3-resnet50-from-scratch-pre-trained-weight-final.ipynb

{"cells":[{"cell_type":"markdown","id":"3db905c8","metadata":{"id":"3db905c8","papermill":{"duration":0.005089,"end_time":"2024-11-27T15:42:38.164287","exception":false,"start_time":"2024-11-27T15:42:38.159198","status":"completed"},"tags":[]},"source":["As we have seen in our 1st notebook that our model, ResNet-50 built from scratch, didnt perform well on due to imbalanced training dataset.\n","\n","\n","\n","So inorder to tackle the imbalanced dataset problem, in our 2nd notebook we applied data augmentation alongwith the custom agumentation which yielded much better results.\n","\n","\n","\n","But in **this 3rd notebook** we'll implement some other technique to handle the imbalanced dataset. Below are the tactics to be used:\n","\n","\n","\n","* We'll use **only the weights of pre-trained ResNet-50 weights** alongwith **custom augmentation** of the images to help the model generalize better.\n","\n","\n","\n","* We'll also finetune it by:\n","\n","  * increasing the batch size to 32\n","\n","  * changing the learning rate to 1e-3\n","\n","  * replacing dropout with **regularization**\n","\n","\n","\n","We'll then again train our model on the custom dataset, and find out how it performs on our test dataset."]},{"cell_type":"code","execution_count":null,"id":"779d2fde","metadata":{"execution":{"iopub.execute_input":"2024-11-27T15:42:38.174235Z","iopub.status.busy":"2024-11-27T15:42:38.173945Z","iopub.status.idle":"2024-11-27T15:42:47.384875Z","shell.execute_reply":"2024-11-27T15:42:47.383677Z"},"executionInfo":{"elapsed":3430,"status":"ok","timestamp":1730261240521,"user":{"displayName":"tannistha maiti","userId":"15250126319063759955"},"user_tz":240},"id":"779d2fde","outputId":"25dbdaed-05d6-401c-f577-17c0e67b4239","papermill":{"duration":9.218242,"end_time":"2024-11-27T15:42:47.386927","exception":false,"start_time":"2024-11-27T15:42:38.168685","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: torchvision in /opt/conda/lib/python3.10/site-packages (0.19.0)\r\n","Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from torchvision) (1.26.4)\r\n","Requirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from torchvision) (2.4.0)\r\n","Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/lib/python3.10/site-packages (from torchvision) (10.3.0)\r\n","Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (3.15.1)\r\n","Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (4.12.2)\r\n","Requirement already satisfied: sympy in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (1.13.3)\r\n","Requirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (3.3)\r\n","Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (3.1.4)\r\n","Requirement already satisfied: fsspec in /opt/conda/lib/python3.10/site-packages (from torch->torchvision) (2024.6.1)\r\n","Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch->torchvision) (2.1.5)\r\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from sympy->torch->torchvision) (1.3.0)\r\n"]}],"source":["!pip install torchvision"]},{"cell_type":"code","execution_count":null,"id":"2e9cc3f8","metadata":{"execution":{"iopub.execute_input":"2024-11-27T15:42:47.399151Z","iopub.status.busy":"2024-11-27T15:42:47.398639Z","iopub.status.idle":"2024-11-27T15:42:48.831805Z","shell.execute_reply":"2024-11-27T15:42:48.831106Z"},"id":"2e9cc3f8","papermill":{"duration":1.441964,"end_time":"2024-11-27T15:42:48.833838","exception":false,"start_time":"2024-11-27T15:42:47.391874","status":"completed"},"tags":[]},"outputs":[],"source":["import tensorflow_datasets as tfds\n","\n","import logging\n","\n","from zipfile import ZipFile\n","\n","import os\n","\n","import sys\n","\n","import shutil\n","\n","import pandas as pd\n","\n","import matplotlib.pyplot as plt\n","\n","import numpy as np\n","\n","#from utils.events import download_google_drive_file"]},{"cell_type":"code","execution_count":null,"id":"4652c6e6","metadata":{"execution":{"iopub.execute_input":"2024-11-27T15:42:48.844726Z","iopub.status.busy":"2024-11-27T15:42:48.844297Z","iopub.status.idle":"2024-11-27T15:42:48.854832Z","shell.execute_reply":"2024-11-27T15:42:48.854153Z"},"id":"4652c6e6","outputId":"41f0095b-fce3-43d1-b583-fc3217946d93","papermill":{"duration":0.017566,"end_time":"2024-11-27T15:42:48.856293","exception":false,"start_time":"2024-11-27T15:42:48.838727","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["['train_subset1.csv',\n"," 'test_subset1.csv',\n"," 'val_subset0.csv',\n"," 'test_subset0.csv',\n"," 'train_subset0.csv',\n"," 'images',\n"," 'val_subset1.csv',\n"," 'labels.csv']"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["import os\n","\n","os.listdir('/kaggle/input')\n"]},{"cell_type":"code","execution_count":null,"id":"7d140a0f","metadata":{"execution":{"iopub.execute_input":"2024-11-27T15:42:48.866531Z","iopub.status.busy":"2024-11-27T15:42:48.866230Z","iopub.status.idle":"2024-11-27T15:42:48.869648Z","shell.execute_reply":"2024-11-27T15:42:48.868972Z"},"papermill":{"duration":0.010147,"end_time":"2024-11-27T15:42:48.871185","exception":false,"start_time":"2024-11-27T15:42:48.861038","status":"completed"},"tags":[],"id":"7d140a0f"},"outputs":[],"source":["# !ls /kaggle/input/"]},{"cell_type":"code","execution_count":null,"id":"c94ac475","metadata":{"execution":{"iopub.execute_input":"2024-11-27T15:42:48.881722Z","iopub.status.busy":"2024-11-27T15:42:48.881135Z","iopub.status.idle":"2024-11-27T15:42:48.884635Z","shell.execute_reply":"2024-11-27T15:42:48.883833Z"},"papermill":{"duration":0.010383,"end_time":"2024-11-27T15:42:48.886194","exception":false,"start_time":"2024-11-27T15:42:48.875811","status":"completed"},"tags":[],"id":"c94ac475"},"outputs":[],"source":["# !ls /kaggle/input/deepweeds-image-classification"]},{"cell_type":"code","execution_count":null,"id":"8eecf318","metadata":{"execution":{"iopub.execute_input":"2024-11-27T15:42:48.896167Z","iopub.status.busy":"2024-11-27T15:42:48.895911Z","iopub.status.idle":"2024-11-27T15:42:48.900311Z","shell.execute_reply":"2024-11-27T15:42:48.899423Z"},"papermill":{"duration":0.011228,"end_time":"2024-11-27T15:42:48.901944","exception":false,"start_time":"2024-11-27T15:42:48.890716","status":"completed"},"tags":[],"id":"8eecf318","outputId":"511f2649-e993-46d1-d79a-01dc04817799"},"outputs":[{"name":"stdout","output_type":"stream","text":["['train_subset1.csv', 'test_subset1.csv', 'val_subset0.csv', 'test_subset0.csv', 'train_subset0.csv', 'images', 'val_subset1.csv', 'labels.csv']\n"]}],"source":["import os\n","print(os.listdir('/kaggle/input/'))"]},{"cell_type":"code","execution_count":null,"id":"fa96d952","metadata":{"execution":{"iopub.execute_input":"2024-11-27T15:42:48.912232Z","iopub.status.busy":"2024-11-27T15:42:48.911981Z","iopub.status.idle":"2024-11-27T15:42:54.541567Z","shell.execute_reply":"2024-11-27T15:42:54.540832Z"},"id":"fa96d952","papermill":{"duration":5.636928,"end_time":"2024-11-27T15:42:54.543556","exception":false,"start_time":"2024-11-27T15:42:48.906628","status":"completed"},"tags":[]},"outputs":[],"source":["import pandas as pd\n","\n","import os\n","\n","from torch.utils.data import Dataset, DataLoader\n","\n","from sklearn.model_selection import train_test_split\n","\n","from PIL import Image\n","\n","import torch\n","\n","from torchvision import transforms\n","\n","import torchvision\n","\n","from torchvision import models\n","\n","import torch.nn as nn\n","\n","import torch.optim as optim\n"]},{"cell_type":"markdown","id":"6d884062","metadata":{"id":"6d884062","papermill":{"duration":0.0046,"end_time":"2024-11-27T15:42:54.553219","exception":false,"start_time":"2024-11-27T15:42:54.548619","status":"completed"},"tags":[]},"source":["## Applying Custom Augmentation:"]},{"cell_type":"markdown","id":"231335b4","metadata":{"id":"231335b4","papermill":{"duration":0.004393,"end_time":"2024-11-27T15:42:54.562138","exception":false,"start_time":"2024-11-27T15:42:54.557745","status":"completed"},"tags":[]},"source":["**Below code snippet** implements a more sophisticated and customizable data augmentation pipeline.\n","\n","\n","\n","It integrates both **PyTorch's transforms** and **custom OpenCV-based transformations** for augmenting images. Additionally, it includes a custom dataset class and creates data loaders to handle images and labels efficiently for training, validation, and testing."]},{"cell_type":"code","execution_count":null,"id":"9865ff0f","metadata":{"execution":{"iopub.execute_input":"2024-11-27T15:42:54.572603Z","iopub.status.busy":"2024-11-27T15:42:54.572192Z","iopub.status.idle":"2024-11-27T15:42:54.627820Z","shell.execute_reply":"2024-11-27T15:42:54.626937Z"},"id":"9865ff0f","papermill":{"duration":0.06321,"end_time":"2024-11-27T15:42:54.629850","exception":false,"start_time":"2024-11-27T15:42:54.566640","status":"completed"},"tags":[]},"outputs":[],"source":["## Custom OpenCV-Based Augmentation:\n","\n","\n","\n","import torch\n","\n","import torchvision.transforms as transforms\n","\n","import random\n","\n","import cv2\n","\n","import numpy as np\n","\n","from PIL import Image\n","\n","\n","\n","# Define a custom augmentation function for OpenCV transformations\n","\n","def random_perspective(img):\n","\n","    \"\"\"Apply random perspective transformation using OpenCV.\"\"\"\n","\n","    h, w = img.shape[:2]\n","\n","    # Define the perspective transformation\n","\n","    src_pts = np.float32([[0, 0], [w, 0], [w, h], [0, h]])\n","\n","    dst_pts = np.float32([[random.uniform(-0.1, 0.1) * w, random.uniform(-0.1, 0.1) * h] for _ in range(4)])\n","\n","    matrix = cv2.getPerspectiveTransform(src_pts, dst_pts)\n","\n","    img = cv2.warpPerspective(img, matrix, (w, h))\n","\n","    return img\n","\n","\n","\n","# Function to perform augmentations including OpenCV-based perspective shift\n","\n","class CustomAugmentations:\n","\n","    def __init__(self):\n","\n","        # Define transformations from torchvision.transforms\n","\n","        self.transforms = transforms.Compose([\n","\n","            transforms.Resize((128, 128)),  # Resize all images to 256x256\n","\n","            transforms.RandomRotation(degrees=360),  # Rotate randomly within [-360, +360]\n","\n","            transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0),  # Color shifts\n","\n","            transforms.RandomHorizontalFlip(p=0.5),  # Horizontal flip with 50% probability\n","\n","        ])\n","\n","\n","\n","    def __call__(self, img):\n","\n","        # Convert PIL image to OpenCV format (numpy array)\n","\n","        img = np.array(img)\n","\n","        # Perform random perspective transformation using OpenCV\n","\n","        img = random_perspective(img)\n","\n","        # Convert back to PIL image for further processing\n","\n","        img = Image.fromarray(img)\n","\n","        # Apply remaining torchvision transformations\n","\n","        img = self.transforms(img)\n","\n","        # Convert the image to a tensor\n","\n","        img = transforms.ToTensor()(img)\n","\n","        # Pixel intensity shift [-25, +25] and scaling [0.75, 1.25]\n","\n","        img = self.random_pixel_intensity_shift(img)\n","\n","\n","\n","        return img\n","\n","\n","\n","    def random_pixel_intensity_shift(self, img):\n","\n","        \"\"\"Shift pixel intensity uniformly across all channels and scale within [0.75, 1.25].\"\"\"\n","\n","        # Pixel intensity shift in range [-25, +25]\n","\n","        intensity_shift = random.uniform(-25 / 255.0, 25 / 255.0)\n","\n","        img = torch.clamp(img + intensity_shift, 0, 1)\n","\n","        # Pixel intensity scaling in range [0.75, 1.25]\n","\n","        intensity_scale = random.uniform(0.75, 1.25)\n","\n","        img = torch.clamp(img * intensity_scale, 0, 1)\n","\n","\n","\n","        return img\n","\n","\n","\n","# Custom dataset class\n","\n","class ImageDataset(Dataset):\n","\n","    def __init__(self, csv_file, img_dir, transform=None):\n","\n","        self.data_frame = pd.read_csv(csv_file)\n","\n","        self.img_dir = img_dir\n","\n","        self.transform = transform\n","\n","\n","\n","    def __len__(self):\n","\n","        return len(self.data_frame)\n","\n","\n","\n","    def __getitem__(self, idx):\n","\n","        img_name = os.path.join(self.img_dir, self.data_frame.iloc[idx, 0])\n","\n","        image = Image.open(img_name)\n","\n","        label = int(self.data_frame.iloc[idx, 1])\n","\n","\n","\n","        if self.transform:\n","\n","            image = self.transform(image)\n","\n","\n","\n","        return image, label\n","\n","\n","\n","# Load the CSV file\n","\n","\n","\n","train_csv_file = '/kaggle/input/train_subset0.csv'  # Replace with your actual path\n","\n","test_csv_file = '/kaggle/input/test_subset0.csv'  # Replace with your actual path\n","\n","val_csv_file = '/kaggle/input/val_subset0.csv'  # Replace with your actual path\n","\n","img_dir = '/kaggle/input/images'  # Replace with your images directory\n","\n","\n","\n","\n","\n","# Image transformations (resize, normalization, etc.)\n","\n","transform = transforms.Compose([\n","\n","    transforms.Resize((128, 128)),  # Resizing to 128x128, you can adjust this\n","\n","    transforms.RandomRotation(degrees=360),  # Rotate randomly within [-360, +360]\n","\n","    transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0),  # Color shifts\n","\n","    transforms.RandomHorizontalFlip(p=0.5),  # Horizontal flip with 50% probability\n","\n","    transforms.ToTensor(),  # Convert the image to PyTorch tensor\n","\n","    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])\n","\n","  ])\n","\n","\n","\n","# Create an instance of the custom augmentations\n","\n","custom_augmentations = CustomAugmentations()\n","\n","\n","\n","# Create the train and test datasets\n","\n","train_dataset = ImageDataset(csv_file=train_csv_file, img_dir=img_dir, transform=transform)\n","\n","test_dataset = ImageDataset(csv_file=test_csv_file, img_dir=img_dir, transform=transform)\n","\n","val_dataset = ImageDataset(csv_file=val_csv_file, img_dir=img_dir, transform=transform)\n","\n","\n","\n","\n","\n","# Create DataLoaders for batching\n","\n","def create_dataloader(loader_type):\n","\n","  if loader_type=='train':\n","\n","    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n","\n","    print(f\"len train_loader {len(train_loader)}\")\n","\n","    return train_loader\n","\n","  elif loader_type=='val':\n","\n","    val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n","\n","    return val_loader\n","\n","  else:\n","\n","    test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n","\n","    return test_loader\n"]},{"cell_type":"markdown","id":"84aeb475","metadata":{"id":"84aeb475","papermill":{"duration":0.004453,"end_time":"2024-11-27T15:42:54.639232","exception":false,"start_time":"2024-11-27T15:42:54.634779","status":"completed"},"tags":[]},"source":["# Applying the ResNet-50 Model built from scratch:-\n","\n","\n","\n","Let us now apply our ResNet-50 model built from scratch (same as in our 1st notebook) but **with** the weights from pre-trained ResNet-50, and **without** the 'dropout rate'.\n"]},{"cell_type":"code","execution_count":null,"id":"6aa35914","metadata":{"execution":{"iopub.execute_input":"2024-11-27T15:42:54.650170Z","iopub.status.busy":"2024-11-27T15:42:54.649394Z","iopub.status.idle":"2024-11-27T15:42:55.975352Z","shell.execute_reply":"2024-11-27T15:42:55.974443Z"},"id":"6aa35914","outputId":"451e2a9e-f497-4978-d6ae-93a54c460604","papermill":{"duration":1.333906,"end_time":"2024-11-27T15:42:55.977709","exception":false,"start_time":"2024-11-27T15:42:54.643803","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n","  warnings.warn(\n","/opt/conda/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n","  warnings.warn(msg)\n","Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n","100%|██████████| 97.8M/97.8M [00:00<00:00, 228MB/s]\n"]},{"name":"stdout","output_type":"stream","text":["ResNet50(\n","  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (relu): ReLU(inplace=True)\n","  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n","  (layer1): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer2): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (3): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer3): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (3): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (4): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (5): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer4): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n","  (fc): Linear(in_features=2048, out_features=9, bias=True)\n",")\n"]}],"source":["import torch\n","\n","import torch.nn as nn\n","\n","import torchvision.models as models\n","\n","\n","\n","class Bottleneck(nn.Module):\n","\n","    def __init__(self, in_channel, out_channel, stride=1, downsample=None):\n","\n","        super(Bottleneck, self).__init__()\n","\n","        self.conv1 = nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=1, bias=False)\n","\n","        self.bn1 = nn.BatchNorm2d(out_channel)\n","\n","        self.conv2 = nn.Conv2d(out_channel, out_channel, kernel_size=3, stride=stride, padding=1, bias=False)\n","\n","        self.bn2 = nn.BatchNorm2d(out_channel)\n","\n","        self.conv3 = nn.Conv2d(out_channel, out_channel * 4, kernel_size=1, stride=1, bias=False)\n","\n","        self.bn3 = nn.BatchNorm2d(out_channel * 4)\n","\n","        self.relu = nn.ReLU(inplace=True)\n","\n","        self.downsample = downsample\n","\n","\n","\n","    def forward(self, x):\n","\n","        identity = x\n","\n","\n","\n","        out = self.conv1(x)\n","\n","        out = self.bn1(out)\n","\n","        out = self.relu(out)\n","\n","\n","\n","        out = self.conv2(out)\n","\n","        out = self.bn2(out)\n","\n","        out = self.relu(out)\n","\n","\n","\n","        out = self.conv3(out)\n","\n","        out = self.bn3(out)\n","\n","\n","\n","        if self.downsample is not None:\n","\n","            identity = self.downsample(x)\n","\n","\n","\n","        out += identity\n","\n","        out = self.relu(out)\n","\n","\n","\n","        return out\n","\n","\n","\n","\n","\n","class ResNet50(nn.Module):\n","\n","    def __init__(self, block, layers, num_classes=1000):\n","\n","        super(ResNet50, self).__init__()\n","\n","        self.in_channel = 64\n","\n","        self.conv1 = nn.Conv2d(3, self.in_channel, kernel_size=7, stride=2, padding=3, bias=False)\n","\n","        self.bn1 = nn.BatchNorm2d(self.in_channel)\n","\n","        self.relu = nn.ReLU(inplace=True)\n","\n","        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n","\n","\n","\n","        self.layer1 = self._make_layer(block, 64, layers[0])\n","\n","        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n","\n","        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n","\n","        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n","\n","        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n","\n","        self.fc = nn.Linear(512 * 4, num_classes)\n","\n","\n","\n","    def _make_layer(self, block, out_channel, blocks, stride=1):\n","\n","        downsample = None\n","\n","        if stride != 1 or self.in_channel != out_channel * 4:\n","\n","            downsample = nn.Sequential(\n","\n","                nn.Conv2d(self.in_channel, out_channel * 4, kernel_size=1, stride=stride, bias=False),\n","\n","                nn.BatchNorm2d(out_channel * 4)\n","\n","            )\n","\n","\n","\n","        layers = []\n","\n","        layers.append(block(self.in_channel, out_channel, stride, downsample))\n","\n","        self.in_channel = out_channel * 4\n","\n","        for _ in range(1, blocks):\n","\n","            layers.append(block(self.in_channel, out_channel))\n","\n","\n","\n","        return nn.Sequential(*layers)\n","\n","\n","\n","    def forward(self, x):\n","\n","        x = self.conv1(x)\n","\n","        x = self.bn1(x)\n","\n","        x = self.relu(x)\n","\n","        x = self.maxpool(x)\n","\n","\n","\n","        x = self.layer1(x)\n","\n","        x = self.layer2(x)\n","\n","        x = self.layer3(x)\n","\n","        x = self.layer4(x)\n","\n","\n","\n","        x = self.avgpool(x)\n","\n","        x = torch.flatten(x, 1)\n","\n","        x = self.fc(x)\n","\n","\n","\n","        return x\n","\n","\n","\n","\n","\n","# Adjusted resnet50 function with FC layer mismatch handling\n","\n","def resnet50(pretrained=False, num_classes=9, **kwargs):\n","\n","    model = ResNet50(Bottleneck, [3, 4, 6, 3], num_classes=num_classes, **kwargs)\n","\n","\n","\n","    if pretrained:\n","\n","        # Load the pretrained ResNet50 model from torchvision\n","\n","        pretrained_model = models.resnet50(pretrained=True)\n","\n","\n","\n","        # Load pretrained state dict, but ignore the weights for the fully connected (fc) layer\n","\n","        pretrained_dict = pretrained_model.state_dict()\n","\n","        model_dict = model.state_dict()\n","\n","\n","\n","        # Filter out the FC layer weights from the pretrained model\n","\n","        pretrained_dict = {k: v for k, v in pretrained_dict.items() if 'fc' not in k}\n","\n","\n","\n","        # Load the state dict, ignoring the mismatched FC layer\n","\n","        model_dict.update(pretrained_dict)\n","\n","        model.load_state_dict(model_dict)\n","\n","\n","\n","    # Replace the FC layer with a custom one that matches num_classes\n","\n","    model.fc = nn.Linear(512 * 4, num_classes)\n","\n","\n","\n","    return model\n","\n","\n","\n","\n","\n","# Instantiate the model with 9 output classes\n","\n","model = resnet50(pretrained=True, num_classes=9)     # Setting 'pre-trained' as True instead of False\n","\n","print(model)\n"]},{"cell_type":"markdown","id":"c0c0cb00","metadata":{"id":"c0c0cb00","papermill":{"duration":0.005283,"end_time":"2024-11-27T15:42:55.988811","exception":false,"start_time":"2024-11-27T15:42:55.983528","status":"completed"},"tags":[]},"source":["# Model Compilation and Training:-\n","\n","\n","\n","Below we'll implement the training and validation loop for a ResNet50 model."]},{"cell_type":"code","execution_count":null,"id":"51832b45","metadata":{"execution":{"iopub.execute_input":"2024-11-27T15:42:56.022604Z","iopub.status.busy":"2024-11-27T15:42:56.022310Z","iopub.status.idle":"2024-11-27T17:56:14.500598Z","shell.execute_reply":"2024-11-27T17:56:14.499632Z"},"id":"51832b45","outputId":"1feefaf2-8692-47b0-a386-f875d34b2f25","papermill":{"duration":7998.513891,"end_time":"2024-11-27T17:56:14.527638","exception":false,"start_time":"2024-11-27T15:42:56.013747","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["len train_loader 329\n","Epoch [1/100], Step [1/329], Loss: 2.3960\n","Epoch [1/100], Step [101/329], Loss: 1.1300\n","Epoch [1/100], Step [201/329], Loss: 1.0039\n","Epoch [1/100], Step [301/329], Loss: 1.1812\n","Epoch [1/100], Validation Accuracy: 0.5901, Train Accuracy: 0.5809\n","New best model saved with Validation Accuracy: 0.5901\n","Epoch [2/100], Step [1/329], Loss: 0.9039\n","Epoch [2/100], Step [101/329], Loss: 1.0794\n","Epoch [2/100], Step [201/329], Loss: 0.8173\n","Epoch [2/100], Step [301/329], Loss: 0.7544\n","Epoch [2/100], Validation Accuracy: 0.7027, Train Accuracy: 0.6702\n","New best model saved with Validation Accuracy: 0.7027\n","Epoch [3/100], Step [1/329], Loss: 0.8119\n","Epoch [3/100], Step [101/329], Loss: 0.7194\n","Epoch [3/100], Step [201/329], Loss: 0.7149\n","Epoch [3/100], Step [301/329], Loss: 0.5548\n","Epoch [3/100], Validation Accuracy: 0.7121, Train Accuracy: 0.7188\n","New best model saved with Validation Accuracy: 0.7121\n","Epoch [4/100], Step [1/329], Loss: 0.7120\n","Epoch [4/100], Step [101/329], Loss: 0.6739\n","Epoch [4/100], Step [201/329], Loss: 0.7319\n","Epoch [4/100], Step [301/329], Loss: 0.7914\n","Epoch [4/100], Validation Accuracy: 0.7298, Train Accuracy: 0.7451\n","New best model saved with Validation Accuracy: 0.7298\n","Epoch [5/100], Step [1/329], Loss: 0.4570\n","Epoch [5/100], Step [101/329], Loss: 0.4432\n","Epoch [5/100], Step [201/329], Loss: 0.4279\n","Epoch [5/100], Step [301/329], Loss: 1.0340\n","Epoch [5/100], Validation Accuracy: 0.7661, Train Accuracy: 0.7634\n","New best model saved with Validation Accuracy: 0.7661\n","Epoch [6/100], Step [1/329], Loss: 0.8910\n","Epoch [6/100], Step [101/329], Loss: 0.5272\n","Epoch [6/100], Step [201/329], Loss: 0.4815\n","Epoch [6/100], Step [301/329], Loss: 0.3944\n","Epoch [6/100], Validation Accuracy: 0.7878, Train Accuracy: 0.7804\n","New best model saved with Validation Accuracy: 0.7878\n","Epoch [7/100], Step [1/329], Loss: 0.5169\n","Epoch [7/100], Step [101/329], Loss: 0.4777\n","Epoch [7/100], Step [201/329], Loss: 0.5198\n","Epoch [7/100], Step [301/329], Loss: 0.6596\n","Epoch [7/100], Validation Accuracy: 0.7558, Train Accuracy: 0.7934\n","Epoch [8/100], Step [1/329], Loss: 0.5004\n","Epoch [8/100], Step [101/329], Loss: 0.8569\n","Epoch [8/100], Step [201/329], Loss: 0.5785\n","Epoch [8/100], Step [301/329], Loss: 0.5614\n","Epoch [8/100], Validation Accuracy: 0.7001, Train Accuracy: 0.7941\n","Epoch [9/100], Step [1/329], Loss: 0.5877\n","Epoch [9/100], Step [101/329], Loss: 0.4454\n","Epoch [9/100], Step [201/329], Loss: 0.8343\n","Epoch [9/100], Step [301/329], Loss: 0.5224\n","Epoch [9/100], Validation Accuracy: 0.7875, Train Accuracy: 0.8227\n","Epoch [10/100], Step [1/329], Loss: 0.2533\n","Epoch [10/100], Step [101/329], Loss: 0.3518\n","Epoch [10/100], Step [201/329], Loss: 0.3962\n","Epoch [10/100], Step [301/329], Loss: 0.4229\n","Epoch [10/100], Validation Accuracy: 0.7601, Train Accuracy: 0.8293\n","Epoch [11/100], Step [1/329], Loss: 0.4850\n","Epoch [11/100], Step [101/329], Loss: 0.2515\n","Epoch [11/100], Step [201/329], Loss: 0.1457\n","Epoch [11/100], Step [301/329], Loss: 0.3029\n","Epoch [11/100], Validation Accuracy: 0.8332, Train Accuracy: 0.8338\n","New best model saved with Validation Accuracy: 0.8332\n","Epoch [12/100], Step [1/329], Loss: 0.5154\n","Epoch [12/100], Step [101/329], Loss: 0.4097\n","Epoch [12/100], Step [201/329], Loss: 0.2135\n","Epoch [12/100], Step [301/329], Loss: 0.2202\n","Epoch [12/100], Validation Accuracy: 0.8243, Train Accuracy: 0.8433\n","Epoch [13/100], Step [1/329], Loss: 0.3235\n","Epoch [13/100], Step [101/329], Loss: 0.3788\n","Epoch [13/100], Step [201/329], Loss: 0.6343\n","Epoch [13/100], Step [301/329], Loss: 0.3831\n","Epoch [13/100], Validation Accuracy: 0.8352, Train Accuracy: 0.8465\n","New best model saved with Validation Accuracy: 0.8352\n","Epoch [14/100], Step [1/329], Loss: 0.4506\n","Epoch [14/100], Step [101/329], Loss: 0.2863\n","Epoch [14/100], Step [201/329], Loss: 0.3678\n","Epoch [14/100], Step [301/329], Loss: 0.6858\n","Epoch [14/100], Validation Accuracy: 0.8189, Train Accuracy: 0.8485\n","Epoch [15/100], Step [1/329], Loss: 0.4048\n","Epoch [15/100], Step [101/329], Loss: 0.5719\n","Epoch [15/100], Step [201/329], Loss: 0.3795\n","Epoch [15/100], Step [301/329], Loss: 0.3369\n","Epoch [15/100], Validation Accuracy: 0.8509, Train Accuracy: 0.8522\n","New best model saved with Validation Accuracy: 0.8509\n","Epoch [16/100], Step [1/329], Loss: 0.2523\n","Epoch [16/100], Step [101/329], Loss: 0.9220\n","Epoch [16/100], Step [201/329], Loss: 0.4479\n","Epoch [16/100], Step [301/329], Loss: 0.6683\n","Epoch [16/100], Validation Accuracy: 0.8560, Train Accuracy: 0.8536\n","New best model saved with Validation Accuracy: 0.8560\n","Epoch [17/100], Step [1/329], Loss: 0.3374\n","Epoch [17/100], Step [101/329], Loss: 0.3369\n","Epoch [17/100], Step [201/329], Loss: 0.1872\n","Epoch [17/100], Step [301/329], Loss: 0.2834\n","Epoch [17/100], Validation Accuracy: 0.8658, Train Accuracy: 0.8617\n","New best model saved with Validation Accuracy: 0.8658\n","Epoch [18/100], Step [1/329], Loss: 0.2157\n","Epoch [18/100], Step [101/329], Loss: 0.6162\n","Epoch [18/100], Step [201/329], Loss: 0.2642\n","Epoch [18/100], Step [301/329], Loss: 0.5371\n","Epoch [18/100], Validation Accuracy: 0.8423, Train Accuracy: 0.8709\n","Epoch [19/100], Step [1/329], Loss: 0.3911\n","Epoch [19/100], Step [101/329], Loss: 0.2310\n","Epoch [19/100], Step [201/329], Loss: 0.2231\n","Epoch [19/100], Step [301/329], Loss: 0.2151\n","Epoch [19/100], Validation Accuracy: 0.8372, Train Accuracy: 0.8627\n","Epoch [20/100], Step [1/329], Loss: 0.1511\n","Epoch [20/100], Step [101/329], Loss: 0.6082\n","Epoch [20/100], Step [201/329], Loss: 0.1660\n","Epoch [20/100], Step [301/329], Loss: 0.2364\n","Epoch [20/100], Validation Accuracy: 0.8460, Train Accuracy: 0.8755\n","Epoch [21/100], Step [1/329], Loss: 0.2817\n","Epoch [21/100], Step [101/329], Loss: 0.4267\n","Epoch [21/100], Step [201/329], Loss: 0.5344\n","Epoch [21/100], Step [301/329], Loss: 0.5481\n","Epoch [21/100], Validation Accuracy: 0.8749, Train Accuracy: 0.8815\n","New best model saved with Validation Accuracy: 0.8749\n","Epoch [22/100], Step [1/329], Loss: 0.2520\n","Epoch [22/100], Step [101/329], Loss: 0.2570\n","Epoch [22/100], Step [201/329], Loss: 0.3701\n","Epoch [22/100], Step [301/329], Loss: 0.4466\n","Epoch [22/100], Validation Accuracy: 0.8161, Train Accuracy: 0.8613\n","Epoch [23/100], Step [1/329], Loss: 0.5426\n","Epoch [23/100], Step [101/329], Loss: 0.4299\n","Epoch [23/100], Step [201/329], Loss: 0.1962\n","Epoch [23/100], Step [301/329], Loss: 0.4006\n","Epoch [23/100], Validation Accuracy: 0.8655, Train Accuracy: 0.8797\n","Epoch [24/100], Step [1/329], Loss: 0.2404\n","Epoch [24/100], Step [101/329], Loss: 0.2624\n","Epoch [24/100], Step [201/329], Loss: 0.2958\n","Epoch [24/100], Step [301/329], Loss: 0.2601\n","Epoch [24/100], Validation Accuracy: 0.8706, Train Accuracy: 0.8870\n","Epoch [25/100], Step [1/329], Loss: 0.1227\n","Epoch [25/100], Step [101/329], Loss: 0.3052\n","Epoch [25/100], Step [201/329], Loss: 0.3733\n","Epoch [25/100], Step [301/329], Loss: 0.1629\n","Epoch [25/100], Validation Accuracy: 0.8660, Train Accuracy: 0.8912\n","Epoch [26/100], Step [1/329], Loss: 0.1427\n","Epoch [26/100], Step [101/329], Loss: 0.0948\n","Epoch [26/100], Step [201/329], Loss: 0.4202\n","Epoch [26/100], Step [301/329], Loss: 0.4084\n","Epoch [26/100], Validation Accuracy: 0.8546, Train Accuracy: 0.8920\n","Epoch [27/100], Step [1/329], Loss: 0.5487\n","Epoch [27/100], Step [101/329], Loss: 0.5936\n","Epoch [27/100], Step [201/329], Loss: 0.2229\n","Epoch [27/100], Step [301/329], Loss: 0.5555\n","Epoch [27/100], Validation Accuracy: 0.8772, Train Accuracy: 0.8872\n","New best model saved with Validation Accuracy: 0.8772\n","Epoch [28/100], Step [1/329], Loss: 0.1835\n","Epoch [28/100], Step [101/329], Loss: 0.1296\n","Epoch [28/100], Step [201/329], Loss: 0.4634\n","Epoch [28/100], Step [301/329], Loss: 0.3763\n","Epoch [28/100], Validation Accuracy: 0.8549, Train Accuracy: 0.9012\n","Epoch [29/100], Step [1/329], Loss: 0.3159\n","Epoch [29/100], Step [101/329], Loss: 0.1626\n","Epoch [29/100], Step [201/329], Loss: 0.2081\n","Epoch [29/100], Step [301/329], Loss: 0.5026\n","Epoch [29/100], Validation Accuracy: 0.8426, Train Accuracy: 0.9059\n","Epoch [30/100], Step [1/329], Loss: 0.3367\n","Epoch [30/100], Step [101/329], Loss: 0.1995\n","Epoch [30/100], Step [201/329], Loss: 0.2959\n","Epoch [30/100], Step [301/329], Loss: 0.3614\n","Epoch [30/100], Validation Accuracy: 0.8483, Train Accuracy: 0.8955\n","Epoch [31/100], Step [1/329], Loss: 0.3608\n","Epoch [31/100], Step [101/329], Loss: 0.2980\n","Epoch [31/100], Step [201/329], Loss: 0.1343\n","Epoch [31/100], Step [301/329], Loss: 0.2214\n","Epoch [31/100], Validation Accuracy: 0.8700, Train Accuracy: 0.9052\n","Epoch [32/100], Step [1/329], Loss: 0.1376\n","Epoch [32/100], Step [101/329], Loss: 0.3057\n","Epoch [32/100], Step [201/329], Loss: 0.1096\n","Epoch [32/100], Step [301/329], Loss: 0.1508\n","Epoch [32/100], Validation Accuracy: 0.8886, Train Accuracy: 0.9012\n","New best model saved with Validation Accuracy: 0.8886\n","Epoch [33/100], Step [1/329], Loss: 0.1756\n","Epoch [33/100], Step [101/329], Loss: 0.1130\n","Epoch [33/100], Step [201/329], Loss: 0.0710\n","Epoch [33/100], Step [301/329], Loss: 0.1801\n","Epoch [33/100], Validation Accuracy: 0.8189, Train Accuracy: 0.9148\n","Epoch [34/100], Step [1/329], Loss: 0.2148\n","Epoch [34/100], Step [101/329], Loss: 0.1551\n","Epoch [34/100], Step [201/329], Loss: 0.3475\n","Epoch [34/100], Step [301/329], Loss: 0.3001\n","Epoch [34/100], Validation Accuracy: 0.8872, Train Accuracy: 0.9048\n","Epoch [35/100], Step [1/329], Loss: 0.1532\n","Epoch [35/100], Step [101/329], Loss: 0.3316\n","Epoch [35/100], Step [201/329], Loss: 0.1790\n","Epoch [35/100], Step [301/329], Loss: 0.2056\n","Epoch [35/100], Validation Accuracy: 0.8606, Train Accuracy: 0.9198\n","Epoch [36/100], Step [1/329], Loss: 0.1033\n","Epoch [36/100], Step [101/329], Loss: 0.1578\n","Epoch [36/100], Step [201/329], Loss: 0.2561\n","Epoch [36/100], Step [301/329], Loss: 0.4584\n","Epoch [36/100], Validation Accuracy: 0.9009, Train Accuracy: 0.9129\n","New best model saved with Validation Accuracy: 0.9009\n","Epoch [37/100], Step [1/329], Loss: 0.3743\n","Epoch [37/100], Step [101/329], Loss: 0.3457\n","Epoch [37/100], Step [201/329], Loss: 0.5704\n","Epoch [37/100], Step [301/329], Loss: 0.1361\n","Epoch [37/100], Validation Accuracy: 0.8772, Train Accuracy: 0.9134\n","Epoch [38/100], Step [1/329], Loss: 0.0953\n","Epoch [38/100], Step [101/329], Loss: 0.1835\n","Epoch [38/100], Step [201/329], Loss: 0.3207\n","Epoch [38/100], Step [301/329], Loss: 0.0801\n","Epoch [38/100], Validation Accuracy: 0.8980, Train Accuracy: 0.9145\n","Epoch [39/100], Step [1/329], Loss: 0.1217\n","Epoch [39/100], Step [101/329], Loss: 0.1431\n","Epoch [39/100], Step [201/329], Loss: 0.1786\n","Epoch [39/100], Step [301/329], Loss: 0.3826\n","Epoch [39/100], Validation Accuracy: 0.8752, Train Accuracy: 0.9191\n","Epoch [40/100], Step [1/329], Loss: 0.2847\n","Epoch [40/100], Step [101/329], Loss: 0.1666\n","Epoch [40/100], Step [201/329], Loss: 0.0626\n","Epoch [40/100], Step [301/329], Loss: 0.1763\n","Epoch [40/100], Validation Accuracy: 0.8683, Train Accuracy: 0.9214\n","Epoch [41/100], Step [1/329], Loss: 0.2498\n","Epoch [41/100], Step [101/329], Loss: 0.2499\n","Epoch [41/100], Step [201/329], Loss: 0.1731\n","Epoch [41/100], Step [301/329], Loss: 0.1042\n","Epoch [41/100], Validation Accuracy: 0.9012, Train Accuracy: 0.9225\n","New best model saved with Validation Accuracy: 0.9012\n","Epoch [42/100], Step [1/329], Loss: 0.0436\n","Epoch [42/100], Step [101/329], Loss: 0.0492\n","Epoch [42/100], Step [201/329], Loss: 0.3093\n","Epoch [42/100], Step [301/329], Loss: 0.5624\n","Epoch [42/100], Validation Accuracy: 0.8906, Train Accuracy: 0.9257\n","Epoch [43/100], Step [1/329], Loss: 0.0722\n","Epoch [43/100], Step [101/329], Loss: 0.0562\n","Epoch [43/100], Step [201/329], Loss: 0.3585\n","Epoch [43/100], Step [301/329], Loss: 0.1729\n","Epoch [43/100], Validation Accuracy: 0.8578, Train Accuracy: 0.9283\n","Epoch [44/100], Step [1/329], Loss: 0.0902\n","Epoch [44/100], Step [101/329], Loss: 0.1424\n","Epoch [44/100], Step [201/329], Loss: 0.2788\n","Epoch [44/100], Step [301/329], Loss: 0.2576\n","Epoch [44/100], Validation Accuracy: 0.8777, Train Accuracy: 0.9305\n","Epoch [45/100], Step [1/329], Loss: 0.1477\n","Epoch [45/100], Step [101/329], Loss: 0.2646\n","Epoch [45/100], Step [201/329], Loss: 0.2953\n","Epoch [45/100], Step [301/329], Loss: 0.1609\n","Epoch [45/100], Validation Accuracy: 0.8506, Train Accuracy: 0.9213\n","Epoch [46/100], Step [1/329], Loss: 0.1698\n","Epoch [46/100], Step [101/329], Loss: 0.0396\n","Epoch [46/100], Step [201/329], Loss: 0.1600\n","Epoch [46/100], Step [301/329], Loss: 0.3505\n","Epoch [46/100], Validation Accuracy: 0.8897, Train Accuracy: 0.9251\n","Epoch [47/100], Step [1/329], Loss: 0.3207\n","Epoch [47/100], Step [101/329], Loss: 0.2032\n","Epoch [47/100], Step [201/329], Loss: 0.1978\n","Epoch [47/100], Step [301/329], Loss: 0.1986\n","Epoch [47/100], Validation Accuracy: 0.9009, Train Accuracy: 0.9298\n","Epoch [48/100], Step [1/329], Loss: 0.0587\n","Epoch [48/100], Step [101/329], Loss: 0.2205\n","Epoch [48/100], Step [201/329], Loss: 0.1407\n","Epoch [48/100], Step [301/329], Loss: 0.2305\n","Epoch [48/100], Validation Accuracy: 0.9032, Train Accuracy: 0.9357\n","New best model saved with Validation Accuracy: 0.9032\n","Epoch [49/100], Step [1/329], Loss: 0.0666\n","Epoch [49/100], Step [101/329], Loss: 0.0316\n","Epoch [49/100], Step [201/329], Loss: 0.1043\n","Epoch [49/100], Step [301/329], Loss: 0.1690\n","Epoch [49/100], Validation Accuracy: 0.8872, Train Accuracy: 0.9370\n","Epoch [50/100], Step [1/329], Loss: 0.1431\n","Epoch [50/100], Step [101/329], Loss: 0.0225\n","Epoch [50/100], Step [201/329], Loss: 0.1100\n","Epoch [50/100], Step [301/329], Loss: 0.1565\n","Epoch [50/100], Validation Accuracy: 0.8883, Train Accuracy: 0.9343\n","Epoch [51/100], Step [1/329], Loss: 0.1937\n","Epoch [51/100], Step [101/329], Loss: 0.2161\n","Epoch [51/100], Step [201/329], Loss: 0.3550\n","Epoch [51/100], Step [301/329], Loss: 0.1247\n","Epoch [51/100], Validation Accuracy: 0.9035, Train Accuracy: 0.9405\n","New best model saved with Validation Accuracy: 0.9035\n","Epoch [52/100], Step [1/329], Loss: 0.1600\n","Epoch [52/100], Step [101/329], Loss: 0.1401\n","Epoch [52/100], Step [201/329], Loss: 0.0554\n","Epoch [52/100], Step [301/329], Loss: 0.3733\n","Epoch [52/100], Validation Accuracy: 0.8763, Train Accuracy: 0.9391\n","Epoch [53/100], Step [1/329], Loss: 0.1698\n","Epoch [53/100], Step [101/329], Loss: 0.0545\n","Epoch [53/100], Step [201/329], Loss: 0.0814\n","Epoch [53/100], Step [301/329], Loss: 0.1625\n","Epoch [53/100], Validation Accuracy: 0.8883, Train Accuracy: 0.9394\n","Epoch [54/100], Step [1/329], Loss: 0.2316\n","Epoch [54/100], Step [101/329], Loss: 0.0397\n","Epoch [54/100], Step [201/329], Loss: 0.1293\n","Epoch [54/100], Step [301/329], Loss: 0.1939\n","Epoch [54/100], Validation Accuracy: 0.9083, Train Accuracy: 0.9377\n","New best model saved with Validation Accuracy: 0.9083\n","Epoch [55/100], Step [1/329], Loss: 0.3437\n","Epoch [55/100], Step [101/329], Loss: 0.1108\n","Epoch [55/100], Step [201/329], Loss: 0.1058\n","Epoch [55/100], Step [301/329], Loss: 0.1220\n","Epoch [55/100], Validation Accuracy: 0.9046, Train Accuracy: 0.9418\n","Epoch [56/100], Step [1/329], Loss: 0.0661\n","Epoch [56/100], Step [101/329], Loss: 0.1087\n","Epoch [56/100], Step [201/329], Loss: 0.1330\n","Epoch [56/100], Step [301/329], Loss: 0.0551\n","Epoch [56/100], Validation Accuracy: 0.9075, Train Accuracy: 0.9441\n","Epoch [57/100], Step [1/329], Loss: 0.0992\n","Epoch [57/100], Step [101/329], Loss: 0.4746\n","Epoch [57/100], Step [201/329], Loss: 0.1455\n","Epoch [57/100], Step [301/329], Loss: 0.1873\n","Epoch [57/100], Validation Accuracy: 0.8897, Train Accuracy: 0.9484\n","Epoch [58/100], Step [1/329], Loss: 0.0343\n","Epoch [58/100], Step [101/329], Loss: 0.1993\n","Epoch [58/100], Step [201/329], Loss: 0.3509\n","Epoch [58/100], Step [301/329], Loss: 0.0908\n","Epoch [58/100], Validation Accuracy: 0.8897, Train Accuracy: 0.9467\n","Epoch [59/100], Step [1/329], Loss: 0.1619\n","Epoch [59/100], Step [101/329], Loss: 0.1094\n","Epoch [59/100], Step [201/329], Loss: 0.1080\n","Epoch [59/100], Step [301/329], Loss: 0.0180\n","Epoch [59/100], Validation Accuracy: 0.8743, Train Accuracy: 0.9441\n","Epoch [60/100], Step [1/329], Loss: 0.1442\n","Epoch [60/100], Step [101/329], Loss: 0.2933\n","Epoch [60/100], Step [201/329], Loss: 0.2775\n","Epoch [60/100], Step [301/329], Loss: 0.1005\n","Epoch [60/100], Validation Accuracy: 0.9086, Train Accuracy: 0.9454\n","New best model saved with Validation Accuracy: 0.9086\n","Epoch [61/100], Step [1/329], Loss: 0.1130\n","Epoch [61/100], Step [101/329], Loss: 0.0513\n","Epoch [61/100], Step [201/329], Loss: 0.2667\n","Epoch [61/100], Step [301/329], Loss: 0.0582\n","Epoch [61/100], Validation Accuracy: 0.8706, Train Accuracy: 0.9496\n","Epoch [62/100], Step [1/329], Loss: 0.0457\n","Epoch [62/100], Step [101/329], Loss: 0.4189\n","Epoch [62/100], Step [201/329], Loss: 0.0931\n","Epoch [62/100], Step [301/329], Loss: 0.2602\n","Epoch [62/100], Validation Accuracy: 0.9149, Train Accuracy: 0.9426\n","New best model saved with Validation Accuracy: 0.9149\n","Epoch [63/100], Step [1/329], Loss: 0.1063\n","Epoch [63/100], Step [101/329], Loss: 0.2580\n","Epoch [63/100], Step [201/329], Loss: 0.2372\n","Epoch [63/100], Step [301/329], Loss: 0.0474\n","Epoch [63/100], Validation Accuracy: 0.9109, Train Accuracy: 0.9503\n","Epoch [64/100], Step [1/329], Loss: 0.0713\n","Epoch [64/100], Step [101/329], Loss: 0.0663\n","Epoch [64/100], Step [201/329], Loss: 0.0279\n","Epoch [64/100], Step [301/329], Loss: 0.8449\n","Epoch [64/100], Validation Accuracy: 0.8663, Train Accuracy: 0.9524\n","Epoch [65/100], Step [1/329], Loss: 0.1135\n","Epoch [65/100], Step [101/329], Loss: 0.2699\n","Epoch [65/100], Step [201/329], Loss: 0.2207\n","Epoch [65/100], Step [301/329], Loss: 0.2706\n","Epoch [65/100], Validation Accuracy: 0.9060, Train Accuracy: 0.9393\n","Epoch [66/100], Step [1/329], Loss: 0.0350\n","Epoch [66/100], Step [101/329], Loss: 0.1088\n","Epoch [66/100], Step [201/329], Loss: 0.4091\n","Epoch [66/100], Step [301/329], Loss: 0.1772\n","Epoch [66/100], Validation Accuracy: 0.9026, Train Accuracy: 0.9434\n","Epoch [67/100], Step [1/329], Loss: 0.0713\n","Epoch [67/100], Step [101/329], Loss: 0.1771\n","Epoch [67/100], Step [201/329], Loss: 0.0981\n","Epoch [67/100], Step [301/329], Loss: 0.1239\n","Epoch [67/100], Validation Accuracy: 0.9052, Train Accuracy: 0.9535\n","Epoch [68/100], Step [1/329], Loss: 0.1120\n","Epoch [68/100], Step [101/329], Loss: 0.0459\n","Epoch [68/100], Step [201/329], Loss: 0.1699\n","Epoch [68/100], Step [301/329], Loss: 0.0424\n","Epoch [68/100], Validation Accuracy: 0.8660, Train Accuracy: 0.9470\n","Epoch [69/100], Step [1/329], Loss: 0.2206\n","Epoch [69/100], Step [101/329], Loss: 0.3666\n","Epoch [69/100], Step [201/329], Loss: 0.1483\n","Epoch [69/100], Step [301/329], Loss: 0.1441\n","Epoch [69/100], Validation Accuracy: 0.8963, Train Accuracy: 0.9185\n","Epoch [70/100], Step [1/329], Loss: 0.0768\n","Epoch [70/100], Step [101/329], Loss: 0.1541\n","Epoch [70/100], Step [201/329], Loss: 0.1363\n","Epoch [70/100], Step [301/329], Loss: 0.1290\n","Epoch [70/100], Validation Accuracy: 0.9089, Train Accuracy: 0.9496\n","Epoch [71/100], Step [1/329], Loss: 0.0486\n","Epoch [71/100], Step [101/329], Loss: 0.0298\n","Epoch [71/100], Step [201/329], Loss: 0.1269\n","Epoch [71/100], Step [301/329], Loss: 0.0431\n","Epoch [71/100], Validation Accuracy: 0.8803, Train Accuracy: 0.9585\n","Epoch [72/100], Step [1/329], Loss: 0.0706\n","Epoch [72/100], Step [101/329], Loss: 0.1366\n","Epoch [72/100], Step [201/329], Loss: 0.1158\n","Epoch [72/100], Step [301/329], Loss: 0.1990\n","Epoch [72/100], Validation Accuracy: 0.8852, Train Accuracy: 0.9542\n","Epoch [73/100], Step [1/329], Loss: 0.1632\n","Epoch [73/100], Step [101/329], Loss: 0.0893\n","Epoch [73/100], Step [201/329], Loss: 0.1275\n","Epoch [73/100], Step [301/329], Loss: 0.0808\n","Epoch [73/100], Validation Accuracy: 0.8980, Train Accuracy: 0.9579\n","Epoch [74/100], Step [1/329], Loss: 0.0729\n","Epoch [74/100], Step [101/329], Loss: 0.0720\n","Epoch [74/100], Step [201/329], Loss: 0.1175\n","Epoch [74/100], Step [301/329], Loss: 0.0103\n","Epoch [74/100], Validation Accuracy: 0.9092, Train Accuracy: 0.9591\n","Epoch [75/100], Step [1/329], Loss: 0.3295\n","Epoch [75/100], Step [101/329], Loss: 0.1192\n","Epoch [75/100], Step [201/329], Loss: 0.0735\n","Epoch [75/100], Step [301/329], Loss: 0.0664\n","Epoch [75/100], Validation Accuracy: 0.8980, Train Accuracy: 0.9624\n","Epoch [76/100], Step [1/329], Loss: 0.1065\n","Epoch [76/100], Step [101/329], Loss: 0.0494\n","Epoch [76/100], Step [201/329], Loss: 0.0920\n","Epoch [76/100], Step [301/329], Loss: 0.0414\n","Epoch [76/100], Validation Accuracy: 0.8817, Train Accuracy: 0.9574\n","Epoch [77/100], Step [1/329], Loss: 0.0871\n","Epoch [77/100], Step [101/329], Loss: 0.0842\n","Epoch [77/100], Step [201/329], Loss: 0.0657\n","Epoch [77/100], Step [301/329], Loss: 0.1043\n","Epoch [77/100], Validation Accuracy: 0.9029, Train Accuracy: 0.9583\n","Epoch [78/100], Step [1/329], Loss: 0.0948\n","Epoch [78/100], Step [101/329], Loss: 0.0615\n","Epoch [78/100], Step [201/329], Loss: 0.1197\n","Epoch [78/100], Step [301/329], Loss: 0.0737\n","Epoch [78/100], Validation Accuracy: 0.9040, Train Accuracy: 0.9605\n","Epoch [79/100], Step [1/329], Loss: 0.0400\n","Epoch [79/100], Step [101/329], Loss: 0.1643\n","Epoch [79/100], Step [201/329], Loss: 0.0471\n","Epoch [79/100], Step [301/329], Loss: 0.0595\n","Epoch [79/100], Validation Accuracy: 0.8777, Train Accuracy: 0.9585\n","Epoch [80/100], Step [1/329], Loss: 0.1532\n","Epoch [80/100], Step [101/329], Loss: 0.0278\n","Epoch [80/100], Step [201/329], Loss: 0.0857\n","Epoch [80/100], Step [301/329], Loss: 0.1401\n","Epoch [80/100], Validation Accuracy: 0.9009, Train Accuracy: 0.9613\n","Epoch [81/100], Step [1/329], Loss: 0.1199\n","Epoch [81/100], Step [101/329], Loss: 0.0615\n","Epoch [81/100], Step [201/329], Loss: 0.0581\n","Epoch [81/100], Step [301/329], Loss: 0.2357\n","Epoch [81/100], Validation Accuracy: 0.9040, Train Accuracy: 0.9658\n","Epoch [82/100], Step [1/329], Loss: 0.0867\n","Epoch [82/100], Step [101/329], Loss: 0.1128\n","Epoch [82/100], Step [201/329], Loss: 0.2686\n","Epoch [82/100], Step [301/329], Loss: 0.1032\n","Epoch [82/100], Validation Accuracy: 0.9106, Train Accuracy: 0.9628\n","Epoch [83/100], Step [1/329], Loss: 0.4748\n","Epoch [83/100], Step [101/329], Loss: 0.1005\n","Epoch [83/100], Step [201/329], Loss: 0.0364\n","Epoch [83/100], Step [301/329], Loss: 0.1834\n","Epoch [83/100], Validation Accuracy: 0.9129, Train Accuracy: 0.9627\n","Epoch [84/100], Step [1/329], Loss: 0.1013\n","Epoch [84/100], Step [101/329], Loss: 0.3789\n","Epoch [84/100], Step [201/329], Loss: 0.1315\n","Epoch [84/100], Step [301/329], Loss: 0.1428\n","Epoch [84/100], Validation Accuracy: 0.9112, Train Accuracy: 0.9578\n","Epoch [85/100], Step [1/329], Loss: 0.0383\n","Epoch [85/100], Step [101/329], Loss: 0.0708\n","Epoch [85/100], Step [201/329], Loss: 0.0779\n","Epoch [85/100], Step [301/329], Loss: 0.1887\n","Epoch [85/100], Validation Accuracy: 0.8823, Train Accuracy: 0.9697\n","Epoch [86/100], Step [1/329], Loss: 0.0553\n","Epoch [86/100], Step [101/329], Loss: 0.1260\n","Epoch [86/100], Step [201/329], Loss: 0.0325\n","Epoch [86/100], Step [301/329], Loss: 0.1651\n","Epoch [86/100], Validation Accuracy: 0.9006, Train Accuracy: 0.9651\n","Epoch [87/100], Step [1/329], Loss: 0.1429\n","Epoch [87/100], Step [101/329], Loss: 0.0457\n","Epoch [87/100], Step [201/329], Loss: 0.1975\n","Epoch [87/100], Step [301/329], Loss: 0.0894\n","Epoch [87/100], Validation Accuracy: 0.8726, Train Accuracy: 0.9643\n","Epoch [88/100], Step [1/329], Loss: 0.0335\n","Epoch [88/100], Step [101/329], Loss: 0.1113\n","Epoch [88/100], Step [201/329], Loss: 0.0700\n","Epoch [88/100], Step [301/329], Loss: 0.0221\n","Epoch [88/100], Validation Accuracy: 0.9126, Train Accuracy: 0.9567\n","Epoch [89/100], Step [1/329], Loss: 0.2884\n","Epoch [89/100], Step [101/329], Loss: 0.0313\n","Epoch [89/100], Step [201/329], Loss: 0.0163\n","Epoch [89/100], Step [301/329], Loss: 0.0299\n","Epoch [89/100], Validation Accuracy: 0.9095, Train Accuracy: 0.9716\n","Epoch [90/100], Step [1/329], Loss: 0.0210\n","Epoch [90/100], Step [101/329], Loss: 0.2643\n","Epoch [90/100], Step [201/329], Loss: 0.0735\n","Epoch [90/100], Step [301/329], Loss: 0.4600\n","Epoch [90/100], Validation Accuracy: 0.9077, Train Accuracy: 0.9614\n","Epoch [91/100], Step [1/329], Loss: 0.0626\n","Epoch [91/100], Step [101/329], Loss: 0.0283\n","Epoch [91/100], Step [201/329], Loss: 0.0202\n","Epoch [91/100], Step [301/329], Loss: 0.0342\n","Epoch [91/100], Validation Accuracy: 0.9175, Train Accuracy: 0.9650\n","New best model saved with Validation Accuracy: 0.9175\n","Epoch [92/100], Step [1/329], Loss: 0.0656\n","Epoch [92/100], Step [101/329], Loss: 0.1193\n","Epoch [92/100], Step [201/329], Loss: 0.0934\n","Epoch [92/100], Step [301/329], Loss: 0.1186\n","Epoch [92/100], Validation Accuracy: 0.9012, Train Accuracy: 0.9692\n","Epoch [93/100], Step [1/329], Loss: 0.0648\n","Epoch [93/100], Step [101/329], Loss: 0.0443\n","Epoch [93/100], Step [201/329], Loss: 0.0517\n","Epoch [93/100], Step [301/329], Loss: 0.0686\n","Epoch [93/100], Validation Accuracy: 0.9003, Train Accuracy: 0.9651\n","Epoch [94/100], Step [1/329], Loss: 0.0432\n","Epoch [94/100], Step [101/329], Loss: 0.1444\n","Epoch [94/100], Step [201/329], Loss: 0.0633\n","Epoch [94/100], Step [301/329], Loss: 0.0962\n","Epoch [94/100], Validation Accuracy: 0.8975, Train Accuracy: 0.9716\n","Epoch [95/100], Step [1/329], Loss: 0.0412\n","Epoch [95/100], Step [101/329], Loss: 0.2793\n","Epoch [95/100], Step [201/329], Loss: 0.1455\n","Epoch [95/100], Step [301/329], Loss: 0.1095\n","Epoch [95/100], Validation Accuracy: 0.9080, Train Accuracy: 0.9645\n","Epoch [96/100], Step [1/329], Loss: 0.0088\n","Epoch [96/100], Step [101/329], Loss: 0.0320\n","Epoch [96/100], Step [201/329], Loss: 0.0507\n","Epoch [96/100], Step [301/329], Loss: 0.0180\n","Epoch [96/100], Validation Accuracy: 0.9046, Train Accuracy: 0.9706\n","Epoch [97/100], Step [1/329], Loss: 0.0611\n","Epoch [97/100], Step [101/329], Loss: 0.0236\n","Epoch [97/100], Step [201/329], Loss: 0.0498\n","Epoch [97/100], Step [301/329], Loss: 0.0559\n","Epoch [97/100], Validation Accuracy: 0.8800, Train Accuracy: 0.9671\n","Epoch [98/100], Step [1/329], Loss: 0.0853\n","Epoch [98/100], Step [101/329], Loss: 0.2146\n","Epoch [98/100], Step [201/329], Loss: 0.0749\n","Epoch [98/100], Step [301/329], Loss: 0.0133\n","Epoch [98/100], Validation Accuracy: 0.9089, Train Accuracy: 0.9678\n","Epoch [99/100], Step [1/329], Loss: 0.1619\n","Epoch [99/100], Step [101/329], Loss: 0.0080\n","Epoch [99/100], Step [201/329], Loss: 0.1373\n","Epoch [99/100], Step [301/329], Loss: 0.0846\n","Epoch [99/100], Validation Accuracy: 0.9203, Train Accuracy: 0.9731\n","New best model saved with Validation Accuracy: 0.9203\n","Epoch [100/100], Step [1/329], Loss: 0.0491\n","Epoch [100/100], Step [101/329], Loss: 0.0116\n","Epoch [100/100], Step [201/329], Loss: 0.1509\n","Epoch [100/100], Step [301/329], Loss: 0.0089\n","Epoch [100/100], Validation Accuracy: 0.9006, Train Accuracy: 0.9718\n","Training history saved to history_pretrained_weights_resnet50.json\n"]}],"source":["import json   # For saving the history\n","\n","\n","# Loss Function and Optimizer Setup:\n","\n","criterion = nn.CrossEntropyLoss()\n","\n","optimizer = optim.Adam(model.parameters(), lr=1e-3)    # Changing the learning rate to le-3 from le-4\n","\n","\n","# Training History and Device Setup:\n","# Initialize history dictionary\n","\n","history = {\n","\n","        'train_loss': [],\n","\n","        'train_accuracy': [],\n","\n","        'val_loss': [],\n","\n","        'val_accuracy': []\n","\n","    }\n","\n","# Add model name to the history dictionary\n","history['model_name'] = 'Pretrained-weights ResNet50'  # Change the name for each model in each notebook\n","\n","best_val_accuracy = 0.0\n","best_model_path = \"best_model_preweight_ResNet50.pth\"\n","\n","# Device Setup\n","device = 'cuda' if torch.cuda.is_available() else 'cpu'\n","model = model.to(device)\n","\n","\n","# Applying regualrization:\n","\n","lambda_l1 = 0.0001  # Regularization strength for L1\n","lambda_l2 = 0.0001  # Regularization strength for L2\n","\n","# Dataloaders:\n","\n","train_loader = create_dataloader(loader_type='train')\n","val_loader = create_dataloader(loader_type='val')\n","\n","\n","# Training Loop:\n","\n","for epoch in range(100):             # You can adjust the number of epochs\n","\n","          model.train()\n","\n","          running_loss = 0.0\n","\n","          train_correct = 0\n","\n","          train_total = 0\n","\n","          for i, (inputs, targets) in enumerate(train_loader):\n","\n","              inputs, targets = inputs.to(device), targets.to(device)\n","\n","              optimizer.zero_grad()\n","\n","              outputs = model(inputs)\n","\n","              _, predicted = outputs.max(1)\n","\n","              loss = criterion(outputs, targets)\n","\n","              loss.backward()\n","\n","              optimizer.step()\n","\n","              #training metrics\n","\n","              running_loss += loss.item()\n","\n","              train_total += targets.size(0)\n","\n","              train_correct += predicted.eq(targets).sum().item()\n","\n","\n","\n","              if i % 100 == 0:\n","\n","                  print(f'Epoch [{epoch+1}/100], Step [{i+1}/{len(train_loader)}], Loss: {loss.item():.4f}')\n","\n","\n","\n","          #training metrics-\n","\n","          train_loss = running_loss / len(train_loader)\n","\n","          train_accuracy = train_correct / train_total\n","\n","          history['train_loss'].append(train_loss)\n","\n","          history['train_accuracy'].append(train_accuracy)\n","\n","\n","\n","          # Validation Loop:\n","\n","          model.eval()\n","\n","          val_loss = 0.0\n","\n","          val_correct = 0\n","\n","          val_total = 0\n","\n","          with torch.no_grad():\n","\n","            for inputs, targets in val_loader:\n","\n","              inputs, targets = inputs.to(device), targets.to(device)\n","\n","              outputs = model(inputs)\n","\n","              _, predicted = outputs.max(1)\n","\n","              loss = criterion(outputs, predicted)\n","\n","\n","\n","              val_loss += loss.item()\n","\n","              val_total += targets.size(0)\n","\n","              val_correct += predicted.eq(targets).sum().item()\n","\n","\n","\n","          # Calculate accuracy\n","\n","          val_loss /= len(val_loader)\n","\n","          val_accuracy = val_correct / val_total\n","\n","          history['val_loss'].append(val_loss)\n","\n","          history['val_accuracy'].append(val_accuracy)\n","\n","\n","\n","          print(f'Epoch [{epoch+1}/100], Validation Accuracy: {val_accuracy:.4f}, Train Accuracy: {train_accuracy:.4f}')\n","\n","\n","\n","          # Save Best Model / Model Checkpointing:\n","\n","          if val_accuracy > best_val_accuracy:\n","\n","            best_val_accuracy = val_accuracy\n","            history['best_val_accuracy'] = best_val_accuracy\n","\n","            torch.save(model.state_dict(), best_model_path)\n","\n","            print(f\"New best model saved with Validation Accuracy: {val_accuracy:.4f}\")\n","\n","\n","# Save the history as a JSON file:\n","history_file = 'history_pretrained_weights_resnet50.json'   # Change the filename for each model in each notebook\n","with open(history_file, 'w') as f:\n","    json.dump(history, f)\n","\n","print(f\"Training history saved to {history_file}\")\n"]},{"cell_type":"code","execution_count":null,"id":"730b2e6c","metadata":{"execution":{"iopub.execute_input":"2024-11-27T17:56:14.579507Z","iopub.status.busy":"2024-11-27T17:56:14.578721Z","iopub.status.idle":"2024-11-27T17:56:14.589044Z","shell.execute_reply":"2024-11-27T17:56:14.588194Z"},"executionInfo":{"elapsed":10,"status":"aborted","timestamp":1730261318019,"user":{"displayName":"tannistha maiti","userId":"15250126319063759955"},"user_tz":240},"id":"730b2e6c","outputId":"313b71bb-efb0-4804-d808-153e79fac423","papermill":{"duration":0.038014,"end_time":"2024-11-27T17:56:14.590699","exception":false,"start_time":"2024-11-27T17:56:14.552685","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["{'train_loss': [1.202370513355116,\n","  0.9096819936866818,\n","  0.7824220284140219,\n","  0.7197981695092558,\n","  0.6675792220968606,\n","  0.607978422953365,\n","  0.6038050217650219,\n","  0.5768644963716664,\n","  0.5062555286206736,\n","  0.4936961183794364,\n","  0.4865525117579927,\n","  0.4567179815747455,\n","  0.4433249170838156,\n","  0.439529150724411,\n","  0.4258477324818043,\n","  0.4188004280114971,\n","  0.3956343453479393,\n","  0.3732747134602541,\n","  0.39114709540431625,\n","  0.34800643879229537,\n","  0.34620165215708926,\n","  0.3918289912009674,\n","  0.33312236900387565,\n","  0.3195035144188484,\n","  0.31705496724026905,\n","  0.30301204110894886,\n","  0.30699751332492814,\n","  0.28804637469056893,\n","  0.26462684186758606,\n","  0.2944117957629875,\n","  0.26883600186839657,\n","  0.2798754331686939,\n","  0.24757887656859895,\n","  0.26640860107939657,\n","  0.22523557693672036,\n","  0.24667663388180697,\n","  0.24144202217708785,\n","  0.2501075078130312,\n","  0.2177452830127851,\n","  0.22607858225386193,\n","  0.21447292017809888,\n","  0.2090301114420398,\n","  0.20127768157825643,\n","  0.20323098784929713,\n","  0.22074044810036214,\n","  0.2179230042821006,\n","  0.20114148774192778,\n","  0.17546565730215297,\n","  0.1772603734104829,\n","  0.19066297259793183,\n","  0.17014640280382431,\n","  0.18144404897211533,\n","  0.16556611274989,\n","  0.17726013515891909,\n","  0.16429240714566143,\n","  0.15870530727548712,\n","  0.14546713563627747,\n","  0.14711204874909575,\n","  0.15303926456204436,\n","  0.152421140956967,\n","  0.14277021703239542,\n","  0.16635311304911474,\n","  0.14172047167722035,\n","  0.14537778700557524,\n","  0.17260876212744636,\n","  0.1563799128411932,\n","  0.12695789079133726,\n","  0.15381461983450367,\n","  0.23605134956946308,\n","  0.13154278813626358,\n","  0.1127274840840883,\n","  0.12653798357467522,\n","  0.11873787171051677,\n","  0.11502100944683227,\n","  0.1017692496187668,\n","  0.12149870578386456,\n","  0.11643345675524608,\n","  0.11032254242488271,\n","  0.11707239923786406,\n","  0.10770967916945012,\n","  0.0964884433207473,\n","  0.10548360045223658,\n","  0.11314307108357277,\n","  0.11305874516468462,\n","  0.08824281330081693,\n","  0.09533242126615395,\n","  0.10888562541692814,\n","  0.1308572823401133,\n","  0.08099094170887351,\n","  0.11342428746636063,\n","  0.09866891472280524,\n","  0.08755301813967242,\n","  0.09847327993933598,\n","  0.08022934625542363,\n","  0.09919770130757412,\n","  0.08459926446819095,\n","  0.0909375640468836,\n","  0.08887780901245591,\n","  0.0762064720264786,\n","  0.08032140438515682],\n"," 'train_accuracy': [0.5808970574231026,\n","  0.6702218836301305,\n","  0.7187886867917341,\n","  0.7450718979144844,\n","  0.763355870869441,\n","  0.7804018664889059,\n","  0.7933530139986668,\n","  0.7941148462051233,\n","  0.8226835539472431,\n","  0.8293495857537377,\n","  0.8338253499666698,\n","  0.8433482525473764,\n","  0.8464908103990096,\n","  0.848490619940958,\n","  0.8522045519474336,\n","  0.8536329873345395,\n","  0.8617274545281401,\n","  0.8708694410056185,\n","  0.8626797447862108,\n","  0.8755356632701647,\n","  0.8815350918960099,\n","  0.8613465384249119,\n","  0.8797257404056756,\n","  0.8869631463670127,\n","  0.8912484525283306,\n","  0.8920102847347872,\n","  0.8871536044186268,\n","  0.9011522712122655,\n","  0.9059137225026188,\n","  0.8955337586896486,\n","  0.9052471193219693,\n","  0.9011522712122655,\n","  0.914770021902676,\n","  0.904770974192934,\n","  0.9198171602704505,\n","  0.9128654413865346,\n","  0.913436815541377,\n","  0.9144843348252547,\n","  0.9190553280639939,\n","  0.9214360537091706,\n","  0.9224835729930483,\n","  0.9257213598704885,\n","  0.9282925435672793,\n","  0.9304828111608419,\n","  0.9213408246833635,\n","  0.925054756689839,\n","  0.9298162079801924,\n","  0.9357204075802305,\n","  0.9369583849157224,\n","  0.9342919721931244,\n","  0.9404818588705838,\n","  0.9390534234834778,\n","  0.9394343395867061,\n","  0.9377202171221788,\n","  0.9418150652318826,\n","  0.9441005618512522,\n","  0.9483858680125702,\n","  0.946671745548043,\n","  0.9441005618512522,\n","  0.9454337682125512,\n","  0.9496238453480621,\n","  0.9425768974383392,\n","  0.9502904485287116,\n","  0.952385487096467,\n","  0.9393391105608989,\n","  0.9434339586706028,\n","  0.9535282354061518,\n","  0.9469574326254643,\n","  0.9184839539091515,\n","  0.9496238453480621,\n","  0.9584801447481193,\n","  0.9541948385868012,\n","  0.9579087705932768,\n","  0.9591467479287686,\n","  0.9623845348062089,\n","  0.9574326254642415,\n","  0.958289686696505,\n","  0.9604799542900676,\n","  0.9584801447481193,\n","  0.9613370155223312,\n","  0.9658127797352634,\n","  0.9627654509094372,\n","  0.9626702218836302,\n","  0.9578135415674698,\n","  0.969717169793353,\n","  0.9651461765546139,\n","  0.9642891153223503,\n","  0.9566707932577849,\n","  0.9716217503094944,\n","  0.9614322445481382,\n","  0.9649557185029997,\n","  0.9692410246643177,\n","  0.9651461765546139,\n","  0.9716217503094944,\n","  0.9644795733739644,\n","  0.9705742310256166,\n","  0.9670507570707552,\n","  0.9678125892772117,\n","  0.9730501856966003,\n","  0.9718122083611085],\n"," 'val_loss': [0.4353443766182119,\n","  0.38696241080760957,\n","  0.34945719079537824,\n","  0.3231105491518974,\n","  0.27114887034351176,\n","  0.2682468181306666,\n","  0.24694821997122332,\n","  0.2821775957942009,\n","  0.17033184963193806,\n","  0.1885987672616135,\n","  0.19605912098830397,\n","  0.19432275302030824,\n","  0.16947793936864897,\n","  0.18608551154082473,\n","  0.16895182423970917,\n","  0.16067833527922631,\n","  0.14899036068130622,\n","  0.17399189533157783,\n","  0.18353183696215802,\n","  0.14732728299092163,\n","  0.1392047030004588,\n","  0.20042233236811377,\n","  0.14610420553521677,\n","  0.13665895418009974,\n","  0.129216952892867,\n","  0.13369333740662445,\n","  0.13674096685241569,\n","  0.14467466656457295,\n","  0.0994950389658863,\n","  0.13353557298806581,\n","  0.13006258081983435,\n","  0.12150753065943717,\n","  0.11856225251474163,\n","  0.10762768631631678,\n","  0.12217874946919355,\n","  0.1064234162596139,\n","  0.10712282278321006,\n","  0.09919309517876669,\n","  0.10417941012843089,\n","  0.10942830534821207,\n","  0.08365643225948918,\n","  0.09763613539663228,\n","  0.10085264689881693,\n","  0.10708446563644843,\n","  0.09938009428707036,\n","  0.09642341836270961,\n","  0.08030184097588063,\n","  0.08393375519663096,\n","  0.09877404106611555,\n","  0.09272881206125021,\n","  0.07683967580510812,\n","  0.08828886957331138,\n","  0.07938236324116588,\n","  0.07334726479920474,\n","  0.07774840176782825,\n","  0.07601959244771438,\n","  0.07369681683114984,\n","  0.08290679737586867,\n","  0.08621151022274386,\n","  0.07286865773864767,\n","  0.08362184673209082,\n","  0.07526213352314451,\n","  0.07246011016382413,\n","  0.08651916943490505,\n","  0.06678844651376659,\n","  0.06454547612335194,\n","  0.0736414436250925,\n","  0.09244268150492148,\n","  0.08048215083439242,\n","  0.07228237214413556,\n","  0.07450215970589356,\n","  0.0774667055261406,\n","  0.07053880752487617,\n","  0.06423810954121026,\n","  0.07788029611110688,\n","  0.06998352549800818,\n","  0.07202542512254281,\n","  0.06186832231892781,\n","  0.08189669337800957,\n","  0.06537979749793356,\n","  0.06303860299627889,\n","  0.06123670652847398,\n","  0.06092592780901627,\n","  0.06247201443395831,\n","  0.06954820391468026,\n","  0.05854250154542652,\n","  0.0711755622474646,\n","  0.057794895306737586,\n","  0.051739301710304886,\n","  0.06273384288969365,\n","  0.05242087004960261,\n","  0.06370160287584771,\n","  0.06766659208488736,\n","  0.05977650926533071,\n","  0.05374817311848429,\n","  0.061416605009104716,\n","  0.06793691828338937,\n","  0.056831479030237955,\n","  0.0513465310446918,\n","  0.05421592910266058],\n"," 'val_accuracy': [0.5901171093973151,\n","  0.702656383890317,\n","  0.712082262210797,\n","  0.729791488146244,\n","  0.7660668380462725,\n","  0.787774921451014,\n","  0.7557840616966581,\n","  0.7000856898029134,\n","  0.7874892887746359,\n","  0.7600685518423308,\n","  0.8331905169951442,\n","  0.8243359040274207,\n","  0.8351899457297914,\n","  0.8189088831762353,\n","  0.8508997429305912,\n","  0.8560411311053985,\n","  0.8657526421022566,\n","  0.8423307626392459,\n","  0.8371893744644388,\n","  0.8460439874321622,\n","  0.8748928877463582,\n","  0.8160525564124536,\n","  0.8654670094258783,\n","  0.8706083976006855,\n","  0.8660382747786347,\n","  0.8546129677235076,\n","  0.8771779491573836,\n","  0.8548986003998857,\n","  0.8426163953156242,\n","  0.8483290488431876,\n","  0.8700371322479291,\n","  0.8886032562125107,\n","  0.8189088831762353,\n","  0.8871750928306198,\n","  0.8606112539274493,\n","  0.9008854612967724,\n","  0.8771779491573836,\n","  0.8980291345329906,\n","  0.8751785204227364,\n","  0.8683233361896601,\n","  0.9011710939731505,\n","  0.890602684947158,\n","  0.8577549271636675,\n","  0.87774921451014,\n","  0.8506141102542131,\n","  0.8897457869180234,\n","  0.9008854612967724,\n","  0.9031705227077977,\n","  0.8871750928306198,\n","  0.8883176235361325,\n","  0.9034561553841759,\n","  0.8763210511282491,\n","  0.8883176235361325,\n","  0.908311910882605,\n","  0.9045986860896886,\n","  0.9074550128534704,\n","  0.8897457869180234,\n","  0.8897457869180234,\n","  0.8743216223936018,\n","  0.9085975435589831,\n","  0.8706083976006855,\n","  0.914881462439303,\n","  0.9108826049700086,\n","  0.8663239074550129,\n","  0.9060268494715795,\n","  0.9025992573550414,\n","  0.905169951442445,\n","  0.8660382747786347,\n","  0.8963153384747216,\n","  0.9088831762353613,\n","  0.8803199085975436,\n","  0.8851756640959726,\n","  0.8980291345329906,\n","  0.9091688089117395,\n","  0.8980291345329906,\n","  0.8817480719794345,\n","  0.9028848900314196,\n","  0.9040274207369323,\n","  0.87774921451014,\n","  0.9008854612967724,\n","  0.9040274207369323,\n","  0.9105969722936303,\n","  0.9128820337046558,\n","  0.9111682376463868,\n","  0.8823193373321908,\n","  0.9005998286203942,\n","  0.8726078263353327,\n","  0.9125964010282777,\n","  0.9094544415881177,\n","  0.9077406455298486,\n","  0.9174521565267066,\n","  0.9011710939731505,\n","  0.900314195944016,\n","  0.8974578691802342,\n","  0.9080262782062268,\n","  0.9045986860896886,\n","  0.8800342759211653,\n","  0.9088831762353613,\n","  0.9203084832904884,\n","  0.9005998286203942],\n"," 'model_name': 'Pretrained-weights ResNet50',\n"," 'best_val_accuracy': 0.9203084832904884}"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["history"]},{"cell_type":"markdown","id":"3ac14fce","metadata":{"id":"3ac14fce","papermill":{"duration":0.026276,"end_time":"2024-11-27T17:56:14.642039","exception":false,"start_time":"2024-11-27T17:56:14.615763","status":"completed"},"tags":[]},"source":["Let's plot the above matrices for visualization."]