2-resnet50-from-scratch-data-augmentation-final.ipynb

{"cells":[{"cell_type":"markdown","id":"a635db0b","metadata":{"id":"a635db0b","papermill":{"duration":0.005896,"end_time":"2024-11-28T03:58:41.268502","exception":false,"start_time":"2024-11-28T03:58:41.262606","status":"completed"},"tags":[]},"source":[" As we have seen in our 1st notebook that our model, ResNet-50 built from scratch, didnt perform well due to imbalanced training dataset.\n","\n","\n","\n"," So in **this notebook** we'll experiment with the below tactics to handle the imbalanced dataset:\n","\n","\n","\n","* Explore **data augmentation** 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":"f5038d49","metadata":{"execution":{"iopub.execute_input":"2024-11-28T03:58:41.279392Z","iopub.status.busy":"2024-11-28T03:58:41.279096Z","iopub.status.idle":"2024-11-28T03:58:50.301222Z","shell.execute_reply":"2024-11-28T03:58:50.300148Z"},"id":"f5038d49","papermill":{"duration":9.029991,"end_time":"2024-11-28T03:58:50.303437","exception":false,"start_time":"2024-11-28T03:58:41.273446","status":"completed"},"tags":[],"outputId":"2f424a6d-50d7-46a7-fcc6-d65f0f6eb3b1"},"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":"65614fb0","metadata":{"execution":{"iopub.execute_input":"2024-11-28T03:58:50.316107Z","iopub.status.busy":"2024-11-28T03:58:50.315817Z","iopub.status.idle":"2024-11-28T03:58:51.685269Z","shell.execute_reply":"2024-11-28T03:58:51.684563Z"},"id":"65614fb0","papermill":{"duration":1.378388,"end_time":"2024-11-28T03:58:51.687137","exception":false,"start_time":"2024-11-28T03:58:50.308749","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":"bdb4a5e8","metadata":{"execution":{"iopub.execute_input":"2024-11-28T03:58:51.698913Z","iopub.status.busy":"2024-11-28T03:58:51.698134Z","iopub.status.idle":"2024-11-28T03:58:51.709020Z","shell.execute_reply":"2024-11-28T03:58:51.708309Z"},"id":"bdb4a5e8","outputId":"67f1d2d4-fd98-4a64-d947-b095d264602a","papermill":{"duration":0.018077,"end_time":"2024-11-28T03:58:51.710488","exception":false,"start_time":"2024-11-28T03:58:51.692411","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/deepweeds-image-classification')\n"]},{"cell_type":"code","execution_count":null,"id":"da429b34","metadata":{"execution":{"iopub.execute_input":"2024-11-28T03:58:51.721540Z","iopub.status.busy":"2024-11-28T03:58:51.721308Z","iopub.status.idle":"2024-11-28T03:58:57.180083Z","shell.execute_reply":"2024-11-28T03:58:57.179354Z"},"id":"da429b34","papermill":{"duration":5.466651,"end_time":"2024-11-28T03:58:57.182244","exception":false,"start_time":"2024-11-28T03:58:51.715593","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"]},{"cell_type":"markdown","id":"d7519192","metadata":{"id":"d7519192","papermill":{"duration":0.004845,"end_time":"2024-11-28T03:58:57.192381","exception":false,"start_time":"2024-11-28T03:58:57.187536","status":"completed"},"tags":[]},"source":["## Applying Data Augmentation:"]},{"cell_type":"markdown","id":"6d49d71c","metadata":{"id":"6d49d71c","papermill":{"duration":0.004808,"end_time":"2024-11-28T03:58:57.202021","exception":false,"start_time":"2024-11-28T03:58:57.197213","status":"completed"},"tags":[]},"source":["Let's apply **data augementation** on the images in a new folder named `images_new`. We did this on colab notebook and then uploaded the 'new folder' on 'Kaggle' while running the model. We'll also update the csv file.\n","\n","\n","\n","*NB: After applying the augmentation & saving them in a new folder, we'll comment out the below code block as we wouldnt need it again while rerunning this notebook.*"]},{"cell_type":"code","execution_count":null,"id":"281a7bb7","metadata":{"execution":{"iopub.execute_input":"2024-11-28T03:58:57.213206Z","iopub.status.busy":"2024-11-28T03:58:57.212795Z","iopub.status.idle":"2024-11-28T03:58:57.218130Z","shell.execute_reply":"2024-11-28T03:58:57.217298Z"},"id":"281a7bb7","papermill":{"duration":0.012883,"end_time":"2024-11-28T03:58:57.219746","exception":false,"start_time":"2024-11-28T03:58:57.206863","status":"completed"},"tags":[]},"outputs":[],"source":["# # Applying Data Augmentation code to 'augment' the images\n","\n","\n","\n","# import os\n","\n","# from PIL import Image\n","\n","# import pandas as pd\n","\n","# import torch\n","\n","# from torchvision import transforms\n","\n","\n","\n","# # Load the CSV file and filter out images with labels 0-7\n","\n","# data = pd.read_csv('images/train_subset0.csv')\n","\n","# filtered_data = data[data['Label'].between(0, 7)]        # Filter for labels between 0 and 7\n","\n","\n","\n","# # Define the image folder and augmentation transformations\n","\n","# #image_folder = '/kaggle/input/deepweeds-image-classification/images'  # Update with your image folder path\n","\n","# image_folder_new = \"/content/drive/My Drive/Agri Project/images_new\"  # Update with your image folder path   # Path for augmented images\n","\n","# image_folder = \"/content/drive/My Drive/Agri Project/images\"      # Path for original images\n","\n","\n","\n","# ## Augmentation Transformations: The transformations create variety in the dataset to improve model generalization.\n","\n","# augmentations = transforms.Compose([\n","\n","#     transforms.RandomRotation(degrees=360),\n","\n","#     transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0),\n","\n","#     transforms.RandomHorizontalFlip(p=0.5),\n","\n","# ])\n","\n","\n","\n","# ## Augmentation Loop:\n","\n","# new_data = []  # list to hold new data for updating the CSV\n","\n","\n","\n","# # Loop through each image and apply augmentations\n","\n","# for index, row in filtered_data.iterrows():\n","\n","#     image_path = os.path.join(image_folder, row['Filename'])\n","\n","#     image = Image.open(image_path)\n","\n","\n","\n","#     # Apply augmentations\n","\n","#     for i in range(4):  # Generate 4 augmented images for each original image\n","\n","#         augmented_image = augmentations(image)   # Apply transformations\n","\n","\n","\n","#         # Create a new filename for the augmented image & Save the augmented image with a new filename\n","\n","#         new_filename = f\"aug_{i}_{row['Filename']}\"\n","\n","#         new_image_path = os.path.join(image_folder_new, new_filename)\n","\n","\n","\n","#         # Save the augmented image\n","\n","#         augmented_image.save(new_image_path)\n","\n","\n","\n","#         # Append the new image and label to the new data\n","\n","#         new_data.append({'Filename': new_filename, 'Label': row['Label']})\n","\n","\n","\n","# ## Update CSV File:\n","\n","# # Add new data to the original dataframe\n","\n","# augmented_df = pd.DataFrame(new_data)\n","\n","# updated_data = pd.concat([data, augmented_df], ignore_index=True)    # Merge with original data\n","\n","\n","\n","# # Save the updated CSV\n","\n","# updated_data.to_csv('images_new/train_subset0_augmented.csv', index=False)\n","\n","#    # Now the new CSV includes both original and augmented images.\n","\n","\n","\n","# print(\"Augmentation complete and CSV updated.\")"]},{"cell_type":"markdown","id":"7dbd6c1b","metadata":{"id":"7dbd6c1b","papermill":{"duration":0.004693,"end_time":"2024-11-28T03:58:57.229394","exception":false,"start_time":"2024-11-28T03:58:57.224701","status":"completed"},"tags":[]},"source":["Also let's **copy all the images** from the `image` folder to `images_new` folder to be used for testing the model.\n","\n","\n","\n","*NB: After copying them in the new folder, we'll comment out the below code block as we wouldnt need it again while rerunning this notebook.*"]},{"cell_type":"code","execution_count":null,"id":"63df15e2","metadata":{"execution":{"iopub.execute_input":"2024-11-28T03:58:57.240333Z","iopub.status.busy":"2024-11-28T03:58:57.240062Z","iopub.status.idle":"2024-11-28T03:58:57.244371Z","shell.execute_reply":"2024-11-28T03:58:57.243519Z"},"id":"63df15e2","papermill":{"duration":0.01169,"end_time":"2024-11-28T03:58:57.245972","exception":false,"start_time":"2024-11-28T03:58:57.234282","status":"completed"},"tags":[]},"outputs":[],"source":["# # Copying images from  from 'image' folder to 'images_new' folder\n","\n","\n","\n","# import os\n","\n","# import shutil\n","\n","\n","\n","# # Define source and destination folders\n","\n","# source_folder = '/content/drive/My Drive/Agri Project/images'       # The directory containing the original image files.\n","\n","# destination_folder = '/content/drive/My Drive/Agri Project/images_new'     # The directory where the images will be copied.\n","\n","\n","\n","# # Create the destination folder if it doesn't exist\n","\n","# os.makedirs(destination_folder, exist_ok=True)\n","\n","\n","\n","# # Loop through all files in the source folder\n","\n","# for filename in os.listdir(source_folder):\n","\n","#     # Check if the file is an image (modify the extensions as needed)\n","\n","#     if filename.endswith(('.jpg', '.jpeg', '.png', '.gif', '.bmp', '.tiff')):\n","\n","#         # Construct full file paths\n","\n","#         source_path = os.path.join(source_folder, filename)\n","\n","#         destination_path = os.path.join(destination_folder, filename)\n","\n","#         # Copy the file\n","\n","#         shutil.copy(source_path, destination_path)\n","\n","#         print(f\"Copied {filename} to {destination_folder}\")\n","\n","\n","\n","# print(\"All images have been copied.\")"]},{"cell_type":"markdown","id":"1ed354c7","metadata":{"id":"1ed354c7","papermill":{"duration":0.004986,"end_time":"2024-11-28T03:58:57.255936","exception":false,"start_time":"2024-11-28T03:58:57.250950","status":"completed"},"tags":[]},"source":["## Applying Custom Augmentation:"]},{"cell_type":"markdown","id":"3558d4c3","metadata":{"id":"3558d4c3","papermill":{"duration":0.004647,"end_time":"2024-11-28T03:58:57.265558","exception":false,"start_time":"2024-11-28T03:58:57.260911","status":"completed"},"tags":[]},"source":["**Below code snippet** implements a more sophisticated and customizable data augmentation pipeline compared to the above one.\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":"b8499d1a","metadata":{"execution":{"iopub.execute_input":"2024-11-28T03:58:57.276688Z","iopub.status.busy":"2024-11-28T03:58:57.276379Z","iopub.status.idle":"2024-11-28T03:58:57.345662Z","shell.execute_reply":"2024-11-28T03:58:57.344784Z"},"id":"b8499d1a","papermill":{"duration":0.076898,"end_time":"2024-11-28T03:58:57.347335","exception":false,"start_time":"2024-11-28T03:58:57.270437","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","        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","        if self.transform:\n","\n","            image = self.transform(image)\n","\n","        return image, label\n","\n","\n","\n","\n","\n","train_csv_file = '/kaggle/input/images-new/images_new/train_subset0_augmented.csv'  # Replace with your actual path\n","\n","test_csv_file = '/kaggle/input/deepweeds-image-classification/test_subset0.csv'  # Replace with your actual path\n","\n","val_csv_file = '/kaggle/input/deepweeds-image-classification/val_subset0.csv'  # Replace with your actual path\n","\n","img_dir= '/kaggle/input/images-new/images_new'\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)     # Increasing the batch size to 32 from 16\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":"6b734236","metadata":{"id":"6b734236","papermill":{"duration":0.004795,"end_time":"2024-11-28T03:58:57.357443","exception":false,"start_time":"2024-11-28T03:58:57.352648","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 (as in our 1st notebook) without the 'dropout rate'.\n"]},{"cell_type":"code","execution_count":null,"id":"b2425ea5","metadata":{"execution":{"iopub.execute_input":"2024-11-28T03:58:57.368655Z","iopub.status.busy":"2024-11-28T03:58:57.368361Z","iopub.status.idle":"2024-11-28T03:58:57.630583Z","shell.execute_reply":"2024-11-28T03:58:57.629637Z"},"id":"b2425ea5","outputId":"eba3b07d-46bb-44a6-fab4-0082c163697b","papermill":{"duration":0.270542,"end_time":"2024-11-28T03:58:57.632874","exception":false,"start_time":"2024-11-28T03:58:57.362332","status":"completed"},"tags":[]},"outputs":[{"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=False, num_classes=9)\n","\n","print(model)\n"]},{"cell_type":"markdown","id":"4efe40a9","metadata":{"id":"4efe40a9","papermill":{"duration":0.005091,"end_time":"2024-11-28T03:58:57.643506","exception":false,"start_time":"2024-11-28T03:58:57.638415","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":"ce0a0cc6","metadata":{"execution":{"iopub.execute_input":"2024-11-28T03:58:57.671324Z","iopub.status.busy":"2024-11-28T03:58:57.671098Z","iopub.status.idle":"2024-11-28T09:37:58.636592Z","shell.execute_reply":"2024-11-28T09:37:58.635705Z"},"id":"ce0a0cc6","outputId":"1feefaf2-8692-47b0-a386-f875d34b2f25","papermill":{"duration":20341.028003,"end_time":"2024-11-28T09:37:58.693019","exception":false,"start_time":"2024-11-28T03:58:57.665016","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["len train_loader 958\n","Epoch [1/100], Step [1/958], Loss: 2.3006\n","Epoch [1/100], Step [101/958], Loss: 1.9633\n","Epoch [1/100], Step [201/958], Loss: 1.9404\n","Epoch [1/100], Step [301/958], Loss: 1.4539\n","Epoch [1/100], Step [401/958], Loss: 1.6937\n","Epoch [1/100], Step [501/958], Loss: 1.6164\n","Epoch [1/100], Step [601/958], Loss: 1.2588\n","Epoch [1/100], Step [701/958], Loss: 1.4926\n","Epoch [1/100], Step [801/958], Loss: 1.8454\n","Epoch [1/100], Step [901/958], Loss: 1.3999\n","Epoch [1/100], Validation Accuracy: 0.3550, Train Accuracy: 0.3838\n","New best model saved with Validation Accuracy: 0.3550\n","Epoch [2/100], Step [1/958], Loss: 1.5215\n","Epoch [2/100], Step [101/958], Loss: 1.4465\n","Epoch [2/100], Step [201/958], Loss: 1.3988\n","Epoch [2/100], Step [301/958], Loss: 1.4575\n","Epoch [2/100], Step [401/958], Loss: 1.5541\n","Epoch [2/100], Step [501/958], Loss: 1.6595\n","Epoch [2/100], Step [601/958], Loss: 1.0110\n","Epoch [2/100], Step [701/958], Loss: 1.1693\n","Epoch [2/100], Step [801/958], Loss: 1.6697\n","Epoch [2/100], Step [901/958], Loss: 1.2414\n","Epoch [2/100], Validation Accuracy: 0.5987, Train Accuracy: 0.4831\n","New best model saved with Validation Accuracy: 0.5987\n","Epoch [3/100], Step [1/958], Loss: 0.8301\n","Epoch [3/100], Step [101/958], Loss: 1.2737\n","Epoch [3/100], Step [201/958], Loss: 1.0243\n","Epoch [3/100], Step [301/958], Loss: 1.1960\n","Epoch [3/100], Step [401/958], Loss: 1.7920\n","Epoch [3/100], Step [501/958], Loss: 1.0419\n","Epoch [3/100], Step [601/958], Loss: 1.0023\n","Epoch [3/100], Step [701/958], Loss: 0.9586\n","Epoch [3/100], Step [801/958], Loss: 1.2571\n","Epoch [3/100], Step [901/958], Loss: 1.1158\n","Epoch [3/100], Validation Accuracy: 0.5744, Train Accuracy: 0.5499\n","Epoch [4/100], Step [1/958], Loss: 0.9641\n","Epoch [4/100], Step [101/958], Loss: 1.1622\n","Epoch [4/100], Step [201/958], Loss: 1.2209\n","Epoch [4/100], Step [301/958], Loss: 1.4107\n","Epoch [4/100], Step [401/958], Loss: 1.2280\n","Epoch [4/100], Step [501/958], Loss: 1.0831\n","Epoch [4/100], Step [601/958], Loss: 1.1341\n","Epoch [4/100], Step [701/958], Loss: 1.7162\n","Epoch [4/100], Step [801/958], Loss: 0.8392\n","Epoch [4/100], Step [901/958], Loss: 0.7705\n","Epoch [4/100], Validation Accuracy: 0.6007, Train Accuracy: 0.5868\n","New best model saved with Validation Accuracy: 0.6007\n","Epoch [5/100], Step [1/958], Loss: 1.4058\n","Epoch [5/100], Step [101/958], Loss: 0.9655\n","Epoch [5/100], Step [201/958], Loss: 1.0313\n","Epoch [5/100], Step [301/958], Loss: 0.8876\n","Epoch [5/100], Step [401/958], Loss: 1.2849\n","Epoch [5/100], Step [501/958], Loss: 1.4208\n","Epoch [5/100], Step [601/958], Loss: 0.9515\n","Epoch [5/100], Step [701/958], Loss: 0.8779\n","Epoch [5/100], Step [801/958], Loss: 0.8165\n","Epoch [5/100], Step [901/958], Loss: 0.9413\n","Epoch [5/100], Validation Accuracy: 0.6064, Train Accuracy: 0.6222\n","New best model saved with Validation Accuracy: 0.6064\n","Epoch [6/100], Step [1/958], Loss: 0.6674\n","Epoch [6/100], Step [101/958], Loss: 1.1678\n","Epoch [6/100], Step [201/958], Loss: 1.1329\n","Epoch [6/100], Step [301/958], Loss: 0.9646\n","Epoch [6/100], Step [401/958], Loss: 0.4996\n","Epoch [6/100], Step [501/958], Loss: 0.7789\n","Epoch [6/100], Step [601/958], Loss: 0.5494\n","Epoch [6/100], Step [701/958], Loss: 1.2356\n","Epoch [6/100], Step [801/958], Loss: 1.0350\n","Epoch [6/100], Step [901/958], Loss: 0.9831\n","Epoch [6/100], Validation Accuracy: 0.6492, Train Accuracy: 0.6636\n","New best model saved with Validation Accuracy: 0.6492\n","Epoch [7/100], Step [1/958], Loss: 0.9444\n","Epoch [7/100], Step [101/958], Loss: 0.7570\n","Epoch [7/100], Step [201/958], Loss: 1.2424\n","Epoch [7/100], Step [301/958], Loss: 0.7966\n","Epoch [7/100], Step [401/958], Loss: 0.4871\n","Epoch [7/100], Step [501/958], Loss: 1.3502\n","Epoch [7/100], Step [601/958], Loss: 0.7283\n","Epoch [7/100], Step [701/958], Loss: 0.9968\n","Epoch [7/100], Step [801/958], Loss: 0.8665\n","Epoch [7/100], Step [901/958], Loss: 0.9800\n","Epoch [7/100], Validation Accuracy: 0.6912, Train Accuracy: 0.6969\n","New best model saved with Validation Accuracy: 0.6912\n","Epoch [8/100], Step [1/958], Loss: 0.8490\n","Epoch [8/100], Step [101/958], Loss: 0.5831\n","Epoch [8/100], Step [201/958], Loss: 0.7488\n","Epoch [8/100], Step [301/958], Loss: 0.5397\n","Epoch [8/100], Step [401/958], Loss: 1.1457\n","Epoch [8/100], Step [501/958], Loss: 0.8839\n","Epoch [8/100], Step [601/958], Loss: 0.7128\n","Epoch [8/100], Step [701/958], Loss: 0.8749\n","Epoch [8/100], Step [801/958], Loss: 0.3659\n","Epoch [8/100], Step [901/958], Loss: 1.0153\n","Epoch [8/100], Validation Accuracy: 0.6827, Train Accuracy: 0.7160\n","Epoch [9/100], Step [1/958], Loss: 0.7979\n","Epoch [9/100], Step [101/958], Loss: 0.6302\n","Epoch [9/100], Step [201/958], Loss: 0.6157\n","Epoch [9/100], Step [301/958], Loss: 0.7982\n","Epoch [9/100], Step [401/958], Loss: 0.4373\n","Epoch [9/100], Step [501/958], Loss: 0.9069\n","Epoch [9/100], Step [601/958], Loss: 0.8677\n","Epoch [9/100], Step [701/958], Loss: 0.4852\n","Epoch [9/100], Step [801/958], Loss: 0.4095\n","Epoch [9/100], Step [901/958], Loss: 0.6188\n","Epoch [9/100], Validation Accuracy: 0.7115, Train Accuracy: 0.7399\n","New best model saved with Validation Accuracy: 0.7115\n","Epoch [10/100], Step [1/958], Loss: 0.6782\n","Epoch [10/100], Step [101/958], Loss: 0.5591\n","Epoch [10/100], Step [201/958], Loss: 0.6838\n","Epoch [10/100], Step [301/958], Loss: 0.6875\n","Epoch [10/100], Step [401/958], Loss: 0.6121\n","Epoch [10/100], Step [501/958], Loss: 0.8328\n","Epoch [10/100], Step [601/958], Loss: 0.5398\n","Epoch [10/100], Step [701/958], Loss: 0.8338\n","Epoch [10/100], Step [801/958], Loss: 0.9604\n","Epoch [10/100], Step [901/958], Loss: 0.4972\n","Epoch [10/100], Validation Accuracy: 0.5587, Train Accuracy: 0.7593\n","Epoch [11/100], Step [1/958], Loss: 0.8305\n","Epoch [11/100], Step [101/958], Loss: 0.5699\n","Epoch [11/100], Step [201/958], Loss: 0.6002\n","Epoch [11/100], Step [301/958], Loss: 0.4476\n","Epoch [11/100], Step [401/958], Loss: 0.5787\n","Epoch [11/100], Step [501/958], Loss: 0.6406\n","Epoch [11/100], Step [601/958], Loss: 0.6906\n","Epoch [11/100], Step [701/958], Loss: 0.5352\n","Epoch [11/100], Step [801/958], Loss: 0.4607\n","Epoch [11/100], Step [901/958], Loss: 0.4906\n","Epoch [11/100], Validation Accuracy: 0.6895, Train Accuracy: 0.7690\n","Epoch [12/100], Step [1/958], Loss: 0.4224\n","Epoch [12/100], Step [101/958], Loss: 0.4922\n","Epoch [12/100], Step [201/958], Loss: 0.5670\n","Epoch [12/100], Step [301/958], Loss: 0.4450\n","Epoch [12/100], Step [401/958], Loss: 0.5725\n","Epoch [12/100], Step [501/958], Loss: 0.4458\n","Epoch [12/100], Step [601/958], Loss: 0.6218\n","Epoch [12/100], Step [701/958], Loss: 0.5237\n","Epoch [12/100], Step [801/958], Loss: 0.5456\n","Epoch [12/100], Step [901/958], Loss: 0.4099\n","Epoch [12/100], Validation Accuracy: 0.7227, Train Accuracy: 0.7813\n","New best model saved with Validation Accuracy: 0.7227\n","Epoch [13/100], Step [1/958], Loss: 0.6162\n","Epoch [13/100], Step [101/958], Loss: 0.6169\n","Epoch [13/100], Step [201/958], Loss: 0.4983\n","Epoch [13/100], Step [301/958], Loss: 0.7490\n","Epoch [13/100], Step [401/958], Loss: 0.5758\n","Epoch [13/100], Step [501/958], Loss: 0.6123\n","Epoch [13/100], Step [601/958], Loss: 0.7505\n","Epoch [13/100], Step [701/958], Loss: 0.2483\n","Epoch [13/100], Step [801/958], Loss: 0.7684\n","Epoch [13/100], Step [901/958], Loss: 0.3565\n","Epoch [13/100], Validation Accuracy: 0.7595, Train Accuracy: 0.8001\n","New best model saved with Validation Accuracy: 0.7595\n","Epoch [14/100], Step [1/958], Loss: 0.5377\n","Epoch [14/100], Step [101/958], Loss: 0.7792\n","Epoch [14/100], Step [201/958], Loss: 0.4717\n","Epoch [14/100], Step [301/958], Loss: 0.3165\n","Epoch [14/100], Step [401/958], Loss: 0.5172\n","Epoch [14/100], Step [501/958], Loss: 0.5074\n","Epoch [14/100], Step [601/958], Loss: 0.5327\n","Epoch [14/100], Step [701/958], Loss: 0.5652\n","Epoch [14/100], Step [801/958], Loss: 0.3174\n","Epoch [14/100], Step [901/958], Loss: 0.5363\n","Epoch [14/100], Validation Accuracy: 0.7769, Train Accuracy: 0.8143\n","New best model saved with Validation Accuracy: 0.7769\n","Epoch [15/100], Step [1/958], Loss: 0.4466\n","Epoch [15/100], Step [101/958], Loss: 0.3310\n","Epoch [15/100], Step [201/958], Loss: 0.6395\n","Epoch [15/100], Step [301/958], Loss: 0.5000\n","Epoch [15/100], Step [401/958], Loss: 0.5956\n","Epoch [15/100], Step [501/958], Loss: 0.4735\n","Epoch [15/100], Step [601/958], Loss: 0.2705\n","Epoch [15/100], Step [701/958], Loss: 0.3455\n","Epoch [15/100], Step [801/958], Loss: 0.6769\n","Epoch [15/100], Step [901/958], Loss: 0.4116\n","Epoch [15/100], Validation Accuracy: 0.7621, Train Accuracy: 0.8290\n","Epoch [16/100], Step [1/958], Loss: 0.5544\n","Epoch [16/100], Step [101/958], Loss: 0.6151\n","Epoch [16/100], Step [201/958], Loss: 0.6128\n","Epoch [16/100], Step [301/958], Loss: 0.4032\n","Epoch [16/100], Step [401/958], Loss: 0.5334\n","Epoch [16/100], Step [501/958], Loss: 0.4632\n","Epoch [16/100], Step [601/958], Loss: 0.4800\n","Epoch [16/100], Step [701/958], Loss: 0.3949\n","Epoch [16/100], Step [801/958], Loss: 0.5687\n","Epoch [16/100], Step [901/958], Loss: 0.5049\n","Epoch [16/100], Validation Accuracy: 0.7986, Train Accuracy: 0.8407\n","New best model saved with Validation Accuracy: 0.7986\n","Epoch [17/100], Step [1/958], Loss: 0.2089\n","Epoch [17/100], Step [101/958], Loss: 0.4046\n","Epoch [17/100], Step [201/958], Loss: 0.2201\n","Epoch [17/100], Step [301/958], Loss: 0.3473\n","Epoch [17/100], Step [401/958], Loss: 0.5577\n","Epoch [17/100], Step [501/958], Loss: 0.4878\n","Epoch [17/100], Step [601/958], Loss: 0.3608\n","Epoch [17/100], Step [701/958], Loss: 0.2806\n","Epoch [17/100], Step [801/958], Loss: 0.4044\n","Epoch [17/100], Step [901/958], Loss: 0.4303\n","Epoch [17/100], Validation Accuracy: 0.7769, Train Accuracy: 0.8458\n","Epoch [18/100], Step [1/958], Loss: 0.1591\n","Epoch [18/100], Step [101/958], Loss: 0.3491\n","Epoch [18/100], Step [201/958], Loss: 0.5360\n","Epoch [18/100], Step [301/958], Loss: 0.2635\n","Epoch [18/100], Step [401/958], Loss: 0.5044\n","Epoch [18/100], Step [501/958], Loss: 0.5500\n","Epoch [18/100], Step [601/958], Loss: 0.6219\n","Epoch [18/100], Step [701/958], Loss: 0.1701\n","Epoch [18/100], Step [801/958], Loss: 0.1825\n","Epoch [18/100], Step [901/958], Loss: 0.3819\n","Epoch [18/100], Validation Accuracy: 0.8129, Train Accuracy: 0.8611\n","New best model saved with Validation Accuracy: 0.8129\n","Epoch [19/100], Step [1/958], Loss: 0.5166\n","Epoch [19/100], Step [101/958], Loss: 0.1657\n","Epoch [19/100], Step [201/958], Loss: 0.3643\n","Epoch [19/100], Step [301/958], Loss: 0.3820\n","Epoch [19/100], Step [401/958], Loss: 0.6255\n","Epoch [19/100], Step [501/958], Loss: 0.2869\n","Epoch [19/100], Step [601/958], Loss: 0.1003\n","Epoch [19/100], Step [701/958], Loss: 0.3692\n","Epoch [19/100], Step [801/958], Loss: 0.2627\n","Epoch [19/100], Step [901/958], Loss: 0.3302\n","Epoch [19/100], Validation Accuracy: 0.7958, Train Accuracy: 0.8654\n","Epoch [20/100], Step [1/958], Loss: 0.2853\n","Epoch [20/100], Step [101/958], Loss: 0.5725\n","Epoch [20/100], Step [201/958], Loss: 0.2899\n","Epoch [20/100], Step [301/958], Loss: 0.6023\n","Epoch [20/100], Step [401/958], Loss: 0.1821\n","Epoch [20/100], Step [501/958], Loss: 0.1448\n","Epoch [20/100], Step [601/958], Loss: 0.1285\n","Epoch [20/100], Step [701/958], Loss: 0.1569\n","Epoch [20/100], Step [801/958], Loss: 0.3010\n","Epoch [20/100], Step [901/958], Loss: 0.2332\n","Epoch [20/100], Validation Accuracy: 0.7949, Train Accuracy: 0.8742\n","Epoch [21/100], Step [1/958], Loss: 0.7327\n","Epoch [21/100], Step [101/958], Loss: 0.2568\n","Epoch [21/100], Step [201/958], Loss: 0.4596\n","Epoch [21/100], Step [301/958], Loss: 0.3404\n","Epoch [21/100], Step [401/958], Loss: 0.2021\n","Epoch [21/100], Step [501/958], Loss: 0.3967\n","Epoch [21/100], Step [601/958], Loss: 0.6418\n","Epoch [21/100], Step [701/958], Loss: 0.2156\n","Epoch [21/100], Step [801/958], Loss: 0.3789\n","Epoch [21/100], Step [901/958], Loss: 0.1654\n","Epoch [21/100], Validation Accuracy: 0.8078, Train Accuracy: 0.8835\n","Epoch [22/100], Step [1/958], Loss: 0.1539\n","Epoch [22/100], Step [101/958], Loss: 0.4422\n","Epoch [22/100], Step [201/958], Loss: 0.1835\n","Epoch [22/100], Step [301/958], Loss: 0.3208\n","Epoch [22/100], Step [401/958], Loss: 0.2963\n","Epoch [22/100], Step [501/958], Loss: 0.2661\n","Epoch [22/100], Step [601/958], Loss: 0.2414\n","Epoch [22/100], Step [701/958], Loss: 0.2291\n","Epoch [22/100], Step [801/958], Loss: 0.2053\n","Epoch [22/100], Step [901/958], Loss: 0.1443\n","Epoch [22/100], Validation Accuracy: 0.8152, Train Accuracy: 0.8932\n","New best model saved with Validation Accuracy: 0.8152\n","Epoch [23/100], Step [1/958], Loss: 0.4689\n","Epoch [23/100], Step [101/958], Loss: 0.1299\n","Epoch [23/100], Step [201/958], Loss: 0.1131\n","Epoch [23/100], Step [301/958], Loss: 0.2450\n","Epoch [23/100], Step [401/958], Loss: 0.2219\n","Epoch [23/100], Step [501/958], Loss: 0.1753\n","Epoch [23/100], Step [601/958], Loss: 0.3538\n","Epoch [23/100], Step [701/958], Loss: 0.4339\n","Epoch [23/100], Step [801/958], Loss: 0.1386\n","Epoch [23/100], Step [901/958], Loss: 0.4731\n","Epoch [23/100], Validation Accuracy: 0.8380, Train Accuracy: 0.9002\n","New best model saved with Validation Accuracy: 0.8380\n","Epoch [24/100], Step [1/958], Loss: 0.1066\n","Epoch [24/100], Step [101/958], Loss: 0.1881\n","Epoch [24/100], Step [201/958], Loss: 0.2398\n","Epoch [24/100], Step [301/958], Loss: 0.2406\n","Epoch [24/100], Step [401/958], Loss: 0.1736\n","Epoch [24/100], Step [501/958], Loss: 0.0898\n","Epoch [24/100], Step [601/958], Loss: 0.2505\n","Epoch [24/100], Step [701/958], Loss: 0.1682\n","Epoch [24/100], Step [801/958], Loss: 0.1643\n","Epoch [24/100], Step [901/958], Loss: 0.3290\n","Epoch [24/100], Validation Accuracy: 0.8295, Train Accuracy: 0.9088\n","Epoch [25/100], Step [1/958], Loss: 0.1629\n","Epoch [25/100], Step [101/958], Loss: 0.1794\n","Epoch [25/100], Step [201/958], Loss: 0.5131\n","Epoch [25/100], Step [301/958], Loss: 0.2103\n","Epoch [25/100], Step [401/958], Loss: 0.1677\n","Epoch [25/100], Step [501/958], Loss: 0.3380\n","Epoch [25/100], Step [601/958], Loss: 0.2073\n","Epoch [25/100], Step [701/958], Loss: 0.1761\n","Epoch [25/100], Step [801/958], Loss: 0.2449\n","Epoch [25/100], Step [901/958], Loss: 0.1565\n","Epoch [25/100], Validation Accuracy: 0.8386, Train Accuracy: 0.9124\n","New best model saved with Validation Accuracy: 0.8386\n","Epoch [26/100], Step [1/958], Loss: 0.2338\n","Epoch [26/100], Step [101/958], Loss: 0.0462\n","Epoch [26/100], Step [201/958], Loss: 0.2694\n","Epoch [26/100], Step [301/958], Loss: 0.2435\n","Epoch [26/100], Step [401/958], Loss: 0.5157\n","Epoch [26/100], Step [501/958], Loss: 0.2130\n","Epoch [26/100], Step [601/958], Loss: 0.2500\n","Epoch [26/100], Step [701/958], Loss: 0.1464\n","Epoch [26/100], Step [801/958], Loss: 0.6252\n","Epoch [26/100], Step [901/958], Loss: 0.1591\n","Epoch [26/100], Validation Accuracy: 0.8398, Train Accuracy: 0.9163\n","New best model saved with Validation Accuracy: 0.8398\n","Epoch [27/100], Step [1/958], Loss: 0.1755\n","Epoch [27/100], Step [101/958], Loss: 0.2874\n","Epoch [27/100], Step [201/958], Loss: 0.1558\n","Epoch [27/100], Step [301/958], Loss: 0.1840\n","Epoch [27/100], Step [401/958], Loss: 0.2552\n","Epoch [27/100], Step [501/958], Loss: 0.2905\n","Epoch [27/100], Step [601/958], Loss: 0.2518\n","Epoch [27/100], Step [701/958], Loss: 0.1579\n","Epoch [27/100], Step [801/958], Loss: 0.2710\n","Epoch [27/100], Step [901/958], Loss: 0.1475\n","Epoch [27/100], Validation Accuracy: 0.8215, Train Accuracy: 0.9247\n","Epoch [28/100], Step [1/958], Loss: 0.1093\n","Epoch [28/100], Step [101/958], Loss: 0.1685\n","Epoch [28/100], Step [201/958], Loss: 0.1703\n","Epoch [28/100], Step [301/958], Loss: 0.1439\n","Epoch [28/100], Step [401/958], Loss: 0.1387\n","Epoch [28/100], Step [501/958], Loss: 0.1319\n","Epoch [28/100], Step [601/958], Loss: 0.1219\n","Epoch [28/100], Step [701/958], Loss: 0.1343\n","Epoch [28/100], Step [801/958], Loss: 0.1257\n","Epoch [28/100], Step [901/958], Loss: 0.1315\n","Epoch [28/100], Validation Accuracy: 0.7618, Train Accuracy: 0.9276\n","Epoch [29/100], Step [1/958], Loss: 0.1522\n","Epoch [29/100], Step [101/958], Loss: 0.2073\n","Epoch [29/100], Step [201/958], Loss: 0.4031\n","Epoch [29/100], Step [301/958], Loss: 0.2814\n","Epoch [29/100], Step [401/958], Loss: 0.1542\n","Epoch [29/100], Step [501/958], Loss: 0.2317\n","Epoch [29/100], Step [601/958], Loss: 0.1291\n","Epoch [29/100], Step [701/958], Loss: 0.1624\n","Epoch [29/100], Step [801/958], Loss: 0.2805\n","Epoch [29/100], Step [901/958], Loss: 0.3438\n","Epoch [29/100], Validation Accuracy: 0.8406, Train Accuracy: 0.9328\n","New best model saved with Validation Accuracy: 0.8406\n","Epoch [30/100], Step [1/958], Loss: 0.2448\n","Epoch [30/100], Step [101/958], Loss: 0.2785\n","Epoch [30/100], Step [201/958], Loss: 0.0599\n","Epoch [30/100], Step [301/958], Loss: 0.2147\n","Epoch [30/100], Step [401/958], Loss: 0.1870\n","Epoch [30/100], Step [501/958], Loss: 0.1101\n","Epoch [30/100], Step [601/958], Loss: 0.0898\n","Epoch [30/100], Step [701/958], Loss: 0.0479\n","Epoch [30/100], Step [801/958], Loss: 0.1137\n","Epoch [30/100], Step [901/958], Loss: 0.3190\n","Epoch [30/100], Validation Accuracy: 0.8466, Train Accuracy: 0.9364\n","New best model saved with Validation Accuracy: 0.8466\n","Epoch [31/100], Step [1/958], Loss: 0.0225\n","Epoch [31/100], Step [101/958], Loss: 0.1042\n","Epoch [31/100], Step [201/958], Loss: 0.1522\n","Epoch [31/100], Step [301/958], Loss: 0.1368\n","Epoch [31/100], Step [401/958], Loss: 0.2852\n","Epoch [31/100], Step [501/958], Loss: 0.0838\n","Epoch [31/100], Step [601/958], Loss: 0.1775\n","Epoch [31/100], Step [701/958], Loss: 0.2021\n","Epoch [31/100], Step [801/958], Loss: 0.0744\n","Epoch [31/100], Step [901/958], Loss: 0.2413\n","Epoch [31/100], Validation Accuracy: 0.8255, Train Accuracy: 0.9393\n","Epoch [32/100], Step [1/958], Loss: 0.1500\n","Epoch [32/100], Step [101/958], Loss: 0.1019\n","Epoch [32/100], Step [201/958], Loss: 0.0574\n","Epoch [32/100], Step [301/958], Loss: 0.1325\n","Epoch [32/100], Step [401/958], Loss: 0.1052\n","Epoch [32/100], Step [501/958], Loss: 0.0236\n","Epoch [32/100], Step [601/958], Loss: 0.1900\n","Epoch [32/100], Step [701/958], Loss: 0.1437\n","Epoch [32/100], Step [801/958], Loss: 0.1248\n","Epoch [32/100], Step [901/958], Loss: 0.1003\n","Epoch [32/100], Validation Accuracy: 0.8015, Train Accuracy: 0.9473\n","Epoch [33/100], Step [1/958], Loss: 0.2507\n","Epoch [33/100], Step [101/958], Loss: 0.0478\n","Epoch [33/100], Step [201/958], Loss: 0.1069\n","Epoch [33/100], Step [301/958], Loss: 0.1449\n","Epoch [33/100], Step [401/958], Loss: 0.1426\n","Epoch [33/100], Step [501/958], Loss: 0.0769\n","Epoch [33/100], Step [601/958], Loss: 0.0919\n","Epoch [33/100], Step [701/958], Loss: 0.0653\n","Epoch [33/100], Step [801/958], Loss: 0.3354\n","Epoch [33/100], Step [901/958], Loss: 0.0258\n","Epoch [33/100], Validation Accuracy: 0.8420, Train Accuracy: 0.9453\n","Epoch [34/100], Step [1/958], Loss: 0.1822\n","Epoch [34/100], Step [101/958], Loss: 0.1138\n","Epoch [34/100], Step [201/958], Loss: 0.1287\n","Epoch [34/100], Step [301/958], Loss: 0.0820\n","Epoch [34/100], Step [401/958], Loss: 0.1908\n","Epoch [34/100], Step [501/958], Loss: 0.1298\n","Epoch [34/100], Step [601/958], Loss: 0.0827\n","Epoch [34/100], Step [701/958], Loss: 0.1294\n","Epoch [34/100], Step [801/958], Loss: 0.1589\n","Epoch [34/100], Step [901/958], Loss: 0.2083\n","Epoch [34/100], Validation Accuracy: 0.8449, Train Accuracy: 0.9502\n","Epoch [35/100], Step [1/958], Loss: 0.0744\n","Epoch [35/100], Step [101/958], Loss: 0.2143\n","Epoch [35/100], Step [201/958], Loss: 0.1398\n","Epoch [35/100], Step [301/958], Loss: 0.0348\n","Epoch [35/100], Step [401/958], Loss: 0.2884\n","Epoch [35/100], Step [501/958], Loss: 0.1105\n","Epoch [35/100], Step [601/958], Loss: 0.1238\n","Epoch [35/100], Step [701/958], Loss: 0.3332\n","Epoch [35/100], Step [801/958], Loss: 0.0311\n","Epoch [35/100], Step [901/958], Loss: 0.1956\n","Epoch [35/100], Validation Accuracy: 0.8392, Train Accuracy: 0.9531\n","Epoch [36/100], Step [1/958], Loss: 0.1162\n","Epoch [36/100], Step [101/958], Loss: 0.0906\n","Epoch [36/100], Step [201/958], Loss: 0.0995\n","Epoch [36/100], Step [301/958], Loss: 0.2429\n","Epoch [36/100], Step [401/958], Loss: 0.0996\n","Epoch [36/100], Step [501/958], Loss: 0.1408\n","Epoch [36/100], Step [601/958], Loss: 0.0807\n","Epoch [36/100], Step [701/958], Loss: 0.3702\n","Epoch [36/100], Step [801/958], Loss: 0.1105\n","Epoch [36/100], Step [901/958], Loss: 0.0947\n","Epoch [36/100], Validation Accuracy: 0.8203, Train Accuracy: 0.9552\n","Epoch [37/100], Step [1/958], Loss: 0.0765\n","Epoch [37/100], Step [101/958], Loss: 0.0793\n","Epoch [37/100], Step [201/958], Loss: 0.0440\n","Epoch [37/100], Step [301/958], Loss: 0.0244\n","Epoch [37/100], Step [401/958], Loss: 0.1825\n","Epoch [37/100], Step [501/958], Loss: 0.0899\n","Epoch [37/100], Step [601/958], Loss: 0.1487\n","Epoch [37/100], Step [701/958], Loss: 0.0577\n","Epoch [37/100], Step [801/958], Loss: 0.3030\n","Epoch [37/100], Step [901/958], Loss: 0.0742\n","Epoch [37/100], Validation Accuracy: 0.7584, Train Accuracy: 0.9562\n","Epoch [38/100], Step [1/958], Loss: 0.0364\n","Epoch [38/100], Step [101/958], Loss: 0.2765\n","Epoch [38/100], Step [201/958], Loss: 0.0117\n","Epoch [38/100], Step [301/958], Loss: 0.0453\n","Epoch [38/100], Step [401/958], Loss: 0.1020\n","Epoch [38/100], Step [501/958], Loss: 0.0696\n","Epoch [38/100], Step [601/958], Loss: 0.0479\n","Epoch [38/100], Step [701/958], Loss: 0.0754\n","Epoch [38/100], Step [801/958], Loss: 0.0792\n","Epoch [38/100], Step [901/958], Loss: 0.0411\n","Epoch [38/100], Validation Accuracy: 0.8378, Train Accuracy: 0.9585\n","Epoch [39/100], Step [1/958], Loss: 0.0079\n","Epoch [39/100], Step [101/958], Loss: 0.1970\n","Epoch [39/100], Step [201/958], Loss: 0.0550\n","Epoch [39/100], Step [301/958], Loss: 0.0557\n","Epoch [39/100], Step [401/958], Loss: 0.1110\n","Epoch [39/100], Step [501/958], Loss: 0.3657\n","Epoch [39/100], Step [601/958], Loss: 0.0766\n","Epoch [39/100], Step [701/958], Loss: 0.0904\n","Epoch [39/100], Step [801/958], Loss: 0.0492\n","Epoch [39/100], Step [901/958], Loss: 0.0866\n","Epoch [39/100], Validation Accuracy: 0.8415, Train Accuracy: 0.9635\n","Epoch [40/100], Step [1/958], Loss: 0.0554\n","Epoch [40/100], Step [101/958], Loss: 0.0223\n","Epoch [40/100], Step [201/958], Loss: 0.1328\n","Epoch [40/100], Step [301/958], Loss: 0.0451\n","Epoch [40/100], Step [401/958], Loss: 0.1088\n","Epoch [40/100], Step [501/958], Loss: 0.0226\n","Epoch [40/100], Step [601/958], Loss: 0.0273\n","Epoch [40/100], Step [701/958], Loss: 0.0970\n","Epoch [40/100], Step [801/958], Loss: 0.0887\n","Epoch [40/100], Step [901/958], Loss: 0.1226\n","Epoch [40/100], Validation Accuracy: 0.7586, Train Accuracy: 0.9624\n","Epoch [41/100], Step [1/958], Loss: 0.0306\n","Epoch [41/100], Step [101/958], Loss: 0.2209\n","Epoch [41/100], Step [201/958], Loss: 0.1956\n","Epoch [41/100], Step [301/958], Loss: 0.1326\n","Epoch [41/100], Step [401/958], Loss: 0.0917\n","Epoch [41/100], Step [501/958], Loss: 0.0325\n","Epoch [41/100], Step [601/958], Loss: 0.0715\n","Epoch [41/100], Step [701/958], Loss: 0.0966\n","Epoch [41/100], Step [801/958], Loss: 0.0545\n","Epoch [41/100], Step [901/958], Loss: 0.0485\n","Epoch [41/100], Validation Accuracy: 0.8472, Train Accuracy: 0.9644\n","New best model saved with Validation Accuracy: 0.8472\n","Epoch [42/100], Step [1/958], Loss: 0.0055\n","Epoch [42/100], Step [101/958], Loss: 0.1198\n","Epoch [42/100], Step [201/958], Loss: 0.1148\n","Epoch [42/100], Step [301/958], Loss: 0.0292\n","Epoch [42/100], Step [401/958], Loss: 0.0915\n","Epoch [42/100], Step [501/958], Loss: 0.1296\n","Epoch [42/100], Step [601/958], Loss: 0.1889\n","Epoch [42/100], Step [701/958], Loss: 0.0477\n","Epoch [42/100], Step [801/958], Loss: 0.0755\n","Epoch [42/100], Step [901/958], Loss: 0.1057\n","Epoch [42/100], Validation Accuracy: 0.8446, Train Accuracy: 0.9660\n","Epoch [43/100], Step [1/958], Loss: 0.1176\n","Epoch [43/100], Step [101/958], Loss: 0.0440\n","Epoch [43/100], Step [201/958], Loss: 0.1007\n","Epoch [43/100], Step [301/958], Loss: 0.2037\n","Epoch [43/100], Step [401/958], Loss: 0.1084\n","Epoch [43/100], Step [501/958], Loss: 0.1559\n","Epoch [43/100], Step [601/958], Loss: 0.1595\n","Epoch [43/100], Step [701/958], Loss: 0.0744\n","Epoch [43/100], Step [801/958], Loss: 0.0513\n","Epoch [43/100], Step [901/958], Loss: 0.2023\n","Epoch [43/100], Validation Accuracy: 0.8675, Train Accuracy: 0.9678\n","New best model saved with Validation Accuracy: 0.8675\n","Epoch [44/100], Step [1/958], Loss: 0.0898\n","Epoch [44/100], Step [101/958], Loss: 0.2479\n","Epoch [44/100], Step [201/958], Loss: 0.0307\n","Epoch [44/100], Step [301/958], Loss: 0.0823\n","Epoch [44/100], Step [401/958], Loss: 0.1060\n","Epoch [44/100], Step [501/958], Loss: 0.1199\n","Epoch [44/100], Step [601/958], Loss: 0.0619\n","Epoch [44/100], Step [701/958], Loss: 0.0623\n","Epoch [44/100], Step [801/958], Loss: 0.0073\n","Epoch [44/100], Step [901/958], Loss: 0.1134\n","Epoch [44/100], Validation Accuracy: 0.6538, Train Accuracy: 0.9676\n","Epoch [45/100], Step [1/958], Loss: 0.0328\n","Epoch [45/100], Step [101/958], Loss: 0.0193\n","Epoch [45/100], Step [201/958], Loss: 0.0821\n","Epoch [45/100], Step [301/958], Loss: 0.0405\n","Epoch [45/100], Step [401/958], Loss: 0.1555\n","Epoch [45/100], Step [501/958], Loss: 0.0252\n","Epoch [45/100], Step [601/958], Loss: 0.0081\n","Epoch [45/100], Step [701/958], Loss: 0.1326\n","Epoch [45/100], Step [801/958], Loss: 0.0836\n","Epoch [45/100], Step [901/958], Loss: 0.0526\n","Epoch [45/100], Validation Accuracy: 0.8312, Train Accuracy: 0.9713\n","Epoch [46/100], Step [1/958], Loss: 0.0032\n","Epoch [46/100], Step [101/958], Loss: 0.0102\n","Epoch [46/100], Step [201/958], Loss: 0.1449\n","Epoch [46/100], Step [301/958], Loss: 0.0681\n","Epoch [46/100], Step [401/958], Loss: 0.0143\n","Epoch [46/100], Step [501/958], Loss: 0.0695\n","Epoch [46/100], Step [601/958], Loss: 0.1167\n","Epoch [46/100], Step [701/958], Loss: 0.0320\n","Epoch [46/100], Step [801/958], Loss: 0.0422\n","Epoch [46/100], Step [901/958], Loss: 0.0762\n","Epoch [46/100], Validation Accuracy: 0.7763, Train Accuracy: 0.9698\n","Epoch [47/100], Step [1/958], Loss: 0.0086\n","Epoch [47/100], Step [101/958], Loss: 0.0138\n","Epoch [47/100], Step [201/958], Loss: 0.0063\n","Epoch [47/100], Step [301/958], Loss: 0.0846\n","Epoch [47/100], Step [401/958], Loss: 0.0418\n","Epoch [47/100], Step [501/958], Loss: 0.0229\n","Epoch [47/100], Step [601/958], Loss: 0.0299\n","Epoch [47/100], Step [701/958], Loss: 0.1829\n","Epoch [47/100], Step [801/958], Loss: 0.0778\n","Epoch [47/100], Step [901/958], Loss: 0.0223\n","Epoch [47/100], Validation Accuracy: 0.8532, Train Accuracy: 0.9717\n","Epoch [48/100], Step [1/958], Loss: 0.0328\n","Epoch [48/100], Step [101/958], Loss: 0.0754\n","Epoch [48/100], Step [201/958], Loss: 0.0256\n","Epoch [48/100], Step [301/958], Loss: 0.0128\n","Epoch [48/100], Step [401/958], Loss: 0.0585\n","Epoch [48/100], Step [501/958], Loss: 0.0616\n","Epoch [48/100], Step [601/958], Loss: 0.0180\n","Epoch [48/100], Step [701/958], Loss: 0.1244\n","Epoch [48/100], Step [801/958], Loss: 0.0381\n","Epoch [48/100], Step [901/958], Loss: 0.2905\n","Epoch [48/100], Validation Accuracy: 0.8446, Train Accuracy: 0.9744\n","Epoch [49/100], Step [1/958], Loss: 0.2744\n","Epoch [49/100], Step [101/958], Loss: 0.0911\n","Epoch [49/100], Step [201/958], Loss: 0.0476\n","Epoch [49/100], Step [301/958], Loss: 0.0101\n","Epoch [49/100], Step [401/958], Loss: 0.0168\n","Epoch [49/100], Step [501/958], Loss: 0.0649\n","Epoch [49/100], Step [601/958], Loss: 0.0477\n","Epoch [49/100], Step [701/958], Loss: 0.0579\n","Epoch [49/100], Step [801/958], Loss: 0.0127\n","Epoch [49/100], Step [901/958], Loss: 0.0334\n","Epoch [49/100], Validation Accuracy: 0.8380, Train Accuracy: 0.9732\n","Epoch [50/100], Step [1/958], Loss: 0.0162\n","Epoch [50/100], Step [101/958], Loss: 0.0349\n","Epoch [50/100], Step [201/958], Loss: 0.0725\n","Epoch [50/100], Step [301/958], Loss: 0.0291\n","Epoch [50/100], Step [401/958], Loss: 0.0266\n","Epoch [50/100], Step [501/958], Loss: 0.0789\n","Epoch [50/100], Step [601/958], Loss: 0.0242\n","Epoch [50/100], Step [701/958], Loss: 0.2112\n","Epoch [50/100], Step [801/958], Loss: 0.1066\n","Epoch [50/100], Step [901/958], Loss: 0.0063\n","Epoch [50/100], Validation Accuracy: 0.8146, Train Accuracy: 0.9753\n","Epoch [51/100], Step [1/958], Loss: 0.0249\n","Epoch [51/100], Step [101/958], Loss: 0.0173\n","Epoch [51/100], Step [201/958], Loss: 0.0049\n","Epoch [51/100], Step [301/958], Loss: 0.0007\n","Epoch [51/100], Step [401/958], Loss: 0.0063\n","Epoch [51/100], Step [501/958], Loss: 0.0079\n","Epoch [51/100], Step [601/958], Loss: 0.2128\n","Epoch [51/100], Step [701/958], Loss: 0.0168\n","Epoch [51/100], Step [801/958], Loss: 0.0367\n","Epoch [51/100], Step [901/958], Loss: 0.1042\n","Epoch [51/100], Validation Accuracy: 0.8469, Train Accuracy: 0.9770\n","Epoch [52/100], Step [1/958], Loss: 0.0046\n","Epoch [52/100], Step [101/958], Loss: 0.0627\n","Epoch [52/100], Step [201/958], Loss: 0.0523\n","Epoch [52/100], Step [301/958], Loss: 0.0261\n","Epoch [52/100], Step [401/958], Loss: 0.0541\n","Epoch [52/100], Step [501/958], Loss: 0.0803\n","Epoch [52/100], Step [601/958], Loss: 0.0936\n","Epoch [52/100], Step [701/958], Loss: 0.1512\n","Epoch [52/100], Step [801/958], Loss: 0.2568\n","Epoch [52/100], Step [901/958], Loss: 0.0098\n","Epoch [52/100], Validation Accuracy: 0.8455, Train Accuracy: 0.9776\n","Epoch [53/100], Step [1/958], Loss: 0.0191\n","Epoch [53/100], Step [101/958], Loss: 0.1320\n","Epoch [53/100], Step [201/958], Loss: 0.0373\n","Epoch [53/100], Step [301/958], Loss: 0.0417\n","Epoch [53/100], Step [401/958], Loss: 0.0295\n","Epoch [53/100], Step [501/958], Loss: 0.0211\n","Epoch [53/100], Step [601/958], Loss: 0.0047\n","Epoch [53/100], Step [701/958], Loss: 0.0210\n","Epoch [53/100], Step [801/958], Loss: 0.0279\n","Epoch [53/100], Step [901/958], Loss: 0.0681\n","Epoch [53/100], Validation Accuracy: 0.8552, Train Accuracy: 0.9767\n","Epoch [54/100], Step [1/958], Loss: 0.0716\n","Epoch [54/100], Step [101/958], Loss: 0.0270\n","Epoch [54/100], Step [201/958], Loss: 0.3641\n","Epoch [54/100], Step [301/958], Loss: 0.1108\n","Epoch [54/100], Step [401/958], Loss: 0.0601\n","Epoch [54/100], Step [501/958], Loss: 0.0179\n","Epoch [54/100], Step [601/958], Loss: 0.0104\n","Epoch [54/100], Step [701/958], Loss: 0.0206\n","Epoch [54/100], Step [801/958], Loss: 0.0577\n","Epoch [54/100], Step [901/958], Loss: 0.1354\n","Epoch [54/100], Validation Accuracy: 0.8455, Train Accuracy: 0.9787\n","Epoch [55/100], Step [1/958], Loss: 0.0268\n","Epoch [55/100], Step [101/958], Loss: 0.0397\n","Epoch [55/100], Step [201/958], Loss: 0.4054\n","Epoch [55/100], Step [301/958], Loss: 0.1032\n","Epoch [55/100], Step [401/958], Loss: 0.1071\n","Epoch [55/100], Step [501/958], Loss: 0.1011\n","Epoch [55/100], Step [601/958], Loss: 0.0072\n","Epoch [55/100], Step [701/958], Loss: 0.1125\n","Epoch [55/100], Step [801/958], Loss: 0.1080\n","Epoch [55/100], Step [901/958], Loss: 0.0111\n","Epoch [55/100], Validation Accuracy: 0.8512, Train Accuracy: 0.9786\n","Epoch [56/100], Step [1/958], Loss: 0.0297\n","Epoch [56/100], Step [101/958], Loss: 0.0227\n","Epoch [56/100], Step [201/958], Loss: 0.0538\n","Epoch [56/100], Step [301/958], Loss: 0.0033\n","Epoch [56/100], Step [401/958], Loss: 0.0201\n","Epoch [56/100], Step [501/958], Loss: 0.0154\n","Epoch [56/100], Step [601/958], Loss: 0.0042\n","Epoch [56/100], Step [701/958], Loss: 0.0253\n","Epoch [56/100], Step [801/958], Loss: 0.0275\n","Epoch [56/100], Step [901/958], Loss: 0.1432\n","Epoch [56/100], Validation Accuracy: 0.7404, Train Accuracy: 0.9800\n","Epoch [57/100], Step [1/958], Loss: 0.0101\n","Epoch [57/100], Step [101/958], Loss: 0.0082\n","Epoch [57/100], Step [201/958], Loss: 0.0455\n","Epoch [57/100], Step [301/958], Loss: 0.0183\n","Epoch [57/100], Step [401/958], Loss: 0.0107\n","Epoch [57/100], Step [501/958], Loss: 0.0628\n","Epoch [57/100], Step [601/958], Loss: 0.0343\n","Epoch [57/100], Step [701/958], Loss: 0.0612\n","Epoch [57/100], Step [801/958], Loss: 0.0506\n","Epoch [57/100], Step [901/958], Loss: 0.0122\n","Epoch [57/100], Validation Accuracy: 0.8495, Train Accuracy: 0.9785\n","Epoch [58/100], Step [1/958], Loss: 0.1480\n","Epoch [58/100], Step [101/958], Loss: 0.0198\n","Epoch [58/100], Step [201/958], Loss: 0.1523\n","Epoch [58/100], Step [301/958], Loss: 0.0134\n","Epoch [58/100], Step [401/958], Loss: 0.0320\n","Epoch [58/100], Step [501/958], Loss: 0.2668\n","Epoch [58/100], Step [601/958], Loss: 0.0530\n","Epoch [58/100], Step [701/958], Loss: 0.0432\n","Epoch [58/100], Step [801/958], Loss: 0.0891\n","Epoch [58/100], Step [901/958], Loss: 0.0009\n","Epoch [58/100], Validation Accuracy: 0.8360, Train Accuracy: 0.9807\n","Epoch [59/100], Step [1/958], Loss: 0.0325\n","Epoch [59/100], Step [101/958], Loss: 0.0169\n","Epoch [59/100], Step [201/958], Loss: 0.0075\n","Epoch [59/100], Step [301/958], Loss: 0.0472\n","Epoch [59/100], Step [401/958], Loss: 0.0032\n","Epoch [59/100], Step [501/958], Loss: 0.0243\n","Epoch [59/100], Step [601/958], Loss: 0.0881\n","Epoch [59/100], Step [701/958], Loss: 0.0513\n","Epoch [59/100], Step [801/958], Loss: 0.0132\n","Epoch [59/100], Step [901/958], Loss: 0.1016\n","Epoch [59/100], Validation Accuracy: 0.6978, Train Accuracy: 0.9822\n","Epoch [60/100], Step [1/958], Loss: 0.0080\n","Epoch [60/100], Step [101/958], Loss: 0.0258\n","Epoch [60/100], Step [201/958], Loss: 0.0245\n","Epoch [60/100], Step [301/958], Loss: 0.0992\n","Epoch [60/100], Step [401/958], Loss: 0.1122\n","Epoch [60/100], Step [501/958], Loss: 0.0011\n","Epoch [60/100], Step [601/958], Loss: 0.0198\n","Epoch [60/100], Step [701/958], Loss: 0.0397\n","Epoch [60/100], Step [801/958], Loss: 0.2453\n","Epoch [60/100], Step [901/958], Loss: 0.0114\n","Epoch [60/100], Validation Accuracy: 0.7963, Train Accuracy: 0.9803\n","Epoch [61/100], Step [1/958], Loss: 0.0613\n","Epoch [61/100], Step [101/958], Loss: 0.0096\n","Epoch [61/100], Step [201/958], Loss: 0.0020\n","Epoch [61/100], Step [301/958], Loss: 0.0371\n","Epoch [61/100], Step [401/958], Loss: 0.2455\n","Epoch [61/100], Step [501/958], Loss: 0.0417\n","Epoch [61/100], Step [601/958], Loss: 0.0029\n","Epoch [61/100], Step [701/958], Loss: 0.1309\n","Epoch [61/100], Step [801/958], Loss: 0.0234\n","Epoch [61/100], Step [901/958], Loss: 0.2263\n","Epoch [61/100], Validation Accuracy: 0.8315, Train Accuracy: 0.9817\n","Epoch [62/100], Step [1/958], Loss: 0.1313\n","Epoch [62/100], Step [101/958], Loss: 0.0814\n","Epoch [62/100], Step [201/958], Loss: 0.0146\n","Epoch [62/100], Step [301/958], Loss: 0.1114\n","Epoch [62/100], Step [401/958], Loss: 0.0074\n","Epoch [62/100], Step [501/958], Loss: 0.1326\n","Epoch [62/100], Step [601/958], Loss: 0.1506\n","Epoch [62/100], Step [701/958], Loss: 0.1363\n","Epoch [62/100], Step [801/958], Loss: 0.0023\n","Epoch [62/100], Step [901/958], Loss: 0.0059\n","Epoch [62/100], Validation Accuracy: 0.8515, Train Accuracy: 0.9818\n","Epoch [63/100], Step [1/958], Loss: 0.0028\n","Epoch [63/100], Step [101/958], Loss: 0.0101\n","Epoch [63/100], Step [201/958], Loss: 0.0554\n","Epoch [63/100], Step [301/958], Loss: 0.1867\n","Epoch [63/100], Step [401/958], Loss: 0.0273\n","Epoch [63/100], Step [501/958], Loss: 0.0112\n","Epoch [63/100], Step [601/958], Loss: 0.0498\n","Epoch [63/100], Step [701/958], Loss: 0.0114\n","Epoch [63/100], Step [801/958], Loss: 0.0179\n","Epoch [63/100], Step [901/958], Loss: 0.1152\n","Epoch [63/100], Validation Accuracy: 0.8672, Train Accuracy: 0.9838\n","Epoch [64/100], Step [1/958], Loss: 0.0203\n","Epoch [64/100], Step [101/958], Loss: 0.0149\n","Epoch [64/100], Step [201/958], Loss: 0.0043\n","Epoch [64/100], Step [301/958], Loss: 0.0253\n","Epoch [64/100], Step [401/958], Loss: 0.0023\n","Epoch [64/100], Step [501/958], Loss: 0.1662\n","Epoch [64/100], Step [601/958], Loss: 0.0006\n","Epoch [64/100], Step [701/958], Loss: 0.0596\n","Epoch [64/100], Step [801/958], Loss: 0.2595\n","Epoch [64/100], Step [901/958], Loss: 0.0049\n","Epoch [64/100], Validation Accuracy: 0.8446, Train Accuracy: 0.9841\n","Epoch [65/100], Step [1/958], Loss: 0.0245\n","Epoch [65/100], Step [101/958], Loss: 0.0011\n","Epoch [65/100], Step [201/958], Loss: 0.0082\n","Epoch [65/100], Step [301/958], Loss: 0.0252\n","Epoch [65/100], Step [401/958], Loss: 0.0215\n","Epoch [65/100], Step [501/958], Loss: 0.1633\n","Epoch [65/100], Step [601/958], Loss: 0.0022\n","Epoch [65/100], Step [701/958], Loss: 0.0077\n","Epoch [65/100], Step [801/958], Loss: 0.0086\n","Epoch [65/100], Step [901/958], Loss: 0.0020\n","Epoch [65/100], Validation Accuracy: 0.8492, Train Accuracy: 0.9848\n","Epoch [66/100], Step [1/958], Loss: 0.0312\n","Epoch [66/100], Step [101/958], Loss: 0.0154\n","Epoch [66/100], Step [201/958], Loss: 0.0367\n","Epoch [66/100], Step [301/958], Loss: 0.0052\n","Epoch [66/100], Step [401/958], Loss: 0.0477\n","Epoch [66/100], Step [501/958], Loss: 0.0313\n","Epoch [66/100], Step [601/958], Loss: 0.0059\n","Epoch [66/100], Step [701/958], Loss: 0.0133\n","Epoch [66/100], Step [801/958], Loss: 0.2424\n","Epoch [66/100], Step [901/958], Loss: 0.0805\n","Epoch [66/100], Validation Accuracy: 0.8740, Train Accuracy: 0.9838\n","New best model saved with Validation Accuracy: 0.8740\n","Epoch [67/100], Step [1/958], Loss: 0.0114\n","Epoch [67/100], Step [101/958], Loss: 0.0022\n","Epoch [67/100], Step [201/958], Loss: 0.0093\n","Epoch [67/100], Step [301/958], Loss: 0.0400\n","Epoch [67/100], Step [401/958], Loss: 0.0117\n","Epoch [67/100], Step [501/958], Loss: 0.0272\n","Epoch [67/100], Step [601/958], Loss: 0.0472\n","Epoch [67/100], Step [701/958], Loss: 0.1190\n","Epoch [67/100], Step [801/958], Loss: 0.1594\n","Epoch [67/100], Step [901/958], Loss: 0.0301\n","Epoch [67/100], Validation Accuracy: 0.8598, Train Accuracy: 0.9854\n","Epoch [68/100], Step [1/958], Loss: 0.0588\n","Epoch [68/100], Step [101/958], Loss: 0.0390\n","Epoch [68/100], Step [201/958], Loss: 0.0109\n","Epoch [68/100], Step [301/958], Loss: 0.0438\n","Epoch [68/100], Step [401/958], Loss: 0.2453\n","Epoch [68/100], Step [501/958], Loss: 0.0251\n","Epoch [68/100], Step [601/958], Loss: 0.0046\n","Epoch [68/100], Step [701/958], Loss: 0.0053\n","Epoch [68/100], Step [801/958], Loss: 0.0044\n","Epoch [68/100], Step [901/958], Loss: 0.0762\n","Epoch [68/100], Validation Accuracy: 0.8472, Train Accuracy: 0.9849\n","Epoch [69/100], Step [1/958], Loss: 0.0096\n","Epoch [69/100], Step [101/958], Loss: 0.0934\n","Epoch [69/100], Step [201/958], Loss: 0.0661\n","Epoch [69/100], Step [301/958], Loss: 0.0158\n","Epoch [69/100], Step [401/958], Loss: 0.0796\n","Epoch [69/100], Step [501/958], Loss: 0.0048\n","Epoch [69/100], Step [601/958], Loss: 0.0068\n","Epoch [69/100], Step [701/958], Loss: 0.0072\n","Epoch [69/100], Step [801/958], Loss: 0.0126\n","Epoch [69/100], Step [901/958], Loss: 0.0533\n","Epoch [69/100], Validation Accuracy: 0.7526, Train Accuracy: 0.9859\n","Epoch [70/100], Step [1/958], Loss: 0.1549\n","Epoch [70/100], Step [101/958], Loss: 0.0170\n","Epoch [70/100], Step [201/958], Loss: 0.1505\n","Epoch [70/100], Step [301/958], Loss: 0.0035\n","Epoch [70/100], Step [401/958], Loss: 0.0159\n","Epoch [70/100], Step [501/958], Loss: 0.1093\n","Epoch [70/100], Step [601/958], Loss: 0.0178\n","Epoch [70/100], Step [701/958], Loss: 0.0164\n","Epoch [70/100], Step [801/958], Loss: 0.0231\n","Epoch [70/100], Step [901/958], Loss: 0.1607\n","Epoch [70/100], Validation Accuracy: 0.8575, Train Accuracy: 0.9861\n","Epoch [71/100], Step [1/958], Loss: 0.0052\n","Epoch [71/100], Step [101/958], Loss: 0.0313\n","Epoch [71/100], Step [201/958], Loss: 0.0123\n","Epoch [71/100], Step [301/958], Loss: 0.0921\n","Epoch [71/100], Step [401/958], Loss: 0.5210\n","Epoch [71/100], Step [501/958], Loss: 0.0845\n","Epoch [71/100], Step [601/958], Loss: 0.1551\n","Epoch [71/100], Step [701/958], Loss: 0.0054\n","Epoch [71/100], Step [801/958], Loss: 0.0216\n","Epoch [71/100], Step [901/958], Loss: 0.1283\n","Epoch [71/100], Validation Accuracy: 0.7726, Train Accuracy: 0.9862\n","Epoch [72/100], Step [1/958], Loss: 0.0045\n","Epoch [72/100], Step [101/958], Loss: 0.0168\n","Epoch [72/100], Step [201/958], Loss: 0.0121\n","Epoch [72/100], Step [301/958], Loss: 0.0376\n","Epoch [72/100], Step [401/958], Loss: 0.0014\n","Epoch [72/100], Step [501/958], Loss: 0.0051\n","Epoch [72/100], Step [601/958], Loss: 0.0096\n","Epoch [72/100], Step [701/958], Loss: 0.0111\n","Epoch [72/100], Step [801/958], Loss: 0.0333\n","Epoch [72/100], Step [901/958], Loss: 0.0146\n","Epoch [72/100], Validation Accuracy: 0.8609, Train Accuracy: 0.9856\n","Epoch [73/100], Step [1/958], Loss: 0.0117\n","Epoch [73/100], Step [101/958], Loss: 0.0748\n","Epoch [73/100], Step [201/958], Loss: 0.0660\n","Epoch [73/100], Step [301/958], Loss: 0.0048\n","Epoch [73/100], Step [401/958], Loss: 0.0063\n","Epoch [73/100], Step [501/958], Loss: 0.0682\n","Epoch [73/100], Step [601/958], Loss: 0.0428\n","Epoch [73/100], Step [701/958], Loss: 0.0117\n","Epoch [73/100], Step [801/958], Loss: 0.0022\n","Epoch [73/100], Step [901/958], Loss: 0.0652\n","Epoch [73/100], Validation Accuracy: 0.8463, Train Accuracy: 0.9866\n","Epoch [74/100], Step [1/958], Loss: 0.1218\n","Epoch [74/100], Step [101/958], Loss: 0.0046\n","Epoch [74/100], Step [201/958], Loss: 0.0128\n","Epoch [74/100], Step [301/958], Loss: 0.0040\n","Epoch [74/100], Step [401/958], Loss: 0.0631\n","Epoch [74/100], Step [501/958], Loss: 0.0317\n","Epoch [74/100], Step [601/958], Loss: 0.0061\n","Epoch [74/100], Step [701/958], Loss: 0.0005\n","Epoch [74/100], Step [801/958], Loss: 0.0013\n","Epoch [74/100], Step [901/958], Loss: 0.0047\n","Epoch [74/100], Validation Accuracy: 0.8372, Train Accuracy: 0.9875\n","Epoch [75/100], Step [1/958], Loss: 0.0014\n","Epoch [75/100], Step [101/958], Loss: 0.0262\n","Epoch [75/100], Step [201/958], Loss: 0.0028\n","Epoch [75/100], Step [301/958], Loss: 0.0214\n","Epoch [75/100], Step [401/958], Loss: 0.1581\n","Epoch [75/100], Step [501/958], Loss: 0.0022\n","Epoch [75/100], Step [601/958], Loss: 0.0423\n","Epoch [75/100], Step [701/958], Loss: 0.0261\n","Epoch [75/100], Step [801/958], Loss: 0.2431\n","Epoch [75/100], Step [901/958], Loss: 0.0055\n","Epoch [75/100], Validation Accuracy: 0.8520, Train Accuracy: 0.9863\n","Epoch [76/100], Step [1/958], Loss: 0.0109\n","Epoch [76/100], Step [101/958], Loss: 0.1287\n","Epoch [76/100], Step [201/958], Loss: 0.0012\n","Epoch [76/100], Step [301/958], Loss: 0.0012\n","Epoch [76/100], Step [401/958], Loss: 0.0122\n","Epoch [76/100], Step [501/958], Loss: 0.0032\n","Epoch [76/100], Step [601/958], Loss: 0.1940\n","Epoch [76/100], Step [701/958], Loss: 0.0409\n","Epoch [76/100], Step [801/958], Loss: 0.0846\n","Epoch [76/100], Step [901/958], Loss: 0.1065\n","Epoch [76/100], Validation Accuracy: 0.8623, Train Accuracy: 0.9876\n","Epoch [77/100], Step [1/958], Loss: 0.0264\n","Epoch [77/100], Step [101/958], Loss: 0.0005\n","Epoch [77/100], Step [201/958], Loss: 0.1083\n","Epoch [77/100], Step [301/958], Loss: 0.1055\n","Epoch [77/100], Step [401/958], Loss: 0.1992\n","Epoch [77/100], Step [501/958], Loss: 0.0956\n","Epoch [77/100], Step [601/958], Loss: 0.1273\n","Epoch [77/100], Step [701/958], Loss: 0.0018\n","Epoch [77/100], Step [801/958], Loss: 0.0592\n","Epoch [77/100], Step [901/958], Loss: 0.0056\n","Epoch [77/100], Validation Accuracy: 0.7135, Train Accuracy: 0.9884\n","Epoch [78/100], Step [1/958], Loss: 0.0243\n","Epoch [78/100], Step [101/958], Loss: 0.0058\n","Epoch [78/100], Step [201/958], Loss: 0.0038\n","Epoch [78/100], Step [301/958], Loss: 0.0007\n","Epoch [78/100], Step [401/958], Loss: 0.0433\n","Epoch [78/100], Step [501/958], Loss: 0.0069\n","Epoch [78/100], Step [601/958], Loss: 0.0965\n","Epoch [78/100], Step [701/958], Loss: 0.0063\n","Epoch [78/100], Step [801/958], Loss: 0.0005\n","Epoch [78/100], Step [901/958], Loss: 0.0039\n","Epoch [78/100], Validation Accuracy: 0.8698, Train Accuracy: 0.9874\n","Epoch [79/100], Step [1/958], Loss: 0.0059\n","Epoch [79/100], Step [101/958], Loss: 0.0357\n","Epoch [79/100], Step [201/958], Loss: 0.0157\n","Epoch [79/100], Step [301/958], Loss: 0.1092\n","Epoch [79/100], Step [401/958], Loss: 0.0110\n","Epoch [79/100], Step [501/958], Loss: 0.1172\n","Epoch [79/100], Step [601/958], Loss: 0.1055\n","Epoch [79/100], Step [701/958], Loss: 0.0201\n","Epoch [79/100], Step [801/958], Loss: 0.0028\n","Epoch [79/100], Step [901/958], Loss: 0.0038\n","Epoch [79/100], Validation Accuracy: 0.8395, Train Accuracy: 0.9885\n","Epoch [80/100], Step [1/958], Loss: 0.0316\n","Epoch [80/100], Step [101/958], Loss: 0.0170\n","Epoch [80/100], Step [201/958], Loss: 0.0020\n","Epoch [80/100], Step [301/958], Loss: 0.0036\n","Epoch [80/100], Step [401/958], Loss: 0.0014\n","Epoch [80/100], Step [501/958], Loss: 0.0089\n","Epoch [80/100], Step [601/958], Loss: 0.0011\n","Epoch [80/100], Step [701/958], Loss: 0.0112\n","Epoch [80/100], Step [801/958], Loss: 0.0103\n","Epoch [80/100], Step [901/958], Loss: 0.0504\n","Epoch [80/100], Validation Accuracy: 0.8543, Train Accuracy: 0.9892\n","Epoch [81/100], Step [1/958], Loss: 0.1136\n","Epoch [81/100], Step [101/958], Loss: 0.0088\n","Epoch [81/100], Step [201/958], Loss: 0.0105\n","Epoch [81/100], Step [301/958], Loss: 0.0071\n","Epoch [81/100], Step [401/958], Loss: 0.0015\n","Epoch [81/100], Step [501/958], Loss: 0.0013\n","Epoch [81/100], Step [601/958], Loss: 0.0002\n","Epoch [81/100], Step [701/958], Loss: 0.0079\n","Epoch [81/100], Step [801/958], Loss: 0.0393\n","Epoch [81/100], Step [901/958], Loss: 0.0023\n","Epoch [81/100], Validation Accuracy: 0.8635, Train Accuracy: 0.9906\n","Epoch [82/100], Step [1/958], Loss: 0.0073\n","Epoch [82/100], Step [101/958], Loss: 0.0126\n","Epoch [82/100], Step [201/958], Loss: 0.0529\n","Epoch [82/100], Step [301/958], Loss: 0.0130\n","Epoch [82/100], Step [401/958], Loss: 0.0019\n","Epoch [82/100], Step [501/958], Loss: 0.0065\n","Epoch [82/100], Step [601/958], Loss: 0.0103\n","Epoch [82/100], Step [701/958], Loss: 0.0040\n","Epoch [82/100], Step [801/958], Loss: 0.0091\n","Epoch [82/100], Step [901/958], Loss: 0.0286\n","Epoch [82/100], Validation Accuracy: 0.8529, Train Accuracy: 0.9875\n","Epoch [83/100], Step [1/958], Loss: 0.0069\n","Epoch [83/100], Step [101/958], Loss: 0.0044\n","Epoch [83/100], Step [201/958], Loss: 0.0331\n","Epoch [83/100], Step [301/958], Loss: 0.0364\n","Epoch [83/100], Step [401/958], Loss: 0.0953\n","Epoch [83/100], Step [501/958], Loss: 0.0267\n","Epoch [83/100], Step [601/958], Loss: 0.0138\n","Epoch [83/100], Step [701/958], Loss: 0.0211\n","Epoch [83/100], Step [801/958], Loss: 0.0091\n","Epoch [83/100], Step [901/958], Loss: 0.0273\n","Epoch [83/100], Validation Accuracy: 0.8603, Train Accuracy: 0.9896\n","Epoch [84/100], Step [1/958], Loss: 0.0035\n","Epoch [84/100], Step [101/958], Loss: 0.0075\n","Epoch [84/100], Step [201/958], Loss: 0.0427\n","Epoch [84/100], Step [301/958], Loss: 0.0742\n","Epoch [84/100], Step [401/958], Loss: 0.0096\n","Epoch [84/100], Step [501/958], Loss: 0.0126\n","Epoch [84/100], Step [601/958], Loss: 0.0032\n","Epoch [84/100], Step [701/958], Loss: 0.0272\n","Epoch [84/100], Step [801/958], Loss: 0.0546\n","Epoch [84/100], Step [901/958], Loss: 0.0015\n","Epoch [84/100], Validation Accuracy: 0.8432, Train Accuracy: 0.9907\n","Epoch [85/100], Step [1/958], Loss: 0.0129\n","Epoch [85/100], Step [101/958], Loss: 0.0027\n","Epoch [85/100], Step [201/958], Loss: 0.0806\n","Epoch [85/100], Step [301/958], Loss: 0.0230\n","Epoch [85/100], Step [401/958], Loss: 0.0769\n","Epoch [85/100], Step [501/958], Loss: 0.0744\n","Epoch [85/100], Step [601/958], Loss: 0.0153\n","Epoch [85/100], Step [701/958], Loss: 0.0409\n","Epoch [85/100], Step [801/958], Loss: 0.0529\n","Epoch [85/100], Step [901/958], Loss: 0.0005\n","Epoch [85/100], Validation Accuracy: 0.8543, Train Accuracy: 0.9901\n","Epoch [86/100], Step [1/958], Loss: 0.0108\n","Epoch [86/100], Step [101/958], Loss: 0.0391\n","Epoch [86/100], Step [201/958], Loss: 0.0496\n","Epoch [86/100], Step [301/958], Loss: 0.0005\n","Epoch [86/100], Step [401/958], Loss: 0.1233\n","Epoch [86/100], Step [501/958], Loss: 0.0087\n","Epoch [86/100], Step [601/958], Loss: 0.0780\n","Epoch [86/100], Step [701/958], Loss: 0.0051\n","Epoch [86/100], Step [801/958], Loss: 0.1609\n","Epoch [86/100], Step [901/958], Loss: 0.0161\n","Epoch [86/100], Validation Accuracy: 0.8395, Train Accuracy: 0.9895\n","Epoch [87/100], Step [1/958], Loss: 0.0009\n","Epoch [87/100], Step [101/958], Loss: 0.0267\n","Epoch [87/100], Step [201/958], Loss: 0.0070\n","Epoch [87/100], Step [301/958], Loss: 0.0045\n","Epoch [87/100], Step [401/958], Loss: 0.0044\n","Epoch [87/100], Step [501/958], Loss: 0.0309\n","Epoch [87/100], Step [601/958], Loss: 0.0518\n","Epoch [87/100], Step [701/958], Loss: 0.0032\n","Epoch [87/100], Step [801/958], Loss: 0.0004\n","Epoch [87/100], Step [901/958], Loss: 0.0012\n","Epoch [87/100], Validation Accuracy: 0.8475, Train Accuracy: 0.9906\n","Epoch [88/100], Step [1/958], Loss: 0.0394\n","Epoch [88/100], Step [101/958], Loss: 0.0619\n","Epoch [88/100], Step [201/958], Loss: 0.0056\n","Epoch [88/100], Step [301/958], Loss: 0.0028\n","Epoch [88/100], Step [401/958], Loss: 0.0772\n","Epoch [88/100], Step [501/958], Loss: 0.0189\n","Epoch [88/100], Step [601/958], Loss: 0.0095\n","Epoch [88/100], Step [701/958], Loss: 0.1135\n","Epoch [88/100], Step [801/958], Loss: 0.0008\n","Epoch [88/100], Step [901/958], Loss: 0.0063\n","Epoch [88/100], Validation Accuracy: 0.8535, Train Accuracy: 0.9901\n","Epoch [89/100], Step [1/958], Loss: 0.0006\n","Epoch [89/100], Step [101/958], Loss: 0.0007\n","Epoch [89/100], Step [201/958], Loss: 0.0099\n","Epoch [89/100], Step [301/958], Loss: 0.0058\n","Epoch [89/100], Step [401/958], Loss: 0.0101\n","Epoch [89/100], Step [501/958], Loss: 0.0016\n","Epoch [89/100], Step [601/958], Loss: 0.0035\n","Epoch [89/100], Step [701/958], Loss: 0.0269\n","Epoch [89/100], Step [801/958], Loss: 0.0028\n","Epoch [89/100], Step [901/958], Loss: 0.0078\n","Epoch [89/100], Validation Accuracy: 0.8455, Train Accuracy: 0.9904\n","Epoch [90/100], Step [1/958], Loss: 0.0660\n","Epoch [90/100], Step [101/958], Loss: 0.0016\n","Epoch [90/100], Step [201/958], Loss: 0.0138\n","Epoch [90/100], Step [301/958], Loss: 0.1669\n","Epoch [90/100], Step [401/958], Loss: 0.0009\n","Epoch [90/100], Step [501/958], Loss: 0.0027\n","Epoch [90/100], Step [601/958], Loss: 0.0050\n","Epoch [90/100], Step [701/958], Loss: 0.0235\n","Epoch [90/100], Step [801/958], Loss: 0.0016\n","Epoch [90/100], Step [901/958], Loss: 0.0023\n","Epoch [90/100], Validation Accuracy: 0.8309, Train Accuracy: 0.9893\n","Epoch [91/100], Step [1/958], Loss: 0.1354\n","Epoch [91/100], Step [101/958], Loss: 0.1149\n","Epoch [91/100], Step [201/958], Loss: 0.0008\n","Epoch [91/100], Step [301/958], Loss: 0.0219\n","Epoch [91/100], Step [401/958], Loss: 0.0138\n","Epoch [91/100], Step [501/958], Loss: 0.0299\n","Epoch [91/100], Step [601/958], Loss: 0.0083\n","Epoch [91/100], Step [701/958], Loss: 0.0012\n","Epoch [91/100], Step [801/958], Loss: 0.0444\n","Epoch [91/100], Step [901/958], Loss: 0.1440\n","Epoch [91/100], Validation Accuracy: 0.8606, Train Accuracy: 0.9903\n","Epoch [92/100], Step [1/958], Loss: 0.1547\n","Epoch [92/100], Step [101/958], Loss: 0.0063\n","Epoch [92/100], Step [201/958], Loss: 0.0420\n","Epoch [92/100], Step [301/958], Loss: 0.1116\n","Epoch [92/100], Step [401/958], Loss: 0.0084\n","Epoch [92/100], Step [501/958], Loss: 0.0582\n","Epoch [92/100], Step [601/958], Loss: 0.0055\n","Epoch [92/100], Step [701/958], Loss: 0.0035\n","Epoch [92/100], Step [801/958], Loss: 0.0526\n","Epoch [92/100], Step [901/958], Loss: 0.0270\n","Epoch [92/100], Validation Accuracy: 0.8623, Train Accuracy: 0.9916\n","Epoch [93/100], Step [1/958], Loss: 0.0115\n","Epoch [93/100], Step [101/958], Loss: 0.0804\n","Epoch [93/100], Step [201/958], Loss: 0.0014\n","Epoch [93/100], Step [301/958], Loss: 0.0007\n","Epoch [93/100], Step [401/958], Loss: 0.0102\n","Epoch [93/100], Step [501/958], Loss: 0.0383\n","Epoch [93/100], Step [601/958], Loss: 0.0489\n","Epoch [93/100], Step [701/958], Loss: 0.0007\n","Epoch [93/100], Step [801/958], Loss: 0.1833\n","Epoch [93/100], Step [901/958], Loss: 0.0175\n","Epoch [93/100], Validation Accuracy: 0.8518, Train Accuracy: 0.9919\n","Epoch [94/100], Step [1/958], Loss: 0.1279\n","Epoch [94/100], Step [101/958], Loss: 0.0028\n","Epoch [94/100], Step [201/958], Loss: 0.0036\n","Epoch [94/100], Step [301/958], Loss: 0.0003\n","Epoch [94/100], Step [401/958], Loss: 0.0009\n","Epoch [94/100], Step [501/958], Loss: 0.0294\n","Epoch [94/100], Step [601/958], Loss: 0.1894\n","Epoch [94/100], Step [701/958], Loss: 0.0156\n","Epoch [94/100], Step [801/958], Loss: 0.0292\n","Epoch [94/100], Step [901/958], Loss: 0.0027\n","Epoch [94/100], Validation Accuracy: 0.8646, Train Accuracy: 0.9911\n","Epoch [95/100], Step [1/958], Loss: 0.0316\n","Epoch [95/100], Step [101/958], Loss: 0.0333\n","Epoch [95/100], Step [201/958], Loss: 0.0016\n","Epoch [95/100], Step [301/958], Loss: 0.0022\n","Epoch [95/100], Step [401/958], Loss: 0.0250\n","Epoch [95/100], Step [501/958], Loss: 0.0022\n","Epoch [95/100], Step [601/958], Loss: 0.0001\n","Epoch [95/100], Step [701/958], Loss: 0.0020\n","Epoch [95/100], Step [801/958], Loss: 0.0003\n","Epoch [95/100], Step [901/958], Loss: 0.0828\n","Epoch [95/100], Validation Accuracy: 0.8398, Train Accuracy: 0.9906\n","Epoch [96/100], Step [1/958], Loss: 0.0399\n","Epoch [96/100], Step [101/958], Loss: 0.1823\n","Epoch [96/100], Step [201/958], Loss: 0.0102\n","Epoch [96/100], Step [301/958], Loss: 0.0351\n","Epoch [96/100], Step [401/958], Loss: 0.0043\n","Epoch [96/100], Step [501/958], Loss: 0.0123\n","Epoch [96/100], Step [601/958], Loss: 0.0152\n","Epoch [96/100], Step [701/958], Loss: 0.0065\n","Epoch [96/100], Step [801/958], Loss: 0.0058\n","Epoch [96/100], Step [901/958], Loss: 0.0111\n","Epoch [96/100], Validation Accuracy: 0.8463, Train Accuracy: 0.9911\n","Epoch [97/100], Step [1/958], Loss: 0.0064\n","Epoch [97/100], Step [101/958], Loss: 0.0921\n","Epoch [97/100], Step [201/958], Loss: 0.2536\n","Epoch [97/100], Step [301/958], Loss: 0.2616\n","Epoch [97/100], Step [401/958], Loss: 0.0060\n","Epoch [97/100], Step [501/958], Loss: 0.0043\n","Epoch [97/100], Step [601/958], Loss: 0.0057\n","Epoch [97/100], Step [701/958], Loss: 0.0153\n","Epoch [97/100], Step [801/958], Loss: 0.0095\n","Epoch [97/100], Step [901/958], Loss: 0.0716\n","Epoch [97/100], Validation Accuracy: 0.8580, Train Accuracy: 0.9907\n","Epoch [98/100], Step [1/958], Loss: 0.0775\n","Epoch [98/100], Step [101/958], Loss: 0.0166\n","Epoch [98/100], Step [201/958], Loss: 0.0074\n","Epoch [98/100], Step [301/958], Loss: 0.0002\n","Epoch [98/100], Step [401/958], Loss: 0.0056\n","Epoch [98/100], Step [501/958], Loss: 0.0104\n","Epoch [98/100], Step [601/958], Loss: 0.0075\n","Epoch [98/100], Step [701/958], Loss: 0.0079\n","Epoch [98/100], Step [801/958], Loss: 0.0008\n","Epoch [98/100], Step [901/958], Loss: 0.0120\n","Epoch [98/100], Validation Accuracy: 0.8538, Train Accuracy: 0.9917\n","Epoch [99/100], Step [1/958], Loss: 0.0299\n","Epoch [99/100], Step [101/958], Loss: 0.0426\n","Epoch [99/100], Step [201/958], Loss: 0.0261\n","Epoch [99/100], Step [301/958], Loss: 0.0007\n","Epoch [99/100], Step [401/958], Loss: 0.0230\n","Epoch [99/100], Step [501/958], Loss: 0.0046\n","Epoch [99/100], Step [601/958], Loss: 0.0547\n","Epoch [99/100], Step [701/958], Loss: 0.0037\n","Epoch [99/100], Step [801/958], Loss: 0.0002\n","Epoch [99/100], Step [901/958], Loss: 0.0009\n","Epoch [99/100], Validation Accuracy: 0.8406, Train Accuracy: 0.9912\n","Epoch [100/100], Step [1/958], Loss: 0.0143\n","Epoch [100/100], Step [101/958], Loss: 0.0019\n","Epoch [100/100], Step [201/958], Loss: 0.0027\n","Epoch [100/100], Step [301/958], Loss: 0.0007\n","Epoch [100/100], Step [401/958], Loss: 0.0052\n","Epoch [100/100], Step [501/958], Loss: 0.0001\n","Epoch [100/100], Step [601/958], Loss: 0.0056\n","Epoch [100/100], Step [701/958], Loss: 0.0035\n","Epoch [100/100], Step [801/958], Loss: 0.0029\n","Epoch [100/100], Step [901/958], Loss: 0.0943\n","Epoch [100/100], Validation Accuracy: 0.8498, Train Accuracy: 0.9929\n","Training history saved to history_data_augment_resnet50.json\n"]}],"source":["import json   # For saving the history\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'] = 'Data Augment ResNet50'  # Change the name for each model in each notebook\n","\n","best_val_accuracy = 0.0\n","best_model_path = \"best_model_data_augment_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","              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_data_augment_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":"3d108ef0","metadata":{"execution":{"iopub.execute_input":"2024-11-28T09:37:58.790560Z","iopub.status.busy":"2024-11-28T09:37:58.790258Z","iopub.status.idle":"2024-11-28T09:37:58.798588Z","shell.execute_reply":"2024-11-28T09:37:58.797739Z"},"executionInfo":{"elapsed":10,"status":"aborted","timestamp":1730261318019,"user":{"displayName":"tannistha maiti","userId":"15250126319063759955"},"user_tz":240},"id":"3d108ef0","outputId":"fcb1e378-4d5a-4697-cf48-39094331d49e","papermill":{"duration":0.05913,"end_time":"2024-11-28T09:37:58.800175","exception":false,"start_time":"2024-11-28T09:37:58.741045","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["{'train_loss': [1.6712732091079425,\n","  1.4006405620833777,\n","  1.2162348470010937,\n","  1.1367398150156336,\n","  1.0312653335126307,\n","  0.9293843909071482,\n","  0.8346907376060904,\n","  0.7876417660825188,\n","  0.717169693093708,\n","  0.6712447785167704,\n","  0.6347205185473338,\n","  0.5993431532494957,\n","  0.5480941055898626,\n","  0.5120575626794879,\n","  0.4685608239257261,\n","  0.4352309761533906,\n","  0.42015899304837173,\n","  0.385886945779239,\n","  0.37548270553257124,\n","  0.3523482079491299,\n","  0.3210443861020383,\n","  0.29799134085549817,\n","  0.27501531029295073,\n","  0.2569073505606308,\n","  0.23912154549114514,\n","  0.23047889479959,\n","  0.2101818059759685,\n","  0.20003687026511902,\n","  0.1865589428393633,\n","  0.1762794346999696,\n","  0.16692602489093059,\n","  0.15141340420000052,\n","  0.15244679014871212,\n","  0.14358627843905808,\n","  0.12947296191980023,\n","  0.12615282331070315,\n","  0.1172407921566482,\n","  0.11640739345452544,\n","  0.1068738395035158,\n","  0.10425035581541965,\n","  0.09941621866497155,\n","  0.09575998791836646,\n","  0.08979192507766295,\n","  0.09061816053322588,\n","  0.08147299766665934,\n","  0.08438591555826412,\n","  0.079309749901686,\n","  0.07372630827966396,\n","  0.07503503921469926,\n","  0.06971607326616441,\n","  0.06833834770481424,\n","  0.0636457348244711,\n","  0.06612781416871469,\n","  0.06092748980840866,\n","  0.061444404086589986,\n","  0.05768112487485098,\n","  0.05987972935757613,\n","  0.0540240406520076,\n","  0.053585477576316196,\n","  0.05637144686410929,\n","  0.05193382912929222,\n","  0.05131400605020335,\n","  0.0459830516106014,\n","  0.046540733753179304,\n","  0.045385076358008085,\n","  0.04808397920291126,\n","  0.043172777626667214,\n","  0.044762216001836046,\n","  0.03961865688756299,\n","  0.04121693550660516,\n","  0.03910593923913083,\n","  0.04150765649479763,\n","  0.03949698636721234,\n","  0.03434596642135353,\n","  0.03825537870684675,\n","  0.03681836791796426,\n","  0.032071422689490064,\n","  0.03665608876490638,\n","  0.03533456787254618,\n","  0.03320206544627498,\n","  0.027615183102493487,\n","  0.03515287844851009,\n","  0.030677643914545295,\n","  0.029012030285412417,\n","  0.030956493924074752,\n","  0.03207645939550283,\n","  0.026715345190068792,\n","  0.028866918064572273,\n","  0.02914436153472012,\n","  0.030713935520687144,\n","  0.030250195269136592,\n","  0.02473098213262715,\n","  0.02323412826334331,\n","  0.026379700358813356,\n","  0.027417325241461305,\n","  0.025941764787654725,\n","  0.02674428714660787,\n","  0.025620907067136014,\n","  0.026294261619941816,\n","  0.02154591452513055],\n"," 'train_accuracy': [0.38384233881493085,\n","  0.48306577916992954,\n","  0.5499216914643696,\n","  0.586824588880188,\n","  0.6222265726964239,\n","  0.6636322109109893,\n","  0.6969133385539024,\n","  0.7160010441138084,\n","  0.7399177760375881,\n","  0.7592665100495954,\n","  0.7690224484468807,\n","  0.7813234142521535,\n","  0.8000848342469329,\n","  0.81427825632994,\n","  0.8290263638736622,\n","  0.8407400156617071,\n","  0.8457648133646567,\n","  0.8611328634821196,\n","  0.8654398329417906,\n","  0.8741842860871835,\n","  0.8835486818063168,\n","  0.8932393630905768,\n","  0.9002218741842861,\n","  0.9088358131036283,\n","  0.9124249543200209,\n","  0.9162751239885147,\n","  0.9247259201252936,\n","  0.9276298616549203,\n","  0.9328178021404333,\n","  0.9364395719133386,\n","  0.9393435134429653,\n","  0.9472722526755416,\n","  0.9452819107282694,\n","  0.9502088227616811,\n","  0.9530801357347951,\n","  0.9552336204646307,\n","  0.9561798486034978,\n","  0.9584964761158966,\n","  0.9634886452623336,\n","  0.9624119028974158,\n","  0.9644348734012007,\n","  0.9660010441138084,\n","  0.9678282432785174,\n","  0.9676324719394414,\n","  0.9712868702688593,\n","  0.9697533281127643,\n","  0.9716784129470112,\n","  0.9744192116940746,\n","  0.9732119551031062,\n","  0.9753328112764291,\n","  0.9770294962150874,\n","  0.9775841816758026,\n","  0.9767358392064736,\n","  0.9786935525972331,\n","  0.9785956669276952,\n","  0.9799986948577395,\n","  0.9785304098146698,\n","  0.9806512659879927,\n","  0.9822174367006004,\n","  0.9802923518663534,\n","  0.9817280083529105,\n","  0.9817932654659358,\n","  0.9837509788566954,\n","  0.9841425215348473,\n","  0.9847950926651005,\n","  0.9837509788566954,\n","  0.985415035238841,\n","  0.9848603497781259,\n","  0.9859370921430436,\n","  0.9861002349256069,\n","  0.9862307491516575,\n","  0.9856434351344296,\n","  0.9865570347167841,\n","  0.9875032628556513,\n","  0.9862633777081702,\n","  0.9876337770817019,\n","  0.9884494909945184,\n","  0.9874053771861133,\n","  0.9885147481075437,\n","  0.9891999477943095,\n","  0.9905703471678413,\n","  0.9875032628556513,\n","  0.9895914904724615,\n","  0.9907334899504046,\n","  0.9901461759331767,\n","  0.9894936048029235,\n","  0.990602975724354,\n","  0.9901461759331767,\n","  0.9903745758287653,\n","  0.9892652049073349,\n","  0.9902766901592274,\n","  0.9915818324197337,\n","  0.9919081179848603,\n","  0.9911250326285566,\n","  0.990602975724354,\n","  0.9910924040720438,\n","  0.9906682328373793,\n","  0.9916797180892717,\n","  0.9911576611850692,\n","  0.9929196032367528],\n"," 'val_loss': [0.4504778417673978,\n","  0.4637623599984429,\n","  0.5520166261629624,\n","  0.4830637509172613,\n","  0.493127919327129,\n","  0.44582978541200813,\n","  0.3642550365491347,\n","  0.3024716801264069,\n","  0.3009622110561891,\n","  0.36617785638028927,\n","  0.2931963943622329,\n","  0.2563274468887936,\n","  0.24851091639562087,\n","  0.2440702286633578,\n","  0.2231974949890917,\n","  0.20520452816377988,\n","  0.1984924549406225,\n","  0.19724704094908455,\n","  0.17646027471531522,\n","  0.17725794444029982,\n","  0.16567582101984457,\n","  0.16749361076138236,\n","  0.1406164313581857,\n","  0.14109432267194444,\n","  0.14638027487830682,\n","  0.12480716508897868,\n","  0.14353639940646562,\n","  0.1607214209030975,\n","  0.1168607388030399,\n","  0.11123207120055502,\n","  0.1130605778910897,\n","  0.1219674371860244,\n","  0.10595478230917996,\n","  0.09782475754618644,\n","  0.09963232590393586,\n","  0.0918858660554344,\n","  0.14008975479413163,\n","  0.10467299432917075,\n","  0.09540308900177479,\n","  0.12006028443574905,\n","  0.08584614309736273,\n","  0.09786666626618667,\n","  0.07966655711220069,\n","  0.13968653231859207,\n","  0.08426079783927311,\n","  0.11075736612758853,\n","  0.08382667628201572,\n","  0.09133963496847586,\n","  0.08712761329317635,\n","  0.07814483600245281,\n","  0.08615111317485571,\n","  0.08325841567394408,\n","  0.07614294575019316,\n","  0.08099258558147333,\n","  0.08237709005109288,\n","  0.1161729434505105,\n","  0.07375067027539692,\n","  0.07658343243158676,\n","  0.11541603790088133,\n","  0.09704684428870677,\n","  0.07606050710786473,\n","  0.06451047523455186,\n","  0.06973886996168982,\n","  0.07702853410420092,\n","  0.0678458676927469,\n","  0.05779106551781297,\n","  0.06436949088661507,\n","  0.06828127005560831,\n","  0.10164394913749261,\n","  0.05810654025355523,\n","  0.09277077969163657,\n","  0.06693134782835841,\n","  0.07095909398049116,\n","  0.060594754259694704,\n","  0.06350541553375395,\n","  0.05695068858225237,\n","  0.09540582874959165,\n","  0.05561901486732743,\n","  0.07075959322974086,\n","  0.061171958159486,\n","  0.048545291406017814,\n","  0.05936332528733394,\n","  0.05790361103347757,\n","  0.06876476281745868,\n","  0.05354181983592835,\n","  0.07405623720789498,\n","  0.06693225950849327,\n","  0.06172370746393095,\n","  0.06095068546181375,\n","  0.05673459578644146,\n","  0.05511239544297992,\n","  0.057138322099027306,\n","  0.05596858952617781,\n","  0.047740199353376575,\n","  0.06771451421928677,\n","  0.064506270008331,\n","  0.0520550576059825,\n","  0.0649282802912322,\n","  0.06665634497661482,\n","  0.05330808752809058],\n"," 'val_accuracy': [0.3550414167380748,\n","  0.5986860896886603,\n","  0.5744073121965153,\n","  0.6006855184233076,\n","  0.6063981719508712,\n","  0.6492430734075978,\n","  0.6912310768351899,\n","  0.6826620965438446,\n","  0.7115109968580405,\n","  0.5586975149957155,\n","  0.6895172807769209,\n","  0.7226506712367895,\n","  0.7594972864895744,\n","  0.7769208797486432,\n","  0.762067980576978,\n","  0.7986289631533847,\n","  0.7769208797486432,\n","  0.8129105969722936,\n","  0.7957726363896029,\n","  0.7949157383604685,\n","  0.8077692087974865,\n","  0.8151956583833191,\n","  0.8380462724935732,\n","  0.829477292202228,\n","  0.8386175378463296,\n","  0.8397600685518424,\n","  0.821479577263639,\n","  0.7617823479005998,\n","  0.8406169665809768,\n","  0.8466152527849186,\n","  0.8254784347329335,\n","  0.8014852899171665,\n","  0.8420451299628677,\n","  0.8449014567266495,\n","  0.839188803199086,\n","  0.8203370465581262,\n","  0.7583547557840618,\n","  0.8377606398171951,\n","  0.8414738646101114,\n","  0.7586403884604399,\n","  0.847186518137675,\n","  0.8446158240502714,\n","  0.8674664381605256,\n","  0.6538131962296486,\n","  0.831191088260497,\n","  0.7763496143958869,\n","  0.8531848043416167,\n","  0.8446158240502714,\n","  0.8380462724935732,\n","  0.8146243930305627,\n","  0.8469008854612968,\n","  0.8454727220794059,\n","  0.8551842330762639,\n","  0.8454727220794059,\n","  0.8511853756069694,\n","  0.7403598971722365,\n","  0.8494715795487003,\n","  0.836046843758926,\n","  0.6978006283918881,\n","  0.7963439017423594,\n","  0.8314767209368752,\n","  0.8514710082833477,\n","  0.8671808054841473,\n","  0.8446158240502714,\n","  0.8491859468723222,\n","  0.8740359897172236,\n","  0.8597543558983147,\n","  0.847186518137675,\n","  0.7526421022564982,\n","  0.8574692944872894,\n","  0.7726363896029705,\n","  0.8608968866038275,\n","  0.8463296201085404,\n","  0.8371893744644388,\n","  0.852042273636104,\n","  0.8623250499857184,\n","  0.7135104255926878,\n","  0.869751499571551,\n","  0.8394744358754641,\n","  0.8543273350471294,\n","  0.8634675806912311,\n","  0.8528991716652385,\n","  0.8603256212510711,\n","  0.8431876606683805,\n","  0.8543273350471294,\n","  0.8394744358754641,\n","  0.8474721508140531,\n","  0.8534704370179949,\n","  0.8454727220794059,\n","  0.8309054555841188,\n","  0.8606112539274493,\n","  0.8623250499857184,\n","  0.8517566409597258,\n","  0.8646101113967438,\n","  0.8397600685518424,\n","  0.8463296201085404,\n","  0.8580405598400457,\n","  0.853756069694373,\n","  0.8406169665809768,\n","  0.8497572122250785],\n"," 'model_name': 'Data Augment ResNet50',\n"," 'best_val_accuracy': 0.8740359897172236}"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["history"]},{"cell_type":"markdown","id":"68680289","metadata":{"id":"68680289","papermill":{"duration":0.047603,"end_time":"2024-11-28T09:37:58.896085","exception":false,"start_time":"2024-11-28T09:37:58.848482","status":"completed"},"tags":[]},"source":["Let's plot the above matrices for visualization."]},{"cell_type":"code","execution_count":null,"id":"09ee1e41","metadata":{"execution":{"iopub.execute_input":"2024-11-28T09:37:58.993180Z","iopub.status.busy":"2024-11-28T09:37:58.992647Z","iopub.status.idle":"2024-11-28T09:37:59.415890Z","shell.execute_reply":"2024-11-28T09:37:59.414987Z"},"executionInfo":{"elapsed":9,"status":"aborted","timestamp":1730261318019,"user":{"displayName":"tannistha maiti","userId":"15250126319063759955"},"user_tz":240},"id":"09ee1e41","outputId":"20c5a296-d73d-49f2-84c9-ae5a1dc8e933","papermill":{"duration":0.473914,"end_time":"2024-11-28T09:37:59.417750","exception":false,"start_time":"2024-11-28T09:37:58.943836","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":"bcb3bc97","metadata":{"id":"bcb3bc97","papermill":{"duration":0.049015,"end_time":"2024-11-28T09:37:59.516799","exception":false,"start_time":"2024-11-28T09:37:59.467784","status":"completed"},"tags":[]},"source":["So from above we can see that our model does much better than before:\n","\n","* The training accuracy approaches 1.0 (100%) at the end of the training.\n","\n","* Wherelese, the validation accuracy fluctuates but ends around 0.86 (86%)."]},{"cell_type":"markdown","id":"86c1ea1d","metadata":{"id":"86c1ea1d","papermill":{"duration":0.04912,"end_time":"2024-11-28T09:37:59.614722","exception":false,"start_time":"2024-11-28T09:37:59.565602","status":"completed"},"tags":[]},"source":["# Testing the model:-"]},{"cell_type":"markdown","id":"d457798c","metadata":{"id":"d457798c","papermill":{"duration":0.049016,"end_time":"2024-11-28T09:37:59.713782","exception":false,"start_time":"2024-11-28T09:37:59.664766","status":"completed"},"tags":[]},"source":["**Let's first test our model on a single image picked randomly.