Image Classifier Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developing an AI application\n",
    "\n",
    "Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. \n",
    "\n",
    "In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using [this dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) of 102 flower categories, you can see a few examples below. \n",
    "\n",
    "<img src='assets/Flowers.png' width=500px>\n",
    "\n",
    "The project is broken down into multiple steps:\n",
    "\n",
    "* Load and preprocess the image dataset\n",
    "* Train the image classifier on your dataset\n",
    "* Use the trained classifier to predict image content\n",
    "\n",
    "We'll lead you through each part which you'll implement in Python.\n",
    "\n",
    "When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.\n",
    "\n",
    "First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports here\n",
    "import torch\n",
    "from torchvision import datasets, transforms, models\n",
    "import torchvision\n",
    "from torch.autograd import Variable\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "#import helper\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from collections import OrderedDict\n",
    "import time\n",
    "import random, os\n",
    "import json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data\n",
    "\n",
    "Here you'll use `torchvision` to load the data ([documentation](http://pytorch.org/docs/0.3.0/torchvision/index.html)). The data should be included alongside this notebook, otherwise you can [download it here](https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz). The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.\n",
    "\n",
    "The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.\n",
    "\n",
    "The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`, calculated from the ImageNet images.  These values will shift each color channel to be centered at 0 and range from -1 to 1.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'flowers'\n",
    "train_dir = data_dir + '/train'\n",
    "valid_dir = data_dir + '/valid'\n",
    "test_dir = data_dir + '/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define your transforms for the training, validation, and testing sets\n",
    "# train_transforms = transforms.Compose([transforms.RandomRotation(30),\n",
    "#                                        transforms.RandomResizedCrop(224),\n",
    "#                                        transforms.RandomHorizontalFlip(),\n",
    "#                                      transforms.ToTensor(),\n",
    "#                                      transforms.Normalize((0.485, 0.456, 0.406),(0.229, 0.224, 0.225))])\n",
    "\n",
    "train_transforms = transforms.Compose([\n",
    "#                                     transforms.RandomRotation(30),\n",
    "#                                        transforms.RandomResizedCrop(224),\n",
    "                                    transforms.Resize(255),\n",
    "                                    transforms.CenterCrop(224),\n",
    "#                                     transforms.RandomHorizontalFlip(),\n",
    "                                     transforms.ToTensor(),\n",
    "                                     transforms.Normalize((0.485, 0.456, 0.406),(0.229, 0.224, 0.225))])\n",
    "\n",
    "\n",
    "test_transforms = transforms.Compose([transforms.Resize(255),\n",
    "                                      transforms.CenterCrop(224),\n",
    "                                     transforms.ToTensor(),\n",
    "                                     transforms.Normalize((0.485, 0.456, 0.406),(0.229, 0.224, 0.225))])\n",
    "\n",
    "validation_transforms = transforms.Compose([transforms.Resize(255),\n",
    "                                            transforms.CenterCrop(224),\n",
    "                                            transforms.ToTensor(),\n",
    "                                            transforms.Normalize((0.485, 0.456, 0.406),(0.229, 0.224, 0.225))])\n",
    "\n",
    "\n",
    "# TODO: Load the datasets with ImageFolder\n",
    "train_datasets = datasets.ImageFolder(train_dir,train_transforms)\n",
    "test_datasets = datasets.ImageFolder(test_dir,test_transforms)\n",
    "validate_datasets = datasets.ImageFolder(valid_dir,validation_transforms)\n",
    "\n",
    "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
    "traindataloaders = torch.utils.data.DataLoader(train_datasets, batch_size=64, shuffle=True)\n",
    "testdataloaders = torch.utils.data.DataLoader(test_datasets, batch_size=64, shuffle=True)\n",
    "validatedataloaders = torch.utils.data.DataLoader(validate_datasets, batch_size=64, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label mapping\n",
    "\n",
    "You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('cat_to_name.json', 'r') as f:\n",
    "    cat_to_name = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'21': 'fire lily',\n",
       " '3': 'canterbury bells',\n",
       " '45': 'bolero deep blue',\n",
       " '1': 'pink primrose',\n",
       " '34': 'mexican aster',\n",
       " '27': 'prince of wales feathers',\n",
       " '7': 'moon orchid',\n",
       " '16': 'globe-flower',\n",
       " '25': 'grape hyacinth',\n",
       " '26': 'corn poppy',\n",
       " '79': 'toad lily',\n",
       " '39': 'siam tulip',\n",
       " '24': 'red ginger',\n",
       " '67': 'spring crocus',\n",
       " '35': 'alpine sea holly',\n",
       " '32': 'garden phlox',\n",
       " '10': 'globe thistle',\n",
       " '6': 'tiger lily',\n",
       " '93': 'ball moss',\n",
       " '33': 'love in the mist',\n",
       " '9': 'monkshood',\n",
       " '102': 'blackberry lily',\n",
       " '14': 'spear thistle',\n",
       " '19': 'balloon flower',\n",
       " '100': 'blanket flower',\n",
       " '13': 'king protea',\n",
       " '49': 'oxeye daisy',\n",
       " '15': 'yellow iris',\n",
       " '61': 'cautleya spicata',\n",
       " '31': 'carnation',\n",
       " '64': 'silverbush',\n",
       " '68': 'bearded iris',\n",
       " '63': 'black-eyed susan',\n",
       " '69': 'windflower',\n",
       " '62': 'japanese anemone',\n",
       " '20': 'giant white arum lily',\n",
       " '38': 'great masterwort',\n",
       " '4': 'sweet pea',\n",
       " '86': 'tree mallow',\n",
       " '101': 'trumpet creeper',\n",
       " '42': 'daffodil',\n",
       " '22': 'pincushion flower',\n",
       " '2': 'hard-leaved pocket orchid',\n",
       " '54': 'sunflower',\n",
       " '66': 'osteospermum',\n",
       " '70': 'tree poppy',\n",
       " '85': 'desert-rose',\n",
       " '99': 'bromelia',\n",
       " '87': 'magnolia',\n",
       " '5': 'english marigold',\n",
       " '92': 'bee balm',\n",
       " '28': 'stemless gentian',\n",
       " '97': 'mallow',\n",
       " '57': 'gaura',\n",
       " '40': 'lenten rose',\n",
       " '47': 'marigold',\n",
       " '59': 'orange dahlia',\n",
       " '48': 'buttercup',\n",
       " '55': 'pelargonium',\n",
       " '36': 'ruby-lipped cattleya',\n",
       " '91': 'hippeastrum',\n",
       " '29': 'artichoke',\n",
       " '71': 'gazania',\n",
       " '90': 'canna lily',\n",
       " '18': 'peruvian lily',\n",
       " '98': 'mexican petunia',\n",
       " '8': 'bird of paradise',\n",
       " '30': 'sweet william',\n",
       " '17': 'purple coneflower',\n",
       " '52': 'wild pansy',\n",
       " '84': 'columbine',\n",
       " '12': \"colt's foot\",\n",
       " '11': 'snapdragon',\n",
       " '96': 'camellia',\n",
       " '23': 'fritillary',\n",
       " '50': 'common dandelion',\n",
       " '44': 'poinsettia',\n",
       " '53': 'primula',\n",
       " '72': 'azalea',\n",
       " '65': 'californian poppy',\n",
       " '80': 'anthurium',\n",
       " '76': 'morning glory',\n",
       " '37': 'cape flower',\n",
       " '56': 'bishop of llandaff',\n",
       " '60': 'pink-yellow dahlia',\n",
       " '82': 'clematis',\n",
       " '58': 'geranium',\n",
       " '75': 'thorn apple',\n",
       " '41': 'barbeton daisy',\n",
       " '95': 'bougainvillea',\n",
       " '43': 'sword lily',\n",
       " '83': 'hibiscus',\n",
       " '78': 'lotus lotus',\n",
       " '88': 'cyclamen',\n",
       " '94': 'foxglove',\n",
       " '81': 'frangipani',\n",
       " '74': 'rose',\n",
       " '89': 'watercress',\n",
       " '73': 'water lily',\n",
       " '46': 'wallflower',\n",
       " '77': 'passion flower',\n",
       " '51': 'petunia'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_to_name\n",
    "# dataiter = iter(traindataloaders)\n",
    "# images, labels = next(iter(traindataloaders))\n",
    "# images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building and training the classifier\n",
    "\n",
    "Now that the data is ready, it's time to build and train the classifier. As usual, you should use one of the pretrained models from `torchvision.models` to get the image features. Build and train a new feed-forward classifier using those features.\n",
    "\n",
    "We're going to leave this part up to you. Refer to [the rubric](https://review.udacity.com/#!/rubrics/1663/view) for guidance on successfully completing this section. Things you'll need to do:\n",
    "\n",
    "* Load a [pre-trained network](http://pytorch.org/docs/master/torchvision/models.html) (If you need a starting point, the VGG networks work great and are straightforward to use)\n",
    "* Define a new, untrained feed-forward network as a classifier, using ReLU activations and dropout\n",
    "* Train the classifier layers using backpropagation using the pre-trained network to get the features\n",
    "* Track the loss and accuracy on the validation set to determine the best hyperparameters\n",
    "\n",
    "We've left a cell open for you below, but use as many as you need. Our advice is to break the problem up into smaller parts you can run separately. Check that each part is doing what you expect, then move on to the next. You'll likely find that as you work through each part, you'll need to go back and modify your previous code. This is totally normal!\n",
    "\n",
    "When training make sure you're updating only the weights of the feed-forward network. You should be able to get the validation accuracy above 70% if you build everything right. Make sure to try different hyperparameters (learning rate, units in the classifier, epochs, etc) to find the best model. Save those hyperparameters to use as default values in the next part of the project.\n",
    "\n",
    "One last important tip if you're using the workspace to run your code: To avoid having your workspace disconnect during the long-running tasks in this notebook, please read in the earlier page in this lesson called Intro to\n",
    "GPU Workspaces about Keeping Your Session Active. You'll want to include code from the workspace_utils.py module.\n",
    "\n",
    "**Note for Workspace users:** If your network is over 1 GB when saved as a checkpoint, there might be issues with saving backups in your workspace. Typically this happens with wide dense layers after the convolutional layers. If your saved checkpoint is larger than 1 GB (you can open a terminal and check with `ls -lh`), you should reduce the size of your hidden layers and train again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n",
      "Downloading: \"https://download.pytorch.org/models/densenet121-a639ec97.pth\" to /root/.torch/models/densenet121-a639ec97.pth\n",
      "100%|██████████| 32342954/32342954 [00:00<00:00, 73109561.98it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DenseNet(\n",
       "  (features): Sequential(\n",
       "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu0): ReLU(inplace)\n",