},{"cell_type":"code","execution_count":null,"id":"971928eb","metadata":{"execution":{"iopub.execute_input":"2024-11-27T17:56:14.692766Z","iopub.status.busy":"2024-11-27T17:56:14.692434Z","iopub.status.idle":"2024-11-27T17:56:15.148150Z","shell.execute_reply":"2024-11-27T17:56:15.147195Z"},"executionInfo":{"elapsed":9,"status":"aborted","timestamp":1730261318019,"user":{"displayName":"tannistha maiti","userId":"15250126319063759955"},"user_tz":240},"id":"971928eb","outputId":"ff18c7a9-fd51-455b-f42f-f846e0426d2e","papermill":{"duration":0.483261,"end_time":"2024-11-27T17:56:15.149938","exception":false,"start_time":"2024-11-27T17:56:14.666677","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1200x400 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["def plot_history(history):\n","\n","    epochs = range(1, len(history['train_loss']) + 1)\n","\n","\n","\n","    plt.figure(figsize=(12, 4))\n","\n","\n","\n","    plt.subplot(1, 2, 1)\n","\n","    plt.plot(epochs, history['train_loss'], label='Train Loss')\n","\n","    plt.plot(epochs, history['val_loss'], label='Validation Loss')\n","\n","    plt.xlabel('Epochs')\n","\n","    plt.ylabel('Loss')\n","\n","    plt.title('Train and Validation Loss')\n","\n","    plt.legend()\n","\n","\n","\n","    plt.subplot(1, 2, 2)\n","\n","    plt.plot(epochs, history['train_accuracy'], label='Train Accuracy')\n","\n","    plt.plot(epochs, history['val_accuracy'], label='Validation Accuracy')\n","\n","    plt.xlabel('Epochs')\n","\n","    plt.ylabel('Accuracy')\n","\n","    plt.title('Train and Validation Accuracy')\n","\n","    plt.legend()\n","\n","\n","\n","    plt.show()\n","\n","\n","\n","plot_history(history)"]},{"cell_type":"markdown","id":"e68854d8","metadata":{"id":"e68854d8","papermill":{"duration":0.025395,"end_time":"2024-11-27T17:56:15.201417","exception":false,"start_time":"2024-11-27T17:56:15.176022","status":"completed"},"tags":[]},"source":["So from above we can see that our model does much better than even the 'data augmented' model:\n","\n","* The training accuracy approaches 0.97 (97%) at the end of the training.\n","\n","* Wherelese, the validation accuracy fluctuates but ends around 0.90 (90%)."]},{"cell_type":"markdown","id":"e4de5c25","metadata":{"id":"e4de5c25","papermill":{"duration":0.025289,"end_time":"2024-11-27T17:56:15.252171","exception":false,"start_time":"2024-11-27T17:56:15.226882","status":"completed"},"tags":[]},"source":["# Testing the model:-"]},{"cell_type":"markdown","id":"2ac1416d","metadata":{"id":"2ac1416d","papermill":{"duration":0.026122,"end_time":"2024-11-27T17:56:15.303967","exception":false,"start_time":"2024-11-27T17:56:15.277845","status":"completed"},"tags":[]},"source":["**Let's first test our model on a single image picked randomly.**"]},{"cell_type":"code","execution_count":null,"id":"201617da","metadata":{"execution":{"iopub.execute_input":"2024-11-27T17:56:15.357926Z","iopub.status.busy":"2024-11-27T17:56:15.357296Z","iopub.status.idle":"2024-11-27T17:56:57.848313Z","shell.execute_reply":"2024-11-27T17:56:57.847380Z"},"executionInfo":{"elapsed":9,"status":"aborted","timestamp":1730261318019,"user":{"displayName":"tannistha maiti","userId":"15250126319063759955"},"user_tz":240},"id":"201617da","outputId":"028650f6-4c32-4455-84e1-06f7c79a2830","papermill":{"duration":42.521136,"end_time":"2024-11-27T17:56:57.850858","exception":false,"start_time":"2024-11-27T17:56:15.329722","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["/tmp/ipykernel_23/3846559363.py:3: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n","  model.load_state_dict(torch.load(best_model_path))\n"]},{"name":"stdout","output_type":"stream","text":["Test Accuracy: 0.9127\n"]},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Predicted Class: 1\n","True Class: 0\n"]}],"source":["# Load the best model\n","\n","model.load_state_dict(torch.load(best_model_path))\n","\n","model.eval()  # Set model to evaluation mode\n","\n","\n","\n","# Create test loader (assumes a function `create_dataloader` exists)\n","\n","test_loader = create_dataloader(loader_type='test')\n","\n","\n","\n","# Evaluate model on the test set\n","\n","test_correct = 0\n","\n","test_total = 0\n","\n","\n","\n","with torch.no_grad():  # Disable gradient calculation for testing\n","\n","    for inputs, targets in test_loader:\n","\n","        inputs, targets = inputs.to(device), targets.to(device)\n","\n","        outputs = model(inputs)\n","\n","        _, predicted = outputs.max(1)  # Get predictions\n","\n","        test_total += targets.size(0)\n","\n","        test_correct += predicted.eq(targets).sum().item()\n","\n","\n","\n","test_accuracy = test_correct / test_total\n","\n","print(f'Test Accuracy: {test_accuracy:.4f}')\n","\n","\n","\n","# Choose a random image from the test loader and plot it\n","\n","dataiter = iter(test_loader)\n","\n","images, labels = next(dataiter)  # Use next() to get a batch\n","\n","\n","\n","# Randomly select an image from the batch\n","\n","random_index = np.random.randint(0, len(images))\n","\n","image = images[random_index]\n","\n","label = labels[random_index]\n","\n","\n","\n","# Move the image to the appropriate device and make prediction\n","\n","image_tensor = image.unsqueeze(0).to(device)\n","\n","output = model(image_tensor)\n","\n","_, predicted_class = output.max(1)\n","\n","\n","\n","# Convert image to CPU and detach for plotting\n","\n","image = image.cpu().numpy().transpose((1, 2, 0))  # Convert to numpy array for plotting\n","\n","image = np.clip(image, 0, 1)  # Normalize pixel values for display\n","\n","\n","\n","# Plot the image\n","\n","plt.imshow(image)\n","\n","plt.title(f'Predicted Class: {predicted_class.item()}, True Class: {label.item()}')\n","\n","plt.show()\n","\n","\n","\n","# Print the predicted class\n","\n","print(f\"Predicted Class: {predicted_class.item()}\")\n","\n","print(f\"True Class: {label.item()}\")"]},{"cell_type":"markdown","id":"ac77c89f","metadata":{"id":"ac77c89f","papermill":{"duration":0.026679,"end_time":"2024-11-27T17:56:57.904326","exception":false,"start_time":"2024-11-27T17:56:57.877647","status":"completed"},"tags":[]},"source":["**Now, let's test on a batch of images picked randomly.**"]},{"cell_type":"code","execution_count":null,"id":"ada5ba32","metadata":{"execution":{"iopub.execute_input":"2024-11-27T17:56:57.959755Z","iopub.status.busy":"2024-11-27T17:56:57.959404Z","iopub.status.idle":"2024-11-27T17:56:58.644025Z","shell.execute_reply":"2024-11-27T17:56:58.643098Z"},"id":"ada5ba32","outputId":"5943e753-b211-443f-8792-ae497afcfa91","papermill":{"duration":0.719123,"end_time":"2024-11-27T17:56:58.650197","exception":false,"start_time":"2024-11-27T17:56:57.931074","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABdIAAAFZCAYAAACczt2vAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9eZBl2VmfCz9r7fFMOWdNXVMPavUgtYaWxCQkQAMXITx8xljY3E9SfICYUQSGMNiBjY0BG5sQAbIwYZvJ2MQVFwOXC8gCJEAICSS1pO5Wt9RDVdeUc+aZ97SG7499Tg5VWVlZVZmVp6rXE3HqVJ5hn3X22fvda/3Wu36vsNZaHA6Hw+FwOBwOh8PhcDgcDofD4XBsizzoBjgcDofD4XA4HA6Hw+FwOBwOh8Mxyjgh3eFwOBwOh8PhcDgcDofD4XA4HI4dcEK6w+FwOBwOh8PhcDgcDofD4XA4HDvghHSHw+FwOBwOh8PhcDgcDofD4XA4dsAJ6Q6Hw+FwOBwOh8PhcDgcDofD4XDsgBPSHQ6Hw+FwOBwOh8PhcDgcDofD4dgBJ6Q7HA6Hw+FwOBwOh8PhcDgcDofDsQNOSHc4HA6Hw+FwOBwOh8PhcDgcDodjB5yQ7nA4HA6Hw+FwOBwOh8PhcDgcDscOOCHdcVVOnz7Nu971rvW/P/rRjyKE4KMf/eiBtelyLm+jw+F48eBilMPhGGVcjHI4HKOMi1EOh2OUcTHKMao4IX1E+dVf/VWEEOu3OI65//77+b7v+z4WFhYOunnXxR/+4R/yr/7VvzroZmyLMYZ//+//PXfffTdxHPPII4/wP//n/zzoZjkcI4+LUfvP2bNnt+zjzbff+q3fOujmORwjjYtRtwbXj3I4bgwXo24NLkY5HDeGi1G3Bhejbk/8g26AY2f+9b/+19x9992kacrHPvYxPvCBD/CHf/iHPPHEE1Sr1Vvalje84Q0kSUIYhtf1vj/8wz/k/e9//0gGr3/+z/85P/MzP8N3fMd38NrXvpbf+73f4x//43+MEIJ3vOMdB908h2PkcTFq//nWb/1W3va2t2157Cu+4isOqDUOx+2Fi1H7i+tHORw3h4tR+4uLUQ7HzeFi1P7iYtTtiRPSR5xv+IZv4DWveQ0A3/7t38709DQ/93M/x+/93u/xrd/6rdu+p9frUavV9rwtUkriON7z7R4UFy9e5D/+x//I937v9/KLv/iLQLmP3/jGN/LDP/zD/MN/+A/xPO+AW+lwjDYuRu0/r371q/m2b/u2g26Gw3Fb4mLU/uH6UQ7HzeNi1P7hYpTDcfO4GLV/uBh1++KsXW4zvu7rvg6AM2fOAPCud72Ler3Oc889x9ve9jYajQb/5J/8E6BcJvK+972Phx9+mDiOOXz4MO95z3tYW1vbsk1rLT/5kz/J8ePHqVarfO3Xfi1PPvnkFZ99NU+qT37yk7ztbW9jcnKSWq3GI488ws///M+vt+/9738/wJalQUP2uo0Azz33HM8999w19+Xv/d7vURQF3/M937P+mBCC7/7u7+bChQv89V//9TW34XA4tuJi1N7FqM30ej3yPL+u9zgcjitxMcr1oxyOUcbFKBejHI5RxsUoF6McLiP9tmN4Qk5PT68/ppTi67/+63n961/Pf/gP/2F9ic173vMefvVXf5V3v/vd/MAP/ABnzpzhF3/xF3nsscf4q7/6K4IgAODHf/zH+cmf/Ene9ra38ba3vY3PfOYzvPWtb92VaPPhD3+Yt7/97Rw9epQf/MEf5MiRIzz11FP8wR/8AT/4gz/Ie97zHi5dusSHP/xhfuM3fuOK9+9HG9/0pjcBpb/wTjz22GPUajUefPDBLY+/7nWvW3/+9a9//TX3gcPh2MDFqL2LUUN+4id+gh/+4R9GCMGjjz7Kv/23/5a3vvWtu3qvw+HYiotRrh/lcIwyLka5GOVwjDIuRrkY5QCsYyT5lV/5FQvYP/mTP7FLS0v2/Pnz9rd+67fs9PS0rVQq9sKFC9Zaa9/5zndawP6zf/bPtrz/L//yLy1gf/M3f3PL43/8x3+85fHFxUUbhqH9xm/8RmuMWX/dj/3Yj1nAvvOd71x/7CMf+YgF7Ec+8hFrrbVKKXv33XfbU6dO2bW1tS2fs3lb3/u932u3O9T2o43WWnvq1Cl76tSpKz7vcr7xG7/R3nPPPVc83uv1tt2nDodjAxej9j9GvfDCC/atb32r/cAHPmB///d/377vfe+zJ0+etFJK+wd/8AfXfL/D8WLGxSjXj3I4RhkXo1yMcjhGGRejXIxyXB1n7TLivPnNb2Z2dpYTJ07wjne8g3q9zv/6X/+Lu+66a8vrvvu7v3vL3x/84AcZHx/nLW95C8vLy+u3Rx99lHq9zkc+8hEA/uRP/oQ8z/n+7//+LUtc3vve916zbY899hhnzpzhve99LxMTE1ue27ytq7FfbTx79uyuMj2TJCGKoiseH/puJUlyzW04HC92XIzavxh18uRJPvShD/Fd3/VdfNM3fRM/+IM/yGOPPcbs7Cw/9EM/dM33OxwOF6NupI2uH+Vw3DpcjHIxyuEYZVyMcjHKcSXO2mXEef/738/999+P7/scPnyYl770pUi5df7D932OHz++5bFnnnmGVqvFoUOHtt3u4uIiAC+88AIAL3nJS7Y8Pzs7y+Tk5I5tGy7rednLXrb7L3SL27gTlUqFLMuueDxN0/XnHQ7HzrgYtX8xajumpqZ497vfzc/8zM9w4cKFK/arw+HYiotRrh/lcIwyLka5GOVwjDIuRrkY5bgSJ6SPOK973evWqyRfjSiKrghmxhgOHTrEb/7mb277ntnZ2T1r441y0G08evQoH/nIR7DWbplZnJubA+DYsWP7+vkOx52Ai1G3nhMnTgCwurrqhHSH4xq4GLV/uH6Uw3HzuBi1f7gY5XDcPC5G7R8uRt2+OCH9DuXee+/lT/7kT/iqr/qqHWeyTp06BZSzcffcc8/640tLS1dUKt7uMwCeeOIJ3vzmN1/1dVdbVnMr2rgTr3zlK/kv/+W/8NRTT/HQQw+tP/7JT35y/XmHw7E/uBh14zz//PPAaHRAHY47FRejro3rRzkcB4eLUdfGxSiH4+BwMerauBh1++I80u9QvuVbvgWtNf/m3/ybK55TStFsNoHS8yoIAn7hF34Ba+36a973vvdd8zNe/epXc/fdd/O+971vfXtDNm+rVqsBXPGa/Wrjc889t77MZyf+7t/9uwRBwH/6T/9pS7t/6Zd+ibvuuouv/MqvvOY2HA7HjeFi1LVj1NLS0hWPXbx4kf/23/4bjzzyCEePHr3mNhwOx43hYpTrRzkco4yLUS5GORyjjItRLkbdybiM9DuUN77xjbznPe/hp3/6p/nsZz/LW9/6VoIg4JlnnuGDH/wgP//zP883f/M3Mzs7yz/9p/+Un/7pn+btb387b3vb23jsscf4oz/6I2ZmZnb8DCklH/jAB/imb/omXvnKV/Lud7+bo0eP8vTTT/Pkk0/yoQ99CIBHH30UgB/4gR/g67/+6/E8j3e84x371sY3velNANcs8HD8+HHe+9738rM/+7MURcFrX/tafvd3f5e//Mu/5Dd/8zfxPO8G9rzD4dgNLkZdO0b9yI/8CM899xxvetObOHbsGGfPnuU//+f/TK/X4+d//udvYK87HI7d4mKU60c5HKOMi1EuRjkco4yLUS5G3dFYx0jyK7/yKxawf/u3f7vj6975znfaWq121ed/+Zd/2T766KO2UqnYRqNhX/7yl9sf+ZEfsZcuXVp/jdba/sRP/IQ9evSorVQq9mu+5mvsE088YU+dOmXf+c53rr/uIx/5iAXsRz7ykS2f8bGPfcy+5S1vsY1Gw9ZqNfvII4/YX/iFX1h/Xillv//7v9/Ozs5aIYS9/LDbyzZaa+2pU6fsqVOndtxvm7f7Uz/1U/bUqVM2DEP78MMP2//+3//7rt7rcLyYcTFq/2PU//gf/8O+4Q1vsLOzs9b3fTszM2P//t//+/bTn/70Nd/rcLzYcTHK9aMcjlHGxSgXoxyOUcbFKBejHFdHWLtpbYLD4XA4HA6Hw+FwOBwOh8PhcDgcji04j3SHw+FwOBwOh8PhcDgcDofD4XA4dsAJ6Q6Hw+FwOBwOh8PhcDgcDofD4XDsgBPSHQ6Hw+FwOBwOh8PhcDgcDofD4dgBJ6Q7HA6Hw+FwOBwOh8PhcDgcDofDsQNOSHc4HA6Hw+FwOBwOh8PhcDgcDodjB5yQ7nA4HA6Hw+FwOBwOh8PhcDgcDscOOCHd4XA4HA6Hw+FwOBwOh8PhcDgcjh1wQvqLCCHErm4f/ehHD7qpV+X3f//3efWrX00cx5w8eZJ/+S//JUqpg26Ww+HYA1yMcjgco4yLUQ6HY5RxMcrhcIwyLkY57hT8g26A49bxG7/xG1v+/vVf/3U+/OEPX/H4gw8+eCubtWv+6I/+iL/39/4eX/M1X8Mv/MIv8Pjjj/OTP/mTLC4u8oEPfOCgm+dwOG4SF6McDsco42KUw+EYZVyMcjgco4yLUY47BWGttQfdCMfB8H3f9328//3v51qHQL/fp1qt3qJWXZ2HH36YIAj41Kc+he+Xc0D/4l/8C37qp36KL3zhCzzwwAMH3EKHw7GXuBjlcDhGGRejHA7HKONilMPhGGVcjHLcrjhrF8cWvuZrvoaXvexlfPrTn+YNb3gD1WqVH/uxHwPKpTj/6l/9qyvec/r0ad71rndteazZbPLe976XEydOEEUR9913H//u3/07jDFbXjc3N8fTTz9NURQ7tusLX/gCX/jCF/jO7/zO9aAF8D3f8z1Ya/nt3/7tG/vCDofjtsLFKIfDMcq4GOVwOEYZF6McDsco42KU43bAWbs4rmBlZYVv+IZv4B3veAff9m3fxuHDh6/r/f1+nze+8Y1cvHiR97znPZw8eZKPf/zj/OiP/ihzc3O8733vW3/tj/7oj/Jrv/ZrnDlzhtOnT191m4899hgAr3nNa7Y8fuzYMY4fP77+vMPhuPNxMcrhcIwyLkY5HI5RxsUoh8MxyrgY5Rh1nJDuuIL5+Xl+6Zd+ife85z039P6f+7mf47nnnuOxxx7jJS95CQDvec97OHbsGD/7sz/LD/3QD3HixInr2ubc3BwAR48eveK5o0ePcunSpRtqq8PhuP1wMcrhcIwyLkY5HI5RxsUoh8MxyrgY5Rh1nLWL4wqiKOLd7373Db//gx/8IF/91V/N5OQky8vL67c3v/nNaK35i7/4i/XX/uqv/irW2h1n/wCSJFlv2+XEcbz+vMPhuPNxMcrhcIwyLkY5HI5RxsUoh8MxyrgY5Rh1XEa64wruuusuwjC84fc/88wzfP7zn2d2dnbb5xcXF697m5VKBYAsy654Lk3T9ecdDsedj4tRDodjlHExyuFwjDIuRjkcjlHGxSjHqOOEdMcVXG8Q0Fpv+dsYw1ve8hZ+5Ed+ZNvX33///dfdpuESmrm5uSuW4czNzfG6173uurfpcDhuT1yMcjgco4yLUQ6HY5RxMcrhcIwyLkY5Rh0npDt2zeTkJM1mc8tjeZ6v+0UNuffee+l2u7z5zW/es89+5StfCcCnPvWpLUHq0qVLXLhwge/8zu/cs89yOBy3Jy5GORyOUcbFKIfDMcq4GOVwOEYZF6Mco4LzSHfsmnvvvXeLnxTAL//yL18xA/gt3/It/PVf/zUf+tCHrthGs9lEKbX+99zcHE8//TRFUez42Q8//DAPPPDAFZ/3gQ98ACEE3/zN33wjX8nhcNxBuBjlcDhGGRejHA7HKONilMPhGGVcjHKMCi4j3bFrvv3bv53v+q7v4h/8g3/AW97yFj73uc/xoQ99iJmZmS2v++Ef/mF+//d/n7e//e28613v4tFHH6XX6/H444/z27/925w9e3b9PT/6oz/Kr/3ar3HmzJlrFnj42Z/9Wf7O3/k7vPWtb+Ud73gHTzzxBL/4i7/It3/7t/Pggw/u19d2OBy3CS5GORyOUcbFKIfDMcq4GOVwOEYZF6Mco4IT0h275ju+4zs4c+YM//W//lf++I//mK/+6q/mwx/+MG9605u2vK5arfLnf/7n/NRP/RQf/OAH+fVf/3XGxsa4//77+Ymf+AnGx8dv6PPf/va38zu/8zv8xE/8BN///d/P7OwsP/ZjP8aP//iP78XXczgctzkuRjkcjlHGxSiHwzHKuBjlcDhGGRejHKOCsNbag26Ew+FwOBwOh8PhcDgcDofD4XA4HKOK80h3OBwOh8PhcDgcDofD4XA4HA6HYweckO5wOBwOh8PhcDgcDofD4XA4HA7HDjgh3eFwOBwOh8PhcDgcDofD4XA4HI4dcEK6w+FwOBwOh8PhcDgcDofD4XA4HDvghHSHw+FwOBwOh8PhcDgcDofD4XA4dsAJ6Q6Hw+FwOBwOh8PhcDgcDofD4XDsgBPSHQ6Hw+FwOBwOh8PhcDgcDofD4dgBf7cvFELsZzscDscoICJk45V40WHue/AbOHr8VawWXZbyFpgCVMLk9ASvf9PraEyMkxCRF5qP/df38oUP/dJNf7y19sab7mLUiwMB3A8cB2KgAqwCzwz+fmRwPw90gbPAwn40JAICorExKjNTqETRW+xidQ6sAcUNblcMbnZwc4wSd0SM8oGXR3DYgy8WcKaAOjANcqpC9NJDkFmyv1jArGRXvv8I5TnYBp4CtnmJY48YpruY8i48BP4EqCbki9u8XgATlOGpDfT3vYWOEeOOiFF7zMT0DP+fd30nd52+G2QA0sMfO4zXOMRjjz3G7/8/v0extgLnvkQgDEdPHyKKA+bPn6O7tsr/+Y/+Ed/2D/8hcRwyNlYlCj2mxmIE0Ov1yNKMp77wBeYuXuSzjz/OY5//PCudlLNLXczg9/CkYLYREwce/U5KkSl8HwIfJsYijh8ZI0k1z5xtkeSaHqAOdK+NNsdP382bv/HvkecZH/5//hdL83MH3STHLnExynG74/k+49PTeL5Pe3WVLEkOukkD4sEtx3UAb5zdxqhdC+mO0UdIifB8sBajFC86EUas/wM3cZHeCel5ABhjBrv3TtvHFozCmhytElSRgCkIpMAisZ6HFAJVKFRRIMMQ3wPp+jWOW4EAvMH/NeUosxj8f6g754PXFYPnBeWVzrAuRu1JU6RESB/peQghEFIMRC+BJ8pLq9Eaa/fwQx2Om2F4LgSiPBdyW14rJcjQI6iFiDhCeh5WmvK1w3PHsn55RVCec8PHHbcMawA9uB8iLvu/5aZinRSDcOp+W8cdQ3mSWMDzPKTnY4xBpQlK5XgCtNwIadZuhDYLZFlGq91BmQpB5AMB2lgEhm6vT7/fp9Pt0e0npFmB0gZtrpwM33yq2k03Y0EbizYGi3WhdRdYa9FaoZW6KWHW4XCMHsOuzH4gpURKD2MMxugdX+t5Hp7nobVG662vtcYMxnkHEH+EACHwpAcCjBqMN4VECB+scn24W4AT0u8g4olpJu5+KTpNWH32C6hsVGbHbhF+AHEMSkGa7vkoMIhixmdmAUGz1UYVBRQZmDsoZ8QqTPocNr/I8tmIdHWOyWOnuPfkfRgBihoy8Dj/3Bkq9ZhHXns/ExNVPlc/6IY77ngkMAaElAL5Mhuj3mLw/x7wmcFj2eCxMeAllFnre5iZXp+ZpDo1g/R9vDAkC1PSrIcnYmbGDuELWJy/RL/b2fQuPbjtxHBo7Q1uBpeX5tgTIoF8aYSoSMxCgZ1TgIFZOPzASe5//atJbMGlZIW02yM/vYQZz6AJJEAwuElgDki59uHsKJEgAsCCLdj9CPEyQVy1wZTz2yUeZUzcrNCllL/XDYSNKJDUKj6FMnQTNxBz3O4IyqAVDjLDLYcOH6IxPsljT3yJzz31CVrNNWZrHmkesDqYRSqQCOthbHli/fWnPs2FuUWOnzzBK179Sg5NT/K6h+8jzxJ+53d+l7NnX+D8/BKrzTbtTod2p0OhNMZubcmQgvI0DQwYA920YHGtTVZYOsaQs6fz/nckaZJw/vw5lCrIM7csyuG4ExBA6IMnIVeg9iEQTs0cYWJihmZzmeXFSzu+9q67TnDkyFEWFuY5d+7sumhutKbbaiGEKPWgW4nvQ62GH0UcmT1M6AcsnjtLd22VuF4nbsyS95v0Wz2XEbHPOCH9NkJcI9naC2Oq04cp+l2E77/4llt7HgRBuaPSdB827xFXayAkXpKjrcDq4g7r7RpQTSySfuscKpVMTDaYiHyMFOQiRKHpNFvkeZ9AZIxVY0LfBWrHLSCkXLFmKFesFZRxbuiGUlCKfkNxb2hzMEEpLO1ZioMgqERUJsbWH9HGQ0ZldkCtMU4gPdZWVrl8aZ0QZrCYZTcNcWVMHHuIL/BmAqhJ7PkCu6TLc2McqtMNjt5zinbaZ/lCgsoLxJjcmJTSlHYhw4msLuXqDxf6r0m5YgXwLJib22U2B51v3jhlT36o0g1X5dzg3JvnCaJAlslOift5HbcvpQWEBwQI4a9PUVeqVcbHx8mzhIvnzmB1QcUXEEiklFhjBxniG8f/pfkFFpfXaKUZE8eOoYFeWpD0U77wxWd46qmnmF/t0upvP/bYYthmN6bHBSAM5NrQT3MyDblxU+e7QSlFu91CK3VFpugQQbl6EMqhobWDFcUOh+OqbLbPOYhsa98DX4LaKVFjO1FsN2M8IYjjKo3xSdK0jxDiqt9RSkm9Xmd29hD9fm/Lc9Zaijzf9n37jpQQhsi4Qm1iijgMWRtYW/lhSFSrY02KaMtyfZMT0/cNJ6SPOD4Dt6M4YPboJAjBubk1esmVJ2/WXmP56c9hVI7O9l5IHlVkHOFFETII8MIYnWXkOwTGG6XIM5pLC/hBxPTUDFL6rM6/QL99QIF0X7EU2QWMbtNrV2mvHENEPrIWYoREEqEyzWf/5lmeigQXzy0ddIMddzoW6FAK4g02LOC6m15zuX2LBVYoU7967IEqNMxu8+m1MxTLRNWQ6liMKVJ0p4UBFq3F8zzSPAPhEcURYRQRV0JqjZgk6bE8fwmttFOqHLeM0A+4a/oI4XjEpeg8Hdsq53k0LH/+Ep9pf5RcF6x1WhQ2Q4u0PJ8kZWekYEOkzdmwVNpPhnNJm4Xi20gHmTkyxZHjh+m0O1w8dwld3KSS3gCqlL9bh/I3SNm6f25ilUBeGNq9Am2sG3s5blsmpmY4cvw0QpYru6r1OoEMUP2MfqtL6MUUSYqPod/r0VxaxAOO3nsPmIL22jy9tYQ8SQCPmaPHOXL8JOOTk7STgqVmj+fnlsjShFSEUBknjhVGFRTGorTB9z0qUQCbrJL6uaaXKTJltti3ZAqyHmjLlix2xwaVWp2oWiNLEpJum6TX48Jzz2KMIbtK8tTxY4d4+P57CKOAqFqh10/467/5HGtr7VvceodjBBnO8AGYMmFwcnKSMAzXX9LpdOh0Otu+/VpIKYnjKgBZllx1wmvrewSNsQZxFGCaPfLulee2H0ZMHL4LKT2ai5fIkx7V8THiWo2016Pf2uH8tpbm2hJZlpD0u1hrkVKuf+csyxACpqcnqFRier02Tz75OJ1O+wpdaXO365aiFHS7qDRlUSk86dHvtAAI44jG+BjVaki9USdLuqzNn0OrW5w1/yLBCekjjkdZS28s9Dl1ZAKkZGm1u62QXvS7tM49e6ubeODIMMSrVQm8gMALKIwh34diJFopus01orjK0eP3UanW6a8t0mdtzz/r4LFotYRWSyS9o/Tac/jViDiugIyQYgZdGJ75wgWyLGFxvnnQDXbc6VhKER3KzNiYUtC7Vi2V9uC2JwjKlNyArF+QqRZ1W6VS97Eqw/S7GGNYswrheVhlEdIjjCtUalXGJhvMHJqm1VxhdXmh7FS6QbPjFuF7HofGp6lMVVkLF+lAmW2eQau9TOvZ5Y0Xh8Bpyg7IMLFzuAJkKKjfCgSlmD68pN9mVjLjk2Pc/dKTLFxc4tLZBay+yVmAKjA5+H+HDWurPUJpi9rFYNfhGGXqYxOcvPel63WNwjDElx4qzUl7Cf2wh84zfEqf9ObSMvV6jZMP3I/VOatzz9FbW6YMfJLx6VlOvuQBAPqZptlNuLjcQuUZmQgRcZ0w7GB9D6k11kLkS+qVECEGJV2MZa3VJy82zq/h5T/XsM2wzrGJsFKlPj6JEIKk1yHPUhYvXdjxPYemJ3ndqx6kWqtSnxpnZbXFk08964R0h2OIZD0QSSkZHx+nVqutP22MuQkh3SOKKgghKIp8V0K6kJJqrUatVqHTH5pgbcULAiYOHcXzA/rtVfK0T6VepzE9BbCzkA50Oy26A+G5bKckiqIyy3xg0zI+PsbERIPz5+dYWFjZvq2D+1s+jDMGkgQDrF3224RhSLVeA2owOU2vtUJr6ZIT0vcJJ6SPOJpSO7K54oX5JghBkrmTYTNCCqQn0apA9xN0npdBZp/QqmB16RJBGJEmvWu/4Tan177E3LlP4IU+/nyAlT6KGsKPqc/cRyNuoI4+ROSF9NbO0l3+Ik4ddGxBslEkFDbsCKDsI13v6ToU1G/5wptNx7VWkGuKjqZDRpFmWDNYQpfnWCnXiwLmqQCbY3WCynrkKiNoVPCqIUKVKWh5kpUZ6ls+68aLRg8TTW6j5F3HPqO1Zq23Rs/vkdls5yK8mtImqUd5ng0L+yr2TcyWIfg1sBqKLhs1EDQHOGK5OVprbc588RydZgezFwL15gLLDodjW/I8o9Nq0miMcfzECSq1OseOnySMKywtLnHmzAucefpJls9+kV6nC2kT4xuSXhewhNUGFWMRQiKEJIhrGOFTq1aYnBhD5xmfeuzz5GlCWhRU6jUCO4OpVymMQRlDnmX0emXGowGMsYMCpBtoW1q5IAVx7GOtJc8PpjaBFKWlgrWlpcKohVopJX7gI+XuLe8GtZnJtaLX66OV4v57TzEx3uCFc3M0WzcmEDocdwJj4+McO3GcPMuYv3AJrTXtdps0TalWq0RRRBRFTExMUBQFvd7uNA8hPKQfIqWHNqXfyrVimhQQhZIg8AjCAC8IGJsYwwtCrPCwYlA3Sgb4YYTnh2wuCW2NwWrN4SNHuPfue8jzglarsz4u09qwsrRI0u8PqrVvdHyNMWSDOgvGGISAVqtNluX0N9l1DXM6hsPZq43QPF8SRB5GW/JM3YJgKhBRDelHeFEd3w9Ium3aqwtk/Q5GO7Ow/cIJ6SPO0Iq0mxasnF0EnNXR5ciBkF70E7LV5r7vIKUKFi6eKa24XgQ/Rmv1DO21s+UfmzL9w8o4L3vj/5fJqQeJ734t2fGvYv7pP6C78iV3kDq24lFmkW/+u8KGVny9mVi9we1AGDitKgVKk2WKrKm2HvPZ1i+UJSlZAp2mYHke/FpM7fgUnpTI1IDStHXzMiH9agrn7hh2+Ia+sA6HMorF1gLSSBKTlFnnV6tqp4FF9rCuwLXxK1A5DDoFnQzmw2/zmaDl+VVWFspVa3vSX1CUExsun8LhuCp5mrK2vEglijh98hQTk9McPnka4fl87nO/z+OPf575s19k8dyXsFiwFiUU3VYLzw8I6xP4lVpZ30AIwtoYRoTUGxPcc/dpLpx/gd/5i4+R9Lq85CV30xifIGxU8ezGNXx+foH5xdUdV3goW96iwKNei9DaUBT6QMYWnoRKUFrL9EbQVtfzSiHd87xdX5YsFoUBpcjaHay1vPIVLyVNcz6UfNwJ6Y4XNdPTszz6mi+n02rRa3fpdjqsrq5ireXIkSP4vk8cx0RRRKfTIUmSXdUYEJ5PENZBCArDoO7EzmeslIJa1SMKfcKoFMunZyKmpi1GxmgZY4fZFliMKVD5QOS2ZfFPoxQnXnqa17zuy2i3Opx5/jyqKMdneZaRPvYYSTIYdNqtQnq6yR7KWlhaWluvqwDlmGrochixMUm33d4IQo/aWITKNUV+C+K5EHjVCbzKBH51HN+PyPod5p9/Eq3V6AXzOwgnpG/D5ESDyfEGnW6f5dXWyIilI9KMkcPkCp2kmLy4dTvJ2heROGU3zoHNCblFSmvxDFZrguoJgmgGT7hZT8c2DLNKh4L6ILEAw0am6WVEHoyFZcZWKyvvD57NBsTDLtT1jDjLrAyjFKqbYn2f2AuQQcDE2Bi2UqPb79NPkmtvahct3REPqA1eOPDJdtyhBEAdbM2SqxyZSow0G4V7dwrbt/C8MwWoHpitY5zbnmv2IUv3CGTVQzZ8bGrQq1cpZD5c6byTkD4Y8YmqRMQSmxhsd7CxA46jcSXm0LFDYGHh0gJZmh1sgxx3FI3GOI3GGLXGBGP1MWqVKp7nAZY8TTEMLQYMxmjsJuFbWIOwGk94NGo1hLAY4WGFxAtjlDZYKQkrFeJ6ncmpKcIoxA8CwNLt9lDphtdcp9PdlXgkpUQIgVIKY2wp3nPrxxjGgjLl/Uh0tyiz0KemZ6lWq4jAQ2UJWuW7bl+70+O5MxfwZJlDarEYYykKRa9/8/0sh2NUEVLiRzEIgSrUln6IlGVh5ahSpRJXwFpOnDxFr9tldWWJPMswRtPtdvF9nyAIkFKWk1ibVoSU9pS2TLITsuy4WTvIAFdIz8P3A6wsV/bshLWWvLBYNJ1Onyy3NCYmqNZraEKUiED44MUURU6ruUSW9lADK5Yiz0m6PZYXFnj+S1+i309ZWlgeZJhLlFJoY5CBjxQCKYKyUHFx9c7U5eFbDOpd6E0xcvMQdviY1oYiU6jiVsxIDv0PJQhB2uvQXNL022sYoy/7/GFl+qtNATiuFyekb8P9957gNa98kKefeYE///hjqE1ZgsP44Yp+jw5Fr4/qJyMz4fFiQeUJZz/3v/GCkHte9maO3v0qfNtidLrgjpFhaAdRA8Ypr+OKHQW8iQheNgNJAZ9bgt7IzNFcXtnv+tFpQW9ujSAMqN11iLgSc3TmCPUg4ktnz3Lmws6+n7vhmpeoCnBy8P8zHGCGv2PfqQEvARMakjwpk3FCCxOUB8qI1CZXCehLgL2zhPRrEpU3/3RE5YExiksZySea2Gyb+JJQ/l47hZ4Q8ME7GeIfi1AXc9QzSRmDD3jCbHp2mjd945vQWvOh//UhFi4tHGyDHHcUJ0/ewwMPvBwxEIrGJibwPA+tNKuLixRKk15FQBUYPJsReT6HZ2cJo5hUxBSiFJHSvMBIn8rkBFNG88DLH6HbaaHTHrrIuHDhEkvzc5QBrMxyvFb2ZhB4xHGI1pokyUpNCoEnJdqYW9qb1gb6g3mtUenF+37Ay17xKk6cOs2XnvocLzz/NEWS7VqceuH8PHMLy1zRZ7OQ7yCgORy3O14Q0pg5DFLS7vRQm2ozBFFIHIbUJmZoNMaYnJjk0Mwh0jThmS98jubqCucuXOLS0jKTk5NMTU0NCofG5eSg52GNIev30VojZIDwfKwusLrAaIUxfYIwJKyXK3vy1B/4dG+/TlYb6PQUAkWzvYDn+Tz8qkNMHz5FYXxy7QEChKDbWuXZc8/S77TKjGtKX/Sk06G1tMzTn/0cpozogwmFCgiBVgq/EhNHHlHgkXS7dJvN3e1QWer41kA+cGvZbPMCG+uIi0yvi+j7L035g1YIrLWsLpxj5fnVcqJ4y/VHUg4GPEqvC1eUYy940QrpnhRUovLwz3ONthZjNs8w2W0TJaXccLdwYvqIYK0T0Q8EiyoStMpIusv0WnPkqSvgMypEXrlUd92JZOjFeVBc7RT1KK9EunxN4AlCX1AJIfShsIAYtWB78/HGaoNRGpUXFNIj93My5K6K8ewJw0xk5/1y5zPs7QuwfVueaxnl7z9Kp5Yt/dGFBD8uH9J3WHb6tljAgPBlmZVekRCIMmhfHg6udb5KEDUPURlko0sLYnRSTIMwYHp6GiEEp+4+RaUSs7y6TK/fw+6j/77jxYEnPQI/IIoiKtUq1VodgSgFbWMwWpNnCVm/iy5yLveu8oQoF8spjZEK7RmMLFdS1GpVavUqvh9QqcTMHpohjnzmzjbpdzukaUqRX584USZvllnSZpgKbsWBna6Xf+522Za3FosxZpCtr7BWY6/jglBOULig4ngRYi3GKLBeKVhtOm+s0VijyZKEtZUVfL+UA/M8JctyioHg7ctygtFohbWmrElnwQx0l42YYAeTW3bT3+XnBUFQvk9cpqoJwbp3ykDDscOuijYYq0j6Cd1uFy+sEYQxSin6/T5pkpRLZ9hkYWktVluMzlFFDkJihY+QHtqynkkvKJtm9KDIaCXGaLOr2H354vxhjv1wz26OkdZsHzHFoICV78nSQ16bLQm710+5360usCpD5xmm2G6lnwBvkGVhkpHpE97uvGiF9EY14OF7phBYzl1cpZcUdDPINLRWm5w78wIrSytbTgQpIY7B86Dfd0K6wwFgrWHuzGdYuvgURdZ3HkQjgBRwV0MwXRFYZbHKsprDC70D1s2GAt6wCqalnCCvsF7UcKbhcWomJJCgfCgwWJGxp1f9y3s/B4RSitX5FaQUrMpFpBBk1zkQv2FS4Ozg/y4x4c7GUP7GKXCeMqt5uLJzBDUGvwKTp8r/r70AxZ2+WiIFcpCFxA8DTEWVqwVCoMV1/UbCl8QvHcc/GpM3O+QrPWzHjMxK3lq1xkvve5Cp6Ule/apX0Uu6/Or/9St88rFPolctZs31Hxw3TpFnJJ0Oh6dneMWrXoWyltV+j1wVVKIIaQRrcy9w7unPooqUDY85gyclcRDhC4+V+cVSwK1OYaIqr73n1bzmta+kVq3QqFaoRT5veMNXsLK4wK996uM898Uv3pBNUVEotNbrWlLZfb78RB268976ohHB4JOHi1lu9by70prnzzzP0soK/c4SIxPIHI4RRxU5ncUFEKXVyOYTt0gEWgqe66wyf+45xEDktsaQpQnGaMZqIccOj6MM9HpraAsylGilybp9jN5YcWO1wprNRTUtoPA8S2NiDCklnbVlshTKAaBAeAEiDMv3ZleuErLGcPbZZ7h0/hz3vvRlPPDy17C4sMjTj38elWeEcY0gjOi0VinyjWWVnge+X64IKlSBNQqt84FwX368SgSJgLHJCQ7ddTf9bpeFc5d2TGQyZmOuwFLGxdAr71NT5j3shiAEz4fxRp2xeoNWu8fiUvMmpBMFVqN7S5j+Svk7bIf0oDYFXgV6KeTO2movGC0hXWy6DTJ09gspBVHgIYXFlwI5/FxAa0WeZihVXOFSN5xAu3xizbG/CCHwfK/0plK3ogLyTm0BISRClIHa6caQJR2yxBXtGSV8WWalSyGQEvoGPGnBHOAwpOxbbQjZsFEVcxBTfU8QBYPsBQF2v2Lt5jV5ByUmWlB5ubw4F6ps1BXZVmKTp9iNN1Sw4e9nh9fXEbH0cNwiLBuC+nAiawQRAryAchXv5qq5+/Jhg/uD3BfD0gsKbGHLBI5hHYnt4t9O+0OAiD1kzce2wSq9oYAdIJ7nEQQBlUqVeq3O+Pg4YXWatEiojdcQ/uC3HjL8v73s3uHYhiiOCYKAKIyQQhDHEVOTkyRFwWq/h9YGrTRaK7QusCq/IhuqzGjUaKFIkwSlDEb0sUaWhfDqVSphiCclvvDx63XSbociy+h3uzfUbmtBa4sQZV/NCsrM9G0Qw1RGyy1xUB92y4YS/kFgraXfK73mdZ5gzK2wSrgKQpTheJBF61ZCO0YaawerbrZ5CtAakiIn6W2NXWXdBoGoB0Shj80KclVgBh0ya+0WEX19i5tPB1HWfvB8jyD0kdJDyGFnZnAv5OC2WfjbSpr0SZM+/W4HXWQUeUra66G1ol6RCOmv15gYno9ClGL6cB8wqE21WWO0dlgLwuKHAZ7vX7Ve12W7dNN3LN0tPFF21ex6pv41NjLQEH1PEgQenrezd/zusKCLyxXLdcFSSA9kiPUGS9WdiLlnjI6QHlIeiXWgSpmd2GTfJuA7/YLHn1sBoJcotIJi8DlhJaI+VSfuJ4OOy8aSkywrjz81Mn69Lw5q43WOv+Q0WinOf/HsVT0O9xMhy5lE3/cYH5vC83xWV5r0XcEax4hhLFzqWFYSy12TFY7NxIx1c2Z1j0zBWra+Ku7WUlBmWAbAGGXHZWjVNrCrXO4oktwwGQnuGffwc4PYj7b6wDTldWeZMkv3oBACKpMQVCBtQbYxKSXDCv7kEbCWYm0OW9yY+t2IoVGBfg7NHlg5WOVX9r9cktedjM9GbYKUsrjs8uB+BFEJrJ2lzByylCtWMvZnwqtC6VGecrAxAMjn++hPKEyqyyzy7c7LkDJ+Fmy7ksRqQ7bcIqeLyYoN+8wD5vjJ47zy1a/kxMkTxHFMUSiW5+dpd9q0z3dQFw12+H0qlNcHS/k9NeWYYARXTzgOnjCKeNP/8Y285IEH8Y3At4JTp08ze2SWdrfHhaUlsrTPhaVF0jTl0OFjPPoVX835s89w4YXnGI7x0rTPubPPIKWPooIVPraTg7fCmadnGGtUOXrkMA/cfz95lnLxwlmW5ufoJzc/I12LfWanKhSFYX4lQenNJ3456xn6IXEQUeiC/jYZnDeLpAwtmxnM7x0Y1hg6rTX63Q7WlFmv1/Kd3w+CMEL6AYHv4/sBeZbS77nkIcftwnBtyc6dfc8TzEzViSMfX1qMyTCqwOQZSgt6ioEN1c7nYFSpMDY1TVypUq1XwdpBPpAF6YP0S5umtD9IHtp5kHfx/Bm6nTae9Dky3kAbRa+3htIFke/jyypK5WhdEIWSuOJTFIY8V6WXeVh+/XWRe2Aj1222UVleWkfp64srnpSMj9cI/dI6xlhLs5vS6e+8Mknl5ZhrRfVotTOKQu3P5KAfQzSOH9eoHzoC1tCdO4fqXQI1op3/25DREdIlZWsqQIPy6r2Py+8LZVhqbt/58XyPqBLhhwFSCMwmC6ehgO4mom8tQRgyeWiaIi+49PzNF+K7EYaznEEgqdUr+H5Eu31jWSj7jdg022gvnyl2vCjoFuVtetLDiyNCZagFAoFF5gckpBtKQWyjGEXZr9vU70hyS5JrfC2wVRB6jxcTD7MShhO3HqW4P7wcHMi5IksRPaqDSiHvbrTF8/Gq42AMqrV4w80LA6jH5e/ueWWmv/TLa5k42EU+jv1GspGsMEbZ12odaIt2xOhyPglBmVjhsyGo7jUBEHOwatEA3VXoS2pQrYrtM8k9yjZfrV9sQfdzaLNlpc9BUXqjSiYnJ7nvJfcxe3iWIAiw1tDutFlrrpK2M8zmrpRH+btbymtDwYYdkcNxGZ7ncfqee3nFq19Db61Jv9VmbHyMuBKR5GXWgCoUnVaLJEmo1RvE1SprKwtsPsG0UnTaayA8CKdBRgxn+JurqywuLFKtVDDGkOcFK6trrKyuUexB0crA9xirhaSZRspkm2Pd4glJ5IdgLWVPbm+v2psX6Q1TyIZlCySDjHnAcGuzwosso+D6bXP2Eun5eL6PH0aEYYQxZksWrMNxsOxUzWB4Zg/Wlgh7VRFLCkG1GlKrBKgiQ6vSS90qjdagst2NFTzfp1JvEFdigtDHaD1IgrblClvPK1PijdqVoNZtt+i2W4w3Jjl1/B6MFvQ6GqsK5KCwNFZhTTkZEAQSYzZW4Uq//Ppm+NUHfasiyymyG/O2FFIQhgFxWEqpFuin174WDOcgEl3ALl5/w0gfgioybhBNHgGd07v0Jcib+/eZL0JGR0gfTpK1B/8XwAzlVTzddH+TonrgQyXcqE6+3fkbBD5xJeLI4UkeeeRuOp0+Z84ubpk1Gt4HoUcYeiilyVLXy98vtDGkWYoq1HUVmtlLSiFd4PuSKIoIggjPG4FUr8uYmJzg5OkTWCz9NCHPc5bmlkj7zsfhxchSK6PQhkrkM3NsiqxQ2LUOWWFod6G4lQKSpMz+lJTCyLBDsw3t3PLFNYMylnwvQ2tAOVkbU/oQB4NbzoZo06VcEXUDCOkRxnWE9MjTLkbt0EmTHtTGwQ/BjxBC0Dh8klr1fvrdHq2VJoRhObAXQDAOJgLdBXN9nb9GBEfH4eTRKvHYJJ0055mFVbJcY3UpXvY6kLswcecxyOgVIVRqIENI5/dIO95Pe6ShFY3Yp+0LNoT0W6XT+GxMZgzHvh3K1QHF4H5ovbRTotZwbHy5DaJlox/tD27pDtvZLwIQvuArvvzLee1rXsuJEyd5+OFHqFYrTE5N0e13+PhnP8r5iy/Q6jS3vnc44RoDx9no/+8w5pQBRFPl/7O16w6PjjsAAYxNTDAxPs7q6iof/L9/m263z4XzC2Vx25N3MTsxTpJlZEpxtlrd8n4JhNJDegF+vYYIItIsQ2nF1NQYd586Tr0Ws7I4z8rqMk88/llWlhbp9W68iEMlFNRCgRCauaUehbZovf3JmuuCTtpDG70v1i7DUhrDzPRhLpsQglPHj3Dk0DT9NKXTT+j3UxaX1vY8OzzwPGpxhLWWbpqhR6QYmSoKtDYYbVGq9LSP6hMYpcjT3jaWfA7HrUAgKtOIsE4UBoRRSJ50SJrLYBWYgaAmQ5AhY5MTxNUqvdYqveYKZcdq42JphcDIAOPHREGILwx50aZfZKjrOMSttSij0dvYUca1KmGtThgExJFP0u2yOj+H3cW5nmR9Li1cKKcEVIG0kBb54LNKIT3NDEoXGCwyhCDwGJtq4AfeelJsZ6VHv3Wtwc5my5nN2V+AtGhrWe308T2JL0AIS5pv30mRAhoVH08Kuqki362h+s2gMkhWUKpD52wPjEand3rBoVvP6Ajpw0omUJ7TDcpl9wUbq1Fybl5I98qsvFxBkm8vpHu+RxSHTM+MEVdDFpdaXLi4su3yiyCQVKo+WSrIMu3S+vYJYwxZnqMLdVX/wP1GMPDD8iRBEBAEYTkLOmI0xurc99J7sVhW2mv0e31aqy0npL9IWesVrPUK7jpS59TMGIXKyWyPJDH0k1sspHuUQrplY2L0KgJZX8ELnX0YnPiU15cKZUZ6OPgbSgG9T3myt7iheC6EJIwbSM9HF+kuhPQxCOP1yeTq9FGmj55ALq3QTs+DNBtFcvwGmBBMxvVWCK1GMFOH2SMxp+6fZaHZZyVr0knKH8BayDMnpN+RaKAP0kI8VtYaKoI9ENI3pzDul+f6PibsAKViFHHr7E+GqwKGE4qwIaBv7gdfi6uZF1vKOJZRxraQg6mFEJSf/cijj/CP/sm3MDk+zZFDx8u6EIViYXmOhcU5nj/3LN1ue+t7h2P7CnBo8PcLO3/cZiG96Dgh/UWJENTqNerVKmfPvcCH/+xPSXspvWbKWKPBg/fdzeGZaTpJQlrkVOJoy9ulEETSw/N94mqMiGIMCpMpxsZqHDt2GF0UNNdWWJyf49lnvsja6gpJcuPL5ONAMFGVdDPLQjPdMTlTaYXS+9dh21zCJmSTpbCAo4dneOj+u1lrd1hca7Ky2mJ5pXm5zfxN43uSsUoFbQ1JnnOdbgv7hlblEiFjLErrMjO90ih9m7O+W6XuOBiEQMYTyOosUb1KtVZBNhdJux2sGi75FSAChBdRmzjK+PQsRp+l1+xRXmg3XyxLIV17EfVIEPuw2sxIBv2w3R7m1lq00Whjtr5HCKJKher4OLV6jbHxMZqLizQXF3Y1aZbnGcurC/jSYzyuIYSgKHLyTXFR55Y810i/LFwfxh7jkw3CMFj/EkVP7UJIHxboGXZuBxFx0PcyGNr9FAGEEjzBVZO+PCloxD6+J8kKQ65uQeKtySHLMRn0u0v7/3kvUkZHSB8yPLaGYsawIJqlXOo5fO4G+xLGQqFB7VB8Kcsy2p02vh9Sb4zR7Suk2EYwFQxEVR9V2H2th/VixxhNliTlMqODmvkXAiElnu9TG2sQxVWCSgWCgCCMCAbL/Yw2GK1Qaf9APID6/YSLFy8ipCDROcZYTt17CqMMcxcu0VxdW5+Vdbx46PULzl1q4wWSysQ0fkPTzlqI/oZSVeSwB6uUr45mw9pFc8PiWyUqrUqsV95UDklvl9vavF4ZthYbNIPnAsps9YLSm/c62mitoUi7COlhrjXoNRr6nVLBDipYGdLv9VhdXKHf6ZYfqzWq3QNTYLNeaa5nd/cjSQGHxmAshpOzFQ7PVEgKn7/5/ApJkRNZgfU8VlqaLC9/f8cdSA4sgQ0gXSuHB3ovElOGYq6g7E0OM8hvp8V518r83ms00AGpJJMvmSUej2lGq/SCTtm3vZbt7ma7lu3aPYxvw0nKy8fJt4pBiErTlHavhRf41NI6/TTh4uJ55uYvcfaZc8w9t0jSzzay66EU0GcoH7sw2Na1xrweiNrgPVOU2eyD5dzrExQ5Gxn/rv9z2+P7AVOzh2iMTyCDgG7SZ6xep16rEkchAlC6oNdfw5KxMH8Ro3NSpSm0RqUZofTR1qCtIQxDjszM4gcRNo7RwqOnc0zWpUi6pL0Ooe8zXq/SrcU3lEgT+GXhUs8b2BEIQyvRZMXoWDAOSxNIoBoHBIHP7PQkR48eZXx6mtljGS+8cJHnn7+I2iNBqNGoMzU1QSWOmZkYJ8tymslz5LfAy1eKssCisfYaEwMG6UmCsJyAyZMuRhduMOU4OCyYPAN62Kj0Rx6fmuXIzDRZv8Pi+efQWhPEE3hBRBj6YArqYw28k6dIe2u0V5N1XcUCyghyLVhp9hEqo9tPCUKJMRatdrcWRhUF/VYLqxT55GS57cF5YqwpJ6OCgFq9gdAGdfJesqTPytI8Su3CHsUaUpXjSUm9WkcKSTftkeYbSwutKYdLSlqKtEBYgWcBY8si7LvZuVs6C3bjzjDQhEqTLW0txl5Rv3pjSxaULge95hrxQghBGFUQUlJkKXofJ08dN89oCunDDu/mAUUAHKFs8U1UP9EGsqK8v9qh3E/6rKyuMDU1y6GjU/QTjby8qu5gAOn5kijwKbwRmTa/Q9FK0+/2BiL1AVm7IJDSww9CxqYnqdYbhOcuwVqLaHyS+tgEqtAUaY7KEnSeYQ8gAHY6HZ599lm8wMevxVSrVV79ylcyMT7OX/3ZX9BurmGM6/u92Gi2M1qdjInpBo982X14nqXbT/GDjU5Lt3MLhPQ9GBfVqzA+TM6OSi06S0pd+poM/YeH33PzDKgaPB4ChykzO6/Tm9caTdpv7u7FRkN7pcxMnzwMlYB2q0Wnma93Om1RULSWQWdspK3uDingnlk4NQ1HDzc4OjvLp59t8Yd/fZFaDA+fDqh4PheahmbXBYQ7lgQ4Xx7666ffXvzcw4z04UoTSbmS43YT0oeTercCBayAV/c5fvo0E/fO8Gz8JL2oA/OUMedav80wZl1tP4/ChNjA473f7bHSXsZ4hqgesthc4GOP/zmXLszxxKeeZv7sAta3G77vhnKF0F2UFjVPsDtrGh/EBGXsVmxYwwSD/+eD7Q0nem6nY9SxLUEYcuq++5maOYQMI1rdDscOH2JifIxKJQZAqZxmZ5EkDXjh7HN0mmtloTshyLp9Yi+kMIpE51TiCqdPniIKI1r9nLRQrOgEnTTJuk26rSZTE+PMTk7T79aQ8voKEAgBUeQT+B7x4L7dSVnuFKOioQMbpQk8KZiuhozVYk4cO8zpe04jgwAvDKlWn+Qv/+pTkO2NJ9bU1AQPPfRSqtUq09PTtNsdnj9/kV5//4V0zxMEvkBpyK+64rl83PN9grhCkfZJu2tuIOU4YCw2SdCpxFTLmDd75Dgve+jltFaW+FRqSJM+9YkJ/DAAIbA6Z3x6isMnjrMyd5ZO8xJ2oKtYC7mR2ELQXmyTtJoEoSSMJUpZtN6d84LKMjpLS+g0JTl0COl56wKyNgMhPQwZm5yiXm0wUZmi01ql027uUki39PIU3/M5NTVLo1LjwvL8lUJ6CspYsqTAakuoQRqLLXbT4dvuNYMvbyRWCIRfZqgXucbu4JZgseS6QFtxTVcFKT2q9TF836fdXHVC+ogzekL61RhaAXjcVAe4nBUazBrZQWGFOMKTkn6WUShNr5+xtNRBG5+wUqVQGfe/9BT9fsbaWossy+n2EvK8wGhBoeSW5WebExwde4MQAt/30AjE9fVd9wxjLUVuULkFJFL6xJUqtfoYQRCVs5FarYvoB9XBMsaQpTmy0PjWgoLVlVVUUZAm2XVno4eeRxR4FNqQ3lIfEMdeYy3kuWJtqUMQSiq1OmEUoYsCqzRR6NGo+yRJRqvVG8kxghj8I4TA93xkFKCSYar7Lhj67wpghVJ4GX7PjK19Jw+osWc1Oq6KNVBkICUyCpGxBCPKa50IoVYBJVFpuqvZgqH1cyjKjIw0g4XVnE7eY241RSlLVsBap1x2ecVpLUV5M/aAqtI69pxr/Yybi1juVojdLKQPM9MDtibxDFd5jBLDIqaDlb7r/uS3EKsMnfkWeJCtJVcvLno5mt0XXpWD2/A3uo4wuSdY6PTaXFy6SGZzwnrEYnORcwsvsLC0SJal5eBzGFc3Z9gPjZqHBaEvm9D06wK/JjHCoISFOhQD33kbbtrGcOK0z8ZqKBfS7giEEARBSBjF1Gt1xusNWqtrPNHpcvb5M7Rba/R7HYzRWOuv+3k3xupElQqXLlQRm4r0GWMo8hRpzXqfqOILxuIQz+Sk7VWaRR+bdlhaXqEShjSqVbqet2P4EAJ8z0MIMMZSKI0QAq0thTYjeTgOQ0auNP1MEVbrTM8eRvo+XhDQaIwh93AwliQZy8urTE4aDh06RBAExFFIFAUU+2zpaW2ZXHfdNjVXrV8RgPQQfoD0/DJzVYDVCp12nZ+6Y2+xZWUDQemeIKWH9Dz8KKY+OUNQ6eMHZfxJkw4qz/D8GapjNaJqlclDR8mzhF67BVJircBakEGEX6kjKNBKYa5Su2E7PN8nrMbEtSqeJ7cteq41FIXFKEshwHg+1cY4UnqkSRet1JYFeJs/fT1X3BqSLB1sz+KJAIvB2I3OgrUWlSkYZNSX93qLm98wyfx6McZelyYldhHtrbWoIsMYhdlVdliJ9EOk563XzzC6LMbq2F9uHyF9kMWzbvdyo5vR5ck7PJQD3+P44RkqYcDZ+SXW2l0WFtosL3eZnFxhaXWeY0eP8i3/6OsRQvLXn3iMhcUVnnn6HIsLa+SFoJdK8rwMPgK7pfbWKHaQbkc8T1KtxGilD8yXXCtLt1OAKDDGQ3ohk5MzWAL6aUE/zcn7ffqrC2DMgVnQqFzRGyxbEkLgBT5PaE0Uh6wsrqCvcyKqUYk4PF6nk2ZcXG1fc1mSY7Tpd1Ke+uxZqvWYV3z5/UxON0jWmhS9PnGlRiWuceHCEp/5zHN7tmx3v4jDKvX6FGR9pFjC7ObiUFBmJrbZuKYMmWDDLx1Kkf0YpbB4kf0ToayFzhr0WgQnaoSHKlAISATC+ogJi8kT+he76F1UeZfAmIRYQNqFeQUL59os9jsU2qKMpZfC0+fLbV3xM/seRH7pg7afVeUdo0NMaYmRUZ4XuxVqL/cXr1JacwxHOhnXtiu51fiUGc914DywyK3LSB+gMsW5jz+LiCQ6UmWsSXbxxmF29W4uwwOfciqUca1P+V1vUVi3WF6YP8fHH/84dx27iy49zs+/wF9+9s9ZW2zS7g38Wi6faClzFcpj6STlfjnLltVM1bt8xu73SaWm4+UYCz1ValS2Tvkdu5QToE3KY3q44shxRyCkJI6r1GsNjh8+xukTp/jYRz/CJz/2V8zNX+Tsc19Ca1VmUQ7sOKQnOX3PKY4cO8LC3Hme+OzG9rTKaa8tE/o+hbYYa5iq+IzHE4RFl7VzX+JikrDWbGKlx2y9Qd33WX3hhR1PXU9KapUIAXSTDKU1fYpy0mcE+9MSiGUp+jS7Ge3M0Jg6woMve/V6JsOFC0t43t5JCMvLKzSbLU6eOsF997+EuFphYqKOVhnNVo80279lNkpb1HWIhDsjIGiAXyWojxM0xkr7B0+ikw7Jxaex6lbOZjrubCzYDtgektkyuUhIrIUwrnLX/Q+SpX06y/NkSZf2ykVaK3N4wcNMHZll/NAs9akJus01nn3ycxR5jrESYwXR2CRRfYKktUKytnxdiVVxvcrMiWOEUURYjTBKIy5bwZPnmna7QBtFrhQ28Dl84m5UmjB39hn6ndb6hJ7d5qYos9sX1pYQQuLLmDgYo9AJud7oLBhtSDoJUgiyTKG1wbeWzRUybijHwFrMrjLbS4Qob9tNKmzGGE233SqvD7ud3ROCsN4giGsYW+pPKu2Tt2+w2Jdj19wSIT2KY4IwxFqLtQatFFmWXf9vu0cDnc0fK4UgjiIqcYTvlaNBrcvlK2mWk6R9+mmfXr+PEB55XqA2zY4bY1GDEymKfIS1CAzWglFmJDM6b0usxWhdzrAd0E4VAx89sPS7PTzfpygKrAXf96lUPGzaO1ARfchwiZEdXG6SXh9VFKirVJTejjDw8X2PKAzwPIEU4lrx33GrGGZ/DjMONzPsZVzFrsBaS5ErsrQgTQrSRBFFVapRhcD3CTyfIBjtOVYx+OJaC7JUU+TXGWvNZfdDLvcTHlbd2q9ZUUEpRAogN6ANtsiweb8U0gsBtlR/rFbXF/sGK0/Sokxu76aG7iafYWuvzESXno8QEiPF4LPcBeyOZRBDRCCRvkcwERIfiVG9gl6/i81NKTruxmYErhzlcNn/R42hpctB2XxYUInasBq5nsz93e5Tnw17k10M4PYcO7BKXFuhUqvS6jVpd9v0mn2SVrqzTd/w+IwBA/ay65wMBH7VR2LLWKcHi3UMW737h9fCzXUxHLc1nucTVyrUa2N40i9tF4XAE4I0SVldW6Xb6Qz65+UxJoQkrsRUqhWU0fSSPrkqMGLD89daixn038PAB+GjlMVYAVqR9rp0Ol1WlpeRfkh1XFIUCt/ziYKIQhfrWe/lZ5Ze6L7n4fve+lJQawd981GNjWw0TRuL0YY0zej2esRxaRcZhiFB4OF7ErUHdpvGGPI8J+knrK010UqVmejWsjtX5luDNYOxqBkGGrjihxQShIcVEosEK8BKhPQJK3WsDgdvsag8vXYtHYdjE8NL4cZZV17kjEop0g5Jt0Vzdbksj+l5eEEIXnk8aqPRKidPe/S7LcQgnVqpohRtB6tx1kVvW4q51xp6+EGA9H10UWavW2MxSqNkQdbvY1Sp3wBI6eN7AVoZep3OYNVceZZbIcprvRycRmZT12WwSHY4HNuIUaUnnMQgpSkv/0JisQPdcaDHCYHWBm3sFUPnWxFhpJAIIRC7KMpj7Q7+01fDWKzR1Go1KrUK3bU1VjutHX+7IAgIwhClFPnQpmtYE9KtnNkV+66WCCE4cfc9HD99N7lKybIe7bUmZ55+FjUCNhHS8xkbn6JRqxHOr1AafJYoreinfc6cO8/Z//m7aA2dlT5FrkiTUm0pshxVGBq1kGPHpgFDkWcopVleS8iuVsLXcV2ooqDTag480g9mn/q+ZGwsQkrLk599DCs8RFRDBhGHjtzF7OFjLJwLaM+fx47Qz260obvaKZeS7jLDWAjB0cMzzE6Po5KEotcvl8fuc1sduySmzNYbZhtuFkmGgsEKZaHMYY9h6D87IEtzvvDp54irEV/3f7yBB19+P6tzl1idu3TLvsYN41UQfsTaWkHr3AJKafRe1E7osNXDfbhfLfuTyRgCd1OK6WeBJhRLc6jmyvrgCwzCKqw1mHx3WVkWSG2ZUN7qgenDNRMnhKDamCSs1Em6TZJec6QH+o6bQFDGjRAqR+rEs3XuOn2Clz7yMpbn5/nEn32EtNmHS1y7yCOwpchozlZBeBT74orSj3xoGXJQFGxkSXfYe6F3jLJoZ0p5LdiN1/geM7c4z8qTqzTTNsGkx9L5ZbpP5WRNg7navveAeODuMihkm/tbm+4FAWG1QtYXqLUMo+yGZVdlsI3NRVkVLp7dIYyNTfDQy19NpVKjElXQWU7W75N0u3Q7HZqdNv00YbNPUBBGnLr3bo4ePcqz587wN49/mufOPUvq5+VKtk3nXhAEHDt2jDiKWFtbotfropI+8xfOstLscubiEpay2KknPcaq40wcHWNhZYFOb2P5TRQFjI/V8GRZYFRrg+xnjPqMjgFSs/F/zxqe+sKT/O8/qvLAgw/y6KOPEkch0xMNjC5otrp7IqYDLC0t8eEP/SnWWnq9HlqpPdv2XlBkGaowg2SliA2vrU3BZSCkqyxDq+bgMUEUBRy65xX4ngRrMEaxcv5LdFfnb/n3cNyeeEJQ8X2wlkRr9CaVtLd6gbSzzNr5Bue+9CT18SlOP/iyMunPC8j9ECPKhNHlS+dory6tv1drTZH0scbSW8kQYjgTLbB658GPEILJw4dpTE3RXFxkdX6etNdj4ex5hChry1kseZIiENQqY4xPHKHTbLJ45nPUJseYPX0CozVrvSZF1if3VDnOHfQn/UEdg9xYsmL7qbVCpyiT4UufOKyijSIrUqy161rccEX95fPq1981uL6YJITE9yr4nkTKhD33O7SWrNemSHq86mUv5cu+4it58vOf58OLc6htCp8Nh7aHjhzh8PHjrC4tce755zEIiCrli3ZdeOzFzS1JO4wrVcYnpzAmR5k6EnFg9hyXIyizGzzPR1zWJmtLLzuVJjQ7CVqBucwn11qD1QopQipxCBgECsT1+SY5dqb0jCoGM6MHlZEuCIJyyVSz2abQlsoYBBUASxiGeP5oZvLq4vqDYRB4xFFIURqZ4Re6PKbdYPTgGXoT+2xkNA9/G0nZQ9jsW8xlr6HMrOm2+2RpgdLghTEWSZYVI23pUvriCYz1yLKMXncPl8ne6uxUQSmmh6ynR9g8w+ZDRWizErR7hkmYFsj07rpsAoHnBwRhjApCCi/AGHNFtpTneyBEuTrI+afftohQQlUQTMRUZhuMHZni0IljaKHwx0OEyrCBhnxjOardaWXG8NxRjJ4n+uVYSlFZcrBtHaZV7YdOtPkaARsjx6v8foLyd7Z7nCibqwKVKrq9Ds3mGp12B9U1mB7XjrUCpA94W2tCD/+wRmALMMlg4U7OVp/+4XceJoC5cHVH4Pk+jcYElUoVz/eQQpKlKZ1Oh37SJ88ylFKDn7u8hkopieKIuBKTpCkra2v0swQ9ODiEKC0kwygkiiKiMCIKQ3zPw5OCtChIUkWv26PfT0pvXErRfbw6TjgQ1TcjhSAMfIQoV3iJgVf28FyD8vFRPCw3hyRrYW2tyfnz5zl69ChQer5XqxWqvZh2pw97JHbnecHy8sqebGtvKY8jawzWKLYuCR3008QwjXb4Wou1qvRIB0QUEtfGCIIAazVGK7wgBlGaV5RhzXJ9yysdLzbK0HGlwKSLDF1k5FlOv69RWpGnKX4UoS0YIRG+j/RDVFFQZNtnSZiBXiCkj5DetTUXIQiiiLhaxQ/LAiVGG/IkBcEmCyiB9AYe7sJD54p+p40Xeug8K4XvPKPIs9KepEwtBwnSE/iehxEWaUy5UuWy8YcduEFYvEHm98ag93JL2v3sDgyz/O3Wf7CI8mZvTBwUYsvmrsBqhTaa0PMYa9SpxNGOixAFZcHuar1Br9tDeINsheF1zImYu+KWqH6+kYTK5+577+bhVzzAk48/wRc/+yT5Pnqe7RZtDGutFlmel3Yzm8hSWFsqr2mqGFzbrugrlJ0w6UNUCcvZ8y4UuXU12vaQjSU6B7hTN3q/A6XKkHfbFEmfi9ay1mrSb67u3tNqhLHWMr+wSrPV5d6TJ3jFow9y7uI8F9c+izbOZPTASSlFgpCNLDzY6NtDmZUXDV47FI0iNoptDkR3ZRWf/Ku/5YtPfYm02yfr9uj3s73J8N4n2u2UJCnIR2BV001RAC9Q/jbdy5+88a7eMEEYrk+j832fMAwZO3aKKLqf5uoil154Zn25uh+GHLv3NFGtysLZc7SXV2+ofY4DxhPEd08RHqly8iX3cOz0CQ4fPsKh08cpQsvUA6fxl9bopPOYTka1AmEAyRKkm3/y4UQQlDUHckYzA307hgLr7dLe62HoW9+ijCvDrPcdVhQ3QqgF0FfQ2sO5yYlDDcZfVkd3FZ/+s8+RtTLy9Bp9/xRYAgKwPlsTPgcTxJ3llOwJhepq7Ird+C0lZYFojzILP2Pjeue4I7DaUPQTqkHI8VPHieKIzz/+ef7yY3/Js888zery/CAZYNgZEmhjaHVbVNoxYRQwPT3N6uIiNrN4EqJIcmh2ktd95VcQ+iHPPfM8FxeWyZIWqkhY7ias9FKMUtSwaAGJBaUUKyuLeFKuF71bb6e1KFUevEWusMYQSQgjr7RO9Dy6WUF3xOuQGGN46ovPcGlunrGJKb72676OifFxXvnyV3Dp0jytzqfJi1ErhLHHCL8Uye1QzdospHvgefhjk4ggQGcGoyzS95BhhO9JwsCjUq1SrVXwfR+tFUZ7RPUZopSyUCQKVSTk/TYuYDm2w1hLotT6/7d/UQ7FGv1WwZee/DRBXKUyOYEXBIzfdTe1mcN0Fs7TXTh/1c8RQjBx5Ci1iSnay4u0lxYGIthujsuNae8ornDk9Cn8IKTXylC5RqWG1QuX6Pf7gEfSbnPxqc9hrSHPEoxR2CIHC34lJBrzqXghtSDEWBgzlrxQLDVbFNskfSmjMPkw6fLWnkdhGBMEUSkRWYs1Cq0SwNDspaWL5w0kqkWxRxR55LkmTXZ4v7U8/vnPMz8/T3NtDaW2HyMP5cxeAct96Ika4fRxtC5QRVZaiV53K1+c3BIhXRiQGmYmpnn5Qy+j3+7ij0jmrjGGJC2XfqjLLEO0Km/XZiObwRg58Fgfbf+72w7LutfVrdqtw4wRMRDQy1UUm1N6QeelqXJbrNLJc2yajGTxoBuh20/o9RPuPX2aw0eO0u5nVxQLcRwQw+zPzTaNsJGNN8xE9NjIuJSDx/TW11truXjuEhfPXbpthKUsV2T5bS6iQ/lbNPdv09fFIHMkDEMa45OMT0xhtOKSGPoCgfQ8GlOTVMfHWJtf3OsmO24RQgiC8ZjwUIPG0Wlmjh9jYmqKylidSq9BPD1BZhT9sWWsyQjHIA4FRfdyL1jKuDLslY/uQpYr2asx1uWXxIO+/G/ORE/ZWvNhB0IP6iFoe1n29022JaqFjM3UWe20uPR8KVxecxWAprTcUUBlkG0+bNDgmMt7mnxBl1Zca5c9n7Fh2zPaGqXjOhGD7F5bKDCWsUaduBrzmccWeO6551hdnifp9ygN9jdlBwNplpCk/UGR0hjf87HaImVpG1CrVTh2/ASe9HjyC19itdUBnYDO6fZT1tp9IgF1AUqU9mnGWvpJD8F2NXPLumDWDFbUWksgy+toHPkEvk+uDbfDQbq8sjq4rWGtJYpijswepsjVyIzn95eBYL4l0AzvfYQMkHEDGUYY1QOVIaTEG9SaCiOfMAoJAn+wv8oLkBdU8aIxPJEjBzOGhey41X6ObbGAupbGYDVYjUotq4tz+HGVw/UYP/KJGhNEjQnyboueEFfXK4QgrtWpT06R9XsIIbGYq6+WsBu6yGa8wGdsepogirG2R94vSDo9sn6/XO0OqCyl29++KKbwPfxKSOhFREFl/axLspyV1vZ+eNYa9AH5e3ueTxBEGCzCgtECq0uNMb2JMavnC8JIlpMnOwnpwOLCAosLC9fcpgVyDf3cUuAjKw1QOcbowYoAp/fshuu6+gkBUcPDDwRZz1Ck1z5QrbVcnDtPkvY5fe8RZibGmGjUR8baRWvN0soKnu/TT27cLLPXSzl3fgljzCCbU4+Ur9vtjlaGfjvBGnYuUHWTCAHjtZhK6DMxPcXE1AQzh49y8p6XsLgwz59/5M/odjpotbUNNs/AWuxVZv9uVyzQ7XSYu3iRtdWVOyLb/o5CU4olmzPRh6tON3umb37tZgHpciHe9d1ffAgBQYwfVXjk0Ue5//4HSLOcLMvJ+i2kCDHShzBG+yELC12CZk6vkFAbhzyDYjdG2o5RwWpLeqZFsZZwdqVg7cx5xqbGmbnrMK1Ok7W5c+h+wvEJj7BSIyprVWHDnO5m0aegnAga2mi8yJBjPsGJGBlIwigAZek920G19lEYCyh77sMs88sZLke5ToudsfEKR2diWM1YSvpbrgXewFpF7zCO3hYLnXM9tNKkaxm2uSlzfLvtNIA6xFMBjekYqzV5O0H3LevudMP3DoX2ywviDr+/4Paa2HFck0Mzhzl96h5q1Toz09NElQrddo9et8/K0gIrS5dI+4MiJwNbgCAMGZ8YJ67EnDt/gcWlJVaaLXr9hLzb4+jsLJ6kFNIrDXSh0RiywpIpsMoDE1CvTXC6OkGRJmSdJgEwFQYYoJsXKHOlRYvv+VQrVYw2aKWRQnBkZoY4itBKYbSmr1psqQI+AgggkOV9YcpTLgjKgqmeJygKRdLrs7K4TGt5DU9r4k3vL7gDTz2rKKse+2xkpOQMs9KtNuheH50V5Wr0ICSqVqk1GmANDOvcqJxCFzRX18jTlN7aHEW3SW2ixuREHUGEtOMkScKli3MUhcsMdVDGqEG94lztcqhmFGQtjO7ROpfTj2LGD5+gMj7NxMwRqoGk126yNn9xvSjzEGst7eUFsn6PWq3KS1/xSjrNNeYuvFDWqds00WONobWyQpYkJN0um1snhMQPQnzPp99ZodfsgBlke9kcdPeymfLhFwYkRNWQ6liVilehElSwCkxuKAq5bqFylZK/B0JRZBhjkJ6P8EOGE23XV03+Suxg0lYISxCCMddO9N0sAwz/DktnTnIToqxH2uljLl5Aa0OhNFIKKrUJMGWNSK1Gf5L3oLm+aWQBcd0jrEq0LnY9dl5cmmNxaY43Nr+cibE6jXp1/QQ4aLQxrLZa137hNUiSnCQ5eKuaOxWjDGlnsH/3UcsVCMaqEeO1mFOnjnL89Ale8tDLed0b38TnP/s4H/3Tv6Df2yawqAJ7hwacfq/P0sICrWYL47IkRgvDhmgwtOe0l92Gq0+Hxtmbcb6xDkoh3avUeeChh/nKr/xKLl68yKVLl1hZmEOIEISFcAwjJSsrfaAHUkKlUfbwnJB+e2Es+YUOzEGyvMKlc1CZqtM4NoXSOd1+i9jCkUaFsbFw/W3N8LLsSU1p6fIiRTY8ovtq+BWfaj2GzJDNJ/srpA/rYsDVE1mvc7wmBNTqMTOHGnSUQMz1N/IuBfiDxF5zA/a9vUsJvcVkwz9/uGpqO2rALEQTPlOTVVRP0ZxPoWcRm4V02JgY3o47syv2omdqcpqXP/RKPK/s7Agp6fcS8qKgubJMa1PxPJAD796QiZkpPN/j0vw8qihYXWvST1IOj49zaHJy/R2VqIZSBmMsuRoI6doDI2jUK9RrMe3WGvOdFp6EWhRgLPQLjd5GOvY8jziqoLUiTVN8z2P20CHGGw06rQ5JkhCOmIgOA8FlIKRrW573vu8RxwFSCpTSJElGc2WNTrOFpzXhpvffbouTdkeZ5Vt2tD02hPRBQDYWnaQgC6hUIQgJ4wq1Wg1V5GSpAmuwOscYS2d1jX63R9peQiVN4umAmYmYMAypVqs0my2WF5ZQTkh3UArpcQDKlFnEuzoorIaigymgm6whPI9aYxJ/YpZoYgZ/YoLV+XM0F+ewlycoWktvbZXe2ipTD72Me176AHMXzrEwf2Fg9bi1Ab1Wi942WpoQAt8PkJ5H2m3SaS4TRVMEQaMU0k3vKl8YhC8IKgGVWoWKXyXyq9jcorTC9wxikD02SrlgShUoVeCHMeEWIX24jPxG7ToHPu8S/AC0Lm/X2tzmlGVPQOyVQzetfZQOyXopWW++rNMgQ4JKTDw9DVaTrs7fgXF879m1kH7iaAODJdEFSUujsus/GNI0YXV1mXa7ecXs114igMiXhL7PPffcw9TUJM+fPcfZCxf37TMd+8gtnG60WDpJXq4muLBAOylYamZcWuphteGbv/kfoJSinXZJsoTPfOLTXHjh6j5jdwK9Xp+5+UVavd3Z1rh6pAfAtvUbLntuP34U92MfDLLsDR06dISZ2VmGQbLf7XDh3Jltq7RvUM661BrjnLr/QfwwIs0tXhBQb4wDUBQ5vV6XLE+xFGVGVd7BC2NmjpwkrtQIKxF+4LN07kssvfAiVlNvV4bxYpBAZMPBfQF2TWOxiEYOnsfKiqHXt7SbI552PnRf2+t4NywGPNz+QEsxQpO/0Ef5HibKQFlUb5/30VCQ3oMRjhDQqAjiSHLi5CHue/AkPXMB8UxzvXigtRt1BG/ItW44gbv5+qQ3PbeZPrAC1rPoGYPKDHkbiv4gac3xoiZJEpYXF0EIlFZoa0iLnLzIabeb5YuELJUCIcpCdcPqngCDgp/1Wp04qlCrVPGDGM/z8P0AZMT5uWWMsRjhE1caeGgEFk8o8ixBqxzPE0hBmWVcShwIIZiaGKdeq9Bsd2m1O+R5wVqrXVqIJjlSSubmF2mutemnKXle0Blm0I8QQgr80MeTgsALEVIyOTXJ2Fider1Os9XCYjl19yniasyFSxfQRpPkBYXebkphtBAyACHKoqHXrUVooCj7YN4460XUhAYZgyeJ4hA/riDRZL12uUK9yJFY8jTF8ySHD89gpidZupTRbRqCsJwdLbKUVtKl2+2hTZkhOlmP8H2Pdi8jyVwgfDEgBqErDHwCP8D3IPRBFAaR5zdmIWss3dUFjFFMTk1Rn5okCPxr1pRsra3ywrNfor22hlF6dx0Bz0OGMVp4LF28iBCCLE0Ai9bl5KHRGXKbAudCCqq1CD/yCDwPowyZzUBDIAIqYzUIBLVKiESRFxp9WZuGuWMHNTw1WqPyFCkllWq5XsfYGGsMaZqgr9O9QBeWXBiMMehh2LrKlxuaUHmAP0iAKMsXCfBChPTAeCAscb1OXG9QFAX9JMMKQ9ruICWMT83AxDid5ipZ/yoTHo7dC+kP3DdNXmie/NIyndUbq0TU63WZmzvP6soSWu/fpVYKQT30GatWefNXfRkPPfQQv/P/fsgJ6bcjm9em3AJXEWthrZPQBOZXuyDOIcTjSPmnvP71X8V/+S//mZnDM5xZOcfi6jL/7l/82zteSG+22qTtDgUWfY2rkth0cyYwt5jhDr/cNWu/ehObz00npt9avAD8gFMveYBXvvo16z/DpQvnWFqYu4aQLoGQiZm7+Mqv/2YqtQbLl+bQRcHk5CwAaZrSbrdI+h2szcEUkCYE3hh3nzzF9KHjjE1OU6nWecwoll744n5/Y8deM7TYaFIWpawOHs8teqG0N8ADEwgunlHMLZgbE1JvJT7l4b2X3gKSsnBzwEb9iZAyCbGrSJ/ogCk1YGD/L3ya0gN8Dz5HCpiZ8Jhs+Dzw4Cle/RWvYTXxER/70pbX3UBtrA0ur7ux3cqoIW2gMxDS79IUiSZZATV6WqPjAOh1u5w/9wLaGHpZnzzPWFpdIMuz9YLYeBI2e3Zf1h8SQjAxPo7vBYTSxxceURhTrTQwxvD0cxcxxqIJqTVC4kDgS+i1F+m1V1FFhu9LxKBq+zAmSiE5fvQQdx05zDNnztFqd0jSjDTLyglKyu5S0j237qeuYSRrKkkp8OOIMPSp1+pEYcSJkyc5dPgwExMTLC0vY7E8/PKHmZ6d5qkvPkWmcrq6Q7qPY/s9QUikHyOkROf9G0jqG1i8eA2ozIDOQM+VT8kGwveo1CpUG3XyXpd+s4lBYJCgFWnPo1qJOX36NJU45oukLGCI4goAadKju7JAPysolEJ6kqMzDRqVkOcurTkh/UWCN5gPrFUiGvXa+uMiKxD9y/3Mdoe1hubcGVrzZ6k8/AjVE0dLS7prsDQ/x8rC/KBO3e4+VwYhfmMcpRXnnnkGq9T6uaZUD6V6eGIg9AJq02alFIyP16jWy6rputDoQpOSMlYb59D0GGESMLFQIUDTMil6Uydlp0XYtwqjC3KtiOIKtbHJ9VVUxmjM8tJ6wdjdUuSGIt9drPIpu6iBgFBAISA1pc0Ofg3hBQiTg1XUpyaYPn6KTqtJNn8RU2g6KyuEYcCRl56mWo954eknnZC+A7sW0q0usFrfVCZ5lmc0m6v0+22CwBIEoNQNZrnsgBAQeB6eFCwtLXHm7PM0W2t7+yGOW8Ow97mPfc3ttEDLoIM7qPqstaXX73NpaQnjSyphldnxaU6dvod7H3yIbrtDt9Mpl/Ald9aozwCFtbu6IN2Cn8txLW71j3B5wcEXCdKTSCkxxuxr3YYrEfhBgBfFVOt1xicm6HS6LK+s0up0NwSFKxrsg+czOX2I4yfuYerwMeJqDc/3sVYMYlxKs9VleWmZuQvnaa6tbrnmC8qq9FFcJYzrBHEDvzKOV53CqAybu87WbYkF01PkcwkqKbA9C9KW1q4MLD1G5dwepttc7h8wjEPDrPEbJaAUzoefM1yZOxTkhnZaQ2uR4X6pDl479O6+CXwJ1bDc7/18ix3p7lYYDSc5h+J/QSm+X4aUkumZQxw73GB8fBwpJXKPbRf9iodX8dCZRiX62hO7FlRiSJYKiq7GKtebeLETBgFhEBCFPsYolCroJz3yIqdQBcZsElF8Hy+OBr7kisAPmBqfIPB9kiDCKI3WZWyLKxXGqnVAIhAYC1lRoJQmywqwlvqhacYbNazpU2RtwGJUgDaatDBYKTl8eAoviqk3aghPIDaJ95vHt8NCgUP7/lE+sodhQCAQQqCUIk0z2q02S4uL5ElKa3mFtbU14kpMY2yMtX4K+Wj7KglKyx0hPYwU17iuiTJ7HYG1g5V5MgAZgjewbRAe+FXK2RKLVQUq6ZNhUHlaZqL7PlHoE0UetdinEnkIU2AUeBT4nkGKMqh7UhLFEcqWkz5YiyoUmQTt6q7dUYRBQKUSo7Sm39+64tsisAgqtTpTMzMk/T7tVhOlivIZcaMrxEoxvN9ps7JwiSzpU5uYRBdqvSDyEJVnmIERt4GNNHkoTbp3+hhjMEUGWpee6tucaJsdSK9oY15gUkEQRXi+T14UZHmBh8dqsILVmnqjRhD6JPkKhdJl04RY7wZqLAd7ypTrlbSxeL5krFFHCokucqQnyLOcIi+G5TwGutPNXxfWu8YDLcAIQRD6COkhRXmtk1LieR4qT+k3V8g314gUlPUeu22MSlC5s63eiV0L6Vm/TaEs9lru9jvQbq/x3JmnWVw6T72q0AW02jeZ7bINUgiqUUjkST7xyY/zV580rHRGz4fOsUv21RN9ow77tQ7DpbU1/t+P/jknT57kG7/2DRw7coive+vbmT15P0898QRffPILtFaWWDh/9o4qyrmeObPL19853/w25VatZxt+jg8yAgyYYTHTFwFhFBJVIvI0J+ndeKHq60WIsnMd18eYPXKEY6dO8OQTT/OZz32BbnOF/GrZ6FEVKg0e+oqv5v/8/30HSlkuXlii1+mTZOV3uHBxkU6ny+cfe4xP/dWfoXVZEG39s6VHpTZOfWyauHGIoDpGMHWa4NCD6O4SxcqzI6S4Oq6HfCFDreZlR14ZbLX0YFRixE7piFLoziltaTbjU17Qb0bIrgN3syGoW2CNUjTvDe4zNkR0M3jdycFnnwU6N/H5QC2E09NQaHh+BdLNp/RuTq/hfpgCxgbtX+SKH9IPAl7+ilfz8pfdx/hEdPlW9oTKoYjGiSr9pZTmC91dFUFNVwrytiqLyxcjdfQ5DoCJRoOZyUmkkFhS0rzPytoihVJbRHSAqFqhMjFO3k/otdrUq1Ueuu8lVOIKndUOeZpz/tIczVaH6WMz3H36NO12h6WlJbTJ6aYJSZqxvLiE0Zq77zvBqZfcQ3QOrM5QeZcsVHTTgrnFgqgS8+Yve5TDh2dZXFqm3elgdoiYI56vDVwZ7621tNud0kKhUKSdHt1Oh4sXSr/k8clJqmMNltod2iNoVbMFURaglZ6PLvo7h1MhkcEEQvrofA2rE/DrEE0OBMXSKoHqYTAaki7oHr2sT18Avge+pF6vMdGoU6/E3DVdJ/B9SJukfYM0HSpBTiADsIYoCqhF0wSdPheXehRFQavZJfVF6bPuuGMYG2tw6uRxer0+z515gWJT393gYZFMHz7OAw8/xPmzz3PxwgWUUkhhEbL0S7/RRNTFC+dYmZ+jMTXNsfsewGhNZ62JHmh81lo6ywuk3XZZbTzYlLluDOT5jh9u8wylisE4cfuzzNjLkgSG7zWWotnF60mqh2eo1QOW+33WVtZo0WRpfp56tcZD99+PJ316/ZQ8SYl8SeBv+KZn2pLn+kD7r8ZYUqXw4wqnTt9NrVql1ohptVZZnF9kdWkVT0LklxZ6e7HgZLPznzDghx718Qae9BCFBwaCIED4PsnaEp2l8xDEUBkbiGICZQounXkWYYvrtqF5sbFrIb2fKJQuM3NvFKM1RZZhtSL0JWEgEWLvB90WUMaSa0037ZOpfOtAxPGix/c8wtBHSkHolVmlnSTfccY/yzPmFxcJ4ohmq03oB0jPp1qrE0Yxwitn/K5F2f0SAx3y9hgk3h6tdBwIV00ruP3xA59qrYYnJX7gI6Usz3shSYuUrMiJ45hqGA8ythKMsSi9z503Y7Ba0e92WF1apLm6QrfdIu33rigILDwPISRhrU4wMU11bAw/itAU5FlCmnTptdfod7us+Dl5PybtJwRBCBYKNrIRjDUkSUq3lxA0DKH0CKMqtcYEme5RiFFTXR27RlvMpv6dVZAk5d/qJvp9e85O8cZw86txJJvWxrKxwme4/aFH+eaxxTBr3dv02utBlNbOlSigVgmJPY0lLQeZN/NdhkL/TtuRHkL49JKUtOjQ7V19VUkce/i+JAwigjAkSTLa7R1WoQyy5oyy+L7P2EQdqwwqVRhtyTOF3WYkbQ3oQR2mvXQPE1IgA4nnSSphBNbS66Wovc7mcewp1lq0MWgMhVbkRV5O8porfzdhLWJgIFv6mBvyPMeXHsKWFixxGFOvGuKoUl7ngH6a0E9T0iyhKAr8wEME5RgBDMKT+GGINQFSSoQQaFsW4xRC4g0y/KTnI+Tl/nq3F9aC0gYhDHlRYBGEhSIINUk/Ya3ZpNvp0Gw1yzcI0NaUxfBuA6zVGLPblN6hWjlY8iR9hB8ghER6G+M9qxU66YLVWG0GYzsfhIc1BdZotCr7XFZ7+J6HxeJLiAJZHqdZisSihRlk2JbHcKENWIHeTnV03LYIIZCeV8aTy56zg9TkQinSLEcZixdEWCERRmFtacs/PFdv1CLKGotR5fb8MEQav1xlazReGOGHEUaKKzPSr/wyCCHLFazDtgwSCsXgPLG7tH0qw7bBaktRFGRZQZErVFHWqzCqIAoDhDeIuVKsN2tzd23bXsNwAszemkGrteXKKK1KqyalVFmDYjCeXG8WN9Z1vOrnbrovi8QPIlLoI5DkeY5VBqMLdJ4BEhmqgfe6AqvRRQbmShFdUK5mtNbeNjF/P9m1kP70uQxrLVl+4ztNKIOXaCoaDjeqhMbQaiUUam/FdKUN8+0eQoA2pa+nu/44NjM52eD0yaNU4oCJsZgkzfjbx56l2dpuUGgBRau1yif+9hM8+8JzVKsBh2ZnuHRphU67R7fXp5/nZEpdMzYHBIQEFCgy3JIZx22O3pSJfofF2ZlDs3zZV30ZjfExjhw9TK1S4cTMUWpxlf/7936XP/3zj3LvyVO88sGHWFtd5YknPk+332ep1SHfJ3HGWkOvtUrSafHxP/kQn//Ex+gnKe12B6vV1lVjQhA0xvErVe5+4GFO3f8QjUaDxz7zabIkYeHiJbrNJl/89CforK4QBB6elBw5doIve/2bmbt0jqcf//R6TZMszXjs848Tn13gy15f5e7xaQ7NHuLlDz3MwguSL809id5G3HDcfmQZPPtFjZSQJiN0Ymes29RuwVJmid9sgY6hdYqitESxlNnvOaUReneb7d/kCMiPwYvgkQcO8fpX3cu5C6v86Z8/TT9RZDdyOg2F/hVKD/yrbMMYw6W5ZaIoYmHlLMtrl5i72N22hpHvCe67d5JDs1VOnrqPu46f4rOf/RJ//Mef2L7m0WBiob+ckq3m3HXqMA997T0URcHa8jL9bsrZp5fod69ec0lSzmVAuftv9igMGz6Nu2pMNhq8/NS9GGX5+Mc+z+Kis34cZdY6HbqbMp21MairiDIq7ZM1FdYYQmnod1t86jOfIY5iZuqTxGHM8aNHqdXqpYiuLWvNJl945otkeUqWdAmjiIcefITx8Ql8D+YunaMocioTE9i2Rnck2pYnfVEozp07T7fTplKvMz4+QRTFt2S/7BdaGzqdFCEErXaC9CQnjvtUqlVWVpsszK+Qpn1azUW01uiz5W/SSkZ/5bc1mrTXAgRmG5Fo64s1Om+Ws5zGB6rIsIJXrRLHFeqNMbAGXaSoLKWdrKLU8MKhQAswglwUtDxBB83KhT5RGHDvfXdTr9cYa8RUowmWl9e4OH+JQlPqLNbgS40XQifXGMM1a1Q5bi8KZeh2c5KkuFKQNKWV8tmzL7Cw3CSKQmZO3IvVGXl3GTsQ042FZrNPP7k+LWFsaobJw0dIel0ufOkLhHGFQ8dPI32fbreLUVCbPkR1apb+2gq9tSWE7yOjCqDRm71lBHhRBRmE6DzFZBtxQPg+QaMBQlB0Otgd6zdtFMkczIGyuNAiW+iitKZQGt8TeLHAGEs/zQk8g8Xg+ZCYskD5ujf4FeeLQIZl0qPJU6za/wxbrQqS1hoq6fHFL/lEcUzoWzbPtVrKc9vsga3L9m3QtJttwijmrnuOElWqJGfPkrbWNtwTigzTXh40aNAQu/01NvR96lGEMoZOmr7oxfRdC+m95ObFbq00eT9FZQUeAk9cOQu3F1ggcxkmjh3wfY9qJaJSCag3YqQn8LyrZZEIhCw9AldXVjAYzl+8QJql9LsFea7RWm/yVL/GZ0uPyIvACDLthHTHHcDt4Oax3ZT/8JS/fBJg8Lcf+DQmxpicnuSuE3cx1qhz9+ETjMU1Dh86RK1aY3J8nMMzs3hAo1Z6ZXazAlkUZWa6Ga47sWX/ZA86HUYVGAqaKynNlaUdX+sFAX4UU6k1GJ+YwBrL8uICWZLQWl2m127SWVuk01xZf8/07CFq9QZxXBlUeC+zTIwxdLpdUhvS63XJsgRhNXHgD5ZU7scV3XEQWAP93gh2kMt57e25kTgkNt0PfdE3f9Ywq3uYib5d13KzN9yN7DJZWu1WawGHZqustfokhSC50XHesA3F4LYJMTDjtLb0ie72EtaabRYWV5hbmqfduspOFIJq1Wd8POLIkQlOnT7KpbllKtWQPCvI88t+lMG+NLnBKIOPx+T0OFoXSFEQRQFzlSZ5WpSe1dvERQGEcmjWXGbFeYOM3zI5TpRZUaaMTVe1tRrgBx7VsYjGZI2jx2YxhdlVsbXN7RHCJebcatQgk283WK2xRb6emaiKgrXmWpmF7lcJpE+1UmFyYnxw3IDWmm6/R56nqLyPlFCpxNQbdaxOyfOstDgbLLhS2qIGB4EdrNLqhQFhpbqerX67o9ZX5+qBR3o5xsnzgn43Ic8Tsrws8pcm5Tl8uwi95nosau0gOUqUBSeE9BGeRAY+QRiB1QgUaA8hhhcMXd4P+pFGFxRZhrCKPO+WHvsqx9oYXwqk7wOGPE/Jcks3KYtNVuPSO1nb0sZjC64Y1W3PcPWq2mQFK4QYXNfKFetpmpHpNmPjY4xNToIGGwRlsvfg2nc94WaYAR+EIUEck/Z6pN1uuTLMmvKwGmQae0GIlB5+2C3rCohBZvk22xVSImS5+nXL40Igg6CMIULs+pAVlNnveabob4r9RlisHV73DUZapO/hBT7kGqXL/SHF9sK0EHJw231bbgpbZtArLN1OmyzPGB+rEIab5Fe7oV3vTxMsWmm0rwexy8NiNsXBMpgIo8o2XGOMKgBvkJF++1/pbp5dC+l7wdzFS3zsz/+SVq/L0+daJHlB6vwPHQdAp9Ph7NmzeJ4kCD2KQtO7is+xH1eIGxNYa+hcmidZXOJ/r65RrdU4dd/9TM4cQuiMyVqM7QWsXaODc+ToLHffdYL5lSWePvMc+g7yU3c4RpYq0GBdOBMeRONlslHWAbN5TqsHtKDVafPpL36eQ4dmOXLiMDVi5loLrHQDHnnFyzh97ARJt023uUIYwAMvva8s9ikkSmuePXOOlbVmacViDd0kZbVz6wpyCiEIw5hKtUbS7XHhzAt011ZZvXABVWTkaVkgOeltNXW+eP4snVaTQudUxmuogS+qkIJaQ+JX4ZmnP8W5M0+Tt+fIW3Okvdb1DVAdo4VHaWcyLKb5Yuma1Sh91+uDWzj4+/LiohlXF+oz4DwbxUavE52CKeDCxWU+8fgTLCykV824vRnC0GdsvIYxlnarh9KKp794hnPnLtFPuiSZRhX2quMohSUXhrgeM3NogodedppvzL6cubkV/uavnyYdZsUJyngbUWbwq4HlhjFMNcZ5+b33k6UZofc3LC4uc+H5VVqrV3orx6HHqcmYyPfw/ApeEHLi3nuZmpkljGPCKKab9FlttVhaXuWTf/sY/eTqP8D4+BgPPXg/R2dnec0rXkXWy/noRz5LaSC/M1LAWAyBB50UbtquWFLuH0F5/Ljcnz1BaoMsLMpCbkAITZ5pVFwlPHSCWhQzNTnGoUPTg+PcsrA8RuyVNhwa0FqxsLxIUmQcnZ1grFFlce4ii3OX6HR7LK20UFpjrCEUPlYIlIVWu0Ovn9DrjbhP+GXspl58mmZ0Oj2s1vh+WTywEsuyaGtU2o6sdRS6uJ3HM8OZ1MtnawXUGhDWMUGINYo8z+j3O2WSQZaiixSrM8qLRbkPPC/A90t/sKzbpBYHnLzrEFHoI3RK0lYYq8AYotBjdnaMdqegn/YxBpJsMFkoAzwpMbq0iPEiHz8OywKRfZeMddsgyxiDKa0zjC2tqrTRWFsWwZ2YmCQIQpJCURhNFFeJ4ipaFSxeuoDAEnilJVrS6w8KI29/MRICfH9jYk9IyZFjxxmbmKTZanPp+ecosgxjIU9T5s4+i5AehbVYIahNTBNXG0weOsrho3fx/2fvv54k27IzT+y39z7KZehILe6tq28VgCqgCugC0ED3oIEWmGHTemw4RtKGD8MHPvCF/wDN+D7PfKMZhw8cTtsMrWe6MewhGw00gC4U0ECpq0XqzNCu3Y/cgg/bPURmREakzrwZn5mHdD9+/Ii91/7Wt741HvTYvHsTq/XBZqMOTJFjq/IB+xYpBY0kRkiJHspjtQ7enU8QRDWiMECaDPYpx62BPLPEIURRSK1W49yl88wvL7C5sUW328caKOxhCW+HLXOQ8sQ2M08L1hjG/T5BGBAnK8hYYWd9Wx2Uz2F3dFWxfucOKghIJxNQAVIFPhGhFEEQYIymGI8f2uevNIZBloFzyFekefazxHMl0ofDIbeu32RUVmz3892M/ilO8awhhNiXwHeURUmvV05/mw5kR4iZVBAS1VvoIiPr7ZAZzWhnhyAMqTdaJLUazmiSMCAK1LFCgVa7yfkLZyhcibwtse5wNdYpTnGKp4gQT5TNiPQIghUf11YK7H7+xQAjyKuctZ0NbOAodYHBMMrHCCE4e/YMH1x5h+vXvuAXnXUCJVheWkQpRbtZx1rLeDTGVNqXYVrrfUeZPNeAwwdIIWVRMuj26K2vc/fLL3CmwjM4D+7NaNBjNOgR1RMaC3MIKSnGKUJCFEnCCHa275GnFW6yAZON5/iJTvFMMPPRMPDaOI4JPHFeB+bwzTlhj03aLzA8zE5mBgMMH383nPGPwSDlzkbOoOfVVk8bSklq9Xg6NqVo7djZObmticGhnUVFilo9ZuXMHO++f5EwUvz0b76myMs9O+EATxTvc3sQDupxwhuXLlGWJV/d/BojSrbXD+/OGirBfCOkHoUkcYMoqvH+G5c5f+UKtUaTWqNJbzjk7sYGtVqNn/zsoyMTGUJ4lfHq6jJnz6xy7txZ0lFGFEVHft79ymIlIQkdcYCvFHhSIl3g7zfB63O/PQcI55DWgfXNkgF0ZQiE9EOcUiRxTL2+Z7+SxBGBlMjp+bbWMp6MQQrOLLeJohBdFQz7O4zSksEk98SX9NfVbA2R5wVQUJav1gk91p/XObQ25HlJIC2BtEjlCAOBc4IoCjDWMUzNAxUwrxYEnki/b+wVAsIIkvq0d6JFm4qyzBHWIcrSk4juYMmSlJIg8OpzXebIWLLQahDHIaUpKYu960QpQb0ek5cOppU22vj3DiJfheN7AhikUqg4ABz6VJn+6kCo6cN6L30cZkqog0MISa1WJ45jbFHhtCau1anXG0xGAwbDIVIK6vUInCPNKnR19EQkhK+0n81jUira83MsnznDZDxh3Ovtcg9Ga0a97vRa940oa805nHMkjSbzcwve//x++8gpvK3k4fsQBgqxb3x9GBzgBIggRIUhQpbsH1ScA6P92C6lJAwVrfk2cT1hOBwQ9KGyoI+4J5w1no1/znDOURU5RksqU2GY9VGYfubncA/PyHzEtHpYSIRSyCBEhQFhHCHKkvIYtb51jkLrAyHM66wDeK5E+qTUrI1SSvPqNCU5xauPMAz4wfc+5ML5VTrdHbr9Lv3+mPX1zm7G8mGuLM5pjE4xpjjQHNQaw+1r1+h3OuRZTpHnpJPxsaT4mfOr/NL3vs3qhWXm5hN2un1+/smX5PnzC77bSchCPaYoNaNJjnEPUmrzc20ajTqj8YTh8PCF7ilO8cpg2kRQ1CFYEYgQyppDSAjaEBowlY+xwquKSAZYJCVj+g3FR5sfs54t8865N1hszrE52mBt5w55OmK+vUg6GbG1eRelBM34DFIp5tt1tJlnOBi/EJWac46036XKUibhNkEYk0/GODuTQPo7PmAvGNq/hDSlJhuMERKSdh0ZhIQKlK1IzBChJ1Q2PfnauSVhTkFqoWdOF4AvE2YiPIknlR1eTfxNjpAFcFbAhWkdsHb+GGT4tdto3/eSJydPj8F46Lh7y5IXjof0PX9kqFpA2AqRSjIixxqLcY/2BtZZer0xVpZ8/MnnPt5JM3r9AZXR/Npvvsd4kvH5V7cYTVLCBFQIsiWQsaDeVsRSgTb0dnqkacbt65vcXd9kkuV+EJrZ6EwhpETFMWES01yYo1avU1uoE7ZC4kZM0qiR7mzw+VdfsLm1c6S1y+pqndXVBm++c4ZvX7xAkRv++X/zR/S6I9bXDlejnz+/wnd/+T2CwJezl0XJpx9/Sq/b5wgB4KPB8nQ8/U+BAJpJSBQqAgFKgCg1WaZRQpBEIaGEG/dusdndxkjD9vY6nUGf7nDIxuYm43SI1tXUotFCNcFmju2120x6CaYsOHv2LMlwTKW9X29e+efneYEQkvE4pSgKRqPxiz4kj4RZvvBh03FVlmRpijUV1pRYa6iqgiQJ+dblJYJAYvQWASlZ9XwUls8NzkE+8gye8ySokRG5qvnGtibD2QqrD67hgjAgrsVgJSJ2NOvRtHntXoZWW4t1lv4wozdIyQuNcw4VhNTmFrxiVPkILe13KLMKU2kYO6yxpzHUqwSrd0l0AFNV5JMR1XQccc4wGPRRSlEa6xss5yn5sE9VFuiyQApBajUwPf8PgRAQR74Zp5tamnS7HcZpRhxHfOe7v0K/2+Xu7Vt7jeadA2Nw1pL1dqgmY4rhkGFvQD4e4lQNwZRMdzNPu6MvQlNpRt2et3Y5Sq14/2FyjmGakxYlxfQ1QSQJYoVEoIRABY5r124RRRGLS3NEcYg2UGoeiJ2EgDAIEAIqbbDPQcQrBEjpD9EB8b51jDoDiklGWVrCVhtblZj8OfWXsNYrGvxeYiufmXBa+bHdWpyS0wXhw9do++t2Xudh6LkS6VllyKpv0ux6ilcBQRDwvV95n1/97gd8ff1Lbt25wVfXNrh1p3uiAdVZgzEZ1lQH2HZrLWu3b7N2+/Yj7c/K6jLvf+ddzp1fZGUh4eate3zx1c3nSqS3kpAL83VGkwKTFZTGHajmF0LQbrdYXloAOCXST/HqY6qSlE0ILwlcADnOV+2GEEjQGegCkgVF40xMPtGk6xOG1vD5zpcsjLa5sLTMajDPxmSL7Z1t2qbGUnMOWxRMBl2UEtilNqFMaLcSkHNUZfViyr2dIx/2eViINhOPSh7kc4zWmLEmiEOaK/MEUUwgQVpNZMYI3cPZ6uREelPB2dCT6P1TIv2lwqzBZgy0p79nfPOJ9FUJ3xLQsbA1JdJT/Gdf47kqLCcTx+QZ+NIHiSJZqmGsYZwVWOv1UI8C56A/TMlMipJfMxzs7P6v3prjO7/2NuM0505/kzEpYQRhAEEoUEhqTUmoFBjLsDdgOByzdmeHe/c63ls54EHVvxCoMCKoxTQWW9SbTeJ2QtgIiZoxcSMhq3Ku3bhGrz9CH9E8bHGxzjvvLPGtN1Z4+9xZrl1b44/+x3/L5ubRavyzq0v8vd/9PknsFeuDwZivr99luNZ/pON2JBynSvSnBCk8kd5I9pa0GoHINEpKGlGIc3B38y4WgRSG/s4W1+7e4cbavUO26KDKcNLQ2RzRAVZXllhZWUEpRTockhUVpdZenVf4Soydne4rR6LDgy1iDvt/VZbkUlIUOUWR+byjhQURsLK0QKMW0u/0MUWKeU5WBc8PDvIxFNnUM11jiSip4yfICYdlw1SgiJIIhSB0lnocTol0mBHpxlVUxjAYZ2xu78WIMgxpzC+jQj/+OOco0xFlBrYy2FMu5dXDrPRsCmMqinSMMd760TkYjQ6Wtt3fittywOnkoZBSEEUSpSROBDgE/X6Xstrhvfc/4L0PPuD2jRus3bl9MB6YlvLkwz4A42gM0RCBQwQJQhiczX1S6ZgUnDWGSX9wsh2ewjkY5wc/uQolSSNECUkoJFpbbt26h5CKD5O3iOIQYz2Rfj+EEEShQgiBMfaRY5/HgRCg1CFEunNM+iMmQNhqEzaa6Cx9fkT6fapRZ83UzkxSWe2rF6SYWhDZY6XypxqA50ykn+IULwICPwiHseLtd97kjbcuUqv9nGtf3CTLDZPy8LHi4qULnLtwlmE6YqfXIarFLF9YAefo7wwoipLhJCM/YZYVpoNrIAkTxcLyPPX4baxQhOHJG149DUwKzeYwIy81mXW7OWUhBM1GQhQG1OsxYRTSnmthnSXLCgaD4akNzSleTeRA13sRa+lwdeAsuAiqiRekGgk2AhNYjKggNMRzII1mUHUpTc7ffvFTbl2/RRJFREHA+vYOX966xmQ0ZH19m1oSsXSuoKkCZKiIk4jSaLrDMWlevHTc8UxT8rBw2BpLOckRBlaXE+pJnfU0pHccZzDrPzrbeGphW/vvL9uBeN0xE8lVeCX6wwxznzVmNaNM9+dpXysCSIAYgjhABiFGlZig9J95gh8vXqZVQggEEK02CRdqVMOMsjv2xyflocfIFIayX2CsxVYap90jd8x0DsrMr507okCnewv+1rwlaqyTlxpdVEgrWF5cZG6+DpUBbYmDkEGnw8BJbuYbTCYZo0GKnVVBHAKhJKoeEzQSVC1G1SKCJEJFIVs7Wwyv3+Sra9fpdIeMJ9mRwgjpBMpKirRgc2OTQb9Huy6wizHt9hxBELKx1WMwTFlenGN1eZ73332Td99+myCQjMcj6rUav/0bv8K3Lp3n4y+uc/cIJfuzwP7b4WEOQ68bBD4BrgTklcbsi02z0jdOM86SVRUgENKrGYfpGIRjkh/uA+ScwxmN0ZBPG9iFKkBaR6UN7fkV4kpjI2/TFsc11LSJ3zcVldZQFGitmQmhJVCVmjt3OzSbCW+/+x6/9N2Ee5t9trtjdjp91tZ3Hmv4FkAUBN75yJjnVMX+MIXtrHmo3ft9t/v09PlK7LnDSIERFWU5IRSGQFaARIWCMJQIG2GdQueW6hC/5jAMWVheJkxqaG0x2jDpd2E02lXFn+LVhrWOsvIK6WdxeVvryHONkF4m44TAOm+JMhwOuX3rJp2dnePvLVNBlXpyVSriJGbl3Crg2Nq4S54d3fdJKklSqyGEIE8zzGN6kwsHyoEzhkwbjPbOFsJaOp2eb/g8Pnw8F3hSW/r+pc8UYjoEhKEiqkU4B4HxwssyL31T1ylsVaKzFFv6pIFUAVGtAc5RZpOpldNzwozxF/6cOfd04oyk3mRh8QzaWgajMUZXmGLsqzO+ITgl0k/xzYeAMFIk9YD3P/yQb739Jo0k4a//5I/pDg1570E/LSEE7334Ln/37/8Wn33+CX/+F/+W5YU2v/X976Kc4POff0W/N+Ta3c1HItLBlygljYD2yhlW5t5FxQlRHD/FD3w8+lnJIC99t+j9+yYFSwstmo2ERqtJnMQsxxHLy4u7apvHnQhPcYoXigmQgtuG8p6DBbyFRQDFAE+cLfm/BaGmEgYSqMe+PHGnu4aZwM0fX0d2FL/5ve/zy+99yPXbt/jz//mvMNp7HS7Mtbjw5hVcHCKjgFqgSKuKtW7vufjgPQ6OG8GsNqS9EappeWOhwcrSAlnnHr3OMS+cSd0r/HpzYGA4HT9e0mPx2mJWp6nhoSUMzwMK71sOu80qnyokXnXfgKieEAU18nCMiaZEep/9PeNePARQAxJB7TuLtD48w/jaJuVHExg5f74eMi3rVKOz6UF83Ptu6m6Qj2HcSbkn9hat88sTRFxhnKWYFEijuHzhEpevnKUcTajGKTov2Fpbo9/P+OyLHfJC+zJih/epP4SDlIEibNf9Y65G2KgR1hOiJOHmJ5/xk5/+jLV7He7e28EYc+T4Kp0g1JJ0MOHatZsMB0OW5gXzzTpvvnmVpNbkR3/9GYNhyuWLK/zgu+/yy7/yy/z6D76PtYY7d26QZxkXl5ZIRxn/1//7f/dciXSJP/2Co7SvrycEkEwJkklWsr/P5exSMNYxykukEERxgJSCzqBLZ9ijPEy+OH21NQWmqhiMSiaFpspy8v6A1sIyKxevUllLNBpijEEIgbUWKdUz/sQvDkVVUeyzTpo65VHmFZ98fo/5uRb/5D/5j/m1X/sOn3z6Bddv3OKnP/+S9c3OAfLopJBS0ogipBCM8pziuaw77vOW2g/n/ckPPrdiVyUggHCa1YmBAKqqgCzDKfCFLZIwgShSRChvC6QrOMRTP4oTzly6RNJokaYVZVHR296G3gBMsdcA4BSvLIyxmKfp4fbA9h2j8d49K4QgqiWoKKCzs023s0NVlNjjriVT+IeKIW7SaDT48Je+jRTw15P+Q4n0IAyYW1xASsmO3sJkj3fdSkA5S1lZRqNqX9LccPfOJkJwdCJd+Oo4Kf3jWUIK3zsjSkJqreYuc68rjakMeh+BbPL8gBI9CCNai6s4ZxlsldjiORPpRoMUyMgfJFc++TKtNbfEWx/8GllZ8fXNW2TZBNe7gz1y7n31cEqkH4IgignjGF1VVEcoFk7x6kAIQa1eo91uU6u1CcM5lpeWef/di2xsjhilHbLCUtwnkiyKjNGwRxTAG1fOs7q0zDtvvQnG0bnXwRlHGHYffX8AiUAKRaAiAhkeaGr1vHDYotM5yIsKpSSrZxqsrK6SZRnjyZhAyWeezT3FKZ4pZoKjCm/dsIn3P57gyToH5GAyqIzznuoJmBLMDtgUrDCIhqU77nHn1hqdnR5VtRfYFWXFxlaHbFqa6JxjNE4P3G9hGNJutnHOMRwN0Yc073mRCMKQMIzQWlOVeyWWWht2ej200eR5isTy0LY0h5mvnhLopzgJZtUMCXsNUJ/GukJNH9OxwIw0eqfETqaN8mYiw+NMg58HFNAUyFix8uZZavMNyragGIzQ4wIyxwFPtofhST7LfZUlDg5UppW5odfJEFIQ1yRBLElHGVv3eug0R2c5wmqkrai0ptLmIIGwv7krkNQi5uYb1GsR/WHqle5CEtfGVCZkfaPPjRv32N7qMxyme4T8EZikJZs7E+LUMMh9s/kgiEiSkAsXL9FuL/DRZ3eRrBOFEfVGkzAKqYzGWTONzxyTyZDhcEhZ3l9w/2whBATB9DTM5qlT+NyfAzktsHioPYkAFYQoJdFVhTXmARLLx+Ze+Wi0RTtoxBFJlNCKIxpxRBSG6OnrakkNay2V1mitOaZl5zcKs6ld4H1/K2PJ84o0LRiNUwbD8bT56mNu3zkqY5BCvByJI8X0Bpw2jDRTb5v9ezcj8qZVVMKBnKrUrfWe1rosEc5RlRpjLKNhzjgvKbWDQFJvtFhYWqU5v0AQ1oAAIQxSStpzC1BVpMMdJoNHO7YCUNMmguaIwTKMY+KkRntujouXLpNnGV9+9il59gJsCV9RBEoSRQpjHWWhH2moFkJQqyVIKcnzHK1PFvCoMJr66PsubtYYbFUihCAIPc2nK++9L6QgUApjDFobrDGoIMA5t0eoi+mX+64T5yzCVDjrG00KcbzEW0lFq9lEKUl/p/uAVc1JYY2jLC2Vtj4xt2/XHAcV/ft3y03dSXRlkUoQxyFhCEWpqZ6BPZKPjfxQYJ3DWYudVpQcV81vnaMqvV++t4N5jp2E9/l7ySd4T4HwY00QIsMIKQPSLCUvSkyZYssM9w2rpjkl0g9Bc2GJudVzjHsduvdu+07Fp3hlIaXkzJlV3njzDebmLwHneO+dD/kv/4vf59NPbtHb+Hfs9FK2Siinp9o5x9bGPT79BZy/sMI//cO/z/mz5/mdX/9tiqwg6024Xr/FjfVt2Ok90v4IJ3ypsVMENkG56Ol/6MeEsZbtzoDBMOQ3fv03+eEPfo3rt27wxZdfEAWK12mhcIpvKCyefOoCf4lfICXsqacllMugzwEtkBeACZifg5sAbwEXHJ9d+4qv/+om1UQfUEJM0pwf/80nu2Xe3l/0oOZ7rjXHdz/4LpWp+OnHP2UwejQPwWeNRmuO+cVlJqMBna2N3QAwLwr+6ucfo6SkFmgiYXmoWc1z9Jc+xTcIs/L4AGhNf9/Gq9OfBDOl+74qifJaSrmW4WLnlYQZz6Wx6ImQAG9L4tUGf+c//X2uvPUWf/Gv/jU//Yu/xG1aWOfBDsFPGwJ/XATesPWQNdBkXPH1xwPiuuLitxvEDcXdz+/y2fZNP3ZYx+JCzPlzDYpSP7igvO9Ynzm3wHe//w6D/pif/fVXFGVBfUGhQoFOFbZUVFVJWVVY447tdXNnfcjG9hghBUJK2q0a7791ljNnVvit3/09zp27wM8/usXHv/iSer3OwvIKQZzQ6XcROIwxlFXJ5199zNq9NbY7m09wQB8dUkKt7r+Px7x246pgj1K4Pyebn7DQQgpJXG8QhAHDXp+iKB5IvkigLj2RUKYlRimuXDjL0sIcgQoJgpBMC4aTlCCKWFpYAgH94YAsz7/RivT7MStgmiEzhrtrW7RaTT777DpffHmNza0ewrnHooOsc4zL0ufvXnQpnwDqAmIBqgaqDmkOg9Ee2ejwC8jZOClANRRhPUAYn2wpc0026FMg6HTG5HnFduoYVw4TBFCLOff2O/z27/0h1km2t0ZUlca5CqkUb3zrXaK33uPrz37Kl/t6VJwEgRTUlMLiSCtz6JQxt7jImUuX+d73f53/3f/+/8DdWzf5P/+f/o/cvnH9yY7fa4R6LWJluUmeV2xsjx5JcR6GARfOr5LEMXfurtMfnKA3mRDUm/PE9RZGOKxwlJMxWW8HFShacy0Ahv0hWmvCICCOQtJJSTFJUWFAVE+8fWOa+bl55oOizUELOFthS4MtY4yuvPT6mHszjiOuXDxPoAJ2NraYjB8tiJuN/WVpKCtzv8X3oVAKwhCsAV36RN94UhEEinPn5mk0EtY3B3S6T7+fhbVgBQhjCXSFqQzZMMMae2xljq40w6mfvG/+qjiumetThQUhHGoa5IlpYmYPR83EewiQKBS15gL1+UUsilt3bqOLnLRzB6NLf2K+QTgl0g+BcxZrzZRAP5V+fBMglUQFCikDICBJaqwuzbM536EWCWL1YGVxlmb0un2azRrppGA0TNna6lAVFdoaVCBZWJjjXF4yGk0Yn7CZYBAGJMk061xkOGc5s7xMVVb0+gPK6sWukoyxVMIgEARBgDWWNMsoyqOlb2L/Q0zFfqe3zileVszigAx/4zv2TGgFEIFN8IRF4p/nJtPn5+ByKMcV5bB6gNRwzpEd0zjYOYc2empH8BLeKM5NVZ73K1IceeE/m0pABo9stXyKU+xCBt632BqHu3/CmIVfAXtqwKeB2b0/JaBdMf3DrHK/4OXyzpCAgiAJCesxaLCDylfRPK9QwfHQHLrnyq0/pIHDKEdRVmSTvXEwyyRZVnmV3lFjxjSR6asGHBZHXlbkWYUNK2QAxchXmT8KtLFoY4mikEacEEc1avUm9UaLubkFFhaXOHvuLJcuXeDM2VXmFxeIawlZkWN0Ra/XYTQYsLndZXO7u1tt9Lwwu1zdMarrVx8zKeHsgx7/aU96PIQUxHFMGEdkabarxrT2YNNrN9sN6Zv01es12q0mQgQgA/K0JBtlhNahKw1CUJYVVVlh7cs0cDxfGOvodPvcW9+i2xswnqR+zfAEF6xz9xM5LxBu38PO2LzDynr3fJKjIKBRi3FlhdMaZx1lXiGAKq+oCo2pvKOCExaUQgUhtWYLrQF8I8oinWB0ThAlSBU8mney8Ne+mKrRHxZuBmFEvdGiPbfA6tlzGF3xxltvoZSkLEuMMQz7fdLJ0VYerz3E/gvlIIIwREqFNhqrDyFIna9cMA+tsBIgptSd0wgEMlCoMAThEMIhgwCkr5x4MIa3WGcJgpB6s+EvVin88/ZnvI56f2exuiKdjHcTh0EQTtcyD45/zjmM1uDcE4lST0Kg3w8h/GHAzV7vcPbwtc1ThfPkvdHGW36egET3r7PYWXXyifZvFhTPjussSHN7vwu59y8Hx/ZWcOy1gXisQ+THmtn51sZQVBZT5VhTHeKN/hxV988Ip0T6IRj3OuTjEUYfopw5xSsIR1Xm5NkYrf3Crp7UOLOywuZSl8W2Is9h4z5P1M2NPr3emC+/WOPH//5TwjCk1fzvSaKQi2fnaDQSfv/3fkh7bp7/77/59/ybP/3LE+yLYHFpnm+9e5XOVofPvvyU3GT8l/+b/5yt7Q7/9X/733Hj9u1nchQeCc4xGgzYWl/n2rWb/OTnn1GWFeaIUrMIP5jECpIQcgu9p+CvdYpTPHNYdglyguljC6+AVb756IH45GP2SPicx7rIB6MBP/n4J76De/ryLUrGowF5lmLM0XPgpICsgmdo8XiKbzIE1Jdj4lZI2inIuvuSTwavPo/wfQxiPLn6pDD4xpyz+3nm5QAwxN/PL1M3Rw10DMaWrH15G11Z+l924QbPl0SfnZojjktYg+Y5kIlly4yxE0F2X6wwGpXcKHxjtSNVejFQg74Z8fmt62QjbwXjLJQjvEXCE4iZLl44x2/9nR9QSyLqtZj5hXlWz17izLlL/Gf/6/+UX//N73P27DLnzq8wzsasd7bY3t7gR3/xp3S2u3z20Rq9bsp48nyJdG1gZ+wv1+pluTafNoSCIJ6Whvssl9UanH2I/u3kCIKQs2fP02y1iOttRpMJ42GH8bDrxwXtv2UWwkAyP9ekWYs5f/EiF86eJ60ck9Kxla1x8+5XKBUwGucIIej3uxRlweQhXsHfdBRFwZ/8+V/zl3/9c5wtcbZiklYvhUPWE8PhbbRyByIFCi89PeKDhaG3YrqwPM+VC2cY9Xps3bmHqzTdrTE4qKbq2khCI4DCGsrMYrOCIq/QxlFUJelkyK1P/5ZJbxspBVII8keIGYM4IKpH2NKQpaW3jTjiufVGk9Uz55ibX0BIwaWrV/m//Ff/FelkzM2bt+h2u/zLf/7f8ld/9mePegRfGxRFRW8wotLuQGJNSsni2fM02nP0trfpd3em8uW9Ca3Smntrm0ghKI7qvSYjSFb8z/k2uArCAJGEU9c6h47riHgOYzWjwQSc9b3NHGRZQaE1Fy9d5uLlK3S7HW7fuHGw99ns5yOu7/F4zM9/+jOUCkiCgNWlM/SHPdJDxr8sy/jqqy+QUpBlE5Sa3jonHBSOHPuFV54jPDe7f3ta+/cIAkGjGeBwVIXGOcvWzhA6krJ6tiWHuvCe6DNS/URwBvRUkHmskFfhO6fMFqOWPdWJb3YkVISMmggxTaQ5g8mHuIc0+rQWiszu/nzfDh6zT3vPSidDJvloKrLwpLp7wMJ0//6+uir1UyL9EJiqwrxgVfApnh6cc6STjOFgSKM5odX27FctaVCv12k1Y5qTiHBQIau9IKMoKoqiYkTGDnvWC416TBK/iwoWWFqa5/KlSywstI/dDxUogkBRq9doNJt0Oz2G4yFWO86fXSVUIUn8cti8OEDriiLPSScThoPxoeWVYRCgpKQRKGpSooQmFBWu8pnx0zzUKV4J7E/oC/aaLs6g8M0JFZ5we7jg/FhorV86O5f9MFp7FcnDnuNO+12d4tEhBIRRiAokSTMiboWU4/uutZkE17BHds9i7lksP7tXZ89n3+8PW4PcvzjYr0R/2Xgwi6+AmRgmvRGDVpdiWPhkwPPej4dASAgScDHk1qDNdFk0q/ZxoK1DzxpMzZIX948fUzV6ZSqGowllanDT1dzTqAZO4oTVlSXiKEJIQb3eJI4T4jjh/MVzJHXvmx7FETYd0e332drpcPvOXTo7XTa2h4wGT39tEAQKIQTGmENtahzwDOxcXyoIIZBqGv86d0AhKYRAypnpLXsevnCQAHrY9vHxahiEhFFEoL1dhhDg9lVbGKaFEUqigoCkVqfebGFyQyEsTijSPEdJxWSSIoRgkqZUZYF5yXqdPE9Y6+h0+iCgUQtIIokx9tUn0WfYvcwe0pB0CiG8DVMcBjSShCoMCYT38y+KvabIs0tZCpDWgXGYqiLPUrSBssgp84x02GPcfwQrl30iTyEFUkmstBj38DWZQOxaElZlRdiocenqVaqqpLKOKKlRbzRPvh+vIYx1FKU+QEIKKZFBQJzUqDWaTMZjgijGam+VMoNz7sG+AlMSdFfpqyKI6r5qtFRgtS/ikV4M74dNgZDhtPLVeoJ2euKtczhjCaKIZrtNlmfTcXDWCOX4sidjNMPBAKUU8eIyURTuXjf3wxrDZDKeNgPVT7fP2kMs2t103lCBP3amEhjjz82zFP9IKRBC+oSVfow3Ool/uJiV7U0pXDH1KXTSP2aBsBAIqaZEukQ4gVMBTnjv9qNEUtbdH1gfjV0LU7tvrJ9WIRzv8b/fy+DVxSmRfopvPPK85P/1z/9n/vTf/Q2/8zvf59d/4zucXZ7nzUsfcMm0+V/8sx537myQ/Q9/zZ21HgPtFdVHbq+o+PTz29y91+HNN9/h/Llzx5Z0KiX54Ntvcf7iKlfffBspltDVFsPhiPFgzOadLp3OgOHoBJ5ozwvOgrOEzlGfBoHTHjpYIIkjfvjdD7mwusz777zFpQvn+eTjX/CTv/krNvsTtu/2sKdy1VM8LhReoeh4bOX3I+OoJoMWT7LNSPZTnOIU/n6YRZEnbILYaNX5rT/4PktnF9icrDMsBrhqh9H6vsbu+4l0h2e2zgGr+ObAY/zY0Nz33Nn+APTxZPP9CbHD8DJ5ot8PA3RBTzS3fvwFG1/cYnSr/6L36gFoC8MCT5ib6RDaxNtlDfHnrAmssLf+q4B7+OM/w7QioEgNvUGK1Q6rn97AX5Q5ne4O9Uad+bk5kA5rK4wpUMISBY5/9+d/zp/82Y/I0pxBb0SepWxsbFDmBVWqCXi6rqVRFPLtD95gfr7Fl1/d5u697ae05VcLSZxwZsXH0ps7dymqalcSt7I4x/L83G4TNqEUQRRTVpo7d++S5fmx2y/LkmtffUkQhORl6Zveljmu4gFe1BhLpz8hzQ02atBYOYvLMkSWU9tOEICxhqz08XqpMypTfeOaqD0KhPD+0GEoUcI9XcLsFUNVeZFBtzciCdYo09Q3ErTQraY9SqeYhZwzZ7Otu7f593/0//YWgVmBLkuyUf9kbyzwItV9Ik+jDfkwP7YhM8DmvTvk6YR8Mubi1atUWvOLn/2EQb9P5+5t0tGQm19//YhH4/WCMY6icLtWJCoMWTxzjjipkTS8Ovj8hUtcvfwG2xtr3Lz2xW6y+DDUGnMkzTlUFBMkTYRSEEaYqmSwvkmV5zhTYLMJRZpRZCkBikUZYmVIES5grKHMhlijSZI6YZIwmWRc//o6xmjmFpco0owyyzEntqcyWGvpD7soKSke0oDbk8uPeCCPg5u6hDzYE5UkUTQaIVIKgsAT6JWudpXqzxIrqyssLy/S7fbZWN88kauFUAEqjHHWYMppIHUUojo05qdkegAIZLiEwGEHHVw+ZjahOVNhigFMG4AGYcjZ85cJo5idjTuMDxlXhFKoegMAk05wD0lUx1HE6sIKQkCnu0NRFlgMBvuAu/rhmCnRX+106ymRfopvPLQ2/OznXyKEoNWGM2c0Ufgd3v7gO8yfEXz47Xdpt5os/dtf0AtgcozgwBjL5nafwShjPEm9iuiYwVIqxZnzK7z17lUWl5YR1DFGkec5w9GIO3fu0u0NKIrnWzJ8HAQOJXyFvRTgEH6R7BxhoHjjwlnefeMSv/Ebv8p7771LICtuXvuMiXZI0X/Be3+KVxoST8RYnp+N2lGVa/vtDU5xilN4zJqCPsK9GcYh3/rwKhffPIu8baBrSBrDB5+4/14U+KajIXv9l+rA4vT/swzvbF/KfX87jiCveHmbN05tp1xh6d/a9pZT/Re8T4fAOiimi9rd2CmaPmYcZwIs4FcdEb4CYIuDRPo0eWIKS/YM/HW01ozTCUJBe66JExZjKowpAYOUjmvXb/BH//rfYEuHvW9NGwFqqob2nuVPPikpJTl7dolzZ5ZY3+jgPcVeP4RByFyzhTGGnQ4HGI9GLWF5YQ7rHNY4ZBAS1hrkecHG5uaJiHRjDJ2dk6l6rYM0r7BOQhARNVrEQqCxhJECIXDWUGl/gWhTYl9jEh08VRNFijhS3nvemBOSKd88WOsfaVbQHw69R7pz3lnMHiTS78dk2Gcy7D/eGwv8HDnr9+PAantiZex4OGA8HNCan+fWjRuMxmP++N/8WzrbW2TbG+j8eZdCvXpwDvYXc0qpaLTaJI3mrnq33Z5jcX6Bqsh3veuPQhDF1JpzBLUGUWvR++8rjS5Sxh2JrsBZg9MlVTYhHw5pRDVq9RArFE7FaGuoignCGoIwJIoTyqKiU3SIk5h6qwkOpBCPYLDhpj2Tsoc+a2YrIuXR6vHHxVFtCoJAUqvt0ZvWWowVmOfQ0KnZbLCyukxVaTbWT9aUXEiJCkOsEZiqeHg8HYSQtPYOppTIuO6XyOkAl+87g87gpqpwB6AEzfY8tXqTQW/bCxzuh5TIOAbA5NlDy44DFTDXbCGFYDTsU5UFGt/b5mR4GqZtLx4nJtIXF1s45xiPM6pveo3hfRASonBaXlntM+J/VMz8dw0v78LtGw3v+7157x5nFhdxkzVM1qcqc4wuqSWOdkvQbiziwhrr2wM2u0crxI0xfPnlVzidcffu3UOfE0Uhl69cZmFxnv/od3+f737vV3jrzXemr9dkWcpgMODWnTv0eqMHy7peEIQQLC4scOnSRbqdLhsb6ywuL/P2h++RZTmfffIJZZ7T3d7g40mf63fXiZtz3Lx5gy+vbzJKc/Rr3HjpVYWKfYm+raB60TGzYU+J/urPtac4xTcHs2rMx2gAaq1lNBwx6NWQLqAZzxHWO16xXOEJ1ghYYo98jfFBf3f6/xlhW+BJgxaeRJ8l3ATeW72cPicFOj6WU3W/HyY9WRXtSwGH//w5e8T0y4QK6OHPxRw+zp0JEs5IuCwhdFAz/nys4z/HCcMdqSTNuSYqUEyGE8pjmjkfhd5gyEeff0kUhjSSBs1mnfG4z5kziyRJSBgqttbXkRbiCOpt0BUMh37R+EsfXGRpsUmt2Saq1fjss5v84hfXHmkfpIBw38orVJZ+v4cQhjR90ZPu88fy4goXz12hqkp2uhuUZUlZHTy/3cEIrS1xFFBPQhASMx5RGUdcbzAXRKSTEVV5sutCqikPMb1GZ1WW4IeOJI559+13WViYJ6kldHsd1tbvsb6+xtbWNklNoLWgLIupSv401nXOUeQVpjIYa7F2aivxRJg1xpiWCcrAe6ZYe0jTupcPk8w3VnbGgPYE+jO9VGbVm08o+theX+ff/et/TVmW9O7docgmGH3URn0gECcJFy9dRinFvbu3mIxfourqFwijK3pbm0TJgKXVMzSaLbrbG2zcusGg38NZixSKOIrBQVHl2H0kU5GOcc7SWlhmbqFNWRZ019aoygxd5EgBSzWYb0BqFakNQSgEgjgMWGjP4Zxl2+UUZY4uSyb7rIvziSIdDb2do8MPjvf5qM1Cvf3jJHieoNGooZQiy3LKfb7ujXqNleUFgkCRJL4ZaZpqrM0eUJDHSUx7YR6jDYNe78hebCdFURgGg8JbuyhfJXCYZdqzQKfTpaoqxqPJiRPt7WbCuXMrFEXO1nZJVWnKwh7ONRoDZebPkwyQQtCqJwRBiAsu48oV0t42Wb/z4Et1xfbGHYIwIpscfn86Y9DTZsIPU6PvhxCCehAgwoCJMRTP6Vi/LDgxkT4/38BaS1FUrx2RLiVEPkGD0U9giR/gF4YvswLqG47JaMT2+gaj8yvYdBObTTyRbiqS2NFqSpbPLVBrL6K1fSiRbo3h+vXrDHsbrK8d7ncchiFvvHGZCxcv8ls//F1+84e/BbDrh+kV6UPu3FtnMBg9tDzqeUIImJ+f4/z5c3Q6O6xvXOCd997hD//pHzLs9/lXrmB7c5OdjTXWbk+42cnYHL0c+36Kx0cQQ9zyJHqV8WIJ7BlRdopTnOLlwz4rxkeBNZbJaMxwkCCcoh63CGsRNNizWYmAM/gy9Vlvgi1ghz1rEDt9rsIT6fuj2QSvRJ8R6Tt4oleBavj5zZavGJH+svm374cGBngivc6etYAFLkq4FEBhYGyhcP5c5pw4DpZS0ppvESURVVE9NpE+HI0ZfjXerVSIo4CdnessLjR4882rrKwssbO1iTCOJIHlJchzGI8higI+/PASb75xhuWz52jPLwDw0UfXH0mZ7r2T934PlGU4GlCZ8kTK6m8aFueX+Pa732Fze4Prt78iyx9UOA5GEwajCe1mwspiE2MdaalBKOLaHEmtSVUWJyfSpechZtfo/iLUGZH+3nvvc/bsOSwV/UGPO3du8+WXX6CNJk4ksrQMB8VzI2hedjigyDWl8Ir+J+eLZ/TdvnJEqbwi01SvBJGeFYaseI6TjGMvZp5Vbz0GOltbdLb+hL2Srodd474sLY4bXLn6FmEU0u91Ton0KawxDDrbqCBgcWGROAzZ3Nnm7q0bu88JlKQ29T2vTHnAErXMJ5T5hCQJiQODTlMG67cpixwZQxBK5hM414RUS1IdkZuAoYFIKc61W4BDFyPGStAbjcgOq3wXAlTo7zF3sBvozJH7/mJ9KQWNRp0oCtHaHCDS6/WESxfOoJS/CMuyYn2je2giKUoSllaXKcuS8XD0xER6VVmqyiKlb/o7s9l5Huj3BvR7j9b/qlVPuHJ+kdFkzDjrkGcWXbnDFfRWQ5X7pGLg3Q6atYQ4SaDlLVmcMYcT6UbT3V5/+M5Yi80eLaEvgCRQyDCgcI7iaTS0eYVwYiJ9NMp8hvkJL/BXEUJIgjBESeUvVicZjUYUj6oedpyqK18gnIOtrQmffr7F2eUO4/6QfJxRZBVFrslzR1EIGo02S8srnO0V9EYVkyynf4h3uXUwGHnvuUl2MIBvtVq8/a23WFpa4u/+3d/m/PnznDlzZl9pk0XrijRPyctiOlM9JjPxDOAc9Lo97t6+w+27a9xa2yK3ApH8W/I05fq124yGQ3qDlLwoyV6z5No3FaaEauK/n45TpzjFNwgz25MjGA4VQ9QQ2MpRjDn8/p9NT5KDDSMfYaxwzpHlBeNJSj8fkemctEi96tywt2Lr4Yn1BP+/mSXIbJqcfbd4xbniAd5FKIWIQkTdIue1J82mSp9XhkR/lTDrJbHfEiW1kBr/vee8sn7aG+uk1421lsloQpEXVOVjqFDi2UNAQ/nLx/pFqK45SqmZVBPiLICwZGEl4szKPB++d4k0LQmjNYQQREmABcI4ptZoEEaP3hxeir3FfVGBKR29Xsp4UpFlxxDBAoJaiAz2GDJTaEzx8pOKR8FYS6krKqOPTEgo6R/1Wszc/AIOqGuLNpbBSFNW+TFrU+/RG8cBSgqS0BFIh84sWttdgsgwzb2VFddv3qA36HPu/CrtuRbWWqqqoqoq8rLE6OM9p183uOmXp3NYZpPVbEB3ftA2PJ3Ow9902Pu+PxKmx15IZNDwPQF0duikGccJ7bllGs0WQRBM3Zi/mVDTJKgQAicUDkFZaoxx08aOAhWEhGGMNYYiT32z5CACpRgMBmhtmIzHB7ZrnaWocm/Fc0RiLk9TdtbuYY2mtdBC65h0MgFtaaiQpbhGq6Uog5hh7nAjC7Zgu7uFdY7xZExRlQ80Zw6UJIlC32xUymnuxOCs8Q2eBRjt0NrbmgXSV4hFSQ0VKIJgVgp43/7mBRtbHYJAoaIArQ3VMc2YlQpoL8xRFiXpeIyunmxec27PmeRlHKvjKCAKA2+HZSuUhIV2kzwsKdIxRh/y+a0BPSXShcSVjsnODkUYTO9PSzE+xCrxGaDSmt5wgJSCoijQ2qBfw8TyiYn0nR2fYXkZL8ZnDSkUSdQkimPmF5dRQcDN6zcfj0g/vuH3KZ4hrt3scetWj5X2Cr3f3aYsKiajjMm4YjhyTFLJ/PwKVy9fIa8UELG2tc1wMn5ggnPOsdUZs90RDywAVpaW+cN/9IdcuXKZ3/uHv8/Zc2d3M7OzC6GsCvrDAeN0gpMCEaiXhUfHWcvavXt88lHER599xc+/uAFf3OCP/+I/oHDUjO8CPnaOktdzXPgmQuegj+l1copTnOIVw6wvEXgS8zBVUEPQviAoU0GV2cMFfzMCe/awnLjJ6AzWWsajMaonWO9t0Z8M6U+GXsls8WS5Adbw5Of8dN9DvGp91oR0RpobvBp65g+rptsIQQYRKmygXEVockxqye5a7Gm/g2eDaXNUwJ8zBQwtDKw/RxvsWQ88AhdmjaW/00fwYKx1IjTwVkEtBasxQgoCLIGTaGvJqOgVPfQwRyQF564kvP/OVf7RP/hHDAcD4uRPyPOMpBlihCCp12nPzxMnCeKQZmcPg1S+wtUYGKWgjSXN/QV83GcTUhDP1QjrewR+McjIXmUi3WiyIqOsiiOPY6ggCaHdrrNy7vyu1/Akzdna/prBYIx76E0tkFLSbCbEkaItNYmwTExFmk8bs+Evyy6Q5hl/+9O/JYoi/v7f/7ssLs1hrSHLMspKk+UlzrrTuPc+PP2l7X2DhNH+cYrjMZsnHwteiS5kRJjMe8Iu3cbqB6tF6o0mV958myRJCKMYo7+55faBgrmGr5ByKsY6SX+QYYxGKIkIFHGtQbO9SJlnVGWOdXgPaxmwvbnFlr73QFNiYw2TfHzEu3qMB30moyGNVp2Lb1/EOcedL+9QpTmLQcLFRgtX9wPSTi/F5T0GRcr1u1uUxjd/dM490DImDkOWW02EnJIPzuKKEqxFBRIpBYPUMtCWUEAthCgOmVtZRAQBWVFRHeLBPxqnTNI7yEBRm/N+evkxSfAwClk5dwZdadZu3UFXDz8mx2E/kf4yolGPmGvVqdcCtM4JlOD8yhJpVtLt5BSHzetWQzEBGQICWwp6g6H3jHIZUD2V3i0nQVmVrO1s+F+m7/k6ToknJtKf/nmZGeVPs1n2BKux/Qqk53i2nHOUpcY5QTpJkUrhHARRtNtU5QEIAY3YpzDttFWwm3YZOSXSXxikdQRANcnobO8wmhR8em2Dre0BvbRkXFnWtnsQr7Ox3WUwGpPlRwf4znGgoU6z0WBpaZGrb1zh6htXuHDhIs1mg2hXuTQrlasQwiClpNFs8s77H5BnFVudLlmWs729zfgQFfzzRFWV5HlKVZUYa3wTFWOIpCCuxUhgkpe4J/ZBPMVLhddxJjzFKwTh5dNSgSleiRLvZ4EwULQaNZx1DNMM87BxeH8l3GFxlIAoiZhfapBFmrQzQhcOU3AwXnH3PQSe7GbftjQPjdGMsfR3RmijyYyv6BLCoZSg1k5ot9pUeUV3q48Rds+iReHV6bNqmVnS2bLnG17t+3wKgiQkrtVBFjApoXK459a5+CDCABp1iVKSOJI4J+j2K4ry8ebPmUDf8JI5YM0O7exjzTzqU/ZI9Mc5/PfFWg/FNOETLzUImjFiQSAXJVWgycMChMXisM6rqionyF2FMgotHTIJsYEg1xUiUFy6fJmyLKjV6lgp6A0HGCkZTka4/TbOJ4C1UFZeWDaLK91MynviQ+Fw0yaC9hWtBpRSIaVEKYUUljgOWFlaJMszhsMhxhiSRBEEEjFV2jnr0FpjrSPPC7KsoKpKT0wdFaQLiQoSgmD2XoJSO6yzVMbt6ptmratmWzHGUFUlnU6Xe/fWGA5HWOtHj0AprLCnfYDuh5i2z5iOzUopgsD7I5cnrSQR0pNE+zekyydoTnaKx4ZzWFPuqpWFinBWHzgXWldMRgPv2V1lWKMpT2ix9Kpgph8IlaLeqBMEEidjjBWMswoxG4Otw2hNVeToqvSEpgNn9PRYeqX3/WO9FIJwWqZUGvMgESqE386094BxYrfywzlHd5JztzsmVpBIGKY5g7JkXGlKa6j2be/+UdJaS15VBFIQKYHEEgQC6SSlhapySHwicypQxzmLrgqUc7QaDYSUFFlGlWcY67zXOl5dL2bvLY6uVTBae25tmiC1xnrOpNmgqiqMNlhrH4kgFtN9nYV7TyKUVoFCKok19oltZ6Z7Bwi0kRTaEVWWvKh2ucY8r46O52c+Nc6Arnw8a8y0+YIPvoVUXpg5g3VY8+QJLt80Vkz7gkyJ82dA2gspieIEIfeaMFVF8dIm6U5MpD9dTFc5QkFY9+Uk5WjqJ/CQl4T40ewR1SxPiqrS9HojhIBOZ+BVIbUGzflF8smIfHKIeWWokFdWoVnD5gPfHGAnh51jOvKe4pmiybQH2U6HX/zNz7i5Oea/+eOv6Y0L0qzAGsv2X/6cIPgErQ3GGIw5+QB+9colfv/3/h5vfust/uAf/wHz84vESXzfs3JgiFQ5URJzZfEM/8n/8u8hZcjXX1+j0+3yP/3Lf8kvfv6zp/vhTwgfNDjybMJw2CWflqfNEMUhFy6tEEjJ5PYW+fjhXbtPcYpTnOKpQSion4GwBpMNKPoveo9eCOaaDX75natUWvPTL24wmjxkHJ7JLSWe+Jb4aWim7JYwv7zAO99+i2F/QFp+QTYqydbBFvdtx3Nae9ua56BtzHC67SOq78qs5PO/uYEKJfNvN6mtxoRKUo8VVy6c41d/6Xt0tvv86b/+EeMs9VYhBq8qbuKtQYbsWblUQH/6nFkT1LP+//XVFouXzlN0BgzzFGfdYzVIfRpotxTvvhXTrAecW25gDPzpj7vc3Xg8GrwFrOIPzzrPNSQ+GWbXycwudIK363keQhgFIpYs/p3LzH14lrAdEbYieltr3PryI6zWWOe8OEYInNMIJ8kqjY0cYqHORFmub66zODfPf/T7/xAp4BeffUyn1+Wjrz9nNJ7w5e07uNjtNck9AcoK+kMeb3HvvOeurQTFIKcaV69sOWAUJcRxjXotIYksyWKThdYvMxqP+cnPf854POLCSp25dkynN6E/yCjLksFgyGSScefOOmVZUVX6oSSrCiKS+SWCQBIGBRJDd5CRZwWxdcT44WrmZrV/S1obfvGLT/j88y+xVmOMQSlJsxGhtWFsslOP9H1QatrQWQikENTrDVrtNllWsL3dOVlTVhVCbRGUQirfDMOOdqB8/Rrxvmg4W1FlHYSQyKSJjOuYfIyr9mKN8XjA11995Ek2IXC4E/cqeFWg8KFOu1bn/MUrhFEIQniblkKTlxprLLasKCpDmaY453DT690WY2BWuvTgeBEGAUutFs45OqMR5X5LD6n2GoBag3GObJp8txa0dfzk1jYf3e1wIYELMWwWjq/GFu0c2j48/ZyXFaU2JApWaoJIwVJDEamA213NYGKoJYLllkQbyEuLqTSjTpckqfHuW28zv7CI0hVkEyaVYVLuRSNCCMIoQghBLg+PUbM0Ze3WnV3SOwgCzlw4S71eo7fTYzQYUVXViRM0UvjqASGmbjVTG7XHnSqTekKtUSNPc8aDJ1PJe/iGsKNCMLGW3BQIYciLiq3tEboyVA8j7O20a/EDvjX+exDXCGrNvafrknLc370eHxdBKIniAF1ZivzZkdphFLN0/iJh7LkzZx3djTXG/e4xr3wxeHQiXcjdzJIQTMvbHufk7JM1uUcwVdv3PCElQkqctU98gRyHWQBgjAUhCCKLeth+SwFJhGzEBEkTaUL0BLR7qbRDrx1mgXJZVoxGEwbDMZ3BhGG6NyjorOBRNV5KCZSSzM23uXz5MucvXKDVblNv1I/YC/+wzhGGIWfOnCEMa4zTjCCKqNVqj/8hHwNSCOI4QAC61AgHpizJJ6nPrO9/rpQk9RpBIA94dZ7iFKc4xeNBIILQxxdTOHtUGfe+uGG3DdL9FMg3F41Gjbm5NnONhCgOcMIRRYqwUmjtVZsPhdj32AeHV2miBEkjxFlHoTT2qCBnto39Ni/HQYITYKeqGudAOIFEEAiJcAJTGUzlK6B2paIzUnZG5Ff7fq/wQpzZcwSeGctAoghqEVWscMrhXmAbkiAQNJuKWiKxzqEfIqI9CSIJbYn3YH3pWPR90DyWncuTQyBrAcFcTFAPUbUIGc3GCp9QcQgMDu0cJgATSHSp0ZVhNMnZ2uqhXECcJCglSbOKbm/M9naf4XBEXpTU6jG6MFRa760HDudLdvG4yxUHuKkqzmr7ilcDuqmyzaCrEqUkYSCJQ0UcSMpQoKRAir0aEmMMRZ5T5Dl5PvNFnw1C9x0LIRDSx6ihkn5b1p/xQEAkBdKBcW4353fYKcvznLIspzG+mAoC3XMrn39VIISg0agRxQFWG6w2BIHaPV5HIUpqU0sQTVVVuCCGMJoSiNInScTJB20VBAgp/T6ceqk/IbzvtxACqRRCBVghDtwnzlqq8pvPazh8FU1SqxOEAWmWU2njk6FSIhEIobxq3Gi8nZRfH9tDVOgHN+7HkwPPkNOslAxASZT0goMwibDGk/QCiRSKXFsKbRgBAwcTDZWQWARC+XvnqLnC4ftUaKZVUkKgRLCrUI8DSTBTdzswzjcZDYOQKAqRUiCEI4kiGvUaOi1IS7NX7CgEgZRIJQlDH1faqcJ8dx+sQ1vti01VsPs6IadjuFKIR7B0un+4eNyROkligiAgqSfESYR+nP4su/skCKMEKRXGBTgnMcJhjG+OmhcVeV5RFPrh1aX7cQT36qYBiFIBURRhtUKacrda5In40v0crBCEYYDYd8CrSp8sYXrc20xjKJ+EgaQWo4vY+7A/oXf+08YjEukC4gaoiDCQhEpQ5Rll+qgWFNPVkLNQjqdlxsdMeDNF1awkGYibTaJWi2oyIev3H3EfngDOkadjyjx9oHHDLqRAzcUEqw3OnL1Cu91k888+ZvP2R89vP0/xAEb4CuPltGBtvUunkz+Vm35ursbCQoNf+e6H/Mf/9J/Rbs9Rqx1GosPMxLXSMM5S2gtw6eIFms15VByx0+0yt7DwxPv0KKjXQt59a5VASW5d22IyzJlsbrM57DNKD04eKo6Yu3SWKAoI72xD/2lkaE9xilO8tlAh4fJVZNLa/ZMebKB79x58rjOQbnpluonx5scpXlP4zcf3f+2X+V/9Z3/IxsY6f/Knf8K4mrB0tkG7jNnaHDEZH6PameUe9lujOOj1+nx67QviMODCxRXKieHLtS10esgieWazotgjumcK94LDG0lOX6NixdL5JZJaTJCAqiAyEWDZudHh//eLP6cqNVkv9/vYnm5vjFc1d6YP2FdzPX2/2T5UwCbIM4qwHZOnAWVgMEqf3B7kKSNOJKtnY4rC8Zc/HTAYagbDx18QLNfgl+bgXgG3et418KXCzCN9pkt4npyWAbQXKmgMVeZ9RdPBBLIpMdeMcFJRTHe00Wggkib9mzts3djhdjHg88kab711hW+/9wFhFPBnf/wRn370FeW03Hx+dY73v/0m/e6QWzfWMbOT4J7RZ7aOYlAgpMC9dCf80VAUOVVV0usp1tYDwkCSxAKjS5bbglYco4ucTp6SlhYnIM3GlBslZWWn5JQAauyV2OzdT0EUEjcahErSlDnKWoJ8jHSaq62A2lKNtV7J2qDaLWaBw60PnHNTUkxQVZosKw+Ut58Coijk+7/2Xc5fOMvdO/fY3NxhMBixubmNMebQdZYQkqvvfMjlt95na3ubO3fueAJFBVjryIpiek+djEgXUjK3fIZao8mgs/3sFYzTROY3r8rcT6wyCInbc0gVIqaksCvUayJZ2MMsvHFRzPzSWYwxfPLZT+n3B5QWhApJmg2SpE42HjHudQmCgGZzDucc4/EA8xAiuDSGztTKtTLGJwEbS4ikgfdLEiyuLHPp6hXKdMzmtc+p8pxI1giSiKocY0zJTgl9DS4KiOeTXUbZGUs+zLEPUTk7C0XmeXvbdAgpubgUcWFJsDOo2OqXFA5GDtrNOm+98xa1JGY47NMf9Gi2mnzwwQfcvHuPya3bWAfaQaAErSQmjEKSKEEbR2+7w2T4YLwehiHzS0sEYUheVGRFiTYGlUQI++gmdtZBVe25oTwKwjDgvfffZmV1iUmWkhU5Ow4G3eFjJVGjuMaVt36JpN5kMJlQFCWjYZfJqE+alVRpgbX25CT6Q2CKDFuVLK2scvnKJYQAa0qKPOfm118/0Oz2pNDVzKHB/54kMRcunCUMQ8Bb/qyvbzEYPJktcVWWdDY2iOKIq1ev0Gw1mU8uYC8ssbGxw727G0+0/aeNExPpQkrfcEIGoAKk8l6PLtAQRVhr0Yd1mH0oHDh98knovglLqAAVReii4JE7/jwhjvRGn+2bABGACAVRq05tcY6wljy3/TvF4dDTR6YNaVpSlpowUESBo9o3QMxwUjfVWi1hcXGOlZVlzp2/eIyi3Hd+c05gpsFlkiTU63Va7TaF1r507DlCKkmj5hNkkZIUAkxVUroKWzkC4SclC36SDwJEGBJGEVEUovXhgfIpTnGKUxwHIQQqrqNqbZz1HpFCPWQMNLOAetpV8kX5dbwAtNtNrr5xEUuFkQ4tLGGkvHhJnuA4HMZJOF+lNRyMaDZqLDfaiNA3mjpyG7PHLC4zHOuPjgIRCMJaSNSIUNIhpcOgEEaSjUv6d/t7QpuAvUl7Fl7m7HmiT7fJrDBqpkxP2SNxp2oviztaXf8cIJUgSiSltnQGJb2eeSICJlBQjyA2L02P8oOYXR+z6+J5YlrNYCuDzjXOaazT6EmJK5y/Nqz3/XVBgFUBop6ganW0kEyyCoYlg80J7UaL0SQl0iFbG33W7+6Rc/PL8ywuzOEs1Jt9ykpTFiXOPLvrzOkXeRU/PThnsdZSlgVZmmJCiRQSZzVRKJEohpOKotKYKZdqnaWqCrQG5/aXxOzPDHoIKVFRiBIQYJDOIHSFdJpmGNKuBeyM9e6lqaZKW3O/MpSDCnRr3VQJfwrwa12lFFEUsbAwz+rKMv3ekF5vCAiK4ugeUwBJvUl7cYW0NCS9gfchNsY3cHTOM3zOcZLVmMCrPuNagyAcnOg1jw2x7/s34YZ8AAKERKoQFcyIMvfYk40QPuvwKlZy7NaQC0kQhDgnKApNmpWoJEYFIVGSkNQbYA06T1EyIAjCaQ+Yhx8059xBOxehEEGECGtIpZBKkbQXaa+cI+13wUmcBik8F2eE9BoGB5mBEEESqN33tULsL/Y88jMaC8bMKgUdUagIlKQvNUbPuruBFZIkqREnEaPehKIsWFpYolFvEMcRalpIgpuqlpUkChQuCAmsrxq5H0JKpFSEUYgKArKi2OMUH6EiZfZZ/HF9/OovX2FTZ36+DdLhhCV4ggp8KRX15hz11jyVCCDISNMROOfHu+rpWaXMXDoEvlJABhIhE8/XxDFFnmMO8+I/brsOnHG7FSpBGJLUEqIoBOew1hKoJ3cpcNZR5rlXzluDEhDVYqQIGAzHBIHCTvsFvAw4MZG+dOFNrDWMBj2qbEApBEbAxYvneftb36XT6fLRRx+fvKHIU0CZ5hgtMFX+XEn0k8BZgx52MMGEdZvS3aiRbt2XRZl5jIJf8J3ykM8NWWHY6qYEUcQ/+P4bjArNjz++R2+4t0JPQkUSBJTGkJZHrwSFEPzqr32ff/JPfo933vmQ4JBJ4iAiQKFUg1oUo6RkmI4wUjHJcvLiIY0mnhGyrOTza1sESiBkRXNB0qzHNOOIcFKwOM4ZaLibw2SU8tP/8Cm1WsJbb17hOx++yyeffMWtW4eoR09xilO8wpiRFPtxgsbgjwghoBZKwkgw3tmiGHVxxUk8UVP85Plylfo9S3z25ef81//P/wcWx+KZJeJGnc8/usF4mJJlR6jRBXvR3myKu++QVQPL6CtNHqak9Q2cdmTjI+I5DWT4+GUqmkJPv1ccrsS1/jWmMnTvdYnqEd/+8F3OnV3ly8++Zn2tg87NwVDO4NXn+6fU+0VJ0+3u/gz+8tQwvLlN+WcZOivQ/RI3di8szqqcY2ANmbLIRd98VPfBPaad7FoGf74NIwPVyxX+eux3W3oB7kvOOAb/4R7pte50wWipigI3cqAcNApEM6L+dy5SP9Pm3Lk3WJ5fZlKE3N0c4ihgK6U/HPJnP/r3qEDR7R1UuC4vrfDhex+iZMjv/U7MxsYW/+pf/H/odfqn8fwxqMcRtSgkUoLxZEwUKYIgQQhHGEVIKagGFelEIyJBGECt3qY9v8IkLcnvdaeE9qx77eFzgLMGXU2Q1hKUBukEkQqpJTVkoNEUNOOIpUaDyjoGZYW2lqIsDohDrHUYc6pCn0FJiGNFrZZw9Y03aDQaWGNZu7fJ3Tvr3Lp171gS3TnH3Tt3GBWWZrvN2++8w6Df59oXX1LmGTob+CZ5elaODofHHhJEBDJAqgilQoK4QdiYx1YF5ih/9Rg/t5T4eetR8I1Uos/gM+PWFBTD7r6mfw7zGDYucdKg3pijqnImo/5j2gE/awhk4G0qrNaH7mORF6zf2yKIQt565z3esBYtfOItCDwBPN96lzNLK2xvbvHjv/gR2WSCfWA9r/AX3v1ZZgFBjJABSkgUlvOX3+bc1bcJooiwVicxIcsX3qKcjBhurFFl6a4D3uxy1KUh66eePJ96hNtjKpg0vmK/ArTWuErQSzWFEQzTgwHdOM342edfE0UBjVpIECp2+n36ozHDyRipBALnjSf23SMhbqp7OHjjxLWE9sICzjlG/QHWOWQYIKSgKkp0WT1UTe+bM+yR7c5ZtHmyhLMxhrV7d5mMeoS1hODE4sb9XoezapoQR4T2h4QoSZBBwKS7AdXk8dn+YzDs9/jy84+pN5pcuHyJOI547zvvoquC61/dpLPVOX4jh6A5N8+Zi5cA6Oc5ZjyhmowwVUk1yUg4qH15dDjfuLequHv7NtubEefOLTG/0GJ5qU0tucpgMObu3e1D7q3njxMT6fW5RazRpMMuVZXvXiKtRo233voWcRzz6aef8eiz0ePDlBWmwqvaXyb4tsbYfAKTgpEpGIUhjIYPPi+a/jzz/DzFc0FVGUZpxUKc8PblRUa55udfbdLb95xQSpJQHVsKLoTg8uUr/J0f/iYL86so9bDU74zRCFAiIgoilJRkZY4oMspKU00VR0KI55a9r7Rla2eMkrC6IElqgrgZEdcSEgzzlZ8a7gmvXLx7e4N6vcYvf+d9Ll48y9q9TW7fuvfNjStPcYrXEsJbqMxkVydUhj3GuxAo3+iIYogZbp3wld+splYnwebWJn/1t30WFhd5+933QARkqWY4yI9+0bS/+65F3iGnz+ZQ5pZSWCZqGscdtXaZ8VYBfmLY75V+lHjIARU47UgHKVVZ0ojqnF0+yy15j3RQPmgJ4/C5kodhut0H/gYU3THF1/vKWAteGPlhcGTWUmARdVAGzPjxifRB5RtozUT4J8XeenNa9v2sYowZ0TQTDj/v+NY68jtD8jvDB/8ngZpBLFjCoEkyv0DrzBkWl89RW15DNGuecCcjzzO+vn4dqQST9ODF2Kw3OLNylvm5ec6fu8T1azf543/1p/R0/3l8wlcaYaCoxZ6cyIscRIg2EUp5tRuAMYKygiiY9fFJaLcXgQwhJvgb+hjzfWcxugRjp0tF710chgFCepuKUAXM1RJK68iRCKMpq4M3pnN7as1TzHySBfV6zIXzZ2g0m2RpzqA/ot8f0OsNjt8Ijn6/x7C0vPGtb7G8urLruV1mKeRjsLMM7WHYJwv3JeAIoRBCeUI9rHnF51FEeoAvaJvxmafndh+cV1efSNDwcARhTK3RRmSSdDx4Oe8hgR93hJw2cnwwU1JpzXA4ol5vsHLuPFEck+sCvc+a+PKFS3z43re59tVX/PjPf4QuS9wDk9/+3j7790H4ZrsyRAqBco75+UUuXX2bQmvSvCSIDc35VYogZrKzfaBdzAzOeCJ5N+7jwbe6HzM3MsHUzspAmsGogPI+44iyqljb2iYIFBfOLtEQCZX210le5r6V4qyQZLZscM77yOMeuJuDIKTRaqGrkkGni64qknYDFQbosqTKjwmSZl1Fdz//k4/T1joG/T5lPmF+eYlWNH/CV86CYfBnZnauFRaBZZp0UQGBBE9iPhvkWUaeZbQX5jl74TxxErOwvIoQls31rccm0uOkxvKZc+RlSe/uXfKsIO8PMWVBgh9WHz/c20uYWmPo93pICfPzdeYXWtQbCc1WhJCwdm/npaBNT0yk9zfv+cx+cXCx5myFqSZYkz9b0k8qRL3tSzHTkc9Qu1nH2pfhUE4RAC0gcRBOy9LWU0gddA9mcqMkZuHKCkJA99o25fib37DjZcEgN3y9k5OMHffS25TaMZocHKwL7RtmVPdlvJSSLCy0SOKY8xfOMD/f5jvf/pAzq5dIksaxZVwzXLhwlX/4B/8MrStufPkLpAqYXz5HqxHye//4H3Dlnbf4yV/+mL/5y798Wh/7WFgHo9RRasEHb1/m3Tcv0rl2i63RVyg5y3t7VFXFJ598yd07a2S9AecadcZVxfA5VqWc4hSvC4SAMPR9rMvqmYkYDiIMkLUmWINNJ9M5dzbPT1lUFUAQUKs3WFxcoCpyOht3MPrk44A1msnOPfJBh3Jyv7/ezAR7v5dIyeuaedbGkOclnW6f8vOvKXNDlj+ERIeDsf1xeZD9yuHjQroKmPElM1uX41jd6ekz1vLVx9fprvW5d3d9j5ivg4wDkqUmWMjvjrHpY4olJsDm9OeHqeWfJRQQwjg1fPmzDG0dk21LVYJ7gqly5hj6MCedGRZbknMLAUmzyfy588RJjaXlVZyDP/93P+HWrfXH35HjEEwfL+LYH4XpNWjHmv6Xd8nGfZq2TlU60qKg3m6i+5ZcDMnTitufbyOkIL8vRhwMB9y6cwNtLnLh/CXUSayVnjLqi02aZ9snjjtfFuRlhXXeNjCUUE8CVhcXcNZy58490jSjLLQndrSjAsaDMVbfJS801qTsdRh+ELGwLIoKhSac3iEGT5Dc6mTcTSs2h9M11/7my9Z5Eu2+m8pNS9dfRWuKp4nZoQqVolGvo6Tk7p27KBUwmRSUlT6eRBeCpLWAihKMCDFS0u10+PRnP2MynqDL/dne+zOr+zYjJWGY4BBo7XBWMxmnGAvZeIjOR1j9EBJufw4mZq+/xikACKOYhbPnCEKv+HPOMdjZIh0ec35lsM/fVmJlRGkBFVJvz72kinQPIQStZpNQKqqy8s2nraY0JWEQ0mrN0Wy2WJibR4UBG19v0ul2qNUikjjkq+GAe9evYSrNh++/R5qmfPb1Z4wn+32pZxed40C5lnOgC5zUOBPjbMDmvduUVjC3sMzZC5dBtWlcvEo66rN55wYZg6On1UeI5WIBqwoSCXHlbV7GJXS1779yWK7JGkt/OGGS5khv5U5elBSl212fVJVmZ6c/FRj6Zs1pejBeLYuC3s4O1phdy5EqL7xY9iQ2WnbG2u/7/RhI4R/Wi5+BgzUvzjmyrKKqDKXp0h+kTEbpycd/EYCMp+UAFUpZFuZbtBfm0VWBNZrojausLrXpdnZYu3vbW7Ls274QguXlZer1Bv1+j8HgJMnJB1GWBRtbd6k3aki1QhyFLK/MEwaSbqdPr/N4243CkDNLq5R5RjcbU9gKYSzCeY1wJKa95g85ZEIIFlZWabbmGPa69Ls7KBmQhDWscxRVjp36AzkHO9td8ryg2YpozyWUuiBMBFQCXT44Xz9PnJhIH26vTX86uLfWakyVYvUzJoFlgGjMQxDiqnJa6mVfLhId/BGdwzfiCqfpuM0MNh+cncMkYvXSeYQQjNdGp0T6c8SosIyKEijh3uGNEUpjKQ8pG1FKsbw4x9xck+9+9wMuXT7PBx+8x8ryhUfah3PnLnHu3CXu3r3Ov/gX/zfKquB7v/HbLC6v8jv/4Hf54T/4A6wx/O2Pf/zcgnfnYJw5igqWz1zg27/0y3yVabpf3EQKjThApGs+++waErjYanK2UWczzU6J9FOc4hlACIhDkAqMeT5EuggDZKvh59xsxEEmbCrnCmKIE+oLy5y7+gbpuM+gs/FIRLqzhknnqAYyMw+0/bLqGWv7+sEYS1aUjLOSe1tDrPFq8odi/6E7CU56aA1wiOD3WFRgK8v1z25xnVt7f4+BFqh2QP2teTBQdfLHJ9JT9hTtL8rLNgASGKeWrz/On9o+zHq8ngQLTcX7lyLmVue58ivv05pf4K13PsBawY0ba8+eSI95udSe0yoGmxqG19YRXUWttUAZhGRlQaPVIK+VFALKTHPvq8OVW8PRgDv3bpMkyVQUJx7VzvWJUVtssPruuUf2kX3RKCpNUWkiCfUAJA2WF+aoSs1nw4Jub7w7DFk99SYvJ0yGE/QJvG9jHAuiQgrjLfGBsfM2SLd7GePefS+YHT7jDr2x9vukv87Y7e+sFPVaDecca3fXplW+BWV1/KgkhCRuLRA35kiLAlMU9LtdOmtre09y+1OEhx93KRVhUgMHxmRYa8gmKUWp0dkIXRzT9K6aPuLpY/b7KQAI4pjli1e89zfsVgscS6Qr5cl0GYEIscoT6UoF1JstXq7J4CCkEDQbTepJQj7KyV1OrnNKU6JUQKs5R7vVZn5uHiEEg96A2zfvsDhfZ75d516/z/bmBufPXeTv/84/pKwqbq/fvo9In3k6+H5pe4y3A1OCFThTYa1me/0uG5s7fOud93nr6puoWgLNFuNhCxcnx7ffPGEsFwMXFNQkiApsBeMKeg9pY2idYzCcAKCmonBj94hpAK0NnWOI2qr0SvT90MUj3IiPsRhSwttT7d/f+82jstzvw+hxuDkRgqr5kkObo5Rlrt1kcXGeMhujq4Kl9mWkvMKNr79gc+2e78+xb46RUrK0tMzy8grGmCci0rd21qhNYuaaEbLZZHFpjsWlOax1j0+kBxGri3V0kWN27jEpJpTOW6BFwhPpuTu8EFQIweLyGVbPX+SeuEa/u0MgA+pxE2M0pc52+V3noNPp0+n0WTnTRoXzVLokiMEJgalebKXYiYn0BzLB0wYIaZZx585tOt3hQ5tvPjaEhCBGRgm1ZgsZRGTD7ZfXEXUaoMtIMbe4RFCPGd3ZJD9kdtZFRe/uDkII9HGlK6d4aWCtZTCcUFaaL768wU5nwJWr3+Y7vzIgCiKS+GGNRvew2wjEWtI0o9IFwlkCKYlqNQia1OL4mK08Gzg8UZ7nBUoFLCzMM1EZtVGFcF4dtDsiCKglCXNzcwytg/HkhezzKV4QpkQRsFfOP1P6vLzx8isH56DSoCzU4wCVKNJSk5fPUOapNTYdg9GHJK1n7xtOn1oxnoyoioKo3gYVUmUTrHmy2VpFMVFjCZDT3TDeS9vkvI6EehJHLC8mZIUh20lx+gRqDAu7g/bLfE9qPPEd8VT3UwoIpl6h1fO+XGY2zi9wPAyDiEatzcriCu+99Ta1ZotakpCm5bNrVDrjBjSeL3hZ1Oj7YfHGsM4x2exDbY38zoD85oBqe4I7yg+7AcQQLoY0FuqoRDLMB4zz0W4D+WeNqBkT1iLeePtNvvcbP2A0HnH79o29Bm2vCMIooNmMSeoJ1lmsM0QBJKEg136BHEcBUaQoKsOk1Nh9Q97MeOz+26vQlm5aEVlHq3K++aw7+Lx6HFGPIiIpGaUZlXEUuvL+uqek+aHYDfG0YTj2zSmK3Pd1Mif1j3eOKh3hjMFMPcGc1aBzEAIVRMhQ0UjmCRQ06glRqOh2egwGe5lbicWZamod4Q2Zjc5wrvLe6ifFTIn+uLfO/mqv/QPqjC99AZCNBjKOsXmOzdPHmvuN1gx6fdKsQEqJw1EWj0AqCoGQEqM1VTpGC4sRR3jLPWcoCYEUWBwz+3BrjD991iCcIQggThToECsTmvU683PzNJtNIhXicLQadRbm2jhT0e32KTKvWh5PJnzx9ZdoY8gyf5+IKEaogEZznlpzDmct1jiqImXcXd+LlR3YKgccVhRYkTLcuMHXH8XEtQbthTNk2Ridj0DnRI06gWpSZSlV9phWPFNFuZACEUdIJVmIQRgYFxXD4uE3h51+eZWHzaez67PqggqswPuJOcqy4O7Nrxj0dmjPzxEnCUVZ4oxGSsXKmbPkWUq/193tzeGcYzAYYIwlzbKpzaZ7YC0mpl+PsiF21mFyTekEw14XU6TUG3WiKCJShlrkqw6qE45/eZayvX4PKRUKhdEVoyyjNA6Lwgl8piIQPlNRaYSUBFFMGMdcunyFRrNJOOXKavUWS6vnsZWhKHKM0UfOv3lW0utMKHX10uioH4FIPwgVRQRxTG8w4BcfbVEUz6iTuQqg1kbVGswvrRKEEdude+hjEs0vDAbIIYxDLly8THO5zbUvMvJDZFvFOOfexzcB72l1ilcDWhs2NrsIAbfvbCKV4vKb7/P93/ohc4054ih5pDJbrTXD4QCtS3CWOAxotedI6ou0pkqA5w4HeV4wGo2JwpAL589RBj3am0OUNQzZvy4WtNotzq6u0q80YqfzEoRJp3huiIEl9hoOGrzdwymZ/lThHOQFKCm4uJww14jY6KbPlEh3ZeHV6A4ePJEz+VYIWIoio9fZBqA2v0KkS4abd56YSA/rDeYuXcE5RZFW6KrEbhmMUew1G3190GrVuXJplW4vZXsrR1fmZET6car1lwGzpm8zCelTghJQU1OxqX3O3MaTdV16KoijGgtzq1y58Aa/8+s/REYx1zc2SNNnKOCYHeSZievLSKQbYBtc39KdW6eb9uCrDPd5erDmez8EsABiCeqXEpYuzRM0JFvDTXZG2xj7HE62gMZSi+aZOX71hz/gf/uf/xdcu/E1/8O//O9JH5dIeUFIagnLq4u0GnWMMRij/cI+EZjMYTQ0ahHzrTrdSU63GO+SNbM2U5IHW05PSkNeGRrOhyizXM7+kGShUefcwhyD0YTNnS6VsxTudUvNPhoc/hhmpSbv+HXto5JnzlnS/jYgUK0lRH3OewWXQ6SKCOKQJIo4t7JIo5Zw6eIy7WaNX/zsI74Y7l9LW1yZ+fPlPItniuF0qHmEnXqSMXpW7XVYj5AX1ftMCMKlJcLFRcqdLcrNbC+L9Agoy4r19XWECgnCACGhmjzC+CIkTkp0WaCLjL2s8otHqASNWKCtYFJZrHM++WINmAphA6LQ21dEWhKViqX5ec6dOUu97i1ctdasLMxjq5zbd+5wb22DQEKooNfv8Rc//vdY5zDG+ARRvYFK6ixf/YBzl97BVAU6nzDqbnJjuLMvVnaYfIzJp6I0AZujNbav/ZTWwgpvfueHOGcpRx1EOaZx5gr1hcXd5qOPixmRLlsNRBRyATgH3OxOGBbjh77WPfrl9dLh6SxVp9kzN00MTpGnEz75yV8SRjHf/sFvcebCZYo0pUxHqCDg6lvvMOh1GY2G2NLfI9ZaNjbWEWIDJ5T3z7fmASJdIqfJ5MOpdGcc5Vhjcs0Wd0gSyYULF4jn50kCzVwdshKGD6k+2I/xcEA6GrJfw++m5H+oYpQKsZGESEJZQqWRKqDWnqc9v8Bv/8E/5sLFi3z2i4+4e/s27YUlavV5+t1Nbl/7HPOQJOhomDMeF6gAovrLUYV3YiK90fIefEpKpJTMLS3SbLeZDLYZbN+hrI5ryfiYEMKXCU2bQAghEEEEYeJVcs8jaH0UKIlqxIhaTN6fgLZU2dETx7EE+qzyZz8OyIFP8azhmyIFGONIixLnZmWeYK1BGEu32+XOndvo1fMsz6/ue/UDBS33/c+hdUmv16fSBWVZYa31Pb2FQD7ncl0pJcvL8zQaNVqtBkJKtLXkZYHAsthMCCvNJCsx1u3u51yzxcrKCncGQ3/Pvspp6VM8GvbHxjPL0qdx+md9efdjZtIXstf0frYyfk1Wv845isqQFvqB/g3P6A0f/n9rwJTY0lGk+ypttN4Nrp4EVldU6RBEiCNCCOllMq+hGh2gLDSDXs5kXGL1q836xM2QIFaUqabK9qmxtKXK/CLkaVxDznkSfb+S9XVCXmg6vQmDUYF1EEhJLalRrxUopY7fwJPA8vRtdcS0NFvsJUeeFE5rKArfgEIfskEJqgkiAlsHq2CcpWxs7jAMM8bbFTvrXcr7re1mBNvu78IvMC2Q3ZcEmxFyR1t/70KXmjItSEcThsMBg16f/k6XNH15iXQhoF6vEYYBxhqsscTThqPOejVaVVYU2lIYb8Ni8OrytKwo9INJQ7HvIe//3e312oM9vnTX6cpYjNYYY9DO+THi2R6CbxSeKMyfvtjpCsrcW1pMuxOK6Ul2zmGdoSpLylIy127xxtVLlEVJlqdU2jIuKl85sksuPccRfnYh1fAX3+ytD+sA+SwhJcTex5zCB+NRFFFrNIjKNlVZoIuSbDgG5/bRXwchpEQqhVKKMAwRKkDUYpyQVFWJKQ3uJMII6zOnYnpOpJLIKETsDnAvbhYOA4UKFKG0CBxSOMKpxYcGcI6yqsjyvbW3NpZKG/KiYDwagnO05+aRU7V9kWVIZ6nFETiDtRprHdX9x0prXFVSjgeMu5sYXWHKgnw8Sw7eP1G63W/OGYw1FPmEYXcTcJhqapphLU6fzO8xYCoWlhCoPSVyKAVBHBGEASqMEEHAqCjJKk3+LESyzxFSKRrNFlJKJuMRuqp8WPKcx3tjDFQlw16HIIyIlCCIIsqioCwKqqp64NZwCJwTSBWggtBX3liJs3bXPtOzr8dzRc5CWVp/lRmHwpFEIe1GA4RmmBXgDrZKPXxDDnvE4O//bhBBRFiv4yJNM4hRUURzYYl6s0m/00FYizaGZnuOQbfHZDIkz9IT9U9w1nvwGy2mY/+xL3mmODGRfvXt9xBC0KjViaOID779Pm++eZUf/cWf8T/9j/fQ2jwb7kxIZBQhwxB/uThcYx6MgEkP0sfz9nlWULWI5OpZhBLc/el17KRA957AdC0C2uxFiAbo87IkdV8LzDcTrp5bYJwVXLvbpbxvUnHO8eWXX/BHf/RHfP97P+CtK+/ctzidRVT3yxV83fNkMuTTTz+lqkr6/QFVUSGt3e1+/DyRJDG/9Vvf49Klc6w0felPVuTc29wAFfDtN1bpZiWdG1tURUUCJELwratX+P73v892USI+/fSUR3+dUABb9/3taeR/Qnzj5v09Jgf4Rco8vhfFBO/RrHlthMnWwfYgpzMsTl5K/SxRZaALygz0aB9j5BzWPnkAXk0G9G59goqaJEvfmpY3ZviT//rRHp3tCYN+7j2DK/vCg8jHhVSClTfmmDtbZ+vagO3re7GcKUtG6766wRZPfg1pB5NpGPaKHq4nwubOkL/KJtj6Ar3RmIUo4szSCnFQp56czIrusfG07Q2mxHQSQCOEvILRk1jPT1s9UOWQFp69PQQihNo7ECxAmkJZwI3b91i7sYXIJLKnMKUhndpd7CLGx/Ez1BUs1aGwcDv132dIps/P2fP2PwwOJp0h6WDMtU++4K/+6kdc/+oaP/vRT8heYiJdKcWly2dZWpojzTLyPEdaizOGIsvpW01RanbGFePMUs6I9ElBJyux7mjBVoDnNBW7bbBR+EM6K3DJ8IdWTP9uipxh3zEuK3LnXsPZ5Olif6h2/P3oTXlsNoZ8/IDK0jlLWWQIW7G5WTDoB7x99Q3+3m/+Jpub69y8eZ1uf8jn129PlbzPeWTfT6Kfmf6csteA+zBj4GeFKIRzKz5Ttb6FyEvmWi0WV1eR8/PI8xfpbW1y69PPsFoj5fQ8ueljuqthHBM3GtTrdZaXF5FSwVR5fefGTcbD0QPn6VCYCqxGBDFSOJJaTC1uTpcFL3YGbjVrtFt1qiKnzCYooKGkp5S0RTtHbziiv0/E5vAHSaqALz//lKWlZX7tB79BktTIhkO27t0hDAMunVlkOE7Z6g4eLGZyDjsZ49IJW6Mh29c/AafAKaw1vu2fn4h42DHKx0NuffYfANClv8hMmlMxxhxjESyAloS6hHZd0K4Jxrlje+hohBG1pRVqcUgcBSDgq50xt3tD9MuwzngCJEmNt9//DlEU8cUnv6DX2d6tTHzen8xozY0vPubOtS9499u/xJvvvM/WxjqbGxtk6QRz/5pJhSADwqRGkux5qOqyIB32cNZgTzjiWuudd/Pce+BHwGp7nqW4xb3tAduDdZxzu06tOY8eumlbIhAE9WVaZy8jpoLQII5pn1nFGs2P/+xPSAd9vvubv8PVdz9g8Dc/5uaXH2GMPvGa0WooJ3se6i8SJ+bpnHNIKWg0GjQbDc6eOcOlS5eYn1/wcfIz+yDOK96s8Z5WSk576sze8Kjc6ouBc4B2OOuoRgVmnD8Z6b3/Y93vv3aK54IoCphvN0BKhDz8BKTphJ2dLcbj4Ym9FbWuKMsxk8mI8ThDa98h3Fhv5vgiTrdSksXFRc6dO0OCIxD+EizKkiAWJKEi0sqXgAFxFFKPQuq1GvV6nSgMeXFd3U7xQjCr9d2PmXHp42C2Gk6mj5miz+LHUsUeOTFrFBWwd9nN1lTf4FWxsQ7zkHtMCEFSi5FSUuTFs7Fd24XzKolphc7jQSDDCCEltipx+7bjnMVVBYgAq3NPpNsTSDb3bXt3PwVeCRJFGG0o8uyhr3wZYYzDPKFdzsuCWWXXA5eyBZuZ3Z+fyns9nc28kigryzC1DMY53eEQEQXUau3dCrhXFW7f44k2YoHSQeqObDYoBAQ1RdgUyMpA4agKTTXWfsU5nm5n1jtuOg+FSYhqBmhj0EZDOEs2uj1thdv708E35aCkGnYrsKy2YC2j/pD1O2vsbGyTDVPy7OX0cFJKEgaKJImp1WsgHFKCqzS2KAGBMRZtLNq6XTW6A8y0gdlhuP/871enz8j1vQaZEje1bXUIsFBpH3O/6PFBSkkYBjjnqCrv+x0GAUJItNG79+n9tpGvvJf71Nt8BiF8w0cBGKPRWPKswlSSsiy9r7R1WOuFdYESaCWRxuySws//M7CXMHQ8f4ZOCAgCr0xXCqEUURRSj0OM0ZhdxvzhOxUEAbVaQq2WkCS+wXs59XF2xkzjrhPCzbgbjZQBURThrEXr6oUTX0ybOhrrVcnSHaxkwdkDSQbYWwcPhkPCMPRr4jBESEGgAsJAESqFkvJAQung23rhgy4NroS9tN/RCKQgUBJjHZWxOGepin1xqxC7ynb3CH0K40DRrodT4rYEIZBBgFABTgisc5TGkh9WnXUfHnXFL/AEwv1jlxCCMIoOjHG6qryS+wkgpCROYqIoOSByfOLLUAiUChBCYLQ+kZoaoCqKaZVNibEWXWnyPKcsi4fco+6Q7T/6J3AWnHSUpSbLSmIRUk8SGklBM0kw2iCrCufcbtH37GycVBPhcBjnFedS+j4JzlmMtmitGQ+HjHo9JpMJeV5Q5AVlmT8SIy6EQCoFjhe+HjoxkX7zq8+p1Wq899a3uPrGm/zgV7/H9773XW5c+wJVkxhhcM9CEViV2P4Wstliof4mSSOhf2PEpL/pjfxlDZxmOiq9cNhxSfbJhv85K09UovlQVHgFumTW0+3VN6J6xbAwN8cH773FxnaPz25sUZQP3rS93jY3bnzGu2+/dchgJx94PkCns8lXX/2Ejz/+lK2tDCEE6aSkzPJn07j3BAjDiA8++JDv/sq3GXa2SYcDbicJwyIlwiKqGoX2zZ6CIODS1fOszLdZWF5ABIHv4HKKU8xm38eJVOaAVTxB3mRPWubwZMXMQ1niyfSZal1O/9fBE+6z576GqNVivvu9b9Ns1vnoF5+ztnZ/ycDLBRmEtC5+i7DWZLx+g7y3/cBzbJWR7VzzQWt1UgJcgAx903KpQVrOXLnMG2+/T3drky9/9lN09ZpeJC8Y1jh2bgzp3Rvv2brMUAKb+34+xRMht6ALuLbV4Y9+9KcszLc52z5LlVu63d6L3r1Hw5Skyqx3YXkiEY/DK0dL4A6wxpHXm5CS5lybZCmgSEfk49wXxWwBdeAKfh6aNVhdB1EIzr17meW3z7DZ6XBve9Mbkd6eerAr61+bs9tfiZK9NUMMLHKw1rrLnlTMwfWPr7Fzb4cizyl75Uu5PgiUZGm+Qa0WMddu0mg0qNVqWGvJJxmT4RDlrF/la9AITqIxdvgpfn+oMTtUs3z7bNkUKskbC3VEGDDMBbmGcZkzyAq0c7uq9ReVVmq3m1y+eJ68KLh56y7WOM6vnqUeJ6zvbNEfDQkCT0jOYK2lKIqXhkx/tKTW4UGiEoJ6FKAEFJMhhTOMprYV/a0eP/2bn5MWJYMsI5CCZq1GPbb0hxMqbSitt3t65pgJSFLg3vRvMwL9RfE6QkCSgApZmKtzYbHGzc27XPvoM6q83E3GGHt4eL443+Lqty6jpEApyWSScfPWbbI0o8gfPUFnywynC8LaCu1mk3Qypru188TE6JNgNM7I8hJrvFWKchBOD4bCO2/FoV/K6gq08WNMCUzSCV/c+JrN3g6r5y+ysLDAwsIy73/wS/T724yGXQIBydQypbivYPDB+2NGEIkjn7HSSjgzV6c7KbjTHT/INTpHPh5SpONj+xE5YGR9vvjS3DxvXV3h+uaQT7vre7kg5+ikBaUxZCfoPhkKTyRra7DHjJ4C4cnyIEQpRaUrKr0Xfye1Gle+9S3iqfLaOce9W7fobD+4HnhsiH3fT14+8+A2pOdLFpYuoIKQ3vYaefoIzRudYzgYsHbvHp3NdQbdbYwxDwobTAmmojQFOpP7Xu4eyfZwlrOPQpDScevuDnc3+rx/9QLvXTlPGCTMhU3Gkwk3b9+mrKpdZbqy/t4Y4nuz78csLXH/HT3sbpNNRv4wC4GQISpZwDlHOhhhrOHTTz7nxt0dJp31R5aVR0nC3MISxmj6nR3MC2yyfmIiPR2PwFqSOGZ+fo6lpSVWl5dpt5vIQCKVwIhnkA52FqoCqohAWALhEKb0ZZgi8o+XSaZtLHbwFBUhMxXmfleQlyNmem0QBIpGLSGJ4yObiJZlzng8oMizQ4Nar7yzOOt8V2whSNMx6+t32draJMs0QRCgK4PR5oUFxlIKms0W8wuLlNmEIk9xQlBog1OGRBu08S0thBQ0GjXacy3CKPRlt6fX5ilmeNxrIcSXyyZ4kmGmSJ9FejNr7Fn8OSPVA/xYGXEwNn0NIZViYbHN/FybeqNGGEw9aV/CEk2hFDIMiRptouY86c7a4U90FltOHnHrbtZBya+QBCSNBgurq1RVSRjFXpn0AoOw1xnFpPJk5P14Uc1Rv6HFVLMwcpjn3F5bZzAeUc05XAVZ/gp6YrlpL9Cnsa3ZRo5xRBFCEIQhURwh1fTJGn+d1kG0BEIJJBKq/z97//VkWZZmd2K/vfeRV7oMnZFaVVZVV3VVK6gZDIABMARJG9IoHvhAmvGFRuMjzWZeSDP+G/NAcGweCBvAYMRwMI0h0D2D6u7q6i5dlVUpIjO0a/erj9yCD/teFxHuEe4e7hEembHMroWH+73nnrPPPlusb33rA7NjEEaQtBPaSx36dQbDwNd3mEy11rP57SjJ1yxYvH+3+kiZnclgzGTw5EJwLxqePFFEYeDr/jiv5AsDgdMWHSfeP7gqPQkqjl9q56g+MFsazMgxJQXNKCCIQoyVIASZrjHWTZcLYteb+0UgUIpGI0VKQRiGWGlppQ2aaYOtvg92CSGQ8iCZ8nLj8fMX09oHAofRNdZoTFlhtSWb+AC6d/ITNOKQTtLyylApMM97zTcj0194Ytt0QJTWZ04HEIWKNFKgKyaDPtYc5Gf2t7yUvv5cFIU0kgiY2vJZTT6ZkGWnvEBnvEraGf8dePXoi1xv1dpQ78vQtOwbgoUfL8Kpf7iY1mCajTHaGEaTMVIphuMRYRQRpylBEFAUY4YDdut32GP3xSczuUkUMt9MKLVFIg7NRDWmPnYA1U4J5CCKaDdbxGmFUxInBNpaamMoa01hNNZZpNinyj/kNIUAifBOEe7wMWp/No3A/11KiRQHhXdSStJGg7TRQEqJc444SVBK+gyU0453zmG0wSi9d4xnHSuED66HcUIQRN4G6YSoq5p8MqEsCp+pcRgxPk3ZdMbyrPEnn+3jX1leUZuKvNAgJFEQMtdsIIwhlMLrlKeZQco5pPNLHSX2ag0JfOATvG0bwj8j3nayQtf7VAkigMl0Y258pZLhYMAw11AcLwAhhJj2QYdSijhJ0HXt62a9QJzIglkpyfLSIjevX6fdagEQJynzi4tMhhOGxfFtLU6Kqsi5/etfocKA0c6O/6WbMirHTKd4qeHYU8p8DS73ImF7Y5Of/eiv6U98Os5hcEbj6hL3WLVhB2Q4V7O+epfRoMfc3ALd7gK/+fXP+C/+i3/GxsY2o9GEbrfDeDxhMBg9XrDqOcEYy9b2Fg9WHrC1tsqwv83D3oj7I4jLigk98tqn7PhK7hFBFLMzHPDF/Xts93snjiy+wiscwMwHfR5PNFTAGnvSM3g8FX7mkT4jNma1KL/mUIHinffe4vLVy9y784D7944gqWc4bHF5jo9z1GixePMDwmaL5vIVZBiRraRP47ROBlsD2q/+hNuVq80vLvG7f/NvMx70+exXvyTPTkrSv8JXArtSHXzwrsKTql+laawL4jL05Zi/+NPfEImQRvQpzsKDB+tP//wreKIpiEiChMCpvYKCQNxO6L7bpdFJuXH9MsI4Pv+LTxluDCnDHjubkD3ow2cjKMyejH4WLDpqg1wCmxyc917CDA1jLDuDCcNJwSD7kiiOuLw0z9JCl7l2m7evXWdrc4uf/PXPKIqShUQxH6dsTiqyyuzGyn0Jw4N4NGa+PxYh8YRip9MgDCTB1Cql0W4Sihg3UiAFeV0zqsoX+siPxhNufXmXJI555+03icKITtpFSUW85TPKjDEURTFVU4qpQOdlGKgEe55HM8Oew1HXNYNB3xNz1oCzGO1FOjNNRafV4I35DtYairygNoZCW+p9j9bXClUN65ueITO1l1M7vwAOQknSCDC1pSq9/c1+vk4IwY0bV1laWmQyyfj1rz7xgadAUFU1Vf3sA854OOCh/pIoTrj5xjuPEajnBiFQYTzNYjxoGTjDbEidJf0LCXESEUeKXNVYpZEGqH1bKaWQSlGYisLVXLtxjVajybiccG91hdwIcuOXmWfRFefmF3j9rZu4B+s8WB9QGUfF6Y4tBbzRDVhKFTfaMUEQs9hq8c3ryxSV5tZWH+csncgQSsdcWJO2oBSCUkqqypFlBwkobQ1CWOyUh1teXuDa1UvMRuUsy7l796G3wlFqSqIfzmKXRcGdL74giWOuXrtMo5Fy7fICS90Gm1s7rK6dTpleFgVffvZbpJSMhwP/yzMogq51RW/zIQJJWZxk1+IXnXVZko2GCGB+cZmqLBmPBidSmh8XFk/NlNN98Sxr587qJsNJhjMOU1ts7S2GAyG5sdAmDQNGgzFFXpJEkqVQMawt67khDAKW2y0aacK777xBo5ny4199wu0Hq4+fgDNgxt6aM26BVGBKmGxP92hPRpq2iOOESvsxSQUxHCFsfd44EZEuhKDVbDE/N0c8TS8LwpBGs4WuDEKMOLoUzLPBaM3O2qMkwNeERIe99ccrPHeMx2Me3B2TaY6OCDrrqx88NgD6adrZgtFglc31Byhxg0YiWF25z1/8+U+ZTKP91jrKsiLPi3P2ND4azlnG4zH9fp/BcMBwNKQ/KeiVkFiDUhmlt+ZEKqaVpAMmeQHsMMnzrxT/8AovAMX0FbCnQN+e/m7mhz7DrLOV+LyzV51vF26qDlm6tMjC0iKDwejpRDo8png8T6gwpnP5BnGrS9RqgvQe5meK2Rphdi3Wm1+mzRZz3Xn629vc/uzTV0T61xUzW6gInwkDT1Unv2wQDRCXIO+X3P5k/WtrefUsEEAgFYEKEU4esG0M44DWcpu5pTZvfnQTYS3rD+9RyCFaZkzGUPXGsPGI+v9p98FweMbGSwbrHFlRAzXDsY8exFLQSSLi+QWuX75MXVQMhhlVWXBtsUEQSAaFJsPsurvB4dug/dPUfqtqiReAJY2YMFDT9wmiOCYIU4q6oii8Cl5U5QtNYivLirLcYW6uy/vvv0ur0US6EKsdKvCLHmvtXur/S+WiOMsPePpG1lpLMa1dMlO2zjLpdsvnRCGX59vkRcnKeEJd12jj0F/X9Z+xMJoOFBEQqV3SWChBGPmUTlk/vkUVQjA31+XatSt88cUdVh6uoZQgjhXWgjmDvWhZFpRlwfzCMvPzywRh+PQPnQHEzPdbCKyuD13O7g/thGJKlgcBYRRQWYtyBjENVs3U1kIKtNVoa2jPdVlaWCRptqidQDtB7cSZBbjSRoOFxSW2+xMiIXazy04DCSwkktc6Ad0kQMqAZpxwtdtme5xxa7OH1pqw7R0RU+VfmRS75HeeH9TKWdyBX7RbTa5eubSbvT8YjHj4cI2q9sdQymc/iFmhtX3QWtPb2iIMQxbn2zSTkE67STDXpiwr1ta3TtWuWtdsbaw9/odnvEXWGrLR4BSf9ItOow3VdKxLGy2kVGTj4blRfQ5vObQfO4MxO/sy2kJgDh+Anm8ktNMIlxe4siQIJCoJAM1GYVBS0m6kzLVbfPDWTebmunx5f4U7D1YPaVrnLbiFQgRdCGJclkN9vMV2GEYkSQOmWWti6nV/EYb8ExHpUgg6rZSFuRajwTaf/nbMaNBnaXEZRcBwO6OuKkxVvVKlvsJXBrmG9RzqJ3jvNRtNLi17q6PH7V8CrFNsbfe5e+8Bn9+6S1kafvTXH1PvS3EzxrCxtc2D1TXefG/CsjPUzzlQ5JwjrypGWc4Xd+7w4O5t7q+sUjpHpBRxKwTtEJPK+1MaR6EdlYXYia+nGuQVzgcT4DZ7qbMGv0mYwbFnXghevT6zhPi69cOptY1SiiiKUJHi1hd3efhwjWa7SZREVFVJq92grjVlccRS/DH/Rf9PGIZ0u/MADAZ96kNUSmmjwdz8InVd09vePJYPZrPZ4Jvf+pAobfCrn/yInY11JjsbgEA1OqhWF1tm6OH2ma0pdjY2+fTnv2Jufp6bb7xFq9HmD//G3yWfjPn4Vz9jZ/ti+8lfKOyv3XJamdQ5Ikohanp/8GLM4ec3Y91mvtP1Ee97GTENErgKH4yc8Cqj8aQIQS2CaFiGozHlSkm5Vnpv9Cl3ZZ2jrg1lbcirilBJlm8ukrQCeh/n9D4fUey8hFLyc8ROb4QxljBOef31nNrWxA1fKFFIb8Xy5tUFrAxY3x6w1Rsfq+tOS+fREJBIaEQBrU4bqRT9vEYbCGWAVIrMaLaLjMpoNO5CPBpGW4Y7OcXYMBoPKcuK/vA0ZI3H8Snsc4QQ3isDB/oIzx4RgIqmykUfcHqUNJvdn8pY8rKirLUvGvtyRRXOFwZsZXlw6x6T3pje5ib5ROOO2Jw551hb2yTPS3q9PuADF3Vt94qBPyOa7S7d+SUEsLZ679kPeEw45zB15YXHT1H5OvAFjq2jn9VMKrOr6g/DgFboRWNJ0qDRbKKCAGM1t+99ycPVe2g0b733Nqsrq/SHfayxR7Z5kkTEcYRwxlsVT2EdTApzgOzcGYz44v4qw0nG4nyLrKopRzn6BJttgdcIhICrDHnudsV6w6Lk1sYOo6Ki9lWYKQtQNQRTr/gIhxTW+3pz+PJITS1DBv0Bn9+6s8uD1HVNFIWowBdilVLSaHWIkwa9nW3KrcdV5s45JpMcKRWddoMgjUmSiMX5NmVVM57kL5ZinNmMPgHLi0vcvP4ao8mY23dvH+B6ZovOIh/jnPYBBinQukZJf2FP2z6pQE2LdguKwmKNQwmFEBJjCow5nWXfLHavjeVOf0xjEhAgiFvNqY23IpaCFjWNQLDUDuh2QkLlgyTf+8Y7vH5lkYer6zxYXScra/qTEqUU7WYTFYREnTYijBgMDFkW4aocV+UIGSDDZMr4a5+N5PyCvCxzrPUF27XWYDUDq7HGTGsDCEQQe4XnFM7U3mP+nHEyRboUtKdE+sbGBr1ej9FgwPLCMs5IVtNtnJBYrU9UPfgVXuEiozBQPMUirtlssLy0SLvVfoRI9ymNzgW7RPoXX9zhiy/usLYx8gPCFNoYNra3aay26WcTCmcO9UM7T1gHeemJ9Nt37vLxL3/JYFBTAs1AETdTXGWRssY4qIyj1I7agnYC83U2pn6Fs8WEPTXefp/0/ain74mBDnu76K/b9DNV1MpQkrZTsPDFF3exteXqa8t05lpUtSfS86ykKqsnFYd/DGEYsbh4CYAsmxxOpKdNrl67QZ5nDAe9YxPpH33zfcIw4sd//C9Y+fUvdv+mmh2iSzfRgy30qOc32GeA3sYWvc0B11+7ybWrN+l2unz4h9+krivWVh68ItJPAom3Q3H4zJEL9tyFDWgteQvGcnJELGZmUVpOX18lzKS8FbgdLmSw46JDRBBcBplYRuMR47GkWK89kT6FtY6qtlSVpqhqSAKWby4xv9xk8NM79D49QRGyrwl2BiN2BiO6c10mZU7tKuKGREo1JRYk168s0m63+KmxrPWO5wO/mygwLfqXRopWu4VVAVWVUThHS/lid/mUSL9I0LVh0M+RouD+2h0m+VERwONhv6nKLGb43CEFhIEfgPURewQZQNDwaf7GD1SPEumzYEBlDXlVU9cai8AJOSXUXw1uGHDG8eDWPR7c2iOthQB1iI2zc46NjU02Njb3/Q7q+uzCSq32HFdfe4thb4vbn/0arZ9TSpRz2GNa08yIdByUmT+/ZgxpCGGoiOOEMIpptLokaQMVKIwx3Ll3m6LMuHTpMm+++xbaar64/QUO9wQiPabTaSJNidR2d2GiDZSVPUCS94ZjvnywRohhab7FMC/ZmJTe9uiYkHgiPXbgakOem937Oyoqbm30qKbEugKq0o8bjdTHtkLhSKR7YgKVkhBMifStnb3AXxgGzM93SQPf+YSUzC8u0urOUxvN9iFEurWWyZQsT+KQRhqTTon00SRnkhUv3tLqGET6737rO6xurHH/4YNHiHTf0coioywyAiWJQh8MVNKPZNYesV6dQgWKZrcJKIzT6NoRyggpFFXFqYl0i0/ILKxDT7MgXptvs9BMdt8TSUsLQUsJFlsBnbavd6Kk5Hc/fJs0Dvjxz3+NLcZsjwsGmbf1met0iOOYxnwLGYa+vrpMsJPeLpGu4va0AE4B1mCdwWEoy5yy3CPiTF1S5vvS9YREhInvsDPUGe6iEenGGB48eMAnn3zC5uYGvd4O9x88oNfvUeQToliBC9CZONF+6rDpTwpJIH0qnpn6wLlpwYor16/R6rTZXN+gt7V9kkt4ha85pPIWTYGQhEJhrKOo9BOXX2koaKcKax15aTAWKnNwHG23Wly9cpW5ue6USN+/bFU4p6hKTZ7l9HoT1jZGDIYHJwMpBGmc0mw0SYKISCjkCyGm/XcWpWU81pSlf5qDMKTRnkOUGiEzAqm4eu06N25cpxhl9AcjhJBcWlqmKEuGo/OrmXDhcFQh4Iip8R5+NTPz8BZAC79qmeUifxWJnLPCjOya/azZKzoasJf3fcHRiAO6rZiqNvRGJfZZnw/lU3dngixnHVVW4azzihjnyMb5tMK7O7JY8lEIo5i01SaOE0QY4qwjClNM6NCmxOxbzJdlwc72FlVVYs2TV5ntuXmuvf46l67fQNcVzjluvPM+KkpZu3ef/tY2ghCFwJ65D54Fp8kmQ+7f/pzJ4jJXL10jUAGXLt2gyCt6vU3G4/4Zf+9XBPvHuv15xs9rqJ/l989sn54AGShkEiCLr2nRhBmRPsvq0bzimk4KDaYHLoQqsAjhsNnBRoyiiMW5edpzTa+6koJ7XzxguD1gMjhYNVdEEDTl3pjtwBTWxwkvYDDqvKCmBUCHwyG//fRLhsMhw0mF04ZEBn5KVwEyiBDHKOQWC2/NECmIAlhqNFjudknShCCNqYxjXOSM84raGKIwpCgv3oLLWM1w3EcIMSUcj3hgj/kc77eueG6PvpB71WKnWbUzVkAECpzAGvMEpujJpHitDeOiRGtDVmu0MV+fvcZpIcDJqeXIeUVUguni4BGDcOsM2tZod77SMDk9BevOZrbXBkrhyc3AWa9wLTO01WyuPyCMImQgCYNgt9hkkiQsLi5R5DnDncGeFdMuvPf8ZJIjnEFaX+RYWusLHz/aj6f8V6EN4zKjqDQhDoQn/o/TnoKpTYsA6Xytx0lWsNUfkhlLurSELCvKgffnnpWZajlIHUw0DDRM6pMnhnqrphKlphOeEMidHfKipCpLmq0OWteU+9SKzjmKosBaSxgI6qqgyHOKskIbiwojmNlcPWr6f0EwGA64dfsL+sPhVF0+q/LxOKxzaONH6N0skKe0szWWYlKAEN7b3IATU4LrDDBL+HbO0SvrA3ZCeVWTOUcoJIQxIog8YV1PUDYlxHF5vslHb99gZ1zQ7ox9kd+ywpYabBNBgKsLXD7G1X4eFtLXIPCTf4wzBlEWh9Y2mEFISRClyCCk2V0giGLGg7636jTPZ81/IiK9rmp++ctfMuzvsLW1Tr+3w85kwtpggHOORjMgCBz5UBz7/GcJWY/2mVApGlGCsVAYg3UWrR1BGPDRd7/DG++8xV/94M9fEemvcCKoCMIUUhnQVilFZaj0BPOENKlOQ/HOpZiqtqxtF5Ta0Sv3xm4hYGlpmQ/e/4CrV67tI9JnD0EALiKflAx7Q1ZWenx+awv7SNqclJJOu8vi3ALtOKUhFOGLKKYgwAnBeKzZ2ip3zzFKUhYuX2cyKVDBNlKFfPjhR3z4jQ/50Z//JV9+eQ8pAt598x22eztMJhP0cxrIXihmubPw+OqtAXTZtd4gA9anP1+d/r3Ez1hbvCLSj4Lj4E4wn75mBQIVz16F/TlgoZPw/s0F+qOSUbZJ9YymnjIUyERM15IOXWvGo/GBturvDOn3RrRaDZqt9InHexRpq83lm28ilQIhcVrTSDuERIyyPsbuqfkm4xF5NvF756dYUl258Rp/93/2nxKnKWUxAaH4zt/+j/hGbfkf/uW/or/9E6RICFHYaTjx7DZgPnLV31nj53+1yZWrN/jow28zN7fEO+98m6X51/jVr3/4ikg/DLPMkNlG3OKfw+eJkL1x8ynTi4hCgnYDWVYgjqdo/UphRqTXfPUKqD4nuBL0g9n/ph3ukXZspQ1ev36D9kKD5flFqrLi3sdb3P/sAXb14FioGpLkZrC737UG8u0akzufXfU1IdJDJUgCwfraBvcfbnobBmsIlSQJLSJ0OBUjoyZCPdlTWQAdBQ0J8ynMJbBweY5rb37o/ZFtTZXlbA36bA/GNKOEKAgYVRdvwVXVFevbD4HHrU0O4ARE+nMviSAVqNATGaZCYFFohBSIOPQOL0XhmtIchgABAABJREFUyfRToKg1W6MxxliyXD+7IOFrACfABgJnxeNGyWcBiU8BEUBu2G9Yr50hNwWVOd+UqEB4slgLvz9/1qsstX+FWJJIg/XWXeDY2XpAoCRvvP0B8wtLBFO5f6vd4Y0336S/02cyHGOr/WchAEGel+R5OS00FqAcRNPA0lFLmlFesbo1QDpLS/qA4ehgMx8JKaATQluCcFBXsN2fMHZrDKImnTffpBhPqLMMU5YM8cuGq0DXQa+Ce1PH5pPePWMsw+F4X11IQb8/8lqyzhwLy5fIJxOqqty133HOMRwOEUIwHvUJA4VUEqkkFkmQJkjrqOraf6Y+xPz/BWNlfY31zU2cExir8AvXw/u/tY7qBBkGALrWDHtDYDpPCImTEcizqTHl2KuFnk1yyPIDf3T4mjEubnkPRT3GFSWqMUfkGrx3Y4G3r87Rm1Tc25ywub3Dj/7qp+RZjVucBxdj8xFmuLkbnfGkuC8IThzhjKY2Y19M+QhIFZJ0l4iT1BeoTWPufDYh2xmeSTscBydTpFvL5uYWUgoG/R7j0ZBRWZBP8t0io7o+2ovrMDgO5z8EPk3A4cDMUrwcUkouX7rEG2+8wa2PPyGMQoyxp56QX+HrhVlNUJkGJK0GNq8Qw8N3mGEoCUM/eE9K61N3jfdKf3TdVpYlw8GQPB+Dy8FFwFSVgQAhCIKAMIyQUh5J3ButqeuaQmsyrb1n2RkiknsRe8s0cr/vVIwxbGxs+cFM+kKF2SRnMs6oKs1Ob0xdazrtJmEUI4SjmuaBhXFEWRSUZUld1+dWePhCwRff3ntFj/w+mf5uRrbHeCX6/upZs7+9CCJ4dp57yRPHVns+d8xiU7PzZfrvbOiv2ZNeXVBUtWU0qZgUhxc+Oi7UbGEZCWTgO58TElsbquJgjRI/dXoFi1QKIY/f0YzWlFlGGEU00hZW+gI7tamxh5Dlj6tvDkeR52yurtCZm+fGG28QJSlB0kEbR9poekXb9IE4r8fCOYcxmjyf8PDBXYaDIVubm4xHGWWp2at2e7EW6KfC/mDf9HkRbYGIp20swBUOO3ZPly6esuPKQJI0PSFWjCvsUQVHjjwAe57sIf46joiwqEggQh9gKkeaOjcXc1x4dPw9a8zGxxfm6fAVwSNDQNxMCKKQIAwIgoC5pUVaUZNYRNSTmqooCaKQpJVSBiV632QaRRHzi12ctIzHE1xpEFb4hdhXYKg5LlrNFvPtBoNJTtYb7qsF7W1ypNTsDMdoK8lyv6WPAkUa+z3f5BF7MoEnjLSFTEPb+U15pTWbgzGjSUZRa7S1lEZjcdQnJC+eF156dfWskOBu9pLDGQtSECiJkBKZNLzTS1VgdD3dnNVTC7dZCucTFJzaYqx9+dvqHPGoDsvNNn3nBeMOnZN1VZGPhlR5di73K0lTojhG1CWyzFEOAukvdVccP9uLn6LumLMOXdvd/iycw1iNUZJsMiaKIhYWFpnvdtFlzU4YE0cRURjttoVzDmv9y7nZgRQyiAGLlXJqfXRw/V5pTVaUFFV1gDc4ybrYAbmdEupxSBoqGq0GjXYTF6YMkhBlY/JuF50X2GyCMIbCeiV6ZSF0filx1LbwaYkOe5fk/HiALwJalaXPvDmkXzjn0MYTLsopFA4rfLv5fuZ22f3nbuy0b4/ir+lgv/LnPtuwnsHZCYWQEpzdVWgfeJZmMnZx9rlHlsMPWWnD9nBMpWvQBY1Y4YxGFw2iWf83BmEtSRBw/dIiWVkz1JrRaET9SD1NZw2mLpEqIAxjkCFRkmKURFfV1At9t0FAKpzwbhFaa/LJGKcL6uccID+ZIr2u+Nkvfo0KlDd4txbrHMbZA418kirPR91yJQWRkggMzumpws0RBiG/973v8x/9g7/P2t0H/OLnP6fMcybDVx6Er/B06ApMDXPtlPmblwn6E9a2hr7q+SPodiMWFxPGo4pP1wqMdRjjJwD7yPi1trrGz3/2S1qtFtasQtABsczsERMIms0mCwsLJOnhqlBrLePRkH5/h/XhkHQyYVifnZZEAIsRzIVT33cDhYV+vfcMFkXBn/37Pydtprx28zp/4+/+Abc++ZKPf/EJ270hP/rpb2m3Gnz4/pt0Ox3qcsTKwzsI6Vi6vMz9u/dYWVkhL/Jjk2ovLRT+9op9/29OfxdO/7+fSGf6/87051lhu9lnn3e9pBnRL/GhZ4030mvjVaYDLhb54vCqytnPcDAVftbGF7jbbQ9zhlnpizmdUhUkhKDZaRCn8e7eQEURYZpSTgq2xmsHLFdmUEFAmEQEVc1xF3bZeEh5J6PTnmP53W+CCnlYTRhOek9VnT8Jq/fu8N//1/+Mtz/4kD/6m3+LK9dvkLY6aGP5i3/9b/ZSw58D+v0d/vi//edIGaDrBGcDqrrAP6gFe53uJYbCB/AcXvUqIPgwQF6T3mJBQH3PUHys/Zh01LTzDM9W0gx57aNlcHDv403y4QkXu7OxNJ2+juq+ApJFRTynKPuGrd+OcdadZv98vhD4wKpiLyvprFHzys7ljCGkYOnmJeavLdGZ69KZX6Cz3ORKcwmtK7ZuPaSochYuLxK3G6xsPaS3srP7+bn5Ob793W9T1CUf/+q3jMuJZ35HfG3ukxCC119/g4/ee5fPvrzN1uAXu2pEYxz9UYkcV2wOvgAhqWq/gV5oNXjn6hLDrOCThxu7vr4zOAfbOaxmECxJ3k8Ctncm/ODXtxhNcoqq9i56dY3Q9Ssl83liH5HurMNUNUpKooYiCCVh5zIyajLYfMCkt+lJ9Fp76fSBXPXH75HWFjPds33t7+CsqR6Z36T0rxmsA1dO33ReQdvCHHr8Sb9PPh7h7NmLHoUQXLp6lcvXrjHcWGXn/m2kswRTEn1cT/l9FSCkz6p8klXEYdC1wTy2XvcFOB8+uM/Ozhrvvf023/rgG3wZ3aG3sYOpaubm5qiqynNlU9/vqq7xIjuJihpE7e6UU3BYU+MGW56omGIwzhhn+ZSAd6dSltQOHtQQGsHvXe1yY7HF8tIyy0tLrBmHqDTjVkgYv0+VFZSffYoZDlmv4IGAxMECT94W1vbkS418MqEscr8+O2Is1tpiBChrCazwtRSiab2FugZjUQgEEovDPo8RQSgI4r09inO+ov2hdZwc8Ow+3SpMUHEDUxeY4nC+0wmDExonns9id5Tl/OTjL5FSEEhBoATff73Jm4sxy0uXuHzpMsPMMO6VpFHEP/47f0Re1fxX//ZHfPlw40CNQPC+53l/gzBOaV6+ThBGJI0YZy3DrTWyYX/vzSqAqIUVPtAu8pzJzhrCafQZ8mbHwYmIdOcgL4qnv/EM4PBeUdbZXRIdACFI4ohWs0HaSIiTCPOcG+0VXmJMA5jG+kinwSFjhcRi68NVa8ZCUdsn2vnleUGv12djY5N79+7SbC0yN5cSBDFBGCOEoNXqsLC4zOLCAkuLcxRlxXi8R9JYaxkOR+xs99ja3qK5uQFCsHTlCkWWMx4OnjmaP1uiHsnZOkdVVrvFnhqNlDAMEYhpGmVJHEU0Gw3arSbgqKuKsiopSq9GL8rCRyi/yivc/Sp0eHxxMyPZZ409k0vNQuczb2HHnmLwAoijZChRjWm0vwZnHLrUF+deHrY+cI/8e4FhrMNUj99oATSSiDBQ1FVNXWkMT+4SApBSoZRCyOCpqlNr/ebTniRjzFp0VVGX5W6U35gae9LCn0IcIMa11owGfcbDAWVZUWtNZAzOegupVreLigJsXWDPuTCVNYbRcMBe4YIn2whcOAi8/EoKgjBASokuamy1b5E6tZYSAgIVIANJsBCiFhRYg3AW0bAHs1GeXUCDUF6o446SMT06fj7KmwgI0xARCHSkscp6Qj3Bj59HxYOm4641DlNeMAZdgkoCUOBih5MOZ9y00tk5YHZY8cj/DzmvXRHoWTXZc5eJnT2EFMSR3ypVlQYhaHXaLF1aYn5xiYVLl2h0Y+aaLfJqwkbtqEvtFbiH8IDOOmzlcJXDltOfdytkfo3g3NTe8PEOMvt9XVQHmiVQkkYSUWmDfCTQ6lWnIJRECoUIfPaVcY5JXjIpqn3vdU/tl0EYIoTE6PqrLwo5c8yUonuNLHH7XqBUgAxCgtDvkaw1WKMRIiAIExwOUx/tff4qBvJkSDktLuoAJxFuv/+23wEKpQ6sy5w1RxexnK3h7BMUr0f82lqDPWTd+ywQQtDudIjjhIWlJeYXFjH5mEEUIYzBaH1oJxFS+gw8644vBnGHZ4k4AXVVIYQXlgZKESi/Jp/9q5TaHT98oobATRUwUgUEUeztl6xFSoVIGqAVuqpwdipY3T1Nf6927+MJnoF6+jjKICCOI5IoJI0iGlrTtj4rahgFoANq4Ssa1EDhjk8UnvSRdM4eKzNxZl1pp31PWOudLw6YiR8dYRBAoBRCQG3OKYtlllEwez6E3BUESSkPLL/sLAPnjOGcxe4Kj88fvm7g3ryqBGwPoKVqCFJk1CQvLWVpUNNspCgMaKcRc82YshJobai09e4L01qYzmis0VgpUWEAaqrG3w8hEcr3TGeNf1aqwltOHANSBd72zZhnbq8TEenPE0Vdo83YT6b70recs0wmQ/r9DcIArl5aYksIhr3+qxSvVzg2+jtDPv31l8gkoPXaHEYbRvf76HyPtBkMKrJMo/UhJPr+eg4C1ra2ycoxt+5s8N/9m7/i3Xff5v/4f/jfcvnyVa7f/Igk7fCd7/+HvPfh97h6/T0+ev8NfvHxLf6/f/zn1FO1TZbl/OAHf0mz2eD+ygZvvPUmrbkl/s//9/8Hv/rrv+K/+a/+S6pnKI7kgKwCUUPmvDDRPrKfCFTA61euMT/XpRU1qSf1dAG0964gCJifX2BxYZ4wjHDOcuuLW/zmN59RFgV5lvuN0Mu+gz4KwfTl8Jvf/bYJMz5ukYOkVKqgHfq842HFLktq8N7oGWcRtD4ZHF4JKdhlbOcWuiy+swBWgIZiULD6mzV0cdF8Xr5aCAPF3/r2W7x5dYEvfnuXe7ce0newcch+3znHZJhRTArmL12iPbfEaDBgc3UVo82uUutRTEYZZVFh9MkLc2X5hM8+/7X/OZs85d2PQyQNRLjn3efqCpdPGAyG/ODP/py5pc/pzi0TBBGdy1f4D/7n/4Tbv/05n//yr7F1dWIF0ekwS3nYHyF7CQiUSCJea6JaEVdee41Wp8PGT79k57cP9t4jgQSiTsjN71wjnU8w8wKTwGgwYDIaIRLrvccLjrJyPBGSeUmypKgGlsmaoZjU3P94E4Ayq3wTh+wVC1bT792XABCmIdf/6AbJYsLq+iqDYd/HOlrAGn7snBUe3odiYqicxRQXbw4KmhHd311GtQLyaoLWNfXnOfrBOU4AAV79bvBj/mHN0sS3a4afx54Vs6ysQ+7Py4RmI+a9D64jpeCzT1fIsppvffAtfu9v/SFXbrzG9ddfxymDCyo2dlZY3bzLcDRg9dNVelt9ys2Da7btBz1+9C9/gnWWyXjix+ziJRhnzggSkM7x5e3brK+vM84fyV6UECQBUgpMobH7vAdVGJI2GxTGEU3Jnt2uJTyxdeXyHAtL8ywtzCMfsTGbjexPa+0gjLj62utEccrGyn1Gg94zX/fXCsb7Sc82ToEUNELpBTrWgK7QRYlwIY3WIo3WApPhNsOtFdJmkyuvvYMxmrV7n1MWX4GMsPPEEZ05SSFJQFQRlBElGm0KHApoIFRIvDSHSvbWZfVwh6q38fjBhEAlDaQKMGWOrZ/3ZuVxpI0G/+R/8b/k3Q8+QBuDNpYvuh0mZUU2GrGzsoLWZjd73BmNM4JGs0WcpJRFRjY+nZOBJ2a9NspWjtpYBjtDVldXGY9HRFHkBWhCYK0lyzK01kghiaMQPfU2j5KY7uIi1kKRVz7b9NoNpHBs3v6Uyc7+e+F9to2zjK1v/5OvigVhEJFGqa9BXuV0nOMjaemJmqLuM6gKtlzNeN/xc/zS7Kwd4sIwJIoitNaUT+E2ZlboCI2oMx8fmmUx4RBPYByiIODG4jxKSVZ2+oyLZ7T+cAZ0wZ7Xj0DGDYQKsGWGqwuIGtBYIAwjWo3W7lxkrWGyeZdqcrJFlqkLrKmeQMA76mqCrvPntF96HMbBpxsl93YqwnsVUbjJXBpzY65FFARsbe8QxxH/yR98RNpI+c2tWzxYXePOxpjP1/Y8zXVd0d9YQYUh3aXLhHHy2HcppYjTGGcNxbCHm805x4CQitbCMlHSYNzbohg/24L3whLp1jmqIwoVal1RVSVSQBJ7Fd8rvMJJUFeauh4TdRLmr7SQ9nHv4Lq21PUTltzSP8xSChyCstIMBpt88slDJuOcrX+ySiONMEYjhGB+YZn5+SW21u8w2LrK6vrWAVWNMYbNzS12dhTLl5ZQzvL+H1zhnW9+i821td2q4KfBruOg9RPi7PXY+6SglTaYa7WRKtxTS+Oj6VJKwiik2WjSaDSRSmGdYzwasbm5uftdF4++OEPMlHszNfkMjr30/EdV5k74iLVw/mfr9gznXqR7xCPzThiGpI0UIQVSCH/PA/k1uKkvFlIKLs23eP3KAsP762wLyJ/Q3kYbT4hbX2zFWSgm+RMJ8tlnTgNjNKPRkxYbT+kgUkLgFQBeDuRwQlDVNRubm+S1IcsN4bT6+uLlS6x+GaKzZ8/CORkuQFrIcSFAKAGRQnZCVCcmvdKlOb9AeGt1b4xy7E4AIpKklxOaSylVAEZCLicwc+c7C4upKVslE0HUlpjSZ+FYbckG5cH3zcbSWQaPmZ4nAhUowkZIupySXmoQFMqPlzPCN2XPxn5/93NgtPPfe9gS8mnK7ONc30k+uz97SQhEIgkXYoJ2SJ1VuNKhw3Py9Zp978xubJpptNtmsNc/ZrY5moN95yTftR9PTH27ADiiHwjh10FCSJSUJGlMp+3nxCCQCCHodue5euUa167d4Mb1m1S6YJzvMAxSnHboQpP3Miabk72KXVNUecX2gx2+jtgfohyNJ4zGk316QoGUAqkEUsmpBa1/2KSUKCkIw8B70wfqse7l8EurOArptFKiKKDWBjO1ktj/3U+DlII4SUnSJkFwYbfJFxfOIdwesSWFLy4rhcA557MyjEFoTRgnBGFMmY+Z1XFJGg2M1l4x/WrxeSpI6d0PpBYIEWDEbKCfDvYiIkjbBM10TzZwZNBCIJRCBAFUL35QD8OQJEl47ebrvPfBh/QHA8aTCesb80TNNmWlMU6g9wtRpuplFQTESQNjDEJMduvvnRSzlnTW81VlUTKZTKhrTRiGKKWmvuiWuq7RWhOHMVIKL1S2PqM0CAKshUA5hFQkrS5SCVQY83jf9wpnpyLAIdxMxXsw++NJ8KJp4RXeWhMAHRzGaRq2pHQVQoJV0hPVjqdmx54WQojddjruuXt/9cfP5klHkFKQRCGh8nP6meAAoS0QUiGCCFGXfkmlQmTcIowTGt15nx1Va4yuEPIUWa/O+Cw34dvNwWP33FlzaNs8T4wKiw9P5UDO5XbKXBQQKsF4MqHZSPndb73LleVFhv1NTDGiN6mJAoWxFjPNFKnLHKNrjNGoQ2phCAFKCSw+28liD4ZSpvvMwyAEBGFMGCdI9ez88SlWCM+6C3k2CCAKQhqRr+g6mYwoq+djN/MKXyFMu6/OagZ3d/xkUZ5ANmU9if6N3/mQq69d5oM33uH919/iL3/0E/7ZP/uXFGXNw4c7BEGbm2/VtNoOUw6wdcbtzz7mB3/67/n8zvruIv9RSBUiw4RxVvFwdZOd/vBUfo7TjH6W8IK02eRv8Ir0x94vBI0kodNsknRaRI2ElZU1QDDXbfL261e5fu0af/j7v0en0+GLu3fZ6e9lg0RBRBLFaKPJy/yrvfyN8I1a4xtzf3r2gF0vYl8gz0BS+ootzenKZBu/yb5Aw9dwe0j9WUWr2+TSlQXCEOIloAF1H9yLF6J85VE6GNnjdYthr0c1LfD74jKyUryUucR3+kNSYMsC6pqo0yHptqknikleIFSEFCE4xXg8xrkJK7dus7O2QW/jzqsssycgmEtofLCITAPohMgowIaOwhToeQFvpb5S1Fbto6bbUJc193+zStAJsAW4GsqdkrpfYiYWehwcx04KgTfTbILsSlSkkIF7nAqZTUQzD++Z/ZXf1dFd7vA7f/RtZCK5PbrD+sN1JuUYQgijiDCJMY2asl34B2XwyBcU+469HxGIRf9dbhvfZU+C1B+Dcvods+KnsyDqYd21CyyCaEQE8y2EEAy3dmDNordrbG4xvXOSbHfZcyuK2Qvcwh4LObPx6eLrY0zrpFMCQ47XF8Lp52dyX4vfR5XH/PxzhlAQtfzP9eRgNnBnucnclRaL84u8/cZbZOOcT3/1CeNRxmTsb3oeWwZNi5ms0/9ywp1PPufP/vX/j8Ggx92VL8mLjPHmZLaXfIUpZkl8+yGRRCKg1Wpy/cYVjDU8WHlAnhW7vsTv3rzOWzeu0WokLHSa1FpgpNx95ARQGk9OrW0MGE9KnApx0QOKvESU9a4b1HG6o65r1h/eJwhDal2StlPqskZXL3FqxXNEEkmiQFJpS1FZpIBYecHRuFYYI3HjCU7WSGEQwlKXGThHkU1YufMZTghMECDbbVye487Z4u2rhjyDuvIuaAeqcgkFqoNKWyze/IjG4jzKaoQzbDpLvn6fAxOZFCC8f7E1Nc68+Pvw9//RPyKKYxrNBqPhkKryGxMhpCcpZYCRAVY4DvpWCxqdeRYuXyca9EBI6qqkmAxxzu16yj+Nl3b45GIhIAwVMlBs7wz4/PPbdDtd3n3rPeLwHj/5yU8YjUa7+/za1BhrSBpN5tImGth6sIoMQqK0Bc6xubmBtZa8BsIOmALsbCItaXcXeO+bv4MKAjY21ijyjPGwT5nnmLrEPCFbwDjLxyvbPOiP+ejaJT66FmJthanGaODNJOFaFNN8J2RY1Nx+uMVmb3z6G/UUzAINx7XOOi37WGnDw50eUgiy8vlsYlutFouv3WR+YYH33n+fMs/40Z/+O4ab69SnyLIRYYwMY8Io8JbWVcWk399V5R/xKbxK5VHV3ykxyxydZdofA4Oi4rdrPWIhmBMQqiH/LP8RcTPlg+tzfOOtN+h2BlxfnOfhzoif3173BWbxgYHR9iYqCKjyg21m6opisIMKQ+avXEMgGGyuURUZMm0joxRbZtji8f5rrWXS3yIf9amLZ1+gnZJIn62WX8Qm1/vshErhrKGqiscM61/hFY4FB7YyFNunkAM7vwG4dvMq73/rXf7m7/wBf/St36MsLf/8X/w3aG3o9Sd05iboqQrU6hxdDthce8hnn3zK+k5+JDkuhEKqkKLS9AYjJll+YlPAGYc75SZo4TcSmqMffIEgDkLSOKLZbJC0miRxDEAjTbhxbZmbr13lzTdep9lss7K+foDsCpQiiWKqWpBXxVfPyHC/p2/AnhXCkIPz1KNjs3CgjFdTJvjhc3zI+14w8klBvl54XuvyPEqBaoGSoEevdEHnial5GbWb8oCH2Lo8iiLLKLIXnfoc4Tu2wKdWHHLWusZRo0SbJE1AG+9vJwKE8KqzsiiptWblzl0efvYFh4f6XmEG2QhI3uwik2lBayFxylHbCtsQsBT6XMutejdyaoyltzKEPp58LvDk+VlYeYDvAk1gDkTilaXyMAXQfuecRwRXpJAup7z9e29BCJ/+j5+x09ve/bsKAqIwpo6gTEs/xzwqNT3KTkSB6PrjuAEnJ9Jnqm3LQSL9Sb7yKbAMoqNQ1xu4wpD/dhM30rDK+WYjpcA8e4p0g59/YG8RUE1/38YvEsBfi8LPUccR7AX4+z6zcjH49rmgCR5CQjBtB/3IHJy0I+avtXnt+hW+892PWHuwyV/++5+yventPYI4pFaOPLJU5ZDBuM+vP/4Vf/wv/luK7IJN6BcQj3YlISSBCGgkDa5evkKtax6urO5mgwohuLQwz4dvvb77mSjJsELskuIzYqsChsOCyaigcDB0e10z5PiOVdZab+ciIG2lhHGINfYVkX4MCAFhIEgiiXOOmQFCIAXWCYyTVFZiiwpLDTafEoUeui7pb6+DUgTdeWQUYarypbaHehGoa/9SgSCdjfUCT6QHKSJs01y4SvfyZZQpUVYzWb17iAjaT67O1J6TvgB7ug8/+shbO0QxRZ7vs/8VICROKKxQOGGmyuHpOQtBlDRodBcw1gdthJAU2Ric2SXSj1MPdWaFHQqFkIpJlrO+vkkjbXFp+TKDwYCqqg5YllhrsVjCIKDVbDLKSvr9PkGcEsQNAMbjMbXR0zk49YEAO/NjMyRpzM233iOIYowMGI0GaOMtHZ01TyTSnYOVwYSVwYT5tMmbixZbV9TFmDAIWG53sVLhopihMWz2RudKpM/a5DiCmf0ZRSftgcZa+pPnOzfHccL8/DyXr1zh7ffeZdzv8aOqoBj0OI2Pq1AhIkoI0pi0nVLlOdlweAwi/bD0+VNiRigd8FR7MorasFbnJOzpZT7eHqEDxetL3+Pa8iJJGNJJQpQU/OruBrPEaeccZXZY/xM4o6lzjaRJsz2HVAHj/jYUAhnGqEbbW9wcQqRz5HFPhxMR6UJKOvPLhHHCZNAjnwwPfx/eAmJ+YYE0Tf1ArATjwYje9rOlNFprWF9b5csvb7Gzs4Ux9dQP6MUP7q/w9YJSijffepvvfP/3eP21d+jMLXHl0jU+fOsdWu0mw3HGdm9Erb1XoDaWsrZsDyvurk8Y5/WRE0ilLVmpkaMMtnpMRpMTqzNn6p8abyU7Yk9QN5uQGqFkKQ2orWMz07t2AUJJsklGVpZkE7/TH40yPr11n+1eRjP9N3Q6HbSQxFGMmtrOpGnC0tICo8mYUT7GXIBF15nCMbOq84REA9+gCt/QPfbsXR79nBcUwCYHbWBeEKSAVurrFE4KKPX0nDLINgpW7AZG6COzJl7h7KCN5VdfrLC2PeT+Wo+Be/6W+ceBUAHh3CVkGFEPtjD53mKkOX+JxesfUtcFvfUV6qrAFkOc3evo9WTCeGMDhyJpLyCilLWVVdLmkKVLN0ijFurItMdZ9ArOpBrmSw7nHNrWUGl0b4KrDYXpIZ0i3xxAr4TJIwW0NbCBH78KDiqUT4MZkQx7LNXIH7dUhqGsqCeH1BiZ7nmjBgQh1IV/UQNjGD8c8/P/4RcQwOjuyBO6lf+7TmqKNMOMNfTc4UrwEETgFff7F/yeQ1AIBXVqvf/yzOrkadc52wWA74YpXuXdnp73UXLX6ebDSYu2NU4b3CwT6YjvbaZwdVFgDDzcclSnnSv2R9NnE3/T/042ApAC4RQ4iUwUIpaYSY1JC+8tFU0HogF7Cv/Dzlmw54nu9v3ugsJZqMZ4y6FHprcsL9jc6aGCiO7tL+htDqjqvRtgjeU3P/4pg2IHkTtk7li7+xBdv3il5otCEHhbFmt8QTh3jCBwIAICqZBCopBYbejvDNGmRu9fczhHURQMBkMG4wmbOz16w/FubaEZZl1TOt/lZ9bqs7ru8Pg5RVHgLWCMpaz0zPdg33eDrnxhOFM/nTmQU+ey3Tp4Lw32G+Xsf3BnBMzxL8Y5KGuHtQZhHW0FoQCrDQ5JNLUNLGZFXI+qRG0dNs+8lcXXXSw3c7h5yq0QEhrtEBVI8nFNXVoqAkaiQbQ4x43XbiCjBlF8hSBOWbh6g6TVROocaSri9gJB5yq2yrF533/Zo8XpL0DHrqoSIST9QZ+oyH0BUSkZ7myz+fA+eZZh9T4/aSFJO4sEcQOnEsaTnFZ3gWvXbzDY2eKz3/ycqsgxVYazh6xXngA9rTk0q02ktWGS59TakaZzVBWU5RhrDXEQEciARtokaTRxYYKLGhgLZTaZmlNYpLPTwMXjxRMd/pYIGbCweIm00WS0tU09rX90FAKm4yJ+TFzpDfnxF/cwtqbSOXONhO+mbVQgGI8HDMrqmWqyHYbderXT2zLbW0oBjSTCWkdZ14e2//PudUJAI40Jw4A8L/38cBScw1Y5wtQ47WebbLDN2he/ZrRxj2zrIVVRMB5scvy8qEe+QlfYwuECwKVPff/0U5x0/H4iZt3rFFmGGq/fmf0MDlPX6KrE6RppDVcXWvy9779Pb5Tzi1sPyYrHd8FKKgKpsM5SG+391DfXEFKiawsqptnq0JxfYOwqhqPt01zpiXAiIl1KRXdxmWZ3Hmv0E4n0QCmWl5eZn5+HSEIgWXuwQn+n90zp2tZa1qZEeq+3jTE19gUZ67/C1xtKKd545x1+9/f/gCvpMp10katXrvPh2+9hhWMwyon7Q2qtPW+q3ZRIL7m7fjQx7oBSG7JS44YTdHQ6Ih32sqy38M/lVfx+mun/W6HkZich14Z+MZ3UlPepHGUTiqomyyYI5xiOM4afZzQfbDDe2WFursPv/sHfYGFpeddnKk0TFhfnEUqwurkBx0zZeqmg2LN1SfEN2cETI2OOJshnKsYLYuUiJXQbkISgzT4ivYYsK8g2C2QK8dUXfaZffWhj+cWtlRd9Gk+FUAHR4jWCRhtblQeJ9LlLvP6tPyKfjMirv8ZN+tQ6x+1jAasso8oyotY8zUuXEVKytrJCnKQsLtwgaTQJ5NH5Mnuj1/4CBF9POCy1rXCVJVvbxI4rnxlT4DNdDhtnNLB+hicxLWLqJaH4WzIExlAaQ3nU2mwqIo8bEDch60+JdA2MYJSP+enw5wc926fXpqnRT4lCighEPP2q/US6FISNAAJPyFO5vUnySZidx4xjmvmNN/CK7xIfRD1srzX1gHfSok3lpbOFe6Jiu5XCe68Jigq2Bs9ApO/3n4+m590AQoHsBIhQIVSEkAFKhkgZUGcZpllDbiExXjFf4/vUUW0l2QsozwSAL95K90g44y1dDsMkzyl2CmrnUFFEPigOEuna8PGPf8zHv/mJn+9HXAhy6UUiDCVKCXRt0bXzXeApTRLKgFjtFTo02tDfGWDs47U88ryg3x9wf3WDj2/dRlv7WKD5wCNykAvf/dthRHqrlZBXmso6r+57ZMzStcZqfaylrJRTAa/1yUAvD2bRtked5GfpKCeba6vaUtXQVNBR/jhOG5xwxEoQ4uujYJ8wsDnrifSvO2buDMcQlkopaHYj4lRhtPVEugipRIOlpde58Yd/nzht0kw7qH3rLKVDhC6J24uozlXIethiOFVDX7yOXJY+z0E7i1QBURQTRSGDnS3WH9zDPiL+ETIgnVsmaS/4WP8449KVa3zru99l9cFdHq48gGGfrCqPbTMyg9YGrc0ekW40WVFQG0uazk0zAwqctSRBTBImNJImabNF4CBsWbKsYH11A20tKk2QQmBsBbp8PNDkZkS6YmHxErquuffpp9RFzVGdY3+cezaFP+wN2OwN0Hhq98Z8i2/csCRSMB71GUwmu5Y5ZwUp96yrnWPX1iUKA9I4xljr61ocUVDzefZEIQTNZkIjjT3B/5RsJFcXXrgxRTbcIRvuAJJbP3+a/9/T4XTlyfQ4OMExzkiJPsNx1spHQOOXyDOEDqyu0WWBrStPpM+3eOed17m/OeDWg80jiHRJHERoq6mNxuiaweYqIEClCJXQandZXFzE5UOGz6HGxomIdKUkl69cYenKNepszHg0xNQVpt6LWimpaDWbRGGEc5AVJdWkQpua8XD0zJfjnCPLMgb9IXmW+0WOMV9zbdorvAhYZ9nY2uT2vTvEV0MupYu0Om3eeOdNRvmEnWzAcDzGGIO1jocrq6yt3mNza/vJpLhzjEZjNje36MgEG7cpi/JM+niF3w/PxnttHVldU2iLdY5AShrtNu25OVa3bvNwbZ3xaEwShX7zMl007AwnOKkIwpBWq0kYTmWJQiCEQrwIOdpsrjrvBJWZijPiIHHykolkfcAzJAgEQh4uNXQazMi/2X29ecsLiSCMCcIIo2vqaa0QiQ9q2TNcQAVxQnv5KippEi8toeIG3eibUL5DFHWIoi5pp0OcRECDS9dvkI/bbOabFFVOq9MlSRu7xzNI6kkfZICKWoBmbXWVwWBM0m7xxgcfMti+R29zDFJBGNPudPnoo98hCAJuffEJg0GPejJGlxckMvW8MFM4Bw5rtS9ANHKe0JuRnucs4JNNhVoIfGHFcXVw7Jtl3zxlLHRMyXOg0U6YX07JRhX99YkXkpXsZf+cZC0sII4lQUNQGUeV741rzoIuLWhw1THV6Oy7npnqfv+GQuKPc9Tj9uj7BT4IW7GXyTRF0hY0u5Ll+YhLlztMMkMQ9Dn2DZ0RLp3pdywCbQiaAXEnAgU29hmiIo5AChwKJ6aKVAdxktKIE3RWMjYDXGCgYfauYaaMnP08882cXduMj5tZmM385F4SuArsyFG4ki3Vo8qqPWJmFkypgcwd3yvkqwrh/ZO18Sp0Y46nRgcwzlDtI1Jd7RCZxFp7IAvOAf3xhHvrm/SGI1+s8gSnOHv8Zj/vh9aGoqi8jYyxh7P/U+JKTW2qjLG7pNmjmHGOL1tcRSqFCmLf9rqaNtSTKg4fXN8HgR8AjDGP722mCv3KgBUO5zQWgT2CLHuF08MXndeY2np1JoCtwIwIREUziUmbKXPzHUCwtb5KnmWIPEPUBVl/E5v1cOXM0+viQkhBu9kkTpJpcc+A5aVlbt58g8lkzPbWJtY5lIqQKqDOxzhrSFpzRM02o8mYz259Rn9znWIyRJc57hn6ZJ7n9PsD5heWCaOUIIwRYu85cTgqo4GSxjRYJwQoKZDCei90bXBC+z8YfehAUhQFD+7do9Fq051bQEiBkwEyaeB0iTOVL9QehVhrvB+3c7vb4tkRpYBA7IXOdK35ZK1HpCQ7g4K89P3oLHHUuCinhaSFMYgXQB0chRmPEcchbZdS1ZqyPI2q4dEA5elhqop8NMJU1VNsXU4GpRRRFGGtpaqqc69PZZ3jy/U+oVK0IkknVlRFSbW5Sd4fMxcYbAzjGvZ3Q2e9Et08JtJx08Bfja5yynwCQtBod9C6pspzzmtMOxGRHgQh7733Pm9/8BFFnrPTH1KMB2SDct97Ai4vXSGOE3JT0RsM6e9sMxoOphWAn+1CrHX0ewPWVtcZ9odUeYWuzUUf81/hKwhjDJ9/eYvgJymt7zV4//LbLF9Z5vt/+w+4/+ABf/yn/47K+VQlYzS//PVv+dnP/ppbX9554nGtc2xsbNIb5VyqBVqlTEajZx7YHHuishn1VGnDTpZT29lmQbGwvMzytWv82V/9jJ/+7FekUUC7EVPWmtoYqlrzcH2HrDREScLi8iJp6g1HBRIhlc+hf95I913keW7cCzzJI/HqvlnJiOOSMhcEQgjCMCYOFUpmHEqk11Btzf7zXE/vFY6BuNGi0VmgmAyoeyXSCULpVX6VLXFn1CHjdpfXvvs3iJptkBIpFYuX3qLZWWau02au02bU32b93pekjYjO3Hcoxn0m659TTfpcunaDpcvXd4+3s7nO7c9/gxMR0dwbVFXFb3/zG6RSfOe73+Xbv/97/OYnP6C3dRfCBNrzXH7nXf73/5f/E81mg//nP/0v+eSTT+g/uPP1I9KnqmIXGeq6gNLhNt1e3uRzeE6DpZD0d9qYXs3kxxq3j6w+dpdz3r6wnMCV3+3y9ncvs3Krx3Ar90UGM/wYO/PePuZ1CaDRCGjOBwzrmmqwj0g3jnJU++NmHL9GxWzNrjlo1Cnw7X4U3zT77P7ghsJfk+Mxb/rOJclr3wi53uny7vU32dmpiH+acawJbSY7i4A38EVfp/Yz6XzM/KUuTkoMCuvAaIm1UFUWYz376Zyg1e2yfGmZ8XhILj9HD0vYdiCm7TgrRGqn1xKzZxszCxJYvOVNiA/wnK/N6pnCTcBkMOpNGG/k3mKimt7cWX58yZHlIL5WmEoM69pQn5CEqmx9gEgXWjAsfUd5dK27srXD6k4PaR3qlJmZh6EoasrycCuBGWZ/C4KAJImoqposO9zywNqXagm4iyCKSBot6qqgGE/woYonWaztkUJSSuI4QUpBnueP1ywTvt7LwDCtCeUH3a/7o3MesNYx2vF9c7dPmwxMTkTGYrtBe77L8muXMabm3hc/Y33lIW6YQVbSe3CLund3mkl8se+QkpJLS/PMdecQUiGEonj7HcqiZnXlIYNBH11r4rSJkJK8v4ExmktvfkTausbG1jaffPYJJh9Rb23g7OHE9XExGAwxBpaWr5E0u8Tp8ACRDpDXOYUuaJkFYKqJkIIAC9UIqmpaLmAWkX48rWs8GvKLn/6YtNHmg29+m2a7gwljgu4CJptgJhlhktBZaKOrkv5midX6sQyeQEI83aY7oKxK/uTTBwgHCzhC56jOuAscxftKKUmSiFobhLhYWShCCNqtlE67wWCYUZYnLSg0W5jtV2KcHlWeUxeFP8oZkt1RFDHf7VLXNTv9/rlbuhrr+OFnK/zVrVX+8O3L/J0PrpGPRmxv75CVFdeiirkWfDmCUbX/c4b8qExXW+JcRTEZMAolCOheukIxGVGXBe6csmtOVmxUQBhGxHFMo9Gk2W7j6oJsn1zIOUdZlb7IiKmojaGuqsdSbU4LITzZF4Yhzgmq2qD1y7h0eYWXHdY6ttY2uff5HdaurrL11g7aGZaXLzEYT9DWkZcVg/GEncGQlbV17t57wGBwuCXSLhy4aZqpFIJAKZSU3u5UQKz85FoaeyDrLhSCQAqMc1RHDBizPf0sOm0cFNr7Se6qaRBexWMt2hiskwgJSgmSUGGtQ2tLpTVb29t0VleZTH3UpZJEYUQQBM9fkz4rrtc3PnV+ZjVwHphZGeSP/P8lGoqsg6LyfoB6moesvFgR6/Y8Ri/0enpmXfA1dfqwRqNrv1DGzfi8o/R3p4ezlroskWFC2u4SRglh7FU3UnmvZW0tWVnhjEFJ5zPFnFcolkVBNhmRNps0mi2SrEGcNjFO4nThr0MopAt9IcBIoZRA4IjiiPbSEp1uh6KskEqSNlLm5ufIN6JzrdV4ITHNunGFw+5M7UmOU5n2WbFPFe4qixlozPjZUr9VDDIEKzVlVWCoUQ3nVcF79bUOykqPcZ5h6EmvLLGQ1HsTn8WP2YLTB1vdIz8fdfmhf4XdmGgxxcSCUiqccl6tbdhTb08F4VpCjmVQ1jxYzxgNKurp+vaphbZmx1Hs1e+Q/ntMbql7NTIOCToR1kKdV96Go5h6xU8LtNZlTi7GlJMMNzA+26Fwe8ry/Ur8WRFTgSeX91cyn9qEvZTjsvPZWE7ag9kV+7MLLvK8+BwgpCSKU+9NWpU4o3cLx82o1ic9Ho/CcfQYZp0D43ZjV2fZ9MflIqz1PsjOeVJ9ZkvwVYCzZlpvTE9J9Cfp+GcQKCV9kWtnMeZgAESIaUKKZVeo89SmPv8s/JcP+2/HMdrm0f4cJClB2qTR7dJsxKRJSCDBGkdVFuTZBDfsYUcTqnwMbv/TezERKOn3urWmKkuKvKQqK4psQiNNaKQJcRQDAmcNzvlCnM4asAZhDVZXVGWOrUqvRH9GUlJrTVHk5HnGeDzCWsuVq1dJ05ii6GFMNV0PO8qyYDIeocKQIEl8bYXZjfYMKQcnnT04azFVTq0UZZmjwtCPQ0IglEJEAU6Crqtpdsnh16WCgDgO0cZQVjW1c1TTTd/Mde55Td1iOmMoKUniiEAZyrrGvkBbIeegqjWynHIwyqdlB0pgnTuha+3Zpsk755BKESXev7DKi+PNRUIgwwgh9wI0Vtc4rf0eT2v0YVlFp8TT1qzaWLSB3qTkwc4YV1e+5oCxRELgAsliKyKxklFekZf66a3oHLouKbMJQggk+P3xOeJkxUYRhGFIEicsL1/i9TfeYEVY+psru89qXdesrK8ghO9sDoc9Ig3uNBBC0G53WFxcBBHQH+RfSRvmV7j40HXNz//0x/zmh7+iUYZ0Fua4tnCZb//Od3AqpKhhVIz5+Mvb7GQ5f/Lv/4If/MmfHKjifSiEj7YHMqCZpCx0OmRpAyEEkZLcaHvF6cNRRabt7qalGykW4pBRbVjPq0N5h5KDMdHC+PT6GanunKPUmryq0bMHSwLKkQSSVpxQa8vmsCAvSv7iz3/Ib379MXdu3wUgjmO63S5ZkT0WjT93/EHX+89+NoKd2isFR+f4fRmP+xC/RCns2jhWt3OEEOjpGJ0qaIeQGxhUF3kpPUWMJ4wqvOrxwp/w2aLIRpRFNk3x8/NtbX34/mQJ8E9GXRRs3rlDo7vIO7/7Bu2FS4RhitOabDKmKjO2Nra4t7aNsJpOYNDFGKNrnLOsP7zH1toK73z0TV57601kFDKpDPl4zPbDe764XPMqBAGOGicK/y+WxaUFfvcPf5+02eTjjz9GSsFct8uHH7xPsbFCb+XBmV3nS4GputllGr0znsqJznkRNKtFNyUR680aMxjgLN4i5RQQEppXBPG8IJdDvrwzoSoM6WsOPYHsoc+I2R1jj61IF7RaTRYXWxSuz7Yr/Fi9jR8nVqdvPM+xWuAV4XPQ/fASl7/3NuN8zMrmKkY67GXhz2kTT+xHQAADYRhPLHdW+/zyzhhbOsYTr9qduYo8cUs2s8Lp4m1d+sAI8s2CalzRuNzi6u93Mdawc3+DYlTgxux5vA+h7u4wuPQQW1vMoIbSwZbbq5Hl9n1Pgleem+m1iOn/JV5tf3ii08uBmb0Q7DV4zUXnmJ4bgiBkYfEqQRgyGW5TFTllWVDX1a5lvuFsi2bPEv9eRPPXdY3WmiAIaTQaGGPIsuzcU+CfB+oqR9clu+bFwNEDpCf5lApI0xTnLEVRYO0eASOlf1UWqprj2f3MbKJmwdNX2MMzjDvNKzdYeOcjrr39LtevLhDFKdbVFGVOf3uHzbV1zMNbmJ01HxNXqfeutxc306+dRggEw/6A0WDE7c8/Z+XefRYvX+baazep8jnm55eYTMYMhwO0qadrZAG6QBQjKMfYaozTxZkoe8syoywLVtcf8unnv6HT6vAP/+P/mOGgxz//5xs8fJh5m1fn6G1vMRz0WFhc5trNNwgO3SsfEchyGuoRRmp6vU2yqqCYZmWKJETFoMucweYOztojRayNZpvFpQUGozFb6xuYfdk0ffbWGs8TYRhwdXkeYyyrmz2y4tmLnc4o45MuQ5xzDIYZo7EgjgRR6G3H0kSgDRTFcXdYhoOKu7NBnCZcefN1nHOsfnmXMnu6pEgoRbq0hIqT3d+VvR3Kfo+yqqh7voblWQSIZzoLy9OX2p+u9rm3PeJKM+RbSynSQUsEtGLB1dcvQZzwk1vr3FrtH+u7s8GAYjRCSolU0ou6zpEoPhGR7pwjG48Z9npUZYEQ4jE/I4ej1vvS9fDkt5ByNxr3LHDOe0SNxxnOCdK0QV3XlOXZFkV4hVd4Khzk44xiktPv9+kN+yy050nTBnGc+ESeWrO2toauKra2thkOj8fsCiGQUhIoRRQEBFMV+mytObWmBPZsUWcb7SfR148qhISSBFHkn826RkiJdQ5j955V5xzGeXV8GEgcgkBJn5k+GFJXFXnhJ3JrLFrX3ivxeK14dmgAGkRbeb/L2oJ2vlbLaYu1PQnPUHjjoqA2B3uElP7e+vTp53xxM0XlSdV+Z2c/99LBWfvYAuEsCfQZrDFU2YQgTHDGegsnIZASnJsq8wQEYYirDEU2Qeej3fRAXVfouqKuqmmWiwMVglI4LG7qbedMjdElVVXghCOME4LQq9St1YxGw911RxgEyH3Kiq8N9ivUnlc23v5imwJvd5GdkWpFgK4MujKYWYW+/c/zKS5RKoEKJEEsCZvTOS3ZU10/l8lptpOIJCIOEFrtkSGz89jvMR74actkjmpsyPrm0B3IE/mUad8QCggFzjjIwU0cum/QscYMDcYYzLDGjrQPQJb4oPNoqkRT+B9mxbMP8znfX8h0xnAyfa/keBlhU0X+7hA+Ewa6M+E2nh2HncNFOK8XiCAIaTRbBGFEEIZIqVBSESiJlgItIJSCWEq0ddTGnq2C/AyPdaLvnQpNvgrE+WNwzs/Bz/D5w26M45QJS09Nv/ka4pRtIRAIIf091jVaSsqqIhuPqSYjdDZGFxNsOQEZgYy5UEbVhyCbjAGBlCU4ybA/YDgY0Gi1vBJ76rXtxVx+MpFSIaQgDkPSOKSIQxpJjMZSnlFwVAio65LRsE8aR8zPdwiUIwoDpBDY6fcYozFGU1Xl9HxnkZL9J/KEfB5ncUZTlZmPWDlDEKrpcf162ur6iZOofzYFxif7HFhmzUYCic+CP1Y2yTPAOre7V1BKTXnD6TCwex/3Lud5jcE+62ifj/1+PK3P+A44O9DZn5wQICXCuRM+rmKvPWfHYcrzPBJ0kdO/2VO0t9j379OG81IbSm1oSJhUESHObzmEIFUSGUrSUJJGCrvrmODQ+vA5y1mDsQYrBMqqc+8vJyLSiyLn3/3xv+KHP/gTjAwxQjHu7Rz5rIZTm4AojgnDiLKqfOSa01M0dV3xgx/8kJ/+9Jdcu3aFf/gP/y63b9/j5z//9Vcmze4VXi44YKc/4Iu792jHHax1nmRSitFoxP/7n/5TXF2z8vDhsY4nwBPooSKNIlpxQhJEPkJsLOOpYZQ2Xo3eFtAQUGjDfeP9To/7JMwvzPPO++9Q1hV3795DBQFaOHK9p0gvSkOtLY1YkXRiwlBxudvEWEe/P2Bzu085re69vb3Jb379S8Z5fu4eW48hHyCEIHmvgVIt3GaG7Rfoh4761jmvBM4LM6v559SUMogJ0hRZVt68+Hm2WROvcMyByTE/M7MTeJVqf66wdcFk8x4mH7Cz9hDtJFevX6PdbEwXunD92iWuXVlg68F9/uL/80NGO5sU44M2VptbW9jffEJelAyGY7TRuOYc0mhs0ceVPbZX2khZYqXgtQ8/QhvNj/7iB6SNBjfffNP7sQLWGvIif3JNtFd4dgh2vbZ3/5/zzNk+zsJkzZFv77txUwLVGS+8Oi00hoqa5lzAa90OWVGxvZxhJ2Buc7Yy2cPg8MRyDoP+kGL1LqauMFWOG2n4pfUktWTPSzzA/26T01mizGxrgJAQFSjqXo3+wvhxModip+DhygOcc9STqeXNLLAwWzSM2fP/Nvv+3Y/Z7mhmIzOTHc1e8HQZUgDBFRAxJFIRCIkZG8zYomsoj+tf/wrPFddee52//4//U6qq4s/+/Z8y6G0TUBKHDuEEgVTMNVOWu01GWcWdjcFuxttXAVprJhO/QHmWDfpp7G8uBvyZW+vI88I7AKsIIaE05bMVETX4MWVW72hWi+EVTo3x2n3KYQ/ZW2ep1cFJyeZwSDYZsf3bn1CvP8TNBltbTyffi90j/7t//a+RQtJut4nCGFNpnHNsb20zyguGgyGDrW0qXSNFQBSFJI2UMIp4++03ePvd9xhlE3rDAVtra/z6xwOqp2WKPwXNVoNWqwmu4vYXv0bat5n/m98niRIajYAokthS+HokU4xHQ+7f/sKT30hEEOFM7RdHBybgx2F1RX/lDjKM6F65SmdhjsH6KoOtLW9h85R72BuNmBQFWpsjidJ2AA0JYwOjc9yDllXFxrbfv8tp8MPomkhClAQEUYAxAmMlWuunZ/Xvw1kMH3Xtdu1P4ZHEnSMgogjZbOLqGjsen/kzVRYVaw/WvWDziAKoM6f9mXbDGUOxvenr2EkFUmKOqDElhaAZxygpyaqK6pT2KAqfnTarzfekVtjOa360MqITCD5oChpAvrmDUIrrTcncu4sULiBzEcNRxsOVrSfyvrPgwIUi0q0xrE3TqNPuEnGrS1WVSDVVmz9yQVL4mxFISRgEGK2fWTxorWNjYwshtrl8eZnl5SW2t3tIKV5ZvLzCLp6voMFRVTWjyWQ6wLvd767KkrUvvqAYn6zalpT+ufEvhdoXka2ndi6wV8YiBjLryE44YERxxMLyAkVRsrq+jhAS4xy1MSAESimstdS1RSuBwyGlII1DjHUM8oqi3PMyK4qCXr9HVdcH/RJ5DvfC1qAksisJGhFWlMhIYEdA4Pya5GUaI/YPls8rnVxIkAFCGqSU2H2ZCVJJ73s5+91Zn8/MLuAkJNdxorLCR+DdcVY/r3AonHU4XWLqkqosqIoCZ2qEsCilUEoSRwmNNMaO++gioxiPHksrLcuK4XBIpQ2V1t6bUk1Nls0AZ3yhmPGgCUDa7jAY9NjaXKfRaLC4vIy1FiWE94o0L5GX0suMmfJ4pkyfqamf8XEyhX+dGSSIACyW2hlkKEjDAK0Mwgr//D+vJIZp3Ya6qtFFBkaDMbjK7NmOzeOJ6Fm7VjxWgPREmBLi0kqUkehc+O+pgRJsbsgHR6QAz5TxM3L9OJip0vfZ/hxr/J6S8CIB2fCKrxCBqAUUcFQtqVd48Wg0Wrz+1rtMJt6DtKpKgsAgpSMMJFIokjigkUbUxj7Tfu8i4jDl3llBCPYski84Zu0ghUBKnzUmzBnd7RMUl36FJ0MXGbrIGXfn6W1uYBBs7GyTT8aU/S3cZP+EM41inxXOae298uAhUkrm5+ZIkoQ4TAiDmLwomJQVk/GYqiwx1iLCEKkUcZISJwmdToeF+S5hHCKkoByPz8SGVAWKKA5xzjAZDymKMUoaggDCUBGGiqrSB5ZNuq6ZjEfTfZdCCYneXdM+3l4zhf0sO6YuMmRdIrhEFCmkM7jqeCRzVWuq+uiJXgCBgEiCOud9s7WOsppa2EkxJYAdUoBSkjDwtRgwnmvc7VPPCfYwMcFT4P3qo+l/xJnvPa0x5PmUbzqC/Jw5FbipV7hzDjMLQqjA97snLLYCpQiVoqhPn9I/01vMfn5SK5TGUeYaEwqqNCC2FvISIQWNbpOkGTJxEcol1LU+lhL/efSTkxUb3YcqH2HqknZ3jms3fodsNGT9/p0DG9vaghEOW1aU2qCNOTPLfeccX3xxh35/QDaZ0GxE1LUhL6pXXMnXHFGg6DZjrIPBpHguahgpBWEQoJQfMqqiYOvePbbu36cuTsoUCNJA0YkjYiGhNj73avevfmDq4vet0nnhhjlFv4+jmIXuAnlc0m7No41l2M+Qosfi8hW+/b2I9dVVVu7fo6wtm/2SZhrz5utdkiTikhFo67h9d531jR6V1ozyHDMtOqWkpNVMEUIwyXLqI1JxzgSXIpAS17XYuEI4hWw0iESNVBWm76i+dC+Pj/n+yfs5jWlZWaGtJUkjbr5+ibLUbKz3kEry3jdu0uo2+OL+Aza3ez68fJYkWM5egbozRHPxEml3nnyww2Rr42wP/jVB2p7j+vvfJWl1mVtcJIgE9+/e4faXX/Kd73zEd7/7EVEU0kgTFlohG//kf8rW6gq/+ct/S39zdfc4jUbK4uIiKggIw5hsPOLOZ7+lzCcw9bHsr62SDQfen0IG1GWOHY/R1tHbGpA3NN1ukzCQe3K+r9Kcf9HS2h0+Q6QEtSxRSxIrLXpoL5S9lQyh9WZE2JaMg4JRr8Qph1OWemLRmw5X8HzG/5kUKIIgDYlaTcw4pxoNYKy9hwvT9wTs1dt4Agn91BLCATDnv7P+1CvR7Zbxxz3OPZqJ4I7T72YKdsfJrbVCoOPP1xgwGbiJoSgtrnLenvc4y4SvQ6HpC+wb7ZyjLmvKvMKICikt1xY7LM81mUwKHq70yCp9QIH5Cntwj/wbxgHNdooxlskww55mQf9cMD0vIUB5vy9n7YGxwzn/bJ8IMyssx5508aI2wcuAaZaTFA0kTcbDgt/88M+wOPKyxOiKanIygdfxEQCSdHGZqDtHOdim2Fo7s6MrJxAWinFOnVf0GeOERAlFIAPquvJKVedwViMDyes3b7J06TKLi0sAbKyu8Iuf/YzxeERtzZ6HySmRTXJ0rVlYmOfqlWWkcPzwL/8Cawxz3Q5vv/Umt+88YGfHBy4cfgzVWhMnCUvLyzjr2N5Yoyoff3jSJKLTaVLXmv7ABzG78/MEUUg5HJD1+5RneD8dXoleWl/r4HkgiUNuXFsgUJKNtR3yvERrjcOiNVRaIJUkbaW7aw7noK6nljbGPNM9fJmggoC01QIHeV1iDlk0RWFIt9HwboyAsZZBlnmhpJ0qC8+JMJ0tD+2+l3t0bxMlEERQl/41RWYcvx0aUgU3U0ErADe0uCyjRJEjKSdPti56njg1kW6qElOVRMvLXLr2Gv3tTTZX7h2YPC2+TxutEVqf+by4ubnF5uYWSRLSacUIAUX5fCNVr3DxoJSklcZY6xjnJefJ3c4ghK84LfDPtq4qRtvbjHd2TnEsiJQiDQNfhMS46eTgR6GZlXRj+v4KqN3p9lmBCmg2mkgZkMRNyqomzyuUymh15mi2u5RlycqD+2jjGGcaFUQ0Og067QZJnCIRbO+MWN/oYazF7IuOSilIkwgpJUVZnS+R3glAClzqcKFBIpGxRFiHdDX1CtR33TNZBjx3POehrNKaSmviRsTiUocsK9neGqBCxbWbyyxenmOj6LFZ9PZytc7syzl7ywUhSNod2peuYo1msr15YSbflwlR0uDS6++TtuYIogDnYGV1i+3egG9+9DZXLi8QRyGNNCUKJO9953fpLl/i9q9/dIBIj6KQdrtFkiS0210GOwH3dQHlnko2Gw48kS5D/3IGTImRAZNRhjGCVjMh2E+kf5Vw0Yh02H02pRQEbYnO8DKlC3SOIhCklxXRgmJnq2Ay1ntE5BBfbPQ0limnxZQYUmFAFCfUeUVV1L7C92w6n5HBM4/yJ+CpTS3x9lgBmFULkxPufk/yLO0n0k8KBbT8v3aqfq8GDk7quT9rO7hwJPOZYdZHnqW9zxqCXUWkqQ260mhqhDCkccTlhS4PCs1Ob0LtzqNix1cH+9smCBRpO0FXhmxUcLE7tZt69ArPjlg3DdZNs3FPc9NnY/VZV6j9uiIAQoEQEVK0fW25Lz6d2obMcF4N7VPDwvYcjUvXcKam2F4/s7W3dD51o5oWoyyso7KOSCoSGWL31zJwFilgcXGJGzdeI236QovDfp8vP/8Ma40PCD0jkV6VNVVZMz83R7fTBuDWrVsIoJGmxFHI6trmgc/Mai5IKWl32jjn6G9vHnJ0X4iz025QlDXDUQZC0mg3ieOYjdU1RoPhoZ97FpR2r97280AYKi4tdQgDxaA3Is9LrLE4LHUNtYZQhiRJcsA33bipna2151Ij6iJCSkkSJzjnKMThaZZBENDc11baGMZF4Yl0d7wF32lbc7/GaVcE8ujeJog8mW7tASK9srBSWGIJi8HUVXLKHWkBlQBdXpyt/ImIdCUVV69cpdloMRhlTLKcN954lz/6G3/E/bu3WV25QzYeobMJztjdMenRyPtZQ2vDJKt2VbCv8PWG1ob+OPeE9vPwZnQ+1ezHP/oRDz75kt/+8Bfcv3eP4fB4E1ukBAtNX1BUhglSBsw1FJGsqCZbbK1+SRIF/NHf/Htkgz6rv/01VZERYvClPw8WGz3sCQjZE3zMMshLYGdnh5/95GckScpbN18jihPmFxeJopi1jQ36gz5VdTDyV5Q1d+5uEschofLVwnZ6h7MAadrgm9/4NnES8/kXn9Pr93w6joAsKxmPz9AIdVCDEohYIEKm5lwC1Q2JZYwsKwo5/tpMtM8CYyxlXhOqgHfee52kkfD6W28xvzwHSZN33/+AL395m89/9sWLPtVDIaSgsdQhaiRcf/Myl65e4V7Vp3f37OehNBa0G4qqdgwm5gVN7grf6S24msOv0ns3NLtzLF65CkJiceiqYmflDlWRsTdCGPZv5ItsxINPfkra6nL17W8QN9u0mykCX0ipKktvt5I40jTh7bdeo5UI/qIxqw7vR6hRf8TKl7cJAkUchxRZRl0dsZlzZkoe+THc1BWT7XXKUYiZbBMEkvGgfxaNd7FwgYcn0/f3wo7dnrf2bA3/opXp++QvcsrxBKEiCBU6tFTnLUVX7KmzJV51vSRIllLmFxcpVYgbTtCipFZjjl/J5JgweFsYyfkU196P2alPgI3p9z3tcmbWXbPABuzttE5zvjNf9vPudwKIfBZ0W4UECCaFpjys0K/E+zuDDzKflAtdBtEFGUhUqPzqTkhc7tD3Kihf3ODwO9/9Hn/nP/j7VNrwq1/8jF5vh8mkjxA1rUQQBwqrc7Z725TlhDRySAPFUx67OFI00wCtLZOsxrkXP5Q8D0Sxt3tQUUiQRIRRQNxIEEFN0AygAlPac1ZYBggC3IHiBsfFzAZEeJuA2bLhAs9fXytMlYzWFmB7OKMPWRuex80SoEJEELG0vMiVN69TL7UoXrvMaHub+599hjml3/IMbhqgmxXJNNNFt3GW0uopuWppNpt846Nv0J3r8tqNq3RaKWsb62xtb/HwwX1vLQhn8oyFoSQKJUWR8eUXd2k0Ui5fWsQ5x8ONNfK8YDiaHBqvrqqK7Y0NlFLMz88zPzdHr9cjz/f2x2VVs9PzdomB9FHV8WBApgLK4nnS3eeHstI8WNkhikIWlxZYvrTE1s42g+EIpSASIDCU2cHrnVnL7lYovQBj0Mwb3e2S1mcLozXZsA/OYY1GCEFzrkuUpuTDEfl4TFXX9CaTXW2OcQ59zFQh5xxFVSGlPPZnDj3Ok/5Yl15NYTQIyeLVK7z9rY+Ioog0inBVxcbP/4ovdzZZVNBRoBykzn+kJb3zSeFe7C0/GZEeKG7eeJ3Ly1e4+3CDtY0eb7/9Pn/7b/8tPl6c4y9/+kOMElAVU1L7/El0AK0tWr8KYb+CR20svfFZSmWfjgf37rEx2kSPK8qtifcUPubgGQeC63MRSRQSpHMINfPVqijHW+R5zqVrb/A73/vHbK4+ZP32fcqiRu6TKMl9r0eFS9N9IAHejjXEi99KYGd7h53tHa5fv87/5n/9fZaWlgmSJk4Idnp9xpNst5DoDGVZ8+Xt9WNdW6PR5Nvf/i6ddptaF0SR8F6KEra2BmdLpPdrCARyTiFTAQG4AIKoQaPbhnEGMuNiK30uBqyxVEVFq93i7fdeo93t8Ma7b9NdmOfmW+/gnOPfFP+WWz//8kIGL4WStK7M01zq8NrbV3n9+lXy7VU+PwevukYiubwQMM4t49w8lwyYxyACEA1A41MujiLSI9pzV3j9w+8hlUI7Rz4ZMelv7SPSA3yobe9CysmQex//iLQ9x/yV6yStDp1Wg0YSkYR+IR9Mba0aacK777zOXDOg0Uj3fbdi1Bsy6g39sY8k/Kdw9oB6yuqaybZXtz+LjfSFx8V7nHZhew7b29fB9xsgXoTsAAfCTU9LQRQqkiSijDQ1+vxOb2blMpuEFTAnYFmSLDVYWFwiCyPKbEJlM3SQnw+R3j/bQx6JmdXIhOMXhg7waXQa/wA/6+U/L1uhKTmulKATRqRCoW1+OJGu8FkBs2DGSeYCCeIyiJsQpJKwGYJVOB1i+gazrnGHpPw/L3zv+3/I//U/+7/xw7/8If/5f/6fsbmxxmTcQ1LTTQNaicLqjK2dDF0ZGhGgQTyFR0hixeJcSlFoqkKjzZk/GRcSSRKSpAFJt0XSbe39IRQErRBX+jnPnSOR7isTxEA5JdNPAk+kOyH8YIu4OFkTr7BreeXqAqOf535YQBAiooSly0u8887N3b88+OwzVm/ffmYi3ddpenSV6olCsy/lOGmmfP/3f48rV674zznHr361ys9+8QvyLNur9XQGe4I4kjTSgCKf8PlWn4WFeRYX5jDGcOfuA/qDo1PO6qpia32dOI559913SdOUqqoOEullTVnWKAmNEByOUb/PV6iOM1WlufdwmySJ+MPf+yaXluf4+Dc1w5En0pXy4shyUhwYZmQUeuXEfpu5FzwOubrGPYO3+NNgdU022HM8kFLSXligvbjA1oOH5OMxpdaUJ6zPN4MD8jM6/yNvxczSZUoKLV2/zt/8n/wntNotFlptssGQ/9edu9y6v4mKoRVBYKAxXVO0JZQCKv1iWZ0TEelCCNIkodVq0WpNaGYlQRCgqxpd11jj/XaCMEDhKCvzXP3x/J5OTQ3offrsUZTCK7xcEKFEtSKfTjqqTmcIfo4wRlPXJUbrqTfb8T9rnaOqNI0k5jvf/wbzSwusrW4z7I8ZFAHDIkBGCVHSIIwSlJQoxO58UeH7+ZNoqf3Pwey9AEkcMddusTzfJVABUik63S4qjIiT5BlJUoEKAhrNJq12m063yzgb0x/0GQ/H00IZZwgHzjrqia/SHCQBKlboWjMpx+jAkL7fwAw15f0SV36FViCHoYkP4c/ufMWxSQ+pJFEcI6Sk158wKTT1zz4haaSknZgoDujtbJ9a3b+ff3smEVOIjxLNMLWbcdZR9CdgHfpatfud54GydgzGhqJyz92eL0pSojilrmFfneMj4CWcUkGcxFjnGI1GFGVJ0OySOhAE4BR1MaAuHg9OC6ARp7QbTZx1GOfoD0bcvn2fy5eXmZufA2fJxiMmk4yg2SZZWKae5L7IjQhABl4BdAEDMF8ZxPjnYuYfva8I5GyvMRv9fDEsgdZQ1ye4JzPSePb8zXxtXyCkVUijYCyxA9ChpQw1emx8fOm8FJMzFXoAtIFYMHfzMs135lFRTO/efcpeTvn5ELNT4qrpGiGfntN5K8gPw/4KUILzL8Y9s2w4T9Xq9D6IVgCBwBUGtPWLntPutBxQgVWQG40WlvowEh18+5X7fj7p92Tg+mCGDiENOIszDltYaFh/fRP89URAKLyv33PQEQ36fb68dYv1lVUfN1LKdx0paLaadNsRk1FOkRVUtaWowSGYT3zAdlyaQ5ftdW0ZZRV1ZXcVpqdBJEBNm0M/h3FIKUXa9AR4PpmcqvC1EALrLNpqTGWos5q61uhcY+vzU6NLGSGETxFxyKk9ywkPsi9331c/e0Zb1f1z1Ss8O2YZO899TnZgK5wW5PmEwXBIMRqR9/tsr66eSaFe7fa636MQTPe1c3MsLCyg65rxeIyua29tMRpRFgXW1ATKF6+cKZqfpftq7ShLQ629iLQsK9Y3trDWonV9LOcYay2DwYA8z3HWkqYpdV2j9wUenPMq3MPON4pD4jiirjVlUb50S+wkiXnttWu0Wg0Wlhb9frPRIE3jqdpcUJXGi/v2XZuv0SB80PGCXbPA2/4CGGue+fRUEBAkye7/nXPUeY5zjnxKmlf5GQoUzxJHXHzaatPszrN05Tpzly7TSBIC5wjjmDc++IAwiREP77G2s0mMF4Rapv79z3jLZRgjgwjhZ0Ks0dTVyQKPJyLSpZB0uh2WlxcZlzWlc0RxQD4ZU2QZuqpw1pAmMYQB2uaY6vkZEkskiYymdhcOhCWzL2aP8gpnC5mGxG90cdpSfNHDZhfL6FqbGl3WT2azj4AxjvG4ZGF+jv/V/+4f8c3vfMCf/vc/5rcf3+bWw5zhwxzV7JK0uyTNPmGgCKRAWDHbe5E94Wsde3u7R4O1c60m33znDeYXlogiX9388tVrNFodfvHrX5/sQg5AIKUiDCPmFxdYXFzg8tVrWBybW30ePNg8+0neARrKrYpKCVqXfdpskZdMdvpEScLcP1jEbBl2/tUGevMrvGKXwJKA+X13fAdP3BzjsoMgpNlqY6zj7v0tyqpm+IOfouuKa+8sMX+5xert7VPPYAI/GcIel3QqpPgCe/ujSpvgtGX4YItJEJC/8c5pj34sTDJLltuzEracCK3OAnNLVxgNxmytbz/l+336dhg4Ws0WWZEz6O9QViXp0lWSxSsoZxHOMVhz1MXj1lRKKuY7c1yaX6K2UFrHw5UN1ta3+cY33uOtd97E6prN9Q22traJF64w90bO4O4t8nIMKoQw9qdSPZX5f4XToo1/Lmr84F+ya4Phzbj2XDHSVNJqSbLMMhgc837MSOMA73dtOZ69xzlCIAhMSFgHsFViVsAITYlP9T7XcxP4oF4MXAU5J3jt+x/w+ve+ye2f/YJPfvgj3JrB/dLhKufZPthLrXjej8HsfPf7jJecLyGrgfOqbQd70dlIIG8kiKbCbGa4ofWTTPaUzx8F6z9rhWOH0gehjrpfhoO2NSeBA9cDatATgx6ZvWMkwGvAEnAXn1LYEtAW/vuq8+9AD+/f53/8kz9hZWUVJSRJFDOWEuEkC0sLXFnucO/LFXqbYzIDwwraieT1uRhtHV/uFOT14w9hVmiKUj/T/CmApoJEwsj4InnnjTCOuXT1OgCr9++Sn3JPoo3B1CXFsGD4cOg9gXf9nc/qbPdDEgQtpIwxwmCEhfqURLrxytjqFEGEx7A/CPUKz46D7nzPEQ7qDExJf2eLB6urbN26xcrHH2O1fmY1OhzdTWax/bl2m299430ajSZFlrFZVYzGY4qyZHNjg8lohJIQRRJrHcYYrD1Fgdx9KEpDWe1ZO04mE377yRcI4ZBYAuVtnp9Eptd1zcOHD726uNVirtNhNB4z3tdm1kFxBKnVajVYWJxjPBqztVlhrcO+RNvcbrfD3/t7f4uFhTnyoqSqa7pzc8wN+iAVSMVknDMa9w4E7dwLSQM+HqQQJCoCHJlze3ZCp0SYprSWlhAzcr6uGa6vo8uSwcYmw82tC5klDhw5ny0sX+bmex/x1je+xWtvv48SkK09JElT/ugf/gMmkwn/7r/+5/xyZZNrAbwe+CX0yDy7u1+YdgibXZRwBMJSFRm6V/ngzDFxIiLdAcYYtNa+YmyjgXMwHI6oqpp2p4NwlqpXYY1FSkkQBBhjntuNtdMmFVOt5AXtTq9wQjhrsbnGGXuuqY7HgZSSZrdDEIa4aYpJpSpKWeGExol6t+PNCEPJHpfxKKIo4MqVDlevLtPpLtFoLpE0WsRJjApqHALrQFuHthbtDDUWO/2Skwi8Hn2fVIooSVBhSFVryqrGOgdCIIRAyD3l+0nbKAxDwjBEBQFCKvKi2F3M2HO4h3JalMtW4ITD9Cy61ujaZwlYbdBZjRmaC5fRcBocaQU3I7ni6c5yVqH78Hokh0Jrw2SSe35Ma7QxlEVFXZaMBxNE4CiyZ9v1nMn6brccOLuBlBmcdRhtGO4M2Hg4LcZzDvPQiyDQZzC6pi4LnNVEkcIaRV3xxAGhLHL6W6vUxqKEIAwCjADnLAEKiUMF6tDPGmsYDvpEzW10XREohVIBKvB+vkVRgdMEYUgUx8jQP/uzYjc4C1bjvVXPrtHCJCVuNNFVSTEZf73U7jNbkf0DwlRFu2t9u6+5978FoNFosLDQQKqccT7CmunteRIUnjSe+eKeIKVfCB9LkQrCIEQqSZFryvwZNkPTsa2aklmmsLsDzLn0hJmKe58qk2nhTApwOdjaYYzFlAY7rnGZe3wR8CK76f4F8v7Cpy+zPcPufbFTVyi3513/rHiCCvLR950W0tfo87dgP1lS4zM+QggbESpQxIsxYSciNxmT3nlGKHyQajQccuf2F2zvbFPkE3RdEGCREqy21JWmqi2VBTMVzwdS0Wok1MahZMWTZn0lBGGocM5R6uPXGgnEXj1W7c7ZUnwfBAIpT7CoegTGWOrKgKoRAnShsXpaLC+cvuk0LMHhZU4OwDmDdRqHz3rgGcmdV7h4CJREKuHnoPPc7wiJVAEzr2bAF+8MArQ1lEVBWZbU5dlVBwzUwefOW7Q4QhUQhwHNRoNWs02apgTTOl55njMaj6nrytdQkX6PG4aSNE2x1jEe55hn8ErZf3nOsau+D9TxrLuFEMRxjFJ+zaxnGe7HgMCTtnK6dz8upPR7AOscdf1iBYpaa3q9Pg7+/+z955OlSZbeif3c/VVXh45InVVZuqqre1qMHg7UAFgO1hZmBI1iuST3G83WSOP/wD+Aa9xvC3INRmIpzAACMGKxg8HMYIGZwbSWVd1VlVqEFle/0gU/+L0RkZkRmRGpKrM7n7boqMi4ce8r/HU//pznPIcwCv01MJaiNN6zT1qq6uRxolKSWi32au3s+fAOx2FmZoaVlWVCpYhVRFEWXL99i/Qp1eLWWkxV7d9jo/U+vzptXjuFkBIZqEnVxaQK8iXMrFRlSToeeTuaUUoUKlTkqxCiLKXSmtbsHLNnzxBkI4bpkMyeMB47Dir0mxAVIKQkikLqSUQZhpgy8972J8TpiHRnGQ5H7O31CKOIpZUVKmP47PMvSIuU9z78kP7eHj/5y79kPM6p1erUagFpmlIUzz/VbLBktpzEzP7yvnxD5jWeBDbV5Dd7fjNzion0eSCu1/jg179Je24ekwpsAavbq6ztrKHLIQXbTMdfiBcSJcBdoHvE+y3Md/jDP/wNLl26xMLChyAvE9U+o9ZaQ4UlVnvrl1GaMcgz+ianbw/q6J9mjMswJGl3kLUau/0hmYYL4zFBkmCxqEAh1Ok3C0EQ0Gq1abXaxEkNIRU3bt3hk08/YTx4Ppu+OPL7gbIHtoDsVk4xLnBngEtQbpfs/WAbxmAGL1dFw2kxbaZnH9w4CrxKOwI6EcxHMMxhfLpnZjAY8cUXtwjjiObs7P6/O+fYXR3Q3RhijitvPwEsXiQIT8nZVHh1nsGTVNPy4Ol7W8tnP/6UG7+4SpkXL2+m/gkx7O+SjvrUkgYLc7OURc7e3uCRypqd9Tt8r7tDozPHhfd+DRlEDEd7aF2ShCGhkozD8Mi/LfKcH/3w+yTXbnH5rXdYWDpDo9OkMdOiVm+wtrFLLYlYWD7jye04uf8NdAHmMUz/E2D+3AXOvfcRvc01bv34Bxj9K1SHpvDq85ADUjvDK1XdA1/cz+MKAWfPXeDDj97i5t07dMtPqHJDNeLRC0uCV8cWwA6nInxUAHNnJbWGYGFuhkajzs2rPW5ffULnewkyBistuzfHCCcw5XOO/KaNM6d2JRPV8n6DyW0Y3O6zcWaD4cYQt4NXn78s08800TIlngP8+QQcVDG8ipAADjPKoBTe1mVKLL7sEBA1IZr3np/53qHflcA9kLFi/r1F6nNNLiytsDQzyxc/uc5PVz95bmubmvzv5vWrrK3fRWtNnudILE2liRSMuwNsXrDTz9krIXTeDn8miTl37gx5Zbi6k0HxcNylBEQKkkixMNPEWMfq7ojiBHG+wjcgC4RXoY/sq7PnS9OKPKug74O5/b5KEbAwYd52J53UToMW3tZvyDHNRCxVNUQIMZmO3K9W4vlXBM1mTL0WMhoXDIbPb0JXQUStMYO1lnzUwzoLzSYujsiNZdDtk2fP1qO93Yjv+9mWFVZrOp0mC/MLzC0ssrJyniiKkVJSVhU72zusbayTpiOSJAQEQkCr1eTChfOUZcXnn19nPH7S0qXjcVLBdBiGXLhwgSRJWF1dZa/XOxGRPlXi+wSyOZXqOYlj5ubaVJVmZ7f/TKx3nhS93oA/+qP/gUazwe/87q+zvLxAf5CzvnEwkXmV/cnmq1arzptvnqEsNdevr5JlL66X4re++XX+8//tf0YURQgEa+vr/J//y/+KGzdvPdX7VmnKoCiYeFh7kvyYe6biiGSmjbGGPMs9OZyVLx2Zvru9wWDQRzrN+2+9TWdhnpV33wQl6fb7WG146+OvsHj+PDd+8mM++/Z3/DjgCTUSQiIaM4iwhgj8XNCZnef8hYsU6ZC9duMgKXgCnJJId+R5zng8JmwFBEmIsZbReIxxmlqtRlFLvCrUuom3UR2HwExu9nE3/FnBPbFr72u81LDuS220dBhCSpJGg2anjYhCqCR7wy5Ysb9RlnjPxpqA+VhRF4Lt0hxp3hhGAQuLsywszhJFDSBBiHCidnFYYzHaeO9ErTHO8iROWz5j7effIAgIwoBaLfYZSwR5UYAMSbOcOMsoqxJj9KlKXKaQUhFFMWEY4ZzAWEuW+bnjuWW9UxBTQiMD13eYgfPE8jwwcJiefUovkS8DgjAMEVISKIUUEq0LjC4RDw6DaXn7YYXq1NbgFEPGWktRlFgENWNBiP2ZVVfmsX5ZJ+n38kzm6akP5CN8hvM0I09fUs+4p8R0TY3CCIFFPDQgHoauSnRVIlSALgsC5zDVZDyFCiW9wlwFoa8EsgcPi7WW0bBHaaBIz+8T1kEQgpCUpSYMAoRQSOm/lFIINfFGn8I9K9mrACGJkjrtuXnKdIyMIywO9wxKiF8ZHG6yBAdWK4+AChRBoGi128zNL7LV7frY/KjI9EEF9uHXndDXVghQgSBKBK12TL0V0GhH1GoBYfSUTOfk2HT+nJm06Y41nHwJDuxQpqr0AnCOsp+T7Q0oB7lfk54kfyRAxL6Jlivds123HkyyTNeOBz5DCqjXApTyHsgOKApLeYRNx5eGB5uMlRNycJrgeVU2BW4SwxzZx1QQioDZ2Q4zZ2aZadRpRAH1SFGLJZW2VM/RHLwoctLioMlKICEMIZSSqtJYJ6i0j0xVIKkFijAK0E7sexo/ClIKokChrUWeQlF5+La/kFstBGLSHE1rDQ+oAI+DnHjKe8Wr8xVzwJFHfNw8fOJjfNwL7JfAnb8kXQB/CSHgkA+z3b/C4tgF/Rl+thAIFSCF8Yv8dC1BoMuSMk0xZflMkzVqX9zlm44yUZcr4atEpJD7XcSm1i1lWVIUxRFEsX+efRV1QBQGaG18ZfaXADE5B2vtqUltMS2PPc2hi4Pp5vmOlMfDGEO/7x0uhoMhjXqNPC9OpUI/DM91SKyVvOizU4GiVks8kS4EcRzt27E8DZxzJ7dHEgKk9FbXUoATX/5NPgKmKjHGUozHlOOMop6RpTlCSYy2OARJvY51DpXU9nPLR9dNnxSeDHPOYo2vCgvCEBOGBFGANSe/UKci0rXW3Lhxk831LS69/R4rF2oUVclorBGBIKgdlHCHYcTHX/8m5y9d5vr1G9y7d49Bd5fdzfXX2e/X+KWAlJLzS0t0oja7azfJt9dwxgfWLQkfRTBfV/zmR/M0GiHDn3ZZXz3Idk+rqWtxwsr5C6ycP0+U+Ez7dBEti4IsHTIeDRn0BoyH4xOXeh3G9LPm6tCMYeXCGc5duoxUnkgvq4pedxsVDmh+/gWtTofbt++wtbXJaHR8t/HjEMcxCwtLtDszDNOUrChIRylFWjw3a57if8An8aZevRV+I90HbuEJjiGv1sYaCKOIC29codlscWZ+hVatyS8+/ylfXP3Zwy+WeClYAgwLKEpfZz29Hqc8b20tg8xnv09a8ig4cH2Yuks8N7xqRMlzQp6nbG9v4Jw98X3KRn1u/Ow7CCHRpkQIQXD5LeK5BeqtDnNnL5KPhwz3tvfXbGcqyt499Gib9VadcZ5ztrpEUKsRTxrgeGXLgNFoRBQndDodss4Cvnfp5H3KDDvu8tQ3LmhCkNCaO8e5i28hVcCdzbvkwwHlxibuGXWdf6lhgB731w4/5qGTSrJyYYn2bIv3vvoVPv7GrzNINXr0HXRh7ifyBJPGxRz4aJd4JfopmsPFNcniuYhWp8aHX32LVqfB3t42o/HQW/JN/RlOOyQs2Pzgv58rOnhP+Kk//AivQj/8uSU44+j+5C6jrW2qjQzuuSeyaRANSfjVBBEJqp/l2J3nkAEu8GvDdN04XC4EtJsRf/e3z7A4l5DbktIafvCzHp989nAPhS8FCj82Hf48puv+4fH0KiTOHZRboHu+ou4wgkgyc6HBzFyLv/+3fo+33rzMp9/7K2794ke0TcrX3q2zN6i4dic/sfrxpDCYiZHg/Q+mEoJGHBApwc4oJ9M5ShhmElhcaHHu7AJpWvHdz+6Ql5rRMc3lrYPCQGhOvzU0wGDiWqck1BWU9vlaxgdRTNLsgHNsra9hraEsHq24DaOIztw8AP29ParyEQphDfQnE8Vpmj9PMcQ/vy9THllICKOJ/86zs/j4pcKDTOYp1sIoUCy1Gggh2BqOySvNaFyQ5dVTVY6eHtb7wo1GIFPS8YgiCHyj+WcIbyXosNpPGlIqCCRpXlBubJCXJY1WmzCKcFJSaU2WF4CkqgxpWqCUIggC+v0Rn39+lTBULC/NIMUs99a2GQ6fvTL9caiqinv37iGlJDuFBci+u6WzCKNh4vl+EqqgyEu2d3pY5zD25Vgoi6Lk+9/7CVEc0e89eZyR5wUbGxsY4xu+vkj84tOf8w//6/96IloMGY3G7O7uvNBjMKUm66Y4CU4++Zw7TUM8t622td7LbjL+Brt7fPHPf4F1ltlzZ4jrNdwDZYVPFdY5i0t7uGxAJQQaGCaKbrtGWaSMx91Tib5Pp0i3juFwQJ4VLOc5AuHJvqpAOYWqKYSQqCDw1i/LK1y8fJlRmjPOS6zW9He3seZ+ldtDeFCN8BSLrpDSl7BZ+0tX1v8riy9b2LCf9XW0anUWGh0SJTH5wcIbC1gKYSmRnJ+PabQj6snD+TPBxMerXiepNx5SoRut0dorSKuyQlf6icaxwD9WtUjQrkuW5ppcPL9MVjj2BhW6MqR5iSgNvX4fbS2j0ZAsTamegIxSSlGr1YiimLKsqIT2qqWn8J97HOz6Mb/I8UTTKwgx6TPRaDZpz8ywsLDMbHOGu6s3j/+jKclTmvvL95/g0jvnqLSZKNKZKE4eP/6movjD9snP5XF9VUiS5wxjDMacTnVvdMWwu73/s1SKqsix1iJVQFyre+U64hCJ4rBlitMF42EPoiadhfmJasQ3dTTWkRUVRaG9yieKkGGMCOPDH34y08jHQSqQMSqMSeIaUb1B2G5jnKWS278a+ZWpTccpIIC4FtNo1+nMzjC3sES93sZpcbQ/+rSR5rTyw+Ln1RM82FIKVBAQJ5J6S9FoB8zMdmh1WvQHXa/+wiLUpFDhSZ7nF8EVTBt0JhzMsRUIxf3uCA7QUO6OKauxT+SOj3zHx39kIAgWI0RNoK9XIM2zn0wPK6APCbcEEChBI1G8ca7NueUavSIlqyq+uPF8PblPhalK4HC/jBdXwX308cAT3SObH0oKHXo/EQiiRkC9E3PxzDLvXLzAvR9/Fz3sEjrHTEtRVnYiZHr2s96DJLoUoKQgVJJACcqqZFxY2gnUYkGjHjIz26C0I7b7Y4ryeFZ3Gk5bfHhx2q48U665jrd4qZ7PJdiHkAoVxhhdkmXjE3mpCiGJ4hhO4qs+raiAJ5vXpn0xXhp4VSRSTSbJ53yDXnWIQ99PuBZKIahFIXKiygbQ2qJfFIm+X5Ux+ZooZk1VYk64ZzgVpvPEdPIAEAKtvfI8imOGw4En0pXCHFJ3e4W6RYhpo9GKLBuTJBHLS7PEUUhwTI+gBw7Bn/ozPC3nHOPxkwULbvL3ztr7VOnT+eY4AZ6xFlN8mQvmw7DWsrOz9/gXPgbGGNI0O5UdzLNCt9vls198hgoUYRRRVpo8f7Geec5YTKnvFxs8AQ5XLDzLqyilREg5qXz2vLCuKqqqYv3GLbS1RK02MozQlefDDo/jEx+LmDwH7tBzoP2Yn75blY/J0hG6ytBVcSp7pFM2G3WUusA4Q62RsLy8xN6gy3g3Becf1lqjyYff+m0iFfAHf/AHfPTBe3z8la+yvr3Htauf8+Pvf4/d7U0+/+QnVOXDD2+t1SFutpjeNl3kjHs7T2YvoRQXrrxLZ26Bjbu32Fq9c+r3eI2XCzKU1JZryECSbWfo8YuPGK0xDPd6xEHE7JU6b148y+xMC/BzVQ1Yrgk+eDOgHUlW7/QojKDbvX+HNOUAKwvjwpAWFcalWBuytbXO9eu32NkZUFUFUgiajTZZrYkUpy8Psvhg69zFS7x1YZFzb77Fpbff5Yurt/npX/45hoDO0kWSOKLSFXmeMur16G/vosvT+9sFQUCj1UCFAfdW1zFGP3WTjcei2fYBRJ55ou5VhoCkXmNh5QwqCOgNu/RHXbpbeySqxvrWJkbUcEJzX1cyh7cRqPBkwuFA/AkU6RiDy1KElNRaTZSU5KMR1SMCgqlNewzM4tfwLrB36PfTQ30RUBw4Frzm3I+HtZbd9XuMentU2qK1OfTs3x9COWtJd+5SjvZwZsxwPOLylbdYPnuOuBZTi0OqQDCqKgZ5RjHqYgYHpD2mesTm6gTd0qbQKdiKe5/9kGKwQdhsceny+2T9LjdW171d1Ws8BGsd22s7jHojBh/7iqMoDmnNtsnSjGyYHiRsBYQJyJaPPU87tb717hv87t/8dYZpl59c/Ta7+YgfffELwiBk5/aQ0W6OCDRn3gxIB5b+xpdhO3ACOLwCXeMJ9QjqgWThrQhdOLbvlVSHpbAjDtT7hzH1JBc8dk4O44izly8RzMSs9u4ynBnCWgE7z0BdNW3COZ2QS3zCeXJ/z85E/P57M5xZavN7v/YBjVaNf/yvvsePP9vg7vqz9bx9Kuzb6fCl83MygFpLgoB8aDFPe5tCoAkmtAzHY8KeIh3uUox3aDcFly7Mcv3OkE+v9xnn5rk0FTycp3BAM1acnUsIpSCwBqyjE/u+5ktLbeZmG8hA0B90GY9z75l8klMNQ2bnZtDGsro79kbxp0BpJy5vz3kMmLIg6+/hnDnxvlTrit7uLgBV9RjSamqxBP6EXsa58KSIY6jVvUl0nk+Uhy+RJdTLhOl9njzzGPwacsK+7FOi8EUPF6NL8nHPWyQ8IJBMGk3iWo0iy8hHzy75Om2qmJXaW026+/9dD4aU164RRTHtmVmkUhQTvimOaygVUFUVZXnQa6yqNPdWt1FSko4fvb5JKWg2YpQUjNOS8jn2bYvjiCSJKEtNluVIAdGEuSv1/T2yRmlBpQ1GG5RzNOs1zl08jwPu3LlH+ktqcXkctLaMRl7g8zRE+oOFIidBVpRs7/V9A1glMdY9kSjxqeCs70m1L5ZwT+SP7h74/tRQEqkU77z7DmfOnOX2zZvcuHqNjXt3+eN/+U/BSUZpBUh2un3CJCIf7VHmQ7pb26cWk7ZnW5x/+yxVUXHn83sUR3jlj/o9zM1rBIEkSk7XsPdURDqAthqLJY4jOp02aZlhcchJpjmKa1y48g6tRpOPP/6Yr334HmcvjdkdZczOz5PmJXduXOPaLz6lOkI2Etbq1Dtzk5OQ5OMBaX8P9wSpeSkVCytnOXPxTdLR8DWR/ksAEQjiuRgVK8p++aUQ6c5astGYLBnRqMUsL87RbNQA/0A1gE4kuLAUkEj49PMxOwPL+Ig4wgHGOQptKSqDswXOZfQHXTY3txkOvU+5EIIkrhNHyake8PsgJHMLi1x8403OXLjEmXMXuHl7i9t3VhFBTHPpIiIIMcZQlSX5OCUbTDvWnQ5SSeJaglSKvb0uZVFSPu+sd63mF4mqeLUZ0wm5EcYRs8uLIARrd2+Tpyl7VQ+MotQVTkQ4AfeZIVs8qaDx5b1Pu247hysLhFLESYcwjqmK4rFEeoQXbrYm3ys8kf6gle2LwFQdD6/2sHjucI5hb49hbw9E4L/ua+dy+I45itEuxWgXLQOGWlFvtskLjYxCIiUwSlAYTV5W6CLF5Sct0ZT4O3aCcgNbgi3ZWxuzt3aLlbc/4Ktf+RZZVOd2cHTD1NfwG85hb8R4kJKO/eZKBQFJo+abho0z3ISREgJUBEECTnmnqNNg5dwi/6O/+evc27jLT+58j0EvY7C2irMwugXFHixeUiyeVQgB/U378pJHUxuUCIghmpHMr0QUqWVvo7qfSC84umnnYSL9MbmiIAiZW1wkXKyxfb6PkBVuaJ6eSJ+yo4etQx9Qss40Qn7rSodzK7O8d/kcxHW2d7/LT37eP/UYeK6YVAC8DJBSEDUkQkCZOsyTWHMcxiSgtNKRFSXjcUaRj6iKAbVEMD/f4OqdjLubxXPrHzYdKtM8RRwqzswkSGA4zNHOUA8glrDQqbG41GGUpXRHQ/KiOvGmNwgUjWaDShukOr376XO0h78P1mjK7HSkoDWG9DQWiVOtzEvoZ3sqBCHUG1DkkI5euiZ3Lx0cPmCt49eZlBP5KTjnOTMr3AtfO501VMXRKuooSai1WjhrnwuRXmpzJIldZjmjLCeKIqxQRFGE1gYQhGE06d01Isv830rpSde9vZPFqFIKaklIEEjyQsMpiPTTqnrDMKBWS4CCPM8RAsLJhqZ6IMlSlJqi1Pu90OtxxJkzyzhgc3P7V45Itxby/NnMOae9b1Wln18/uBPDgdPsNxB58nd5tpACESjOnD/P+++/T5al3Lx2nV53l94PdpEqIm4sIGTA5sYmAGW6hy6fbA6pNRLOvblCnhas3dw8kkgvspQiS0nqCTNLM4+vHDuEUxPpU+R5xmDQIwgU586dI05i2nMdjLXs7g7JioL/3x//GX/+7e+ikRgh2NncZDQcUOTZscFVMR6Bc5w5d4Er775Hd3eXT8Zjiiylyse4U3o4BWGNKG6h4g7EMz47o1+899VrPB1EJAlnYoQSFHuFrx7Lv5xJSmvNzvo6OsvIeju4YsDKTMSvvXsWVaSEaZ+50LGzrsHCzYFjJ4PRcUNXSISKEEEMQiGEIqk1aHZmiId9FBlKWALlUOph56PHQQCJgFg4yixjt99ncP0GN9d3+eKLm2htSGJFs9Wi3WnTaDWJJs1ITyrzmpmfo9FqMhwMGOz1CMKQRruDc3Dz7h3G4xGj9Anr20+IX/vtv47Vmu1b10l7XbL+HsXoJfFxPQ0ml7vMCrburSGkoCoKHA6jc2wFxho/Fx7lwzD1iH2GrLFzlnw4psxy9GMSIlNRvMGLHEMACecmJJw2vgS7FYETsFZC+np/9ZwhCOsdwkYbnY8ph3s88rl20y6uj4ceD2B7FZteJEwkKpJYXaGk4N0PP2RxcZEfD/uku12vRLcH40eGMbXOEkJIsv4WpsyYON5yf/fDxxGHARAxGmpufHaDqsyokiZ0HIwH8IL9EV8VOOfY3dnj2rXrrK9vkGUFRenJr2azwW/+5jdozTT5yd0fs9Zbe6JCn+2dXb73wx+ys7tL727BlINygBZAB9LCsnMH8vFjiIAjpEGHBZwnFO89OQ5bh1gIm5JWGBFEBhmJg7l3SogcVQrj8K87hd1WEAS88c6bnD13gfXBF+xs3PGf86TDepqjmh7nETHFKC357OYeg7Fm5fIqUb1OPsp8y42XNdHxABQHtS2PvdQJfrEqOToBcgJY48gHFgSYZ8DsRp0ara/MgTMUa7sIqfni+meU6Tbbt+7RXd9ie3v8XCs4olASB5JWs85Mp0kzVsx2Qk8olyVKOmZmZgjCEKRjd3eX/rhia1CQ5ebESsA0K7lxd2vS6PxXeL6ezi9PaMf3UqEqYTjwZUwvZZnRS4gKmOqXpsV5k/XmQbSaDc4sLiImjT0rrbFPKrR6RpBSMjM3T5QklGXJuNejzJ9tBVMx2YNEgSJQirLSaG1RUqCUbxwaBL6542g0mDQTDZBSkiQ14jihXm/QanUoioxeb/eRvceE8D0YvHWhVzeP0xIpxYSgPzlO+xSUZcV4nBGEASsri9SSiJXFGay13FvdJE1zTFVhraHVbNJsNr0YrqiIazFlNkZbiz0meAvDkFriBRRpmp64gmiKL9tp90XgmSuyXzGc9PyFEMysnKHemWG4s81gZ/voFxoL0h67Jvjk3BApA4KwjlQB9eYcQsxQZCl5Om12d/DsTSvhFd518nAEMR6k3Pz5HXSpqR7DX+hKM+6NEfI5KtLBX8wsS+n3uzRnZrh49iK1Ro3ZRe+VOhxepdfr80//u3/Nzu4ec3MdOp0mSaCoh4H3lTuWSB9SjIe889YVvvn1r3FvdZ1bN+/iZA9d5qci0oUQhGGdOGkTJDOQzEE5BJ3xq/tIvJpQsSJeqeG0I705xGZfXqbPaM322hppr0va3Ya8z7m5mG99eB492KHcGhBmlu3ViqyEaznsGB8bHQ3hiXQVTQxXFXGtRbMzT7JToDATIt0v5qeFAOoCagKKCZG+d3eT3X5GrztAa40KFK12i85Mh1arTaAkYTAl0h/z/kIwu7jA8vmzrN2+y6DbIwgjWp1ZRmnK9Ts36Xa7XpHyHPHrf+3vUBUFnyR1ttfu4W6ZV5NIB3BQZDmbd+4ilCRu1byHtc4x5SM2mdNN2LM+HOvIhidTVFk8kX6Yi1hScD70Coqx8WXgF2v+tX39mkh/vvCawqg5R2PpInl3g3LUfczGdlqC/WAHrIdhxn3MOMOM3iWsSYJIYHSJFPDhx1+lLArWfv4Zdz+/CYzuI9KDMKG1/AZCSqpshClzDoj0qa5m2kHwUYiAJqN+xdVPr/o/rbUhTqAsXhPpj8D29jaff/4F91bXSNOcqqhwDlqtJv/jP/wDzl5YYecf73B7fQ37BMvu1vYWf/Xd7zLu5/TuFJRTUZQE5oFZGPcc443HxHZHDUV3YF0OL6Dv8DSvPGneHGhFK4xQxiBjceAfb/DR9XT4Pkikn/I6BmHIlQ8uEIc19LUeO5/eeXi3cFpMy30Pq9IPYTiu+OT6Lnu9nHcv36HVqpMNxy+XGv0xOFyN9MjDFngVaB0fqD0xkQ7Z4NldoHimztKvXcRWJXvFCMYZv7j6Cau3JeONgmyvZKv/fDnKOFI0aopzyx3evnQWJRwK32BzPBygleXMmTmazQZ3VtfZ2NphZ+RY653uuMZZwdXbG8/tPF4ZTBNtvwwoS//1GidHhe+rofDlzXBso+pOq8X7b79FUVZcu7tOlWa4J7D+fJaQSjG/vEx7Zoa7N26wt7X5zD8jz0uEENTrCUGgGI7dPpGehIogiIhrDYwxdHs9tLG0WjPEcY0kadDpdPbfq9/v0u/3HumJLAUEyieQp0T6aPxibAPLsqKsNAsLs5w7t8xMp827b13GGI0zlu7eHmWWYqqSM0uznDl7hiwv6A5HOOcoshFFWWH00UFHFEbMzsxSliV5np+6j9mT2J68ivhlPreT4CTnL6Rk7twFFi9dZvWznx9PpO8/SMcQ6c6giyFCKJSKESqi3pohqTXo722Sp9Oy+/uJ9DreUnYSnu9j1B9z7Sc3T3Ka6Eoz7J2ieownJNIFIKRDKud7fUlJFMV0OrM4hmxtbbO5uUlve5O030NVY6phQqPRwLY7VNbSnJ2jyDKy0eDI7qjd3R1+8bOf0u0PcEKjgtN51sCkYcHGXcDR31mFagxmaqb4Gq8SrLbofokzDveS7OSstQz6Xba31mk2Ez76+H3y7XsM4zFJZVk6o0gzw51rPfSwYmQgPWLoKRVQa7aIG01K57C6otZqsbi8TL9f0tvN6LRaRHFIEKlTPweOSe8i5xhlGUl/QL+f0+1lpCNfHSKEJAhDgigiiGKCSROIR0EIQVyLCMKAssjZ29omHY3A+XNKGg2qaRfxqnjuZZ1ra6tURUF/e5Px7jZV/stReeKsw1QaZxxhlBCGtf3fCakmX6CURUyZHutIRynVSZVdAT5wn65Nh+u5n+C2PfgnqYU9A80ALrcm+mHnX/dWB8442BnDoDjaVvikUErSaddRhzJO+SinzMtXT9wllS+Ntna/McrpIQhrLVSUIHAUg22qbMjJ18DHvy5ud4g7iyRzMxhdggtoNuoYo1m9c49+t0c6TieNQSMQMUIpRBBCkFBM1PFGTzcmDyrQT8I6To2SI3AOIQPixgxYTRlsvnr3/gXBOcfe7i43r15ne30TU1bUoojF5WXOnTvDytmzLK0ssXJmhTNnlunrEcPB6SqLiqxib31IkZXYwypdxwFheZLhHeInjgcsSA4bAL3wyM4BCMJIsrhSp54aehs5ZWoOckFPkvOPgTaolYCZpXmaszP0B12KbJNRb/Bw/4unwXSufwClha0c5Fiz2e2TVTn5hBRrtgOaTUU6Ngz6jznBNtAEpUAFEls6dNcdKOqf402bpuSm/2044nZMVZ9TO7QvuxpbQH2pSetsm/hcExkqjIZKOKxz7PYrUicpeoZyDEkieOeSpLKOXLuHGoOeFssrCzgHZVlgrCEOHFJCVRYMuj2kAIlB6wpd+eZfuqwoixIh8M361DHVcq/xGq9xMkwTKhJkLUQcmqRtqXGFQQYBYbOBKUq/XxOCKE5ASrTWWGM8QfwCqwGctYyGQ4x1FM+puWKn055+GkLA/NwsQZiQjccMBwOQlhhP7EVxQmAdQRSjwhCkxDjQRqOrkrzI/R4YCFWAEILKaOyhazZVon9pRRXOURQl3e4AXWka9RicoyxLEAIZeCpPKIl1jqRW42yjRVYUrG9sk2UlZp+09PyBEBIpJLMzs7zzzhWccwwGZ8jynJu3bjMajUkEhIfoBu0gP1Q06DmAGKUUbrL7rMrysarfZ4JpKaLg1e8l8aSYJs2eQd8JIYS/j87tN+Y9LZxzjLp7yCAgHfQf/Vpr2d7cJIoidrd3jxRXT6vwweFs0x9nECGTBs5oXDXt3lICbl9rM9Ww3K9Zf354YmsXpSCMIAh9KU2t1mDl7CWMW+MXn33BjWtXMf0tXJEyFJ4En1leYfnimzhg4eIblFnK+vUvKI6wfLh1/Rr37twmTBLqi3OENcFpE61GV1z95Htc//kP/MAwz12v9BrPCTYzZHcn4+QlqSu21rK2epsvmgEffv0Kv/+3f43e7Z+z/sOSlnS8364xGOTc/X//FHWnTy+H3hG8ZhBFzCys0J5fZmQttsiYXV7mHScRtgZZwMzKCo1OjaQbnarkBPyIHzmQxpF0exQmY7dXsL2XY633mpNSEiU1klqDuNYkUAoVRI98X6kEMwsdkkZCd6vHxt3V/TLeII7ozC+AUkhdQpbyvJ+973//+7iqoP/5zyj2Nh9ZqvdKwTmqsfcp7yyeodZs7/9KBhEqrqMCR5JUCOFQTmONZvX6Kr3t3sk+o4YvbR/j5eSSgy7fT5B7fJCL2DGeSP+oBb91RWI13Fi1WAt/Y0VSj+E/3Hb8YsvR46Ax6WmRxCFvXF6mXo8Brxy5eXODtY1XUBUVRNBoeTX1SD9RsCSkpDG3TK2zwGhnlcG9z59gY/Xo17bOn2fpw6/RuXSJKh9BolhaPEuR5/zlH3/BrWs32d3aAxlOmC2FiBOiVgenC/obN3C6mDRum5ZUHL5fJznWwv+NU4AjCGI68xcQWLrrtyjonuJ8f7Vw69pN7ty8g7W+SdXS2Rl+97d/k4uXzvPhxx8ztzjHRx99SKpHfF5cZ7h2OiJ93M1IBwU4hz3cCdABw8nXY2tGuX+OOjQ8nlMRzqmQ1EPe/WierLR88t0tdqvMR/IhT0bMtoD3ILocc+G9N2jMdPjTP/pX3L5+neJO31+DZ7WcTmXbDxznWMMXA9izFT+/fY/ZhmI49vd+5UzM5St1Vu/mjD4dPjJHLs6AeBvCRFJrBug9y+hHGjfGq7+fI3E9rWmJ8UOo4IHhJvHjKsCve6fJMT4PTHaCCx8uceUP36dyhkGRUWSQC0dhLeONEjEGl4Mr4KvvKn7rqxGpsayPK8xTsj0ffvQu1hr2ervkeYbJUkyRk45HrKYHSi2BQwqNkoJ0nGGNRQKtZp1+XoDIXm+3XuM1nhQWyECEknClgaod9Hyp9lKqYoxKYpKlBdw4RUwsTeqtNomzpOMxRVFgdYV9gRV5xhg21zcQSuGqConE8fQJvsO4ePE8xhj29vYoioI337jMpUuX+Ozza/zghz8hQlJ3DiUVrWYbJyRBVEMEIVYG5BbyLGc06FLkKc5ZpJA0ogQlJYM8ozzUKdo6nqga71liNByTjlOiKGR7e9s3RZTCE4dRDFEMKqC0lsXZOd64/CZb23t8cX2NXn9Mte/j7jPngYwIVcjF8xf5O3/wN4jjEOcc3W6P//t/+/9hPLrJTADtQ+0qhgY2q4NpXSpJe26GpJ4A/t8He316L4pIr02+p3z5CfAXDSFBTeYE7Ynkp4FSAXGcYK0hf4T99qPgrGX71g127tw6UiB9GNZafvHpz/niF58dT9w7S5n3/T621QY6yLhG0FnE5iV6MAZXAl0OU+ZT5qrgJSbSHY6qKsizEa2ZGTrtBnEckA6HFHnG7OwMi0tL7JUj8qrAGQNOU2a+FFAFIWGSEEQJM/MLVI0Go0GfsjjIXhpjMMZgcUT5lPA7PZFgdPW6wdwvC14SAn0KAURRSK1eo96codlZxnS2GHc6NKWl1k7IrUJLRW7BuIf/XgGBEKgg9MS1CkEEtJodmBdstXaoJxFJHBIGChXI03ukC2jVI8JQ0ahHJFGAkiXmkLJfVxXdnW0EsLCwQNJsMH/mDGffeJNRv8dg72Fq0znQ2nux6UpjDnnFFVnG5toqWZqS1Ju0ZmfJRkP0c+xanXW3cVpTFdljJ/GXEVIpVBBgjbmvDC8IApaXF6nVaiSdOYJagzCICIII4wSVExidk2VbgCGIFUodVC4I/Bhwj7IfnqZuD79gamNwuO/jEz6C00xxpaGXQ4BgthWjhGC+KanFgnMzjsI4tooKmWmsA20nz9lEeZAb/xxN308K/1U5X+1hnSMvKoSYiCwdSCGoxb7ZUPUKjQuhJCqMfFZeiCOvfRAGhOHBMm6tpSxKz5NLBSrwShFrCYKQWrOFLgvKY6s1Tu94KITwCnMpcc56ZU+gIAioRRH1OCa4r3mcA2uwusRpv9HjPsu2E3y2CvygNuZQgmGy26lSXCXRpkJKcHENai2o8tcWL0fAOIM1hk67w9LiEmfPrnD5zcusnF0hSCKMBCMdRjjslJU8Utp7NJwDp4+J3U5TGDH9zJcoP1pVln6/JKoCwkShpqbcDzbyPClCfLIgAQyYvGJve4usSBn3+xSjFGP1QeQ+naOfZlo7au6fvq2D0jgGaYXEIJWlUYd6I6RWrxNFJ/jgiZWNq8AUDnv4EXzOdr7TU5s62Dx0mu6BF70kIWZVVqTDEZU1ZGlGMUgxI4sbg8nxmaOJCi8rHL2hJTOWceaeQZjsiRZnHNbYScWiQCpJECiUEARBgDGGNB36hncyJAgTbFlRlAZnLZH02qVp3Puow5pqpNwD358XxETcNW1a+Bqv8cIRSB+8TiClQsoAqzX2kB2Os+Aqgz1UZemMAyEoyoq93V2KNCfPc8qqxOArV77Ucb0v4X4+bz8VbEVRhJTSq8grH/cnSYySEusMzjqckyD8pkEIr9g2xlBVFWVRoKtqX1dinANrJ/sL8RD5790XPE1XVRXO2ft6QAjh92txHE1+FlhrybLiscIuKaZuD/7TjbX36V28UthRVZo0ywmUwgQKJQWTYgTyomA0HpPECf3BgOFoRDHxzn9wPDhnsc5SVhWj0ZiyCMBWpMMBIZpGALUAanISZkzU6NN9l1ICFQisMVSVJgiC/TUiigKsdaf2jz81pu6Tv8xTuFSeNLeH9zpMAutnd+LOOay1Tz1vWGP8vuwEMFrfH7oKASJASB9jAN7S21mqMqfMR+iyxJkSZx9uBjdtPTTdJryoYfFkinQHvb1N7t6SnD23wNe+coXeIOPTn/yAotT8rb/11xmnKf/yX/xLrn7+BaR9KEaM+z3y8ZBaZ5b5y29Tb8/w7ntvE+D43l/8e9Zu337oo6qipL+xBY4nLjd4jdd4HpBKcf7SOT74+D3OXfmY1spXCCpQu9eIqFBJQMWAOybiWgYPWmdGeOFZWyiiuEFQaxHWF5AqZu7KAkpbqm7Oxo3bNFt12o2ERhIhT8mkx1HAV95dYmG2TqAEUgrKCtY2x/sTzaC3x7f/5F8xs7DIWx99wMqVy/zef/L3+eB3f5cf/Nmf8Bf/8l88NMFaY+lu9RBSYB9YMFfv3OKf/7f/iFqzzfl3P+CcVHzxw+/Q3Xp+HpiDz37kVY/Vl61PfDLUmi3q7Q75eMSwu7e/SM7MtPlP/5f/Uy5ePM/eKGVclMzPn2N27iy7uzvcvnOH9dU7/Ls/+TFllXP+ylniJN5/30BCJD0pXRwXy2V4z93p76fEQgA0J/825Imz/jHev6w3gH/1ueXiTMz/6puLLDYjpFIgJSvLjlLDF7f2+OTqDmkJ3cyT6Mstv8beHMGwOhB7xgpqIWwb+KSEPK+4dn0dJQW1SVli0mhyYXmB7mDEdu/V8cyPwoh6u43OU0aD7pFBwexcm/ml2f2f03HG2p1NtHGQNEGFVGUJgy6d2UXOv/E23a1VVq9/OlGAP4jDfhQnW2+t1ui82PfuV0JQCxT1esJ7b1xmNorZvHGdNQCnQedYk1KVu4DlfmbtBBAS1WghwhAzGuKK7OB31Qi6N9BFi36nBlGCmTsL7QXYvAXdZ+/X+apDRiAT+MZvf43/zf/if83s/BwXr1z2JHpdsVEM2Mj6rKc9xnEOy8AILwJ5kRuYFD9PvUREerdb8sMf7tGcCbny9RlUKLDCHlhlTYn1k2IOuIjfEWzBYNzlT//ZP0PWJOOdMVVaelJgngMCuOLplNQFj2xsWFaOGxs5rVhQa1reaMPymTbthbPUtjdBPHoguD64e1BKSyUsFF5NvT+9TKtznwNyDtyDjtxvO/yY4qhffgmYJEa2b2zQ+9d7OOGwwuFSi75ZHljPHNol3rxn2NixPvH8yGz5yTAYlhhr6PXGZOmIWDlCCY1mi+XFeWpxzFy7zXCU8Zff/RlpXnDhyjyzy/NsXb3O2uYW2jjmEigNjAqvgTmu+l5xUPw2Pa3nXakfhiFKKaqqQh/jHfwar/HcoATMxBOFCIAgqnWoJbOU/R7jtdUDK0xjqXbGHFZQOSSoiK3NHf78j//MCyjyEufsge2GCjh1Gf8zgEAQyxAlA0qbUWGf+bM8HI6QUjI7O0cUxRRFwbVr1yjLivPnlimKnH6/jzUOYwVCKJpRHRXKfdFXOhoy6HVx1uxft1GRIhAoERDJCO005pBFVZIknF05i8Oxu7dNWRYURYUxFilBSpiba3HhwjmkFAghyfOCa9duMx5nx56PEpAEEimFF8YIwSivKKuHY3CtDYOBfy8phE8eRBAFgtF4hNyWrK5vcPPeKllWstfrUpX6UBNR3xxFW4OxJatrq/z5v/suEo0Zb2PKnHq+w5sdaAkv+q6s7281DRWUEnQ6IUIKhv0eeg9m52eptxrUahHRQocsK+n1Rs8voTOp2Nj/719KCIgbEMaQj6E8LIBysF818fTX2BhNnvsV+EtLwqkIkjZBFDMzM4vA0Vu/RZmNGPY2GA+2PeE/TRS6AyWJxYfB0zhv+vUicGoiPYxCAqWI4pAwVMRxRKNZY5TmjIZ9Ku2oz8xOPJcjkIFX2apoknLz2TaHRAYhndk5QiWIovjoD3TeH/g1XuNlg0BQazRpz81Ta7RRYQMV1wmTGsoqDJbKCTIrSO3DHORUVWu0ZtDr0ey0aYezREoQxwlxLGm328zOzRI3akinCZVgdm4OrSuGw8GJFN4CiAJJEvmgyrmHhWDWGEaDPkEUogJFvdlgdnERlSQ0OzP7kmYpJi7ck4nWHJNxrsqS7u4OlTGoMCSIYk+Y3ndgPtDAPVm1yYNwalJKrF+GHfEUB8onmCq7gkli8IHtopT7yt6pAHkaWC3Mz7GyvESm18mKwisCpPCCFquxWlPlBWWZU+UVUsrTWdsc9dLDK9G0fOLwbnequjyhIlLgRSppAYWGOJTUE4UKYpCKeuIXx2EnodtJGBeWSFqUcMyGBge0lE/Mx0yIdAmJgJHwh2ecoygqP74FaAlJ3REFwQOq6FcBDvEYG5YwCqnXaxOFjfZVCGIaSvgZxlrrVQIOpAz8M3esFHTq83BydzmrK6p0jK0KP76lQAmBVIpOu0WVZtSSBKkUToDDgtM484QJr+nEKYRXwUs1kW05/90UUAXofDyZ7CQE8aSR86sHqeR9yVNr71dBPS3COCRuhswvzHHx4gXaszPMLS6AknTNmKq0nrsTAivF6cnhoxBwP7M5bXz5KBz+/ekLJ54LjHGkqUGEgjzVqEgc2NdMz+kkxzidS6cZx0m3ZmMNg7WuV6hPSVTHgQHklIR+GlXWY/7OE5uKSghQDqUc1kJZOE4Umlf+fNy0qGYqJHoBm98Tbaa+jE34dN085uB0VqF3qwP/+gI/Jo6YMosKiurZPQjWWN9wzjiwEMYBtViR1GvUGg1qcUyt0aDUDu0kpQahQoIoARlgDARBRK0ek1eGwlVoY9HGHLmWKSEIpcA6qOyzJ92OwlSRftp+Q6/xGs8MgYRQEQQhSgUEKp7E/g+PyWlFl5AKISQqiFBhjNElw/7R4hCJRAZf0vh2DuG8yj4gwDqLeYZWm2VZoZTEOYGUkqqqyLIMBASBRFcSZ61X2Fov9bZGY4yePPfSq2H1/QuRnezT5GSv9iCCIKDVaoGAssopihBjhphDsWwQKGo1byZmjEGpo+/pFF7hLQiUREmx//okjglCh640Wld+Xpwq5yfV5HZCpGszYdWsJ+MdGQZBUWjvlX/k/trb7eR5zu5eF2kr9HgXYQoiWxGFPuwIpxVF1hP+MFXeT+dPOxFKO78fVRIZBlSVt/0y1j0/b/lfMgJdCIEMvV2LqSYeOtM93ZFj6Nle2GfBwzwd9jd3fi7E7Z+3NRXWPJrvemjHOnnWhZA+sTjZD4PDmYpn4S0PpyTSgyDgytuXmVuY5atf+4h33rnCxTeuENdiLJqd3U3GaU5x7w55XtLf2oI0Q9Q7iNklZmc6LC8u4BCU1hJENeaXz1OLQuJa4/EH8Bqv8RJBKMXS5fd542u/R1z3sl2nQmytQT7UpPfW2djosVcU9HmYSC/xzdmv37vLP/6v/kvOXjjHf/Sf/eece/Nt4vo8cdjk3a98heWzZ9lYW+fnP/uEhcTyX/wf/o90ez3+H//NP+T6tauPPU6tDRurO6RdRWkd2jm2e9XR6iCpWFha5uyFSwRRQlLr0Wi2AAhUQD2KsdaRltl9zViO/2xNt7tLEIaU5VQb5om8KK4TN5roqiAf9p56Em/9QR1bOvIfFOjNl6N6JQi9hZWuCnSZEsV15hYv4Jxld+suVXmgUsjzHM0Aa0pkrAgkJKGi1YpQSlJVmm//xV/x459+ShgmhFFMWZZkWUaeZWRpinWG9dvrKKXI0xzwSnT7pFVgBq8+VfimcQo/aLPJz7N4v95tHrmmT3tsL0h4I4Qlodno9tAm5r03LtJpNsmykqKo+OC9M7zz3jlG44KNnRHjUc7dm5ukWcV8ywvkY+e5pKqCUntF2/zksPqTwy6cV1I0X6a8yilQpSOG63dxxuLs0eO5UW+wsLDE1sYW927dpaoqn9xyDooRTnh7F1RAd+se3fUbVGV2zPsJCGoga2CGh9QOj8Zw/S5Fv8tSLKlHf0A9ToikIgoC3vngHc5dOMf3v/cDrl+7RdatyLIdnioCthYzHvlAK66jai1cPsYe8u+lLGH1rveZr9f9NShyHstgvWSQUjA/36ZWO+hXMRxmdPdO11X+WAh449Il3nz/Eh99+AELiwskjQYiDLATklYIQavZYX5+icFq3z9kT9MkMgDO4HdpU6K5O/k6CUL8A3/Y6uVLjv/zseH6T/oIKRiPq/sX+5MsRR18edo8XgKW4q+HxU9ohy1jAg7sthQHhPqTDutw8j7V0ccaxBELb5+j04rYuL7Gxs6Q9d0t5M8GpOPift/7ozDES8OnOGyl8hLZqbwwhPhkicWvnUeN3REH8j/Bw/2XnyOiyuEszCY12oHk4htnWT6zSBQGxFGEcg5pzH5O3QqBCmLCuEaj1aYzN8/KxTd5472vsrPX44ef/JzxaMze5hq6LB76vHotZqXTIC8qNnpD3Etm4fgar/HMIQQogYwCLpx/i/nZZTZv3GLj+k1fUXsU6SwEzc4CSb1Nu7NAZ2aR7s4at6//GGsezmhaU+Hsw5YezxsOR6ELKlEx25ml2WjSH/XZ6+0+s8/Y3d1DSonWhjiO0brCmImxw8TmxVtSQRwqEILxYAfT2yWuNwlrNSo9LYt6+FprpxHuYWuXVrPF+x9+SC1JKKuCPC/49rf/irt372InWo7prUvTlDt3NiiKkizLH/oMOKjGiQJFp9lASEGpNUjJR+++xezCIndu3uDO7VvYSTLSWl/pI6Sg0fB7wzwvGeeGTieh06mTZRU7O0OMsY8VVI3GQ27cvgY40AWRtLzbNrSjA70DBVBB5A56c4OPT9szdZxQNFsJcS2gygxVBaGStOoB2ljGE6vO13g0omaTuTffxFnL7vXrVGk6UaJnE2vjF9lC80uAKSHroQtJL+8BUBXH2ZA+HkHcIKx3iOodmvMXsE4wGueYqqDY/gyTnXTT8ZjPOc2LpZI02w3m5mdYWl5i5exZWu0WxhrKqiTNxoxGY0ZZRZYVlFnmU2UqhKRJ1J6ltXgGXZVUvR5CBsRJnST2fr9CqhfeYfo1XuNJIYQgSBpEzVlUMJHrSokLE6xMyfOSLCuprMOIh4f1tNdwfzzi6qc/I+3vMujtsFydw9kOApidnWV2dhZd5th8QFJv8s7HH9MbDGm22g8f1BFwzpGOc6ggN47KOdJjqsyEFCS1Go1mk9pwRJ6XRFGEUopASCIVYoTxmegTPKfOWoo82+8gf+iTkCogjBL/zD8Dn7PgvMLmDvEL8ZLwZWLiex6Bszjrz7fR7GCtpR9uoKtiP4FgtMEUJVJo5MSCJwwVSkmM0RRFwfraBtevXn/kp2aj+2+u42F//hPD4TfwU5uXGL/JB7+mN/Br+tSA9hjsc10CGgoiLIOsIAhBSEkSh2jt+2K0W3UanSajcUatJul2AzbX9ii0oSYsgfABXegmIj190BvVcCBWNUyaBE2SCM+y2dGLgNW+0uBRCMOIRr2BFIrRKPVKwimMBmHAGhyOMh1Rpr1HvJuYVJBFYE+u3tbpGJ2OKYd9JAIppOf7pKTdaRPHMY1mkzhOqALFwwPlCeTFusIhEEkTEcWgS8REme6c82UL6QhkiJASoujVJGiEIIoDapNmToCvuDjZ9Pv4t0fQarc4u3KG+bk5akmNMIr8Oibs/hYzDCPiuIaSwXF7z5ND4bNh07nD4h/kkw6DKZl8uPDiSybSjXYM9qrDghpAIPYTmJOTelBUNH1thCfQIw7Oa7J5ZbqPiDmw2qpzoFaefj9cLXQaSCblPBw5hwslqc+0qHcSzI1t0hyqNEfnR5MDD6HihZHArwQkfu08vFgdxtTkc2ro/oKnLTVREMYqwEnHTLvN4uLCvqrSVRV2nPp5VgiEnKjHhCIII+IkoT0zz9L5N3DxDvV7mxgUcudoW61QSWpxhHVMPuP5r9SvfdFf48vFdD0Q1OstZmYW2ZP3qIbHWw8KIIxqJPU2zdYsMzOLlNnoeLXzl+iTbp23SwnCgEa9SVYcb2vyJMjzEikFaZqhtcE5H0g4ACEw2se8Am+3AniP5cogAoUIA99r5JjA4bjmqEEY0ul0aDQafonOc5LkIDabEunWWspS0+8PKcvjFz8JBMLHylHorVK0c0gpmV+Y59yFc4wHXXY216mqCgrjc/STdToIFGEoKQrv/S5VQBRHZLkmz6sT3f9KV1Sjg2OsK28eESvvEg9ghb9SCq9KF/4i+fg0ChFKEUaKQAmskhgpUUoSBhN18avsY77fn+r5n4AMApJ2G2ctcuIRjtWTYTr1CtwPMA/hVb24D8BZMCXO+IqPp4VUAUGUENXa1GfOYJCUKqXKU8q9m8/ggD1OTKSLegsrJXfurLO9vUtWlNy+d5coSUjqdXq9Adeu38FaQb01d6CWCQQu3YN8j3455GaREyhJFAZUhWJ9fZ0kjqjNL3H2va/Q31xjtLv1zE7wNV7jeSHPc/7RP/pH/MVf/AX/8d/7W/zh3/3rRI0OM2/+LWzRx1x4C7e6xje/c4N63uWLPmwdNTcECtFuITstknpCknj/RgBcAbYgIKcRKcJYUY8C8kgh5TEB1AMwFnZTS78QGOebUR1Xku2so8gystEIXRRgDAtLK3zwjd+kGA4YbW1QlAUiP9lna63Z29lDKEW530DHe4FURcq4r7FGPxOSKzUZToB5x3gf31vA87NkfwQEca1OEITIIERKyZmzl7l0+Q1vbTLW6EoThR9Qljlb67cZDbue+HQW31zS+lL40mL0Hv/ff/bfUavVuX7j1rM4PJg2LjppQ6DDykiLJ30mDcOJ8N6+JV59+AhSq2/h0xJi67h6UzPbgLi1xZV0RFyLCeKQRqdBbWWZQBuipSVmxiXDmTP0BilXP/+cfq+HdgfkvLJgBPRKr0J/sG1lf5ySlxVF+Wp65x8HIQRLK+d4/6NvUlaSn/74U+yDF985qmyIqTJM9bAa0L+RhKg9aXRcB8InskEZjcbcunWbvCg5u7JIaTS9nT3GwxHjSqNaLcQwuu9vhAyJajMIISizHvY+u5ep9HaacnwQDpeNsFVJe3aOuYtXGA+6bN+7OVHhlKhAsXzpCrXOAjvXfkQ/6/EqBZ3OWnp7vipjiiKvnqnWoFNvcnZhiblmm1iAwFKgKa2mO+rRHw8YFRml0ZjAegL8SexEpiRwQxBcjBAdgR5UuNz496rhk3Q7x7xvOHmPCE8q10DM4G1DbnP0EHlRUHhF+TTZGAjaK4vUZ2YY39pj+MWO//cZcT/5XZ9IvHrAJl6Fvo5X/T9Iak/J1Wni4TCme/np78sj/v4oiMnxTkn6qe3KoWupVEC7s8zsbINGtErC1O7lNZ4IGj/Op4mPw+gAdUiaAfVWQNm3jG/5jeULg/BKziSOESKkLCv29vZIkoRGo05W5Gytb5BmBStLc2jjyAvN2tYepYZ6s8Nud8hffef7hEnChx99zGgwYLC1RZlNB5ijJiFRYKqSu7t9KmMorXtkTuwwhfA0uTOt9cQi6yTvMi0HedoM4mu8xgTGQa/AjQ2b0V3G/YzezvZj/kigAkUQR/T7O3R3N0mHew8IlF4eOBxpmiLY87YrzxBmkqC2zk5IdB8w6EpTTpuHusk6Nbk+AkcgQBfZxNblYYJ7uqWebkcDGaFU5D8LGI81P/nJp4RRiLEGrSv29u5XtQ4GI65fv0tZVo9tthlGIc16glIKJyRBEHLxzBlqjQZvvfU2586dZ352jo8+/IhrV6/yV3/5Hx6qJpVScv7CMkmSUORjsmxMVZV4K1EIJvs0XZ1MfGGlpKp1qOohiR4RmoKgpgjDgJlcc86UlDjSkcYEjvaSIkoiAuWQTrOyOEenPcfW5haffvIZZWmOLLB46SEEYauNjCL0eIzJnlwZfVJUacrOtWvgHDqblqRNb9pUJaGAGkIFhPUaYKnSnrcq+RWEEtAMDmyHAMba94MLTE696BImNcChpKTZaFKFijIInlkMe3IiPaphnWNrpwumpLKa7e7OxPvKYIylKCxRnBAlDYQMD5QuxRhXpaSmIjWOWq3G4vwcVVmy190jjmOiVof5c5IyHb0m0l/jlUBVVfzZn/0Z//bf/lvOn2nyh3/7mwRJk6BxAcEYmEXP3uSt5SZmDTazo4l0IRWiXkM06oRxRBQGByS502AzJBVJKAgDRRRKokByQh4dB4zK6X897rWOqiwpiwJbaTCGVmeWi2+9S29zjby3gzbV8RbLD8AYw3A4BCn9++0fERhdYPQx5N4ToLAVTgBngSU88fslEOlCQBjFRHGyrxZZWFzio699i/Eo5cbVm1RVRb3RxJiKQW/HE+l2qh728AJBS56P+PZ3fvBsD3BKpJ/U88VxQN5MlZNTrnVKkBUcX6o+Qeog1fi9dG6YSRzfON+jJlMWludpN0Kox0Szs0QCGghqheFsY4GkP+LO5j0Y9Q5cASYFT9b4jz6KS0vzgjR/duPsZUJnZp7zl65w5869iff5w/Ak+iPeREiIGqASr0S30v/bKZnSPM/Z3NwmiCLGRYEVjp2dPQbdPoU2yCRBTqP6CaRUhEkbIQRVMfKlffuYFr4+wOwdgqsKXFVQP3OOpQtvsre5ys7abT8g0EjpmFs4Q2f5AuON6/RfIRId/KM5Hud+cD8n1OIac60ZWrU6EWLimGIobcUwTxmkI/KqoLIGq6wnXZ9ELKKABog2yKUAOaOwymCG5oDQlcAuRw+7KYk+IdRFC8Q5oA9ulS+XSJd4lXgENEHEUL/SYfbCGWypPZEeAh1xfyPSGXx5zRBPpj8K0/3zg9F/hCdgp6+ZWt6clF8J8dd+Su7DfddSSkW93qHZbJMEESEHDTxf4wlwjPIfgR9DcxDNS5pLEdmaJr1b4Z64pOz0cBMCJgwClBToqmI4HCIENBo1yrJiu9vDaMtMp4WUikobuv2RV7LHdfbGKRvr1zh7/jwffvXrjIZDfhDXOSh9cETSb4BHWrOTn3xLOy2+eBqcnESffuLhBjGv8RpPCQeMK5zQ9KIdBmmOeYQafQpf4aro9/fo7WxhTfkS+Bofj6IscBbKZ7jPA/bb4bh9It3DGEORl75aPFAIIfYbE4qJh7epSswxopr9bfeEhFcyJAwSrPPkfZ4bbt26i5BQmQpjDcPR/cFZluXHWrk8CBX43hPTSFsqxcLSEp2ZWc6cOcvKygorKyuTPluC7/yH7zDNqk4LEYQQzM93mJufZX1tleFwj6o6eM005DaGkyVkhcRETXSSIIqKgMp7+EcRGsFcVpJqGGUGG0AoJbVoOj8aZjpNLl26iNaWonKU1cs7Ph8JIQjqdVS9ga2qF0Kk66JguL4++enBPdi0PM0HwlLFhLUOOIPOh7+yRLoU0Ah8ZccUpfVEujIVcTVCmcxXV0hJnMQEUjzcs+8pcAprl0lDwAmDMeqNcMZNMoIOi8PgqDdahGFIGNWIooggDDGVnDQZKmC0R5WHDMoheRjhRrtEUUzYaBEG4X1NtU6Pac3k1I/g1do0v8bLiSSQNBOFdo6B1dip4uvQ8Lr92af8+b/4Jyyfv8xbH34dFSpIGjhmyESTVNTRouCoHVScJFy8dImLFy8w01igrjoEYqLcFIH3LJahDwiwBNL3qXmmfZKkQiR1ZKPjy/qNZm9nh62NLYb9AWEUIpXCGIcxj28eosKIuNnEAWVV+E3LMT7Pzwrn3l3CasvenQF5v/TKyQW8uu85ElEPwjlHWWRYo5mZW6A9M0sYRezubOIczC3OUJUlO1sbFPkYi4Ew8JHOi7CfcM4r0af/fVpMG95Nr2mAn3YF3uN3ynuayfcj9shi+ifacXW1IB8aPpAZF8OAOB1Tz4aE9Rb1hRUiJzk3J5hJc7K8YHd9k+7aXYa72+AkwkqKbsXsKCM17nFc/qsPIai3ZoiSOpmBO2ubGBnxzkdfYzjosXbnJs4JZlcuEkQ1hsMBRZ5jixG2POJBcBbKIcgcZNuzgPvS2keZ806zKV5lONje5Itv/zuy7bf58IP3aXdmIUoIW3a/gQ77XWu9ukIEIUlrDhlEBEkNowuy/iZV1oewDuEc4EBosCXkOz65+ACGvV3WbnyGEIKLb31EmRdsrW1grWR7t8uYkLkLb7Jy8Sxbt6+zdu2zX2kLOakk595ZYmaxzftfv8I7712hNT/HTlySi4zNUUaaZ9y+dY1hv48tC9q1GpELfULN4kk/g2dVT3IpDX4uDgHjN7dOuIPhkPFoMvxwb+bDVlIhviImwRPSX8J+IkhCOm/MErVjOmfmSJo1li9dZnZ5mc+2LNtzN32ZddcdyGolMJic/+M5lONhOEhyTkVL0wn2cSJaiVfSd3wPlEApzK6hWtX7hLxDYBBoJGiB0jATwGzdK396LyCBEScx77x3hXqzzurWKr1Bj3JUUY5efV28CKB+MSJsB5x77ywLFxbYvrnF+hdrlH3zQkl0gDQbIQTYWBIEElk4kI6BtZRFznA4YjgeUVaGohyBUCwvLdGox+zsden3Byxdeovf/9bXCKKIwXBIf28XraedUvz5zM42OTfXYKOX0dsanHg6fvG2+tNPPH1UIaOAoBHhjKMa5S8mvnuNVwfOYccZThvsCa2yhBAsr6xw+cI5Bv09Nu7dnjTbTJ8rqR4KT1oZe/AkHN6CHvWE6Mm+T0+SVkIIZPDo5psngZIghKMsKsykEat3GnEktXhCtPt/DyaEma6s7+vlHnb+3HeVe+DxFFIglcBa53sPGcco9Z9jTIW1BnOEP/1xSOKEIAgoyoKqqqi0ZpyOfTNEKSmM4fqN29QbO9RbMxTaMTfTYabTQQUBcT0C5W0tHVBpjcksw+EIFUjyIn94bpzelBNOPdpY1ncH9EcZbZ2TWEM7CWgn/ronkcQKR1g6rHNE2hBpQ+lAO9jd2qHKNYNRwYWLl8myjO2tTW9N8wCmYVAYQq3u74o2vpl57gvivzQIIYjCkCiOMUp9CWHl/TdsZuUCnaVzTDfcZaXpDVNMmeE4WkSlgoAojpFSEgQBzlrG4/Gkye4xEBKC+IBgcoApTszfNAJJfEgeXljH+DkmU6yDUcV9wtLSTr9bBiWIQQ915xNkWCNI5nDWYMoHq2Qe9Eg8OU5MpPvM37SbAgz2hgwON7xSQCywFqIwpFavE8cxQRjilPR7njKDMkMDvT3/Z1s3BUEY8dbXvsXs8hmUPHpAnAwS9gtPj5N9vMZrnA61SLLSismcYVwZ31zrkOLLOce1n/6If1Pc49d+89d542wL1ZyD6BKWOVLZYiQbVMc0iajVarx55QqXLl1irrVMI5g9+KUIQSmciPY7igfCl6g9Sx4dpRDNGf8lFc5odja3uHH1OkU2JIojVBBgtJt0DH/0qhzEMc2FRayuKFdXcUc0mXrWuPyVs1SFJuuV5IPSN8NcwSscXyCRDlDmGZXIWTl7npWz51BKsb21Tr3RYPnseaqyYHvjJnnWxYgJkQ7eD+15w+E7kD4ppp63dvK9wYEac9pEbYAn2CxHEunTmVppx89v5dwNBfV2jU4nIBiPiMZ9Go0WyfI54qjOpVoTrS1xs0V/Z5dr3/5zVj/LUWGAigLK1ZS52zmhceT8chPpQkgasws0O/OkWnD97hrIiA++/htsb6yyvb6GRbB06QNq7XlW797B9Lro/trxRHoxACQksQ+iCPB3qOR4ZnLqCZECmt7mGv2tdcbbG/zG3/hDtEtIajWiIERGU0uXw7JXg1QhSWeRIG4C53BWY3U1IdIb0FzxgZ2QUI2g7HsLpAcw3NtmuLfD8vk3+Oibf41snNPb/T7jNGdzew+VV/z27/0GH3z0Lj/5s/+e9euf/0r75KpQ8ubHF7j84Vk+/tZ7fPiVdxiGsBUV9KsxNwbrjAZD7lz/lHSvD0CnXichPPAzb+CHxgNJ5WMxsSRxwSQX4tyB2FNzQKQf916HvbanRLrDE8bThGnOl0Kkh0nI4ttnaC13ePP9d+nMzbK8dJ6ZzjzjO9v8dEHgBg623LMPSw33r2+CyeTKwRx8HAR+nVyEqB2SNGOKmyXVrr7PHkYj0UiEgaCCTgztyFfZ9cvnT2wmtZhv/MZXWTq7xF/9+K+4frdiuJr+UhDpMhQ034lpXkz4+m99wAfvv8+f/z+/y2f//CZWv/g5apz2PZEuQgIUIndgHel4hEWTZTmD0YCs0GzuFiAU7dk5WlHIbn/AjVt3ufSVb/B3/94fsrOzy7f/6tts72xTVRnTYEAImJ/vcOXNZcydHW5uD080Hx8mv14cnvyBVXFAMt/CFBqdlsc2DX+NX13Y8RjGp9ugnDt3jrfeuMTavTsU6Zh0PKIo8sne7NlDALH0qs8CqOzDTs2Oh59PbS3a6P0OnEIKwih86s3rtKC2KCqcq/YV5PV6TKtdw2hDnudIfJ8pAWS5wLiDnt2Hd7H3nYc79O8KZCBAG4zTGA2lLsE5nCl97HzC+UEIQa1Wo5bUGAwH+0T6YKyRQiCDEOdgY6eLUgH11ix55Xj7zTeYnZkjCEOSRoTcV5g7usMMnTv6gwEITZ7nD8+RpxwS2ljubff3Q4MacGneeSJdCWqxwAqIUm/DFWtDXGpK64n0rdEGa3fv0egs8MaVdxiNRvS73SOJ9Gn4V4sEi7MShyMrDVqD1l8ykQ7EUUQ9SSiCgGdrTnQSHL6Lgvnzb3L5q7+9P1a7e332fvILysrBMUR6EIbUm03CICCp1bDGUBTFY4h0BVH9kL2ng/xkQkgBtCJJOzo4nmFlyaqHTEefGYyDwTGnUxhHYQyUu9DfQ4UJtZlLCKmOINKno3G/o9uJcXIivSq4ryXxQy8AtMNpiy41OijRVYnVh4OHIxh/57DGMOz3cCogf4ryCRkEJI0ODkcxKl5a77DXeIGYrvYx/hkpOVC9CfwT0OCgYdlUGXfowayMY5BrSixOO6SDJJEoJ8gKizaOnUHOZ6s9ki/u8MZ3v0tn4Qxn34+RWcrZs+fIRmOuDa9C/2HVQRLHXDh3jgvnzhHH8UG23jmwFZiCMh2wt7dD02ismbKYT3lpDn2MUopmq0mtXmN9fZ24VmdjY5PBYMiwt0N/Z4PB9jZVVWGMfax6KFCKRr2GLhV9KV5ISstOml815us4B5UrMaFGG4veOKEX+DOEcw6JQ2F9B3jnSNMxd25epSwKdne2yNKxb/AXx74xszYURU6ajsA5xOH7PBGSvzT835RIn/TsCeKAVqtDEsacf/cC9aBO716f8V5Kd3eX3UMekNOaoeljZ6xjZ1hwa2sErZKF0mIri9MaK0sofNfyZruBlHDmjUsEVOTZmDQdMTsneOeKoTuuyNZHjCrLs3lKXj5IKVlYWGDx7AUa7TbOOa9IGo8ZFxWqMYNwglxbXF5ghURFMSauQ9EE4+eUh+F85CoqP+9QcVRjUBnGICROO5w9xLapCBfWyW3InburjEvLmfNnCMMAKwNEHIOaloAelCsaaxBaU2UFptLo0gGRH+zF2Dc/nQZ2qgkoMEeZSDvKIqe7t0ORFRgzZWYNzjmGo5StnT5ha563vvk7jPZ22Lx5FXsKRdHzwJfVh0lIv7kb5iPWuhvsupK7NmNsCjarPjoviesxoWuTDkaUVQkTj3QRSkQtgLHDDquTPWiT9VYEvqxSSoWxYmLzhPeNPkm+dVLp4jJ8wk5zkLh7hguNUIKwqUBANTK4Q6Rm2EqIZ+o06nXm5xZIZussXThDfa7JxeU36MzNEtVqCCEQSnnJldInrK2GoBZQW6hhjSXbzrAnVfRM453pI3b452Ner2ohsilxAsq8QisDc4KaSFgM52jUaqyubrGztcdub0xmoCMlcSLpKMn5UJJXlt1ReXLB7XSfdcLTClTA0tISF86f5/bmWdIiw/U3GT5kFv/qwTkoco1MS5wUhI2I+lKNzpstimFJtplzn9D0OVt2CzVRjE4CxKIsMdpQak1RlZRlxTDVlJXBWgfCkucp41FIORFMdPf2+MUvfkFVVdRqCc1mE6UUQkCnHlGLAnSlubfRoztIX56Y5hnDakM1LrCVeYkCt9d4VSGE31P0u7vcMAV7u9v7JPrzFgZMC2OsOxy9HeDoJNeUM/KLkXPefkU8JZMeBp7k8/shN3GodPuWqCpQ1Ot1nHWYSmOs9XMVB8viA0fpl0l3/zkYo6mqAmPMhMeavHLqLXPKyK2qKgTCJxc4KA62AuRkX+2P07C9vY2QknazzsL8HCIIOHfxEnmeMRqPsdbRWVjGIaiKMYN+hnnA3kMIJhY33k/eGHfiPaTjgCrpF4btUYV0BmUcyjlaAVjhiEyJLC1JGBEEAb3SMUorjBgjtjbJ8xIjJEJFOHu/UfuUCaw0jFKHkC/JHBkoXKAoygLSEQhBUmtidEV1XK+p5wAhBPWOrz6ut1sEIWgjKI2kNAZX9CDvHyvAs8ZQFgWBUszNzGKsZW97h+KR3owWdDmx9wRw+3GrSmoESQNTFuh02ujlfuTGIg8Nw1w//+bhJ4PDWo0uBgihJjza/RBIAhUQx/VTVc2c3NolnWZMj7kkE7sLm1uKUYYwgjIbUxbjAwWZUCAiwIA9GIzWGtbv3UNsbWPT4ZFvfxJEtRrzly7irGHrRo8yfe3k+CuPaZJpDq+W3cGvDlOHgSZwiQP1VgWs4UvEJxgXhrT0E4kFwkCwuBgRB4L13ZLh2PDF+pCbWyPubvTJb3/GG1fe5g//5zFhvck3vvF1zl++wk83Un5x92H//3a7xa9/8xtcunSJRqN9/y/LFPI9hjur3Lz2ObNLy3xYZeCejiaU0n+5CXcWRRErK8vU2zP86Mc/5RefXyMb5ZRZxfqda9y59nNEVRGk+aQj+6OnxigKmZ+bo8xzdl5QWVQ1IRsW35pn/o1ZBit90q0RqSsZfp5/KWyVxBC6avLRId3dHT77+Y8p8gxdapRQvPnue8wtLe4f3+7OJndvX8dZTeCq/c7pzkFW+Ez9S4Gp13kEWKiFNa5ceoulhWX+J3/7H3Bh5SI//s4n3Lx6hx997zvs/cW/3x83h/NVBh+cf7E2ZHU0wiSzvHnRorMKnWVIrSEdI6RkYXkOcW6FuZk66Yfvce/6F9z49KfU2y3OX1hgYyel+0fX2erm9Pjl9PJVSvH2u2/zzkcf0+sP6Q9GDMdj7q2tURSaaP4y1gl6WYUoelihCBttvykwEsoBjLc58oHQBWgJLsVLj+9/jZCSoN5Bqohq3MUUw4PXhA3oXKJvm/zVd37I7MI8v/P7f435+QV0kCCabUQ89ZuYjmlHVZVoKxis71GlOVZboAWFhnITkhaECcgIohUwORSrYB8m0UbDAdev/sJXwpS705PCOcfa+g7j0nH5jTf4g9//m9z68ff403/0f6EYj57JfXkSHFZHfRlJH4fj7u4albLcHfX4ZG8dowSuGVIPYz5evEBjaZG7n12l7OeQVLAEshUSLjSwO5pyw5yswkXiG4TWBGEYolSI1jmmqHw/i01OJjmdJu8EuKmz0A6n8wU/AVQsaV6sIZRgcD2lGh5MvM2zMyx8fJ6LFy/xW7/+O758NpREccyVd96h3emwNlhjd7zrTUqTGkQFiJPpmpK5hDO/eQadaVb/fJWif8KZbDqgDlenPkJkI4Qg7tQIl2LyvTH5XooLBLypmG3P8duXv4Uea/7yj77H3lYXXWmchaUwpNmKaQcBb4QRW8OS793okZ+E8J86MMKJqxniOOb999/n/Q/eYVyl1BstTO8nrF99zv2UTkn4PwmsdfS7GWlYUQHRTI3Z92a4+AfL9G6MuPdnm5hicgDTaoMAv7hNr98zjG1kEvr8i/Lz8ygdUxYVw3FBt59P/II96eMcKGnp93Yw5ZAs9XPpjes3+Of/7J+xvLzM17/+dSI1eeal4I2lFkudGjc3B3z79g7a2l/a6iCTVWRFf3KPfjnP8TVeHBQQCsudG1f50dYa1lqM8fHN83yGHN5zeCp8OfEnWQPiQPftLFT50+8Ga0nslzhrvf+A8Y4JwcQoOYoiWo0WZVmxtrZJURT3aUAfnDKPm0LLMqeqismj+yAFf7rr7ZxjnI4ZMz7YB7mp0Nfdt7EzxvDF559x/dpVpBS0ZmdRccw3f+u3GQ6H3Lh1A4HgzTfeJI5i/u2f/Ds+/+wLGg01sUjxkFKSJBFCgJReBJcXjuqElU7TIj879snTTug4W/M2Pys1f9yiSBFaUJubRTVChsOMnV4OvYLV9V2QAS5sIOIIVww43LBpGpaMC0e6YwgDmOk8Y9va00JKqEU4pRiM+4h0QCtsMju/wnjUp+o9rinws4NQiqWLbzKzco724iJhZCkKRa+QjPIC27sF6eBYgUZVluiqoh4nXL54Gescq3fuMBo9gme1BooH90R+vMSdBRorFyh6OwzvXsM9oJxwQL+wDCbeKkclp75MOFORDyeN8x6wwZIIJJJ6XGdpcRGljlb5H4VTeaSfBFZ7dXmRF1RFcV8zOyEFMlAoFZDEde/XMxphjcVOymWOKts+MQRYJb3v5pf6JL7GC8EplDkqCZEtRWgFYSgpC02WFkRJxOy5GUQgKIYFOjNkuxn60Kb5wRjYWdDaIpAHi7N1COvIRgVbWwNqzT0219ZozMxSbzWZD2LiWnLksUkpieOYeOJldfhzizwl7+4y6HcZj0bEjSZ5llGW+hTNkh7GdHKbnpcQwjeXUpLRYEA6zlAiREnls5p5htQGYc2JfPiElIRBgA0Uz9iE5liMB5nPwMcxUoVQCEzfYgvrZ7oX7PYkAGcMtvLNwqx26DyjyDKKIgcDTjmKsiTPS5I4IopjavU6rU4HrCFC45ylLL0qoqw0+gl39SoIiZMaKgio1WpYZ713aVXRnp2lVm9Q5Dl5nvnPKsv7B/5x480AOciaJJEJ9aBGu95mtj3L8tIyVWZYv3uGhcVl8ixjNBrcR6gXDgIHDSlwSjIuNVu9lKo2pjkYoeIYGUcIpXCFRliJCmPqnRlas3PMLi6Sjcfs7e5idYl0jhc36l4cpJQ0Wm1qjYa3TLOWIs8ZjoZkee6VPkJQb9RxCDQBFt+fxFmLCmPCWgPrCsxxKlUVgIwQ1BA4nNU4nQMCpMKpACclTgrcg9sPZ8EUOF1QlgVZmrG1sUWeV5SlJo7rqCC872+stVRVibSSIIoQSKp0jCknbKDzSj4hhP/RTdXyR8cj1mjKfIyzFicnrKLWuDyn6HcZO0O2MEulBTJuMHfuDdJBl9HOJkZX/vyF9HGIsxMV/OTfUL80g2qq0N/t9hAmJOuVrJcDtvNtXKSQto6uNTCz1ivIA4kMBEIJkOCkw0rrY63HxYbTyq9EIDsK0VI45zClwY2dJ9FP68fkOLCDmRLoDk/QTns0Se63gzklnAATTAjFSYPTKZJWjfmlBeYWF+jMzxGFkScfhWBnbZO9jS02ept0R136q7u41EB58h2FUAKV+OtEcug8T3qNps1MD5Pq02t0KMR2gEstdmBwhWVfOyScv8eBQwtLUZYU+YEZukVghEBIMakwOMWDMR0P+zLAx/+JMYbeXo/dnV2UDJjpzJAkR8dTzxzPexfovC7CFI4iK0nHY5CO5kyDolEdr4ySPJe4Ji8qBH4KVAK01hhrfGWftg85iLrJ30gBlfYHUuQZ/d0danFIPh5T5jnCWaSDSlvyQlOUhkqbX34Dzte+6K/xBBBSEIQBKlC0Wh3CMCYMFXk6Ji8yimoSnz8Bgb7vCX6C10rp9ePOPhTxnQz3Hd+zeRace1Dl6n21rXWeTzIWM9mrOuce5MxweHuYw+uWc6CPUM865xBSolSAz957AYK3xzg9mf44SCm9gtxojNZ0u13WVleRyhGEDmstM+32hOOyVFWBUoIkjgmU86GPlISBQgUCIQRKCmpJzZ+jyan0yYMiB8S1hHajTjOw1GKLdBahy0l/RIsTEAiLdIbAWZTz/unaWpAOKbU/3mNO3+9RvKivqny4/eVNm85/uHDEUYgKAqQDrb0n/ouCDAJUFKEmvSatg6KssCjiOKGIE0SYgCq8uOgYXmZasTwYDCY/+wby02djCiHlZF/p0NUDjW+k8lXI+FgsqTfovPkWVVHQ3dn110ZX/j0nnzENQZ9WJDRlxKZFclHoubJSV6Rp5sPIQ+4Kj8Vj+CvrLJUuMfa5EOknQ5alfPbTnyCkpDwsNwSCMKDWqtHpdLjy1hWKPOdnP/who8HA+6eTP1XWXuMYU+GwmJcmB/IazwUSryaP8SXhx1X4Gj9BNBbmiM82OZMkLEUxq9d3+Px7d1leWuLv/sd/m6AWcOvGLQZ7A77Yvcr2zu4xbwjaOLb2/GZDT+rd2vg+i25Q8ZOrhs3hKnHnX7N84Ry/9w/+U87N+g33URBCEEUBURQ8tGlaX13l5iff5vNffM7G2j0qa9lYW6MwjrJ48i5fUyX6FFII4igklIL1e6vkRcmbl95iYWmFnUBCPsJaSzF5Ph9XrKOkJI5inDH75XbPG9c/uUMQBJy5dIF6o8b454buf0hxufMN1Ur8WHmBU4MucrJhD2ssurJkoxG20vsbX2stq+sbbHZ7XLp4kfMXOswvrdCZmyOQUA98l/m11duk6Ziy6lE85O11MrRm5zj/5rt0Zmd46513qYqcP/2X/5zdrQ2+9Tu/z7tf+ZibN65z4/pV+oM+m+trB8ka5yDPjzaty4A1UJWiVTbp2DYNGjRkg6+89wHvvfk2nXqDeljn9u2bfPe7f7Hvl2eADeeb58y2aswuJ2ykJX/041tc3spQsk6t3aR2/iwiish3t7FGMLvUobV4hgtxyNLKPF/8/Bd8//s/YnsrxZYlk5D3lwpJrc5H3/h1ZuYXkCpg/d497qytc29jEyF8oN+ox8zNzuFQdMeaorIMh2PKsqTWatPozJDvwmCw9rCKQQhodKC2QKh8Q2Mz3qHo3pyoNNqgArQSCKGx4oEHqRxC9waIZTBvk6Ypf/bHf4KxjqWV8ywtnKeod9g5xJ4ZrRnudQnjJksXLhPX6mxdqxhsDA4dlkAFAU6XmHIDdDqpyHkYRhfkgy1vB9OYBxHAYARmxHD7LmNpCbIRTtRIkjq/9T/73zHYWuU7//S/Ybi7BY1Zr34fdyEfQdyAxhzIGILmoXLHZ4MvS4muteHnV69zde82QVeh+gHVnKE4r5HthOCNBWY7M3y4fI4kUciWIqkSgjjwBYWVpsrGPgv2uJgtBtogZwPir7QgERQmw24b7NUK7vFkZHeB738xRQjMTD4vmfy8DTyhaNkIxygwEIBtuvssU1Yur/CNb32LhfklzqycI1ABwsGw3+f/9X/9h3zx6adUA40ZG9LxGDcc+9r4k95sAUSTjuKLk3PZhccadU5vRYODdkHTL4u/Zn0OjkM78mspYj3DLVlfuVc6yA1ZmnGnv4kZGcoHymBzBwMjCAKIpSUV9uTLqsJXBwIn9d8aj8b86b/+U37+s59x+f0rvHPlLb740dWTfuKT40U8nA4YgwscO7e3uPHZNaqs4sKF84i9gBtylYOGPBz061T4arCCx4+LU2BtzT9U041rqxFSrwXHkkDWOvZ6GT2ZoycezdmwR5mNcemAm+0ZqiJH5DmhdaxvDNiSgvRp+rS8xmv8smFaTTRBEAfMLs3S7nT4jd/468zOLvDv/80f89knn2CEgLjms12n7D81zWPC/f27jzwkKagnIUJAluv95/vkeCLq/bEoJvuH6X65LA2VthjrcNaidYVz2nvGG8NhQ8FpzFWLFTOtYH+fUBnHXr86Uq0dxQmNVscr8id2xKNB99Fe008A76MeIaUkywq0Nnz+859z++ZNWjNNFs/OsTA3x9e/+hWEg08/+YRer0cthrfeOst4NGI8HpGEgngmmrynIwgClhfOEIUR127dJcu7pzquC1cu8s2vvYdyJYEeQ5EhtjdxZUmaZ2hj0KLC5APqumRG+gqGoQNnDbYYsm+J81Cr1wNoA/2JWPopdIJPB+sgK1BByNlzF+l0Zli/e4+dzbVjxIsP7gmefswLpUhmOoS1GiqKcMBwVNDNB7Tnapw7u0Ii4M7Ce1ThLvSuQ3V8n4V+v8e3v/0fkFLinKReb5HnKVofcEhxrU5rYRFdFvS3t7CHx3ZchzAhrzTl1jofffxV/t5/8vfZ29rkv/8n/4Tuzjbj3ja6OrCMqUloS8gs9J7CWVcJ/xUFklBJzpxZ5sKFS2zu7PLTz7/AGE0UefuisjywcHpS5EXG2tbpelY8cyLdWUuWjvHmi4nfyE4wDcyk8pmPqYLO/+HTDD7/xm7iPeWcgyBAhBFO68dmIF7jFYIUyHoCgcC2HEQOygrSR2QKHQgnkUjarQYrCzNUI8vW/IDFxXkunrlIVA8xmaEX9livrdITuw/tewUHTU4ezFzvu/9bKCtLmhXs7e0SNxMQECUxSRyRRKFfdIxFSkEYSKIoQEo1UaNPuzP65yIdD9ne2qS7t0eWZeRZRpamlMY90+yoEBAoiRSCMkvJxpm3H6kqHyxYrww96RPqrEPrCqMfbwPzrFCMS7SylH1NqDV6aDBj6/egkxskpb9HL67K1oduRleUaU5V5Pen2t1E8VVWlGVJVRReMV5voHAEaJz1iohHUcNCCIIo3J9PBYIo8p51vuwTZufmmV9cYmZujuUzZymLnIXFRZzVLK6ssHL2HKN0TH/YRwYB43SM1hqtNc4YdFEcff8nkakrHDrVlOOSdJgyHo5J4oR6O2F+dpalxSUG/R5xnEzUHz6Mr/AcU+kEpRPY0lCYnM4gpbs3IK8seaOJjGKcDQFFVVm0FYggIm40ieIEKbySY/osJqHwPSq1Qx/iIV5ZCEEQhgRBiK78JmE8GjEaDAjDkCROIAyIoxCEIsh9/4YwDCb+kRIpvZpfqhCLOOStJ/1aLQOEUoRJQi2JqUSFTev+wUnqoCRC+UBYS3E/zzRVpFcZpkixCAa9EZW2zC+sEKoQqSIIooMoWQQ461U2QklUoFBBiAwj/5xah1QhQRThRIVT4Iw7/vl1Dmf0PuEthEAI32vAFhnaFqS9LoOdHcT8HMm5s5RFiYqbiGiEC2JQISJKkM5iwxpOxj6WCevPnEj/UjCRimRpTibxRPMu/jnugAotia3QVpNVGVkZYzCIQICcPGA47xl6Um/0yUMpQh8V29RicwNjd3wS/HGYkooST55HoNohoiYwaJyw96uyn+DhN8b6KD7EE9OT6rcoiWi2mtTrdYIgQDhBlo4Z9vus31nl7vVb3h7ulASnCCQyUohITeZeg5tOaKdBKCCZrAWTc98vTxf3rz8utT6n1uLArkuDqTTjNMVkBvPABrIykGlHHEIoBW5/XDwfGGPY3trGOcOZy+cIlPIJ+qe4ty8NHD7JnzvSvYzeeo9AhURRRBSHyFgiKuE9+qdM0NSa8IFr/ixuQVkdbKAFkEQSE8lJs/ujL7U29j5VvDXGNzabxKumLHCTMZRX/oVHkXhHeRe/xmv8ykFMfL6bTZrtDstnzjA/v0Sj0UBK4WO3F3UoYvp/Lw+kFPsx4HR/Y91Eke7sRNlcYc3DtlFyqmwNJEkc7L8HlZ2o7w/2uWJSdaWkRE5iP4nzscVzuAeeIxP7XwBZlpFlGdpVhHVFLU4mxyPAWZw1SOG5BCGEd9PZfx9/rZRSE6W7eqJ7GUYB9WYNrMJVxotVpcJJgVISISzW+Dg+wBILMMKHGj7smMYPjw9kXorWhn4woRAoIXHWoKcqfimQUhFFEc5BWZQ4axFSIYRASoGUPmY5LtGiJlzLYe/+hzAZqEZXVHlOKRylMCRxhi1znDWoqI6Mc+x+Y9CjYYxhNBoihKRWayLlw9SvUL4q2LmjZhd/LGEYkiQ1ZmdnOX/pMkmcMLe0PKkwHZOnE6spa/fD/qd9SqbDVU4I9TgMaTUaDIaj/feejvenwTS6sdZg7OlEqs+cSL8favIR/uGpCt8AtByPGXe7WGvJTtmt+kiEMSRNrArJdrtIpYjOXEQAxdptzKD39J/xGi8FVKdJ5+/8JmqhzSAfUlQ5fP8G9O4d+zfOWMZf7FHcHfLmP/iYP/zd32PwtTFbf7PH0tJZfusbf5M4SkjfThn2+vzffvB/4jvX77JVwM4hIVYtgPNNvyG9N4Ls0IQ/wFenn6vDO7NQrxcMehsEkWPz1nWqvODCbJ3f/spb3Fjb5tb6DnMzNa5cnOPdN5aIowZ+t96bvKOvUb9z8+f85Z/+CZvbXdY3djAiYnP1HhrprZOe+EJOPsIAlS/1SZIGQRSh+7uMt7e4Xlas3V1jtHPv1MzzaDjg+uefYbSmfJrjPA20wuaw9W82CPrbFC73/vebwD1/uo3En8oo901enjeSWkhnps725i7rm5vkVXUfKSGEoNZoEtWbdHd26G2ssrCywuW33mIwHnL76ucUeU42HmO0piiOlm42Wi3eeO9doiQGfHO2r3/t17h44QJZXpHlFXGtTmNmhjCMaDabAFz8L/73WKNptTsktToLM22+8v77dLt73Ltzm+FwwK07txkPh9y9epV0eLy32mgw4kff+SGtVgudOs6ePcfv/NZv8e47bzPbavDe2xcIlWFza4Ner8ft29f3m5NZB9fWMtZ2Sy4tt3jrXMza7oB/+Z3PQUis+Qm1eo3f++u/wdnzK6yt73F7o0voCiKb4VTC7//+b3HvXpert35MUaV88506jRnFFzdy7q6XlLzanulFlvHpD79HnNRYuniZ5swsexurDO7dmgRwipmFRZYXF5GBwOV9RKlZWVwhrrfpdvv0+31U0iZZfgeTDym793DGQjgHIoLS4kyfs5fO8/YH7zPq7bB9bxnnDEJNSDghcNawfq2knz68fpejPps//QuCpEnr4leJOoskUkA+RkRtxML7noQ1GqUC4noLiWTv3i0wDlWr037jK+SDMXlvRG1miZXLb2OqjD1VUWZ9iu4dbPkIb3OjYbSNCGI6y5eIkgaDzTWybsFg9SbFsEf7zCW0FpRlhTj768TtPuXwHrYa07zwPo32AuPdAcPNPQgCP4G8XPvJ0yMAlvDnMgL2OOj6OyHpanHC5UsXiZOYL25d5ZaGubhJLYgwsfWVYFOP65NEygXQBVtqip8NPJE+1l753HsGVFkNeBNUO2LpozdJmg22/+oWo6u7/pxmebJqpMrCTgmxIJiVyFChuxo7tvuRs9GaPMvY2dziv/un/4TN1XXufHLTL+NPIFarX56h/ZUldFGwcXUdk2r0vcoHFyeZvCx+t7FQg4WQMAoIAuUtxozG7mj0XnrQOc7hExkT9yZG+HOL/Ab+Vu8mroAiu//Ddwclo1xz5kyL+XNNoipDiP7JTrLCB0zTRMgJUBQln39+g7t312jPNsBk9Ie7fiyWk+N/VWGAPbAD9/9n78+e5MzS9E7sd875Nt89VuxAJnLvWruqdza7uUyzZ4YamWZMNxozXelOMv0ButE/INOYrjRXlMlsTCaJ4ogURzSKQ7LZa1XXXrlULgASO2KP8PXbz6KL444IAAEgAhFAZhbzMfNEZIS7f/s573ne531ePv/Xd1n/wTZXvn+Rt/7u64TdgN43muQDxfTzHFu4fSsjw7614ezHiNMdohwwzWuKyvhGdcf8vHWOotLo2pC7Ry/TwfzHXIwbPcYHlHb/Vv0aX+PXGnPz8ZmLXKPV4c03vsPqmbO8+863WF5eZv13f59Gq8Xtmze5eePGAUGCQIUBCJ8AfZYvxsFh93mPlrWOrNDe6ukLkwg/ieXlJax1TCYTyrLaPw4JLoDaWKpx4e1oZvs9V6L3Ekk7VnR6LRaWemhjmGQFUmk6saUShqy2aOtotRs0Ww2qUjMe+kqduR/9y2hSb60jz0uEED6JfwD5NGP9zibVpORXnS7tdovXLr/O1StX+dEP/pa7t9fJq/phPzcBNBox588sI6Tk3tomRVmT5scvXxqOR9y+dxeBJ+6zacada7sIXfOtVcVCQ/j8vIVG6MOPcQ2pdzA9MM3ve2cfKPD7UsIaw9q9u2xtrJNnM7VHHEIzZmF5hW9+67vURcmHP/gh09GITm+BuNGk3+/R6XbY2dzg3u3Pn0zkSMGF5UW6zQYbe0N2Rk+uqZ21lMMxlZpSj6cEQYiliaXBTvQJ9xo/gjim1VsgaSwwGgboI3C/zlnKMgPE8YSYZQa65Dvf+y6/+0d/nyuvv847b13l0vmzhGHM7vY2P/z3/4bNB/fYfHCb8WCX3ELt/Px9kmtcz3ozBAakc09UKjjnlejAU5MS87Y8Dh9+HvYuh8FwjMrKAzg2kS6EfMTX0D1XSb6/0nLWoqsKXVWnQ6DPIRUEMU4ITFHhopCo2UEGAWJ7/fS28zW+cMgoJH79PMH5ZdLhrm+C293yi0f7lCfWgR6W2HFNV7S4cvYKlau5eLVgsXOGN1beIIl8rfF0YcTlxSVuNhWZtQxq91AEFEjohLO16oEFDPi1XAXYAHptCEOH0SVlmZMOh8RJm1YUcHapz/bQD5yNOGR1ucNiv4lA+YyuS8FpEAk4xXi4y9r9uwxGOVlW+Nd0ihESc5LU7cGmZAAIlAq8J3qVo7MJI7HDOK1wxfEbANd1zXAwwFl7sv08DozAVVDczWDdwUU8aRQAlR8mohmRrpRv4XnSMqDnQSpBECqss0yzlNo+puoXoIKAMIqYTgZkezskjQbGGIqiZGt7myLPD/ZnfAJCCKI4Zml1haTp7+MwDHnr3Xd4+623maYl09Qr3cOZr6y1FiUlFy+cJQwDiiyjripajYSV5WV6nTbKWQbDAaPxCOl8eeCzUNc1O5vbTIYTbixdZzye8N7bb2GqK0SBpNdt0e93WVpcBgRrayG6rrEztcIoNYxSQ7+tMdYxySs2x0NsbSlHBa1mg2//5jdYXq0ZlZqsNkSiJhYVkRWsnl2lqhRJEhGFBcv9gMWlgM31yvcxFPuT6Fex75cxmuHujq9Y6C0gw5hiOqGa7tugJHHkVRBCYusCp2uSKKTdbpOlmVcKqBCVdP3cPS8TExHIBlgQrqaVxCytLBOFkroqcabC2eyhssQaw04YHrqfVlfku+sESZuFy9+mkSQEAt9kSEXQXEAYA7r2Kog4QlhLureHLiq6rS5JbwldK5gYgqhJs91B1yFRawmEoh6vP1sM7btaIbBESUDSbZIPIwopqbIJVToBETLcHWFFAM1VlGwjyz2cKYg6SzRWL1OV64iNFOfEr4caXeJtNWK8xUcGSIEIvVpcKEEcx/R7PVSgGDy4j8lKomVJoKS385krYY/qS2PxkyMOs1P7fcjwq6vTyGwFQA/koqR5sUez22P40w2/jbnNy4twAAbILFiQywrZlIhUeAdCvPpKa02R5Qz3Bnzy4Yds3HsAe2Z2vC9wKJ2YxuU+2faQ9HaGnWpIOfr3CUAJZCtG9mOCKPDzT11DJXwFwONM63yVm+IHxwTfWsBqJvXEb/sxzqCoLUVt6VcOoXwVy5GVbo5jXXch/AJwOByTZRmDnV1Gy22/IJx7hH/VUfrXKJ0yYsri+T7CgYoE8WKAdYYilOjqMc/fA3HB3DL9tHN9Wju03idnDl7n51UbOufQ1r8sAivEE5+Zf5sUECo5a1DmPXYfC7MfeT98eUmYr/E1joXHWUXhexp1O4v0usv0+4ssLCyyeu4cw9GAvZ2dWb8q/wEhmFUK+mrU59UOH+e5eZzQ/TIgSRKMMaTprFL3oTTVvxyOuvYVc/OobX56QyVoRIJGFJAkCVWtUUWNkY5ASZyyyNl8FwQBSRKja01Vvpps7dPOt64Nus6IVMDuzh7WGK5cvESj0UAJRZkVlNpSHbCmCUNHGHqLl0maMU1fzAOsqiomswaVzjnGk4J7wxJpat5cTOihHlYFBMKLDsvZz7gvzsLwJHDOkU4PiHUEngRKQuJum9VLlyjTjDCOkEoSxQlJo0Wnt8DC4gJZmiKExDmzP2c6hxSCViOm32kxnD6FB3UOU/mgT+fz+67J3BNvhCBeWGRpaZFARseyzzVPSwDNF8aHzelWg9UsLfR5+523OXP2LAv9Lq1Wi7fekyyt7nLz04+pyorR3jaT0QDjHPoUFtrz59ZY53ONzs36Nex/9/N4nIeOEc/Z0tE9Fx7FsYh0FYRcuPoe7d4iaZpRFCXZaIfJ7tohu+jAlkA9+1PE0brivECuqi4hG6KabZLVS97HNc2wpsaewEf6PyoIIA4OMMSzdNKX7PRJpWi1O0S9Hlk9xVCg31nCNK7CgzFc23kqS+acY3t9j+vv3+LspUu8+43fopG0CVT08D1xFPKP//Hv8d03HD//8w/54G8+4U4NH1SQa7g79+8y/o5+vP9XGAW0+gn9hR6vvfseYaPNzdu3uHb7PsPhiDiZN9yDpaVFvve973L+3Bk2737I7prk5o1PSadjuq0+jbjB7U9+ialLhNMkgSMQhixL0ZyQSJ8vwGY7b4xhOpmgguCh5YYrJ6A9iXZcWGOo8/yld5N/BFvWqxzL2fY28ZYFjykRG42Y165eRKiQ658/YDA8fqLgKHDA+sYuWVaSTjPK+URw8D3WUYwG1NmUuvBBznBvj0/ff5+qKn2H+2eQ6EmzSbfXY/nMKpcvXaLRbGJnTXbef/+X/PxnP2M8GjMejllaPctrb71Dnqbc+uwT6qokiiOCQHH23EV6/QXmN8XW+jqfffwRWZazN9ijrKr9rPxzoHXN7ds32dnZ4vKlVQwZeZ6RZylpPuX8co9eEhJZxzRNuXnvJpN0/xps7OX85LMdOq0Oq6tnkZEk6LUpgf/+3/yI8M9/xuXXzrNydonh3i4721skFCyIEcI6fu/3LlJWhl/e3uCjBynFpKbThNV2RLsfM0o1NzeKh/0NvggECpqxxDnISnuk6oikkfDOe+/RbLXY2Nrh3rVPSB+rtsomY66//xOEVORZhrWW8fYmYZyQVYKsFtgqxWQDrC69DYpzoIcgM+L+eYJmn821DbJ/82/pLy1y/vXLTEe7XPvlLyhyH/g555imJSQLoHPQTy4ynHNkWY6dTGk2EhqNxC9MGgnCGIRVPsARwjcwNiOEzYjUJRpxTCUKqNbJdoY8+GCLuNXl/JV3cM5wa3qPST547jmzRjPeuEs22Ka7sMKZCxcZbO6y82CDYjRh4/0fQ9SgbK+ACmguv4vCknT6CNmhuSRRQQ9jK7RNXzjQ+rJABILGYoxoC8pWha4MvbMrdFeW6J3tceaNM3R7PV5//XXKsuBvP19nWmZcv3MdYRw7n+zBNUDOCNnyGDGCxiuR5z/PG2CeFFJAEiIaCWHUJAybSBH4oayYvZ4xhj4VFj935KBrjQgFNrdQwWhjxO27t6n2csbXdxgPRoxu7cHU+LjpBSGkRAbe6u2hzcpRvy6WiCsNwn7Ce7//XZYvnmFt4x47O5uU2xXljRSbGpgvtA/mheZWIXNFcDk7/nmz1qfsw2CY8atPN8gL7YmLU0YSCVYXQ4SELKsx1nDjs5usPdhmY3fXk/+/DkT6Y4htQL+OqeqIVi1IopBL37oAtWNzY4c0zckzKEvvbhjzUMh6YszX5I+vTdtJQr/dIggC4jih0pq1ra2HfsWzT89eM+pEeFuEIGlw4dJVyiJnuLdFkU+9dZHZ72bV7nR47+prGGu4dvM2RZbRCKAjfLm/NRCEgijeP0ptHaPC8LXd+tf4SkJAc7FF1IopRjnFKH847k73hvziRz/gzNlz/Nb3f5PVMyssLixw8eJF1tfWaC4sUpclVTrFWYepPEHmvgxWtmHo7QC1fileHbs7u4D3Kg8DiZMgQkkYCKJAgHREUmCMo8iNb0Q6++yktFTGMS6nDCYaay2Vrv051I/ua5blaK2pq9P2Qp8niF9c1KPrmnt37hGHIVdfv8qli5f48JNrfPjpfu+Qsqq4v77tf35KNfNRYFFoEZFOUrY3dijKmrr2a4ePNituDiTKOKSDRuhoNRXSOqIGlDXsTSyV8eHU6ev4TxdKKbqdDkodmGekt6qRjQTZbhCEEWsP1rBa0z1/nmRhASUUUkjG0yF5PibLprT7PW+7E4VgLfVkgnCWUL6IKKfC+wo2oNFDS83o5q9wpkYXRxcmB4Hvxae1foSbqYqc0dYm1hrsUxakeztbXPv0I6ytefOdtwmUotdtEQaC3/+jv887v/FtfvGDZe5cv8b21gO2N++9wHEejto5jIXhJGVjY5PhaHRkbmkeyr6sCohjEelSKZbPX2H5/BX2dgeMRxOctUz21p6iBJ4/uBH7tcBHIdIVx1r5WA2lRiYJcXcBhKTcvI/OUzhGd+L/uCEgDHyDK5iN8Hhi8ksEISRJo0HSbBEnAaWW2AtdTHe2nzd2nnmLjQcTHtzaYHnhMheX3jygsvWfD0PFb//OO3z/HUl/a0Dys0+QwEeVLzHZLmY5Bw4XQ6lQkbQT+ssLvPXuO1gZ8Dc/v8HOYEplY8IofuhP1el2ePONq3TaMcOt25R5ys9/8Dfs7uxwdmmFbqvD5r1bGF2DM4QKJJayKKiRT2l8cUQ8liK21pBnGVIF+wS9LnCHEGRHwbz65JViYB9a1eDw5fWH7H4chbx25SxRnLC+uffSiHSAveGEvWd+v6PKHrWoSCcT0mdYqBxEFEX0FhdZWFxidWWFZrOB1t5v/cc//im3b99mtLvLaG+Py1ffwgrFcG+XH/35vyWbZduVUnzne7/DxSuvM78x1u7d5YOf/xRd62MrCYwxbG9uMtjd4cbNz4jb+9lyWcNit0knjolImExTNrY3HiHSB9OKwbTizHLM0mqCCAJkAFVV8zc/+5ThaMTf/bvf5ZvOcvvufa7d+JwGJSvBhNXFDn/6x99Dqog//3SbD28XnGnAQgwr/YCL5xs82K24s1Wgv0ACRklBM5ZYB0Vlj8QFRVHE61ev0uv32FhbY2ftySClzDPWbt145HfDzQf+h7jvX/UUcr8QeQibAgVh4wpRt8dwa5P1T7d481vf4De+95uYKmNv4wHT0d7sAwIaCxC1wJlDiXTrHEVZYrP84ZgilSSKIoQ1SOtXEs54z0VcCnaCkpYoCFBUUA+o6j12xw/orV7ind/+ewRhxIOPmk9s7zA4a8mHOwgpOXvxMufeeANjJDtre9RZzvD2NWh0CF5voZpdkt4F4riJChwIR9RJiDpLmDqjmGwe8Hr8akJKQdQOUX1J3fMeEa13eixdvcD5c+d5+6236YRNLoSLjIdDfioFpS7Z294mn2ZwD3gw/7ZjxgaWF/dDfxaE8Av3KESpmEDFs34j7BPBLwLLw/nDpo9e93SQsrm1wc71Da79q5+jT7BIPQghBVIphJAIK46XAIgE4nxMeKbNa+++yWtXrmJMzXi0RzUFfat41Cfj8bYbB/3k501Aa55J5k/Siux+5dsjvITxNAwlywshSsIemqKyrD3YICs3Tn9jXyKETtIyEU0dEGtQKuTylSUkglqPESJH11AWPg5tnOK25b547pHLnkQRS90OcRTT6nTJioKtvb3HiPSDhv4zn1QhCMOIpeWz6LpCVxlWlz5+nd00DkgaDS5fvkxd19y4t0ZtM7ohNJTP/RgLkRI0Eu8F7PANAtPK2zB8ja/xVYMQgriT0FhsYWrjiXQHGCimGbeufUI6GpJlU0DQ6bRZXl6i2+8TtzsgBFWWPoyjvhQQwlvhKeWtZ17Cfk1n66M4jlBKEkg/cYUKQokfxIIZkV4+6kNd1I6idqR5wWRU+DFq1j8rnNnjzFGVNdUpze0H4b3L/Rj7oqdHa8PO1jahCnjnnXc4c+YMO8PJI0R6XRt2945oufYMOCGxIiArDBsbA7S2D9eFd0ZeIDsvdDzflyy0JRGCDlCUDpP6NnYFX34iXUpJq9UimlfcCqikL+hTjQTVaiJVwN7eLlJIWouLNK2lmk7RZUmep4xn9jlJs0kQBjSaDdCaoipB16gX8vOeKSuCGNGKMVVJun5n1ufqaPeotwD1vbLsY24Bpq7J6mffK+PxkLX7d1haXsI5iwoCms2EKAp595vfJs8KJnsjTAVVVZ4qka5nivlpXjDYGzLNs4d9W54Hx8u9745FpFuj2bz3OdPRHnmWUxQluippLfhuwJ12F60129sbaF3T6XWIkmjW7M1RZ1Oq6d5ztnLQc+J4i9Zms8nV169ineXaxj2macYXypbMMWddK158UXdS9GJoRoStNnG7hzWOqjI44zPZKghYees1kk6L3V99yOTO7RdXVs3HiJcQ2+q6ZOf+HYJ6xPT259TDPazSoGrI00e2GQSS5dUOYaDY2ZmSZxW37txHCkmjt8g/sAX7rpIaL19OQW8iqgEXz4f87u+fY3o3468+GpFZ3ztsFuc8oS4GfLO+qEmQtGl1++S148b9PW492Gaxt0gzaRFKx3I3pt9MaEQxk3HBX//gE8bjMXdu3SNNUzrNgiSOSbd2me7WCAnNJKHZiIlCBVacSv8XGUiCOEQox876PQSSyjrfWLCuvNfwKUAI6DQiIqWo6wqtDbWF6jR5qbv4izI3rH9s17WFrAQ7qbh24wEqCJlMXgaz83IghKDXbdNIEozzi8ek0UQImIxH/PxHP0Yp5ZtlGMPaxjppllE7i4wjpumE6x9/SJGm1AeSHNY5NjfWKMuCec52NBxizIv5hT38Xuu4c/MeZVESNQKihm/oWw4rQhnQay0glePsyhmSpMnecMcT6lKBCklrzZ0HayRxQn9xBSFjFs9eoblYIzrnGNAldzEOgZYhadBnowj5tz9Zw1jY3PPXNtOznOBezcRkjDP9harRAbRxpIX1gfQRdyVQAcsrSywtL9FoJMffqCmhGvt/D7uyzlKNt7B1ic78/THc3uCjH/8HsunwsdJW58lzZ+BpFSumpt67g8uH5L2ErNmiqjVG14hZ6b6zFltr/5pVr2R7G1gtwMHia+9RpRPSnQ3KvOTep58gwxgbLpCceZt6vInJR+wn4GG/om02AMgIpwIGgwH27udoW3P29QsUacFoewTOokyGNBJTtagAYRRCSqzTaFvRaYa89e5bKPXVNkm3laO8W6EGit4bPcLliJWFJRY7PSIZkI1TVGjJGwl1XdNsden2FphMJuRFDomDFj6O+bJUqxUObtWYrYyd6S0mSYPs/ujJcrFTxGR9yP0f3yDdmWBPkShQgSRphtSJ2g+Fj3jLSSlpNWKajYRgpqZK4gaddh8d5w9tyQ9CSEH7QoOoE5JNC/K83I9T58n2ebBzGOxsHXfgi+c1pSc59Y1GRK/bZLHf4O23zqAkrK9vkaY5pU7Jfk0rTRt9iNrQX43p97s04oR+s0coFGd7S9ha44qa++oBdZExnRQPL9HxV0yHwzqfcFtcaBJHIaNJTpqVNJsNVldXyMuKte1t8rKkfqKZmo+OZRChojZB0gYhCcKApYUOwll0uUcUeIKnOkDCT9OMTz+/iTGGvPBzTW1AOW9ZlnQi4kZIu9ugqmuGk8mJY5RnYW6T86zb/2t8jZMiiELiZkIWHlCVHlhD50XO3/74b7m/tcnWxhrj4YC79+8/qsYUXqQAeDXpF5xXClWACiOQChEbdFU9suY4DfhmmhIlA0xdgzZYBXXgW/koC9JCRzps4IncxykNd+AHY8Fo+9BG4vCNSlCzkeGJ8uL5f+YMwSH6VxX4JDkGhHmh61SWJfcfbBCFIa1AESqF0Zp7d++S5ylXX7/EZJqyuzs4NevSyWjKg7vrpNMc87g96Qxz/dpO6dAj33C0I70iPbX7bm6K/TMzD3FiBd3Er9H3iv2iuVcJ3zush5TiYYPXObTwivTlVpuLly5Ta81knPpq7jNnUVJy/dOPGU8nmCzD5PtrJakkZZaBteiiBGsRwzHjrGCaPV2o+DQard1IWDqziK1rihh0VTIZ7qLr5z9fzjmMMVhrX0iIube1xfWPPiIQkktXXqfbX2D1/GWUCgliRUzI6++8QauVUJQDPr/2y1NzJIilT5KZumRnPKKo65fmdnAMpxzgmES60ZoHn3/iS1IAHDQXztJdvUKn0+PS+csUec64+Ck2T+mfv0Sn3yErcvKyINvZpEoHz6llmS+Ij3+CWq027737LnVVc++Hf8N0OuWLY65nEHjPyQa+ruWL2B0BLDbhTIvkzEV6F16jKi3TcYWpDHZaEEQxl/7B32XxzAqfDKdMPrl1su3ByyHSy5KNW9dgN4FfXYftPTjXhJXEd7Y4sNEoCrh8ZYlGM6Qoa/Ks4rMbn3Ptxk3OXb6ENalXGAqJZ15vgduD+gGUu7zxWsTV8BLbP9pm8ZMx0jpKHjUoemJhqkJk3CZq9uj2l9HTko9ubfH+p7f59ptwcUUQScfZfoPlTkI7TthY3+P/9s/+ks3tvX3Pu9l/+zgWHHS7EefONmg3E5JQgfGWCCeFDBRxN8Fqy+adz30jh3YPmh1Ix6dGpEshWGgltBsR6cSX26X6lIn0z3nmPWesbzI6LSp2R7eBJ/06v8wQQrC8tMDyYp9CGwptcDOmZbg34NMPP6Kuaz++CoFqJYjIZ9VlEjMajxhs/MQf84Hjdtby4N4d1u7f3f/dKZwXay3XP/uc65/dpLvUoLvSJJ9UDNan9Dpdfu+736bVaHHx3CVWly2f3PiVJ9JVCHGTaVVz4/Zdmq02b7WWabQSVi+9jQgiRL/DjmswpeE5XREzCVvs5BU//Ms7lHn1MFs9qf1rK68RO/UXvcYA/L04yY938wdhwLlzZzhz9gzN5tEU2Y9A5/71NDhHNVqjGq0DXaDDzsY9djc/BOyT90SdPXM+c6am3PqcKghIV88R9c9QlJWvsJnBGoupaqzWPuh3lsn2fSY72yy99husvPWbTDYfkA0GFGnOjfd/iQwT2hdWafbPk5qfz4j0g43N59YCs1E6iEGF7O5uszve48y5i1x49w1GmwMme1McBmWnKG3RVRPrLK5OcDKi0hmFntLrn+Ub3/kGSRI9cZxfJbjKkd8oUQ3F+Xcusnh+kf7KGbq9BaRQpIMxIqpJbYOqrGh3eixow/beFmTCWzR2nM8of1m4zMzBZxVGVGx+es3fCrl7qbHW6O4Oo3uzqo5THFCCUNJoh5SNADFXiB8RSgg6jYR2q0GoFEJAo9Gi31uibIwP/YwMBAtXO3QuNtn4bI/8dulXvIex7ofAOXCPhQhz7v8kZbStVsKli8ucWenzrW++i5KOdjNkOByxOTDsDL8sN98pQkBrGbrnYelCzNLiAp1mlzO/fYY4iFhotCjTjOHGBoHRDHY32KR4pGz5NKI163y8trrUpddrcufeDmlW0mq1OHf+HOtb29z9+FPyQ5vI+zlNhRFxe4kw6foeLmHAxYtnfc+NYoc4NBRlyWi8X4k2nk755cefAj7+EECt/b3U7yYsL7ZptBp0+10mWcogn6L1yzHbEvgaaoVfGXxNpH+NlwIhCJKIuN0kiGZj9H5bOXCQ5Rl/9ud/Rtxuk0+nlEXxhO+SEAIVKgSC2r48gukoEEIQBxFBGKOSGCEgn05PnUgHX02rVIAsakTpiXRjQRpf/aoE9EMgAF36xNwcB+enub93ZZ+TmBMSwgYg9pPNB7/RHRyFn/SuE0GIUCHClN6r6gWQ5yU3b90nkJKFZkikJPfv3QMhOHPhPO+8fZW1tU329oacVnAyHIwZDg6PIeaYF//luWMrd3QlXAg9OT61++FYyP6pk3idaS+Eyx3IjV+rPZGffQWI44SVlXMYU3P//m3K8sm10qXLV3jr9TcYTyd8lt4gjBRvvP4acRTz+fVPGUyGuGkO+bMr+dOyepikfRqeRqN1WwlvXlxFa82wE5OnKUU2PhKRDpzIEnhrbY3t9XWKLKOztMj5S1f4w+WzNNsxYaKQYci733qPN995i/v3rsG/e+FNPYGGgqaCqS7YeM75PQm83ejxPnPsZqPgHuHBTV1QTAdIU7EjwVhDs5kQxgpdZkx2C7QQODHnbtT+njo3G0weT+u9mAbAaM14PMHUxt+gKgDrbTFeCQ40FHiIuUzki5Q1CIGMImSzSdJq0m61yERFOqkJwpDGUpMoTpCzzOaJLEMkXuTt8Avt057PawNrIxhmMCh986w97VWRI/0wzakiSNoBF86t0Gk1uPP5gG0ms3JVx+7WBu//5IcsLXS5fG6FMPSKdGFGMBwiRgPAITpdgmRCjCDCPTG4PX5446zm+vqE1G6x+P5H1AaunF8iCAKcdqzv7qF1hQhCagvDSc44LdDaPJI99rGUo2bWA6y2yGlNJlOSm/ewQrLQamDPrbA7mjwzs/ksOGvRhcYZ67ubO+GfSStf3LwNH0wFQTA7X5ZAScIwIAgCrJCU9iVknY/xfe6heuDlQoUxKogxusLUJxz8nSPNcpSUVMZSWzu7IwVlUWK09tdQKd9s0oGwdtaE1+KM9v6JTznslxF8z89zVWqySUGVzTwJq4rNnV0aSYoMGjghiVsxC8vLFNqRG0AIXBBiHIxGe5RVSfdMiygIvZVAaWi0Fll9/RsURcZosEtZWpwKEJFD1N738JH9OfUjPBqkClCBwhrjm4EeA740XhFFIVL6svYgDAnjCKMN1hiElAh1cDp3WK1f8Bl2zEJioPTNco6ynzJChi2cM9h66rctGyBiqtr68mQBzVaHuphSjLZxRmPrGukcnaUzBGKV6XBKmZZUeUa6t0eRjnCuBCfBGpzR1NMxtlLYypM5MgiQQdM3N67nSp95p6OZd6gF4SRlOmW0+YB0lOJsigwSGnFM0GggIoFQlsrVaOsIlKMRRDgc2zsDoujwBqunjgDoBb5b05yVnBjf/PIU4LRj+mCKUIJqxzHtZ7N7SLK8tMj531zy95kKCcMIKeT+bTFrffOlwvw2r92ri7VOY7ycOWEkKw3CfowIYfDZFunWCDedJSyOeCzWWYo8Q2UKi/WWWGGIimNEEkBDeKu+CoQShJ2QsBHQbrfoNtoMxHT/2h7x0OZer4GCRuIVNnnuTlzNX1Wa4SilGcdUZU0gIUtL0rSYlRSDcxI3T4Y/J2YN2LfiO9Gt+5JlytZ6EkGqkEa7SdJoECQRzgn2BkOmownrm2PWNqak2ZNHIo6TeXkGBIIoCmkmMUuLXZRSBAE82NhkdzBEP/UC+3hESkUUBOAs45Ff5K81AqJQURSFLzEXgkD68GQ+TTvnkAJaSYASYCtDbRzTSiOnBYtRzFIYksQJnVYHKUsG0+kzK48P1iodZ+Y9uDKd10h/GZLwX+PXCM5RjHJUMKJKZ4mpx1XTxpCPx+jai0CUUsRJSBJGpMKRDfbw1i6zZnlf9E3q3ENSr9nv0Go3wRrS8bOJ2ONACd+U2NYGbbwgQ8wSgCqQNBsJZzp9v93hLlVdEwmwwocI8yIqi/9MqHwFYhxGOCAvCvShIjIHpgahEGGMEAFR1EAFAVU6oc5TEMpX1ToN9lFxn+e7BNYahD1ZCGGdpdTmEfX8cDyFaJfxZHoq4ckcQglksD+3OOuw9dMnQIc/z5NZKB5JCNz+3+aVuJGEdgCNcBZHzFTsofSh7qvg08MoJGrEhEFEnqUYY/xaAektJw9gMhpx78bnWCnodbqoQLG29gBnHdPpdLaGPuTEi5k42FmEEDRbHaIoJs1S8uLwqnjHLKEr/bkx1p+zIs/Z2dhAKL8wUJIXdyeYry2OAecck9GI29euMR1NSFo9uv0FLl25TLPZpCgyimlKdUgi4iSoLRSCV9IPxc1P/hHxAkT6oyjTIVU2ZiwEW3ckSbPNpXe+RRDF3L/2EePBNo3lVeL+ItY4IPYkehR6grtMHyO65x2Wjo88L7h39z7WOkonvT1FWb0an3QhPIF1mOnVfPNfoDlU1G4RLS3RX1zizNISQzlluD0lSGIuXn6duNlESUk2naLrE5yvAF/6bfHHe9o5jLyCn9+dLWZmpOCgBFnNCEOQEUR96K0m/NZ3foPlfp+PP1rj9u2th19z7Vcf8N/9n/+PvPf6ef7rf/z79DoxQk7BFHDnNuxtQGcRVs8TdlI6QlDyfLH9vd2MjWFOv7HOx59+yvJij3/wn/wjOv1F/sk//Xf84GefsLLYZXmxzbSy3F7bY23ryYXJfOFX4ol0kWvulSnxZsbtWzu0GjHvfedtvvXWJX7w/mdcv/tinqGmMuR1xkNlncCn7p1+SEC9CIIgoNVqIQVIagIpaDS8l1aJYlh98fHeq0Dc7NPoLFOkA9LBOic5auscm1u7bG3v4dNBc/ikpLW+uRdRDErhcDhtcXXtx0DnTqfu/gWQTyuK1O+Dc5DmOR98+hlBGHD20hLNdoPuSpfFc2dY39rl/voWqBCpQmpnuHvnc6Io5q2Fs8T9GFuD1RWLZ99i9bf+iAc3P+Puv/0XlHlF0GyiEksxmjzsfP5FI0oS4maLKs/Ip8fz5A8CRafTpN1uoJQn0uNmQrPboUgzyixHBiFhq8V8hHLWUE2nvpnoCyHDs3jPwvxm8sGjjDpEvdexOqMafu49O4MVXNBmmmrqnS2ajSZLq+eYbNxkuvkZVvtsa5C0ufKtf0R38Sw3fv63bNy6wXSwR5p9htNDnBmCiAGfEMo37iJc5X8PBHFC3Fn0HoWj0YHHzHkVvhAQdnAuYLS1zvjeTW8rYyxhY5F+v0PcXkAgcGiGRY2uIWkmNDptsIYPP76BOA0/raMgkch3mtBRWDlTOn2aw+3TuZ9tbdn40QabP9lEKImQAhEICOHqd9/gvbffJmxHxHGTJKlRIvRzeY7vPfFlxDx5/1VCCISC/veWWfjuKsOPdrj5Lz/AlhY7MU/xjzscRmsGe9sUQYZxhqAREbYahO02qpfAEj6Y2AMVSrrn2zR6CWdWVljod9lh5JXox5gbvK8stJqCC2cU1jpu3jVUJ1xDTSYZaVqgS8P43QmhEmxtDNkbDNF1TSORaBegXeCrWZ4zzjeADvu37wvNwmL2RQH+PL6Ee62qoMhAhQ0WziyThAlhGFNMcj6//jnb6zv85Oe3+eTjBw/tyeaEkEAgT4tIl4JWq8FCv8NivwM4bt/d5m9/9gtqY6gfI67FbPuOAIciUhHtRoLWhvv37iGEYG3tHmEoWWhJklAQKkgCr40pD3xdqCTn+wlRILm3mzPOa7JJxsY056044c1GgzCMsFYyyXI2B3fJn7Fol/iiYPCz2lFu7/lQMifh50mYL4FR6Nf4NYKzjuH9XUYP9rwI5uEf9n+0xjDa2EAqRe/cOZr9Psv9HqsLfTYfhOysrWG0Rp9yQ8wXhXOOIpsipeTi5bNcev0yAsfu5tapiXUSGXiyNq88kTz73kgFtJKI86ur/N53f5Myz/nRD37AcDiiKz01MZwppC3+GY+VImk2vT1xbwGE5MHmGpPD4nRroMohCAnaPVTUZGH5Io1Gh9171xnlI1AJhC2wJVTlI2y5q6uZPO7k58E6mBT6EcJv9GATubaNdS9m3fE0qFgRtfarMW1tKcflE0Klg8gdPNDekmM1hHCuM8XnIkoDrQjOtDxZLAVEwqvYKwF3an+dXjaa3TbL51cp05Ld+9sY7Yl0RYihfoRMX7t9h60HDzh/5Qp/8J/+KVVd8Td//dfs7e0+nTcTEoKZulRXSClZPX+Z/uIK9+9+Tr5299CPzc9sEkEUeFvavIbB7g7j4YB2p82Vq1cIlEWJ/TrcI0PNVBAHM9lHxNaDB+xtbRG1Wvzghz9k5ew5/lf/6/8tb779NoOdDfY2NxgPd4/1nc9DZvzrZfNGjpnd6jE2dGIi3RMjvvO6NVBXBVWRYo1GVyWmrtFliSpybD0v6BCePHfHWCUcAcZa8iyf3RduZtnxCj1NnzZJHDQx/ILgqgqbZdR5RpGlVFmKmU6QkUaXBUr6kjDrDPWhJZtHhMDfVbPLfOpwPFqfNcdBo2Hnb6+6MuwNRggraLYSzp1bosgLqrKg1iX317ZJlOCTT2/Ra8U0ggLlalo7Q8I0I6RJIGvaccjbl5doTUs2tydkxj61/5axDmMdU2HZHmqEirDWoqTAWusXIEIQRhFBECKVIggUSSRJIkFVu3k+4KFFqT3w3eCYmgoElFlBHAbYk/YBeKSC4oC66wRBj1LKN9jAUuc12lqKqsY6qPXL87X8skFKhQoi32BW+Cx3EPpJVdfVsQPLIwVHM+/AOA4IAkWVplSPEw2vWmLlZop3CQT+51prrLPkeQESrIkIQ4k1Gt87aDbBO0eoAgKpKLMp6WgXGcQIFTIdjYmTgCLLiJIOzimKdIw1h1iRfIFwzmGNOcI+CRqtJo1Gk7LISWcqB2O8in9rc5O6rsmmqf++WQDknMMZ+1Ca4BdlL3D8IgAhvY+jlDircU/zQH/K/j8y8DsNtsIUY+qpo8ZSh6Gvnnio6jQ4q6mqmqKqkWFM0u6irUJXuY+6kX4+F9Z7S9oSZwrm1IZQATKKEdYipPLB78FHZV79ZjRW177/w/xPtqbORl4hmbSRKkQ44xuiOoVwMc46zKuQQswUygT4mtcEH/Ayk+ucIuxDX63Z/KGAEKY7UzZurhO2I4Ybe0zSCfWg8iTrU+z1TxUv0RruS4fZRG+cQbvKN2NMay++OOa0LqQkbjZJWm2CKEJKRZw0aLe7xK3Yk8AaXxQaShrthEYnwVhDnueeiDnOsCEgCAWNlqTZlLQ7EVpDnOTUxqI1L9ybN5CSRqSQzrK9O0ZJGE1L0tLS7C4Q92NG45TROD1SnDJ/rE70BM2/5BmWO0Eg6XYT77NaVRhriZKEIAop0oIsLZ5o9H4QtvKOWaO9KQ8erBEFMYlqUExybt96wO7mgPEkpzpEDSgQBDI8cdy9utQnCCTWWKaTnCQOiKIAay1lVWOeer7dw5e1Gl3lOHxzaYejKHLqCvrNNkp5CeI81gVPpMSBJA4UgZIoKR7aiLrZmi4vawbj1O9bXpIV9dP9jB85N/s6kfn/H8V+yD32Am+hFM56EFj83KuN5eWYzHyNX3e4I/j8O2sxzqHLkrooqLKIIgqpyvJI498rx6z/TVWWZGmK1jUyVFhrfZx6QshA4mYhnnGOOAqJgoBmM6Ld8n1CpBJIJX31ZhiijZ7ZWj76XdY5jLVYHHESEwQB3W4bqQRFUVA+0Y/DK4ud8bGx0RW1rrFzqxZnfdyLBemrch6K0mbVwr7q+2jnIQoUYRCgjaGstU/uzbwn5sIOYy3WgTEW8zJUUu7A2tPxTAL9wEcekpIHq88d+7ytdp5QN24W7szmRukgcj4Efhl6zIOwxqCrGmctcRJjtKEo8pnw97H3WkulDXlRMh4MqeqKbDqlzHL/HM7XrE/AHXhOBdY4bG2QThLI0Cc+nlL5a92+ZhT8WKBnz1aeZbO15dGuuRCCKI6QUlJpjXniHjzaWGJn2zeA3N1BScndz68jrGHjzi2GO9uMBoNTnRGP8l1zoZl5xev/kxPpj6EqCu5d/xghBPWsOVk5GlJPp74pk9PetX9+/V400j5s22XF5tYuIKi186TAqyLSn9V+uYanMq+vAs5RbW5S57tsDqeMJynV7oT8xgOquMGG0cgkoahLjK6ptjZffFuK/QXbK8xhHIStoRzA9nTKf///+TParYRvfes3+N5vvcet69e5e+sWk6nmL9+/y89+9YAf/s1ntAPB1djRC+E7lyTnepLllYrFpZx3ltr87/83/wm//HyL3f/uB6yPMnZ5tiApq+HmLgyN4faDHbJSk2VeNRM3EnqLC/SXFllcXKSqSi6cSUjCnPWdmqJ0D+syHr9lNLNemmXN9U8+p6Ekw/yUpVHzzOoJBqJGs8H5y+epq5qb1yYUecFkWvlx4cvQAPgVQaqAMEyoVAgIorjB0sp5nLPsbj2gql6O15eUknPnLrC42Gft9h3WJ+n+6nFO1IG/iV+lOj0GFvA38p5v7rOzMZx1Et9FCC9xTKIQU2mqPCUKYy6uXESpgO1bn3DvWkWydJmot4L59H30ZIP20irn3/oOZTblxk/+gmIyflTh8wWjKnLqsnj+5C7g6jvv8vY3vsHt69d5/8c/QmvDZJJRFDX/33/xLwmjgM3NXdJp9vAYra6pppMDJKR7geMXEHQhaKHiBmHUQBcD6sn9p8zTj9IQzhh0meNMOTtODXoLtKTaWKPelhSLFxktXsaMJzj6eGbWq0xv3fycYH2XfqfDpW/+JoO1dXbu359VrHX8fB6WIEqoRxz0vZBRQtBdxKoppLnvDlTljw6gRQ7Vk/6idT5l45MfEsRNlt/6PRoLZxF1RlBmCFlhFahQETTiU2nw/EzMK7o6YBMNsYNmBEpCcjpq06diltjYvL7Ov/g//TNEKKhcjXGWcpRDYX2fl5cJiVdpz42ff92hAesY7+xS3BlTbdXHsnM5iLjR4MpvfIPupSV6S6tEUZNzZy+yunyWYnuLj1cELgRSCFsB566u0uw32Nrb5M7mlMFgevTtzsjk9mLI5csN2o2Ycys9qsowzNcIxwXjPf/IvQiW2jHvnO9R1I5//zcfUGpLWdfIIOSP/uSPefPdb/K3f/kf+PFf//mrWzAJvG1hzFOLdXr9Bn/8994iaQZcv/eAcZ5z/s3XWTx7lmsf3uSjn3yGK/HS6Md320G+B+UIfvIXH7G+sz6zQJFUU8PGryaUk5rRKH38Y97yIQjpNnrehukE+C/+5Pepqopf/vJTbl2/z7kzfZaWOkynOeYpdPH+TOBpjzwbUpUTeovLvPnuN9Fac+2zj6nKgktn+7Q7XYSKKQ64jzUixYV+gyiQhIF82OPkINa2BwzGKeAFK3Ym0DguIvwtPPf1fRbm+d753rTimDOdDg4ojaU2hvUXOEIAAQAASURBVL10SqX1oy06vjzhx9f4dYBzTHd3yYZDplHIZhRRltWpKo9PE8457t+/x+bOpu/X0W+itaaYk44nQNCMMdZR15rKWM6uLHF+ZZFmM6LbTgiDgMF0RFVUtNodhAiYbg+YluUT63ZtDKMsoy0FCytdut0OqxcWMcZw7dpNbt++9+QOGI2e7GBkwF5lEGELkw5nfyvAZt5auNnxx5qNwRlkq4OMEkw6weXpk997CFZ6Xc4tLrA7nnBnawcpoBsHKClQUgGCUVGSHyYwPCXoWmMys+80cEQyHbzFy1b9KB00J9ZHFWSzHIMQPtxdsT4MXnTQA/aACS9P95WOp5RZQbfb4423Xqeuaq7fuEaVPWbLA4g4RrWbDPOMv/q3/x5rLVmaej82o2cJksf20tlZlb+vb7JWMt4ZoicVrnYsNFfI65RpOXpi3xzehKEQT+Zdirzgzs07ANRVdaTpJggDLly+QNJIeHB/k9FwVnUxr3p9KG4+2pk2VcV0a5Nib5f/63/zfyCKY+rK28xm0+krTfIJIWi3E8JQMZ0WFMWrW0CcOpHunKXKH/X8cUZjHikxfzIreDwcJlnynsBVVeGQp9at+FTwKpWfT9m+K2qcrKkaE1xzhBlPsdMpaEudpQhjKNIJuiqhOKmXM1/88WqorWFre8g0DfmmczSbMVEcEAQSbS3DaUGOQKYFLQFCGXqhYCFqYk0EKiMQEd2VJudXOqztpsRKcJT0jHWQa8hKy3iS0YgCjK5REpqNhH6/R6fTptFo0GjENJKAJFZI6WepZ52+eRa+yAp4GZ5Rzxj8hJcKP3cSFVIQRqFXclqv2NGnoET4ysFZnN2X5gnh/UOdEycwNnveNv2kGAQBUZQQhiFSBTis90iHF/JGey4EoIT/avOMMX6u6vO5BbQ2niwVPtoKSYhChXAWZ33lkpIKJSRVnpLlU1zSwwQNyuEuxc6ab/IrJEqFRFGMiWLqsnxqlv9Vwz2vidEMAm+LFCcJURwTzNTbxhicq9jZ2UZKSZ5XPjH9cAPuyF7mz94BBSJAyAhUglDRzN+P5ya9nbM4Xc/u99nRunr2T4mrweYTdD6BsgA3V5kHOCGp6xojC+h1CZOm92S0FUJIZJDglMAyu1ce16lIiQgChFJPiOL3d9AeKm1x1lBlY0xdURdTwjLH6RLhNE6XmHwKOsDK+uVbu8wrugIH0s6IGevlmq8iMe2gzmt27+088oxScDTW6aQQj72+RGHcS8FMka6zGjcymNweTxV+EEIQJDFhI0GFIUopdF1j570xBMhQErYD4maMCpS/tFnJZJxSlce8uMLflkoJhBJY531AVejtXk7C5wZK0IoCtK4YjlJybZFhRBwENDsLLK2eo9PrEycJRuvnNrKbneaT3U7uwL9zz48DXyokRLFiZblNqxOxV45QmWTxTJfFcz2adxr+hMmn74WbrcdHuyncqR6ON3Vq2V0v0fmTn5VCoIREyQAp1ImJ9GYcIXHUlSad5uTdBmWZoI/c28Mr0q3VWKOJotBXkFuLnqng5tXkB8NIr6iXSCF8ZadzD5uOzt9W1ZqqPmaPEfYLCA4OLUetUJi/x4tABUr6/XR4kkh+GRXBX+PXElZrrNY4rTGlr3h5GYlEOetxNYfDofVRKiofRVWUVHVJlMTEzcRbyElxYn5NKIkQdtbXRdFoJHR6HZJIEUUKKaU/NzjCKCbUFifUY+I0P4Ep5ePtRqtF0mwQNxIiG2GtedgTx/cC8eUx8wSfnCnNvT2hn0uDMMTaGmvK/W0ciGWE8PsrZuPHUSClJFSKMAyIowgpHIHylTFSCBwC8bKDQzdb083dFY73UeqnHKx5TH86t9uB/cKvg2P3vJD6NJkEayyVqTDaoJTCBg9Vvg/f4yt0BUEUETZbmLpmPJqJtazdF9I+Lan10Dtd+Ri7qiis2P/uZ8zZ9il8mrWWsjjcPULMYrMDrkez3wtUEPjeiM0W2gi01mitUdJXWlljKIsM645g4u8ctq6p6prNB/ef/d6XDO+uLQkChTxut9AT4tSJ9FeDmH0H6Xr2/02sbVLWfqHtKgOVPbb3z68tMqAEm46oNkrvX1uXiCBE1TXCgbhzH0ZjyE5gcFkCW+yPdl/gYthaKHPQleav/upDfvazG6TTCVmaUlUW56Dbi/nuWytgLZ/d2CZNKz6+UdC8W/H9fsm3e7ucvZzzVtXg3tqAqbYUHG0gt0BRVVz/5DpbzRCTD1jtwW9/723+5D/7R7RbbZYWF5GhY2FxgbLSBFsDnqV1V0AX7yWWhBBIbw//KswbgySi2WtjjSEbTB8l8h6D0YZ06i1FvjyKiTlLBa+qRCQb71DlU7SufGVIWbCzdR9w6PoEFkpPg3NQljitmQxHgMSpiMXzFymzKZPBro9e7OxsOIlAoPGljSeBSCTxxRgkVPdLbHrIda+AHXzVyhv4yOgWXnKQADHousKOZ7YlzlHVJQ+27oHAe6E6RzncpE6H2FkD18neFp/97Z/R7HR473f/ACkFn/zoB+xtrJ3omF41nHPcunaNnc0NlBJcfO08RVawub6F0YY8r33jmUPKDk/u1+OgzsHY2f1gfVzYWAVT+jKfZ5D1zhSYfBvQT3/fdAfKbKbeqLx3YOs8MmrQXzlDnDQwzrI33CPPR8CYpLPA4rnL6Lpkd+PWzO7l0THSOYsxGqsLL+k09bGr3Yyu2Lv9PuO1a4T9CwTtJYqde5TDNR+YBi9ZEQ77FV2J8wO7E768yQl4VR6ocy/0gwmJOWH4sofyA4qnX3sSfQ4H9q7F7jrfPP0Fj7s2Fet795i2pnxLfY+kHfPLn/0lH/7sbxnd2cWuWRb7fd77B+/irOXerfvk04xsN6fOasxh4/XTMFPcjnY1N/IUJXNuRClCOkR48go5ozVZmlKWGuUcsVJ0V1ZpdrokzSYAV66+BQjW79/h0w9+gXlGPJKz75bzwreVBUb4Z7SJD8SmwNgnDppNWF6KePPNcywtdzl39Ty5sVRRjFYhTRX7ee4IO5HvGXRxsNrHYcpHPzQngttJi6VWH60taZafuMj33//Zj7DWMhiMUMI33tsbCrK8ODYBVtclg71ttLEY4wn0je09JmnGcPyoB3FeG+7uZZ6wnq0bdGVI2C/qfRGEwMJsHNPOhyDzkXye03/W0JZIH2+rKCCIQqxxbA5HWByVlyfs9zg6nqDva3yNF4I1huqI4owXQa/T4fWLF1DKPym11ty8d5/x9Ggq6oeYzRO1rbGlASmRYfLcjx0FUgj6vSYIwaXXzvPm1SvsbG+ydv8enXab1y5exlmoK0GQ5bidMcU0e/icqzghaLY5f/ESf/jHf58oCqkme9i6JBvvUeUlzmjfyyGJaDUTqtowHGcIIekvdAjC0CvPhSVprhA32ox2t9i6f8v3J8pnDVZn44OsaqSVuFofOZTaGY2Z5gUL/S7f/9Z7lEXOxoMHlFXFpK7Q1lG/bJHaK2rgXuPpowBo48fumUkObfyUWwADTn+InU4nXLv2qe+xWD4qJm11+jRbXfqrK5y5conRaMiNa9eoigI9HfOIj50QPqv+UOF9EBqHIC1HFPV8BheYE/SjOwxJImgkgrJypOn+maprzdr6FnGzyXd/6w+4cOUqt2/d5PbtW5w9c4Z33nmTwc4OP/yLP2M6HlMXxZeqsvv5+GJ8tF8dkS7EKcn852TY/BGb+acS4VyAMT6L4uZyh68DGo+ZV4grS9yk9NFj7L26hNYIC4ynMHiyvORYmC/CYT/S/wKvgTGedHrwYOfQv0ehYmmxiTaWkVRsa9gYGgSGVl7TnYKL2ixfmDKZ5NT26f7oh0Frw97ugGoicaainUjOnV3i7XfeIFABgYoY7LWJo5gwDJ+reJT4tFEEPit9ko7Nx4RSiqjpFWBilD6TvLdupj56xuL21WM+djheVedfXRfoen9StlZT5C/ZH8F65UhVlOSZZ8SSZgurNWKeoZ6r6BDIh4qGExLpSiC7ata48ClkyrxjV4iv25urXueWDhE4bTHV/uRtrWWaP7rotlWGrfYrn+oiZ7hxD8wqC6urREmDzz/4xYmO57l4XDX7nNP3tGf7cZXPeDhgPBywuLzAxSsX/GbmXohPDZgPur++6HUUM0kkOBNhTOR/pxr+37lh7dPgDM7M27k95Y114V8PN6kgaiPiFo1mm6SRkE6nFEWB1j5RHoSCZq9FlQuELUE/WTHlnMNZ4xcvpvQllseFs5TjbUohaMUdgtYiOh9T7D3N2uYlwIcy/iVmxoi18c/Mq+ol9uqGx8PxVYrbTwluAkxONv5aa0nzCWQSKywqVOztbPD5px/CLjCFeCHi7KVVyqLik19+xmBr6Mnh4+Z0Z7taFZaqmF+wkiCAxTMQhEf7GvHwP/vf6fAqsbqqMbVB4gikotls0ep0CcMIIQS9hUUuvXaVqiyQSj0z1phb5Z0Ysw6UsicQndmXZp5ITxJHqyFZ6LdYWurSEgG1kGxnJaNSE4jAf/4Il9mUDlM+fwwTQBREtBsdsqJgqNMTCxcePNiazTkWJZl5xpbUL2AdYIwhz7KZCt0Tf2lWUNf1E97DxjqmpX54XAKfWw85mU5EAPFs6lLuSXv75zVpC4RvlBcqSRgqcqvJqhLjHPX8yw5e06/XnF/jJcPxZNx4mojCkMVel3CmSi+rintHHdQPYj6ma4vWFhEGqEjNKptfHP7YxawPVECv22ZhaYHRaI80z4jjmDDwC4swaRAYcErtjyNCIMOQsNGmt7TKm9/4FoGU3L/+K7LxCOu8Utc5h5S+90UjCR8KzKUUxElMHIUz4hTa3SbN/jJ1lTErwQH92DpIW4TTR/ZHB8irmryq6fa6rCz2SacBWxte0V5qQ/2qxKKvYDMWTx/Nw2DYH/sDvMbkZekz67pmMBgc+rcgjEgabbqdPsuLy4BAhRGirhHO7Vd6Aw9nr3lw4x6fHBzaVjMngZdTeqkURLH3Vj9IvTrnSNOM2kJ/eZWr77xHXmv2xhNWzp/n9bffodluE/+oQZ5laCG+UtOZm1WxveoisVdCpDc7XZJWiyJLycbjE36bIG42UWGTurAzH/YamEBV4raNX5wrCY0G5OrVem2+etHri8EClcO6guzmHVAKg4Nuy5syPa0D8XHwZT32AxhMS/72002shXG27zMlgJsFlBbGuxPOrd1iuFuQGnNkRTqAto4H45oklKye67Lcb3Dx8hUuXXobMGArNtbWefBgxP37Q8ri2cu9uXLGCgiCkCCQyLp+Cf4uTyJOElbOnKUqS9KdMeawElslIQho9npcvPwadVVSpGPSacp0UrzQYuy0oMKYRnsZ5wz5ZBtrnrzHo6RB0uygdUU+GT82QR4Dh7lPvUI4Z0lHw0dstuqqPDT4FkLQabYJwpCsyMhe0NjWlZbqQQli1sjwWQLpAriJHys7s9dk9joB45FNJ/zqh3+FUgHj3cOTZy8Ewb7NxaxntrgQI1YjXFHh0tJHgDscOjgsLy3Q73d9r8wDf6/qmvWdHcpDrAnSacaDe2voSj+z+sPjiGz+E5AIoQgbLaRU1EWO0SVh3CPqtNF1RZlNPZEcNP0CwDJTW5Q8erHmxtaHXfSDAeOBv1kDRUqgFKtnVugtLXD/9l2KPAcVQ9SlKDVbd65jdY2pD0/Q6OmQzBpsVT72zErCsIuUEVqXGFOxn4B/CpyjGq5jyim6rqGxcEBd8pIeaF9Uh1hWyG8mEDmMKHwk/ABPdo7xq4t5MuprfCUhBAQx3taqPL0cjasNentKFQimg11Gkx3KST5rqsKj+S0LpBxjvPXPbxQEKCmptUYfoqIyFiZDkBKq55DzoYILS9CIeOjUNEhhkIGuLNuDEicl/W4TlTRYPXeW1sISK2fPsnTmDMvLiwh7lSh0XPvsl2TTKdPh5MiNt14UUgq++9YZXnun5xMQBehiQjnZYGUV4tCrJdvNBk4p9tKKOisxp1xRMo8FW50OFy5fYjgcMRxPqY9pffI4/v7f/z5Ga+4/uM9kMmFaWnbG5aENTp+HqizY3ljzDTmLDGcM+VRTKflUO55AwFIiiZWgGStCJdmYarazFzuuCtic3ffz9tSG/aVaAz8bPC3qKSzUAmSpkdqLs4IwRDiH1vVXYZnzNb7GsTBOUz67dRs5I7yNMaSPWfZ+kRiOcgKluHRpgX6vy5mz5zhz9gLpNGNzfYtQBaxv71JWmhu315imOaOJFzA1u32SVoeFsxc5+/pbnDlzhiCMiMOQ19/7FlWR89O/HrG3uUkxG0vLsmZ3kKKNxTqHwqGcIUCRNNqEcczrr1/i0mtX+VVQ8+DGx2htn+DLjSm99csLCD32hiPe/+Q61mjKqjq0h8SvCxw+PMnZr8+ftxWZrzBeJfI0RdeOIp+yt7tGURQUgx1Mrb27wyPwa4Wk3aLT71MVBZPd3ackuF903fZslKXDWm8L1O0pjLak6azZrtboIueDX/6c9e1ter0e3/zmN8kmI/7sX/8rJuMR03Q6s0b66sA6R5ZVlKV8gmcKAkEjUd5yOdfHyWMdCa+ESI+bTdr9BQCyyeREynQhIEwSoqSNNVO8O8JMb6IL3LAEFUFvFcIIyldQjv3IDrJ/Vl99hcHR4QDtcLqizLe8oVK/C80GaPMfDZGeFppP7g6f+L0D1mvYraE7yhjvrDMdW3JrKDk6kW4c7OYGVTnON1ssrS6wsnqW1dVLWJNTVyOiKGZ3O2N7a/rMBqZz+PWwQKqAIFAI8WrqrsIoor+wSJFlKKUOf5OUEAZErRYrq2eoq4LtjTZSGIq8+mKJ9CCk0en5QCTdO5RID8KYZrdPlWcU08mLD1UHxcdf0HNQZlPKI8S+QggacUKj2cRY8+JEunbonQNdZSRPJ/xqYB1PTr+BJ9Iznr6aPSKqPOfOxx+d7EsOw3xclzxspCyWQ+SbDewY3G7pyc49Dn0Uu90258+tIIxA6Bml7Bx5WbIzHB5KpJdF+VQPvMNx3Btt5jooFGHcRgUhRvuG00EoiZsNRGYp3WwiU7H3953XWqK9gv2R7T/tgs/Lkx4jo62FqkDGCQsLPZbPnmFnc3v2kRDCFnVdMNp8wLM6uJkixRRPlh0LIQiCNko1sXaCMfOb89nzW53uUad7EHehsej32b1EmXaE76e6rFBXE5y02O0Sl+KTMxuz981FYV8T6V9ZCOkdjYTwjdFPjfc1DjMs0JEkn0xIsxF1Vu6vRg/msObSryOPt75qKVSh99F09tDcvbNwxB5qBArO9KHfBEp8OwUDaQamtgxrSxQHdFdbxO0Wi0sLtBeX6S8u0ltYoNuM6TQi9vY26C71cBLScXqKJ/RwKCl48+ICv/PNcw9/lw622Lq1Sbs784cXgkYSIcMQ5aAuauwpN1mfX8pGs8HK6goOQRCEJ14kfvc7b1FVFdpOQZaMtwrG2YutB3RdMxrsPvK72tTPHH2VhIVY0o4ErVZIGCrG2vr44EX2ARg+ZWqcFcE9c+as3czj11qoLZFSNOMYYS1iplr9Gl/j1wl5UXBv44S90p6Bkz4zaVoShSGNpMlCv8/i4hILi0v0+7t0Oj3qqmYwmjCZZty8t8b0gF1t3GzTXlhh9cJrXH3323Q7LZQKCKOIxfPnMbrmZz/+IZOipppNclVlyIqDPYm8R7rCkiQRjWaLi+dW+I03LzHceoBSgsO0L/YEFh6TNGOSZgRS0I3VfDd+LeHwSc+DqHiW6e3LRVkUlEXNdLzH1vrz5kIfaEVJTHd5mWI6ZToYPKMK4fSvYl1DXTuaTUGnFVDXljw33hLUWmxVcevz69xZW+cP/uDv8N3vfJdPPvgFv/jxj6jryj+fX7V5zUFZHq4yUkrQbAQY4yiK0ydmj0ykJ9/6ezhdU9//BDvZO9ZGyqpEZClFWZ744jjwTeRs5tViT/xVz8ypM5ABGAkPXfZOM5ANZj5I88X9bJVysCz6q3QfOqAoodb+dZo4SEB9hcq2Df6uKZ2ltHrmSffsRqBPhyMrKkaTgmmaUhZjrKmoy5yyLKmdO1IBw3z961XpyldfnLC5FMC8USQCbyR5cEcCCaHCKEeaTaiy4qnlw1Gc0Oh3WVpa4vyFC5RFxt3bXWpdI4OMV1se8iiMLsnGuzjnsFaBSAibDVQYEicJYRQRRRFJ0kDgTt5YcE6+Kp4rgv0i4GlP36QyLTIqqymeJyN8FuZDrYBjNRLYY19+8EVAAC32SUrYN08t8YrOuRQigOBSgmwrbB9sleOqykd4Fq8sVjxRwj8aTRACLp87x2+89yZ7wxEffPwpkzSjPjCPvRonrLmfznyyelJprYsJxWANXVdQpbN5e9Z8xooDCu2j+rI/WpAZNto0Fs4SJy0Wli8SJy1qbdnd3qPdbfPam6+j7CWkqRlsrnHrV+9jdDlTwR+2rQBEiPdm3w+3nXNoPcHa8gnrhyhp0V5cxRrNZHcDow95QE0F5RikAhW+PB+tWQ7CFQazm0M4m2nmLnYRsyak+GflsBWFwJtIxnjv5i+PgOzo+CrFTMeEwD95nWbMN757gTBUfPj+Gjs7x/SdPQzzrlzaVwONhrtsb6+RD9J9RXoO5aRia2OLOtfUh93vT4WbuQzVWGdObNkmgDgMeOvSKiu9kE8+22FrmDIp9j2sNVAZC9OCRISsIpBhQBDHRElCqSvq8ZTBcMB4d0Q2meJeQUNzYxzXPtujLDTLy5IzZxTOaDrLl2g0e2SVQ09KttIhtRW8/7Nb3Ph8jfufb75w9Pg0OEAGirgRkzQSGo0mSp2Mbtja3qKua9J0SlVWRIFjoakotCWvjrb/oZLEocJYR1HpQ4/68sUznDu7zNb2HrfvbqBwNAMfbo4rS6oFka5RypAWhgBoxYKFpqTQju2pV8x1YomSMC0d1aG9Q54OjQ87jrPiMc5R1jV2Vkb+Nb7GaSJqhLQWWujakO6lL73C5lXCGYstqxPHUWfOLhMEAXESP7L+jRsNFpZXyKYpRb7tvcw7LaJAMckKaq29uq22xEFAr9+j0UjQMkKLECUlYRzxjW99l6WlJX7yV3/O7uYmMLNRnW1HYJmkhe9rlmS+SqvyIpTVlSX+8A9/n9FgzK2bDyjKkixPMS9iN3gIrHMUM4L/YeNTDpWpfKkQhgFvX32dXqfN7s4W4/GItKgYZ0+Zr+aWn7NqtS8W5rF/n45Gp0PSbqGSBrk2lLNGt96q8fBnWQQhMmrgrMbO+oCdBmptSac1xjjsQQsg57BlAdZy//qn/KQu2dvbodlsUpWKbDrBGvMV80d/Ep12i36/g64rsnyK1i+nOfORifTm7/3PcPmU6XRwbCI9LwpKwJWn0FzPOco89/V29vEHcMZ2OAPZmH0Gt4lfVZ7i0yhC/6KeSWnmK2G+dITZkeAcZC8hAy3YJwEKvriU4gtgntsqnSXXNaVxGPdiE5VzMEkrwmHGeDIhz4ZYU1MXOUVRUDl3pNvG4DmSWgi0VF61KU7hugl8RyWB7y1w8FGJFLRjtHJMxkPqonqq1USjkbC4tMTZc+d4/epV0nTCtU8XqOqKQA1Pvp8ngKlL0uE6825hQiUk3WWiVovewgLtbtc3KdQFzvmO8CceMiI8uVzwpbR6MlgfV2bTkxNv86FWwJHLNgy+u8wXCYn3a28d+N2cvBzjictZIkRIQfRGk+BSQjEZo7MplM4fr2G/Tvyx4G93b8jeYMhr5y/y29/7Dp9+fpN//Rd/xXC0b3V2MB32cm+TAH+wGu/t8OSFqtMhdTp6yp4cLDmYO8w+b48fJdujdp/Fq9+k11vknTffRknFzTt32Rtsc+HyGa68eYV+u8NCp8unv/wl967fwpjJvmz1iV0KQbRAVF7me0B6W9ej2T432HdehLjV5czrv0Fd5uST4eFEui78K0ig0feJy5eBWY8clxvMZubHjN5sd2P8/zdm/zr8ZXv8lCtgafa5B3w1iXT40o2Rp4W57+dit8Hf+zu/QaMZs/FgenIi/bFqGVdY9ra3EE3BdHsMQ3zclUMxKHhw9wGm8h7kR4d/fqvHPV9PsMuNKOJbb77OxZU2165/wNpOypRH7dqVtlTjjJYLMDNf26iRELdaTAYTxsNNdna2GGzuUhYvT0F5ENY63v/FJh/8cotvfzvke98L6faWOXv+LYKwwbQEU2SsD6ZMUs0P/sMnfPzzazOV1+nvTxAGJM2EZrtJu92iqqLnf+gZuLd2D2MMo/GYoihIgoBmVzHIIK+OFhBFoaLXjikrQ1mbJxavQgjefvMyv/db3+Tn73/G3fubhDgWEx9+buaW0oCYjfXz6Xg5kby5EjLILYO8wjlYbimiQKCtPjaR/iIqR2Mt+VecYPgaX14k7YSV15Yps5Jikv9aEelYiz1WleXhuHz5vK/6aTQ8kT4j5hvNFqvnzrO3s8fWxi4CxVKvSztJqPWuJ9K1hcqQBBFLS4vIIKRWEUoGKKVoRAG//ft/B60122vrvP+Tn6AkhAcCdIdjNM4QUpI0EgIJVZmj64qLF85x/twZ7t/boEz/iuFoRFWXmCOOnc+DdZA9ZrOl2NdrfZHtbZ6FJIr53e99m9cuXeBXH/6Cu3dusbYzOZxIF/i4N+DUqbsXw9OrYR9He6HPwvmzZKVmnFbUjxDph4uOZBgTdhawdUlVlTNB0MlRV5a6Ony/XZ5i8pSbHw659dEvaXV7LJ09RxWEZMMB9jRcKb5gLPS7vHn1Mjt7Az75dOyf/5eAIxPpb37jDUyRs73726TdRdKNNYrdbXwY8pyB0RhcVeFO5SDcbDHNM8wlDyrt5t6spz0ZzV325mUCX7HVnxC+27RzYF7i0Huw2v8rEA8IAa2mIookeW7IC0tew1bqGOR+Enux7xV0+gssnV1ChSHj8Zi6LMgmIwZ7u8dWeDlASIVQCvFE+6SjQwqBCiQykKhG6BuYVOUjRLlUkiDyCtZsPEWX+ukNrYRACIExljzPyaYpw8GE8XCKVI5mM2RhoUen3SbLMqbTCXVtyNPqFVYSzW9KjcV5GlD4ZopRHBO3YpytiTttRJGji/L4mdn5sSh8QDAfhiTImc+xHXNo1CPCENlIvPi3sgeGFudNdR9RNnwJPGTmsOyvSr8Cz/rDbmYBDzv4Ro2AIArQtaaq9ExCir9dFLgQTFnDAOxYQ+r21edzi/BDEia9TpNOs4HRFe9//Cn319afILIcr0qRPp8f53MYmLrAWo19ZC542p64R/8NY0QU+5+FIAxD+r0eOMdw8wHVIVZB1lrqskbX9cx+JaDbahHJgDiMEU4ikEgpaXY6nL18mXS8x2ArxxxmD+WMJ9HdQRL98X32NUYiCBBhFxfEZGVJKBVvvvdNbF1w7/ZNpuNDGm4745+9kyrS51UPhyXV5mFKOfv5YFWDnP1/Pfv70y7NfJwJ8ff2vKzqMMyrZeZK94NGwS/7JvyCe0h8EWg04NwirKwanB1g6ogzKyFl3mNnL2c8eQalJ0A0QARgHxckzBl6BUz9szW6tocdWPKN6SPFmKY0pFsZVjvMC3henxa8U4ahSMcUiSYRFUsNn8dONQRKEEWKJGmwvHqGTn+By1dfZ+HMWUaTPT797AMGGw8Ybq5z9+atJ+OnuZpt1s/i1Pff+WqXvDaMSoHUlkIIXF0zHaxTVbC+aZhODKPB5FE12KlDPGx49/B1AmxsT7HWkGaaqgYpLEJ4Jf7zkERzj3hLWWkq7as4hYAkUkjpd05IQRT6/1/otXnrtYtUeU4x3qPW5mGc/XgYFcx+GyjoNRTaQqkdtYVGHBJGEqkUUkryomacHr3M7WC7bgEEUiCFIEp81WJeFOSvKFnzNf7jQ7PdoNluoGJFNsyoihp7zMTQi+KgJOKrELqHYegrhoXAWkdZ1uR5zmCwx71795iMx0zSCVVZU9fefqnd7BBGLeI48YrcukJPxwRJk6ATESiJkworJFWRUZcFrXaLC5cvk6dTpqO9hwOSlIJup0EYhawsL9Lvd0iiiLqqqaqKMs+p8oyFfhslHaPxEGccxvqkopDeBXWOeQzsnKOs6pc8X3gEgSIIFFEU02o1qWvNYDA8cbXZ02CsYWtnCyUNe3tDsmmBqWsi4XmVL2sC4Lgo84J0OKJyEsfsQkcRYZKwurpMFAYMBgPyPEcXBaYsccZgy9yvv4SadQh9Nd7Q8waddVWRTyfUdf1CDcsDKZAItLMvzJOdFpIkIQxDGo0GSioaccLK0gJVVZOnKcZYKmtObT+PTKT/p//zf0hdaj459wY7d7e5+a//OWs/+HNgwPMkha6qcHV9ep47rvCvOTP1BJHteNJh6ZThnrVo/wpABZC0fDIinx6ri/SxMScGvgKnSko4fzZmaSHk7oOCB+slezl8tAGDGl7U4ltKxfnXr/LON98mbLW5f/8+0/GQnY01bly/Q/WUxktPh0AEESKMHp2Rj4kgkLRbESoKiXsdnIOdfIfywIFGYUCjk1BPS3bub+K0fYZKwj+TZVmxvb3NcG+P25/fZ7C7w8JCRG+1yW99/xu88/ab3L17hxvXP2M0yLl3aw/zChqmeni2ymEwTlM7w7ytRqvT5szqAs1Og73xNtlkynhjA1Me8/rMh6QIaLMfgMUQL3mHiuojsJMnP6o6LeIL57A1FMMKNw+mnYXJNpSPKxgVj2asviAYvE84fCWedQK8gjfmoSVG+1yT7nKT8XrG3q2xf895/DQTA8JRDqeIbXBT59USDfw1Nnj1+mPxjxDw+oVV3nn9PHfXd/kn//f/F3VdUzx2T726dKxmX64scBaKdIjg8e7zz4N/r2z1kYvn/IFKQa/f5zvf/g6Ymp//j/+C3bW7T3zSVDXZYEyiYpyxBIni8upZsI5SWOra4aynNhZXVvje3/277G4+4Gf/YYO0LtnX78/P2lyJ/iyUQIVsLBMunMFIxc5gwJnVFf7z//K/JokC/p//l/+Wa7/64MmPmhqKISdmqeZjQcqjBPecvZmXHFX4x3q+uQD/bB1eQPAkmrPtZHhF8uMQs/fM7/02PpRb56E6/qViXov8Ze4jc8pYXITf/T1otQqq7DN0GfHtd7u883qfv/nxAz76dPfpH5agVkG2Qa+DPfjWecNmC2yAMZp7tz9HSIGtzCNJm3JSsf2xJwS+SKWjA2pds7t2lzhVLIkp7yyBHcHOBJI4YHkhYensBb7zR3/KwupZvvlb36PRbvM//pv/gY8//oDN63fYunEHXetHbR4l/n4O2be1eUkYV4a7U0vR1HSEYDrNeP9nnzDay9j63JCNLfqUm4w+AcGMSBdY4XDiZA/ULz9e98S3sgQS5vRacQSB2mIXVvqwOzKs7/qFqnMQBpLFfkI8k3UKKWk1/PLzyoWznFtY5M79Df5/f/G3pIV5Yojbn6IdxloiJbi8GFHUjju7FaWBq+e7LPYaxHGDKEq4v7nHJ7fWjkRKzW2X5itKJaAdSqJQsXT2DK1un7vr69xfXz/aSTwOntKH+2v8x4Xlc0tceesSOxu7fP7xLXRtXtkYHeGHyyPIIr8USJKm5xoRVNowmUzZ2dnj2rXr/OAHf4PRc/sxgXIRUoScXblAHDcZZwVpWVGnU9K1ezQWFmn3+8RRgAsiaikYDvfIRgNWzqzy+3/8x9y8fo33f/ITzOx6NMKQyxdX6HbbXL36OouLizSbXbI0Y7i7x8b9BwgBb1xeIc06jAdj9oykqCq01YSh75MyRxAGtJoNrHVs7Qy9iOclo9GI6XRaLC0t8dprrzEajfnpT39Blr0cj82yKvnlB7/gWhKQj3Kq1NtjdYS/76Yvp2DrlWOys8t0b0DQ7REtrYJS0G7T6vX4gz/9U/oLfX76k5/y4P59phsbZFtb2LqiGu14El02fC8q82ql+EWeUa0XPsl0TD5QAs1AEUhJWmvKLzC2FELQ7/dZWFig124SyoCFbo/uO22KLGft9m3yrGCvLClOaT+PTKRLGiihiZpLxN2A/vmr6Lc2KfMt0jTGVQUmG86yKId1IZo/IqchRTr42actbF/FI3nEbchZ6ZG1vELZ7bOhFDSaXo1ezH0nXuLN70BFChUqTG1Orczp9CGIGjGNToMgMkBJaWCvhPHMXu0Fv5Y4adBq99DasLOzQ5FNKfKcqiyP79skACER8+zli+KhmMkh3OG3p7MOW2qsNljMMxdqRmuqPCdPU4aDMeNxijEOh2++Yoyj0+1w/sJZpBQoqZiMCnrt4Ssk0sFXtlisLjFViK4K6rqBrktMXWDq0nfktgapFC4MUSpASImpNaY+3Fv6Ccwtm+e21PO2CgJUx1cl2soPmyoSqFAS9kPiXozTAqUSrHZUVYXVGpv55Z4KI1QY+YCxqv0XBTMP53kJuZnZXM0aSnqP65f83H1JhrcjwxcmPFR42tyiU4st7f7C9qAA2wGV81bdFfve9yX76sfHz4GDqtbkeUma5Uyn6UNvw9OGEAIVBkghUUo9VO0AaK2ptUEIhZAhOIupZubu7kW6P3gGIGm2aC2tzlgIQa/bpdHqIIymu7iCrkryaUpVzvxvnMUajS6mlFnMZDTE1pqGClFCop2hdpYikEwUTCcT8umEMs8OVIYcpt8/yv47cAana+JmRH9picXFBVQQIpRiafUM58aXGQ8GpNPHslzuFPzRE/YbXRzEvJpB4Ym/WQUEzP6/5vltXvYLbTwOEvFzHOzbMLeKmQ0Nr/zZ/TKPFeKxn+cK54pjr22UglBBI4YkBikto6H3vorDLlEoUfIZ99a8YsDi+90+fusfVCJbQPt5+JHDEX4/nOOESnQx2/SjF09JRSPxVVR5mR9pIeasI8sqskASKGi3FFHub0TnHMZ4ZVMQxYRRhJAShyMvMibTIdPJmHQyJQgkzVbklYlF7fdsngx6yfdYZbw3d7Oy5Loir0rGw4LxsCBLLWX+2A6cYiVGq9kkiWOiMCLPC4qixFp3YjXjnLzTPBoPHuVrAyWIQ4lSDussAkESBcShop3ExJFCCF9pJBwUWUFV1uRpSV6UYN2hK7p5wZc2jqK0CKWIA4Xzmj+wjlpbytoglUEFhjAM6HbaaGMoywrrrCfCDjkOKSCS0lcBWosQkiRpEkchQRiDCpAqQAYRzhrcKfkdf42vMYepDWVRUZX1KyPR5/lseH4eR8zmmGNpLV4SwjDyKtq6wjnNNJ0yHA7J89zPcdZSVjVCKJphgAr8+k2pgDAMiKwlDHxFTCAFkRKEAt9kWfqmza1m03uf1zXOgQojEBqjNdY5qtpQVRopJFEYUtcV9WjMeDxiPB4RRSGNZIE4juj3u0ilKLVGG0NVp1S1F7M4wFk/Nhl7+Pj03POhFJGUaBz17Pj1c+ZgZx3WGO/qGoWEYXC0vmBzT/rn3AhCCL92dn47zjmyosSYGqs1Ttj5koFAChoi2F9qCUfljK/I+DLHiYfAWYuzFlvV2Kr0z7HRWF2TTSdEgSKOY/qLC+jRkEzMgrk5MeAO9Fw8ZQghCKMEISV1VWGNRkrlr5O1OFM/DC95yh7MKYZHjhn/O9835NR3+9gwxlDXvhqlrg3WWrSuqasKbSzmif08mdL3yET61jWwVmHqs8StFb73X/0v6f0v/iuuf3KdX/7kfYqNz5n84l/hqilebnXIQyaU9zJ11iu6T4yvSAo/aUAYQZFD+SUpDWy24OJlKCvINJicoxsbvwAEdM706JzpMtkaM7y396W8dEJKWqtn6b++TLJ3G+5M2alhOpn1KHnBfRZC0u0vsXzuItubd7n9yYe044ildos6L46dYBEIhAoQQYg4QbNR5yzG1DhhsVnmB8PHArhqUqDzChc6XFeAETCyhxIK2WhEmWVU4wyhY7SuUWFCq9Njmk7JsoLzF17jD//oj4jCLnG0gDGOunrSR/PlwvrF13ALPd5lbErqqsBkASKNGA1HDB88QBtL0GoShiG9/gpR3GSwscl4Z88z4PY5z/MIrzDtAateNFuu+76Frfe8xX36AKoxdC8k9M4lqKRN0GwSyBbN4Ay6sty7e49sPKUsEkxW0lq6RO/MBdK9bQb3P8dFMWr5HMgAV1swBjve8I0SgyaELe/1XM87Z34NNLDDvjVCAJONjDQusMp68qzGq4Dh0Uaq8zyKwpOiI55aF+uAm/c3Wdvao6z0SyPRAcI4or+8TBTHdHt9wjD0pIUQbO0O2N4dEkQJcbONLjKmG/ew2s5OgOB4Tbk9w3fl8pu89wd/jMCBtSglSZIGOMtv/uGfUKRTPvjpT7l/+zbUU9BTdDEm3fwMPW7zYV6QNNqcWTlHI2liZzTdXuBwoWWwtcG9Tz6kKjOKbF6N8eJzv8lG2DLjtW98m//Jf/k/RQYBG1ubGGP4vX/wp/zRP/rP+Xf/w/+bn/3grw58Sh04RyfAOfY73B2Ufs3J8pB9Be28ge38kjzvsswV6PD0aVwBK/h7uY8n0wf4hr8Zr66Pw5eZh5onG+YIgPMKGgLWDOwd7wT1WrC6ACuLXlMxnVg+eD+lqgK+8W6Xfl+hn0ZuB/jrpEAPQezMcqEzYv3heZwXZj4FcQLtNtQ1jEcvrudQBEgkBo09cBE7rTbvvfEOxlo++fxTJun0ud9Va8f6gwI9kPSXG1xcDrlZ57BbkBeaDW0hyUnTgniasbmxQRiHDEc7TPMxlfYPULff4MrVRfKs4ua1HarK+Dl3XvHwEjFKId2CQhX0RtukuxWbNyume4Yn7OTnjYMf7Yf8QpBC8O5bb/HWG2+QpyU3btwmnWbkaX3ipnatwN9KpYHiQD7mefeMAOIwotUIiac+u9yKI64s92nGESvLHeIoJIwSlAqwlePOjTs82Nrj+p0NTFURVBUt9gu75ijx03CVOcrK0Gwrzl1okBhHT1WoqmZre8z63pReJ6HTjml3enz/+98kyzLu3LtPUZSMRxn6EMFGEgYsNWMqY9lNC2wYsnLhKp1Wl6mpGdcGG7Zo9laoy4xyOuTUBsqvyDL2a7xcbK3tMNwdoWv9Skh0ASTKV19UBrJncZYCwkghlaCuDEZ/sTdsq9XDWMNo8y5ZOqHWGesb96nKijNnzjIaT9m5/QAlBUvnFnw8KhW1qWk1E3rdDstLi3R6C3Q6XVaSCCMsG+vrWOBbb15hud/hVx9+wMcffURVlvSXlqjKkvHeHlWluXV3k0Yj5tKly6ysODbX19ne2mMyGbGzt0m/26fb6dBsxPz2737DT/4ywiH5xc9/zocffIh1DjMjUCdpCQ70Ma1VpBAsdTv0mw0qY6i1YVKW7EymzxxW8rygriparTbOzQpKZ04kT+XgZQBh7N+gn81bBGFEq9PFGE06GWONYTTVSAHdpqTZC7CFxeSWZhzT6fYRM0GBtpZ7wwHTvPrKjo06m2Kr8qF1ynQ84K//5T8naTT55h/8Aa/91m/yflGw92BtJmQx/mWeF8S/OFQQcebiG0RJk60Hd5kMd2m0+rS6fepsTDHaepgxn9X5PrIXUgqU8nZKB63eHJBqjUC81PXtUeCcYzAYMB6PyfqL2NqSZxk7O1torcmrCuMsZnacAm+P7LC4FwwYj0ykjzf2sE5QZAFaSxq9JZbOrrI11jRvj6HMSZOez0hojbc9mWVWZIiQCoTypvtWg/4K26IcF1IiwgBXv6RGZS+EmURJzWVrp2Cu+BxIJVGhQqoXJ35fKgKBiBQiiRFJggj846EdTE+6IBOgwpAwTijLip2dbej1WO12CdTx7wuHV84Ya5FSEiqFsV4FdLwv8mU8zghcrbFO4B6THjnjs+VCClSkcBqsOHw71hisMeRpxmg4mVlFOKT0SiSBoNVqs7i0TLu1Sq974WhZ8JcChzMarEGXBWWRUQhJKhX5dIIuSgyOQLSQgSJutmg0O0wG45nC+wj38ZwAm5NhFlzp4w8RgkxAJEAFoiVQXYkMBTKyKOWI4gARWmQAQs3VWgIVRARJGxVNvOdzEKGSJkKFOGVx2iCiBGdLRJh4Il1JkJVXH8+UD9ZqcM6r7ZXCmsd9sn+N4di315iR4qa2mMx624su+wpfx75856C9+FwFOidG5IHvO4CirCnKl9+8RSBQSqECRRiFhGH0kEifq66lUsggQKq5JdD+p4+zpbjRIoxadHt9+r0+zhpMXQECJQQOSdLsIIOQpNUlTFoYV2N16ptO2YwKmAz3KPOSZtTw1RUz1FJTy5rR3i6j4R5WV09WVIj5RTlQ7fU8xcxMUSidoZlEOCHJywKjNUn7Ap12i0a7i4pi/6w4x0OX3pOOVXMz0se/xrGvJJ/nszOOF0vPv6M+8P+Pf37uXRCAaClES+JSg+NABcarwJfZI/1AOCQDCRHYhvBjwpEj5n1ICbNQgqKELHcMBo6qNEymmjiqn06kw/79Uvu54+E9dNh+z7YThaFX05UaXRu/SFYgzOGKoqPCn5YnnwEpJVEYYaw58nzuLGSFI8WyIBVxIyQI/UBqHdjaetVfWVEWJXmWUWlFVVdYq5FKECUh7W6DldU+41GOVHuAORGBrpQgVAJjHbV2s/vAH7w9KBZzsyndQFEbsqwgS2vK3FLPk2EHw2rJI/fWie79WS+KZpKQTUsmkylFXmKMObEivdVKvOIyrWdJ1kM2P3spJRDS+/sq5a1QpJDEYUSnHdCJY3qtJkkUEqmAQEhCqZAyYFrkZGXBYDhhe2+Ico4OjxbSzIcrKcWsugqMBWsFzgmcm/3dQVX7xqZxpIhjX6nZajYAR5zEWOcQsuCwQVUKSRAEGIy/LEISRA3CpIlJx5S1xglJFDVwRlN+Rawqv8ZXB3VVH7MB9Mkxf74ODRUOjOPe01ugpECLL/7ml1LOVKaaqi7JMok1GoF/jqWQnuwPHDIICYJoVm3ifCwsBdZayqqkoWsCOYsitcYAURTTbLWJ45hABRilUbPeC+DJuqKssQ7yoqQo/Bg8GAxJswnTNCMMI4qyRAUBi0t9ojgCmWCRtNttwjBEGz8/+8WY/24l5cP1/VHn6TBQxGEIMyJTzXtmPOPz1jpqa6iqmqosqar6iGI2CUewDxNCIKXEuf1gxRhPVVrncLPq2UhIojAmDhVK+rWLtpZ4GlFq6+e0L4PM+biwFlvtq2WshlFZksUxpipn8+BhsdLLS6IJKQnjBkmjTRQ3CKMGQRgjVUAQhMSRr7iys3P+OM0zL3CeDw1CSqLIexTVVeUrKr4E0FqjtaYoC/KiJMsLpmnqKzWYF3UKfyxuHlC/AkX6X/w3/zucCtDLb+PaS7jvv0doXyNWDb7x3rvsLjSpyynlaIfi5ifYfAz1AGxBsvou0dLrmHpEXe5g8xF655a/s46DL/Pi62kQgigOCZoJdV0+te/XK8d0DDc+8SuDau7F9HKtXSZbY4pxTl3UX75rGCm41MV1YqadgD0s+SnupHdiCRAqYnd3wLVPP6P73e/yrW9/h6jRJ4r+PfvS1+fDGMP27i4TpVhodznTW2RzNGB9ODjWfmkDWWG9mjT3R2z04avQKIroLXUxlWE4HD31fQBVVTPcG2OtZjoeIoTm0sUVFvtdLl96g6WFqwRBfKx9PXUIQdxqocIQU+WkGymVglEg0LXGGL+oqkZjXJnQfbPL4rmLTAcj9tg43rZS4D5ekdYEU0D64YyX7fnfTYqScl0jZI6QA4SICeRdXOHIPhuiJxV24uXQVZmRjsdoAuKVKwRxTHtxBakC5sakbqnvrZuCGBHEKCkIAoHRmnwyRZcF4/U7mLLg7OW36C+dYWvtOttr10/3PH8VMCfHNY8SH+ANWufkycGkyKxB6UPSJsKT7xbvzfsF5CPqqmKwvY2UkuHWDmJGogOUZUVV1WgpKQfbs4BpHlbU7GcFno8gDPmj/+y/4N3v/i61tdS6ZDyesLa+RRgolhf64Cwba3fJs5ROr8M3fvM7bN6+zfrNuZdOjtUl6egBuQoppyOUmp9QixMVlhJtgWYToUNc/liz3WQB4jYUYygGECbQ7Pv3pINnxhj3b3/OP/0n/y3Nbpezb7xLmDT42QcfoLVmKkKufPf3KIuCIsvZX3KeEDeYyTee8nfD81Xlz8KchH+ail0DuyAyRfe7V0je6DG5uUbW3IRNfGXFy56bJdDCM2YZJ1bnnhhz5s4eeNUQtAM6b3YRDck0nKJdjX0BLcQ4haqG9V24sw5aw2DiF8offLpLHA3ZHT/FmfZg34nH3ffcYz9HwGsQJSG/8943OLewzPt/c41rv7xLVcMkBWOOZtHxNMyV6Paxm2SSTvnVjU98+XZ+NI9VbWFzBGUuWHmtQdDoIALNwYejKAru3X/AME2phSZqhBR5ShBKLr61Svvdc1y9conf+/53uXtrjWsf/zOKfPT0jR4BV84mvHmpweZexUefp8iGY/Etn/geDKGcy6MLaC5A5xwom/P5+xtUY0s9l3HPG8A2Zj/PDYhniaxHkmfHhLWWazdusL6xgTMCaoE1oGtx4tLqP/nH/5CyrPjrH7zPg7Un+1+F+Mc3iRTnLrRpthKuvn2V3mKfu5/fYOP+fd688jZ/+iff92PpaECWTrl57TPyNEVKBUIyrg1TbciKCuvcw96wsO9e1AuhoaDfbdJtN5E4FIaittx4MKbWlrTUD6dv8DYFnVYT5yybO7s4EbB49jWvSB9/Rl09WS1hhULLJlrWOHKsc2SmRtQF27t7DEcjuv1FVs+cYTSQZOPtV1xB+TW+UjiQ33/la92DAo9nwAGFnRXuPLaPKlREzQgpBIGYk8wGZ91RONQDXzQT63lfz+McxTMxGu2itSadTsiylCgMII4oyoI0K5hOc6yzBAhEoCBQmMrb5WSjIbWpebCzxc8/+oC333mHCxfOk7TbrMzKxpyArCj4nT/4Qy5dvszf/tVf8s//6f+DqiwfsS0z2vDpxzfYvL/J3mjMcDJFG01dV0zSXdLxL1hc7PMP//Tv0Vvok+WGqrZcuHQW5LfZ2d7h7t27KKXodDqomcjQe6WPmaZHcDA4wMVOioKd6RRtnk/Cz5c0O7sDfvqzX1LXmqKont0uzxqo8/0veAZ0XTEZDXHMBH4HiP1pbikqx7tvXOE7773D3s4u1z/9jDCMuHLpdYIoJFhskxYFD9a22d0bP3tjT8OzMkVfBKREW8tHP/4xn3/0EcPdPTDViysbjgXFPNiVSnHp9beRVyVbG3fY2bjHQr/L1W98E13l7G7cpSxL6lQ/s8l4v7/A93/ndxFC8POf/IjdnZ1XcBxHxySdUtW1txy29uE9LxA04yaBCimtpbIzh4UXFBEemUi/99M/990RrmaIxfMMLy0wPb+KRLC42EfrkvjsVUzURazvgg4R1nv3Bq1zRItvUheb2HRWvvAiyq6vIpGOzzAGgcKcoCnkqaOu/v/s/VeMnWme5on93vezx4enZ2YymbYqqyrLdvW02R7X3TOzszsrrAwgs7qQAF0sBAi60oV0IUAXWl0IkgAZCNICWml2NYsdje+dttPd011dLisrKz2Tngx//Odfo4v3fOEYJIOMIJPMyqdwKpgR55zPvfb5P//nD8P+kz2GcP/nzdQkKquo0s9713wfSAGdCOZiCl+QGk11goOb68ACiyDPC4aDIUpp5ucX6c05PzXPkzvFTB76fbMNqxKShbkFFjpdJvmjFwmx1m3sj7Kr8zxJ3IhQQiOleKDoy2hDnhUYU1HkOb5v6XaaLC/P0Wn3iMKOswOzljqvzDWXp6dOF7gsAT+KUMUYlaY7rgv7rqUsMcwUYK0WQRDsfsFRUXscRzhbBQvVNm5ynxWrrJSmmszyvkUKeGAnzuphK4Pp3gVcRVnkWARes0MQhsRRjLc3u6HRAiudBZDn4YchYaOJKkusN6BME+TGJkYYWt1l5lcuMh1vIoScbRKfs4H2OKgvt77F9fOS7HpVV3veU79vr7q4LkhqeLS2cYIwxsyIX7gfW3ufXJJHOo7neVy4dJm3vvM97ty8we2bNyjynEF/QBgGtKMQrGY06JMmU5aWV+h0ukw2t3GskrtJ1mpUOQE8ykyz66fj6lNAAWET2VlCCIvNJbuUiQA/hrADalbRWvoQNl19gIeMJZPRkI/fe4fuwhK9MxfA81lbX2eapMRRRGf5NH6SYn0X4DyRzJnhQ/5+DHJt57N1TuZh32OB3F1L2OoQLy2SDQaOrB1xfKXsUSDYsVJ60jXhjwTBbr3mGtapbaL5CNGUZGWKUhYhH/32lJV7AWwP9/7FstHPHsx5WO6t/laPQQeJdAn0QHYkp15e5KXT57j24V3A6SWK+9VweARYDq+lUKmKrcEDiqUeAmMhLcG3UFkfK8NZptculFaMx2M0lnavSZSHVFWJkNDuNVmZb3PuxTO8fPkyuvLw67n5GOi2PC6cilHaIgV4PjQWXaHwMbgFwoy5DSJXaqiaaEYbCSpxSnvpAaFE+GAaBlsHY+tnKdmdJw57HnuHmtnf5UxlWCvO+8Mh/eGQ2ItpBk0EHlJEHHfyefnVSyRJxk/f/eSev0kh8HFLmHYgWenF9OZbvPbyaZZPr5Bsb3DnOizMLfCNr7zJNE24duM6QgimScawP5oNy4Khskz2LCL3NuuaSI8kND3oxAFznQZCuCIAeloyWqsoK70vkUYKl5Hl+z7WQprl+GGT5nwP4RdOaHAIrJBo4Tvf3lnwubKaQivSNGM6Seh052k0mmTTEOGkJ1/iS+yHYOaRYXf79udBpNc/H3Ls+3FkQgqCMEBKQTgjIitt0UessSTELIPE98F33tf2BIn0PM/QWlGWBVVVOdGThapSpFlGMbPU2H0eTqmtjSXLC7IiIc0LxklKo9GgrEpioNloOOJdQKUUZ86f5+zZM9y6fg1VlqhyP39hraW/PSQZTZkUBUmxO1kXhaaY5lRKYSwEQYAo3AjX7rQ4dWYZpUvW1zyCIGCu18SbeYprbRiO7qe2uBd1MyuVIinKR2pyWZaTZUddiNkjC2DNXkV2vU+anVilnIiv0epy/oWLs/e7zMi41SBqNJiXmrgMGQwnDEcJ1prHy7Y6YmDpqUAIrLVs3b37lMjznQPvebn+2e7O04ibDPur5NkEOd9hbnGRKpuSDO5iTeUU83sHCSEQUrptsWdpNptcuPgCQkjef+/nT/F6jgbnk36IfFlA4AWEfojSClBgxWNnMj5CouoGaA82foidtLnZHjEd38D3Y0K/hVKabqNFa8mj8+a3sWVBSIFEkfsdCtnAjA3V+gBTTh5PFrPzkeeIUbeWajpFFyW6uI/q6AsIL/Rpn5qj1W7y/W98g9NLS/z5n/0V7/7s/c/71A5HpeHOGL2VsrmeMYkCJqvHUzbthdWG1evX+LgVoRG89I3vsJYo/pP/4/8T35P8/d/+DZIk4/f++C9YXT9aVK8mSPMKJqmiKJ/wTFGn9dQqK7ivrb7WBVm6ibUGaxXg4SHwjGD12ie8+5d/SDOKaDcbxM0Wc0tLeNKD1tKTvYY9sNZSpimqKO5ZIB2E1opbH33A9t07TNOS5tISKh1RjjIeaRxSuN14rW4G5+cKu+Stz4xrNFAVs2KY+49RpSOsVljhAjSVJ1HD+ADZJ8C61GuEpL14hsUXX0dVJeOtNXftVc0Wu881Osssnn2DIk2YDvuzZ/eI1/g5IIhimnPzGKNJBn2MOqYcvMSpDmtFQ03+wa7HbU2M1PN0xa6i91n2gJ5B+gFRuwdAMR1h1MPzpYT0aHTmiFsdbJmRb91GpU4tUmYp/fW7oEvGN99HCjB+jBWStRtXWK1yxv0BrsEfNOOuK3DWqfd7bqIqsclgxrSEIELHVEnp3pqnIEPonnaL02zqihEcMc0wT6dc+dmP8cOIaaqplMGTBik1SkZUXoMwjOi0O8eqR/FUUKuW73fpIXAWTFMzvXaHfL1PMRy5MWnI0+nmtW2N5OFBg1q97uPiQicVh6/7896MzgP8oy4U45tjRCSoGiVWWqy2uzUVQlyff/T49Q5qLvWRZu76fD3cfWmwq3pWUCWKd3/6Cdeju9y+ue4+MlPZP0siWimh3YFWaPnF7SG/2My4tZXue0+RZqxdvUIQhfTv3MDzJRMxIScj749YlxKdwrmVc9xeW0Udd9wH8qRgtDEiHSlX0C6D/sfgNSWdi026Z3xGIiXTJdkYzMcQaGiWEAYevTedJcDS+RcIGk1+9u6nXLt+d19R6732MMDuHrfOcqoxa1+tZsSbr50n8D0+/OQOg+EuyVKZiqRKiIMmvUYPKR4jbWIPfuX7v85oNObP/+Qn3ODGzpByeq7DC8vzxIGk15Q0mhEXXj5Dq9OgN9fDk5JX33id5dPnOXX6InGvRWEVQjjRxUTD2AhOLyzQjmOK4YjJeEooBA3pgvd6phqrN6baQKFga5gxTjXNWNBtSTzghdMt8spwezul1JZzKwu0W02SPOP22oDu/CLLZ1ecDZ7vo6rqviGGsiwYTgY0mi1e+8rXkZ7PNMmZpEPyPAGTMx6so6qEPEse3ULxS3zhISKP+PICshlQ3BmghrOJ4Wk3lQcF6A7AjwRSgioth5VW8IOA+fklhIW123fJy+KBClWAIAy5/PWvMbe8NPMTE6xevcaNDz44sSyOza01RzYPp2RZTpEP6G8nVFVFUVYo5Yh1pTWjyYCsyBBWgIEsT0izFKU1voDJaMBPfvwjlk6d4o1vfZt2s43vu/MOw4jQ9zlz4QJvffNt+lvb3Lx2FWsMnU4T3/PwDGAFcRjiBwFlVZEVxU6e57Qo+PjTT9kebhPHLTw/pD/YYGtrDaVzTp1ZpNXu8NKLLwNw984dkjSl1+vieSFJmpE+gOi21rI9TUiKElUUdDj2suTksY+LFawsLzHX6xJGITdur4Hw+N73f5Ukz7ly8y6lUswvtonigG9+62v8aqPNtWvX+fjjjylLRZ5VR1vL1NuIvQmln+fQbQzH8tZ7bMzUItbDGlCV4u6tT1FFxnCwCcBwNOKjjz5Cq4pknKGUojog7OzOn2J++Szz83NcvHiORhzTbLXJ0vRkLOueEqy1ZGVGoQqUmBVA90OkH2K0QpWPxnk8ApE+dN873IaRYD3UbEyGRFGXZnORZqvLysoFRKNF1ZgDC80gxJce69ubFOMhpjKo/gRs+kgnuYN7iPR9v3xmobIcjhzx+2JA+h7t5R4Lywv8+l//Vb5y+WVWV9efXSJdW9hMXRP3xicewTTGsLW6ShRIOkvznHnlda784mP+6b/657x26QX+l/+z/xEAP/rZB0cj0sVMKuX5lNqS5IrqPn6WJ4U6uC9q5W2tgDzksMZUlMVwz2dnRLqF7bs3+ewXgrlOh+W5ObrzC/SaPgTBUyXSAdQRg1tGazZuXgOguXKBeOEUhS0pH7V2p2a/UFjgVjwGxy+muHvbwrXJTO0vKld/TTFFF/tTlB92JQLLwguvoZUiGfap0hSURu4h0uPmHL0lwWSwzXRUgM1x5OazPc56YUhrYQFdVWTj0fGJ9FmQevcAuNmyFkQfZpuh2Q2KPAeQnk/UmXPB3iw5IpEuiVo9mp0eVAXlYB2du89VZcF4sEWZjNCju3ieZPmlN4laHfrrt5luP8gOaSaVPgxGYXOnWoeOi+R5wS6RbgpXTTHuOpuy6aYj0o9IdpR5zu0rH+H6QBunik+AAlqL0DuDxaPnRTsemc8sHlaU1AdOA21LdnNr1zYEnl68bK8y92HHEzhv8toa4yQT2gLc/TjY12cwlSFdS9yxl3Djsma37mwT12SPMTw+tmCxVqDXhH6EuxYNOtNc2bzl7vFg9/1HFBM+NUgJzTbEoeWDzSnr03sHT1UW9FfvAM55CAHeSozo+IySEpOWNMMmt99YZXNr+5ELtR2GKq+YDiryxO13dQHjW+C3BBfebNA8E1JsV2TDkiKBYgvaAcw3odPxuPxii85ch5fffJ1GZ4H1G32uje8++KD1JjRmV6QArm1l0IhDXn/1LHEUcGe1v49I11ajtSYIQqIwcoKEY+ArX/0ag/6AuU53p6atARbaTd44f4pGw6M75xO3Ys5cukjYiN0lCMHFFy7y8qst/KCNHzXw88yp6C2kGlIriVpder0eW3kF4ym+ELQ8D2Ut01nwc8cZwzgNQTYtMKZgru0TeSFh6HN6oUFWGe6OC7QxLC30OLM0x0fX7rK5vYXfXCBuz++M2Z6Q980qUlqh0ilxq83Fl14B4fHz937BYDSlKHOwFcmkTzJ5wlm8X+K5hQg8ohfn8RZiVJahptnnJ6g4wqQiBPiBQPrOscMcQpB7XkCnt4BAsnp3g/IIe0wvCHjhtdc4f/nl3dOpKm58+OGJEYjD0QCtDdNJRlEo0uTwtasxhmkyxS8LQj/AE5KizClm+z4JZMmUjz/+kNF0whvf/CaNOMabeVcHQUjcaLC0copLr71K3LjFnZs3MSja7QZhEFAmJbrSRGFINMusrol0BaRlxY3btxlOhpw+fZpOp8N4PGAw2CYIfOYXe8zPL/LipZcwxjIcjSgrRbvdxPMCtDEPIdJhnOWMs5w2bgVbS1KemV3bflEzC/M9zp05DcDa5janFhf4yle/yvrWNn/6k/cYTca82XiJRjPi1dcu8/Lly8R/6XP7znVkKsizg0Kchxx7b3T280y2rjPwPxc4L3BjnTXw1vptxv31nb8mSUKSPDgLotlZYOnsK7z00nm+971voKqS9Vs3yLJ0Rg7N3vjMNLz7o5gVqxdBjPR3feJ1VaCq7JGu4TFKJ+EOMN7AIlB+TB62MO1FKEuCqEW7u+yKNXS7hEHE5niLohyjdOFYOFuXrxe4meZR77o98PMZQK0i3et/mPC5+OQ+C2g2m7z99a9z6swpeq0OKq+eSiXyE8EjRPSPCikFi4sLXDh/nlE+ZfXObYaDPtZajNakidtEPkqhR4NE4zEpKtIqJauerG2OqhTp2KlxhJB4vo8R+tBUb88PCZtdrNEU6RitDasbfZI0p6wUV2/cpRdHLDaanD59im99a0wYhrzx25ee6DWcBKp04lKcrKG9eBpdFuSTEfYRmIogDDn94osEjYiN4U2m+Yh4oUV8qkmZ5aSTyYn7KxbTAVtX36MqCkzWd/ZOpsQiGG5dR1VjiiKjyHOKNAFbD2DP0Dh7H+iyJOkP8XyP5XMXsNYwWF+jSNOHf/goqHnevc4iXwRYPXu8tWTjwWN0oxHzq9//LmfOX+D1V1/hzKkV7v78Az79xftMJwkrC3OYTgPTa2CMpsqmlOmYKqsXaHVEYq8hfU38PGiFWxPYM5NhrcDKPZ8pwKSgypnn4KN0HsHueqSuHOvugxfF+L054maLVreN96wT6TXqWsgHg3AKWMMRrwP2xy6eZgGXhy3hPKAJIhQEy01k6FGlGfo+G+ZHwt6iwQZ3L5q4IOZBLrdeB2S4+1Oym5HC7N/B7HtOYq036x6eD3Fcp2s7QtHUliJ7ryPEEa/zs3PYYMcRaZ8C+hmEMrA+deVpHsXxz6QKYSzWaPBge2vAT//yPSbDhDI//gWPcrgxgHG+f89rKsP4akber4hFyNmLIXMyZF6GNKmYJ8XzwTc5NjH0797AD/ukk4f4u9ZKdI/dQPvMUswDohYIUfLJlbt40mM8OVxrGMUNTp09R+CHh/79qBAze5QXXrzAV776GsPBiOlkymKnRbvZwg9ckXajNKpU+IGhO79IGDe4s9pn89o6SWYYTxXJNGH97irjyZQs3w31C2Cx2yXwfVSWUU2nGGPuqdSUG/BmRLwBlLBIYcEaqqpElQbPGHxrSKcTBlKTzywOyzJnOOjTaDVZWj5FEEW0F5bQ0kNlObqqMEZh9khxy7JgdW2NIGqwsHKK7sIid6+WjIqj2yx8iV9OWGspshyZGrSvXL2cMQ9XuHxOsIBSFmlwNS8kMwW5T9hsMre0PLOLjLDaHNkwSlcVNz7+mNH29g65tnbz5okSiGlRYg1HKm4ohUDiMiatMQhjaYQBlVIUVUmaTFm9dRNdVdy55ixclk+dotVuoY1GVwVLSwt853u/wifdLr9496dk6ZSyLDFaoyqDURYhLAhxjz2rUprNzSFJklFVmlarwebmNtvbYzqdJlEUMR5PeOedX6CVYWtz0xX/rB7hfs0CsVaAFYIA6FqBspCagxVNPl9YC/3BCGMsK0sLnD21QrvVws6CELGASkoW2m0We3MIBNM0xUhozXcxMkGMcpcd+CioH0v9MZ/dOVexy93Bro3nFwxGKyaDDbKpT5k/+t54OuqzfusTqqRPOZ1gdMVkuEaaTEgmk883SPGYaHZaxJ0OZZpTJKkT4T3iNTwekY6FwW0Y3kYhXNStfY7RxNDoLtN+Y44waNOZX6DR6iBXPyHL+xiVsbuRjnAt93HiZs/gkwqAHruptvVm65eUSO+22/zmr/86F184T5yXlGmJqZ4TBuoJDAZCSs6cPs2rr17mp+/9jGufXWG0McQY63xAR0OAI6cmWwQGH2U90rSk0gX2Ce+aVVkx7g+dR5YMEYFHJcyhRLofNegsnkWrkqpIUVXB9durgOD9j685S3oE88Crl15EbY9oNmLe+O3/xhO9hpNANR1STUe0FlfonXmJIhlTpFPsA4qvHkTUaPDqt96mszDPj/8qY3prRPtUj4Wzpxjd3CbbnGKN3eUYTwDpYINsuOWe155FrQU27n7I5l3HGtn6l8/iOHsfVEXBaH2DVq/HS1/5KkEQUKTpyRHptfPIFwp2V7Vt91bluf9zb7da/J3f/Vt87evf2FH3/cVf/Ft+/pd/TG9uifMvXHbF5IAiT/n4vR8yGmztaW8BTnZZsUuk7yTy3+fYe89txm5pH3QdiDfHzGWtU2wkbte7K+v2Gw0aSyu0mg2683M7xaCeedSq6dpuvkYJ3Jj9+/Ps3g87tg/Mg2h6NM728OOQ6dYWevuYRHodtxHs2jN1gbnZvw8j0tnz+9p5qFai10vZOoX4uPd0tn4MG9BbmNUtLkBVUG2AVQfeGwMdYAUXCPiQp1Mw9gRQGbg5y4g48ulasBOFnSiXtdWC1dUN1j91Y8xjeagewFYC28m952VKy/YvpniB5NJ3V1i5PMcrc23eWOgRlFMak3XSLOfK3QHZeMpG/jGKkGl/cOhxdlAr0UOcnVjKTpZB4EO3A0YXvPPzqyjFfa+x2Wxz8cXLRFHj0L8/CoLA5/U3XsGXlluffMb6rducme/R63awtqSqxuhSUeUlfhDSnV+hM7/Ezz64zV/95Bfcur3Jx1duYYzdqYNjrMWf1XARQnBmYZEXT51hfX2N65MRxpqd2FZ9hdWB6agCR1hZTVVoVGUJrCG0lulwiMrGpInrJFmWsbG5xqJd5qVXXsNY6J0+h41bZFtblNMpVZXvI9KzPOf6zZu0Oj2+8vVv0Wq3SfqrjLZWj31Pv8QXG9YYsiQBv4RIwQJujnnMOolPHNZZuuwsr3xmWc4Nou48K2deJPA8PCOodF0l+eGoqpKPfvITBM6bHAG2HgdOCNOsnI33DybSBS4QJ7GkyYQyL2i3O7QbLaZZSpJVTMdDbnzyMdPBgCsfvE86mdBotGi0O2ilUFScOXOG06d/l95ch3/5L/5LCpWQF5m7RuWD2b03St1LpN+9u4mQgv5gQKMZMB7lTMY5p09blpa69PtDPvroA5Qy9NoxYeATxQ08/4g1P2biACsFWgoCJB0khbEUlUE9Q+sBay3rG5tsbG7RaTY4tbREsxHjLgAaAvAlK3NznF1aRgjBYDJBe4Luyjwaibg7fPQDH2wqAW7tVosOJI67q0URz9A9OyloXdHfuAXCPFZga7S9yri/xk3R4Oc//DkWBXYE6If2xWcSQtCe6zB/5jT9u6tMttYf6748JpEObkU7+wlQpdjJHZTJmKzNo5IRjWaIsQXlaAPbX4VkALZml590K613S4ftbp5A/kG9ca2jXOAWx7CrYnqe8Yi3LMtyPv7FR0xHY95+9TUWTy2wcmqZM6cWQGusUrMGq1HaMkwr1Alsgk4cJ6BM9z2IQ8HSwhznTp/mFx8ETPoTsmnmFgNak2cZnhD0GhHLnRaTvCCvHkCqW4s2CotFWzujv55sI7MGdG4R0oLQWCucKHRv/m99zX5Ap9tFVQWTTQ+948/qzrfmIsbA5mTCp9euE4fHU1I9XVinRJ8OqfIUe0QbiTCKmV9Yot3r0Wy0iaMmL156g/nFFSppURZk1CFeXEaXJWUycXLEE1qE3vc87eFF5J40pBT0ek3C0Gc8zsiy4wSDDFqVjLc28Xyf6oBtT7vTYW5+HqMURZq5flcVbrM/K4CmlMKcYFGkZxFRo0GnO4fwA0TcxGjtfPSP8PyNsUwmU7b7A0bDAWmacvfObco8o8gS8mREEEbEzTZRHLNy+jztTo+iKJ0CKK1cWubOoWoGsv73/WD3vOBoaUP3MUUUEqSH9Dx8P3RemrnBovHiFtKT6CLHVCWmtFSTKXlZMTAa7ykWQz4W9hK7B+Mjz+A0u4OamJ55RVvPotICW2hMeQL9slb/wu79mfmHCwmy4ZI0TE2y77Uu29tUaxgej0Dfrft0b7OuQEvIp7NDVi71vj4PryvwmhIdG3TLQmtW1C2w0FY737GjtPqc9jeeHyA9j0bcpBE3yLKE8Xi47z3Haoq1WKXixLMd73teBoyyJNsFwzhhqj2KRozUlStMKgWhFFRYNvoF01KRZA8RR1h2s0Hq9jXbzBsLhXDPX2v7wLIP0pOEjZgoPh6RnqY5WVZgVIm0irlOA39liZWleXrzHXxf4IcLeKFPZ6GDF4YMBgO2xwl37q6xvjFgNJ6ilCIIQubnuwgEZVEiLKiqYjQe4/kBnu8zTBNya6ns7n0PxW5CTa1GN0ChLIPM7NQAr7SlMu49VWUQ2B0PZzfPJ5R5B7BIKYnjiGYjRgUS47nEpl0IfD+iO7dIo9UhLyv0eHp4sbIv8SVq1HH4BhBI51lV4QJiz7qQbs9AJwRIz8MLI9CGcX+bMIyY684j/QAhPFyvOzzbT0hJEIWusKd2mcq+7+N5HlVRzch4EI5+Pt5pq1nZ64dMII5IF3iiPqalUgpRFiilEFgEFmsNRZ5z47MrZMmUsy+8yNziAhU+nvRQVUGVJxijWFpeQmIZbY2oSrWTHelLD096h9pHWQsYS1G4PXtVOg/3PK/o96ekaYFSGq0MRakwxqKtxPMU5RHGnyD08QOPUAg8KQiEJBYBKIWnFGa25/68EQb+PntEIQRKVSjloXWF7wvOnj9NmqaMJinFrVUakwlRu8l4OARA+oKg4SNKUKV+/EVEvT6y7Op7ay6j5vCehZt2Iti7eD0en+AC4xWajF3R0fN6oyxFlpEMh5RZ9tj35RhE+gHkA1j7IaXX5FZ/g6A5j7IjuqfOML32DubTH80Iob2r+ye1o6t3S7XiXR34m8fhu6JjIMApm+rscImLSFfANvt9kZ83PMYt629t8w//H/8ZSyvLvPW//9/x2tvf4u13f8b26lVsmmEmU6xVWJMzySt+en3EJH/GVh2SPav5x/wKCa0GdNser19+ke9985v84M9/wOqndzDGYK11G4utbeIg4KWFHl0h+ODOOqvDyX2/12LJlYuI7xKgT5YhMWVdsHtmfyDYLXKWsS+NvNFscO7iRfI0YfPWJ5R7LARq3iCdfazY2GL6h3+KLwT/6yd6BSeLfDqiTKdYO0szPwJ6c/N883u/RqvTpT23SNho8Dd/+79FZ36ev/jhX/Kjn/4Ir9Nm/o3TlMMh/Y8+wlQ5z38k7nAEgcdrr51lfr7F++/f5ubNoxXavReOEcvTKdfeexcE6AOBqPMXLvKtb3+HbDpl6+ZNsixjvb9JqSqUlBghSMcT8vQLJz3fh/mlFV5/621KbdgYjMjSBOmtcpQ2VlYVV65eJ1OWn/30x9y8fo21O7fJJiM8q+h7hmanR6vzKo1Wh6999zeQ0mdjc4vReMr6zeusXr+65xstD/efuB+D+SDUSneBm4T3RvlCCBsEcYN2bwFTlYzXbmKMobF0kaDVI13fohiMqKYKdfs2mRAM9noAPuuofb/rAH/tWPOso4FTVwdAQ2KxpGsDKMFOTmB+83EK7trapbZBkeB5EC6CyqDsc3gWysFTqCPCj4K9TbNGvSSeJWqUGQxH7j1G7HmPgOiiT+NSQG4qEltB4EHUBGHhfOLMqAezc08OuYanASGIWm3CuMnFsxc5f+Y8t25d4/3339mnAD4Wcp6+bYJ1JM76JyM2PxvTeqPgtLDMhZpOy+IJQSfw0IXlk88m3Nq2D69dY9ndH9TtSwF6ltSaciRrVS8IaM51aTRax7rEjY0hRZ6ST0eYcsyFU/N0zp2mO99h6dQcrW6X0xfPIaWgKCakWcYf/9VPuXZ7lXfevcZHn9ydrW1hbq7D229/FU9Khut98ixna3WNzbW1WdcTGGsw2u6sC32g54MvoNSgLOQWCguj3DApyp14G0BpZuvJXFPmUIkZl1lkjDZzGmGIMYYgDJjrdggkmPEmFBa1I9V0ipBGs8vLr7yJH8asrm+QpAmT6fO8efsSTxwBsAg0BTR9CH1IJGzy6HPD00bd6Wa6wyiKaLY7VHnGtQ/fp93pMvf2dwmbTbwgRsoQayusvXev7vk+ncVFvMCjKFO0UbQabRpRg3F/SD/fRCDw8Y69jDKF2Q1GPyCRUggIPYnve0ghZuNEyjTPEFg87KxGFEwmI/7N7/9LWu02Fy9dYmVlGdHtgR8xGU/or10hz0Z85a032drY5Ed/9hMmWe2lLGjFTWI/QN1nfrMWkmlBmogddf5olDKd5o44nwWDp2nuQg0im33uwQO/EIJOp0mzHe8EKZqez5wfk2YFgzRHGLXHsPDzgZSSTqdFtEcwF/iSJJmAVTRiSRx7fO9Xv02SpPzrP/0ht1fXWZhr0G1H+J0m/lwHP/RoLjYo84p0K8M8bm24WRB+x5a5rjVTr5UV92wdnl8cZZ/1KKjYTbV5lpU5D4GF0fomk83tYynqT45Ix7iqPFagswFYTda/gydLyumWWw3uoI5gWI7qzfpoECB8JzEyB4fsvSPvCZJT9Sao9t88KKB73vGI12GMIUkT4jTGCvDDkEazQbfTplCKzGiM0RRaUypzomlfzxqEFE4tFATEUYQUEq30zjULhPMcl5Io8InD4MhevPYxG5mzZ5EzP0yJNYaqUg9/Dnu7qXRFZQkEttzvlW60Jk8TyjzDPmCAEjifu2leHDFx8BmCtRit8DyPuN1ywY00f+CALKUkjGKCMKQqKxAeYRDT7szR6czT6SwipMTzfJKgyeTmBpVIMMX4QF7/wyGEcM/XD/elCBqjqYrsc+1zUkqajSZRHGAMFIXCGIOURyMNDodFSo+40UBKidIKa8wsvdw4T39ceytVhdJq1g885MyyI2o0kH6AKkvK/FnfBT0ahPQQ0sf3Y4KgibYlqixRZfHAPnrIFwGC6WTK9tYm6XSKtQZVVRR5hvB8kukEZSyNZg/pB/hBTBApvCBE+D7WqJk1y0m1wT0SXyGQfkCz2UNIQZ5OUKp0wS47U2NY49qGUrO6FC5aanWFqd+LAV1hixTr+RCEcAJMuhCCbttlYWRFQV65+iHmJPNv98ZWn6d1SL0sEziS1ILNzMll9dUK9L1NT+MU6YFbNoahIO55aG3Jcu1UwCeh+6izt2oFYy26qJ/PXuW73TPVziZG6bs6u0EoCQKPKtfsiIJCXHHqcPbfz8Bkao1Lvfc8jzCKCMPIFXPSrtjVyRzkZL7mUaErg65gMinY3M7wuhLRCbHAMIX+1JLmhqI84gnWb6tFn3uKBh81UbOsCgbDLbLseMRvf2uTssipqgIpLFEU0IobdLttenNzNDttGs0mpapYvzNiOBqzurbN+vo2SZqyN0XDWoPRFRhJpSoqpSiVolBqJ2lib9MHQIAvnbi3nKnNdxI27K5Cvdb3xL5ASpwaUwiUNqDtDlGGUZSpK6ZtigxbFkhr8aQk8H0C38wKsLn2WhYlygjSNCVPU4Iwpt1boMwzyuKLHWT/pcVxRFP1nKWtK2ghhCsA8SS1gseAkIIwDBHCZYm4dbc326NGNOMGmVJMjcUiiJoNwiim25tDVxXpdERR7PZxz/MJ4xgvDPACHyGlm8KMy7bWSh/IGHLfe2zU9/YBX2WtU6BbaxFCEHgeGmczJRCO0pcCO/MRt1ajVUU2HTMdDvCDgKjRQCAIgmi2j3IHDsKAMA7dtRk3aWujMQ/ITnZ7mz37ZGPvCSxL6SGE41CMtQ9sQ54QTuFtQCs310rP3f9SaaoZz7A3Ae5BeLK1Iu3seg1SCuRMjZ6kKYHv7XIexhXPLoqSNCuIAhBWEUlBIwpQRYnRxlmfHvdM68mn/rnXLOOoN+2XFs/g4HZUCPCjAOlJVKnuEd09Kh6BSK8raD5kdjAFZNfRhcfGj6+xFQRU6RSnP61RdwCBW/nDifqfCAlRA2QAReFyY3fg4TxX9uZ1nAAyXBGvEKd4Aud3WPF0i3g9CTxOzEHiPCzboDyFEhWtbptTp85wdzJlc2ONcaG5mxpKbUlPIm37pHEMJfoOBAjfRwSB++l5bqG1B57v0+3O04oi4tYGYV7i+Q83x/ZmURvD4QU/H4QwbhC32sSRT68VURYld+6sP1Iaq5CCuNfGa/nkKqHaU1l81O/z/o9+6IqNZvcpjiWciAMLyXM8Jnd6HV596zWU0nz8849IJgeNdu9FVVZsb26ClJy7dJml04Lz514Gr0vcatOZW+DurVUm6RyT7Q3ymz/EZIPZp/dKMe7fSOMwJggCOstn6Cyd2fl9Nh1z57P3UeWTlPPtzY279+G2W23eeuvrBL7PZ1c/YjS+jTElcQxV5V5HQV0YrV6gtVpt3v7ur9BstdgeDsiKnCLPqIoS5ft8dPUqyWjI2vVrYKHZaiM9J9kVwnLqhRfoLCyycesWtz/55AsV4PMbPYLWAn58GlV0ySdbbN/4jDybHLkteL7H4sopVs6dp8wz1m7eRM/y44uiYHN7GzkcsdEfEjbaXHrjm3R6i1RK4Qc+frOB1+1iColJpie4FptpFKWE0KfV6/H1b32LIAj48BfvMtjeRGVTdJG5YqS5oipSxskQrEUr5z2Vbd5CSM8VnEFDJUF5eJ0e8eKLCO/42oMoDPi173yFC+eW+fmNq1xZu0s6LJhuPIHAzfOWdZkCd5kNb2Z37XFSugeNE9LUiQp1alQOtEGEsLgc8vK5Hkmuee/TIWmqd9dyx0EELM1+9nDDdzL73k1geJ/PGUegN+chaEI78mkWEWbVMP6sgI6BlzKnjAyFk9avGyfj/byS/KylSBLKPKc8nWMFBFHE3NwCRZExmYyeT0/NA7h2a8r6Vs43X13kG5cuMypS/vj9Tdb6FeP0MQa3Lm7/MOb+7eE+2Fi/w7/+vX+EkBL43z76sWf483/9e4CBckQc+nR7TRZ7Pc5fuMArr76GxpCagturm/wf/u//Pz67cYdJklCWFVFoWFnymSaG0ViTTKdc+fQDV2CuX1BVmqqsMLhdWBMXL9tL/UsJzQaEEsYakkNqf5nZ5xq+4NKCTzdythLS8/l0K2OynRMGgk5DEuiMtWsfIYVgvLmBKgqEksRBm9CzdJqWNEsZjEckyYgPPvgFCJ8smYK1vPjyZRaXFrn5yfvcuvIBJ7Mx+BLPFOLZZiS3MH3EfltnnY816IHb2CR6t+D3M4Yojrjw4gWkJ7l97TbJNCEOW0SNJosLy6ycOstwOGSaFjTnFrlw6WW63S7tZovpaMT7777DzaufUfuatXo9Xrj8mgsijrepyoIyKSjznGKUIRHoWf0oi0WdpIDRHvh5AEopNvt9PCnpNBq04ghhDWJ2DgLIlWY7yfH8gFOnVmh3Omzeus77wvDqW2/TasR0mm2W5r5GkRvW19YZ9LdZONWju9CkmCSoomSaVIzSsSO/HxOelPS6LXzfZ5KmFGWJ1XZGGu+HFIJ2HOF7kmyaMxmn9Hoter0WaZKzNdhCa+PIdPYbHR52hgJnQwugjuGYcj8YYxlPEqQUtOKQOPDZ3tqmzHNevHie11+9TJFlfHTlKv3BgGTq9tHjpCTJKhpJQWswpKgM01RjtD0Z8Um9Ri5xPF4doYWHZjx8iecT0vdYfGmFuNekf32TydrwWN939F2hmL3V1uaR94MFk7t12KReHtXEz8HWWBOKT2C2kd6sAvVhivSDRNQJdcaZgorm7HfPmxrsJDG7vVZYtNVUupqpcZ2qL89L0kIzznimCmGcNAQCISVCek4JW1X3bCCFkPgzv0hjQdWR6CN9/9HPxHXBmQpeCOeH5/kEQYDRGk8KtLhXASWEQEqXjra/0JVABh5eGOwoemtoVZGMhzyIAdk7Rz3PRbI936fdaaO0JopDitzfl3GwF07x5FTryXiCETAdj0jGY3zPY3FhibjdpbuwTJ5DZ/E0RhuquxFm34z+8CcvhGt7QRjTaHWd0t33kZ5H1Gw7f7qqvL8aWcyO99gLw3vP0fcDGs0GnU6XZqOBkJIsqxiPM6IIguCQIftBR5i9WQgBwl1fq9Wh1WmTVRV40lnvWNDaMJlMSKZTpmmKFIIwbuJLD6RASIEfhERxjB8cscjPcwQhPIQMsHhoJVCVRRUVqqwOXajv/6zACwKCMEZ6zpdCKU1Z1JMezoexqkApCmUIK0UynSD93VROY50fuzjhKd8PIvyoB1JifEGj3aU7P+9UQ40GMgghnx3UWrAai0br/eyos1HaAyvAKoSpEMIe+7wF4ElodyLm55u0BhHh1KfM1K59Wn2MHRkmxxscn4WB9aibkaNY0By2uTnqNVp2yeW993fmdW4N+J6k0wsgAC8U7nxOQplUK9Ej3Bqxft7l7He12O2AQl3MlrLSn6nSjXDG2YlwhKsEUc2UdaEEOZtRn3bNmQPt1hgFxlBVJUVZIISg1e7gBz5KK7RSVFW5kzH0PCIvNHmhGU4UWSFIcsHWxLL9uDZEtVdrXeKpxhG+riwLtrfXH++4ezAd9REC2g1BFEdEcUgYhwjPo1RuPV+oimmScv32Oldv7BbiXF4KabUiKuX05korpskUawVlZVDKouyu7OPgZcIuoePP/vigYUPgFOkNX+AHEuFJAk/gCVdk0BcCoRXpZIRAUCRTdFmC3yLwIqy0M/VmhfQ9rLWOwBEeVs2yDRstOnMLhI2mWxNZ+JJI/4KhNt1/nPm9HsMtUKj9wd+TGoLlnu3bUb9TuDWtNfvX8EII/NB5l/uBj+/7+NK9Qj8kjiKiKCaMG4RxTBCFBFFIq9N2prlxE+kFzprVWnw/IG620MbAyGUjG2UwypGdB/cPJz4rPeALLVAphRYC0Wy6fTYaD7kjPlPWzsh1g+95+J5HmWckoyFVkYHR+DJ0Na3iJp4X4HkBUUPjBxJTFKAUQliMNY8satsH4ch0z3Mvpy63mNk+rFaXg1N0+1LiS+n2iJVy+05jUJUizV1BVoHbU3nCfaaa2W4dduydpM7jbv3uA601WoPyPbSUlGVJmgryPKeqFGVZkU4T0mkyyxR1mUJaWyQG31SUemZ+ccRjSiFclupsr6jNIQ4Ie9dceynCL/H4mGXE1+32WYIAZOjjxwFe4CG8md3SY07rRyfS514EqyBZhepRUwcbOP3BQe1BPQPB01uY1KZHHm43Y9nNjz0G6g2gwW1ufJy6RAIbPF+q9HohX4v2HwcamIAJDZsbm9xev804n2B9S6YtW4klVU9/r/fUIQR+HCHjkJt3bvOzn/2M1bW1fQOLwKVzKW344OYq126vMkrS+3/nDGbWZx46cQuJiBoIz8MUGaiKMs/RSpF5kunAucT1Qg8bxAyzknJPKl633WSx1yHJcjb7o12SX7jInhf6s2KF+89ut28ffn6lddmQ9bufd4RRyEuvXyLLcm5/dovxYHTPe0bDAT/9qz9HSElRFFhgsHGbdqfLd/7m7/LN3/wbGBmh/Yizpxf5nd/6FttrZ/mDOx+xOVE4aWTBfk+Aw1FUBZWq6KmcgIrO/DIrF1+mLEuWLr5IMh7yyY/+jMn2xj2f9cKIoNnBakWZjB/N+gPY71Owi9fefJN//7/532Y8GvH7//yf0d/eYjxxXmszDvaRFm91nQHhBwSNNtaLuHv7NlEYUpYFSmv62xv7itzpytnIWGA8HrqMkKVFwkbM9toqazdukKfpMzf5HxdVPkKrnLExrAVdtKroLFwmLlMmg+uoB8zrze4cL3/lbdq9HldvrXJnc8DmIIGwBzp3rxrWgq6osim3P/kZfrBLpOdFjsoyZ+1ygvf30je+xVu/8btUWcJkaw1PQqPZQquKIIgJoxY6Sx5jlnd9TGcT8tufcFwmve1DIBU3tj5m7N1k6FfEF3wKT7plSAjMz96c49YNW+xP6HveEANt3FpizPEH+y4wh7s39T1Kjvi9hxHpM6jM1QDJQktSaLLKoIU92Q1VfY7B7BW685BdECWIHMjAJqDXwPOhd9olV6ZDyEeQbmd4QUmVOtmYF0ZECyvQ8NChwCiDCtaxpLtWMsdZyx0Fe2sE1SU9SgvacPf2dUbDbc5feIG/9u/8FgJBVRWk04Sf//QnDPsD0nxCWT0PJv6H48btEf+ff/I+WakZT45xHVN29xHx7He1BdBuzPKJ4uULPaTnMo/iVosInxCfv3jnI/6T/9v/l6X5Ll//ymW2ByPSZDfbUAi4cOYMr10+w6dX19javoFSltFIEUchl8+fJvB8rt3dYDBJd7ruwW4rpaDdCGgEgkFSURVmx862ho8bUiJjmYwVpQeJ1VQIjFFcbIAylirVJHnKOLvj3DdL92zOLnfptZtkZUVWVjRaHZbacyhtmWYVvu9z7qWXabXadHodtKqwwoeoA6oA9fCswy/xHCGzUNnj0QACRynEuH5cuxwdE0JCPO8hQ0Ex1KjsCIOAgHCuid+KqCY51TjdGTvKvOT2tdv4vk+302Zxbo48Lajy3C3AgU6nzSuvXsIPA27fuo7n+5RZSVUpgnaLUy++SJYlJMkUE0SMp2NUVTLc3KTMM1RZgjZPbLzyZ/OmngW/j3JDLB6GgMjziXzr7FysBmOY88FYxWhjnWwyobh0yR1HSuLAxxMCrSxLS+f4e//ef4+tjTV+8Ke/x/bGKoO0ZDpOCTyP5bkmWaEYp4+X7WuMYTRJnO1Us0Gn0ZoVdwRdVVQzC9QQb0ak7//8dJpTFBVWuzx1TwriKMDzPIJGjJCS0WRKlhcciK9grVOiSwmtZoDnSdKsoixPfneelxWlcjZIjThibX2DP/jDP0JYg8mcVeTB7UHp4vL7rL4ehjD06bWbBEFAt+0WKLfX1pg+iF85uEb8Ym0DnxqiRoOo1aIqCrLJ5OSjMseAUYbB9U3GdwfIhqBzqUc5KMi3Hs+67ehEetQFU0G2+ZiHqe1UDuJJWXrcrwfUvUSwKwE6gZTqvVHoesFbFzB4uEvHs4U6On+c8XMWI7G5JU1TxtMxpSqwwlJZS1a5QkLPTtd6MhBCIGcq4NF4zOraOtPpYYtwizKW7fGUtUMI2MNw9Mi3QPg++D6icuSt0c4buOYiQk/SaccIIXcitzWiIKDbbjodtBjvVzfMlLz3yoj3SOrugz02oM85nOeeLyXd+R5xq8HG7cOVYWWRs756e9/vhlvrCCF57Rtv02nFVATk1qPdavDSC6fphtBq9tj22libgy1nJO+Dn7/WGo3GaIWwbjO7sHwKZS1eu8tkuM2N939KMnBKLLtHgiCkhx81MKpCpFMe3Yt//3trv/bllVN85/u/yt3bt/gn/+i/YHt7a8cv93Ez/Z1KQ+IFIVZ4JJMJpe/PFsqWbDIlGd3bpyxOxecZteOrnU23GW9vP96JPOOwqkSrkiIYk0zHCOkRxnNILyIZ335gsDcIIxZPn6PR7jCcTFGDIWmuQIZuXXDPwQxWGyaDe4M0JwnXriTzp8/w8tvfJh8N6F9voKsCXSSoqkJIl3kjjkGCW1WijkOQzRD44EnDMN+mmEiqTgu/GSPbAtEA2wSWZmPp1Lq1xPDYh/184eG0FCel7I5w9hf145A8WqDhPsOYVaAVVAUUlaXS9sms/evUYZ9dorsBwoBIQUxmM6d0SvSo44j06RaUCc42YM/MKTyfoNFGNHxU5GEqjQ2GaC93Su+TLEVwP9SOiTXpWxe71ZbpdMx0Oub02bOcPXcBP/DBWCbjMdc+uUI2zcjLA9XKnzOMJgXvT4qdxNTHRl3kzMdtT4RbY6HtA5KBXcbg3v8+Dua7MZ7ns7C4QKPTw2QaWxpurW3zB3/6Ay6cWaHbiEjzAqU0UoiZuELQbrVYXlpkbXOKJyVaG8oKwkAw32nTiALubg2A+6//hADfl4S+JBCKgHtjQI5MAt9CWRi0gKEyZAbmIuhFkFazfoxiWkzqpJOd2kBR4FNp7RS6QUQzblNWFVk5wvcl8wvzdLo9ACckEBK8wEXbnkqn+hJPDSe1Gan77Swp6EQgwYslfiyopkdPT/OiAL8do4v9vUdrzWQ0wfd9FuZ6tNstdKEoVY7VTtEcBD7zrRiDZTIeuboEBqyxyMCn1etgpCBXGjyfoixRZU6VZahZlqIUAivApZW4c3Y/jt9vPM99lzEuafBIXyl8hAzwfEvou4wprd2+LZKCShuSNEUpjaqqma86eDM/b6stzWaHy6++Ra+7wHs//jOGs3pnZVERNj3n4a3tY48O1kJZVs7Pvd0iiGYllQWoXGIqhYcgFD5SAGK/rWtVKapK7SwxPE/g+x6+79NoREjPI80yitkjOXiOzsNdEAQegS9nXvgnT6S7OhbupwWSacJ4OCD0JIudxmw+2X92xjrO6KgQAnzPoxGHRFHEfM8R6RtH2d99EZR9nzOk7xM2GjtZFM/SbGmtJR+lIKB1oUOj10Dnj680OTqRXm2B0Ydvmh+KWqLylGTZ1kA5dgsffb/FucbtvmoG/KSOza6iZIpb7X1ePpVHxcEKE7u1144Naw1ZNmUyHTCZ9plMBwibsdgVTApBktkdVfIXEUIIokaTqNliY7uP/9kVtgeDfe9JsoyPPr3i/p0eX3oopSQIAldopapcwaciQ1QSqw5vjNpYhrljOqoDjGbgB7TiFnmh9xHmVluytQnFIKPKC7eRnm2gvSgi7M1htaYYDrH6We8Ej49kmnDlg0+dV7c1aK2P5JO+F9YafvYXf85kOOTy197me3/z76IF9AW0Oi1+4+/+LUb9PrdufspgsMFg9SbD9dsP/2Jg2N9CKUV7fp4XXjhDVlZM0oSo2ebtf+d3KLOUm7du0e/3yfubFNubaAXFJMUPfBZOXQRrGG2vUZWPF3R84803+frbb/P6V97i7OnTtJsN/jv/g/8hd+/c5g9/719x9/atx/reGlZXVOmERrfL+fMXCMOQK598yGDQv2/xNc+TtFoxnueRT8dk0yl58gjZVrPMnTD2iVshqtBkg+KICpmnAOfAsj95wUJZjBltfzpTV0uwBhlIQr+NyvOZP/h+aFUxGfapqoq43XZfrK3z5TJ7A+VPL/VKSI+X3/5VTl96nblzL7C+sUU6HrM1zCiSCaO1GxTJhK3V2+TTESo/fGyVnk/U6iGEpEhGaPXkCL30ldmypGPwPYsmw5gSMy9pvtkh7DZpv7iCqiq2rtygGmWu9sqzgr1B9s8rCipwbTvEeY0nuESdEzqf6aji6gcTlDZUQ8NOVcTjorakqe9hTbq4Lkhdc0x4YBvAsqvHO1YgFFQPOYdWs8XrX/0ajajBaPkm+eaQmz/9lPWPbj75jWGtS6mLZZo9v5+tJ+/evskf/f6/dMXRLJhKUYynNITP9DnPo1bs7iaOtZyt9Qch0IVuu8W50ytUmeLWe2sU6b3j64WLL3D5lTcIwoh4Ztl2HPS6HZQ2vPvuLxhOcuKgSeRFjAdjLp87C57gz995Hy/weOONi7xmzvPxxzfob4+5fmONaZLRaMT89d/4BqpSTKYJHoJmI0RYWGyFeDqmyCuqQjtr2j03rVSWm/0KXwrGhdnZPe7dpmhcAo8RMB9B6IFM3b0bKxeDVGY3d6/uOqEHnrBsDwdM0pQoDInCEOELZOizNNfl7a99Fc/zyEqDVvmssJ1B5xMoJzMi/Qu8afkSj4c6ud0CzZk3UaJngc9jfq+2j1bSzUI1ydBFhc6rQz9njGZrq894PKFIC1e7YGOVaZ6CFEhPEsQxC2fPIaRk8/Yd0umUqizRStFodzl39hzWGrSqaEQNvvWdbxF4HlgN1mKQWCRpmjGdTEnTlK3trXsKbD4qOq0WxliStKCstFtz3yfI6BEQhQ2+9tY3OH/2PGU6ocoTinxKlgyZTMYMhxO0ceaZ0mhWb92gKnNefPlldFUikeC7vfB8b44qywgbi3jxCCGdWKQoFUZn5JV+7NFBCFcIVkpJlReooiRsNggaEdpoyrKcTamVI8vFLGNS75/gPSCSEEhBHDrbWDkTkkg5C0Swu+bYC2staVohpaB62KLjmEiLks2BE+dZ44Ky46zEAmn5+JzB/FyblcU5irJiNJpSlpqFrsHzfBpeSNuPKUxFdcx2+CXujyrPSQYD9KzY7zMJC+UgxxQadcja6qg4OpGuRrPw3+McrOTpqk0MqIcRIoYTUaIfRB2JNrhJteL5I9JPsJaOtZa8TEmyMUk6Js3GCHJ6LYGVApE/Qp7Ocwgh5cxvLmYwGlFVmuEBdWye51y/5YjErDh+m/Q8jyiK0FqjlHKqtKp84G3W1jIpDm+ovufTCBuEfr5/u2ssZX+WChOzWzdYgQxD4vkFjKqoJpOdgoRfRORpzu1rxyOCAT75+Tt88vN3+Ntlzt/43d9FScFQQNxs8q1f/x5aKX78o3lu3riJKosjE+nT8YjpeMQrX/kKZ04vMckKrly7RRQ3uPCdv4YfBKgf/4Ti2nWMFhT9IVZDleX4sk1v4RRCQDIZPDaR/tLLL/O3f/d3OXXmLMuLi8zNzfE7/+7fZ3V1lZ+/89PjE+lGo4sEYZqsnDpFFMd88MG7DAZb9/2MlIJWq4HnSba3R2TZI85RHhBA0PZoL8cUk4p8VD7Ub/ypoCYba1uHWlhiQZUJ0zKhlsYK6dHotfD9Bkap+xDpimQyQhtDEMdIP3ASEWWZLdvhngT8JwspJeff+AZv/tpvM5lO6Q9GTCcTNsY56XjK6vVblNMRZrg+k/IeDiE94nYPKT2qInuiRHp+wf3MsCBm6WsG4k6T9kqX5sICK6+5okvD4RqVyR6pJPwTR00Cw+dHpNdkdBNHpA850TI72VRzZ/qEvHTkgdesj9rCcQ/s7U7zbqOb1LYsD1mTNeIGb736VRbmF1lfOc10NCBZH7P+i5tP5lr2oh5vAty514meNfljYHNjnc2N3UytwPM51ztD6IV4zzmRXjuvHBs1Ez/zLmktN3jx9XNko4K1T7YPJdJPnTrLt77zfRqtDt355Vmx0cdHu9UkzQo++eQzPr12h4XuEt32HGWacWFlmfXhiJ99fJVur8m/+7vfp9NusLW2zWh7zN3VPrdW+3zn7Vf57tuvUZYlGxvbVGXFdJhRFRXdhk9gQqbGkFWaxLitUg1lYHW8f3DZW2ULdu+3EM7mIfRA5u7viYb8kLFJ4gqYesIynIypDJyan6MZBXieIAg9lhZ6fPdbX0cIyc/e+5DBcISpKrTW2Cp9DFvTL/FLg1pEJ4CGD23PrZGOS6TDLpH+CGtLlRSQ3H9UMsYyGo33/S4bFPQHu4rdVrdHZ3kZ3w/YXrvLaGvXkaDZbHJqeYUsz+hvbRKFEW+8+SaddtMJLo1B46PxGPSHbKxvMhj0mWQJVXW8dWIzdqrlolRUSu8Vve+DQCAJifwmr1x+nTfffIOt9XWG29uk021GAw9jQVuBNq68iDCarbVV0mTCsL+Nrko8z9VM8j2PTqtN1ukSRHPIoIuQgQtclIoKhToGd1IT6Z7nkSSz++RJ/EaEsZZq5n1Zr1BnSUv3LMWkmDnHSUEYuIx4IQUGt++Rsr7We++bta72x9NAUVYU5f62MHhAmz0quu0mF8+vsN0fsba2haoM2lh8HyIvoOmH6MpQfUFy4p9FqLJ0Fk/POKpJRTU53nh09C1akeNkJM+K3O6kccLJBzVP7/HsE+lPkPdRSnHl44/RImNw4xaTwYjtccZGYkhK+4X3SDfGMM1ytC+wZpNxMGZywNolrxQ3NwZEYcClSy9ySXpsbW0xmU6plEJVCqUN5YG8psB3E2al93dLrV3k2hxWVOMxMEkSbq2vk2Y5vnVb3nsmII1bRGpAgh95tBaa6LIiGUSQGVeI54v+wA/AC0O8MERXFbo42gJhe2ODd3/wA+Jmh2Z7jkbgE4Q+loCXLr3AXK9D0yuJAxj3t9i4fetIxdpWb93gD/7pf0XYaNNqL9Fpt4jjCOkHfOXN13jxhXNs3jjLxksXGGxscOvTT1HGMur3EVjUMRa/N65d449+//d56+tv88qrb7gCre02vbkey2fOsnL+ApNBnyxJaLc7dLtzqKoiT1K01mRlhjnCNRZ5xpVPPyIIAqaT8eFvmuWFG2lJsxwxK5z5yJh5P1eRJqlylDXYeLbheUp+tuHpmGAlRvVLiju7PphIoAU7efF7iHQqHHMhQcQewpPowGCkxYrD77GqSgYbqwRhn2Q8QEiPZLyJm+T2Rl7j2b9nbcUPdzPDTkz9ISCIsWFMlleMB0OKWduUFnxr8bWCbIzNhqCP2G6FIIgbCClQZY5RTyAo0GXXXc6wU++80WqzdPY8RsHWL25STjLUp4UjiZ8Ff/S6bsre+il1rfaH3Cbfk8Sxh8aSC3X8bpEAm7jA7RB3LxfYtROpm2Nts3ciDOcJoSZK6+BWgVt6prjrynCZjHtltHURvNpT/QCpbpKS8rM+46ngo5c+pLM4jzAVVitU08DS7HufBAfo7TkvZueWHfh5n2HbGMM4nxJ6AZ1Wl25njlE6YpJOTvw0O4shnaWIbFwxXMufJbvOw5EDA0hUxk27SpkrqvsIHWr4nqTVjPHk8bwk/9kf/oiyUnx09S6b22Na8TyhH+I1BL70aXQ7nH7xLJ1Ok6+/8RpxFDBYT1lZWMELI0QQ0ozg6pWbhGFEu7OAihT5ZB0lNK1mkzDwqApLluzfaNdJG4HvSPJSOQ7xsMelcX/T2tk79GJoBIJhaRGla3p7hyYLlMatmYUUBFIgAh8bhMwtLvLiK68jhODTjz5Ea0OVFcS+xAgfYySBd8IVsh8CAbRC8KWzqXkUW4NfShwUhD1t1PO6h4uKyllaxHFhoMosurI8bT1SVRZs3bmDlJIy3+8fnIxHrN26TlVVJMmEMg358H1JHIWztZ4ljJv4QcRwOGJzc5s0TSiyDKOP15in09wRyzM1+v3G8yAIOL14ml53jkbDFZ3Ii5zxdMxw0GdzfY0iz5lb6M72zRXGWCpVMZlMeP/n7+IFIZdfe5Nvfe/XEFKijKXZavOt73yH8xcu0Aold25eZWt9nf7W1mNpEIMgZK7bc9+vK4wxCOkjPFCVJp9MkdJj/vQpVFWSDEeuqGvd6A/YZzh7LABDmubOqrOq3OBnDIEfYIzGWoPve4RhiDGGoiifXfXwQyAEnD27wtLiHJ6wjIdjiix3gVLpkU1SSumR5jmZLlH2ywH1S5wMjk6k51/kSPzeBdIJDSKWJ7NxeRJ4guNmWZa886Mfc+Xa+zS1IDKwujXlxtD8UnCq2hiG0ylTVTDsj/CAZLC/YaR5yUc315jrdfkH3/8eZ0+f4p2fv8vNW7dJ05QkScly5RYNs3smBTQil6KVZG6DUMMYQ56fXLZFfzxmMJng4xHbEA+JxrhK4jWq2WumtAsaAb1Tbcqyoj9ooHwLOnM7o18i+M0GUbdDOU2OTKSv3rzJn/zzf8mZ8xf4tb/+t2g1Y6JWA+l79HpvYoxhaanH/PIK1z58j63VO+gjLNg/+/ADrn3yMRdffpX/8H/8H9NdXEQFMcILeOP1Sywu9rh+8zbXbt7mo5/8hLt37lKlKVtra7NUzcdnpN7/xS/48IMP+DujMX/33/8PaMcxc3NzWCznX36ZYZZx46MPyZKE+fkFXnrpFbJpwvbqOnmRU6oSox9+jUky5Wc//SHg+sGh8IEmaOsK+zx2oLMJLEKpFGWp3DjaYjcL6Uk3dQHxSx2631kgeX9EuZpha58siVPr1t7Ue9eMExxR40tE1wdfoJil4t6HSC/zjLWbV92yfWYd4JT3ewfxiN2qkmpm99ByvrL5GMzjFXK597oFxB2IW0zTgs21dbwwQEYhEoispdIVTLewk/4jfK0kanYIoybppE/5JIj0Be7NVtPQ6s1z9rVX6H+2xvXf+wFVP8fesu45PQvzZMS91h1qz+sB5xj5HvOtBqXQlELz+InPM4xxbbhGB7g8O790zzlpYMCzR6TX907i2oDFXVP92mZXle7jrq82ha6V3nv4RzMqyN5do1wY81e9mPhUlxdfuECv06bsKDg3+87ae+QkEbAbsKuDdFscKdlTW0M/HeB7Pq9feJ2V+RWurl59IkT6wtkGL359no3rU8abBVo9C53qAUjda7yR8MHVqwAPzXQKfI+5dhPfP14Ky//5P/s9wGKMRQjB2aXzNKIGRA1ow8rKPN/4xmu02w2WlrsILM2gy8bmkFZ3nmaryzs/+RF/9Ae/x/Kp03z/117HWsNgY4gnS7q9LkIYkknBsL/7rHd8fSW0I2dBMMrYIQ/3Xn0di1LsEumLTUHgC4IR2NLuDLF7P1M48SpRJPF9iReFmChm+dw5fvVXf4W7d+7wX/7Df0ieF1x+9XU6nS740pW88p9usStPwnzDiZvXp18S6Q+FZFee+3l075pIF7Cz+DsBQaa1UEw/HwFjmefc/vST2Xnsv6mj/hbjmXrdWrcsW791Y+fvQggWFnp0Om3G4yn9/tDVYjoBorY/PJp1ZhRGvPzSiyzOL9Fpt2e2JVO2B5usr93lxvXrNJsR586sAJbxeEpZlmwPJ2TjCf/2T/8NP3v3Hf7+f/Af8v1f+3WE72MKQ7fX42/99u+QZRmNOOSjD97n3R//FRubW4/V9OIo5vy5iwghWF2/Q5ZnCC9AWI8qLymThN6pZU5deoF8mpAlibMRna3FhQZR+9AzU6T7bg80mTglRhRLPCkQXkDkB7O6ss4Pv9drz/zVK/Rz6rUrhOTVyxf5+tdf5bNPrvHBux9hjKHbDMFIpoMpRltGeUKmnqVF4Zd43vEsJQ0/BqRTu9njeJHszYG/bzWfL3EMGCPQSjBNK5JMkaTlLwWJDoC16LzCGosVboJTh8gK6iIjd1c3KMsKCywszFEpTTmYoNT+KtYWV2HbFQd5GpdhMRhXvPJBZU5n3JoqS6bDAUopTF46Av05jXQfB6ZSqDxHIml059GqosySB94LrS1FUZFMM9bvbNCaZCyeWSRqRLTbLcIo5MzpFaoipZj0+WhhiSJLKZOpK4p1H1hrZxYdY6599AGdhXXChRXCRouzp+fwvAUajZhet825C+f45q98h+HmFtfe+8CpUfyGGyZVPvMJPTqsta74qdYYayirisF4yHAyojvf48y5s2zOFuFBENJut7Fagy+RxqPdaqO0plAl2hqiMMQPAso8J8/2E7T3JdB33sAugWXZn4xUWy7UhBd73lPPlrW6u8SpR+u8c9xPEUiipRbSSIpRii6qmSWGcBkZx90QC/AXI2TLx5sLsFIgez7hpRhdalReuuN1cORbPLuGFEfgChxJJy1Wa4QVsynU7hLxh8HO+v192259Yw2OEhG7L2/GtBl137bjyQCBRNvqgRkW0vPpLp8i6i7QaLWRwmK1QueWYjJivHGTdLR1ZIsWaw1VnqK9Emu0y+R5QtIvP4yx2mKSCluZHfaoyBP6G2tMtvqYkcJO7KN5oh5Aq9ek2XHFo4XnUWYlo63RkYJRh6LuKx6uTe01LX5IMp+uLPlEYZQlageYylJmCnOcDdvej9bW/HU9gBp7++WzgNpIW+MCXCG7Y0qCG0sydscH5Za3fuiBBIXBKnv4UtdarFKU0yHEiixbIIwDZKtJ68wKVZVS3nm0uh1HQn3vazuSx6hNZoxhmk8JJgH5CVjbHYYwjmj1OnQXLfNnxhSpJhnqk0uS2Yv6mdb6nCPY8twX9l4C6yDG4zE3rt8gS1K6rS5BEDzmwRzUHrWowIlhsizDkxZfWpKR5fqnnxJEIdfbDQyCO2tDJklOI1FEcYL1Qt546xssLC5x5uKL6Kqkv7FOFPq0WyGBL8iTkiLNaGhDZAxVpZmkFda6LEthH57AaCxM1cxWXnhEgcTzNJ7QyPt81uJsLbQ2lGVJnqVUM+tDKSVxM0IIiyctWGd1ZozGHDWz6YQgpaTXbdOOA8bVlGlR7CTafIk9qAn0p5swcC/qQOLBuekJQjDLsNhzCuY+GRyPiweNP3v/Zg8Zq/K8RMqUoigevi5/TOxZZe4s2z0pifyQKAgp8pwsS3ZsU5RWTNOEoiycv7vWZEUB1lIU5YxMdtnceVFgJlNGowmD4YgwbiC9EIuYZchaokaTVqdLEEb3ve9107zfHTDGUGQ5gR+wuLCMNpq1zW1Gk9QVTRE+ujIkwzFlUYDvI6xbv+9dj++0A+uChjU/YIFKu3p0dpYtoc3+zz1LS6XHgRDQ7nRYXjlFnpZk05zxeMqdO2soVWErizHc48/v+T5+FBKFEd1eF6UU2xsbVOXTHe+fGQiQgQsam0p/OeEcAc83kS5DkBGY8hhqN4GTFvo4mdOXkaqThUAQIWyDzY0pw9UB5peGRQerLcUgdSLOmcLMlodff5rl/Mmf/RVRGPLt73yFy6++zDStGH56+54FirWQ5dzXG+5JQGPIZv3jgUS6hmw84faVj0Bb9LaeFSX85XnuNao0RWUZrfll5i9cpEinbN+6gn6A2lUrQ54rNtcH/OjfvkO70+ar33mThaU5Ti8vcOr0EkvzTb7yxkt0WhEfffYZo61NNq58jC4fPn5tra/yL/7hf0rcavPS299h/tQZLpxb5Nz5s3TaDc6eXuTimUV+6zd/hU9/8QH/p//V/4bN9QpaCxAEMFmDYvTQ4xwGYy2VNZTZlA+vfESSJly8dJGVMyusX7/KjU8+odVqcvr0Cp4vuL1+F9+POTs3D9ayMRmQVQXLy0v05rps3F3l7o1H9ABW7BLgtedzrWLaa59Qb4pqG4v27OcER6IPcQrSDk5pPIPfCFm6fJEgiNh45yrJ2tBV/oklFAbS420ohC9pfn2O6FIbEUiUtXgXQ7ovLFKmBZPbfbcAqrnscHaNN2fnLHAWI9pgs8J5M9f897H6qMLdnJptnXlSCAFRx/0sxk6dfvCahCQOOngyICvHVPr+83kQhrzy1W+yeP4SXhDiSUuZ55RZxfDWVa7/9A+o8gx9RNWJURXJcHPPb+wT87lvtBawypBtDtBJ4dS8MfT7qwx/tIm9Y1A3q9367I8BIeDsy6d44c3z+EFIEDXZvL3JO3/4DkX2mOubmjhv49q7wvWB2Rz0IBRTRZlpoqbP/IU2WMvW9SllekLBCo1rdgf5w2dtd1gA67jzLHBdpI1bem7gVOM1EzAbj6QnabdDpC+YrBWU2f03NqYqSdduUuQRg8UeVSAJTy9zdqHHkJtsfvwhJ17Zvc6q8NjVoTziYzXWcGvjFne37qIeMUB7VLR6PU5dvECjvYUfThhtlnz6w5TimGPxPfBwQZI6c0Pg5ponEx8A4Mb162ysD7lw/jy2qIij+ES/fzqdsrGxQSs0dCLNrdWEH/3xOlmlWasESnj0lk4TNdpIIUAIfuV73+E/+p/8T2m0WnTmF0knI3SZMR32uXhuiU6rQa/VpB1BpUrKKmNzVPDutQGFMtRD98OG4crCnQKCClrzAd2Gj58UBJ7GMzMP4EM+V1YGocCOx5Rlzng0IC9KhBScObtMkWWEgcuiLJIJVZ5T5U8gEPUA+L7PxYsvsTLfISmuMBmvUfF0q40985C4fRXsPujPU5Fe27A9JZsZT0Ls7ydQM3Xyw/zjYjpNSJL0idmF1ENs7ThX948oiFjpLeF7PoOtDYosQYrv0el2yMuC1Y018mSMtZayrNja7mOtJUtztNZUM+FakmSMkpzbd1f59Mpn9ObnOXP2PEJKsiwnL3Ka3R5LZ87TaHcOPcf6/AS7y+yDqMqS/voW3U6Xb373e7Q6Hf70B3/JaHIdISOEDMmmBdkn18D3EO0YGUWYJMFW1T6dg49zFBoWu45wAshK60RwwuWS14/ELZMs4jlnTIUQnD57lje/+hbnz1/k9dff4IMPPuHDq/+MJNmzn7B2RytlgajVoru8yPLKCl/72tdIJ1P+5Pf+awbb2/c50hcbwpcEPTeolsPcCX6+xAPxyES68HyEkEjpIaXEGDuLNFrXMa2dqSLrHcET7JxSgue74xhvzzEfBQJEAMIH83mHtL94sNZSFYoirZzP4xe1U9ZK1sOSI4wFKejOzRN3mkz7Y5L+5B7pgLWWPC+oKkWaZmRZRllV9w082Nn/NRsxYRiQFyV5/mQDQUftzdYYdFnuL3T4BUEUhIRBgNYuFc7zfKJGA+l5+J4PWMaTMcVM5WCtxWiNVi7vM252UVVJWaTYQyRxSimyJMFqSRzmeJ7PZDjF933yNKcqSzwpaTWbzC/Mc+78BUIp6V+7ciTuzWhNOp1QKcVoexMhBas3b9Btt7HSDYMyCPCimGanxYWXXiBqNNnKLYUyyDBGUGFU9VgqLYsBIfEDH8+TpNMp0/HYWWn4PqVWTKYTsizDzv6njcbWc42xSE/iB4F7+T7GmEdTvOxVo+9FTazXfzd7fn9weqnbdr16r3PNK4upNBoFgYdshojYR0QexmpMWbq+f8+z36ureVBPs5jKYHINdparLg14BisNNASEe2TCtWLr4GEMM5NZTmjzWX9BPe9bd34GkDNS/Z4N1U5CP1YGWBmCDJyn+v3mcyGciiQIEEK4saZSlFlOWZQoZV166iNcz2H98ElAygArNQK5e37C1bXQSu/PlDgG6kfs+R5RMyJqRoTNAItx6igLZan2zy17ieeDyu549mrOftbdXnFvHzoAa8Eqi1YWM7Ogkr5ABsKp0o87N9SVB2uyfy+J8SzZIdTnI3DnW3vk+3v+vTcLZtY/bTVbKtyvj9ZdKBDIwMcLAqTn4UkPpd08bNUTVBbV51pnUMyO4/tuiV4PB4EnCAO3ZygKZ+1XzbqpF0hXJD2I8XyfsihIpyfnjVhkFZN+hsoVrXaISg2xD1ZCdR+y9bEgcMxFzZzArsf9Y/TrIPRpt5sYa0kzp+rU1X4VYlVVGDNhOhkzHPaJw+gB3/iIsJasLBknCWhLjEGXOZgCXSlGY0VhJDJq7xTsMxaGk4RxkpNryDRk0wlpllFUBWmWIz3wfcn8XJssnTKdpISewfed5kI/QnPV1qnXtXEqSyEgCASBtvizsjyHEYvWMvNwbxNFIUYrlKpQVUlVFegqBOm5dc6seOLThLWQlYokr6i0OYlp4YuJeh1Xb98/z+CpPfDzCSMKA5bmmxhtmE4TlDZOtPWMNJSTsnI5iE6rOeMWCow2SOESPp1SfDaVGoORZqc9ZHnGdDpFeB6dXg+jS8ZDsMbimz2TgKhpZYuxs5exM/LZZdZKO9uZWEuSJAzHI4qqQviB478OrCcfFtMXCDwpEUJQFAXS9/GkR7PRAMDOrs+XBhF4yFaEsZpUVc7JzlZYbfGkJPQklTGoWfbh3q3MYVoZYyxKKZeJ9Iy0m0dFGPqEYUBVVYzGEybjKeNJQpLlaG0wB1T7+56FACsE0vdpNJtopZDHLNj9vOMI5ci+xB48GpEuJFF7CT9q0Wp2iBst8jx3EUdjMdpgtHIWA7rCSTGeYHpEGEKjDXkJSrKbO/sIo4GQzm9VRlCkoE7Ix/VLAGCUYevWFtKTqC+yP3ZDQFNAbmFyoP0J8KOA3/ydv8Ob3/4Wf/YHv8+//aM/dLlX2b33xBjNp1c+4+7abUaDB1ea8zzJG29c5uLFs3z88VU++PDKSV7V46NWpn3BBmQBvHD6DC+eOcug32d19S7dhUVe+drbtFotlheXsNbwr37/X/HpZ5/ufC6fDNnMM1qdBV545Xtorbj56Q/JkuE9x5iMBhSfvE9vboVOe5kir/j4vWvEcUToeWTJlPnFHnPzHV69fJn/6L//3+Wj937Onfd/RpEdvTKhKgpuf/Aeq0HAnfd/TqPZ5PXvfofXv/tdpHR+es0o5j/+X/zPmYyn/F/+r/+Qjz+5RmvxApEvSft3Sfq3H+n+OYOgimajzeuvvcb25hb/+P/1/+bnP32HYVnB/Dw3tjbZ+jd/jOd5hGGEVortzfXZYs8VK9TlPABhFNKZ61CWJenkMZQvB4m2uniei4fsqkMNbmoxh3wmYTeRyYCSJZuT60jfR660iVdOE4kWoWiQjRPGwQiKAiajfSsWQYTAx1JgHzBvWmVJ3xmQfziGV4GXBSQW+gYbC+yKh/AlAgXGYLZwU3HB/iCBxU3PJ67gmjGbFlekXMCODZup5f01fJBdrAzI/S7C98GAb0OMSTHm3vZsjctoyCZDpIwQImDSHzHYHDCdVNB+ERFOsZObLlvtGYLnNTBGI6zcrzM4mPng49rVYxDB1sK0P2Xz6ga9UwvE7Q5RM2TlpSV0WdFCgrbcurXNeLxnvRPgSHLN/mWUB7wEnGZXaZsCo9n5bhztvKpcsX1jivQFQUcSdAOyvkIdVxVc+3IftlN9loj0Ggp37wTOw71+5mdwGS5DduZPnVkmd5y0zGT3uU8B0ANvLqJ36SWihS4rK2fodnpcvXKNOx99jL5bPNnUtTqYgTt3IWB+AZotkCmIHE4thFw612KaKD77bEpSGFZLKIXk7PkzzC/OsbByhoWVU1z/9DN++hc/QB+zKF2N6z9fZf1an/MvNvn69+bo6JyklTBVhrUc8pNap/i47KQ666D26xe4YOsjDkenzizyq7/5DYpK8dNfXGU8Tkg2Rqg9mSXGlFirGIzW+PDjnx3bI30vLHBna4vN4ZALXR9/IaTV8Pn6m6cYZhV3390gTxQqH1NQMC4gKeFP/vLHXLm7gRQCIQ2eVTTMCM9WvBcG+J7k8ukeb73xIrdvXuej9at4lWK+bcgqGE6ddeGRz9NCnhUklPhSsjDvE5UGkWkKBaPycHX7xUuX+MrXvsbifI8qGzAerHH96qfkWcbZU2dpRDHCaDdvPWV2oSgrfvLBVQLfI83yp1W//PlCPe54uDHUY7c+xi8Bzp07xd/+rW8zHk/48z//AaNxgvgCb7Nr/MrX3qBUio8+vUZ/OCbwJZEvsQiaVqKsYWO0SRw3eWXpMu1Wh0+vXuHO5hq9+Xn+5u/8Hd75yY+4fusmgRQ04hDpCRqNBsZYcp1RKLWzfY3imLnFJeK4QVYUyJmYA+An7/yEH/zgB0zHI/y5JUyeoqePlrEbhiGLp1aQQvLDd35ApRSLCyu8dulljBRoT7A4P8+Fc2cRtkJVQ/Is5cOPrzAajRn0R6RJRrPdYK7bJklztgZjjLV7p+VDUZQV/cEEa11Nt+cNnic5e3aRTqfF9Wuf8V/1N9hYH3DnziZZmpEfqE12MCBZVYokTcnz/LkttHqSsMpSjdz+84FWn19iB4+04hJCIKSP9AK8ICIIYyoF0lMYYRHW+a0i6lz5JxwaltJVW5R7K2A9KgRI33m4iiOcr5gZkv0S2lQ8Lqr8eMEUgbvtngs5Y7R9rNyDJ4o6h0oL8MW+6LYXeITNJitnz/Pi5Vd47513XNuVh1+BtTBNEooypSqdV+L9I/uCbrfDysoyd+9u4HlO8fUsTAhiFtW1z9aTOh6EoBFFzLU7qLxgFEV0mi1WFpdod7qcPnUKaw29bpc4inYi/UZrjMnRDUsQdfGMQnqRGyvt/swdrUoyNSGKWlRVhfRKylKTBjnbWwNanZgwCuh2W8RxzLkzZxmsr9Nsd5hOXLEcp7B9cH6ptYY8cenKyXAAQHdliZWXXsTzPKTnIecXWFheIG40CAIfrMHzGwRxhBdGjhx1X7bvWJ4XIKRAK4W1ximIfR/pe+RFQRjHNJstslZGOk3ob21TNRoQhuRpSj6dEscN5ufmUUqR5zlqZocjhPve+rul5528gqBWOO0ViN8vMFT7pe/cAUtV5uBJ4pUWXuTj2QDPhki/Aj9wkjspEQjiMEJISVVItAYx85pxffgwKR2YicJMFJzC+SpPcQRcS8JSgKifvWXXgkPtfn5XnnL4IY6P2Y2ytdLnfpqcWk7rY4QPIkAKf3YPDnum7nu0UqiqdJlxQlKVFWVRoJQBGSFkhRV14u+jXuCTk3TZyjrJ5d6vP5iEELEbuHlMVHlFNslozSl86eF7Hn7kI7GEMyL9nj4jQUYCq8AWdjfAUrvf9divWJ9yb7bDA2ANVLl2RHpXIn2xM3wcC3VA6HlAfa8Okh1tdgu67lUva9D5TFV3P3Jo1oWEJ5BBiBdEeMLDQ6CzgmI0hvuR8CeJvckNwrmANaKZd3XocWq+wYVTbSZTxXQA40wzTTWZFcSNiCiOaHZadObmiJvNo63Hj4hyWqDTgnJeEJh5QgSBhEA6j+ETw167sL2/ewybIQHEUcjS8hx5pWj12lQW8v70QPOxWKupypzxeIjnnWxRzKKqKKuKNAgockMzimm1fJQA3xN4AtAKq0AriVKC4WhEdeMW9cQZSsNSQxF6rpFIITjbC/G9OZjZK2htCHynHvdnpa8exe9ZK0tVWvAF0pNI6c7Nu88YIwTEcUy31yOOo1mWYEaaJRRZRlUWBJ6HMBphHzHj7QRgrGWcfCnseijqsbLuY3vr3Hz+26AnAiFASkEch8zNdbBWH2sNLIRw2X2PoCCXQiClwNrdukRP63bP99qUZUUjjggD3xUZ9mZKcivQylBWJdL3scIpuqfJlFxXdOfnaHe6hFGENnY2/gsEbtwQwkWC9/Z2YwyVUnhKIaTLnvKle39VlWRZijIa4XmzNKz92KED7ndBQiA9D2stw9GAPM+Z6y4QBQHG9zC+pNVqsTA/D7akSksyz9JpNVBVxWTk9nFOfBSQ7/H3ftgzsdZSPUrU8hmDEALf9wlDn8lkwmQyYX19wO3bmw//MO7ZqqqiKkvyLKcsipn//S8prCPTj42ZzZuUAn/Wtis1q5X3BRuXH4lIt8ZQTrepshHlZIOx56O0Rinlbv5sEHZq9DrX8wlihwio1e+PyQpYAWY22j4IUkKvBb4Hk9Qp4b/EE0XgQeRDp+lxYSXGKli7nZEXhjHPoKN9K4ZuB6fA9AnCkHMXLzI/P8/r3/wWb7z+Jj/oLsC0hAcUfisLi6qg2WwyP9ciy3JGo8k9ixwpBfOLZzj/wmusr084tXiXNM8ZTe5979NE0Ihpn1nAVJpJso15QsX7Pk90Ol1eeOFl5hcWOLvQI240CTyJRfLdr3+DS+fP8vMPP+SjK1fA70C0TEaD67fXAEsZLCA6TWy2AdXeNHbHzGbpiOtXPyMIG8StDn4Y8Kd/MqH14wbf+/7bfOd7X9sZd+NGl9/6nX+PjbVVfvhnf0R/a4OdnYWY+VTbmRXIA3D9gw8Ybm7SW1rkzMsvcVNp/vKf/lOyNOX6R9cw4xSi8xBHEDahs+iIwdLMCklO8YOAcy++RaPVY+3Whwy373Lx5cu88tZbLJ05zZ/+2V+yvLzMd7/7bVrtFr/6t36bpRde5p333+PajetuotWaMs8YbOtZQaDd9mOtZWt9g8loRFVVLr3TmJNp7zmOlKvNBvf5hz/idxlDeWeI2pxSso20HtrzwQsdmd7pMdft8Tu/9bfodbr84X/9B1y9cpUgaBD4bSqVk5cP8WW9iyPSK9xgGBmYVljp5mssTuVav6dWa9U/n9p68X672grMEGcY3QB8jMmweoy1B8YM4UPYwYYt8tSSDp2RuBUeeepMiG2lsJMxRuVgQnZ9NI64UZCz4JYp4eDxTwDTd+8CFqXz/QRbnUwngbOzfyc89hJqPEzIs5LFhVNcmj/HnankxzeGpEmKPyuskST7Z8+4E9B9sUFZKMYbGaa0ru2we55eQ+A1BLq06MS6dvWoxSW1Je8rkKDLX6INi49T/BvcOHNYYdQmrg0UONW6wd1juH8TroAh6KJg/PNrpJ0Yf1oxWJhjPLq3HsHTQohL1PvG117gK5dfYGVxjhfPncYQkFQx07TiJx9eZ3Mw4ae/uMYnH1zhzsY2zVvXGa9vnyhxeX4OXliA2KSM/+w2o8xwd6BISle24sRQ4sZkyW4gqg7APsJxfA9CH3xPo4ocPwh47Y1XmU4L3h2kFJN0N4g12/bkRcHaxgbiBAMQ4J5jAKhUsakNujQsdCV5ZZgPDF5sCciRlcRvzTO30KUylqKYgJR4fkBZaW71t2d7w9movDXms3evsjWdcqtv8Dy3fA59lySsDGwlkN4nSLaPN7WQ5iAqyDDkWHcO2hHzte5pJ+43+/BkOODW9WvMddvY5UVGo/FOQWZPgMTSnwxJspRxeozI5l4ceG5f4gRQB3ZrZXqIm7uerq39U0OvE7AwF+KJKe/87CdMpjlbo4I0dw5EjyoF6HR6NJtNkmTKZPLgOaOOUyx0WizN9ciKnO3xgEobiurpaAxbzZA48nnj0gUunl4iS1LyLCMrKpJ0tyBFURZ8duMz4jjmpZdfYS4M+PTTj/nw4w+4feum2zdISakVPh5xGLrangci/Fc/u8I//kf/OWcvXOR7v/YbdDpdus0GUbPB3/8H/4C3v/0d/vBf/FP+7R/+/j2F6uspAHZlI3v/ZoA8z7h5+7orElrkGKO5u3aL7cEmcbNL1OhQTcfoZA0hDEYVSCm4cHqZ86eWKVJXWPOXEVob1tb6bG3tZgEUxdGVFaYsKUea9bLiz8ZTtFZMx5MncarPCer02PrfjwfZaOC32yzOz3HpwgXSLOHDTz8mTzPIHsx/PW94xBxAi64yqJ44RX50WDFTdB4sl/0EIAXEIQS+W7V9iROHC2KJnUVy6EMcQLfhcW4pxlSWfKtiqjSpMRTPgPJ6H3wf2k2QHkJG+I0GvYsXWFxaZunMGZYWl2mE8cyY8/7n7rJJLb4X0G63sNYyGh02uAviRot2d5F2u0ur0cAYw6Mllp08PN8n6rTQhULKwRdHkz57ZLW/ZhjG9LpdOnFIEM7UD0jOnj7FYq/NnbVVpBBYL8KGcygko0kCwiL9FtKLMMUQS3LgIJqqKhgO+vhBTNtavCBgPBkjhOD8+dO89tolp4zwfYIg4sXLr9Nq93jvJ381U/PNlryi7k17J8jDMdzcZLi5ydL5czR7bZLxhI9/9FOKNAU8hPSxtX+19BBhAyssVBpkBTZFej6duRU6c8sMNm8B0J2f5+LLl5Gex61bd6kqjdaaOI65cPkViFtcW7sLN2/MOFeLUYpcHT7TZElClpych+4O6lT8embUPH51LwtmUmD2hvsaLegGLigbRkTdHq++8RWWF5b4yV/9BABPBgR+iHlI0ANwRRb3TkUZLqhREzk1aVf7F9fX9KB15l4i5iTHVyEO+b46N3sP02QV1pbcwzwJiQga4DfQlaVKK7QwWCSqmrUTa7BlAboCZlW47FEf4CybTvi4SnSPd5kPQrU+cedUb/b3ds1q9u9ZHJZjCEuLvKLIK0xpmYva9EVINsqZTu5PBvmhR6MXInPBJMnBs46I2BMDEQF4scB61t3ix1kIWlAn5qXxHKHOWttrg3IQdWWw+v110OtBmPVxqzXl+pBq4jNenKcEivwJW7rcBxIIhCCSgrNn5nnrrYsszC1y7vQFvDDGby2QpCU69Llzd4t3379Gf2vARBUE+Rg1zbEnqArrxo5MV0lFdrMiLWGSH7vu873QnAh5J4Uj0z1hMLrCD0OWlhdptiqiaI8H+p7xQ2tN8gTmxNrtzCpLojRxUJFnOZWxNDyLDcDi7E+C0IN2g2mWk5U5wnrg+WilGU0y1N5i6KOEkYSxgW0LrRC6kVOjh6EjwIcPEGQfFCBXFeQKptYytfZQF7a9RLqQgrIomIyG+BimjZgsy/f76VpLXuRM0ilFdUK73oOZbl/i+KjnT42bV1vsBiC/gIgjj14nQFCyunaXJNWkhaJU+5PEjtK8hBCEYUSz2aYsH75WqpcmzTBkqdthnAom6RBrn14h3DDwsb5lab5Lr91ke9tjDGi9fyeltWYwHhBkAWfLi06Es7VFf9hnMhruiD+1MUghZ1mi9yZDDQbbfPD+e5RK8fZ3v4/B4gU+QRDw2utvcPbseT740Q+w5eH2IPU0U+dIHkSlK4bj4ey97t3TZMw0GdMuNW0lkTrBt/0djiQKQ04tLRFFEXF0gnUxnjNYa5lOH7+zW62xWpMUJcnw82ZOnhUcf2KSQYBsNmnNz3PuhYuMxiOu3LlBrksoxBfKfuvkzPQ+B3heiBc00VXx+M/Eaig3Zyq0nAfmhGkD48TlCp6033cdAP0lXlxFgcdfe/sFLpzu0ew2iFoxZZ6QTUesLC/w7bffxPOajCZzjCaK//S/+Mf87P0PP+/Tdihm6SpeDpM+raUFLn77deJWi06jhS8syWTE9uY6WXr0zU6z2WRxcRFrBRsb2/dM0sYYrt24iQxjbt66TZFXu6TS54iqrJj0R5jKoJ9SIb+nAYvl7lafvISFboezy4tkecbNW7fxA5+wESGkoCgztKq4cHqRxq9+m3Eh2Zz6ZCVsTkFIj4XFOXwPBuWArJhyUCJsTUlVrGN0A61ihGiQJWNUmfOzn/6QabbFa6+9ym/8+q+5TbeU+GHAmZdeJuy0dzKohlt9Rtv9A+PKg5fayWjEjQ8+oipL1M7i2mCtIh+to4sJZZljy3ynmpcMQhrzL+D7PlvrN+hv3GAycul1d2/e4K/++I947c03+dt/7++ysLRIEMQgAy5dusT8wiIffvQRN2+ukpk10vHnvKCp7VpOeiyuSueNPttIT4zi9//lP6HVaLLd36bTm0dVKWk+Qh/F37tWPda7G4Uj1zlw7nUJkQeo0IWQzJ+5SLO3iMAisSSjPturt45FagkhWDx7nvbcPMPNDYYba/e+yVqnAjceWB8nza0vQgE5jWaLF978OkHYYLC5xXj9DjboYv0Grbke7cVFlC5dGpOQQOS+S2dHfI6zcxDKZVc8KRicWk7gNv2d2c82jmAdc69i+TGxubnFj37yLv3tPoEvaEQBRan2kUU1SgxjKnRs8c42EJlBjwpsauA2MAXxio/s+YiGgsXKtbkjeqT/0iLCKdHh/oV9FeyYIBvcfZ1ntz8fZQrVwBhsbkg/3aBYG1NujWBkIXs66bSBhNMt6DQ8vve1V3jh4jJf/+plXrx4CWslo0mFkAYxNWTFvWsVnZVYY7HVyQayglDQbkvGyjIxhty4/BeB62rP2gpFG5fwmuaKcTKhHfgsLi/SrixRPCNN6rbyhJ9rnZAVSbf10VYzGBf4vsf5U12slUySlLJSBL0IrxdzR2WsTcZuavImrkjqgaC4kaC92SXM+oStnDvipHRak3qbFeO6UckuP2r3vAQwnSXI3C8rXQgIAwh9yZlz5+jOzbF8+izLp06zvbXNj3/8zkyRm4M1rG9uEoUBeDA310XphOSYqnThCVrLEV7kkW4VVNPPf63+hYLFZfJkfKGJ9Mm0wli7s4qvKlfMG3aHg6DRwgsiqrJAFQeEf3tEGq5g5oSqKlFVQRgIV/g5DLHGUmQ5do/H0kK3Q7vZoN1wk5o2TmBa6afneNvtdlwxUaWxRrviwoF0Ba7RyAOLJ601t2/fpN/fJs0SstxZeAAoY0myCs9zNpxC4Cwo9mAyHHHjyid0Wm20qhCzey+EoNVu4/kBF1++zBvf/DaDrU3Wbt5AYmn67rZlapYZw71E+u5O7H43zwlFKwVJagkCn26n4URJd+66YICpWFpoIwQMxxPyovpcAuhf4lmFwIXD4XFEx412myCKd/67Kgqyaa2yORw6z6HfR7dbYC2h57HcbtG0hkFSUVbP2qrr8fFcE+lSBvh+A7zpMRbCGlR/9u/al/U+q1NrIXlCSvRaXvHFaVuPjCDw+O5b5/jOV88zf2ae7lKX/vY2a6t3OH3mPL/2m3+DuLUIjdcYjCr+5KfvPjtEegmUtv4Hje4cL718iUa3TdYfIY0lS6cMB9vkeXbkSS6OY+bm5siy/NCUXWMtd+6uklWaZHubqqxQ6vOvvq0rRTKazlYPXyz14eZwzOYw49LZ05xZXqAoC1bX1pES2m0fz5c7I8jppTkuv3CG1e0pn9zapp9Y+jkIL2J+YZko8EnXPyPbmeT2EOm2QpV9rInRagUhPbLJgHw65qMPUm6tXScIBH/v7/5tBMZ5kfkBK+cvEPe6GGsw1qC1cUT6PjyYSM8mU7LJQWmdBasppltUqXCPdc/HPenRnD+LkJL+rfcok91jbt69y+bdu5xeXuTNN16j25vDD0IQkvPnz7Ny6hRnzpynN7eEnU6OYw99MngcK5ejQFXuNUOSTvmLP/1DhPCYXzhPs91jPErIyyOmFtbnWU9dtdp872ON2S2e+oB9u5CS7so5Fs++hDcj0rfuXKe/fgd7jDQ8ISVzK6dZufACRpvDiXSsU44bCVbiaK4aBf9/9v7rybIsS/PDfnvvI69yLUJmpM7KqqyualXVPbrRgzHMYACCAIEHYgx8JB/4B9D4zHcaH0AQDyAfQNKMmIGRNhwObGZ6prururpLdYnUmREZOly7X3nkFnzY57oKjwj3UBlZGZ/ZjXC/fu895x6x99rf+ta3oCRJU157/R2CMGHj+uds3r0NrRWIZwlaLZZmZymzISKcfkbkD4iVpx8P3bNRoh/dBge2LQscxLgtDhI4T6mz3M7uHu9/+DG6rAgkJHFArQ32hCZCNZYRNTJSyJkEMXGYsPbj9wawC2JJIkSITIC5+qBB6ks8GNNkSc1BA9mTiPTDeTMFzDbPZZwuLrTN52eWQu/4ayrjaMXKM0bQEOlLPcXvvP0a33j3TS5dOc/5cyv0Bxn3Nvo4VwMFRaXvI9JtqbHl0ycXw0jSaismmfWVjBZix34P0Bct7DZ2Sqb7RmhRp8Ps/BzGSqI48i96TqKbaZGWEb6dlHGO4biilUZcvtQlCgM2ti1ZXhD3IqJeTH/PUU3GmIdVXQowsgmFp0S69kT4KPOWO9OZMsbfQhOOEumH/8+aX6YK9eOYEulpIrl0+RwXLl5idn6JmblF9nb3+OCDj31PGi9MZXtvj1BJllcX6HTbjMY1T9S4AnxDw/mYqB1ST/RLIv1pYzq3PoNixRcJk9wwyU8etfz9IAjilKjVgcnYj7OH+xgd68uU5xl5nnmv8UAQRwGddoo1BqoK4/yaUgrBXKfN4vys34oQWAeldmdqDvykaLdbWGvJx2N0JQiUJAgESnoiXRwbGK21bGysnfhZ1jrySiMEaF0jxf2NjrPxiGw84tzqeZzWiGmFnoC0lRLFCauXLvP6u9/i5uefsXH7NgpDuyHSpxZTx4fsw60z7AMHcx/UawNZ7mghCMMIcKxvbFI2CYHZXovhOGc0ydH6wZ/2El9HTMsh4Wh58qMhhCBptUk73f3n8vGYYjJ+KI3lyhJdlpjM812Bkiy0UmJrGKvhc6teeR74ShPp1np/9gcrXgVBq4MMYnQ5wZaPSFFHTfPSWnuDvmeF/VpJDlSPX2Ml+my3w9/+/W+zujDDaxdmiVSKNApXGGbaPbqvt5lZWCFIlsgKwfs/+yH31nbZXD95YnyuSASEwkf905FBeG+2tbt3CHcS8ttryNqwsXKOuSQlG5++9lepgDhOCILo5N5bzlEUOaPRiCLLmoZ7ZxsonwmMg1wjpCCa6SAcVKMc+7QrOb40OIaTMV/cvbPfrExIQRxLlBSNv6ng0sULdNopRZ6xs7nGqLDYCaiohbMrCBnRWzpHEIdM+mvkw437tmStpphso8oRTk1QXY21Y/JBye0b1/iLH/2AMIzozXRBOia/GrK9tY5zFoejrEuiNEU4i3IGIQRK+uYfWT4+4j9+WviGts0vMgLVxoqIfLCBwGHqk9mbLMu5c2eN+aJm5fwFlIK9wYjxOGN3/R7DtduUTYnj1wkOS1GMMEZTV48hpTpERtx365tDfz8EEQSEnQ5J2uatN31yo7Nwnrg9g8QrbkJp2N68Q5Vn5MO9pontyej25lg5fxnrHONxhsPRaieEYcji8jlarQ5pd4F49gKmytHZ3sHOOgd1hnCGV19/naWFVe7ducHtm1/4BFE0h7URt65dRQpFPhmDELTn5ojnLhB0OozLgtxYXJj6zysHjdf5C0STDWkkSvjzMfXkn4YbUxPNB9UAnxFlWdHfGWCNpihqjDHYB8nGJhZ7R+MCiwstrnIHBtLNIbQbhvqTysdc01L637C4RYaCIJU469CZ5YkdRioO/Hu7HDCT+thrYL83g1CgQuFtcMQZD/A0rnwObYqOQwCxgNhZNu/eRQlDbWuMhM+vb/LjX1xDKpiZDbA41kYFo6xkmD9b5muz7/j1Lct4ZNksvdJ54h6vTYQAFlPoRVArQS0lRenoj+0TiwBnZgJarQMaeG4uwAlDXmbcuHOjUSU+3zRzJ4JWCKGBkYGyAjOBXBtaOyPiSJHnJXVtCIoMFw2ZCS1vn+uRV5r+KMdYh26aLMdxQKAkiTPEztszCrwCfdAkgWt3VM5U4bnRw45I06N0/Pw9SKjvHJS1f6PWXs1a1TV5VZK0Wrz61luMRyO21+9hjSYAAueYjHKqypI1PSWUEIRNM8Lano6wigLJQi8hjCRhKBDCUHUcwkKdweNM+S/xElNICUmiEAiK0mAOhzyi6ZOE9Z6hTZAopWR+pksUhvRHY7K8wFiHMBAhSMIIIzVlKJHCIZ3vRVAWOXt7PuGlgXFR3B9TqOho003zdKv80tg32ZyMx15lnuVNr6QaFQiU801ED/c1FMJbZnU6XdJWmyzLGDW9xIQQ++u4o7r+o9jd2eYHf/pvOX/xEn/37/97LCwuYo3DWsfFCxf4/d//HpGA6598BHWB44RjcwgKiA5t1+GL2xGSpaVV2u0uWtf+UdeURUlZlfucVzYZYbTGNI2Zi7L2Y9sjxGuHCfyvMfX0NcLU/hruO9uH7SVP0g8DVVkhVI4zBmsNdVme+pqp84z+3TtUVcXO9h5Zljcc1W8OvtJEujEGU5fwoJMiBWFvkbA9S7F7j+phRLoAEgVJAGP7bBcgAQcLqql68DdLtHsmLC/O8r/9J/8Jb105z9qdzxmPdlFaYCeapXPLXHr9MjKZQ3Qusnl3k3/+T/8Zn392lVvXb3y5Oy6AroSWgD7em7gZlPI849rnn4Nz5B9cRRUVb547TyeOGfb7p95EEISkaZsoijmJWXEOJuMxtbaYLMPkOdq9ANlobWFSoZKI9qUZpFIMb2xS/cYQ6bA96LM73OLI7NMEZAstaEWCditmdWWFyXjA3ZtXqbTBWJDpLJi3kKrHwsXXWeAV7n32sxOJdGc1k/4dhBQECzFhK0APMoqtik8/+AX/ozJcvnyFf/Qf/Md0uinDwTZ3bn9xsEtWkXQ6BAhChCf6owhjNFVdnplIv48skAkkqxhbMt6+2ZCXJ1+Bw9GYTz67xuo4Y25xiSiKWV/fYmtrl7VrV9m++tHXM6pzjmyyy9nbRDV4mIL+AX7oMopIlldZXFnlP/gv/gmXL73CsN8nP0TUJGnE2vY2470dymyEqR5MSi8srfI73/8jamO5ffce1jlWzi2StOJmMQft+fO0liXlaBNdDA8trixUQ5SL+M533uV3fv9v8md/8v/jzq1fo4IOaXsFayyf/OLnWGOwtkJIwczKKnOvvkNeF/SzCYU2uKTrj2F+G/QLxlBMizSmivOpYnhq9ivw8UHAUyHS80lOkRW+bPgRl5XrW9yggql94eHAuiF+9Q2N3tLeduQcL56U9ylAxYJ0KcTWlklZ+7L2J0HZPHrACv5+HHI0xiyaRwykICNBnCicBCv02cPD6TafMyTQlpA6zc3PPufOrZuM8oLMwJ/+1Sf83/7pXxCn8PobbcJEkccxtRXsPKLB3ZPixrbl9o4nuqen83HPqhTwSg+u9GASKcZRwFbfMJxYTij0ODWEgKWlkHPnDvxugyQCaRjnI9Y+/ZCiMAxHz8/2TADzCax0YG8CmwNQtfeXT0KDsHskYcPTCQgnQyyG5Sjk3OuL7I1yPrtVUVSGvDk4c23fKNBmFa6oGoW5o7KwfSj/fnj4Oe7UMR0mT9IgPYgYcs5bUGgHdeWJ9LIsIc9ozczw3u/+HtsbG/R3d6gyTeggdI7hzpjSjPctYwIhaAcB2jn0KYn0NA54/VyPKJaMw4JaaJh1xB0Ybr4k0l/iyRAoyUzPi630rsUcHoiEBBX4vjGHGqgHSnJxdYmZTofPbtwmywushco6WomkFcdYqyjjAK0cYUOkZ+Mxo/6IzDkyvDXMkXtACIgST6ZPUefwFCuNummXQuQM9vZYu3t73/LUam/zUjux32JmulXVWFMtLS2ysnqe9fV1xuOxD72E2G8l5cePk+/qtbt3+af/z/8Hr7z6Ku++9016vRmsMTjrePutt3jzjTeQOH78wx9QTYYwqbCYB44RgYC2OAj1DI01lVK88srrXLhwhTt3rnP37k2qut4n/jc2d5oecj5hUGowpyxEP95b4kvIt7/Ec8dUXXECphfDCWIr/1ZHWRRobTF1ia7P1nenGA7Z+vwziqrm3uYOpa5/41yHXkAi/fDt/YjlgymhGvv/H5BKsVWOloFXYba6OF1jq0PRmsAvXhQQWpAG5HNgtafR3uFs0NcMSkIaQC9WdNttWmkHXVmyYU6rPU+v1yXtLKOSc5RGsHvrFndur3FnbYt7G3vkxZdcHCKAOIVORBAGqJmAJAxpxzEmkEyKElPVmKpGGIt07sxCwygKabdbzM3NcuHCBfI8Z2dnB9NIDoSAJE5pd3tMtKbCPqRE7MkghSAMAhxQ1/XDt9IMzg6LzkqElNhn3KW50+vgnKPIC8xZ6wwPuzodUXP43oOIJga1cDA2GexxuWITVBYNebmxMyC9fY/NnT61sehGye1MRTbYwOgSIQwIS1U8rFLBN8VxlfFOFdr4CS7L2FlfJ0DywYcfYK0hCGLm5hYZ9fcosgnCKQQKh8Dhz2EUhb6p71Mg63AGTNZ4XPv9IggRUtLptEnTmLrWVLXGSsna+hq10czMzhEEAR999CnbW9vs7WyfaYI+AqG8Mh53aD4ImkVECFKBqcC86E2in99E4IxF5xnFoM/Nzz+hGA6aZpsSZ72qZTIeAwIh5H3jVqvtffOqsqSqSlQQoVRAVRdkoz1qXRMoTRxHyChGBiF5VmGtABeASEDU3tJlqgOylq3NNb649gl7Oz5J5ZxDa6+kNtY1xKaXbQdhRJTGuFDgaomdDBH1EFePGvXV4+IRMo3HxfSrTouGCrxaOcBXqdXNY/r3w7FBwFGl+tQG5hE4qfnVg1/Mw7+ubvY5x0tEi0e8/isIZ0DnFqvd2Y7daSHxdkuSgz67UzSrWleCGTlczZMr4p8jpJgq0qHKNGUJu9sD1tc32OsPKKsai2B3rySIJXXHYoSgnkooAz/fKjzJYK13wnLu8acG8O99kHf24yAIIE6gwGFri3uCD5dSMDvXJklC2h3vo13mhjw3yKxkUo0wMiCTNVVlqcuHdYp+unB4W4Jx6X1+p8OS5cAbWRvQEpyApBcQJwkgcEISBQEz7ZRWZClDu+/yV1YarS3aekfEB6nLp5iGZ9Ohb9oSZLr2P+noBxKS4EB76YBaWySO0SRja2/ATJAg2g6DQAUhcZqysLhIOUmIyxHCagJpveKz+RDrHLX1LQGl8jFUGMRIKSnLcj82B1+lKAOJCCSltUgnmWl3EZEgzidkVYnulwy/jKzXSzwVTE3k4KAQ6FEQApJQIqWgrC36STJwU0wHSOf/MbpGlwWmrrxg4YSJxBqDMTUS68dbfKKx1oZRluOcpah9VVYcBITSL5KUdNTGIGtzQvWHQAYRMkpoddokSUI1GVAMFabW1GewNn0Qbt+6R11XGG2J4wRjvFe6BZ8AM6CEQUrFbLeDlJJxNqbWNXleMBgMEEKwsrKClJIwCDBG09/boa6rB9IyUknSJCIMFaP+LnvbG7Q7PaIoxmhNXVW0W22uvPoaw51N1q710dYg3MmfJxAI6f+iLVghiOIUEcbUNYwnPrkRx4lfczuHtdar6PHkuW3mybMe0ZOSkC/xNcQpLgRnDVbX2On6/gzwTdBzaqNRwhBKX33mnJ+jpWDfKsk6X432UEzXSoYH5wZO4DYeVhnypHgBifSI+9vKPADl0BPpD5q6nKXsbyHEDuHsIsnKK+jRHuXOvYOLQQFLzSbryhsBnrWU9qyYLqSn3rZTb/Sv2YjWCuDSLFyaC5jtzdFqzzHYybh3Y4uLl17jtVe/hZq5CL232L39KT/8n/57rt+8w49+/jG313apn6cp20kQEuaWYHWOdG6R7uwCq91Z3lpcZXd9nb/4V/+Kot/HVTUISSQFrUASytOzl51Om3PnlpmZ6bG8vMLGxgY/+MEPGDf2MEIIFheXWDl/kVvOsruz+cBs+pMiCkNmu13fCX00QpuHHP8mujROM1rbASs8+fsMcfHVixhjuHfrHpPhGUvFGyUgBV4lOj2ECuSMREgwA4sr4aBr48nH2QHD0i8++x99wS8/u43WmuqQb11dZqxd/TFCNEtIAVY/YoHsQI8qGIv98Wu4s8tkMOR68jm//Ohj0naHt197i/cWz/Prn/wFg7uH7Y+Ez/3EKWmanLiJx4LJIL/rd9AZX9LZaiPjhFfffZNLF1fZ7Y/Y3h1gg5Cf/PTHpGmbTz+7jjWWX/70r9jeWCfv7zz+PqgYkiWf7ci3/MJBtUFEkM5C3IF8EyZ3n9a3/srDViX