**"]},{"cell_type":"code","execution_count":null,"id":"ab8975ea","metadata":{"execution":{"iopub.execute_input":"2024-11-28T09:37:59.813602Z","iopub.status.busy":"2024-11-28T09:37:59.813250Z","iopub.status.idle":"2024-11-28T09:38:38.034319Z","shell.execute_reply":"2024-11-28T09:38:38.033388Z"},"executionInfo":{"elapsed":9,"status":"aborted","timestamp":1730261318019,"user":{"displayName":"tannistha maiti","userId":"15250126319063759955"},"user_tz":240},"id":"ab8975ea","outputId":"f792ac1c-f3a8-458f-8272-12897327ce7a","papermill":{"duration":38.27456,"end_time":"2024-11-28T09:38:38.037496","exception":false,"start_time":"2024-11-28T09:37:59.762936","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: 0.8668\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAakAAAGzCAYAAACVYeimAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9ebxkSVnm/42Is+R617q1dVWvdNPsSCOr0mw2guACCogzAor2DM0mrjg/QBSGD4oCwzbiOOioDDMgqKwKDujAILKpbN00TXV3ddd66665nC0ifn/EiZsn896qulV1a+vOpz/ZeSvz5Mk4S8YT7/s+7/sKa61ljDHGGGOMMS5AyPM9gDHGGGOMMcY4HsYkNcYYY4wxxgWLMUmNMcYYY4xxwWJMUmOMMcYYY1ywGJPUGGOMMcYYFyzGJDXGGGOMMcYFizFJjTHGGGOMccFiTFJjjDHGGGNcsBiT1BhjjDHGGBcsxiQ1xhAuv/xyXvCCF6z9+7Of/SxCCD772c+etzGNYnSMW4Hf+q3fQgixpfsc4/zibNwnY5x7jEnqAsKf/MmfIIRYe9RqNa655hpe8pKXcPjw4fM9vFPCxz/+cX7rt37rfA+DJEl4y1vewiMf+UgmJyeHzul3vvOd8z2808Yb3vAGfvRHf5QdO3YghDijc/2CF7xg6L473uNCmfBvu+02brzxRq688kpqtRoTExM89rGP5W1vexv9fv98D++0cMstt/BLv/RLPOYxj6FWqyGE4Pbbbz/fw7ogEJzvAYyxHr/927/NFVdcQZIkfO5zn+Pd7343H//4x/nGN75Bo9E4p2N53OMeR7/fJ4qiU/rcxz/+cd75zneeV6Kan5/nh3/4h/nKV77C05/+dJ73vOfRarW45ZZbeP/738973vMesiw7b+M7E/x//9//x86dO/m+7/s+/vZv//aM9nXjjTfy5Cc/ee3f+/bt4zWveQ2/+Iu/yA/+4A+uvX7VVVed0fdsBT72sY/xUz/1U8RxzM/+7M/ywAc+kCzL+NznPsev/uqv8s1vfpP3vOc953uYp4wvfOEL/Jf/8l+4//3vz/3udz/+5V/+5XwP6YLBmKQuQDz1qU/l4Q9/OAAvetGLmJ2d5Q/+4A/467/+a376p396w890u12azeaWj0VKSa1W2/L9ngu84AUv4Gtf+xof/OAHedaznjX03u/8zu/wn/7TfzpPIztz7Nu3j8svv5z5+Xnm5ubOaF+PfvSjefSjH7327y9/+cu85jWv4dGPfjT/7t/9u+N+7mzdc8fDvn37eO5zn8tll13G//k//4ddu3atvXfTTTfx3e9+l4997GPnbDxbiR/90R9laWmJdrvNm9/85jFJVTB2910EeOITnwi4Hym4ybfVanHbbbfxtKc9jXa7zc/8zM8AYIzhrW99Kw94wAOo1Wrs2LGDG2+8kcXFxaF9Wmt5/etfz549e2g0GjzhCU/gm9/85rrvPl5M6otf/CJPe9rTmJ6eptls8uAHP5i3ve1ta+N75zvfCTDkLvLY6jFuhC9+8Yt87GMf4+d//ufXERRAHMe8+c1vPuE+3vve9/LEJz6R7du3E8cx97///Xn3u9+9brsvf/nLPOUpT2Hbtm3U63WuuOIKfu7nfm5om/e///1cd911tNttJiYmeNCDHrR2vjxuu+02brvttk0d3+WXX76p7bYK3hX9D//wD7z4xS9m+/bt7NmzB3DXe6PxHC/O9+d//udcd9111Ot1ZmZmeO5zn8v+/ftPOobf/d3fpdPp8Md//MdDBOVxn/vch5e//OXH/fzCwgK/8iu/woMe9CBarRYTExM89alP5V//9V/Xbfv2t7+dBzzgATQaDaanp3n4wx/O+973vrX3V1dXecUrXsHll19OHMds376dH/qhH+KrX/3q2ja9Xo+bb76Z+fn5kx7bzMwM7Xb7pNvdGzG2pC4C+IlrdnZ27bWiKHjKU57CD/zAD/DmN795zQ1444038id/8ie88IUv5GUvexn79u3jHe94B1/72tf4/Oc/TxiGALzmNa/h9a9/PU972tN42tOexle/+lVuuOGGTbm/PvWpT/H0pz+dXbt28fKXv5ydO3fy7W9/m49+9KO8/OUv58Ybb+TAgQN86lOf4s/+7M/Wff5cjPFv/uZvAPj3//7fn3Tb4+Hd7343D3jAA/jRH/1RgiDgIx/5CC9+8YsxxnDTTTcBcOTIEW644Qbm5ub4jd/4Daamprj99tv50Ic+NHS+fvqnf5onPelJvOlNbwLg29/+Np///OeHJtUnPelJABd0LOLFL34xc3NzvOY1r6Hb7Z7y59/whjfw6le/mmc/+9m86EUv4ujRo7z97W/ncY97HF/72teYmpo67mc/8pGPcOWVV/KYxzzmtMb+ve99j7/6q7/ip37qp7jiiis4fPgwf/iHf8j111/Pt771LXbv3g3AH/3RH/Gyl72Mn/zJn+TlL385SZLwb//2b3zxi1/kec97HgD/4T/8Bz74wQ/ykpe8hPvf//4cO3aMz33uc3z729/mYQ97GAD//M//zBOe8ARe+9rXXhDx2YsWdowLBu9973stYD/96U/bo0eP2v3799v3v//9dnZ21tbrdXvXXXdZa619/vOfbwH7G7/xG0Of/7//9/9awP7FX/zF0Ouf/OQnh14/cuSIjaLI/siP/Ig1xqxt95u/+ZsWsM9//vPXXvvMZz5jAfuZz3zGWmttURT2iiuusJdddpldXFwc+p7qvm666Sa70e11Nsa4EX7iJ37CAuvGeDy89rWvXTfeXq+3brunPOUp9sorr1z794c//GEL2C996UvH3ffLX/5yOzExYYuiOOEYLrvsMnvZZZdtarweR48etYB97Wtfe0qfOxG+9KUvWcC+973vXXvN35s/8AM/sO44nv/852847tFzevvtt1ullH3DG94wtN3Xv/51GwTButerWF5etoD9sR/7sU0fx2WXXTZ0nyRJYrXWQ9vs27fPxnFsf/u3f3vttR/7sR+zD3jAA06478nJSXvTTTedcBv/2znVa/N7v/d7FrD79u07pc/dUzF2912AePKTn8zc3Bx79+7luc99Lq1Wiw9/+MNccsklQ9v9x//4H4f+/YEPfIDJyUl+6Id+iPn5+bXHddddR6vV4jOf+QwAn/70p8myjJe+9KVD7phXvOIVJx3b1772Nfbt28crXvGKdavezUi4z8UYAVZWVgDOyIVSr9fX/l5eXmZ+fp7rr7+e733veywvLwOsnYOPfvSj5Hm+4X6mpqbodrt86lOfOuH33X777Re0FQXwC7/wCyilTuuzH/rQhzDG8OxnP3vo2u/cuZOrr7567dpvhK24nnEcI6Wb8rTWHDt2jFarxX3ve98hN93U1BR33XUXX/rSl467r6mpKb74xS9y4MCB427z+Mc/Hmvt2Io6Q4zdfRcg3vnOd3LNNdcQBAE7duzgvve979qPyyMIgrWYgMett97K8vIy27dv33C/R44cAeCOO+4A4Oqrrx56f25ujunp6ROOzbseH/jAB27+gM7xGAEmJiYAFzs4kQvpRPj85z/Pa1/7Wr7whS/Q6/WG3lteXmZycpLrr7+eZz3rWbzuda/jLW95C49//OP58R//cZ73vOcRxzHgXGT/+3//b5761KdyySWXcMMNN/DsZz+bH/7hHz6tcZ1PXHHFFaf92VtvvRVr7bpr6uHdvBuhej1PF8YY3va2t/Gud72Lffv2obVee6/qSv/1X/91Pv3pT/OIRzyC+9znPtxwww0873nP47GPfezaNr/7u7/L85//fPbu3ct1113H0572NH72Z3+WK6+88rTHN8bGGJPUBYhHPOIRa+q+46G6KvQwxrB9+3b+4i/+YsPPnKkKbCtwrsZ47bXXAvD1r399SEa9Wdx222086UlP4tprr+UP/uAP2Lt3L1EU8fGPf5y3vOUtGGMAZz1+8IMf5J/+6Z/4yEc+wt/+7d/ycz/3c/z+7/8+//RP/0Sr1WL79u38y7/8C3/7t3/LJz7xCT7xiU/w3ve+l5/92Z/lT//0T7fkeM8Vqtalx/Es6CoJgLv2Qgg+8YlPbGiNtVqt437vxMQEu3fv5hvf+MYpjniA//yf/zOvfvWr+bmf+zl+53d+h5mZGaSUvOIVr1i7ngD3u9/9uOWWW/joRz/KJz/5Sf7yL/+Sd73rXbzmNa/hda97HQDPfvaz+cEf/EE+/OEP83d/93f83u/9Hm9605v40Ic+xFOf+tTTHuMY6zEmqXsQrrrqKj796U/z2Mc+dsPJxOOyyy4D3Mq2uvI7evToOoXdRt8B8I1vfGMot2YUx5u4zsUYAZ7xjGfwxje+kT//8z8/LZL6yEc+Qpqm/M3f/A2XXnrp2uvHc0k96lGP4lGPehRveMMbeN/73sfP/MzP8P73v58XvehFAERRxDOe8Qye8YxnYIzhxS9+MX/4h3/Iq1/9au5zn/uc8vguJExPT7O0tLTudW8Ne1x11VVYa7niiiu45pprTvl7nv70p/Oe97yHL3zhC0OS+c3igx/8IE94whP44z/+46HXl5aW2LZt29BrzWaT5zznOTznOc8hyzKe+cxn8oY3vIFXvepVaykZu3bt4sUvfjEvfvGLOXLkCA972MN4wxveMCapLcY4JnUPwrOf/Wy01vzO7/zOuveKolibSJ785CcThiFvf/vbsdaubfPWt771pN/xsIc9jCuuuIK3vvWt6yam6r58/szoNudijOByf374h3+Y//bf/ht/9Vd/te79LMv4lV/5leN+3q/0q9+9vLzMe9/73qHtFhcXh7YBeOhDHwpAmqYAHDt2bOh9KSUPfvCDh7aBU5OgX0i46qqrWF5e5t/+7d/WXjt48CAf/vCHh7Z75jOfiVKK173udevOmbV23Xkaxa/92q/RbDZ50YtetGEFlttuu22drL8KpdS67/3ABz7A3XffPfTa6DiiKOL+978/1lryPEdrvRaT9Ni+fTu7d+8eup6nIkEf4/gYW1L3IFx//fXceOONvPGNb+Rf/uVfuOGGGwjDkFtvvZUPfOADvO1tb+Mnf/InmZub41d+5Vd44xvfyNOf/nSe9rSn8bWvfY1PfOIT61aUo5BS8u53v5tnPOMZPPShD+WFL3whu3bt4uabb+ab3/zmWvWD6667DoCXvexlPOUpT0EpxXOf+9xzMkaP//E//gc33HADz3zmM3nGM57Bk570JJrNJrfeeivvf//7OXjw4HFzpW644YY16+fGG2+k0+nwR3/0R2zfvp2DBw+ubfenf/qnvOtd7+InfuInuOqqq1hdXeWP/uiPmJiY4GlPexrgErIXFhZ44hOfyJ49e7jjjjt4+9vfzkMf+lDud7/7re3rVCTof/Znf8Ydd9yxFiv7x3/8R17/+tcDTnbvLdHPfvazZ10G/dznPpdf//Vf5yd+4id42cteRq/X493vfjfXXHPNkCDhqquu4vWvfz2vetWruP322/nxH/9x2u02+/bt48Mf/jC/+Iu/eMKFw1VXXcX73vc+nvOc53C/+91vqOLE//t//48PfOADJyzd9PSnP53f/u3f5oUvfCGPecxj+PrXv85f/MVfrIsj3XDDDezcuZPHPvax7Nixg29/+9u84x3v4Ed+5Edot9ssLS2xZ88efvInf5KHPOQhtFotPv3pT/OlL32J3//931/bz6lI0JeXl3n7298OuFgowDve8Q6mpqaYmpriJS95yQk/f4/G+REVjrERvMz3RHJma53kt9lsHvf997znPfa6666z9Xrdtttt+6AHPcj+2q/9mj1w4MDaNlpr+7rXvc7u2rXL1ut1+/jHP95+4xvfWCfbHZWge3zuc5+zP/RDP2Tb7bZtNpv2wQ9+sH3729++9n5RFPalL32pnZubs0KIdfLurRzjidDr9eyb3/xm+/3f//221WrZKIrs1VdfbV/60pfa7373u2vbbSRB/5u/+Rv74Ac/2NZqNXv55ZfbN73pTfa///f/PiQP/upXv2p/+qd/2l566aU2jmO7fft2+/SnP91++ctfXtvPBz/4QXvDDTfY7du32yiK7KWXXmpvvPFGe/DgwaHvOxUJ+vXXX2+BDR/Va/WRj3zEAva//tf/uqn9WntiCfrx7s2/+7u/sw984ANtFEX2vve9r/3zP//zDc+ptdb+5V/+pf2BH/gB22w2bbPZtNdee6296aab7C233LKp8X3nO9+xv/ALv2Avv/xyG0WRbbfb9rGPfax9+9vfbpMkWdtuIwn6L//yL6/dT4997GPtF77wBXv99dfb66+/fm27P/zDP7SPe9zj7OzsrI3j2F511VX2V3/1V+3y8rK11to0Te2v/uqv2oc85CFr9/9DHvIQ+653vWtonKciQd+3b99xr+eppiXc0yCsHbF/xxhjjHsMfu3Xfo3/+T//J9/97nfX1IZjjHExYRyTGmOMezA+85nP8OpXv3pMUGNctBhbUmOMMcYYY1ywGFtSY4wxxhhjXLAYk9QYY4wxxhgXLM4bSb3zne/k8ssvp1ar8chHPpJ//ud/Pl9DGWOMMcYY4wLFeSGp//W//hevfOUree1rX8tXv/pVHvKQh/CUpzxlrW7bGGOMMcYYY8B5Ek488pGP5Pu///t5xzveAbiaXnv37uWlL30pv/Ebv3HSzxtjOHDgAO12e1OVt8cYY4wxxriwYK1ldXWV3bt3r6tDWsU5rziRZRlf+cpXeNWrXrX2mpSSJz/5yXzhC1/Y8DNpmg6VG7n77ru5//3vf9bHOsYYY4wxxtnF/v3713V0qOKck9T8/Dxaa3bs2DH0+o4dO7j55ps3/Mwb3/jGterDY2whRPmYALYBMZCWD1v+uw4oICr/HQBtoFl+VpfbmvJzRblvWb4flvsYvdN6wHL5+S7QKT+7WD5M+dgIYTkmWY5JlvtJyn3I8n1ReQBk5WMMdz0V7hxnuGtYwa4HX841P/QQ6rMt7rrje9x6yzdI04T40r3UL78MEYbunBtAa3RnCdNfIS7gkr5iNpX0egl333mEpcUOOoZ0EkwNd51Wyu8NBEQKhIBVDcvG7XeMew1O1iPsoqjd96pXvYpXvvKVa/9eWVlh796953FE9xBUJ3HJYHL3XRRmgV24CS0sn8GRkZ/sbeVZlttVJzz/WsgwYXRwE5UnlgI3Ocnye0z5bz9hecIKcARZL99LAd9r0H9PdfvqGP0xjzMD3TnzC4oNzkee9OgcPUyerdA/uohdziEpyLsH0d9dQljprl0CSsBkU9CqCWIhmbYxLULCPCczlloMiYZjh6CvcYui3UADaAQwVQcl4bYM/i2B3vFWJ2PcE3GykM05J6lt27ahlFpXxfjw4cPs3Llzw8/EcTzOmD8X8JOVv2cioMUwSRkGpDI6uVmGSWv0teq+PcGkDKwmPzd597RhQCrV54AB6aUMyE1VtjMbjG+MAUavywhMXpD2eqA0RT/BJgZSi+n0MVnfnd8u0AcrBXK2Rm0yJpKKQEmkFCitiayhJsAYkD3c4qaGW2RMAi0J2wIIJBzV7tqOFxJjVHDOSSqKIq677jr+/u//nh//8R8HnBDi7//+7+/dlX7PNbyF45+DyqOOmyj8MwxIxZNUtQQmI3+P7luObFO1ukzlPXDL8kiCtrBqoWvdGOLKvhQDV5NkMLH5sXorrDrRjYlr8xCQCcOyzQgt9LIC07PQx53XgqFzbCz0E82yzAiEJJOGusgpTEEn06Qa0gKK6rXOKd2MATKqQxzCzhgeWIOuxs4n2EN9dx+Mca/GeXH3vfKVr+T5z38+D3/4w3nEIx7BW9/6VrrdLi984QvPx3DunVAMYkw1Bq4yH4cKcO4YyWBS8dZOxmDC989VkvL7iRi4ET389rL8XjmyTUuCCcrvK6CrHfm0cLEwy8DFZxnEpkZrR3v3YRXj+e7kKGOJfVFQkCCsRqcZZsm6OGL1OpekY4xltZvT6xfOcywEEoG1Fm0NtiQy7UlK4+6hBKQNUfVJRLMGVxm4xGJTg/naPHohLf2DY9ybcV5I6jnPeQ5Hjx7lNa95DYcOHeKhD30on/zkJ9eJKcY4y/BWSZUoAgZBde8+g40tqI0e1X1X767RGFHVtbj2XQJs+fCve/hx+QludB9U/j22mk4PlfikATKrwQqsNtjCDmJ/G0Briz4Vq8cveKwEoUAqqAVQB5EbaIbOqha4+8LHLYyFcbnRexUuygKzKysrTE5Onu9hXHzwE78nJ09Gk8B2nPXjJ3iBs1wmyu0zBtZL1bXm3WqjltREub8ctwLPy0fCQNHn1X3TAcyWk1KuITeQWbjTwEHjvn+mHI9hILbwLqfROHvOwDU5xqYR1CT1yQAVCfLpkHR3hI4k3Jlgb+lBf3MnVAYgvLqzhDXOMMYCU8BlQAvEdAN5yTSiFkJcg3oDa8B+4yjma4cgswQzs6iZWSgMxR1H0AcWx0R1D8Ly8jITExPHff+iUPeNsUXwknAfwwE3yQscYdWAVZwE3McevNy7VHIBA3Wdd7eNuvz8ZxQujrGII6pu+beXgvfL/cUhXNKEWECvB7oPwgxbSAXrLSjveiwYELBgTE6niagumdoRE7cUHVGQdVax1kDHOuXDJiAkqBBkNMRRmDImZapqTQO220ffnTnhxMQ0zAbOqoos7BQIFMF9L6F232ux/YLk//wb+vASFGOSurdgTFL3FlRl5rLyWnUm8VaUHnnAQNHnSWAjxd7oPi0D95xX8iUMYkppuW1RuviMf9gB0fhx+/2eSLAxKpQY45QghEBFkiCSSA0i1y6QtJGS84T7AemCU66SgBAIadGFcRaQv4c0kFunqtBAlkOmB26+mnMDyokaaqaN7eUudhWFIAo3NjO+0Pd0jEnqng6fd+StJ09QIU4Y4a2PQwwsrTYD9V2vfN276wQuxylh4DIMR/YNAzJaBuZxFlo1N8fDAos53NZ3Y8wyN3H5mMV0+T0tnPVWMEgE9eq+auKux1jGfGZIgAXcee4yWKx4dzFs7GrFVZAJpCSsx7R3bSeeaNHv95ifP0S/13XXZgV3T/h7zQLdBOwxUAFICbu2I6KYeNd2JuZmMGmBvnYvWVpguwn2zqNwZPlsnoUxLgCMSeqeDomb3Gu4CcWr9CIGcaNl4CBuQtqJqz7hKwN0WW+9JOXfAS6+4BNovZWmGciVF4EjwBID8cOo9baQw3JREosduBGncbEorzSsjsmTnc+b8vsaE9PWoI+7bl7RV81h80ndGetISiBQQhFKRb3ZYvuVlzO5dydLK/N07ujQX+o6gjpQ7nsbg+olRR+6qSOoHTvhkp2IRpPaJTuY3D6D0ZbkAX26rRhzbBX6Gfboyjg+dQ/HmKTuqfAuMh8bkpXXq+KH0dwiGFb2ramwKvselZ9vBB/Tqj77/UnW72/UbVOdGP2YfB7XRu7FMUGdHF4pZ1k/sXvxXGHRucXkFlu9bv5+igTUyo2tdbGhyq6EEARhQBRHhHGMikNEFCKiAELpHrLUpBcMLPSAYYs4CBD1GqIWIwKFsQZjLFbYgfXs74fqfTi+D+5xGJPUPREK50bxybiepEzlfXCrZT9J+FypmOGqDT4uVZ2wauXDk5mfFLz7Jy/3XebCrH2vFzpsFNcaha9ooMvvmgDaopyYyg96K0oymPD8GMYT1TCkQNZCRKRAW0w/wxZOnCIDx10616wcTlGhIO1pdF6exBhnyQbA9hh21N353deHO5OhfLQwDtm+Zzdzu7dhayG6plhIVulkfQokqBikZk0q2sNZ8RGwowZ729AICXftIN69GxlFpHnOkTv2Y5KM7i23Y753N7afYs0yzDG4r/x96hWkY9wjMCapeyI8SU0zrIDzq2FPGl6tZ3FEAIOCrTAgKa+sy8p9e/fhKEn5Z09SXigxoujaFCxuAuvh3EGXCkdSAghHSCpgQKonyOW5V0MKZD1ENmJsXmByDYVBCBcCEgp0YVg5mmJLkl8ztmJcmkIs4NIYrmk7gUvPwF3pUFWIIAyZu2Qnl197BX0K7sxXWUxWSfKEHAkyBpmDKFc//j6RwGQNpqZhska4Yzv1nTsQStE7vMDSgaPYbh97623YW+90mvbAwjbr7i9fcsnf62OSusdgTFL3JFSLxFYVcVWXGCN/U3kNBnLvaq28qipwI5XgqNVVVfP53KrTQVVq7i0lnx/lxywqfx/veC4m+HM86u7cCphSSj7iWrUWRElK1rjHEJR0SruaRNQiRBiDsdhQYX1BYb8AWcvHtqVBW/5njBPEZHYgjFkbV7kPbZ1iT2swGmst1lqM0RidY4scigIK7Qbpr3+19uPFdr3HOCnGJHVPgWSQv+T99Z4gqhOCqGzvJ0RPLhZHMMsMiK5Rbu9Xqf61WmVfvq7fEZxiyxOVfz5T60YDSxbuZuBC3IiU/HH744MBwV0MULhzGzFIgt6qsWuL7ufOgjIWmztTw1qXw2RKN+k6DYIAMdFCXjqNaAao2RZB0ARtyNop+Y4lSK0TQ3RAG8Nq0uFoZ4FMQCrKA0gsHMpgvg+pgWwDBk76MH8U0ois1cTOzkAQkCddrM7A5GC1G2S1lqRv01JdEIUj+67GXMe4qDAmqXsKfKzIV4iA4WoQfhu/Sq8WZvXbgiOBTvl+i0H9vqpoocZA4eXjQAlwFEdUW52vZHCTIJXJaVRyDsMWnj8HVavwQod3pTYYSPi3iqSsxSb5+tNQktRxIQSiWUft3IZox0S1OlHgcgFM8xj5jBjEH7uuWHQ37bPYW0ErSR4pZ4llBo7lcDBb+951SFJYTqEIKJanKTrLEEaQ9UuCKkpTj4GC1C+G/Lny96raYP9jS+uixJik7knwVsOom8+76Ly0m5H3RmNL3mLyVpD/rL9bcpyowf/tJwwvb99KrKm9cMRYMKwCMwy7fDwp+9dPdVI6m2pBKUoClVCLXaUNa50LSxu3+m8JR1TSQKqdH656rQrrJnyLS3pVsrSGbaXM+BZACdeQUElQYAsNWYEN/Yl1KkFhwQqBaoQoGxA1a0STTaJWi9wYbJKQ5ylFJ3HW24nO6dqiw4IuXM6cMdDpw0ofein0i4HYx3sANlL3jcnoHoMxSd1TYHAWkCcYX/4oxFlEtcrrXg3nV6F+O8OgY66vkZcy6MY7UW4/j0v0rErRi/L7twKjcSafj7WL4XYhXijhxRk+YK4rnz2VCWtUiOEnwq2AAOoK6gHUY7j6Uti7wxHU/CKsdst8MOFk3v0MjrpcoLX2JULAYgYHS5dZM4KpmiO/lRSWkq2rwNAIYFsMscLUgYVlRCfAzgloNAaxywSkULSv3EZzZpZas8nOK65idscuOvNLHPt/X2PxtrvQSUa+2j/xd0oBgXIlktIE5ufBSrhzCfYvQ1LA4R4s2OEYaNWl7a2sUb4eE9dFizFJ3VNgGYgVfAKv98uPtuUIcAS1usF+vKLOW0W23E+z3IcnowOcHR//Rm48VX7/NM5iW8IdQ8FAnOGJ60wnIsWAsKtloc4UAkc0zQAmanDVDnjgfSDN4M4DML/gyCiWzoLp9Z0CridcblFNlbWGgKOJu35x4EgqkK4o73JyohGcGmIJM7EjVsB0eqAkptUaEGGpJJehorZzgomrd1BvtJnavZfpmUvAhOiljO5thzd3XYS33CTkOaysuO84uADfW3DE3MXdnyfCuHbjPQpjkrrYUW337idWwfAk61eWo4+qK82XNkoY7vPk99dnWBRxtlalXp3oj00wiH956ynEkbB3R1aTkKvuvk1ASEk80SJqNbDWkKYdsvxks+BpwjdV0k76TSDBKGw9hGattHSFIytdOBLSwbBbLxBOCu6rhnjiGiX2M4GgFCdod84DBUHoSCrLKRZWwRjsaopNwWpDsZiSHF6FhmW1tkSgGqx2VsjzbPP3ShghWg1EU2HjGCvOlt91jIsJY5K6mCFwQfZm+e8eg9ynaov3FGcF+WoPVaWTVwVuY1AJ3SdEVqXkBc6C8SKGs7VaDcqxKgYCDV9KqVMej3df9hmOQfi5zFtYmxijikK2P+BqdjzwGvI05cDXv87R276L3eqJ0eLakPQNxKFrkRQrCMDMtrE1VcafykdNQtEr26eUFRqshYZ018q3YZcn/NZTQzX2ZbSz5goJky1oNUAp8sUu5o55yDX6WA+OGYywrCZHSb63StCu071/RuPSo6RHllldXdnkdwvUxATqsj2IiQidZRRZ4tyhfuFS7W82xr0GY5K6mCEYuOJ8jMC7vrwFpRgQFgyEFVWS8smavjZem0ELji6DhOBVBrGas3U83t3m29hXi+B6V2ZceX2JjZVc2ea+UgaK9s45tj/wGrJuj8W774TvnSU5oDFlrpcLpIhAgFLQqjkLyppSYl36Lhsh2NCJIrKyInkkHEnnrK8kshVYS/g2rio5skz2jkApzJEVzF1HnaijD/TBWkva6ZDSQbZj0nZMGOaYxR5JunkXpKjVUdOziKkadmUJ8spFrKZMjHGvwpikLkbEDCo+eKsCnFuoKZwCbMkOCCbFWVnVz3ky8Ptbq82GI6mQQSa/ZrCa9RPi2cw5GVX0jUrqg8p21RjSqcSmAuncWPUIFYdEoULEIfWJCZqz29BJRrbYQ+ebZLuTQQoXi5qOYbIJjdD1avIZtKYiURMChHQutiiGvHAxpzWUGbNClhXDFaphUVMSCoNOUnSanmQ8DFSb1WvphxAGyHYdUQug2XBjERKDxBgxcBf7eyhwbkrbEFijsf0cmxSnpDi0WY5Z7SJkgUmyMstYQKsOcxoSDXkK3VNwIY5x0WNMUhcbBAOlW1B5KKAuIVbQs3Czhm65cl/CWUET5Wd9wm9cefZ3QhvYy6DhoU8o9fX6NE7Zl3J2Jgovl/fuvnb5unc9VsnVW1XevddlEC870dwogEYE7Rqi3aQ2WaPdiNGhZNuleylyS7rSZfE7++h0j27NcQUKLp2F+++ARg0z10KYwsV2dO5iUNgBISvlVHRBAP3U9VxCl+UclJvAVQRxA2RAHE7QmFKQa3qHjtA7fJTjNir0500xnHjtCUuAajSp7d2FbMcQBhBFWGPJhCLLwHpLznd3bkloKAgURufoIx1MJ8Vmm1zNWItZ7ZLvPwSLITYsFY1Swc5p2LYNegUUh+DYsQ2yjse4p2JMUhcbfDLtJANBAbiJYkrChIRVCzUzyPfxi+q4sm3EQAFYrZLue095MqpaW3UGkvWzhWpel7ekfCUMP99Vk5Or1TU2XV1CQKigHiLqISoOiEKFkVCfaNPaNodSESvRga07LilgsgG7pqEeQSNylpTRjkx8LSIryuMvLSkfyxIVXb4tT5JQrlq4ClFhjUjUINekS8trxc6Pd/hr59f/uwoLMgwJ2i3kZH3wujZo5MCd7AU3AS5WNuFk8tZqTGlJWX1qlpRd6TqxSDOGIC7doXWI6tDNobE4dvndyzAmqYsN1ThUDecOCmPn6qvLMuBuIPR+ugpyBq3hWww3LPT7ruZPxcAO1icInyxYHzLoVSXLpFApnIW3aIZjWnbk2Y+jKkX3K/y88rd/TnDWXrr+cI8L4WTTqhGjaiErK6vs33cnJtfM336AlQPzpJ0uRX8LJd3WQKfrcn9qEWvtMrSGlQ70E0dMYeAm5jSFpRVXhSFJYbkLWeESWnPtXLq9FLKOc/k1BLTi9a1MNhwLA9XmRjlFgOll5IeXkJ1BbpPVBt3rQc2WCkvh2nZ4N3MECIEUCkWAMQbRDsGEbryJHipGC7gYW72U3U+GjpwaCmqh68CrVGnJBa7un6wWN9wACqd+VD7huHwUxsX1TtEAE0ISxDEqiLDGkGcJphhXMT6XGJPUxYgGZbM4CVMTMD3j4hPauofMIdYg0uEfZR/Yj/sh78BZYw1GKhowaJHREjBdLsmXrXv4jrwnG99VwCxloD9yMaC7NPxr5vZTRVVt6EmwOtF6sUaCmxy9FD3DKQ0XObUafUIQ1mPi2TYiDDh44CCHDx3DpDn53fMUR5cxRbG1JKWNI6hbV1wybz9xCatFAQvL0Ok5116zAXEEqz04cNS9nmtIcjfRGuOEFwZY6TqiEgr2CJhsD0v4jwd/7jaq7eiHu9Ql+fbd7rp55aSwmCjHTumBi6+pKgsREEIgRUAgYnQsEabuFkS9Ao4k0BtZSTQV7IocUe1owFzDVeKInIsRKSGO3TnRuJyxE5FwKGBGOQK1yhGkkdDLYTEZie2dHFIpGhMz1NtTFHlK59gRkjFJnVOMSepigxcU1HA/xHYIU3VHUklZuDMuVWNVawTcj9yXM2ozWE1Xt/OTvca59ybL131Nv2pJouOtSn0L+mncSntSOvdap1SnyZEPHs+Kqo7dE5lXs/nJNWfgzjwFyECiogCUpNfpk3RXnJptfgGObVI2fSow1llEK6lTrU11od90oohuz5FR6MaDtY7EVruOiNbUfSP7TAvolAXrsny4hNJJx3Pit21WoJeKYWtW4DoltyndxcItlKrfJ1zumRAKgUHUVEkWtrRuRhAI5ypsKmdBxaHLDwsCF8eTcpDgq2TF7XkcSAbWmZWgFRg14jI9BUhJEMZE9QZCSqQ62QpgjK3GmKQuFkzgJogasJ1yshcugp31AeFWi73C1TdrZbCbQTVt3wvKx6AmGbj6qkU5fdVzg+vXs1Z2psIkVfWddzsG0rlq4gAaGlql/83HVqLATW478oEAw6/kE5yVR/n9bQZuq2rTRD/2gkGO1AyOTFNcOafNiPGsRa8mZIeWQCkXZ9nShKONvhN3HRZx1kTQgUQ6C2uxtIhqEdgI2tJdw8S4CuPaDhSL1eRsL14RYPIU3XXkavLTYO2NxrvR31BZ0Fg3+cNaHycrNbpRkMU5Ns8xqxmsZKUyb4NVTWJgoXC9qWqlhYgYuPkscKQLi4suJnW0u3nRhJQgyyBrYkBu0jIWwrkb4xCrAlKbQWcZjCFstmjXYnSek3ZWt079OcZxMSapiwECR0wPxJFCq1wpKgE6h2TVTRhLCaymrnzMdA7X4CbzQ7gJPMaRnRde+GRQn0DrhQotKuRh1zeR8y6lENcZdRtuFXxJG2YaTibcW3QTmHfXNCJHKFf2nTXnZfFFObbF8jsny/0JBgTrScon9fq2ISGOiGOcyONWNkdSxpIf61Cs9CFQ2NkJmGpt4oNnAINTWPYApWF5AZrL7rx2tGtl0S4nVIu7jp0Cuno4UTnDXdNKFRErLbrfI1s8BoDu97BboX7baBdVS63QbvL3x9YFGwqyuQwRppBlmGMJHO2VFvoG5ltPwwHjrKSGhitKNYwK3H2TGdh3BL58wNXu62Wbq08oKImu7ogqsSC7J/0YUKYL1GG6hTGW3kpCstAhjGMmZ3dQb0+QdFZZvPv2MUmdA4xJ6mJBRNlCnYEVJYRL/tS5W23nmQu4F9Z1r20yyH3yUnWfK+ULzfpJxyv8vEHh40CjMQtR2c67HZs4t89M6IqS9i0UCgpRrmbLRyhcgN9/n3ct9hmUNKqWaPIlj/yc5Lep1hSMy+/vcfJYTAU2164qdyChXQwqOpxNZbNPqpZl3FDng1y0FDdRp9q59rw4YjTOVi2kWz0eozHlhGnNWUpiG/WWGdx952NcKU5SX2g3BmPccaReubjBPv31VbbSDLGSLwaOmOa77tycbHxVK08K5waXm3ATVvchBYQBohaBNhiRYooMGQbIMCCs1SnyDCnHrr9zgTFJXQzwk7ovEWSsi+8IC2leJtxaWMidKKGwjih8+3Yf5y1wK95+5d+jqFat8Io5i4tzTeGIx7sLAwGzAUwoaMTQakKjBUHkVGv1eln8VMKqgY6Frl2rVECXgZrQzyEpTgzhj3eaQTUNPfK3wfWwOloe1+l4uYyFbgpy1bne0jNfGYtIIWsBAoHJCkxarHef+WtSFY1kBuZ7LtaU5rBarD+mamfiswmvIA0YrvUoGbRo97lSPtbZBUIDK4m7N3PrFHtR2xHMUrqeaGYasGfaVdeYEZB3YaUHaQi9VUfYnZO4+AKgpdw9KnBWU2ZBphCsggygk5w4sVjgKr/XA2QU0pybobFtG0YbUrVK3ugjEST9LjpJyJM+xckSpsfYEoxJ6mKBT26t4fKgVspVpzKgsjKvqVTgVWvveRIAN6n4WI4A9mzwPVV1X7WcUl24WAk4F5/BrVKjustnqdVgahJaE85CiCIoMqdK8/LpFeMIaJSk/IRdjUGFuITluXK8vsGdJ88CR84HGRS+PZ0GgcY6JV23X1ppZ84AqhYQTNcRUlAsJ5hMD0+ylmF1nX8rLeBgBw6LUp5u1lsf1e3PJmLcoiRiOH7or5EfCwy5+wgMqC7kPQhD2NaGuOaaGaZ6PUntmIBHXwEzTVg9Agt3l4uG0lOQWUd6J7ouoYBtCqaUi20dLJwLFQ2iXAEZ6xZOx4MQzt26vY6MY6Z2bGdubg9GW5ZaK3Q6PXSvT/+Ou1hZWMAaiykulpbPFzfGJHUhw1tQ1UrnPni+Zk2UgXVfOSBnMPFXrQ8x8nlT+XcVdoMHDGJQVbeJkM7vr0phRBg6kYQULklVKdASpO/GqpziilLW7sftV+l+nMK7BK37Tm95+DmmWtLHx2jOBNpsbZknKRCBREgxqFI+io3IxnLKEuktR9X9GwlHVt4NV1VVVu+P6v0ocOezwFWLkMLJxgO58bkIytqFEzVIFZgCiryi3qyIM447VuGIKpJOaGJsufDxgzvZMQtQAlGWxlK1iDCOiKMYrS1BFKNi7co2GT1sQY2qXIVAiPUSy7USWGOcMsYkdSFjGmfteCn4MQYKNq/sWisIysY16yTO+qoSld+3ZJiwfPyqXb7WZzAhCUqJs3DuExmADKHWhqjpVsvNSWhMuO1rpRXQKpzSL9ewsATygKuufSyBXs+5YKpqwZ0R7I4cOUUphPlAek5lnNXnMbAITLmAsKd7YrxoJgSmA9gdu4l/PneWUG4HsU1YX9AYyuuo3OeQMJ84BWqvcMKHURjrYlZJGbvyD+8tyCws64Gr0RNjgHMz16X7rpoaLHBO5fgbETRiRKhozk7TmpkmCCPqUR2rNUYbRzCjkCUp+pQB4+73KGpSb0yjVKUcjLUk3WX6ncWzFy+8B2NMUhcyZoGH4NwuC8ARBnGA0coCx0tmrdZpq7bBqDPo0FslKV9hvOraMQzn4AQhqJqLPbUmoNGGqAbtaWhOuC+QvrtiubK1wNFD7t+dVTBLcLjsp7EmgxdwSQwPbTkrasnCSj4cEB8T1IawgF4jqdNEjLvnGsBsCHuazkJOepBkrmKIrz4PA/fxEEk50QG1wMWGjvRhpXR3jlabAPdaWqoEk9Id2Ndw1MB+M5CtV8U2BnevzAQuJirFsAhosxDCkdRcGxmHtOe2s2PbTpRSSKGwuizrtJEFpITrshypSoK1JWq1mZq9jCgaqEWt1Swf2U/aW0GPSeqUMSapCw3+xygorQkGxOJXrrayrYefKIYsHwZy8aDyHDCworLyO3xcyNdiq6r4LBWlVPksSsWU9C4/5dw7QoIMECpGyMhJocuVqPWVA/LQTWRyZJwS57apydLdJAcTk3/fT1JVV+aFhtKVab3L6lx8pbVrdfJOW35evQ6SslK8HJQYgmG1p2HYDbg2mMrrvgpK1b3p28RL6Z6NdZN8bpzllJcWVGYHizL/m1Bi4N4LxHDDT+/dO4XDF0ohohAZRUgVgBHl7opyWBqdZZg8xxQF1hcBrt67XhEoACWRYYiKQqyxayQnhETJAITB4ruV2fJaXYg38YWDMUldaPAlj2q4Va1XTvkmft4NN7og8/EZX2fPd6+tVjmXlWef/KrL75zCkdUUTqzgVV2e3KLI5a0IvxMYtPtWgwoXgFIhtdYkYdTCFBlF0sUUOaYWkU+3sDEup6suK8nC5fdH0rkRlXXvG+EmLU+8PVze1wID9eIFBpMU5EuJi9cnxdmPRViL6ebkZV91081P7zu9GEXjlH0+Vhji3IB+YeObT+YM12GUbiysFpBbhBGoMERMxdhCo3sZtjCuVckVO6Fdh1bolIzHCjjch/3aNYZctYN73JNiIGC2BlPRQMbfywdpDBkDd+FmIAS1epPGtp3IKMSkmvn9B8FYbJpi8wxjLElekOcFRhdoUpgqqy7b8rsqvytbgyJ2qVnZSpfk2BImL5C5Zqa+A1sz5LYgw8n0s6xLfrY6Qd9DMCapCw11nKptkkGXXJ9H41eLvi3F6G/RVyPwSbm+d5TPi6paXl2cMq6LI6lJHJntZtCjyvv+Ec4CqpWJV351DIPVcKVkjQoCGq1Jao0piqRHpnO0NeRxTDHZwtYkHFlxFpOf5CwuUB8qF+/yJKUYiEB8Ps4KcID1OVwXCExaYLLS93ouFskWTD93hAinn+/lc7Z8nUR/bkMGNR5XcCRVtZ6q1r/FKeu6GoIA1Y5RrRCTFJhMD0jqfpfCjinodmBxwSUvH03g7sKRVHX//joLAbMxXNZySr35FVjuu9/DIu5ePoV7QghBrdFkanY7MghY2X+QpbsOo7Mc0+1gel2wA8vUhgImAyerz42rgJFpd98GCkKBjUHHUETQz3sszx/GJBnT9UmmmnMoqejZlJ5NKUyBsYY895LbMTbCmKQuNHgBgbd4qq6TNRdK5e8q8Ywq8vx7emQbGFhdo+5Bv/2oKnAtKC1dfErasiI1a6t2IRUiCFEqQEmJEgIrJSoIEUajwwARBmACR0YhA8WgV4FFpTtH2kFOlH9UlYDeDXihekvO9Zgsp2+x+WscSKgriKWroQelIGBkv6PnfOTfSkqklO5+sDi3l8DFb7DuWeHuI0rRQVWlOuoliMraj7F0n5XCWdijv4WTkVO5iJKBS8qVQUAU14hU6MZqcASV5egsx+YjQV4pwJTWPXbgfhSijLmBKTQ6SxE2QOcpRufuYQqM1e584Nx/UkqkDJDKNcB0CsALcNV1njEmqQsNfgL2cSHvvssY5D5Vc2x8DKlKUFV/eYFb/focI6/G8j9w78rZgbPiWpV9+4cnKJ+7EkQgytI1UoLOkdSoNScIp3YQhjFxIAnICUJJPDEFxtCNBEmxjA4FTMYwLUu3ZAjNmpuIdsbQFmCFUzMeKsezzMCibAGXMFhBn6kE/d4MyaD6yPYGPGCuLFicgO25LriJc99tVOliDeWCIQgVExNtms06udZ0uglJt4etBdjd7cG17ixB0XNV4E+UbxQI2N2GPRNlpXXtlIK5dtc9VS55XRrnCfDxytG5XiqXIiEl9blZJi7ZTVirMdmaZFpNYLWhn1pst4vOMuxG5Y60dWWcCuPIqVa6ugvrrL/CUPRWWV25HalCim4fk3ewaHqZ5Winj5QKGdURUQ2EJKhPUItqGJ2T95fR2SZLN92LMCapCw2epLz7xE8Kvtq3J6jqZOHLBQ2RSuXzSzii0jii8512Jxg0P5zBTf6e2Mri2kNVz71QIgxdAq8XT2iNtBDXW9QnZlFSEEpBQI4MAoJaGykUWmaofpNcade+Y1K4O3AyhOm6U5Jti1yJpdxCIlw1iQznyknK42uUY+6Ur49J6vThY04BLt5z352wsw0LC3BX1yVjp3pAUqMiCY/y3pMo2s0WM9sm6fcS+st9ipUEwgZsb8K2psuj6q26CiRKuYrnx5PlSek+d+02Z3kdWYFjHedmSy2kpQRcWoiOY4n5/ZR5fLVtc8zc9xriZpOJTDKZSkyecTQz2F5vY4KiPPZEu/uxJl08raGca3PJxdIK0aGgQjSlFZoUKWmxgpCSOttohDFCKoKoiVATmCLDFOmYpDbAmKQuBPiJwgdgPSFVXXBVVd9Gk8SajJtBgDsp91ON6VRVUJ6IfFWKaut1gfsB1st4UxAMCMu7aETpj7MWW2SYtI9Oe1ghUEGAFRKpQpfgKBUWjYwUgQkxUYCJJGSyXCGXE40nQ580XLUOVWXsF6KL72JEKF2lhbqEdq10xZWLD3+tpXX3p2Y49rQBBpevTGqtKga9Na5xyd6FcYSVlTl1aVlDEZyEvV627WiV1fWhVJFKkBYZxIjYqQONyLG6vDHEeiYVQiBk4CwZqcp2IoI8z+l1cnSakqfpCZWRQglUPURGpeuxEUJNYvOcQmos5XEc5+RYF+DCmAKtc4QxaKUxVmF0tnE+1hhjkrogEOMUfV6Nt4wrM+NJx7v7qgmTsvK3f/bt4Ks1+rzwokpAMCh/1Gfg8vNuwRQ3oVzdgCsmIVYluYlS1ZVBnrsvFc63aHRO//DtZP0OQgWoWg2pnBQ3bNaRQYAhpz5Vo2YU6VKL/nQNE2Zli/rcTVzETt0nlXMpUh5rHWf95eWxeYvy3vy7rlrMx7NwToaJOuLBu2Bn05FUo74mw7b+PqvhVJ/+Hlzd5L69KzHGWez+njUasqTsNGxcsm5mXCHZQjtX2qXTcO0OR1QzNZisu/caKcQ5UkFNRIStEFMUZN0uRZph0xyTdl1JrgqUCqjFDVQYEakICkuRFRw7dIi79h1AJwkry0cxJyidFMQRU5dvp7Gt7RZWsZPn9492WJo/RNY9eZkkay151seYBYQQWOHiddZodD52CWyEMUldCAhx6rpJnPWzzHD/JzmyfdXC8s8+adf3VjqKi+n42NPoBGYYyLdXGcR2+jg3mgQurcHklLOofBtzY93kkg2zgzWabPEQpH0IQkSzDWGEikMiUUdFAWEcUWvVkCrGTtRJ2iHYSvkCWypFRFCuhuXgGH0vqbQc74WcJ3WuMLpYOZ1z0YgQV2yDK2YG95IBa0trVuIIplk+r7D5hFkvAvLuRD9Wa9wiJ8tgNYeDiUvm9QglzLXgATtdsu2aN0FDFEMYIpQiDCeom4YTO9gQTQ9sipDJulOhpCIOYoIoJpSBs77ygpWlJY7ceXvZhdkHbjeGigKa26eYumxu6BwEqaQTzG+qSwxYiiKlWIvDHc8/OYbHmKQuBHgLp6qqq74OgwnJ//A9UanK6zCscqqutE80gfk8Ey/x9p9PC1fcs1DOeiqygfvNd1n1ZWiULF04KQiLtQWgsFZgigyEQQUCS+w+ptQg98ofqJJlTKwMjGdmUIndE2qGswz7DGT591ZshbLRWmxeIPK8VPIVjkSSvrsOukyw9da4t8iPtzvhCuWnwpKHEqabyKaERoSRuLp8We6aOqZlKaQN+0OJgdu3TAoWhaAeNIibisAqJu0kDRpkSYpJCorMYJTBCMeGUoUEtRihAsKwhlASLBRpSrK0hFCSvNvB6s2teKy1WGMwWmOtpTAuGTfLC8xmelwN9sTwj/xEqP7w/YRw71qZjUnqQoC3anw8yk+8XoJdtSSqq1MYrr3nSSavfKb6/vGQ4EouecFGiOvKe7QLNxeuBXxk3ENJqDVcrT4pQUVO5ecyV53LJq5BM3Qv6Yy8lyKkQNCm1mq4uEIthumpMtZQKdWujCub1C9bO/ik3RUGDRB9FXTN6bXnuKeguog53XkrL7Arq9h5oEggXQGduZYl3b5zsS1o2G8HydMnuJ80lo4y6EBj2hFcfSm1doTp98kWjmE6i7Ccw6E+dL1q8ARBViudldW3hDZgT+tSLpmYoCZC5tQUk6rJ4uoqXzM3c6c+iLYRRi2hEUStFpN7LiNqtjBphl7tYYqC/vw8q/MHMdaQrXYxhb+xTvxDscaS9zPS1T6Z0XTSjLQoKBa75KNy9RPviUENs+NdOE9M1Wx8X4fq3mV5jUnqQoC/Z73Pv7rI8n9768nfs7Xy317x57cbzSWCk7tnqpUDagyqC3QyOJwNis62fC5TVJakUa68kYrKQqF5GVOwYFy9JWtd7glAWIvdKl3ghBiNRim+8CoR4yyoJHEk1dOD/lPzOJXiGMM400W1MdBPoRtA1oHeMUdWa5XNravZt8SmqntYIBXOohF1BXtmCHZMoI8tIlbmnTu4V7iW8p3jkULVly3W5mYlFdOT0+xp7aahYnZF08wELQ7Hi9x28AjB0iqEOaKMZaoopjk7R21qmmxpmV4/wxQFebdLZ2keo/PjfP9xjs1adFGQpxlZoen0evTzAvrJiduAbHiWToaqqyRikI3vXQv3HoxJ6nyh6qrzBOTv3WqcoXqvbhSL8vX3qgmNBQNr43S9A15F56ut13ByWl+PrTBgS0JS5QeMcT/WPHdFSUX5xdap9oy0ZPUQHUXkSwvYlVXnVopM2Rq+dO8oVaoOxUCheO/6XZ59rLmOhasgXg9c/ptsQBG465sV7lpHGlQ+XIV8FGXcSTQV8bYJ6ru3lS042lBvkIU9cg06KVy1ho34qR4NlHz1GFKDFJapaIKp2SZ1GbGztY3JxiQxAWHpTpBSuty8WgOZZ6S1BqR9jJAkqysYo8k7XbJ+F52lFHl2Wkmz1lryQg/KJPULyHKklgStCURksNqg88Il5+oCW7gSVfV6g0ajiRCCXq9Lv9/bVI1FqRRxrYUKmgShpd7YThB4taKbBLqryywdO0KRnxrpXiwYk9T5gsTl+/jW7l5eXY05VQms6pr2kl7LoExQVXLuk3ZHa5+dKhKcoMJ3BG7j5MhZAf3MWUNBXPZ8si6GkWWgCxfbCMqDss48zJdqrK4eQgYKfWQBs/+g236mAXPNspipkxe7ytgKjokyH+Y0j2GM9fD3mAIaEmZi2F4HHUIeu9himkG35yzbpT7EXjLO+mr7Cmd9t0FNR0w+eC8z97/aJWc3IogCup2MNBFki2U1dW3Wj2l7G67Z5bo8N2JY0oSR5v6X7eXhex9ALYhoBjUaKkZoi1rOoatRKqDVmmJ6OqcXRPRXl8ikIrWahf23I63BFoWrImEMRheY02huqY1hNUnpdbuYRFMs9KFfENRbtC+5hDCuk/cSkqVVdJaj+6sUq4sIa5ib28EVV9wHKSV33rmPO+74XkU8cXxEcZ1tu/bQmpijPdVmz+W7aU40CZQijEKEgO98/at8+f/+HatLC6d8TBcDxiR1vuAniohK/hHDZLTWaLB8z1a28ZaXt6Rytjax1bsgq6RnKMvXGOfWQwyC3paBJWW0IylZvlGSlMn7GNN3ZLS4AssrLimzEYAt6wL6RBtpnSXlC4eOLamtg7+3FK6aeD0or4ECHbjrmCTuumUFRHlZfZyNFzyCtaRw2VbE2ydo7pkb5LoBOoxRmkEy7Lr9CEdMOyadNVUWi1XGsi2a4Opte4nDQY8mm2vyXgeNRghBGMXU6k10kSHrTcgSTK9DsrLs5O5bAGstuS7Is7JmX7+AXoGMJVF7krg9gYh65DlYlWGK3J0DK2g0mszNbUepgIWF+bIx4skgUIGi0ZpgYnqWmblZLrn8aqZmpwnCgLgWIwQsHTtKGMVbcowXIsYkda7hXXy+DUfEepVeVa1XcsGQq89LxQ1Ojl117Z0pvPhCVv4dMqj4oHFElGQQGpCJy5UqCuglTqYuxaAGny4tLGOhYdykaFwpJXesFvo5HO07K8pXu+gaF2D3bR62HNUTuhUyuYsIfnEDldqIoqyhVzDojyQGj1HlaRUCd72bAlMTZOR08wSrNbrTwaQpvUNHXOUJr/TeaD8GRG4ROUgrUUoSqxAB5DrHaM3CkaMsH1tC55qiozF9zWqWcnhhnpVuh6TfRRvj8ux865jT1uePnrcy7tovrfvSGrPWUhQGWRiKvEDnOTrPXI2+MEZYS2YsK50uUkr6SbquHKIQgiisEQYhKgiJG22C2JUaa2+botauEbdrxI0aUS0mCBRB6KbvHbt3832PeCTdlUUEwkV3tebA3fu5e/9+9CnFyy48jEnqXMLnMvk4qE9QhWEi8r8rzaDSRFXRt4xTvXnXi+/IuxX3YlVd2GNAqA2cuy+wYDNnKUXSkYgpkzCXe07d5+McgXTxB1/vbCIEVbgkyDx19dYUrm3HfA5aODLs4hrm3a1PXC/ujOBLWMBwI657ASyDKiQprvxU4kmqvLZZUW4jIRcDcc1Gp0gCTQXTEjMhWRUJpr+M7nbp7dtHPn8MfaRPdmjZ3bvHuVdlYZF9g5SGOAypxTXqUc2tY9IeeZLx1X/6Et/68r9SGIsJW5ggRltLj5zUakyRkxvj4msqdDl3Q5WazwBaQ6cLRd8ttHLni9fakmSaPCkokoy018OkCViLqLeRArq54cDhowgBK6urmJGYmJIBk61p2s1J4kaT2b2X0d62DYIA2WwgwpCJ6UnaMxO0J9plTEoiBFz74AfzoAdejUSjEARCkPT7fPKjf83Hj/wVvd7FXWppy0nqjW98Ix/60Ie4+eabqdfrPOYxj+FNb3oT973vfde2SZKEX/7lX+b9738/aZrylKc8hXe9613s2LFjq4dz4aHaeNDHBarw8SYPv3pdS4TETSwdNu7Ee6aoqmM9CgYrYMOgnE0unUWVCkdSae4eWjhBhRUux8YXKI2ArCxro/VAWJFpWNWDahLLDOr1nbU8KL8a4Gx+yYULbzx6K6kQ5b9LV60uxQ2+2viJFNprAgyJjQQ5mqRIKdI+3aVFsvkjsFg4i/l4sX3hbgep3SMIJZEMCKWLa+a6IE0T5o8c4Y7b9lFYsK1pbK3lLPaodEfaMu/KN+Ycspi34JwV2t2vRjmiInCcqw22MBSFdjEvXSCkRAauLFhuLN1+H4Ely/N1ZC+EIAwianGDer3N1Mw2JrfvwkpBFrqviuoRYRwSlLEof1zTE7PsmGkSR4Fby5bijK9++Z8JwsBVtjjbPc3OIracpP7hH/6Bm266ie///u+nKAp+8zd/kxtuuIFvfetbNJtNAH7pl36Jj33sY3zgAx9gcnKSl7zkJTzzmc/k85///FYP58JDNXHX5zNV1XvVBHSfuKoZkBoMXH1bhep8vdGCsxqfKirbCAsrhSMjbVyJm8yPs3TtFXawv6zcvi9cbKJbHtsKTuLsZfjn5PfkL4I/wIv3R3zK8NZxANRcTTtBiNU5NjFO+HKy3FaBq0LRdPtgRkE7wjYkue4huovorI+tKcRkC9IUq3x/mAEarTZTM3OEcYy6ZBa1YweiFjp3XlZgs4Qj37udbx5cIu8lzN91JybrYpHYvO6sJS3WYj8U2knqi8IpR7W/Ybfg+krpks8bgSOpIgStsEFEURTINMFYDbXYJQ5jXZsSIMszujpDGEuW9teRhjGGftpFdiRGgRWaKFbYQKIihQkEYS3AoMl1RhyFtBp1AqVo1AKUFAhrCQJFPQoIRJ0HPeBanv70p7K8tMQt37mVfd+7/aJ0/W05SX3yk58c+vef/MmfsH37dr7yla/wuMc9juXlZf74j/+Y973vfTzxiU8E4L3vfS/3u9/9+Kd/+ice9ahHbfWQLixU50ZfkdznPqnK+xZHUMsMkxlsLkH3VFAtTHu833SGs96qysPCwpEMFvKBcMIAoS1L2IzEAhINR8okrtw6v77GuS7ny+OKy8dZR9W9dy8iKBio8Wog2hIVRAhq2DxDr2hslpVFhUfrcY3sYxuuSWZNwI4QZmJMqEiLZbIlJ18XzQAVzWCLLibqYUdkmlMzc1z74IfTmpxCzTQIdraxSnDk8GEOHThAnqR875v7uPnQPCYv6K120f0+VgYQlKoiKZ3oIxTOkl/qOnWiSV2Fi63yFysFrQnXtFFL5xUoJEYq8ixDFOWPp9lwUbA8R6cZGE2v1yHtroDRaK3XFZPVpmCls0Snt0omc64QGfVWWOYTxthIUQtraHKSvEejMcHsTItGrYayBmU1AkstVEw0Y0Qz5slPup5HPvxBLCwc40/e+2fsv/OuMUlthOXlZQBmZmYA+MpXvkKe5zz5yU9e2+baa6/l0ksv5Qtf+MKGJJWmKWk6uLlXVlbO8qjPItbcLAz/dnypOv+ez3cabdF9NlC15Daal/yYPIFVk4Qz6x6jyMsPjlqJpjwBOQP3Ycogkd6v8s8J7sHkVPVwjR5mVTmqcLENJNaUlm9mykWL2HjB4u8TX9OvRlmVRIESGJu7pF0kSoSu0WVQNiv0uxASIQVxXKM9Mc3E9AxqokbQqGMkrKpFpHbpDr2lZZYOHMAWengMpiiTaEsXny4FPHnpdt4yNZH/zrIbQBg5YjQDWa41rh08SiJdIpNbtJVlw4zRmLxMzzgOCp2DzsnyFIt2HstAIKMAEYcEgQJpsRiEhCgKqMWh22eu3WWRgkApAiWob5tlx9wUx45NMjs7SxRFJUG6C2qtXXtcyDirJGWM4RWveAWPfexjeeADHwjAoUOHiKKIqampoW137NjBoUOHNtzPG9/4Rl73utedzaGeXcjKo6ri8wFkwTAx+Qn7xPUuz3xMvjJ1TUE7dL79VQ2L+XCpGm/VLeDumBZucvLv+WPxDx9bsriJsMZwY0YYuDU1g1p83kL0sveEM+eRuByrLPfnW4zfUyFx18f3BvOGhD/PXs7vz3ebgQfOUNZLtK7way9xFu/h8tm7+Fq469qkvIetmyhN5mJYaQGmB5nELinoCexCiu241VZ7YpLduy+l0WyxbW4Xc+1p6mETaxW2K9AYal1Lo2eRfUvXCESosFKACBDS6ectBvJeKR6yTimaa1cxY6iV8yYRxRDGrLUUWUteLusZRiX5FHqQbqGFe84y9ywFVqlyu5IwjSW2glpjamgNaIwhyXqk+bBEPi9yDi0eg0N3MTU9zdV7djO7fTuhCqiHdUIZ0KjXCEO3mlNKEaoaUkAQBlghMKXKz1qIopjve9j38exujyRN6fX69JOMTmeV797yHQ4dPLj5c3QecFZJ6qabbuIb3/gGn/vc585oP6961at45StfufbvlZUV9u7de6bDO3eodtr1JAUDj5O3MgSDOJSfTM4WSQUMkonbgevKGio4lMBqsZ6kVstx+bytBusV3H7h2sdVYe/hFIzTDFSMVYLwn1vBkYdvH7JFYiwox7m9HPcxBi7WeyokMIvrXAzuWnjRyxEG91UXd30mqCR9ly7YvoGjKdzRc+WpcutUmgJXqf8ShjtCa0qrJnGEtVoqOjsC811gXjixTeq+aGZmG9933aPYvmMXsYxpBHWkUOTGkKxqtLY0lg2tVUOQGFaMcARhLCKoI1QMxmKzAtKOy+cymRsDdmCtn0osSkiIG9CaHLg6A1kKMRK3fyndoygqyloLaQorK46oALvWC806hZ8Q1BvTzLTnCNRgyi10wcLq0dJyGowzyTPuOHqQI4HlSnElj5zbxgOvvT9KSmIZoIR0ScmFhjIO1QhdXCos29mbck0hLNRqda5/wvVc94iHk2YZh48cY2FxhbvvuosP/e8P3HtJ6iUveQkf/ehH+cd//Ef27Nmz9vrOnTvJsoylpaUha+rw4cPs3Llzw33FcUwcX6TJahtJy0ctDzPy8Cvfs0FQnlh8HMondEbSuWuCapJWBVV1V3Vs1bBOdfwpbhKEYUIbhZ/kqvvcCre5/75q6bMTNIC9x0DgjjOm4mJl4E728Oe6wBFQod2zf6Ql0fT0YL/eFVtjOMlcDD0hCl3WYATTtW4RUoFSAc1mi4n2FMpKIq0QVqC1QeTaufUyg8gNojAI61ySCOtck0phhUWQY03hSErnJUmdAaQYNFVU5cMKEGVvM1n+cHVJhP6hCxf7Ko4vXZRYAhWg5GDKtdYifWPICpc6CyvF9LukWYqUkjiOKyQlXA1BYzDa7UNKiZKi6lHFItwaWComJiZoT06QZTnIEBXVSJKEdrtNvV53Lr9SMWi0pihOryrH2cCWk5S1lpe+9KV8+MMf5rOf/SxXXHHF0PvXXXcdYRjy93//9zzrWc8C4JZbbuHOO+/k0Y9+9FYP5/yh6tar1t2rljTyAglvQcD6QrNb6ZoKcC6aNWUX66tanEyxa8qxHmMwCVoGOWBh5TUYNGA8XtK/ZevVitVjq7OesEZVlPc0VBdGMLDYN7iX7GqCvvkg4tAyNkmwS6VFsFo460fgzmUdd14nGDTXVIAUiEBSq9eJ4jqqgIYqiIQmE5oF+vRGgqpGG7JeQtLtEcmQUNVdTbvOKkfmD5OkKccWj7CwskBWpKSFRqnY/XxUgFQCi6VAo01Zh+9Mu9paC2kCLJUWk3Dub8Fg8aatc9/Z1P2dlvl/hY+NHWfXQJbldLpdpFQYW6BtjjGaZAOlny0KimML2DSjEzc5cuBu9s9tJw4j2vUGURASCEksJZEKCITAGoO2AoVASlFZC0ustRhrMNa4hYCQhEHI7Owsj3vCE7j8yisRAoJAIaXg7v138cUvfPGCsbC2nKRuuukm3ve+9/HXf/3XtNvttTjT5OQk9XqdyclJfv7nf55XvvKVzMzMMDExwUtf+lIe/ehH37OUfaOTPwxIyqv4vHvMy9HPtiLaN1f08SS/sq52TT0ZDE7l56XiPj4d4dxMLYZJyquOT0Z8W3m8IS7W4lf9/tg8SUnu+aWWqq7YE+U4rfQxX7+r7AdmyzwpW96DdtAVeRZ37iZxlnGIs8ADgQwU9WaDdm2KKIPZQDMhNKsiI6VYT1JFQdrrk6x2EVEd04gRUtBdWebAHbfT63dZ7i+z0FuiMJrMGmRQcz8dJddIytgCXfgKyGcK68QeWcKQaRgomJiAZsOdmyRzvdW0gaSs+l+69U607yzP6XQ6IJxEPzd9LGZdUi+AzXKyI8cQ84t0whoH9++nNbuNRr3O3NQM9bhGq1aj2WoRByHCuMK2xhoQEmkVUoDBxaYsoI1Fa02hNbIkqW1z23nyU57irDAlqdVCgkDyxS98kTvvuPOeS1Lvfve7AXj84x8/9Pp73/teXvCCFwDwlre8BSklz3rWs4aSee9RqLr3YNgtttHjRBPJVo7Jl2Maet0637o1boV4oh+9twB99QFPrpJht111F+fDazBqufq//R2vGRT1PVuLgnOBNStRuNV+iOvJ5WsqVgUqG10HY53QYaP9VvuXxaJsESPc395VXFpTgQqIgojIWGIhiBBEFKgygA8CqSRCSFQQIIWrlmCsIcszJIIkTUjThDTpk2YpeZ6hrUH76vjgyMCUpGC3eHWzEdl4ld6ae88rCUvhxCZdYtYatHEXQRtN4eNnx4PWWGPIs5xut8fK6ipFXhDLgCzOkNpg6g0I7FozRi+UGHwprKkP1w7NVapQSqGUotFqOVdihaRmZmeYmp5icnKCPC9IkuS8uv7OirvvZKjVarzzne/kne9851Z//YUBgSOCVvnvauPPnEH7i2q84FzcA96SmmF4cu5p14QuF87Nk5/kGlbl6FW1XrWO4PnsGuCVgZrheJTFTaq6fN9v49WFFxvWKpcI2N2GPRPu+PorsLzqFh4rDKqTnEol+YCBi28uhEsjqEuYjmA2doTf7UCvh1KSdqvN9m1zqL6htrgEdNfWRDEQxzUmpueIaw127d7DzI4dTM5uY/HYArd/91v0e31Wlpc4trLgEl+TDv1+B21NWfjCTbZGgJCle+tstqYQck0+Tlp2pKa0NE8D2uZkpu88zdYwvFoa+fELUfZsC+gUBd+6+VYOriZEUtJQAaFQ3PeqK6k96lHMzc6uEbcApLVEktLlZ9cWMVIFZT1DQ60OiACpFLVajSiKnKCxDGhdcslunvnMH+NRj7iO73znu/zff/wcR44cPa3j3gqMa/edDXiS8oIBr67y0l8/WZzr1XuAI6ltDJNkv4BDupSN25MT5kbxHI2bDKuikPMFL9wocK5NLyTwikavQOwxqFW4FXL3cw2Fc2fGAq5owcN3uvP/dQPf6rj6h8sMykudyvF5iXkEbAvg0gY0FEy3YHbCWTEHgX4fKRWtVou52e2IXo6+O8FWSKoGtKIau7btoj25jW27djKzYzsTU1Mcmj/Ct2+7mYVj81hjMcbl8fRLktrIHbaGs5nfI4Sb1MHlXGVF6YmQm3eNV2Bs4apRAMPlYzaQsfrKFnFMp9B865bbkHccRhQ5ot9HFJqlRz2KB1xxH9q15tpuhBAEAvJAoqR0GhDhjkWI0nqVhlrdJXC7PlwhQeBWcFK6lfTu3bv5sR9/OqYo+NTf/T3f+ua3xiR1j4F3MflkVzPynse5mAyrSkLvtvGqLG9N+LFYSlfGaYxtNNZ0IUz0/ph9FY2qu2/0fctpTToXAkQcICdCqClMM8TGwrluQ9zDxwJPZ/E/6oKWtjxn0nVjBoJaDdVoUAtialGNOAhBWhIrKLTFGpBSoYLAPVSAVBIphGtVIQTGGnKdkxf5mtvKWrNGVmeViDaF0tW4psKrxOtOGXbkGZforIKhMJiVEislWIstNEXiOyUXrsuA1nQ6HRYXFplotYmjiEa9jlJqeFzVr/PD90pApRDl38K/ibO+AqUI6w2EMTSaDeJ6jageu1qEyv2YiiwnT7Nzcn3GJLWVqLZ1N7iGgX5CPJcToU/o9KQ0Wf7tK5krBorCDGd1eGvD539s5t7zS+XRu8hwdhORT4YY59L0ibzVChmesHxcrrqovZggBfGl22g8bC+yHdFvpfRkgtU5TGVwKQNLcYVTXzzkDMpgdcrJMSxATkKjjgoCtsUt5nZcTl0GXNrczo54irzf4+5EsrKckSUQ1ieYmG0R15pYAXmekeYpSdIj6IfkOkdEClkLyZM+adrBaI3W+fmthGDL+JNUENecZWM0pL3S/WcHcb9ThgFyRBCgds6its8gpECWETybZqRHjqJXVkGk7tyrAEQAKgKpuPvuA3zibz/F9OQU115zHx7x8O9jamKiXJMJJ23HVjjExfCcFkShSrKRQiGQCGuR1iCQYMAWCmME9Uab3VftoScz4naTxsw0QikO33Ynd33zNvLk7HcjvRh/nhcm/IRd9u5biwUIHDnUz+FYFANCqgO7cERVrTLhXV6+qaBXvq2vAXp8VIMOVfj2IedLMFFVoXlryr/nLT+vvPRiipFclQseQhDtmmHikdcgp2rY5f30F2/H2hTaOey27v47zOkdm7+GCpfcm6WuNYUE6jVkHDMzM8Vl0QR1FLuymG15SE9Z7s4EnU6B1ZKg1qIZhigVYoUgLzLyPCVNE8I0ojA5hBIRB5hMk2U99HHzjc4hvBUncQTVbDn5edJ3rr8zQmmiKoWamyK8z2UIpQgIUCj08grF0jK63y+toDI/JapDawYixaHDh1leXCYKArKkx4Pvfw1yooUUtrRU/XFQuf4ubqWUcnlnCKwRWOsMcGklAoM1EqMtxkBcazC3dyf9IKM1t42py/eUld3h0K13jknqosNojpGfGLzMfKul1qOotgDxFs5GzRRHVYdVheGpwn+uOhGeD3KqFsn13Y6rLk+Bc9CLkrEK44QFp1V/1PtmpFvdCoGrerBZE3RrYEtZsdW6VF+VE2vOoNLEmc6nFpfgGtegFiCDCCkCAqugsGibU1hDkkM31/TTlNwaUBJjBIXR7rRIiQwUYRRhrWV1dZlC53RWl8nTBF1kmEpdufMOWSbweheaLtzjVMa3pkoUZS7XqHKw3KasZGERjr6qHQn8NQVnyZWJw8Zocm2wSrG6vMyRw0cQ1tKox6zUXVPEZqtJq91ACEFeFBRFgRACFYTIIMAaS54WFLkmkIJ6FBAphTEGo91DqYB2s83kxBRxs+VKMQUBtXaTiZ0zBI2IrJuQdvpnzfU3JqmzDV+Lrpq4ezagcC6uKQY5Wr49vVcVgnP7RQxXX/B1AqtJxJuBP54qOXnCO9c5SDEwK0ohAa4SOwzH5aIQajWwAropHEvcMfc5BW7xmmwJQR1qk65dRLYKyQKYrbICqquJjU6oJev2WT24gOhGJKaLNdZtdhj4Lu64Ftn8sVW/0sejhID2BOzdBTMxtYkZmkwQFBKz2GO+s0BgYMlIakaiOxkdkyDaMaZXsLy4Qm85Y3Jyhu27LmF6ehvdXoev/+tXSJI+nW6HhYVjZHmGzt3ke94hJNQarkwSuLyodMG5/8rSR5tCEDhyl9JZoml63IncYklNhrEGq/sUdoMFj86hvwypwiDIhKSQklu+8S98WGuarRahEkShoBZHPOIRD+dRj34EQRBwdH6eY4sLCCFptiap15tkScqRuw+ysrBEu9XiyssvZXZ6uowNOml7I2py7VUPYMeOS+jKguUgJ8ew49pLqe1ok/YS7vzKzdzxz9+iSE7h3JwCxiR1LuBdJ2cTEufe284gWD6qdPV5Ld7aqlpTXjZ+KoshP3fakb/PB0JgUrhzIGCtoWJ14g0DaNScjtlqWBWuVt0p5YNWgoyqBrXpsigpkC5t6SENM8aIz85C0c/oLawg8ogiSrBxKX5ZAu6i7LrL6ZEUlc/VGzC3HTHbIAoatEQdpS1mZZmVw/MuZ6j8mEgtkc1QjRBTGLpJj+XlVaJanaheoz09xUp3mdu/dyvH5o+4r7lQrCcPIVyx2UbbWS6rPeiunvp+VElSSg4I7gQkVdic3JSFeu0GZO1jYgx+bhrB3XdYFhc7zkJCI9E0GnXa7TYPeciDiKKIYwsL3Hn3XUihmJ7OaLdz+qtd9n3nexw5cIBts7NMN1s0o+G4RBzU2LPzUmbz7RzqL9LrHKTQGVN7trPtAZeSZzm9xVXu+uotZ22KG5PUVsInucL5TWCtTjaelKoCB09KCQNJ/Om6IqvuwnMNJRGNABEobMti6xYiO6jqIYSrSVgL3ERRi1z+iRFQ09C0EBi3Qk2LUziGkjRMAbqs6aS3qvLB5mExWFu4Fhs6H5TniYC2dOfCGGctjmKtNYsYtNEwxhGOFwT482itq8WXFSAsNpBYYbEyQKgIjKboJegkRaQWvVygVjV5vyAKIpqNFrUoxmpDnqYUeT7s2ltziZ3FGylQ7tpLAXkBWX5i95S2gw7Upy2QcGIFNxfYdYc2EFA6JaQtjDvPuT6F77RoXZBnfbQukGgEGiksi4vz7L9rP1EccejwIY4dO4YQkjw3dDt9siSh3+9hjSVLM47NH0MhUEFArV5HBYrCaOK4hogCWkFBW/YJTY5qhqhWjTzPmdkxx9wVl5D1EpfQrUJ0UbB49Bgri0unee4GGJPUVsEycOsJTt0q2Up4j5QXEfjWGp7AvLLvMK79hu9Z5VfQo1XaT4TqNuf4eGUzJLzPLGq2gZY5edDHyMLlstRDR0z1BrQn3arWF0bUFna1XZ/yfgF3LEBnaZM+dZ/JXNZt6+TOnaN9Fe5zB2tztOmCCbBpF5KeW21PG7h/5AQPtxbQ1cPXxtc0DIE4gOkmxCGsZnCo686Jj+0FOAJccslWeqpGXncS6TBqoeqgbULv0BKdOw5hU4NcNIiOJVIRk/VZdk40qMU1dJKxfHSe7tIKunDnSkiFUGWLc+1ar5+VG6nZgJ1zTgSxuASH5p0QYiMYW5Y8SpxFk5+mC9Jot3jxi4h1LGVdsrJx18d0Uugm0Etdi/pNQucJSccRkBNHWLJ+wL/+61fQpARhwGo3odtLnOAmjAmCkFAo2mGDuoxYXl7mK//8FaS1tKem2HvFZbSmJqlPNJnctY2gFlG3c8R6J6ktEKFEhoqiKAh/ANqzU9jCsHtqOzsmt7G6uMzff/hjfPmzn8ecYaPFMUltJc6FW2+z8PlBPpuyzsCC8hLxFZxrqIqq+GOzqrDzRMYiDlA7WgSXTCB0SpECRQq1EFqxaz3SnIKJOdfFNe9CvurIZQo3efQKONorV/ObPZDSTDbaxRrOCywW7dR8pnCToa9j18Al3nYlHDAgRkiqqspsSpiJoVlWkTjSd4dXdQ0bDb0MQoltajQSIRVhECMji0kF6XJBZ/8SNjXuvupDq9Fm12STbdNzbsS5pp93yPp9TOkiRAikVC4O5BPJz8b9FMcwMwWNurM4jy6cuCpKrl1Vc3sG5WCscTFKUQonNjgwYyvWVlJAN4d+7u7RTcLoHKOHDybPJfv330FOigwCjJHoNUWGQ7PW4Kpdl1OfmqPf6zN/8DDdlVW27dxB0Kgzay2ztYgdjRbNyTZK5iAnyCrnQxtNcXWOmAqRQnD1jsu5cm4vxw4e4Vtf+VeXD3eGGJPU2cSobx8GqrMzieFU3Xlr7TYYbixYTeKtKvf89/safuHI+6PjvYBhC41Z6aNrEm1ybJa6SUFLV2NOSbci7SYgC6QSSNmCwGJsikkT58I6hQnh3OIkJmq3cA0JGwoaFpoRiNJFVRjWVQZXlQdsXI5rozlFKYhjbByjNeSdBGMEHF7FHFxFdxKKxT42tUP5cdpoer0uy8Fwq+Vur1PWsQPKBF5E2SH2bN17fkGhRFnJfKMvGhGrWF+O5HRJyjqrXZTHJSon3kvcu32YX3Ivd/OSHM2ZnwfrOpp3VjpIFWBlgC2/32qN1QadZBwNDqOTlCxJWVpcoN/tYUPBxF376Xa7LHdXSchpTLSQUw2C7RNEcbCmPDRG0g4abGtMARDkgv5il3QloRY22TZ3yZrVDFDkOd3eCnl2vLYI6zEmqbOFaoUDv4iSDNRnvmbc6YjBPMEonBuvwcCF4y1rT0S+ukTGsJvHd2/1HVr979GXPDpfcaZTgOlnZHfMI44urVUpAAPbLdRbTkacZrAwD0ISTs9R234JAklSHCRdWcH20jIedaEd7Kiib2R8BtegsqehJuHaBjyg5a5tN3HVvA1OyQiDauZx5fNZ+e8TzcECqMUwPQkTdfI+dPcfQ/Q14usHEd86jE1yspUOdOyQgjXLUg4dPcCxpfmhXeZ5TlpOUtYYjM1AiDJGdZauQ5bC8hIkoas5uKGK0AdvYdAZ8gykqtoMivfKAILy5Jdt4ikKOHAEllbc+1ETgpqLlxVn5iIzxrC6tEKauH5Uql5H1WKssRR9Fz9UUjJ/191EYejap6QZWhc0jrQ4cOgQ9bhBbbJJe8c0USPmvt/3IB71Q0+gPTsD1jptkpXEtVm2x210oekfXeHI7XfTWeky09zNAx/8gxg9uKarqwvsu+1fOTZ/YNPHMiaps4WN3Gbe8olwP+TTrULhCcgXAW1TCXJXvqvau8p/n/8dRpVHNfG2KiO/0JEbzFLfJa3C4HzXi/KYpOv500vACmRzjlC2ETIgt/POvZKcmmvl3OIkF6FbuEcoYE8NwsjV8esXYMvWG34XVRdfNS1iM9Z8EDjpfr2G7vQxy33oZNi7F7DfPXzcmI02mk6vs+F71WM8qxbU2mC06xdlcicp33BR4n8g/gdzhpJqy5ryEXDuZ8SAII2FTs89VAATEpph2Z7+zE9IlmZkaQZSEGlNYI2rrL7apei5isrLLK773Opqh95yn0jFBI2I+FCDoB7Smp7AJjmREWAFwlgEgjhsIKIWRVZwIFulO79C0k2pRW127LgMUzmWKIq5+67vnNJxjEnqdDHqFvEE5CsZVB/eJQcDYpI4CyhmUKF6M4snUX5mguEW8NXE1aqM2H+Xv9I9nKqvW/m+qmsQBkR1tpOPzxSVVfugq6pw7r5eAbIUSZTHYNI++fIxEBLd756+9bRRwvbxNlUhUjmfqtE5Vm9h0HIydFXJawpmI1AjUkt/r/j+YVV1py9/lRpYzlzl+5VsQNitCHY1oBHATL20CnKkEAT1GIygCAP0mYcczi78b8KYsheUci7eDa+bF8WM+r/PHEopVBQ53ZIt0MVoSoGFPIWkhzCGSEQEYRttNblO0RtJ0jcLC6bQ6NQ1iLQnETIYDJnJnCw+K9A9jSpC5g8eZt+3b2Hl6AJZkpD2eggh2XXZJey6bA/SuvAn4FqwpAndzgqFLsjSHnmesbq6QJqcWsuBMUmdDkYndf9aG0ce3nqx5XaTONdajutou8JwY74+cJRBu/WTfXcLV+rIu/y8BeXd59UcUBiIJzKcmi/FVcf2C8Vq7Mr/7X+vF0CFmuPCu6xynKKvEbtVf67gWOqUd7FySj8pyFcX0WkfrMAsptjTSRytXnt/vo9LVAIV1QnrEwDk/RWK/uqJPrB5SGBPA3ndDLQCbNNiQ1sJ0Fs3zhYwx3o3rm8ZUxSQrLr4XWEgKc/J9hY8co8jqFbk4jgdQyhj4uk6Ii5I6rHr9bSlqJZbOMPzVL1WuoDlUiCjzbCFswbfU8d//1ZBEEUhjaar/tBD08/6w2skY6DfhTQlkCET4QTNRp20SFhM59HFZiaH48BadJpi8twR1knue20KennHVU0vBCKVSCn5zr99ndi6ivfHjh7l8MGDKKW44Rk/wg//+I8RxzGysGvf0VldYv7oAZKkx/z8flZXjlEUOf3eqeWcjUnqdLCRteLdKXUGJOWTads4ospw7TBg0GahxoAcNotqGwWPqlVRFWyMuv1SnBWVMMgR9eOpEutxckgvOKwJQoQLTMvAqfaSwrn7hIDYgrSYLMVkqTse31n4dDAabzzBdlIGqLCGtRYh1dadTyEQrQB21mEiAHKw2SBQjyhTEIS7x3wPLa+E9gRrrMsbGj2+euiIaq6sulDmSokYgjgE7drGb+3tcaqy0k3uUuCOU+uT7PYs+bkFri1GECCkQMnRVWT53ToHXSAURGFATTXcWkOcyuSwMax23Xs3tS0WbQvX/9QvVoHlhWMcvGM/9VqdQwcOsP+O2wmCgO972HWYNAcVuu8pxTBZntLvd+n3OywvzbO0eOi0xj4mqc3Cx3z8c8TAnRIzEDE0y2187lFVzSdx0mff28gTTYizpE51LP7erYrARl1+Pv4Awy5FT5Iw3GnX40J34xwX1rmulrRzf3Vyl/+j5EhcXJSJrAZqhbsWXmByIo9cTcG2COoKSUhAhERh0wLTzVwb7zTD9B0ZGp1RJC4uY4t8S+dA62a/Qd8jhGup0YpcWaikgJVltzDKjBNZJObkxX8tTnxx4Bj0O6h2CzUz4XKajvXIlpagl6OPrLhSTFsC7xePGSQa+pXEGXxHdaF1st2UNfQciW+gjtwEhJTIICgbOfn1ghOFJP2ec/flJ1ohWYzV9Asn2shMWnbxPf9Ik4TFhaN0w4hOZxljDFoX7Pved/nMpz9FFEccO7bAwsIi/X6f/XfcwdLiIdI0Ic+8cuvUXaljktosvDLOE1St/HsKmGWgpPOE5CuMe5eDL0u0m4GbLi4/dwDYv8lx+P150UP1msuRvz1JdRmo+Pz9HjBo59Fn0M7BEx1cnERlccKBhcRNNL6qghTDrUpaCppBOamXFohva3GiOaEVwn2mYC4mUE2a8TRKROhjPYq7l7FJTr6wTJblrhdQ2kPnbpXg3ItbyFJSYlUIQSk9F5GzHFs1V7IpyaF7ALrGCSzmE1gxm5snljrw7TuhqQiu3EtjZg6hQtL9R+h/ZR+2m2GOpWesQisPhFJGyEAF1GMgNz3NGJGPvW3WMFMBhGHpDs1OS1AjgoCg2UAqt3BwCjiLyXM6K0tgKr2yNhwwaJOxki3SyZex1l4wJNXprHB3miClQBcWrS1GC778xS/yrVu+iZCCPHeFbI3RJElKlmYYYykK71byLp/Nn9sxSW0Wnhy8qs4/13CTfTUVxF+HvPJZP/nXGFhb3gLzbbo3yqsaHUPVfScr21Zde37b6lj831Vry1/9anzNv3chu/hOhsI4Vd/oBGoZNAQ0waDkT2jctahWUi9XwOuuSaxgIoKpGjKoo2pNAhkjCrCLKQaJCEPWeiXYzbtZNo014pUuCda3OpfWvRbGrq2DLSfdQIEyYMXmk81zDZ0+GIlMCxQSKRRZv0AfW8V2U8cjZ3yfVBVG/gemcGb/Ft2Mm/m4r1guy6rnpxlrE1IgA4UMAkTpebXWovMcXeQuJ+ykw7WuhckFBq0L+roor4rzJBgsS0sLHF06coLTPKoeOzWMSepE8BJv74XwQgf/W/K/oVXWu+AUTkTh4x8Jg7bm4ci2EbCz/J4eA3FDFS2c1Rbhqp37kjXVSacacx7NddrIJVj9nCdMU3nAmcVuziW0KeXk2sVYRt1QEphScGUINYFq11BtFxAsljqY1a5zE84q6ElEGKGmp5HNFliwWoARyKmY8PJJ5EQMuSHv5eR5VtbuOwdS9tkYLmk6qfJlE66ckQhKV1XgjvtQF1YWXNvzA0uwmkBfn5plEIcwXYdWAM162eZEwGQdsXcW283gUA/S/inKpUcmf1WHsI0QIbYIoCgtmc0pU84cSrnCw0I4SyooK18Up0dSUimCeg0VRWvrSKxFKYFCYLSmyPPS5XdxoTqdKCEIXW96AhQBAoslN4ZinZvUOksfQApUGCFUSd69k1dsGZPUiRDiiKGBs4AmGbjYYEBAi+XfEYPJfrJ8aGAeRz7e8vJVyD1JxcAVOKI6wqDoq4cox3EfHGnWGcSTqi6N6sKzWtW8mkzsLYWqn95bc9X5wHtauqd0xs4ftIG+q022YddUAcyF8MAGtBRBo03caGOtgSVDttJ1cao0gCxEtNqE19yHcNdu0AKTKmwhCIOAZlwjVIr+sWMs33EHedYl0Iao7Hx61iCAnQ147E6YrUEcQb1sBSFjkDVH0HcegZvvcFLrvHDEnVrITmGyr8UwNwOTEUy2B/L+2TZcswu6GRSHnQtx0yQ1ujoCgiayccmae9J2y3p5Q+qjs4gwdJXxZWmNIpybWJ4mSYUBUatJUI/dOrQ0xoswJJchRmv6nY6zqi64BPITw18NAUgpiIOy9TyifM/SLXKKjTwHwoA0rhtxM0JFEdaYMUmdMbxiz5NPtYyQf/geTF76XYlhD7nw/KLQS4H9QtFbNlUhxkZJvp7gfMUI/x2j4/VxKbvBc3W7UWWi/2x1u9NNNj5fMCeJjgcC6hIaEhrKVVA3GpFJl1tlLEQKigDaIXJbHbmj5VSCicIWEoUiICJAEsRdVCAxqjKnWU48htPBUFwtgOkazNScBYUqJ1fpxqmFsyhX+6VFyamHAQROaBIGrgdXUHHTSAlRALlxxHUKByGka1U+dHZkADIEGQ1W20M/sDOEd9lulDAsxMBtKis3+5msNIRAKIlUAUoKIuWmcVmAKkDnBXnqXZlnm6TEwG1ZvT+3oLKHANdAUYg1Kb2/TdeiG8KPgbXwhJQCqSRCScQmFwJjkjoRvAKugTvJKY6U1lpBMHCVWYZjO9UEXR8a8Xk9fYZLG2W4qgk+yXajuEFebqMZxFWqZOPHUlX86cpzNW5Z3d4rEb07str6vcvZ/x2dK1iceOBQH1YletaSyhQk6FiD8he5AdSh3oSG0/gLKYkaMYKAoBDITCAKmGtPc+2VDyHKNPO3HuTOO79LkiSI49aGO01MhnBJA5oB6j4zRNtmEJM1DBJtpatPutDBzB9zBJX2oB1CJp3KcVVvLufN3xOiPGFFAZlCpgVB3yClRh7qws3zrlL3fLdcGAhXwDcISleZk1JXEcV1JqZ3EtdbFNqQ5RptrDPy8hVMulJWgihbn2xFwKsWwFTdkWovg+W+I9dAuOoPslwpjrZAt/b0BSEGyARWCiamJ7hk5zaiKCTUgsgIiiznlltu5pbOKnl+Fq1EIVCNCVRz0ikOhUAIidUF+eoCRW+VwUq6WpbmxGNaE0sKiZSuen2uczKdY7AYaVx7NSkIo8h18nWMBsJ9rlDBKS1BxiR1Ivhae00GLbk9Sfh8KL8NDC8Afb+mqkDJMujdFDNw/aU4VVkHF99a34R1kGOVMSCpai1AH3cerSrg8xyqi9RqnNpbWv1y32nlOM+Bt+WcYjWH/QU0BIXtoxsKQoGt12GyVdZPm4CwrKHWcLXWZCCJ45ggrCESg1zOQVt2Ts7x2N272SZr/EvvKyzY20j7/RMUMD1NTEXwkCmYqxFu30Zj5zZUvU5uLJkBkxcUd81jbr/T1SrUxn0mMS6peVFvLrTj7wkJYMq+SxLR1wQ949ZAd63C1w85kiqMIykhXZPAqO5K/tjuepKqNZm75CompneQ5prVbkpeaHpLR8kO78OmPbA5g6KuW3Dz1UPYPekq4s93HFHlxsWdmqVMvK+ht0Fc7XSvnxbY1P3AphvTXHvF1bTbTSbjOtP1FlmaotOE2757K/nxWoVsBYREtaeJdlyKVCGBlARCYbI+XaMpeh0GrqKQwWRx/HNe1V0hFUKFCAR5kdMtHEkFsSWMQAWCVrtGvdkcskxzbemklrSwmz7HY5Iahf+h+uvnf7Tehh2tbVe1ZjaK6ZiRz3h3u668r0f+vdG1s5Vtq8q+6j1VjUv5z1Qfo+6/6qM6hlNTiF74WItgw1r16bJFNlY6CZYqXT7Ku4CEW4FKiSoTMaMgRAaGIBAobWmFMZNxk0lVpxXViFVAqCSF2OIk10BCI3T5T40QGQRIpRBGO2sn166QaTd1xUmFKO8PMTyIzZBU1UtgbCnN19h+jkVi+5lrJZGMJgB7tSEIpUAF5QLavRZEMSqIUEGMsgUqBI1GKIWw2tXUWzPjt+jMSen6ZdVDZ015X5R3Q/mTYsypf6UQKBUgZPlDLD8fhhFhEBKFEXEUEccxtbhGo96g1WiSBQH1Wp0wDMmLHKMN5jTysTY1RKkQKkIEAUJIdy1kCKJ0EduqTHjzY3AeQ+tCv6KclqzFVrphCyGQSqACgRCDB4VF5j5YPiap00MNl8s0ySCXyS/uvGVcjTVV6+L53xkMLKaqlSIYJIzmOBIsKttW6++Noho38m5DWe7DVN7z8aqN4kleCFEdh6/G7sd4lnrOnTd4d20E7BJwqXL/niwnciTOX5a6jP++Bt1B1tvEzZ3UGw0aQZ1djTkmohbNmmJHPaapFdM6ZC6PqaWC2bjNFXsvZare5NBdB9m/0sXozSi4hHOTSeXGURSsSyKNQsRkG2ZbiFbTxYKsxcwvUNx5ENNL0bctwL6+Iyw/CVvrRAjbcNfVu5SPB1WeqxAINPScxZgtzrP6vS5CQ3rXAnbUFSYECIWQIVJKavUWoZSoICSuNwmiGCkjEhuQLa1grahUJfKmftWC2iLEgRN6zDad1XdwwX2VsI5oEc6yOg3UGi1m915GfXIKtMZmORhDa2KSbTt2UavXmZ2dIoicW9ZLaqRQzE7PcvXlV9Ptdjm6cJSF5YWzIqLQuaFICoSEotAIrTF5Rp4IsL6go1+Jr9lIm0JmClazHkJAVuTYkqiLsiyi0ZbCaIzICYKAZqtJrVYjSXJyu4I1Ln9qM8WexiQ1ihpwKbAH94NeYPDD9rLxKgFU5ehVt17CgHw8AYD7Lfo4UFnNZkhMMVpuqYpqgq4nJl/V2i+ITkZSSeXzfrxVkronERS48zONu647pSOpuqicZ+Emcp26c7LShZ5BtgviPZZms8lU0OKy5ja2h1NsEzXuJ6aZpUax2ic/vIJOU2Zrba64ZC+z7Ql0P+fgHXdvruyh8PGc0JXt8S3cqwgDxGQbMTOJiGvO6vMk9c1b0Ms9uNvAncbJ6Nc+h0s0n2WwMDoRSfmCxTEur6qfQarJjq2SH0ohK3O+NlBvCSERMkCFEfXJaRrNFlFcoz29nVqjRb/X59jBY/RWVgmCgDiqIaVfcfnyLFuMOITZFuycdJXwG6osDWWcK/QMODFuNtlx1TXM7rkUm+WYbg9bFExPT7Lnkp3U6zWkFCil0AhnhQJKSmanZrnPZfeh0+lQ6ILFlcWtJykLpjAUfQ3CYvqJ65+mNTYBF8OoEpPhlEhK5+RrLl279n9duEcQWbTWGHJEIGhOxExMtul1E7rdDkWiMWJsSZ0efCzRWzmjOWhig4cceW+jfYoN/vautVFXn3e5VDWfo7CV56rLzu/veIq+jVyCG712T4E/hwZHCKF0rS0qbj/nsxDl+ROQCUQhiExAnZCYgMgqAiuIhKIuQ+oiolAFQgVopQmlIhCyTHo9VXlYefKrn1PSBf4DiWi6wrlWSGxmMHmCMGCXE2wnc83yvPBFj+z2VFKNqudKW1dKSQCpxqb5MAGu+2zpNjMajMaWVRWkUqgwQsrcdXHNXbfbQICVErPVUmwlHDkFEurRoMxR1btUdXmfJiy4ih9SYoXAGOPapFtLoBRhGKy5lI2wWIRzgwpJGMfUGk0KYwjCsJRxbyXKSclYbFGAkGuNDp1rc9TXD6dzMuwJPmONs6a0Nmht18SVQgiCQBKGaqjP1IkwJqlRjMZ+fOkgH6uC4Vp31aTcar+e6v5gsHKvWmIpw0m+3uvhZeYpA5HTKJF4eG+JH9MKAxdeXvlOHySpVibx6kPf+sO/5gVW9wQkuDy2COgHENRd0dUsc60RECBCMDVHUKsSVgSR2MauZI49xXZqNqLZlaByRFggG5YgcOqlxtQktlFw5MBhipWCZCGh6OanmOC6AbY1UQ/bAzsnYCrG1BrQF+QHluh9ZwHRSSnmF7GH8oFi1FvS1VioF9sYTmxFweD+CQCpIUicKKJfONI6HqxxwgddoKWim3XJliJq7Sma7SnqrUlMkZOtLJMcW0RKTapypDDk6SqmOHmuzKYx1YAH74HtE84taiQc7cNiBj0z+L2d4eUpioKllWWKY/MUqyv0D96N7vXQl13G7rkp6nFIoTVZXiCEoGhOoNpTKCFp78qYKyS1lWUaR+4eXpycMUrZsFXYpMDYBbe4WVfMs8PwSfCT3tbAGEuvk5NrQ61uiGuuZ5Y1hol2k1a9RlFo9n9n8aT7GpPURvA/cK9IhoFyTjCoJF4VMcDAAtsoWO3vEW+ZeUn5KElV1YKy8j5sTFSepMBNRt5lVz0GnzxchY9HwUAd6F2Om+/sfOHDu7kCXKKuip01tdamW4ANQNddi49uAKsBYTjFXDbD5XoGaQQiFwhyRK1Axb7MW0S9HSG1pR7G6G5OtpRS9IrTqU06BDFdR37/pYj7bsdkGnoZNjEUd6xS/ONtcLRbLlfLG8xfY38v+uvfYZCQfbKJ2cethN9BJaZ2os9aC3mCzVM00O9BgkBnCcWeq9xwioK80yFdXsTdYCsIsjKWsYWW1EQdHrwX7rsT5nvw3WOwmMBKDn27vpLLaaLQBSudVbI4Jl2YZ+WO75GvLNGIFFlyLVZPUGQ5aZKCEGghUM02QRDSnDPMEBAsLVFrTzhBwZbBl8mR2FRjs2VHglHDqS/XpMJbUtPquLAG+t2CJCnIEktzKiGoh0RKMdFsUAsD8tHq+yc4ojEEgzJDvhW7d7NV86H882jSbnU/a6+LwcKlmkg46vYbFdZUVYUaR1je7TiqxINBpXUYqPJG1Yej0nRYT1y6sq0a2Qeczfv57MJbtxGuRp/Wzm1VmOFzZcsLbKUjLiOcuysxCCWJAkkgJZEVCG2whcZoKDIQ2iIRNBoNWq0WcRxvOlERcL9oa9y90IxABNCMsAKENk6x10ucgq+flpUkNlj1VnPkRhWcp4I1y708hk2TSPlBGWKVwghFnvZJO8tk/Q6mSHASc2f62zM11/1vqHqMUiKiCFGLsVGOFcIR+RZ3/7XGoNOEvNel6PcxRV66+0yZI+ySVmXglHRWCApjEMYgAkVcrxNlCUEUOym3sVirt4CwZaneU07dV7YFsRKsycB6t8uJvqc68Z2Kv3gEtry1S7entRZjLFmegzYUx+noPIoxSYGbxPbiVFARLqZYjUf56+Ufnsx8zNdbLz5BV+CSBcOgTIotVWN++6oFEzBY7Rfla7Plc4KrF6gZkFTKwE3oBRpeuFGVy4vKa/VyvN7N58fqpfAZgzqAXlzht7OVz22dN2DrUOWCjX5Hc8CVuGu6PYPOKiTCyZAKPZDjBtWTBzY3pMf6dGvLtGox2+emmWrWaZiQYLVPJnLSzLDa1djComTIfe93LXv2dumZPt/63rfpb6YDqTVl8quGiRjusx3mGth2jOn2MfsOY7t97Pyi6yx7YNWpEAPWC2jaDCx9L9rx99apQknXlkSUCrisOIkLuFxxBSFMbIfGBLkUHD24n5XD+8mShKSzyCAJ7wxvJr+Y8+eh/F1JFRDW26j2LHpVkqtjZ8VzrZOE3sG7SBfm0WmCTpz7QUpBHIXUaiGKENmouZhQIFnuOtGIikO27d5O1KzRmpkjbM5i0wSTdTD5qXWtHYYAFULYAhkS1FtEjRbWGvLOYfLe0VIkdLLvCEFOgIjAJmBW2Hxl4gr8grrCcWmasTK/QpE4+f1mMCYpcDf7duCy8t9Vn37VavLw5OUncE9SvoWGFO4HXovKZMfS3+stHX+9fR4dDNw0IU7+3sSRhbekvDLLu5R7lb+9m2c0sdc/exl2lST9+/5vf1wZg9JMoyvyi42kBO5c3qd8toVrK1+FtC5GtWYiux0abchWEpKoS7MNU9OK3bKGNCDTlMKk5H1NspKjc4NEsffSvRRFwa13fJcgGL1pTgCvkgpjuHQKrtoGusCkfeh2YbUDh4642oQL2m1fdevB+gRzf219rPJUoYSTcCsBFJB7k+V4KFdFMoLmNEztpEg7LB26FVaPlh/dQlPGLxYjhnL8hFSEcZOgPkERpxTyFK7DKcDkGemxeQamnIOUThQQRQopFUKVgpdA0E16BCpgYnqGyekpZKhoTEwR1CbRBNjCB4TPACqAsA4qImhNE01uA5Nj0qPkxQpspu2HCEC0QDRwQc3jlcE5CfxpqXBRnhcsHFums9zddC+yMUnB+onfT+S+823EcJLjqDimar1Y4R6I9a7A46kBDY5EBu7k9d8x6nqryshhOEHYu/d8z6lqrKJqFZrK50TlfU+c1dzKC9Xd511Txxvf8ZSLQjhRgFCu8KrOESYgKiICGdNWDSaiGpO1mGYYIrQmS1OkthSpRmiLyVxZH2staZbTSxLSLKOT9DDrCtwKgjgiiOKh+8JaS65zjClc0mmoXMA/K2AlcyV7Oil0tJNNp2ZwzKPnYQtUa9Xx+tp2MgpQKISx6LxAZ6MTligntsCdzyKHtOvKM+lia2NOI1/rYq4K2Wohghg51cauJpi7FjCHlrCLHVjtIzKNDGNELDC6wBSjXT5PF8P70EVO0u/S63YgqmHrynngpEKGIUIpsqJgdbXD6mqHNOljdIbVuSt2fFrwJ0IRxg2iiUlkUCNuTxA3G1idk4cRrm5idXIZ3ocQpXtFhFgfKLdnfp6ssWRJQdLJyJKcIjOYwm76thiTFDhSOsTAE+HbbNdxFpbPHWkySJ6tysWjcha0winEhIBYrlflVRN/qyKKFs7FVyVAbz15QYWpfM67cczIvik/G5f7nGbQZsQTjxd2GAZKRMFgARfhlH4ap/TqcWEn+J4ugQYhhJEryLpQwNIKgagxV59jpraL2cYk99+2m2t27UAKi+33Weh30VlB1u2js4I4qNGI2igRcPexI9x8x+2sdrrcsv9O0pGSN1IpJnbtZHL3LtdGvkSuMxa683TTVZhuQqsBQexiUDcvwJFl55pMUucS1BYKu56kvNvWLzrOFFJAKBGhot6q06o3kAi688t0jiwMu2qEgKABQVkCp7MI3WNu1Z6exTL65WJMTreJH/pgwkv3YLop+c2HyL58O6bTxR45Cv0+gaxRb80h24qss0yyPI/RW5+b1et2OHDn7fQ7yzRn55i45FKC/5+9P4u1LMvuutHfnHO1uz39iTb7rMqsKspdgV3gC5/ByPfK3xUSFhISSBbi0bT1gowACQlh4AWEMI0sZMEDQkLoIq64XMSHxAWBweUqbMrVZGUTmREZzWl3v9rZ3Ie51tn7nDgRGc2JbMoxUjtPnH32XnvtueaaY47/+I//CAKCNCZeHwKC48Mxk/duMZ9M2L99k2p+gK5K3CMVf59nIZAgZMja1nUuv/5DhEkHGUSoMEJXBW5yk2wvwZ3UpJ3eaAgREagNpOxibYW2c5wds9ypPrnp2jDem7EY51hjKfMS/RjKYc+dFPibesLSISzwN3wPf9P3WRIq2gW+vW6x8E5KCg+JGOlvWntO4nyVWCFZCsy2UE2Ad5DtubR1T2fnSBtBrS5GrQNsjxfjIa5V0gecTq63eH67uMEy6rJ4B3UB6YNPpUnlHRUCFgb2apSS9C8lbPc22YoGXO0Nub42oKwqRpNjFkVOVVYsJjPqqqab9lHDiDCE4/mEd27fYjSdsn98iD6jXSeUJB0OGV67etK1FaCsC+bjmkWmod+035CBj+7uzuHm+PR5r7JMV6dYOyfaDdTTmqCRiZKE3YTu+hpKSuq88sn4U/kEASqCsOMdU3EE9fQCTuIjzq+Z26IXE37+GtGXv4B+9x7lN9+n/u6HoEso5mA0arhFtNMniFOcMZSz42cyr6uyYHx0iNUlLozoXTL+NMOAoJPigEVRcPfOXebjMdPxEaaYYc3TOAIvjSNETKe3zvaV68Sd7slfqyLjKO2AaHeo93+WQCFlHyUHCGYYe4xz86c4p6VZ48imT04Zfu6kYJlvaWG+9v5ro6oGlgdOR1IAiYOu8LmNEk9zFQ4q0+SQHMwtJ/ofrWM668NaOPFsNL4KM7Z/W21OuCo220J7Z1UxTqI50TS4aRhPsmE9rbaiX81htJDhWbbf6hh9Fmx1QZfNE06CCXwkFUjoWKyQFLpkNpsQCsvB4QHDWKK1ZjafUVYF+SLn+OCYIitI4w7j3oQgiPjwzh3Gh4fMFguKLDu3QNVIz3KXARjhsAJK4dBxALph3BwvoDBwMPOQ31k7y9yMVv7d1sW1vz9MveSjzDnfxsSAKUrK2QIpJEYbL9MkpZdHMs1Kb2swTS8o94x2NSdkCdGQlyQkXsnB7E3Rb++hbx/7AmeDH3DnPboQESpMUGGKjFJE1EWgcFZDG8EIiZDKD6/1TL3HtTCOGW5usLaxRtzvUeEwuoYix80mgMAIR9rv4bD01tbpbWxRVxVVvkBXT8KR9zkK5wRVmTGdjIiqijAMCYMQXfkajCDsY0SBNRb3Kez8+yB77qRgmZNq4df2HqvwDQtbht+qqn1rfWDN+r9PBUyanEHHePkdx7KIsGURbrNcPFrYsHUILQmjzRNFLJ1R60hapXSDh+bWVs5RsmQNnljjmAK5zHtY43MHtjmByi07CLd50k5zbHvmkfHZqaVa5UTASjI3gDz1zqrj4LJDa8XheMz8sOIoTQirMXs3h/7ljZrA6HDMu2/dYDKaoKQiDCKEEGRFwXg+R2tNVRQYfdrBOKAMHPPYQejIAkOhHFY7SmII+16l+9u3m066NYzPUTZbjeLb/mKCZbsXWDbFfAonJYxFlhVCC8qs5vhgjkBgggA57COcw84W2EXeMBTnnHQnflYLoASG0usuKglpDFGEdYLi19+j+h97uLzC7E+hEv4aO09rleGAsLtJ2OlSW0FQGKhKbDHGZiNPHQ8TZOiLFG2V4erHn+SDjXVe+fKX2L1yiUltOSg1dT5H6ZJgOkJJRTfqsvvSdaq8YDKdkNeWYjHn8Oa7jPfv8vj4tdc9dK5gNLpD8fZbBHHK+nCdjbV1nDEI2SEdXMfUOWV2i7p4tnVSF2nPnVRr58FarXLDw6yFkRUwcl7dwOIjrLZ2LsM7wS6wy+ko52zEtMqqY+W5CL/wtA6t3VGHzXFXjxmu/N5GUODhmzDwjf2sANnQimPhz7eF+YqVY6crY9NCShcoEPCx2Gok1f7UTXND5Ek0aktBdliQTwvqPOKOMpjFBCkkgVQoKdm/d8AHNz7g6OD4sc/BSKiUwwWOReTIAgvKeRadjT3zcH8Gd8cPP1Y7N9p5IVmWQsCSndnmG5/ErENoXzahq5oq9wlWtdZH9To+fXrSh8k1jukZ784F/h7oN04qbvKKCzC3R5ij0fJ8wG9AnE/GCpkgoxQVd1BxgYz7SBF5R9TcH0IqRBj7KFKXT7SER2nKxu4OW9euUo4mlHtH5Lr2BXX5AiUVyXaH3voQ3ekw3NpmuH2ZKJkwObj7cBLQA63ZPTpNkc8ojg+QYUogJN0kwUvkhYTxECFD6jL6iON9uuy5k3pa0ywlaVYV6O3K76vw4YQlvDZguROeN8dZVZhYPcZq8XCrzt6SH1qYb7V7cLf5dzvrnfBRk2mo8M7cTwBZzWe1zRjbQyQsC4yffcH601mKz8dFwAbLBXs1smjhKSP82GcgaoFaBAQuQOia+XiKygrfgVRKJILJaEpdPcZiHEmIJC4JMB2FTiREirgToxKFzQvKgxH6eAKTwveielRroVnB6Q3WqrL+kyJvzvm6TylWjuFwtcZmfmF3j6gY8FQm8N2UE+FhvqSB05310WYmoGx2AEp6LDXqIlRIoALSJEYpRdzrE6oAaS2hVKRJB6MUZRVRCNHUU2tcXfrvaZ9s4OqyYjKekvaOKYoKKQVhEGCsxRiLdY66qskXGbqsqRYlNqsxucbpJ8XQ25tXeXjTcqqAWUrJ2tqAfhJTl3Pu2XuUWdDAmX4BcDisrRGUWKsbUsPZOodPxp47qae1tvC1zVu113OVQNM+VwG3gQP8IvoFPKuvBPZZ5r/ahXS1sLaNsARLrT2Lhxv7+AV5Gw/PSZYUYguUzie6jWua4smGWmOXTnWVddgyxDQeWqT5jCHLZo5jPr1Oah34Ev58W0JKu+aE+O9uGwWHUsANBzcdAknc7ZKkKZSWg4Nj9nLfY8UHpIK6rskXj1jLIoBeAOsRdGLqzYi8r1BJxGBjnc6gT3U85vC332H27oc+F5U9BsOrZil51DphWBbwrkLXj2leGN4hcKe6jdu8PHFOTl+EQsJHmAQ2A7gc+hyqrT05Q1sYW99DyyrQsW91H3eQa5cRyZBuv8flSzukaYotcvRsgtM1MgyJ19axRjOpp1RjgcHh6hJr9DIn9QS2WCy4/cEt8rzApClhv0cQReRlTa0rrHZk84yxOUYXFYvDGdVxgc5KbGme8J5aYV65CIxDSItork0QBOy+eIXtYY9sPuWb1R6jgw9xbsn0c85iTIY1DkeFc22Y3qpTfHI3+3Mn9bT2IPbbeZsPi19UVns6BSxFXWe0bNIlOaJl97VzpY2kWqJOGzmt0s5hSQJp++eA312Zc+hfzq2WWizn5modZKvE0fbB+jRbwrJFBSxp+6vRqNONRBIwcbDnEFKhZEyYWrTR5LM5+eQpiisFXiewo3AdhY0lOhKISBKkCZ1ejyArCCrtW4RU7vHYvm2kfR4d/Wk3v42Du29pMgZnnjQ8ewIT+ChqqLyTyjQUrknyGV8/JqxXWlAOEUpE2kV2h4TDId2dy/R6XarREVk2x9QVUirCSGJtQBaESzjcNa1InsJ0rVnMF4RxjBKCcNBHBApZ6xPR/VpryrxAFzW6qLCF9g7qEVXB7zfJifyGU41PcScXT0pJv9dlZ2eTeaJI044njZwsMB5tsU6zbCrXHted/P2TsudO6uO0VrC2dSgFnphRsIT5WpZWC6u1c2bGcu7M8HBbSztfYwn1tabwzqmFulqGnnG+lqvdHLVwZOv8VtXR4XQe7LNiOcu6t9V6sZolrf7YLXuFzfwN7RxU2iIqg60t5mmVzKG5RtIjMbrEzGeILGdxnAN30JMp1b2JX3gf17m0tXbthqd97wWvJy0bHZYo0idiDn8Nc3y+adD1reHDCNlfRyYdkB2EGiBkDIMQ3ZXUHYGrQsJ+lyBQuLrCFIVnLzp7n49/GouiiLXhGpsbW+RKMpsuMMLLRVoHUijiKKbX7aGDmihqddSextpEsW4SnxZsTOjW6ESCJARTZkyPj1nMJlTFoqmXWhX7bGtRVim+q/mAT84+S0vPZ98CvD7gRvP7vHm0G5rVWheJvxlzli0UPFy+jK5ax3SJ+xl9gVtugFq4q8JHDaU7/Zmr0VvGkt3n8Is8LOWWPgs2Ad7Gn/OLeCcV4cf6AD+Ot4HbblkwbcEKR1FbqlzjavPI/W4eaoHwxJRQYIoMe3SIqSz6vTHTOzNcraln82bh5fHWgzbqbdl9FyWicMak9IRQ8Cjb2ca8H5u1pJ4pvh38lS3Y3EB0u4TXr6I21iGT2D0Jc4Hrp1TrCpUKQhGT1uvIokZPJ1RFhjE16kJEXZfWSTtcvnyF3StXuHV0zM27d8jqmqjTIer2UGFAp9thc2uTOq9IO+kFdOpod7UC6gWYY0QYE7PFsCOJQkk1n7I/GZEtpuTzEb7z4Sqq0rLE2kVjdTI+d1K/c6yFeTtw0sr9vIWljbJbuK7NTS04vdNeZf6d3ZCdratq80ztcVc3S6u0+tXPheUMeZCayqfRVguyWxZxG1G20Ooc78zOwGvWOu+cnjZkaGvSmoJYlABrcGWJyzX2cER96+jJx3OVWv+MNw8tQfRs/fDH8qGyya22c6/doAUNBX2th+z3kLvrqO1N3MxCVvlaoFhiQzChIwglKgpRVuCU9BGUMyd5m4sypQKSJKXT6RKMJ9RVTVkUyDDyt6gApRRRFCEMqDPagk8GrK0kH9t/CoN0tQ/ipcPoijovKfIMrVejpHOO8Smz507q47DWSbQRzdlNTDs/WofR5hlaGLCVSDprBg9r/Tbe8V3Gyzi1M/1ESqlZXiQQ2KVqertAtwzF9rm2gLddECx+QW/zrDM+1c5KDhThyxGyJ3yU2QNi0D1LvW4gdTBtEu+rKjHS+VyVLpteU0+QnxDAVg8uDyAKiNY7xOspTgqqqqaaV56VVp2zIAgBYejVMKxdqWNbsSSCbur1/XQBdd7kGnlm18S6pmZX8NS9HB9qbT82JQi2hwS7G6AEupyj92f+mkwNZKDCgN7mFsmrL0A3wV4a4tYSTKQpZgYTOEpRMsn3yXJBJ3OIwhJWjmx+zPToNnWRMZ+PsGfH+AJMAJ0k5dLGJnldI6MYESQoGSJqKObeaejKs2yFEwQiJAoSrLMYW2Mfef61i0uL+2qccywWGQf7hygVYasKV2nKckFRGpbdMZ/hxLkge+6knrW1eYNWGaLd5a9aCwevEmkcy+aKrTM5O5c08D5wF78Q/yRwDT9nW4knIUCoZkdvIXJLokQLH7Y4P8170+bfrW6fwefO9llGYJ/ieR1sBnR/tIvaCVfapzhyW2NkiS0tZDXM7HKM2+9vK9+/qY2mHtekgOvr8BMvI3oJqUoZyg5OGyYffEi9fweXV1Ces+uQCpIUoshT0Rf2fifVSeHKtq8ROhzD3Qpq/Uyvh3XL9lXP9LK3pROJJPziLt0ffwOEJPtv38f895FvYW88VB10IzavXGHrh99Ex4r5IKBIFdWsoqg0pmvJZxnl3WNEVjCsI8KiQ6wF4/Ed9u68TZXNMUZ7cd9nYMNul5evXKE2llxDVjtAQgXZJKfKSuq8RmiQVhCqmCTqYWxNUTvsI2v5rSpT+4SydZbJeIr94DZChSfIgK5LFoXBuZhTuaxPscmPfsnT2d/6W38LIQR/4S/8hZPniqLgF37hF9jc3KTX6/FzP/dz7O3tPetT+eSsJSW0EU4bNZ2FfduNjV55rOqxnV0hHEutv9WclVt5z+oaJ8RpiKh9tK9vN/crZRfL4leWfYoMHyPu8/gmAoHsKdRAoRKFEgrpFEI00FvQ1Ny03/FEGb4ZvLYJ4aNAQe0YypVHEsAwhbUU2U1RUUygIiTCJ3R0Q/1v4CyhFDIIkGEAQeDbLahmY9GWEogGOgxUQ7WO/Guc+Fhy288U6V0dv6YZp+xFqK0earOHDEOfRy0a9qMVCCRhmhD3u8S9DioNkZFERBJiXxttAkttCyqdU+sSbSq0ral1SVllVFWOuSCRWSEEUgjEykMpRRSExGFEFAQEMkBJhXACo60X6XUCKRVKBgQqPHlI8ehLsxAKKSOkihCiiagc1HVNkRcUeU5RlpR1TaV1Qwj6WMHbp7JnGkl9/etf55/8k3/Cl7/85VPP/8W/+Bf5d//u3/Gv/tW/Yjgc8mf+zJ/hj/7RP8p/+2//7Vmezidjq5HUqqNi5efqIrOaS3qcLYQBjvCRlWTp4EIHQ+ulf+B0h962OFeuPN/KM7WONVo5Vnvc1aZ6rZzUp8hMaSmOKpS0uNzC3OA01EbjTFNjs298rVdLnGghVtXkksBTm89bmtv7O1ENu0yC0VCXTfEpIMFZS3H7kOntDCpNmU9xxvloq5+CDAmjmK2tXXr9IWVVcTQes8hyrwoSh/dHc93EO6aqqXv7xKh2j2vNxBaiOedmRxTjtS8Vfq5GIGKBTEOCbg8QyKihrUoBg8R3Lb4yxGx1qdc7VNKRu5x5XaNthY40pMarxa+HEFvKGRwXBWEFC11iL3DcwiCg2+kQBgEbwyGDXp9+t8f86Jh7+4fkVU2Y9gm7A6QMkCrESgFOsX55G2pDXWQcH0TMJ13yIuPewYdUs5Zt9/Bz7a+ts7X7CkGYMD76kKP99zBao6s5xXwPIUPipE8Y95BSIEXLojqnHOVTaM/MSc3nc/7En/gT/Mqv/Ap/42/8jZPnJ5MJ//Sf/lP+xb/4F/zBP/gHAfjVX/1V3nzzTf7H//gf/MRP/MSzOqVPzlo1iFUNudV81Nm1cDXKeVTTeObaO5wmOXTxeaikeV3rLC3LHNmqsnZbg9Uutu160nYjrvGRW0uJb3NVnyIzhaE4KKGucUcad7eGyuFCcFHDbNxvKOirEKvE1+Sohq8vz1kg2k2EADohXO76n0XmOzBj/ThJwFiKm3tU//N9qA12M4H1xEdzax0YKqLegCtv/jBXr73E5PiY8n//Fos7tz1EG8QeAjxldkU55NO/wCxNeDWIlsHSFpMnwKbw94fDO+UIZCciGPQBiYzjhnEgYb0Dl/q4y2vUuwPKzR6lqZnPF8yKAmtrTKSh00BYmyF0oXA11YHvK2V1+Rj5no+2KIpYHw7pJAmbGxusD4YM+gNu7R1y6+4esyxn58p1Lm/sEEYRAuGrPoKAzRcusbG2gS5yjj5MmR0MmMzGzLIp49mY8xeIlVEVgrXNTV7/0pdJO31uvCWZjG55okQxQ5cFSkVE6hphZ4B1snFSq7DLp9ueGdz3C7/wC/zsz/4sP/3TP33q+W984xvUdX3q+TfeeIMXXniBX/u1Xzv3WGVZMp1OTz0+U/YojM5V7T54/Pnj8Jujtlg4YymztAobnnWGreNsHdVqU8Q2wmrzOjFLqvvHwCp7UnMGbGGxWfNYWOzc4hbWq9QXbll/dhZ6fdD4rI7TieCw8BFPpDzbTDYUOGO9mkWhcUWFzUuv1FBp/zfrvPMJQ2QcE/e6pMMByaBPmKaoOEYmMSKJEUnic1RKLYtOjfGwobGfhTXmjIn7WagNxEeskN0Y2UkQSuFKjSvqpTMW+HGOFIQKpyROCqwUWAfWOSwO19YHSncyTx0OYwza6AsnSiilSNOUXq9HJ00JwwClJEIIL25inT8353xqqIV5pSSMQjrdhKSTEIYBQnrY0I/Rg28wIQRSBaggJI5Tur0+3X6fOEkRollInMVZjbU1zhqctb6xojtvsl+MSSkJg4AgCB4LsnyYPZNI6l/+y3/JN7/5Tb7+9a/f97d79+75gre1tVPP7+7ucu/evXOP90u/9Ev89b/+15/FqT57awkQLbzW5XRr9/Znu/BnLAt8V2WWPsoMHr5qC8hbGzSPVqFidVFuqevt4tuSg1piROugWjJFmwM7wufAHsQ6/KRtbnHva0gELrMwbXIZIUvB4LYuadUcTQ2ZaQqvJQzk6ch29dELPOQXKi/NM8WrRjCB+fugFOFoQbjVAWOpnaPeX0AQoDYS1KBDnHRJu326/QHWwfbLr0C/j7aOQju0dejxmOrWLWyW+VyZbXJmeXE/seJTa87vHs4mWNvoCUFy7Qqdz7+KShLsTDP7D7+Fy2uK736Iq83pLbUFV1jM3GCd8TDqWSstHJUwLeCo9tqImYaivlCa4trGOj/0lR/l8pXLXL56lcGgRxRINtYHvPrKi8yLklpGHI7HIBXDwYDhYECAoGs0HSTzesFbx3f4/ru/TZ5nzOZjHiZJFKd9hhuXiOKUy9df58rVF0nTLvu3PkDJIUuZFYuzkqKosGKOszVV9aTNFR9uSil2N7fYXt/EGMPdw32OxqOnPu6FO6lbt27x5//8n+c//sf/SJIkH/2GR7Bf/MVf5Gtf+9rJ79PplOvXr1/IsZ+5tQt7yVK+CE4liYlY6u8d4mnlCx5vo2PxTmpy5vk1vKbfKnTXOqi203DrKMEv4iu1gSdOLGHptCx+Qf60slfnDpfr5X3anmMbAcH53YYdfmGr8AW4wwAG6rQTZ+VnHHiSRBD4Sv+p8OM3nsD7c4QShJsdOtsdv3ndm1MfLBBhSNDfJIg6REmXtNuj0+9DGLJlDMHWFmWtGc8zylpT1jV6OsUeHfmTPInIP0s5Kefp/ef+qXFS1y+x/lNfJeh2Gf/bX+f4//tf0eOFZy9q4yOok7c4bGExM42V5ozTaXYRlYWjAo5yGNcwKb0+YvEYbWEfwdY21vnyV36Uz73xOZI4od/vogLBxvqAV8SL5FXN+/sj3rlziHGgoojhxgaBVHSdYF0KXOYYHd/lrXd/G6MNxj5cfiTp9Nm+/Aqd/hpXrr/GlasvkCYdbgx3kWrIElopcU5Q5DVF5TF6p58NPTdQikub23z+5dco64qyrjiejM/trfZYx72g8zuxb3zjG+zv7/OjP/qjJ88ZY/gv/+W/8A/+wT/gP/yH/0BVVYzH41PR1N7eHpcuXTr3mHEcE8fxuX/7TNjZwsvzck6rcN/q3x7XUZ21VhC2LSZfZfStwnpnWWpu5XXw4Gji07hGOs5l1cpAegadAOsM9kHSCe3mVQlPihBNfmqVCenwxIXa+sirtif0aKzzLd+lZ/M56xrSoPOvkc5zumuNq2rqsqIsSqqywmjjF2BjcXWNq2pPR9eN1uAnYS1jDe8cnnbRAU7mnIgDRC9C9EJkr4vspog0AYSHSLMzfWGkbBiQCodrICzj4SzXQKDa+PFaZVE65+vOag3WehacdA389XTjKqUkiiOSNCUMQ4RSICQOgbE+GjauIWI2cJ8Qwld/1DV5w8CrqxprnIcjHzjGfuCCIKbT7dPrr5F2ekRhRBCEhGFMFHUIogprhO+DhvDf0zYFgReUj5MNdCul9ErzUUySJCRpigwUnU7Xyz8ZTVXVmCfsPnzhTuoP/aE/xLe+9a1Tz/2pP/WneOONN/hLf+kvcf36dcIw5D/9p//Ez/3czwHw1ltvcfPmTb761a9e9Ol88tZCZSlLuG/V367Szlsn5UlNy5YYTzOnCuAmvs9Vh2VxaxtVtQ6qdU6tanhLSW/hvFWUpo38Wgf4bNCDizUh6G6sMby2jVSS6Z0jJncOHywo2hbWxpG/qXXpGXwVnjRSASqH0PqFc155R7VizjmqWYWzc8BRzyt8CwyNPhxhi5L5YMr70YDpaEGlNaP5nLys0NmC8mAfk2XoyQSbP4XQ7ZOa8JMjjCLiOEYIQVUWVGXxdI6qrYcKIXx9l85PvoHaHJBcu4xLYwzu/CkvJfS7sL0JG31M5Kj1DO1qTDXH6gyXV7iDMWQlYuFQnQgpQuyixGTHuGmGMiFh5xLCgS6n1OXoqR1Va04qbJjgVMBBPuJbN+8wzXJqoYh6PRKliNIEGkX9GzduMnvvA7LZlNmkZK27jdYVi3JGpc9e8xBkD0TEcP0FPv/mD7O9s8twuE6adpFSMljf4fLLX6A3nTE5OmB6dIC1Nbgc7JH/nu7pO5ZGUhEr32NtMBwyWBuSxAlXrl5lbXsb6xxfjCOuvPAC8/mct999m3v37j7RZ124k+r3+3zpS1869Vy322Vzc/Pk+T/9p/80X/va19jY2GAwGPBn/+yf5atf/eoPJrOvZckN8E4oYZkfanNOrUNoIbaWHdZ2wH0aJ1UCd5rjbjTn0Tqp1aigdVIxS2hwzun+VrB0Uj2W5IPPgJMSQtBZG7D9yguoUGEry+zeMeZhTioIIY79DlwXPpIp8OK0GSBWvvyK6vSJOdDzCr2oTn4HwFjMaIIZT7GdKbdFysFojrGOvKqojYHZBD78ADeb0mTfL3xMHm7COykhCKKItNtDCt+qvarKp4PLQvz8SSB8ZYveT/4Y4ZUd35JeSUxRewLE2fdJCd0ObK3jBh1s4NB6gTY1Jl9gywwWBRyNYZohREyQ9JFJiNk/wuQjmE5R8S5RZxshvZy/riY+CrsA804qBhVwXFS8dfsex/M5G5tbbO/uEkURUeIdfq01Nz+8zXvf/C10WaLzkkG6SaULKlPe76RECKIPskN/eJVXX32Tq9evexKFlBhj6Q032bn+Op3ZAmsTZiMDtgC3ADfmoorqAinpBCFhELC7vs7lq1eIk5SdS5cYbG76HlZbWzjnODo+4nh0/OlxUo9if/fv/l2klPzcz/0cZVnyMz/zM/zDf/gPn+6gnzboaZUF1qjon6Kfr5IYzMpjNWK5qO/Trm+r0F/rKNti9Tb31FobXZ3UdomlMxPus8JePWXWGHRRYnXgYZBAgpM01LDTLxY0JAXjHUTtPCmilT07+f6PMAjnvaTJJ1lt0EWOmM8xzmFq7RvuFU0DxI8d4mtrxJYJOIGHp2ggv48+xApe/SBo6QReFoggQIQBLqsw8xyblZhpdu41kWGATCKCJERIgTBmeY3aQuy2E64BpwOcqXGLwsOA1jPenK39y92TJVaDMKTX6xFFERubmySdDiqMCMIQqVRTxiCwrinaFaCCAKUUdVkyq2p0VmDqijBUCBtgCoexumk6+JBzck1OzlqMNZ5NFyrPKJQCbSy1Ns0GrN2Jtjc0XISjcrRsRetbj1QVSEVd12ijCQgIIw9BZnlOoAIE4rytx0fax+Kk/vN//s+nfk+ShF/+5V/ml3/5l5/+4KssuVXVhE/SJEtWXVsQG7GMnlbliFpITbBsJbFgWTB7kY4gx0N/Byy7+yp8S/vXWMKM7boS4okXDjCBf+SAqKHSSymnz4A5Z5kfjLhjLFJJiqpEDlOwFjsvcW2zwZN72UJdQK49mWJklqSSC2Q0urqmPtjDzGb+xm/zEXUFVfnRB7hIE9LXMq0KujrhH49abyAEBAnI0ENLuoSzskPtutk8nANnHMV3b5H9/76NOZ5Rv7fn5aNWTCpJd71Len0TmUZESiLnBRKDsBqE9bJWxQQWE9zIot/XiJnDTua4Wem7Wbg5hbmFQGH0AvcEUerW9hY/+ft/Py++9CK7V67w8ufeYG1nGxmEyDDGCYhDhZIOKS1JFNLvdlFScvD9dzj4/tuIWrMmAt545UWKPOOt74+5t7+HtjWVPgeScw3zVNQU+Zz9g3uIQLCzs8n27lWUUvBdy729fUajCdn0EGun+AnbLkoGfxM/3dyqjWXuKqSR1IeHjPKMOE4okRAmpGmHq9deYGdnF6FCkrTzxIHEZ1+7b9VJwcciEfOR1kJ2G5xuHNgWerdqDa2TaotlLcvaplZS6yJRnhLY47QETbu5uo7PE6yOXas3CFArqKMmEW0bxtUFn9+zNAf5eE4+WfhalLUUtd5BOU9iMHl1mkwinN+NV3XTHsItuxRfpBmNGY8wjJcn+kmZaApuRRtdthdXLMfmI036BoRB4p3Tg2SH3PLhGlJJ/f4Bi//rN9F3x82CfHoshJTE/YT+zgARNktXUSOEAWWaCF9DlUExxR0VmO9N4aBRbmgOZ22OrZ8ux7c2XOPHfvdX+JEf+zHiXo/hzg5Rp3Pyd2sdYaCQAqRwhKGik6bgHJO9e7z1G18nsI6vvPlFXnj1cywWc9694ZgVxw+p43JNTklTFTmTyTFBHLC23qXbb8hlwnI0GnN0eAz5FFzbQE3ib/BWb+3pnJR2Fm0sGFhMKsR0QhzFdIbrDNe3MA7CKGFjc5eyqoniJ2d6f/adlBRez0yJhmFlPrn7/CxEBqduxlO6fLCkoLcKEK3TelZQ2moeipXPyvH09dV7o6Wft32kaufZUoXzcjPtOT9z9ccLtOb6OOkXRdcokUslkWkEzhd8nloknPNzLJZghWfnVfbhzrltMUGLfDX/RpwwzZyxZ3bwn/TOasWEIEoiOnFCICUqDAiiAJxDSvkRG+LGudkGhjvvlWGAHKaIbgAiQN8ZQabR+xNc2RQ8n3dkazGLjPpwhEgiVK+DSGMPjVU1mBzmhaeaj2qYab8mPAOavnWWutaUZUkQJwgESkqscxhrMc4ihCAMAuIwIpaKGN9gsZ8mbG6so5yjP+iTdlKMrQmCAPGQnjhSKaK4g1QdeoMe/WGfwbBP2klQSiGEIIoi+v0OVVlS2RFlXnOa+r+ahG5/B/AagmcLcK2z2AddxxVzzgN5QRASJSlRlDT5y4qq1ljbVm2fqZN7BPvsO6kwhH7HN0FblFBnzc3xSZwLSwit1cU7Kxi7wO/MI3wDxJaA0HbbbR3VszK78rMlatxrzqHt7NvOn25zfhJPX3V+58TcLFvJt9p3nwULlS++FQJnDOZ4gVSStJ+QbvWx2rI4npFPs9P3UCzhclMoNtewV0D24IskQoWMoybfIhCBd1BSBCgUzlqqeYbOnp5l9axse2ebL7zxebrdLpPJmMPDQ6qqoi5z5g/r0uecJ5nYeglPnTG1PiT+odeQOwOYFsz/X/8TV2j03WPs7MERjqtqFt+/QWUmqLUe3R/9AvHmC9hC4/bGMDqC/Rn81gjuTPw1yp6NwndVVhzsH/DhzQ/ZulSzeekSSRBS1jV5ram1RkrFxmCNQIVsxxFbuiIQkuDFa+yGP4kCrq5tsDtYYzwK6aQ9JDEOjaO+j+OYdjvsXnuRTm+D17/web70Q19g59J2kxvzBY+bW2u8+earjEZj7rwz5vZsjrHn3aAJqztjJQPSZEAQnI546rqgKKcY+9EYt5SS/mCNS1eukyQdjIX9oyOOjicUJSwjuVap4NHss++klII09WKc2oH4BKi6J+fCsheTZOls2q6p7fUpmtekwBDvuMYsmXTPEkJzZ37CUkV9tQ5I4CHsYXOuq2KUqx1hz0rLfZpNCV8QKgQuq3FZDYEi2ujR3ehhakO5aBzH6mY2kNANvTxRIOH44ck4oRQiDhFKIkKQkScfBCJCiRCnDbr4dFMiB/0+L7/yMmtrQ27fvk1e5IhMEITBCqniPHMe5ntI+wvZSQlfuIK6vkX1v25QfONbmL3xR56T05pq75CqHhFsrxO+8SJhrLAVuPkCDkdwbwE353Dr2a4DWmtm0ynHx8ck3S5YRygVNRqtDVWtkULSTVKchX4Q0reGSDmSzXW2B32fJQoieipEG00UJ0gCHA5zzkIQRjFrW5sMNy5x6eplrr5whd1L20ihCITCGEu/1+XK1R26nYjFQcJdUWBYHQvBkmK8ZG1JqQjDDnHYPfWZAkFZzR9pTISQJGmX4domYRhhEczmc+aLDK0FSwHTx5v7n30nZS2Uvv6ESj+T0P6htpoTa+Gx1kmtQn2rUXYTaUkpEC3BLHAr+R9O+YRnbu6cf6+SPM46ojan1WrfPak9qU7hRdrq554tbG6vm3aQ++JbCu0hv4cd0lqcNg08ppBWeWaTsxhbYbXBfVKFuQ+xMAxJe32CKGZjY52NjQ3W19coi4LpZMJisSCfzZBCPNVld9qgZxluPMcs8kcfi5OygAgRxYQyJHYSgSKQESJIIKxxoTy9BuMXUCH8dbDO4h6kfvEQk41GXxiGbGxusrW1xfbWFsN+HyXE/VCxtb62TusT6NM5R12WLOYZwoGKYkQYUxQlgQro9/pUuiKvptT69Dkao1lkGSKcMZv5R6ebnrT5sMYymYw43r/LZDwmm099Ee991g7McpGxznpRWnH6ZtemelDV2v1HtYbx+JgPb31AnCQM+mt0uj2s9ePtTuCkx7vZP9tOSgpfuT+an1T3PwjPfmYW4jcmCl/g6oWbT3e1baMox0mtlIwFUagIAomJHFVXY4Tz+aEW9vs4F+/zPutBvaNW821PynRb7ePUQqKfBmsJJat5wtLAJAdTNrJJD19UXW2wrkJIQWA7hNLjqHWeUxe5d1b1p+ULL63X6/HSqy/RHwx58803+NIXv8D6+jpbGxsMez0W8zm2Kvnw5vvU9ZNTHM08p3z/LmI6xd45wlaPOBZCQrcHm0Pk+oBu2GXdRBQuIYuGlB2N60h0L8T1WDbz1CBFSBT0ECJAm5xazx958W0tjmOuXr/O5tYWL730Ej/ywz/M5z/3OYI4JgwCyiyjtstco9Mam+fYfIHrJL7kwFomxyPu3rqDs471To9B2iXPctK4y/VrL5EXC+4dGMaz03BwnhfcvX2H4GhO2ol5//2bFGVJEMTEUYo1lvfe+h6//ev/hen4mMX0LkafHdtVWvEyQW2tJisnyDNRk7W6yUl9tFV1xdtvfZuD/bt0ewO+/OXfw2uvfRGjLc4W4ObN5z3e3P9sOymB363kn+AN30JfbTuOtDmvnNMtW9r7oYlCRABKSQIpEYGlDoXvmtu2kPk0bLRXSR4XbasRqFv5+UnaWZmoE7KLg4U+rZz+MLMOV2mcbOA+qzwKVhnqvPj4o/1HtDiO2NzcYH1jg93dHS7t7rK+vo5wDl2WzGdT3u73kfLp2DKuqtGjKZgKpotHrwUTAqIYOl1E2iVSER0rEQSEKkFFXUyUIyKFa/OrRftWhVIJUoQ4Z6iFVwl/HAuCgOHaGtu7u+xeusTVy5e5duUK2jkyY9B1jVmRjXLW4uoKV1W+jxmeYFBkOZOjY5y1iFLjKkNd1QRBxNpwnTAMOBpH932+rmum0xkiM4yOR4xGY+IkJgwTksRgjeXocJ+9W+8xHR/zYLma+2Ea5yz6PNr7Y5g1hsPDfQ4P9xkM1njh+usYY08iqeXi9nj22XZSj1pM+QytbXODBCEb9pbw0IAIJVJI0kGHJEya7p0SISROaeo0w7iaWhtEbpakis8KrftJbFX3b/W5s/DoszDrPNtLsALZOWpbk+scaw1GaR8Zt/ViBUsnugrHPsSkVL65nZS+G6uTHvp71FqjT8jqumI6HYNwZNnCz22lWGQ5t27fZTKZMBpPMU+rflEbr0iuLczKR0c/nIOsglGGQ1Edz8lHM4pFjr47whwe4Q4nuHkNFQgtkSJESEmgUpSKkDLEuABRi0eeZkoFCCkRUpHnBZPxhPlshm6jFOc8rGct2SLj7mhEVlYc3LtLPptSZjkHd2vseIQAxocjyrJECkkQBHQ6XWxsuXQZ+r0Bo8kR+8cfcnB8+jyE9DlBEfqJmGc5s+kMXY2oy4q6qti/cwutWwjk6W8kKRRBECKEwFiD0fUjFeRqrTk8vMeNG99nPpswX0yf+Hw+207qAuX2n9QC4YWwReDrOIRQTZ8Y3zI8imKuvvACO5cvexFGFRHIkEU+5ca9tzia7lEUNfmoph6b03p5P2h2tmcWLPN0rQN4ltFbbZd1O43oq3WWrF5QFqVPWEe1J4tolrJQbcPHiKVW4UPOMVAhSdxFCkWkYkIX4JyldOJErOPTaFm24PaHH3B0nHDt2pUTSvP+4TG/9vXf5PDwkIN7H1I/LVSZVXBr5Hd3tflI+PTEjIWjOZQ19rhgsX4PJSOq6Yz8W++ib+/hshK3n8HCQ3yxGKKiCBXERHEXIRXGlSCUrzn6iF2RkJIoSQijGBUojo9HzOcLet0uedFEHtb4rsym5ujuh3zzN7/F0WjCeDbl6OiIqqq4c3jIdH8PjGUw3GQ43CRNUuI4YWt7m0AFvPzyqwRByJ07H3L73g3ev/XOqXNRSpF2EoLY12MdHR5TlTV7H37AB299m2wxYzaZUOYTTkN6T25BGNFLhwRBRFEuWGQTzENIMa2VZc73vvtb3Lr5HlrXTCajJz+HJ37ncwN8Kuyk47gQCCQSSaQCIhmTxCnr6+tcurzrF68gIVQhk2nKwehDpi5AG4soBHyCxMSPzVabJa4WiLaO61kGG+fJHwHa1mhTLyHI9lwMS9WPtsyj7WD8EJNSEagIJRVKKJTz9TPCfbojKa0188WcSpcUjaCtlJK8KLm3f8je/j75dP70TQO1hfkT1C1YTnpBOaGoJwuKyZx6PEfvT7B3R97hLWqoPctShTGBSlAqRKkIISVS+pqiRzHfXFARhD6aKMuSsijJFtlJvse5htFoNMViwf69e+wdHFFUJUWeoeua44N9br/7LlYbrr1gSdM+URijVECapsRRzHC4Rq/bx9iatJPefy5SoAJFGAWAo8gLpJAc7e/zwXvfZzEdnzNgT2dKKqIwIQxjtKkfedyMMYxGh4xGh099Ds+d1FOa1VDnXjEoCQLSxN8Qg2SdfrJGFMekdDEZOGURssZIR7GoqQrrxbUvtr3Np9tWC4BX81GflDkaRQlO5xfb6KmFJtsat8eRgnIObSp00668NtWnNooCWFtb43NvfJ7h+pCXX3kNVEBRG9bWN/jyD32Z4+Nj3n/ne7z31pj6UaOfpzQhJYP1Dfrr6wilsIHABgIXKmxWk793Gz3PMON5w7w8v3jXw/IWKb0CxKPiylJKer0eg+Ea1liKLKOuKuo8596Ht7ixvobDer09HLPJ2PeCkr41TBRHyMa5tOaa/4QS9Ad9dnZ3UFJRFjUHh0ccjzwceNacrqmzMVZX1L3Yazw2R7z/AU/qpOI4YTBY9z/7Qy7tXCGJU27fvcnb70zR+ccL9Tx3Uk9puvJwtJKCrogZxkPiKObKxotc3r6ODAJqIanHDoShwiJExWxRUEwNVeYl4j6p+uOP1Vap+GdzU5+Uo7J4WC/HQ44beLhPNj/bYusRy3zhI14rh6Oscsoqe+TK/U/Srly5xs/833+Wl195hW5viAgSZnnFlevX+T//n/8ni2zB//X/+X9z+/23qT8mXcEgCLj88su88qUvo8KQymq00+TZgg/e+x4H33kLWxvMrGyaGXJutCyFI5QGpRyBtIhHVD0IgoDNzS0uX7lGVRQc3LnDrKwoplO+/1u/RTk6JIwi0l6XIAzYPx5R6hKn8AWyocTokCiOTkUhTjhUoNje2eZzr3+Ouq759re/x/s3bnJv/w7zxeK+czF1Tj65gxCKohtg9evt0TjN0Ho62Zpud8Crr77J+voWl3Z2+Nzrr9Pvdfn6N/4nt+++T5bff27P0p47qac0ZxuSkAChFaEJiG1EV3UYJEOEksxcRV01CcdGPqQuNLq2GO2w5lOcqLhIO3vffFrQr9XyjVUWZqte3977j1WD6HBOYKymNuXFNAp8xpYmKZcuXeb69RdxPsmKNpa00yVJu5RFztra2lOz+x7HhJQk3R5r29sEYUSpKypTo5RC1obqeOLZlw9SahHLH62O3jKSesjnNo0JgyAgiRO63S6qkTnyAiw1s8mYo/2QOE0xVhNGEUVReDaboBkniRA+klLKq41IJX2jwECSJAn9fp+iKLHWMl8syLIcfU5DTl+64PNgpi4eUAO1OomfzIIgoNvtMxyus76+yc7ONoN+j+Fw0Eg3PZma+ROfz8f2ST+gFglFohSBkMRWYLMaYxS29sWc1liyfMFxMcMYQ55nVHVFUWaMRsfkWY4pDOZBXWJ/0KxdH86y+1blmh7VWkdytti4FfJ9lPtI4MsG2lo3yQlt+eR822Ot5tLuOxfh1SiEwEhLWecIBNrUjwflrnZG/pinRFGW7O8f0endozcYsr61SxjFVNpQVYbSBGj38Yo1CgRxGNFL+6gwxOYzKlOvlAfwQH6AUpI0jXxOJZCEceAdlZLn5lakCpFBQBzHvPLKK7xw/TpBGJLEHaIwJiTgKEhRIkIQenlCbXHGIZ1AobDakGUL5nlBv99nfX0TpRQdFXF18xLCOS5fvsrlK9fo9/pcu3YFGQgvjvuRJmklbayJqHNDHVXoSuBcB4TGNzR8uoi9KDLu3r3FfD5lPNpjMtknTRNu377Nxto2UZgyn0+ZTEZNm/tna8+d1FNaqgI2o5RQSJQWmFlJXTpsqcGBsYbJaMy9wz3KsuTwaI/ZdIKxFm0qjGnahesfZN75iq0qWqyIbN/390ex8zodw1Je6lFIaG0Hg43m9xyfe1o9n9VC7FXIctUCCWkIUqArgynmPmp2jwG7tOSMtpD46TfFj2WLRcbNW7epreDaCy+xdfkF0l4fs6ioipLC1Gjr26J/XCaEII07rPfXkWFAbWqyMl86qXYDcc4Qh6GiP0hJk26j+esjAJWr+6WdhEBFEWHSZbi2xv/t//iD/OE//NOYWnPj7Rvc/fAuUxmxF/cI5BQlIqwGXRlsZJFWEiAxtZdLGi/m9AcDdi9dptvt0f/8F1hLu0RBwM7GNrubW4RBSLfTRQUSpHsEZCHA488xpk6o5jWFyKlzcK7vi52ZrDiqJ7PFYsaNG99DyoAgUERNj6yNtS0uX7pOGEbc+vAGi8Uc8zHkJp87qac0JQSxVIRNF9OWQeaMb0hmrKUuK4q8oChyFrM5s+n0kz7tT5c96aavZeOtzuKW0v6o62jrGGKWdVFnnVAbRbURznmOY4kn+bS4M49fIiHOPD5mM8aSFyVZXlBWNQ6BkAFOGIyTGCtxyEZiSD6SA/ZlGcKrZNsny5Msu6c4nLVYY7DG4Ix9aOpFCC89ptTKYLr7/nFiUiqUCrxG3voGV65cpa5qjvaOOY6OiYIIJYNGXkk1iuXClzLYRv1cG+q69oocDqI4JklT1obrXFrfJI4ittc22FnbQEk/htbZZtOqMcY0G9ezLQlgOdkDcBKrHaY2OCsQIkTICGcDeEoWaYv4LMdRIIQkjbskcUqadojjxNeFfgz23Ek9zNp26oplm/SVuSOAKAjopSmxCgjCmDCIECogz0veff8GlTHcPbrH0fgIrSvfevt3mj0MJjtrLbMOliryD1qIWicV4ttoWNncoCtYmZS+lYsQvi7GrbRyaaOjVZZh25zy7Pm2rYJKPBOw3cGftD1xUGr/OQ3U+1BbdaSfaA+0xksLCSqFsA/hkNIlHM0shavIS0NRQ20VKtlksPUaIhpRLo4osuMHftc4Sdm8dI3ecI18seBwb4/8LCGg6Y90/wAoEAptJbc/uMk3//t/RUjJLJsyLxaUecZ8NHroOFd1xWR6RJavSv04smyVRt/mjRRx0qE3HNLr9dBaczw6xhpLECrWN9dxuEZt3HfmHa5tsb2zS14XvHvvDkVdcnc84vB4xLwoyHOfnxJAFEf0B32SKCIMQ2ptqJxmOpkxny0Yj8Z8+9vf4Td/87eYzcaMx+Pm8gTIsIdQEX6ipwgCCEIqbQlKi5QRw8EGSdyhLGqy7PhxxTQeaGnaYW1tnSiK2dm5TH9tSBwl3kk9Ih39ae25k3qYtTTkmAcqlMdhyFq3SxpGJGmftDPAArfGx9y+d4fK1EyyCYt87tst/46g8Z2xR4XzWlWHtltA688f1IKmjYJaJ1W1XRzN8g1KQRx5Z2Wr0xTlBzmp9q5wK69pOnUwY5mktyuv0bZRTmgO9lFOpy1sfnQ29DMyCbL50qoL0RDidXKTcjAxTMuqaRMuqG1A2Nlh7dKbBJ0x473vUeaTB4q1xp0uL7z2BpdeeIWj/X3y4jfJ83unX2SrlaLa1dNSIBO0Fbz/7rvc/uAG4LDOYK2PNHT1cCZLVRUcjfbvW0yttSv3oU9sCqlI0y7r6+v0B30qXbN/sI+SiiAK2drZAueIYo8tR2HMxuYuly+/xI29m3znrf/NnaN7LIqS0cxr+GVZ5p2hECRxzNqaZ/5KA5XW1JXm7t0D7t7Z4/DwkG/8r2/y6//zf1DrkqrR0BMqQsUbqKjPKc2uIKKqHFJolIxYX9vG6JrxeEGRywtLZ3Y6Pa5efZFeb8Da2gZr6xsEQUCcJgjx8eQnnzupj7KzEMx9UYFo/i9OZI+sc9RakxU5la4oy5Ja/6DKSDwDO0tLf5DArcRLfoQC9Kp8+QqlqymyPvXG1evY5oDOgwjba9yiLC3b74EbSNH4qJVE+IOcz0dBeqfeJ0DKEyX1C9sm33cywisxCIVxgkrbpjFko95dG6wDIYOmIPbhi5RAIFVAEISEUUQUJ0RJ2kBc/pjOOJwu/cbBtV98ZTF2gqqsqPLV0PXRPLpzDmMeJTHpi/DDKKbT7dLpdAgC5R2M40Td3OGV4pMkIU58NBHGMVIqyroiyzOKsqauvIaf0V5Pzxrjv68fSbSx2MpSFRWLRcZkMmc6nTGfZ2R57ns3OXcCqwrh1ZiFFCfjHoSBZzhKL70mm24KQtyfbnsaE0I0MKjyhBPZnNNJUbQ6A/te/G7ruZN6mBmWQrHgWWApp1rA51XJ4XRCLAOCxYIoHGNwHCymzPMZ2hqvZvCDai0bbdXOriVu+dJV4fP7dnstSaFl1523HZT4qCYCugHsdGEY+WaEZdHUylgIXcP6M1BV/lPbPJFkqVgf4IkT/ebzM5YqE2fPCx7caSAOoBP7vJQ1vj2Dc76Lb3WOU1mFMVfHqVXOX3lexV2i3jYySNDFhGp+iDMX1Y/KgWt0ntyyqryqNePpAhVU1GVJmeVUVcm9W+9yfPd75IsJxeLoATRob2WRcfvG95mNjwjCmBdffZlX3niDrCg4mo4pqopiPmZ+dA9TNxp+toFjnWpQC+dXX8JmQJ5NV9AgCHjp5Zf43V/9CXq9Lld2dxj0B5R5wa3b73P39h2wjmvXLvPClcsMBwN2Lm0TpzFxFJIiSI1DVxq7WKC1IR+NGR8cgbZ0VEQiQqIgpJyXlPOSoih45+0bfPD+LebzBVlWs7a24zUkXYlFYy3UJkPnGZ3egM2d66SdPv3ukN2Ny8RRwt7dDzg8eJ88m5PnR9hHkC16VMvzBXfvfsholLCVXyIIE6IoQWtFJ91G0KOqSsqqWRBPmuddnD13Ug8zg1+0wC+MA/y9kgPNWpiVBQeVIWi2MAKBxTGxmrnTzQ7q018j80TW5oTOUsDPLLLtzxbNkyxbZt03Mm1bEzg/ulF4CLYDDALYHcJGDw4XsF/4NuKwzC3VTb+x1Q9rG07uNifUaX5v66XOikGvOo8HOakohLWO7/5bVVAVXqF/0bYwP2eMzq7vbSrtzBqsoi7JxosEyZBi/CE6n2AuzEnZxkkJ76Sak6qqmmoyBxGQzabMjkeUxYK7N9/h+O53KPO5Vzx4mJPKMz587/tIKbny4it89Q//LNdf+RyHkzFv377FZDFnsn+XQueYbOEde6X9RqIdZwfLndBqserFmgoCXnnlFX7///EH6HY7CGOQxnBca25/eIv/9c1vsrm+wR/46k/y+Vdf9+LBQYCSiiT2TqpjHWVVY+cL6qpunNQhttIoI3AVBFIxG82ZH8/IsoK3vvc27757A2P8BF1b2/XqJK5CW01dZejRXXQxI+wH7O6ss7Z5iUF3yO7mJaIwZjG/xWz6PuPxAc5ZT9q5IMuyBUWRI6WkrDWd3jpp2kNrSSfdRamKxWJKVU8b2LfdSFycPXdSH2WrC+05Ea11jtoZ7MpqanFoLBb7g+qeHsxE+4gv/Ejpl9Vx/ihIzDn/sO5+57j6ge2xVh3r6kOeeawW8K/mrKzwwqiR8GSJ9vxCBUHgSRotxNTmvh6HOHKeCYlQISIIEeoc+vRTm98NOATWCR/QaIt1FQ5NtshYzKaU+YIizzG1xpqPbiHtnMPo2msmV6X/t66xRuOMXsr6BAGEYTPGxkOlq7iVY+WzLvaOUkGAChLiNG0gvJgoihG6BikIAi8Y7Zw/pyiK6DS6es5aHL5/lNW+1tG0rENr0XVNnmUEQcgimTNLZwQqIM8L6lqja01VVRSFL/YOw9CrnFtL3aiNnzgd58dbKUkYBgShIgg9RVwIh3U19hFavD+uecjUfy9da2ptCLRBCEkUpwgR+r8ZgzE1WtcYk3OR1+m5k3pUa7XbmiZq7T1T45hjTrVhcDiqH9T4qQ2HQk6lDk42UOc5isbaHoKCB0RRnA7MjDuni0Mb3Vb4TrnJBLIcJrUXFl2tm2kjk/aDEnzU1BJiziqyt9+thXSP8ZJJrLwujJBrA0QY4YzD1o2DjEMP9wnhIceFado3OE+8aaOxJ0BihAQRgoh8yuhi6ekhvtgsRJs+8yIgyQRlvWCRj9DaMNm7zdHNd6mKBeP9OxgdNydRsGS3PNxm4xHf/o1f49a73ycrC0azKUVVUymB63dR/S5uMsGWjUZYEEEYNfOqgPriG60JKdnY3mXn2sv0+n3665tkC1+D1eskdHtdLI6rL1xjMh0zHAwYbPQJYonRmqpYYKqK+fSYveMjbh8cUZQVuql9HI+Oeft73yFOU9Y3ttna9pHPxmCDzf4mMggIV+SSgjAgiiPquqCaj1ksfMRs9DljLBxWaKwUOGk+lrWmrg3ZrMLqijju8+KLuwgEVVVS1SV1XXD7zne5d292oQSx507qUa1dYNoFubEah68oOW0/kA6qtZaBt/ql2+LZh4RKq6mdB1nLKAcfiJybt2rV4msN4cw7rRzI3dJRnvWCAu8sBngoMOV+wsQqu9Dij3t8+jhiPURdWkMMe15toDK+/qftJeYAFk27eeM/sy02fpQBOM+aJpki9D8v1hTQA5GibY+sCpgWsJjlHB8dUBUFx7fe4d73f4u6WHgCgY1Z9md/NGmP+XTMW//7m76+Cl8z5QC5s418+RVUJ8XqGnt06KMGJXyDQwe4EnR94YQRISRrm1u8/Pk36fUHniqfe6x30OvS7fdAwOWrV8jyBd00pT/soSKJtRZdLaiyjPl8wuFoxN7RqAnu/XhMJ2OyqkCqgMHGFmsHR6Rphy+8/gWu7l4jCEPCpjcUgkbhPMTYgqqYsZju0UqpnTWHw7VOSnw8NQxGW/KsxlnN1laXy5euE4VRc3qOolhQlsfs77/z3El9ovaQBfgH3trI6exzqz/hsQfjLHJ46u1nj7X6uwVqt8zVnkPWOBXtSU47pVUo77w8Wvv8qXMViEAhI9++QSYK4QR1VVHlOVYbT0d35xzjYeNy3xdvn/SwkqkKRBBi6+oCFmvhKd5CIkSCEAmQ4kTg5Y/yirIoqcqcqsypqxyti5Ud/VlmzEebh/7u99BOa6hKD6E6UFECQmFF0DAZm886WajvZ+oIKQgbtptzDmN9JOPbbAS+YZ/2cNUpDUWhMMZSFTllGLCYZ0xnC+ra0O2kJElEnmVUdd10l3VIIQiUwsoWO2kgOesZi6fOq2HGSRUQhtEJIxAERVl6uK+uMEb7PFRdIQNBXWtfBy4ChLO4Bg5w1lHXFWVZoJRgNlOEgSLPnrR9SlvD0Vapr0BE51iLrDvnkFIRRRFhGFFXNXWlPfvT2gvv6PDcST23R7OWqt16Ek1DAWeJzz0hDBXig432sDXn+of7rYX+2jedR/xajZoSluubXXl967jsynEecL9KJYm6IcEgZr23ztWta6RRyt133uO9b/wm2WQKeblsib5KtHjQ5rKN6E7Bk8vB1XlJdu8GMggwxQKrn5I0EYTIdAMRdpCyS6R2ULKLCIYc7U+YzzVlMWa+uIeuc7LFPs7OWMpxtA6qvVJPbnY+h1u3IAyJ4i7RlRcRSKrxMcXRgXdiuo3WJB6abCqrBSAdcRJz+co2w6Gvb5pmU8q6JIpTer11wjBiMpqyf+eAMl9CZ04Ijg6nmP/9G0RxzOTwiP3DGWmScPXSGrtbA+qq5PaHNzk6OMQNh4RCsN7tkuPIlHoo2Jl0eqzvXiZOU65ce5EXXn6NKIwxpeW9929R5gV3791lMjnEWstiEaIChXUWbWPCzmWcLdHlBGcKr614sMcsrxCuQpocYQ2j433KJ1ImDxFs46mtBY4DTmuCPdiiKKQ36BAGIbdvjfnw1h3yfM5oNLpwMeXnTuq5PZqtds+F5Tq1uhF7QicV4P2IwN8iDyLQ3WcWnxJpRUbPe2Obf1ptWOhWXi9Xfq7mjR6g0SmUIEwCwk7I+tY6r732GsPuEDWacWuaw8Ho9PvO5dqvHpDThb0nAUqLPSpsVVEe3/MHuogFQAaIZIhI1whklzjcJVBdCEKmozlimqPrEUW1jzU5ZT7C2hw/2KsyIBdgeY4tChACee1l0s1LyDDGVSXlvZu4UwotgiXNVjR6d44w6bG1+xKXLm+RlTlqvM+imNPpDNncvkoSd1G39zk+hrJaLsIOy2Q8Ynp0ExVI8gKmeUiSJkzHfcZbXawxTMZHZPMZSRAQIOglCdQ1wUPV4AVxnLC2sUXa7XHl2gu8+trnCFTI++98wK333ydfLDg6OmQ+n67AYwIZhISdIUGSYnWOqXOcKanrmtH4mHleUWUj8tEdTJX7erMnmhcBsIZgB5jhmPIoTkoIQRgqOl3fO68oC+7euUuWz5nN5g8tS3gSe+6kntTa+flIdLUfAGsX0LOOyLAsfFoVjG0X/bPH4P7nV9PhTzS9z+KFJ7CeWBbgBoByy2hwdX1pWc2rQcKDYF3nMNoia9MUaxqcNagwoLM2QOuauiips/zhC4dUDeTmPKPNnUNTb7+Do4H4LmiiOYezGmEqLBHW1b4luAVlFQKDMQWmWmB0jtXlCbvsQia8lJ7Nt6r9JiVhJ6Xb76GiGNPpkkUdnJM469U8hAzp9NZJOjsIKQlDr8vX63XY2t5mfWODpMyohSbKYzrdPuvDNeKkQ5nVbGxtEccdXwCrAnCOMgspZjVC+OA3m88wdck40oTO1/7UZdNqZeVrG6OZz33kMJ/N0Q2jU0qJUiFSSsIwRskAKRTWWMqiRCuv7edVL1YFiJcPZ8GZEmckztTgFBDjrMJUGkSB0RYnQlBNbZl5kqj27E0T4qGH9oY4fTxjNGWZIYQgLxfkRUYQBBTlnLKaU1cLrKkufD187qSexNoOrm3Bz9OjHp9+a2uE4HRaoH2uvcdWc0BnHdrq3F/B81YlER9rj97SyVcjJIunh3elV6OIhQ/TJP6GVuZ09NKewFlBg9UUyMrvprbks4IyCFjEcxbTKSGCeNDlxa98mWK+4PD9W9x761108QAwSEpI+xB3/AJTTH1751WMUzbfQwlPe9di+fen3ahajSvGGFPggpzchkhVEdoQoRKUUtTZPsXoA0y1wNYFzrbFmhcw0dMEtjchWcrXCyEZvvwir7z+MlGUcEtrslFGleeYeYFZFERJyqs/9KO8/IXfRRyFbHRjenFAFAX0Bz2SJKYocg6PD8iyjDjtMFjfJIoSrlyesr1zjTyvSJIunc4QgJs33ua9t36bqszRdcG9999DCDi8GZDEAXEcsru7zvpGbzltHcxmC9763vd5/733+HB/n8XcRyBRlDIYbBKGMb3+OrFMCVxINs24fetDpJRMRlOcaaLik+F0tMlV5yS6LDE6wFmJswnQxWhHMVsgF3OECgi7OyAEpphSLw5xj0xBPy9B21a1y+Y8ZpxtoJbnMw4O3icIYqIUumsdgiBg/+g9xtN3KMuCWi9wFyzd/9xJPYm1m452AfsBFpQ4sXZ9OkuQaKGx1mG0876FAVet9UD2/qefiAu0eq+15yjwi3tPermkUPjf29epcz5plRq+WtMkzvyOJzHUeQ1RRbEoKfOMMgwJ0pjtV17wSeS84ODd9x9M5BMSogQ6fc9aK7P7B0CKZQR4dtyeNpixBldnYCuMcTg5QIQCREhkDUIoTDWlnh9iytlTfNADLAphbQC97vI5KensbLFzeYc0SpkdjIkG+1iV4eo5JpsTRH0uvfgyn//KD9NNY64OO2x24lPafEWeMxgOybOMKEnoDgaEUcRguEHa26QoDb3eGmtru+AEKkg5Pi6YzyaM995lfLCHNZqj5njdXkqaKNbWe8CSPFAUJXfv3OW9d9/jaD6jbDYkYRjR7Q5Jkg5J0iWUIcopyqxkdHQMCMqsaiSXzjJ1GhVrB1YXzZxM8e05ujhTovMxUBF0hoT9ATKMwTnqfASP7KTOkRA7qXAX+ERvdt+76jqnrmukVPTHfY5GuwRhwGS2xyLbo66fjXj2cyf1JLaa4L5oGbVPq61uvM7Sts+LnFbvP3HmuYuCSNvxXw2/JB5Ca4t727IBAQSuKRJ15+fP2g1H26foPEq9c15JwvgCyxbRC4KANOkQKe2LQR9WcOucd05V6WGas8ys9r3t57QL2oWNnfPwoTUEUpJ2BoTpJmEQkCYxSklcfsxCXJxQKbBCvDGgC6jEEvZEUizmHO8dEAcR8+MjdD7FlgVON1pVLvKqBrYpoLW+xUV7SU8+JggJowQVxAgZ4ghwKIyVGGdxzk9YISVRnNDrD/zSPGoL2pa4r5IJw36fyzs7bK6tIRBkWc4iy5lnObMsIy/LE7Vz4ZopaB26qsnmC6RUVHVNrQ0Iga2ML12oa7AOeRLWe+kn/985xdKibXoocFY0+SjtHdpj5YH8YAnhG0MqFWCdxRmFs82N3oxnFMf0en1UEFIWJYu5F82typLpdIIKAoqi9tCjcA0sfLGKIM+d1JOY4bRC9w861Ad+pvj747QSQ8D5uSez8hpx5n0XNWYtcaLmdJde6ZY0cIBcNDl3Bx3hoxTl/OtYOT8DTPAFvJoTfcbTn2l9YWmloPa1O0II0iSlM0zBOo4HN1DyrFbUmWPkMy+d5GzDXmtMNiqhzjWagzQO6pyc1ZNa6ySFJu3HvHD1cww2XkIKiVISIWDfVkzvfuviQIJVJXlVwuLQq9aHMSQ9nAw4uHmT8vYY6QTZ4T753l2M1rhaN5p+GqdnmCpHK0dRSjLlaxSVc17/3kGc9AjivhdmlRHGKSpjKGpFXlkSLTxXUAjWhuu8+OJrZPMZ1ewOh7cd1iypoknc5Y1XX+IP/PjvJVQBAYp7t/e5c2ePm/fu8f7ePWqj0XW1THsahzKOYjLj6N4R1jrCJCFMfT4sDUM6YYTVGlkaQhdhlz1ncGg0C+x93MEQpIcprSmpZvsIUWN1hXssvT5/YwoJcTciSjpoo8izHF3XYJWHmB1sbGzzxhe/TH+4xu2bH/C973yLbLFgNDqitG8jpSJbFBgGHqWwc7AZF7koPndST2JtZP47xdpoqc39rEYYZwkIq+jFao5qNQ91YYsty0ipdaLtbt1acG0ep/lABRhx+jutfkfHsl/UgyIW55pEtT4VAUVhSL/bQzhIPrLXjvOO6TwqeSsH5FwTrT2jHVDj9EIVsDbYYnPz2qnzm6dDpLzA5WH1GkkN1dwr19uOV5dQjsV0zGI29huMbAKL4zMRQoSzFdZorPYSPJX2cYhrnBQiIAgTlAhwSIxTWCewVlEbSa0Fxgq8ypEgSTqsr28RhzFpmiJEu8PydQ1h4Nje3ODVF17CaM344IjZbM50MmM0mzGaz5oRa8EG50mH1lHnJbPRBK0NQZwQJgVKKeikxN0OzjqEcSgU4pRiTY25T0ASvEK9L9ZwtsLUbVnAk5hDSEcQKcI0QtSWslJgGsy+Gfa02+Xy1etsbO1Q5DlhqABDkS8oNJxEgCIBacFdPOT33Ek9t0e3U5Ba8++zkdFqLucs7N3++6LX3fMiOUsD+7HcUORief4Jy+KsVeJai6icdbYtpCiFh12EpCxKjvb3qbKcNE6ZpiOkENTOcvXVVymznLqqqarat6doWj5YYygWc6r8DO4vhdf+U9I7wMo9OyfVmEAiZYBSkWd1C8A5gqYI9smsmSChgl7kFeKlgaD0DsqJZhE8zVIRgUDFEpTE1hIrBFIoNra22djaptNf4/LVK6wNu6hAUemK40mBM5Y6Lxvmm8LJGKQiilP6ww3CMMI0Ea+Qkrwo2D84RADH9w44uH2PIpszmxw35IMlflzVNXfv3uV733vLT5WyxNaaoihJo5T13vpJS3ttDbXRZMWcStcUdYFxJQbbzEOHdQpr/bIrhCCNY4b9nu+GW2WUdYFFN3AfzbkUfjydwDP9BL5F/CrL5qH9Bc41Zw26niKLfbSusHoCtkCIChmlSBHQ6a+zsb3Dzs4u9+7c9E4WGocZ+XvBOrANKvCA3mJPY8+d1HP7aDubC2kT+ktC0unXPYg48Qiw+VmuwhMt0a1ToTm3lj6YORg3SYMhsMbpKM+wlLKDpTNuW7bUeMagUiADJpMpb33r2wRNb51AKZRUXL98nR//mZ8hDCPGR2NGx2N0bSjLkqooqYqce+99n6PbN087ISkhCSEKvOSTMc885ymERAUpQdj1zHAFQjjfnO+JnJQnYCCa1iWvbsFOD3QG2QFUCygtzLRXC1m5wkGkiMPINwU0IeXUExG+8EM/xu/5fT9Fp9dj8+pVhrs7lFXFzQ9ucbB/QJ4V7N05YjpeNJ/vL9yly5f48pd/iI2NDbTRCCVRSnE0HvPueyOqsuT41gcc3HiHusxYzG9izOlKvWyx4Jvf+CZH945Ik4TdrS2GvR7j8YSN3gavXn2N8WLCrcPbFMWCrMqpJ77RojHW6/jhMFpSW4mSijoJwPVRMmB9OGBjuEZZl9w+/ID5ZILDYE8msAbGwNQ7KdNA126V7dNOWomfqI8Gt1lbUsxvUuX3cM763lvWIKOUpLeOihK2r7zK57/wRa5eu8LoeI8oanZ2MkQGXRASV01xZsKDuyw/nT13Us/t0ewsfBfgF9BVgdlVW4XTVt/7LM9v9d/t4t5GUqt1UBIfRbVFvO35Ok6LB7ZsxVbR4kSdwrtS34xvBnq5c1VBwOXdq1x66UU6nR5J/xCVHFLXNXmWk2c5xWJOdOfW/cMmgKDxFI3q9jM3IX0TQxWhpBckl8KiVPCEn99ceKG8QOxaF3YGUEpQY8ibXcECTm1DhK8xCmSAtA4d+NycUorNrV1e+dybdLo9kmGHqJcyXwhqXTOezZjPMm7fO+T4cHIihwSAVLyW5wwa2SHRQKlFUXJ4dEyeLTi6d4eDOzcxVQaMOCs1UmvN3v4+utD0ul0frzhHWZYkYcqwO6S2BqmUR5+tQVf3M+PaTZBzCmtrfzsIQRxFxGFMWEmCCVhKTktTt4nXM8c6Ze2kVSwlnB/BUTiD0TPO9oUUIkBFMWHSJ+0NWdvYZHNri16/j2wiKSEkyLDZEDgP811gi5BVe+6kntuDbZXR1+aiWoJC64SW+V5vqzJDcBoqc5x2ag+IEtyZnw89v1aWafU+bVMKqw5qddPeRoAzlnIXrWTSgiXMv4qgtNqA2vmeVYGCsoZFecpJOaWpFjmzeYZxklrXSCUQBvLFlOPDQ8oso8gXD/1+IggI+j2kcdhao4vct4C4AAvjhMHWLkmvz8bW62ztbrO+PkBK/7UElqTbQ4YdUIlffB6V3qwksjtApD3oxUAXighXBVjT7AjaeRPg8xi29lCY1dS2QiHY2lhn/dJVummHKy+/SNRPUWmESiKCOCTUIWEcESYRKi8wxYJifEQYp3TXtgjTDml3iCWk0pL5oubwcEqeV8xmGcY6hFCoMCXoDCGIsHWOq087Zikl3U6HtbU10iTxTDgDVaUZz6aMJxMm2cwz9x7BnIO6qigWGbVU5MECoaDWFXmV8WS9E9qJ/pCb6iNNgoxBKHqDLV569VWG69tsrK9z5/ZdFvM5h4djOv111rcNlVYUden1Cq0D16oyrzaEuxh77qSe24OtLVqG5ULdRiGwzN+0TqqNOuC0I2odRgsDtsc7B8974C26igO292Ej4H1yPqsswoLT9+3ZSGuB36CGwCW8o2qjpTmnI7/VY1QGsgKshryGSeGfay1QZEcTDo+OScsaV1eoSCCtYzLe54N3vocuS6rJ5KH5JhVHdNZ6hEFINZuz2NvHmItJSie9AS/9rq+w/eKrDHtXePHqawx6u374hCch7N3bIkjXIJyAyT1Z5BEWQBGEqJ0d1NYl7/EIYKqwpkQb6Y+wZFKD0P74RqELg80MoQp56ce+yu//vT9FbzBkeOUS6c4AFYbEaYc4TtBA0u+Q9rvki4xqOmJ25yb9zR36119nsHOF/voGhpSsFByNCt6/cY/ZLEMIgxDORwzpgGjtCrLK0fMFtd47NVcCpVhfW+PqlSuEKiAKE3TtWGQldw/3ube/R2UqyvPaaZxjzjnKrGBSjwEoWFCSY52lrJ+UBNHWTLTqAk/g6EQAwRBkysbOy/zEV38vL738EuPRMd/+1nfJsgXz6ZT1ret0Bzsc7R+wd/eOzwO6AI+fW/zNs3iyc3iAPXdSz+3BtloDtbpGrS7gq3CZOvOas4/29YLH3/Cdhzy1Tu+kt8fKo42mHmRtfrmFANtzbxmDD7rHrPPMPo3PG1X6tJMyDlNUFIVXLVc4VEPYq8qCxWyCqSpfI3WeNZ8rpUTFEUGcYKoK8VCduMczFYR01zdY271Cv7NDb9Cnm6aAa4pVLWEUI1ToIR1bsWxD8hEmJSJOkN1ew1SkWTeVz6mcQHHNA9fkMhxOa0xVoZSl2+lw5doLDNbWEIMuMg4QSiECiVQSGShUEBCEoa/rMjWmyHFaE4YRaadPGKU4FNoIqsowXxTMZhlRJEgShRASqUJklKBwGNXSV5cmhPTHSxKUVCihGuKlJS9L5nmGxfDo7U19E8HaelgvdwsWrt0VPamt7qKe8Dht0zIZEyc9tra3uXLlElVZMJlMODr05c1h3EGFCWE08Rs1W7Ja43V/m+6nt+dO6rk92E5YcpxW2ZArz4tzXs85Pznn9Q8z4VmtLdPOtfVQZ+H6giVJov3bqs7SOcdFsGxBH7DMOQfAuvB5ocrB1PkeVUpCJ4RINRGi9TVMxnippZBmQZY4pZgtJty+8TZxp0N/0Kc/GKB1hbEtFPIA8SfbQInOYWpHZQQmzNCLzLcAeQYWBILuQDEcBBR5xnQ0oiwKinwfW8084cFWD2cZCgFhAkGEi1KsAb0oEIGEjoKm/5IznE7++y+Nb1svEIlCRCkiiBibjHc/vEF33CdcHxKsD1FBQC/pkcYd6rrGOEm3O8RsOK6//kXCYEjUHZL2h/7I1lJVNUpV1LX2fb+AOEoZDvooCeX0CJt9SF3OsdWYduJIBEIInDEcHR/yvgrodnq89MLLbG1s46RgfW2T+bxonJT2BAlrqLVu+m5Zr4m4OlSAdYaCHIdDu/MinybUFEGT56n4aJXi9meMx64dHip4xOjbGbALwGD1jLrOfWQnHGmnQ39QMZ/OGB2NqcqS+XSKtW3Fe8HSQT5af7HHsedO6rk92NqIpM0htBDNKiNuNce0yn49u7F7HAeFP67ogVxr7p8jcGfnf9st+ayjfBA0v5pHWwd2WTIV2+cvKbiiYOHgXe2dVKRgswP92OehpnMP9TnnVSwCPDNPhTgpOJ7sM/utKWEUcfWVV7n+2mtYY9G2YCn2eM4JGgtZCaLCCEE2mfsmgdY8MycVxoK17YDtrYijvRH37nzAdDRiMf0AWx5D3cKSDwl9hYSkD50hqBBdgRgtIFXQS6ATgHU+32MlGLkCpRo8nVoguj1Uz3c93qsnfON7/5s4Sujs7JBubROEIf3OgF7SQwpFJBPW17dJ4zXk717n2ms5tTEs8opaG4y1FEWBc1CWFbapuUrTHjtbVwmUYHb3O+jp96iyCc5WJ8l/JSSBkDhtuH37Q/b39rh06TKf//ybvPTKy0Rpyo0PblNk7UT3P8uqYpEt0Eaj65q6Lk4+tzVNzcJNcDjsuePaTH7RAVGCnfBgJ3X2pkrxuy6BJ4I8YK6dNadBT4EZptqhKKZkhdfhGwz7KCVZTBfs3b7HfDZvCBftDrFm2Yn04umoz53Uc/toW2X1rdY6PcjpnIX4eMhrH2ZtneDD6qseZ+1uj9NGhSnLAmXwHxQLTzO3eN0/gS8eigJPD3cW0ap3C+HLVmhe0+B6dV1S1yUqDCmyOXVd4qzDGL3SyO8Bu027/Jt9rC/36CYaJp1SChVIwkgSxgKhNHW1oCymjaJ1/WiMLSFAhV6PUHhYz2ndXBvbKHu4Bt5rirFaKNDByWRR+GgqCKgwzPI5RV2jFx1Mt0ugI6SIEC4gVCFhmhIEEXES0B0IhOxRFCWVGaFNAQ0sV9c+svGKGopABURh7EvShMPpvJFeWl4TP1U8xFlVFbquKcsSqeRJ88IwTAhDL6Ukm2vqrKBUNTgJCpw1uKZhonWWVvRIPxSLFn4cT4QbVzH1B7z+FO22LQB8jOVdgJTOX0rpEHiFdiEFURSi4xilvJq7qXXTWmT1Rn92tRLPndRze7C18/6sHmUbdbTW3tvByu8tWaJ97SqV20JgIHTLVO99oIcDl3l1lpO88KPa6mc+KDcVhdANliK0ofDOQ1sf0SjnndhAQhogOiki7RIlXXpr64RAURXMsjlaa98ufupZau3nO2eZHuxzW3lK+Wz/EFeY5jMe4/ucsrPc/sdfHJIk5voLl3jji6/Q6wxJoxBX18yOj3j3u9/i3oe3OLh7k6o0LFs3nDeIzbnIiGC4hty9DICuSqzREFovHbWwniARNDJIoYC4gaFWuipLo1CEKBkRdLqojXWCKKGzucHa2jpBEJJEKWEYEogAqfyiba2lrkvK0kdN3U6PJO6htWV0PMcYi0Dy4gvXvOOxkvl4htGavIhQ6SuEYoapj7DVMWAxzkMDcRhzeesSm8MN1tY20JXizp0Re4cLJrVkITtIpwlsiXQOp0Lifp/IOaJQkkS+g+/R6IiDw320qTGPxX5TIJqunScR7WrS1YGMEWoDISKk6hKoPuDQVYauHrY7lKBiECHdXpfrL11lbX3ICy++zMuvvsLVq5eYdFKks+SLnEhJuk234tu3bnLjve9TlQXLNgLPxp47qef2YGvJEGejqNYJtPdMu062vZtWe0y1GzuFRwQa1l3oPDFPsmyue8pJWXBz76geey1uGYiKZX7qVG5MeCfV60IifFuPVPoc0ziDedEwBwXUApIA2e8iOkPSTszu9oBuGjGeTSgP7qCLHO7N4bCEXPtxCD2yNbpzm+m+Z4yZTOPy+qny26cvyqMx7s5a2kl47bUX+LGvfBGnFa6IsHXNaH+P73zj13nv+9/F6LpxUglL2uPZk/bcfxHEBJubRC++gLMaN9nHLiYgnNc5nAJRDYMAwrRJm8hlbrNZb4WRKCICGaP6A4Ir24RxSn+4w9ZwByUlQkiEECgUUnhn7ZylqnLyfEYYpPT7W4RByvHRmA9v3mQ6XXD96iW+9IVX6fe63Hr/Hm9/5waLeUaWJQSdL0C4oJq/ha0n4HyWyTpIg4AXr73Emy9/Dqli6lJx44MjjmZzjqqAqeoSmJLE1ihrUFFI2ukhA8XGsMulrSFKCt5657vsTe5QmuIxaebKQ39YEG0pQAux+esvVAcZXULIlChKiWP/+mI+wdQS96BoWChQXVBd+lu7/NCP/z5eee0ldra3ePOLb7C1ucl4OCZWkmyRce3qLl/84ueo65pf+6+/zt07I6pygmf0Pbt+Rc+d1HN7sK06pdVIavXvPOC5ExkhTkdjsGSwnXn6PjvrXB7nvJsDq1ARyBDhBFZYrLC4QOCiEBuqpnhWephPC19Iq+RSZy7By/oEAQQKEQbIKEQlEbJqyBRGegirrRlZSVOYqsZU9TKn/NSoiDjn8QiLgxAEgUJKRRxHJElEkoSYSlAV+JxZXVPkGdl83rzpQZju2bBaIlSAjCKsaeC89oueMPrcg09/9bhNwa2T3slb6Y8npERIhRSe0CCRCE9vABzWGozRSGnAeVaec1DXppGlgjiO6XRSwiDAaENdaYzFExRE5AtUV7+hEEgpSaKYbtrFiYDSCeraoLXFOIEVCiMkRrTFxwFhHBNEAUknpdvtopQgiqLmu7qzt8H54yuE/w44P69cmwi+P7nbshSFjFBBjAoiBBYp1cn0EMKPn8N5AomzCKkI4gQVdel0e/SHQ4br6/QHQ9I0JYoi4jgmSeKl6nwcU9c1cZI043U2B3Dx9txJPbf7rZ1zEctwZ9VhtHBeW+8SrDzXbtqildeu6vs189iyrOw4tUc/oSbz5E6q/RpScP2lF/niF75Mp9PlOB+ztziidDWjQcnxoMAGzkdRifSLgWwirEr4xP/cQyI27SLCiFwYDsZHTCeOrJhTL2aeTl6vMODaje5Zu1DYvsk5iOYWdi2Wer6lnZSXPv8au1evsHXlEiJ07O3dQpgIUfYRJsbUoII+cbKJMRmmnuLORlEybiCiJm/nHKgQ64Rn0FUVbjyDo2NIFFzqwiACYUEWYDIwFSeq7q06uhXY1FJFGh1UTLJD9G1LFCXISqOIiIOYYbdPP+2gUCQkRIRUQpNnNcfHGUrVzCYWpeKmrURFGIJnlzsszkvgSYWTAlNPqBc30PUCU3lBWykE3TAhDRIGaZ8k8JNZSUknSiBIKZ0lCPxENTIgj7sIl9Ld7PPCi9v0OjH9NGa9lyJwDPsDOjJhFed1eClZfQb7FUoSdboE8TpGF9TZMabOWIadqz8hCBSdToQMYuI4IUm6gMWUETkCZEB3/TLpYAdrLYvpiGI+ozcY8OaXf4irL7zIcDjgpRdfZG0wbByrn1dxErOxtUldVRwejLh39y7zecZodIwxnmjhGYTPxkHBcyf13M7a6gY5whOFFH4errLrVp1RylK95WzbjAqP550hGbW16YIz2Y42X8z973nsryIEL7z6Mj/zR3+Wrd0d3j78gG/fe5tZteC97B7j/A7WGYglRA3jrNNpclMSBjHkATiFsyHOepp2dXiIKHKsqbAm9/Uidb10Uq0HftYmAhBtkVjBw3TT0m6HN374S3zxKz9CmMTIyLG39wEBPVIgxKG1IwgHRMkGugKrj3FudQES3kFFA0B66MnUOBn4iKQyuKLGjmawdwTDGF6IYDP2ebg8h3ruc34tBNX22gNM6jCxRgSCKjtgMjskUBGBiwjDNTpxh07cIw5SAhmQEBERkImSbFFzfNxQPd3ckzecp4QHIUgFrnFSVgickDghMPWYav4OuprTirZKJL2ow2YyoJv2TpyUVJKkkxAmPTKtCQI/UY0MKIMIJwTh1hYvvfYS22tdQimIpcAazVpvSFcmjdP35mkU1X1OSipF3O0R99apiwmmuoepJs1f3ZmfgjBUdLoRYRQTxx3StIezhmIWNaK6Ab2Nq2xc+yJaa+yHNyiqu/TWd/ix3/N7+N0//iMoJX3NmZR0ux2CRv4oTmK2tjexxjIeZ+ztjTk+GjM6PsKYFup7Kvz6I+3iKgRX7Pbt2/zJP/kn2dzcJE1Tftfv+l38xm/8xsnfnXP8tb/217h8+TJpmvLTP/3TvP3228/iVJ7bRdjZ+XeWvXc26n8IxLd6iJMAazXPJc95PAozUAoPy6UhshsT9bok/R7JoEvU7xL1usS9DlE3IeomqDg40XI79VDKM9WCEMLQ566iwMOAoWfzGWHR1nhq8cnj2d6oS1tdpB7984TwDf463S5JkiAQWG2oq4rFfMZ0OibLFhjT5hYeUBwqWrKERAQhMk6QYQTO4soCVxW+hkxrX/SM9qQJYZZjJUAEASIMEaGHUWn7bxmD0xpbVugsR2c5ZZZT5Bl5npPnGXmeURQ51uiT2mBjDFpr6rqmriqqqkQbjZSCIJQgHFVVUxQVVVlQ1xmmzrGmaKjnZyJG13x759BaU9UVVVVRViVlVVDr2jPiAkUQhYRxQpSmhElClCSEcUwUx8RJTBz7RxIlJFFCcF/RsL9xpIoIopQgTJCNdqKQCiG9oDFSNay/9tFOBXfyEDiExEtxieXNKFSICmPfCFJFIINGs1F5lqcKfL8sFXj9xCb3J6VsiqYDnLVk2YLFfNaQVFYhEm+BComjDlGUos5t9eIjO1TkUYtHsAuPpEajEb/v9/0+fuqnfop//+//Pdvb27z99tusr6+fvObv/J2/w9//+3+ff/bP/hkvv/wyf/Wv/lV+5md+hu985zskSfLoH3ZWYue5Pb2149hGRlOWdVGrBbqWJUminadtPn/1NWevTeN4rGtEsAGbgOusfE77+nDlOG2U9iAbpsjXtmEtZb035NrOLt0kZf2ll7gnNdN8zJEpEWFIQIRyQFn5KKhV7RYCXOAfVoKIIGyelwqE9KKpdcfnsMoCZuWzZN+eYy2uKvyC3+7CP0J9WghBIAIiGaNkQECCsiHHh1O+99vf4vhgwuHduxzuvUWZH2ON7/r6wONJRbyxRbK+hhOOslxQv/MtXF3hxkdQF16NYyFgNvMFwXUFRiDjhGh9Haki7LzEHmW4ymArMHcn/msUDjKwKmCU38QsAsIw4qDzPr2kQ7/b54de/yIvXXkRR0lVzsgWI2jEbQWSJE0YrA+I4gjrKt557wMEiv2b73Dvxm9SLKYUsztYc1pw0jrLrMrQzpDoHHUnZl5kIEN01MUGESUBdZgwvLRNnET013pEccjOWg/VHaDDmG4nYn3g2XEvvPQSn//855hNJtw53OPOwT1q2xLRQ4IwZm3nOr31HayVZCWUlcbYAJdeRoYbYCy2LSJ3GZgxuJqqrJiNpiilsRuSuNtBCIcTEl+xvqJD1W7EwpDCGN5//xZhqBgOBrz+8itsbWyQBAlhEJ5Eig6BtZbJbMTb3/1N7t6+w+j4HrpuMX9/0wYq5NKlF9ndvobWNXfuvc/h4Z2GKOLHV4QJarCFSLq+Vcjdtz5yxl+4k/rbf/tvc/36dX71V3/15LmXX3755N/OOf7e3/t7/JW/8lf4I3/kjwDwz//5P2d3d5d/82/+DX/8j//x+45ZliVluaycnk6n/h/tIvZsykl+51rrKFqkpyWUtZu31TFf7cDbvqbF8s46qZUoy9qmJEjg2V5Dlnz01fW2Zds+KM/THnqQIN68hLy2znDjMq+/+CZr3QFKKg6EgXxGZWpEGBAQ+tKdsvKQlRKeLIECp8BG/mRE6AkTSnhIUAkfYVUJRAJmFjL5cOd54dYOyMqvj2ACgRIBoYxQIiR0CcpFzEd3+OZv/BrvvP0utlhgZse4qr3wD3F6ShFvbtF76UVsXWK++78ob3wPZ9qIyUEtIa9grk4dT8Up0e4uQaeHOZyji2OcqNCTOfZ4iqu0L9KegxUBk/I209x4WaSGOLG1vsWV9Q1evnoF5yqqakGeTUAopIoQQpF0FL1BRK+XMh7VvP/BhxSZZnr3Oxzf/Dp1MQVnmohgSUiwOBZ1zqIuiKoMh19zNJIZigJBPNhk85Uv0V/fYthPuXp5g143Jg5DgjRGB4qg12Vta0goBVeuX2P8ystMxxPmNuf9ow8pbU1LiVVBh7Wdl9m+/nnKouTO7dvMjkd+85TsIqXC1TVikflOxeYYjGfV1WWNruYIqQmTBOtKT88XEkTrpIJ2Ipw4qdJYbn54h7LMuLJ7iZd2r5PudIiDiDAIfO+oBhrFWOazCe+989vcfP/GCZFimbB2KBWws3WVN17/EcqqIM/nHB7dbS57E9EFKWp4CTXYwpn6kZzUhcN9//bf/lu+8pWv8Mf+2B9jZ2eHH/mRH+FXfuVXTv5+48YN7t27x0//9E+fPDccDvnxH/9xfu3Xfu3cY/7SL/0Sw+Hw5HH9+nX/h48LYfmdbg8ik7XR1UrJximHtArjtRp7rWpL23QwYkm+aKjbJ8rm8PBrvAotBgKiABcF2EBQoiltTaZzpuWMSTFlVs1Z1Bm5LqhPVL2FxxtXH+3zSjVOSi0LUKVYttNoEucNLvSpm4tCCKI4ptPrkXa7BKGHVzwy5PxCYzzkV5UFuq5wdjU0vu+IfuFTCiclzhhsWWDLwkdQpm3z3obIDioLhfE/jR8jpy0u19h5jcs0rjC40uAq619XO19vph0YiytrbJZjsgy9WFDP55SzKbPjI47395iOjrCmJgxD4jii1+swGPbo9lLCUCGkwGGp68pDdXWFMZ7l5k7VHi2/s//NYaxtGhiWlHVBWeWUZY7WJVJCFCqCQKKkQAqBksuHcA6jDVrXWG2wzWcqJFEQEqkQ1ba6ED46lSpEBj6yCqOYMI5J0pQ07RLHHVSQeCcsY3yX3hgIcc53G7bWorX2n2ltcykcpi6pizl1scDWBdgaq0vKfMFiOiObLyiKkrqq0dr4ptMOtHbkRe1VPIwjiCKiJEGF4QlqolTgqe9JSrfTo9/rM+gP2FjfZHtzh/W1TeIooY3kpPTwYkvO+Ci78Ejqvffe4x/9o3/E1772Nf7yX/7LfP3rX+fP/bk/RxRF/PzP/zz37t0DYHd399T7dnd3T/521n7xF3+Rr33taye/T6dT76hWm+09t4u3VgqpRULayH6V7ZfjnVT72hbyaxyTcCBjX9BoASsbbEyzJGK0TkuwdHgaaPOyZ6MosfIZzWe6rsSmASKJOK7mfOfm94iF9EW5gfHMLldibI61hnE5wijZ4OIR1HGz82y8o1KItIuIuuCMJ0g47R3TIISOAJ37kLAyfgH+lFkYRVx7+RV2rl5lMBwy3Nz0fsIaXF0gdEV1jmzP/dYMuJBeo687xElFMTrAzO6BrqlG+01ovGKVg33ji3kTCZsSUoE9zCk/uI3QgSdazArQFlvVuNwuI3GHH9/p1Oe4VvpbZYcjfjOvufO/vgEyxcS7XLl6hW6vy5Xrl+kP+tRVzSJbUNcaYyqycsyiyClrjXND/I5oipcPWtX0Wpp1hnm5oDIVFkHlBAZIuwndyLLeC4gigakKclcTiZTOMCaNJFQ5R3fmYAx7H97l4O4B2XxOqAOu9S9T6ZrjfMq4aOuMWrZeyGBji7AzQClFksSEQUg2yziURxSLAl0GVAacrvCOqoOTAUWpGY/HSOnIswXOaZyxzI9uossDrLPk2QKKjDoPONAT5vsJerHgw5c/RycZMFjrk3Q7hCpiNMu4dTBikZdMSsH1N75Ef+cSh3ducff999B1zWC4wdr6Dr1Oj1df+TxvvP45AK5e2mYy+XGOx8f8z2/8d95+7/tIBIlUxCrA4pjfN+L324U7KWstX/nKV/ibf/NvAvAjP/Ij/PZv/zb/+B//Y37+53/+iY7ZJh7vs+cw37O1lqW3Gm+3EB2czhW1iE7MEl1QgBTLJG7ocKlH1Kjwhb2rqhRwGtob45mBZ9fQ1mkGeOcWAqnAxT6SmuY5s4N7iLryqgexBulw0oDy+KOrnIfvXONNTfNFVVNkKiUySRGdPk5XiLz2Uj+B8GKzCJgq/53rRqXiGbd6f1wLgpCdK1d47YtfIu2kdIdDzyhzFqM9/bCuywbu+ihrLlIQQ9IFIajGt6gmd3xe77zvroFj54V6+/ii6RjsuKR6d+Gfh9PvvY+k42CRQZaxSjYogLc+vM3bQtDfusLnfs//g91XXmVja50vfOl1tnc3OTwc8fb3bzAZT9Gmpqhm5GWG1gbnevgdTtvT5XzM1jpHVhdkZ9poOL1GGjgGaYAQYHSJNQKXBqQBdGOBnpdMxxN0UTHaO2R0MKLIchSSne4mtakpTcmknLDabFEFAd3BOknfM/cGnZgkDJiM5hQzjTMhwgXoQjaixc24CKhqw3w2QwhLVRa+kNfVZOMp2aQ8Nd4awWgxZkyAcoL9vQPW1y9hgZ1c0+nAbFrywa0jRrMF81Jw6aVXWb+0i3Oa/Q/fx2hHtztkZ+c6g96Q61df4KXrLxAGIa+99ArOOW7fvcUHtz7gnRtve4UxKUmkauDCj7YLd1KXL1/mC1/4wqnn3nzzTf71v/7XAFy6dAmAvb09Ll++fPKavb09fviHf/iiT+e5Pa21KMhqDeEKxH2Kkdfm9FedDuDCJuejLCeFne1rVo/rzjzaSGm1rcaqBQqGHeiGMOz7353zTDMMDuPrc5Tz+nHtZzqxzJ85mgJcH235ot0Q0UARwjVV/ZKmSFU2fGYgDHBx6DX9YgGJA2mbSPBT4LB8RWrD0lJEYUSSJBhjyPOcuqopigJTV56JtwrVnWvORzat2K0xD3/PSkR9opF4Qsxx90deDzN38r/lU875aWEMUgrCQKGkwDmLNv9/9v4sxrIkO89EPzPb05l9DI8pMyIzK2vKKg7qJimRRKuvhiYuBIF8EgTwgZIeKFAPAiVABAmQGghShPRAFKQHAtQTBQ2AcAHpPjX7Crx90U0NpEhWFYcaMqtyiozB5zPu2czug207Z/sJjwiPDI+srGIs4MDdj5+zR9u2bP3rX/+qqSrXMiXLMop8gc6nmHyOqeY45+Q1mZ7+XllrlgXEQoCw7jpXZcEiTbGmgqJwcKU1aGOotKbSNdpajLVUWlMbsFYiRECSJAwGXbQV6KyiKg1aQ1XWCGMpyxqtTQPjWayPbn2NnLVYA7oWCAxGFyydn2/F4TF6n4ezFoumLHNOT0/Z39/HCsvVxTWiXpdFXpEWFVleURsIoxhrNUEQLjsg13VNkRcUUYmQgqTjIj9tDMYagjhCBiGIEEtAra2DFPXFooxLd1I/9EM/xNe/fjYZ9uabb3Lr1i3AkSiuXr3Kb/3Wby2d0nQ65Xd+53f4qZ/6qcs+nBf2LNbupdZWC/fQXDsn5GuDclYECAW2K9CDAJFILLVzHG0ZJZ8S8DktDwOaZj87zXtTXFTVttEQ/sxn4OUrq8LjunJkCFlCUDmYaaBcBCSse1mgqF1UZaxTHi8KUBGiO0D2txx2jkGUcwQGFQpE2PQNafTs9EhTX1lgOwl0cwjnUGiY1HBaf8sjfZejsNTaIIRkY2ODG9euM53OeHBvnwf7B+zfvUt+egTp2Dkc86iDtm6DRQ6ThrhUFI+f3xUuguqyEvN9DqakoBMrBt2QKDDMFmO0LHhwcMT7d+9wfDxmtv8B6f2vUM6n2LLC6gIe6vVycTNak6ZzptNTlBQEgctJHVVz8sWYMJBsRDG7SYIECl0zLVLSLGVR1aRVjbaaRaWxxERRnxs3rvHG5z7BIiv42lt3OJkcg4XFRCKsIF3kTGdz8qzAVE5FwzmpBZgDsCXaB4aAqXOwhVsYhj2ImhrAIoPak2MqoGQ6OeYPfu+/89ab3+Dl1z6B6fW5UUn2j8fcPZwwXaTE0tLrb9Lr9Ol2BwgpsdYynUyoqjtsjOYYNFeu7xCGIbMsZZEXyH6MSAYQbaJlzCKvycXU5TAvYJc+bP7e3/t7/OAP/iD/9J/+U/7aX/tr/O7v/i6//uu/zq//+q8DLpn70z/90/zSL/0Sr7/++pKCfv36dX7sx37ssg/nhT2LeefRjno8BNhuIe8rcnNWTmoZCQlsILGxdPJBlVitvH201c4ttkkYnlRRNdtdd1KdBF55GT77KpRzmO1DtQCrQTbhV9QoR4StuhQfGVrTzE+Va2BoQagAEfddvYnJoc6RUqAitYyultYt0aM+NlAgJdSla+VRW5h8HPJUbsWujQUh6HV7bG1soivNYpFxsH/I6fGJU80o1i/uw9sC64qWdfNZ84QoxC9oPLJ2+f3wgIYgEko6sSKQkOcLakpOJ6ccnRxxdDwmP3lAcfoeejG+lH0aoynKnDxPURKiwJElqgLmM3dMZjRiaydESkmtNVlVsqgKJnnJeNk6xAARSiVsbW1y86WrjCdzvvneXYoyw9SWOreYGsq8JMtyqrKi0XNqXgWYYyBzHTcemvsVBBFEI7coq0XjyDzMqcmyGW9/8y2EuMOiqnn5u/9n5GiXw/Gc42nKPM3Y6im2+n0CoYmiTiM9ZUnTlDS1WO1WnKPNPmEUUU4MmdDIJISoA0EfIyR5qYGmu/UF7NKd1Pd93/fxH//jf+Tnfu7n+MVf/EVeeeUVvvCFL/DjP/7jy8/8zM/8DIvFgp/8yZ9kPB7zwz/8w/zmb/7m09VIvbCP1tp0cq8m4eEcn7PyDL12Tipg1XPpvMLeNUb10vF5B+lrLAVNq3oFw75zUHtbECuWhZgCR3wIAogTl28KRLMP6xxRVTUPqnW5JCtcnqXfcQWGSmF1tYq6lMBIkD6SMLahWVtsrbFKuloqKyE3kBlHGPgW+SchJf3BkN5gQK/f59r1G1zbu8pgOCBQAUVRorWmkyRsjEaU89myHubRtvTqbgFgGzmNJ7Xx8Av1Anf9pXXMvdyuQbey9dP//ihNLOcshRAEUUwQhoRJQlFkTMZHCCVQc4lUgpPDE7KTA6rpFJ3OmuLiyzHfq2q+mBNHIWHYQQQBuqooiwJrDKdGc7cqUMDRbEwmoQgldeUEkVaD36DrgpOjI+6+8x6zNGc+c3k0U2uqvMCUmrossdUCdNXUcHi6f/aIa9UyU0GdN3D4w803ZRAQdzcIwgFhd0BeVkynU4qiJIxCOkDSCUjigABNnPSJOyOqWhBHA+J4yObmFsmgh1ECLS21dP7QBgGDjRG7V69QGcOi1pRGP3n8NCas/Zhley9g0+mU0Wj0rT6M73xbj5h8RLWBg3F812iFG/MzVpGUXzkPFFyNoCubCa5aTvZou2IIuqagZyntxzjiFaxo6qMBfO4NeOkmdALYbXJSuoKqkSgSGlTjaEzm6klMm8FMBgABAABJREFUDeMpHJ24VWjYca8ghK092Nh1J1oaRGVcDq0XIuIAYSyqrpDGYMsKvciwVYWtLSbXzuF98wi+eAemuXNSufmWOKoojnnje/8nPv3572YwHPGZz77B7VdfdfmSuqI2NWVZcnJySpqmvPuNt/jP/+//yLtvvXmBrTdslaVeoJ/sHmFe8siPhUi4RUNmYWwb6Si/kvGDxmPJvqtsm6njvV5NEEVsXrlOf2MLFcXEoy2CTg9d5hSTY3SRkRcl08mcsqowRYbOLg4xPcnC3pD+zU8QDbeb9hYvMxj0mY3HHN29S5FnkC1gMcbqmpmRjK1Aa0M9PqUaH7PkeWMJww471z7BxtYNaiGZS0UmJCbPqI4O0Ys5xtToKnNlAraRlrLWXSd7HsPImwAZOSUVi3sWlv2g3M/OcJfrr/8Aw92X6fYH7Fy7TrffJ+z0SIYbqChiFEl2OgplNV/8H7/Lb/+fv0WaLrh69RpXr11nNBrwF/4fP8gP/9D3IZRkfzzleLZgMpnw5T/4I9595z3Gkylfe+ubHBweuWO4/0UmkwnD4fCR1/qFdt8Le7S1J9l1BRQfKfm6Jolj9sFZVmBkXV+O0H+xWZF7Bp9nCNL6ruerVzgKumq23QFGEdzeg0+/6iZJPQeduwfeV9MHCuLIbSs3UC6cdtw8h6OJS/x3resnFSuH1W/uuG2cjrFp6nYqA2woEdoga9N0yK3QWYYtS/cZFTmoz0pX3Dv51iailArY2bvKa5/5LKPRBp/45Ke4/fItirLgg3sfMD1x+aRBv8+g32d6fEQUXkyeZukk7AWrl33KxxPQQh9N0Yqe21XgXgjSDwI/kbadlPuilIqk12ewtYM2hvliSjk9oZzPmN17n3I2veA5fTirtWa+WCBRhInryCyjCGMtizQlnc1IT/aZPHjfEVO2dmH3qou6Az+4V2OlqlLuv/8+998/RiQJ0dU91MYIU6ZUixP0bIxbEBScXc1dxCyYwr0eYUEUM9zdY/vmbQDSvCTNTxhtS0Z7V+n2B/QDQS+SSKPp9DbpDK5AkLF99WVeevUVhsMevc1NdABWGEphKYXFRiFbeztYKegcHPLe3Q94FOX/3GO74Fm+sD+N5ueEVn3rEo1ZL/D1UdY6Y8/gHESFY76pxll5J9VWrPAwn9++757rt1UChXVKEUVTt2RK95I4SrmQLqqap26lWcwhm7v3ysox9JqKe+IORE0X06ppp6GEY+sFDRW98cxWSqy0jvnXibGq0VETgYMMN7pwbeDo6VXdqFlYV5xaPt+QSghBpzeg2x/S7fXo9oZgBbo2LBYpp6djirJkMpkynfpmfxl1VbG//4C8ePTk9Uzmc1Ih7rqH0jn0wjS0fe+AmhWPCBsG2qoMwC1oNGiNEJYwiQgimnYYLj1grcVUJXVZossS66nNUiIDt4iwWruaossCjozGFKkbOvOYxWSCEpIszZqOttKNRW9aO7V8Id3vWIRUdDc26AyGIAK07qB17CDnMHJRvRZYFSOjHsaUGF09Vq7qw5quSuYnx0h1DxmGqKSLDALUfM706IQyzakDgQ0V0hqqsmIwGBBHTm5pPJ5Q1xXzRUZRaoSUSKXoJDF1VVEZzTzPyOuKIIro9npYXS+bzj/OXjipF/Zo8ws+eFjsVa69YNWyw5vAQWtZ7bblnY7ELQoXnO3g67ftpdR6rBxlgYuqEg2zOUxPnBMyhfsZBzDouP5O0xkc3IU8BV1AlTpooaqaSEtArwejLQf3oWCerSbSpNsqXnZFpFYprAiwKkREEdK0PSvY2tUfsSjhdAqHY6ddd1rBSfVc9f2kVFy5/jK3X/8cnW6P7b0bGC3Js4r9/SPStKAqS/YPHjCenJKlC+7fv8tkOmZ8dMRkPH4+Bxbg5K56OPg0ShzkNK0gy6HSnCl2kx0Ies75R9K9sJDOIVsglaS/PWSw00cpSRw7dXKjNWW6IFvM0UWBaZgDMogJBhvIKEZnC+r5GFtfjoaVrUvM5AgzV6S64KDTYT6eUJcVWltkEDoZp4bmTVG4omQE5C43FMQx1z75aW5+5nNYoVhkNVmuqfOK2b0J+alrTR1Gm6AGVNWMPM2XNW6XacViwb23vsbh+w8I+yO6N24R9IfM05xisiAKQ2Il6AYKJQRal7x04ybaaCbTKW+//T6dTsJLt27y2rQkikOiIGJrIwZrSKuCe+MT8jwjGfS5cu0apq54/5tPPrYXTuqFPdp8vdL6e/BwJOVRm3WFGYuLpJa1VI1H8w6wPmd7/m8/f3mUo8JFJj6SwriEsDVNFIWbDE3tKFaLmfu/brWbkMLVOYWRI1eoJulW1Y1Gn3J9kHxNkC90lcrVpUjpiBnrNurA3gDyElQN+cJBXQu9qg17TiaEoNsfsnP1Jp1ul053gG3yH4tFhtaGsomkZtMZs9mUBw/uc3x8RDabUj6vSErgxRDctY0C19ipsE3Nmf9QU98gQqcAIoMm0m1a15euFkJIRdQZ0B1tuttoa7yyuq4qdFGgq1UkJZRExh1klLhcVDuyeVYzBlu6OKCKYtLpFG1WDFLRdBFeLby0G7cCR7+zIJWiv7nNzq1XsEISzlPCtKCcZhSHKVXlFkJKxVgVY211pjHjZZquKxanJ0BGtFliRttEKsJWNWFREUlFKCVZoJBS0O93GA77gGUynTEeT8nzgvm8iaSUI1lEcUAUBlS6ZlHkVHVNEIV0ez0Hg17AXjipF3Yx851qfbRU4GjdcdOd1gBox+SzhqZK8ayShGd5CeHIEh5eXyv+XVLYdeszPgkfWxC1o3vLpkhYNEoRxjhnU+uVT5DSJY1pJkbZFONG4QqelLhciWgYgNbntyRIp4lma7OE0C24/5elU0KoNWSVqz2x2kVfS8z9PE///MxaS57lGO14/LrKMXVJXVdMJq4dR1WX2Lqg1wkJ6cKVLcpBhyIvWMwW1A9zmM+3gKb+rPWeodHcgzN1b7K5tsK6yFqfE1oq5eBX1WrfYVehujGWIq+YTwsEBqEXCFNQVyVlOkcXOUbrZl2hsNpi8hSra0yRP6YG7EOYktCJIFCYWFJkU0wrwrEWysVspapgdDNmJVGvR7y1QdztEfX7+DYxIlTIJCDQEYPNAVFlqaqK+WJGVZbUlK7OcH1MNZ2Fzzph2yzgLhh1Sekkr1QPE0aUdYnJFyACQlURSkknjgh7HYSVzKZTZpMjaKDNW7dfptPtMNocYtBUuoK8ptKCqirZHA64df0qs+mc96dz5ukCc8Go9oWTemFPNsGq3sU/BwscHDPsukaBmGaSrptVY+Emb0+CsDjHlHtmH6u8qXeAHj1rNz2ctfbfA3oN06JIXRI66TpHKWgcVO1yT0tH4xQkXEFj0yNKKeh0nJOVrIgd1kCxgEXmJsleF2IHVxgvkipYRQHjCdy755r5BYkjYCCbiaHkbKvij8Zcc7oxVVFQ5hn377zF8f6dZb8lYzSdbodrL19je2OIHSbsbPUxRnN6eMp733yP+ewCimoC19G4K1pREc5BzY1j8HnihMQ57zoDI1wkVZuHtxeE0O2t7pcQ7nvCJTqNscwmBWk9Q9gSigMox05hoq4bBQOBNY65YypDNR27elejG/HcS7I4gCsj6CdUacX05AEyP+vcdV2tJmJdgdHIIKB//Sqbt24Td7r0dnadcoMElQQEsSCMFb1qF7kxYDqbsXhvwqKcYkkxa4QLAGQCwQarBpjgnsdpUzR1Aaw5CGA4gs4WWgWkeYqoSnIUhY1QKLY2BvTiCBkKjh7c5/6ddwDLd3//9/N9f/b76PV77F7bpLI1uqpJiwpr3bh76eoVdjaG3L33gPfffY+D08NvXTHvC/sONc/Y8/NRjYNwCFyhIMYRI/zqWTcf9Is+X/fkK+LbsKEnTKyTMjwk6BfTnhKPcTUvgVwRIbCrupG23E6j2u3yTQFE0YoBKEQLZmzqoOrKJbiNgiQCG7oITdcuh+KdlBBOfWE6g8UCOtpBVSpo6j/W6ZDP2SxOqscayqJ0qtbpnKP9fe69/94Zfb7BaMDVGzvEURNKdtwKocgKpLognOTh3Wgtkmom3KV5EprGESZ8ecGZebMFxSrlruHyXy3c2EJd1dRZQ5bJUyhnawfWYvBYR5awzyMhKKVbHHVjbKWpywzS/NGft02NmZGEUURntEHU6aKSBJpSPhFIF+wDcS8msJLSFBBYNDX2zIPTMqFAxg87KaEe/uyjTMimyWfsQAztmlZa25RgICnLGGOdzFOeZZwen4CwCAHbO9v0B3063RCDdkonVeWaaFpLJ4npxBHTyRSwrmniCyf1wi7VvKNpa/UFDfRmSjfJy6ZGSTSOQrKSVmrXZorW9uBsHssroHtUI2rt78yBrIn5eUcDbpU7GkA3Al02EZ5xsIhnmCnhjhMgS130VNdwOnbEDCVhOHeOqjIw1Y7ObkQzyQrXzO8wdxJLvRyymXOci8ztr11bZluvSzZjDZPTQ+68/TWUCinynCLPqcqCNE2bycssnafWmtlshooDgjiiM+wTxAEmUmejoseZxbEW07UT8k7FQ8O+VMHfspKzPcaocSF25dRIUnPWSRkD1Yxlm3JdQBk0zM7zcmkNvCsiF9GaC0YSbRMSIi8jpF10XWVukbPRh27cwL01HM2crFZ1cTq4NRpTlWglSbM5LFyH3LibsBGF1LVhns/JJ1MW6QKjIOzGGFGiF+LhIWRK0DM3vmlU/bE8lTairiAbNzBsgEsmBlhSalM3iuVjDuyYMFBoW3H19nVUGBD3O0znU0pTkeqApAwR0q8hBUVZsH+wz2Q6Yf/BPpMHH2CnR02d2JPthZN6YRcz7xsEK7miyIJoiAnSQlS7thjarMpcfE1m3fq+j5z8ZBW0tt8uhgeXh4K1keqX4wHLCUiwyiv1Yki23UQ2G8NJ2uQDYpc/U0HjbButunkG8wLyCt47hv2p+39fQiLcxDqxLgoscBBkiYM1fQ5rWMNu6iBEYV1UKWlahTTH95zQP6M1h/fvMDk5BEQjfup6CVVaYGXcRJjOWVdVxfHJCYsyo7sxYHerR3cQYzqhU9C4iFkcdFuunZAvHUhYwbghq5KDnDVn3WC6VkAxg/qAs6EZTYTc7KcSTsYAe76sjlCguo4paDKw+dMvDKSC3jYM99zkfXrHOak4hJf34NoWTBbwzbtwMnV51vriN9bWNSbPqK1hMhdMKYijmFudK+wlfWZlxf35CfcO7qGNQYeWZNSllhXFRDoe0JnrU7hxiAD6IEbu96eh2+sSpvsgT3AP3RbQwbCgssdAST1VzA5ClAq4+urrvPrdnyfu9uhs9Dk8PSKYKcJZQNgJUIFk2O/S6ySMJ2P+x5e/yFvf/Ab5ZMLx22/B4eGFj++Fk3phH86W9ZUNzCasi0y8s5AOnjnTev48puB6hOFhwSXxgTXmX/PhhtXVdPBrWHjNjiSOIWYb8oM1jYxMSwXCNuw9bR01Pc8d+WFRONUIHwV6sewpK23CMWcLkME550y77wTCwWD+Oq0zF5+DlUVOWazDTcLlK2R8Zt/GWsqqQhSSoO5gBFglL+6glhvi4SClXcjdfq1Hz0vzDBncouJJCNBjo1HBsieYUA2R4CkueqMY79ifMTbuuSjcR3ZSuhq6XuIWNMa6KBqeOOEKIRBSopRCSYkSAilAW0NtagLrqN2JUhRSYHRNXhWuKaOwSCWR8lGDyLNQm5WQbbBzgbsG9gIhvDXOIWvPZPKQRol1yWTqWlIXEqkCLDVJv0On30MGiqIqqLSkloqKgCBQxFFAGAbkRclkOuXw+Jh6PqPMFlA9BhpdsxdO6oVdzDxU46GrihXcV1o3KW+xiop8RORVJTyU55sktp8bn6vyz5+Hh9ZZyhGu3UexgLmAInQrwChaRS/ey5ncPXiTBpIra0jmcGTcJEQzkRnrWpwXpVMwn5eriXLCKuo7b/L0UJ4/Pn+8lV01dCzsU6SnJIjEwTa2Buupjc9gtqlRa3kIoSSi14VRD9HvuvzcZZm/zxY3QSYhDAIHAyuP+16uCQKUGiBlgsGibYXVTd7qgtdPSEVnc4/Oxh4ijDHDbexgA50vyNMHlBPhoqVxCsEEUWrk1i6yt4lJU8zJCfYRVH4hJXvXb3Dj1m3iboeN6zcY3biBDAOqSFGHkiSMuLW9y9XhiJOkywdbdzk9HVPlFYvjOVVaorMaW3sors0ePXenhMkmUbwJaMr8lCqfPubzIYQbTQQqHfRt5rjVmAQiVJwQdvqoMKQWkuPjA8LFlE6nR7fbQyhJkElkpJx24jQmigIWi5RcV3QHPUoMVSehTuOGAvlk9uELJ/XCnmx+sVvCUlZN4KCssoJ55bT5uqH76WE7D/P59FGFUzIvWYnRClYOzUNFPg/VZqguYfIaiilMcudsFuOGONGYoMHoMwcRTWo4rJwjlSWIhrlmRCtH1nhMY11OxB+zl/Tx9Pf1p0W2jtVHD6I5v6xhMJ7nlB9pAcg+iI6DqZ5ChPN8s42zq1d/A6gAMewhtjegm5xf9/UMu6RqXqGEpAMbCVA2ckCXX4gqZEgY7BAEm2izoCjvobWfkC/mFIVUDK7cYue1P4OIY+pOQJ0EVPMxJ4dvUz7A5Z0OZ5BqRKdDeOUaqt9HHx5SZtkjnZSUkpdefZUf/It/mf5og/7ONr2dbYSSlKaiNDWRUlztDdnq9DjsHvPu7h2OT05JT1MWsynFQYY1FabyxYNt7PSc8xGSuLtJf3Mba2vmJ9+kKmaPjvhkBPEuhJtQp5AeO7h0mYhWhMmI3s4eKoqpheTBgw+QStIdDumOhkgpkaFABC5vZqxX4TcUdUl/Y0guIet1KbJFo8jywkm9sMuydk6qjTjUtkEGGujNM+bOgyX8NtYjizbT77wR6ffp0SjdqEfIZvZXknZrceekCjfJ58axyko4w3tv8y48GcQ7Y3+cPhJsw5XtZ7wd6bVPt32elhXUdSHzJ3pZRZvnscHAIrBCYBEYYzBNM71LM08hX0JuzxHrRCJEiBARQvg+UReNoCRSKlQYE0Rdwk4fGcUQWWwARilE0CjrS+nyrUUFUYwIQ2SSYGInZfSo++zY9SFxp0un16Pb7zMYDkEKyqqgrEtCqUiimDgIicKIMAgJgxAlFWiLKZukrvVbbL/WHyQ3AIWQCBk2DRkVAok9F3OnYWWGoGJXXyXOgQ+EREiFUAptNDovEUogI4WKFFIppFEII11ArTW1dcfspaKEZ8aus0AfYy+c1Au7mPlISuIiGq9svWzN0eRhEuHaaSigDpyDUA0kqFkSj4CzkVJ7HvNzpYfH23R03eBJuXHOKWmo6F4tQgqXUC+Nm1Bqe3biOC+iWWcZto/HzwE+N9UmP7T1B30HYU9R7wgXrfn8nMERDR4r7qDBpI6MQv2MUdSjzZQVxeEpOs0xnRi5yJl2YuYHJ65X0bNYIGAUO/FeCcwqWNSugDd/0vl4RXRYkWOe9Hn/nad3gkJINrdusHPlNkHUIdy8SqgNOsvIxlNm5Yy6Lig7XfjE6648IZ1DmSOqCKG1S8MKiUgi6HYcJFhWZyIWYy337j/g937/9+kNR3zquz7Pp7Y2CYIAk1foNAMhmBUWHRVMxnNErujpAVpXSFNg7ZSzA07gIipYVUxb3ACbYq2izGoWpy56KvMMa73O2PLIWNaECIEIg0blQ2KFj9RWVudjslNX64VoSk6koDqNyToJMgzpX9mlu7vjoL8oRAlBXdcs5jPyPKeazaizDPLiBXHihV2y+clZ4qCvhBXM5eeKWLpOuABd5QZh1tQu5dptoympOvNcwcoh+P+tLxaXcKN1+SVwTLpecwyxckWgUjbFxNpRx9v7aKM/647rPEhuKeWEe15N67M0f3sI1DspSSMB1CSrlHGwqG6c9GN7TWmwc0f4eF58dcAUFcWDYwopyTsx+TQl6CRUswVl8YxwXCBhO4HdxDmmO3MYF01k+aTz8UrokpXUyKO+48PudhOzpzMhJVs7L/P6Z3+YKO4yL3IWZY6pCrL9+5wc3cOECnt9BC/twWIO77wFiwlUCbLWSG2RQkCSQL/rZLFq3RAQnBljuHv3LvuLnG5/QDwa8onPfZZAKWxeYiYpBphEFXMVsJhmiFzR10Mqk7acVHtMeNhPsio+9A+JE0su0wll5iJz56D6rMEgLBcDUiK8FJVRWFnBmvxrXeTocswZmS/ROGkhCOIYEQi6O1sIoVBhiAwDKAryImc8OcXMF1SLhdNufOGkXtil2zob76FJX7BUrvbvicd8tw0hsvb3OiPuXLjNrtpAqDbDqflwexutw3qIUdh+71Fw5KPID+1nfr0WbHldPoy1T/7yHZbVLtI0SlGXNVZVaN/t1U9CT7tLf50D4fJRZQPD2takdqnmdyiwGCx1oxD+pH3JhmkXEcUdur0hUdwlqyt0VbjJuMjQeYa1ATB0NVKhasaRBWuwdY2tStcEE1oQ5/oxCowV1MZSGeM07qZzqiiiXOSUZY0QglhZpHLbUDIgCkICpZrStfOi0NX5r2w1Vqz18KCnqjbfUQ10aRXYyOUshcJiELZpxWLPGezWnikKb+/Rn7+pNbb5nKlrrNFOU7Es0UWJLctl09CL2gsn9Z1ughUDrR0xfFizuAWWH6vLaEOACSBsanLq0v2sLEytqy2as6oxajuddm6n7fjaCuntRaD/Xju661mn+qCMgyIiexY5aj/j/hx8n7i2g/Kwnt9+i9n+kHNrL/xl67vGNqQH0fzeTPjarpzluX5HOXaVCGmA/OZ4K7A+lLtcs9aitcE0dT4yiZCBwtY1uqguNpn4SNIv7j2PJVSw1XfkiaJ2zLhiHcITrS/AiljxpIG6onxaC1V9gjET187iMX2TAMKoT9TZJoo6bO+9zI1bNwmCkJPTu+y/+3XyPGU+P8Kmp2BCqELQ0kGx1CAFpioojx9QLyaYssSU5+xTuuheqIDhtRtsvfZJwjjh6HDKb/2//neCIKC/uUV3NKKTJLzy8oDt7W26SU52rUQJBSolSprxcObl4QU4u3pq64q1GYDN+Ali2LoOgx0Hiy9GTmIMIDvB5qeua4C+SBONdbMYY6i0hromOzqkGI+XcJ/OcufUn1LQ+IWT+tNgHo67MBX6MebH+/o4U9I90FHsJIR0wzioGgc1xjmFOWeljtZTCZ7y7eH1ghWq40dr+xw8bG4tbNarxos+9+Ud8zJfxNmi4Xbk5Nl4/vm8CDoVs6op9mSryjrquWVFU/fba8OX6yYUyKYQFbdad6v2vLmez8NJgTYuqpJCEMQRIrSu5UVVY/UFnZRvw+KdFTgntRnBUMIsh7R8jJPyF8UzVS6yU5cUtbamrsfUy0H5uOskCKI+3f514s6Ard2bXLt5HSnhK1/OObjzFnm2wNoU15Y9hrLrxrZpIEgpsHVJdXzQIgGckxeTCoIYEUUM9q5x47OfQ6mQu7//R/zh//gvSCm5+V2f5/pnPs3GaMjtVyI2N7foJgXZXoESkqqaEMVe7sivnPzA8oO6fb18otibd1IFUDroebMP1246tuLxEGYpVDPs9F0oTliOvac1azGm0YisKsbv32Hy7rtYY5roqnkgn0YJgxdO6k+X+ee6XWDr338UAWt9PD0qCli+30QA2q4a250Hq7WhtLa14cPznpPzYEb/7Jas6PHtAtL2/jyrr+0w1kkS5z1D5/2/fSx27e/zmH2POuczJ9d82V/HDwX1echubdOPuHG2aUW+nEiWMNFTWPtQtW3YnjgNx5ommnyW1ZG3Nn4LZ1cZ6wPaHxhIFRBEMVIqesMNRtvbxJ0BcaeDsRqjLVpXGF1htGfIGJbq5UUBukKEytH2rXUtQUxzk03r2qrAsRqDEIIAKxXGGOosw6iKKsso8gIpFXlWkKY5cRRRVTXGuGhE65qqrqh11UBs5927Rz2w6wOvOSbZNPvEuCLlWrs6Q1u5qMo+gqyzFPt9/D204ISMm+aTdVWhqwtG44+xF07qT5MFuN4+AW7B6mt4fNfu9ZrOdSisDYmtR1JeeUJXDbRTNgoO1kFwHVYRFJz/zMFZJ+KPeT2Caveham/nfnMuMQ4e9OfpCVHeifn5LGbl4Pz2znvuPaMxYCX15KMzX4TcRlkeZesO7aH/azCLZsXe8rT2aaIoASpGqPjs29Zgde7oxWesOQlbYmuLzhwj0mqDveiK1xNIDI4YEpZNPt46bb8SR3Z5KIpqb+AiZBHBiloKZ6vF/bbXw1sXSfQ3r7J363WS/pCrN17m9mufIel0Abh/dECZp5xODjFmwYqIgCNAnJ4CGSKMCa9uEbzyMrqqqGYzB/PNC1c/lZWuT9nmZkNJb0gLQjA5fMB7//2/IKxgdjDDGIEVkpOTCcU7d5htjTi8dZ3J3jbpIuXu/bvcv/uAo/17ZPmcswO+fT3W4Yg2m6dxOFLBaAeGW+6Ysgm8+2XHfs1Ld29M6eqj1k0FiDh2OayqwpaPZuVZY1iMx+g7H2Dqmnx+ATX9C9gLJ/WnyQLc5B2zYquVuDzCkLMogZ9Q9drv/vUQrGybSbbRs5s1L8/Uizmrou73sf5322GErARKPR+iDdWtR0jeCfr8VczZeSzDQY+GFUMRzs6P55HE2rT7hjy1dKb+OrSlgB5lj4oOl2ZwBZR+p36j7aKuJ5uQISLocubi2hpMjWXdSRmcsoXAaovOrYMrn9b8YqCyENYusqiMK6b21PNzN/u4sHrd2riiwWHH67kTibuxnm7pvGd3sMvVV7+LwfYVXv3EK3zuez9HkiS8+9Y3eOsrX2U+mTBbjDF2bQWmteuoW80QoxHhq68QvnSTOsvRhweYRQpiCg/GTgV9FMNw6Fq8GNMoNxjmpycs3nkXtMXSB9HHWsF0umBm96nygtOTMYvZjNlszsHRAff27zI5OaAo2kng9rV4FMNo7bNSQn8Ddm+6qPDe23B495zI6JzrryQiil1EKISrT3xEaYQ1hnw2J7fCnXuaXUr0/MJJ/WmwJQyDm7Tb855//7xUQBu2audtHgXDGeMeamNWg7O930d9Dx7tvPzE78th/HE0z6UQNHCEdSSydh7eOx2fn6pa27CscvTt3JQ/lvVnt33u50ZBnHVCHlI9D+67kK1jmk/zVRfVCimRQeiIEFpSa/nQ/CKkRCUxsqOwQjvBWOmKe7X+kLBfbRohWLsijTzxS+2fT/qsvxHtG+ZwbCEjZNRFyAQVCKJEopRguLdHf3uL3uaIqN91NUGBQoQBMmpebe3CsGEoKlzNWyIgkpi6wCwW2CJ3BICqKRoPlKNvB2o1dn3RuTFQV641hbYgKxAVQkAcB8SDHv1eByEgLyvKskJrjbEWaywPF+D66/CkQdkyfyx15XLG5lFU1bWIdv25ftx+rG32U67IQ5dgL5zUd7r56MRHQAb34PlFZluyaD0KWF/k+td59Z7WuCLGzDQtLZoJpMY1SCxYLXq9/l97QoeH82LeMfnFc6/1PeNIcGFPISOBqSxVbrC1XdV2GtwE05euwHZiHPRY4QRjT5vteWKJbf29DnFmret13rPno0uPwESt989Dap5o7cn4STDY2e9Zk2OrGhVGJMNdOqMtdFUw388oysWZT4edhNGNK0RbPeyiQB/OsVlNvihYjFP0hVtQNGasGwNV46DWmxuee54XjRJ96OrD6DbENwISVNKje/0qUb/PcGuD67dfpjcYMNreZu/lWyTdHv1BjyxSlEJj+hH9q1vIriIadJwiQiBgN4adyNV9dUNIAqwVVMf3qQ/2sVpj8txN+LVyzT97fVANHFFVrmvzdOom7rJaQa1mAbYkjLrcuvkpXvrMZ+l0OkRxzP7xmCxNyc/Uq52HQdc4JpIf6I8ZH8bAYgrivnMcWXrO52XDKm0wbdvgt7rGZBlCiidTx611PdZ03cwTz1gY3tgLJ/Wnwfwc4CfMdar3ufDdU5q1TVdcs1pBt7fdtAwCVgy7dqDwKJjN56QiVvBcYyIWqC2J6gh0bqknxiFXy2PCrYiHqhG+tau81ATnqNrQn3/+PBNy3TE/7plrz5me4daePz4U6vEh2XymwUyVJeqEdLaG1HlGPg4fFm+PQnrbG3SvbmCmKbURmLQAAdk0e3p5W4sbA09VE/w0F+e8G6FwydYBMhqQbO+RbI/Yun6d29/7vWzs7NDtdxhtDgijcLmV0lpsEpJs9LBKE3biJo0kYBjA1aRxUgkkEXZRUr97BEdruZawB90dCDpu7FcNzFcUMJ833XHbpsEWKCHY2erxiVdeIgjdcU1mC4oso3zoO+vmsfoLmLWuQaQRbjFZnfc90XJSPg+Kc3CmwD4ELzzCqurSnJO3F07qO8Euwspbf//ZoeKV+kQMjIRzCG340C96zzvGR03cbUZyzErYVXBWieK8qM87IF+/5COi3DgViBQX1fm8Uhvd8MfZhjg/7DWSNH2lcJPWR27SsctkCFI5fbVQEXY7xKNhwx4rMUZjpKVc5MjTOTYr0E0xprbng0yPtCUDDJasRCEc/CVlA3k9XRHnE3aIT1gG0YDh9g06vSsE/Q7J9jbhsEuvNyCJO8RhjJIKba2LDKxBG40xmvHkmJP9D8hnU9JiivVkGtk4GqQTQbW2iYZMw1UQ7voKBaopljMN3FVUjpTwBGabMZrFdMzx/TuoYJUQttYShwE7u9vEyjI/ukqApSwLFvMp9blO5jG2bEvTFOk+SqOxaQty/jF/K8axsxdO6tvdHpWsf1Qt5LNMvusWA7cFXBNOt28oXNFjaWFmYGZX0JhvfNfOb63DfeBGpI+gNoHt5js5Lk/uSQwNk3Z5LiWuFmuGI4H0ms9pC4farSKPLeyz0uDzdPw2WWM9gvowFkkYyCZibODPC2/rSVz4C3xfJe4VRsgoRsYSGcUMbl6je2WLqsqZzo8oijmVsJy+v4+6c4hQcql/WBuNufD+hYO5VNBE1OUqTzPsQxK7iX4ydxP9pZiH+HoMNq/xZ/7i/5Pbn/kcldEsyoxS14w2N9gdbdPr9KmVJi9yTGXJ6px5mVJVBffe/APe/73/RjGfMpncpd5tao/CChbaOaN5AxfXxp1biHNM3U0IO64JYyFcPi/LYTxr8j/1ox0CUJUl7339jzk9uI8Qq4dga2eH7/9f/lc+8ZnPMhuP2dve5OTwkKOD+3ztj/6A0+PDp7xW1lHOTd04rPOiNOscmL1gjusjtBdO6tvdzosq4KMZZwrXQ+olmlW0BCvOQnztz7ZLW9bzXd58JBXiEJwRq+35KMkz/trmC3EXOOfko6nUNgXE1jmxE1ZNfdsR2pPQlYuaX+DHjZMKn/YmiNbPD3kDRQAydhNpECADASIgjoYAlGXKIkhhUaLziux4BnmN6EWo7T7EAdbai9dcCly0pAI30emGmSMlJBH0Ou73eRvzfVbzLL4BSXeXlz75WT77536ALE3Zv3+f+WzGYNCnn/TohDEZBQudU9U18zJlnE8oyoz7+3d49xt/TJnOICmg30SAVjtqtm0RP/zCSuEKlTtdiPpOJ7IoV+KyWXohZ2x0zenBfU4P7p95v779Cr3OX+b27ZeYTTegtoxG24RhyDtvffXDXS5TP8I5nfkQZ/XDPh72wkl9u9s62QCe/xjzTiLEOY4xrgFigks6S3uWjdcmY60X07aP+Tw6epvcQfPTyw9VYDCwkJiZWfV/yppjyprjWzTbKnh+16ZdsmJw6u9CfAg6t78Qz3Cgtql7MQJrtdMlECCEzywYN7GmtVv9a7u8P65w1BD0YjovdZEaynlKcTpzen/Lk/XHyooBZjQPa741nw0VDHvQiVyJQlo4aCwMnTirkm6izy8uPIpUIEKsDKiNcaw4Y4jiiJ7pEndiVAhCaeoyY5GekOuCrMopyjllVaAxThhWGQd1iYbmalgVovtFjJINg69ZBJgmeqo0Z4p5G4viDp3uACkVRb4gS+fnat+tW1XVnJyc8sHd+9RlhRCCXr9PkiQoec6KVEh3LaCB8z4Mq84fv3/Y/IA+P6IXUrk2IsJ1ETaXxOQ7z144qW93e1Q933l5qsswiYtwujhHNLZOAqgj4QawIVz+x/dl840O67XX+nG3aOXLObpgVdfkYXjPzCvBSEsVGoQ0bl7O7Kouas6KxehhxuejLOSs3bZEG3ddLE9QPT/PnhWPtWAK56jqEm22VypU0iCUxmQVTHM4yFjWMxmw2qBtjbCWzrU99m6/RhR3OPnquxz83lepFhln8eVWAk/Xq8nxPIirk8DOyAm1jmfw3n0XWfX7cOOaqys6Oob79y8GCQoJYQJBDx3GpKVmMk9RAgbDIRujAUEkiDsWGZRks2Pu3/s6s3SGtpra1GijKSix13ah7MN8Hxbp2ZyqZlWO1Q3g9jbs9N19HRvIs8ahtamgAILRxhVu3voMURTz4P473Hv/61TVkxlKaZry1a++xaySDPt9bl2/ztXr15hNjgij6OEvqNDBjlK6tuzVh6lP8g9IO+nrV1wP5w7CMCJK+gghKIuUIl9cYr7xrL1wUt8JJtZ+Py+6usx9+S65ghUMV1m4wmp8+0jqvDyURxXax7m++PYr2HU5Nh9Jpf7Pcx6MmodrPJ+3rUp1ONOS40k6qc/DrAarsUZgrHFuRFiQBiksFo31kdSZ77m8hEUQ9BL6L+2R9PpkR2PX+A84iy+3TuxJUkphk5/qxm5C99uLQlf82um44s/zIoXzTAgXPagIKwMqbSjKijgM6HVjkkghlEaGJUhNrTMW8xOms9PW6Vo0BtvvQqWgOoW0GZCeCesj8wUukgo60BuAqMHMXSTlr93a4cVJl42tPeKky2x6grjgudV1zcnJCXxwjys7O9y6cZNev0fS6SDVOs6Nc9jKkWTQbfru05pfIPmH0x/vw1GSkAFhGCGEpK5KBIKnpNpc2F44qW93ay/e2vVGH4X5tuoxjjhhjXuYM+sebA/LFZxtEf8osodllSvyz2Jb2aHdjv5ZrX3dLuPZWoc1n5UhKBoyghANlFY//UpVG+rZjPzgEBUq4o0Y2X3MI19bSB38Z3NLbSSVUOikC1vbboLOiqYX0AWTnoqG6WhBF46eLg2Mei6qGnYbNqcmGvSIb76MqGu0durs1hj0fEq9mCGEJOpuEXZGBFGPwfZrdIbXGe1ss7W9QRIHhIFCyocfgCpPmR7eZ3J6iOzEqH4HJFTVGJseQ1nAYgGLpoQis2flrwApFUmnQzAcokVBHhVo2dwX7VhxcdShv9cnQLKxc5Wo2yEII2SUoII+UgcESUjYCV3sUlZUvuC3KkG7Qt7FbI48OcZWBW+GgpP7d7jz7tuki3Okhox1jt/gHFWn2xyTcS9rlwuXh+28Fa1svSxnYT93z42uqcochEDr6rk5KHjhpL79zfAwNfujWrmHwBUBO6IpeNVwrF0H2rFdFcD6nJCPoMJztuVTMQHO8Xkn5ieKGavWGpfhpNpz7GU5qfUarWfZtgwg7jqR0qqEfOEgvKc5pLomf3BAeXJK0EuQr10j6G49+gulgZMKlKC+oslrhRER5XAbe/s1mOfw4D7c/8CRBJ50cp6KHwuQGoq5Y8JJCTe2HdEmCCC2ICu6V3fYeeV1AhFQFJosr9FlSfrum+h330LKkP7eJxle+yy9/oBPfPrTXL95k6gTs7G3TWeQON8u5UNzbzo55cGbf8zB/h3ia1t0XrmGjAKyxV3s8btOw+60ci9tl6Lhy2gYCIKArc1NBnvXyOI5B0cZaVq6nJSuoTYMNvZ45frr9DoD4m6PXn8IQhB1N1DRLiEl/b0h/asjLJbJeMJ0OsdWJUxPIK2oyoqjg33G85z7wvD+n/wBoTDkWcb49PicG20c7V0o6EbQH7hrn1XuvIyBOmvuWdt8+L8enbXrQGDlnFYPX10VGF2DANNWmHkO9sJJfbubHztt5/RRRFLeIXaAAQ0sYlrwX+vlYZP2d+H8Oa4Nm/kJwucInrFp7Bl71tTP896eEI4tp0KX61lXNb/QMVl0nqPzHGtqjG9r/qjjNDhHJcBWFm0FGoWJYugPQUQwPmnu3wVPdpneaFb1xrqeRt3eSp3BHSxBJyHZ3iEKIkhrdFpR5zkq6YKUCBUQdkYkw6v0RkN2rt7g+ss3UYEi7EaoUJ5R46Kl6l4XOdnklPT0CDNUKL2BtAF1nWLzuZvMvWyfJ+yspcakFERhRCdJMFGFWorI4s7LGMIgoj/aYtDfRIUBKgqxxiKDCKk6yEARJH3igXNSqtSIosaKhh0JWGMo8pxCS9Alk2wM9dlW7uv3eVnjJCREUUOZb+7nsunaeXYerCHWXr76fjUGrW1ksz4Ce+Gkvt2tvejxEkKGy2P6+n14skTTqHQpVBuyYof5KMk0Pz00dx60tp6TWs/Xtu3jxYg933weTvBo6aSnMWtWunC175T6DJurNfnxFIA6LdDZ4xP4KgiIu12ifo/BrkTrhDoryNMJ2R2FvYiqgDaOHGFMc3/dTzEMUBsdRK/rRG21ASvQ3S5Vv4NVEcX0lPTwBFOUhPEGW69+F0pGdDZ2EUIgpaQTh4y6CSKQyFggAkjzlAeH+8zTObpeUBWHmDrj3W9+hXw2gapGpwuK00NErKizhpnj0zht8WA4E2xoWzH/4IG7louMav8EJgsXoRQlaEM2nbD/4C7T7pTBcMTWzi5KBQy2R7z06VtUVUlpMhaHB2hdU85m2PnCFdr6Il1rXF0TOAak9Tpb55MYRBAhwiFCBhhpsOnCfaas3dhZ6id6EcsWg09GICKCIKTb7xNGMVVZkE4n1BcgeXwU9sJJfbubJyj4cdwWf74sk8AGcA3nfPo4h+XrgHybBt+ZVbCSGfLPg5db8+YdajtH23a47XrWdoT4cXVYCnfOEnc9fAPED2u6bjqmNgyrxxSFXmhzRUV695Bs/wSMcV13H2mCIAzpDAZ0NjaJ+tt09wLqouT49Ijij//oYj64quF4DqfpmehLvZQQf3KI2tmkqjQmq1151WhEvjlCScX8/X2m791FVIbtm7fZ+dT3u0k4l5hSoKRg2I3Z3egjlKWWNUYYZtMpb33zS9y5f4d8esDkwVco01PyRcpiPIG6op6ekj5IIRSYrMSiW4QXzvoBP5ZjqE3G6VffZvLVO1hjqIuqcSJ26Qhm9QHvFBoVJtx86TaD0YggDNm9eYWdV65QVQVv/+GX+caXv0pV5Oi6xPpFiGdHWg1l6lh6KDC+aLDCDayzV19EHYLhLkJF6OyIenoMpnFOvt+VDZsT8bi0cfCg6oDsEfb67Nx4meFog/nkmPvV1xtli48qwf1oe+GkvhOsHYU8r5xUu/Nqp3l5p+GJArSOwb/WYW1af69HUc+bmfg8zUdS7fN+1vtwmbUn1qLzp1i5CIlUATIICFQEcYIqKoIkRlwUerS4fM26S6sMUkpkGCCRSC2dD44iTBQihHLBaFkhKouKEpKNXYQMqCY5tS5QShGFijhUIC3GVhhbU1c5s9kpp+ND0vEDTvfvUMyPzyyUrK7RhXF1TutSTefBts3iydaGapGer3PZXBNdVWTZAlFV5EWG1k5mKoxDwkFEXQUoJajSlCrPwT6i9YXVHrPEPXTrD1Rr11IhVIgIQgSiUTpfx9d9n5z1bSgQAVJFxEmPpDekLHKkPIdFuNzWR7tSfOGkvh2tPV5DVuPPwxUXJF59qH2uPyttdXUvN+TVJlJWEV27fQasYDFfiuGfn5AVoaj9nFyQmfyRWtCQApRoIlrLUn0hEu488qZw9uMaAZ5rgjwrOdofE9oQqyKsSjBlTbZIXaL8GczMUqpv3EEfnhLs7LJ16xVkt0fc69MdDRFIxCsvEZYSoS3dnRuIzT6hCtjb3mIkQga9Dlf3RvQ6gulixte/8SfcPbjHyeSU+x+8x/z0hDJfoCMF/Q5O1LUp1B0IGCh33yyQmhXM507/LPHFz/frpJ1mTIokRm2MkFEMUYzs9hEqpOiFfDA5JFqMUQ8qlC3QVcnRB+9j6tIRYZ5IOGg/1G0csvWJKkenJy7SLObnRN2+nsP/3vzfWpYNNo3XLfMn6hNz/jv+wrTbBVwm8+jR9sJJfTtau3DUw2awIhhcFgPOm49uPBGoHbF5p+RRBF+jNMex+rwjao80y9nnph1BtWsV/f4+atbiRS0UTi07EiuFAnA1QZFyx3taulqaj9uxP9KctFU6L7h35xix0KgoRsUd0DXFbO7apj+DmfGU4o/eRCjFxvf9T1z5c/8LydWrSCkRyt3wbmfAxsu3wUBQBwgdEKmQT1y5yqc3d0gixZXNhEEfTsZjfv+L/4X//ge/S601RZlT1xqjcnQSOJKGLJyqhNTQlTBs6P2Fdb2vSnu2oBxW49Qz/eBsrqp5DuSwS3TrJmo0QoYRsttBSEU6L/jm0QfYqoaTYzg+gKqkKkp09egOt2tXi7PY/cPfMeUCUzcUX/uo/GVb+8tvQ4NOXdRVhy3ZJL/PgrPJ1nYE1q6zOP+4LsteOKlvVzsPGluH3i5rH4+D8NaLdP2rXTPE2nfW803r9Pn118fVBE0U1ZycP17Zeu+cmp2P1KRwjK+2eTaYtW6iltL99F0jhUAjsGWFyEqUEQRCgTEuxREEjolmzIcjdGiDTXOsEIi8RFkIZDOwjPMUQRQRbnbBCERmIbcoFRB1Yjq9hCSUBIFECIsxNYt0xmRy2jQKNI7VF1XYRLhFgwyan7hmhlKczX36+XadzON/8Z+TzfVrf05KRBwhkhgRhsgoBCnRIicvcwezLuYwGZ/TtuMi9oRrbE0D73l44ryHpv3e8sBX27fGqeI3Ekf2XKXlcy8OZx/uy7cXTurb0dq1Ue2oqS0B9GEdVTtaSnC5pxDYDt3qU1kQjcKzTzR7dmz7IW8/Bz7S8wjCeePf/+7b2rcd4noNkjcf3XmG7Eet7uCJIevC0dqsdAKf2PTvMfasz75SqO1NxHAAQmKExCKgKLFHJ04CqNuFvT3309hVjmZ7C6stNiuQUmETHKtue5vg9U9hswxzdIg5PmLZvsIvMi4KN1tLdu8+h/+//4twNHLU9ChBBhHbr73OlU9/hiCMsZmGrCawgnGe87W790hCxc3tDtvDmGlZ0Bv0uHb9CsUiZbL/gHyxcO1jriroR4gwRsQjhBSYPMUumj5PaSsn1V4sdaR7Sekkh4LI3descqw5bZyye22w2lBVFboqEGWOmE0R1lLPcsx44RTRn0aT8GlNKJCxW2iYRoH+kTdAguqCTNyipHlVleT04AOyxTF5OqUqUn+TeHilSetv/xAGrKDJF/2kXph3UrCC09qTw7NEIApHLQ9wjL4tHJy1E8JG1204TyGvV1BfzlmH0V64e0jSj++8deze2sfqlSq8taOudfMwYsCqWPhb4aTqtfdqP9njnNSHPab1KPlpvx4EqCu7qJeug1TUIkAjYTrD5jUsSuiN4NXXYXfXTdp57iY5qbDaQppjw9BNsEoid68g4wSb5VRf/RPM6cnKSfl56ilUQbIP7lIcHiKkgs4QepsE3R6jeMj2n/1hksEAk1aYrMSUFSd3D7l3cEo3UlR2i9L2meQ5vWGfGzeuMjs6IvvgHbLTQ+h2YLgJOzEi7qC6I4QK4P4D9P4EstIpS6yvIxQwlLAZOFWM3giSgXNOxwuYF6549lSDNpjaUFYloixcZ9rZAqoam9Uwq9wYyHMuLiv/lCaVc/BCNg1HfRXyOSYkqAGoESCWtWtlVXK8/x6CrKmBakOD593M9iqy3ZF0wQsn9cKc+THYrrG7DIisPfGr9ku0oCux2s86LL3+Oo+59ziC0NMc/3rE1s5bPWobl4lMWBqa75onfRa4cnk+wvV3ksLJFPlC2KfdlhQIpbBSruA8qRBR7PTy4tg5oaCZCsIQjFrJ9ACUcaMJ53pNEUXu/1HoJnFhXYQtbQsSay7Ek5CqukZ7CKyWQAxWYssaJSVKBcjAYALl6qmUxErQWLIyZ5YKFnlKbZxGn1AGEYKIhFtcRRIiiQgDZBAiZIC1Cl3S5KM4C/UpmnNUkIRO8SMO3e8Ip9/nGzx6Mxbq2tWOlXVTn1Q7Cn7dRF3mI1pBLY/Nw7fLK938lA76DGK38DANFGCFc0yeXPLEY12HD1Wz+wCkW9RYu8ac/JD2wkl9O5pd+33dST2LJQI2ZcMatE4he30x5aG1NqrQhttarTRo0gBNPt51PvXCsxWPP971JLZ/r02maNdXeVp8m8zR3r4nnMDl9JDyWnfynAnI/1mZs8fuHf2ywLJlEnf9A1Bxh2i0TRB1qBcLiqNjTFE09+JiN9kag87m2MmRK8OZF9i8RoYR0cs3CT71OhpBUVeYB/cdlVkGCCGw8ynm+BDqCnvlClpXiCjCCpevsLLCbPbg5esOzipTKDOWjsna5vpU7ho8rWmNyEtEVGKbqDQIFVeu7xBe2aDOF9z/4Ou89dZd0mzM/QdfZTK5R21rxKcT+p+6DoMQ9jrQDRA2QtQSoaGcQH0f7Jyzz4zE9S+LFFzZhL0tUAqijpvUgxwOJi4qqvSKRZcVcP8ITqZA0DDlIoc25Llz8PpDaC9e1EwNddrkFwUk7kGQJkAZR+DRpsSYClSEGoyQgx23QBhPsalXt/fV+p799Lhw2K9Qm5yniBBSEvd6RB2FNTXF/JgynTzz6b1wUt+utu6oLssiARsNhbrSq8neF+O2ocX1KMoXQvq+TjVLpRbfE1FHrc+uF/ium3dEfvt+lX5e08N2y3jPMlyPqvxnvON71hyWtk6v8HHW3r4nVCyjrLWdK5yTikH2O3SuXyXsb1AcHVGlc4wuV+oeF8r5GHS2QE+lW9XvT2GSIq5cIX7jc0SvfYLq5IT6629iTk4gSlD9ISIIMZMJ9v13sXmKLTJXv9RJMB2F7rraJEZdkNdcxDU5dvqK1jQ3HUe9r/TTOylrEbVGFBWiKJfRmAoCdraHbA87TI4P+Oqf/J/80Zd+m6qakWbvUlVHBBt9eq9fp7s1bBYxTWSRB4i5wJZgJiD2wc44ixr0cWoqiYLdDbh2g2WfpmV0Yp26RNWKbPMC9o/dZ6IBdHZdw8k8dfCfftJq7BnN1q6oGCDuQNwDoVB1TFDHYCzWLjBkCBmj+kOC7S1MUWLTHL1I3cNpuzis39MZn4TZNsljIUG6Oq2oP6K/OcDoAlMXlOn0mc/9hZN6YQ+bH1M+32JwrTjKZgJqT5Sm9Zn25wEEBBK6UhBIQWksKdYNfQ8rthmB/v12Ats7lPY813Ze7b/XX76kY73QuL2f9vm2t3fe++fZZc493nFZ4Q5PKfd6ZGHlk7ZnV91iq3rZ0txWNbYssEWOLZo8io/SgggCDUXhlBDqGluW2CwFa7Aqco3/sI2oau324eHIpYYcF4wcmpsnBCqMCTo9gm6fKIwJEG6CEs65S+VgO4IAAoUNJDaQGCswyh2CDIBIIRIFtcFkTtWd3GDnFiqBLerVrtcjcn8fjHHnJVmxHw2sFMfXVgoNK3HZKNFDbfYy4I2LWLMPJRr2okKqkEDFy3EgtIUwRqigObz2sYnm4jVN2MxFCxP9A+bkm6ypMaZ2ZQpSIYMY6zUUacaHfbrV4Qsn9XG3Z0yeP7XVTXRQA3PrVsfCgmhW8cqCcOrHy+aCM1aQYPNTKkcguhIoPpfEbCnFnbLmjxYFE21cRDNozm/R2kbMSlqp7QjbrT58bVgb+ms7NS9R5J1TO5Ly872PqGxr27BaVVvOOtzLsDbEd94EbnCJ/BJXGOxhPQ+dVf4hf4r9zTLnoLR1yX7ALObkf/LHVHfvYrIMfXICWY5VATo6BCGxWcNKsxY7nWDeew8Rhti9EYhNt/2DI3hw4hxVkTutQU/PhoYB94QLKCJQfZAhoyu3ufL6Z0iGQ166cZudMCGUIXkckMchQkqKOGQMLMKQ7o1rXFt8kiw/5cFJSbmQMEgw/QSTKPT9gvJLR+jj3OW7KgUa6jTFJsZN5mGzkhLNQMtNE3Weuu7KQQjdDUh6MK1hoSHXjbNavxEflUN6hAmgo2AnQgQRidpgKLeQSIxxihzaChaE5NkcU1ROyRxABci460R/6ykmn4F+jKjt0izYDLTFWEkxm2HKCCEEQkZ0t29jrKUqa2pdgy6xxRj0xRu+XbqT0lrzj//xP+bf/Jt/w4MHD7h+/Tp/42/8DX7+539+KadireUf/aN/xL/6V/+K8XjMD/3QD/Frv/ZrvP7665d9ON/+ts76fN7PgLau1UaNc0DjZp+ydrBCAPRw+R8PXXsH4w/Z55cVbIaKzycxLwchPVPwDVMyqXHw9wYrh+ALfyMc7OKdYDtKg5UTahczr1OfPUPxSdeqDV16B+i3vR7hXYadB/G1bUm1t9Cxq3O2PGJSfIIZC4vCtWr32wFsmlJ+4xsNBmvPHNOZ0/Xvz+fYxQKrFKiyKYQFjk/h3v6qbceHGZsiBDVAqIT+5k1ufOIN+hsbXL1yg40gJhAKGceYQYKVYklwLoOQzpUddvUtZumA0/gYxgW2G2C7ETaS1IuK7E9OqN+dnTl/+k3eNVBONSSU7n9p7dQnhHH5pTSDMHYsv0EM89o5qOIyK+Uv0wQkEjZCRBwRx0OG8RWUUEseS1XXlIcT5pM51msPAsgAEQ8QcRdbWEQVnN9+6jyzTesDLSjnUC5ABgnd7VdIRtfQxmKyHFNW2CrF1um31kn9s3/2z/i1X/s1fuM3foM33niD3/u93+Nv/s2/yWg04u/+3b8LwD//5/+cf/Ev/gW/8Ru/wSuvvMIv/MIv8CM/8iN85StfIUmSJ+zhT5GtOyhxzntte5qF3HoU4t/zdHG79vLalk1OeCnKbN3nBaC0S1WEAgZSEEnYRKICiQ4lJmxyMqa13/Vzar/nHZB3FgI3qfSVq2HBspSWETSeEedwcnO23gpWLUP8NfLOLWi9F7KCIS+XSftk88QK4RZyOi+QixSTFy4q+bB23ph4WtUIzwQrK0hzd6y6cqQR1XKoFzChAoK4g1ABUWeD7vA6Qdhhe2+HXq9Lp5MQxxGhkiglCYQgsBatHVW6qirKYkGlNTYIkFFE3B3SM5sIaRFzjU4zzGnhOhDXaxeghXAtoWzb/KxxUZVvcaFr5+RF5s77ScW47cJa86Sk60UuFqvCY79YedQml8+x+7zWFWW5QIkAqRpmIyCkQCmJDRRGBVgVOEjTH7vRzfrkaXDv1uesG79Ga7TWGGOxWmO1bnKWgavrshbsk/UkL91J/df/+l/50R/9Uf7KX/krANy+fZt//+//Pb/7u7/rjt9avvCFL/DzP//z/OiP/igA//pf/2v29vb4T//pP/HX//pfv+xD+vY3D2W1O3i3f8LDsNWTzMNtHvaSrZ9t5QdwD7JvXOijDO88BBCDqmGUQ6eAbSn4XBiypyQyihC9iINuyFjW1Jlw0UKbwHCe+WJiL5Pk87iJghsbsNlxPYCOJrDIoatgJ3b/n9awXzi4JsFFfgI4Bg5Ysg6XTtofS/v82/2wnrf566AEJC7foqlJ795DPThwPaGKj0HbBGvhdObgMCmcpM7QuPuScra+7TEWDzYZ3voU8WCDl156ic991+cZDIcYGaFlglAB25sDhv0EGYVYITBFTVFXHDy4x/7RIdqUVHZOHUTI3gbXbn2WXfMK6YMDDv/rH7C4cx8zKTCnj7lu1jp4NbVNNG1Wz0+qVzJKpwcQTpyDmqWP3h6ALiA/djCCLp9+MbBuSkA/cFT6ykBWO6fbRg7a1owlYwyLxQF1dogSAXH/CnFvB2shjEKGwz660GSFpDQxWON0/7IJmLRZdMSsEs0XdVTuAbJGUOYZmlPHMi2c2rsFUENXq2UNZO8/cYuX7qR+8Ad/kF//9V/nzTff5JOf/CRf/vKX+e3f/m1+9Vd/FYB33nmHBw8e8Jf+0l9afmc0GvEDP/AD/Lf/9t/OdVJFUVC0HtLpdHrZh/3xt/XIp+2o1mGri1iAg9V8ewm/7bYqBKzGpm9m6NtxdFv7b4QoehqGBVxTgu9B8apVnBDwzSRk3AtY1Aodt/b/OCclWDkYWKk69BS81IMrIzdhVAv3v6GE6xEMAjgSzoEtmnPcYMX622+25c9ZsooM26Zw3/+oTOAmpCiASGFKTXl87HT/Pi5mcf2h5tmKrj1kFXVe0EkFnT6D66/S3b7K7c9/kj/7l36InZ1Njg4n3Lt7RF1pBv0O3U6ECBRlrSkqjc5LFgdH7L//LkYawi1F0A9QQcRmb0QUS06nEUfv/HeK399fHfPjzic3MDHnRIL+AfCyKhc0U7lo87JMiUb9QkEhHLTqo6nHISfWkKcz8pMZkoA+ETYcIqRCKUW3E1ArTZlYqAKoCmw+c6UEy5WZdw9PQ3RoVnpWUBcFtZm5461K5+RVCMkQwsQRKL4VTupnf/ZnmU6nfPrTn0YphdaaX/7lX+bHf/zHAXjw4AEAe3t7Z763t7e3/N+6/cqv/Ar/5J/8k8s+1I+/NVDacjD6yGWdifSoVdV55r8b4HA5H0UsVcrt+VDcejS3LsW0TkdvvqeEpENALUISESClWEVqVWsb7SimDdH5qC1qCkk7TQFpEECSwNYGRImDAPuxe6B7BQwtBBV0DCTG4ZBdAwO7yle1X22Shp90P2r/IKRjznVikKXLkXzkB/EEixrx3EAgtkLEVuCS5sYTJ2BZ6GoFaNEUO69MqpAwDImiiDCMCIKAQAUEYUDYRE6LLKW4dw8rBamQpFKQ5zmzo33S/XtYYagyheoJwk5MfH0T4q7T5xt2YXvgIr60gUrbz43AQXvrQYL/DMIl/oV7+Iy2jy+kbi8g2/nTx17DZur1+oftZwe7YkquL+bah7E83tb7xjSF19I5tMpg0egspQonCKmQuBYpdWmwZe4IMrpyz4hSDkI3/mGQDprDOqdiLxhV2QaK12XzewPzWMAUDQvyYmP70p3Uf/gP/4F/+2//Lf/u3/073njjDb70pS/x0z/901y/fp2f+Imf+FDb/Lmf+zn+/t//+8u/p9MpL7300mUd8sfbzNrv3sH4wbve/XYdqmtbW/KoL1z00RFno7HcNolwzj4cPtrwTspr0/l6pzXquREWLS2xVFyXPSrRYaoMkVcHr4AJK7KAd4BRc4yeUFHgoI6NBEYxJDGMeq5gsTeA6y85BpbQIAr3s5dCd+zqWSjApu6BERXEpVO8PgaOOEuhNzgiiIcWL7Nx5OPM368whO0t2Bq6jq/zCvKPOjH2GJMCtjpwbQCdAPXSFsGNTSgrqj95F/2New1xRblXJWEaQn6WQh8kAwa9EaP+iEHSo6MiYhXSTRL6m32youTNt9/mT956k6KqCTcHhJt96iJn/w9/n6M3v4o1GhkJRCDo7m0T/4XvpzN6BQYdeOOmi6CPJvCNuzBNV/3QJM5BTZqcZW1X4zYUTp1CCoIgQAUKayz1vETnj4EoPJkoxI1XTwJ65DXswZW++z0vXORl7YrGbwzUpXsO/bPurb1Y9e8v6xitc8yL1EVg8xzmFdZUFPl96pMxoJDECCKssVRl5fYjGmmlqONgz0I76FJ1IRw5FYl6DtVp46ieZMYVGJusGd/NA2alY1bU6vEkorXLe6n2D/7BP+Bnf/Znl7Dd5z//ed577z1+5Vd+hZ/4iZ/g6tWrAOzv73Pt2rXl9/b39/me7/mec7cZxzFxHJ/7vz81tl4n1I5oqtb77Z/n4dW+/1SMYwIla5ibNm7b/kFo76/tpDwBwTvJFiJgYaldGQjJqNH2GooQ5XsveWFavzBr1zZ5woQ/PyOcLM0ogTiGJHITercHV69Cf+BW8fOp+xkmbhtV4R6IogbdJIb9vgvghLMsPo1zUhcnHl2uSQW9HmyM3LEEH7I+6nmZAHoR7HahHyNf3iV49So2K9CHR+gHwk08YeOkCgVFBHVwJlpRQUISJXTihCSKCWVAKBRhGBJ3EmphOR6f8uU//gppkTO8ts3g+ja2yJm88xazb3x1xUoD6lvXKf/nT7s/4hCubbpINJJw78Dly3yzzgCYsmKwts9NCQidHJWMFEEUYmvzeAcFq5IHDxk/TqxBCOhGsNM4qVRCJpxzqhsnpbUbs7V+OFryr3Zk2H7OtXbCt1I451c4eLAuJtRMAImgi2i09qyHQYLYRfFh6IgNnjgiJAQ9kB33/NSTCyJ/Fuw5hB/Lar64oF26k0rTFCnPFoIppZaN0l555RWuXr3Kb/3Wby2d0nQ65Xd+53f4qZ/6qcs+nI+ftaOWD0v88asp/3v7p3cscu3zLQjO7V80EJdYQQsGp2fmFSMkjlxhcU4tZkX79oy4RuJI1hDVbh5AWfZDgw3AmhIzm2Mqzd0spVyYFZQWrb6/pIDDitgQtF5R4B6iOHKacz0XTYkwRAQSa+RZqTJvonVRlFspg3EU7x4rJ+tXo20H2Z4EnlWd4lEmhYP3osBFiUXh6M/TxSqi/TiZ9BCUwY7n6LcPsWWFzSoHxSIa2C0GHSI7I0TZcbCvVARCsn3jZTZfvsrGzi7JqEtRV8wXKYeHh7zzwV3macrRwSFa11itqScTCpNhdYURoHa3sda4Fxa7MaCwlnSRkmcZpiwbyMu4hVhXOYmvwLrjP69O1YJvA2+FbVAHgZXiYXWTx1kAdBuY06tU+O2DY9FJ46SUJK52rWryTKV2UZ7RK6ahtA4aE03N2bJYmNVCsj0H1A25QorG0TT/9LIvyCX8ZoXABtIV8UoBpmxIGbWrCzORc07Su4k2Jv+oCexJybKnt0t3Un/1r/5VfvmXf5mXX36ZN954gy9+8Yv86q/+Kn/rb/0tAIQQ/PRP/zS/9Eu/xOuvv76koF+/fp0f+7Efu+zD+fiZT9b7ie/DkH/89zwG3h6kPirxVGoPrXmyQDvK0sI1fKubB8RYt8o8aT4/AG5ytn0HnHW0/rQqR0LaysBIyx8ENVpBVRuyOwW1UUx1xaQq3fe6uKR7wEpJ3T94i2Z/Q5p2IRJ6MQz60Ok6xe6NLUQYoPpdZBxiqamlePyjEblKfLRwzD9tVrVeXgnGH4snVLSlzJ5V6+/cYwrh+hZsDRqV7QncOWjyKRdkInyUFliX51MV9Zt3MfvvYoXFbhiXBwpC6G9DMkCEfYLBbVSyw0CF3Ij6DFTI9u4un/jU6ww3RnS0ZbJYMJvO+MMv/yH/5bf/C5PJlLGuKesKq2uyd+5SjQ8glIhbe4Tf+zmsgNqWaKup+10musbce0A2nVDOZpBlQAUbCsKoyfs05QrKOpr5umnrJvbQYiOLHQGlgNnjGD4tE7ixOmzypyJoXmItetBwNG5+1U0eyTaFwqZxlk2eSlgHUyPOwpPrC1V/OmntWIBCNNJNzYGp0L2w2FpjdemcT6/vcru1hnTi4OW4Bxt7TmKpbI6r9rmEJ4lfrk9Iz26X7qT+5b/8l/zCL/wCf+fv/B0ODg64fv06f/tv/23+4T/8h8vP/MzP/AyLxYKf/MmfZDwe88M//MP85m/+5nd+jZSf3NtO6sPYRRYqPuLxMFY7kvJmxOr/VbNi863fDSv21vooWU/e4p7FuHABylzAA2WYCCirktmspFqfb31k1u4q3CYt+AdQ4WqjosCpb8ex633U60MgkVGAVAKjxOPZgn5bUrp9dazLW7Q7EsAqkvLXzytbPC8GuBLQT9wEP03hgyPnqD6uJnGOSmjsyQz9jYm7P58cwG4PomYx0R0hOiPU1RuowVXiIGYj2WArTNjaGrF1+zqDQQ/Gc4p7J+g05+DBPm9/7U1OT8cwGsDmCIxBj8foO+8jkpDw9h7B3g5WSYQpELbChAG5NcjZnCJN0WUJdbMySyRY1YzxBjV41DgxNKoqgAIbCyyPiLweZQHQk65AWCiXy6FxUnWz/7R0hI72c1RbJ0ibnrdqPeeBf9Qc4NvErJuUoALnAEVTWyFxx5mEjTPKocrdc5b0nNKGLCCbs1oVN7JJ52KN7YO7PLt0JzUYDPjCF77AF77whUd+RgjBL/7iL/KLv/iLl737j7e1w/TLjYifvF+/EPJ/V9Y9EJVwq0zvINrH6NXENatIaj3ib1aIdQXj0o3pXMBCNozZ4hGlIl5SKcBFML4Fjk9we7hP4I6vyCFtpC2yFLoZaIWREVjVtM82KzhHNlGTlS1ySZP0Mk3uROqzDEaJi/C8Q/cRZMnTTVQXNYG7OLPU5XHSwkVQH2cz2rHBBKt8g7Ywq+EgR/ZD4t1tor1biLhPMNpFdkdEQYhJupRhxCIJOKRmrgtEnSOrDFNlzHWO9uFs1uQ0jHaTpDVYYzCzCfrgPlYJrKmwVmOSiFKANDXlYoFJc9crKq1gWjmZL982wiMNQ1blFj6K9+O+HQz4iLo9LtpwvW3+356za7NGCpAIAqR1061UEtWJEAiSICIOQnRtmDMjIyOJEq7u7bEx2iBNF9y/f5/ZbIpAIqVCCIHRGlOVDSFBODgPsSIoAMSBW9x5mI+mmNYXNxsDZeGiPt9SBEBIJCFCRshAoHoCEdfoTFOVE+wZbP752wvtvo/a2mH/R+2k2g9gYeG4XpEpErscx8tXgZM8aqtMeNkgX+e3ABp90g8WsD9rUEQa9nHdFN6vm4fP2osxiWuyuNnaDzhIZD4FlTtl6eEQwhAbBWjbRUehq/OwtXM80rpVo7FuFV2IJmKSYMNl7gFZN9APK7bUZvOyrJxbhnOol2l+UtONTtzp1E1u6ceIzfeQ2RUMKVhq+6GBBzmclKi9Llvf+xrDz/wgBBE26UMQEQUBJonJAkUZB0xEjqxKgnJKnJ8isozDakLNHOwMpoUryNbaTaTGQF2h9+9ixNQRc7BgLbrbJS0Kis1NTJZSn0xhsYBJCg8K16QwsBDZFQHoanNKJ7gC73YZRRuGl7jc5XqZRJsV2pbpMsaRMsAN/NqtcFTYI4y6SKkI44ioFxGqgN3+BtvdEUVV8nb4AXlwyHBnhx/683+Bz33mDT744A7/n//8fzB7802kCojCDlIG6DylnJ5iatNEbE0tia1crZakidB77riz3DEJtWmU6ZtjXcwhz1gK5wISSShjlOwSdhzCroD0RDBdnDQNET9MnuLD2Qsn9VHbR+WY1vfZjty806r8w8QK1mpHU23moGq9L9b+1m7Bu2grXqznydat7azbTkKwygUtc2jWhWq5cfVRZeGYe8Jg67BRP2jNMgK3ehTNjGJwHnOZxLYuqmrDr6zt21PjPfT+PBaOAjc5ZIVzhO08w8fVjG1ICc3vNFFppiHTyJ4lCof0Nq46wdogwApJEChsElIHkiqA2mqs0US6pFMXyLogMyWWCkTtelPNpmfDcGuxWYodV2fuh60r6sUmOkmweYEpKkdIyGunHJGaVT2goclz4u77nNXiDB4er35cenKxRxRM63vtuj7LKlLRtmHBGqRw/bAkIZGKieMOURAy7G2wNdgiLwuS7iliPiXq99m7eZPXPvUpbKDoDoeIKESqEBV1UDIArV3/L1FjRQMtIjhTexQq6EQur6UL55zaAsDWNir2/qGVzSkLJK6eKlCSOApQAqooRog2pPLR2Asn9SRrQ1xtLTlPnX6W3NLzMrH2Clq/+0XQeQ+jZAV9tN/zP/34rHG0Xn8dvLVhQL8PD6G1HcG6+QjPr0g9zBbLpnZFweYARh2XzA2bzrDauOhDNnClUC4yihUi6LhJtNN3rb91TRjGxFECxlIeHVEcHbgHt2edKoXWrsYkzx+GfDqs7rXvsfUsdp7z/igh4KexXuBEZUMJvRCKAEellK4coFXoauMe2gZUpUVIA3WNEAKtKhamcGpBpqCsFxhTEZeaPiUq0lRbXZJXbyAmfeo79yln87NOylgn8DoVZ6IaKTVRZQmNQJeGYp6hZwtHwe4rlydr10ksF20CuhKuSFYFx8IRbPaaHFupYapXIr1+fPo6unaEtTa2E5XQC7sEKuTajdu8dOsTRFHsHE4YoJRiqztkI+lTVCXBoMuVa3tsbW6xtbEJwHAw4Ls//91sbWxijVi9qgKdppi64uj4hDv3HjhVHtkgCBJXBuCfe20b5qB1xfBSud8rT4rAXxRsnaNnD7BVSjLYoDO4QRR3qNM5srsJInJU9Crno5j8Xjipx5kns/ifXdwA9RIw7ST/x2FyWXdOPuLxq0A/wa7DbwXO6cCKSNF6Zs+QCPznvZPWa5/zD7B3PCFNt1NWD3L7YbasWHUCt8r1tVx95WSQ4hiu78HOtnu4wtg5FCHdQ+YPQoSgFCJJkN2+65tTaWxZISx0+5tsjnYQ1jL+5tepvvlVh+sbs4povnkfZvnq+GnOfYhzVDlOGf5ZiRQfx8XNeSaAzRheG0CsXMIxpaFYSxioMw7bdreoTUSRGqSEkBolLJXUzIOCUtVUiwnp4R10vqA7GLCxvU3QCalvjujLT9NZpCykoL7zANPO0Rlgpl10JMTSYShT0cstnVpQ5jX6dIY+PnHHuxW78ZKWMMlWund+jG8EsBs3FO3mhMMArm3D7ibMSvggh/HM/d+TfWpWdX7+Oq1Zf9jnZv86vU6P73/jz/Hn/9e/SG/QpxCGQroDCJQikIqqqvjk8SHj6YQwitjY2QFgd2eXH/nLP4LRmjwvmZ7OKIoSJRShdPVnv/cH/4PT//y/UxwfsaT1ShzTUjaRrm/Bg4ROAnHinNO0aCIpP5lZTDmjOv5GE6l9ksGtT9IbXaGqDOp0DuHc1XbpiYNQnrO9cFJPsvbk264PWhd7/TjZuiPwUUw7L7Vu54219e0sYQLOF17118pDgj6SCln1iFrfpv9emzwkYdnFNpQukoojpzKxTBDbVW7JWM60AA5CRNxBhBFEBmKDQKCG20SbewhrCY4OEb2mCNg2XlVJN0FZsUp8+3NpK3x8HO/58zKBiyz6oRPv1TSQn3CLguZ6+TY8IkpABM3CzSIwSCxW1FQmpxAVZTonn46pszlCQbI1JBQKEoXaGCCjANVNWMortU03OTDRuj+1QWlDoJ1SuqwNotYQS2ysnGiv1m5M+e8so3fhdPF8B1/ROKluCN0EUQmEDJBGYbFYL7Hka/sewXMRQhCYgEQmdIMuO4NtXr56g8FoSEZNSu1yao3VdY1Qgm6ng1CSoJMghCCKE7Y2tojCkCzNOI5PKfKCQIXEYQes4O333iWKY1QQuGO01gW6Qp59TpZRn3SwOcZBq22pGABTY8vaHV2do6QkCEJUELmi36B2rMXz7s9zsBdO6nHmmT2+5uh5MLwu287LP7V7Mj0NTFXhVs0BZwVYfWQFqyaBPlrrNvvxSEAP2MVFIW06vKeWCxyJI6NZ2Ur3Urhkd127hO/xsYNvwhD6Iwf7oZ30UW1dzVSviwgDRJQgwy6oAGMNtTBYK7AoNAYlBeHmJoPbr6LLguL0iHJ64iasUQTXGphnUrrVp89VwSoH8Z1u/p4rnMR9noMJwCauWDdSBN0BAX2UCuj1B3R6XaLRiJ2XbzDcGCCtJSxyVF0zTjOm+98kn5+iqTCNGm2dZiwePCAIAkJtiPQjZv118wuaUGBsTXZ6grY1oZJce+024adepZSWeVBTSUt1OCablphKrxirwrrILC5YFt4KIDbIHoTbIUEccuW11+j2XqJIUw7u32E2Pn3kYi9UEZ2oi5KKOOxQ1Ya8rKhqjbUWgSCMI7pR7HZnQViLNgYVKvrDoXs/CEBJt50oIlABoQoRVlKVNUooAuUSya+9+ho/9AN/npPTE05OTjg4OKSqS1I7Jz2ZYX3C2BcFZ8VKycUEjnRh62ax5lerbjGYpwWH9/eZzjST0wfUswdQpGBKSEbuO1XmXs8JTnrhpB5nCje5xpwfAXzczI+RdfJDG5676DjyMJxn9/mcjM87eaKFl4MxOAflGXEeCukDe83/PKxYA7Fwlfmyeb9oDmwoXf4DXOV8Xrki1+IBHB46qtGNW7AVuYeuBkSF6PcJ4h6q20UEMSLqglRUGKi1WzyLEINFSEGyu0u0sYEucsbf+BOq2dhV3+90oDN0D3U9c9CSX6x4KPNjplb0XCzEFXOHuJVzlrnOtrID3QFCxkT9q3S6u0Rxh2sv3WTn6h5xJ2Hn5nWG2xuIuiaYWGRmuZvOePuP/4j0g7exGwPsrT3od6jmc2anJwhr6PX6BIPhxdYAArfIiQWaisXBfdLjA7ZvXOP29/0Qu7deYpYvuDc+YlFkzNUHVPeOKLPcjc2UxlHVjuXZfrh7mmDXEr4c0et0eO3zn+D692wwOTik+q2S2YPTRz5LcRiz2d8hDmKkVBSlxlJQVrXjLAhBnCQkIxcpSWORTdQ+YtOt4RqnZdwXkE3EYrqGQW/oojma6NVaJN/FRm+XxTzja1/7Gl/60heZzWccHL5NfnAfrasmh4vzinXu4E9CEJsg+2BzMDmuv9MqAb+Y59x5931EeILJj6hn74POmzqqHefc0yNXa3LhLolPZy+c1JOs7Zzs2s+Ps9m114dJ9Pvv+einze5rw6BtNQrPePLsp6j1MqxGnM87nZnwBUTCkSVsa5vWuOr5qmoUIxrlgCYJLrBNaZRCyhCkQjQHKazANjfQoR7GIUVBgAwCUAIZRgglsUo6RlSsHF7vC4TXz/Xjvli5DPORSkAzsTVYUSQRQYQMYsKkQ9wfECcduqMR/c1NoiSm10nohgHCWpR0gq2R1oj5HDMeNyojmxCH2KpEZ6kr2hUKG3ccXKUvMOEtn0vjpJAAW2vCTofuxogqVURVSqUEQRwjggbWw7Zq5x5+qIUyiBKkFqhAEiUdOp0BRZqiVPDYZ0kIiZIBSoUo5dpiBGGIVAohBVaAkMIdh5QOeWt2LaXESomxllrrpZScNyssyjrRW2ssxhqsscRxwnAwIpAx/d6AOOpQhiUBzTiuW0lz689Zu5vrW2ifGdgO4gSFRVBVNZgSWxZN4WMBdJyChRDumRTiuc2LL5zU48zXAa0nyT01uz2BP4v5CaHFWHpoImyRk57odDzE13YyH/ZY/HF4Jp+PJtorSc94bDuxpPk9YgUbeqgsaA5y2YlCOmUAISCQq212QicbZKwTyiyNw9lrDXmBjBOi0ZAg6SLjBBWEjnxY1VRlikGgawOlwVoopGaqamQgkZFFRgYrKhgqkmvbmDylMnN0pl29VWJdNCFYOdSMlfNs18fUnO36+2FtvVD6W2U+Wg6b45gLUJLu7ga9rVtESZ+b129x/epLhFHEYHOD7nBAEAT0VEDcKHpr22ReqtqRDw5PYJE5dYM4crmiyrVzqJKEWaeLtJC/cxf7uMJmXyKw1kKjSismiznhYkyhNbLXJe7EhFsTxObQrfbDpkq9sk5pwReSe9OWYFqSPEiRoea4c0AVZyyOTpgvHt/0sLSGia0Jqbl94zpvfObTjAZDXvncp1HDLnWsWJQZ8+M5Uko2ej1GSXfVGqQhb4gGfhRCLBvzGut0BS2wWGQcH56SFyVH+yfcfe8+WZqz/2AfYQWhigjDLmE8RKgSUxcY7dlJHqMPXIQlSqByi0EgiHskmy8TxINGBX0TZEg5SR3qa8wqtyWlgwtlgmvF0bwu0V44qcdZzaMb311mVNUeN+2Jr70vr/5wkdySJzZUrb8vam2qfZuN1y5eXD/29rH6CMq32xCtY2lHI7Cq04qlk2aRYgUjClzyOgwcrDcpmyLFhtGXF6gwoTMYEW9tu4daKrAWW9WkeU2t3UrT160UlJQUCCUIBoYw0AhhUBuKTrCDWaTY2QH6pHFSnZaT8ufiRXZhBQcrzkKqH9b8fhQPKx981OaLXiOcfl0qEFLSu7rFld1X6fZHvPH667zxiVcJggAtGi4FIKRE1hatDZl1+gRUtStY3j8GBLx7nyUBpjnJUggq4S6urWrs45oHWhxE7Ofdxqp5xelsip6dIqKIYNCnE4YsJlPEzsjlZKKmdUulXZ2Ubx/fmDCWcFKS3E0hKDgMSu4Hx1TT2ZOdFJZTW6FswOdfusYP/shfZG93l9FwQLDRoxaC48WMe/MJUkluqT2SXg8lRdPlqTk3I12QIgSBdI5KW4sRzlEt5hnvvXOPyWTO/r193n37HbI0Q9caLERBRBh1CeMNhCqomGB0yWrF1QzkZQv3lRZZ0OkzvPo6yeia+7xQWGDOmPJEuLyesC4KkwpUI0RLjWvN8cJJfbT2URRXtiGldjTVjkzan7uIPe3k5rd7Hk3cb695Lf9t3eLLk+POCN+2t2nXtiHWjk/goDUpW/ChcNBbGLiJRckVA8saB/kZ7Y7FQw26dsdUG6zWWG1dYlqw3KlTz6b52YSEEtcWI/D78MfEyjm171Fbnf0ycpWSBjZhtVjx+baP2kmdGWvuWkgVEkRdpIyJwg5BEBMGMZ04ppckBEFAbQ01BqxzTtZobFVSZSl5mjq5orJ0zuoRUeLTpEyXX2j/xN37MivI5ylBD9RAOKgtCBBhIxOkPOzH+ffONk4yK7GBogwFpTLURYlebz1B45RV4H4mHWS3g4o7xP0eyaBPMugjooBS1xhryYqCNEuRSlFWNQaLpNEJ9A+HtcvclK401rhxa2rhxDdqTVnWlEVFWZZUZUVVVksI0GKRUqGCEGsdlOoHsJBO8ggrXPeAJeTZGvgyBBm6CE9Ktz3loNslycQzBxtH5nD0y2cVvXBSHzdrOylfi+EdgIfZLstx+gfUr5rbTmXdsTSRlJSO79ChUVWZQD5lNfH4vFPFasL1pIM2I8pHVd5BxcIlwruxUzz3kZFUbsVb2lWeSDuBQB1IstMjKmNWsJEx1EGCSQagQuI4ppc4tlUV1ZSRk0KSSiK1AKvdA1+lWJ07YYogaurC5CqX5q9/BFzDFf/mONX4Z4miAgGj0NWDtRcquYGTRmXjjElXsIk79ktdtbaj+FCBjRBWsb33Ctevv0EU97C9bWzoqI7SWkJjibGESUgYB1RlycnRIbPJhMnpCV//0hc5+OADJkcHTA4Onnt0WE1Sxl96m/T9Q3ov7RF8fweVxCtWaA3MLSymTh+wtA8dj9Ga9HSKqTU2CKkHfXSni8kydP1wdNcZbLBz6xMkgxG9zQ22blwn7nW5ffsWi0hyUGeYyTFmMUfXNYeLKYfzKXEUsx11uL65jQgCpJAIBMZYyrykrmpm0xnvvPMOpyen9Hp9ru5dpdvtMZvNqBuliDiO2dwc0e0kTGcTxqenVHWFkoLN4SZVXTI1hqrQCBXQ39yjM9xGVzXzkwn53FNrHSxQV5LZ6Zi8cFT4eGOADBU6DLBBFwLtmIF55uB3a1zdla0d27ZcX4U+m71wUh83ayfp240B23ZZ9987JV/H5AuV2xh920k1UX5XwKZocrKZY4ifiTZ8ewuv+efhQ+/ILGchxQDnpCLpGhtuDFmqlRsaeK9h+SFc4javMQKy02NEVTtSRbqAusIOtuBKAklEHLk6kzAMSOWCVM7QaBd9Gedoy7omrzNs3WjSBZHrPxTKVU7G589CVrpvB8ApLt/2Ye9JIGAzhO2IM3Un08rRo9edVFunzVRNGHtJA0LixkFA46RihInZ3nuNz3z/X6DTG3E0mbM/dkKGykJoDTEwSiIGg4QshVk+pzy+z+TOHb72f/9/efMP/xBjNLqqnjsyUU1Txl9+GyEl1XelDD/zCvHuprunvYHDJFUOqXDO6pxLZ40hHU/IplNsGML2NoxqbHG+AHBnuMG1T30XG1dvsnNth1c+fZvesEugAtJQkVY588MHTN9/nzovmGYp0yyl0+1ye+8a9a0aYQVSuOjH1JqqKCmLkqODI37/f3yJb779DteuXuV7v/d72N3dZTpL0Q25JI4jNjdGlGVJUS7IywVlWaCUZDTcQOuaMiuZTQuUiulvX2fz+k3KNKfK3yOfN61Amptfl4L56QTmFcnWCDYSwkA5iaugC8qAFY7xKXA1V3EMNgCdXnpH6xdO6ltpj4LxTOvnOqPO22WuRu0jXn5BZNy4VNoxkUKfL7XN+82YNedF+t4xeXLFeZJMy2LDZtLwRZUeAjQ02Ldg2dlQNBuuCmyeunqqKneQn3ZJYIHTTJPCqUcHMiCUAQqBNhqtHYyCAdFge3bZBBJ3AWSD2wt79n61YdhnvRdt/NRfH3P+BOqhIOfQnmHHbUi5Hd0s7/9q1SFVQBjFRHFCJ6kZdGoCJYmUxGqNrivydIE1OXmWMp9NSedzssWcPF1QZo/P41yqWetURtDovKTKcsospy5Ll5/05sdY852HN2Ox2oLQbgHkocrzJP2FQEqJVIogCEjimE6cILBgnHq7BEIZIANNEkVU1tCJY5SQGG0w0mCVg96stdR1TVWWlGVJnhcUeUGaZkynM6IwIs8qrHU1f1EUYrsdojCg2+3S7SaowG3L9TO1CF/oLgRBEBJGMabSCNnOK6xe1hqwBqM1uq6RdeUIE1I1LT+Mi5zOfM2cey2f1V44qW+V+YjD07fbuoB+Ze4hJP/Zdr+xy1it+PHkm/q1nNLyp3Dw/WYKm7ZB4Logkmbh3TgnLSAPoGorTETNdrLmnMTayxcAFxrGhVu9RxX0a7c6ixL3CmunZJ03iV+pHMyAhskDmMiz5AYTI8kQBIiGcieAnkwYBhEGw8n8iOP5GG012IBIbmPFgrq8j16UzumJ0BWu1hp06SYsf63aRJZnMWMdRJLqFVGnxDEZi/MSN8YVUgrx4aOoAEcIiVnVwvnGmL7hIxa6zjk71QhNKC23drf4zPXrKCnoxwF5OmExqfjq/h2OjvepypLJ6QmL+Zzp0RGz2ezDXJVLsXy2YP+tdwlnE/I0oxpPXUG41dCPGsKLdq3afXfqdTMG5gvnqHxr9jXTdUW2mBFOT6mHHaK8ohsbMDWiLhHWst0b0HntdQRQCEEuQAUBu9u7FIsSHWhUNyGMI+q6ZjweMzkdc3x8RFk6evF4PObLX/xDojBkNBhx9co1et2ErWGH5MYVhIAreyOu7G2Q5zn7B0c8eHAEViClQFAjRUgSRwx6fQJrCQPLCq9OgcpF681qtNYl6ckJMgyo0wwTddzzV0whmzhZJC2hbJxd1Rb/vBx74aS+VSZYKVm0cxEVZ9tYtJP17bzRZdCd206qrUG2FrUJAYMMrms3fhcBpHFD4muCjUpArVy95xLm85NgM/bP9Ihqj7zS0cQJNAxasF4ncFJIqm5axzdeWim306KC6di1IYgC15ogDBCmh7Q5ghjR8vQdkTAIIow1zKtTitkCbQ1xPCSK+hgRYCqFzpriR5RznMIrReuVsKjv5PusTspaN3FmldvmCStG6bn31zQr1mfYp8IVWfdx9yZnRe7y5RZhs3+xclRKWq5vDnl17xpKCk7HJ4wnJ8xmM/7kj7/MV77yhy46baKBYjZnvrjsHicXt2KRUr53FyanSyKCW/FrV96gBIjS1eA19OuHHJUxkKYO2npElGB0TZEtyOdTdLZBVGiSyiCqClHlSGvYHW1xfWuHIAjRnRidJE4vNysoshKtFEkYQgxa18ymU46ODhmPx5SNY5xNZ9x7/x5VUfHqK7fZ296mmwzYGA24srNNGARsbQ/Y2h6R5Rnyq9/k5HROVWukFIBBSEsUhvQ6XURdESiLG8hekLR0K9DAQijQpiabTpsz1c2iMYR67qA9XTZzx2XBCg/bd56Tehzb6hw20Lnf9z8fRzdaFpo+YXuP2v46NLYOt7Q/v05oeI6J5/PYd86nuClfCYFQkjASjkcgoAihwLJY58bb1gbWa78Mq9W7hwQD61pVp1XjpAsn26L1+d1Gl/k7sWJrYcFobF1AlSHqCmEtskHsMK7AVxEQyRhjDUnQIQ772Fgg+5uojd0zsI4pcupat8RO7dnz+7DmD9k7vvb1eN7Wrp9bLoCa1ZCQBN0uyXCDoJPQiXtI16+9KSQ1WCsxxqCNRVtDbdxL6xpdVpi6pswyzHn37aMybbBp0WKONg+dla77LMrR2EXVRKbnX3gRBEvY1zbt3oWUri5PKXr9ATubm2xsb7M9GtJLYjphiJAWqRzU10lioihydP0wdC9jyW1BVdXUdc1saijylDRNmUwmzOZz0nSVexLQsO0ESimiKCJJIrddFSClQmtDmuUs0owsLygaSSYZhHR6fcI4wVpLlmbkWU697A3VLvjVjpxU58ggJIgSt+26oK5qrG6axJ25Xs9v0H5nOanzWGrrtl4U660dQZwnx9+2tiyQL+y9iLUhPj/BrjseLwLZjqDcIgjy1mee50TWppE3v/ZlxG4QEUeKzjAm3onRSjCWrs5zWpUUp2PmiyaZ2p7PY1bQnz/uonm14UBpXaFnphvSwtit2qRYqU8Lj+vhKOODGLpNxXvj+W2ZYsb3EKkTOw2HNwnpIIyl0gaLpSOG7HVvghAMBzv0+1vYumIRbZLfeqNFy4X8aJ+j3/+/SRfvshTlbOeoPox5mFewbBy5HG/P23yRet0cwwjX6FEmEGyDTNi+eoNXP/Epev0Bo+EOYRVh5jVZnHMSTlFSMsszcl1TGoMNFaobo1ND+mCf7PgEXVWU6UeYj1q3RQHvHbsoO4mh323aVESw1W8WZMewKFli6/rsgyWCgGA0Iuj1MFVFPR6js4wwThjuXSPpDXj9k5/if/tLf4GXX75Fv5OwPRwQBQGBhFBapBB0IpenklKiVUCtAqpac5IXTMYTiqLg6Gif6eSUsiqZjE9JFwuqqiZLU5QUxHGIGPUxxrC9u8nVq7vs7uwShSFhGGKt5eDghC9+6StMZ3Pu3t/n3v4B2ho6o21e3buGtYK01HzjzTepi5T5dExLfsOddJ3D/B6oI7pb19i5coO4O2R6dI+jO/eostRJhtlnhREuZt9ZTso7Dz/5w/mTuZ8s2wlj7xg8XOUZaktVhLX9+EnmnMjjseYhr/XjaeeB/D7aBbXeGT7vSKp9TI0JIYhFwEgmdIOAnU6frVGXOhAcJjAJ4SjLeadIG1oqZ3Uq2w0V/cKtxDknjaNxT/zOClDF2RqkSMHVPmx5FfTm4JSEbrMi1tZBhgaoC+ziBFuEiO4Vgso40WcLdZM8j0SHjditPrf6e4xGu1gMs8EWaX12Yp3f+SbTr3+FlDsNoaB5oB8XtT/J/BjyKYGPMuDwsJ7G5Q6HNOzjGKJNUAMGN25z+/XvZWOwiagtVBabGYpFyTxJkVKQ6pJSGyprsUohowjynGI2Y/Zg/7kk0Z/KihoOmxYb/R5oBbGFQQf6Gw42nhegTly0bs65mVIS9HqEm5uYwvVwIstQYUR/a4f+1i4v3X6V7/ue7+bTr7+OrStMUbiGjmFAEgdI2YZNBDUShXJSUFXJYr5gPp/zjbe+yZ0776F1TVWVGFMjhCSUIVIoZKAIArdAG476bG5usLW1sZw36qpmMpnx9tt3OB1POJlOOZlMkEox3N7l6s2blGXJ5JvvcP/ufUxdQL5gRaNt7pcpIT8BBPHmiK2NAb2NXUhPOC0WVIsJl4N1X8y+s5yUOOf3Nmy3/lpn17ULWcXa/1l7f93JXdTatULnHX/bicnW736x92HHxfq5+O3DCm5bs0gIYiFJlGKU9BgMR3TigDCJsSoC6RjUkYDQamQpXUTQJjG0a2/WnZSfKNvn5VvW+26oESvmn6+TCoWLogRNkW/zPyrQxv1PAdJQm5Isn6GtY18J1RQoqsBBJCoAKdBod2ukxKoAaw11VbkJAwhHm3Sv3EDPU6ryBKOzD1+32B5v/u9ANGQZ+/yffSWR/QjRUYhYIgcBMpZo0acmdPpyCKSVKCMaX+MemrIumWYLhIBZPmVezMmyBdPJCflsQrlYOKr5t9pBeWsOQwYhQW+A6HSg14NOD4RAByG1L04NAvcSgjBw40MECiEEJs8xZbnUFLRGU2UZ+XxGlWe47rsOAq91jdEaqQTaOhUU99wJl6urKvLKic5WlW4kkeRSCkmpgDgOUUoghSSQEUoqpJSEQYiSkt3tbTqdhDAImM9SxqcTsqzg+OSULM8pypK6UV53xDuL0doxCY3Pz/nJaM1JtS6e1poyy1FhRlU2dSUyavKiT7M6//D2neGkznMqPgoBF4G0m/R5SK0Nuax/bz2S8c5jfbJ9msjGcr6jaSsNRLiEdptQYXHRxnnQ40WsrZrQVrVoyxatMdUksK0ibgYxvTDiU1df5vVbtwgjSd4pKP//7P1pjGZZVt+N/vbeZ3rGGDNyrOqq7q5umsHdbRCYa+u+INBrGskDYFltIRmBBZKltmzxwQjJyAZZQrL5YIMlkKwr2a8Euh+urpHNq4vdcoPbNk3TA03PXXPlGBnjM55xD/fDPieeE5GRmRGZkVlZ1fGXTj6Rz3Dms9dea/3Xf8UFVjgiAUMLWRkQjELYPLLtgEUNVjvU2myzuSbNtvfwob+mHulC/Z1C+7BMFEA3qRPfCjqxD+FUFaQz32Zeyvo4HfNqxJ2t15Cqg6iVB6QMWV+/xoX151BBiA0kM5thBRQSKhFTVQWT2YR0PkEZzfBDH2b1hfeT3r7Jzuc+S7Z5ByIH8hEtSpvRmShveEsHYwv5k33wZTciev8l1KUBoUzohksEMiHLKsbjjLIyXuSj8Bqi1jVzK8fubMRmuoU2FXd3b7O1t0mVpYzeep3p7ZvYsqSYvn1kiWPhIO4PGb74AcKlJX+/hBHOWWY7I6ZSYqWAbge6HVQYsry8wvJwiCkr9u9sMrt7F6s1Nvcx9yrP2b99g+nOFvuX1r2hwvnC2XSGriq6JgEFQa1wIaTEWMf2/pjtnTFaG/LSEgQRYagJw4QwTIjjiGvXLrK6uoQUAiW8YHK302F9ZY1OnDDo99lY86G+N16/zv/49J+yu7vPzdt3uLuzR1EU5GV5QGIpy5J05sOHumqHZBqq8PEDWZ5mbN++S7hXkE1SrBiiohhr9nFVxtPwpt5dRuroe41GXFtstX0t2t9p0DZMR41X4xE0Ce5HuT7HeVKNtyFYGKn4yHca2a3Tom2cDqk8sDCEzfqP/KwvFJdUzCCMuby0ysVL15ChZMftkzHxdRr4os7YGFSqvDFthy0DfCip7rF2cN7agqztCcUc70lF+DCUw89EtfWGSsiFZFIU+VlxFPnmhVRH8oOO0qSU0y2/QqUgCFBBxHB5jSiKUWFEpSB3ld8tEWBEQEXFrMiYzMZ0goCly9cYRBEqjBl982uwKw6fv9PioJBZ+OroRHrjNH/y8VwRBaiNIeF71oldj769SOS6iMmY6fw2VKlPvVULTgESHI55nrFfjSiqght3bnBr8010lmFu3cRsbXqP93G7Fj8BBEmX3voG0dr6wXvOaKq4w6ypmUpiGPaRcUz34garFy5QzudM725RjceHvEOjK9LxPgDpaB+j/UCvjSEvC8qyQAaCxMR1aZ/3lIy1TOcp2zt7GGMJwg5BGBMEjXJ6SJJ02NjY4Nq1S96jxWv6Lw2GPHf5Kv1uD4FAIbDWMp1O+cY3XuH2nbtM05TpdIY2BuOsv5Ocw2hNWZRUWmONbR1KE8o4Hroome5PUKH1HrLoIIII4VLcE1Q+b+PdYaTa+ae2IWoGyqPe7NHwHa3fNNfMtV6bdbWZaGd5cdrhtrYYaxuPmrNohzfb+a7mPcmxkj5OQC4M+6KiQpJR92RyjnmasZONMM5SWkdlHbvTlDSv7p2QtY/tvsW8HL6G7d81k7zU1qyj2qMKlG9MiILYeLaRkJ5oYa2v33AWQoWME5AxTnjShFOa3E4ZpXcJwhjZ6SFVDx+Oqb/j9AF5wuqKbDLyrXP29jH7JYycVyw47rq0df0aY9y8354kNPVIpfMDYHFv4v6xIPBeWnzkZhqEnpgCSCmJgpCOSOrJhsJUFZdWN+gkAUEAJQYtNcZZrDJIJ1B1bs6WJbYqcFof1m48Ovlr339Keo9YSs/aLI8yxc4epizIR2MMkrjfJVkaAAFBP0H0OhBIwqUh4foaYRQRDXqIKESUYV3wehTeM0IICl2xvb3Nrds3fZG4M8jA55CkUkjlq9+FFDjni287ncTrAAp/opQKWFtbBWHpdhPW11cZDAZ+LiO88Gw36SAQWGNpWtFYa5FCEicxnU5Cms8xeo7Wldf9cxaMIp+HzJTFGEOVj8HMPYPPPlhWy1mLrUqcK7BVgdUpzmqv+vKUQrrvHiPVNir3m90eJUm0q+6bUBQswoFHw3q29f5ZXp8mR9Ps94yFunPzfDxO4WhjHI7mpeSRz5vPpM8h74iKDEsPzQtU3kxpw+0723zt9qtkWrNTGMaVpdCa3en83n1skvTt8HXbcDb/t62/m9cc71Up5xsgKgOd+iJlmc8f9DLfziMJYbnjNcSqAvIZOIvqhQSXViHuYsoZuhhjXcWeuU56d5cw6LB+4QOsxC/iFSdKrLNYk9M0cSuzlO2tW4j5GPPGDsW3JnDdevXs47yGDp4xJ/GeZeNddvDNH9v3kAUm1ufSDAtl+LNAIGAjhgvJ4UlPrwM9/+iHQcCwO2AQLtNf67Hx4iqJjAmkI1IghKN0JalLqTDoQBM6hSslwpaY+RiT5bisXDSubHvpzdJmysYhrA997dssg53JsXJDZ4liPGHvlddQ3R7rH3gP6y9sEHQi5rsriEtryKpi8J7nWX7+eYIgoC8VgVAYHCK6d5gUUiLjBKkCJmnGl77yF+yO91m7sMp7Xniebr9H2IkIk8iH++p8k5SWwaDHhQsWrS1pVpFmmk4n4bu++zuI44AwVAx6HZIkRCIIak0/4QTSCKrCz3pEbaRUELC6soLWhjTdoUg3yYvU67/ivAdXbDPdi3HOUpSFf0aawvAHwGpNOZsihMa5Gc7sAxXOPmru4fR4dxipBu1Ze3sQbIeTGiN2NI911PNq0B48n+Q1abbTKBk0+ylb7z/q+HXUS2lwv+Opz0smLBmWFMG89qScdcxmKVvbe8yqiluZN1QP3PZJzttxx9YMbBJfcCnwYb++8AW+YeATJlEILoGVnvekGtFVZxGRQi51EEkXlxUI5WtdsmJElo4Igg69pcssO4cQDudMvWhcfVGMrshHu9j9bdgew24Be/e5GAIfuu3W+5233m/yc7BgjloWLSfOGlJAP1joAjb3ehx6nUS8JxXHEd24w1qywovLLzAMB+TllFm2gza+42plS0qnQYJEoKxEYLBl4ZvhVeZw3dXRnmRN7hO8F9xNvJiwNq0atycHUxSY3T3EPMU8d4GoExINOoSDDmLQhaoiWl2mf/ECgQqIygpZakQUeo/vKISXixJBSFFV3Lm7icXihOXaC9dQoUKF3pNSQXDgdAjhtfZ6vS5aGyrtELkmCAI2Ni6wfmHZt1UTXiVFCgiERCLQpSGf+VqlBs45pJR0Ogm9XpcgAF1N0eXhHkOmlOQHg8nJQ0HOGlzZzKBT/IzrpDU3Z4N3l5Fq4zh23tG/G0/puJn80b9Ps832NtyRz9veWzNISRZK4W0x1qbg1bVenwTabLw26hCnqRy3R/t86fZ1nDW8NR6xX2pybansE9ophx+45xw+pxpYtt4I4MMtKF9oyTwFXfedsv5BtLrAzEcI3YQqahdGGFDa62TqPcbpHYRQWONj9rqqCMqUxPrvRL0+1mrMkqUcBNg+i5xzcw3b1y5t/d2t912wMFpNacOD2J6PDQFOgQtRQUivPyROusggJOwMkUFCXy6TuC6RjVEu8F1QnM9nGAfaCeZZzvZsl8KUkOeQ5eiiYH77Lm6aelmnpgVHm2hE6/9SepFXlDdOTXPLKIClDlRhfS5cfZ+3ZlalrkO7jwFnwGU4Y8j3t9h77VuE3Q7lfEpyYQXrIFweQKyw1pHu7zPf2qOczigbIkhjmKQnQjhrQVdk0ymbt25SpHM6nZjqQDpJHHhAPoQM1grKypLlGmMsUgV0+x3CIPBsQOPVIZwSSNWcAf/bovTsvTzNvRNkwRjHrVu3uX37TXb3dhmPd7H2uHPVPESu9XoSNKEQx73dIZ8O3p1G6iiT7WgOpHnvft7Jo+auj6uBatZHvT8Jfqat8WG9sv7/Cj4c1HgPR2uJnuRg1mbgtQfe2ohXpeZL16/z1vYODpilc+Z5iXHuyRkpy+L8NP93QN95bb9IQE/6PFQU+jqXrV0/GMUK+j7vYrMp1c5biDDChYH/rnAgC1ApVqaMsjdI9zKkk0SlI9QOYSWxjugYhQsEXNjArayQ64j9y9cpGgO6V+9jBPRYKMnv1fvdBVbrv2fAiHvDnU/MQxfgIrAdomDAlSsfYuPSc94Q5WArR1f1WHZr9EyXWPs8SWkqSmupnKB0gp3RhG+99RrzbAZ3t+D2XVxZks9TbOpbv1O1DuLocyDwob3lFS9zBdRMDOhF0Fvxk4ymU65rZmh1GHSUwn76eDkQV4DeByEZvzWj2H8TGUXEL77I0gdfQiYJSacDSYxOM0Zvvcnk81/DFAXFdA7OIVRA2OmhogRrNFU2xxQVe3dLvjKbEIUhwhm+/we//+CwvQ8kwTqvzmEcaaYZTXKcg/6gw9KqL/INo4CyqlBKooKAIPDnyFpPgJjMp7z2xhvs7YwxGsoCjLZ85at/zp9/6X+xv79NUaRU1f1CeI/isVb4G7dpy/B0aqPaeHcaKTgcKoPjSRNtht7R0N/jbPPovdAMSG1du/b+NZ5UwoI40faknnTot02Bb9+D9T546vGM3UaH7VGN+GnQeI/tPGEzUy/qAU3XiTYp/Uw+yz0F3UVehQIJpsTlM5wOgG6txSe8JyVLHJJCTynzEOUEriYJKhcQMSQi8Yo5SQIk2F4H2VF+QtEmuDTsz4BFi5UmD5XUfzdG96lORiW4ACljer1VllcuYytLaUuMNXRkQkRM6CKUUweN9qz1npSxvknfaDJhNpt4I3XjZi3Uep9jOXiWWiHGQNa9whJfC1aYuqZN1SQKvIHKrfemDtx4AzN1usn/cXAWXAkWqllONdtHBAHLly8yWBoQ9Hso4QkOVjjy6YzpnbueENIcVl1fJ+t+Wl4mSVNkmiLLQAjG+6ODPk8HnlT9t/ekQGtHUXuGUkmSTlTXSHliknCWhaKJqz0pR6krJtMpe/v76AqKDHRl2dnZYXfnDqPxzglOxGkNVTNbvg/D6ing3WWkjobq2iG8o57Uw35/UrSLVRMWifFD26jpXhLPqgqkfxjndTWrwYeH2vTsJ+E5NfmSdq1UO4zWzpMcNwDdL7f1pHA0r9jsa1PsW1gYFN7oGL0ohK3qQdDhJV4KA1J5GfdO3Tbb2LqtvIOgxIkU53zDOWccuJCAkAiJSQvmm/tU05Ti9g7mbupD8801a+/rWZNqHgVBBCpCdhMGy9foXrxC0u3TCZehCpDGEQcSF1sSFRMHEaFUiEBQWV+wurd7h1vXXyadz7i7cxN95w7kKUzn/tw9aKLSqKME0pM04sh7sEr6MGypYZJ770uKOkxaPzRN+4xKt1pjWO8ZI3weMgjqa1t7WtbVihH139r413bZSMyiV9YB/d9RqYys3EPNU0SqEXONmWUUkylOSn/f1C0oXK0QAWCNxtVhNRkEhEkHFQZ0h0MGnT6DuEsSRMiaMCFwSIFvEx9K4iSoKysM8yyvc4MhYRB4ooMxVKWlLAomexPyNGdne8T+/oz5vMBo3+PTaEulqxPccvfLaZz0d+3fP128u4wUHA6l3G+Wd7/fPQraxbdDfMfWdtIYCTIBkYCT4EKwCuYVjEdA5h/oEYtYfsSiaPgsBzyFn9k36288ugw/6LYp9/czUE8TDemiGWyaPMcuXkZpyUA09+0BRD3YCeEVKOZV3YIh9QOYALCeXCFr0drC56ZclFF/irMaW1lwEaFTdBxkOyNmn/kqkzc2sbMKcyf1ob42I9S1/n57nmUPISHuQWdI2B9w6ep3cfl9HyIIQqK4A3mIEoI4lgSJIBIB3SAhEl4QNbcZlI4bN77FFz71fzPavkuhC8oy9wam0t4I3O9+aFIXBuiFsLIGq4Pahaj8kuaegJLWCflmUtcLYRh7gzUrYZrXIcEQ1jq+zq3f80W3lsU11sZ33qwqb/jm9b4G+Bq0UHgD1aOOGDgIHS5wlOGEyfwGIg8x1yeYG1NcVmG29nzfJGTtnRucNehsjslT793UIsRBHNNfXyfqdFlZv8jacJX13jJCKAQKnPemVD0eJHFArx+jraWymr1xRRgoVoIBSSdG4NBlhXaa8f6EV775Ojtbe8xnBdubE7J56cOADpy1FFXhez09EPebeT4MTTinfYGfLt59RqqNo+fzSZCImlBZiJ+t+Q7Mh1lNUvqB0ck6QRzUHrRc7Ge7luZxikQfhMYINjmDZpbZhKDeRsHqB+KoN1XWS+QgN94rldKTBJTwHlITCtR18B58LyBr/HWw1tckCWqSRX0BnAZrPN3XViinIc/Ru2PKzV0fxptzPBvvfh77036upYIgQoQJcTJk0FtDKm/hhQUpBYFUhFIQyAClpE/W4zD4wThLZ+xvb7J/9/bptt2cAwM44dmWcbIwUNZ6o1JUvttyGwFeXw/pr1tRh816QS04rLzSSDf2I7StZ4JBXT/nak+q8cYEnoYftF/xavt1x2gjK7ROwQTo+RQ9GkOufThTyPqeWzyMzpp7hxUhUWFEEMe+OaQKiVSIb3AijqTSHEIKpBJIIagqR6U1CB/qa9TYrbU4YyjLiul0zmg0IU8r8rykqhbtUJyzByrpJ7s494cQ7UFH1LviDoktvx149xqp+53Xpl7orPIq7YH/uHwU1Dd7baSaTrNtpl+zX81rk4t51BCwwBvMWlT1YGYrWBSXt4suHYs7wba+8yyh2a9QwjDyvaYi5/swbflwzEFn32UJl3XdkdF4gyWAvRRubvvBLq98E0UlCbUilF0CJCs6YiAEshKI0ZQ0nZJvTTBF/uD9MyzqwQyL86nxXio8HSUGIUBFEPYg7KFUh0AkRCqg141J4hBrDTrPsVojnMOphacpsDjp6fiP/IA0bMfS+HOu63Bsnnnjk5b1JOIISgvTqqbu140IwdfIicKXHeRAXNaSWB1Y7nuDV5besLTh8BMWgTdoStWelADhC4hdWWDmExAKKypP2Q8VyiiUSHCVwUxn2KxJMrbp2/5hN9qQTaboovSvZYXR9cSpfr7TPGeSplTasDOZsTOe45wjSRLiJEYp5Vt3FKX32IoUU5XM0gxdtzsRQnhShXJMp/tsbd0gy1K2tm5RVQ7/0D9YQeJ+iOOEtZUL9Lo9lAqI4gSlAsbjfe5s3iDL3z41+3evkYKnE7JqPKl2bcihGbSoZ7YBWOkf2KO/bXviR4tfH2WfJT431mXR7bVZZ6Nj2MgwNUaqnlk+1Z5GJ0V7bIgkrPZgveMHpckYdkooxKKE4zI+ZDSkZoQ5/MFOoChqooWF0iKCkMR26YQBiQy4TMIFYsoqY/vubSZ398lHuVe/fhAaSrlg0Z8JFk0fn1q+SkCQQDJExENU0CcSHbphwqXVJVaXexRZwfbmXWbZDBE4EBonF3FL4RxCPsaONtcrN7A1gf3MTxaKYpE7MscYqUL7cJ3gcE52rn37FgFIT1Cgm8BLz8H6kg8fzua+zUsbFm+kHH7CEih//4hFawNbZLjxLkiFI4alGKkFYSCJYokrKm8vs2aH2iEHPyvVhWa+u4+UkvneiDIrMaVBBA4RKhyOyWzGze0d8rJkazRnazRDKsW1KxcZLC+hpKQyjtk8xxpNns0oi4x0NqesiRhCCAIV4ELBbLbHN7/5Ofb3tw662/swTqPafLrr10m6vPeFl7i4cYU47rA0XCWKE15/41tMpqNzI/WuwNHQTnuyJWoJFYR/++Afd/i3tH572jGivQ7JvWrqR/eveebapRP38wSfFTRGP5CeEWaNNw45te4dC6OQOz+xbCYNwvmEfYo3YBVQOUQIorSoyqEUREISS+UnFEWFTlNMUWKNefB1adtCOFyE/VRIUeLAYxfKh/pUGKNkgEAhhSIKIjpRAsaipPJNIJ318jzNQTh8CEmfwU7b+pwbFoSJY2t4Fps/VhbKuoVX1Vgu5Vtd+BxkEzZ3HIqtNRODA2ZgawbZXCdrcZWua9pqj7sWgxWBAm0Rol3c2KYH10vdDNFai9MLpXFhPVPPOkepNWmekxUlaVGQFaVvgGidvx+lV0g3Dqzx18RY/2pte/HbKcuC+XzCbDZCiAQp+yAUzhqsPTkVUtQ5wUAFxHFCt9Oj0+mxtLRCknTp9wcodVTj6uni3EidFZq8UsYh70oIQRQoojjAGYfOC7StcLbCWuOVd9qDX9tYnCRPFLHwihrmUkOQqLutH3h5zX42r822G325ZyHUdzQEao+8L50PGeWlDwk1ag3tYucMr8Y+xXuTQ/w5yJ2fyUvhk+IqwrmAcpLD7V20Cpl2NUliKNKMSZEy0Rm60pjK3tMb7hCa1iLtHOVTy/MJiFYhWkdGCStXXmLp2ovEnS6DwQWElQgrCKUiDgOsDgkDSRAIsmzGzZs3ybIpVCUuzXBas3X9TfLGe1TCh1mFqK+JOJJ7Ewtj4ZzPPZn6RBnDAff6LJvkVRru7nvDV2nYm3q2rGl5ac0zafDaj6O6vi5xPs8lBdIlSN0FFHbPYPcnOA06Awp8uC9vZj/tG7J+4ERAFAb0Ol3/2h1gjCHLM0phKecWbQ03trZ4484d8rKi1KKuWXZUxlGUDqUcsfSpM6UU/V4fuh1cBemsYOvuiPk8Y3t7RJrmbG/d9vJGwMr6BS5f/SBhlLCz9Rabt15GVw+PLSup6HcHxFFCr9unSHP2drZZX4fhoMvq6jp3t4YEwdtrJs6N1FmiYqE0kPhXIQWdQNJLQqzWZLKkNHOsMVRGc0/Os00Tb9PBj4PgcBHpQfdaDntFTTiybZgag9TkqfKHbOtpoWHywcJwNscTsPCIsrLOK1lvpNotUObArXo9F1h4VNrCvJ599yPoJzgExX5GOSkow4jxqiVcchRpxiifMy5TXGWxlXlwKDRkwR5rT7wrWjP5JwQhIb4Ag+9AdQasX/sQ7/mODxCGIZ1IIoxAGEmkApIowmlNFCrCULK3N+Wb3/gKm5s3cPM5bncXVxSYqqJqCCeB9DlAIXy4WsujO+DD2UrVRskujJN+Qieg1HB7B+7u1fnIOi/pWHhTh4hpdb1PIGA58IxBpZCuQ1j1wQj09gj71ghX+WdT197zgUrJoWOQIP0MMYq7rK2s0eskDPpDKqOZ53NmumRU5hRa88adO7x8/SZlVdHrDul1h75+WTvS0hIqUKGoT6M3ekkYkM9KZpOczVu77I/HvH7jOuPplCrbJ89zhBCsb1zkL33f99PrL/GNL4fsbr95IiMVqJCl/grLgxWklOTzlDIv6HW7LC/1uXplg5u3lwjDcyP1zkc96NeRvYPIC8LTTgPhCIXFCocRFqEsRjpQIILWRLMdTTgp2gSMpmarvV+i9Z2jeZF2BOW0IcYnxVw7yfE3J2zR6OjeIuTGALfVOnTr/ZiD43aVZ7SZEHRcUgYlZVaiS4PRpq6/am3ruGNuE2ieOGojIeSClKMSUB1QHUSQIFWCVAqHxRqHtQ6tLVprqqqiKnPKIqPIUrL5lHQ29UZqWuftjkNzgwuo+0/U7x/9+7j49ROANn55EJpNNwRO52r2J4e9c4MnbpTG58Ws8WrfJ4AnNAQEQYhzkKYp09mU3DkqDMZatPUela7VI7w6ujpg1LnWurzSl0Qp30eqqjRFUZIXBVmek+WeYh9GMQJJFHcIgggVhAipTj6ECLzCei1g65xvjGi07wxcVgVaV+fsvnc8SmAKIoTuRehdqiNJ0kdJpLTEak5kLApH0tdESYDuGWZWUA4hzWB7378eGmBP49k0HliHBaOvecbaUkwph0N6j5L7OuiYyyLUdlZo1x21aw8bD6YherTfPxqJqcUlDvQQMxbqLmP8QHWp4kAYcA8YgRWScddQJWO00eT51A9amYOJOyxRdT803lOzT43s2VlBJqD6fnYTDyBZ9my+aB2SHiaI2B3n2De3CUPFsB/R74bM8wopS0ajTWaTXd745hfY2brObDxmevNN3Hjss+/6mIHZWB9aFcKH+kw9DEpVF7s6sJWvLnXWM/meNTTCCRoY1TVbymDVhEpVCCcwo7K+tkc9xfus0NUuvAtpbopbt2/xX//7f6M/7PPc+97LBz/yYXrDASvzlNXJhFIblgbrLA/XCMOIwWBIFMU+zRo671FJX8xblI4szxiN99neucssTSnLAucMa2sbvOe576HXjam04tWX38QYy93bN6hOqChvjSFN5wgriKOI5eVlOp0Os/mE//Unf0yUxNy8dZ3xePQIJ/zscG6kHhcNvTuE7lVYvwhhDFEFYYWvlBBzhEmJhORyP2E5CKkK2FeCdA57Y5jlnqQEPBq7rgn9JRxWrRD1e6Le1+Nklk7rQcX40FYzYJ+lkbqf0kZTOBtw2Hg1+bS2J6jwRiqu/27O6zZwu/l9CZ1a2udN4LqPUE3kjCkCArB96/MXJZ45mHOyc1VPXJ4IS1LGEK54z6l/GZafBxUfeFUWxe44Zz/dJgwDLl4YsrrSIwwMWTZnczNnun+bV/78j9m69U1caTFTjavcvcSDBtp5j6UdtfNS3fh+SE0e6hlqG38Uzb0q8EolEx/6sJRYMfMfuKA2OPBwl74xUgKcp34757h1+xav3nwFJx0//H/+n3zn9/5llpeXWJmnrExnlNqwsnSBlaUNAhXSiUPiMERJCANDqKyfn1pDaQ1ZnrM/2mNnd4u8LCmrHGs1K+sbfPT7/w/W19f56l98nf/1x3/C/v4+ttr1zQlPAGstaTpHF5p+v8+F9TX6vS77k32+/M0/ZzQde+WL+2oBPh2cG6lHgJTeWxKiVmGx+CJJ/HMbSkgCQUdKcFCVDl0ZpIQwtHSkQypHGPj23EEIYQRR4slPD33W2yG+hhRxdPJ3JHx+iBrfoD3YP4pRfJpMQFkn5ps6s8otwnBNWDMQC4+yCb05tzBijZFu/s7r3xYceD5eKQ0/qDWe2INyg+1z0FacOLP83uF8jlABKu4ggi4u7mDDBBFEBIFP4CMkhgiLRCmBdZpK5ziryewEq+bMJ2PybE5VZKfrMt0+Htc2ao5GOujp4BFjzW0P3HHvf2ToPUMBB+1eTrBS5wzGaLSuKHTOvJxhseR57htF+vgeQRBiUUip6kmFP5YmxNeo/BljyNI5ZVGyv7dLls6pytwrpAtJEIQIIdDa5w3LsqAsS6qiBKtPfFocvoDYWocxtm4doinL0ocVs7ePdt7GuZF6BPSHsHHJe0yjPdjeqlVYKkgK6CjB5WHMpWGCKR03Xs/ZvFkQhJb4cskwNqTCshsYqhCCAVx6HpYKmE9g+44Xj7wvOngppobF12Hx3KYsPKlm8G4G7PYgepREcZrn/bgQ25OEkjDoeqUBrC/gnWd+dpBbv++xhNXIv0ZAYv0xpwam2hu1jMWx7rE4/jH3Hr9lESa8H+ux3VoF/LmHM2qr0qZrLi5ob2nIynveR9hZohBdMnqIIOT5qxd54T2XUFIxmpSMpxXaVGTZLtPJDVyVsjd6C9JtymJGeiIx0ofA6hZJ4WncCOAZdbX35tq6VKfE0esjJIRdRLwEzuHyfTAnUwMuyoLd/btMgn201RSmQEjBZDpjb5ahBgWlCYijNQJlcS5kNk9RSqGkJI5jJA7jHMYadne3+cwf/w9e/cY32N/f5+Wvv8xof5sg7jMYXiSIupR5xp9/9o8JFGxvz8jS+UP3855T4MA6gbaCLC/Z3NpiNB6R5nOKhxWvP0WcG6lHQLcPl5+Dbs9P7Pd2/POqtC+G74RwsRvx/ktditSx/7JGbxaY2BEuV/SpcMKnFarQT+DWev6m2bsL470HGKkmfLfMgvYcsfAIjhbGt5lxbRp6U5h+2rGl+W1bqf1JQ0roJ7A88AW8W/swzhf7A3WRbwTDmonSSORUFezqRciv+f6YhRLEcZ6kZfGb++4XC2+r3VblTNC+aIvi06Q/4MLzz5MM15jljvHcIVXA+957iR/4vu8kCBS3bu9ze3NEms15463r7O5eR8/HZG9+jXLrOk5aXGIf/+m3T2uW0qAOL4qG319f59PMCO77VYEIEkSyVJMrUh/NOwGqqmA0WYTEHJ74MJvNGac5UVZQ2YA4WsJYhzYlaZ6jlKLb6eAQOAEWh3aW0f4+f/q/Ps3/+G//zRMZjMY5Q3/pEmu999IdXGCyd5Ovv/YVinSMZYhllUe5oLbmjOSlptrdQwqHthXlQzr2Pk2cG6lHgBCLnHEY+uL3SkE3go7yk3mdO8Z7ljK3FJnDmbqesfQEidKBEoJuKNDGkZUOrX3e+tioSZul10SA2l7Q0VBGu+awMVjt0M6jhPgaNNt9mpR1IQ6KHr28TZ2wN3XIyeI9q9JyiC5p3GFlj6Oq5Y+L467BmaBOMooAGcREcR+pBL3hCv1Bn2TQI4gFUeKZZUvDLkkcEKiAbidi0I+RoiIMBM5ZnDO+oaOuFl51w/hstCIf5FU/qet8UBMrFtfxgWjt5GnCixLvkQvqGP3R3zr/gNbeoZAKog44gzMPKUKGexhwrqbDC+fJgwJxQIw02lIWJVJJiiwlDxXCGWb5CMoZd+/cYToeU+TNjNOfFGsNuqyoigJdluiqROsKRAVS18/8aR5Kr8tnvcIgOIPFYZx5plKL50bqMSAELA9BvQewcPkSXBuCFI7t13O+9TmDrhz5uPJ94TTc3vYEiTARLF2I2FhS7I4t37xTsj2ypDM4tsShkdppjFRTNNx+qJvnqAnxNTVQYxb5mIzDauenRWPs2iHDJw2Bf9KV8x7TsOMVJyoN09TL6eQGNgvPWGkTAJT1LTrAs/smnN3kv63Xd9ZOhYhBrYHo0V9f4tJ7r9Id9Lhw+QrveekDdPp9Op0u3f6AIAgY9LsMuhECwaX1PsNexGQas3m3c4ghDhxmugXAgIUk1lFvsO11PwkEws/spPATjOwBCuu4OldUh3gfdsLbk7pEwSDyNV9zDePysLqFc7hy7lcpFUGnQ7C0gjMV1WQbMx89wqEJukLQFRIt6ki8tcynKXu7uwgs+d4Oo46kSGe88fUvsfnWq0xGY9549dV71lfkGbubtwnCCWUxQjcECVeCHbOgsp7swXY4jCuwNfNJ1IOCcw73VD3kB+PcSD0GBNDvwuCivz0ursLlrveGXrtT8aW/qLDGR6mG9fu7Y5jMYXlJcvFiwKVhiMs0xUSzt2mpquNZwAfFuk3+oxkcm/ooWNyb7fcN/umYcDgM+Dh4jFTAI0PgjZQQ0IshDn1Bb1bUmm8O9qt7f7NcLw1LeMbZGZMnGe0SAcglUEskw6tcfO9fYml9lQsXlrj23AV63YT19RWuXFknCAKyrCRNC6x1xEsdVpY69DvQ68ZH1K1ZTDTA3ycdPBuyKeg+LtLzpPQclfAGJKj3sbDHSyMdwHIq5YqGYBQrL0wcKX8c0+rIdhxUGa6qEEGEWloiXL6E1QUmnz2SkVJALCSJEHW60huAPM0Z7419w8SoZB6WTEZ7/Nn//GO+/sXP1bJK965PlyWTvT38TdzMNgEqcCmLWelJz4/zBuoZ8pqOw7mROiFULRenJHTDOj9fh8hl4G+PqoT9fc/GLVJQxrP+FAtGYCeRJLEgjiRlAdOxZT61lIUP95mHDQZtA9SESnTrfcnhGWQbz/jNeF8IgYwjRK+DMxbnCt9w7mEiqI1RbjQ3n4qG3qOgvpAyQEVdRBAh1RAVryLVgKTfRwQhrtaQ82wwgVKiljeSlBJEHZqajkdMJxMmkwnT7Tvo2QiTznD6IdTkxgNvDPpRya4nAcci9NaEbs96/Q4QAhF4PT4X+gXEomki+DCyDCBQWKux5RyrSx/ueyAOYpZEcUyv3yOMIoQKuHNnk7w0jFLN3lxTVoadu9vs393CWU0eVsyDivlkTDafYbSnsiNCBL4YG9cOXTQKHu2buQkJHmaCHr+fPOQ7zx7OjdQJ0Y3g0rInmK0uw0bkJ4Ciznc4A9vb8I273lhlI+gbb5x6dUudTiK5djlmfSVCV5bd7Ypb10t2Z47dXUuW1UIKD5sINQ3cBAuyhGBRwNo2Uo0xewdDBIrowgrRi1cwRUFx5w56NPekCPmQk5UBOyzqxJ6dKEaNpsAtQMXLdK98iHB4kSjqMByuE0UJcbeDjLqUWqAtuFrtIZCCJJSEoaTMKoRNqdIZX/7MH/Pnf/q/SOdzNre3me/vY3XlW1Ich8YjVPj7qiGNzHm4NNfjorI+/CZYtFo5KzSTFOuVFcIkQiQhRgt0Dq6qmaJp7Tp2Yuj1cUKgqyn27hhso933IDRhDsX6xWt810e+h+HyEqV2/MEf/DeMteTpnHw+wRhDnmXkWQ7OoaRFSUtVlext3cU5CyJAqQFCdXC2xJoxzjbaX3MWMdl2glW3/j4ObVHMZ63NwYNxaiP16U9/mn/9r/81X/jCF7hz5w7/6T/9J/723/7bB5875/jn//yf8+///b9nNBrxV//qX+W3f/u3eemllw6+s7e3xz/6R/+I//Jf/gtSSn7qp36Kf/tv/y39fv9MDupJIA5hte/TIcOuJ5FFzTVXXs5sNoHX3vQ5pQF+UdJHGnzvN8HKcsiljZjpRHP7ZsHmZsWkgPmskdt/CJo6oG79/0bdoNmX8Mj3T1I8/4xDKEkw6BGtr2KylGq05WnlyvjM9INQcbbFxmeOhmoeIsMB0fILxBfeS7cTsbLSp5NEB9/URtQK1x5SQqgEUSAIhEW4El3OuPna1/nCpz9Jnj2ojqFGezLe2EtYjGNPejyz+BDfk0LtSUkhUEGAjANc4qBbk2yM9WM+wre473XAOWy+j52MT+jZNYKTIYPhOi+89CHWNy7w9a9/k7/4/J8xHo0x2T5mvnsiqSWBRKgEqfo4kWNt02unkTC5z0E+ZK2HlZvfxUZqPp/z4Q9/mJ/7uZ/jJ3/yJ+/5/F/9q3/Fb/7mb/If/+N/5MUXX+RXfuVX+Ot//a/z9a9/nSRJAPjpn/5p7ty5wyc/+UmqquJnf/Zn+YVf+AV+7/d+7/GP6AlBNKSywOdeRV3AWxVQ5j7EJ1NYtl41Zjn2SxBBsgLxKnQSxzAxJKqiVIYwcF5QsuLe5PahjeOvVMJCYbv9WduLbyZXDZOvmYC9c+7Je+FAGIcw1rc/EMpXQCsecuLOEM15PpPwl/AqETIEJ3w3WieRMiDpdOgP+oShQsoA6wRxFNDrRYSBYnWlx3DQodcJyWYTXv3WCDCM9/cZ7e0ym0zY29nGHtev6UHHJVmUFjR5qacR7jsRmvh2m1Z6QtS3h9MWOy/BWFyqvcyTro1Umwl7NGImBbKTIKMQqRRxGBMEAQKBdAqBpCwt83lNkjKOnb0xlROM9kdU+RxbZThTeS/pBHDU3B8ErvHSRP3bA6N5v/j1IowSRDFBFIHjgA34DFzMU+PURupjH/sYH/vYx479zDnHv/k3/4Z/9s/+GX/rb/0tAP6v/+v/4uLFi/z+7/8+H//4x/nGN77BH/7hH/K5z32O7/u+7wPgt37rt/jxH/9xfuM3foMrV648xuE8OSgFnQS6Xe9VyToMMt+CnZugCwjn8JL23tOlC3XBbwLJRYjXIVCWbicnjipk5Rh2DNOOn9Ddt2VLc891gFUWDL92e5u2F98MLiN8ZKCdk4F34j2KcA5ZGVRWQWkQQQSdnp8hyJwn7io1kwTFQgbqcc6jDPwNkazWM/kZlDlR3OHCxjrrL1zFaEOVFVTasLLS5QPv32BpmLA6TLh6oU8YwJe/+Hk+8+k/YjoekU5GzEd7VGXJzuYdT00+6bGF+PsqxTNBm/vlqNzU2wKBD6Ul9Y7UPTRO+tM63G3zkvLWBCEFTjtcu1RBurq84chvJcgoJL66QbSxRhwnbFy4yKC/hHKK2HQIbMDu7h6vvPw649GE3Vzz5199lSgMme7eYr53B11kNbX9pMZVYJ3EWQkuArkK0ixyAa45D2MO0y4XF1PIgN7SBYZrG+As452bTPfu4Nw7K9QHZ5yTeuONN9jc3ORHf/RHD95bWlriB37gB/jMZz7Dxz/+cT7zmc+wvLx8YKAAfvRHfxQpJZ/97Gf5iZ/4iXvWWxQFRUuZeTK5T2z9CUIIP3mPIm+EZO0xV3OYbUGVexLZKp5UcaUHV9Yh6EDngh+T/IzNx5KLwBMxotB7Z/I4h0C0lrZoajup3XwPFp5U0zJkwjvtfrwvhLHIymCN82yVMILAPB1Pqk1Kaf7/WOdVQtCFaMWLsRZegFCpkG6/x3BpQJmXjEuDrSxRGLK2OmB9rcdyP2J1pYPEMhvv89UvfYmdrbsU0xH5ZL9uK3HKYzuQkMLnok4QJXy6UCwq1k9ZZFo/P66yuKJYGN3m+jXH3o5ItH8bSIJhj/DCKkmvx/LV51ldWSNwIV3dJzAhLrlNdGcP8opUO+7c3fUptvkuOpvhzAmNagvOCZwVCKFwop7ByrqW68CbPC6W70OPgpAwHtJbuoBzlnS660Pjz1IB1AlxpkZqc3MTgIsXLx56/+LFiwefbW5usrGxcXgngoDV1dWD7xzFr//6r/Orv/qrZ7mrJ0bDP+hEipXlkPVlSSQMHVEinMMsg70KOgcxg2rqf3cQKXFQVKDrPlNGSpyQ7BeOncJyN3dMSu9N3YOj+YJ21KNZmnB4Q/xp2sM/K3mYQPheRIGsFcXNgk11HNlIKc/CkpIwjjxLKg4ReUVxawdjK2w2gTKFecm9DbneAZCCsNtFLS0TSEH/4hqJtCxduMSV5y6ysTGgKiuW+iG6rFgeRpTZhPFeijI9hkl9OouMKk+p8hTzDLRUeDIQyCBCBp7RYaoCZx4wS2icCcnh56f92kabGCcchLWnEgOVhEhihKEyBUUpGE13sWgiYipREdMhK+YYXdSim145QgBBFBBvrIPTVGlKOZufbBLhLLgMnCSKuqysbNDp9A/NWWezHfZ2HWXZJnXUs1kR4WSAtpDlXjVd31cl4NnHO4Ld98u//Mv84i/+4sH/J5MJzz333BPfbiNUIIGlXsQLzy9z7VIERYqY74PWrHXg2obPSd15FW6+vGgWC947z2Z152wpKKOIKgjZmRm+NS55Y6Q9G/B++dT2Q9Tko9oKB02TQ40P8U05m5DUWaETeLeyF8KogNtzX3jbDlMeHKNAJiGqm6DCkOGFNQZrK1itGd3dYfrmbaywmED78Edpay/knQWpArqrq3SvPk+v1+P9Lz7PxQvr9AY9rr3nCssrS348MQZnHelkj907b7F3Z051cZ1e5AhDxWw8JhvvkY/36vb2z8IFP2MIQdDpEfXWwVmKWU51P5Yi+GekR21kuLd4/egpOuAQOAgsdGoyjpWgImyoKGWJLqeUZk5ezQi2QzpBj/XeVXrhgNF0l7KYQZVjjcGZCuEsnY0lVp+7QhAqRtevs/f6G5gTsaO0L861MwYrV/nu7/oAV66+D6UkURwipeSN19/gzz6r2Nvda50rDoytk4KsdJjJFJymKItnYjh4FJypkbp06RIAd+/e5fLlywfv3717l4985CMH39na2jr0O601e3t7B78/ijiOieP4LHf1xGiclyiUDAcxy8sJNq0wWuIkdCKg7wtw9+96lp8zhz2psqpJo1KQO0llFZMSRqVgr1GCeNgE62jI6aD+g8Ndd09ecP50ENQc/GHoPal2XPMYT0oohYhCZBwRD3t015bRWY64cYdye+RDHh189GchafeOgpCSMOkQ9wd0l5ZZf+49XH3+ObqdkAvrPQa9yLPRhEACW27O5ptzZuN90l5MXuQ4F1KVBbrIMeXpw0mH8CzdL0cghEAGIUHcwTmDzB4yZLVzbPBw/UVoPUu1oWpYjlpC4D0pa0u0haLyKyyigkQOwEryKsOayodujcZVBQ6HChSdlWXCJCLd20PIk1JtHThvzMLAsra6xJXLlwnCgLgTowLFdJYTxksgy8VvwBMspJ8EaguuLMFpzDsx4lDjTI3Uiy++yKVLl/jv//2/HxilyWTCZz/7Wf7hP/yHAPzgD/4go9GIL3zhC3zv934vAJ/61Kew1vIDP/ADZ7k7j402A7dN2myaexoL4xns7Hv6+N5eXcQuYDcDuQsiEhTDiEp5l0fYAIyiMGC0WAy0xw0UMT4PFeJVz9tRjqOeSLsdx9sw6AgpCDoRKjp8S5meQluHy403Uu3Z/tH6QwFEAaKX4KKQIhLMpEYHlmoQw/qgjsk3lMUniAAYCOgIf7FTd1gO6kQQEPYg7CFUQH95ie5wQNTpc+H9H2Dl2jX6/T6XN4asL8eEYUAU+osqBcSB9M3wAnFg23fubjLbuwtW88bL36S8XxfdE+zagZxTQ7BJeXaMftiBqAcqJFwdkKxFOKOpSglTcX+vsSkZavdOO4m2osPXTO3XCbmi9F66ciBSXGYPpax0UDEbK0y4TzbJMGkOxhGoiLjTRSlFpzfwbTnAT9Y6IQjjrUdl8PdH5CnAzvm6P6NBSlScIIMArUJubd2mtJLh0pBrz12lH/UIIkenl9AddNGVb9nhrAFXgPXSIa5UWCQ4i6vyd6ynfWojNZvNeLWlK/XGG2/wpS99idXVVZ5//nn+yT/5J/zLf/kveemllw4o6FeuXDmopfrQhz7Ej/3Yj/HzP//z/M7v/A5VVfGJT3yCj3/8488cs6+53+Ew2dM4H9KrDLx+F77wdUhTGFSw5Hwu/8YYXq+ASCKu9RDJgEDBUqXpOsusAl2Kw1Tfo+gBV/GeQ9MjqfG42uy+ZmebBPBRTb+nABkqOqt9omHnEJmhQjPXJXpWeNpvk486btAQAtGJECs9iALmXUmmClxkKNa7EK772cBoH9InbKQi4KqEDQkzB68bmLf2/SQQEjqrMHgOlfRY/+BLXHn/++h2E56/dolLF9ZIkpjLGyssL/lZiHVeQS2Qgl4kiQPBOJIo4dt7v/nqK7z6F59nPtlnPp2QzWenP7Z2kbfBty1p/n4WoqdC+o7DS1cQUUR0rUf/WhdbFhTzgGzrAeNtU4isWeRmTzKxsA7GBaTV4v/WghC4UQVyemgVpZTsybtIobA2oNIdcAHxsMfqxiWiJCEcBqigDnPEoS+yjAXMi7qJpISkD90lv63ZFLIUqSKi4Sphv0/pJN949Zt865WXef75ayyvRQyWFFEHhqt9KmdIZxMmozmmKsFOweyDM1gDLvezQF+f9W1ipD7/+c/zwz/8wwf/b3JFP/MzP8N/+A//gX/6T/8p8/mcX/iFX2A0GvHX/tpf4w//8A8PaqQAfvd3f5dPfOIT/MiP/MhBMe9v/uZvnsHhnD2Oy7k6FhL3aQE7Y5jNvSr6UPkxOtUwqWemqgwIbEQoHJF1BMZRNYWZD5rlNf2iutwbGjuOidS8Op66RyWEQEYBQSc69L4xIIrSP4QnURSQAmrZGq0AaXHKYWLlNfsUMHsKFcqq9qIGYkGkOs35FMIPQiqGsI+IB8RLGwwuPke3m7C8vszq6oAkDhj0Y7qJwlpPsqkMgEM4i3AC4SzOWaw1zCcT7tx4i8neGfSDau6RZyV/CYBoUWm7EMfITkTQk9hQIkNx773fRhP2aEcoTnps2vrl2BUehi8lq70ukdQUXYkUiijukHS6yNDf715FAgiVr4mTrbCHDCCIayp8Bvj2CjKMkXEHU1ZMphN0XjJc6VFWGdZVICxBKAmjABVIhKj301W1J+VzDs/MZX0MnNpI/dAP/dADWURCCH7t136NX/u1X7vvd1ZXV5/pwt3joLUmnc+YTQuEzkgSQxzCxQvw0ouQZbAkYVkuJkXTKRgccp4jJwIlIDOajjWkmSEtzEIc9jijkuMlfZqwX9PosO0tlfX3GuWJk4Q2HoRGu60hY5yQJegQaASlkzhtcGmOqzTaGqwpfZgubzH7jl2Jw85yzNYI4gChlnw+Swro1zpUcwEj9WTaYwh8OU4EdJwfsKZ4D+rEHoaAwQpi+QKoCCeWQCiEEIRRRNLtknR8QagfbP3FFCiwBptnmLJiZ7LPN66/RjbZZ3d3jzfeeJPZdMrWzetUj9KQrl3r1Yy7z5jwgEoGBN1lhArRyRCtApyAPMsZ72W4qqDI8wezGJsY/aMcXyB9CxioDVYz22taXzcW/RhDZjU4iTAGpSVSK6rxnHw0wriKdLSHnWSeYaUlqK5fr1U+rOgcqAg6Ahd4Zp7L/eQuiEOCUFGYkldee4Xt3S12tvbZ3brBfJqSZxm2moKtwDXtPd49eEew+54FVFXJdDJiHAt6iWWlbwgkvOB8WLms/ORb4WfDN78Fu7tQlXXiSqU+pzlzyAysdZSVW1yB4wbBFLiFX+kG3qtqnpeAulsZPlyjWZAmHucejfBhRslhZtRDYIHSSoyV2KLC7Mxw8wyHwzYtxi0PVri2Djue4WYZJCGqGyIvDPzgsZJAKGAsYTN8MgOswmtZDeu/Kwu79rDg9MMgBGL1MuL9H/V5lf05bm+GEIIoSegvLdFJIsJItZxfhSAAYzDpDDOfcfPlb/Bf/7//b958+ZtooymLEmMMuiop8kcoZGpqYmMW+adnpUwBAEHQW6F78SVkmJBbizEGJyzz+ZzcTHFVhZ2nD88tPUzG7vjNQxx4JpTDd3TWhsWJC1m4ZkcYes6CKUBYpNYorQjKgNl4zvb2dcoyxZo5Vs/870UPVM972lp54yKEv1/iAU44SuMQ8wwVKrrdmDAKyHTOF770RTAGnacU0zG2qnwL+ANx3sZCv3twbqROCGcdutKUFSQhBzJJSQzDgQ/RNG2MbAFEdf7WsWjG17Dvmjbj4sjrUbT109rt4Nvfb0IbZyUE2p44NstxyfRjPD/r+1HjjMVUGleePsEhjENY4yumjT8gVxdVeul56WcDUniv7KzJIk1L+CZH05YKOtEBAGGE6A4g6iIyiwtzVBAQhAFhGBAGAUpJpJQIBK4ekI3R2KrCVCX5fMbO3U3u3Lx+Nsd1tM7uaeIk10cIpAoJ4i4y6iLLHGEzH+asKixF7YWcQDHhUY5PiLpVgayJSM4z5Vxz0pqQx9H2Aq2N1Y0OMQ5nHLY0VPOCqixYsDio76/6IXP4+1zWYU7pL5BzlWczWl8zKANFWZXMZlN0nnuJm3xWE4laFVSiWTfeeL5DyRJtnBupE6LSvg9UEns1Hld4xQgRweoGWAGjDPbmPiJVLIG7wGKAH+MHuoyF2n4zGN4PHfysPgRW8KGoEE9uaxMumvDco3Z8brf+kHiD1xioLou6q7L+Tqy8sXDuQKTTOYOdznGV9kVh1UMMVOMNthL2QkqGK0sMV1ZwccBspctMWhzOK1Yj/EO5HMG1nt/2uPBhxMaTfBx2WsN0a1iHDU4lUiuQShAmEtkJGCQXGV6+TCdJeO+LV3lhrU8Sh6z3A5a6Aboq2d+6yc3ZFKxFaA3WMp3PfQHmWaEJDTdhsKfF4mvG9ONTO/ib14tSdsIl1ntLBHHC7mhKmt7xHgqZX6zxrLtm1GqKGNto6w7eFwG+Bb30xeZhbSBCBaJeeWT831aACRBGIoQkDJZRQmCtpiwLrNVIGRMEQ6SMsS5gNNpGqRFlnpIEK0RiQGV2KXWBQ4MtQc8OEYywAvIMqlp+RoUgFdYairJEO4spK+wDi4EF4WCFaGkdISXleIdysn2CtgrPNs6N1AlRatif+slWKqEIff+0C8/B1Ss+nJztwNTBVEK+AvYy3qBs40NyzYPaUMYb43A/dICLLMgTnfo3OYeFY6PW+48C2VpHk4tqjGgjmTanNq6i7nIaek9mXkJuPZtoMoPxnANNtAeh6W7QGFcDUkqW19d57qUXsZHiRpQykRkWB5WoJ6MG1mIYDH3Tw7sGpmZh/B/XSM3qYz3usxNChYKoowj6EZdWL/Lc2mV6ccwHr13ipY0hcRSwOowZ9iJGoz1e/cobvPzytwjDiKXhCknSZTydnK2RciwmN2edy7sfmklPkz89drsRsIQgphetcGm4QhRHVNO32J7fwJSZD7uW9QqkWxinZtLWRnMPPOg+EAGIrvc4IgVdyUFv98Ybia1fLIhCICoIZEAnGhAFCVVVYu2IssyQskccrxMEHbTO2d25g7WGJBjQi9aRgWReGiqz5x0bW3hDdfRkNc+XCqEzhKiLtY48B1FVOK0frFghBNHSBfovfDdSBcyuf51qtnd6qaxnDOdG6oQw1ued8sK33bBeuJoDQe46bG2kX1yAH4Qbg9QOxR31no4bMNrhmSb8djTMd9Z5mVbU4NA+wOHwnxJ+ORp6NPZ0YbGj2wOUkgRBgFXSd+GojoZ3bJ3grrXcEuXlawwP7y11Epx2Fc2+S4lQAaiAMI5IkoioE9Hvd1ga9uklCf1uQidSxKEiDiRRIFECqjJnPpsQhhGBDLDaUuTZ2RRgitpLoIlFP/4qH20/7vO2UCgVI1WCUgHCWU9CMBXClN6TajxZwSJqcMy9c9xGhWgeHNtSIW9i2qomtcg63FZ/JhwOtzBcB2Q8UX8/QAjD4gGVXpkfiXNgTIUxFVZ26mMUR7oj32eW0LwtBDhTh+sEzhi/76Yp4WjyT62TKzwrEBl4AWYV+PvxwSfoHYFzI3VCZBncuQOTfXjxmuQ73hewNBR0VkFHgkqAPkgO+S4MqusNme2Ca5rJNfdWY2R06284HAbUwD5+dr/EwmA19NqGONG0RH/UcF8TztP42WnjsbU9v1hAWD/MMV7cVdcPS1t25qRoanKb46ypuvl4wvjGLZwSFHqOM+nhFfdCxPuGiI0VXF7i4hjmOeyUMJn6gsynhUb3VAmClXXCS+8h6PR5zwe/k5c+/J10ewOuLq1xdXmdJAxZ73dZ6UQoCcpqylSTz2dMJvvsj3cxpebWK69gS81kb5fZeP/x9k9KGHS9fL8xMEshe0x1itOgff/c5/4YLi+xceUDxMkAa1Ju3/gaxlaMxrcxWXV/r6jtGTb/h0PfV0GHuLOGUgm6mlFku1hbN1+TAYjAk5lKgZACkgDiwJN9bIazFbjaYDnnGxYWCl1WGGsw1oKQWGcoihlS5t441Z5LZebMi9tIAaXerxXITwBroMy8UWomGULUZRy1mnpbVT1MIBqCCqmsYr63BwLK+ezE7UGeZZwbqRMiy+BO4VMxFy9I1q+FXLqoKEMoIoF2oAU4vPaXiLyRssobKNeMtW3jlHJv14EmV6PwD9s+i8lQDz+TbIyUxhupRq/vUb2qxsA1FOxOvQ9NOgAgkT4XdTCDtQumyKOo/x83+FjnjZTWfgzKUsjzw+u+2Ed+cANx9SKuKLGdCJfmEM7grafQtqMNRR1yEqjLF+h8+CNES6u88P6X+H9813ey1O1ysTvgYrdPIBRSCqQQOGfROqcqS/J0xmQyYm+0QzaesP3qm8y3d7HWnrzdxn33rzZSq0s+DKD10zVS8FDPbbC0xIsfeIn+cJXrr32Zb33la2TzMdZZrNX3v68aI3WcEaxfVdCh27tCGC2RpXepyok3UkLWIb8A4Ryqom6HE0Lswx+uKnC6bo+B92SMU9gKClf6SVXtbVlnKMqZJ8G4xc5UZoY2u/73p2HfOAtFuvDsjh64ax0kQJBAbw1UTGkl1d4uOIubzx5c8vEOwbmROiGcqyNP1oseFDnkmaOsBJmGyjny0n9HGz/ROfBO2iG/Qyt90AY5HNJoDFxjnExredyw39HwyUHYAZ9UhsMhR9uik59hfsPhapq1V/S2pfZSSm2UxitXZLqu2q8T3oH0BrytY/ikIWTdCbNuvRyGiDAkCEPiMKQThiRhQCcMUEJincVZgzWG+XRKOp8y2t0lnU4ps4wiyyiy9NEo5sehYZs1k4mTXKfjQmiiZlO2P6iZnI87CAopCMKAIAxBOIwp0fohIYE2W9AeeW3ic0IgakPkQ3QKIUIf4hDNw2h8OM2CQPhjEuBEE+prjntxzK7JtzbhO1Ez9Jxpnd6FW+cOJclOc66OMUYPOiFC+sVanNE+XGjeuSoTbZwbqRPiQDHOwc07lj/9nyXLfYHtCOzAe0y7xrBjHbmG8V2oNn2UxTVeCniPpZkBHjeQNqE3gfeclvGDb4z3mgS+T1RTG3Xa2r2j+aYmzp+woCeP6/eXArhcs41yA0WLRZfhjVR+Bg9BHdp3AnKrcVkGDvLaozqEWYH98h3E5gTXVbiLMfSVPz/rwq+rad73pJ2qoAO9dUgSbLBMmRe4YEqZpdiyxIYRylpiKVBAWuRkRcp0MuHP/uR/87Uv/wWz2YzX3nyNre0tdFFQzs+wmZO1MJnVmnDWJ1QfhIbM0s4/CnwTtU63HtybdTsYZ3554C3QNm7HKDc4S2kqCl1grH44Y1pJH3a2+EnK0R+IEIIByBgre5SVxboc4wJUso6wQ4wpMdW8HtBDjIgQSnqGnRM44XCBABGDMJ6s0XbZnPU5oJqBh9U15bcxRk0Mv/33kzQWzocIRQVVCmVNTa/Se8/POxDnRuqEaEfENrctXxpbEglqCdQaEEE6hPlSLVSwDeZWPdFM8A8/9UoepFTuWBQjKjwFvVO/1xi3Kb4tx6OE2Q4S/Sxo5wkLqvsEP8A7YEXBRuzvkp3CVykbtzACZ+FFNTnsOg9XVJqy1IuJ5FGkFe6bd3HfEnCxC99/Cfp9nxtarY9hnwUb8Uki6EDnInQHmKBLWZQ4OUfnObZmYynniKVE4pjrgiKdMNq9y+f/5H/yX//gD6gqn8PweQyHO8vwjHU+DzWrDd/DBqzGSDX9mBoWXS/yxYDt9tFN6/XJw4RL2xz0tgvU7KKlMhWVqdDW+BDagyCllyAyDt/E78hMTwQQDEH1sSKg0t47QypkvIrEQr6LLfZxtsTRxSH8LNO0ZnASkFFtpFrSK85ywCZSoV8MvmGca1Rt262wnwKcZ9diBZRzSHfeNV4UnBupU8Phn8+s8ve0KiCoFQkyBWVQP78FuDahoD2hfNC90/Zw2iG2ZmbbftZPeg+2t91m6wWt12Zbjddm8YNAYX2yrXRQuUfTRHsYjuQTHjr5M/UPtAXrEA6EVMhuB4HA5hojm6KgJ4lFmCUOYvqdPnE3oR9Ffpw3hjydsWcNAkeazynyjCLPD5Yn3kLhfiEjWZNgnKuLpllch6ZOrF5EpJC9CIIAV2hsVgsMmkc7v1IpgihGygCpAop8jhCSssgeTpd2+Otv73ejuINBWwhZH6aoC3UBJFZKf28fxIWbaxAgXONN1Q+ZqI1iFC1C66Zh9rkjodEjSbHHwoM90ENwFt9w0Xmvzj5pz+3p4txIPQIyC1vULOxZPdGSoPdA16oqxRRcwcIoNLmShzFCm7YcAYe1+oJ6sXhv56TMUkHNQGv9v9lOI7MUsQj3hcCwfkAqA68Vfpu5WUwQm1qbs0JDJGn+PiGEdajSInJLFHXpv3+FUEmyV/eY3L5ONU8fvpIzgBCCqyvr/OUPfYTl5SHPLQ1YE4KwyHjjjZf57K3r4CzLqyssLy+zv79Pmh5XjPWUoBT0un7grSqYp55U0SiiSGoGJ6AE0XKXzksXkVFI/vIW2Ru7uKyCtDzBjKJ9Qf13O4NlLjz/fjr9IboouP3G17BaM5/uoasHhCQdPifZSADpY7btKqjGYDJU3KeTdAnDyBsl4fcn1QGVELVpqvChAUlgQoIyAiXRyqCl9SzApcQ/E2UJ2/tQzYAEIZIDW+LOlOndXIBGiqkWjL0fdO69JyHr2fE7n9HXxrmRegSUtNq9N/dQg6PeUnOGFyUVD0a7s2iTH4AF/dzxcKWKNhoDd7Tosd2rqjFUEogEJPXKb1m4bZ68Snab6HDK3LLUDlk64mHC8OplOv0e41nEPN6k4ukYKYD1wRIffc/72Vhbo0fFgAJT5my+9Rqf/dz/xlrLSy99gPe+9/1MJ1OKPHt4aOtJQUlIEuh2fJ4qL7yRsizYpoID9f1wkNC7toKMIuybe+S7KW5+UpbgvccYd/usX3uRweoG29df5farXyWd7Nf27iHnRLtaGul+mzNg5mAyZCSJI4iT9jDn0HngiRLAgSflFMpYQh2Ck1iED/UpCUkH4sT34xnv0ySHhbCttZ4lmpll0174IVXqpvTLuxTnRuqs0dyt7RBduw7pYdGdJifVqsFB1WGZyh3W8TvNPh2dXDVhdsfC+EnqJFr9ALdV1Z8kpFgUBx+Eck6wXW1xswo3yjFaUSUz5MxgshI17BBqi81LzLx4sLDt/dBmOwI4gVABKuoiVUC8coH+pcuEgyUurK2RxAlhENZOr69lcQgqbdC6Yjwac/fOJvPZnCx9iFDqk4SvOPVelDW1By28t1GHl8UwRF2IEUmASAJ0liLKou4CbI9LLz0QMgiIOh2CICRKEqo8I53sU6QzjNEPVja//1q5t6CPg/87Z+t6Jo1znvXmnMXo4p4Jgo8ICkIlQUqcVJ64pwJfZ9bt4iSYbgebeakLh0HYAufKOrbfLng8LYRnHsoAIUPCaIlAdbAmp8wL7MMYj+9inBupJwGBnwRF9d8lsMvhmer9oPFJ/wJPq05C79lMDOxpb6gafbmToDF6baquq9efs/Dwmkp+Caj6yzMe65k7ce6sU8ssCTy1PKsW5KgHTZpTjXltH3NrigsDTHcbFQTIYULy3dfoxAHF69vMv3IDOztlfZDA67opUXt6PqYT9lcZXPoAUW+V5158no/+le9ldX2V1eVlNtYukMQRHalJZERVhBBG5MaSpjlf/eo3+MoXv0JVltzdvPsQHbYnCGN8A7Qs99c6cdBVEDlIDIQQPrdM97tfQPUT9HjG7K3XsXmF3p5hpVlEok6YUuv0B1x+6YMM1tbJZ3N2br1GmeUU6YzqkSj3Eh+vjljEKZtZlxddNbYkq2Zo4TBlSpWNcaZEV7lv934EcRDQT2KkUhhhsdJBEuEuXYSNZap5ytRp8k4Mhcbtz3DZxHtvrt1d8RGuqwwgWYdoiTDpsHH5MsPlZbLJLluvl8z3n15U4FnDuZF6UmjLIqUsimIfSgrAG5AKr68UKq/+jVsYr9PQzptntnl22hPOZtxu2HXymN89jpFqXh+2r6GEfk11xx5u+/0glAa75R/esl4Q0PnOqyx/9AXCjSGuNGTfuuNVtE+7/815cbX1NgIV9+isPU9n+TJXXnwfH/1LH+XK5QuLnwmIlCIMlBe5VgGVcWRFye7NO+zduYt9RMLBmcE6aNrOxwK6gb/HEuubPEYOdbVL8qHLBMM+86+/Rv7yNmaSwsQtQsjtMO1DECYdVq9cZfXqc+xcf4tb3/o6k+27j3EQTUisob42zDpoZkbWaSpdYIWkKqaUs22sPv4+EECgJEkULFq++x3HLQ1xF9coZx2y0ciHG6cz3yFaTx7jGNo7ICHsQ7JO0O8xuPgc65fWme7EjO68zHz/bDbzTsS5kXpSaIflmjHpJIYlAHrCh1+60ntTDRMrYKFQfppwy8Mmd0c/P6oTeFocDXU23pTEx/gFfqBsEt/GeWKGED7Zd9SYngSh9A0SI4XtB1SuwpU5Wmhct0W7rOxif45bv4CDtgntdgoHhbH+74WwgKwXA9ZgnWV3tsvNyRZ5Nuf2zRvMxxPy2ZyqrM6WYn4WCIRvJz2Q3pPqSggdNhZUusKWBbrSOOPqUCyPoRvZvqke9zw0FlIjhEOGXaRqtPL8toQKcFZjqgxrCpy0fuLR3nfVFGQHEAY4KfyCL9x1zmKqApOnVNkcM5/XXmhWs/xOc+zNLPCYk+ccmByqKU4LLBojRePAf1vj3Eg9CTgOt9I4TT1TV8K1APrSh8EStShgTOq8wWnD0w2luNm34z6n3s/jxGxPC4mf5DZGtWEFhgp6gTe8mYZ5LZiZ6kXbbuMeTQtwGMEHV2E5oerHTMspYj/DkGMvR177cFzCfuGNYzOJaEPgz3NQDygiBhd6tpQtfP7GOJy29UKtKKxwusSVc0xV8NUvfZ4/+d//ncl4n93NO+zcuYOuSqq8fMTcyxNET8L7YrgU+qJV5fNS1ZJimk4RVYGZzrG5WbirJafPi545mpi1RgYdukuXibpLXsxVekHXKp+STm6jyxQnNC4wi3uyiQwmMfS7EATYXoKJpO/OYVydzyrJpvsUWwV2OqO6fRtu3fXe1Kk6JAf4hwIWfW9asBryHSinOLVKZa+RB1AqgT03Uud4IjhFvP4QQlH3oa+NU+AlXlDC5wEsC1r6aXBSpnA75PeoD8ehUFlrPVJ4/b9Q1kXBtZV4GGPrJIgD2OjCRten/mzpdepEBYPAjw/awrR4sGepxMJI2aA2UnXOwRmcrQtuG2koK/xiHK4qMUXO5q2bfPELn2N/f9cz54qTULXfJkQC1hRcag0FQmBDQVkV9WBcLQx7W4rrbUWTbDUImRAmQ+LeBkIKpJIHquNuv8IUs0XbmYZn0bTFCBR0YghCXBzglH/fOId2Du00eZGRzTVM5zAZw/hRQnxNQaKgyZkdfiitLwgmxVUKQ0klBUaKt/9Uv804N1LPKhz14F2LbKYG5m4xk31SY157AH+Up6OJaoT4AVC4w/2pntSssDSwlwH2UIhRAkGvA12L1RadZf67c473Bow7qKfB+XMvVUDcXSMIA5bWr/H8i88zXLvM+uqAbDZh964jVIZE1YbWWj+4V3Vh5dMyUA1ZJcQb5cbZOMoRaBeKR84r2gcV2NowWwEzDbszX8h9N4Op9XnVU3jxQRjSW1oh6nRIBkOyImNvZ4vJZHRGvbL8eXW2osrHCKnq5rZ+YleWU1yoPNVeGpC1+6SEj1IgoRdDr4MLArSCTBe1jl+9Cetgmvpi9rz0ntfGsq8hnGX3ae5Z3+Sy7uMj2hcF32HYAEIg4g5EiQ8hV14E2DpJvr+HuvUWxXQfIwMYrPjfFXXzx28jnBupZxbOa+WlxhurPQvbbhFGfFLTq3Zo8rRjaxMmDPCq6YnwbERhvUFo5HaeBOYFvLYLN+Uij+4E4QvL9L77IrIbkncF82CKyw1s4gkt7efdUcuF1ASOWgInGqyw9sIH6K1d5trVq/yVv/KDXL58mWw6Z+/OTe6+WbC2NuTa1XWvHGSMJybkBU+tK+oBMxOv97hcH9sOXkKrDYmvgYqAvoUk9/L+OvK5O6u8YXp5H2YG5hWMjV9f1VrHQ0KySa/Pez703axdeY55Nuf21m1u3L5BNZ2ekYiuj2NbnZGNb5DPNr15aLyoSGH7ETJIcFWGy8Y+rJYEXu1cBbAyhPVVnFSk05xyNvH9FFVCrGLf7XRv37dqjwJYH8JzF3yb7tdv+06oh9DMAASEHeg1clL1BXJ1BCHLQEnkygXk2kWcsdj9EXY2QxvB/uuvML3+OlYFlJ0uXHkfpBPYvgX521gI/jbg3Eg9yzDOK35XbiHq+jQ6UTwu7VzWIbOgnlFWdWjjUcKUJ0VlYZQv+m3VDEh1cUCYxKhBTJVFiIlYNKQ8bl8sLEZeH9uSUpAMVuitXWL54mWuPneZ569eZvPGHbbefIvJ/oQkEhizgpJq4Uk9acmjNg7OPf7Y+vjzMObeyFJb/ipyEOianFOz2pzwk6O7cxjphdE/5X2hgpDByhprl6/C7hbFzTcY7e966vuZdR12OKfR5VFjAYguMlzz3o/QUNTCtEr68HAQQCeCbuJDfGmOMQXSCQIREiv8tZznPsTX78DVdVgb1h7rccNn60KoEMLYb+dg4mf99sEz+uIOor+MMAabVpBVOFNRjkeUOofuwC+9YV3T9qBW3u9OnBupZw3Web283PmBYsJClPZZD063PbCDcAmLonldD95YH3I7yyhYm6DS8gb1uKB4Yw/RC6nSGS5vUdwfuH1Rq1wHGKGYT0ZYdZvZUgdT+phXWZWMxmP29nYpijGT0U2kcLz5+iuU5VPo29RIZTXGSQCRQK13Udc6uMpiJil2Jz+stNMBruLJJD0gc7BZa1OJ3L+WxUKrsV1n1+A+XpRUirg3JEw6dIfLaAuz6YwsyzE4nwNq8qxPGtrg0gyhNZS5Z44avGdP5WvEogw6M2+kTQWBxFmHLjLyNMdqjSlqJp+ri5iV8gYqiXw+y9o6tNs+IfUJOiAsmcU6kgTiC75o2Bns/jYYg0snXrncGg6Ec63xIb40rEN9z/ogcPY4N1LPGrSFtPI39wjYcg2J6ekJKzchudMy7I4WDMNCNirHDw6i1pI/g15Eh3A0TFn/Xd2dMpsWEAjsoMItGX9uHzaRlwriPoQdSgJ2795C7m2z2lUUua/NyrKU25u32bx1h2K2zXz/BrrMmE0nZNlTKL6MOKzvCIhEEr2wSvw9F3GZId+5RXG98My9ITDAq8V/BG+oxg7ecnAd6DhY0j6XmFpfFtDU5J3wUqkwYuniVYYXLqOCkFLD7vYus2xKhYM49LkV+RSMVFni9sc4KfzgbuuJUalhbrxhcrXYbOBbyRMHOG0oRmOq/ZkPwxntDYypfxMGEEcw6HjjVJQwzXwosW2chKsX62WLiswb55VVWF0F63A7dzE3XwNrfJPFxvs+YNxqmI19K5Ay9+fu2wznRupZQ0OYaFiqOQ9XqThLnMXYcXRCeYjpeFrLdwocM8l0uUbnehHy6rHIpzwQwhsqFfpxrcigyLyMj/btNaqqIk1T5vMZ490ddm6+SVU8xXxBE7ZrjJQAIonsRailHi7QiChceFpN7WsPWAEusOhBM8Grmnfswog/Qu5TCEmYdEl6Qx9Csw6T55RliXVuUYP2NOCcl346CtsYEuc7FheFz8OFdZ5KgNUVNssOk16sW4Szm5YhUVAbr9YxHTq8+vdN6/dGVb3b896Tc5DNjnhIrbyWc94wCQFVybtNPPYkODdSzxpKfB4h497E/tPAae1Ho1TRGCNLTS+vQ0WFezTdvCcFx8nspLNQ1b2SlIQohECxP93li1/4MzY3N7n15pts3niF8e4O2XTPz7ifJjT+HglAXOwgL3UQocIUmuKrW7hSY2wKV92iN1kXb6ga5l+G70/WvucCPOHiEeTinANTGcrCYK2mTMeYMqfQBTobeY+gKJ5uvg7qeqieNxBl7f1Y543TtPKtDJSBoKgbRB5DoW2o64XzodDGXqiaxh6GrUJnUUuiFSBLb2hcvZL5HHZ2vGHK2o0JA2/EVIjq95FJgjMGUxa4IvVG7jzcd463HQV+gKgJZk/dSMHJDVUzO2/k03IWbTfGdqE48Sw9Vyd15JzxDeSqzM+WOz2IY7b2bvHJT/7/iIIO5WxEun0bU6RYY3xzvaeJhl4eSeR39Ak+egGkwHx9n+pLb+GwuJUKPsBhFf4u/pqkeH3GPWCbxf3WlCA9ggfvnKMqDXlWofM5k7vXyac72Frs1fd6co/ci+qR0evCtSteLHaawt5kEarbaxo3Wh+aa/bv6H3i8AYqdT5n3ISrw2BBjtC2Vk2pPy+OdMd1wtdazWb+/YPPBYgIRISME8KNKwSrK9jZhOLWm5hs6r937kmd421HM1t7p6AJJT0wxPeMwB1ZHvr9moZuoRnAyjJnb77ja4qKuacFv50K1fVxiEghBpGPEBmLGeV+wF3GGyVJLfVEXRRelzMUHFaReNRekUIgEAghsdZhKoOuKqoio8zebsq08N5OHHvSQqF9WM+4+nmztYfyMEVj32CzmXh5aSwHTVPFJuZq6vpAp30ezDr/uaiTvaauoWvU6Jt9bB4mGSCjGJV0cFVRl+09aw/T08O5kXq3QOBlh4L6QWg6lzazuyehGdcY1NaD+8zC4cNZe/hzldbvHaVnHwdjIS18AacTYAL/qsu3b/AQwIqCCwo6Crsm0KYC67DHhdICBct9T7cuNdyY+dqyEbCPv46PpMfni507w1WS/jIgcKZktnMdU+Xo4m1U75YBBIl/lV2opPf2swrSuc9HVWXNpHv4DSyEIJKgAoGVjtJWixYaojYyrs45uYYc5K+JZwN6qruX/nLeMBbOswqbh8n5EKQwBmkcsmnw+G2McyP1boGX34ZO3d3QWP8wmFbo4azRGKm2+PSzjJRFCKvZV3Hk/8fBWJjXzCzX+tHbGX4RwLqC746hp7BDgTWFV0Ywpp7ht74fBrCx4pfNDD5XwTcLDjotP6KBAm+kBmuXWbn0IrrM2LnxCpOdWzhrsW+nByBDiIcQxKA6UKg6zFnBfObzRe7kRkAKiANBFAi0chhbYatav6851yrxBAwRLEKGFq880e97Mk5pvfdmjGf9lfVMyfmHSViFNBqlHc54p+zbGedG6l0EEQaIuJFeqY2TNtjyCbo576QHqMm1wCK6cj8I6hBNzbCy7tlLWit8q41EeIp5aWojZQ8iR/6e8KEuF0e4OARVQSl9PuosIARShQRxgnPmQDn8yaN9AY+5EUXN0JQ1u6ch9RxM3k55PevwnDAaYczx6xBNLsstDGA7JNh0NBD1NRPHzJKcrSnpGmf0uSf1du/AOc4GMgjoXdigc/Wifwhq70nPM+Zv3qTY2X+7d/HZwtEc2hGIMEStraD6PWxeYHb3selZSPmcISoLM6/35vMctcL7fg7KIXsxww+8QO+jV9AKJrIkQy+kq2IWRJfHGQedw2LQrkI7jTt2QtSuOG6Slq2POOk+BCw49w3/3vJQORbrfJhTuzpse/oDtmVFeWcbM8+wRYEdzSE74inKoq4JlD7vZKoFEaNps+NY1GgdE2+2uqLc38IUGbbMMeUzdt89ZZwbqXcJZBDQv3iRlQ9+ENmSTsl39qh2x+dG6jg8YJwSUUh4cZ3g4gZ2MiXPcniWjJTDe05T50OYowL2XJ3795Rz1YtY+o6XuPD//ChFlVPdeJVs+/ZifI9ZqEk8RlTOAcYZKleiXYW9v8Q8hymfDevjyMrui8a6Nsapgz+IhvXxACNlrNfCFLY2Ug89rHvgipL89hZicxucwx0rHlzHTxsPvPGibK10ourjaHvqR7ejS4r9LcSotZ1vY5wbqXcLnG8h4bQ5zHh9mirc7zY07K1T5C2eKhzeQ2gMk3ELCvmBhqLERb7XuxAKYSXOSZ/Yf8xDEkohpUKFvmDY6Apj9H0G1fvEVsV9/n6osRJeIYJ6Oa4zoHM1u65uqGkq7+HY0zR4a29WIKVASOkJKs7e57Y4cr84asWLI3V0DSX/OFjzjoqkP0mcG6l3CWylmV2/iU6zg146ADrLKUbjt3HP3plwRUm1uYWZznBF+eyF+qAehPGDYOy8ioTBE0QysNYwLabY2Ra21OgpRLMBdgYmV9iCRWL/lBBS0VvdoLe6DlJiHIy272CqgvJYhfMmxNfs8JEhuIkGNpJcxzWlPPoDFYCM61XLe4/DVlBMvR7egYCuAJ0/EisziCOGly+SLA+p0pTJ7S2K6UkSew7KDKZ7NVW92T/r3z83Rw/EuZF6l8BqzfzWJvM7dw9/4Pi2Dxc8Clyl0Vu7IPb8G8/iOWw8PagVzVnYgRyss8yKGXm6gygFpIJw3sdmGpsHj6Qo0UBISXd5ldXn3ou1lp3NG0x2NnGVxhb3M+jNObzPoNwUh8PhztbH7oCoKeZR7S0d0wPGaihbRuRQ7uv0hkFFEcMrGwyvXSbdHZGNpic0Unj1En0MmeRZ9NCfMZwbqXcRnLWnmxWfOLzyboRYLEohlO/144xezLJdPeN/Vs9No/LR3j+LH+w7AjqSIAqIVARYTJpjRzluWtd8PQKEUAgZoIIIGUQIFSCFQSmFkgqnLIjjusk2MUhqAyNaeRu7cK4s940MLtbTfFGDK33R7H1v/FatgWuu+TGe3AkghPcgpVLIQPkCYRXUHu0JOPznBumRcG6kvl3R7mZ9Fgyvdxxq5oBUqOVl1HCAMxqzfxc7rb0nJVq6hM+YsXJ4UdgbHO7TFQAXA7gaEKx0WbtygfWV5yn1jK3XbzH6kzdxaYHdfRT+uSBIlgi7a6gwIkyWEcLnaPrdHuHKGlVZMC1Lsvs2NRS+2LWf+ALjovSEFGMPe07HjvmNcap8Aa7eBzv1Rso+jPIe4IkWCs80aTS8HhFK+bqnUkNZQpqeYY+sc7RxbqS+XdEQpSQLxetnaRB+omjobQnIENlbI1jfwOkCl8+ws33A1Xp3YkFQeNbOT517AhaOYSLgmoKrEWo1YWl9mUuDDbL9gNHtOdWX36oN7qMRB1TUJ+5fREUxKurUckiSTtIhcZYyzyhGexxvompLGgS+pXtct1bPCu9NnUhKq/mCBpufIgzbGKm62P1xWwsoBZ0O9Osuu0VxbqSeEM6N1LczjnYHOEFL8HcNhAIRIVSEkhGBCHDCoI/2t3/c89EuGn4S5/YeBrTzAqhjC8oiMucFvpFESz06l1exRUU1SbH5o4hECnAKrMRWFpN7A2OzDJtnVEWBNU3M7ujOucX7TYH0PefjJEVTjntPZrvmqLmx27VZTcjgRB0vj9+qcxhTYaoSU9XK5lW1UPg4KUSj49eEOr8dHrhHx7mR+nZF87w2z3GTsDYs9PjetRAQdCFcRwQRUTCkJ7pYBIbAV9s0IT7LgkX3KAiBsB54S/fkxIObMbsCrmuYWFhP4L0a3m8IRMza936QaHWFfGvEzv/+OvPXN0+9GWEUogzAKsrRHJtlOKsp5iPKbIrVhiLL8WG1xmA0N1P9am3dmVn4Ts0Hkl2NVDvcv3jrmPUdtB0O6t8U9avCq+s2Bb9pa72PUMxrDMV8ynwUks8yzP4ejOfeSJ3YixKgIggT/98qO55QcY4DnBupb1c0jGDwd0HEgv77TlJhfyQIUDGEAwhjQtklIcKgSduelIXHttZK+E63UE8AnrD1N8Cu9cukgh0NqUURMHjvFaJLq8zfuMvk69c5tTa5E2AFwihwEqMLTDbGmJJstkeWTuovNkW7x3k8NclAGzwd/Dh3/mGqv0c/a8K3EYui3sZIxfVnOTDn4S2ZH7BVa6mKnGI+o5qn2Pnc94Y6LVQIYYe6+RZPt6vpOw/nRuochye8zyDT+vHQzM4FYdQhSnoIGUJ4ARcOEVIhXEU538PoDKuLs/Ui2xP3s1hvi5R47PUSQC9GdEPEhQFi0IE4wlaafDYn3dknG43RzkInquV62t7McVBAgJQBw8GQixfXEVIynkyZTAuMLmshWQdCIsIYoSJwBltmi7yR8Iw+GUWEvT4yijAypcpLnGlCgaK1zfZBnuTGPEoLbBhB8FjMoHq/HQKdV5SzjCptwppHvypRMkAIgbUGc7SAFzx7tJFL+jbsD3VanBupc/hnuWARtn9XhfpCoIcQIcPVF9i49h0EUYK1EmslxpSM915mtPM6xpRU+ZQzPQGVW3QmPovxSOEdBsGi/1N7dwOFemEV9f4LhKtD1HsvIlaWMKMJu2/cYPsvvomelWROw8Vl34F2fw7Fg9znBFhCyYT3vvh+fvCv/GWkhD/73Igv/PlXKcsco/3vhQoIllcJ+svYMqfa28SmtdJDEIBUxIMl1q49T9zrM9vaZm9WUFUpCyPTsHqa0F/DxnsQ2ta7gcF7T82N/ShVy8J7PirAIsh2pxSjGVYbTH5voVkYRHSTJZQKKMqUNJ8cUYJ3YArIa6P+bdwn6qQ4N1LnOBz6e9ehDvmImLi7wfKFlwjjLrbKMWVOVc6Y7hRk4zvY42a9j4uzFqBvSgcaCbyjUAKx2kW+ZwW5uoRY6yO6CXY6I90bsf/mzdqpiGCQ+HqlycPUNEKgi5Bd1lbX+MD7ryGk5eWXO+gqoypbBkRKVKeDGi4h8gA9qdl0QhzUFQVxQm9phc5wCZOWSBVxbw+VxhqD3+EHhQDvV1hleayK5QbS77dzlirNfRv3+31VBkRRh0BFGGsQx+2bfRY7gj67ODdS53g8tCevb2fkQoYQ9UGFSCUJAoWUkjBcIk7WUarD8qXn6CwPCIKYfKYpKod2Duvcs+08NorlbS7C/YyfdbhRhr05wmZg5iVGCEwYYJeHcGkN5tr3lJoV3pN6KI1b03DdtS3IdYWQDn2sF+B8mMtUWNNSRD8oeNX+tWa0xd0eK5euUS3lFHlBNs9wxiFlgJIB4NCVxlQPap4o8K3XO5556HK8EWhyXPDYKrrUobyoUwciBUpKJBBGEd1ujzAIfSSvPkQhFTJMcFb75QHG7Rz3x7mROsejox2VaZhlb5ehivuw+n6Il4g6EYNBlzAMWFq5xPrF9xJFXZIooZt0wDqqTU0+H1M6g347TNRpxA86wAY+6jYBdliE+Y7+trLYN/ew2zPUxVWqj36YUgiqToJ94YrvRHt3DLdfhrsjn5OqHjZ4ZoDGEZPrEftFihSQac1R8+6cozIlukpB54tQl7Oeri3w1G3nEEIwXN9gfeNFhJNs3d7l1ut3qPKSOIZOLMBp5iNDOp7g3H2MjFCg+iCXwfnt+m67jTcm6xOW8Tg3qFQhvc6AOEoIpKIbhARSsbK8ynve8yL9wZC7d+/w8svfYjqdIMOYQC4jnMXkc0w+Pc9BPQKOEbx6MD796U/zN/7G3+DKlSsIIfj93//9g8+qquKXfumX+J7v+R56vR5Xrlzh7//9v8/t27cPrWNvb4+f/umfZjgcsry8zD/4B/+A2eysOrCd46miLQz6QDmbJwwZQbIMvQvI/gbRyiXi1Sv0Lz7P6nPvZ/0972fp0mU6wz5xv4OMAoxw+Ln+2+hHHU2jHIcA6AEDvEfVhGePG++cw01y3J0J9u4Ek5YYATZUuKU+XFiBpYFfR1r4XNRDuzY3+ksZ2hUUuiLXGmPtMQbW4azB2sqHTw9L8tdq4AtPKup0Wdq4zMqV5+ivXiRIlhHRkCAeEHf7RN0eQa2yfn9If/2lD+simpmToq4BYCGv8uiQUhJECVGnT9IZ0O0uMegts7K8zpXLz/P8tfeyvnaJMIx8kbNUyDBGhgkyCI5ty3GOh+PUntR8PufDH/4wP/dzP8dP/uRPHvosTVO++MUv8iu/8it8+MMfZn9/n3/8j/8xf/Nv/k0+//nPH3zvp3/6p7lz5w6f/OQnqaqKn/3Zn+UXfuEX+L3f+73HP6JzPD00oaezZK89DFJB3IUwRsiAIOggVegNVH8F4h5Jt0OnOySKI8IgxukKUxSUVY4pMkxVMtm9xWz7Ncp8RpWO3t6CyibH1DCmm/G0WWL8Oc7q16Zk4LjUhgQGIfQD3MUEE0GV18W7r2/B167D9gSmJ1R1F4KV1TXWL10hThKCMOT1V76ONZbd3W2skF7o1fmCViklSbdDuDTEZBn5eP8e0rcKQuLugE5vGWsUu9u7WCMY7dylSrexeUFlIdPeGldlAa45Kc3NJhGqi5AxjsjfejZHYJHBACESFkZK4MwMY7Lawzo5lAroLa/SGSwTqJBuZ0AUdUiCkJXOgE4Ys7y8SqezRBj1GCytcem5F+itrKCVpAol1ll2b73B9lszdHmeizotTm2kPvaxj/Gxj33s2M+Wlpb45Cc/eei9f/fv/h3f//3fz/Xr13n++ef5xje+wR/+4R/yuc99ju/7vu8D4Ld+67f48R//cX7jN36DK1euPMJhnONtQ3ugfBrjfBDC8kUYrCLDLknvMmE8qBWxE5AB/X6P4coqcRSTRDGuKKjKitl0zGS8R1Vk7L71FXbe+DymzDBlev9Q0pNCY9QVPowX18s6vv60ra2YAft4olrFwpA1Eaz2eVcCriTwYg+3OqDsCbJJSrE5wnz2VfjjP/fe00PJEh5SCK69+D6+///4ETrdHq987cv86ac/SZ5lzLMcIwIIJJgSTIUKFMsrSyxdvkgxnbK1s3VPl/ogjOkvXWBp7Qo7mztcf+062SylGm9TjG7jqhIrHUVTXmY0zjXeUOlPggyR0QVktIazBlNkuGrii7M7F1BBTNvS63ILl42w5nREijCMuHz5eS5eexEhPb1cSkkv6nJlZYNB0ieOuwyG64RRwvqlkA/1OhQ6R3RiZK+DcZav/en/YHTnOro8r4k6LZ54Tmo8HiOEYHl5GYDPfOYzLC8vHxgogB/90R9FSslnP/tZfuInfuKedRRFQVEsLu5kMrnnO+d4m/C0HRAhIUog6SOiPqq/RpAs+4/qcEoUJURRhyiOkULgtME6TZXnZOmcMk/J5vsU021fF/V2o5nwx3gDNcAbKe8ELDzWkoXKeUNBP0p6EwI6CpZDWApxAZjKYLMStzuFm3un8hqFlHT7AzYuX6XT6/PaN7/G7vYm89kMgtirJ0gJtq6BkpIoDEk6CRQFSqp71imlIohiwriDc5L5LGU2nsB86ltrmLKe+zS1U42buailEkiESpCqj6VEkOFchSBEqgQV9g4dpjDtavWTQ0hJknQZDJYRchGu68Q9+v0lht0BQZgQRh1UEJF0eiyFa1RoVDchGPQxztAfLiHl6bd/jidspPI855d+6Zf4e3/v7zEcDgHY3NxkY2Pj8E4EAaurq2xuHi/T8uu//uv86q/+6pPc1XM8axAC4gSiyOdMitwn/a2DvAA1x8WSMkixNiKKYwbDIWEUMeh2We73iMOA+WjE1tYWuiwpqpK8KLxSQnm0wOhtgsR7Un0W9U9tz/Rorq9trB4iX7WQ0XUIFlVHJ0Hc7dNfXiWME2TSY3t3n2iWop1gZeMKnUFKmqZkaYaQgsHaMr1egggCbJGx/9Z1yjSlzGpWXqAQ3Q6EAbaXkOuceTamkppoZUASB5jdAj3fxZmmCLfdf8oXCqtoCRkmCBkhoyXfLkQ7bC4PvivqxbkKU6VYW2GqKc6dnl1ntGZ/dxMhBUI0xlKy1F+hL4dI2yHuOIJeiIpjQhkwCDpYaZFRhOrEaKOJ457v6HuOU+OJGamqqvi7f/fv4pzjt3/7tx9rXb/8y7/ML/7iLx78fzKZ8Nxzzz3uLp7jWYaU0OvD0rLXRtvb8UbKGJilUCpMbMkZI0pBsLLC0tISg+GQQRJzYdglUoo37tzgra/9BbPxmLDTJer1AUOV5bhnQdhT4Q3UKgsCSmN8GqNF67VhgzcCCg8xUjGOPg6FO9XD3lta4epL30XSHxIkHW7cuoMQgtJKrrz4ElWRs3njDfLZhEAFbFzZ4Mp7n6PIC2688iZ3X30dawym8OE1EUXI9VVEr4tZ7jGtZpipoJSQXFlFVY6cgvn2Jq5qWCFN62DvTQkREXY3iPpXQCiECEEoTCnQSh0csxAWKSzGpJTZJkanOJfjHqEOripzNm+8zs5m0xPFyyytr16hry6DXqK/2qV7MSbu9ok7IYPlBBkq79lLSVUW9LpDpDg3Uo+CJ2KkGgP11ltv8alPferAiwK4dOkSW1tbh76vtWZvb49Lly4du744jonj+Ens6jmeZSjlc1D1w34A5xDWq2gLHEI4pBSEYUAUx0RRSByGxEoirCGbTZlPRiTOIoMAIRzW6GfCkTpwcdrkM3efBRZElZMwmZ1DGIOoNEJrxCm6C6sgIEp6xJ0eCEGa+e6KzlrCOEYIgQrC+tIIoiSi0+96+SCjKWazIyLlAsIQkcS4QGGcoTIVFoUII9/dPYwQKkTIEOdq2aADIwUIiQxiVDTw94R1OJxXLTp0ouoiMqextsCanEetj3DOURYZZZFRd5MEQtJ4Tp4XFKUm1haHBKWQYUgYd1BRgLUWWxf0HlvUe44T4cyNVGOgXnnlFf7oj/6ItbW1Q5//4A/+IKPRiC984Qt87/d+LwCf+tSnsNbyAz/wA2e9O+d4p8I5yGqpHGt9YzlAhjGdjatEy1foDZe58t4PMFxbBxHiVAeAOApYXe7RjRS3+glK+cSNyccUdh+w6GwPdxY1K4/X7HURvstYlPUovCEa1euc1ksz1j5sOxoowExK9r/2Ou7OnGp3Rrq5feKdzGYpWzdvEyVjiBQkXjnCZVNIp1ij0UrTf+4CQRigQ8F4OqFIM8qqeuBmHIJKKKQIKLKKbDtFFwZRRAwvfhB0RT65QT5+HWcNBEsQLCPDhM7wMoO1dazRzPbuUEz3sLrA6hEww9qSKjdYHaN1hjNzFqrojzsrWRQDltWMnb07WKeoZMVG5QlfxjiyTCMqy9at17nx6pdJpyNe+ernKYuHSTud4zic2kjNZjNeffXVg/+/8cYbfOlLX2J1dZXLly/zd/7O3+GLX/wif/AHf4Ax5iDPtLq6ShRFfOhDH+LHfuzH+Pmf/3l+53d+h6qq+MQnPsHHP/7xc2bfORawFuYz37W1Uc4GgjhhcPVF+s99J5c21vkr3/9hnn/uCju7U77+ym32R3OSJOTCWp9hN+LNYZegbg1fZXvo6Ra4Cqw+NR35HrRFux9V89CyEOhuSnpU/d4eTXkSjLl/AW8bzTiag85ztm98jb35X+Byjb6dnXgf08mMIn/Li/EOElju+mMd7cJoByGh/9wFlt53Bakk2lh2R3tUWU75EAabFYJKBAgZkM1S5m/toGcVg7WLrFz7AEopRjcl5ewGxjkI1yB5DzLu0lu9ytqlS1TZlHTnVfLRWzhb4VwKFFgjKLJ9mtb0nrV5tLXHo6I5uRVFMWZz6zrj2RwbWt5bfgcO0NpSpBUGeOWrX+Z//Jf/F6PtO5R5SpE9SDXjHPfDqY3U5z//eX74h3/44P9NruhnfuZn+Bf/4l/wn//zfwbgIx/5yKHf/dEf/RE/9EM/BMDv/u7v8olPfIIf+ZEfQUrJT/3UT/Gbv/mbj3gI53jXwh6j/SNE3bq8S9TtMRgMWF4aUBSGJAoIlUAJwJo6pGd8OEh6lp/ThTdSZx3re5xoThPCawhs1K+NgGzTeeKkjp9xvncVFj1J0ePM/784uVG21mDLCqSDUkIV+JBdVUJZICU4CSqJEELg0pyqLKnK6lh18OagnPOLtRZjLEZbTKkxpUY4RZD0CYKAIO55SSEkLuxA2EOEHVTUIQgjrA6Rsu3CLu4V51x9Hp9E8Z5fn3WGsioQRUZZFRhrcc6htaYwBm0Ns8mI8e42472th6zzHA/CqY3UD/3QDz0w4XySZPTq6up54e45HgkNAVk4gbAOU1bovCBRjhcv9rg0UGTTCX/+uc9SZhm337qJlI5Bv0OZ9SjMAGcrsAW4x6SfH5czepR1GLwxaph9x/XlO+n6jYP9+ricg7SEzJy+l1WgoBN7BfAkXKglRAH0kjo06YcPZy35aEaxO8FUmjK9N6zlLNjKIUpHOcuYiU1kEFNNHTYOQUSoYY9k0COMIvSll/xpMAajVjBqmSAICQdDCBOUcww23otQCVUxY773OsV8B0SICroIGeFsgdGTelJytrDOkpsCXWVMq5xJVdKrSna3b3L9ta+QTkfceuPrFNm5ks7j4ly77xzvMHgDJZ0A441UlRck0vG+S32E6/CVv7jDH332T7lze5MAQSQEw0GHGT3KfIhrCjrNGdDQHzet1XhMFT6c1hRHN47BaQ2gdbBbwH59bI5FXdRp9jVU0E8gjHyBcOO1RAH047qZY+BThtqSjWZMb23jjMUeJ7PkwFUOV1jKMqWa594DcwNcfAGRxATDPslSnyhKIPwAavgc1jhKA6X1uxB1FSKUKBUyuPg+kqVLFNMdqnxCMR8jZAcVriNVD2smWOPrp84axlpyXQApsypjrAs6uuL6zdf4s0/9f9jbfAutK3R5nod6XJwbqXO8AyBQQYRUAVHSpZPEdJOIJAxQODAaayuqKgNbkadz0tmM+WxGEoQEUYQUAiEEUgVemduqunroCVD8hPQLcP/Gdu7wn41xqupFszBWp93Fmvn4OBBKIaMQEcVYDLaxmrI2ThKsc5hS8/9v79xjpLzKP/55LzPvzOx9F9hlCwu0RakF/VEQpO3vZ/Ir0WpjvUUjQcVLNFUaQROlsan+YRASExM1pkYT6x9W0SZt1UZjELDKL5SbgKW0QMttgV32MjuXnct7O8/vj3cZWAqV0r3MLOeTTGZ5z9nZ5/vy7nn2nPOc51F+QOgHhH74hgeFTdPEsCxEKVTgIwKGrbCdGKblYDs2pm1g2kZ02Ddho5SgghAVhFgmxOImMcdEQpMw7iBhSBBPEE+kcBJ1YDgY5sV10/EM35QoN6HyCUOfIAgIAh/PK1Mq5ikVdMKBsUI7KU0VE0Ul2PEkrbfMp6FtFg0trcxf9E46uuaRSsRodgTTzdHf188rhw8zNDhIb28/mYF+QreM63kot4w54pBS9Q2IBHgFD7eQG59xzKmHVHPkqEoFKA1fMXhfnD6FlwIngpF3j+i3Mrjs+pvZjxojknUpWmZ3YqfqGM6lyQz2EoY+JExINiII5ZKLvHYeCULczJUaRxOPxWhsbMJpaqZcLJBLD+B7LvUdbUyf34lT10wy2UC8DkwzxInFsJ0UosD1fcqeT8w2mTk9xYzWJH7Z4/ypgHSpSCJZz5zbFmLMmUNxOEfP2VMM53pQyh2XWRQAygcvC0GRoDiNQraPXF2K4nA22gvVjBnaSWmqmOgQkRVL0TLz7XTc9l+0tDRx54LbmdPVASpAeQXEHSbTc5Y9O3dx4sTpaFM+CBGRKIZAGWBAKpWgvr4uOlMT5PCK45FX1oB4HTR2RNm4ZQDKV0YSXlzLCyN/5XKpNt/lWxhjFZR2AyTrUkyf2Y7T2ETfOZd8ukyoyuA0QEs9IoJ76gLumX4I1H/ci45ZNs31DdS3tJJHUezz8IvD1CVMOm9tp66tHRUIygdUQMxJYob1iDKwXRfLdXHiFu0z2+jqbKJUKJIbHGCoD+KJFO0db6cxFaO/9zTpgRNkBi4wrrMpCcCPZktBcZBiboB8XT3lQg4V6iSyY4l2UpqqxDBMnGQ9jlOPU9dMQ2MTdXX1pFIpkokESSdO6AtlNyTwPXzfx/N8PHdkL2ZkucvAwDDNkcOUChX6GKjo/M14IRLVaTLVyFLflYPl1fdsrtU0UdixGE4yhWXbOKkUSoUEfhmlAmRUdXaJwrvViM7rOCQsogh9j9AtRympVAiiUEFA4JXx3RLIyKlmAwKvhJRCEBPTsqlLxonHbeJxOyo2aBrREi7RMi6GhRhWNHuVcVrGfb0qAMLApTycpZgdxC3mrygXr3mraCelqUriToK33bmEW99+F/FkHQ1tt5Bqmk5DfZIZrU001acoFhRDfUXyuSGG83mUKEzbgiBA+VHFWTuRxKlLYVom4ucpZvoR5eN7BeRNZGC4fgTcEmTS0YDp5kEuJtu7PBSw+gaylvYO5i9eSkNrG5lClt6hc5QvuJTcAmGMKNIPBcXiSJi7d91TUa9UJN19mnx/H17g4ZejgILhzBCnj7xErK6bxtZ2WttnY5k22XOnSR87hWmY3P5fS7n1XUuJO3GSMSNKmOuFmEqIm4KpFMWiS+AF5Ic9guBi+qLrSHA4BhSzg5x+aRe9JxsoZAdxizqibyzRTkpTldixOLNvvYMl976PWNwZyXBukEo6tDQ20JBMErolvHKZXDZLsViMnJRlIgEQBBAE2MkkyZSDaduUMmlKw32oYJyTy3petF+ByaWNpTeq+V4dNLS2MX/xEmZ0zeXoywc4fvpFhgb7kbgNiTjEonLulF0IVHSPr5PAdcn29UY7g7YFThwsi2I+R+nkCYx4gpnzFE2tHZhxg3x/N+cP/x+2abLgjrnMvaWFmJOgNFzALZZQQYgpCtsg2h8r+5TxKRR9gtAkSt1xcUNvfP8gKA1nKL+Wq2QeGZNMJpoK2klpqhJRiuJwlqGBHpxEiqbmVurrG4nHLQLPJZ8XcrksQ5kh0ukhCsUisViM+vo6AtvCC0NUEGDF4himFWWwHslCMH4RfXb0rqyRn3N5Ncjxjja7MUzLJuaksKwYTqIBMSxCJYRKEHXZuUczKsURlZsPo/fgTQ7GMlJsXl3MIGJE+1luACrAzxcppQfxYzG84SwqKKNMk0I+w0B/D7G4QzmXxy0M45Y83HIxCmMPAzyvRBh4lEs5wvDKmev4I3K1KsWasUA7KU1V4rkljr64m8G+c7S0Ted/3/dh3vH22wh8j/7+s5w6kaa/f4ADBw5x7nwPlm3T3NpM6/RpFPMFhgbSeK6HacewYnGAS45qPLBS4LRGxRe9MlFURsilv+ar00kl6promLOQusZpNM1owy0bpPuzDOdKhAHRPpFhgzVS/jxfgPQwBGEUEHIjoe5KgeeDEUDoQuAiJuRLpzjT049pQnGwh9DPIqbJsZf3kQk9TNMkyA6g8kOYlkOqcQ5Oqh3fKzNw/ij5dC++V8At9hNFo9xoripNNaGdlKYqCQKf82eOc777VWZ2zuZ//vu9TG9tZHg4z8nXhjjbfYreC/0cf/U1znSfY9r0abxtwQJaWlvIZvMEAqWyiygZ2aMPL51dGg+sOMSbonfJgF8YcVKTED/+Jog7KVra59IyYw6xpIUXQJgv4pY8VEiU+8gwwbSi5Sw3hEwpclI3isil7w/96GUYlLJ5SuGZkftWAgqEYtJz7gS9wwUQhWR6Id9PMtXC3DsTtNd3oEKffLqHgXPHGR27r5kKaCelqW5E8DyXnvPdHD1yiGKpyNnTJ7jQe5Z0OoNbyCJ+CUIP2wLbtrBtC8uysCyTIHAJ3BIqCAj90jUO1t4Yhmlj2ono8Gi8PsrOYMZQplnFbulKLkXIiUAYKBRC4IeVfT1cRiomGtEM6CrBEqZhYRoWgqBUGB2Yfh0Wl7LxjrSLilJUGQrTMjBjSQxDouAIf+SMQOAhbiGatQVR4csw9CkX0uSHzlMqZAi8AqNnrZqpgnZSmqpnOJdlx9Y/8tK/9xEEAfl8llKpiOd5ZLI5VNnF8JM4MYO6OgfPc0k4MVQY4g+nKfSfwvdKhF5pTMODbaeeROMtWPEUmHGwHMDADYYpG0ZNDZUCBKEiKHmICeVCCVUsQqEABQMGR5ZJXf91IecGJo6dwrGTKFGU/GH88Mp0QBZRCWKLSi0RQpAyhGkwLGJ100m13IJh2ZQzZykN5ZHQh1Ie/JF8hEFUCDHwSvR1HyY7cIYw8Cjmh4hmUBeTzWqmCtpJaaoe1y3z2rEjvHbsyDX7GGEJ2yYqfBizR2ZUJoQuXmEQr1wYc7tMyyGWasV2Gi5dFCG07GhmMuY/cTwRlILAVyhDCLwA8fwoUjGQKDntNQQZhoFtxolbKUIJ8IISr8/zYBANN7GRry+eXg5BSiAGVmw68YZGTNshcNMYmZF7GLgjzukSKvQpZHspZHvH7A5oqpOadFJVUfZbU1UopfA9D88t43tudHA08FFhMG7Pi4hCVDCqLLmo6KCwSPVG9F2OKEUYeAS+izIgUIIyhDDwo3NkIiOvN/gMEUQUauR19ft9eUmNq92XqOqvCgMwzMvuoWaq85/+nw2pwSfh7NmzzJ49e7LN0Gg0Gs1bpLu7m1mzZl2zvSadlFKK8+fPIyJ0dXXR3d1NY2PjZJs1LuRyOWbPnj2lNYLWOdW4GXTeDBph/HSKCPl8ns7OTkzz2pG3NbncZ5oms2bNIpeLEjw2NjZO6YcEbg6NoHVONW4GnTeDRhgfnU1NTf+xzzgeHNFoNBqN5q2hnZRGo9FoqpaadlKO4/Dd734Xx3Em25Rx42bQCFrnVONm0HkzaITJ11mTgRMajUajuTmo6ZmURqPRaKY22klpNBqNpmrRTkqj0Wg0VYt2UhqNRqOpWrST0mg0Gk3VUrNO6qc//Slz584lkUiwfPly9uzZM9kmvSU2bdrEu9/9bhoaGpgxYwYf+chHOHr06Kg+5XKZtWvX0tbWRn19PR//+Me5cOHCJFn81tm8eTOGYbB+/frKtami8dy5c3z605+mra2NZDLJokWL2LdvX6VdRPjOd77DzJkzSSaTrFy5kuPHj0+ixW+eMAx57LHHmDdvHslkkttuu43vfe97oxKG1qLOf/zjH3zoQx+is7MTwzB49tlnR7Vfj6Z0Os3q1atpbGykubmZL37xiwwPD0+gijfmjTT6vs+GDRtYtGgRdXV1dHZ28tnPfpbz58+P+owJ0yg1yJYtWyQej8svf/lLeemll+RLX/qSNDc3y4ULFybbtBvm/e9/vzzxxBNy+PBhOXjwoHzwgx+Urq4uGR4ervR56KGHZPbs2bJt2zbZt2+fvOc975G77757Eq2+cfbs2SNz586Vd77znbJu3brK9amgMZ1Oy5w5c+Rzn/uc7N69W06cOCF//etf5dVXX6302bx5szQ1Ncmzzz4rhw4dkgcffFDmzZsnpVJpEi1/c2zcuFHa2trkueeek5MnT8pTTz0l9fX18qMf/ajSpxZ1/vnPf5ZHH31Unn76aQHkmWeeGdV+PZruv/9+ede73iUvvPCC/POf/5Tbb79dVq1aNcFKrs0bacxkMrJy5Ur53e9+J6+88ors2rVLli1bJkuWLBn1GROlsSad1LJly2Tt2rWVf4dhKJ2dnbJp06ZJtGps6evrE0Cef/55EYkenFgsJk899VSlz8svvyyA7Nq1a7LMvCHy+bzMnz9ftm7dKu9973srTmqqaNywYYPce++912xXSklHR4f84Ac/qFzLZDLiOI789re/nQgTx4QHHnhAvvCFL4y69rGPfUxWr14tIlND55UD+PVoOnLkiACyd+/eSp+//OUvYhiGnDt3bsJsv16u5oivZM+ePQLI6dOnRWRiNdbccp/neezfv5+VK1dWrpmmycqVK9m1a9ckWja2ZLNZAFpbWwHYv38/vu+P0r1gwQK6urpqTvfatWt54IEHRmmBqaPxj3/8I0uXLuUTn/gEM2bMYPHixfziF7+otJ88eZLe3t5ROpuamli+fHlN6bz77rvZtm0bx44dA+DQoUPs3LmTD3zgA8DU0Xk516Np165dNDc3s3Tp0kqflStXYpomu3fvnnCbx4JsNothGDQ3NwMTq7HmsqAPDAwQhiHt7e2jrre3t/PKK69MklVji1KK9evXc88997Bw4UIAent7icfjlYfkIu3t7fT21k510i1btvCvf/2LvXv3vq5tqmg8ceIEjz/+ON/4xjf49re/zd69e/na175GPB5nzZo1FS1Xe4ZrSecjjzxCLpdjwYIFWJZFGIZs3LiR1atXA0wZnZdzPZp6e3uZMWPGqHbbtmltba1J3eVymQ0bNrBq1apKFvSJ1FhzTupmYO3atRw+fJidO3dOtiljSnd3N+vWrWPr1q0kEonJNmfcUEqxdOlSvv/97wOwePFiDh8+zM9+9jPWrFkzydaNHb///e958skn+c1vfsOdd97JwYMHWb9+PZ2dnVNK582M7/t88pOfRER4/PHHJ8WGmlvumzZtGpZlvS7i68KFC3R0dEySVWPHww8/zHPPPceOHTtGVavs6OjA8zwymcyo/rWke//+/fT19XHXXXdh2za2bfP888/z4x//GNu2aW9vr3mNADNnzuQd73jHqGt33HEHZ86cAahoqfVn+Jvf/CaPPPIIn/rUp1i0aBGf+cxn+PrXv86mTZuAqaPzcq5HU0dHB319faPagyAgnU7XlO6LDur06dNs3bp1VC2pidRYc04qHo+zZMkStm3bVrmmlGLbtm2sWLFiEi17a4gIDz/8MM888wzbt29n3rx5o9qXLFlCLBYbpfvo0aOcOXOmZnTfd999vPjiixw8eLDyWrp0KatXr658XesaAe65557XHR84duwYc+bMAWDevHl0dHSM0pnL5di9e3dN6SwWi6+rqGpZFkopYOrovJzr0bRixQoymQz79++v9Nm+fTtKKZYvXz7hNt8IFx3U8ePH+dvf/kZbW9uo9gnVOKZhGBPEli1bxHEc+dWvfiVHjhyRL3/5y9Lc3Cy9vb2TbdoN85WvfEWamprk73//u/T09FRexWKx0uehhx6Srq4u2b59u+zbt09WrFghK1asmESr3zqXR/eJTA2Ne/bsEdu2ZePGjXL8+HF58sknJZVKya9//etKn82bN0tzc7P84Q9/kH//+9/y4Q9/uOpDs69kzZo1csstt1RC0J9++mmZNm2afOtb36r0qUWd+XxeDhw4IAcOHBBAfvjDH3DiEN4AAAFwSURBVMqBAwcqkW3Xo+n++++XxYsXy+7du2Xnzp0yf/78qgpBfyONnufJgw8+KLNmzZKDBw+OGo9c1618xkRprEknJSLyk5/8RLq6uiQej8uyZcvkhRdemGyT3hLAVV9PPPFEpU+pVJKvfvWr0tLSIqlUSj760Y9KT0/P5Bk9BlzppKaKxj/96U+ycOFCcRxHFixYID//+c9HtSul5LHHHpP29nZxHEfuu+8+OXr06CRZe2PkcjlZt26ddHV1SSKRkFtvvVUeffTRUQNZLercsWPHVX8X16xZIyLXp2lwcFBWrVol9fX10tjYKJ///Ocln89Pgpqr80YaT548ec3xaMeOHZXPmCiNup6URqPRaKqWmtuT0mg0Gs3Ng3ZSGo1Go6latJPSaDQaTdWinZRGo9FoqhbtpDQajUZTtWgnpdFoNJqqRTspjUaj0VQt2klpNBqNpmrRTkqj0Wg0VYt2UhqNRqOpWrST0mg0Gk3V8v/NiAss2ZavTgAAAABJRU5ErkJggg==","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: 1\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":"3e432743","metadata":{"id":"3e432743","papermill":{"duration":0.050985,"end_time":"2024-11-28T09:38:38.140913","exception":false,"start_time":"2024-11-28T09:38:38.089928","status":"completed"},"tags":[]},"source":["**Now, let's test on a batch of images picked randomly.