       "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (denseblock1): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition1): _Transition(\n",
       "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock2): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition2): _Transition(\n",
       "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock3): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition3): _Transition(\n",
       "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock4): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): Linear(in_features=1024, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Build and train your network\n",
    "model = models.densenet121(pretrained=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining model\n",
    "\n",
    "# Freeze parameters so we don't backprop through them\n",
    "#for param in model.parameters():\n",
    "#    param.requires_grad = False\n",
    "    \n",
    "    \n",
    "class Classifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(1024, 512)\n",
    "#         self.fc2 = nn.Linear(1024, 500)\n",
    "        self.fc3 = nn.Linear(512, 256)\n",
    "        self.fc4 = nn.Linear(256, 102)\n",
    "        \n",
    "        #keep dropout to avoid overfitting\n",
    "        self.dropout= nn.Dropout(p=0.2)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        # make sure input tensor is flattened\n",
    "        x = x.view(x.shape[0], -1)\n",
    "        \n",
    "        x = self.dropout(F.relu(self.fc1(x)))\n",
    "#         x = self.dropout(F.relu(self.fc2(x)))\n",
    "        x = self.dropout(F.relu(self.fc3(x)))\n",
    "        x = F.log_softmax(self.fc4(x), dim=1)\n",
    "        \n",
    "        return x\n",
    "\n",
    "model.classifier = Classifier()\n",
    "criterion = nn.NLLLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.001)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.is_available()\n",
    "# Use GPU if it's available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseNet(\n",
       "  (features): Sequential(\n",
       "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu0): ReLU(inplace)\n",
       "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (denseblock1): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition1): _Transition(\n",
       "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock2): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition2): _Transition(\n",
       "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock3): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition3): _Transition(\n",
       "      (norm): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock4): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (norm5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): Classifier(\n",
       "    (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
       "    (fc3): Linear(in_features=512, out_features=256, bias=True)\n",
       "    (fc4): Linear(in_features=256, out_features=102, bias=True)\n",
       "    (dropout): Dropout(p=0.2)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing your network\n",
    "\n",
    "It's good practice to test your trained network on test data, images the network has never seen either in training or validation. This will give you a good estimate for the model's performance on completely new images. Run the test images through the network and measure the accuracy, the same way you did validation. You should be able to reach around 70% accuracy on the test set if the model has been trained well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/5..  Training Loss: 2.745..  Test Loss: 1.468..  Test Accuracy: 0.576\n",
      "Epoch: 2/5..  Training Loss: 1.182..  Test Loss: 0.858..  Test Accuracy: 0.748\n",
      "Epoch: 3/5..  Training Loss: 0.685..  Test Loss: 0.546..  Test Accuracy: 0.874\n",
      "Epoch: 4/5..  Training Loss: 0.495..  Test Loss: 0.645..  Test Accuracy: 0.820\n",
      "Epoch: 5/5..  Training Loss: 0.364..  Test Loss: 0.544..  Test Accuracy: 0.873\n"
     ]
    }
   ],
   "source": [
    "# TODO: Do validation on the test set\n",
    "epochs = 5\n",
    "steps = 0\n",
    "train_losses, test_losses = [], []\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    running_loss = 0\n",
    "    for images, labels in traindataloaders:\n",
    "        #move input and label tensors to available device(Cuda).\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        #activate gradient calc\n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        log_ps = model(images)\n",
    "        loss = criterion(log_ps,labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        running_loss += loss.item()\n",
    "        \n",
    "    else:\n",
    "        test_loss = 0\n",
    "        accuracy = 0\n",
    "        \n",
    "        #turn off grad for validation\n",
    "        with torch.no_grad():\n",
    "            model.eval()\n",
    "            for images, labels in validatedataloaders:\n",
    "                images, labels = images.to(device), labels.to(device)\n",
    "                log_ps = model(images)\n",
    "                test_loss += criterion(log_ps, labels)\n",
    "                \n",
    "                ps = torch.exp(log_ps)\n",
    "                \n",
    "                top_p, top_class = ps.topk(1, dim=1)\n",
    "                \n",
    "                equals = top_class == labels.view(*top_class.shape)\n",
    "                \n",
    "                accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "                \n",
    "            model.train()\n",
    "            train_losses.append(running_loss/len(traindataloaders))\n",
    "            test_losses.append(test_loss/len(validatedataloaders))\n",
    "            \n",
    "            print(\"Epoch: {}/{}.. \".format(epoch+1, epochs),\n",
    "              \"Training Loss: {:.3f}.. \".format(running_loss/len(traindataloaders)),\n",
    "              \"Test Loss: {:.3f}.. \".format(test_loss/len(validatedataloaders)),\n",
    "              \"Test Accuracy: {:.3f}\".format(accuracy/len(validatedataloaders)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results accuracy 0.859375\n",
      "Total 64\n",
      "Results accuracy 1.765625\n",
      "Total 128\n",
      "Results accuracy 2.484375\n",
      "Total 192\n",
      "Results accuracy 3.328125\n",
      "Total 256\n",
      "Results accuracy 4.15625\n",
      "Total 320\n",
      "Results accuracy 5.015625\n",
      "Total 384\n",
      "Results accuracy 5.90625\n",
      "Total 448\n",
      "Results accuracy 6.890625\n",
      "Total 512\n",
      "Results accuracy 7.75\n",
      "Total 576\n",
      "Results accuracy 8.5625\n",
      "Total 640\n",
      "Results accuracy 9.4375\n",
      "Total 704\n",
      "Results accuracy 10.234375\n",
      "Total 768\n",
      "Results accuracy 11.097120106220245\n",
      "Total 819\n",
      "Test Accuracy: 0.854 Test Accuracy: 1.355\n"
     ]
    }
   ],
   "source": [
    "#testing on test data\n",
    "accuracy = 0\n",
    "model.to(device)\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "#     model.eval()\n",
    "    for images, labels in testdataloaders:\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        log_ps = model(images)\n",
    "        test_loss += criterion(log_ps, labels)\n",
    "\n",
    "        finalps = torch.exp(log_ps)\n",
    "        top_p, top_class = finalps.topk(1, dim=1)\n",
    "        results = top_class == labels.view(*top_class.shape)\n",
    "#         accuracy += torch.mean(results.type(torch.FloatTensor))\n",
    "        accuracy += torch.mean(results.type(torch.FloatTensor)).item()\n",
    "        total += labels.shape[0]\n",
    "        print('Results accuracy', accuracy)\n",
    "        print('Total', total)\n",
    "\n",
    "    print(\"Test Accuracy: {:.3f}\".format(accuracy/len(testdataloaders)),\n",
    "         \"Test Accuracy: {:.3f}\".format(accuracy/total *100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the checkpoint\n",
    "\n",
    "Now that your network is trained, save the model so you can load it later for making predictions. You probably want to save other things such as the mapping of classes to indices which you get from one of the image datasets: `image_datasets['train'].class_to_idx`. You can attach this to the model as an attribute which makes inference easier later on.\n",
    "\n",
    "```model.class_to_idx = image_datasets['train'].class_to_idx```\n",
    "\n",
    "Remember that you'll want to completely rebuild the model later so you can use it for inference. Make sure to include any information you need in the checkpoint. If you want to load the model and keep training, you'll want to save the number of epochs as well as the optimizer state, `optimizer.state_dict`. You'll likely want to use this trained model in the next part of the project, so best to save it now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torch/serialization.py:193: UserWarning: Couldn't retrieve source code for container of type Classifier. It won't be checked for correctness upon loading.\n",
      "  \"type \" + obj.__name__ + \". It won't be checked \"\n"
     ]
    }
   ],
   "source": [
    "# TODO: Save the checkpoint \n",
    "\n",
    "model.class_to_idx = train_datasets.class_to_idx\n",
    "\n",
    "checkpoint = {\n",
    "    'epochs' : epochs,\n",
    "    'input' : 1024,\n",
    "    'output' : 102,\n",
    "    'lr' : 0.001,\n",
    "    'optimizerDict' : optimizer.state_dict(),\n",
    "    'modelDict' : model.state_dict(),\n",
    "    'classifier' : model.classifier,\n",
    "    'class_to_idx': model.class_to_idx,\n",
    "    'arch': 'densenet121'\n",
    "}\n",
    "\n",
    "torch.save(checkpoint, 'checkpoint.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the checkpoint\n",
    "\n",
    "At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n"
     ]
    }
   ],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "\n",
    "def load_checkpoint(filepath):\n",
    "    checkpoint = torch.load(filepath)\n",
    "    model = getattr(torchvision.models, checkpoint['arch'])(pretrained=True)\n",
    "#     model = model\n",
    "    model.classifier = checkpoint['classifier']\n",
    "    learning_rate = checkpoint['lr']\n",
    "    model.epochs = checkpoint['epochs']\n",
    "    model.optimizer = checkpoint['optimizerDict']\n",
    "    model.load_state_dict(checkpoint['modelDict'])\n",
    "    model.class_to_idx = checkpoint['class_to_idx']\n",
    "    \n",
    "    return model\n",
    "\n",
    "model = load_checkpoint('checkpoint.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference for classification\n",
    "\n",
    "Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called `predict` that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like \n",
    "\n",
    "```python\n",
    "probs, classes = predict(image_path, model)\n",
    "print(probs)\n",
    "print(classes)\n",
    "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
    "> ['70', '3', '45', '62', '55']\n",
    "```\n",
    "\n",
    "First you'll need to handle processing the input image such that it can be used in your network. \n",
    "\n",
    "## Image Preprocessing\n",
    "\n",
    "You'll want to use `PIL` to load the image ([documentation](https://pillow.readthedocs.io/en/latest/reference/Image.html)). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training. \n",
    "\n",
    "First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) methods. Then you'll need to crop out the center 224x224 portion of the image.\n",
    "\n",
