pH165dxXzjxo1qhye5ePGi8drJhAkTjNdOtH09Ly9P7dDmSadOnTK+PtJyW0hIiJj/9NNPakfHjh2Nr9O0eWXnzp3VjrS0NONx/9ixY8ZrOI0aNRLz6dOnqx2jR482Ok/aoqOjxTwpKUntuO+++8R8586d1s3AO6UBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4Bofpxtu2LBBzIcPH652pKSkiHloaKjaERAQIOZpaWlqx7Vr18R87dq1asfly5fFvEWLFmrH4cOHxfzYsWNqx9WrV8X87Nmzakf16tXF/J577lE7Nm7caPQzbF988YWYly1bVu1YvXq1mEdERFimMjMz1W06d+5svI+VLFlSzH18HB++HqFHjx7GY8PRo0eNO+6//34xL1WqlGVK289tISEhYj548GC148iRI2KelZWldsTFxYn5hQsX1I727duLed26dY0fsy5duqgdBw8eFPNWrVoZnwebNm1qmSpdurS6zblz58Q8NjZW7ahYsaKYX7p0Se3wJNr+4+SY/PTTT8U8JibG+HnbunWr2qGdf7Sf4eTxaN26tdqxZcsWMd+zZ4/akZ+fL+bx8fFqR7NmzcR89+7dasemTZvEvGrVqmqHNk/65JNPjMeGXr16WaZ69uypbvPKK6+I+QcffKB2zJw5U8zHjx+vdniSW265RcyrVaumdhw/flzM/f391Y6WLVuK+fLly9UO7Xy8bNkytSMqKkrMK1eurHZoY/KqVavUDl9fX+N5gzbOORmzt2/fLubffPON8bnDyf4RFhYm5kuXLlU7JkyYIOZNmjQx3j+czPe16+dy5cqpHZ4kOTlZzCtUqKB2zJo1y+iaw8lYuWPHDrVDWytysg6krRWcPn1a7bh+/bqYd+jQQe1ISEgwHl/S09ON16Pee+89o/tqe/zxx8V84cKFaod2joqMjLRMjRs3Tt3m9ddfN74d2jzKybzACd4pDQAAAAAAAABwDYvSAAAAAAAAAADXsCgNAAAAAAAAAHANi9IAAAAAAAAAANewKA0AAAAAAAAAcA2L0gAAAAAAAAAA17AoDQAAAAAAAABwjY/TDbOyssR81qxZakeJEvIaeI8ePdSO119/XcyvX7+udgQGBoq5t7e32nHfffeJ+caNG9WOihUrivnEiRPVjt27d4v5nDlz1I4RI0aI+bRp09SOgIAAy1SVKlWMbqctNTVVzBMTEy1Tf/3rX9Vtjhw5IuYRERFqR0ZGhpiHhoaqHZ7kyy+/FPPw8HC1o1mzZmJ+4MAB4+ctLy/PMjV9+nR1m1GjRon5lClT1I4uXbqIeUJCgtoRGxsr5v369VM7cnJyxPzQoUNqR8OGDcX8woULasfhw4fFPCUlRe2Ijo4W89OnT6sdbdq0MRoHbT4+8um/f//+akfp0qXFfMuWLWqHJ6lbt67xufbGjRti3rJlS7Vj4cKFYn7t2jW14+jRo2I+ZMgQtWPBggViPn/+fLVj9uzZYj537ly14/jx42Jeo0YNtWP16tVi3r59e7WjU6dOYj5v3jy1QztHNW3aVO1o27bt7z6PeuGFF9RttNv6yiuvqB09e/YU82+//VbtuP322y1PoY0fu3btUjuqV69ufI2lHZMhISFqx/bt28X8lltuUTtWrFgh5vn5+WqHdj729/c37nByTJYqVcr4+jk3N9cod3Lt6+fnp3aULVvW6L468eabb6rb/POf/xTzdu3aqR3x8fFivnfvXrXDk/Tp00fMk5KSjOe/1apVUzu0ecGJEyfUjp07dxofk5cvXxbzsLAwy5Q233NybatdPzl5TLU1QNtbb71lfO6Ii4szHuc6duxofDtMxx8nz39UVJTaoY1j2nWtU7xTGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACu8XG6Yc2aNcX80KFDakdhYaGYT5s2Te2Ii4sT8+eee07t2LZtm5jXqVNH7di/f7+YV6tWTe24dOmSmPv46E9PRESEmJcuXVrtOHz4sJgPGzZM7diyZYuYh4SEqB0FBQVi/sknn6gdFy5cEHNvb2+1o0ePHkb7oC0pKUnMz507Z3w7Vq1apXZ4kipVqoh5Tk6O2pGYmCjmTz75pNoxZ84cMV+2bJna0aZNGzH/4Ycf1I7BgweLeVZWltqRnp4u5k2aNFE7Vq9eLebXrl1TO/bt2yfmAQEBaod2f1u0aKF23HHHHWL+zTffqB2xsbFG46BtxIgRYh4YGKh2eHl5GY3pthIl5Ne169atq3Z4ksWLF4t5dHS08fiyfft2taNv375inpycrHbs2bNHzJcuXWqZatCggbrN9OnTxTwvL0/tCA8PN5pn2dq3b298XGu39fTp02rH3//+d6O5q23RokVG5ycnHn74YXUb7dzgZNy/ePGimFevXl3t8CSzZs0S8woVKqgdrVu3Nj6eevXqZXyu1Y6n1NRUtcPJ9aAmMjLS6NrYlp+fb7Sf2/r16yfmvr6+aseSJUvEvFOnTmpHt27dxHz58uXG162VK1e2TI0dO1bdRpsnObnW0+ZJubm5aocnKV++vPG6x+XLl42Pp5UrV4p5mTJljM9xTjq049bJuoc2VmqPuZN5gZP1uXr16on5sWPHjG+HkznQkSNHxDw4ONiV+YvGyTXDxo0bxbxs2bJqh3ZN8Nprr6kd8+bNU7fhndIAAAAAAAAAANewKA0AAAAAAAAAcA2L0gAAAAAAAAAA17AoDQAAAAAAAABwDYvSAAAAAAAAAADXsCgNAAAAAAAAAHANi9IAAAAAAAAAANf4ON3w6NGjYl6pUiW1IyMjQ8xTUlLUjp49e4r5/v371Y7g4GAxv+2224wfj0WLFqkdQUFBYt60aVO1Y8uWLWL+t7/9Te04fvy4mF+8eFHtiImJEfOAgAC1o6CgQMxbtWqldmg/Z/PmzZapS5cuqdt06dJFzAsLC42fWyf7uiepUKGCmOfl5akd8fHxYr5+/Xq1o2TJkkbHvRMLFixQt+ncubOYlyhRwvi43rZtm9oxceJE42Nh69atYp6dna12jBgxwmgcdPLctWjRQu1o0KCBmH/99deWKe0cZ0tOTjY6nmyBgYFivmfPHrVj2LBhlqe49957jeYVtjvuuEPM69WrZ5lyckzm5OQYnc+djFFOztc1atQQc19fX7Xj/Pnzxud87fziZNzfuHGjmPfq1csy5eQxffTRR8X8k08+UTtuueUWMZ82bZrx/nHw4EHj646bcbz8mTRr1kzM09PT1Y4ff/xRzGNjY9WOJ554wvh4OnfunPE1VmRkpJjv27fPeHzJysoyPl/feuutase8efOMr7HatWsn5j4+jpcV/kfVqlVTt8nPzxfzHTt2uHKdpq0XOJlnr1ix4ne/Zvgz8fLyEvNDhw6pHREREWJetmxZtSM8PFzMly5danw94GRs0B6Pa9euqR1JSUli3qhRI7Xjm2++Me7Q5icJCQlqh3bMOZmbNmzY0HhOqO1jTs6lpuOgk3nj4MGD1Y7FixeLefny5a2bgXdKAwAAAAAAAABcw6I0AAAAAAAAAMA1LEoDAAAAAAAAAFzDojQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABcw6I0AAAAAAAAAMA1Pk43bNCggZgfPXpU7ahQoYKY16xZU+24du2amL/22mtqx9ixY8X84YcfVjs6d+4s5snJyWpHixYtxHzLli1qR0BAgJhv27ZN7Vi+fLmYT5w4Ue1IT08X8y+++ELt6NOnj9HjZVu3bp2Yv/7662pHRkaGmFerVk3tuH79uphPmjRJ7dD2wypVqqgdnkR7Xj788EO1o3fv3mIeHR2tdrz77rvG+7Fm6NCh6jZnzpwR80OHDqkdV65cEfORI0eqHXFxcWK+ePFi43OHdn6ybd261XjM1m6HNh7btm/fLuZdunSxTAUHB6vbHDhwQMwLCgrUjtjYWDEfMmSI2uFJVq5caXwsjBo1SswTExPVjnr16on5xo0bjc9xOTk5aseNGzfEvEQJ/X0TlSpVMnrMbadPnxbzESNGqB2VK1c2mrvapk2bJuajR4+2TDVp0kTdRps3aucFJ7p27Wo8r4yKilI7wsPDjX6Gp7l69aqYHzt2TO1o3LixmN9xxx2WqVatWqnb5OXlifmsWbPUjtatWxuNP7aEhAQxb9SokfFYGR8fb3wsOHk8evToYTRHcmLu3LnqNg0bNhTzWrVqGd+O8ePHq9usXbtWzBcuXKh2lC9fXsz9/f3VDk+i7etO9sGdO3eKeceOHdUObb3ppZdeUjvmzJljtH85OZcePHhQ7fD19RXzDRs2qB233XabmJ88eVLt0OaNoaGhaoe3t7eYp6SkqB2pqaliHhgYqHasWbNGzLt162aZSlDOLU7Ox1OmTFE77r33XjHfvXu3dTPwTmkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGt8nG7o5eUl5n5+fmrHxYsXxTwxMVHtaNy4sZj3799f7Wjbtq2Y169fX+3w9vYW87p166ody5cvF/O8vDy1IzMzU8zvvfdetSMoKEjMP//8c7Wjb9++Yt61a1e1Y8uWLWJ+9epVtePKlSti/thjj6kdr7zyiphHRUWpHdpz98UXX6gd8+fPF/NOnTqpHZ7k9OnTYl62bFnj523hwoXGx1OZMmUsU5GRkeo2a9asEfOOHTuqHWFhYWK+f/9+tePUqVNiXrp0abWjevXqYr5z5061o0KFCmJeuXJltaOwsFDML1++rHaUKCG/Frx48WK1QxtPtdtpCw0NFfO3335b7Zg2bZqYZ2dnqx2eRJs3jBw50vhnODkmd+/ebTSvcHK8jBs3Tu144YUXjMe5UqVKiXlaWpra0bJlSzGvV6+e2nHo0CHj2+Hv7y/mcXFxakdubq6Yp6SkqB0JCQlGc2gnrl+/rm6jzaNnz56tdjRs2FDMz5w5o3Z4Em3/qVatmtpRtWpV4/OTdn3k5HpAu27V5oxOrm2cjLfaOKY95rbo6Ggx//rrr9WO2267TcwbNWqkdhw/flzM09PT1Y4jR46IeZcuXdSOkiVLivm3336rdgwaNEjM//Wvfxnv69q808n5pUOHDmqHJ6lSpYrxNVZWVpbxXP6RRx4R8yZNmqgdly5dEvOzZ8+qHdp5sFWrVmpH69atjc/5X331lZiXL19e7WjXrp2YJyUlufKYHj58WMwnTpyodmjrotpj7oSTMVvbl7Vzi23ZsmXG45wTvFMaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK7xcbphqVKlxDw6OlrtWLJkiZiXKVNG7ahbt66Yp6amqh1HjhwR89DQULVj6dKlYu7joz+0AwcOFPP09HS1o2zZsmI+Z84ctaNDhw5iHhUVpXasWLFCzC9duqR2TJ482fq/YPny5eo2lSpVEvMdO3aoHadOnTI6Jj3N1q1bjceG8+fPi7mXl5faMW7cODEPCQmxTFWsWFHd5qmnnrKKgyZNmvzuP6Nz587Wn8WwYcOMO65cuaJuM3z4cONzR82aNcV8/fr1aocn2bRpk/EcqHLlymJeoUIFtSM8PFzMExIS1I4aNWqI+f79+9WOPn36iHlcXJzakZuba/QznMxfDhw4oHaEhYWJebly5dSOyMhIMd+yZYvakZmZKeaNGzdWO8qXLy/m2dnZlikn8+yTJ08ajT+2du3aifn27dvVDk+SlZUl5u3btze+LtGON1twcLCYnzt3Tu1o1aqVmI8aNUrtSEpKEvMqVaoYXy/27NlT7dDGD+1YcXLN8Nhjj6kdQ4cOFfPk5GTjuXpgYKDxXF177p1o27atus3cuXON56ba/U1JSVE7PMnZs2fFvHTp0mqHt7e38Rj18ssvi/m3335rvA60d+9etaNWrVpGuW337t1G807bs88+K+Z///vf1Y7bbrtNzO+//37juWmnTp3UjsGDB1umbsYYpHEyV9fmOF26dDEe52JiYqybgXdKAwAAAAAAAABcw6I0AAAAAAAAAMA1LEoDAAAAAAAAAFzDojQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABc4+N0w+rVq4v58uXL1Y5bbrlFzCMiItSONWvWiHmjRo3Ujo0bN4p5jx491I5OnTqJ+cKFC9WOH374Qczr16+vdqSkpIh5Xl6e2rF//34xz87OVjv8/PyMn5dXX33V6DG3rVixQszPnDmjdsycOVPMQ0JC1I4OHTqI+bFjx9SOpk2binlSUpLa4Ulyc3PFPCgoSO24ePGimIeFhakdEyZMEPPGjRurHc8++6yY/+Mf/1A7WrZsKea7du1SO8LDw433wVGjRhmNg7bFixeLecOGDdWOtm3bivmhQ4fUjoCAAOOOgQMHinnNmjXVjiZNmoh52bJl1Y4vv/xSzOvWrat2nD9/Xt0G/09gYKCY+/v7qx01atQQ86lTp6odjz/+uJhv375d7Zg4caLx+LJ3714x79y5s/EcyNvbW+2oWLGimH/44Ydqx1NPPSXmsbGxakdycrLxvEEbbwsKCozPg06O+169eon59OnT1Q5tzudkjFq2bJmY16tXT+3wJEePHjW6jrNdvXpVzA8fPmy8D2pzbCdz9Z07dxrPgbRrUtu9994r5mfPnjWeAw0fPtz4Wq979+5qx9q1a8X8jjvuUDuysrLEvGrVqmqHNscpUcL8PXc7duxQtxkyZIjx2sjIkSPF/MiRI2qHJ9Gu0V977TW148477zQaB23z5883XivQzumtW7c2Hht8fPSlvgsXLhifa3NycozGdCfXg9q1j5PbOmvWLMuUk3nDp59+KuZjx441Xgfyd3DN0LFjR6P1XSfX2HfddZd1M/BOaQAAAAAAAACAa1iUBgAAAAAAAAC4hkVpAAAAAAAAAIBrWJQGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgAAAAAAAAC4xsfphq+88oqYR0dHqx3JyclinpmZqXYMGjRIzI8cOaJ2bNu2Tcxr166tdhw7dkzM8/Ly1I5hw4aJeYcOHdSOGTNmiHnfvn3VjsOHD4v5yZMn1Y7IyEgxr169utqxY8cOMff19VU76tSpI+Zjx461TNWsWVPdZt++