**"]},{"cell_type":"code","execution_count":null,"id":"807227e1","metadata":{"execution":{"iopub.execute_input":"2024-11-28T09:38:38.284278Z","iopub.status.busy":"2024-11-28T09:38:38.283976Z","iopub.status.idle":"2024-11-28T09:38:38.937269Z","shell.execute_reply":"2024-11-28T09:38:38.936393Z"},"id":"807227e1","outputId":"075050ac-dfb4-440e-a1fc-83ddec090a9d","papermill":{"duration":0.710233,"end_time":"2024-11-28T09:38:38.941711","exception":false,"start_time":"2024-11-28T09:38:38.231478","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABdIAAAFZCAYAAACczt2vAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9ebBl2VmfCT9rrT2d6c4356ysSSUJDUgqIcwobCTxWQiMo7E/4SGE/AGyMRjCNoShHTZyA7abtkMEYGGHu4XBNI7A0W4IGlBAtAQYDDZSUZJKqikrK6sy82be+Ux7XMP3xzrnDjlnVWblzar1RO3KzDPss84+e797rd961+8VzjlHIBAIBAKBQCAQCAQCgUAgEAgEAoGrIu92AwKBQCAQCAQCgUAgEAgEAoFAIBA4yAQhPRAIBAKBQCAQCAQCgUAgEAgEAoHrEIT0QCAQCAQCgUAgEAgEAoFAIBAIBK5DENIDgUAgEAgEAoFAIBAIBAKBQCAQuA5BSA8EAoFAIBAIBAKBQCAQCAQCgUDgOgQhPRAIBAKBQCAQCAQCgUAgEAgEAoHrEIT0QCAQCAQCgUAgEAgEAoFAIBAIBK5DENIDgUAgEAgEAoFAIBAIBAKBQCAQuA5BSA8EAoFAIBAIBAKBQCAQCAQCgUDgOgQhPXBN7r//fr7zO79z59+f/vSnEULw6U9/+q616XIub2MgEHjtEGJUIBA4yIQYFQgEDjIhRgUCgYNMiFGBg0oQ0g8ov/ALv4AQYmfLsoxHHnmE7/u+7+PSpUt3u3m3xG/+5m/yYz/2Y3e7GVflJ37iJ/jWb/1WDh8+jBDiwLYzEDhohBh153n++ef3HeO923/6T//pbjcvEDjQhBj1ymCt5X/9X/9XHnjgAbIs461vfSu/8iu/crebFQgceEKMemUIMSoQeGmEGPXKsLKywvd8z/fwwAMP0Gq1eOihh/j7f//vs7GxcbebFrgO0d1uQOD6/LN/9s944IEHKMuS//pf/ysf//jH+c3f/E2+8IUv0G63X9G2fP3Xfz1FUZAkyS297zd/8zf5uZ/7uQMZvP7xP/7HHDlyhLe//e188pOfvNvNCQTuOUKMuvN8x3d8B+9///v3PfZVX/VVd6k1gcC9RYhRd5b/+X/+n/kX/+Jf8N3f/d18xVd8Bb/2a7/GX/trfw0hBB/84AfvdvMCgQNPiFF3lhCjAoGXR4hRd47RaMRXfdVXMR6P+d7v/V5OnjzJ448/zs/+7M/yqU99is985jNIGXKfDyJBSD/g/MW/+Bd55zvfCcB3fdd3sbi4yL/+1/+aX/u1X+M7vuM7rvqe8XhMp9O57W2RUpJl2W3f793kzJkz3H///ayvr7O8vHy3mxMI3HOEGHXnecc73sHf+Bt/4243IxC4Jwkx6s5x/vx5/tW/+lf83b/7d/nZn/1ZwB/jd7/73fzQD/0Qf+Wv/BWUUne5lYHAwSbEqDtHiFGBwMsnxKg7x6//+q9z9uxZfuM3foNv/uZv3nl8YWGBf/bP/hmPP/44b3/72+9iCwPXIkxv3GP8hb/wFwAvAAN853d+J91ul9OnT/P+97+fXq/HX//rfx3wS9k+9rGP8aY3vYksyzh8+DAf+chH2Nra2rdP5xw//uM/zokTJ2i32/z5P//neeKJJ6747Gt5Uv3Jn/wJ73//+5mfn6fT6fDWt76Vn/7pn95p38/93M8B7FsaNOV2txHg9OnTnD59+qaO5/33339TrwsEAjdHiFG3N0ZNGY/H1HV9S+8JBAJXEmLU7YtRv/Zrv0bTNHzv937vzmNCCP7O3/k7nDt3jv/23/7bDfcRCAT2E2JUiFGBwEEmxKjbF6MGgwEAhw8f3vf40aNHAWi1WjfcR+DuEDLS7zGmF+Ti4uLOY1prvumbvomv/dqv5X/73/63nSU2H/nIR/iFX/gFPvzhD/P3/t7f48yZM/zsz/4sjz32GH/4h39IHMcA/JN/8k/48R//cd7//vfz/ve/n89+9rO8733vuynR5nd+53f4wAc+wNGjR/mBH/gBjhw5wpe+9CV+4zd+gx/4gR/gIx/5CBcuXOB3fud3+KVf+qUr3n8n2viN3/iNgPcXDgQCrywhRt3+GPXRj36UH/qhH0IIwaOPPspP/MRP8L73ve+m3hsIBPYTYtTti1GPPfYYnU6HN77xjfsef9e73rXz/Nd+7dfe8BgEAoFdQowKMSoQOMiEGHX7YtTXf/3XI6XkB37gB/hX/+pfceLECT73uc/xEz/xE3zbt30bb3jDG274/QN3CRc4kHziE59wgPvd3/1dt7a25l588UX3n/7Tf3KLi4uu1Wq5c+fOOeec+9CHPuQA94/+0T/a9/4/+IM/cID75V/+5X2P//Zv//a+x1dXV12SJO6bv/mbnbV253U/+qM/6gD3oQ99aOexT33qUw5wn/rUp5xzzmmt3QMPPOBOnTrltra29n3O3n393b/7d93VTrU70UbnnDt16pQ7derUFZ93PdbW1hzg/uk//ae39L5A4LVKiFF3PkadPXvWve9973Mf//jH3a//+q+7j33sY+6+++5zUkr3G7/xGzd8fyDwWibEqDsfo775m7/ZPfjgg1c8Ph6Pr3pMA4HALiFGhRgVCBxkQox6ZfSof//v/72bm5tzwM72oQ99yDVNc1PvD9wdgrXLAec973kPy8vLnDx5kg9+8IN0u13+y3/5Lxw/fnzf6/7O3/k7+/79q7/6q8zOzvLe976X9fX1ne3RRx+l2+3yqU99CoDf/d3fpa5rvv/7v3/fEpcf/MEfvGHbHnvsMc6cOcMP/uAPMjc3t++5vfu6Fneqjc8//3zIRg8EXiFCjLpzMeq+++7jk5/8JH/7b/9tvuVbvoUf+IEf4LHHHmN5eZl/8A/+wQ3fHwgEQox6KW282RhVFAVpml7x+NS/tCiKG+4jEHitE2JUiFGBwEEmxKg7q0cdP36cd73rXXzsYx/jv/yX/8Lf//t/n1/+5V/mH/2jf3RT7w/cHYK1ywHn537u53jkkUeIoojDhw/z+te//orKvVEUceLEiX2PPfPMM/T7fQ4dOnTV/a6urgJw9uxZAF73utfte355eZn5+fnrtm26rOfNb37zzX+hV7iNgUDgzhJi1CsboxYWFvjwhz/Mv/gX/4Jz585dcVwDgcB+Qoy6czGq1WpRVdUVj5dlufN8IBC4PiFGhRgVCBxkQoy6czHqD//wD/nABz7AH//xH+8UdP22b/s2ZmZm+OhHP8rf+lt/iy/7si97yfsP3DmCkH7Aede73rVzUV2LNE2vCGbWWg4dOsQv//IvX/U9y8vLt62NL5V7oY2BQOD6hBj1ynPy5EkANjc3g5AeCNyAEKPuHEePHuVTn/oUzrl9GVorKysAHDt27I5+fiDwaiDEqDtHiFGBwMsnxKg7x7/9t/+Ww4cPX3F8v/Vbv5Uf+7Ef44/+6I+CkH5ACUL6q5SHHnqI3/3d3+VrvuZrrjvbfurUKcDPxj344IM7j6+trV1RqfhqnwHwhS98gfe85z3XfN21ltW8Em0MBAIHkxCjXjrPPfcccDA6oIHAq5UQo27M2972Nv79v//3fOlLX9o30PuTP/mTnecDgcCdIcSoGxNiVCBw9wgx6sZcunQJY8wVjzdNA/giroGDSfBIf5XyV//qX8UYw//yv/wvVzyntWZ7exvwnldxHPMzP/MzOOd2XvOxj33shp/xjne8gwceeICPfexjO/ubsndfnU4H4IrX3Kk2nj59emeZTyAQOJiEGHXjGLW2tnbFY+fPn+f/+D/+D9761rdy9OjRG+4jEAi8NEKMunGM+kt/6S8RxzH/5t/8m33t/vmf/3mOHz/OV3/1V99wH4FA4KURYlSIUYHAQSbEqBvHqEceeYRLly7x6U9/et/jv/IrvwLA29/+9hvuI3B3CBnpr1Le/e5385GPfIR//s//OX/2Z3/G+973PuI45plnnuFXf/VX+emf/mm+/du/neXlZf7hP/yH/PN//s/5wAc+wPvf/34ee+wxfuu3foulpaXrfoaUko9//ON8y7d8C29729v48Ic/zNGjR3nyySd54okn+OQnPwnAo48+CsDf+3t/j2/6pm9CKcUHP/jBO9bGb/zGbwS4qQIPv/RLv8TZs2fJ8xyA3//93+fHf/zHAfibf/Nv7sw+BgKB20uIUTeOUT/8wz/M6dOn+cZv/EaOHTvG888/z7/9t/+W8XjMT//0T7+Eox4IBG6WEKNuHKNOnDjBD/7gD/JTP/VTNE3DV3zFV/B//9//N3/wB3/AL//yL6OUeglHPhAI3AwhRoUYFQgcZEKMunGM+r7v+z4+8YlP8C3f8i18//d/P6dOneL3fu/3+JVf+RXe+9738pVf+ZUv4cgHXhFc4EDyiU98wgHuf/yP/3Hd133oQx9ynU7nms//u3/379yjjz7qWq2W6/V67i1veYv74R/+YXfhwoWd1xhj3Ec/+lF39OhR12q13Dd8wze4L3zhC+7UqVPuQx/60M7rPvWpTznAfepTn9r3Gf/1v/5X9973vtf1ej3X6XTcW9/6VvczP/MzO89rrd33f//3u+XlZSeEcJefdrezjc45d+rUKXfq1KnrHrcp7373ux1w1e3y7xkIBHYJMerOx6j/8//8P93Xf/3Xu+XlZRdFkVtaWnJ/+S//ZfeZz3zmhu8NBF7rhBj1yvSjjDHuJ3/yJ92pU6dckiTuTW96k/uP//E/3tR7A4HXMiFGhRgVCBxkQox6ZWLUk08+6b7927/dnTx50sVx7E6dOuX+4T/8h248Ht/U+wN3B+HcnrUJgUAgEAgEAoFAIBAIBAKBQCAQCAT2ETzSA4FAIBAIBAKBQCAQCAQCgUAgELgOQUgPBAKBQCAQCAQCgUAgEAgEAoFA4DoEIT0QCAQCgUAgEAgEAoFAIBAIBAKB6xCE9EAgEAgEAoFAIBAIBAKBQCAQCASuQxDSA4FAIBAIBAKBQCAQCAQCgUAgELgOQUgPBAKBQCAQCAQCgUAgEAgEAoFA4DoEIT0QCAQCgUAgEAgEAoFAIBAIBAKB6xCE9NcQQoib2j796U/f7aZek1//9V/nHe94B1mWcd999/FP/+k/RWt9t5sVCARuAyFGBQKBg0yIUYFA4CATYlQgEDjIhBgVeLUQ3e0GBF45fumXfmnfv3/xF3+R3/md37ni8Te+8Y2vZLNumt/6rd/i277t2/iGb/gGfuZnfobPf/7z/PiP/zirq6t8/OMfv9vNCwQCL5MQowKBwEEmxKhAIHCQCTEqEAgcZEKMCrxaEM45d7cbEbg7fN/3fR8/93M/x41OgTzPabfbr1Crrs2b3vQm4jjmT//0T4kiPwf0j//xP+Ynf/In+eIXv8gb3vCGu9zCQCBwOwkxKhAIHGRCjAoEAgeZEKMCgcBBJsSowL1KsHYJ7OMbvuEbePOb38xnPvMZvv7rv552u82P/uiPAn4pzo/92I9d8Z7777+f7/zO79z32Pb2Nj/4gz/IyZMnSdOUhx9+mH/5L/8l1tp9r1tZWeHJJ5+kaZrrtuuLX/wiX/ziF/me7/menaAF8L3f+7045/jP//k/v7QvHAgE7ilCjAoEAgeZEKMCgcBBJsSoQCBwkAkxKnAvEKxdAlewsbHBX/yLf5EPfvCD/I2/8Tc4fPjwLb0/z3Pe/e53c/78eT7ykY9w33338Ud/9Ef8yI/8CCsrK3zsYx/bee2P/MiP8B/+w3/gzJkz3H///dfc52OPPQbAO9/5zn2PHzt2jBMnTuw8HwgEXv2EGBUIBA4yIUYFAoGDTIhRgUDgIBNiVOCgE4T0wBVcvHiRn//5n+cjH/nIS3r/v/7X/5rTp0/z2GOP8brXvQ6Aj3zkIxw7doyf+qmf4h/8g3/AyZMnb2mfKysrABw9evSK544ePcqFCxdeUlsDgcC9R4hRgUDgIBNiVCAQOMiEGBUIBA4yIUYFDjrB2iVwBWma8uEPf/glv/9Xf/VX+bqv+zrm5+dZX1/f2d7znvdgjOH3f//3d177C7/wCzjnrjv7B1AUxU7bLifLsp3nA4HAq58QowKBwEEmxKhAIHCQCTEqEAgcZEKMChx0QkZ64AqOHz9OkiQv+f3PPPMMn/vc51heXr7q86urq7e8z1arBUBVVVc8V5blzvOBQODVT4hRgUDgIBNiVCAQOMiEGBUIBA4yIUYFDjpBSA9cwa0GAWPMvn9ba3nve9/LD//wD1/19Y888sgtt2m6hGZlZeWKZTgrKyu8613vuuV9BgKBe5MQowKBwEEmxKhAIHCQCTEqEAgcZEKMChx0gpAeuGnm5+fZ3t7e91hd1zt+UVMeeughRqMR73nPe27bZ7/tbW8D4E//9E/3BakLFy5w7tw5vud7vue2fVYgELg3CTEqEAgcZEKMCgQCB5kQowKBwEEmxKjAQSF4pAdumoceemifnxTAv/t3/+6KGcC/+lf/Kv/tv/03PvnJT16xj+3tbbTWO/9eWVnhySefpGma6372m970Jt7whjdc8Xkf//jHEULw7d/+7S/lKwUCgVcRIUYFAoGDTIhRgUDgIBNiVCAQOMiEGBU4KISM9MBN813f9V387b/9t/mf/qf/ife+9708/vjjfPKTn2RpaWnf637oh36IX//1X+cDH/gA3/md38mjjz7KeDzm85//PP/5P/9nnn/++Z33/MiP/Aj/4T/8B86cOXPDAg8/9VM/xbd+67fyvve9jw9+8IN84Qtf4Gd/9mf5ru/6Lt74xjfeqa8dCATuEUKMCgQCB5kQowKBwEEmxKhAIHCQCTEqcFAIQnrgpvnu7/5uzpw5w//+v//v/PZv/zZf93Vfx+/8zu/wjd/4jfte1263+b3f+z1+8id/kl/91V/lF3/xF5mZmeGRRx7hox/9KLOzsy/p8z/wgQ/wf/1f/xcf/ehH+f7v/36Wl5f50R/9Uf7JP/knt+PrBQKBe5wQowKBwEEmxKhAIHCQCTEqEAgcZEKMChwUhHPO3e1GBAKBQCAQCAQCgUAgEAgEAoFAIHBQCR7pgUAgEAgEAoFAIBAIBAKBQCAQCFyHIKQHAoFAIBAIBAKBQCAQCAQCgUAgcB2CkB4IBAKBQCAQCAQCgUAgEAgEAoHAdQhCeiAQCAQCgUAgEAgEAoFAIBAIBALXIQjpgUAgEAgEAoFAIBAIBAKBQCAQCFyHIKQHAoFAIBAIBAKBQCAQCAQCgUAgcB2CkB4IBAKBQCAQCAQCgUAgEAgEAoHAdYhu9oVCiDvZjnuer/7qr+bDH/4wR48d49F3vpP19XW++7v+f/zxH/+xf4G7XZ8kgBQ/B1ID+nbt+BUja0G3B3UNwwFkWcrrHjlJksScfuYcW1vD3ddmEd1uQtMYBoMKd9uOY+Ag4l7GDxxiVODA8hBwElgAjgIRyBSowJ4BxpOtBs4Dl3bfeujUg7zrL/1/0cLxJ3/y/7K1dQmydYhyZh5s0T2V4aIam4yp1x39/wF2AFyc7O8oMAOs7N/v1chERCQktTPUztzeY3ATRALeewreugxxS5C0xc51Pc4dz5619HP47DZs1K9484AQo15NxFHEX/6Wv8S7Hn2UZ1ee40vnnmLt/BpP/dkzGH3j81+2JMlChNOOekPjdOigBO4+92KMOnr8FF/7F97PoUNH+cqv+ioWFhZZXJyj1crY3O7TH454/rln+dxn/5Q0iTl8aBGtG5555hkG/W3OnjnD1uYmw8E249GAP/fn/hx/5a/8FYbDAY899lm2trb44pNPMR7nHDnxAL3ZBQb9TYaDLcp8zHjQ524OMLqtFocX5tBa0+9v0mhDacDcpiZJoB1BpAS9mS5JkqCkRApBVdeUVUVZG7bz+rqHIRWSlpA0OHJrrhjaCiF46I1fzon7H6YYbTHauki73eLIoSWOHDnMt33btzEzM8OnPvUHnDlzls9+9rM8+eSTGGPQ+t4bzwZeGvdijHpNIAVxL0bEEj1qsOVuP2hmcZljDz9CVeSce/pLNGW585yI28gopbN4ku6h+yn7Q7bOXcDpHMxFoNl5bQwovHp1s1e8mmyHjxzirW9/C1JGDEcNRVnx9NNfYmtrk9bsImmnRzUeUPQ3r9hHHCle/7r7WJjvMR4PKcucja2ci6vDK14LXiBVAo4ud1maa1NVDXlekteG1UG9E5uFEPTaM6RJhkpjZBKR5zn9fp8kVszOtIgjSTtWxErS6c0Spykbm0O2+yNq4yiNI44iut02whrqwQZW17RjQaIEUgqEgPljpzj2yJezuHyIL3/7O+jNzHDkyBGSOObM6WfZWF/j6aee5Jmnn+Kdj76db//Lf4kzZ87w8//m37CycpFz5y5SFOVVvy9AkkQszs8QRQql/Pbggw+yvLyMcw5rLXXTkBcF47zghfMXaJqGCIVEMNvJaKUJy4sLHFpapKlr8vGI2tT0ywFCCY4eP06n22VmfpFWp8e5FwacPd1nbe1FnnrqMxgzPVcksAhkQG/y5xgYABUwAuwtnEW3xs3GqJsW0gPXRwiBUhIpdwO8kBIhBc7e7s6Z27Pdm1i722d1zmG0QUuJvcqJa63D2le4gYFAIHC7cPj7vdszCDAODH7Te7bLYp11jrquMTic1mCMD54ChPT3HSP3fM50n5PPQ+D7Izcx9nipd5bpV3q5OoQDagN5A3EEugYhHAIoG2gMNPau6h2BVynWWYw1WBwyElgncDdSsRw4A85O/hG4cwj2xTAVR0RRhDEGo7U/+tNApG34Oe4hhJBIpYhUhFISZy3WGuq6RkpB0zQYrcE5lFIIKSbCq8E56wf4zmKnA4U9Qpu1Dq0Nxhicdf7WKSRKKeR0k+oV+65RFCGEwDmLnYwN3eQ+baz/Dta99NN3Kn6APwwC4fsJQtCJBZEUREoBDmsdTkzHWA6Hv9cidvspUzFhZ7w23a7ZwN1jL6UgThLiOPLHW8p9+5rinENKSRzHe553WGNxuJ02COEFJSn9+WKNpWkaAoG7hRASxOQ6uUHHNIoipJT+nmVe+USVGyGEjxUA+4KQkD4eCDC6wU5i8RXvnbxfTrQvppu58nOUEFjnEM75OCV8bDCTGO73x26MnLzXWktdN0jlY/6+SOksztpram4OsNYfe+vcDccRSkkfL6OIKI7R2npdT1wpSE2/v5STe8tE+N6JWUIg8IfNGIPQ2rfD+vuXP32m7beTmDd9HJwTO/ri9J7HzrjL7dnPbixVSu08t3vOXftLCwFyT4zdF28n5+30M6btk1KipEThJ2Wn9x9/j3P+PufsbpsdO99R4PcdRxFZlpAkKXGc7Jxnk1/8su1q7RfX/V53miCk3ybSNGFxcZ652RkvqCtJZ7bLzOIs+SCnKW9X+pzDz+wJrlBc7hHqaqIFWb/VteaFFy4hhSTPCwCkipBKYaxlNKp9Jy8MjAKBwL1IAWxD3IvJ0hbGaMrtAjt2cA4/wT7CT7LvJAsIkJLBaMznPvd5nLOMXzgP5QDua6AlaPUS5uc7FMYxqHOonM88n07YC/wk/gywceNmNs6gncXeQqckTRRpGtE0lqJ8eYNa6+Bz63BmAFI5pHKkwCxeRF8roDIwDolrgdvMqBhxYeMCla1on2ijC02xWmCba18LtrY0W82OoB64CQQ+tWwv1x/f+YnAWXZHLAJOvP5+7nvkYdZX1zjz7Gkfs1K/OkCfGeBCkLgniJOMVqvDwsISJ48dYXZ+nmI8RDcV586dRVuNRCGEpKlKDh1aoCpLLly4QFVVjEZjqrpiNOjT39oAAWmWoaIIrTV5nnP+/AW2t7fIx2OMsbRneswfOYJqpUTtFoP1NUb9rZeVJXtT3zWOeeCB++h0OgwGI4qipG4MVWNwRrO61cdZS6OtF1xusTlCCE6eOsWhw4cngrkkS2Jmui3SOGZxtofVDY899hnW1lbp5w2jyos51loE0Eq8GN/qdhBCUuYFTaOpG0tjHNpZxs5dY8I9ARGhtaMsS2ZnZnnw5BGiSJLGEXNz84DEmF2xzFqL1pqlpSWOHz+OtY6mMTR1zfr6GnVVUdYlxhhm57rMzHSYnZtn+fBRtja3eOJzT1DXd2l5WuC1jRC0erNEaUY5HlLno2u+VCnFA697HQtLS7x49nkunHvxQOVDqkjR6rYQQlCWJY3WO4kESadL2pvDWc3Kc6cxukHvm8ASpGlGknWZ6faY6faQxtHvdjG1ww3lPrmq2+nQzTLKqqAox6RJSq/XozGGS1sDtLH0ui3iOGI8LimreifveKM/4PHPPUGatTh8+OjOpCRAlY9oqhJrrn7vt9aysblFUYx2JlHNNWQ0KQWLy3N0OxlHDy2yuDBHf7uPW13DjEvEsGH6ZoEgjmLSJKXT6dDqtoiwlOM+WaaYm+mgpMBVNdZYLl5co6oNVaOpG40TCicjbCPQdYXEIW2DdI66sdjGkbYyoihDW8F4nNOdqZAqQkjJeDRCCEFRlmhjmF9Y4JHo9SwtLZHnBYP+gEurq6yvr2NMg1K7yaxiMtcRKUmSRKRpQq/X3Zn0UUrR7bbpdNoUhT8vtDaUZYUxjrnZWZxzxEoRScl8t0cny1ACqrKiqHK2x32M0VS6QirJaDzG4lg8fJSZmRlaDy5w8ljEi+eWMMIxHGxx/sVnaOpp1nk5+fUz/Ikk8Z3IaPLviF1tdO8POs0ic9xJvTQI6bcBIQRRpEiSmChSO7NMURKRtFKqccXtnTO/NwX0KdaC3dPvscYyHOT7XuMv4AijG5omjFADgcA9TAOUILUiVglogSuK3VVqA2CIt2KZIgQISdNoVldXwVrsYARNAU5ALIlSRZbF6FoharwgNbWJAX+Hj4CEK8Wrq2BfQs8+ihRZmgANZdW8rAlPhxfL14rdx9rAEv6rDfDdqZCDFrgdOCaZ6MZQ1iXDYugHBZ0IIaGUN8h0sWCr62X4iDsuzt2TXF6d6VqJRlMEPoalk78LQe/YHMe+7BS2Kzi3dR7jLDaLcbVBnJMHRZ8I3AClIpK0RZZmdDotsjShqkuqumJr0KesSlppi1bawpiGLEtp6orBYEBVltR1hW40dV1RVQVJmpIkKXKSjdc0DcPhkOFwRNM0OARRnJC2WqRNTaY1eTJ4Rb6rlJLZ2VlmZ2dxTiCERFQaKzR1mZNX1UtabuWzx/3qtNnZOQ4fOUIkBUkkaGcpi7M9WlnC4cUF6qri6aeeYN1ZqlozLprJPiBWgiwSxJGinSUIKbFNDc6irUNYh3VcY6J9OkOmJqsANGnSY3FpEYnPTEySxK9ANmYi4oidjMkkSVhYWMA5n1xVlhXj0Qico9EN1lqyLGVmtsvS8iIn7jtBFMfEydNorXdXI1zz+Oz+eb3XBgI3ixCCKM1IWh2a6tp2GeAdCmZmZ1k+fJjNzQ0fn6zDXUvJfYURQhCnMQJBXdQIY3yCgBCoJCVud6nHA0bbW9MlePuIoogkSUmTjFaWUVU1Mk1wLsbsWSEk8BOKWZbhbIOuBVms6LUzKm1Q/RHOecvfLI2p6sYnBeG7CWVVU65t0G63mZ9fIIp2ZUyrG6y+9ujAOUdRFhhTk7VaJKnCuj1t27MKR0pJq53Sm+nQmenRnpmhbjRJNiRu/PPC7q6WkdKvqEqThHaaUiQRkfIxNUkilBA0dYO1jvEoZ5hXO6MtISNEJLAGdOOQwpEqn3FujEXgcE6AUDgLTdOgtd7JDK/rGpyPucZaWq0WaRzRbrdpmpqqKsnHOUUxSVSV0yz33UUDSgmSRO2I6dOVU0op4jghSWKqusY6h5lMfjoHSZoihCCNJJGUfpKk1aKpKpqqRDeaqqn8as/Jud7ohrpp/DkXx7SzLons0WjN0vJRpIq4eOEMDSV+tKfx9xbHrsmPZP9Sa8eVFi97lzHufd3tJQjpL5NjR49w9OgR4kjwyd/+f2i1O8wuHmI4GvP8cy8w6vvZ/MCtkaURnXZCXjiGzSuTbRBP/py6IgQCgcBtYQxocPMOUzrspoPP4yfbN7hquYsoa5H0FrA46s2LOKOhqXx/oO0QMxaTNNSuxFiNspOlfz3n+xcpPqj1Jn+/zavXW50OcZpy8vgRTp44xsrKRb7w+S9hjSGZCGWNffmxtAa2mNi+cGPNLRC4Way1PP65z3Hx4kUuji8xGo1xDVCBayz2pXqeC1g6tMDMXI/+5oCNta3b2u57ErFnm+ZGXC6oX4vpxS8F8dEu0WzGzH2HWDx6GBNJxrZhtN7n3GdPo4cltgh97oNOq9Mla3UQ+Kzkja1N/vhP/oRWu83M7AxRFE8mkwWdVptWK2Njc50Xz51lPBpy6dw5tNYIGXn7s0YTpy2OHjvC4cOHOXr0CNZZ6qZmPB4xHo/98nfg/AvPs9Xvo43BGE0x6N+RCS+lJGnqheWq8mJvvz/AGMPGxgaD4QhjLFpbjLnSLuFy4slm8NpSHMcsLi6SZhnzC8tkWZv5hVnSNGVz9SKrK+eQ0gshSkk6WYaxhuefX2E4qChqfyH2uh2/mlp64Ucbw2CYo41B4BBSsTjXJU0SiqJgNMrR1lI2e+/uk4vUmZ3lOUVRsL665m16qoJer8PS0iKtVovt7W2qqtqxuNje3ubpp5+m0+5w6NBRup0ODz70MFo3XLhwnuFoyJEjxzl67AhLy4c5euR+er1F4rRFv9/ni088wXA49O11oJsG0xhmZ3scPrxEq91hefkwVVXz+OOPM+i/MpMngXsDgRfEJJAJ//fc+cWkl6OkotfpoaIIIyRVXd/QqsUYw4vPP8/mxgbdmR7v+pqvYXXlIs8/8+yBmNixxlCMcrJWi7c9+k56vRnOXbjI5vY2VW3JN4eYupiorxEi6QLg6hHCWWZm5llYOsrh+x7i2ENvZH19A4Qg76+xOnqeackZBwzznKpp0LpGa4cwltpqpJIcO7qMihJOPfAgnV6Xp59+lnPnLlBXFVVZIoQkiiKcg5WVFZSUJLFiaXGe0TinLKtrfkfnoKwsTeOodIUqLNXELaLT7XD0xHGcc2xtbWONprGCwbhmdOYCz525wKFDy7z+TV/OcJTTnllhPM558dx56qZBSUmkFFI6BAaswTWaBstwYx0lJML5Pqex+1OWnDMTH0sAgZOCKI1RSiCaGmsNjbFQN8Ta4JAMh2M+9/jn6HQ73H/yBFmWUVcFVjfMznSZ7XURwNrqKlubm4AjjhXdbpcoUlRlg9aGSKlJInBCu90iiqLJ/UJ5S5soYmamx9zcHMPRmH6/T11V1EWBBYzyE8SxSoEIhEUIh1SCKI6ITEykEqS0xLFARYp2q0fWyugPBtT2LDPtBea6Nd1ewld/zTu4cP4CL5x9hrIyk6xbCzuiesSuWpfu/XUnf17ukToV26eZ6RW3ewQZhPSXydLSIq9/5GFGowH//U/+CGRE1JmhqhpWLlykGJXexyRwS6SJpNOOMUZz9TIQt5dpLoUkiOiBQOA2U/jNjcBUDjtwcBrIr/0WlWRkswvoYky1cRY3LcAS4/sPHYeNDbWrsc4grV+OKNoTIb2Lz+JssVvd5zYhhCDJMtrdLkeOn+CRN74BRMQXn3gK7K6Qrm/D0lUNr8g9IPDaw1rLM8+d5pnnTvvrpIMXbIe8rPNWIJibn+HIiUM469hY3wqzP7ArnE+7xLeSJDRZihLNtEmOzdA+NM/M0gImVhTCsKEucm71S5it6wTVwIEhy9r0ZuZp6pqyKBgMBnzhS0+QJimHlpfIspRut0eaZuhOQVO3OH/+RT7/hS9QjIZsr67gHLQ6s0gVYa0hShIWFpe4//77WVxcwOHQRlMUBeWewnhrF1dgfR2VJKg4xlbFHbk+lRJ+xZi21LX3c8/zHGMM2/0+w8Gt3dkUfnF7PdmmQnq32+O+B15HpzuLMSXG1IyGQ86cPo02juoGQ9B2u8Xh5cWdfxdlycrqNlVd0WmlxHHEbLfD/EyX7b7C1BWVhmpiQbPLNA1pYrVQVWxv1Whdk+cDxuMO58+fo9VqMxoNaZpmR0QcjUaMRiMWF5Y4cvgY7VaL3swMDiirCoRgYWGZw4ePs7B4iKXFo8wvLDN/aMlPsFy8QKFr5MR32VmLaQydTpsTJ44xv7DAw697I6PhmGeffTYI6YErmJpFdIXvYmuguEpckFLSbXeJ44TtpqG5wYoI8L7QqxcvwqVLPPpVf44ve+tbiaKYs8+evgPf5Nax1lEWFUmS8sjr38CxEyeRn38C++J5Ni9u0F9bBzsRqYX0QroQOF0ibEOn02Nh4RBHjx3n/gcfoNXu0O8P2FaGzefVvjyhvKr86psJkXFoa0gSxdLcHK12h4de9zpm5xfZHuRsbA1wTlCV9WRiMMZay/r6BlLC4UNLtFsdmkZfV0gHqGvra0DoGqksdqLPZe0Wx+874TOucVRFiXYOXTb0t/qMh2M6vXlOPvAQ+bhAipSt7W0ura7tF9IFgJ9MdMagrSa3NUpK4jTFIbxn+N5GOYdz0yMkQShUFBFFCjepiWWsw9UN2lhAkOcFzz57mk6nzexkIlTXNc5ouu02Rw4fZtDfZnXlAsPhALDEsaLX65CmCfm4oKk1cRwTRRFZltHtdnea5H3Wk4nA3qHX6yGEYDQaYZqGpq6wwo/xpJS0XQQohPCDPqUkggillbegAVQUoaKINGuTJCmj8ZjBeIyZsyQyoje7wAOve4her0XW6oEcgWsmE8zThNrpVHKM77RPszMcux3L6T1o2sFU+MGwmewnCOkHivX1DZ56+lnqqmQ0HiCjiFavQRuDrmuwNzJ/DFwNpbxVTpo2dNoR2liq6s5J3NNLMPxagUDgTmELQ7NeYvvmaqsj92GqgnJ7HdtU+w2YLXABXAl5UyOcIMocca9FiqP7DofOHcVGhW2sn8Qf4UXC+9nNgn85FizOUZclzlpePPsCuqpYvbSKtZZ2q8Xr7j9OpCQXNzbJq4rhqKSqgiFL4C6xd4Xnta47jZ/wug2dAIdje2uAdY7B9jB0KqZMj73b8++9qYCafYXJhALVBmIwHRCZ5OiJoyw+cIymKHj8s/+dcX/ExsUNxhf7mDrEmHuFLMuYm52lv71FfztH1BJjGooowtRj4jhmbnaBLGsxMzNDr9dh1N+eLBdvdipeNk2NtBalfAZ4p9NhcXGRsiz4s8c+y9raGsZolBKYqem41aAdzhmsabx9yTUuUiV94bQ0SUiThKqpGec3N1nTbrc5duwQ1sJoWKGNL5RXltXL8vWOBXQlKNuwtblBXpQknXk6ecNosE6ZD1i7tEpjHFN9TwloJxIlBVIpEBJtHcbrSgzHBdb4Aq9V0+xk2DbaF+YbjsZgNcNxQVEbtL1cRAcQOCcY9rdgYhWTqt3MfOfg+efP0m63WVhYZmnpEGfOnNm3h6quuLR6iU6nS6vTJUkS5uaWiKKMOOlSVHBpbcDW4DkQFisrhoM+5bBAlw1SeD996/zSfyEUgghnBXVVY6xlcWkZh8QisAj/Hil3bGYEDjVZ+2Z1g7WG4XBEVV1fpAvce7SShOXZWWIlaSuJEpDoCmk1aVXRrWtq7cib3YS7SAhaSYJKUqS1t5Ys6RyrKyt88fHPsXrx4l2zfpuZmWF2dpY8z9nc3PRiaKfFTG+WTrtDO2tRlQ3bm32KfAS2xN+cJXGUMbdwCCkjxkpgmhKLIi9L6onlR6MbxvmIssxvOMnQaMP2sCBJNEh/5RnrcELihAIZkXY6qDQlVop2EtM0DevrqxitGY9z6rq5IqZKpUjSjCiO6c7MoJT30nYOBv0+49FopzCpNY6y9NYoedFQlg3CNGANUkV0ZmaI04zGQNrq8PAjr2c0GtNox3AwxDUarGE8GtEfNIxGIxrjSCNJK41RUuJkhHGgpEBNkgomt7EdqyulFCpSCBUjpEKLGuOMX3EMjPISub4OCLTWdLpdHjh5AgnMdNsszM0RqYj+Vp/B9jbbm5uMRyOSOKaOYvJxQVmUlGWD1pa5WS+Up2lCq5UhEDvWNlmWTYpFx5NC0c5PPAiIkwTtLLUpEUIxM9Ol2+7QjlPiSGEaP3kURzHdVtvbgU0Kt3ZabbJ2G6d8HbAsS3FO0zQ5o8E6TTVkcbGHMSXb6w11VbC7Ftmwm2E+9UufMrV/mV5Te21dInav4NtbnCAI6S+TCysXWbl4aeffcRIzO1/4RW5lMRHSA7fK1HO+3YqxJqasfPGZaxRjvi2EIVggELiTmFxjVjT0uaKS/eXoMkeXBVfc8A1wBngehlXN0NbMHW9x+MQM8aJAnYRmZGj+UFNvWD8BX+ALjh7BFzfduvHn34hiPKYYjxlub/PsU0/v+PV1e13e/va3kqUxTzz9JJvb27x4fiMI6YG7x7Qffb2aQw23rxPgYP3SJhurd76A4T3D1cYu098ixScMTcfqE0QM8YJ/3nZBtiT3P3yKh9/wJj7/2J/y2T/8ffSgoro4xhUOV4ZjfU8gBJ12m6XFRapiTJkPsdYyHgIINoTPcltcPEy73WVhYZ7Z2Vm2Njco8zFNXe3cb5q6QkhNq5WilGJ2doYjR47wxSe+wO//3qepqgqta6QUWOtFFIwGo7HNjVegKuW9b2d7M8zOzNAfDsiL4qau616vy0MPPQBOMBxU5HnJk089xXa//7IOXyygG0PjGtYuXYQoxUZdWt0xl158lu2Ni5Msxz3fQwrm24okksRJhlSKvHFUxvmJv4EXigeDgS/e5yYL4WufKSmMphgLysYxrq911Pwy+q31NbY3dsfFvV6H48ePUlU1w+FTtFptvuVbHuHBBx/i8cf/bN8eiiLnhRdfYGZmluMnTtLpdFlePsb8vMUgGOWOwWiDjc0zRLGg21VUxZjx9ohmVCGTFkJJrJmuMY4QxFgjKYsKawxHjh6nNztP7RTGCVSUIKMEaw1GN0gsySSrtClH6KbmhRdeDEL6q5BulvGGEydoJzGdLEIJRz3uY+qC+eE2+aihX8Ba45BMyg1JQSfLIGuhXsKE2Lnnz3L+7As7MexusLi4yIMPPsjFixfZ3t4miiLmZueZn19gdmaWTqdLOS5ZW1nH6TG4MdPrKUnaHD18H1GUcknElOUY7WJGeU5V1zs+3sPRNqPx4IZCelVrqnpEEivAYKylMRYnIm8jIyNa7Q5ZKyOJFDNJTJHn9Lc3aZqG/uDqRV6jKKY9M0u73eHEAw+QZi2c8YWVn3vqKUbbu0sPjbHkeY02htG4pigqXJUjrGZmYZ7ewixxq0PVOBbmZnn4oYfQdUOvPcv21jbPPfNFNjdWWV/bZm1zE+scFkhiRafdQklJ0YAzjkhKYrnbJbLWZ3Z7z/BokrmdeCGdisZphBEIC9UwZ6RXfCHt4ZBut8vD99+PcoITR7zV9Ob6Ouur6/S3N1m7dIlhv0+aJDRJSr/vVwJp42ulzszMTlZ+JXS7rR2rNSkk7a6fyEzT1AvpzmFNg5QRcZohdM2oGiIlLCzOsTA/D5XGaYPAr/hMkwRBD4dFY1FxRK87Q6vTQaUJKo4QzmJpqKoh25sNVTnk2LF5skxQjkfU1bQy1nTFU81uFvre62ca89nz/FRsj9ktUjoV5G/PtReE9NvA3kBojKGaLC25a75XQiCjCITANtNlEfcWTaMpi4qybKgqQ9NcvbzNK4mMYuLOLOCoR33vWRwIvJp4ZYpcv3YpgU28pctNHd9rRL1p/2GyjMaBn91XEhlJZCOgJfw68OlkvmHXZu42BtPLBwNVVXP+wiXiOGJza8hwXKDvVJ2Qqf1dOF8D12N6flzvvJ/WMLJcWbPopX7sne57TeP1dGyguTez38XVH3YWTAnSQacriYVgeG6Nc/o5+pc2sdZga4Md2zthfRm47QiUipBKUpYlG+trjMfDy64TLwBbaynL3eJoTVMxGA3RdYVpNG56E3R2ks3nxfemrsnHI4oip669v2yaJljrKIoKY66WSX29FguEENRNwzgf7xFTffE332Q/KFfKZ3yrSBJFktmZNvNzPerasLE+oCzzG3opXw+Lv33byUdqvAjjjCEfjzEW6qrCXWXsKYSYCDQ+49Mai0XihEQbX9CzaaYF7K78bG0dtfZ/Xu9oTZeWOOeI45g0TWi326RpSpqmzM7O0WplbG5uYcyz9Pt95CQbfPrZWhuqqmZjY4u6NsRJGxl5IUQItVMsVkoQQhInguP3PUxdleRFSa0NzjQ40xAnLcZ5hZCK8TjHOrtTqC+JY0SUYJ3AToQsKSWRVLTiBKzBNgVGXyNABe5ZZrKExW6L+W6XhXZEogTSVThjMGVOXZbkRcOghlzvl+4iB23nvI0QICe1HG6WuymgX96OKUopet02WRrz4gtn2djYYHtjDWcqv4IHfLyTLUTUQsUxUZzQ7naIYkWr3SLNUpyDvCgoioKqqnyB55v8rm7SJusM4/GYZDAgz3OquiFOU5IkAaPp9/tUZYG1xk9qtNvEUcS4KCir3YkNay1NVWKSlFbWpt3p0NQNRmuyrEXa7mB0ha59LcPBYOALadb+O7c7bWKlWFhaYnZ+HhVFrK6ugYO6OgkOb4fiHL1ej6rMiba2sc6hpCBTglaqSNIEgKasqBuDNvaKpFCBt+acTtxGcYqKFLKqfFFTplKeQGuDmawUstaSj3OGwxHD4ZBet8PmxgaXLl5kNOzT7w8Yj8dgQQlJEkW+TKd0mMmKrq3tPkkSUZY5SimyNCWO40n8TinLkq2tbYqi9MWircHWFY2p0bpBKjG5/woMDuusv59UNdpoqqr0XXApUE741QbWUZUVtiqJlCRRgqapKUtfg0NJS5ooZnptsI6yHFLXe++dewV1tecoTv+c/n06SJwOAKZFSm9mUHBzBCH9NmO0Ydj3vnd3K1BKpUgmfkbVYODF9HuM8ajANg1VrcmrZmfpy90kavWYe/DN4CybzzxOM355WSWBwIFjr5XYVJQJAuXto4+3Vtlr5/ZymIp+TuBigYgUKkqxaMS89M9vTj6vmHz+y/R/vhHb/QGf/qP/AUJgjB+wGnOHTqIY34tp2LXQCwQuZyoyX4+E2+aR/orgk8P81sJf6wNuT1w5ILgG6k1IU8HRhYjMSs7+/mN8fvRniGWFWFZeQN/i3p1EeM0gEUKSZh3iJJkM9i9grbnqWMk5x3DYZzQasLGxupNRbq3xP/P0Pc4grEUKiKRkPBqycuE8mxvrlGVJFCnm52cn96FN6toysZ29aZxzDEcjtgbbODsRwYQC1fIvMDk4QxZHJHFEbyalN5Ny6uQSD546zuZmny88/gRb2wOa5qVnNTdMwtjEAnYnH88YNtYuIWSMbcZXfa+QkqTVIo4k47ym0RojM6yMKeuK8bjAXeO3AKi1o9HuBpfY3tlIS7fb4fDhJbIsY3Z2lpmZGd72tneQJAl//Md/wrlz57l0aZUoSjBGY4zGWkddaawZ8+STT5O1Wpx66BHmFhdR0US8ixJU1sFZh2ks3W7Mm9/6LuI44pmnnmB97dL0yFDnOSsXt+h2C6JYgYAiH6ONZXF2kVanx3CcM8wnkzZCkCYx83M9nNVUxYA6JKK/6ji1OMPXPXKSNI5otxKM1mxubFEWJfnaNuNRyUrtWGscmt3upcIvoOpaS2ItUnirJHELQvpBwFqLMWYn4TNNYo4eXkJKye996nfZ3h5Q1RU0e26sKoV0EZHNE2Ud0ixjOT6MM5o4iZEqwiFY21hjc3ODYX9ANSnyfHM4n7lsGlYuXaSf16yurTMYDGl32nQ7HfqbG5w9c3pi76VJ4ohTx48y2+tx+oVzrKyu7exNNzWj/jaxiliYW2B2fp7hoE9VlswsLJA3NXl/m8FGSVkUnHvhRe9H3hQoKTl03wlm5xc4euwIy4cWWb14ic9//vOcOH6C+46fpN1qsbS0zNzsHKP+FkoqVre2cEArlix2YrJ2SnumTaMt+caAPK8oG4e2k/zpyWkjpLfASpKEOEno9HrIKKayAq1KtNYYYxAORGUmk6UCYyxr6+tYa5mf7WKqkjPPPsvzp09T65qyzqmKErQjRjGTtX0RbqcxzjIajVjf2PaFpiNBu93i+PFj9HpdTrZa9Ho9tra2OX/+AhsbG34VWFNR1TXaNBTNGITP6FISameodUNRFOTDMXVdMypyEBKZZCSppa40SWzYGvUZlwW9TsZMr01VlgwGfXSjiURNry05fuwQiwsN5869yMZGuedcafAd3mlHWOIzxxS7Qvo0Q12w20GcPs/kz5efmR6E9JtACEkU+yqxuqlvKJDfeQF9snRBXjYL6pyvFDwptIIQd199fokYa2ka7WfuDoiQJ6RAxglYe8/dNAOBm8ZdtgVuHzcj6N0KE19n1/jMNOF8pprbayM3/dwGnwl/hwVn69y+rJA7SjhHA7eL6fVyL93a91pB3kvtvhy7Z9vLZMLRGYeyjsg6dF5T9i1RlhK3U2zpQnGbewSH2/GhNtbsTLRe8/VuN3v88kRuIQVSKtqtFlEU0eq0SZKEpmnY2NjwWXg4pBQkSYy1DiHEzQ+JhPB+sZM2+/buacRuiuDOuTfNNM2yjMWFObqd9mQ5vKGq64mHr9vJpAYmdjO3kLF5jeesmS5N8xeRUhKlJNY6tPbjwnqystcJiVQCVIRQMVJpHAInxM4HTMc407bd+FYrUNJnjBsr960S8Md9f6Z7WVaMx+OrZKy6yTG33mrFaKq6oCxzkkyQRApjGuqmRiBRMkGqmCj2RVHjOPaZ5mKSJWkNrklRSpLnY/+7ComKJFrr3axZa0hi7xMshfNie1NT5L5Q7c2sJJB7jlkIRwcXAThrsXVDbXws0rphkJdUZcWw0uSNpTJgLru9+Nwit3NuWqOxetIBB39+TVZYHBjx4jIuv7anCfVissKnyHPG46vYpTg3iS8OFUmUElR1hW4qjE2RUUxSVSRlQd3UE4uom78SpBBEsUIp5bOTizHWaJQUWKMpi5yqLHxWudETn3Hhr/k0QU2Nx/c22Vq0bhgN+0gpJtf6JOa6vTHNxwUhQMUpSRyxtLjE0qHDdDstcBZd14xGQ0bDoS/g6Rxz3VkipYiThCj2vubT/DNfg8LXoTDWJxRp63ay0ae3ECemh9dhjEFN9DupFGmrjYgS6rr2K4a0xtQNQsqdQqGm0VRlxWg4ZDuNfVyta6q6JM/H1FVNXfu6F9YYP13h/OaswVpvxWKMz3ZvmmbHc76uay+K5z5WT8+X6Z9SCKTw9zFjfcz2m8UaO3nM+vu18Ku7zOQzzMRmxzm/8ssYQ56P0Y322ezagtUId63M8emRnl6hZs/jl3fm997Bbm9nOQjpN0GcpiwuHwdgY/X8xPj+biFAZSAiSGOI1O5TTQn5EGcM9XCSFX9AA/mNqBuD1hZ7j04EBAL3JHuLvYVL7+CzBQgws466MqAFVmt0abD1ZATQ4LM2N4A1Xl2i09Q2b9pvCsJ64JVgKlzfzfNtOrbYm2xzr1LiJ/iu1l2dCuzNZH196aAAfb7GrGpobFg1dU/gU6irckRdeS/YdrtN0zS37D0tlCTOUjqdDl/+lrcwOzMzyWa2vHD2Bf7wD5/COUMcS1qthF6vizHWi7w3tWJDEEUJQgqM1jT6avYExmeiT78bUNaaShveeuQoX/NVb8MZw2Bjg43VddY3+gxHY+JI0M0iZJQglKIoasry5aY8O4QwCOGwk7Z0OxkL8z3GecX6Rp9aGy6sD4kixaHlBXpZhotSnIxxIqJowOgKUzaISTE5hEDX9U1llAohaLVaxFFKXliqia1OMynkqpTPBDx37jxxHJPn+SQrVqP13gQ1n20gpKDdjUhTxdbWRbYHayweOsT8wiKb2wNWVtfotGc5cewRVCwZDPpIIdBNQ6QESRSRRopkpkXr/kP0+9s888xTCCk5cf/DJFmLS5fWGY7OkU1sZ44tL/DWN7+RlQsX+ORv/T8MBgN047+//x7X//5ZkqCEoGga9Muw8AncOaY5qtvrAz43LNBCMBQ+AaTRGuMc48bQWG8pFnMV2c06yqKg0Zqiv0VZFriJ/YmMY1SrjdManY8PXDJjFEW+oKVSvqikgDSJUNKR+yIV2GvZ1poaym1UJ6HVUkQRnD/7NNvrq0S9BWTWZbmoOCIkw+GASIFVglpcv3syzWFIlGJubo40S6mKPnk+QgnN/GyHctTn2fUVjPYrV8B7fFsHIo6Jsgypri5pjocD/vj3/1/SrMWpBx6hNzNHPhwzHo6pS+8JlyQxcwuLRCoiiSI67TZ//t1/ngcffognPv9Znjv9JGsXLnDpwnnQmie/9AWWlw7xFe/4StqtNmmrjYy9rzlAXhtqbWkbgWoVGGMpakeld0+JndyBST+yaTTGjEgaTXfREsuIYyeOkrY75OOcPC/J+9tsr11ESmilEXEcoZuKUX+b506f5uL585i6QilFlRdcfOECVdMwLEuMdQjc5Hz2jVBK+toAShEnMUp5CyytDRcuXKDdbtPvb1OWhRf5lfKTLhKsk6jGkqS++Guee0skUzU0jfebdw6kjIjiiF63QxT52F/WNUhBqlKyJCPLWgwHA9YuXaKuaspxgdGWunIY49BlhSTC+WmsPb/uVECfbgK/RDNhfwf98r/fPg/H15yQLqVEqWgy86MnJ/QNFqupiE53BoDtzVWoy7scHCUIhYhiRBLjrJ1koMudwZ17hW7iYjK77zNHbt8xcQ7MAbsBOed2Zp4Pgr9ZIHA19s3DCt/RuOHZehCEoYPG9CAe5OPRAGNwhV/ijDaYymAK4wvvVXjxabqV193bfnay0w7wAZj2oe71jNzA3edmVuFM1+I6dzDE2+l4YK/94/Sxe4m9yUTXeN40Dq3Aafz3qxyuCmLVvYa1/jeLIjUZP9x64BZCTAb+CfPz88zPzTEaDamqkrqu2d7ukySSVivat/+b67b7Ngk5GdtwvVXG+y806wcuRFFEr9P12ZN5jrN24n8rSWJJpAQq8ZnURtuXKaQL/5/wwWvajZNSEkVqJ0vTOagaMxGfJkqIkAjpMzpbnTa6kpS69GKLvDK780YoqYiiCCl3M159dqLP/ta6oSxL7/FrzJ5M9cuPrwPhkMIhhKOscoyDTt6mameUxZDxuE+kIqTy3ryNbhDOorX37Y2EwwqLTFNarYQ8V1RlAdJ/5yiKaJqGIs8ReE9jCXRaGXEkfdbpoH/dsZ7Y8z+fmTk5d275yF2L/b9BFMdIpTDGYPRk9tTda8H+7iCYxA3niADTaEaNpga22c3HcPhussZbuOw1gphinaMxGovzEy167yTLJCP9JVw/rwTTmDvNfgZHNKntMBXQlRJE0eSbi0k9helkmp3YeCiBkPhCv8M+SkQo4+iVBXVdo3XtvdXdzWXu+MtI7KzUaeqKxoCbZJ7bpiYfXZklb513MNDGW35NNSkphf/USfZ5f3uLKB6xtHyUJM6oqwrTNDv3IyH8ihqlFEmckqYZszOzLM7PEylJmY8pi5y6qiiLnGG/TztrIQXEceQnKCIvRMvJb6+dz0TXxnlbsmvUn5jinK+zOJ2Ek9IX/OzOzCFljCNCl+XObzj9LKO1HwoORzRlSSQEkfDneFWUVFpTNQ3W7eoDcpIhLqUgiSOkUsRRhJByx/anmBTVrqpqsprKr/JhJ85N7zEKay1aG6yxXg/EgWD3/unY+TxjDNaBiiNk5KuuWmv9xFRRUlcVdVlijcNMFntIIYijCG19MdrLjtzkz+lgcJphsve5vWdayEh/WcwvHua++19PkY85d/ZZ6rqiaUq/BOUa9Ho93vb2t4MQFOWI9TVJU+aXBc9XGCFo92ZJZ2Yot9bINy5yy8Z/t4HWzBxZt0c5GpD3t1/Rz36l0fmQ7ec+Dw50eXUfwkDgbhIBSwJSgU+lULDdQP9GZRI6eHuxfLK91onxx2Oa0e2TpA4eE6uWZrVm9OIAVwua8xI7cpjntPdjn44KbkFEF3GGiFs4XePqeyDWhQmgVz2R3PWTBD+A0rdTQ6jxNQSutoo0AhTIYwliIcJeanDnm4Nxzhl8HIiAefxIaQs/ifYqQTdwYQWUgvxuLggN3Db80m5zU9nOlyOVIu126Mz2OHT4EHOzszz55JOcee4Mw8Fwsn+LtZqqGlMU53DOUZY3yiqWqDjzIrLwS+5F5AVvX7zy+p2A6RB9e7PPc8+eZXamy7GjR+l2exRlxbjIKaoCYy2d3hxp2ubZ02cZPn3GD91uMZ4IIYmSzNcjaWqsq5n6BozGvoCe1ga7p7KdsZa19S22ogEq8sLsyVP38+g7H6W/tc1zp5+jLHI2N9Z2srFh1/388i/s8IKRAGQUofZkZRpjJwVf7aS9gqqqcM5nxqZpShRdXYaw1pCP+1SVIi8amsaS9/tceP55qsYL8i0EWWZIU43VObquOf/CU6xdvICaiGmLi7OcPHGE8WiIMzXSKZpihMIRC0M7kdT5kHF/k1YieHa+y/r6OnGa0mp3KItiR2zbS6QEaeyFLDWxcyirZmLlcBtuTCKGaI5pQVupFA+/6S0cPn6CF88+zwvPP4etR9h8jYNxIzq4CKDXykgiRaesyZqGBJ+3KvGC+V5nsRR23BCn297bqbAGPdj25/NlNeisbtCj0S3bmrxSTL22x+Mx29vbGK1ZWJwniSTIBiklp+475MVfGWOlYn19m9XVLUCBSBBJjIwESjoQDVBi8zVctY09NIeQDlOPyFefQ1eFT0C8DtN59KJqOPvCqhei3cT2ZGJB0tRX34fWmjPPv8D58xdpjKXdm6XX6zE7O+eF5Dynris2tjYwWvPi2dNcWjnni6HWlbfEAuqqZHN9hTiKMZ1ZhGnYWFnhUitj9dyLXLrw4qT+oaMuS9ZXLhA7R137jPbezAzLy8ssLi2ysLaAEN7CK0ki0naLRhuEGNzUbySF8DZTWZv7Tt3PoWMneOGF8zTnVxgOBuRFDbahHGkiJTFzcyRRxHjgJ1IjIYikZDTsU+saYyyR2zVBQQhmZnt02i0i5cVwN3lcSkkcK4SQjMcjiqJASkUcZzSNt3rRRlM3FUoJslZMEsXUVclYSmIUMoqJUz9p0AxH9Pt9oigGGREnKZ1ZRZyoHRvqra0tNjZXWV9dZfXiGs5aWkniJ4NTv4ak3Z0BkbK+1efS+tZkguZq58Te7BLYvSvLPf+eDuinr3151+lrSkj3S886LC0fYzjY5tLKOT/7o6/vm5ckCcuHDoGQZK0OKk7R9a2k9t1mJrNBcZKStTvo/obPkr8LxGlK1un6KsevcqxuqLbX73YzAoFrIoC28JtQ4GIobpS0J/CroDL8fWl6n3kto/BiumT3nnwQj4v2mx1b6n6NzaE+jy+WeAHwNrG33m4ZQZROsp0O4hcPvNbwfoy7/77tZ+R0ZejVkPiY0JOIpQgxNAdnjDwdE0wVgQh//b+KumTWwegemM8L3DzW2pckoouJd2ycJCRpRqvTptVuMRwMWbmwsvM650Bri9aWsrxRJsF05xKpYpAC57yXLBNRh6uIqVfdBVAWJVub22RJTLvdRinJyWPL5GXBxqBPrRtmZ+fJsi4rF9eueYvdyaa/muf2xItZxd58wjTVPk/mpjE0zZVtds6RF368GMcRSkmyJObUyeOsZRnr61teCN9Y2/f7SEBNVzzv+bI7i9Z2MkHlHg9mn6UITASa3ckTIcQke/0ambuTonbWCqqipq4tZb4/y8NUBZGyRJGhrEuapmA42GBrY3XP71EzP5tRFoX32xVgmwYb1QgskYK8qRiPRgwHfTY3NxiNhiilvOdxefWxtc+Q9N83ibwHf17Wt8/SRUiQLZDe713FMQuHT3HiwdczLCznL20BDlduMC2Ks5shP9Vwr58B+1pBCEEcKbI4pt1oOo0/n6fb3vKE7Pm3uWzbwblrax7WYu1dTLK8AdMVIM1kMkopaGXZxCXYIgTMzrSRMkKrFCMjxqVGbI5wKD+wVH7pm9tjq+F0MdkqhARnGpp8G3MDS6SddgHaWAYDf40rJn2+SQq1ucatwjlHvz8ABK1Ol7TVotXuMDe/gK5qciEplWK7v4XWjuFg+6r7sdZQFmO0ishUSh3FFKMR4/6A8XDAeDSgriq/Os5oyvGIYjzGaF/nI01T2p0O7XaHdrs9EdKtz9iOY4y7rKbh9RC+/kcURXR7M8wvLLK2MUDFmwipMNpijUabgkhJ6lYLnMPWNViLmgjpRVFOPMjdjoxs8cczzVI6ndZkItB7+hvnfDxLvDTs61c0tFpt5J7JUe9x7judUnjBW2tNU9eoOENJiVQSFUucwPvoG0tV1TgkLTvJWJ8I6WVZMs6H9PsD8lGOFIIsTnZWCUgpyVpt4qTDuKzxRUavF9imArm4yqYmzyv2r0N56bxmhPTF5WMsLB7BWsMzT/4ZVVWQ5wOMbq6bjQ6+U/TC2edxCIbbm9T58IYzbHcKoRStxQVU2kYbTX9tleZqRSFeIYqh95BrrtHZCAQCrxzTLIpaQJYqklSijIHqKjFuaiUW4YX0e8HK5HYyzdoHL0TtPUQKf2zM5O9T7/gD6iRgV6H57+AaYJNdG5eX2EdwugJncfbVZKgeuJcxFuxltdVvmjawKH1wXLe3vrpkEh/chQY7sLiB2e/JPxWxBV7AvhtxQuPXqEteVSJ6IDClN7vI0pGTxFlKe65Hp9NiXNY0etN7rr5MpBQkmfcGL/MhVusdodjdhJA+Hb5vb29z9uzz5PkAoTRxEtPqtYlaCZe2NxiNR1y8NKSqHGvrm1ytlloURRw/fpJWq83FixfY3t7aea7dm2XxyMk9md+ardXzVMXYF5PbI4DHkaCTRVjrGJV6X/1DL2o71jc2ePrpp6kbQ9pqoY2+QuDuZJJuohjXhmFp97m++b87Kl1jK4meZO7XdcNgMEYpRZo2OKdQKiKZZBuCX/EdRdFk0sNMshQ1UkKnrRDCURSWq1VJt0ZT5QPqUnDuhbPkI2/HspfRaMzZs+fBWoy2XgM0Da4pGWxvsr09oG7qSVv7XLp0ifFozHg0oqrKq2ajgy8kWFSaNE2Y7bZxzjEoKhp9u4K/ACVI2xlvfNObWVhY4MjxE8x0Mx564Ai9dsN46yJrL9TEkeTQ4hxpHNNOUiIh2NjYYDQccmlryMrG8Da16d5CSkWrM4OUkropqZqCvNE7vudTsdznFO9egtPnptnoV1eIIp9c2ekQJTFVPqIpD/5y3m6vR5pltFoZKlZY01AUhc8ut5DEEZ12lzhN2RiWjKqGOI44dvIEZd0wGFdUuuKpxz+LcJbxZdeblIJIRairTJCJiR1MLCGSzpc9uYaUZgHh/NygEDsLbdifXbz3WnM0dYW1Bqc1xXiENcbXeDCGGImKEiqjMdfR/Iy1jIohFs36+kU6WcRgc5NqUCC0oRtBN1G02y2Ukpx/8QXGozFZt8OJmRP08wEulhT5iOFgG2O1P/f0jZMvIqVotVOSNMNYzSjPeeKLX+C58+fob4/oD0dIJXjgwfsxuqYpB0gB3U4HJSXjQZ+qKnwsrX1dj1ar5a1atAEhUHGEUJIs8hbXeZ5TVRUq8oWa4zgma2VIqUjTFkII6rrxhZpxfvI1kkSxF6SrskE3hkiNKJOaTuaLNvtipQ21NrQ6viB41k6I4gjrGprG0TS+Hz8aDRgMtymLkjRreYusKMYJQTH5HrUZEceaui6IpMU6e51avtOrVuEFjqnIMZ02g13xo+Bq95Zb4TUjpM/NL3Py/jewcu45nnr2C9e8OV6NqqpYubDiT7pRH13dvWAppCSbnSHpzNBfW6MYDOAuFj+ti5y6OPg3j0DgtcIkSRkZS+JMocprmOZKdmtyXM0I8NWOwN8Bp73pvSh213tO+20HOMy5LTB+1eHVV7vdKqbBmbszWRwIXA37chJHMgGHJRTO257cqpA+WZXi1g1u3exPcJkK6dnkz2kB3Fcai89EDwRepbR7sxw79QhRmpB0U+JIklc1eV7TNC//fiWkJEomRcqseUkJUw4YjoZc0GPKOkekjtnZWR555GES5zDWMM5zzr6wyfr6tZdZKBVx5Mhx5ucXGI1G+4T0rN3j0MmHkFLR1AVNXZIPt3aKdu4V0iMl6bZijLHklcHuCaLe8sWwvd3nhRdeIE5btLtzaOOLfe4cFyBLFDNthQa2Srv7ZXe+t6PRDQaBnoyvfVZ8gVIKrS1xnE2823cz0TudzqTorKEstfdU1xVSOFqZ37OSVx/jWmuoyzHGai68eIZBv0+d7xdF8rykyEuSSNLtTEw7bIMzgvFwwNbW5s5rx6MRGxsbFEVBkY/R+to3CmsdlXVEMWStzN8GVP+ar79lBKAESZby+i97AydOntzpomfHlzhyKGbjYoxqVmlnMa9/4ATdVovFbpdYKp5/7jlWL17EWvvaFtLbPZSK2N4sqV52Ud8pPrNVCEXa7pG221ijD7yQLoSg3enQm53ZKRjpjKGsCpRwSAsujYkiSZrGNJt9hsMRWWee5fkFBuOc3GzSlBVnn3kStOZyEVJOsqGvttJEKIFQkESOdKJnXktI3+nuXdHv25tVvH8GUjc1uqmpipz+9u51rYRkJsmQUew9tq/rU24pqhzrNP3+BpvthHF/SD2uEAJaEtqRJMtSpBKsXrpEUZS8/i1vYvHwIQb5CKsEmxtrrFyAosjJq4LG2H2x92ooJel0MqIkRVuDrQqePfMcRkikjBBCMZOmHDtxHKtrxsMWOEsWxwgcVZlT1QXaaEylUQrSNAXnMHXtCyK3MlSkEJEC5yjLksFwSJIkdDqdSX0QvxqnlWVIIaiqTaqqJI4jkmTiGRvHaK0ZjysaNErm1HENRqATS91oqrrBGkvaTomimCSLUZHCOo3VGmsc1ji/GqjfxxhLkmaTNkQ4oNKVr69hclTdoJsSJX2V2WtPh0xVkKnErfZs0eScySZ/bwhC+itAoxs2tze9N/ZlnbU0TclS3zlQfm0MQkqMtQz6/Ste/3KRQtDOErJ2Ri4MRZ37SsqBQOA1z9SvsnZgIwVpDOo6K272pl7cviLWB5PpfXQqjO/NSJfsF7/2JkDdC4Uspyss2Z1vP6DJ84HAK0cy2ToSkSZgLE68fE/EqzKNKQc9VrxamMbzA7xSKHB7UFGGUilx3EFJhRISgaCuap5fOU+djxj0X7qI2e50mVtYRkURySQjuxwP0HqSMneL4UIbR9U4tgcF7sV1Ohtjispn1r344hr9wZiiuPHYUBtD3Wisk0CCiGJkHCOTjvdFN5rRYIu6zKmr0tsMXJamZ4xjVOidYnd7iZMEFcWkrS5J1kNrzaWVFaoyRze7nUEH5LVFAHl97YtNSu8dboTYd0nutXGpmgZZFGxtb7O5ucmhw8ssLsxR15aq0hR5zsb6qreVMQKBIxaSREq0dfvEqDIvePH5szhnqYsKYd0V4Xf6am0dZaUR0nvjSgmJEqSRQFuHsVBXFdtbWzR7fN1v5jcajcc7hQKvxuzsDEeOHJ5k6UqapuHc+fMUxXWS4KyGeoAtJVtrl0jiiEOHluj1etSNoW4cqJRhXlFVNSuXNpjt9bj/5CkW5+YxFlqtHmsjS3bmIjh8QUkcpQUhBQszPZI4oj8cM75MZN5nBXGPIZUiTltIqagm4ra5hcSQKGohZYQxFWaPviJVRJx2EQhv1QE0dYk1Fbo6eMvApFSkWQuAqiywzlKUJVb460pgMbpBVzVKgpo8utkfkZSa7a0hw/4YZIekA6ZpMOMBtm7ATgaNkbceiuLIF+pMU6TQSMy+a9HbEXsRF1vSTCYp01hgreMqLlT7ETHIFBUlxFmbKIpodzKkFNjG+7CPhn3K/EqHBqUk3V7X2530NfXeIuUyBpVNqpZOlyZ7C6oXL6wwGudc2tqmtNBtJyzMtIiSlDwfY6xjdWONvK45MhqQzbQZFUNG4z7jcZ/xaIs8LxlsDajrBnvZapWptdCOQY615GWFbAyR3kDIiEZIrBDErS5xq03STVlankcJEG6RidcMWMfRo4exRpMPxxSjHOc01tYYrSlHY5y12MkVrWHf/cBbb8UkSUKWJERR5DP6nfO2Lc20WNjUHshbs02tvKIo8T7oSIx13lIGX7RaiRiEJM9zEAJt/ISunHSYi2I88Zr3Hu3TtvjfLsJaqJsGWzXUtZ4M4XeLOl/79jwVNaYTL45dAWD6WMT+iZlbJwjpN0Fd11xcvQQ46sv8njrtDvPz88RxTJqlCCkRUURd11RleduFdCEFvU6L3kybPhrKmyteEAgEXv04vIiOAxNH0Iohvo46Pr2T5/j1ja/mJOSI3epCe+9806zzvRi8Ncr0tQddHJv0xPZ+tTskFwYC9w4ZMAdiRqE6Kc4ajKy5o0J64JUhwq8aavCrcwOvWqK4Q9qaJ81miFW849dalQVP/NnnGGyuvyS/9Skzsws8/Po3I6VA24ayzNnauERZl6DNLRcNbLQjt5ZhMebC2hgVwTPPnQZgNHJofWPvaufcTlafMQpEhkq7RJ0eUWsWISRal/Q3LlHlI6rx6Kp+xLW21KOrJ1ulWYus3aHdmyPrLLC1scbZM2fQdYW7LEFrUBqGpblm5BQIlBREStBclozqfXcTVBSRlyW11qyurbF66RInTx7l/vtP0DSOqjJsbmywsbGKswahFRJHgsTIiBJDvWc1+Wg45KkvfNFb8sTKWxBfpYEOL6SPygYrvGgSSchiQScR5A0Y63wm+vXE7augtWZru+8T7a6Rwb60tMhXfuWjxFGCQDEajRkMBtf/LKeh2kSLikvnztBUFUsLPeZmD1PUiqJOceo8G4McYTW6blheLPmm9x3j4Ycfot2eZfPQOiurQ9o8gZSONBI0TtA4i1SKE4eWmO22efqFC/uE9Gk/0u3Z7iVUlNCZmcdZw3BrHX2TPt3gC/imaY84blEUW/uEdBWldHqHEFJS6dIXMx5tYu6iS8H1UFFEd2YOAK0bbF0xGo0YFQUY44snT37cOBJIFJW26LVthIpYW91mOMyRyQytedBVjd7exE6LLguJjNuIKCHrtEgnSZ4RDfKyrCwhBFnaIU4SyrGlbBpiKeikglr7ycfrnmcyg3iOqNWmM79E1m5x5MQx4lihB2voasy555+5hpAeMb8wRxxFbBVj8r2THiqDdMGL0WXhrzsM2hieOv0cQgic9dN38+0WR48eomo0G9sDhnlOiaQ96nNs+yTpbEZ/uMl2f43t/hr9/irjUcnGah+tr7w/TUtxTZ1DtTEMpsVg7GDfhddeWoaFRdJknuMnD9NutZifnUU4GA+GWGNYXJil08rY3Nhga3OTqiwYj/pURUl/fYOmrhgMh9RNTVHW1E2zox0rpciyjFaW0Wl57/RhntNoTVX7FV/W2knxaovWGqUkaRoRRRFxnBHHMQ6JNhbj3ERIF0QywxjNYDjEaE1eFGhj/D1BKaqqpKoKoigmTVs79l8AUdRgrWA8HlIUJRKFnAzKJWLHqf/qTDPiFLtWL9NsumjPn9NfIQjp18ArRfl4zOb6CqPRNu4qPRiBoNPpkqQJddVQ1Q1RpIjSGOcEjWm899xVvJUEAmstdd0gI0USRbtFYm4RsfP/q58czljG21tYranLMHoIBA4KQkAS+YychYV5Wq2Mrc0B/f4I47jucrLbxY6zh4Oy1kSFQMaK2YU2da0p9g6oHP4eMy2m+WoVgqYTzgnsmCNO0wD2fv+9mMlz00z1e8QqPIoVC702DsfGoLiNfp2BwD2Ixk+IxRaXN7jaQuJ2+9cvVXvbm9iSgswU7UM+S7Qoxpj6VbK05yDXzZgmG4UQ9xrA4ZxBNzXj8RgigasFRT7aGeC/FKI4niw5bxHFCdZZynxMUVQY7ZePv6QaI4BxDqEisjhGKUEU+yJ/iPq6Q//dfTjMJBPQCxYpToBtSuoyYTgYouuCuiwm3sD7j4ESoJT3GL6KhgP4gq/GGIz1tgMWh7N+u5zpYhuJ7+v67+hF8izzIsrc/DxpmrHhNKauiSIviCRJ4r2Z09T7MSvF3NwMaRLRbmeTbERJmiiUgqapMU2DU16ISRM/pjZVRX1ZRryzFusERgqEwBenQ2KsuSIDH3zW+PZgRFV5MUkbuEGZtOsyLWo7/fvV0FozGo2IohjhFHmeIwTEkUIbe1VNYoq1htGwj4wSVs6fwzlN2m6Tdtq0Ox2OnbwfUxeIpsSKiLzSjPIaIxQqbRFnGWnLC1m1NTTOT+I45yirkliJKyYAprmZURQxO9sjjmNaLS9wjcZjyrKkLKtbnnS408RpStZq+0mmpsYafcuxwQFGSoRS2Mt0HOesz2x3crLvK1eA3A3SNCOKYpxzPrHaNDR1hbOWuvb166btdG5iPG7tlbFtYusRJylRnKDUEBzUk7hbVjVOqIlEpX2xyImobmpB4zRYQxJHRJHat2vnmExoOKQQJElKEknSSEKlGU8KeU6J4hipFCqKUSqiNjG1dkglyVopWStFRQKlBDJLiZQlTmNkLHFmfwyz1jDKcyKlcAjiOMFMz43JyZ6kLWaXDuFcQz66iNUlTlc4ozEIjHWUtWZrmFM3hlFZIaRBlgUiiijKkrL0xT0jpVBTayx37bgg/CFHTJ/fO2t1WQw2VUU9GtFfX+eF558nyzI2ez2kkFjdeMMb6bC6gzGGJE0n+3ZEMqKaCPTW9tFaU9UVVdOQtXzB7k63w+Lioo/ZaQpAu932sWswmNwrLNZ5ixg5qZ1qJ9nnWvsVVzv1sfdUHNCmwRiN0X6CYhrvpoVv/aknkVIRx/GOkC6EZG7O25JJGRHHY+pKUxUNCEGapP6QCeEL6Orp9X759N+0EoJl1yt9Ots79VCH3fSzW7vpvwaE9BSIWL14gfW1F68phkslOXH8JEvLh1hb22J9Y4t2r83c0gL5eMiLzz1DU1fXvCLqpqHKxyRJwtxkJuWloIREColx9qoFEXRTc/7Zp32n4jr+bYFA4JVFSZjvQbcd8TVf8wbuO3mcP/7Dz/Nnn3mSwsDoFRjwO3xyXulADiuKpqKzNMcDp+bYXBtx7pn1iS8m/p7RZ9ffN8bfb15tWektduuKxOyOEhpgPPnz8rBes3scpllOB1FMuoxOp8Ujb7wPay3FE2fpDw5mpkwg8IpQACW4UmPEJNNn1vnioxu8vKKcFh83lyGZSzn+lgdQQvHC1mlGw9volXu32GtFehDF6gYvpN8DcTnw8nC2weqK0ajPyqVzGKBwBl2XlC/DUqHV6dGbnWdmfoEky8iLgrX1bYrxiLqsfDb6S2CaONFtpywtLyOVQEZ+ify4XKOx1Q0n5511lEWBkhFxEjE7N0NejBkPNxjUOXVdY3VN1d/CTrzR9xLH02xPx6i4unRf6wZbFbR1ReM02lqcFeCuzKiYussqCVEEjYVCQxzHHD16hE6nzX3HjtPtdPjSkwJdVvRmZphfWCBOYrJ2G2MNGxsbgOMNr3+YpYVZxqMB25trdLpd5mbniCLHaDTCNA1zcQslFXO9DCEjzGaf8VWKyjrn/JJ/IeikGXESUdQV5VUykau64dkz53yy28QS4eWMpK11VJW+7gqD0WjE2bNnkUKCFeimQThLp5WRlxV1c+0WaN2weuEFtjbW2NhYodXu8mVf/lbe8o53cOjIMb7uPf8fhv1Nnvr84zgBF7dyomyTSEii7iyt2TlmF+cZ5DmrW30vfOGT8jY2txgO+uTF/mvI4UNrp93iLW96A3Nzs5y87xSdTodnnj3N+QsXuHDhImfPvvgyjtztZ2Z2gSP3PcB42Of8mWfRTXNVvedG1JFCJzGm3C8GW2Moy6G3VdINzpodIfluIYRkfn6JXm+Oxhi0seTjAVsbF9G6YTDxCt8R/I3x25U7Ahkho4S5uQXSrM3G+hAYMhqOyN0KttG4qA1SQzMGZ3F1jhNQVIJSCNypU8z0MrazZF9CqXOWfDhASMncbJd2r0uWKLJEMRjmbI92Y5iQks7MDEmW+dUy7S6b6wPWLm2RxILFpRmSLCGODUpZsrk2yqWsb7RJBgm61Ohi93epmoYXVlZ8jcGsQ6czQ1GMqarCC9aNYfbwEm/7uq/D2obnn/oMxWgL01/BlSNGeUNeGrYGOaNxicOhrSOKImopKXXN1vYmvdke1hjarRajJPFFM6+XVDsRo/0Bmvx5jVtONRxQj0c8u7HO2Wee8cVBWy3iOGZpfoZWlvHQfadYWlhgdnaWmZkZ2lkb0ZmnGI9oyhohh9TNBUajnO3xgKIqefjh1/HQww/R6XRYWFigaRo2NzcxxjAb+8mZ/vY2W1tbjMYl43FBlsbMzLQAhTEK53zM9sK6nxjMspQsS9HGohuNNYaqrv3kphA7HvrOuUndjJg0Tel02kRRTLvdIY5jZmbmiaKYS6trbPcHXLq4yrnxClmSMtud9dYyKsJYx+b2gKquuDLDosYXR0rYLTwaT/6ecWVW3a1d068BId0rIL646JWzk0IIsiwjiROSNEWpaOITlNLtzXDo8GEG2wkXXjyD0DXusrRSYwx1U0+87HwgKMsSa8wtzYRKMfH7EeKG4wJzm+1iAoHA7cFav0RUN5q6rvysN9cf60vpK50LIbz3nnUY89KzHHbux9ZXRUcKokShlLwy43wqkkzvH69GUWK6TjVmv5B+o3Wr7rI/7wGmy8F9Zso91PBA4E4wvb41fnZxOil2o/B6s5nY031ph6k0TrgDkaF22zjoIeSgty9wW7DWYk2NbkrKIsfgqIzG6OplXW/e59VQ1zXj8YiiKKkK7zfuXlIfbG8FYn8PNtbgkDjhsNahIkWcRJja+MzJ65zDWmu0bkiTmDiKcK6hKh3CGZoqx+2slL66n4l1VyQ27n+Jtf791rdDCIFSCmcV1lzDJ2Wy750Q6ZzPatd6UuzPm4JEkbcKmJmZ8aJPlqCNIS19hmyWpmRZRlMX6DpCOEtdFZimIlISYQXGGhoDQhiEE9ibEEVVFJHECZXR10wKma7U2+tR/HK4GZseY3xyXFPV6EZ7P3t3Y4sfnKNpSqxziIGkrvw14IAoSZiN51FKsXz4KMJanFAUdUOqFLEUtLo9jhw7htzY5NL2ELtXYBJeiN27Fl4IdsoCWOdoGr8qwjmLEI52u8X8/Bzb232klDu2F1dFxCBihFQIFYG12DuQAKhibxskowijG4xusPbqSZNTpJREMsI6NymuK0lbbUQUYbIUFyuE3K/HOLw3NA7cJBv9eqsJXhnczsoSISWxiojqGBXFEx3q5iYDnfP2KkpbqqoBqh3Pf2sMtipRMqLV7WJ1TT0oJsfXTbKu/WSdNQaz87leJE0ybxWipPQrTLKUKI5AMHntlbqcimPiNCVOU6IkJUnjiY0IOFthGkNZlCgpSDuZL47sJpnows/2TbOchRAI5/y/owilYsS0GKpz4CxSQJwmGOvVbef8uBy1K4Zb56i12R02Gu9EEVUVZVFSFCVJHDM/P09V5HS7M1gjSeIRDXqy8sXfF6b7uOlht3M4YzDUfgG5VjTO+qKgqcJaQ3/QR03aKoQgkhGpjH0edpwSJxXG2MkY0U9YWGdpmmYnQ3+6+dUXvraGMQZrLM6aiff5bga5Umrikz7NMveZ6/7epdDaUJc1xlrqppkcVx9nlPIZ6Fr7e41SkzqTYucro7Vvq189MJkQ3jNuF0ISqQglIYmjPZnzl5/3lt1MwamlC+yus5r6pt86rwEhvQJq0jQlTbvUdb1vOVKapLzhdW+k15uh0pZxUaGSmIWleR55w+v52nd/HS+ef5EXVs5iNjdoRmPcniyF0XhEURaTpVIWIQTbW1uAL1xyMwgErShGSUmpGxqj75nxgRcBd1cKBQKvVbSBzSEMcsMf/Y8v0v3iM6yuFfSbaw9mhIBuNyLLFHGUEkUJo1HJ5tboVm059+HwRUelu0lLGR8m783KQjcixk86Z/hMVNhN3d/gVZGBP+0K5OOCL3zxLM5BUYQi1IEAsJuQsnfi8Hrj+ZsV0ivgElSbFef6z4EQ1FsHr+jYS+KgZqIHXnMYXWBNRd2MyfOBnyi2NTiL1S/9PpePhlRFwfbmFisXLmKNpi6GWGswL2m/00y3Gqgpi5JLK5eQShInEUpJep0WszMdRv0hZVGi9dUTRK215OMhzhjuf+B+Dh1a4sK5FzgnSvKqYXvkC3K6awhllXY0E6H+WmHMGrMjfgnnMyzbM12aKqIYVdg9kwnTkDnRMzCT/TZNw8rKRSIlGWxt0EoT6sbSamUsLy/x8OsewgmBMQ7rLMtLSwC0WilNY5npzbAw12NzbZWzzz5JkZccX+xQ1wmD4Zj+qKJ2BcYJmmsU85wihKDX69LrdNEblvwG9qdXc/W7E0gZkSRt6qpkbW2NuqrIq2Yi+Fy/Bc5ZqnyAEIIq30JIyXjweowTtNs95uZmOCYFb3nTl2ONZv3iedYGOe0oIpGSBx/5Mh5545v4zGc/y5lz/5FicgyllPR6M7RbKbneZFw1RBJihS+8aiAf5zz++S+SZRmDfp+F+TmOHDvKl7/lTehGc/b5F7xQdtXkPgHxEYgOEffmaM0t0eQFxdr6S5ykuhaC2eUjzC4dIe9vcvaZJydi+vXPlU7WYXFmkbKuWd3eImm1eODL30ba7bK2tc2oKCjzmGq4913+HMY5rK78tfdyfIFuA845NjfX6Pe3WFw+zPzyYRCOuvH2Lvlo+6YmG7W2DIcV43HDcHgGKSXl1De/LkE3zBw6wqk3vIlyPOTM54dUxZWdqLIs2d7cZDwa+czkdoeHvuzNtDodep0WkZJsrq0w7G+SDwds9repG7vPikUISdaboT07S5pkRHHMzHyXJBU4p+mvPY0xmrwYo5Tiodc9RG9mhrrIqUc1LmvDfJc4adHtLqCkJHIWYS2mKrBGIyZFaHEGdIltCupyRNOUDLbXyQebdEVDEimk9N/zCsMQYxj0B1Rlxcr5i0gZ8+Vv/TJe/8jDvPDC88zNzLB68RJ1XlHkJc3YYo0l1zW1M+g9cfRmieKY1kwHEJPyXpYyz9FVxTPjnFgqut0u3W6XudlZjhw6TBLHdOfmEVFMXjT0t0e0ehntToftrS3+bHuLubk5jh8/4SfHnE8+Ho/HNE3N9tYWVVWCM6SJJEtjWq22t+zqdrxDhqmw1lAU3ke9LCvK0hdCHg7zicDtEELQ7aakacTS0hLz83MUReELkeLve02jkbKmqmouXLhIXTc0jcYYQznOiYVCWKirmiSBXrfni5Tiz/vt4Tbj8moxSQPbeOl7CS8KwK5bvWB3OfrN/yqveiE9inznJYqmsyb7UzKFFKRpRpa10EWJtpooivzJ2m7R7rTJsgwZRZMZrP3vN5PZN9jNQ2huMWNc4DPS5aRte3++qTeZsZYiz19WQZ3biW/qxJ9KCsDuWkYEAq8xJvWL0IbJEqMR4xxGuRe0r4eU/jpSShJHk8zx28Bu4uUk2/16S8wORli5M+ydcJZ7HpvWHHmVIACjLcNRebebEggcLKZWTo7bO3E28Vp32lJS+Ivw5ez/oNupBAI3yTSz9XbgnJ1kgjqMmfRs7MufKLbGYI2haQxlpb0oZkpeao6yEApEjHO+iJ+vnVX7zFbniGLFbNImTSKqcU61d2n/FbiJP3pNpAStLKWVJbSyGG007ip2LvvefRNJFM65nax0ozXOGL86Ul7ZqGl/UjDJdN+zj6qqaIQgjwSmqVBRilSxt3RptbDOUVYNwsmd/q0XXywqjchSn9lotPddztLYf46F2lhK7W7Yj57ifZXVjnXAzSCEb8/UO/x244Uph9aGqqqoqgqt7XVXC0zbBQKcmWQM+5tLVfqiqHHiM4+zNGFxtoc1mq2NDbQraKxD4ujNzLC4MMfiCy+SZtmOR3EcKVrtNp1Oi2R7hNrxPd71OTbWMhyNqaqa0XBIlkQIoJWlpHFMpNR1NAkBJCAzRNRDpgtInYOqua03NwEqiomTDOscRT66qcAjhSSOYhpjESiESmj1ZmnNzDCsGzSOJrpMInN4OxM3zUw/GAMnrRu/GQ1CoFREmrWQQtBU3g/cmBsXTTbGZx83zWUTUM6BNcSRYmZ2BiXdFdeXlMprZA6aut6xHpZKkrbbtLtdZudmSOOIfLTNaCgx1lJWNcbunutSCn/9RjFCxTjhaxBKBVmq0E1DXRfYuqYcbiOVwuoSSRspJu1IUmS3R5p16MwuIREoXeOMoXIO6nKyEgOmmcrWNNRlTtNU1FXp605EFievX9HCaEMjGuq6pq4qkjhhfm6e4WDAwsIiZV6SpjGm0Tipfb2KiVh4vQXR12JaB8JNfhchvOitncXWDYX1942yLHHW0u10aGUtWlkHh0AbQ6M1bSmJk5iiLCjLnCiKGI1GKKWIIp/ZXZYFdV17/3NrJ+eWnGxq8tp4ogHu+uztrMAxhqqqKctqJ9lWSoExEdZ6XTZNU+/sUdc7Kyum8dJaR54XlOXumNZZR6Qi7w1v/MTEVENVUhIrOWnP1ZgOAKYDgukSddgvFNwar2ohPYoivuxNb+bw4SOcee40L7zw/I7oPaWua549/QxZ2uLEqQdZXj6EjBQyUqxurPKJX/oEw36frZWLNFWJu8aMuJKSJI5x1lI1zS1dHBZHoRtv63BZYH7zW97Cd/y1v8bFS5f4xU98gvX19Vs9DLcdIQStVkoURXQ6bdJWi0F/wObG1t1uWiDwiqNagvaywlnIVzW2hryAm7HYdA7GY01ZGqQwSOkLRN6Ovry1PtNJqogsaxHHJbvTfa8hNH6SeVp4cLr6+jb35+8mt6a7TZewTT0vAoFXOdN0ypsNfbc6PrbsxpaXMbYWCwp5OMKNLPZ886qJT4HXFn6A7IuEVVV92wR1nAV3fQF9v7nKze7XTAR0buVdV3xyq9slzXoU4y3KfP/gv6kbIiU4ubTA3EybL4xKtjfH1xRTrbXk+YiqLjh/7nmqvI/AsLy0gFJ9tja3aYx7eSFikl4+3NrgnHHouiIf9r0tg3MTcXkijuzZrmbvLoSg083otlNqDY121E3FcDikKAourq7iHBMP3IhOyxcoTaMWrVRx5Ogxjhw5xvraJp97/AlqUxBlmlg2VEUBN2EJ4pyjGI8R1lLdxIpwMZnIaLUyWq2UsqwZjW5/Ac3hcMjp06cxxlCU9SQT/frvUUrSbaVIKZDSWw6M8tJ7vD/9DNv1b3Divvt49F3vpNvp0tSWSEqyzgJR3ME1FZgGozJKrejOLvHoO95JPh4x6PeJlOLL3/JG5mZnUNFnMKahrGqKstpXY9cXMRS0Wy26nQ4ba5fY3lqnv7nKbCelKKGum6tYCTrQ22ANjdSMnPIWQmnKbR2DOMdg7SLFsE9TFTc9e5eXBSsblzBOYFWCkwllZZC15cixk6hI8Vw+JL94bvejrMHU+WRW6WCI6HsZjseYS2t0e11O3n8/1mjG4wF1VbLy4guU+U3WTNrrrQHINCXKWnTm5lhenCcVbl/ClxCChcNH6M0uMLe4tLtsxUFVVbz4wlm6vR5L82+h2+3iEORlQ1FZqgaUEnQ63la5OzOHjGJqC4P+GNNUWN3QVoauMsxkEYeOzFEWFU9uDrEGDrVaLM/3qO4/SdKZob14lN6RU6StHrPzR9FVzaXnz1CNRwwslEikmsqfGsgZDi7xxc8/hjUN+cY6pi4YCIsUjqa5/m8thKCVZPTaHUxj2docIJziyOHj1KUmTjtQGApb0OgGY+1uodHJhPPN5qHquma03SeOItqdzIvHcpKMq3yy8Hg8Yn19nfE4xyFI4oTVtU3KsmA7H1FZjYojWq2Wt8afxJg8H/uEO+d/+0Y3WGORShEnCfEkIc8XAvV/jyLpi/tq/9m+8KgmL2rGuV/VNC0BIXe+r8VYizaWRlv0HnsfKeXOJsT/n70//ZEsTbM7sd+73M1WX8I99ozItaq6ullV3exucqbZzeFAlL6MIEjQAAPojxJEQBAgAYIwH0RhMBIEDMUhoYGazW6STfZaVV1VuURusflu693fRR/ea+4eER6RHltmZJYfwNIjI9zMrl27913Oc55zoJdlaKWOh4xePMANJWVVcjQ7onUNeiGRQlAUBca01M8UM6/MdI6AOSe+6ba7Fp5/T/ydJtKFlGxtbXPr9m3293ZpzggpsdYymUyIooLrt96h1+ujEo2KNId3D/nbn/0Npqqxef7MK10KQaQUVogw4T/nytGc4RElhODylSv8oz/6Iz779FP+u3/+z5/rNV8ntNYkSUTW69HrD6iqCxuBC3yzOF2D/DqpYqkhGoWkcHkocHiekR30BNrW07Yn+vFXBU8YsoRcVY2/08P903HaxqHhRPX5fN1bbzzO/1FWJ+CUNOICF3hJHIcHnfLMfKPwuve9r4D0FqlAbKgTccwFkX6BbyGUksRxRNsamka8wrHgRTR853zdJzxVnw9CCKI4Jsky2mbxxL8758B5Rr2UzeGAREV0dstPRWsajBUs5jMUltGwz9p4SFlWKCGOXape6ox4T1OWWHuIswZTl6y8jVddjKe/v6d9A0JAFCmSNMJVLgSX+qDIL4qCo6Oj7jxY4jhGIoJK28YA9PpDxuN1PBFCf4KXBqljpBMIcV6b1OCp29Q17itsYFbHHI5bk2VpZz1QvrrCT4emac7kH54FKQRxrNBKHa/dq7qhBo4OD5nUH+Gc5/Z772KMY9AbEccROsqIdEJb55imxglN6wRx2uPa1Wvk+ZJYabRSXL16jY2NNT658wlZEtEaGxSzp7AqTGmtibQmz5dUdUlV5mSJDuLEM5eRHlwIc3R1DyeXYbOkkxc7ic9AXebUZf5cz2mtoS0LkBHoAV6oQOoZx0Z/QG/Q50GaPfYs/42Hiz4LTdNi8zzYqIzHgCfOEqqy4GBnh4pzEukrtXlHiEutUUlCnCb00pQmiU8pugEh6PWHrG1eIs16HYkeLghrDPPZLCiNne+yHgTGBKsP5wRaSeJIEScRw1EfqSIOck/dtjRlQduUqNgzTDxp2mN7mFJIQeoFrfVkWjNIYkZrI0oi1i5fY+PmO6TZkNH6NeqiZHk4BSdQUYKU5WOKdEdTLznYfRA+c1mAbc+dRS+EQCtFrCOccVRFDV4yHIzo9QYoHSOUxnhH4+wjRarnbYoOKuwaETv0IEEJ0FJ0OWthzG7bhvl8jtYRs9mcKIooypqmqanbFis8Ukl0pNE2InIOKcPzQifVyffnvUcKGTIIOoJbn+rUEEIce56vfn9lz1KW7anfO/l53AnlAqHunD8Jmj3unhdhbNahC8Z31T2hIwQRzrtjL/6qLpFCUFUFrTHnWDZ7gvIFoE9YbL94UNyvKbNygjiOeeutd+n3h6S9PkVdkyqBijSj0Yj333+f5WzGvY/vYJ4xEVrnqJqma/l5+Vn4xz/5Eb/7u3+ft995h6YuqMocHcfEaUrbNN9ooFUIVK1pW4NDUTWWonj1VfwLXOB58HvjQBxPCqhPrXUWBFes1wVrPUURWnlt1Ek5vmG/cSFgOEwZDGK2tja4tH2ZfG6fbe/yXYIg2J+tCs3zM/59lT3ya4dVWxtckOgXeBUYDAf80X/5h2xsbvDw4Q6z2Yz7X97nwd0H3/ShfavgZg4+60LtM4JYpiTcsitnwSEh62E1fjWE8e3iVr7AG4I0TdnY2MA5R1nWGGNYLBZYa44J2kCkvPpFkn/s5+tCFMWsrW0iEMzmC0zbUhcLTFPRNCeE1ekVl4DQJmgNw75m61LGsmhZFqbzmx0AAms6RXqZ45wjzgb0RxtsbF3i8pUtnIxJv/gCGodtzxFY+VXwNljl+BOtue8U6f1ehtYK14XRta2lPkMpYp3ncLIkLxo++OAD3nrrFlGcEKc94jgiL0ratqVuGqqyZn9vD2ct9Ts3wG2zt3dE29zh4HDCvQcPaRrTBVXqZ/nfAKFwszbsk0QRw36fJIpJ0oi0ioPKPD97f6q1QClIEk2cJOh6FUT3zQ+m3jnqoqaREofAI2i68+6bApb7HHwJ//GPBRubl2h+9DuMx2Nu3rzBYDAkUusoKZhNJ+wcHrE/z1lUNQ7B9tVrRFrROsd0vqAogoWDPUP17wnhrHc+v8fDvQNcF0x4aWPM9997h52DCfvTHOfOIpi7jZA5glKC6kFyKYRBvgS2Nq/inGOxnNK0z5lJEvUQOiXL+vSHY1rnWFQtTkccLRYs6orZ7pcobzi498VLHefXh0A8eiQOmM+mfPLLv6M/6HP1rZtkvZQrN28yWF9jenBAuVw+9ZW01ly5eZ00Tdl7uMN8OgtBnBJsWzM5OmQxmz5RqPKAE+I4kPW4k8U52nxJ3jb87K/+kk/7ffIiWIgMRwP6vZi2bsiXS6qmojUHSB2hs00GUYrEI+OYoTKMo4ZLacrVXo+5FcSdj/svfnGHO/d2sTLFyYiNLcGl8QZeJtSVIc9LJtNDlpNDJge71MspSis2ty9TFQX5cgHWQDELn+Sx3Im1tTGjUZ+6swBb2WGFYldEFEUsFgvu3r1LFEUADAcDNtY2WBttcuPGbbL0kNnRkro2BB+KTuhGl40qOzuuc9dpPB6DjjRXrlwmSVIOD6fkeXFss+z9Kn/DY22wTllf32I4HKOjmKYJ3e9KKbyn6x7zWLOyV7F4PEJKsuykqBTmb4MQUNcVQgiqqsKYMEdIKdFKkMThekrTDPA4G9pCpQzzS9u21HUItZWddbaU4ViapgpzkAQdKdraYKxFSYXqgkp7WS8E/hqH9R7jQodWlsQkkUZpjdIxVdMwXSyews0+Ttac1W/1bPzaE+k6irl+8y3W1jcp6oayadBJuBEGwyG30lsc7e2z89kXzyTSQ5rvq6tU/uAHP+B/+1//b8IF21bUTYnuUoytMdhvuK2oaVqgxXpB1VjacwarXuACrwu/NQwhOV80MD91Kwqgmx5fC5yFqnLhjVZ5FSup0DcEIQSDfsrGesbGxhrrm5c42Jn/+hHpCYFkOqcQ49cDz2cEc4ELfBX6/T5/+F/8IW+/e5uf/fTn3H/wgLZpL4j058XS4ZYujFtrnBDlnhOV+hDYJHTZtMCye1zc0hd4QxDHCWtra3jv6fVqmqahrkuaxqG17lrAzWsh0uHroUGjKGZjYwuBpCotti2pqwLvT6nweIxI75gNby39TLGxluC8Z1kYoihiPB6DF7QNwU+7afG+JU56ZIM1xpvbbF+7SVGWJInCOkPVvopP23nOn/o6VkR6L0tI0wTTNpi2oYAziXTvPdN5iZQ1/3Bzm9/+yY8pqppFXqCUYrkMgXLL3V3KsmD34UOKIqefRYwGPSZHE/Z295gvcnb2DvAIxuNNlNKPql/PgJKS9WGfXpoy6PWJowgVK6IqYjZfkhdnq8y1FkSRIIo1URw/orT8puGcp+4C81auvscwFd5UTOqCyeGEze0rrK9dor58mZu33iIbDBgNh2RpyrJq2J9/yWRZUDQNkVKsX9oiiSKMMzR5TllXQc18RnCtB1pruftgBwikkQIura3x9q2bIDXqw8+e0oXbWSXYedic6TGoYVCmvwTWx1tYZ6nq4rmJdKFTRDIiXbvE5uWrFE1NcXSIdY7ZMgdvsbuf4fPpSx3j1wsJQgY/cWCxmDPfvcfG9iVuf+894iRh8+oVsnxIVRTPJNKV1ly5eoXR2ph8sWQ+nXWqZ3Bty3w2JV8snrhWfNfeEVxBHL4ji71zmKrEVCUf/WKGkIKNrS1Ga2N6aUQaD5lNFxwdzbC2Jc9rlIq4cm2DLInoSUniLT1ZM5CCjThhK0vRrSMSAmssd+7co0Zwafsy4/VNlJOsD9eorOBgYSjKivliynx+xHx6SL2Ysrm1wXhtLVihLBfgDNRnn5fRaMCVK1vkZct0WYVsjbpGSslgEKyqiqKgqip6vfD/16/e4NrlGwwH61y5fB0pYz756FMgD12bq/96wvmNTuxYzzeBeTwWqeDS1iX6vSHLZcV8nh+Pdd6vmgp8yO0AxuN1hIC2KWjbprN2UThnaRpzbM3ivcd0ORyDwZAkSY/V5qtsSCGgaeoQhFw3GHMypysliCNBmkaMRj289zRN9djrm9Cp4x1CSgQCIRTeO9q2wnsXuumFpGl8Z4njUSoQ6WmSYo3BVOF1rQ9EehpHjLKEOMmIk4x5njNfLrFnEult91CcdGo/H96cWeM1wFnLvXt3qaryqd7iUkoG/QGj0YhmMqVqWqwzGNcyn07Y3bnPYj4/s1L7OrFqnzg8POTjjz7myy/vsljMaev6jQkcBbDWQF1/7efnAhd4HEULCMHN62sIHbN/uGA6L0gsJPZF3a/OAQvknKicX7rX9tUgjkNQcZqkJFFKpKJv+pC+Pnig5sT27AJPIEoy1rauAp7p/kPa+iKk9ALPCQkkYBPLdDnhaDIE6ekPMn78kx/x7q33mE4nPHjwgHyZ8+DBA8w3sVaQBHW3IKx6PUHp/aaSz5ZQ/DttSLxy/yoJleHVuj8GxoS9QM7XX8B9MwScrxxKC9JEo5Qki1K880yXxbEq9AJPR6/X4/L2VaqqYmd3pwse64JCvT1uhY90FDbTz/Q0fXE8ZvX7SmFMy3R6CAiatsT5QAicxuptlYBUQ6wc+5MJRV2ye7RktmwoqzAIOeeoqwatY3r9oEyP09DCPlobE6UxB4cHHB0dcLi/S14ZGuMfud1f1N4wSRKGgx5N3bCY5zgfXtd7T1XXOO/wznYKQUGSaKwN4ZlnoW0bqqpAqajrTIDGtJRVyXw+oywK2rbFe8/R4RGfKUlZluTLnKpp0XF6rEqEbp/5LPhVcKwhSWJ6WY/Jcsl0tqSq6qd+/8Z6EJ68qDB2QVlU5+4oT5KEfr+PtZbFYvHc+/JVQcme8gd+7CMF8z0BqQqcQG8wIE4SGutpDETpgGxti/XNLdY21hmOxmS9PnGScTCZUlcV93Z2mMznOODqtev00oT3br2FkoLP7nzIcj6nKGvq1obz8RU4DpnFg3ckkeDyWkZVB2sU5zzLun3stVqgBKegnRImrhfHbH6I847WPDpuCCERSoN3OGuQSpEORkitO9sLicrWkMmIOE1pvMPiUVqBsdh8EkJ8n1fl/hqQpRGxVlSNoW6eff3HWYaOYryO8SrCuoYWT11W3P3sM5IsI0oSsn6Pze0t4iQmn89Zzk5adaMkYWNriyiOWRYVZdNQdiGPpmmp84KFkOxEEW3ToNOURErasjweF4SUVFVJXSxZzuchkPUxeO+pymAfNRoNgwd2nJD1+7RtS1M1SCEYpinjfp+e9CTCo80S3ba0rWF375D5ssIai6JrPpYQIRBOUC5ydh8+RMY9smxMrQTNdEJ5uI+tC/AtbbmkEgbX1KRpirOWpm2DHk4Fd5teLyWKNGmWYFBUpiLPg0e+FKDwx+G9eR66h4QQTCYTrHHcuPEWKtLceOsWaa/PRx9+HPLp2wJrw2cxxoaMa9PZ7p9z4LbWURYtUrS0TY2JI6xtcc4cjydRFDEcjkiSlMFojPeeyeSAuq7wdSjWKnXi5AMrqxbZPT+o0KWUHXluuvW7OP49reOOjK8f8UgHj9aaOI5I06QrzAbrrboOn7upW5q6JtKKKFoVTCXOCZyLuutHdnNBS1lW9DJN1tOkKmVNrmFbEwo7psXXPgTuWktd1bTGUdUtRV2fY1x/8UXCd5pIt9by0Ye/5OOPnu7Rp6RkbW2Nzc1NZssc53KMbWmt4PBgh1/+zd9gWvO1+30Gj8GY3Z1d/sX/8C84PDxicnBI84Ypv23bYl/TIvgCF3gezCpIYsVvvn+Z7e01/vpnn/PhnYKsgcyeZEu+8jvZEIgNyYki/RuudQkhSNOUwWBAL+uTJT0iHf/6KNI9Fyr0r0DWH3Dre78VFrb54oJIv8DzQwMDMKlh72iXKFEgPeONEb/727/H+7e+z4cf/oo/+ZN/w/379zk8PPxmiHRNsELUBMsUB+zx5hLphuBJdhqrOWVOsHccEFTrCbBFmOAaOLex56uAOPV4c/QdrwRxrBivx6RRwtZgA2c99ZcPL4j0c2A0HHP71jtMJkc83NmlbYP3sDEORwha6w8G9Ho9irJ8LUT6yv96hVfve12zt/ege+2nv7gnEBWDBJQy3N3ZxSFZFIaqccfHZYyjyGt6PcXo8og4SYgihVSSKI3QkeazTz7h41/9Ctf5y57GWZrt837kftbjytYWy8WSKi8wZqWW9BRlSVmVRJEO6nApyLKYtg2qxLM+elVXLBYzNi5tc/nq1UBoNBWLfMnhwT5FvqoSwsMHu+zv7HWkjAIVEaf9YAu0DCrVr4LHY1uD04pelrK2NuLzBw85OJw883s3xmMdVHUBlMGH95zo9XpcvXqVsgxK++ch0oUQJEmClJKqU1M++ZnCNKCAfiJJI8nlq5uMNzZYlI556eiPN9i+cZvBeIPLl68yXFtnOFojzfp8+Mkdvrj7JbPZlOl0wkYv4Z333mdjPOLHv/EB3hq+vPMrJocHLJY5eX2+cW2lFXLegTf0E8nt7SF107AsW5rW0R65QGgdo1N9OgPVStP+4tg7eLAyIHrk74WK0HGGsy3OWpSOGG1fJs56SBUjpUKnY1TcpzENVVNhcESxRvgaM93BVcUTr/t1QwjBqB+Ce49m+TOJdCFCyHE2HGFsCHBsrKYVkOdLfvHzn5P1evzwxz9mtL5OHEdstTX37nz2CJGe9Xrc/uADpNZ8ducOy/mcahl859u6pq1ryrxgNpuidEQ2HBH3eiyMwTRN8M9WiuV0yvxonyrPz7Yg9pDPF+SLJZGO2NzaIk4dg7UxTVVj2glSSjb6PbbHY3qxJFGSdilo5gV11fD5lxOKKnBPChhI8AISL5FOspjM+eKTT1m7tMWtDy7RRoJyf4fFw3vgS8BQLxp8LlBxwqDXo2lbWmOQeLII4khyeatPv59R64waRVEbZrNFUEP3U7RXNG14zuHhIWVZ8uDBg2OP77/3935MkmV88IPfYOvoiF/+8kOEVCyXhzRNwXJZYIzFdQ1BzwNrPYt5g7OKuixJIoVpa4xp8V3eR5IkbGxsMF5b59Y772Gt5W//9q8xk0NcpwgXQiLlo7yAUgopJVkWxqi6brq8k+CzrnVEkqRIqYjjBCGgLEuEMFhraNu6G+MCid7vB0W6lIqmaZlMcqrKUJY1cVyiB32SJGQnBAW97Gz2fadyt1RVxWKxCCrzLCYlZTwa0zah8NLUNRqHbQWmNRR1fex+aDxnhCGfhQuP9A6rC+LE5+2sRU4cx6ytrzMerxGnCUorxutrqCTGE9rpvA/G/uECCMuU16kGF0KwtXWJ0WiIMS0//du/5dNP75DnOXVdn1ndu8AFLhBQtYDsuBHhsR5aA60LC9LXzpmsVphvAFftvWe5rDk6WuL1PlWrONg7fKO6WS7wDUGGzSpRglAacUY77+MQgOr2PtZ+09uMC3wjeHxcW6mkWzBFy73P71MtKtJ+QpRobl17myiKGAwGXL6yTVmWSPVy3qgvjBUrsbJIWY3V30aswpNXa/4VkS0J0izJyYT3tGTAl4XihLn7ivcQGoQSeOvxr5ODFiBDbmHYlL7k59ZaMxgOSaKIKEsQSN597xZ1a3nwYJfJZPbSh/xdRd1YZouG+bKhKGqqqjn2bU10RKQkkhDCaM4RCvki8H4VbPYkib4ayiIVwhyNcy9kzXlegZXz0FiQHoxwgcSwJ97mIqSpYUxL3dQslwuiukbqLnQyS9CRpq7r4zH08bXcaih4EbRtyzIvKKuaLtPt+PZxLigJrfX4rptASoG17ikktaeqCqazCUnWo2lqrG2RqxA5/+iAYb0H68OQ4gRChPdTStLv90OYXFVijCHSmkgpjLWPfl8+kOnOe4qyQqklddN8ZfHEd7Jv70Ph4HmqLcZYyqru3ud8zxOdt2+kNWtrY+I4Ji8Kmro5Lo4YY6g6JfDqTHnnu2YHiRea4ShjuJERpX3SKALbsrvzgNliRn/QZzQaM51MgvLUO3QUIZSitTBf5vzyV7+irUu+vHefvYNDyvJsEcWqgQtOps/V0O9MS1HkLPOCWVHTNIayNrTWYZ9ZkHj5Sck/5fkCj1iFI8YJKk6J4pQ4TlBxhlQRrRM0dYOQkCQZUrUY2yKM6mrQ3/zqVghQWhHFEYNBH6FjnLUnpGLdnth3ELpjmqoi7vXoj/rUSYQm/H5ZlZimYXZ0hDWGtJcQpSnyMRsjYy2LPEdKRV1Vwf3gcR9057BtR74KEcy9pUYoTxSnpFmP5fSIuixDlt9TPl8Uxyit6Q0GDEdjhNIs8gLnAg8W/LNr6rrA1pYCR53PArFvW7ypqRpD4Tw13RiPx7UNbVkgl3Pk9AihFcvZhGIxw9oafIvCBYe8NGGQxsS9PulwxGJZkBcFOBuWVD4U59qmobY1tYpwSNL+oFNgd1Yk3Zew4hpXY8FstuCLL+8zXl/nxlsDoihhbX2Dqq4oqxltYZ5rP66VQqkQ7nu6SGWtZTabYUyDMS1aq2O/8ZDdWBOVJYvFsgv2dAgErTGUVYX3GiHCtaCkDPYtna2X1hFaa9pOTBzGJ4sQJwr1YA/TddbYE2/8QNArQBy/ZxRFncgv6cZC0Y17tus6EsfztZLy+DhW6nel1LHq3XlH3da0TUvV1MGGBo9QEiU1Eo0TXbSQ83jrEFKR9QYgBMtFTtu08Ehk94uNTd8xIv2sYf9sjMdjfu/3f5/RaMxwbYyMI979/nvEacK9L7/g/qmQCSmDOhzCwu91kVFKKX73d3/Cb/3Wb/DRRx/xz/4P/3taY2lqQ12/vve9wAW+C5iWkDooG0NtWorasShh6UPH++viFB7BG9Kc4Zzn7t0D7t8/Qqn7SKXCIuwprbgX+DVClEB/DdEbd6FPX31NSBlUUQDLyvGarG0v8KYirIdPsBpMuyDffFHwJ//yT4lizdUPtljbHnJ9+yb8BmxsrvP3fvxbRHFE/K/ib+TwsQRLlFW//OrntxG2e7ScTGirfI6t7t+mBNV6y+uxtkoIpL3hxELrKdCZRA8ltnA009d30mUE8Xr4c3P0/Aqvx5H1e1y9eR2tNVIIsizjn/74t1kbjvm//z/+3/y7f/eXL3/Q31FM5hW/unPIcnHE7u6EtqmwLoSzbQ4GDNKU/eWSyXz+Wrt9n7ZlWpGB4zRl1O+zqCr25/PXtj40DqbHXKU79sYFkDIQ0847iiqnairKquwC3Ro8jn5/QJImOGcZjUfUdc1yvnzk3L3M+na+WLIsCrzzT+Rvrf7XWnNK4f90PyfvYf9gjzufClpr6Q/HFPkCrSRxpJ7oijyuaXqPcg4hLdp7ojhm8/I2Skru3v2C+XzGqNdjbTBgnucczGaPHIHzQdW/u3fAweGE2ezxlp5TkCfHGgjq5z97RVnxYHcf1/kFnwdKKgb9lF7W4/3332U4HDFf5FRVRV3XNHXNbDbj4cOHx/v9EEDosThaJ2l9xDs33+Ld995nmVfsHkyZLeb8+1/8HOvh008+YTReI8pSVBwhtGI4HiK9Z5q33L33kP/+P/4xi+kR+3u7lEX51C6xldpXAAsXppOke7T5kp2dB+xPlnx0f0rb2uOChHsOZf+rhOiuIbRG9wfEaUq/PyJJe8SDMTJKeLB3xP7RERsbG1zd2qatS6RvqV1LIcWb0aQmgtVK0usx3khJkpS6LFkuZuRFzb2dyYmtkvcU8wVlXnL7+9/n1gfvUxcFi+k2+XzO3Y9/RZPnfPaLX6CjiA9+8iOuvHWDqFMAr1CVFZ99/gUQbFBc86QdhncOWzdIIZFCBV/rKAMR0xtvsHHpMpPdh8wODvHOnl2YEoLheER/NOTazRu89e67TI4mFLVBqAVy/wBvDYvlIdLX5IspdVFQ1DWLqj4ullg8pe2srbrbVyzmUJSkTcthVbMxO0JrRbmY0jZzBDUJnljAra0Nrl/ZZLC+wfr2Fb68v8PO/j5NbZEOhPEUiyV1VZBrTyktcZJx6cZNbNNQz2cI7wglYbei1I9x9+4D/vW//je8/c47bF95iyQb8u4HH7C2uc7ewQMWOwWu/er1kCAUF/ppj37Wp6hKpsuTIn7TNHz66WdEkaI/GJNlKUVZg1SUTcve4RHzoqJoXBcK2oCQLJc5R4eHDAcZo0FGFEX00hTnPXXbhnyMXp84jqnr0OpojKWuG6x1CCFxzqHUHAE0XWjoKltDKYXWUSDt6xatNf1+H/BYa0JRWEqcM50VmDoufEupSOJgKxNCrkPxJckypFJYaynLisnRhLYNBT3vLP1YE0WSJMnQUYQXAitFV3xqSLI+N9/+HkJqfvWzTzg6mBDYoZqTDc3z4ztGpD+JOI5RWpEmCXES0zQNZVEwGPTZ3t5iMBwTxXHwH+v16I0GJHspoBiN1njv/fep65rZbE7btrSv2cYkeArFtG3L0dER3gfz/fYbsJd5KQgg0uFna8Mq5wIXeI1oPSjvaY3BtCGeR2uQDvyvoYrWmNUW5aIV/dcaQoaqvpRBqRAniDhFSBUUS7YNbW/HarGz8Tqnn9Otzk3TfDP2Hxd4OlZyx4iwajSchGC6oAYqi5K6FiymKTKC6XzKZHlEaxrSLGUw7HNpaxOPYz5bYL5Oi4zTBPor3CkLgjenJ5BlXwtW9+EqiFRyUuhYdc1n3c+CR8U2rworb/ZVsPbq56n3iFKFjBS99YRsHFOImma2fL0TcchcI+1H+ETSVC32HJvVs+Cso6lN8PXuXryuK5o0eUKpd4FH0TaGxbKkLGqs8XjnAwFCp9xzHu/cUzuGvy5IGbzalRRfOf+dB0KIk8/42L+ddwsUgtZCFSiEvTmUCgFsoiPdX/U95LzHnUNkcXJ6zj4ApURHjjjquqGqasrOukd1ikop5bHq9NFjCMcRCUmWBRVxL+shpSBJYuJIBy9dJVFdh7gQgkgrtFRY52mMxfoa0akZ4zg+DsY7hgCtgjLSOnds5+JXSvmnXAM6TpBKP3LNmrbtruOvPHUdzea7AEBH2xrapiWKNFL1iSJNE0XUZ9i3Og/Geeo6+AS3rUHg8balqXKqckE+O8J6KBZHxMqTpJfoxRlxlhH3s7APr2qk0pRVRV4Ux4r6rzpuyaONTwrw1lDXFVXT0LaW9lzqitPtVK8eYWyxCDQ6Cqpn0zRIBKgE2XrapsU4F/y46xpT19i6xDXVm9P170PHQ9Ma0kySpEnwgD6+9h4juLsxVWtNr9dHSRV+z3uSrAcITNMEQrHIKZYh1Ks/GmHalrqqcN7RVOHac/YpfFOnxJZipTImVKI69XbdNJi27ebHk+dLpcISoTMBd87hXfDT985jbfg+nLXoOAIrMc5QtSVlHQqLZWuojAljdqTDmOXMY+NqqIwF+zCHaSvmRwdU+RzX+XYHF27QUhBrQZZEDHoZWZqghAi0uA/ZuI0J6nUjPF4JlNYkaUbjoXQW6T1SaSSSKIqITYyx5nj8m0xmbC1zvINIK8bjEc4ber0eSRzT+ob2VMHsmZeE9yGrAo9cdVp1z6ubFmstUdwipD4uwhljKMvg3a51gpQKa9pw/n34Lrx3IV+i80FfvY/wIoTG+qD+dt6F71/J0BW1Cpd1q3lPIoVHiWDvqGSnjBeh20F0474QkjRLUFpi2rbLiLB4Z7oVQrjQRPffVQeU1oo4jlBKHh+3cRbrXTdAyS43SCC6h1+p2VFEcRzmlF4fqSLSLCNOS0xb4+zLtTB+x4h0zwlp5FFKcvOta2xsrPPB997n3ffe4fPPPuWv//KveOvWbf7RH/4BSdbjzmf3qeqWzUuX2dze5mDvCKXu8kf/+L/kn/6TP+STT+7wz/7Z/5GdnR3a9j7Wlq/n6L1nNpuxs7MT2rKqCmMcdRMGnNeVcP9aEGu4vglKwsMJLE+1jq1m42+zKuwCbxwKwDrPbD6nHzfEccmVa1AsgKNv+ugucIFvBjrtoXRM2uuT9nqgY4gzrLV89vkdbNtQmRaiCIw5U8ZnHeS1O/7zq0YURbz//vsMh0Pu3LnD7u7uq3+TC7w4VmTsNnCFMJ7e44m1p7Oe/S8nTHYW/KdL/wk7NFzbvMb3b/4G19++xv/6f/e/Yu/hHv+f/9f/yMP7O1/f8Z9eGr5CpBo2euGeOCi+RjIdToQ0EYE4X4WOxsAlwur+HrBLkBK+Su/0BBgRzmlGUL+f2otILdj+/gajK31uXr7Ota2rfPyzz/gPu3/z2rqivA9WbklPc/tH18iylM9/tsPhvRezYJlOFvzsrz8OCloJWkvufv4pSaK59+XkFR/9dwuLxZLqiy/BNUgUUsU42yK8ZbZcsCxyLIJEx1jnaI+Jl6+HVF81cyMEWolgU/AKkEQRkdbUbXtu33fnPM55lFLEse5sDdqOxAjno64qmqZBdm4Kp1vo3xRIIRgNU9I0Ioo0Ve05miz54ssdWmPIsj5ta0mShLquMeZRawPrPbXx9Mc9vvf+eyRJgtYa5yxNOWOQSGIliaRHqfDZ0yTm8uYG3nsOJ1Oa1nRkDYzGa9zYvMRiseDg4OD4fCmpGY0voXVMWde0xuBcsCVwtsG1JSebVACLkIrNG28z3NimqWvauqGcHzHbv3+2D/Tj54bOFdw58rykKGvKn/6MOE74/g9/yNVr1xA4hHcoCV988cWJIh2oLLTWU9x/CLv7eG9RyrGYT3l4/y55UZLv7iOVJro5pmdb3tm4ytUb21y6ep2tG7eYL3MePDxgf23EvU9ukyYpy8WSunp6No4nzG1udfxAJMOjaWsOZxOWeXtO/2EIk8ThOX/3+WGdpfYVaRozHo3w3rH7xSeYtkXIIULGNIM1bG/AdDIl39/DNzl28RBvW+wbkhPknGf/YMrRZMHtt2PWL21RmTk7B1OaxjzJBakIoRJ6/RGbm5sdGXqF+WxGbSz5Ys7Bl59TFzlffPgxDz//ku0b1/nRH/znHOw85JO/+wXOGmy+6Fogzr6mdaRJ+ylITV6WeAemyvHW8vDeXRZFxXxvn9PjuNKawXiEkILlbI5pWhazOcUyJ05SssGAo/197t+5g5SCS1euoLWiXh5RLOYs85qybI8DkLNeyvq1S5jW0N4/wDWnxtlYQ5zSXx9w+coG5TLnZ3/+b2jrkrJYAqcLQRWmXqDZYNRLGWURwwhEC0Wn/RTWI4Sjt53RG60zGAwZr22wONpndz5BS8GVS7dIk5RYRTRVzeHsiNlyTlHkPHz4gM1Lm+AsWTrg+9/7HlVVcvezD8FUHBzscXR0gHVh6wUnuR4rx81AlnvyqqBsaoR3RCLck61fEenQCEfdzpFyGboVnKVYTHlQFegooTc8REcxg+EApQQ6Egz6CbEC39Y0bUNTFJ1gRiC1ZrGM0FHEslpQtCUq0Yz6a4GcFgotNakeIBHESbCfblUcbFqUAC1wzlKWBcbGZIMecRxx+eoWSkkePnzIdDLFGbCVP7aDEarrThIy2MAJzXDYJ+tnSKFRElQk0KlGOknUj4Klk68R3mKVCQUAJ7BeoHRCb7ROvz9kfW0TrSOu37hCv5+yt+uZTRo4dlR/fnyHiPQzFkJCEkUxaZZyaWuTt27doGlK7n65wcbmOuPxCB2nwT+stTgXUshNa2lby3htnb/3ox8RxQmbW9sURclk//Vs8LXWoSLdNMznC8oyJBIb42iab4n65bhkrSCJkMMMoRVu/7GWydXvXeACrxCh4z0o0tsmRIuq6NFE6gtc4NcLQQEglELHCXHWQ6gIEafUVc2kKLBtE8jzr7hRXmcdV8rghzoej49t1C7whsFzokSOONlZP3ZdtJWhrQyHk0PuHdwlTRIsFhVrNrc3MM4QJdGz3AFeGME+WOC8/0qFz6uAVoJBJjFOkFtorKc17utpbV9ZvDjCSn7l/e4J31Ha/VQ8fxHhaUPB6mMpwjWwkio+ng0iBDpVJMOIwbjP+sYag2EfHQWlnDvvYPK4ndBX/K6UoaU4GcRk/Rgdvfjkb1rLsu0Sq2UYHq1dEGkoStH5e34919m3CwJrDLbMkdjj/PXVV9laGxRqSiOV7qJH4bUMCE/B6l1WfrBP81x+XsjOy/VFiXkpZacUfJQod86Bc7iXFM6Hwzo5tldFxovu3kviiDSJUUrhvKA1lqIsj8fDVcu/6trzV3+3+uZXnrhxHJHEEVJInIAk0rRxhFgpIwkqV60UsY6wzoVMJLtSWsKQriNdqScOVkkVLJuMQa7uYQm4RwYxQKC0QmlN1h/RG60j8iXeF0ilu2S8841loVMBjHVgHUuzJNI1zpjga4xE4Yi0euQ5Kw9mJ0Loo61r5vMZk8mEfDFluTiiLGtMVaC1RrQ5wvSJpaGnBWuDjMub68RRxCJvKJcLBqMxdVmQpClREYivZ1nHCnHS9CRF14DU2SU07WOtSM+E5/X6X/qO1PcorbHGUFdlUFoLQCQQpZCmtHVJWyyhySFfhLblbxTiWPEdfKBD14YXEhnFeCGpm5a2Pes4xfF1HekoqH+1xFhHbzTGAVGSYtuWtg4P7wXZcEA67xOnCaaRuNYc+40/+vIrZXC4dx1gjT1RNntHU1fkiyXNYx0VQoR7aOV1DYT5wRiqsqLIC4q8oCpydBShtEZHmtJbGtPQWHOs2gZASVSi8au1x2PHSbcGiCNN5R35fBpUx533+QrBc77FOQPeIQXEkSLSiqJxXXYDgKenNHGSEcUJOoqQUgWfbgRKR0RxQpoZtNTMi2ApZa2laWrapsa0Dd4ZBqM+aRoxHg0ZDgcsFlOUCkflVrf9Gd1MENT81jmUgKibB1Z61JWvuHPm+JxLKfHOUlcFrTF4GQVVdqJDqLMPYmPRqbtDZ4AHGb5nRPDe950Vi3UGHWmiOOqOUyDFyic+WP14QAqJFzIMFFIcK9o9/vi44iRGa3WiLncOby2I0PrqRfBN7wTtnV+7RuLBh2RZKeVxZ0J4ngfTdGtRj8NhvcBYECqQ8qKbm5WUJElMliUkcYJWMc63uHPkhZ2F7waRLiJQj/o94Q0OuP9gl6PDI27deguAd99/n9vvvY8x8OkX9zFO0DiBUzH/8l/8S3Z2dplOJkwnE27fuI51jstXrvBf/zf/DV98/jn/7f/l/8Tnnz7De+0FkCQxP/jB9xiPRxwcHPLv/t1/ZLlYUlbmqR5/bxwSgjJp0ENc2SLu97n89nUUgt39BcU0P/nd1We62IBc4BXDezDeUTvL3tzz5T4cviG+5Re4wNcPj6kLXFsjh0N6/R5J1qM3GrNYLDia7mNNJ4cwzev1b3kWBCS9hHSQopIYnnOTeoGvCYeE1p8IuEpQIu9zppDj3s595j9dsFjmrI02KYqCjz/6kKPDCUu9hDWCqvolvaxX0EpwfSshjRU7hzWz/PVbx6xvDPjJj6+jtKQ2UNSGv/j5ffYO8xNS+3XDEM5j2T2i7mfW/b049TjP8WQEtTmcrNVU99zV9xVzQtIrYM7JdSDB4dh/MGW2zNFtilIRNja891vXWc5K7n+6jzmTEDj1ft1+7Ziob3miaHMaSay5fHWESiT7hwfs7Dvmy+IcH/gccEGhVi6hVrC23qN/LWF6VHJ0+Ho6VL+d6Kps3oI9wOFpj/OqHv3yhLPgV7YYX58afQUPzMqKyhhaE1raJZB0zimNf/6G2bptaa19wmf8PJAStF4VZhTOedrWPrIHPCs49bwI4Z3JcRCd91CWNU3zcgtkJQWDNCKONVuXLtEf9NFRilQxAkWRF9R1zWQ6pa5r4jhmOBwym82w1jLs9RikKXlVMctz5os5P/3pT0mSiO31EZFSNPkSjGFWlCzLmihOuXL5CtY69mZLnIcoG6EzKJczTFszn8+pqqpT958qSljDfHaIkIHMd35lMQT+MTI1SlJuvvd9+sMRw40t4qzPvaN9Du9+hGnqr7QCUSIUd4M1y6k6pBSsD1KSOKLKpzy8+znONNi2Zv9wincODfRFsA7L4nB9HNaQG3j4cJc8LxCuRZkcYxyiceAMy/17RO2Ez2LF/HAPpeHmW2+RKsml9U0iJD/67X/A9OiAXn/M0cEBn3/6IXu7D874bmGYQKRC2LwQsCyhbKBYGnyzKhy/1CX0yhE8lMtgjdvfRKUeFQ8QKsZUM+zOHlgTHt049I1CKIhSdBxzeXuLNE1J0oQoirh69TJbVy6zWNS0bQj9VVGg7mznS4+p8c5gyoJmUaLTDB31iFPB1pXbjNYqBtkGbVViygLT1jS14ed/8XcMhn1+/Hv/OXVT8/DhQ+qyZLG/iznVqZD1B/RGI9qmplzOQxeND+0xcdoPnRA6QnuDfOxcWmNYTueBmH3Mzq9YLNm9/4AizxFKYZ1j78GDYF8lazwO+9i80LY1k8U+zjjs48RnXYFp8bMBPluSWLh87SpNXXK4ex/b1FgBrYBJUeAOG1qZYkXKoqi4cu0mg6KifHhIWbfHqeWj8QZXrt/GtIayaGitQo+3ibRC9taQWcog7SOcY1IuYXJIEksG/QhnF/ziZ/+B7cvb/N7v/y7DYcr21jqTq9ssFkfs70O/l9DrDajrlslkcab7hJInY0njIJKCcRaug0VpsM4TCYUUgn6/T6/XJy8LJvMZzhrqYkZbSdpqEQq+pgLbIiOBjyWtc1S1QylBFgfJe7lchPyOtkU7RwTEMtj6mI5cL5zteOwwjlZ1Sds29McD+v0BEJGmEXGSsLY+QmlN3RqKuqU1QYXufbChkV3YqbEm+LRLSZr1UToCIZCIYAHmBGmvz9XxGtYYFstpKIo0Lc52hXxnqGtHVVkQJbNZTtrrE+mUJE5pyoZYKK5sXeHS+iZHR3vs7N19odv3O0KkS5CnVWy+szpzLOZLljjyvMB7z9rGJldu3OTwYMKf//lPqWtDb20Tj+DDDz/i7/72p8evcnQ0wRpL1uvxw9/8LQaDIb1+/5UfvlKay5e3uXRpg3v3HvDll/df+Xu8diiCAmoYIa6M0cMBo6ubaAdHyRkKwzds4r3Atx+r6dbisd6T1zBZnmTMXeACv47w1oSgMBxRFJGmCcNBD2vbsGDFB9L6G9wNCQRSS3SsEUqFOV34ixv3TUPRPTYIwZaKQK6fwYnOFgtmDxesDdfYm+1RLHPu799nPp3TiCasF15hJ7VSgnFfM+hpJos2kL6vGWkac/nqBmkS8mDyouEXn+4F65uvS2B7WuRXE2RKMSc+9vB8HYAR0Ov+vFKar9RSodHrhEBfKd9XXu2nDHWLRUVRV0wvLZjlC5x0bFweIZXk4ReHwbP3aVj5va8g+Uoho1KS4SBFRDA5PCQva8yrtLPx0DaBTEqTiLX1lKo0hBXGBQJWFZAGfFh5PXVWeRlW+BWhNob6VB6HAHRHpLcvcGgr1eCLwSOE7xrDBM6BMa9mEDlWjCfBY9b7QF68LIm+eu0kUqRxCJMbDkc4r/EovBc0dU1VliwWC9rOKz3ucskg2OEMsgzTKdTrumZnd4ck1qTCkCURtm0RzlHVLbO8Yk2nbPYHFFXN4WyBRzBaG6KUoqkCUVjX9Zl+49576vp896zSmrVLlxmtXyJOUpTWYFqK2fnsSTqB7BNlJCEEaRyRJRGmLlnMCArpumS5rPDeh6iLzkal13XWzruva7nMWS5zUgXjJPDAwoLA0eQzSlEy2buPMZbF9IMgpCCil/Xw1nHl+k36/SHL2YIsHXCwv8P+3sPj83P6+OMIEgVpdwxVA8ZD03rqrzPn5DkQilBtR1D3kJFEZQOEirDFPiwPvulDfBRCgI5RccZwbZPBoE9/0CdOYtY2NhgM14mTfkdgQxRa74JNmge8AWtxbYttDEI7IqFRGnqDNaK4JRIxpm1pFjPaquDBzgN2H+4S336LyzduUtU1i7pFLhcUkyPMqcWZjmOywRC/8LSNObYzElIh++Pg7KAUEn8qkDjAe/+ESn2FpmlYzhc0TZhYnXPkizkAcV8iI/FEtoR1hrJahtyzx4dGa8OjqvFFjdKCwXBIFSkm+zI08AmwAsqmReQtUbIgPTzCIhkMR6AT1P4ysNUIBJ4kyRiM1sgXS8plifUCmQ6QWiGiBKETYi1RIoQkQ1iPpqkCV7O7exchGvA/Iol6DPopw2GfuFN3R5Fm0M9QUjKdPpkjs+oEUSrUqJ0P10waB2V5XoeOYSkEkVBkccqwPwhzkZiBd9i2Dhn13diXijC2OCWPu4Fa4zoX+eA1b5r6+DsUHiSeTkCP6JKaTbu6BoHuu27amsxlQfkOgCRJEtI05ExUS0PdGJzriPTuPU7bWbW2RkgVxl2vEV0BeDUjRlFEOhrRti1VE8KAjAknyOBxzmKMoWmCINm6iqZtOZockiYZqYhRQjHsD1BKU9dFR+o///z93SDSvQH7mPLEd+1GXUvBp599xv/v3/wbkixjMBxiLZSFw3nJ3nRG0xqW030wJ7uvD3/1d/yf/6//LVJJiqLk8PCA2ezF/Ba/s1glN3jCpjiv8bMDmnrBjm+QFqrOm+oCF3idmAAJnlljyGrPILPcvgT3q0CoX3ByF/jm0LFLxzsrD+7r3YQsZxOcc8S9HkfTNUzbMIgsaSZZ5icefd8EjDU83NlhkS9ZLhZc3K1fAzIg6jKDABGHv/MVuKeQ42TdY8BJr/fTsAAc7LS7/NvqT9GJQg0k0SBG1CKomF9Bt1Av1dy8PiaNJT197H78bAhOAjmrFz+O/aOCP/uLL0gSyXAUQpWWyzIcwjdVl1opx1sCwb0iv5/3llKETkM4sZBZ2UhOu9ftE4oqluDP3nTvfer9FvM5D+49IBaanjinbZMjFAVO5+l8xfE3lWXnszlCQVEarAnX8quG9zA5KqkqQ758Re0U3xms2gbOecGtxh5OWtRfFFkWB2/uqqFpnm8yW9WKBFB3x/B1377e+9BK78GYVSjly8+DUq5CQD15XnVBn2Hgbl8BEeo9IfTNKaIoIkkSHu4ccHA4O27lj6KY3mCAlIrJ0RF1pxQHyMsy2IQ8FnrpnaeqgyI0SxJ0pum1lqptcbZl/2AfoTRra+v0hyN+5+//Pr1en//0H/4t9+9+Hiw9mq+qpMWApjcakA36WNPS1FWwyVARUil2vrzDwYMvUFojhORo/0nl9tOwUqI/8TV6H0JQjaApC7xtWeQVi6KkaQzKe/ppxFtX18hiRdR157Q7M5iVVC74pmsJ/agbImOQ0iNNS507Pvv8PubulNJojg4mXH/7fX78n/0T+jpDXL9GtbHO5mhAkedcu7rFvS9+k0/vfMKvfvl3OOew3hwXlLyDeQHWQ9OGJWw/DiS+NdCU4d/eFNGSNQ3F7BAhNT7pI4TCzJbgLK6cf9OH9wTSXo/Lt98jyXqM19aJk4TBcEiaJvT6A+K4h9ardG+BF6sWsTByKiQKgW0blss50rTk3pEXJV98eY+mbhCNQViHtgZpHW0d7r/pbMovfvFzsizjrZvXMU1DufuQennivFAXBbP9PdqmOQ7nhVCcG/Qzsv6QK5cvsz4e8+knH7I42jvX51Y4EgxpIhln6zRNy/7hNKida4drBe6x4Bnfgu2s3J904wn7rGWZ82D/Ll6CVcGixLZtuJ5dINJ9A6WFyi9ZlA+I05TeeJ2mNbjjToVgs3J4eEh75w50IalN2yCkxFrHzu4ecaS5ur1JP0u7gMuwaLG2Ii/m3L9/j6JY8hd/8R8ZjUYcHh6EMcB4msaz8DVtO8UYi7XukXsoTRRaCyIdLFnqusUVDVIFH/PAqRvAdqLiLni5NVhrUXR6iG6tvspO9wTtVNN6Gucw1mM8eOuoc4OQnfobcIT5SIqGVErWx2OuvnOdpmnZ2dnFWsugN0QrxXQ5p6xKkl6G0iE8OV8u6BnDJbtFFCuuXr6GkJIvHbjWkmhNGmm8c1gXxp3Ih2vdeU9jmk5cJY+DYF3VWf80DdODA9qmpqlrXBcu7ZzDGBfWgcfOLy1He7toHdGLemgZIaUOvuxCc/v29yiKJfv7D57L5uU7QqR3Vahn4Mu7d5mXJyT4YDDirbfeR+mYWWWo6oZ8cQTmpFL96Scf8c//u/8nURzRz2LqKlTVL3AKK2UShI1P2eDnR7Sl4qBYhBGreEXttRe4wDMwAxIPc2MYNJZe4uhvQD4FLmo5F/hG0W3VRdef512XJvP1bTuK+YxiPkNmKXoxItGSjX6MF4JSvpYsxnPDWsve3j7T+YxlvuSbaPX/tcKKSE67tTcg+iDWwc/BTXg6kb5GIFhXRfSnYRke+7MD9ncPWNse88M/+D5RXwci/RUpxtM04p23AuEwPZpSVecgNwVBdR0TPucLEumTWclf/PQ+SQJXrgXSKg/imG8OnsBo1Jz4qL8IJCdEesUJmW4JRZIFsE5QpDuCHUzdvffKzQNYLpZYWtb6Q7Lx5vnee0Xan1bDfwXa2nJw7+uZ6OezmvnsVcrdvyswnHsm6Rj007US+4JDvhCCLItJ0xjn/HMT6ZKwGT4OcHuxw3gphJBRG0JzX6EdoRAQRUHhXpbNa2gC8FgblPhaa5IkYTab88UXXxz/xvrGBh9sbCKEpCgKivxk8C/qmuIMxarzPpDrAvqDIWmW0SsrqqqirFuOljm9/pCr17fZvnKVf/if/QFra+vsPLjLdHKEc+4riHQBxCASssEl1rcv0dQF+XKGEBIdx1jTsv/gC9r6xfawzp9BotORWNZiDZi6xLc1i0XFwaI67unoJZob1zfoZ3Hnme2Z5zVNUUIbiHQloadBK4h054JlDY0x3N3Z5bCE6WTOw88/4R/84T/hD/7xPyaKM2K/jTWO22/dxDvHle1L7Ny/zx/r/4k7H32MocXa4JfddsTVYQW1gZ6AREIvhtEAmgqWVbBCWk053zScaakWU4TSaKWCbcjiCNe8GWGijyNNM67dvEXaG4QCjlT0hyOyLCPLEqIoRql4ZRgdPKhPQSPQSFzTUhTLYG/iLNP5nM8+/5S6qsnQKCTDKCaV6thabTGfs/h4zuUrl/nRj34L4T0f/3X6yOs3VUlTPdnFIYSg38sYjga8dfM6165eZTE75JNfiXPlL0gcEYY0SRgOBxRVw2Q6xxiwzdl7AG/BPnXtGBalRVVSVKt9xKMwhL9uWqCFRVVwNCkYDodcSwfBT9yaTugUjmEymTBrJWma0O/3cNYihAwFvcMDlIDxqE8vCwtqKUOhw9qGsvQ83HnIfDGnP8gYDPpBee091oQut6ZpyfMnB30hIEkUaaKIohitI4QQlGWDlAIR6TCer1jyFZHuCQUB58LaXpz8ilvNb93PpvWY9sSX3TtoynBtnNZgeCBTDV55RtuX+cG775KXBWW5oDGG7c1NkjghmsUsimXoBhEKax2LxbLzWvdIqdne2iZNe8wPJ6ErJonppQnWGqqmBC8QaDyBRHe2DR7nHZHuPNi2pl066rpmPjmiqRvqxvEs/tu2hsnhPlJIqnRErE82MptbW1y7eYujoz0OD3d+DYn0c6AuG+aTk0W2d5qybhCN5WD3kCIvqfLisefkHO7cJ05T7No6pm2OA1Ieh1IROknxztLW5XOFuFhrePjwIcvl/FtH1ItBihhEpL2E3iChaRoWsznet6FH0gH1hY/LBb4eOA+zhUe3YdB2Do4uOq8v8A0jilJ0lNEfDVnf3KBtapbzo/BzMcMaQy9NiLSmqhvqV7mTfgzeOVzb0lrB0jR464JP+jcI7z11UWHaFtO0b0Tb/3cSHXeAJhCgKXjRPeKw9iWD+DL4Gsw0KICOURMIVE8gojUh1a0hWL484zIyzrAsl0gB6bUYn/Uo92rM8uW23nVjuPtgzqAXc/3qZdI0Jl2rmS4MO7uHPHi4/+STVh10La+kgmQtLOZdIOWb0O3uHvt5HvQI10RGuEYE4Xv13U/DCbF9Ouir4tinPZKa7bcvkcZxeK0I6qaiaSqqec3u7Igyb84fNgqn2oaf47Nc4BuBiHsQp9BW+K8iH7vvU0eKNNFY6yjK9oWGfe89VdV2lg7PfwN6HiUMXhqPyOzP9xTnQbhHp72VL7U9y8bgOaGUZDhM8B6Koj7Th/fF4THWsrO7z2xRMJ+HfayUIahTKd0FDLYdqXM+os0SiitRnJL2+vQHDa3z+EVOXjVY07JcTOhlCcvZIZF0FMsFRVEcK96fhASZgdQk6RCtk+CbfrgPwgcfYmcoFwXOtHhvz3285z5bXdFEOEfd2jBxOMt6DFoKEq0YJpK2qimdDeQpMBxkSKUQRwXLowLlu6HYcWx1UbVg7JMdhnVZsvfgIf3BiCwdIOOoC0D1XL+6xSCLyJe/g7ct9+7f5a/+5i9QwjEc99FK0IqcsjEIE16/bkPROFKKy9sJlfHk0wprXv9AvZp6vuqdvHe4ugQRghffFPRHQ3rDIVEUkyYZQkUcPnxwHIYopWK5fomsN2Q86DMe9ljM58HLv1u/w4kNj5aCWEqsbVguZxDHOFNTzBe45RRf1xgR4ZAUUtBKQV3lYKrjLJdyMuHjn/88+FRLSX99nTrPMc3ThQnOORaLOdY56qrCO4eUCp32sMbg2rMLWWvDPr00JdWCTAsi4RCmQbx0TtPjAXxfbY0VacUgjpBSMJlOQ5aBjlGpxDUF3pnwnaweeKxpMMsJ1rS4pgoB096GsHMpuhBNumBYSds2FAV8+eWXJElMFAVCPM8fLfyLLlMBntQGa61J0xRrHUkikXJFlgdLFA9YZ/F4ZBMWaE3T4vCdrZdGCEHkgktHJEDh6UcRMoqOXx9rsWWFMZbpsqIx7ngai4QkUZosiugnCUrA9uYmTdugFDhv2N7e5Gp0maJsKMoaJQXLxZwoSo4dRLO0x2AwZDzeYDHP0dKHR6RJsvAZyyIcgzFdOHmn9A/HEj5bmZcYa4m07gJEwzjZtg3GmOD085TxSCuB1gKpIoTQXQCqRKmTUNzz4teGSC+WJWV+wqiZVrC4VuI83P3400D+ukdv+nw+o1gsSHsDzI3bofXuKYs0HScMRpsY04SE3ufYTTVNwyeffIxSkqr6FiUjCpDrfdTVEWtX1rhyc5P55wfk/3qKLVd9xVxsgC7wtcE62Dt0LAmCx4rQqntxCV7gm0SS9egNNrhx8ybf//4PyIslX355h3w5p/6swlvLeDBgkKUcTOevl0i3DlPXGOeCete7b/wG8c6Tz5dfn6/0ryskwZYlJthyJIFEtyL8mQi0hrQHlJCXYE9fijmBMAfY7J6zQSDY2+7nU2Bsy3R5iE4Vg/cTem3EwV/MghXKSyAvWn7x8QFr4z6//Xu/zdu3r/JB4ahrz5/9+7/h4c7+k3szzyvtUjIGjo66y/dNuH5fxFpmRPC9h/BBDCfFkZbjINFHQkYF4Tw2wBSSYcwPfvwuG5fHQDjNX3zyJXc/v89yWvJgZ9IF+z3HSfq6Qlsv8NIQvTFydAm3OMTX5zB68BDHmvFaj7oylJV5YcKyKGqK4sW6BF65E5OE4576c24FvQs2ICsIEcZipaBpXt56TWvF2toQIQTGTCnLV9NREWreoYBx59MvMPYUwRdp0qyPjiKatqZtA5GulMBa/8yxctWhYBHEWZ/+cA2LRCUJDsnhdE7b1EwPdtHCMj24j7Als8kh8+mMp2qjhQK9jtAZ/fURWS9lfvCA6d49smGf8eYarm1YHu7grEEqhVIhmPRVkeneQ12FGN6KoPq+lMKVHsSRJEsDqVMVBU0pgm+xEGxsjLh+PcPe2WHvqEAD0nfzjgn7n3wZSO7HV5D5YsEXn3zM5qUtvv8bPyTrJcdD+PrwFvAWN29c5bd/8hP+9E//hL/75c8Q3nBpa4sklngekOeGZQ6VgbIKavTtzYi3b6+R14YHeftI5sDrwnmJdJzD5m+elcva1hbXbr3FYDBmY+MyB3t7/MW//VPKfAkEW43xpVtk/XUubayzub7Gwd5BsFXxHneK3BZArCWZ1pimYDLZx2uFi2PK5RI33YGmoZGhOl75FuHtqZBnoIZ5UfAf9/bQUcT69eusX7vG5P79ZxPp1nF0eMBisSB/912stQgdEw/WMFVBfQYxLoXg8uY6V7c2MVWOKRcIHKIpEK1BvFTw6+lWtvNdJWkSsT7qUzSGh7t7OKGRww2iVNLMW3xjEEKgpESJ4BNOW1FPHnYFDY+PYoQzKCnQWnXe4K7z5/ZIKSnLiv39XcAzGo1IkoTJ9NFrUwpI4k7nUT966uI4YjDoAZaq0ljnqOoaaz22a3tpXRuC0a2nqQ3WG6wHJSW9foZSAueC/ZoWHik8g0Gf4bDPoD9gc2ODtmk43N2nLCvaLw2Y5pQiXdGPYgZxwlqvh0lTzI3rVHXFztEhTVvz3q13uHLtOru7++zs7BNrzXy2REcRzgah/6A/YmN9k62tq7QNmGZJWy+Ik5hef0BdNzysd3DG0jSGpm07Il0cy+vLomByNEEKQW80QGuN7goiy+WCsiyoK0dl/JNXgIAogiQRRHGC0hlZLyWO9Clf9/Pj14ZIh0cvSoFAa413vqvyedAJSIiTmCROsM5g25akN2AwHOCcRamzT7JzlratcOb5F4LBEy94I70KT7yvFZXBLyrYMEglEVJ0Cb5P+f0LouQCrxGeE4/LmpPWpDcSEY96DL9MG/4F3mgopYniJLTmKYFUAqUUOooY9Pu0WtFLkxC81U9xwoWK+3lsKs57DFGMjGJkHCGzBNe0NHXDS61bXzUu5obXD3HGY9Up3KnarAW8IBoLVCwwS4drui9nNb+fJjifJeBwQAOucJR7NSoOayhvPbZ6NRef6zwh9/YPiWKB6OTQWms2NtZompaqqnDOv5QSU45jZE/j8ha3aB+9Xr8tBduVmnzFomjR2bj4k/XZigCseSwlj0fV6PCI6tZax3R/jrMOGYFQAoFgbbyGsgXl3GJbi6nPyy6+xOe8wNcOHcdEvT5tvaDpriUpn91gZK2jrg1ta89rh//m4xxdFCtO4KxzI7txeTUWvwh/uwoYlTK8hhCOul4REoFYt9a9MDmstQpWBni8CYSOd+6Rz70i2YUQKKXx3pPEMd46mqbpyOmzT1OwuLFIYSjKkjiOKauKumloj4VqHu8d1himk0nwL64rnl5965I/fQMOTC1pRI1tS7w32LahqUqcNWRpgnOatrNIeNVYbfWVDPeIlp0iVSviNEHJzhMYMNaFbIZFxby2OBxbGxmpsMS6Ad8VWjz0MkWSCJraYlqPaRqm85zpfM50ekScxHhnj7NRVlbb3hGCYJMYHQVqyFjHfFkSRZKittQmFHtOd2+oOGXj0jZp1bDeWyCtCcWUU90VhlcSh3KMmzevY53j8HBCWb2Zdi1nI9z049GY69ev472gbSrapsZ5i8ODcwgkUji08kg6G8gzrkEtBUoEBbRU4L3FmCp479clTVEEJf7KSlKI8BypMdZiT3tpeY+zNmSUl8FVIU5i5NqYpqqpzzzPnULeGKqiYDGbURU5rq2Dz/hjt2CkBFpKFA7hDN60tHVDpCVZGuGtoC/D+Ff5J7fD56eP/Ll/u7WOojHUrcE6h9CQpSlCaWyuwkhiWlxdYbC0iiCWdSdjjPeOqixZLhZYa4iiCGtbTGMIhHqLZ2Xd5THGoJQ6VpJLEcYAuVqH+5OfxjgaEWy5hCyoyqYbjyRxFOEjSRIrnBcURUHTNCitiaOE1tBdW56mtUgbrhHwoEL3jRASpeOuOyIF6zFtS9u0GO+Pv4OVSMR7aOqW6WSG0JI4irvDFVhnqeuGqixJk5QrV64gpObwaIqQkiiKkFJSVTV5XlDXDcZajHVY133Wtg2PJvifr8beMJeFQVJISaQ1caSRUpJEGh1FZIPBsbLetA22BdN6iqLk6GCC9w4pBVoFhX6aRkRJjI4S4iRC6sBhPi9+rYj001BKMez1cM6jlAapoD+GpMf6lS22t7eoypzl9IhBv8ftt9/CNDV7X37KWdmZbV1h2wbfDSzPi6Zxx4uqbw082IMFdrrE9lP44CsOfuWp+k0GcV3gOw1P8Eo/3VX7Rt5SiqAMjU793ZITtecFvlOI05TBaESUxlhvcDikVqRpyo3r18EaUiwaz3CUYNU6+wczPv9y75WpoJLBGr31LeIsIR32qJZz9hZLjL3w+v21weMEOpwYBAPUwZqkqIIScvxOgpKC+S9rqr1T2xrLie3HV6GzBWmWhsP92SNCIde+uoVAWdX82Z//Fb2+5url26yNt1BRxG/+5gdMpzO+/PI+bdtSFM2LCRakIHl/TPzukPqjGdXPD088w78tWPmeq+BugAY3UhBLEGHTd6xAL4Cj7nnD8LtPBMyenmAVVFXNz/79h0glyTYkUU/y1o3b/OC9H7B7cICRgmpZMX8wfyJE7ALfcghBNhwyvHyZpS1o9gIxEEUrUvTs/U1VtTSNQXhQziMJl98buW47L86xx4k6tbkxcLrRWXBCqrZdre5FlgBRJEkSjTGOqjI0jaOqQgBomsb0eilVVT+3n/wK/V6Pt29dxxjDp5/fpWpqtIRIBesP25HogQgRpGmGczHj8RpJUpLPZzRNTWs85ozx2DlHnpdUVc2DnR2m83nXst+S52VH0Adb4KapufPRR6RZxmx2xNmDsiAMYgLaCd54FoeCpRB4G1QsTWWY7C3JspTL25fw3vPw4e4zbGJeDJ4wLUpgFEGqIY0EKIFOUvrr6wC0tQmWR01F01p2dufMSsM71wb8/R9u01Y5xeyQtvHMp8Ey5/rllCRR6PslctJSTJd8tCgg7vH2nQ9pTcWPf/xjJH1Wg3nbWlpjaa19ZG1Q1Q0ffbEbvNed6wJwT2hKgGS0zg9+8/eoiyWTB7tMoprF3FE3x3mNzIHJKzx///R/8T+jqir++I//lLt3773CV37NEMGv/e3b7/CHf/CP+OzTT/mrv/xLZpMpXglkrHGNRyDopZrxUJPFoL1BPXZNSwGDRBEpSRJJtBI4akw9oyxrZvMlzgSSGwDfIhAMen2yNGaZlyzyJ7sBnbXMdneRSnH91lusbW6we/8BO087zzaIRw/2dlACDnbuU80DaXl6FJcCRqkijRSRbzDlnGq+YDmZszbKuLSZ0WioIkFu4b6F/NSwsKKPPOddcp1vfZHXDVUTPoP1nkRJLm1eIooT7i0OacslNl9iyxaXJpimh2vK7vN17+Qc+7u7LJc53jQMBgPyPCfPS4Qx1G0bOAkXiopNY5GyPRZ1aCVIk3BH2e4+WwkzitJQVrDMpyg5Q0iPVJ4k0YzXxsRxyni0hRSKj+98xsHBIVk2YDwas8znlG1Baxyz+Qm5IPBkKcQaRmNFFPdIkz6DdISrHfPZksUyp2gtFWHUlATBijOeydGUX/ziQ4bjIbfff5sk68HDh9SN4WD/kLYxvPPeB/zkd77H3Xv3OyLfYF2w+zo4OGI2yzk4PGK5XGJtg7OWunWUTUtdVUxmM5qmCbY5HQmvtUIqiVQSLQXSWYQUpFlKFMdsXb1Orz+gl6UkcUysw/X2xadf8if/339L2zb0MomONevjjDTrkwzGxNmAKM6I0ggdaZ7T2eW7QaQrHRElKc5amrp85qpDKU2UhOpLVQWvxmAq37WDSImKYqI0wzqLjBJ01HkaeftEtUJrTRRFISHWuqDGVgpPqO49z3LwW0Wir2DCxsvkNdWkoFnWj6Q6n4aKNSrW2MZgqzfBSPQC30V8q7bnnhM16Gml37dlLPi2He83Bo/H0bQV88WMuiypygJnDNp7pBChK8nZ43ng1XcnBfWWkJI4zbBNzXOvGC7w7cZqF3L6sSKDVzsVA74GL4Nq3COeXJt0ZKsQIJUADVY843rtiKXn9eKXUjAY9NBa0TQ1xhiMcZgzfA+98yzziqYVZMkS/ElglnMOLzz+tBL7eQjwrtggEonINCKVweLEPOfrvAlYnToNxKB6GjKNqz2+cSdFktP+8ZaTc/aYCv+0wbR3nroIAYE+kkRWUuUNddXQ1iasDb9VE/QFngdSKaSOurC1E0Xg6WlGStmFJ7rO5ocQQsYxzfkERKdC8+5RBbUQAh2Fdm5rbVBHuue0DnpJBOV3+EzPUniLx9ZKcRSRJoqqtnhvOpLSnzQIrRpEXvCjrJTnZwm0fMfUvMxpWpHkoZP6JMROwPFXv/rM3nuapgme1V3QnoevXDd677uOhaa7ZuwxQaeUQgh/7F9eViXO+6fmmJ16VcIk54Ov+CPv58LfuaBc9P751kfPsxxeGVBoLckSSaQkWgu8lJRtOEdtExSsKkqINZhZTVG3eIIlRax6JNJR1xZjGrzzRLFC6aC61IEBp3GOPM/Z29un1+szX8yI4og4SpBSYV2Y52WnSE/TlH6vjzGGuq7PvKZXf9NYx2xZYaoa4YP3cqIF0kONpzHheo5W+xxx9j3+PFBKopR8w5avoQIhpURphXcOY04VYIQg6/eJk4woSXBdV0a+nFOVOWCRMnQkSCmDWtg7vG27fL6TBO/VGKFE8ORekY1IEdY4OLwLk7eKFCHY0p1Iin0YXITqxobH5mRn7XGnh7MWpSRpluKsw3QWR8eCUR/2FU1dU+Q5TV2f6UcvAK0kWkmk8HjXjdc2HFekBEJL+rEC7xi0HuGgtv6RAGglBakOlPqKdLaPZUs8jxuc92C8Q0pJHCdkWY/hcBg6iLvOjNAR0OKMwLYKrAlznAgdLkJKrDG0dU2kJXGaUneWOOE0dd9bN+gZa2lb+Uiny+nupNNdOsf/7xwWUDo0EXoPQii0jhiPx0Q6ZrCzz2KRo6O4y6eIiKI4jGvePyL09Z1fubMeZx1ta6mqiqpqOocMj+vG6dVYZX0oejbGUhQlOo4x1oEK/uVSSJqmYbnMadsWpRRJkjAajSirmvliibGWsixp2hZjglLfOUfbmnCclaWua8qqxhpDmibHnuWr684LEMITRfrYdkeK0GkhVYQUCiEkvSxlfThgNjoijRXSSyIliKQgiTRp54suu4KskOLUBHZ+fCeI9LVLl7nx/g/JZxM+/9XfYp6R1L22scnNt9+jaVt+8cs7NHVFvpyFVi9bQXsqLUrIIBlY3TBn4PLlLa5du8piWbF3uMR1d7OzhnxxiG3P15r/uEjsjLHtzUQ3Ys0+OaDcXWJrg23OWMgIWLt6ifG1TWYPjjj8/OEF+XaBX19YQmifJISyrUq+GYG8eHWOHq8PHRHzrTnebxBNs6RY7jOfPuDORz/FGoupK5QQrPd7aCmZLZdUTX08EbTm1XlyAlSLKU2Ro67fpH/7NlgbFt8X+PWBJcjDVv69KcH3vCSETa4R7DwOweKZTxqEBDN/dDUiGpBTiDJJ/3KCN575XoN5xauWXi/lj/7ot9naWuPzzz7h4GCX3b2S3d0zlFQOigWU0lMuH6L1wfG/GW9ofBMW4EOJ8uCWDt+e4/4ShHOTEMY7CP7yVwldRHt8SxZrHNvsAPgERF8yuLZGvNZnef+Qcmca5qWVqHO1lFuceo3Tp+zxBeupluRm7miXnjuzu9z7aA9jDFVdB/HKhRr9uwmpQEdh39ThEUtNIciyHlHU2XRUNauLZ6XE9hDCE0+9bJwkxGlK2zRUxYmyTkcR12/dIElT5keH1GVBUdQh++NrgFKhu3nQtZTP50uq6mT/KVYt+yJ8NgiZE0IIblze5tKlNY4mUw6PjmgaQ74MxJvohMEx4TysIgrOg677ncendq0Vo1G/C7krg5DsJYr1eVFw57O7QW3YkUamK7gJRBe8p4LXd1Xy+eef472jrUucszR1E0j4c5Dp8/mCPM9J04QkiYjjiPFYBUvVJhDLyzynqptnqMc7Av34z0+HtYFUCsWN849Vq0JQ19vz7M8FeCEYj4dsjTOkVEihmOYVdz7ZxTuH9p4sS/nJb/+I8XjMYfO37M8LEBpUyvb2Vd59+yZ1VfH5nc8oioLp5IiqrBDKkybBzxwDDx7s8q/+1f/ErVtvceXqZa5du8bVa7fpDUYMBkOStMfa+hrj0YB3332HH/3oR+zt7fHhRx9Slk/PMfn83g7/t//+fyTGMmzmaDyDkUIpwd0jy9HCkUZwpQszl9FTX+rc+OiTj2ibljzPX/7FXhEkMYKYfn/A2qV16qrgYO9eR4AH0eVv/L0fc+3mLYRW/Pl/+kvuffEpdz7+KITwupY0UozWR0Q6RktPUyyxRUEppuSLKd77k6YyERrJtAxFOR1FiCxB9GKkElQEBXqqIpz1LA6WtLWhKErqqsZHEA8VtvGY4smr1TvP3oOHHO0fsLY+5t3336UoS46OprRtS7nMT0hZ75nPZlhjWS6fEj4jRCCq06izSPYI4VEKIi3IYg2R5Nr1PqY1XMlbmtby4dxwr3DHd+9WL+L7VwcIHLPFgqa1TBZQNSccWsXzN3dn/QHXbtxmtLbOD37rRygdsXv3Eyb7O6xUBa6p8SZHaU1vOEZ0diaCYJHijGG4vsn6xhpCSg4PwhrUOY6DRIWAZV6zzJtjRbrH43yw4KqbrijZDVGrz3S8xOqyEITwGONQKuL9D77PeLxGayHr9WlqQ9NYkqzH1d51rLM0psI5Q1svcdaEQo/1FEXN9GjB/GjJw3u7mLalbsATHb/rSm9TOs+iNZiqxi9yGilZP5qhIk2sEwY9z2y+ZPfwiP54jbWNTZzzvPfeexxNJjz8q7+mKEsWi3nInpCKNNVMqwWT2YyiKDg6OsK0hqpuUVJy/VpCHEe0bUNdB8WG8I4o0vQHKXhB21gsjqIyWNkyOZxim5Lf/N4H3HjvferZnCubPYoSrDEoLdkYDhiMxiwaR1kuSaREZ32es3YKfEeIdBXFpP0hbdOgVISV5qkJzUprsl4PsyxYzBfB98m2XXnGgG3xzhx7vSkVKh6mbTFt+4TaWuuILMswXtGrgyKhNS3WSIQ4P0khHvv5reKYPZiiwRTPWrwKVBQRZyk6Xi03vlWf8gLfQawmKalCRd92CpuvBaZ784gTVfqK4Po2YCWL+A5ysVJKpApyDddJAo45omdtrDqFwuNjm7WGtq0oi4L5fB5WVtailaKnJE4p8rKkfGqwz8vPDM60ONOG4q7vXvNpkrULfHexUlG3hPFmteuQBJK14dgb2zh3wgychu3I9FgQaYnHf6U6LKjXA8HmOoXMV411UkpGoz6bm2PmswHOLlguLUpVQS3z2PO7Dn1MW/NI8qkisFIatHqBZW8XHui9x9vgo6kGGm8dTnUH8W0Zt7vuAE/3ncQKnWiUVp3q14egkdOf5wUaCIMgzlM0JcXi5QJlL/AtgBBBLdZt0uFkWjk9vUgpUUqhlEZqd6xMFCIoLIOqeaVkD6rqOEno9fuUQtLUNQKB0oo0yxivr5P1Mrxp0DL4rNZ189qntFO5Z8cBc0GZ/njxoLvPRPg8UkuUkPR6GaPBgLIsibTAmvBZT0/xjwusVn/3VR/t9Fh8+liCile8dFYEBG/7vHjsvl4Fwp0+WAIxXVd1p0g34N2JhcE5YIzBWkEUabzXx59lJdv3dLYDHWO1Uqk/ifNfFCv/9vPieC/B+fWMnjAn6ihCKY1SES5vmOcV3vlQt5UaHafB/zcKLLTz0FgPMqI3XENHJb1hhpeO+SJY1WqtiGOH8g5hPHVds7dXk6UpB/v7JHFE1l/DeIjilDj14TqONcPRiO3ty1jrSJLPaZu265Z88oSUVc3dh3ukEm5l0ItAa0EUS4R2uK6ok3QOuvJpbSfPgdls3nWnvUmd7QKBQkcJWW8Y/mZ1LwiBlIrReMzm1jbT6SGHB4dMplPyfIHwjiRWKCmJ4+D97J3DNA0OgSNwUOFdAomuhQje2iLcC1ppdJwQZRkWT9yUCAGJjnHGUWiJbQTOum4NI7tcO546qDR1TVPXjMfDMMYKQVbVyFpS58VxcS+EDbdUZXlynF2nzurL1mo17ndrQOeO143hNIU/R5FECkXUWqzw9JQgWt3jHiIpGCQKiURYTdMImsoh7Qn7fHoJ89W3sEBIQRTF9AZD+sMRw9EYpTVRFAdB7Uq271xYc8rwfQop8DacPO8dzoa5IM0yoih6dG916kCsOSkgHm/BOCHU/eNzgDj5P9+te8MWMnQDxUlClvXp98PDuYKqDj7sUaRQzuJFsFZxRiIQwUzCB1unpm6x1mI7VTheIKQK8y8iKNkB6zyNdUhjKZsGXTUs87Ibl0FJTWtM54GeM5/NiOKYNOsH33hnaNuQjSGlJEsyZBR3GZMhx2ixWGKMw3bncmXhtfKWF6H1MXSldOtV7xwO181rPliWFQXWWtI4IUsSelmMdw2lC1ksWkoiLRGNwZgW7SxnxJKeC98JIn12uMcnf/vnCKEZbN7AGsPy8B6mfTIcYT494uNf/Cz4geV517LR9aXWObQ11XTINO2RZSnXtrdpyiV/91d/QV0ULBePpuweHBxQlgWXr97g93//d5jPl/z13/yUuq6fq5K9ap04bo37rsF75g8PqeY5TV5dEDdvCoQIsprjhLlfHwhgPYJUwfWbV9m6vMUXDw/4xaf3v9bW4EdsFVZE+rchdcsSSLhvC4H0HNi+fIVbb79DXTccTI+w1mJcaHNcHB3RlE/OLUIpdJqB95iqfIRwr/Ic0zRh4X9q8W+dY5rnSCFonrYpkBEiGQQyv1muGKoXxvJwj8/++s8xzmPSAegU8tlJJ9YFvvvwnCjRV197BRxw4u/rCaS64Il73BuwORjpMY09Dm1/FnqDjNsfXAMleHi0S1lVNEcWe4YaaoWyrPmzP/tbhsMeP/ntd/mDH77Pz37+EUQfspy37O9W51tKrJTYLccdc96ec3D1hPPUeJq7M0xRMLq0xrWf/JDl/ow9dRdXWNjn1SaqvU4YYB/czJHHR5TzBTKK6b27TftFQbM7/8qX+EqsPNW/jfY3F3guiDhCaEmxf49mfoQpFuA7FaE5aU8HH9q6m4bR2iW2r62zmB1yuP+QSEvWBhnOe6pFiXOetVGfLIu5/e4PuHHrPR7s7PDRJ3cYjoZ88L3vMRoNeffdW2RpzO7dz5lPjvjFL/6Ojz76kKaxVNX5LjylJHEcLDqbpsX7oJIUAoz1Z+X8HS+ZnXPMZsuuQCAZDPo0TUNdB4Nzbzv3LAFxrHn77WuMhgPW1kb0soy9vUPm8wZjHSvHqhX9dDoX+HS8VPAGF8d2MGcdl/eOtj0Zl611zOd5EIa9BgJSCMF4PCaOY5bL5XHwnT22gujoGO8ReNJIoaSgbi3NOTpUvA8ESXvKUD7Yy1ikjFjf3KCX9ajbliiKyPO8U1KvzuRX93lrrYnjGK0VxtiOnD/fPHF+vfujn2nvcEm+qLh8eZMrl8dEebDcc3gaoDCGuzs7zIol807te39vzrJoGNw54K//7i5aeWJdo5VgfbzF2qWY/lpBWdSohxOW94+OtRO+bbh39z7LvOCTzx+gooTf+vFPeOvtd1kfDrg0HnH12nX+q//qf8n9+/fBwd7uLp/f/ZzFcvHUz2IdzGqoLQwI9huXhoJYrSwtwnl5FcvMz+58ifOO4vFCzjcIh8NjiJOIzUubzGeCnfsSIRVpf0SS9VA6Bu+ZHk348u7nLOeHKOXRUjLoRSipcKamNi1F3tI2ll6S0YvTY4JaCkijziPah3Pb6w3oD0fcePc2N965xcP9XX756YdYYxHO4VpL3wjasmY6L8mrBt84jAv2Rl91wVZ1xWQ2YTwa87u//RNmsxl/+Z/+kjw/0X2XVXl8vwP0+322t7fQWhJrhcDjqgJvWg5nJU3dkghDGkFpGu4dTLDGczipwXmu9QR9DW8PJVuZYF44pkuHLFs+ujtnPEj5ne+9S5Zo9nYnFEVFVS5p6pJZAZMFFP5kSfs0ZP0eg9GY/nBMOhwR9wZ4qXBI4uEG/c1r1PkUUy5DMGfHl5TLGcDx+Np0hLyzWyRRhFbqkfqE54T2kjKMSHEciG7xSEvfo0crdVCyR3FEHMfUdUueNzRNy+HhlLJs+U9/+dcMhmOKRY5SCYgK6y1tY8irlhBCGyx3TBv2sdaEeW25rDCVxXpP6xxxFLE1HpMgqF1O3BjKtqK1hrw1tM6hyha9LIkPJ+weHZHEMZuXNkmShFilJMOM/d0D/uTgT+n1e2xsbtK0DUoGS6plvuzyihZIKSmrBisJj9NnwHvqpkVXMmRQGkMUhzHaO8diNsU5T1MbpIpZQxBHMbVQ4AS2ddR1TRxpbt+8xmw655NP7lJXLYvFHCscs9KwrA1eR8SDEcaZ5ybUvxNEel0sqYslSX+NjevfxztHMduFM4j0qiypjtuUHovItS3YlrbMKZdLsjhiNBgwqwp27t+nyp9sWcnzgjwvuLR9jZs3r3NweATCY7oAhufBm8yZvQpUi4JqcZGm+EZh1QN6nCLzXb8KTyCATMFQC65tjrh96ypl3fDLz+5/fadhRZavyPSzPJ7eVHyHQ4MHgyE3bt5iWeSBfzMtrQuebsX8bJJJSIWKk0Cg14/OPauOpsfhvad6qgr9+IUROg1Eevvy42dd5NRFDlEC69ugIiiXF0T6rxsev+y6FvBH8LT72wU3PN8Gf8XzSH/iNGLr6gYiEkz9FJO3mIV7JsfaGsPnXzwkiSN+9x/8kFtvv83+0SH3HsbBT3jvnFPWaowF/Bn+6l+Jzi/cTmusrYkuXWL96jZCwv7uvcB0HT7/y35jcIRCSgXNpARfkl27RLw5wB84Gha81OSzYv9WheELIv27CwFCS0SkafMpzeTgkX9+nIQ2Jqjf4iRlfeMS1jRMDnZQUpElMc57dNHgcPR7CcNBj6tXr/Luux+Ainl4MGFza4v3fuOHrK+Nee/2DdIkYphETA/32dt7yBdfqI4APd+FF8LMFNaKEHwqfOc9HAjCp90LoUDgqeswmA6HPZIk6sikbr7vnuo8SCSbG2tc2hgTRUmnzhTUlTlzOXW6U/mRJaE4pXZ97NhW46G1jyoxwnG+vkpfCDFNybKMug7dQM7aLivs8d8NCtVYy+Cxe04YYzHmydfzHrKsx2A4pN/v07YtTdOcsiQ5nzwt+CTHCLFSez7d8/4sPO9y2APLoqEuGtbW19BxjOw6plZXb2Md08WC1plj3+V5XjPPa5SY8YXYJUsV169mDPopW1vXGQ5HCJWS9ioO5hWak+sGa5hNZ7TWklctDsHG5auMNrdIIo0bjRiNR/zghz9kNBrz07/6W4SHnb2dZxLpDigNeC9whKJSPwk+7U0LVdt5Wb+CbebR0fTlXuC1IOQgKa3o9TKaOuk6agQ6zoiSHlIG7/SiKJgcHmHavPNFF8SxQgqJtxbrDFVZUJUtygkSGXVe4qGAFikZAonx4CGOYrKsx/bWNm/ffhsVa3aPdmmbFtO0uLYl6tcYKSiKhoKuCHhOMUHbtpRlyaX1DW5cv0aWJGj9KH1ojMGcWkDGccT6+pg40vSSCLxjsmeojKGoDMtlxSiFtAets8yWJU3r2ZsahPdsxREykmwkko0E9g144Shax8GsRqiYSxubbAxTEu/Il4rloqYsSrQFtwiX+1cty3SnRM/6A3SSIKOIUOoT6KRH1Bth6iI0kHedBcGi6gwxVdeVFanQXfA0yE6FHmlBEsuuIHi2jZSUoZMjSTRpGuZHUTQ4FwpJrXF8ee8+vf6MftIn1jEIgfUWYw1N2+CdDXYu3uKM67InwtxcW4OrzbFDax+JjlOklCSlASdpTEMLtNbRWgcYqGqUgMV0Spok6ChhNJIMBhlJEnM4PeJgcshgOKCqK4QMHRSRlhhTU9cVq5nNOHDHqnxxbK8SCm82+KdbF7IrBERa0baOqq6CC0htUVG4F6RUCGRQ7VuHMQalJJvrY4Tzpwj2CllJqspS1ZakbbDOdpmZz4fvBJG+gncu+M7qiCs33gNnONi9S7GcPu0ZZ/5tk89ZeIeZ71MePsQ5x+jSFXrjhvnB7pke7Hu7D/mzP/ljiqIinx1inhK48FU4rUy/wAVeNUQcIyJNaO4Rx0S6txZj7beaSF/nRGh5nu2ClILB5pCNYcLmtS02r27T3z0K5+WsOzAj2LBUvFpP8JWh4irQ7TtMUH9bIJFor7CV4Wj3gLqpcbQ4b2nrR7/8Xr/HaDyiNYbFsjwOPTuNwWhMbzikzHMW0ynPNcI7g68XCCGJ0mFw2qgXOPNyF6EQgkhIENAKcTHnXOC5YStPcT9snlxz9hWU9iKGaynDtT5KS6x1NEeGamawVXefnCZdDfQHKW+/d5U40UGNKQR3791j/3844P69HR7cKygL8/VOV55ggWNgcWfCvfpjqrzAHbqgWP82ksWeMJ+dd9L8KmiB3k4RkcRManxpjs9L0tf0NmJM7cgP63Nv4i/wZkMKyfqwT5KlzP2MZfNkQVYpzXBtnThOWdvYJM36DHoZvSQlk1uME49taup8Rppl/M9/7x8yXFtnfWODrJcxvnSV0cY22foa4ytXEMLjbMve7kM+/Nu/pC4LltMJVVnw4ME9lsv2uaxLgid2E5TT3aBirO+6+c9/ndZ1izGWtg0X/ebGOu/efoumrrj/xWc4Z/ji3kN2DqfcvnGVS+trx89N44j+MAuq4c6mZLmsaE8Rx8f1Sg/CvVxY6KuG957FYtEF1j1JMj36u1A1htbIjph5MfT7fa7fCB75jTEcTiZMplNm0+mpYzit9nw2bBeEdzok9XFyK40kaSRpjKNoXn6hvhIEz4uC3b19FvMlqrMz0BqSJHRqDAZ9drrwwyiSxLHCGRcCSVvHzmFDNLcsqi9J4jiQqMYwmeYnJmceisZweHDIMs/ZPZpTG8dw6wql87x1/QZVY0mjiFF/wNbly/zO7/0ub926RZwk7O7ucP/+fQ4OD3A+2Cms4AhTiVCS8dY2V7f6VFVJ01TsH+TMHi6oLSzMt3qb+QyE+3Q+2+eTj34ebE6MCevsSKO1Ym9vl6LM2d95QDmf4lyDNZ5ISJIkQwCTo5KmNtStxRIU05cubcH0iKPlNAT3miBA8CLsY0ETqZTZZMGndz7jcHqEqcN+Pot0ICRFCItMNPQjQeU852zYwbYtdVFwdHjInTsfky9zhDfEWtDas8ehuqo52t9nbTzi3be+R5IkbA5H4bx8eIf5sgyFlwKE9MgqBFzmzhNJie+toUYpidJopZADR7ZuOcwLFnuHYB2TeQNoesMNBoM1nLNUVUmaOtbXHb6BOPc0/unLs7osmR7ss8wLlg0MRznr61eJkwRrQUp9bNPsvcM+RSSbpilRFKye4zgmiiK01hhjcJ11WbfdCvYtnbUKrAh62f27PR6ppJRcv36V9fUhWgdbnIOjKUW9gzUW21istUyPJiyXBWnaI9IxZVlQVQ3D4YAbN29irWE5n+KcQRJCn/PpkqaoQvCpDe+pgEhpsv4QKQT7RwtK02K6z5sORmTDMVopkjjC1BXLvT2Mk1gnsU6wzGuKsmE2XzBfLKjbhsYYlFZkvQRrHXt7E/K8REUCIUHqGKkSWmPw4uTcOueZLQqKsiaLBbEWNHUD1oZx2Um8JYRRty1H+0dUpaXK57RVwWI+ZXK4h6Ll7XduMR4NuPPJHZhWFIt5yAsQEoMAZ4iVIuo6CZ4H3yki3TmHaYNP+vbV20RaUyxnz0+kFwuaYkFOaAvJhmOuvv8beO8p5tMzifSD/X0O9vdf+jO86W4OF/h2Q8YRMk2QSNQpc2tnDLYqv9XX3piwmKs5HycgpKC/MWRta8jalU3WtjfpDQdni1cEgUhPeSSw7ZXgdKjbBd4ICATKS1xtmewfUdclyJaws370d7NextblLfJFzuxoij2jbbo/HLF5+TKTgwOWs9nzdSt5GyxdVETUX0OqGGfqlybSJYJIhtAfw8W8c4Hnh2ug2n02mZBkEZeuDMj6PaT8/7P3X012ZHt2J/jbwsXRoYCATH1FXc26xapqks0ih5zuNpsesxH91P04X2LmY8znGGuzabPusW4OOSSrWMW6Ja+s1BIy1NHH1VbzsP2EAAJAAInMRGZiXTs3gEQcP37ct2+x9vqvJbHGY6aO5ujUc7L2MG9V8d1ezvd+dJN+PycBvHX85d+8zYcf38VW4B6dJ//FooyvVTVjtTc7GSsuki73IiIQB0zFcyHShZbo3Q6yq/Ari5ufUqh1NRtXu9RLQzFt4CWR/o2AEIJRr8tw2MesSpY8HACotGa0uUOvP+Dm699htLlNqBaEasVGV3Nlq8d0OuG99yf08pw/+Rd/wquvv053tE2Sdaicp3KenevXefV732MyPuLd3/6Ko4M9/v3/9r9xuHf/c30H7wPNAxsA9hnaZ9OcfYg2Nzf4yY9/yHI+YXl0h/liya17+zgkm6PhGSI9yzQ7mwOEFJHcsFExfJ4Cm8DTzSG+BIQQnir8Mdq5fL5Os9vr8eabbyKVYm9/n6Ismc1mMYfmDC72Oc65Y2uKRyHTkmGuWdWOsvGfe960nv4vipKDQ09ZGlQIaAGphk4qGHZzBv0uqV4T6YpOJ6GuHVXjMCZQTuN88M7BnUd/WIDSWCaTCWqh+fTuPqva0Nu5RBmgqC0y63N5c8Sl7U22tOInP/0Zs8kU2xju3r5DUzZMx1MsFn8qpOR4KJGK4fY2l69tU1dLTFOwqg5pzILCwrT5eg6VT0Zsz4v5EYv5iQ5aJyk60SgtOTo64PBon9nhPtXypI2GVJJmGSEEympOcSpvLu902dzcojR1m0MRsMYftxulBIRIpC/mKwp7i6IusY2LVhq5PtYACCBTAp8InIHqgn2cs5a6KJgBnxJidW1wMdvBh3OHctPUTMeH5Ini8s4lRsMhRUuk376zD9yntlDbGGzfnFoEd5QkdIeowZA8y+nohI4PbFiHOhzz6eGE4ALTZYOQKa9e2WLQSZlOxzA+IssdSluaIpAU0Xf8UVtpTV3R1BUsCyZlYFTUvPLqgm4nem6fJdLDcXjsaQghyLKMPM+PCfUkiaGqYZ2zRThWolv7sAGAEAIpxBklrVKSq1evcOPGVULw0YddSO4eHkENzsSKn9l0CkKRdLroJCXYqN7eudTh1Vdfx5iG/b0EZw2pdhA8Ywsr61k2DaVrfcOBRCk63R4gMAQqa3DtZmLeHzDavUqepgy6Xcr5nNXRFO8dLgisg7qpcc4yX6xYrlYUZcl8uSLLUra2R/gQODycsVgUJDmoFLKsR56Lk7GuLcEKIbBcVvGvo5REJZjG42qDFAIpNfhAU3s8hunRlHJl8K4Cb1gt58wmR2xtdHn11ev0eimjYUJVBabLJbUBOilkSSTSpUQ/ppLgUfhGEenBGepigjclh/ejl1FVPlgK/7BfmtQaqRSj4ZC802E+nbKYzzkuOzCWxdF+fIjMl5MG/xIvcVEMNmK7XS1XFItHJGY/AO8swfrjDjsGPr1YE/OnxZJjQeOFIKVkY3OTS1d2IDimB/dpzIzOFlgJoacIPmBmMWCkc02ih5L6nqMhROLhgoROpqCXgvGwrF+Sli86ZrMpH338PtP5HOca4OFqjddevcnVq7v0B302tjbY3zvg/p07eGd58FEqiyXTI4X3jq3Lu5imYdES6jKNoTS+adq8jrMQUpEkGUIqnClxpsK7c1ivpyxl8t5ii0UMkvky8hFellp9KxFDRiV5lnH18iW8DUxfnTLqdzk8nLFaVSBB6PYRU1CZms8+u0+eJyggOM90UuCa6Dn8lcNxdjP1YoLHFxM1EMDeqxDNHLtfPrNkMPiAW9Z4Iwnm7I0ylWVxUGFrd3F/+pd44RGCpyxWCCzNAzZlWif0+kO6vT43b77CcLTBm9/9DlvbO8zHByznE7QMpAq6RwccLuYMBkNkkuI8jI+OcB7u7t1n7+CQweY2l67eYDGdcrC/x9HBAeZzrMk2NjcZbWzSyTsMB0OssywWc+q6Zm/vPvUTlNVPwnyx4IMPP6QqC+aloVwTKCIwGU+4owJVWdDv5XQ7OUmaIaREA9JapHq0lcZLRNXrvft7bSigIss6x4HWzwsSzsxdvPU0tcPaz0+irw8tgMY45oWhaVwMANWSQT9jMOhx7foVRhsbvPvR7XhOrQWN1oI07UX7EKXx3jOdzR56Dk/DGst4PEFKRV0UOOuYjo+4d+cOw/6QyZWr6ODYH3Txxrb2EZbt7W2UktSmZnNnk/2DPe7evY2xlrK18pEQNwASRZ4lSDISBVmaoKVAiW9uv5/nOWmaRhuUqjyZD4i48ZGkmrqpMdYck7EqkaSZJu1ojGs9+R8Ye+vGMF8VlHUD4UTHFduNQATR+mYXDPIhedqhqmuKYoUIHlsKvHWM5wV1WaN1ymCUUS/Kc+2Pz4NzAWM8xsSwyBAC/X6XEEAnOUJIDltV9BpKKXp5TpYk2KahKksWqyVlUVBbc2ad/uDGivOevckC0xhG3S69LFoeKZ2wcp6SQGMsH+0d0V+sWFYl/TyhLBwyH5EK0DJgdc1oOqWygZWL0zZzzufBiW2L94Hx5IiiLJBKMhgOaBYpp9340zRhNBwQQmA2n+OcOx6HFos5s+kYKeDatWvUTc1sscBZi20qvD8JMTbGUwl3HDhq7Ym6X0lQInB0eIj39jhMeTydYSuDP86VCKyN7r2psN5ELscH6mrFfD6jbiomkzHWGhId5fCrsqQ20WZtHdEmBOhEMdzok6QZ31XfpWoseb9PkuXIvIfsdOnkHTYHQ+bjI4rxFFvX9HpDOnkX46JFStf7qItxltoYqsYznizx3mOaWE3q2uoULT0+MZGX8qG1MIptPNEKqQRSxk0bteZwhUTKpLVSEvggIAiscbGvUQrTlEym91Gyx7IMOL/gyvUBSe5YfLikKg2udgQHydGERH3CZDp7qnxL+IYR6c7WFNP7IBTzozFCKLx/kFhcmzdGIl0Igc4ykjTlxuuvs3PpEh+9994pIl1j6obDzz4G4qTxJV7iRYGUkp0rl9m+cpl7n96+EJEeAN80+FXJ13f1/zDWGoCLfiOpFLvXrvP6W6+w2LvD3U/ep6wPGF4P2K7EXtcxNuG9htAEht9P6F5WjPOGxtvI3F+QSO+lcH0IRQOlgQtkK73EV4i9g3scHO2dLe091bCklPzsZz/mT/75P2knQYL33nuf3/zylxhTE+zZdjifTllMp2zvXuXGG2+ynC8oVjXOO1R3gFASu5ji64dZQqU03f4GgcBqPsaahoda+Wlv/Qu2rWAN9WztZ/sF9wNfF9//l3jukEKglGTQ6/K9115HSYVyjsl4wt//3QesVlWcNLdGrkHDqiz5za8/YC0OCQGawn91SvQHcZ6f/NcRay+0AsxsiRGr1vj5GY/nPGa8ilPsB+5VtbDUy+WxNcVLfDMQQmA+m1IUUFVnb2yWd7hy9SYbG5v86Ec/YWtnmx/95Cdc3r3MR59+wq07t+n3OmyNBty/f4/DVUmWZcisQ+Phzu3bzKZT/vqvfsGvfvVLfvizn/Mn//V/y+HBPp98+AHTyfjYj/tZcOXqNb73ez/g8qVd3nj9OxTlik8+/ZDJeMx//vM/+9xE+uHhEb+YTvCtHcPxBnsI3L17h8nBXdKsw9ZGn6zTIe90EUohkwTdWJT+OgUvfPlYrVa8994HdDpd3njrO/R6PbRKnutnqJbQOYlS86yMf2650usg2aqyrCp73DVmiWZna8TW9gbf+/532L50ib/99btADEWNytecTmeIVvHv1ljefvfdxxLpTdNw5849BGBCAAT3b99mvlyRJymXd3aoiyVKgvSBZjYnOMeNV25w45Xr3HztJqtixd//7V/z53+2ZFkU1E0kVxMgFYJuntDvZZhE4GxGrzMh1aB9JOu+if1/v99nc3OT2WJO1dSRK/Jx/pN2UrJOyrJaUFQFphXCpJlmuNNBSkntLNb4h6ykVmXF4XjGYlXSxvWyjtBNieThsijR0wW9jSH93oD5cs5sMsU5g/QOZxyHBwuMcbx2fZetzSFFOOJoeVEi3eOcR8qGqipJkoSNrQ2yNGX38mXSNOWXv3rnDJGeas2o36eXZdRlCd5zNImWQsuqfOzzY5znw7sHfCYE26M+w17OoN9nNBoyM5ZlAFM3jD++hZKS3X5OL0u4eWmLSxuXGeSazW6Czqbs7y9Y1RbloQmw4FFEukQrjfOOO/dukaQpw/6A7e42xfgOp2tcup0Or716A+csH3xYUaxi9mJZVoyP9kmUp9sd8P3vf5/lasWnd25TliXj/YMzVl1146kbjxKgZWv5EuIzkmpQ0nPn1i1u37qFdWAd+Da4+QxCDLZ2dX2muH25TNk7uE9Vldy9dwdrG5SUCAG+iuGdayX6msxPM83O7iaD0YjXf/B7qLTD1Te+z+jSFQ6ncw4nc/q9PruXLnF45zbL/SOK+YzN/oA0STDOYINDpjlJZ8BytWB1uI+pDUeTeQw6NfHZcHXc3NDS4tIqVnP7gAigVLQt6vY0SaKQPuB8ACRCKKTQ0RIGySiVeC+oGklTWdKuIE81Vb3g7t4c6/rsbJVIPG99b4fLix4f3P+U5aGhMQ6Lo1zdY35/n8aFc6vKH4cXlkjvbu8yuv4GzWrO5NP38Paiw1b0Fss6GVIlhCAAg6kr7PHgcqJGD0BwjuAcQsRdjJMQl5NfD19gMZKQkt5giNKacrmMZSbfBqyLA+CltcUzIhYMBXzwF0oaDs6Bsa0C9Zs1m3nw2wiZImRC8IbgH55YBh9YTGcc7h2w2JtQjGcUtkTpgMglcpRgbUBuGnwT8JnHKoFPQrR4ccCAE3Vi4JHtWAJJgFRCP4lEumwHzdLDs+TfvcQXh1jCd/7NFEojtcb6QFHVlGXJcrHg3v19rPPn8lCdPCfvZPR6XZRSra9hLNULzhCCfMhX/fS5WNtACG0QyjmNRRL9+5/aduhLaniak/3r9fD7TSAiv2XItCLTJ4q/gD9W2FQ2PFSJAWCMYzWvKbo1dVmTJglaK7I8Y2t7iLUelzpc6qmXhmVZEhzYxh971RLOLdb4WkIpGPVjH9AYh/OBpgHzVT4Px56Cn7M/CMT+R3HuWPiVu1GkILoch1nFkuwEKSTNzGAL99AlkIkiHXURer2rE1VW3jt8aaMP/LcZIc5Bg4/BaFqLNrs+0O91ef21V2Pl385WVPfVJZPxISF4BsMBaaKj169SdPt9Uq0xxlAUK8ZHRxwdHjKdTilWKw739/n4/XeZTafMJhOKxQL/lIve01BKkSQJWZbR7XZIU83NmzfpdjpkWfZMxxTipJ17H2hsbFMP9o3OB6wPYC1eGLyQCF0ilELo6G8dQjzHpw29/DogTTVSSoyxT+Vnfxo+RJuJRtYs5jOqJMGcE+r+rNBKcXknEoZKCCQiqjcDLKuK8WqFdY6qMRfq25RS9Do5CEHTBuXh/fF7BdDNMzZGA7qdlMGoT6fTYVWUyMkMgaDXi21TSo33gaIokFJhTLQBepI1DQA+rhYFgAjYqqJaLpgeHnD3s89oyoJRv48WklBXyBDodTK0Uq1NiUKq6OmstaTX1Tjro2ez9yyXK6bT6THhVzSWwkL9tY7hEmRZymi0gVKSOH+P4cnee7RWVFWFaUxcix8r0gVJomJ7F0Dw0Y4lT8i6Gd1+L+ZyeRDBIuRZ/imEljx9MFQ4ngG+DTkNwSKEjxs/7c6Pd566bnDWEYjzKeMMVVNjz7EoeRLW/VkAiqLGWk9R1bgA9oEJWgghBl56S9VUeDyNtXGNRHhsgWoAbJtXsTIGKoFOFX0rEa6kR6AGKh9wIhA6Xeh2odeHbo+kkzDopxjj2d3ZZF7UTA+XePNwP5OkKWnWQSQ5IktQicLj8cHFINhExBysjVHsa+oqhlBbg20V1IG1/iBQVTXz+ZLGBKwXlFVFXVWYdrPpUV9YrF/Enz4AbYVCIIbDPm036a3BVEtMXeOtwVsH0sdr70840ONQ6+NsiPhv3V6PrDNgOBwyGo6onaC0gk6ng04ysk6P7UuX6GYpKQIZYnBs8B6lErIsp7GGJMtBSExTQxA4zJm775zHGEcIAa1llDDr6Buf5xmJ1rjG4K1FSomSKgaqOocQHlB4L9oNH4GQKSrRKB2QyuN8YLks0EqQpCndrmBns8eqcBwuG2alxXlPbYlj8lPihSXSr//8T/jD/9v/g71/+Fv+0//z/041PXzym1roRHP5xhXybg8boin95P4dZvv3eCjJLwRMXRMekS7+ZSBJU1773g/ojTb46He/5uDu7a/kPL50KKLvdCAaeL1U6T4DArVpKOoCc4HNJl/VUDdf59nMhaHTDXS2ha0nmGrvoX83xvDOb37H/Y/fo5rUNPOGcNmTvgJipJE3+5jgqXRDU3uqrsEES5OHaMjeIyaclsQwBdP++Zx2rDxkDhIBegQESAwED5+UMHl+c/+X+CIhBEmvT5J1OFyUvPPhZ3zy0cf89le/pmkayqI6l0y8em2Xm69cxwZJ40WbCW/BGezCtEry8ztAZxtWizHAI8l2EuKmzlop+6L1pet8gTUqojTkm98NfaOw08+5Ouod/917Q92sqK3j9sxRnbM2W85rytURdiXZe+M+vV4HLQWDQY+f/PQtnHPM65KFqdi7dcR7B5/hreeh4ewb0lZ6ueKn3+vRyQUHk5JVZbl/EBhPvwFf0BMrtZ6iMubLhNgA9V0IKi5KlZZcvrpNnqTc/8sj5h8uH1oiJIOcy3/4JrrfdmAhMF9OKasC8+mc5qPZV/FVXjgIAVmqyDKFtZ7GeK5fv8r/5f/8f2Q02kCnGuc9H370EZPplOuvv84rr9xktVoyn08IAq7s7iKI5fHLxZzf/e433L51i7t377Fcrnj7t7/how/exztP09R47y9GHD4CUkq01mR5Rn/Yp9ft8JOf/YiD/X3+7D/+e+7eeYzf9LnHiy/vWwsXHl196BHYIChKQ70wKFWQzpaRVIsMJz4I8k6Huq6jL/E3BFIKRqMenU7KZLJksSif/KYziCVuwQeapsSYig/fj9fOXlh092R0ux3++I/+gN3L2/TSlFSpuHTy8Nmd2/z6d79jsSq5vT/FXKAddvOc77zxGlII7t+7Q1EUrCpDZT0JkAGv7u7wx3/4M5SWNMHgA3z08S2s/wwfPDdvXmWtY18ulxwc3G/DDOMOjrnAxpLkVJFggGo2pVzMea8sObh1i5uvvY7wgk6e0U8UaZLQ7++SdTKYE+1JvAXh6XYEw16XunHc2SsoneXjTz6hmN2l1x2Q5x3ujZfcLaKX9gs4LDwRQgiEUGxvX+KP//iPyPMcMHjvmM2mlGXFJ598xu3b99oQxJOxXEnBoJ/THXSZjAUiWLJeihqmbGwOuHp1h+ACdWGoyppDNefBwJLjfe4HYNckumiQskIqg9YeLT0KqKxjMl/hrCdRkGjBolpQ2oJl+fTPiXWOZVlBgMNmjlKK0kHeyVkUZ59hGyyFKZCVYH9ySJqmlGVDYxw+BFSrwH4Ub7nWA9WrisOiJrBklEmyxvMqngL4FHBKk998nd7lS+ieJmSKfj/j5qDDztYWo36f+5M5H/3iHyhN8dD+/mC0xZUbr2KBwnm8AKECQVpkGkhywe71y2xvdpmNDzm8f4dEeRazo3acs2cI7qPxjOlsgRASKSW+9VUPAfwjmHBB5AgEJ+HDp+M2nnVmaOuC5fgOxjp8U0P7/U4f9JhEZ70J7KmrCmcMW1vbjLYusbu7y3B7G9Iu5EMkgiAkvcEGP/n9n1Mt59z/+BOKxYLKGqwNJElG3u0idUJtHU1TIwNY07AwC+yptawxDrv0pFrQ7yuUVK3HvGZzOCRJEubTGcVqhdKaNM1xzsbssgCg8UFQVwLnJUJ3yAd9Ov1AtwfOOz69fUC3k/L6zSv0uoqf/9jw+vUBf/m7A2afTGk8GO8f+aw9Di8ckd7b3qa7scH2K68xvHKD5f4d1GAL0TSEanUxWVIIBO+iek8IhJTRBz1JCN4/7AcbAsF7mqqiWK0e2NF+lsv6dBBCkGQZWd5B6Rfulnw5WCvTv46j7FcEqSRSyRgEUlbYi0jaQvgKSPTTvg7wZTxTJx/1GPPaEKjKipVvqFaWpnLIBpQFEQJB+rjbnQeQ4DQEGfAJkRi0nKhsO5wEtp1DZLoQQ1WCJA5krWDBfxW34xuAWPbVXrt2MnZhW7N1JcxaEnH69bi3KR1D0za26PYHhABH4wnj8YTpbPbYjIF163fOUdcG09StOiFcyErhkQS6Io7iafvnZ+k/BSRJgpASa8wjJ3unfr1VDGiUVBhnsY9aRD5o6SJ4uDt4iS8cSknyPCUAtWmiwvEJRUlCCLqdHKVUfEnJ1kaXzWEX5yzWWKwVhFDhASnO72uDD1jvqMqa8XgeFVIYAh6pVCzbbATeeEQIJJnAKYF7wEf7Sef7dUEg4H0gBEGaxQWJ1t8QuT18qcP7U0MBOUgtkEqhlIZ2XEeGk/ik0whEciQEZK5BCoRVcTDXLzuyQJxmeR/HZSXAnVK2WWtomoqq8RhjONzf4+DoiKzXI+10KcsV8/mUYrVktVpCCKj2es9nM4rVkqauoxqyrmk+h5XLg2iamuViwXw+Yzod4/2Qre0NtFaMRiO2trYwJhJmTWOeq9rZhzgv9MdzwYC1rvXqbYliZFso8qI+UM8KgdaKJE2Q6snhbidKyQf/Jc6fQgiYxrTHTVBa4lz03P2cp4nWmjRJSFNNqhTOebz1KCFQRJX6k7DesMk70aJCiKhOb7/BmZ/R71y2qnWLsY5FUcfKBiHo9/tUlaEsG4yJuQSPq1ZYT7fW3bKAGHrIqTWI93jvsU1z/Iw1dY2WEp/EwMSiLHCuYbVaUhRLQnB0OzlSJKTaoSuDlBXOWcqqYbl0WKcpGlhWBvM1WuvoJImZRNacEViurR69dwgcIUT1bWg39Ow5mxiBqOT2zsc1ggsICVJIVPvyrQRZhBBV/1odL9XF+madAynW6yCHcxZnG5omhp7kWRYJVBc9x7MkQWuJVOqxx3wcQojq4Rh4avEuUJZx/ocQJHl6wrEJMNZRG0tZVzjvqRqDNe7cNYZcB3q2/3fsAiEFUolWpm2i/lLFdqs8BCFQaYrKO4hUIlKB0hqt4wZQp5OTFw1IiYdYWSIEfh0CGkIb5BmialtACBLpJU1dIkUgWHvcz0BsA01t2grks43at8/SRSAe+HnmWl/g/bKtXnjQDujkXBzWNDjrWy6EGNApBMHbM7sYUknSTKGUpCpLVkVBWRZkVbQU0lqTZSndTrSW8c6jkoRer4cKDiElLvg2sNkilUYGGfszrQnekaZp7H8erLrwEFzAK3FM6EspUFKgpEJL1aro47W3zuGswxiHlJIs0wihCEhckHFc0ZEfi2G8BmPAaHBOIIVkNOijhGZro2BzUNI0jqaJ+WZPOxt/oVhboRR//D/8D/zxf//fE5JL+HTE7NordP7xf4u7/xnNb/49fvFkzzhrGvZvf4xUmmSwgcxygk7pX7mOWS4op+OHenTnHJ9+/DF3b98+5Yv3dU6Q+hrAEZXokqhWFERF77e8UvYiEFLS3eij04TVfMn8cPqCKlYkkIFomU8AX0H44kN7bTPBmQUhnN+gQgBTWKoaFk2ITbEEeQBBG+x8QkihyRwhAatjAIa/RCTOF8A4fj36xC30u8Q2XHBGVDAz8J4DBMc7wrJVLNUvN4+eGsOhot+XeBPLSasGZqsLTtRTYkWBIPY1jugT/JjHR+qEzsZluv0h/+pf/yteffUV/vJP/3/85Z/9OVVVPTGo9+joCGMqqtqyKhqctU/tw3YutoCd9tyr9rs85QQ5SRKuv3aDrJNz77O7zCePVlgKYtNPhGB3+xLDwZC74yPujh9RMZbQGoASn4ukfZ2t7HuJLxijjT4//vGbeOF49/anLIqCZmxx5aNvQreT8we//xO2tjbY2dqm1+2ilEMpx+xozP3bt6nKEntkMaIB6XjcFHQ8WfAf/vTvSFPFxoYiTSWD0YAsz7l9Z8Ldu1OSnmf3zQRjPfOZOSHTA5gZuG+A611Ren75bkGeCd58U7Ozm3IwbXg58fkSIIBEknRTNrY3wQumHy4w84ZmYeI8tOZMM24WJQd/9SGqmzL8yTX0ZofG15hQ4R4xt/i2oW6igq6TBXQaw2SN8dy+fYf/8X/8f5GmCcY2Eu/8OQABAABJREFUWGO4c+c+88WS0eYmg9GQNNVknYRiVXDn9l2cdShkq7CN5QGfJ1D0cfjs00852D9gNBry67//Wy5fvsQf/9EfkaQp/+S/+C/42U9/yt27t5hOx3zyyS0+/fTWY4/X8v8XmofUNtC4QJZlDHvZsZ2c1opuNwb5TSYz6sY8eiP9awohQCUZSdZBqdUTfz9N4yZN00CcNj24WyeAaHeyvXuDbn/A+OA2s/G9z3WezjoODg5wtiGRcZOoXK2oVgWT6YLpZEbZWPwTstMG/R5XruyyMRrx2utvYI3hg48+YVE2x1YCjsg4TJZLPvz4ExpjuH3/kNpYautBSH74wx/w6qvXeO+9D/jww09a+5/HN7a10n09RZQSOnmk/xe1x55ys+p2e+xcvsqgv4EzgZAK8u4Aiec3v/0li9kRRbGirkok8OMf/jCSXiIwmc65c+/vWa2WzKY1rhTMTMPSKsrafm1IdCkl27u7dAcDxnt7zMZjQgiE4JhOjvibv/7PaCWRWELwVLXFOB9D08+Bs47xwYzVoqaclLilRSaBoMHrBN8vMcaynC6w1jHq9+h3u9R1tEHJ0gQh1hYWp85TQD/RJEriTc1i5Tg4PIREIZTirTff4mg8Ze9ezGgYbIzodFMyncT3hCnLxeTcc9ZaI1rrjNOksHeeUJ3MyZxzTA7GKK0YXdpi69pl6uWcaj7FWs94WrMqPErvkWhN0zRR1V6scP6k3WmpyXUHkLgg8MFjXEHAsrXdZWOjw5AC/AotYNgFbaFfxHadKo3WCUkqSHKBlAFfVwTrUCJDyBSHwAvBRicn15ppVbFqDPPZmKosCARcCDEUoddBKMVivI+S0d4oNA2uqbF1gwCKZbuBZR0JJz3SQ6yhEMcByN6d9TbSIlap+xD92/15738EtBL0uwkhwLI0DwlPABrjmS2a1mqttU/b3EbrlGZxhKsKnAcToNfvcOPaNkJIPvjwQ5Jbtzmclww3d/ivt3a4evMmWxt9eqMhZVkzm69wIkONhig8Vd0wmy1ZLhfUdYlOJaqOGxadVJMpSaokTV2zWi3P5ji0X9rhqURAa4+UGiH88SaHqy2maKiCwfoiboR4R7/f483v3KDb6dCIDIdE0ECwqCRDyg4iNOASbCM42GvIs8CNa2/R6XbJOh/wxrU7fHbriPffv0dpYWKfTpP2YhHpwOb1m7zyj/6QxRzGBwGVdkm2rpPUFqMzzsRnPwIhBOqyiB46SYpGRF+vNMXpRweRlKsVDxeYffG9/3qXz1n3yJ2lbyQCJwuW49jgr+50vi4QUqC0JMkSdJpQLQqq1dOWRn5xEK1U+GSCt47TWZu3fTk3OXhDeAQ7KmVUk3jnsR6Mizy4tJFM92XArExUQYr4FcK6fSbEBXfFibp53TWtvaAf+IomxM94iaeAaNUYgYeIaq0hS0VcfIToH/fkkaGFJJLp7X3FcUJCnzN6CimQWpN1+nT7Iy5d3uXatesIIRgfPXljV4gY8LRYBOraUBTPMTFxvYlTEXm4KMp6KgghyPKMvJOjTvlfn/u7tCJ4IeimGcNuj6PF/NG/vH4+DGf7+m8WL/DCI0sTLl3exAvP3dUhXntYVVGB4cO5i1ypFINBn63NDa5f3WU0GmJ9jQ01Pjhm0zFBgF6kaB+VVEr66Ol4zvGMsRwcTtFaQEjodKK611jHYrZiNl4ykIrRtRSsRFatCh2if+jjm+aXhgv3M4+A8zBbOKpGEERCmsmouHqJLwceRBAkOgEraJaGalLHPuqcOWiwnnq8QlYNnbKGvm6Vijaq5jQP2cF82+CPi6tOqquEEFRVxe3bt5BSYm2Nc5a9vSNWq5L5fEbeyen1Ogw3+qxWBXv372GtAxvfPxr1ybL0mT20n4SyKCiLgqosaOoSa2oOD99gMBiwtbXF1tYW1lZIGdjbO7jQMS9KGK6vGSKqs733BH+S1RXa67lWxAohvlk+6a19TbjAhGWtUNRaIqVqlcHxWkSVtUTIHKVS8k6fTm9IMk0/1+nJVrVb1TVFUSKCQ+Ap5guKxYJlUVE3JoYHPuG2aK3pdbt0Ox3SND2uorTuxPl63Rwaa1kuV1R1w9F4St1YjI9CwwB0Oh0AyvLsmk+2QqUH1bDrKZg887snfz+zHdEq54VUeOdx3hMthmC+mHJ0dIAzDc5Zhr0+o/6orTqxZKlBijhfb5pA4QPT0jNtXkSB12MgBDpNyfL8jEOAIGBMw3Q8jhtBwgKh9cJ+tH91CIG6aqKHc+PARTsWHwK+sbja4IzFVDXOBZRKUSqGJyI8QoqofA8P31clBVpEgtaYQNW6KnT6fYb9DcrSRFf/IEjShCTLSLUmaVXvp/Mczl4CcSYrMFbJyGNF/mmYJuY5KK3p9ntI3xCahFBaGtMQEBRFhdYSY5pj5f6ZdidkGxLcKs2CPy5rShJFlmu0VWBi202TuNxJW/myaHcvRXuusUIghkYaG//cdrUkSpIqhWoV8NaYsyJEpUDF580EIpfRNARjEMEjW88u5/25Bbbr+WEM75Sxgk2pNdF3/DtAGzIqsD4c56SdV30j2r5ybd8UQkAKgVIxj+RRPWj08D85sJAaneboNMOVOor62jYgpSTNMpxzzOZzhCzRe3uUjaUsCkLwJIlG64zgPQsJqPgek6ZY56maBmPiKwiJF6KtgBEIKciSBBECUiqiwempL9nuQsTqjbZKwHu883jp2kogh/WCxrk250aAkHS7Hfr9Po1McUiq0mMagw+SgG53t1OcC1RVrHztdAZsbmxy5fIYbwvqRcn9TBGER7jwVJP8F4pIDwH2P4V3fhFYrQzTacVyFri8dYOeDdxOdnAUPFE+eOqAdrXAVSVGCoSQeGMhrP0YXgxmyxrLZx/cIkkPWTxGDfiNheckhvqlwOexSLKEjUub6ESjsxQhoNDLr/q0jpH3+nSHQ0xVsZxO2om/gSBOrba+2pucJpobO1skSjKeTJmUFU1bzuNrEBMIFYQF0Xf6e0CXE6LVtK994BNOFtGOqFJf27u8xOdC2k/pXe3hjWdxb4GvTyQMpvCUPiBlQDxqFvEkaGCj/fO6GqbkTFinzlN6OyN6/SHf/c6PGAw2qFcFH7//AfPJ9LGHFwJ6vZQkUTSNY7WqnjshIFIikd4httVFbLvU7fe4wMdZY7n32V2UVqwWT1aHrfGgJuzcX3jQ6mi9afEtJp2+CgyGA3784x/Q6XX43g++R1kW/PKv/457t+9ycFAyHj+spmqamg8//ojx5JCdS1tcHVyhsZLGSbZ2d8m7PZbLOWk/pyhLRG9MUTTc25sxXzxZOu594P69OcYuKFbxoSsXnr0PGkIaqHsBNKQORFzPf+XoEPeuqvb1TGgr8EIeqKRj5QP2CYrGl/icaJmkMAf3a0+dNBzmR7HSYWxif7muTnqUp7WxLD7dQx5prI1kUlAevqNhEeC+OzE4/ZbC2UADpFlKf9jF+cDe3j5SCnq9rCVu4gVq6kiquKbCVovonWv88fUPIbBalVRVtLD4IiDE2tc8LtzrumJv7z6r1ZKtrW20TtA6od8fkabPFj76JNS1wTnfGrl4bGOo6xheubaS6Xa7ZFlGVVUPEahfR3jvGR9OmU+XVNWTe1JjIgf13e9+l9dee4P5ouRovMD6gHEelaRc2r1CkqSsFnOaqkKIZ38Qsyxla2tEnqWE4OMmz8ERi+WKYAwYQ20dRWNPLCJOYT1USRUJSCnBOcNkMuavfnFI0xhms/m5cyhnPXXZRJLeBdaPhAzxWQI4OhqfPd80ZXd7ByEEe0eHVKfsjwxn9/lCANN4JFEUkQiOvctnkzGmaShWK4ab23SWHVbLOUpCmmZcu3aN7Y0Rg16XsqgplgX37t3nl3//dyyWK5ZliQlwVINqoDqHYnlB9sMfieA9k/19FpMJVfusJSohTzIEHuEtQgTSTIEI1MbFDREhTqw+Twl/nHVMjw5jQKIFKRTGOWprYLoA02BcYFY2+BCQQrXilh5pmlJVJfP5FGPNmY00H2DVWEohyKxDS4FMZvgAOwh2r14hy5KWFI+bJFJKnHME49AyMOppGuspqrODnrUWIcQxab59aYfLV3aZz+bcvXOnVVaffocg1QndrEN3Q7DV6zIez5nP7mKtYzxZIqQ4Dk02D6jKlJLknYQQBK40xzYkIcBsUVC7ht3cM+wlaCnoeIHQgY0aaikx8yPmytKrUrKuZi48hyIwnix5+4M9ZmVNZg2biSJYS+ncw2GrQoBW8eEtKkJLMIu2TeA9UkKSCLyLoZRxE69tNw/87PW6bG9vYJ1nWTTY9nO9cyRao5SimyV0Uo01BlNXIKMtjQ8hVi77QJJnSK3pdXt0Oj2qYsVqPml/J/Y/7kkCXJUiexuxCqg7QmtFKTTGnTTV5bLik0/vx3EoRBupTz/6kKxzn1sff8Qrr77K1u4VtneH2KqgWs7RIXDp8iXyVDEr5tw5uAvWELyjE1I6IqExhrJcoZSiP+iTakWv04HgYzjvA5kWIsTNFFtZvPAclkeIICjKmtpCr5Ox0+ugtEKnKb1el14nRyvJfLWgNIbx+JD5fMbu9g4yXEEGj7QeRaAuA72OQiV9BsMtvvPm97h57Sobw/dI04ZbBwWHb09w5wTTPgovFJEOsBgH9j72FJVlvqpxBgbdTVR/hVI94lKm4kJEOkSDfepTlLkkfu0Xh0j3zjE9nHDCgnzLsCZcXuKJUErRG/bQaaysCD4cKxFeBOg0pTsYUgqJmE3bXfT2OftKTG4f1g9qKdkc9MgSzXi+pPQn5VRhHdZYEm1bhsCV9jDrxfZ6VroAjjhZfPv237/FC+nnCZUpujtdbG1ZHazwzQmR7kzAhIBOQT+r8EgSN0gkMSDPc4ZEB1CJJhv16I+GXLt2lUFvSFNUHC4WlMWTFrSCNNXkucaY6L/23KFBPLC+DymxTV6wT/XeM58+QlX+BDyRTH9wLvItV25+VciyLJaWbwx5TQiaumZ6/za2nrBaGcbjh99jrePw6JC6KTHWxLBAoVHC0e336fR6ZIsO89UMXaQYZylWFePpKvaNj0EU+gQW84rl6mQeZuvAsnZxmtcHoePvSnghiPR1MZIjDgfP1NW3VU0hAYOn8cSS4pf44rCWZVYQlmBxWC6+aQiADzSTRRwr1thWcEnFnZ59vvVukN5HBVzW1fT7HYqyZjyeIIQgTWXr/dtaWTgXg0LbSirrT1WgtDDGfmEkOpwondcha9YalssFIXh6vRiqrJQmy/Lop/8FYH0dVFvmH3A03pxpRmma0ul0cM59I4j0EKC4YBVttJiImRI7O5f5/ve/z8HhHJUeYZynNI40y7j5xmtkWcqn77+DqVZ8nomGUorBoEeWphACjWk4HM84HE/RRAbhSVMsufb5VbF9uTYgd//eHsYYKuPO7Sqi/7TDWn8mnDOEwGKxREpJURRn3qOVYjQYxirJ2fQMkf7QlCuAsxyTgEq0/x6gKmN1Rpp3WK6WGGuoyhWJkuxu5fQ6Ha5du8buzjb7e0fcNXsYY7n12W1WZUXTrqWKRzyy5yl4XzSEECiWZ8VpSkoyncbsPWMRkui9LMSxurw1f34orCkET1XEBO5cd9EywXtonEMWDtU0mBBYmXbDhNhukqyLUgrrLMviYbFcAJpWnBZc7DvKokIqxXAjkqVr1TmAaBXN3kaPckkgT+O2Rok/0xbDmYpy6PX7XL5yBSEl9+7dfWicE8Q2mGhNorsknQ515dpn11OUj7fmEkKgExUvnTi7qigrQ20NI6VBaWQQkcwO0NUBEQR1taJZemrZwZJS41nhGY+nfPrZHUrr0ImmqxSV93Fz6sE5V5T4x86psceS8DPKeSVQKgapBBseG2+VZykbG0MaY2ncMirC25shlYrZC2lKp5NhpUD5BiklSaZxPtBUBkIU/ukspd/vMxxssJJgiznN8UbeYy9te94KmfeRaR5fUhJQnHaDqRtDMzZoBb08ckrLWYFKFkyPDplPJmxsbdHJNFqCrSuUVvQHG3jbUJma2XJOIkARSLQgOIVtGsrVkiRJGPZ7SCnI0gRrM4wxZ4n0sCbSwVuPD+tQ8bbqI0CSaEaDLkmiybod8jwnTZJY9WZqqjJuPB2Nx+RpzubIoHxAu5g44hsHwSNkSpb36HYSJJtU1Zzp9FMqBPLd6ZMv6im8QER6TggZ9z76gOZP/2d2rl/nxne+hxAKbwTzTPFxmj3t1PcrxnrIeNKAHiCURMnMSynrSzwazjnK5RKpJU3l8NZTFy/O5ktTFswOD7BNc2qgOp4KfklnEZMXs36HtJthihXV8oQotNYxPpyQaUW3k5DmG8yWJcvzLDcaouq8S+wtBbEgpiCSRWvVbzj1+rZibXEDkWX6nITpepJPqxoTp8JF63YnXTiQDdHn8aLX3gAzYjNZ17wueMjXHoAQkN5RLxf89u//Gi00zliccxzsP95/M4RAUTQ0jaOuv5hN2zAH7nCiSG+I+8wXVKNfFN1OlxvXXiFLEjrexIlS8sUo9B7C2k/mxdn7/kqhRPRW9CFaRl0Ee/cP+Z/+3/+One0Rf/Dz79HppDj7+J0W7wOrVY0xnj//i7/hvfc/odNN6HQTyqpmvixwzmDNAvBIodB5duwH+VDK2anjLhaOQnnq1Mc+oyC2W038ewKUUVdgyvh8++foiPSsWHdrGxpeT2Hu4Fb9lM3SA1UkM/Y+88yywHz6bR44TtAZpnQHCY21VKbGG3CfjxOLeB5TkHXl5GlOwPt2PhC+dWr0NE25dvUKaZKwKhaYpqYoKuqqZlU02DDHOh87LAHLVYlAHBPja8O/tRveeZcuaRfISZKhdRoJJqXwPsTQuxBiqGTwVFWJdZadnR22t7fa0vpogXH37j2893Q6nZaIjL7E3W6XXq+D9566rk8FN4rj0M91KOY6HPKLgg8nOowHr0VVxRDHM76y31B0ez2u3bhBlmVsDIcopZjP55im4fr1m4xG2xiXsCp9tETwHmMtH73zD9RNxXx8RFWsKFbPzhaE4Gkai5QSlfRRUrYE2ole5nFd0lqUHFqLguWqJIQDnHNUjTluu6eRCEEiJf1Oh9HmNrqskOMZp0eXqqpRckVdn20HjTHsHR6AENRPaCOe6Me8Ps8HBNRAJEW7nQ6dTodenpImiq3NDt1csZjPWEwnfPjhp7z99gccHR2xaiymJf0FkOsYFFjbgDl1cCWgq15sIv08CB+QziEFqCxHaUGnnyAUeFmT1pbGxe8arDsn5DZOhqw3+OCwPs69pJKkuY4MoY3EabwfgaKsaKx/6F4rKeikOpL7SYIUgqIoaRobhyARsN5SlCVVXeMJ+OApihXONbja4K2jqUwbZhsuPGxprekNBpi6oSqKY8vNEAJH+4dUZcX25pBLWyMevMtCRHJZKUldt5ZILYw1zJYLCFHh74MntLuqoV3rjeeOYKDv4YoTGAfTOlAjaMYTQrlEd68w6gwYCugLgcgqDhDUCLp5RqY1g94AqRPuHh5RTU85QITQ+q+GhxaTm1sbDId9CI4QLNYYVosV1nnK2p3ZO+l2Ejp5wuZGn9FoyHy5oq4PqevmWOHvrY1ViN0MlWh00qPX68TqAS2wztMEFZX7WoC3zOczlssCU1eUtcV5/5g1b6scUCki7ZD3huxcewOdpjE81RlkuzG8MewxGvWo64rVYhmJ7kxHNX7TEHxgMj7i7u3PuHRpm1zCIM/YubQVx20lEUnCjddepTE1B3fvMJ+MaRqosdRVSVOsIEtxpkZISbCPmC+FeL8dAesCEkE3TdFSYpzDecfO9gavvnKDJE3odnPSNGN7e4eAYFWXOGcQAWzjMJWhKhpEAGEcMXm3ols17I0X9DaW3Li+xaVLI0pXUlQLlu4OSt3naWb1LwiRLoAOhA73P/mI+4v/lZ/+03/GH/yTn6B0D9hgLPwXVlr3xWFtuPgkhi18O5XoL/HU8N5RFsvI585q7FOUn3wZaKqK5qFSzS/zHAWQgkjI+5v0tgasxodUywXrZ9A5x3Q8JVWSrRs7dAZdjPOPJtJvEXvKYTw0B8DkS/o6XydIIqELT15pXACCOIdARHWPkCeKtcbF1zPBEonz9caIav9+zu0XtET6asnb736KqZ+udKaq1iUOXxAW7TXZ4CRRam3r8hzRybu89cZ36HW6ZLZEOMN4Nmf1RFX+c4DgpIjsW674hLgYzSTYABewZwVgf/+I/8//8h/YvbzJjasDrl7ZxLnHt+X1RlBRNPzir34JwNWrm1y+vMF4uuDu3hFZrtjd7ZHnKZcvXUFnOUKeItKjeOfMnNR7WCzbUINLRGuiwAmR3uXEasmBXfLCWGXV7etmAj/swu0G7jRwTs7ToxHiQXwNB6sXa/z+qtEZJGxe7VE0FW5VY0vw5XGl97PjeWzCrdvoaay+vbt7aZrw6o0b9HodDg7vsSqW3N+L8yhTGYrKRAVfpiDAalWeuY+K+Ljr1hHhvFucJAlJkpDnfbKsR6oVeZrgfaCuLd47alPinMVa0xLp27z11putZUu0wTg6OsQYQ7/fRWt9rHTP85zNzU1WqxWTyeSYSF8r1aUUqESTEJDqi638DHDsk/sgqqqirp9CLPA1Rq/X44233mI4HPLK9eskScLdO3dYLhZcvXqD4XAL6xRFZY+Jqdl8xt/+1TscHR0hiTY53j37JMj7gDE2qnqThCTRJ4GBXGxqG4jqcu8D1lYUTxA8JVLSVYp+3mE02gS9inlT6+OFQF3V4KMt0mkYa9kfPzmvZ31ezRPakVKKbien1+sy7HfJEs3mRodOJrn92Wcc7u/zm9/8A3/5i7/DhXCmBxQC8kSSKUEI/iEivaO/hkR6iES61opOlqMTSb+XI7XACU2SNawqS6gszodz2kf8xpFAP7keUkmSPMUbhyijCjpW60FZVVAb8GcnP0rKuLmhFYN2Y/CesTH0lhCJdGcpq4qqaQjt/8piRdNImsLgjMPaY8vuC0NpTa/fp1YVTVnh2jsfQmByOGZyOEaHG1za2nj4CghBJ09JU43z/gyRbp1lsTq/jDG4+JqawHRh2eCkImROXOqY6RRWAn1zh2GeMpCKvlTIbMVhACME3SwjyzK2L18m63SZVTWHZ4h0zr0gQsDm5pBr169gTENTlTFs19c0jaVp/Bk9f7eTsjHsMBr1GQwGNMZR1zV1XR9XKzjnCD4yyVIrsjSll+e07vlYFzdmmsbQmAbrLKvlnKK86PozGjcJ3UF0NskGW+xefQ2dJBTLGU1doFR0NRgOu9y8vsNiPmfPxmzJJNUxnFRYXAhMJ2Pu3bnFd7/zJrmEfidlZ3srVpQZg0g011+5CSJQzKdMDvcxtaN2gqYqaaoVIli8bZBSxUCBc4j0sL7fgPEeKQUbvYw80VhX47xge3PIjRvXydKEXjcnTROGw02c8+wfHVBVBdKDM46mttSVJbiYR+Cdpa6XdKqG/cmCwWzJq995hUs3bmJ9jWkK7k956k3zF4RIh+NC2eouTC129grL2RilEwhQzKY4u5YtPX4YE0KQpBlSylgWcObheLkaf4mvL4IPmNZ47useTJvKE0KouiAh9Dhc270WQ3GqQO3AmppiajDlCkEg0Yp+N6OTZdzYuUSWJFShxgbLaJCjNayKhtlpf9/Aibr3BfHpfWHhOSGjnwM/ZI1lMS/wjcNZf6bsey0k/1xOIevzFTySC3HGUcyi8sI/B3/zfr/H5uYG1lqK5QrrHGVVPxTgc1HoHqgdgdMBWxKHx9Mll0KytXuFbn8QSzqNpSyWzKZH5weXKRV3LZw/U6KqtGLQ7zPodtFGEWzDrf0D7k+OWH7RpeZrrurbXvHRwrVK9KfMwwGgrms+eP9DJkcDmrKh3+mT6Itv4hdFzXi8YFVUeBewtWcxa6gLTzATpFI0oSHpC5wJcQ34YNMWRLI8IW68pcCIE88U98DvvoCYOfi4hrF5WM33QuL0psYTuhqhQCZx41JqQfBgy/CQ7QZw1inxOdjz1YVlcVTRYGKYlCK2kdOfXfNt5a5fKDjnmS3m1KZmMltQFAX1A8GCUiqyTi8S06kl+EBZ1ljrjm9h8K1Q7KHnSGDt2magwlqH7A/I81E7XtY47/DCIB0MR0N6rkvTNNy+ffvYG3i5XGKMjRYDRRXtEmwkp5qmYbmMPt3Rfs2wWCwIIdDtdsnzjNlsTlGWLBZP8Kt6BjxsPPhofLUk+tOc6edDVVXcvX2b1cYGly5dRuq09eARVNWK6TQqu0fDrL1fK1xTEVwDfk0ccqxofRakacL29hZJoplM53G+Vn6xYjclFWmSgg8UqxJTNXTTFBkClTE474/brXsKBjSVkGmB89C06uMnvXu5mPPRe2+TZhmdLCXRik/7CYmWTI6OWMznHB4dkWiB8tEvWinFaNhHa0WKRRKQ2pAZR2U8RePwbTXpVzKstyUqnV6HLM+oy4pyWTz5fYANntIbEufRBoKXFCuHUIKiqCmNpTY+kujr3UIpUW3VprdR5RweINJ9a+PjrD/n8WrLcNsA15P3eMomEvZZ4tAqevR7oGosclWTzJYcHhyyWKzwzkfeoGlDN0OIFb4XuAlKRb/2qq44ODygquqoDA7+kb1BURTs7x8wmy3OcBQhRPsQ5zzO+mfuUdY6qLawD0NcrggLZVGyXMzp5118t3ccSOl8YFFWGOfp1jVS6wd4wUcjhFgJspgvWqumhCz19AcD6sowW82PCXgp4NLODq+/dh0fJE1jMMa2m7oihnsGSFNNohWXLl/ixo3rSCHQUlDVNYdHh1S1YVXECinr4oahtU+xRhQaZIbQGTrJcC5wdP8eSmm8sHhv6XY7pJe26Xa7Z5ZWwQea2rZLwEh6zKZT7t+7x2w6ixalISr8vff4JMF7z+bmFnVR0O3GzWqIm9tSCYbDAUmaoqRECEkn7yJlSpACqZIYKuodMgRUW0FhWsJFSUmiFQiNcHEjSQpItKLbzcmzvF1TexIZf2dzMIJdQbc3iMJ8KUmSlKYJlIuKxtV8eucOXjqu3bzM5auXWVYeKzKcTLlIAPZpvCBEeiDW9pYwn8PiPar7lxnv3UaqlGADi/27ODMlGhQ+Xp4kpaQ3GKK0ZjGdULvTC/3wxPe/xEu8qPAuUC6/GeWdHQXbWfTTq58Dkf6D7/wA4xzvHRwxWZXUi0OK8TT6bgG9LOGV3U02Nzf4+c//mLzT4e9/9bfc37/P7qUBeTbi1r0p82V1smhZq9DWkqkX3eDvq4SH5+m9VVeGw71pTLl/gDxJ2lfD5xBfr33uHwNTG2Z7s7g0ew7ryO3tTX70o9+jLEru3LpNWZTsHY6pm2cj0tMtQfcNQTWD5f0Qh9HTqj+teOW7v8e1197ErArsquTenU+ZzybnLzTTBBINdRNfLRKt2d7aZDQYIOsC01RMfvdb3rt963xC/nniOZF03xS4AOUzcgSrVcFf/MVf0eto3njzdbY2tulkFw+rns0K5vMSiP6QjfEcVXF+dV/EQKlsU9DZVtRTTz07p11rYJuT5E4NbLZ/PgI+a38v4YXta+81sNdciJd+MSCJ1/OcHIgHoRJIByATgeopvIXirj1fvaY5IbqfQz7IalpTzGpUH/R2e/zRqeMGYjXY198m+msPaw17B/soJRmPZ5Rl9dBYoJKE3mgTrSQ5Bpxlb29yTKQ7eMRueKzmbUwA4ymrBQJDlib0hyO8cwi1jFU10uKDZiMborXi/v09Pv30s+MjhRCON6rn8wUgjs+zKAqaJoaAG+NZrUoODg4pihVCREL13r37TKczDg4upvi9KE5PJV/svbjTSvwv/kwX8zn/8NvfsrW9w+tvfZ807+NbIn22OMKYJRsbI65evsx8XjA53KepFgRbgCufyxnmeYdXX72J94Ff/OJvGY8nMXDwC4TWmm6WIzzMjma44NjodGmShIP5HOc9jWlozNPNeLuJZLsjqZrAzLrjYtHHXafx4T6/+PP/ENunELGtto117Z+dKUEnkzgXsI2jkye89co1sjShXM2xTY21Dd4aDhYNReOwAVZf1VxOSYSSDHc22bq0zWT/kGpVXmj+2gRHYx25E2hvUAIqE/ACJpWjsoEgZWRSW/JYKo3uDQGBKxu8cwR7VqDiXaCubBtc+cB5SAlS8eAOtvWBeVGTqGjtkiYK4z0OWJUNRdVQGYf1lrq22MYSXKyOFRIyHTM1L8Ija61JkoTlcsmyKkBIpFJRUf2IFjSdzijLFca4MwKhEAKron7IJ/+pxSDEaaInsoEOYoirC8zmSw4P9ulv7eB6sd8IgHWeg9mKJKnpjwqUkg+HjT4Gy8USgmMwGLC1tUWaJHTzhLKs2B8X1I1rtQqCN157nX/6T/6AT2/f43dvf0Bd12gJXgliTJag283pdjJef+N1fvSTn2CamroqOTw84tbd+yyWJZPZkrppnm0DVaWg+8i0S5r3cMby2bvvIoSguzMiyRIubwwZXO4hMPhgj614nfPUayLdAcKzd/cuzjl+9o9+TlE1IBWbgz4eMC6QpCnXr18nU5L3NjbJsgxfG+q6oNvpsLVzCSklQkRDt+FwiPOSZNmlqGtc02CbmuAtwdVY384OBCSJIksTpAWnFInSSAJpotgYDuj2+ly7dhVjHJlOkQau7+xy4/J1CmNYNg1KK7qdDIrA7LNoQ/fL3/2OW/dvs3v9CpeuXGO1tFSyhxE5T7vweEGI9DXafZHgqYoFR3v3kDIh+EAxGeM7m8jRdXwxA7OuX384eTcgsNYQgn9mpd8zQ0pEGm9EaJqHgide4iW++bjYXrML0Z7D+yhK/LwT4NliFv0SqwJvKvyplHMBWO8pqoa0rFksFzjv6fZ6XLp0Ca08SnmyrDr//Nf7b4bnxpyoZB0UrhBC4l3sr7wH91ReAV8xnkLt+FTwEOpw7kJ7vYMuAS3jwviLumTPkyi21lEUFVVZUdU1tTEXrywRArSOk3UsCB8nMjW4ikgutRUBUiu6wyFZp0ve7aG0pglQG4N9zAxaKo3UGd64M5d87UVrTYOvi1iuZ5oT9c1LfOnIs5SdrVFrOSAIBJq6OaNWq6qGZVGRaEWvl5NoQaIdAcF8XmPd4olBUA/iwedh/dcQourKGYmo4yLx/AMQp27r+Jh1FeW6Xvd0yecLWjz4tTP0WPfNF3hcQwBvABltN8TjNo/XF0IQrc88x5t56TBFpQqzMtgH0+cetYoOJ5/vq5NS3zNE+ssu54WA94GmqZFS4pw9d5wUQqCUQmlFIgEnkVI+fLCHEABxUh7ho5q9bmpmsyk+OKqqjKXaTYkPUdGmlKRpmkcqdtcKS+cc3gfyPKfX62BtDANPEk1RRKsYpTRJoplOZyyXK5rm27ebK4Qgy3O00tR1hXlKEvdZ4Z2jrir27t1rw+OOqJsSQZ80CUyncdPDWkcIjjRNuLp7hV63y9H46HMHsnrvqRvTKkGjIlQriVQCZ6Py+DTWQ9izjAnHBrDe0ViDC+AQMWjPGqx3DwcjPgWUkiRpgvEOL9yFKqhCCDj7eKJRITBSooSk00kYDgdcv3aVPM+4f8exXPpohdE4nPPHywQhxVezP97OT6w1VE2FsfaRZPCDkIAWAn1qU0FI2W4unPjdi+gJhZAaIRXBGkAQvIXgke1VWNut+BCzHlwIDxOm7fk+ikn1IVAbiw8n7TG0/+esazMf3Em/3IrepVRoLVpVuD953zlQbSimyhJ01i5YhcInjgSBs5ayKHCnbFripqRFCs1g0ME7T1mWx2uF9WcJoNfr0Ov3qOuG2SzaryY6XiVj/bltda0FeLBANQDLouFgWtBlzoZMmM5jcPT63533rMoSIXiq/txaR1U1KFWRJEukCGii2rzf7ZC2CncBOGeYzecURRHV5C7eAwFkiY6OGYlGa0W/12Nra4tiVWCdR0iNMT56oxPb0zOtP4OL1TlW4upFXM/ZFVJpOvkOnV6Hbm7oJA7vBN4F8ixjNBpR1wZnlxDiM+tDoKpKFos5i8WC5WJJmufkvQSHwBHQWtMfDGjKsg2NdRgbX5kPse+UClBIqegNNlAqY2Mrtv/VcslqMceaiqpc0JiGsojVG+uQ3DRN2zzfOMY3dUNdVWitscbEqg4fWn90g/cNQQqyNIbuJpkitYok03gctalZLFfcubvH+x98ggjREqxuznkWn4AXjEg/weH9u1R/+R9Bagjgg8de+UdkWz+k+eC3uKP7wCFnzIrbTswLWC3mx+/7MiHSHL17EwC7d4tQXax06CVe4puBiycDljYS6SnR4vnzTq5+885vCCGGD/kQWg+yCA+sKsPH98b0pgVS/Y6N4YDv/uC7/HDnR8xmE5aLOYdThxB3Hh681kUzjnO9tJ8WUkE+gCQVDLodsiSlXDXURUNVO5arrxFNo4i2DOswtudFfBmiCR48RJ6sA5+0EuRaYXygMO5F5NzOYD5f8vHHn1HXNUdHY5yLJecXgpSwMYJUA1OgoraB5n4gTIE9jvmHpJfzxj/6KcOtbfr5CIBFseLg4IDFYn7+5EwIsrRP0htQ20B9ajHqnGG1mBBsyXK8H8O8iosrmV/i+ePypU3+m3/5R3Q7OU5KnA/s399ntTwpC/nszj5vv/cpg36HH37/FbSWUYVpDB98csRicYfGPMe+JkAz9zQLHu1rbYk5E6eTBjeJz/uC2IecttF6SZx+flxAib6GayKRrbuQDJ/wy4Z4P4fAm+3nfAiykWx+b4ve1T7jt4+Yvnd6ns6JyPURTc9X0Ow/5ru8xFcO7x3L5QIhwDwiq0dKSZKmpGlCJ4XgHEpPzv3ds2hpkiwD3YHaQlMxmRyxXM4hhJZcDGdJLB5teyGlpN/vopRkuSxoGsPly7u8/vqrVFXNchntae7cuYcxFqVunyLdn78o60Ei6EWEVJIrVy7T7/e5d/ceR0fPV5X/OKyWC/78P/47pFLIrkamit978ybd/Crvf3qf3/32I3a2d/j5z/8x25sb/O/+5F9gjOXf/vt/y4cff/S5PrtpGvb2D6IlhjEIAb1eSp4nLJcxTHcNRXQqg5MlwtMgod1Dbhpm1hAQuPXgJyUh8FjxwxOPn6X0h32srDFLgw0XD5d8HBobMM4x6qVc2d3m2tUr/Ms/+ed0ux3+43/8d9y69RmTyZKDo4LaBxQxKDNNv6K0UecJ3jOfTSlsgV01F34AMyEZKo0UUc0tlUTlCUEIZF2A8UgpkVojVIJMcoK1mGIR16Htw57IBCkzbDC4EO1ZqsadP6R5H/3RHzGRcj4wWRbQbrrAieWlMw3zebRyOe63PIgAaZLS6aYEV+Gq+ngtdR6SJKHT6bC1s8XWpe02LDd6uSMEpmn4+P0PWc7nx++Jn+nY2Bjyyo1XqOuajz/9OHqEt1j3fTduXOMnP/497ty9z1/99a8gOLZGGVIIjmY19TlBWI74nK2Pc3zMALf2l9w9XHG/N2Z/+AmHZQwiPjk3z529faSQT/VMlWVNVTXM5yv29w9JJHTTSD6/du0aaZpSLhcY07CYjvm7v/8VVd20lmE1wTmkCGyMuiRak6QZSZJw9epVvv/d73Nv74BF6fBizmJlWRXREiWTqt2Ieson1pXga5wVVIVsbYU8Sa/PjaubbGxtk4cl2tc01YqmkvQ7Pa7u7rJcLvno/Q/id3Ye7wLj8RHL1YLbn33Kp598yuVL21wa9XFSYI0g5CnXbt5kOByQd3KqakVZlpRFTZJkJEq2gsGMNOvw5ne/z2hzm83tS/QHI27fusUnn3zMYj5l7/4tFssFq8/iRoREItEM+z06nZxUJUwnE2xTo0SgX5QM+xtxz6lxKA9Hh0fM5nM2r2xz+eYuUmuSPEEnjs3LQ8qyYFmVLMuCf/+nv+A3v/uY1195le9957tMJvapbZNfWCLd1gXF+D4yyVF5HyE1Ku0jtYfeFq6yuGaFM+dMykK4sP/R54ZSiDb9NgAizRBpdtzRvMRLfJsgpUbpDO/tmV3q8+CJj8madv+8T0tVP97D0IeAt46qMcwXi1iF5+NuKkLiEAilyfIU24ZRnYHjuSrS1zgRHbzoy6qvAI+41oGzC1EhBDpRx2WnIXx1GQLxXBKklGitj3fQjYkKp6IoMcYcK50uBglKo7sdRCchhJKAw3uHL3zc3LHEYNZckXRSBptbjLYvkascLaKRcVWuaJo2j+Q8hADOI4SMtmrBE7zFOcd8ucCahqauMG3F15fnnPoSD0JJQZ4qup2UpNeNvn7esurmx+1rc3PE5cs7bGz02d29hJSCw6MJsqpJdIVUDmmfj75aSgmi9VV8UrNeK8/XivSKE8PLBx/uLxiJgtM5gs7D89xb+NphLYTz0S/zieqc9eCdifjGtrwsG2T0NrvUmxVms8HWhvoUAXWmkukcZfrXS/L/7cR6/Fq3ESkVUqm2ui6qyJ2xWAEGQXiculaIqFprfW0DEpFkoDOC0QREDBetn65hrFXxWivSNEUpiZTruWIMg4zFXgopRDv3M5gvWIAePdzF8ZzlRYRAoFR8iS82a/UheO8pVisQoEOGckn0CJexEmI6nZHolGK5REvJ5e0dANI0/dyf7dp52mlbIClES6RGz17vA857hIBESYQQWOI6Yu1rfB5OF+Octrk4ViiHdeisOO4eP89Y6H20X3DrEN21RQufL98j0NIcUpLnKVmWtM9WzCYQxGtnbPTvlkSf6CRRx5teXzpatXaoG/xj1qdKK6SMqu3jPq69Y+tHVWsNUiLbB0NIgVLRjkUIRaANlDzdDkKIc3QRbS5EiH7Q5xbehbMTISEEUsU1zprfikr0cDyUShEDXaU8Uc4rLfE+HFcIet+ujcLj19xCCKSUKBX7dKVUXK9Lhw/i1Nzg4UYU17TxegkhUFIhpXzomYh931rR3x7nAmTAo5qtsT7qMaRjLGF5ToaNtQ8G8TwZ6z5aiPicB0JsR4mn08no5DmuKdu1kqUsS6rGtFUBsepYEKvPU33S/rXS5HlOmmboJEUqjXOxgiMGXop2k/r8b6zWE9e2DXp36loGh0AeBxYHPAKHkgGtoJd26OicqlDUiUJrSZbq+H4hT5xsBXhnMQaK1YrZbMqg3wERjnNIhBQkaULabhBoHZX3zsc+IHgPEpJUkyYJSRotgwaDAZtbm5RFwWq1QivJfHZEYxqEFGAD1jmMte01FDhnqSuHkoKiWCGlYLlcQBBYY9pMAN8+J5amrknwJEIhJaSZxroU08RKivmyAGYM+wsmkxXLZf3U4/ELS6S72W3qak5n5zW2fvJ/QKU9nA0EH8h++EdIazl4/99w9MndkzeF8PQxxJ8TycYlkq1dPB4XXJSapilY85JIf4lvHQajDTYvX6Nczjm49xneP/p5XA+dNTDmyxMpmMZw584e4/GM7d1LNNZwNJsyXS5xSvDm915nOV9y69O7J5sBgRPrjOfQxXgH5RxqGShliRRVO9nxX78QWceJEv1LOvU1B2ddoPGOrJOws9EHAqZucM6zXNWPVMp9kUjSlGuvvUq33+fa1av0+z0+ev9Dbn32GbaumU6nT6dwExpUF93tsvudN8g2uxT2LsbNKT4YU34yO/Yn1t2E3isbbGzv8NYPf8zlyzfoJimJkBzd+YzDOx/hjDl/ohAC9WJMs5qT5EN6269iqgXVYo/ZfM5f/PVf08ky3rq5SzdLSYhKrIaXFuZfBcqq4Nbdj9ne3uTHN3/OYDji9TdeIyD47NPb3Lt7n5tvvMV/899eJs8ztrdGVGXJr375G+azOVevvooxjrff+YAPPvrkc52LlJLusItSimJZYOoLtoi13ciUaHgZOPHy/hIalRBwdVOwNTgZfaarwGcH4esRIvoFwvtAU7cLknWh2SPWdEIKZJqABLdjkE6ye2OHa69c5ebOVezPLXfevsvbf/puDG12ROuYLK4BbQ3hZXTR1wohwFrstx5OeqMdeoMtisWE+WSPpiw5vHU7kkxtZl5dnV/Sp5OU/mgTlMaiCUKhOiNkklMeWOpqrfd9uoaSpimXLm2RpgndbhchBGXZUJZ1638+pdPJGfYH1FXzuWw0ngbdbk6aJlRVTVk+hzLHLwAheKrVDBkqbPMVBRMEcEVDqF1U1Hbz1mYCptMJf/2Lv+DSpUu89fprjDY2yPPsc39kVVbcvnUHAtR1VC5bazFNoNdJ2Rj2WBU1R9MlidZsbvRIdELIB3ih2NvfY758OIBHEIe39msd/7SAjloJvGufKxE3y6EVJT9ls1wT9NNVRVEbJJ5eEo7FDyFEovFpMgzPQ5podjb75Am8886vEUJwuH+PqljQGHM8jCdAniZsbW20dnRfDXzlCE0M4DwPUgq2dkd0+zmT/TnzyZI6eKwLx3PeTKbsDEboNOFwUUPZkCUJebeDcYLStH7oD5K4wSCCpd/p0stzyqZivlo+4tae9blLO10GG1vYpmE+OTwjFs0kJAJUKpGJQieaNM/wQGYd1npW8wprHKtVTVU2iNZu53HrbqUUSZLQNA1Hk0m0OjKOpm6YHB5hmobqETZKxbLk1ie3EFLQyTtkWRaDn+3JxO7WrbuMJzPqumkJ7sB0HvtCs26Yx95HXFjENmmgcFEUcZF53KP28x/ExsaIq1d3cU3FcnJAN8/Y3tygk3dYzGZUxiC9RwPLZcGtO/s452gaR6oVmUrIdUJtHcY27SauJss7bGxscXhwSGNKrK3Y3t5Aa4VzE6x9+BoniWYw6MbNFdUhIFhOFzRVxbrtJN0+na3LuKZhdXSA94JiNibX8PM/+hlvvnaDYrmgWC6idWdTcev2PX7xN79jXsT2kSmiVYwL7N27xa9/+dcY80O+83vfRUrdBqkSsxSUYnt7m1deeYU7d+4wXywwxrCazen3B1y7eoMs70YPfR/YvX6NTq/Da2++zo2bN/ns008oi2XcoJIa6ysOpnOmaolzDa7pgoiLhjxPWK4mdPIOq+UKgWL//j7LeUGWddjpdFiWc97+zW8Zbg155a3reKA77KOyjLLxGFtT1IEQLO9/eJ+9+xWH471ja52L4isk0p8QNWBKvCmhv0GiFEqnKDwEQbc7RAvJ9O4o+ucFz4OBDF8WVJaTjjZx3hJM3e4ev7gKg5d4iS8SSifkeRfb1BfeSLqgdetzgw+Boqpx3rNYLOktFsyXKxZtgnu328E05mHVxHM+UW/XFiVfc+ndV6QeXE98fAhkQtLppFHtQZzsyS95I1NKSZIkZN0O/dGQ/nDI5vYWg8GAvXv3o5db09A0T+szKkAmSJ2SdjtkvS7WdgneIEVyxk5HSInuJOhOSt7t0en16ac5qYpqvJge8+hG7K0BDGlnA50N8C6aJRvrOJpM6eYZ5to2yFh+GaNjngECRKuieGSIl1h7BHLC1LzEMay1LJYL8k6CEIEkVXT7Q5I0YzKdMZ5M2bm0zZtvvdWSSB1Wq4Jbn91DCo0xBuccn3bzz38yIqq4lH5Ktdn6tpr2pTgJGf2Cyx2SRJNoycZQcXlDtkogi7EBIZ5D+vXXHCGAt6dsAB57WwUitEypjtbWWmsynZINUxjC5O7k7JQgCp/ii2/95f5a4sFuWeuUrNOnqQsQAu/PWoQ9DlJKdJoiVAIiJQiFzjtIndEoTRxpnn4CJmX0V12/QBz7tFdVRV1XONsnT7OoeG5bohQxICB4/0xt80n+tkpJkkS/0N7rofWUNvWXWOV93nm0dhH4EJWKUiBlzEI7OjpEa4X39pjQ+bzw3lMW5QP/LQpdVCbp5imNiWy3kAKdRIWlzPO4AaTU+Qfm/KGtFaBHYWk4cTZ7Hn2isZ7GejIFg/Tk8vgHFMnP+nlRcSwJ3jOZHBFCoCoLnDXRckRESxEBqNYf+qsk0vHhkSQ6AEKQpglZJ0OnCtEOa3Ydxkhccyit0UlU4QvRPs9a49rjn/fsr73RpRSkOqGx5jEE7tn/qpQizTvHlSzxVKPyXEtIZGs5oyWqJdM94FpZ8fq+OxuDSTUXJwCttfiqinMC52nqmuVigX1M2Y61lmJVoBJNf9CNyxglEW59LoKyqinKs5XkTSt+OtPTP2XjND6+IKr0o9f3+b8rTv180kekacJoNKApFc1ySpKkdPIOnU6O0vGZX6vXjbEURdUq2eNYsv4fbfuwxtLUMVA0zzKyLCXRGtO2rUSrR2aKrKuolE4IOsGHtQ+5bNXiAakTdNqBIBAi2kSZqqQpC9IkYTgckipFlmhMU1MWkizLCUITUGgZkCIQ2tDWsiw4OjpgOp2wKFYkmcOrBNrvKKWkNxiwtbXNbDaPFWBStWsNi1ISrSXGNLgg4k9no4VQ3mUwHNDt9eisuiRpimo0jbVYE6iqmiqRbQWDwzmDUvEa5kl+vKaBgE4SRKKYrwKr5QqdKeq6ig4iQiKUihVuAYwNSBzOFpSFZ7Fcxrb3FCVYXw2RLjNINwEP9QTCox/GerXi/ofvkXY32bxyM3Yk7S5tsvE6+c1/jlvewRy9x1dhnnhl9yqv/eQfsX/3Nu//7pe4poamBGcIT7CaeImX+KZhuZhgP2uwpjnjUf4iYc371s7xya27HE1nzJcrlsXJ82oa80Rrmpd4cZBmCduXNiEEZlLR1Ib5suHLYPil1qgk4fqNG/zzf/EvSNKE+WpBVdd8+O57zCcTptMpy/miXag/7QdEY0ZrPQfv3ELlGttrcKnFLs4SfrYyLD+ZYA4sf578GYOtS7zx2nfZ2brM9d/7Kf/d9Zt89Lvf8Ff/7n99bJhUmnbp97cpvKQQFTqBzY2EXjdlY2NEv5tCoil5tivc6ffZuLqLbRrGd+7jzpmQd/p9uqMhTVmyHE9fbk4/gMWy5nfv7tO7M2NS1ow2Bvz4J7/P5d0roGH76hZblzbY3ByQJCl53qXXG/BP/8t/xmq14j/92Z/z8SefPLSQeRZ47ykWRSxvf9AS66kOxFl7ly8IvV6Hf/1f/TE3b+7y2u6IS6Mu7//D27z9q19TmgIhZl/sCXwNEAzY9jKEtaXZg5ek3fgIjce92z7DdcAGxwd/9iF3f3nv+F4uj5ZnA2hDVKIL2v2954mEszKzx+8fvsRzgpKKRCZo3UXmmwTXEJp1qclZrAkMGd0QUNJjmwqhHDKLYX2ZiGGG1efwFWkaE31tE81oNEBKdbyZvR5SirJi/+AwBv85j9aaq5d2yNKUvcMjFqvVYz7hBLIleNM0I8tymqZh9Yj3Orf2E37mr/aFI/jAbF6yVJKm+epLRharinsHM6x3XL66QV3WzMcrJvM5/+bf/Rs6nQ537tz+Qj67bhzGetKstXhoJQS1sdybLJFSInRBQFAUUZSjdQxrdM5hjCUQhzcpotJTitj3BQ9aS5JM4RqPaEMi137/zzL3CQ/8NB4WzVkJ4zqep59CnkBhoGh/R3Oiln8cFosV77z7KXmq2d7IUUpQFiuMMXR14HJfUTaeog5UteHu3viFrtYPITBfLqh9RRCewWYPvCNYi/ABZxyNcMyWE3St0UlgNMpJsmjnKFVA6YAP8pFDTlGXGGex1l54liFVVC4DCJ2ghGQ06JImmlxFIr02UaijRBT1GGtpqiqG8j7Q0ay17o/6/BACi8WCqqpAita+I6PTG2Cse2KbDHga35AG0IlGKcXmaAPnHVmuSRLFbLZkPJ6ded9D12w9fq+r4p5Cmd5LBbt9hfOB+wtHc44+4sHn5HHodrvs7l7BNCVZIul1Orz2xhv0ul2MdfQHA6bLJYvFnKqKCqcsS9naGiKAyaKAEBht9Ol2c9559zfMFmOu33iV7/3gJ8jvvsn/6b/7vzIej/mbv/1bDiYTjLMoFS15Tl9yax3z+SoS5HIFQdDUcc3bGW6Qj0bYpmF5cBdvLd7WWAf37txifLjHv3EVv/y7v2F3e4vdrS2K1Yrx+IhVUfL911+LuSHzOcYYGudw3lMvltz64F2O9u7w61/9FbtXr/En//t/zWhjk+HwMv3eNv/yv/qv+eFPfsJ7v/st7/z6l0z27vHZu79lPJ/z3gfvkGUZW5euknV6/Of/9G/5z3+hyPIeWbdPr9/nrZ/8kCuLm5BJJpMxb//D75hOJxwsS2Z1jJkNITDqZSRpAhiqsiTLMr77vVdIsw4f3z9gbzxj88ouW9eusCxW/MM/3ME6h3XRzq0oa4x1NBkkSYlWHdKkT209qruLeIp8za+ISNeQDKKKvJk9tgXbpmZxuE/at2xevoaSitb9CdnZRm++SfAGxu9/+eseIRgOR1y/egMznxOKEl+toJjx6KStl3iJby6aqqSpvqIS0KeAJy4SxpMpy+WSVVFRVk+rFH6JFwVaa7q9DgSoioYQxCN38p831kq6rZ0dfv6Hf4jSinfe+QeOxkcc7u/x2ee0zQBARX/B5f1JXH1dSqAnoT476AXjqcclzdLzUecDstERWb4BqsfVKzf43k9+ijOWv/kP/9/HEulKJWRZj0YbEAOUgn4/p99N6HY65JkiKPnM7hs6zxhe2qYuSqb393HnHCjJM3obQ4QULCezl6r0B1A3jnv7C5LpkjosGA477OxeI+/1QEJv0KXb79Dt5mgd/QulVGxublPXNX/5i79iuVrRPA8z4MDF7VyecJwvo7olTRN+8MM3+dGP3uKVy5fZHg5xleWz9z8mSwOCOd92Ih0P/klD+Tow1kHYPyllDwQOlocnMq/Tr1MI9gu4yusF92krmqdYfL/Es0IgiJ7BQqUI3QUEQawe2XcLWl9fBUJ4vDUtiRfi/rEALSSP1vc+Gc45lsvVsU96LJc/GftCAPNAJo6Sko3hkF4nZ7pYPAWRDkpFRWunG0mvoijOV6eGgHMvdvVyAKr6qyfQIfYpVWOYLwts8AxGOVLCdFJQlBVvv/sOSkoWqy9GwGZdALf2mhbHZLDzgfmxNc/ZDjPm5MTWu25f625IyJjNIWKhPUpKlNZI56CtiHpktd4Fcbpl+QCPihdINXSTaPMS4ytPOMsTc5HzUdWGvb0JWSLBZKSJPP7wVAZUJqKlRB0w1lEtyhd6ZA0hUNc1NhiyJCXvJmAF3gS89RjvcMJT1iXaSZSCPE8QbTWelAIhfasaP3/DoLGW5ilFNbEtJVhtES0P1u126GQJmYqq9LAMsZoaiZIKJxzOWqxxBKJHfjgeo5881arr+kxIaN7tkWRdnHtyu4zae0fAH+dFKRWvR7efkedJuzk3e9KBIp7kQ3MOEiXY6kqMCxytPMadH7R70faYJCmDwQBrUpyp6Xe7bG3v0Ot22do+xAfPqq1ysq2FjdaK4bCHd5570wW2MYy2+iSpZm/vLnt7d5FK8vt/8I9J0x1++rOfcX9vj7/95a9ZlTXCOVpB+5lh1PtA9QjOQucZnY0tismYZrnXiho9PsB8OomCl6bi009zfvDWm2RvwmKxYG9vDyEEu9ubOOfZE5KyrKiaBuMctq4ZH+xz585nzP52zutvvcEPfvQdtPRsb+3S6/f4zu/9gFffeIt+t0smAh+8k/DRu7+hqmvMQUWaJuTdLgLHnb0DJrMFeW9Ap7/Bm9/5Ht//6U8Zbm1EL/bhkI8+/gg/nbBsDJyq3hJKYqxHG4cxDWmiubS7xWhjk/2iwE2n9AZDNjc3uH3nHvd++x5NY44rqI+nhV5gnEPrgPEKh0RkA+RTdFJfMpHe1i4FB/URcaR4QmfiDKzGMcTKLXFBURQeazyLg1s0ex/i7QIGV8FWUI6/cBJbCMngyk2y4SaLxYJf/+n/xvToAF/MwTYvF/wv8RJfA8QJk8Fah3mpPv96oSVKNjb67GyPIMCtT/fwPmCsb3eeLzAOrP33nsGDco1r12/y/Z/8jM2tTfb292mainfffZfJeExlDFm/h62bc1XXF4L30JbKQyyV5UjCXMLyUYtGgQ1dZOiTdLfobezQ7ffJsySGPT3hI8tiyfjgHnVlCDLBesN4cshyGbDNhERLxtP5Iz8bJEJquv0RSmvK1RRTF8Qbp+nmI67t3mQ1n7GnP8bwsEdsnuVsjraQNjBuSwtf4mE4F5hNLFVR8Vd/+Tvee+ckN+ZnP/0xl7cuM58veOftdwgBLu9eIwR4++13+fjjW8xm7X3UxKBIRzS+/wZfcCklG4MROxvb5Fm0trly8yZ/8F/+M5Lffszfv3dIcP4p3Zi/5siJ978hBr+exqOcGNdt5Un952kSXYHsgk4lWxt9tFKM768o5s9pI1sCfeJ3mRO/y+ddEsTcr/iMSKJMc62SWzuOfKsaSySN8zyGr1WVx7nAarXAh30qY2KYn3vYJ3g96Ia20Tgfl4HCOZwrQTbIxqJ0SlflZDpBCQWiA6HmWcMTvPesVgVSymNSM0li+Jm1jrppooe1EKRCxOffusfbQDyAdXB8kqR0u73456KItlEPEGfr6jT3FVqmfK0QoGgs41VNOSuZ7y8wtWmDEwNVFe0YL0LyPS2EgEGvR5amBDwH4zlV3TxxE8R7j7WGLE3Z2hhijGE8nUd7DBeJ61xDIgVpnpLlHZI8Ix/0aJqa2eQe7jyVwdOef/vzUWdbm0joexf9tkOIgacXKQzTCnoZ5Cl0OppUK7RKEUJyb7xkXBhqE6j5XNPsLw8BXO2jrZlsMLJVc7tWhR0iiVnUBmUEx92ccHhqrA9YC8G5C3NRghjIGftDd+41qsuCo727WGtwpoHgmc8WFFqRJ/E+dDo5u6NdrLPUtobg6HVzQgjRBhKYzQrKsjnuq54G1jQsZ1O8dxfOeLLOMpvP2rDSuMEw2HiF0dYO03kF7LW/uaY4H3Hc9Wb4U5xzZQJ35i5ajD2CRH8QWmu2tzdRSjIeT6lOZXocHh7xy1/+Gu88VVmQ53HS1Ot2aZoC60EmGZ1eH5nkdHoDdKLo9BKkkIxGbxBCYFksmBYLNje32djYRGYpR7MjlquCDz76mIODI4rlDG8ahI9j6NPcq3q5AO4inGc42MBaQ1HMIQQSlaOVZtDbYDDoMegP6XRyVqslVVXEzcq7+/gQSKWKdqFJglAqVp4WKypTU9cl8/mUjz/8gLqquXrldZLBBk5J0JqtrS1uvv4GxXLB5vZlTF2hRNxUKYqSpjE4Y0i1oixWTBZLdJby7jtvY63l3t595vMZPgSSJMG52OZSCZkSpMGxWizxJmXYzcmyjLzTZTAYRqW68PhgsbYmOBOLFKWg28mQUtAYG8OiExW/m22omjEehQtPF1j95RLpQkTzxOCgGV/sPc7AagpS4N0C5zWzVUNZWJrDW5j770GvBxtXoVlA9cWrwYWUDK68wuj66xy89ys+/ru/4GswPLzES7zEA6hfYH/Kl3gMNJDAxuU+b37nGgf3p/zmbz/GuUA26MUd9yctqNaym9OqyWfA1es3+C//5F9RNyX373/GdDLm3XffYzadApD1e1F99qxEenDQFO1fWn3QE0RXAYn1HWTok3Y26W1s0+ml5Jkm0U/W95WrBWV9H0hB9LDeMJ0eQWjY37/ISWukzOiOLpHmHaxtWiJdAxmdzohrl28wS+Kk7jzkWYfN0Ra2rJ/Od/tbBu9gPo3hQgd7/3Dm3xQJf/yHv8+tW5/yv/zP/xPWeb73/R+RpjnvvPMen3xyqgxeE5O01n7l3+ApjZSSUT8S6amKD/+VmzcYXtph6VLy/+U/Y2rzRDXeNwoZMABWXJxI90Qi/XF4oG8VGuQQ0q5k9/UReZpQrczzJ9I7xO/yeQnu9Tgh22Mq4vVpOFG+2+fwOV8zSCnodhVKCayN6upitaQoLdGnVT1iLSZZkyYB13rxA3gw64a3QimNHG6R0Y1Euszb+vZnO98QAkVxtmGnSUKv26Gqa+qmQQLZmkj3LZH+lGxTCKCThG6vh/MenSTRL96dPZZzZ+07XuLJKBqLLBqKacFsf3GGXaqeR0XUIyAQDPs9hv0+++MJh+PJhd7nvcM5T5b2uHZ5m6Ksmc1XuJZIF0A3FWSZJMsTsryDTDeRnZsUxYzl/OBzE+kXiRtpDHgTN8cyGUn08oIxIVpHCiZPBN1ck+iEPOshhObTccV+8fUrBXJ1HLQsHoQ5vhBCgEra3qs2x7Zk3oN1De4ZyOl4XEEi47zcB39un1OXBXVZnPlv88USgDyDREN/1Ofy1cssFjOKvTkB6HVjJWKv10EIibWWum4esgq5CKwxWPMEBfkDcM4xX5yIbqSUvJpkDDcvk++3Nj8BLkSkP+X5VjZwb/50/WuSaHZ3d0jTJKqxHyDSDw+PWE8K0jRlsajpdnOuXb9Ef9BFJDGfqjeQKCUJeGyoSZOU69euo5Ti7379a/bvH7J15QqjS5eQWcJ4PmY8HvPe+28zGU8oF3OCaZ5puGtWC5rVgl5vyNbWLk1TUVUrgvdkqhOV9d0NNoZD+r1B9HhXgqoqmC5WfHj7gIDg9WvX6HU6aCGQIWCDpSxr6pZIX8xnfPzRh5im4Q9+/5+SCPBKITRsbm7hX3ud2WTMxtYlmqpEYAneUaxWOBsrKxIlmS7n3B9PQEo2ti8TQmA8PqQsS0LwJEkSQ++9J1WCQSJQwVEslgSb0WwOcd6Rdzr0hyPSNBpTed8S6d6QEEiUZKOXoZSM1i7O4ZXCS0nV1FTFWuyV8zTlD18ukR6eYUspWPAzXGNZ3X2bejqgDhuYkOG9AUQ0WiyOoiL9OZDoUsXAm+Cjr9BDp0SgKivkfEldW6Ih4zph+SVe4puJdXmRlPI4tKmu6y9EAfK1x1q59rJb+ELR6fbYvnyFqhQI8QkhOGwbFOt9W4L7qPFmPRx9zlQnIRVSJ/i6YlXUrFYVdVHSnApZ8636LMkyOr0e1hjK5fJCi3ShEpJ8RBACa6tYpmfrNfvwyO8WHGCjxY3SOnopJim9Xp+N7V2K1YLVYnZ++XAw4JaobIO0NyQYQT0TF5x4S1ApQSVY58AYdJKT9zawJt4f01TMpwuW8wL/iP5jtViwd+c2y8USleQgDO5lxdeFoDUkCRwc3OdP//Qvmc8noAJKwni+j5CKKith+9R7BhnZTh+3MNTF8nOXlT8zvoTkyaqq+cVf/YrDowmvXN9me2vAslqxKFfc3t+j8f7b120bYl0/wAaRGF7x7PfiEeajwYEvwPrAfLaiSnUb3Pec4Inn3XAh8bKUcV7zSGLhtL9BQxzX157xp//9W4IkWZMyMYiyaTzOraX5xAsp1mEHHlSC0gkb25dRSjM72qMuntywvPcslzO89zjn6HRyrGkw9dkOQusY9uedP1ZZPurID+4HWeuo6uZYoR4AEwLBOaaLBTpJqJ8iHDy0atWyLJlMJlRlGYP63MOE/JoYeJGtXV40mFVJcTihWZVf7jxACNIkp9vpsTEKCKWpqorFYhHDNltxgms3XmIApSAEH0NKvcd5jw8OKcKZ2FwfwLvoH165ApXmpK6mrgNCDpBK410BuON9u6dZVvRSzTDXx240QgqyJFI/ZVFgjYuPq4/dWxMdbAi0e4cqPjelj//9QVgHq+r/z95/PVmWZemd2G/vfeSVrkNHysos0SW6u1qiIXoAcMZosDG+EXwY0mxeaDQa/xc+kg98IY3kUJiNgQBhA8AwnEE3prunq7u6RFZmZWboCJfX3a88ais+7HvdPSI8ItxDZUZWfGY3PNz9+rlH7r32t771LTDGE8uGSFq88jihKMoGBaz026yv9VBSECk5t+f9+iLLIlQUKleMnjdMBRCBYnKEZIPgeN54HoX3At577CLe8ucPgRa5uMmkYG9vQFGUTGcaISBJw/0Y7LPkPDn4+L6G5pXhQI0VeDc/2HMGAL1+n/7yCuVsxv5g79TxTdcNVVFgmkU8v6i2FUgV1MDO6q8k1rfWcng4Ioqio14aj0IISaxSlFDMihmNrhHSkQ1TqqqkroP4RymBigR5O1QBF0WBVBFSKlqtNktLS1zY2GB5aYk8TWnnOSv9Pt5Youj89Gwcqbl9Tqj6UlJi6hqjw3n23qOdBgNNU1PPkwRpliKlpK5rmqbBzc+7sQ5jgyVMsNZqKJoC4wzaGuqyZjQc0m53GY9nTKYlSkWoSJFlKZ1OhyzL8AKMc+i6CuS2NjjniKVCKXmU0PbOsbuzjbOO4eiApq6pqgprLXGkSGLJar/LpZUldF1SjA+J4hiPwHpPVVUUxYxISXqdFrFSCOdIlGS538YD3U57viaOMdbROIF2YORiTFrELl9XIv0sVi6PoQa3g50JBp/cCQv0K78H3ctga0BCPZt7rb+cwiGVtIjzPqae0pjHa5y9g/F4wowBZmZAtMA3HK9C3uItvnkIE21YSC0t9RFCMhgcYO3jyabfaAhCQjOG5+7G+BZPx1yd0Fta4fq3PqbREVL9HF9rdLEgsBe+LYs/OAUv4dpIqYiSFDebcXg44/Bgwmw4phyfsD6Zf3yr2+XCtWsU0ylbt2491ad8AZVktNau4oVkOhsHMnk2gKc1//KBC3calIpI0oQ8zejmGSvLa1x+90OGB3vUZYFuTnl+XQGuJO32WLp4AV0m6KniTDkzKSHJ8SqmbBoi60izHnlridl4j2mzSzWbsP1gQFlMME+wVTrY2+Vwf4CKU5K8h7KGanqI928fqGchz6HVgps3P+eTT27SW8r48ONVpBLc27lJbTWTThGUu3Nk6z1W3r1OvTllcO8mVn8FRLo48XqFHz+ZzPi//t//P6RJzD/5p7/P97//IQfFAYPZPndv7FFY89z+/28sCsJ8tQxc5liZ/rwc95OunwE3hDq27MSHyASq8iUumC0wmP//DJuNojBkaX1MSJy6Tcvzuop8o5BlKZevrmGN5e6dHcrSEJaSCvz8wfUaqOf9sNqk3SXe+60/Is1zfv3X/y118WxVo/eOwWATISSd7gWW+msUs4bRielKSshShZKCpjZYExJgpxF+8LCTmwPqpnmIKHdAFUzTmQ0G4b3nIHNC8t4zGo4Yj8Z4/2QP9LNaI7zFMarDMfVw/Nr5NSEErVabfm+FVrfPmjPs7e0xm80QQpDlwR6snJWB9ImDF3/TGJrGYK3DWI2zx2T4QmDrnMdamFUlM12Spop2e4bzHqVWQdToegvnSlKCY1XF2UPX1U7K+2sdlAKlPHGsWO62wXu2H2xRzArqGrSB/QamzfHQHUlYiUEJ2KnhtGG6MXAwgUg6yukMIWBsBbWHyHlS4IPLK/zRj79FGke08wT5Na4wlFLQ7aW02jHjYclsUh+JyJwL5wlebgGSx9O4MLksxqjz3OLahNfu3pDhcIQxnqrxxBF0e2JeNVQTxG/NqfOcVIK8pUAIZpXEGhEcIc4Zb1+4dInv/OAHbN2/z3B4iHmkEtd7T1UUjA6GVEdrtfA0CKGIkxyEQFdTnH39ZV5No3nwYAsQTxyjIxnRSjt47zk8HGKdZntne548e/jKdbop164v42zCwcEhUkVEUczS0jJXLl/hWx9+SJ5l9NptIuD65cvkUcLP4vhc+y0E5HlMlkaE+TjCGU81nWKsxnuHx1ObCm0NRTEjlhIBdLodZKSYFgVFWQYRjVBo46gbezRWTaYFh+NDhPRIBUVSsL25ifeS7d19Wr0LrK706LRzOt0OsRR0ul2cgNpoRqMR1hgiKZFCECUxUknyVsayUmhj+PLXn2OMZjobHyWghYB+r0UrT3nv3Xf47rc+4mCwx5effYIQHick2jrG0zFxlpDGiourKzR1Q1NWtOKIK5dXwAvSrI0QksYYjHVMZiVl2czXwYsjPR+n9dU0Gz03wsF5SyDiyzHINFi5LGQnL3Fx7Z3DGXOKIktAnIGMcdrCbIbT1fyz3wZFb/HNxCKISNOEbq+DlOJIJeTO4R/5qpHlrZBxbRqc+wrItpMeqotS8MXrBaxD3uIUOMDCdDhl884WB7uHpyib50oHKUNrDudDJHwSzzKPfAo6/SVanS55u00xnTIZjRju7zE+PAgE+aPbFGFRro3BnsWTX0hQMV7F2EW5p9WhD8ez7m/voJlBNYa6BF2jKyhnjqbWOC/wXj7jsD2IEDCJhQXOWTCv1feA0xLrPXmvR6fTQalAsqgoZjLap66K+SU51mi1u32yPA+NjqoKKRTOmq8kqH7ZWF7u4b1nNisfam73PMiyhPX1JYQgqEico2karLVsbHRYWW1xcDBjc3OEE5LS1ygE5ArpQFj5UNjiI/AKRK6IL7SR0wYzrPCvk1B/RhHJy0TTaKy17Ozs0+22GFVjRuWI4XCKS+eysNfkFS9zhUwVrra48itKFC0YnojHG3vNr0uSBwWRrm1Q6j0v5ipuU3mEeXpxzXNv/wyIIsXGxTXyLGU6LamrhrKsKctX07DwmwDrHHXdYIzFzqu+oihByBhrLM4tDOTnF0EKvBRYCPOOSEDmIcv7DDoqENEWa2q0Lh4TbSwU4Pgwt3rCNU3UceO/KIrI8zyo0OZ/VDUaYwxlVVPVTbBsUDIkoBdVzXOyzzt/buL2aQT6W7wA/PmvxXkQRRFpmuDdYh3hcT5IjsuqZjKdokVoodgYCyoiiiI63R54j2nmhJX3WBu+ShmIUq1N8MX3/qGh1TporMfMlc3WGpqmCHYfkSJyEd4oHBI/r5Q6z8grBURKkMSKVitBSYEQHu8skYI4kiF29pAqaMlj34BIgvBBNf8kLApyhAM9V25r57Ee1vo9ljttLm2ssdbvkESKLI1e3KpP5Ugp6HQ7JEnMdDykKs7WEPgsSNOEPMsoIo33NVEkSZMIPFgnsM5TLvz55xAcN4R8kVt0EQKdBWJ+vhe7Ya1HL/pO+HBvae1xLtgMhR4C4c2RkkSRxBOeKbmI8QXIKMJLiavtU7LLT9onQSSDRUreagUys64eGg/LsmQ8GlLXDUpF88qcsNOL/QzvfgnB4KNdOs+ARUJ0gTRNSeIE6xzWOJSIjuamxfO+OL4okqhIheSZtlhjqeuQrE2SBqksdVVTa0MxnTEZjRDO0W21wHmcCWsdycOVK0eHwxPcSH2YqwL5HxbIdq4oN86c2L+ISMVkaUqeZ2R5RpplpFlKnCZE2iBluF71XGQVxzFKReR5hlBLOG9xXpOlCRKBM4aDgwPaOzu085hOOydSCpEmdDodVtbW8R4Gu7s0tSaOJFJKMg9CKaSyKCUxxuFcUKurOdnearWIIoUUNtgSNQ1FVVBUFWVtkHIeVyCx1mONpalr6qqkriqqoqCpa6wJTXidNUH8phRKKcp5o+hIKvI0nY/55ysveUOIdAi3TgxOwd5t2L8HtiTkZl/uzGqaCqst3puHt61i6F2FuIWdjrCH98HNCFKet0HTW3wzoVSwCVhbX+aj73yfsij55c9/wWw6+2oI6yfg4pVrWGsZ7GxRvsSg6syQQJ9jpydHICYW37/5PODXB/O1+hc/v8G9Lx5gtEE/5ncfQg2VZqg0wdYN9iRBskh8LLZ3jiFcCMEH3/0+H33/R8RSce/GDbY27/HpT/+aspg9rvKWAqSg1g0HB0NMXT9bkZbk0FrGSsWsKvHO4Kd7wcLsWX9rNRzchfIQd/AAO9xgVCj0gWJvZ5+ysNT1s8vKhXAIpRHyHDevN9AMAYXWLVyccekHP+CdDz9kf2eXwdY249GA21/+HGssWi9KOCqkErz38Xe4+t6H7Dx4wIN796jLGZPDAd7ZY5LjDcXv/OhjtDZ88ulN9vfP5zX5KC5dWuWf/bO/R5JIdnd3KMuS7Z1tZsWM7//g+3z00Ud89tmv+Yu/+Et0ZNluBkQyZePKJVpxzN5OA9P6KDJ30tK4GrGk6P/Dq9hxw/C/v4fZL5+5Ly8Vr/ESW+v46U8/55e/vInzDucdJrPY9Xmd+w6vXoUsIL3aJr3Spr43o7w5/mrCydb8lXEs2V3AB6Xe6oUWnX7CYKvgcPfF7gtvQc/tRr+qIpO81eIf/KM/5crVy9y7fYv9vT1u3rzHjRt3vpodegNQVTX3N3fBe7S2CCFpd5ZI0g7Tyd489prfwAKIBE54pkVBbUHTg/gymH2wwzN9ZlEMqapxIDVPPBzeQ1nN56b5j1f6OUv9NnEcSNFer8cHH3xAkiRzKwbHzvYuk8mEO3c3uXtviyRWtFsJ3rujBKdQMV4IirJB669PnPsWrw7dbofLly/RVA0H23sYbah0jXWOze1tdgb7+DjCRVEgn7MWebfDO++/B95hTc10YtHa0TR2rgAHfCDNtDaPiY8KDaU55m1CwmiTNM7YWFpBipSxT9HaUumG+gnNKJ8EITyRcHRbHS5fuYTRDbu7mzRNhVCCNI8RwhCpYNqeHutPsC6ECM1TyHRPCJ8dIOaPiSNYgP7wt77D737v21xYiri6FiOFRwh7HteEUyHbl0jSmO/+9vdZ21jhk5/8Bbc++8WLbXSxbSlZ6i2zutpnNrFoO6PbSbm41kMpSSQVtTbcerBPWR0HB0HxH3jns+hknoSzaq4EkKiwtGjmDU+PGp/ON+AshKWwx8/tKhb3X6uV0OtlwX7IWqx31Cb0hYnaOVLG6KHBlecMgJwGXZInEZeuXqeYzdjZvI+eV/5479nd3WYw2ENKRZK0jpJH3jt0PePIh0jOeyo+b/YsZEgXGarn2wawvrbOxvoGZVEznZY0TcN0MsU6M5+TjtHu5HR7ObNZxXA4RVvNcDgiTmKcB6kUg90hxaziZusL0Jrr199hpbtEUzVMRiNmozHSWlJCGHpyzyVh2bowIFnAA1rrecVuuNbGeBodVN0ej5KKXrtDnuZcvHiB1dVl1i9u0F9dYWltlZX1VUQcczCeUTeGg9EhQko2VjfopRlXNy6ztNqmKguGwwPiJKGVpKANv/j5z7h7f5tW/p+wvrZCniYkacz1q9f43d//Y27dvMkvP/mM4XBCloSKnVa3SydJUM4TaYuWoV5MKUGn0yFJEt5//3067TZffPEZW5v32drZRgrLaDTl/t6ILE1YXl3DEaONp641w4N9djbvUZUF5XSCdQ5tLFKGcTuKY3r9FeI4oZhVCG9pZylZmmGso671uW6510OkB2MuhJBIIecP7vOuTjzYan5naV6Jb4J3cxL9Sduep/qOujK/2Yv7t/jNhRAcldnZZ6jLQ0Yyx1l/pEj/OkEIiRDnyee/7B0gkOYJx5Hl21LwVwcPTdXQVC/YpO4s3ZiO3ivI8pwkSVleXWNt4wJ6VlCPJzRVTV0WNPWT1YzOOkzTYM1ZzHsVMkmD6s42YHQgyM+UvHJgK3wjKUb7jAY7iF6XpNdBSEmWtzBNRZq20UJhdH0qSe2cwTSLyqtzzHM++EpKJYmiiFa7TbffoypKZuMZxWxEU5fzKoKUk54ecZKRtdq0uj26/SUEjsmBe+NJdAi2WN67l1LaHMcRK6s9klhRVhOiGLTp0+6krK2tsLK6Qn+5R6efU3pNLRosDiM9KB9cGE5UZARl0m+eICAokB9JfPV4vdNIJBCpQLUjkl6G0xZT6rOvqBd9ul7GlLyYt04mF+efoRJBlEqkejkn55UR6Ge0B5JC0ul06C8tsZfnRHEUlMlv8UQ459FzWzHvmXvBhiaDUi7K707AO7w11OU0jPdCoZIWzo/OfP29t0+MNx8dsqIoIs+yoExPItIkJksS4iTGGDPf5yPB+Vu8xRGEDPGKi+xjqmmtNdpavI3xsSVOEnr9JXrdNu12G2f13Bs9VDdIKZDCIWUQLDQLX+A4QkqHNRbrF7pbgT/S4Dq8a8AJvF80Gzz+91lR0GL0WhCy1noqbUm1pW4sWlumlUY3odmfiCW2djTOIqWgnYZgWApPYwORfpawYKFMl0CaRERxRK/bYrnfpd1yRMoHPYlUL/zsyShBRglxmpFmLdRz+Ek/DdZ6jHZHpPNCoCoIPtTOe5JIYaKg+j25dJZSEokQ530Va+ST18pzvFxwRz8JEAKUFHgvwv1nA8numduTPEcseOzOHXzZ0zTB6AYpHp5TjQnduZMkI01jHipF9OHOlSoOva7sV9tHQghBp9NmfX2NyWiGMwLhBRM/DkLCJ+yaFGKupPbzRKygaTRKhcQF3lNXFZPRiHoh8PIeZ+3R709DpCStKMZ4FypgTvzOw9G58t4fiUOECIprpSKiaE5itzK6vQ5xEiqekYIkDfOkmDeANfNOutpotDFHa5eFr7lSEqsNTd0wnUwQKkPr5qihgBCCJMtYWlmlu38AUh1Zr0kH2lgabY4ab4fzDUpK8laLNE3pdrt0Op0wLs+r4SbTGbOypDYGFUUYH7apjaHRBm00xjQY3WCMDl7o2qHUsVXboneYihRSKZRQpDJCW4tAfv2IdJW0kUmLNM3J2110OWU0uBeapp0ZnsdZqVf1cC3kpI9s32oY3wcRBV/0NA9l9m+Vpm/xhiKNI/qdDGsdh5PyVDLdzruSa32yrPLrtwrZOxjgnafRL0isPi8UgYDJT/xsC5jwtmDlK4StG5zWwdrlBREnCT/6gz/i0vV3+Oijb/POO++x/WCT25Pp0//QhQDRViVlowO58AxVeZKk5P1lTFVQ7N7Dm2ZeDn8WeKDC6Ia//Q//ms9++mf88T/5T/nDP/3HXLp+hT/8kz9iuD/k0/Yq0/GQ7Xu/pCofV0jXkwH7N/8Gb03wZj8HVBSxcvky7f4yl965yuWrl/HOUhUFxSyfL1QdwY9uMZ4oGmMpqob1i5d45713uXvzCw527uOar1fi7nmwubWJsZbqKcmWsyKKY3pLy8SJJDrcI5Ge3//4j+j3l+h02rRaba6+c51vT7/P4XTMrd371N6wNdrDSJg15fHq13AUXun9itlP9nBTjR3/Bva/qAge26+xUXSjK2xlWXpnhWs/fJ/pgxGb//EWtj5DcBkTKqE8MOY4iXteFBw/iouaYjv/PmdeEGoxaL5WrVgXlUULaaQA2oT5uOBMyWzvPYejMQ92dhlPv4JqtjcJ/oQVz5yUThJFnilm00fiQmugnKLrkt3PC2SUkvffY+3yVSa7U4pm8NjmXxR5q8vK6iWsaWiakqKouHXrNs5adnZ3KOuKqgzEwHRWYz1U2qKn9SPWLiFesG+9zH9jYK2hrEvqqmbWBPs1510I350BL/DOgBZcufYxP/6TPyESIKops8kYQahyWltbpdNpM5tMKCZTjHUcjguyNOHqpQt4PHe29iiqmnYckSpJbSy1sUgMETMwJYPDWWjUZ5tAij3jXpSEticRocVFDexOaqaVIYtmfHbnAOsdo7pGKsmH19fptFK2R3tsD0e8u5rw0UaGdwbTVEwrx34VrEEkDxfbPooFQ5PGim9/cIn1lS7XNtokUYPRFYeHBUkS0+t2EOLFkpVJkhEpxXg4Bmspi2quXvbnE3ycAmstd+9usbm1RzVvxjiZ1TT6kFYWs7HaQQjBhdU2xmbs788oSo2zoB202m2Wl1eo65r9/b1XRqZ7ghL9pLXLaXhiqxJjqKpynlQUGOOoitAUFzmGuWXHWZFIiARkkSSLI3QckccRNo5Jknhe7aMfIsWzLGFpqUNZFtT17ChxIaSg286I45jZdEZd1c/njrpQor8AES+E4Le+9x3+kz/9B9z88h4//7vPGOzvsbO/ibY1/pEzPJuWVFVDmkSsLXdotGEyKamExusRcSTp5W1Wu318Zdi8/YCLqxfIYkUdR8ReIr1Ae0HN49dvZWmJj69fZTwr+OTWHeoTHvRKCCKlgu2ud0eHncQxq6t9IhmBEyjluPbOJb710YcgLHfv32U8ndDp96m0w3p5ZDUFnsPRkMl0wmi6x85eSpYmdNstGm0YDe+h4gyZreN8hG5CTwFtg2VV1OrxwcffRTtor6wwbWriuW3N3v6Qwf4BWZqQ5wnOQhLFtDsdPvr4Y9rt0JBVKUWWhT4Uw/GMyazBWEvjPJF3zJoaWUXsHgwpa01RlggRkjlxHNMYzcGoIUkVvdWcOMtDpXqS0ur0sD4miSPSJMFaS1k3Xz8iHakQKkbFGUnaxluLEOqRG/Ase/26AponPbIedEkwi0xBybeyhrf4WkLKIMla3J72CV0CpRAhsy4EcRSBdThnH85qz5PEwWdt7h81rzJ56Dnx8FUyxmbe5dt/FfuwIB9igsAWwqk4zeTsTccL+Ip/JXDuybH1o17AT4EQwW91ZeMCl6+/w8rqKp08I1YKYzTWmqefEg/eOrw9GyEtpSSOU9A1wjaBSD/XSbd4bznc22Z4sEcxGRElEe1Oi7WNdaSI6fb2cVYiVcxpsnxnGprpwTk+8xhCSLJ2i1avR7vbpd1p02q1SPM0lNnL+fx5dHFUUEEYS6M1/X6X1fU1Dgc7L+6p+TXBrCiw9uUplTzzHA0glGR1bY2LFy7inAuqqTQl73SonCGOYrT1lKamERaLRyiJ1x7mL68drtDonRmu+A1VCDiOHQNf09jtnMEbj8wlrctddKURsThWhT9tFSkJVVCOF8tvn5Y4EBz7psceJ1zo1yD816f3x6Nj+Ml9fgZf4wk9K5pGUzcNVR0WaG/xDDxyzYPSVJwyTnuwBm+DRYZUMa3+dZI0QUXqsc1KqUIlkzheWwVV5Wk9q06HlAoVxVhn530jPOPRGK01O7t7lGWJsWHcNHah4gNnHt3+Ny1we4tnwTkXbAKNQTuL8Y9wFCcMsLM04eLFCwhnKfY0poqP7v8sS+l02pi6phIC56HWhiSJabWC0kYpFYYqIYikxAg3X0Z4FBbrLZXW574LI8IyZGF5XRlHbRwxmumkwhLyi3ESURPTijJKp5gYgZWSLI/w1mOkxHiIIodyoXFoCDTmz6V/fKXlCb9f6be5tNaj04qJZPA9rmywkXsZiSkpgoq6LisKEfzoVZzgrQ0WiM+pqIbwZ7PiYaGDsQ5bNgg8jTHESpIkktjNfeylQXtweJSKyLLW/ONfbdz6In7szjmMNUgR/Kqd8zgLzp4mWn02BEcOlkfL4cX3UkqEEI8145RSEkXBq/rk7wUCpRRxHBpJvxBegpq9221zcWOd4f6YVp6FxAD2MRIdwBiHMY4kUqRJPM/tCJz3GG0QXqLaijROaKqGqq5o6gYlRQipnAte5/60rYdxZ3V5CSElSj4S4AhxvDz3ft4nJJzjLE1QUqFrgxCevJXS63cYjQ8ZjkaUVTVXaEd4Lx46bY1uaDR4Gowp6bbbtLMUay3FtEAllqZuMFofJfus98HKJg6VO93+EmmeE6cp0hhwjqoq0U2D9444lngvg91PHLO8vEyn0yFNU/AeKYNKvNEWW9v54Qq8EBjv0S4Q4FIpjLXBbUEKpFR4LLX2oDxCRsh5h3kvBDKKiVNHmsS00jT8rVTnqoJ4LUS6awq81RT1BD07CPau0RJi8ene4c0E3Jugfpr3VDbT4NHuviL161u8xRPQbudsbCwTx4pOOwx2N29tM5k87mlaa8vBuCBv5Xz3ux8ipOSLL+8wHk9JEkEUSbR2aB2a7WxuD2i0gbRNImKi+QRpjAkLl6bGNV9No67f/a0rGGP5/NYuh6MCtJ77op00Kn8FXisxQQaScWxeVhDMzb5JPcsWDVRbhGTBbP56UyE4dhV5RgcnFUV0Vlbp9HpcvHCJS+sbbN+/z2c//wWbd+/w5a8/YzadhLK2lwYJXoHMoLUCug7NQ8/RmU9IydLld2n117j0zvtc3lhj4OFwexDKOJ3C+4QgabWEC/pyjkEpyeraKqsXL3Hp8gUuXb4wrzMNja5u37hGVUwpp4Og8sLhvGFnd4upMWh9jTyLKYrZN8ZyZHd3Epr41C9O1j3Y3OO/+n/9N2R5RLsDeZ4yfn9GX9fcf7DJ9u4et+8+4BeffoFLQa1lRElOu61IlQhJHecpfnVAdXeI3i2Y3N7ElTY0mfpNheP1Vhl68GMHwjMSI3R8l6apsR+nUCsY1yHRccjp84km/G6x/n1Zj0oKLBPmtCBkYtQ0zIzBdxytDwRmBM3eC6zmXwYW18uf+L7gOIm9mJNPGd+rsuIv/4e/otv/lDiJuXjxKk1pGezsv5Zd/2bAUzcVQkrMM6qWnLNMBjcpR1s0xeFDv2u1O1y88g5p3mJl4zJxkqG9xzrP3c9/zubNXz+8sQXB8ogcc29vQFGWwffX6DkxL3DOMSsarJnfCv7pSs6XhzdNefCbi6qs2N0dhHvnCeKjBcrxmK0vb5BnKd1uTqvnyPKMLEvJsow8zzlEMin1UaWvEIIojo7+74HCWGobEpQna+FPqYl/JjxBhb4wpj3ZlFBzYghUCicUe8OSWeWwRPR7PaaV5ZObU3rtiMsbfXoZ/LAdPN8392YUpcGkXWzUYjSbcTCZPLYPcRTx8XtX+N4HV8iUJJGOSWUY1prYOGprX9jarhoeIARslbvEMfTWVvnutT9kPBlxeDhAlyXV/v6Zk29ngSCIPCajabAcmScVup02K0tLzJqGotGkWRoo1q95zNpoh5t6hHAsupZGmUJ5j67cuSt49Vz8PZ6VDAb7FGXNeDSkLCvqukafIFkXKIoCay1SSpaXl7HWMh6PsdYymxWUpUTP7Ute1dlckP5P0k147/nys0/4/ynDwcGE8fCQshg+895akNitLCW5GPpzJEkgvifTiv3DWbAFso7RtKSuDdPpjAf37rO/f4BrmlMrQNI0Y2VlBS8EnTQBa6isxc1JZmOCiMZ76HRbrK2t4r3D6DK4CjiPkJbR+IDdvS0+/+JzvvjyC9Sc+B+Pj3vfRUIF2x/v8DiMdkydx7kCOU/8WeeJSel3O6z0+wgiZoUligRRLEnzlO7yCmsXLvLRx9+l3e5w5/PPmEwmWGtCkrFqmLmQDEvyFkkSsby0RKvdZn9/n6IomBUVxgb9ZhQJ4ighz1tkWUqStpBRwrSqaWxogp7EKaDwQqG0wIiCWCrSvE2c5QwnFdoWeB9EoU4ovIzAy7kd19nvoddCpHur8TZkVnVJ6LicbCDEQpHg8LZ4HbvyErDozFXzNjB6i68j0jRmZaVHniesLLfQ2vBgc/9UIt06x6xypHmLy5cvEEUR9+9vMx5PiaKQdffeo3Xw+DscTUIAESdEMiKJohAUNQ3ezFVDXxGR/s7lPnVjub875nDWhFndLlz7IuahEC/9uVUck8uLBHEFlHyzPNIl4VhzwvEa3nwifaFafIZyUShF3u3RXlqm1+/R73S5+dmv+fWvfsX2g3vc+vKzl0/2ehGIdBFD0gahoH6Ghcyj+y0kraU1+hev019dY6nXphhNjvywgx+iImSBWoRl2Msh0oVc+A/36S/16C91KYslyrJieDAka6/ivKQqDuai9FCWO5mMmFnLUr9DWW7QNN+cZPVk8vLEAqPRhJ/8zSfkrZiPPr7AykqXsq5pjGF3f58bt29z++42X958QL7S4tKVS6h2StrLiSMVKnc8aF9SHQyxNNiXdO3feLxuMWoVKqnKvKDs+jA2XYhAS4gaKH2wCDsNC4nhy0ZEsCo7sUooSwu1JV8SpF0BztPsvYLPPi8evV6LeXcxvj9haNZac+PGLeIk5tvf+Q6XLq+Rpjuvbj+/gfAetNGIpsY+y67TO+rpHqeNgkmasbpxkU5viSvvf4e01aG2QWk22t95mEhfyB8XO3Di+k6mUybT882Trw6PNKJ4i681tDZofbZ7pykrRrt7uH6X5ZU+aeaI45goikiSmCRJcEjqE41qhRDIeQ+GBZesnXusaeDi6/MQ6YvOcY/W+x8RhUIglQIhmJSaSofS2SzPqWczHhzUGC+5cjkjSyTry6E/ga0qRt5iWzk27aGtPZVIV0pycX2Zd69uYGYzTFkyxVEYSzRPbr2oTtuUBeAYFROEMKxdvcSld98j2t+lwIJSVAfPV0l5Ghb765w76qeibYhxV/rL9LsdZFFCWSFVhPNf/3qWsDw+vstkJEg7MQCmeUoV7xOwOOaqbphOZ5RVRVUW1FWNMY+T6ABN09A0Da1Wi36/jzGG6XSKtZa6fj2x6LOLkj17W1t87isaK6iMoKmLZ1e+C4EQkihWdOIYIYPK3nnHYH/K4Wh29LlF1WCMpaoaDg8PGQ6HeGNQHBcaLj4tjiPa7TZlVZNGikYpGutweIx9eNRIkpTV1SV0U7O3G4hrIQQOKKspk+mIB5v3+fSzz+j1uly4sEFZ1vOqgFAZJkPXBpwP94uzHukbplKjpCKOE4SALE1ptVqApG6CqjuWoOKIrNWm0+tz4dIltNbc/eLzh9Z1Rlsqa0m8IGmBiiStVos8z6nrmslkQtNonAdFSFDESUS71SJNE1ScImREpTXGGiLnUTIiigQOgYhMSEwKgUpSVJxQjAuKqiZNM+I4CUQ6an7pFOcZfV+PtcujcBpvRjwUYJzZ+/WrxuK2lie+fxsgvcXrRZal/PaPPmZlucfu7i7j8YT9gymD/QllWbO9NSCOFIO9GGcdRfF0Aidvtfn4ez8gTRJ+8ckNdnaDIkvMm0YAGKOZFlPiJGV9YwOAw+0tymIWAi3ncOf0UX6Z2Nwaoo2j0kCUgkiDWtxF4JIwxhhFCDPn7mMiCilOb57fW88SiPOF+F0SSI+Sl8VJfvVQwCqBRE8IM8fXZa36IniWbcIcaZJw7do1+svLDHZ2KMcTvvz1p9y7fYPpZPySSXQFKGSUkbTa+MJCPYGmPGOj0RMQkGcx3U5KpKBpajyePM9Q0YTx9JDxbAyRIMpSrJbPaAAnCA+V5FnNviOl2NhY4+rVS1jr2N3bp6wbkjRnaXmVD771IcP9HT4fb1IfjRseV5V4a9m7e4tfFRPGh4dfu8bGLwuL0ld3DnVk3knpLrVoGsN4OEWXlp37Y6aHDT9b+Yyd/X12dvcYTSYU0wpfQBMbDnYnRF1N1m4jI8Xs/oBmNKPefxJDezYsQs6v+8Lxa42GcAIPDUQVZAJ6ct7UnvB63WFmA+wTHvcWHK3q4Pklk89Ar5/QW0qoG31EWEgJ3kIxC5bb58JiHwXH9jcntuG9x+ogANi6/4DJaMzB4NWo0eNYkabxfMH8pqx3ngypFJ1OP5RSa0Mxm6D1w8cVGi8eW9XOfwoI1lZ7LC+3ybOMbrdL1TgGOw8Y7G1zsD9AxSk2auNkjMp7fO+P/zHD4SHb21s4XePLSZgP5/eglMGi4OLFC2xsrLO/f8Ddu/e+4mqmt2vDbzqkjMhafbyI0QaKomZra8BwOGU4fLjvjHceOyeQnnRfnmdoVQT9zqLTDBzrXRZTxmI7QipklICUCBWBEJRVTSNqkiQljiKa+XYOCsPte2NaWcSFtQylItKlK/Taks1RwWB/n2kZhFmtVLLRj8iTiAv9Lv12Tt9X1Pt77ExmHBYVNRGNWqLUFQejgxduHB+1MvAO29R4C4PNLbQO3sjFdIapyud87k9Pei2+sw6q+fzhHAjnGRxOmBQ1IlJHVhuLnkwqzfHW4Jr6+deXLwApBWkSyMGmsfNmog9DEOZY8JjKhnvmOUp1Fk6N1llmZUlRlAxHk7kS/fHtnSSHhYBIhcanT8LCssjycpfVJ2NXBcQSVlqSJJLkaUIcKXq5Bz2jLizDiWZaN89UpDtj0VVN0mqzuryGVBLrLNoYVBSSPHEiiWNFo0tu373D9tYOm/uHjMcTSmOxQrDcDmRx8Dz3dFsZToDxjspYKmOfWP0wm5U8eLCD95amCgLHVisP3vOzKbu7O4zHM5oaJuMa7w4wxiKEJ4okEo/wYaw6OSZZ59EVOOkQztJEDQf7+4gk587tO0RxxoWLa1y8vIFxHuMcKkm4cvUdBIpPOj+F/YMQ0HmHVII4FqgIvLcIgn9+kkTs7Gzz4MEm4/EYmI9jcUyc5rQ7XeI4RqgY6wXjaYGzmshWRL5BCBkaSCvFxoVV8labzspaIM9Li/KKOA9NTWOlQCnKqmJna/tca86vhkj3Bq+HX8lHvzgWU9NJA8Zv5iL/Lb6+yPOUv/dHP+KD96/wy19+woMH9/n1F9sM9idUZc12+XBzjmdNi3m7zbe/90PyPKfz//13AEeeZQvlhLGG6WxKW0reuXABKSWHD+5Sjg+fsuXXhwebh1gPpREQZRAlQAw2AhOBqefRZcNRoaOMg9LX+ecPdBwPk+aC0PTtm2brskZQKC6Infgr3aOXgzPKfpIk4Z3r1+gvLfPgzl1uHg65ffMLNu/fewU7pYAEGeXEeQenS0Q1DkT6OSEQ5FlMr5sRRYKmCZVUWZ4jIxmI9OkIHwmiPME7xdPjhwUjtfBLePKbVaTYuBCI9MZadnYHKBkRJ6GL+wcff4vdzYzbv/4b6hOH5usSX5fsTkfs3rl17mN+k6BEaLVi3dmJ9FYn48K1FaajkulhgSkt2/fGqHiKTz6lv3nn6L3ltITSo6XhYGdCXBsuXr1I5CPK+4fM7u3BCwi3FnbU8HJdRX7j0Mxf2CB160nIo3BTWP/V6DU0x0T6wof9pH3KK9in/nLClesdprOSwUHoCRGrsN6q6+ck0iHse0Ig0R/ZhjUGa2Dz/oMX3f2nIkkiOp2MqtLUtXnj7aqUiugtrRBFMbvbmxSzKY+m04SAKOKox044ZIkQkrW1Jd5/b4OVlRUuX7zE3Xtb/Kt/8+fMZuWcpJCI9gYi6fKD3/sx3/7h3+PWrZsM9N9ipiNsMXsosayUIooirl27yve//z0+++xz7t9/8DVIwr7Z1/ktng6pApHuiDEGyrKhLE8v0/HeYRZE+lOIuLOuRCJCX+WFaaVjIcOYf96J9wqpUFkrrHdEyG6Vs0O8qel2JUpFND4UmdpC4wodPM6ThLyVkixfJYlafDn8FZv7m0fbbWeS9y6krHQyfnj9Ar08o09Ftb/H5mjGnWlF1lkn769TVkMGh1PcuQfyR467lYN3OFvhrWOwucVg8y7IBFQSxoXn8m16cvWIJ0zFD++6Z28YhAgryz2W+h2sC6Iz5z0qbeGtwRuNf4ZV0KuAlCH+B3DWoefn5KH7Yh6Deg+6ev6xUkpQSmCdYVaWTGcFw9HkVCX6o3U6UohApLtjvuFRRAQdl+blxpqLJeAiAZUpweWuopMqVvst8jRh1mjKZkozrTkcFFT22XSBM5amrOnkHVaX14iiKDQyrmsiFajXJFG0WjF1UwQifWePzcEhRREWREpKlrodVrptrLNYZ+m1cxwe7R2lCUruJ6EoQkJDScjSkMwPFTMJ0+kU5y2T8QzdgG4appMGKSGOJVEkEDacIf+IG7ybNxJV0iG9oZE1+/v7aKG4fft28DpXgrULGxgbqm5UnHL12rtEKibr9AI/4x3COpQSxGn4m3C/eLI8IYoU29vb3Lhx4yhJH4j0lCTLaXd7qDkBbp1lNJlRVwWRLYl8Q5pkZGmOimLWL3TI2z26K+uoOCUaFigDSatDlucoQpBSas39rS10c3axw1dDpH8jcFKa8xZv8foR1D6KDz98j2tXL1JWP+WzzzfDomX+nrPenXEUs76+wfLyEn/4J3/MysYqt29+wd72FsbMJ18XJuOmqtnf2kQIaOqvD1s8npShgZQWxwaBCLDNXNlnwWsQPijWZUKUZAgVYUuPq0+6E54DC4uQhTTzZRm6LcS/JwtgGl5/3m4RYUA4rikhcfCmuHE9DYsKgmfUmhpjODg4oNGa/f0Bk9GIqno1936Ud4myJYQUzPbuoMvx3EP8/PDeM5vOODw4pJoriELgLJDeYqohphyGZofO4Z9ZGeYJTNQTTIdPQCBI4lDmbGuDsZbZdEJdllRljcWTdjp88Fs/ZDYe8uD2DYrpi6mj3zS4+VhxljXf9XeucfHSBbpLLZbXuxzuD6kmDWVRMRkVeOuZHJYPebvORjXehxJkmaUIIopbhygkZqcKz/ILynsWo+Y3JhI6SRov+vsmnKUI46notGMub7RxOA6KKY1xlJMwPR0hBroEBbiUYVxqA5GHtp2vdnm9Hu6LRLEBkQWnKVdDU3vQgmxJ4rSnmbkXvgkcHosja2dczoNyqtvuUVeG2egeTfV8k067k9NeyWmnbdbaayRxQrfXw1rH/bv3mU6nHBwcMite3aRmjKMsG7T+KsoLXj6csxSzCVJFGLOg8R6iaILi3/KI36jHe4euZpSjA+pYoHstTD1DWItwbp5kcHgd5ixbF5i6QSHotFoY4bBmDadrqskIZ4N1gLWWwWCfL7+8we7u3hufrHiLrycEUJYFW1tbVNrQv7BN0zRoByJO8UY/xLQpEZSukbeUs2kgZc+R4FmEqI/lLoO9LxJI5x/Xi8NnpX4+fSkFKqLTW2HjyrtkWc7yyjpKSurZIVY3Rw9oUUyZTSf00pgrvRZpoljqZSAku0PNbDalnSVcWl9Fm4ZG16yv9nj//at0EonAUteamaqpIsdk1jAtDEsbfd774NvouuDixgb+BZNbtp4gBLQ6HaTsUc32aUodSpecCV/PPcYKkCrYIbYy4iiiLGfUT4jz57biR7GbMYa6rlFxRtbKiZzHRQajG6bl5BkClVeHRUHWYihUSgQ71rlIznuPc496QoffP5EtFgKiuZLKBFWxilPiJEJECZZQ9ZDnoUeb0SGx8CinrpSYq/gdk8kMBPR7Xay1TKYztD4OdBZK9Fdgzro4YmJCqJdKSJVHOY0wnhSPjASmk2Nkm3HVMN0fn6q0P9pf56gbw2Q6496DTZRS1LpBa0NZze2BtKOqLPuHY27cusNoNMGY42P23jOtKgQeITwCz2g8Zntnh4PhKKi5eTZjEcURKytdWq2Md999h1a7jXUNzlnkyWauYs6AzCsXlA/ruEfPE8xXgd6DddhGs7e7w7Ss6HSXmRYlneUe7330IRJPJ40RnZyNCxt4Z1ldXWFvqU819ujSYL2najxpKsiTmDhSGGPwPjxXYaz0CAFpGtHp5mRpjHcah0Pgw/p1fhMrFRER7OLyTgcvFTZKEUJxMBwiZISxFqlUaAJrDFVdo6uS8WSCtgZzjjX3WyL9ufFsIuEt3uJVQynF3/vj3+fqlYscjgz/9t//BJ7t3vUY8izjg/c+4Pq714naCTuDbf6P//v/A7e+PFbcBjWFxTQTbh3sBUXAS2zm8qLY3QtllEfHLvw8gpiBG3PEkEgFeQcRK9J2ThRFVPvMGwA+o+vkaZAEi+lFpPuylHqKOYnCscX7IYHUeF1YRBcLQt8Be8CAN58LOIdHetM03L17hzhNGexsU0ynNK+IdEmX1mitv091+ICDG3+DdxZ/XkuXObx37A8GFMYzHn0fCOqPWCqEa2gmm1TjffAnw6OnbpGzlloIIcjSlHaeoU0ZGhsf7PLg3i1UnJHmS7TXVviT9/9zytmUf/P//D//xhHp9iHLgydDSsmPf++3+dN//PdDlZAUbG7eR+uCw4MRNz57QDGrGTyYIDaPz6F3HjyIKCLpdsF4hn9xDzdpgkLqBRnwRVrlG4WYUHmzUC97wjicECqNnnP83VjJ+dM/vEKD5qdbdxhNa3a+hOIkkd4C1oFYQBR8bFmJwHioKohs2IfXedItMAQUqMsgczC7nnof8mVJ/2pMM3OYu815+iCfCofFoFlZXuHq5au0O30uX/mQyajkzo1/wejw+cbclbU+1z++zDuXrvN73/1dlpZWeP9bH9HUDf/yv/5X3Lp1h5/97OevlEiva03T6HM1sfo6wxrD4f4uzJuRPQwx/7lA60d/F74vxwccMiS2Bcs51OMx0hqU9/NhyUM9wjcTzGxIPZ6ivGd1uY/rtrDdDroq2L31a5pCB3tBa7lx4ya3bt3Gf83i07f4ZmARLo6HQ0bjMf2VNVzSwQtB5QQi60A1xetjO81YBoJbohke7GM9GH32gWARpj6axxUiJDYjDx0TCPuL7VDMNPNzu5c0wWctrl6/zo//8B+xtrbOD37wQ7IsZzYd0dQ1eztbjIcHFMWUYjrmwlqP7390BTwcHBSMRlP+/b/7DwwHe6x0WmysLTGeDhlODvjwvSv88R/9EcLU3P/lT5nMCuraI6RiMLHsl5bvrl7m9//kT1Go0JXyBaGn26goYfmD79Pur7Fz51OachwI9OdlrAWgYmQUs7pxkV6nw/bm/VOJdAFEcysTMxe/13UN3tBbTugv97EOksZS1zXV+CAkLF43PPNGkcdL0yxSJLFEKolQkqY2TIvm4XlJnugpdhqZLhUya4d2gcUUrCfO28E2MI4wRKgkZWm5i9Ga6XiMsRat58Xf883EkaSVxWhj2d3bJ88z3n/vGkopbty6+1C/goUS/VXhqP2X8HQiT1s5YlsgnaCVZKgkodtdYj1fZ2t/xINRgXmKalkbx9Q0zMpDdvdHJyyb/NG8VNeWprFMiy3ube3ircc2x/ev857BcMyhgCxWpLGk1JpxUVBVoQ9cLCWNc08N4/Ms4513rrGyuszv/8Hvs7TU59e//oTt7S3ieK6Sm5eXeg9aB9JeSok4seVFJcFCY4KHugl98YZffI6Uip39QzqrG3SW+/zwx79LN4vZ6GT0swghP6S/1OP6O9eZjA7Y9Q2jZoYxnrqxyEjQyXPyNKbRFWhBoxuMMcxzP7RbGRfXlwCwtsI5iXRJ6PnlQ9ooihJSldLu9lhaXsIiqZ2isZ579+5jPeR5hyhKsNZTljXj4SGH+wOqYkptatzTPU4fwlsi/S3e4g1CnqdcvXKB9bUVrly9xsbGZZJIoKsxzpy9kZ2QEiEVaZbR63VZu3CRNE3Bw87WNrfv3mYy96Q6gvfzbL8PpXlfsxXh4+ojDYTgJkxhMgQIct59zIdpTUiPiBUyTfG2CWqSc33w/KMWEfazu5c8HRHHpeh9XlgJ+Vw4STKnBHJp0V95IT77GmHRTnZB7J35zjxj6YazlmIyIapr6rJE6wZ3jlJNKSRSiKDKeILKQ8Q5UsWopDVvniLnz9nzn+xQrlkhZxN0Vc33WRDHUfCWi6JgYsv8eT4Z5b4gjNFs3ruLiiK8inBSMhsPmQxHJKlB0EIRkSQ5zjpU2kIkWXj+njNxcDYsSjyeo/rkK4LHMxqNeXB/i36/x9r6KmmWknczyqZEzBuY+VM8MAEwDjcOyjOnLD5xUPtvIAt+BqhHvhcnvgpCUjQnnJt5K42jsfAFxnUpBXEkcV5imNuiJxzXK5v519liHzxCSuIsRTjQSuNOeEK/VswfFV+Dj0JhFw6c9ujSYRv/QuFAFIep2TrHbGaIZM1+PKOcCYTfoyhqhIS0FaMbgzPn+zBdG2ajArPmyDttWt027XaLOE5YXVtjNivI8vz5D+CM+JqFTC8EKSVZliOFnPvgOqwzR6XZD7vgHiNWgYRSwuGtpyxqBoMxo1GBfWz8CjeerkrK6YhqOqaeTnFG45oSU1ePWWQ49zCBLkRo8nb8FZpGfw0sX97iTcRR0an3uLnatioLpIpotVsI4ZkeBLV2lqVkWYIwGqsr7LypuuN8ziPHJNzDcBBU8PPvpQAZSaJEsJzniCRBtrqIVo+11SVi6cA1VMUE01Tsbm9SFgXOGuI4JoljdBThvWdSFOjGcOfBgOFowmA4ZlKUXF1dYm19lX6/xXLZZmN9NTQwdJ7GQW09lW7wXiGiNr1+TruzRCtvI4UC+6KtRjkaSOMkIctzukurYDXaaJomKG2NnlcF2DOuCDzgHd5ZdF1TzVWxp0KAVAIpgl90cIXwGGOJojg0znTgZ3VoMCufodR5SQhko5iTiiFuNMbPOVIR1v2EfZVKEEmFkQ+Pn1IqukvLRHHCdHxIXRY8dv68DzH6oupbKJx1mHm/EW8s3howBmcsSoSdM+J4feGZO9c5h7Vuror3REoFmy4hjqL0lzlthjkgtNDEe5z34RpyIrzzHrzHO0KDSgGxlFTGMJnMKMrqmT7yi2dWzBuEeE5fwnsP3nqcN6ce6MJWxTiHMB4ajShKdGPO7GUv5HzNF0V4bzFGM5sVjMdj6roOxQenTNfOe5jfT48e26P/99aGZryzGURDJuMxk8mYmDa+myGVJEsT8jyl02nT7XYYpRGRmp8XD5EUqEiBgNlsEu6PR57BNIlY6mV4L/Am3Mt1ZdHGYpsGUzd4GSMihZiTMs760NjWWOoyjMORVAhn5+YFnqYqqMoCrZtQsf0sdd0JvCXS3+It3iBcvXKB/93/9p9z/fo1fvTbf5+lpRXufPrv+PIXf83+zi3OOuWoJCdudbh+/Rq//we/x4cfvE+306GYTPm//Z/+L/zZn/8549HDjXJwFnQ5/4g3YEXoC/AVR/uqImTaCVl0U4LzeCvxPiFqZ4ishZmO0MNzSg41MCKMpn2O1ePPiw5wiWN1ogVuEhqYvq7TnhAsBmIC0QPBL7fmuKvR1wgxsMSxePJM/OAiKbBQ2j8FWmt2799HCIG1Nqg8zhDEHHFzcUISJ9S6oWxOUXQLRdK/StRaIemvEGU5Kk7OchRPh3eUh3vU4wOKw33qskYC/X6HzlIP1V9CmgZnhuCboLZ9SbKP2WTCv/x//FekecZ3fvu3uPTOFTbv7vLgzg6t9jKrGxFuyaOupiSZJOpvoFam2NF+aCT3ygxDYgJTaQgH/PUfy7zz/OX/8D/ys5/+gj/+k9/nP/+f/U/J2inLV5ewiSP65aPs8MNwk4bmkwGk4C8T2Kw7FkZf/2N/qVjIjhYrtMXPFpm4iDDuLhEGk5TjwWTBDb4gtIPdAg4qsMsE9fuAMHAdEsb5VQdtTdRRrF28gPIRgy8byrL56pIfDuwA7AFHK8Jm6jBVaKx2DgHPQxACOiuQd6AoNbdvGqQv+dQNkEKiop+iIkneV1x4t8/+gwmz0fkmocHmIcO9Mf1snXipQ9JvI6QkSRK+893vcGHjAp9+/jmff/nFqxt2vmFIkpR3r71PmqQMDg4oioJZOaGqZvN3PD6pSgErObQTyJLwOO3ujLh5a0plHaU2j5MN3jM+2GHz5qeMRkP2dnZwToOp8M5inyF8UErR7XaI44h2u42Ukp2dPabTb0K39Ld43XiUEDNaMzockOUt3nv3GlJJfv2Ln7Nbzrh0aYN3rl9id3efL7+4dWS7tiARz4pFmPqYVMjBSM8LRwWkQuDzhKgT8/FH73P58gXy/gVaSxcpSs3g8ID9YsjfHOxS1zV/9R//I/uDAX/yj/6U73zv++As3tbs7k649+AWB4cj/vwvfslwNKUsK/DwO3/w2/zDP/wxRB6hHLoumI13KadTDhsoG8HhwZS6dlz5/rf4rfe/wzvvfky3twRCYrx4CQlFiRSKTrfL8uoKK6uBzD84PGBnd5uqmDE8GOB0BcWQs5VKebA1zjXs7TxgIORjJN4CQgjSJEZKgbMa7xzWOIyFdrvPt7/9PapG82B3wGQ8YuvO62koFUWSJJEY46hrh7WeotBICa0oRsWCRhuaxhCpmCROjqxbF0izlB/86LdZWlnlZz/5CXdv3+a4MdYczgYluhDB61rFlGVNNasQOEToUIn0HiWhHUmQkgaDPXEXN9qiTSDQPeG8xlIRq4hUCnI513u8pPlYCEGet0mSGOkahGsoG8u0OnGdfbBdWlQaeCmIhCCPI+7tjfmbu9uUxj45yXK8GSwhtIx5cjLsoTefAhkFD/vGOqoaRFMiZnU4v/bpSvSjbQhJkqYopTjYHzAeD/n888+5ceMWRWFQ8zZxJ/VLHtCLhMKJnz3187xnOtynmE14cPcmN774gvryBTZWg5d5r5fjbJdr1y4xmx5QDB9QjubCjAbasSLPU5y33L79BdpYZsV0sWm8h5V+i4/f30CiEE4xm5bc/OIOuigoh0OKsqG1toRKM/ASo6Esaw4ODmm0ZlIWITJpCpo4xfuwnh+NxowOD4iEpJMkiCeZ9Z+Ct0T6W7zFG4A4VrTylOXlNisrXVZWuvT6PTrdXmhWp0uw5qFu7QuEySNHKUVVV2htEDLYAwiliJIEIRWzosAaw3RWUMxKnAMVxXjnghIO3jBJ1aOz0/E0oBZNVJ3DGY330bHp3fNg0XcxCH6fbyG+YF0TgpVAzrFacXEor0tAu/AJXpBLDo5llK/g8573nM2x4MGem+daVBM8KVLwPihcXgjiMb+5418JZJSgkhy8wOoad0qgJqM4WHsQjtVae/xsPgHeGpyzTMcjBju7xFFMEucIKVlZW8NLz3RcYxqPrS3+0Qv8aP8lOSdtvXvqeOCcY3R4iBxLhvsXaPcyysmEpmyIlcbUFm8ckYoQUpC1u+SdHlUxQb9K+yIhQczrF94MQToAs1kRVCSTCcZqvPAkWUKWp2SdlKpuglrXuscjdufxi8WCUmHV/XpEUl8/nOz5sHiQFuNdNn+lzOV+HI97iwbLMc/qs3sqjHFMC83UaprKYxqOx1c134/FerVm7vvjETaU2ZIoyFTo92H8V0P4PrKe9g5s87JWuWCtp2l8YIia4/EviiVxp08SR+cb5Ofvtd6Fqn88MomRcYTFI4Sg3WljrGVtbZWNjXW0NphGo7V5ZX0wvikINlPyqDH9Y7+XEhWFhgNGLxoOBFgX+upWjWUyM08tdNNNTTEdURVTdFWEniG25iwPwKIk/Gh/xZMb2r3FbzaEFCglg7e/Ob1J4gJSSlQcEycxc8EtaZIQxRGtVk6r3Q5fWzlJHC+G86d/Pk++o08NSwnbFIQklSU4pzQOhFIkcUQcKeIoAl9TlzOsh7KsqKqK0XCf0fCA4eEhw+GIcjaimExodE1RjRkcjNgdHDCZFGRpQpYm9LodVleX8MLhpWE6towGmqrRaC/QRBiR4BS0OkusrF4gyzshiSAEToB/CRlpD9j5WN3q9sjyNrOqeuSBlwgVmld590Qa88RGA1tnzmAJ9ej06+d/zlyBrpREyvl486jPtBAPeVP7uaL9eSd0pRRSSfI8ppUnVFWD1mWomvAe4YJ6Xolggu3mn+ecfyihI2Wo2ml1WnR7XeLkKQkAHxa7QohQEakdwnucs3hvj/VJTuDV6cFmON3+xPceow3CQ5YmuHaOqxqMflnVQyI024wTpAeJQPsGqW0IC6VERh7tHNqAjRahokAIibOOuq7QL94O5lxYJBkWc6xzjoW4/6z74X2oGpnNCqQUzKYlZVmh9TH1ETzzH/7c0/C0ml5nDM57qmLGZDyiWOqGxrtChKaisaLTadPrdUmTBCnCuCsiQawkUkqc94zHkxCLzdfBSi3+XhCrsKPehp4IWjfopsFqg7MmVGAohXeepm5o6pqmqtE29E+QCCQeJUMViTV6/jJIpTgvNf6WSH+Lt3gD8O47G/zpP/oe/X6Hw+EDEDUffvQ79Pt9+t02dm2dtU6bJSmonGd2XEFFlmf8vb//R6yvr/GTn/6MG7dvg/QYU7C7t8lf/PVfcvv+bfJOSitv8eH3f8jK5Wt8/umv2bz/gOnokPFw8FUe/suB1bhqnyhO6C1vIJViNh1QDAvwwc/Tv0g3eUvwr51by50bCxJnHbjGMdlTE5oCvk5v3EXTu9N83xek88si9l9CI7+Flfu5XHUWf3QyWVBwVgvwM2ER6FS6obHmCZ6tAkFQRyZpSnW4y/jOENeMH4pqVJTQu3CNOM2JhEViGR0MmBwePHs/vOev/sN/4MtPP+Xqux/yvd/5Q/JWxn/5v/wvmM4m/Jt/96/Y3LzHwb0tivqEpdPCZsgR7kOhoLMcvBimQ2iezXh757h36yYH+1sI20KZNqIGVwhUP2Z9aZkoVXz/ez9gbWWNz/+mYmu898ztPi9kHKOyFs5U2HL2hiUHoahmbB0+wOFY626Qx21m/7BmNBlz6/MHHA7GQdV8mt2zA0oLTjx7Vf9NhiBU/kSE510TlOEXCONuBEiBWg/shN1y4XymhAf6kKAiPwe2BwX/5s/uYr1nWs5lhJcJCvjFOLQg6I2HkcWUJYPJHYRS2MsZ6voF3I0R/sEsPI+vs1/GK4L3MD2EYvzkqn9rHPubE6QU6Pock8TclixfadNe7dB/b5W810NkGYemQgpBa32Z1soS//P/xT/nf/JP/yn379/l3t3b3Llzn7/8y795pvLsNxVaN2xuP0BKRVnO0FpjH0nqZu0ua+9+C2cMe7c/pykLDksY1xw1T7PuWCvwpBFpMtynnE2wxswbOZ6dQjDGMh5PkVIwmUwBQdN8BX7Fb/G1R6uTs7zRp6k0+9sHD5HpC3evBZaWlrh47RpJmtHqLhHNG9xJ6/joww/56P13kcIjpUfI6MkiihPbj5iT4efcb0/IPVrtubXbkMQGGW8xGs5AbSLiz2m0ZVYZEII4T/DAylqPTj/jl5/9ir/66Sc4a7BWk6WSfjemrEJfhzSJ+fF3P+DKxio//q0P+fiDa2zvbnN/a4/DwwO2dg+oKs3MdXBJl7X310myNr/1O/+Ab//Wb6O95daNe6gkJu20EELCxdVzHuVJOKyuuf35r9m8u8X3f/fHXLp0hb37Nzi4/avQ3FI3wX6q00NISTUZYpuXU07rnKcoNQKBfcSfejIZcefOl2jr2B8XFEWBcR5kPFfGe1qtjHY7mzPJ0GjDeDJ7rBnnY5DyWJo7hxCCtQtrdHtdLl/a4PKldR7cu88v/u7n6Magm7m1iw+vhT5gWtWUWgc7LR+IylYnodNNyNuSpOWR0eJuPH2sFQg6SUwUx8gkRQBVVVBWxRHJqr1nPE+Km2eUYdR1ze07d8mzhI8/fJd+r8Pf/epLbt7besaJORukEHTyDp12hyiXqFSgRkOa/V1accRKr420lt3DEXJmuNRVdFNJ5hIyn5FENattmOrQsuYsIbRj7ifOU4hpcXxpH70HjJl7g2cJrU6K8D4sl41hPKuC/cpjG+Q4K+fBGM9k3NDUsL89xFrNwd44iDnmRQXehfgrNPh8+jGlAnrz1j0j88iyfd7s93Bvly8//4wkFjTuB0ditzSKef+990mU4s5nv+S2UGR5TCtOyDo5KomptebWnXtUVc1sWiAEXNjIWV5KaeeOwe4OVVEz3B8zKxpub04oa4PTBgjC0aWlHsPhmK2tLYw11E2NUhHLS8skScryhVXSPGNvb5/h8DjJb6xlskjInRFvifS3eIs3AO12yvXra2RZjDEVVTXD6AprGrz3iFDoQiweD8KUVKytrXL58iXaX36JjMKI7ZyhrGbsDfZAwJ179+j1erS6XZK8zc72Hgf7Q6pyduo+vXkI/oA4QaSioLbXFlu+RK+SFxEtRxz783YI4/rCo/dVd1p5FIt6NAhRwEMlDo98fRGcVIS+wPYWmziXyFaAiARE4IU/stF/FXDePcVTPZwEKRWRUri6ohkfcGxKP4eUxGlOkndII4+SjqooKKLxvGrk6VH4YHeHwe4OxgkuvPMtLiUXeP+996jqkv/xJ6scDkeo6BF2cJFsWKhkhYA4gSide6s/G95DWUyxtiCLIFM53nq8EQivyJKEJEtY6i9RVxVpkp5pu8+LRSWOcG+mLLHRDePpOKiQhCKOEzprLVzLEm2pkHR7EsHqCcm4xf3+mwTBQ+ONSAUkAr+QCqYijLvz9wolEC2BsOD2g/MRav4q519Pk+Q8AVVt2do9kd1YsCaP7hvzbdZBcVM3M4gV0YUOqpfj92b4Pb5R/vZmsdJcKP4fgffQlM9xwPPqA5Urkl6KymKcEBg83mqUVKRZTiIj3n3vXa5fukwrT8A3zIqCNJ2rqc1bP+2F16/3/siHvChngAg9Q06pjJJxQqu3hDUaGcUgBI3156/m0M1c0X5+eO9PJENeZxD1Fm8KpJQIJYjTmCwPvaIerbA4GaYKAUkc0+l0iJKUJI6QUs2VjtDpdkliRVlMKYvZ6WTXie3Cw31+ngcLdfq0dsjGcTAqyBU4CiwjnBcYJxCRJDUpUkmiRCHjjPHmFg+2D49sHNqtGGdbwXfYWWIVs7G6xLVL66wudem0UiIl0E1NU9cUVU3TOFAZMo5pL6/T6iyxtHqB5eV1BsMBB4MBURLTdhbxEjzDvXfMxmOKWUNTlUgBTtc00+FxLBzFqChGKoUU8qW2mbI2sJQLAfziEuumYTwaYjzUdVDLht8ds5tRpOZzC4E5FYTkwtP28Lis5jEiPUkS8nbO0nKfCxc2mE0mJLEKljOL5pX+iLcPYaB1mBNrEikFcSyJYokXFu/NvIn0U+5dAUoKYjlX3wNaBnfpk+2n9FlsMOde3LNiBt7S7bZZX18lz+8/82/PDBH4kFjFxGlM1IpImoo4jYjTmLSdgrbM9gVeQ20FmZNYL/E+9LlKo1DxcR6c5Zl+Em+7yJsoKUnjKKxzXVD/i5MlLE8pZ3HeU9UGZ6GZTTC6oa4NQZPvw3pgTuafpT+3DKHzQyHrQtXuCTdZXZYMDw+ZTWdY50LFhQjH0e/1KVfXaLXaKJUQxzFpmhAnoTrZOcd4PKEsaxaF4Fmq6HZjlHSURcFsWnJ4cMCsMowLTWN80KMcVYSEHgfT6STEK95BArFSpElMu52Tt3MmkwlRpOYVJEENf5aKlJN4S6Q/hEdr2N/iLb4eqIqC7fv3ubixyu/97g/otLvs3/0Zg9u/4N//N/8tv/r5r/jkxjYj64+qvhcwRnPv7k2K2T7FZEAWWXTjqCuPbyyu0iTGsXXnHsNWi+3dA2aziv3BPpVuMN+wpkzWGsaHO0HddgZF7WvDEnAFWCWoFCuCCn3C6200Cg8TRItYyp/4/gxVkmf+HDN/vSCx9ygX9SxEcczq1TVkqji8NaCaVMfSgdeK4CmYqIg8TimVJzB1DxNHzmjGew9IspwPf+tHrF68zIWr71JPJ+xt3ef25796rPHaaZhOx9y58yXGlnz0rXfD8y0TkrSNko+EBBHBZkjPd2neOAdhzmzyKYRgfXmZldU2B/s1+/ubLKuEtSxBpglWSLTzlGVBMZtiztvs95xwugwNkY4aN76gr9Brxt1bD/i3/+I/ICMBKVhpmYgp2mtmZobqgyuecESW0OtA8lIrL14rFmT2wm7qLEgJivO52lxmiv63N4j7GdODCeW0RHYksh1sH1QUFilR5PHGUbRqrLfHdlcJYZweAvd5vvHZArsEdXsxP5aFR7sHdoCWRH7YRnVjeusrJN024/UZs8koXMcZb9Kt+2wsfOleVpKnAQxUOyV2Yvk1n/Ev+/+aldVVvvvRd+m0OkRRjJcQSUekIEkEca64/t5F/rN/9g8Z7B3wk7/6ObPZ1yhW+ApwcWOd9965ynA84YsbtzDGoLWe8zqPXDAVh2Rr2sbHGV4lsHwR0g6MD85UyfSyEEWKdjvHe5jNynM3GU2SBBUptNYY/Q3KXr3FEa69f41r713jcP+AB3fv09T6yM98gUUBZieLaKcRVpfc+PJLhJSh0WirxQ9/8AP6/R6HhwPGwym7u3vs7Q4oigJ7SmwmgH4MeQTSgnBQuEDUnXdYX1DTi0amD/ZLDmd6Xu8oURKSSBAnMX2/BFKyezCmagwXLyzz8YdX0cZQN5rxpODB5oBGG6z1tPOY73zvW/zgux/ghOaTT3/JZDzCVgW2rqhrTZy2+eEPfo9Wd5nO8iXirIMn4teffs6NW1/yyWe/xAuHmDNw/+kf/Pa5r9PD8OBKvG+48elPONi9xXCwO7dIWZwMh60qvFQh3jsLBGR5hIolTWVPrYCSAvJcBqsJFZTOs9JRVI7hcMjnn31O1m6xcvkiCIUQFvzxIifKUrKlXhDAWINrNIkJtiamLPGniW4W5PkjSRnnHAf7B0ynU1ppTLebo41mZXWFYlaxXQ+xxjE1BiEE+pH7UEqII1CRACmo6oaf/+RnKBWzu7X91FPlnKMoCipZHRlXGqsfCsskQR8Gc8e6U7aTZQnLS91QodRUJHGE8ZqyKbDOvDRmzjnH/uEek9mINbnOUr5ElqUsr66SKImKIxyaKJWhkrh2iMaTxQ1JXKGNnic9zq2jeCoWy6qnvym8wj1jw8sDEvJ2jFCCemYwJ31n5l+rquLu3buBKJ5XdLU7LS71+xwORwxH46P3P61AdyFWE3PnPUe4tkIKut2MOJbB0x5BXU249cXnrCz1Odg9pNftsNZrk8Yx7777HivLq/zot/8AU1uK6ZDp+GBuJRxstUzoVcu83ylaO6rC4pqKurJMJxXbBw21dhjrj6gE5z07e/tUsxnjsmJU27kHvydyGjvYp91p8fGPvsvlq5dZWV1nPJlx794WIrpNVVWMD4ePNVh9Gt4S6Q9hQcO8LJboLd7i5aBpasYHB6z1WlxZX6XTbnHv9l0O90f81f/wU/78z3/F1ENxym3rnOVgfxetJzT1lCRywQtZO6w22KqmUBHDwT5FOuP2rbuMJrOjgeS0APBNhneWqhh91bvxONrAGoHwSQgkQDl/fRWX4KTfu3/k5y9zf15CkuBcli5zqEjRW1kiyiKmt8dUdfUVqnQ9kVQkKkKKRTHgwzvjnaWaHGLrgrzdZfXSddAV3tR477j7xWfYMxxAVZUM9nfIWgnTWag2ETJCRSlCPqIyVwQS8mSpoJvXH54x0BACuu0Wa8tLTEa7TGdD8l6BSCJEHOOQOB+UPHVdnZvoOC+81Xi7IOsXGrM3Z74/2BtysDc8TnJEBCV1DKoLogUiesIROYJi/U3GQrpnOTuRvrCqml9ukUtal/pk612a3FOPPVIJVCSRCqLYI/FEWBwOmTZhoZPOPzsP2yECHjzncXhCovQkBGHsXzSw9hLZSpH9jLzbptXtUHbj0NS64OFB7825hZ+Mlz23zO8PbRr0tGE73eKnv/o5ly5d4uKly6Aky84QExPhUSIQClEiWF1fYmWtz4P72/z87z77jSfS+70u7797na2dPW7evoPW4J/UYVYqiDOIUryK8dJDp4+QUWgk/RqJdCklWZYBnrKsnk1YPAIVKeIkxjn3lkj/JkLA6sYqH37nQ7741Rd89rNfnxqDLIYlFUlaecSkbNg9HB+FQb1ej+R3f0Sv22awt8VkPGKwN+D+/c2nfnweBTLdS2BukyDOSaQv1PJwHK4fzhoOZ81R3jmNoJNBlqa0Wi28kOwOxkzLmo8/vMJ3P7pKWTfMyop79wd8/uUWzfx+V5Hi0tULvPfhdbbu3efBg63Qe2fuLWy0JWvHXH/vA/qrF2gtXUDGOZs3N9ne3ObmFzf52d/+HcZpNBX+pU1WGrxmb/MWe5u3Hvut9x5vNE5Y/FmFH0CcKuJUYY0/lUgXEtJEEseCLBYoCdp4igrKoqQsSvorS6xdu0iMRAofvDPmUHFMnOc4Z7G2QUlBlBmQClvXpxPp4YBO/fFsMmM2nTEcrTCZTTHO0ml38F6CGGNxT1zHSxGIdBkBQqCN4d7tu5jmbANl84xKoUVII3iyTimOI/q9NnhPVTgiJXHe0hiN9+4hhfuLwHvPtJgiSkF3pQveE8Ux7W4XRYj5sA4VC5wT6MqBhboxNLrBOHu0FpICpH98efz8+/asN3C0/vLWhUpKglApzhQykuFe1SfeP4fWmv2DYwtQJSX95SX6/SVmRYMx46NKm6cdzMlxZuF6FRO895fymCyLmHloEJimYnd7i4O9AdPxlEQq6LaII8Xa2jqdbp/r777P/t4+21v3qHVwBnDO46zD2ofV8cZ4tA5NfZvGMJ41DKcW6/wRhbAIH8fjKdV4+pj7YWMdxk1xwtPpdLhwYYN2u0tR1Gjr2dk/RIwlk9E4+K+fEW+J9IewMKpseFsC+BZfJ6z0evzo29/i0qV1kiQOzXC0CV5wzyC6oyji+juXuXhxhbXdJcbjKbrx6MZxOJxw584WdVly68YNkiTFeUErjZmOx5Rlga5OM9t9AyEUKkrxeJypvhJv5FanzZVrV3De8+DuNlVRspDhqRaoNbAS7EKNvk0gvl6XUvpk080Fgb8QRy8a7r2s0/YSZAYxIUjrRbCaQePhoAyNzJ6aDpXQ7rT58fd/j06/y8+Kv2W3vc3B3j7jw0fZrVeJ4J3ifUJdVqhohGkqnrb3Hih1w6SqWF/qsNK/wOHe5pm7jFdVyf7uFkrAp5/+Gu8992/dZLC3SzV7xMbJc9xg0BMWA/UMtAJ7tjnSe89gOKbxmuEksLgWQyGmFEwodUEqE/rLfaytuZfnZ9ruQ5AConkSQJ6odfSEjnYL6cZRSH6y8PRrhAhoy7BbM3d6kikhqJYVxzYYc3LcORAZtFYy0tWE6qBhtlV97Q7zhfBQjT3H1ldPO0ZDIJ4zYAV85pgc7FNWU6qmwTuPKwy+tgjhsdIjDMhJUKTb+YLqSIK0CmJ9Tn68SoegyuFuFLDdMBkJqm5Cq92h/zvfZ/z5PoezzVBZMHnG8UccN0l9iU5mZ8Lier1Mpfl5Ma8iiDJFt5WisNy7d4Ph4R5OWHrdPn0Rk0cR6XKHd927DIYDvrx/k73pwWPe37+J2D845Jeffs5kOsM5H9RtT4g9W50u3YvXUFmOdgaPYHl5BdNpMx7vUc9e3xzrrKUugs9qohxKEBrJPeVeFFKSdzqoKMI0NXVVv5BXvpSCLFYgQnNVd0ZS7y1eHsScMITgJ+4X45GH/Z19vvzkS3Y3d5+tRJSheaKXDw/83gd17nQ6oapq6sY8ZJ3xKBRhWKw1jB20Ymjl0G5gyYamoTPONmSeHFof3fvFzxsH0wYqZ9GDMV4ItNFI4bm/uYsxNXVjKWvDcFRgrSNJFBvrXdZXl4ikoypn7O1s8+DOXWKVkEYZh4MJOw8GjEYNn/3qE5bX9/juD1KWV2MS1aBUQ1Ufsrt7D6QnyuMzx6pPh4CoAzIBU4CrUFmbuNXH6ho9PcB70DrM6c9aJx9tVQjSJCXPE2JpaGWWutLMZsclfM5BWTkaDY0USBn8wnvdCN04ytoFlbF2OOt5tGBnNi1gZ8AiwDba0sxqlJS8+8414ihia3Ob8Xhy9tPhYX/vkM+5ia4bqtmM+pTKikfhHDQahHVoF2xonmxDOT9HHJOHhqeHHicrhRc97q0P92WaZeR5TppG82fFgRTISJG1cjrdLj/67e/z0be/zY2bd/n0sy/PpRR+0v5IPMIZcJpYCuI0Q+JJvAMpyVeW8XVDvTPB1g1Oa7ypWV9bZv1b36J2kmGtmMxKfv7pF0ymr44jWYwTzoTm53ma0ltawgNd67E4Zn6Gdub4AJ9xirz3lE3NpCjQzqKSmCSKaWUZcRTTbncRHorZFK01k2JK1dQhITdX40ciXMfKg3CeybSmrg0iT4hiRT2dMhpX7O9sczjYIxEesb5EFCmQEhEpev0lVjY2qHTFpJgwm044HOxQFBXuESJbqSD2qquaclZRVgbr/anj46Jx+WkztvPBRml5ZYULFy9hHmwzmVXk7TaXr1xhPxkw2Nl97POfhrdE+hEWebNFN6m3RPpbfH2wutTjd7/3MUvLPdIkxlqHMRrdNM8m0mPFu+9d4YP3rzJYW2cyOibLvrx5n/sPdqiqiptffEEUx1x79306nQ7jQUlx+A1oMjqHkAqVtPDeBVWqf/3qok6nzbe//x2scRwMZlTF3HxaWFQbkjXQBdgxgUjf4uzR9MvASaPGBYE+4tVYy7wEIj0hCE2XIljvhT6KaR0WSo9auz/0uRI6nQ5/8KM/Yv3COu7AcDvrYLX9Coj0FHxCXZY4hjRN9aQ9B0IQVNQNk7Lk2vVLXP/gHTbvfImQZ1uc1OWMertB1zW/7H2C93D3xpeMhwdwWhO2k01lvQ9E+jngPewdjhhMR7g5iWeEZibHzESbmS4gguXlPmkkyFvPQaQrBUk058oX507NaybdnEhfaLNO+gh9zQiNWMCSnBOeTyDSM2CZ4+dHAwfAvAzSt6D1fsbK9R7DL6YU29Wb1k/16ThJpC/8+5+1mrMEIl0BPXCpZby/BwMB7RYiTXETDXsnFkQlQW2uOW5MOiMM1x+BCFXbr3Zorh3uiwKnYLQ7RvQkH/yT3+Xq732H+/Izhpvb+JF7dpXBSYumJw6Mrwhzn/KXrjY/D+b9R+Jc0WtlSCx3bn9BmufQTej7NS50luhlbXrLHTa6Xeo7DZuf7bI7HWDcWyXy3v4Be/sHCCFCf4anEendHpffeZfGeYZVjZCS5dUVhHfU9758rbkcZx3VrEBKSDJIJLjy6US6VIrO0hJxknC4s0NdvdgeKylo53GwPjCO5i2R/tohJaTJnLrU8+s/V3kOtgcU44Kqqp65nvLidCLdOU9RzJhMEoqqom7ME0lMwXE0UhmoDSQJZK25BXYVCKrzFKM+6X1HWggHdQM0hsNiFMjQwGlx78E2D7a2afT8PXO0kpR3rq2wsbZEpCxlMWVne5NbN76k215hqbPO/u6ErXsDVDYh7v6M1Y0NPvjgffILyyRxQxTVVOUBOzu3ieKY3soK8oz9dZ4KISHug2qDH4CribIe2ep1TDHEFEO8tfP+FudxGRBkSUo7z2llHu89o+GMojiOo7wPRDochyL9XsRSJ2Ja2ECkO49pQi+JR8nf2WTKrCyRUhBFAm8dutS0W23ef+8dur0uRVGej0gHBrsHDHYPnv3GE3A+EOnB2PpsvNeCLYPj/ujPghQhvFUsdGGCVp6zsrqK9wbr6iDUkRIZReR5i16/x0ffucLy6hr/9t/+93z26xsvTKQf2ZN4C7YhiVKSLEfgUd6gXEQrXoVGs71fM9UNVhucrri4/h7v/fj3cSqmJmZ794Bb9zZfKZEeAUoEa8/SGlpZRn9lhVgpEhXTWM3NvbuYSoclwRnW1R4oqxqUpLGWKInJWy1W+su08jaXN64gPAy2tymKGcYYqqYmEnMiff5yHmob1qSjaU0ioKcUWRIxmk7YPZyxt7XJ4d4OrViCvRbGPSVRRCwtLbF64RJFXTOcTKlqzcHhAVXZPCReCDFHTBRnjEc1+/sFxoVmp6fhSS3lPPMEqlAsr65y6fIl9g/HGDeg3elw9do1BPCllOdqQfQKiXQBsgUiDlGL/ypkMHC2wTPmWKlW843q4vQWbzSOOrmXFbtbO1RFQWUcjbb84ue32dnZp24Ma+t93KxmOn3c+NYay97uLnkKG6uXeOfqde7evc+tm3eYjicPlbw555hOxmjd0NRvqonu6VCRIu/keOeYNZOnLqTC+DUfHt2zWJqzw+NxThPFER98611mlwsebN9jPB3irMNMfChnygiq04Wv7+vySV9ERYKgdDxJop5UgL4MvIRTuiDSuzKUxyYClkUY0Rd06YxH7KDniydda3Yf7OKNYGPtAq04pxyXzMYFVVVSPKrOfmWweDS6HuN9jdVPL3sXQpBnGe1OB+FDWWdTNc8kS5NWiyRvoXVDXZc0Rclg6wF4jy6LY0M6OPKSPjICfMFr5e2JTUSgm4Lx9n1EPeWTfvAXTWQKTnDxnavE8R9xMBgw2NvFNA11cfo5SdIMFccYZ9DGBhJdeZDB71AiSXo5ykNV1DR1E2wHVATOgtWoKKa3so6Qisl4gm7qsNJ+lYm2R8OThWI4I0SokqBMzyBWAikFxgUlY/tSi/57PYwxTMYFtrA0+w3eeGQOsiOQ6fwDFpYmDeEh+CbwNwv53eIeXZhwnu6IdPw3i4a5+xwXHjoPmcHHAqbmYauVkxU4C4nLom/XmOCNPua4KuBlTBOWJ1f+KEB5mqammExpxjWMfEgQPOlzF0WWkq8utF1cr7PMG4vnYFFtcfLcLxpvPw9s2E41LNm5sU273+HKt67Q6nYgljRec39/E4xlQ2VsqIzN7W22vtzl8GCEiWy4x550f/1GQISGeGJumfDQhPPwgCYFKCWOFtzeWWbjEd4YjHFz76nXk1nxBOWckjJ4tCaK8bCkLjW18dTWL24PlJLkWYaKIpxuaKzB2Rd/aJzz1E2wBbBvWlbzpK3bG7Tr3W5Kp50Qq4g0SXBWU1dTtLHUxmJPHIuZqz21fjaRqLVlVmrwsNRroY1jVtQIIciyjHa7Ta/XByEZj04XZZwQwx+d3kKHakppgnLee+jOnRrKJ+TUF1ACOnNmc6aPbRcWOClSlVLSyhKUUrRaKSqSTKdTqurY9mhtbZlvf/QeeRax0oN2HjMc7ENdMzqY0NSwU014MGg4GI45rGpaKkLGEqE89+7epphN+fWvb3L/3iZ37t0Nlg1urhC3L+NGEkRpC5n2kLlC+B5Zf5X28hqVdBRC4jGABSGJW915TBjGJ11X1MVpMb7HOou1lqYJtjV1/ez42lhP0wRLCqEUXki0cThnHyd/5wIP78F5EdbgPtyHOzu7jMcTiuLrWwkeRxGrvQ4CGEymlNo8cXhYhGXSz0MkMRe1SYlUCiEFRjuKWYUQkKUS72EynREnCZ2lVVq9p1d3nAcLMtVai2k03W6P9bV1dF0xGQ7w1hIJiVSSTksRaclSP2ep3yLLU6y3NI1lWBWUdcnaxipEioP9IVX58jkTO99pCwjvqZqGyXhMliTE3V74pXewSNg8Y5yO4xilJGmWkWY5daOxxmKNQ1uPkBEry6soKWmKCikV2eiQRMxQ80BuQRktvi7GMwsYazHahGuZxUjhKKYTylnvKElpnKcxjqpuKMsK3Ric9TgX4gohfPDtV2HellISxwmRSkFEmLn1VRyFe+VJ94ZUEEUCKUIfCwF4b5ER7O7scuvmHR7c32Rne5e6NpSlZjp+enPo0/AKiXQJaj2YduotsK+bSF/kCZ/lRSA5rpOe8frkOidbg7zFW5yOlCCG08Mhn/3iV7S7bZbXLzIrGv7Fv/hL7t4bcOXaBu99eAVzb8DuKUS61g2ff/Ypw/37/K/+i/+Sf/gP/pT/+v/9L/n3/+a/Y384fUiB4axlsLsT+NI3LeB/BpIkZnltCWsM1WQf+7SmhkJB1J5LPKeBCXwJ8N5hTEWr3ePv/+nvI1TEv/7v/h3jmyXGNJhtE8jzdcLQcIVA1jxq9vWqsFjbah4n3iLCkPoSGoMCL2WYbQEXgFUFlzKoNAzFMbfkgTuc0lfRQjmp+NXffsbFS0P+4Pd/j42NdbyRVKVmd2eHsiheWP3wbHiClNhSzbYRs8fVK49CCEGv12NjbQ0c7O/uMx1PeVak315eYfnyFSb7Awa3b1EOh9wc/x0Q+ig89PcpwQf6USeU54RfSAQSIIOyOKD+ZMROKrhx78/IOm2+8+0/ZmXlEt/9g9/l0up/xt/+9V/xV//xz5gNxzTV1uONVIWg1enR7vWYjIbow0HY3xhkHJHGCVGcsNpbIYkSdu7fo6lnELUg7YKpoRqR5m2uffxDoiTnxuefMzo8gHoU6v9fFRbhyeLcLpphRgTZjgDWo7A4bymSWFLqmspoNr67wnf/+GMm44IbX9ynHFSM7h5iGkO8JFBrEtWaE1s5YSyZERpbfhMcKhYR+8K/n/lXS2j+eZoN1qLOcwrc4OixC30g6vBHi0XIAicXJI+OvbtBBEdMOMcJLyfZuWi8/CjmEjCfwayYMtjeZbY1wW/y9BC3RQggZsw9119w/54H5xk/csL+5gQfeEOY/xpCAuR5Lc7mZPzo3ojJgwmX37/Mj/74R/Q3lvG5pPQln9/4lO3tTd7vrfJBf43PfnWDX/7Z59S2xvZcuMYHL7APbziElAgZg3dY++hJeHhAk1IQK4EXkChoas3+zj5NVWFKHfynmCsQXzE8wSYjlRHvX77AcjvjMNqmHI3Zn1n2C0vJfHqKYzbWVxECDoYjmqbBNC9enWydZ1I0R/vzRmHhLfDy9CSvBZcudHj3nWV6eYe1/hLj8YQvv7zJdFYzmVXoE+RLXdc0TXOmeK+oNFWjWeq2uXJxmbJquPtgH6Uk/X6f1bU1hEro9UsOD4ZP3M6jw/x+BYd1qK68nEHqIdHBkmXLQ/mUXUsjuNwNBPGdIUxPPFYn+wcF8kmxvtIny1JWN9aJ04TPP7/JcHQcJX/4wXX+N//rf47Ecvfzn1NMJ9y/eZMbZcX4sKGYem7t7vLp1gDnAvG8GiXEeYyK4Sd//Zc0tebvfv4pX9y4gzF2zh17ylLPzZhfDEJKsu4SSecCcZ4QpTF5p0Wn12WSSIa3VdBAYRAyor22QdpdIpUQSxgNdqnL4tS42WhD0zSMRzNm0/KpVkwLb+mmcTgHBoWMU5yMKGqNt6dYOTkPzh6FIQvUdc2vPvk1QohX3ivoRZClKe9dv4oSkubGLayeoDk9/FmImQKJCcJDkkYkcUyURIhIoEvDwcGYSEmS9Q7OOXZ295lMS/LuMlmnR1U/y7/vbFgksJpGUxcl3WttPv7gQ4YHAz4d7OCMDh75KmJ9NcK3Yi5fXGZ9dRV6HSpTMSkb7u4c0hjPBx+/y5Va89Of/ILtV0CkH4WE80MfzwpoGrqdDp1WG/B4a4Ont/NPPUUhiZaRJAndXp+826GYlZiqplEJVWORKuHa9fdI4xirHdnwkOHBIdVwhvQN+Abnj/dr8XGLfawag8ejpKLfbRMJz3Bvl16e4azBe6i0pWwM40nB6HAc9kGHBqreWYRwtPKQZgzEf0Qrb5HEHYSY0ViIIkkrjbHOU1T6VL4qiSV5WxJHMVnexntPVdVEseCzTz/nYDDi/uYWe/v7WAOm8UzGo3PZusCrINKFQi18q+I+yDbO7b8sHuqsOxGIMCHnbvXPevjcia+PvvcMhkPPjTcoKnmLrwRZIllKZGgmuDMmG2sGM0lZGfZHJdPaoJKUbq9Lt1vSaecYY6lO1uchwEu8k+jGUBYl3nuyNCWOKx69x71z38g703lHY5swWJ+FIPULcvHlnY0kjllbWqG31KfVznECkk5C0o0xqcVhjtWTC1/ylxM/nB+PfubX8KbQBG6sw1yYquBCT1FrQW0s2nm2NaeSU955dK1pKo1EBI+4PKfX6zIeDV/TESxy+QK85alNmIREpDkqb6OSFBVFNE1FMwtZ/Wc1cLLGoutQpriQZTxRbbcgK2OQHfAN+ILHo6fz4ohU87jIQARWaLzzjA4HiEYwiFJkrRkfHqLrGquf8AB4j7Um2FuZxTGF7XvrcdrgvKCpKnzksItj9e64ykSGZqd1MQv9JnQdfveqCZ5HD2eRvPLMTQfnP5fHzUOzLCZWEXk/JU4ikiym1cmggdlyglfhuP3Uo2NDrRp0afANbxwJ8lScVDgvqmf8idfT/gaOieeHCOhznpzF5y+0GuepHH8eeEIytfDoQUUZjdGj6mG16KM46R//MntbvEoszFcX1TCCMLjXBEL9vEnchOMEMBBFijzNSFsJxjQ0ukKaBIdkVk8ZFiN2jCeZNQz299G1Dk3aNK9uKTB390IJqP3XtyDWA/7hRK9UEUJIkjRHqYjFg5Ek6VEPn2YyoW5qTDHFNQ1pmqDimLocoavTSWo11xk59/JOufceUzYYAdIYYu+J8ERAK45I8ow4iYiEw83nRmst/iXNBW/C43cq3jAl+gLaWMqmIVIVs2pGWVfU2tKYY0XlSZxVNOH98X2phCBSkjxLSJOYuqqYTadEUUSn2yXPc+IkwVn7TGLUzbfd+KBAXzjUncWx7+Q+PesohBBEKkJJRVU3NMai56KBKFIkSUwrz8iyBOkNcRITx1GwnHGeWdVwOK2ZVDX1CbFBow37+0PqWjOblDS1ptENrTxBG4c2Ho/AvSybLO9xTYGtRwgSvItRvqL0FfV09JjwQkpJpBTLSz2Wum2kbTjYvPcYAbfwVa+roEa31j9Vo+Ln/zgflLYOh8fhjKYuZnjnzuzPDjx2nwSiXoSXFKE/2ktSZz8vnHNUVY0SAmPtQ0XLJ/GoLmHxNYoikjSUUFR1TaN1UCMrgvI4jinLmkZbtrf38CJiOBy9NItCT1gPNXMry1arRTFNw1jvHEJESCFJkxjhEyIVmrwUs4LJ5oBpozk8HGO9QCbt1yC4OobxntpaEmPQTY0XHokMz3RkkT7YCi2cBiRintiOkUoFYjqKEEqGwWWR1PLgvcA6T900OBt6PsxmoV+CkgopIiQe5x3eWsSJw/7/s/enP5YtWZYf9jOzM93JZw+P+c0v58oqVlVXF5tdpSZItigBEtmCAAkQBX3Vn6RvBCgRhISWSKAbktCiqru6WjV0VlZOlfnmeDH57H6nM9ukD3avu8fgEe4R7vHiRcZK3Ax/EX7PtXsGs21rr732cYgtQMjwPFjLdDrl3r27SCk4PDzEOc+kaqjqhsODfYYHB4wOD5mMhrMqcI8Qs2auUtDrdUnijEjFoQFpKGlAypg47aK8C/Yy1h19FWcDd+O8x1qPUsFhQEnFQqdHEsVo7ZhMcoppSZVXGO0w2tPU9bmv54UT6VJ16K78LipewKsuXijqw0Pa9tmdq0/H3BATziz3EQKSbiDTtYMn1BMnMc+XzX9+/LPnte0XnQl4q0R/i+djYzXl+ze67EwM//2/+xrnBVIpnPczGxfBwto6777/HjbqYkTCcDzh3sOdo8VbCEk3W6TXWWLz4T5/93e/pCgK3nnnFqiYr+7vndrR+01CYxr2pnt467DPC+a8AX3avPACmBEeK0vL/OM/+I/oD/o8nO4yrqcMrnVZkUtM0xFFOqvcmQBDjj3SX4eWDa/hBv+AGb/i4YcWFjLFh9/vI4TgwbBgVGl29uDe6Mn3eufRlaYtNaZucU3LysIC771zm7rI+UqIVxQgne3EijghufY+6eIKydIacZqys7vD3uYDdnZ2njvWfDymqg2urZ9PDsxEumoAyUfBna314CuCsvVF78dZpS194ArBLmEVjGu488ufIaeK38QJkVQ0TUNdVaE09hRFUDEZU+ZT/NxPb0Ycem1pmgmtENSH46DwmW/69My6RSXQWaTxcOc3P0N4h9YtWHv5RPrjh68JSTPhQZw4uZGATCG6gvXbSyxe79FfXEBIQdZJuHbrCuVyTV0bylFF+3mBvquxV3KmKyXm0IWGxfYpn/ltxUni+nHbgWcZxV7GHKoJSnSe8dkXAUu4jnuQ39+ljA5x3kBnRrxWPLprPfIL4NnWL68bZk1Bj3YmmYANGZ6Nwh4nFM4SjgtgDVjiyDRzaXWBj959j6yfMRrvUOoxS3IDmSbsTXe5O95kc/8OP9traA802phwXUezY17GNVbAdQV9AQ8tHLyeF8t7iz8ylg7l+Wl3QJykXL1+nX5/MP9NnJBU4wnFdMLu3a8xbYu1DUoprn/4XRZX1njw1WfsPnjS3F8K6CbBu7lsQq/oi4BpNbtfbVEpQVcYUuFIbbjdVlcWuPXRbeq25eHONkUdeog0rX2z+ku8CObNxr9l2B1NyH1BJ5EsdBRN7djfb9Hao08z1z0DvJ+9XPghjSOuXV1BSsWDB/fYPzjg+z/6HW5eu8H21hb37t6lqiqm0+kzqwbnS9jUhF4/mYQrZ7QStw7G5axg9LFr9Ti5LoUkSTKUlHx95wFFVdHOqi6WFvtsXFlhfXUhNFjEEicxaSej0+uBF2yNd/nNnR2ax/xjptOCf/tv/xYlZVBgC3j/3ev80R/8gOGkYv8wpygKdnf3L0Rt7Z2mOvic+jDYgyCChYOQAm8N1hzzPgJPhCNRnt/90Xf5D37nR/z//vzPuPf5rzAaTg7He89kUpJPxZEdzbNwJMM0PnhI4fBYGt1yUJUz0vbFgw+lJHEkA/mcJBhjmRblN9qwuK5rvr5zFyFg0mo0x61Q5ozdPIf/OIQQdPvB/3ya52zv7OGMwTlHFKUsLK0QxYq7dx4ynRZ8+dUmURRTvwDB+SyUZYE3Fd55Vlau0FR1SJxag5BpUDwvDEhsShQJ6jrni7u7/O29CUaCTSFKEpbXryNEhH5af6lLgHaeibPYqmZ4sI9SkkylRJ0Em9TgDLrUmMqghCCRijRJWVtYRSlFi8NJgYgijAB3ZKwerlzTaO7ffwAePvnkc0bDId4aojgjjlPi2GN0Q1tNwXmEfVRXIlSCijOKsmJcFhS/+Yyv7z7go48+5tbNm6yvrXM4GlGUJX/7V3/JJ7/5DcODfQ73dhFCI6UlEuBlUKPfvnmbwWCJ4UFFPm0wrUDKlKTTZ3F9A2MtrThAGoNUgUlvywLb1OjWY7Ql7QmiQZd+f8B3Pv6INEkY7ezz4MEu+9v7jA+GaKNpdIt15lyJL7gUa5eQLUAohEoQMgrlgByrFY5+DzhbhH8eKUj47OOPOEsJ0Wkn7WRR1Fu8xauHVIIklkgF2noabanb46YnkVK01lFri7EeOctcPwIPxgT/tqpqKIqSum4xzmHd+eUmQgiSOHReD5nkbwdD473HGB2CWSkQUj5pF/EILu57zTPwvV6PQbdPt9NFlTJMlRHIxCPU7DpoAlkwa2z32+3L+mzM/fesCKVeWaJY7sdIJRg1gtaHhkpPg/cerYOiWTcNum0QeJIkQs0lcU/DPFp8NRavj0LI2Wu2cVAKFcdEcUKUpFijZwruJ+GMxdHCU1ToUikQAj8nrWc5ZBGD7DCTkXCc034ZzKec49gN7z1NWcL4fA5Gztngdf748QkKhbCteXzT5sN7ZAj9vLO0dfHkcV4lTsvVO49vPa7xYEGKcNK89yAEURQRRTFChQ2kdAqsx9UOVxjc3LPgTSOD5qS54dFQcr6Le5Uqylc1B8yqQVxjQuXSzCbpaYhigZRh3f/G+mSeJ8Sf42RCxHA850QEUr315yP25nPM7DgiEchEgoLWtPgWqrJA2hZjNV55qrahGE8QWqAGMVh/XMUzX0suwt5sPq6YoEifq9Jf64f1hBpdBkIumflC9wd9jDZYa2haTdPUNGWJrkqcNcRxIITiWBFFCvUUqa0Uj4rkLnTkHupGowRkSdgmSh9ylYkSdGKJ8JIsVlirjqwIzoMoilFKYYw5roB6i28EWjvKyuFs2LPoGhrtMZqnKtLPj3CTSqnIohCXlWUVerV4yGYWCnGSYLSeEczPt+7zBGGIceDOGG85HxJOz7pn5UyJnkQREgE+WIlUJ+wosixjdXWZfr83U3Wao1jTOoc2llobikY/Mf0558hPNF0UQiCFoN/r0GhLkkQ0bYSU8sIIUW+aR2bLZy0LzoaKQ29MsFVx/lR2x1nPeeuy58p0KQPpHaoE5r7VL/59T86Fs/APcalOCc+H856qaR8xTz4Zfj0v/yOkQCqJ8x7dtkdCmWD/4xBG0LSaum6p68shqL3zWBcUy9aGe7ttDV5rtNZEyKPG2ohwLau64XA4xSqB7Cni1NLpV0gVgwAVq1n/kONE21kgpUQpNbN+fXZwM98qWBeqbKWIiNMYJSWpdRgvca3DCBO8xSNFEkWkcYyQEu30/ENnHvXqqMFrkqRIFVE3Lc5YiqqkrEoiKVAiWLUFNbhESUkkPLEI66j04b50hD4gYXwOa2vqumZvd5eHDx7QVDXDGZF+uLfLZDykzKc0dUkcQ9qNECIEW3GckMQpcZISRQalHGma0e0O6HR6xGkHYXSwnRMimKILkFE843YCHyajDBmnqDglijNUFM84MYMzDuHFbD/vEB5ieb5myBdOpDtbUh7+HBl1yFZ+F5VdIZh/XiMU4Q9nEUwX8GBLnh2RnkMNLqLQwRnA1OBnqRJizifJmvfTfpGdwFu8xcWhqDQ7hwW9bo//9B9c5XDa8Fe/fEgxK4m1zvKTn3/Kb764R103VHWLNo9m1LQ2fP7VJg+2Drh9+z1u3zZ8/WCLf/OXP6OqmnM38eh2Onz3gw+IlOLTr75iNHl6Q53XDcJ5otYipCQe9MB56rzEtpe/4bm+scGPvvd9bly/jjeOpmpQ0hNFUJsxo2oPHekwI+8D9wgq1YJvty3D3F/zkhSxcy5pJY25caXPSi9h/coiQsJ+XVH7liR6OrdmjObgYAfnNJv37yJtzWi8R22mGNfw1JMugRWCh++QYzXqK4DXLXrrK8R4Bz35LsZc472P3+N3f/+HfPqLDaqmpBgdMrx/56lkukKgEDjEIxp4GSkGV9aIkoR8eBAI7UVgEcQ6qA4hW9ESEjsXcS9WwCbBw3nuc33qMj+vDIMLLYuwGqoRgVh/PTNV3kG52VLvaeqxZvvOiGsfb5D+YR9vBa4R2Imn+fuCZr9kabBE+n7GZDQkfzAN/VK/rXPH8zBvHjqHAHoct7t51W15XjXmTTgfm9iUguvXUgaDiO2thoODb6icaa4sn9uTnQWacO1KZh74M0YpAm4J8AK+dLDH89cUT1hLx8BqeI2aCb/65FMWlhf4cOVDIut5+OVnNM5AT/LOe7cZ5g84uDshu9Fn+U+uouuWwzub2EajhER6gX3ocMOXfLBS4KYKC1h6CczxJSOOY65dv06vP+DKlRW6nYwH9++xvb1NXdXkeY7RGmtqOlnGhx9/RKfTYZpP2L9/SDkdPno8GRomeqDSQdx5kQ4GBthzMBGQ2pCXSSIYxODbKXv3viLrdfnBh7eptGWv+IKieVIxfxqklNy6dZu1tXU2Nx/w8OGDixv8W5wbug5LvBsIXEdivadpQ2LxQnhcIUGlRHFMp9vHGMu9h1sY6/jx79XESpElCd1OB4kHGypcirJ+qp/vPOeXEJYx6aE2R054KE53ELMeJmbGWPhwjKMWIISwdbHb4+aVK0gpwTp0a2YVJsd47713+Kf/2f+EbhZRFiXOtFgdmgc+3Npkf++A4WRyJqew8H0sghaBQUpDmiUsr1+7oETG2eGsZby7Q354wP9ndMDf/Pm/ZjQ8PPI1v0j0F5ZZv3YTbQzjSY7WLfXkAGdejBA2NgjeWuOo25DQlUIhlAwK6m8gwHOEJRqOw/b5vZkI6M5y3sY/ec96PFrbwFW0Gm+O+zPVTcOdrx4gBFTlxfuNn4SMMqIkZTSp+ftPvmR78wFf393GmwYziunFivdWuohOTCoEUnlaYOyC17+YWlTV4NglihVpN2G9t07dahptMHWLnj5bFqSUQCnBYDBgdXWVqqrZ2tp5LpkOs0R2lJClCd2VJWQS0TUttTPs+n32qoZelnJlsISSEUpKjPfkdYn2juvXrrC4skI+qYhGJYvrV3jv/Q9RMqJsLE1dkjdTCh0qegWQmYiOjIjj0J9KSFhUEusFRePR1lPpmmk9xjxWdbK7s83/5b/+r0mTYKtnrWU4GlLkBUoJ+p2Ebi9hZa0fLKbSFCEVxsFolNPtL7G03OW6lxivaHXLtCooigm2rmmbCtntI6KEeHmDTpoRxwlxnJImCQv9LkoI7j/cQ3iHn1ahSrG/zHJ3kbYqacoc7xzW6XPtmy5eke4ttj3EmRRnKpS34BUhWmw48i+Xs7bUQpxhwOdQo8sk/L4uQjb1SNJynkD1pJ3M/PNfd6XIW7yJ0MZR1IasL9hY6SGkDH5PM3gP+8Mx+8Pxqcdw3jPNS5pGh0yjc+R5xfbu4bmUAWKmgOxkGWsrK0SR4s6D+y/1/V4lBKC8QCKIZjJlIS5CYvt8dLMOV69cYXlxEWccRuqZvYbD2JZWVxwVB+QEosDweli6vAzEidclYN5vsKNkWIi7CVknAQFRIokigZL+qNTwkYDOe5qmpqoKinxCPu1Q1wXa1BhnTp/t5+z9JbbqfhIieL5VU6yfKWu8ZbAw4PrNq+ztbJENFkN54Sn3tCB4ej7+vYSQxGlKlKXIfJZ1yEAsgOiGJL+TIGzITV/IMmg5jsRnYcFppJgQsyZ3uFn/hpdT+BzBP8/27RvCyfy9B1sGTb2OLMIKlq61M99NibPgWw9Tj5xC90qP/tKAtmypdI1z7kiRL4R4RLL2rbcseFxjMVcux4Qk5OsYsj0+F77MJt7zRF5JyFAK3utGLC5EHH5TJDqEyTnmfLkvy6NrniP0DEiBZRHkw+lMAnWWc9fMXgthPG2jaXONjwXaWfCS4WRM1db0e2v0+32qToaKI+JBSuf2AFXWqLHCVwaJRDjC+vyyUCLYuXTEsQpf8uorKl4QUkq63R79/oBOmpDECmc0dZlTFiXFCYGFlIEs6HW7TMYHVPkI0z6a6RICYhXmpdyF/Mmj//7iVmvzR67lWPFrHaBC4sk7TV1OSRLJQr9DYjyxUueaQoQQ9Hp9lpdXODw8eKFxvsXFwdtg2aF1iPutm/mIX9RzNVNAChUTRSnOm+CtrTXGWJxzCERQbipFHMXhs4U4dRBzyUDMo0WPc5XvadOCJzTUhRCenmQ85mxGEikGnSBgzMsCq83R8yRn/tuLCwvcuH4dZ2rq4hBjGrTW6FYzzQtGkylNq8/2TAgAh/d2Rqh7IiVJuxnev/rEoa4rNLCZT3lRo+GzIIpjev0FGq0pGovn0Srx885jQdAerry1DiGC37VAYIU9ZYIK1zO8/4Li5ZNj4unLuifca7EMtjjCPyUMm6nOjQ3PyMkm78468mnxxHEvBUIiZETdaA6GY4ajCdO8AlMzRmFTRdOPMWmEmlVJaefRfu7g41HOUVUVsVGkg4Q4TbFSYITAPc+TTMzjNUGWJSwuDlAqqL3P6l0nhURJRZamqDTBWIWyhiQKjgFKKrI4QQh5dB9pa9DeIZUkSVOiNEUmKWm3x+LyEt54poc5rTYYpzH+mFRW3hMJUCqaVV9J4jQKFTE4vHaUbU39lAbdVVXx5eefzapqw/3sZ37unW5GkmakaUKn0wl9Gvp9QDCeaFpt6Pdjev0BQqWoOGNa5JRtixASZw3OGgQCoRRRt0/aXyBJMrK0QxIpOkmCs5rpcB9rNFlrUQ46cUyqImIgMgbnLNbMg7Cz4dKoAO81zfgTdHEf0+RAESQH6dKsND0OpdTlWYj0M0JKRBLq0L2Zzoj0F+kE9Thb8C2Iat/ijcSwcrTWs1lM+frwHlVrqJrzqTKVFKwsdej3Mnqd+IXH8s7tm/zjf/zHDPp91lZWaOqaT+58xe7+t2PT0IkTri6t4rxjbzii1RprXk35rTWatpxQKNi+/zVEgl0xZKoLiocV9gH4+SZ6xLHa8NuOS27EtwS8A1xXkiyLiBJFazQzuhUZSbrSsYynIuQoTkIiwHoO9nbxbcWnDz7j7u59DncmTw92PUGJnhPIOjguYJrbEVwwVJTSHSyDh3I6RhBsgjrdFOs0k+mY0e42e199Sp1Pcaf4MSqlSJIIMJwUrDtryfcPEZGitRXE0FmD7AOBi8FIj3XgawIpdZH3ZQs8JETbjwlQVJqiOh163T6rK+s4a5mMxpi2IZ8MMfoNkhzPd7sxiB6hYeqE43haQP/KgO67PVZvrLE4WKItDE0+IRUxP/rHv0PsE370/d9jY+0am3cfsPdwh68+/4K/+/c/IU0j1q8sIJSgdi3WOiYHFW35BtkPzElqRZD0pYR76jxeQZeJroB1FaLudLYbe+ienJReEHEq2bjVJesoup1vWOEsCZPzADgkrGnz5rDPwtNszCIgFcg4hkjgpcYLe1ztdNbjzs9zSmCbxGxjaVqatqZT1Ig45sb7N3n3vfdofMuEgjiJWd+4gq1bpsMJurqg5JsEugp64niLcd0F0n9EaAAyJ9hfQ0gp6XUyumnCwfYmui6pxkN60mHko2tn0zR8+cUXRFFEkU9o24b2sUpA4QjtITyPNDEDWFjoMRh0KYqa0eh8ZWAJoYgsltBPg/f6UMOuBmOCLdzaQPHOIAMcX332Ja1xLLgK2YHDNtjzPw/ee5pWU5QN+qKM3d/ipWErT71r8Rfc+iSOMwYLq1jn2R/myCji9/7wH9LpdBiPc/78z/+Ch/fvMxlP8Pb5pWGSkFszwMRDJ4IbCxIloW4d2sJuE5Tnz8Lc7ezkVxVAXlZ8sfWQYPUZmihbY0iF4PY7N1hfX+X6lTWaomZ76wE/++lfYUxNEjVoo/n06wOG45Zp+fx7O5EQSU9b5owO9yhnoVqaJPQXV04Ve7wolAyvYKkxy7G/wJ5j3pf6ZQr5qrpmZ38PozXFeIw1OpB8QtAdLJKkKVWRU5ePE8aPr9mnJFukQmV98B5TT2f31hwxkNBfGLB+dR1rWibjPYxuqYrqQrzpT8Ncstrrdljq95jWDeNJ/tTqCzcj0r1SRN0uHgde453HNRfYYfoZaJoGYy1bu3u45A75+JC6tWDhMHdUNax1C7xtKAtPVTnu5e0j1RTOecrSIqWjrCcIVcwqCNxxT6bT4EMCS0rJwsIC7733LsPhiIP9A6qqoizbZ/auc9ZSlgV4y4J3KBFEggIRrFacxzQNo8MhUkYomeIESBRxFIFUOKDX63D1ygq3blzlow/ep5iW/Hr/72nbCufmUrVASLTehla6FmID3ThlY/0aSEWzP4G6IfXgVIzVNfbEHs3NmqRKIBYhfOt0EpI0NDfu9TtEicR7jVIJG1euIGVEVW3TNHWwA4oUkyJnPN1jMhmzuXmfpq7QrUepLhs33qW3vMLy9XcZrG4gpULJCOEssm0ppxO2Nrep8ikUI6RpuX3lOivLa+jpkHy8T+McuTfnqvS4PE2dd5jqsbyf6kG2fPzflos1wxMSESfgPf7ouC9Cgj8udXqLt/hmULSeovWQV7D/YoyAkIKFfsriQkaSBCX2i6xTa2ur/KP/8I/o9Tp4D+PxhKxziknra4hYRax0+2hj2Gn3aetXR8R5Z7BNRaMkw/09fATDaEJuKprDBn/AcRFMzWvZ2POFcMk5yC6hZ+WyFCSxQqmgNLTe4YVHSEEqBH38U2f0UArrmY4neF2ztf2Qe5tf0xb+6eP2HCupTx4kIiwZl0KkR3S6S3jvqcsyeGPHEUka47ylrEryyZDJzibmGQ1vpAx+f0HxcKwT8c5RT/PwVx0ghnhB0LsqaIwnb2bqvVnDvguFISQmnjbeOCbqduktLbFx8zq2tXivaKqKqpy+eUS6IBDpA8CCL3gkDMkWMhauLdBfGdBNe1BX+NoRi4ir37/NQm+JP/zRP+T2xrvcv3GXrYcPsc7y87/9KXESsbo2QMYizDnaUE3bN4tIh+PzOPecdrw+RHoiYE3Oavc9wnj8gYf8YibIKJasbGT0+hGi1hfXpfFFIAiT8yIcZTDnupZn4XH/85Sw44pARLNydmmOEyb+xPuehXlD1pgwx80YEw9YazBG4xsNlWbl9gZX33uH/YN98i8/I4oU2eICNmup8/LiiHQhAuN00tZFiJB8cIR58TUl0SEoK9M4IYkidoZDpqM9pHckwhHhH1EiGmPY2d5+9gE9CHP880l0uynLy6GZ6Xicn0vRGRNyOqmAxSRweL/RsHvinumgSLMEow07mztY6+h4SNNAop+FSAcwxtK0+omy9rf45uA1GH3xQaiKYrLugLppmBZD0k6H9z74iJXVFX7y13/N13fuUEwnVGWFFP6pPQFOQojQH8D6oFeIlWDQl2QK2iI0SB3P+mQ8C49PGfNPrXVLPnx07kqAWEqurq3w/vu3WV4YoJuW/b19fv7zX2FNzcJCeBw3d+E5ThVHnxfNpjbT1pS5pHUx+IQ4jlgc9IMv8wVCCYhmRLojeMafl0ifLyknfb9fBK1uGU8nOK1pi+ms946bNYjv0un1MVqfQqQ/Xq725CiEkMho5ofYPH6MCEjJOousX72FbkusLWlqSVM3l0qkz3QgpElCb9CnFRKm+VNPpPOBSEdKVJbivQnNrI3Hta+GSNfGoI1hOJpgo12aMkcbBw5y69HSMi1rEiHZ3bccDi0jHq9qhradPXH1+QNNMSPSu90O6+trSCnodTO8s9S1fqa1mXOOtmmJZKhwCfYr8//NHFW1pmwdUkQkCXgpkVnorSVE8DNP04TlxQGrK8tc3dhgGA/x3mBMi/fzqxruYYvDeof0Ea31dGRMf3EVqSL2C0frFJHWRB68s48Q6R7Qzh0VjiIhSyK63YxuN6PXzUCGChalYGlxCSljomgvZMeEQChJ3dTsHe4zGQ3Z3d4KvRyAOE5YXFln+ep1rrz7IctXbx1X9TYNejIJ+8fWkRc1ZnyI1yXXV1ZJ4wicpSpzKm8ZYs8Ver3S4nSMhnJyTJ67ea34S0LEIDMQEl+PZinJb+kGUc6ayTl3senzt3jjEEuBEGCcf6bnnHOe4aSmaiw/+bvfcPf+Pp9/effcZbJSCpJUIYRjeHDA4XCIbr89ZFbdtmzt78+a5rya+SFRklhJhNWMR/uMRkO+vL+JxVPImsZpJtt52OQnhDXrLZ6LBQJPc70Dt3qwsaLoDTpEsaB1Gu0cPopQSUKStqSZIzptE+KDGk46wcbgCtG1iJ29Ifenu2eriJyTzJcU/FmjKaYH4D3WNsRR8HtbW14kzyfsDkNQ8bzn2ZiWqiqw9pTBzj2m+yD6IJKwPJsWnH71ViDWamgK8qlkZztFIokihex26C+ukGQd6iJHt5frpfhKkAQlenexw/o7q9jasjPcp52XSHooN0u88ZiHDcX9KbrSTHZzFgfL/MF3brGxcZ1Bd4D3nvu7D/m73/yMzek+3ZurKGvZ3Z2QdRJuf3SDKI2ppp9R1M1RE8tvEjINIdys389x7bDn2I/heZgbd57kCl7DWyNKYpauraCiCDoOn3umXx5SbZ5Tmv7YvtsYx+6DkjRTLC5KkhjcNyVMnxPBNceJxxcZSwNsAx2PVxrfEbAcBDP+wMLBaaXtj6EmeKXP9oVN03D/zn2iToxMYxZXVnnn1rusr63h0oh8MsVqzaDXp64KDn6zSzupqccNtnEhyfWyaDzc08HaZZlgF5YAvSgYsSbubMmHbwjOWqaTKVYbnNYoD0WjqduWqnm+/cP89u0LWFbQSyVXl1Ia5/n7/Yap9kdUUp5XeA9V1Zw7brUE233twloWCqIFvUggZrYH3li2dyskDik8TsJhDbWF6ozn33vPaHRA2zbk02C1uLiyyPV3r1NXNQ++fIB+Ssn7W3w7YYyjqFqsg6y/gBSSX/3iV0RRxO7uHmVtqZqWuq7J0phBJ0MrQVtLPIKk00UqRRRFKKUo85wiL46em8Z6HowdkQSrgwpUSVjvQKmhOOOaPWcRHn9qIiW5vrrEYq/Ldz58j48++pDDg0N++td/w4OtLcazD4hnW7zn9SsQQKLCGLMYkkiwsrzAtY01Hu5NebAzottzRMlCaOB4gbA+xKqRkiRpUCSl+NBQ0gRiNokC4W4NOANxDEkSGl4a7bE+eNK/tHuhAKEEwoII+l3AI4Vk/coy61euUK/3actV6rohL0pa45iWHqli1tY3SNOMuRPfweEBh8MDvGnxugIhkVESDIefEKKGEibvDKYNvtFlXtA09aWS6HB83iZ1gx2OqZr2VF5C1y3OeZxwWOHBObxxIQNygfuMeXJEinD9BVCZYO01LwM1xlGWFaZp8QQb0ERApgRpkpJmMVpVTLAXHk72ej0WFwdYa/n008+YTnPGk4KmaZ6pRg9SKI/zDq0N+/sHRPmUylu0d9RlFfZu3lN5RyQtyrUIoQhBBpRlgR8p1vs91q+s0EklD+78mnya411JEjtWVgb0+x2WlxfpdjvkeUmeF0ihiGWMSDtMaxksaqKULPOMp4fousA+1hNAEOYGCUdNc1EOpMH5FmMk1mjqtqbNG76UnyKk4uG9LcbTivG0JelsURsoDZB1uPrhd/EIjJdEScri9ffpr67TX9qgN1hBSUEkJd5oXL9PliVcv3mTbifhwBa0BfQHPZZXe5R5QqcrsEYgzklrvWIivQ2vi4ZIQC2Ab/HVIa9t5HkWSBVemIvttPMWbxRC0CJQAirPU8un5nDOcziqgIqt3V8CL+Y1KZUgzSLwjsODbfb3Dmib15CpOAV10/Bgd/eVfmaqJL0kQpqW8eEu47Lls60hrbHH8cL8h5ki9S2eDUEQOq4Dt7rwzjqsrSr6C1289EzzlgYLkZoR6ZYsM2FDcNrmwwUy/frCBtcXNlDtVzx4sHe28q65keUlwZqWYnJsyitFxOKgy5XVZSaHe2zdv8f48PC5TLfWLVo/Z/3tA2vAYEakN2CaoOp61XCmxdWGqdG0jSFLMzZWN4jSFOdXabXGWftGEOkiBbEEvfUu737wDm3eMvx0QnuiBKC8X1DeLzjo7vP14G74Swu3bsGttXf44N2PSKMu3nu+3rrPX/7iJ3g8/dvrNMOc7U8fsDDo8R/+6W2WVgZ8fX+LndFBIDq/aSK9A9FS2BC7uR3UvDLnrGSiJzBmrzmiJGHtxnWyfgfeB28drrUvRqSfqJDXxrF9rySKBPI7HQbLUdikfhPwBGsSOG46+iJoCNZPGbieDg2QVzLERgSmxe+cMdaveSSpUuc1X6dfEy+m3P7BRyyuLvPe7fd59+Zt7h1s8fX+Jt47FnsDxJ5m+Msh1d4F31yNh69MOD8fEcj0QQyLCg4MpLNF5ayJpFcMax3T8RhdNzitiYCyajnIz3ae5rfvkoQPIljsKW5f75Abz92JodTmqKAtzyvy/MVKS+ZEeu1h2syI9L5kkAiEdQgLrjVs7hiyGNaXgp7poIXJOdY97z3Dw32Gh/tHf7e0tsQP/8EPGR+M2X24+5ZIf4OgrSUvW5SK6faXaNuGn/3dzynynO5giSTrUFUtdVORxpJulmK0pE3CpD1YWiDOMrIsI45jNje3GZ/wh64N3B0fB5YSuN6DtTS0aDgrkX5afX2kFDevrXFtbZkffOcjvve97/Nn/+Of8dd/8e8Y1w3DwhIpSOPwPDyvKacUkEVBGZ6lgaheW1ni5vUb7A3vsLs7pD+w9PqLRNHFUk/Whe+o4rBHFQKU8Hjn0GGDTD8LRL8uQRvoxJL+QGKNp6otrYX6Kb0ZzgshRCDSJZzcHEglubqxxrvv30KYNYRpGI3GbO3sklcWs6+J4g7vf+fHLCwuIaVECMGnn33CuHG4OsfqOvQMipOQl37CIif4SzrXYtqKti4ppgXtKxC8zYvJRlXNqHp2TK6bBt00Yb8bnyjDuGBIwvKqBPTjuc85hF6eofOA1g6bV3jbhLaNQKogU5BlKVknQ0eaMc2Fh8n9/oCNjQ3yPOfXv/4NTdMyGufBO/4UzNdN4YPXfKNbdvf3QQpsFCxiq6LF29A3ocGFSgGrkcIRyl6hyKdUpuWDKyv88KN3Odjf5+vPf0HdaLANSexZXV1ECMUHH3yX9fWrbG3tsbm5izaWutFIqRjXEilARimZkOA0un7Sfk3MqkakeIxIVwbng866KWvGowlTKSnHBc4LHh7kFLXBsINF0llao7u8QW9pheu33gUZU/sIGcUs3nyX/mCRwcpV+gurpElEJ4lC5kw3dPsdbt6+Ra+bUI/3cMbQX+yxstYnH2d0ewJdgzhnzPUKiHRBlIbO1ka3mOYSnhZvwFUzFfpFMBrzW/WCDQrnHUOe4ekolELEEb51FyLWf4s3F8Z5nHg2if44wq8++ftxHLO8vBTUEZFAKcn161dZWVmmrhqqsuL73/uYpcEiAs+NGzdJ0w5Z1rm4L/RNQMQg1LE8ys3kCnCsoHuJTax2nto4luKYpeVl0r7BqISqadnaH9Oc3FgZQjDx27DXOllHeUZDQsGxaH8ALIjgqTrOIc4teV4RpTG9hSUyIZCLnq51vOdHyG7O/VGN3y+ptGdUeqy1TKZjjG5ZTGO8IzQmlZ7WzBR1c9uW+W7kGyU0RGC3RYdIJqQqQlcV44MDqvx85e7PxMxn0lmPbwgsRMmrtxnwHqzDYzC+orWWiTpESUndNBhjMc9LDHxL4DVQQKMa9u7sY2qLaU8J2+c2FQAOymHJz/7qp+ze3+X2Bx8wWFpkdHBIeTA6Km3UeYWzDmMs+8MhjW+piyaQdK9BnOE12GJ2i833nppX+8zN14DLsqPSHkaOKJUsDZbJlntMRmOapsY9q6TsNMzHeXLchMR5PjZY62mq10CMYXnStPdFjjEi3K8di0/AFy94wHkvEhWKlfPxGO8d+wf7dLKMna+22P7iPniH945yN8eesy/NuXDyGhoHjQi75N5snA3f6Nqz0O/T7XYoq4rJ9DjZIwTEcUSWJkRyAWFTRrWGMxLpXRleKx3JyiBiYZCxvLRA0jrWuyXeGg5beNlb2BFuG0VoZioUyChCRCp4RWsNNpxyY0HVs+nnAh6dMi958HVQoi9fWaLb7zA6GKOb34Yg781G2zSMh4ckWZfByjrIaObR7TBaAwobGDuMdVSNxlqDth4hJUQJMs7IegtkWUY6mhIlCc5anLWz5vDhs+xMqCsjQZQIMusZmGBdqJTCOk/d2GBpwtOniiSOGHRTkjRldW2FOIqwGHYPD/nlJ5+xPy755Ku7jOsWJ2BjrYf3Fq1rrPbP1fUF64aZqLgNP2/uTbBim73DaehHoTXj0Qh5wYr0ObrdAevX1mjqmtHBHtY4rA02U8gIEUm0NNQ4nPXY2uOcpzXHY39ZOGuwVYkzBn/CVcA7z3Q65mB/l24syCKBxyFlsPcIjSYF2miatiGOU6RSdPsDrly9QTncYVQeBn/xusEj8CKZ2Uq2M3eHQKS3bcl4eEDb1lj34gGeigRKCqz12BcxnT8FR8YLgLf+0mLQOJYs9RPA0zYt1vgTlj9h0fXO4kx9VHYrCY5yGWCblqrytMYctfu6SJRlxeHhiKqqqKoGfaIB8EkIQlNSqQTe+rA3kwIXqVDVkiQgBc62WG2JpaCXxhjraLVFeY9xNtgJ6tCIJqMXmmwKgcAjhSCOI1qtKcsprbFYFyNlTFkWFEVOVZe0uqHRhrJuAEFVlcSRYmNtgSTuspOkT23SrZRksJARKUkkJUpKFhYzOt0k7POMRwtFqQHhMUWLFwLjFUSSxYUV0l6fpSvXWbl+G1SCS3oYJzC1xTvLZJzTao+KM6yDpYUe3XgA3iG8Q3qHEB6Bx1uNMw1NU1NWFa3R4caU5+/fcMlEeiCkO4MFuktLlOMR071LUIT65oTS/SIe9ohA1xgerRN+ScSEp1PzpM/uDCqJkFmG9Q77DL/bt/jthieU/l0Uut0O3/ved+gPenS6MWkn5n/2n/8n/MHv/5jd7X02H2yz0Btw8+p1hBRcvbrB/v4B//2/+LMLG8Orhwh9G2QaHvkIaEuop496sL7EIl8bS2MsNzodbr/3LkrCd0zLeFrwr/7yV+wenthQzZs5vobqswuHJHjVCs6siJ0r0bvAVRleVHC/gUZoVrZHDFYXuP3++ySDPnWvj45i1m494Me7e/z93W26n91jZ+z41X2L1i2b2w+IoxjpFIsDQ5Y44thR1LMJWs3GOVe7fqPXRoIcIKJFsrhDN06oxiMefvUFw73diyHSZ0leqz26DeQmu8DJxpevCg6wHmdaWqvDsjk8ZN4kEMLm5I1ACb6GyV7BJ3e+ADjahD+BlkeSbYflAf/t/+m/odvv8T/93/2XfPCj7/DgzlcMv3pw7NvvQ5lzo1s+v3uHZDdhvJ+/Bvd0gC3APi0mepVjO2kHeRk8V+nhniGJFDeuvUNvY4HfTH5NVeTYF2Ht5mvTkelkgLOw/7BFiNfEHfBx3/MXgQbuAxJ8oiHSL6Zgm5+rCEjACsPew4ccbkdkacqoyrn3519x988+n+2aZ/YA7SVOfvNYQwGNBWvDOK8Q5t2CcyWcL3RoQnD96hVu37zBvYebTPPiaO5VUtLrZiwO+qxkCyQS9ouaB2dsPr8SwbUErq3EvHujz6Df5+b161SN5uOdIYuy4ddjeI648bmYK9KlgH5CqNhIU0hT6qogL/XReZUWDmdiuvYCzvX+7j6jvxixsDzgez/+Dnj41V//mlEzfvmDv8U3irLIqco79JdW6K5uIKNkJr5xNFVDU9vAKAOtMQynFdZZam1RsWQl7hF3FllYvcJgcYFRXpHsH2DalrYskQI6M41PMXOwipKIpKtYFIZUGKJIkWYdmtaxc1jSGE/F06fbQTflg5vrrK6t8od//AcgBP/yX/6/+Pyr+/zd53cxVmGNQRvNlbUe3/94jao2/PI3OxSVfq61n/PBckbAbP30HP7mIfLTLaxzwZqpLHlw/z4X2hvvBJbWrvDxj36Pve0dNh/u0TThxEkpWIwyVBpTVyVDGkTjYT6v+4vLn7umoZlzNydOmnOWnc0HVOWQa+srXFldwloz610kiKIIISVFWeCATm+BOM1YWd9gafUKW19/wnjna5wxlKNJcC5IuhB1oRmBqZiX8JV5w/1iBPhnqpufhyyLSFJFXRmqC+ynE8XhpVu4zFZHnW7MrXcXabXjsy+HlI05cY1n2Wvb4o+CT48iCLV6ztPkBcZA2fiLZAKPcHAwZDgc472frav+iedMEB6XNJFk2dzj22ClxHZSZBSRdrpIKTHjIa6p6SYJSTehqTRTXeK8p9Y6+IxjkLGis7rMYpKSSQXWEqng1V43NQcHO1RNSxT1UVHKwcFucDYYTpgUY+qmZVJWWG3RRU03y/jue3/E+uoyD+99xg5yZmp0/GWSJGLj2gpZmhDHKUop+v0enSxhMpkyHk2o0ew3Am8dqqwQQkLSRaYJNz/4mOu33uHWBx/y3nd/wO7BmF9+fp+8qKnGY7TRlO0OUimmRcnS0gHv3rzO6iBDOouwGukM0luEN9i2RNdTptMR+8M+RV3jlQo+7U/LBDwDr8TaRQiJkDKclKfmKs6K+XCflnN9zjFlhFAK72wwyTr1+HOZ5NMbPbwUFMfWDQnz5OEj8M7hrcG/xOT3Fm8upBAkiUIAjbZnVrJFUWjE2O30SNKUwWCBxcXFo38fDPp873vfodvtknYj0jTixvWbrKysUeY1cawQApq6RRvN1t42u/v7lOXr0sntBSAEKkkRcRcRh7bvToDVNeBChn/euE4QCKxzPpZKBpWBQKCNwSuJFCIoUp6G14DUukjIWCGi4+86tzE4KvY5x3Iw5xsiZuVhM0WD8dAKRZt2MGkX0V9A9ftEnQ5IRZJlpGlGEsdESqEkzLcazjmsDc1JnXc0pkV7HbLT+OPaQEf4+UWn5YtSuwqJR1LkJePDQ6bDEeVoTFuWL2liPrtG8zWp9VCDb/yxMvhVY36PeAIRDPg3uUzLgcefzcvyxKUOTWgrnHPsPNgi6WVMDsd47Z6ofnPWUU4r2lYHxfvrNN9c4ljSRNDvRUghjl6JihAI9ic1eXXsRX9p44iArsDGjsnwkFY0lMMJzTgPCsa5UvrxCpjn3Q4nxxxzVFHyqnsaHCHm0RD6okLpeaXC/NgvMhXMz9WcXW3DGuClId+ekrhDyv0cc4JcvXR4jnukwKPFsHO1+tMw365cUpPrORqtKaqS9jFhj/Oetm2Dn2uUQCQR3j+3kG9e76skqJmBrREC7aHShsY6sk5KX1uiYi7Hf/T9zzr+0zCvLotiRZwokk4HlXWop+0jz4mDo/L++eXQvPiy761HNxrdaIzVCCHOZhf3FueClIJ+J0VKSVE16NOS0BcI7x3eG5xzgdtQoJIMlXRIkh6RSmlqRzurONLG4LzD+WAj2BpLbAxeKqI4Jck6dDpdWiFxbUskoTOzgakrjXaexniKJrC+cSRACrRxaOuCLdozxhtFEf1ej143QwkZ/JWNpdGWurXoE3OINo6q0tSNwbonyb3TMO+nOt+WpmlCJ0up6oaiDBmxlyF2T/3cKAoqdxXRWHAyJhusIOKatqmDn7SIMcSoKCNN5Wwi8jgbmjZeKJ5ywryHum5RuWCcJCghqZuWoqioWo9pHQhLmY+xVtPpdkljhZQJQkmGWTrLP/hQwudc8PFw4onPC8Ts058BqSKEVDhrAh92CkLFUUwnSzB67rP3ckgihZKCbi8h6UQU0/a51owyCjmDuWe8c2d3iBazxUaIp4V24W+kCLwKPvSakyI0y1UCtPG09dwK5uLh/dnjfSHCPDdvSixmDLuQEhlHRFKRpAkeSyQlAo9SkKUR1nlwbtaIN9zzTdVQJSX5dMp4MiGfTimKgrKsaFpN22qsa1HWk+cThJDkeUFdFTRaY3SD1RZjaqwFZ1uc1XSzlOXBAkVbU86sf+drtjEWrQzGieDXLhRae/Kipaha6tbgvMcLSRSlCKWIOgOiJKO/uMriyjqd3iJSJVgnKMqaaqaMl1KhpEIpRSwFsQTdlIwP9vBGY6qcYjqmKoPVkXOB5m/alqIsaY1BqBjkPOg6+zp9yUT6RUbRCtQg1OXZaVChn+fdnQGyu4Srp9jpwVPGJIJJqeiBH4fXRQc8CSHVZTlWpo94ZPWzVYWt64upMXqLNw5pori1sQBC8GBnQlk/XzonpWB5uUunk/L97/0ON2+8wx/98R/zJ3/yp0fNSpSSpGl65MsmJPR6KVka8ZBthsMDpmLC9KDg4dYO/+f/6/+NrZ1dDg6Gl/2VLw1CSrorqySDVWSSIeOUcrjN1LpgDN1MIfawQZgpdzi3B283TehlGcJ7Nrf3iJOIbielqJ7dTOSNgIBktUuylB39lZ40VNvTML+dsKc4K2LCNOoV6ChMoa2A3qBHdeM9svUV/O0PoddDmQaMQcYDUDVe9vBkBG3BY/I+FY45aqaU7ZhpVYbYNCZ4186JlxeNJePZS/PiRU4hksJ6zxeffYZzDZ/83c95+MmnWGtfQpE+ow28CtdEgRgaRGoRQxdq67+JSomXrAb5bYNuWv7q//1v+Om//kvKvHiqgtW0ht07B6FJ9WWqbF8zXFlN+Qc/XiSLFd04IVGKq4MlYhnxf/+3d/ibT3c5qt29rPt8QcJ3YqZZwV/+2b9CCEE7abC1xU1MSNj2CJ2UDbMOibM/n3Wp/Oz3YmCJQK6WHM81r9L/PiLMl/FjY7hInmLum/8i12l+rqbA5xzt8LxwbN29x266iclfIYkO4bvcJ8QX7wFXOb7uNaePZYGwn5gSGrtewpi99zzc3GZndx9jHi07161me2uPaZoTrQ9Y6CTYpiXh2dz+3DJfKrAJlAL2a5i4lpHbR0jFwsYG6arn1/kDyCdH750n0ufOT+eBVJLB8oBuL2Wwfp20v4BtPKO9Y3X4vCglBgazxmj77iVaL8zut7Zu2N7bAgHNK/Ar/m1DJ4350Xdu0ssSfv7ZA3b2J89/00sjBChCOCIVIVRMtrQB2SLXN66yOBjw4M4nPLw7xViPqys8QXnqsQzHY8rWsnHrHeLegMXVda5XFcV0zKFwpJHk2nIP7zzlwz3asmFnYtjPLesDxZWFhKJ07B5UaOeprH/m8tXtdrh96wZZJ2U8HFLWDeNJS1E9STdMpjWffLYTrKtPs5h7DFKEJqNCQK3Be8F3PrjFh+/d4LMvH/DzX395cfaDjyEbLJANFmlkxp3tEUom3P7hH9E2DQ/v36WtK2rlcQ66gwUWlyKIQkPnMi/ZebBz6c04Qw+FnPGoYHd7jFIKgUfgsM7TaPBIJuN9kqzD1fUlNlavEScdoqRDvndvtl+feft5oJnNTGctPROCrL9I0ulS5xPq/FmVMYKlhSVWVxfB7zOdvFxpkJKC9UGPXpqwfnuDxbUlvvrqIZPJ1898X7YI2QJEUpAIRVU4hjvuuZ79wOy8WrRxT7HADc13UqXoJSnOWuqmJZ15/ScSDvKw/Sm+wVD5OAEgECJY7SQKIiVAKoSKyLo9kiQh68R4qykmI4rphCSKGKwNMM4xrWq0cUxrQ2sM25vb7GzvoasJ+eSAsiwZHQ4pqobROPyuEBNAMJlMg4WUdUfVJW6uovcO5xqmo11SYbi+tsKVpQ5f3r/PZ/fuhTVVAtZweDBCSEFRWowJiUepYqxuMLrG2XBdVZySrd4iTrr0F1fJuj1uf/x7vPfhBzihOBi2bO6M+eLzr7BesLRyhayTsjAYkKYJV1cHLC90mRxu8ZPPf0aVTznc2Q59t3SF0Q1aaxCSw8kEhAuxRWeAtBWIkvMEVK/A2kXgnDuR/Xra4E5j/x//+7ms78XGIsTz3j+3dJlLQi4YJ63XI469I0/CXaYk6i2+7ZBSkMQKMVPVnYQQAilDI1HnPFGk6PcHpGnMxsYy/X6H27ducevWbd5/7z0+/PBD5BPK6HDvee+p65ChzPOCIi/ASia2Zmtrh3sPNtnd2+dbCyFDlYpUs1eEECEYJkqCGn0+/8ynjReYeqQUREpirWVaVMQ6wnlPWTchS/yGQyqFihOUkkSxotZQi2m4y86ZR5hfgvkd68SJV6Qg60KnG/7MOvjS4oXDuKAsMFZgvcT7J6sBPB4ngjd63TRoM9s8nLRNuJxq1HMgJKadM0zHE/b3DsjHE9r6ZZttCoRKgvRDzKSsxkPrQpZibiXyim/XkNAL89nFWLicvIBv3rPnvacYT59N+ngwl+n3/JpCirDx6GQRG0s9sjhmPc1QXpLFJ+aDy7wtZiU1zlny/XEwvp0LvTTHc006+3lOosbhfUeYK7PnoaIUyERB7HGxBQWyIxGxxHmLN8+r1uTiijAlyF6ESAXOm6C+vejb7aLU7Y/xmbpt0ap9tcm7+a03lz03hPm24bhfyvOk3Ze8LrVa02qNEJIoikN8OfNwdlpjhKStYxphccY8dzjzYctIoVKFVxG1E1gDXjtUpOh1MuJYkCYRSTTz6L2QJSBI+WZFTo9ASon0/khVP38cz++Y+iSccdRlDSJUBb3FxUIgiJUknlV8vkp459Btg1AOhERGMWnWodvrE6dp2Gt4hzuhEPbeo1uNUA1NXVFXFd454jghjmKUUiglUZGaKd7DdzKWQMr7IH4SMlh3OPfsaSJ8aPBqb1uDHk8p64ZWm6dq9qz1lLWZeb6f7TwIAbGa7UGFwKPoZAm9TkqSXC7VJFWEihOcENStIYkVSZqhUCSdHgiJMxXaGtI4IolTRBqanFvrEWrWTfVFiH4RDL/FXCrs/dFrrvie35LWOqwFrcPfh7gk0D7Ghl/0bTiHUniSSBFFiigKHurHmF0UPz94GHeSJKRJgtaGujllXyAlQkXPtdcRApRSJHGCkhfhaR8qs4WUxFFEksSoSD230vdYVR4EfuKcz7fzz9Kl+qPPEEKEdckfS4Dn772osFDMzqN/gZLB+W+L+f/5+bPvjyquAp8ThXM0e3CFFEdvFjJYCOGgtWG9LoqS0WRMXdZMi5K60RjrZm4H4Y2PJ5nk7Djee6y34DV1VVAlCVkq6Xf6ZGl6ZEujZklpM+sKXNcabRzKKWQ0N8uP8AJUDCrpEqV9orRL2l0g6/bo9BfpDZYoqoaiqGkbjZv1m8iylDTNiCNFJACjsW1FNR0x3NuinE452N1CeE8ni8BZhBRIFc2SLZo0ikniFBUZlJTn2nNewuwmOA4/wquaFLRVgdWPR9SC4zrQxwvo5toDf/xvbhp+158/Mrf1FKfrma3LKaS9nz2xl7WZMhxvjo6bOb/FbxmEDIUVfuYeclHo9zssLvaoqobDwynXr1/jv/qv/jdcu3aNDz74mMXFJfr9BTpZh+Xl5WcsSAZrNX/9V/+GX//6Z2zf3+HBnYccjqfcub9FXpSMxs/KZL/mkBF0l/