5vbuU6+v8jzeuE8Uxb/7W77F88QpVVVFXNePJ5MQyUSklKxcvs7hyjs3NTbY2NgiSlre1ysfcvfZrJuMRN2WMUAHthQvE3TnKAnQpMDoEOQ8UYHaYZq+M0fzkR3/KL376I6qq9ASYNphJBkgcyidFrAQRELW6dGZ7tPGE8U62y87oOhTZEfXV2dF4bNznwfGEOC683cWPlS18s9GCxuea+4n06WumCpIC2OH5VrNNlc7TXOKLaDD9hNCFxWxUfjh7msd2ykBKYBGfMNniaJjbJFFs5ij7/sA+0WX8nCGBnvD27nd3KgZFjVY3WZ/0+eLOGG0dVW65/kWGiEAtC0QEZeEPtGqB6vri0K6CuoTBji86tfaJ+ZenAwFxCzozMBoY8oGhyh5/38Io4JvvXWJ5ZYbxeIs8H7Dbr7h53Q8EQg6ada53/jXPuapvN4Nh7t36anyo5PAE+vbQE+h9oJIwd67N/MICeVExnpQkUcxrq0v7iZDaWD69ucVWf0LmHLk7ICIf1t972q/3sP+54KC1wEm3aSeSnOsGzYJaoC3sTSoqa7m9tsG9vSGv2ZArvSX/qaGkMzvLu9/+LarJmO3PPqQY9cHVxNIy0t4SqHYOrTVSCcLYCxNmZxYIgoitra392BxAhYqkF6MCwW5R0pGC3zv3OvNzPXYHe4wnExhusM5JfUNe4quAEE8hgO+7fZqUiJKC5ZmYOJSs90uG2ZPd0845jPEV0dPkbzkZUWUT/7u7nylz1lFkGaEzKFvTDn3SrDCQFSW31rcBT9wGUjIzl9KKo/3Pl5MCM86orSM/ZJ8nhCRudYnaPV5/+w3OXz7P5tpd7t68Tj7o0799y3uvPwH+1b/8c6QSzC3EzM8vURYZdVUQGUMU+FL/EE2SxHzz7TeI4ohffvQp69u7rG9ssrm1zZUrV/je975HHMd0Oh1GwyE//MGfsru7g5R+fLHuaAPVNE24ePkCC4uz3Lr6Kdlgl29993fpnrvAaDBgd2eXhfl5/sN//B9x7bNP+Gc3viCrKoTz0eSBlZin6oUUiFBgrCMzFiFDerMXCeM2g1HFeHKXKHLMzi5grWV3d3P/+DvgIQWiD8ThK+E3LHR7icfBg5Toh2DqCiPO1sB2mowqypL17RqpoBP7BPQg99duGnoxrXRev5wb6NcPp0JFC8QcuAzcA9Y/QSAJArX/uzH2mbohvIBE+lnyZPbRKx1rcBic0f7n6eun3vtT3n4aHXJIHvGscFhG8TVOCbbaLa68tsKly5cIwwCcJW216MzMkHRnCVvz2CD12fFRzs3b69y+u8F4UlB+2RYhSkIY0Or1iBaWmF1aYXZxmZkwJohipFRQ1biyadamvKJTOq/sVlJhnX2k4m2aVbPW+q7qWh97jyCKY9JWizAMH0mih4FCKYk2Bn3GPgAOH6TZ46V8D3pxI1VwtX0+ZI/wiuvH9kSYkhzTVd309xPVcA8/As75W9xU+gHX6oN9xB+5j4e2bY3FmoraOoqdHaqipFiZEDiBqWvcPgnqGTmHwBhDXftU7tPJ0rpjbI8gCEOCOMY5R13VTK/TMIxI0pQ4jpFCYHFUVUVxWMklJEL6LmQP8+Q+CtGoqQUyTsEarAuBAGQIMiRIOoTBAqauqPLxC8LKfIlwDmcMxhjGdYVUAXs724RpC6VCpAr3j5FUiqTdRYchVZ4zXWBprX1pq3NoXZNNxuTZmKoqGq93DSIgaJeIsMYYhRBBc4eeXHVWFjllcYhddBZn/TXkISFMEGEMSuFwmKpAFxk6H/jKg8diH30TJ6EUurYnNuR8Yhz/yKleYPp82fx+EqN0mIi1+MPR9FM9Y/+gx8fhFdhUijYtC52eSnXsuceJcYRXewL+PDzG+4/s71nQ5AOfOiz+/E7FE4dltof3szleDw1tD79n6nMBz+86aBAHgnYkUUqRRBGzoWBhBmYCx/qwwGrfULSqSpwwxB1JXTvqwveUCUrvE70flisIIogCQRpKlIBxYDyh8aIo8x3kdZMsr6CoPMH6uJBS0pvpsbA4R5YPyDNDURiqanrjfLlf/IDE9xeWEN4WQh36e20spYFJaRhm1X5FoBDSN1+3jqLSlMZQWktl3b4cAQ7U5Q+6dKe5QwWoRnzgpr87e+QIhUFAEChaqSRNAqx1VLVuesb4GLYqK3CCySRnPMmQUqKkQjcNcLXWTXzOvh3Q4XDBNQciTiKiOKI72yUKEwbDgU+EukOvdA5joKwModJMJjVRWJFNNEVuqB8pwXuJZ4YnLDybvn16/Z3lI6zzFU9hoEgSgdZnX5Md+Tzrjq58nD3gO06Aw/keARJqY474iTvnjgilHN4OMk1TdBMzCimb9xz61qIROtAoTsOQJE1J221a3S62rrzNzBPG3lmWI5WgPRMSuIA4SWmlCVmeoc24WWPj+zUZgza+N1SgBMaaZg1UU9c1QeAdC4IwYGZmBucs48mYqqqQzq8prfWJCmstdVlSVxVSQCCbSk1rfcW2UkRhQBxFpGmL3swcOEE2HnvV/PFzIARO+mphGQiEipAyRAhFXRtq9CH1uf7aL1le4kvEY158XmVu900/EL4VZSR875AoFEgL0gqshMS5ZmXo519zuB8bgGxcAgJw01h6PxaWR6o8nhdeQCK94kCu/fSgR3uYfOwJdee8uuui9AvRxB4sbGqefcdvzYHi7GtMpL/1zW/xv/8//O9Ynu3SrcaU+Q7vff+7fOsPf4f2yhuI+Ytko236t3/B++//Nf/9//tHbG7tstP/kluyKwEzCUGnxW9953d55b33WFldZXl1mfWr1/nkr37G7p276J0BjCe+/koFjaTHEsmAdqtNVVcU5cPJ1KIoGQ5H3Lx5ix//+GfkeU6eH5BMQgh6sz1Wzq2wsXb7oZ8lhGBlqcfcbJut7SHrW2fzJa3qmt3hsFE/PCqFyYHS7jmtA7e2h1hrqarHKH0u8ENPmwMf2xFgwIzs/ZYvLxq0huGQYjzmg9EIJeWhprZTda1HXVu2tjYAT34+bSglmZ/rEbfbrN+4ye0PP2Ju5Rzz5y9x6eJlfuf3fp8kSWilKXmRM+jvknS6bF3/jEGREUQpQdrF1iVV1j/TBC7jhNa5txFSkG/vofPSJ72EYOHyG1x89W8xWL/FjZ/9Gbp6WU59GNZobn32Ies3r/GN7/we7/zW77K5tcXO3h6t7gwr3/0+uir44v2/ZjzYZf32TbbX71HXvhHS1prgfaAsfNNjD8/sWQO6DpBBQBIFlNpQmt2GIH/UjVWC3T34NYiRK5dQ7UXqKCLLRuxe+zXbH/8EXUx835THgFIBy+dfpdXpsbl2i8Guv0eeKUYc+KVPObMpST3tnzLF1E8dfN4ogeCcJ33NOt4h53lB4WMniR8zaw4SArP4+GqqXj+puHDKwp3UFRBQoaS16L2is+0SU51xEpkyb6dwCHxuyIF7+OO2wIGYI+agcetpMLX4mSZQErwk0uAlkc/P+YMrCxF/+FqHxV6Pb7zyKq0oZCYEW2u2/+0HbA83kKEkbAXMnI+5PN9j3K9Z/2iCrhxmgLeAa27ZNIZuD2aSmNVuh8lYMxkPEYX1HrAvwBxsHHy0Bl9sQ629smra7O1xEIYhr739Nm+8/QpffLHLxx98TlW/KBctpGFAO1IUlSGrNEmkmJ1LCJrKSmMdg35BXhg+ubnFvfU+5xc6vHFuFoegtoJxqfng9hbDvCQvNRp/Caf4y/YB5pz3QaqAKIxxCLQTOGdJ6pzwEGF4fnGO1ZVFr3VRgtE4486te5RVTWGdP08mh6zkzs3bDPKadpIy1+uRjUbcufYJusxJTIF0hnHtKLU/v9P8oAPiNGb11fN0uh1ef+0tkjhlXI7ZHe7uj+GmMmT9AiXAKBjLgj/Z/QCpFNoYrLXkxXO8YV/iKBL8eDqdp84AxYH+brv5/7Rn0hjHdr8kVJLFlVku9lpsbA5Z33y8/gfWOupCe1L/lAIAbSxru0OkEM21+OAxTCnFyrlVlhfm2N7eYzgaYbKSzBnsND4XCuIuToWURUmt+/sx9szMLEkYstlqs725gS2fPPa21rEzKuhXht9+723eef0y165d5f1f/xIlIQ5Bm5Jff/oZCEUQWBZmU0aTiqzQrK2tMRgM6PV6XLlyhVYr5ft/+AcIAT//6Y+5d+8OYRgQBgGjccluf8JoMOSDX/ySS5cv85//Z/8Fb771Nq0wpBwN6bUSZnsXqCZDdjfWCQX8vT/6Y/r9PX7w53/Gzs7OfWOcFSGVbCPDmG5vDiEV1gnKsqLWOcbWWD3B1JOGSH9x5oWXeImH4Ti9qS3UhS9Y77UhDAXtKCUK4v0EYNcIFrRAYxi7ktpY+sPqSDwkpEAEAhIHPXfI4lIQRm2kDDFZhj40xjwTi8ZDeAGJ9FPUGTzOpxrtSfQpJH4WjHgqTb3OtjO8MKScwE+SQoBustLPC+1Olzfe+QbznYTxtQ+xZU1vYZGo1YH2AkRdynqd3a17bG2uc2d9l929p9eU6iw5KyG9ukYqBUpiohARhXQ6XeZn5lieX+D84jKjW2tk/QH5cOQtXfbtTBzOWKwxSCGJosj7az8inqjrmslkwnA4ZGdnm7o+Wd0MjjAMSFttjNH7ynV3TMYVBIokCkiSiDSJ0cY84DNP2Ipz1Gfptvyc5/yyrBpv58e4iA8TLiEHSstpJ5bn8l1OIY85LLg//FLvgYEFxtXRFYGQCiHD5nrwVTl1fZyle/J9F1ISRSFBGJFEEVEYMCoKRv0+aXcWZwxKStppm1YrpdfrEBcRaatNFCeoIPL7qkJkEHtLEaEaueIpMo4ChJTIOEY2FSPUGhWFyDiiNTPD3MoFTJkhlPra9qY4EcLb/hSTMcVkTF0WBEGAFMIrYqQkbrVRSvkxkPuV42WRMejvouvKe+kBU3bYWos1FiGNVwzsF+WfZjw5vET18lsRRYg09bdnXVOO+oy37jxCxvvoYxCEEWGcog6VBT5TTG/D0zjIGI7GDaF/TBv9AUfGByEO3TpPG1MifSo+CAS45pFaaDl/P+K37457qU+J7uk9eGw/pRLEaQhAoaqzh0tTD4gX6f6eKtKn8d9hme1ZFM1T0ccUEYiWACNQqUJI0LV+8iapp0ASSBZaitVexOvLPdI4AiEpypqolSBDiVMCLRwEgrAlCXPZXJzeA/6wpHP61UIpiYOAKnBIKaa9il8YjBvrtqcFGYSoKKaqYTx+sbx8pPBWFHLqQica9WTzhLB2v3RyXNSURU0nCam1wSGotKSsDf2sYpAfxCbTW/TA7uB+iGZekkKihEAFEUEY+6auxiGs8apQe9DPII0jZjotbCP4cEJRakepHQTKE4fa4JwmzzJsfwAdSydOKYuC4WCAqQpULAlkQwC4ZmhtxiuHX9AHYUgYR0RJ5KvsAnlkDHYOXKMyrg1oLMNs/MLk9r7WODz+PkYV+uFofVoIdNoR1wGVdhhrfTVPEh2xIjgznCfQHzVGeivJg3ulPMXaTwqBUpIwComiCKEkxoHFYQ4TBkIgggik76PlGj9lITzHEMYxQRh6Jk0+xgE//D0acUxtfP8cGYSk7Q5xkhKGIUFtCQKJqS2jcYYFZmcSwkg1alUoy3K/p8Hc3BwAadoiikKSNCGKAqIwJAwUZaRRSuKsIRuPyCeT/Z5hXpFuCFREFMdEYYAUPkHa7XUbG6jgxGvDCYWTIULFBFELkFRVjrUarSuMqajLnLo6u3hQSNEUxLxIQdBLfJ1wvDDUNYuCKBQkkSAKFaFq7iEhmr56oByUzo9VUp7wQdP1wnTIVIATCOWr2R3P1hP9OF5AIv0saPT9GM4sw6mALesn0KnP59Ru5WskVGylMW9dPkegFJ/dWmMwzp7j1r1sWUYxrUuvgHUE8TwEKagUgF/99fv83//b/5rba9tkk6ewb42aKzQQa78Hj+qXFqSKeCai3ely4dIrGCe4dnudsrb0722x3vqChTAlXlglX9vk2o9/QjYaoxEQxd5730E+yRnuDYnDmIvnLrDb3yXLsvsbVh7C9es3GQwGjMcT9Ake49YaPv3wA+7eusXlK6/yn/+X/xXr62u8/8EH5NmE4fYmpiG/nXNs7QwZjnJee/Uif/AHv83tOxv8+KcfnppMf5FRNGp5exay/zgyYIMDAv25Voy0mkeO70B4DALvkZwceuTAgIMmgCfsa29xlpmlZSaTIbs767jaQOme3vdSEURt5hbm+YPv/x5JFHLn1lUmowFLMx3mkpCiyrn+4fts3rjOtfff5/yli/zj/+x/gQoDdjY3WL97GxEkzJ97jbJ25LXDkXrfc2ugzBs5YsmJWUjlIDZYlZFnu6AcptdCzEa88tYbnLv4Oktzc1xYWiaRYz6ZaVNLC3n5+FLC3xAIKUlbHaRUFPkEXVdsra/x0S9/znA8YbS7izU1u7evYuuKbHxyMrMuC4a7G00y69A5co56sosuKqTUCFlj6uK+JN+joYAuXsfobWfqrCAXQ+q8eOJyYWs0W+s32dtZQ1eGOO5hTInWz7BB7WGLlClOaUviCjBrDVkzJSR7QAxJFBCHimpoyHf003P/mLJfsxLeCP02jUAQEEdzCBFQTXYw9YRESxIjqbYNWaEPYqypJ3yIP50xXtJ3qEVCEqdcufgaAJ/uXKXOH9aI+QS08OPjhGdfYXhWGLxX/nQREHOgLj8NUnwVQAJ0QMUhyWybNEy5/FuXCYzi8x9/yt69J+g5cUrsDSs+uTZgNIKVhU1a7RZhewYtJMGrPWZbS9yjZH13h3zkGG1ZdOEOKgw0R4j0rO9VSzqtCGdGZKVlMrRU9Qtk7fKUkRcF/+pPf8BfffBLbly99WXvzn3IakNlLKaxYxnXhts7xb7toHOQNWa9U83B5iDjg6sbCAfWCQpjKY9VCk5fO10PT/NpMLVwEXRabeIoop3MkkY9pJRIKanLHL23gRAwk3YQwjcQLasaFYSEaYvtnT1u3r5LXtUUWhPEEW+++SpJmnDti1vs7OxRTcaYuqIXhczMz4NwSCmPRBjT/QpCgYq8n7HVjqIsuf35XcIwZO3aJkoptja2ThxgLb7B2vTnl/jqY7p2jAQsNpPrrj5Fs7wGFr8m253k5NIxLB6feLDO202Br445CUEUk3Z6WKPJR8OjMdoDEAaKxW6bJAoZ7O1STMasbe+xOxxTHLufpVKk3Q4qir31k5K0WjGhgN3+Hrdv32a8u4MZjX0H6SfAwpVzWGcZZkMqXfHJ9Wus727SjkPeevdb7PUH6Ks3Geclg12/dh5PKvJc3tdbLc9zbty4QRiG3L17hyBUKKWZX5xD1xpba9qtmDAOcdZhtCUJHT/68z/lxrVr/M0/+iOuvPoaWmvy8ZjllRX+/X/0j/jogw/4b//P/zVbmxsMBv0Tv4dSIUnSRcoAQY01hmyyQV0XWOsV6Nae/VjFSUi3m6K1ZTiYPFdS8SVeAhrnbO/QinEQh4rluRZRpGi3QoJAUhY1dV0QhTFRGJPVJTv9EcZaNAaLQ8WOJIE6966dduxwlTvIYArhu78j0JMCbIF7Eg4IOKpYfDS+4kT61LLgMQYJw8ECa8ILoxB/nhBCEIch5xZmCQPFrfVtHq+w7HFhsbYEaQlnZvHncgZH3PzdsXZ3nb/88x8zyCvqp1G63kieJBCZhn98xOUjQ0nYDmnNtVi5fB5t4PbmgLLOKYZjhls7VKMJsjbUwzG7t+94e5E48ZYu2nfbrqqKPMsJpGKm16MoC581fsi11+/36ff7D/y7c47tzQ22Nzd44623ee87v03ri2vcuHMPhGC0u33k9ZOsYpJVfKvd4t1vvIoDfvrzj0958F5s6KdQLkjNQU5uKpo40/DyJOa8IQfFzifIpQUHTVG7eDIlwEfzgpMb/wmI2wm9pRlsUCOGjRrlQV29HgdSQZiQdmd57a13aMUhg807lP0dgjiEOKTY2mVvc5s94M71LxjsvcUf/8N/SNpuMxmPGA0G9NqzpN02elxgyswfjyD0RHrtwNYNY3jCDSMA5XCypq53/Gqit4pMEmYuznPh7UvMJ20W2j12NzqIJIIy8Aa3L5Rs9flD4JXYKgipqgJqGI+GbKzdo6hqirxAlxnF3trRqq5jsEZT5f7vQohGvucVCKbOGyPhggN/krNC4tnDFN941PcgqGWJqc2+yvVx4ZwlG/sZMIq6BEGKe9ZeEscTdVOV3GkSeAbs6NDv0yq7NoSpJEkCnIF8VyPcAVl1Gni1qbtf1Tzdv1TCvPJKdCkRKiTo9pAyRN8dYYYZgZMkTuGKQxufDm0h/lTO4JODGUeI9ED5BuQAYRCecq8P7eP085+n3c1pYfG3gOLAWuC0MfvxYzcPKlLEnRbttMvqhXOENuTOhzfZexb7fgx5YdjcNURhzt54QiUccdzBKIGYjUlkm8luyZ29HN2H+h5HL8Jj13ld+EdYGsa2pKgdVeF4AuvgFx611ly9fgOxJtG7L57NR20d9aFxoDJuf5w/juktPi5qxCHLkpPyRNMiwOMFFoeFwmkYkcYp7fYsaWtx/xVODqG/hcCQRDFS+QaJQmikUsggpKgNm7sDtLVoIAoUi0vz9Lpt1tY22UHgdIXWNcJq4lZKnCf7Svv97zS9NxWIEMTUtsUYhjt+AN5hl0fha7jEfLFxfBx6jLcbAAHtRkk5EKcn0qdDX1Zp6rw8lTr8YZ/1qDFSKUWUtjC6ppiMTpXRUULQSSLiMKDIMvJJRn84on+C4E5IQRRHBHFMGCqCQBEGCuEceZaxvbVFORriqtLbnT4B0tkOxhgmegLWsdXfY6u/x+uXLvDqpYsgA1qtLbQFKf2+enuI+790Xdf7a+ytrS2Ukrz2+jnm5rrkTXIuDAPilsJZhy41gYRb179gPBrz29/7PkEUec/1qqLdbrO6usr6+hq3bt1kY/3BjYSllARBhBASgcW5mroaU1VT79/HQ6AUaSumqjRi+LL09iWeP5Twoeo0WR4qwWw7IolDojhGKkVdDX3j4ZCmb6Cfx81UNSFBJk1D3sr58bbiqI2lxJuuC7BVdSyTKA4l/M9yD5ytYubLIdJlAFHLL3qryRnLsQVEHa+CrEvQJ7FHJyBtQ9qCsoDJ6Kgx3+F+fPC1GHMun1/lD373O6RxQCI1RVH6hp/PEVtbW/zJv/nXnL+wyrvv/Rbd7gxR0ELJmJ31mwx217hz9zo7lSXTT0nJ0VwqSQsWVyEvINs+5MBy0lsKQ75T0Akt7aBDmMb81lvvkBclg61trt6+S762wbUPPuLmhx/4RkvOss/8NyWmH3/8MVtbW8RxTNxpMecWuOI0eZ6zubmJfsIs2vLSEt9+71s4a/hJu0VdZEhx8grdaE1VFOjqJZH4QBw/LAFHx9epar2BCiLml86jgpj+7h5FnoMrOb15cc6B4dcRnwPEzCIEAY49yDNPpIC3V+h5DjE85/envuc3yyLQgXHUx+5aymGGzQ1NXeYxTOtbH1Zo/QAYC1XNYGubH/27PyGOAorRjhdeDsfkWUGWHzZJduxub/L//af/L4IoZO2Lq5jJgKwqqVRIVWtPuroASAhaLea++duIQNFfu0E1GULeh/qQSrWuYNSHwELXgHCQDXBlyc7256hZy81cE4w1u+vr+81Wv/qYVmWdxhvkZDhnKfKJV+LVnvzIx0OctWhjqWqNNfWpFeTd2Xne+c73abU7tLopCMf7P/kpd659wanmahE0fiXKP5xuvEEsvlJj2sVYYaqMymbIKKa9+ga6GFH2N57M4gXQusQ5gzHPh9gKGiciFUCU+nXmeHRsvTlVgx8WS0yj1IagVjMKOS9ZWj3HucVl1j/fYrx9E6edV0Q6HukzvXrhHH/7j/8eWmv+7F//W3Y2DyVj7dH/41abxfOXUEEEroWrHHp7g/qapZQOJyx6ZH1ycvpeifdR74A610HMRNgsx+7m/hIuIc9yrn58DYBskh+8b6pePmnKUiCXgaRR6b+o5OtUSWPxK40pcxhwwC5ODaThgG2cqveb5ENrpktvcZb53gJvv/IOpjTcev86k90Rw/7Ts8A7grTZzybhPAFuF5DtlES/WiOMAsbRNlpKMgbUFOzs5tR7YA831X0EitqxNbZo455JwdA05wR+GfClXioWzNAgMosrvvyLNmjGlsfp8dtNAtpRQFVpxoX2Nj1iqr49+T3HnZ2kgG4AgXQUVUZW15R0aVEhRUCgQvIKhnWBsxWFK1FKMjs3y0oS0e220XWNbZK+YaCYaSWkrRRnfePbtBWxsNjj3MWLnLtwgTBsERuDqmtomp3ljQOFCQUi8KpfnTvvCW14GTb/JmDqLvcYoVMrgNm4iZzdg5XgD4WDOq8x2mCqp59qOUyhCikIA4F0p83aeh/17dEENU0uOcjKk2NnqzX53jYqjJhZXECFKXe/uMrmjWv0BwOynR1MVT5GJeL9+OzaDaSATjui224TBAqlFFVZ8PHHHyOl4sL5c8wWBf2qZjjJqSrfQD6KE8IooixL8vz+cjVrHdvbQ8bjHF1pjNakaUy3l4Lzx9EJR16VDMYjfv6zv2Zjc4eLF86zurrM5vo6H/3qV3z4619TVQ9aZ/izUlUZg8EGnXaHS+ev+KLj0FAUGTvDXbIio9WKSFsRZVkzHp1OLFZVNf29CdY8ptXpS7zECZhWillAP4KYrpv4wblp6yeJkKF/NA3Dsb7SUMqAOGnRMtCb7WCdQwRe8VoHBiscJiupqX38GeLn4AqkCom78wgVUIcZRmu/ZLSOS6+8yhtvvkV/t88Xn16jKjOKfLvpzfUAyBCidrMGPR2+JCJdQdzxKx1dnG0GEgLCDoRtsDugT1nym7ZgdgFGA8jG/uyeNCZ9Tcj0c6vL/Id//HcQWK5f/5Tt3V3C8Dn5wjbY2dnhL374Ay6/cpGViysEsUJJjRSO/vYd7lz7Nevrd9ir3EOJ7jOhWajGCcydh2AI63s8lN8xlcVUFbZjaak27VaH2ddmqauKP/vXf8KNTz/j7vUbRLNz1KOhb8Tp3JHyNescV69e5erVq7z9jW/w5ttv0wsVURwyGAzY2dl5YiJ9cXGBd95+i73dHdppShZFXhl60ncymqoo0XX9G3+tPzZOItIPj5g1RwLwIAiZX75IlLTJS0FR9v0scWoifdqd7xhU4In0OIW9HJdnB9tt7BFkBOE8UIHebYTbi8AyZHtDsv7Qc5AP9DE63JD0jIGus1Bpxru7/PwvbxMEkkuXztFqJQxHE/b2jte5OAZ7u/zbf/kvjjyb32dnkwCSsDvL/NvvoZKUTAsqselrvA4T6UbDZOiPRxvPlGRjHBP29m5QbpdM1vYY39qC3PrmrL8RmJpVCx6fSHdURX7kuSIbU2RntNNo0OnN8Vvf+7vML60wvzqLkJbt9Xvc+eLj0401UoGI2I+YXAlmaunTKHWcAyfRVUZdl4ggIl26QjXYpBpu4R5rVXsAayusfX7JlkBBGkEYQ6vnL888O0akN3mFI/7fU0K2IVtVVxEsBCxcWeaVV96kLgQ3PriFrRymPvTah5yH5dUV/uF/+o8pi5L3//qXR4n0KWk/JdKTlJVLVwijlGKkqUcVw50Arllq5YPg++SoU4eeOYFcaaHmOugth23lfnwqocjL+20upt8bTp6vAxDLIHpgdjjRHeuFwfQYThX6U++d6fMBfhwD9jszLuHV+12gBa3ZNkuL57m0fJHvfef36W/u8dN/8Res3bznrWOeNqYVUVNLMQeZgayGwV5FMdzEAXcNVAIWl6DVhv4u6P7ZNlVqKJ+hDF1ykKd4bi1QHgQHdvLi6JWnRLp9DCK9EwcstCN2J7DXEOmNVe9DP+zw8ZdAO4RQwkZRMNEFWuXUsiIIJFGgyCsY6xJjSmQNoVJcvHSe1cV5wAtFbDMHhEox120TpzHOGOq6Ik0jZuc6vP3um7z327/NYGvI2hfrKK0RzuGAQjRraemrtlzp0OXLYPk3Ck+QJ08CWG778DcvQD/mpaFL/UzsZA/n2x3+PgwVYHnguvC+fbOWvfHxTuEnwxlNMdhFBSEzsz0UCRu3bjHY2nic3X8obty8Qxgo3n7zMu12mzgKicKA4e4OazfXWFxY5L1vfZuirLm5uYXFMqGmri2tVou01WE8Hp5IpDvn2NsdHXluxkKrFSOlr7B0QFGVuMmYD97/gBvXb/M3/9Yfsrq6zPbWFn/9s59x7epV6gcS6QCOus6p65wkEizOzRAFES1pyPOMvMzIiowkDZmbbzEaFkzG5amKLuvaUNfP06b3Jb4OUEIQCYF27nQtnQ5dq0aAkAFS+BJM1yzjcKBkQBglJNbR6bZwAlQS4oSg1DW1MUxkY+E97fLciDmkUiSdOWQY4WSEqyts6XAazr/6Db7/9/4Bt67fYmtTMxntUpV9zMOIdBV4fvpLJdJFAOEMoEAPwJ4wQ0gJUdikIhRYffqSbOe8Et1JOItazFio9aNLik5TUv0bgLos2dvZoqprPvj0Btt7fUaT002YT4qFpVlWLy2TRIrPPl9jczeDxT9nbmmRlfnXSJMZPvvpT7n54Yd88PG1p59RFZDnsLnhF2yq44lHkz1crTeZTPj8449pdTrMLSyAEFQWiFKsNtTDAYEQtBaXcc5hau95VuRjX77SYDwes7G50TQpsZR1/UQNQaQKkEo1XpIRmJqdjXX6u9ue1D8Bm1t9fvXBNdY3dw/KaF7i4TiuQnrAtSKEIE5TWj1DXUNdy4b4fcxo2RrcaA+KCY4QknmoctjJ93lUW3teWQLJJRAWokshai6AeaCAqq/J79bYvCGanEB0YlAKlwVQTrur1p68D1PfJb6u/WrBPUCG5Q530XNYaxmNxk0zn8chIyMgprOwwsqb3yLo9IjSFBXHfOd7v03gNPnG5xR7d9m6e497128eOlZ4mWSI90mOHPXekMwIal1BS/gVT1lDZZ7ICuRLg5AEvVVk0sGMB5jJkMcl0Z8mFldWeP3tb9CanWNc9Sm2Mm6ujdB1QSbGzL25SrE7Jt8eezKux4HIfEq2OryVj6wOnnTNH0QI4Twy7jC/sEIys0RETuAqxnvrDLbuoPPhGRVPxxeUX871EEhPHkWRoJ1KqhqkODbATGOTqZx22q+1USurVHHxtcvMvTLHwsySf8+0wGXqiHSa+KYhy6WQdBZ6zKzOkvUn1IesGjAOSke1mbH9oxsoAurMYgpNtT058jlHKv4ioCWR813kYszyhUt0F+fY3viC7Tt7ngAen7CPAtKFlKgbUZiKUlde8pJZhASVKIJUMXuuR9gN2M0HTE5JAHxpcOwr9YM2qBhCJYmCgFpbxoX2w9PhSgTYV7CLQBIkETJUWGt8JVyBJ7mfBS8rgBnh79std2BPoyCMJXPzEcY6NjYqysqR5z7cfiiX8CVhOmNNf36JA/zRv/d3qKqaX334CVvbj7YsOYyqMuSypqoNjqZ/mHj0kHOY8LPAWPvScJ9LEdRVRp7t0O7O0+rN+QZ9UiAtdOOAKFAMhkMqrYmUJJSCqi6Z7bUbz3NLpSsGwwFSSfJ8gq5r6rKgznNUIJk9t0LuNDJOfCZTensy53yD9pdN+17iMEpzEIIHcCSUkEGElAprDiojnjeSSBAFPtQtaoeSkETTZs7esuhBybJpkZQQeBW7EEilEEKQV5r6gUlOh7WGbNinrgqM0YRpitV6v9rxacE5R5llZMpRCN/Cvi4zBJayyFhbu0ttLFIIkiTBuojAOIIwAiAIFK2Wbyo610vRxrCxNaSq7j9fVaUZDLP9BIRSFaXeIIwTpPC2Lr/6xc/Z2rjL+r17XP30UzY3N6lP+Z2t0ZTZAC0VW3tbZHlGUfkZqiw1w0FOkdcPXK48QT0xcKKR6Eu8xH0IgoAkDCm1pqyqM10ztbZsDibEoaITSZQSlEWF1pb+OGOkt3Fao6vSLx3qyrsg1gZtLfX0vmyU6HHcprewhAhCXBgig4BLl68QRTGmdjjtWF5dBSHozszwznvfYjTc48ZVQz4ZkI220NUJawSrvVPKKZON8CyIdBlCeslLJMefP5hIT+ImlavAKR9xnypQcZ5EqmrOZICpNRSlJ9MftJmv0UiS5xlrd24xGE340x//mu29IfY5EaoXrqzyt/7+73Hnxjp/8s//gloafrh+nXQu5Y1XX2euN8sHf/Jrrv70c6y1T3e/mjT9aOxL51UL4lVfmlfceziRPtjb4yc/+hFpq8U73/42aadDYQUkPUw5woz7pAtLrFx+FWcd5Tijrgq21ysqc3DD7vX3KE1NkiR0Oh2KsniiIF1FkW8k00qZaSW4uuTOjWsMm+abJ+H6zXVu3t7wzTlfln6dDqeMA4UQtLtdVBQxyiN0EeOKcWP18xjH2mjc7ppPOi6ch+4yDDdgM/fqxEV/3VYlqATm34WkJ+jMpiRp4knlEEbrBZufaOodRzEEZyRysYtIY8w9hyunEtcCEcSI7gJCG+x46BXfruBkhsaAO7i+rXXs7Ozhs86Pc22lwDxzl97ld/7n/ylGCG7dXCNQkr//x3+PSxeXWf/i1+ytXeenf/JnrN28fUCeGnzz1RBvH9GFYmOH4rMdON+CK22oLGSF//+rCKmIV94inL9MfvsXmMnTV/w8Di69+hr/s//lP2FS5fz0w1+wt7bN1Q9+ymi4w+rbr3L+e2+w9f5tT6S3gVfxU/htPCk3Veg67a+345AxpJcJ2nOcu/Aqc8uLCF0ibM2t4Qb37n3iEz9nuuZeDCI9Vr5MPE4lvW5EVjoCdUw6fthQuIVnAkf4KG4BgrmAd7/7LV5/900muwPy4cTnIQrOlGdxzseSUgYsXFpi7Casf3r3KJGugcxRrA+5/eEvfaORhhTerwY4bkk63e9egDq/Qrja45W3vsHFc6t8sJOxffcL//02ue80CCnone/Ru9Rj1wwpzQBGBtYtIhREiwFJO+aVNy7Rbrf5ePtzJpsvOJEO/hwKiFYgWYVON2RmJmW4U5N9ZDDGHVQiHCPSZRQQtBNkHKCNxlQ1buz8NfEshjYJLEpYEVBa2LP+2oshnlGsvN3B1I7rwwGu1IyGT9y24Jlhmm99ifvxX/2v/ktGozE7/81/d2YivSxrhlW933dI4Alxx4PbAEyHtGnezTjYq6bkjg/Yi2JIUUxI2iG9hRaoFCkFUkoWOjFxIFnb2WV0b4NuHNKJA5IkYWVpHm01ozKjqiomWxnONX1ihKCajKnGQ8L2LCurlyhDiUw7UGqElAgB1pa+FNy9pJte4gCT2lfjpApWk8O8iyCIUoIopS5GXwqRLoB2S9BtSQYjS1E7AunoxM73sQi8PVH5ANpl2oojENCOFYESREmCkIrtQeZFKSfC4aymv7XhBUXtNmm3S5VlT59It5Z8OEAWY8qipqpqkkTRagdMshFXr34CKkDGM3RaHVQk0Vb4vi/WEUUhvV6HuZmUt99YoSgqRuOrJxLpeVFRNKKgg3FsDxUE4ATZZMJnH/2a0XBANpnQ7/fPxF0YXZENN3HOcXvjFqMs2187TcYl2eThSvQQPxVXnL0tjDj0+IquiF7iOSEMQzqtFqIsGTc2aKdFUWtubu0RCMFyKyQJBNPl2s54QL/aJQ0k83GAxTHRNdo5irqxmZtuqrHias/McOWb38ZYx/rGFlIq3v3Wb7G0vIo1DmscnXYbIQSLy0usrF6iv9fHyIDd7U3Wv/jZyUS6qSHfO9NxeepEuhCKIGwjVAta53G6i6kybF3iV1qFV5yVuSfST02gT3f3eE3zKWE0VEVjt/ECB0Oh9LOXbUzXptHlU8YkK7hxd4NKaxZn26SxZFQWVFpT5Jr6GZJMk1HOvdubjMc5C+cXqWxNMaqoS82m2CBvTRjsDKifoPnKI+EOCAPT2F2cxlPVGkNdVQz29iiLkmoqu7IOnMDUlmKco6QiiiKk8A1FjqBZXWijmYzHVEXpfRcfB0LQbrdodTu00sRnzJ1XBZ9EYgahRAYSU1vMb3IHr+eAKEl8c8y6JhuOsdaSjfsYownClG7cQ8UBcZVgyxSTBOiqIh8Pzu4T6FzTdLOAPPAyvyYzS8Y+oeGsV6YL54hTjYprlFAoJVGJIJ6TfhQ9J7G1wiQSJwVhO0CgkEikS7EqwEgFCmwU4YzCaoNzsrGrmUpbG8mkbJo+NyVT7lG13FIh4y5CCEzp7yHV6iDjlJm588wuXGb5jdfozM0ilERKSRhIejMtokiirWVUVpQPsmixeGWmAMrGpC3TsFPBqOaZGO8+Y8ggpLVwgSDtkq6+QjhzDrtznZKYA0n3dAkEfr49xfcUoMKmWYu2OPN4x2YyGnHj6ufUGGqd4ajRVYUuKvJqQqAj6rj0FRJdDgjCiAOJ6EPmOhkEJLOzxO05pFIYY3F1iasLdF0+Bon+5SOQoCREkVckG+HojyxF5e4vnms1jw4wKyD317UIBNFyTDrXoj3bpdPtITQooYhbKSIQ95Pap4CzjiIvyCeZVzsfRuFg28LE4QpzcO4OH/64eUy3nShYSQjnWqxevkBreQZnDDtbm+Q7E58Ae1A1soM6q8n7OXWsIZGQOOgFyFCSdlPiOKSsS9zEoU19tiaeXwKEgHYvJIwEcReiGFphRByERMIhpt69x5vPKgGhQIYBURghhaIua+qq9vfuGasqp1H1AyNqBcxKSAXhQhs1F1LHGcbm/pzXIKxEKW8kfdg24GndjkIIWq0IpSR5XlHXjxkUB/hGuXAQ8E3DM83BMPqYHy8DQdRSCAHlxGAf1+/hS8Qwr8kqy9ziCpdeGbO3t8d4NNyf8wVeC4U7WroNjQJdQqoC4jDCGYOpyiN+61Ol62E17EmXrP99SvH4wcWZDFPuIcyYdixxSlEbi7GO2hiMdVTakEtvkxWGIVY7qkqjrUE0nyqVRApJbRzjUmP1CJNZtre3vWJXKZzRONzBGvUrNre8xLPFNAEkHL7/iBCEcYIjIIxTVBBhqi/HPdfhNYNF6dDWIQQYYxmPC+8h/Ij4d9qyQwLGOL/8qAxCOoQMiBKF0TVG+4STDEJwDqPr/XvFObevRLdP2Fz0OLpp1FR0O+pKU9aGUjuktkTaopREBgJroSwrDJrKSIwTSCQCQRQGdFtteu2YUCqMUnTbIdb4Pk3GuP2lPRzc/gdHzo8N49EIISTZaEA+GlFXJdJ6webx+TQMJVIKtLY+Sd5AG81gMkYJwWynTTuO6Y/H5E0514OGnuP2PWcZoQ47BB79Xi/xdYRQAhEdGq+cw1bmyFrZGENR11Rn4m0PIB0IHKU2WCcRzUVbW4t1Dm0thTHeeMQ4387t2NTb7nTp9GZJZ2Z9HCgkvZkZlAqptWU0nuCsF/QZ69DaW8cEQcUkzwjCkDhNCZIOssxwusA9YS+spz7KSxnSbZ8jiOdxs6/jgMnmDfK9NWAbuOelkztr7HeEOtUJUfgVpMLX/k6tBE6JMvNK9hc5GBJAL4RO0JhE1lA5mDz9fb63tcM///OfsjTX4Y+/9zatVsgHd2+zMx5y5+aY7Weo5rp57S7rdzZZfWWV7/+D75FnBT/8l39Bf3uX8Qd7SCmpy+fT5M1WUDZNtR/VhG2Kqiy5+fnnCCnRNgQnmxkzJh9VbOT36HTbXH79MtYmbAVHbzMZBgStiHKUs7O2gTX2gRYsj4IUgvMXVjh/6TzLKwsPf7GAVi+m1YkYD0rG/eKxtvkSHrPLC7zyrXcY7vT54lcfUpcVa7c+Jwhj3vrO91k8dxnnLBZHXZeURcZwd4sbH/ycunyMY+8cDHZguOvHzaktRo4XcZ/3iaHh5168rkVOGhZ0VIt23CLuSeZeD3G5xC2m6EyyfTegzAWd812SoEVMi5g2+WTEYGcDpxQEAdZZ8jz0BFVZNx7jGm8DE0HU80R/1T/VjSSjFvG5txEypLx3A5NPSC98i+TcK3z3+7/DH/7R38BJhRWKJIl45cIccSghH2Oqgs29HT69s8bm4AFVFwZY52BFoICtCtYbEv0rSHCErRle+cP/hM7KK0Qzy6i4xfW9DYaff45nIId4En0OPzfucZoyCiEF8UyMDCVFv0Bnj5fAvP7ZZ6zf/W9oL/a48v23CEKH0BZXOHYHa/TjDWzHwHscRP3TaT1svsJDdjdKEy68+TpxZwECRTaZUI42qbM9JuO9x1wJfLnJxFYMnQQ6XUE4KxhN4Pb1ikq7ozb+AjgHXAJ6AhaVjwvuGYIwYP6NRXpzMyy/ep7l1QtUM/PURcHG51vezkjwkP4IJ0PXmq07G6xdv4vNjt3TOxYG1YE9yUmfOw+sNn/PgbkY+e1LtFcW+Bt//++wuDDHX/7Jv+KTTz8k/+s+fMwDCX9nHf3bfQYbQ9yrAbwWQCShFxGFEYudORSwub1BWZaMy8KT+F8Oj3EqBKHk0psd5hbj/f66KgoIVYh2GSIf+XHq8IrX4Y1uWwFRK6XX6hLJiPEgY9zPMLU50yUt8MUhMX4qOTGPkQr4TohYCJm5dJ6016N/6zaj+qA5rGhJQpkiAodAnvQpT4QwVFy8OE+aRty6tc3u7mPqybsKLkUgLejKs2DThq8DvEx9jFf1nwaHmQwgTCVLb3SQUrD5+Yh88OVbbp0Vv7q6Bjje+vbv8fq73+Uv/+IHfPCrX/rgwmqkhDiUvs1UbY/kpJXyrp2zM22WFhfZG+d8fGuD0pj9ISJWXitUWl8U9mitkGPa2V0XW+Tb7yOt5tJsRFlL7u3l5JUn0QHy2lBoQ9yGVidFTxz9YUmta9LYl5QnaQupIkaV494gZ2dnjVu37qGLgsBBN44ZjjLM4SrCr17I8BLPEAE+7JYWJpVP9LfnV+hGLWisRmxdUH5JVtWjiWWc+aSVUpAXJbdubyOcQz5ikpj2uvb+78ZXiuQGJwSt3hyzMx2y8ZDxYA8VxiQz8zhjyPvbRxT4VVFQl+VTt0W6sjyLtZbh3ojxpGJiHbkFIy1KVcRRRJrGVMaxvdunqP33dQhaaZskSZntdLhybo5ACQIUTlpeOdcmm5Ns74zJ8ppJDvlDJN7WWtbu3WVjfY3AWpSzKOHoBT65MtKHnO0EdDshaRIwHFWMxgfBblaWXL17l06a8gfvfoNuK+WvPvqEmxsPrzadWro8hrSUUEAifHhWnpWFf4nfOMgkJFpse0szwGlLtTnGlgf386SqyBsr4rNeLhLv6CkcDEuDwRyIOJoPq4xjp/Dbuz9x5XHhldf41m9/j/5wyPU7d4nThNffeocwitncGXBvY2f/fXGYkEQtpJAEQuGcI0piZuYX2OtfplZd6sEddLbNfcHcGfD0lxnO4kyOMzmEPe9rKpoOF4cPyf5ROu3pEL60WwRgs7MT4i+SomBqQDaNDw8hTlOCmRRRGkSpPZEeWUxtyLPsiJL18Ck/6zdz1mLqEqcjYuVoBYKZdowRLbbi4n4V1BMgSiKUUsRJQhSF5JOc0XBENimoy9p3xi4NtjRUz8Tc8yFwpyfQD2Pf+0zgr8nm2rLWoKuSugoxtW78FY8eROs8ca6N9qr7J7wuHQ6Lpax9xq2sHzLzO+fVCC/KvfAVhgwUURLT6rSZnV+kzHMmoyG6LnCN+imKY8IowjqDsV2SSFHsXSCfjBns7p7aQ28f9gTf5Cmn3dhK29JXIdd9h2o5KmGIYoMzFqVo/MMdQjiC2GG1QwiLtRqLxvqW1yipPK9lNM6aQ0nP4xoGd+x5/JgvRLO/DhCNXE0hVIiIWsgwQQYx3YUVpKnpnrtEev4i86ur9JYWMMZSFDVJEtLttIhCyajIqLSl0oayrjHO+U6N5mjmHGjqxDkYb7Xz4+lXDDIIiTszJLPLpLMLJDPzhK0OMohJevO0Fs5j9AhdBzgjsJUfj0TQBmlI0pAgVJTZhCo7eVXnnHvisb6uK+p+hVWWfDhCSIFtlMy21tgCfx4UR5Wf08dxwvAYBMKrJgKBFA6BpS7G5OM9dJmf/KYXHRJc0PCRGsranaxGh8ZfHE+MJyCcgK5CRSHt2S7tmR7aWMaTCUV/TDnKyEZnj5WqsmRjbZ2qLikmOabU96/QpufsYQil39eqEUzEAhKJTAJanRadjiccdVVja/PwmmQBLhAQC9xUaW4B45VvJjM4C+WooixLTGH3rWZeVDjAGueVac2xdNaC0GjdEI8SP15HQCwRscBpAWOHmpGELkRZhakMujYHt8AZarWF8iS+pPGzNsdioqbIRcQQppGPUafigGZ7WjvGE411EMaKtKWoyqOqu7NCAEpKojgiSUJarYQkCVFKPfZn+oPu/IfH4qCAR7j7LXQO78v0+WZsmk53cSxJUkVdW4rCeCsb4xeZ09suUT73IZucSGV9k9YXdbSSga9oiqUnilqtDq12h7rKqXONQBBIeWIVpWimeSW937+S4r7exs75+OQ0w5KUEiEk1hqcM+A0zpQIZ5HCk1bTbU7Pz/TYG2upjUEbizHWWxgKbwfjl4O+ctMYS1kUjPp7OG2InEMKgULgkAgp/O2kBEJKH7s/y2rZFw3T/gyPaFL9dYWlGVIcSOvnOqVCf+3KL68kyjZjlJR+XPcKTeOnlAfkOgPhq+Qi5xMEzRTrwzXn/FqzqXaermsd7sjvR/Cg558QcRhirUUphZByX7CkLVQapHKNotxhtPHVtABCYIxGa+0bEmtNbaCuamrt/Zpto+BX0vcdVEY0Y4Wfj8LIq++rqsY6h3DWJyeE8375UhAqAcKh7LSCRyKVJAwVYah809Ijh8lRa01d102jZIMSjlB5Y4JHFdC6Q4/T4DBlOFUFv7y1X8I5ENMqNEf8AAEAAElEQVSL7YSLzjnn19xPsg0OxhQ4WPIp2F+rn0TXhlFEEIakrTZpq01WVk2hvqOuLQhDWVYUZYkxBmssOrZY01TR78/Z3g3CO1BKnIogSJr5zU1fciZO/akT6UZPGG3+FKFaBL33kPESul4HbuJXSg6CENpdv7iajB7dABTpVY/dJU+mjxt1+VcVIV4GZPDql2mFqVS8euUNVl9/BeUEygo/yBno7+zy67/8K/Lx5MjHBOw3rz0TZhLFa4sRvVRQrN9FpDGXlxe5tLpMv69ZG45wJd5f9QkQhAGX3rjE7OIs73zzG1x57VV++oOf8G/++b9mZ22bP/v//ABrLeP++Mk29GXBVeAOjr4/XZKiMKzdvgM4qvJon4Ain1DvFtjyya0IrHOs7WwysBnvX/uUn3/6IV/cu4M5Trg2OzcZVuST+qWty1PE4soq33jz20yGQ37wr/4nBnu7THZ32RMRl954nXOXLtDtdVhcnMM5gy7/PtsbG/wP/9f/jru3bj6dnSiBtebnJijKxlBEUH6zJPtmTSwcLekQVkBQI1PFzNIMui0ZXd9iuGWQ2iGNI0y7tOZW0VXOZPcepi59U1rn/KQ03RD4Rqquz36FkVAQ90CFUA5BF378DmJka4Zg/iIyCBFBQtJq8f0//vdZObdKa2mBeKZHe9b7mgWBoNOWJJFE4NDasTU2DIaG8ZQQTyNY6HjZyPABFUc1B40sv4LoLF/g9b/7H5PMLNBZukgQpVipcAjOvff7LFx+i/7OOhu3r1IN9phc+xhrHcHia4TtNt/47kWWL8zw+V/+iKs/+av7Pt8ZRzkoPVn5BMTXFNlgwuc//AgkFDbzk9SUvz9EeODwKulN/PkJ8Y0MM04gVSXaGIbDbWJnWF2YJwkUW1tfsHn9Q3T11ayuySOoUxiUDtH3KnTzoKE5xRcbxP5XEQcEKzHt1gyvv/Ye3fYMX3xxm4/e/5ydX9yl//E6470xZmTORILcunGD/8v/8f+EdY6N9TV/Lh5nuuiksNyCooDhCFoWW+W4skAFkjCJ6M3MsrC4TD81VA+TAUtB8o0ZoittCpdRksHAwm1NWRbcySYIC3WgscLiJs6PiU/Kd00522dAIpnacuuzEfeuH8R0yZwgXRLkE4NNrd/+OaAlCC90kO0Q/WmG+euC+DuC2eUOgVSUoqYcV9jm/hWxH4aPhSf3Q4DugUx8WN4NoBpCvnPo+1oHkwqRQidKmZudZRInRz6mP6j48c83iVPF8qWU81Gb658O2dt+vAbbU8enXifljXdeJUlikiTEOUsUnM23+whGBr5w0JHwWuzvJacbuzLrk63HrnURQjirfLKhsgjjKHNvpXnlcodvvjvL+kbG+x/sUGvD9rWxL00uLFLAm7NwuQup83mwWxn8aOfFbdHxzXe/ia41V69dZ7DXZ3FllW9/97us37nFzWufoaQkDSOsdRRVcSKhXlU1o9GILKvuI9KqQ0r0h0EIQdpuE0YReTahzA+SwMYYykmOtZbZlqKbBuyOK/JDBzXLC+5tblGWNcZahJC00g5hqCiqinKSY2pNCLgiZ7K5jkQQtHtIIenGbQgscRQQBIqgkxB2Y4b9AWt3Nx5pj/EbAYG3YYs4qH58CaBpE9L87CxQa7LddaQKmFk+T6szi3iSpN9TgrWHQnYaWYs5+Pkw5hPfSqisYDA+RKIfem02HlPmvpEogKkq8v42OPfULVwehChJcc7SmbGEaUw5zMiyklxDbSDRBssE50DWhsBatJRYB1mWMZnkmHxIOfATnTUGbRy7RYV2liS2BAG0VECaBpSlIctqOp2UVy5fwBjDjRt3KYqSpfkWrVaEqA3U1if2gNoYKHKQgrTbIQgDVCCRApTyk/JxTWVRVfz600+JlQJRcKEr2Sscg+LksWbaXPS4tOlh2FexuwPXy5c5spcweY3bOIjBnXO4x7XQOwFTt9Xpz3Bw/cY0avVQINsB2jhGY70/bgkhOH/5FRZXVllYWkUIgZSKKE6pasP7v/oEkCSdFBkoBnt9xqMxvdkFZhcE5aDP3o0vsNZAEOOEFwwZY7DhHLSWoSwgzw6CzzPgGSjSDbraA5FBMkKJFtZm+CmnuVWlQoSRV6+Laa/hwxAgvBIB0cjUVOwzBypqnnvGOJ6NeJqjzOHuOscQhzGdtEMgFIEMPClmLVVV3ee1PR2ET0ycHDbOOgFRIFlox6ShpMxznDHMLAsCFaEC+ZAPPht8MJzSme2ydG6Zi1cu8vlHnyMQVGXNzvrOk2/kzDvFUzyf9+eBHRZjJEWW+wz+MWbEGuO9p56KqMWRFwV6DNt7u9zbWGevv/fABqJG2xP7+D0MXvEjH6w6+JrCWYeuNaqtWFhaJo5TkjRlPArRVUWZTcBa4jim2+mwsLhIEEiiSNLtdZmdn2dvZ9srKB/k9X1aWDwpeQimBCNAnbOIoUUEEIeNequplFDCgrTYsqQel96yRWucULREk4GuS0z9MELE7Xuj70Mq75s+HauF8JUbKkbELYQKkDIkjBMWL1zi/JVXSWbbRJ3YezrjbYtUIFGBQBtP0hcaCi1wIiAMI2QQeEW6esiccBapxguBZv6TEhkERJ0ZuquXSHrzREkbqQKMUzgUamaOpN1Dq5DBJAeryIIIZR1Jd5GkN8v8+cssvzLHvY8/4kHd/2z99Fgdqw2T3ZGfQ9p4gnxaMXE80994LGM4sOJ4wKl01lKVGSKMsHWKEwF1PqIc751ux0QzqbnpsuVLgDj6s2kU6Tb3j4euaKbVJApfOi4FMgkJWjGdziydtMfaeJ3tjS22rt9h95O1x/JGz7OcG19cf/T+P+qeUgqRRDinvQ1LABjjvYedxQm8X2Gc+KZdD4MA2QqQMyGyVH6sqy0MDbaAYtCo5qfufzln77r1gO0+KzgH+eSotN8EINqCunZeeR/iVxgJXpEeBZAL2HG4gcVlFhdIbOBwVhBGEVESYZuEwiP3X4BVYAMQUfM4iSwzDowlkJIwCJBKHKke0dqyNyhJa8X5qE3aDggCub8Nby+gCJTCWq/me8RueVcupWinCUnqs0d+mnyCk2KArGmSaoWXRlvhJ8rpqZiKkgSe+IgkcTtASnBUuNqhlOffO+2Qc8ttysKgpD9vZe7l5l6V7ZsIr6RNewMHY/3gbxBFIUEYUNea+hHH6Fmh3WpT1TVCSqx1JGnK3NwCw70dfF2Qn5sR/n/BQZn3tLBJW0tZ1dRa31+ReYZ9UUoRhiGVlAdLGueb+OnGpzWKAqyQBFIiDn26sZaiPPDTl8IryqX016DWXlmu69r7uFcFyAAlJUooZBgjAksUBgRKEaUJcSelzAuOfuvfYEzXqg+o1PgqQAgQyssc99dFpzl1j1hDT6OI/fCyiZWFqX2/FikIw5AkTTGN2vjwx05/eFZLqn3FsTiy1WabJ280ktCNfPXMWAqk5T4bB2t0Q5hPj6XFNlXQoikNedbrRKUU1knCKMIJQRjWKFX7BKZzKAt102NOCgiExDW9O2rtm3jnaIbOe7obbdAOxk3rqUB5OxwpBUJItLZI6X3VO+0W2hiCwPeeSpOIbjvBFDVGNGO2AyccYeDHnDQJCcIQs9+77OB8HD4z1lomWUYlBTMdQRJJxvWDg8LHWdoc17LY5knBs7sWX+IrAOuO2Lg8k02c8Nz0blDC328qEA3ndIioE4Iw9tX/KgiodY0xFhAYYxn0fdJsVgniOKIsSoq8IEoKqrIkzyaM+7t+3IoS36NNBb6heJDgwhThQqRVOOmw0dluhGfgIOkAb1dhJh9h8uu4eg8wvk4miIjSFr35OazWDMdDtD4cMCZATNhZIp5/BRWERFGK0TXDjTvo8TY8lNR5Ckh86fT+EKed9yN9WoNMjfdgPLbIddZy86PP2Lp9j2S2QzLXpZ4UlNtDyiynzPP7PsZwwsWpmtlQCBiXTSeUo5if7fH7336bSVHx7355lUE2IrpXIIKAjb0hdsJTaXLqnKPUNXlZcm9rE9sK2djd/vKC0OkscthC4BnsinGGvJo0lcTHDuTUzOxpcDkO6lGByWv+6t/8OXc+vs725hZ19TRYBI9Op8XMTJc8L9jdfYAn9dcQg60drv71r1lYPEdLzCEQvPLGt1k6N2Zr7TZ3bn3C3PI8i6vnSeOYqqyJwpTFpSV6vS7/5H/9v2FrfZ1/8c/+Bz55//1ns5MOqhtg+lDOweRCEzDVQGVwd4a4iaQcaKgOGojU+Yj+vWs+aNbHF/SPqPX1HgueuDRHr0Ob96nvjkl7S6y+9/eYWVyhM7dA2ErACXSuUbFASQiigE63TVU7Prq+R6UdQkWIJOLCK2+zONvDbPW5vvdD3FOwSHphkMxBPMvchcucf+c9ku4MQTKHVCmd2TmiJKHTaRFHEet3tti8NyBOA1YvXyLrJJjRFkkY8L2/+bdZXF5icTWm3VXcXJiHXuprXx9m/Pi0MJUgVOyrqPc966cL9BDvoz1tnPuQOceUGePrH5GHIeV1RSAdo931U+6MQLSWkck8Nt/BZQ/3nnxmmGn+by5V2wXXbiq/jjfrPAZhQNTglMApSVNIjIxiOr0enaTLcGebtS8+p9jLHvl5Z0KEP29TcrfA+0k/BCpQqCjGCIHxUTLUBjMp2NvcIRAKox1x0iYIHiEBsY7i7oC6zjBd61WSNb4twFQxHwCLeOX+PZ4OkT69Hp/T0JKqkMU0ZhxoRq7ANE2kkQ5zbYKpCtymH4/X763xVz/6EUsLy3z7G99ioTvP3/ijv8tkOOaXP/spG/fWHh5jCP+1yonvYS16gHxARYT0+1Dbisrk2Lbx9+0E7y3eoK4sd24MCULJeORPQDKrSGYCLq9e5L03v8HW5h4//MHPybIHV5EYfEHB7iTj1x9/gWoSpc459gZPoXoxs3Ct9Ku3qWVZ4Q6ql4CZhTaX31wkbUUsL/Ww2vDJr67R3xmRpF7Ff3G5y2uXzjMZB9R6A11ZAud9wN9dhIUUXm/DanLAR3aqfRvSI1BK8Xf+wd/iu7//bX7yV3/Nn/2bH/gKoefsInJv3fftUVLQ67VppSFmYZ5hf5dAeZvOqiqRQjCbKJxTDEtNaRyZ9mpzVVdsZ9Y3DHsC5bYylrC2tKUiiWMiY5kMht6asmlsK/EL706kiIQgqw2FtkghCcMYIQytyFtSjIYZDoc2Bmstt6/fYHd7lzLLCAV02imvXbxIHMVgLc5aBru75FlGFAR04pRJeEa52lcZ0z48krOXPb8gaM+3WLg0S1lWbG3vYirrv9PD1rcBPinreOBrpzlOgw9zjl7lnoD91m99h1curPDF1c/5yx/+AGc1rQiUFIRhhEAyzCuK6ukruTuBJFUCohgRx0z5DK01g9HYN2s//p0CRTsJCGNFkEaMa8Nwd4I+UsF88qI5CAJ6vR5SSobDIdVTXIMeh4wSJI6WCkmcRbXazFWavCyZZJkfXwNvQdWb941JR6OMsqwZu5rcGArjsLk/7s75wqtpU+RJDkUJYEBYAhHSSWZIopavllWSubkZut02V65cYH6uy8baPbY3N7DGYIz2boVRRBAEtNttgjDg3voew2FGVel9vcjhoxkEinMrM7STkDAUSAlDO4KJD7Yekds5E6YkehgKuqnEGMc4s4+0kXmJl3jaqAXk0ifywiaBtx8iKYmTko31ewyGfZLOLK3ePHlR09/L0MZSlr65b//WBCkkVRggw4RisMfWzgamLLGm8sKJ2qDikEvvvEt7foG1jS12d/vMrl5iafkCRVmys7frK/BPiWfUismT6bbaPPq0EKBCVBiRpi1sXTGWx+VnAZAgowWi2VcJwpg07aKLMeO1275p6LP20Q7wZPoUlYDsKRLpFr9KOAbnHP2NLfqbW6TLc6R6jqo/ZnJr68QywgdysUpA2khPs5ONQltJwsXVZbYGE9bHhrXdHLafft2ew3cLr7VmMB4hd3YYTSZfPud1FmXdY8A5S62ngcSxDTwtEn36caXGlppbn1/n1ucPUBI+AaIopNdrAyDE6CWR3qCYZBSTDFErBpeGxEmLuYVzdLol63euM+xvMRkPKfOCqqgw2htBt9ttur0uv/397zPo9/nJD3/AJx988MyIYLvnH3UGRXu68/i6vnulXygcw8NV6IKjuobjcF4OfxKqAjuZIKKI7sw8vflVoqTlFakOnHa4wHnLWumzy5XRbOzm5KVlYXGOJInozS6i0oBu2kXk1ZHeEV9tCAhakMyRLl1h+Ru/j2zKg4UIidIWSTtlbr5NuxUx2NnBmoogVPTmZlFo9uYXaCcRb7z5GufPrxBFJVLWpK0WIo38/fu8SrQP+6BPpQdTL/Sp+rzb/L3PA8jfRuWkK6o9T4DnlJw1DhBhF5EuInTx5WkJ0+b/6eUagQs5VU8S4UA073NSNAp75Xs1xAlRFFNmE4Y7Oyet7B8fAn+uIjypGzWffajI8KT3iEb14QQY5Q5Wq7Uhm2SMhxNvQRRGSPmIEngHZlBiXOkrW7qBP/0FB0TjtAKig+9r/zTwnC+UQCraQYQWAqkrTGD3m2HanRqG9f5xHw5HZDduggb5liBNWlx583WKrODzjz4BvfbwjTVDuGlUeL67HPd/50P3rHUGbWtsZPxxNvhkRvMeYxyDvRIhG72LgDCRJLOKlUtzfPvbb3Pzxj1+/Fe/5r4SqkNo5DjoSpNvPoGVy4OggZ2Hjx9JK2T54gy9TsLlxUV0pbnz6W3Gwld2hRJmOjHzszO00zHWeAuFxs6eix0434FzEcwHB8RF/IBVl5CC1996lT/4u99jc3ubP//TZ6/sPAmj0RjnHFIK0iTGxZ44TlPftIumx4+QglYcIhBMagPG7du2oC1PI5MlrfcZlkLgAm9kWeWNDZmQ/nn85RkFEoWgto5CeysXKZUnLYMQrTWTMj9ie9jf7dPf7RNIQRxKkihkYWaGJPEDtTGGfDIhm0xQQhIFXrEuvqLq7MfCc8i5P0vErYjZ1R6TPGMnH2Ck9WPow25/xUHy/wGvlc1LNCf18fZE+uq583z7u9+m1jU//tFf4JwfOwIlSKIAIRRZpU/ewBNAALEUtAMJcYho+etZCCjLmuF4cuLUppQkChVKhIg0gUIj+6frliqlpNVqIaVkMnnMZtCnxNQyJ1R+MFVRTMtaxlmGw+6PmzKQtLstVNOLBW0phA9IjYX8AaxxXU/zRj7gSqOIKE4IVeRrcqQkbcU4GzE7N8vCwizDwS5OGCwG7WocCqlCZBASRRFKeYuY0bg8UpjpwFcY4iugur2UXjve/1sQ+nnyuP7vSTGlIaSCJBbUuqlGe7m8f4nnDCs8zSrFtObtEPyTjEZDRqMR4TgnnhToGor8EC9rHXnu73ExP4fsJtSTIdXeYR5agnUoJLPLq8xduMggq9jbG5D25li49Abj8YRhHSHPYFP1jIj0B8AaqCt0oZgMBjijMcd3VoQgUnRRkG3cREYpRWcJV1cYneBNVEc8Uyagchyx63yayq6HQEjByhuXmFmeZ7DbZ7C2iy7u9xg8jiQJaLUCqsoymVS0Wi3e+uZ7qEDx2c8+YFju3feefn/Ir371kc+GF89O4W+NZffeNpP+mP5mn6TdYndt+/kvEI5zf1Nl3XQx/tR3Z79o6ll8+HOBCnypTVmWbG7uUpb1by6JPiWIKk5McgH+epEckIMNJuMhVz/5BYGKQbSwxjEeep/U/l6f2zdvEYQBV964Ql1rdnaGGK25desqw/4eSxeW+N7f+R63r9/i3s17z+47joEbzc/TBqWPdev7kqozXdemcZQUKUSXqcUK2/2KQg65t9anrGFhsUWvF9PqRHRmEqwQTApDVhjfKEnX3L15DVPnDD75KdmtT7n96UcnerQ+ER701ablzc9kPhAQzoJK6ay8RXv1dboL57AVVBhGZUkQlqj2Hp0yZ7EtiRJJKxJ0uxGBtMTCUIQRyZuLREphzIjRUNGbiUgSxcLiMq+//U2G65tsja8dTT6cJI95WpiqumoOmo0eXklMy6qm3PhxBWYQQtwmCBNmZxZRauqFohnubpKPh6fbjWKINRJXPcSL+1ljqnKbEsCD5nGMlJZAu2kSpmNvvdHqtkk7KeOgpu+qxifQ2yflZYk0AXrdwC08ufkIyChEKImtNU6fEDQKIAIRSebeXiZZ6jDKB4zzEbJyyBVwlUUPav/inmcXw3YbFUfYxRDtDNZq0DW93ixvv/st2u0OkYzIRxnSStIwJVCnCEWn45X2CQWZgrwIWHBNYsZ0msTENDmTc2Bk+xXAeFRz51ZGOtfit954ExfBWE6oqpoN3ScblfuliC4zGFvAnCGJW8RJSlFob1/wqHtY4r1Gpop3hSfSp5UHEugIuCyRPUX6Ro+gG5JVY6q1gmxn5GPkunmPBaXZJxilFCxcbBOnEZoSXVQEVtBrtWknqbcGaSCEYGVulk4rJYpioihidzDg9vrGlxJvdGZDOnMR3a6gHOwxzANujSdYY4lDw/JCzEwvod0KWV6eJYk6hEECQtAK4Z0Fb+dyviuYj6GrHKmEwvrc9eG+ykd0ntZy9dpndP4i5MbNaxC4L8WBqpPEOOcolG+Ad/f2bbY2Nlhfu4u2hlA4gkAhgbz0FnEYtx9On7TLURhybnGJMAhY395mnGcoGfiG5tag7f2yewGkSUQnTQhkQiAERZkxyYYYa9G1xmHIKnBCYIzzxXCN0rasKvYGI7AOVzmssygnEEgMB2pcAbTShOX5WdKkRVGMKYoJW3t75GXJaOjFECNbsDXcYzyePNA68SWO4TD79yVpHeI4YX5+iTkHi/PnyUc5V392lUn/IWSvxscsjgdy3AGQysa1zvr/a0AKyfLSPKuXLmJswWeff8D6+m2ss1gLk9Jbh4SNK+HTzslMx5ZpGFsWJWVlaEcBy52EQlq28EKV1W5KKwxQQiMwzKUKhEIFAWkUUyMJlEQK80ClcpKmzM4vECcpi6sXABiMJ+T5s+NoRkMvxqutxeBwIsBJRRh1Wb24QF1VZOM+zmj6e95iM5Ah3W5ElGpmtWGcl+yNTxcc1KZmlI+QkQCReMW7GaDrmrW7Wwx2h+xs7VKMa0ptyWuHVI40Ba0N99Z3sU6S6xCZzIPOcfrg+MzOzvDuu+8QKMF49w47gwFZYalqR3/sifQwCmm1UhASJwKstWRjv5ZMEkUYSqrSUDV9Ih40Qk3riI/YEr3ES3wJkIH0NoFNVWCtHcNxjbEcVLIZ2wSWflQzeUFldrAyBtXxzxvh/aga68uk1SLudCitJq8r34ezMkRJi5XX3iJpdxFxiywvufL627zx5rcwxFhi4kiwsnwe++Ur0h8Aa8FWGATZaISz9v7mFCIEmWCqCrNzB6I2VLJpHx3jV6LPeLcrPJn+nCGkZPm181x89zWu/vhD1j++capRLkkUs7MJk0lNltW00hbffOsdoihk/ZMbDLfvJ9KHwxEffTQiN1A+Ox4dZy39zfu3/9xxOLIAv3BM8JFPyTOaTb7aKlmlBEEoKOuS8fir2czv1IjxxILgZHJZcNDd91hwnWcjbl79sPnjgldMugngGA6G3Ltzl7mFOd+Zvdbs7Y4pioyrn33OcLjLwrkFls7PUFXVsyXSJzzSjuF0eIzwy3nvdYIOxBfRYpadYUnGiPXNIdpIur2AKI7ptCMWZruMS81wkJMXPog3RrN25zqjvS1u/pt/yeYvfvQ0vsxR3CcVOfT81JLkmXTmkZ5ID+foLL7O4ivvEacxthaUumZnnKMCQavdx9UJbrlDZH2Pi243IpaajrLoNGJGLSCcw9oJ47Gh1ZqHpMXs/CKvvvEO95xi+9r1o0T69Hs/q65D0zYpjwMVQKtH2Oowf+EKURT5hYipqMrilES6wxVjXKH5UjumtfHHd2ohMeLE46Lw/RADCUUCOoG5doteew7BmIHzhvNCKRCSoqqQlcJsGrh7iv0QnkiXUcDUI/Sk15CAbElm31xm9vVl3Podsq0K5RyBBTsxmDva+4+eD6EVEi3NErbbFHVJpSsvoNA13XaL73z3d0jSNvfurZOPc4QTxGFM8ChFOuwzFWJKpCcQrvo/uRCs9A8Hnkif3sdfISJ9MjZMxobL6QzvvvoqMhJsjraZjHNGOiebHExOLjeY3CAyS5wkxHFCUXiS75HWeRJPnE9D6oiDZNrUh78HfEMhZ2JalxYIopDJzg7V7gS9VxxUMoXedkiZQ3oFKZg712Zmqc3u+i57WxOUE3TSlFYcN/6Xza4IwdLsDKsLc7Q7XVrtDl/cucPdza37hTbPAa1eyPKlFGUt5WhIBYzYBeeIQkdrPmJlpcvsTJvFhRnisEOgEgSCNIJ3lr2ly1IoSKW/5ROcv+Wbvr8nEenWOW7e/IJaDblzdxMn3f1x63NAO4785pRD1JLd3W2ufvYJe9ubGGsIlLc2wDkm08QN/rJ5EF8aBgEXl1dI4pjhZMI4zwhkQBhE1KY6mUgXgiQO6bQTkjAiDkKGo4CyqLCmRlc12jom6BO5zqrWVPUYiSBCNTYwIJE4HAa3f7224oTlhUWklJTlhKKs+OLWTUaHlbWT0yVsX+IQphZuT7kK9yyIo4S5mQWSJKHT7jHqj7j30b2HE+mGR1uXCUgaIr1woJokmZCChcU5Ll06z3CwwRfXb7G5uYazDuu8q16gHN3Ws2Eyps0kBYBzlFXFoC6I2zEznZBQOO9oJQTnuikLrZhAligOPNyFUkRJRNlUhkrBfv+D44jjhKWVVdJ2l/lzV7DWcuv6NeDZ9T8bjzKsc2Rao51DhC1EmLDUabN07hxlPgZbU2QZO/1djNasLi3SbqV0m4txc29Ef5KdqhBYG402mrhOECJBCIczYGvD5voOOEeVj9BFTaZhUEMcQZJ4T/bd/phKg2otIZM2trBHiPRer8fv/u53EVj+/N/dZXc4YmegyYqDmyaIQtozXYT0DU2M1pR5hjWaJAlIEgUOdGUfeqt9tVmJl/hNggolKpS+33tt0NpRlsdmczvlG6SvzCwKqmICSRdmeoDyli0WXyaIT+61222c0eRV5e1MTUHUnuXCO++R9GYYTUbkRc2r777LlStvcPfuNtevrxGFAQvzrTN9j+dLpDfDsHMWoxtFQJD4lZGpwRlwObjBwYhdl5CHTYS2R+NJ8Hx3+znBWcfunS2wjsHG7iOD57QbECUBEsFoVFGWumkgKGjFEVEcEycxURKia3Ok6WVuYK3w9un6CYL0tJUSJzFlUZJnL3BL95PKlacNXwOaRM3R182GMBNCu91mZn6eUV7y2b0dqpNIh99AWAMar/IRjZ/qb2z6etoI8QE+kEIIOt0ZolbMZHdEUR1mZ4Qnz6cXlDtgI8tsyGDnHsO9VUaDMUVRMc5L6rrEyQgVtRn1N6iKMUkY8dqr5xiNMrZ3Bl++/dGzQBhBbxZaPd90WgjiVJJ2FZ2ZhO5MmzjxZY1CCEQQIqTGln3qUZ/+9ffZXb9FPtw4ILanBPD/n73/epIkS7M8sd+9V6lx5+7Bk1dmZdHmPd3TPTM7vT27QyEgi5eVhUBk8TcBAhHgAZDZxQ7BcMz07DStLtJVmVXJWWTwcG7clF2GBzVz94gMTjIjq+OIWJqnh5ua6tWrl5zvfOczHBNEgpovfdBHNYzq4ohhjAxC0kZKq92gnM0Y3bxZB3wtD0hqCGQQIYTAmaouPHXfjwhEo4dorCPi2n8nCEPiZgtflTTKGWGo2Fhq0u00iAOJtZZeNyWO17HlDDMbIFyddh4GAac312g1WoSRRErIhofc+PwThge7eOnq7rqQR6Ycj4Oeeop9wJTudqdFp9MizysGg+mcoH+C5tLWQDFFhgFJIyKJE8ppxf2H4Tq1dnljizhpMDw4ZDae8KWbDp9Eg+NskPyLpxJJ6ETzbMYAXCAINxOCdkgRO3Q2pgg9UdImkAmtoEtQhFz74afIDKZ7wwc7Dw9uXlfg9mLYR5AC2QpQrQgRKIQTMHS4K9VRZoEvLT6jZhFKA8qj50W27ays5XfaQFFRBBOu37hCo90CCzGe/rU9Rtv7VNmM9lYLnWmKcfnFINZibM7ATxwMLYGDZhBgjWfWr1O1/YIQHtZ/ew/3kCcDyTGDqHli86PygtQqUpWwcupF8qLianrIwR1SDUaDEb/4yVukzSZJs4m1ljBN6a6vcfrMKdY31zHOUlnN6HDAxfc+xnhdn3fEsWXPYr+SAD1qBljUGQVFWSCdRZclVmtIHWoNOmHKatTBa081qbDOkfkCLz2BlCgn6cYpadvTjWJcobHVrfUsvPf0JxOsc8STGVGccNDvf2V2XfnU0N8pkN6jnCMMBK1WQCAFwpqaoCo1vijZv7xHeVMz2dnnzVVPQwnWW552DO0QEikWzjyYArLKYwU05iKOmZ7vExUgPbMs4/DwkNkkq8dfw5e+5uosL+GB2GqMNZy/8CLOwc3rLSqjkaZCmAycJQpqfVRp633E3e6YMYbd/iFhEFCU9UNpvQWrUUrSidoYa8jL4igLQQhBs9Gg1+1SFQVZnjErCiZVhbGGyvsHSg6Lwoi13gpKSrwtcM5yOJ2RVRXNNKGVxjQaEaXO8c5TFoZK69tqdz3HLdGfB300T2YCf0WYDCdc/vhKXfgzSimyguIe9Rnuh6aChoJosdSnLioshUQmKSIMOdzbxVpLZWZUdsZ4Or0lu8b7+pmQ3j6U8vF+UMCSqp1pm6EnDgSxhJYStCMxL7xZ/43FI6qSSlhc4FDKM9OOSeVQoSCqcrLKUNnaN3tx9s1mgziOKcu6oJ+1luFoTOUE3Q37RWuGp4AoSuvzSebxdScwzlOWmtFoSp6NGQxGVEVBoQ3eWkbTGYVeTNKeSV7cd48VhBFBEJIkKc1GhziKmUwLhISk0SVKGgwGh+R5jq0Mdl4jAmqbsyw3IBVhYxVFhEp7yCAlbLcI2UJJT6Ac62vLWG+QwrO+tUWcJOQXd8iKL873AkBKVBixvL6BcwbpMvAVSSMmSNK6GLOxWOMo81tdDU4m5ntqfVNWuHr99Mu453yOrwaKOtrouIVkFEIQz2sGqFAhA0E5yzHl/Syf6+LBKm4QJE1EmELcwLk6Y8TjUUFtEymDmtput3u0mz1spSknGWmzhQrC+mi2tp0ejybs7u4ym+ZIWdvplZV+qIfhSybSAXztH61d7fEZNusUxmJUy+/9BOwJ014bQbXIsRrxRHcrzxi8c9z44BI3P7p8302EENBZTeisJQy2cw6uH3dCpRSdRoMkTWi1U9J2Sj7JqU5smCcGPp08ZmqPgG6vy9LKEv2D/rNNpC/cKBZz/Mmidwl3NLrbSuDVFpw6u8w33nyTi3tDrh+O/9oQ6cb4Oo1eglhwxL+sl15wT9JFSsnq6ibdlSVu6isUo5NEugSR1O/ec3L3Ox3tMxsfcLC5wsFeHxkGaOXweLxKCBPF4f4l+ts7LLdTznz/NS5d3qY/mMyrUv+SIUlgfROiBqgAIaHRVXRWQ5bXW6xtLB09okIpVCwQeY6b7VL1b7Dz9n9m+7P36/Fx3uRHhbAs9TO9TD2J7/FgalQhIG1CFKOaSwRJm5XTa5x76TSD69eY7u/XRPoD8q9CSsK4iZCSKh/jzQMw0lIiuxvIpZcQjR4AQZTQWlomKGYUeZ80CXhxa4nlpRZSCExVsbXWZWm5xeHuHtcvTRE2qBcqUcg3Xj3P2vIy+3v7TCdjhjtX+ehnP8Q7hw/c8aZYUqtP4xPnM+CBifS1tWVeevkCO7sDRpOrWKPBV7cElB4LpoJJHxUr2u2UpNmqAwf3PL+a4QzCFudf+z5L65t89LM/YzZ+0OKkTwkd5hlv3FKkcYE0gHMt8Ar2AzCRIH25S7De5KA8ZDjsE3WXSDvrxKrBcriKOSx471/8kGJngnuIucmVFe4emgQRCILllLAXo+IQ4RRsG/y7OTYEm3CcnRMCs7qYT6VGVEUGBwUclKA95DDLDvjgg3dorXR4bfMFGiJi//3PufSzj+l126y+ssR0L6Oc3GZnt5DXFdTBh8jhpSNMFb1eTFU5JtcdpvI1+auorW2eimXbbQiov9NRZxc8ofkx8IK2CVgNO3zjG6+jreen//ETLvFF3/O97R3+07/8tzQ7bd74te+QthrE3Rbr8QV+/w//Dr/xN36dmc4Zl1M++fkHXL94BZPrevxMqYM7EccBnoVCve1rIt1WTGbT2ocgz6AsCDoQvgCnmh1+ZeklqsJysD8mKyuujQ6orCZUitBKltIOjajDRtLEzgpsVt5yf5333Dzos33QP/KK9d4/ecuuB8RkUDEdHg8uzUbAhXMBMgKhDco7mBU4AZeu9tn/PGNtCf72C5BGil7DEiloR4JQiiNiqXSO0cSjJfSaEFRQTGoimgB8AIPJgMwOKYe+DlB9BU2wdvrU0c/ee4IwYfPsOT788H1m1qDHA8rtK+AtaVQHAvKirlV+N5Ra8/n1a4DAzdlHYzXGajpxm9WlFYqioKyqmmCnzlRY7vXYWl/n+vXr7I+GDLOMwzzH+/vmXBwhTVMunH+BOFTY4hCjC8rrmryqWOq2OL2xQqUrZvmUsqgY9sdoY6nsL+c+85GxKDZ957Jbd8dXTKQf7vbp9wcIL47G58fJdFkK4VQCZQXTHBDzugehIlzpQRhz7fLnfPTeu0SdiLAToafVUb+H+rmqyhKneABBwINDCTgVwFIAKvKocC5eZM6gC4gEbAVghYdsSlYIgjRCRgE3Jporo4ogNKRpXaA3r44LUAoh6PW6rK4u0z8ccGAO0dqwvbtPuzCcumAJg6dPK6XNTt3uUYSTivFkis0LsixH2wPy6Zj9mztYY47mmmIwRCCOs2YeYH6Jk5S02WJ1eYszp18gm2VcvXoNpQRnz24Shoqdg0MOhvVi7uQRjXUMJyVBlNLdPEeYdJFhWos7Ok16nSZRYEljTRKDcRVKwYVXXqGqKvYGP+bg8A5ZMELUe4wgZOPUKYIw4PDmp8xG+zR7TeJWC6MNZV5S5hW60tgTROYiY2FRukgbz2jyfKx7jieMQNbeV5W/ZZCTStFZXSZuNo+Tv+0h2eh+6fJ1BDdqdmmuvUDNuwiM0Uz0CCeZ11uLUGEtZFtZ3mBj7QxFVTEcjZBBQBDHtZDIekzl2N/bZzYtEDImCJK6LkpRPMPWLgt4XyvQhaxfX0gIPPlQW469N74ks/KnhWCeq21dLQW/A7xz3CJgvJvHnAAVKcI0RAblLXOC0ZpB/5A4ieqIsTZ3LVb6OBAIGs0GKysrFHmBEF9NcaQHwsKXd4EF37lohPm/KSl5+fwGq0ttXlxKuNBLaCQxQeBQ4mve/x4V/sTrrym8hzLLycKQKKpTGfM8YzoeEycJvdUtlArBBzjrGPYrirwet7z3TMZDrl36hEany8rZUwipyGc5ujBIHxOqNkUx4rAckxcFQVQHMexTE0UtHohFNKniaRZxVnGHIO3iW+u4ICCMQjq9Bq12k2a7LqCJVFTWY6zDGMckL9jvjxkfHrB78WOGOzcphiN8ZY5VlGp+CYZje4KFx2+XmiSqOLbSuFt7WgNGIrwHIdF5wWRvl2w4fHhlpPc4ZxBe3n+xLgNUYxURNlFxB6FCVBAShCFSgrMlkXKsL7dIYoUUDms0USMhCmufobIo5xkTbaJAIU1JEkZIoagqy+7uPnu7e4zGOSpIsb7Cu/LYj3wRiIDjOgAPgbKqi1flRYFY2BFYXwcoZFy/LzLOHhkeU1WMD3cpZhMmg0OqPMdUd2aCpQpIWstESYMinzI63K3J3YeEkBKlFM55nH0CSvYh9+yHzkGp6xiErevi4BD1S0q8qtUWYRQjrSCfzjCTHF1UtdL3CcI7ah9uqcm2J4gMymFxnJmx4BoXj8fUz8lYU/ejoYXZ/HcV2MKQzaaIWJDlU1AxRmpc4hBNRdBJUJPqi6SLP/Hu5987rQO9hbdUpccthjF5h889AoQAqQRRIwYB5azCmTuMAyfXEU9wfgxkQDtuEnjF5xdvMitKJnfxc220mpw6c4pmq8lyp0MQhozkhApNGsd0mi1CF6FsSHe1R/NUBzt1lHFeZ6YsiK6T5roLpf3xTme+83a1P7ur+2aWa/bNBFM6xqOcQmuqzKC9w1YOGzu0cVTGkxc5s9mEvMi+sE48IkYXauT515/MbfnS4LltPe2YzSy6Am0cgXd4ZcjLCulgbTkmjR2TqcYmsNQIUFIQhCFKSiZ5RV4adqZwbVLXOM0jQSXAF/74Ai3YAjQeW/GVrbe6nTYwz0L0nt29AG0MxhicMfMNpsBTKyw8jqMqyPdAHRjxgEAIQbfbod1uIeb1T7wzRIHAWoGZk9jGaKqqxNrai93BUd8J5vZA9j6kurGG8XRCHCoCU+KsBuvqaUpriqwuQOp9vVYz1mGsf67OvBselBRf/N1XaOsCdaa31/7WPbSQCCVppA3CMKTIM4riwYRgpYOpqYdCmB/Wg7ceV1bgIG0mRGmIjAUyhsp4XFLvDXRVZ+ObuQL4Sdrte19PucrOtRHCo4IAFQR4oTBYnLfkzqNdrWGU0iMqC06QaYf1gHVU2hw9Eye+AWMMZVnW44FzOAHOaHSRMTzYRakA/TT9YoHJbAJC4KoYL+vsTCklzlmqIsM7TavZwFqDriqcq10Q3HwMeVBYa9FVRZ5PGQ37lGWFc7Up1GSWoUKFilNavWXKPKMqizmvFYBUyCCulbMqBRkhhEIISVlpJtMZYWDQlUZXgiQKkcIxm04p8mK+fzyGpB7zBPVY6pxH67puWdrsEEUBKg6QUYANLYEKCVRJMa2w2iLrCRZT6Xo8fXK34zme4xgn15OL/z8B7z26rBDieLFuqgcnOpyx6LyqbS2DEGsd3mi8tTgZIZxH+BaBijDGMR5PqLQmzwtkoIiKFKEkla7mwfw6K0hJh5SOIPCkscL5B98IfzVEujNzlTkctfJd098XRqLwtXd3aobQS2rl1mH+YAtlSa0cctymsBKEzZh0uUV4cKs0bzab8u4vfopSir3tXWbj/I5E+uNCCMHpU6d581tv4q3n8sXLT/w7nhgWRbROVti4QwAsTSL+x//ub/Nf/863aZ06R2vjDBd/9hN+8m/+JdpMnt1AwVOE/2VWoj8gnLXsXrvBwfYOr77+Jue//+tc/fwz3v/5WyyvrvI3/vbfJm3Ulhy6KvnLP/6PXL10LDm9dukT9rav8cJrb/CPXvsfCaOU4Y0Bs3FGIlYJOj0uXvwRVy6/TxBZmj2PqWA6mCvWnigUx5XmOvP3fR6oQuEjorH2Mp3zv4VGkglFs5Py0mun6S512LqwxfJqFxuEHE4rJrlmmFX0d7b5/J1fMLpxhQ//w//C7HCPKp/VxEMPWKIm02OOJ2tz4jK+Mb/MHaBPrQDu3+HkfK3MqfM1O9CA/rWrHLyzjbMGUz2gNPvocA5dTOqTuo+ti4xaNM7/PkFzHYIYIUPSRpO03UYFYPJDeu2Y1149i1KCSZEznpSsrJ1mebnDcDDh2rU+rUbCufMvoLxHvfgywvnav3FvxJ/8yU949513GU36tFYuUJoBWXWjTrcT1O05oh4Te/M2e4hhbv9gwCjL6wyKwKKEq38WCtJlCCLIDk/M+YuLn78/YP/Op2M+/slfIES9YfK+JmDuhChtcOb1bxGEETc/e4dseIDRD7+5C6OYpNHE6Ip8+gTG//ep2/Yu2S+lgZ1J3XQGQILUYDTYMIK0Sdhu0W51KIYZ25cuYvsl5mlsXCuHvpJhpGDn0xlCymOy/raCy5TURU6FB1mBqOpgyuLeetBpRX97m1k5pBuEpHFC3iqQ5xXhcoNkaYmisHVA/mQHvL1/TIAZ5MKxLSt87LHr1MPaoojrYyggpQClIG5FrL2yhpCS7Y/2yEd3IFoWmTBPONDciBPOr51iPMv5v/8//jXX9w6YTO5MpJ9/4Tz//f/l/0QSRwyubzObTBntDcj0hHYYsdnuUQWeMvLk1YzTf+sVkv4eO1cuURX5PAWXW8n0k0VIha8DjX7uA6jruJit4NrBkN3rU4QF6WuionQOIQWzRBMGmjLLGBU5IghptTvs7Q+w9wlKhdTC+EXNv69y5V9Wjms3MoQA4Wtf7Xg7IxA5v/vqCr//O6tcuznhR2/dpNsQbHYatJKQsNEiCEOu7u3z+c6IT/rw7h7YtkRvKZwFhz4KDGGgqkCLrzbF/pUXzwP1c+Cd47NLF9k53KE/OGA2HuPyHOHqSIsXAXV+3aJS9L0g6y6mFEpJ3nzzDb797Te4+MknvPWTv8I7Sy9RWCcZZBrnPePJmEQJ8vzWxboSgtZc+ToxBnuPBptmMz68+BGhkqwnAaEQ2LIiBKajCXo6I2rENDvNepvl627+12+l/wRx0gbmq04iX2jwjiCQQUwQRJx/4RVWVla4fOlTrl35/IEO169qD+wG9RLUu1qdboQh3+tDGPDGr77B1vktsnGfYtxnOs05VJ4yNwwPc5z1zKo6WPUkt+YauFzVer03Y09PeJqthFa3iykr8skU6yw3tWemIRZzHUVhMMIeFfiz1h4VC73dkmY6HqPLnKLUVFrXandRMaty3v/pDxAI9F0EDk8Kn135vFZmqxhUQHdphVa7S5mNySZ9Go2Yl146g3OW/v5BnWkyzigeUmxQ5hlVkTMbD9m5eYUgSGk0VvFC8OnVayAFW1vrrJ+7wM3LF9m7caUu3hJ1kVGDsLOJDGJ80saqACnr9tw7OCCbjQmlJg4qOq0EfW4TazUfvf8u4/GIsrx13xEIQUMJHJAZh7YVg4M+QaB46dWXWdtYYzo9ZDbr13t276nykiSIsdoSWxDOcX3/kMF0eucLfo7neFyEok6NCe68CHfWMtrbv6VOzsOov6vpDJ3vIpIU1e3hTYWfjMBoqqxEqJil7iqtdInRaMLlyzfrILxzhHFE6TVBGJBVFcZ50sZcOCAtQagJQ0G7sSiY92D4aoh0uC/BcCu+omX03dTgjwgZBqg0xlmPDYt6Bj3yMZiHcYz7gvL8lsiOP/5ZSIlQAiFvveHWWsajCVIKdKWfCom+QBAFxI2EIAyebUX6yY3uiTa8/V1ISFoRreWE3lqPzuY6h6vLdNpN2s0ZzURQ6ZrweIrN+hzPIKwxOGsxWuPsCW9DAUpJgjAgCAJUIAniGBVGOGvxzmJ0hdEVk/GIyWRC2oA4jhFNAYUCqwnCBjJoIlSJ96busgG3bgIeV/koBPiF7PB2A8sj+eFjfMHt3xfVXuhhExW3QEpSVWeypK0maatBGEWoIKDQlsrk9AcTDg5GDHdu0r9+lfHODWb9Q4rx6Pg0F3mJcCxdXDjqLJTpC9uCJjXRp6mDZwsl9knMo0XeVHhdgnUIGaJkiApT8L5W3nmP9zWJO1+t1qplKXHWUpXl0e/v2Y5CgoogaECQ4oOUKI5RQUCaxqRpSBJDI4JGLGkktUJ9WjiMtUwnEwSOLKuVQUVeMkIQCkFLSbx1TPtDZpMpg/6Q8WiK9RAlbbAGF3SwlUYXOUcGmIsN50OmbhtjcEVJEIQ0mk2sMWTa4v18wrpnW4gT3e7e/c47VxN/DwSPdwZrBVWRUeb3Sxu822H8ifv5BLBQmt6lfYWAUEmEmifrCUUgQpQIkV6Bq71YwzBAIzBZic3KpxFtq2HqwpWmusPxb28Sc7d/mMM6rNaYSpEXWX3O0hEkAQSi3sgLd//RZ65wrMXgJ4JBT8g+IIxD2p2UMA0RQs7XNPc4q4fxC16s5e7DOWptGAynjKYzDg5HDAZj0iQmjSO0MWhrCKOIKE5YWVth6/QmURCg+yPQhpXlJYRUNNMUhCfPMg4Px4z6faw0+ODE2nMxdtr5z+H89545CeYhWlhB+ePfF2AKh5lVCH+8mbDUgSCtLVWlkdqgjGGWFxz0R0zzgmarBTIgy/K72iw8aGwiDAPSRjyfyeoN0yyrnpg1Wu1nfOuZhMojlSeJFb1ek51+xVgLfAnDzCMDT9IOIAgorCAztR/6TNfK1bovnBh359Nwq5GSJBFIiRCKsqyYjL5cAYcUizobdd9Pkpg0TUmThCSOcN5CleCsoSzrzeji9G5P+lRSEsdxndFX1urJMAwJw5AoCgmDgCiKSJMYgSeNAozzVORY5zFGM8syiqqimitgpRCo+csDgawJeqVUvefRBnOiT3nvqXSFM4JCeqwUWFeH6pxzaByBC+p91PyYX4ul/aMs1x5nied58Ify9s89a1hkNYQhcZKi1INTIVJCqATK1fWjFlkz1oN1FuFEvbwLJGEgsYFCSXFEbh6dAvdfVtxNa6CUJAyCo6Z182fF+3ls10HuILMgjEcYjzMObSzaOrSfl4Xy9XdUvvZMP0KdklUPS3OuZrHfMdYiNCdUzR68wPuHWZ89HvRCQOEEwjqMrrCmwhpdZ5z4kCiqvYjanTZxXDHN50UHHwj1g+K9q7MGnK3bF4UIFFIFxDJAKkmj06PZbtPuDSmyDCtitGhCkCLCFKFCvJDz++3xwmF0SZnPcMpCaMgDwWSaYY1mOs3IZl9sR+8d3phaXCHquaOuWSKpyoqq0mhtMdohpUQFaj4mKhCeQAFC1MVjeeKx/+d4jhoneTbHHYky9xjWWt5ZvCtr7lOXeFvhrQFn8bYC7zFViS7L+XNRHmfWSOr6dDisrYP1xlSUZYFUMVKGCO/x1iAe4uH46oj0rwMiahLGUqugHnNd3u606Z7dIhsNORQZ3tn5TCYgTuud804B4xODveJWD+8HQFlUXLm0ixBQFk+xYI4AFylsI8BF6tkelBf2DouTjKjbFTgqVqjBSMcH2TbtYYvvri3zJmc5s7nK3/2d3+LU5ud8cOUzbhyUfLQN06ddyOw5njl477ly6SJ7Ozcp5j5as+mMTz79mGarw8bpMyiliHsrdE5dIB/sU4wHR58/PDjgP/+nP2J9Y4t/+Pf+IavLq/zsx+9x/eoO5177HhsXXmH7+od89sGfYaXFd7l18zPm0WstywCCeE6ELPwIJhx7e5yM1D0JSGicgXgdki1A0O51WX7hLEmrQe/UJmkzRkUxzjlubB9y2B9z9edvc/FHP0JPDigPLmLKnGp6Qi3vqZXlY2CTWro4oVbEJsCbQBNEWhM6bAKr4PvgW9Tqv23uSGbZ4T52OmLlzMtsfPd3j9LPvLdoM8E5TZkPMKYArfHa0EgbtJttJoM+Vz94H6MfYMyN2rD0Ei5skWUlgR2y/o1XWVpb5dRmk421Bu00YLkTEwSSMFA4ZwiFQeucP/6jd9nf7/PGm2/w6muv8sknl3j3rQ9oJjGvnNnCG8OlDz9gMhrR7/cRXtBqrBClTXyg8fE3yEYHXJ/9FG3z42KD2bx9HmKd44zFW8fyqTVeffM7zGYZH374AUWWQTmqCVN3hzZxzOe+sO56pX5ihHCVT7n+4c9ACMrs0dU3uqqw9k4pzk8HnUTxzdUUoQR9KdFRSBKvIYMOZVYwzgviJnTTBkIUiN0CBuVdreKeKXjAeFxh6B/sEwV1ofSlThdjK3b2b1JO8odv54r6eVZAe/7+GNg8t8Vv/cFvMJ1M+fGf/hWT4YRq9gTWUeH8/Bz12HWPZ+zza9v8X//pv0Jby+FwRBiEfP+NV9hcW+Ly7jbbhwecvvACL73xTV5++WVe/8ZreGMobu7TVAHf+Aevk7aayE7CYXbIX/7ZD/nX/+xfMRFTDpoHGGfq9HtLbTd0kkxvUCcq5dTjauBhuZgHLOf3ZgRcpx4v/CKoUcMDeM9oNKE0GQ3pSYXj85v7vH9xRHdpib/xN34TbSw/+MGPODw8nh8X0POvfhANy+kzq/zqr79GGAikrZhNC37wo0/ZP5jc55OPBingwhKc6cCrp9qsb57m4kBwQ1yBmaF6N2OpEfLb3+mxthQwMZJKgRHztnG+nn8NdWC3AiSoVPJbv/sm3/3WK8ikhWp0+OSDi/zb/+U/UORf3mJztD9ASkl3uU0UBbx04QIqilltNZB6RjWbUfQPmU6mfPzp50zzAjsftwPqbqLnr06nwxuvvYbRhg8+/oS8KNjYWKfb7aCrik8//RiJ53u/8m2iIKDdSNBGs31jj2yWc2P3kO39w5r8sxYlBO0gOM4el5KlNEEFAb2VHmmacGN7j+29gy9cl/Wew6JCCIFxdcBOSghCUJEkSkKM919KscTHhVB1opf3YBbOp/f9EMdOfo+iEl+MDw/yuZOZv88cPM5WOOEfUtBX43RHcb4XMJla9g4NFfVQ6QREqUKFkvFggBeeSBpCAVVhGe5nGONwD+i9LwWk87mssDVRv8DKcpfNjWU8EucleV5w7dpN9Hzt6YDPM7hRgJpkqN2ShvesutrWZWGdVHG8/T0JFcYk7R4Cj9IF3lmyPMdYS6UtxtgT/JjieAH5ZRQm+SImw0Oy8SFS1MU7IcL7ujDqSy+/CMCf/emPmc5uPsRRvxh1knFEuL5Gu9vj9ddfo9VsUWiBsYKNs6+D0/QHU67vDCgrzXCaY71He1sH96QlwOHKEcz2IAkhTJnNZnzyyRBnzVEmwO3QRcn08JAwjuksr2K94EZ/QF5qZuM+YRQSJhFRHNNqNVhZ66ELTTbJsdpg5+stJQTNUFFaR/VcDfgcTxrGz609efIpN0A9xlh8FWD6o7kAbD4x2RHeefq7MePxBMIYFaa15ZMuEdZSVFOUD3CuLno/6N9gODzg1MYmK91zFNmMw52bD1VH4xkh0p80gfOEsFh4POZBpJIIKUnSBu1OB+81wSTCOYPDIqREthKEDLADg7tdLnlSNLqAB2cd7g4Ts3OePHu6qVVHkOCVeGhf3S8dt68UTqiAjs5dgpeeqS051DNyW4I3hIGk02zSaaa0EkkzrjNXnuOvJ/LZjHx2rHA1RjMejbDOs7yxAUoigpAgjpG3KV20rugfHhInDdJmg97KEo12k7gRo+JlUtdlONzBOIkTth5/5oVejwpDPiqEqHeODpBzg0Z/wgdBKGoPnycxFs8r1KoGBB1k0CAIA+JGSme5R9xoEKUNgijEeWovs9GU/b1Ddq9e5+bHH0ExhOl17ugrtNiplxx5MB8FOxUQzZN84NhPPadWqd9haBRS1qS5d6BLwjCmubSJVAohJc5p8iLGmhKvDFIr0BqsJUkaNNptqqK4xfftnhAKwlqNbp1DWE2chLQ6DVqtmFYjoNOMWO4ktS20c7XPo67QZcHu9i5Xrt5kfX2D8+dK+odDLn1+jXYjpSkVWMP1azeZzlX8UijiMKXR6ELkIXV1IWEVgiw5GiAXAceHwdyjNgxC2t1lkCEyiECVderO/Yz+50rAJ8lfOGvJJ8PHPo73tbfmE4O/7f02BErSTgNkoKhkhA5DlIoRIkLZunCPMB7pPcI4RGZr6dnXgEcHEM7jrUPnBV5JQi8JVYAuKvJMY8sHNIc+uWRc2N4pUG0JSuCE48HLEd6KMI7orS2BFOhCU0zL42LljzM0LtaTD6Cen+UFn1/bPkoSSdOY9dUlzp3epPAVJYaNUxuce+k8p86eotPtYIuSKApxUcTprU2WVlc41BOGeka/f8AnH36MTir8OX9rgebFGLp4LewEF+cYUPexxTC84EsybilIfLJpPLWqntwShIIgFEyzkv3DHBU3WV1bxVpLGIbcCSeJ+Xs2qYBGI2br1AqREgidM04zovDBoylHrjayHorM3MHm5HeEgZpPn5JACpY7sNGDbjshTlNEFFEIgbawM7bkpeBw6ogST6Y9pbstbuL8cWbUvL6HUILV9R4vvnQakXQQjR7DwRgZyC91i1SVFVJJnHUgBM1mk7WVFQ5Wllhd7lHEARNbzdWVYN1xFkkoJYmsN8/aOcIgoN1soo0hDEO0MaRpQqPRwLs6s6rXabG2ukIUhnSaCVprdFYyCRTXdvaZZvlRGYJEStIgqO/D/D2MY1QY0GqkpI2UKArv2Fz1PHoru+vnewC/WAIJQRiFeMSRL/uTwiKV/UkcU8yzZx963D+ZjPgoyvKH+cwzuKU/gnd4b7HW1Erjhwjgp4FgKRHYsl6zLB5lL0AFgiAQmKqkmNb1QIJYYI1FVxb7EAVsBV/cZyolUVLQSGO6nSYeifUhSgVEcVzb3c37be4EuQNhPUJbOsKT4in9Mbd1p1sqhEAqhQxDpPeE3uDtrf331ssQ1KPok5gkHx7e+zrj1xnCUKJkgPe+HhuikJWVZaRStNttkqQeX+5Kkom6gKGUIULO9wP4uZDCIYIIESeotEF3eYVup8toaikrT6QEkZI4NWCYC0SWI/IKYQwLZ3ZnNRaDtyXYAm/BuxjnDHk2xTmDv0smlXOOqqpqvshbnBeYqqIqSqpiBngarTZpu4OScu69bjDaYLXBCImae6wrKZDuGeXdnuPrjVsGlcddMN/pGPPVoXPgLPWktljvWfAWXWZoOyZodoiiOitTCA/C4bxB1EUtAI/RBc4bjC4QzuJ0RZlPv05E+iL/XnBsbvkMQVMrhx7JTkFAGCOCkK0LZ1haXeL0q+c589oFDkf7XL6+Ql4UDIcDVKB46bXXaDQavPfvfsyNg4v1Ifxt53BifPXec3h5zPQgJx8/nIfvc3CsBkqoje7mm1wRCdJGRCduEOsKhgd88ot3+OP/+V9yaXfATz/NGGSQPW/y55ijzDN2r31Od2mZV15/hWYjQk8GjLevYcpb1QWtdodvvfkdusurXLt+ncFoxObZdc68cIYPP/uQTz7/hKJTwQtN0CXkBZS+JoEttxAXDw1noSogjJC9ZfACN8nqitpJD8KwVhBXo8f4EkAEkG5CUKc34ktWNpd57Ve+S9Lt0Dq9gQwDwqRWsVy5OkAXGZ/++Afc/PhDpjc/g/HV2oT3foqhAceEuqUmd94BYnBLIBLqKSYCvwNc4bgN5+tIqQKWz5wnbXeRJEgRIpM2g5s3aTSbrG1uUhWaq59eJ5sOsfkO3s5oLi+RdDoMD/bZ/+wzqrzA3MWz+wvQUzj8BJI2wenvE7djVtYStjZSWo16UZDEEavLbfKi4PrOHsPxhL966xccDgbs7/fRVcW7P/o5V3/6KaNpTr8/ZhIqZv0hoZI0g5ju8npdUArB5tYW65tbZMYyrCqEiQhWL6BnQ/xot+5vIfWqYFGc9UFud5QgoxStEvrjnCwr7+lZewsW1TUXP/+y4xbZ7jGUqH1NhVJkYYNmr8s3vvMdaKa8M7jIbn6TEksz7pEPZnz281+gb5aYXVP3+Wds6XQnCOcJKoMsHBQah6ByispLipsVervCze4fFJBxbUPqKnAnhtcwCLhw6jSNTsK1aof+7NHGsd1ru/yv//yP0VXFbJjV96pF/VzMePSMIM1xLYL7rNEXRLKSsJTAUi/md3/nt/ne977HSGtmRtPsrdBZ36DVbNJqdsjs+MiKpsoyinFEkECn2WT59DIb3zvLbDphuLeP0+54zFzEThfr3JJ6vgmo10eSOktFUPe1hVw85+73ykE185gCtPRMZB139B6CQNHuJFhrUZE85mAeco3dbNav9TXF5nID4T2j4cMptyPgJaAr4dSGoNeBXxzCzw6OT2apm/Jbv3KGpW6DM5trtBsJSym0ImjGKWUS4cOAxrx5+sBYW/7TJ/ukVwaMi5xc1wUKieYHzbm1SO08IWy5t8S5F85yOCnZGY2Y6RzfCsAHkJkvJWBW5RkqUBjbwTqI5nZhp1aW+OZrLzMdj9iOQhpJzFo7ITQF49KirefFsxu8uLnKld1DPryyTT6d8OknHxCEAadPrSGVIgwVzmb4ua3XykqH73zndYIgIAwUVVERqYDRcMLO7j7FdMxMO7J5TQ+hYHllmW9/+ztIpdjr71OUBbM8Y3www2hNJ02prCW/T32Tal40snQls6JPu9PmV3/tezjnePedDxmNnkzdmDAMWO61EMBoNKWqzGO59DlbK9Hv5zr1pWOheF+c1+NaET4GlBQEsr6/5i72AtevXuJgb4fp5MHvs3WOSlskjm4Mga0904WUdJsJSawI8hkym6ITgUkkelqReo+hHl4XOi74YvMc0UIe8vk84TxIKfjmS6c5s7FEmiY0GzFWJeiwQ6UdndXTzGYZFz96n/FoQLvdJk1TGs2YVivBlSVuMqnrnAymx9VSTyBKm8TNNs4YitEAJTxBKI9ss76IhZ59MRF/yWs4N1+ozvcJxni8N3S7AStraywvL3PmzDnSNOUf/+M24/GEP/+LH/Lzt9+FMECEETiHL4pa/NTbJEibnDr/Mq3eck1CG8tsNmE06ONlyKgKmB1k/Pmfv0USRZx74WW6S8scHI6YTKYIIej1Okgl2d/fx3lLJD3CG4rhdUw2xJQzoMBqTTnJ53o+j8RDoPC+LqDqTux/tKvNCmSume4e4jyU1WISr+9NWRRYayhnE8aDPs5ailmOFIKw20WFQV0c9kTw8Dme45mDVBA359RwVtfVRFGPjgtlnDjxmsshgnQuBGlDFOOUo9JTwjhiebmHDAQydCAceIcAGo0mSdxE2Ywblz/GGvtQJDp85US6oJZiSPDPIDN50of3YSEEIohQcUxvfZ2Nc6c4feEC585fIB02yEXJbDbFzJUV5196kW6nw7UffMSNkwZWC7XV7fAwGxTMBl+tv0i9kHtU7ddXiEURnJD6mZQcKYOiMCANQgJnoJixd+MmP/mrn3N9VHF1/3hx8xzPAdS+dsM+gRRI6QkjiSlnlHdQxMZxzObmKZqdLv3hkMlsxve+d4qtrVNc61+hvJSjEwsrEWQO8rJOk3oScUZfq9AFETJtABKfV7Vna9SAuAGuBD3msTyhhYSwB1GHhYFxs9Nk4+wpgmaTcKl3JBc3xtDvz5gMR9z89HO233sH8j0ov5juf0fkHK/hoX6mb1LPq9XcyqVNbVcwpibePcfKLEAqSau3Qnt1AyWaSJGQ5SWT0ZBAQCgF1ntmBwPG/QMo98BOieKYpNMhn04YXL/+cG1mK8j2EL5EhY6gEdBshXTbIaEStTIuUDTSGGsqqiJnOh5z5cp1dvcP53YKnu1LN7iyP4UwgqRBLjzj4YA4Dnnl/GmSRkqEIACWl5bYWl9jlGn0KGcWF8jmcq0SHh+CL4/lmQurhweAUCEiSrEiYFZoirL26jxOCbhfW/w1GlDv4Y2uRK1KLVVIo9Fk/aWXCDpN3n7nMsPJGDyEQUKZTxiNB7BH3aefwaXTnSC8R1qH1A507S9rvcJ5iTnQ2OsPFoQSQU2m48GdyCRXUrHS7dFdanOQDKlZ64fHZDhhMjxhCyKp1wgRNQvyqES645hFecA/D6WgGQt6zYiXXnqRN7/zHXySQhTjVYQP6oYQGEqVHakGTaXReYFMEpIootlr0j23BNsw+vwQijmRfqf+uFBKRxzvUxak74h6LnoAgsxqsNp/IcFFSkGchFgrkUo+ciZKFEG7De2motMIsdYxEQ+nsguAdQEbAr7Rhq01yWHhePvguGkaachrL61yar3LN1+9wHK3ffT5bJqTjWcQSGIhKJknRjnPcH/2xS87aa2x8KSnfhdW0EhTektdRmWfrBhQmgoiCZGaLzyf/kpbVyXOBVjn6gQvIYiVpNtssLWxyihSZKMRpipoxgFVKMm0wzhY6bV58ewGeaX59Oo2pirZ39uh0Wzw8uY6aaPJbDaufUqlBC9pNhJOndpAqTqAXOYls9EUKRTNRkwaCkonwPhaiS2g0Wzwwksv1oT/547ReMxoOmEyneKsJQ5DPLcuDe4E52tSWltLXmQkzTbnzp/BO8+nn3zO6DE1BQsEStJuNepY1CzHaFMnBD7qAf2cW3jWcLLIqD/x+ipORdRkusffcZzz3jMa9h96lnDO1z7heNKwtmwS80BYEgU0YoUfT6EosVZgrcCVnrD+k6MyKSeHvZNNdFLfrd3xvykh2Fzt8ur5zfrvBJigSZUuY5wgbq8xm864ceVzppMRSZLQarXoLTVZWm6RzTL63uLyEj+a3ZHbUGFI3GhRZVPMOKvJ1iC5y1ruZKr8V9QZbxPaeO/rehZC0Wy2abc79LpLtNstVlfqLKiLFy/x85+/i1AKGUV4Y/BCImRA0OoRtXosX3iN1a0zlEWFrgxh/4AyuEFVGaaZxuWawc0DQilYWt2ku7TMNMvZO+jTabdYWV6u1ePU6x4lLNIbbNanHO8dn681GFsipSQKorrOnFAwL454smNY5rEPZ8n0nWv+WKOxRlPmOSfdMAOlcM4djTmLVnuuSX+OZxJCQhjX405VzDuspF5ALXyZF733BJEuZa0+CWJEFOLxGFsSqYBGO0VIX9uyeosXdZZNIw5oN2KKLGc0HoJQqGBB1D8YvloiXUhIG/XOqMhrcuFrjjBI6LY3aLU7fPe3vs/S6jKl0Ghh2dve5sr1i0yLCfvjPXRWMt0eoqTkA+1pthsEqeDlX3uN2WQ8r45t6tTihRr1GRr1vPNc/vgi2XTG7rXtZ7fQ6J0gqUn0RWaxAFogEkGr1WW5uU7aWoP2KuHqGq2zG6TxFHE4umMk/zmeI88z3vvpXxEnKYP9/Tv+zWQw5Od/+Zc0Oz3Ovfwy7W6Xw4M9pPQYk9NuxwzzoFZeJRCeF/hSYD73+EWhzEd9zMIYkbQQQYQHoijkwnffpJE2SFo9grgBegZ6xsHNa3z27ts4+ygLZA/SIpRn5dQFWktruKTJx5cusrS+zgsrXax1DA9HFFnO/ucXmfQPKPavQrZTn8PjYuFDXMzfQ45Nd+enGCQxy2dOEzcaNLpLKBkjEAhhqbJDxttXKPsx1fAK1nqMzwgaEmsE3kI+HGKNoZrNHjnw4HWJufkJenoIv/oGMmmysZSy3kvIx2Pe/ul7TMZTLl27QZbnLLd7JGHC9pUdJv0xZtKHqg906w2PFKDAec/U1guQFze3WO106bS6AAwOdvng7XeYzPpUB1fw1Qzyqt4LnQzypzxQFoQ3Hlc4ssGEXXuVKIl5+dU3sVZz7YOfkY36j9Q2f53g/FxxlGku7wyZyYRvOUEaxKw2l5i21zk83GM8GmKmVe1rvRgPvibwucd8ZhCBwDlfq7DaHpEIfHWfCxEQdGNUGuBKjZ1VtSvVicfOaMO1z7fZS/uMB4/ui//FE6du64K7PwsL25YFSXrivNKlhMZqg2baYm1pjSIrufj+ZxTZ3UUQvW6LF1/YYnVlid/5jV9jY2ODF974FrLRhSAAFWBFgPMSITyCACUjmu0m1pTc3LnJtRuO3ulN2usrbLZP8/d/5x/z+Qef8G/f2ibT91A3L0Q/UX1NcSS5sNSmEQSoCoSF4iCjGOSMS9jLH25KKo1mZ66Wqyr9yKrVRhqzvBQRKkF//xDnDJPpIVmeYe/DMnZDeHMZegF8K4aVAM6sSZZakjfbnv2uZ1DB9Rwiran6h1Sxo9v7DuunT+EReC8YDaf4cMrSZsmFF7c4HM24uDug1Cf680krjUVAYkGin7gHzjk+/OA9Wq2CqzcmfHZ5xP7BAD0saun0l+Rn++/+9b8iSVP+q//2v+H8iy/SSBKSQFFmo1pRPRzx9ltvMegP2B1MKEpLJ4aVVBD6GZPJAVFQceF0Pd9IWf9nMNhjMKoLggohGI0qiqKi1exwevMieEc+mzHLCj765Aqj8ZSr24eMSkcYRGykAYGUxEoiBXO1p+fipSsMx2MmkwllWeGtw7va4uKkK94Cgtp7OjyxT67mxRm9tVRZXafBPcEMqV63x+/+9t8kUJI//7O/YHdvj6idEqQRYRITJTGTwZTda7tP9HtvwUm/pKfVlRb9eiGrvr3xHxfqtuPe4fgLjYR3dRDPPeF96WHhcQPopRFbGw2SwrCzPUNbTzbMsaFkVTlaqWLiPNOpxxf+2AJmfpzb3xdYaBikgNacH8pMTRLnkzGjw5BWo0G70aCwFcV0hDEOPS2Q1vLtb72OdXWGexRFVFVGVU2pKs1hf0JZ6bsWYjZFQTE8xBgDvrYptJWupzfvavLeL5Ydd7uCrw4yUARRiAgU06xADsZ8dukycRyjiwqtLU4GvPzGN5nlJcNpjowlQXeNMGmy/MI3STpLbJ4+R295lbLUlJUhLysKcxPnFVGkIHB4WReRzQrD4f6A2WSKqQqsqYPbEkcsK4TLKPp7OJ2jy1v3No0opNtMECrAhw2sc/Uaz5hb1Oi3Y143lCgJkVJgrMc5j7XujvfWOcdoOiVQirIqMMYdFVx+jud49iEQjR4i6eC1xFfyWPR3NBEIEC0QIUEgUWGANRrjSvRUM7w6IQgD0pUuUkmKvMAYgxlOGXiBUAFCRTjn0ebhFqVfLZEuFSQpqBBM8LXaFN4NYRCzsnSWjc0t/ts//PucffE0b33wcy7fuMq1Dy/xiw9+jkVTUdQKkxslwkM+OCBqhbzw2qu8+P1X2Nu5AduGfFJQugpf+UcrEPMU4b3n6qeXuPrppa/6VB4ei83i4gmYE0eiJWm12iw1V0laq9BaIVhepXl6ndQHyE+eQNXZ5/ilRJnnfPj22/f8m8lwxLs//gmt7hLtbo8gCOj3D/DoIyI9Hs+LaSWCcE1AKbA7Hr9QMz7iOCmCGNFaPlKXBGHAhW+9wdrmBp1mjzRKj/7247d+zKUP331EIh0QDhF4lk+fZfOF1zmcjfj0yuecl54XeRVvLIP9AdPRmIPLl5ge7lAcXINslycyyHlq4vwe/x5GEWsvnSdtd/BZgtcLnzWHzvpMdz9l4h0HV0CEMdH6OYJGgJvVhy9GY4rHTf02JXbnM8x4D0yJSpqsri/xyqkOH7wz5J23P2Q8nrK9s4eQkrXTG6z2lul/tsfhQVYr9/VuPX6Fa3U03oPDMbM1w9VZ73Lm1GnQAmFgdLDHxz/7EVpPwB9wxw6Vzl8V9yHSBd54vHPk5Yx8OGNlc5Pv/sZvI6Snf+WT50T6A8D5OZleGKbFCB01wAnSIGK50SNr50x29ikOx3BAnXXxDK0FHgQ+B3tpnoINICE+7VBd7u/LLyDsxITLCeXODN3/Yqc0xnLjyt4dPvyY8NSeHfc5vyPJ4W2ZjEk3YeXFZVaX1/jGC28wOhxz49L1exLp3U6Tb7/5Ei+88AL//f/wf2Zj6zQVCQ51pJNx82RwPAghkSokbTeoqowrV64wOBxwzgMyYb2zxYu/+W1+6pf5T/wbMnOPYOW8xsTC5ilJFK+c7rDSSgilQALDzzxjm3N9CvsPSaRXWrM3GOCcQ1ePbleSJBFLvTaBEgwPD7FeMyv65EWJu1Nx4xPoRPDra7CZwist6EawFAsageCwJZh24EoGwwIiY6iGA6qmpN3tsHZ6C+cVzguIJpR+zNJ6zrkLG0T7Q672J7cS6Qtmb0Ggwx3HVOccn3z0IbPJ51y7VnDxYv5YiWGPiv/47/8dnW6Xb333e5w9c45GmhC3Wwz7O4BgPBrx7s9/wXA0Iatqwm+9IVlKBYHLmE4PiQI4v9U56hd5ZfhsZ4+8MnS7PeI44bA/pt+f0kobnFpbxlQVg4MDxtOMtz66wmh6rCffTENWO82j/5eiLtxeVJrLV64xuMM8vNCv3ZFIl8eFHAEyWydpOOeo8qL2RH6CgplOp8tv/8ZvE4Uhn334CbPxmM56j3SpSbPbodntsH1lh70be093e/G099iL40ccq9OfJBbVbO8SjOLE11rPUVHNJ4lBAYPC80oS8e2NHuGkpLGXkxlDNiqpBJxeC1hJFHZqmc3qejT2NmH83c5sEe+IBLTmQq/K1r/LZ1PGfU8qVkiaaa1ALsfYymBGQ2QQ8uab36W1vHJ0vJs3rnDtam0nNBhOMebuncBWBUVV1PMKQa3orExt8QIEYr4FOTr5Z2sRooKAqJEggoBpVmIcaHcVKSWzUYYuNUQRL772Gru7h+RXtlFRRKO3TNLqcuqlb9DsLLGxeZpWu0NRGYrKcDAYUVoQXhLPazCQpCgpyQvN4HBEls2wusTNawJJ4YmlxvuMyeA6uvhicD+NQzZ7LXwQUUZtKm0Yj4eYe+y7FvpbJQSNJCQIJWXl0MZRVebORLr3TGZPQKD0HM/xVUCAbHYR7U1cbvCZBm1BLzzyzLy+QVQrylWd0Y2rMLpC5yV6d0zUSEm7DYSKqPKcsigYD8fY2Yy006O5tIbWmtls9lDC4K/Y2mW+exT+8awEgLs7jn05UEFEGDcJw5SqzBn29/nRX/45H3/S5fKNKxwMD9m/eQM7yrB+njurXX39HszU4I3n8MoB1UjTaMV848XXmQzH7LBNOSsZ5mOsc6i5G47Rc6/953h4LKxdBHX+59x2SUhBq9FkudMjiVJA4hBoHPbraGHzHM8YLPgCXY25efkj8ukBp88s02yuseqXiCLBoH8TMa5wcYXtzMeI5XqO8AfU6shHgLcV5GPitMnK1jkarTYSRZmV9Is+UiiK6YRiOmH7yiXcQ1puiCAk6qwQxA2WzrxC1FzC5xm7H31EngSQBIx29/nov/wAj2BSlVRVhanKE8O2p9nq0uosUeYZo+EB/n4+6Y8IU2mGN3bIGxOiaBWlEsrZBJ1nzEYHd5hIPUIqmssbSLdMPh5QzR6OSFdRQpi0cVZTZQsLHY8zmuvv/RW6GNP63jdZsq9y8/PPuPLh28yyjOFwgpASPR0ipCLr3wB9AHbO8Ekg8sStiOUzPeI0ZHmtSdqICJuKQpYc7O5weH2Pq9cu4tykZjbvNqItlOj3jaN48Kb22JABqJiyKrl+5XMCJWl0l9kIFOPDffLZvSIbz7GAB6yxjAYDglhhS0PgFDKXtbXGPW7b1woe7DyZw93PgwGYWyrTXGrQTROKWclkf0qkBBtNBQL2ppbCfImNs1CiB9T2UQvS1ELaSojTkJffeJE3f+1NOq0em6tnmKxO6f/KgP7eIZc+/5zxHfwjprOczz6/gRcxo8mEzlIBUQgqmN/6er0r58o35Q3CVlR5TlVVLK9s0Gwts3n6PKubZ4gbCWmnydr6Mi+9+TKHB/sMhofoqqQca2x1Yow11H1sXnhYF47dyzOqluX737rA1kaPvmswVDHRdsZoPCK30L+zgwI0520zLwqtg4qD0R7eevT9ChHfAY2GJI4F589t8q03LhDIgFCFjCcTrl6/wWickxf3nruUqFXpvQASB2EFMqzbdXOpwXeTiK1S0J0JVCRYThSpFEgE3kuEDFEyJE4c7bYniWMwVb0ov33eWCh0H6BbjscGKev3ryrBczrNCIKoJoIihZK1R3JVaSbjMVIpXnv9G/T7Qz7+7BplUeCsx+p5oqcURHFEK0qI4phOp8N4mnN5b4IuNUVW4oynkcYkWymNVoNZUVIVOePJhFlWEElHI5zXqvZgrKXSGmMdla390rXcRhtLWd25D50ULQNIIWjFEZGSLDUi0lAxzjLGs4IgSTi91qbdalFWFdoY7BPYXC0vLXPh7HlObW4xneVIWbC6vonxMNYjxoMhzWaDXqvBOImOrJm+tjiSg1M/709rfyp44GfqSePUapet1S690KMrjdWapvREATQUhBKsdhxMoDKeRBw7Mz5MQqnzdYBHsCDhPQeT2lfMe0UqwYqIWKUob7BSI4TDZiNy6RkNh2RZxt7BAdv7+4yGE9xdsloWty2aZ3xoD5lxCKkIG12khDKbYo3hKcQmnhicteiiwlR6XoTUsL97iLOW6XhKVVaEaYsgTskmWV04tTJkgxHCKZpxRKfVIgxqakxJiJWg14w5u7GM0Rat60Km7UYTJSVmMmY8nCKFJwlDqtmYG5/1KbMps/42VTk7ItdvR6kNh9OcMHIkQYLy9sENJcQx62Wtwxj7SNksURTSaqV472vFvnMURfX0MmOe4znuB+9AF9SdPAClcNoiswy8REYB3kd4kcxtwW39GVvbttjMI2yGNVV9HFPb2lpjyMYjZBhQZRm20vh50MqUBcW4j7UGV2ZfIyLdMy8w5h6TSF/E6B5DrvmYCOMGnZXTCAvZbMx0fMj/9P96C+MqnHd473DOfXFwmntWlXOF1bWdKwgh+N2/+zv8/q//Pgf7O3wSv8tgf8SHuzmlLUmaoCLIxlA+yAb0Ob4Iy3HBJ0ltudQGqQQr3SXOrJ8iCFrURDpU3lF590vBXzzHVwkLzCizjA/f+nOarRa/+hvfpNf7Jt1eivdb9G9cQeznuKREd6mDPGdqH0Zf8shEOlWB1yVpfJrXXn2dtNXBest0OGU8nZEXBftXL7F35XNcmeHMw6nRVdygdfZVGt1lvvntX6PTWeZnf/SXXHznJ6SvvEj6xmvsfXqZy7/4z4RJyPLrZ1FJhDYLeV79dC2tbPDCy29yuH+T6WSAMU9nQafzgpvvf4wKI1ZeeI2ks8Tg+udMd2/MJ9EvPu1SKZY2XyJJm+x/9s5DE+lh2qG9cQGdTTDF7Ejxb6qC9/7zv+DDPxGE/+B/S9v8PT78qx/y1p/+G4zWOF/bNyBWECLA2UNwGUe+AYGH1NFcT/jGb75Kq52y3AqJA4mUkgkT3vvsZ7z9Jz/AlTnWTLmn9G3h4ftAA15ZL2ZEA8Im02LGez/7MXES8+o3v0mz/Q0++/mPnxPpD4FKV+zcvEFRZWhbEpkQOZKwwy9F5h5QCwgGHNcteBAIwfKpNuurbQ6uDJj2M1qR4DtbCVLAX17NKe6huHviUNRrhwjozv8/qO1PuudbLG10+c3f+3X+yX/zv0GpCEHCbJyx1txkb3uPf/ZP/+kdifT+YMwPf/w+/WHJzt4BnaVlmt2UQMU4IfFztkrNSfTQ5UgzI5tMKGY5p8+9RKPVY/XMWXrrGyhpCZRh/NJZfv2/+k1293f44MO3GA2HDC6OsSeLMi6yUOa79FJYPr0+oNdK+Me/9/v87q+9wcHyRQ43r7D27k30lQl7lWNi7mIfvwScolb1DyGPcq7sXsIbT1k9XI0fKWFpSdHrBXzv26/wB3/r9xiNM27sHDLOtvnFuxMGwwn2PmxPJGEzga0EWiXErvbdFzG8crrLt5fXmSE5dJJhqfnwcIAMFBKJc4IgSlFhSqsdoURKu3kNqgxfZV/w7X3QbYn3sLtbsbdXfWUkOsDgcIRAoQS00nl1VCEosoyD/T3CMOB3/9bvsbd3wI3df0tZFJjKY7wn8IJGKGl0mnRWV+j1lnjplZfZPRjz5z+7RJlNEHZGGWacPnOWja1NhBAMJlPy2YyDw0N0VZEqQ5hAPwdr6iyGWebJtGWUa2CM2NmfbyHvPI+ddDIBCKRko92kmcQs9TqkacynN3YYDAtOr7V58/WXAJgVGVleYp5A7Y6zp87yj//eP0IFioPDIdZZzr/0KqfOXeAHP/ljLn1+ka2NVTaXu4x3+8ivO5EeUO+nFuuHp4XFln/x85f4vLzxwha//yuvcbi7y5VPP8PkJcvKIwVsNeqx5eLEcbOoi5F2I8jkwzeJ8TCeD8uLy7t6MOPaYUY1y0iLEY1mk+7yKh5PFBQ4IdGDbYpxn08++pCbN24yLByDorb+uBs5tHDMaQaKdhIz04bclIggJFneQErFTF+l0E/QMu0pwGqD1QbdaIL3lGXF1UtXyWcZk8mIqipptZZJG13M/FHTZcl4d4gvPd1Gk7WVJUQUzQMLEIewtdQifOkss7xk52BKEIS8cOoMoZL84q9+xP72NVZXVlleXuLgxkWufPwznNV4V9Qe/XcRA02LilmpaTcSzqcJofeI+wiHFk5hcv4/3oPWhqJ4tOzhZjPh3JkNnHOMpxlVZTg8HFKWz4n05/iK4CzkExAKwjaoCF8YbDFENduoVgMnNGbaBB9QrzzN3OpFYKo+ZrGA9R5ECCLFas1wdwdkbc+K80cku84m6EmfmpR/OB/tr1iRDnXE4WTRigfBPH4axBBEdVUj/RBVnJ4ChFIESYzXlnI8xeqy9qJyD+j7Pj91N0/LacQNttY2ENqw02jjGoaVVoMykKhGbZtQZYZSuC9ctpSSNIkQAvKiuqsf2i87pJI0Oi1koDBG16nERYUtT0w4C2W6Yi4ZEEgRolSCECGg0MYxzTLysvx6+cA/xzMMj7MGoyuyWcZ0PKEocsoyZ3TQx88Vgn5Cncq62BU+vIDvtq/1GF0yPtihnE0wzmCdY5ZlFFVFPupjilmtrnvQ8VRIECFR2mbj1BkanWWEDCiqCkJF2ErBVuj9HfSwj8738C6k6MfIOMS4Auc03lTIIEZrzWQ8IM+md3jeFtUwn0zhNe9cXZxnNgEEpsxxd1KieYcrCyyCMs/BS4wTECQ00oR2q1F7/pYl1jnyUmOdB2Q9v0kFMiDtrbG+uYUpu7RiqIqcwX4fXZVYo3HWs79zg4sfv8/uzeuYqsQ6jwwTQOJMAW5hxOwQUQMZNfBJiBMZxgSMR9dxLqURr+AIyUdjqqxgeLiHLrJaNXmHeeOL1zx/X6RnH8lf7vLH3ta+dV5gAa0Fs/EQb0p0+agVGn+5sHKqi3Oe6SBDl3ff+Bht2NvZpyhLRDcljEKUl7cWKHzKEEoQLMXIUCECiZACPSgx4we8l2GdRXMEy7E91QIP+gh7cIXFTCuKpGSSBxS6qsczB8PCEkhJrxkThpCVJaW289TuB/yOh8GcML/lffGcuDpJQ88M+bBkdDBmb3eXdmuJjbUeQTvg1NYpYhVz5sxZsumM4XDIbHZMUnhfq8yyvOD6jRskacL5lzt049aiORC1UzfM30MlWVpaIopiOt0uSaNNEITzccghMQg8SRQTByG2cOiZwd2NdF6sST1UeAptGQ/H9PcOmIxHlMUMrSv8PKvybpCJRHYVTjhcZnE4iklZJ7I4D7JWiEtRl5+5lxDOe6gqT547tneGfPjJVSbTnL2DETe3DyhLc0cSfa2VsNZOkLZC6ozzLejG0IglzVaHSIYkDUcQeaI0JooVTijaKoRGxHoUQtrAasN0NCExAWEsGR70Odg9ZPvmLgejnOGsmrf3FxFFkMSgDeR3EcD4J5Gc+5jIy5ysyBgMhuztHZKkKVEcEyVNtk6foypL8iyjKi1xEKB8bT0xE5CVhjwv8Cqovfy952Bvl8k4Z6kVky+3KcocayxpnNDrdMmzjPF4Qp7NmOW1or/VaiOAqZ1RGI1xjtJYtHHHntcP2VAqUKytrdJrNWm3m8RxxLDQTErNUqdDEkUY5/GleWLr/CzPuL59gziOCZOwtlgYDyjLjDCSrK8vk6YNrAHrxC+HUOdht/OPcsxFusEdrGz9idfjfWfNTYSBPA5wCIFxjsk0ozKOOG1hrULJDOHr4qa1u17tiV7YOqiaP8By6064/TPSg/SeSNQBq0RBjMFYiylyjBc4McKoEJ3nmErXRZ/vsGYTQBooAikJhEAJSRJGBGFCohytoIGKYpJGExBIqb54kGcUVVUxHAzxzpNnGWVRq1KVE/MXGG8wzuC9IGmmhHHEbDwgOoxp9VYJkwb5dESRTaiKAiUFOEM57VMh6AtNIAVlNsE5R5mNmYqSfDrA6hLv756ScbJshvcej0CFQX1/lUII+YVMXEE9Rwrm6wPmQUTBHcerRd3tL/QhjusB17VLHWVZ1kV0jcHaJzf+3Y5AhQghsM48V7w/x/3hXf1aCK7FfL0r60KhR1lJ/vYq1/MFuV8ItV09EHuP13PO+ajGxlzN7gx4zR39wu6Dr7jYKBDUBAPyYWbeGAigvQXdDZjsQP/iXaN+XwZUEhKttqhmOdPtKaYscP7RQ/Kn1jf5tW99n8uNLoNr2/RIaZ6bUhU5s7CkwlLmY2Z5/oX7Hsch585sIKXgyrVdprO/nrL1KEm48O3XaHSaDCcDiiJjePWA8c3B8R/VAuGal5qCSAXKNwmChRQ4ZDrTXL25zf5+jn0CKpXneI4FnHXsXt/h4gefcv36JW7evMr21au4sa2LRlqOeWPHo6vRT2A27PPuD/7oKI3Y4/FztYq1Box5uE2qjCFeort+gd/5vT8gbXd47/2P2dm/Cd0Gy2++yGz7JpMff4g3UzAHGBsw+OQAhMRzCJSoMCFsLjMcjRj2f4yz9aLuGIsKmAG1vPHJFKf21jLevoaQ8q4qfG8MerCDkQEHeYmIGjjjoLXB6RdO8/1vvkqR5+zdvMk0L7i0fUhWapycz1VpB+IWGy+f5zd+5ZtICc4aRof7/Mm//p843K0roXoP7/z0h3z83ts1KW8tMmoQLZ0G76kG2zhXsFgshMvrxOun0XpCUdxkOr7Ke2+/TdpqUHz3N0gbbS798C0OLl+nGpRQFA+XwLUoGrYozuznTX+nz9kSisN6Po/a6NJx+YOfI7Ho6sncq687fuUPv0FVGN77s4scXB/e9e+m0xk//oufkTQSfuX3fpONs6cIffT4gbSHgGwEdH9tg2gtJWynqCig/4PrDH+y/UCfVz1Qq8f/7zIwN3gAu6A7wEN1kFH1c8rplL2Zwg1tTUyVnp9eL2gmAb/9yiYrrZhPr+2xMxjTL6D/NGI4KbUCfcHYLOJ7i+cjh8F0zEhO+bn+OZSWN17/Fq/8o9dIug222ptMxzMmB0NevvAyf/nDH/D+B+9+4WvGozH/6T/9ER9+9BH/u/9und7SRt0cQiC8R3iL9BaJpZkmfPs738YYjybBElA4yXgyI5SaRGlcWdEJIsY+YHJ1xmBnXI9j94CvLweM4d1fvIcdXkeUIyin7O3nZNpT2rsQRQKilYTkpQbVbkFWTNFTy/DqDAw44RERNAKIJcxKyO9xv7yHft8wHFoODt7iv/zpB1jr0NphrGU2u7PC/TdfWOcffuccUbZP2v+MVFm2OpA0IlZe/SZxZxmZDRDVDCkVQgqSOEK027STlJW1UxgVMZxkXP3wc7orW6TtJd77+Tu89ZOf8uGlXX788R55qanu0p5LPThzCoYjuHz1zuTWs4D+5AAjND9/5wNU2OL0hQusb22xvHmBPzh7jtFgwOVPPiFUTbpRzAgYaRgaCAdTvJ0h5CFSSuIk5PJnHyKDiDfOL/PymVXefudzdvZGrLSXefn8i3z+2Wd8eukDZmXJ4bSgkSa88dqLNNOU8TsfM84OyCtLqR2PkxOaJgm/8qvf59zW5lHB09WNDc6e3q6zDaKIotKIe9QueFhcvnaZ//n/+/9hdXWN3/7t30Upwds//wHD4T6vv/kKr7/5G3jZYDyBPLt3QOprgadBokO99ljwuIsaFHeZC28rUfFoEAKiAKUU3XZa++1SX9pgMuOn733GUqvFqa0XmI5GHO4McdYipEKoumC9x7Gv60KhTyr+3aSeejbSiLPLbQIlCUTBVJeM9g8otMXFA5xUlMNZvUS+yxcHUnCm3aATh4ggRqoATYIWKWkYs9xoIQJF0IzRVnO4c+UJXMGXg+l4wkfvvT+3yTPgoSMSYhWTyJhIBJhiRj49IO30WH/hDcI44dLH73Lz6me88e1fZXVjixsXP+DypU/p9lZYWz9FlY3Zu/gWZTblWj5BeIdsnkFEHQ62L7Iz28NajfeLNfqdsXCDWzgoykCRtNp4IIr6lJXFWn0L2awEJIqjAA3OE2iDsuILlj1SgFL1fHl7gl5MzWyU1JZDeV6wvb1XE/JuUbT0yU9OUkhajQ5hGDKZjSnK+xWeeY7nALSuCXQVgJpvXwWLqCJ4AXZRlGNOhMsUVAI2r/0jMfUGxAmwi8X6AnMi/eh5ffhJ+BnwSDfUT/Bid/8g1VLrfxcqQIQxXoXz4hhPJA79cFAKoRRIgTVV/XIa581jnUsgFUkY0262WV/bpKki5MGAIpuiswNKbe56+CBQdHsdlFLc2Dl85HP4AgQEUd1lrLa1EumZRi3v8QuZz93IQV8/p4kKaKoAWWlsliGlRSiLrUqKXFNV9itXCz3Hs4DbpTGPDucd4+GQ/d0dDnZ3OdjdZToaH6v85jZhLIaTJ7C+cc5SZk8+RdM7hykKdBCiqwqtNTJQxI2EQlhctjB4LsAH2GpKfXFToEQKBTLEWo0pbl9kyTu8nlxOrzO37sqEDBAqBO/rLCEhUEEEMsAZi3cli7nKe4/zrq43GIaE2s4rgIMKElAxQWeFoL1CY2mNqNVFeIcuc1Sc0uz1qKqcfDrF6Ioizyjy265/Pn75o23iLZoW6g5S4lxFmc2Aiumkj7GayWTAbDKsV85zkTwLpbCfV48y/s7T53xaFkqgYgVeICxgPeYLc8Bcle7F/B10lc/n+EfE3UqfLC7/ayZq8f7u6dW3otYbOy8ospJsPMOUXyKLvkAAPgQaEtIA2gpast7NLaqn3eW0ZCAIkrlM23uc9lhR1xmRx79+4DnV2/o7rZV1d5jXFnIGcuNR2hOIuoigelr+uYv+GFA/Q4760TsZ7Jw/os44nHeUWUk+zTCFJhABcRBBLPFp7a0dRzFpEpPGCcYa9Ilgnjaavb0DwjCiKBab8zqfWxy9HDhbq83jGBuC16oW2vh5Sj++VoQbi6kqbKmxpb3VG/0Ol6rm98k6MM5zOJpxc9cjbYayBYNMk/uaDPAnPyhAJAJCiQhF3V9OtJ/Tfl5RD5AQBIIo4IH87a0Faz3GFEynxQP1oUYUsNpKSGREs5REChqtiKjRJFpaIuwuI2QFM1M3LwLikKDVQKVNgl6P0gdM9g4ZTysmuSRqFFy9tsP1G3vsHYyYlRp9j6BEqCCNIHsG8oDvBesc2ljG4ymH/SGt5SmNbkG7ndButtHa0ur2aHe6NBspaRIxrQzGOSrtqEoQwiIkgCXPA6IYOq2QmIAkiojDACXqADXWIvEIz9x+AoIgIIpCAiVRYjHcPP4Dba3BzOd6jycIFL1uh0JrJkVBUWmKsqQsH88jWAUhQRjWdgmzGWmzjfOgpCKIQqIkotFo0Gq1KXRAqT1BGNFdWqIoSrLZrM6Mu9c1f0Vb3nviaZ/P4vgPQhM8DqRABYogCAijiCgMMMbgnaOsNGNrCETIrGUojUcqhQwCVBSjAkkQ1D7/Mw/lQ56nlJIwDPDeofWttRKO4rZSEClJXQy0Qlcaow1aGyonMMg6I8sfD713/C5AeQiUQoUh3gVoJ5EyIIoSvBT1M6O/XtnYztUqa1hoQSRS1ap7vMNZjRKeRhwSBQqwOKepihneGaajAXEUMRn2mQwHKCFoJinZeECVjamyKXY6AOdJw2VClWCqnKqY8jAPgRCCUEoCpZAqwHtfFw6XAimjei6yFucsgfDUd/zEdVo/pznu/n132iWdPEPnHJU2x0tt758OzyHqvq1kQKAUgZqreXydveG+Rv3rOb4szAd7b+cvDa7CmwJvdf3vYvF3C4g6S/6II1jAMV/c3fb7x89u/2qXdFbDdLv2wfERqCVwM+oiaPdCXQUtEhWRsmgpKUipd3RfrsVLuLREtLKCy3P2Pv4QbwxWFzxKesBJ6NIwG+Zsrp/h//B//B/IBgMu/eQn7O7c5P/9b/4516/dZJbZW/vA3Dir2W3zrd/4NaIw4tr2IcPhk/GmDZOQtRdWUYFk//IhxfjJKTeeBqq85NIvPq6tXebRXVPcedefBgHfWV9jc71NfOMS/R//Gc1em0anRbV9ldHAM53eO+34Of66IJy/NI+rijZa8/aP/5IP332bsiioqhJdVccE5YJPPVr1PNbXPR1YA8WU/tXP+Q//z/8bYaNF49yrhO0OjSREipAqqhhwyHFEwAD9+QHqZ9JWU6zO6wnzFijqanUn/RMU9T14IvqjLyBqrdFYfQFdVMwGI1QUs3ThJWQYMrz8IeXwgDooUHLjsyGj7Us0kpTV3kptueY8QkoavRWiZo+tN3+d9Ve/TSphrGA62OHqB+8hMXzr9/4uCsdP/9f/wM2Ln37hXJwuqAY3AI+/xU/eovu72MkAnxpo11YXlJ7KFlz+4BdIpcgn05qwWgzXDWCd2gCyHdbNd72C7MS8Wc5fUf33YRKysrJKoAKkrknNw5t9ZsM7pEg4C9Xk+OdHRTj/fsOt5suC2pdazX//FfDLj4qf/5ePcM4xObz33Jk2Gnzj+98lSRvcuHSZj37+HrPxl+tParVhdHUHMQ6IXl5FrTYpzjpIUpgYOCjrDJmb3PEepElCp5cijEEUmjKwHAiNl55ms3Z+mM3gYZMVGp0WrdNL6M6MWbSPyzx6pw4QjoZ9gkqxN8rZmcE9eOJHQ0S9am4BHerhf0r9DA05lpid+N5UNViL11gKl2iKBomrC/LarGTvxg2uX7pIJ0n59jfeYHt/j2s3rx8N99ms4O233uf6tT3+yf/+AGMrEBIlJMpbIq/xpsTmtQ2WkyHOw2xSkZcWH7VQQeNo25BlM6589hnbN3eoynv3wTgQrDYU3sN+ZtHW85NPhrx7eYKoDSapSk/u6kfUwHHSUCwIv9Mg2Iqww4rp+2Ocdsfr1Xk8QASgQmh3QjpJgHYV0+zBAm+PYoMShopWOyVtpay89AJBp0v82pvIVg930eFdhVUCIwVyZZXghZcQSRPZOUU1yvjRD/+Ijz/8nGuziH6hmE1nTCYTitJg7mOhGHpoOZj5W7dxzxokKd4GXL2xi0o+JfcwLkvW11dxYgspI8699g1UEvHam68RxoKLl64zGE4wFeRTiBNIm5AkMd3lNcIwJgkTnBdsrfeIo4Bq2ueTd9/CasMLZzYZjmeU+U0iHLrIKIWjqSyrDcm08sz04+3rZlnGX/zgL2glMc5ZPJ6XXn2dl155lU8uXeInb79LWWnKymCtI79XasR9sLpxis3T55lMp+zt75N0V0laPTrtJn/wh/8ApeyRnZuxlhLL6XMXOPvC6+zvH/Cn/+W/MBmP6kJpd8i0FgHIBHC16O6ZIdM1x0u8p3HcBZ7y9YZBQK/XIYwiWu0eSikO9w+YTWeURjO2mhu7Y969skszELzQiGnELdpba4RRyLq/QugHmBIGDzm/dTtNzp7doMhLLl/dpqqOL3zGXEVM3QSzacHu3pDSWIwxGAuXh5qxrouFVrbWSNwJznnGkwyjJGtrDVrNlCKzZLMZkReEbU9Z5Ny4+QlFkVHkz7Y/+p2gELQICJEECDyeaT6iyvtsbqzw5rlvMZxlfHT5IwhC1s+/jBSed//qz7DWMpnNmOY5h9c/5+rbP0BXJcVkhKujuQC42QGmmuLKMQ+6QVuYvjTjiF67SdpsoVRU12UQtY3OxtYm7U6bYjIinwzRVUUxmyF8vQTxHsqqFtXcTkK7uaZlwTOe1JyU3Br4ttZTlCfX6f7p8hwCWs0WrVYKpkLoikIbhkX1zAxjz/EsYV6wx2pwEjvp4zKF9wH4mLpnzwuOHlUPuNsKy5/4u5O/ezx89Yp0M5dcqhhkBL54gOuqSWrhLcoZjF/I7OR9PvfkIYMAlabooqCcjJ8Y06orTTbNaDXbnD33AnlnifLmDt6D84pZptHzgAww35RIgkgRNRKa3Q5RGKHCJ3eLpRREaYQKJVJ9+W39sHDOMRs9WBBBCUEviliOIuxoyOjmDUTZITAdyvGIqvRo/YTXb6JOd1pYazzH1wWL7JnHJ3C99wz7B8ec8u3f5Go/r7sVv/xKIer0aO8BZ6iyCduXLxKmTc5tnCXu9lDeInGIIzZ0cQ2eoyDEvEaGdydIcSFOXK7guIrVCRbmyBPtzpBS1dYxzn7Bb/A+F4YKU6LmCsgSlUEQp0TdTVQYooLLCCfwvi5Kkk9L8umYdqtNN21jkbUtupSEcUqUtmgvrbK0cQrKkirPyF3AJMuJQ+htbJHGIe3eEnGS1D6FJy1mvMPpOweXfVVgq7ldS4vjAL5xzIbD+veLGhCLVwyiJSGR+KWg3mkdynpjsGjzxdfPu7oMJXGaEAYBQnu8cai7zgH+WIUuBALxUGomoRRSSlxg8cp98RYvuoOaX9uTS0x46hjsjsHXa8J7QQUB3aVlkkaDix98wMH23pdzgifhPHpagtRQlUgbYpsgVIiPPVTzhg84VmKfvAYpiVSdiyyFwSLq5auo/aKD4O5e0XfEfEcYhAFxktQ1FVKBt762PPSeoqrIhCCvLIV9CnHHhcVRBCQL31x/PJzdwRZVeBBWIp1EeYXyEuFAOI83BmcMURAimk3iUTQf++oOba1lOBgjZUhZlDhv63HSC7wpcCbH6wI3G9fribSFQ1IVOUWhCWWMChe+qh5TaSajEdPxBO89Uoq7KtACJWmnMdZ7BmWJdpb+VHPPyNViaoxArYQEp2JMrjFTU+/ub9OXzG2IkUoSzAsj33Ksk+/3upn3UOdKQIg6Z1VKRRRHhGlKvLxC0F1C9Zag2UYkMT4I8FLgFIg4QrZaiLiFihsQGA4PBly/eoOPDuDGw+pTFnu4ZzEYfgsk3gkmkwmH/T7LozGtyZRmq0VZWeIkpNVu0ux26Sx16PbaJHFIIAViTsJ4X9sLSCmRKkTIEOfqf0uigFYzxlvNdDQkjiJajRStDXEgCUSdIWaMJJTQiBSltfW88xhXZY1hf3+fkRI4V5vEbJ45hwxCtLYcDoZUlXkiljtxktLuLeNkQJwVRGkTIQOCMGZjc5U0Ddjf2WE8GiKFQEpP0miytHoaZEAQxXVdlVvWQScg5vqz+c/PzPz3tJapX/LyVwpBGASEQYAKQoRUWEedcVJpbFVRARk5y0nIhUYHGQaIJEXEIUEUEKm5BcFDQgWKRiOZn8etayxHnfBXeaiMJ6ssw2mBcR6t6rp509Izqh6suKnzC8qiVqF7b9G6Qs6FZ1ZrZpMRRfEEPCW/AtRLRYlCHs2B2mlKV6IkLLVbaK0x+RSvAqyusCpgcLhHPsvqALEHl09w44M7foMzJcLbuUL2wXC0i5GSKAwIA4Xznjr5VSClpNFI6XQ7KKcRpqDAowsFwiNsPX45B3cbFRdz+qILyvniy3t/yxTk4cvhH/xxRmYQ1BySwCGcwTh5ctnzHM9xAifSkDx4U78Qca3C8IsUJceRx+J9+ZIn29GenSRD5ed+N/4BFpp1xEFPDrDVDFeV4DOefr7XF6FHI1xZ4qrqicqVP3z/ff7ZP/2nfP9XfoXzZ88iESRLaySzkkrEzHQ9aQLQCKEVsrG1xbe+8ybCe37yzlsUs5zD/l0YukeALg0Hlw8QQlDNvp7F407Sb7emSDnKgzHjIuffX/8hfyISTm1EbKyF/PizfbKxQT9cId/7otlusLK1RFVo9m8cYm83M3uOZxSPVpDiYRHEMc2VFYQQ6HKGM4YqK7CVOe7IiznkK0DcXiburaGzKfnh7nzyqlAqZHOtw/KpVS6+/ZfsXvqEfHTInR4eISRpu4cKI4rpGF1mEDUgadUFT7PJ/HM5x7oKf9v7F6GCiK0Lb9LoLLN37WOG+9ce8KoSIELFqyS90zRXA9ZfCPFCYCRYZ2mcukDQW6bYvUI12KZm0AqKouDqzWuEccLy6hlU0oLls9BYYTKace3994mbHdKlNRobZ/mb//UfEqAJRIXwlr/59/8Bf+Pv/B1++qd/zDs//tHD3YwC2KdWm2/Nf9en7qrt+atTN2G02iY9t4YXnspUOG3ROsPnBqYaijnplXG8hpnfOmsso5tDqqyimN6bBRVS0mx3UUFAPp3cVwG7+Mzma9+ke+oMh1cvsn/p4+NbLKgNHhdk5iK2ks6v/9lOkALmBLq/o8jwNtSpvVLFCPEVFfkywB4wAV6sN3hJu4lcaqGTCbmw0LF1RkoO7HJL5kA1LZntWYR2iMJRFQ5nPYGCtSVIE9AVFA+SRCiAZQlNiWmU5HkfPS5wBx43F21qD5/0DZESDAv3hMoR34YEaIHcSJEvtvAHJXYwqW1uFkT6bV965co1sixHa/j7f/hPiLoRSSNBBkv8wd/7e3znu9/ln/+rf8mf/cWfMcuyOwedvMdVBXY2ZvvqVQ53d7h+8RM+e+dtnNV4o+ktLfE3//APaXeX+PTKDoNxxotvfJvTL3bQVUWWTzk46PPxh1cYjUasLHdZWe7S7/fJsgJrb13CdnpLfO+3voezDv+L9xmOxoyzuojrPSFBSEEjapImbfyapBICtjV8UtR9ZH5zXAFUcKgrJsqQFfMTmGfCEANL1GPANnU/Wwz9i2ZqzP9GA4fcwh4157dsSXq6oaWVNGj2XiBa7qEufBPZ6eC7KxBG+DjBhyHCWZS1qMoicoOwFY4clxVg7aLe1UNjfwSlhqKqrXKeVXgKSq354L2/4uqVj2l2Gpy5cL4utI0CLymMwKJYWuqSbSxTTZdZa0NoCwJbEoSgIpjlFe+9ewPvFUIECClpd1IacXhUKDeMFHGsaDUDNlbSuhjzaEAxU/RaTXrtNuHeAG9GVK4u3PggWCyRjn72MC0dgRT02ilpHHJz+ybDP/9jdg8G6Mo+sS2c8wLnFatrW1x46TWEkByMcmalZWN9maDRwGhFnjviRovOUoPKGAYH24z6e7fVhuHYI3ye+uEN2MX8/Az3pa8rtNYMDgZIqQgORyAkRZZjtMY5e0vS0Uwb3j+YkkQFG0AYBez0M8YzmD7Clm4ymfHZxet4ZwmEIY3qccN7WG6GNGPF9rTiX7y/T1UZsqwOEibUQ9/UHO9Q7oUwinj9G6+x3usxnFSMC82wKjjMDohtgUahTXmrqONrBotnikYgEHNxR+gtTSA77PPRu+/jpeT81iraw2DnKtqBl3XwpBEowkCRG8X4LiYp1taWiu4Bsi9DMX+Mff0o52XF3mCMmmQcjKY1gZ7GtNsNWs2YOATVa9FsRDitsWXBZJZz6fouVpsHmoYcECrJ1lKTJFTsDDPG+Zdfs8h5xyyfkJfZvFgkCFcr+83c3uU5nuP+mM/s3oEdc+skOOdlXA6+mjPuTx/PDpG+WOk8UAS3libZcoYtn4xtyaPC5TnuoWRVD4bd7W1+/rOfsdTtYsoSKSRh2kQ1WhihqE6O2ZGEVkR7a5mXv/MGk+GQ9//9uwwP+hTZk4skO+OYHn49I9MLnFxcu3lZaz8P7ZppTq5Lbt48pBhbzm/BmQ24NICqqDc/TzJiGiURvdUu+aygvzt8TqR/bfD0SXSoValpp4OQAjH1WF1hSo3FHOfrfYUI0ibp0jpCSIr+dq3QBqQ0dFoJS50GenzA4ZWP7noMIQVh0iBMUnSZo0sgCGsivcxAzOas4yL95sFYDKkCuqun6a2dZjLYfQgiPQASZNAkSHskzQbdlWWcs/QPdnBVSdRdQTbbmMmYajCcn0+BNprheEjSaLF6LiVt9yhaPUzco8oqqnybxrpELp+m00545fQS0hbsXb+IqXIuvPlt2q0GNy5f4t2f/PjhPCkX3gpzNehR7KGgJtEXxFQD1FpMcmYZZyxuNMVWGrNs8KUEUaeWMqGW0nJrkzvrmY0yivH95zwhJFGSEkUxVVHAAxLp7fVN1l/+BuVkzP6nnxwPupJjJfqCRF94vVu+FkT6g9YfF0LUhctEsNBxf/lw1P2goi5mjydIEqJGbUye56pesy1T25vUNYOPYEtDOTYIA6KERRkCJaGVQrMJYcjxOHav7i6AhkT0JDY06GqGKQxu6utz8/X8vJ89RemioCa0EhCdCLXWxFYCxLQOPN1BjQ7QHwzpD4a8+MIrVGWFs444iQkixeuvv87Zs2f59//5P7Kzf6+sA4/TFbbIGe7e5Ppnn/DeT37ED/7o/1fXcAC2zpzmtW9+E2cMezdvcDCcsHX+AoHyaG8oi4LpZMbuTp8iz3j55TPEcUiWTSjL2mv8JJGYNhqcvvAizhouXrqCLnOyUt+bSF/4o0tBFMSkUZOsPVexHwKj4tY2MvMa2pUjwx3PqgF1gKxJbUNlqQODJ52tFoR6RF34tQAGx/dgQS41xdziX3mSOCJqNok6K8iVTUS7jU9beCHwKsBLCc4hrUNYh6gsYPCBxlca4fxc4X6PW3UXTIv69azDY7DOsLN9FbWnGA9/FwCHQCORXqItOCRpI6XTbrK23KQRFOjcYooKKWub0jI33LwxwsyTnVQgefmlLVqNGCl8rRgNJEEgiCNFuxmhtaEsMiohWF3doNFsMZ5kDNV81fUQSy9x26syHiNAqpAkSRiNR9zY3yUrLdY+nuL9ljb0NZnebHW48OLLFHnB5YtXKCuDMRJBjLUCYzxNFdFudxlPxswme2SzSe3PfvJkFnPf4tr9Ykx+jqcBZx357M5rnNu7X2k9N6clYVBhYogixW5WMXnE+1OWmrIcoiQ054kJel6wtBEreo2AvUnFJ6NjMjQEetR9fBGnvB+UUmxubXF2Y4P88g4H2ZDMGGbVDONByQnW6a+VN/rt8EC5uGPzxdeivEk5y9idZTQ7LTZfPENpLNu715gVFUlvlTBpEilJI1TY4O6KafcQNYAU1Bk385PT1qLz+b5pMiWKQs6cPU2j2SCOAgIJQRJDEiOdRdomKhzDzf2H2oFKIeg1Y1pxyHBW8gDL96eCSn89BZjP8SzhhFnRFybBxZ5Vf6kT5LNBpAsBUQxho44k3Pf6///s/dezZUmW3on93H3LI69WoSNVZemqFtXdVWgAjWZDjAA5ZqSRZmM2YyQfiQc+AsYxvoJP4F9AvtFAGjgzHDQw3ehGC3RXd+mqrNSZEZGhrr5Hn623u/PBz7n3hhYZmRmZFV/msRsR94h99vbtvvxb3/rWzDT+U1Wfz2rpoo7rHpuPZ7Y0nwyG0ynXd3f5yRu/4F//6/8XYRiBDBiMRkx1DY3YmYvW2ikIBzmDm3u88aMfU6QZk/0h5SR3vpTPChJEA1c5kXGnZ93nBO1Y0Y4UL3/pVX7zu7/N9u09/v0f/keKLOXmwKnZ8sJQA3EK8RGMU8hnirdnOeKyLGd/75B2q80/+Ie/i64NP/nBz+n3Bs/wU17g+cW8e/RcO+m8NxaWFtk8u+UIhto1vfK9ECUUJq6RUlBX9XE3+k8FQhE0F1F+SBz7BKHH0sY5Vs6c5/CWYLr9ISpqsHrxywRRg1sfvsvO1Xfp7T4ugX0KVQ7JwBHoYeR8tqsCJRUr61tEcYP+0R6T0d33iSQIG6yefZmo0abRWniKL+rKTvLRPv2r79NZXWNhaRnfD1leXaeqCm5feZukf0hZ1xC3oRZupzOznNEmYJoLSlmR9K9QmY9Yf+UrLF94meW1Zc5sRHSaHkuLPr5ssdYNkFaz2GkQ+R6/+/f+HotxxNUPP+CnP/jhYyldjlEA28xYA9xSOcCZa4buUQ1SpoMdrDXUeYmpDXZYOkPppHZ1w8lsYNXutXVZM6z7YHlk00ulFFEUoTwPaQ2mKma2PQ+G9AKWzrxM1FrA1JLDjz4iyRNYbjs51jS7o8Lv+JY57ckag1IBYdDGGkue5mAtoQpQQqG1duXKpkSb5z+gV8oRTOJpWLtncgA4krxh0SbBDmts4lH5imqcwVEFhZkPewcBqg0ydPvWfAgYEDU0GxHf+toGvge23CMZ59TzKq8INz5dvfy9MMDIYEtL7RtyX2NSg55bCH9a86APxOA3Q+LWItWKT/JSjh1WkOSQW2ed5AteffVVzpw9w80rN7nyzhXGoyFvvfUGG5sbvPr660RxRNiIsMKysbHK5YtnGI2n9Pqjez42zzL+6A//J6688wY723scHvY42NnmdnJiy5IcTvjTP/sLlleWWNi6yNLaMv3xAW+89QOuX73BO2+8y+7uDmGkaLcWeenlV4ijiF5vQq83uUeN2x+M+P7f/gQpLJPpY/rjzvoXyJZk69wltl69zMGgx9FwwLi3y5H4AHsXBSCAVkMQ+YJpaUlLexLez+YfBLCKY4vmyv909qhx1TizREbswa+t+6w0FGdWV1lst3l9tUEgLX4co1ZXkUtLiM4qNo4pq9KV9E+mmNEIZQ3SGAhT6kmCiAQicsmPb/3at1haXkf+4APUh7skGqYz6/cvEq8phKDV7RJFMaNRn3ff/Bnn85Sou0CrFdOMu/hhzNbl14maHWos4rDBwc0b9EcTPE/gB4JpCWNOpmjPWoZJilSWpW6bTrtJWVdkyZQ8L7DHahXXJHcwHDGeJPQnCUntYvBHwcORVadJr9O5OmMtgyQnrWrKuqbUNVVt7jOF3B2fPT4mkwnb2zv0e4ccbF+lrmqGgyFhELKxENFfXcHqilarxd7ubX755k/JspzhcEKSpJTJBOripHTpGTabfxieg0LH+2Nue/vUc/0n35lVG8toUroGuQg6kU9Ra4qHNCB+GIx1oQ+4saxg1ttaYGYjWgrw5EzlPKNFPM9zVayVfmjDXGM0B/0jJBqhPFbWVpmUUzhU1LomyQdEoc8rl84jJNzc3mGa3G9x/nxhXgDnCwgFFHXJQa9PbSwIi+9L0CU6t6SFc7Cri4xAOmuqx23VIAWEylUTFlq7XPvMcMHYe0fk/GegJKEU9A6OKOuaZhzSjAJ8JYl8iRSWhU6DrPCYJjn6McqbamPYH6YMPInRNd1AkGtL8YD55Olnvhd4gU8ap1UUzweeDyIdAX4AUQPK8WM8/5NdEO8PAdKDxjJ4obMd+ASJ9HGSME4SKlNTZlPanS6bF1+m1IZJXTki3ZgZke4ew2qPXxYTTKnJ95NnS6IDKBAtXFXF3c1fPidoxYq1hYDv/tZX+Wf/5/8DP/7hL/jbv/gBu9OUm8P6DheBVuL6941rR6Q/68Ayz3Ly3Zz2yy1+7w9+Fwxcu3L9BZH+K4G50fPp3YGTEy8srfOlr32TJJny4dUPKHSJ54XgBVhdIz0gmVm8fFpHKyVBa5mg0WFpqUGrFbJ55ixnzp3Hq1KuSEHYaHPm9d9AKp/3/tP/wHDv+tN9WFW4hx9Co+OI9LpAKsXamfMsLK5SlsVdRLrbAvpRizOXv0GjvYgx9b0l0o9ECZQUo32KsUTUNXz56wRBRHehS13n3HrrJySHe+7pcQsK6453tiYZG5IUgtzWjLdvUE6nbF24xOrGJmfX2ry6ERHFAZ2lBlGgWO+cIfIkARqFJf7d3+X1V1/lT//9v+MXP/7xkxPpu7PTEcx+DnERsQf4UIcp9XZ6wi5oHNsxN9U8PdHNlO41NcPR8LEOQUpJq9VCKUU984C2j6iZV57PyvnXaa+cobfzIUfXr5OSIJba2EnqSErsybHNI/xs9p0joAFeENJsLqNrQ6mH2NoQhW0C6VNVJVVVU9XT555IF2JGpCvJXTapnx48YAVoW4xNMePcNS6zOPZwcMpT/xSR7nVAdaA6gOqUs9xiJ+abX3UK6Td/MWQ8yZ1K3eJI9AWcsv1BzfPGBsanGlt+FgiACPxGRLO5SIYieWkKBwVcLaHQjkhuSF799qv8xnd+g+//8fe5+u5VRuMR77z9BpPJgAuXLxE3YsI4RHqCjY1lLl88w83be/QHo3uUb3me88f//g/5S8+Juqf3Gb69aoL+879kqdvin/zv/mu2Xn6Jg/EBV3c/4Mff/wV/9D/+GUHgsbreZXl5kZdeeoVG3OCNN97jftX7g+GIv/nhT/E9xfpCA+9x+uLMifSmYuvsJV575Vt0jg5o94/YuS7oiQ/vubRCQKsh6TQEempIjT1ZFisckR7gxqKHI88LnEJ9zmAfnrxfHAh+54zHq8sBr7x+lvWtTcw0xSQZXhyhVjaQS0vQWcX6HmVvnyrJHZE+HuMLQSAENsgw4wRpFN6iJQwDvvntb3Lp4sv0tyck13c5xFVClHyxiHQpJZ1ul1anw2jY4723fg6+z9rFlwHD2lIHP4jYvPQlmgurDKcjKqnYvt2jPzb4gSSKBdMCxvbkflXGMkpTpC1Z6rZotxqMJhOy4ZSyrByRbpxixVrLcDikrA2D3JA85jLoCde+QHPSaPH0UmeBYZJjHlpcOzf7n9NdT8ZgTyZTkmoXW00xWe+YEI/jJouLS/RHU85vLbK82OStt7b5/vf/nLq2VIVLjN3DvX4yPdXvwfxbP37t36eE0y3Qnvg8nE4PfHIn0RgYTSsEsNgIaUUe45ynJtKthWJ24ygcMWsRMyLdQQoIZs5vrtmzwPM9PCVnPQke/NlaGw77R+gqp7tygeXFFQ4Ghwih0KYmLQaEUZdXLp3H9336w9EXgkgvZ4+GgFBCWVdMe/1ZhbpwfuW6oK5LysrZpnmz59aS4+qaR0EJQewrLJbKGIy17hrZk+F8ekRaACEIlSKQgpuHPY76Q9aWOqwutomjAL8dI4Sh04kJCkVeVI9FpGtj2R+lziEvEnQDAaWrprgbH2/me4EX+KTxXK1MwPNCpAtXviKbDXTmPTc3rlQRymsjlcILA6wQ5LrA1BnoT8djKs0Ldg+OGCU5WkbU1lJMps5Y9K6uOKYyVJMSW5tPpHmE7/msLi+hPMnhtE+eP99kxP2Qlob+tGZn54gP3nyHbDLm9/7+b7N30ONvfvom/eFJImdsYbuG1Lg9foXb5z+zM+vkBZjKUKQ5SkoWui1WVxeYTFLy/GOMsccpl3+BTw3SU6jAx5MeYRAhhAShsBaSyYCqzIkbDaJGlyAMSSYJZVXRjFt4smA0OKIsC+oyQ89U6sJ3jfzsp2G4ag11NgZTYzoeQkSOzJ6MyfMUa6EqMo5uvo+UijJ7PAWjNZYyTzG6Rld3jXejnTp93r0MS1Vp8rLGqggVLWDqHFvnuNAvpi4VR7v7RKMEL/ZAQjUvJzxdHn1fJu6uKifrlHnKEwhlyYuUqsjRCPACKBOoUqccc34GLtmq/OPafxlGeBaMLinHh9QdwC5RFwXDvSmBLwnziDhQdOKA0FMEStFutWi2WkTNFmQZVVk8ma/UPAKeE1Kc+t73a+JXc1LoNd9JP2JYhY0mXhDgez6B75NnGZPR4A4FtRCzxqtKIZTnmr7eZ3MnhSAOPZqxx8ibNScta2yRIsoaQg+swYp5M1pOrDYQSN9DSR+Fj6lrTK2hdsRMWeYYUVPrGqMN+nH9VT5DlEXB9vWPCMKQ9HHVwM8KCpeYiIGOB23pZL6+nHUnsyBrd3+WOKJzfut6oLOZtcHstltcaLO1uUIcedy63cPUFYeDkiSB41xghSNIn2bJm/nkq0CxuLlM0AipjUZbS1mUlEWBmVZUB8WTrYce0FHgS4JOCxn6hOdCvJUAujFpmlAOUriRw6CEyqI8xfr5TVqrHdY2t2i3F4miBgLBcDTi57/8Jf3RmNe//k1qo2k1myglOX/xIr/+ne9g1S+48tEt7F0bXDtTJ2oDD3JW0cYyyjVaFrz73nuM0oQ6NGjfMk0nbiOvNUmeE+U5RVXiByHK8wjD0DU5vk+nRWMMSV6hpKS+e62Z18lr3LUTgJQI5REELcJwgTiuaDYNYdi8vyeKAN8PCaMAL8tBz5on57P39XFzUXN2rUNO7Ks8nP3QKZ99KSBWkpanCKMAvxEjGh0EHsHiMv7WeQgCst4RVpdU2x9ipgPU6AjPWqQKMH7E/qDinRtX8FtNNl8tUb7PsDclmeZondNq+9SVxpSGSQVZ8ZypeJ8SL108RxCEfOWb32RlbZ1CSyojqJHc3tlhOBqS5wm6Kti9tUc6HZHOYlWrBMYXyNCdd1/WBNMEURvmzoVSCJQUTKdT9vdhkuT0RwllbZgWFdpYKuuIJ1sZrHa9FR4XGldUNS9ckrgpDe61178b82oqEOR5NbsfnjyItrrClKnbJ55at2tdc7h/mzxLyMZNWs2Q7e09ytIp8I19tvaRj4so8mk0QuqyJkvyeS7j+cEjRYgC4flwqlmxrSu3Rh2/wSf4jaR0PtyzHGCpDZSW6mPG5ndWUkBaaAyQz7xdjYXKuMbQjXaM5/s0FpdQns/e7h6jwUl1U+h7tBvRbA4WeJ5HGLURXpP+cMzhKKE/HNxh5VJWJbd391CeIs8/B75UT4DaQmbBUx7NdstVAKc5tTYY7eYfT4FSrrKSu+2WHgEhJX4YYK2dWYRZ4sDDV/K4wEJbS20NxliqyriaZCnwlGSh08JTkk4zJA49al2z3xtR1ZppmlNW9bGt2+PCwkyFbqkewBGdvtWeqzngBV7gOcVzQaS7JlYt/MUFsunBc0OkK79N1H4JPwxpL3YwOufgxs8o0v6nFu2MJ1PeSVKiIOTwcIQQgmnvEPJ7Ta5MoSkOZxnjT+Dw4jDiSxdfJQgDfnbwBvn480ekD6c1o6Tmrbc/4s//3R+zdfYM/+z/9N+w3xtw45//346JdAvsazjUjkQ/g9vn5zxD5ZEBStCZJhlOCEOfjY0llDJcu7r99ET6aTLsebmZfsXhBQHxQoc4illeWEVJt7UzWnPresGozOkuLrC2eR4pJUcHRyjPY7G7Qllk7N+4xmQ0mN3WFhl4qNjHVhr9uN23Pgas0eTjPaRU6OUG0CFNEw4P9xiPR1hryCdDrv/sz933ekwVtbWGbDyYcdh3TVq6njUbdTDWkuYVIi0xqo3fOUOdHlFPcxzb0qHIFNffeR/pe3TPLhO0QrJ8Jj2b+Rsfexnf8XGn1ZYnvxAKvEgilGU8GFJkKZWVELQgPYLpHsdskvQgiJ1NmZAukG53kI0muk5I9q5QtgHOk09TBjs38aTBrLdoxj5ifQXRbBD5PuHyMotLy7QWlxDeGN2vME9KAN+dLJhb+1c41e9pHNeVzr7OI7pVCSFpLi7RXFii22qz0GpzuL9DMrnTlkJKiRAS5fsoLKaq7quSkhI6LY/lrk9vLrFKcugNIVCoVoi1Bl04u5ZjIj1y8kNPRkSyBQjqskBXFbbKsbUmLefm8fMv+fxrR7Mk4a0f/RCEwNyH3PxE4QPLQFPCWgRtDyIfPOWkkrUBmYMp3Ti6iRtrM3uWeoJTC89O99kzq/zD3/8OBwd9/uiP/obxJJkptE7d8vPKgqep2PSBJfAXfC7//sssbi4zrUoKUzPsDxj2exQfJdT9Evu4ddngJGjnfWQnpPHKWYKFDksbK7QW2+wf7LK9cwt7c4r96RgyDRn4zZivfPubnHnlPK9c/hJra2dotroAbO/s8D/84R9y+fJlvvLNb5JkGS9dvki30+Gbv/7rXHj1MiWKv/irv3G+OKduEwtMy4cbGlYG9iYalSaM/+zPaMSKzS9dYPXiJv1RD2stZV3TG4/B85lkGcoP8IOAVqtFmqZk9+n3o41lOHXk9j1hb8CJ3cq8+kAphOcTxcu0WlvkOkTToNe4cV+/f4EgjNs02i38Sd9Veta4YMufXwtcrwc1+xnNfpa4Cpy5Oh2QCDqeYjH0aLUahAtdguUz+MvnUO0Owfom5XjA4M0fo4cHyPf/Cjnapxm2CP2I0m9QNVd4++o2/+rf/IBGM+Tv/PZ12q0QaxzhWVcTltdimkXBUlGwm8LBEV+Ihmnf/c63aTQa/JN/+l9x6aVX+PG7H/Hu9V2ySvPmW+/geYqoEWK0ZjwYYKqcRpG4GdYTmEghW02ixWV0ktGYFhSlayQrcNOIr6B31GN/Z4+8gmlhKQyManuH+Lo9s194ElFvZe+c4efqSri34Opu+J7PytIKFsv+wQFaP+VaURfYev5NTgZFVRZc/eAt1wNDCIRwsZJ+gkTBJ4FOp8GZrUXGo4zrWflYKtdPFY9aAqVExk2Emk8YFpMlmGLekfUTXEOFRCofIQRSu9YyWVmRWP2xtuHzbRzMqiuAUVIySk/mYTNTrSvfZ3FtmUaryeaFlwniJkXx4zuI9GYccunMKlIqDBIhPZrNdZQKuXrtA7b3t+8R4CVpxhvvvgd8BnHIJ4zSuqR02ws4s7qGsYZid49aF+iqRmtDoxXSbPjURUWZPhnfIZUibMZYY5FZiTSGxWZAOwqOn1MZTa4rqsqQTErXU1hJfN/j7OYq1lqkrRC2Yr835sObh2hjsBYs9qmaVk8rS1I9PMz6HJoNvMALfGZ4Loh0hEAIiZTq6br4PPPj8UF6WBE4T1VdUWYjjCkwuvpUJQMWsMZQ1TVpliKEuK9q6I4XfFLHYi11WSF5eMnYZ40wClhaXsBaw6A3pCrr4w2OxV2+8TTlxs09stLSXLnKYDQhy+5cKOfhV43b48+r0D82FMcRklMvFNy6tU3g+wwHY5Iko64+5lJ2uuT+C7C5+7xCeMqpcX2FEZZK1+Rp4uY6HJE+bzJbVSVZOsHzA8IwxlQldVVQFjl1dWfTHztTogspCZoRRhvnXf20c5PyXBfAOYzhnnp/a7HGkKcJ0+GAMk/J0ylZlhG0FtBVQZUmTnX8RHh8+dNcJeEHAXGjSV6NZ0GfY4iFtHhRgPQkdZlgpxn6dIObhy0vQiCDEOkHGNPEmAAvimg2m3hhiLCGfKYoQVdurfJ9jrfqUoBys0adj7GVh65qjLFkwwOGgWAvlFzrtqAuqcZ9JDX5oCL0JVVykaXFBabTCWmScPP6daqiQNc1z64N2qkT+aB/fywy06LLkjJLsXEDqdTJmDaGqnJqPjsjS7XWWGNmglWFtfaOcWKspcgz0mRKkSbUWYouS7dT1BZbu4NS0pXKGn0X0Sg0Wp6QHqbWMzuZ04bqx799vHP0GeOh6/wnBQH4AtmSiJZCxgEi9AlaTVQYUqY5VV5gbY1OcNnlOTs1Hzd3ne4sK9g/6NPvjciLiro2+L5ASCir2WbwscfdfWA5bohaYSiVRvgevvSITZPKVsgRlEsTqCw+EgyuP4B2Stk7whkJ+OC1fLpb6/iLDbpbW4TdFs2FNlEzYoDETjNsWjif+LmXrTaMj0bEzUPGnQ3SxgLVrHLPWktVVeR5wXg0YTQck04zQj+gKip0rU8UZg84D486PRanWs/yGmNqhv0pIh4wHaeupFxJ/MhDBYo0T5BKIpQgjmOK8sFEgX3Qh/tAy51/ClChT+fcJo31RUTsk9c5o90DDq/dYLx9eN/GdRbI8opJUlDebVc2T/xJTuwt5vqCuZ/KzD96Tjz5AgJfEfg+fhjiRyEqbqJabWTUAKEQBlSaQZIiiwxZFwgvBOlhipKSjCwtGBUVKfDR3ohGwxEg1lr6o4JppqkqS1HySGLi84Rms0UURRRF4ZLkRhOFAVkxJZ2OCcKQIAqQymN5ZQ2rS8a3DpmmNSrqsLR2ljiKaDRaIAI6XWepFRZuMRCiIi81eakpSkNZuzyUFZI4DrFCoGcVaLEEH0FWVA8uxXgITltrw73XyPc9gsCnrmvKosJaQ1m6xg2PsiO7F4IojvGDgLo21JXlJNh3eyZrZ2vXk46WeeJ4jmcsF1XKIwhilK8/u+bWHwcuyDg5JZaniEOf+sNdbAzML9KzuDyn3+OOn/d5Y6EUQbND0GiSa6iKikpIVzk5hxdgpIcKAlqNFkoFBEEL8EAptLE4C6I7F+LnLqnyDGFx3y/Lcow11LXGGItS8vghhFPvywhkbcjz6o5tlu8rlJJUlb7rXFmsdg2VY18RyHllmT6+nWurMdq6x6ltkBBOlS6EwNQaU4OUgiBQVLWgLGssrnkzAoy2j+FCIGZ0wL27CXnqlp9zJI+LwFNIKalqjX6OeaEXeIFPCs8HkQ5IoVDCRwr16Cd/0vA7EC5RI9FVgihypv0DMBW6/nQsXe5GVVccjZzk57Miseuqonewh/Ik5XNc5rW2vsTv/5PvUpcVf/E//zVHB31KDadFH7e2D/mjoyHC8+D/9+fUxtA/lb0/jQzY50TI+rERA+3j/TrDcsC//bd/AgbKssIYTfUUm4ZjzMkMcerxYn37TBA0Y4J2k9pqMlOSJxnj/dPSNUs9I6yHvQPGwx4Ly2tsnLtEniTs3rxKWeRU5Z3zjq00ujbEC03aG8vUWcFw+xDzNONGCGi1IIpP/i3PYDy6J6Ky1nC0fYv+3g5CukAv6iyx9NKXqbKE3tV30MUn2xJeCEFnYYHukqJHQjaYd9bsoYImC+c3UZ7PcPsaxXSEfWyfdEG0skG0ukGe+6RJSLy+yvlz54gbDYwxZMmU27/8CUfJyEnrukuOhTN6tofSGFsy3d8Fo7HWAyTb/evsKcWVzjI/+Ku/pNOKubC5gi4zPnr3J9Rlxne+93c4c+4Cb7/xC95/9x2yNGEyGroN+Kc1559u3vkQWGuZ9I9IhgMaymN5afn4d3VdMxwOjy1eHJnuwnfP8wnCCK1rylPEna5r9vZ2GKUZR7dvMN7fxRrHTtpKo8c50pOErQghIJ/ms7HutpsVGbUoTh/gqXP2gM7uL3AnZkykbEqiiw1UyyNabuPFEWtnz9PsLnK4u0/v4JCitCTXR07+Ob+95iHBXaf3xq19Do6GaK3JsgKlBCuLPoEvOBhUTFPz8ZiHCui7DXFvMibLBN21ZRqdFkEnZmF1mUnjCFnkqBqWiRGVYXD9gHySM5k6W45jhMAStM92+I1/8D266yt01zcIopisTCnrnNEH1+D6HhyaWY8E99IyL3njL35CGIbY38sR38zp7x3csXUti5JbH92GStCJW1RZyX5/l8Gk7xSEH7PDl7UwTSHJYPTeAepaj7rQWGsJ44DVM8tEccT2wQ6+CpC+ZGV9hbIqmU6e0EaoBZxnZjoL0WKLb/xXf8DS+bN4y012x7d584/+jLf/339GleaY+wgEjLHsHgw5OBrfax2jcX56JW58KVxANifYa5y1iz0Rq3elpNOM6XabdJYWaC8vwcoqrGwgUFCBTCviox5m2EOVOcLUiNI1bssmNaNqwuBoxKGxZNOCmz+/fYdl1bxxsZ0RaNrcGV9+nrG0uo4Qgg/f/4CPrn2Eai2z1Owy6h9xtHuT9sISK+ubLC4s8bWvfw0J/Ov/5y3euXmNcxuvcvlLv4kpM3QxpcxLOoubM6sEN8/vX3mXg94hk9oyNRwnWzutkItn1vGUxJp6lnQRzo7gcMi4/zj9s07gOs6cDJX7TTGLix1WVhYZjSbs7xy6Pc7RAQDVQxOZc9HBfLy6RPLG2fMsr64wHo4ZDUbHFYTGGIqiwGhNnk6cIOtJ4HHnbv10QukZwPNj4vYqaSFB7D67N/60YA06m96ZbPi0ykOsxdTlzK7R5+FqjSfD40bzKojpblzGjxts9/ok+ZCRFsjO4sl7xRFTEdKJO5x55UtEUUxVCqpSE96+CX4EunAikV8hpFnO9ZvbwIl4odOOCUMPKQQIQSMOaXUDplnB5GBwnPCWQrDYbdBoBAyGKaPxyd7Hakudl/ieZKvbRArYGyb0Jzk+s9t55gVUG0tpT3zJAaLAJ/A8kqwirTRh7LG50SHNKnYPJlhjidoBSkmySUlVPHyfo1AIBBrN6YbfSroGp+D2d9ZCaR6PTFdSstRuEfoevcmUafb5cyl4gRf4uHhuiHTnMfucZcKtwZoCq3MoM7CfbcGLfpKmc58AjDFkaY5S8rnMUgehT9SIWFjusrKySFWWLCx2qPKS0TR33sqz55ZVzaiq0UDB5KH71nnM+kl9Y13XjMaTZ1996PYhL/AZQijnjW61QNcGg6Yui/sG+VrXM4IxpywLyiIjz1Lqu73D57D2ocGOHwUo33ObWDPz/XtEpYPn+Sg/QBuoZeoI4rssRXRdoesKz/fxfadM88IYawzKD7G6xuj62VfuzJTNVV2jPDFrvoTzBbEzNY1wskWLwNQl5rQa/aGqVwvCosKIoNlBS4UyChWFSCVnimunTPE9D89TGCEwx2960oHImtr5tusat5VX1HVBDZTaktSCOm2yEFp0mdE7PKDKE/Z2dpHKY2dnm93tbfd9PjVV1Z2n4nFgtMZoTVnkFFlGVZbHL31gsvcBY8JaS54mWCEp8wxT3zXjzr25jz+AuyZki33gBPoFYbg+Dub91uDB68w8szvvgTwLyaQAKSRKSGSNk4/m1i2Kp9/rAae5qmqqU/OOfFaL0unvhBtDhS4QVUakCzwTQlVjc40Emp0YVVm8XIABpdzjnrBz7hEfCYQvXQInCIjCkDyfUucFJq1gUjuTVXPnMeTTjDItGBz2Odw7BGPZWN+gKAqSaYJA0O/3CfyA/b11R67pAqU8GnGDxcUF8rwgSZL7KrgfB2bGGuq8dkb0s2NUStFoxQRh6JRs1iCVxPM9hHz4dRFAI1L4nqTQmkobbAg2AivAerNz5wGeJS9TSlMy7feZ7vUe+t51bagfFGHNi0pKTmyn5qqGU4TivGVCIARBEOBHEcpTSCGwSmE937HdlYaqQhQFssgRxiBnqlKspS5L8lRT5AWVtRTGkma/OuRSGIQAFEVJXpSEqoGvIgSWRhTQjGPazRbtdotup4MQrtG4F7UI4jZRo4P1fawn8FRBWWhqXaOrmtpoamMpakupXR4u8H2iKKTZatBut/GUpC4zt7ZoQ62Nm4e4V6F7Gg9rC/SgPJ2nFFEUkiaZsy4yro/Ag97n5JPmn6bu+DfPUwRBQBRH1FVNXdcUxccnlzxfIUOFrk8qGJ8ltLEUZUX1Cbz3pwb7ZB7WzxLzddLznJWdrupP9VisddZeVlvyWpNXGuEFBI2ms5wRgiD0kX6IUD4aSW2hKAvKoqLWj6Ge+ILCVYqdxCd39vgRKCFQcqZQlxIlwIjZGiucUnyuXL8DQiClQor571wVpjHmOHwV0n2GkMJVz0hBWVXkRYkCjG+OhVaWu+Yx4awTpZJITyFPDTkx+3yOK2AeDCUVUei7CHqWIK4r89DEsBCCIPDxpETN4oYXVMML/KriuSHS75kkPktUY9CnulNbcw+Z9KuIsqzZvd0HAeUjsp+fBS69dp7v/P1vs9ztcvHsWUytEb//PYaDEX/zVz/l5o0dJ6Czbv81zx0/asw5sv0Zjs8MKI8t0k8U5J8Enr98x+cQd7eqenz4vk8cx66czljKNGU0TB/qIZ6Mh9y68u7M9uXhG/hymjG8se+Cs1NGotJTnP3aqyyd32AyHDMZjsl6Y0Y3d+8tAbQWplPIMha2LrOy9RLj0YC90sdUOWS9+85/W2fOcunyy6RG0tMK40e0189TFxnJ4TZ1kd7zmo8DYw2DyYhJbTBlga1rqiKDoOOOTxfoWjO8dRWA+u4+EhUnas97LqMjUZrtJZY2L5HkoBKL12nTn4wJ8gxdZFR5SmOpzebLFxnefpfJwR7zRqgnb2VPNYI+XRKiseUIO0yYThUf9q85b/l0ijWad3/5C65+8B7JZDJrkvVcrIaPxNHBLpPxkKosHxm0a11jjL6PHX5F//Y1pPKo8oz7yXKNNhSTjC+iX+cnDg8n17U4Be/9lu8GsAImMOR7KTJWGM+g6gC5HzIdTRm8scP47X3sqH5qxZ/WlsNBhZxZuzw1ApwieubfoJuaQXGAHA0Y1UO8gxhzI0V/lLC2uMhXLrxEMU65/tP3yKcZdV46AvjuHWAkYM1jGuX89N2f0tzp8K3q11nqLnPlynvs7mzTf+M29kN7MqfcBWMMb73xFh9d/Yjf+s7v8N/9d/9Xrl25xh/94R+TZSl/+ud/gud7/OQXP2VpeYX/4j//x/zOd7+DLgye9Ll27Rp/8qd/+vEbvN3VNSxuxJy9dJY4jollC4zg4NYBSZE88q18T/J3vr7Mpa0G7xwO+KA3olqGogm1hnwKeTHljb/+Y4LlJkvnLhB3Fuj1noHCtcZ5oc/Z1NOP2VThAR2g6ym6m+t0LyzjSYUeT2GhAAk2L6E/wfR62N4ednwAs0S1EiCFJZ9M2d9JGR5pavvg9NwXFUsLCxhjGSUFeanZ3Tlgmt1mfWuTf/R7f59Wd4mNS68ShhGtZoyua7717d9gdXWDZHhEMhmw3OqyeWaT0bBPOh5Q5wmD27fI0pTeeMJYu+lDAZfOn+Vb3/46YeAThwF1VbG3vUOWpgz29plMp+gip+nPlP/6pD/26dnDF+4ano7x5xT23SS7nD18zyMKIjzPd8n3h665p9uWzsp3VOTeVSdAjS5yqjRhZbHLpQvn2N3Z4a1f/pK6ru+ydnl8CCFYW19jcaXL0V6P/Z3DZx7X9w6PSKbTY0u2F3h8SAVRpPA8j063i1I+R4c9kunT7ZFn/Zpdw+7HXB7TLOPdDz5EhREyDJGeYmljkzAMiQKfRuCjrMHXGl3XvPfBVaqyZHi4R5GmTKYTqLJnL375HMJay3Sak2aS5XaDZit2to55itYVrcDlYpPSWRKWVY3KBUYbJHJmnWLxvYDFhVXAsne0R1Hk+EKzEOLsjBHO+jMIsLhCdWvhxvYe1rqGzFIK1paarC7GpGnJzu6YqjZobU4S30IQthcIWoHTmxiLUgrf96iLgqR/iNE1GoO4j7FLu9Xk0tlNjNWMpxOKquKwN0EXD957NhsRr1w+j1KSWzd3GA3HVLU+7kT0gnZ4gV8lPDdEOjxHc7itfuXKmx4H1liy7LOxtnkkBLQ6Tc5d2qIdNYijCFPVLK8s4nuKOApQnPAHT0qKP9OF4VQ3pYcewwt/8+cI4q4/3701u8+FEmIW0PiuSZkwaPnoKbeuKurq8eYfU2vK+6iIhBA0Ftt0N1cQoY/1PGypGYkHDCpdg67xEDTCBoVfILzY2ZYIccdL5o2y4rjB4tISstAMxzlGKbwwduJu+ewtuqy1lGVBJRSmyLFV6RSb0juWd1hjKJPp/b/jYySspPLw/Ajfgm9BeB7pTG1tipS6zFGBT9xqMlUC6oIHGlcCJ2NlFl4aDaagrmByF88/Gg6e6Hw8Lyjy3KnuHmOuct7o97s2ljJ9BJlnuSNZ9AJPgLna3OK4oNOmwfPLMfdCkBaTaaw1VLnCKEs+TjA5FEcJ9V7qMsBPuTZZoPg4BPoccna8c2W6ZyjrHIqSGoEsSnRvgt6ZsCBCmpdjhKgpk5J88hCCWgjwBJWt6fePyIqcyeaQyPoMD3r0dvZJj6bHliIPwnAwYjgY4X/X5ytf/grKKn66/DN6gx43d65Tmxo/iBlPU8qqpttZYGNzi1deeYWyKIjCkLquqD+uStQ6pZvyFGEc0Oq0ieMYX0fY2iKFdBUkj7gkUsL6YsiljSb79ZSoABGD9mbJiMolxAZ7O8jMh8CnWZXkj7qvH/M73N0gWeJUoEIKpC+IEbQFtAOfII5QUQTWYPIcqhJRV1AWkE4x2QSbJdhinrRzJiISqMuKbJox72v8rCCFQCqJMc93jyE1k38bK9DakiYZ4/GErTNbbK2v0+wusrS4gPJcY0cDdBcWKSvNdpYw7B1ibEQQhHhKYU2NrkuKdEIxTSjrisqe+Jc3GjGbG2t4nocQkqLICXoDqqp26vWywhqDJ50Twh2qS+4kx0/H+PNjm//uOIITAmHtTKnrmoJLqZxVHZwSGtx98e8yKhcChOeeJ9zzrdFYXRMFPgsLXQb9HlVVPnY8dzeEcGrXOIpot1tM+p9A5SpQFMUzUc7/KkIIgVQC5Qn80MNTHup0v6Enfj83r1kx01M8BrTWjKdTZFnT8T2U9AkbDRrNFq0ooBWF2KrCJAm61oxGE7I0ob+/R5E9g/n5CwZjrcvazeYpay3a6ON5yJp5JZvbC7m/uD/P41tXwepjjCEvNWlRsRCCrwRWCIwA6Uk8X2Fxr6u1YZpld1i7NiLFUjeirDRpXmFm85OYHReAVD7IaHass8oYP0AIBUIxb2Rzv15LSikacYyxmrIuZ0p6p6KXUiKlmK2Ddv4/ge/TbsWz2MGSl9XJMZ1675ko/gVe4AuN54NIt85XVTyGou0LCYErJZY4CcXzJ/Z+vhEDEZjAUuc1k3zKZHdIMkn52U/eot8bsbNzRP4EGf7PGmFD4YeSMtOU+QM2XQ+rZX2BZ4i7WlUFDfeYoy6hmHDahsMPGygvoNteYrm7wqjfZ29nl7ossZ+iLZIQggsXL7Pw1SWu/OJNjt6/fq8P7SmMjnYo85TSSIy5/2ag0+nSaDTAWnZu38ILm5xtLTOdTviwt0OWTtHlJ9BDwRhM0kfkE1orK8TdZZJ+n+nBviP8rb5XHf4ksJbh9k2ytEJ211HL5xkOh/zspz8j8j02lzp4QlCXJQhQcQd/YRNTJOj0fv0VBKgAZOAsBcwza1f8nGBGLIQN96hyyB7BLr7AZ4N5+ZMEurN/m+e6Jjgf6hTY49gj3BaW+mqJFjWT+gCpPcpeduJR/VlD43yzPVz8ZIGDGlKJ3tLYBYNpWdiAXjrgB//hDXRe0d8vjnUS1sI9HNfEwAc1KI0ODLmf8fbbf0MURAyHA5IkQQ8fL3EEMOkNuPXuB7S8gP/9f/PfUlQle709irJgmGbUxhA2m+weHNHsdPnO73yXhcVF9vd32N8/4I1fvsU0eUqyYya93by8zkvfuER3YYEzZy5gtGXv2j7ZJOVw+4Bxf0Q2ffhnCASxF9AJIsLac+Mmmv3S4tT5FXDNYMKKUe82aeuQ4vbk6Y79EbjQDrjYCVhYarB1foG273MxatAKFKGuOLh1QBQO8X2PaJoR9wfY6RBzcBtGPWz/KhQJRuVYacB6GE8xTSy9AUySZ0sCbG2ucOnCJr3+iA+u3D62EHne8LMf/8hdThtgUEjPpxMFRL6HANIkYXrtCkIq/CBEa83B3g5HRz2uXLnC1SvvE5iC2KQUecZw0EOYmhYVnq9oeAJbOysKbWE6Sbh5c5e42WRhZQVkSGtpBT9u0B5PqIymzHKqoqCUlgqDJwWNwHO9OrICbexxu4b7xfgGZ0/VaDRQSlFr7SoEZcCokhC32Tx/iaKsGI6mTkFeTMHUuEHuc9LddnZTWQ16tt5Z1yPFE4JASpQQSGsRcwP4p4BSkrXVReI4pBl4mLR4uh44X0iIGUFoP7NqcSmdNRhYilxTFpaq7COlRNc1cexTVZr6Qcl/5TmmUes79g6ep2g0Q7Q2JElxTJw+9FiUR9BdwosbNBcXCMMA6YfH9xjAeDDg9rvvOOuQyqB1fZ85aJ6Omvtp/epBScm5s+t02y0GgyE7R2PiQNKOFbWBaWmpDVhP4vs+5y6+wsrKKlc/vEqS3Dh+n6zIubm37SznZhaTxgqMFeTaUmiIEQSRT11rRkmO1q731Wn0hgl5WZGdItHBKc/zpERmtbO0FB6LK2ssrKwihURKSToVTPYtBo1EIE554ZkZsT6eJrz/0U18T9Jo+EgpaLdjGo2Ii5cusLK6TFmUFEWJqTW6cInBnVt7lGXFNM3u6/Dn+4pGw43jNH28cfwCL/B5xHNBpFssxmhX9v2rmL6amzt6uEjwBZH+ZAiAhgDPYipDXlVMj0YMeiPefucqvaPhZ32ETwQhwA8lYcND1xYeRaTDC+7qE8VdJ9cLIWie/F0oKE41aRMC5QX4QUwUNmjFLaZ2QDocf3pNI09heXmVyxdeZbrXR0rFvc0XT5BNh2TTIfgtaK7d83unRI9ptztgLaPBgG5XsrEcIrKEajqkSD4Z4gQstkhdKWO0Tnu5i07HTOv8GY1/SzrokU4y4rMe7aVzZGnK/mCPKPBpyXM0Qv842SuDCBV3cI2uxvffMCsPVAg2//xk8Z4IArwAopb7az55MRc9j7C4uMIDmtzphV5y0tCxnP1+wb3MDDWUGj2uTnwSnhfMuwjCyVo4sVBp7LJBY1xSoCOY7qRM30kfL7bKOV5zLZqKnB2efk7Lk4T+3gFnzp3n27/52yAFR6M+aZbxzkfXGEzGeEHAcDJhcXGBM+fOoeua1197jWajwXvvf/D0RPpMrd9Z6/DSNy8Rhy0W4iXyaUGe3mQymjIdTUmGE8ry4apZISCQikj5KC2PLeqOa7nnfuVHFqQmzwbkEfAJFdosR4pXugGbmy2+9OU1ulHExXYXZSz9/R3Gg4RSGQIF1kq8WmOnPczBNUgnqOQQUVdYH6wClMYITV4apink5bOdyha6LS5f3MTzFFeubT+3PNWNj64hhERFHZQX0l1cotWJ8Gcq26osGA1HICRh3MQYy2Q0Yjoec3B4yK3b21STHmV/l/kZjHzJpZUmoScJpKCeKW5rC3lR0usNaWtoLq0ilSJsNpGeR9RoECQRdtbfRVoLaqbSDp3aMykqtNHudD7kggkpCMMQz/MpqoraGGqhSDV4fkR3qUGWF0wKMEWBLVPs8eYs5E6zGAkYV718DIkUAk9KR1d9DBIdnP9xp92k3XaiDVNW2M+zh/mzhBDOYPozJNKFcES6MVBVFms15czOMI59fF9hjH0AkS6cJ4wQMxHIyW+kUoRhQF1r0vTxSr+EUnhxA7/RImw0XbNra9HGHoedWZqwe+sm2lhUowVC3KcyRrr9jBU8txPUJwwpBcuLXdZWFhlPE4ZJhrUBrVhhrCWvXXJC+hLleyyubrB15jz7e0d3vE9V1/THw5P35cSup9SQ1RbPgERhrSHLK/R99ofTrGR6PycAC3UxL29385C3ukq37WJxAdg6RwgnLhLI2cO92M6MXvKiJC/6xJFPGC3hKUUcBiAk585vceHiebI0I01SdFFRpRnDwYhb128yTbJj29u7oZQkinzq2sycDF5sDF7gi4nngkgXuBIYPwyplXoIzfPFgGpH+BsdUBItnKeWriZYXUNZQfHJL2AyAK/lEuH15PFiEekJwq6PkFCManT5HMjSBFy+eJmzr55jbWERXRh85bO1uUXoNwj84Nl/pICG78rvsgo+rtuA7wk6LZ8w8Fnd2CCMIpI6IdcFVk/JJg/YxD/EVeSzhhCuMaNUiqDRBCHIxuMHN878PKHK7lCQYAxuozWXfZ5ckGHviDwdMx2Pn7pxnIPPHZkTKZwkxtQzpfOdMMZwuHdELmGluYHYFGxunufv/2f/lP7BPm/+5Efk2UM8zE0J+WDWbPTkuzr/QOelee7sBS6cu0hRVuzt3WYyGR03xvkkYa0l7fXQZUk+Hj9y/IfdLkGzdfz3Os/Jh4P7JzV0AdZQ9W6Q2BIRtGg1t/B9nyTJKPOcdDKlyhPKLEWXOcsrq2x9+VWSNOPm9i5VWaCnAwSCtYuXaC6uUUx6lOmIbDJk0jvgfgetohjh+ZiiwlQVIoxRrQ62LtCT/mycPU+YMWhl5v5cly/qOJ9XGBwHpHFT1bxwQ+KMpede46c9ECpOyNJZfBAsSoIFSZUYir65T9PXTxGz6VY2PKJXWhAJ8nKCsTVQuN3qc2DRp3yfqNEgajYI2zHK81gJJWVdYxsB0yxjd3ePq3/7A155+SW+FgT4ccyvf+e3WV5d5y/+0/fp9T8eGz1Np9zavY0vQnb0kXMvNJa40eK1119HaMP7H37ARzeu3/NaD1gV0DCGtz7sceso5drBhMHIiSmrHEwGdt4feB5LDmcvfopWGR5OHzEftvebVYq0YnII7Tgi7RcQGG5PDb4U+F4Dvx0TqYpAaHwKmG6jigmeysArMZ6r3p/mrn/uj24VXM00e0cVO1PYqx3R+6xwcDTgjbeuMhon6E+xKu1JsXN719mfeD2E9FhZW2NhaZH2YpfJZOhUlVWNUB5EMVIqlldXiZst0iKjudBl+4O3uDLcQyAIfZ9ASYrKNbPT1s6sY9z1HU8mXL95k8UkIep2kUIw6h1R5DlJ4gZPUWsmRYW2lkpbjBFYa2YiLHcuvVnjc11VaH0nwY30MUiyrEIpQ9Tp0ohi/Ng1VhXKRwURyiikmKKkQDXXwBiqPMXUKW5ibOOyceXs7/P9xZ3zzHDYZzwdcXh4eE/cFwQBUspH+pEbbRj2R+TTdGbMANNp9sDnf1EhhKDRaBAEAWmazmzk5j59M5sLKYniBlJK8ixFP2EcqiQoJdDG8riFIsZAXd8/X1JVGmMsW1tbbG5uMRyO2Nneoaoq0ixDKI+FzTP4UUwyHFCkKY24QSOO0WVGkQyoa1c1IYCW744xraA0TjM+143XgCkLyoPb2LiF6rYIPJ/RsE+WTElMTU9XTAd9jK4cmVvmCCmIwgAZ+sfHbVEYPLQuqfIvWgXl40Eby+7eEeNJwnAyAR9yo+lPCqraYKwlCCPOXrpMe2GByy+/xtbGJlevfICx97dPAXcmcwO1tVSz7EZR1QynCbXWjy0iFbOtXxD4bGxsEYYhh0c9JtOEdDph+/pVmq02i0sr+NKj2+pSeCF5nmO0OfZex9g7thTGWPKiIAx82p0uQRhRlTVHh32McU1IpZRErTZhWSPkvRXLAtd3wg8CpBBUmUbPekO8wAt8UfFcEOkIt9nwg5BCPXtv3ecNqhsRv7YGvkcpncLRHAhsmsLo0ympkiGEK27zo9PHJ9IbKwFCCercPBdEupCCly6+xG9/97cpk5RsOKLR9NnaPEPoxwTBsyfSpYBmCL50JPrHJ9IlKwsh7XaDr37jZdrdLld3tzkYDcgmNTxIDfccr01SurI3LwhpL68ghKDK8y8OkV6d3tD4nHTyq5nfv9ZaBr0jyvzjehDOVVHy5K9KuYcu7kukW2M52juknya8cuZ1RCnY2rzA1n9xnusffsCH77z1cCJdl+5xH0ynE6bTCV96+XW+/MpXuXn7Bh98+B5JOr1rA/vJIen1SHq9Rz9RCKLuAs219eN/ykdDivHowUS6Lqh7U+reDaKlCyy9fg7pOSLdGk09nWKKKWWWUhcZy6uX+bXvfpf9ox5H9c9JxyNMNkVi2bx4kfULrzI62mcyOKK/c51p/97NNUKgogYyiqltiqksMurgrZzFZGN0MnoOiXQAe5/74QWeO8xzfHPV8Hz4eTirlw4n7OXc4mVOpJ9iMsMlSfOSR7anKSZmtov/9L7GHZh9J+V5NF9ZRDQF9e2cMpsT6cVzIarzfJ+o2SRsNQnbMX4QEHebWGBha42iqnj3gw/5y7/5W0qtOXPuHM2owW/+1u+wsLhEo9l85Gc8CtPEEelUEj1QBDJkY/EszUaLy6+cY6HVYpRM7kuk+8CWhNha3vygx9FpAeUI2ObEXx/c+DI4Zf9TYl44Macs7xfqlEnNJKmZxhFpP6P2SzKZEgQ+Z8+s02xEhDIjEhWencLkCGlLAi/DeCWlshgBgxQGOfx/Pyj44z2nOP4kQquDwyEHh8NP4J2fLbZvu+awGkAIimJKka+wsLbCZDREzvZo0gvAGqTns7K6grECrxGxfv48P9c5V3/+tygBjShCCSiqitIaamOcM8osDzeaThlNp0zznOWNDUCwfeM2eZbhS40UJ0T6HLV2JNQJBF4Q4vkhJcmdcYiQICNns5+XSFnT2ejQWVpBmxJjK4TyUFETWUuk8JFS4kVLCCEw+qMZkd6ePTLc4Ja4rOT8bLlRY61lNOgzmgxJ0vwO1a8QgiAI8DwPY8xDiXRtDIPeCMkDexr/SkAIQavVotlsYoyZebnfqUSXUtJotfA8j7qunpxIV4LAF1S1a4b9OLDWEen3Q10b6tqwtXWG3/md3+L69Ruk04Q0TcmLAul5LG+eodFdZP/2beygT3dphZXlFUYHu+wc7jtyFddAtx1AqNxec06kB5wUsNuqoNy/jY2beC+9QuAH5NMJ/YM99HiEGQ7c+TIVIDBlhlSSsNUi8E9oIIOiRlEVgrpIfiV1EcYYdvZn6vIA8N38U0xOxlsQhFx86Uusrm/w0qtfYm11lc6PFx5IooObHQpt7yjsK6uasnqysSoEeJ6rerh8+QKdThfz7gcUtSaZjhkcHbC6tsnSwiKBVHRaXUovoqqO0LpEKYGvBKY2VHcQ6e7eEkKw2e3SbLWpKs3hQQ8/8AhCnzCMiBptwqK8h0ift6oJfJ9Go4GuasqscET687h1eYEXeEZ4Poh0Y6iGh1hdIz2P5uZFqnRCOXoMouRzCGMtpdVI5RN0mtiqprphMIPSrZKfAnylaDcitLLkMntEt3oHoy3FuEZIMNWnOzM22k3WL21hreFg/4AyL9CZq7Havb3Lmz99E1O5hoDLi4sst5pk08nH8txvBtDwBd1Ol9W1NaIwZLHTRinwmFLXBT96e5cbe+OP9d2ElARhiFQet3cP8Hoj9vp9xtOEbPq81dM/Hqy11HWNRZBOxgghnji4fX4xDxkMJ3Xtp2vcQSgP4QdQPqvEYM0xkW6Zkap65uF5P1hsXmIEDA+PuL19C98LiP2IqqhZWd3EVwHDYY+qfLoxNhgOuXbjI3r9I4wxH7eS+ZOBtVRJRtYfMr8+VZo8tsWOLqdkh9dQYZOwtQJCUE2OqNIBdTrEVhmY2jUc0jX1dIioC1bPXiQMI+JW15H5UYTodEiHDRcF6/oOYlxKycbmOp3lFQ73+vSOhgg/RGc5ttSgYrCeI/qfC4PqF/hcwnKiTJ//fc4J+Zz4XHvcUwSDgGpqyfc0ZWJmjUmZ7eQ/peO/GxKM0BR1ClqgI+My3fNjE3aWeMRxXnNW6iHH24ok64uKurYc9Wsq/ZCXSNy5mnNqAnc+5/lUCzu7u/zNj3/Ia8mI869dptlqEcdNrID+cMAkTanqikYcEwYBSkqs0aTJmCydYh6345wHLIDwodmI8X1FOiwoJhXSVwR+iBASG/vEfoON9U1acQshBNM0o93pcOnSJSaTCUdHJ2XqGuhbd0pze9d5mFnqepGguaSwBpIDjS5OntWOfVqxR1XW5FlFFAWsr3cxBm7sDcmLiq3lBp1GgNaGWmsCXOubaamZDu/0Vw1xQ3O5JdhqSpaX5431GiyvrxKGIYvLXUJf4U/2ENkQJSyBssiqRuQZFCVaG8oa+kM4mEL6rBzCPuc4PdqEta6CazKGsqDpS4QU1NYifUmjESH9iLK2VNrQOzriyrUrHB7ug7WuoqCq8JWkEYV4UuBpH200RVrewUQWec7uzg4CGI2GVGWJkgaJazj6cFgXX1rX6DyMm3hBQBA1ENJHBk2ElPjCEbNWeozHY/zANfsr85xiklIWtdszWENdjMBqjJ7HR6dLdKyrpJaKIAi4cOkirVZEHAQESpFmU6wx+J6k22kex0UCnKXHY0AKiMMAT0qmZYV+CluXL0qzPyndQ4j7/14IV/ng+z5xGCBsTVXpx678MAZqbVFK0W75Mw8OM/ts13ixqmq0MQhpnbOMcM0ltbbkpT65xkKwtLRIs9GgLEveffc9Dg+PSNKEoqyxXoD1Q/LaIipN0GzTVT7dpWW6SyuUeYpRAcaUdyQLhICFpqKFQhqLtJBWhiLXbjQqH4RktL9DkU7JjvYx05GzQ7T6TrXcvG+u1WAEtXEKemMF2krquvxCjJtnBS8M8JsRWIHREDTbSKXQxnDl6jVu3rzN7u7+Ha+RShFGrkrCD3wEgmQypnzK/dYcQkjC0CcIIqz0MELR7i6whqRIU/IsY2llmcWFBYo0Z3R45Gyimi10VON5EiVB5Dk2z9DWHivi80JjbMnhYZ/JNGd5eZFms0mSphwc9YjjBouLmiRJCeKI2BjqNMdoTaMVE4QB1kJRO180qTys0Aj9xZiHXuAF7ofngki3RpPuXEVIRfelr9J++atMb1+lnAyeUxXex0NtDbWpCT1BZ2MRkddkQ019K/3UOJIwCFldXKHMKoayon4MQx1TWaZ7uQvOPuXLsrixzO/8l79LqWv+9q++z+CwT74zRU9K3v3Fu3zw5gf4gSUKLRfOnmGz2yTLSnT99ArZ5YbgTFfw2qtn+O5vf4+V5SW+9tpLKAUHRx8xGA/p/T/+5GMT6Uopms02QsEb737IJM2pM4OpnLL48whjDGVZQlkeK5+/OOVdCjd1zr3pNM5keA6JCEJEFN/pnf7UsLjN2ynMrfEe8hI7SbHTjN2PrvOLRotO3GG9u06RFVy6/DrT1RHvvPljRk8Z2N3e3WaS5rPje371Uml/SDZIcOxW5cbhY47FatpneOX7+HGXpUu/ifQj0sOPyEc7J+9T5Y40KHPKo9soP+Tl7/xndJbXQLolttVqsdCKSUc9RBBiK+EUszN4nseXv/waF199lZ+/c4Pkw9tUSUIxHLo10O+CqiHvOdudF3iBp4EBEk7ygAHu1k05sQI+TQrPRUczAiM/0hRHGhvjlOwljjj+LG7/GYmtRc0k70MgsG0988+dPUcBasYCd2bHOn748a52Fd/9coMk0fwkSZmkhgkP6CyhgAbufC3jPveAEyV/BW++9zbvXnmf733ve3z1219nZXWVja2zWCG4fusWB70eRVGwtNil1YzxPYmuC/r9PoPewePbZYXAZZAdyerZLp1Wg52f9zh8f4Qf+zQaTZT2kSKk01jgS196nVajzY0rVznq9VlbX2dleZkrV67Q7/ePlbQlcGMW790T9nlADNGCZOvlCFvDjXFKdopIX1uIuLjeYjxKOdwbsb7S4Hd/+xVKbfg3//FtDmvNNy4v86WzXdK8mPmpulO5P8rZfvuIamZ3ODfXaAEXVxRfveARLTVoLy3QXVvjy7/1HaJmA08JhNXk7xVU+RBfQkNZbFlgxgN0aahqS1rCjR3Y7sH4xbQKnNwa8/xQkUyZ1Bkyn7ISKoySJNqgIo+FxQ4qaDCaJJi84PpHV/irv/xzssMdjDVgLNM0JQoD1pcXicMAo2uMNWRmyCQ/qbacTCa899ZbgBMbnV6jHyd2rIqMSggarS5xq0uzu0h3bRPPDwgaLZTn0W60wMJbb/yU3Z1bLC4tsri4QDLqc3jrJkL4ePES1lqqdA+j81ObnZzT5TlSCGLls9hd4B/9wT/kzLkNrrz3Hge7u0ymQ6ypiUKfxW73mAHW2jAcO0L1UZBS0uk0iQOfejChqJ+s6suRvfPz90Qvfe4gpdMf3MdNwv1eCBpxRBSGmDrD9yFJctL08WLbWltqDZ22z+pyF2EttiqdeY8fIIRgMk0pyhLpW6RnXdWC9MhLzUE/P1aySym5dOE8Z89u8cGHV/kPb/xyVn1gQHnYRgeCiGlpKNOKxcU1mo2Y5cVFVhaXKKsKE7zlvM2reXztEitbiyHNZkBVW8rK0p9WDIsMhEKFTawQ7H/wFtbUWGOxdl4+dGoAzNd1YbGmxtSGoiwoqxpjLNrw+R8wzxhhu0F3dR2rBVUOcdRAej5lWfFXf/23HO4fko0P73iN74d0l9YIwpBOt40QglvXPqTsfzwiXSlFo9Gh0WxghE+Nx9rmGVY2tsgSV/WwvLDEua0zjEcjdm/eQinF2vIySknm5jP+ZITCUtQ1U63R2jJNSkRaMhzdwPM8vvWtr7Oyssqt7V3e+eADOp0uW2dTMIbGYhevEZHtHlLnhu7KIgsrC/SP+hwdHOELn0bYQGiNmO2RXuAFvoh4Loh0AGY+SlbrmbrxbunL5wOekrQbIVIKtHWNPrK8pDrdbb3UMMgweFRJBqXG1uZTFRpKqQj9EGk92gsxfuhRFBVGm+MIzNYGUxmkknhR4FTGefmZkLuVrhlkE2pdU5UVptbHzfu01mitXc7FwniScnv7gKKoHtlA62HYWFvh6y+vcPHyy5y9fJHFdouFxQ7CatIkpCx9VroBG8sBk1STZE/HJmhtmKYFCMizijKvXQXe88tNPhG+OAT6HHPlueWEsRFI5dHqdFB+gAlirDhtOPxp4pRy3QryacJgfx/bLmmIkDLPmE5HpNPJQ0uLHeZyTjhdvgw4v8c0RQiLlJaqek49Fa3B2vkN9aSTrNts6CqnTPpIP0RXzuJljjSZsL9zm0Gvh5EeQvqkWY5MEuJGC9/3QVu0rp0tteefMtacqZ58RVlVZGlKp93k8uXz9PcO2ZlMMbaaKdirpzj+R0MIQdRoIJWiyDLq6rP3lQbodtssLXZIkozDo8EXYh6RK7Ofs+Fo7u/M9ORQuFv1zlv0wZg7yM1vWc1JnfhMnS6tIF4MkG1JNi6pc+0SdPP3V84iLliWSAShHyCFRBkQxlLlFVVWUWnIP8liJGtd/FTiSPPTU26B479KTnKeDzg/sYKmBx1lUZVG1gbxqDE3d/UKOE4+qCWFqKT77BJMpimmBUdHR7z11ltsbm3RbHUIZwr0dqvF0uIidVUd2xcI65JrnuchHrWGeEADZEvQ6IR4bYUtLfm4pNvp0np5geZiE9/4+DKi1V6gHXfxlJo1RQQQNBotgsCjubfnKmxO4UGzjrLga2hZyZofYKUliyVJaUhLqDQstpuc3VglX6hZX1plabHJxuoSZa157eIGq4spW6sLdDsNwFJXrulaXRuksCw2FA1f0AoEvhSs+R5tT3J+02dl1SNcXqC1tUp7eZmo23a9lrIUU1YUtaasLUpovKrEZjUmNZSlJc2dCj0tIX3GfuifZ2hmYtXZ36PQp9OOaLVi4oaPlQphDFrA0f4uRnrUVlEbSxSFrK2tMdYFdtRD1xVVkQOCvHRJbGM01hrquxXW1j5GPPJgxHGMP7NRimJnJ5MnE5TnUdclUnlQVGChzBJMmVNlCXmgqMpiZlkjnALd2tke9Fi6Oz/I48+TUuBHAWEjotnt0uouYqxlmkzJ8pyirFGeTxSFSKnwPIXWhqIyIAqK4uHeR9ZCWWl3LwqB73tobU5ZxZw+rhPl9PH++TmHknLWa9M+0B9aAFK42DLL8nvHzAzWWoo8xxoz83J+urjbaEtV1rM1Rc+E2zUCQa01tba0miGtdojvBURRRJZXVGZENSOilXTNQj3Pc9fwrj2okCCEpc4ShLUEK8u0u13iRhPfD1GemiVvLMr3CBR0FyI6kaLbiWjEHklaonVxnDM2WBeTClxlxj2VqgIhJJ6nZsu9Ob7fhLW0m01832c8TRmNn4X45wuCWQ8YJX3iuIU1El9BGIYgFFpbiqwkTTL0LDkmlY/yI7woQno+UrleYULMG8xKNwakE8rZx7QSmsNaS1XVlGVFkRcoL0DM7v2yLEmThIV2hzCK6CC49NJl6qrCm7UXnRPp/Z6rEJqkKdPjvgPuh+d5KOVRlBXTaUqtDUEY4fm+27oYS1GU1HVNIw4JA48wdL0flOfh+T6e9PE8f1b98Fnsg1/gBT4dPD9EOoC1lFlCOjiiTKc8l6TMI9BphPzaa1uEoc/UGAqtuXrtgIOjUz7Xgwze2KNaHNObcW0m+xiGkk+BwAtYaC2ChCAOyYuSvd0+aVIgPAVKUg4K8qOMoBWzdGENXdb0PtqjLj59oqU3GfCf3vohVhvynUPMpMTeZYOjNWQZ3Lzd5/Dor7EWkvTpvHulEPze3/se/+y//scES+tEW5eRdUUw7VNnE8JDQUPBN19pEfnL/OL9Cb+88nQBSJpVfHjtAIDqVELgBZ5XzAnZOcnspJFR3OH1X/9tGu0OH127Rr/f59Mn0ucNsE7Kh4+29xju77G0ukZxaUyeTrn+wXsUefYYti4KZNv90UxwTJtDnudUpcXOGLn5Jvn5Q83dm84nha5SRrd/CUJg6jvP2e2PrnK0t4tWAXVjBa083r9ylTC8zZe/8jXWVtfIspwinVDpGj/uYMMKaQoEFoRLVu7s7TDNM775a9/hP/8n3+QHP3qD//5wQJkMsemea5z4OM0snhBeELB1+TJRo8H21asMT9k6fJb4+tde5X/xe7/D2+9e4X/8n/4jRfH5l4yG35MIA/6RQaaQbkNx+OjXPRQSZyitcIUxj7s8Z5yUeHPvn4O2x6Xf3iDu+Fz94R79W6fWt8A9gpZk/VxAIw44s7JOww+JMotfWg6vHdC7fkQvgZv947z3s4cGEn1nfnOOITCY/Rzx0Fza2Qa83gVf1VS7KUVhsY9iV1vAS7hIugKhBPGrTbw4QJQgKsjfT0l/mfLee+/xL//lv+Tll1/mX/zzf8H5Cxc5t7nJxfPn2Vxdoz8cEQU+eZ4TeZKFhUW63QVHrDzqGF6BsOPz0uVNQs/n+hv77B0O+P1/8Pf5zv/6N9i+ucfV967RbS3x2sWvEgYRvhWUaQZWolTI+tYZ1jfX6A2G9xDpD0KjgmUNW02P3/I7qNBwaSNl0qp5Zw8OE8FXL13gD/7ut2l3Oyyvr4KuqJMRuqp4/fJ55xObT6irgpvbu4ynY/KqYjjJsdryja2I2JN8bSNgKfbYWm2z0I5oNgOazYB46zLdr/wWXtwiXFpD1zW9t7fJBgOyYUKZGRpFxqQYwTjHHEBZQi+BSQ6HGfQr+Hya6D17FJxENp4QbG6s8NL5DS5f3mJ9cwEhHRl30B/yH/7439IbJVx69ct0l1e4dOk8Z86d46N33uGXQZN0POBo5zq6Ktk96h8rZC0W/QyrjZVSnDlzhqWlJaRUCCk56vW49e6HGGsRM+ZKigYgKYsR1DnTYkx6JPGjBq2lJXRVkgx7GK2x1scZDM29mu76zMCjs95hYX2Zxc2zdNa26E8mfHj1fUaThOEkwQ9iFhcXiaKITqeNNhYv3GMyTSjLkuwh+z5tDEejKVIKGo2YhYUOaZqRJLOJWyrAgnFkexiGKKUoioKqqp5Pq70ZhIA48vE9jywvyO+j0BeArxzpPByMGAzGD7RqqaqK3d1dlJI0myFBoJ6Ku8uygsOycmSzdY0+IyGQAnJjqCxsntviSy9foN3tsrq6xnSacOXKFZc8ySqMhU6r8YDvbfF9ixAl6cENlPRY+PJrXHzpJZSVSKQjH+sCiSZqNWk3Qr72tbNsLDmbECng9vY+02T35I2NRs+rXy24oMDOHq41aRCEdLst6rpmNB6g65q00iil+NqXX+e1ly7xy3ff58e/eOtzkYj5VDCzaItVi9Wlc0ipsNYJEQ0hWWEpkppqXGBnsXnY6NJevYCUAqms6xIrZtdDeeD7eJHECyV1XlFNniyuraqa4XBEkuYE8SGNRkYQRyjPY397m/2dW8RBSHdxiYWFBX7/H/0BUkBvZ4cic3yItZbr16/z0UfXuL2zTW80pJ4lMZVSLCwsEUYRg+GU0eQaKvA5d/48nlKEvk9WVRwe9LBG8/KlM7TbTTJrKa0liiIWlpaQQhGogLIoENMxj93F9wVe4HOG54tIB0xdUec5+jlRxT0pfN9jdXmRRhzQL0uyqiYIBnc+SVvIaghK9Ch1NVvPeJKRSjqCXpvZ/O2hlHLq7bpGCkngB3iBh98MqGpNrSXpNCeIY7wwYOQN6eVHBFEwU0Xw2QhsAV3VTEcTd+7yCqp7FfxzAUlZacry4Q0eQwkN36kx5SxjKuRM+6VcNnZlocVCt4NsNZFhiBCuVK7WhrwoybMCX1qaocD33IlRnsIPfefDKBTWWMo8wz7Eq89ae1zq6RqBSILIZX+LPCfPnuEWT84lDJ/Pio/nB6dPngDpIzzfKQOkwBqDvcsH+/Fw+gZ7NhdIWPcwuqYsU8oyx+gKa/QTBMz33vjuXpirpO/aCInZZHH8/p/1YPuYn28t+gENNauypCpLCBtIv4M1hjyZoMucfDoib0Rk6ZQ8TSiryjXpsdIpTYXzUlSepKoq8jzD6AolLGHg0Wy3UbYgG5rjQP1ZQQiB8n2CMDxWoAjxgNrpZw0hkFKglCIKXcO2NM3uUCQ2GzHr68vs7R/SiF1ju7Kqn1ty4HGwsN6B2mKLFGtqCu8+X0Y4dWHciJBCUpSFOy/Ht9RdCr65//cpK5Z5Wf9DyZT5PvsBELUgaCiClo8K5UnOUAIR0JSIlkS0QMTgtyVhqOjEPlEtSfaH9D/peGFOnhecqPLhhDA/PS3JU8+/DwQuIFYGKA2iglBAJSB50Hk63Qd6ru5X7t+kEohQIEJ3UYqiYH9/n0ajwfbtbYIg5FK7TTOO6XQ6M9flmQpeiOO52VMCz3NevPe9lrMKAuELpBAIA3WqKcc11OAHCmEtZVJRipIyKxFauFgKgZSSMIzwwwAv8I+bST4uhAFqi84tQlo8685bw5sp/GOfxU7MwmKL9dVFyjxnkE0RUtOOPGrPcpBasqwiyyuysqbUBiMFvlJ0PJ+mJ1gMYSGAhVCwEAmi2CNshERxQBhKZCAQ0lUkCClASKqqJs8LSAtsWsG0xmaWrISDFMYFDLTLsXw+dxzPHvMhNhNj4nseURTgKYmpK4RyfsplnjLoHdEbjlk7c45Gq0UUtWm1G4yWV1heXcNTgtHRLsZYqrKYJdofPYHLWSxuzMPa9939GomnFMZajK7RVUmRpadiHMFJyY1rFGFq19BRev7x+wgx+3zpI5DUdYW9W+ErBFIppO8hlCTLc6ZJSlFWVNrtEbQ2s2SB89T2lEJKi6ckSsnHInqtdfe88hRB4FNVNUqVjpiTyi0JVs2O+VNauz8GhHBrvpQCJeVsubr/iTj9r3WtH5mIresarSGKPLQWT0QGe0rhKeUq443BWEs9I9LtLJEphHTz6yxGcu03BEpJoigADNWsurwsK2SWUd/HWlQYA8JiygJERZVnVFlCNVuzjK5ptTsYU+M3IuJGSKvTptVpziqvDVYoytpQ61P3x7EF0Z0ZcSmU68cWhMRRg6oqmSDQ1inZxWyPIoR4dPXTryIsWG0wVY30JUEYIaWHVL5LDCKxnFQfK98narQAg66dB3lVlk4NbvTx81z4/+ixf79RrLVBVDV5liOQaGtQvkeeZRR5TlkU1FqjjcFY49b4OEJJN34FEMxU9UJIN5fNLICkVKgZB2KZ9Q7AEewgqKrK7XmqylX7Mos1Z9Uyx/2nrMVY9/kv8AJfZDx3RHo5GVPXYPPp85tOfwgWFhb5u3/379HtNLmyv0t/POHm9pjbO/17n5yU8NasQcX02antVKBorzQQUjA5StGFZm3zDAsrq/T29zjY2cb3AzqtRZZXl/nKN75GGMX0D0aURc0rl19la+Msf/k3f8W/+5N/T/+wz433r1OV5XH50qeOpIb3x25VSY2Lh++en2cb2OPej/fBPMT40qLkH5xXxIGk1fTxPEnYDFG+j1rcQjU7rK5IfvbzN2h0lumuHrrFrSpIphN++osr9A4PuX7rkMEgIZ9dv5XNJV7+xiVE0EBHq+Rpzod//TdMjx7dOFcpwcpSk2Yz5Gu/+RucuXSBn3z/p/zor3/ydOfsfmgGEHuQVG78vcDHgCuSFH6I311BS8l7v/gRwtZkaUpVVZj6Sc6xx51T8kMG8kOPqeR0KLZ+9iW2Lr2E0QVVNSGIBC+98hJVUXLj+g2S5GFJJz1Tos/+fAcM923DJwQELecPXhbOluSRpu5fAJQ5ZrDN/NxXUvLBD4+4HgYYFaFV6BIY1mLqmnI6QUpoLbbxPR8p3ebv5z/5EW/94hc0Oqv87t/5bQ53bvLTo+vkTzSWHo2w0WDj/HmUUiTjCaNenzx9eALyWSEIfVqLbZaWFvjOt7+BAP7sP36fnZ2Thk2tZsTmWpfJ+VW+/bUL9Ppj3ruyR/4ZVEQ9K/xvvvdfUuYFv/zLH3OQ7lLIivQ02zsjRduLLb73936LVqvJu2+9zdHBITYSEAiSMmc8TxTPFzQfjhuHFhCEEIZOeZs/XVEWBktpKqQV6Ng4T/SZQbVcDfAuxhir2U9zgsqgkgGdOmZ18xXWO6t8+GHCB/s7lPoTUqPP1ecZsIfzKl/BqeVHQAKiKZAbEtuwGGHcc/e5n8CUwxTeLGHNhy9F4Fv3M/HhFzkU95u+SpziHSB1xFc2SBBeRuNMg2A5vOclu7u7/Kv/+79ic3OTf/4v/i/85m98h5WFBbrdNlVVUhUleTJhe2+bo/4+ywsB6XqLw15Gej+PnMp93yIvuZHvohBMdwvMwPDmD3/O0f51BnspBx9NUMrnjR/8kmajxauvvUan0+X85Vc4c2GZqs7YP9xjPB09NnmZArvAYFyy99N9AmHpFCWBhZUmnGlbLrZzlr0RnrZkE8V0mHD9/Y9IpwlHvZukWcq7H03Z7xdMdUGiC5bW25x/ZZ1WEHOusYZXVwyvvcVwZ8R4f0grVJx/9QznXo0pRj0O3/05frNL99wrSC9iZXOLammNwds/5/DKB4hBhuhnCAwK2C3gfz6EXgX7ufseH6/TzRcL86nEdRVxGB4d8uEbv6A0hlGecTgcc+v2R0zykvPTMZ2FBRpRm3YQ8epLL7O1usbNj64xHY0Zj4aUlXHkIQ+3KFNS0G3FeFIySnKK6tF7DmMM/aMj8iQhL0uKsqQsy7vI1Pm3Evd8fpVnTHq7eF5IZ2kdz/NpRCFSwMHebaaTEzGL9Dy8IEKFEVZJxmnCH//JHxEGIeP+hMWNy5T2NqNJQpHlHO4e0GjEKOOSPHmaUBaZa2r6EEgh6LYiQt+jtdAlajTwwwYqaM4sFBRKSuLQxxrD/v4+WZadsn55viClJPB91tfW8DyPwdERWZJSP+B4DU5f9iQNU62de6OLx240CrCxvMzm6irZdMpwMKCoKkZ5jlSSxeUlwsA/WW4mU3758zfxAp8ojghCn+5Ch1rX3NrZZzLN8PwdpFSkd1VE21pTzqxTrDYYBO/87V/w0S9/chxGr2+e5ff/l/9bdF3RO9jDk4aljUWarYCjoyMm2YTtYcWHexllbe6ztp7OkAtarTYLnVVazQarK4sk6ZTxYICpNRqX7Hnz3Q+4duM2kyR5oUa/D4aH+7yXJnRXNnj5698hajRptdoYY4mWllHTDJNW2HxKs9ngzLlNsixl++aYqswY7vUxdUmR51DX1Bp0Jk5IZwQo5SycxJ1Eup4R4ndD65rewY4jwWfWLnO7qNF4zPsffkBVFvx//vUNAl/xnd/4dVZWlomCBr7yuXF7m5+99TZVVdJqdWfX3c6q0QR1rVlcWabZajGaDOn1+tRlSTFNji12AT66voPnK+LYIwgV06RkMi0Rs/+sNVhTI+UXsuXhC7zA80ek26pCk8IzJg0+LQSBz+rKKguLbfpVgVGKIAzu/+TawPDZW7pIKQgaAUIKpMrQCIIootnuMB2PUErheT5RENNudrhw9iLNVpvl1piqqPnal7/BpfOX2Ts64Mfv/JSqKCnTnOqzJDAqC6NZQP2gyXiuyLM81MlBAN1Q8HJX0YoVS90Az1dE7RAV+HhrHURrkWGl2d0/pJ1psP5xyWiSZAwnBcNpQVUBRiGFQilJox2zemYVEbWpog2ScYIX3LuZvhtKSQLfo9OO6XQabJ1d5+JLF7jy7tUnPVMP+NJOoigiH9EMsbV1Cq0XQdPHhHWVDL7bzIz6h9jqae9pCbhgygUzGqx2jnZPlNW3nA6m42aTpdUNsnTIeDBBCknUjKg8f6YyeNR7PWgj+yBJq3AkugpAambdi7jXd+ELBmugPNk4GWCSz8ptowWI2q7ixHMBtK6q2XxlZw2cnWdlf3RElmZceCXi5a+sYPIJXhAgc2+2Sb77vM/ubSmdt+exvc48HL//OVdSEjdcufB0NKZIM/QDPEifNZTvETdjOgsdzp7bBAtRFB4fl1SSMPCJo4B2K2Z1uYMxBk89/6q7h+HCyhnyLOdWs0MSDRmqu+6v2RrmhR4r68t0u222t5sk2RgTgQktRaEcHwQcC6EkLk81Oz2eJwgj4ZTr2RPec2Lm46qgNppK1xhpHVnfADogFyXeWogpKvLckSFJVaCERPsK1YgxfkhufayweL7zHJ5vCKVyB260+Xgs+/z0zfM/c8Fr6f5NxE4VTjw7duu+F/repa/QMNTQMu45voDOTIg/39zec6SGE4ucWdNVnWqQGr1k7jvl5XnOu++9y1HviGQ8wROSyFcECspSOWv1XJIXOVVVEkeKZiOgP3xAZZoFKpf4SEYZGKhnzU4Hhz1KOyTtaUZHzv7miEOaTefLbrVBKuU2y+OCosyp69pZTj0G5unRojJM+zkBsAW0JCx3BYtNQaRqTJVR5Yp6GjIeJ/R7I5LJhP39A5J0ys1bKdtHFZUPtQ/RskW1QsKoSaeziMxLelpQpjVeWVIqy9pZ1wxQlznV8AhT1eilDUQEYauDP0suVWmGHhfUgxqpQAbQy+FaAkf1CzX6gzBPk9fWUmtDMk04Otin1IZhkdOfTMmyhKLUVFXhemtYiyck7VabdqtNMp3iBxFKBQgxL2l5sOpVCKdUDj2nEPY9Ra2dQvihBN8pj+w0y8iKB1Vx3j+OskZTlxopFH4Q4QcBcSNECot3l7WSkBIv8FGej7GWsirZ3r6NEIJG0CCMWnizBpVGa+djLARlkSOEROt6Zh3z8HtMCAg8RRj4hIHz4660oKjcHs/zFJ6SNOPouJLr43jMf5KYW0VJpYiiCN/zGAqB1vruSOaOv1t44pCxrp+crYujiKVOh5ExZOOx81nHJTP8wCeITvZw09GQZDxCKIn0Ja1Wk0YzotaWaZoxnp6IEeZ57hPVuMWeSgxZYHS4z+jwREDQ7XRZ3diiriuKPEeaCuFFGKkoNKSFJsk14+zO6o4HRXue8oiiiDiKacQNjNF4SqGEdPcVluF4wnA84QXuj7LIKYscL4gxxgISqTxXGBIEyDBC1AGm8vH8gDAMqKoSbbTz959OMcf7QoHV9k6XxnlV/KxScx7lWmsxDyhdsdZSFPdXSZRlwXAwYDIZ8fabvyQIPC5dPEcQ+tSRIfBCRpMJveFgNt96swqZk9FjjcX3A+JGgyRN0NpQla6Pk53vQyxMk8y91gRgPMqsoszuXFE/B8UyL/ACT43njkhHF2BreC69dh+NLMu5/tF1lsZdVOjRWegQBP6jX/gM4fkeCyuLSCWZHqaUSUVvf49kMqbRbPGlr3+ds2fW2Fo/w9baJpe2XqXZaPLB6H1GyYjd/R2SMqUUBa9/9VWksbzx1z+n+qy3G48oRXc7uoc/z589YkIacoGFMOL8+iLKs0yKPmVR0rt1hcxKfrSt+Pmex5mNFb7y6kWarQ4bZ84TxjG/9Y//KZ7vMR4ckKYT/vIHbxD/6JecffUVvvL13yHVlmsHE052+A9Gp9Pk9S9dYnGhw9e/8VUWFroMjebW3gGj6TNQiEoJy4uIOGLtq6/QPbtB/4NrHL3zPuQlTLIvNL/5ScNUOfVwF4vF6qe/R0QYIsMWUaNJu9PFGE1RZOiqJOvtoh+HoBcKP15GyIA672PqjCD0abdjGrGi3Y6YDkdce+dtsiQhy55SrvpQSPBa4MVQ1jiG6QtOoj8KVQqmwgiopDi2/TEWkuGUXKVk4xTleXQWFllZXKYTh3g6odUIufiVX2MyGrN37TpFmuDYuxrCDoQdmt0FljY2KKdDjq686RpOxcsuoZEPZszanSjynN0bN5BSPmU+zeOEwX3M9XqWK1paXeCb3/oa7XaTGmd7URtXgvr1r1zi/Nl1vvzqRXwvZmVphW985SvcvL3PL9/Zdo2ZP6cY9YYIC9/7te/AV3+DP/zv/5bDm2+dPGFWhDLtJfzNn/6QqBFCFxpnF+iPB4wmYyrqO21W7vY5B1ZWVtk6s8T+3pBksv9kZe4dReu1EBlJDpMxNoVUF46IntmYxHGT1eUz5EnC/uEUXdUc7mYMbUmevEW7/RGbm8v8t//HPyApKwZZRm804o133kdLOPOVV4iaDXb+9hrjax/Tk78EDnB2Mx6ONO8BE2dtZzOL1K4ppu9JumsRVNDvFxTlybj1cXbjVsNeCp5w4nZroaPdCJ9wksMAnJR5Z/bnmtNCQIo4p84rdK9+4NQnhbNjUdYiDIRC4YURstWmXltH1yVb586g0RwOSib3qSITAXirOAL9JpCBTd3vsqGhzi1VYe84hqLI+fDDD9jZ3aW1ukqJodVusLK2SXexQxQJigIe12HR1WadXIqpFHzrzDKvbTV45/aI//j+DzHKQ/shZVEz7k/QVYXJxpi6YneqGeMUqMZCryy4XfeopOCVTkyj3SJ6/RuY8Zjee1c52DvkpUzQ8j3yIqfo96iiPmMNfqNL+zyoqMXqxVeRxmf/nfc4yN+jzCzpERxWkGl3vL/Cq9IjYa3l6vYh/XFC/OE2zZ+/z8rKIl/96ssoKVlsx4RFxXRwyK4xoF22bR6C9wcjBod7TEYDdJVy/1JSh8D3WGi1UFLiKYuSgvPnz+OHMbu7uxwcPriZhAXysqSs62Ov3/vDTZbC80BKPE/OiHKDmPUqqcohulbocmbJVN65djbjBhsbm9S6ZnCwh7aWMG65xqJtULGl5QtW2xGNOKYVh/i+Ik/dTSlrTcBxzvMeSAHhzP7FCkENRM0O3cUVajtgkhSURcF4MEUpRdVqAs7a5HmBIwTv/YZVVbG3v49UCm0MQaPhyqbqmsBTREFArTVJUYIQhOGsUs9o7KmEa20M1eN2B1YgPOF4v/L+r5FKoAJFhaGfZVR17axdas1hf+js94RrfFoUJUXlficqQ1ompNVtrHUe66cRBoIolJSVJc1nSeTZ7x5E9+/dvsGf/+G/wRhDOp2ANVx/xyfwBGmaUpYlg3EFXsd1KjcpSglazRAhIEtKdG3QCCyCJJ2gdUXW7uIrMFqzsrRG0co5HBySl59uf7bPM5JRn/d/9tcEcYvFjZdRfkxdWTqdLu2NVVqxT5GO+eCNn1GbuxsqC6TfQCgfU+VYfeq8W+sWP6XwwgZSKeo0wdQuOflwScy9GA0HvPf2G67CrSrRuuaHP/wxb731Np7ykUKxd3BAkU4RAspTzXmFlESNNp4vZ7YtggsXLvD6l79EPhkx3LtNkiZs725TliV5XmG0Jc9ryspQVXOrICeyt/ZeJfqzNzB9gRf47PD8Eenmfh2nPz+oqoqjwyOsremc36QRNvC8T/c0S6VotBuu9G/22dPxiOl4xIWXX2bz7DnW1pdZ7Cyy2FlidWGDRtzghrqB0COGowGjYoKmZuvsJoc7B8+H/96jZtz5bu4hmLu/+Pj4NIn9JsvdVYTUZMWIos6YjnqM8px33oM//xBeuTBECMPi8iqivcZK2OY3v/5rrKwuk076FFlCv7Dc2t9j6/xZzp57hWGacaP/wWN9rTgKuXzpDOvrq/zar3+bxaVlvv/mm9y+fZs0fwZBjpTQaiIWOnQuXWLttZco84Kj2zfdijbNXyjTPw5Mjc5GH/tthO8jopiw06W7voHWmuk0pcpT8tHhY8nmhFCooINUEbpKoM7wPDXzcAxo0qIuDf1en2TyCSlQhAAZgopnUoRfcRIdQJegy3umKGuhnDUcy6Zuo91utmk1m0SBj7IlYeixcvYSQWtMb3tAkc4pqxr8GKIFwqUtli68StLbpX/9PbSxELRBhTAbB3ejriqGR0dIKWl2uij1pOuUa7L7WBPv6ZcoaLZjLlw4QxAEaCx5VWGsRUrB2a01vvGVy2xtrOKpgFazzbkzZygr+6mvpc8a2TQh9Hxeufgay40uP/3ra8ApIn12KotpwYdvX0OFikvfvUh3qUud9JnmmVvA5kzM6dMxI9WFELTbbdbXN8hSgxD7TzS9q4agcSnEeJb+zoQymzXyDGafJ3CNy1qLeEZyKCWVsUyGJZTQz7eREfyvvvm7fO9rX2OQ52yPx9za2eOdo6sgYeVbWzQXFhh8ePDxifTTDHcHN09OcSS3mZEnCpQPgZR02wHUgvG45HT/Wg9nNY+BkXFft4P76vHsbXPuItIrTqxdTkNA3a+oTeWO5QGYu9JKnOeulBJP+hBGdDpdkmTMwtIi4+kYz9+9/3t4oNpgC6gnYE95lFSZpbpPRUJd1+zv7xMMhxwc7NPodmm0GrQ6XeJGAz8QaI2zWXxcWwUcTTrGJQubCy02Nxb44Y92+aOf799hE8LsO7dxMVnCjNSeGXNPdUXfTGmIFjIOCFRMY+scdDKO3t9nPDqkLgShktS6xI571GlCbkPqZkJj5SxK+XRWNpCiweRogP7offLMMpzAxLhjeTzH7l9tHAynHAxPBvHLl8/y6msXkVLQDH0EUCQT6srQbG8Rt4pjIj1JMpLJkCwZc18buFPwlKLdbKCkoCxzhBSsLC/TWVhiOp0+lEgHqB6LSJ5lHZWaWbR4BIEHaIRwo0HrlLp2/C6We3yuw8BnaWGBJEnYHt+grGp0V+CHESaKkIFHpATtOCCOfKLAQ0pJXZau8akxd7RzuPcIXZ8kR6SDFu6942YHf5ohhEDXNdPJBKUkambH8Dyp0cVxVeUJrHUVd6Px2Kn3Gw38IEBpjalrAqVoBj5FLUjLCoT4/7P3ZzGSZfmZJ/Y75+62++4ee2RE5FqZWZVVxVpINpdmN9nT+2BmumcEjYARBAjTLaEF6VkY6EWAIL2MMICAmQcBgx5AvbKbZLNJNotFVpGsqszKzMrKPXYP3xfb734WPVwzd48IjyUzI7Myq/2rsvRwc7Nr184959zz/873//64nlu1g+JIph1YJSgfc+RWGaOiykIS9tguWGVvSzSWcVEc2OMYaxnG1brMFZNyZnZSXqI6EygKRsnxGfSeK6gF1Rw/rSsrD9957DcY9PYZ9O62Ar1z74ucRvVAgEmr7MLIRQqByspKizg5fl6k5EWCwBA3WjjSoVFvEgYhvVEfToj0x0aexmzd+hA3qJOqGl7UQggIw4jZxRXmFxZZv/Y219/6McJ1CWfn7nq/cHyEGyGM4r6SR9YgrEB6Po7rodKk8hyn6jMfJc8iTWLSIzaNWmuuX7/xwNcfneGEdHCCGsKtshUA5ubmuHDhPHF/n/1Q0B/0GI/3ieNq7JbGUpQGVRyeZVW34ZBIv3cd8RDTgBOc4AuFL3ZU+jmEKkv6/S6eD0vhRbx6Dd9/gLXLk4Ynoe7AjAdtgfDAPwV+BKoLZgwqS0j6O8RuwWDDx8sLPnzzNeqNJr7rsLi0xLu3rnJne51c56Rlxsb6FjowVdSR8YWe/aZL+aujjH9zq0trZ8z5vRiEZSfpkaqCUa7IFYiZc/y1Xz/D01cu8q1vfJVGs83C0jlqjQa1xjzIGl4okU6LF176Bm7Qopcbbt+4TVwonNKhJiOWOk1qWYtenJIescdptxssr8xz5vQyr3zlFWq1Gteu3iDJ3uUn719jdXObnbWtT/ydvSDgwstfpnHmNKYRMOwOUArCxgy6lJSM+EJf1J8T2CLFWFC+Q5F3MBaEsEh5f9AV1sNKrTqBKjXxMMZajcpGCJlhlQY8dtY3+an4EcJ1Eb5PPBhUhWI+tS+iIe9XKmz1pBfpH1Wb8Qg8Kqo5eJ3ErQdIxyX0IhzHJRkOJurwe0/RAbeJ9AJOnT1Pq93G8zw8z0VYjUAx6O1x472fUpbFNKuTdjMgCFx0EbN++zae52OfuoQrBe3IhdzBkRkVQzghDYoM7IB0G7a1Joo8Xvm130SVig+ubpAkGYuXnqNZ99lfv0V3Y/W+07WTtHgpZaVkf2xMaShBJQfWPHK3Z1IAsrvd48c/ePPAWqgoCoaDEcYYrt5YI45ThFPn8uUrbGzt8R/++C/Z2tknTj6NDIrPDu++8VN810PsxMzV2wQ1yVd/6Tl2Nnqs3dy6L9iw2tDb6JFmKakscBciTKEwWXmYgTXxYHCly/PPPsNCc45hv8+HH9ym1x09tho9bHk05gNkB4wp0cpgvQnxMJ1qJmR9Ohix9f4thCuYP30anZb0ujuUSQ6bVVKhuOwhWnXCWsBsPWRgCmTbQ2CQvsT1JeFTLepqgWIjptxKPlnjaipSe0qiF9Bp1+ksNpGyUkdF0uVM2MIUhr3dgjg57O9NH874UHNg1oNcwZ0RJBr2qHJAHttw0E7OQ0/O5biXGEs8jhn0h4T1EC/0DgywesMeb739Ohubd3jv5nW2tjaJy7RasU+HXQBEYAWoG1Wb27wKXpeXXOp1ye6eot9/cAiutebGhx/S73Yp88oTuNnu8Cu//ptsb+3w+qs/IX+gTcaDobTl9Zt9dvs5V7fiY12xhYDQB88FMQtFDeyE6Dx1cY4vX7zC6dkVLj/7EoFx2El+Spok+E+dZrYVoSPYurEKusAvMlAG299BZwnJ2g3KRg8vnKFz/jSnR8/imIzxVpfd4DYy1rg76sGuZZ8Gphtg03X0FxS93pAfvPpTEIK9wYDSQDTr4tUk4/IWdzZHzMzMsri0QqEFXmsBX7iU8d5DM/bq9QaXn34GRwr297cpVYnjglIp5omJqyY3eqUwxhA1QjqzLfIiI0nGaGUp0vIu9fPU7swRAkeIKmgucwIJK/NzFEqRabAqR5oSaRViYsUnEDjCQQpZdXitGccJaZZjSoUvJdpatLVIAd6kyGqhDdLayhZPGwpVUkysIuSkoH11fIuDQQpBIC2uA525WRrNBr3+kL39/hNqt/shpcR1Xay1KKWw1uI4DmJSKPler/bqPiSQ0kFKh2ajSa0WoRoNjFJVoWUMIhcg0so2pygnRTANTOzvrLGVUOAxYbWtJu6H6Dl6vQG3WEVby/kLZyiKgkF/QKk0WVFijK3ebqFWj6jVItIsZzh8eMZwJH0b1AAA5YlJREFUUVpGSaWen360maxh5fQZ4SKEg7EK87CC8kLQmjtH1JwlHnQZD7pgq4hWa8M4rtaShTKTW0R1fEd6uI6L1rDb3Z1scFi00RTlFze772cJowqS7h3coEZr4RxBvc2wu8Fw+yrD/W2sLbBaUY57k0zlKl3NqGxCoh+/krDGkqcxUjoopT4SeX4ULhDIKrsrO37v6AgmPn7YySLCUGQJRinSNCHNaty6cZ3NWzcosoRk0K2svAqNxEEbQXmMQ99UZH9cwXt7z88TnOCLjBMi/QlDqZJubx8/kERRSHNm5rMj0gMJs8EBkU4A3lkI25CWFZFeZjFJb5OxTekHDmYw5F3p0OrMcOWVrzO7sMB3X/0L/vJHrx4cdndvGx3qQ+/Rz4/w4SNDTR4fjHI+HOUEwAzVV+txGCgLIfibv3mRv/Fr3+TZF17iF3/lN/D8ENetH1RtB/DCBl4IL3+lzQtfepk/e/VNvv/Pf4fSSvzGAnUnYqXToqUSCqXuItI7nSYvfOkSF86d4xe+9gtopfn//tN/ytVr17h+fZ2dnWMK1H4MeEHAM195haXnn+O9q++xvrmOUhA1ZykyQym2H32QE3zqsEWKLVJU4JHnKUI6B7v69zLpUT1kZrFz8HuWZGRxiioNKhtQ6fwAXLbW1tlau4UIQ2SjgVUKU3yKNSisgazL3Z4TTwpPOCnwaJ7tQw4nHInbrOOGAa36LKEbYo1+MJEezOBELc4//y1On79Iox5Ri0KELXBsyu1r73PnxocHRLojBbOdkHYrYH1rxPbaNq1OB2stniNo1zzIHVyZcZddVJFCYUjjMen2LueeeZqv/5f/AG0tG/v/jCzf5vSVL3Hq3Fk++MF3HkikFx+rIuXU0iWcPAoei0g3sLfVZW/r+Pnt/at3+ODaHU6dOctftbC+ucu/+YPvMx5/QqL1c4CfvvYmnuOS39lnrtEmnJvnG7/6Im+9epX1W/dbsBht2V/vwh54l2u4ZyJ0L8eMy4rZ7VI1eQpe4PKV33iJ57/0DP/u3/4hr/7wrY90blHbY+npJqWnGNghWumKSHeoCECHirgvIOmNSIYjmkszXPz2i9jSEr83oFQ5rAMDAX/Fr4h0IZgTgn2d4sx4oEqkL3ACSXS5Tb1t4LWdT06km0l7HEEnaHJp6RRTcWTd87jQalFkJe9+uMPRsdTy4Wyr+rkUwW4OP05gX1eC94fraY/BmIeq0a21jEcx/e6AWVcSRD6GilTbH/T48U9eZW3jDu9c/5D9bq9SyB5NAJksXmwK6joH6zLHgzNnXJYWXbS2DyfSleL6++9xU0qCKKQ506HdnuXXf/Nv8+G77/HOW+9/LCK91JZXr/V5nf6B8vteSAFRBGEIzjkoFwRWVHYWZ84t8LVLX2F5/jTPPP8KJi/ZvHaVkYDg8hmCy6fR3Q/YvPoBjdBhvuljRUGWFmh3QOJEOI0ZOi9/m9rZs3hCMdOsMbx9jbbeodzL8HoaHtce4knAp5LgTwoCf1FZhP3ekO/95U8OfpeOR/NMG7/lc2frBnCDS5euUKt3KLTE7yygpIPKBg8n0hsNrjz3PK4juXFDkiTV4FEqxdgnSaRTnYcW1EKP+bk2w5EkTWOMMRRpjtH3XxxHCHwpca0FVeALwamFeUqlWN/ZIyuOEOlM7Q0OiXThOBgDo1HCKI4xVMcrjEFbiyMEoSMquzdlsFoglUFIQ1mW5EVeeXdLWcUgkzlNonEQhNJiXMHppTmWT69w/eadT51ID4LgQGk+JdKllJRl+cCip47j4boerWaLVqtx8HyWJ8TJCGEtdlKkMJ/ES9KVCFndD+0x1+ahmGYlPQT73R7DXo/l08tcefYySZJwyyjSLKNQGmP0AVFYr9dYXpqj2xsyGiUP3ajOS0te3nNPn1w4j2oOFNID4aEMDyXShZB0Fi8ye+oK2zffZNxdZdqftbGMx8et6S2O4xJ4DZQu2N7bwU4XYSf42DC6JN6/jeP6dBZPUavX2bz2Prs3f8rBxK6hGN+9Y2pV+tBp31pDcU9M8XGiKFdUdUqUhUI/grIREkRANVCqcVykMaVMSZOEtJaytrnO3trqQV/3PZfZTg0pHNSESL//u1RE+gO/60f8Tic4wecVJ0T6E4Y2hixLydKqKrv4LGcLZWCskKElEDUC12Nmdp6wFqGwmAWDzAT9QYyLw35jAEIiMHiuJIoiGo0Wzz39HFZIbq7d5oObV8mLotrW/AIbSk5vRjPNBu1GnSRJ6Q+GSCo+Yvqa0HW4ePkicwuzfPmVr/D0cy+xfOocrjctmHR8yuKo32ewt8bmzRusXb+OES7tuRyhSlAlgStZmuvQbDXoDWP6w5g4SVm7s43E49btOxhj2NraZW+vT5Y9IaWAEOiyZOPdd4gHPfY21ol7XdRgRNkfoobDE1uXzxlUkZH296oFDmDKAnOPUthqg87LqgBMqSjyclIEZ8qyTJXCcBA8KoXJ80OZwKeKo5qDT5jEJx1EUAN5mBBt8wTUExgjZnJIb/JTc0iq37M4vEtZIR6yvLUG1BiTG/bWPsTkfRqtWaJGB2EV0hYMRykrZy+giwzfsUhhCf0SKQ2Neg1lHHSRcu29dwnCGq32PL4fcercRRrNDns7G8TjEYcl4apFcDLc5eqbP8ILQi5cOMPZs6e5cOkCs/Oz5JcvY7IRo94+O+t3sB+pgO2DMTs/z+zCWUbDfXY2r9+Vgv1xYS3cur3OH//pD3jvg+uU5WcpHf30EHctjjRs2BGjmuFMs02z7hE1HGptKHPIU+4ePhPuSeQWpzCYxFQVEgsON7YnfdYoXXlP68e/tq5fPaxWDLYybMNgzzpVlG/NXUVMyWzl3eEDIZT7Bf3Xd6GwqI0S+tU5WWtZ29rkh2+9gfA9ZOizvrtNaQuMNKTFGCd1QVgC3yd7ZNHjjwgPcCApM3b3erQaIafmWnjGkuztkSc5TVWw7MBc26UZOZxzNUuuwhWV9awtIbKVb7rDYU3TJ6XhK4qCN958gzJXfPUbr/D081fAkeA61Bt1Lly8RFCLuLlxm9beHmu3thgNksP9q5JDTxQDvi9YWQpoNjy+9PQSK0s1iuE2arhPlhuS1KAs5Ob+2dhay+7WFh++8w7nz51jYWa2IrUfcO6LCzPMzbboD8Zsbe9j7eHW7XT0T6fRpu9R9z2SUtE74qUjJdTqUKsLZM2lDCQGF4OD7/gIKruKcTzClhrpu/j1gKS7Rx6P6O4NMbuawLX0PLBSoDyNchSDvU10OOZp90OWkhy5t43sb+MUMfVGjWYqaYuSBP1Aov9JI2oENE5FGAUqM+hMk2ymmONYiC8QrDUUcQ9zZN7f3wq52WzR398hH+6hkhH2EaryOEm4evUqURTgBwGe77K5uc1wOCKOn1QmkgQhqdd8fN9FSjOxnkkp0gJV6gfWdQ/CgFatRqfTYq7dxBjDKI6rDDyjQJcMx+PKYsZqgrCOQdIdxriuS6NRQ0iHhaVl2qViv9sljmNqYUjT99GqpJwIKFqtZlWYMwpwXJc8L9nf2ycej4jHQ/KssmO0xlIUCkcKHMfBdR1G4wSzuctw9ATqKz0ER5XoU4LtKHk+Vabf866KlDaCQpVkRVndY6xBa4UrqwKzvifRyh5saEhZFWFUxqA/YRAqpaTRrN9VRLZIUvI0J8sLhoMhFpifn6coCpTaJs8K6o06fhDQatZwXQ85Ebo8cil9XxKlRUqHWq2F5/m4bojjeAzHXUbjR9xdRPWfqDXH3NmnKYqUJB5gtMJmyV0nE4Q1PD/AKkupcxxX0q7PoXVJPBrcNV5P8PFgjCHubVeimnGfT4sgedyj1gOXRujhC6hJSJVhMC4eWtBdSoEfuBijKfMjY9ZakvEIIQVJHFc1A5hwKVIQhhFCCrxxSqEmw5hq2SgnavgH7KV9tnjcrOMTnOBj4oRIf8JQSjEYDIlCH12WCGs+OzI9M5BnOE6Npp2lFtSIzoVop8B9rkSiufmXm3z4x6vE4wxfQq4Vr0hDFLp02m3mFxb5T37jb/DXf/Wv809/55/xo/ffZJyk2JH9GJKszw+mddmeWlni+QvnuLO+wbujMZkxTJ2i6xIaocff/M1f4avfeIXnX/gKzzzzItJxcRyfh+0L76ze5uqbf8lP/vJ13vje93C9kLNnnyL0XJo6o+57rKwsEtZr/OT9W/SHMft7A/q9EVubXeZnFwB47/0bbGxsPVDN8ZEwqfaRZxlv/c7vIITAaF0tTqeL3+MqgZzgZ4oiHlImR6WMdlIl/RA6LylGCWlWkbLW2CNBy1T1Na1IOHmvUtjx+DPaOHmCqxbXQ84sI7yJv4S16O4m9lFBx+PAUDVRSEUOTkXV93gRWDvxxXycPQirINtD54Jrb2wgpENj7imimTNM22SmVePFr36TyHdoBAphSlZvfEi/t8/8fMTsnKXbG/Ddf//vWFw+y7d/9beo19u8/NVfpMgzfvBnfzgh0qc5NgCC/a2rfOf/d4u5pRX+4T/6v3D20hV8BxxhWejUOX/lWT588zX2tzdQT4jAuXD5Mq9869e4/sE7dHfuUJqp38gnww9efYvX33wXrTV58TMudP2EsH8bEJr9zV0cv0vt7BJPzQY0F11mTgmSoaXcuKfWegYUIGODE2t0z1QFLu/JorDWUuY5eZyiP8LGQ9iAegeyccHm2yXuaZfm03VECDgGSgVKVmzj0MCGqdjlGch2Ytb+7EPIKxL/YAPKwuvv/pR38veImjVa8zNkKifVCcKV9EZ7ZCZD4NGo1Um9J1yQPaoe3WTA8PqQp88u8o1LC+hxwu2fXiUfpSzlmqUAfuFCxOVTIWKcIEeaOLPs9sHk0Jl4pU+/1ipPjkiPk5h//s//GX/Q+Pf843/yj1k+u4Rfiwj9OvMLC3z7l/8Ke/u7FK5hY3OdP06/yyhdPZzeUw4tQizUaw7f+FqHU8t1fuUXXubM0hIt/RrNLGa3W3JnIyfRsFMcT6Rfe/ddbnzwAd/85b/CM89+CfsAJ2chBM9cOcfXX3mWn757g53dHlabg3aatpWevHu5UeNyp8WdUcxgr4eZTKCOCzPzknZbMO5E5HUfZT2U9Yn8OghBWRbs7m0jjMWr+zR0k97td+jeuclgI2FtswBVXSvhCGSzapZXx/uMhMPf2unx8rOLLJuCZZPjWMPswhwLOuG0MwI0Yz6CZc8nwOxCi/MvLqMcS+xq0v2UtT/eoOh/Fp/+6cEaTbp/5y6Bye3hPlvbe+hsSLp7rVKAP2Ljdm9vj+/8yZ8wO9PhN3/r12i3m7z9zjWuXbv5ZNbDCJA+0nGYW+jQ6UTE4z77O3dIU0M81NW9/QH392arycryIgudNhdOLZOmKUkyqmwaVI4pMrZ2U0oDpxYWOLW4yHA0Zm1rlzDwOev71Go1nn7uMr4f8sbrrxHHN+m0WizMz9MbDLi5vk7geaycOkUQhEjPwyLY3t5hsL6FKjKKPD1Yv2sL4zjDdSSznRq+77C9u894dQvzhDbKHwStNdmkhtN07am1RuvKfsZxHIwx9107YxRKWeI0wwiJ1QXWlASeSxj4WO1SCxyUYymzamPDlRLpOlhdfuJNL89zOX16iXq9Vp07lo21bcbpDqMkYWNzi06nzdNPX0FrQ5ZmjEcx5y+cZWZuliLPKYpiUq/lESKRaeA51bdMPtFxHGYWz1Gvd6hFIb7ncefOh4zG+w881BRCCGZWLjB3/in6/W3W7ryHSseUOwVM/PyFELTaczTbc/S7O/T2N2nV5zh94SJFnnL7WkKRnxDpnxTWKPbvvEd3TT4RAcmxn/ERXrvYCrmw0ASqnjlIC3bSPuoh5+Z7Dq1GgFIlg1IcqMittXR3t+nt7WLtoVe7C7iOS6vdQUrB/mhEqQpKVTlROQ4EflVfJftZ39oklagCPpub/An+o8QJkf6EobVhEOcEo4T9nR0cRzLXrvH8lfPsDwZ0B32MBv1pCexs5ZU625mhOdskdiVKZgibgy2J6j5+TRDUHMJaQFQPieohvu+ys7PBOM9pzcxQazRRZUHSH5GPK6Lui0KiB1HI7MJcpWDIK3VelsZopZhdnOfc5YvUOh2CRofCWsZUN51IQD0KuPTUU5xaXqHd7uD54cTK5X4FqjWGbNynyBLW7qzy3tWbrG9uU+QFWIktM6TjU29EhC4IacmLFD25+JWiQ5MkGVvbVSGlLMvRH0FN+FBMiXJrUdkk+j7hzD+HmPavCQNlLfZhXolUAUtRKFSpH6I+PcJqHTz1EQexkMgJgW3K/J5geDomPuWtfmuhPFJaytp7mMZPAJdqv2G65zAphnnABE2/mrXYQmEQFEmKKDXqoT7z1XXUqlKM5+kQ3MMgyRcpw75HGbo4DR8pDIUWKCtRZYkqS7Isp8hzknjM/u42tXqTeqOFkC61RptWZ54sjavAevKZ1mjKXJPGI/Y21wgCn6WlBYJmA6U041FCnhdPYC9FUG+1CMMatWYL6TpI+WT7glIK9ZE82z//sEGVaOK1PfzIBd+QlymONHTqIb5W2JqmLCxxYQ6FRAZMatADXSnSj7EistbS7ffY2N4kK1KCyEMrgyofPlaMruJvXYJWFtdUZIP1wfV9jHSQroPQAu3nGKciLxkDMZhEH+vooxKN3teYQiCESykUBoOUAmMUWpWYrsKOQE3S0qWsCNZKXT8Z6tMDTsfqdIxOa+4KKpsTCU7oIRyJpsQKgxYWKy2ZzBkWI0yRkZclSmsaoSAQgvlmwEKzTl4q8nGCMZAUlUe674AWMNaQ2yerXLbGEk/sJNY2Vrl69T0WVpY5HZ0FoQnCgKAWEdVrhPUa0nPuX4Yc6QNCStwgwItCfD8gDDyWlzpcvrLC7FDTWVCMC8PMqCTJFFvbA/LicHxNSbCyKClKRakPS3FKB1xHcvbMGdqdNkuLc1iriUKPxYUZMOYgXs0nHsZlXqKVZqYzw9LyIkO5i7ffwwoIAohCcF1b+T0bDaWiXmsRRDO06218L8AaS39vF1OUbK9vkwz6bOyO6PZy/FjhTbijogAci0yrvYW9TBNjuLU7Iqg5xGgK1MRKAXaHOWNjyaWgU69R8xxKrdDGkOeKrHhy8840eSmKAmZmZkgpyc0Yx3MQD9ZmfLFgzV33FFWkEPewRVIp0R+D1LXWUpYlaZbS3e9S5AVpmh5bRFMIgTdRFJdKP0YdiMkay1qwVRZflhXkmaLINaq0jzxF13EIfR8pBHlRUJRVQUwhZHW7NxYz6ft5WRKnKWmeUyiF63kIx0M4LnmpUbag0BptLUVZkmbVvDRVd1dFBi1hGOG4LkmaVZv5ZYDKq8xYKQXWaFSaImz1uUWpKZVGPaLwqJSVZ7s15v62E5UHe+BVfud5frdNizMthGotSt39fmvtQdaunFQaPK5ZrbUUZYHMJaHvEARRdV2KolKmOxJhHTQGg8VYOznXR1zmx4B0HFrtFq1WE601RmuazZh4nOC6lS1NnhckSTL53hbpTHzftaYoqr8VRfHgfieqcW8fsCSyVAnkhYZQOuC6Ew/HB0Mg8DwXP/QoTUmWp5SqrN4n5F2vBInjePiej+M41aaLUuRZQpHnj1035QSPhjUa+zP2u637ksCV1DyBtJrSWDIFaaEPNq6nEI4L0kHaqiaB40iEMEhh8SRIW/XNKuy5Pxa1gLaWUpXVHHRsXxJ8tnYMD8GJEv0EnzJOiPQnjDhXvLs2YK2fMfed73B6cYZfeulZ/u5vfJPf/973+L0//VOy2DDq2U9NGNqo1/naSy8zf2qerXiVuByTJH3yLGa8MqJ/pcZSe4bL585xanGJs+dPEQQBf/QH/4Kt/T2++dd+i+de+Rp3Vm+z/pMblHmJUV8cBnbp9DJ/4x/+HYLAp7exRzpOeO+9t9nf3+XLv/pN/vbf+3uEfp1G1IEDN8NJlWkhaEWSwJN4tQ6HCcv3w2jF2nuvsnfnKr/zO9/hX/z+nxJnOUopQk8RqJiGAxefuUS9EfH2O2+zsbZBd3B3mupgOOYvfvgGAP3B6LiP+vh4xIL6BJ8DCB+EC7aoHo+BNCvJC3VXUaz78cnHrPQCotmzICDbX0MXR32MHart/imj9SmhLNHd7QkjMSF3nsROpAu0Jz+nexkeh0TdlKzLAK3RgyFGCLpygEQcbIg9DrLhFnm8d/B7vCPZW3UJwojT5y4RRhFpUlCWDr29HoNuF62rwLHf3eMHf/YH1Bptnv/Kt2m0Zlg59yyzixdYvf42a7fev+/zRv0ev/s//0/UWy3+/n/93/DS17/B+u11Xv3zVxnu3vnECj/Hcbjywpc5c/EKYVgjTkZkeTrx3zxZtT4QZ8HxJedemGdmvo7XKNnaX8WXBc+fXaYY5QzkiFGieHcjIy4Or1O5pVB7ClvVrboPRVnyozde4ydXf4KLw9KZDuNBSnd3/NBLko4gTw65LtdxaNUiqDvkXoRjICTExWEc7xIP9yt7l+tUhPoD9pPMPjCGtJ6Tz5TYJpjzGulKrM7RCfRe7RJ/GKOzaiz5IbRmKmuVrFfdvnIDRlCN1TpQmzxGwBYVib4Csu7QuDCHG/mM394nX4uxbdAzsOcPeW3zPdzU4qiC0IFnll0Wai7Pn5/nqaV5NktY2+6RZHB7UH21VgSBgJtj2HvCtSktlrRIyVXOH/3x73Htxpv82l/7df7+f/H3Ea6HG3gE9ZDabJNa3sQJXB4gEq+OJ1xUOIsK21gnQErBL/zi83zjl57DSA8tQ/qDlGvX91hd6/I//s/fZ229d99x8kLRH8aM4wxjq2LXUQ3arRr/6B/9r/nFb3+Lv/j+d3ntRz+g1fD5jV/92uGWqrUUhUYpzfatHeJ+zMsvfplXXn4R5403+eDWHVxfcepsRaaHQRWo63GKLjLOrTzPM89+jVbUpNOaJ+kPeOeNP2e43+edVz+kvz9kY5QxzEtmlKWjYWxhb7LxIsbVXS83YLD87k+3+KMPdnkaeBaL61Z9bLuwvJ0ptO/zrReeYmG2ye54yChLWd3ocWvtydSngcqz3vFgcWme5559nt24x3DjGlLmD76YX3CYbEhZJJV92Efc9I7HCd//sx/gSMkoOb5ugu+5zM40sBb2e6NHWH85VClngM0wyrC7XbK/JzBGY415rKTMmu8x26ihtWF1fXtyD51kq1pZWRtM5tn9wYDBeIwxBqUMfi3Cq7fBD7i1uU2WF+z0+6RKUfT77I+qQttm4jc+Go3RBs6ev0hnZoaFxUWKosSoAlvkeJ4kCj2yJGb12jWSOGGzNyTJH+xNPoUQAj8Mka5LkaZ3CwKEBNcnCDxOLbWRQrC5uUscH3o9R5FPu10nzwu63fGxcWxlM+OiBFUb36PDstYwGvRJRpIrly9y9uwpdra3uXP7No6AWuBhXIciVdWm3ETEcC8p+HEQ+D6XrjzN4tIC4/6AIs2oBzXmOm0Ggz5b29t0u1VBRYGgKHMcVxDHI5Qq6Q8GDAYDiuLBGzjSB+mCKap72b3QxrI3TnGVi61F2LpLeWBd+GByvtmusbA8w63bV7l58wOM1cf4qjuAS+BF1MIGQ7eq0ZaMh6xe/xBrK8/9E/x8wJFwZTHiVDugKEqKZMh+arg10ChtKe/ibgROrYMT1vFNim9TpKi87BwU7cCiNQxyKM3x/uwKyFTJTm8fIaEo7479LAJtxUQE8jOOBabOl/AzP5UT/Pzic06kT3OivjgjwFjIS02alYyHQ8aBoBa4LC/M0GnVCENvknb96SntHCmp12o0a3UGZYi2BcYNEJ4mCkPqjYBGM6TVblCrhyhVYIxiv7vD9vYWWzubdHa36Pd7FEn2kfxW7z4RMfEd/mzV7H7gM7cwRy2qEQmfdJyws7+FRhE2G8jQpzUzy9nli5Vq4iisgWKE1RkWjcmGlUJjIh3SplKL5HlBkadsrq+xefsW65ubbO12D2gkKQW+J3FdWalOtCHJCsZxSn5kEVOpSgzDcUV2HKe+OcHPKybyOOFMqqZPJNFH84sPqqnfPQarAk+PY874SVTCEiFcpOtzWPX0o+ATeqMfwICeSk8PVZJPDJWA59AFRx95buqhbgFtJhm6Bv1Y5piHsEbd5RFrqPywy6JgNBxRlBql8oqcly5BVD94rVKqsnEREs91iKKoCiyNwPECpOtXqpgjhIXRmv7+Lmk8ortTpWf2u3sMu12yJJ30OeARmQ8Pa7MqfdulLAvKsiBL449lGzQt3vykPNs/1/AATyBrAtkU5LZgGBucrMDVYI1ACoEjxH1qVWtsdbmOayYJOJZSVPYqQghcMU09P/Ka49RxpkrJnabBCk9UKiXXIcTDQVJ36vjCQ9djitoAMzbo7Biz7aOYdHfj2Eq17lIFNf7UTsyg4oKyn1VToTOZDidqvoMgbjo2AyoifWLbgpr8DKiI9brAnfHw6gHBTA0xttAB26kyNcZlhldCUwqEK3GlwJPgCokrKhJDGcg1JAqUqGxdSio7l09q6SI4nEKnSypjDdYYxuMBe/uS3mCH4Xgfxw8Rfp2sTEnLhEyl4BqkL7CurZSOcF/7a1y08Coi3guoN31qNQ/reOAG1BsJWVJSFiX10MF3mBS5OzxGnmf0el3Go+HBRm3liwqNeo2ZTpNWq0mz1SQMIuq1dlWksSgwxlCWGlVq9LAgxMMPQwp7uOJ1HWjUBEEo8HwQrqAZ1aiFPvPNDgvtGWpBnVrUQCcZuiwos5QsjknGMeNEMyjswd7nmMqa38J9Y6OfKkgVTSo3Is+FMIeuqch2IQSBJ6n5Dr4jkceMu490jQU4TnUMYy0ICOsefugS1kJ8z8d1PMRBp/45hdXYj7mW1cYwGo0P7vZHISdKZ99zCUMfYyyOFDxgb/G4EwMqb2/U8UTRQ2EsZakYxYdFJgulq7jA3m9xcvQ7FVpjlSLNC7Isxxg7yc4xaHPIwlfK/Coby3VdgsDH2Eo5apXEuhLfc6jVPKTVOLIqPKqNRT1urCbExG773m9ftYgQAtd1cGTV3lNfZCGqe4Mj768Vdffhq9cZXX3OcS+Vonq4jsR3Xdzp54hqTtYTYVO1QVdtjH0SOI5DGIY0mg2iWo0oirBFiScr+0tVFuT51KrmkGzWusoOyPMCYyxZmpHl+bHr74mF+cPnkMma3wiJERKNRVsDUuC402rSHCjgj0LrkrLMKLKYLB7CZL6xWiOEg5UWR/o40gVRrQOnsUM1N0/uYieK9J8LVJofQeRKGoFkWFriUpEXhvRoRuMRVOuravwKOxmDEqBaFwkBojh+HpkuH80kk0ZO5p27N8mmbrGi6puWJ2TN9TFx0tVP8Cnjc0yke1QqAk3levi4o+FnS75LJnW4LEhjQWuG/T4721sIYGVpmT05pre7/9Grjz8urEWqElEWqH5MkYyp+R6tYIais0h5esDpuSWeu3IRqxTf/7M/Ik0zdsdVGuV3/+xP+A+v/oCrr3/4CMXrQ+AJWAiqzfG9AtLPbiJ1pUsraDI/O8+XL7+EFJIzF8+yvbdFRsm/+vf/lq+9+HVOLZ67n0jHAinYMdnGLbKtO7hBiF+vUyjNYJQxGse8/tbb7Ox3+f7rH3Djzg5be727Fv1+FLLy1FkcR/L9H73DYBgzGg3JspxSVW3qey61Wog2hnSysL534fTE8Rm5cZzgUZDg1Ssl+tQ6SLggaqBzKGKQHoTt6jrl/Upy95EQUs2jH4cG8kHWQIaT8zuusxytzHnM+xFU9MmT6NPT+eMJdlpNpax1gWUqkq5HVcBvagZ438cJ8OuVxLD85AVPy6Jga/3mxE9UIwS8/PVf4LmXvnwQjW3cWeXP/v2/o1aL+Na3vsbKmXP87u/9EXeur1Hg0166RJ70SXpb951wWRT8ye/+Nq9977v0egMGvQHg44XLWJ1T5nsfi0zXWnP1nZ+wdvPawXNZEn8klT5UadZB1EQAWTrGfGqeZ58TZBWpsjbqsh0MkGOBTIANg7xpsMpgy0pJlJfmaIUDWBAwK6BnYevI5rQDNMCpuVz5pedYOLfIzb+4ztqbd9AT33LhglerghyVPOCSt6jGwTLggud6nJtbwvNrrHRWaIYNbjjvc9s6jBixv9p9+BrmqD9sQTWuNqtzpUNFgk/g1cCvVeq93l4V+xtVpcabBhVZfkZWbVAaKC3MTo7jVH+XkaDRiai1m5z59TMERCidU6qMbHuPwXvXiISlteRTU9DfU5TbmtPBgLqF7V7Mdlp5iO8AsYFeXM2cwycwhYUezNYr4np/XP0EQAjqMw4LZ332klv80V/+W4Jak+bcCvuDHt959ffZ2d8hifq0Loekd0pyqw4Z/gmUhV4BUSmpzZ9i6cIZHNfgOIYsSUlGI2ye0wkti014atFFZi5rXc0oPbyOt69fZdjvkWcpWZZiDKQJWFvwnT/+Lptrqzz/wnP87//bf4wwAqkder0e77/3DnlRULoSa+Gp05fRieKN997jj/6X/4XhcEhPaxYCh/nlkHpNEng+nh/ywtd/heWzV2g02jQabYJ6h8bMKYZ7O9i9HfqdNnY0orcXUX7YI99JyCbd6XG2VjeoyPZQQzOttB1NC64qGWzdRo88PtzL2R4p0vzjqzV9X7CyHOB6glRVfs5nn73E/JkVWjNN9nZ7DLMYo1ysuWej6wQHmO5l33td61HIQrtJEPq0Z5qUSjEajrC6pNQPqqk3TSuDe3daQgmRU22qxI8Y4+NxyvZul2GcsNXtToq7V6RrluVoYx6YA5imGVev38L1fMIownEcGmFARJ1SGwplUFqTlSXaGOI0qzyuRGWlUuYj4nH/wJpGaQ9LwGA85s5en/EoJntMlbG1liLLDmom3f1HXfm9l6bKRHYkUhh8D+r1iDAMKFXJcDigLO2ximxrLY4j8H0HayRFDp6Ehi8RE8s7KSWt2VmCqEbDc8j6fQIBpxbnMFphshRlLKFrcaowmk8SLksJCwtzfPOb36DZatKqNbDK0Ol0cByJH/gEQYA2ht29PRzHodNpo7XhztomSZoxkBlSCkqlJpY2d3+GEOB78qAoqtH2/vusdMFvI4OIzvIiQb2BpCRL+viBw/zKEtNeX+YFg/3uwYaMMZqbH77H5tYuyXAXPdhFYDGi2oUWXgs39JjttAg8D1UmbG/fYDweAuD6DmEzwGhDOsw/fmx/gs8FJNVSahrlAcSpYa+rGenjHA+q4F/FQ3QWU1pNima2EXFqtomxhmEqyUuFzDPQ+oE0gTGWPFMIAUVh0EfmXmMsZWkJAp9Op45SmsFg/OQsa09wgs8ZPsdE+tEqAR8VT0oJ+RE+caKUcIUgFBB4Lo6QCARlUZAmMQKoRRGBXyA+pXOslFzVysOqEpUV6DTH9RrUvJBmVKfdbNFpt5jptEjHI3Z3thiNxhR+DStcNrc22ElTujtd7Mc9RymqVaorKmX6ZwgpBL70CL2QuZk5fN+nF5/Br/msbayyvrHGhTNPoVSJFLJSXByVENgSTEYx3GG8dQ0vrKGaLbJC0+vH9AZDrr37Ohvbe/z03XWubQzvOwfHkYSNCGMtW7s9dnbuTxWWsvJ5FKpSDpjPYmHjCISUSCpvR6sN+ufMh/gLATEhzuWROW4q0ZkS5kKCMymwWUx9Rj5KH5mwwUIhJorLSkhtj6iXBQeSn3vPDw9wJ8GSuf812AMJznT8iIkhrDHOxCDyozBQYuI5yuHxDk7n7jlE68O0WiHFRMT/EceP5bCA8tHbzfSU77VREJMMAtcDx0faEmE11t5fUOuxT8EasiOFZYUQNJptzl26ctAOxhi8IMDzfWZm2szPz+A4krwosMLBCxuVJ+0xtxRjDJt3Vtm8s3rwnOO18aIZLAIp3YOiw1Um5mO2obWM+l1G/U9mgSCoVO0HXrM/71BgpSXOc8jAjmxVyLsL7ICwVaouHCahTL1WZd1BzDnYXGPuzWhzQQaSxlKb2bMLrNXXKUqNABzPwfHACytVu07v0fZNN1cDEG0QjUpN6Dku7VqdMGywNDNHp9ZhNLtNv9NERY+4Z0yPOb2kpvruxNXzVlmsNlhhQVb8guNXU1+Rc9iPRfXd8JgUMRGQiopVmRDo0zEqXIEjJZ7j0Jht0ah3KOKUYpxghymlEvjG4viVorLMS9LEEI9z4jglzkqSEhINqYXYVnYuT8q0SgoIXHBNpcq2CLSx1TVywQ0gzgbc3rhJrdFmVlq6gx4bO3fY6+6BAK/mkAeqapMjU7iUEum6WOlhnQA/ahA22kBRrWdEgS4VVmsC11LzBLMNl1HTZ3+UE6eHQXM8GhKP7l7TaA1Fblhb28BB8+JLL/LMs89iS4vJDfVajY07qyR5hvJdrKjUaDQM8Rtv8N716wfHMlIQhA5R5BJ5IWFY48LKeS499TxTSarfaFNrzSCKgmazic1TZjoRlBGhP8Tho20PJ5NHaCtfYpdqq9ezhjyJsVrQHyi6o08W7EspiCKJ7wuElhgpmFvpsHJhBWsteVFSFApdTm7zJ1zWA3Fc03iOQz0M8MOK+JRS4DoCR042ph7YnsevQxwBvqjqIDwqIiuVIkkyxnHCYDR+bJsRIQTaaEajEY7r4/k+ruvguQ6u5yKEwdrD9Yy1UOrKHumAqLaqKso58U/HGqSoaiqN05w4/2iz1EMFO9bApEioFAJHClxX4vtOlVEdVxkt6iHLUTlJYJwu26QAX4IzyeSTEhqhT1gLcbGoPEdYQxQEaFUVGcYYXCkwEuTEZgL70YfM9BzCMODUqRUajcZEjSsIggDf90iihCQM8YMA13VxXRffD1BKo5ShKBRCVG12sO4UAteRd32O5zpICbYUYCsl+90xtATHR7gBXhgQRAFkBbrMcRxBWIsOPkOIKlY7Uv2R8aDPOMmhGEORHB7ZCRC1NtILCaIGoe8x6o/IsjFaF0gpcFxnUpehWq9/7Nj+BJ8LTCM0D4uwVZZbUUKaW4pjL201EKwuqwLJTCJKYwk9B20FrnJR3B9v3Qtrq0wNQaU+N8csKKV08H0fKdUjj3eCE3yR8Tkm0sWRx0fBz0Zqu7S8zLMvvEA9cFmqO4SOpeMVBG6Vuh6PY5RSeJ6L4zzYd/uTIPAFraZLI9Ts3blFMdxnffUG4zTmq1/7GpfPXmbea7MYtJlvtji7tEDWrJM8c5n+cMgHGzt0x2O63S5bgwH5MP/4TVka2C2qQDr72exEjvsD/sMP/gVlmtI4NYvfiujU6nhnzqBIefW9P2O2Nc+Vc18i8CYeisZAfxs7XOcnP/whP/jT75Erh7TwSYxlOy9Ji5L17W3iJGOnf7ff+TQ1sShy1jY2sBbyByxyy1IzGqeTQjr2gMw4iBKfNL8tBa1L56mtzHP6zAXOX7jE+vsf8OPf+31UdiQkPVlfffqwppKGHl1gTP9t77nwwsGrLyCsQaUpRhVU9E5ZFbDyIrAaUyT3EKFVJ6otnKW2cAbf86gFAfm4x+bV11GqwG8sIlyfcryHKeIj56eAEaZIyHsJYDH3qq+jNkQdwsil1YnwfY9Os4PRmtUPbjAeDKsKho81/AVerY4bhMzMzjK3sHBAirmuQ6NWq+wmpMVaw7tv/ZTVm7eptRs05trkScZwp/vxVDZ20lQJlXBt6iJTcCC4F65L2JlFej44HkK6nJq/zMLMHJtrN7n54dtPrIBTt9/n5u07BJM0ZOtFXHjp6+iy4N/+9m8TBT4LZy7yd//u3+DHP/wL3vrxq6gi+8gbCUHUYPHUCwhhGCXdSrnbH6Ky7NFvfkIwRpOlVRr/z70aHWAfcME0DGIosHu2ksqOOSAJDpR3AnDAnXeRdYcLX73CynPnWH/1NtduvXuoBjfV+7UxbPf3yIeW8EqLL3W+QiA96k5QBeNZRjJKeffHNxgPjngPzwIzEMxLGudcFhc7fOXyc0S1GkHYwPMCnmp1mIk6NFbOs5hL3uveYENuHtoXTO9d00wOeeQ7+FTWKxIoQcea/q0+0pfkfg7noYzB9I8hFy0V+Z4Dm6ZKTvRtdcyp0HSy5NNjxe73t+iLfXbnt/EaAaar0HuKYpyQ7GhyC9dlQQN4URpaDUtvPObDtZztQcFmWqm6P41bYK5gdwxR4PDC5SZSCq6ujumPSlZv9Bh0U4KZHaL5VWaXZrjy5TGjYUz/2ojhXlZt+ikoR6aaqyZN35prcPFLZzl1eon/7O/8Tc6sLHJusYHVfiWkKApM6eA7baSTYShYaLb4T371a+zvj/nj7/yU6ze32Uqr5MEHQWvNtesb7Oz0eekrt/nq5ia+FxD5NdI8ZWt3lzhNMJGPMobVt2/Q3dzj6q3Vu46jCk1vO0U0A86eWWA26rDgBHSsQDTaiEYbrMAmQ8xoSNqLGXdjujtD9nf7jLOClLv3Oz0OI4VpAsRxt52Sylo/FNB2qpT2/YFGC4izT37Vi8KyuVUQhi5nn1qkPVvnKy99lcsvv8Tm3i53trdQNzI2/2KVdJBQJidexfdCIIgcDykEmS5RRyy/tLEUyqDTAmUHKKWw2k5ES/fEfkJOsvwmv1v7wN0Ll2qKUlR957ieMIqTilQtS7BTKdTD+0wYhLSb7aqAcZrjSMtMKKiFgkFqSXVFgvlSYqY2Jsagi4zClYxHA+q1gKXFJS5dPM+d1VvcunWdNCnpD5KqYOpHJNEfBinBcyVh4FKLInzfxfMctNZkWUocx2RZSV5wn+85HInYrcZqhTF6YnlTCbym9K0xlkGvV1n4TARniCoGajXqXLp8iSIv+OnbH5JnCbN1H8d1yJWl0JArRVoopBQEgXe4dLZUG1VH1K/TZdHUIs9zHRYWlvGDEMep+ky/P+TW6h36vS5lWaBUSbdbZQkoVRGBc3Mz1OsR/f6QwWDEwsIcly9dxJHOhFjUDIcjlFI4wkcKh529bXb2dw4byJpK8Z8LkkGfssgJHIsrQDgebhBhyowyHSGUouH6KOGQlkVl/6KSyubwHuN1IQSu5yMdl+FgxFhYhClxHIf5hQ71hs84TtndG6CV/tlabZzgE2Fa8WGq81EGbu5nrI1LxknJ2PJQq6upiegUSZpzdaOL77s0ZxsEvkV2M0AR+i6uKylKTXGkaL21MKm1fF99idnZWRYWFiiKnPF4RFGok+K2J/i5xueYSIePT6R/9mjPzPDMCy8wUw+4MOPioUj3N9B5CkKQpRlGaxynSv36NOC5gmbdIfQ0o/1dVDpif3OHJMsIRcjK3Cnq1qWBQzsMmW21KXyP06eXqTUibuzuUZY5cTyqLADSR3/mA2GA0c+OGLFAGsf89C9/wGi/y8u/9i2WnzpLLfIJmyHallxfe49k/hxPnX4W606VHwbiPvS3uX31Kn/+wzcYjmG3WynUtqiCsQcuQyaiYq0U3W5vcsM5PljSxqCPLoKnpMPUS/YJQwhJtDxP++mLXHj5q7zytW8RNur85I/+CFUcIUlPbNo/A1gwj6enE1LieHUkEl2Iyrx3WkVFuki/htUlFPdaYFW1GPzmDI3Tz1IPQzr1OuP9dXZuvY3WGidq4/g1dD6+m0hHT4IhKOOjzx+BH0FjDq8d0l5pUatFLC8uowvF9sYO4/F4UiXwsb5lZaHUaNJZWubsU0/hOg6h5+F7HvMzHTzPQboWYxTbm5us3ryNXwtpLc4Q90aM9vp3+YQ/NqbK9JwDsupeSOngN1s4QTjxC5Usnz3PU2cvo5Xi1tV376tu/3FggfE4YXt3j2arhXA9rOuzeO4Sw36X1/78T1BZwn/z3/4f+drXv8zq1bdIh7sf67M8P2R28QxCghkIsnxMmaSfKZFurak2Af5jwZgqktkHm1vYplKjHxm2BzHHxBpFth28WZ/lp07z3JdeRK+XXBfvY6cT9WQjyApDPx5SJLC8vMTcxdM0nYgZt47NStTeiMHukOtvrzHmCJHeAJbAn5M0F1zmFutcWDpFLawB4EqHU1GduaiOMzNPLYNeu18V3J0e4zCBpbqHTRNozOR5f/I6XX3vZCc+VJovTIqyHTfNTDe5CqBrq/HZmRxvqnK31XFNYRjd6Vck8wLQpGrfzcPDVSSZInVALEMUwjjLGGcZ3QmJPlafzipSGRhl4PqSM0s1Al+yvpPSH5Xs7yTs7yQ4LfBmYXk0oHU6JB3ljLdSsl1VrceOmZtqjZBzz61w6fx5fvmrX+fM0jIUu1gVo3MoUw1IHBFVqnYcWmHEV557inicsfrOLbKtbcbq4US6MZatnR47ewO2NncZ9PvUaw0CL6AocwbDIaMkxpY+hda8f/UD7ly7xSC/x+dXWeJBSc06tETIglenJTzqCGRQR7TmKbOEbNDHZClFnJONM+JBwniYkBbqruXR1E5xeqc5Wl/sXkyNIh3Ad6ou2E8MqfnkHvgAWlv6fUUYCZ6ttVhcmOPC+Ys88/RzGD9kM47RpaT34R5F8iQ+8ecPlXq5yugtzKHHM1RKXaUt2igKVWBM5SkgxV2z0fRIVarLActqKuuSe0gdISb9gYO9vmMJ4qwoyAs1+X2i7nzETOF7Pu1mG6U0ZVHiSEvdg0YgiCvRMhJRKYYPbCYtRit0WZBlCWmW0G6f5vSpZfq9ffIspd8fc3t1F/2Es1ilEHiuxPMcgsDD9yvlP8DOTkGe5xSlqdTox2AqJMJarFYw8U+2TDKlp9/QWpI4qUj26WdLieNImrUay0tLZFmB49zC2pSG71KLXMaFJS2rNpoS6b7vHCherQWlNPc51kwUtHmWURYFQRhRrzcoywytFXGSsre3T5KMJ9mOhrIs0NqgtUYIQbNZZ2amTZ4XDAYjWq0mly89hes6BwT++voWaZpT8xt4TkCaZXcT6QBaYUVJlsQorZE1H+E5uELiuD5W5ViVIbQhcB1cJLkqqw12PRXR3NPuQuA4LkI6JOkYq0vqgSHwHJqtOksrLXa2+2ys7Vd2byf4wuKoZmGa87s7LsnH5WPJSKdaByavzQvFuBhTr4e0lmbwhEA4Vb0Cz3UIAxdj7N1EOod1Xu79vEajwalTp+j1evT7A8pSnVjyn+DnGp9jIl1RRURfjJ3T/b093nj1VZbn28y/cplW5NFut3Fsg9wYlLVEwxGB5+M67icqaPQgFKWlP1QYO+LdN97B8zxWd3tkSnPtyk1mmjNIXSCVZmN3jbf//Pt4rmRlpUOnFfGNX/gaz+ea/h9+l1tbvS9K09+HvZ09/vj3/xBhDKurq+RxQvLaGzRv3GTu9DydpVlOn1ni3JmniIKAna1rhK5PM/AQqmT13Xforn7IWzd3uDqAvKhI9IKH7/TCRPRCdXPa3R0ClvLIDUjwkJS6KWlQ8vHI7IBqqzpyoR1VWQF7KRSmyvA2hnh1E51mfLA3ZHTtOkkSc/E3fhHPdVicn8cUBT/54z9jf22z+rJf0D7wcwGjq8K3wkGVyUS1WykFgqiGH81ihYMVLlqVGKesClqajOrCVUxc1tticPN1kqDJuD5PkY0RtUVcL0PnY3Q2xBTTXTMHcHCCGkFrpvLCy6uAP4gCHNel3W5XatX2DEF7lk6nwdkzixhjGI2GxMWwWvCrrPoOj4XDMdHf2+V6Mq6ssqTEcRxqUYgjJ4p0LPu9HmGrgVaa3vouRZpjP67K5qjqdcptOJOHBRQYo8j7PaTvE7XmcL2ArbVVxjtb7G6t31MoU4AIQTqV+bPjQjGEcswjYS37WxtYa5hbOQUClDYYa5COSzS/TJ4m/OTqDbbjgmu3NkHUJhkEj6dM8zyXZqtOEIUTMlYQBg2kdEmd7hMhlE7wEEzn+eNYm0lRTeEJgk5AWA954Ze+zOK5ZZYvnGNubolGq4WYWpqUdx/WCIsWBhWArklGRU4aZ7iFJTJQTu4+UgqWz8zRaNeYfWaG9lNtWrMh86caCGvpru8wQFJ3G4RugHU0UjrcvLbOD3/8Brc/3KoK5DlUUs6pzcrUF32qgZhOQ0dLHGiqpZ2kIruj6qeYBZtTjcXj2myqwp5+Fkc+o6Tq/tMx3Ju8/p4hZyZ/Hlr46QhuH9nDSXTli56b6u8f9zb8KGS55sPbI1xHMEruZsZtDqoPA5nywY/WKEtNXpR3205NFyJT6BIbD9DjHlm/TxbU8H0fx3FxAxAioIxH5MN94t4uG9feocxyiqQgjktubHe5MYbBwfQx8eQRE1XAwWLVViSQtbz/7rv8u9/+Nzz3wov84l/5NcJak5WzF6mNhmz39iiSjDhTjHJNoe6Zl031HWyqSLb3GWcl+e4OZXsWN6rhdlpIq3G9AM+LCIIaUdhgpjODNYag2wWSSaG1al/lFJUPcxhUTXMnh8RUz7kSRqZ6TFEAO7r6pvGncK1Vqbl9a4/BIOPiy1ssXNxlde0OH169yvbGJubEL/aBMBYyXSKFwFhzl+VKUZYMRqPJ/GKxxpDlqhKl3MPW+L5H1GgRRiHz8/Nopbh94yppUk0wApiZneH0fKeyJzAwSjNubu1Q3EM2WiDyA2pBRKk1ea4wWBR2YpX3IB375Fw8l6X5Do4UBL6HEJLllSWWlpcxE632KM0Ie32klDQbDeqNOi+9+CKLi/MsLszTajVpNVu0W02yTE0Z6yfU6hUqwsxgbMH2ThfPc4lCD8eRpFlOlpuHFrm3TGIgO7U0sdUyysCorKx0IrdSvsuDcmYCawWtdou5uTk8z+XazdtYC6dOLbK4OMto1CMtchaWTtGeX2Rtc5v42g2qIqB3z6Gu4+BKp/Iy11WRVtd1UKrgxs2bbG5tsba+Ta3e4Fu/+G0uXryEkR6d+SWuffg+P/rL76OUmvSJqii2FDAYDMnzgjiuNqF73T5vv/0uzWaTM2dOY7Rh2B8yHiWohiH0I1rNDs9c+dKBnUpalGx1h1VfLVOMKUhSTYZGYpAYjMpRWVYVFy+qttQPKMQu3RAvauEFNdqdWQC6aY+8GJMZgyogigVJLMiz9CNnLZ7g8wdDlYjnAQ23cs8tdBXuH7pbiSr+sJZDo/7qL65XFdYulaZU+mAGsYDRgjAMeenFLyGly3AwIEsS6spQKk2SJPT7/WrOe0DdqsFgwK1btw6Kj08hpWR2fg4/CIiTZFLgt0QVH1E1OJXkTxvipEuf4GeMzzmR/sVJ9d7b2WFvd5eLZ5f5ypUlan6bzuwMNc9hmKakRUEURVVxJefTafaitHT7ing0xPTfQgrYTqDE5ekL12kFNeY6dRZnGqzdWuUP//W/ZnamyT/4L/8W8wvLPHP5JZyozY/eu8n3fvjWF5ZE3dna5vd/+3eBQz+7qztbCCF57qVnufzcJTqtNgtzKzhGs7XxAb6A080ajla889ZPuPbeu7xxbZMP+h99njYWslyR7fTvel5M/gcPULJMCZaPixCYAWZ9uNiBuIQyr9gBDZSW8c11xrfW2fvpO7wTwelXXuYr/9V/ztzCAi8/+yzlOGb99nX29zYr6dYXtA/8XMAqyCuv2ntnwqA2T2vuDFqVFHlGKUoKx2CnnrjWMGWDs+4mWXcNghmon0V6HkF9CdcU5DvvY7KjfriVNsuNZqgvXUZpQ9Gv/h7NtgmikLMXzzO3ME+rGdFuRczNzvLUhQsMh31+9NpfEg8Hh0T6R/vCWGvp7myzMehNFv3iwMb9AAKiTpuw0yIfjRmtbX+yxZTlPsLtYLE2qZVqlSbr7SMdj1p9FtcJ2bxzk9Hu+jHBiQSnDtKH2nyl3B+tPh6RDuxt3GFv4w6nxs8QdmYQ0sVaiXA9ooUVbJry4/evkb/1HrY3qIrCmgzsYxLpvker1cDxPQwagSQMmvhujYG7/ljHOMEnwHSen2zS3IUJMS0jSe1sjdZ8m2/+1b/Csy+8SGkNCkuj3a68KabvP9L9jDBoYdA+qLokLhOS0ZBIuyzbJsWUSHckZy4scvr8Ape/fIkLz5+n1WmysDTL+voqf/RHv0ORFqyEy5igiekYnNDl2ge3+YP/8D3U0FYpvT5VkdKpncuU2DZHvo/D3UT6dLyJ6r0iAtogDDAEm3D8eJ4aXU/r0B899tSSKT/y8yFNnxt4fXj3vGK9ybE/5Xtemhveuzk69m8mrx69cUpvd/VQbu1yuFofcVe/sbqEuIcZ1Ul7XRK/jjs/j+tHuKGP6zVQcUneH9Pf2uHa2z8hj8eUw5g41VzdsFy9a2qaeAoJCd49a1VjsUbxzk/eonfnQ7K/XfDtX/51wnqT0xcuUev12dnvU8Ql41Tdp0avjgEUYNAkG7uVl+/WNmVnFtlpQTmDsB6uF+H5EWFQpxalzM7OIoUgvBMDyYGlywxwCag5VTHXAtAKugYaDgQOrKt7iHQLm59ieKGU4ea1HTy/x3Pf3uDUM1vcvHWbd9//gGS9j/44mVP/kcBiySbWFdMppXoe8rK8K8PT8uDh6gc+nU6bmZkZnn/+OYoiZ29746BelQRm5+Z46spTB+/Z2e9xZ793H5EOEE2yeNOsYKBTlLWTqc5gS8XDinYHnsdMq4nrCKytioovLi/T6nTQOGgc+sMR3sY2vu9zenmZdrvFV7/yZRYX5w+O02636bRbjMf5p+I7bCwUpaEoC5J0D8cRzM3WCQOXJKmI9IfBHhzHYGwluXcnxx0U1cZW4IHjVHt0IMBIMIJOp8OFixcZDAa8//77BL7HV158kSgK+clPf0p/NOLZlWVeePnLOD99mxu3blGWmiw77A9CQLMW4nsucWoPiPQo8lCq4Oq1q1WWcGGo1+p865d+mcvPPM/M4goXn3kepMP3/+xPybKCsqw2cdzJufb7A4wZHHxWt9uj2+2xtLTI4uIiQggGvQG93gCjLKqmmJlb4OL80wjhIB2H3nDIsHyHJEuhTNG5JkuG6I+Zlef4EWFnhSiImJ+ZBaMYbhek5ZBps0Q1y3hkybPsxGLj5wDT5Y4RMO9DICHOKgfdg6srJtk41kyk44dadc9zCXwXlRWoo/OcFRgt8L2QV175CjMzM7z77gesr28dvGR/f5/hcFhlbTxg5u33+/T7fTzPI4oOK8o7rsPC8hKtdpvtnR36gwF5nHx0It0F6hysI04y6E/ws8bnmEj/AsJa4jjl3Q9usd2pk6+0aEU+eB5Ih7IoKtXkJ7mZTVK98QR2pLHp/cfyXIf52QaOhF4ZU+SGna1drr9/g3ihhV7qMO73qUcuoS+IBz26voOJ1nCiATOtGi996Rn29wbsbHUxhUaP9Rdq5+/eBUNVT88w6A1YX91gcWGRjTt7OFhsPCb0XGajWUJH4LaWieZGtBYt8/uQJgnjYf+JnVsQRvhBCFiwBq0VSZJ88kWOS0UwWAXdpJLY5aYKuiex8UFkYi02h2S7x+ZrbzOen4F0iM4LRv3hI/xrTvCzRlkkpKN9tNGoskAre48qGg4v4GQhpXMo+ljtoG1V2c/qexcxlV+CKWLywTYaB2urgK0Yd7GZoOspyuEuankRh2WEtXhSMuj32Lhxnf7eLkWa8FAIB+FHgMCWCRiNLjRCluhCHZkj7fT/h7CgigKRSHRRggXhSBzfw1qLzstPrryRXlX9zx41Yq7mkHw8xBpDmd3jSe/4yLADSEwhqpVuUUya1AJepU73plUVH8QYVkhHQ3ZWbyFcH7waSimS7i55lqKyHKMUYeDjLy1SJn3SfvzQ4wVBWD3CAK1zHAu+X8day7C7R54llPkn8fM6wWNjKmCckHlO3cVrBUTtGgvnFomaNU5dPk291cD3Q/q9Htur23S391l79xa2NPfVHhYIPMcn9CI830P6DqVVDOIBSSxQu0OKYU6RF1hj6e5VTHKtVcP3HMzKEvO1GUTm4FPD4qBKSY5lHOcMximu4zM/M89YpvTKMXbKZh4tzHvoUHD4KMHxIGw5SKcqcisdh5kzHWozNbwFDz/1CE1Ao2zS7w54/fW3SdPs0AB7miGVAYPJ50YcWqJNSWfFY2dTPekljeMIFhYiotAlH2WUSUGiqj3tj4Spyn4qOj3KmUkm9/nqkZWajfUxju5x+8YqJtVcDOsEYQOEDxLcqE00s0xLG1YuPk/c77L2zvvkWUxoKlV3Y6ZBrVUnas5Snz2FcBys65CmKR+++w7xaAQThXBnboHzl1bADXjrnfdJkpTr12/THwy4cf0W+90e4/j4e4Cliu9zZbnd13TTkpX1bfxaxNxMi7lTS1hCrPDR2pBlOVmaUeQF5RGVm08VT9cERJO69nkO2oEzMw4LUkwytgTdkfqZWA0aY7izdoc333qT1Rs3Sfb6FMPk3upsJ3gA7DE/DXeT6w+CKkuS8YhWq0FndgZVljSaLZIkwfe8yjouqmFs9dosy0iTBM8B3xWU2t51ey/KklEaY4zFDSQYQ56rY9ZdUA9DGmFIFIXUfEHgWVzH4nkOzVYLz/dod9rU6hFpYclKi+M4BGGAFJI4TRAShsMhYeDhuZXVTb83ZH1zn25v9InihamgR4hpxolFYHDd6hym7SuEQAqBVlXcKuU0lnr48bWulO1aVzlvUgqiQOJKEG5VZLpQoK0l8lz8wEMZRbffYzweo42mVIL9QZ8g9cmUwjoOO/s9nA+vsd/tUatFFIWCLMcYi56E1lOV7dT2xhhDWeqJ/YnE9Tzm5hdotTtoK9jv9dnZ2WV3dxetLWcvXGI0GLK5sVEp0+1ByHQXpr+mWcba+jogGCcxpS4nm9WVt3ySJDjSwXFcTFlQD31cYVGlxeiq+PLDuEApJe2ZJp7vHTwXjxPiUYJRBWUywLUKa9oTAc3dJ5pnisEgI03KEyL9CwzfldQCD4HFsRYXiys1QtgD7cLh1Z0o0a3l3v5Qao0oq0zXo7AWlBEoI5BugBfUmJlbQFvJaDhi0B+itX3sPlSNuxIhBPV6Hdd1UUVJPBoT+D5zc7PktVr1yHJGw+HjHXuSVuh5HnNn55BIuttdsjSjPtcmbDfI05Q0ibGlxqSfklffCU4wwQmR/oSx2x3wb//wz2mELt96ZobFdsSpc+fpzM6TpinWmGMXXo8NT+Be8JFtSflBjk7vDw6iMOCZp07juZKN4S2G6ZgP37nK1vVVLpyd48pTS6DGLM3ViEKXnfVVhr093N0+Iqxz/tQs/8V/+lu8+u47fPfNH1N0C7IbyWFxsy8wNu5ssrWxDcpjcf4iruMCllajzsrSc8xEDcKVLzHDLGfUIk87i2xvrHLj/eEnLtAyreDeaHWYX1ypbC9MQZomrK+vo9QnNEYPgDaQFnC9W0nvRvYwHX6qanOoyIgEeu/e4c1r/wrRCfnLry6DIyhur38yf/wTfOpIx/tkca/6xYIVHsjWPa/S3LVdrxKI72ARhyYg96moKhaqTApU1gWvhm2eAwRJbxVUzGhVIoTg3PNfRumvsi0lN67dYLi/wzt//h3S0QD9qL7seDjNZRASPVjDFglFkk+45aMM0vEo4oQyTg8yO6TvEsw2MUqT7Q+xn9QH0qlBMA8ihWSbaTtaoxnvVUHTvfO4DJp4i8+DthTba9g8g3gi9bQaqINfh2anqq6o1ibFz45Hf2eb4f4eIqghZ1aw1qD3N7BFehDUtS49x/z5p+lv3CYbbj202Gqr1WFhYYlCG7JyhOdbGvUIXZbc2rjJoLdXec6e4NPF1KbkSBcP5ms0n5vl1LmzfPtXfoX5uXlefP7LBH7Amz95jZvXb/Cjf/093v7O62ilMKW5b3gIIagFdVq1NlFUw6t5ZCZjY28DvVlgX4uxqUGryrPy5gcb3L62xWivT/fWOs9/6QXONc4iRg4NMY8jM4pcYErNbneMoUfoN3jm4tOs97fpRzcqYcC0y0yHw1SdPp1+MiAGry1ZOO3j1RyCdgMvCHnp/EucmT1Dy2/R8loszy3wzNlL/OQn7/F//j/930jz7NA6Zmq/NKJStNeoGOAImJ+05XDSrjGP63T0ROH7Di+9OM/Kcp29D7YYrhesjeDmxyHSpyLF6c8pu9WYPAygYZjnvPX6LtszBU81fsDmmRXa86eZmT1VpXe7NYK2h19vUl88Q3N2gf2Nde68t0kyiumUcF4InrqwzNlnz7F0+WUufu3XEI6HFZLN9XX+3/+P/ztxd1KLQQguXnmGX/r1X6Q3iPmX/+b3SUZjdtbvMB7HvHP1FsNxglbHz23WgtKVF/3NngJrCFvvEe+v8XwzpHP+NNZvY4M6ZVky6A/o9QeMRyPieIyaHLdGddlnJTS9SZ34GLxA8LUrPs22w2YZ0lce+3dGMHq8jKAnCa01r77+Gm/eeYtyVJD3csjsxyuK/R8hjmul+6mh45GlCXmeMTPb5vS5cwAsLK+gDLSaTcIwpFkP0AZG44Sd7S2yIiPyLY5wGCYadSTmGacxcZYQ1ULa7QZlaYnTAquPpuBUWGi3eGp5CYRFCI10LL6nCUOPpy6do9XpTGplCbqDlKyf4rouzUadoizZ3d9jMPRYW1+gKDJaYY3I91m9tc7rb15Fa31XQc3jMHXXOq69JFWBTyFdhCORQiFFSa0WsLAwi+M4QEWGDfpDirySfvpuNXYftbwqy0NLSwuEvmBmxseRQFlijCHNDEUJ9VpAu12nUAXXVm9iSo3WVUHM66u3EVKSlRrtBbz74Q3eePsqjUaN2dlZ8jzHdrsVeV4YjIU0LxF5efCdlTIoleO6kijyK+uKr36dhaUVSiu4dv0Wd1Zvs7F2B2Ms3/j2r7K1sc7+/j5KKR7lwjQcjnjttTcAJn7qEisFju+TFRl5N8eVDp7roY1moVVHaZ84hrKQlInz0FuV57tcuHSW9mz74Lnb1+9wc7SKLhKS7iqi0UbNzSOlvC/beTTKGY+Lyo7jZNr5wqIe+pyda+AIi6tLrDEUaYpSGiPuSW48UKLfjzQvyY6MjymMhVwJcu3ghk2i5gznngpYPnOWD979kO2tHmX5+ByI1po0TQnDkOXlZVzXJR6NGQ0GnD5/ltmFecqipCxK9nd3ufp+/MA1w10ogSFEszW+9LWX8AOf1/70VfKNnIUr51h69iK7Wxts3rmFGuaYjTH8HHBXJ/j84oRIf8Kw1pIXJcJq9vqV0rI+k+CHCapUOEIihXz0gaaY5iBOpRgTZbWxD/ba1sYQJwWeI1HaVCmRRcnYmGoH3wpcx8Wt1SrVejfBizXzjQWimmR+Zhan2aGXJuwkI/ZXu1y/fQN9bwWXzwn8WogfBpRZQZ4cMsCe77Ny5gy+77O1ucV4NALHRTguSVqysbaLlAJrCzqtFnvdEUL6hI0O8yuCc7kgdmqA5ua1q6DKhxJfjwutFEVe+VhLq1H6Hmnhx0VJRdCkQHqUQBfQiMBzkVGlVDS7CSYdY7VBpTk4hnK7XxmKluqwz53cfz6fsPbu4pZWgigP/vaAN30EpbaZkGQllFXBZKsL0OqgkFMyHDDY3UY4Djgucb9PkWWo8sFhgeMFhPUGVgYox0M6LsvnLxG4lQuRUpDnY7J0iFIFeTLm2E5ouXv+MxZT6qrY6JOIFoypxrtS9x3veC92iZAeTlDHKosQHpZyYreiOeIRU2UG6PKRY8saU/lQkyPTGKzFlAVHK2mpPCNPRlWbu371t/uyDCporcjzHD3pBqosiYd9VFlQFhlGf3Gs1H4ucLT7lhoVl1BagiAgrEXU6nV816MsCsajIcloTDae3N/kkfdLIAJbgyLLyPZjnFKiR4pkfYjay9H9EpNVx5+iKqRmGA9T9neHbK3vcfvqKrktqPkNfDdCGBdHOFhcsrykVmtyZuUsTiMgDwx5kROPY1SpSEZpVcjMmX4pDro9Bjzh0mk0iZoBzblZwlqNc4unOD17iobfouE3mW93aLUbzM91uHTpHGHdYy/fJ9c5ZWYx0/SUqZo/p1ojTZM7pmbXH2cKmK61HhIrTuup3luT2HNgtubSqLm0AknNg5X5JgvSQ20m7AzHBzbuHxkTRbpb95C+JOoEBE3vgEgv44Jka4wqC/Z29wldl3ScoIoC6UqkKyvCzAtx/AI3CAmiGp35BfKspN0dkhY5NQ98x9KshywtLiAcDy0ciiKn1Zmh3mqTJQlGK4ajmI2tPXr9ERtbuxRpSjwYkiQpeZ4/sMC6pJpax0X175GaFEzzPGQUIVwfIVzKXJEkfbJxjOs4+K6LxCKtxbeWkEo34FHNZYmpunbXgG8gTg2eJ7GuxPWr7IefFYqsoBwrbGywuflUCsmf4H6EYUSj1aTeaJGkGcZakA6u7xPW69RrNTxPYIXEIFDGgpDU63V8pUnLGGN19b5p1pvrIBw5UWVPJ6L7VcDWWrQ2SGkRjsEYSJIUbSxFWaC0whEuUkqEdJCOi+dboijCcV3KosBzHcpSkSYpOs0ZCsjSlCjwKYqSVN89m0gmZQ0cByElYnJ+xljKY0g1MaHZrTUTwr8677JU6EncaHSl5i5VtXErHVEd9xGh4LQ1psVHKxcaO6khZTH20AFMW0tpLHmpSbMCow1GWYSw6KxASImdxMxKa7K8IAwDhKi8z8MwwFEaS3lwTztuCTi9JmWpSOKY4WDA+p079Hs9RsMhSimKoiTPMuIkfuzNLmst5RECUApLXmQk6WGxD1c6BK6PtppM5yitKfIcpYrHEmgJISa3pqpkqxUTk3ljq6xmVZImo6o/YfC8wxQmras+cIIvAgSO5yGEOJhffNfBc13qoVtlVAhB6LlYo9FliTEWIcwjpEeAqNIGhaz8+g8SCKXEdV2iWlWAN4pqlEqTphmjcUKWpYzjmCRNqo2rj9iVpsp0Y8zBBplWGq30QSzleh6tdouiKEjj5KFjQkqB5zn4roMpC6wjmJnp4OKysrzM0tISUpUUoyGpHtMTcbVuPMEJPiWcEOmfEgpleOf2kMBzUE5EWeZoL6TmRQSOz+E09gg4VIorw8Sz2lKOCoQGmx8/OcRxzltvrSIFjCZGaUlZkpUK43h0OnMETkndaTAcjHnjtesgJH/7wgucXT7FuRe+zOL5S3z1a7/Ab+33+fPv/pD/15v/PaPis1f0PBJCsHThLCuXz7Fza53bb39wsACaW1jgf/dP/gmLK6f4H/+H/w+v/fBVotYstUab/aHlD37/LzA6pch3mJubo1lf5KmnLvO1V57hzJlFni9K+qXit//Fb/Pam++g4xFkg0n63MfHcLBPPB4gXQfPr9L/nohnZpdKrTcJsPGBOaDmwdOXYGaGaHEBv90k+95PSbdfPyQJEwVv70/S8nWVOn5vUbMTfI5Rgp56nT/BDS+VwehO9W9zNwOwt3aTwc4GIqwjWnOYIqMoHm7y35xb5PyXvkFRKlZv3yEMQ/7B//Z/w5Wnr7B5Z53e7j5rd1a5ef06/d0NVj9849HqdkAXiqw7rDYYnkQhtyyGIqvG+iMDncpXXrp1gtYiVhkKZ49qWTvg0IDdQp5C2TuySeXwyOulckxvq3rDPZuZg81VxntbaMfH1udAFTDuHpNpAINBj/F4RFhr0GjPE48G7GzcrHz2s4/n0XmCJ4N8J6Xs58zaDmZSnFECGMPezhZrt28yGh76s95F9obAMw6mDjura+zf3EAUEqkkaqRQvQKrzF0k+lFsbQ3Z2xtz+/o+P/mLtzl76Ty/+Z/9Fs2ZNrVGGyElWxvb7PfHXDh3ha9/5duM8hG78T57/X3eeu9Ner0+b7/2HsPRqFJLB1TrlWmBUAUNv86L559nbr7NpfOXaTXbPHP+eZbmVpBOiJQh0miELjl9Zpl/9H/4X7G1s82//MPf5vb6HfazjPhocc5pYW4J7E+eKziO13o8TIuYCg497I9AALNUSV9dYG/yvATmay5/60stZpoupq4ROuelrz7DU4un+MM/fofRnR8zMJZ1HsrTPxDSk7Se6xAt1biycpaz80vVHyzsrW3xzndfw2Ypb775NndurvHtb/8655ZPE7Qa+LUawvUgaFUbgwhqzQZf//VfZtzvoZzvU796m3qQYJMenZrDM1fOgxOQGhc/DHnm5a/gRHVuvv8O/d0dfvTaT3j73Q/RWqOUJnAk81FlxaLL4xcN1SxZ7dN/OJkCtYYwkITnzrH44gUaKxcQ3iyDrT1uvP8uRVEw02oQSsPglkuJYWFyeepUS+PMwNUSEgurgFtY9NWCeV/RuNIiWIwQ7iOsxj4tWDC5hpGubgNV/fkTgcJngJWzZ/naN7+NcDze/eAGRVGQGwibLeaXlpmZ6WCyBJOnGNdHOVVfPzV/riJVr99gOB6TZxqtDGEjpN6qNsrzQqNKhbXHe0iNk4yt/R5RIGk1XPKiYHO3j+t5NGZniIuMZnuBqNZAOz5+w8PH0uzMoJUi61Q2HWVWsrOzz/bmGv3+ProoePHKefZ6Q67e3phstE/2Up1KMxM0I/woQmuFUoq8KFHj7BhrEovRJUZbhCdwHYc8L9nc3MPaStA6JdOttQShg+d7GKsP1OaPgpRTj/GpQt1Oiqfbyn1LwliVmCQhTUvG43wiEqneL5ISKSWNeoDv309XBEHA0tISWmsGgwFFUTIcZ5Vg7B5obUnTkrIc8sarr+J5HkorrDE88/wLXLryDFubG7z907cm2Qwfr2CVsYbt3U329ncOnvNdj8gL0caQlAl6kplekfsPb0trLHmSkbguOQ4lkrSksh7UGnRBniWs334X6UjqoaHW8Q/eHyeacXwSyH0RIB2H5sw8nudTqgJjNMtzbRbnWug0phj08HyPzsxMtYFiLTZN8fIcH32gWzjmyODWEI6HH3i47uHGclSLmOl0qNdrnD1zhiAMGAyHjEZjVtfW6Xa77G1usbOxjjHHW1k9DGVZsrW1hZSSWq2G67qMByOs0lgpsI4giEK+9JWXicdj3nvrHdLkwffrMPRYXGjieS67N28RhCGvfOVlZuYWWXnqHLOnlti4dZubtTnWbq/xg5v7FOqTFJ87wQkejs+ISL+rpNNn85GfOibb7Md4UEG1UZwUhlJb0qwgz3McJ6i87uSD1TFB6ON6DtoajDEYaVDOPVNjbrGSA5JTSoHrSoyxKGXQxjJOCyQgPYdwskhypSQIA1zPx/NcQl8Sp4ZhXKK1ZTzKSMY5UkPNdZmpNzHCY2FmhlotrKowTxY5ulSfbXqqYFqdZrILf/gn6To4vo90p0at1R89z2P59BlOnTtHrVGfFLL2cPyIMimJxym6HJPFXQB29ro02n2E49FstcEYXG2YX1xkYXmFcc9nuDXGfEKirkrL1EgjscKrqrM/CRXtPU4eTOtNegLqETQbuHOzhLMzqEbj7vcaC+lEHhZwqCo8wRcIk4svJkm9j7PgEU6lfjAPknFaMJXyyfW8u7JptFak4xyURkofdPnARZaQDsLxcPwaXtREeJp2Z4ZaLaIzt0BnfpEkzjDasr+/j+O4CPkRMnesPWLn8jgOqo863oNTI++FkD7CbSD9OtL1QViCegsjBEW2h1FT1dr0uNNzfIxBNi0oNiXGJ+ngU3JflwW6LCCog1ebyL8mRqb3BPjTecfx/CrILgvSeHyiRP8cwCqDVoZilBN3hwz9iO7WLo6QDPd6jHpDyuwheuZJ/Qs1LlCJrexAMirGMT7+LVVPEniOh+8HYCEep+RZgeP4BF5Eo9Gqxq7YR5WGMIiYm50jKiKCeojvemx05rClxcU59CeXVCR3OVFJegI/cGnXmszUO8w1OnQaHWYabTr1FgYPg4fVJcZooijgzJll/NBhaWGOJB0jxzmRKEmSjCRJD8WglaCyasf7fOMfbG9w14sOLsTDXlgtmH2qW2QAOAJ8CR1PsNLwmGl49AQoo2nXAhZmW8y1I2ZqDqowyMJ8ZCJduuD6ks5ci/ZKm6VTS6zMn5qouhTFIMZxBEpU2Yil0hRFTlFkuGWAMbqycRCTAqJC4rgezdlZHNel2Yqo11ys1YyTDKU1oe+B62OtR61eY35xgcFwwOat6wDEcUJ8xAM98lwaAoy1lYKXqlaPPDKHO9bimir4T/RhPpEL5LpafigrQbgYbSmylDIvMGWOVSWeIwgcl0AKpjTRlDgobdXNx4CHoBAeJQ44Po7n47kuvivRxh54J38cSIdJdtZj2CRM69FM+9dU4HCCTxWO4+C6DlEUUW80Udoy7I3IixwhHVwvwAsC/CAgL3OUMVghcX0f3/eJono1BqI6hTKoMkErg+8H1JpNTKFQovL+FeJ+wqe63JUKWNvqYayoOo90MBNltDEGbarMIKU0Ugocx63OPQyqOixliVaK4XBEv9/HdySBlJUtjLj/c4UAz3HwPRc9ES1X/f0wLhJw4LlemBKjFXZyMK1tZRVh71/+PI6quWqTivy1k/ORslq2WGOxojqOsVT1vhAYWynmlTYodYxy3mi0qdpzOuaMNROLp6qI59T/3HHkQZh4/PlVGd3xeIQQkqLIMEYzHPRJkoR4PGI46FOWxUcjDO/5TKVK1JHUE6MUQlcZVdqUkz6g75pDpJi0EzBNvJqGukopyrKkwFAiqw0UKQ/Wk9YaiiLDkQJZD/F9d6L+/XnhW/7jgRACIR183wcsYRRRiyJKayBLcN0qw8oagXBchOMghUSgHy7PnMaFkxSRIKxqJoVhSK3RIKrVCKII3/MolcbogtFwSK/XYxzHFOV00/CwvoIQAt/zkFJQFMUku/5uVFku1XwZBAFSSsqiIJUC6blI38V6thq7jnMQ8jwIUghcz8FxBKYoMdIlCgJazQa1ICQUHjUvpFlrUPPDRx7vBCf4pPiMiPRp6FFwaPj4BUdQq3aEi6r69nHQgLRQlFVKfbPp0Go1iaI+x41ux5F86Ree4fyVU/SSHvtxl/7umLVr24c3RE0lhZIc5AnPzdVZWmoyHGasrfXRxjIAAk9y8dIcrXbE0vwy7VaHdqOG1wwJoxqt2RnGzg5b5h26vQH/8l9/n/nvvMnf/Ds9vvGtTbQXof06UZnz5a8+wzBOGOQ5eVGydW2DuP+ACP3TQOAg54JqMdbNoTQT5Zllf3uPVGvS3uAuQlq6Lq25eWYWl/CkhDypUjVdF1n3iLwGReqQpxvEScr3Xn2dD9d2eeWVZ3j2mQsMukM2ugOeOv8U/91/93/l/bff4n/67/+f9Lv7Dz7PjwCjK8WJtfYTcX4P/xAq2Qc1hGgx0znDwqlTbDavMTru9ZaqXwlOio1+ESEkIqohHIlJ08qe5EGQLn59EeEElMku5iEZJ57ncfb8U9QbzYPndrY22d5ahyLD9Hcmi/rjmYKgs0I4fwEnrLHbTZhfmOUf/tf/FY1mneGgzw///M/J0oyyKFi9+QFX3/pzyixFf2SSV3K3rPTTZy6C2TPUz7yC8EJwfIJawOXf+E0cNB98/7fZX32PQ1ZzCvvoc3Pcyq7FcaqfB6S6gXQMRy10ygxGkxuDW69eo+Jjlel5ltDb28Qac+KJ/jnDzo0N/v3/8K/wA5/frzcBwc7OFnE8Ju0/QKWTAx+aikwv7CFhd7917wEEUBc+gXT5xtd/gS+/8jJpmTLKhkTNGv1xipJD2vPL+EGIMKAzhYekGUTUfJ9O1KDlRBRnEjacLd4y79CN+4f+7xOWM1x0aF9yWTnV5MLKaebbM8z6beqihq9dRGFJ05hxUhBFPs1WHcdxOb1k6TQ6/Kd/9W/T6/eQwgccfu/3vsvv/u6fVEPdq4ZHVMWcpDEcnTICKsG+5lCsft+M6FfHQXOY0fWQ+7EAFoFTwEwI52dhvu3y1dkOYeRxc9xnlOe4aUqZJ5xacvmVX1zgg+2UW+8MUA/IDDgOjg/NZZfWbIO/91d/jWe/9ByN5gpRbY7u5g47t1YZyozB0MEXNV74ygssL85BKNjqb9FxC5p+gWcDQifCGoHjNBCBj5ytI8KY2VOnmB8OeGMt590P7tC4tM1vjod4NYPnt5hrN/kbf/032HrxBbp3brN1Z/W+88yVZmtUrQdLbXCk5OKpZebazYNGy9KEeDAgKUryYVpZaQBlqfnBD95l6+ot/q63yMUvfZUg8JmZ7dDd3OS9V/+CMk2YrQfMrixx7U6MJWZ/crmm+ylTA60o9Fn56jOcmW/h1X2cwGVlBE/HBf24YKMbf6xan9IRNBZ8vFAy3ivIxw+ZOyVV6oIPNKk6YUmVoHSCTxXLSwusrCxSb83Q2+9ikFS8o0O7M4sAoqiBlD5xnLG/vUMYhly8eBnH8fC8GtrAhcttkjTjxodvs7ezweLKWZ5+6UWsKjFFQm+/y5s/egOdHsZ/vgOOhHo9oD3XwlpDogxhvcUrT79MGEUsLDQIQx/pemhdsLe3y9rGNn4Q0Wq2Cf2A+ZkOVhvW76wxHPRJx310lrMbJwzGCXmp7hL0WCa+5bYimgIpEX6E9FycYUx/kBzERo7rcO70Es16jY3dHfYHfbCVYtwYi3rA/FcUFdH9oAQ9IaBW83FdhzQtKAqFdCRe4CAnm0rWWPKi2ucPw+q1FllZvTxgTFoLcZKTpsVBgcQkydgs9yako0BKQeg/nITzPId2IwRgnBRoY2k2IoLAZ2drg53tHdIkIU8TwDDdA5zW73xQOCQkOFH1b60nr78nUUEZTVKmBIHPyuI8FtjZ6ZLlh2u4ZihpR85kM8NQaMsgtxhj6I1GJKqYWLsI8iIBx9xnOyikpFFvU6sF7Oz2GAzH6BN/6C8MjNGMez0cz+fM2dN0ZjoEvofCpd6JOLW0TJGndLc3UWWBdVzcsIZwJqqFBx8ZygQrBKWSKCl4/plv8tVvfJM4Ttjb7yKkAOlSmkpFrlRJd3ebnTurKFxkrYVVBTZPkVLih3XCMOBLT1+k3azx1rvvcmd9/YFnYK0lyzLKsiTNU4QUtGc6dObmGPQHrN66TZkXZNnD1eMaTWYyQjdgtjFL6IfEO/tsxRmDrV38Wg11r6LiBCf4FPEZEenTSoc/L8q3asLB8UFOWUc4VplOpRJSE7XkVC3hOhKj5UFqHlQ7fJ35JivnF3FGoIYFhS4RjqgkV1Ny8569CD9waLVDSlVVJjeVOy9SChrNgLnZGqeWZ5ifW5x+EHgeMmhi/YQEh2GhWb2zQ3+ry9bqOv0LixA1ISqgLJhb6BA0QmSWkWY5e6u7T7hNH4FJ7qLQEzX+EWRphuoN0HF618JCCgc/iAjCGq4jq2rXjkR6HsJ1EZ6HtRnCcdHWsrPfQzkh/3/2/uvJsivP78U+a21/fPosX4WCBxrtu2d6mtOcGZLDO0PeIGVCDwo9Si+KUNxQhMwfoAc9KmQe9aBQhO7VkEGKc8kZksMxd1w7NNANNHz5qvSZx22/9zJ62Cdd+QIKaACd34iDSmSes8/a5+y19lrf9f19v9M0o6oVeV6QxhndTpdvfGMVoSs6nQ5pHKPUU0hAt3xidfujjn+o2pMI6xL5bbrtAUM/enCe4wmB/sWFAOE2nuVClg+ZSgiEkEg3QrohSt7vVnC4KpHSodXu0uvPNepvYDIeNX80utlQvO/bNCoIJ+wQ9FcQQlDWGul6XHruWXrdNm/8+Kfsbu9gTOOZN53sMd3b/AT9a78y5dFSBCHksac1VTaP+76NskOGHfzBaZASYy1+EDJ35kyjoGwPQIb32OLM3u3hh5cS4QUNU+gdJdJtE2RKfXgMo5uH9MCLDvwQ7wejNZX+FVkdnOChyOOM2+9cf7IXaWDy5H3FReILl5X5RZ67fJmkShlmY7S0ZEUFTkN2BgiMthilsdpgtUYCvnAJZUDX79D1OzjGudejXIDrQthzCLsOoRsQOAGukUgNulRURUUWZ8TTHEyHXq+D4ziEfkPgn10+zXynT6vbxg08fvGL9wgCH+tY8C2uA60AsJYq18e2p/bdWvY9zY9rM488aX96+oC1qLzr0Qb6ApZdeDaCuUiw4Pm4jktkbKPeqmrqqqQVCc6cbrGrLK6c8uB0m3shpMBvCVo9l4unT/PyhcvIaAUZzCMKSboxJXA6aO2CJ5lfXGBxeREjDHEe4+YeIhCEpoXjSIzSCOk1XsfSxzUSL2rhRyFxmXF7J2U4zdF1jasUMrAEnsvZ06cIfZd2u3XfdhpryY9YurhS0m1FLPR7B0Nx4oApMrS1CHH4LRhj2dwaocZjhqMYbDNPC8MAKSzTnS1UkXOqewE/CPBd52Cb9H68tHAkraU+ndV5YJ+0C+i2PMpaH6kkfUII8COJ33IoYweV2wMRhOUuhbqgIdH3zdxPqvw+M0RRyKDfQ7heYxEgXazrN+SP3ygiHcdDCAc18wIOw4hefwA4GOMhjKDT9fHCmiBq47ge7U6P+cUVrK4wVYIxBs/3qMrywNNXiKZKxfMc/NBrCNG8RrgBC0urRK2IVtTskWvTEGdFnjOZTIjCGkd6MzJcYiTkWUY8nWJVhbSWsqyZxFlTGXzXee8rmIVtlN6O4+B4Ho7rzvpbAykk7XZEv9dhbzpqrLYNaNUc80Fdwxj7CJc7geu6+L57YK3SKNLFgUc6gDUCY5u1sOc5B+GlD+uSdyvVldIHa2poRGieE+E48oFjqxSCwHcaf3tToJVpPNYDj9E4YTxNDyrMxayAYB8PHS1EM90SQmDF7Pz08XU9gLYG4Qi63TYAe8PJMW2F5whavkRrQ7mv+qUZY6paYR1xMN5oVe9/0Y3n9Uz+7zoOftAoja2dUFUni7kvFKylrkqMNniOQyuKZpyOwPVCuoMueeKya9dRWmMR2Obie4xjq4ZzMACCTjvizJkzjEZj0iyfVZM1bdCmsWAqi4I8jRFBFxFETT6CrJCOix+2aLVaLC8tsTDocvXGjUc2wZjGzsgog8EQtltYYyjLguHeEKP0IyuBLRZlNRrbVPBIlyLN0GVNnhY4XoDbDvC70RPb0JzgBB8HnzKRHs7eQtMUXn5Z1G+2IY50hfQinKiLsAppSoxW1Hl+hASyZHnJeCrI1R32hiOEdfnd33yN0TThzXeukc9KtrUxvP/mNTZv71K3Ksp2RZrkmIFtPkpoFnpNTuUBxlmO2tmjTGrMkYFDa8Pm3oi0SphmKd31G7TDNp2oS1Vr0vR14jjFqWNO9T2+/9rznF2a59kXLuL1+vzyg2v89K33qIVEuSFR2OaZ5TONIv39DSaMP4PPeobKYLeL5nOtjyu4dV42flv7gS/SA7+LCAc40sVzXJafeZaL3/g2F557hdMXnyfLCsbjhOl4iPHBc1xeeuVrzM3N8ZO3P+DG+iarq6ssLS+RTGN2NrdwHZ//1f/6v2F7c4N/80f/Letrtz+78/84ONh00XB7hIhdFl50uOAvMnFbiCda0p/gCwFjMXmz228foEYX0sMJ+wghUcUYrMHUx4lV6QZ4UaOIhWYxtLm1y+7uaDYh0yTJ9N6DH3sjQbh4Hq+7yGDpFHOry81CKwgYDLqsb64zHPkoYfFbIVd+8Tob168w2tn6BCS6pbnoBY+633hBxOK5y3jhTE5kLaPN28R7W4/3Vv1z0Fmlbi0yTWMGcwu89OLLSCkYbq9RFin05hg8+xXynY8oRzef6Exa3UUGZ5+n1hVJvNss1qXXBIXKEKMUZGMojnwPRjdKdOx91eiPhCsaplDbL8/t+gT3wAKZram05odvvcGtvQ0uPXeBr3/nNbb2dvi7v/t7pOOQpjGDfp/tjS2SScyf/sfb/Jt/9ccHx6nqisl0Sppm7G2PjpHoYQ+iXqNEGv6yorq6zX935S/od9q8fPkUvU6EqiRaCabTnOk054WXnuMHv/dbaKXZ3dgmnk558/XXGY2G+JGP67u0+13+d//H/wWOY3Bdg7Q1bpkwmaT8t//2Ta7fGh60L3Ch50KqIa7vyiF1mV3rNEPGA3QenoBnfeg7MFDQNrAYwkoLugEs+yDKig9+sYZ2JOVciQkNW7evEY+3CTtdLl+6RFLv8Eywy0gbNtVjZk4aC4XGphXT7T321jc4dW6BhbkO0emzzHstAsfnvbfeQ6iKKIpQSvHe++/wwUeC8SQnjktOry7y7KWzLC8u8LWvvIjjWiZb68TjMR/d3OXdK7tsjspmL0FryqrE8XxCP6dIU25+8BYb6+sk4+Ejm3xwjSmDrTS1rqm0Is8KqqoJLjw6vltgu4LEwF5eU+UZ0hp63Q5Jp40XBlijEI6L47gsR5JnuqBK2LmP25ExhuFoSiAhK0qqWnFtbZdrmzFF9cktCaWUPP/Cefphn3Q6JR6NmSYlGztxUzkqOKiWOEhEnVkvneDTx/bOHkVRgHDB8Wh3B5x+5kVcz8XYCqv1wfXnBQFRt0en32cwN0+Rl9y8uYXSBjfqIhyX51/5Jpdf+hr9+QWMjkjinJ3dHXSteP6rX6PMc65/8AHpdIrSs5zyg2WYRDgBVWW5fWcdz3cJAxfHaUhXgFrDqVNnqYqS8e4emTdtvI6sYTqJqYqaM6dWmB/0mNsb0tvdYxLHbGztIKyh7Tbk/f79Ok4z4rJGzKwSaqWRno+YrfaFIxklU4q6IE4z6vrIhtCRruHc5aQZeg6eJ6lq02xI3QUhBEErImqFFLWGvKJWFpspfN8lDEOEEE3AK43NSa0tZa6oqsaH/r4QQKvZKCRXUN77PGMMSVYihUA9wL9dK006LQA7CyS1xHFGnjdV1kdtWq09Ulz5iOHCGtA5uL7LyumzeEHA7p0N0vGEdrtFuxPhuR5hGDKYm+O1r72G1pq4+h8wO4dzTceVaAS5tkxyg5pZ4DhC0I1CwshnOIxJ0xxlDFhNJ4xYXl0mcD167R6e59LttJGOYHdnAp/lGv0ETwkGY2o21teYjIfMzy8wNzdH6TmkhcILWrzy2jfI8owf/fRn7OyNSfOamsfVwDUTnw/efZ8kjrHSRUmfRlwl8DyX02dWaXUivGC/urdBq9Nl7tQq3U6b5y5fpBUGtD0JVtMOfea7LfKqJi/vnd0IIQiDEM/1sMJihUWXNZt31qmqurHmfIzg3bo0THZLUkdTDNdwpNPY3QiJcByEdOjNdZlb7LO7M8Q8IoPgBCf4pPgUifR9SYZPQ6J/ycz+dQUaZNDBac3h2BLHJOiqoi6KgxmJtVBWNUlmSbIcxJDV1VO89vJzbO6Mefej2wdEujWWteubrF3fxDvt4Z310FZj27b5pnIOLV2OjDdpWZFOq2YxeOSmb4xlHCdkNaTZmNCTzHXnWewvMRrHXL2xhlUGX0OvHfHqi+d47tJZ5s4s47barG1t8Vd/8zf0FpZ45tWv0WqFrC6doq4VYRDymUJZ7PT+S09b1+j6yN+kA14bvDZSuLjSYbByiuXLz3H5lVd57oVXGY7G3NnYJNzrsDNN8V2X85cvM+h1+fDmTX721jv8g+99h6XlJfIsY3tzk06nwz/5r/45O9ub/NWf/+fPP5G+b9NiLezGiMKjW0mWvC4dGfyqW3eCTwUW6uqhc38hHRy/B0CdbGDUvWpy6Xp4YRchJdZarDGMRtPGj9sWDZn+KAiJ31siWr5EZ37AYK6PH/i0Om38wGVvPMR1JBaLG3jsbt7hyts/+5jnvQ/L41Y+OZ7P3Oo5wk6/eaW1FMn0MYl0Aa0lmHsWJQSqyJl3Hc5duIRWFWs332Uy2oWoTStsofItytGTnUnQ6jK3eomySCirtFEhOWFTCu22MNo24aJHiXQM6E9gn+bQrKAtDZl+gi8tKjSV1Xxw/RofXr8GAfzgH32Prb0t3nvvXbQxzM/1WZxfIM9z6qrmrTff5d2ff/hYx/db0J6HbGSY3DJM7YT199+mHXkUo3PM9yO21kdMxxnxtCCJC4bb3+KZS2fRteLW9VuM9ob86K9/yPbWFk7g4HiSf/k//6/5l/+jf4jvWAK3hrrETnbZ2Bjyn//yo2NEujuzfalq0PWRaZPgkEjfV6I/4HJ3gTMenHKbcMtAw2oI53rguxC5kJWK6+tDKqD7ooPvCUa7W4x3tzj/3AusPPc8p3YUK55EOrCrH5j9ehwWqA2mUOSTmHhvxJlTin4Q0J5fYL4zoMhKnnnmWeo8wfctWmm2bq+R5gkfvH+HG9e3eeHyGUZffZ4XXnyer331NaQjKLKcZDJhY3vKjY2YSXpYQdl4M1dIXWLKlJ21G6zfukWePW7gfCN9s0pTVzVFVVKVdZPhc9e4YoGphthAXClUVSKwtKKQMAxwPBdn5gsrpWQQwGoEa4YDe8OjMMaQJBkjRzBJUrK8ZGN3wtb4AVVTTwgpBWfOLHHx9Fn2NjbZWTNs7SZs7SYY7GExlMtxEv3Eq/UzwXQaM50eGhfOLa2yeuEF8MVMaNTQuACO6xFELaJWm067g6ot0+mUqtb03RDf9Tl1/lk6vQFVVVHXNVlh2N6bEIYBFy9doi5LNm7dIp1Om/3nozYlQiKEpNaW3b0h0hH4voeUEikbcqk3mGNuboHxcMj2nQ0K6SBUU7GRpzl1rej35jhz5jRuGCE8F+k6bG7vImk4ZlfM3EQMxEVFbg87hnQb+wcp9r2NIclziionL8v7Wn/sR1IJDs/FcyXRTNH9ICLdCwOCdgtnkjRRLtpSaYuU4Dj+rNKkOUZaFFS1oq41VfGIbcXAgdBpdijuQ6RbC2X58HmfMZYybz6Xffea/CE2Eo+t5bBNwaFwXQbzy7Q6HZK9Cel4QhD69Ac9wiCk2+myuLzMcy+8TK1q3nznTcbp4b1qfy+kMoakPhK4KgSR79EOA4ZqTJEU4DT7RJHvsrI0TztssbKwjOc0lI7WmsDz723rCb4AsFirGY/2GI/AdRx63S6qVhSVJui2OX/pLEmSkv3wZ+xMnrS6tCnBW79zh/U714l6cwxWLxxkUkVRyNkLZ2i127iux9GbVxCGLKyssrQw4LVXXyT0PXY2NsjimNB36UR+U532ACLdn+VQ7N8L0zQljsdP1HqtLHnchPHED7CKXlgcUNWLxNP0oFroBCf4tPApEumWQ4/ax9LefCFhqgyV7KKtRtrymNoBmpvxKFVU2tKJPDotDyN8HOHiC5cezU39br2+TjRsNKEq2JkPW6d5kr6bx6pnB7hrMRiEAd/6jddYWR1w7f332bxzh4W+T6/fx3F9jJYUecHe5h5IlzPPPMMLX3kBp9PDiSI6gz5hu8XpM6f5/ve+B67HxnBClmVo9fnd5QuDiMXT51gYLHDjo6uk0ynj4QhhDFGnzcLqMu3FOebPnWIymdBdnMNqTbvTQlvFzu4am3duI8oRt957k6LISeKYixcvcen8GQa9Dn/wB/+Mr7zyKj/84d9x48YTluJ/VjA0XfCIV66XK6JpjVt+fr+/E3y6sMaiy/3Nu+OTjKC9QNBexHEdXN9Hq4os3sWoGquLRuX8WOVyLodMFdRKkBWC2hqMmxPhYU2IcBoyykPgfsaKPWsNRVlhZE4ex9RlSRo/LlEEJCOwd1g6/wznXvoK0tR88Hd/Sllk7N1ZoygyMDGYgmr65FZYRTxk9/rbuGHI4up5pOPgOh7WWja2tomTlLnTF+g/8wzxcMTe2jrW1GBzPrY/oKJZOZ+Q6L82sHf963sey4sD0izn+gc32Qi2OHV2kU6/heM8PhtYJhALqLLjV2OtDDfXxmzvpSTTnDKvKUtFqeHK7U3++E/+EmMM4+GYPMtYG6UkFUhjkBVcu7bGmz98nW47YHnQQuoK4iHT8ZSXViVB2eKjzZLtiT40zr7f7U5yaOnyAEenfUeCaQW+hgstmA8gkFDWDUGfF1AqMA4oCze2DWoqeemFFc6em0O2uqztDLk9nrJWWUaqed7jwBjIYrBG8cu33mcyntDtnuXsqWcBB0+6zPX7vPTyS+xub/Hemz+iyGJWTy/Sm5/D9Sdovc3mXszr791ilBuWl/+awJNMttYpipxzl56hv3SasXKItcOp+Q5/8R/+mKWlJb7yla+AgItnV+lEPv+l23lgW/eHb0NDWu2OJ6iyolYKpRWlUmRVRaXNQSDp3UiThO2trSY8rNWh1QpZWlmlSBOE56MQSN/Fa3k45X4QwHHUynB9c8T2OEEoBUYTuh4XV5dI85LdafyxKp5aUcTv/fYPuPDMaRZ7A7pRm5+Mc26uvcs0LRs1ugO0aAj0xtL/8EM5GVJ/JSiylFtX38P1PLSpkVLgXngGFhbodkIWBueRQjCZTBiPJ0ynY5S2dOYWGz/1MKLb7TKZTEnTHM8NWVo6h++5BEEPR5TMLS41YZEzGxMLrG3tNup0LfF8n7mFBYwVTCYjlFLMz8/T6bTxPZdOKyKbulSqQitNmRfNGBiPUarm5tY2BbC7u8P2zhZlkdGPHKSV+G5jAaL1/X2BXden3evjeT7tdhewTHfXSNOE+gHq7X2bmMeCELieNzvHZQZzA/K0Jo3zg4MJx6GoFI7jEHiN7VxdKeq6erS9pQVy3ZDon9CqZP+UPHH8k3pYAd7dxq1R5BFFPrXSFFVNu9PlwqXn8MMIEfgIx+GVr38D75sOi4sLLCzOo8qSMo3p9wcsLi2itOall19jfmmFqN3GCwJ2bt1i5+YNFgddLr60TF1V7GxvYq0hCPft+ppRttNq0e23CF2XZDKiiGOy8bTZtLGgtGE0eUTF6Am+EIjTlM2dHRbn5+j3e2hjSIsKIwTf/PpXeebSBd59/yM2t7YfcIT9nV0HIRzmlk7T7g7I8ylZMUEISTraacJrXZ+6Ltne2aUoS1bPX2BufoH19Q3W19awtY+wjR3VtZu3kcDazesk0wlbW9tMs5KiOs73OVLSCluNrbHjzqyJNEbrAx5pbm6OZy49Q1mWXLl6pakomqHVbRG2AvK0IE8ebzM8zwt2tkeUZXUSuPsUIF2JkI1llT1ZG96DT9napeRLp0S/C6ZMMeWDQzctsBsrdmPFqSWPoONjpYcrfELhMje7TVccv5GbqcFMj/isdcB7FpBghjOuZB8V91XmhGHI93/wfV565TJ/9P/Kuf7hbaQM6A8GdNpd+u0+00nMaHeKdT0uPP88r3z7W6S1olCa3sIcrW6bc+fP8Y9+93fJyop/+6d/RpamHyME8LNDFLW4cP4i/cEcH73zAbeuXWO0swvG0Ol2WDq7ihcFhN0OSZpy5tkLZGnCzvVrxMMhm1u3+OiDt7ny8xSKZrfXWvjOd7/Lv/jnf8jS8jL/0//J/4zpZMrOzs7nl0g/upc1W8j5uSKalHi5PlnY/ZrCGoOaVcHcTYqHnWX6p15G2AqhE8pswjSfoqonUTkL7mYSagVpJnC1pnZKjAgwtocQDqErQArcz9hD1hhLXhRUBvbW18mnMdSPu/iwEA8hrll96QV+83vfY+2Dt/hPf/z/JE8SrDfX7H5Wt0FP+TidLZ/skE92mTt9kfOvfJOo3SVwwOiaaZoTJymL5y5z6ZnLrH3wEcPNHGtTsJ8gZPXE0uXXEvbgPxAEHqvLC+ztjvjonSvUpeG3f/9bLKz2cLzH3+0qk+ZxN6pac+XG8L4RHe9eu8P719cO2wSHpGdpAc0HH9yk7SlWFgY8f+EMrlU46RhV5Xz1tORSr01amoZI308XvZt/2VcMuzw4K4RDHnRUgRFweRGWFsCmUCRN+X9VzfafZkT6lQ3L2Fhe+NppLr70LJt7I25s7nBjb8LNypA+Qf8yGpIp5GnNm6+/xfWrH/LCs1/nG699F8+P8IOQ+bl5vvLVr3H16hX+6I/+iK2tTf5w5Z9weuESXnAbpWFte8q1nSl3NocEqiLyHZSq8DyXb/3Gb3Lm3HmsF4Ab8PM33uBP/tV/x4UL55kPBIP5OZ69eIYzZ5YZDHr3bef+x7m/H2GsZWc4YsToYBlf0RRVPhiWeBqzsb7O0vw8C70u7XbI6pnTZHGCLivqukL6LkHLw0nhfkR6pTQf3tlFCpj3HdquZGFxkbOnltgcjhnGCfpjEOntVot//vv/lO9+9+skcUyRF7z91jWu3hodZh05NOGiPo2ty/4K6/6c/wk+A+RpzPX33zro567n0gkDPE+wdOEsF86dYW9nj5vXbzEcjhiPh1gki0qBEERRRL/fJ01zqkrheS1WVy/hSEEYONROzuLKKmHk47kCRwrWb6+zdnv9YFhptzv0Bn2EEGxsbJDnGb7vzYh0j16nzdh3KaqCMi/IpylaKTJVNtfq2gbbaclkb5vRzibtwGG56yMPYlMsdWXuuwHu+j7d/jxRq83KqTNYY3hnd4s4fvic7nH5EiEEXhAQtFosLp9icXmZ0V7M3s7REjxLXiocaZEiQIqGSK+q6kH59MeRq0cNHo+EpemCTb7H8QKRkvuf791JOxZotwIWFzqkRcUoTllYWeQ7/+C38XyfDz/6kLwo+Pp3f5OLly6ztLTE4uIio+0Nbn/4PmEYsLy8grGW1772LeI0YX5llXavz4/+6i/Y2d5j+dxFvvnN75ImMe+89TPyvFmDNrZRzf2312lzZnWFMk2YDndQtSLParS2pBqUOTHt/LJgGsdkVY3jSM6dOY02hqQoCAOf3/qNbzcWUHHyCCJdAgFC+iydvsjKmXPsDjfZ3dugiMfEW3car/WwixdGrG9skuQ5Lz3/IqdXV/nRX/8Fa9fex9YhwiiKPOPDK9epq4prH71LPBk/cHPacVx6nR6u61JrhbEGVdWoqkbP7HgXFxb53ve+x2QyYX19/YBIFwI6gzaDpT6jrfFjE+lZWpCln6Ay9wTHID2J9CSUzDZsT3AUn1HY6AmgCUTxfY8g8AlDn6jl0x/4GOExTB8uU7IK9IQm+fwxBf7GGKbTKaPxiDAKOHV6lSD0mU6n1FVNOs0oi4pev0Ov38WPQnA90mnCKE6YTBPyrCTNSuI4o6gbf0lhHy/I71eFusoZbt+hSEcIPcELPEZpQqUUwmqCwMH3HQJXULsCz5c4hSVJp4zHQ8oswZZF4zF9RLE7Gg354Y9+yOLCIu1WB6M1p06f5lvf/g5ra2tsrD84sfpXCmuhUFi3ZO3DK4QVbN2+zQmT/usK09iz7P8MHNSfz0IqdV1SpXvURfIxPOYawgurqLMMMY4xWYGa7uFEPl7ZQnUikkGEKiW7u1tUacx4b+8pnNuMxBcC6XlIR6KrAqvvs9MIONbgGA26BJU9IBR0H0fr8wXt+WWi3hmMkFx/75fsrt1ECQ/r+GDSZpPCPMQz4rFgqfKU4Z2rBFGHsNNDSEkYBCwuLmCKhO0bHzDdXceabHZz8Gm+16PxivBgWaQAr9Us0lTxiM/gBF9WbO/s8fpPf06lcnrtNlYZXGebymoQFiENQjy9e8aDjvQgtfI+xtOC67eHjMYl06nCwSDLBKEr3CpFV9WBT25pYWIgs/d5P5emqzxgvbW//Ny3Ua8B4bn4kaSoNIXRKA2VbnpaZqGwTVaWtZYkSdnZGWKEYGFxkd6emZVPP/lCxFpLmmqEqJnGJXGaEyiBtj7TSczanTV2tnaJog793jxFodgbTun253nx5VfYHg65tbVJUlRc29jFdx2MrvF8n9WtMTLo05sf0OmHzM0NeP7551hdXabTDolCnzAKMdLBkQ/eSDE0gX69wMeRAl3WWK0PCuIefdaCMAwY9LpYY1hbW2e4vc2t22vE04Rhpihrxe5uShxrRvexeDjWHgultggMS35Av99nUlTHghefBMZasqIgyTOyqqRUNbXVTejt/gdgaAi//aH36Obw03GWOcHHwuEAYLRhPBo2OTJ1QTIZoWqFMRXG1NR1gTIwjcdo4MaNK+wOdxntTRgOJwRRQKffxnEkdSkwWhFGXRwhKbKUuizR6vh4o5RiOp0gpUMYhvi+R7/fp9vtorVmZ2eb0WhIlmVUZUWlaozRB5lXehYAaEzjZ661oaz0wYzEWovSFmUPZ3TCcRGOh5UeZVXjeHWjDhcC1/dxgwCtFPYx53dKG8paoO5SkDuOQ7ffpdVu4/keQgi6vS4rK6vYWZvruiRNJ7MKH4Oxs/DSR4SMPm3st9zahqTb1/DfTaILwHFE097ZBow2jd1KrRRZXtBqd1k5c46lldOcOX0K1w9QVlBWNSvLp+h1e7TCiMB1aYchc/0edVly5b13qOqavSSlNoYFBFEQcPb8Bb727e+yvLTCuQsXKfIMa2qqokAASmviScpwd4jRCl3mVFVJVqimikE1n6s2Jyu7LxOsMei6Js8zxqMxVVnguY3ly87WFlopXGG4cGqRSZwxTbK7CuxmP0kJ0sGgUbbComdjQUBrbgFjobIO0nEaWyZjGE/GIATKWOaWVgijNkVVYssSVebUVUVdVcdIdClmtlBCIoUk8D1aUYDjOFRxsxmulKLWCj0b35RSxHFMWZT0ewOwgjiZUquaMi9Jxikg6C8MqKuaPMmOE/dHJ2onF/9Th9UWI8wnzpf5suKESP8M4Xke7XaLTrdFf9DGESXnzndojTTrN1OS5MEqb1tAdWv2P4+5DlNacfvObYRX0+t3+fo3XmMy3OP22m3yrGC0OyYIAs6dP8fKyjKtQQ/r+Y2C6tYdbt/ZZDSM2d0ecWdts/H+UwaJ/BzT6JDGIz5854cIIfillEjXZf7MRdpzCwhT0+sEeK5L4IL1IIwgTWs2t+6wdfs26XAH0uk9M7ybN2/xf/+//V/p9ft861vfYmFhgde+/nW+/9s/4I//3b/hP3xeiXRjIS7R2ZTX/+Of8ab9M1Q1+QSBjif4YkMD+x6iB8WuNKySB0CRTRmtf4jRNdZ8nB3oCqwiHw4pYhBqCHoH0VlErDxLf9Blvm1xUbz9F3/OcH0NVd2f7H4ySJAdhPTwe10c36OabFKn9x5bWPCMxjUKWcdQj3nwLGw/86MhkoSQrFx+mdMvfZfttTv8+b/9/6JVTe12G9PS5FpDzD8FGWI23uXKj/8MJ2gRnX0Zv93n1Kllzpw9ze1fvs7bP/k5RusmrAcP2FeNZjTf9f7GZ819bdak2/i9OyEk641fxQl+7fD+ex9x7epNzp5b5h/9/rdY6M/x7pu3yNIS4RiEa0D+6u8Zt9bGrG1OEULMiN1m2dhy4dsrhrnAkqVNO6cWEn0f5xZBY8ER0hDpBfc8ab/Hu8zU1AJkOyCaD8mKnJHOUBqKmetAYqCyoDRIYdna3Ob99wvOP/csz3/lFTbSAOm8y8exOjQGhns18dSyuRWztTMkimo6HcHt2xv85Ic/YTQasbCwSrc7z2hSECdrPPfMM/zGb36fn7z+U+782X9kb5rx129da+wnLISBj9M5zc7E8PIrz9Dp97h44QLnTq3SbgWsLs8TRC26vS6yqHA9777tszTcceBIzsz1iDyXrd0RSaapaBSfj3PlDHo9Lpw9w9rtNV5/8y221zf46d+/znCa8vYEJnWT12GNeSx7nEQ1FQDPtDqcOnuWuNYfm0jXxrA9GnFrawtldFNib0pswGFVqKLJ9zvcbz3EiSL9cwFjNLevX+POzRu8IyWOlJw9u8qrr76IMTlZNqWsFWsbt3D2dnj3w3dQSmFqia0k80sLnLt8Acdpdklc12FpfhXPdbh15SqTyRZ1efziLMuCO3du4/seZ8+epdvtcubMGZaWltje3uT27evs7Y3Y29trFH/2iKJYCLS21Koh0wHq2jCNy4Y/mj1xn0Tfn60JL8Jt9zFCMokztBVoY3BdB7/dIaoKijimzh/TLqHSFLW+h/j2fY8zZ1fo9Hq0oiZQ9NTqaRYHS+haoyrNeDLixvUraFVhdA3WoJXhsyxs3h+jJODOxvqK+4/GUgp83zmi+Iey0hhtybICVZe8du4Sf/CH/4zB/CLnnn0Z1w947uWvoa0g8n08x6UVukSuxO20aZ1e5eb1a/z//u2/Jk5T5p95jtZgnnPnL9GJ2nzrO7/Jd7/324R+QL/VQdcVX3vtNYzSSARVWbG5tsXVK1fQVUEej0jSimFSNdfF/TaLT/CFh1E1RtVMRiNu3LxBKwop4hFKK9776DpplvONFy/wD77xIu98eIt3Prp1IKNpYJvFjuuC51FTUeoYZRq3iLDbo7eyRFVVjPZGCCEQUmKM4frNm5jrNwgcyeWXXyPLMkbDIVWRMd3dQtcV+q6NNVcKAkfgOi6eGxCGIfODHkIIRsMRWZJSa4Wyh+vKLM9Yu7OGFJJzp86zPFfwwbX3GU/HTPdi4lHCwsoiZy9fIB5PWLt6+1AZvT9Rc3hwhz7BJ4Ku9H1dL07Q4IRI/wzhOA5B4ON6DlZapCtp9SIqq1iYCvBqkrSkrB4wu3hCPstow3A4xo8kTm1wrKUoFZNpQV2WGGPwXJfVlRVWVpcpipLd3T2shU6nS7vdpdVqEwRBU7rrOAwGcyhl8aIQXNms8D7rBcIjfCettU0wH83NRNQOVZHjFzme0XQciZQCaS1SK3SWo9KUPI7J4yYw9n4yCaVqJpMxSik21tcpi4IwbCGEg3Q85uYXD5qj6po0je/xoP7VwDahhHbmS6j2zdNP8OuLu6/vpvBV1xlVPkKVSeOLbj8OiX7kmLrEkmJV1oQ7lCmkE0pZM9n2cawmi2PKx1zMPRJCNBNGx8VajVX2gX3QWk1VpOi6xKjHUY5bhBC0+/P4YQtHCop4hyLeo4wnsw0HBWY/xOLwfR3XR7o+Rtfo+sn6nrWmaSPgJGOsMRRtD89WlFmMKo9KavdNoQWHA+SjiCMLRoGowHHBi2bJjJ9f+64TPH0opVEqJ54m7O1Omv+vNcZYpuOU3c0RefIZl8sKIBJNAG5uQDXKQH2fzT3rQJKBq2FWMbxfG3Mc++olxb2eejRv5bvN2tPOlKXBQTMEVki0EM3LLWQz696RmanWHfAlJFnFxk7G4FSFNQZpLRHNnbfiyUkPO/O+3drY5v33PqDfn2dhYYWdnW2yPKMsC8qypK5rwjAkCIPZo5lzNsewVEfybaRjCFptenNzdPpztHsDBBZpDYHvEsz8e6Xr47iCufl5Vk6tkEwT0vReS0MhJWEY0Ao82q0Aaw2mVpTKNB/7I3KMp9Mpa2tr3LyzzpVbmwx39tiLCyZZTVJC9oRDkp19bmlRsDeakKQZH9f0wGjNcLjHxuYGWVZSljV7O+N756H37Nqc4PMGrTVHPUWSJGU8HhPHCcZorDVYq7C2wugSpRWeE+G1AjrdFnMzi6OiqHCkwJUenuPQilrUvR5ZNkKIw+oNIZgFjEpc18V1XTzPw/Oa4FFrDVI2tlpKCaqqGXh8P8BxXISFuizAGDzHQVrTKKQ5Xm92dKYjRNMfXdcjDEKCIKSqClQNqq6azfe71jn7VTiWe5d1d+8/SQGe6xD6DqHrEbouWItRGoHAdRyEEeBKXMfDcVywupmXmUPW13UkriNRxqDU018v7Qen7rdZcpj/+7C1qxSN3/1+FUDzHc4IeW2plaasa+Ik5fbtW3h+RDRYwvECxqMxuq4Z9NroXkQxGTPd2mR3e4c0iSmKAlvXCFVTJgnJaIwXtfCjNnWpyKdpsz61BoylyDKKLEdrjec6SCmw9jCP4kQT9eWHqmuyPMcYzdgVKK1I0oy8KEjSnDjNUUrjSwdlLcYapHQIwhAhXUTQQroeAk1ZZCitGuU5FqXNwSadEALXdfA8D4FAK43v+4S+T1WWVGVOVRSour5vtbK1jXbP0mxISacZ94SQBGFASymSMj+oGoTGMz1Lc6SQFEVJNeOnmuNZrLaoWlEVJapSx2+v9q7HCU7wGeOESP8M0YoCFuZ7tDshtagRkWT1mbPMlSWd0xVJVvP6W7e4fvtpWBxAWVa88fovCd93Obe0xFK/x83be1y9MqQTSFb7PquLS/zjf/R7LC4ucPvmba5+dIVXv/oNvv2t32Bne8oH713l4oULtFsRYavDd1bOMJpM+ZvXf8y1nTuQVI0k67OCpFGSSRrB5ePsPlpDEU8QRtOrKp4LAjJrmOgamSTE128y2txk98oNdtbWKdL7p2BrpUjjKXma8KO/G+P5PmcvfMjc/CKuH/Dd7//ewWR2PNzhlz/7EUX+pInanwKsgWoMSIQNEfgn95wT3IVmKz8b36BI1rBafUISHZoNnCHoMewfK5/C5vtkO5J377gImjCwpwZHQr+FlQ5VvAtVjn2A7ElVBdt3rjali48kty1Q4fouL337+6ycfY6P3v573vmL/zeq9rFlQEORjZpzNce371uDZdrzK2TjHaY7tz9WPbOtK4qtq5RScnOtsa2pi7s3IBSw7/O+v0o8mnp3HxgF6RZIB7qLECxBsgvJ07kPneCLhZ2dMf/pT36EBZI4QxvDWz++wntv3qDIPuMNWF/AMz50BFytYefBY5IysD6GsYT4Qc2UNCp0CYw4lHAeIVTaPpztNZYtdyaNT/mqA8sOREJQWEElBLUDSQ3rFSQWrlkwEl7owLwHN9cT3r6docJdLl3ew8RTThtDCGzw8QQ+Rhv+83/6z/z93/89Fy5d4qVXXm1s+pIJaZpw8/oN6lrx2z/4HucvnKPTDqhtgbb3HwNdz+OVr3+Vf/Bb3+P02VWWVhYRHC6oHSkQUuA4Hp6o+M73fpOg2+XHf/8Tfv7GL+45nuM4DBbnGHTahC2HMs+4vT2l3EtwJfgzH/lE3Tv/sNby+s9+hpjc4sZOyhvXhphK4eYl2sAnyUe/cuMWdza2qGuFUh/vQEVZ8OPXf8xHN9/j5gfb7KxPSJIUm5/MpL7o2NkZEsdJY5lSVkjHwfcUXuDgBRJjfRYXTrE4d4YzZ0/z6ldeoshKrl+5TV3VBI6DYwXPXDiLuHiKt96q2d3dPLjNu65Dq9U6Rp57nkcYhkRRSKsVIoRBiBWyrGB9fRdrBcvLZ4miNnu7u0y3tghdy0I7oqwVSV4ckML7MQ9H93SEAEcYBr0uFy8+i9Y1O1u3KIqUyc42ZZYekFX7cAREcuZQpI/zzI7TPPRsozL0XZb7Ee12m/nAJ5AuOq+oioZ4xjZjJzQirHbYpqoc8qJGaXsQAtjvhvS7AdOkZHf09NdLroTQkQfjj5iR6dZCWRrul7cqhEDObKyqum5EZ77AcyWqslSVZXtnzJu/fIc8r7hxY42w1eb3//m/YGlllffefofN9U2ee/YSz16+xNat63zwxo9JkylpmuJIh7ZWdMqSrQ+vMrq1Be02tFrE4wlbt+/Q7XZ45ZWXEULw/i/fY7S3x+b2Jr1Bm8B3sY4LYl80cTIGfdlRFAU7OztIKdjadLBYyqoJ03z32hpXbm/ia4e+16bUNYnKaUVRE4QbBLiBwErLdpyyvTEB1wPPp6prxtMpxhqM0viBT7vdpt/v4QkHh5nwUEjyeEq8u0VdVZiDddVxq19lLNoarLREXlPwimjGwNOnVtBKc2PtDjvD4eG55SVbaztoo9ib7FLVFdVda7LpaEIap40llGnCUw9UEPuTqZNucIJfAU6I9M8QnufSikL8mdzJ8Ry6gx6RUshA0S0U0UdbT+39jLUkcUZRCeajNt0goCxqylLhSwdjPKR06fd69Hs9trc2SNMU6Xh0uwP6vTnmB/P0un2iMCJqtXA7cwjXozvXo9XvUNUJ6h4y51PGvmzCoZnpCZrZkbH3Ve1bQNc1qiwxVQmzm0BVV+TTKZPtbSbbO2RxQpnlmIcstIxu0qbjukZKSdjeoVaa+YUlooXOwWTW8wOCMJwpW46W3Vn07BifKaym+XB8Tu42J7gXzdaK0SVGP4os2584Pc52zF2MidVQ55ga8k9L3CqaXmh13VRiPABNQNeTNMIisLiuhxf46Conn+wAbWBAQ6QX3E/mJKRESnfmkfzxIa1BWNB1ORON348gu3tseZRcQzTlnEJgROOjuG9hc4JfPyilGY+Pp4RmvwolugsEIHouoisx/v497P6wFvK6IbMfKmzcX/Op44cTjkA6gjByGPR88sog4gIzk08LQCtLVRkqZalN44meWIgtJDRTkFqAFlAqQ1YaqkphtMYVll7kUmvNVvXxw77jZEqap0SdFos7S2AtulLUVUlR5NS1wlqL40ikI0FKpCvxPBdEo/46+lHImf2dF4SErc5d6XozVRgCKxSe5+EHAc5dydCOlASBT6fdImq1iFoRVrcJXAgm+YEq1JvN2R5E/YwnMTfuKO6MSrb2UoQ9NKr6JFrVoqwoyk9Wm2y0YTQcU+uSjfUtdtYmn+h4J/j8QCl1l7WmxRrVJAjP7o1h4NHvt+l2I9qtAIklDDyktdhaobFEHZcgCAiCRklurcEYOyNmHVzXo9Vq0+m0cV13RmRbpGxUoEEQoJRBiEZx7Lk+nheAtai6QkgHVzpoKRFSIGZKaUHTVY/d5a0Fa3CkoBVFVLVE6Zq6airw7rcGEQJcRzTLqiOq8aN/P/5cibvfDmMwRmFoPNy1MQgcwMFai+s6GN38bLQ5NBV0JWHgUZYKzxEYa9FPUZguaJTlUgg8OfNGtzy0MuXgc7QzL3dzeM77VS5FUTIajknSjLU7dwijFjvbmzOic4ONtTW6rZBeO2JzY4P1jQ3qMqeuavDA1jW2rijTjLoGUyt0WTHeG7K9tU2WZSwtLTUBtZubjPaGFEVxMK5L10G6Do7TrH0/F8XPJ/jUYK1Fzeb71V0KwqrWaN3chx0pcYTEEQJXOoRBQBgGuBFYYRBTTV0VeI6L53uzaovmNUGrGbvCICDwfQLp4UrZDCXWImSjULfWEoQRIKjrCnO0w87GJqSc9WVNXc/yGQDpyHvs1bTW5EWB1oosz1D6XoWkVno2d9kn0Q8hhNOMmcZg7eejI4gjDxyaAu3PR9NO8JRxQqR/hhjMD7j03AV8z8ULPVqtDqe+8iyu66K0R14o3r024d0P15/OGxqoU9CFJW7l+EKiVU6/A9IYdncL+v2CPC/QumZhYYl+f55u/xTCW2J19TLf/Mq3OXPhIi+++DWCKEI6HtMs4fs/+G16qwu8/Vc/4dov3n867X1c7M93uzRXcORB4MJIwVZ9P6kTZZ6iVMX161f56Rs/Y1rk7MQx169e47//V/+a0XjCMCmo6pr6Mb2ajTHs7WwxHQ3ZXLuF5/vN2wFR1OK5l7+C4zjUZRMcpIxGW8PO+hq7m0/pO35CWAn2qOvDCU7wxIg4TOn7nCWjawXj7WbF84QWKo+Dui54//W/4Pq7P2Y63J79tgD2uLe4+hDZeIcymza2Lh+zDtf1A5YvvITf6s5+Yxlt3GCyfftRreYw/e5eSDegs/oiTtAmHd2m2rv10A2IE5zgiSBp7tOWx/evbAGXQPYcWi/N4bYCslvbVGsPPoAG9jQ4ugkZvS8Mh0PWXV21vRTSO9Pmwvwy3730PHvDmBt/9mOmk4SrGjYtyI0cNa2YpJq9BHYMvG8PLbKx8H7aKB8vDeArq3BuyacVtTl9eo7f+v5p1vZS1t/cpUg/hpmngMXzi8yfmaftt4jzPVSlyKc5WZpTqwylNbfX16iM4cWXnmPpzGmWTp/iwnMXiKcxW3e2D/xFq7rihz/+Ibt7W/yTP/xDFk+fZj9UWWBBmIao1zVJXvCjH/6UP//zv2Q0HB1r1uLCHN/7ztfpttsMum08V2LVPFbX7BTvUa2PCDxBt+NQKEus9H2JnzujklGmKCqDtrPLZn/N/Suer9S15taHO7ieJEtOrPG+zDDaMN2bHG58C8GF1TMsL4d4bs7m+hWMtkSBRlrF7Y0bFHnG+fPnCYJ5giCg1+tRFDlxPMVaSVW5tNsRP/jB9zl//hzXrl1le3udOJ5Q1wpjwHUDHGfm429tQx5VCmNqDBV2Vl3mOTCIGprGoRFNVcVxAtqoijpL0GUP0DgCwiDEakUeuFgtUcoc64ee59Dvh2htqSZ548vOIXlszOH0xWhLUSigZHc8wvczBD5YyebeLnvjCf3uHAuDJazVRJGLkA7lsKQoCvTsjYPApdsJ8ISlJTRpqdiaVA+0f3pS1MZiakvoOrT9AAvEZUmlDfUDiC1jDHlez35uKPeqsghlD1T24+GY937xDkpr6jRFFzl/+e//mCAIiKcxRV6we+cqv/hJmyrPSCcjMBpHa3zPpRutkyVT5lc9Wl5EMp0y2d2jzAustQx3d/jTf/evqeuaJC+plUbqFGFr+r0OC0vzBFGMNRVlqRhNK/TT+tBO8IWBFJIzi6eZ6wwo4ilFMsWVkrbrEzoWo/ZQKsAxPXAcpC1xTcp8b4mFU+eplaEoFWEYcvbMKXzPxbWmsXhDIIWgLCuKokQ4PgQduq0Wr37lK0gheOfNnzDa2zloT7vTpzc3R1XkTMZDpknJOC5nVknNzTzL8wMLKQuUdcneZLux5X2kpeRxwz4hHXqDFTy/RTLdocg+HxvcAdABnADcgUAb2Btb6hMP9y8dToj0zxBhFNCf688EPwY/DFhYWSYMQ7T2yHNFq916qu9pVVNeV5WKPC8xRuO7oCtLXmiyvKYqS1Rd4/s+vi9x3QgrfFphl6X5JeYHi3Q7fYIwRDoO0pGcO3eOWBXc/PkHT7W9jz4hDvkqn6ZMuyMRLRcqi3UU4sjkSMzmFVorrDWMxiPW1teJ84zd6ZS1mze5/sEHxHGCjbqNGtNa5Gw39VGkV5ln93Ubn1tc4tkXXyYII6qiRCtNZWq00UxHw/u84jPEgaD4pCTwBB8HLk3n+xzOCKyB6kEVMo1NweFzeWL1gjWG0c6du377cKUsCFSVow7a9fH6nXRcot48UXcOrMVaQzLafvQLH0FECSlxWwO8qEc+vAVlcv8nnuAEHwf7FWT71WMPu/T3700BsCAQfQdvEOCGIdJ/iD3R7LAPJNCP4gFd1Ys82gsRvaU+i6fPYJwhjutigAmNRfs40yS1Jqkgq5rI5hGHtV7Q7Oej4ZIHgw60wyZ4K4p8llZa5MLguh8v8FIgCDshvaUujpZoVaFMTV03AXgWhcGQpCn+eEKtDV4QErVb9AZdtNbNGDj7DLQ2bGysA5pvj0YYBMI2X5gV9mCuaizUWrOzs8udW7fvCRiLwoDzZ07RabcaT1NrcXERVuMFQfPVS4HnSZQ9vhA+irQ0pOXhsYUA4czITG1+pdMVayzJ5DOuvjzBrwTWWqrirs1krQh8wFTE0yEgcYSPEDVZNiFNEqpqBTtbP3ieR11XWNvkTGjdEOSnTq1y8eIF7ty5RZallGWJUhqtLdYe1TE2ym2tNMY2ix5Lo+SWAnzncDAVViDEYeXM7CQaWztrmmwC2VgvOY6DIyVSMiO2DjuVlBLPd5Ha4AiBxh701H0ivXkTgUXMvMINRdkQ356wCCuIpxP29nZAQ9tvIx2B44N0BFpr1Gwjr1G1OwS+h1QKJ3CwMwX/0zL+tjR2Uk04cSMNrfTDraL28yiOwphZbsasWY1f9OHqzyjF2o3rx14Tjxt7vP1v1KG5tRmlSZMEgaUzXxEaQ1mUpGl24D2dZyk3rl2hqkrwQoTjELkG3zFIp/GbVqoiDB2sNfd42J/gywsh5MH37UiHTtRhrjPHqKzImc6qLySuAEyJNRZr2wgrEUYjTEXgunTbHbQVtLSg3W5z9uw5PNchGw1R1eG1rZSahZA6SC8gaHVZPnUWRwquvPf2rIKm6Rie79NqdzDaUNW62ZSaVYM9qM7VGE3xwHXb/XA4NggEnh8RRB3ybPwEx/h0sd/XHQlBIKhNE0J/gi8ffn2JdCnBCwELVXlQwvqAJ4Pwmh/tk8dE7d9EB/0BFy5cIssShrvbaGPIiow4S/jZT3/J5sYua7du0PIEtbYP3C1/UlgLk0lJUShUrRpLgCbDhN1pzN//7d+yuDCg3Rnghy2k28Nqw+b6TXZHQ3YnE965coW5hQW+/zu/Q6vb4bsvfY3nz1zi1hu/5Oc/+3HDqX0WIp19VduRHL2wHRLMdyEy2HlNaB0GBLga3BxMrbm9tcskyfjpT37Czs4OiyvLnH/mGRwheOkb32A6mXLj6jXKIuPM+Qv0e302126z8zGV41kS88Ev38JxnYNSKGMNxlrSOKFZdh/P1v5MYOtZEsjjhCue4MsBh0MfpKdxveUcqpw/J3LBx0Bn8TSD088cqCLqImX3xnuo8lPKMRACWn3wQ8jiJmjViyDsNmGe+eQR952DAwEuxrgURYGRU7LhFlU2JY9HD3+lkLSXzxF0+2R7W+Sje63DjKpJt64j3YD6c6LmOMGXCAaoQXoQLsx+FTe3oNo2t6ODSdKKgOcE3lzI4IUVhJRktxPy6Yh6+JT66f5q7q6u1/JbLHaXKTPFD1//GZNxSl4Us3SE2R5+ADJqXlqVh6Op5fBnQ0O43B5BnkO0nHHuzhZX1/f4kx9vsDctifOPNw5bLMPJkHy94JXLL/Cd176Bh4tnPIa7I/70v/8zRsMJXiBAVuRVzDgekRX3/+yM0dy6tc5oNGV3d9iMR3cRMgKB53h0ojY/+Ae/Rbcd8eabb/Huu4eViFVdszMaMZxMuH5zjSzLERiENaxt7QKQVZbNiUKbB4eN3o12t8MrL1zGWnj7g6tM4oRO6BF4kqxU5NVnZ5EnBXQD8OQsGugki/nXCjev3SQ7kp8kpcB3nMZrO1NgJfE0JghCJpMJk8mUui6bub8xlGVOkkz58MMrFEXJxsYGcRyzt7fLzs4OWlvq2qC0BRkiPZjEQ+JkSFE0m9uFUpgsR2JxMTgCAr/psPvb+UGrjRsERK0urW6P1ZVVzp85RZpMufHhiGQ8oswL6vrQp9wVTRWNYzVZkqONpTb2WB3bPpHuRT3a3Xmk1dS6BMelJESIkNOrp+hGLWo8DAFGK9a2bs82xCxKa6q6RgCh34Q6r851uLi8zMb6DtuTPbJaIy14CFzZVMdURswI/SbQQiBxcGYbC/rxTAa1YVoUs4HcHJAfd/vL3+84Qgrm5jpErYDpJCOePtm9aP+YQkLblzgObIwmiGnKduEQdXawszTTPEsZjfaaYFh1l4WH0iil2Y0z1N6YIsmYDgtUbU7U6L8maLW6nD/7HL4XgvRBSCIrUBZq41ArCEKXftfH8RyEtphSUckJVkqSaUw8Kuj0M7rjjMWVVZ55/iWqumRt7XZjd1kkiCPZUkHUot1us7g4zzPPXCKKWniei0CwfOY8XtRiuL1JOh0/tO37Wop9PNEVKwJwwibTyWQHr7ZWk0x3yNMJZfH5EQGVwBgQNThDi7FNtsQJvnz49SXShQTfn62A6keYFwkQM72RvctY83HfDmi3OywtrTAauYz2dprJVVWS5Tlvvvkm167eYm8nwXdEowAyT+/GmGU1WXavgnSaZ7z3zrv0uy2WVs/R7vZZXjpDr9VmvLdNnEwZjiZcu3mH1TOneemrXyFohbxw7hkEgn+1stw4PVg+GyIdOKw1BAR4gU/UaUEHxIqgbV1O2w6+EgRjiy4Ue5OYSZJx5cOPuPLhR7zylVdZWFhACsG5y5eZjsds3LhOpSqWlpY5dfY8eZZ+bCK9LArWbl1/wF+92UPz2RPpekbenZh1/fpA0lxviqdzvdWzx1Gv9M8/ot4CixdePvA8zad7jNevfbpEetiBqNNYpZQpuD5E/UY1X0wf86NriHRrJVVdYUrJZG+dYrzzyFciJdHcIu3FM+iyuC+Rbo2inGzRXCcnlgUneMqYCZClD36vucpU0VTK6f1Au33V+hzwssSdC+k/twwFxG/tUNyaNibknxT777PfriP9L3AD+mGfeDTmvWvXyNOKcmbzpmevtS6IEGx5j8X6sZ8tsJPCNIXzeyV7e2Nuro/58fsj0vITjMEW4iwhHiZ89dVXePGV52kHLQZBn821LV7/0RtUVY3rAVJRqYIkj6lmhN7dMMayuztkNJoQT2OwjeL1uLpR4AqXyA959dWX6XQidneHx4j0WmmmcYzSmnfe/4jReHr3W1EpS6We7F4RRhHnnrmEtZYPb60hkoTId2iHLtrYz5RIF6IJog3cRs16QqT/emF7c5vtzcMKMCkhCMB1XQbdVcKgTZ7npGlKmqZkWYqZ+YBYa6nrmizLWF/fwBjLcDgiz3PGkwnb29sHIZ5CevjRAIkgzccYfaiMr7Wh1uZA7eg64HuNSfp+brIXBLQ6Xbr9eQbzSywtL7O0OIcrFCpPKeIJdVUfI14dCb5sMljKolGXK3N8lbBfoOsELVqDZUxdUqZjcCS19fFFQH+wyNLcgL1JxjSuGE122dvb5O6JjhBNPwp9waAdsTwYMN6ZkmU1pbZICyDwpQQk2jZrYjujvMWMSrcIzGzkfdTIoq0lq+oDIm8/pBUOCb79z/BuCAGdbsRg0KGu9BMT6fvtE0IQ+M0NaDtOKTQMc4sXjOm0IrrtFnE8YWdz7bBacjYYWwvKGLCaOC+ppyl1mpPG9VNT75/g848wiDhz+hJR1AOnBVaS7G1SJBOUESgDoePQ7gbNpWMsRmlMmWGwFGlBnlYUaUWRloR+xOXLzzEc7vHLt98imY4JbYG0+1UjggXXJZybp9ftcmp1Fdf1cJyGPuwvLOP4IVkSP5JIh0+wWpQeyDZQzIj02fGspcjunW/8qnGw2lY0QTon+NLi15dIt6aRFAGPTumYKXiFgCBsFmJV2fjxPgJCwKAX0Gl5LC506ff77Gxv8uGHH1FVip/+zCcvKt57b5O9vYxpUlMqi3qKJPr90A49lgdtXFewvpuyNSrYGhnCMGJSKN6+9hFbm0Pu3N4mywqGwzGVteTxBFXMY6M20vV44aVX+L3f/4MDRfru5jbvvvGLJlDl04SlWVgrqNoZ+OBFIUG3jao1w3hMaByWvA5SOAjnuMyqrmvSaUya51y7dZskjinyHKylKivyLEd9amZW+8rgzzhwlMZRUeBhqbGc+CB/eSHAnwe3TWuwRHuwTDHZI964CaaiUZVDswsmaTrTURLVpzEq3ifLDQcd7gBfrMlBrWuSMsP3AzrtLtb0aC2eQwQdquku+olKCx8Mxw/onTqP9Hzi6ZQqHsIs1NQLIqL5ZVQ2IY+3jhROPxpWK7LRFtJ1UA9QmB60wfXpL5/Djzq0+yv4fovE8bn/xse+nvaLsylygi8ejIJyPAvtrJop2LFpjqAhBGoDSgP17MkWDEStZjuwzKH8uNEM+9LD/Z+PYLw75fp7tyiznHiYU1f6IGiUWdMSDbsVpIJm6NTwoNtox5f0PEk6SXjz7dsM45QX5g1xCbcmD7cWOIajCnoBSwtzDC52OX9mlaX5OVzrQFmDUbQ6Ib1Bm7mFPlGnRW1LdoYbjKY7ZPkUbQvafZ+6khRpfez8sjRhuLdDFLbodHtNSB/7ZFNjt3D69BmCIODZy+/y4fkPiZOEveGQPC+4ev0O1ppPFOrpeQ6+77CwsMi58+cJw4BK1RRFAVbjSChqhTaWon568ychBWcunWFuacB0MmIy3qPONenkMHPHWkjKhkD/DPn7E3xOYWcKQ2MMcRJTlDVLRY5SisFgwHPPPct0OmFzc50oirh8+TL9fp8zZ07R6/WYTFpkeYLr3GVZZTW6zgCBNfe/0KQA123Wl2nd0Mt3C5LTeEKRZRRp3BBoWYK2Fs/3KapmF/NYIN7sGOVsXL57CRq22nhhhNdqAyAcFzfqIIA0r6lr2N4dU9eand0dhuNtsjzlmA2DANcVOBIc38HxHaTnIRwXLSSlbaqU9l9RGoOQEPUX6AYRrgBHWvIsI5lOZ5W+D/6O7p7RWMBxJHPdHp7rkKYpVVVTGUNlHxw/ag1MJxl1pcmyT5IL1FQmCwGeZ7GOwJqSqjAUUuM4iqpu1LZSCoLQRQhBWTehtXOnL9Kam6cqp9Tl5488PMGnj6LIuHX7I6Koy8rq83h+i1G8w3hvnSybkJkCW0nkpMJ1JVHoYgVkiW5smIrjvEZeVGzsDpnGMTWSGsl0ew9TZpw+e4bBYI4iz7l+5QpZnjEcjnFcl6zIsFaws7NDnmeUyiL8NpUWTJOcsqgeb3/nbk+iB73IVEAyE7Oe4ASfH/z6EunGNCuyx3tyY+ki3EZd6Lhgxo9JpAsWFiKWF9usrg6Ym5ujqip++dbbbO9MuXI1pig19SMmBE8b3VbAC+cXyEvFGx9ukOYVodjFFfDm1Q9w+y7ZVBGPK+ysZrC2lmQyos4Wsb05pB/w2te/2RiBzvDOT3/OlXfe/2yI9BhIoAxSSielvThHuNBpyownEyI8FhY6+IGLcI+7c9VlxXQ0Zm9vj3ffeIMiz7HW4jguZVGQJmmTNP2p4FGeyp8eJB6CADPzWjzBlxRCQrgC0Srdiy+ycvlFRtevkG4rjEmBfTXzAofVEUeJ9ABYpGFxJIfeTXePeV+cq6hSNdMioes6LLRaOK5De/USsj2PrvKnRqS7Qcjic6/gtzrUr/8N1fahii2M2swtn6aYeJSbV9D3Sad/EIyuSXfWeJyxw/ECVi6+Qru/NJuoWsauz6FZ9d3f28nk9ASfLkwN+YPiQY7u11UGlELsM9TaIDS029Bpw3jvExDp8MBirOHWiNH2qOkZ9xnWLDCpYaOctbVNQ6LfxyVNAL3QYbXlMBnF/N3GiEHH8pUVmFawnT4mkb5vrCsA1disnFpZ5PKLZ3n24jlOLS2g8ppxMgJd0+1HVLrL4socrW6HtMpY3xmxN9omSYdYa+nO+6jKpS41Rh84IJPEU3Y215mfX6TX6SCFnFHpdub7LLh48SJnz57l3bff4+r7H3F7fZ3heEySZrz/4dXmSJ/glhAELp1OwOVnL/A7v/t7pGnCu+++wySZAgrXgaxUjxFI9mSQUvLMy5d47ivPcuv6h1y/WpDslWTT+jBc0cL0pGDnBDNYyyw4zlCWExwnJc2adcPCwjwrK4vcvn2L7e1N+v0+v/Ebv8H8/Bxzcz1cV7K712aaBLiee9dxDapKH/rewgEvaAqp47tCRptjWKaTEclkwvZ2j63RFIFBGvCDEJlVCPQsVvhwr04ZyNT9hj9B1O3RnVvA4DUzENfD8wNMrYhHE4QpWWvtMU1y1rc32d69t5pXiMYv2HEEXujheC7S82dEukNhBHrW4QwWZRpf+fn5ebqDRVquR+i6bG2tEyfTY+Gn97b4XuNBC7iOy+riAlEQsLG5xVTHjVuofvCAbK1lPEoYjz5ZWZS1oI1BzgriHSxZXlDVBUKUIAOqsrmhuI5DpxsAgnqUYbVg8cJzrDz/Kjs3fsnWtV98orac4IuJLE+4cvVtolaP3uAMrheyO95kbfMj9q/0soBcQ+A7LDgtrIXt7Yy80PesHpKi4ObWNkVRUAmHCsn6xibFZMTpM2dYWFjk+rVrXL1yhVIp0qJASofW3g7WCqbThGrG94iwS2mgmmSYurhvFdwxCPH4RLqtGgXGCU7wOcOvL5H+xDhSByxEQ6a7XlOf/BBFuxCCQX+OU6uL9Pvz+EGE0Q7jUUkyqbC1RtyPQXecpm5Pm0/FWKmsFTvjjLJS1No0Gmlrm/K9UiETS1lozH4proRCl1zfuInTdmkNFgmiDov9OS6fPk9eF6RlRqsdHngQfybYF1NWoNKKYhhDqrGjEmMVW/UITzjk+fEVUF3XJElCnmUYrQ8GfGsNSTxprB+yh09mv4hoFLD1EylhT/AFhLWgYihdytFNpncUusjpnj6L0QW67mKUpppqTG0gnAe303gtaAW21TwOllo+uMsg60aeYy1OGOKGEbpIUJOdxjbogJDdJ21rflWbRndDFTnl3h49x2e+16OuKjYd95OPV9IDJyBoRQyWlnF9nyqeUE4nB8rxueVVevNLyNYCjgywfpfu/HnqMiWPdzDqYRPEmTcG8LiWTEYr4uEWuq6YWzlDq9sjaPfA7zbKDvUp2dmc4AQfB/tK8QS4DXqiSdUUatA7GpLGGsZxm0mry/Fe8dSa8Yh1XykglbP1nwQhXRb8kEpZpnF+LIQzU4ZhKdC1oVYWW0AvhrQ+nDLuj67329o6wH7ERQi4ULkFaRVTVAl1nR9ksAS+z6UL51lYmCfstnF8DzOuKesUxxUErQBV1xR5gToy52kg8H2fdqtNEAQNgX6sQY0dlpSNEvbUqdO88pWvYITg/Y8+eqzP7nHg+x7tTgQo9na3KMsSrRVYizGzvNGnsXcrAA/80OPy5Wfoz/U5e2GVTjdkaWURYy6w5YzYuZmhH1mxeoITNGuHyWSM63q4rsRxJNPpFN/38X0fKSWO4zIYDIiigK3tLbI8Y3FpmSwvUBrKSlBXFdPx3kOJXSkEnusi9f4CqAkSlVKitaYsmrFIegGe7xOFPo6wOE6IriWxTA4KaWbuD9Sz7AI5y/ncv+o930e6LlG7Q6vTJcuKxsJBSJAuRluM1jhCIl2J6znHQ92PfUaglJ0VHjVeMXtxyu3dEcMkQ1l73E6GfUK8OV6WZ2S1IssSrDVPFBa/v4I3NMS4xSKkQDoS8Qn7+OMmBUlH4rcCpBSUSmG1BdE40WttqMoapfTBiQshjm0IGGvRxqCqmjotUOVJ1tWvIywWpSr29tbIspg8jzl6HRjbFPUJZUmyZnyotT02zyiKkvEkIZir8cMIbe0sUFTitbtYaCpQPBcpZVP9oRSqyHFcD6LoYBwSQszGH6eZVxiD12nRWl7AaEWeTlCqJsuK5vo+OBF7nEg/sSg6wRcQJ0T6Y2N2C54FgoggRDgutsyx1YPlUVJKzp+7xFdfe4Hz5y/T7syjdMDtGwnJNMMxloBmKnTsVh740G5BWUKcPvUBZpwUvHV1q+GhZ4u/g4lVqqHQB0p0HMCHaRXzF2/+LVf2brJy+hIL8ys8f/YSl0+dZ22yyfW9m9wcdJEPmER9aqiAFMoqo5rmkAJ3LELDnpgiEMcHbyDPMrY3N0nS9NiE1RjD5p1bbMnbmC/hAspQ0CiLT25YX24YyNcg32CSfEB81aF3/jVOf/ufYa2kiGPqNGX3lz+nnI5h8Cz0ek06XpbfZd8vmtVV5zy4sgnKNJpg5RTtU6cpN64Sv/2XWJ0D+wRth4ZMn3BoI/OrRTUaUU/fZ8ULePbsWYqq5NoH73xy62WvBa1lemfP8vV/+DvUecwbf/JHTHY2sEohhOSZr3yTV777D9jdirl9c4+g06Lz/BJlPmHtvb+iSPYe8gaWB/pHPACqylm/8gZ+ELFy5l9y+txZdna24M5246+R5JyMASf43GA/tmMD2IFKFGw6a82fqsaCwFkArwOBaUynaj7bkcUCsQN4jT+yIyFqRbyweJo0q3j3vTXyvDp47nau2c31gcJ9XEOdQWUPrUE8mulVxQNqQiRNcZAHLIJtWSbeiI1JyV58ljTZBeVgraDb7fA7P/gttDHsTUekZYa+ljNNhoSRR3+pTxonTKdjqkrdUwHZ7XRZXVnF8wKkaDSq+2S7EI39hONIHOnwtW98g9NnzhH88R/zV3/zN+iHkH5PgnYnYml1DqUL3n3nzdkmpwNWo7SlflqaEhfoQWe5w//4f/kveOGF59jb3SKJpywstHn+hcu887MP+PDNNfRDNzlPcIIGxhhu377B2tqtg9/5vk+n06HTaap2XdfhwoULLCzMURQFnuvR7fVZOLVKVQuyQjIZDXn3zR9TPETI4zou7TCiVgaZZRhhCcMQ120CydM4xo1aBN0+nX6PpYU+nrB4lUNdloyGE7K8OCBplWksXaRogketaIh1pKTV6xG0Wswtr9BfXKW8cYXJ5nWslUCzDvbCDm4QEoQBrW50j8p+H9ZCUViEtGhf4zqWDzd3uL2bEE8S6ofYqwDs7m4z3tnGmBpjHt4vj0jfDqBpvNKVNmhlEFLiei7yE4xf+0VD+8d/WPsd36OztIB0JNkooSprrCyAmrpUqEodW+ofOJLO9jXruiYvSrJJQrozwhp7Mo37NUVV5Xz00U8AeU9lq7FQKqiUIZs5L9xNZ4ymCRO1QbC4Sm9uATcIkI6DdFy6p85h6prO/AJhGODN+rOpK6p4jOeHyP4A13XptFtYIGq18cMAVSt0rVhYmOfypYvUZcra9V+SJjE3bqyTJHeJeL6EPMsJfr1wQqQ/KbRqyHS9H9r48LuYtZY4ydndm7C9O2J7dw9t4cIzlxjtjdhe38TUClccswNtRkKt8YOQbruHrmumo+FDVQpPAmub4JqjkDSn1uq3CToR+TQnHaUHM5K6qtne3MGRLmubd+j3BoR+gO95aF01T3IsBKLh0D6rzXIzey9hsa49sB+3ioMgmrsRhgHz83OzXVSXo3nSxtxt3vplw5f53E5wgFmorFWq4cXzKXW6i3AChOMj/QAnauGoGhm1kWEHVRu0KBGeixM2ZaXWWHAkwaCL9FzKIkNXJTJoIbwIrzOgs3oedIG1OVZriolG1wYnHCC9HtqYZuwyqgnetKbJnfgMr0XXdfHCCOlKsjyhKEqqLEPlTZufFI4f4vgBxumgpIexkjwrqPOCuioxdQXSQ7gurXaPubkl6sJl1Ckp85xkMkWV6QN9UJ8EfhgRdXqouiaLx1hjMLbJ2iirirwsmswHo2djm8Mhe3mCE3xOYDjYMzp27xZNJnxZQq3vvXIfVxH4MAgar2zXdfCjAGMMeVo0498MSkAhGoLDleA5IDyB9OQ9FcrGHm+jBsyMuXJN01ZPCByaasAHNn5G3u8/tFCUuqQoU7JsjC5gsq0QVtJqtRBCYFTdjGmmUUNooyjrilqrB9q61VVFlqZEEQR+COwT6WJ2bvtu6VBVFVmWUlX3DzE9fPaTfSe1UuR5RYkiFyVSSjzPoyrrY2r/TwwB0gHXk7TaIZ1uizj2cDJJluXE04QkzRCORTizYqsTnOAR0Fof21TaV4gXRcHe3h5CCIbDIY4jKMoCYwye79PtdCmqpk7UD0IcN0DICmsatfI+HClxHUnoe4RhiKMMQVDjaIvvuTiO08hkrEEKge95OFJibTMfsMaANYSBSycKUFWN0QbPdQh8D2ssVissDamOlI2EzBiUUqiyRNc1RiuwEqhwHEG3HRGEIaouiKeGunq4B5K1YLRB1wLjghUCx/OIWi2UVlRHcxaspaoKiiyhLguUqvgktUjGWvJZiLQyGitAyKaC4MibHgtjfei5cNw65qHPtaC1xe6PwffYWhx9rqWuNUJK/FYPV3g4no81pnk8zfHwBF9IKPVwa0jLbBl4v7+pGl02GQrjvT2Urmm12gigLjKUlGgDeVlTVjVK1Witmmtv/6DWYlTdcCatCCklUgg0Fq0VZVVSVxVlVVNV9aOtXo5COghE815PUHnyNHC3FPSEMTnBw3BCpD8JjIZkAkLMSsN4ZAdXSvOjn77FL97+gLXhlK04ptNu8b//P/0fuPLBFf4v/+f/B+nWDj23WZiNa8gMsxVjzerzL/Ib//ifMh3u8rf//t+RjMefyqkJIBTgu5JXf+NlLnzjWd770fu8+V9+3iwkS0h2U37652/Q6XeIgojLL/6UZ89f4vTyKrnVZEZRyRK7KsCTsGUagvvTxr5naYdGqiYf/NR9x+eL587xh3/w+1y/fpuPrm5QVnFjp4MAk88CLe6naTjBCb6YSLevcfvv/j/4vVUGL/wOwo+Izp7Fr5cJO128MGSic6bjFH9umd7Z57BCUlYG13U4e26FqOVzZ22d0XiE02pUVoOzl1l+7bWmJNgoyiTmoz//D0w312if+yqt1fMkaUqSJJCNYXgLdAbV9oxM/2zQW1pk+fKrBGHE22+/QZ6k7Fz9iGw6wdQP0rYedRDVx37fW71Ib/UCcZwzHKWMxzE/+5u/xdY5eRw3pc/teWTYZWnlEs+ff5ZBNMZ3u2zc+pCrP/s78nRCXX5yC6mlMxd56Ts/YLK3xVt/+58piwJac5igxfrukMR+wO76bZjuzVihcHY+BSdj3Ak+77AWRhOYxLO9IA6vWkHjggeNsvLjFu+5riTwHAZLA86+eI48LfjgzasU2SEplNDU3OyT6Mq1tN2awqkx4uFv7PiCqC8xBhZijdLg+C5CSkypqO4Xnilp5jTh7OFDTomsa3ZGa9y58XP2NlN+8eObCDwunr9IFLWQgYdwBXk2xpKTpGPWN9caGxhhDn3XDz5fy8b6Om//4hecOXOOF55vI2Yk2uwZNJuqYIzlow8/4O///m95991fPrByz501/0li1Xd3pkzGKftmDq4jiCIHa2wzpj0lSAFhAFFgcUSjumjK2h3ee/cj/u6vf0qVF4hI4bqNS9pnvJY/wZcAVVUxHo9JkoTd3V263Q7TZMjKyhJFUVDXNVGrzerqKtOkotQZftgmaC2gjU+VjzD6cPzpRgHz3RaddovlxXkqpTCuT12rxn4BKMuCooTId1noRjiuII2nOFbjVwnSGM4t9mGxx9rmkN1RzPygx9KpJbK8ZGtvgtYGh/1Ns5KqKhE45NOEeDScDbLN/CEKQl598RnCMOS9937B7u42VfmIMAELdaJR0rDQ9ZlfHGAGFrVkSOOUO7fvoGdVxMZohlu3Ge+uo2pFM6J8/DlLrRS3d3ZwpCDwHVxH4gYObf9w4aiUJs8fn/h73PFN1YrJKEY6EnWXWEtyKG/QgFKG8bjADSNWX/kq0WAJL2o1QpQTFe8JPjZmu/l1Aipj65rDX//HgPmlJV779tdxpODqLwzxaMg0Lsizio3tPeLpCFVWx/qEMZp4uE1VFnieQxA0m2llmbK7mzGd7FKXBcPNNeqyfPzcOSFxgy7C8dFl/JD12aeDu6ZHjQjiM23BCb5IOCHSnxQfQz0YxylxnLK+scXN27c5c/Y0Fy5foDXogCMbLbQ4VIRD44EnpUMURvQXFzFaI6XzsLf5RBACwsAjCj16813mVuaIOtFh7LkFUxviUYKqFRtb6/h9l/l+l7l+F+1IHFfiepKg7VHkHtqvG4XDp11+dpTv3mfK74LrCKQU+I6L57j0ex3mBgPGcwnzcws4btBIlLBkyZC6yrD2bh/RE5zgiwtTF5R1gRUOuoyR1iCkQTgghUbaGmkbkymBRjoGHAfPaXw2W902rVZIp9+ltpogahFEEb1+l8GpFaSQWKMp4ylRf54ySQj7SwSD09RuQilicDxsmWBrH22noEWTQvgp9DPpuEghGr9OIXADH8dz0EYxHu1SJClVlqLLnPsvhcSRx73tE44zC8qqwYKuapIyB1XMDAoFftjC7/SJWm2iIML3UqRsNhyqfEKVT5/KuXpBSGewgFIVYbuLFQ6162OFg9KGqq4wZmYgZoGDZfIJTvDFgHpQRre469+PCVc6hJ5P4Hv4gUtdOffkJ5jG/rPpORJqDJVV1EY/Wo0oQMtmLJEzVbsUHFclHh1yZpZ6+0p06TZ5964ET1ik1eiqoioyppMREo88XkRoi6hdcARFllMWJWVZUpQVEvA8B0cIbCjRDtQzO4HpdMr6+gbtVgetFRJ3ZgS/b2U6M+61jRKu3K9yeQie9CtRtUYd2VBwHBoLCduoYg+OJz7ZLUMAwgK6IeiLPMMYjZCCLC3Y2dxDYPHkvaLRE5zgcWGtPVCpl2VDSG9sbDS+/zMIxyNoC4yx1HWFUmo/gOGe40kBjhR4joPveSAkvuceVNNYC77n4nseriORTcfB6hpjm3BhgcH3fJwjKmwhJZ7r4jgK25hzHxDzui7RxlAVBRJ5l9rcIoTBdyWeKymLnDSJH/h5CNHY2wghMMYcBjtbixTNRmbpOgee6PtQdfVIUdZ+P33UuGCBsq4b4trxD6xIj471n1bOV6MyVwgt0UIcC7Y+OvQ3z23IdKHBCVp4rQ7WaFSRPSJT5wQneAzYZgO5yhNGOzt4noek8Tz3XB/P9ahrRVUayqJE1fXBuNVUlCiskai6RFUlqqqa+YC1eI6DNoYsS6nLgqKo0PUTKtJnuSyfeGL3hLi7H57gBI/CCZH+GeL9jz5iO92lvdBm/vVF4p2Y3XhCTaNEF/u+dEBvYYn55dMMllbJkpQsS5uyvE8Jnu/z8tdfYmV1gf6Z1Yc+V9WKGzevk6gxz50+Sy8ICLsdWoMe+WiP1559jt3BkDV3kyItKbYrdP4p1sZ6QDR77JunHhkFHSk4d6rHXD/k/NlLnFk9x2B+mXSasrK4zP/2v/nfYK1AWUNR5vyHf//veO+9d8jzlKI4CeU7wZcLKh0yfPe/gHQws6C63HEQUlKbFtCiGt9hPP2AoDfP4nO/SdD2cJ3GI/errz5PqxXQH7QZzHWptaGo9r2ALVUxj1T/kuloivHnsV6HOaWolcKoHJWPKdIRm9d/Sp2OYPsjeEqE8j6k47J86hytTo+gE+BFHtPhhNtv/Xi2bpEYpamLMY3C6W7zXY+GxTLcP1PAEm/dopjsomoXKo+DVZFtLFRcz+XVl15i9dyznDtzBoCdrXVe//FfM929Q109vcVQrRRJkeF2enztH/0z8iThvZ+8QRanzPX6LJ09S53njJMcWxTY6XTW3vtvEpzgBF8UWNso0fd//rhY6Pa4uLRMXBe8//pVyqqmLO7qowoowThQSZiqEjXcxhQWLRXCO1LMdhfSynJjpBEW5Ow5tVFooalnNgIyBNkB2wa9yIH5rpTQn4cohMutFmfCiBfnznBh/gWiesjuuRxhJOd6yzjC5YPrH7A73uXa3oS1SUxlK5SxdNoh508tEXoBg2AelODNNz9ic2PI3/39T/jwo2v8wX/1T7h46RKtqEPU7iGkmKknLY5sAg1ffPFFOp0Ii+Qv/4e/va9H+r4S/ZOMLsZAnpsmeI+m8sBxG1FEXVtq9fGOrmsodmFcFfz8xz9jZ+MOC6fO0OkPkMaBbBbAaJtr6kSNfoKngbIseesX7xD4/sHv5lbPMr96jngSs3FrnarMiSc7aFVjzHH2OM8rhkojkCwvQTPfaMjxMIxwHZdep4tAsjeesrG+TrcTcnZ5DscBKR2ElWhVUWtDpRQVsDeZktUlShmyokY4DmG3hxQCUxToqiJTY6okvsdOosxzPnrnl7iOQzyZ3Pe8hQTXgyj0uHB+Bd/3GI4mFEVBkcbc/DAhcD1avk9eVg9Z67o0g+KxEJ9GCBY240JRGB7Hqc8ARamoa91YhdkjsglrPxURldKWadboW41tRketHt5YozXT0YhSQ759h2o6pEqf7nz5BL9OOF5pb8qUcu8OY53xzk9C/CjCGoET9Um218gnI5LJmKooDvqlqktGWxuNsL0ssdYw3t0mmU45d+ESl156jfFkxM1b10A6BL0uWimqJMGo4/1WHtkvnOWUgjXoKkHXEqufVjDK48Fy72rwZIV0gofhhEj/DDEajxmVY5w9D29yE5M2XnIWKO9yEQmjNoOFJcJWh7quqauah8ewPAYewpk4jmRheYFTZ1eh03roYYxp0ukJFWWR4wpBy/MYtFvMd7uszM0jtGE0nmAdqEbq0yXSHZoref9xV12OENDr+CzORVw4u8xzzzyDxqMsSvqDDs+/+BqeH6DRpFnKGz/7Mbdu3UDrmqrKZ5OqT6/5JzjBZwmjSorhrWO/O1gahachjDDllDK7gWNSPPF1AkchZhUai/MDVpfnGMy3mZ/vkOYlO+OkqT4B6jpk+dKzhIsVeelQ1oedUVtFrVdIp7vsTjfQno8dr2HLrOmyopEaWvYZjI9nrySFIGx16PQGRIMWYScgnUyJdzdmfXm/uudBkzRJQ6bv+7jf24Y6T6jzDIjAdjgcYBuWTArB3GDAqZVlWlGEMYYkmbK1cYt8uot+Ct7o+9BGU1YlQavF4rlL5HGM98Y7SJ0QhRG9Xo+o28Pp9DBIdJI11VUn49oJvgR4Gvfn0PMZtDvko4rh9hh1PzZmVndvbaMwr4xGFzlUzLx2m3/v16+UgWlpkUBbSoQUFFqjjj7Zbch00wGWaIaUWVVzFEInhKXQ42wYsRR06IVzlC3LfH8AWtD1W6Ahn0wZbm0zGmaM4hIRWGRL4LkevW6bdtji1GARtOD995t7wfrGJusbm7z04gukSYIULmGrh7ACM/uAHdmoNefn5/E8wfLyEp7norW+x+LlaRjjWTurRKCx0tkPeXUcHossexCEBSqJzmF3cxshK8Jen3Z/0JDms12AL3VUzgk+c2ht2N05HiyeG59StklGY7Y3NjG6ApNyPzMBpTWF1lRV40ts7cwvG4PrSDzfpeW3CDyfOElJ0pTAtbi2gyccHN/FWkuRVY3nsTUNoVzVlLPqEmsFjhAIIRt7J2Ox2qB0ed+NMaUU471dhBBUD/BGF6Lps37gsLDQJYp8jK1IUstwJyeZlBjfxw0VSqkHkNj7Nnv3VtM1SneBlM0Y8bhjg9IN6b4/Vn3aSlRroaptMwCZ/Zy1R73GUhUFRiYke9uUw81PsYUn+HWD1QpdJBSxYGdjjSBq05tfwXV96rKmSNPm+jtCgFtjKPPjlpRlnlPmJRIYDOapVY0UzTzH8X2QzXhyFEI8uOLL6s/O9vOe9/6VvfMJvog4IdKfNiSNkBEaDuaomWfV/L+pNXVeYJXFPkBRs7y8xGuvvUKuNOPxiOlk/PHDlgS0l9qEvZB8kpMNs+PtovG9TNKM8TTGbwu8QDQ75fvjnjn+3PFeQlFXvP/We8wRsDA3z+ryCtPRHi+unufsYJlTKxeIk4yfxD9lY/op3vwdIOD41XzUkUEIgtCn3YlYmO+zurLItRubvPGLt3D9iLd+eZV2p8uLr7xM1Ir45je/yYUL57h65V2uX/+Q4XDC2vrOic3LCb78qCeNpFI33tlVOmHr3b/Fjbr4y5fxOwNW+t9iac7hg7c/Ymdrk5XTp3j1a68hXRdtoVYGVRmSXs04ESQF1EZQGYkXSNodh6paZm65S54mbH34EvlkTBC0cb2AKp1SJSOq8S7ZzStYnQMjHkx63wttNMPtdeLJEHfTxfEc0vHkSB9+1EpL0bghP4gOEnSWXyBauEg+GpFsboMtgZh9FkYpywdvv87G7eus3XqZ1XPPcvWDt0m2r1GX2VMJGd3HdGeTKz/9G6L+PPPPvIxF0D57jmjlFM+8+jIXn3uWbq/P6uppdtfWuPnuu9T5lHIcf6qVTic4wRcFO9MJSmvSsrEyuC/2gz9nG/bWNPnz1LPHETW64wgcR6D18eC6bq/Pb3zjOwSez09+/jqbO1sHf7MFqBHNvEtzsN/nCjgTwmoXno3aXAzmmffbGAuD/iK/8c1/SJlXbN/eYFJMWStSbhUpL3ztOX7vwjmmkzG7wy2MMqhxxkhnrH04oi4te7vHVaS3bt3iP/7Jn3Lh4iV+8Lv/mKjVRs5KrMVsOAyCECEHPPfcs/zT3/9dNja2eOPNtykf5Y38CaBnGfDGWoTiscMAj8LzIGwLBr0+r770MlErxHZLtNa8/dYvqH/xc258sP7p2xGe4AQzpJMhRiuqssTodCbJlAgcWn6IJx0cYZAYtKpQdUWSZLx3/TbGGtI8R0pBELVwPUuSTJgqTZrGCKDIK9bXhywuzPH9730dz3P5i7/9MXd2RxgD852QolbklSLq9Fg8dR4DJFmGquvG6mUGwWHusaJJWDHWkqkSAei7U3kl4AqEI3ACSRBFLC2v0u91OXf2HFjLxtoue1tjkmnGaG/Kfj7CcfgcKtFnC+kjsBaKwiCEuIdEd7wAL2pjlKLK42O7rvYR/z5tCCnxghApJcHM3svWBVYrlDbUxuBIF8cLZlY/NUZDvrOJdPdQ6YNtc05wgo8LKSyqKthZX0M6Lrubm0ghKcabVOmEqnj8HKfNtduousDxfBbnFynriq2dTbS6d/129zTrsSgW4YETAQZUxol7+Ql+1Tgh0p82BM09X3CcrLY0cwANtjTo+OGdf27Q59Kli2zt7bF25QppmmA+JvEiEISDkO5qF4MhG8/sSo4czlpLURSkaYYtAii9RmEkuWdWYY0lmeZkVcGta7eYly4rc0tUS2OMA+fnVqgFLDsOkyTj/R9+wAafMpG+r0Tfb+sRIl0I8AOXqBXQ7baZH/T5oLrJB++/jzIS4V9nML9Ab26e1VOrPPfc87z66ot02hJsgpSC9Y3dEyL9BF9+6LR5zKDKlPGtX4IbQFrgdRdJXrmAqZe5fe0qb/zkZ3z921/nt773TfwwoJ4R6XWlaLdqhGuRGeRagpK0uwHLqx2shcHqGfK8RIenGI9iOp15gqBDNtwk3blNtnad7M4E9AS4f8nwg2CNYTree9gzHvVB8FCyXQjCuXP0z38DuEqyOaaxgDn0Wzdac+f6hyAEcZKxO47ZWbtKPt546uR1Nhny/2/vznokO7LDjv8j4m651L50V+9skiI5Q4/YM6JljA3ZECDDD4afDPjR/hQ2oC8iQIAf/GK9WrABGTJgGxoTFj0eamY0ZLO72UttXV1rVlZm3j0i9HAzq6t6J6ubHJvn18gqoFCV92aib2TEuSfOSfsHJPPnqDpLBK0OydIycRSxcu0qV65dpd3qMj+3yP04YefRNurIUx5pKVsgBNBPU/rpS0q5TQLpJ3a9+clQ8URXTWMUQaABdyro2261+fBHN2i12nx5/6vTgfSqedDm1D08rWAphsstxYVWzEo0RRTGeK/odma4fv4qg8GQna0dRlXOfpmzXRX8k7cv8E//8cc8XN/gq5uGw8MB9+9uMhgW3F0dkuVPj3E729v8/NP/Q5bl/PQf/QHtdgulxq1DffOiwzAiCA2XLl3g7398g9t37vKbz798o4H0cXUZmnySbzYXM6Gi3VEsLXf4vR//gFYr4db2HXqjHvfv32drd5diD1mbi29NkQ4o0pMB0iZcrZShFbaJg5BI1RhqMjxpVZLlBfuPdo+vgjAMWBrXt8qyEXk6Ih+XpSrKmv2DAd3OFG9fv07SSvjLTz5jp58x302YasV4D2lREyUdli5epbKWdG0NVz49Phiavsclk6J3nuJ52aNaQaDQgUYHAWESMzMzx9LiPItzXVpJxPzUOtvTO6yv7XB4MHkfTm7rUTzebny6pMuE99Ak1D89LuggJGpPYcuCKh/hnwj2P120781RSjdjpzF0goBAKbzy+NKRek/lmvI7JkxwzlHXDu885fFc9tstcyH+/6fwaOVxdUl/b7cJZo93BlP2of46jT49B/s7HOw/Yvn8BT748McUVcHuwe5z/+JrL4VUAEGnmXjZXOquie+cBNLP6mTm8+QxKatpObUY+jo21lb533/1P7HGoE1IqPUz79O/Cj+uw1aWJa1OQuvqefJhTm+n32Te0JRr2TvoUfuKuB4S9hKOBkfEcy1sWVONymZxMfkcL5rxa3tzh1uVo7yY01EhUTuhNTcNSuNrcPXr2Xb9QpM2557TJV7GP3PWs7l1RJrVpNmv+er+AQ9WH7LT6+G8QgUpo3zAJ5/8D6amp0kSgzGKjfV7bG5u0OsdSRBdfL85C4MdXDXi3i9+Rr51h91eM8HaenCPv/iz/4CJYmw8i4lbXLj6DnF7iq2NNdY295lZXGZueYWpyDDdbra5tMKAsq3Ir55nfn6WugqxtcFMzxNpTcuEmCyjzPoMjhaw1RD6O1CeDHZVPB5wn80k7ebcKoutapRRhEmAd456dPbeE63ZKZbfvUo52qP/aA8/TolSStGeWSBMWpRVwfbabYb9/Tc6ltR5ymDjDjpuYzrzRK0uO4926XY6rH7xBWu3bnLYT6kt+Kd60wshnqJo+q8ENOVWZng8r6uAI56VJDkOnrunMqdNGDK3vES30yVK4mcesqNgJQQfQM9CqGC5gpUMVkLDuSgiSrpEC4vE8Qyt7hzELS5ff5f27Bw3igELK0vMT88zPCqpS0UctWklnunpWSDDmGc3WW53O6xcvsDC8gI6BFTzwpTy44ajUOQpWZGSZc+/8RDqJuOycvCqyeOT0i0TJ0u7PPc4kSbpGlztSYf1M9fVy8tzLJ+bJ4k13W5IEoU8uHMb72G9v8OwSMl7KUE2CcgJ8V1psq6892RVSmUNsbKEeLK6arLAaYajydLHWcfgcECd5xgcoQmJdE1MkwulgTzLuPnlTbpTXX7wO9e5fOli03DeO/p5TS8taU/NsHx+ieFwxEaZUqVHxyUdWgqS8VrXv+qGDeehBq01nbiN8YaNtYcMen24usLsTIepVpf25RZl5thYfQhY2qWmcoqituOSppbHpV0inkp0UIowiTBBwOzsAknSIs8LiqKkOz3H/PJ5hkeHrI761GX+xhalkzIVxii0Gu9GOlEfKjCKqVaE0QZX1FTO0YlbxO0WejTCDkdoHaBNQq09eRQ0Oxfr0fObbwhxBs7VlEWK96r57FSKIO6gdYAyHbAhdd2snZqsgefdzHE0DUINWscUuWNrc4swCrl68QoeR293mzxL6fd6pMNXz3I/bXwNKN1kpnsLrhifmxDfPgmkn9Xks71ZM407mJz9ae/duc39r+6wfPkqH3z8+4TGPLeW1KuoqoqiKJifnmX50jz7j3r09wbHdXqttWzv7NI76mH2A0wSUDlIlrpUaUHtKnzlT71Gn3s27j6kt75FPchZSNrMzM0yMzuLJoDa4iYlht+kkwH+yb7DSSDdgnWeB2s9Hqz3+OVvHqKUwnuPOzHBUUpxf/UWzQdB87NJwxkJoovvPVdDfxPbV3y++xVfKEV35XfoXvqA+7e+4Ff/9T/iTQwLbzO1eJ5/+a//DZfearN27xaf/c3n/OjGR1y7NMt0K2S+o9CmqXVZO4iSK6QF7GwOOdzPIG7B/BLF3DKtqQXSUZ9s9Tx22IPiMyhPZjcMeVkgPWxPEU3PUaY5dpBikoB4roWvK2xR4M/Y9LO7MMfCSovBdovh3s3jwI/ShqmlFTqzC+xv3GN79dYbv6tYpwMO7/4aggSWPiDszrOx9hCtFX/76f/ii5/9N4Lp8yTn3sdhZFkmxMsooEuTJX6BJpg+oNkkM+S5VaeeLOkyEUYhyxdXmJqeJm61nnnIGQ0/isAGcMs2JXUvlnBNwZVWwGUiTHua4Px5TDRD0F4imrG8ay0rwwF117Dd2yZQmn6voCwUSTyFawfMz1YoRgRmn2eNnVMzU1x55yrnLp7DhIC2KO+bRA7tQDnSbMBh74B0NHzm/EgBsYZINwWyXrVmcWAgCk+8h655vGjYjFqaueWYKrfkmT0VuJq4eGmZn/ze+0RBQBLG9PcP+NWnf81wOOSwdJTWYQwEGvTr6wEtxDdk8VhGZYUCEhQRUODJeVzNcrIcc87R2+sxUIqF+Wmmui1iVdLi8fIrHQ35xWefMTc/wx/90T/j0uUrHBz0GQxHHBaOg8KidUAUxOzt7vDLYkg17B2fUVfBrIbMwdC/4qYNB5SeIDRMxR2MV9y7/YAoNLS0R1WLXDx/ieWFc6SDjC9bX6BwlGVAaS21c+NGx5MBJKAJpJecDKRrrYm6LeJWwpW332Vhfon9gz6H/SMWF5e4cvkqO9sPebh6l7qqxkG31z/7mdR8jkJNYBRF6bDliUC61sy2E7TSHIwKqrJmcXqOuekOyu9SpznKhKigRYlmqFwz907TszWFEOI5nB2XPBpTOiDudDFRjCJE4cjTHGtzcNX4hs6zNNkFWscY0yHPLGv311hYXOAPfvcf0uq0WN9cYzDoc+/WnTME0id0k5mOg7KWQLr4zkgg/az8E4/X9bTjAG6RZ/T2d8lHI9wZPkjrsqYclbjIosb/njxeWdQ47zHeofKaztwc88sLDAZDsqLGFzXUZZNeND6VykJWeXZ7A+6uPmR+VBB0p7HG8CgvORyO3uh2X6CZU50ck1OaTLETcyU//uKf07xQAuZCvAr/uKlodkTe26IcHGDrqslmGPUojGf1N/+X0e4mBw9u4w4ecfjgJmtJwczSOVz1AXGrxczCLB7NqDdklNZonzA1lVCVNWVeNVVClSYKIubnlimTNml2SN2ZwWYprixQ4Rw6snhb48oMbAl5bzzZCwGNq2rqbIgtm4wJX1vqNMXb+htnoytgaXmWt967zOHOKjvra4wOtvHOorQhancxYURdVqSHfaq8BN+UeXidHxRRZ5qwM01dVhR53iy6yqZxa5gkhHHC4c4mOj/gaO8RzllsmVIPd7BlKpNPIV7FJLh6QDOvSGnmHBmTYeZxX+InjZtsLSws8t577zM/v8DG+iawyXBwuubtcldzeSZgccZxtaipbRNMZ5yRPmOh1a4JohJTOQwGtMGaAB8aos40bW1YXDiPDgPS0YA8S2m3O3RbbYq8JElmmJoZsP4oJWkdcXh4RH5ijnZwcMjnn9+iLC0//PBD6mlPtxUQmADvm/HLaE0YhgwGQ27dvsvGxkPqukZrxVQ7whiNLUqq2r60YafWisXFebrdNkZbtHJYa6mqmrp2DEcVKE2n08Fozf7BIWn6eLu5rTzZsKau/HN3eR8dDVlf2yYwhsiEDAdH9IYlWWbJao91EEfNgkjJNFD8Fhnnpx8vaR7/7PRwE4aGyBjiOCJOEuq6yYb24+btQWAoq4rBIOX23Qfs9UeEYZPFXdUeZz24msoD3jE3O4vCMzgaUFbl6aSxlzrdttNaz2iUozW4uiIwiodbu5RFSRS2abc6oGB2dg6lR+z1U2p7ck32giwyrfBaNTunVUmeZYzSFG00U1PTuLpma2OVw4M9bN00LjPji/xkK4Tn9Ij+WianO7mB6p4x+E2C7Up70I60yPADS1ZZvI4xUYfWzDy6qtGHu9i6kPIV4tvjHXWV470nCIOmQah34zXVy0sLeW9xrkQpjVEBZZHzcHODOA7Z298lzVKss4RJjK3tqSamr0TRlIwCsNW47MF3f32YUKG1wk7GUvG9IYH0szp5s/wNGAz6fHXnJq6qm8nMN+GhGBSUZclM2IU5nkon8B6y4XiyNM7KXjn3Dh/d+Cmbu4/YqUvK4QiqXnP3r2ieI6uax5cPtlnf2Gdl5RyjGrzRPDjqM8wyjg6PzvYmvMyIZmG7z+OJ3ptJOBBCjGW9LfL+LjjXbD91Fg7XyI82+Ks/u4M2hrJMcHXI6sZnbH5SMH/lA976/X/O3Lll/t4/uIGJQh7dvs3h4YCV6zdYunCd/v6Qg7Rp+Ge8pxO3WLj2AdY71hfPMUgHpJvrFHu7BHPzRIvL2HRAvr8B2QE8+jlUI5oU0pBymFKNDvEmAdPBliV5OmyCQt80kK4VH354jT/8Fz/lZ/9li1/8+X+nKvNmkRi3mL1wDRMlHG5ukg+2xuVeYpqJ6OtLeeyev8L8tfc5Ouyzu7WNz4dwuIY2AVOzC4SdWVb/9ufc2V9rtmYCNuuR5f1my7STmptCvJCjyT5XNIH0yRzD0yRHdmnmG32eORfUQYCOY35448f823/3xxwdHvHv/+RP2VhbZ3Nj89TvfrQS8a9+t0NclLQOhtSB5/2VpsTLe3uwkMMcBYkdotIKhcGqkMIkYDSdxYB2XfFuHJCmR9y9d5ONzfssLp7n8sXLTeO6tKLX62Ndm0fbO3z2q9+w9Wjn+Bzu3L7H6oN1fvKTG/zgBz9k5fwKb12JMe12M2Z6CMKQTqfDw60d/tOf/wVZllEWJXEUcPXCHEkScG/tgP3D9KXTsCAwfPTRe7zzzlWKoqAoCrI046g/oChKDg6OCIKAt66/RRIn/PWnf8P9B+vHf59nlnLLNuUmnjOcP7i/xcb6Dmq83dA7T13X4yDjuAqzakroSH108dum5PR9OkezBJswWtHpJrSTkKnZabrdaZJ2h6m5Kbx31N5h65p0MOBokHHrP/8lNZqPP/4x7733brOkOzEVcLbi7etvMxoNuXnzJge9g8c1Yl7l+lCmqWU8lpeORzuTOt8erWA4GJBEhrKy6EBj8Vy79jYPt3e5vb5DVtYnDqV4MjgPNAG1MMAD+TClSnP2dnfJ65qlhXMsLSyzuXqfX/76M2xVUlUFCk9kmj/NbZMXdvKlneXy9+OyN0XpXhiYV8pjQof2Ndv9faq9mth0icMZWtPnmL/0LqNRj+29ryAbnvGshHh13juKYa8pTTk9j046QAVuxKsEVZwrca7C6Jgw6DA6OuLTT36G9w7nLB6Iu23as7MUoxH5YPj1TlArdKjBWVwxaILp33GwRymI2wFhbMiHFUUqCUrfJxJI/y3nnWs6pzdFbc/wRM3i4UU37h4na48XF0oTRTFBGKKMaYpXak7NZyanVFlHZkuKomw6jTtNVVaUZfXMu/Kv1ckkcxm/hPh2OId3TwSFvcVbS5FOCs02F6e1GbYYUoyGFFlOWZQ4Z9FOY+sSWxUo3HFtyXG4Y9wIRxEEIRqPjhJUXUMQgQkhiFFhG8IaTAw6GheqnDw0eN80mFL+uCFxE9g+27gUhgGtVkxgFLZqgugAKIU2AdoETQmp44yL11+PXBuDDiN0EIIOmiLD4+PocSasLUvKLOX4PZm8H0KIVzOZYzw5fxr34HzpUKIUYRQxMzODrSxFUZKmKfaJXYahUXRjRWQVoQPjx3WJNYQejG2qq2Adp1K9FeAVyhiU9wRBRBhGaN0UHDemmcs5B94ZkqQgiiOiKDz+nYm6rqnrmizLqKoaayd1ipvjee9RSqG1xlpLlmUURXl8HibQhMY0Q82L35Lxd0UYhiRJfPxG29oShgHWOoJAEwSGOIqI42hcFuwE39zDfRFrHdY+f/LrOTn/FeK3z0uHGK1QSjef+6opnxd4g/NqXP/Y4b3HOkuaFRQWyqrCOt9kZp84gPeeIAgIgxCln5i3vNI05vjiPj55dyJpwdM0QcVZyqqmts0cKQgCjDHHN7j8s57zqUONGyO6JvPeOns8rjbP5SjzHOce1xh/Tlj+tc7QXjqUjDPTnbPU1hJoj0eBNs3N10kmsATRxbfNu2bMmAwK/vjLqz4Bk/+3zjmKuji15oj8ZLw64xXnX+8O37NQWqE0nKkGs/h/kvJSz0IIIYQQQgghhBBCCCGEeC798l8RQgghhBBCCCGEEEIIIb6/JJAuhBBCCCGEEEIIIYQQQryABNKFEEIIIYQQQgghhBBCiBeQQLoQQgghhBBCCCGEEEII8QISSBdCCCGEEEIIIYQQQgghXkAC6UIIIYQQQgghhBBCCCHEC0ggXQghhBBCCCGEEEIIIYR4AQmkCyGEEEIIIYQQQgghhBAvIIF0IYQQQgghhBBCCCGEEOIF/g4NM/NkSQxkggAAAABJRU5ErkJggg==","text/plain":["<Figure size 1500x500 with 5 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Image 1:\n","    Predicted Class: 0\n","    True Class: 0\n","Image 2:\n","    Predicted Class: 5\n","    True Class: 0\n","Image 3:\n","    Predicted Class: 0\n","    True Class: 0\n","Image 4:\n","    Predicted Class: 0\n","    True Class: 0\n","Image 5:\n","    Predicted Class: 0\n","    True Class: 0\n"]}],"source":["# Evaluate the model on a batch of test images\n","\n","dataiter = iter(test_loader)\n","\n","images, labels = next(dataiter)  # Get a batch of images and their labels\n","\n","\n","\n","# Move images and labels to the appropriate device\n","\n","images, labels = images.to(device), labels.to(device)\n","\n","\n","\n","# Make predictions on the batch\n","\n","outputs = model(images)\n","\n","_, predicted_classes = outputs.max(1)\n","\n","\n","\n","# Convert images back to CPU for visualization\n","\n","images = images.cpu().numpy().transpose((0, 2, 3, 1))  # Convert to numpy array (N, H, W, C)\n","\n","images = np.clip(images, 0, 1)  # Normalize pixel values for display\n","\n","\n","\n","# Plot multiple images with their predicted and true labels\n","\n","num_images_to_plot = 5  # Number of images to display\n","\n","fig, axes = plt.subplots(1, num_images_to_plot, figsize=(15, 5))\n","\n","\n","\n","for idx in range(num_images_to_plot):\n","\n","    axes[idx].imshow(images[idx])\n","\n","    axes[idx].set_title(\n","\n","        f\"Predicted: {predicted_classes[idx].item()}\\nTrue: {labels[idx].item()}\"\n","\n","    )\n","\n","    axes[idx].axis('off')\n","\n","\n","\n","plt.tight_layout()\n","\n","plt.show()\n","\n","\n","\n","# Print predictions and true labels for the displayed images\n","\n","for idx in range(num_images_to_plot):\n","\n","    print(f\"Image {idx + 1}:\")\n","\n","    print(f\"    Predicted Class: {predicted_classes[idx].item()}\")\n","\n","    print(f\"    True Class: {labels[idx].item()}\")"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"A100","machine_shape":"hm","provenance":[{"file_id":"1fcK8meVkYIyNl452Duw6_KYKOz_iJvyn","timestamp":1733403415206},{"file_id":"1CXSibep108UBOlFNVzpu8ie0c6nV3XDQ","timestamp":1732368422653}]},"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":5944322,"sourceId":9755289,"sourceType":"datasetVersion"}],"dockerImageVersionId":30786,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.14"},"papermill":{"default_parameters":{},"duration":8065.568,"end_time":"2024-11-27T17:57:01.322705","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-11-27T15:42:35.754705","version":"2.6.0"}},"nbformat":4,"nbformat_minor":5}