fWJesWJFtSMnJ0fMQ0ND1Q5PUq5cOTFv3bq12tGoUSMx379/v9rRrl07MR86dKhlavjw4eo227dvF/P27durHd26dRPzFStWqB3aNsHBwWrHa6+9Jua9e/dWO9asWSPmlSpVUju2bt0q5vfdd5/aceHCBTEPDw+3TJ04cULdRnvMfHx8jM/p/fr1Uzs8ye233250DrQdOnRIzNu0aWN8LMTExBjvY0lJScaPR2BgoNqxadMmMS9Tpozacc8994h5ixYt1I6QkBAxv3TpktqhzYEHDhyodmhz8YSEBLUjPT1dzLt27WqZuu2229RtgoKCxLywsFDtOHv2rJh7e3urHZ5E2wfj4+PVjs2bN4t5QUGB2jFgwAAxP3DggNqhXTM0a9ZM7bhx44aYN27c2HjMdnJ9dO7cOTEvVaqU8XHtZKy84447xHzZsmVqx9///nej62vbkiVLxHz8+PGWqTNnzqjb5Ofni3lWVpba8fnnn4t5q1at1A5P0qdPH+MxXVsr6tmzp/FcXltbcTLease9rX79+mK+fPlytUMbC50ck6dOnRLzRx991HjMbtq0qdoxd+5cozHMyVzcyfNSunRpMd+zZ4/a0VS5v0899ZTx+q2Tcb9t27Zi/vHHH6sdTtZgeKc0AAAAAAAAAMA1LEoDAAAAAAAAAFzDojQAAAAAAAAAwDUsSgMAAAAAAAAAXMOiNAAAAAAAAADANSxKAwAAAAAAAABcw6I0AAAAAAAAAMA1Pk43LFmypJgfPXpU7WjZsqWYX7x4Ue34+uuvxfzy5ctqx4ABA8R869atascdd9wh5gsWLFA7ypcvL+aHDh1SO6pXry7mU6dOVTt69+4t5mlpacaPaeXKldWO9PR0o8fLNn36dDEvUUJ/HaZ27dpinpCQoHbs379fzB966CG1Y926dWK+atUqtcOTlC1bVsxXr16tdrz//vtiPnr0aLXDz89PzBctWqR2VKxYUcxXrlypdnTs2FHMd+3apXb4+vqKealSpdSO+vXri/nMmTPVjjNnzhgfkykpKWLeunVrtaNBgwZGz5utUaNGYr5w4UK14+677xbzKlWqqB116tQR89mzZxuff5YtW6Z2NG/e3PIU+fn5xuen06dPi3mrVq3UjszMTMtUSEiImAcFBakdqampYr5lyxa1Izc3V8yDg4PVjvnz5xudz20PPPCAmEdHR6sdzZo1E/M33nhD7YiPj7dMdejQQcxPnTplPFZqY7qtW7duYr5p0ya1Q7ut2n31NPXq1RPzdu3aqR3e3t5GY5htx44dYt6jRw+1Qzv2nRwrUVFRxtdHN27cEPMrV66oHdocJzw8XO3QxiAn87kVK1aI+auvvqp2aNtoY4cTH330kfHt6Nmzp9px4cIFo+tJ27Zt24znt55E29cjIyPVjjfffNP4eRs4cKCYHzt2TO3Q1qxiYmKM11+cHE/aXN3HR18urFq1qmVKG5OdrK2VKVNGzAMDA43nYgEBAWpHXFzc7z7OzZo1S91myJAhYp6RkaF2nDt3TszbtGlj3Qy8UxoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArvFxumGnTp3EPCEhQe1o1KiRmE+dOlXtuO+++8T8448/VjuaNm0q5p07d1Y79u3bJ+Y1atRQO2JjY8W8Xr16asfZs2fF/NKlS2pHZmammEdHR6sdy5YtE/Ny5cqpHaVLlxbzzZs3qx0PPfSQmM+YMcMyFRERYXxf5s2bp3Zoz12HDh3UDk+SlZUl5hMnTlQ7Zs2aZfS82ipXrizmoaGhakfZsmXFvG7dumrHtWvXxDw3N1ft0I65EydOqB3BwcFiPmnSJLVj/fr1xuNc7dq1xfzdd99VO4YPHy7mFy5cUDv8/f3FPDs72zLlZMw+dOiQmJ88eVLtuHHjhpgHBgaqHZ5k27ZtYl69enXj40l7Tmx5eXliHhcXp3bs3LnTeB+cM2eOmLdo0ULt6NOnj5h7eXmpHQUFBWKelpZmfP5JT09XO7R5wcCBA9WOxMREMW/evLnaERAQIOanT5+2TN1yyy3qNgcPHhTzsLAw4+Pl4sWLaocn8fPzE/Pz588bz19GjBhhfK3nZC62bt06MW/cuLHa8dNPPxmNHbakpCQxv+uuu9QObb6fkpJifCw4uS6pWrWq0eNlu/3228X8nnvuUTveeOMN4/HWdPyxjRkzRsxXrVqldgwbNsz4mPMk2nqTtrZi6969u5i3a9dO7dDOHQ0aNDA+Jr29vdWOY8eOGeVObuuVK1fUDm3NauvWrWqHNtfSrq9tkZGRxte+mzZtEvO2bduqHfHx8WLepk0by9R5B2PD7t27xTwmJkbt2LVrl3GHE7xTGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGh+nGyYkJIh5QUGB2pGfny/mrVu3VjsSExPFPDs7W+2IjIwU85kzZ6odw4YNE/NNmzapHTt37hTzI0eOqB233nqrmIeHh6sdubm5Yl69enW14/Lly2LepEkTtWPOnDliXrp0abUjIiJCzENCQixTzz33nLrNo48+KuZXrlxRO2rVqiXm169fVzs8iXZcr1y5Uu0IDQ0V8/Xr16sdY8eOFfNDhw6pHWfPnhXzixcvqh1JSUli3rNnT7UjKytLzI8ePap2BAcHi7mvr6/aER0dLeZ79uxROy5duiTmDz74oNqxceNGMa9WrZrxc1uhQgXL1Ny5c9Vt2rVrZ/SY28qWLSvmOTk5aocnKV++vNE50BYTEyPm48ePVzu0cSwsLEztqFKlipinpKSoHd26dRPzVq1aqR3aWFixYkXj+W1sbKza4e3tLeY3btxQO+rWrSvmx44dM74dcXFxasfChQvF/N1337VMOZkjBwUFiXm5cuXUDu2awMl+6km08WX16tVqx4kTJ8T84MGDaod2/tGun2xpaWliXqKE/r6sRo0aGc0rnFzLLV68WO0YN26cmMfHxxvP59q0aaN2ZGZminlGRobxPFu7rrUNHjzY+r01aNBA3UZ73LU5ku3AgQPGt8OTXL16VczfeusttePTTz81nke1bdtWzP39/dWOgIAAMX/vvffUjgEDBhjvP7169RLzqVOnqh1TpkwR8+bNm6sdeXl5Yl6zZk21Y8uWLUbnOCe3QxtLncyzndyXGjVqiHlycrLacfz4cePreG1M9vPzs24G3ikNAAAAAAAAAHANi9IAAAAAAAAAANewKA0AAAAAAAAAcA2L0gAAAAAAAAAA17AoDQAAAAAAAABwDYvSAAAAAAAAAADXsCgNAAAAAAAAAHANi9IAAAAAAAAAANf4ON3w1ltvFfMlS5aoHT/++KOYt2vXTu0ICwsT89GjR6sd27ZtE/OGDRuqHaVKlRLzbt26qR0xMTFifvLkSbXjs88+E/MhQ4aoHVFRUWJ+5swZtePChQvG+4e3t7eYnzt3Tu3YtWuXmJctW9Yy9fnnn6vbrF27Vsx79Oihdrzxxhti3r17d7XDk2zZskXMy5cvr3Zo23To0EHtWLRokZhnZGSoHbVr1xbztLQ0tWPo0KFifvToUbXj2LFjYl69enW1w8/PT8y///57tSM6OlrMmzdvrnbMnTtXzBMTE9WO+vXri3njxo3Vjh07doh5RESEZWrMmDHqNsuXLxfzrKws4/NxZmam2uFJevbsKeYBAQFqR1BQkJi/9NJLakdSUpLRfM/WoEEDMf/qq6/UjrZt24r5jRs31I6EhAQx7927t9qRn58v5v3791c79u7dK+ajRo1SO77++mujsdRWt25dMd+9e7fa8corr4j5tGnT1I7HH39czMeOHat2PPjgg8b7aefOnY3PpZ5EG/e1ax9bnTp1jH6GLTIy0nj+op1LL1++rHakp6cbjT9O7ktgYKDa8cEHH4j5wIED1Y7Vq1eL+Z133ml8/RMeHq525Obmivn48ePVjuzsbDGPjY21TJUsWVLdRjtnV61a1ficnpKSonZ4kgoVKoj566+/rnaEhoaKeaVKldQO7VrOydrJ+fPnxfy+++5TO/Ly8sR8//79xnP5Zs2aqR3BwcFiXrlyZeO56aeffqp2DB8+3Ph5Wb9+vZiHhISoHV27dhXzI0eOWKa6O1gH+uGHH8T81KlTasekSZPEfP78+dbNwDulAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACu8XG64fnz58W8Z8+easeKFSvEvLCwUO0ICwsT84CAALXD39/fMrVp0yYxP3PmjNrx/PPPi3lCQoLa0bFjRzGvUqWK2rFo0SIxT01NVTseeOABMV+/fr3aUVBQIOY9evRQO7Zv3y7mZcqUsX7v597WtGlTMf/iiy/UjhYtWoh5UlKS2uFJrl+/LuYHDhxQO7p37y7mhw4dUjtq1Kgh5hUrVlQ7Vq9eLeY1a9ZUO7755hsxv3r1qtqRm5sr5q1atVI7tMd96NChasepU6fEfMmSJWpHdna2mAcHB6sdN27cEPPExES1o379+mJeooT5a8XHjx9Xt7l06ZKY+/r6qh3ffvutmD/77LNqhyfRHi8nj7l27GvnDVtaWpqYV6hQQe3YvXu3mEdHR6sddevWFXM/Pz+1Qzte4uLijI+F0NBQ49sRGxurdrRp00bMO3fubHzuuP3229WO/Px8Mc/MzLRMrV27Vt3mrrvuMr4d77//vpg/8sgjaocn2bdvn/F5MioqSswbNGhg3LFhwwa1w8dHvsS9ePGi2hEYGGg8zmnXWE7G/ebNmxvN1WwhISFiHh8fr3b069fP6BrMyXG7c+dOtaNt27ZiXrp0acvUDz/8oG6j7cvNmjVTO/Ly8oyv4z2Jdkxu3rxZ7dDWirS5ia1SpUpivnjxYrVD2z+cXFNs3bpVzGNiYtSOdevWifmVK1fUjieffNJ4HWjQoEHG13qRkZHG41zXrl3FPCgoSO3QxiAn51KNk7m6do3t5Ha88sorYl6yZEnrZuCd0gAAAAAAAAAA17AoDQAAAAAAAABwDYvSAAAAAAAAAADXsCgNAAAAAAAAAHANi9IAAAAAAAAAANewKA0AAAAAAAAAcA2L0gAAAAAAAAAA17AoDQAAAAAAAABwjY/TDVesWCHmQUFBakdGRoaYFxYWqh0xMTFivnr1arVD+zkNGjRQO+rVqyfmtWvXVjsuX75s3JGYmCjm165dUzv69Okj5kuWLFE7/vnPf4p5tWrV1I777rvPMtWkSRPr95adna1u8+OPP4p579691Y4xY8aI+V133aV2eJKwsDAx79atm9px6NAhMT98+LDa4efnJ+ZnzpxRO5o3by7mBQUFakfdunXFPCkpSe2YMWOGmPv46KeQQYMGGT8epUuXFvNRo0apHatWrTLaf2zh4eFiXrZsWbVj7ty5Yt6sWTO1Izo62ujcYsvPzxfz+Ph4tUMbg06ePKl29OjRw/IUZcqUEfMqVaqoHUePHhXzihUrqh1Vq1Y1GjtsW7duFfNevXqpHb6+vmJ+48YNtWPs2LFi/v7776sdo0ePNh73S5QoYTy/1cb1ffv2qR1XrlwR81OnTqkdNWvWNJozOnHs2DF1m5YtW4q5l5eX2pGamirm3333ndoxYsQIy1NERkaK+YEDB9SORo0aGV+naXOxAQMGGF+nObnmmDBhgvHY0KpVK6PH3Obv72887mtj0IULF9SO48ePi3mdOnXUjri4OKPxx8l1mHZucaJ///7qNnfeeaeY79q1S+3QHnftnO9ptGPOydqJ9rw89thjasdtt90m5ikpKWpHrVq1xLxcuXJqx+233y7m5cuXVzu0a5d58+apHdevXxfzDRs2qB1paWliPmnSJLXj888/F/NHH31U7XAyBy4OatSooW6zdu1a47WAhx9+WMxnzZpl3Qy8UxoAAAAAAAAA4BoWpQEAAAAAAAAArmFRGgAAAAAAAADgGhalAQAAAAAAAACuYVEaAAAAAAAAAOAaFqUBAAAAAAAAAK5hURoAAAAAAAAA4Bofpxv6+vqKebVq1dSOMmXKiPlPP/2kdmzevFnM/fz81I4RI0aI+ZYtW9QOb29vMV+4cKHaERMTI+aJiYlqR4MGDcT8nXfeUTv++te/irmXl5fa0bJlSzG/fv262vHMM8+I+eOPP652xMbGinlBQYHasWHDBjFv27at2tG6dWsxj4uLUzvuvPNOMY+KilI7PElOTo6Y5+bmGu/HTh7zpk2bivk333yjdlSuXFnMjx49qnZUr15dzJOSktSO559/XszXr1+vdgQEBIh5SEiI2nHy5Ekxr1GjhtqRmZkp5rfeeqvaoT3/EyZMUDvefvttMV+0aJHa8eGHH4p51apV1Q7t/FKzZk21IywszCj3NNo+uG/fPrVDe0wLCwvVjoSEBDGvV6+e2jFgwAAx37hxo9qRl5cn5mfPnrVMValSRd1m6dKlYj5mzBi1Y/fu3cZzD20efeHCBbVj69atYt6uXTu149KlS2J+48YNtWPNmjVi3qRJE7XjxIkTRudr2xNPPCHmycnJaocnycjIMJrbOrn+qVWrlvFxq82RbC+//LKYv/DCC2rHlStXxLxx48Zqh4+PfKldoUIFtSMiIkLM//GPfxjPCZ1clwwePFjMv/76a7WjVatWYr58+XK1Y9iwYWIeHR2tdrz77rtiXrZsWbVj8uTJYl6ihP7ev9TUVDFnHvVrXbp0MVoXsX322Wdi/thjj6kdly9fFvM+ffqoHaVLlxbz8PBwtUOb769YsULt0PZTJ+P+gQMHjDuCgoLEvH79+mpH586dxXzKlCnG65XadZzt448/FvO+ffuqHReUOd/Vq1fVjk6dOhndTlt+fr6YR0ZGWjcD75QGAAAAAAAAALiGRWkAAAAAAAAAgGtYlAYAAAAAAAAAuIZFaQAAAAAAAACAa1iUBgD8f+3dB3RU5fr+/R0S0iAQIKGX0Duh9x5AkI4iIFJEsKBi49gVC2Iv2EGRpihFpffeQ+8lARKSEEJCCRBa6rv2nBf+es753ffo49nkON/PWi6Fa7ySmcy+97OfTCYWAAAAAACAU9iUBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JQGAAAAAAAAADjGx90bnjx5UsyDgoLUjubNm4t5bGys2nH16lUxDwgIUDsWLlwo5o0bN1Y79uzZI+YlSpRQO7Zu3SrmderUUTuSkpLEfNSoUZapQoUKqbfR7m/9+vXVjqioKDF/8MEH1Y5u3bqJedWqVdUOf39/MU9JSVE7Vq9eLea9evVSO9LT042f656kUaNGYp43b161Y8KECWIeHh6udmzZskXMq1SponZ06dJFzA8dOqR2aPd38ODBakdcXJyYjx49Wu1ITk42OmbduU1YWJjaUbBgQTFPTU01fo5FRESoHUuXLjV+jp09e1bMDx48qHZ07NhRzI8fP652rFmzxvjc0b17d8tT3HvvvWI+depUtaNo0aJiXqZMGbWjSZMmYj5p0iTjc76vr6/a0aFDBzGPj49XO/Lnzy/mGzZsUDtCQkLEfNq0aWpHWlqamPfr10/t+P7778W8WLFiakefPn3EfPv27WqHn5+fmF+7dk3tKFu2rPF8SUxMFPPAwEDjNbJ2LHga7TpNe466MxvcOdcOGDBAzJ9//nm1QzuXnj59Wu3IyMgQ8wYNGqgdR44cEfMVK1aoHd7e3mLev39/tWPnzp1iXqpUKbUjOjra+BqrePHiRtc+7lzH33fffWrHY489JuZfffWV2nHHHXeI+YULF9SO8uXLG51bPI32eHz66adqR8OGDY3WBO7sFbizfqlevbrxbNDUqFFDvc3hw4fFPDMzU+3Qrktr1qypdmRlZRnNDnf2m44ePap2aOuCsWPHqh3a+UW7jrPlyZPH6PGyvfrqq2Leu3dvtePNN9905DqOV0oDAAAAAAAAABzDpjQAAAAAAAAAwDFsSgMAAAAAAAAAHMOmNAAAAAAAAADAMWxKAwAAAAAAAAAcw6Y0AAAAAAAAAMAxbEoDAAAAAAAAABzjlZOTk+PODRs1aiTm2dnZasfDDz8s5j4+PmrHrFmzxHzUqFFqx759+8S8WLFiaoeXl5eYT506Ve146aWXxHzOnDlqR0pKipgPHDhQ7Zg4caKYt2jRQu3IzMwU8/Pnz6sdJUuWFPOiRYuqHWvXrhXz6tWrqx3h4eFivnr1arXj8uXLYl63bl214+rVq2L+zTffqB1HjhyxPMXYsWONnz/FixcX8+XLl6sd2vwoX7682rF06VKj481WqVIlo/tq004PhQoVUjuio6ONjhV3vnblypVTO+rVqyfmefLo36P9+eefxbxmzZpqx6lTp4weL3eOa22G2fbv3y/mw4cPVzsOHz4s5klJSWrH+PHjLU/xySefGD8Hjx8/LuZNmjRRO8aMGSPmgwcPNv48qlatqnZkZWWJub+/v9oxadIkMX/ggQfUjjJlyhidW2x33nmn0Ux35/MIDAw0Xpu6M/c1Z86cUW+jPZfr1KljvFa/44471A5tBrmzRpo8ebLlKb777jsxnzdvntqhnX+0tb6tXbt2Yr537161Y9WqVUbHm+3s2bPG11jr168X85CQEON5686sDA4OFvOMjAy1w9vbW8zz5s2rdhw9etQypc2xrVu3qh3a/XVnL+D69evGX5cGDRqI+a5du9QOd64H/y5++uknMT99+rTxnpY7s2HPnj1Gs8MWHx8v5l26dFE7tD2JoKAgtUO7DmvevLnaoZ0b3Pk8tONam2G2yMhI4+vW2NhYMW/WrJnaod3GnWvfl19+2XjPc+XKlWLeo0cP45mtXQvavvzyS/U2vFIaAAAAAAAAAOAYNqUBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGDalAQAAAAAAAACOYVMaAAAAAAAAAOAYNqUBAAAAAAAAAI7xcfeGrVq1EvOYmBi1o0GDBmI+Z84cteOll14S88OHD6sdFy5cEPNGjRqpHdu2bRPztm3bqh0LFiwQ8+rVq6sdhQoVEvNr166pHU899ZTRfbWlpaWJeVJSktpRsWJFMd+3b5/a0blzZzHfsGGD2qF97ZKTk9WOKlWqiPnJkyfVjsjISKOvvacpVaqU8eM1ZcoUMQ8LC1M7ChYsKOZZWVlqx9ChQ8W8QIECasfx48fFvEiRImrH9evXxfzAgQNqR4UKFcT83LlzasfZs2eNPk935v7WrVvVDm2evvvuu2pHnTp1xPzFF19UO/bu3SvmiYmJakeTJk3EfP369WpH06ZNjc+DnmTdunVi3q5dO7UjIyPD+OumzbH8+fOrHZs2bRJzPz8/tWPEiBFi/sMPP6gd2vEye/ZsteP06dNi7u/vr3bkzZvXeN5qX3/tMbf16dNHzDdv3qx2lCxZUszDw8PVjkuXLhmvK7XHfceOHWpHcHCwmOfLl0/t8CTa49WvXz+1Izo62ui5Yfvmm2+M1hXurPni4uLUjuzsbDFftmyZ2tGyZUsxX7p0qfEx6c6xoM0g7RrMnesSd74usbGxRvfVnevjatWqqR1r1641WjO687UNCQlRO7S53rNnT7XDk2jn2oEDB6odzz77rJgHBgaqHTVq1BDzPHn0130WLlxYzBMSEtSOGzduGB/Xe/bsEfPLly+rHdr1jzvPY+38k5OTo3Zo13JFixZVOx599FHjPZyVK1eKeUREhNrxxhtvGM1jW7169cS8efPmaod2PLiz9+oOXikNAAAAAAAAAHAMm9IAAAAAAAAAAMewKQ0AAAAAAAAAcAyb0gAAAAAAAAAAx7ApDQAAAAAAAABwDJvSAAAAAAAAAADHsCkNAAAAAAAAAHCMj7s3TE9PF/MmTZqoHdOnTxfz/Pnzqx27du0S89TUVLUjTx55L37BggVqR4kSJcS8cOHCakfdunXF/KuvvlI7srOzxTw2Nlbt0B6zXr16qR0pKSliHhgYqHYcPXpUzEePHq12vPfee8ZfF39/fzEvVqyY2lGgQAExT0tLUzsyMzPFfPjw4WqHJwkODhbzIkWKqB316tUT80uXLqkdpUqVEvOwsDC146GHHhLzF198Ue3QnmPLli1TO5544gkxX7VqlfG54/r162pH6dKlxTwmJkbtiI6OFvPjx4+rHUWLFhXzUaNGqR1bt24V8w8//FDt6NOnj5gfO3bM+DyoPY/decxCQ0PVDk+inRfOnz+vdiQlJYn5sGHD1I5vv/1WzFesWGF8LKxfv17tqFWrlpgfOXJE7fDz8xPz6tWrqx3ac71jx45qx8iRI8W8TZs2akdGRoaYh4SEqB2rV68W86pVqxof1+6sszdu3CjmV65cUTu6d+8u5snJycbnH29vb7XDk+zevdvo2sedx3THjh3Gs/Kv+NoXL15c7fDy8hLzhIQEtePrr78W86CgIOP74s769ty5c2KeL18+taNQoUJifvr0abXDnWs5zc6dO42v0ypXrizmkZGRasfevXuN11Ha3oc7j6kn+eijj8Q8Li5O7ejdu7eY37hxQ+0oWLCg8fpFWyOfOXNG7cjKyhLzzZs3qx3NmjUT85YtW6od2p6EO2sPbY6dPXtW7RgxYoSYX7t2Te3Q5oc7a2RtX3TSpElqR0REhPHzQzsff/bZZ2rHnj17xPypp56y/gq8UhoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGDalAQAAAAAAAACOYVMaAAAAAAAAAOAYNqUBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjvHKycnJce7DAQAAAAAAAAA8Ga+UBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JQGAAAAAAAAADiGTWkAAAAAAAAAgGPYlAYAAAAAAAAAOIZNaQAAAAAAAACAY9iUBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JQGAAAAAAAAADiGTWkAAAAAAAAAgGPYlAYAAAAAAAAAOIZNaQAAAAAAAACAY9iUBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JTGbfXa2tesul/Xvd2fBgD8R0PnDrV6/dTrdn8aAPAfMaMA5GbMKAC5GTPq9vO53Z/A/xqv173EfEybMdZrbV+zbpdRS0ZZm+I3WQeSD1jVQ6pbex7e86c2il9f97rrv729vK3SBUpbvav1tt5s/6aV3ze/dbsdTD5ovbr2VWtn4k7r5MWT1sd3fGw92fTJ2/1pAblCbp9RcRfjrEcWPWKtiVnjmidDwodYb3d42/LJ4/OHFg9T9051/XfePHmtsgXLWoPDB1svtnrxD/X8N32x7Qvr/c3vW0lpSVZ48XDrsy6fWY1LNb7dnxZw2+XmGTVlzxTr/nn3/8fszOgzVtF8Rf8WMyrskzDX+ulfjWw40vqi6xe35XMCcovcPKP+r8/vx7t+tPrX6u92R26fUb+9Fr2papGq1pHHjty2zwnILXLzjGIdxTrqz7j9X9H/MaefOX3rv2cemOnaHD362NFbf/fbTducnBwrKyfL8QNnWN1hVuSpSGvfmX1/uqNmaE1r5eCVVmZ2prUpbpM1bP4w62rGVWtC9wn/dtv0rHTL19vXcor9eVQIrmD1rdHXemrZU459XOB/QW6eUVnZWVbXGV2t4vmLW5sf2GydvnzaGjx3sJXXO681LmLcH+rqXKmzNbnnZOtG5g1rcfRi69HFj7oWLS+0euG2zyj7cX96+dPW112/tpqUbmJ9svUT647v73B9HdxdjAF/V7l5RvWr2c81W/71wuh65vU/fOzm5hm1fcR21+N6k/1Cho7TO1p9a/Z17HMAcqvcPKNusmfLb2dVsH/wH+7IzTPqt9eiN+WGjSggN8jNM4p1FOuoP4O37/iD7M2Um/8U9C9oeVlet/585OwRK+jtIGtJ9BKrwcQGlt9YP2tj3Mb/+CMBTy590mo7pe2tP2fnZFtvb3jbKj++vBXwVoAV/nW4NefQnD/8+X3a5VPr0caPWhUKVTC6n/bgsu+T/SrpfrX6WQNrD7TmR83/3VtufLvrW9fn6z/W3/X3qddTreHzh1uh74daBd4uYLWf2t7am7T3d73vbHzHKvZBMdfj9MC8B1wD6o9qVKqR9X6n912vCPDz9jO6n8DfTW6eUcuPL7cOpRyyvu/9vVW3eF2rS+Uu1pvt3rS+2P6FazHxR9jHvn2fygWXsx5p9IjVoUKHWzPq5v15a/1bVskPS1pVP6/q+vv4i/HWPbPvsYLfCbYKv1vY6vlTTys2NfZ3m+ZPL3valRd5r4j17IpnrRwrx/qjPtr6kTWi/gjr/nr3WzVCa1hfd/vaCswbaH23+7s/3AX83eTmGRWQN+B3n5/902KrY1ZbD9R74A/fz9w8o0Lzhf7ufi6MWmhVLFTRalOuzR/uAv5ucvOM+u0m9G8/T3+ff16L/V1m1G+vRW/+ExIY8qd6gL+b3DyjWEexjvoz2JT+L3h+1fPWOxHvWIcfPWzVKVbHrf/HHgDT9k1zvbLu4MiD1lNNn7Lu++U+a13sut/9mIC9IXw7BPgE/G7T6Nj5Y9bPh3+2frnnl1tvEdJ3dl8r+UqytWTgEmvngzut+iXqWxHTIqzz18678lkHZ7k+/3Htx1k7RuywSgSVsL7c/uXvPs7a2LWuH0n57fAA8PeYUVsStli1i9a2iuUvduvv7qh4h3XpxiXX2/KYsBdBv51Rq2JWWUfPHbVWDFphLRyw0MrIynC9WjnIN8jacP8Ga9OwTa5XEnT+vvOt/+/DLR+6fuzsu57fWRvv3+iaXb8e/vV3H8fOpR+bs7vstxayF0435fHK4/qzff8B/O+so6btneb6htLdNe62TOWWGfWv7O7v931vDas3zPLycv//AzzZ7Z5R9isGQ94LsRp/09j1DW/71ZB/txkVfT7atdlUYXwFa+AvA11v/wbgf2NG3cQ6Cu7g52D+C95o+4bVsWJHt29v/0jCuI3jrJWDVlrNyjRz/Z39Smf7u1oTdk6w2oT98zsuFQtXvC3fJbY3WGYcmGG1L9/+dwfftF7TXN8lstmf67ZT26zk0cmWn88/X738QacPrLlH5rq+w/ZggwddP8Juf5fsgfr//E7Z2PZjrZUnVv7u1dL20LLfM8z+0QwAf68ZZb+/8m83pG03/2xnf4Z9IWYvSpYdW2Y93vjxW3+fL28+69se3976US57sWC/AsD+u5sLBvtHwuzvlNvfDOtUsZNrRr3Q8gWrT/U+rtx+hfOy48t+9/EK+hV0zaj/y9mrZ10/zlUs37/cz3zFXK9eAPC/s46atHuSdW/te10XQn9WbptR/8pep9k/6Ta07tA/fR8BT3M7Z5T9se1rMvuayf4JtJGLRlpp6WnWqCaj/jYzqkmpJtaUnlOsqiFVXW/1Zr+/dKvJrawDjxywgvyC/tT9BDwJ6yjWUf9L2JT+L2hYsuEfur39qmP7fZLt96H5LXvjt16Jerf+vGrwKssp+5P3W/nH5XdtrtifR9fKXa3Pu3x+K7d/jOLmhrTNfpsOe0Fk/xjEb13LvGYdP3/c9d+Hzx62Hm748O/yZqWbWWti19z6s/2LwPglFsB/199hRtk/JmXPqIzsDNcCxF7w/PaXetQuVvt37y1mzyj7ftg/0vZb9jfF7Bl1sdRF63Taadd7QP/2R0ftx+q3r0DqXb236x8Af+8ZtSV+i2vdMr33dOvvPKPsC0b7rZRKBpX8U/cT8ES3c0a90uaVW/9t/79XMq64frHyH92Uzs0zyp5JN9mv8rQ7y31SzvVTtzdf3ATg/8Y66v9hHZX7sSn9X5DPN9/v/mz/6Pa/vleN/eMFN9mbubZF9y6yShUo9bvb3a73TLa/OzR/wHzXgWofYP/6xvH2d6Z+y74PJfKXsNYOXftvXX/ml28A+PvNKPs9t+yfqPitM2lnbmV/RLvy7ayvun7lmk32jPrXX+Dxn2ZUg5INrB/6/PBvXaGB/+8bbKbsVw/Y75925so/79dN9p//6H0EPFVuWEfZvzfDfu97e278Gbl1Rv3WydSTrp9Ys9+KDcD/1oz67auK31z/puuVjjd/WvXvMqN+ey1ZpUgV14YTgP+NGcU6Cu5iU9oB9kFg/0bO39pzZs+tt6iwfxGWfbDb75V180cjbjf74K9UuJLbt7ffP9r+8Xt7WIQFh/3H21QPqW5FJkRag8MH3/q7rae2/iWfL4DcP6Psn4x4a8Nbrveev/kbmFecWGEV8Cvg+hh/hL0Q+aMzaubBma6Pa3+8/8T+xpo9o1qXa+36c2Z2puvti+z/94/MTntBtOrEKqtXtX/+QhH7u/v2nx9r/JjbPQBu3zrKvrCZdWiW9XbE23+6I7fOqN+avGey6+N1rdL1T/3/AG7/td6epD1WIf9Cf2hD+n9lRv12JtuvdBxUZ5BRD+CpWEf9E+uo3IlfdOgA+32/diTucL3Re/S5aGvMmjG/Gwr2e2ONbj7aemrZU9bUPVNdJ91dp3dZn0V+5vrzTfYvDfx82/97C43/xP4Osr04sTeI7bfOsP/b/ue3bwr/32D/Ei/7/Yfs34Jqv7+Z/YsKN8dvtl5a9ZLrvtueaPKE9d2e76zJuydbUeeiXI/Dv/5yM/tVlNU+r2adunTq//xY9n357f2yb2v/N989B3L3jLLfy8te9Az6dZDrR6zs9wZ7efXL1qONHv3DF1N/1MA6A12vYrZ/C/OGkxusmAsxrvcXG7VklJVwKeHWjHpn0zuu9waz3//Zfp9G+z3Cfsv+ZRj2jJI83fRp65td37gem8Mph61HFj7i+vHa++ve/1+9j8DflZPrKNvMAzNdFyr31bnPcoqTM+rmN8vsi6kh4UP+7dVHAHLnjFpwdIHr1Yd2t33d89X2r1zvA/vb91n9O8yo0ctHu3652s3ryd4ze1veebytAbUG/FfvI/B3xTqKdVRuxqPngDsq3WG90voV69kVz7re08b+zZyD6wx2vW/zTW+2e9P1Hay3N75tnbhwwvVjSvZ3bF5s9eKt29jDwf4lWpLh84db607+v9+QWm/CP98DKOaJmFuvYLZ/m6j9pu9/5Zux228mv/jexdZLq1+y7p93v5VyJcX1o+r2d6Fu/sKvfrX6WccvHLeeXfnPx+Gu6ndZjzR85HdvLm+/l5H9W1Tt9w76vyReTrx1v2wfbPnA9U+bcm3+49uHAMgdM8q+oLB/O/Ijix6xmk1q5vrRMvtE/ka7N27dxr4AKT++vLVmyBqrbVjbv+w+2r8QaP39663nVj5n9ZnVx7p847Lrx9Miykfc+m76M82fcb3X2JC5Q1w/5jas7jDXe4pdvH7xVs/FGxddM0piz7qUqynWq2tfdX2D0P7RtaUDl/7bL3kEkPvWUTffH9D+JTj/6e3H/g4zymb/uKn9iij7sQTwvzGj8nrntb7Y/oVr48h+D1T7VYQfdfrIGtFgxN9qRtkbSAN+HmCdu3bO9Zi1LNvS2vrA1t/9PiMA7mMdxToqN/PK+e27euNvz/6OUZXPq1iHRh6yKhepfLs/HQD4nTUxa1wLiROjTliFAgrd7k8HAH6HGQUgN2NGAcjNmFH4V7x9h4dZHL3YerD+g2xIA8i1M+rFli+ySAGQKzGjAORmzCgAuRkzCv+KV0oDAAAAAAAAABzDK6UBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pT2A/Q4tDy540Cr8bmHL63Uva0/Sntv9KQHALcwoALkZMwpAbsaMApCbMaMgYVP6NkpKS7IeX/y4VWF8BctvrJ9V5uMyVvcfu1urTqz6Sz/O0mNLrSl7plgL711onX7mtFWraC23/r9RS0ZZDSY2cH1udb+u69b/cz3zuvXooketIu8VsfKPy2/dNesu60zamd/dJu5inNV1Rlcr8K1Aq+j7Ra1/LP+HlZmd+afuG4D/HmYUMwrIzZhRzCggN2NGMaOA3IwZxYzKDXxu9yfgqWJTY60W37Wwgv2Drfc7vm/VLlbbysjKsJYdX2Y9uvhR68hjR/6yj3X8wnGrRFAJq3mZ5n/4/x1Wd5gVeSrS2ndmn1u3f2rpU9ai6EXW7L6zrYJ+Ba3Hljxm9ZnVx9o0bJMrz8rOcg2A4vmLW5sf2GydvnzaGjx3sJXXO681LmLcH/78APx3MKOYUUBuxoxiRgG5GTOKGQXkZswoZlSukYPbosv3XXJKfVgqJ+1G2r9lF65duPXfJ1NP5vT4sUdOvrfy5QSNC8rpO6tvTtLlpFv5mDVjcsK/Cs+ZtmdaTrmPy+UUeLtATr/Z/XIuXb/kyof8OiTHes269Y99mz/q5sfQpF5Lzcn7Rt6c2Qdn3/q7wymHXR93S/wW158XRy3OyfN6nt/dh6+2f+X6vG9k3vjDnxuA/w5mFDMKyM2YUcwoIDdjRjGjgNyMGcWMyi14+47b4Py1864fYXi00aNWPt98/5bb362yZedkWz1/6um6/bqh66wVg1ZYJy6csPrN6fdv33mae3Su68chFg5YaK07uc56Z+M7rmx85/HWG23fsEoXKO36UYntI7a7/j7skzDrtbWv/aX3a+fpnVZGdobVoUKHW39XLaSaVbZgWWtL/BbXn7ckbLFqF61tFctf7NZt7qh4h3XpxiXrYPLBv/TzAfDnMKOYUUBuxoxiRgG5GTOKGQXkZswoZlRuwtt33AbHzh+zcqwc1wEisd/LZ/+Z/VbMEzFWmYJlXH83rfc0q+aXNa3tp7ZbjUo1ujUspvScYgX5Bbn+PKjOIGtVzCrrLestq6B/Qdffe3t5u35E4aaKhStaIYEhf/l7Evl6+94aYjcVy1fMld28zW8HgCv///988zYAbi9mFDMKyM2YUcwoIDdjRjGjgNyMGcWMyk3YlL5Nv33UHYfPHnYd/DcHgK1GaA3XQWZnN4dAWHDYrQFgK5G/hJV8JVnsXjX4r33zegB/H8woALkZMwpAbsaMApCbMaOQm/D2HbdB5SKVLS/Lyzpy9q958/i8efL+7s9eXl6u71Y5zf7OV3pWupV6PfV3f3/myplb3xWz//2vv/305p9/+50zALcPM4oZBeRmzChmFJCbMaOYUUBuxoxiRuUmbErfBoUDClt3VLrD+mL7F9aV9Cv/lt88iKqHVLfiL8a7/rnpUMohV25/hyq3aVCigWsg2T/mcdPRs0etuItxVrMyzVx/bla6mbU/ef/vvnO24sQKq4BfgVx5nwBPxIxiRgG5GTOKGQXkZswoZhSQmzGjmFG5CZvSt8kXd35hZeVkWY2/bWz9fOhnK/pctHU45bD1aeSnVrNJ/zxg7Ddor12stjXwl4HWrtO7rG2ntlmDfx1stSnXxmpYsqHRx4+YFmF9vu1z9b2G9iTtcb23zrXMa67/tv+xv/tkO3XplFXt82quz8tmv1/QA/UesJ5e/rS1JmaNtTNxp3X/vPtdB37T0k1dt+lUsZPrYB/06yBrb9Jea9mxZdbLq192vcm+n4+f0X0C8NdhRjGjgNyMGcWMAnIzZhQzCsjNmFHMqNyC95S+TSoUqmDtenCX9daGt6xnlj9jnU47bYUGhloNSjawvur61a0fe5jXf571+JLHrdaTW1t5vPJYnSt1tj7r8pnxxz9+/rh19upZ8TbD5w93/ebUm+pNqOf6t/1G9/b7Btm/2fTouaPW1Yyrt27zceePrTzL8lh3zbrLupF1w/WbTL/s+uWt3DuPt+s3sj6y6BHXsLN/2+uQ8CHWG+3eML5PAP46zChmFJCbMaOYUUBuxoxiRgG5GTOKGZVbeOW4+y7nAAAAAAAAAAAY4u07AAAAAAAAAACOYVMaAAAAAAAAAOAYNqUBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGDalAQAAAAAAAACO8XH3hp9//rmYr1q1Su0oX768mHfr1k3tmDp1qph7e3urHenp6Ua5LSwsTMzXrFmjdgQFBYl5lSpV1I4bN26IeatWrdSOlStXinnZsmXVjt27d4t58+bN1Y5GjRqJee3atdUO7XmYkJCgdpQqVUrM69evr3bcd999Yt69e3e1Izk52ejztL3++uuWpxg5cqTxce3r6yvm2dnZasfevXvFvFOnTmpHaGiomA8ZMkTt+PLLL8W8ffv2ase8efPE/MiRI2pHRkaGmH/33Xdqh3abZs2aqR39+vUT8zfeeEPtiI6OFvO+ffsaz1ttltpKlCgh5klJSWpHsWLFxHzt2rVqh3bOrlu3rtpx9913W55iwIABYt6kSRO1w8vLS8y3b9+udmjrpF69ehmvPSZMmKB2PPnkk2I+atQotaNr165ifu+996odmZmZxo+ptp67fPmy8aw8ffq02vH4448bPea2wYMHi3lISIjaoZ0b0tLS1I4DBw6IebVq1dQOPz8/o6+97YUXXrA8xejRo40fL22d/dhjj6kd2jGnHW/ufO3z58+vdsycOVPMIyIi1A5tve/O9dE999wj5qVLl1Y7Tp48KeYDBw5UO7R1UqVKldSOdu3aifnGjRvVDu3+unO9uHXrVqNrUts777xjfJ2mrSvHjh1r/Jj+nfz6669ivn79erVj//79Yt6yZUu1Q9vnqVq1qvExOWjQILXjlVdeEfOhQ4caPx7urOXz5csn5rt27VI7OnbsKOYBAQFqx7fffmu0hrb1799fzH189O1T7XlYp04dtWPSpElG14K2ypUrG607bQ0bNhTzqKgo41lp45XSAAAAAAAAAADHsCkNAAAAAAAAAHAMm9IAAAAAAAAAAMewKQ0AAAAAAAAAcAyb0gAAAAAAAAAAx7ApDQAAAAAAAABwDJvSAAAAAAAAAADH+Lh7w4SEBDFPTU1VO/LmzSvmy5YtUzvq168v5p9//rna0bdvXzGvVq2a2hEaGirmwcHBakdycrKYFytWTO1YsWKFmJ85c0bt6Ny5s5gXLVpU7fD39xfzI0eOqB0XLlwQ88jISLVjz549Yl6kSBG1o3LlykaPl61NmzZifu3aNbUjMTHR+HnqSZKSksT8oYceUjveffddMW/fvr3aERgYKOZlypRRO9atWyfmU6dOVTuaNWsm5rNmzVI7MjMzxdzHRz+F3HPPPWL+1ltvqR3btm0T8xs3bqgdL7zwgpinpKSoHe7MIM0777wj5j169FA7tOdQQECA2lGyZEnjjkOHDhk/P+6++27LUxQsWND4+aWdW7R1lq1169Zivm/fPrWjQoUKYt6xY0e1Q7u/M2fONF6LRUdHqx0zZswQ87NnzxqfO9auXat2lCtXzrhj586dYt6/f3+1Y968eWI+fPhwtWP16tViPmTIEOPzj/a1d2cGXbp0Se3wJGXLljVev2hzzp3rgbp164r5Dz/8oHbUqVNHzL/77ju148knnxTz06dPqx3jxo0T82effVbt2LVrl5i3bdtW7ahdu7aYHz582Ph8ffLkSbXjgw8+EPMOHTqoHdqaT1szunPsu3PuGD16tJifP39e7XjggQeMn+vt2rWzPIV2fRQeHq52aHsjx48fN96PcmcPZ82aNWL+yy+/qB2NGjUy3gdq1aqVmPv5+RnPKHfOHVqHdt1i8/X1FfOgoCDj51jLli3VjgIFCoj5G2+8oXaMHz9ezCdPnqx2aI+Ztldg+/jjj43X++7gldIAAAAAAAAAAMewKQ0AAAAAAAAAcAyb0gAAAAAAAAAAx7ApDQAAAAAAAABwDJvSAAAAAAAAAADHsCkNAAAAAAAAAHAMm9IAAAAAAAAAAMewKQ0AAAAAAAAAcIyPuzesUKGCmJ85c0btyMjIEHNvb2+1Y+7cuWJetGhRtSMqKkrMS5QooXacOHFCzOPi4tSO1NRUMV+0aJHasXHjRjF/+OGH1Y4CBQqI+Ycffqh2pKWliXmrVq3Ujj59+oj5jh071I4iRYqIeZ48+vdhli1bJuYPPfSQ8fND+zxtw4YNE/PixYurHZ6kbdu2Yn7x4kW1w9fX12j+2AICAsS8cePGakeNGjXEvEmTJmrHzJkzLVPa8zQnJ0ft2L17t/Hz+NVXXxXzyZMnqx1lypQRc39/f7UjLCzMaA7a7rzzTqPc5ufnJ+YpKSlqx/Tp08W8cuXKxsfcr7/+qnZ4kitXrhidA21HjhwR80KFChmvgbQ1gTvn9L59+6odly9fNp6V33//vZg/8cQTakfFihXFvHTp0mrHypUrxXzChAlqx4svvijmdevWVTtiY2PFPCYmRu0oX7688eOh+eGHH4zPg1evXlU74uPjjT6Gp9GeP0uWLFE7OnfubHSs2Bo1aiTmNWvWVDuqVasm5s2bN1c7tFn4008/qR3dunUzvh7Q1lra9aQtNDRUzIODg9UObX64c52mnTsef/xxtePnn382nlHR0dHG1/E+Pj5G1+i25ORk4zWhJ9Gu491Zh7dp00bM33vvPbWjdu3aYl6rVi21Y+TIkUZrfVvJkiWNZ7a2ttDWWe7MsYMHDxrPymbNmqkdhw8fFvPq1asbrwnd2fM8duyY8ZzbtGmTmHt5eakdFy5cEPNr166pHdoxdfr0aeuvwCulAQAAAAAAAACOYVMaAAAAAAAAAOAYNqUBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGDalAQAAAAAAAACO8XH3hhs2bBDzY8eOqR2vv/66mPv7+6sdtWrVEvOFCxeqHQ0bNhTzKlWqqB27d+8W8507d6ods2bNEvO5c+eqHVOmTBHzK1euqB0HDx4U88GDB6sd27dvF/OyZcuqHb6+vsZfl6ysLDGPi4tTO7y9vcW8QIECakeRIkXEfNKkSWpHcHCwmN91111qR8uWLS1PcejQITG/dOmS8ePlztc+NjZWzI8cOaJ2nDt3Tsy3bNmidgwZMkTMz58/r3bcuHFDzMuXL2/ckZOTo3YkJycbfx7Lli0T84SEBLWjevXqxnOuWrVqYv7999+rHcWKFRPzd999V+3w8/MT88TERLVDO7+487X1JIULFxbzV155Re14/vnnjWaHrVmzZmKempqqdqSlpRnNH9vSpUvFvEGDBmrHvHnzxHzmzJlqR9u2bcU8PT3deB313HPPGT8eTZo0UTvWrVsn5i1atDB+nv7www9qR48ePYzPP9r8cGe+tG7dWswvX76sdniS9u3bi3mdOnXUjqioKDGvW7eu2qGd05cvX652ZGdnG89K7dpWuya1xcfHG10v2ObPny/mo0aNMp4vffr0UTs2btwo5oMGDTJei/Xv31/tKFSokJi3adNG7di8ebOY58mjv24vIyPD+Bpcuy7V1tCe5tq1a2J+/fp1tePAgQNiPmDAALVjxYoVYr5+/Xq1o0yZMmJeunRptWP16tXG5+tSpUoZP4+1r4v2MdyZ2dp9dWcfSNuvsjVu3FjMT548qXYEBgYa5e7MlxMnTliaxx9/3GjNaOvdu7fx9bM7eKU0AAAAAAAAAMAxbEoDAAAAAAAAABzDpjQAAAAAAAAAwDFsSgMAAAAAAAAAHMOmNAAAAAAAAADAMWxKAwAAAAAAAAAcw6