EqoZy0VNN9hIxAykBELC9BFUE1DlPPLmGKegEhkXcO7wwHwwnbh+OQUVYS6xx5+bIE6OsOgWgyRN7nyo0Vrt9eZff+Fr++N8Scaq91Nhwt8QpEDEk3YbC8Sn95hag7QKYpzaSgKTXbexM27+6yvV9QNBGVsY9M8R5ovKF0DdOiYjwssMbNv8LFrJIX0TjRO9AjrC/48osv2dodMdp6+WSWiCI6azdQWQ+XTHFRgxI5pipwU4vfty9X3/6CSDopWb+LblrKSfFim5yTkCmIJFSzuec9zCdt6N4u1N927By0/PnfDHn/1gr/0f/6Y5Z7KVu/+ZKDgzH1ZduTzVm50sHnoaEVrX+SEIcw18x7RwgCqX5VPToHjRzkMxK+hGQlY/DDdZy3TLZ28dqy/P4qaS9j8tmI/O70mWOLliSyI7Bjh81f8BmbzZNqkLD84xtESwmjT7eo9yZwQBjr646T1+NVQAF9wr0xJ8z3CQrziuMGz/Mty+PjmnDcX+QVjLnXW2R5+Qq6qZkcHhBJwaJ0pLRM9reYesN4WtKeZTgCeqvrrN5YpW01+1VDrGL6akASJ8SdBYSAG+t9ElGxNdLsTszREvoiX9dax3CYM81Ltg9LvIyoxsEbN05iOt0OGIstS7TzlO740pz2HY4SXM9Z23VjObw3CTvd+rcvkXnZaFrNV18+JFbipZWz50VVTLjzm58gVIyWfUSUIqUgzWKiJEUmHbxt8eb4untn0SKvizYAAQAASURBVMUYU+V88tO/5utf/5zeYJH+YIFg2xLqLobTHGfdkVXNfFlQQhBLQT+BqwNoDByUoWHm4w6U8yrO0f4hf/aXfxdsaARY5zgcPl257zxoe75nTSnJ0mKHNI3o9fvEcUKawu7BPnmRn+NI50cQkcmwtGpL1ZTsHxZEUcTG9ZsoHA8//TmT4S7R8hWSJKPX7bO4sYRKh+zuDRF1jalqvHPPyC0//tADnWWIuyT9Dmk3w5sW39bYpqI+3Edg6XQipISqspgTie35eZ5/iJSShUGXbq9HJ42JpMCYltoYmqZ+TFcqiOJQAmC1xjvH9z/+Lj/+we/w1d07/OVP/hpjHptrPKHpJhXmCT7uaedVzl4vn5xyzrGfF4yqGtuJKHTNJJ9wVMJ0ykRbj0PrMoFDCo81/kxqdAgKf0+EP/KEexKNtdjaIf3MKc3DQRMU6dkAujGUOUxO6Wt4VkgV0VlcQSpFNRliTkt0POPbeO+xzmNNILfNJCeOa6JYkiYx8ewba20AQVlrhlOD9Z7KOlQcce36NdI0Zf/wgLzIKcuCe/cqtA52TnbWzPRZGAx6XL+5Ttu07Gzu4I3mwd3POUxSfvzD73P92nUebG8HN+tI0uvORItRhnMeIRwI6PRXyPrLZGmXLOuHyhrjQEbEWZ8oThisb9Dr91m7eosr127zxRdf8fXXD2lazcfvf0SaZdy8dQslBfe//pLp4Zjx/SGunTI52OZw+17o/dA0pFnG6jvvEqUJemmRThqRRBKHIO50WFhaxscxg8n4XBzFJaUJ5yS6AiRW11j9tNrOE6S7eLyOW4GfF/GZ8Kd/iQ5QVuPt897vX36z/pzDP+Eb+ToL4+bppKfJN97ihSEkSCVw+HMR6fNlwPugoH3cazGOFZ1OgnOOKFIsLAz40Y9+yHvvvcvHH/+IpaWVZxzdn/gzeP/t7m7y+We/Ybg7ZX9rxNb+Ab/+7HPMJZfAXTqEgCgFlWAqA8YctxEfRKEjlZ7VKDqOLUheBN7jnaNuNePyt7Ck10rQEanqsNhfJM/Gz1VCnBXeE7LwClQcvNDjNAtNn1SEdcHrMS9bRpOSvDJoK7Hu0c/3eIy3aGdoW0P7+Eb3Iob7+PL2ogfxYUM2GU0oaoE+QQLOg93zls8KoYg6PaLuACNMCHJ8hGsFriXc/98AlywjRZwleOdeqrPKEYQClYRmfqI9w5p2IZ/6Fq8B6saxtdewvOhY6C+wvJCyaSx1UR2paC/tSs/ZD0MgwT08wTbO/S5Oqoz97Oe+CIT6HK0IRHzYK6GyiO61BZwzVMMxTmq6K316y33arYYqqoKKeK6CeqzIU6QiEOnFS5yFuWosFiRLXeKVDtHCPqKU+MnrHujOcBlDfNYpneuIFMee79XsdRY7HM3lNMU9BXGc0OstUouIQk6IhCcWnhhLVZVoXdO07tSlQggRaCgpiKQkyjok/UV0UVFXHktMIjOQMUYlKAGdVLHYURzmx2vyC/uVe2hqjdbQ0gbue9ZRVEpJHMc4BIYQm2uOybRTccbYwFtPm7/Ci/VbBmcdk3GBEsf9I18VrNFMR/sgI1Q/IkYFxSk+VGRGCQ73RGs2NzN6Hu2HGO7KtZt0uz289zNFpAs+xdY+4it+skA2koJOPGvlM9uqP553k0AsoK1bRvXBmae58xYBCiGIk4g0jVnod0jTFOc9VV0FG4VLhgDwwXJCa8s0b8iylE6nSxKFJ1k3FcZqjPegFHHaJUorVBIjrYFaHB1r/no6kT4L6IWAOEOkPVS3TzTo4tsaryR4j5AC4UMzUaUEUj5JzZ8MQ4WAJI5Ikxilwqc7ZzHOYh/zMxcQeg9KiTOhiebiwiK3bt5iPJ2cSn475/Dm7P3VXrgc+zF4oNYGYQxFWRInkka3x6fzFFgdXsdHOR+cE3h/+vid92jrQ//x2a/VQThNR0GSgrwIrcWsJ5uKYoQ8n/XU/FL6WfzmZpyc08FurW0amLX2VEJgbbBfMbPKEkvQASYKOp0OvW6XvJjStApjDXVjMMbTntGCL4oVg0GXOpJEKvRpyKdjWqkQ3tHLMtIoQonZeh/N+BUR4YVHSIWUkKQdsu4C3e4i3d4yrfV4bfFCIGSEiiKirEec9UiyLknawTmYTnOUVCwtLNLpdFhZWgDveWA0bTGhGO1Q5QfkhztM9jaPxq0wKByxFKRJDC6dWSs5hFRESUKcxLOegGePNC6BSJ8ryE8W4T++skVAN6jD0lWESugMesRJcvQbbV1QTUdgS2h3L36YT8ARJB4VlyafmZft1kDOyysULxliZYC6uoybVriHB/C2JPFCsLg4YHl1gemkZH9ndCbiK51xvHjLzsF05l336HNVFDXWOlZXl/mTP/kOH330Md/97g+4du06nU73OZ/gOJZAOU6aqw7HYz7/+g7TWUO7bz2ch7IGYcC2oSwgWYW0Fzqf747A1Jw57f0MVNpgnMe8CeftvPCephxh2or7n48Y792nmOS4F+zcIgitJboADmoLvUzQX1OsrMR0+ylZJ0aKUFLZNIaitNzbHvPLL/eoiHCqS3B2mTCffL3zjEcHFOWEuiyPP2wuPJnbL7wuc7X36GqK1QbXhAhvod/lysoydduyvXd4xmSXBJmBykLjN29pRiPaaohQLUJpbPWMmuFvFQTZYEDaW8aYBq0rXFtjitHbJPFvEUzdcHj3IfQjDoYHDIsxHd9yI4GphfFlkjBz3cjTmizPtCIMOZ5vamaSWBve15u9f+RhPDtGFxavLPF7v/v7xFlC870c4eD69Zv0ewO23nvI/t1ddne3uXvvLu20onw4xlt3pHWx0uEkOPESz8FM72LHhvFfbqL6CZ1bywx+d4Op3yXf/RbbwJ0HJwtyO4Q1JOfp1Wx29m+CJ/cCr6FWQUiJiCJUkpB1MpS3GGfwznPQakptqU9RssWxYm1tmU4n5frGDfr9AdOi4MutnDTJ6C7fCCRcL8V7y73tPdq2YnNzzGTcMKle/oR4D1qDdIKV9VU6/S6j/THj/TFGG/JpDs5jZyXx84jtVF3ayeT42yXkG4UjFPwIgivdNzMIh6uG6Dbn/peGg60+aRrz8UcfMh7u8fB+gX/GPqCYjtjZtHSzhLW1JXTbsL+7Tdvqo33XXIPXtJZp7jHa0RaAgJVeOE7jgqp22EBpIImgH0NloTwzURbT7fZxzlEW0zPt+4xx7B5WRKphf9TOmpsmdDsxVdNcaphVTyeYtmWwepWlpSuUoqWcFrjWcbi3QyT9kQViPh3Rtg15M2FY5+imwQmJiOIjxnL+bY/tNCKUiCGKIM3w3mGrMQJPd2GReGEVLzy2tdgiRx9ug3dkvR54R6trfGMw5jnn0YekkNWGfDJleHhI0umSZB2UnBHaIkLGSagqsBqMObo+Xz14SPtXf8Pe3jb2Cb4mBiHpdAck3R7VxFG3pxsBhuaoFdPpOKjhLwjew3hcUteautVhbbyk7XHdaHZ2x6HZr376GtKR0IugmyquLGY469nfr6m1YzMHX8P0Keu3ZH6vnO3G9t7T1g1C2jPbewmg201IkgghLN6HngxxFNbjOE2JIkWSpsRK0RQ5tm3w1oBz2MfWY2MM21s7JEmEE5BlXYqims0xZxoSAEVe8fVXm1hrKWuLs6A9GAFOBXuWbiZY6SlaB5NCI5Un63hUFLNx/V2iOGN54136i+u0GtrWEylIVTin1nu8gMl4Ql1V3Lt7DwHs7myjmwoRKVQEbd7w6S/3aJuKr3/9U6aHuzhb4m1DW+ZAIPOTOCKWMBnuoaKItmmDL35d0bYNVVkxGk1om4aybM/FdV2SIn0+gNOCH0Ug0jsQr0HSJV26QtrtHf2Gn+xRtQJaCXrvFQUqL1m78SzM8wrzM17z2leMy0EHeXMN9sa4rcPXMrj/NqLb7bC2tozwgoPd8ZmI9FgEIr11jp1J/dRgsWk0TaO5enWDH/zgIz788CNu3LjN2tr6c48fFBQnO3Mb5h5beVGwubOLtk9r2PEtRKj/47iDpAa1BGkSarjG41n1y8s/oNo69G9xAso0JaYpaYoh+5vP//3nISbkIv3MvnshliwuSPp9RdqJiJMIIUJlkTaOurHsDSvubo+I+otEC32QMynnDN57yjwP8/M88DiZB255zZKeHtdWuFaDDzujbpZxdX2FaV6yezAKquvnQUiQCUImSCGQWGxeor/Ntk2nQUDS6dBZXKTVDbQpppxiyvFbIv23CFYbpnsHRIVkmk/Jm5IUy0oUCJhLJ9JPM1ye56/z2eskCh/et0LIJOYEvccsq9hb6vH+ex/QXegDEKmIW8u3GWQL3F+9y+5723z22accuAnlnqLanoRKuFnhqJP+Yry2LfjKUnw6RKYRCx9eZeH9DfSdipzfMiL9ZOJj7nn+ODzfDsubOYRAKIWMIuIkQVqNdQ3WG3LjmD6DwVRKsbIyYGGhz/sff8jyyjo//+Wn7Dw8ZHWlx9L6KkksiTuKtq3YG43J8wlbhyXT6cWpWa0B7wX9/oCVtWXaomXMGGvtEz6wZyrs/u0N7V4reEKRzjcLh9c5VsPBVsWBiPngow+48e4tpNdsPRDP3ELXVYFuS+L1VRYW1qkqgbaWptWPqMM90BpHWTmshraGOIbVHigFrQmat8oEIj1S0InBilkh3hm+SaQiOt0+1hiqqjiTqMg6zySfM/U1Ariy3sf7jLa9XEW6rit0XdEbrNBJM5x1CCzOGvLJCOkdeuaK0NQlTV1SmIax0UgBEhmsJ+b9Ex47vhASKVKEivGdLt4ZXDMF70h7XbLFRZqqpK0rdFXTTIYhGbG0hPeeaVVj9dkmC2/9rKdCRZHnyCgi63ZQM7NwIUHGKeCxVYt3xzPV9v4BB6XBNsVjRGBQBgkikqRDp9vDVNPnLD8e3TZUlcSYi+wWDmXZUp41q/MS0NoyGpfgT9eBxhIWIljoKK6vZrTacThq0Bqm9dMLw2ZXAjgHke48pjUw62twJghBlsV0shitm2DVIzxKhebZaZYQRRFxFKGkxLSapqyQYlZN9djHWOsYjkYoKVhYXiLrdBCi5XEHoOehrlvq+skz4wA3sy5OY0E/lUwbx7CyqEiQZJ5YKZZWNuj2FlnZuE1/cY3xuOBwlCN96GXkvMdbjfeOqippatjb3SOOIkbDIVY3OCKki2jahoebDynzCTv3Pqca7xPHAqU4sjaa9xZUwlPmY6SUCBFKQOuqpihLyrJmMg42iIGTO/uCcsnNRk+DAYpAArQSb1PqgxYz7RLI7BpdF1CPwFacudvFHEJAmgW7hqYJ0VOH8Jo7xcwx5w8dcyeaQJpcZIAUzY6tCYqjM5kIfvNwRQlb20gf0f34XZyDtjF4Y/B7B1DVx5vDV+kt+S3HdFqy9XCfoqjObMPQehjpwPOdVpG1urbC+pV1fvfHP+JP//Q/Y2PjKp1O54yjcnhf0TQFv/jFZ+zuHvCXf/VTfvmLz3m4uUd71HziTcC8q5cMjUVlNiM4wtzzpMvgW7xKdDoxvW5K2xqmeX3EczpgRJi+Mw+JhYEQyDhCRYpYitBoxHu8dVSNY1pbhrVhv9Z0EkffKYg6LC5fQeuWPB/jnSXLekRRRF1VaNscq81agvpzXnr/jWPm/+AtgbkKi1lRVjzY3qNp2ydKQJ8JKfECdFvjnMW550VU87oASXiGTvy+UKDSsF7bhpddEEyjqSYFRutz29U8AQ/amNBIts7RxRinm6eQ6HNbupP+a2/xVMyFAd1ZX5ncvSbPyOkYFS3/31/tsJAJMhoEQYm+p6G46Cn/ZH04cFRf+yJOJ55AnmuOd3Ym/N3w7gE/+R/+goW1Jd750Uf0FxeooorYxMQqZmGwxMbGVd7/4APajZpq/QbVtODuZ19S1+Ux8XsRXRXnw7WO6Se7tIcl5f03KDE3v56n3Stzb4W5JVzExTdb/YbQ6oZpPsI0NUU1RTqDnwkuzClrjpSCJFGkaQxIjPHcvXuPhw932HywRT4+xOmatpqSZQnLSwOMadnfH1OWBW178SdPSsnVKze4/e47VJOWrfvbF/4Zb/HbDEGv3yFNuzR1ztdffcZkPHru/mn+79NpyYMH21ijwTnSRLE46KCkwmiNtYbYW1pr0TbsWqQFXQbKw84cvGIFV7rBNczMHCxPHbEITSC9c3jvgzfx4jJmHjM1DVqHGPE0JEnMxpV1IqXIJxN022C043BYUdavxtYojhW9XorzhkRJrG6pxvtgNdZYUAlplpAkCUl/QGd5lbauGO9tB5/xUwQoUZLS6S5gvaOucrwzeGeRUtLNUgb9Hod1SV6VCCnpLq4ghUB4QhXCKfFrp5OxsDBAa8NoNMY5R1VVWGsZj0eoOFT/JNEKi0tL3P7gA+q6ZjItsEbjZsT/HFZrqMpZ1W+KjCLSwQJCKrwP2XKtG+xhRVs9sy093kNRNhjrqOsWKTnqo/ptgffgnlM11M0k60uSNBFY29IaT+U9FaeHs2F3cN4T4XC6AiEfSX6chigKvv9pktDJUowxtG2oEJYCkljR73WRUlLXNc5YilbTmiC8jHm0UercQcf5QOo3jcYjZ37qZ8Oc7pvvjh6Hd579/V3u3g3Jl6vXrrLsYNkIsk6XG7feJU46WNnHixjrDZPRAeNJyWSSI1SEStOg65AmNHK2Nc47poeb7LqSvd1dtrYeonDsSYu1jmlZY3RLGivihQFWlzjbIGbcsXWepjXEkaKTBkusKFZIqVCyT7+XUdcNZRUCdHnO6sxviEi3wBS8gKYAIqqyIBTt7wKHvNQGVkro9kFFYIeBSO8Dq4SNyLzh53woc/HpnPCGi+XR4tmrITQS+pbAT3KsyYk2rtL7ne/jhMJNKkxZ44sqEOnz5AO89pvo1wWTUc5k9Ljs7NloXXg9CxtXr/CjH/+AP/qjP+af/tP/JZ1OZ1ap5nm+3MziXElZHvLnf/7n/PrXX/Czn37C11/dp24N7Rulqp5LwSREKxBns+e+AF8dqXzf4ptBr5uycWWB6bQmL5ojEnXufJADfQ8dD2tCopKIKFbEUhJJgXAu1Bo0lnFp2S8126VmKfMoFyHimOW1Hm1TUVcFxjt6WZ8s6+CNQzcniHTLa6QaPOEb4BtOTrjTsmJ63qaJs3bqXgrapkbqFvtcaYIkSC1jjtMa83+KIOmBM+DO4kH+bOimRTcX9yxqrXF1jc3HmPEeT48x5oziN9Bd9duEeUSdCliZEemNP67oeE1xkLf8i59u0ksE/+QjyZW+YGRg6zL2+Y8r0B0v7mntCcVTJ2HC6/DLPf7dnX/F8rV1eoNFxG1FGRfEJiJWCctLq1hn8MoGogQY7R2yd7BNvVuGR/miiXTjGP9i63jsbwpO+tc/7XuddLacX/83xBq7bWsm4wOMbijLEcI5jJh9xVOusVKCTicmSWKEUGjt+fKLryiKiqqqqOua6Qh2NgW9XoerV6/inGN3d0hdX05PGSkVN67f5nvf+dGMRP/VpXzOW/x2QgjBYKHHwsISB/u7PLz/5ZmEAN6DtZ7JpGAyKYiUoN9VpHHM1bVFOmlCXRbopqYsG4rCUjuY+KCrGJ5YH6SAmwNYycI2vaxmBPtpY1YKKWWQDlhLnKYsrqxijKUsKhpZYZ15JpGepinvv3uLTpqyef8e08mEg0nJQfnqAugkjhj0M5zTpJGiaS3FcBvbNhBlEKV0+gv0Bz16C0ssrK4zOdzn8OuvMO3p802UpHSXlmnrnHJ3GzczvBdK0et0WBr0mRzs0VYlWSTpr6zjjcaVeYiJTjnxvV6XGzeuURQl02mO1pqyLKmbhuHhIV5IVtdWSSLF8soy73/ne0xGI77+8kvqytM+tq13usVpT5iVM1Tco7t2CxXF4fpZS7l7h2ayd6bzmRc1eVEjJUdE+reqRdpJIv0UdDuSjfUE7zxt29IYR+H9c/0pzk2kex+I9DNACIgiSRQpsiyhk2XkRUXbepQMXudJHDFYGOC9Z3s0oiqDHY2xQer0uGPcnEjXs2RI3WiM9eci0uedL+cFlE9+Rcfu7jaiGdNfXOTGzRt4pfBJxGBxhe/98PeJ4g73tidMipaHDw8YHg4Z5xWjSUGWZSzEC0gpiIXFS4dxOdZoRrst9XCbnZ0dNjcf4HUF9RCkRCULqChiqd8h7SxSjBuqVjO3OXfOU7cG5z2RgFRJsiQiiiIG/RSkZHd/yGRaIGSgjs+DS2w2Cs/fiJ40mZu3n38ZJZsIm3mpZpOXPT5US9iICEKKdv7RJznG+VCeNexYhbvc2LN347CcoWvNa4g0gUGKSzPa1uLFrORBSuj3wUF/bYlsqU+1s0/xcPPbK+ITIDoxSIGvDTzPy+wloNIIlSqcdpjq2busLIYsFhgTPKQeL6gAyLLQHAE0k/EhVZUjRFADHd908z/niktO/L2lKKZ89tlvODjYZf/gEK01t995h/X1m0zzgtFkwnQ6ZWtz883wSQfCKtuElUVIcAKe2hT55ZElKf1eH2000zwPjd/e4qloW8N0WlNVj9ahzsV+c3GmBfZKx91dg+vm3Np6yKDOyQYdZJyyOEhxboFr11a4eWMNLxKKoiSKY7JuH2HtiS4uILwIyd1LhFIRURIaAuvmnGudkMi4CzLCtQqc5ohNexHMlOPeW2wNXorgrffsN3HsdfNohJp2ewyu3kDgcNUy1rTkh3vP3KC8OnhcW4OcBCX6qef929AF/OWg1CzIPl0odTZ4gh9KPYvMX3XFUkLI6cy9pp8xpcaJoNsLO0GjHbHyjLWHOnjHXhr8KT+fgqgbEfdjbGtpx2esw2emMCprHnxyh+n+mMniPt2si58JHaJuTHfQp2lrDkcHTPNJsLKYE/Sn2Y+8DN7UR0gQqlsjwjk7zbZlvr94Q5Z6Zw1tU+GsOSIGjX92P1XnCHsHrykmOUopqrKibdvHrFQ8xhiKIsc5/4TNyotAivDys3HMx+is4+GDByilGA7HxFkfZzVWvw7r1Ft82yCVIuv0EEJgjZ01jEyIlUIKgfc+SCDE8ZJ5FoSmmQ68ZTytqGqDboK9Q9M4agv6FHLceyg1KAGNDr7pc+sbSfBNh9CY1XnodTt0uh3KoiSfEbqT8RhrHW3ToI0JimYRz6ohn5zUvHM0dYvwYGbNLF+6kvCcKPMpOw8fUJYVTT5CN9VMZT7jg1ygL6I4Qrc1o70dykmoSn0a4qRDFKd0en06nQxc+0gTT+8c+WHo4VeN9vB1jlWCxgi8tfi2xlk7s059ElEUvOitBSEj5nYI3nuqukZNpmxubgXpV92gmxpjNNaYmc/2fD9/0tXdEqcdOoNVBkvLvPv976OimP2D/SAcGj8891I/V6J/m9ToZ4V3HtdatPGMS0fR+ie8xedYX13h1o1rTPOCr+/dxxgbemgKGXzsVUxTV+gL2POEMTiKssEaT13rYE/jZ7n61jAZ5wgpSNIUpRR2UmGtfkQ2edolm6+xcz5HSkEciVmTUvfI9Y5mCfO5C+Dju0QhoJcoEiXQ2jCc1JRWMm4ccRbTHXSIkwytG7yQWNNiTYNuK5q6oKkr2qbA2wpMjhB+5gnvqJtQoeHimEpFVNMRXhd428yeaYezNcIrmtpilaTVOrg3PDZO5zxl02Kdp9vrkaZp2PILwWK/D9fmfurqXFaHl0Ckz1VzgrOpuuZEwMHsv19Gia4g7oYS83ZGu8yVtDlBWbgIXOHY0uXkxz2Pk5ACumnYieZVMCM7C1qebrT0uqPfhxvrmCijmDYgxOzBkrBxFbHm2fj9H3PtOx+w+e/+HXd2t/HGfTtLWSOJXO8iEoXdzvHTy7tgyWJKd71PM67IH5pnrk5LXcm1RUleevYPLdodtwSFMIEtLXdYWOzgfcX9e59zsPfdE2T3nBTSsz8THiXSg+R2b2+L//a/+x/Y3tqkaYOk6h/96X/Mhx/9Dlubm9y/d49PP/2U/+e//Jc0zZuy4fDQzioDGnH8d5eAxYUl3r/1PpNiwud3Pqe9JML+TcA0rymKZraQP3o9GsLKUs3+zA8MD4eGD6Y7LC39hPWNVRaWe/QXl7l5fYlr11e5v/k+xrZ8ceeAX32yQ3ewyNWlDSQqrBmAcCAsvEy/vbMgyToMllfQTc34YP+ZCp/HIaQiWlhDqA56PMbV8xKn81W3HMHbo/tfN7M54bkJHsdxWdWjJ2txbZXv/OHvI4SiLWvqIufLv/0Lpgevoln48+GqMa6aPGc38G1cvM4OKSBLJFJC1Qb1ygtbjcytSoaze/hVn7oB8B5hQfySZxLBvZ7k1ntBdVSMGpz1PKwdmzWMLms5ewECNVvNWPp4iXq/5vDXh7gz+qoClKMpP/kXfx78XKUMG/4UiOH3/+Qf8Z/+r/4LDkb73H/4gPH+AW3bhjHucfF2hm8yBLBM2EvsE4pon4Y3RIk+h2kbjJ4nd/yZLN6tdZRli0SjJxVCQON8SIg/vrY3LbuzprQXIdZQMghRnIOmPVbRGaP593/9l/z0J/+eKFugu3iVtppQ6dOqlN7iLU5HkmSsbdxEKUVdVnhn6XV6ZHFMJENcFQnozvR9hT3bXeYclLUD4ZiWw7DZ8z48eXNi85T3euCggmH96O95IJKwlEqkgIPK0TrB+toKGxtrbG7ukE9zqrJk88G98OxUTbCdETGoZGa3++T+xRjLeDShimOqWqONf+W59YOdHYaHgRh3pgkE9jymtS04gVKCpJMxPjjgYHMrWBqekrjr9JcZLK3T6XZZWBighGXvBJHurGHri18j5CdhznKOFtBH20k/++PpJyJNM1ZW1kEkyCgNmRHX4p1nNBwxzSt29/b45S9/weLCgI0rV6ir0Byx1RqHDOJRN/cTCwKX3mKfGx99j+s3b/Ef/9N/iooifvK3f8vu7g7l3l2mew/PdV6/dUr0c8C2lja35DU8OOQoQXUS8wKzH3z8Af/sf/Gf88WXX/Pf/Hf/nLwokJFERYqV9St0egP2t7cYHpxN8X8aQmNsh9aOth0jmPmG+xBmtwbKtmFSbJMkMe/c2iBNE7TeoWmC77+YZbjnt97jvLAx5shDHCCOJYNeHBoMlxrrPMaG92dCkEoRkg6zh/rkDBBJwbWFhF4i2Rs37B0UGDFGC8HKUpd3bi7hnGMyHZJmDXVZ0JYNZX5IPh1R5AXFpMDrmoN6PEvWBc7q+NkRCBFiA+/mm5Ywubl2igPGcx7H+6fOjcY6DiYlSRxxdWODhX4/9EdxjuWrfTrv3SKKIpI0eyRh9jxcIJEeugkH/9ZnaRROwwVE8d4fT5p+/t/++L/nnrdzT/SGY37xzMP0gXX5bYi3hAhJA+dweY5UEeniElIpfJyFe9gY6v0DdF5wTn/+1wseMC7cumdY/ZUSCATWuXNnab312NbgzPPl+9Z5GuMx1j9it/oIBAjhWV1d5cqVq1y/fgMp505WJ3fIpx0hNL9o6pZWW5aWVsk6XZaXVxkMBmx6z3A4pMjzV64wuHzM54fL/V7GGqomKLHevHN4sfAerPdIAZGS4dE8YS10clOgHdQOylozHuakaUJTFCRxAj7ByoS2qambGi8l2SB0rBdRhLDq6EDGalrdnM9f/AyIo0BqKRUjVcTSyhobN27SNjX9boaZKUusc0zHY5rmSWpCyog47YRNjLd4WxO80efB82l4nqEvHN//5/ne8ysw9zkIHgZSxkRxgpAK01qkio8V/68DnrlAnbWK7tsNT1C64cTFTHnzJWb+82VDACmIFHzG8fL2nM+2ztM0Pnguzspa0jRBSolU88bTz/jMYPEc4sXL2FDOHiURC2QkEdH5nxvvPfrxBlANEMNo95Dtrx8wnAyZ7AwpRlNca48LS97QTfJlQfYS5HKEKzTuTWPMn4XTJo0gy5t1N/NPvMXPNsQS6CUSIQXOOKz1R76tFmgvsNrR+zCckz6xc2it0VqTRV2iTCJlhIpinLNH6tT56vZtLbJ9i1cD70M1hXMea3Swz3IWvDtyd5pvK897d89yVtgX2Fx7AnEvRSDPnT/WFc57bflHftvT6XRYv3IF7wXWRxhrafysT53gmdyO856yqtHa0LQaPcvSRzKQgK+CVHfO4tomjNc/JbPvQwxgbbCp8TieTrkFJGlCt9dHSqjLnLYun5gDnTVPrJ9nja2MsZRVTas1KopQcYxtNfgwRk+oRkdAOlPRG92EsfsTM9Njl8U5S9tq6qahLEqkUhTTKeVkitG/RevVGeB9cH82FhobLHSfpeLWbY13hk4WY2yMdg4QdLodFhYWmAwPTnn3i8G5J5/9o3nBOqyxGG1Qsx4H8zVrrpE52qXNHBhP0pez3BwASknSLMUYC+Ws6szP2tQqRaQkzjiEt0QeYvwjrcOcdzgvcC700zP44KTdtORFg4pztrd2iJOM/cOKqjJURY4xTWgeqiu8qY+U5u5pz+8pkAR+zJ7hwXPehySBMRgz77/lMUbT1B6tIrR1iHNI0i+OSJcpJMthAmuH4M/LUF8AvAX9mJHknFifyxGmcGR+NL8/z7qJcB6K+kTA+NsBP53i7++RLC5x/aPvki4tI+LguLT/73/C1r/4l5iqevYM9LrDOOxuEWaN59TeSSkY9BIiKZiWmqY93y60GVXovME/wzttjsPCM6lmE9cpv+usxlrBP/kn/wn/h//9/5F+f0CWpQSiba5W7RPYgKdPDkII4iRlYWGJP/0n/3OuX7/N3AD/iy++4P/xz/85dV2j3y7CL4TRZERZlcHSw7w9h2dBHEUsdFOs84yLCvuUSHyeGx1NG774fJd8rHnv3Qc005xKHtJ4xSe/+ZRf/foT4uV3ePf3/gNwEm/AOosQEucdk3wUfr5AIl0KwdJCl26WMFhYp9db5oPvfpff++N/iNYtw4Nd2laT5xVVWfEX//p/5N7XXz1xnLQz4MqtHwT/uYef0lZ5aGD0zMVr7mE2rwy7DKYsC58hVEiii16oWnIeYxzG2HP3Cf9mEPwkw7mqeZNZxeCN6BFc0MbWc6y8vey1f1bFLK+DegfcBOwOZ7pkxdRx98sgPXfekyaKGzfWWRhkTPM9RuPJ6W9WwAbBzmOH0Hz4opGeeF0kZvm2L37y92x9eh/rDHUdSmVNfUKm+xZnhxRkt9ZJP16mtntU93Zem7j3Am3uzwGBjDOkirC6xpsn1aqz/BeZhPfXUpZ7McWoppy0tBYqExyGNrm42de44H72TApyJrhSKibrLmKtpiknCO/oEh79kjeuuOAtLhBt27K3uxsILFMj8HST4AKLNUdN/+ZK9Fc9VXRiwSAT1NozrjzawWHlEGJGrAuo65LpdMT16zf4/T/8h4wnBffu75JPpzTlhLapwT67aldrzeb8PMzi0yxWDLoJjbaUzSsoWfN2xvmcfpbbpqacTrDekCx1sa2hnVRPst9CsLi4yI1bN9jZvMedz/8eo/UjKt6XxWgy5dMvv8I6SHo9ZJJQjlqsDtYX7sRc2kQwHUpabbAzS8bTvmc+zWnu3GE0miCiGOHh01/8gsnhAU1+drX0XCbzbdZIPg/OgK6g1jD1YT/5eEg0//47uzv87d/8DWVdce3qgIUq4d7DIdbC1WsbXL91k3x6yM7m+RT/LwNjLQf7h0RK0FQtiuBW2/iwA8wAqQQqCT0QdGNxLohBpRRYG1TmWdZhbeMqdVUzGjVH4jUBxElKJ01xrca2LbF1xNZi8EwJ75+WLU0D2nqiaOaO7CAvWr6+e4h6OObzL3eDBZZ1OARJZ0CUdNDlCFsMUdKTxg7nPY0+W0JKAl0Zxlm6s8UP3jsm0zHeNWRZQpJEHB7mDA9HISQW8lz3+wUQ6WKmRphtpo/yIN/QY/e8nfvLqm+ekh16Y2EdtAahDWiDMBYlJEpJRKTweExeUO2+XBnLK4VgZpz4FHnAGcuo54vLeUo/TsLbs3tAWhdejuMmD/O7L44VUaRYXFxkda3P9Ws3effdD054o/sTf54e/NT1lLquyLIM5zzLy6usrl6hKErquqWqKg4ODt4gb/RXD2stlT1nM8i3QAhB6Ev/dCXMUaGR8UxzQ7/X0tYNuqqYtIZCw3SSU9Y1i1LRW1xCt4ZqVIRqktkxgxL94gjUKArzpFISKQVKSlQU0e31WFtfxxhNHIeO6f2ipior1q9cIZ+OaVsdyjatCeoRIZAqCioUq0PG/vQzhpCBcfTu1BqWC4QgSjKitEOUZtiZN2ZbV7R1far35DMhw3cQMnStd87h5jWG51h6hTxR5vfc953sDuh55THM3OryPIn9F8Q5T+MZDniRB3s+hAKRhLCThrDzec4YnIOmnv2SDJsI5yUISZImdLoZRptHGy9JQoQcc0xyqycOfXEQ4J3HahvUu6nCC4HXZ/QCeBpm76vzijp/u/5cFAQSIVSYa4+tbV/1IBAnXq9i7jhtECEWfvpaowinKBGQSkEvkiwkAq8EbjZ2R3iML3q1el6y0HsX+oJ4N5tQjkcwb6ym+MZ3tW/xGsN7h25nhKdrEXi0btG6xTl7dEdd1vQgjsOcU//9aI6YwZ64mQUe5yxW6+DvniQkiSaOE+IkJo4jrI0e6Y3wNHjvnxBa+ViipHjks19033x2PPsp9c4dfRdxNHc941jeYXVLXRUzAcujkFIhRFDinuaFfhqss9SNPpp2HhnL3MZnBmcN1ujguT7/rJOOCyfgrKGpSsoo4mB3B4DpeESZ53AU44gT2yoPQqCiQAk68+xr/SbgaMVyoSGpc89fxtumYTwaYpwhjhWJVbO4jeP1+NL3XI9h9tw5K444mpPMj5oVi8k57zW76I/PC3PPA+/Fk0+QECd2ReFNYbU83pc7ofBShEadEqQJJ9XOGn3SGsqymR8uPHsyDtXawhNHgkgK0jiQ+602z6wfnv/bvBeK4EQcdHLohL2gUgrvA/fmvafVLXUjZst+6EkwLcoQRsnzXcOXJ9JVB6LFMNx5g8+3eDMwzKFukVlGfOMWMuuwu/UQsb+LdS3OGZr9nW96lOeCXIhRgwhXGOzwxXQm1nnyMgQd5qydY14ShqCMmU9mSRLx0UfXWF1d5J/9s/8tv/8H/5BbN9+dLcRu9g7LMf1eE1iHDiFPGXD//l3+zb/5F1hr+f73f0iSdFheXgGgNY6qNZhLbLz6Fm9xGrSxjPIqBAWnJHHmgc+oga/2QEeeptHUZcG//8nX3N+e8GDcEqUD+kurbNy4zsHuAXc++4omH6MvUF0yR5pG3Lq1RJJE7O5M2dmvOBg1qGiHpavrGG9RcczC8ipSSt7rDZBS8uF3PqQocn7xi7/nk08/Y2/zIXc//w1NNWX77i8Bj24e6yUvVHjNSllVnJItreO9px4f4PTT9BUXheCNduujj3j/h79H2bbs7BzQFBNGX3+OqQvq4hlK36dBQLo2IB506S706S8ukI/G7H+9idMGKn2mryOVpDPIkFKgqxqnbVAoPvW98/lRguoGQYArwD3PAfgCsQCsEyb5bd5kUfyLY7YzsPth40MJTDg3iakEWGP56qs90jTm9ru3+cHv/IAvPr/HJ7++c/R7IgP1voI0JEIvNbRtAAMVFaY1yE5C//c2sJWh+PUBrnyriX0t4IDWU/9yj/brMc43cItQ+LfPqyHTZxvVuCOJEkEWezqZoylhvPt8LdFFQEJI8qjQc8cajfDNE0RSSrCTzwRc6UAWeTJvMK2ntY7Gzzppqdke/xWHmrrJsaYGL2ZOoPaoOeQ81u4pGMigKC7fhsJv8Ti8A9cc/eyBg9GIyXQaLIS4vARMpASdVGKdp2qebjNaaY+2/qkWR0fQLb4p+fqLz/nss6+Jkw6d3hJKCDauvYvWLXs7D6ir8/Xjcc5iZlYPEAQm3W7yCsj00yElRBG0taYZl/jTuq57z9b9O4wO9qjrp5PoQkgWVq6QdnrkowOK6fBcY4mjhN5ggbapme5uYbTGnbInCfZYDucMVre4WUPIp1oW2hrqPWoz5N6v9kEqahPPmkYocDJYRUYpuBZ0RZp1uHLrNkLA7v371GXxRicPO8xqag005cxQ4TlftihytrbuI2OFTJOjxdZYy5dffMn27i77O6+WE/PMLE38cZvHI5I5EiRZUFdrZ7GOY/GYDU1V5985nxbc+epBsK85ISjxwKSqKJtm5pEeAnE/s3ZxQBzFLFy5Sa/Xw3uD9469/UPKec8TjhPTEohFIPhT4YiEY31tlez2LSJviG1NWVV8cX+bun0y7k2VoKOCXZR2HikFURLcFkStj/2rCJZWsYJur8P6xjpaWzY3d9Ct5mBSMsxrlAxiN60NrQaVRmSLr9ojXUSgsnAHmna2oX9TH73fMjQ6vJYi1PU+RBHldBoeyv8/e/8VY+mWpmdiz1rrt9uEN2mPd2W7q6tNtaHtGZFDNERAIq8kQIAGEDCXgu51IwGCAJkBBAoSBoKEAQRIAzZJQEPTBNlkuyK7q5pd7tRxedJnho9tf7+MLtbeEZGZkZHh0p2K92CfyIzc+9+/WeZb73q/92tynKkhz595mFcJIhSIlkJoB/J4pMxhaF4wuex4tJabUpKFhQ6XLy/w7W//Er/z23/rsXf7HWs3qRMgpN4j2Q/uNI9GA27c+IwkSfnur/023e68r2TsHE2jfaXw50A2XuACz4J1jlofzV5NA73awriCrPI7zrqp2Vjb4s7tbUZRFxGkPj2t1UIF3manLvLnYtElpaDTiYnjgPX1IXnZME0OH4/94opIEEQRYRgyv7BAHMesXr6Ew1E0jt64pC4LX4ndGIrxo757Yk9OoHB7MlmBDAKiVhfnLPVoF/tcGdlJYaOZlJVrl9na3uH+xgbFYMhoZx1TnUYBK1BJRDiTkC7OMLOyhFOC3uYOlAJXPabEedpRpCCIAqQEWwsw3hvw6GtxflNCRi+ORJ/GajG+eOGLSCJ4neGAElwfL2E9YaHQabdx1jEclKig4aOvt7h2fZXtrR5SCb/od0AgEHMCkYAZiGPVTjk1JisSUxiMMERRQHspRRWGPNh9ft97gZNhMuGY7RKzW8ISfgPsRYVI06QZBTISBKkkig2tFISDkXpxe3ACgRPKdyhd72UfHRy+AjxhkQroKkgCUDjv7WodjfOOEU68HMLGmgZrmslZe7vK6ZnsZX4KXyyyuhiXL/A0PLbLWlXVSaemU8HXEfKqaim9xcJUETstSjrNaj6IKUk09UHGGpxuGI7GbA0qujNzXAlbqCCk1e5itKa3s37yExT7xQIFIKUkDNVzIdKFEMdTUYvJRpm12Ppo5XWejciz0ZHfGSct2p1ZyvxkmwwAQkqCIETXNbouj/YvdxMfausmPvxHBLTOgCkwBkbVyBck7ayCivclyFKBCgGfjaPCgFa3ixACFTzP1LtXAwGTJEPrbUiOM29q3ZDnGWEckkah/6UAh2UwGJAXJUX24jkxh18vP25qJKQn0611uMc20x5v9k2jaQaHt+HaGOojXBSEEIRph7gzy1TEGY1zpBQHNnv2lzeB8IIWiUM4SxLHzM3PE9iGsM6RQk7q/D0JJTyZriebAkIKZKBwCIQ0CHtgzJl8TxwFzM50qKqGMFDopqGq9ZMhvfCbYzKOEOr4RnlnJ9KNhjr3HXdalOJCTvWVgq0qqs11EBKnPRHrrPaysNc0XThIFdGlFqYy1L16UgX49UEQBFy5fI133r5Gt9Pm0eTTGsgp8jF3bn2Jc/DGW9+g011g2uWdq3CupKoGDAYDtDY+/ScQ5LqhqTT/9g//DT/64V9y6+bNX1xbFyGRSQuEwFaFH+8u8Mqh04YPVmB1yVHrgv64YW1ouDeEOqzRAcRbfZJ7a/TW17D9NShzMOev9Kxrw717fZSSZNmjfrFffPYp/5//1/+TheUVPvzWt2m12/QHPeIk5vLlK3S7Xd59/y06My2+uLZMEEr6Ozvc+eJz9CR9WErJyvWrdGZnGY0zsnGORKCEoDs7zzsffgOjG34y2mLwiIJ9mqg+zVg5Hzy8eYMqryi0pV8ZdJFhj2lddRiEswhrWJxd4L23vkazXPH+6kfk44y7t25RjDMG6w/9RsgTCIE21jiKUYUQFlN7r/ZnD/FuX4nunvT5fS5YljAnEW0LbQsNuGnC0MVQczimSRanaGLOgTE+wA6AwHgCEiBZbrPwzRWqfsn43tAvkAIFkUDOKpx2uG090eFMMLV/gX0GcfrzNKUJrP+csAIZBrjGvVT13gWegql6eoBvj8ewFzoTAry0W+HNxA2EXUmyEBBawFiCWeguvBhC2rsBWDBTSwvfJ2YCr+CWSqJUgNAWSk0DjIWiQqBqENqxXjt29L6rVeVepl3/o744lmm+lf+1tRM7jAtc4DljuvlkgIKj+7O2jrw0RGHAtZVZwkCRJglKSYb9IUVWMK4No2q/badpwlvXryKl5M69B4yznEHeUNaWsvHvK4ucjfV7JGmL1cvXCCI1sQw9HqIAlIKZbky7FVFWlqLU3ilFm3Mfo+Y6HbqtFlmeM8jGhzq3TlGMfdZXoCSLS10arcnGhS/CLgNAeCvBZ6x5w0AShgGrK0ssLK+iywGDZ9SZbMUpYRBS1hVVU1HmY7Ye3kXr5pl2r1Xd0OuPsBNP6xPBGij6njzXJT7WbcAIwiSitXwZAWw9vI/Vhqo4uZAkDQShElTGUb2gTP2zoB3AkoLcwAPtx/tnhWsCiRIhcdhibmaRqjF0OyVSluiqoilrzGObIQqIJxvFz2OOc867JDjBE5tCxjoK7el1I8CecygpBcQBhKFvn+Q1aaKIwpjF+UU6YUhd5hSjXRptGRaGxvmuJZxDjjNkWWOcxQJpKJlPQ1SgaAd+nycIA6SUVLWmaSxJGBClMa7RVHlJICQzcUoYBszMeWX69m6P0SjDWqgd1LWlKiuMdbRmWgRp7Is/OyiLgrqsCdOEuN0maSXML88/lcg/DGcn0p0BPclL3TOfuMBXCa6p0YO+77F5/XyVWS8IMlZEqURnmnpQv3YFt5RULC4ucmn1MmmasE+kT90mC6qqz4N7N7FGsLL6Ee3OwUpmDdbmNE1Glo2RSoH0Ss7KaPLK8OMf/4g/+Bf//GVc3qsDKZFRAlLhmtr7aV7glUMaw9VVWJi1aFNRasNuYdnOgdBAUDMcjOlt9xj1erisB83z0QwZY9nZyQ79t7X791m7f5+rb71Nd3GZzuwMeTkiSWPmFubozHS4dHmFS5eXCULF1m6P9Xt3eXDr5h6RLqRkbnmZpSuXCDY2sQKUUAQyYmFpmXfefoO6qvjsR49XLpyyfufbhnfX19hd34CkA91FHw+ceuPNgbMILDOtDldXryGNQlySXvHRWAa9XbLezlOI9ABo46ymLjJOdq1uP0X7RUAAMwKuSFAglIPI4aaey0+vJfWLjTOWM5hmQkv2vRUB4tmY7ltzyAcjxg8mKjQlcYHwtXQtmPCx6pwC3+TEYy84Hdk/+YxwIAKJDORFhsKriKlmoZi8njcUPmMlwId3BlQqCecDVG5h7F2pWpfBvQAx4d6wZB8dX1sKFiJQgSQIA5raMKo0RghKIWkQ0HjjlN0Gtux+CYKXP9w9+u1TOiR0vj++ZkuEC7ymCPCWExpvOHdUn7AWKuv9hRdnW7TTmNmZDmEQsIGhbxuMc48Q6XEUcu3yKioI2NjaZpzlFJWhOPCepqnp93doNxWXr1wlCI6vIhdAoCAMoJVGdLstwrBBSYluDGXZnHtHbycJizMzCOcYZeOjfa7LmrqsmZltMTM7Q1nWlGXpMxaVV7Y6Y488RSF8VngYKubmuiwvLbJ+Pz3yHIUQJGFMEiUYa6iairoqfSHXY0Brg35Ghu7T4aB5bE3iDJiaIExoLczTlCW923cx9elEJJGSpKHAOUv1igeuAkgUdEPPWu5qnlBzH/o5IZEiIFQx7dYMQaNpJTHaGOpRSV0/ud6Q7BPp9XO6LXaPSH/09waoD6zFnpr5dYi3+HGgJkR6oHz2uK0aoihEiIBut8tiJ6Ec9eibIXmtGVQGbUBP46eqgqpCKuU33tsJrh0hpCRWAqEEcRz4uozWIowlDBRhFFFb0Na/JwojkiSiNdNFBQF5UXoi3U3MUoylqTVIQdxKCKzDOoF1oI2mbmqCJCKZ69JqtejOLqDU8QOpMxDpU3Ub7O/lvNqd5wLPgpzmPfPIs7QOaj1ZPLzez9gVBtNr/IAi5ESt+Hpc08GiMmEY8M6b1/j6R+8yNzeH74s1+6FXi7rus7m5TdNodnceoBT0BgOyLOPmzS/5/PPPsLbhgw++QafbIVAB1lo63Q5xSxLH0dNO5ZVEK/DzQSeGaJJxLAU0BurG15It6qM5qtmFBZYvXSGKYjrdLlVV8eXNm+RZdrrCiRd4IagqWN+E0aihrLbQTjIYTkjRiQdi0dtm+9YnWKOZvfwGuq4Yb69j9QtSHx9AWRTcv3+PmWyOJHmDKA5Z39xgnGcsLMwzNzvH0tIi3/rm1+m2Er784hPGI69GkVKhLYxHBdlwTN4fEIcRYZpSDne5/fknNE1N9QTRfLB2wnlisoGnS8j7nlw5rVGvg2ZU4Yzl3s9vYEeabmee5eU3yfMcUzVe0fTUeUgDI/a8Mo6E90kQQqHiFCEEuspw9jn7UQu85CwEcgf3DEiHEw40yNSTYe55PKrjnt80tDu6VjXgFwoceMurOJumkWChHeCsoyo0xkBpoXKWO3fWycqaPDXIFEQMYhls5LCV9jKeIV5ONHqsTSUClverHUmlWFpZJY5TeutbjHeHkFkYHvOuTBTpelCT3dhGKsnM+4u4xjK+1aMZvMCNnlcdU5uTqX7gq4gJq6ZmQjrvL6NaIbwJNA7ikiKsqaxENOCCyVR3zqcQxxFRFFHXNVV1+FyZSO9H2prY8JbWMsobGuPIJ+uGorIoKQgC5RVmWJ/p59yeoP9VHDumCTAXEooLHBdqYiE2VT2eBFNJ4nGIvSkabdjtjymKilAFJHHEsKjZzTVVbZkYeGCAsqq5ff8hSkqKZyiPtdb0e9skScrb776PkIp7d26yvblv8yIlRKGcFPDb944QQjAzM8Ol1SWGwzG4ASZ0xEFy7jRCU5cU+YC6Lo4V+QFUVUO/l4FUpN1FjLWUWeYLbT5DCOIcaG2RymKdBWGRUUDQSrFaYw/xdnbOUdQV2ponirIKKQniyPtc1/Uzv/9c4KwP24uCbGsb0+gzZZJWxmIR1K9B6o4DauP3wCvzaF8TEsKOQkhoMottHIvdLrPtNrFyhMrSbne4du0a2lpybemPxty+/YC6HiKlQEpvp2Kt8/0jljgHsrZP2Cydy/W4/Rc8WkS0mT7Sx94zRRBIglBitKNpjvf8AwlxBGGg6HZbBHHCzMoSYToDpsE673CgpJiQ5CFSgnjEwX0fVVUxHA6IQ0mUpN5x4dp1mqbGWIu1lrzpUxcaUWucKGm0IRIOaS3DcUZZN6StFlEQMtdq4ebnaOqGqq6RSMpSk7RS3n7rTeK0xfziMnGS8uDBA7a3tyl1Q940tNsdZmaWXxSRHuKLFnr16wW+ApCKva2pg73NOqi+GmGkzQxkBsO+4uR1wCPVlR2EUciH77/Nd3/p67RmF/DMh8ET6RHQpq4DHqxtUBY5727cQYqaL27eZH1jg3/5L/+Yf/bP/jW/8zvf43/5v/qvaLdb3mPOWGa7M4ggIU2Sl3a9p0En8AP85Rnoxr6YhZSQlzAqfDLFVuPXoE9Teiwsr/KN7/4qM91Zrly9Tn93h/V7dxmfsMjOBV4sihIerIGi4ctbaxgHu1POyfqAMd9eJ9/ZoL2wyqWPvoOuSsphj/olEOl5kXPr9k3mhwtcubpC2kp4sHYfB3zto6+xuLjE6qUVZmdm6HZb/PCnf0nQ66Nrfy2NFYwGGaPekGxnF9FKaUtLXud83t9Ca0OeP66KP6OU96mYUCC6nKSNng31oKAeFHy5/jE3/+rnXL7+Dr/0GxHaGJqyxjVH+bTs+9E/GwIIESoiai8ghMSaGnPIIuhcIYAuXnbWs/DQK0UARAvkChCDnlg4vHD42+LRcOSKfsplwqNE2Ku2lOrEireXY2xj6W0bqtqx1kBpHTe+vMeNL++z8N4Cix8uoxIBlwActmx8Icm7+P2Zx59HIuBKsBdJB1HClW+/y9z8Ip//7GeM72TwEBge80FOWAC9WzHKN4gXW1z73Q9QSUgzKC+I9IOYVq86LnPyOiIAZiBYiVn41pvE8y0AnHXsrj1kuL2NcQ1as59sdPyM5GMhTVM6nTbjcXYokS7wIoZUQTuENIIst2yNLRovs3IApV9cp4kiVJLSSRC+iod5hQU6U+eeC1zguFDS+wA3k3IuJ2nd04KBJ0GjDZvbQ+IooJskYCz9cc3WuEbhp/Np9FeWFTe+vO2ty55xYlo37Gxv0Ol2+Z3v/jorl67xR//mXzxCpAdK0E4kxnri/WBoNj83z/Vr19nc3KKuapwRWBOce4BQVxm5Kyhrd2wLpqpsqMqGpNNl4co1rNGU49wXtT8GtLEIbTDO4KRBxiFBp40pykOJdICiLigOGUykUkTtFjiHOQaRfy5wDpyhyTKa7PAM2pOg1I7yNbB0maLWMJ5Yuhy82yIQxPOB9xZvalzjWJ2f5+1LlyjLMeNsQLc7wztvv40TEi1CdvtDtrf79AdDlBKEoURrS107pBIkcYB1DtE8n+3ix5uLEJ77cJPrPKqQdxgq0jSkqvTxifQAOilEScDM4ixx2mbl6mXidIbd7U3y0QiUz9oIVIBSEd4pZS8aeARVVVJVJd1OStxqESpJp9PBaM1gMKQsK7aHJZXL0JWmqjUKH34ba+gNRgRhyKXFBeJUstDt0I4ihqMxveEIIRRZoYlbER+8/y2WV1b48JvfZm5+kU8++4Tbd+9y/8EDbty6RafTZX7+Mkodnx4/PZEula/6a82FJfrrDKmg1faGZmJCpBcjOGa60QXOH1JK3v3oPVYurSAtSDfJGJcwHIz4+Oef4qxlnOX0B2OCtCFuwVS+1Nvtc/fOxzx8eJ+HD9dw1lDXfmfvzp0HfPzzz7h/fw2tDcYYrLFIqejOzGGs4Mf/6Ufs9oc8uHf/5d6IE+KNawlSwGIEaQBGa6z1pIk1Xq01FXk+bSrLxyPW799n2OqRj3Ky0ZDqoi+88lAK0tTnrRWFozFPWSQ4h1KSViulUZzIB+08EIYRaZqioohiOEQYw+3Pv2BnpsvcpRXSbhdtposBRRCEpK0Ol1Yu0067CBTOwvbWDvk4Q08KAWtjKIoKxIRXsha7J32Q+BHkJBqnVwDOF43RTU0xHnsfvabGGg1KIcKQJG0TRTFVkVFmJ9zsEhJkOMlMKibJWM83mIkXUsDR2Bpb2H3Of/JYnAaXTwT9L4scdOzHdCeI+x0nevsLhcOhcahIsbQ6T904djbHFEUzGSccVV4z3hlTmgJnHHu77RqeKpmtHexaSCRqNiKMYpKkRZK2CIKJ58tpPc6dJ0yt00ghiVYTUtuh6VXo4eskA3hOmC4OX8UGd16wQAl2qMnv9tCjkvbqDEErBG0wRY2dLn6VQETKZ0ecI5qmoSzLJ1SUU3hyPKATSSLp6bqDuU8HH4+vUeBl8y+k7s40Y+EMbURNDmN4CUPy1DbqpXz5BU4LO5k+TkqinxbO+YKJUltGWYnRhqpp9obIx5uP2/vf4QiVoJN4mxNtNFVVM85y0vEYiyJMOlhdY3RNoALa7Q7OgZA11loiZQgCQTBRdSqliKKIprZUZXO8oqAnQGOhaBzNEfJ/EYQTscSkvtsERhvKosBac4oxSRCGijgOiOOAJA6oG3kCkZ6PD1QQ0E5THI56nD3imBWEMUGUYHRNU73motWpVZ05RarGOaPCaySekCY40I1FOm+ZghAkrYTZ+Vlcv6E/1FhnUUGIUIpuu4M1jvnZWbIsJwgEUkGRV9R1gbGQN3avIOiLwFR5LqQgVBLrnBdhHQIlBKFUGGmP7fCigoC4nRDFMVGaEiUp7U6bpNVhZ3OdPMtoXEPuGuqyIC9qygmjf9h3CHwGS9No+v0hcRzRThKEElR1TV7kNBNbXSUhkoIwUHSSGOMcsqpQShBIh8ARhRFShtQa4tr7rxvjyPOSu3du0x/0MSJidn6RcZHTanfpzMwxN79EmrQIoxQpX4QiPYohbkNtoTj7btYFXhKiCK5ch3iiPnYOHt67INJfIsIo5Pf+wf+Qv/33fpe4gWgyqQrg448/4X/zv/3f0+iaBw+3+PL2faLOm3TnwEscY25++TP+3//tP2J3Z5tbd+7Sbre9LYI2/Omf/pB/+s/+gKbxwYwxlqqsgYAr195nNBrz3/6v/3f82Z/+GWXxek3av/UbiwjrCAq/8zsYZoyzgqywvpCOhtIdve+3tfaQ3a1NhAiQMsZZS11fqNFfdUSxYHFZYRrHODdUzdPXnVEUsrA0S5UHqODFEumdTpfLl6+S5xkPH95n2xge3viCOEn43n/x93jzw48oa68iEChUlDI3v8S3v/Udmrqh3Z7DGMsf/Ks/4P69dfRE3lJWNfVEBbOnCt4L2sLJa6982muFuizpba57dUWRYXSNiCKU6LB05U0WFpfZvHebtVtfnOzAUkHU8p6O2e6ExX6+bMXce4tYbRnc2qUelE820grM1uTPL5NIP4UE81Ul0cGTepkzzLZTPnj7bYwR3P8PnzEoBnvvyXbH5FWGixyu5XxX2ebooqFjBzc0ciYk+XabVtphpjvP3MwicZxyVoNz5yx1VUBq6H5zls6HXXo/2GL08/6ZjvuVwFdZiT5FDfSgyUq2/vgLwpmYN//uN2jNLCDKGr078gbeEkSgUJ3WuRPpeV6Q50+PBaUUzM+2WOrG2GyMKwvvBsnhj6eqm9Nasp4M05IgZ/RlifGzZ8khpMvzxNQCLMAnfb/K/jcXeATavlgrIIenYkxjWdvcJRSCkbZ7Q+RJz6WTKN5ZSam05cuNkrGB9bUNytrRWEVn8QrlaJdiuE2cpKysXEVKqMoR1miMrsA54tintkVRRLfbZTTK2dke+M20c0TeOPLmiI4hBCppI8MInY+x1b7tYdM09HZ2vXBDn+xOCQHtVsTsbMpsN2a2EzHWNflxBzghQYVEScry0jzWWrLdPs2BzJ+0M0d3foV81GOw/eDcNyFeGARe4RZIyBvvqfISMcIT6Y/fTWsdZd6ABGdAIJhbnOPqO9fQt2ru3L+FtpowjomimMsry8x02mxtXSOJQ6wzOCxb231G45LaOHZy365e1N6Bc16lHgWSVitCa0um60OL1IZS0QpCbGOP7ZUeJgkzyytEcUyr0yVptVleWaHVnuXmZ5+yvbVFlY+pshHCOYSze9bQ08S5g18jpfRWU3nJzdv36XQ6vPvuO6gAeqMh/d4ORek5yVBJOrGk3W6xvLKENZphbwecI1WghKXT6iDDFBG0MCKhqCp6owHFTo8/+nf/FqkUy1c/pj2zwK/8+q/xwdc+xBDhZBspJWEcI04Qu5+YSBdRjJABVglf9fd18Q2eFoF6EYHIwYJTr3rg4xw09aO+IS/imZ6DUuSrDItFO8tcu8tS0mU8GrO9uYluNItLizhn6M7M02rPEQQR+w3Oe3M1dUVZVYxGBXXt+PLmPeoaNje2yfOCbrfD6uoMq6urdDqztFqdyS5eQJ7lDAeDo07vlcRgpH0RkdqhjGNUWgYFjGuorLd0eVZzs9Zi65pHEyxfn9W6wPsVSiEw1vrNkqOcML4qEN6LzVqHxtLgnvrUTFOTD3rUZXEmT8ATnR4CiSQKI9K0hZ6kbxqtfVYIgtFoTK8/YGt7h5mZWeIwopXEFGVNUxvqWiNEjjGOTqfDpUur9FzFIOsDR6kdjsjre5kQk02MvdM+/ByNbsjHQ4T0ukApHEEgsQTESUSSpgRheOhnj4TDE+fO+oj5OZPoANevXELXGr2ZY7IKaxzu8Sb4Cj6qo3AWO5dOt00cx+R5TpE/x817IRAqACko6wZjnlTEOuNw1WSSmDqlTS/qaQsMx0Sx7nBjjYk0dV5RtUuSNGVhZZW6GlP2h9jaYktzvBs16bKusTS9EmesZ/SE856sF/jFwaSN2cygZUM5zAgGAbqs/MQ+dWS04EqLOCOR3p6Zm1gMeHWp1sfwzp1mT1iHMc/2aj/4b1MvVYfbG4LPLV5xPsU9iBU4aBpzYn/mKRn5UkKoqZz4qx6/vaYQ4lG6ZSrLMM69lEdmHTTGYXGn8mKelkcJpSSOA5zwLd8aw3g0QKqAVivl6rXr7KwLqmyAlAFCCJ/pmaZYaxgOGpqm3suKVEoRxzFFXtOcqWjm4TjWlGqtzzh8fP50Fqcbf5RTkNTOGZzV4Axiov1/WrggJuSQm5AfQkpUGKFU6FXE9sl2EwQBSZLQlCH7uTHnDyEEoQoIwpDV1RWSJGZnd5dsPKZuGppjWt4cCqlASoIoQUYKXTnsS7ayeCr95LymBsleLG6dwzhLYwx1oynKmvFwRJIalFIkcUy30yYvuhRlQVmVhIEiTSK09p950Ziq0o3x8/LjTVuK/TpykokyXfi2OW2/xh1+n4SQKBWggpAwTgjCiLIqsVZSFjl1VdLUNY3WKAHhJCaRk+M92T/836SShFFEEIbYCX9h3eTcJxeglCQKQ5RSOGuw1oC1k2vVaK3RrgYtqaqKuqlpdI3RGm0aqqpECMmw36PRlu3tLWY3FxmNc4qiIopj0naEPEEcdTIiXQWEy9dQ7S7N9jq6v3XCxeeLZLMfw9TSveH5ygrk5HsOEulHKZpeNuoKHtx9NP1YP2fVosLfI8OF+eAhMNby+c2bhD/o8De/89t896Nf4Qf/8c/5/f/vP0Ubzd/4a7/JwsICv/nb/wPefuedifJtH1EUMb8wz2hc8HA9Y5xtc///9P8giiK2tncA+OVf/ha/+7t/g/fe+4Bv/dKvk6YpSZSiGL2MSz4X/P/+aIdAwtUZX3D0Yc+yNbJoA40+qYBtUvUNeJ1WMYESXJ1PSSPJqCopdUNWQPYVTzBxBBjRphGWISOySXr5YRjubPPxn/47nHMntwM5JSIZEcuY2dYcKyurBIHizp1JcecoQgeKG1/eYW17xL07ayytfMzl1Uu898679HZ3+eLLe+RZxnA0ROD4pW99k7/+136bP/qDf873Nx4849un/hSvUDtWCsJ4X/3sHE+6FXpk4xF3b31KnCasXFomDQJMK8Q4xWy3zcxMh90kmhTKPsE1Wg3VpKzXCyIn/2e/9/fI84Lfr/57PpMZRc9QP17A8jWC4/ShTRAovvHtr/HmW9f52U8+4ec//fQ8T+0RqCAg7XTQzvHJjdtUlWGUPVaMd9pNSny1N4VP8gLfNKd7q4dcsM015c/7mHbBg7nbjLIhV66/yYff+iXufvoZN2Z/Qr1Vkn02xB3HR9T679S7mt3vbyIiAW8DXTDjr0a9mgucAAYYg2k06x/fRG4omrLaL1KgwJUa08/PPMx/7Tu/iTOawc4aZTGmv7vLeDh86vudc+RFReQMptDYCqoTLCGiSNJuKZxx6FpjLJTNOZDpE/Y7igNWlts4B5ubY6r6ZCNWxb4Y/IXCsV9+7EJ09MpBKUkYKs9qaJAIokAigHHTUL0gkcZBOKAw+38+KWKgDcyEAa3uDFQaKXOaquHOjU+I4pi/83v/I37lN36b//inf8y/HfRRUYDWmiiMuLS6inOOtY0+WztjLl+tEc6RxAlRlFDVjlFRPbVo8XODc5hyjC0F7vFYzxpoJrHACeNA5xxlkZON+jTFENeMwdRPvfcBERKFpsHQEMcJ3dklgkCSZTVGN0+o9dNWwsLCLLYp2BFqEuKef9sKg5DluQVWlpf5X/xX/yXvvPs2v//7v88PfvhD1je2WVvfPN2BpYK4iwojFpaXiNKYXnOPLH9F671YfPw3/bOAcZmxPdxmezBgu18ggy1+8lc/Ym5+jvc/+oj2/Bxvv3GN+dkOX96+y2AwIokirl9dIctL1jZ2zj0L4zhoGoPW/nv3MhkmBHocCkIliEOBwpFKx2LkiwYzcd7JtEW7/VJJUwghkSomilvMLyyBUHx54wZ5VnH/3h3Gg54nwoEoUMy0QqxxFHmNse6QDBm/kmh3Wrz1ztsESlE3JbqpAUEQRghZw6TPzM7PYI2mt7OL1ZoyrxAIRtGIqq4ZF33yGrKqYFRmfoPfGpxzOGdxzjLYecCov8Gf5QV/+ePPieKEJE25dPkSly5fJYqiY9/nEyvSZRQjk0n6oD1mtCTkRH02jfomUYGbbv28AIgDrxf1Xa8DnPNk+ovA9L7IA3+/wJNweF+ovMA5SJIWTBSrUgkudy8xNz/HzMwc7fYszhqMrimriqosGQ4HVJOCDGWlyfKaLF9/5CvSNGVpaZG5+TnSVhspFb1ej53tbZr69dzd6I00gYS2Aq1hUMDwTATy67dyEUKgpCBQYq/Y6mktep/+HZPt0GMo/F8UrINaCyojaJ5h32N0Qz48fsaFkHKyI376QEhM/pNCeBWTEAgh96sIO7znuRVIKWnqGmEcs+0ug36fQX9AnmcMhgOvJABaSbznQXk0XsIqXDw2yLvHNH1CIcMYrMPWmr2I9RBYq6kq7QvT6QYhHAKLwGGtxujGF2c68SVO448Xd28uLS4yisckSYgIxP5tgmOnVX4lIEEGkrm5WVYvrXD75t3n+nUOMFbgjGE4Lqhrg358YeMOecU8EjuKQCCUwFkfv+4VcbJgc4MRDXVZUdc1YRwzOz9P2m0hU4WI5MliHgdo5/3QA2A4OY/Xz53pdHj8Xv2i9I2nwQCNox6WPoaevqawQHmacfBRRHGCNYYgjAiaAPmsAMJBoy1lY3B6vx7Ns+DVcD5ekULgpNuLLc4NbqKylZ4gkFIgxYEsmmPcq5fKYb++e6y/EBBCINx+TCwR59+GT4izNJm95bnzatRHMkucr5zaaqUsLszT6bQJlEIp6dcZkyNY56i1pWoMxnhVqY99/WBlrMG8iPoIj2NC7B2Kk8T2e5mUE4LSaKxucNZMLCz2Fb0HE/33Pn6gdUipCMMIIaCpm0mm6mNnOSH/hACpgkki5fkT6QJvsRGGAQvz8ywvL9Fu+1ovZ6sl5dc6QiiEUCjhaz8FUYSz1mfSOvdiCqweF5NT8fdcoAKJDAOCKCSOExCC4aCPUhKcz5Btt3w2RjwhYIUUKKFQUp7IJuQ84W/tY+1pEstKKVBK+PwIZ5FCEIcKZx1G++cyreU+ZW0F+OxvIZFC+utTAQ5BWRSMxxl1VWKM3stSCQJFGAZYaamVl/l7u5fJ+CkgUJIgEERRSKvl60iN+2Pqupq0yYggUEghkEoipZpYSPmMbuf8OtpYi9aGotJkhaVoSqq6mpyLp7uN3X8WzlrGoyHDEtrtLt3ZWfKZwl+/Ov6sfyIifToYBiKgOUFpeJUuotL5vb9bBNoBpobxQzAvgMidqsKfd1+dpvpOC8RcKAn2keIXpjVe9XVxXw6FEILl2QXeunSdue4MAEsrK/z67/wWo+GQze1NrNilqhtwjtFggyLv82d/8sf80R/+IQ83tvnpJzfIspLsKenyg8GA27fvEiYdBuOMtYcP+b//o3/E3bt3+eTT56cMfJ5oz/qf2wXsjL2q6RcNjbE87BUoKdDW+AKr5xxzdRKII8hLeFVEBaOi4bMHQ6yFWp/fIC/DkKgz472KR0PcKVVGta3RTrPd30bdDCjrkiCOiaWiritc01Bur1GrgGrrPjthxIM45pPvd2iahvFwhJOKdGEJFUb88b/9Q77vHFvrr2BBYBlCMgsyIJQBEmiKXWyzrwBOkg4zq2+j65JBfQvTVDxLZdPUDRtrGwghMM4v0PJBSRjfohiNePaE8nhG3MvJOnEOisyQ9RpMOfneX6AsLaEgmlOknZj3PnqbX/3Od7h78xTteOqBPM36OwKjUc2NL3d98dpaY62jfpqn6kGZfcV+LAd0riakCzH1WFMNNaY01L3Jl0dAIlDtGNVJqY1hNBwx6g0Zbfcww0MWyceFAR4AG+yrVL/KUPgs0qn+xuFjxldorf1SoIGHwA6wAswf/fbT4N6tG+AsdT7ANH5T6CgYB9uZpl8YhHUI62vwPquld9oRnVZEWWlGY+/fOrVSPU8ruqo2bGyOkQIiaYhTcEgcgqqxVEd5K1/gAk+BtZaq8n7/fsNGUE0K4unX1MdaM7HjH1f0b2xijKOqNXGc8Kvf++usXr7G7MwM63dvUA3XmQlLZtOUy3Mtaq350cefkxUVvb7PYLHaYKuKoqzIsoJRr48SlvD01fleKmQQEaazOOdo8r4P5mwNpiQUhlYoqZQn6gIl6MSe+B6XeqLEbTDoPeNJKRShTGl05Qsg6hr9mE/77tY62XiIVAGzK8s0dcV4d+vcbSkbrdnu71Lqin/yz/4py8uL/Mc//yFffnmbojrDQs86RNXgakP/3j1UKJhfWubS1auMszHD8QBdlBS7/VNZ6zwvCAGdbkSShnzwtff53m99j3ff2uDDt95h0O9x59YNinLER+P3SZOA1eVFVpaW2d7tsb61yW5vwPrmNk1jMK+SBfZEs6SCgDAOMNqQ5RntVovl5UvUZc3mw22MNgRuYrIxJd/DCBlFdNOUOIgIZIBtDNZ525ZAiL1Ng3Y7pdPpEAWSdqwwxiBkjtYaWVQYa0jThDCKSFoprXbK7Pw8rUhQFiWbD++j65rlxSXixUViKdgRllA6xtnE3V6FBCokSRVCCFABjYW8qRmWJWbim9lqd7h05TrWQW8wxjqYnV8iihK2Nx8y6D2grOfQhUYaySfdzwij49uFHn84m5jpCCY7sMd/bIggRsbd/V844V+69KuqF4EXWZjo4Dr91RkXXj4Unkh/Snr0BTyEgDROmG13ScIYgCRNWLm0igoD1jY3qCrvBdXohqrMKLI+t258yvf/5A/ZGZbc3cg5bIN3qobV2pBlOVmWM84z1jc3+ZM/+RPu3L79Yi/2HBHEXiSQ5aDPgeAVQvju+wpN7s+Cc5A9xyIuAggUJCHUr9BGRaMdvfH5n5CQChV75YRUyvu1nUI5YbFYZynrgtFwiLYGKRUqcIjK4ozBFJkXHU4+M8DzZlMEcUo8O4cMQh7eu0fe74M76poPztIvsA0LCWGKUCFKeCJdV49aA8ggJElnaYRCiOMZhFhryR+z46iK6gSWLpPMOHfQdPbFMnPWGKwxmNqi631PYAJ8tNpM8ihfnyHn5BAgY0nQkszOdVlaXKTdbiGlfMK3/FnH2fOxfIaav2ks/cEJJoVp/GbY859GQNQNSZdjRCj351cx6YMKCIQvpqUk1ll0o2nqhqaqsWfZ4HP4ylgHMFX0Oji0iNRrDYkn0g8+4wOepb+wcECG31SY5bmME6NBzysEmwJnj+GPDhTaL3qmIvnjPCbvIRvQaEtdH6EWPSOsdeRFgxQw24JQgUPgEGjzi5QGdIHzxJNqz1coRfOUmEZidWMY9Pd3bGOlWL50lWtvvktAyXiwiykzIqGJlaOdBNjcsL3TYzj2n5NSTLKlNXVZko9H1GWJFO7M9ZCFlI9Gt2569ococKefmcjDz1SsU0hkmICzaOlHOmcNzjQo4YgCRaCkr/sgBGEgcXgDBx91PjrOTfJUcRbqskbr+olYtixyyiKnNTPH3Nw8Ukkycf5e6dZZiqrEYvn8iy9YW1/j/oOH9AZPt/U6HhwYX4CzHlcI5YiuXGV2cQnCgAqNcI5SylMLlZ4HhBBEkSJNQxYW5rh0+TJJEJIKyb27kk8+/hHOGeq6xOqGTrtLFCW00oQg8PW6RuPilS0OK4T0MbfT6EbTloJWpzXJPhA4tx9+Tf3UVSC9Kj8MkVIhEBhtJmPhRGk+aeFBEJCmMaGSBKFEaEMYNQgp0HWNdJCEijgOPek+O0OnlRJIwBry8QjdNCSXLzPT6TJIU4o4xDlH3TRIKQmUQkpJFIUIIWiMw1hHYzS12V8bh2HEzOw8xkHeCIyD7twySdJm1NsEnWFJaKjJxwW7u32C4Pj0+LHfGV99H3Do8YBmsIPJjp8ab8s+jZlKe8T+oGeNV6V/lXCQE3g1+8/LQwmPMEUXeCaMtdRa83Bjgz/7D/+RTqfD3/27/wVpmvKzn/2YH/3oh7x5fYXF+S69Uc29zZyy0odmqkkp+eCDd7h8eZVf/83f4nf+xt/i3r37/Nf/h/8jG2tr7O7svPgLPEcUw4lA4NhuUQJkih8GjSfYlCdC4jhkYXYGawy7W1uvrd3NecPhVei19r7zX3VY3VANh8RJwtsffIQKAh7cusl40D/hkXwkoqKAqBPiKks9LNCNPjYxHwYB169codXtUu9skvfqp6ekygDaK6BCyLahyU54vkdcx55U9PDiHyqQtGcShFCU/R51XWH1o5kxdVGw+/AB1lQYfYaGNA1SjwxWA0ARzl8h6CyiR+s0/acpoA8WNzl/fP5Xf0xR1cSqx5VLgmHpyCqIl9qkV2dpdirGH/dwzVeXMXQGmoGhqGs++ewLhAoYl2Peevsa41HG1tbu8RYf04SC55X1Z/HZAROuTUhBJ2mzPDdHFWuqrma8kVFtToqq1WAyzfYXdxgPtln5rd9i9dplRsNddgabFPdH9Nc2fOHQc8D16wt89OEqOzsZP/3pA+rm1VmEnhWiFRBcinGNRe+Uvn/P4J/FmOdb5+hVhgTm8N79AV4+muKzIabq/TOiGPVxTAIp555QSB6Fk+iHau1rt1TNi1kqOedjFyUhDn2Zjgtc4AL7CCSkAdTWx/fTftk0DZ998nM2NrdY6MZ0WyEbG1sMhiVxGIGrSQLNm0shecuQ5xqtLcOtLT4rc6+dBPK8RGs4K1/6rV//3b3zFUA26lNmI+oipxyPqHVDVmYIIWgnCWEQMDc7Txwn7PR2GYyGvsiidMhAeTsraxmPxmj99ILEzjQ0WQ+HwxqNlLC7tU3gSuIo5M3r10njPnWpabQlrwzGenLvMJTlmJ2du0ilmJmbwzlLkWdo02CayhcxnT6bUNHptCilO3NB6aOgteb+gzXCMGQ4Oo8aUg5HjZSKuZklkiQhUQm2qJhJWsxdfYNRf8B6bamrinw8wlqLEsKXVnXu5eyfC0GSprTbbbR19MdjqqYhCAOMkGyPG4LCcuvGLfLBkK9941vMdNpILLapcebVXSA7B0XZ+OLbxuAMRLUhr0rqpsJKh5XeKc4AbSkIlWRmZoaZhUWEktTjkior6fUHaGPY3upRFiV55teaeV5irbeMUUoQSEmahEgZUiOoDIRNg1KQhPOsLPj0ulGvz3g0oilKrPGWRkEkWVlZojvTYm1zh/trm7SSmOV2FxUowijCOkd/a5e8qMinRVomSvogCmh12zgk2kkcktXLK6StLmU9xgQRcwtXWVx5CxWEBNOaW8fEsYn0cOkKzjSUdz7BDHeP/QUAth5DPd6/Kv/bEx3jtcIFgX44Gi5I9BPCOoe2lt1en5/9/BPee/ddfu3Xf4M0iflv/pv/K7du3eBv/62/zszMB4wKw1a/OnJH/tq1K3zjGx/yK7/6XX79d/4am//sn/H7/91/x+iIYlKvC6r82e95AiL2L9eA0J5IjyRhu8X88jKmaRj0ehdE+gFUzcmKib3OcMag84wkDFi9/gZJ2qK3uXlyIt2b7SEDRZgGNKZG10+mcR6FIFCsLC0yMzfP7STkyPoiQkG6AGEK1fgciXTwRLrCh1iHEOlK0m55c+lyO0OXTwbjuqoYV9tPPcaJcCTpKoAAREjYXSFaehNsQ9N/wOET9fNV8d/74idoa4nEmMV5gc6hrBzx5ZSZDxYp7o3JPusfnWjwusOBziyuarh37wEugqLOuXxpmU0p2d7uHY9IP14iw9lw4PgCSMOEmXYXnViaroEKhNzZK0TqCsPwwQZZFiC/9z3mlxaZv7LCwu4VBtUWQ7l1RBnkk2FlucuvfOcNbt3e4ZNP179SRLqMFWoxxeYasTOJadr4FUvFLy6RLoDO5CXwGz0HSfRzEFhXxenJk5PsaWkDRQ0vqtk6oNITz3R1QaRf4AKPQymIAq9xPFgRUGvNvXt32Nja4trqPEvzHXq9PlleU3YacJpQWVZnAqooZNcayhLy/oBhb0CcxrQ6KWXZoI07M5H+1kff8QUTpVfL7mzcZ7CzST7oMbKOoirIyxwpBK04Jo1jLi2v0unMYIylKEukdCjlCKOQVreD1oayyCeey4d/r7MGXe2Pj9YJRoMByha88cZVVpeXscaxu7vDONMMxhXGPH1EbJqSpilJ0jbLc2+AkJ50ryuc0ZiDRLpSpGmMM82euv55wBjL1vbJOL6j4XB4z+xuZ5ZOe4ZABrha05npMDc/SyJDxr0BRZBT5vkjRLqFl5IVLoA4iknTFOsc4zwHrVGBwiLoFxppa9YermHrivff/4AoUEjncBPP/FcZdW2oDwS4pTaUukKbBis8kV45b93WknLPB35xfp6yrhmOMyqtGZQ5ddPQ3+1THygiXNf1I7ZwSRKSthaQQtA44TfrjMFoiALJfLdDUZTs7PQpxmOa0pf4FsJ70M/OzTDLDL1RybhoUEFEGMcEQYCKYxptGFcNg/EBImgiqZehIk5ThFQYJxFSMbcwT6vTpT++RGEUl6+9xdvvfETd1PSHgxPVcTg2kW70ZOfiTA36gmG+wCEQICZpvO7x8sAnQLA6T/TWFewwo7pxH/eaSWbDKOKtD95hYXGRX/rmt/nm+1/n8tIqoVI45yjqmt5wyI1bt0jThKIs0drw/e//BX/5wx/zox/99Nj90x9rxDh/dVOPTgsRxAipsLo+Up4eRBFf+5XvsnjpDXZ3Nhj2dymrgqzMCJVkXFTouj6Z3cAFvpKw1pLnBdqCnigN5heWmJmdZzQcsru7O1GHHx48xa10MpFLdrd71FV1gnYlQAQ0WnDv3gPSnT7jQ5UiYs+fXMZt2itvoKIWWZHRFA7PutT4AfZgv5AgZ4FwYnti8f5tDhEFyDTBZ5BZwjDm+psfkLY69HubZNmAvLdJ0dtEBBEiaUMQkeUlUgo6i0t05ucZ93ap8oNkvmbf9Pj5jT9CKjpL1wnSGRqrqLY20OOj/NTdYz/PF3fuDNDWsd6vGZaOPHAYCdWwYHhzG6kFy798CZM19L/YRRev1xx2bEyKOM3NznF55TJfbI55+HCD8Th/ZecjZx27DwcYbbA4jLNIoXjvV9+mymsefrFG3TR+j0k66qYgK4YMR7vs9DcpmiF21vp9qIwza0nqsmKw20eajPffklS1QmuD1rC5A/lpvdQToONTfwMVgAHda3D6xT0Xmxv0wwLXGFzl/L0a4Qnjk+xpq8lnTlIf6fFSCqeBgLgdTbJzOsRpQjUuKIbZhLA5w07ZZNOGGE+iq8nfK3xB2vMOV4QCKRFC7Kd4T42hrQHnMMYX3j3JaG6sQxuHcwIZhDjrHlFgPi84fB0V63wq+AUucAEPKSRhIGnco7vUzlnqMsPqhm1RU4z7DPoDtIOyrtjZ3SFUgiAKEVISjhoabWilKUEco6KQMIqxssD1CswZ5/it7S0EjlD6agc7aw/p72zQFBllPqLRjVeNW0delmhjKMZDQmeoy5y6qRHCIaUvlNwYH+NrbU/E2UohmO/OcWmxy3xnjk7cQtqAbKQpyuPXRNG6YTTcBQRVmfsY4zEi1lhD1ZTUun5lY6SjYJ1hOO5R1TlyrJBKMujH7GwllFVFUZU4YGl5CWcMo8GApmmYnVug1e7ghMVJR5kX9HZ6z704qXOOQT+jbgz3HqyRzs9gxjl6MOTeg3WMNmhreLC+Q5aVLH/yKYPBgJu377K21WOUla/FcwpCQRgKLIat3cx7pmuLsb5WiQNKY7E44qIkHg5xQqLCCGEdRV5Q1TXmKcXYlJLEUYiUcs/qRmvjs9eMpWn8uJNMLFRbrQgp27zxxiUEMDfTIQlDNre2GAyHDPp9X4rSaEZZhpASPRzTaOPrBh6AVBIVKbTVbG1t4hCMhzkCCUFIq9NlZ3OTwc4uNBVlf31SrPRkz+7YRLpuNM6coVjSHl79hnWBFwwJIvU/3ZhTLwTCayt0//Nfo76zTn1v47Uj0pM04du//l3eevdtfvM3fpNf+9avgPClG5xzjMqSzX6fn3z6Ke00ZZRlaK35V3/wh9y4cetEhG9elmz2+gyyV5e4OBWEQEYtRBBDOcTWT28DURzzvb/9N/nmr36PTz7+Gbdufsnu9hbr9x/Q1AX9cQ9TVehzSsW/wOsLYyzDLCdsDI02CCFYuXSFN95+n3t37tLrZTh3uNUJQKvTZnZxgVGvz+baxgnnUQkioW7gyxu3kEpRPOFbOM1hS6F7FdWeZf7qB0RJG7M5otkFz7IM8WlBB/tFAGoZRNsXTrIGlH/JdotwaQ4AZ2ranS7f/lt/l+WVS3zx+aesr6+x9flfeSI9TAhmV3HWMhqPCALFG29eJ00THnz+6WNEusGzic8XMgiZu/Y+rfnLbHz+OeP7d/Hu80/D8x0LP/tsB+NgJ4dcA0vALBS9jGIrY/bqAtd/+wOaUUO2nn2liXQhJYsLS7xx9Q0+//ENbt9+8ErPRc7B5q1dtm7tTvzQ4dpHV/nO732drJexs75L3fdEulNQNBnD8Q47gw02dh5gqxoWrCeqK85cVLYsSnY3d1Cy4ltf856cRWEpS0dZnYFIbwGXQAaSJE5xpcPkBnfeVauPgBtrdKYf7Y6nuV8hfpUz3UM8DqbGoCfxKXkMUgrSmZi4HXPpzcvMLS/Qf7DNzp11iqyiKvXp2rrDD98VXqHfPvC7Aujx6NB+HpABqNCnZyuBEt5nHGdBV94nttpXmR63lRjrQPviAzKIvZ9yfbB+xfND1biLIqMXuMBjkFISBhGBNQhxQJntHHU+pAby4aR+1OQfs7Jic3ODVhKxurKESCRhLyOoYWGhw9ziLAaFQVE7gXF9zrp/9XDt/iTX0CKcYevBHfqba5Mszf0B0AGjoiCoKrLBLmFdUGYjqvqxlKbx6SZLKSXLc4u8cWmFmZkOnbiFsiGjXkNlzPGzc5qa/u7Gke8x1lDUBXXz9KzzVxnWWnr9zcnfvKJ+KqxXUUTUbpMmCZcvXUYAX1YVRV2zuLTClSvXcMpilGZ3a4dhb4B+zkS6tY6dnSGyN6K7fAcdQz0YUW7v0u+P0VpjtOHm3TWSMCBQgnu3b/PzL+9x5+H2a8N0RpGg1ZZUpeHh5vDQjaTcOHLjUOMcpXrErRbtuQWEMWSjjPKIYrSBUrRbCdpY+oMx5gCfoo2lwSKkIE1ipIBGJ7RaIYtzKVJ4OxmpAvo7O9y6fZtR5T9fN5r+aICxjlHWHMrTyEASpiGNaXi49gCjLaPeyBeGtpa022V7o8egN6L34Aa3mwGnqXVxbCLdjne9sZV+TXKOFb6vvpi47AKvAIQQCCURgdhfQL0iz18IQWduliRNyUYj8kMUpVprNjbWUZFiOB7TWMuXN27w5Rdf8IMf/IBxv48whp/+6EfEUcS9WzcZDfoMhyPMEblyUkrm5uZotVrMr1yms3SJre0ef/bv/h2f/uxnZ/MoPgOSOKCVRjTakGUV51MvzRdTlVIgwgglHMYY7IFrFFIRpR2iVpdeb8z9Ow/p7fYpi4IyG1MOd9FNjS4yTNM8953vKULlX9pAfUrOQuBFaoGA2dkWSRrRG5X0xuWzPnqBI2CNIevvooIQXVc+TbwsGQ8HKCW4dPkyZZkx6G08oSQB0HVNkWc0dXWKzWgBTk54iwIhhReOE0HcQsQpURSRpi1U2iVdfhMVtYnSFKSis3qJIIwoil3KfNen/ZjJwkEKpIppLbxFEHUJhEMKR6NLGl3iQokLfQGlVhqRpimjrMBubtPbXGO8cZ9q7IlpZzW2HHshp7VYA9mgjy5CGl15GeNUtvis6z0PSaiKcSqhKHKs6tNUI3AZZ2Ywz4AoAWNBlEy8BthXJxto+jXDB310rjHH2Qh+vpbuzwVSSTpzLdJ2QtXkbG5tUORjpHAoBUHoi3AhQqyDqmrOXExTKe+LLCQI5ev0lMUpUsynMfaEZLXGomsNUrBweZ6oHTF0I/TQsHtri7vxFxT9ETPdLnVTUMiK83L8tMbR1JbSGLLC4JxFCj9/RCF021DW0DwrZJ92t+lpTRJWnHNo1+BqeGJyVpPXNLllSj7D+dntnEebPjiESI43pCh8/Gg48VAhpSCOA6SSWG1pioZExiy0ZsnFgLJoqM9CFu9V/2JfhT6Fnvz+vC1LnAU7UZA5gdtrK84PZs5hTqhG94c12AM+sj4V/tEjTImW15A3usAFXjto68gbQ2WOVmYfJHKNdZS1RSpLY/wcVDWOsoGyrCnygkILci3oj85HpZv3twCHwiGcpSmzCYl++NxqnWNceQK6PAcOSyCIw4AkiohVSCgCmrJhpMcUeYl27typB60bijwDC/PLixhtGI0ydKPB6KfXS3oFoZIIGQV7NygMI9J2mzBQjLIxzhqayXOqq4o8y4jbMa1WShaczL/6LBCTDKy6rsmyDF0UNFVF02icryTip2Fr2RqMqa1jlBfPfO5CCITw/ehlz23GOOrKofWzz0Ub4zehlCIoC5q6wj72ITWxW5JSIpRCSYnWGm3sE33fOF+PISsrdkdDyrKk1+shBKRRSKAUZV2hpKZpGprGYie7cM65SeaGL5R7GFQQECcxzkrqosBoi9M1TgiKcR+ja5piDLrAmYmI7BQ4vrXL2pdM07tfeQi86kfiVRpfUVHXa4OD1vjw/IqDTSHxBZg0kPP8fVSPASkl1997h9Vr17j1yafc/vTzJ95TliV/+cMf8PmXn/Gf/Y3f5TtVwT/+J7/P/+X//F9TVRVZnrMpJfdu3EAIgdEa5yzNMwiXMIp4/8MPWVld5b1vf5cr73/AH/2Lf84f/JN/TF1VlOXLIVnn5lq8eW2ewbDg5q1ttD6fsUVKT6QHrTZKdqjzgmI02vt3FabMXnqXqNXl80/ucfd2j8YM0WbM7to9du9+4cnQ6ST3ggKUVgzd1BfgarLTdZFpLbK2lHzr7RWuXF3gB5+t8cMv1s73ZH/B0FQlm7e+BOFJdZyjt7uNrhsWV1b55V/9FXa2NvjRX+5QV08OOFk2oqwz7KmKSApwCqehGvXxhXFbQBc59wZy5SqzC/NcvXKFTrfLlevXMRZu3lwjy2sufevbhFHM/XsPeHh/DeoGysJHiKEkSFOufOtrdOZm6CQt4iCk39tl0NshHw8YbK/TbiW8985VpJTcu79Ont+k99lfkK3fxE6qo7uqQDdroAJIuhhr2Lx7G2E1BA6ZKFxjcfWz7sGUDTqDf7oMIJnHqpjdrU3Y2cWNHgK7vEzWeXbJewJvF3gSbIQvnjhBkWfczm6AcZjxM65d4G/Ti/AKP0eEUcCb712hO9+mP9rmpz/tsb29TiwhSWF2BoQMIJhBG9jYGFCdsShDEsHyAqhAIJMAbWH9gabIz9YWdK0Z9zOCMODd73qLl59//1N6D/t8mf2M2z/4hIWPVnnjl95gGOxy79bg3DzStbaUuaY30Pz8RoMQhkurfsOg24ZWCpvbsHtUAgbs259MeQgNZGCFoRSFt9t7XEKY4JXrBb79SrzVCOw7Nr0KONgvAp50tToMIV7pfVTWwFP2+oJAsbTURUrB1taIQlfMfqPD2/NX2RXb9HbGWHsy+4AnMB0ey8k5Tjc1aqZ1lc8XtgHbYBD+dorpbdlXbp3mcpzRmGcUZJPST1PWvh5Lzwtc4HVGXhuKxivRj62mNjDMHcYa8qpGCckwdwzHIFVGU5dsZ5a1kfWFN88hw3fn9seTP01INWs5atKxzrE+GCKFOLOtDICSkoVWh3YS0w1aJC5muDtklI3Y2epTToj080RVlezsbNGdmeX9r3+Ec/DZZzcZDcdQjKF5Tep4CUG8MEM8P+s36K0jDiNmWx3qsuTB3VvUZYmtPfE6HAyx2nHl6mWurK5SJBnyBRHpwaSWRlFkbG9vQ14hxjlV6e11plqY2lg+vb+BFOJYSvkgkCglMNp6cpeXtyqpSkddHS97otY1w8ISmQbtLFVdPyEcS0JBFArCKCaIIqq6YTwqMNY+MYeXBioD6/0h8b175HnGzvY2URhwaXWJJIow1qKEJMtK6sLuhW/OWJrKeo3H47d8Ep/FSUR3bpZyVLCztYPV+2R+f/3efmaNPdsTODaRfpTX8CuJi6Dr9cGUCDhtW04CiEJsHNDoBiPBzbZ9FF4VXjXzSkCAgChOaXdn0VpTN7UPAqyvP1AXJblS3Ll1m49/9jNu377Dzs7OXue3xqCfKTHzkFISxwlJmhKGEVIGWCTaQlGWDPv9l5oi5pzFWoPAkUYKo85jcvQ+m84oVJgSxRGBDIjDCCV9dWcVxkTdGWQQU2RDymyMJcdRUuUZRjcvRQJlJwIve4Ig9tDjMNklbwxFUdO8wJT8VxoSP+MdnHhPcGssPg09iCKkkF7dUuaURU5ZZNRH+Ko5azHandIa7QBT6pyvJj87T5DMEl66TLhymfn5OWZXVmi327Rnuhjr6Mx0kEFFq9sljGNmsjmyssJWDSYvsM7RACpNSGZmSedmSeOUOAgZFTlaKIx12LLCKEFTG6RyNI1Ga00Yx7RmZjHGYIzGaI2uSrDCZ68JJv66gigOCCJJU9Ro6+sOHG1FdQ79zxmwDbaebBqYqT/8y0NZ+4Xn3qU/Nue5xmHG+tE2elR23Wuo1HTO0dQNVVFTFX7xZ5uadiug3Q6Yn4vQVjIsvErmPC7Sq2lBCUEchwQWpHx6TYNjHdBBU2lG22PCOEBGEmM1zjjQoLMGbRqqfknTr9CjBtdMpLtPKww5JUkP2oo85fLrxjAc14wyTVk5hPB2LnvD/XR9cMxr2cMBJbbDPeovPj1v+dhPOPtjOni889ocenyRdZxzPDhPtCbnUT36WRmCCAW2do8VBnZobZFSYIzFGks2zunv9smzHGvPqECbSuBq9pXpsJ9RcBIv+KcgTlLAYazDOYc1ekJU7bPmTzTbiX+6OYbCTknhCS3rnlCzPY7JdAdMfp4xNrrABS7wdDhOt/QRwg87eeGLSlba0lhfi6CuoW4s9alEJIdjKt440Wfco+NNGIbEcYRz0FiHEIIwDBF4QcNR62whIJCCQAjqsiQbjRmOxgyyMXlZPTlGTZTNj2uyhBBI5SdV58Tk2g7XsjvrMI3GGUsQBDgkKgyRQYCV8on3v1IQCiElcRKjwoDZhSVaS3M0VU1dlkRBQBAFGC39eqIxe7dA64aqLMnzjOFwSJ6f0Y52Gk8fvfeyB+egKRuKUQFVjaga6lrv9ZPp/HQyC1hfG+RVseg57mlMVfjGemW6vw+Pfti5SRKbdThrccZ58cBTbo8DyqphOBpTFiVFUWOMpSxrXzelcQgERdWgJ++fhpzWHp0JN80Y1Y3GGovDEYQRIDD6yU2A00K4Yz7J51kl+LlgOq68KhzqBY7GaZ+XAN5bhTcXEfMzqEtLOG0w4wz6GfzZp7D7/L14nwUpJdfee5f55WUUMVLE7A52uf/wLqauMNnAjwpSIJVkdWWVmZkZdra2/U7oKdBqd3jrnQ9IkhZpmhLGEW99+5dZfuNN/vzf/Cv+5L//py91IO+0JXNdRaQUrShECsGPv3yWfO7pmI5RQvpJe/nyW8wvXWZutsvS4jzdbpcrV69RNZrPvlxjOBhy+7P/RH9nnemsanSDftw/7wVBCp8WNSXUT4sA351m05A0COhXDcMjvOJ/YdAF5tmvc6nxasrj3GspodNChiELM/Mkccxoe5us3yMIQsIoQmtNWRwR5B1GnB0L3g1yyjSpKOG9v/X3WHrva8wuLzGzsEScRHRaKVJKAhF4v+RhiW4MQvlgbzAaMxhnFEXFYJhRFJqt7Ywwivnmr36LucU5IiUJpOSTjz/mk08+wWw8RH/xCUGaMvPO2wRxTBwplJIszIS0E8VokDMY5Ix3tti8dQOnSzB9hJKkS1cJkzYrswkzSUh/d5f+9jZVVZJl4yPGn1PfrP3Py0nELCbsn23OJbvkLGNmKxE4fFLAofsIj1tkCHy7jfAWMKf1vX6FIIQgTn3xockvuLwQsboQMz8/x7Wrl9naHfPvv/8po7EvqH3WaSqMIO1Apx3x1ptL4AQ//3ibfv8MY70AFSqSVkzQCuheb4GA7c96lL1qb8GmZgPChQijDU1ePZop93g2ehuYwXteT99Tc2hXCANJHCm/6Kj8xlUYPJr1rPUx55KD3W2a6XCQIJ+u7ePJv8V45fbUmsg99joppqr4FK92L/HlHM4Dj2dDPgtzwAK+z7Xxz+Em+zYqAtJLimhOUm1byq39xZgQXpUOTArXOWa6bVppTJYXjE7pxfsIpqrzFv5epZM/j4E1fMLSGfy/3/3oG75IX+FT2PNRjyofPfX9AugkIWEgyStN2Tx9cSoEzLb8HDLKa/LqeHFJGHh1oDbwmpU+usAFvtJoJ7A6J7EORmNBY6Csvfp8LoJ2BKMaeo8Jps8SR50HH3X58irvvPMWpXZsj0uCIOTy6iWkFHzyo79ka/3pWbyxUrw5N0MrDIitQzlHv6oZNg2ZNvQOkvACgtjbyumKiS3j5DhxSLub4JyitjHWGqrx4PCNAgEIweziAh995zsIqbh55z7jUUa1u43Jn7SLfSUgAmTUJYwT3vzgbWbm57j+3pssX15h7cE97t69jXAGZQ35aMzNn3xGle3Pk1IqXwQ3DCbrrIaiKE7XfgQ+vorx2aDPmI7F5H8qUMhAeZbYWpx1NNp6K9VJzFXrYwoXeHGWZd7m1sfZR9n/HhetNKTditCNpSwbjHU0+lE1uxITLkP4jSbtHJVxR4ZeQaAIwwBrLcZookixvNhGKclwWFNXlqryosCp1mSqGzgq5JRKogJfP8g0miCMmVm8hpSK4e4D6vJobvC4bez4ivTXDS+CQD/rWv8XBEIKhNyf+Jw9RJl5lucVSEgCXCDQ1vgFWTf1K0h5jDzXF+QzK51AOr+LHAQxQRghgtD7m01PwjqsNaw9eMjag4dn+j4hJEEYE0QxFkGjHdkoI9zdpczzs1/QGSGFIFCKMJCkoUSePTYCvNemcxacRQhBFCfMzM4zMzvDwuIiZdXQSvuURYlpCqq8fz5ffEZYd2qLrkcwXWcOi4ac5iU6Qr8imLarAE+OCDw59bjl1MGPCK+aE0IilcQphQlDZOAVICoIJ9XHvTq7LI9BkJxZsSkQQYJK2nSXV1i8fpW5hXlm5uYIlCQKQ29HNFG+y3bqlYTSgnAQOFQSkBUVhIogbxiVoMLIq+yVgomqsGka6iLHFjmuyNDOko/GBI1GdUJkGBAEKXHaoqwgKB0yiBFC4bz/jeeygxAZJcRxi1YaU6cVdcuPPVk2ZeCex81yx8yim7LW7rGfzwfFs3jbA0pcMd0Riycv7f/NncHx5lWAc44yf3RUMjN+HojCgCiKicIaKeQkhffsgZZ1noBrDDgrvAf70zr/ceHA1IaszgkqhZwFqQRmKgmf/hhrzMG2OFWkHyYim/5+SmQfcYqNtjQH7dCc36A57bU88ufDuo7wrk0yEhh1wOl9+njOFMNNXuHk1XCmxy4m99adhthX+Hlial8zfV4Hj69AhgIxrcUzUbc5B81jRPJ4nJFn2clrwDwtLp3W05t+9/Tv5sDPM0AFAdKBaiwGiVSBv6GH3EwhBEoKojAgDhWVtr6TPfWSBEGgiMIApU7GiE/9aqeQ+yHz88VTbHwucIEL+DEgCBVaO4paUzf7BFdj/bx7/knhB+O203VKKRVRFGGlJQwNQRTTmZlFSTlRrT6J6R5zKCBRklgJ6jxH1w2jRjMy9tD11nTsenxKmxKdFolAIg7KbR/HJF3AaoOua4QKkFIRhCGN9LZbQkqklJNMolcjSPTtIyaIEsIo8ZmsrTadTpe01SaOI4SzKAy6qp+wbbHWYDFo01AcZ5115MmwH2ccgxpyk//pxhw+rx14ruIE8cqL1C8eLAx8XrDOoY3BOrd3/TBp3847AQocFnes2ilaG7Q2/lhSYi1UdYMQMBpXVAeKxh0Mi586rAhfK89ah33EFlIgJ/1mL0g8B3x1ifTnjQh/95rJ6wKHQkhBZ7VL1N6fmIp+Qb59TipxB9zvQS9DfHAVdWUZZxrMeAxV9uj272EI8Io/h9+hfE5zj5SKd994l/fe+4iPP/kZP//JX9AA9qyL+SNQVRV3790nSlosX3mDJA35yQ//nCofMtjZfulpRW+/+Sa//WvfYLS9xb0f/xR9nh5vztHfWScfDxD2XRYW5qmahv6gj0MQyJCZTrqnHvsqouFJweMvJBI8ETmtm2Hw6r0j0uA7ScpCd4a43Wb20iraWB5ubVLWFfnODmPdUL+o2gIygmAembTpvvkerYVFvv5r3+H9b37gSQ8VUNcNeVF4WwnpfMCfWASCmbkucRoxKhvGZUNVa/K8Iis07cWMpnGMhhmjQUaVFei6ZuPjz7Aff4ar+mDH2LqhWt+iloJa7CBFRT8OCQNF0xiaWqPrCluO/JjrLDhBU+Y4IXGdBOkCLi9f5c3Lb/Jw/QGD/hhtal4aKywiUF3/ZzM5Z3JehaIqMob4ukSlAtkSPvCfkGT1pqW8a79SpM7mTsE4b5hpZ6yv7RCGId/92nWqxvCjT+/RH55t49dqqAsY1JrPyh1AkGXnN9+YyjK+nyOEQBePteeGR3zwn1B8H8S0po878PMVec5SwtVLCTPzio3tmq1esz/JnOnAwCJeKabxxzzDGkcoSOcUMhCUQ4MuTngDZ4E38XPDYU3EQTW0NI3DhA7ewKvytzh0PrGOE3kOA/ubvtMNCoe/LwcPUkzOb8w+oX4Ok/3Du7f9BnLSRagIF6aojsTVBbba74dKSrrtFnEYcml1kXaacvv+Gll5RAalECStFp12SlY7yA+lnRDKF5RzpgZn9+ywpqR5GMBM22f3DMeO5+pel+CfxdST/qyY2hedtDrrBS7wCiKKIubm56gbw2avjzuQMlJN+u0ZEmQORcAi4DCMcZOV9Ek7087OLj/9aTXRXBhanS5cukqQpJON9ifREnAlgG6ieHdlnjiKuFlV7OYFpbPkHDIEO9BTq7TH/rGuG4YDi0PgXL5npXUUinHGlz/7mCCKaS0skczMsN3fpQFmFheZXVoiGwzYXVt76Wt8gCgKuXx5mSAI2b57k/XbDdaUZHlGXowJgpjZmS7Xrlxid3ObOz+7QTF+jkK/ED+enwOP6pxXok///KrBObenRFfKcx1nUaZXtS8aqqQgSUMUEAjh3fCk3z7rF5a8dgQKWgqqyWbacW5P0kqYW5rFGsOoP8Q05glr2mlYdujxpEIISTRxYKiLkjovmAZSRtcMdx74OL06v/TeCyL9tFD4u/dqbPq9shBSELYi4m6y9ztd6vNV849KGJWISwtIOfES07X3xH1WKr8CkfhzcRnPkUgXzM/Mc2XlCp998jN2t9YQcYrqzCHcybOPjwNjDKNxRtRYZo0jQLK1tsbu2u1z+obTQUx2C+dmZ3nnrTfZEoIN4+CYab7HRVVkVEVGPl6mqmtcVVLXJUqFzM6uEoXBCyta8jLwC0+gg+9YIX4xHE5+NyUljujrYRDQSVPaMzMsr16ibhp6gwGmaSiKnLp4gR4bQiGiNiqdIVm5RntlmaXLq1y+vIg2PoU2yyxl6bDSsw1C7qfXpe2IVjfFRSEu0sTaEKcxUaHJdEhRaHrrPaq8YtQfUeUF+eYObmcHzxDVYMBMFOS6WQOX8axQ1zmH1Q22rnDGm9mlSYv52VnG4zFCKjCSl8YoCOkZayfYl3K+nMLLT0BBMCMJOoKgJZGhnBQpdNgcviq9ezoXFJUlLyvKosJWI+ZnZ3jz6hW0gU9vnr1QsnNgGjCNZfc5bIA562hGT5m/HEhzICNPggsm7f3x6ecVzjYQQtBpK+bnQgZDjZhmR5w50wZvT9LFDzcHEvROd54QxBIVC+rsFGNLPDmXmn1l/GMwtcPg/IK8yzO744lv0eP1PA5rE8+preTZGCElSdgiCAQiCHy4/hjBI4QgDkPSJKLb7tDptIiinSOPLQClAoIw8qqwp7xJSO9H5qwEZ/dUblMoKWilCudgnJsni+GeJ6YKxvMSTJ1DMswFLvC8IMTxSUEhfH+O4wSE90d3B/7NTDYRz3uYkqSAxex5NJ6cUCjLkrIsH3Eqc1rv73weglDAfCCYCRULaUwUx4RSYp3FuKcPEU/T81nrqPfWvccbYHTT0N/eIYxjOvNLJNG+TV6UJLTn5ry/+0ke5HOEUopup41Sis21Idl4yGB3l/b8PNYapFQkSYvFpVXQDhU+SUuem6r68Yy/c8Bzz4g6I5xze3E2nO1emonfuYgUUah8zSH8WjOUDoElqybzvIQoEBh9/MEkCAPanZS6atitHU39ZMc5SlsipERIRRBFhEmK0Ra/++0DNOcsdXn+FkgXRPppMSViHn/OEkjlxD7A7qseX/HOdl4IkpB0vou1lmJnhDWWfHtMNdxfvDZF81zuh9vYRf/Hn0HoIGkgb7xJ6BFotWKuvLuINY6H2Q7lc6p8LYRgcWGWN69dYnam489XN9hsQOosV4RFShhb37SGnJ7SETJARQlpq8vlN94jSlqouHVel3ImCCF4993rXL26yhuXl8iLDLCsLi9hTp2X/jgi/Ezp5XK7Wxt88uMf+N1Zq0mSFm+87VAqQOuLdJKvJATe3zaC8JIimFfosaEZmP24+4jYu6wrdoY9tITZ4Ziqacj6fbJsfOxiv+eFzvwMb33nV2jPL3Ll69+kMz/PbLdFlRcIFaCUIo5COt3U22aZiXefDBBCMqgsm2XOYDhmMMrQ2lBXNXXt6A8NurFo7YvGmHGPpr+LKbaAAV72WICrJlUMnf/zceAsJh9iq5x1XTCIIuIoJokjxllGEEiEiHEuxDmLMbW3ZDoJwpY3oNQ1NNOA6aQks8UzeNPJPORcqvadAa6G8r4h7kZ8/e99jeV3Fvn0r25w+7N7mOyrEUxIpXjv/Q9ZWlnl3u1b3Lt7m6qB3TFoV7P+cNN3Y1Gz0oJhBeUrSjIfxDRr1E6SHK68tcRbH17aW8hkZclnd+9SFJXvYtOkjFd5b0QBytE4S9VYdO38MHCac1Z44lzhu5rEq6rH7Cvcnyo5ejacg7oySAv2JARrNDmfEfAF0AaxJP2Qpw5cqADmJcxJyC08tD5YO49umU7OY1rHwz32egH4xq/+BkJI4vYsKgi5++UNNh7cw+lDYmMHVlt2trYZ9CTj0b6Xul9MC5IowAFFpbHOsdsfMs4L8vwpEa5zXok+tQh7DAKY6Xb45jffQRvL+D/dpB48xzpIJfvZABH71dxPi6lV0FdjGL/AVwiz3ZTF2TZZUbO1OzqyGPDSbIvLi10aY7h9v0/TGHRjiKRgeWWGJA3Z3hkzGJZ7TV3KgDBKz+xxrhkCFse0gPzxJyLFvntXjLeyijotZBhw47OPcUIy6O8+8ZkIWOi0+ODtS8SBIisL+qMxZV2/lI0xozW7aw9RYUCZeXJw3OthtKY6rYf4uUIipSKOWywtLxElCY2tKMqcj772Nd5+/30erj/k3v27WGMQwtd/6q4u0wjId/voqmFhYZlud47hsEevt3XaU/Fiqune7ePZXV9xHFSmn6VdzM52mZubIYpCOq2YpqoYbGzQ1A1mUvRpJlLMJoJuu81sp8v2KGe4vos5bMdBRiADWp0OndkOzmp6vTFGa4Q0hJEgTWKUUjRVjdEabdzhLjtC0ElTwigi6nQJkxRXNxSch9rjaFwQ6afF0wIpJfaJdDuJfl+h9NznDRWFtJZmMI2mGmRYbSgHL0bl53aHuI+H0JFwNfaLsWeYTidJxNWrixht2f5iQDl6fkT63GyX1dVFOu3U/9JorNGEAq4qv9u95aBwfg/ttHdNKkUQpaSdWa6+8Q5xktIbDo/n4/ycIYTg2rVVvv3tD4mEo6xKcI6FublHUgLP8A3s+y75Fc9wsMtwsB8YtTozzC+uEscp5hTV3y/wGkDg/W1bECwr4tUAHkKzY/bH7iMWs3VTM8waVBjS5AVNXVOMhhT5iy9c3Oq0eedbHzK3vMpbX/uAdrdLEErqqiaKBVIpwkCRthIfJBmDFIIkjHBI1jdztkcVvd6Ifr+PabQPSgzUpcBZAUYBDlMMaQZb2KqPZ7Yq9vLZ7QnHD+ewk2Iuu9ngkX8KgpAk8cVkjJmo160+BZGeQNQB8gNV6E5DpE+VTdNNuJfLajoNzaYjKAXXr73Fe999m7WbPcrhnVdGNH9WSCm59sZbvPfBR5RFwb27t2kMNAVY17CzvUsoBanUBIlPWHodiHQ1IdLdxH1n6fIs3/iNt/fIg93+kNv5GkWv8vtUrzqJfkDFpa2jNtaXdTnt1DktlBlMfgJs4Ansc4D3KbcIPxQeHyGeWcmBAsQlgXhTQuN80soUAuhKWJZwz3lLl/PCtMDpGL+39xLw1kffQAhJkiRIIemtP2CtnDbUJ2GtY9DvY40mn/iRTvepAylJ4xDrHFVj0MYyOkbavntKfYvpcdutFu+8+w5NY/jpzx/A8yTSp/adU//+qRf9afELtB68wOuFTivmysocO/2M7f74yEyPuU7CO1fmWd8d8+WDXbS2BEAcKpYX2szOtiiKht4BEZ1UiihunZlIt3sVrk9Okk3NBBK8m5gKFGGnRW0t9+/eojxEKDNVrc8kCW9cv4LA8dnntxiOcuqpGOsFb45ZYxjuPmqjVYzHFONXo+io938PCaOY2bk50nabytVUTc2bb73Fu2+/TVUW3Llzy4t/hEKFIa35OUrTUI0zTN0wMzPPyspVnLP0etuc6iYL/AOcMp6vesz1HHAeGyudTpvV1WXiKKTdSslGIwYbG+hG4xpfimtxXtFpB8zMdZmdX0bLHmKjx6HPTYagEpLuHHOrK2TDHtsPdrGmQQhHEApa7ZgoDCmFoy4NOGgOGZeE8KR7miQEaQuVpJThiBfxoC+I9KOgBETSE+L10Z6krZkuC5dWkKGEVKK1ZufOQyr98snLF4Hrb73Bex+9T5xEtGZSRsMRf74+ZFi+QKJympnvHKxp//f66MGj0+rwzXe/QRiGLMdXGPYzfnbjcza2t/c3umtOTV5EYcA7Vy8x1+1QDB/yyU//DDO8x7tzkNXQz6EbwfUlQTuUXA0TKiR/vlEyGD793kkB3VgQKMG4so+4ogghCJUkChRxGBJIwXD7IcNBD1mPmI0FlXanJiUk0JrsFRX2eGuKOI747nd/mUuXVlhebJOEinJ3l97WBvmoYOfhDlafbcCbvbyCQBIGC0gRM+rdJx89ucq1xjAa9imj4nx92V8ghBJIJXDWYY+bOvWLBMee3bXe9pOv3jGeoNDs++M9pck5ITBSUTQNGzvbNI2mOcy78MzB83TjZ1oB9ckTEsKhqAlEjaJBuIa8EDROEFQSFSqqumZc5ARK0G2FKCW9WYkx3PniJnfvrZPv7FDs7GCNxWiDs6C1xAmFSGdASPKNL6i3H2CyLfyg93yYS2stTVN572Dng7wTk+gAjd+I8yaUBlSISObBGlw5eHpOLfh/Mz4jZt8C7Fl14F8MZCRoXUpI5xOyeszW+iZJN+Str11heDdjdzjYV+6+Yji2BbBzlGXFaJyxsLzKt7/7PUbDPpsbaygsW2ONdBZdW7/pM3mUrQBaUyWz9IkSw+LVSbE1xi8opuuW7bUBH//5LYQSEHhFepU1+25CR3EBU2sqAYHyPuW6AvussGrqAfoMC6tjYWIvYhsY7hrqwlGMz3DQKf8h2I+rznOYcT6jw5208OZUpdYGOvh6E19aT2gfvN8O6HurKobn3Ojqx34eA0pBEPg2p+zZxZEOgTGGB/cfUhYF/V6PwxqpdY68LFFS4ozfBHXWPeJIg/Dk2XlaDEgB4yznJz/7EmPs05Xt540p+za1ZH6a5c4FvtoIBATSk8zNV4uNq6qawXBEnpfPJN7KomR3q8c4q3CTyVcBWMtwkKGbmqKcZi9KkIrZ+WW+/o1fJnxKMc/jY9rxTj6u7HXZQBJGCsIQ7QIaa544WiQloZQIa7HWUuqG7f6QQEpEmBC2BDqrKJ6DD/zeOShFFAQ0xlBpTRTHzC0vAdDb2qKpzhYEpp0O80vLNE3D7uYWDpi/fIkoiemvb1AMB888xmHw4piGui7Z6W0TFWM2Nzcoq5JPfv4x/UGftfU1Bv2Br7OkHVVVUpWaMGzxzodfJ0RSVpbNwYBxcYa0r2ldGomPOSwXtQ1PAWst1hrKynpxWZaT1YbG+CleOOiXltJqbFCiwoyqrJ6c/mWEkAHt2XmSVoeknU7qA1ivRneWKIyIopCrV67R6bTZ3dphNBwxGOUUg8M3i1xTYwWUTuIqTfWC6phdEOlHIZTQCUFbaJojg8HZxXk++JVv7/k7lVlGvtWnyn4xiPQPvv4h/+P/6T8kBIKq4sH9h/z8L37KsD98cSehJ6/cQX8ySj5j3J3vzvG9b32PuYUu418bM8zHjH4/Y+Mn2/tpxiO8KPMUY3gaR/za197j8tI8u7u3+cHaT9G763xzCTZG8EUB87HggzcUC+2AdHYGI0Pu/mCHm0cQ6UrAYkfSCgUP+o7qAJkqhSAKJHGoSKMIBOw+vMXW2l2WUsFyKuiVUJ7SVzIQMKf8gkY3cBweN01Tfu/3/g7f/e4vc/uLj1m7f5v+7i53f/QzdgvNzX6DPiMTsvzWdYQI6CZXCGSLu58XhxLpxjT0e1uoIKSuzqN61AuGABVIglhhGot9hn3RLyQcnjQX0DhDMzK+Hw95hJx6GollhUBLxaiuqR4+xDlL87hS5aDX6ambrmTf5+Dw6nYSQyhKQlEQUCNdwyizjCogkIggYJwX9PoDWknA9SszRKFAOmhqzSc//hk//08/xm1swubmgXMVOBQijJBXr0McYR/8GNe/y165+ucEaw3VeRR7aXL/mkBEHdTsVZyuME3OkVXoXAPm8UXCq2FSrRLF3Icd0qWEYdnH3dW052K+9uvvcidao3dviCvcK5miOk2dnk7HT4NzkOU5/cGQlStv8ME3v8PdW19S/eA/UOQZD3e3sFrv2VlOn0o3hktt9vKt89q/6ldkGLSPNaGHt7dZv7OzR8S5AFxn8tCeVax+oqQSAUSxJ9PLARzpgibYL5CY4VXvZ8XED31nrTm79eqUZW3Yt3A5z0WtA3ua9VM9ec0CC8AA3E/tfgHUA8dn08LWcyj4O3HSOgmCANIUAgvxZBPnLLAO6lpz48aXbG1s4Yo+h23wWmsZTWqFTKfBaR236R6GExIVBAhrz6xCBW/7KwX0+yO+/x9+6pvSi9pBC/EZFNP97l8w284LTBAqSANfSfMrRqQXZcn2TkPZ2GcS6dk4Z73JybQvKDpNXBLW0dsZMOgfqCUsFciY5dWr/LW/+Z/RanfOeKanv+97e9eRIurGGBmRu4DGCuxj25CJUnTCkFpriromrxrWtnaJoxAZt4ijlHpnxNgWz42XTcKQmSQhq2tqrUlaKW988B7OOYrx+MxEend2jne+8U2y0YjRYIwFrnz4NToL83z5F39+aiIdJpsPVcb6xkNUEPBw4yF5UdAb9Eg+bmOdwzqHXt/kk08+R8mATqdDO23zzV/6ZRbmF/gP3/8Lbv78U2xxxqyjaWA64CuT1fmi4ZxF6wbdaMqipCgrRpWh0fsxQJkbVG4woiCQQ7L8cZshiQxSZBAzv7TM3MICjfFCNWsstm6QAuJ2i3anzTvvvMfS0hJ3bt9lc2sTzRa9w4h057BVhTGavNBU5Oj6ORatPYBfGCJdqhClQqzVGF1776awjQPqOsdiac10iZIYbTTaaow0NOLoitDpTId0pkPUbTEYDb0Sq2qoi5KmbL66gZbyhUTffe89rl67wre+8y0uXbpEPuizfv8+O1tb6JdJ8E3veeDTrFtxRBgEFHlNVWm63TbLiwu89cYbXF69TmsmZbD9JZU0LF1e5p3ibXbXe/S3+k9IfAQQBwIhoDYOc8ScrpRibn6W5dUl2jqiNiVFC8qoQqma4U5Oahx3+47t0hCUFVoYesXhZI7AW8B00ohf+tb7LC10+MHP7mHv7+KEAqFIW13mJtYl25sP0FpTlfnEJwsaLY4852feWud336XzgdNRjsJxHHP9+hUWF+eJopCyLHwhq7SFkIq60d5ytB2fOQEnH+QIIXH5DoHMiJIOq9fe9/pS56jLnGFvw0/uRY5SCoP16hLrHpE0CikJghCHwzTNC/Gba7dTWu2UsqgYjbL9XXTYI3xVFCKVJIgCgjBACI01U2X6K8IkvQxEQEdMVKqTDltNalRMWT3HseWyYRSTzHbBOGytEVaQJAnOWrRpvKe3c6cklKYMvJwwZAnIYCI1tf51QJ1d5gVrt24zHo4gaZPMzmHULEImvqCurSnLiqYsyLVgY70kCBRJGmN0Q7W7hu2tQd4HPX7iPBwhdhwhqhBXl+CmBUBfIwQxIkgQQQvXGJw256qCfNFw1tFUDaqUk80yx3grp+iVZGu539h9zbu7kIKFhXmuXbtC0p4hbXfozHTpdLtIYalHCo3xBY7cfnetDYxqmJ2JuLLSYpAbHuxm1GfMaHpucF69u5dBMB2TpvZTKZ48rYAYZDpRVE/XjZOmbCZDxhOuF9PClAeLaHmnpucSe55Lt3L489S8ejFyhd9wnWQ0PXVf7bTnLL01KEw2Xabq5lMeL1CQRiDM5HVGvnrzwT10o6lGQ1xdwGGZWDCxD/MF28tGo63d45ed8IWuwVHVDc6BVAEBEmvNJAPp5BfsHNjJRo551ucP1kE5j/Y19a2XAmaVD4KdeTXb8AWeH6ZK9LMsol5RGOMoa+utE57RnmsLY+33E6ZvnSYaxXFEFElK27AnV3XGZx1KcfZB6jwgFYQJKkhIk1lk3SAHu+x3aIiTmG6rTV2XBNIQB1DUNbUxNKWlsZBpvTetn/pUgoCk3cHhqMZj7AFPsjAMSVstT9QXBVprhru7PhHzHNZ8WhvyvKQsaqz1xPZoMEQ7dyahWRhFtDttgjCiLAu/X15W2LqmynKcNntDszGWpjEYKamshapi/f5d8n6PYW8LW+e449iwTlXn04lIsF8sehojPaXpBYGi3Uqw1u1lZMzPdUmSiDhJiKIIKSVCSrIs58GDNbQ2r/My4wj4Pupv1f5cXVU1w+EYYwxNVdM0DRJHMC0szP7Ksag1vawkq7zaRwhBGEQIKXEqQCifFu6cJQwC4jhC5zHgC6OqICIIIpAhTgSIIEQFCVKGSCT2iYnd+Uxk460hDQb7DGvn88IvDJEeJx2S1hxVOSIf7RAGKYtzb+JwbO3eRduaS2+/zcKVFUbFmFE5Jh/1GGw/wB1BKiy9dYVr33yfQW/Azds30XlFtdnHNdoXMvzqzbV7bgQqVfz9/8nf5x/8w39IkqS0Wm0+/dGP+dd/+UPWHm5Q5C9mN+ipkCBboELB5dVZZtstHtzvsb425I1rV/ibf+17fPjR1/mlb/42OjD8aOcLNpoxX/vlb/DeNz7g+//+z/nh1l8+cVglYb4tCZVgZ2zIjrCPCcOQ6++8wUfvvcnsQkLSCtj6/C/Z+jzg4Wc7pHfv0qssf3jDkGMoRB8toHyK0iEQMKPgynyX//J//g/4xjff4x/93/4x/+YPf4CVKU6mdOdWuHz9AwaDHv/ph/+e8XhA09Q4oKwcrnZUZxj8DTDU++v3kH0e4HEsLMzy9//+32F1dZkkCVhbewgO5uaXCOIWWaWJ4oivrc4hlTzkCMfH+pdrfu0k1hBS8eG3fp03fvVvY63FGMv2+m1+9hf/kqYqGOxu++rViYJ2BEWz7x2AL0DTmpnFOUs26GNeAEl9+coKb719jYcPNvj0k5tYYZGpvyc2t2AFSadN1EpQKkDJgLpucKrANg11lnkS9hcRXQEfKIgExHKiGmx8MbicfQVCgo+Tn5Fh0pqZYeWNN2mqksH2DlhLKjpILFkxoGkqqspS1ye93wdZrwhUBPMLEAYwslApb1ei93vTYGubv/iX/5qo2+XS5gad5RU+/Og7rCxfZadXsjWoPDFhHcOm4tYXfaSwLM3FKNcwvvFXcOcnnpw/rH1YgdtYwwkBdpq/XvM65T7KdA45ewVXacwoB1O+2n3h8WK3j7VFayyj3YzaNOhrBtfAw7/c4u4PHnpbnumc8xoH8UopPvzwfX7zd36LcaXJKk1Z5Vy+cpnxIELk25SlZZBp6gNpT/3SFx795eUuv/ntd1jfzfn53Rvkr3p20dSKX+AnzBB4F2/U+gDYArkMwVWwPdA38GPVRKFc5ZMm8/gzD/Aq6qnnuMBn35yhYOcLwcHSBK/SefZhUsvuucTvMoZwfiJIKCYWNE+3IH8mkggWZvyUUdSP7MGeCj/5/p+Cc2hdP7GpexCBkqzOdAml4uFgwLiq97JQAimIQoW1jv4wQ0hFmrYQQlLXJVrrSZr4yU7W4ROEjwMx2afGgT0PC6wC316XA3gzhdLC3Qwqd6bnd4HXDLWZkMMv+0TOH2XjqLS/tmddXq6hmNafn/yuApySzC90mW3HVHbAqMj8IGcMzlReffqUGggvFCqGZJaoPcP8ynWKsmRza5uqmqYgWWZnZ7myukKT9akGGmctvfGIxjg2Mk1lHLmxZ04MjNIWy++8C86x/sXnVNm+4CVttVhcWsL2e2yNBxR5xs2PPwU4l3VpUVSsrW3TVCWmMRhruHfjJiIKMYP+qY/bmenyzvvvUzU1d+7eoyxLdFHgtKHIK4pHNlP2RUmN8L6xu3duIoSk0Q32uMKYePIq8WNygLdqm9Y/maZN/P/Z+68nSbM0vRP7nfNJ1x46I3VVluruaj0KwwF3B9ilEcsFCBoIgxnNyN0b/hW8II0GM5jxhv/AXpA0I9dILLlYYIGBHtEzraZ1VZeuShmRoVz7p4/gxXEPlZGRkZmRorrzKYsKke6ff+LI533e5z0B9VrM9avrKKW4ees+Wmveeesal9aXWVtfY3F5yRWyjGI++/Qm/90//edMJinqN24oEE7YJQSenDkPzObr4XDCeJwghUUKkFgiLNaDVDsyfZ7x0UtzBlk5yzqAwPfptDpI6ZMbl/WNVVRVQbfTYXFhEVnlbAiBkJKoViesNdEypLQ++HWCqMIPRngiQFiNPqwmsjiLOaNQunCuzs8pyvElINLn8hoBUs4KGLjvxswiDtbwgBdqEBx5rfC82WtBCg+BxBiFkIJmu4n1LM12i3qzgYg9AhUTBFCVE1RaUk6yfR+ww9CVosoKyjSnTDNUWlDlOfY3MFp9BLNgUBiG1Jt1TKWZ9PtMBn3G/QHZZELoQz3y9t9SKoN6QjuRJ4HnSzrtOmHs40nPRTxnn++isBlFUSIEBJ5Pt7FAodwEaq3hg9qHbjN6bKNncWIEKey+D+TxkbRer3Hp0iXW15ZZW1+nu7xMI64IfU078tCNENWJmaw18JOK9/oFibYkmBPX43EU0G3F1KKI9e4iF1YXCOM6yljiWky326LQEbl2A32tXifLE6qyoCzyuZ3syRvxM+JQT9wX4MyP+7BDCiGJooggDBgOh1RViZipbkeThGqmBM+VQTxllzGH/M6FlJRlRVFp/CAkrtUI4xZCxE62JQXCE7Q6LcI4ZNobkZWH1LoWrHHt4HlFnD1PEoYBnn/QZ44TbdYYjDYI667BaA1mloY5P1HhufR781skkxKAZ2cK/mO7gHmY3B77OgVWa1RZospq5tlmMdLuE9ZzX+/HxzyHw2NfJmGMM1aWODsxI45YzhhjKLIcLQST3T20sQw6G3iVZjjMGQ8PCMSyKpiMhwhhCdIAzyrK6Qj0KUyCEAR+gJQeVWUxDxuj5axdnnuU/wn8cYSr6BhEEX4QQq2FDWO0ytCmdIbODzwf4diV/V8FUjr/XqNfoEfKCR8rhaAR14jjAFsqyklKOS0ok5c/uHGWLiY9ifQkSZrQ7/cwXgAyIIojFpdXCHzBZK8N0mOcTTgsv7e4BbsxBmEqAqHp1pxSe1qcnWgLA6cwqlSFfqyqlOeAuXo8YBb8s1Bz3vi+JzABiJpFKEHoBwgEWVailDlQXc2//BN+DnA3yudg/fKyLEkPj8cv4xR1eK54RhDS9YEgirEaClmgS31A3h8m8R8xPJqZ86Qxblh8WpP0Kp970Z7+cAQCT0q82R7rMOZ7WGtBG4ucpflLKfbnzWeW5ScOfXGOe+nDA9u8YmE0qyqcv2yN+BWeKb5kj/txSvkc7y9ytmQ6HvN6GE9lASkFnieRUhz6bFcHx2j9XIRJD4PnB3i+T1ir49caCD+YeXPnmENBQyHcPn5psUsRGBI9wRiL0iAqjZpOyJV+ojiw7/v4vu+UvTPLSDNT/h4/ltaavCyplHL33FjUWdTZxyA93/FmWh0Ze7XRFHnpCkZaAVagywJ0BWd4TmKWYBAFIb7vY7RBaY0vJVVVzY4738C6SeG0sd/MJr7iSdrInCiXh373Z1+P6ASeJ6nFMapSBJ5EWIsnXM25er3O4uICYRRRq9cZDEa0Wk20MUyTYp9X+o3C/jx9+E8WrTUGNy54OJvhk+CoHfcsxew/FzjXLvMBgapKqqLAGoPvSeQsiCKlJIxioijGWKiUpqoMldIYbZn/d+R0cVlqavbZz3OM/hIQ6QHIDsgAP2ogfZ96s0YQ+iTJmDSdQpVAMTh4i+/jLy8jo4h6FBP6PunuHtP+FpKA0GuDMewNb1Nr1Pn6H3yHzvICYRzjBwG1bpO402RvuMutjQuMt/vc7r9PpR40Vtq9ucFoaw+lNFVZOvLtt4FEnyk6B3sD7m7cZefmHTY+/ITNm7e499FHlGXB1UUfIYP9t9zby9gZPofqaC7mQqtZ42/+3rdptRr8+Ifv89kXO5QzI9Xbdzbo9Yf0hlP+/t/7L1lZXeVvf/VvYwRAjjYlN//sJv9+908fUJpoA/1EIwTo+eb1mLfJ22+/xT/5J/9nrly5yMpSi9Cz9D76M6b3P2FZD7nYiXnrayv87rUO792b8KN/8Rmj8cPvzY2rS/wXf/w1Ll1+jT/82/8Q34947xff49ef/ggrfb7xra9yb6fgznZB3GzT6bSpygRfSjwOXJjne/An2WuFQGt2mePZJZccEOsnQRvDNM0R/SE/+sH3uL+5QZYaysIiixSpLVVS8tmk/4A/3WPjkDGqtYI7dz5na5hx7fW3+crXv0M0miKCdYRNCdqSuB7yR9/9KpfWFvnB93/Ee798b//9SlUk46FLJX5IavN5Y5+cnc9ceqZEh9kNtuSThCJJmac/uYIu5mDXKiR+3EBID10kGPUSViN8FtDW5ZiWAvJZuvXUHvjeag78iM8wwU4HA/IkcQsHpcBakhlZ4FLTnzDAImKQizNCd7ayG4xBGmgbV0kxma0CNUfsO1Re0PvVrxkEPr1f/QI/DlGZQeUHvc9Yg9YaAex6s9T68el1Knw/ZO3i60RRnZ37t5mMezxwk6SPjJ2vpcmnJ/hLPCkOh+fOuCUREuIOIqix+vobLK5dIE0yppOU3GSM9d4sDfTYOXrxgacC4AcRcaOF1RXZaAtzWrDhvPGIhV49jvnO22/RXIjZuH+HrS/ukew8h3or86joUyxCH1k/U0riZoMgCvjLv/oLfv7Ln/Hut77FV7/5TbpLbf7G3/rP6e/tYH2fYW+PYfo+RXng0znfJ5XJlO07X2Ax/OF1wyTz+P5Nze7JdYiOwPd9Lq2tU6/V2NzeYjAePvkFnxXzTV6AU5BHOK/dQMCyhqYh8iSt0ocQ7DWI/ZBry+tI6/H+r+6xtztxk3Bz/n7cmDY5dOx5mpjB+YFOceu1F5wguA+N825/mcj9wzieHv6MUGvVuP7uW8jA49PPPmU0HLlnNfdJT9hPXMLy0AyDcQJZDnEEC+0D4uvJ8fRju9YWc2iOMMaQZW7d4ubOZ7fLFaH7sgrME9Y2OhXGQFm6/rzsO0V6fsZiQa/wCs8Z85jtkwxnnoR6LBEC0tycWnLmxM8WroaDmTnhaKUpkgT5ArtKe2mZzuIy7XaH7sIy49GQj9//JWVRkGfzSpQWgeC1a5f4oz/4DoPdTbbvhFjpQdRgOM24/aP3KMrJwfAyX5icIZNpcWGBleVlBsMh97e2KKuS3b0eWEuljpLke8Mho+kUpTX2CccYKT0a7Q6eH5BORpT5wVqyKhWj4QSMwcwpwSzFbUBPf+ACCAMIfMHrl9dZXVxmPJ4yHI3JqoovPvoYKz38eoNaLSTNS9SznFQPbyfArYWaHFjdPcyDFgj8kIX2ArpSdGs75HnOdDhhB8ubb77JxfVLNJoN2gtdEB5f/8Y77Oz0+ODDL5hOf5NqIdr9/Z2yc/HlCaS1cbdyPss/rMl7CALpg7GMJyOsmFkdIrCqIhtNaQYhcnkZOTtKGIZcWL9Io9miLDW9wYhef8igP2KSJGhbnUikT/XRekrPC18CIl3gdgUBQoYIGeAHdYIoRBYliPwoKQHO28fzEL6PDEKk72MFaFU6osmTGGuoVE6gJWEcUqvXAbBG40lJHIXEcUzcbJJPMoQvjqaEzDabVV5Q5S95SvF5Q0AcR0RxBECe50xGI/a2thju7TkCymha3QAv8NDGYgx48imJ0sc8RykFtSimEcdY4/yd5lH1siwZjyqmkwlaFUgMi81lp24kQ+uCdlDHr2aRtWOHn6venLJoxmMaCMOARr3G+oVVvvqVt7h8+SIIg1E5Iwm2ygiFoRX5lDMlT9TIQYpT1/v1WsSV9UWuXlvn7a9+BYvHT3/yPfZ6E5QRRLU6Uc0jqnlIP0CrEqNm3lQciNXmT0Ac+/ks3Mm8J84HztPe4xQJPp7nkaQ5BsvO7h5b97dIUkueW1oBdCPnMz/O1TnsqQ8dwUKeJuSmT55nTqUtPbfDkhoRSLwwot1qsdxZoBZGRw81J1CfI4wxKKXQh6Ufx26K0fqRs4QQ0l3r0wYmvgw43JAVgHUkRMWBj/RhH9ozKiC1Ug88//OZnIWTDUoPz3dsl7Bu9jehwETWBQPmC7/DMIZq6oyTi2zkXnOSA8vcIv6Ma24hBL4f4gcRQh4Oix2eUzlgaM69Wc0zMM6qmxIIP0SGMbV2l9bSGsIbopTGpB6CCnuIDJJSgpBYKbGHFenSQ0p/tqh7sZBSIA9JO+I4ZLHbod2tsbe1waTUCGsRnptnzkwOzYLK8/npUa/d/3oKpfBZ5hH3TAT9QY/BoM/6lUtk6ZRWJ6K7tIS2hnq7S16URPU6RZ6jquqIFYTWiiLP8D1oBRZhBL48WxsSuFTT0A/cuTwvzO/vPKotZn/0ccS4BlnN2kEAQejRatfwrIc/z1SaFVklxhHpEke6Hj7ufPx4hC/oC8PLaoMxv4eHC788g+HBGvCkR3uxjV8LqPfrZOTO11MYl/2a24N2cVypfuictJ4lNHkumen52Q9bjLFocXJQ+fjfHtfG5YlxOH3/vD/y8HoDmOW3v8IrvLQ4lqDx+O9/gjHlIGPTHvlcozVZmj51logfBLM1jdn/HDv/4CPnLvD8ACkFvu8jpUez3abR6RDXWwRxHSYT0mQy8wM/dLbC2X0sLrShnJK1migklR8TVAakPBiGZ2OO8IQ7k0eMO57nEYROwQ1ubCyLnHkm9GFUSlEd3osIR4wDR7zUT4UAIT2k9I5lDwmsAa2OWXjp04qDHD2uJ+dWXj61KKIMCyLfJy9K0iRB+AHNuDHbWxw/qeOrfvv08+1hYmKuSJfwKCN7IQRCeEhpCTwP43lUZUWSOP/4slAEkaKsnM1JEIYEYXguRbRfPtj9b6c9DgsH1/9A33P/JpEIXL9Qutp/j0BgqgplPVeHTis8IajVnE10q9WmVm+QZAVlpciynDzPqdSDJPocL2pZ+SUg0nEd3GhUliA9D9uMgRCqKWQ7s+jJoRurFKrXQ0jJ1PMQQqJmEThjS0o9niUGGPIs570f/4QwOiDS/G6I3wmxYYBthOTTBL3kQWumZrPAQEH2Msppnj1qtZi//ff+mGtvXOXNd94GwAqL8Zz9wRzS5Vqw0csZZ4o0f07N3LpA6nSc85ff+yVR6GNUwpX1GoNRxWiiWOnWef1Sl6/dWKK96BO3wPPmkh8LSDqe4EoAEw29E3ywhIBmJIgiQZJaMmX5w9/9Nv/7/+ofsn5hjZYsKcc9gsYyiCY2aGLjBpEH7VrE997f5L/51x9zf5izmVRkPHz+rdUbXLh8haXVNXzfp9KCMK4T1hrcudVjZ29Kd/kKX//WVXY37/BX//5fMp2OyZKT5XkRbq8oJfieQAGjyj71QCSlQPqShW6H1167BsD3//IHlGXJ3u4OWWb2s8WCyKPR8SE3iPIZ1BMoB6ASsv4iu5u3Gfa2MaZA2ArPSGRlGd2/z04+JX3iyuTnh9FoxN27gtFw8uSKLWtQRQJCYJ+Tkv6FooZTHPg4b1uFU2HOp4TDKoSXQQFpM9DbxM1lVt58m6AWU1uugW+5P/ycad5D7RUYmTgF6cNE0g9LBQlwilWLU6meoQmoqmTn/k2k55FnswqHInRmvrZymR5GY7LZWHKu1i4+iDYgwA55+AUfQHiC+kKdqN1m/coaV1+7QjLpMB502L1jGdz29rNIpPRYWnGFlwfDPsn0YDzUOiOtJmDNEfXkc8Vsh7u4XOfixc7+onRhucvX3nqHzkKbbrvLaDLkZ7UPKO0XlFPIh2c7fNwQtBYkZWGZ7JnTbePn6tc5DM/EZ9sYQzZNjhDYn332KTmKN9/+Ghev3aDRrLOw0CXwJfX636DIMj5+7z22793b78oagbKSKrf0dzWT3FKcMXqktGZzZwvP88jy56wmUsAAN2bVtJP2lAYU5BODHpX797xqeegVixeccJwKN0ZYDlRXdQ6KK8+f34SXl7h+EXhU1sWSgIsCJha2rLvP+Smvf0yYAqo+iEbIhYtXaC21iDstknTK3c277PV2yPdy8vupm9+WcM9vgAsO77Dvm38YRQG7vecXM6m0YWs8QQrIq5dnrWFLnNPnec/1s8CV6IR47S42UeiNocuAe5qiQ6/wCi8pjHFKdCHOXlvVWkueFSQYikJz2N6639/jhz/8C3w/AP5PT3xeb3ztXbQ2DHo9siyf2fxadFmgioPBMa7VefOr36Dd7XLjrTfpLizSn0wYT1N0paly5fyg9zcKh320JLXQY6ER4i+2CNQqmzt9fv7z9xhMUtLErZW90AUx/TjEjwKqTJGPThdXDgYD0iyjLEu316sUZjR0H/swAddMDRfGNRaWV7EWBve3Z1Zcp8NoQzoZIYRAz6xkELPK5n4EQeTsH23ubBEfNnjOg5SzvZQAAguBNmxubLG9PUBqjVCafGbpYlVFMuo7pfBMbS/9GOkF+IHECzwMmsqWGG0wSTnz5ngCZBwlzOfp+PPsvLlV7wnIs4KtjV08IWlEbSKvzt5wTL43ZPCv/iN//oOf4fkeQRCQpClb97cpioIs+y0T0h6C70natRghYJzmVIcGiXoc02jUKIuK6TTFzGyLpJA0600CL0AID4SHKXJGu7sstNr83f/y71NvNLhy9TpVVfGv/uRfcefObYo8p6pKVPXyZdl/SYh0J6eypsRYiZ37ousS1An5qtZis2w/G/LIP2HQ9uCvWil2N7eOvqgloCWIFzq0L15wvs4NiYh8pxQxFiYvLgolZyHi/Ujsc17D+YHPtTeu8NVvv0Oz0XW+zdagMc7na/7CWerHJFX0p8/Z29VCWSru3dtBSriyHtNu+iSpG0Vrkc/aQoPFTo0oAj+wINRss2QR1hIL6HizjM0TNqJO1QZxAMWME7i4vsLf/p/+AY16DSE0pkixNQHSx+BhhIf1Q6Sw7EwNf/Fhj3Gh9vfDD3yGEEgpiOKIZrtDvdF0dh7aID0P6YdMMs12P6ezGrO6dpHB9iZ3b31GUWQ8TOc9t3j0gNATlBaEeHQ0+JFqw5k3Xr1RY+3CCmma8eEHnzCZJA9cl+dJgtDD0/No8CMO/rjQBeiCKhuRjIfk6RRrXKRdGovQUEynJEJTvQSF6vK8YDwezybmJ+/UZ6pu/psCn4OFUo4jPRJcf53zdGdJt3huUGAVfqhprywQtVs0ryxAAMPbe6SDKTLxMCGnc8ondeu5mjLGXW9ywmtOgLWGNDkWSBIeiLnRMm4OfibWJ8KR9giwZ5P2CSEIawFxM6LVadBdaOP7AiksSb9+RCEipCCu1anXm0yno9kmwcFSPXaq8rNCXAtYWmkgpURKycJih+XFJbqLXaxnaacdbt68R9Sa6QYeJbyeqUGCUFBrCqSExHNrhoeuF+ZpS8yOPZexPYO+o6ujCb6Dfh97z2Npdd2pnDxJXIux1lBvNFBKce+LL/Zf7/ZxAoNAa5hOLUlhOWvs0FrLND1jBzlvWNxY5eMIuMjub/B0atHDgxseYtDKYPwTBjGDG+8OW7rMJ/WDaMNZYlO/PRDHvh9Wrs3/1hCwLJ3dVn/2godMyUIIxMz3+6QaSifCzFzoKo96rUGr08H6grzMmdgpU2+KNpo8wRVKW+bAKykD+icfVhtIn2NMyFhLWr6EjesM1gpPhFl2gAg9ZBBhhUBP7W+toOoVfvNh4bHXSNZCVWlKKVDKHOFEsyxl895dxFNmgS2srKKVIi9KtBX7xRCt0YhSOJdLAUEYsbx6geW1C7z97jdZW7/A57dus7GxSTJNKYsxVhxe5BwSAgoIPEkceJg4QDXr+LsDdrd3GUwz9vlo6bh4P5QEcYCp7CPXTXlRkB/ec7qU+dMvekZie4FHrdPCGhjv9qjOtEizqPLY8YUPMkJ4IcJ3/rRWGDfvnVBkwmJPzFr0rBNNTiYJhU6oS0ldSpS1zn4UUMVRsl9KD+kFeIFPGAdoFNriAgrpU9QrmvsK7l8jBwmvCrcWeshwrZVmOkkJfZ/YC/GETzbsMUwTBuMEuP1k5/QbDCkEceAz9zU/HG0LAp9GLQYLmkOe5gJCPyQKI8ys3phRiiJNWVpc5K0336beaLCwsESSTMmmE/a27/OSbeSP4EtApCucpMZ1amsE6aiiSEPKbPhsPrKYpeZEjlhsdzrceON1dF7x6Y/eY7o3hOLFLJ5qccQffPebrCwtsLGxSa/fpzecsDs43Qf3PFGWJb/8+S/Y6W+y0lmk22yzffM2dz/6hGF/wk6mMMYy3MtACJLixbIV1sJgVJGkmiRz59Kf5Lz/xS617h36n/2KYLJMbWHRpVoNtmA6Zqn/KW8K8AVs4lKYFqKD4goWyEvLsISych383s3P+df/3f+Ti+sX+J3f+Q6NVgtCizKWD378Iz76yfcZJ4pxpvn43oCxMqeK/r7x9bf49rfe4RvvvsFbX/s6aab4Z/+/f8o0yRiP9jC6oioKitylvWR5Tl6VlNagsYQctW47LtIVFryZEv20faDgoCee5rXXbrVYXl6k02mdmDIvgAtriyx1WyRpwlZ/RBjGvHl9bT9d7bwx6W9y56O/osgyTLmLtZpiKhGlj1FdHBP74lHklSvCYTR+IPCDgGariTGW0WCCemlUX3MT2Wch+3pM5Dil3lxtfviU5uvBw6f7khCntVrE1asXiLtt5HINJRX+jsSMNTa37poeJx5Sx/kuz60AFI+Zcj7ve7NRwmowpVOkP1NosHMS/2yfJaWk3e7SWlyh2+nQaTaYDPrs7ewwHg6PpMYabRj295hORmRPRZweN8c6J8zHXG2wRcXS2irvfuubNDstOotd/Dik0WkhY49wZqUmQvA7LiZgDkdgBciWSyRYWVtmcWmBKp9QTHu02hEXlzuUheHmFz3StNpP2d4vrxCAaAIKzLwIxnmvW+fN7Fg/zEYTjNZ8KH6OxVKr1em2OjSCNuPBiCIvj1peAVL6eEGLwIe1y4Z6rvn8zhSSl2WcPIb5GBV5cLUFsYfoAAHYSQK93I1nh5BnFZ98tIXnS8ZF7qYqwYGlU4lrlg0OahhL3ERdcqJy+bca871cCP4S4IESYAWIVgsRRzQuN2heaZDtjRnpTezEuOdyiDOuteuE9YjXXrvBW2++xb3bd/nr7/9wv3DcWTDtT/jxP/8B9YUGC28vE3Ujmo0Frl6tc1/cY5KlEBjwZ4WoGxykqh/CXBQxj6v8RiL0IPAcq/ei9hMFYMBWBSrtQ6mhekkWFK/wCg/BfNZ8XhSUsZZBUjLNFWl5zB5RV6TJ6KntMDqNOlopknqMVBV5JijLilrcwqt1iKKIVrdLVKvh+zFlqZkkKeFozGe//jXv/+LnaHy0CElHA/R+sTOFlIKLSwt0mjVW2hEBBUUyYHvjNtu7Pfq5Zlwd2LuaarZc1ooqy1zR6Efc7H2NgsAR+UI48Qo4W5WT1A6zAV6XzklBCElQayBkSJlN0Y+p1K01G9Tbq9Rabdor63ieIPRd7TfPMwgsaTKlLHKy6ZhkOqTKU9JxH6MterZ2TJULOijjlnWZtVTG0Ggv8M6laxRVxb3NLcqiwKocrEKrCms07c4yy+trWGnRnqVIUraSW1T6nIRt83os8+y8U8owFVXJ7qhHIH2aQR1hXZHLV3g4tDGMstzxQzOboXotIAp91pYXWF9dZac3oDcc7RdklQgC331N04ysKGi26sRRxMrqCt/87neolOKTjz9ld3d3VlvlGZLowp8Jx2a/W3NEcHUWfAmIdM2RKkkW8kPp2c8EJVBat3AC6s0mb7/7Liot2fjBp0yHL65zRVHI73zza7zx2jV+8YtfcPOmhzHmuRLpVVXx6ccfs7l1k0tLy6x2uvTu3mf7i9tMcks/d7X/KF4OtYq1MJ4eZdHGScE4KVhb2WZ891Naukfkr7pN7taH0N+mO77DVeEyggQQSFiM3HdwxPO9BKblQQff3rjH9//9n/D2mzf41puXkYGGrESrkpvvv89P/uyveW8Ivx6e7dzfevMa/4u/859w9epFrr7+Jp9/dos/+4//jsFgxOtvXKder1NVFWVRUBQFRVlQqopqlhswJ9IPE+iH6y66P54+QB0OQD+KimrU66yvrRHFIfIkT3whWF7s8NqVNb64fZ9b93qsLPpcvXiBIHg2w1E63iMd7x35W5WCrALMC6wefxxlqShLhec7JWkUB3SXOhhtSSbpS0Skz8sXzZmhF4h5MdGHwWVoHsx0L8m6KIpC1taXqS10qFqCwhT4kcR4GltaR4Q9DmKccnEuTjlm9/hoHK4zMiPS7SnyjXODBvt487kQgkajSafTpd1o0qzFWKUY9p11y2HPc2sNk3MpJClwTOUzyESzYLXBVopOs8l3vvUt4madzNMoNHWvjhd7hKHz95AheC0QOZiMo0R6A/yWYPn1Ltdev0L/3habH/dpNELeuL5Mnmo2N8ekacXMppx5rWJ8kHWX3btfW+A8cVgddMznuZhmFNOMLE25P9riwvol/rO/9XeIoxrD/oiyVPsL8f3DCR/PrxN4gjCGINcEW/nLS6SDu27fg0staAbsi+GqCjt4MEW7LBW3b+25e9fA9XM4sHWZcGDHE+KCaHMiPWV/DfsKh2Dd3slfBiIcISAFcrWO7LRprC2wdGGRUbzFeLSFDQzcO/R+IYiaNRoLTd7++jv8rT/+z/nZj/6an//4J49FpKfjlPf//FeEjZCv1r7Jkr9Mvduh3aiTZBmifxcrlCPSDQeZRscCpPOEhHlt6pdTs/WUCKQrzJvz4oj02aLZphW29xsbsniF3zA8JKH62X2ehXF28hxstKbInj4TrBlHaO3TjCNsWUClsaWmFtVp1Fs0Wi0uXL6M9DwKIagqTZrmBNOEO198zgc/+Wu8Roegs4opMrSZL040UkhWF9tcWOo6WxcKqnxMf/c+/cGEUamPLDHMbFOtK0V1xkLNBw4pAi2Fk7V7wSzz8yEKhtmDNJWmzAuk5+PHNTw/RlfFYxPpca1GZ3GBztIK69duEMY16gsdPN8jDJ0iabi3SzIZM9jdpL+zQTrqU6UDtLX7S7j82BZBW0thLZ1Gk+s33iZJc3YHBZWeYrWeuUtUaGMJI5/F5SWELxGhTzIas3frHtV5LVw07Hvmzte0D5kgK60YTMf4wsOEFk9I1Csi/VRoY0nyg3YnBNSigGYjYmmhzfraCpXSR7OEBfi+JPAFlSpI8wRtFGEQsLi4yNtfeYfBcMgPfvRjNrfvk5fn6Kt3EoQHMjhEdOkH7cIfgS8Bkf7iYIqSoj8ikx6j+9voXKGOpzLOCzsdLl+LGxfjho/0BbayWG2pKoM6XHV5zs4CVObU51aLI66tr9Btt1BVRq+37Yoo7h/o2UN6knq7jvQlZabRZU5bJkQIRtOcQWbJqye3t3oemKt3akBLwrq0tERFXCWoW59g0ATVBAlcvtjgd7+zwnQrR34+oTIwKJwyHdyc98Ca3ihskWGqwlnvCIEI6+BFlH6NVEYo8XCWYq55nAtrw7hGu7tAVG+irMQIn7DWRE5L7twbAGP8sMO111Yoi5z3fvaX7Ny/h9F6n087TKQ/iVX08cfpC2hHEgGMS0NlYGVpkU67xfLKEouLi2R5xu1bGyRJQnlMlTCv4D5vJ8ZCoS3mORdt0sawvbtHkWeMx4/LXD47WOMsCqpKk6U5xtrnV6jrLAgDhB9hqwyegtw/Xg713AWw8yIz8OyIwUPc8+NgOhzw0V//kKAZozsSJRWj7buYZIKtHmMRObd0qOOIthLY5aDQ6kmQHvgh+15KM4/Go1Us56PES9TuZjBa09+4Rz6dIKYJm59+zNb2HnubW2Sj/gPFmp4OPu4mhzO59rOda7e3e/zJn3yPRrvJ5RtreJHk9tYdBpMRWVFw7cY19u5P2LjZd047h9uddcS6spbe7RFMN4hCuPLGayy0Gly/vI6uDFp7jEcpw70tsnRKVG8Q1OoMkozN4dTFT57FHH54Rz87fqMZEkYeQRTiRyHal5S+xBjFYDAgCjOKqkDPkkMPY5hW/PrehNgXNEMoKkP50gQbH44gCFhZuUC02KDeaOJLj43777F3+xFiiPl4M09RnnN5882ixpGt8yLEc5/031AIzxHi1hxLnJkHHTwO7L6OjdG2AtUD/NmfPYhWY8JmE72T0//iDlk5xubmQXbaWookn30vsNo+VX8x1pJmGf5kSrdRd5syKcA7VIRZGAhmSsUVXMBkAiJxWYW/mez5IVTGWS++UoC/wpcaAjHLvLXmWaR8/XZACGeRWBYpeTolz3KXtaYqiionKSbkKkVIiQK8IEBGHu3+ItNcEXWX8cI6fhDiBT6L9RhhFaiEQMKVixe4uNwlCn2SyYRBf8T97SH9QYY+B4IjjmPqUURuNFNdzrJp50r0049vjCHPUoSQ2NJgtd33Hn8cFOmEcW8TZQ22sUTcVKw0O8R+SKfbIQp9JklJNc1J04LR9g5llqBKeyC+OAWqLJn2h2R5gS4mWJXOJuoD65gkK9jq9QmiiHqrQVao8+WPDm9jTijUDThCx/PcQqLSGGvJdYFAoO2r+eZxYK0Tf6QC0ixnmmUUVelqcgvH9VgAIZCex+LCIu1OlzffeJNvfv0brKwssXH3Fts7O3zy0a+5v7npCtYCywsd2s0G4/GUwWh8aoDwLFtz6QUIKak1O8S1OlmWkyRPFuR7RaSfApXl5Fu7TIuS3XYHowxVfozkmKfSVxwhLqQnaC4FRDUPNTWYwpAkCnVYAesJqM/opGk1M+M+Ge1Gjd//+jt0WnWqPOHevQnJQwpJPiv4oU/3QhfP99i9vUM2zalZEKpgd5iyPX304PqiEeD2misCrkt43TMsiJJGMSL54gNMniAvX0R22rz1Rodrtcvs/qyHvDklV5at7CiVcvxyrVboPMEW6Sxf3kPEbbCCLGgykTUKkXPSECBwTcnjIIhaqzdYWFmj1mxTGZeKVmt28cYlv/7gDpNJwTe/+5/y+tvv8NMf/1t+/Ff/ynnFzSpwn1dm9+HrDDzBet3Dk4JyXKEVXLm0zuuvXaXeaNJotbl1+w4ffvAJeV48UDjTGJehO7fT0taSKksgnm/jUVpz697GzKr25Wm4xoApLZaKyXTqPMbOWunnmUMgahGi1oCpxlaPLnRzEuZ1DQ/3pVPqwDw+xOwDfB5Iyz83zGUlJy3OHoHh7jY//jf/wg1ICxJCgekId87FY9yFEEcatYEuruDqHqd3fD+EWvuASDca9Mgt5Pcv5OUk0cEpf7Y++wQhBLfET11FeD/GBnWsKji9oubjYl69NQaxwIGk+tng9u1N/t2ffkCn2+bv/i//J7S6Nf78pz/gztYmX3n3bb72rXf5SN/mzi8GmOOZRBbMBEwC9/d2uW/2+Orvvs53/vDbLHUavHNhCQ/LmzfWyNOE93/6Q3buT1m62KF7YY333+ux8R8SzCnrkKfGoWlPSkF3IaazEFPrtql3W4zSgq3+BKUrtra3iIIYi0WjsMfa4864ZG/Spx44pxSAL0PdpziMePPqdToXllldv0QU1fjehz32+OL0N87HmwI3ps1vhwam7Md78GavOaVm2G8CROhqpdkCjpQF8YAF3GKqx4Ef3WEivYRq89CxfEH9a3Wa3QVGH95m7y9vYhcsvGZPLLibjRKycUo+TrH6MfzRT4A1ltFkQjUQRN0OdZhVgQ/AzsYboSFKwLdwBShA3AJRuH96WTKtnhlK7b5e4RW+zBAC4TuLNltlL/+G+SWFoAJbkqdjksmANCnI8+pg1SqAewe7Cy8IGGUZzYUVxmlJbfUyHhIPQb0WcWFpAQ8DaY9QGt65cZ3LK10aXsmw32d7u8fN2zv0Mo0+h2GoWa+zvLjIIE9JkhG20lAWZ1IhGqVIJzPhV6HOXgX2GNJJn3Q6wM8Kev4CrYUKuXyZbhzRWn2NbqvB/d0h5d6IyShl987dxxKplFnB8P4uRZWjssGxvaIznB1NM8ab2zSaTVaER5WVmPNk0g+n4T/MidT3oBa5IK3KMdYwPan24iucCVleUhYlo1ZKezolK3I8abFylr0BCCmRnseFpRXq9Sbf/c53+KM/+iPSPOXzTz7g1q1b/ORHf8Xuzi7GGKQUXLmwyvXLF7h56x7paHyqNu7Rbq4C6Yd4fsji0hILS4vs7uyRTJMnim2+ItJPgdUWnRukErRjV+QxbobkmYeaRQJdJQUefFpC4Ec+fuxTTgvK0uynJtfqdbpLCyij6E9HaK0e+vACCfVAEArN9l6f0TTBzP4bjRKyrCDJnozMelwYbSiSAuEJtDJYY8kyxXhckGXqS7EmaNYDFmsBYakYZCW7ac792xvkTR/GKVIrolITVJq7uxk3v5hwcy8/Mr+ddJlz3aIpDf2xYjDRpIUiykqK7R7TUrM7SOiliqw6fTKSUvLWG6/TXVvh2mvXkX5EUWl2dgbsbA/o9UYMB2PKosTMRiYppSNcjX7mizNjLUllCQLJ2oV1RFSnu9DF9zyM1mRZRlEUaO0GwHazge/7BEGI5/ssLi3SaLZYvyjwowae5yOEfCFksZ5Vkj63T5bezC/h6Z+DNZZqRqoeD0Y8DYSQ+IHzA1BVuR90OeNZuaryRc6ZK/udfBQ0pweljkMIZ4nieR5FUZ6ednc4XH1eD/d4gbrjPz8OLBg181JMDVQzlizE1eg4K+bFBOek2ZxEinEE0kmPyGhQ5cH1GHNMjQ5hFBNGNaqqfHQq7kGe6nMi7ty8a+1hjXLpTuIxU/IejnnVxnj2FYGVB8GHJz6qN7tNR8pykxeK/jBnmpRO0ZHm3Lm9Rb0XMdhJyUeK/v0pkdxjtDd9+NAyewbucTqCzxqLrhRpmmGVYvv+DlmSYKVHo9MFKchS54X5PAVy1lryXOFPS5YuNLh66RIb9/e4d6dHVkzp1bcI/MAV4VSaPHtwc2Os2/9MZoGyh00hQRDSaDQxRpOkqcvYeqxx73yh0JSmYre/hxQeyVn8++dFZo838XlW+uHV/PF6Ec8KcmbtasE+52QAq53A7YhobH4vGj7UJeQzoqHgwObmhHHcGku5k5Dd7FP1Eqya+aIPeXiBMmvZ3dnl1796j7u376CfkGGxxlDsZWAFA38HXVSYUrO6fIG8LJgkI0cuaAECojBwaee+wkg9Hw7dmPBcm7RA+v6RtO0gCPH9gKosKPKD/iqlJG40kJ6PFMIVoTMWrHVzTJ4hhMCfHU/O0j6LvHji+/oKr/CyQQpBOLOwLJXEvMA56MsMKVypcaU0RalQ+mBFZTnyP8D5l6fjIdZasrxClRUiiPDjOp4nCALwjEHpHGEM9UDSqkfYomKauZpm/cIyrg5t607aD5wRZVUxTVPKytnSoB9jv2g5ZND+lAs2azFljhr1yAUM+j0MLkMqDgPGwyH9nR3S6RRrzL4tBxbErO3a2fyjj5kqaK3J84xSPWyPaQnCgLDZIohCSmVQWp/rXnffG/0UsVMYhMStFtIKwjYoVTEeDI6KXl/hzIhrEXEYEIQ+SiuEENTrNWfPmJWztmLQxhJFMe12mzAMUVozHA758KMP2dy8T5HlB5n4FsoyJ08mhB6sLrXRxlAY5Yw9fA+sJUkKqkrv21Yq45LZtH1wK2yNxmiFMXrfPUL6PlJ4BF6EeAxh5ysi/RTYwqIqTdgOeX31GsKDu5c+pwoy0l5BOVUHhtPH7rmUgqgVE7cChtsF43G5P06urK/ye3/zbzAcDPnBn36PJHn4BrYZCq4veJQ65we/eJ/SHFRodhVv7XMjIFWp6G30QDhSHaDfzxkOi/ONIj4jCODShRZfudrh1v0Jv/i0h9ru85f/5nustkLW1xZoNGJqiyWxn/MffrHH/+Nf3mJHGdQjfMQbuFp/emr4ICuQrYKdYUEpxtz68B69ccKvPtngw+3kkalhYRjwv/5H/yv++O/8Z4RxDHFMfzhm595tbt26y/vvf06/P0QpjZA+4pzYj7kLxqNaU6Hh7lTRbET8z//gb3Dl6lVG/R7JeEyW5WSDIaPBAGsNtSjirdev0mrW6S4tE8c1ao06YRTx1tcWWFhY5f7mfX78ox9TPKpq+TPAuTpdCglRzanJ8vSpiGYArQzZxAXJznNx4fkBjc4yANPhLupxrEQAm06xWfJUgQLL44vEpZQsL3Wo12J2dgcMR4/IyJn7hJ/XrZvbiB9OE3wazIOwo9nyvz9fHD/GMQrcjazPfo6Ad3CK9F8DJ7lFqPJY27QPPMvWwgrLFy4z7u+yde+L05UoPi4AoHhxnsy6PCZLfVrUZl9N3MgucZWhnvaoTmGd4exK5hgMC8aT0pHe2jIZp/z5n/0CIQRVVaENfLx9j8/8+6hKP1wBe+wxGaWp0pxEKzbyjGyS8LMf/JxkMuXNr15i5doNBr0t9u7eYdg/503MI2AtDHoZo0HOt7+9yn/6+3/Ij374S374735Bafbo7227vWoCtnp4+nKhYXM2FDzstrTbHW7ceIs8z/n85hcUeYZ+6Abv2UJbQ6pyVDZi49NPmIynZBv3Tn+TxfXpuV3k4ev0cAuQuZXVfFw5PMA+hQ3VaRABeA1HouvJ+R77UbDHu/ycRA8lrDWh64OcQFS44s3FodcczwvWlsnPN5i+dx87FzpMgS9OeO0hvPfzX/DJhx9SVYryuO3jGWFKw+iDPYQv6H+xiehKrn3zK3z7D36f3cEu73/2S6oSjBJIJAv1JjUiRvGYqUxBzlx8NM91/JVSEtWbyJkFjUDQ7S7RanUY9LbZ3ry9P574Ycja1evEjQah5+NJiakURmkG/R5b9zcIfI92q+E8emeFlXe2dpxK7BVe4TcAnufRbNQAGJXZ+SbP/RZBSoWQirQoGE2dzdZpU4/Rmv7mbYSUWCuxVlBfXKbWqhOEUA8VosqZpntgFQu1d7m42ObuRsr9XsGdoeLT8YzztvNzYMaF8Njz3mg6ZZKmTgwyt3M56zGshfL8SF6TjCjufETV75IIn9bSCteuXMQYxe1PPuWTn/4UPdkGHIneaARIa/GV8/GtrLsn2THnrbIqGY4GKKNO4Kjc781Wi5Ur1yjKilEyQeXlkRpHTw3FQb2ph8zhrVaLi5evUotrLHQWSZMpv/jRj5iMR+d3Hr8lEEKwtLzA0lIHKQRlVeIHkpWVJbKsIN/qobWzYPR9RbvT4fLly8T1GpNkyoeffMx/+//6fzNNEsosO0j4tpbxoMdWNaXTbvGdr1zH6IIqn+BLwUKjhtGWTz+7z3CUYgIwvivTmBeQWxgfqQFs0VWB0SVVmVMUhdOh1WrEccTiYhcpz559/IpIPwWNRoOlpUVWV1cJhIexmigIiKOQQlYHm5IT+r21lqrQyECiK4Mx4HkSz/eo1WKazSZVXuBJeSLX04hDGnFIOzDUQ42noRZJpIa8qtDGkbsPpHc/Y5hjA6Ix9ktBooO7x5UxZJUh14bSWtJKMxwmhFrRXegSxBKlBFoJprlhN1GcZX8YSmgc6ndGlQwHfWdgaSWB52MQVCc8LykFQegT+h5r3QU6rRYrF1ZZWOxipYf1PLwgIgxrhEGE5/n4fkCt3sL3Q6oqp9/bIksnT7yRnYtKz7rX1tbNS2EUUa/V6CnFeDKlKAuKPCfL8lmUz3lj+b7H4kKXVruzX6jE8zyklFisK5T6Aoj0c4d9kJR8usM9m75l5zlWT9JgzvkaHwUhBIHnEQYB3XabVrNOkZVUZUmlNOVpvqnnfZrnTEQBB+Tnk26qxKEvOHVe2scjCERrLEpbLB5+EGO0mhGZDznoSzEFnOdJzG6gEM6IWXog/ae2SLcz1ZREYJFYjBORH5tHrbXk2VFirtKa6jEL7RVZxXBvQh54lL4gT1NGk4w8K9BW4PkBykJWVJTP0tLlIZhft7Mjs1hjsMpglMZks2ud+3+fggenVYEQIKWbY4IgJAhCF3zmWEt5RiTzA5iRuEZosumESiqKIqPSFUaaWUobD7/Wh53f4b5/2uvmJPt5CnzPMtY8S9hjP89/nxVhiVp1/KBGKSoqVbq/l/pgwQP7Cn9buoJ1R473CL6iLMsnJtCPXEZlsBWYift8lVag3TgRyBA8jQ5ipNVYLdDGYAOBrHuuBlNlkMLV4zyvKg5BEOJ5EqUVRhuCICCKollSjgAhsF6ARez3X2Ms2hiE9AjjuiPbowg/DBFCoCuFsmCFwGiX1WpmRJKd1YIRRuzX+Hmegb1XeIUnhZRuMHlkLSPBQQbH8yltdiY8rynwvKC1Rmu9P+6cBUbPhI8zWxGjSlSZU0pNOrUIVZDlOUZYp3L1XJZ0lpcUpdpXth7BE94sJ4B8STJt7MzSpMww6ZQyjkmnY5I4pCpzjNFIKZFh5KzEZ9fseR7Cc7X/7FyWfgjGakpVYOyD9W38MMILAlqdDktLS4zHYwb9XVRRnK+4wfLIfVUcRSwsLOD7AcKTX4r2/zJgv6SdcBxWvdEgDEMWFxdYXOxQVQpVKlcTT2duzTJrPFobtDZ4vk8UxxRlyfbuLr1ej/FkQp7nx2uqUylFXgjisqKoFEZrtNLumRmDsAZhLcKCLwXCF3jCI/Q9QmOxyjqPduFGO8/3ENKjXqsRRSF+mmGNxmrtssUfo2bfKyL9FHz729/iv/qv/7fkecrHH/+aZDJmIWzht6Hc2SRT5UMHUlUadj8fI6Sgyt1qvN1psLDUYv3CEkuNFiYtiKKQPAyoKnVEZfb11y7wR197jWQyYmfrDgQhby+voYTgk60thknK3nbGZPSqevzj4Ob9Cfd7KUVlMBay3HBzoyBpCxqrITqusVzGBGVEon1STrTIfAALDXi9dfB7nT4/+sv/wPLaOn/zv/gH3OgssfQ//LsT3xvHAVevL7O8vMTf/fv/gCvXrrNyYZVcVUTNGrVWh3ZniRuXX2d5aY2f/eQ9hqMJF65+hTBu8OPv/yk/+v6/IU3GZzjTkyFx+ksBpJzNq9paqCpFXhR88tlNPvj1B3jS2RHlZYXWBqUUg9EI6QneeutNXr9xg08+/Zy7G5vs7uxy794221vb3L13l7J4FkbWzxHWOMsTwTn7NJ8vlCqZjvYAnK3US47Q91lbWKDdbPL73/wWKytLfPHZx2xu3mVjd8TNzd7zOZGXZN17BBEHwukYp1jdxJGPT9GdxqOENNvGD33ay9dRZcakfw9zkup7bm32G7UCTYEcghiiCKI6tFdmMqSnOWqOQBDLAF9IMlNSPkNPjM3bu4z6U4RwJVmENIg4JYwEaraRn5SWrdQwLp9rfOwI9vp9Pvr8Mza3tx0ZoYG5EPUJ+p3v+3ieT73WoFFv0Wg1QbpUeq1KtCoOLnZesLfinFOUjiEEulAECbd++gu8WkTttcu0V9qM1/ZQBS6DZPiYxz28Waw4efMocdd5zn7atgI1X3a8DP1fA4WBnQSZ+1z5nW+wcv0y927d5+6tTdhK4KM+SOtqSoDzUX+WMfzHYalGwBRGd0fcvnkLZRTdYAETtKjaHYyumGzsMJxO8LoRQbNDtZ1T3U3peILrXYn3lPZT4EiSC+vrNJothoMe08mIS5cu8cYbN5Ce24CmWc5Hn91iOk0pJxOqskJVhuFoTL1Z59Lrb9Nd6HLjjTfJs5Qf/9VfMRr0ERiEsHh+hPRDyiJFlSVaMMuOEwjprkF9CYoHv8JvN6TnEdfrAOQz27AvGwIJvnQ2CI9wHn0pMB5PKcsK9UTFh106aTEeUWUlIwm7vnZWJWVBqxZSVJpACrI0ZW+vz2SacHzwNl++x/wQaCCHyoPefaoi4+7HHzDaWaCqcjrLbbzSR5ZNinTCtL9D6As6Cw2EgHEvISsfLBKqdMXEjACLOeS/JoRg+fJVOmvrvPnVr/Lut77Fp79+j5s//j5FOsVW57wIe0R7Xl1b47u/8zv0+n1+8ctfMBmOKKsvORfxjOELqM2VlxLqjZjf+YPfZWVtlU63Q71RY9Ab0usN2Nzc4uNPblNW5Sx4JChyhaDED2NaCwt8cesWn/7pn7G3t0M1e/6HH5sFkrzCKs3OtEJvDhHW4ltN7MOltsQTsDepyCtYbHq0Gh6NZpd2Z4lCaUZphrYCJQOE9GkuLjnHh6AOXowpczZvDkgTSTke8TiRzldE+ilotVu8fuN1hoM+H3/4HrqsCJH7m+BTYaFMjy4C/cAjroX4nkRXCqOUE7zJBx0I6mHASquGpzJ2hCTwPZa6DZQU1EYBSeUh5UsU0v6SICs02SFVX2VgmBkCf6ZUrwzDVGF8RVJqKs627wx9aMTO29HzfeLYI00TpskUay2e9I54SR6G73u02w0Wlztcu3Gd66/fcMUYhMT3A4IwJLSSyEhqtRrtThuEz/LKGkHUoKoy9nYekRp+BjysRXueh+d5GOs86YQQBL5PGIaUZUWSpIwnEwajEaEviX2JmvlfWmtRqkIpRRTFNJpNQJBnOWlWMk1yhqMhWZrtD6Dnhhchs7D65SAUToN9sirvniedqkyfXQVyHnDqEI/Q94nCiFoUE0chtSgg8E/O6HmqDzuLuvNlwdwbeZ4No9nngJ+GMFNVhdIZNdkgiNwmUUjvWEHSGV72e/TEsC6dJvAgipDNhrsHT3VEp3aSQiCFwPf8fc/AR6rZngBFVlFkB31dBtBaBXyPojDkuaJUlgqJFtYRjPPTEAdxg3n6spTgSTFTnp7feaZZRq8/YJokB2PLU7TfIAwJw4h6vUGj2XIejEo5da2d+ZHO+7o3+5oXbXhWbVkCPlhhKNIUTytqWLzAQwTCEd1P2rzmffBhiUaSA0X6eV7jeSvczwAhhXNaMvbkTbIFKo1Qknq7SXd9lb0kgVEPkgoiD4SBaPbmwwGGFz2OKfdVTUuS/gQ8gZQC4fmIUKKNzxQodEU9qOHHEWZkkH6J70M9FPhPSaQLIRBS4gcBYRgSxxGqimk06rRaTZfyLCQWN4bNG5UAqqqi0pq4HhNEEXG9ycLSMsl06mwAihKjC6zVeKHCCzV6VqfF2jMoes8ZQgqXFTnLinmFV3hcCCH2FemP7HmWA8Xtix5rDmGeoONJgRXSKaaP9QeXiCJeiiyRoiipKvVYHvNCCDdKzcZHazQqz1AYCkoEEEmBigLAqWyVUmSZ25u+BJf9RPC8Wd2umf2NPVHFb1x9oSLDeD7JoAdGoaoCz5OEcUwYelhdobXFCIGVrtaF5qSMQLfW1TORiJAeiFmGkZBEjSathUU6CwssLHap12qYqsCWz1/NIaUkDAInIkwT0ix9+eeCp/DnfxrI2VgXeoLYd1mfwhM06jVWV5e5sH6BuBYThAFloUjTgjCMENJDSh/PcysFIQRYgZmNM+PJhI3790km44da+yhtKdDkWpPpA21I6UPTd+R+rhynN8/6jQKfZj0iVAqLwSDQXg3hB3QWukT1OqXxqayHLyVWu37ustxfEennAl1psmlGkRVYYxHGEEwzwumUEEVYcx6Nj2vNurGxwZ1bW+R5wWg4dhPCsXDexuYWP1IJvazki/6UbrdJ66pBCMleL2Nzb0qR/saERF8Yhgp+MIUVU7G2eY98HPAnv7zF/VzyydaQMWfbJwa1kMZiwMraBa6/+TZRvUlz5TLaSv7Nv/8LRpOUTz/97MT31usN3n77HS5eusBit0OjFtNtLVOPW0jPQ/oem7fv8IO//gnTtOTS1be4YAW1ZhdjLL7/dOQOuP3ovDTU8eu9dPkSb7zxBnu9Hh9/+im1eo2vf+MbxFHEZ59/wS9/+Sv29nZPPb7Smlu371Ioyw9++BN+9d77KD0rFlMUZHl2/oV3Gjjv6IwDn7RXeCL4vs/FSxeo1WK27u8wGp1kvv1sUCrF1mBAbzJh9Od/SRSFJNMJeZ6SzIqXnBf8Bni1Q8VJSjiTt9OLgseBP3kP14mHuPN+munB5GAVQnjIMERiIWyDKKBKeO6VBZ83/GXwutBYgs4itXabpcsrblPyFFgIu862RWcUtmJ97QJLi0vs9PbY2Np8IAX2vGEUpH0oRoafpPf5IO5Ruyhory9S2BwG031y0gsEC8shvi8Y3i7Jh4b15YALywF7A8WdrfLc9jy3Pt1g0BuTTLKnrvkipcebb73D5ctX8fwAPwjZ3dnikw9/TV5kVOWs0G4DtxKfr5c9XIZHhZszzhsKSMFrRbTevIzXiLGBJJsm6LFy/fZJPtdwoKSfq9GPd08fd70lZ0uxe96YBxFmRPJDIaB+oU7YDsn2MvK9/MHXSKAdIBYjWsuLrKyssbm1657zcgzfWYWigMHQZY5dC9ybNioYPsGgOQ9QPOyePkGgMbszRE9LaApYEwStiKW3LiLimHHdh8qnQZuuWMSsanRDERYF+WSEeEqrxXqzjQB6e3sM+n2uXLnMG2++SZalfPTxp5RlSZKklGXFaDxBG0t3YZkorjPs7zIeDZn2FWU6ppyOWFlaxBrLhctXaHa6bG3cJBkPMarEGnV6/Y1njIXlLgsrXSbDKbubey8FSfgKXy4YrclTt3t6FAGnlGI8Gu7//LJgbluy2GmyutBlnGbc2+3t8xICQS2K8T2PrCyonkCIc57Y2evPCo2eTTkshKDZ6BIFMfgh+D5ZMiYZ95kPznEgeXOtyUq3wXK7RhT45NMhO5s3mYwKXr5J89GIazUuXr6M7/nkpVMED/b2SJMT6kuZCtIBqpxy/1cT/CCgxKIFXL94gRuXr3P/3h16m3cplWanlyCAsjx9zvTDiPbqRYTvM5lO0EazcOECl65eptFqUJQ5lbDQ6YAnYTJ5rhndX3zyCf9sPCEvcnq9PWdJct6ivvNEgMs+1rj14nNslovdFsuLHZq1GssLXTzfww8D6vU677z9DguLS2ztbtPvD1BK02g0ef3GG6xfusZ0mnDriy/I8wLt/N3Y2LzPZJpy794ddrc3UVV1aD1wNFpQGKjsQR2k+bLXaLg3cbGiSs3C+hNNnhmKaoQ2CiskSvj4QcRSt00Y12kvLxPWGuwOp2TT/FAm0dzT7xWRfi4w1hUb1Uozi2XiK01QKTxp8YLHS++Zr9HG4wn37+2d6i0+mSTcrRL6FWwkUPoVpbH4wpKmiumkOpv/xiucity6/VNZaMbTEZES/PRzzXt9+8g9HcxEbQKC0COqR3SWF7ly4zXCWouoe5HxNOfzH77H3Xv36fcGR98rnNoojmNWV1dZXVmjVqsR+j6teot2YxFjFMYoymnCzU8/xciQ1vobeEGEMZayyDmPxATLg1ntcwV9p9Ph6rWrCN/j81u3iGs1Ll+5QhCEfPj+r7l35+6DN+XQMYRwVb6HowlesMe9jU1u3rrz9Cd9GgSOYKzzW9pPzlda6XmSdrtFo9lgMBghJnI/4+CBzxU8nqpAiNlbTn6PsZYkd4TJcPqIAqNPCRk4It0eJj+epUr1aSE5UNOmHKjRn7rNK0eWW4WQHkL64MczBWr2ktjcHF5oiWPP6WkemABZB3/BBQ9qdYJGnXbHFcJ7GtS8GG01qc6orKZeb7C6tEI2t4R61u3MQpVBheX+NAGRcK3bod2q4ze0W6Ab3C2IBLVljyCQTLcVYGjUJKsLPmVp5yKnc8FwMGE4OJ9op5SCxaUlLl+95hR0wHg8YDDoUc1TdiVufgg5KBw8T1WFg6Ke54nZJCutR7zQwWvFZHmOKktMoZ+8387J8/n6/6Tznl/vyyq08nAbxNNU9TgFsd/wiRYiqulDNrtCQOhBzSesxdRqDcIgREiBrftQ8yCRMB25+9Xxna9B7zFuvjj0/Rmow9S4QI0LZz/jS2LbcIkTno/0JcKXBDakZuuIBtAQME1QecbTpooEQYS1hmQ6RquKG2/cYG3tAvfu3WV3r0eWpQz7w33S0POcz2mr3SGZDMBUlHlFmSfEYcB0NMILAhqtFn4Y0N/dJMEpQu0L9keI6hGdpc4s0/LFWVu9wpcX1tozE2/WGsqXsBaUwRFUURiy3HEepVKIWQWX2R7XDwj8gFJVJ+8VH1Hw8zyRZNm+z/JZIBCEQUwcNSCKEWGEViXJ/sQJvidZboWsdWLqkY/nScoyZzoekudfzrEh8H263S5BGJLmBVVVMR2PD2zzDsMaqDJslTPNBoCFZgcR14h8j6XFBaajPlIIKmNJs7O1een5xK0OIghItEariqhep9lqEYSByxLEQhxBWbi0x+dIpA96fQa9/nP7vKfGfC2neDbr1FMQRyGLnRbtVou11VX8ICCKQ2q1OktLS7TaHbZ2d0jTDCFcLb96s82lVpfxaMx0MiGZJmRZgVKG8XjCeDxlb2+XdDo9djFHU8O1fTDzYT5uHR9+k9IiSksQ5sSxQfoBIqrjBxG1KCKu1WjWGwS1OoNJhtV6Zq192MLg7Df2FZF+AtqNBq1Gg8urq1y5cIG01WB89TXShSXyxVWKLMV88HPE/TsMbcmwONuAkkwztu8birx6ZGXikSuITD5LU5gkBT/51R2kFAwnz0Iu9duBTlPSbEjSzDIYaxYj+MYSLEbQDt1rNAe2v6fBk4I315ustCO+++7rfOsr16jV6nieZDqd8vHdXzFJcqbJ0VlroVNjoVvj2vXr/M7v/R4rq6t85zu/R7vTYf3iJeqNBnHo7BR0lVHmE0yZUA8k+AGdVo28VPzZn/8HNu/d486tL574foQeNEI3QE0LEEKysrxAvVZjcXmVVrvD8vIyzUaTZr1Jo94k8COGwwmelJTl0bbvSQjnKr8K6o0G3/6d32V5aZHVC+tEUUwURYBLqfKk5xai5+XVLXCe0T7uIfZ5Kq/okzH305jjsCTwEZASEbqGZovi3Fdnnh/QXbtCENfIsxxVVZTJkDJ9MgW553s0GjG+77G312M4HHHttTf45nd+n7sbG9y+cxddFpTplCiOWb18FSEEOxt3ydOTVmtH4fsBF1+7Tq3RZPvuXYa9vSc6z/OCyt24a3wOiv+9zJgXYpw3yYrHOOf5igxcJ3nwjWU+ZbR9BzxJ3Apc4U0VgFEU4zEqe/5zkfR8WqtXCWpN0rQgLyvq9TqNZoOyLJlOJpgqR0+2Hz9dDAABnQVE9yrtpQW6ayu0202uXFp7akW6O7pAIvHwZuH5FwgLSZqz1zNMByWMwa9JGldD/EgiGh7aujRegN2BolKWSWIeVa/2hcCTPr4XIoV7Thv37nD3zm2Gg/7RehAWF3QqOLBGOkykPwtIIAZNxfjmBl4tRDTr4Ptu0Hmazz5L7WjziH9/EZgr0VtABO12i06rRTJNGfRHWGkhnN0YbbHGkqc56rainDxkYq8s3K0wA9i8+Bm6KjGV4p133mY0GLB1+46r9dAIwPoQhc5f0atAHmoj8yBlAHSE+3mmnGi2O8S1OukoIR1M3EJ9bM///mbApqUaFuype4hIUpgEaxVj26MyGZeWr3Bj/Q3S3T22tvcwT0mkZ+kUrHUFP63l3t27pEnCdDphNByilJql6Av8MEB6HqNBj2QyJpkeXWck0wmffvQ+YRjRaLYRUtLpLhBGIZPRkHT6YlMFi6pgnI7Jy+yl6xqv8AIx9w2AlzOD5xkhjmO6Cwtk2h6xIhVS0um0qddq6D1FXh4EA9qtFlcvX6SqKm7dvUfxHGpdRVENrQxSHh0/hJQITxJFMZ1WF6M1/f4eSinSdExZ5jSCZWphHekf9dSVUtJut+l22s7fezpme1pyawpT9fAmUMc1lZwnL7XhSUk9ivA8SVyPEVIwHE7I8oMjRlFEo9FEKcV0OjmT/YjnebSaTer1BpdqdayFbDxmNDiJOD4sp3PuB6tLXVpLy7QaAUkyIM+nj521o40hTXOEV6GnE0xVcO+Lz0mShNdeu04t8CnGY+xoBEkCX8IaA88Vs8zGx13PeQg86dPpuMKqw/GArEhPfu1sn2ON5nAd2TzLGfT7DEdjNrb2CAKfdiOi2WrSWlxiYalgZ7fPTm9EmqRMpylxVKPV7lKVFVlaUJaKXq9Hnh/sHZW21FuLKKUoi9wFdaxGSEljYRk/isjSlLLIsdo4sYDVYEpX2LzeQAhBmSYYrZjnj4jcIIYlcc2jXfMxCMZJSmUl6/UGncUl0sJQaegudOkuLVEWBel08ljt/BWRfgKatRpri4tcWF7mwsoKRb3O9MJlik4Kl6GsSu4NtxhNdikzy/CMJFqWFmTp2YbaRLuvOdKs5Nef3He/HE5HfoXHQrMhWVvy6Q01w4mmE8LvrkAnhLoHpT45M/okSAnXVxu8fbHFt999k29+97tkSeI2FemIjz/5jElSkB3zym+3Ii6vt/m93/0q/7v/+h/S6aywuvomvh898Bm6KijTEbbKiD2JDH2a9ZhKT/jFT3/Ar9/71VPdD9+DduxS+9IShCdZWVxgodvhtTffYW390v5ra7U6tbiB9DwmY1d85XgxKCkdLzCPHNZqdb767rtcuLDmrkdrwrkHnZAEfoA2+vyJ9AhXuOvRXO4TYM6yzjEPvZxh4JUSEUVOvVFV575okH5AZ+0ytdYCk9GIPE2xRj85ke5Jmq06Qgj2dodobfnu7/9Nvvmd7+L/9OdsD6aU6YQymxLGMetXryOlx6jfOxOR7vk+61evsbCySjqdvHAi3RTO/oIIt5l6Gcmnwyh4ikJ5h4n0k+cwVWRMi/v49RrtK+vIwAfqYC2qKF4IkS48n9bKZWrdVUx/QpVk1JcWWVpdJpkm5Ns7qHSESfrYJyHShUC0OsiVdZpri1y8skqnUePS2hLeUxYbnUPOyPQXTqQDWVbQHxTkI7AT8CNJcyXCi925GWWxs9McjDWD8cu50REIPM/H9/x9EmBn6z6/+vlPOdHXf+4KUufAnsMd6Nlg1t0MFenGNsL3qF29hN9sHBDhT/LZh1Xcp73mZRvLBAeFXuvuq3WlydqVVXp7fUY3R2iJU1sLoLRQQXGroOifMuhpYEtjfcPOp3eZMmbt+uu89voNNm9Jtj/73NV6aATufvghWAledrRQjI8bHmvAqnA/C0fW1C92aHcXsXd3SW3i7L8mz6A+SgHsWLRXMuhvuXNYBxowNSOmdsSV4DJX1i/T14JdKZ869lvkRzfX21tbbG9tPfA6ISVe6JT+k8kAc0Jh0CxNuPPFp0RRzLXrbxDXGzTbbertNqqqXjiRXqqSaT4lr85f1PAKX2LMM1rnWT4v55R37gijiFa7wzDNjhDpUkpa7SbtVovhZHTkPc1GnTdev06WZWxu7TwXIj0MY7TU+970cwgpkIFPXKuzsrxGVZWMx0OUqsiKBIqEqNvGCyTSO/reOencajUxRpOkCXtpyUb68GF9vt2s4abXpyHSG1FEEAZ0llpIT5LlxREiPQxDOp0uRVGQpsmZifRGrUan3WJt7SKe5/Ppxx8+5NXzxj67NiFYWmhx4eIqnjSk6YiySB/bftAYQ5YXCCHRaYItUrbu3mGnPyAOfK5eXKeaJDCdQn6CVdsrHMXc0uUx4EoBCULPZ7G9RBTXyIvsRCJdCLFPpGtr9udFCxRFzmioyCrNKKkIA8lyp0an3ebya2+grKQ3GNEfTNjd3WVnZ4c4rtFtLyCFBA1lqRiNBkwPBd3jRpd6a5GyKKgqizUuG1pKQX1hibjRxvR7VIxBaWylQJdgKqQniepNhJSostgn0hUgSoNQhhaatvAwCJIsxwiPuFZjobvAKMlJi5J2u02ru0A6nZAljxcw+tIR6VLAUqdOPQoYTHPGyfmnSSV5zt5wyEeffsK/+Xf/lnoU0QpjtK/56c9/xm5vl49vbrA7yEmyF+AbMR8/X9J133x6euGCNU9AK3TfJxWUmrDRpLnSZqqmQJ9CweYIBj4kxpJpy15xthtrDQzHKVuhYbc/oT9KuHNvi5+/9zFZXjKcZOSlYmtnRFEqLl+9wOtvXQVbIGyBJwVR6BMG3swCZbZwsRZsibWK3s4W9259TpLkdC5cYjhN+d5f/Bm7/T79Xu+xb8lcfLew2GFppUvkC5oRKGNZyCwWSafbJopjPM+dV73Rcmm7eUWtUacsS3a2NtFak5808QnwPEEj9hG24pOPP2br/n3SrKAsK8ajCcvLy5RFSZ7n51vYY06QKM4sEn98uFV1EDeImx10VZCNe1j76BW3JwSxFyCkwAgPqzVlnj1R8c/TIISg0WhQi2N02n/ieILRhmTqFtVaaSyC0XjC1nYPbSVLqxcQdhnvyiWk71NqjTSGG2+9Dfp1kmRKcSjynEwT9nZ3scbMUqkVOxv3mI5GVEVOo92gKirK57AYPxGxdF/WOFO2F2oLNKtEbecs2Dkd1Q8RYR3fj6jFS64QDBXWapJRjzJP8KIALwrRVYXOMowqyYcD5FypYC36WaUoe76LyGl9zDtNghcRxC2u37jB8sUr3Lp7n93ekLjRRMYNPC2IajnSWLRszt31ONNOWHp43SvIuENt9TLRcpf19UVev7qKL0GY4qlV2AsrKxhjEElAURVUpaK3NyCQAW+89jpJmrK9s/1Aoa+nxoy09H3BYisg9A82yUoq9K7eryMhrCBQPiIXTHs5KtOo5IXP5o+EkIJmrUEcxYz3+tzSnzDY3eORC6V5H58X5Kwe/ZYngsYFdmcLJCst6v4UE5XoUc6ZK5vPEeICfho35532iOZk+8tGps/jzyXgQTJJ2Nvbo8hLms0m2oOiLjAYtHXpN2ItRLQ97LDCDk83VJeejx+EWG0p0xyVF85Is1KzwsnCKZuw0ARWxEFWb2UPPEj3rLvfHQGx6yPCSuJGne6FZUqZkW6PeGY313CgjJ3tfUXkIwJJUuVs9O8DFVfefOeZK/qklARhCEJgrHYOYI/wZddaMxz2CbOEerNJEM58fF6wbVqVVqT9jCqtXqpu8QovGPMxCfbHVRlKhC+wyoCybpv2JWk0j+pmC806jThEq4pPbt1hMBofWYMYY+gPBqRZRnpMPJGkGTdv33WFhp+Tt3Rvd4DRhqpUgKTZalFv1NDGUhmNEIL+YA+t1NFMNAS+5xEFAb7nHbkvUggatZhmrYa0BlUWWG2O3Ld57HdWNxxPQGQPkpWeFNoY0rIkMJpgKvE8iT4WmFTaOH/6qjwzyZfnOXfu3KFeb5AkOb4fMJk82h5TSvA8QzoZMNiR1OsRjXqMUSdI88Xsfw87J6PR6djZ7M2ELWa2d9i9c5v3JextbaHVb0m06gUgCkLWuouueHg9REi3tTwJ1lq0dn0oCHynYVAKrS2lMiSFppr1C2UsSVaBl7O1s0tpIMtzPN+n0WiwtLQEQqKtRXqCZrON0ZrRZIznR2TphLLM0VVJkTqXg8VuEzAYHTperEgptcKWGT5m1tk8jJIo5camMksQQmBmfX2+3C0sTA3YSlObpEivAHJqjZwkdccNAp92u0mzUadRizFlcSSIeBZ8+Yh0Kbm21mFtoclHd/aeCZE+mk4ZJwnlT39Cr7/La9df5x/8/X9EFcD/+Bff44OPPsRYV/n4hUykL/F4M59gHt9l6BnAl7BWh8iD2xOoNHGnQ/fyZUb5NkIMSEvL53turfRJbpjog2IGj4Kxlp3+GJ1PWNvqsb435Mfvf8H//f/7PaIw5O3rlzBYPr6zQ1lV/KN/8C6/8+13+PUHH/LBhx/je5J6LSCO/GMd17pifzpj8+5NfvrDv6azss7lt96l9/HH/NP/z3/L/fv30U+wYZrbwr52cYVvfPudeQFzjLUUSqGNJclAaYEfONV1p7vI1etvkJWaZqfDsN/jzs0vKPLswQl95hka+JLQ90EX/PWPfwwItnf6ZHnB+oV1rly6xM7uLpPHTKF5JCzPSIV+GM49P24ssnz5Mtl0SpEkaFXiSLuHMxqekDSDCM/3MfUAg2W8t/1MiPR2t0sYhmTDLR4/5OKglGY0PFh4Sc9jd2+Af3sDLQTrV6/TbNZYWV5kOp3wwa/fR2D5G7/7e6wsLXF/4zb9/oHK/P69DQa9HsoYPA+Mrrj96ceApLPYYWF5gclw8uKI9LoPXR8mCgalG2tf1CAmJIgAhAFzfvdDBDF+Z5U4brG8fJXAD5HCxxrN5ufvUeYb+PU2UbdFNR2R5QmmzEh3jgXNntXk54cQhFDmLkVo/8Q98FsE9SW+/q1v8vo7b9N4/yO+uHOPygZUhASE1Jrg2YBcLuBWXX3OMmkKLyS8+C7+4jUW19foLC5w4/V1vvG1q6STCdt37h3blD0+1q9cwRhNsBeRpilFlrMxus/Fy+t85Stvs7WzTa/fR5fnvK7xgBjCmuDa9Zh23S39rIWteym7d/U+gSyNJKxCTGGZflySjcuXICr+aHjSo9vq0Igb7N7d5N4nnzOtTk5dPYJ5AU7BUWX6eaPCZUnNIQzlsO8MaI19/HscA0s4svdRzcXgusDLtHack1WCfYXVuDdmWk5o1BosLHZRnmQUgzKKoqowwuJdi5FhjP4oQZ9KpDvrsCCsYbQlGyWU0wyKEqrSEeiCWeDOg0Xr1O9zz/whMJqR6dMZkR6JWR0BgTCSRrtDvdVmSp/8s8mhglXP4F7luDY0dt/FYoCsR4zylE+3PuNCe5XvfPf3CP3w9GM9JTzfp9ZsYq1lmk7cOvQREUalKnZ37uN5HpcuXyb02wiMa/s8A0ucM6KYlBTT8uUKLr3Ci8dxebEAr+bh1TxMqjCpwtovhwvFo8o4SCFYW2hzabnDnZ0BP3nvA8yx+kdaa7Z2dtwxjq37RuMx733w4Syw8Hw60uYdlyFjkQjpsbi0zNr6BZIkZTyekGcpW/c3MEYfPSfhfMNrUUTgH6W/PE/SbjRYaDWRxqKyBwVOs2UUAdCQjusQdq74fXJoYxinKb4UCEp8KQ/qucygtCLJUrRSj7QGniNJEj744AN8P+DChR2iKGZwoq3LUfg+eJ5lMtiiTPZYWl4lWlnDnFTcVc4scjQn7gmsVqjJ0R2ozRJ0lnBnPOTuRx84K7EXWHT6Nx21WsyNa1cJw5CJKimUeiiRDq6/SymI6q7AsEksWisKZSjUwXNS2jJISlIFN+/cZTAeE9aaeGFEu9uh0Wq4jNf+mEB6LK6sIIQgKxRxfczO1h3KMqcqM6qyoNtps752ESkEShVorRgOxhRlBcLDly7I5Hk+VaHQOVityY9lycy5x8y6rzyvoD8BaymKinq9wXAyYaUqCeOIxcBnodem22z8dhDpMEs9kIJaLaTdrlGViiw/NOBJgWxE4EusEFgEZAWcsTgCuAmhLCvGk4Tt3T1++cH75EXJKJmirHko2yqlJJpZVxRF9RsxOCwvLfKVt99wnth5glGKPE9RqsJag7EGP3DVe6eTjI27Paw1NGsSJEwzwwlZn88exkKqQFviVkxQj1EWdrdGTMfZvuhoMAuyFvrBYganQQhBo9VkoRszSkre++gWtzd2KSqFlJKsLF2BWmkRgSSOAmpxxLVrV6g1G7x+4zqBL5GzzGWlKoZ7W5RFTrMuiUJBMhkz6PcIGi2k54qTKqWequK7wA2URVlSq9Vod7tkecHW7bsUpSKMO3h+RFxvOi/QuIaU0vnP7RPv5sRFUxSFdDp1ylIznRSgFXrq7nVRlijlPLek9BBCPreF17OAMZqiyFFVgd1nKU6/HmstVamwVhAFNTdWnZNdxMFnMFvka6qqOmVzLxBBDNJDzM7bquoEO4yDa7LWMhkPCbY38eI6Mq4DFikFaZqQ5TkYw9bmBvlkzN7eNpPxiEazSbPVot3t8Pobr5OlKbs721RVhTUWazVVWVJk3gN2Qc8TXuDj1WJ0lqPNi91g+7WYqL2IVhXFdIrVClR5ImnhhSFBo4ZRinKankpyW6MxReqErJNdKj/CD+tYBBoLgY8xCpUnmGruNsfTEedyRlYZPfPOOQohJfVmCy/wyUpFpdT+dUa1Bp2lFYKoTqO7Tqu7QKvTxvM8wtCnFgeY0pIWBVWVUWYTqnyCNTkP834/8b5Yg04GCC+GxRaCRbQ25KXCImi06pinLI5XVQqtNWVZUVaVK7RkDXZW1iuMQpaXlsjyjNF4/ETB0pMvDtAg8Wh3Fug2Q7LJiLLIEMYVYmk2AhZXYvxmgOfJmUcyXwoSHdyclBUuuJuVGUWVUx0LfAjAn8231Un83fOM/s/vrXgCEh0Oik2d5uE7t0552Vf6GqjAJmCMpcoVeVWgPYEOQaOdNyYWm2lsVmKLR/QNayl7GdmdEcGlCB03iaOIC1euUKQpg9376EpDombFzewB4zQruEuMu8/l7BwT10BKkZEmB6nJ5fAEUcGzwFyZDjAxWKOwUkMLdKlI04RKPttihsYYVFU5IZGZ7YXOcO12RpbkeYb05Ey9+hKs/16CU3iFZ4DDgdFzmMM8z8P3fZTQ6C+RGj0OPRqRT6EM0+zkzAtjNVprtNEPzYY7bXwzZ1WfnRMOzsViseR5xmQ8Is8KijynKsuH7lEBl+x5jCsTQhJGIWEUoY2mKPQD66+50hWc05gR4HkzF8h5ttdcTXhWj9hDMNZSVgYt7QP3dD7uGmNmIpu5Cvz0RYsxBq1nc8MZswaMmbmplRprIE1SJuGILE0fvKfW8qSpRdaYV8Pv84C1aFWihCUvMnJVPVQUNLd2kUJgjcUITRSFhGG4336EcFyUtVAp56dfr9VoNBpM0ox8NMYYg7XGcR1RSL1eZ2GxixSSXq9PpSr8I8EsC1YjTYXvSeLIR2vByLp2LwOXAeJ5Hn7ggTo7b2IsKO34GYN1Tn6+R+B7TKcJ02lCMh2TZ1Pnw/6Yg/vLvrw+FUuLTYJmTK8/5e693v7EJkKf4LUlZCtGeR5aSOzNXeydx9NlZnnJTm/Idn/ETz78EGU0SZJAELjU0BMmnDD0WVtbAmBra488f0HKynPEt77xNf4v//j/QD3y6d35gjwZs7HxOdPE7HA3AAEAAElEQVTJkFLlKF3S7DZpL7f55Xt3+G/+b3+G1iVvXg4JAsGHdwr64xewG68MbE6Roc/qt6/SWu/S+2iLO3/1MVobrLGkwBfFbIJ8zBHd8yRXr7/GW9fX+dmHt/lnf/6vHTmiNJWnGI2neBKasSDwAzqNmGa9zje+/XVee+sNfCmIPIGUBiEsRZrxyx/+R/q7m7zz1a9yYX2drXt3+OLzj5BRhCctviceWAA8CZI0Y3uvx6UrV7j6xlvcv7/LL/7lXzCeJHzr27/D6lqL5bVLXLn+GnEYn/m43U6HG69dZndvxOb2LSqlEcIpWbU2+xY2QsjHjvq9bMjzDNPfwZQ5xsz9ZE6HqjTTUUoYRnRbiwRhyFie4zBscemN2qCTBGEtxcMsOKSPbK0hwjoeGolGTUdU08PR3aOrQWsMG7c+Y+vuTZqrV2ivXkUbhVIFSlWk6RijKvZufQ6qxGgXaHvjna/wzteXuXb9NX73u99iZ3uHf/7f/w8M+oP9cTuZJKTT9JFp4s8MAuJGndpSlywbkuhTzBGfAxrLy1z4xjfIpimbn91FZSmMdhyZfgzxQpuFG9coJwm9jz9Hlw9fLNsiRVU5WgjyvdsIPyRcuIgIYgpRQrNGVUxQOzvYQx55Tw4BURPCOuQTKB70xfX9gIs3btBod7j76Sf07m/uf+7ShUt8+2/+z1hYWuatr36NWr2OHwUgBI16wGInouxPyZIxyXDIaOcOOp9g9S6P9rw4BFVS3n0f5X9B1a5jVy6RJiU7e1OascfFK+t4TxnzGo2cgnM4GpNmCb4v8D2BtppSF9QbNb759XcZTyb88r33mEwfnYZ7JmgghbAWc+Xq2ywvN7nz3l8zHOwilLvPVy61+NZ3LjPRmo8HE6rySyC3OwStNTv9HYQQboNm7QN+nh7Qnu1BR9qR6ft4ESl0T/OZcyX6aURRC+jMXje3KXnZcEhtbccufpaJgkKW7vYIsBFww0Ad9P0UPRZOJX7aYbVl9Kttxh/uwh9o6u0ai4sLvP3u1xjs7vCDf/uvSbIRbGZOtrQinL1LZSG3jhBZZeZTjpsGNyxWaEZyh7HcO/RZxlk+PGtYnCJdgBkUIErEjS7Rok9lcu7mN5Gcb2D+OLRSJGMXRHjcDacxhkF/j9GwT6X0lyZI9wpfQgSzL8VT1JFxEAjCICCKQpJEo5+oiPmLwXKnxmsXWuyOcj65N0QfW19bLEVZME2nlCcpjl9qGLCGvd1tBr1drBUYKxyB9zAR42GJ/iFIT9LodGh2uxT9HtMkociPNpzZMso5wFnwLNRiV2tMl7iMoZDZPIKzy3uMMc5YmOQVgge1mrqqyJXCCg+80PkcKzVbJ6tTP0jPiq4KIc4k7lRqFj+uKoRQlMUe0+GQvNQP3lfzIhZOr/A40KpiOh3hScFuMiSrSrLiZD96KSWNWg0hcGrxyrC6ukyr1WQ4GDAYDJw9UhRQKcNoWuL7HhcvrrO6tspffu8HfPzxZ7OjWZZX13jjK19hZXmFt996A8/zmCZTpC/Y24n2X+fIixJRjIjiiNWFJbSx3N/UFHlO27PUZEAQBoS1kIlRjIU40xpEG0NeVbMaChIv8mnUY1r1Gndv3ebW7dts3LnDzv27ZHn58LHjIfjSEOlSCpr1GnHoU2/UCWs1hM7QpnwwGiqAQELoIeMY4XnoyEc/ZtDMeQUZKq0Y5ceKO0gOUnIPZw4Jge/L/Z9/ExBFAcsrC7TikCAbkdUCJv0NbOFTi2OscAUyFlcX6V1Mef21daxWvH69gZRwd3eD0Xj6Yuw5tYVKYyqNqRRVUVEcyl6Yq9KfBE71a1BKkec50yRBSkkcR9RrEa12C9+DKneq9DTN2NrusX71MmHgUeYp470hYdxhKVxHq4z+3jbbm/dot1qoqiJJEoK4RlFV3Lu3wfb29mOr0YWAOPTxpMDXGqEtlVKMJymtScp4kjBNEtIsJ8sKykpRVZqy0lSVQlUJSZoyGg7Js4yyLB5KdmptKEpFqTTazKPq1om7ong/AlkUxVOp6l8GWKNQZY59iEr4Ie9CG4XWHlVVIjjflDZrDVWR4aVTPM8FKx5uR2HdeZu5mt4gPZ8gqjkVw74f3rGBU0is8FHakJclRiuqqsDoClUUGFWhkgRbHSxC0yRhOp4gMeTNmLIsH7DGsuZB4utF4GVRGlmjMVWJURXWnJw+Lzwf6fvIIEJIf2Z896h5x4LRLnioFRiNKBKk0RhdOMsDrZ0C/nyuhP22duz5SikJ45gwivGDEOHNliTWIr0Azw+Ia00arTa1RhMvcP7peZ67VNfRkGTYx5aaOJDoSBJGAqUFlXQk3NmblEAIH6SPEAd1K6QQWCxVWaCfck5Xysz6u0BKD4tBG0NRlkynU0cC4zY+ZyKpTsvZPg7r+phWClUp8lyTZG5sbkTQiiWduo8tIRTCeYG+JH3hrBCee17KOnWXxJHnngdBIIh9yXLdVY0sxjmmMpjziBW9CByWx52Ew+rqubXLy0pczs9rdo4W67JjTnoN9iCf/hH921QalAZl3HwoBUY4RZKMAmQQYFThzDRzXLrC8QLOh++Zmv/pBfrkzHkL437RaUU+yZChpWxEyGe87/CCgFq9jrGGPEkw+myNyo2nbo1ojOYpk3te4RVOhz32/WkgZkGjY/v9F4l9QfIjEAQ+jXqNaW721zJyNnbOknyoKk1WVFTHFNhSCuIoRCAoq9IJ0HDvCcOAKI6cV7lySul5xvGzhucHbgicTd5Gm1nmrQThP/zGWIuqSoosfcA6xVpQVUVZluRFQVkUMxXrsUNwMJ1aZsJYz6nTj7zgCflle0oTs/bwweFgkn/0ZDjfZ0op8TwPY8xD956H6S1jLZXS5IWh0i/DDu0VHhfaWLKiQErnDFBWp7tlOBGKyzSxs4wDKSS+7xOGIZ6USN/Dlx71hketXsPzJFJYtKqoisJZsPiSwBPUo5DAE2TJBBAUWUKVZ/ue5oc+eSaCMbM1nvt8Yy3aGCfGVBo5y+x17d3VwnFrC3Vi37fW+blLADHjdcuCMs+YTMb0+gPGkwlFWVIp9dg8xJeGSG/Wa/zx732dlcU2UbuBF/ps/OxDPvv8FkqZo4SMtZS6Qlqf5uIKUbvNdHtM6m8fjICPA2uhPKRAFzgv3VBCpiD7zV4RWgyaDDxJq9nBN5JJL2Hr7jarV7ssrrVYXl3m4pVLXFhd57vffBfhRXQWrzKZFoz+j/9Xxts/OZOV57OA0YadD+/T+2yH6jHsfR55XKO5/cVN0t0NRuOMTgz1VpPuyhrLS4t855vvEviCvfufkyYTfvTTD/gf//1P+Pv3NtBFn637m3z46/e5dPk6//B/U0OVil/99Pt8/P4HfP9738cLIq688Q7f+sP/hI8//YJ//I//CePxmMFg8FjnGfgeb19fpNOM2O6NGE0zesmE+x8k3Nnsc3dzhyzLmU4TjLHs9vqUBsJGi0maMez32NvbZmd7i5uffExZFqjqZOXC1s6A4Tihqo6mxflBwBs3btDtdLm3cY/Pv/ic6px9wZ83TFlgVd9NNGck0l1fKihUyeZWgZCC6hz9kFVVsHf7Qzw/oL6wRthoURYPKfFtNHq8hZASjRvWmgurNC+8RTadMNzbwerSyRNmE4sQkvbaazQW1kiKnN5wTOBBLXSFZNSoj6mKB0jYzbt3GPT28HyPMA6pyorx+JzUtueILCsohlNM9uKVOcnODnenU4w26KJ088+xPlNbWKa+tIIVgqSXUyUPD3I9FFqjRjsuQDJXoJ83y1FMoUwfOG6t0eTGV79GGMUkecl4OKYq3DU2F1bprF5j+dIlWs0GVle8/8tfoGdqY6M1H/3sB2x88RGvfe3rfP0732UyDdmsG6ajAXezPcoEqGbBgUfBCwjWv4bXvki4dJUgimm16qyuNEjGQz789efOM/Pv/fGT34bK9aVGs0u90WY07jFNRuT3Ntna3WaeeqiNfqCo1wOYpxDDmQtklnnO7Y8/pFcP+OiTIXs9uLEK33odVpcti9bgacO6hljD5hNf6fOH53usrq4QRSG7uz2mkyk1nDis25JcvRLS6TR4+yuvo6zgT/7qY+5sjUhTOG9L+heOeeHUKa5myJxMf1nFY48qhHr4vBeANrDFQcLJw7r3zNYmbsYsLHSYFhm/+vXPKMuCcKWLjGokn2+hksyR6Z598DyeIEX/eWKw3ScZTVlaW6H5ja8ShM9oWyecH8LC6irv/sHvU+Y5v/qrv2I6HJ3hrYI4DpFSUBQlSr2MjfAVfqOgOJ8aNwKssFRZ6dZKxYvft0gp8DyBMRb9iFTqRr3B6uoape5RE3sYCa1ZknG/cJamu5OMQVKgjgXF6rWYr715jcD3uHn7NuPJFGXcUvS1q+u89fYbTKdTNjc3ybKc+9sDyurZ8yGtpYtYa0kSZzGLypwkXPrgxWCVC6Ce8PD3tu4z6vceKNpZlQUbt2+jR3voLEOXiiQ9eQ1mcUsuIUAHAhkJN6aV9sAG7FnNtdaAKnAN0+fAoV3yqGi5EIJWq0WtVmM6nTI9IeNRAk3f2WgkGkoDpbIo/YJqAr7CUyMrS25u7yKEoNLOY/9hdkxaa5LU1RYy1rkIFKkiExWBV2d5uUFlFIUuqEcRV1aWieMYawpGvW1MmRFKWOjWWVxss7a6zGurXfIs5z/+i/+eJE3o90dkRckkOVrQzjnXWYTR9PPMKcm1RgPjvGJaKuS0QMgJ2hiMsXh+SHNhBSEkyXCXqnxQaa8NpAUIYfGFRlKw8cXnBFXGz376S37+4WeUZUlRFE9U+/JLQ6T7nmSh3WB5oQ2NOjbwMUjS9GTCw+KiGEIIPCGRUrirnU+uZ4QxzgLkgRLdAvAEXhDgWR+jjfMMh/2J7WUcdIQQSE8676OHRKQ838PzJPPQsx/I/ehQEPgoP0DKACFDgiAijmPq9QaNRpt22+PixQBkBMFF+oOUWlR76qrWTwUL5fT8d8rW4ghoSgSSTrtOs9NicWmB5eVFVlaX8CVk4/uoKqc3GHPz9i537m6ycW+TjXt3+eyzL1AaRv1ttDL093bZ3dmlMj2MlSxcuEqj3aVUhs8++4w8f7zrEIAnoRZ5NGs+/cADT1DmislUY6zF8yVKaYSQ+IF0HqWVIk1TJpMJe709Njc3GPZ7ZMn0VN/eolQU5QkeyEIQRxGNulM7p9kZisC97LBm5tv6IOZqVnN83ADAYCwU5cOIMjHzwHPqAQszRTKPIOxdD6vyDCULwmYHT8WnRJ5dKtWcY7SzI/hhhBfk+xvn4+flhTX8ehuUQenURaelq0eBqrAnBEiKPKfI84MGaZnJYV4uGG0xlXHn9oLHb12W6LIEIZCeq7thpbf/nIQQ+FFMUG+iipJitvh/fK9e67IqniX2sx6OQnqSWr1BGNfIqxGUB4yw9AKCqIb0fHRVoY2m3+vtezwaoxnsbDHc3qK8dpVIaErPEEWSMvAQntzvR2d5lkJIvHoXv7WKFzXwPB/P9/B9idGKQa//1EGveUaOnRXzEsKNt3lRkRWVW7c8ZCPkSYnc3zwfCuo/BowxTEcTVC4ZTysmqbMoW2hLaoFAFQpTKERpnOXLeS5iBEjPnbDR56fsE0Lg+T5B4BRycRzhe670l8Rl9zdDn7XFGguLTS5f7FJZwdJSg3E+y6Qp9alKsOeOub/vHJbH25jP28XpGd8vH8RDfvYOJV/47nfmHMIpfUAEAhFKhOfkm2WZMxoN0EaDL5Gh79JWXrZCrI8BVVSooqLRaKINePZZK9J96q0Wnu8j5dlK7AnhyD8pH/HA/v/s/emTZUea5of93P2sd489cscOFKpQ3V1dvc+0pqVWyziUzWhEmj7KjCb9LzSZ6ZP0RTSTPlAyUguNNuRI7Ob0cIbVzWZ1VdeKKqAAJIDcY4+4+z27u+uD3xsRmRkRGZEZkcgE8jG7iEDGveecexb315/3eZ/3FV7hvHBeROasL5Q26ALsWT1ALwCz0Pw0xSeul0yA5/n704o/fQxnHy8rQ3nERKGkpFmPCHyPMFB48kAxHcchSwsdfE/Q7wWuibrvOxWzNvuq1ouA8nyssUjPR1iQQiMVWBFgpD91fFGA2XcGmFW8lkV+ZBxnjHGVs8pAWWEqcxBnHYKY9rSa+XtbptUBszdYnpx4FcJZs5yyv8RjsK6qUUgBQk2LPffLlKYe1m7t+GiPrJnX/0m9uSRTP2opUMIpk1/A5dornBLGWtIT7D4fxaN9EqrKUJYaP/TxgwBhFFoLgiik3mwQBv60GWmBNRolIfAVceQTBYpACdKqZHdrg9FoxCQtKCtNZexD0YDFoq2hsobSGMfhKoXyFFobdDVThODWxVIhlYdSvnsuj+mganHmFMK60NFUhsloxKDXpdfv0TskBhBCEvjBmcKUl4ZIr6qKvb1dFCWr7TepdTqE0QnezUJitWXy4W2y1FBVCSwELsXW16dTb5UFo/EIYx8pgbE4JXphuPGtd3jjvXfYWl/ns48+QmvD9m4PjD1VU4fnjc58h4WVBSajCVtrW4+RbEIK3v7WDa7euARlji0zPvjONZqNkFrooX2D1wr54//lf0haCUSxgSh3aa3coHX5HYQskWLIzs4e//V//v/k3t1NfnP7NiNeaFHPU0EIQdRpUZ+L+a33vs0b734Lzw8I4xiBQFgYDkb89Fd3WV/fYLfrsr8//ulnrK3vkSQJ/X6PT78cM+n+H5HW8smvb9LrTsgrQWUE29u7bG+uM+z3zqw09SQ0QvA9w+ZWj91dRW9SMM401TQYzLKSnZ0eca3Bu9/5baK4Rr3RxPedF1WapgxHA3Z3d0jG41N3C38UVVVx//49dra36fX7T7WNlwVCCFqdOcIwZjwakEzOqLyO6tBYxI8iOvNzWGMYbG1QZimkw6ka4RFID8I6QirCMER5HlG9QRj65N7pPVOTwS5lnrhmYkU6DdimzW38JlaFjHJD2h0glaTdWSAOJZ2GRzYaMNqUnGgf+YIS6DP4NkTZBtpqSl4MxXzQmKPz2ndAKCZ7XXRREkU+vufhxRGeCCnyCenOOmYayLwsKMuC7d014nqd1eUrhH5IMdhluLfDpL9NmacMNkI2b/8Ka50llJklk6xl1HM+xfe/+ILxcEhVVaR5TlWUlMO+q6Y4pXWSUIrm4hVql96mvdCh3mhgjKK7N2Ht9jq/+rsfkz9JJf4E/OHv/SFlVfLFl1/Q6/cI/IB6VKfQKdkT4oVrV+e5tDLH5nafO/d23KNZciBIgicq8IrCcPtBhlKC4bgCAZ25ea5fX+DOZsIP/vYBeWkY5SVlaUjS85u1w5pi6UaMBXbvpOTJ+dyn9WaTt7/9LfzAJxsNKY/oCbGyusKf/fnv02jGtDotEIL/7cJVhknOX/23P+YXv/yCSQ7jo20jnztEXaKuBFhh0WkBlXV+qzMf1mkV+2Nk++yUmkP/9jIg4uHVSAjUpr9bkBIaFlQCkwSKAucVflAs9RiEFLSudYhWaozLPh/+8IdU0pBJp8jSVYlJNPqY6rqXDUZb8kmBuahlx1QUMNjZ4Rd/8zcYrUlO2cPBGEuWFVOruRdo/veFswGtrJNevsIrPIpp4+Eq12hhnntjzSMPaWq5cJrlmNaGIndWnTkuHBrlLmx40qNoraYqUjyr8I0mlE7rUGhHZjfrNfIsRVuDH/i88cZ1tDHsbG2RTBLy0uyvNc8To+4mQkjCeot6o8bS4uvMdVqMJimD4YSqLMiSMUJAGIUgBKPhgCLPMXmKOWLM11rT7fcQ5YRmEONJ77E1twpD4rkFMIakt4cpS2xisZk9E8Gh2i1Uu4Uej9Hd/lOR6UIpau0O0g/JRiMX9xgDVlNvtli6dIk8z9lee0A1jS2ttYzHY9I0PdZa1eBoMmWhNRcT1wMGw4z+IHtxK9pe4cJgrWWUjEnzjHa7TTsMaDTbXFmaZ9ZfoSwycpMidAkUtNsBUhomk4QdsYeQn5OlmasCwVIYQ6Etka+oSzltiGuQ2jAcJ9RMzNxyDd/3eePNmDyvWFvboNvtO+sm6RFGMe3OPMZoJqM+uiqpypOFTkpA3YNIaB7cecBgt0d3t//Qexbml/n2t76H7wenPkcvDZFurevMnCQeCEEQhCj1BDWEsZQbPcrdBBY9mPemhtinW8AZY8iPU6BVFipNZ26OG++9hZaGL778lCLJmYzSxztFXDDEKU33ozhifnEeIQS7m7v7yji3EaeAXVia48abl7F5is3GrCy3CUMPz5MIBSr0uP7adxC1OcYPPiTZEcStVYLGFSDBYkirXX75iw/54vO77PR6fD2WKo9AuIk1aNS59sZrfP8Pfm//T2mSsn5/g7KsWNvqcffBQaPbB+t7PFg/+P/e3hg13MYTMBoZigKyCiojmIwTRoMBaZqc2bdJCoh8UMIyHmVoA5PSlfLNoLVmMtH4YZ2llUs0mm2UEvv3U1G4LuhJMiHPs6dWKBpj6A8GKDGkeMm90Z8EIQRhGFNrNMmzsxFvQgisH0Gtg9eoU1u+jDWa8TihstKR21XBY9GMEOAFCOWj4hqe5+MFAcqTJyoPHkWZJ5T5UdUCwpVMehF5acmznEYtJo7rxKGkVlNQ5afb14saiFmQeHg2wL5AU6MKY+rL10H4FLmHyDKiekQQ+CBcosNWmmoydNYsLxG0rpikQ1CGRi2k1WgTBi6Amd2LE2Bv8+TtDLtdht3uMx2LkIqw3iZuLxLWYoIwdA2pJxmD7oDNu/fJHilFPCuuXb1Gnuesra/RH/TxlE/gB2h7MvslBLSbNa5cmnOEFM7rdLbI31fnPqERiTbQGx6Mv0JAGNZotedIH5R8fGdwIQtfAC+QNJcCsNBbOz/GOggDli9fIggDNm7nFFn+kIOoENBsNXnjrTeJ42C/WujS1ZjKGj7+8CaffwLPoSL9dBAgQolc8EBYzKjElvbAvmdmZXKUav1Ftm85DrP79/C6JcbZtwBodw0DC34JWcpBo9UnFGgF7Yj6apPeYJfe/V1k5OO1YtdLpCwgNXBChd3LBKMNVV4h7MU2G80mCRu375z5cy8UgT6DFM4X/2V6Xl7h+cNOq6hekBvlyELXY2CMpaoMWtv9qaPY7/Xw5B2ZqkSjEZj9fL2xIIUk8D2UUk4dLiVz821AMBn2KYuMStsLiSeKdIyQilqrQxRFzC8usXpplbDXR4sdijzHTuf5erPu7CnKisriKmaPINKNMaRZRiI1kfRRnnrsakvlEdQbWG2Qgz6G0s3LZ4SMQ1Sn5ew1u/2nOgdCCPwoRoURRZa57PJUGh+EIe35BZLJhN2Nhw36TtNQtrRQGQhjn3Y7Ji80Ypi9kC4Lr3DxyMuCvCyI6zXs9P5aWFikKEu2Nrcoy5Iin2B1DmjCUCKEpShKkiRhr7tHVVaYqchLW9DWooTrW6Q1lNNBrchL/CDE8wOCKGber6O1cSQ6faeqkD5+WKPRnqfMMwZ7m0daujwKAQRS4GEZ9IYk45Q0yRBT+bkFarUG16+9RRTFpz4/Lw5bcAwiX7HYCpmfb/Od7/02nbkOv/niPts//YQvv7x39IcqA+sjJ8mdTEe5iQFTQX6+JftrX96lKkv63R7lIMdU+rl7uly6dImr167S6/a4ffv2idYbM6xeWeXt995hNBjy8x//nDzLee29GzTnmkySjJ/8j792Ng26pFlfRlcaEQXI+RuAhwicXGhSGbq5JvniU4a//oidvT1+/elNdne7/MMvv6TXGzA+xn7nZYe1MEkL+qOMWr3JG6+/7hqPphn3793j5z/7MRsbW4yGwxO3U1XQHVh84ax8pZRce/0qjc4ck6Lk3/7gh+zsddHV2RZ8lYHR1KFDm1nDhaPfK6aWHX5UIwwUYLnzxedsb64xHAxIpmrPs9tGzLbv7JmUJ6msObVK9GWEsZbxaECepWRnsLBZfePbLN94j8oLKfw60vOIohBTlZTLJUVzgrd8GalLRrtrjPcOBUhGQz7BCklRpVRKIXSTKgwpjlBonhkWxzSZCkJ37YIootnuQJUw7HbJxoPHyggdztjl+StElaRYJPoYb8TniaDVIV68RNxcoBHX0WXF3mSbfNjHjiVKCVerhqVIx0/9bJ4EGQSoWh2rNdVkfO7PbRRFvHbjdcIo5P6dOxRJzt7Ozrnu44mQIdSuIOoL+K1FomadqKaIQsHO5jqf/+wO/fVblJMdOEWwduKupHTKrdffYHlpme29bXqDHt3uNvlWgTYVxj6+D2tha7ePNiV7vcnDSVWB8y+RHHhhnxLWwqd3+6R5xfpOcqFqO8/3aS8tYq3FC1LOq0YtSRJufv4pyvMY9/uUeU42FUCsXF3kvRsrXHnjEuO0wghFqxEjBOxt90gmKfkwwdegXoApqb3aYfH6ElVkyNo5BoNpNDCVZuwPKZPCkcgZTp2e8fDQOvV9Ek0QAdgJ2K9+KDsZFkeKH74dKthXX+ipSk66W7ycvfcJBIbFMknHlIOKSpfEcYwRlirJXcJRa7ePF0Bheh5IJxPu37p1psT5NwFRFLG0sgJC0BsMKMqSKsswVeUEUdY8fg9IgVfzkb7ECx2plg8ziuELUrLyjccsi3hgZ/F1hZSKRrON5/tMRkPy43oeHQNrDKYsCW3FylRLaPWUUOdkWWFWVNxe76KkYDQsKQqnRgfodrvc/OwzhqMxk0mC8pyCWylJo9VCKoXpjsjLi7HxtNaQjvqUecJW4JHnOaNBj+7uDrqqKMuCer3B9ffeIQxD0qFrTF8LQoIoIstzJukETwlqkUenHrK8usJcLUQmOTovsI+subUxJFPlt34G0crC3CLLr79NV95lbW3zqWJ3azTpsItUHlWWgimZ+XQmkzHrd+9QleWxynM/DPDDkFotZm6+TZ7mbDzYOOSkIBBSIT0PIeTUPuYpv/ArfC0wmUycehxDo9MiyzLW79+nKnNCr0QJw2iUkWfOQ1uIknpp8bwATylWVy9RVZrq/gZjk7jnqZg2EjWW0A9oNOssLC7w7jvvU6vX2euNSNOcS+MC6ccoP0IFEcvLy3zr/W/T73X5t901BkWOCOugfKwRrvuv1S7Os5ULhIVAyHBqCROhVMCV5TlWvQBjpbNTXlhB+TWQXyNFehhILs1HrKx2ePeDb9PozPFXP/gpP/rRL47/UGVg65GSw9S41zlj694aW/fWzn27Z8HyyjLf/a3vcuf2He7eu3sqIn1pZZnf+5PfZ3tzm08/+gxtNDfeu8HK1WV++u9+xs0PP99/7+s33nRNSLwA2bnCTDpkrSGpDP1Cc//Ol9z69Nd88sUG/+Vf/YzkBWjWd9GwFtK8ZDDOieIGN65fJ00zBoMBD+7f4+Nf/5y1tU2e5OxRaRiMLJ6AeghhILh09TKXX3+Nf/jFx/zoZ79+quMzFianvQxCovwQL4wIYx+BZXdnk88+frp9H7EDpCfxPIUov96KdKw9u52LECxdf4f3/uh/Qa4N40K7YDBPqYqcatFQ5hmhL/GkQOvqYSLdGigSx3fnbntCGKqqdmxj2DN+KXejGr2/6AzCiEarTToo6fUHFMkI85jq7LAn6osfheksR1d2qvr/auE3WjSvv0kUNahFNUozxiR7lMNNynPpovVkCD/Ab3cwRYFOE+w5E+lhGHLt6lWU5/G3H37C5oOvoL2lDKF2HRrL+I05gkaNMDaEgaG7s80vf/Rz7HgTkj2e1TdBCIHv+Vy/eh1rLbX1JsHOFpUx7OzuYMlB5xx1bfe6QwbDIdWjjekPE+lPkTP78sGQLx+cnOw9Dyjfo7kw5xbb3hNKDM6ALEu5ffuWU5eCOznTOWbp0jzvf+9t5jt1JpkGqel0fLCG/u6A3l6fYpThaZAvwPDUWmpz/bdeIzM5u8UeerowNkaTk1JOCtc8NMVZmxzmKA7J8GUTRN0N1y88kQ6OFD/8aOW47zYtILW4r3wmWEiyhGScEQU+URRSliV5cqiqpOBrw8Nlacr6vWPERd9ghFHEpcuXQUoKISBJMFXliHQnjXvsM0KC1/BRkUfcrOGFzpP5FZH+omDmZ/b1J9KFlDRaHcIopizypyLSrS4JrGbRsxQWEuP6YipOzkcWpeb+Vv/Ivw36fW59WVFUmjTJCMLI9X2RknqjgecHjCYFjC6oH5a1ZJMhTEAIRZIVJIMuo+7W/lsiT3JldYV6s8UXn3yEKQviZotWrc5gPGSSThzxH/u0GhELS0vM1SLGDzbIivSxeNcY45L0RyXfTgshmGvP8drV15CjCetCYp/iHrbGkI+PbvScJQlZcvJ594KAqFFnbmGO11+/xrA/ZGdr54BIF+7ek1IhhHwZlm+vcMFIs5Q0SxG+Ym4wIEkStjc2KIucdtPH9wXDUUWWH9zP2gjqtZxGrc7S4jLWQHe3T5VlZJUmN3Zf/xNIRa3eZG5ugTffeIt6s4m6t8FgNGYprZB+TBC5140bN/jDP/oDNtYf8MMf/DWD/gARNMCPQUusFjC1msFkoDNAIkSAkB5ShCgZsbRwiWZnAY1HZVzFifJiEKfr/wIvMJHeqvkstCLqoUez5vy5pRAHnS5eYR9WCmwgsd6TG/l4cUBtoYkNJdv9PfYGPbSu0KVm7dY6o96I/p4bnGWsUA0fr1NHeB2K0mdn7SbaCJZWrhKGMZ9+/Bk/+eEP2NndZn1jjY3tAdUZldMvG3zf5/L1y9QbNa5fXmK+02Rhfo5+rzctcckwRhNGEVEcI4cn1yDPQkHle6xeX6XVqtOeb+F5kosWFy2srPL2tz+g0WyzsLiIkpK1O7cZj/r09/aevIFTwvMU167doNNusb6xwV53j6KoyPOvnrB0cM+O9ENUWMPqkiobPafqEg/wkMolMkxliISmkiWmKjGyco08pw1klBJ0Flbw9fsk4z793Y3HFQ3WoqcdqJ/dA3Y2rrgAUkqDVBIlBVKCqQrSUY8yS/ZLtx45GGaDtvI8wtg1nM3TxwPVrwJSSuYWVgnjGoPRkEmaPPeqoqMwa4yik5TdvTuU2cT55J8UdEsfGbYBMPnIVRA8ye/jBNiqpBqPsFV1rtdKego/DjGe4M7dewghSJ4Q+J8/FBAg/RbRyhXCzgqt+SatdoBH5jyU0yGM1iHvcXKj39PhZx/+PZ7v8+br79FuzxPEEbVGjBdItMmnFi9HXytjjrF8d63uD6xdXlAIa5BF4h6t87QgstbJ5ORB7BPVFL7n0VlqsbiyxGQ04W9+9AlKCjrtmuvzlQ6oipQsG1OPBFJYWsBIw3bx1ZzKcW/E+udr1OZrXH/zOhrN9nCXLM94KBiYDckzUeZUiQ5TK5soIGy4Bk0mMGSJIRm9RHHZjCcTOL/0mWr9LBfFAgMDBnSzomg5KympwZZgD3vOv8LXFkWes721BVKSJglVVSGDAF8pdFFgTupNYSEfZpTkLon1CidDyKm9hLngGOqwj9XXG9YYJuMhRZ6daMuxP1z6AerQXBEKiS5KdKnRlSssnVg39D2TpGla1RP7Ps3ltnu+0oRJkjAcjsnzkiw7h2rYIxD4CiyU06amRZ7CsO/Oj4qcEtWUaK3p9QdkhSbPMqyuSLMUjCHLXFLMWktlNGVVMZ4keFajtd5vUgqwcvUqr3/rPawfUEQ1dFmS3F+jmEzY3VgjGZ9BiGAt/a0t7n78Md2NtWPtGCXHufUJkL771RwfLz4JxgoqXCV6a24ZREjUbKERlFkG1jAeuZK3JLmY6/gKLyeqomA8HFAWBVHk4ymotKHSzkIKQCnwlMDzXCVHUZaMkjFYgfAUQRxTpBm2KPdDWC8IaLXnaDTbjovxQ+YXFokbTSoNfhAS1+rUGk2arRZ73R7dbh+tNVJJ5hfmiRodlAxRwmc87DHs7hH5MZ3aPOiStN8nLQvqpsK3FZNkTGkFftwgrLWZJAO2Pt86Uz/AF5ZIX2xF/NabCygp3cKgHuJLEPbF8Sl7UWA9gYkkxn8y6xo2Y9pXFkBK7m4+oL/do6gqyqLk5i9ugmC/wYZqB4RX6/grHfBXyfKST379E4o85ft//Od4i5f44d/+iP/0//L/YGIMI20x1r4QzVguElEc8d3f/S6rl1f49puvsbq4wNLiHNtbG+7OtBZdFdQaNerNBmpvail0AgzghQFvfedbrFxaQkjn76bUxTLp1954k3/xH/3vUELRfbBJf2+PT3/9S+7d/uKxRrTPAt8PeP/973Dj+g0+/NXPsF/oabD1oixOJKDwohbR/ApVNkbnCdY+D/V8CAQov04Q15HaIsuKsiioiqldlFBYoZBSoZRk6fLrXL1yg427Nxl0t7H68eMs0wmkz+bp/HAnQ2dM6ymN5yk85RI9ukwZ721OCftHn/2H/98PQ1rzi1RVSZkXj3UH/yqgPJ+rN95mfukSn3/+MZP7t77qQwKmNK+FbDhi87M7VHmKNSNOYpOEF+O1rrt8c3UHaxLckukpg+08p8if/vPHwYt8GosttLX86qOP0ZXGjJ+3dNYH2qhwkfbr71BbXGXxyjydhZiin1KMM/RkB3qfg35SEfTp8N/8d/8ltXqTzuIyC6tXqDXrtEyTIJKU5rhElIPWx9g5P5Vc9yuAqZDZEGtBmHMcVw0PNf4QEhqLMY15n9XrC1x77Qb/8JOP+c/+5d+RZjkCQegL3r0e0qlLRJrTbghWfaj5lvsZ7JVfjeNHb6vLoNfnzQ/e5k//5/+EUmqSW7+kHBnEo7HAo0T69HilFCw0YjpzIbpeoivN3mZBOtYvQn7wdHDTsVuh1HDXeJYTPC0ssGNhV1MuaypbIITLt9gJ6Ps8y9D4Ci8J0jTl7u3bLhEVBAiliGo1PN8nHw7JTyDSrbGkvTHVpHx1n5wGynPnuTqwmbgYWM7LGuxFhzGaQW/P5SeOGcBnbSY8IZmLYkLf3/9bTSp0WlBlJeW0R1Yfnr1vmTFQVjQaTa6/9jppUfDRl7cZTRL6/TFZVl6I3SBALfSxFkxWUGlLPhmRJ2NQIfh1RzAXFWVZsrG5TRiNSCYTTFUyGpeMZz1mcOe01Jq0KOj2B+g8oFaWeELsyxLfeP9b/Iv//X+EUT6DypKNEzZ+9Qmj3T1+/nf/7mxEOrB5+zZbd+9hrT5SoDK7nhJ3lz90pwsFatqJ246dbcVToEKAkai4yeKl1/DjHs2FL7FKMd7bo8pzut0xvf7ksaarr/DNRpFndHe2kFLQqEdoHdDrjcjzg3vR9yS1WOJ5glJrbJGz2+8hhEQEHnGrQVrpfSIdIAxjlpZXmVtYRgU1VFDj0pU5EIIwqtOZ69JstenMzTOeJDxY32BrY5OyLPGUx5UrV5hfvkyt1iQMazy4e5vbpmChVefd65dIJyN+9tMfkeUpLVMjMIr+oEfVHzG/vErcbjPob/PzX/2I4gmNSw/jhSDShRB0Oh2iOCbwBIGSLDYknpQEgU9crxHGEfcePMBIn9HojLYJX0cIaM91qDXqSCnYuL9Of7f7xGZz6Shhd22HWbJ13B+jp2XQjxKntjDocUlvt8tHX3yMLQo+/OhT8jQhNXWa7QVu3bnHpCjJrLMb/CZASEFUqxPXG6RZRndvF4VG2IrRaMLObo/79+/T7SWMJ+V+hu44zELC0kJZVRRFwXCckGYF/cHoQr+L7we02h0EkkHYRfgKbc2p7IFOgksCKIIgZGVlmUajge97ZFmK1hql3IL/hYEKQIUYFFWeY4ryzM1dzwzhgVCE9Tn8sEFpLDtb2yjfw/NDjKnwPYlWUJYFWZYSezHCV/i+R+R7+EHwhBqU02KmfzhswzKzD7GH/s1giwSTDsjGAcN+QDpJnCcZkieRjUYbijxDn8Jvf+arb63Ldl8UhBA0W00W5ud5EAZT+5qvnuDXuqLIJpRFgqkSrDkkyZTTBauZ2u0gAR+pYvx6ByEkskoxRUKV7WKrQwkV5YEfus+V+SmUY+f/HFhtKLMCYyw6LzDVM5TKPjWclNualGK0hwp9yuLaVPngEzRq1Nod4vlVqmxEMd55ZiV1pSuKImNt/R7K8zECpOcj5KxM/esHiQs0/cqgBynGgj2uWcc5wFooc002kWxsdvn489vcXd8iy0vK/Y6igqryqDSQW8hc6wclYK4W8H6r7iy20oSyMvRSve8Ne6HQYErjLLIsKCFp+DV0UJG05/CkIpUTCpE7JXXIgdmt+1pYC9m4YuwJdKndmJuZl/PushyQ3Ud9gSe135h9rgI7tXGxGmwG5+KOFQE1gVQKzw+wlaEa59jKHjSHfYUT4fseURiitSbN8gsh36y1YAzCWgSChYUF2u02QhtkpUnThH5/QJHnDAc9rLGYXGO1dXPTq+t4OtjZuXp1ws4X9sQwTUlJPQzxpSIKAnypXLLaanReMBxZRmlBYi0zc6JZS5WzQk5ftrLkeUlRuB4UWk/L5azBGHthJDpAENUx1iBL7RqAYd3EZ1wvPCkFXr2JF9VIsoyi0g95hR+s7QSe59NqNWjGAZ7vI6TEGovWel+V6nke9VodEUYEKiCvpVRLu4QIavUGXhBidIXRGiElynd+8fqYJJ21BvuE5rWza/PoO6TnEbXbCCEpM+GawRbZkWKqIzFNdoW1OvV2h2ZrjmarQ1VpvDBG+omzcmFqAGFeuUC8wsOw1mB0hTViKp41+/yh789EdpaqslhrEKKkqgzWyKlNkAArUMrH90Iqo6lMhbVOLJTlBZubW8TDEX4YIaRkPEnQxlJVmjwvSNKUwWDAeDxCa4PFonVFVZVoW2GFRnrgh8q9AonRHnOdBlkkqdcjQt9H55aqKhinQ8TeJv1hl7Iq3Hh2SrwQRLrn+7z/wQdcvXaVpWbAfN2nv/2A7bs3qdVqvP7O20zygr/863/N1l6fna3nXQL+4kFJxTvffZ/X33uLLz7+jP/hL/8dVVlRPcF/evPOOnvr28wIM2McoXEUqn6OHpX8Rvya/9N/9n8mn2R8+G9+xqQ/xg/+C6SUjIZDkm9YnKmUR3t+ic7CCvfufMqku8Xl1VUura7yq1/f5K/++u9c9+8kQWuDfgIJqHG2p0JruoM+iIpf/uYO99Z3KYqLrTuO4pjVS1cw1rK5sw2TEM5BBa+UR61WY2VlhX/+z/8ZrVaLL7+8yf21O0ySIXJaAfrCIG5BbZ4qS9A7u27VfaFkqgTZQPoxi69/m+bSKsNc89Mf/z0LS4tcvXED31PUY4kwktGgy6A/oOavUIsCwjim1WwS1+rndCIDDoyWJY69mJGvhxgkaykHG1TDbXbHe3R39zDpBGMi3HSScFJ4XuQZ/Z1tsJyovgXwPUWzFqCNYTQp0BdEtHpKcf3aVd58623u3/7N1GT+qx/RinzCoLdGlaYY2+VAkikgaoAfQTaCfIJjdNp44RKtS28gvYC8cwVdpEzu/5iif0Cki6iOWFiBPMPsrsMZAobzQpkV6O2+47lm1/W5y2ULoE+VaXq3fkbSXWbw2nWC5gKrnRbzjQ6T3nfY2hoy2blD95O/wZxBpXAc8jzjb/+Hv8IPQn7n9/+Ud97/HaR/+sY2LxtCAS0Btawg/2KPyoJJL5CVtjDaKxj3Sv6m9yt+9OGnFJOK4qhFrYVkaMm7Bi+Gdg3eutTmn/7WOyRFwce3b9Edp/z9nZyt8XNg0qc2PbYwlGWBL32uxJdY9OZp1hpMqgn31r5ka3vNkejgGo722B+mjbZs3kuQDxyrbqf/9gIMaWdHxYFX+qPTymyqOk2D3QIYOQJdD3Hn6jwu55KENyRBrU5rbgk9Keh/tIEel+6avCgFdy8wWo0GVy+tMkkS7jxYu1BrSGHBVx6/9Z0PePdb73F59TKrK5e5desWP/mHf2B7a5Nf/uRHZGlCMcinLiUv44PzFUFX5/NcvcKZEPgBl5ZXCX0fURowBpX3kWVKMpiws6NJjGVvei/P1M5P0yIiYuq2lZd0iworx8yPB5TagK6QxiIueLJpLFzCaE2SPXh4jWxyKEr8RovO5dcQUrLd7aOrkiR9tGzPVdvW603efPNN6qGi5Zf4VqOHCUVW7AvKwiCg02wRtzvUli9RZgXzpaC/tc3Nj37J7t4O2XhEPhnjRSFxu4UuCpLe4BhLxJNJ9JPqLaJajStvvYPwPPp72xRZQrKzTnGMX/pjCGuIMGJ+9SpXr9/gjTfe4PrrbxHWGkStefysRKjd0xzmK3xDYYyhKt2aPElKtHHJMyGg1Yxp1COGo4TBIEFIjSed96SxI6RUzLU6hH5AFNTxVEySjRmnY9eDICspt7tsbP09CPDjGOX5LCwu0Wy1qcyIJC/pdrvcvn2bYW+PsiycBVYyQo5C8C34GoKSeicgbnh4scELPd577xq6LJBagIGi16ccjVnf7TG8+wlaa6rqbLzbV0CkH65FdRACanFAu1ljrhWy0AwwSY1+EOD7PsrzEWVFNiUnq2dUy34dYMFd8LIkTzOSyeRUA54uq30F+hNhnD9bMkpYu/+APMnZ3euRDmedrr4ZUErhB77zd44iWp02ZZ4xHvYpshRdlQwGQ4yGjc1ttnf2TrUYEIdeEmdblI4TxgJGo4Tx5ALq9aceho2m85iaW1wEoCwKBt09+t09dFk9UeilPGf3YfTD5eJKKTzPI4pi5ubmmZ9fYG5unnq9RlEUDAZD0jSjLPUzq97PFXYqVTMVVp/OPdDzHaGtq4o8TU5QYBx1NsX0Wii8MCSI66SjCXmaoMsCJWelUQFKGObmWigJnu+5537qRya8gKjRoSxyyjxzFSnmaaV2lgMrl5lC/YjtGI1Fo/MELUdQFFMS9BT7tBZzhutuL7wuQGCtIEszJpMJ5ZH2NM8XyvOQnhtvjKmwU2XR/pJnpmIOIkyZuqOVHtJvIMMGKohQfogxEql8pJolORzjJDwPL2pgkBhxwU0YjoMFM1OvfaWn24AwWCGxQrlnEmerNauYmYqszu04rbUk6QSZpxRlMW0M/JTJsJeg35oEAgGegTLRaCDyQIeQla43/LlhehqNBrQlGeckOj/SwkMLKKWzxhOBIqh51No+9UZMI/TAVs531vD4h2f7uqB7t8xK+ts9wjiCyuJZRaACSlmhfA98Ma3lt+5ngCOw9lXQFoxFP6fiGiFA+uqheiZTmacnIWfjwowkP06NLo/526OYqfZnTU2P+sxZiPnZFBlLRNOHyMPGElvKg+N/hVPBCUndQvzCz5uTWKKkxFOKOKrRarZotdq02m2SyRjPV4hcYM2F1yS+wiucE5wC3BqDNU6l7RmDtO7/y1I/5Ix1lvtazeZU+7CLmFSKMPBRSpEXJWWlKSvt1KEXPOdog1O9H/lNDFIJoloNEOR5eaJNqbV2uqaCUlQIq7FaY4w5WNNZp3iXQhCGIcoKojAgDAKnYBfTcyogiiLm5+ddw8/B6BztKwVIiR/FLK2u4IURUeSTpwkbkwHFZHgKMYpASIlQinq9zuLiAq1WC095KOkhpJpWRz4HldsFxk+vcDEQOGs8CU4kasy0cuOI9wqJVG6BYqbPjwulDVWlkaJCGzt7tACoKu16ZQlBVjrLF2+SID0Pz3dNQut155Ve6Yo0Tciy3H1eyOnYZNGmoqxyrNV4ngBhKKscabVTzEsfXbhqTW01ZVWSFzlZ9nSc23Mm0j2Q4bS28kD1J4FmZFmoGxbrsFBTiLkG1eVLSM8jy3LyvKTd6VAhydI+efbN7p5utObmh7/h7s1bpJPkQgek4faAj3/wEUZb8sk3r+lEZ67DtRvX6Cws8OYH38Gaipu/+BEfjfp85903uLqyxIcf3+KjT+4ymaSnJog9IJ79VKB0yfrNe2xIyeCimnuEMfghv/fnf8G/98/+GVFco7u7x/q9u/z1//s/Z2djjUmvj698KqMd2fXocfuK1lwTISTD/ogyP8jetVodlpaWWV5Z4TsffECr2WJ+foGyLLhz9z6ffvoJRVFQleUTlfrPFWkfivHUhPhRS5OjsXT5Gm998D0Gezt88vMfUeZHjUkzs1fDQ1IdAUwDmiAMiOOALE8JlaUeeSy06zSbNS5fnkdJwVtvXiHLMn7+kw+5f3+N0mjGaYaJOrz9B/+UybDPnc9/Q5mOIdubejqfBTmOYajjlOmnSLYVI6iyKVMzO2fnd03LSjMc2+nEeBEDnOtklxeCv//hj/nww4/Z3b53Afs5bt8ePGSh49BaXqa9vEpeFST54xO7EJJ6a56gtUBiK9JkgN+aI1r9FkG9RRA3Ub5PGNcxVUm6tkBGD1dhMCGImzRX36Ac9Rhu3cNUX4Fs0hMQSne7ZPorDahlUCO6+ltEi1epd5aoxxG2mpAMMwbr99j55JdUafdMCaDnAh/3uFpgzAurBAyAjgBK6O44F613V8EL4DdrsNk/px2J6c5mUrsZeVrx2P1lBPR9QxkK2ldj5i4rbqyu8p0rl9Flztagx94g4bM7E7rjgkn6yLh2zLB+Xti6t8W/+b//axqdJu/+/gdErZhKaTcrWR/8GLwSVOnugeb0OAYgNcyFklgJej3DeHQOD9esTcYxpLYX+bRXGyjPEcnGWIZbY/LxU44tGjclnTQNOzcrd92fdA1SDuSXx22vgQvIxhwUYx2HFlAH71KN4HIHoy39IsEmBbpnYcRLa+E8S60+r+hsOBqR5TnGXLC4wlpMWaCBrY0NwjBCWIUnPYo8Y3l1GW1y6p0GRmiySeZsXV7hFV5wlGXJ7s42npAoC9LCnF9RU24AnUWbNdywNMsrP+nu9iS0IpcgHeaGUlvy6edfW57n7dcukeUF3b0+SV6y0xuRF/rCxY6bWztYa6iO6bEVhRGry6sgBFII0sSnGu4+MiQ73fd4MuSzm58RB4prHY+aJ2imFX51YFdRViWjyQgV15hD4Ak5tQkz5FlKMh45KwjP49r16/zpn/0TNtfW+Tfbf8mkOqeJwPchqrN0/TX+/f/VP6czN0+RjsgmI/6L//T/xs97Ozwxcy5mDSAVb7x+nT/9x3+MpwJ0WVLmOcKCeB4k+iwRfUHx0ytcDCJPUPMkpYFJUmDsw46c1kKaZRhbolTA4mKHsqxI0xSBwJv2bhhnY8zE7Ovv9LSfxnDY59NPP0IoHxk2QAh0PgZTsbfbpTm3yFtvv8XS6ir09tjt9ZiMhlQopOcR1mvEjRpFmZP3UmyVEYaCqkp5sNHHl4J24KOATOeUZUlvNGZ779kSXs+XSBdTCYl4XHlgdYnVOVKEBApC3yOOY7QQFGVJUZZIIVHKf3r11ssE4by4mXlUHYHxcMR4eLEe2gBVUTHavfj9vGjwPIVSHnEtpt6o05prs3LlEkWWMhr06G9vIN5+jWa9Rp4VPFjbOtP2Z9pfT7jyd6dIT5nZa54nhJTuFYQQxsyvXuLNb71Plub0druM+n32tjbobm3hCYGSEr3vd/j4kStPIaVEKYVWxikhrMX3fer1Bq1Wm8XFJfcMa02W5YxGYwbDIVZfrH/eU8GU0w7oJ2Cq5hcAQhDVm8yvXAIgjGKsMejq0QY705oDcch//KG/WzCV27d2L6FL0AXShkShRxD4tFt1yrLis08/RyqJsZaiqlDSJ27VKY1E+jUoKw6WwmfBjNA9w2Sy78/9bBBSOnHC1IZgdn4u2ht9Jmk0Brp7ewwGA4r8efffeHwuU55PEMfo1GLToxv9SKnwlI+cKsqF5yNrDVRUQ3k+SnkoT2E8iRfWkX4TayqsyRAqQIU1TJbueyF+JZAvgiRFggxQtTm82jyeH+JJiakq8iIjn4zJRz1sMea8ZVYWqKqSosixWIIwmi7cLNZYqsfGEgchnVreemA8s+/289Q46rPneFlm89y0NxkWiD0IIvDVEz58lv2I/bYTbliaTV9HDFEWS4FFCUMzFEipCNsxzYU5xoM+O3vb9Cc5w4lmnBgeG4Zmxu/nZREy+w5SoKSiKkp27m+TTXJeTwv8OKSqNJWtsKUFLcFME1Eert7egColnhaEgSCUODXOeV3ME+4xKQVe4OEFylWrWZjsPWNF3TGPm5DCxcY+4M/mjSd8xyepzKdMk5g5nKkTqlDE9D0xiFAifQ+DptQ5VJVrFnRE8uYVjkalNdVjtgsXBGsxRpNMJgz6fQa9Hv12jzRPAVeaLpVEKvl8CKVXeIVzgDGGPC+oAA+JFFBKi5ZO8Xm48nlWxHPaaEYJ10do9jTMPqd8j3ojxmAcR1MUFIVTpV80qko7n+ZjuBEpJGHgY4XE930q3zuWN6qqitF4TOVLkjBC+JK4csVes/jLGENVVWitD/jfqnKCsLJ0/uvWgIAgDOi02oz6A4Q8e3wthUCImeL+AEIqpB8S1RqsXrrM4tISVCnpZERnfp4gitB5ji5OTl7L6fo+DAIatRpaW4oso8gzV2H+EKEonBBeOP7APlRR/AwT3FmqyV7hhYEUAk8KKmPR+uioS2tNWRo8LySKQqSU+wlypZTri6U15RHOGFVVMR6PEMrHs47rrZIETIk/HKGFT14UeL6HlJLKaLS1COXtc1xIga3cfSoB3/PQRcUkmeALQSwaeEJQ6opSl1N++dmSXc+XSLcarAtYDj9BRVnxk198yudf3ufP//ADVn7rHYIwpDk3R2884Yu1TfqjhFu3dxiOU5LJ1994sNaJqc/XyMc5o+3Ji0c8fo3hKdfH7/1vf4t333uP3mjA+s4mpidZX7tHkeV0eymTCVy6fJ3vfve3+fWna2fezyyYmV1ZgxMyzbTB5wXpecxfukJYbxA2OvhxncXFJTCG8XDAl59/QXd3lytvvkN7YYnexhr5ZILODfoIolRrzbA3xvMU8wtL+EFAd3eX0WCAH/jUmw2MNdy7d4+iyHlw7w7j8Zj79+/tN1F76SAkfnsZGdUJghDfD1h6/V2u3rjB/MIcQSgY9rp88rOfMB4c9qpzV1gGAarWxFYV1XhKylUjjEnYvPlTuvd/Q1EUlGVJsfUJe5//Awurl0j+8Z/Rnpvn7Tdfp1mr8d3vvMel1SVGw5TxKGVne5cvv7hFmacoPySMGxSpwj51LDtraGlxbIHhIqV1Uik6S0sEUUSaTCjz3L2y51H5YoAcrKAsSqpSYJ6UTDk3HFaiP/xAjHZ3yZMErSvKsngsuLXGkuztkQ8zymwMKLQ2FEWKH0bEcYgfhAglsUSsvP9dOjdu0Fu7TX/tDqVcYDQxmMxgrMdBBcJzfDC1hVR/xdYuMbCIlFeI4kXi2hxKBVhr2dzcYbz3gO2Nu1BsTSs8zplIN4YvP/s1u1vrLCwu8Rf/9F8AFl2VjEYDfvHTHzMaPu572V5ssPLaHMk4Y/3uLro0T0fmztybPKYZXZyiu8RNROf0dScWHmi3iwhno/ubNTAKek9S/j4JAqSCIATfE8wtBPiBZDgpSDNNkUBxxD6MgUFSMUaQFZpYSxajMYOlhFtbQ37w4RZpUjBJK0rrFOzAwb3aBJZw6uVNTjxXh8mLJ12mhaUFLl+/zGgw4t6te0gEkQ0IS4/tL9bY6e0wGY1hkjlPnBInMwwhDgPefPsKkfBJtrcoJs96cg/hCZ4AZV7RXx9Sa9b49vffI67H5L2PSfrnS5AKKZi73KLWiferApJuSu/+8Mw2MjOOYza0xgoCH+ychI6inBjS3SOqZQROkb4MmgnZZuXimszAxLiutRrnXf8Squ2+7hpsozX3bt9iZ3OTW599Sr3eQBtNWZau6eh2l7Is3TV9hVd4CWCAxOgpYW4QAtLc4pXga0vAfrR7ph7L2jglugCqR8bX0WjE2oP7aGtRvsQ3HoEnnVL8GJLtvHDj9RvoqmJ77Q7pEd7guipIBl2nUlUWESg8eTSRbgxkGaANaZojK0FhFcqKfaLeaENVlNiqwkdQliV3bn3J+p27DHsDKO0086q5f/ce//qv/5rxcER+ZJXy8fCV5Mpck8BTbPTHjA71r4ujmM7SMksLi9RrDVqNJvPNeawu+MM//kN8T/LFxx/z5SefHLt9AcS+Rxz53P7sE/6rQY8gCKjFDXqDAdsP7jEZjaimZLwM60gvZGFhkaWlJYaDIetr6+iqwFbPIC6ZCRFmlX2al7aC65uEvDJoYzmp5U5ZOkvBRiOg1WoRBBG1WoPJZMLnn39BlmVPtj62YI1AKI/63ApKSkpr6fb6lFVFrV6jMz/HtRs3mIzH7Maxs3opCqpun8XFZdrtDp60BMqwu77GvU+/QJclG56HkoLIEygBafLsfPJztnY5WhZijOHB2jbrG4LfeecG4Mi/MI6xScbucESvP2GvN2Yy/mZYi/iRR30uBgsjcTr/81c4H8hp+dOlSyt854Nvc/P2F9xav4NMxwz7PYqsIEkrilLQaLZZWVml0Wg81b4O80gWF+ict2mRlJJau0O9PUe9NUdYaxDX6lhjyNKM3d1dkvGE9sIiYRSS9rtUeYYojg48rLHkaY72feJajUarxWQ0YoRAeR5+GGCspdfrMRwO+OUvf8l4/JJXNAiBjOt49TmCuE4YxtQ7izQ7beJaBFLT29nh1m8+ZjIcTkXVB46CQklUFGEK54mMNWByrIHR7sPNk7Mu9B7AuP8ay2++S2Usb79+gyjwubS6RLPVYGury7bqs7fbpdvtYnVF5Hl4XkA59Qp7OlS4qGbGrM3q5i9mABJCEDcaRPU6diqbMVpT8rzGeTehm6+g4eZxlEWeTMiTk0gwSzmZUFLiRgw59XkssLbC9xW+74FSIATN1cvUzRJZVtDfHWFknby02NJireIgkn2Ok4zFKTe/UvhAGyHa+EEdP4id6gbLaDRmZ2uX0aD3bAuGJ2BvZ5O9nU06c3/CO9/6AIGlKjN2d7b5za9/yVGjZtwIWLjcxttRbH7ZRRdPeWwzVdDsUQ9xuYUMRxCfEwqgb93mA5yeYmfIsz/h0/FCSlf1HATQrntEkUJToZVGH5MXsxay3IAEM9GkBQzHBWlRsDvK+GRjTFVUtJmS4IeFZRaXEehMf59J1E44zCe8ZR+1Ro2VSysopVi7u4YUEh8PVUlGWwP21rcgsZDZhxejBjzPY2VxjrofsjbsU06SJ+ztDHiS4LsyZKMcXwW02x1ac03CKDz5Q08BISBuRbSWD+Itm1t64mw9e4Q4INLtdFHoSwgVWF9iA4UFxN7DPWD2EQJ1MJSYUekubom72WcJqZm90Cu8ULDW0u926dP9qg/lFV7hXGCB8vBK0jrHPLTL+c2I9JlP+mkjLwvkx8RpRZExHBik5yOjCOUJlBQoIdDCXmjv+M7cHFVZ0Nv2OSpVa3RFkU3wfB9PAJ5TeR8Fa50jSiWgLDQl7neNPKh0shZTOUGLxGKriu7eHjtbW+Rp5oQh07XeoNfns+Impiypzqh0VVLQroXUAp/eJGV0iAzwPY9mo0Gj3iAMQsIwYr5TRwnDjRuv0e/32dvcfOI+PCUJPUV3Z4udrQ3q9TpzcwskScq43yfLMlfSJwTKC1FhnWZnkeVL1xHeDps7fQwCWyU8ddp1ViIxs3iBV0T6S4DKQqVPfrDNtH+BlIooiqjXWywtrdLr9TDm84ebAx8HIRBWIIQijJt4vs9wOCBNE7TW+IFPHMd0Oh083yctCmcZrA1VmuL5AY3WHIHnCPPhXp/RMCWfVr5JAXP1kNBTRyrjz4rnS6SrwHk7mgqKhMf9XWA4HrGxvU2lNUVVkWUpvucTBD7ym2DpMkU2zOnZAWVWvVKjP2cIBdITrKws8f633mOv22Ww3We4OyQb5kS+z/fef5PI9/noo8+4+fkdfvGr47PAxyHwJJ3Aw1jLoCjdIHUBl1ogCJQi9HzkdPV47859fpD9DVkyIRv0SIYDNm59STIeMej3ydP0iT53Rmv2dnYYj0bMLyxy7fprZFnOrVu3HT8jBUWRkxcvT/JLBnVUrY0tc6qkd0CgWUM17GKyBG/hMn4Ys3b3Nn+zc4d6q83q62/QWljmf/bP/gVFmvDZp5+xu7vLpLtLMuhhioJqNMJqDdYilUdzbgHleYz7XYojmlwkyYQvbn7GcDjiu++/Q6fTpFaL8YOAsrBo7ZpkvvXu2yTjETub96l06RqOPjWmN6AKIOi43820bLHMHQtGznlFPcYYBr0hSVoQBh7NVhu0JRufo5ryawI/CLh6/TWCMGT9wSajwYiZztemFXp7m2Q8Ya2o8KKY2vIKfhSzuDxPoz6PZzT1VpPROGG318cUBhqXoUwgWQf9nMrrXxCIWhNv6T3Czgpxo0UUhnh4iEowWd9k9zcfk21vcqErwilC36NVr1GWBd1RnyLLpkrZx605ht2Eux9vkqcF+lnKqGcr6lkUmHPwaJ9j3mBWeVUA/em/ncvoMT0tWkOegi4tw72CzBeME01SQHXc1DMr/0qhLN268bO7XbL0c3b6CWWl8QPB/KJCSkGyV1HkFtpADfzLNYKrTapWTi6GkBjY4shSMqvc6zTFPf29Pp9/8jlBEPLt3/4t4lpMFMfuy+bWkejlbHGPI2oTYBOihYA3f+868+0mcWrpxXsU6Q79Xv9s5/UZkKc5n/7iM4IopL/z7PutBYrr8zU86YgbbQ1pf8L2pHDsUADZoHj6pqaPwkKVGcpJhc6Ma+mEq57A4pJMBueh3nX7J8Qx84HnCJWwmtnvPrzCKnmpyIIw9FBKUlUVVWUeagq2DyWQSrKwsEgcxfS6XUaDsyU1vkqEcZ0wrlHkGdnklGIPKcH33Mk4DSnwChcA4RZqWJ6h/PJriVnbiMOkucFFio3pv5+WAvU8j1arhRCC8XDoLHan27fakBUlnoUoCBFCEsUx0tPoJMNUFyfO2FjfwGh9rOK7yFJ2N+7j+T5+GGGNRZdHPasHpWbGQjJtRK0DDvqCAEVZMhqPEbt7BF/eYtgb8ODeGuvrm6RJwmFbTFNVlEnycLPSU0Jrw6A/JvcUDU8Sd2r0s4JhVhGEIZ3OPM1GCymdWKowbp3tRRG1VosgPDl5ba1lMhpRFoWzabGWdDRi0h9QlBVlOkYBc0urRGHM8uXXaHUWiaKYOK5hjSS7npOmY/Z2JFWRUeUTrHmKie0VrfXS4SytgUajMevrG9TrY4pCM5lMnAXSCQiCmHpzjiius7h6Dd8PsEpgrKHMU5LhAF1WZFlJnpcUeYlAcPnyJZRSzHXmiaKYRmuBqNak1awz127SiFtsr20z7O2xce8LyjwjySvy0lCcQx+U50ukez5ETdegrkwfi8osMJ4kbO/t7f9bnuX4nofv+V+tn+tzRj4pyL8BFjYvIhyRDotLC7z91tt89OtPGO4OyfOc7ftbLMy1+fP/zb/HyuI8/69/9d/xi1/ffKr9+ErSqgVklWErrygvpKEiIMBXHqHnIYVTr67dW+PuzbvEoWKuGZAOemzfu8NkNKIoiyObjD4KYwz9bhcpJTdee4N33nufTz7+mE8//cxNrE8zuX7FkEENv7WCzkZU6eAQkW7Rkx56ItCNNoYlNu7f5ea9T7j8+pssXH+DhaUFvv97v4fvKcxf/hX2s5voqiQZ9A4sXaZQStGaX8SPIoosPZJIT5OUu7e+JE1TssyFvrU4BiEpClc+WVWaLC3Z29lme+s+pTkPiw4Lyod4zkkxq6kXuh45H/dzrMOzxjIejhCTjJVLSzSaLYrkm0XonhZBEHDttTdoNJuMBgNGgx1cp8EQm1foYod00CftD1BxjQUJtc4c169fZmlpiTCOaC4vsXH7DjtrG1htobbiCPR89xtHpMu4gX/lTfzWEmG9QRSGKCRUkGxt0/38JhS7PI+I3/c8mrWYJDFURUaZZ1Ny8HEifdJPmZyHZYY99AJH9F3QLTCrRTx32mnKoxQaqgJGuiSQMCkgO2mIsuyr7qvp69bagFtrB2XisS/prPooBVsj7VTgTWABvNUa8eUliuaQQoywAxyxetQXlGADsLMdnYBhf8iwP+Tqjev8zh/8AWEUAZY8S6GYKtEPfweNu2YpBF7AjYUrXFpZQAxzdv2YjfWMg/TFxaPIC7746Na5ba8WKN5abhB5MB5r8rLgk50Ju2Prhr46517CpzNDPnHzvpj6pbtSCtz1NTgmajDdv8TJ2X3feSGEeipz5PH74SUJiYQQhKFPECjS1GKMwZhHlmwCkALpeywuL9HpzFGW5UtFpAdRTKOzQDIakCfj0xFfSkIYuGtdVs8l0foKj0BI1xDDdXz8qo/mhcFhp7bZnDsj0iXOBews07xSik6ng1KKIsuoynJfROyIdEMoJKG1SCGIohClLGleUe4TVOf/fGxvbTuP9GOajRZFRnd7Hc/zqTfbSKnQ1aOD8aG+VVMiPZs6+FX+oT8BZVkxnkzQSCpzj0G3z/raBtubO9O12cE9aLR+6sb0xliGw4RMCpbm69RrIdpaR6T7Ia3WHPVGc+pX7nLq2uKI9EYDLwieuI90MiF9xPatd+h3PwiZX1im3Vng7Xc/YHX1KmmakSQpSnpYYxiNRyRpQppO0FV+diL91ZD5UkLhEnWnaQ00mUzIsoRaLaGqLEVRPLGZuB9EtOdWaLXavPXWWwRhyDgdkxW5q7bQGl1WFMUBkS6VZHVlmVq9zpuvvUmr2SbXilILlpYWuXRplcCvcf/uOjsba+xuPqDIM9Ly/BocPVciXViDNE7VaD0FRjwy4FiyLKXf74PyEJ5Pb5Swtr7HeJJRFC9eFCr8abA9g3NsOPtA4fNwj8CZw8IrPBcIAUHoE4Q+3/ud73L16iXKQvMv/+W/4uc//8VDA4A1hmySkkYT9DOUhQghEdIDqTmKMDnz9pRwvsjGYrXBD0LmFlfw/IBkPCbPMhYu36ARN9G+wcejyic8uLtBMh467ypdPblx1+NfhCCMqTVb+EHoAtsLskJ4NszCzBkD8bj5qTGWqtQn+GNaynGfTD2gHPfBCrKsZGNji8rAu2+/SRBGzkc9CFBqOsQqD7zQEdJljpCSuFYjjGt4novaVBjiBSG6LKiyFHSJHvcohxGjXo9Bf45ao4kfRCAsQkIQhjQ6HSprWL58hWQQMxhvUBbPVtYfxRGd1RWk8pFCoauK7ta6sxwpSicDPWtz0n08eq8LLC7AE6aaKjyeEQrEso+IJXavwg5e3sHU8zxqtQZRXCNLEqw2KKlo1BsUBRRlBlTuuTMKyhwrxmRbEWa0ywOdMLzXQTVaqEYDTyk6cx3KsiTPC0xh0EK8oLHto+bU5wHnvSBlDT+ICPwABQhtKNIJlJYq60PZBX2O9hgnYHNznR//6O9QSiGEoiyObjQK0O7UWVzq7DfPKoqSzY0uRf6UNHXFgYTtEKSAWux+pvm0j/ELDGsh1846/BxEJgghUMJHCYGgdHPaBHeu5qdvmkntC469Ra2YqtJnTWFPcStbwFj3gqmP9xM+Z41BJzn5KOH+gx3uPdigOzhFZc9MYniWmv8LhgJ8AaHr7402sJsYxpkhnd3m09JwWfPw4gBbGsp+fnDSTtq+gmbd3dt55u6dhUaT1lzETjVh0jt47u0sFp+FDfBwmUUCIlbIRohqKOpvzCMqSHtjqrxCT711bR9IX5AT/ERYyrKaNtk7Ro0+vUlNqel3p5aH52kndAFQSiGlYnFxmXqjSaGdqtPaJ89/tU6HertDVmaMkqGbbwMxrTSxL8yz842APS6G/2ZDAGHg4YtpJYk2aB52nAKoKzfdZ+bk4VJrw2g0RgpBWVZY3NjpK/ADQeALjLX0hxnK82nU55DSo9meo9KG3d0dBkf0eXlWmHLaa++IHl7gbg+jLRWaNE0RQh5B4tnHPpNVbq7tCkOlICldILG3tcWvf/xj/CAiqjXJ05xkOHKT+z6cR57yffwoxOiSIj2bLeDMx95gqezUHmc68CqpiIOI0A/xpEIp6ZpuCzdWZ1n+ZO/pEyAFxIHCCyRVkTOZTLh7+ya7W2sURTklLzPG4yFZnpEnQ3SRY4+5Bk/8oiXupvRxl+Ic+6K/wsXgsD3UkyCVhx/4WFw/hbIsMeboTwoESkjajTpvXr9Es9Xm2uVFpPK4t5FSFIZaI6azOAfC0t3dpchSOu0Wvu+x0OkQRRFoTZ5O6E0KJmmJEQYZhoyKimhukVpZEXRWyQkweYrVFdaU0yDv6fF8iXRToqpk2vXexxrjXrMIzbpygK2tLUQYI2pNtnYGfPrZGllWHNuh+auEjEA12F/v2wLMTLR56o3gPDcPE/JTldErPB9IKak1QjqdNv/Bf/Af8id//Cf8J//J/5X/+D/+P1BV+qGSFGM0yWDACEN5TEb8VBASoUKEKR/Kfj/1d/AUKvYwpaHKSqJGg9ff/wApFZ/+4h8Y9bo0mossLV7b7/y2OdjjNx/+krLIMfppMkDTBib1Bp35JaJa/AIT6bN2dxYnKTv8XV0WyxhLkVdOhX0MkZX3t8j728wKncaTjM8+v8UgyfhH/+iPiGp194rreN50iPVCaMxNZZN7TpHeblNrNNgMQkAS1FrEnTny0YAqz7Blju5uUMiKvc01arWYlSsSz/cRwiKVJG7UWLjkEzVqlFXBqLvNzY1PKCe9I4/9tGi1mrz9zlv4YYwf18nzgl/9SpB3u9AvXaPIfS+Is2KmBJneI1NCcNDtMdTF+VhZeQL1boxc8al+PnmpifQgCFlaWsXzfEb9ASM7IPA8FuYX6A/6FOWAfaNr41YDJpMMx3sIfLofhwjhc/17v8fV3/5dAs9j9dIqWZaz1+tTpoZMqRcwhj1Lm8azIARaKNUijhqEYYxvQeqKdDghJ6cYb0C2zvOK7L/4/DNu3/qC5eVL/M5v/yH5CfHO8uo83//9d5HKjVmD/oTR8FfPRqQfEUcqBQtt51ixufcSEOnA5Bwl7wKJJyOUFAgxDcZ609cih6xV7IHlx1HHJUHP2hCcEtbimjpN74HqFD26rTYUg4SJEXz08V1+ffPO6WLmWZuEs8atFwhfQFNBTboRoDRwt2/Ym5gD4mdaZ6zqAfF8Bz0uqSYlNn/yl/A9WJxzjeVHAzCV5NrCIssrC+jRButZcvwJny34DftxuhAeflAnatS4fO0yUkm2H9wnHY/Jh2PKJAVrsHsv3ih7FKyFND3Fw6QtRldsPlhHCHGqSsavCkIIfD8gCCPeee87XL16nS9u3+PW3fscs75/CJ3VS1x+9z32th8wufkhBu16ShjhOiq/IM/ONwPmBV1jfLUQUlCLIyJPkY1TMl1Q4YapCFdA40nn3FgBOneOYcehqkp2d51DwMwy0gsgCiHyJVGoGGeGzd6YWq3O1auLNBsN6q02yvP5+S9+fiFEui1ObuJi7dTLWVfkxeiEufPgL8bCuJhGnZmhKwzD6Z8f3LrFxt277n+EwPciFhdfQ3khYp9MD4AYP27QXJynzMZUeXqmpsUWl6tXFhaNm9NnyyHf82nEDepRjcDz8ZRCSJcAzKbEd3mkfc3p4CtBp+HheYoiS8hyzb1bn5IlIyeum9F01kntDh6/p3gOZ7HkrDcPvOK7XgKcRcPt+QFRvUGR5+zu7rr75pi1vRKKQHgsz83xux+8Q6vdYunyFbS1dPs7jMeG9nwLFSmEhLW7dwmjiNWlJeI44tLKMr7vkeU5k0HKxtYu2/0hgywlQVGUFc3LN6iCOvHqXYpgj2JvC51NoBxhn7FH2vO1drH2oeyVNWZ/GIs88DxBFAZ4YQ0RhAjPR0kPY+xzJdGDyCeIAsqiIk9OJop8TxHGCl0a8rTCTj22zgIpBPVmHS9UTMYpRV6cq0fpy4CL0B26zlS+S7Vm5ckyNSHwAh8v8CnKgslkMrXUOLj+URiwsjBHPQ7pDSckWU6Snb2uOPA8As/DUx6ToiSvqnOpDrXWYiuXnMIybUCop9l5V242Ho3o7u1O1auaUX+PqiymzRbPrkT3wwg/CBiNhty/e5tBr/sCl7qeQsWiKygnoLPj3yc9Z30y7fZmhSIbDkjiiN2tXTCQZSUgCestWourlFaSWemCD+tscdI0xQqFRoAfoHwfz/ep1CFzPmspy5KtzS2E73zw4noNsPi+pLIQaNAmojXfQYqKaH6RrMzRyRhT5MgwQoUxpizQ6XEKxRlp6QpCyzxn2N0miOrUrKUylmazgQDG6S55eliNPusacwThKabbtY80mhYC4YUghAvSTAVGPzuJ7glkWyFqEnwzLTl8uQdTbVxjRFXNvAIsAosQiodHzpmkdPoyGisEtjJARp4MSIZ98iQhS4auwqsqEfopy9MPV8bOfp/x3o/G88pZZtmzRGJCgpx5KkyTc+diF+WkpKbKKJIRQiqyZILRJbYskLagKs7DIukMR2QMxhiyLGU46FFWJVIKfN+nqh7peyAkRgWuut1U55GDPRrWCTWsmK53JAS+I9az0tmnfJ1RactgVKKkoJw1L5kOZ8pXBIGPqEVU8y20V1LspdjEOFL98D0+Gx5nNfFHKL/nlhdotFsoKVFCUW+2GA+HZGmK53unKostypK7a1vs1SKGowR92gW8eOTnCwCDK1tPK8vuuEBbS16Zh9WTM3cHYdBl4aoDTzmOGQNZ7lw6itJ9TEqPIAhdFdlJm5mNb4cX/iON2c0xhaJYypG+pNIllSkxxdQW6CSOYzaUv6Qi28OL5FmljH34Py8EjDHoqmI46LMbRvie4uqVK/T2FNm4h9YV5pgOxWVZkiRufWZn5SKz0pEX5ytePGZz/SzU+IZBSoVSHsZo9FfSpP5xCJxVqJKCShty3NLXV46b0KXdH7asdYlJzVRQLcSJ46adllIFgYeUAqSzbRHTGyAvHT9TVZrhaIQ2BhXEhJH8ysWPT3OLWpw8yNoDvtcaQ2WMi0mVj0Si/ADfDxCzrtVTva5SgiiOELac2hE75bgQbvw5zRxlgbTSjAqx7+Hsxlj3eSHcGsAYi7GG8WhMr9sjTZ+ejRZSEvq+mwObDVARyWiX6jFLnP1PcC4y8m/oOPJ1hVTSVdzXa7Q7bYosx5dyOn8mR6rSLRaDcQ2M+z2KImOUZZRas7WxTq8/ZJJn5KWbu6kqWp0OrVYLKQTWGrTWZElClmf0+132dnt4cYP63AClfOqNBkVeENXqZElCJSXa2rM7MByBZyDSzz6bGq0fPonTAUUKWGwqWrFkeWmOzup1t20pGA6K/cDseaGz1Gbp6iL9nQHrtzZPbGRUr8UsLjQYdlOSzSFG2zPzNr7v89b112i269z8+BabezvfqIFlZrgBbuI6t68eKOTVOQg8zP0uDI6fZKSUxI0GUaPGg7U1Qv9Dtra2HnrPysIc/+u/+EdYa/lvfvAjNra7ZMXZ2YS5Wo3ldpthkvBg0Ecb6ybpZ4QpNKacLrYt6KJiNOgjpKSqXFnNvTu3WdvYhSqBaozRFbo6oS79BEil6KxcIqo3+Pzzz/jo5z+hKM5JUXwhmBnKwuPfd1pbVo6hmrj/P07xUmtDvYNAIpDosmB09zb0u/zk739Ka2GR7Z0BBp/FK2+xeOk1dje3ufvFnf3FdFVVbK5vocIBqQHZahM0mtTrdUyWPBSepEnCD//Hv6c+d5OgFtNo1BFC0GgEqMpiQk3U9Kl3IkajBTZ7a7C0zPDmx+TbG0QLq9Qu3yDf22J09yZHyq9ECCJwJTU2Z7C3zSc/+RuiRpul194lrDV57doVfM/nN+k66/3bh47Qw2ldNE5PcWj7MnLyF50f8uA2CBXgtRdASKrBHrY4IXFxBsi6JPx+E9EU5PmEapy42u2XGEWu2dyeIJBgc4SwzHXqRJGP5XAQPzt/U0ZbiSk5MwZbMOrdhy9rFFnCZDgAJErGrmfJ05Rnzsw4ZwS6j1PolTjV7qFNyrq7FczEvU63/RCCJgeJgRKyaUn9MyEDcop0k/7aTfzaHNoYV30hBcJUJONzNl4+JSaTEV/c+g1CCIJAolSD0WhEcWieKVCMbYinNV6eOnueC1iwWuNyikK5JmX1CJbaTqW+3odPt07lovHSIs0qPvty4KaFaYk3ERBC2Apod1qYZo3GQoN8OGFH3HHWIvfY918HDjy2mf48yIcBbuHxnT/6Hh/80e8S+zGNoM6D23f5u3/z31PpitbiHNY628OT0BuO+a/+7Q8RQpBkL0+D76NQWFcUNp6U7N7rgYXikcSA9N24YqqcyW4BuVPwnWr7BWxsu9+tddUX+BG1ZhM/fLLPLCMeusZmr6C418UsTNiNJaImGfe6lFmK3dHQM3CcdbjA3VcKNzS9xAkqqRRSqX0rGGvNM6u9zgvWOp/Wsiz4zce/4LPPfP7oT/6MP/3Tv+CTT39Dv98nSyfkkz72iDmm3+szuX2HKh1gS+0y3LMizq/xOPgYDhvlvsT36tMiCmvU4gZZnjKeDHgRLr4nJXNRgBTQz3O0sby16LPajNjslaztFWjcsGUslLkrpKikcnGiPr4CFyxSCjqdBlEU0O8NGYxThDAI4RTTxkKaZXz6uWuU/dabllar85AQ7XnjaaN+ixvaj6SIlQ/1BURUp9aZJ/R9vJ2ZlUABlERhh4X5OcaeoKcCjAE/DBBCUObpsYm6h47dwuY4Z3uSo6fXxRqNLjNMlaMwKCxVYSjKknt37/PxRx+xs739lN8aPKVo1Rs06nVWX38NP26Qjvfo93aP+cQ5DHyHjfwfxdcpYXd4mfY1hx9FeGHI6uVL3Lh+g7IoSMcTRsMhn3/+OdkR4lNtNdZqdrs7/PrDn1FUmnvbu+RTSyFtDSpuIYMYkyeYbMyNN9/mypVrYC15nmOtZWtzg/FoyOdffMmd9U0G44TKeiyvXub9D36Heq3B4tIKaEO2vUZe6XNZxDxfRTocOVgLXLmR7wk8z2V7i7IiTXLSLH9+xJwEhEBFCr/uoYbq2LdGUYjne8RRhOd5+L5HEPjoUlOW+uyq9KlHkEB8Ix424ICAgQssi7QgnnAxpAAlpgpuw95el8iPGI0eLh/zPEWrUZ9mlaGsqlMTGA85qUlF4HkIKSm1wZzn/b2/Keddl6UpUsx6EViqIqfSwnn/VqdltGabFEh5YIGCkFghMECWpkxGL0OTqSed61MoBvYNQ6dKAV1hy5wyTejtbFOUFZPhkDzPiKKYqNbC84cPCYatcYO/ZwVKeUSeIghDPM9DqZlprTsOYyx5liEnE3o722yvP8BKhRWS3EomViKk+3wUR8wtL2GFRfV3SUxFfXGJ+uISoyJlfKQX9qP1IHIqtNJUVUE6GWKNpmy1kEHoPMUetcUR3hNO7yN/EAKpfJDSCWLOK1KSAuLpK7XOBPIrKbl+ksRQOCYIpl5gJyiChEBLZ/8jzHTxMuuI/FCSeZoM4vD9M+uvrtFFSj4ZUuYpZTZ2iSClsbo4Pml02q86ex1WqT/0JU78isfAPvw6t2HS7v801qKtKxO3RlMWpetNcKwK52KhtSbL0gNV5xFjkZQKL4jw0HhU+GFFGPuEmUdZ6HMj1S2OzJxNn4Kpb/cTfFVfdkgJnu+achflI54q09+t0ZiyxAqD8CUiUBCJA4u+w8UiMyX6jICaVW1YkL6H53s059osLC9R8yIafo1Rt4fveWCN82iXknanjScVVVWhjaYqSooiRwmJ73tYa5kk2dniCQFhHOIFHmVWoguN1QZTHbON5+inbnD3WVUeMzZNfVZtabG5PROpN7u3wU0ZAFlenr4X06OHpC2UGjOpKHsZopDoSYkttMsKlIc+c7hVy2zIfoXnBOc7nOc55AVVWe1Xdh2MtUff2LossUmCKYrpQMjXg+R5hTPB8zyiKEIIizY5xhiXMLJOlf1VCImEwM0Twq0XKmOQWIKpnmIGM6UXtD1dIYWzQ3J2H1K6Z2S/d8djHxZI5SGnfaHszOP7K8K+UHyKs2jVjnuspefjN+cIwxp43sMW6dNPGVNRFRmmKpESp+I/catHQ1uLPvT2siwYDQcMh30GvT2EtMSmQakrktGI8XBIkT+euDgth2utpdKWojJOEKfyp26aempYnr7V1iu8kFCewg/8fT5CCkGZZSh1PJ8K03CuLBmPxmRlSb/XIz/kJ+mJAIXAFAW6LKdiwgZxLcbN35oZu2GNQVcuPs7ShKosHLfsTSvnresz5nkKUymsllMO4ukGrGcg0i8mghDWoqxlbW2bX3z8BWlWPL3355l2jDNkjCTMC+eBOeLIIFcpxTvvvcmlyytMyj6TYkhn3qNWr5GOc9Zu71EVpx+AjNZ0d3dJkxHpC96s5zwhfJBNXJfsIa56/zx3UGnMXg884bqIHHUMnkQ2QoSSDMdjkiTl327/AIViMHjU200gpE+g4MbSPBGG+3t9+pOT1Yv+VLtscVZGnpRIGSDlBeWxhAIvoigtm3dvIayhyKfHqJOpbcnZJ0jPD4ibLayFJM0xFgbdIWIwpkpedoOzM4xn6QDyCXY2bE8tJ7Kk4NN/+Duk56O1C6yvvfMBrdffw5MRojJTxZx11i6jMcovuXTjKq35Dp5SKKUoJ8NDPLpESUU7jAik4u//8v/HD/+/xXRcEohmB7lwiYWVFb7/x39CvdHgz/7JP0FgWb/1PQY7O8SNFlGjxRc//zF7n/6S6qHgaMp82sK9lALpEzfbzK1cQpcF/bWbGK3ZvfNrpJAko/7D50N6ENQdIVw+0lxHZ2AeJ2qlkIR+BFKihTy3OMoKS+VXCCUwPQNdnEj+ucID5nDMWR8nM3z0LTWIL7vzkqyBOWEMierIS+8gpI8qBkhbIgMNnnGGlQ9hJg0XYKZ9AKbqumzUo9C3sGWByVLAoKcku2vedEbMLlrMQUGCE+U89iiZBGx2RjF5lcFD6h17xg0chymjGTRgfhmv1qbZauIJSW9tnWzYc8/gV4CqqhgOH973o5YezWaTazeuu2ZTRpOMBnQHXfp7e6zd7TPsn4+aXlvoFw/navpduDuAono6Mn0WSr/IdsK1hsfq1YgiN2zcTymLQ190qhiebPYwX+aIOEC2GlRFgQmNew46uCFgRjp7h14R+00qpZS0Li8QtxpcfvsKNy5fJTCSUCvM8iq//cG3KYsC5Xsopfi97/0Ofhiys7tDr99n4/5d7tz8lGZc47VLV8iLko8/v8skPf319zyPt771NouXF9nc2mB3b4esnzHZmsahh4UOAqjhhpeUI4e15wkzAJscymnPcoZngBRQj8FXhi++uMuXtzZInmDneOIxjUvSn+6BBzqoQB6RyI2AhenvCQfj5qwcc+aS9oKSCyfpX4zWD1l2vtgks2VtfYuf/PwjNta+JBntoh+10ToEk4yxeebU6tp+c0n0GfF1tnzdfu7oxahPeHq0mnWuXF7GGE1pltFak6YJRVGytbXjkjTPGzNtzyGURUWaaKrSuNytFNjA2UtWpXZEutYnWruEYcCNG5dRSrGxsUOvNzq2oWUcx7z7zrep1xpEUYyU8qBH1HOGp6AeO5EOUmIRJElFWT7bA1trL3L9d/8MqTyyrTskkyHlI/HZqLfLrTRBCkMYFASeqyartD222eJpsLO9zo9/+G+5e/dTpFeysLTIm996H98PePDF52zcuUM5eVj8pwRE06Armw5bxyEtKm5tdFFqwJebfYRUjEajpz7eU+HwvPfosX2dxtcXdC4/bwghqNdrNNpt2u0WjUaDQVkyHAwZj8dPvP/TLGN7e5vKmEd4CtDZGF2khLUG9aXLXHvjHf7kH/0JAF988TlJkjA/P0ejXmOju8dgMiaQMBnskXXa2KpAFyn97ja93i5xLaIeLJEOPPKJpLIFlXm6sfsZR7ln90eSUqCkQHkKb+pNWBUlk3HC9k4ffQ6WF088Bs81bSCW7hUKjGew6vF9zyaHdrvF0vIC3tiiRyXar1B+idbmzFY0xlry1LEMunrZw4wzQDgyHcMjmd1n2+aB4NBCWYKebvxQwCCVwvN98ATGc8rqsqyoTMlgPJh65B58TAqJEIKirPCkIPAUceCjHk17H3E4AoEQwlkzTCVI5oTGC88MIZxa3BryydSmZIYzmRQ/DCklXhA4xWPuSlvLYsqaPXTfPnoxvy6z4RS6cq9HYLRh1Nthn0ERkjJNscbuKzu09LFT32f3rEt8pYgOlZOLx86fcKJGo9ne3GDc39v/i+osEEwKV96UptRrNZYWF4iiEGEM9VaHMAiJgpDduXn8IHJ+zPvXa7av2cpIOrW47xHW6xSJpcjGVHlGdlx/HyFc8kbqqSf64cD8+KZQgpnaVTz0r85PUDjh//74f4p7SDgi3ViNMMIpAZ8uZ/QMmBoiqxoCD2tTXEfwqc3K7LyIALy6ex7Fo2OIOMReCoQXoZpzCBUgE5A6x8oES+HOu/Cm2z2sSJ+Rzgfn3lQFJh27Z7VwJ8bydA2GgYcDXcVBS/ejzrd+Cg7cXlRDsSmRrkJkVENFMZ6n8LCYYkKV9jHVV1OS7FRtJ8cAQsipmsJDyBCvLIhqAXHq43kSKWc+xQfPlbVP12emfGQx8xROZgfHfeiIzsFZ8yEcVEqd/X5RSqGURGuN1gbPF8R1D6n0VE12eP7EJf6LgmyikTZGRQGmKrHKOpI5xD0Hsy8sD/2UhzanBF7Tx+9EqMj5z3pIAhS1KGJxYY4iLzDGoDzF5StXqLebRI2YaK9OlU/objboNJqsLC+S5SX3NvewQlBVFUYbtDFHxxlC4HseQRQytzDP8uoKhc0pyfGtjxm5/iqFLh6+UNOexpwc9lwIpCdBimkfGAvVdGh9RnjSES/DcUpWPKMgoLLo3jTR3cDdD4fP13R6IMSd18N5Qn3oPad5OL5CJftJh/jiWvs9jkmSsru7x3A4RJcF5iSLM60d8fhNx1MQXBc59j9veJ4iCn2M9fCsh9alU0OKw+rj5wuL4xHEvrgHKm0oyqlrC26NbeXM2VwcVIGe8LxKKYmiEKUUxhjy3ClBZ+r0wx9VUtGoN6nXm9N4Q39l11kI1/9C7Febi2lM9MgRnfFmVJ5P1OwghGRiDHmZP8ZRVWUxVcAKGg0PK6cVbM9YxpfnGXmeIT3Bg7u3SJMRrU6bMIoZDfpkkwn2kWajgocrEh7F7JxY6yoZkrwESkieU5Z8pkh/ha8NfM8nikJ835+q0MU0ttaOBxPi2BjBGENe5C5Pffg9Qrj+mkajlCJutKg3W7RaTfTUMryqXE8Cf+oU0qjV8D2FqVyFsa6ckr0qS4wu3ZpPhZSeRykUwj59UPv0RLoXOr8oXcJTLjp9X7G63KIWh7x1aZm5Zp2d7R4/+eGHbA8nz5S9Oy2CKOT6d96k1q5T1TO0X5JmIzY21sj6D/s9+77PpStXaTQbXL16jUvLq7zx1mtErZBPP/uMf/vf/4BkmD2xKdSjMNrQ601QUk6bFL7CU8HHqX1mVthSQlwDX7kEiRUwSmCS8dq7b/O7f/qP2dve4kc/+AFZkmCsI/b0Ixn3diPm0mIbpeAv/+aHaG3o7nXJ8pxxevK97+YJiUXSiEJCzyMtS25vb1JUxfnausxgNFQp+xHVecMaKNOHyeTpAkT5AZ4f7ROBxmiqLDlW5fP1wkxvM41crGDr3k3Ggy5zi3P88V/8IZUuGY0GZJMxdz/9lGwyZuvel/S2H+xvJU8mhwhkg65y9rbuORVEVoHXcOSrlJgCio11emXJxz/5GYsry6y0G7TDRa6sLLOyMO8CZQvDt9/h3X/8Fwx3trj/0U8pp6rkh2AqsAarS4yd2g496R41FRSpi9jqC4CGpA/6eNbNVAVJdwMQ6HIatPkheD7tdof5+Xkmk5Sd7S6mKqF6ROn+KHwccVEzmL0chrgmmzNiK5pemgvLUwpHjqsGMmxRu/LbyKBOMtimyiZurpTKmVOmEyeFFPJIdllGDWTUwFgfQ0ht5TrXfuv7COmxd/dLivGAZP03jIdbVDJAdV7H5ikmGXAgb3ykuStAVZxvY7RZEJxwQKDPeru+yI+73wH/ClH7NTorl/CDAGVLbD6h3PuIfOcWJut+1Ud5LO59fot//f/5V0TNGu1rC5R5zr1P18mGIwK/5NIlRa3WIo4b+58ZjzPu3dujOqnh9gXj8JrpPGelKAq4vLqAEIL1zT3SJ8zJhyGl5NvfeZtr11a5dfcen395CwJBKSTlUYZTc0ADTNtSYiHNkBu7WAEmlK5CpCyhYQ+qM2a56xmJ6nq3Ya0lzXOqyZi/+8UP+fT2Td6/9ja/9+Z3UbHi29/+NkWWsbG2hrWWq5dWmVta5PLlq5SVZvI732f0Z39OGAR0mnPoquJ/sr3BeDziZz/7GRsb69xf22Z7p/fY927U63zv+99nYXGR9773LRYvLWLNH4I1TEZjBr0ud27d47/9r/81yXiqTrc4T+jZRZzFWs8hZPVrPpe+d4WgFbL16QajreHB+X0RYXHHp3mYTK9Nf59NvbNE78wB7PDrSWjgqoHO8plTYJbDPW7a/zpxH/29NfJ0TJ5P15tKQDyVcKZPkHC+whMxG/K+Tu5F6WRCb2eXtMjpT8b7TUe1NhTFV2QJZwyDaeV+ZQwG2ElhVLhi7AyQQuALgZXTecriKkZPiKuLouD+/Q2UFFhdUI8V9TgmDENGk5T+8KCCvqwqdrZ3GfgjhuMueZEyGPYv9HsfB61hnJoDohgei32EDypyyVh9yhZNyWDAnZ/+HLDk3c8w+YgyPVq1rbVlMnEjpT5HL7zRcMgv/uEfCOOYX3/0Ecr3uPXZTWyRO7KRAxc5a50S3SVaHt5OHEfEcURRFIzH3xwnhFe4OAghmJub4/Llq4RRuN9ctNlsopQiSRLyLCeZTNBVhXkkxvZ8j3qzTqk1k+EEayxhrY5UHkWaUZUlVy5f5YPf/QNa7SZ/8zd/S5omfPbpZ2RpSuhJPCVYnJvn+h9eZZiW9CclUmtuf3GLLElYXpijHgiy3S3KZIy2BbmePBNH9fREulSuidwz7FwpSbsV027WWF5ZZr7ZZu3eHvdurzEtSr9QCCHwAp/Fq6u0lzsU0YDKy3hwd8Jwe3DQG28KqRSduTk6c3PMdeZpt9qsXlth5eoyvd6I0TgjnaRnJkethTT5BnZtOW/MSqcLXOQghAsYAt/ZTyAgL2GSMb+8zAd/8Hvc+/xzfvLf/y3mGOsXgCj0WZprMpxkfPL57Yd8m04Dl/0X+F5AFIb0kxH95CJLpqwjNy9q29Y6G48jVNlCeqggOvh/XVGJ9OIf5hcGD4+H4/42436PztwHvPbudcrKsNcbMur1WP/yc1KdM+o9IRlj9NRORTgSXYZu/JXKlSslA1IkG3fvURUlVZrhYYmaDZSnqMqSqqqYW1pm5Y1v4YUx65/+ipIj1Hd2qmQ2TvV3qqHMWkeai6ldhjCQjU5cbVujKZNH7DOUh/ADomaL+eVVRG/Ibs81hkQnJ881Hvu2A3ZSuUhSc+DdfW51xUctCadyU+E7Ij1oEy5dRcZtclGjGk/cOOQFjkQ3ffedjuk+J/0IFbfAhhhbJ2itsHj5GkjFpNfHaEtaaPLhCNlaQjbmwQ6nKpLZ4HfEubLVlBiQ0xPyjMvb2b1x2M7lZXjOZQ3CJbxogUarhScVYjxAVyl6so4e3f2qj/BE9Ha79Ha71OYbrHAdXWq2NwdUScalS4pWS9LpxDSb7f3quL29MQ8e9PiqMxwXcXv4nmJ+rokQgp3d/lGj2rGQUrB6aYn33nuDcT7my/u3QUGFQE/n7X3MrE3mwMaunoOyQue5kzV3wmlCrwLfPmwpdVhtPCMqceSDznO+fHCbLx/cJpIe377+Fo2gxvKlFfI0Y9TrUlUV7VaT+bk5vCBG+RFUBaIqkELheaEbU9Mek/GQybiLoKDXHx1JpAdhyGtvvMHlK5d54/U3mV9eoN1o0ohrTLIhvfEe9Uadf/eX/47k8EU7bD9yIR3ij4YMFO3X5qgv1+jv9Bh1h+dK4l/I4c+SiocrEoLp77Nc58w7/bDf9mkPJgSaR3z+GfEkIp3z2c0LgTQZkh6OQ4SAYKpMy74u6YKvDrMinJddhX4YZVGQjMeMkoTdQe+FqMAwQPaoxUgJk/JgaPAAb1qpZqU/JdJnA9HRqCpNvz90mrRAEXiSei2kXqtRPlI5Z7RmMhqTypTt7gZp9tw9FQ+OxYLLaRw/KArlllJWgj5lYWaZpnTv33drpGwDzPHRhiuIP/94K88yHty75yqH79911jV54aplpvfizErJAKk5+qv5vke9HiMEJEn2XISrr/D1hhCCWq1Gp9NBaz1tAmoIwxBjDHEcO2eHLHeVhY9Q6VIq/DCEqkKIxFEefoAXBOhSU5WadmeO115/nTRN+OKLLxiPR9y+dZsiz2lEAWHgsXrpMm/euMHGbp9C98AYujs7VFVJox4TSMPeYBeNxdgKbZ8tkHx6Il2XU0JNuxEJe2ZSXUlJo1Gj1arTaNZotGoszIesLvvspYbx6KA0KIwD3njvGlE9ZJgOyYuMweaE0U56sDiZjZkesCDdz67hqJXV0vICf/DH36c512bpjWsE9ZC+2SY1I5Qx+J5hIBK27/VRnqTRiKjHEYsNj4WGx3e+9Tbvvf8+9VadRrPOJ/OfO/sPT+I1JWjQSYU9pGjwIoFXk+jcUk5eDVq2AjNiv1z6JAgFKlKOs8u0e38gnf95vQZxhNeICeca6MGE/LM1N7EME/A9aNQhDGhduUzz9Zi5G9dc2ddMMjEj3o7AeJJzZ32PvKyo9Nmvm0VjrGGcZ+S6InuW+vivAK12h7mFRfI8ZzgaoqvqkGL6YZiqoMxmPc9xfpnfCDX6cXA31c7GfX7+t3+L8ANsEJOnKWV51vvAgsmnRPfU/sO4VXSVJfTv3UMmKetf3kFWmtWrl2nNdbDSwyhBo9nh2o3X8Izlk3gRcjFVej/OLhdJSn9jy5U7P7HCZsoIGA3JNDA/RWf6x1AVWGtIJ2P2egOyrMQPI8Igpj63ijCafu8eRX4oCeXhyIkmrq9FCLSnf9sDBrjxf6YOfEqoqE7ztQ/wak2CwEcqhdElxlSAAuthrcLYCBXWaV27jgpifN8jTxLXhMlTJIMBI6nBhK5CplSQqEPHJlhZXWXl9ffIRY0JbfzWAjkKjKS5sEQchcjB6yS+R6ktZVlidc6BNPyYFYEfgh/TaXe4tHqJNBmxdvszyuL4RI7yA8JafVpyl7rnvtIPsyxPsY704wa1uSWsdaonoyvKcQ9bnc/YKJRHWGuCkBTJ0FU1zBDXYH4Zf26JTnMeU+RsbX5CNtihSF+eXg9FWtC7s43RFl1UWAvDoSHPLcZOMMDi8irXrr9O8GCH3/xm43SNFC8KCuchbjnwhj4H5EXJ2vouQgjyY/rpREFAu1Gn0pr+aPxQOXYcBDTrdULf2WsVpWbQS9CVRYQG3xM0wxjf84jnfIKmR88k7OxOYGbnYq17Lmal/QL3KB7mKcrp955BWPRmihEHxyxvVNTDiEBIp8ApcqJahNYVw36XqiqYX75Cez7eH9eZEf5S4ochKvdZ29jj05t36faOTtYZY0jHCekoIRQB7ahJKH2ktmyvb/GLj3/KrZt3HldYHvZGFkDHg4XANXXezp0X0MnczJngNwOab88TtSP8ZnCg9M44l+oiayEtQJYHzUfPDbNzpTloXRHg7oOZIn02XJ+CCJ+5CD4Ues3iV49zSyzMhnbfd2XYVfV0tlAvMoTyEUqxtLTA4tIi/W6X9fsPnGVQMr2xvmbf+XljljtSYqphsC9Prv0kpHnOnh1QVOULQaIfh1l/a5gWvFjn0W3cInp6MY4eqKVU+EGA5yka9RpKSqQtEBh8f9qD5xEhhtYVg1EXIcRTrG0uFkIIomYHzw/JkxFFOnG6kgn4YUDrWgejDcPtPrqoCHDnruZD7MGkgl6JW38VWzjLyicPtgIXEgS+G7+zAp6CQjga1mKLCqRAeT4yCNHTRowGJ9XxlGK+XsMCo0nykJ97rTnH4uVraA1LVyGdjFi/d/PE9cArvMKT4HkeQRA49Xmek04mdLu7mMoR6r7nEwgfXVUIJRFSYHSFrkqENY4bqfRUyGcpsgRdlSwuL9Nodai3Wmxtb5GlCf3eHkWeEwQeAkOv10WXBZ7vsbezRa4VmVaoIMIvLUWes72+Tp5MSLtdysmI6hz4uGcj0nU59Wid1quekTCTUlCvRzRbdRrNmFojYm4uZGUxoOyVrI/1flAXRgHvfPd1Okst1roPGE6G6Mo4Ih0OvCf19FstC4gEpNa9HsHC0gJ/8e//T5lbnMd4ARrL5jhmmPeQokIGFWq0y44Y4HmSTqtGox6zUFcsNjzef/ctvv/7v7+/vaW5JeftqwRe6GG1xeT6YSI9lkQdRTk2lMkxacJvEjSYUyathRJ4dYU1FlNMfT8DCaGChQbMt/E7c9SXVik2dii+3MSWFYwyN5OFoSPSL62yeuUa7SuXnVrPtTt3988xDXTG6ZMtXE7CrMloUmRQSF62wthmu8O1G6+zt7vD7tbGiR6+/3/2/uxJ8iy/7sQ+9/5W391jz32pvXpDL2g00ewBQQAkJc2DSI3ZyIZaZkzPI+k/0JvM+CDTg/QwMslsRmYcjSQbDkGQFAgQhgYINNB7d1XXmpmVe8Yevv/2u+jhukdEZkZkRm61dR0zr6jI8J/7b73Lued7jtEKc4RS/dcXjujub63T31onbLbonr+ANQZVPcWs15Q4JmEON5NWRcb47l2Ypmxcv41nBb1uF6/XRQuJ9ASNVpvTZy+gsgo/XgJfzwbUD1+vKs8ZbW5zsgS3GSNgDGTP4LmtKlAVeZIwCCdYKwjCiNCvs9S7gLCGNO3fT6QHOJXonEgPcWSdwpEtIw5sR56hvfWiBp1XvkVt8RS1Zp0gDFBVhqoKsD7YEKstqjR4fkBrYQXp+XihI9J938OTgn7oMS1yrC7dYkiBqy6YQQhYXl3j9Te/RCJb9OUClQhJrY8AWr1FaDbRowtIP2DS36YY7M0kNdmjDzIIod6kvXaKV7/6ZYa722zdvf7IgbMfhtTaXbSuKBONVZW7zg+WvD/huQ3iBu21ixhrSacJqszReYJ+TkS6lB5xq4cQHrrM7yfSoxr0lgm6i3RaXfLxkOHWJsneOuSfHSJdZSXD27v3/dtkYplMLNJPkUHJqXNnufDyy2gbzhLrP0H4uOf0QYuQZ0RZKja2Hm3FE4UBy90ueVUySdN9Il0giMKQZq1GFAQAVKVmNMqcMjeCQEp6rRr1MKbXrdNsRFwf7bLTnzhrjR7uoLSZjSGsa5YfJDYVBwH2NbeNHuTuXMwgx5p6FCE1VEmGUoq4HqG0x2Q0YDoeUmt06C4sY6WHxZs5d1mkEHhhiOcHbG73ufrRXY6D1YY8ScmmKZEIaEdNwICx7Gxu87Mf/5Sdzd2H+6j5EH8+3u748FodRpVbRM3sc7Vb8VshC19bIWrH+GHg2LiK5xZ0anHkxgvBnDWcjyvn3uhz68EnJFTmRPq+29qcx5oLQeaLN884r7B2VszpSzxPYK353BHp0veRfsjKqdO89vor3Lp+nc17686WM/91Fn48H+zHQ8ymV5rn6yr3SSIrC7LPANk4rwbYTxmaEenWMhNCPmJbzyOMYuIoYmFxgcCTqGKK1dV+HslDRLrRjJOHq58+DRBCUmv1iBttjNGUeYpVFq2cNdzC2SV0pUmHU0yp9tc8FwLoxW6NeFiBtSVU2yf/XtwzUIucZ7vSz5FIB6xSIAR+FONHEaW1+0S6wfn5d1tNBJAXxf1EerPD4qmLeH5MELYY7G6yvXHrCyL9CzwTPM+fLbY5kdQ0Tdjr7+FJj167h+d5NMI6woIf+niehypzyiIjSxMGg10qbTDWcaRlniNlxeLrK5y//IqzrdrdocgyxsM+WEsUBEjg7nDAZDQkyxPu3b1Fo7lIs7OCH9cJjSDPc3Y3N8nTKXbcxxYZ+hMl0uewM8PHJ+giW606F8+v0WzWOLW2TLMeYW1Jlg2xviDqdPDzhMOEUaUUd7Y26BdD+tM+WZGR5sVBiM+8Yj2aHdXIwtTeN0kB8DwnUPYoGe/tgKnQkQuEUDrD05ZymDG5O0BNU+odQSA8Al8S+R6LCwssLy0y2tvj6nvv4wUBMghpNtr8p//4f87O3i6/+vA9kiRhYiZUhxVSQlFOFKr4NRuozWv7njDp/TBqcZ1Tp06jjWG93KLIC7eAYz0WF1fpXb5Aq7fAwtop+n6Nd71fUHoVslVHhCGtlRWidpNL585x6dJlVJLy1t/8kK1761RZ8ch963bbnFpbIcty7t7bfGwY3PE4PKt6ekjpvF09P0BIQVVWD/m6P09kacL29ibT8fhzN6F6MRB4UQPhhS7gUZWunbQKU1XkQ1cO+kwLDjIAGToVuJkb8ZZUVcKdO7fIqorO0gJ+FGL8AO0FpEWFsgLrh0TdBWJdUe7uYcqjWIl53fmJvF04YKof7AseLBd6PHRZUE6HWCuwRoBfkIQdBAL9oNJ9bgleckDOqdmuz39/lkfOj6G2gGiv4Nc6BHGL9kKXWj2iyBKKIidNSkbDHKxAWoFvDKpK8YxEmAxPlDTjGo1aSCRahN4KYBBSU0332N59i/klsNbS317nxnu/pAy7ZLU1onaX1QsvE4YhzbCJtHU6XCRdbrN1p86O71OMFGm27pREsfPGLrMMa+b+AtZ5pBcJSX+b2x9dIZ2M0erRrJdWFUUycROwclZ18Ng2QDjbN5hVJjzc36kyJ9nbAemjZ0xQVGtjPJ+qSO8j1L2oTtRadGJUVWGNcn7wR+y75wdEcQMhPao8nT1n97+v3qjTWl1iodem5lmCWsj5yy+RLjTZfv8qWbnzmOP79CPPDMOB4vadXYJfvsP21h6VUG7h6WOw4gAOZvJzAjGYvQDaHCi0X6CtbLNep9loEIe+Ezo88HeLm4yasqTmCZZbdZTVlBi0seRphbKWiS0ofENZKUZRwCjLYIojRHPcrLtrZhLAGWOU4dyb5k1gdcSxPthtW4G1viuLrTSqUuR5jrWGbq9HHNeIowjmHyuEy3qeMasWH2RIb6HHqVMrjMcTkuThxaEwCrl48SLnz52n02nje54juqzGlz41v07kxTzS/ulwkz63LTmswn4K1bisC2TjIPTJX/Dx6iFeHGBDH60MNhAHXvOf8HDEb0LQduuY5Zj9dWfhCRqLMX4sSScFZTZXOeP6qqfcbykFrVaDIAiYiJRsUtwfZDsfaz8jrHUev+7/Pz9jPul5zhJBCCQWU5aU0ykqPwF5dJiZ/DWbwj0NLG69/Rmnfl/gKTG/Rf2Z3swAStuDhbhHwBpNVbp5y2g4xJMSXeVYq5GzwMDsJM/MJwQ/CKm1WgghsQiEkPhhDSu8WTaRxQslfixp9GKWlrqosmKv7u1rREtzKObkKW/e/e5Rerim5wU8CTPyHMA8wE0obZikKSAw1hL4HmfOX6S3sMTZSy9z7vIr7O6NuX59g/Fg94nz/b7AZxDigZ/PsUzIWstwOGR9fZ00TZlOpySTCVmSOeGqdlUSsR/hCQ9lFFJKwsCj024T+B5VlZOXFYlKwDpHhDCOydKUuzdvIsMQL3RWhm6/Dx2INVijKbIMYQ1VacjzAhnE+IMhxhgkmjDwSI1CqQL9qIDxE+LZiXTskYrGR2Flucs//INv02zGeFKA1UwH24xGU2woaaydIsx2QIz3W/y8yPnV1SuIWKCtdpYRiT0wglKzo+m4XWLLHPgPHkLoQ7sJvsjYvH2D8aCBrksIJHGjhh8EZOsjtt65hzWWzopHYD0iK6hHIefOnOHU6gobt25z98ZdglaHsNnmwoUL/LP/wz/jyo1r/LP/6/+J9c0N1oNNskOl4snuiOnu8NdvRDEPWprzfU+BVrPDV17/OmWlGAx/QsEIAGElF8+9xJvf+TssLC5x5swZrtV+zrV/+cdUVYV3ehm/1eDU2fMsdHt866tf5Wuvv8Ff/emf8Yf//L+jLArUYzzPT59a5e/9znfY3Nphd7fP9KmJ9GcffQsBQSDxPI96q4HnBYyGoxdKpA8HfSaTsSsLfA6NzuceQhI0lvFrHYpk4Eg/U4LWqLJgvLHu3vcsE1Sv7mbwKoFywKx4ljzX/OqtXxJfv0mj0wIEstnCa7UoKyiNwIY1WqfPYsOA4eQ2pjzKr39eHHgSuO8+wGEdjDf7fZ6o9niobOoCOmeo/DrWRgjhUT5YMqpwhFWAI7bm7UyF8yh+VpIubMHyG4juMmFzlVpzgZXTp+ksNJmMx6RJQnZnm53hAE9KGrWYAE0tH2KlReqMAMVis8bKYp1qKSY/30P4AV69zmR3gx++/+eUw4OvvHPtfe5dv4LXWsZfusDZixf5zlfP0e7V6C0v4QcBk/MLFGnJh2+/w7Uopn+3Itv5AC+IaC6cAgSj7U1UccjXpsygzOknA4Z3b2Cxj7XtUUXBdL5if9L7VXrOKx+gGD/gReBQJgmD7CYyrBEunsbzA+qtZTyjGA/WyQ4R6WGjR/fS19DAOB2hy4zq3lWsGj30uUFYo7t4GqVK+jt3ZxUD9+/3Qq/DpVcuUY9rtAODCGN6v/Ud8umAH27/kGxw42TH+SnGeKSZjDXb/Zv86tptrLUooV2GyHO0VXkk5olXdaDLARHF7N8ssA48fBmfGxZ6XS6dO0tVlCTJ9Ih3WExRopOMtu9xeblDWimGZUmeabJUU1WGnbGzKxNiTgwdklfOlOusmQPfah9Hou9ysJgwH6eCI5mPeJys9TA6RKmSaVFRFTnTcYInoXfxMovLy4SNJliLQCClcNypdeSAJUR4Nc6ePcPrr7/EtWs3jyTS67U63/j613n11VepN+sEQUClLEprQj+kE3eZhilSHMPKHvblZnaM8ew467O/7fLERLq34BGcPpiWRAshYbuGX48wsUQrjY3lwYLQJ1z8Fi9C62XI+1Bdm1X7G/B8yfIrbeoLEetv9ykHU3fNpzxbZZQnOX16iXa7yY2b62Sbh/roGq4qSx639ZNBKYPWRzbfn00IgRcECCnxkEgsKktJ+n2KZPr4BYN5eza3bPoCx2J+JhVf5LV+UpiPuGs+dGLIKujn9kSORVprdJaQZzCdHG0P9mleYItqNVbPuqpNZSXGCiplZ5Vork8L6h6NlYjF1RYXL65RZDn3PgooFZRTKGYFVql9+sfdAlYIfD/A9wVSPqPH5DGo8pwqf1gQVSnFznDs6pZ9QRiHfPPbv8WXv/ZNllZWWVxd44c/+jn/7k/+inTafy42F1/gU47DmS3wJFPzx8Jay/r6OqPJGK01SimX8zMcO5KdIb7ns7KwRBzFswoZy9rqMsurq6TJFDBM05RBkiOE4NS5c7TaHW5fv8X1D69S7y7QWlwijpxd40GFDDjbW0UyGZNOBbA3y4nyQcSEcY2V82eoxT5jU5KWzyfH4TkQ6Y9HGEiCwKPdbrG4uMjZMyssLS8ShQFFnmA0tDttTDOm2h2RltP9kKw5rAGVKIQSGMwBd3P4bYfVHnMl4oOY/a3MSrbXt6k1a/i9Gn4c0G62aTc6tGpNGlGNKAhp1uuEMqLjd+m2Opy9cIHlpSXG05K80Pj1BsHMQD/JUipV0e11SIuUjbsbTu08g67Urx+J/pxgjaEqK+cHrJSL5fZ8hPCox3UWWl18YxlsbjMZDDECrARblZjcIx8MmZaadDiiSFPKLKcsykeS6I1GnXq9RqvVdFpbIYjigEoFaO08nIw9YSDjc8DcicYVcjsFmhCCh3V2BxtIz5Xzm0NBJE8Ka4zzRf8UD5w+FRACGYR4QcTpc2doL6wy3N1gNAhQZUaRehitUHn+1NdC+jHCCzAycr6497E0zo++yhOEgK07N4l9gazX8ep1tIypvBbTyZRGp+sWMOtdqqIAlc+U7Q99o1O+w8xW5rhG9bjjsY/5+3GbHbzfGIUqpyDkTGF9BAyOUJ8HuT3Dot39n6ugmELRAAxCSDwpCKQgDnyIQ7rtJmurPYx2JfCBhDgShB6IIEZaS7sZUY89kAFtKZF+gFdr0LAdzpw9Rw3NYG+HPEvd82YMkS/pdVu0WnV8T+AJi4/GR4Iq0WVOmU5JJ0PK3JkvW2NQZQ5C4kcR0vfxA4H0xH6IXFUW5EnqBh0nwUP36uyDPM/Vq2rtXoffv3+djrvuBmsqjPIwxRSUj5plSZgHrrH0JEHkrDcavRZoha576HTCYHuDZDS4771+HEBpEBwtvVJakRc5oe87Vx2rKfIJeTZGf45sqawFXRl0duiZ/TilgfMCLMXMsJODsMV5tcgLJvTLsmQynVJVFVmakZfF/VVVFoqiYJpMyfJsNsZQFLmmrPR+EJK7jY7p6+dj0XnG79yzunrgPfaB3wEZOjvAWq1OGEa0uh2iqIZE4HsBxlP73y89D9/38YRAWGe8LWfPoqMHxL6NnBASKeVD4+g5qqpi/d494ijiwqWLRFHkxhEW8qxgd3eP0Wh0/7k6XGy0b5FrQWuk7xGudEFZVFlhlMZkpatgqXgs4e3U9VCLIpqtBlY65yuvEZOVKYUoobKYSlOpkvu6vvl99jGi3gkI6x5BRyM8hV+XNE4H6MyS75RYaSnSChEIZCiIFwNUZlDp09/wznJF0mw26fa6ROv9h++pA57omTG/14+658UsC8Daz5ZfxzzbRwiQCIosYzAcusWmx8p0+UJavQ83t/DDCKylKlzVjPQjpPQwRrtsJGPA6i9O2QtCFEV0Om2EEG7YZQyj0YjqkCWXtlBqqMzTTTs+i/M+ISV+EBCEMY2ogbGwt7NDmR7YBxplUbkmGxfsbuxRFSVlrjBqVkCMU6WnlTt/hyEFxIGPFIK8UqhD/aRg5hYrBF7g40mB0RZlP5msCWvdKEZaMNoyGg3Z3tokzVL6/T027tykzEboMn02cdcTIZg5CnwKVsN/3fBgwfhJEInZmPbRq0rWOh9y6UmMNuhZZYs5VAajjSaf2QfVajWiICIMQ6SUaGNI85ysKGfcmiVPU6SQaGPxwpAwiojjmGBmVam1IkszqrJwHOuhfZmP2YWQSM+NieU+Y/b87vWPhUjvdWMWejHf/MY3+P0/+H3iKKRWi6jKgju3r1NVcP7lN2h3mvzsp2+xN/jw4Q9RYHcsVtoDkeODg+cKmM+pjxlYWw02h+HmiB8PfkoYh6y+fIrWQoc3L3+J1869zvjuNsO7G5xeWeXNl1+lUW9yauUUzWaH1770GzRbXe5tDhiOE4SQCClJi4wf/+LHTPMpF185R6MT8+6P3mJ496BM/LiAxs8FntzB4YlQFAXbm5tUSqGmU8hyRLOF9CNWOou8tnaOa1ev8rc//T57G+uUUkMAam+A7g+5c2Md30qWvZDFeoO97Z3HDhDOnz/DK69cJggCxpOEoqxYWGhRq/kkk4SqUuSFRn0MkgshHG8FuM5YPP47pSeJWk2EgHwyxTxGeX8crLUuuPULPBLC84l6S9TbXX7vf/R7vPnml3jv/Xe5eu0Kw0GfrY175JMpo/W7D5XgnewLJEF7Bb/WpcxzqnzOTtXZl2YbRTXdRiWSH//ZLr8IwtkKtCVYPEd48au02gu89PLr6PICg60dMtuG8S3I9x7+ThlAuOL+v9wGc5w57XFt27OzZVaXFNO7gHiYSJ+HrZXADgeE+vMi6YopbH0I1QSq7wKWAE2IIqiHtGs+i60Gr148xXA04fqNOwirWO361ALJQrtOPQwIPIkvJY16jV676QjusEa2VqPzj/5j+lub/NW//zfcvHbQ9y0tL/CV3/wKre4CQRAgrMUrMzyVk+5uMtwbsfnRe9x+9+eYcrhPok927+AFEe21i4S1Ju3FZeJGAyGdLdTe5iZ3rl51lj7FkCfNNtnvgBsNiENIEjis9rUa5lUOx362W/W2qqIcuEWA+ZKzfaDqxfMDGs0W3YUF3vzK69TqMWiFKgv+w7/8f/Huj/7y4L2hR70bUxYgNo+WZk6nU9Y37qGXlziztoAqcu7dfofJYIsseYHy6E8CR5GYH9dcaZ5NMCeZY2B59v3bHDynLxA7/QGDkVPDzF/qUF9mrWVv2OfOvZDNrU32+n2SytJPLdq4JWvpz9aFHnXeNE5ZL3FKdIk75uDQex6YtAgPogWfoOFz/sJFVlbP8PKXX2Opt0KeJugkJxeSiZQu5M0PCIMQTxikzrHCx4oAi0QJxygLo1Fa7S8AHIfhcMgf/uH/wOryKv+r//w/Z2lhCWUVQntsb+zywx/+hDzPKecKtbkH99zBy8MtilgNWUnU6bD6xmUQksH2HmWaUwZb6P4U+sDw0dfJl258c6q3yPnzZ6l8SxZapibnZn+dUpezihqNnlQHvuDzRYyP0WVAeoIzb3RYe6nJMB3RT4c0VmOWv7JIPlTc/dttyoli6/YQT0g652qsXewwupUxuPL0Cih323qcPnuOs+dPs77e59at9YM3zN3Ynpu67HgS3Q/d1FFVM4HFZ2FqYy2qLF3woO8RSsHezjYb29voeQjjo/AcslY+FxAChEdYa9JbPo3WFf3N2+hKETcW8cPGLD8mR1cZpkq+OGfPCXMx1fzZXFlZ5lvf+ia+76O1JcsyfvKTn7C7e5CdklaQqxc2Nf9UwpOSOIppNNusnb0ECPp3rzO8e31/DlEmCpVrkq0ttj8a7JOA5lDc37iEafnwuQs8j3O9JqHvcac/YZgdsIuBgLoHYRjQ6naw1jIYjihKRfUJWqO60OiSX/7iZ1z96KoTE+Y5RVFSJE7Ac2JxzbNCttzLjMAMP57v/AIOh3NuTmIFJ4AlCV0PtjXsPPoeSSZTsiQ5JD65fyBhjGEwdsr0N5Ze5+ypM/i+m6slec6tjW3Kqtofp9+5ddM5MLSX6K2epre4wNLKMqqqyNOULM24d+MKRZ4dmzvnBS7ryxH2YmaF+PzwsRDpcRzRabdYXOxxam0ZozXjyZiyyJGewMenUpo0K0jTgmlSUJRHEE1zcuTwxT9G6TOH70ukFGht0NriS0kcelhrSZKMUmuWtUVagUTgCUGzXmdlcYHFbo9Os0UcOwsaz4NavU691aSVaowIGE3GjMcjRpMRmzub5FXONJ+QJ9m+MufXHs+hB9dak6QZWmlnY2INYqbIttpgSkWV5STjCXmaYefJNjPyOPIiQi+gTDN2NreZjsdHzxIO7/Zs4l2WJWVZkiSpI7E/YRGOtWCNdX5mQhy9ICBAeB5hFCKEoEzSE811xHGf9wWeABbf9wginyAM8INg5mfvIzwf4QUIw/Hq6kd/9PwGmLE785ndoWtmNBZNlozJDpl6+oTEzS28WeMpvQAvrONFdYznH3lPCyHxwhiQaBVgzdwv6+O8R+zBuTrc7nPo/+fVSHMy4XlN7K0GlWOrjCpPKbKUIssoMg8hndeq70lqYYRWJY16gLCSRt0R6XHoEwRyVkXnrplRFViDMgJV5Bg9syp74LkLw4B2u0mz1aQWh0SBjzAaqw1FMiWZjMiTMVU+cRUFs5NitEJIOSOkDX4YEtUbbn8lBGF8xEmcr4Q++sQJIQmjhiuZDQO052GlfPhuOBE5P1MMzNKXjq1p0AqVJ6iihlYKjCWuNyGuE9WbeFEdqyqMrrBGo1Tp3ifEzA/z6H1x4VsVVZWRjndIRzto9en1/XxqPMOjKn13Go1yzU4QCAJfopSlrB44r7NbyPclnpTOGkKZA2Jvbrlkcc/qxyBE0lo/1vdz3nxU2pKXmtJlHj/ySfBmIXpzCA9kIGfpemAFVJ7zWX9Ucyl8gQwEUS2i2WwQ1yJ83ynP/SDEDyPiWh2BwfM8N+bBIubeKtZgBYCcEZquKikMA+r1Or5//xBfCOH6psDHk56bVMwa1aosSaYJSZKQpum+qlFIQdSMEVJQTgt0pQ9OnHsH0vOotZsum6DSVFFBPsqoEPuLnbqqKPP8foubg49wOgxl0LlB+069p7Ub11WmBF1CpZ2IwHcXQfgScostzcfSJXkSfA/8ELyagEqgrHOzsrVZeHQA+KALF5hlmO2vb531D7PgUCucHsKCqU5W2WitK9evqoogDGi1mpRlSVHM2J7jKnGfI4RwlkJO6TXTdonjB8Xz0mvzaRESzSb2EoFWiuJJ0v++GBrfB3tM1RdCIr0Ao7+winiemD97vu/jeQGtVpterwcIxuPxkXM3y9xeRyCkdEpp37uPP1NKfa78sQUCT0o8z72wbhxpDofZW6dKN0rPLBCPgPTB8/CsIbBusa18YLzq+x5R6KO0QR9qS3zPo9VoYKxlPEmRwsz67U+uETHGkiaJq8pMM1eZ+oyQvpsLmVmVPjMPfTceedSxftGYfiw4as78hNv7UYBXD1BRgZaunZCzz3zwEruK6oPfwzCk0WiAhUpVGGMo8gKlFdpotNFYpTEGiqKkVOo+sYuqXAZgN4ro9rrUGw0838casz+2qKrykSG5Qrh8GQToqsTOvNSfF144kS6E4PTps3zlSy/z0kuvsrS0ws0bH/FHf/gv8Dz49ne/RavR4m//9ifcvbvBzVt73Fsfkhfq6DIYCaILogF2AHbIwQ0yD7UCyF3p3vJyk2YzZG8vod/P6HXrfPnSEqOk4O1r22gC1tbOc+r0MqYs2Fm/w4WVJV7+/d+jrDRpVtHf2+Vnf/s3tNpdzl58jU5viaWlZTo9wf/n3/wP/Pf/9l8yGQzZWd9AV5oqr1CVYndr9+H9/7ziUWT53ILnGdrNNC24eXMTa82BSgpHhmzfW+f9n/+Srd3tg8Y7q2AW9BqEIb/9vd/mwqVLfPjeB/zb/+9/T5Ikjx043L17j8FgD2vFzL1AURQ5xjiCwH6MpVou/Ongd60Nk5GzQHrIH10KCCVhPWZ1dRUhBNU0RRXHD2rdBNt3762qL8j0p4DViqK/g82mvPfOW0yyCZsbm+xu7zAZT0gmOUZB2FnCqIpyvId9TNjj/V9gKCdbVMmeUw+YuV3PfHD24DW7/9/1aJfsyo+Qg7NsLJ9GBjFhrU5ncZkkuUNxhFguDCNWVk6B8Nm5V5CrCTDhY5UAzssxBODNiJS50nZOHByuiHmu83XH0Jf5mHsfvkewuYtOztNZ6FBvhMS1gF6nxepSj3YsefXcAp6EpV6M78FwZ4/BaIw0bre3S0WZFxilKfOKZDTk/R/9OeO9LUaD+ysC6vU6p0+t0V1Y5MLFMwhgsLFOOpmwfv0Kt2/dZbB5A8rdh5QkWlWMt27jhxGdToeg0wEhEZ6HsArygWML98PC67gONONRxvJhFPPKV36DZnuBO1vrDEZDKpm9yLxI0sEWd375fTbrLdZvX6PR7vLNb3yLXm+BqLPC4ktfId3bYLp1m2wy5d6Vq1gklRdDPYJ8Ogs9dUr6Rr3F6vIqtdhn0N8m7W+w8c5fMu1vUibDF3gkny1IH1orPl4gmO4qysRy5lSdc2drbG7lfHRj6vyT58KGAKQvWDvVod2us70+ZndjckDw5cAmswXuF7zvnLA5EIJGZ5HeqfPc2pqwMxZoPVejH8IDTWsngOXo4He/5tO60MKLPLS1aGO5tZ2wPSzccT9mvlqXAT0/puF5eJ4miDzido+42WZ5ZQlPWFqNEGtKhA3whMCiZwZvHta6hTNVpKAKLpw/T+gHbG0NuH799v731OKI1ZVFVlfX+Cf/+J9w9vQ5zl04h7GGm7du8tG1q9y48dF91ZNxo8Zrv/Vl4maNqz96j717OwcLIcoDPyKqNVnpLlOr1YlPvYywgvGFbfLpdL+d3rp9m+u/ehszNbDFfWS6Fc7KZf36DjvrY4wAI0HXNNVa4aoZeh7UfWhVkGuC1RrBSh11r6B8e/LCqxs8AYt1iENLaRI2JhXDtGBQWUResjcdYHJDVZstHvmueR1NM5J7hcsouAh+IIjrPp4RRJmHLWG0XlAmj++4yqrk7bff5sbNa6wsrvL7v/89rl27yTvvfICt7AvNG5jDWRd4rkPzhLPdNEfXQAR+QLPZxFrLZDr51JB1QjpLJaGfkw/OrxOsBaspsyn9jVtYa2fkhCWf7iLkkLDWJggbWFO5Nf4v8FzgFlk9zp8/z4ULl1hZWeHSpcvs7OzwH/7DX9Hv95lMjso8Ai8IiBpN4ihiZWmBYLbIao1h/d49+v3+x3koLxRCQOgJpNWMBttorSnLJ78Rm71lWt0lIpVSr0ZM85K7gwml1tzpTwl8yeJSm5V6yNbOmL3+FG0h09CuNXn11TdwQiDBaDRmMJ2SFuUnSqfnaebmIM+hLZa+pHOqR1gLGe8NyaYpXhThxTVMUaLGx9jFmDHY5ONTwP+6Yp7rcZgm0Nw/MH7MsEMIwXJ3ifbpLnvlNrujbRd9JBwnlaaPXi+5cP4Cv/97v482mo2NDSaTCW+//TaDwYCNzQ0m4wmeL/F9ySRJHl5wF84K9ZVXX+X1L32Fvf6A7Z1drBXUag2MqpDi0cEwxmqUylEqIx1tYrSiKp5fx/RCiXTP9/A9j3a7zdLSMvV6A60N02nCnVu3CSKPUn2VmJjNrR2uf3SLze2cvf5jSJrDXpsPXsAHzmcYetRqAf7MTyfwPRqNiFwZFAJpBYEfEgaxUydkCc1WncVGk3GSkmUlZV6wubFBkqTkRe7KAI3BGMluf48Pb1xj2h+ye2cdq6zjmL7gIQ/wvBTpyaxRnkXBz0tGksmEva1tptMRulLONkPb/ZQbaQVLS0ucO3+OD959j/U7d0/0nVmWUZa5m1h/Ctr7B/ujI/3dBY5IF059EISB84SSj54wHCbS5yo+JzZydYSft9t53zvePKVx4FGwFlOVKCzD/h5ho85oMCBLE/IsR5WuDFp44aya4ggl7+O+QhXYE5HYD/ssWVWgpwVVXCeZjPEig/R8wqhG5s896+x920khCMMAIXyEnEntjkwzm99zM4Wadr225/vOM3NfGfqU51oId19L7b7+8K1/bPsylzk+6/U1WK3IpmNKGzIZdZGeS9cTIkLXIzxhkb6g04yREqLQR2Aoq5I0TZFWIq0gSzOmwwlVWZGNE5JRn407t5gOH1509X2Pei2mWa/RbtawWtOvKso0ZToaMO7vUqQTMEfcD9aiihSrK6wqEdZgjcBogdXKEctWI70AkBjtfj6uzk9ISb3ZotnpEvX3XNtiXywZoauCbLhNlkxIbUi9NWF86SVqQejsjupN5Nj5+GulyCZT8AKodxz7NfeJFm5UKf2QMAwRaLJkRDoZkA63yEfbL/Q4Pu2Ydx1zpwYhwAsFfjh/ri31us/SYkyS6Jka1e5v7IUSP5DUGiGNdki057mCgPnz+TF4oj94PI978oVwZZ9BXAcvoKhO1lz4AmLpFMqBJwnqPgu9GjL2yfKKUhmCQN7P6B+FmbBcWotn3U+XxSDwghAJNOMGvrBgUqqyREpnL6J1RaUsCB/ru+DgLJlSFBm+5x2pSJdSEsUhzWads2fOcv78eWr1OhZLlqUMBn3SNHUEvRQEQUBUi2l2W8StOn4486rZv56O8RZG4ltJiE+r0cKXPp425I06FqfMHud70BAPZxtx8JFZUjAdH2rPWkB7dqGE727QAIhAND1kL0COFCKQWMwLub/2VfweNGpQiyC3mrQoyJWiNIAxCD3z/wkFIpZuMUKD0hqVazdnaYIIBV5T4GtBICV4M7XUCeA8kIfkZcLq8hoLiz0aG1vujx/DAtUcYvZfgQBh9+0mHnqfFPiej7HmWL/+TwoWeMFd1+cYFqMV5QOZIka7yhEbNZzt6fMy7P8CwIEivdFosLy8RK/Xo15v4HkDhsPhI8lwIQWe7+PPqpXCwLXlxhj8IDh2u88ipBQEgYfvCaoyp6oqzBMoUIX0EMIjjBvUmj3iStLMM7Rx7Zg1lqxSlEay7EtqcUgY+PtKbG0BKanVagggjmKKqCAqS7SxKKNRxvBJREwYrZ8LiQ7ufgyigKAe4Y09kALhe8gwAPUohlbh/NG/wAvF3PrUcj8l8ARiMyEEtSim3WiRxGM3zscSCIuoeOxAu16vc+bMGbQ2qFLhez5BEGCxpFmGVsYR6YFHXhQPiTil5+EHPs1mg16vS5KmGKORQhKGAWUQIuRjBtrWYozCGk2RTp+rGh2eB5E+D/154OIEQcDXvv4ma6dX+ObXv8obr73ClQ+v8Uf/x/8Le3t7XLveR0rJ6L/9c4Ig5N7dDcajlCx/zAEap0RnAvbBBQWDU//g9sdi2d1NGI9yVF7RAEbDlB+/t0FRuTApY3Le+snb3L7S5Hd+99t06uf5xY9/wb3rH7G8ssilly9SFQmJNuhKc3Nnl7J2k3/7L/8dv/rlu9wdbR1891yl83ljHZ8G8wd47qv5rCpRq0HP7Qsc+WnzBF0W3HrnVwzu3KKsKvKyoMxz9INeSdqC0o8pNbofURRTq8XkubN1+bTDq4X4tQhdKVRWUEwS7l6/iUCQp8eUrs0QhAFrp9aQUrK7vUmeZzTqMXEYkqc52TRH8/Tp5Z8mhHHMqUuX8YOAzVs3SUbPV8alteb2h++zfec2ZVlSlS7UtiyqA+9aa53Fx3OHBK/uSEOdgn34O8okYfODdwniFp2FReJ2i7R3kczWIRlDOlecTymLhO177wKCMp/iZuoP3AV+BHGboFZjYWUVMAxuXUVlCV/75re58NIrXLt6jQ8+eB9TFeg85YkayXk5hgaUPWhTHgUhoNGEIIA0geJpFfQSCEHESC8kCH2W25KVRUF3qUGr16bTarLQqwEWrUPGozF//v2/ZrA3IB1PUEXF8sIivW6HZDxlsDOgnA4ZrX9ElU/J02MURAIiKYkERBjyMuf2R1fZvLfOrSvvsH77tiPSHwFrLUWekUzGjJKEyTQhH25jjKHeWuDsa9/CWsndD6+STSYcvUhygKrIuf7eLwmiiMl0Qp7nmOpjqk7QJXq4Tpbs8su/mBLHNYajPmk6ocpm5RS+D3EMCCgL195r7X5vXYL6GTKxzO7WLnqyTn73R1TJkOLz5o3+FOiGcLoBqYI7U3fapjsK6QmqzCAELHSaXD67Spnuci0YUgmXPxRFAa++foZGKyKZDEn7ewR+xdKaT54aJqOPx3pjjsPCmxeBceXC2y6sNPjWG8u0uk1W3rhEYQX/+k9/wfXbO4zT6pEWNtZAMVCoqWE73sEvJadWz5KnKdZ6SCvAatJJglYFd25dZToecHp1keWFDlfeu8IvfvgLuktLvPobXyMrCn70458yGIwYjqdkRcG9e+v3fWdeFKxv7OD5MWmeUqoCpSs849Fstzh15gzXb91CCMHaqVN89w/+HhbLO7/6FePhkNHO4P6D2Kvg7THTuOTd9zKCekTtco+gFbGyskqj2WRj8y6bW/dI9wbH2ntoPcslPO6CWQtFOUsgNVADZTNMqhBNSfj1Fmasqa4mUD7fq95baPHb3/sSrVaM8HMMFW/dvc3mnT2q0EAEcVyjs7yGNBLb0uhUMbqyRTnKXZVswH4IrfGMK18uQfc1ZKAetEk6DkJgI4GJ4ertG9y+s8lwMHquFYQ1lx9Jqdw1eRDGWGclw9yuxR75PnCWEePpGCyfGjW6BTKlKbS5LyTwCzw/lPkYrfL7rTS+wDNDa4W1ri+u1UKkhCRJKIuMRiQpaj5poZ2l2IPbVhXZZIzKU+6pyoX7zSxdkun0iG/79GJOXB3Xx7cadd545SIIwdWbt8gmE3R1sntRSJ/OmS8Td1bpdLs0223k8A626GMfGB8bY9jeGTMcpgjpsbTUJc8LkknK3nDEX/3kp0RBQDOKWFpa5tVXX6ZWi7l39wYbG3eY5Jq9qfrMUkbGWLJJQVVahIloRD7WetiUzwdZ8FnH3KVjnmkDBzkq+ez/j1vrnHG7IhCsLqxw8fRlmllAK5OUVUaWjyDXiOR+H0QxG+sE+Pj4qKLk1ke30dqwubnFdDrdd5UoqxKllbMYFNLZvBway/i+z4ULZ2i1W6TTPr966ydkWUGV57TbHc6dPcto2OTGlQWqSqCq5EjLXKMrytTZOdsnzgR7PJ4PkX7EhfA8yZlzp3j51UtcvHyR02fP8uOfvMUf/7u/QM2C9iywsX31yb8zPWaCZLm/XBRIkpIEVxkaA1lWsZcdkEvWKNZv32NUi/jmN97AKMOdW3f4yd/8mDfefIVz51bQunRjc23Zm05hb4/v/+Vf8md//Gd0zy3TPbfsKuQVn43QnY8LcyJ97h7AswhELdgHWuaqwlLR38zob64fvdl8a2ucSvYJvtz3A+K4hv4YwkSfB2Tg49UjNzGb5mhVMSpOVq7nez6tdgvf85iM9lBVTj0OadZjpNboJH9cYPNnBp4f0FteIYxjBltbz51It8Yw2Np6rp95cgiQkevNTHkkka7LgsnmBkF9Sm9hgSiK8OoLUEqofEjnE94ErUsmo41Hf6UXQNzEb7Rorp1FYJhs3sUUOWfOX+RLv/FNpoXi6o1bAJgym6lUn+C5elJ/VSEgiiCKoSyfA5EeID0Pz/NoxIJuXdBrh7S7dZqNmEbdrbAb4zGZjLhy5SPu3lmHwnl66/MGKTwmgzH9nSH5eJvB7auYR3hySwS+cOpXHwOqpL+zzdb6PQbb64z3HnNdALCoqqQoMsbDAbt7fcjdgCKI6yyfew1rJdu3NskmKY9TpGut2N06WUXPc4fR2GxElcHd8dwG54FyBCndddcWsmxGoht3P8SL0LpEJZpMx1OK7XWGV36OrR69yPjrgroPazUYlrCeOEFRmRycXyGgXgtZ6LZoN6ZEngDjSLQw8Dm91qPbq/PRlSGTJEFKSaMtsRamI/OxThaf5ruepDnKjXudDwLOnenRW+xw6sI5ktKSp2+xuXmCslELOjOY3DAZTNnzfSbjCVVRgAgQhBhjKcuCIku4d/ceuzubSJ0T2pKP3n2Pv/qTP+XU+bPUWhHTNOMH/+Gv2NreozRHD0WV0ozHUyaTKaUqUcb5VBprCKOIdrdLXKsB0Op2+Mq3foM8y/nRX/yAjRtHPPephlRTULB5fYSoe4R5l2CxTqNWp95oMB4NWb93G7Ly2JN8XLDl4XO1r3CbNckGhSkUXhQTnGsidhXqRoYtny9h22jEvPmlC/QWW0zthEzlvLu9SzrYdmr5CHw/pNXoID0P2zaorGK60YdsRqTPK2ilU0FrYzAV2MRgc+fSdlJYX2B82NnbIx89xsz/KRB4s9Otj/5oay3qhNlPxhiKp+57Xxxc6N9nY1z/WYRRxSPHNl/g6aC1xc7sMFyVvaUoClRVEQaCKJTkleYo239rDKrI0ZXEaI2AWbjmZ4+0mNPZx1mkRFHI2soCShuuflRQ5lPMCTOphPSodU/TXHmJRtOJ6UwxQiGxD4yPrYXJ1PX1vV6bTquGMZapSEnynOu3blOLIr708ss0mw0unDvL0kIXqSbkow2Msew9o+3tJwoLVa4wWuDhE/ihUx5XxgnOP6vH9XnBXNAaAo3Z7/u2fDy+gk2C8ASteovl9hKmk2C6U6b5GCVSp2sTD1xobxYtICQRAVpp9nb7GG0YDyekWbpvRzz3SD8OnidZWOiwsNBjnKSsj4Yua076+F6XXq8DQBA18PwcrXLsEcoVawz6KaydTopnJ9IPe+8cglKKDz68xm5/j8XeIi9durT/N4kbW1qc7vFwM37Y6vZ5PoMKtwBTa4Qs9+rkpWJ3LyHwfF6+eJpeq0453ubGuwm+nvD6K2ucPdOlXveIGgv8/b//H1EYwdvvvEXys59x556bVOSjhCFQJvnn1lc6btborS2hK01/YwdVHvP0zXxSqXGgGrWA7ywivvvV1/jayxd4+/1b/PVPPyQSlpXQcR7bxZMIiSRCSBqtFkEYzVbVFVVVUuYHyvE4jrl8+TKtVot0mvLh+x+yt7v3iM+9H7VabRbkAqPRw0EuUkpOn16j3W6xvb3L7jGfLWbhG/sDFiHZD8F7DqtjUTsiqPlooCpTZ21zwqcnCEMajTqe57GzveOscpKcsjKMpzl5oSjzksJ+ftaIqrJg49ZNwiii01uku7DE3tYGo/7J741PK4T0CBo9pF9HqQbGFNhijC3GjmD3GiBCUAqdZ/R3twnSBCuh2e1SoijRUApIBzzyPgprENaodRZYOH2RMK7RaLSQAi68+Q2EruievYxX73L2/AW+85vfJAp82o2YsqrY2t4hTVNuXP2AyXhMd/kUjU6P6WCX0e7TLUTIIKS+tIoIAjKjUE87SfBDCGogYhfKEcSYskBnY4rpkGxiiOshgS+IhIVmg6pSjKcJo+HEEQ3WwrSArGSgrqG3b1DkmmxaoqoM69fdgodKj2wHxIxEl9ag8pxsPOb2R1e5fu0ak/HJFn+ssYwG2xRlRprNFhSUAREhRUggXRvw8pe/RDY9z73rP2e0d+fpztnHCvvAzxnmpn0WMPN+yklCo3qXYGER8inTG1dR0w3sp0Ql+UniVAgrISzU4HQELQ/KLiTKEerzIkFr4dqNAUpZQt/ne999Az+IqLV6GGvYHW2wfmuPne2EwcBghcUKQZmfLEjxKASBz8ULa9TqEXfvbNMfPLoC40lhrWU0nbC5t8u0SLCBPZEFTQPnOmKHOb/45QbtzoDL04zSWpITPpuHoZSmLCp+9davGPSnnD59hm99+zvEUUwYRxij2Lp7h+tX3ifduMl6r8kHV26xpyzF3gjztz+nrCqGUze5Oe50Ly4u8rWvfZXTp0+ztrZGq9XC9wOstbzzzjv84Ac/4ObNm67cX0haXkyjHvLmG6+x0G5z69YdRsPjj8+WBnU3xfYr7o6uMGiu09/bhkHhgkIL3ETuJI+dxE30tnERDi3h1DANe6CuysFkimo0daT0SZXdD0IASyE0PZevkyi63Rbnz62yvLpAsNTGturE1kdWJUEVwYD9MW6pMvpqHeEJrOfK56tq5u0+V6PPv8cTyFCCtlSlwWac2CrWGksxqtC5oXt6kcaXm4zuDdm9sbtvE/SsKKrZ/MtyrGXLZxW+LxAClHr6NukLfIFPCrM8afK8ZDxKqIUVXqUw2ZROLYKyTppPqdTxpLG1FlVVM1HbbJGc+3kXKSD0XEFfpT99fOi8uTxuv8qyYGdnh6qq2Li7QX8wJM8eINJk4KppjWZu5O8RIonJS4PNcqI4oi580kIxGIzJiuJItT9AljlbiqKo7mtblFLsbG+TjMeMphPiWszuzgb9UUleGgJ7dP72ZwHWWnSpsNqirQu8NsbOXjMeQoJXk/cVu5rcukyPL/BioYAER37Ob/+5Q0Rx6P8fg9QaRkbTXl1jsbXIcLhH806DNMuI5YA8LxnuJZSFws4q2eq9BouLC2Dhg2vvo5UmyzKqqqJ4RDDofbuvNPfubdLvDzFWO0ttKTHSQ+mCuBaTpQUyEMSNCFXJR4oShHTaOiGgeEIBw6PwfIj0I54HpTQffniNa9c9vvEbv3Hf2yWOa527oRzG3CnmeYcxzBdguo2Qc2c7TKYFg2FGGPq8fOkMKwstJv0b3NjcY3Ghw2svr7Gw0qVe96l3FvnKpa+wN075P//X/5wPrt1k594OAPk4JR9/+m0/ngVxo86pl85RZgXjveHRRPq8Jw6AzuzfEtyF9EEGgr/7d17jf/mPvsc//5d/zd/8/Cp1oTlfh9LAoIKTC4kkQng0Wgs0Wi3KIqcscrJ0ch+RHkURb7zxBgsLC9y4foO7t+/S3zt5oEocx3S7XYqiPEihPrwXUnLmzCnOnj2DtfZYIl1Kub+9tdY9zdKf2dU848xHQNyJqPVipuOEbJQ+kb1QGIZ0e12qqmJ7c5vqkCVONf18pgSpsmTr1k2CMOTNb/4WS2unUVX5OSHSJVGjhxd1KFRBZSrM+K4j0kUEweLMS1VhTMZgbwcxndBeWqPZaTNFU2JhWkEmHn0fBTE0F6gvrnH64mU8z3UnnuexunaWKIrpnT6LV29z9txFlhsRvYUeFy9dJEkz3n7vA3Z3d9jd2mQ6mdBbPc3yuctsXP+Q0e42T9MDeGFI8/RZZBhT7Wyg0qcsWfUjqHVANkCugPQwVYnJNGXSIJtoarWQ0JdUYYBVClUUDPsjRoOJW3E3wCSHccaov8eIAY4BisHzIa47z3ddHEmkSxyRLoxGFQXZZMKd69e4ceX9Ex+GtYZJf5vJaNctoIjZyiURQgTO97ce0XvzSxijSMa3P2NE+gPQ2inR9zFftq8RNTrUewskt/tMbn0Euv/8RlKfUQhgLYIvNaARQTeCNHDd06iCfn5ApANcvzXk+q0h3/nmJf7gP36dTrfLqbOXGI2n/D//2/+O9Tv32NkyjKfPRwoRhj6vvHyGhYU2yTR7AUQ6jJMpW/09JkXqxi8nsI5qAGuAHub8YrhJsyHIp3fBg+n4ye4pa0ErTVUq3vnVO/zF93/AN7/5dV5//WWChQWazUW0UmzdvcvVX/2K3bpHK5asTwx7yrK7N+Lm3i9O9F1LS4v8zu/8Dmtrq6ytnaLRaAEuF+Xdd9/lX/2rf7X/Xl94NGVMEAe88fprLC31GI5GjyTSURZ9L0OTsX51/ETn4T7MJwIa2MER6ReEI6TruJOW4KpTdxXVNfVs/ugCWArgVAR7wK6ie67JV/7Oy7S6bfylNjYKiWwdv1D4VXw/kT7O6G9lbv9qHKjAYh6KnhC+QNY8TGlRhcXmT2JzBuVYUXma899Y4Pw3z3PrZzfZu7vniInnRKSDK+6Z25p/HkhnIRyRLqWb6H9WKk2/wBc4DGuhyCtGwykq8olUjk6ndOIQqWtsDzMeSctai36AaJ83UXPexRMQ+Y5IV88xRup54XFNfVmW7O3tkuc5m+ubDAZH9FleAGETdAm6QFiBT4wkpig1ZZbTbrdAeGSlZmswQRt1bBOb5wV5/jBBqLRmZ2cHKQT5nTv3XZl53J/hxVrRvTBYiykVVhi0qY70nhaeQDYk4hDbaLU+qlj6CzxvzInPp4E9+JFZw9gaLq2scrmzwt76OkFhmWYTvFCSZCnptKAslLO/B+qNBitnV9nd3OPD9z7YV6E/CbTWrK87UV09lkSBoJLuVZQpCIm1Am9GpGcT+cjDlRJqdfdTVZ8mIv1RMGCV4YP3r/Knf/J93nvvivP3esQmlmO5+SeG53m8/OoFFpe6bNxbZ2tzkzByCuFGLeaVy2eoRTWWVlfotOoIPSD3KtLEMBkOKFREczlnb7rD2zd/yGCcsn5zi+nuFFV8FtcPnx4Cl9Acd+sQCMppjqkO3YU+ByWssH8BPV9y7vISvcUGSyttAF5/9SL/2X/yD1HjPubeR4ySHD9NnoBUNiAkvi8IfY88KcmzhGq2yiWEQHoe0pOkWUYwmaCUcmFHj8m+EUKwsLBArVbD8zy2t3eYTCZHVhsIIWg0W/QWFonj+rGf+dC21jgS/QHyTAhBrRbjeR55nlMdFSb6KMxNzJ9gszAK6S0sUpYFk/EQmVuqSmN+Hbwj59U0+uNUJ0lHZsLMquj56vytsVRpglGSereDX+sxraYk4z7IGIJZk+9J15tgwSiqdAJGoaYTyFJ3L8VdN8iskqMrJ1QJ2QRRdfGkxPd9PCnBwnQ8YcqEZhwhjGZw7yb921dptVvs7GxjhaRUmiiOeeXNL7N65hyy3nEFLFFEvdNDVyVFmnCS3sALY6Jml0any+VLL+MFAXl/lzwtXI36E6LeadM5f5EgaNOITwOCSqf4geDlS+dZXe0QN9tE9Sa+9BjtDRmMpnx07TaDvT7FzghGU6imQAr7AbEaqNz5VLMb8JiqlJ2tTX76t39DrVan3ekwHg4Zj48hp4QAOQuLnY8OZrMjEfgIz8Mqi1XFbLZkESgCD7CKrc118ix7jFemdC8vdh780puNSFL32n9bAGHPfXk5mSnD75c+CC9CRi2sUZhi/Fwqc46EkFBbhqBDZXzy6YTKAq2eU+cn8uRy0M8gmu0GC8s98qxgd2sPc0QfG3hQD91PjWsaFn2oS3htEabKiXRLDaMCJiWkScLG+jqqUqyevuAm4UagDYSRoGGhLC0PRpU8KZTS3FvfZTxJiSOPS+cXCaOAMAwYT1K2tvsoZXmEAO+RsLjJwO7IJ60SN34JcD89kA3nc2H2DBQH7VAO9DlQkhXKcn3PgAfprLRubsvU6nboLiyQJCmbWztHltIrpV2JvnKB1Ha2d1ophoM98ixl5fQZXvvq1+hvb7A12GNSPrldzng85u2332Zv7wyXL79Evd6gKivKoqKaCSR8zycMIjw8xrt9rIBbN26xvbNNMv0YRSMWN16bjytL65pSZpL7BJjixjwRB6XKT3JSPIG3EEHNg3aECALCpZC420U0Ym7ubLCgcxbPnUZUkpu37zAajtntD9z21Ww/5KFXxr79DB54SxGyGWAwGDS+L4ljD50ZKqlOxP8HUcDSuSX8yKc0ylV7NAT9bERa5q4Je85jmPmYaP7T8yRB4DsVotaz6JKTtdtSSmr1BlJKsjRFvZB8mEfD7a+defH/Goxvv8DnAm5e6KqGq0rtL7qOBiNU5BFUIWmasDNKSNIMYzSBdNPpo55OT3o06+7zAukCMifJhLzI95uQuRLd2E8fiX4SpGnGzZt3qCrlArrFEfFoRoHK3E9rsVg0JcaAmG4gTMpY78K4ST7Zo95dQZU5JCOMNegnaHAVIKx96HoIHHXySY5AgzCk1myitSadTFxI9hPAeU4fvlGEG3djwRqssZjcILz5qixf5Ix+BiA8QdStE7VqnDtzhtfOX6KpJKoosMYQBiEik4wGKZMkoaruv4uFkHiej/R9FxQveOxcS0qJF4auaqa83wpQaccMayHQQqAig1Iazw9Z6C2gtUKlKZ7wKMscdSifQ0pBEHqEoaTdiZBCuHFIpZ5L+/ZiiXQNRlu+/2d/zV/9xd+ilEZr88g4M8vza1TC0Od3f/87fOM3v8Sf/+m/56//YpNmXSARdLotvvbmeer1BqfOnCUOQ2pBSjaUvP+re3zwzj0uTQT1U6dY3x3xh//+FwxHKSpXGG2xvw5E4wPwQp/26QWiPGd4c4fisLdshFOiz+vDZm1xGPl8+7df5pXXT3NpaRWA3/273+Qf/5N/zPX33+Nf//P/hlvru4Tbd5/Ax9ggMES+pBb5DKqcyXD3oExNSsI4Qvoe/cGAvCgoygLhOYuVRx6j53HhwgXW1ta4evUq1659dKAkfwBCCBYWlzl79iJXrlw/fm8f7JisOXLRwPM8Fha6RFHE9vYuVfWEatoKN9F8gluzXm9w9tx5ijxlMtohmcJ4nD9RwvlnFRaw2mLUx+hbI3zwOu7b1YDnT6Qr8uEenp+zdu48i2fPcy8rSLaG4EUQhwcyMwChsdqQDXfIENi8gLwEX0LzjCNIR7d5KJ8AoEigTBELXQLfJWhHQYSqFBt3bpIkCbYoSQZDPvrVT/nwF39DFDsf3t7SIt/4zm/T7vb43j/4H+P7IW+/+x637twlqrdYWDtHOhlS5tmJErbDeove+ddZXl3lO9/+bQJPsPGrX9EfTXka6cPCqVVe/e636dR6nOteAqMZDdcRUvON33iDtdUlSm1RBrZ3R9y+uc699R3+5m9/QToak91chywBu8P9D6UGsllp3fw6HP3AfvThh9z66COEH+DV6lhjKCbHqEGlB0HkpHZlDsK63t0TeFGI9CN0ms6CXmebiJIoAEzJlQ/eY293DzU6ripjVm4kAgiW3L0U1pwFTnrvfiLdq0P7FUDC8DqUYxz1eIhIj5oEC5cwVUa1d9UR/C8CMoDeS1A/TW48it0d53O5cgYmClL5GZQBnRzLa0t87be+zO7WHqPBmCJ7+DzXAujU3dWpgFDAhQiQcG7JRSdsTGCcwwd7jkgf9ge8/6t3SC5c5NU3vopFoIxAG0GzIWjUYTgyjJ7WamOGoqh4590b+L7HN756jq++folur0W31+bq9bv85V+PyDJFmj55jAI4Qm13tMOYPkWlnNRcAj6IusQ7GyCsoPpZgT1EpI9xHO7+1LGAu3fcDsz3oyYdP/vS+VO8+fWvcfvOOnv9wX5Q42GUZUGSGMoHqv3KsmBv5w5lWfLKV77Mxddf44//+N/z8+vbTzUB2Nzc4o/+6F9z6dIlvvvdv8vCwiKT0ZRkmuyXvkdhTKe9QCACNm7cIcszfvrDn7Kxs33kQswLwXwi4AFN3PgytffX9I+A4ez/2zhCff77CSECif9y25Hp0gktFs4usLS6yGh7h5+8/z6nxiu8+qU3Ucbwlz/8MTdu3Ebfne1IxkHJ9P6Hzn4GgC8IFlqECy0qW1LagsCHVihRhSIVBfoEM55au8Yb33uDxkKDUZVS6JK9asit0QZpOnaLss/50jx4f4VhQLNZR2u36KO1wZijx8YPwvN9FpeX8YOQrfV7qOknI0esvrAT+AKfMbh54QJxXGM8SsizgiKr2Li3QSPyqJoBkyzjo/U+eVkSe5bYh/yYsOAwCDi1vEIcRbSiBlJ6XL1zg6Q4aMi0hfQzrBgeDsf89KdvzwqbNL44oonUJej7V14VGZgcdt8DIdm6C9sIWr01Fk+9SpmOMMWHqKrEcrw6/TAsx2eLzbMg55mPn0TrFDebrF64QJFllFlGVT5ZEtpD3vPSAxnOKu4L0KDH5n4R4xfN8Kce0vdon12kvdzla1/+Cn/nG99h6/pNNj+6iVWKeq3OaDxh4/aA0XT6ECfqeR5BGOEFEcKLXWabznnUxfeCgHq77RZ1hkPMIevNsrK44bHbvgoURalohjXOnz+HFBKTKQZendFo+z4i3Q8krXZErRayurqIFILxaJ08V4/Ssp0YL5ZIn0EptR8wCjNnAR44nULcT/A8Nn3oBBACP/CJohBPehh90LFUlWIwGpFkBZX1CDyffHdANU2QQcTpC6dYXFkgjmM8mVAUpZsA/RoGKOiqYjoYY4Xzgq/K8uEJ1VyaNStpDSOf1eUOnVaDy6urXF5YphfG+Mbia0WgcnxT4Umxb31yUrilEElZVmRpRlVW9w/mZzYz1kJRFAghSJOEsiz304KP/WwhiOOYZrNJp9NhcXGRPM+ZTCb3vSeMIqIoJssK9voDyrIiDAK0Mein9N211u5PpIU1BFKgrX14JX0OCXizsvCsQs/DuB7RI3tBgOd5tFot2u027U4bpQqKMkdVCqX0r41aRwhBFEXUa3V8/2NpCpG+R9hsAJZiMsWquZTsWc/5XOnugdVYU1AkA5JBhDaKsNnBSBc8iTi00uVFrjxKV27gU+mZgluA54F6MMXiAViLsC7LXliLUcopyZMh+XjMaDeAqiCZjLBGU5UlyXSC53tsb6yTTqdkSUbgRwy2t5gOdsmnzqJJV+Vjz4sXRARRjWanx9LSEu1WiyydkhmDKouDFXAhZ/3Jyc5zlaVMtjexcUKUagI/IKr5RFHknB+UQuIRSI/Q84gDn1oYUI8jbBZRSA8jPGengz0YREocYWM0VMUj98cY49orpZDGKWaOU4tIP8CrN/El1L02WEOqMhfkIud5DPd/l7UaVRVYoVD5BJWPMfq49lG6YxH+TPFegQ7cv8sa1JfdaRaCoNaluXYGKX10XWOLKVolaF3gSYEnQRNSVhm2yp6wvTkcM3USWDAVQpeEcYsgqlNmU8p0Alojah3QJbYsZ1VC1RN89qcfcS1icWkBX/pcfuUiySRha337PjJ3qmA7d56otdkl9WY/Aw+sgNhzOcS9GNbqsBAJ6lKisoIr1+6QForRKCc/ZFPxvKwT5qpXYyqsrdC6QqkSX0KvXSP0K7Qu9pXpT9p96dJQptbtr2a2ADX7oy/c5x0xPDl8l9gH/wF33qxw4xvf9/E8776/SynpdjvEcUwc+4ShT78/Ikn6TCdTrl65RlyLKMoErRVFWlCVisk0OX5M8BiEYUiv16Pb7aKNIU0z7q2vs7e7RxCGvPbaa9TiOs16h8VOhyAMyMscpc1TleYeBV+4RQZwWaVHfupsMWPfaufwmuO8wMVywEI8xs3gOFhjsVOF8SV+K8Sr+QShhx96CE+grCYrczZ3t/GFx3SQUI2q+5mRB6/F/H6RrrlsNRu0FxdRtqKyFTVp6PqWQueMVqaYUGOymUpvfmwzBKFPo1Wn0WugPUNhK7TQGGnwQp8orKE6JflKiM0MZqxe2PwkDCM6nQ5auwDRqqpQanzf3O5YWDcPtELMFIxf4LMAAXieQAqB0hbzCc0NPOlEUNqYz6RC+lng5oUlQkjCwCfwAwLfw/M9tDFMkoS8qmjUYsLAx2qnGBXi6OcsCAKWlpZo1Bs0whoCydaozzCdUFXVffaeANJzpY3G3G+WLoRACjd3F1LMQog/HVLjedWMwKlRZ/961DuP/jc7U1PP3qHKnCKfoIoUY40bi8/eHQg3pFd25pr4BJjTJp+krYtT6/po5d3PwYEbB3mcKDcmimP8IMBYibYSo9xYzc7P4q/Zc/tZh7UWlZaUk5x8MiUdTciTjKooSZOU3f4ug9GQSqkjhcVFqZhMMvKsmAUkP77ft4B5QLgqD/F5h9v++fzYaE0tjvE9n2BWFe8JD0/4s2fVzOwTnYJdlSVCCqS0BIGzeHlWfcjHwx49AIVT88ChUxv4rqZ4/00antTa4hGoSsims4kiMByN+eCDO5TKUBkPa6GmKkJr+N7vfJ0/+J9+lSDwCWshSWbwpPfoL/gcIxlOufH2VcCl7FprMeqBO6/EybQaQBd6Sw3+8R98hwsrS/z9l97gfHeRcjRFJSlmZ5thv8/07t2nCHuTeF6MFB79nSHDvbHzSjoCSisGwwGe9Ojv7pKm6WM7eiEEnU6btbUV4jji7Nmz3Lp1i7feemtfWe75Psurp4hrde7c22Bze5dRf49up0OW50weaY9wPLTW7O0NkEJQ86ATeaSVIX3wXIMb4c68ONNpSjbOnB1LwPHJJULQ6HSoNZt8/Wtf49vf+hYbG3d565c/YTwaMxhOKfLy18PWBUdkLC0scfbMWe5c/+Bj+c6oHrP6ylmMMWx9kFJM4SD14xkgIpBrgAC9h9Epmx/9hJ1bPvHCRXqXXycfbjHeuII1JZDNkjdWwAuh2AM1BRuADV3bm89I9Mek3UvcbSe1Js8y8nTK5N4VRrtbTNZv4QUNpE6oxyFKKabDAcl4SH97Eyk9pIwQQlKWBUqVGKuxRmONeWyZYaO7zMLpS6ysrvLlr34FbTRXPniP6XjEaLgLVI4V9ALQaqZAeTx2b11ntLeOFBJP+iwur/EP/8n/gt7CCnmasVuVtNodGs0m3XoESz1CBP1LF+i3hry30WeqZuQz2g1GJU7uG0un5t+799hzC4AxmGJW/XPMLDKsN2mdPk+nWeeVcytYo7j60VUm0wnpdEpZFA/VU2pVMR3uYi1UozuYye7xpnEigKDnBtrVxH1WmQERLKxC70vO3sfz6C4s8LVvfJMoikjGA6oiZzrok6cJ9dijEfvs3L3G1V983y12mJNKn+Z+54ITPzNWQ7oDxrBw5pssnH2Z3StvsXX1A0QtwF/7MqiKanfLHY/aBZs9/nM/I+j1erzxpdeRQvDNb3+dna1d/t//9b9g/c4G4Aau16aOSL/YhK8tOvJPNmfVucatu9U8J+5/cwFe6UKrGbLY6bA1SPmv/h9/xCRXTKbpPtlqeTqF+LGwliwfM0oUlUrJkgSU5s1XzjCZply7tUGaVwyHPJmdjIVyZBCT2TzP4nytO64Z1LMqZfuoMspjYKREeRIjj944DEO+9a1vcObMmZmORPDuu++xszPgxo2b/N/+q/879WbMhddXCALJ3StbTAZTxtOnvz+Xl5f53d/9XZaXV9Dacm9jkz/+kz/lyocf8q3f/Bb/5X/5v3MEifRQRUEyHFFikMccw0MpdY/A/K1ND16aVUBcTWB61GMc4lTmc/Ney8GkfoCzU2kBqzhP8y1OHJx1HypDdWWICDzq31ylvlQjjgP8WZchfBimY/7yJz9AKEH/6gB2eXzTI4AGiKbk8kvneeU3Xt+37AlsSU0nDCdDxmbIaKhIbhrUCPfZh2IAOgst3vyNlxF1yUCNUaMRc3qnttam01thL96h6hrUvZz8JyNngfMC0Ov1ePXV17AWylIxnU555513TkSeaa0YjkYIT1I8oeLxC3wy8HBT8mbNEbfjrCL7BCxNhYA49gl8SZYripOHaX0u4OaFu3iez6WLl1leWqGqSqqqIB2PuHl3nTgOeeX8GYSU3N7YYJKkCK1cZswDaDabfOMb32JpYREpPGcH6VlE4LHX32N7Z3v/vcKThI2ac+lLcsyhxVTfD4jCGM+ThIGH0prxZPLUQrIXA4HAn63DPr0KMp32KbIJ1hqMcjLQ+XppO3Djo1HlihyfBBWusIqn3rNnh+cL4rrEGvkQj05DQE1AYmF6/B4KIVhaWaW3tESW5qRJRpFlTCqNMRr9xJ5rX+CThik1wxs7ZOtjbrz8AR0RU4wT8nHC7dt3+PHPfkiS5OTHOEn0+2MyfZcySzBVNpsQPPoeMMZQVpWb+1uLAMLQ9UNFeb9Lq6oqJuMhtShiqdcjjmKuh5GbZnsxOrBUOqfUKbrSJJOcqigJgwrfEwReRbslmU4smXq2e/MTIdJhPt4V+L6P8CR+o4EMA8o8ozqxxccjIAAsZVmQpZlTEEo5e3kIodBaU5UVk1ShtSWMQ/wwoF6P6XSb+EFIGEU0miNkKN3I4nOiSPcDn1otwhhLlmaPJE+NMZRHhGgcidmEyvM9Fnptlpd69FodOrUWm9tDBrsDlIXKwPrWHoNpwTSvjk3CfhTcAN6i9QO9l3VKI6MNZVEghaQoikeq0efq5DiOMTOllhCCRqPhVKj39TACISRCSLI0IyOjeB73LG7QpIFIzg03Hw+j7czM7vj3eJ6P9DwCPyQIImq1Oq12m729kCxLybIMpcyvDYm+j7k4+2M97Jn+wJPg+4RhgOdBVeSOWDwSj7YBmZlhs+9PZ52KQpWGoKMQfoDwA6T0sbZyg0EhEbPhoDXKlTo6DRIuMeQkE14xs+XWsxCkjCLPUEWGLnMQBdYG+Gg8YbHWuOdVu47QYSZ/3WdCTq4cF1Ii/RDp+QgB1miyJCFNpqhZ2aaQAunNVqdPOMbXZYkeHxx/GIYURUpZ5gzLBA9LWSqKoiLPS5Ikp8gypBB4ciZRkRI/riMDgRUGKw1GGoxUj81qeAjHybDEzOve80F6+FFMZ6GH1Qo/CBFCurZBH6o0ESCFa1uKPJ8tjD6O0D6kKrHKKbdnKgM/iPFbi0gpCXyPsNZCOAnA/nscKzu/zq46wKryCUj0p4VAegLpS4IoIIhjgjDA98ELA8JGG1MVaNnHHFuA+9mFqhTpNHXHPFfaPnDvFRrGFsYKJhUYD+pm5nQhJZ4VKGPIld1vHULPoxFF+EnOdDRmnCkq/ein1hNuwilwhS/WHkQ1lMa9joPFUpSKJCnwRY1a6ErUa3GMEHKmmntKPFjaebhISABC4NV8bNNgC+1CHU/ysTOFWl5WTKcJWZYfUX0hEMK1L0EQEEYRXuBR6Yq9vT5JHtLuBwShx2AwZDJInibyAX/Wz9TrdRqNOnEtxuIUhNM0YTSZEEURa6dOzQ5ZMBoM2d7YYJok6ONWReYB8xw6Z8eMkUNP0AgkTc8SewZtZ2qjk2D+uQIn2ihwoaPzvz0NiT7b1hYur8KmCpspTKHQSmGMxgo3JhsPJ1AKVK4c+3FCCAuxF9AKHOEkPQ/KFJNmjqiMwWsImt061p+JdUIostLle8xgtCGf5pRCO4ctXxIJ8MOAoBbit0Js3eUA2Rc0mHEqTzNThp3M0mV/W0CpCmHkMdu9aGODx42fvsCDcAtfAt9z/bon7p9nCYHLxIH9+dvzrGZ1c7JgNq6Yh98+6cDp8wH3vBkXhSNdXoG1bsxbVpUT/QUBnu8RxzWUgUIlrqN9AFJIgjAkCEOk8DDaEMUxtVqNIAxcG3JfhffMEvWBcx/4AfVaY7ZPLmfq03Z1hBAEQYAQElXah21IHnw/c/tXMSvK9fE8H60V1WMqSE8Cz/OQUmC0cdUVz/yJzw6jDVVZzuZjz2NvZpPqX7fSkc8hTKmpbMlwb8DW+iZUGlsq0ixlPE3I83JfYOqsk2d8lHVzjzzL0EW5X4UmPDdYtPo4IlXsjy9mvzrfdE8gpLmv5ENIQRD4+L5Ea42qKtSsCkIKSeiHGFvBbK5htEUpQ5krtD+rdL2vmZP7/dmT4hMj0gGCMGBp7Sy1RpPTr7xEq9fjg5//lBvvvvNsD+FscK99za0bN/BsQTIesrrSpt1q0Wy0qNeaNOtt8rzg5rW7VGXF9771Ci9fWGNlbRGd5CyeXebyl79GUetSP/2n+CJDb1X3eWV+VrF2Zplv/0e/QTJJ+Zs//xmT0dOpqPcR4LwsfSABP/fpNZZY7K4RdVew9TY//OWf8Zf/vz9lM4dbiZtgjsYTikoxTk9KYhi0ztGHOvUHS0WN0ZR5TiUEZV64iftjZGq1WsxXv/pVms0mt2/f5p133uHSpcucP3/+4T3QmsFwiD9N8IxGWENRFuRlcWSQ2NMgqwyFsujjngPLQahVJCAWLgwtf/j9nuezsHSaKK7jBwHS8ymyit3tHTY3trhza4M8c9Yuv04w2rC9sYtVsZsofwwokgmbH7wDnkcZBXjNDi+/9hWWl9e48fbPuP3+W0dsJcAP3EBWVRxr6OXNvX66HGgdcoqyxEymSC+mfe4NVDYk2XoXaw1BvYkMG1Sqf1Kx9iG42vuqtIwGQ7TWDHZ2KLKUPM8RQtBb7NHsrjDpbzDa6WMe2vc5NedxsKpxcsxDco2p0DNSNstSyrxyHToQRTXCeoMynZJXJ1zwmhtGz5BPU95//21u3r3J7uY2aZJSC5vEYQ2rLVpZED7Gb1DkBUqXyEiw9pXztNa65MWUokpItzcY3rrhltefR8hlrQm1NqUfMRyndNodTp+/jFElP/vZz5mOJ5TTbBYQ485HGIbUGjX8wGNzY3224PiY9tdWUO3gauzmljstIGBxeZVTX3rDlfgiKMYD3vrTP0JlE7QeYE2J1k20ifFEjicKynz08ALoY2E4CG092X0iPI/66lnC3jnCziL4IfVTKyzLLxPHAd1ei2I85vr6TXJVHP9sfUbxwTtX2N3Zo96KWTnfpcjLhwJr50PaWxnsbUM7hEspdGoeb5zr0Ah8fnp3xEebxb7ryZdeqfHmyipVMOG1xS32pnBrBOkjLuliCN9dgcDCrR3ISljuQqMOH47d6zgYA3fu5mxtl3z1yytcvHSGVqPOylKX2/e2+duf32E4LJ4odHQudj5yHfXQep6IJfWv9/Arn+TdIcXt5ERNVakMlbZcu36Hja0+eVHcFyBeliW/+MUv+fDDK7z2+qvO5zEUdFabFGlJMsgos4qbH24hhKBIC7Q+IjTtBFhdXeHVl1+mVqsxGY/wpMTggsYanS6dpWV6q6ssnzqFJwSBlOzt7fJHf/xv2d3ZYTAYHP3BdWCR+81e7/KwbzhwsRfzdy+0KIuKze0Rk9LMHVAePpVzv3MfV+VogI3Z36azz58T6BOeef5vtWXyYZ/01pjitS6lzkimI6xvMakhuZtBDiY9YftgcN1vBrVdRXuvpNttsbDUZX2S8PN3rzHKxlTjnJoI+cZvfouV5qrLoVbwzo8/5Cff/xWj/oS3f/IhSCiFxvgWLniIjsRfqtGgQxiEdOptsggyMXhhxMzW1jaTiZsnzK0T8vyIC30ErDFUeQ7CLd3jecxSP3GpvoFre0+0eP+kkLgyE4vrPz5fbfyLwLx6RAL1KKQWBySFgvxgUBSFAQvtJsbCOMlQ2qCUem5zoDiOeOWVS9RqEZvr60wmj+ggPseQUtJqtQjDkDRLWN+4R7PZpNVqkoUBSIEWltIaYj/kwuWXMEje/+AKabbx4KdRVIqNrR2yvHLEkQWkYGFxgTTPCOM+Wql9ocucu3pwKrq0tMzLL71GMp2ysbGJ1vMJ6adHkR6EAafPnSUIAu7cvcP4EfeQFJLYr+FJH3wnTFldPc3S0jJbW5vcuvkRxioO+3pZYFw5e7zHiVqlFKys9Gg2a/R3RwwG408FkT4djrnxzrV9Yel9SGacwmMuqbWW3e0thoO+WyTQGmMM2qgDa5cv8JmEVpqf/uinvP/u+6ytLnNqbYmN7S2m02pmBexI9Hovxgs8slFOlSp0kWOUU5djLdKPqXVOAZJ8vI4+nLE49ym+z1dxtpga1wkDj0pnVNXBs9dut3n9jdcJg5CPPrpCVVbcWb/OsD9gsbnAwkKX/sSQjSZuqGjAlJa9vnKiO2tnAeTu8+pxg06jC4B9QnXlJ0KkCyHwfJ8wjKg1mtRbbboLi3QWF4mj+PnUBDvTXiaTCf3dPaoyJwx9fN9z5fq+IGqHRGFAp1lHlSWn1xa4eH6FMIqx2uD7Ia3OAq1Ol3q3SW06Id3T6OLT01EchhAC6Xtg7WP9LON6xKmzq4yHE2q1iCzJZmqTo2+eB4UAD75NeCAjgedJ/CCgEdZpRE3qUQMjfXID69t9Prh6m9spXJk8fdNqMY/e2LLv4X5SX08pPdrtFq1Wiw8//JB799ZZWFhEKXVfqZqUEiGFW8FVCl9rpDUUqqJSz09dqS3Hk+hzmNlrFgwHHLPIJwjCmChuzN5jqcqK6WRCnmbY2Qqi73v3beY6w89vB2itJU1SJuH48UTic4JRinw8At+DxQVE5NNcWmLh9Fk2blw9fkMhZ69jPFD3n8+ZV7r1cPXvAq0MtqyIQp+g3gE081R1LwjxwgglPQ6mT4dfHDz8D5GMrvPT2lJkGUopkonzNzczRisMQxqNOtnYQx1LnB42wX0yGK0pywKRQH/PVRwhBEbrmTdxgO/7+J6PetLV5kPnWVWKQX+XaZ5yb32DyXhKJOsEMkIgkFYQRHWaC6uoSmMxSF9Q69VprbYRiYasohgZ0MmRJbdPBemDH2HwMKXCWEG92UTPglaU0mjlAmVhpiLzPeJahJQeVVHMnvPH7c9hkmPmoT87QWEQ0Kg7ZTBCoqcDBut3KCZ7OB+GCsQSiBbYFGyCm4w8TT//ZNsIIfDjGn69Cb6PsRYZRcQLHepRQLtdJ9UVEuMYrM/ZgH88mjAeTWh0Ykqx5NRHDywqm9njN9VuQphpV65spQAZ4vkB09JjJ53lJwLTyhmpB4FPt+5jrWa38rDlrN+wFmPca5+Q8eBszXVXmQ+JhrUQ2jFsZRBJ1+8dNyFNM02aaapKEkV14lqdWr1JFE5QlXgySxdmRSNHeC/uz/vmohgpiJbqhDKkuJVQHMn8PgxjAWuZTFMm04ft54wxTmU+mXL6zGnKqgIJUSPEWks+cVk0yfhkZOWjEEcRvV7XZYFYi7HGLXx5HmEcE9cbxLW68zmVknCWW7O7t8vO3u7DY5v5KkSI4yiD2c+Sw/Og+1APJKebIVMPdoRAWKfYPuwOc3ByQMy+UgrpFjwK65zGcusI+wInJnjKrnvunTufUKlxiRqXBKshYRa70DXrSAQ91Y68f5Jmeybw8wpDWBhiDU08/MqQjqZkRYqonA/62vIy55bOoJXGaMPtq/dAQFUqhnuHyJ8A6HmIwKPKS0ypEcri4+Y184KfF4E8z09AnIsDVfyDmRzzeZ038+IVs4dPzA3l7QtqfiXSCwEw+vOVgfGiMfeY9jyn1pNy5ostBVEYUq/HaGNJi2pGMj7u0+AkF1lK6fqWbpt6vcbe7s7sdvp89c8nwVxVHQQBSimqqiKKQuaZP3Z2Wg0WKyT1ZgvpBYRh+MDnSDzPqbOnSYqUPr6UIEAbgx/4eL6HlAd2ZLMu7EhEUUy320MgCfw+VVU9bA3yCUNKSb1eJwxDPP+YjukQPOHhSR/pRwg/oNFo024vMh5PZ5W+Dx9gdUKeWAhBrRbRbjVJpxmeFK5q7RP2D1eVYjo6Rkh2Am/0OYo8pzjhwuoX+OzAWkt/r09/r48UhnojIElTlDL701gByMDDizyEdINDZ896cPMIIZF+zbk5PGSVLRHCc3PIgy3cX6Q3qzi/f2AT+D6tVgss9Ld2SdOEJJlSFBmiZYnDkMD3EIdGl8ZCeYz1XeAHNGpNHMP4ZPPzT4RIX1xa5uvf/E2k77M7TSkrxZW3fokqC/a2HlxBfQpYoHKm9ZNRyl44YmNrwubGhHqcMxhMqMURy0sLtFtN/ov/4j+h1+1QCx2/JbwQ4QWkSnD1yjVMoflf/8/+MzY2tvkX/80fcvfGvWffxxeAhVNLvPz118mnGR/+9F3y5AgvTR83+ZlZzbbaDb7z3a8xGU54+62r7O0OH9okCqDbOAgENcYyTAzloXHTcrPG2dMNLl++zD/4R3/A0kKPL12+RCAFf/2DH3Dv7j2+/4v3eHcMyYvLQ3pq5HnOO++8RxD49Pt9AO7evctkMmE6nWKMoVars3rqNADbW1vOwsIaBPb40uePA9WsI9b2SGGvNYbpJCHPNVqlGF2STXe4c/MK5y+c5n/7v//fUBYF165cJcvcPWOM4d33rnPnzuYncUQfC7RWrG9cZ29vg8l0wMda/qsNjCbYoiIdTxl2U4pK4x7OB0YvArdSJTweSiObwxioMpAawghE5LwSdM0Fmek9TKuFqDcQ0odZGVNvYYGw2WM3HTHNzMH3mBI0yDAkXlwBLPnePcx9eQQaKEmTEXdv3cbqiny8jalKdFU4Ib3vEYYPB+0dwOKYkMPa0JOf/yqbYLWi8DyyvQAvCInaS/hByKXXvkIUeNy7fYPdrXX0g2TQnAia2wU84hGu8pzNK1fw45jG4hqd3hI69zGVv5+J5wUBfhwgA0G4WENZhfLHZKpitHWL0eY61Xjqvuh5TTjyEtTEVSwEEVJpgrBGEIR0Oj2mnTHDoiIvS+qxpFaT9Ba7nDl/Gc/zETIgz3KS4SZl8QjvZeGD33L/r2Ye6YyBjP7NH1JMb+DVF/B75yjHe6hqGycVnZ1YOwLS2XaKj4vMMNaS5hnFdEJe3MPzBkRhRhy6ALN67GFjkDLFSV0/nYvkz4oirdi80Xd++HOf2xkZaiKwIe5S5a5pUtL9OkglaSUZK0F6sAlv3xsw+P779NoRr371ZWr1Gv9o9Tz4IR+8f5XNrW1u3Bpw886IBQ/Oh3C2Dq80HaF+KnB+h75xn9nsuL/dSOBHe49XdwFcv7nBv//+zxgOp0yOGus8AlJAq+miecaJe4z2UeFubSwmtYRxwMVzr9NrL/Le9R+R3Bk+Oal61D5ISbvTJo5jkjzhxp2bGDTnz16kUorsbEae5Ny7do8ie7aF3vFoyK3r17hw8RJ/73f/Ps1Wm0arjQUuXb5Es9NmZdUFBlsc0X7q9Gn+6T/9p6yvr/Mnf/In7OzsHOz7aQ+55mOUxhQzu5Mcd9Mcw6cNhwXvfdgHbfBSQ01DYyZ2ezArNAwE9bqk0Yg4e2YZ6Un2koKiVOxujEmq/GCjk6x/BbOXBgqII4+11SZSwu4gIS8VqnQREVZojCixhYIdIJ0dm8ItFswjGioOiGt7/HGDm4zubO2ytb7OJJnQ9brUogaR7BDEIWsLKywsdnnvJ+9z+8pt1q+tH81gaWDDYAeW3b27JD8fuH7Glqiiwq6ZT5QnbrS6xI02eTohGfdPtpGdXZQXZAVQa7U4/dJXsdawfu1t8mT0+I1+zTEv9rAWplmB0opus85Sr8vC4iIra6dQWpFlOWmWkZX3IMtRai6+ma9C+iBDhPTwQt9Z++XT47NYgHq9xtraMs1mg8XFRXzfRxtBklXoo/KiPudwIZ6OpC5mVU1ZmrK7u4tWbpwtfA+8GCsjysoilH4o7HtxcYlXX3kdgeTuvXtYcwcvcAtfaZZQlAWj0YiyKA4WvYxFzypJ8DyE72OrCrRGeh5+GNDqtLlw8QLjyYjRZO+5CsqeFWIW9i29A/7iOBhryVSGj+J0d4lWs4Opcu7d+YjhYBdrny3LSgjJ8vIKZ8+uzaz2JHlRMM1StDIUqf78u6E8sgzwCzwR5m6Z4MZdH8P53N0dUOQlWZ7fJ7I0xpIPc4QnMTrEi2sYVWDVwcKK0Yp87HgW/YDqJW40qTcXXO5DOsEaPbNZgiwrKIrqvmpOgDwv2NreRleKrTt3KbKcPEtn+6PRusRH0g4bIDT4Lv8vyc2RVZ2LvQXefO01rDWU1VE2jMfjEyHSm60Wr73xJlZKsivXKEcjtu7eZnQo5OKZMfPrLNKC6TRlNMroDwuysKAsprSbDbqdJlEU8t3vfpML589w8/YdBoMh2noYfEoN25vbBPUGf+frv8nWmT3+7F/8OXf5dBLpjU6Li2++xLg/4vqvrh5PpEcgfNepxHHESy+fJRmnfHTt7pFEeuAJWjXpvH9xE+0kt5SHZrutKOB8t8nXX77Af/o/+Qe0my0oLOPRmGvXPuKnv/gZ793us/4pzXFTSrG+vn7fvw0Gg/tKmoMwZGFpBWsMu9tb+6rPTxzzES8cSdAZa8nzAlkaqnKEVimT8R7r9+DsmSV+7/e+R1XmNGqSyditTCutWV/f+VwT6dYaRqOdQ//yon067/tyyB1rVeQZWVFQqVmZ8/405tB+7SvSj/tA48hvC0R1l5Sm6qADKAW2nGLDEGbe/nMvwFqjSa3VZhQ2XcLgXKapBegK6dcJ2ysIYSnHOw8Q6W40VBUZw70953edDZiHWoqZgmnuC3j8+X36wampCsqZXUsOeGGM8WrUGpLF1TP0uh36O1uUWfLwxnN/X8FjuV2jFOPtbbwgpLt6jvbCEvlEUKSCYOasJHwBgcR6kqDpGHrj5VS6IJtsMt26/Uiy5amgKlA5+LN7R5uZ6sijVqvRqNeZ+q6bDwNBoybpdeqcWltBzlQB02lCEDxGrSMkyHj2/zNrC3IgJ9lLSPZu4XXOEJ0Bk48xeoJjoObI+ERCPC2UZYXIC7JkAGZCpyOpLUqkJwhDSRkIhJgbL38+J+qq0oz3HlBFz6Xi/oxIn/lMG9zjXwFJKai0INf3C3/vjjLujTJevbjI9773CmurC7z6ta8R1es0ooJrVzWTccGtOyNaEs4HcDqE1UjQDOFUHayxZGOocmjW4exsNPqTPo9thq2Fnd0RP/zJ+w8NsE8CISCKnEggLbj/4Azu1g6ByiKMx9LCKVZWznCr/b4TITyH51gIQRzH1Jt1irJkt79Hp9thYXkJIzQFOdNRwuatLeeD8wzI84y93W3Onj3Lyy+/QqPVYjhNKMqKpeUl4kadVqvt3mwtBkun2+G3fuu3uHPnDj/4wQ8OiHQBoiuR5z0YWswm7t5JcTfNMY9Qmik2MkUkoOG5pje07nQ+yL/7PtRrgm4z4uLiAtL38IIp06JispeRkLvvOiln4zG7nkABQSBZ7MVID6Z5hrIu99nxewaDwlbGrQVmh44rwo2h1eyY5xXJhscOH6bjCYO9XQBqsk4oLJ5oEIYx7UaLRqPGtD/m9ge3GOxMjyZVDDCyMLZMt4dMGbp9qs1ei7wwRfpJEMZ1Gu0e1miS8YBjT8h9pJbludicHbtPMctnz2G0ZufOh+RHDAW+wMNwoztLUVZYo+m02vS6bc6eO81Lr7zKNE25t7mNP54ShDt4ZTVTDR4SpAjPEem+jwwjrNGYIsU+YswXhgGLiws0mw0ajQZCCIwRlL9mIaMHsDNiSFAUBUVR7gueAk/QiARCSqzwsfgzlejD1eXNRpNLFy+Tphm//OUvSNMEGQiEnBP0FWVRoA/7o1mLrRQIgYxjhOe5PCStZ2N7n7jmEfg+ni9PpPr+uCHmVRSPVa9YlKlAW+q1mF6nzc7eHoPBgDSbYO2zLRBIKWi1miwsLpJmCWk+xU8lpc2pSiiyx4TMfB7w9MXHX+BBCA4Y3I8pyzWZpiRHVFdiocpcu+HFNWRQd4Ghhzz+rNFUmauSf3DOH4QR9VaXLBkzHQ+xRiNnN0o5U+w+GGJcqYrxeExVlPR3dylnOYUCgbUarRUSQexF4FWIUKG0JS3NkZRDo1Hn1NoaxmiyPHnIMvpR+ESI9OGgz49/+Df4YUThBY/f4AkhJdRqAb4vSdMUXZVkSYYALl44z/f+zleo12O6nTa9bodWp4f0IxqtDkZ4/OKX7/HW2x9QbzbpLS1ifZ/qvasMh2MGw0+vmmE0HfPuzQ8o04KqZpHtAJMqhIZLL19i5dQK2pRUpuT06RV6jTZSQJoqxBElS3M06g0uX1ij3Wlz+bVX0Fbwh//mr/joxj0W6tCKBF85u8j33nidl86dJZiXbkYexP4TJEp9uuH7Pu1OG2sMnv/xPDqtep1Ws+l831WJUprpA+Gwsh4gIx+TV5j0CLm/tU6JLjzMzEe61WrQ6TRZXOq6Dr7d5bd/+/dcAAOGsiz54IMN3v7VI+xGPhd46GR9rN9ujKG/cY+sVEzGUwhaYHIOG5YLKam1O8ggJB8oVHbUgG5mFisikHXwPGrLS4RBjXx0j2K4jsoKkk2FrgqMjhDCJ89yrD9FRgG1XocgjgiiEF1VqDwlqtdZPX3erdKOdlH2UHtdpe5lSihHzvrlUOdjrWW4t01Z5GgLi2dfpkgnJIPtR3RST7eY4QURcbODH8W0uh38IGL93l221u8y6O8dvdFcDA+P50/ng5YQKgylNgS1NnHcoBF6dCLPESqxQKGplzUKlTPc+ojd632yyfDgMzwc2ZE81aE+gNmkxygoS/rrN/j+v/lDvMBnNC3xagucf6mONIpRf5NRfwsvnFC7cweEpDKSPC/I///s/WeMZWme3on93vf468Onz8osX93VZqZ7egxnhmZ2tSR3KRLgLqQVVquFoE8CBBCSIOkLIX3TF0GAIAkQxF2RWmmwhpR26MThTs9whjPNad9dpstkpTfhrz/+Nfrw3hsRmRmRGVmVWZlZlQ8QlVERN84995zzuud9/s/zUI90DXqy//3dvwQMphhSb1/CqgL7yIb7AohBBITtBn4SU6cj6ungoX959PFChIhJGgl+t0E7immEIWdOdblwrodRNWU+pZQ+giaOMct4/LsdTw+nzpzk1TdfRliDrlKyrODDD2+QTvN9VVDN/tzWg9LA+gQGuSGtJnhCspse/nyEccKJcxdYW1skjGM8z+PChQu0212qQlAMdznfCPl6L2Exgm7PEgpDnaWYWiGFOwlfOGuXhnDO+1ES8PrpBQzw89tDhgeI5Fu3t/k3P3ifnd3RbLx6ABo4j+0Kd2sNeyILVc840CMOEUQxC6fP01jtYoMWpQ04/81fZnHtJe784mOu/+Q9J52fL2Ie8bExxjCdTKjLiqXVZTqNDp1ul85ij9qWoDRl5SEe4/ypVjWj8QCDATx836PVaOJ7PnEUujyZqiItcm7evMl3/+AP2NzcZDgcggRv1UO0JNbH5QVNZ2RzzX7Tuad7mDtflsBtILawYFykx9kGWAkDBZl2wbeVhoVGzOmTHZJGTBRBpRW7u2MGk5y8KPfJawui5+OfirDaoAbFXYFUe1AHvoCyMtzZnCKlICs0yjg3NAHUaU16c4pKFVZY9kqODPsk9TzWY35r5puy9sD/h2ACw6UbN5jWOWtLi5xeO01e5PQHu0g/4OTSKZIkISxj1EAz2s7Z3JyQPSgzaE7a791U9ttxzVMlKvJsgjGWqjhicBMCr9VGRhF6OsFkhyzKHxP8Rpuwt0LvxGlOv/IqxmjubFxHN9qU/U109hlzob4EsBaK2qCMZXMwZZzXDDPF7e0hVV0znqaomX1mFEVkWc7BRtDsdOmunKYqS0b9XbSq9tXOR0ApzXQ6pSxLRqMxWhtGo2d33f2kIYQkjmPCMKAsK8oDczVPQuhLfCxVkSORdLuLBEFEEDhrF98PCcIYi2Rnp09dV/i+JAx90nxKreqZHYMTi/lBgKprinxf+CBw1gdBGHH2wiusLC0RRwlplpGmU7Y27pBl7p49SzDGUBSF8+s+wk5RCgh8J2CqlMFYw9bOBmk6Ic0y8qKYBY1+NmituXLlBoPBiPF4xHgypqprilI7W90jA0OeEoQg6fUIm02K8Zjynmyd4x8HZDiz3JxPvu7RigVJSGOxhVGGdHeC+TSJ6l82GPbXr8ddRwrcxvtcrwePJkh42OGFoNvu0GgvMh4Kxv2MfYuE+QN+P8o8Zdxfp65LrHGWehYzc347fEJTFQX9zS200nflbVkskzxDab1nhYU2oA3G2PscakMPfM8JB8fDMRZDpeYhPMfDUyLSB/zg3/wZcbPF+a98nTBOHuvxpRQ0mwFhIMmnGZNKUTr7SS68dI6/9d/995zHGM7ftd1ZQHghzU4XGUZcu3Gbf/xPfp+Vk2tcfP01KmPYnE7J0pzBYPhYz/VxYpSOGF39EGHASyxShtjKIC1cfP0iX/ulr1FOp+TjMd1ui4VmB6MUJSnCOIvCw9BsNrl4/gKnz57id/7Gv4sWku//9DJXrt5msSE41RG8fXaJ3/rKmyycPeuIdCnA853k61Mm4T5r8H2fbreLNuZz23lvt1qcWV2lUhVpmZIXJVlR7nsaC/AaIV47Qg3ATA9bzTsi/SA6nSZnz55keckR6Z1Oj6+9/TZx1AAURZHze//4u0/88z19PF0JgDWa3fVb0N+FKoCgMyMj9hPUpOfR7Pbw4wSdjVD5YQtAjbOmKEH0EH5EY3WVZm8Fru5QDndQ+YRpPmGeDGdlRJEXaCkdkb7Yo9Fu0Wi3qCtFkZY0Gg1OnT6H0ZrN9XXS+sBzn+1Anc6I9EMW/tYy3N1kuLvJ4qmXWT73GpPdDbLRzp5n9904KFd4tPvihzGtxTWCOKbb7WGBm1evMhmPoTxiAniQSH8YZkSJDaHGUhpDr9mkHa/SawSsdCKEZyE0aKHpyTZZkfKTd3/MzsefQJd5Nuf+qPtYCI+ZDdBsUtW/M+K7//gqYdLgxOu/TLO7yLm1DgvtiHd/8iNuXr+DtWMkNzBWkmlJXevjEenqKOJh5tVZjqi3P+2CVwBNEDFhd5V4oUO+deMzEOkSiBAyIWk2SDoNTi+1WOokvPnKab75lZfY2e7z7vsfM5UBQjR5qDfFc4jTZ0/yl/7t30LYmmq6xc72gNu3th2RPl/QHLQ18lxm9cYYwHCt/+DFU5QknDh/kbXVBTzpxqSXLrzEufMvkfd3yNY/4kKnydfXlmhI6EqDUDWZUShrEdKdhD8rupkT6StJyF9+5QQauDHI7iLSb97e5ubt7UPP5z40gBXcptXcFWtGwGrlXNGOchkIwpjV0xdori1C2KIwAed/6Vt0f6PND//ZP+XGJ++74PmMfTeuR+i2rLVMxxMykbKyskK32aXT6dJZ6lHqjDrPCAoP8RinT0rVjEdDEIJGu+t8JptNkigmDiPnclVVjEYjrly+zO/93u/tV+X54J30kWseaqdGbdSuyUxxTWa+ZroHsyJIKlwOaQIoA20BX21CK4CtHKYVjCsYazjZiHjt1BIi8KkDQZnX7G6P2Oqn+ypwcPZjPZ/gqy1ModBXK+xhGUYjXHjp7FdVpVnfSN0iP5q5pkn3bz2tqG7OqrtmFRuE3E1gz3/O7DPfq7STQBtsYPn4+g0+/uQGv/Wrv8LX3/gG/WGfOxu7JCLg1PJZmo0WYeVTF5rhjEh/IO51P6vYH8eeMt9YpBOKNOVINkgI/FYbr93GKvVkifRmh9apl+mdOcuZV17FWMOlmzco4xa6yF4Q6cdEqSylsqTlBJjA+g7wyd7vwzBkbW2NOI7xvLnPkWsQrU6X0xcvMN7tM9zcxJQlD+sklVJMJs5Oc2dnQHVvAOKXDK5yKSJJ4r2w3zmkEESexBdQ5TkSjyRKaLbahHMiPYhIGh2s9djZ2cVavUek7/ZzsjwjaTQIo4ggDAnCkCLPKYsD9gZCEHoBcRjz5ptf4atffZvbt25z/dp1tre3+PCj9+8KAnxWYK2lKAuUVugjBnopBXHoOQsd7Wxat3Y2H3t9staGK1euH/5Lwf7G7KO5Wz4xCCFoLC7SXFmB27c/NZEupEBGjhOyymCNvW+zPUgiuqeXUWVFMc5eEOnHwXzz/FEgcBOw+aa/Zb+S8DFACEG302VpZQ1bl4z7u+xPumGfSL978VsVU6ri7r5t3gSsPXwCXBUl/Y3NQ383LVKms9Kzh7Xj0BfEgcCqivFohBUW84gX5LET6Z7v0VpqI6VkOphQF4eckBDgSTSGcX8b3w+oHmNIgTFQFApVC6raoNW+6sgai9aKotCk6RTpeUjP4gcB165fY6ffxwsi/tLv/A7aGmoMgeezsrhCFudsBhs8o+4kTkq2W2EtmNyAsljtgpQ2bm8QBiF1WaCKnMWlHlEcUJUVVy9fZzyckB7hMzpNcy5dX6cSPnle4Ach1rgs5kkJW1PLx9f7fO/7H/LSqGL5lQvUFn704Q22dga88951bt6eMp0+nxOiM2fP8tWvvo2xsLO1RZZnVMXns/NeFAX94RBlFEVdUdW1G4gEeM0Q4UvnYze5fwEphMQPEwSCus6xRtNpN0niiDMnV7l45iRnTp5iaeEknc4CvhfMdv+c7cdCL+H0yQ6Tacl48mwpDb4wsEDt/EFltIRoLmAzhTkwJxW4EkrvkMCN+49noc6w1lIObiPKKUEYsXzh6xTTgunOZKYoLjCmppzsoKoIo+fp2iXaaIIwpNNrE0Uxyrq2vry8QiOKGPR3SdMp94eP3gtB1OjgRw1AMNlZJ58OH1Iy9WgzyDCKiWa+70U6oi5SqAt3GfKJ841/aJDmIZjLKA13K07nO/kWJv0+WZUyjWAUC4Q0EFQYDLlwljPZeLKvvpjzs09cdWLRWpNlGdYLKXoRVezveWZWtWYyLfDDiLjdJUbgRTGqVqTDAVX+2cgN54fqNsd1ld8VOHMv/Cihe/Ic0o8oqxBjPIS0lJMMNSP3ZdgkaK5ihcAIgzUKM93B1g+aM7gbZm1NWddQ1/hxTHthgaTZwvNDGs0mp9ZWCBCcuXCRRrtNf7OkzB++2H9uIMReWFmj1yXwA771K19hZ2fEpY+us7s7ut9J6hgfPQ59Os2IXruBwMdYD18GCCzpeESR5ZiiJJGCOPRJ2iEh4NUlQniE3TZes4FNIKoEJrDYwHI+Kfh2NaHVlHRDyLQ9KrtyH5L9rIP5/Hxe9RGw72c9F8a4y4IfhgS+wKsU1Pc/oyqrGH1wh2ono/WtVaJGi7x2rHlRz5SXAW6TbL6oMeAr8AwsNUI6kU9/otgaPphkqOqavCxgOEZ7llLnjIoh6TQl6PrEMqSaKEx1IGh9bukxV4M/4L7VSjPNSqpaE0UxURhitMFaTRLHruopivClRFrcvNHYu+0BrAvdtAGY0QEl+nyD4oj3n3ehElcckAAtoCmcX37Th6W2R9MKktLQKA2RdMS5lZLK88hqhQwVSRcq4d6u2evQ7LZRXUvp2f37bQ6cT8B+n+3PrtPwwLnOXEXus1HxD1zXeai7PfB1kEg/ZEPUD31eevk8rU6TwUaf6XBKEIRkRY0ygkbSJQhD8qJGqSkbkzF1WdJqtPjG22+zvbPDxvr6c2ole/TgJoQgiEKCJMb4PuoJ2ulppSnykqqqMUiE79NdXHMWGTcvc3BGK6SLGbHGVaq8wPGhtSZNU6SUKOUqXToLSzQ7XXrLa8RBSO5JxBGBBp7n4XkeWuu9r2xWdfvQiqMvAaQUNBoNms0mnXYGVlOUNWVZO/5VuE6szDOssQwHfcqyQgiPdnuBMIyJoogg8BEYjNXUdU1d13uh061Wi3a3S5HnZFlGda8wxuKsGowhCiPa7TZxnCClCxe0z2gvZa2l1gornDr98NdArVxuUTQj1KvaoA8zUj6I2XzDLZmFW5c/4uMqpMALXCmUEW5sN8Y83U5fCIJGjBeGrKwss3zyBDfH40+9RyuFIJEh0pcQhK7KxRRUByyEVFWT7o4xtcZ8CXMQngr0ga/HBGst0+nQ5S5kI+4m0Q1uMi72f+ZFzoZWV+7r8KPe8+8jnhPgCQh919bLe0wbPOm5UFIBSjuRalXnT9cjPYgDTr12miAOuP7zA13UWQAA9yRJREFUq4yKQ5qfFJCEKGvZvH4FtAs3elwwxjKZuJty0Dpa4Ezo67Kgqkuu3biGsZat/i4g+O4f/hGffPIJ/+7f+Nv8z/7n/xHvvf8u//K//X2iIOLcyXOkac7ldz5h9LRlH0ch1XB9ZrZ/4NkzwAfvfMBH73+EwCIEnDq9hrCWNM35/vd/xmScHln6tDOY8Cc/+oBXBzn/3t8Y0em0sLPXbk0sO1Po96/xwU9u8mu/+hZfe+sMW8Mp//v/w/+bS9c3GKU1Ra3vsiN5nvDNb/4y/4v/1f+GD95/j//d//bvsrW5eeS1etwYTiZMplOs3XcVtNYiAkm40kA2Aso7E9Qwu6+fkX5Ao72CEILpaAOjCk6sLnNibYk3Xj7P26+/zCtvfIXz594iipO7SFopBWdOdvnaWye4cn3AeHJMBeALPCIs5CmIHK97kuDEadSOohrdONB5CXw/IPQj5EOJdA35EMSEabpDKiSnv/IdTr31l9m5doVs8OcYlQJjrNZMN8ezzROFxSCmK9BaZe3kCS688hJC+JSVQQAvv/QKntW889Mfkw76Li3wARBC0lo8RWvxFOOdG2xefc9Nxo/s6x+9f2h0uiyfOEM2GbF5+zpGa4YzZsPY+YT0U/Q7AY71qdlf+80DXbRrg9vXrzG+OQAKhJhv62d7pT3WWoxS+yRbjiP8PofyTa0Ng+GQaalZ6cYkobfnJV2UNWWlaPc8LlxYIYgTrBeiteH6L9757ES6FxB2VgBBOVxHV0dvPcedBV76lb9M2OwyGE4p8oLdSx8zXb+Drd3f+Y1lmud+DTyPUpaYOqO88v1jEOkV1vqkRU6Z5YStFqunT9Ne7OCHMQs9n3YUM1hZZTSo2d7c4ud/tsN2PmKfPftiII4j1hbaSCl4+dULpGnO3/u//X8ckf4pPmqnFXPxzDKnVhfABhgVIKMEISyj7Rv0tzaoxhPanke7EdBeahJojT9WYCWN3hrW82j5CcgAhEZITW9zh3PJFYyU2MSyXVg8+ZD26wEd9snzeflqgFNMp7j5+5xctc4uK0oaxHHApJhAcX9fVg1Tbv/L94gX2qxceJnmiQWGeZ9BqhnlQ9erJECPPTJVGEimECl4+1STVxZb/OzyhO1RdWSQmAWysmAwGTOcjuHOJpWuGFcjjNSEJ0L8VY/R5QnV0OzfrxZwAqcIv8UDF0SuzddkRU2r1abVbDFKCyyGlbVFWq0WnjFIa5EITG0x9ya+GtA7Cj1VjkSfe00/5PbMufY2sIgrElienX7Pg3YIvW4Asc+kqJgUFTtZxrVLObWBwoL2wV82dBdgZCC3sPSNE5z92isMhgNu3rmBlrgwUMl+lUAy+1lndp7buCDZA9fqPuvbmcsUNU7hPSfkD1p7zjda5xYvB9uQhKQV81u//hc4f+4c7/74p1y7fIVG0qA/zgFJt3cCBAwmOXU54qN332fcH/LNr36T73zjO/z597/P7sYGytrnrD7mwQ+DEIK42SDpdlA7uxRPMOC9LmvUcMx0MaNGEIcJp8+9SrezyujSOxzUV/oBxC1XpaInx9AIvMAetNazqhWBtR5CBJw8/zLnXnkdKQRSCPLQR3BvGIVDFIXEcURRlGRZTl0rBgN3dx6FzPiiQkqPbrfHwkIPiabZ8Nntj9kuR0hh8YXF6pp0PCCTKcKLiOImUgScWD2LmFV7Bb6HFBplasoyp6pKjHFE+vLKCqfPnuX61ats3LlzCOnsxBlaKZqNBqvLy/R3B/hB6AQ+z6jxtbGWoq7wjEEf0ai1sWSlIvAFC+0IzxMMxhX6QZ78M5tGIUF6AiEFpuSROQ4v8IhaERaLttptbtf2qW5MSE8SL/aI201eeeUCF89fQA8GXP9AHLLj/HB4SLpBQhgEBEkAnmSz3qUq9iuvqmlBP3PqYvuc8kTPHWoeewGutZadrTvsbq/P1vkH76Vhf9IIICBsQ9SGYngMIv3TI/Cg15BoA/3UcHCvxvcDkjBCCktVp9RVOfNpfwoe6UIKZCiJWjGLyytEjYTd3pAi06iyvC+lVVjAWozWHBqh+hkxb+9J5BP4zrZeCpBo+oMhFoMfBAgpabXaWCR1bRiNM8qyxlqBEB7SD1Hasru9S5pmzm/nWcYR936+099qJnQ7DaIgYHdnQJoVFEVFrY5uTcZajNJM0pQPP75Cs9lgMnUNwuIsKbUyKGWYDsdcv3yNnXHKoD9mPMkplbMoeq5wwJfJD3wajQZxkuD5PsLzEObzSdi21qJniuC73s6CqbQrl9JH7GBbi1YVQogZgQllVTFNc3b6I27e3qS1sMmkv4NuNIjjBCGgKFPyImM8njCelpTl87WUey5hDbbKMdkQq2qImqA11BprfKoswxpDEMY0O0tUZUFdlbMV372Nayaxs2ARVFVJlhdOZWLn8kEXXOoHIUJKdO0ChKxLvUEaTRy5mvdauT5a1TVa1WhVgi6Zh4oe+ZGwKKWoyhJV1Rh91OsffTGdNJqEUUQYhFSFWxRYbWYBJ48Bhv0A0oP5r9ZQpRNkEFIVI7SegK1wLLnCMXYHzmBOtkTsB9V9HmsOazFV7ghJrbBWYg+8sbXWjX3tDlHSoBYeSmn8x5H9YC1GVbiF9VwC7IGQeGGAF4SzayDwooRiPKAuS4pJSlVW6DJzKnYLILHaoPMpIgzxGz4iTlg4dwGvWqbIM/e3WUqRHrBEEB6EHUTUptnpEXV7NBsN4jgkCn0C36OqKtJJymQ4YTLsMx31UfV8t+SLMZmfTqbcvHGLZhIy7Yf4niAM56Fln74KUGDx0NRFztb6JnVZsHZyDc+TbG/32bi5QaUM7eVlGgsd/E4HWVfYWcCyCHyk5yOaLUTUQAjXj4W5otFqYbwY3WnRjCwvLbUAS1m5jJBxZRkfnIbNq0Vg3/9x3s4k++T6/LW4vqmqNUII9FETZgum1qi8YnxjExl60PAQsaBUuXsvgWv6HtCYWYTM+opQShqRT+Advflp59dSSgLfd3Mt634qhYfwBDIAY1xg2sFH0w88wraPFoayWbtF0RHFFFKCJwVCGKqyoCzDme+rQFiDnFUuCGA0GvHxpY+5desW6t554fy63y8sPRbmFeyhhEC6Ia6qIawNnm9oBQFxFFLpGlmXjrxnln1twQgI2wkyDAiaIda3yEAQRRGi1lRmVnA1P7e5Z/i8GqHioefdTBLay02qoHZVPcZghJvv6fzA8efP1b354DNFvGcloQhYXlzGVppG0sZqQxAEJG0XMLt+Z90FB1qI44Q0z9jY2mI8nT6nanSHOI5pNhvUtWIymewRopbZWqE2RypEPyukHyGDCJIWIorwwhCkhxUeykCt7axcfN9o39oDYbPP60V/ipjfX+H5CC+kqg3TNENoBbomnYyOvN/GGJTSd/3+BYG+D2vnYaOaOI7wZJc0dfUUxoBSzk+4rkF4ClVVeMKnKmuqeh4KKoiDhE67RV4UbG3rWXWzu+baGGqliOKY5ZVliqJgMp4cuA8C3/MI/IAwDAgCV72stEYb83QV6Q8saplVNQj90GfKGFDaYK048rVCgO+78VxhXCzU7P2POrwQEIUBUgjKWqG1odFIaCQx0veQkY/SimkxffwbEvPxCR7Bek7g+xLf9zB1RZWl+MLSazVRdU1VlHvLo+PAWktdKwSCKAnxhIc8xPf6BYH+OUEd+PcJTDKsNcfkxqzjEHQ1azwHywkfDyQzNbonCX0fpS1C3L2+k0IgpSQIfJI4wOrKhS4/glj2sRHpXuyRrDZZXFvl7V/6ddrtHkUaIBs3GN6+zWT7bkWrMK7neZJNx5OC06stFrsJUlqksASy4Cc/fYfeQo9XX7tIp9Pl4mtvgJD843/yB/R3czY2Bty6tc5wnBM0uoy2dvj5n/wr0mlKnj2zxi7HwoVzp/iVb77JrY1tfvCTDyjKkqI4nrfZ+sYO/8f/yz9ASsFgeLdfVoBTHPVv3uIf/u5/ybgybG5PKerDs5+eaUh5t6/77Hs/COksrZBVinF/G3WYL/RjxlGUjlWGatN5fFpjXY9xz4u1qsnGWwAzT3XLnfVtNrd2uXT5On8cBPzGr9zkzFKXlaVFLp57CSkl1zZusDvs887PPuAn79xBHVL2/gKPH2r3Fnq0iY3asHjOpa4NMrRS7N66ge9bTpx/g86Fr7J5+wZbd246f3JzUEE8lz/vo7+zwZgP0eMNjBniBqsE6fm0llfww4DJzk3KaQVFCpUmOrXC0kIXYyVGTaiKiv7mJuV0SjbYhLL/UCIda5n0d0nHBaZ+kL+ej3uAD0mqOwRCSE6ffYmTZ86xfusG169dxmi1nxvwODBXovs4VaMFMjBKMbr2CWPvqvNcDPRsJ3He+A40QIFTQiY4dXtjdtySozJXHgNm7KGx2PEWJg+gPoOUwX22QEmScP7CBZJWm3FakucFN6P4M5+BURXlaKYumU9Gwgb4McnKCu3VVSflERKVp1z94R+hZ+ooa0GbBGw4W6FE1OmEybWfEbTaLLz6Bu2FHr/2V/8jVpYXuH7pQ7bXb3Hzw/e48u6P91czQQNWvkLQWebiW99k6eRJzp9dZLmbsNCKaCcB67u7vPfzD9m4vc6P//i7DPs7lMU2j7XW8Snj8idXWd/cQAqBZx2JfvKEh+db7tx+iBfzA2B1jS0n7K7f5I/+xe+zsNDj13/7N2m1W3z/z37Cez97l1/6lbf4+q/9OoudiGilAenE+ahWlVOxeT7BqTP4C6vzfT+icJHGyEAQ4Z06R8dY/oe+ZTocc2drk8FozPe3DN/fPEDKKJxyOGRf6pyx387m7feABaPWlt1+ihDsWR4dhTrN+fgf/TFeEtL61RMkry0yKtZh2TqbkGtADN7rTlxvCzBTQWQCumFM7KdHHnvO7zaSBovdBSrtLNx8GyCtxHgGkgqlajymdz2anXbC2rkF0mnBbbuLTg3c4dDchygUtJseoV+ztb1BmqcYa/A8H1uv4FmNQCKE5L333uP//H/9PzGdTskOeljP+sC97z8lfAmd0IXLjqYwzWBJ1zRjxalzq6yeWaVxZcDNy7dIjSUPnZd9pkAZycpLp2idXkJEHmk2BmlZXl0i90Kyso/J9P45jnDPx1wWX/LQ6ouLp87yzV95i+3hNh+ufUhdVVTTDF1oxpddvvYe35HgnrF77HUsoEYVZlryjTe+QedbHW7evMXVq9dodXq8/MbrbNzZ4J/93j9nNBjyy9/4BstnF3n/vQ/57h/9a8qypH7C66MniRMn1nj77bfY2dnlxz/+2Z5VhDWWfFpQMaUqDoYzPD5EnTWShTPQiBHdNq2VJWSYoIXHYJIzGKUUlYcblEugRNVOjQ6fSnT5Bce8nvshG8xC4DW7yLDJ5u6QndEUW0zR+Qita9QRIrSyrKiq+gV5fgSsNaTpFN8XrK0s0Wm3KCvLzdtb1Moynmo36zcC6bkgb6kVg+GQcZYRhRFRmNBrn+GN115hMBzy4aUPGY3He4r06XTKzs4OS0uLvPnGa9y+dZsf/uCHe8JBKQXNRot2p0Or1abRaoEUTLKUvCyeXpuZ+4ofQRIbYymrEqG8h9oEaWMZzexnj1KWB75HtxNhrGU0Kd2GoJq1iyOuQeB7nFztEYU+dzYHjKcF58+e5rVXXqKoNWleM56OuXL9CpWqHi+x6eGIGXBVZMdgv4WAKPBJQo/RzgY30hGRrnn75YtMBkPWb96iMoZ53ebDUGvNznhM6Pu0GwmJ7+E9oxUMX3gcFIU/C3qhcgzVFIhAdpwozU4f24lFElq+s6JsN5qUyiCn+/lz4DLo/CCg025ycm2RzQ3Fx4MB5SPwe4/X2kWAlJIkadJsdVheXaWqFNVodDeRPlOj762eniCEEK60afZVlCXbOwP8IKLZ6tDudPG9AG0FjUaLbm+BIIioa4WxFil9ZxUznpClTy4Y5/NCFLoHJtodkmU55SMEuSil2NrefeBr8rLmzuaAqYaydsUGT7utPiqCMMDzfZqNJo1Gg0ajyWAwmE087JEpwuCeN88TjhB6wjJ8O69P8SXC99z/3/WeFmPuHjlrpagVe+GC/d0Bo/4OkTBMWm08z6OYjqmylDwryPNnvALjiwRdY3UNnqtPF57Ei5ugKlQ1wBo1S7H2Zwpf767KiX3c3eJUlaPSoSPJ98yCHak6s/fbn9ZYDXq2qpztLButUHVJnk0o0jGqLmYk+sOeb6dMNkaCedBzNJeNHgWB9J0//NxPM2m2aDTaeH6AquuH+K5/CswnGYccVlczd9V5Wf/Rpw2JRLQENjLg2/29gsfO1Yp7vpzEzmow2tlqBUFIs9WirmuqsnTXMwjw/AAhqkdXygvpHh5r7mEfLPa+6gO5p0rf+7tZpUyZpuhibkaMkxB4B/xvjMJUU0wpsFWGrWMEAukFzqNTyP0+WXggQ0TYIugsE/dWaHW7dDptoihwe6SzuYCqFePhlPFoTDoZkqcjHpy68/xN/quioqqrPcVsEMw8AQNBeVgoI+7azPeRjxJlaGMpK4VIC7a2+hSVYn1jh3Zasrs7YjycYIWk1esRN3xk6GOrAqTEStxGj7BY7ZSpwg8RQYhsdgh6S+AHyCRGasNSO6ZlK8qphy0gOazNzdvUXIV+sO0ecduOOz5bYymHU5gIvN0EBh4qLfZzD+a2jzO1s6mdurUoDZNcU9ZHv08QBPi+TxCE+H6AAXxjkFIipcRITSUMFoOUAuHNipAszv9+lmey1xc94BEVuDnceDzEYkiSJtJ382OnqDMYA2mWsrW9db9PLnwm3nOuYqtxjjvWglKuubcrSyQtwggizyMQEmEEwliEcZY5xolbsUoglEBrRZXvP6A2t/sboPPhZF6+XLH/uwOQnqTbaRMEwSyQzrC8uMjJpVWEZ9hOu5RFQSYMta/RTUulodY1WhusmCnU7w2b1Ra0I1l0rVH1rGzfOO95ozRaK1Rdo5Qbk4XEtQkM5jkm0cHNg6X0kFLeM0Wx6KrCloWzPXuscOOKFyaEjTaiESObLaQXkI5GgCAdDcjHA/S9wYgPUJR++eBumJQz72b9KIyLm3/U1Uy5Wk4x+YQHSR+ttS9I9AfAWktZluR5gDEWT3p78x1rnZLaWtBYrDHUqnZqceX6Fl96WE8jhaCRxOR5OOvr9ztzbQxKKcIwpNfrMRwM71nnCjxP4nn+bLxy1fza6Jkt79O9f9J380CX6zFTxM6U8lobhD1elcNDfdHB+aIfbBLH+ZO9S+m+icKAVquJV9QoLQm8wPVBz4gq22qNqRXZNEVWFb62JJFPFXiuksw82mxYG0OtNXWtqH39zHzOLyWeJXcIa9yXjBF+6CpkPsO0YFbovDeWe1IQehJPyr23uvf1Uko830N6AikALJVSD3TpuBePjUjXhSZbT8llhqkNcRzz1/7aXyeQHv/lf/qf8i+vXj3waoMx8523J9egtLHc3pqw2U/3LrDvTfD9Lb79LZ//yVu/RKfd5v13PmA4HPHtb32Ht776dRaXlkiSiNAP8JDIu2qDX+AwVDjryd0CPtyAysK0frba7HEgPcnpUydZWFrkr/47f42/8ld+hw8+/Ji/95/9Z2xurHPn+hXyLLvPqmiOZiNkabFFWSm2dyZPPixHCMJuF6/ZRI3G1KNHS9bOJ1NufXSVYbLBrQ+uEkQBJ8+eoBWGBE9ONvsCD0JZwGCHsNVj+fVXwSi2L2VU6Zitmxv0N8ZU9SNs6qWbUI4cw2Pndg41Rgsmu7mzN9jzm3b170U+YXv9Dkpbtjb7lFnKzo1fUE6GVNXcgPYYfbfJwZYztuEwzGsPPY5SpPthRGflPFHcYGVlkUaSkDSaiDDGi1oEjS66rtBlerxzehRo9lWYhzjoPLA0zhf4F7rIMxF6a4Lup04Vu8njJ9NlCF7LbXCo6f7JWktRlEwmKafOnuelC+e4feM67/38pyhtmEwLSi3Z3Nkmy6bkxXErrgQ0205pnk8gnz745doxFfnmBtXu7b2VhTWgTeRMavXEnb9JZ8/N3LZIAAWqyBh8MmUchPzLax8QhjFFuktVTijSqZs9JYuw8BbJ4ioXv/MXaC8u8NKFk3S7DUK/pChyilhSFiGjYcr169vsbu5Q12OcVOOomyJxXh7PGeaWE7NnVCnY2nKWJlV1+IMbhrC06C7n7q6z3rgXo0zx0brBkwX+eobv7/DjT/qEvo832cazgl6ny7lz5/BUhiiH2FIhlIZKobMUjKGYZBg/InntK8SvfAX/bJPuynl0kVNsrWOKDNlMkGjEVvDwz1rhWNoc12792dc85+6zDMfGkn2yQ9EfYcaVU6PPnbIKMB+Dkc7+o9KWP/9oyHvXp4yy+lCSzvd9Lly4QK/Xo9nq4nkhkQwI/Ji4EbO03KWsC67e+YRpPSZuSmdNkYMqYTzOKD+u0bVGT8wD1dZlZRlozc3b23z/B3/K6uoav/3b/xbLy2tEUQRY0mlGnhZMJ9MnQmwVOIvyvoHNyrWmE0BDgAmc73k0yIniPv3hlKm1VAaiGQluaqg8w+bwNrvRltvrFPvnaYxGz5Mi5+GzE/aV4vfygR50F9r8J/+j/x4vv3ye25t36I+GvPbaRd588xV2RttcWF1jMh1z89Z1yrJEnQFVG66u32JzuEu5C8U2++1sjlmb01rx+//iD7h8+RqnT5/kpZfOsb21xe2NW5RVycXXzlJXa4zSLUaTDV5/4wJ/4Td+hZ/+9F2+970fPqME43y3Co7qL7e3t/nJT35CUZR72RwAGIMabCHGfcy9ZPZnPSe/BV5Mo3eCxVNnCVodoqUVqtGQP/p//ufU2YQ03ULVBXo8ICDHSo2dbU49zoK25xcusdfzA5qdLkIK0vEu6gE5J3uwFp1n6FK7i2k11sw7pU/3HHuzTdM5WfxlhFKaO3c2CYJdUIZ8kjKerfPmQhgLKOtscrbGoz0bsEYQEQcBcSBphB6NKCCN/PvEYMJahLU04oSVpRX6u32kvHf95zZuk0ZMr9cmSaIHiso+F2hACFprSzQXepR5QZam6LKinkzdJnhWgBDORviYOKoGo1aa4citlY67tq+VYX3LbUyUlesLpZAEwqeyGqEstjaoQqFK9XhJZo0bA+ffH+dPlGZwZ5uR57ErHQl5YrHBqeUW1svxQ4svQcw1Wcc9Fa25vbuLJyXF51DN/wLPD/wkJmgvoosx1Wj0qTp7AcShhyclZa2ptSGKI7qdhKpSbPWn1NqiDrZbAXEc0uk0ERgGg12GkzGpNYcVdh59/o98tkfAGosuFHVeU5cVRimWl5botju0O517XsznokYHyIp7dxUqIGOSlrTaPVqtFtNxynB3yMrJNc51OnucufQkWhuMfvo7ro8LxlqU0o+d4DW4NWWqYSt//gj0OYQQtNotlpaXeO21V/nV73yHza0dPv74Iwa7u2Tp9NASRSmdz1IYBsRxuHeszwMyCPHiGJ3ls1nV8Z/VqqwZ7Q6p45xp4BNFEb3FLgnixa7x04LRUBZ4TUNjoYs1Gi+IgZQiy4EKpJmpoY/xjOnCfd39JmBBlfc+y07KqVRFNp2glCGbjiizlHw6oMyGj/hh9ANI9IdDSInnh0SNDkmzRbu7RLORuE9gLFZIhBcgtH6IV+KnxL0EyWG/P/TEAU8g2gFyIUIPM3ecPTnmYzo/IZwSRwYgI2cifODELMwU6AXddo/lpQ7pZEIcxa76pKxQCLI8I8vymS/cMS6kwKWtByFUx5hGWAvGossCnWczxaIAGRIEC4BwxIp2hJg9xDbIakU1KQBB3p/ipi9zD48ZvBjRXMPvrNFdPUF3sUez3SSJA6QqZyothVKasqyYjFOm0wyjH5a6M/fUfQ431A/cSmuhKO754T2QEqLQvfaobONaW+q9ham7bhu7U6SA801YioXzgk4SKBQ2n9v5ube2dY2tFXVeUgP+mYtE0kM2YvxOEzGdIPo7COkhowipFTJwlWKBr4g8lx2iDtvcmsue57dT8Hi8IC3oSekyIlL2SmTFrHjCTvZehgF2xhU746On40IIms0mnU6HIEzwvGCvwKUZN+i1ehRlhmc8RC2QPngRqJnKWlUaNdLuM5fsF1McYvNmDFTGkuUlu7vbhFGA53nEcYycsVVKKUcWPyRE+tPi4N5hahzXneCu1USB78G0UEzTgrxUVNa6jzRTv1ntXuvGwEOIPR9n6zP3yJ+7hR0SJiqlwIs8mq0Gr712ga++/QatGw22+zucO3uatcVVQt+jyieM4xbFZEpRFdAFbTTb9TZ9DWp8yPFn5zzvY27eus3773+AlILTp09SVAX9cR+EpdGMMUnA1p0hZZHTbCWcPXuKq1dvPOpU7slj5qPPLGlvruYCF0R4kPQvioLdXbfOuNcb21Yl9mCf/djOzwcZIv0QPwwJ4wZJo0s1GLF5+TLltI/z+qn3Ho+7irhegLmwQQifIIwRs8qY48Jq5QIN7Hze99n7kqfN1T5tWGvJ84KyEEwmU+IwoCzvHlescNXfGvZEXokfEngevifxPYEnxSwnzvkCH5zlWePyhTwpieOYKIzwZhUl8/brmrfAkx6+7ypDpTd/Pp7mTbJ4QUDYbKABb+79LgQYMyPQxWPJ4rMW6gdUmB3+N5aimA0SQuBJ6RTy80wnY5x6flax9FhheXSFr7VUueuf56vGbgO0DbDUSM8tP+/tNh925hbIyifQ77/Acw/pefhhBDpASM+1g0ed/AjXr7ksoHlVlcQPfMraUJSK2tj7uEmXIeGqa7I8p6gqavtozebxWrsAk+GQP/q9f0Kz3aLT7hIGAR++997dLzrYuJ/SRDFLU65cukSr2WRze5PJJOXVr32dcxcuujIh3+POTp+fvPsThv3h4WWuzyGu39xwCsVp5kIEX+Au+J7PG195i7fe/ioyCvjxez/jw49/wZ0b18nS6ZEBBC9fPMMrF8+yvdPnytVbVFX95NXo95573IAFgalK1PR4PlObO7v84Z//iLMn1/ibf/XfIopj/ps//B7XNzZ599LlJ3/SL3A/bA2khJFm5fQi1gq2ri6TKwvFCFQKIgIxl9091jcHYNLf4tJP/40j3vICrRR1+SSsreYy0rnR4T6iZpvu2kmiuMHC8ipSeNy5cZUqmyuHNel0Sp2NZuGUzwjrEDCTWlq0qDAjgRko2OEuH93HgWTlLMnSGcq8IJ1MoBJuITsXpBvN9p1rjAfb5JNzFPkZFldO8x/+J/9jRqMh77z7M/KyogwTlBXk2oO4A6pwstejYC1kE6gKOI6y0BRATdBZIOico91uc+LECkncZHXpFALB9uZ10umIW5c/YHfz9gMOZnFTfMG9052k22Xxq2/TWlymu7RM0ojIJinVRBHrCZHJaRhDGoYM+5vcufYDxsM+df0wxd3cU/eLX6VTVbCz626xOrzw6hiwTAcTNq9uEEeCdtJ2rGlnBfwU0hJqha5dqE9VZBRZThD5+GGICBt4CyuIdo/OyQtuMd9ZZHnjJo2bO7y+ssOlfsUfX0+pzOxx1zieLONuj/Q5ofo42p17jPceu6gpaK8IdA3jLYN+hOultebGjRtsb2/ztW98iwsXLwIWMbNx8TxnkbB+fYPBcBflF/ubAgpH5M/DMzWu3zmJe1SH95zrbB/ITwI6i4u0ewvIyMd6YHABp9KXhHFAEPh4wkOjMU9QEmFwYrkKGJUgFZzdLDg70dzKFNft3fmdD+1lDI5fD3BNNeC+5hrEEHcFy4tLfOeXv83a6iqra8tIITlz4hSry6u0Wi2kH9NqLHB+9WXS1oRAexRlTlqklFXJ5Tu3HngqRVHyb773Q5rNBrdu3QHg1q07FEVJe6HBiXOLlGXNlXevo7Xi3NlFTqz0uHzpCu/+7BIbG1tH+vQ+LTQ7XborK2A90D5xGHL29CpSwHvv/pTt7c2912ptKMvP0/fagk7BlIxuf0g13qR16iLLfkA27WPMCFcO5p6oeTEZc6Xzs3Wp9zEPTd474ScN15lYqzGmRhwMDD/O35rSSVVnF9ULAvyogdGKukgfeY5mzb7u7ssGz5MkicusyfMCrQ1bu33G05Qsd/MVBaQzzuneuxR6hkagSUJLI5GgM9Zv3WA8mdINfUQzYViUVNowHo0oioILL71EkiQsLS7x6iuvMRqNuL1+Cz0LFa2VYppmDIZj4kaT1958kxtRwLVLH+xXA33esJD2B9R5gREeRvpYLfbbtLH3eLEc65B3/Xsc+JGHH0lUZVDF/lrGE4JOHBF4HnEY4vse1XTMh794D+F7eKFPXacIzBPRAj0OqKqiSKeUeUWt3MPW9cEKgR+5MOdxXpFXj9uu6wW+DHDbtxYvahCunEPXBdlwC/MIE2pr2VOiIzzC0COvNbd2J9RKU8xIdHvP32z1x0yzEk9KPOmRl9UjBY3CEyDSiyzng5/+7OEvfMqS5bKq2NzYJG0mjCZjiqKi3e5w6tQZvDBEBD5BFHHt1nWyccr90qfnE7uDEbuD0dM+jWcW0pOcOHWKV15/DYBrN69zZ/02g/4O9QM2U1ZXFvnKWxd5/xfws3cuPTFV11EQQiDDEF94aCFQ6fRYI/JomvKLT65iESwuLhElMT/+4GP+/J33n/xJv8ARMGArfN/QWWhh8PCabZjkUA8dyYkHRE9IrmMp0glF+unDCB8Nh0++giiis7JKFDdod7popbi2s81we4N9T4VnED6wALTAoCGvnQRzyjHYoEdD2F6kcfIidrhLVtVY6v0wGZwaZjLcZTLcJQgS/KjHyVOn+PXf/HWuXPqYP/wX/5zd4Qhv5SwiTNBGgJ/M6twfoh6pS/d1HFiX9udFMeHCKTorS5x57WW6rTYvn34JgeDKlQ7DwQ6D7fWHEOlwlJd52GjQO3OGVm+RRqtF6EvK8ZCiytB6gjIFWRJTljXZdMRg54rbgHgo5tYuDzLF/2JAa5g8xKnnYbAWirRgtD3E9hq02l1soN0mjREg3XW0WmFUha5KqrJCegaEj/AjZLODsBD1VhGej6hT2omk6wkuioxQwPduZihj98mWDHeLCj6T1+KRmIdWzuCH0FqSqBKmfeMCC4+5EjbGsLu7i+d5fOOXBCurK2AtAo1SNVmWopRmuDti0B8SL87iM+aORyV3N1EB9HCPacXdEvCZg5YX+iTNJnGz4XJV5ExBb60j7wOJ5zk7Q/GEVYaGfa5/qqBUUNaKYqLYsrBzCDn00AMeVOXPScgD8AJB3BYsn2zx7W9/ldXlVbqdFkIIFroL+H6AlRIrJVHYIGh7NPwGKs8pyozhZEhapIR++MBTUUrxySdX7/rZYDBkMBhy8twKJy8uYIqa9Vub6Frz2sUTLC/0+OSjD/jwg5uP8qk/N0SNhO7KKsL6UAe0Gg1efe0VfE9w/drlu4h0a+0jL0Y/M4xrEPmgJB9sY7yA5ulXqIoUa3P29ZUHCjaeZRId9jcCP9flp6upMUYjDxrOHgvqruspvYggbqCrElVkPKrzv7Ff3mIBKSVxFIJwgaxaGybTlMl0f4JncDaq90IAvrSEniEKLHEIVlcM+rsURUHDk9goZFrVVNqQ5zl5nlNXFWEY0mq1OLF6gtAP2dze2Kss0cZQlBVpVhBEESdOnWQy7LvqwqeIKs2o0gwZN/FbXaw9eD7mU7XxR/0TGQj82HMChANEuhCCRhiQ+D6tRoMwCJjkU24Pdmi0E7qLHZRyZarPIpEuAKMUdZGjKuNycyw0ZpFHQRQgfJ+8UofVib3ACzwUAovEIoMQv9GgLlPy8S6PpEwBlHGVz2HochyqumaaP3iNOkkLJum9FfuPhsdOpD9xCPCaHjKQ6FxjigfPMO71uvJ8t/gZTnb4vX/2+yRJjFaaMIzoD4eMJxMa7RZx4LOwssLb3/oWuxvbXH/3Y+rii6FKfxIogD77UYbPK+pa8aM//wEbd9b3fnbt8pWH+qtp7cp41WMPUHoAAsCHuB2S9BpkwwnldII5JrklgMgP6CUJoRdy88YdvMAnzz5bp/ICjwdCSLwwQcqAsLtEWBlUNcCUGchgZiA5L6s8pNV5EUgfgtAFedQF5OPDX3usE5op4O3hXuaPG54f0Gi0QXhsbm5TlSVlmXK32bEPhHQWFjhz8QJhGJAkPlorLl3+hOFwgC1LOCLT4Inh3pLxOS/9mBfExWATayFud3npzbcohjtsTbfR5Zxt20eaZez0d+gPVxhPM9KidKpHY7DTIdabYqvKGWnre8c6D6Sz1MHkfNr7L4VPIBJ8YoQJmIxyvn/7+xhVU+RT6rpERk16J19CK4WuaywWYw1GKVQ+cdUHh2L/gtdVzfbGJmjF+NYn1NmINy6ssXyih9KC9c0x/WH2CKrPF/X/x4IFVUFlYGezz9VPLtNux4x3W0RoeirDqwuk1ghrCQFpBWLjBrUxiMYicuE8Im7gLa056yDPwwoIkhjZ6cBSD8opzYlHU4zoNHwuvtTBCsF7t0cMs8cTziKYiZoFNFs+ni+ZZIq8vP/gjVDytdNNbAk3twsmhX7oVp/v+5y78BK9Xo/llRVX5JFnTKYTqiJnOhoyGPSpa+exXmeuWd7XNOeYK/J93GZaeeAE5qp1KyDwEIGPF/r4YeCsXYTgFx++zwfvv8c7775Lresnqkafn1LBfsEAwGjmi5N+FudHg7sOHvftBarKkg0g71qsYi/IFG3pb+1QFiVho0HUaLFx6xbv/ehH1KpEk1OrmlvrfcZpzrU7O2R9qB8tkgaA6STjk0s3kJ7gzMsrCAsbW302Nwfs7HyKA35OyKdTdm7dRCBBe+TNFpvLPcIgoDwsSOGpwZVsFNM+Ozc+pE4nGBG7sG+dfSaruc8dcyX650aku47CGEuZTUA4n/9PC60qqmyC0eqRqxOkEMRxiJSCoqju9rb9AqHdiInCgCAMCMOQNMvpD8YYYyhKV3l3rz1SEASEQYjSzo7L9z3a7Sa+79NouH/VdIgpUkplIVcIr6KZFmR5QT9z/9baIIRgaXGZdrtDt7NAWSgm45z1jR2quuLll99Eeh5Jo00cJywtLtOMG7SSBu1mkzh+Sl7pQhDGMdLzqcsSXVd76Q3CDwi7i1itKNIJ5jMGIIRALF3UTz7b3El8t92ca0NtLboyVFZh7hFdGmuZFCWF56wlQs9nWhVkVU1hIasNZa1Q2nw27uQJsPCdhkcSerRaPl4Y0PQ8GrGP1oaqLDHGkNcaVSj0C4eDF3hUCA+Eh6pK8tEmYmaRbIzGBgl4IVSZS5p/BCTNNs12m+l4dGw3kWbk00lCilozzMpH2j9+Lol0v+XjtTzY5aFE+pxmmkv6PR+SFgzGO/zX/80/JQwj3nr9dVZXltnpDxhNxsgwIG42WVpZ5Zu/+qvc+OQq6x9ff0GkPwAFB/Uezy9UXfPnf/q9uyYGx5kAzv0wHyXp9zNBACGIUBB3ItoLTep0TF2OXfXEQ855TguFQUCv0yP0Q65fu40VkGUv9pWfBQjp4YcJBDHRwjKxFuSDdcxkDGJGpNujrCYE+BH4MSJpIxptSAfYYvIp62TFjEif+VJ/DotR3/NpNNqUlWJj4yZ5OkXncyJ9jhBo0V08y9vf+Yu02g0WFxOqqqCvakZGw2iE/TyJ9MN8VxWPXY0OUPbXKfvrtL/6bS5+5WuM1m/R/+hn6LLkXrldmqWUu9vsDk4wnmZkWYGxFozGTPoPfiPhgd9239fVp77/kn0iXeqI8ajPj370Z5R5yvLiInEUIaMWi6dbVEVBkWUYa9BGo8ocXWZHEOnirq+6qtkc9KnzlI3336Uc7XJ+6TfovHaOIqu5vTFid/AoRPrB93iBoyBwRG9Vwc76LlcSS6sRMuwmdOOAZKFBLDSeckR6ZCAwUN65TnXrE0xjBbtwh2DlNM3V88ikjfLAYgiTBDodRLWItCWtHU1TCHqtgL/49gpWCO7s5gyHj6etS1zvEkrBcicgjj20tocS6c3I45tnWnhKUE0VutAPjK4F8IOA115/jTPnzrPYWwZgmubc3tyiSKdMdraYTieuEs6CepizlgIGsxNPubtoY06kAwQehB5+GOCFAdL3wAref+8d/vHv/UOGwynK1E9cEDEn0g9iZGdk+meBxl2HQ6BKUKUl71moDxDpyrCzscXu9g6dxWW6Syt88O4v+K9+97/AoFk5sYCx8MEnO4zGxWeymphMUj76KKW32OLbv/oGge/zoz/+iK3bR5z0M4J8MiE/UL3TbHXZWDlFFCcU92WtPE045rmY7FBc/YX7X5lA4DvV+vNGpH+umFm7GE2RffZ7alRN9SktP6SUNJsxnidRSn8hiXQhBJ1WTLfVoNlq0Wy12NrpMxhOHJFezIn0uzucMAhpt9oUZUFVVfi+z+JilyRJWFlxAdK3r35CfzOnUoZa1SAqGtOCNC/YTXPymV+1lJKVlVVOnzpLr7tIVWrG44w765tEccK3f/VXabfd3M/zPFaXVmglTdqNnHazSSOOnwqRLoQgSpoEcUw2Gt5FpPtBSNLquo2cIv/sRLqArnTq/0q7TZ627+FJgSottXZEuq7uf0aNtYxLd25lWRNJj0wrcqOhUDB6DOvtg1PTxzRwCwHdZsBC2yeYzRXiIKYbd1FKMxiMKMuaydaQrKhQz7OC8gWeDqQPIkBVBao44JThh9BadQ+hqh6JSBdC0Gi16S2vorVhPDzevKoVh5xabDHKKiZ5hXqESd7zR6RbXCq4gDgIaS83KIuKdLrfGXmepNttE4QBSTvGj3x2NwcMtkcY7WxdjbaYWSBQfzAAAdl0iioLtFYYYDwace2jj9m8tY6qvzjeTwLohR7nWxGlsVydFChjOduGVgC3p9D/kmdCPJA8FxAE0rXx2mKMpT8Yc/nKbba2Bo/sbSmEwPddU1TqEZQbBqyyFIMpQknK0RRqw3FGtPkrSq3pZxm1gHevX8MCk/wFkf5sQCCEB9LHD0L8MJqF/9VIP8FLYueHHYBVNapInakk4DrKGhDYcmZHUeWPMMmSjqx3yYCzQ858C47tmXn4Z9o3rj0o177/xMpKsdufUNeGuhIYHYBsu0FWZ2ArFldXWD39Cq2FDuN8QGEnVHjUVUExHWHzHPt5VonAvlcvFora+TNOntSKOADh43kxjSigDD2EmMk677mmVpfoYsJgd5sPPvqE3Z1NauE742BV3XNfhavbFBI8D+EnhM0FwFKNRtjj+KIfAlWMyQc3GKo+t82IPJ9ST3fRVUk+0qggcG5FwqLqmrqqsHZWVqwqF8x0KNxnrYqC0cYGYaNNp9MklBZpLKYouXPlBrFRBFGTIGmRZsUjqOTmptQviPTDIIF24BacbU/QlDAuKy5tTIhDn1Y/pxF43GxFtH3LW3FJ1zOEBjwBvjEYq/HqCtIptpliigIrJaoYYlSJGA8gLygmOekwo+VJvvXaGmEkkTXUWrMYCLJEMqoM+UOa3HztudSJaMU+w6xmMK1oJSFri0200ox3pwS+x6svX2B5ucPa5oT+MGNjZ8TtzT6+hlZh8WrNjUmBqQWjSlPxcBGp1pr1O+vUtcJ/LaTd7TGdjrl9/RpVWZBPRhR5fp9FRiuCRiSQnsDzPddVewJlLLuZoq6O8EQRIDyPIIkQoWRnvIUSFYutJUI/Ar+i0ZJkueufw0DQa/lYC2mqUNqFuz7TdNa8Od9DKpw9d4Lz508AEqygmSR88tEVNu9skY4Lms0mH3x4idt3Nmm02jTaHa5e/oRR5vofdjOX5lHpz+7XPLOgqdKazZt9F/qcPX8iHVVX9HfW8YOQ8onkp3xG6BrKmcVhbRyB/pnmLi/wecJY57MfBB6nTq0SRRE727sM+yNmRSRfAFiqqibPc5S2pEXNZJohpUBYyTznVd/zYZVWFEVBpSqstUgpCeMGjWabk6fO0Gw2GfV3GfQH1LVB1RUyqMlrRanu7sOstYzHI3w/oNPtUFYl0vdYWlkhihNazRbNZoskjgh8n93dHX7+s5+xsb3BzfVbXL929fOtxJ5BIIjDkDiOqVK3frZaocscj5ig2ULiPxaSX0g31vrGEhmNmAVWSymIQh+MpTpis0cgCIIQT3okgQuArcoMytk6+3E9x4/BpsrDzcfiKCDwPFpJQhKHSM8phX3p4/s+QkiSpIGQFVqOqezx9vyEL1wGrH5U37YX+ELCzgJ/9ja3Z7NyK53NoRCIqAG+j61L7AOtXgR+GCI9n6oqGQ/6FHn6gNffjSAIaLdayEBRWB/9CDzec0mkq7FCTzXLZ3qsnVhiZ2tAlu4visMw4KWXTtPtdVh4dZVkqcnP/tXPGWyPULWrXJ93OHVdc/3mLXb7A3Z3tiimI8qFHjWwfvsO3/tvv0s2nqLyLwaRPl88nmuG/PsXl+iXiv/80jaZVvyFk3C+Df/iBvS3nvaZPruQQtBo+nieZDqpqErLtevr3Ly1iTHmvjK8h8GTkkbibBPSNEUdx1vSMnO4sIxubjI2227B9wiN3wLTqiStK8Sozy82XChW/Xl7W77AoRBCID0f6YVEcYM4Kck9C5SEsUfcaQNtYAmVp6Rb17HqwIJc5c5Pvcqx0zFuqjMnsufP6JzUvmcWJgLw2o4812P3elseeO2nxVznOT+PuS7x/mcuTQuuXN3AWg+TCTAxBGuuRLu6DXWfC2+8wm/+9b/JzmCLdz/6KUoXhNsluioZb93CDoeff1KVwoX9TYFR4T7ek5o0igREmyhss9CKUEmAFIfXgpsyxVQZN65adrMSpRSFiCERkA7u9owQTilAEEAc48dt2mtnwWhG+SZ1/emIk2K8TjnZZCQE6x/JmZeuG1tHk0MWPLN7t/ffB95LSz4ececXH9JZWeXMr38L32ty21om44x3//WPeP+Pa868/jovvf01+v3RI2x6Vri02Bc4DIGE0wm0fGjEEj8QXBtnXF+fusmwcAtKKWAl8fmfvt3jtYWA5dgQB84j0bMaW+aYqo8JGujxCIqc6va76GyEFT4WyWhjwPDWLsuNBv/hX3mdNK+5fGObLKs4Hwt6Cz6/GCry7OHVir4UvHqyzYUTbd6/MWQwrVhdaPBb3zjHNC353g+v4gchf+kv/BpfeesCG3c2GfaH/NH332dja0CiLMsTTaE03+/XZLUjPY7T3Ouq4r133iXwfVqdLmcunGdnZ4Of/+j7jjy3FmvtXfMJAax2Bad7gjAJiFoh0pd4Dckk1fzoZyl1dsj4PZv4eYFH3GshEo8rmx8RDUMunLpAr9lFJjlLayFF6axeWonHa+ebYCzXr09Jc81UOdueZxaz5iw93HpslkH97V95i7/9H/wOWB+rfK5fvsH/6//+X1BkOd/5tV9iYanHn/7gHT66cnP2vAq0dgQUwPbIqZoeZXF1JGb+9llV8v4PryEQn7+f+GNAWeZcvfwuAvGZFZ9PBPUsMPuuW/aFYF+/FDDGMplmxFHEb/7Ga1y8eJYffO/HvDMaUZkvBpHuNilT6iKj0oLKOII28GfzYymw1qKUvWvTvyxLqqra+5nn+bTaCywsLfHW299gaWmZra1tbm9sUdVThlmBkgFxXlLVlatE3DsHy/rGHTY2N0gaCecuXMSPAi6+/hphGLG0vEqz2eTk6hJxFPKjH/6Ajz/6iNsbN7l+8wpaKerP2zoRR263mw067Q7FZOyihOsSrSp80SWO1tCeeCz+7dLzCKIQqTVt5YScvicQnqQV+jSEZJQVh/oxSylpJR3CIKLZiAkDn2q0w1QVWPNZPMwO4DEcY54CFErJiU6TRhzS6raIGhHGKLSuCfyQMAyxFnw/dEr0jT7T4/gRCPAiifQFKteYw8z9X+DLBXNP6NDcagF/b63nt3pIIajH2+gHVEkJKYiaHfwwYjodMdjefCQ7sUYjYWVlmQULCyvHc6KY4/kj0mGWtG6pS0Wel9T3JAUba6mqiqIqsMIifYmUBywQDlyfMAy5+PIFlpeWOHHiBM1miygIkQgajYS1EycYhkP6t7efeQ+oOInpLS2gasVgt//A85VCEHuCbuTz+mqb0ipW2iXtWHO6KxgawTDX7KR3L+Ii3/1b6kfibL9gsG5HFbPH63waAv0gjj3US4EXBSBc2aQ1FqvtA3yDHwzLrMOwFv0Zzv8FHj/qImdw+zpe0ibwBM1Wk3pxDU8V4AfoKscLQsK4gTA1QshD4mrsbLf3oMHVPb8/1FxvvuUm2Q9YfAySh7uO7x94//thtUIX+T6pK4UbeM3+DnalS8bFgHE6YDrso1RJ4JXoukaV9QOJVyEFcRQihNgLc/pMH+cgKvbtXJ7gHmzYaBA2Fmm0285n0/cenD9rrfOjm4wAgRdESCnR+ejuincpwQvAD5BBiBeGhFEERiM+y8LEGqx1XpD3dlmPY79DepKwESMDnyxP8axGl1PQKda6OCdj+ZQLmC/tgPdQWNycwBNglMWzkNeGWt/fZ2TSkhUVeWaZGoXxDZ40eGK2wadzbDlGDdYRUYgpMmxdoVSJNhY18/Y3yqCVdvMcC54QdBoJ0vPppCkjKhT7uS0Hz0IKZ8cSB5JOM6LTilldbHIu0yx3IqSpiDzD2ZOLhFFMqxEQeNAIQceCTijoBBAJqEoXlFnVlvoRhmEpJb1ul0azief75EVBWZaouj50LiFx13dlsc2F8028wMOLPBSW1Nb4tUJwxAbX7AJUdUV/tIMSBVGyiPRgPOmjq4KiTJ0SUriYUW0gKzTWWGpj0Q/bx3pceASv18NeGviSU6e6NJIAiw94dNox09EIrIfVPtPpmDQvyLKCje0+WVUxHE8pyyOU4U/ig1vQ6tmfc3lBiPR9pPTxvMD90IIxmjKfYO4tuz44AH3em9j34tO8/3yp+Ozfmi883NLEgnB99srKAq+++hLjLGd3OKaqNWleOWLPk24DzJhHrgp+mtAGauxexY9nBVLIvSn7UYTOwZ9ra8nLkqKqEEISBAGtTpeFpVVqBXY4QRkolaZW+r5juvHGUFYVWZ5jLXR7Paek9p2qW0iJkAJjLUpr6qqmyF1I5tOAwNk/BkFwD79jMaqmzLPZBp+r7LX2/irN48Jal9MjcEJNi1s/WFzAoca4zQkxP7PZyGRdvK7WGiVrtI3QQsyG42frGRVA4ElC3yMKQ6IwIggCfM/HiPmnktS1coGqyqC1xpeCyJfuOjys3Rnr1hnP1kd/gacE3/fxPM+1Yz9w6zPjoREUVoGxJFELPwiYZsOHVD0IPCnxpZzNTx5tAFdKURQ5XhDRbrWZlf8f73M80js9bRwUTxrY3Roy6k/Q+u6Boa5rbt25TTyKiE+3CdvxkaTy6uoKf/fv/q/5+tffZnXlNK1WF8/38RC8/upr/O3/+D/m6sef8M9+9x8yGY4OPcazglPnz/AX/9pfYdQf8ge/9/uMBsP7XmO5e1F5uhvzv3z7BJ4Pg8F1ynLCX12N+Le9gD/4KOP33k33CPPQg9Mdt5i7PYH0+atGfSwwBtJpDeJ+77onDT8KaJ5dRAjB9NYu6st6E74EGG3e5gf/6P9B0lvilb/0t1g5fY6lhR5VmrPx/vfY+PDP6aycoHvqFOVUMlkPMbXGMbcHn8t5YtVhz+rDyPH5DrHGscKPSz4hgebs/wsOZZtVCeMt8GO89ipIgRlvYavJHpF+c/sK2Tv/P/KdMbvvXcfUCqGdekeVD2awwzDgzLmT+L7HrRsbTCbHLwO7Cx77I+n8ck7YG6eeJJbPnebEq1/j9NnTLHQ6ZI3ELcIeAJ2nFNVNgmabzumLWGsYp7uo+oCqJIwh6cw2ahLiRod2t4tVFUPffxJ2748FSbvF6hsvY6XHtauXUOmYaf8TKO6QLJ0n6azR6nQJRYCH98Ko5TGhNnAzn229TQ0I97PDoLVhsDNmI4NtAVJYTi+FnFwKnR1DtYnuT8jeqSBq4HWXwQ/JdjYoRiPKmSR6khbcGm27xZsxJFHAhZdOoRAU2Q2C0S5DXAi64u7cSd8TXDzRYKkT8vK5RU6tLbCytsQ3v6JJRyN27tyi2Wzz7//1b9Nud1juSMrpFpEe4PkTTicFb3YteQkbfSgMPKrOIooifuO3fovz58+Ta82NW7cYDIeHEicCiIFYCr7z7df5q//Om1RlTZ7XbAyn/OCTDWydYuWYQ/tSC2gYDHf53s//hMWlHn+5/duEgeTSJ+9RFxnbG+t3/ckkU/ziauqqQJXBfB62LpL9gqmHvNlRL+20E/4Hf+vbvHpxDZF0IEj45KNrfPeff3dGrAp2+yn9LCMran7wzkdIT1K8yEA6FI2FRRq9JeKkS6O1BFZgjaTMxty59CPK/EBAqpRwD6nF86S2n8sy4YmEg7/Ao8NYS5ZOmUxGfPs7X+dv/s3/DpcufcyPfvADtnanvPfRBlpb2q0Ez5NM0oLymfLsfzAqDbXen1lrY2fz+P09qYdxQmVVcWNjg0lVkZclUnq89MprxJ0F3vnxT9lY36VSgkFaYnSNPoKcn04zbt/ZZHFphVdffQMpJEVZYgSUWmFrkEFAo90miCKEeHp7ZUIIkiSm1WoRBMFdvyvynI1b1xBCIkRIGAbUdXr/pt8xYbSmKixBGNDpdVyWWFlSG8MkL8lrjRHWrQWEBzJwN60uMUYzyUfIUmJDj9iTVMo8UYHNp4HvS1rNiCQMaXe6NOKYKA7wA7cJYayzWxzMgnCVMiit6USSoBszSCvSQj1wRakKs6/peoEvNYQQdNodWs0WC90FlhcWUdpQVBXTLOXytctoCyeWL9Du9Liej9ge7z7geE5EEYUehSfuzZl/KMbjMddvlKysneIrr7xJGEYP/6MZni8ifY5ZK91TI937a2updY2oIZ/mpOOU6gilifQknV6b3lIPPwow1mBUjdIKrRW+5+F5z8eCO4xCFpcWkULSbDWpipJ6pm7yPYknJEpr92Us40oTKMNJKQh9QRhIjPFIYokMBMvtgOVughUS3w+JPMtSopDCUgc+WS2Ypjl5sf/IPk7NKsy8ybyZBcAzpN5xE5vH80kt7JXaPfSIwvmhvgi+++JDq5p0sIOxFqO1Cx+NmhgbgRdgaucZ7XkSIR+2e/ppnlXBg+XNnwaW+7fzDig47nqpAVuBkWBrhBXu/+1+X14WOaP+DtUopSoy7EET34d0FwJXmSOlOKIpPVgaKaRw6mwfV4lmLHaeT/BZVRfHUmUKwiii2WmTJDGBJ/GOoxa32p2n1vhBgLUWcR/5fuDNhVMi+b6H5SGK96cF4YEX4MUNGu0WtTaUtydU4wGmcoyIHwSESYKQkroq0erJByp+WWA5vuWHsTAqNbs4RXcgIG8ZZ7mnjSPTVYHNhi4csNEEYRGqROgSicTzJFJ4CBHgIZCha49+I8EIQSfx6QZghUALsUekWyGw0iMMJEnkEfpOwZhXbh4ZepLUWtKsxA8TWklEqxExTTMmqcFkE0yZUszCw4x1eWGlffS1oRCCMAyJk4Qyy9B1faT6UApBrxPTSUJWVrssr/QoC0WeVZTCp9PNKUpJ3IjIC42qnJL8XqhaMRmO8aVkPJjgacl4d9eFm46nFGW9p1i0FopDglVhRmIf6AekBM8XWOuU+YfpCw7OWB7HTE6wXyt18HhSCnrNiJVujEgSbJhwSSu2Nnf3ru94WlJrg7aWND9GWfqXFgLpBfhRQtLs0F1YQViJtYIs8PFm2T7+TBWqjTmSpHseID1J0Aj3qjesMW4ONGcMn+PP9tzCWvK8YDKeIqWk023TaMT4vsT3ZlNU4e6d53l71kyPUpr/NHHYmnl+6seOy5oFkzrf9BptDGHcoN1doNHuEiUthBQopd1aQrgqfUc0u81taw1lVTIZj2m22gRhiBQelVJ7FcvKaLTRM/His7AeF/vt8wCsNdR1hZQeSdxASA+t8k897hgL2hh8C37gYwFPu+tgbOWquSV3ryMETtVqLcbomard2b5J3ydMYozSqLktzj25Hp83hBAEoY8fujn+XSs06eHjODelFEYblDZoY3D7B+LhbIS9598X+NJj3le7vAEPaWfranAly9YijJp9/5DWa50zgz1m33SQdbA4RXqWQ1UrpHRjyXHxfBHpx+wFhRT4iY8MJZ+8c5nLP79GOjq85HWaTvnn/+r3+cXND7hw9iJLvSVEECGDkD/94z/jv/p7/4DxcEQ6/ZRqxc8RgQxoxx2ipYhvfefbjAZDLl++xGQy5uzCIkvNNutbO9xY32BzWvL//WSHwJc0PtmhHQt+51XJmV7IKCtJqylvnD7BK2+do91b4uT51ylrxSeXr1PXipMnTxNFCf/on/8hf/LnP907B2Ug14+vr2y2Gyyu9iiykp2NPuYLmNxutCbLsr3vHwRdK6a7w9n3z4/q4gU+PVSt2VjvMyxjdKkwtSZLKyDGGElVVc7L1dbcr0b/lHBpOrPBq2Jf1f5ZMUta2zNdmNMhTRzVdfCZ1kAOusRMHNlh1d1BuOXWBDUtMUJjm8YdqjH75QYuH/IIVFXNzZsbSCHI7/M29IBgdg6Ht7OkHRO3Y6x0XL8qarKdFKs/jW3IAQQSfM91pg/yihCQRAGL7QbtOMQXBg9zzO01g5CWIHBedPf5SJYZqAodNCiVJZA+gaex2GeTSG+tQfc8jVNvcvbcCbLBkPVr16i3tzB1gohOk/RO0FtdpSpLrl/+Benw9kP72xd4/Jhq+Jd9aEn4pg9nPYhkjWc1iW/pBSB9Q+QVCKEQk6sgPRZCi12CXIcUOmZpYY3zZ14F6WOMs4KqxpvURUq+FdHRQBwikmivOEQHMWXnJLUVrK9fZ2c05uZOQaVvu6AtoKhqxnnN6jDn5Mod4mib712+zeYoBa2wRjOd5ozGTmw7z/Z81N6xVoqPPv6Y3cGA0+de4sTaCe4024eSQEkj5D/47/86X3/7LCdPdhBxi0bs0Vr06axZ1s69wp3tEcNccGt9lzsfbzHt3z/nNRnUVwz9WxP+6PqfEvgBqi7RWqOp0WjKqXoogdMNoXtABNhoepw4nVAqePdKxji9/2qEuF5e4eIjDr1ej1B9HwtoCigsTA50ubqq2bp8k3YxxoYRRvp89PNr/PwXG3t/q5ShehQfni8xPBkQegknVk/zxpu/hCc9fCHZ3bnD7Y/+DWXmcer8S7S7XTZu3WJ3a3P/j58TMnOOZqvNa2+/ibWWj3/+AdPhGBoxhAHkJRSPqnN7gc8KpTQf/uIqN6/eIU8LRsMBlz+5ygcf3SLNKpQyIJwITAsPITyk1Bhjnhsy/bPCak09mVB4Hjv9Ea2FEcaLaC8sc+bl18m1R397kxsff4g1mjhq4HkezVYTz/MZDHZIp2N2ttZJp2PS9E3OXniVOE4IghAh3cZSWWk2Nje5dv06g8HgqV5fayGvaiZ5RX1EOVjg+6wtL+L7AXc2C6afsoJbWUg1WCsQnofve3SSGGUto1KTVerA2KVBGMADmbgf6sJ5hHs+YRjROX+OuBXR39jk1idXsMK4kjOAnKei2PZ8j2avSRh4jLIxo+mITq9L0khoxgnNpIk1zo7MaAtWYDRM04qsKCm1eSE0f4Fjw1rLeDImzVL6gz43bt10gllj0Fq5jWwLNy9/hOf7FNndi3lHwDsBmNauv58Mh6RSUqsHc2MCNxf1cc2tBPLKsDOq8RsZm7s7hFF47M/yfBHpD8DBZGYhXQgEAsa7E6pMHTo5FwKUUdzZuoONDFEUoY1CRjEyTFjfuM3Vjz6hKp+NydNcdWqPqPMSCKQRRH7IyuoKURSxubOBNjWLvS6negtUVUV/OEJrzbXJ7HMNcrqx4Ntn25y0AVmlGWcVa6shZ15aZmH1JC+98QppodDaUlaKN1++QKfV5Ic/+TmNyENri9YG85hIFulJPCmJ4pC4GWGMeSzp288iLBwvYBR379W8uuKFp/mXAq60taAOMmytsNpQ1xrwMEagaoVW6jN5AB6Og/rBx/mszY819253Q5qUGintgbyBmR+B1dgjNo1MoTClggRYYJ+tsbjR7QHKbmMsWeqI+blCx/loz6xnrDz6j3HBfWEUYGZEulX28ZTkSIkIZrvhyvmJH0VIeAJ8AcJqdFViVH3Ap/HBJyIsiKMWQka5L+FjtQFjEMIipN0n6sXBPf35P09jYSUQYRPRXiVsL9JsNrDTKUwmmPEY4hgRJPhRQhDHlNmUyWiXMh0/I6qqLxeUhTul26Y6G8CiB+Pc0sg0JnDkaKAsAoV0EmeEAC9KEEGAMRKjA8JOi8aZU646R7ugsVxOqaY1vbZH2ZUEiUeY+PskaxRRriyQadjevkmpFJv9isF0r7BkL9rA8zT9wYTQ87h0dZMbu+OjPtIDMVfcuODQ/fZhjGE0HmOFYGXtFL7nH2rLJIAg8Hjp4gpvf+2s+4kAzw+JwoQYj04vwAtiFlY7jKqCMA6QnnBVMgebpAIzhYqa9XsT5aPZ1zG4hkBAw3Pqb8+TtBo+a72AojbEgSSV5r6+MMBxBRX7MdOH9hbH7ELmW52K/ZFKAMIaiklKOhDUQqIQ9HeG9If54Qd6ykrAZx0CD4FPHDVY6C0SeB6h74MuCIMQzw9otTv0FpcYbG8/U+S5a3dH+0zvv07i+R5JI6G3uOSqeP1ZIJQnIfDhObIL+SLBWMtwOCUdp2xsbNNbaLK1M2AwTJ1FBrPKQuGqlA6qCZ/HEN9PBWuxSqGrmrysyMqKKGoQBiHt3gLLJ09RV5Ubf4zFD0ICPyCOm/iBz2QyBKAocooiZzQakOcF0vMJmw08KTCmRitNUZakWUZVPd32YHH+5Eqbu8JTD0IIl4PkvL69/cqSY2Iekg6gmYWIz/zivSBwitn78vdmlStCzlKvYT5Pnitw40aDzlKXfDp1pyRBhDN+p3zCHmqHFQDPlMB+5ON5knKao2tNpWp87TzhnTODcGHq2gBuflHXmqrWxzvleymcZ2eoeIFj4hEibB6Kuq6p65riAWG16eRwS22BmK3Z7Z74pK6Pt1E2T2gL2Z/uagOFseSVIity1CPYQD2XRHoUe3i+pNaaWhviOKbVbO0RrcYY8jR1g2jkEyQhOq0w+f6FCWKP9mrC8soCX3vjNc6dPU3Q7KCkAKMQVT7b1Xg2WnrUiFk9fxohBVvX71BM71cbDTY2+dl3/5hmp8XChdO0Fzo0ug3qquKNs+c4vbKC9CKkF/GTn/yEv//3/z55luEB49ryTz/K+dc3SopaUSr4duLRvhBRDkvSDy8RNpq8/a2vE4QxbTSe0fzWd96g3Yaf/uIG//pHl1D6s0doCCH4xje/yutvvMr1Gzf54IOPqMr6C6lGf1RIzyNqtkBAWaQY9YwZrX3p4bFvAvd4jI6MNkwHE2QVY+sKtKLOckBSTsf0r3+MrqsHPAtzhqTG7b8+ACLE+SQE++WSj3PkvA8WqJHS8sZX3+bMuXNcu/wRH3/w3vHetAm0cSNia/azDMfRB7PfFTyQHJKex9qZ8zTaHdLphDzLKPOCYpLxoBltMSlQlfMERDilxmF2Co+KRrNNY6GLVRpTKVRZMh0O799AtZbbVz4mHQ8Jo5BGkpBlBcpr47cDdNbH6qMXO3WR0r/+MSCoSwMimdnm7C8+m50uyy+/SRBFWNymjQtVEiS9NcJGB6VqalVjygw1HfD5jpktIGbl1AVOffuXaPYWMbpE1RnYEuEZmqvLBK02IgooiinZeIPJ9kfoKsfaL8lC+xmEBj7UsG4gGUAjh9MSvupBr6N51c9IYkEcufXoRFZUIqAIJHmQ0BE+3U4X4YfOirnMSLcU2uSsnu7SaZ5jd2vKzuaEWkOuwO8GtM8mRH5Ac3WJIvTYysfoaeEW5riNqaYAVZZ87+NbIAT96UP6zQfg5IlFXjp/gsFgwqXLt1H3qOestXzw3jt89P67bG9t3RWS5EvoNmCxZVDTEaPNLfwoxA8CiBcIZAu8AOsnCN8SBBFxHHHmtROsnF5k6+Yug40DGwD3usLNpfrMPvwx90yVcr7wr768zK/+8gV0lTHeuUVhFL9yyqdY9mBcwwFrGFVBlUMtYCKdCmhd84Al1D24h/DOrXuGpIUuEHjQbUK3BYnv7Ap+cqXP1e2MO7tHlCZ5wCJu/BjAUVmt951KNLPBvdtp7AsGD5BYE2B0yHhYcPPaLbrtFmdOrdGIG5y6+AbNpRVOnz1Lp9tm69atp33Se+h02qyuLlEUJRsbW/e1u4O4+Oqr/MZv/yaFNtzY3WE6yaj1bDMoL6FS7qF/gUfG49in0jjhw+Ubm+yOMyaTKWlp8D2f5YUWcRyxduI0QRCys7tLmqfO5qQqqWtFVZVY+3xoj6QAf2aTVatHu2rWGrIiY5KltLoLLPQW8YKI1sISYRRz5dothFWcWHDkeJbl5GWFumdtPRiN+Om777G6usZv/Mav0Wm3qIsUVRUsLC+xtLaKpWY82n6cH/2RIITAD9v4yQLSjw99Ta1q7mzecZtinke3t0CeZVTl8Uad5V6HhW4LW9foosTzPGqtKJRi2B9RKUN2lDWY1aDn8waDNTAZ9cmzKUoWKK9gWo6xDUvSbHDi5dNYbVl//zbl5NPPNx4IAclKRNj2XWCsUZjKoDKDjgyZrolkQHNxAQ8PCahSMa7HFLN2t73dx1qL7wm0sWRKUQJ+CKEPdQ2H7bEIKYi7TbzQRxiLsFDmJWX6wlrteUEsIfGcjWP2GZwnHjQmPGy8kMLDkx5BEJA0EqzRVJnje0tz+KbavO59Js0DIIzcZ6krN7Q7JgLyqqS/u03gH58ef+6IdCHADz2CUGJqi6oNYRLQWejsEel1VTMdTSlLRbgc4jUCbG3uItK9QNJcjOmttjh/6gQvnTpDv9ZkxmCNBqPR+tmZOPlhyOKpVaQnGWzsHEqkp8MR1959n6WTa5x882WaCx0WV5bwpOTNl1/m7IkTrK2e5szpl2gtLPAPfvd3qWeWIrmGn67Xd62hli94fEv7FGnFMN1gcXWFr/3meTrdHtXGbdR0whuvnKbTkQymFd/9wSePxR9RCMH5l87ynV//ZWql+P6f/fjLoyx4CISUBFGMEFDLT+/59gJPAgKQB/z67Mwa5bO1CWsMxbQAlUJdOg/hsgYkqsiZFkcHcDj47NcNFg8+H+GDiB1z9UQJ9INQCCE5ffYcX/3GtyjyjEsfvnc84UgE9Nj/iDUu6LPGjZzznz0AUnr0lldYWFljd2cbMRpi7ZBicqQBAQB1qagfEmj6aRDFCa12D6s1RimKNCUdjw+tRBpsrTPY2g8JlGGToHcGL47Qxdg9K0dA1xXpzjogQTYdOzSrAJgjbjRYOnUaISSqKtAz/2SBIGx2SXprVFUBZYkSEpUOP0dF4iyCUbTpLK5x7tWLCC/AGoVRFRaFkIao2ybuLSJsTVUVlPmAfHSHZ2WT/MsKA9w2cBscgZnByzhO80RuObVcIhsQNl23VFhNamvKRpOiYYjx8JImchY0JjyLFQZjKzoLDXptn2xSU0x2yGsYlxB5ik4Y4ccRcbdNgsYLCyzF3gTbwy0YdK34eL3PZ515LCy0efXl09y6vc3lq+twzxGNMazfvk1/Z+e+v5US2jG0I4spMrLRmKjZgCTG9yzGxAgZgWiCV+B5AWEYsHSyhzGWdJzfTaSDk4PPcdAkUt93akdCzwLyFrsdvv31i0z6u7y/c5PQKhqLPsYIBBrmFi/Wdcv93HXHTc/d8h3jrFmOhYNqNuuOU1vn5NXBKeTXEugkEEoX2ndlfcyPLw+OPqac/zF7z+BxIAM3TAr7RSbSJeBhrYc1PnlasbvdRxqLPLlGGIYsrJ4iaLZYWF6m3YiIouMHdD1pJEnM6uoKk8mUra1dHvRwr508wW/89m+z2R/wyR/8IZO8QuvZg1kr9/UCj4x79+w+LeYzn62dEVs7+wpF3/fotBo0mg1OrC4TRjHGaryJs9/Ic4GUJUpVd1UDPcsQUuB7AmNACftI0ylrXfBoURb4QUCz3caPGzR7i4wmKVF3GWkV3aUGHpasWKdS6q7NW4A0zbh24wbKCqJGQnuhRzkRVIVHq92m3e0wGe0+ssL78UIggxgvbCFkcOgrtNYMx0OElPQWl4jjhLquj02kt1sJJ5Z71EVBMZFoa6mtoawV/dGU4oH9gp3Zbe6jyKYA+E2JbAjKOoMIwnbEyukTGGXY+WTriRHpQkDYCUiWQypdg7KoDBQG40FpFcJ69Jot4iCinEyp84JaV6RKkU5zxuMUsASBa5eVdmNx6EMUMstJOey9BWEzImxECG1hpmx/QaQ/Pwilm7tJPpuF8z1TuUN/dzSRLvGET+g78ZjVGlGVzizWHG4vNGNm9nQjQoAfuEIzX0Og3DOsgbJWTCYTfO9huXP7eO6IdGuhLjVaGZQxWA2LvSXe/sbX8P2AUERUdcW1E7eYZimb6S7TKr3PckTVmvFuRsCQj3/xMcUo5dSrr3J6eZlLly9x6cplbt64dt8A87lDSgg8amvYvH4HIaA6Ygc0VzUbkzGmlaCKAlvF4LtwrU6nyYm1FdotJ9t8/fXX+Tt/5+9QFAUeoOqaazevMJmOKPKcqqxYXEq4fGeXRqNJb3EFPTH8yR/+CZ4UjDduUaYT0mpKUReoepe3Xk4YTxW3NkseSTwucKvn+XMrodIV2STDl4ITJ5bIsoLRaPLcTIaeCIRTJ5fD1H3/wuPz84PwiBdP48UdqvGEejoFW3CoCbeQJO0efhRRTodU2eRTvql06nCvgR81EXEL6/lgFKbuYnQKagJFzoOHtBrHEBzDP93Wzkh3bvxtHxDy4cXgJ6ArUCl+3KB3+gJIj9HtW9TZlH2Z44HPtLc3PJ+EGqxV7Gxvcu3yJfq79xNKD4XCMTXzfYt5X2K5v5wQiWPgLVBijGZnY510MsXIABE0EV7JvnHA59vOisnElepai9UujMhaMQsuuvteRM0GYaMxm+hPsbpCp3332Y69ETyf8B+8184x2hofXRt0lTG4c5kqm1AXGRZLOd7F1BVqFs5tquIxLapi3LWv4EHZ60LQOX2WZOkleqfPEgURk0nK7TvXSXf7qKiJ7Eis0ZgyRVmNwRwaUH48fDGtxR4GIQQryz2azZjBYMJwNH1i71UDYyDR0J9AVcEkc9OgUanJa4vujFEdQ7J0AlMphLUYlaOLFDt75kNPEuDhz7z/jYIyBS8x+NMCX3qcPXmShdUl+nnFNJiijKXWFr8AMeaBzb7le3RDH42gsgJlLNOqREjByRNLNBsxVV2hdM3rr1/km7/8bcLkEn/+ww+o63qmjjSMBwOKLCPP7mZwQwFdD3qdiF/9pXMsL7U4sdglED6BjPG8Jp5MkMJH4IH1aYUtfvmVtzi/uMZHVz5ku78DqrxfhX5vd/4pmuyJxYALvYBuXHHr+nUwirWzpynzktuXtlBpxVqzSasXkI5S8klOdeB9pL2bz38o5nNED9clHOja5nunhYYqhaQ2mI/7xGHA9uAhi3RP4C03kT0fVWdYv3LdTzI76CaH7qWaytng2ods0j63EJI47uAHCZ1Oj16njUDT39mkyseYekJZpty+doU8n1KPtokj/9DNoIfBjyLCRhNd11Tp9DH6LtvZsR5eGbizs8MPfvRjhmnK1tYmk/EY9QwJqZ5XPOnVmuf7tFptOp0OJ06eJGk0QEpakzFZlpLnGePxGGMMSmnyvHyWnIcAF4njCReJoyzOLsPlex77XFudNq++9RV6i4u89sabLK2eoN12VfpJEtNoBSwuLtBb6FDlKaPRGKtr0iyjqGZhmQeglGI6yZhOU6ZpThRnDLadj/qtm9e5df0y42H/qds4aeujbYDd67Tn2UsOge+xvNAjDAOCJEF6PmXqHbJfejd9J3BzDqUUeVmiqppKa2qlSYuKSpn7rtmDIGYGMTOjRieE3ICqKrCZRceaPMux2jy2yvvDCEmLCxwvKwFSEIUxoqwplULXmlGdUwaaTuSCWvOqoqpKirwiyyt0VWOsxfNcMKkVILQCbalrV/FxVOGOtZayzFE4pwcBVOYLuwv9hURtXGUnvsdCM0QpwyQ75ialkI4zQGC1W2va+/iB+8cMz3N2XVo7a0RjLcrUFJXFToyzO65rZwl7RH90UIluceHBkwpKBdLzaTZ9slpRVYqqqtndHuLJLzCRDlCVd69wlhZX+Ma3f5kkbNDyWxRVTe/sZfrjIel7P2a4nh1QiTqoyjDazjA5fPDuB0x2+py7+DLnV1f52Y9/wE9+9H02rm0/fSLdkxBHVMawceXmA2vT8rrmdj3EtmLqPMcWCTbxkL6l221x+tQJHIEEb731Fm+88cb+3+YZf/qnv8/t29cZ7PaZjifsbN7h0q3r9BYF/mKb0bjg3Z9+lzwdsrlxkzyb0lls0Og4tevXX21ya6tiY6dCPwrhLXGLl5mdmJCWSlWk45TA8zh5cpnhcMJkkmLMl5Q8ni2ErdaUgydHZLzA4RDSI165SLxwlsmNm9TZBpgh2Ix7u34hJI3eEkmnx8iaT0+kCwkyRngN/KiNjDtYP8CaGmV6GFFCZqHceciEtgKOaVNl6+OzA37y/2fvz34ky9YsP+y395lt9Dk8xozI8Q51pxqbXc1mdZPsYrMbZINoEJRAUQ8CBEGiCAkC+MQX/QUSIOiJgASJbElQi+yJbJJd7KGquqpu1b11p7x5c4qMOXw0t/HYmfakh20WHhEZ85SRN30BFu4ebn7s2LFz9vn22utbC5J1UDnogrjVYfO97yKDiGpSogrN55NzliS2xd/a/CTXOc3h/i7WWI4GB08/mdb4tQLHsiP93pe8d8fxEkQffGqN4fD2LYSQ9M++SWfjDCIsvb2Ngyc+di8I5WxGObvrnBFy0SkQLD7n431JOx26mxvMR2PqfI4zCj1/2nbbJZEu4B5dbox1AUY5qjzn8LNf0JTH+1VNB1TTZ1j0eCQEfkLUxjNZDyfShZT033iD9fd+g9WtDbI45TAf8PH7H6GKkiDtIuMWOIOt8mMHi2du0f9qEulBIDm9vc7W1iqXP7v1Uon0Bk+kJwaOZlAuhFnOwWxqqUqLXBsj1yb0ts9jG4VwBl2O0dX8jpVRJCWZCIgWxbDV0OQQJ55IbyUJFy6dwbRibld7DJNdysZSNI5giE/DfESp0YtDLnQyGieZWUmpNaVSyFBy8cI2mxsrzIsZVV3wjW+8y2/81l9EmZAo+u8QovIkibWMh8MHbj+VcCqGsysZf/U3v86p7RXiRBIIQRRkhGGHIEiRIkIQIWxIL+7yF977NtPZmMHtKxzMxzjdHMtxlpKc5yR/BXB6I+E7b7YQNFy/coV+v8fb716iyRW3/3yAOlSsf2uT06d6HLoDBtPyjqmY4JhIf+IratF84lUffI5IVwAGjnIIMOwPjwjxI8gjNxtIoq0e8lSCUxodNj5rYw3YAQ55IJHufqktXbzndNbuk2Z9VvprrPZ75HnO0WCP0ZHjYOczmqbk9m3/dRD4qUpZPiLd+yGIkpTO2jpNWaLKwk+yXwCW3uhPUkvsHxzyx9//Uy9GOhqg6tp7FJzgufEyK6cwCOl2e6yurnL27Fk6nS4yCOgtiPSqKtnfPyDP51RVTVU1r10AaSS90rMCtPEkj31KS5duv89v/uW/xOb2ac6ef4t2uwd42iNrp7S6fdbWV1lZXWGGYXhrB1WXVHWFNvpzGV1aaWaznOk0J89LonjOzv4Bk/ERN65d5cZnH7+ot//McAiMC9EuwbplzVhyD5EeRZw9tUG7ldE4h7aO6fhBtNe9tLOU3mJHaUVRVRilMcZQNYrxtPDe7E+xr3JBpLtFAlM1Laimx3S+TQ3lvMDdyYd6Pjy0E8ThOxBqR5qlpEmGKwRClWhlmVQFZaTYWGsIo4hS1VR1xSgvGU6KOw6agRBESYwTICsLxngV+iOGTOccVVn4e/cy2f0rSud8WaEsFBbaacj6SouqMRSVekIiPYCojbeLqxadz5/3Erx7S0IIoihaeKHbBVlusE6jG0XVPFnnxt1E+vLn2aI0Xu9FdFsJZl6TN5q6VhwejJ9qtvelJNLvx+hoyIc//YA4TEiClCCMSDf6bGYp6cfx4u5074clHAQWIusIDUhtGR8O2LlxExrN6dV1ysPyiw+4tNa3FVr7ObKs3W6TJAllVd2jaFJaM5yMIRR0wj4yDjg8OuDy9U9Z7W+zvnIWIQRBEGCtQTcVmJrNjQ3CAFZ6qxTzgtPbp7l08e3FiR0QhZK3vv4tnKnZHpynKHJu7Nzm+u4hrW5Gd6WDDgqcmPJU5dNycrc4052A/Z1DPow+YTAYMBpNmc/Lr7Ya/Sv81l8POFRZQ1zgopRwdQurY2xtwVkCDDKQZK02YRgRJS2ck08p2Ag8YYoDZxBhRtA/hUy6BFGIkL54M05gZQxRl6BlCaXGqZJmPoQHLjS9qAZbv53Wyhpx1ibpb5OunMbpElsOCbM2UdrBIelvnyPJ2hhdYE2FUg2qaYjDjHZrBXBYW2GNJp9MUKqhmJcEweBz6dwPxJKFcXj2zd73/3CsXPxcxWsXv7yblPYlbpKkdHt9VD7l+Cb/ZMdOSEkQJ+AcuqlfnFrH3W1kfO82VVVTTqc0ZbmcAuDV3OAPjCOKYoIg9Mrxh6aZ37+v/tiocsZk7yqqzDGPsIl5bggJrVUIYt8zqCqOYwQfchydo6wU01lBp9NgGgPaEVofUOa09mp0E2ABXc+wqkRXT3l/undHn/Hvvryw1t0hz+cvuQ23Ag6A2kBnDu0QTkWQiOMSLu2t0j63Rnt9CyEkOBBO+MmqcQhlMVWBUgWhruhnIMKUIuvR29xg7dJbpP0Ok9DSKK80q40DEZBEIUHbEW1aQuUQpUUbmJe+jFx++t3+CmfPn6VxMDNQqgYxHCADwfraKuurK5w5fYoojjh9ahvd1ATCsbHeIwxgNJ5jHqE8Uw6mGlqV4nBnB9FMiBbZQCunBV2ZYEOHkAlCRECAamr2dnYYDo/Y3xkzOKgo537wa/darJ7qUc0bhrfHz6V6c8Bhrvl4r2Z7rcWbp3vEUcjocEA9a1CVwmpHk5dUCVTzmqr286ZOCC6Ebidg7qAzMSjtR6pHzqmX4/wTTL6Xo/uT9BI54zDDEicMUSsmvtBHyRplq5cb+PaawwHGaLSumU4O0KqgrErm8zkSRxk4jPGLWGEgSZKAMBAoVdE85QKDFJIoCLFymS/zfMiylDRNCcKQ4WhMWVaPJaeaqmIyGNAYg83noNWXw1D7Kw6lNOPxGGstOzdv0+31iOOYzc0tPvvsM3Z29phMJlSV90p/zTh0AIJAEEYgG/fMxKJqFIP9A7S2KCXJWh1W1zfodLrM9gdUN3a5fuUK0+EBxXRMVUyxWpNkLVIZMteaulGEaUbUatFfPcW5N7/B2vo61mmK+QylvXLdviYB7c5a5uMx1sTUjYK45T3HzPH8QQBxGBCHAUYprDMPqcvv/T/n/PBf1YrJrMQZg1EapQ3aPf2tQd6ldHfLb+7ymtCNJj+c+KyXF2AjJYTPerEshjEBYRIiQwnWYQrjw2ILga4MYRJjhcXUXtk7meQ0taLMC1Rd0yz2KQgD0iwmCiVRK8YBaaOxIQgRIqQkDGPCOKapauaz2b0LV8um6OXC/knTz5cKQvhFJuMMs7pBKfNQFfjn4CyYGkRAnCRIJMKGCNegjEI9aH7p3EI86xBIpAj94vgLWoFxQK0M80phnaWVBLSSmK2VPvKXXZF+P65/do3bN275NT8h2Dh1iv/wP/mP2Ti9zQ9+8CdQN36AvQsSSC1kVpBZiJTl5qefMTw6Aq34zsV30UPNj8QHX+yimbFQfH7yKoRgY2uLjY0N9nZ3uX0XkV43Nddu32RcTnm78w5RFvPhlQ+5vvsp3/nab7H2nTPHwaxaU04GWKN4+9IlxNtvMi8a6lqRtbp02n2uXfmUf/Z7/5gsS/iLf/Gv0+m2yKcjiiLn//R/+b/xJ//sM86/0+GdU9sU8Qj3MBnPw2DxalKx/NHxi59+zMc/v+xXoKzFOffVJtLhhEz/AuGsoxhPoYpJVrdobb5BMx9RjVeQThFTkcQhZ85uEycRk/Fs0Ub6FA3sMgXZ8vJJVyPTFdLzX0cmbWSQ4jBeW24FBB3IIqLuOr3kAiofoK/+APvAFdqlnYrl+SoXiZAhG+ffYe3sRdbPv8nWxXcwWtPUFXVVMxgM0Vpz5pvfQzpDNZ+i6oLpeMR4NGS1t8obZy55j/+6oqkqLn/wc9RwyGg4YTzae6AX+OcQLh4O766zfIvLVBGLV5U+0PVGc2zJc0ykCxzdfo9TZ86i8zEHNDgsxxXvo+8EMoxJ+6teVTI6wj6UtH5aOHAP/tyK6dSr1+/0AYf4hFWAMUIY2u0uWZYxm03J84ft04P8HizFaJ9yugvOYl9mq3sQITfegmwFt/cRbrz0MH/4IoZzMB7nzHYGtOI2Z9YVrjbetMc66rrCGIWVMSYQlIOb1NPbuNdkIvhlgbWWm7cOuL1z+Ejy90VgglcRJwquH8FKAL/T82T60pFn9cwbnP3NX6W7fQoZBAgswcLLWdYWUSrU4QAzPSKtGs70BZ12H7n+NfqnznLpX/0dwiRh+umfMx/uM6sUs8bQCmO6SQsZG8K1GlkbkiOFLh3XdiEvj+e/22fO8d1/5S+hcOS6oagKNneu45zh7YsXWFtd4Wtf/ybnz79BWc+Zz8aE0vDOm2cZHLUoihsU5cO7LUoLtxqoxnM+/NFPOOxIonZIGIe89S1LFLSIwk3Eem/RqSIp52Pe//OfcPv2LT746U2u7kx8zeRg68w63/5L7zG4PeKHh+9Tl88np/5or+LyQc1f+V6Xv/rbFygmEy6//wHluKKYGmwN+cGQeC4YjR3j3E/AtmLvD9s+H5Nbx83K4bRlzGNisB3HNeLj3Mme/KmgLOrKGHEgWf0Lp2m/vcL01gGTm9UTOaH90sI5lCpxOGaTPVRT+Fbsu8bOIAhotXrEcUiv2yKKQpqmoniShfC7EMqALEogVPc3Dj8TVldXOHVqk9F4wtVrtzDGYB9j51XMZlTzuV8+douQ0deRdT3BPSjLkhvXb5AlKapo6PX7/PZf/R3OX3yDDz74kB/96GeLuaP3/HvdPlIBhLEgTSWNsz7F7xlQzOd8/P4HpK02ce8Kcdrie7/xF7j45jtc/ugTPvr5Lxgc7LDz2YeoqqCeDQmCgNPb58i6q9jaUueatH+a3rnzfOPrX+dv/a1/F6zhxtVPGRyNKesK/Qj7hFcNazSHN66BPMSlKXTXfQL2fMyxslyQxTHtJEbrBmMV4oH1373vyS40NuNZySSv7vzaPcM55OVHDsldM4iQY+fIBpqi5uDyLjiwz3gO3A0p8R7m1sckCSnJVjPCJGQ+n9McKZTSFE1J2ImJN9rYxlDlM7Qy7Nw6XNjf+/phSYbHWczK6RXCQBJFEussPWFJlELGLWSQ0Oqt0OmtMhoMuP7xp8f5dg6vlHiUQfYJXmsEoT+vGqeZTnKsdQ91fv0cnIFmhgwiOqvnSeI2UpVIXTOtpkzK8QP/TGvlhb8iIw5itLVo++JEXXnZMK8U7TRgrRNxemudX/uVrxPHD85deBB+KYh0Y8w9YZTzPGfv1m0apSjrEqIAUoFwAU4bqP3KtHWgtGE4mSODkM7WJlnXq+BEGCKewmz+1SAgjCLOnD9Lp9tha2uLfr9PWVbcvnWb5aiktWEynvpgyiCk024RSQfGIpxPlLKq8u3uWiF1icARRRkyinFEhJEhy7q02h16/VVObZ8hSWK6K+ukacz+/h7D0YQoa3Pq/Bt0V7o4EeDEcxyzuxcutUE/s5ftFw8hBGHovWr0IqDvBF9uLF01ZBATZm1PbqoKYRtCHSAjiSVAW4EyoIzDEkKYEUhBGEqsMaiHeUkHgY8+txK0wBFi6xohIpKVEBkmXkVQa7+6i0AIT24LGfIwNZcMY4IoxVqN0bX/W/s0pKhAyICs1SNKUrJ2lyTNQEjKBRnkr/0AZ/3AKmTg1aLCM95CxoRhirNQzicIHFopmrpeELQOZ80DVprFfV+Pw+vuuMP43NVj8bi563cPvewerEppyjnz8YC6XAaNPnlRK6UgjhOcMVSvqpNp0boeRjFJluFchDFt3wZnGgQaIQOsE09Zs/oD7JzBPbMVytNBLmzgDW6xgPEoBCAigjAhjDMapRkejZiNRpj5COqKAIMQDqsKtDJYXeKe6rw/hggTgrTvz+mvIPyC9st/neVyVYN3rQos7HsLThIDsQUrA6IoJFjUZ85o1DxHFzNsOceWBbNJjRoqprVhWjuKQKNViVIFVTknsIpiNqfK53TjNmfWT5PImCSI0U1BVVRI6UhiiBxk6aKxcdEcOK8q9odDDI7KKozVtNsZcuHJrpRCSkmSJhyNDrhx/Rq7O3vk85Kyap6IjLB439y61lQBEFoQDhFERGkHGcY4Z72AxAmkDOh0unS7fUIZgvE+pEJAu9Vm69RZTBMS9WJ0YHDq3rQot7ytPAGExSvblKVqNNo64jRFZYJSFjTOMmscceAoFNTWK+SEW6x3Bt431ol7+4KChZLOBQIbCi9iauzd651PjMc9VQZACNY40NY7qcUQZiFxJ8GmBi2+mpI5h8MahRECrWqM9t1N9x7VhSpdOJqmxrl752Hgbe5k4OvgpSBmeZKFYUQYRcRxjMR3lDwfvJDKPyTWgTbmyaxinMO+IEuZF4FgcSjMybThkXDO0WiNEA3T2QwhJGEQ0Om06ff7rK+vUZYl0+mz5hS9XDi8Vk4Z93S5YgsEQUCSxLRbLdpZRhTH5NMJ8zynzGeYpkbVFXVV0tQVui7RqvKdekJgtEZrjQgjwqxNkKSIICSIYpI087W5ECD8a4Vh+FRKzZcJISStbpso6WPiGBNFKDmnqRPfmWt9FonW2ueSGINwFrkgtYMwIIxjjLE09XJ8uxdLe6jnRSC8tYtYbms5f7nrvmb1i/NGl1ISRQHGOITS/vMLI/8IFDIA1/ix0SqLbQxOHb/+soNHBoHnwZxFOIsT3hrGWIkyYnH9GbR2SGkReKV7VTeoRh8f0eUpc/d7PsErgxSQLKyT64V91LPAehrR59Iv2zaedFtCQBgQJjHbp0/R7a5QHB1S5xMqUyDLB1UYx/84LG4xO3wc4jgkijwHIZE+H6OqHjLr99e4sQ5tHEEQsrq2Spo+eXD6LwWRfj+moxH/4P/5XxAkEbOug9UOci1CEmKHM+zNI6xzlAaaouFf/uxj2lnKv7d1irNnTjN1jtxZqtdq0hxA0KG3vsn/4n/3f+BXvvMrlPmMuir5x3/vH/DhBx/jnDfuL4uSTz/4lLX1Nf7GX/83+dqbb2HKGbYp6EQBQhTo6Q7VlR8hhCRdOYtM2ohwDZIWYezVXUvS4NTps/y1f/tvIaUgzTLm+ZR/8Yd/zM9//hNWzr3Nf/Qf/6842L/GzZsffbGH6DVCFIWsrfUBGA4nngA9wZcbIgSRELdW6KyeQnRXkGubmHpOPdkDo5jMFM5VTOc1daOxQQ96Ea1WxHovpSpmHN66inmQWjlNoNPzxHDtsBbKK9dIuz1Ovfk22do6TTmjnCysKYTDWYGz0hPYD0HaX6W9fpaqLJhNxz6Arhj5FeLHwrMLUZpx6Rvfpre2TtLpEaYZ46Mh124NSVsZvfVVnNboeYHTBiW8HrDKFXWpESamFa9STwd8+NmPcUbfsb5RzfLOfj9pIfC3qOVX8E37i5j4hZrjjnPN/Xezp7zknHPsXvmQw5tX0KpZjKdPXnFEUcRqr4/RmmJ4yMt0Qrkf/fUNzr/7HkbBZKzQStMUXYxWGFtTNAr1VIX6cjXi1VS9AkdiSgIdUbqGB2evLyGBDoIW3dUz9M5eZDoc8Kd//EP05Db1rR8SBCHdtQsIEZAPr6OqCc48uwo36m7Qffu3keGTF1cneHYYFg0lFn4vh0jAtxycA7a1IUYRoZHOoIqc6We/QM3GNLevoGZTfvHBjFu3a+bCkQsIuxOS4kOK2YBP1laQccL1y5fJ8xnfeutdfvs7v03ZVBRNyd7ODd7/6SHSano9ibSglGXechyMYTKHDy5f5urt20gBoXCsb6zxO//Gb9PpdpgMBkynM958J0c7zfs//Sn/4L/6/zKaVtw8mKO0oW6ejKC1FuYFJA7CFkRC0FrdZvXCN5FBhDVTEDGINmmW8Wu//he4dDTkp+9fY+/6LjoAE8C5M2/wW7/117l86hN+fP1D5HiMOcqh8fcAB6gJqCcUE3eBHsCs4PL1PdqtmPNf+xrTac2fXfmIg2mOncOkgrnyvpoR3uY8NRBZqJ1XjhccK/U6IfRiMO2AZi1FVYbZreqpPYMfBxlC1gZCqMLFGqzwhFx7KyNb3aagYPTjwRNN3H7p4BxNNQUhFyr0z4/H1hqKYgoI8lwiBHcR6f6mHIQxnU4Ph1cPe3W4J91XVtdZWV0nCIJjoul59llGIAMaIygqRdNo34n8JROxBBLasf++aPxi2gkeDOMcpTI0ukLv3GY6m5KmMefOnuE3f+NXSZKITz75lD/5k++/9G6qB2F5Tt9PEN2NvLQU1bM5CXU6bc6dO8vKyirf/JVv4pzjn/6zf87B4YB3Ll7i3KltdDkjcA2oEpWP0brxXZNYJqMhRVkjWm36/XVkFKORNBbKxtvJijAkTGI6UpKmCXHyetRAURzxjV//FptnLzEtCuZlxeFuwi2tcM0cigHGGMaTKU1dIdEIDJEzZBI6K11Wtk9R5AU7N/cWY9eLP0cEglhEBAi0Uxjsse/YCx6apFiQpklMp99HNZqiHoMQxEmHuJWhRQqJQrkZupphSk21N1s0vt77/pNOiyhLsbrG6BrtDHuHI29BswjENcbicIhYI8IAMS4Qcohtak/IL2OxBF6RfjKevXKkgeBC1xsM3ZwZimfUByjtBSV3TtunOX+jgGCtT3dtnd/9d/8ab56/yI/+5R9y5aMPcbs1s+ngji7ubiybrS01guax9ZgQsLnVY2OjSxJkpLLF0XjMx1eufy4L4m6UjaFRlgthi3e+8U26nfYTv7VfSiLdGMNoMPAjStCHKF2EG4h7Qkcd/kbcaEOoDdo6nBNYHFaIR978XjWElIRxSpp12D5zlnMXL3L72lUf3iAkMowwRoDVOOuoypqyKLHaIpHIIIIo87ZUTUFdTJkOdgmCkCDpebqqKUGGyLBNEC5PDUcch8TxKssytwxKv4quDVGS0u33GU9bfnYiJM/Snyml37q9S3wkxN0rUl8ueC8p8WWr4U/wKDgNtgHT4FTjbd6iDJwjjNs40+D0HGfAIbFInAwhSJBRQpxmGK0QYpmYdt/JIcRxJSTxamOjwRqSJCJbeNMJsRiZFl+tWfiIPeRkS9KM3soqcZqAFOhqTlVNni7USwjkQtFgrUU1NWXeMJs0GN0lbWU4rX3IsTZeySAERlm/f97cHd00lPkEZw3HqaAhD0gE5fNq9Ps/j7sO4YOe8gzXnqorVP2MPtCOB2ZZvAoIKQmiyHshxg4hBM5kBEFIWTZoY57B2/IVvg/nwDTeG/2xgdICGbeRUQcZxCAkqirJR4e4/AjqGTKMEU4jhMPpCquKx2zz0ZBBTNxeJYiz59rOCZ4cy2ltY/0IoQNfJ1hn0KpGNTWqKqmKOYPBGDUdw6TAzCuGuWY/d1QBlAFEoaGVl5goZ3BwSJCkqKoC6+i3e2yvn6aoC/K6QM3n9Ns9pK5pRxphHEncoBvHskmxrGvKuiYAEglpGi/8QQOMtSitqJWiaRRKa4w2vpW6rO8EsQshaLV9poaHw2qFVscLPknkfCeREQRhQpKmREmLMFkU+cITmsY2GGtI0pQsaxEsVMDLYTGOE/qdVVZX1tg8tUWShNRWYKqGqqrRyjx4CH4EjkVmAiek7+IMDVoIGuGjDubOf63w6tpl3EOjHMp6ZfvdzthJHNJrR1QtiUkkxriXF0sguBN8igVXasxMLX4lEearl4dwN7y42/m8iYc8Z6lctA8ZswViYSPpu+ek8HMrnD//fYiYQ2uFtQYpA6Q8tuJ4uh32/xhjaRr1HKHSXyyW9lF3l4NfdVfLR2E5j6+Voqprmsbn8bTbbc6ePcPR0ZBWq0XTNNT165cQbO2zc4vLa0gIHyKyrM1VVWKaGqcbhDMEUiCXXZ93GHt/vzFKEca+I8fhMMaijUNp6+cacmGMjPN/+7pMagVESUTaTnChIEpjqiKn1VvBVAHG5IShH8Odc0jhO7SkOL7/SikRi4dvCXgRWVKfxwO5pPv/4xm6rh4OP4q4u26ezjiccUgZEkQSFzUQN3fy8pxz2MCfG1EYIgNJ0mkTZQlNJagrg1WaRmms9TES9+yrXM5BnRdpLZl2cd9uneCVQUoIAkkcCkLhOwie5yPw6u1n/WvPvwohkMHSJiYgSSLCUN5pKn/Uqz+ZqEEQRSFpFpPKhCxIyYv4sXmXzoF2DmXsUzti/FIS6XdgHRzkMCoXxLjEKX3PmZDEMV9/7xKrKz2CTpf92mKCRf/pa4QkzTj3xlucOn2WTtYF5fin/+0/5g9+739gNFckm+cwVY2azT2hZ73X1eWPb4JLeO+tNzmzfR5XG/Y/fZ+9a5/y0z/4A9I45tvfHNPudolXrhKkLXoXvkd76y283LPGnybHBEKWZfzu7/4Nvverv8qfvf8JP/vznzI3DU16Ch1rnnZGFgaw0g2REia5oW4cYQJh5Adr/XAr0dcWSmmOjiZ3vj/BlxzOQHEdqj3y+oDq5scEq2cJt98ljjK6mxcJpSVlDlbBwR4iz1GFQJUGISNklCEj5ZOrXQimuLeXvqkhn4IL/O/jlGB1m6i/Qm+1S7eX0VlpMys7OKVwyl8ceZ5jy/yhROmlixf47l/8i1hr0cZwuHOTf/nf/X3msyeRTFuwDbp2XLv6GdHuLr4fX9DUDU3VoNa2Ca3D1DXDG9ewypD2VgnjxFu8SEE5njIfDLBqvCjkBV6feHc66P03uuVkWnCsVn/Izc094FevuN6vy4Ldm1dxzqGaVztoTfMpn12/QiBjkqBLIAO6qz2Es9Q3j6jn48XixesJZzXV0S2EDDCPSWKXcUz/ve8SrZyhNiH5J5+gBh/jdn8GugJbY62jKI789szzfxZxHLO2vk6YtJ57Wyd4PJadzwGwBrQFvLUp+FpfEMoxn924QjY5YrWYsHMw5h/+dx9QTma8bSpa1vCDieGKhdBBaMBMHKoyxLtjPrz5Z3Q7Lb77G+9w/sJZ3n7rXbbfeOvO2uTkjff4lQsXqSZHTD7+CcV0ymywT0X10EmIMorBaEBja5SwkEYMxlMuX73NhUvv8b/+3/+n/OgnP+c//7//f5jP/aJOmmX8tb/x73Dxrbe8b6tzDG5+yu7l9++0k0tVw3iHCseZN7/G2TdOsX76PDJKYGGnVeUF+ztX0U2DmddMpzMmdUWOv704C4ENWZEJ3zr/Hqf+J/9b5vmYzz74EePhgD/+4z/n5o3dp+rgyfHk+LurK7z79fdoijnXr1xlOi5Idc1a5FuIB8qT6QX+2EoDWW4przY4YL22pMANfLV59uIbfPsbl7g9HfDB4XWeOX3vMbAGqtzbu6QZBMpR/fGU+Q+PJfkmNzj1mpBGrxhSClZWeiRJzHQ6Yz5/9Jj8eXhCReua2Wy0sHiJkYFfoHDAZDykmOfHrxmE9PobWGuZTkaLYOynINSNAqHJJ5ZqXnjl7etC+j0NFs15QkAr9uVW0Xg14CMRSk96mgXZ+TopwV4yHH74KrTmRz/+CUVRcuntN/nLf/kvs7KygqorDg4Pef/nv3ilZPrL/gjyfM7Vq9eJox1uXr+BEIK6rumvrpBGAmlK2hGs91OKcXyPxk0AkbCEQlENb9E0DWQb0N5iPJozyGuiUOBEBNQM93eZT0YUs8lLfEdPDmsMh/s7GAcX3n6Ht999h3PnznPhzXcoxgMOrvyc0Co2W45EOgI9B9OwKyWVBT3JmVU3fCeAlARx5K2UfOInT27+/Jj9xFFYhUCgH0YVLgue532txWJ1UTYoPT7OmLMw2Rsgo5BsfYtWp0/YWiU8K2m1WqytrGG0ZjIaEwjJGxfO02m3mZcz6qZid+cmO7du0NiKWjW+A/r+E1tZ0Ms3chfPZvE00tJ28wSvDL1uzNnTbWxtmO3PaZRFfVGfgdLY4ZTJrOS//jt/h067zUa3RS9LiBdE+otaZ/FZtn5rxi2uiyf8293dXf7hP/xHJHHIf/C//E+f6G9+uYl0gEr7Bw8+kEEgWel1WFvtQxCQNxoROuSiZeV1QRiE9Hor9PsrSCFRjebWtRv8/Kc/JeqfIl49jXMhcm68H66dLwrSOcPBFHMxIMv62HrAZLDPwd4uV2/s0Epjzmyuo6qcpCoI0oxk7U2yNY11Nc4VQISUIc4JtDEYo9nc2qbVbvHnP7/C4OAQm6XYVgsXJE9VOAgBgZQkiSSUgqICbSxh6O2inXXoL2E1aK17LdUPJ3hWONA5kKMb0NRI2SJcsxAmBFlGFDhSGSJsTTydEDUNpg64Y1QrJE4E3ohVGjD3rcEaDapZWMgsmjtaLWS7RZRExFFIFEeESYR1Bqcd2mpUU+FU/dCl4m63w5nT24t3IZBW3dVx8iSwWKOYzSZQVr5gMuAPREMYRDSzGaqqmQ+HGK1xIiBMWsRpShhF6FrRFCWg7vJzj4HgeJXZBX7WuDze9zR7P8FY/NKHiUerVYzRFPkX48WpVMN0NiGOUmQrQYiYMAyQIgAMVr+KsUh4ZQ+wPEZPFBwL4BymfjJfCSED4pUNko3TFLsDivEQpodQ7LM8bxwObWqElc+gxH/QawriKCSMnjyA5gTPh+VVFgOpgE4K/Y6gdDWjyYTKGmwUsLc34Rc3jigmJVEEfQG7GvaB1EHLeVV7rhwhDcXokH63xa/+5nt0Ox163R697gqRDIlkSC9NaIeW+WCP67euEFaKSASL4vy4Vf/u/bTGUhQFQSQ9aSglRVkzGs84s73FG+dPczjM7yjFAcIw5Mz587z93tcIpSAQsBNrgtkO1misNegyZD4NsMLR7q+yurlN2uogZOjvJSJEG8t4MkLVNVSaPJ9Ta80i7ccrgIQkkSG9tMX2xhp5PsFOBhxEIVmUHsv/nxBq8SBO6K+uMHOOPC/JZwXSWWIJWoNyPtZ5Gf4pFi3h7Zm9cwdwHOtWsk6b1a1NxtSIQ17emL5sTTcQRN4Hv5o2VCclG+DVmnEckWUpRfG0JPoxnLNo1SCEJJbRPRkTTVPT3LXgnKQtOt1VrLV3de091auBc6hGodW9wahfKrgFhychCr0kvdb28etcS/m6u0vC/uWbOj0zHKCt5ehoyO3bt3nznbc4ffo0hwcHnDq1hdKaQL5mCrnnhNaaPPeLUePRiEBKequrZO02YSAIhCWOJO0sJksioii6E74qpfB9s85gqjmqmINLQa5Q14pSGSzSdxwBVVkwn01R6vUYJJ1zVMWcfDYhDCSrqysEUYKMMmZpRD3eQ6qSLKyI0YSyBm2QUniP50ZRN2rRzZl4ZTpicf3pF3rt6CdZUnlBr+fw3NWxgMRvuClrRKNorUmSOCNJU5I0pdftsbV1CtU0iCAmkAGnz1+k3+0yHO4zn0+ZjEfIIEIIxUObhe7IlR/wyy/pUPxlRxwFdDsxtVSMtKNWj0+femlwDlc3qLrh6uVPCQLJ9775NdZ7ZwgD4W9fL+qac4Bduqpb7FO0dBVFwdVrVwmfIgvil59IfwyU0tzY2eVoOuawqMi6PfLpjHw2Y3g4/Fx4zkuBwAu+A7zM5wEVU5JEnD27SW+1w+//8R8R/DBkUDWc+ca3qZWg1hBEMUF/FasKyukYISRJ2qPdXmd1fZvN02f55//kx/w3//X/m+l4yN6tG7TjgN3dMa0kptAxVsb8ld+d8e1fu8nt3dtcv3Gdbm+F8+ffYjwr+R//5KeMJjPqukBrxcHRhNF0TnvjFN3WGnNifzN6AmRpxMZ6mygUpIkPiTl1do0wShkXE/IqZzYuffDfV6ggPMHrjhhEB6cD9HyOEWBdB20deWPAGBwhcZSgwgCko6wK9vYLb+2SRgShw8zm967Qm0UQ6JJFlwbJmUUwp0Yp7/EXSkc93aE+uOYJF117/+eHBCm6RSDlNM/ZPxhwuHsL/bRdEs5BPYemXIjE3MLU0dLM9xle/SHWGEw1wVlHPZ7SBCF1kPmg0fYKq++9RxhBmnpCXxNirUM1FcYoyvGQZj6FagblBKIWtDb8pLAovUqEEV7e8IohpFfYC4nRNc4YgiBASvm5sOt7IBNveWUacC/PNN01CqYzlCyZzUoCKSmiGAEU+fSlve7d6K5vcurSezgEdaPQTcXwxmWa4sUvLkgBgXCI2TXYvwzVCHDIKCNsrx1bjL0gMqUcHXHzh3+ECGLgyVQKJ3h+GGCM90r/6MhRzi1qb0aTNFRRzCTbZ1o0HJUKDXxoPDl7tKgXFJ7INXdtLwfQits3byDNnO2zF9na2sbFLUhCIiHoLVpQg2Uto0A0sBpC1oFpA+PGD4XKwTiv+OlPPqHVyjh/4SzdbocbN3Y42B8zHBwymRxx7fr1e8aJplG8/7OfcTSe8Ou//ut87b336GUh505vMti5wQc//EMkNSurGVkS0Vk/Q7Z2AZn0MM7T+c4Zbt7e5e/+V/89TV1xeqOLc5bR1KsGNzc3WV1dZXt7mziKkEKgipLJ4Ig/+/6fcfPmdcajAXG0cFZ6ws8lxdueLvsUkyzhzBtniTs5P7p6lQOl0W6x3rp4tIFVjklzDdwCZsvPBPjs6i3yvKDQBU1VYyr9UifhzkFR+/HkpHHQI2v50NwgWHaC+Q8gSRLa7RZKKfJ8/lRqb+ccSlXecuy+zigZhARhBEIwGQ8WvruGZ9Wntft92r1VyvmU2Wjw+lhRPAQB99obAVQOIiE5t75KlsXc2J1QNY+xJ9N2aZAMcQhVA+WXsJ33OSCloL/aZ2t7iyzLsNZSVyXT8YB6PiELHUSCSrvX0i5nqRh/2Cm7dFlZanOsPfbQ99yRwzYlLoTNlTZvXzhNublKef40775xjnffuMBkMuHTTz4mn8/ZH46YTQqUiyFrgZUwHdBMhownOVEoUeMpuppTVgptno6UeplwztHUDWVRMR5N2N8/ZDKecDgYInXN+kqfwCRkxQGRE2yvrpHFgt1hxa3dCQrfBeWsw+iGMAxZ6/WRUjIdG+r6xXE/d8tLXs3RW76KBNoIGZL0OkRJytk33mF9e5v+Sp/eSp9ABkRBSFM3SBFjjOFoPONoPGUyOaQoZhxNcmotUF9xu7MvG2Z5w9XrU4y2FNr5BaQveJ+EgKQTkaQRG9trnDl/msnoiFYsKI2jeswaVhwJkjhAa0tZf744dM4xGs/QusbaEGdDyqp+6BxdyKVnpMVZQ9kYdo4K5FNYVH/liXRtDIfDIZM8JFeWtD3m4PY+g93DV7cTAj8ribhL6nMvwjBgba1H2kn54KNfMJ579d7a+UtMhlPqwZggDAjaGaYWVDNPJERRRpJ06XRX6K+uc31nwN/9b/75nZOqHUJxsE8s4HAKygasr3XZ7Gh+8eEn/PDHP2Nra5v6e7/GzuGY/9d/+Q+4vT+8s1+bm6dYWVklyVZIXUxM9MREehwHrK+2CEO5eI8hZ8+dptPrc/Ngl72hQNUOQX7Co5/g9YGIQKY+6LOqMXGMdYHPW2is9x4XkiiMCKRESKhVRT0vEVIQxSEyADuX9+Z9Wn0PGS7i5I6vnzHWe346RyAcrhyijq488S475yiKgt29PcaDo8csEN6vucT/rB7sHa6rMXk1vuf/1Hz5cwtI6PX6dM6dJUkzOt0uTgga5zDWUBZTdFOjghaNGIENoSwh6ELrjO/NqsZgKjzl8uonh0IIgsj7IDurcc75kLSFwvTBx1Msug+ihWfgS0wfNQZXmkWh5GmpJ8wNfGFIO31Ovfk1EJK8KKnmOdOD2y+HSAevlSr2Yfzpnf8XYeyJdMDUOe4FdZU18xmDyx/y1EbSJ3guOPx5XAK3ZqBmDkuJoeQQuMy9POut+z7u+ycODj96SGMYDgZkVBTTMa6pcIFP+Avwo5ZaBCDesY1S0An8wzhPpDt8N7OuGq5d3SFJYq9uD1KK/Ahrj2jqAmtrDg4PsXedj1prrl+/zjif8yvf+harGxus9dvIc6e5miZ8+MN/gXCKdiem3crIemvEnU1E1DpuQcdwdDTij/7kx9R1yTe+tk0ch+TFHCEEvV6P06dPs7qyQhSGgEXVDcV0xicff8q1a5/hHEQh1E9xakd4Ej1e/hzHrG2u0YiIXN7k0NyJhb4DCWxwfAVpYIBfKFli/2DI/sGQOBG02gFWu5dqz+GAkxz4exEn8SLjB+62VonjiE6nTVVVzOfFU34uDvuQ+99SEWqNpihmi5dbdq09qBZ5NNJWi976OghLPhq89nOHZUrM8t1aoHSAkHS6XVa6GQfDEt/X8QjYRQ97J4Qs84PUV4xI97kTLXorPeIkvuMZXuRTmqogDsCGgsa8fkS6WES4PeqykvI46k2KxULlXUS6w2F1DVrQbyWc3ljFWoc1lurMad48/waDw0NsU3F4eMjB4IiqmHsSPe5BXUE1xRQ586IiDAT1rMRUJY3SPljyNTpuWmtU0zDP54zHY0bDEUf7e7STgLMrLUIbIGpJ5GB9pctKJ6bf2iFd/L3X1jucMQgp6WQpURhS5jPqF3jpLK/tV0tiLrt6E4SMidtrpO02a5unOX3mHOsb62xsbqCVpixKmrrBGG8NdHR4QFUVTCYjynLGrKhQViwW8E/wZUFZGcrq2TvKXgaEFMStkKQd01vtsra+Sq/bIo2E79x4jKAhDCRZIqkFVM2Dx6P5vKSuSrSG5nECCSmRQYh1PgFYacsof7qum688ke6co6k0WlssY8JZQZk/XzDZE0PipT2RIN7KkIlEqRpTfb7gnM9zfv6zPyfJ2hRRgggjLr7zNpunT7NzY4dbV29SzCvGRxOEMKS9LYIw4PKNHfIavvXdbwDQ7vQ4ffY88zxnPDpCWcdeCaGA3IBxlj/+4CoHVcPewSG3bg/pDBQ3xpbZvGI2v5dMC5wmtA3NZJ/hNctscOjbop4Ada05GORI6QdnKSV5eYMkSRnnU/JyTpHXr9WN+wRfdQhIY0g60EohkhAKnBS4AGQYYgnRjUIVBVmS0E43qYqc+cy3DBrVgFaPnYhGUcjaep84jdn96CcIaxmOZxRFhbrjUyjxtIZjWRbej5tXP+NP/vk/gTAmTDKSLCNdPwVFjpqOsPp4vBFRgogSnFa4x3hVP9GxWjzq6ZTpzRuE7Q7lxiaYGjW6jTUKHSZYIRHGkGRtjNFo44i7q/S2T+McNJ0OVjc0dYTVOXZ+hKtejdIaFi3qTY2UkjRJCGSKMQv/wYd+jA5s7Un0JxwTX1eIMCbqrCGDkCj0wS3lZIQqF3S9ACFj//s0obu6Qj3POfgw8dP/IAMZEWUZUSsjDEOSJEHXJdP9W/6aeCw8xWl1yuzmDarhhHoyvOcZVtWo2SEiCAmSGIIILcULIFM0fhHnZCLxKpAlEWc2+gDsDCbUtVoEhT1f8F4kYS2FThawsbrC6toqWdYlCBKEA7s4D2W7Tdr0OX3qLJFIqN0hR+WcVghJ8PBGB60NOzt7TCYzIiEIhGA6POTGjZsMBkeouwIQ4zjim9/8OhfffItzZ04TCDjY3+XW5Q8Y7t/GRB1UYLm9PyOOLF8/mrCyOuLajT3GhUIIb6X0iw8vUyuFCELa3S3SNCGKJjg3ZzQaYa3l9q3b7O3tE0iBtYp50RBHHdK0z2xW0NQK9RSE8vJqPZrNuHH1Olo1HO4fcTQpyauapbv13aiAA45V7BbvfS8EzO29dy+joSrtw9vIT/DScPbMWay1TCZHzOdz6tqfGE3TMJ3OyLIWX//mt9DasLd/SF1XNFXxUKL8cbDGoJsK5wTIdOmVxFP5o9+FMp+B2KUu5l+KucOSXAvwlVwUQKstCAIYjSZMpwXzsvJhkMv386j3VTV+kPwKWktqbfjFLz5iOByhtSVJYrRuuHjxDeI45vKVG6+tGv1JzlVnwS7KasPxvTAQ0IoFcRRwan2FXq9DrxURopBhgEwiAix1EdDOUk5vbpKEIaPJjG67w0SnFDZGJC3oh2Tr27TTNlEoSTorNFJymNdMBhOq12Rxxi1snKqqoqkrTN1QzedMpxPmtmG2kxO5hp4a04oc753r0m132WoHnG3DkfLZA8vDLoQglhAHglj4azFJY+Ikpm4U86d830EQkmU+U6cpCow1PPDCvdcN8YXe78IoobOyTavT471vf5P+2ir9jVVanRaN0ty8vYtWhrpsaOqa4dGApm6YTUY0dc1sMqYqcupihp7PcFqDW843l+/nvh0W0oePOHfvvEcAceBP1sYs/NR/ibD0/TupWR6LZRdEu9Wi1+uTJBn+AD6+Z0Np6y2gtXvomGnMXU3zj4GUEhmGpGlEGrZpGkWeP51Q4IRId1AtPNSLp1yFeG4EQBtkJkm324StmHw4wow+X5Dmsyk/+sGfECYp577xXVY2t3n369/gW7/x63zywcekWYeD/UMmRYmQkHIGrOXDz25y9dYBv/tv/Q4A3V6fcxfeZHC4z3QypjGa2/esGzj+2Y8/5p//+BN/OjsHYoD46bWFBdbxySXwRHpka5rRDoej28yKCmuejDSqas3O3r1kmLg1gsesyp/gBF8o0hT6XUgyXxhEErfwpxRRjMSiGl/UbW60WVtdZTweoXWDris/wdPNY0f5OA7ZOrWKs47P/uUfkB8NvN1JEIMaLZ61DALW+FaWz184Vz/5kGuffsSZS2/zjd/8bdJWi9bWacQ8x1TFXUS6QMQpst3HVXOMql7Ahbgg0idj6kIj+ysE1uGqCebKn+B0hVx/A5F1SbMNslaPmhBNi3R1lVNnz+GEYFbOfUv5pENTF7i9X7xSIh3nME2Fk5Ksu06WZeT5nLJ8sEr/DmzDwxY4vkyQUUK6fpYwyWhlXQIhMfoyqrR31ktEkPowo3abM2e2qYsZH2WJ/2XQgrBNtLJOZ2uDNMvo93uUkyPmw4OnINJ7OB0xvXrFGy67e4l0pyvUdA8Zp8SdcwgpUVK+AHcIDbzC8+0rjlYa87U3fK7DOC89kb5Q4Um82PJZkARwtgP9TsCp9XXWN0/RbvcJQ69RM02NFI6w3SVzjnOnL5CIFqX7mP05bGU+u+VhRIwxhhs3biMEdKOALJAUxpIrP7u6eziNo4hf/e53+NVf/3VW1zYIpWB/5xbf/8PfxxgNYZcmEFzbv07gSo4OxmysDvng/Y/56PJ1T5CEATuHU6qmIWu16a6cptVqEcbXATg6OuLo6Ig3Ll7k1q3bxHFMFAXk84o46dJqrTM60uRPFDx9jHrxOJhO+ezyZeqy5vatIyaFZlo82CKmWDxa+CEjBLrSk+naQXPXsTHGYcqTIvCLwMU3LtI0DUeDI4bD426ium6o64ZOd4Vvf/fXUNpQ//hnTMZjjNHPTKQ7a9CN8Z1+UXcxl84Xq1VPfw4Us8lrE4j4JLAcLzrF+Nb1zX6AE3D7cMy8dBB4fgruWsR72KGpav/4CkJrzU9/+j7vv/8BaRqzttbD6pp33nkThKRxf075GgcIP67ctgue6f4rI5SClSygnUa8cWaD1dUVVtsxkWtIoowkiwmco4giulnKue1teq02ZVEz7E24Pmo4zBWitYrobNDZPEev1SUKJNY1FEKSTyv290YUrwmRDtDUNU4WNGWFrmqKPGc0GtLkI4q9K8RoznUcq52EQFyi30nY7gZc7IIrYOeuslMAiRQkgSAWghjoZyndfofxbE5R1k81GoVhSL+/inOOYaPQzQPGM8HnfV9e4OkZxQnr2+dY2zzFv/KX/lW2T28zLaZUTcXNnT1u7uyilUXXmrqqGR4eoFRNXXgb0vloSF3kPh9M5fi7dsLxGfiA9yRDP0e9r8PaWxgHEEuYOdBftMnIC8YyRAe+eP+U1xgCQSgD4jCi3e6wsrJKmt1NpD/64CntUI85d4z1jyeBDAKCKKLTSVjrZeSzgqIoMU8xyfjKE+lfKJaziQgIJSIM6K60cSqinNeUeXXPk4UMSJKUN995m9MXL7G1vUmahKxvrnHhnbdoraxgwgBVlKjhBNMoqrIgkAIXBJRKMytKRqMp80esuPjA97t+d9/PUkrW1vqkSUIrEAjcHbVY8AhfoX6/Q6fToqoqiqIgzVpsnNpCK8POzR3qenGjen3rnBOcAFQNxQxsAEEXUwUUswmBFIS2WJCnjjCU3v5DSlpZxtr6BlWRc1TOMc4eGyI+BMZo5tOJD/l1DoIQogjCyHsZali49C6+BjzsRuSco1GK2bygqirq6QRVzHHLRa8ogyDCCYmrS0/0v5DrcLF9CxiLqwx2LHBNsfB1N7hqBtZgZIIQoS9AV3q02q1FAJAjCQTSOnLdYKvieL+RIAL/Au7lVy/OOT9OOUfTNJ5AeNJAzSdEmGREcYZWNap61QYtD4YzGj0fI4wiW90gSVrM+30aVWPqAtsUKFUxm+Uo43BCosqFmlEIspVV4u4m/Y01+pvrPsiulZHFIfadb1DlU452b1IXj3q/FqhAGEj63jKnmYP+/N84a9BljhDyiRd2T/AEuNtt4SXcp6MwoJVFtJKAqpjhrEMYQ4gf5Qq8srnk2ayLtIVxDTZwzPOKdjbj4PZthIzpra6ysraGEwZrKppyxtHeLod7A6qqwgDFwuKlum+oCaSknaUIIZiXFdoYlHEILMrYB9ZaSik+/vBDjNZ893vfo99t0et1uXDpTQaDAZ988inldIZSGodj73BI0k6ZTPNF5kXJrFIMp8UioFEShhFRlNBudeh2e4RhSBCE9PsrZFnGPM/54IPPGA2PODg4Yjad36OSf1okccLqyip1WlPkGkJFMLjblf7zcODFEsJ/jvrx3bwneIUYDocopVAPaVEoy4JbN69jjKOYTWiq8s4YG4b+/m2MQS9kYY9VdgmBlNJ3EcU+zNkS46zE2ubpQ0OF9A9nX1hGxrMiSlu0e+sEYUiSRlhrGO7t0NzVbr8cUuMkoN+OiWNJ2vb5MUFQIIW+s3AnA4mIQ6yx2Pq+q0Ymd7Htdx/zxffOgtM8+cC9ZIW+XEmByzDNo6Mjrlz5jFAuuLvpDGEsPn799YPEi3UdnjC/+xYbiIXY1d376SVRSLedIhfh6o2GJE5pt1rUVc3RYEiStUlamqpSVHXFvCg4PDxkOp2h6hrhHNJZpLMEQhAEIVEQEsoAZwxHgxH5+Ih8OqIuJ5hXElz/eDjnMKpECNB1iWoaVFXQzMboMsdai8FRNJDUzuf2qBrnDGJhkRNxbP9mrGU6L4jqgGoR1q2NQTUK8wykr7WWslyoFIUkCGK0bR48JonFNAZwfvr4QhBGEWvrG6ytb5CmbYIwoa4Nk1nBeDxjOPSZVlZbmrpmXpQYpfyJJiRBmhJLCFxKSB+tHHVtcUYvhpK7BVwLJjlKvFVQUy1yv5bjD6Cs//lZlRCvMx6yrvClxuIjTeKYKAxoak1TP58XnnOOulRIKq59doNAS27e2qPQluYLOC+sMQjVEAUter0e1vrulKfBCZH+RWJp7ZKCiAOCOGLjQpvOdsjO1QNu3UWkCymIopTuyip/5a//G3zj136VMIoJgoBL777J2XffZTyZ8NatW5TTnOGn16nyObu3b6KaGheFHBU1u4cjrl65gVblMxNAURTy7ruX2Npa5+j2HtPBkEBCFAiqQCw80u+9IIQQXLhwmjffPM/+/j43btzgzPlz/MW/+lfJZ3P+0d/9Rxzuvz4r3Sc4wYPhYD6BYgc6GkSbpqk5akpkIEhDRyAsMZYsi0iikEiGpKtrbJ3eZjQaMplOMAKoAh4V79ZUFQe3bwEShYC0Ba02RDHY6cIq3Pt6+cEk5rj8/vy1XVQ1O4dHNEXO9PY1TF3i7ILQb61Aq4/LjzDTIx6avv60x4oKEGDn0AicEpjZ1Xva/tz0ACckjXUorVlfP8Pm1llEEPkJNoZ2DNo5hsUEPRl6L0fwZKpMwSkw5QvY58e8I+eYzWb3/Pyi0equ0107zXxyyLguXov2HNuUFAfXyDo9Vr/xHXrr25SmRqUJ9eFNqoMB5XzCzu4eIoiwV29jmpLZLEdIyfr5i6y+8XW21vtsbfQXa0gOZy2X3nqbYjbhT//b/x8HxdVH7IUGhv7zXnkTklU4mkI+/NwznVZURweL2eeXiwh4rSHx63WWl8J+dlox57b7BFYzPtxBKYNoLAlQODh0vi9gwqK1/Sm3Xxm4PoV2bTh3MCRQNXX1Q658+Clf+9736P3mb4KrMc2I/GiXT376I3b3BkxmUxRw1MDwAWuMcRRxfmuDIAi4urPHrCgpjaUyDx+RirLkH/29v0eSpvwn/8n/hvfePMf5s2fod/9NfvBnP+Tv/v3/gWo6YUMYsijgg4+vcHPfZ/c4YH+Y8/OrBxhr0cYihCBJMlqtDhubp7BO0ul0aLVavPHGRdbW1rh9+zZ/57/8O4xHI5RqvH/uc1wfvV6XN9+8hKoasqjNwWjOD29f9+HQD4EQIEL/+Q20D1Y8uUJfH1y+fPlOpsqDMDw64vt//IfgfOeADzP3RFPWatFqt++IZawxmMcs1MggIIwiwiil1Wr5XHMJxiiaymLMUxJ3MlooIhXox3SMvWR0Vja58I3fIuu02Dy1iqoKfvA//rcc7d668xwvQIJeN+b8xTW/+CVjGmVIBrvUlaYxXmEXZTFJN0NXDVVjjmsDIZFRD4IE5xYLd8vHsiZ0GvT0CQUHwh9H8MfxS3aFOuf47LPL5PkR/U6XzdVVhoMRgTF3jBBft3cUSUil5xiVOZbICPxCgBRQ3+eI0WunvHthm6puuHJtF2Oh0+mysbrGZDzlk08+I2n1SdsrXsnuHIPBgF98+CHT6ZQoiEFIhNFIq4iFI41CsjAiDiOKvObyx58xOtzlcPca+fDWS82seCo4SzMfo8sZZT6mLOeU4wHF/g3fHeN8ZtC4BIsjnxdU8xlGNwjvyEnCsQxJac2tw6NFyejPjrJuCHA0jX7qmYVSDaPREVJKoqhDGCfY5gHjmeesCTL/vV5O6V4Asizj0lvvsL65Tbe7ThhkjMcVt3YG3Li5x7Ubt3ywtJToRjFbEOvtdkYYxqT9VZy0tJIW7aRNns8Z7A8xdYmZ7i9Y/2UxGPlH1oP1VcgnUM7uJdILffz9Lxtex9W554EAIhChYGWjQ7fTYng4ZXjwfES6tY7ZqCQfl/z+4I/4fvB9Sq0pF9fYqz41TFNjVEMab3H6zBlkEHH1yk2e5gM9IdK/aDhf15iZQiuJICISAWkc02lnaK2pauWf5ywC6LVarPd6aGuwzmGcwzk/QFdlTplPmI2PKGc5xWyM1opiPqeYF7RaLd58+y0m4wH7t69g7dNf/c45rzjRijiJ6PY7aFWh6hJjzT3qdSEgy1LiOGJzY4Mzp08vQq9gddNPPL1HDMcT9F/GQfYEvzxwxhcQuoB6AjrCugwnJTqSOOEQrsGhUY2mURppFKrJUeWcJE4QzmFVF6dirDVYZ3Fa3dMKt/TjFkGEXFvFGkO6skaUtcj3DAVePWCqEoRABOFi99Q915AIQk9uIqmKElVWWK1x1uClgdJ7kurGm9O+cBWXO/7q8AZm9/zaqxScKqGeocsJTT5ERAmBbYM12HqEbipMNQNV3DlOMgiRaQtnFU45nDX+OL7EQeRlTySElAgZEKcteivraNVQzmcv4XWXAy8c+x0+AtZgVUMxHSKDAFVMsY1XwxJEiDAiCEPCKCZOWugmYh630VWNJUAbh1z4RgaBIAwlRhvm8zkyiB7boXGnhctJ0KXv0HCPKOqcPbmXvGCESUDYCjCVReUvnkk31tI0GmE1rjZobdGLj3GpfVKL75cZ7cseHCmgl3hCQqvFUOaO//aO0Gsh+dNKoRrlFSmLrjohBMJ5eZiQISKJkbEPGV7+6d2nVCAlSRyRJTFRGCKlIItjjDE0SqMfQVILIIkjWmkCpqGeT5mOxhweHlKXOduba6hOynoIcSAJkozGCBqlUFqTFzWN1vessy2Jca0USjXUdYWUgslkwv7ePqPhEG0UTljCJPQisUrdE4L6NKgbxWQ8o6kU40nFLFcEzpFILz67e6tBIImigAiHwatsjeSBPuhBGJCkEcZYmurxeSIneHFYdlw9LJDcOW9d53FvIOhSDSylJE1TtFJUxjz681uo1p01Xu3qwFpfn7hnGcDvkMjL+9vz0aYikAgpcNbinlAxFycZSZrRaneRYUgQRiRpC4FDBsE9z5USolAQBP44Ouew7tgGSggvpBLOy5WttlizHIkEIowRMkLGGSJIMFr5jj2n7yXNl4PhE+PlXXNCCLIsJQojZCCRMliE2L64DryqqplMZlhlEVYwneZoa78QsuZJ4JwXAksJaSAwDozyV4CUEEq43x3EWEfZKK+2tg6spSgrZnmOsoJ4WpC0SpJ2dedFJuMxtVJoY+/M1xvt0EbQTVqsrW4QyYCDW7co8imTo13yyQGqKZ++O+QlIxABgQhwxtLUFdpYCBZEglZ31P3G+ky2sqqpG4NeTHOWiypLCd/9i8rGWhptHnkffxSc8zkffqHxvu6cZbnr/DUeR4G/vqXhGWiZh7w+GK1RSpPP52hrmBcFVV1jHcRRjMMvhLrFjgjhPayDIMRKgxPCe57LEAj8GGjhWFUR+e9l7FfInQClF0bV9+/Qi3lfJ3hFWAyWURSTpRlJVBPJEOus76p/1s0u7m9V3dyp559nZLnrUnrWHUIrRVmW3i7qKTd0QqR/UVh2zTXgtKHYH1KbgK0zkmA1YqvdZfWtjOF4xtVb+4v08RKnak5lKZd6PaZVQalqitmMwWzGzSvX+NEf/ksmhwOu//h9qnyO1powDLj2G9/j7QsX+OY3v85/9n/8z/jB9/+Y//z/+n+meIbCxRjL/v4+RZHz1sXTvPe181y7do2PPtpj3txb/IZhyFtvn2Njc5Xf/ld+i+9+6zsUVck0nzOaTfnk1k0GwyOaSEMb37t90ud7gtcaDZBDWUFzACIDuY4TEXUYI6SgkhVCKqzSNFpRTXbJ9y8Tt7psnH8PIUPq4hRGK/J5TqNq9PQAkx/deZWs1eLNt94kaXdwgSQIA956513WNzf5yQ+/z89+9AOKgwMmVz+DICDsdgGHnja4u1oRg9YKUXcdayzDW7s4tcwxEHgVu4T5FMrJFxiK6XD5AFeMmIx3mN/8BVF7hdbWBayume9/iqlLlGoWBZrfz6Tbo711AaNrynKKbWrU6GBBpn8ZcUwmr21us/rue0xHAz55/weo+kWr60KQfXz1PuVR3RFLNFXJ5R//ITIIUUphrMYFLWhvkfS22NjYZGV1lXffewdVN/yTwRE76jbj3FDuHbC1vkqvt0q3m7K10Wc6nfLjH/2c2WSGfmz7bAiseluj0RWQ2reOnuCVYeVUh7U3ekx25xx8OsK94NS2vGi4tjNacF++tF5yQRX+vxt8idAG1vAFeA6kIfyrZwO2WoLBvmE2cZjFfG4M3OJ4uSixjqaoyEM4/UbKqVOb9Htt4kCAjHH0ibqK9XfeQfdWiC4XcPT566/dSjl/aoMo9O3wwsHptVW07rI7HDPM84e+1ziO+dVf/TZnz2zTywQ7n/2cn/7sff74T/6UjfUN/qO//deJY9+Vo5Tmo4+vcXQ05vqVa9zY2UFpcx+JbinrEllIDg73uH37lu/oEZLpdMTgcJ+imrN+aoX+ZgeXgLGGg6sDivGD1cePw+7OAb//+xPKynFr39AoS2wV2zEcqoUVzgKddsqprT5GNcxGI5SxvjPLwf3ppN2VNmcvblEWNTcv76Kak6LwVWFJZj5Zp8K9tGRd1xhr6XQ6bG5uUszn7O7sPJSUB99a7axFNw1N6TsZlqrqZ1pAsQuhg5P4/BjD8ejxlBAQtVOCNEbNS/T8ye43p05f4MLF99BCUuoaq0OiKAKbIAPp53+Lw5ukkm4nIIq9atYHsRm0cTgsIoAwCJEixChDNfKWVzgQYULcO4MME8KsiwgiyukEW8zBVaDH3OlWdHe7sT8ObqFEX3z/ghEEAW+9eYmNjTVanQ5Z1uKzz67w05/87IVZ5U0mc/K8RMohYbCDMZZaqdeWSFfO20b3E8mZtYBKOW4MNMZCEgdEgaCxBnXXPXeSl3x4dcdbDWpN5Cwff3aVvd0dam1RFqKsT5yt+OvJGCQ+K0RGCYfjmrI2jKqQXEW8tXmR3/ytf41bV6/wj/+L/wdVMaIqrmJMiW4e3mX0RUAIQSftEkcpptGMhodU1hCsbSKqAjM5AGu92txaBuOc2/sBg6Oa6djXBR28vVjBg8+JUhsqbZ9tQW8B5yxNM19877cjA784Yq0fruIgZKPXwjnHoCyo3YJMf84TtWka9vYPmFWKeVMQxRF7gwMmsxlRHHPu/BuUZcF4PAQnSNIEgLTTJgpDirpAq4amcQihqcsGM13katmAY1sFAXHoM8N0DYcHXph1sgD+5cWiLhNW0E17bKyews4kOjZUuibX8+ceR/Xi8TzbWQjn72zvWe8ew+GITz78iKKoH1mvPAgnRPqrxlLAcTcs2MJ4CY+yCAdSCMJAIqU3SgmCgCzL6HTaSGsxdYWwxou4taIu5lTzGfl0zGwyYjIcUC8K4iiKKfOcYj6n3c7Y2Fjn6uUNpJQ8C5xzVFVNEAiU1jjpPZxrbXwIwN2LrkKQZRndbocoirDWIURAHCc4BLNZTj7Lsc4++Nic4ASvHRZGuU4vFn003p8pwhmLExLCBqRGNYqmUVRFyXw68peGtchAECUZQZSSyYBQNZT1DJMfJ1cvL4dAStJejzhL2NjaZPPUFv31dbLeCnpeIMJ4kcInliKleyCCEBllONdgTeNrmyDy/YRmMQbcHwxzzwbuT7l6SVjsg9EKU1dYICz6GFVT52Os+jyRJaUkjCI/4ZTBE6iaX38EMliEAsYL+67wJQ6Ly6TQhRmeCPzPzjywBdw5SzW/L3QzTUFGBFFMmqakaUqr3UIFAcHiPNNKQ1HRVDWqUagmQDUNTd1QVxV1VWGfqHhZ7K+pTkj0LwBBHBC3Y9KOptVN0cqgaj+m3HHUdc8+f7LWUTf3ngfL6dpi1L2n4VLiz4g0ELQiWE0E6yk0AVgBRvjCuuGObgoHhDis1hilsFr7rAbjxx6BRYgAJwKUkzRWPvT9SCGIgoBA+oR06xxS+Fbpxw1FQnihQRzHnlczCt1U1EVOuLnO2TPbpFmGFYKyavjkyi6VchS1Ii8fbHchhAAhfKeisWitvZ/6dMLBwQEEjk6vjcFQuAqnHUI+++hS1YqjoaKsYTz1yr8w9iTB/VsNAkGShDTCogPhOw2WT7r/niUEQRg8c416gmfH8xCZ1lrMQoEuhHhir9GlFYl5hmlwEIQIwZ3X9Z1vluPR4TnvnsuU46eoLZZ1idEGVReoOLiTp3JncWCxiCSl786SUnilubNI7LFtzmIfvBua9zO+s2uAEL6DTS66woRY1IF3PNGXruBPOyi/TEU6hEFAHEW0Wy06vR6tVos4jtBaP8Gi+uNhjMU8Y6fNF4E7BL/w4aGhdI895Yy1lLW962dHPi9xWlE0ikZbkjYkOsQZi1WKKJSs9dqAoFaaslYoIzEuJIoSut0+URgyn0woixFaTcC9frWWQBCFEWmcIgDVNDghiLIWVjhkmYDRiIW1VFU3zOYlRa0ptadbHgd7p4VN+PvkwhngafHAv1kMKVIKpPCWuM8+Uj1Yk2utpapLRJEQjAOCKGCWzyjLAhlEvj6/0/kiiSI/fgRBgAwCHAJrHVobAqEwSnuRll1mcgEyRMgAEQcQB7hG45Ze6Ms56Qmh/uXEYlCSIkDKkCiMSaPE36f15y2cn2XzzwspBXEQIBC4RQ3wLAvwSinmeUHdPH0H5AmR/ioR43uRDb6X6O7O+sU4GMQhUSthsDtgZ2dA02iMtWxvb/Nv/82/ycbGBjdv3WQ4nXDx3bfZPL1NM5kwvXqd4uDAex47C6bxoYiAwzEdDdnf2WFzc5NWmuHMs7e9W2sZjXKm04LZtOTnH1ylKApmM4MxjrtFakIIWlmHTqvPH/zBn/L3//4/wTqLtZa6aZjkOY1qKCbFsd3zCU7wWqPmXvVuhQ+7jED3fXubNSAdqpQUMTSNvxbr+ZS9T39MlLZZO/curXafN99+k3a3w0c//AGXj+aL7Rfk4wkffv/P6K5t8Bu/+6+zsbVF1k0JUsHK+irnzr/BIAiZ53N0WaDGXol9vxo7CEKiJCVKM5KVPtpaivoUTjUwuAn1nIcOBjJEJL7odnX+ihTr3h1SV2PyPR9Iah8WcGQNqBpXzlBHe8dtzV9WCNjY2ODSO+9wuHeTD3/0R9TVQon/wqHBTjx5HrchCAmTVWSUYeZ7mPneY/5+SXH6r0mcsr62ThQn3LqxR5nPKEc3YX4T5Swmr7kaCorpBGc1RlVYazDKh0TV1eMU9xqvLYYnUc+f4MXDhAKVCrbe3eZXvvNNJoMxH/7gfVRVk0UhEhhPDWX14iZOSx1liR9plyVCBRwA3UTy1mZCLxG0Eo1zltx4P/PY+ZJrqZta/m1oHXJeYnTD3iefkB/s4VRNK0vJOm1WNlYYj+f83j/9Obdu7XE4mPEg1FXNwd4BURiSZl6ZNcznVI2mesw12zSKH//kfT69fIU3zv9tzr/1Dlm7zaULZ+mubHD+7V8hiCKsg8lkyq2/93v88P33yecPVo+HYcjK+jr9fp/t02fQ2lIUOVVZYIzm6OiAzTMb/Ppv/xpFXfLPfv8PGAyOqKtnH1vyEq4f+mG4Nv74jtXCSuchfIMRUAbQLFruH3TrySdzrn50a7EYcFIUfllgjcFay3Q8piwKzOLnl4UwjNg6dYYwihkc7lHMZxz7Qy5dpuGZJzsOVFmhmwannvw8PNi7ST4dYZxDW0d3dZV2K8I55zvL7jokYRiQpgmB8PdS5wTKeK/kSkHVgFs0wN8/wbemoZnuIMOEIE4IgtArQevCq/KB4wnncxyHFwyjDTdu3uBwcMDb77xDkiT0um3eeedNZrOcmzcf3cXwy4ymsUwmimbBVzoH88oiBY8N4zPWMZhWjKRAW7+wu9nP2Ng67cMkp1OUs4yrGmsMo+khRVljky2iKCEKBXEAa6srvPetbzEd7XL98h519foR6VJK1ldXWemuMLWO+Swn67RY2drA6Qqdr6DLgsmt6whVcfvwkKoYcWVcc8t4knxJOTxuhArjlLTVwWhFNZ8+t8WNNZ6mSdKATjvGWcfwoMAaR1PbB9qd3Yu7LRnBVzkhXjJwfD/XpmE8G5DXBTf2NMZYyjJHNTXCCYQVEIWINCWKIrZOnUIKgVba2/c2hqpUNE1J0ShMrXBWLUzde4ggIO61kXFI3I4J05ByPqKYx17ooiM/kM2Lk6yiLysczMqGMC8IspTTb5zhaDxkvDN94R2pTwMpIYokWZpy7swZAhmyuzegKCrqpkaZp5sj1rVa1CtPT8SfEOmvEgGeTF8afX5OLCHuKEurRjGaHLcFp1nGW2+9xdr6OvvDIwbjIStbG6ysr1Hnc8rRiGaW+zZJY49HagCrqYo5s+mEbqcD1j33BVAvknuL4nE3WIGUIVKG3Lq9y0cfffpcr3uCE3zxWOa8L+HwzYERuMQT6gs/cKO9V62fFDiMVhSTAXFdwRlLFIasra2zvrnB7e5lEMmd61Y3DaP9fYy2vh0z8W3BFksUR3S6XebdHnFvBXA0g8aT4/dBCIkUASIKkWmMsA4RZri68uPNo8pIIRBB7N/lK1V7O5xp0MWjSR7nLM4orGowVbHwfX8O3KWic24xPIvlay09Rl+iUgv/OXe7HQa7luHh7hMqtZ8FS6OMAGQHggQRd5FxB1uNn3Bvl4p2gZSSOIoBwXQyo5iO0dUMVI6r5hiTMD0aYjFUVclsPCYMBP1eG2HVY0Pp7nWzPMEXASfBBIJspcWZs9ukWcCNjxPqwNIOJQKYzV9s0MlyS/efHQZPrmeBoN8O6SeCMPBt2LUVFIsaZ9n2ebe2OQSktghpKSdjVF0wGx5RzKYEgcDZHnWt2dkZc/v2iEo/eIzUWlMWFhWGyEDihGBeVpSNr48epdmx1jIcjpjN5mgL3ZUVIgn9LCZq9WmvbyFk4Mk3K5lXNYPh6KFFvgwCkjQhzTJai9BHYxRGN76TpCqRUnDq9CnyIsdYQ1lWT+z7/CAoA7Py+H0aoLQPdudbWldb59CLboGHSfBUo0/sXL6EWCrLm6ahaV7G4u8xfNdCSJa1iZOU8Whw957woCtPygAhuKP0vhPK+Qg4ZbwK8ykuk7LIKYvj+ZvAkM8mCCEw9y0MCSEJAolELjrDBM4tukqs8ItNDzMkcRarCpzVOLsIhbDmAQGhrxeJZZ1jlueUVcl8PkcpRRBI+v0u1hqkFJ+L0vmqwFqoauc7dhYlp1qM0Y87BR1QLjq6lup2KyRBmiIdOBn4ri/tu6+qpqRuKsLYEAQCKUFiieOIldU1nC0JwohH38m+GAghSJOYVisjn5fopiELQnr9HtgUkwma+YxqsIOxDUVVIQyMapg85VsJwpAka6ObGlUXPi9hcTzscrXjKeEWzgNJEtA0hmqivRDx8e+c4xvn8muAr2ruvWd6wWJJYyyjWUHTaHRdemGScWAcYatFGobEUUyr1SaU0rsEGJ93YbTGlJUPELdLWYMEEUEQEaQtgiwibifESYiyi/wwI0A2i5CHL3+n8FcVDmi0pqwbOjIm66TE1fy5+ideBITwcQhRHNDtdQmDkNE4RzUGpdVTi3KttTQPU388BidE+quExB/xBFjFj3lT7qRoOWfZ2RkwmcyZTu/1Lq/rmlu3bjGdzRhMp1Sq4fKnlzFKMZ7kDMdTirJgNB7SVCW6PiYcjDH89Mc/ZHfnFn/5d/51Lly48NLeohCCKApwjkWLnuaTT6+ws7vP/sHhS3vdE5zgi8WSRsjxRU0KhJhi6D3JVXFPsWWdpSpLwqQkDFM67RV6W2dYufg29WyP8mjm23LNjLqU/OKjD7g5PGTr5hqdThtVK8I4ZHNri3aaMdnb5cP9HWplub+Nt8nnWH1ItrpCZ22VpqqY3T6AqgD1mFVba3D17M73rxuaYsb04BZGNS8gCEnQO/sG7c1tbAO2higSdFoCYzTDwYCmrqmmR5gX7lfu4Zzj9o3LzPMps8nQdxi9bDgLKgddYnSFDSJs82AF7r1YnGdSQBgxmsz46Z+/D7ZBTXfRVUExHfrn6BHYgmYYMytijNa4psEIyOcROIuun82n+QSvDs4JnJXUpWI0HJO2O/zNf/9vo6qayz/5MZPhiNF0DPNX6KcqJDJOIJaMghjpLDeZc4OaCF9yKWDOsTK9HQRsn15nq5cwLubMm5ooDOh12sRhQJPPyaKA3/7Xfo29/QH//Q9+QXE4/txLGweFcUinqWYFUgpaUUAnPg4ULBpN/gBSOIoi3nvvXTY3Nzhz9jwiyIjaklaQIaMEJyOckDgsMozY2trkjTfOMhyOmU6PSbo4juj1e/RXelR1yTSXKN0AljRLCENJmsS024lX8GVdYhlw8dJp0pZk7+Mh8/rZPq8ggDTybfLT5tFNhUVRs7M7QkuDCaz/YJbruO6+xwlO8Ai0Wm22T59DSMFsOkYdNVTlQ+4fQkKUkrXbfPs736Hb7TEaDJjnOYP9HQYHj+m8cvd9fQbUdcXejesgBHV577VmtKGuGuIoIc4SpAwIopRAGWQwPX4PyEUNuawJlj5+Ec6G1KMRTViiH3YcXiMIAXEYEUUhh3v7lLOcqmmYFyVV3Xxl1ejgO3mmyiuml3o3y5NT2fc818H46BCtNUZrmroGaxHO+NDghfAmikLiNKEo5ly/cZ3R4JD9g12q+ZR2tkUkW8yLQ7R+OXXvs8A5R101lLkPCNRKEQhJu9Wh3UrYWnsLU83Z6UTUszHN0Q66mGHF0zspn9o+zTe/+5sIKTHOYKyjqLw14Wc//xGjgx0ee/MSglanSxQnVMWcuixoGst4XGON9ba3+LD0ZcAwArT1fu7eQiUC53DLDt2w7TtKjfRedncQAjHaJozmDSJw1MZhhcCJEITz6bWRRCYtwjAjy3psb10giiLG6YiqLJke7jMbH3rzfmMgiCHt+nGoKRFGEaerxO2MMJSIJcnlUn+ITbKYJpwQ6V9WOGuZHB1RzudUrRZNq0VeTJ8rN+CF7JcDbb3N9O7ODlEYkSUR6cYaB0eGWr060dUJkf4qseyCz4AeXsR6yJ1OHAeMRjNGo8+TGFopBoMBZVUxKgrKquIXP/4xt65dAxn5hzMP9I11znH1yqdcvfIpFy9derYAnyeE9/sLcc77ahlj2NnZAyFeDSl0ghN8IVhOcEr8hR6BC7H1FFsNP/9s52gaRV03BEFEmnXI+mu0t04DFeWRAAy4CtUIbty6TjSdMh6u0G1n9FdW6fVX6K/0ObW+yX4Q8GmYUFNwzEx4mKrCVGOSVkaaJNAo5HSGKec8NtnXWdwDvMlfF+i6RD8jCfQ5CGitb7J66V1MCTqHLJFsrElUU9G4gGKeo4rZSyPSAYaHewwPH2er8iLhQPtjaNXDwxEf+HcYT6QHIXlekl++5kn58Wf33otMDiZHK39cl7BA9fR51yf4ouAEzgqaRjPLC7rbK/zmX/ptdN0wPdz34ebRHD8OvhoIIRBhhAsD5kKgjWUgKg4e8Nwld5sGkrW1FbbX21Q7DbNyTiAFWZZ61WhVEQeCb3zrbTbPbPBHH9+ABxDpFqidW6i7agIh2O5ltOLo+DmOBxLpYRhw4cJ53njjDdbWN0HGhGlMkHSwSKxYlucCEYT0V3psbm5QlvU9RHoYRXR7XdqdjEbXiEpgrEIIRxzHxHFEliX0Ohm9Tod2lBFIwantNVygGN/ImT/j5yUlRLGfY1fKH4aHoW40dZP7rswVjp2hHMfm9y+2meEEv6SIk4Tt02cwxnCwt8tsNnn4k6WEKCHu9Hjnm99h69Qpbl65wmgwoK7KxxPp8NznpG4ahod+RLrfps1Yi2o0YRATBBFBGBGlGSIwd2UEeB90b9253KFjIh0nUfOch8cmvm4QhEFAFIRMRmMG+wcoa6kf0vnzVYJfnP38/z/Np3r3c+fTCfPpI66PRR5FHEfUTcXh4IDx0YDJeIhRFUmyQhSkVPXktSLScQ7dKJ+7oxRaqYVKPWOl3+fSm+exTUFipswnR9yuZ0zLEnfHLO7JsbK6yrtf/wZRkiKTDOMco6nvpti/dZ3RwR6Pu3kJBGnWWljEaOqyQGuLXpzzi6UyQrHIGAkAKbyeyuF9yIMUrMaJhTdwkHhluF12SS8Jay8ZMDZkXqlF4eO7bZ1YqslDCEJEmBDIiCTOWFnZIokTnA2IwpzIGigmx9uMIkgzMBqqOUIaoiggTmKWY5JwAbhFuLGJXksB1gmeDuUsp5zl0O+Aayib6qXyiE8Ch89UapRiNBoShTHbp86QpS3Gj6oHXgJOiPRXgaUsqo0n0JdipSc4D6MoIssyZBBw+ZNPCKKIsmlQWjOb+JOl2+vRXz9FmU8ZHu48kLCWQUgQ+NZrq2pOnzrFv/Pv/C329/f40z/9Pnn+JArEx8Nah1J6Ib5dtKNZ64n0k8CJE/xSQoJsLeqICt9eMgMKHhbSY42mHO9jm4Krn33KvFTM5iW91XXs/ICJOBawO61pDnYw0xHDYcY8jsg3TjPe2CYQklAGCGf49X/rdzFaMZtNUFohrANnObi2z+H1A3RdMx+OCYTkwtfewdQle9c/oJpPOGEwPObjQ9ztEKcCbBNRCEM5qP1xHY5p6hrzOBX/Vw3NHOaH3JkKmOYVeemf4FWjGhRMpCA5GxGd6hG22iAlWlv2dyfcujF6Aru3FwtlLEd5RW0iNlZXyIKQIJzjSaV7sVzqDI3lcDwisgVxFHNmcwuhFLc/u0y712Pj9Daqqfn044/ZO/Tq1YfhOB560W4aCqJYEEYJQRhRWGD+eQJCKc2VK1eZTKa89fYlLl666InvNOUeIxr36NZxYzRlPcNNNZ9e+Zg4TrDC0F/rUZUlTdWwvtbnrTcvsLm1QbubYsqaeT1lVo0IVzUdGVDXFtU4L+x4CB8XscgPW1gOGAvjGvTjHTLuej8cN00ta+HjqIXjW9HJ/PsED0FdVezcvuEn0s2Dx5uzF97gG9/+NjKMIUrodrv8xm/8Kmtra1w4e5p8lqObgquffvhydvIuCbGzFl3NYRGIdjeaxjKbgzINTsyJ44R+nBEEAadOnaLXW2VeKqpGo5QPsBdBQBDFnquq7cJyYWnnsrwQX986xTlH1Si08dYR1ljMyfzwi4GDpipwYsThbVDzGUoplCowqkLNDzC6wjwsp+iLgpCIpIvI1tD1mKpsqEuDqhRGOQJ8N1anfwYhMmx8k1JM0VgeKyC6D43STPM5a2mLN968hJARg1nDbFbw8zPvE44q7PwQW3xeNBWFEac2t0mSdCE1h0qKe+6jcGzFkyQBoYRSO7R2Xo1ugUAgotBbsSkvIEhaLUSY0kxHGLOw6pRtCDOI+ogkI2h1iNKMU1tbpElKU9boRhHFCVGc+JBTJ4iiiN29XYQQVGVB09ToMCHobdxZuxNxgswiXGPQco61UMzHKBxZ5n3Wfdi6wFmDrUs/Hzi5tn8pUNcN06m34npqPk+ACH2krtXPntG4dApqtVI2NlaIwoAsST0XEqSIuzpDQuE7PIy7O7NHEIQRpy9eot3tUxdzVF2STyZMRkfPtE8nRPqrQIq3c+nhlTiGJx7H4zhmbW0NpRQffPAB6i4SZ3kir66ucentdzjc32F8tId5AJEehiFhFPsgiabiwrmz/If/4f+czz77lI8//uiFEenOeSL9/v87GUhP8EsLEfriBee94ajBlRyr1D8PZxTF8DbVJOKTZIWbu2NWNjdZ3dhCjW975cDyuVpR71wHBMVCcJCeeZNkO7/j533+4gX+3f/gb5O1Wty4tUNRlAitEdbyk9/7Fxze2EfXFfnhEf21Vd757rexumE6vE01zznpqQecYzbcYVYNEKRI2rimwk2PFkqwpZ719Z2gfiFocv84wS89iv05xcGcbrxK9FsrRJ0eQki0Mty+OeLqZ6/evq0xloNpSaktW9ttWu02YfzggnhJpGMNu4MBdi659MZFtjY2cU3N1Q8/YPv8OU6d3aapS37+/k+5vbPPdPrggk1wTKSbxX9EsSBJBVm7RZy2mdYajj6vkNFa8/HHnxCGId/53rf55q98k26/T9Ty4c4LE+fF4+ELncYqinJCUU85nB4SRjHvvv0u61trjAYW3dRsbq7y3e9+k3a3RbfforQzZtWIcXlItAkrGwHjCajceLvBis/dvgQQS0+mWxZhbQaOnpbwdvj6N3jA487B4YRIP8FDUZYF165+9sjnXHr7bf69/+n/jCj2GS9ZlvIr3/ga3W6XsjE0ynD9yif8i3/yj1/8Dt5tY+zAWYMqHnyPrBtH3RjKukYZS6vVottfIY4Tzpw5C0h2D0aMpnOKokbZEhlnxJ0+VhusmuDscvXrNSM7H4Gq/vLs6y83HE2Z0zSO+fCAPfMBcdals34Kq0um+Q5GvUKrtieFkIikj2xtooYVRT6hnBvqQmFqR0BKGGX01s4TxH1M8hFzMUSJpz/v6qZhPJuxunWKd977GnHaZn9qGU1LVv78A+KRQu3aBxLpcRRz6cJb9Dp9hoNd5vMpcykIpF//WnZxOQApyJKAKJSUU0V9t2ezEIg48qvWlUDIgKzTJUwyZtUQUxcQdCDqQNKBzgYiTog6fdqdDu98/VusrqwwywvquqadtelkbeqyZDaaUMzn3Lh1A6UU4aITRoUpwdppn0XlLEEgiaIQK2uMyHFGM58OEbUm3NggiSICAaEUGGewVfGAvIYTfFlRVQ3VM4bTCykIFpaHzrpnzuYRi46Nbjfj0hvnSJOEVuaFjEeHY8qiunPrjSS0I0+iq2ZZQXuRy1vf+A7bFy4yPthjPjri9o2rTMfDZxL8nhDprwIaX1QtJwYKmOFndF6sTbsVEYbHKqSq0lS1odPpcOnSRebzOcPh8J4An6zVJo4TnLMcHe6RT8YPDRG11mKMZjIZc+vWDdIkpd1uUzfP4C0sBEmSEgTBgiB3ixXs+wim+8OkTsbSE/xSwCtvZZIQpi2sCdCVuSuVfNmkB8eMwIOuS4tzhjof4ABpc3Q5pKkrOqfeQtc15WzmU9L13G9nsRlT5qjxIVHaIu2tEEWh96cTkiRJMNZyePOI2WjEdDwEGqwOUVVJU6XUpS+CrL1fxfQVh7ZQKRzCP3TjA0zdkip7ttAlEQiElIsC4mQgPMGXGA7KsuTg8BDVNGQioJjk5KbwooGlTccrgsAX6Q6YzgsabaibRy92WQdl45gLhzJghcAiMMC8rNjb3SPP51y6dIl2b5Xr+VXG5cMXiz7vACpIkphOp8XmxioaSVGUHI3G2EWNFgQBZ85s0+v12dzYIEkSwiD83PAiBIRBwJkzZ5jnOdNpzv7+8YKFlJIojQnCABHFRHHM6kqXtdUenSRGra/R67YZHQ2YTiT7hzvMqgm9OOLM6goHs5y8rrHCHSvD77Dlx/vhnJ/DL7+3QKvb5fT6JlVVcXC4/2TexncPpcvXawMtPBdYclIrnuC5MRwM+NmP/pww9NPcJI4Z7NyilWU02qKM5cbVK1/Y/iWRJAwkMpBIKQlCSZJERFGEEAHOSRqlMdZ3fiCCO2GkQkhkEHrvYbGcaC3rzhNRxAkgTiLCMECIACklSmmq8iGEuFVACYvmpyiWrKx2sSbGqTVUU1AVOUa/PiISh0MZQ60Vxpbg5lT5gMFuTOga9s9sk6YxYSAIwhiCGCdjnAgev/H7UJQlewd7tPs9xqMRSUuTzwVFUeEQRFGCkQ+m0xxgjcEZTRJHCFqMZhHWff4qdc5RK4uxDnMflyNlsOg2E1gRIkSAkBIhlyHFEhklBK02Lm5j4xZJq836xhm6vS4bG9us9Ppoc4DSdhFq26CUxjiLcQZtNcZqAhkihCAMJVEcsrBrxzmHdQbrFoGjToLWONnQVCVlIGnKGbYY4+pyMWbZhUf6yZj0VYZzDqutF/89h7B2qS2pK8XR0ZgsTZFrEAYhrTQhDkLiyFsrLpXox45hAmSEjFLWNjY5feYMqApTF0RRdO8LPYWt/wmR/ipQ4hU+vcXPBXATz7FZCCPJmVNtuu3jD3L3oGBnf86pU1v8lb/yOxwcHHD58meUixuhEIL1jS1W1zc5Otzno/d/jLUG+xA/Kq0atFLcuHGV73//j1hbX+fSpTcZjYfopwx2kVKysrJG1mrjrMJZw2w2YzQaHT9JcGxIemcnOJkgneDLDxGCiIl6m3TOvoHK58yu3fSE99KzkhA/qamAms+xEgA4nFX///berEmvIz/z+2XmWd69NhR2cG2STbZ6Ubc6QqOWO8KjkGYu7Llw+Go+woQj/H18PZ5weOwYO+yxLKmtDnnk6ZZaUnewyW6SAElsBRRQVe9+9lx8kedFFYpYCiAIgs38RRQLAKveOnWqMk/mP5//85DtXSHb/4SpiJAyYnT+Lc5/919SLBbsfPABupxD/gnYQ6uCZrKHnh0Qn3+Z9QsXGfZ6GO2wxtHrd5HS8XcfXubDd9/HHOyAW6AbjZlLpLDM7t7GOdMqTYINxz0KDaUGaiwZXk62eug/fXVQJRFRJ8ZUmiYPSqzAV5vJeMJ7v34fGSn+Tv4DpmmY67EPUZ/yPC3SvcerinEIbu76Q8n58tHG+9bCdAmiggu1oEFghMQIyd5kyviffslwNOTP/uzPyCrNr2/+j+zsX3mSS2Jt1Of0mU22T5/izTcUN3Z2+f9+8Svqtsifpil/9Ed/yBtvvM63vvU2a2trqCg+tD5uC2JSCDppyg//4Ie8/tprjMczPvzw8FqiJGK4MSTpJAwG63Q7XV575QJntrYYDUb0e33u7Nzk8m/eY76Yc/32ddKO5Fs/OM/rZ0f89YeXuZ1nNKs1W4Ivah/vgHdQVbCye3fAK+cv8sc/+jG7d3b565/+FXl+gqBDixfOuvbrpcCwfX8D//sTCHxOPvrN+1z9+AqrTYgQ/vBKCHHPerIsvyDP58fUsoWAUT9m2I+J04QkTbEItFXEcYqUHayVzBY5VaMpa7yvMQ0rv3SVdBCiOdK9uNrOP+eTzMALhxAwGvUZDPtEUYqKUxbzBbs7t7APyiqzebu/SIEO/UGHV18/h7WWXk9R5Et2r31Evny+3sOPwjnHsi4x5ZKqmYC9y/jOPrP9jzg49xIqSVnf2OCtN14h6fQgGWDiAU7On/hrHYz3Wb73LkVd887vfZfuYI2lTslLjTOOTtpHq5gHmUw5a6nLgjqKGfZ7RGt9DmYTHqSnsRbmmX/AHhfsxnFMrz9EVxX1bOkV6lK2oaQKiEi6A7qb2+ioTx1vMlzf4q1vfp+N9Q3efONNhv0+VePIi4ayrlkuc6zRmKaiNDWVqTC2IRYCpRRJJ0JECUkUEUcxZVkync0wZtWhC9QVGMikoyhzzHIfO7/r98mq5+XDzYsfgBz4grFgq89fAHTOZ/JMphnzxacMeh3EaxcY9XucOXWONOlwc/cOcFyJThs83ifurfGNt97i9773XWLRoOuMzu3OU19TKKQ/LxyH3pMl9xWVBZDEik4akyTeV1PbHoYB3W6X8XjCdDprlUyHJ3u6TeFu6hrdnKQ44xeOk+kUB/R6fQ4ODjD6CQtpzmGsQesGZzXOmc8+nIMoIvA7i+95d85hrEUlMZtntjF1zWJ60HporwxfVydJR1RDK+WC9dUE1wbFOBwW54XtMoGog0i63mNOyvvr8M7ijMU2NXVZUhUFZZ7jcMyXc/I8o5hPqRczqEq81MTgTIWpC7L5BJz9whQmUZT4hHvdPPRw76EI1W4aW5wF13j/wM4AGUVYa7HOf//m86RzrxoH7ru37X/EAyQjT4AQguGwT5omyDRCxIp8kYdCeuArj6k11Sz3npyAMwZT6Yc33zyElcjSmda95ClwFqrGYq1DYrxy+jGhdQ4oHETGsZ9XJNMMmdaoNMYAtYO1omH73IS6sZ+xqzv+Wvf+7KBuLEVlmGcF8WxJFHeIkq4XZR37TL+Gq3HWIaVECtHW0N2R17RYa1gs5kwm4/uLf4J7we6i0VRVjRCSoswpyg6dNKFrEpwxRE6gnEMY43fpWiO0QziLALqxnw5rDWXMYaHbcXgWfGxKVEqRph1GwxEXL77EYrngYH/vvs7JR9641dnzykomnOkGnhFaa/ST7m2+QI44vbTjyOGsRSCQKsJoHzqKiBEqRogIJ2scrlWMOize0gEEVjdY3fjJEwftvx8GD77YCCn9IYC1IT/rGSGEIO10iaKIOO0io9hLeJrm2D5/pcpedaMevf8SIZRXcWvfEaG14yHN7l8iPiRVxbEvJmN9FoGFpq4oyoJO1cNY67tBhfQBm0I+9pWPY7WmzjMWsyk3b+7Q6S/IdUpZG7LpHiabYJsHHcr5Ud/UNXVV0k27KBERCUHUCnNXzVmrt9V9Pn67rTGYusRq7ce6UqgoJooiZGvxKZUiilNE1MElPeKkSxx3UCpBa0dVa5wVSKlwzlG3c4iuK7RuUJFEyIgoiVBSIhsQWGwb5qrLAltluKbArfazbR6SbQQOjWsK3+EgALmanwJfV4SSqEThrMPU+pnVBVfr3rpuWGYFOEjTJWncULXrT8HKU30Vsqu8A4ezKKn82JH+cH1lk3v4BU5+LaGQ/jwZ4y1djimzpZT0ux3Wh33OXXiVjY1T1CahNgk3d3b49//+f6EoShaLAv8j0zjn2N/bZTLef6JC+HQ648onn6BUxPu//YA8y1hmj1ZvHcday3R60CbK+53VA1t6V5Y2K164B3Eg8BQ4Dc7QlFMW05jTZ87zw//yv6LJS37+H/+Cyd29VrUufUubWxnCJqA60N0CLOS3wR4tAqdAD20S8qqhtgI52kTFCpPHDywyZMsF1bVPaeqKG1deQSUJH9+8znw+5+7HH8De9bZtE/+1nKaYz7n6231wjqZ69oosqSJGG2eJ0y7zyS5F9oQqFjWC5NTh320F1S5RJDn3+rcZrG+TlRlVXbHcu8n89qdPd6FHu2aO2/itLAc+R3EnjiN+8Aff4tJL5ylrTaUNVz++zq/HiwerggKBrwhmVmKLdrPUjhNXaz+GTrpvEpD0/ZTYZP7taai04fZBjhR+BpVAqR+92NDAroG71rHz8S7x9X1OxYLTsWBu4JaGXifivd+8jxCwu7v/wNe5543eYqxjd1qgZiU3x5+i1HXOntnm4sVzTGbze7Yu4MNGP/rwCtPxjJdefpW331ZIBML5LeoqoL1pGpbLBT/96U949913uXbtlr99yj9mGtuwtzfxLd5qTBxH4DJ2N4ac3thma7RB0sC5wSajKEG4Gisqiv19lrKBqqAXw7kBnE5hZwwfmbYbO8ULQG5wWOx+ABcuXuSd736H6WTM//Tv/i03b954/A9OAysr+9WZ84vjHBAIPDMkvtED/HCyQFk2YDRWdVDdlCzPuXP3gMFonbMvrZOkXeImohEVdblgWZU4EaG6A6yzZJMJmAZrSn/on/R9ga2ewAtkwXEcKX1hwxd6I+qyRD8kMDbwZERRzEuvvsFguEZRLqnqksV0wnw8xtiV4E0Bg/YzMu5f4Ep8R22Ko0etC+7sLVguZuj6xTmUApBSMFwbMdrcIpt0OKoz17phuZwTJzG5rolljI4ibJLg1JNbu9gyxzUVN5uG/7X4PxBRijExxhiKOx9QL/c+U0gXSCQJGMVkMqbKM7rRaXpRn55wbChBaR25bcWU3H9mfZymWDK/fdVb1HTXUGmH7mBImiQsIv/wjOKEtL+GiNcZdM8xGKwh6aIbyY2b+yglyAtNGvfJlhmLbEFTFRTLGVLCYH2IkpI0UQigqqeYrCSfzKgmU5wtMWaBcwJsxy9C9BwwOC1wQrSFdeP3vbo6YosZ+DqSDFIGFzYwVcP8xhhbP9vfhaJquHJ9FyUlkbqFkIK88GNRSUgUuCjCpH2sdeg8hzqmaWqqxtJogzEPEAM/AaGQ/jzRPLAo4xxY6zAWup0ea2sbVI2ibBRxHFPXDdY4BoMh1jryfIYx7QnhcV/yx1A3NcvFsj25jKmrCqUi4iRBN/rEfum6aU5WJP8ii+er+N6V/UIo1AeeC17B4YzGVCVSOEabmzTdiqjTR0YL7GpwOOVb4FbvRQyyA5jPKiNEBLKDExHW4k/8VdSqKB5s2LVSShTLBfPxBJUk5IslRZYjcXSSCN2Y1iPMgdM4A9UXrIoWUiFlhHgK9Ycf116qKqSP3HbtWFdRjEpSlG6Q1rbqrGdxwdxvf3787ye87ChS4EAb4zeLcUynk+KQWDTqWV1vIPBlYhyu0IeFdC8Iu3/8PGLsxHGEVAKVGGTkUBHYyKvL7RNK35xbeQj7FCI/VaxyDR5Ojb/GsmygbBAK4hhmBvYb6CSC9ahESiirk6+zGu1ocJTtxrrb77Ge5T5c79g1OXeoQLuninHOt20ffhTWWoq8YLlc0BztPmwtYJxzWGP8mlA35HnGMhV0o5TIwoCEhC6RlKyP1jCuwskKnKGnFMMoYpgYBomjk/imKSvBJUcv9rPfq9aaosjp9fucPXeOtJPeC3d8JKvX0g9+3UDgdwkBRMr/qWk9G4x1NMZhrUAIhXWCujbUtaExDmnxfs4yQsgIIZWfPoRPKLRN7dXnR4OIH7JOfNEQApI0IU46WK1DIf1ZICRCRSRpl06vT1mXGOu7qarq6P09+tBenWD63xulUqK4TxR7ayGtoa5qmqo6kgH1ouDtR6I49pZoKr43HqxzvlBWVyzznFjHaGtXpzgAqCgmTjtYo6mr8tHrBWdx2tKUBdPpFFSKEClYR11m2DpvO0GOX6G3karqComlqiuqyhfgV3fff9z97x98CQZjDQKJihPiJEWpCNnaVYEjimM63T5EPYg7RCoBJzAG6kajjMA5f98coI1GmwZjvGVUFCWoyOc1iLbz2hqNqUuaYgmuAopD2xaEX8BY0y7C7uvPe/o2w8DvDkIglcQqyWpVK9v9vLP2M8u/J7FQlwDOHVmf3/8cUVKQRAKjJFa2IpVWkb76XZVSopRq3+QT7z8gFNJfCBptuHx1zM6djNff+B7nz57mV+9e5h9/+SFnz1/kv/s3/z1SKYyxzOYz/sN/+J+5evXpVJjZcklT12xsbfPq+UsIKTl15hxVWfLp5Q9YzKYne6Ev27pFCFgbQJp4/62mgsZBGXZlgedEXcBUYzc2qQuHtR0GF96g6ZwiW0xoqgJc39uS1Aby2lcmbGtbcqzXXwy2kIOXida2iaIEoy1Ga9/K58A/Nuxnr2F2h3lT8Mv/JOgMR1z85uuc296EU+u4suCT937Fp++/+zzuCIC3IZjeQUiFrmv8ov0JAk31AmyNSvqk3W2sEVQlmKbi9ifvEd28jDF+Uamrz2HGfFRtHuGFOEe7hZ5wjhsOOrzy0ibaWD69dkBVNfzyl7/hww8/xRhvPZFleVCjB363WLlY9TmMhWjatwcIB9I04a1vvkR/0OHm/g7T5ZT+KCXdSCmWNdOD4omyiNI45szmGp0k4dTWJnEc8+Gn19ndP3j8Jx9h3vopNu3XrrXjk30vGMjrpx+z4/GMsqh8qNeRsZ+mKT/64z/mW++8w5tvvkkcx5+xOPBBoxG9bo/f//0fsLm5xf/7n37GwcGvveNVDd1hwpkLI4y13N6dYU0rKnCO/Vu3OSivo5DEKM6fv8h/8eM/IU4Vy3KHusm4ePsjlot9ps2YuZkRK+inUAuoxKOnwJ2dm/zk//lL3vrm23zn+7/PcLjmQ+gfxdF5NyzXAl8DokiyMUoQwN1FjWksVQONgaGI6PW6lJVBRF2K0vLBbz4iSjr01zaJkpTR+gaDtXVm+3tM79zBWdcWNleFdAv11CvT7XGRRGsn6FzbHfnlDjoh/EHqa6+9xubWNpc//IAb156yHSkAgJAxqrOBSrvULiGvHZODMdPxbrsGv++jW09tBa6L10InQMTZi9/k1Te+gxUwm9fkizkm34NqCuYFO+wQYKXCqhg5OkN6WmOyMXq+S6MbposZi6rk9sEeCKiaGoNsLUng3Dfe5pt/+GPGt2/y3t/8BXX5+L1Et5Ny/uJZuoM1ts6+gpAx7/96wJ3dHZjtwPLuvY91WAwV1gomWc28kMyrjDiKKIqc0jiMO0zPWq0MHrfSSNOEM+d8x2+cJPetGc6cOc9b3/k+WSHZn0lUlFCUNc4JtjZHdLspeWGpa4uSDlvXSBzdfowSkKjGN+M6X+SsiyXZcoGpF+AWrLz7hJKoke+AMYXE6QZ0CWaVzxAU6F8qR4VgX/Iaq8kqZtf2ccZhtPEZi8MhSRwzWy4pjlgVqlbM4hzUn9Wd3EciYRh7l8JF89lMAYBBt8uZ9T7LSnNrlmOMwxmFEAndtMOw1+H0qS10foFsvMed7XW0sWjjnui2hUL6C4C1jum8JC8MxkAnSSnynFs3dzh79gLf+c73SNOUxjTs7+/zk5/8+VN/rZWKfThap9PpEiUJveGIqirZufaUFgnPGaEUQklcJ8X1OojKgNI4LFTmS584Al8TjPFvVY01YEVE3F8nbSSFrtsNTtuw52qQxZEF7PGFhkBEHUR3DZH0fCK7kGCd3zTd56J35BfcGqgLGuu4u7NDfz3jG996k43RiHg0RDnH3s7153M/VjhHXa3CZVbKlyeQd7sGTINwEqUsAocQYJ0lnz9ZceyxrFatK3nI0bOKE16uEAIpBZ1OzPapIU1j2Lk1o6o0e3fHz/Z6A4EXidVZoMLbgEQcnpk9ZC8VRZJTp9ZZ2xgwLScsyyVpEtFNYsxjvM0fhFKSfqdDr9thY2OdJE1Ib+0+8evUDlaOMEIIrINZ+fkPvaqqpqo+2wGklOLMmTO8/MorrK2tIaX0yvL7DtoEUkgiFXFq6xRNXTPo9/3/cl7ZE0cRw2EXrQ2RXN4Lj3dAkS2pxgvvvS4Em6dOc/bsJbrdlPEyoq7mdLN9KlvxcZ6xLASxcHSUnwr1qpDeChftsca/ZbZkmS3ZPHUKpSLSJCWOE6IowhjzcO/jsEYLfI2QEm+XIEAu/aS5EnK6VlkrpfdqaoxjfDBBxSlxb4RKuySpV57mkwmuaY5UGFaTrW1tAh8wXwna7scXo2vXNw5JRqMRW1tb3Ow+fchboEVIZNRDRl20k9TaUZQlxXL50I9HSDARfnJPgYT+cIszF15hmS/ZvXubqqqxOgfzPNPDnwApvVd4OiDub0JTohFYaymrClvXzBcZ1jlG62sknRR/kCDor21w9rW3oM0oOAlRpBgN+wzW17hw4QIyTrl2+xYHeYOtZthsvx2bfqA5rFeka/9MzuuHW2m6VWjwY1QESikG/T5x2kUbhzbad7NJRW8w4NT2GdSsYZaXIBRNo71yP4pIkwStI5yVPt7GGKR0yDhCCosUulX5gnAWYxoaXeNsTdvD1953gUh8p4xpvOod01q6PPYoIPB1wjaGujncDAglSZOETpKQFffPKz4QHJwTPhbOcm88HB8VkYRu5NfseesqeXzoxJGi1+nQmALXNG2DRAoiIo4i0iSm1+0yHAwY9Hv0eh0abai0O7ksnlBIf6HQxvC3P/8nPrl6k51bdziYTdmfTLhzd49er0ucCOq6II5jut0edV1jzNP5ls1nUz78zbt0ewPOXHyZVXvwi05nNOL1P/4R6foau7Mxy6qg14nopBGLm7c5+OBKm5b17EINAoEHIhOIuoh4RCK6WBS2sejaJ7mDIO0PSfs9hFMIG6Obhmye4ZoMqm7bDugLza7QWDfDqA5mS2KJiDpdQGPiLZyOwM7APWAxZhrIx5A4ImeIgMnBhDzLmM0yvNz6SQyMnxWWJ/ZIaTFNSTnfBWewTznPnQjBYUji6v3q7OIEl33p0lneefs11kc9Xr20RV039HprTKdLPvr4FuPJ4oQXspL2hsVo4CuC5X518Sp34BHZAkmS8Oabb3Dp0jne+b23qZqGf/zFu/zTP/6autRPpEYH37K8O54QRxEHiwypFOPpyXMZYuW9FE3bnTwaDblw4TxVXXP9xs7JgjOfgqIo+PM//3N+9atf8a/+1X/Nj370RwAI8dmCel3X/Oznf8+7777Lp1f9weip7TUuXNgm7UYM1zpYa0idwmhNpxshTM281IwzzYWtLV47f5aLL71C/9Q2cSyhuAUONomQpAyTbV6XA2bFnD1zQBY57vbBdiEVMaYU/MNtza3F4XVFUUKSdEk7AxARw7UB/81/+6+5c3eXn/7kL/j4yodfyL0LBL5K1NqxN6/aP9//bJ9O5xhjKMsGqwtvA6hicD5HQdU1jTYIKWmEgsHavY4TTOPVwo+YNKVUxGkH5wxN2Xyp+zwpJWm3R5wk7N65y3yRMZlMv7Tr+V1BSEWcdJBRynS6RMgFRfHgoq2QAtVLQChMvtqr+JbMqtbMllPKskDr2lt+vKB1AakUm+cvcOaVN4gHffrr64yvO+4c3EaXluXuXZwQ2MZ4G4n+AJcKGG4jZI+7kyW/+I//O/l87K1rToAQAqUknU7CpYvn6A/XkUry9ltvcf2DLXauDCgXY7L9nRPdtzY5i36vx4ULF7HO8cnNm2QPVMev1D4KrMVZQ2MtBsvGxddYO3uJ4ekLGAGN1VRlhjGObDGn00nJT/eJVYO0DamCQRoxGnSIFKSpoGlKpge3cdbQ7/W8LUas6HZimqZLUw193pYucdpgFjMf3ljXvuJpKj4bNnXs+leByJ8nXT7weNyx98+YNI0ZDDpoZ1lUBdbYw/CPx16bwzQGjfafdwRroWmgkyZceuk0kVTMp1PqumJe1hSNIZXQVTDoxZzZ6oEQbNbeUnEyzqgqfa8pdp6XXL87odIa6yzImKi/Rrq2znC0zsZojflwSNXvM+r2GMRdSlfT1MUTPSdDIf0FwlrLe7+9wnu/vXLv32bLBZPplEY3DIcJWlf+ZDHteHuDpywwFXnGjWufMhitsX7qDNED2opfRJJ+j5d++AcMz52j+fA93P4e61sbDNdHoGLGN657x4zq8R6pgcDnQkYQ9ZFRl1gkaCew2qIb41XkDpJuj976FjLuIdMRVZaTNzutym+V5NYW0iuDqzPsoMZY336pkhRnNUaN2lC/4sGFdGegXiLqGOUsCsFisWR//4AsL1mFFD//QvrT95Y5U9Pkz0nRveqtPHq5J7zs09ub/OD732I06HJ2a0RdVTgH48mM3buTpyiktwqzQOCrwOoscDVe1LH3x4jjmIsXLvLmm68xGA5IkpS7N6f89ODvn+rLa2OYLFprgMkTBhvjlS2xgspBY6Hb63Hp0gWyLOf27TtfWCG9rmt+8YtfEEUR3/72t/ln/+yPWn93r1h1be6LwBfUfvPbD/jPPzu8R2trfV57/dy9j8cZRrHA2oasyalNQ95oxqXlQtLj/NmLbJ8+R3dtDSGdV/M5x5CInkjYjhOI1igiQWYPWAi41gErBYOuomkkn04Nt45MZ0pFdDp9kqQLIqLXH/KjH/9z8izjg9+8FwrpgQDeD32WP3ivlmU5WZYf/oPwRXTnBNoYaq29LzoCLSSiN2itXZy3U6gf7b8kpSSOY+95LcSXui0SUpKkPVQUMR5PGI8nZIuTro8CD0MIgYpThIxZZjla17gHdEEBIAUqjRAiwpYSZ1rPdBHTaE1eLKnrCmMa7AN8v18UpJSMNrfYunARJwRxktJM97krEmxjKScTVn7wIopw2vqQzN4aMlljOrnD5PJvW7ujkz/jlRSkcczp7S02t07T7fZY5gXSFCyyDCEk+f4tTmIOsWrkG6UdXjt/Hm0dO3t3H1FIbwvRbSHdWIMRgsH2OTqdHr2NUxgB2hrqqqRpNHVZU3cSyvwMva4gFoZIQCdW9DsJSSzp92KyzLFXlhhd008TpIyJlSKJI2yc0sQ9P9/oCqzBFvm9a/H7lZWtyyO41wkR9jdfOF/gPB/HitGoR201maiw2t5vi/qoy3JgtfE2S8f8yK0FrUF1Iy6c3iaNI+7YhizzXR1FY0gkDBSMOhEbmwOkEDgtaBqDXZYsKo3G/zbmVUN+JN9IKInq9oj6Q3r9PoP+gEG3y7LToZd06MYpzlikc9hQSP/dYW/vDn/7t39DmiakqULrBq1rtrbWsLah/pwhLc45jK4R2BMHjX6Z1FnG1Z//HZ2NdZbKgYTsYEJ1MGV5ex+y2ivSQxE98EVjNTRLFge3+c0//WesE8xuXaPKc2yVgampZxHCCeK+o6PWcDZCxD3QBkQXv4BrT/Fbr0slJd1OB2NjnK0wSYw5/yq2LsjnXZpy6tvorPEtl81h8aiuSq6+/y53b1xnPB6TZTnFdJ8TP+VaRNQhSkc4a9DlrLWiOTamog4i7uBWTz9nWn/Or9DYU1EblNgevD3Fpd+5e8Df/+LXrA06vHRunShSREqytbHGH/7wO7zzzQqHD/m5cfM2v/3w8kMCTVZK9Bd/Hg4E7sMCGX4683arh7kDxxphrHOUuiarSoxwRFFxn0/i80bbthmlHXZZlnP12g2iKOKNN7+BMYbr16+zXC6x1j2zpYVSku3tbUajEWtrI6AVmbahXc4dnk4IKdne2uLihXNMp3OWWcZ0mnHl8g5xLOh1JEmsWB916aQp69ubyFjR7S05s51xbmsTNUgRaYxxgljGDE+9jBlu0UsSkuUEt3MNt3sbOW9wMxCJV/4Y4dC1xmjBN7D0unC9gdsa/A+2ZO/uDf7v/+t/o9PtAj6g7tbOjWdzowKBrxPOgC1wuqGcgS4SVnYUdd14Baij9YbR3jbwEam91lqapsHhEEohhfAF0i9hj3Q4vxm0rn1Asj55kHPgwQghiJT0Dj7NAlcXOPOQ4rA1mHIGIkLECVIluEbjzJyqnjKd7+OcxYkGRMOLup53CBojqBqBIQGVtuKklUCpxF+7xFlNvZhjtEVrgzUWl5dAj8M92OO/T2MMyyyjl+eAI44VZ89tYZ3jzq1z3Ll1GpGPORCPCRdZvR4+HnFWlly5edOvjR6ijo+ShLQ3pNPr09iGpjQsZjOMsfQ6Q6JRj25vwGi4htMR1RnDYr7g6t0d6qXgYHeIKddIIoeSkC9moGvqxlDnDUW+JFvMMbpBWkekFNliTlUW6Cr3WXT2yH1yDat5qf2BPJ57SvQX83cq8GhWPRGxjOikXbpJwvDCJbRz7O9NfBbQZIbJj/qeC9JY4hzo2qCkoN/rkqYpWVN+5gzLOdCNZjqdkESKuqlw7tAm0FlfcC8LzcHdJWkSs7kxIpIxSaSIJciH6PeUkgz7fUaDPrGUCGPIZksO7uxjGs3G+gZqGTHPcow5+SFiKKS/4Ozs3OT27Vv3/p6mCW+++Spnz26xXC6Yzz/fab6zFlP7E0b3FQjBK+cLPvirnyCThNEf/oD04jnmd/apDqbo/X3cvApF9MDzwdZga2Z3Kv5hbwoInFu1Qnqfg8pAlWt6WxFpX4CLIR5681mxWsQZ7hW6nSFSkl63j8MSKecPuNZPYa3B3BrQzA6gLqGuoNqDZsGq+FoXBR/84mf3oq9P4rn3IFTcIx1dwjQFps5xn0lkBxH3EP0taBpcWbT348tQvT8lQkAU+fe1e4Bv/cm4cWOXnZ07rI86vPHKFhvrQ77/ve9wanODd95+h16vj3EK6xR//Tc/48PLn2Dtg9RpX5H7FggcxwJz/Cp7i5Xlqp/Wjk0J1jmyumRW5mRVgQCyIv/MSz4vGuPfVswXCxYfLTl9ept/8S/+lCSJyfM5VZXRNGAelGr0FCgV8fLLlzh37ixbW5vel93aewt40fqaO0CIiHNnz/CNV1/myifXWGYZB/szxgdzeh3B9qZiNOyxtfYWg/6QC69cYLg+5OL5nMWi9D1PQkAnwVoBssP6ubcAS3L6LLKaY+YF5sOPUdMaO/bOZQMHjYB5ptEGfg94Zwh/vVwV0jVQcGvnCv/u3354n8IohCoHAk+DBbvEWcjHUw6DKFY82fxjraWu69aWIkJIR9PYL6cD2Tm0MVjrKPMM3Xwx3T5fN3yAq/J/aGa44uGdWc5qdDYGGRMPLyKiHjo7wJkFRdHjYNIjjhW9bgKybrshXjy897ikaBRaJBD1QPaALn5BcsRrwkqvUJ8VoH0GEyIBMQBRgF1yknGlG81sNqc78MGbaao4fW6b/qDH3dtXuXv7Jnp+h6srv/PHYIACKIqc8Scft9/Xgz8zTlNGm1uoOKbWDabRTG/toOuGU+dfJ+kMGAzW2NzYohN1SZxi95bmN+NbNE3NnbWIfDoiTRVRJLGmBlNTlhmL+YSyyFhMJhijqZcZQgiyfEFVl1AuoT5aa3LtAZ4AGeMXfo+7hW0oROAri8IXjdMoot8b0Ftf5/Tb72Ck5L1PLzOeTVl+ZO4rpMdKMOzHWGMpGosSguGgR6/XY7KcewHOEXzQaMP+3l1iJVuRr7vX4WGd1yFmWUORzRgOu5w9tU3cUXQSRRmBesi5slKKtdGIjdEaiVSgNYvxlN3rt9GVZnv7FCpS7O+PaZ5g2guF9K8ARzckTaOZzRY0TfPAAKsnxRjNbDohiiOStMNofYMyzz+30v2JWPmqSqAX+V7rpYHiwZOusxarNfX+AeBoJnPMYonNi1BED3wJGJwtgAiiThso2uI01BPMUlLuKzQJggEyjrC9bVAdqO6AXrBKumzKgvneHiqJiHuxD4zRfvMh4w5R2kfEKaJrsEuNLsatEtwrMLwlwOcbB85odJnhTPVwBYFtoM68ubCt2iL6V2z82dbD/XPeL2sdVaUZT3NqDZc/2WEwmDHandPpdDBO4Zzk5s7tr0TnTyDwVDgO3aoq/E7x2GO8qRs+vnyN5SLzIZjAnd39532lDyWKIrrdDmkaM59PiSKFlIJer0OWPX0uzXGstUynM5SKyFuFG7QFdIRXF+I31tZZ5oslB5MpZXW4SXHO0WhHXgjSjmS0tsXW1ibDwQbdTpfrszHXrt3izOltXn75EmtrmzgE1rk2vUJgrMUZg13bwL30KlZH2Nt7OGeRjUM6iNpHQKmhNof7YWsddW0wxmL0k/vbBwKBx/F5B5XDtSIBZ/AFic/xkpHy3S9pkjAcDXHOki2XaK3JS41+hG2Dcw7TNNj20DDwbHDWUlU5QkjsSZWUzmKbHOGMX+ej6fUTTp3eQEmBUg7pSsTqQfSC4ZwjL2vmyxJJRKc/JOr0IEpB12BXB1DeOhNbt12n7cm+iHw4im2TtE+A0TX5dJ+836XIF5RlhjUbCBzD4YCzZ88yvjG6J9J+0u/n0R8A1llc09A0c0yjsboGZ1DK2zeBoNE+ILRpKkxT4pocW1WU+QIVATbBxYq6LmnqwhfQZ1PqKkfnC6zR1CpGIDF14a1c9CPWPEL6Pe8XmWP1oiGASN4LexcI6qr+jOf37xor09GqbpjOFhTaYq9ex0hJdveAJltij9UljYWq9na3pq1PLPKc2hjq5sHdSMY68loTSdG6Rjp0K2AxztswtqMaURt2JwuUkswqTW6hechQssaQz8dMJPzyFz9jfHeHD959l+tXP0bXOU21ZLFYsiyKoEj/XUZrzY0btxFi1SL3+ajKkutXPyGOY86/9Apn+gNu3bjGeP/uM7jaEyLxHVYdAZd6MErgcgY3Hp4U7rQm/+AKuZTgbOtJHXZxgS8DDSxAdqB3DlS//XfnA92W16myq9R3f4UcnCO68GNU2qM5/U1so+HOu7C8zcrHfDneJ3//fQabG1z69jdRcUy1zNBOEHcGdIUkShKiJKG802cxLYEl2Fs8NOHvCTF1gWluA6Zt4fvs2HLVEle3x8n3xt5XaAw6B89QEZWXDZ/emCDEjA+u3PUFMSkRRxbpjdbPZN4OBF5IHF6ZvnIlecB0sFxk/OX/+VOklPdGRl2/OO39/X6XCxfOopTk008vA44ogq2tdYyZUlXPZo7VWvPxx59y48YOf/Inf4JzGoEkkpH3SlfWt7lqizGaGzdv8dsPr3zGt7ZuYH9iSLoJFy+9zcWL5+j0E4SEq5/+A3/1lz/jT//0n/Nnf/o9ev0hTioa67y9lLCYskTkOVx6Dc5dQq/9HD2+jFs0qDsGrLd4aRxMc5hXULY/16axaO3n0LD8CgReQJwPAnQ8m9VhL43YGHXZPrXF2++8hdGaTz+5zHKZce32jHn28DWVc5aqPL5mDHxetG6Yz/YB4YurJ8EZTLGHF5L4k9JT20O+9Z030LqmWC6YJYYbkeQ5yupOjLWOvfGCojfh4ukNtrdPsbd+AzrrUFuoBD4wLQIEmJz7bBNlAnHs1eknrJnVxZLxtQ9QzZLx3VuM1oacPrOFsz3Onz2L+q5gsXvtCzl8sM7SNBrTNGSTKdZorKlQcUyaxnT7fZyErMjI8wXZckqxnGKzCbYqmU16VM0c0+vTTVKW2ZxFNqPMMmaTA2xTYMt9cIYarzJ3q0OHR4l/VORDRN3J7+NXHiWR/ZgoiljrD1BIJnsTyvzLsyh8Hqx0MU2WM796EyEE8iPfSaGNt4d2x/a3jbbMlv7fnAOB4eZdvz82DzlM1dYxzu6fdVaPiwo/vFfMipr9j2/76zPmkQ6tTVVw5/pl9m5K/ocr76KUQjfai0B8MInvCrX2iZ5PoZD+FeRJTkpO9noaIX3RR0URUn4JJ9CrQ+FY+jf1+CNd96hT0kDgudJWjYRsk8k5tHhx2r+z4HTla0xC+LBS1Z7m3xsAztstNTVGa69MPCpvEAIpFVJFiPbtMKTyKWQQj/p+3Mqz+2EPlHB4dRTnaE/NDY3+uqwoA4FjPKSAfu9/O0dZvIhbc48QgihSCOELFKsOEinl/XPxM0C3a5h76syjIrr2edC6dKGNvvfxx/HZgwIVxURxgpTewsFoS1nWWGuJ4xilvGrt6Lmncw5hnbe5iiKIY5wSuDb3WNhWdIa/DntsoxIeAYHA1wchBEr6ObKTJmgliCOFUuJk82OYML4AXHvA+oTdlccKpFJJ4iRCYKmV9LWAZ/vIe2b4XBOHMRa32hdJSZvY/ZDPcMf+/qRf1PkCtm78e3uYLaeUIkkSIqW+mFvm/Nd3zmGNaTsPVvtOca9uY531WS7Werve9s1Z016zOXwzBmPaQqJpW82cwdE+/O9lNj3uXq30wV8TvFeeLyRLiRTPfm34IuOcO6xDnqAOd3RK8uP28WKyB8aIHXmNo69dP8F+2x9AwfIZiuiE+1KM0gKBQCAQCAQCgUAgEAgEAoFAIBD4avBiml8FAoFAIBAIBAKBQCAQCAQCgUAg8IIQCumBQCAQCAQCgUAgEAgEAoFAIBAIPIJQSA8EAoFAIBAIBAKBQCAQCAQCgUDgEYRCeiAQCAQCgUAgEAgEAoFAIBAIBAKPIBTSA4FAIBAIBAKBQCAQCAQCgUAgEHgEoZAeCAQCgUAgEAgEAoFAIBAIBAKBwCMIhfRAIBAIBAKBQCAQCAQCgUAgEAgEHkEopAcCgUAgEAgEAoFAIBAIBAKBQCDwCEIhPRAIBAKBQCAQCAQCgUAgEAgEAoFH8P8DmoxOfF9IurIAAAAASUVORK5CYII=","text/plain":["<Figure size 1500x500 with 5 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Image 1:\n","    Predicted Class: 1\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: 8\n","    True Class: 0\n"]}],"source":["# Evaluate the model on a batch of test images\n","\n","\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()}\")"]},{"cell_type":"code","execution_count":null,"id":"36c183b9","metadata":{"id":"36c183b9","papermill":{"duration":0.056281,"end_time":"2024-11-28T09:38:39.055596","exception":false,"start_time":"2024-11-28T09:38:38.999315","status":"completed"},"tags":[]},"outputs":[],"source":[]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"A100","machine_shape":"hm","provenance":[{"file_id":"1wTz6BWPcc_mXbIaDJwabYEm6EPPvn-Ek","timestamp":1733402596705},{"file_id":"1UQjtNGKN_P98UNwBXbdQPjaVNmV9aT1O","timestamp":1731944811279}]},"kaggle":{"accelerator":"gpu","dataSources":[{"datasetId":5944322,"sourceId":9755289,"sourceType":"datasetVersion"},{"datasetId":6036447,"sourceId":9840097,"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":20401.826963,"end_time":"2024-11-28T09:38:40.734101","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-11-28T03:58:38.907138","version":"2.6.0"}},"nbformat":4,"nbformat_minor":5}