Y0AAAAAAAAAMAxbEoDAAAAAAAAABzj4+4Nr127JuZHjx5VO06fPi3mYWFhase8efPE3N/fX+3Yv3+/mBcsWFDtCAkJEfMGDRqoHcuXLxfz2NhYtaNYsWJiPnjwYLWjSJEiYv7rr7+qHU2bNhXzEydOqB07duwQ82PHjqkdJUuWFPPAwEC1IysrS8zPnDmjdmjPoRdeeEHtSE1NFfN9+/apHZ7k1KlTYl61alW1Y/fu3WIeHR2tdjRr1sx4zmnPseHDh6sd2vOjWrVqakdcXJyYV6lSRe24fv268blDm9m+vr5qx4gRI8R8+vTpakfLli2Nn2PaHOvWrZtlavbs2cYdpUuXVm9Tq1Yt43OYJ6lfv76Ye3t7qx179uwxOmZtP/zwg5jXq1dP7ZgxY4aYN2rUSO3o1KmTmO/du9d4DfTFF1+oHYcOHRLzsmXLGs8Gd47JihUrGq3VbOPHjxfzxYsXqx1t2rQR8wULFqgdV69eFfPs7Gy1IzMz0/jrkpKSIuYxMTFqhyfRztfJyclqR69evYyvKc6fPy/mw4YNUzu0tYW2xrYVLVpUzPPmzat2+PjIl9q//PKL2tGwYUMxX7lypdpRrlw5MU9MTDReV27evFnteOihh8R81apVakfx4sXFPCoqyvg6zZ3rZ+3x0K4nbR06dBDznJwctcOTnDt3Tsz9/PzUjnbt2on5zz//rHZo+zza8WYrUaKE8dc+ISFBzENDQ9UO7TGrXr262hEfHy/mjRs3VjsuXbpkdH6ypaeni3nnzp3VDm2Oadektv79+xt97d1ZAw8ZMsTSfP3112K+fft2tUP7OPfcc4/1V+CV0gAAAAAAAAAAx7ApDQAAAAAAAABwDJvSAAAAAAAAAADHsCkNAAAAAAAAAHAMm9IAAAAAAAAAAMewKQ0AAAAAAAAAcAyb0gAAAAAAAAAAx3jl5OTkuHPD/v37i3nz5s3Vjvj4eDFPSkpSO+644w4xL1OmjNrx9ttvi/mLL76odnzxxRdi7uXlpXYMGTJEzBcvXqx27Nq1S8zLlSundly5ckXMixQponZoHycwMFDtOHbsmJiXL19e7fD19RXzgIAAtWP79u1ini9fPrXj8uXLYl6pUiW1Izs7W8zPnz+vdnzyySeWp/jll1/E/PPPP1c77r77bjHfuHGj2tGlSxcxL1SokNoxceJEMa9Tp47asWrVKjFv1qyZ2nH16lXj41p7HhcoUEDtuHjxophXrlzZMuXOrDx+/LiY58+f3/gxdeeUvGfPHjGPiIhQO7T50aNHD7Vj4cKFYj5p0iS1Y/fu3Zan+PTTT8Xcz89P7di/f7/xc1C7zYgRI9QOwIQ7x/2NGzfE/Ouvv1Y72rdvL+YrV65UO5YtW2Z5ilGjRol5nz591I758+cbXcfZkpOTxbxdu3ZqB2BiwoQJ6m1atWol5gcOHFA7zp49K+YlS5ZUOx577DHLU4wbN8547frzzz8bX1Pce++9Rms1wNRF5drY9tFHH4l5/fr11Y4GDRqIub+/v9rhzjmbV0oDAAAAAAAAABzDpjQAAAAAAAAAwDFsSgMAAAAAAAAAHMOmNAAAAAAAAADAMWxKAwAAAAAAAAAcw6Y0AAAAAAAAAMAxbEoDAAAAAAAAABzDpjQAAAAAAAAAwDE+7t7w6NGjYl6+fHm1I3/+/GKeN29etWPz5s1iXqxYMbVD+zhLly5VO8qWLSvmR44cUTsmTpwo5hEREWpHTEyMmD/77LNqx+HDh8V8+vTpaof2uB8/flztiI6OFvMiRYqoHR06dBDz2NhYtSMoKEjMO3bsqHZMmzZNzENCQtSOzMxMMU9NTVU7PMmSJUvEvFKlSmrHgQMHjL/2hQoVEvOpU6eqHa+++qqY79mzR+1o2rSpmK9YsULt6NWrl/Hcb9OmjdHx9le59957jeaxO1+XQYMGqR3x8fFivmPHDrWjdu3aYn7o0CG1Y8OGDUbnWtvVq1eNvvaeJi4uzmj+uLMu6NKli9rxxRdfiPmiRYvUjrlz54p5YGCg2nH//feL+bvvvmu8rmzRooXaoc3TqlWrqh2dOnUy/tru3LlTzMuUKaN2VKlSRcy/+uortaN///5inpSUpHY8/PDDxh3Hjh0T85ycHLXDy8tLzAcOHKh2eJKwsDAx379/v/H10datW9WOypUri/mpU6fUjlKlShk9v2xr164V84CAALWjaNGixudr7ZohX758akdkZKTRusL2wQcfGM0O29mzZ42u42wFChQwfp727t3b+Dn2008/ifk999yjdixevNj4XOpJsrOzxfz7779XO0JDQ42Oe3e+bpcvX1Y7BgwYIOanT59WOy5evGj0eLkzK729vdUO7ZohLS3NeB2VlZWldsybN0/Mu3XrpnbMmTNHzO+88061IyUlxfgxHaA8PxYuXGh87nDn/OPr62t0PLmLV0oDAAAAAAAAABzDpjQAAAAAAAAAwDFsSgMAAAAAAAAAHMOmNAAAAAAAAADAMWxKAwAAAAAAAAAcw6Y0AAAAAAAAAMAxbEoDAAAAAAAAABzj4+4Nr169Kub16tVTO7Zu3SrmO3fuVDsiIiLEPH/+/GpH3759xXz16tVqR5UqVcQ8MTFR7ShdurSYx8XFqR3t2rUT87ffflvtKFOmjJh36tRJ7Thy5IiYlytXTu1ISEgQ8yeffFLt+PHHH8X83Llzxs+PSZMmqR0NGjQQ8+zsbLUjJCTE6HjyNMnJyWLeuXNntSMoKEjMP//8c7VDO15q1qypdsyZM0fMO3ToYPx4dO/eXe14/PHHxfzQoUNqx8SJE8V8/vz5akevXr3EvHfv3sZzLjY2Vu1YtmyZmB84cMD4uC5cuLDaoc3TmJgYtaNChQpifuLECbXj9OnTYt6nTx+1w5O0adPG6Lxhq1atmpgvXbpU7cjKyjI+rh999FExP3nypNrxwQcfiHmTJk3UDm9vbzFv0aKF2qGtPbXHy53jNi0tTe2oXr26mB88eFDtaN26tfGs1I7rRx55RO3Q1lraes9WqFAhMR8+fLjaER0dLea7d+9WOwYPHmx5isDAQDFfs2aN2lG2bFkxr1GjhvG6Qbt+sr3zzjtivmPHDrWjRIkSYn7p0iW1IzMzU8xTU1PVDm1tMWzYMLUjPDxczJs3b258/nFnZmvX2O6ss8ePHy/mGzduVDu0GeTONbj2XN+8ebPa4efnJ+YXLlxQOzyJtrbQ1ti2U6dOGT3PbZGRkWKeL18+teOXX34xmj+22rVrG91Xd2bQ888/r3ZMmzZNzF955RW1o2LFikZrE3eOJ3c6tK+/dp5059wwcuRItWOYMtdbtWqldtSvX1/Mz5w5o3b88MMPRs9jd/FKaQAAAAAAAACAY9iUBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JQGAAAAAAAAADiGTWkAAAAAAAAAgGPYlAYAAAAAAAAAOIZNaQAAAAAAAACAY9iUBgAAAAAAAAA4xisnJyfHnRuWKVNGzB944AG1w9/fX8yTk5PVjpCQEOOOli1bivmsWbPUjrNnz4p5tWrV1I527dqJeWhoqNpx4MABMU9NTVU72rRpI+ZXr15VOzZv3izmjRo1UjsSEhLEfO/evWpHq1atjB4v26FDh8Q8ODhY7ejWrZuYnzt3Tu0oWrSomE+bNk3t+PXXXy1PMXToUDFPT09XOxo0aCDmXl5eakeePPL3+sqVK6d2ZGVlifm3336rdmjH/vz589UO7eOcOXNG7QgPDxfztLQ0tSMuLs547jdu3FjMf/nlF7XjwQcfFPP27durHS1atBDzgQMHqh0FChQQ88KFC6sdJUuWNDpf23bu3Gk858aOHWt5iu7du4v56NGj1Y4tW7aIeVRUlNpx+fJlMb927ZraoT3HtHWFLSUlRcxPnz6tdly4cMHouHfn8di/f7/a0bp1azE/f/682hETEyPm+fLlM56V2jy2Pfzww2L+7LPPqh0dO3Y0nrcDBgwwfn5oM6pnz55qR58+fSxPsWrVKjE/deqU2qF9Xdw5P2lri1KlShk/BxctWmS8Jly6dKnaoZ0Htc/TtnjxYjFv2rSp2jF79mzja86HHnpIzN9880214/nnnxfzKVOmqB3aXF+9erXxNfiJEyfUjhs3boj58ePH1Y7ExEQxf/zxx9WOwYMHW55i/PjxRtdgtosXL4r5zJkzjdfy7hxPvr6+xuvwjIwMMb906ZLakTdvXuO9k4CAAON1pfaYaXuR7hxz7qyBtLWY9pjbRo4cKeaxsbFqx6ZNm4wec3fmmLYOt915551Ge6K21157Tb0Nr5QGAAAAAAAAADiGTWkAAAAAAAAAgGPYlAYAAAAAAAAAOIZNaQAAAAAAAACAY9iUBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JQGAAAAAAAAADjGx90bvv/++2KekZGhdrz44oti3r17d7Vj165dYh4YGKh2nDp1Ssx37typdkRERIh52bJl1Y4jR46IuZeXl9px6dIlMa9Ro4ba8fLLL4t5r1691I5p06aJeXR0tNqhPYeKFy+udkRFRYl5XFyc2tGzZ08x3717t9px9uxZMZ80aZLaUbp0aTGvXr262uFJcnJyxDxPHv17cFeuXBHzH3/8Ue146aWXxHzixIlqR7t27cS8ZcuWaseDDz4o5h9++KHaER8fL+b9+vVTOyIjI41mmK1x48ZGs9S2efNmMT937pza4efnJ+YjR45UOzZt2iTmH330kdoRExNjdCzYBg0aZPR52hYuXCjmTZo0UTs8yWeffSbmx44dMz5fa8e9LTY2Vszr1q2rdqSkpIj5smXL1A5tjnXu3FntmDp1qpiXKFFC7fD19TU6L9j8/f3FvEiRImrHhQsXxDw8PFztCAkJEfMRI0aoHe+++66YV6xYUe0IDg4W88cee0zt8Pb2FvNff/1V7bj//vvFPCAgQO3wJHPnzjVed1arVk3ML1++rHZox+28efPUjo4dO4p5Wlqa2vHTTz+JeeHChS1TiYmJ6m127Nhh/Hlo18fuHNfaeu769etqR3JystExa1uxYoWY16tXT+3Q1kkVKlRQO7TrQe05aFu7dq2Yb9iwQe0YPHiw5Sm0tUflypXVDu3Yd2ffQ1vfaudRW1hYmJjPnDlT7dDWSfXr11c7tPPgtWvX1I5atWoZXU/aWrRoYbQmcOfc4c6M0vaK3nzzTbVj3LhxYt6qVSvj52lqaqra0a1bNzE/cOCA2pGdnW20/nUXr5QGAAAAAAAAADiGTWkAAAAAAAAAgGPYlAYAAAAAAAAAOIZNaQAAAAAAAACAY9iUBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JQGAAAAAAAAADiGTWkAAAAAAAAAgGO8cnJycty54ZQpU8R8+fLlaketWrXEPC0tTe1ITEwUcx8fH7UjOztbzP38/NSOc+fOiXlISIjaMXLkSDF/77331I6iRYuK+ddff6121KxZU8x9fX3VjlGjRon5vn371I6AgAAxv3jxotoRExMj5pmZmWrH9evXjR+Pzp07G30Md57L6enpasfTTz9teYqxY8eKeVRUlNoRHBws5iVKlFA7tmzZIuZVq1ZVO0qXLi3mAwYMUDtee+01Ma9Ro4basXr1ajGvU6eO2tG7d28xz58/v9qxYsUKMd+5c6fakS9fPjFv0aKF2rFgwQIx9/b2Vjtq164t5vfdd5/aMXXqVDGfM2eO2qE9Zvfcc4/aERERIeZt27ZVOypWrGh5iq1bt4r5+fPn1Y4rV66I+YYNG9SO1q1bi/m6devUji5duoh5hQoV1I6NGzeKeVJSkvG60p2ZffDgQTGfP3++2lG/fn0xr1u3rtqxcOFCMU9NTVU7PvvsMzH/8ccf1Y6SJUsaPQdtp06dEvMmTZqoHUePHhXzPHn019Vo1ybt2rVTO5544gnLU4wePdr4eNIuK+Pj49WOnj17Gndo5yftutZWqlQp45mtXTNkZWWpHR06dBDzhIQEtePQoUPG18+7du0yeszd+TzcWSNr5wZ3vi7a2tOd6/igoCAxP3z4sPF6PzQ0VO3o27ev5Sk+/fRT43VDnz59xHzlypVqR2RkpJi3bNlS7cibN6+Y16tXT+14+OGHjc9f2tq0TJkyxtfPJ0+eVDu0Y86ddYO2nps0aZLaMWPGDDGfPn262pGSkmK8Ri6tzIbLly+rHdHR0UbnFnceU3eufZ977jn1NrxSGgAAAAAAAADgGDalAQAAAAAAAACOYVMaAAAAAAAAAOAYNqUBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGB93b5iQkCDmmZmZasfs2bPFvGzZsmpHnz59xHzy5MlqR2BgoJjfeeedasfUqVPF3N/fX+2YO3eumDdu3Fjt0O7vww8/rHZERkaKedWqVdWOiRMninmnTp3UDj8/PzH39fVVO0JCQsQ8KipK7ShXrpyY+/joh83OnTuNO27cuCHmRYsWVTs8SfPmzcX81KlTxh+jbdu2xsdTy5Yt1Y7XXntNzAsUKKB2LF68WMwPHjyodhQsWNBoltoWLFgg5unp6WpHaGiomGdlZakdrVq1EvMxY8aoHdocu3DhgtpRu3ZtMX/mmWfUjrNnzxqdJ23nzp0T80uXLqkdMTExYl6lShW1o2LFipaneOedd8T89ddfVzv27Nkj5i1atFA7tGNOOwe683kcPXrU+DmorTttQUFBYr5582a14/r160afpy0gIEDMt27dqnb06NFDzBs1aqR2vP3228bnn/Hjx4t5RESE2rFu3Tqj84KtS5cuRus928CBA8V8yZIlaocn0Z5jq1atUjt69+4t5keOHFE7tK/LgQMH1A7tebpt2za1Q7v+adq0qfGxkJqaqnZoc3316tVqh5eXl5j/9NNPaseWLVvE/NtvvzX+PLS1q61SpUpiXrduXbVDu4a6evWq8XV89+7djfdG3HmOeRLteHHnOm3+/PlifujQIeO1vDvrhpSUFDEvVqyY2jFgwAAx37dvn9pRp04dMY+Li1M7cnJyxLxNmzbGx4J2LWhLS0sT8xkzZqgdP/74o/H6Zdy4cWL+zTffqB2FCxcWc29vb+NzenZ2tvGsjI6Otv4KvFIaAAAAAAAAAOAYNqUBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGDalAQAAAAAAAACOYVMaAAAAAAAAAOAYNqUBAAAAAAAAAI7xcfeGlSpVEvMiRYqoHQMGDBDzcePGqR2bNm0S87CwMLXj/PnzYh4VFaV2VK9eXcwDAgLUjgoVKoj59OnT1Y7r16+LecuWLdWOxx57TMxnzJihdgQGBor5mTNn1I6MjAwxr1y5stqxf/9+o6+bbf369WJet25dtaNq1apGua1Hjx5iPnfuXLXDk2RmZhp/3bQ5NmzYMLWjZ8+eYn758mW1Q3uezpw5U+3o1auXmBcuXFjtyJNH/r5lYmKi2lG6dGnj43rFihViHh8fbzwbxowZo3acPXtWzI8ePap2LFmyRMzbtWundhQvXlzMvby81I5Ro0aJeXp6uvFzrHfv3sZfl7+TsmXLivmCBQvUjho1aoj5vHnz1I62bduKedGiRdWO3bt3i/ldd92ldqSmpor52rVr1Q7tuR4dHa12PPTQQ2JesmRJtWPNmjVi/vjjj6sd9evXF/PIyEi1o1ixYmL+5Zdfqh3PPPOMmF+8eFHtqFixovHMrlmzppjfuHFD7di+fbvxtYsn0ebLnj17jB9zf39/tUNbI9epU8cy9cgjj6i3iYmJMbomtUVERIh5aGio2qHd5sKFC8Zr06CgILVDuz4uWLCg2lGuXDnj88+5c+fE/O2331Y7OnbsKOalSpVSOxo2bGh87duoUSPjx8OTaNclv/76q/E+kLZWsyUkJBhf22h7J8nJyWqHtu+lfZ7uXHd88cUXakdcXJzR9ZPt0KFDYj5y5Ei1Q5tBn376qdrh4yNvj3788cdqx7fffivmTzzxhPHjkZOTY3wNvm3bNrXj1KlTYh4cHGz9FXilNAAAAAAAAADAMWxKAwAAAAAAAAAcw6Y0AAAAAAAAAMAxbEoDAAAAAAAAABzDpjQAAAAAAAAAwDFsSgMAAAAAAAAAHMOmNAAAAAAAAADAMT7u3vCnn34S89GjR6sdP//8s5hXr15d7bh06ZKYv/jii9bfxbvvvuvIx9Ee027duln/K1q3bm3c0aFDBzE/cOCA2hEZGSnm0dHRakeDBg3EfPv27WpHz549LU+xevVqMS9RooTa8eabb4p5r1691A4/Pz8x//7779WOadOmifk777xj/LWPiYmxTF29elW9zYYNG8S8TZs2aseKFSvEvGTJkmpHVFSUmPv6+hp/bVNTU9UO7f6uX7/e+Nzw3nvvqR3BwcFiXrduXbXjgQceEPP09HS1w5M0bdrUeKZXrFhRzIcOHap2TJw4UcwPHz6sdnz11VdinpWVpXacPXtWzGvUqKF2zJo1S8zbtWundkyZMsV4DXT9+nUxr1+/vvGcc+cxXbx4sZjff//9xnPu2LFjaketWrWM5+26devEPDY2Vu3o0qWL8X3xJNraw521qzbn3DkvlC5dWswDAgIsUzt37jRe47gzo3788UcxDw8PVzu09WtKSorxuqFatWpqR2JiotH5yTZv3jwxb9asmfHz1J3z4OXLl8X82rVraseiRYuMr8EqVKhg9Hl6Gm2dXahQIbVDmx+NGjUyvraZMGGC2vH0008b7zckJSWJeZ8+fYyvn905rhs2bCjmK1euVDuGDBki5gULFjSeL+fOnVM7tHPUiRMn1A5tvebOXkDVqlXFfNeuXWrH0aNHja85tfPga6+9Zv0VeKU0AAAAAAAAAMAxbEoDAAAAAAAAABzDpjQAAAAAAAAAwDFsSgMAAAAAAAAAHMOmNAAAAAAAAADAMWxKAwAAAAAAAAAcw6Y0AAAAAAAAAMAxbEoDAAAAAAAAABzj4+4NO3fuLOaLFy9WO+rXry/mCQkJasfmzZvV2wD/Tfny5VNvs2vXLjG///771Y5y5cqJ+eHDh9UOT+LjI4+zIkWKqB1TpkwR861bt6odRYsWFfNVq1ZZptyZlQEBAWJetmxZtWP+/Plivm7dOrWjd+/eYr5v3z6145lnnhHzli1bqh2zZ88W8xIlSqgdv/76q9F50tasWTMxT01NtUxt2bJFvU1mZqaYDxs2TO2oWrWqmE+fPl3t8CTac/2RRx5RO8aOHSvmFStWVDtatGgh5u+//77a8Vd8bePj442P69q1a4t5Tk6O2tG8eXMxDwwMVDt+/PFHMV+4cKHaUbhwYTGPi4tTOwYNGmS8btC+LhEREWrH/v37ja4HbP369RPzqVOnqh316tUT8zNnzqgdnsTb29v4WLh27ZqYt27dWu3Ys2ePmN93332WqaFDh1pOcGddYKpDhw7W/wrtuHbHgAEDrP+2GTNmqLepUqWKmHfr1k3tmDZtmphv2rRJ7fgrjof/FYmJiWKekZGhdlSuXNnoY7iz1po4caLaERMTY3QutnXv3l3Mjx49qnYsWLBAzGvUqKF29O3b12it5s4cGz9+vNrRtm1bMT906JDa4e/vL+afffaZ2hEeHi7mQUFBxuvsim6s92/cuCHmo0ePVjuOHz8u5snJydZfgVdKAwAAAAAAAAAcw6Y0AAAAAAAAAMAxbEoDAAAAAAAAABzDpjQAAAAAAAAAwDFsSgMAAAAAAAAAHMOmNAAAAAAAAADAMWxKAwAAAAAAAAAc4+PuDadPny7mTz/9tNqxfPlyMW/SpInakTdvXjEfO3as2uHn5yfmqampaoeXl5eYjxo1Su2YNWuWmIeFhakds2fPFvPWrVurHVeuXBHzy5cvqx01atQQ8ylTpqgd/fv3F/MFCxaoHXXq1BHzjh07qh2hoaFiHh8fr3YMGDBAzC9evKh2zJgxQ8xfe+01tcOTNGzY0OjxtK1YsULMe/TooXZMnTpVzMeNG2c5YePGjcaz4fXXXxfzd955R+2IiYkR80GDBhnPOW0euzNP9+zZo3Z89NFHYj537ly14/777xfzRYsWqR2ZmZlifv36dbWjcuXKYv7CCy8Y35fJkyerHWPGjLE8hXZuOXbsmNqRmJgo5iEhIWpHkSJFxHzixInGx5M764b8+fOL+bfffmuZ6tq1q/GM6tevn9rxySefGK89Dh06JOZVq1ZVO+Li4sS8ZMmSaoc2PxISEtSOkydPinmtWrXUjp9//tn4vkRFRYl5mzZt1A5PEhkZKeZPPvmk8XNw9erVake9evXEvHfv3mrHww8/LObh4eFqh7beT05OVjsCAgKMrzm1Y//AgQNqx44dO4zOT7YbN24YXV+7Mz+084KtfPnyYp6UlKR2vPLKK8YzKj09Xcy/+OILtaNbt25ifu7cObXDk/j7+4t5o0aN1I58+fIZHSu2YsWKifmSJUvUDm0GuXN+io2NFfPDhw8b35dWrVqpHefPnxfzXr16qR3aXqI7x5P2tdOeP+7MoAYNGhjP7OjoaLXj0UcfFfMjR44Y74tp15PunNPT0tKsvwKvlAYAAAAAAAAAOIZNaQAAAAAAAACAY9iUBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JQGAAAAAAAAADiGTWkAAAAAAAAAgGPYlAYAAAAAAAAAOIZNaQAAAAAAAACAY3zcvWHFihXFfO/evWrHvn37xLx69epqR7du3cQ8PT1d7cjMzBTzuLg4taN8+fJivn37drUjOTlZzHv37q12FC1aVMz9/f3Vjm3btol5gQIF1I6EhAQx79mzp9pRs2ZNMV+4cKHaUaFCBTHfsmWL2nHnnXeK+datW9UO7f7u379f7ejcubOYL168WO24++67LU9x4cIFo2PF5uXlJeaBgYFqR05OjphfvHhR7Vi+fLmY9+nTR+3w8/MT82+++UbtGDp0qNFj7s5jun79erVDe9yXLFmiduTNm1fM77jjDrVj0aJFYj537ly1o169esbnsKVLl4r5+++/r3bExsYan8O++uorMW/SpIna4UlKly4t5pcuXVI7zpw5I+YfffSR2jFy5EgxHzJkiNpx9OhRMT937pzaUahQITFv0aKF2pGammr0PLe9/PLLYn758mXjObd582a1o1atWmI+ZcoUtUM75oKDg9WOK1euiLm3t7faUaVKFTEvVqyY2lGiRAnjc/rq1avFfNeuXWpHq1atLE+hHfvunPNPnTol5k2bNjX+uj366KNqR1RUlJjnyaO/Lqtq1arGa3kfH/lSO1++fGqHtt7XjhXbp59+KuZPPPGE2lG8eHEx79ixo/FegDvXnNr1c8uWLS1Tx44dMz7/uHMOu3r1qphHRESoHZ5EO0+GhoaqHevWrTO6fnLnWu7jjz9WOw4cOGA8X7Kzs8X8qaeeUju0fa8HHnhA7di9e7fx2nTgwIFG+1W2mTNninlQUJDaoT2HQkJCjI/rZs2aqR0rVqwQ86ysLLUjKSnJ+Jyu7Wm5s2/qDl4pDQAAAAAAAABwDJvSAAAAAAAAAADHsCkNAAAAAAAAAHAMm9IAAAAAAAAAAMewKQ0AAAAAAAAAcAyb0gAAAAAAAAAAx7ApDQAAAAAAAABwjI+7NyxdurSYBwYGqh3ly5cX83379qkdBw8eFPNSpUqpHSkpKWIeEhKidkRGRop5lSpV1I769euL+alTp9SO9evXi7mPj/4lzpcvn/F90R6PvHnzqh2pqali3q9fP7Vj1apVYt6xY0e1Y9euXUbHgjvP5YSEBLUjICDA6HnsabTnT7Vq1dSO+Ph4MX/11VfVjoEDBxp/3Xbv3i3mhQsXVjvCw8PFvGTJkmrH5MmTxbxYsWJqx4EDB8Q8KSlJ7bjzzjuNH4/z58+LeXJysvHMduc51qRJEzGfPXu22lGwYEExnzFjhtrRqVMnMS9UqJDaUbZsWTHfvHmz2vH0009bnkJbJ2nHiu3uu+8W88WLF6sd2vl4+vTpakeLFi3E/Pr162pHenq6mI8ePVrt6Nq1q5jPnz9f7fjss8/EfPv27WpHhw4dxDwnJ0ftSEtLE/Px48erHRs2bDDuGD58uJivXbtW7ShRooSYb9q0ybhDW+/Zjhw5Yrye8yTnzp0zPl9r546goCC1o2jRomK+bds2tSN//vxifuXKFbVDO/Z79eqldkRFRRnPhszMTDHfu3ev2qHNwrCwMLXD19dXzC9cuGCZ0r727szK7777Tu1o166dmCcmJqodoaGhYp6RkaF2TJo0yXhd2bt3b8tTzJw5U8xHjBihdtSuXVvM4+Li1I7g4GAx79Onj9qhPT+mTp2qdowbN07M9+zZo3Zox752TerOXpE714vadZo2023Vq1c3vgbXzlHuHNd169YV85MnT6od+ZX7684en/aYanuz7qzXmjZtav0VeKU0AAAAAAAAAMAxbEoDAAAAAAAAABzDpjQAAAAAAAAAwDFsSgMAAAAAAAAAHMOmNAAAAAAAAADAMWxKAwAAAAAAAAAcw6Y0AAAAAAAAAMAxbEoDAAAAAAAAABzj4+4Nr1y5IuYNGzZUO3x9fcV8xYoVasfQoUPFfMOGDWrHvffeK+bHjx9XOypVqiTm9evXVzvWrl0r5gUKFFA7Hn74YTHfsmWL2qF9rtOmTVM7KlasKOaXLl1SOzZv3izmTZo0UTtat24t5hcvXlQ79uzZI+ZDhgxRO7799lsxv/vuu9UO7TE7f/682uFJtOM2ODhY7dCeH9rssGVkZIj52bNn1Y6yZcuK+eDBg9WOGTNmGM+oWbNmGc0f21133WU0O2wrV64U8wYNGqgdSUlJYr5x40a147nnnjN6vNz5OG+99ZbaMWHCBKPzkzszKiwsTO0oXLiwmB88eFDt8CS7du0S844dO6odH3zwgZifOHFC7ahZs6aYBwUFqR27d+8W8x49eqgdc+bMEfPvvvtO7Zg/f76Y9+rVS+145513jGf23LlzxbxLly5qR3p6upj/+OOPakdiYqKYP/3002pHcnKy0VrN1rt3bzHfvn272tG0aVMxP3TokNrxxhtviPnRo0fVDk+SL18+MS9YsKDaUbVqVTGPjIw0nlGlSpVSO2JjY407tOfx888/b7yudGdmFypUSMyfeeYZtWPy5MliPnz4cLVj586dYn7jxg3j86A7HT4+8vbFiBEjLFP58+dXb3P48GExz5NHf+2fv7+/mHOt98dmgzvX+doaZ9WqVWrHsGHDxDwiIkLtiImJEfOlS5eqHdraYtGiRWpHhw4dxHzdunVqh7bmc+e6RLuWi4qKUjv69Okj5l9++aXaoT2HtOt8d+acO9dY7dq1Mz6Xavuz7qyjtGu9M2fOWH8FXikNAAAAAAAAAHAMm9IAAAAAAAAAAMewKQ0AAAAAAAAAcAyb0gAAAAAAAAAAx7ApDQAAAAAAAABwDJvSAAAAAAAAAADHsCkNAAAAAAAAAHCMj7s3LFWqlJhfvnxZ7YiPjxfzBg0aqB3axwkODlY7Lly4YPx5bN26VcxDQ0PVjpSUFDHPyMhQO5o1a2Z0X20xMTFifuXKFbUjLS1NzH189Kdao0aNxHzNmjVqx5EjR8S8bt26akfTpk3FfMyYMWrHgAEDxDwsLEzt+P7778Xc399f7fAk2rG/aNEiteOuu+4S8+TkZLXjxo0bYn748GG1o3LlymK+Y8cOtePee+81fjxatWol5nv37lU7vL29xbxHjx5qxy+//CLmW7ZsUTvy588v5klJSWpHnTp1xHzIkCFqx4MPPijmH3/8sdqRlZUl5ufPn1c7IiIixHzcuHHGz4+WLVuqHZ4kISFBzA8ePKh25MuXT8wHDx6sduzfv9/ofG7z8/MT89jYWLWjQ4cORmsC25IlS4xmqW306NHG5/waNWqIeYUKFYzPYYsXL1Y7ChUqZDwbrl27JuZDhw5VO5o0aWI8X7R1Urt27dQO7Xyr3VdPEx4eLuYTJ05UO7R19uOPP652dOvWzbhjw4YNYj5hwgS1o3///sbnOG2tNXz4cLUjICBAzD/44AO1w9fX1+ia1Hbq1CkxX7ZsmfHatECBAmrHgQMHxDwxMVHtaN26tZgfO3bMuGP37t1qR0hIiPHM9iSpqanG+1FVqlQxWh/bTp48aXx91KZNG6Pnhi0uLs54vqxcuVLMV6xYoXZo6yR3jqeoqCjjPb6ff/5ZzMuVK6d2aOvX559/Xu3QrkszMzPVjqNHjxrPW+25Xr9+fbVDu82qVausvwKvlAYAAAAAAAAAOIZNaQAAAAAAAACAY9iUBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JQGAAAAAAAAADiGTWkAAAAAAAAAgGPYlAYAAAAAAAAAOIZNaQAAAAAAAACAY3zcveGOHTvEvFKlSmpHUlKSmPfo0UPtWLJkiZiXK1dO7di4caOYly5dWu0oUKCAmM+YMUPtyMzMFPPq1aurHTExMWKemJioduTJI39v4pFHHlE7fvjhBzH39fW1TF27dk29zbPPPivmN27cUDuCg4PFfNy4cWrHq6++avx5+Pn5iXlWVpba4Ulq1aol5gULFjTuiI6OVjvCw8PFfOjQocYzatiwYWrHe++9J+Zdu3ZVO7Zu3SrmQUFBakeZMmXEfNGiRWrH9evXxTw9PV3tqFGjhpg//fTTakepUqXEvEiRIsYzqnDhwmrHlStXxLxVq1ZqhzbXR40apXYEBASIefHixdUOT3LkyBExz8nJUTu0c8dzzz2ndjRq1Mj4eXz58mUxL1u2rNpRtWpVMf/kk0+Mn8fHjh1TO/r37y/m8+fPVzvCwsLEPDk5We04evSomG/fvl3teP75543Pg15eXmI+ceJEteOuu+4yfq5Pnz5dzKdMmaJ2vPzyy0bXJbbBgwdbnmL27NlifubMGbUjMjJSzGfNmqV2/OMf/xDz/fv3qx3aHNu1a5facccdd4j5nj171I63337beF25Zs0ao+s427lz58Q8JSVF7fDxkbcN6tSpo3YcOnRIzJ955hnjc2lgYKBl6tKlS+ptrl69KuYlSpRQO0JDQ8U8ISFB7fAk2h6NO+c4bW+kYsWKakflypXF/NSpU2qHNoNiY2PVjieeeELM161bp3akpqYaX+sdPHhQzKdOnWr8mLpz/bx8+XKjOWirXbu2mK9atUrt2L17t5iXL19e7Siv3MadtXqHDh2M16baXqM2w9zFK6UBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGDalAQAAAAAAAACOYVMaAAAAAAAAAOAYNqUBAAAAAAAAAI7xysnJyXHnhj169BDzIkWKqB1NmzYV82XLlqkdpUuXFvO6deuqHStWrBDz2NhYteOee+4R86NHj6od//jHP8R87NixakdaWpqYjxkzRu1o2bKlmH/yySdqR0BAgJhPnTpV7bjvvvvEPH/+/GpHSkqKmGdlZakdmzZtEvN69eqpHSdOnBDz4OBgtaNgwYJi/tNPP6kd27dvtzzF2rVrjR8v7XhyR1BQkJh36dJF7ZgxY4aYr1y50njevv7662pHmTJlxPzrr79WO7Zt22Y0020NGzYU87Nnz6od/v7+Yn7p0iW14+GHHxbzN998U+0oXLiwmK9atUrtePrpp8X85MmTakfevHmNHi9blSpVxPzee+9VOxISEixP8fjjj4t5WFiY2lGrVi0xX7dundpRqFAh4/NTgQIFxHzJkiVqR/ny5cU8IyND7dDWfMeOHVM7zp8/L+Y+Pj7Gs1I73tyZQe4cK9r6tlu3bmrHgQMHxLxDhw5qR+XKlcX86tWraoc2x9yZc9q5oXr16mrHK6+8YnkK7bpEuxZ0Zx3lzjWW9nWrUaOG8fo3NTVV7ejatauYf/rpp2pHRESEmKenp6sdXl5eRvPHnY9TrFgxtWPChAli/tRTT6kdkZGRxjO7V69eYr5+/XrjdZQ7exIXL140Xpt+9NFHYj5s2DC148svv7Q8xYIFC4z3X6KiosT8woULakepUqXEPE8e/XWf2hwLDAxUO0JCQsR85MiRxvtAzZs3VzsSExPF3NvbW+3QZtCGDRvUjitXrhjfl/j4eKOZbvvll1/EfOPGjWrHE088IeZTpkxRO95++23jDu2awJ3j5ccff1RvwyulAQAAAAAAAACOYVMaAAAAAAAAAOAYNqUBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGDalAQAAAAAAAACOYVMaAAAAAAAAAOAYr5ycnBx3bvj555+LuY+Pj9px6NAhMT979qzakTdvXjFv2LCh2lGlShUxHz9+vNpRsWJFMQ8LC1M7li9fLubvv/++9Xexdu1a49uEh4erHUWKFBHz1NRUtWPevHlifuedd6od69atE/MOHTqoHenp6UbPH9vWrVstT7Fv3z4xj42NVTvefvttMW/QoIHaUaBAAaPcVqJECTEPDQ1VOyZOnCjmbdu2VTuWLl0q5tWqVVM7unXrJuZ16tSxnHD8+HExX7x4sdqhnaMqV66sdkyePFnM58yZo3asWrVKzGfPnq12PPDAA2K+cuVKtePatWvGz1NtbfF3MmvWLDH/7LPP1I7evXuLeaVKldSOXbt2ifn58+fVjnfffdf4nF+uXDnj89fChQvFvEmTJpYpPz8/9TYlS5YU86tXrxrPqOTkZLUjIiJCzBctWqR2aHOsS5cuakd8fLyYZ2RkqB3r168X87p16xqv1bXnj+2DDz6wPEViYqLx9VFISIjR+tjWuXNnMe/Vq5faAZj48ssv1dtoM2j79u1qx7Bhw8T81VdfVTvcWfP9XWjXLto50BYdHW20l2BLS0szWt+4c35y5zq/Vq1aYr5p0ya1o2XLlkb7d7aOHTuK+Xfffad2lCpVSszr16+vdpw4cULMs7Oz1Y7hw4eLeWRkpNqhrYFbt26tduzYsUPMixUrpnbUq1dPzMeNG6d2DBo0yHh/rk2bNupteKU0AAAAAAAAAMAxbEoDAAAAAAAAABzDpjQAAAAAAAAAwDFsSgMAAAAAAAAAHMOmNAAAAAAAAADAMWxKAwAAAAAAAAAcw6Y0AAAAAAAAAMAxPu7eMDg4WMxzcnLUjp49e4r55MmT1Y6BAweK+Ztvvql2XL16Vcx79OihdsydO1fMfXz0h/bdd9+1PMWhQ4fU2wQGBor54sWL1Y4WLVqIefny5dWO7t27i/nOnTvVjgoVKoh5eHi42vHll1+KeYkSJdQOT/Lee++Jeb58+dSOBg0aiHn+/PnVjj59+oj5hg0b1I7s7GwxHzNmjNpRr149Md+yZYvx83jo0KFqx4kTJ8R87dq1ake5cuXEPCYmRu2YP3++mKempqodZcuWNTq32Dp37izmq1atUjuCgoLEvFWrVmpH1apVxfznn39WO5o0aSLm69evVzs8ycmTJ8W8W7duakdiYqLxjGratKnx+uW1114T87179xof1zt27FA7HnnkETG/fv262nHgwAExv3TpkvG5w9fXV+0YNGiQmI8dO1btiIqKEvNTp06pHSNGjDBei2nrk4yMDLXjypUrYu7n56d2XLhwwfjr4kkGDx5sdA60eXt7G19jbd++3XgdHhoaKuaVKlVSO44dO2bcceTIETGPi4tTO2rUqGH0MdzpSE9PVzu0c8Pq1avVjtq1a4v5xYsX1Q5tfmhrV3eeQ40bN1Y7fv31V6NjwTZ16lQxnzhxotrhSbT17enTp9WOTp06Ge15uTOjtHW6O+uGgIAA43OcO9e+2rm2dOnSase5c+eM7qutffv2Yv7DDz+oHV27dhXzhIQEtWPSpElivmTJErXjo48+EvPly5erHQUKFDBeI5cqVUrMGzVqZDxvn332WbUjMjJSvQ2vlAYAAAAAAAAAOIZNaQAAAAAAAACAY9iUBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JQGAAAAAAAAADiGTWkAAAAAAAAAgGPYlAYAAAAAAAAAOIZNaQAAAAAAAACAY3zcvWHVqlXFPDIyUu24ePGimLdp00btOHHihJgPGTJE7ZgyZYqYjxkzRu04duyYmKekpKgds2bNEvOkpCS14/Lly2LeuHFjtcPb21vMDxw4oHZER0eL+Ysvvqh2bNiwQcyrVKmidixZskTM69Spo3YUL15czBctWqR2lClTRsyjoqLUju7du4v5/v371Q5Poj3mGRkZxvMlLCxM7Vi3bp2YHzx4UO0YO3asmFesWFHt+Oabb8Q8PT1d7bhy5YrxbLhx44aYt2vXTu2Ij48X8xdeeEHtePTRR40fj23bthmfw7Rj352vbe/evcX83nvvNZ6VFy5cUDtq1KhhfMx5Eu187M5sCA8PF/MSJUqoHa+99prRLLU1bdrUaHbYEhMTxXzo0KHGj6k2f2xz5swR80qVKqkdaWlpxsf1zz//LOYJCQlqR9euXcX8vvvuUzu0tWdMTIzacffddxudn2zBwcFivmPHDrWjffv2Yl6+fHm1w5PUrFnTeA1drFgxMf/uu+/UjnfffVfMd+7cqXZo16VxcXHG177aes+d2eDOdVr16tWNzsW2/PnzG89s7b5o88edz6Ns2bJqx759+8T8jjvuUDuOHj1qvJ4rV66c0bFgW7lypZjfc889aseKFSssT7F161ajmW/z8ZG3vz777DO1Y/To0WK+ceNGtWPmzJlG88edY6FVq1Zqx9q1a8U8NTVV7Rg1apTROsud87E761ttXZmdna12aNf67qyj1qxZI+bXrl1TOwICAsS8WbNmasf27dvFvEKFCmrH1atXjZ+n7uCV0gAAAAAAAAAAx7ApDQAAAAAAAABwDJvSAAAAAAAAAADHsCkNAAAAAAAAAHAMm9IAAAAAAAAAAMewKQ0AAAAAAAAAcAyb0gAAAAAAAAAAx/i4e8MjR46IeVZWltpx/vx5MQ8ICFA7jh49atzRqlUrMZ82bZraoX2cpKQktSMjI0PMq1WrpnasXbtWzGvXrq12nD17Vsy/+uortWPAgAFifuHCBbXDz89PzPPk0b+HUrVqVTFfs2aN2qF9ruXLl1c7Nm3aJObx8fFqR8GCBcV87969aocnCQwMFPOTJ0+qHdrX5YUXXlA7Vq5cKeaTJ09WO44fP240S209e/YU8wIFCqgdN27cEPNFixapHYUKFRLzxMREtSMqKkrMR40apXbMnz9fzLt06aJ2ZGdnG30MW4cOHcR8zJgxakd4eLiYt27dWu2YMmWKmD/99NNqx4cffijmI0aMUDs8yYQJE8S8U6dOasfy5cuNH/MGDRoYzR9bSkqK0TFrCwkJEfNatWqpHYULFxbzGTNmqB2hoaFi3rdvX7Vj3759Rmtod2ZyWFiY2rF582Yxnzt3rvHcd+fxmDVrlph7e3urHe3atRPzEydOOHLt4kmuXLlivG6YPn268Trq0UcfNZ4Nly9fFvMWLVqoHdo6+6677lI7li5dKuY+Pvql+NWrV8V89+7dxnNuy5YtakflypXF/JlnnlE7tHPDL7/8onaUK1dOzBMSEtSO6tWri3l0dLTaoX0cd+ZL/fr1xbxx48ZqhyfR1i/+/v5qh7YXoF0/2ebNm2d8furdu7eYv//++2rHE088IeZ58+Y13o/S1nvu7OG485hq5x9thrlzzi9ZsqTaERQUZPyYaudKd9ZA0coMqlOnjtrh6+trvG+anJws5kOGDLH+CrxSGgAAAAAAAADgGDalAQAAAAAAAACOYVMaAAAAAAAAAOAYNqUBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGDalAQAAAAAAAACO8XH3hitWrBDzsmXLqh1r164V8wYNGqgdqampYl6xYkW1IykpSczHjBmjdgwYMEDMo6Ki1I7ixYuLeWxsrNrRtGlTMV+/fr3akZCQIObz589XO4KCgsQ8MDBQ7di1a5eYX716Ve2oVauWmBcsWFDtaNKkiZgXLVpU7ZgzZ46YN2rUSO2Ij48X83bt2qkdnuSOO+4Q85deekntuP/++8V82bJlakerVq3E/O6771Y7bty4IebNmjVTOzZv3izmTzzxhNoRGRkp5jVr1lQ7/Pz8xPzgwYNqx9mzZ8U8PT1d7XjsscfEPDMzU+2YNWuWmD/33HNqR5EiRcT8zJkzakenTp3E/NKlS2pH6dKlxTwrK0vteP/998X8mWeeUTvuu+8+y1PUr19fzL28vNSOAwcOGB337nxtV61apXZo67WSJUuqHdpxO3LkSLWjdu3aYl61alW1o0OHDmK+b98+tSN//vxifvz4cbUjLS1NzAsXLqx2aOukevXqqR3a2nP69OlqR0pKiph/+umnase1a9eMvy7aOjsnJ0ft8CTa+Ulbm7izBlq5cqXaUaVKFTFv37692nH58mWj54Ztx44dRmsT29dff210HrX5+/sbX2Np1xSdO3c2Pof169dP7YiIiDC6zndnRrlznaadf9x5fpw/f954Xaldl5YqVUrt8CTaNYM7eydhYWFifuLECePrI+3ax3b06FGjc6Bt9erVYj5s2DC1w8dH3g4cMWKE2vHll1+K+blz54z3tNx5PLRjrk6dOmrH1q1bjT8P7bhNTk5WOy5evCjmFSpUUDsKFSpkvG+qrdfKly9vPPdtvFIaAAAAAAAAAOAYNqUBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGDalAQAAAAAAAACOYVMaAAAAAAAAAOAYH3dvWK5cOTGvWbOm2nHy5Ekx37dvn9qRkJAg5kFBQWpHp06dxHzFihVqR1JSkpgPHz5c7Vi4cKGYR0REqB0zZswQcx8f/UvcokULMd+5c6faUaJECTHftm2b2lGqVCkxv3r1qtrRuHFjMT99+rTa8eOPP4p5SEiI2hEfHy/mHTp0UDu053JsbKza4Um++eYbMe/Zs6faERoaKuZdunRRO15//XWjj2ErXLiw8bEQHBws5hs2bFA7tDm2fv16tWPOnDlinp2drXZ07txZzM+dO6d2TJs2Tcz79u2rdjz55JNivnfvXrWjfPnyxs8x7VxZoEABtUObY9HR0cbP04YNG6odnsTPz0/Mvby81I7w8HDjNVBmZqaYP/jgg2pHenq6mNepU0ftCAgIEPMiRYoYPx4zZ85UO/r06SPm9evXVztOnTpl9JjbKlWqJObNmjVTO7Q1zokTJ9SOtm3binmePPrrWVatWiXme/bsUTsyMjLEvFq1ampHWlqamG/evFnt8CQDBgwQ84sXLxofC9rz3Na+fXujNbY712naWt+dtcWZM2fUjilTphitXW1NmzY1vn5++eWXxTwxMVHt2L17t5jXqlVL7ShTpoyYp6SkqB2jRo0y+trbzp49azR/3Ln2ded83KZNGzFfunSp2uFJtHWS9nW1HThwQMzvuusutePee+8V8+LFi6sd2h5N//791Y78+fOL+eHDh42PyZIlSxrP7AULFqgd2ppPW0O7c7y4M2+14zo5OVntWLt2rZiXLl3a+PGYplzX2u6++26j85OtR48exvPWHbxSGgAAAAAAAADgGDalAQAAAAAAAACOYVMaAAAAAAAAAOAYNqUBAAAAAAAAAI5hUxoAAAAAAAAA4Bg2pQEAAAAAAAAAjmFTGgAAAAAAAADgGDalAQAAAAAAAACO8crJyclx7sMBAAAAAAAAADwZr5QGAAAAAAAAADiGTWkAAAAAAAAAgGPYlAYAAAAAAAAAOIZNaQAAAAAAAACAY9iUBgAAAAAAAAA4hk1pAAAAAAAAAIBj2JQGAAAAAAAAADiGTWkAAAAAAAAAgGPYlAYAAAAAAAAAWE75/wDJpE57kbWl0QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x1200 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get predictions for test samples\n",
    "n_samples = 20\n",
    "test_samples = X_test[:n_samples]\n",
    "test_labels = y_test[:n_samples]\n",
    "\n",
    "with brainstate.environ.context(fit=False):\n",
    "    logits = model(test_samples)\n",
    "    predictions = jnp.argmax(logits, axis=-1)\n",
    "    probabilities = jax.nn.softmax(logits, axis=-1)\n",
    "\n",
    "# Visualize predictions\n",
    "fig, axes = plt.subplots(4, 5, figsize=(15, 12))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i in range(n_samples):\n",
    "    ax = axes[i]\n",
    "    \n",
    "    # Show image\n",
    "    ax.imshow(test_samples[i, :, :, 0], cmap='gray')\n",
    "    \n",
    "    # Title with prediction\n",
    "    true_label = int(test_labels[i])\n",
    "    pred_label = int(predictions[i])\n",
    "    confidence = float(probabilities[i, pred_label])\n",
    "    \n",
    "    color = 'green' if true_label == pred_label else 'red'\n",
    "    ax.set_title(\n",
    "        f'True: {true_label}, Pred: {pred_label}\\nConf: {confidence:.2f}',\n",
    "        color=color,\n",
    "        fontsize=10\n",
    "    )\n",
    "    ax.axis('off')\n",
    "\n",
    "plt.suptitle('Model Predictions (Green=Correct, Red=Wrong)', fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction Confidence Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average confidence for correct predictions: 0.9995\n",
      "Average confidence for incorrect predictions: nan\n"
     ]
    }
   ],
   "source": [
    "# Analyze prediction confidence\n",
    "with brainstate.environ.context(fit=False):\n",
    "    all_logits = model(X_test)\n",
    "    all_predictions = jnp.argmax(all_logits, axis=-1)\n",
    "    all_probs = jax.nn.softmax(all_logits, axis=-1)\n",
    "\n",
    "# Get max probability for each prediction\n",
    "max_probs = jnp.max(all_probs, axis=-1)\n",
    "\n",
    "# Separate correct and incorrect predictions\n",
    "correct_mask = (all_predictions == y_test)\n",
    "correct_probs = max_probs[correct_mask]\n",
    "incorrect_probs = max_probs[~correct_mask]\n",
    "\n",
    "# Plot confidence distributions\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.hist(correct_probs, bins=20, alpha=0.7, label='Correct Predictions', color='green')\n",
    "plt.hist(incorrect_probs, bins=20, alpha=0.7, label='Incorrect Predictions', color='red')\n",
    "\n",
    "plt.xlabel('Prediction Confidence')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Prediction Confidence')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(f\"Average confidence for correct predictions: {jnp.mean(correct_probs):.4f}\")\n",
    "print(f\"Average confidence for incorrect predictions: {jnp.mean(incorrect_probs):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Feature Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x1000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualized 16 filters from conv1 (filter shape: 3x3x1)\n"
     ]
    }
   ],
   "source": [
    "# # Visualize learned filters\n",
    "# def visualize_conv_filters(model, layer_name='conv1'):\n",
    "#     \"\"\"Visualize convolutional filters.\"\"\"\n",
    "#     # Get first conv layer\n",
    "#     conv_layer = getattr(model, layer_name)\n",
    "\n",
    "#     # Get weight - it might be a dict or direct value\n",
    "#     weight = conv_layer.conv.weight\n",
    "#     if isinstance(weight.value, dict):\n",
    "#         # If it's a dict, get the first value\n",
    "#         filters = list(weight.value.values())[0]\n",
    "#     else:\n",
    "#         filters = weight.value  # Shape: (out_ch, in_ch, kh, kw)\n",
    "\n",
    "#     n_filters = min(16, filters.shape[0])\n",
    "    \n",
    "#     fig, axes = plt.subplots(4, 4, figsize=(10, 10))\n",
    "#     axes = axes.flatten()\n",
    "    \n",
    "#     for i in range(n_filters):\n",
    "#         # Get filter for first input channel\n",
    "#         filter_img = filters[i, 0, :, :]\n",
    "        \n",
    "#         axes[i].imshow(filter_img, cmap='gray')\n",
    "#         axes[i].set_title(f'Filter {i}')\n",
    "#         axes[i].axis('off')\n",
    "    \n",
    "#     plt.suptitle(f'Learned Filters in {layer_name}')\n",
    "#     plt.tight_layout()\n",
    "#     plt.show() \n",
    "\n",
    "# visualize_conv_filters(model, 'conv1')\n",
    "\n",
    "\n",
    "\n",
    "# Visualize learned filters\n",
    "def visualize_conv_filters(model, layer_name='conv1'):\n",
    "    \"\"\"Visualize convolutional filters.\n",
    "\n",
    "    Args:\n",
    "        model: The neural network model\n",
    "        layer_name: Name of the convolutional block to visualize (e.g., 'conv1', 'conv2')\n",
    "    \"\"\"\n",
    "    # Get the conv layer\n",
    "    conv_layer = getattr(model, layer_name)\n",
    "\n",
    "    # Get weight - brainstate stores it in a dict\n",
    "    weight = conv_layer.conv.weight\n",
    "    if isinstance(weight.value, dict):\n",
    "        # Extract the actual weight tensor from dict\n",
    "        filters = list(weight.value.values())[0] \n",
    "    else:\n",
    "        filters = weight.value\n",
    "\n",
    "    # BrainState Conv2d uses shape: (kernel_height, kernel_width, in_channels, out_channels)\n",
    "    # This is different from PyTorch which uses (out_channels, in_channels, kh, kw)\n",
    "    kh, kw, in_ch, out_ch = filters.shape\n",
    "\n",
    "    # Visualize up to 16 filters\n",
    "    n_filters = min(16, out_ch)\n",
    "\n",
    "    fig, axes = plt.subplots(4, 4, figsize=(10, 10))\n",
    "    axes = axes.flatten()\n",
    "\n",
    "    for i in range(n_filters):\n",
    "        # Get the i-th output filter from the first input channel\n",
    "        # Shape is (kh, kw, in_ch, out_ch), so we need [:, :, 0, i]\n",
    "        filter_img = filters[:, :, 0, i]\n",
    "\n",
    "        axes[i].imshow(filter_img, cmap='viridis', interpolation='nearest')\n",
    "        axes[i].set_title(f'Filter {i}', fontsize=10)\n",
    "        axes[i].axis('off')\n",
    "\n",
    "    # Hide any unused subplots\n",
    "    for i in range(n_filters, 16):\n",
    "        axes[i].axis('off')\n",
    "\n",
    "    plt.suptitle(f'Learned Filters in {layer_name} (shape: {kh}x{kw})', fontsize=14)\n",
    "    plt.tight_layout()\n",
    "    # plt.savefig(f'{layer_name}_filters.png', dpi=150, bbox_inches='tight')\n",
    "    plt.show()\n",
    "\n",
    "    print(f\"Visualized {n_filters} filters from {layer_name} (filter shape: {kh}x{kw}x{in_ch})\")\n",
    "\n",
    "visualize_conv_filters(model, 'conv1')\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": "3.8.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}