    "Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so `np_image = np.array(pil_image)`.\n",
    "\n",
    "As before, the network expects the images to be normalized in a specific way. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`. You'll want to subtract the means from each color channel, then divide by the standard deviation. \n",
    "\n",
    "And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html). The color channel needs to be first and retain the order of the other two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_image(image):\n",
    "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
    "        returns an Numpy array\n",
    "    '''\n",
    "    \n",
    "    # TODO: Process a PIL image for use in a PyTorch model\n",
    "    size=[0,0]\n",
    "    #resize the image\n",
    "    if image.size[0] > image.size[1]:\n",
    "        size = [image.size[0], 256]\n",
    "    else:\n",
    "        size = [256, image.size[0]]\n",
    "        #image = image.resize(size)\n",
    "    image.thumbnail(size, Image.ANTIALIAS)\n",
    "    #Crop the image\n",
    "    \n",
    "    l = (256 - 224)/2\n",
    "    u = (256-224)/2\n",
    "    r = (256 + 224)/2\n",
    "    b = (256 + 224)/2\n",
    "    \n",
    "    image = image.crop((l,u,r,b))\n",
    "    image = np.array(image)\n",
    "    image = image/255\n",
    "    \n",
    "    mean = [0.485, 0.456, 0.406]\n",
    "    sd = [0.229, 0.224, 0.225]\n",
    "    \n",
    "    image = ((image - mean)/ sd)\n",
    "    \n",
    "    image = np.transpose(image, (2, 0, 1))\n",
    "    \n",
    "    return image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your `process_image` function works, running the output through this function should return the original image (except for the cropped out portions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imshow(image, ax=None, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    # PyTorch tensors assume the color channel is the first dimension\n",
    "    # but matplotlib assumes is the third dimension\n",
    "    image = image.transpose((1, 2, 0))\n",
    "    \n",
    "    # Undo preprocessing\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    image = std * image + mean\n",
    "    \n",
    "    # Image needs to be clipped between 0 and 1 or it looks like noise when displayed\n",
    "    image = np.clip(image, 0, 1)\n",
    "    \n",
    "    ax.imshow(image)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef764c1dd8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 231
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgcAAAH3CAYAAAAv2/y/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzsvXnQfddVHbju/IZv+k3ST5JlywO28SjLNsayZTwgW8YD2I4hDIFQpDtQod2QVNLd6SadoagOnSJhcAOdZqqESjWEbgiB0IR2Ak2YMcYMwWDseNb4m77vjXc6/cdea9/vPklgLBlZcHaV9H7fe/fde+4555539tprr52EEBAtWrRo0aJFiyZLH+sGRIsWLVq0aNE+syxuDqJFixYtWrRoI4ubg2jRokWLFi3ayOLmIFq0aNGiRYs2srg5iBYtWrRo0aKNLG4OokWLFi1atGgji5uDaNGiRYsWLdrI4uYgWrRo0aJFizayuDmIFi1atGjRoo0sbg6iRYsWLVq0aCOLm4No0aJFixYt2sji5iBatGjRokWLNrK4OYgWLVq0aNGijSxuDqJFixYtWrRoI3tcbQ6SJHlCkiTfnyTJJ5Ik2SZJ8qEkSb4tSZIzj3XbokWLFi1atD8vloQQHus2fFKWJMlTAfwSgOsA/BsA7wPwOQBeBeAPALwshHDpsWthtGjRokWL9ufDHk/IwXfBNgbvCCF8UQjhvw8hvBrAPwPwDADf/Ji2Llq0aNGiRftzYo8L5CBJkqcA+ACADwF4agihP/XZPoC7ASQArgshLB+TRkaLFi1atGh/Tuzxghy8mq///vTGAABCCCcAfhHADMDn/lk3LFq0aNGiRfvzZvlj3YBP0p7B1z98mM/fD+C1AJ4O4F2fygWSJPkvAA5g6ES0aNGiRYv2eLRbAByHEJ78SE7yeNkcHPL12sN8rveP/qQTJUny7of56GYAGZCcBYIOBgCkif4U0GKfKyTT9zreYhsPZY969EYXUhtTvabo+56HJKNj9ffQ7hEIA91XonNm6elLIOChb8JDUw/xcZpl3q7T7fT+4D9CEnauob6374XO3t9u6tG1kp0Oz3idLM95+v7B7fbxfNCdjA/QX94B9trU7egedkNzu6dNkgT9zjHqj9CP7zvL7dtJmuwc7wMOAH6+ru3sc7YxS7NR2xp+/pATMBm3xcebHz/oGw83ufXBMKijP3WPp8+oz9Tu3Wtprup1mNNmZVECAA4ODgAAs9kMAJDnxfg8PH/f8ftJ6q8aNx3T9ezLdNwPMs3Fth2Pv8ZGY9Z33ehaw5x/8HOiay02a37Xzr3i38fHV9UhAICizMb3FexaHZ+PbOf5erhnVpM/SZJT4737nOgYzlWCtlrvNCYaX53H+0/zKhmP5fDoa570p2fGQ7f3k7WdtQ4P6oeHm8Sn1kIekvNZzDLNE3vl8KNvx8/k0PTx+z7PNE+0zvpatduGoY1B6+DOuqZj9Lz7ehICrl0+8XXhkdjjZXPwJ9nDrmd/CtsCySwvKvTBRl+LZcUHsqyq0WXqzRYAsN5u7Pg0QZ5ko5OqQXp4ETRoD1p6Rn/1XKhCP54Ebc+2lTZ0aWHfm80nAIDJdIIt26UHJOfsS9m2Zms/sOs16RlBD61dsyjs+PmhnbOY2t91a/fpv5Uh5fuNXaflw94HgH03PdgHAOzNbfGuKjunFuKOP7R9Zn9vEmt7Huya08K+1yztGh9434f4PWtDxX4A+/dgfwoAOHPmPABg223Rhob3x4eS18rt98UX1J7HpUnOv+3zIrdz9o3d090fv98uaU1F2/Ch5thm/uNi5y3LAscrW+xl+/wx23Is2mCvR+dtjhWaa5xO5cR+9BLOA43xlav24zHJ7POjvbPWX1tr/H0P2L65qdtTU876PK/sPmdTu9Zsz8ZKG5ROP/L8VuI/5DsbHc15jSlXz0avW/Z/6Pxk+rGu+GMeeI6utfcLztkysUHaLFbWZnbITTfcCAD4grteDwC49dZbAQDXXX+DtZXPU5HZ9xcn1v95an9X5czHLeXzfXxyDGCYq+qujPddlTZ3Lz1gSVE953vJfqymdi/LxZLHW79O+No26gfOly5gymv9yu//DgDg5OQKAOC3fu89AICfftdPshHWlxefYFnbeibX64Xd37E9m3tTa2PLNazjKx9x9EEbFrvnvKyQpdaejj9OPt4cm4wPStvYHF0trS/XKxuTjnM443MzmXAtmti9TfjM57xmxx/LZsv50W/QcU52nX4gOTbcmPTss0xz7dTGAgA6LgNaf1NtLnkznRwhbXSgtvCZTTY8DtDv7/kLdsz+EY/hGFx9wE56csn6JQvWT12j4ya8A663tY17wWc4y6wt07nNiwO6tCHd8l7zYcPKeTyZ2bN59uy5Ufv3D8xvnk5tjWqaBj/9Q/8Rl++7+iE8Qnu8bA6EDBw+zOcHO8c9rIUQXvhQ7ydJ8m4k4bY2bQHu3ouCP45cqAs+JB0fknrDH2oOlE1MTlY9Yf3upuBBLbLj+cP8YA9qfBr/FjcJiX746TAlWY+QuUsLAGj1Y8C/M24osLaz17xfBLuPnvdQbfmA5ryWvFV6Rllu/VPlc94/f+ibFms+KFcv2YJ7ia+pgyx27T0uJHtHNvmnswOei30Ku7Grxyd2bnbj4d6e3Ruv03Dj0nNMrl264p/rR7Ca2/iVJX+Qei3WXEAbey25wOpa68u2GD75SU8HADzz5ucCAD70wY9a2/dtWm5W9qNw7333AgDueNnLAQB33nknPvjBDwIA7rnnHgDAzTffzLbY3PrV9/wnAMAv//ov2jWvcbE/Y/2Ssh/ywvplTc8yn9jfmTaXfNk0XOx4b+alyNOzY7QxO+F917Xd8GQyHV0r5RxLivGC2vDHTpsN9d92y0VucNusjUmCoB9/LtL6Qdl9Oji9/QF42ee8zL7HH/vbX3w7AOCu177O7uHqCZsiL5Zt58Jd8Mch5ZK3Pt6i4dwp+Tyk3MSA83hb80eP6Feo7Bw5215yPgmtaPlcTEp7Hjr+KJysrG2tfiTkKnY9Um6Cn3HLMwEAe/v2g3r759r93nLzEwEAv/RrvwAA+Pi9H7FrcDO4l9svy6K2eZVwPqh/A++720Fe9COJLgGgTTF/tHlIU3Pg2O6c19zfs2d0f2rP4IYb39UxNyon9rpZy5ng88fNlZ6XgnM/T/Lhl4iX1Dnbtub9FGyKtSUL4x/7fvx1n3upHmJ9kAnlGc84oSFlnmO2Z324t0cnorD7kNs3qbhhza39zWYzaoMmr54T/dA3Db0J/r3Y2Of9VW5k6awgadFwTQ56rDnHPv6Jj9m52C83PZGRg+ICAOD4+BpareeP0B4vhMQ/4OvTH+bzz+Lrw3ESokWLFi1atGifpD1eUhmfCuCP8MenMqYALnyqqYzkItyGIgfRL+zNbWdcEmbXTkqw4XJB7y2nl5sXHuvzeJzidA6D2ecDlM3j5Tv5CbxlvDa9l4QIA9EBwYuH547YhhTrtcF9gztGzwjjuNWSO3z39Dr7POfuupoxnDJheGVGeJQ70yylR8nzJfQcMwAKx2kH3LlHYLvszUk7aqOQj4xhgj0iAzdeNPj43rvvAwCsr1nft41i7tZRz3+OefOf9STztFr2/5lz5zwe9wd/9D4AwMfvNu/r6Kx55TffbFD0vfd+AgDwwAOX7VxPoTc3N0/npS+5AwDwBXe9GQDwh3/wAesfehAbhmmuXTMA66abbrI2HB15Gx64ZJB0Ta/06NBg4mxi7f3N9xol5sd+4scAAO//gO2Le3qzFaHIxdKuIfjY52Bin1+9Yt7qtas2R5M2eRD8r/BHQw9HUy7leHqcU2ESXsNDZTtoWM5BzvX9bAzthj6gzAU9GzpRMyS3IuJzdGjz+LnPfB4A4JV3vBIA8KLnfw4AYH9iY9Zu7drLE+vzjBcp+PAK0q6JDiTE1a9etTk/n+9jUtkxy+UVts/GRDF2IWTizQhx6h365n1zfdCc29Jjzvm9OUNInSM0dp3Q9SiJnF1rFMpkn/F5KCo7x+VrNm9++/feCwB46tPNY1xvbXx/8md+AgDwy79iCFRBBGG+Z+dfbm0+BOdHyA/OkBT2/Iv3o88arVmd0CmhMZwPDHkKeRH66c84+6HtFKtX2MleK957mZdIGMrSmjudkkPC8KS+o9CMUK8tPegtw3JqUypelUKdHlIkosLF3N/Ph5DSuXM2Nw8O+WyVC17b2rC4au2+fC9P0jLsSmRgvSLSonBKoXCcECnrl8mUYQmOtUInk8nEw9BaP/f29/m3vX/mjK0bZy8YYtArjJYX+OWf/A2cXF785sOh5J+sPS6QgxDCBwD8exgL82/sfPwPAMwB/IuocRAtWrRo0aI9cntcIAfAQ8on/z6Al8Dkk/8QwO2PRD7ZOAfJbfm0QEEPYT6zHaTc+w1jS5ulvYpHIAJO1wN9M47LajfoYIez7NPTfyLIb0tEhbWXPFEwTrG1bvRa7dvu88yFM7xee4qQKCNzn7tQkfw26+3ovup27FGCRKWKRJoJvVYRFjvG1rhZx0TeSBC9aLhD+VqblV2zIxmpo8fYilHEnX3LrAR5mMpWaGo7043Xmbf/+td/AQDgy774LwMADucW71WcPM8KbGq75mpjiMpHPmwef93Zfd/8xBvZD/a5UIqLF83zP9y3vq0ZI9wnklDTe73/PiMozmc2D7Srf+CBB9hfhSNJK5K4ytLGTUQiIUD7Zyyeu93acb/wSz8HAPi3P/3jAIA/+IChH/MDxjvJLUChDBPGQ9nP9ZKExzqgI4dks6KXZd90slorpql/IE/S2paRcyDEyBn0nA+FCIv0rFv2e4bhfc17oQtFZXPrla98LQDgC9/8RQCA689ftO8Gu59rl8zzPX/mOgBAt2F/Lu0ak2xMcBR5WLwCPT8dPck0zfw+Os6DlryVYUkco31lYedeEZkTWW7CdWJFzokQuvne/PRpHKGD4v9JAsGUK87vCfkuWW7n2JDfJA83LeSlNqPj+on9/TM/++/s9V32um3M621SzgOiI90p/lA2oUdPDo7a1CtWzmdV/IgSNiZJz+edBF95wBrjlmjIZtPw/nkv4kK1QlV7RyfUWVUp4qiNY8W+L4RSCtVS9slUqBhPw7VMyIEySIQYiIellaqoDD06PJji/AUbt6Lks0WyYt9Yf1y7Yq/bhbWta6ytx+S9tOzHnCio5vp8nwhjY/NEQJ7m4fKE8w8pCv6mCG0RCjOZWNuUrXN43gjIR2dtzUmTFO9+129jcXX5FwM5ABw9eBGAH4RtCv4WgKcC+A4AL411FaJFixYtWrRHxx4v2QoAgBDCRwF89afr/EkK5GWGCT3lnLtTeZ7yhJRbXCqdkDvALpzKr/VG02vqtQ875TXgdA44jefyHHzPyxb3YCcvfOdyfd85Q10xwlzE5OEoe1859Yxzpi1RD8VatXdkFsOa6YTdRClc2r0rzbLx6yUe0xMj3HbNU3oC8gSFsEyY+RB68TdsZ3zrs18AAHjbW95q37tqscEnP+kW+/xW+1zpmYq1TyYWu2zqtXvpVWKIyZNvNl5rTe+84M794MDShM7ODElQSH1x2Xb6k8o8xHZFj2hhu/mCHlXKftrQmy0zpbJNTo2fvAnGsXmf9ZrM9s7arwyL17ziTgDAZz/DdMB+4F9+LwDgo5/4EADg6KwhDe/9/d+2NisVNGV/E82Y7E9whRkjyhioGbdNlBmyo62guZURhRHKoZi7ELOaHmLD+1YmyXOe+yJr+2dZf6+WS3z8E8bruO4GQ36e8rSnAABue/6LAQDnzxkysF6OM4JyZh20a7LvyTk5e2Qpq/3GxqhkvyrbIdDzkvemuG8aEvc6G95PGpTXLo0MerjKzhHHRunFvFZFtGYLu3/l9c/o5dVEDLaB84Kx6BAGz/CIKWmCKeTZV0r/o0e8OVEaMjNrOLc3JzZ/vuj1XwwAeNot1uf/4H/5n+y0zDjaP2NjI12FJEnQN2OthGIifQO7ZjsIeozeV2ZISPpRPylTRNktE80jLkSrzZj9X2Uz5zv10pDg/a74jC1bQ0AEDBTSUBCaNSUiRfJSSdKGnknPGGC6cpcK2RV/SigpUFW8D6ZWtQ05JVs7x8kVa4t71v041WIysWcvK9VRRC2ICivtvFZGEdeZ+f5ZtiXFbM4EPM7JK5fJMeIze3zFvnv5svGnDs/as72/f4Cm+YuVrRAtWrRo0aJF+zOyxxVy8Om2NEkxmVQez9FOuG0ZJ6eXL5GY3Bm/ZiEE99oVY+49H9cPshdIjc3eVl6uduXK//aYmjdS8V7FDMe79tNKCYPiG3fPO968bw15rowIQruVmBG9NObfNhQ7kujRIfUN0lwxRO76iyGX21nB3LEXjB0eMdf58tK+u6XnvGX89zlPew4A4B1f+/X2N7MRxKfYJ3t3sZAIjHncUyIQ4jQUaelep45NhZzQu1heotdJTy+o34TSSHBpYh2R0lOY0GMUM1yiWYHiOpkEZ/rBCy05ri7WwqEo6EdJ9Kcm4/sKhXnmhXl8X/c1xsddbe39C9cZ2vFDP/yvAAC//p7fAADcTQ9d8yOkAfsH0oaQRgaRjlriLfSMMfaMC8Xx1TaQIc5snb2p9dstz34WAOANd90FAHjpCww5OJyxX7seC2YnHJyxrISWz8e1K4biXLvPxnHGczrCwmyOXrwY9l9Jz7HvzFsb8Dl6axLBkffPMarrxoXGZALxNO4552ofFCu3z5Wfr3N1O9kLBdcPz3oQc16ZR0QxsjxHmUrkZxynT8Q5yuQB2981IZFCuheQ3oE9Tyf3Wj9+9i3PBgC85a63AQD+9Y//MADg0oboUaZ1KcOaXJtqKuEoIkkV53/J9gsFldpkOdbW6IKEi9gRzTjbQ/ySCdeo9XrQMkhgz624BNKS8HNzbWrIRRJ6pQyJNVXRcv6kZXxG+07iWtIN4JiX8uaZYUO+xKQC+sDYP7/Tbe0ci2v2nZUtIzjc5xxkZoWUU3OKak3m0r8Ya470LecFESgup1gzi8GOkUCZIQjTyta7y8fUb6GmmjCCaxQ7W15duJ7GI7WIHESLFi1atGjRRhaRg1OWJAkmReXb9JberDxiRbwUJ1YcUxwE9N2p7IOxJz94/mO98nSHS7DL8tdu3HXJ6QF1idS7uFMWBJGkzh5XExTrc/EBnlvIR5Er9iqN/dNHAYG72I5b3MB8XslRHhyRcT9RHnjjfSgpUukRKPe76RUzNY/h2U83r/NVd7wGAHDr858PAHjGM0xr4OMf+ejo+xtu3xVznUnVkKiHpEzzskS9plfRq8+p20CvMxD5kCRzSfRBehEpd/oV4/jyOOXlKL9dXp/QIiFQ9WrjMU8hJ4r7pxobTxlg7FMeI0fh8hXLfLhwo8Xkzx0ZYrBgP3z1l/7XAIC3vflLAQC/+7uWD/8d7/w2AMC22bia5v5Z89rvv5ccXk9SGHNONAP2GEPV7BFict1Fi/e/7a1vBwC8/CU2dpVU65gVce+Hre1ZmqEkB+IqY8hBaAudpjm5IhNKViu/XfLaLZ81SZqnjON2crXFCxAappizao9IlrZukdFdFN+lbSUzno6+E3bqP0gxVcqJ8kqzTJkSdr6G/VirP3m8Hoo6BEcECmXpiCvBaZEJWZQ2BU8xY468y5Fz3qypobC6ZHP3ra//SwCAl99uGh0fu/fjAIB/8m3/BABwslyiqqzPE0mZi6Sfj5FSdbF4G0KeHKvk5z1vSvyaVt8jelhSYVD3uDnZIhHvA5JJHo+Bzr3tmKVB5Ehik/14aXMjlcB5E+o/Ha/vH+wTDawKhN6QRKlxMlEA1y7ZtZUg1DMZRZeu+JxIs0XZCkIe2s0YVd5SgbJ1qXyeL8lw370mi75emvKluBMF52qRz3i/VPGUqm0xwSLZYFQr4lO0iBxEixYtWrRo0UYWkYPTlgQg67BlDvGmtd13Sxc8V16qdquMITXMRU7SzD3lpJOOAT1/r8LH7+7EO70J0LnHDGpXpVMsVds6AQaq0obgRZp2t9HyANjsoeoYPag1PWyv8EflvwEN0Y3zvhn/22zGuetJ3ns7Hb1gpzWsVvSkJ94CAHjJW8yjuevz3gAAOMeMAe2mlwvDJ7zAkuLBvNb+vm3f7/mE1TOYZvZ3Uoj9HFxNTXnqUgjsVUugkIdAFn7JvPUTZTNIfdCuvalVGVJFCqhN75UxVSyLTc5TR5+aesye97oD0rHgXJK+RUvuxNG+efv1Qup69r3lifVnlVlMcr8wxvPtL7J+zd5hbfmn3/mtaJUrL32CffNCtizak++ocQohmzIroWI/3fFyO/eb3/hGAMDNTzBVyvUJY7NUmMx4r+cPTcWtrRtkjNevmdmi4nEz1iOoOOfE19AzpmqF4hgQMELXLtmN9GK9KiXj5PJivW6Aii0FlMyNF8okTQTl6zv4p5x6F78ftBLsHlgvgs9R6nOd80xVCfV8iNNUb5D0QgBZWEvPqLgiQdwJKWESkdMipLogzF7I2eYpVRkXrOtw3ZGNwZNuMWXFj73Nnpfv/v5/jhX1HXL1lSRHyEHQYy+egnzSzlUJ1R+CoISOCkEw63shDULWyL85KNEubD70vmzw/vk8bNfjeh1FpnGlSqNq4jVSUuQ1vW4M/xYSxTeEep4/Y5yNPO89+0Lr3Iq8KCZ4OCICrvHHC9aN8XowZotj+4J+HzbSdmkFC3E+CcHVHECGlBkTGajXsSZ/g5lRoRVHRcWy+DxlxYOqxH6qFpGDaNGiRYsWLdrIInJw2nqg33RoyIjvNvLystGrWM2KzWojmCenYv+ZYmdkLmfyCBjfVt5yPa4aNmQr0AtR/FaZErtVHDtpiJPFm7SuAa5z5l64QDADv0PeQkZ3I03kGTDeF6ReCN0weCBf7WXNoJyqNE4nFTq2Z0Ztd3mjN114EgDga77i6wAAtz77JdbUmrUH7mZ8esf7mBW2w18znz0tqPZIb+08NflXrGZYStUvT7AgwuHxZ6hKmmJ99ELEVu81dsp7t7apSp9KEYt9LQRhN8sBXpa4RMogp9QopWSIfqxjITBCbSuoD1GyHLAyRiSkeTQxj6dba2zsBIu1eTMvuc0qQ/71r9niW7/znwIA5gfmpe8z33q7FhIihExzzv4+c2BoxBteZ2qUr7vT1AxVtfEKmdJZa8zqwxnV2lLmdeeqqLd1XofGwpEvMt+VCRM6ZemQq8E5LFRMKJa4Bg3j4OuNcRly5/LoMtQsIUM+L3PP3pHa4JZVGcV0dxU+xdaF/vnwMtNGGQRUVpXYn3t1ylQhV2HQMEmGyoScrz3vR+uF6jG4L6j3dd/sr/199Tn7kQz5UnOSi9SGcfM3vdJQn6defBp+/Kf+bwDAb/7Or9i1GAuvV5yDShzgPybM2lFdC3j5Y2Vo8V1BEOowmld75NtlWiKbqtooPWC90utWfYYhwYrPZiJNCnu/k2pjpzVKyCvnNtUgr7ve5v7BETlLh7bmF2WKzZpZTCu72hXqnDTkbEnHQCiU+B5dolLlbAKfJ80r8WXESdFy3HAd7oka9U3joJWee41/y4yGVM+Do5dQBzyId/GpWtwcnLIQArpNh3YpoZQxXC48TfCsatBrLHoMMJcIh3rItailgsc1mxOGLmoJkfBjXUwPlhNquDiIvKIfNH/e0qGGO8+VFwpx6Knlj6Jj/iR5cXLqIW87pcRosdwRYBIMrQ0MCV1JqLzEare0c1+47noAwJe8+csBAE+6+DQAwPoaST61092sSezHOfHjFArhEKqjGMyyYuoboe9tz8WfHTKpMqARjKuSqSzJynQ5bW46fl7zB1wtUqnuNcVsNHQq1avFv9MmZOd9hOCQc8+0MP3oi9S1pvSuFrNUG9JCAlP2scre9rXGWDK6PA83MFMKMNUUrnr5i1+B991pRZx+8md+yvqGqVbTUj9E/GFiu2+5wcIFX//XbCP34tus+NHVK0aWUoGpQxaPGgr08Add5cTZ9jLPUXNjVekzktPqbCxRXlBwSvPIU32VlsvNlRfiSSWHy5RZLp4SuJKgWUMhmzIvPB1O81dCXXqogs8ApZxxM12KaEfngEeVU32fpFuJ/XCMZxSRkghQ13U+R4ogUjCfcye72im1BnVeglmbQR7HeSPCbsoCXJV+O1qVXWYTeY+vfvEr8PSnWlHbv/vN/wMA4H0f/B07iAvIIX/MEm6C5ESUSi+kjHiHcRu0hKmtIhmq6JwcoA4dUkkwC7FnyEJp1RqJ9NS/ACBPbR70neawQj073hZTGPn44OJNLA1f2fqbT20u92GKpqHM8dLOJbGihBLlOTdHChvJKfBAsTa+kJ4021qwDSSTikxYq598ozyQPrXBKPfo7HAJa+oV75ezj2vvtq5jWCFatGjRokWL9umxiBycsr7vsdqsHP5JHBkg3OMCRmNzuVCcQgoEST9ITpmeArfwGQVi6kJysSLUqBgKiUYufqQ2jeFGQZ72jiDbsdiRIFqhDYJN0508oAdTJTud4PRhQxGdVOl5lBVenmBvTyVXbZf8/OeYzPGznmniRqz/4p5RInnpXh6i0A5dVDKoJHvRy79CGeamMRLcnGV4JUyz3TYoiD40Egeh9yUIVn2skquF0B2FWXqlqim9SmEX9QfHWoQ1Qbsqw1umw26eSECutE+HYkU4IjkpUwoe+0FhKc0rkrkaDy+RTMlHekNPsajk5bb4Uhan+tCHPwgA+L33/761k3NOcPodt78CAPCNX/c3AQC33GQktisPGGKwPDav5eBwn21mP+YSz7Jb3fSUHT/lDesYsBRxKgGpTqmuKulMF49jVZOQJhQiEEFSMaOqGgvSlHJBOQYugqTU3zzHluJmaq8/Y0JvOAZCLTQPhLA4usW2Tj2dVqJJds3ZnP2UCvUbnqeGc66XaBhjeCotnDgxmc9quvO+yMecq53kgb1UOue8DiM6qHDEx+/5OA7Omxf9V77sKwAA3/49Fn7aZvZsHV+2Z+vwPAu2OZHX1i6hedjxpPWspsnOGkgb0FH4miLIvSJ5r1mNUTyhEuWu0JTKQjvaOSZHev+0Eo+jbDXF1JRLu950fsxiseL9jZEOqcJ13Xiche4oHVVExGHOiTWpJio9UWQDNwHlAAAgAElEQVRyZ7ojxzjtepgyEsfTSfh7oXTIOkTkIFq0aNGiRYv26bGIHJyyAIllKHVJnoPSELUjHgSHgAEdyLKAvFCijzzd8TXkIdZ0mVW8STHSXVNKz+Duj+N6IrJ1Xm75VOqQyI3ulJMg5aRH3vdOtdPTVz99L4qkqT88U0+SwDqgaz0Qq7LHr3+NpSrKyZhTDnReGpFQJYYzjPkefa24sDW2yixuq5RASZFuKFjFqtLYkFRaZJ3H6cWZkLzoigJC2uEPHgA9RzohjciVleSiVcRnxyTMQqSiVkw6HcZHnS7uSUPPV5wUeR/yEP1aqdArCTAJSQD7gd6cSnwrnst4cMh6HFKK9W9/w98GAHzTP/x79l2SGd9wp5VL/tqv+VoAwDQzT/HqfSQccrk4d2DiRyK66N7SgumC7N+OhYg6eeJIECSo43OSJE8WUpqy/LEK6mxXYzKXvFN5dwnExZEwGSWAiQLUbIs8ZT3DIXRoWwnqkNyl9Fcn/sgTJgdHzz8fLKUfSvxGY6NrKTVNKXt6f+ANBXdwhUqEflwMS6JnQhKUEswmOcrlqZvqJ5LcBCiGndmqZWk2n2O5ML7LC55n6N4//uZvAQBcfJKlFX/PD7wTAPBvSFw8e72RYLf0nOdEa5bM9RNvZDIZS8D3u0XmaGmSDCRGNmy+b8JMGoqtEARJfAv164T6SbBOYljV6P5BTou8+u2WxGYSxpWGmCQpQq+UZ6IsJEeuapFlxYsZo7laETWvfPUU30zon6OjfBa0smoM+wCNUO/lzzkP+MCrdHXg2tZyvNvuwav4p2oROYgWLVq0aNGijSwiBzsWkqFcrrxyBSWVmrO7N1OaXpaEQWo0G3etUg6DRDlUUEQFmiTO4ix1Md3H6TJunvPIw9LTSIJYxTpYZUl5TfcudAOKZw5SS6NL7fytuG+VMrMgMB2Lcb0sy7FgSdGDp1oa3A1MYTy5Uo/u2zMexDlQVoYK68iT5vsNvRVq6KCSfDK9enVs8PSp1L0Kxe+cW8GTVBxv9bneF6PbOQtEEKod715sdOeecOxbcTzQo8/E4JcgDttXC0GSAAo9hl7y05KbHscWleWgdKp8UNfi+WxMVvSQ0AeA8f4nXrQshL/z3/4dAEOZ69fc8ToAVrwFAC7fZxwDyQsrI0Kez5JpgYdnDAUKzHrYMJafVWMvvmkadw0TZXImQgLs72qfssmcoxuP247RvJbXVlpZUaptelbHsWZxVDRmfd8NolVC/vS428sQv1ZapFLP6BlW5I1o7La9xJSYMVLJexUHhffifILEn1FJMCsWLk9fc1DzQGOglE+X8pb6kS8bRNY4L4RiyHreW5HnLlmtsbjh3EVrP2Pkb3+TcVV+9Rct1XFxbEjS9HDKixHVUyqnUrybHfGxh+Ee2GfsB7a7YraNEAQhiLvog4paOTKnTCKhvhLR4hpGWohLxC+ZJVSxEFPX5NgyhXO1lnKStWXLAlUFM45K1qIWv2njhfo4dsxQkqy2AAbPJvMS2HwmXF2qw6mkTb7HpigVPhuLYalA12azQZ08OtmMETmIFi1atGjRoo0sIgc71qedM8J3905tq5i0mLJy25WbDkiDQ16Ix/8VltoxsYldiMfLpTJOxRNsV2TvSnAlG7N1ffeZ5EO7PANb8W69rS3qmDPhcWxtOyWnKtqDPCyx+RXeY2wuaweRl+c900osv/bVb+QxtmWfM5eciq1Ydytem1666Byd5GLHQkPiZswocaw4eE1vv6bgiMRlijwbhIeCYr07hXLo8UjHQHnNiv/LIzpZshBTSg/QNVut0ZIXlkiS5lGHxDkjuXtX0ilQaWnlKRPlEDfBZbcl+2q2UYxVMXfJZ3t8lwiCigCFFoEezYZyyU++6RYAwPGeeU/bhb3fUIp3WpCFT80BeYReBEz53Xw/DwPb2q5djfqxdXwMSHLl41N3oBiz8dccMxXFmpJVLhlkjVGh7yl2n4zn8q4n7pkCbe0onUucFxKhUSspwEWJ7i3RB4kEpRRukleaevnfMX9kS+GaEMYZGWk/oBCNkDTxOMR3kMBaOeaeSOJXRb9yjBeYkIib4u/Y6VUSWaWwy9IlzQehKIqfcY5df3gDAOCvfvnXAAC+9Z1WtMmnPwXfOra94rj3EgfSvYlXsIOqJknqAgdKsGr5jFbSnjg0vszieDE6p9ZkByIJCUg2OZDTokQB6RwUQrmIBnSuG5JjcTIusKSfSc0XfVe8jozPtHg/LdfdiXgBvG8hCoOk/lhHw4vxYciMGwSkklP/hy/pLVEISXNXkwrrRT0c8AgsIgfRokWLFi1atJFF5OCUZXmC/TMV1sfMzw47zGHu5pWDPmEsrW0GFS+xixsdy2NUNll5+227s7PzxFVJbHL3nakMMr065qRLxVAetXaleZZ43ra8EPlrhfMUxvKliTclHb14Hr9iZbW4C1KdY6YFlcBuuPAEAMAXvO71eMNdlp2Qkgl+fL95p/szYzrL80sr6T7syAjX8l6YB874pQoYee60NAt8d25/z2dkIQ8JwWgYCx+QA6rIUaVv05zwvsX/GCMN2ugrdlx6hkEx+nu7ViGbgbsiT8eLq8jV6XWfzOenyuKU8Wx5n4F8Bw2JOAfKkdbc3ErlUBoUmQc6B1lNysAK6chYYOwKlQ+PKMW7aS2ur7i259hLa6FU8jxbpefAlUXL0fE9EofCdIxiqCVRCh3bCFGhhkDJNJTFtSt27kKsbbLWk53YujIHOH9mLBW9WlGRtG0wEQLGPl4RGRC35ODQ5mrJQPWK8yetxBTnPRBa8zLh4hSwX7aOcpC9rzLVdecx8VYoXqky8DvxaGW3cO516nM9N6kyJYi4FfJak9F5PAf+lAS6intJlbEklyiRh8s8/+c/6zYAwDOe8tkAgI/e9xG7JnUdSE1BSTdd3JPkQVwD+1s6IFmeuBS1oKUMY/RT96MS1VsiS5onabqbtWFtuniTFZw6Okdtgv4Sz6MMFXYDMxTqtsSSmhp1bW1Q5pO4SWwCspyZNr2yl5RJtqNIy9tvxUkgWizl2qyTMinbkmVeQErPh0vMPIhMIHVa9mVZnYIXHplF5CBatGjRokWLNrKIHJyyoihw0xMuYru0XemVK8bKvXaJHpSnP5PFTM9LO8UWrZeI1Y5PUoCd4lby1hWeVeEkbglTxT2lmCcVLXq3CvOtW4ls6/vcfSLDsHUcoxGuprbzKdzT0Tk88M+/6RGVyr2VEp69fsVXfhUA4M1vequ1MZug4JY8sH0VY8clvfWkkqvPsq+FshJUgpT3xc839Pi8iA09yo2+7/G7sQZF3/VDedtO5X8Zz6dHU5DfgYUK5RD1Uay8c3/d/uYuvZyM4+RpN2Ydy2Mq8sK9dlddd3VGZmFo56/CQr3ystvR58pymO/tsfuo7EYSh843nQg5YWx/cyrrhCjTlPHc686Yd3XP1Wu8NgtGsY9reoYDyYQoFbMRNGfzhp4QM08yr4ugIkEBhb4jNUJIO4DxbnppGk9xJlqOnfQOVMJbr12iHHP7fMH5omyG3PPYeQtZ6v/uONeUt18QtSlmZKnzeW9U65xjN5uOc+SljDjIfYg3Y20smfUiLkrbdZ6vnzuj316C1DWJDDTk1KyJhKgk+YT1Gnq9z7k6YcGeDYtqKWNHdUKkQJnnuXvI4hwItVwuFt5OALhw9joAwJveZHoY3/Lt/ysAYJ81OtYbQ95aZk6cObJMJc3lS5euqGfsOuyXJgxsFHnPQ50Ce8k5l7QOLle2JrsnHZSlxLkqvYS5HX/99abNcfVY37PnZbVWUTb7wuq4c8VQlZiWbsH+0Zx9B/YLtRcSexYzoYJcb7JUiqlEbbrxMy50yzMtvK50N3RA0HeVzeUEML7oH2xr3Q6Vrx6hReQgWrRo0aJFizayiBycsj50WNUL35Wdu2Axx7192ypeut/K4K5YtTGh9z7hbhZd67nT8h5bMpszacrnYw8mkQZBIg+bXANJhNNEWtVuW+6JGPY9lfDSKneNfPEYFOv0WgGJdNn5t8qbjpMvhuyEoMqI9GKIBtz2IotBvva1dwIA5iyrHNrEPd4Z6cGhEGtcKAS9cu7gh4Abc+OVt76jNV/4rpyeAj0i7dpzeWeK7/WN378q1YnJn0rPXkpoO7nmHiNm25bNim3kGEqDgl5eo1vQK3fwxlHRtTgfpJonnQtu02dz80429GJPTmzOVVQOLFj5T96qPAa/J3qayorIdd2kQ5AOPSeXmO8lvcnrz5unpzK5QiF6clQ61+jgvGCfF6op4Y61am7Y63Yrsfnenw9liqSBr6qhIO+caE4vJI1jMKFXm9HT9HvgWKrc+pqe5bkz5E/wb2VJ5GXpD6Hi+CXj0JM563MkapOeLWW3cAx4TZWBlpqdeA/Kf3eNAo65MjGSEPxYoTSu5KgMEFX4VFsJX8qDTnnOzkv5qn/HmRhSfRRiKd5UXddIiCTNOLeEFFVCMwpptNg5XvY5twMAbr/tcwEAv/ienwcAnLvB1E637M81EdiKfA+9brb2ft8qMycb9CcUf/cFMvFjgGE+71aIlWxIkqsfbR6cXDP0Y3EiZUl7XwjVciNlUTv/8XGHtmafe4oZkbRKnCzBG/moTUsqKTZck/b2pUFQjfpPbe77sSaLlDe7tnVEJMO4DYPsQRi/LzSiH455pBaRg2jRokWLFi3ayCJycMoCApqw9t134K4tn1k33XCLeSGXH7DY2uJEibCeEIucXntOBECOUNJJX3vQ8AYGRMGVrgJjrGA9c3qnyscV+7zg9zdEJsBda7c/hJykKqhcZ+U+y/OTN9bw/S1jhYmKwG9UU8G81u3SrnFmbh7CF77+CwEAe4wLX/rEPQCAJ1z/BGdw12vLUuikyCadA3UMeQytdsRhPCWV7xwKMf+pDEhmeSGkRpr7jYLKyjTYIuM15Z2LtN+39J5UVE7cEffahpgoMOSpz1j5se80RvRu2mx0nNCPpOmQZ4qhm8e3IuJzUrNuA5GBkt7G+njsjVaZanDQUxTvo9V9sgLg3jjGXkgnHj06cUjojWMyfAYA+/Rwt/z8gauWvVCwbRyKAf3gNaTOtg5UkJsom8XOu+LcLbuAovHJaE1gX6K3Z8qrNjJWXDOHXkhczskcmJWySqnJsGF/rOUFWxwY1MVYdZyHdJ3LYuIIkchE4iekjrCQ9+HqnHb4HtGwlDHlruZ9iy/AW3QNCvbzlv284DNRZpmfS57+hvUJhIwJYdvUylYhN4ljpfVBnIyZUA+hPEL/vMiCMkrE+QmYEVFL1kItlCnCQ9jHHT19ZeV8/Vd/nd3PZUO3PnS3ZS8UM/EdyIdpjMtycN6Q2Jxr4QP3328X6DKUXPecc+GaAVwfk3H2hdbLBoJYiUL0mk9sA9GL7cYQhLRinQSiv/Mp15vWELtQt0Bf8jt27GSiuh280lY1drhWc/nP2JT10p7tazzuHBE56YYIVXUejSYWxDeCV1ltxL3iq6O6zE4RspD0O0jCo2AROYgWLVq0aNGijSwiB6csAZCnp4SpgzTCFYO2HfaF6wxBOH/OdmnXqE1/5coaaa7qiOMY8FCfgTFW9Tx3grmqLQaxrvW1sVrb4NUqv9k+9Xrezamd427SgsvI6ZS8prxMXmPFmOFEMXnGjJ/7tGcBAG5nzPG2ZxvnICzs2vusW9+F3mOF8r5rej7r9YCy2OfjDArXxPfUEHrhOg9jhdKGLxmTbV1BkBkJXrWtQ92JG8L87S3zrBl/nDDmLt1+Z47zb8WvK3plE/IC1myL12zwSoE7cd8+eJ62PJ5LiwdG93105oDfoQ6/mPPkUEj3XkiK2hg25L+k6u9idB6p/YU0eN9KXVMZE8qUka85oxphqYqRbENL70yMeI1dcI/ZzjdlP7XMJNnydb6/j5xe6orelWcR6L4qMft5HGEdeVJy3welOCJrW3m39rp3uM/D+VzQE62JOB0eHmDL2Lf4GarTIR5DqzmsfuJ4qlaAdA229PZ130IFpWaqtm7Zn00jz7xCQQ6Fs/N3qq6KB6OTZLli50QMGtV5IPLEe9gwV19Mexf253mk9pikib+33MgF5v2TRCPvvCSac+maefz7BzZn3/jmNwMAvv27v8O+r4kkiQ2iPIr/n+HY3HzxZgDA/fdfchVT8RySoWynvQrp6JUpxXUwGa+Pus0qVabMuCpnVTHbh4iBxr4l76oLnfOfhL5MiBxrCgbp32QP9/Np51pTafKB+y8DAPaotClOSylhCLaxZ+VFdMHnu2eOiZegHyfxxPxB2MnWehQsIgfRokWLFi1atJFF5OCUBTC+KOY35J1rV8nMAu7SCu46j87ZThhpwIK69fKyphPGwLxa4G58ibtUsXKV5+98AeYe0yOUqpjQDAc52ObQN15dMZE6oCsijhUQVU2yacbxyD3mTl+7xzyiV77oDgDAO/6rbwQAfNYTPwsAsKX3Jw11BeWOj4+xzzx8ec86d9iR+FIusJi8fi7F2FRlj573Vv0n3oTQHSIJa7G9C+m9t5gyX107d3mPFT3oVDoHRBRUCW7Dcy6pcDebUd+frPWhEqJ0E3bQDt17mqIsVNnNri2vVdfKec5GOfXUx1CsVaiGV+krpELIfPfJmEHf0FPOvIpj4uhUwTi2nC7XNdijR0NW+fzIPMMNPV5n1JPNLt7DlgiMsiFSeq/LNbUpxLeYVc7J6bZCuqhTwTk6pRphq2lCz1i1Axy1kGfd6Xmxw+U5ax5JWTM4d4PP0bZ273SfSMlu7Yg8FQ9gzN5XGzq+X6SqyjpWQRVqJnVMIQcay7KsnPcjrzQVQibOAO9LWSxCeYQ+BGVfsN+2tdz2cVscFZGeivg1TetonmoedAk9ZqIaw7Nrf545c57XsvXhzs+3ap4gx+a7/o938hpjtKKlpsDlxvQODvaNg3Du6AyWrB4qjkAbpBmh9UO8H3rQ0nXhfWjd3OVaTMj3CIQzPJNgT/0lJU2uM0mGzU4FXCFuQaQbVfOUyifbOFzb/qbUhGtvnFzlPRLtK6TSmAhVVEbGqfmvM3pmw1gB1XUONDh/XOXLP6VF5CBatGjRokWLNrKIHJyyEAK6dqjzfkqMAMDgrSnepbxW5cOfOXfo3sKC6mKBWQeFx5cUY5dKmnaltvOrW+WYM6ZGj1NOai2mq2KJOztMq1O/mxNrJsIyqLYlVboM2n0zVnbVPIKLZ81DePsXvh0AcMsNFiNcXiHbmjveaWEe5zoMaoWKierVvajOo6s8ln3KXbdqDriSXS5FPXoMQj34D3EZlJMutEQx+3ySuuKb4pHKlKh0LHOiF0u7r/mRZWNIZa+RBsUeGc3KYqG3LkXNPIzRDnmWZVa5PsXJtWu6c7vWvlj1YuGP1RhdXY9zTF6Is5N5rSlZ7xPOF1Xa0z3nReYAkqpQKmbuTgf7WvwN5eAvWPM+TaQXwTnr3hq9LuZzd87uJ9ImfYBp6Tr9QrcUr8+n4mlwXFV9lF55Jba6uCWcR6utfV9x7fM7qnyO/rFfK6Ji2/XWOSReJVFcAc6PhnNLj41nSshr51jssWqlIzGcL+q/rSpo8tl1dcKy8KqKW/bhcrsatUWohhA4r2/BNgmd6odwNYBBS0BtUOaM8vuVabKp16iIOkivQmqM0oQIUufMNCeZ1cL3xR8RgvDhj30IAPCjP/Ej1phqjDx0HIsrG0MQjo72UWl+K47vDP6xJyxOiRY+X03VD6oDwg8OWc1xMpH6pebZmPSlmixJljkKmfGBKSrVxOB8l6ImK6gKcZTeiQsd8oZdEVFILX83VOdDmiXzqc3HIi98TgbXQdG6IPXRMVdLVTytOuujgx5E5CBatGjRokWLNrKIHJy2JAHS0r3Z1Nnm8mYVQ9bxY138NE9weMAYMuOKreKzQfm42l1qN6pqa2TZazea7eyYpWq3w97VflI7xzR07n11nuGgKoTjSn1iwirvOKutLbe/8GUAgK94+1cCAJ52o3EMrj5gXu+soK6/x9x5j4UyNRL0zomgBydvczfvWl6naiEQ5xjiv2N2spADZ+Mz7uf69jvV+PaODv3YVlXgpHDIvlPMVf2i4ZVXV9LbVOwc7oEzvk2mvIQyFZNfLc2rLSclEt6ndNSnM3oJRB+EBKmNrrZHXoRir87ZIJM8sJ/LxI6b8/iWnrj6MUsH71pxbuVOK34ZPLaszBryGZjlcfWy6R4czGz8O097sWtNy9no/pUpUPEesyr3uOymkzKm+A7MLiBXp+3HXpcqgspbF6disyUngex0PUfKghCvRFlAaltW5JjSUxNM5WgVxmiWnDYhB4W0NuimF7m81vH86r0+Bj3xZKx1kWSpe9PiGEjP4Ny5c95OYMhSKne4A34tSKOD/SRelLgoipPz1auiZglmRK+0tKjyq9Q7RYwv0zESIjRUfBlwHbnzNXcBAN718/8BALDc2rrR6dkXIMvrXb7/MqYH5AVNDJ1rqROjKqXBNQSYvcK57EhaJjSLSprM3piy/kXf00vXuqMx5DoBKnXmWeYZDaHnuiABC0FDDn4OugQA0EkiVRSsTJkX47W89cwLZabZmF3bml5EUeWoCtW+GCMEfg42IsvEd+JzErqHPP5TsYgcRIsWLVq0aNFG9pggB0mSnAPwFgBvAPBcADcBqAH8DoAfAPADQe6LHX8LgP/yx5zyh0MIf/kRNywktvv1nf/YK+3ISldcayDe2/ubTYMio2Ibqao9a74vueOtt/SkFTtMdvZn2uFjvKvUDllchEGrgH+q7nfXDd6ENqzcfXYep9Nuk2p89Jyf+oSnAwC+5C1fAQB48k1PBQCUsF3smnUQJoxRqtKbKsVNyHKflBlW0rIXQ76QvkM+el/IQnaqUh0AbKmq50XHkrGHPZmSva1qbuzPdk2OQ8m873Tirk/vHiAZzARxxKIuJT6x47WCiIJyzPfoIW+PlzyBNPUZcxdjnOfJ0xSrE+OgKGZ6dM5i4xqjmm0IZE+nvbQnmFHR0pvl/OmkOUDHp+MYVNSakJLiRm5OngwMdWXjkMmdtuPceiFKJefNHj3sTWb32/P+MsXYOfcq1ZZX8UeiYAfUcOgyYC0uRCU2PdtPZURVnRSSJF6EEJVazHryJuaMyTf0MBWbnxHtOLlElUchELwesmzg5Hh2Enj/PATJ6G9lOiTiPYjv0knvgUiTcueHJcy+TwRKGhVZmnltlE0jLYi90bFqk/pJGgvykMXp0VqkOiGaq8pe2bL2gjJPxJ+azStXIWylGdGqsiHv3119cm34mHi10x1E8sKFGwAAz/rs5wIAfu4XfhYAcHRk99awLVXGjIG+xobqjG1tc2xCzznjc398bOO4JaqhzBFXoWTfS2k1dR0YVfMkosR1oak1r4RsDrwAZQRVfD6EXmotTiCEkQqryl5xmsRYzfVUsRq+jDPWvGqu1uUu84yRoYTCGAXWOqlzaqaleDCv61O1xyqs8HYA3w3gbgD/EcBHAFwP4K0AvhfA65MkeXvYzXsD3gvgxx/ifL/7aWxrtGjRokWL9hfKHqvNwR8CeDOAn9pBCP4ugF8D8DbYRuH/2vneb4UQ/v6nr1kJAiq0rTxHe7eXlytNglZMaHqk0v9OgdTVteTJUDVupspcVC5TDNoVELkbTaR4Ry+vVqWvetxU7ealGCap9LbxmKj2j8pvV3yqYZ5tqh0xvbJbn/VCAMD5Pavb3qzs8/19svJrU4I8IcN8/9A8ge3a3t/QI5hlUzS1Mjno6XTyqsYxU91/L30Hj9vSc2QMTboAYoJr1ziwuFl7YCfRBJ1TIpw70lC5TGzinvUppvv06PjVKpdSopAG9ii5Ber7tNN82akcycNyJNg24iXYl/cYY19wPpSq3EYPVxwCORBgGxPdi7gaqufA+bGlWmdCVTdVw+xDgoIseeVZq+aE+lBKkWvG5YUwTMhiPyTXYHGVsVF60kK1Mnphmn9eQdQFJDu0YnwzkJ9JY4KvnVT1xAgnItJTQU+6/rkqIzKmLI95Sw5CyaVNiJo/DJw/pjuRju5fKo3+FXpg8ip9TvIac6+xQda+FBdFPpFXq/x1IgrZKfRMyn3K8JhSz8B5Q+24zofy2l0hkc+ZqjMmnLPiB4iDsyQzPmG/C91AgmF8sFMBlXNVaooi/EhzRboNFbOB1qxOqXXydXda9sLlq6aoeM/dH7Pz0rO+Rg7LZG+ChvNU+hdbaQb0ys5QNVa2tRZrn+/nelDMDg/mp5vsKJi0K05WC55XNW+klFg750ZZJSD6oInhujdEtbSeik6m2iVpEII75irpVetG8DbA2yydjrBTnlfZWf2u35yc+i3adak/RXtMOAchhP8QQvi3pzcGfP8eAN/DP1/5Z96waNGiRYsWLdpnZLbCDq1/ZDcmSfLXAZwDcAnAL4cQfvtRvXoYVLi8Hrt2imLEOkl7rFqFNHE1QuVhizugOGNe2uebY8Zeg5jR4/im6pP3zjWwvwemPXeKiZTD7PO2bT33XexaMXs7erTiDLQnrORW2S77c1/4UgDALTcb12C7YhvpObl2vPpBev5CAxiLbraNe8+KkSpe1+3oGEgZcUBG6EGxEqCq1NX8fEYVP3EVyqkq4xGB4L0qOyJF6n22oQe0XrLCJSEAebizUvXsqXXONs7JfK74+XYlvX+x8XkeqT0qC2BpY7terR3SmDKWLF0GISMzZT5QB6BI1TbxGOhRMadcbdlj9cFNa4zw9YK6ASCfYF/V7IC0lDohUa1SsVPqGqif7LY95i6W/pT9cNJZfvq1E4sPn903XYicbt1iLW0PtkE56CG4V9oE6XiIiyItfPDYsapgyjj/4VmrawJ6yp3qItDz66k5IW6HsoVcs4BefZ7lXnUx42uyg8K5Yh0zHlTXo6aHfDgzLkUjpDHI+7S2rxW7V82Ondz6/tR9VpzH1bQ6fYjrXgjFUAZEv5NTrwdKNUgyflCzxoK0WBQ/d85TmlYVhHoAACAASURBVLi+hTxgIR+uiLmjOqj7S3e0S6STsGX/vPC2FwEAnvlMy3Y6PrF5c+/ddwMA3vubvwkA+JV3/wo+etnea4mQTcjZ2mj8vKYG5zPGWhIb8Z54n0dnTH0xyak3I8TBY/aqYkkVUAPcUOU5MJXPLFIN7559lhGlbImU1jtjketn1eHLT86VP53V0Hl203h9H8Z79zuCHQICHt+cg4e0JElyAF/JP/+fhzjkTv53+js/B+CrQggf+SSv8e6H+eiZn2Qzo0WLFi1atD/X9hm1OQDwjwE8B8C/CyH8zKn3VwD+EYyM+EG+9zwAfx/AqwC8K0mSW0MIy0dy8dAHtOuNFy/Uzq91nfIx+7jz97nzrlssSmni2y56AvME5mTfihGfB9VOt+PrRh714NkAg3698twnZNsqRiktdYEXYZt4TnCWjxnhhcflrU3SInjihVsAADdftFex+OWdSo98OhvXIy88/ikEhbv6Jriy25YB/y1VxxJq60/oxbb0VlZri2NP93j/zAjoTkQpZ9tTZkp0qoRHbz/QQ+J5+5wdUiRoqRi5bVWJjqdUHDujCl2nXPFGHWDHcyM+4X2eLMzNKOjl1QxNyoHYqlIi87+PVxtU+8zkYN75mkXgm4IxRMXIGRNPVUuBk1Ger3T5p9SGL6qxqt2GbP5iRVQjG7x3r55Iz2K/GOdtbxt5adLmGCttpkQ/Ml5zddVixmdyPSfUHmA/5+yfFEOlvYRIl7JMAsem6Omt0sVbMSacrVVlkbn4M2l4UMWObSoT6TkwY4IIS+A8EfJQkSeQFBk6xqFrolkz1S9R5dBOugR2H8uljXtOpKgjCrjmtXuiWtsVa3IsrB/PTCwzpeTcde817dAQIJ0dkp1f0mOkdoaoRdJacBVTzjGhWi0RFCECiyvmpWuBnxOZks6E5lUxKRzpUeVQcQjEiBfao1efD0JYw7giZgiqg2HXnJV2/wcXTXH1xotPAQC88EUvBwDc8f7X4YMf/EMAwK/92q8DAN79bnuVfoWrMjqCyEwHZjXNqXx49pwhBi3rPoBaBdWBlBBVaVQ8ELunDXUfQhZwcMSKoCsiP73IZ0R12rEiosZQa3ZGnQRxCPTsyhxHIJdBGiiD9z8gCMmQ4sDXMbqV8B8l29BhgN4fqX3GbA6SJHkHgL8F4H0A/srpz0II9wH4eztf+f+SJHktgP8E4CUA/hqAb/+TrhNCeOHDXP/dAG5Dn5zSGdZTIDLLWMxigOoIz5ZAWgrfF6HE/lSBD6XsSLzFNZFEjpJ6qNKkvBQxW6S0IUFYhPaUqtOF3iEpwX7FRIJBhN4dyrfXpz/dQJN9FhZS8ZMJF/dMoQ+J42gzIKlfL+CjW2+RlyrV3PL+7X6mhCp7LiQi2AwCTeNUR7VZ4YiwQ/LyhBa+CFbdMO1ytVyiUPEV9qXaIAEUL4bVjyVY81kyOj74mDINiveQERJuOKYaswlDI3XbDmJQSotMh0IvAJBxoRXJr1EIox6XhT44bxC+IE4BiNN9kuN4T74gbQjTHhz4OEnUqOLremUbsxXhYO2OPazmhFvr23NnbbHfLGwvvlgSug1KdWMKcKlUMJ02dWEYkT1LjrdkgFUUa7GwNinscIapnyuluHpaJsdAwlSZyLgS+9H84uaDP3whSbydOTcBgoMTpSwrtY/X1A/KdE5RLG4KRDJt+JysuSHRRqUi3C4Cs36El4uFH1OSDKo0UkH1Krwlq0lg1OZKKXvseiy4gWkZtlLxJJXPFnFRZYM37drDBAcHNrcUkjuhBLyetV0JZ5El1dY1f5BnTH2VCJg2Q9pslir8xXX17OF1eObnPw0A8Ma73ggA+P4f/H4AwL/+0f+T92sDPGPZaEkPT+Z2rptvpmhUzs0Sf+xV0CvdKQGtEKJCKSoZneclag/ljtc7PT/dDhnQVfaV+axriQieaK0ayy97bvWOJckg2bz7E+2keKXTK21R62eaoO1aPCjP71OwzwgRpCRJ/gbsh/0/A3hVCOHyJ/O9YIW1v5d/vuLT1Lxo0aJFixbtL5Q95shBkiTfAOCfwbQKXkOU4E9j9/N1/kjbUhYlbrz4RN8hL7lzlusjr6NWqlerMqCCxtdYb094DCE57uBmKrbiJUgZNvBqJNqFiw42lkOVR+1ER6W85UoVJKkqpA4HKrund9iKxp1ww539+aML9l0hAryftBQUxya7dDPfJxrSNUpH5G426QcIUvLH2jXL2xZUKa80E3LC+3RxlzGBSt0l2WlPgQwSsuH1lF623OKgoDAQoUl5q4INBc0pdFPlIoexhDGh/JNrhGp5HhEzXbKUwjLFXJK9dtxqs/JQRoIx2iJp4T2Yx7OisJKHEZjaVu3RWyWMLtKYwg81v7dH+e4TwsoKMxyV16Hj416WmltMc1NpZXo4ByxWc+3E5rJIgcWMwkz0avYPrV/rlV1DYRsRH9N8DJNmaeqyxwpdiRyrolWXj+2a8kYv3nA9z82wVCmEgcgIx0ACTHoWvfS1np9KUr8kzYXeH86C0HXC0uV6DmQ9vXCFsJqMIUEOYsFz93p0+ZwIofGSvyrDHoYUYifYEoVbsfCSCmg5IZEIyeLEEBURfVWobcVncdXYM32WY5j68kIiK9EsRtCwWG8wndixQpJEAhbZbxel07MpVKbphAqy1Ps1a+OE5eqn5T67hfftpGSbJwfTCvWxrbU1x+1tb34rAOCBB+zn4Ed+7IetbVxX989am298wjneD1GshYW6RCYtMgkYcU1MFX6a8ngOmgt69dhwDBKo3PN43fPwiUK+jgaOQ4G75oJ3OxLH6hel6RZ57iRHRyd3xPJSL+YkU1rko5THiMcYOUiS5L+DbQx+C4YY/Gk3BgDwuXz94B97VLRo0aJFixbtk7LHDDlIkuSbAPxDAO8G8No/LpSQJMlLALwnhFDvvP9qAN/IP3/okbap3tb4yAc+PGSLKNQkeUwvF6qUHsXTKUSSJygnSh0bYkAA0G6UTsj3WbQkIekgJGPSj0SEnJCiNjImW3phDpbfVbpi0w+FlohOKI9nKHbEWCjjc096wpPtsGZcBEmx1yHWPo7vS+Kzp9cScqIaReZxe+2KFbfPM6Vs8pxK0UrlTQitGBerGZADIg3y3nkeJ1o5h4P3kiWYM0YsAlFBbsFKbpXQB3oX5YzIAeOSbTMWolJ52V6EEXkM9OIlzNJu9fkQK1Y8d0oPtk7HCFOvmLLQDHrK4jVI9EflouWd9yIR0utzwSsJs/RDvF8MS5WoXnFOXXfBvHSlZlZNxePs0SxJMDuzZ6Sva5cNnSjo/S5JbHNS5Q4ZJXQ9Us6pgihNQXRFHvFqZcjB+YsmxDWZUwaZZaPFh1D63SCepXkw5n3kE5WLtnvJiXrUIaCQ0BB5GXPK+SbBvFhxUvaPzPOV5HNP2KOmB9jRe1cqo8a4ovcvj1Honzxt9L2ne+Z8nluMvVJBZRIBU+y9qMYExIbE372jsYjSlqiOyLNCWhZEi/LJBNOJxJyULtmPXmXuxXLOzVg8bBI4l5naN2dq9IxptgIuVXDIBZ4ka95skZDkJ8R1dmD98WVf/OUAgJtuvgkA8Nv/2dIfP3bfxwEAa+YgJqn6R1wUPldcm4T25BLbUtr1SuWoySeZzLystesw7QCvEsHTcugpuztllHtPAX1o09xNdwozJUidKxIKpUcLnR2LenVCpVwtrbOfp0cBQHisait8FWxj0AH4BQDv2K1cBeBDIYQf5L+/BcCzmbb4Mb73PACv5r+/KYTwS5/ONkeLFi1atGh/UeyxQg6ezNcMwDc8zDE/D+AH+e9/CSvU9GIArwdQALgXwI8AeGcI4RcejUZ1XY/FyRrwcpjKRqDt7F/0517JXLYiGXaPfuw45i5XrvMgNBnAYh0zD1FlXcXWbmulqtj5FEtVnFDx73bboiQyoLaIJS1hHWVM3HDhCQCA5z7z+fZ5Q6Y82dnO2mfbNht5ztzV7vACvGhUng0BK3bHHr13sdK3ZBsHedTkN4hVrJhx4SJK4mRoh5yN+snlppV3SO/14Ggf033G88mudzY6Y8mK206YqiTeg2LlShN0WVTG7Hv2iyR/XduV5+1VwjUbYr9eiIreRrNTSEsiN2KTV8xCSOQBk3uQE1FQFkjN7Ax2l5fm9fM2vXMg5H0+qJT3zjmFSqgkr1JZNVfnRAzWCxtLpYNV9BzlYQ5FYRJPCy2Iam0p2nRlYyJOe0yBne6zdK/QGaVLKjtBz5fcOjHJO/FeiF6Q/1BSvlyIQ9b3zp5XrFgcDM2Hit9R2lzPvq3pQXthNmZxpExASp2lLpSI32dbr5DLAZwqduXIEdsgNFKZvGybOBqzvTmP247OI/GwROXJVciK96jy8iqaNtube/aE5H7DZswtEDrhMtu8H82x9Vpy7HaeOYW+fGq7vDjPL26TSjiHHEkrMSvdsL2eP7oIALjrTstieOnLbgcAvP+D7wcA/Mz/+5MAgD/68HutH7jGCQXR/MnEi+JzJ8l4CWENJY8HEbzdTCi96nehcq9eCwDXBaGm3Xhd9MwCISg75Zhdljn0niGh8fQUXS9hb9/ZMENkyzeKPEO9rgeU9xHYY7I5YH2Ev/+nOP77AHzfp6s90aJFixYtWrTBHvNshc8kSxIqne7Ejlz1QLLB4iB4/JsM8qxCK8a+56PqHGQqO5N1HMgKjjDIxeT3xGClsEbiHrRkmuVJcVeetkMJWmkFSHqZpYoV333S0w3Aue6sxZpP7lvyHCxQxDimirSEjXJqmf9ON7UJKh5zyhNzbgFG962shF557cl4l74bXRq4BuR3kDk/pbiUM4TFVRAbWYz6s4fO8O/SscSykAB5aRnvp5gKCZJMKvUQNM70KPtC4j8q5MSx5/SRaFAxKVFvxqxocVHEf5An03DeFPTSC+XUE61IK3njQm2IsOhzhrMrFf+hB5aGzOOUPWGMJXUNDuiFarA07j5DnWXOnPAt2ey8xvGxef01EZZprsI95MPIq69bhEYFduzYY2bM5GftXAcUscmIlKwZn3e5Zc6f1LMeFKPnLcgFF19If3PsJFsdkoHPI02OxTWb/xsy+88SxWAyg2sHSAdF3JKS/KG0URsphrODCtScuypQlBbAZiVtCWlnEH7YydJRESRlPigzRGWiVSJeyJF3QKcYuxCJsd5IVU18rVLhNKEUys7y519dyzatyFsQqqkxaHmfu3oxWlb1tpdVRgZkEonj2kPksCUqkZMfNS9MPvvVL7sLAHDLTbaGfcu3/88AgCuXbG3rOVfTXopu1JZg27JkQHuBQW68b3vnb/mcIpKgdcOrJWfKPBvf506lbkc7h7LaY69eEvtCJELXYUWRNHENZnvG3xBqo2WyTcR7I3I4KZCkDR4N0sFnhM5BtGjRokWLFu0zxyJycNpCQOg6JLtbJm3TunT0Z8ItYqKYI3rfZYrKmsnD89fxKb1Iz87FpLaneLAkXIUQdNn4TBJSC3nv11LI63BqnuFW7Hkyg1/wvBdYuyV3mg7nsMPI7pdso8jufK1mjN/1LEOt8tJ95/FbuU1eeIk3OvPiT2TMU99htxSpirlIrW/IQTZTjF4x5g3bMp/YTrvcm2DN7zRQCV77dkkuwppaAGL+qhBTw7FRTrjkgDN6aT0RlJYd3bJQ1URqfDN578CWWQSebUF3tKDnt9wseU5rg/LfU2YltJRa7b2YFhEDqdXRiVHcX16rVOxCF7yIkzxEcQpmLG+rGLM8GGVpqG+dxc77nhDdUIGeckIGfLkjAxuUkRGcJb7huM/Ypr3rTGsj8Ls9Pf1NLVSKMXcVtyJK4WWic+Xi2yW9YJOY8SpEpMyCcnJKDpgIUGZtm7BE+YRS3q4+qvz8oOedmQb0epFQSVGljaVFwKyHYyJ2islP9iaONCbMJHHP0BeIbvT3ktLd4v/MifqoJLGQlJx+nytpsr/EfdmbqqRx5sW89BzsZgiJ77ObxaC5lTLmLsRBa6C4DIq5C1mTWmPDe2i2HRJOYHEqMmVvECERGnF0aFksx/dYP1x/+EQAwJe+9a8CAN75nd/N/rH7PXOBegYnhm7NDlSG2q4d2O/Sutist86lSsQ541ImvoayGYYCXdKDkFS5NFhU6tlesMPNEvLkY90PmgxaL1ccE+cv7NureF8ao0qcrmIYv0dqETmIFi1atGjRoo0sIgc7liC4dv7ADxjXFpD/78pnPL5Bg9Bno2MGmW16RIo/cQs/5IRrty6OgfL4tXsf6wB4vF96/tLBzwuETPUbbId7PjPN9L/0JV8CAHj6jVZL4ZYjK6W6pscbhEboFuQxcutMp80zCzLGuXNpOzBW2zSN5zzLm5jSA5ZXDnodiksn3I1rxzyUZFW9AztuW+9kNcgDkJPqGRPclecJmrWYy9R2Z9+J8X3lAVNVUwZIRq+8ZgxZIUXlynuJW3pjikWXVEiUUlzFNnfrzvkpmlGqiZHzHNJBqJSlQO8ykw694psic6vEtZATzkHVblCcuO+JtITU8+0V85YGvupRqNRuybz3NbkWcm2cN8L+m1AHYs7Mgmx5jJE5b4Y8gTxFyz5dM//+ibdY/vqW4xzYh7Vy7pULvqOoKRVPTdkZ+1y1KLoT8WPsRboAORG4YjL18V6wtkRD1OngjN2P5vWWfBHVIFAuvTxElV1vXAdlJ5+/ESPeOuTgwNT9ZgdTz1ZpxT1Rlo5QLHrQ145tjuq5clU+ZTntZG+I3a7Mmy3I1RGPgKz9bVM7D0Go3YRcEqFc4hYoc6jkuCtTqFv3o/sXZ0lzU/F/zyDh85Jz8JIyA4KNn9CsxDM+qB7I9ePkChVE2aeBmVcvufVldtx/YzyRf/6932X9dmKZ7/MjltduyKvaUViVauNmUzsiUjkaReRDmTCaWrnGTFwUZgy4rgW/L/VbPQ98zXa4bdLm6NrOP/PaOZyr18jvUS2OlHVzpizsFlxh95FbRA6iRYsWLVq0aCOLyMGOBWDY2im+L66B9P/5cUuvr/P87mqI/XvsfIw+uEfr746VEDOpEiqvVex+aBdOj4refMKd44SIw+L4Gkh0xcU9yxF+0ZNfBQB4zfO+EABw7tBKpx5fMm9kG8xjTMg9mBU7OdSMB7bMvc3EA6CXt1UpX5ab7dcN9hK7xjahJzRn/LVkOVvGnNOpYqW289UmPCvoIRXU9eduPSUPImVHr+jVJS3rAiR7o3tYbxu09K7ALAN0qghoXsZszvsnKlFupHXOSppUfpS6XuO59XbNw8y8kmRPe21W6eM45MUcoHpeph2/vHF6Lsve+mO6b+3POa6F+A6cNytW3ZNqpfgBNXkkJTU3JmxDnlJBLgnYknuxVvz2vOnS15xrCRXtFvTqK3pzIld3S3p6zCQ4JlKUHDG3/rKqfpKrcMJc+mTONgdsTsiqJt8DzFJYze3+nZXPEt0ZvU6VOtdzU7MsaU7eS8NY9fYq0ZwgNI9x7YpIwyH7qwuuaNitzRuT4qVqR2yE6oj4r2eaKM8ZcW7EJyE/oqLHKOVQxY0LclDyQyoKTnt0FEfYNNaGvFWZcCIcjIW3LMHdUJ2vOKBHOScvJiengHNSqEa7VPVG6+8ZUaHAdabdBNRg+/Y0v4mUcI4VrHyYC8WDuDb2uu1ULl1o1VjbZagDM/bWt/Wgr5Hs8BZUJl7rqPpec0vFOaYV59HGxv+lL7C17hnf/NkAgO//F/8bAOBXf+NnAQxzvm0NLeo4P9bB5l+bNCgLznshfwTQulYZUqreCfaLauwQLU2kg8D1Rr8JzgdhXZ2aHBVVYnVV3MJTOjrV4OFc7Dk2a+qaTIhuleJJoMejIo+IiBxEixYtWrRo0XYsIgenLQGQJR7nhzN+x4pnyu8W03q7tV3ntt6imoxjhfK6tAuXB6hk2K5XsJy7bXkpEEvZUyUAAFWl3HH7+3Bm3m/LmH1xcIQ7brfq1a+6404AwHOecat9llqb7v3Y3dZ+xmlrejY5vZUJvc+t2MvMTnCVLinJ1eN8ePccmuC53t2OvoNuV9XxlOe/XY3rVihem6oeezYeC3Es1D3q34pjUhLdaPseIUjxknE8ZQrwmJztVnVB1ZtX/Fs59l6dkMxgXcPvNZPnTC+VHkZVVq7K2JAZLs2I9Qmr8DGOu8/Y6IzcA9HLpTEQnO8iFno/el/9t+HYdJ41MnBQDs+YlsCgQ8BzSzmS58w5D7K8Hl1Dc1iaAZoB4jsI1ZAmQUU04HhxjOXK6hZcuME8uGwiTkUyuobU98TFEYdiy3leSYuCz8uKfIkhS0j6Dhj3l6rzJQX6RvnnjLUzrq02KOPGuRapNPT1HBDFUhVUNmaxtHvsSjvPjPMlZ7XOVAhU2qDR07OjKKq3pWTYeV0CsfqpnKn5H8ZtFdlJfA8tOxm5Bnp2T5YnArrQqwIkPdwprzGdzcdtqsXJUSETBdP5ovWSWTtD1sNYq8Qzk5KAvh+VzXEkNfXqrsyQYWbMUDuE1QvZT1fWhpTN57YufvmXfiVPaNf8nd//VfuT3v0eM3Warao2tr6+SRdGz4enHZAHIS6BHgNPzlGLte5IKXSnLoJ4Ba3za4hYFik0EaTBIQ2WpWvNkMs1U3YL18eQ4nTu2yOxiBxEixYtWrRo0UYWkYNTliBBnuQAY4+D4p+6SWxb++uI1dpCYjvsy5euYMucZmejq3qigsfKeODu088sJGFH9yDz3Th3kMm4ml1CD/rZz3gWAODz73glXvV5r7Fzs2b5dskdO+P8BxPbLWtn2O0w3sVr8Gp63mb1guo7MP4ntj6r+bV18PxqoTDyMRVLzJ1Vzzz+/hrvR3nr9CTlCEmHnNvzDbkG5Yw55zyumImNzrhuWA/1DKQZwbQLISTySo83l3l/vE8xpSuhEMyR97ako+MCtQgy7vorZpzkfY6U41QyjjlhhcPt/UQOjqiIWCpnmuqN9E4b1aBgv4nxrayF3bZm9AI1Vtt6g5ao03xu3ALFfp2x3Y21OIRSrXccETHkU8JcyrhxZvWGsdWJqlTaCRbLE3flD84aQiJShXuKXjl0rD8vlKbeUrWTnpSUAhsiB3sJOQz0wgo+L1OqGOaMUWdJicv3PWDfVfXF88y6YJvEznftBOblTzN73reay+R7NGTOK4NIHrd0JEAeSUu+RBt6f95zxbE7zXt7f90QESGCpkwY3f+qYR2UVBVFqY/Rq0YJ62RI1ZDIzQkrcgZ0KKjXULLvCs4PPZsa58VyxX4gHwBsmqNYrHCYCCkYVxh1rRMpMJZDXRRVZfQsBaknOjIo3RfOf04TzY81eR/KitpwPlx/wbJh/uY3/o8AgPe895cBAN/7fcZFWFM3opBSYlZ4TQhlxjjPAQMKZ41Q1VGlpCk7gd/jMqHnwp/JIGSRBzRjpDHLEn/uNd5H58iPqu3+xAuZMIPKEZRtc4rv9sgsIgfRokWLFi1atJFF5OC0JTAN9iHwP7wPIKU31/ODzvUQbAc5P5xi0puHcvmSecIZVdf2ZmShM4aknW+iuJZU2JinKpW1zD1w7jIZ/ppwt/+6Vxqv4G1vegsA4PrD67A6tnOsWOlO7NuOOuXaCMu7Vm70lnG3TPevvNt+XDvAc6tToRzj+GCSJkMMXC+uz08PhzveAuXpwwaeLc8V6GUlXlHQbEVN+unMPFB5IeWcMUnxBtretRIEQ1RUblP9AXEOhGooV156/NODPd6vKmiSJ8Edfy/+A1nLaS7uBvu3Dig5flL468k+D0R15jfQ43ONCb4IOZJOP+egtPJ1nBCHWSU2upT37PO27zCbWl/Ji+xZl0EsdMWQE6IS8sq6fsdjcpqMxn/s/W6pgJdozBnfXi4Xromg+hRboi27mgh9M+bo7FazdESE8euE3mhBtMKRB1XvzKQoR/b/tkHN6qMlEZ7ZIRE1ksyVeSQWuepf5HPperA/OGmXx8tRv+0dMouFeiDK3GmJ1DSh8fFRO70WBNeUDZGBjt89OrDaAoWqTdLbb1WjpFW/kHMifRS1kZoF0uI/ONpHwRtW5osQk7qWDoShDculVD7Bz1U5VfwgsG1jroG4HWpENVH1U6Ib29YVAL2Wiuu6YGSd852IpBEy3GO1UoGcmrMn12x+lJwXL77VshkeeKON1Q//6L+y87WfsPOUc9Rc32sqyjZMV1ANBK130k3Jdt9XlkYnRE2tF1+K62iq58s+FUradf3Ql9UYARI6KW0SjYUq7S6vLZ2P9UgtIgfRokWLFi1atJFF5GBkCdI0Q9OMY68SNkjptZSMHSb0apXf2qPDhDG/2dY82ONrax5jOz5VOptxpytGtxCFifJaO8XJ6caoVjwZ5RcOTEfgjhe8HABw48ENAIDL91xFxbjqPmPq0j6X4ttG3gPjVnOyqduE2Qce32Ms1T3pcYzd65S728dezNJBE4I5vDXGldo8a6Mbu6Py4oup9Ni586enmRTWn/LWxUr3WORUnpSq7wVU+ZjpXkzHcUup5yXS8+d4Hk5NWdKzVYSM8LiG2Q0ZkQJ5gYmzmYksNMG9hJ4KgYurlmctTfk9Vl1Li3EcXzFpd/3Gshn+qnmUEQVY1sqgocc5KTDhnGvJdA5itItDwPtKGCz1uh9+X+N4uJTwEq9JQUa160DYq2oLrNdLXHiSzVNKDKBJx1kYvX/XXqbipCgzQBoCYRyDVlXDzqsagp9zbNn/4qosri19Xp+9aM9SSg+/T1UhU56jPRfy1pWdEpwXYp9vuG7sUatCugZdEUbnkbeLrnd3UZwiaS3IK60Dr01NhekelfA47lNmIHWJUEBl2AgFsvNvt1IstfePzlrGSjmtkCmfn976yYnF4ddr8jmIIEivQO2fSsUzFyLALC5m2gjdW6yozeEJNVJeHeo/CDHoXIVQ2Rma8OP5oXW0IArY98qAUGYJ1wFOh25r57/C8f+8l32BHZfZWH3b//5NbHuHgvoEivur4UIxhCArO6XVo5mM33cowFVzxWWz7ZlzqAAAIABJREFUv+patV5Uy4W6Ku0WZ444PlSjlbJj3Uu1lM+g4K3T13x0KAcROYgWLVq0aNGijS0iB6csQYIszdGRJ9BpS8gdszT15eVo915MxNKuPV57wEwGxfEXV80DUFxXVeaUjXBwuMeTSU2Mr9wBZ2IU8/UVt5mWwVPPP9nOe4keNv5/9t401pb0Os9bXw17PNM9d+xmj2zOpChSomRJluRIdiRHHoLYliDEM5DYMeIAcfInQAbISP4EMGDHjuE5TmADlhPFEhIP+RFrimM7cqjJkkWKZA/s+fa9555pzzXkx3qftU/t7hZl9iWaEWsBF/uePdSu+uqr2t9617vedxKqaJXUv1L0PJu2hc9816WQuj1a8tQG15suS5963oZujvFOR0bZBkM5BU9DLw2pR6rHVyxbsk/qlaVWzHx3Uh00L0FWuvrtkXEUZHNbV8vQTNBCfilm8yDJyUzIQRYaCuvOvre4OWo7ZOPoBuwpu92ShFV7hItgKerz9PnTATI98HlSql+ZbJSkI5TipIhZ7LC1d3UjcOTEvQ++yGg02mrhJ3V6MKapq0UBAxpEIRAivQ8fiFSiXaHMUY6CpEaAHZfSg8+yZFMx9ytpQmyEYjDX4AYsxAfgvBahVtpV40vKiFGvhItBJ8HBocYXlz+Nz2J134Y6zkN1TlTqCKg11rhXMt9Rn2Tugo69ce5dLhud98mhZ310YtTSiaCrBxRpnJWRjTZoQzC/DR6LtikfEJCltuGcCYLRHEWb4Fx+EeeX6gLSPeBIGhdjuX4u66WtLpXhq5tpueD69+OdCnHKwgtAXT+aT0njRXfX5aVv50IulKCEIA1wGkKzZN2E/0m4lupes1VdRIWwq4fA9YFK7VqeC7UQFLQpcFJENyKJ6/Qd3+qdXa/f/7yZmf3Ij/xdy0bq4hFwkO8QHwC3gNS2r3Z9dbb2vqn7esPvSNcvB06D5cnyEv6CvEJa5r1vA1+Q6OIwn8vDwdCarNkicO8geuSgjz766KOPPvroRI8c7ERXZaDb/9+2OytD1ZBge6eN2VrGBpOJ16uPrqmf2zxjhOE+u/T3IaGA/vrhoXTqtcpcKov96NNfZ2Zm/9Z3fK///aQ7K2ZzrTDpcx2UxmkNDQG9Rhbe0vKg2ioZ8Hjo2clq5ZkEnRJ7yjIq1PlYzVu3JtuWsJALW+s7YUBHXXrYZTJvdpTRgpUPJWFHhG2k7GMun4CNWNmstEs4ByAJ7VV2MAgKHALQCmXQA+q+qh3iykbmvFPfJ/O0rLvyz4z+eLZTROfDubwR9pVBH9zw+UHGWGUoZ1adx5ShW9BVEiR7ZV/JnOkkGYqrUYwG8R72syjoT0dDwf8eqZ6btlZ/er3ofC4UJJtun3ZcP8puL8/pJS9sAO+l1HGqXj3CZTDcJ7tckkraAdSx96Q1wNwu4EuUQCni2QjNWGtfSqFc89XSRuLmFPKKqORPQOaMGyE8GJAhkDayM7pbJkd+zY+FGm6EQFL/J+ujuyNvU3Ql1eGhgT6HbzMXWz3lqDaS+QIdWWffUIA8fXCqv/0Nt2/f9Pdprp4JUTibnVmzkgaHuDOHNx1dGGnstpYSeCXQndDNiGshavOlIwfonhxSP5dWRRMOiPJwaOpAJbkOyKrhSNRxI/CHrXIs+h5CnsQbEU3C1mtQLZ8vB4fSQRB6SEfCv/sDf9TMzF7+4mv2f/74P9L+M9+FOuk+UtX8LnT1PdilDXyXHaVCUJHQjekKJlqdttdfASrX0uECeaLbxYP6a8V9MH94P+k9ctBHH3300UcffXSiRw6uRKt/qPFRq7ZQkPPV2UjZGCu+KLWPBraYS/td2dREfd2lVq4bsdVfe+V132YDC5t6ty8nl5eONHz9h90X4Qd+z/ebmdnHn/6YmZldvKxaItmMWP+pyCwXYsCKfjIRGpFT9/asAj36+VwrWTQUCpjAXX3vtRCFkswhamqspFFUbK1RbS9XPT+ycOrb8bdvgXol9UgR38MpkfcNhRwsz32cUUKboPqnccxVa2zqtRXKljb054MEkcFxvtHMb2HhK6uj80FHS0JQ6Nii53jHxz26RIqB7akO/cqL7i9/eNv/nh57llkHMuBBLZXOhzz853Hn89dHqK+pjj2XtjwZ9Vh147IstroccAkMnQacLhFX0Jg3aFPo3Gjv4I0sxJwOVbsdLwUY8tSebz12J1CaDQ6eoTrZraUzEGSpK3gO1o1B9NRv+R1m2/li0Q3k77uvfZmvFvbMY66eB7fi/NJfu1z6GOYDOm58Y1Op1aEoeH6C46WP/f71Yz8mPCboCtH4cc5w4hymIrgUTQhc6L1CCHBArOliEPO90D5UeEXounn91df880Iob99yZ1ZUT1E5XLeoOI5teCDOECgf5z/ER9CxAHECAZCKo9CcjfQspuJHTKfTzvZALtGmmM0dYahXjRUtaokhouGPoX+hx+iC0hjDvdD7QR4L3Q/XQsXQNOFccI3CAcqEYH3vb/s+e+GLz5uZ2QsvPeef4frgegbNAr1jl3WutpSEbheX7dAAwr0yqBxCMssyriW6dOI+gDWPcQ/rbvThKBx0d7uPPvroo48++ujDzHrkYCdaM2uutJB3PRWCplqxfFOdWI+D3GxZ+2q4EfegUIY0Vd1+b9+zC+T5X37BEYShvmukxycfeb+Zmf3B3/mHzczsG9/7m8zMbHbfV7oj9ee2ym42yiCypjWTQt1Sj2Xp2Smr7UaiCWSAA9VcV0I1xrjuVV2nt6zpjgPAAXVCdPOLzGylzBXnsgHKdHRpqDA3UMawJzY5dbpKypGDA3fvq6XXL9DGLlvPgJaqRe+XXu/NcETMyfrLK6xhjyqsI+gVVramc3UIQ16Pp6j4aVwOKrITKQtKfa1S9hIsCurp1tqetDFy9a0P98TKn2hsB2hIwAdB4Q+lTLgGyijIjAtqrF0VtzUZGN0f44lVFTwFMbo1RpdCX8aRvcLXECIASz1MGKmZSoUPFj7Kd/BlTnyummra+4fXLI3oy/frJBLF0C/oKiFWUn6ja6ecih8iVUM6TQrM6uZCfyr/nmG2r2Hyc3d69oaZmU2ORjZUJwPuoxtlso3qvcN99fHrcEfKzg3+w9qPrxn4ORvs4UGibhh4ICoXh9CktBdWbRbnM193ORaa3lZsUFTtapXAB0Cz4v5rjhis5fqJkuJS6E3wQtQFdCi+TD4wa3S+VjouNFbyhFQkfh5C4HQBzYVCoC0BV2U06nY3nJ45yonSJCgGqEeeBpa34kdJEwN+0HBEZ4Ppdc053atA2EAxybDhNZSh3dHlLqBxgedJvXKU4/FHP2R/4c/+NTMz+wt/5c+Ymdn/8qN/x7c1QkFS10/sKxws3f/gLHGNwpuAXxVzXmhxQruAYyitabjnSr8C/lKVd7ZRCGnmXl1VdaAo7zR65KCPPvroo48++uhEjxzsRDBJzbYFYJrJ+ZOadUN/O6z2PBS78EoIdi0ZgrLJxx93pTgyI1S1Cq14P/HxT5qZ2Yee+bBvT9lXJpRiPIFJ69ufL+Qhv1lZ3qIaJq8A9YwnsW7xra+03yi6sZKvtMJfiGMwLdRjrZUtnINVi4ujshJle1neGtkn9cYUDHaUC+FzaLWNFkPUGJUxjehTFscCPXNlvyt4IMpIUcIjm6mqynLVSKMeGe5nSDpqH1DAwysCLfQdVcJCxzQS2rHaqT2GMDrifa0jUv5Zf24iJAnCyrrBt8KfppbOXNtqMGx1C8y2ioiVsrgCRUDY/3QeFJnlynhwKlwZGgrW2f9ix+Bh23ve5TeE/4FY+A2Mc73v9PREx+R/Tw8OrEHRDtZ1BitbY1h19SsuLnxeH+2pq0N6Bgt5kCQhCBtl/+0Kd0NlnoU4KnMhTWLSP/7kYzHZzsXvKeF1jMiUu5ojNZm1rln0G4ajicaBDpFuN0+rY53KpXFD/X+xCjU+vqsiI1bXCR0EU5xU8R7QdfTg3n0zM7unx+NjV3vck27KWtsbwsUp4fyA/lWBpI3HKKpK1XQpzskaboGPIYgBPAA4TaAeiwUeDELSxDEgoUUvYoyDZF1YkoLhCgNEHEMr6vhkxl2kYDKWZ42u8c0Or4F9qoVk4ncyGnR5E+dzqVfmAxsOfL8++hHvEPvf//6Pdo63AVkM58guusc9jDlMVwZdYVk4T3ZdHOm4Kcp869sRN5+ut05ITgRpy2K8HpJAYo8c9NFHH3300Ucf3eiRgzdFZm/H+UyRUelvMq5g0pY2EGt+pqx7GL3x/ngx92zjeN9X+I89edvMzE7uPjAzs/O76JArQ9DnB+rfp/ZKN0AoLZZbH3P6sofKbMnKoj6ns072xvPoIoxVz7y/8PosPcFku3AMMq3SI9MM1m4by86B9gVltyVOj3i+UwtEbZHOAX2uzlCOI0Ps9vOimAjiw+q9gmlfbawUQgKqk1XdPnVQDboVNuG22GWbx9kP3f6u0mSAIQ3qZai1FYFaoKWA+lziuNDCR70SUCNAq7bzSPZOdtYEQoWSoO/zBJJGdmX/hJTgzMe2cs2tSJHiq4X+cJ55jBRFGZT+Rgnv1QrnTK/tj/bGdiFGRsxF/AzWYvCrfvtA2TydDsdyOET3gXN5pg6Dkfgfq5kfU4mKoebb6Yln1vvKcg8P9kMZkF5x5hweAaH8KNfCgS7GtTLBVnPx8Pot3zeGy3bQkJzx0xvok6/rLTiprBEdFBQSJyhH6rHU9fH6K84xeHBfx3XDOQYHN8Rp4v2aD+iJwHK38MXIrIgMH90TqQ0uVY8XitFoPk/FV2BuwhNYLrfaIv66H/BY6p9l0f3cAj2J2Syuyd0uBThLoHzoGRB0v6zV50+WzhykI4nrr93xSajpCtG8KnKzByd+L37q8feamdkHP/gRMzP75c/+ou9LQTePfidEStkqSDIuQo50cYIY0HEBYsC9cusXUcacbEIbQfczbTuD/gXay33goeEG/eLgTdG0zRYW2glO8lXyh5lZ0sROuVmlm9Jar5Ui1kkxM8oNZzOfgMf7TqR743WfpB98n7cqfuu3/GYzMyvo6bPuJNnK63Ztgy21ttZNH1vkieA7bF9pMeOHaa0WJG6OYxGKkFq9d/euPy875Lbq/mhu+8ZUGikLsyDZ+EOQH0WwyfWZyroXCC2O/D5tWkyCRGDUPtNOen7uPw7I7U5qtY7qxyOttlA8Jj5hwQtEmVH60OHohhSA3k4JhOeBS9MY0SktaHSTxKo2lZOA3hElajJuGIL6BYvmej5a2XbKCtxwOSbaLoe5ygyVfsBY8GHLXVj8eoegkqBWZJRLbgcQNcMUqNs2FgJFBda+Ku1ke519qvRDtC+hpzTOr5gZqZUO2Vd9d9IP0GLm8P8QwqEWVdtxMe2bCHpaHJ9qUc4Cd6FWtXXj8+M97/fFeGt1iGMxN+uSkpUWnpS6NKaFShUXWlRAZqNMN5Cw1Wrh+97qehsJrmfiRMtnWcQPa7S9sf7SPOeaY26xGHjwwEs21675/WP6iMyjgNGxlW+7BFckvSHkNU1tpaDraolsvHalRS5dPzwiOEMu5scOSWrIg2VA/AhU0QqNAZce9UNuldkQmfiy+wPLDy7HtULITdfiQvey7Xzotr6ymIDgyFzlnrXQvXLIvW8ysPOZj+0H3ucl3e/77b/LzMw+89lf0fExVyF2Y1zlx7taijRZsxBjUaAF69UkyrbS4LRxmyXbVrcpJ3btoVlXIPFfcZ8oHt5Pel9W6KOPPvroo48+OtEjB7vRbLP8fAdCIFurZKZU1spqGmDZ0kaC3ueC4pvUhWBZybdafUOguX7osODv/d2/z8zMPv7RrzczswuVGU61NB4VmNuQtSiz0KpzUczsUi1ZIAjFRrKcykKQO14qy2LnCuBU/Y1pzck9Rw4oLxSyoi2VAS21Yt6opS01W0MZxJ2aHTlgshMMRAI5IEmndanuZpoD7FKVSQLRhXARZEFlc2m2FQoBqkMeullD1pLEMC2cNcShLknyqpGS2TYLSSKHYlm83LEhLovMFtYtm2RFt5wwLLAmVrlI5ztKG8p0JoJLF5L2DVMgSdNuZhi1iDQlVMORA6DKrokTmVDWdElxRerCwFt0irZKzi0oiI/XXMS1SjbKB5LjXaVNyIGDiJGdYyi2hDyrzBBrc66bgLxr2gdV6qpEOm1pt/NxnM0cWTq441l9MYQAuraxRHrWIlY2mSzLda0W0vCmlZEkHCImCNtUFsU1YmGXyigHCDIpuwNJQiZ3NLQoyRglLUSN/FxMJaJ2LjnkV+96OeHasSMGt255SWM+QjSna10NCW4tpAAoG/S+KHMr5Z/N8SIKBZkxbJLDFE37JkRyKDRwLWSBa537yqVMwC4vu5LFU5VgpwdTK6/YN18N5j+y2aAYQPDtVqmpc1wgZ7sgO0JNkGT39vweh+9yXVdBBtzoPv+t3/ztZmb24ff/QzMz+/wXP+ef1f30bObnBuGxCMi2IAg8jUR4yLTvtivXcT/jPbS/8jzCTEg5b+PhlRV65KCPPvroo48++uhEjxx0onXCDt1UghCyndUs9R9q7tQDW9uupgcj2oKUMWpVzuq8lOhHVvvfn/r6bzAzsw888UHflshAA63f9rRS3lfrzuyiazw0URYzmYxjBT+e+nMXsm9FAIWWpcXat4E8LEI6IApDtV7tH/h3QvbZCJHYu+HZSwobVTKjZJZDDAQpUBYStrXUzpCPptVTSADZFsgB/ADV1rEVxvwIYuJgSF1P2Wy2NbcJq+0W214//tGe2puUfa9ryeIiyRrtmB4puCfdY6sjYeJzen+ZRw2UOnyu7KsqugJKMxHxGIeYWyHp3K1fDpRRIKe7QnxLz+eqAztyoMwEkhetig2GOtR3VVMFvlEhvA2WWIyEb7roImjIclM3H13zeXS5mdlaxLixiIHU7Qu1CzfrK3PIzCYis0W75AIpYj+GafAhNG90vSAjfTR1vsO1W47MzSRStkmZDdQWWeaI/fjDOkSfRP7UnGuEKM2FXuwpey80B+fiDzDHW9pqde6Y60XCIjwLyWIs2TdL8RGEasAteEmy2zduuCjYzZuOGJBpY6bGtb8U72GNpbXO0YC2XlqFs9xKLIR1zZye+thtlOHzmaMjtZNybW9AWnxurzfK7iuuvR1Oiu6Jx9f8fjKU8VWZ55YpXb+4nHU+w70WJOXgwPcBhGAmbgrkRlA9LMBBFqL+z7WcmLsSsqsgYS7tYN+RLrhJt248amZm3/u9v8PMzH7pz/23On4fH7gTxZvMw/zBujSKQOIGw66gF++vd5AGPxBaWP3Ppu5uFCG3lF/53ncYPXLQRx999NFHH3104l1DDlJKz5vZk2/z8utt2955i898m5n9F2b2LWY2MrPPm9n/YGZ/oW3bt1hu/etF224tTj1kHqRMguyUIiTvhYFtmzaWtGPMSditppultBL9eOTO42Zm9nu+z7kGN3LPCCrVCFXms5xaoVarxwe+uj0VW//igT8WZWbUnbCGRaSE1TirzP092OUSXtH718pysaKFzX125h0W5cr/HiPZjEiOVsKpSltb6Gj3vNLJYNsujmUu62p9BwYpq0je4AuozqtMEpQCNICaajDrdUzloAjkhywhhKqEgNAOFfX8KwZSZlvGdJG6HSPUEhs4F3RxwCwOsaX8ivWy6vwjDIjUikmtuMYGnNa1HQlvBXXrgd6XJLNbSZZ6K42tCZSaEOWhTY7acLRg6THRHrrpducMJV4TRXnEoqIV0j93oRrzSHyBcur7cFrNbDBFJtfn2FRci5Gy85PVvStb3mZXtODR0teyrw3dLd0uoZXG8fpNb+2jJdbK7T6vU+hoXz2cqDnD86CsO1f9/lIZ8/Gx2iuFyNG9lIWcOMPUrYvDq8lTFi2Xmb7kUMjjhRCkZ1943szMbt3xLoubj/ojNXUya67dGKchMsvK0jVPaEdMLR0Hpc2VrZ+fnus9vn/TA6/Hj3XeFxtdq4wxxlqXfl8ADSoHyK/7vh3JsplWRiyQkQiezy9stfTjjQ6HvNtWisgRoNWlEAPEjkDUMKAaiwdCi2MTrc4gi11jo0HJtTq19Ybr2R/vv+HH96lvcAn7b/rGbzYzs3/x6Z8xsy0iwrbqSO/fuv4PooZQGfwj5kdVNWFUhysTZnEMAGMXVyLXQ0oPCzh418sKZ2b2597i+cvdJ1JK/7aZ/a9mtjSzv2tmJ2b2u8zsz5rZbzaz7//K7WYfffTRRx99fO3Eu704OG3b9oe+1JtSSgdm9tfMdUb+jbZt/189/1+a2Y+b2e9LKf1g27Y//E52pm1bW2/WITRCcQhGPXXxhJ2mVpbNhoyhtTNJqsa+U7fFIENM56M9z2i+5VPfYWZmN44cKMnPdrR6E1oEvkqfIbd8IOlj1e1mCIosLm2pLHotidmJVtFNhvWw2MfqBV+q9g4yMlbGh15KGbK5vk9np87OnVzzzClTzSz4AlUTWTVZ+4BsIkPsp5t90s9eDug5R7ykmxGMlBGvxSTmcyvxJSrMpgZI35Zhexurb+xPsWiuyNbVr4xBzAYJbFovutyDLDJn6oE65p26Zts228wGTkSJxK74D2L4g0ogSJXCjKUrEkOnSDDhle6t1nAzukhN02ysNrRpu50ddFlEd86mK6iCIc+eMsnIiGDxg0Qoo1yuvfZ886b33oNUFMOhmfYHOeOk/naEdhAxGuQgbWLOB6KiWjmdBNr3RgjCqeSWD1WbHtFJUEoeV9LI66axVplt1iBZLDZ9JnlfXVutun0uxZjP6J2n+wdFGo0nqE2W0MvoXgtXfcDiPGmqXc78O1547jkzMzuWqNHNWze138pqkSfHfrvCaKibhSIAkOW0S+ll5KnPzuzBmV/PSDkfSlCpwCxN18G6Zpz83C3E6xiPuDbVrUE3h5C4BB9mSQcJWgV0xywsaaz21X0yGOgepDGdXfq8QLeCuVvqOgFxhKMTEZ1GOyinhIzgB+Sggc32O0uJyD04dzRrLCvqP/SH/oiZmX32s79sZmYni/tXdzWQtix1q/Z5zGnNYT3PdbOdGI1V+MuDOkWXSbcrY3ucaJ/n+v8771r4/wvn4PeZ2U0z+2EWBmZmbdsuzcsMZmZ/4t3YsT766KOPPvr4jRbvNnIwTCn9ATN7wsxmZvaLZvbTb8Ef+G49/h9vsY2fNrO5mX1bSmnYtu3qy96b1qypW1ujJBej4ythmOCtetGpC9Xmq/2yGJux4qPmTqasGvt07Kvy3/5bXHXr2z/pSoj1uTKjldcIBzJPWus7WdlWYntfagWNlOBInQmbs8yS6nMwvGdnvsIfSJ0v0+sYyAzgQSgTxAY0OghkXXtgnr288MXnfd+EUOwPfEWNxeulLWwzUA19pBUsPfWyaG43yCjLtGmIdK9qplq3jrXKXihDOlOdM/a11HHrWJqVOAiSa67HI1sr0ynI3Kjjj6XToKxqrFrgQqY0c3Ezho3q3lLCVNk21DAnqt8usZuWRHCettbGWCmXYYNLD7QQDzHZYWPD0t9IW4LsNBfiwHwy1VRzQAEkrZGLRWGtKGxDxi80K+fy13s3em+p96U179ffQn9W1O9RedRU56Kls2TvUPyCQtfHuLTZhnOBhoCf99nc5+is8tcPpY0gKoXVdDPQaSFEbaR9unjDrwcQiaefecI/rwQ0C/a6/91s1tZKxbMRh2h+qc4fZbxT62Z4K3F2xlJCLFVrxkgpg4sEQgVaJHQEN26UKPPaLAtzNH/8wnOf17b9/F4XcrDW+1q6fDQu0EUqXRAYuJVttxsEwdJMlr8zITTz06WNMz+e4z3vhKAuv1DHw1KaGpi7rdXNtHfg33H7tqOeJyfeWUEnRBPGQ/65xdy/E54E6Nje/r6V5TWNXRcBg/d0ofsdmfOe+FJ4pyGsyDlYLbtI4hjujcYHzY6EUqI4HOvVeqtJo7k2TEjXCzFqUFplWxpz3aO4Djg3zJ9SGhtwnlAoRQW2qa9yeOAYWCdC+RIVU2SwAQ7SW3zoy4x3e3Fwx8z+1s5zz6WU/mjbtj915bkP6vFXdzfQtm2VUnrOzD5qZu81s1/5iuxpH3300UcffXyNxLu5OPibZvZ/mdkvm9mF+Q/7nzSzP2Zm/yil9K1t2/6C3nuox7M3baX7/NGX+tKU0qff5qUPpSzZcDqK2k8epSB0z8ne+Ui3b76ut9lp1Yg9r8/QS/7h97uJx3d/+/eYmdlY8MQlmdOFuhTC1AcVO2UCaC4EmdX37VwqXVZXocc/HnpGMNKqeS7ewlJ1zWhjz9A/oN6vmqCyl+AU6PWDa77Kf0OeCwdjbzqJ7Hg0DlOmVVKPc/L9L5RlwwimUyIfUltn2a7sU5oM1PPrls4R9cGrj/3s9FXfnnwNin2x9622ubKCkWqo4/FEr6m3WftwnU4IWe7OtQ/HhnKiv7yQyp8pix9wDnXM64ZzSH28Cd7CVgvBHwYDvBKUneSqV5PFo6QoNGINGx19+5Y+eSEF2gfq3im4Cdu++7YhQ6H/3vdlqwiqfVKmTO00Dbo+IdRr4VjUFz5eaOwf3XSOQir9+VS01krHoMw4XmX+yoyrFnMwcQNCIVNzWnVx9ELIvk7vetZK7fn4jroUxuJNwLfQ/EmWhWJk9KFndNKoO0UQERbFS/XxP/6YNAbgrAj90amzCpvh1EVvYNwzX4ZZskpj/NwLzjHgevjg+13XH8e2Fm8JrIvhw3BrEveimmv8lIUOdL1t9PyZlBaX4pFMJ1Pbk1Lhcu7HcXJxqn0R6qnxGWpfDsWXGguJXMnuuBE3g3kxk1IrJltjIS0grmTQw+HElOjbxYU0FioM1vz5wyPNJc095geIABbdqHPOZc29FiIzEn+E+ynIAXybJBSjbsxWuj9ybkIboQYJ8vdeP3bE5OxV72Z4k1gh6pfwpkIZEbMkVGPpYtkRROiEOGs7ipChGwOEUthD0zl41xYHbdv+6Z2nfsnM/oOqqXcWAAAgAElEQVSU0qWZ/adm9kNm9u/8OjcXnjkPZ+/66KOPPvro42s33u2ywlvFXzZfHHznledABg7f/HYzMzvYed/bRtu23/hWz6eUPl0UxTfcunY9au1ke8sL1d6UeeOQl7Hiq3C+y6OGHlz8vJuNPP3U+8zMbKZVevTXqoa8ph8XpzyyPRT26HqA4aoscKbsZljm0ZVw78wtl6+LNU5PPNwJVsQL1bVXF1I4U/Y+zR15GIq9jWIgddAHssF9cOor50foZ27NSrQFdh0NcVusN53nAxEJRjd9vF1badzT1sqQ6eMPTQIhNIVOQLapo5d+QAokZnxWdf0OAIRgFdNBgBrhVu1RfgghQqHMeYWeu7ojwmkvD52HrWa6Bwp5U2kBFBXsc3XIwD7XcNXiaiR9d2jRa7trfW6q/vZUps7r/hm6Nei+0HzeSTlqZW90jNCNsl2Kd9Ua71/4PEClEl8EfMurVFsuTklRoMUhfwrOr7L3AXwXxk3nFcShkYPgSp02Zxd+6V+/5XN9T54E9xfK6oRMJBAqs0iJcfpMe/BCNLaarOezS+2zzpUUQ2vjHHW9JqKXHl18kBm6n6qtfsrnvvCsmZnNFr6tr//kp/w9QExorOi4mfcB8iibzcW1GZiuB81VxuX8RIiBupoGyqSXdWuX9x11AaXCf4JeergBXCfhiCjEgH2biHt0LpvtvbHcOCew/9UVo/kyF7Jw//zEllJjBL2aCmXYKh3q2tO44IS7EtLKffFS+hDVmu0JYcJdmYHTvMKrY6uC29pI3SiZuCdrKV+eCVG5Lc2J7/6u7zIzs8//LbdyxhExu9J1YGY2LHHlpDuq6+qrU2hDuGJNbpUQpNgSDTGR/uq86yecH3JHEH/jdivc1eP0ynOf1eMHdt+cUirM7GlzBuCzX9ld66OPPvroo4/f+PHViBx8qx6v/tD/uJn9fjP77Wb2d3be/51mNjHvcvjyOxXMzNrWmrqytbKSjepV9PsOxDRf1Tic+cdIxMsyswwQQYXMwrq9v3df82z79m99xMzMzlWvGolTUO6ptog+PS5+u5rgWhHjR46q4WiS27VDp17cP/WM4HTm2cOeWNZT6baTwSylh1Cqfj0TM/hSLP+GzEjL1kJZHX3sd19zpzgcJsvB0KZadW+zKA/cA0PPn8xYK/dN9GXL70HZ+OW51yIzZSEFymEZngz6e0XNVZlp09ihet0L9e+/Jmc7VAjxVmjQPJdWfni/4zq4o2uAcyJIC2gH2hZbNCRF7b8ou33YbShAmraBLoa+WrVlNDVaOi2i9CgkCS16bW8wRElS200pSuyRdRRdjwTU8/JQBoQPohox/gZx1xAqps9fysMDLYeBNAXQFlhUK6MpZQwyJHRmo8eBjn8iVb46QDzNI1jmQn1O7/r1g9PmrUfe75/TADbB7pZTopHFbXUu0EwYCmXJdzxV8BQ5vn6scdP5T1IpROeh7upnxGNIL8pJVefm87/yWZuLjf/hj33ctwXKoGwz3EnXui6YW/60rZeq95/LF0VaDHRtLJWdDzTwB8f7nb/bPMW9BxQTh8jwRtA1NZOr4mohHRXNzYM9sny/zo4PQbW6SBRz/PLc7y/n6qKqmybmK5wRVBZBK1fqRuJ64R7EuZlvumqfdPuQjYPioHppO54LoF9N3QTXgvegwsg9mHvXRz7i/LFr152DhYIsMYTPkHdVTutwVuTZLkrQWhv3nIbrBD8fvSftPEYXzkMsrL8ryEFK6aMppeO3eP5JM/vv9effvvLSj5jZPTP7wZTSp668f2Rm/43+/Etfod3to48++uijj6+peLeQg+83s/8spfQTZvacebfCM2b2O8w9E/6hmf0Z3ty27XlK6d83XyT8ZErph83lk3+3eZvjj5hLKr+jqOra7j84ibr1WDW0OnTvUdcSex0Nfi00h+XQUrbpfKYJL3i4Bf74+quuujVRn3cOS10ZMwz7SjWx0RAlOBz0fF23r754zuSqurDZUr3i1xxBmIm5e6r6437u3ACyh4lqqIVWsqEUSBKrOh81dLKT23KIg417cs85Dk889ZRVFY50qIV19fwNDQAe8VLQUpiOi1AGZCWtcR2N/dw06sWn3gfT+Eir9mxlRls+2WalbAuGP4gAxzfEtVIZQ7PTtw7rehNue6gW0pSPS6U6VdLWpTMvBG6hdLbBTY06r7JPZTI1+7CjDU+QzVTMRWgO5c6lfaX/mbrrtp65o56nbaCdj95Hs+MtEdmYxvVS3JXJoXQvpJhX5zrn6zrq7iup623u+Vw9P3HUYSiFPPrWo9YajpL+xEiZ9epcXBs6aa779YD7Y/SFozyp7eSpCK2ENi7SLjI0E9dgo/O5fyQvEvEmmgzVU1wu8852cMzEOXVPCNZLL7xoZmYnZ2f20Y98zMzMxroGN2vQNzgGyphVn6YDAOdL2P2mThEul4l0T64/+ZiPD9wcrkPxJfKisKLtsunrNd1XaLRo7ED9VO8fyVNiNPTjWq266qcLkIa4jqQnoWOg5j4cD8Kxci7uRS7vmZhrXZK+bXR+6dLYl3ohfKE87hvi4ugcLHTv4vqCBwFK2FT1FjkTWsd7VkIpOJ73PvOMmZl906ecxvYTP/WP/XiEgmy7E7TrNd4JHEw3N2eul0UWc2r7WleVFN4baFX8kodj6juPd2tx8BPmP+qfNC8jTM3s1Mz+ibnuwd9q265AZNu2P5ZS+i1m9p+b2e+1rfHSf2Jmf373/X300UcfffTRx5cX78riQAJHP/Ul3/jmz/3fZvZ9D3+PPJJ5JpZQbwMhCGEDrWJhoSdqzrBPF1Y3vipuDMc/X20/+sjTZmb2zd/0Tf7Rlpq6+talyldqNXl46Nn9UtkLSl/U1uEYVAtc2KRG1y5trtVxIWb/wYEjCCcP1F1w4rUxWNdHx14zs9AeUJaqGvNYzG9Y+BcXQgNUez049DrmS1/0jOj2rdtWUCvTtmI9y9/bJ/whtCSyzuusvukMwAENR7MkDwbc+sjS2GyR5duvUEbItsgmWIVvVNfb2/fjwfEyshayelAceo31Mj3TWd6tuVZ1bUNlcu19Zcyq27L/ufQOYmMhOSCuAmp9xdZ9zWw7RyumpN7PvItyd9VGvTLbQQ52x6dVN8flzPc1PDRUkw81vhbUwj9/IYTqtrL3SN4q0J6BVdKheP3Eeccv/YpTi85e83n+dd/0SX0XLpMaJzwUNnRI+N8Xql/fePxR36eReDPrmV09yDzvdr9kWXoTGjHQWKKh8PIbL5nZth9/IHQifDwGnH85gKpTANY/PJBjoXsvv/iymZm9+opzXj76sa+ziebaqoIzorFX/XopNCa4N2Ljg0yONO+nN3w7B0f+XUNpEGzEtQApIIXKDVfGJuC6JrLytjMuOZoaSDxKi6JSdvtAHSEL+D6aR3RWtE0XcR2Kj7Q/wDm0jDENDoC4Feg57Coask9DeTBk4juAdtBpg/vnRoqLoV6540zakHE3rZV0DeieujgX/wvVUSFGzJ8zeVPAc9if+H314hL/kNR53DYS0JGjP+H8lIVt6ITRS3X3J2jLC7Hu/TVrrm7/ncVXY7dCH3300UcfffTxLsZXY7fCuxfJLJVZUIZxQCPr2GpnazWqNoVGq3PLBzaUIuJI2faNG94TW9bocfsKeXxDNWi0vldayUJwp0eamrKyL/QPWCFv1M97NPXsvyyPI+M7e+CPI9UC96eORqCqRQ80mcPNG44wLJWtrVVbG0ylSqcMih7ySqvwO49658X83LO1577wBfvgx6TwVndrobCQCZjf1MjhHIA8lHSKlGQKKKJpA2TB+vxKtVg6J1K+Xf+ych+JU3Ax826OJii+en1CDVWdIEIYSnUxbDsHlGnTk9+SIegY9dXLzcoG8rBHK2EhXYq9a57x7Wb1u7ZrZK/0vZNBSUw9OAcgC+g/kCg1TRMaEyAhjEdURrXJSzkbolK3tz/RtvW+DYenDhM4OdqniRT3VjDIB2g8bOI7kvb35RcdbTqTN8IHVj5v0NinEwS9g0zzYHPZZdB/8Kbzm9Gar7gmpaeQqRYdNeyURfa1Zaf7wwIGvBj/dCmEm6cQtY0MC/AwKSt66n08jvY9i3/lBUcgXn7ZkYNPfOLrNU5Te0MaITNdc9S1txmy5qQQkdHIr1H4HgdCHlKie0m6D5IchPcRPhgtnSlXtPtb9ruLSoH6LcTROTvx+wWqg9CFKtAqXTdwckYNiohdrwWCjgPLs5inJTV1ZiXXOY6f0laodE2udW/DJ6MWotqgE6END3TfGAtpAcFEnZBdq6s6EBPQST4LHwRU96f+qYPfn/3MZ3yf5ecCsjYWkrDUuVjqfgo6mEKDoctTa6s6fheCY8C9JTphQDH0vIYpPSTUwKxHDvroo48++uijj53okYMrMRiO7OlnPmAvfdF1+tfK7qAYUFssaJXWItRKf/708r49Kj35p55ylvAJWuYLfQdZBjVUPN/pDKjwgPeAVUumUKuGBjOeVer5ua/qR2Vph3ueXeypfnkuJAHVsP1DdTjoS0/P/LP3c8+kcW/kwFERC3l6ZX37E9XRtUp/71NPmZnZ5z7zOXv9FR/D20/4OOAFH45nO+x7+BENboXUtXe4CTCHTcffqD7OKr9ScS4U5soU+vwZfgbhANld6dPVQLfGaOTH90Buc+950p3+1sqoY9WOfwHJCtyEK1dXjs88dXoUEMmEjfHQByqOX1lcqDhSO1c03e+Ec5DILKJvvrAs+qlhPqvDgUxGpwhHOzK+kXQxgj+NUqI6LSoUJ+WQOTnwzGqluv9k3y+YZrMKDYgjaXHQOZJpHpyofntNCJCphx7l0LHO3f2XXjczs+NrntUfHPh1t5TewVZhs1sfDkAmpbi2mk3XTXG5RBkTpEnXQ6jr0YXS7dowHcNIN4hXXnKk4PkvPG9mZh/92EfNzKyUvsZssQpuyHAgNb2xtCPgPWl36XMno2QuN5qLsyTEQQgDXJ21jonPF9xHangSW44Fl2izVseUFFNRV6x1nkfa/0OhFjZVdw9zOLoexM7nwgC5QimQadRuuLXEfa/M0aDQOQrtDaGadRdpZS7C3g+tkbh/dOv+mzUdaOgcSJG2LENBdMA1m3z+SzjTjo4cpf25n/t5H59Lv38e0s0itBTuEbwJtCq4RlHYpZMGNIcuOLMtR2Y30nYw9XcQo97y/V9O9MhBH3300UcfffTRiR45uBJ5kezgxsAOl75murzEHyCgA/9bq/PJxDOktRQTBwOzg2v+3H2ttoeF1+Eef/oD2pZvY70Sm1pJ+eFYmQ8ij2Q6ykYqrWapLdMhMNLybi5oYj5f2MmpM8H31I1wfOhKhmeXzr4/vecr3am4A4/ecKb32SlKh77SHal3GK31Gna+VvVrpTWrSv3tuWeOt977tP3q59xde3zkGeKBfBdCFQwmNApmaOhTyNxhwlsG78GzFGpwZHuleq7Xib5w6dc3hTXhlEkjMduSE5tY1xwX7pTHN13HAa34R/VdlVQpRyMf36qmW0VKasqs1Ypt2WQU+21TXBaVK0WPvbL4yE513AZypPFCO0F1SubHRvOmavlS9CPYXrpSY9e2dljp1CtX4sXQYWEaW3Qiosda+1Jp/k/UzUDnzGqjvnCN73CQWy1t/4myceYUPfNL6R/Mls5B2D8QG13J50gZ9Wzm18/RbXdINHlTrHVuyFbp60/BB7E41jpSZZQ/pf2v7wB9oW8dvs+mdmQBfgM+IntCA19/3a+jF55/3szM3vdhV20cymtivpbrnzVbqEh1/1ICD3iD4AyYoegn/IZ5QqfURgNUBtKogIuiP+nIWMykPZC2/J7VpY/d2WnXhwGlxwN1reBwSHeOpFpstUGTQ+jmAE8SvS6eAPMvVAnbJuZ3hgNqZPbiFAkiaHXO8GzhB6ySFkUbtqceoHqMBxyt2Bed+5EQmzxP0V1Bh8Na1wN8qfsnrufyH/3J/9DMzKa3fJ9++EdcvHdU+Hfs7QklVncT9wnugU21k+Ujftq0WyQ54DqQAZxEdZ5DQVQo3kPs6O+Rgz766KOPPvrooxM9cnAl6nZtp+uX7MZTvhQ+2rCKlfJXjTKY2NtaW60WvpI+vr5njzzu2eZ7pGtQmL93+UArWq0WR8ouEyx9ZW9tmPDBPtd3CilYqR56X6v7gdjI+/uemU8mB3aquu1caoHW4iqnlb7qdSevuu7BdE96B4UjBeixN+rG2N/3GtulEIIaRz2tpBfKmPF5uHH9ht1avMfMzH7+F/+lmZl957f9Zm1Tq+qxFOFQX0woHfouZ7jLIago/XdI1gPGZUzvtP99oYw8qRY7sKE18DNwS1R2lkMe0XlNgmFWysr3NOb0TNd0QKDsRu1UmcVCaNDBgY/XZiGkZVTYZqi69sgfV0Hg8Ic8AJOQBNQLYieDMGTdumQjevfaVGtnQ3q+pf5rzbYDxhR4CMBg17xYKwu7du263qddqruIA3NzoXmxP4LvoLqudcfnYNRYrgwXNGeKmuKMPnff1v0Hjn4dCb2ZiM+QSxdkI6Ts2m1/fSbEZJV1+QNluO0p48xQGk3hDYBPCbV33AhL+TuUQg5QzKOPfyQjlWN1Z5y+5Kqnzz/rqNlT73MFvcNbzouYiw/RQD+xbXdOLs+Hmr5+IQYJPkww+4Wo6XjieDWRCpRFNfaiQ8W5q9SBMc7VebNZ28U959RcnDpqw3V8eOT7PZIuQdA1dI1WmquN9A/GUxQn9br69mv4EkKWUhy0xrnNLd90uwvgVoAoomxoeMjoXgavJ9O8grtEoA/Q7PBr6B5i+23Cw2Sz7QSK7gp/RGOilZTsvjqNptKRAQcYiYsFR60NMpJ+Rxr4JNtr0+yKt0LbBC8j0xjTtbPFBXSf07xgPmT1tiPnnUaPHPTRRx999NFHH53okYMrkVKyYlhuHfR2105iwNJLPFTN+ukPOUrwvvc+ZXfkJ79aijU/81XoRz/pTOVn3vMhMzPLllLhUh0zMmTY+ngzaBFIfz+12qVqrrOFPz647yjA3mTPjsQur4a4C4rYADMXBrc2vlJXRqEVcSW2LRrrK6nQVSqiXyzl8qga7U3pIFxoZZxVlT3xqPMYTl71+uvnPuuu21/38a/z987OtS/KwrTin+s70o7MF94MaLC3UnhDkwBvBdJi6uEpT1GPi15vEpcCBrSyjobMx8fhQBnhufzu16pB0sWAbzsdJjzid1Cov7+1OvwZ6M7YMpLTzuPbRGJfdSjR39xl4e/htZHjsKcX2jbm1q4yIt0Hc+n0wzafiHOyTRm7bH3qnitl8aXGBYSqniuLVfpWbRrL8aHQ+b5566aZmZ289IqZmV1oXm9ed+Tg6Wc+aGbbDpNLaXfQM74vRUDq2KAfHGMW3Rs77O6UohujVCrcKtNF3wJ/gshGlaXTzy4A0U7FSXnpdT+Gx977lJmZPfKYXwNzbY/snlq7tU1c50mZIPX6LOsqiYIkwrbHrQ8lwXHGfYTz7Q84TJLW5knIiXRVzt64sAv5U8AVufOYo36lxrxCO0DZ6UZzOZOPTPihYEGifYo5rufp4qBHnxp81mTRRQF/C0VI6v2oMY6FYkx03TM1B4YPhOlz4hbo3repQI6EGur6AJGBj1TVTYwZHR94KaC9od0PpdnP/vKv+DbDIMYf5tKs2KgjBzpEgcst52rn0m+a1rJQlu12WcCHuHJZ+3HYW27qHUWPHPTRRx999NFHH53okYOrkZKlMtm6Rjuc1Wi3Vjs69NXrxz7qWfBTTz5lZmbjchAr86Hq2gdi6z8hn/kkpcTVTOqLYuFv1eq6bOMahUGteAfabqNVPrXVSs53s4uLyIj29z3zHSoDrsPpEGYw2gH+/qlqgvTx0zmwlg/AdN+P++zMM+mTlxwVOJaf+UhZa7Opor74qU98wszMfvbTP+uflWbAvvaJ1TUZQquaecs44EJI7ZgMkOSL3mqNC6pkKBHWTWUNmujGZ8jg0BzwMR6L5wCiUo67Hgr00F8T6xxYpxbzuVXGFN4bSvPrZmOZaryDEZfcDlP5Sy7TEYjoKiHGq/rKKRoWPI/mwtbMYquixlNKP9DxBzHg+G0nw9luiZqzPw419vh+tLgchvtlG50QI3Xn3H7UkYN7r3xR2/TzhtvgZqlrUFID52eepY/I6id+zuaNvExKfABAkrpa+tRj2/YKmqDn4L/gCXD92GvunE86BOgouJg5gvaCPEWu3/DOiTuP3DEzszOhetxPosMg9Pxba0FzarpXAkL05/VdoF/tTlcPieSgwYtAXToZegni7oj9T6fSyV1pmuQDu3nLFU7zEdeaP6Dv3+RCMROZM9ksGhLYdurexPWjc4EsRtBspEmAl0u1TNaIUoAmCcgQPgaoso513nOhNyADaK8sdS3SjQA6Eeca5AWFRBAFbEDbFMgfyOBU18PB0O9Z98+dW3Kg+9373+edaL/0K7/g29Rx1ehnsA8F2iM6dxoWFHiZqXVbW968lavqNriuQeXCQ8UeXvSLgyvRtI0tN0sbM6kxf8HMQzeRO+/xi3966NDWiWR4r00PbCLYazjym1+29L/HuchtKjOkVm1RSPFy0+/yaYJIs1yofarh5iepWsFriOpUlmKbp5JmHYQBk+8DEqLA58CoSz2fVC4BdqctrNFVPt7z7XCDOpFBUSuy08HRkQ0kHYtY09NPPWlmZp/5rENwn5DN6Uhtg2HRrAsljxYkiEkiuYksONSiAfIgxLa0kInUFfGkNkhdgto1xrQYbRYiL2L7jISt9gVi4lLEyy2GJxhaP6rFnrYPyckw09mYSVYba+ElIj/sJe2Exc5dYDfC0Gp7fL6vIi7qmNoQd9nC6PGJwCJ1UxKEyw/3kRZ7YZOduhA3N+xWP2jxI1Bo4avyS4ENub63rusoWaw3fvwH1/2Gm4lY2hTdstocEtie//DOVXbYO5AwU84iEAMzwcXaR0iHLf1jRukoGZME4a2t8pg/TGQfzXke6jpaqW303qmX8q6rlHjruv/IPpCoGPcPfqgR/ooFim1bFCG51iKDVrQy60ci7VgRM19CwEpE50GiVCKTKBE9X33BSx6zM5/bj97xksf1g2M7z/RDTP+kQXJUe6SOm9IdZTgSl6Wul8WCBazOhRKfvNaYy3SrXXdbBW2ZWbZCDMy3PVFpdLine5XKLEtaVVdI2K8644GYFveLLMSwdGQt5T2RUHfmi2W2nfcN815Jk0iiECxJKg72Dq4OW/xwD2l9VYsnwWKaftXdH/S2acOAixLMm4LaqBYwSe9v32qDX2b0ZYU++uijjz766KMTPXJwJbKUbJCXIWOJJC9Sr7duefYCiQpBkqVg93uvvWanY2+tyje+mjzKndwz+LBDrsOx2gUhCdKbB6oVxjDKoJHN1fto6SHr36aQrNormwvODLgUO1SR/RrBYSETqmxzrpVwIdMe0hIg7CVpygj7ZBmSqAwxVwvk/TfuWqVs4lBSutdv+LicSojp8899wczM3v8hJ2hWkL1yiGUiCSr72sKr3ax3IwLXUGYvp5QIcrUp5mnbJgmKrLGG5LYS4gG0uJL/ca22wbEQotdFkquUSRclYkBqbRxibc2qHvi1jnYmkIOFkCAiJJeNj+4iCED0lEKAppVh6FhoL92lO7ZNE1lIWJBrbBcql4AQjVSy2iIu2gbQPPsMw0rjWozo0ZOQT5AmkY/d2FiZGmWkXAJb0yMfu1OhOGRpc5Fh8zu+zZlMod7ziF+LdQ50S9pmGh9B4ZDIaDslG7T8TWMMUsQ8SELKQN6A9t94425nfI5vePlhJdGgIGyGv66uaVAekIOqsrXKABlVBQSqAr2UYBeEW5WuWrzegMVrf/381K+vpvLHqVqGsfZ+SmTJQ7U+pybZwZ7QLJEBW8oemo2rjc9VELUoOzaU0yQ9jFCZEKS0EZoVvtndkmBZIly1CeM0kNdG94HVjqjVGpE4yMU6RyV225Bha6Swu2l0MgiJtAqCxAnqb5o4bVENEcIBskIZFpGo9z/jJeO9iSOM9+76/ChGQndF1F3s2EZzLYMSFFdM4qJexLUHssg1FSUxf6irbqn0YUSPHPTRRx999NFHH53okYMrkSzZIBva/oFniocStxipVW+omjw1tvsvOzHllbtuArM4O7OhbFubC89cf+d3/n4z2670Zxe+Cm9WZGlqm4kVr7+vjXYh/7sYDPV+7EN9BX156cQt7GWHeR6kHcg2eKtulFU/OHeRpOnUjxNLUltjGSqSHxbViP6ojbIMG1y1vA27ZKF6s7lC8qINyrf5xHude/Dpf+nkncMHPoaH1yWvTL1OxxvWy6mbKUQdPOvuI9Kmj+gcbqrKhqod065EPbuILFvZJ0IjqnuvWNHruFrJwa6FFAz0uZGIeKnAFtiHMyn7aVMb5wBCIm1iiF/RUpWutNr5trpI0q7tbbRPFki1qs4PcQ3iUmPBe8iybtYxn3k2PtRn8zFtcfSm6buVnYS4i/aB8TMIVzVGVPRbURdNtkG8Sey04cS/a3LsGe7FG5j8+PWBDfipCHS0oO2LB3Le+HUQLX5NVwQpatCRUEGc2DbLMhy03u1JFGyM7bXOwRv3fa5SM75xQ23LuhZT2zU9oiU420UoMB1LWx4MpM2hPksmndNmCYlP1+ZEoklr8YVO3/DxORf/B0Ri+Jgfw63bvq974gItJKa2WK8sXQjNqUEYyaJFdoYkCdIoQaqi9HEaSAK92Koj+efFLajV1p2LUFUIqZzp3M6blaVsrff4Jlo6MEFItel9EVkRf+NcYIG+EGrKZUKr+LbHWcjbjuR5JNztVqQJoDR4CUN/flnDF/K/n/3Cc2ZmdnHq302r83Ljfw9BWJHApjXS4BVY5/vM2niy3SEQbJEO2qe7xObMSku2todBPOiRgz766KOPPvrooxM9cnAlBoOhPf34M2HaQ1a6vBTj92W3IX79DUcKLheecWEsSJwAACAASURBVN246avyo6MDS2Lkvu+Zrzczs9/2W3+bb5wVqzKi4KAmsjCyOrIVVrZdu0/0U0Z0FrAgbhBNqmx/6lk4S1Fa8NaqAVJfW4hFS62xlGdPJavWRiI+hTLJfAg1GmUW32eMqTbU8qcjG+TdGuDFUixzidY888H3mZnZ51941szMvvn2p/w7o51MLW8yN5pJChpL5jr4EP6dmOYMVS+/0Lm5drQflsx1qLToM2T6/qfNZRRE79VCmd9YrUxDMeR5fpI7IlFiIoSRzI5NsKUm5Fmps4LKkOGVo502FcVuXZyuFroTMmXpYWKz03ZLppHyFK2VYXKFXLLq+4fXrvEt+jBs6u7fCDnN4U1QB83gx/jTZDWxD0UW+8Vxw285OPZ58Yoy4IEyvrVa0rAAvy67XJCCKlGvFdqxg7RExLiEU80W4YFUIW7EgdpB4SCcnXj3AUjATUk6J5lFXQh5KVuQI3FSGA94DqA4avEssmJrNU62DeKmawtp3aGy8oHuHPP7jhC8oXHBZhtEpTzyOTlWFksXwP2Zox9LoR11W1m+GHTGJjJlcS3YRzg68BfW8DrUeVJLbj26MdVpURaggF3OxnqO7PIorKoD1WKuJRmtFV0xIDqs5kJAQoY9jJS6YkmLuSTOxQ9j7mOZjThX1mZRt6fO38CZEdeCe/Ke0MmLc5mFCb0Ycyx+JCGiRIPBIBBoCTyBsPGrkMxSxhxCeMwfwrRqB0HkPhtchIcQPXLQRx999NFHH310okcOrkTbtLZZrG2pjoDTc9cJmCvrJbt76gmvm187dk4CXISLs3t2cfeBNkavq0RIoqCkuh2SrInVqK8i6Q3PcwxilN0pkz6TOcpqgNWxb4ba5eV8biutom+rqyLpu3KMX1Rvg4NAR8RA3zU78+9Yn/u+gRzUyjAH++Jg7KnWLvETdHmaamMzrZan0kSATT9TxnL99m0zM7uvPvZffdYRhGekh0Adk/rcRHX9c2VptRjSRYu9rmSYVS9eiz+xNxzbgl73EArpMn9Zna/FJZlIPGoZaAwcBf/OJX3LYrUPhSyoxBrv39aa26jjlnShqHNgozGlLrkrMHS1su/HyT6rvp8z9iBPHGR79cEspZgrIcVcdzkFw7FnQk1wDeLD/rw2jV0u2VrwI4wOAfac2nUVj2NZKy/EgAfNObrmjP9q83ntAzwHH5/Luc/J63fUKST2OgIy4fQdCByci279OEV/+LZ7gDHjOiZzPpXg0kb7f4MuJWV1yCIPZAqGpgAnEY0Pq3f2qcYEqNl2Jcn3OFrgd/QMLs8dvbj3iqOWjb7rcOoox8Et6aigpYAxEzwCXZsIHe1pjueDwpIAMzglITnNHGNeawxX6ijZ6DiKoAUgGuaxkUnWhc7d5Zmf8/USeXJHOEd7Q6sKdUqQKO9wcOiIAkGtQ8cDATe0WYrO6xdnl51jKgNhE+IWOtNwfXLL6QhRJ0y1RtpaSKm+g3vbex7xjrTxWChHXIvS/YAXw8XNOEcnmpAJkM1kccKG2F5jnKW5GN0rVzg0Zld/Z9559MhBH3300UcfffTRiR45uBJZlmwyHYSxymNPueLZe2SgMhyq3sdKWPKpL774gpmZvfziy2YLX8E9OkU6FEle6kldpn/0rYrJi5wyK1pkYEvJ72KOM5/5SpqaO7WzYrxnlzKCeeFllze+plrydOIrdayHW2XlpAbNPn3JqgXyPq1Wzy/8eG2tTGvjK+XRoWch+5Xvw7AoQndgrixjMvKsNFcXwpmU2p558sNmZvbLv/pLZmZ2V/ax73vkCd9XZYjoHCDpi13sSJkhvdSTI68HV3ddva5cttYqu1hqrJbiFmC5vCe0Zq19HR8o+0C1EYa49v1caAdGOjX6sEoNUG8LBnSdgsGPbkOhS69SBpg0hqAOYbgSVq2qwcZ30TsPE7xrv0ztPbvSOx01du3XQuNQ6FzBIm+jo8Bju4XUeb6m+yOaH9QPru3nOTwZrGvXVskeuw1FOjIkKWom6tr+/IMHnikPH9Hcuu77ukFsVl1AxQCkATQDRAkuB9cTxld1yCAXKH8KOXggy/OFstUjcYqoDW9WOidIoSN9DA+EDppQtqWLQ3/TvWFZdCMU2hb6F6cP/DqYqRuJTBgEbf+2JMtlJ00NGoOmRhocuZCaJGgFvQD4A23b2DK/ZMd8W8hdJ+5dAU/qwbc9AHlr/doWCBrI6+xcyqua45is3b4uG245V83bmTUiPHGdYHIGr2EmRdFcSCxztdD5zFbiIswlp0xnAJo1Cc0O6YUAo2nc5tJPSI1ZLp2OXO/JB3SSOEK0lj34QnyYezpXKNPCB7sUwsi+hp6K7kcT3fPVuBZy/a1t0aUtjwciDxLX8FuEyqWtUV9v2dxHH3300UcffXxFokcOrkSWm42mrR3eUCZV+OrtxVc+Y2Zm59IHeHDqK8XTB84vQIN8lE9tVHoG9O3f9lvMzGwM2/411eALf526bbZbr1V6EbrjZIxapZM5VNS/l3gu+L7uT6Z2IP2CB1rR3nvDe/+X0is/OvB92lP/fiiAZTB9/Tv2xKkgO12rdnY68+M+F4pxX0jKgVa5B5M9O1R9lnruRjyGUmnlWCvfTF0In/ygIwg/8+l/bmZmx2IpP3LHfSxmGh96iFfBttZ4KVul7neqfd7UtTXUejWiMP3JKmA2L853sw6pGsJn0PvO1EvOSh+/gA11UGq2ZHF1HWY7W6VHKV4qbbgWpfCd7gTMjcjm2y5SEIhCMO61nd0uh7aNbYS/gfgOU80L5iIdDyT3zdVaqG11DVBWZHsYT8Ub0X1Xdtw0yebKAFPKO48lfes6vuHAz+PZPZ9rTzz1XjPbqnJehGGOPkemtcPW3tU34NxUVRX6+gNt8+zSuUULjcs+fBnNWTos8CZuOP/6yvAzkEVvHnXtGDg/tlIaBeu1nYnXcHminn+pjNLxcnjkaN+Runzwe4CDFMcrVKPIQLP0NNdBS11b9XPq6E0VWWmRgbZ01TgTkqJNFwGpsEZQJ8CpztWJ7osD8QCYX1Opw6JN8GAtJLJsLRJjbRTuxEio1kjI466uR9Z2EYCKnWu7980mYJxudw8cBbgL1aoKr65SiGJ4TdRc75jr+fF9+EN+75qO/PhW6toYqeMGNVy+K9/52c3xWEDtsK5C/wROUthc57teIXxG571p7OHgBj1y0EcfffTRRx997ESPHFyJlJINRpldLrwP9/59XwE3kRGqjqeV3tGx3Ampi29Gtld6PW2Y+yrywV3PDLKlr/xXWpZmqgkOB29tQczqPRzvUE7Tqhsm9Grt2eylWPwXlzMbDrs9/0PUF7U8x9kuamra9qrFZQ3dfynGqb+fzOHw2Oudw0Y9xBU6Cb79xWJhD55/3j+jzG6k7GFfvcB4LphW59Ops9U/8MTTZmb23Gd+1czMDsSgN6lUlthKa5mNPfBAdcFSvACYxW3aZiG2o6tO33lR4iWh1TdW3foUNrhbZbiy8zwdKXxnZC8NdX+Lei5sYjgiS3wwwvLQ3jLieGC6h1KajgwkAa5B1kUOmivMeOrXnHfQmN3IQnWw+3xY0BpzGJdPHYqBirEdPB1S1J3J4LB1hnsTSJB65zeyYoacvV6rp57uhEI1d/Z5V42QbgAyRe3LeDQOlAkOyaWQkMN9v67JVtEQKNDC57gT9WCdE43LUDwH9AzoOKAP/pX77pB4eXkZ52+gDPlQjpjHaCnowNbK+Beqd+fqpaczKRAn1B5h2gtxQ40P+2X4FlmTWYMXRiKzFdegBmFTvb6BJyPNBCEF98Tv4bwfSosClIPOrNBuweXywI+5buroKhlKl4C6fKksvd5wTfkDiAlzcZ1xfHobSBt6BnHv7iJN2HMDh7RNu2X+78x73Hm5hi+lb/CJj7umzfd87/eYmdmP/m9/z4+h6KIaHNPWD0FIBHO22P4c813gACXdFC3XyU43U6B7vc5BH3300UcfffTxFYoeObgSVV3ZvZMHdnbhKmKXqoeTjQwKslJ//3wu5zhlf2W1MZnG2fPPv2hmZs986pvNzKw2uhdQfuvWK/H+DrMt2MX0wGqFWyhDnEu3eyx1vuNjz7wn66mdX8pPnrUfNdEBev7KQoQQkHXWJZmlf/c91Q6TujRG6sBA0KBQRnFDTPupmNPnp+c2od6ulXyF8puQk/OFr7qnylrnL/nAPabOkJkcEF/4gve9f+BTn/B9U495HFPerVXDckeTYL5Y2FCqcZHKokNArTzr6hJEnU/Z1UYIwSTz45wIkeF9jY4bVchWWV4VHg7lFoUQ0hEeCIWazHdUFclO2h3EIHgDMOBh4edfep0fvvRiWaOeV9BvvQMRkJ0F+z5UO3VcynRQfVytuwqa0XERR5+ijrvSGNWxLd8XfA1WQrdgYy/XqsnrMR/LI0C18zKj84D50B1PEIPQtsiLqJXfveecnJu3XHsjtPGXckCtUDj0o4CDEIz3QGT8TxRJz6VJcqGbAuqeIC13bj1qBwc+N7Mxap1d9Al+TC4fEBxBN4mOki7UFNoSCCYEO1/HD3s/5DMayxrOc7eO31Y677pXvf6yIx7nD3QOdE+7dugoB94q072x9lncllrZeUZGLoSx8ftPluc2Hk70WrfLoN3QQZF1Hmudd5Qe19aNuLb1N3yRaoVCYrdmXyrLL/JiO1cMfkrdGRe6OZba5kaEJ3xzQJpGoKMSkoCjBIIAj4BxyUEc89zq4BZ0jyu4BxpDEAYgldGgeEjOCj1y0EcfffTRRx997ESPHFyJ2WxuP/P//Gwwy+kxJQNs0Qyn/qO1Va6a/l5e20qa/q+86qtsll94oKNxXuQ7NUJ9Rx3662+9jzDMqVet1atOh8FgWNrh1LMvEuWF+o5Xi26fbVugS6+VshwDr13zFe89ORzeO/Guhxul1xL3VBdk4btULRUmejYsLR9u98fMLJWQKjg+ZQjKjIZSRJyJrf34k65z8KyUE+/K+fL6E65Glpa4r5FRq46pGjSdBVVd2bCb4AXygyLZkIzJn47ujakyoQWrdFjdOXoXcticOJ8iPOKjHLhVCKxVU13XKJypd1rdJ4EQ4BlBlqVt0UMd3Su8v+H9dCt0dQ6I0Ky/8hq97m/yIdh5H8H74FrE3ztKidtsHc7ClmFNZw8aEpsNffc+hhNlnacnjhwNxjgGSt1TvBiU40rVy3EthFtQ6TpBByA4PMXWg+HkzOf1vtCKqc4jjqdRK+YzQhBQkDxXB85cXQ7sA9n/QN/9yCE6KWM9jy9Ktq1j5zsOiNxjSjpIgtChXdP7cfajIydeD7lOM9vOyYH4BLn2tV41lrVdB0i6OF5/zRHUF5972Z+XKumdm348h1O/H+RjcZbUzbQSBykpo+Z+ybHB0cqHXHfFVo/giveF/72jFKn7w3rh9z2cUPMxqCgqnV3uFs6zSXO3Zpz0Prw5RsUwULlmg++NHtFU0D4yl1BQ/Fe/5FotOIeu5eOABgvjEH/7LlgV+4oyZRvboEOi2elG4vrhd4P7ycOMHjnoo48++uijjz460SMHVyJZZimNQpUvXAeV1ZKCwi6NnvXQKDDLB/6ZV++pPrdQx0PjWUlZa4WqVeKF9PwHJX3fqi1qNUrGN0IRDJ933MnkITC/9Cx2dlGHqh7ZJ/22FKJYTdPFsK2xUfvyx9uqwd59IARBegmbyuukx0IYyLxPhUwka698p8Ym9PrpN/c/cUwkO1sbbou+zUeffNzMzJ5/zd3nBje81jxVF8NKWZtp5Q+Jg/7oqqq3zOaGHmn1NgdCpHFp4Rx0e8hBORZCCiboHZw6t6OFKdxwDvHP8O/ZbNbBCWga/CykjFeyPv+1q4RhCbDDRr6KCJhts/03mxKmrR49OgzprVGG7Yd0WDsOcGhMUL/fqOc+28lutsxp+DNrS5tudbjUGM3E2yHrXggZwoXxcq6eeKXAfG65pp+9WzeHewCSUBTdW935xXlcH7ev39Rx6jzCZFd9enHp1/B6vdMZoMfRVGiVuASFstQstCg4earBN2SklbXS4W8SGaLOX5xWEIOuImYGxybUO5qrbwulTOrkuSCFTPe0Yev7atXQTO+BE/HFLzpf6p4cMq8dOp/pkduO2tGDT7aKYmA2RCeF7hVUCZVpi9uwxjdjw72hCv4C3UgbkDZl+ktl4RvxZUAYxoy1BgyOU74zDzgXzF3Q1KXuvyAOVVsHGol2RLgz0tEQoC96EL7tJ9Rp9dLrjrTAh+K+QQwSCoyl9s2fb7j/Nrm1uM0yjxP8h03n+PIBaAPoTK9z0EcfffTRRx99fIXiXUEOUkp/xMz+5pd4W9O2XlBMKT1lZs/9Gu/9u23b/uA73q8ss/HenhXSxifTpIcefe6Ud9W3qmAAV9te6EzZlUCH8YHc6OQShja2yveW5FeO1F8RmbeymDUrRn8632GSkzn5alSucXP/rtfun2hbqrHvaCeslRlOpWdQLdFC932kJmvyX7931+vBtVjZt284W/naWEzzqrJi3WVbb0J2bMsKNtv2W+N3UOf4kkv/4Jqcz0b+uS8879Pgfe9/v5mZjXC3rLpKgXRm1M0yMmBW1FvRPHQeVI9WNnqmvvfwnKBuq4+F5wDuhOuupnrCn/3KEp7MpC3IDKnHqr5Jxm9vFyAu3eycv8m42x2W/lU+AWzpXeTgbaPtZuX0hM/USXBweKDt4V8Q5gH+PNmuHoumtazY5TH4/r/4gvuAnMm/49o1OZ4KMeOwybLYl40Y9eO8q1WxdRbsanmw76vVym7euOXv5ZpdikUvP4OFstU1vIhRt6MCpTw4SnXd5RWBGGy5S7qvcK4su+Kiif+ExozsPDQj9D40JlDro1ulBJnqvM1M2XkhrkG2ErKWy+ukbu2e9FyekzYJHKsn3vOkmZkdqROKnHQd1zJzUh0WmR9/CdIKErfa+haYmRU4ZYovkmWZZUr5F5d+T1nrHhzXqDLogXgbzc55zkEgM5BVUD/fzELnfSmuAhwe7tep2fIionsDQLDu8j1AjuhWunPdx+d3ft/vMDOzf/rP/4m/rnlTrdBQ0KPufUNQQ7hJ8IlSHtopW7dWIR8bHS9chOhmEgrzJS7pf514t8oKP29mf/ptXvsOM/tuM/tHb/HaL5jZj73F87/0kParjz766KOPPr7m411ZHLRt+/PmC4Q3RUrpn+m/f/UtXv75tm1/6Cu1X5bMLN+qF8Jaxckres3p2yU1pE7YZtbKVfHFl71u97O/8C/MzOy7vslXlRu5Ka7lsz7UKWi1wp8UghIaMkEyCzT1UcoiKyMzoje5suUC/QLff2rkZCllMOF9Gyutps+kazCR90ImNnGp2uL1azjB+T6f3PMOgkoIwp3rzlEYW7ZVjVNWTcaIdgCd/0nHndRnvKQmDz9iJf95acvfVpL63LNfMDOzDz75lMYD9Tr/4L7eny4yq0mnCvqsydx4XijFxI8b5CDqelk3e2OVfrAv34zIpKWnQCaKEmGWX+lXTp0Hsu0mWMdckl0FNPwMQk0NuYcAEnYU094CgwhuQaAKzDF7m8900YwVGaA+P5L/AU5/b+qQ4IPhVbGx0UR682K03737ih4d3WKOohS4Vk34+Ib/DboDcpAnv16o72ahseDnjI6McAoUt+H4+nXLlbmRVbZ4JeDWKVVPnAABhPDQWDOvSACz7vEzztFpECiBMu1qE3V3rodADoIHQ1cKioDKGPUI6gHrnlNQCikodT8yuRYOpHK4vvTxu/vSa/b6iXclDCd+n3j8Mb+O96RfssaXQchHoXOwqtRBgh0j3BK6WbRv5Y4DbVvR9eLHvlosrVrv+Bao06NUZweQUSVEZaXuL65tXGy5NlGB5byDAhe7irMgb8yb+gpfJ8RrxV/QvQt+yKbxfTg7c7TrZelAxDWpczEcoaiqUdI+wn+ICywMF1Pw3HaRQq7/VjvX6rcI1CHTqP+G0zlIKX3MzL7FzF42s3/wLu9OH3300UcffXxNxldbt8If1+PfaHdtpzweTSn9cTO7bmb3zeyftW37iw/v65NZXhgd5ZNWq9dG6apWzih8Ncpe2kIr6jQyM882XnrRa6h/8S//eTMz28uc2f+d3/zdZmb24DVfbY5Kf34mLkI9QTlRK9oGprxWwDgFit1PbT1qUk1jM63cx+qhv3bb/eiD2I66mrY12Pjq/O6JOitU/z9QPRdXQvS7j+X6OB17v/Orb3gnwedeck2C6zfv2HTfM/dGHR6RfUh1btL6fu+rhohnPDX4SivkC405y+4bB55BLs59vO7LIfPWTXdvXCsjzcTibcZZcESGaKa3ZHRSQNR3Dqee+U+nqkurp3o0Uo0x9M59X9BSOL90/wzY6C190qIcJ7uCVkStFL92zTYhJtRIyU6q5MfTDhadfTVlUPT5J1Qfk5jixnfjiLjV48hyZrjms/5KhscC3AEuQc3BlZw1Szot/ByuF2Rvyna03ab28dus1GlQJVvPhcpJEXF+4dfJvsg3G2X253qErf70oV8n1GsL9dxPhbThsUGXC7ofK6FaDy58bo/VWWCD1i6WF3qvev81Po14P5XmyTLU6ODR6ByACASpReOrP0H50CJpQsdf13hVW6Z5UcqLJR9rH3KuG/EUlCFSt0YmBcXU0VxoXwOnR3wIcX+WUnt9WV0/r7zkj7nltvcevz9MDuWVkvt3Xl6+of3XHMQXBg0GkBFGIaOW7uM5EA8kNBjCxdH/s15onlSVDaSVMN7XOIxAa+QAKgRpJYdDkAb0LDZCEhq4TnAtWtBTabOoGwx/DLgMtT7ftlsfBzxW2jifGlvta72UVoeuadDNjfZ5oOx/NPBjOqvc1ZesH/QHtVS8GFKe2wZ3StApxlqfQReCsW8DOX04qIHZV9HiIKU0NrM/YH68f/1t3vZv6t/Vz/2kmf3htm2/+Ov8nk+/zUsf+vXtaR999NFHH338xo6vmsWBmf2AmR2Z2T9o2/bFndfmZvZfm5MRn9VzHzezHzKz7zKzf5xS+kTbtrN3sgNtY7a5NFuuWI/5Km1KxlV361SDkkzM12rj8dRmp3gh+Mru9MxXi3/xL/1FMzN76lFn2T920xUAV2JIH1/31XutPt6tE2T3O01ua9RLyUAbZXtVVlmmuu5CNbEkPfrxZKj99EcU0ZZz8R0qZQ6quZ88cLe1a0eelZRCAWq848U9uHXbWd/3lc2/cnLXBnM/7iee8uO8tuer55Nzf361dl5EcA9K1PpUS6MeXsN4RpXPh+GJJ3y7zz/v0+Fy7Nk7Ou90dwyHw+g2aHaVyPgO6vnqfIBzMV/h/+Djgl49WUqZ4TqnuvbqsrOPKWrQjdUw1ZFmQ6mObCyydWrm1G3J8mGzq7aszGHdUtf1d2U4we1yD9oUrHJQpm3//U47x9sEtdX9g6POprcqc9SSyQhB2vx9e/uHNltealun+mqpch4d6Xl/vV36GE+FKBwc+HkFGcFJcKPrDCXOckiN1r/05MTn8EAoz8GeIxBVW2+1JzT06wqVwi43g86aXb3+4AfsdCWQ3TMOdaiaCt3Q9xaDIjwPCmXZzY5zZlNRS4aro+PUTuQ13QdCSoROoG764IGP88m9E+2bf+7oEUfgbhzfNJvy3fCadG9hKPO9zvEG74GBAxlgQsQ+c7/w7S5n6v4QcjAQejQ5mloGaKUxXghtmQtp5BG0kw6C8L3QvgWSIoRoMlB3g87pEv2YoqsOioNtvaltLfXIUJnUA+q0VuIy2fW/+chHPmxmZsfHPlfvn/k5qAWZwCtDq6YYdr1NiCxtkYFNXP/ale7UfPMLV/b3ncZX0+Lgj+nxr+y+0LbtXTP7r3ae/umU0veY2T8xs99kZv+emf13X+pL2rb9xrd6XojCN/zr7HAfffTRRx99/EaMr4rFQUrpI2b2bWb2kpn9w1/v59q2rVJKf918cfCd9utYHPxasV5u7IV/9apVa+q3QgjUxw3zHkI5PfqomllKti9fAxPKMFFPNP4Ef/N/+h/NzOw//hN/yr9Tq2hqyNPSM+xdbXS6EWqyEa2cYd+OVKPL80mwYFF6nK991b2Y+2p8qNoozoVJqo6jPen8a6V7ee6fu5h5Bn1Y+rFUFA9hRouNO73h/b62v2cXd6XL/quuS3DnpiMjN6SJUAv5WGkfYwhV52uCla8VvzJBvOHJyu7cca7Bi1J1S1J7O1Im2qyaqNNFXz8P0QlCn7aOR+OQmdj5QkhqZWUwpsObQR4My5WP71h6B9uafWOtVOTqSOjVC67e8Ebcgjzq2DDA4QVISz6ygh2zeXwM0ENIuPx51LZFiiwY7v5X8FtsN+BJiAEO0//Ys86qgm0NikEHjZ5GDVO3mcVqFbVRHE0PD31ezETbWKuPeyE06+ZNV+Xbm/q8KQv63P0c1ZVvG1W+qvJxev2+IwbwCJgP8EYspWDGr1Z4Iug8J+aLxmfHDyRFRqksP7w3dM5S11FzACo2BPXYqh4y5isU+ioUAH1f0G/IhZSMM/E6lJ0v5QT44ivPm5nZPSElc+biniMvNx51Fcjr4h8NxMmwZKFFUjfd/Q6VxkS3iWrwuvxBM5hzIChkwpk6I4Jnk+PlonsaGXm1ia6UFk0NvGY0lgPxQpLuXYMCHpicLjVeE6F8lZBGVExXaJGoI2MkHw1Y/iBvVV5Zq7FtdhRF4XIt1A02EaolYUz7kR/7n83M7PXXnEcjMCfuNyXcFg1sgbOqbgp4L2RZHt1bePLAWeKawnuhjXGybTykdoWvlm6FL0VE/LXiDT1OH+L+9NFHH3300cfXbLzryEFKaWRmf9A8FfobX8YmvkWPz/6a7/r17EubWb4ZW7GjHYCf+Ub8gBrC+FIZpOrJVdvaxX3PpqhTmrJIVnqvvOG9sK+J4f/4HfcOWC+UMWzgM+BGp4ySLEyry4XcHxdizNL/PR5Noz+71nFM0KFXX/LFhSMBpOt7qrEPlUkd0q8vhvS5eAKlOgAG2Nur/gAAIABJREFU4i5EhkBGitNgObA9Ta3Nia/c53d9G9XMM4Ty2L9zcOw1YPrUUYZDryAnG1F2OxMfYqwMANTi1i3nPbz8yku+DyMYwEPbhJ7/jsZhCNHrPPM3dch9+vi7bnuoWZKFjpRBLKTux2UVfJB2Y5aj+MZ3C41KnoWQAZi8Nxp1MdRCDFrNAzIKsro8FPS6XIMUlzZr7cwSPe8ItwfVeXdc6s5nQQjI/PKCeS89jQJWvpA1umGECm2EXA3K3CplZYfihlyXr8Fz53c7+zKdeqZ/6/ZjZmY2Gqn7ZccHYDohA/bxfQPtDb1++7o4KHB5dIx13UZnQwqFx66GSIvHioaHzgIUMekEwFmxFBo2lK8HCB71cZQDyQLrpo36vUrnVha6tug6EKdgnPk2N2c+H1561vnX9153pKDWcR3f9ON97Cm/r+xf93HLJ1JoFQ+pZm7mrWX4nui+Fhydeoc7AarX4IDJfVJqp7p/4JMgk8bQGMCTYcW50DwbjgbWlqnz3nJM54O8ZvTdCSRVmTJZ/UAKi/A60LOYq7thLM4JSMoub2QuNMDqFFoZpVBZOgfg1Dy4cJjrxtTn7rNf+JyZmf3oj/09fx+qrQmdExA1dFL85SpQLH8YgiamFH49bxfMGyK/QkJ4WDoH7/riwMy+38yumdnffwsiopmZpZR+k5n9XAs2u33+u83sT+nPv/1OdyRLme2X+7a37z9Y3G+Bi9eaQCuRZYoQohDEt5pZnSFR65/FrGOjtq7pkU/Spz/ghLp6JliIlqXGJ8hKfxcFBCWReDTxBmqPafW+pQg7q5Mza/QDvCci4UBWzGNscXUxn838x2x54pM9K/3C4Yf4UO2I/HCdPfD37+lmt1/46xjN0F5p1cbGIgLdvO3brCXJfDb3hcmDNwT76uK9IVLjBGGdsPL1vxFVQnBnRZuYYLfr17ykwULg1dd8EfbInUdiEUMpBrJXitZDpEe77ZRY19KKtpUF1mHqnOQ7okK1bsAQupp6bZYkD43wFOJPIpjWahezTHBnvH+l7/K5h6EOYklhxASZDIlv3emrdql9HkQLbqLlMHUv/4C7265U92It8qBKICmnnCDiYdM9FyyWZoKKH5z6uX70kdvGFXxNpK2lfngu5z4HETc61o/6zZu3tG/dhTjGWqnwfb6Q5DE/Fo9qsdiGaIyOsU7xPkpQEDThIUL6TAl7aUp/pnERtCvy4zhX+6jGnjIDi8ySc8UXaB8G+cAa3YKLjMWwtqmFxPyB3zc+/+ILZmZ2etdbMlm3vOcxXzzdeNx/qBJEZUqeOsXrTO2DmpsbpHxTMhSts6i3aRxYJAZxV/sYku15Z595O5bnLHQXKiEtlkjKK2kRaTAbZZapdEeJotm5xkInCMl6LdC4X5Tal6UWHouVCM/abqmyE2WKzaZLluU/1aaKEk7CervsLrSRz+YH+if/P/bePNa2LC8P+9Yez3Tn++4b6lW9qnpdUzfdNEODoeOYYByMgRg7DG1FdixHsRLZUmJiW5FNFEt2LCE5suyGzI4xIQQcIIkwAWxhmqFpeu7q7pqHN9Sb77vTmc8e88fv+9a5+1RVD/RD3ZH3kkqn7r3n7HGd/dbv+33Dh34TAHB0bPemv8G4eC7stVAtCkl+9Y+D7Pg5P0IRpZdzUcMFrvHz2xITH+D4WlgciIj4Vo6IGj8O4F2ULd7g794Ds1kGgP+qruvf+8M5vHa0ox3taEc7/s0aX9XFgXPuGQD/Fr44EfF/A/BnALwPwPcAiAHcBfDPAfxEXde/8yCOp65qZLMZ5loRKwWYr8marezinFAmV53zKVfEeeCrC1UPVULIkSvXz730aQDAZz7/CQDAH/l664qMSZw5s2mEIVWhisNVxbOsagR9CeJfRp6qbXBwx0iBkWyQCcEnfN1ISJ5UmA1bFAsSF2XuEbP8SGD7Or5nxyo70DNnGVnLaiWqa2Wy+BVvf4PSIrYRNtiy2Kc5ze0rBhpJ0rZ71iohERU93Mh9KEgnJCogGPEMiY+3bhlycP2N67j0yCU7foUxefJXM+zGRzoHTUmXKocFK0it/AVVL4VIiuIFtyuYPocLBHdahdzr2XlKBluulgq1IUF1JZIbCYm+ghDqIeRAH5RM0R8FP5cARCFQyyiJMHetqk3hXfosq82SbMHAjiGMhBiwCqtkkS17WTv2e/dtHf/Gtav2uaTG3pnzdv4kcV5/6WUAwGhs6ESPrZxLj11svC9jS6zLcK+YJNqToVGOxmzpPHTuIs+XrSKVWN66l6fmAh9O5Ct9X42JkCyZMJEUhYaxPTOfqyXA8/fEPrVfmrHRkt0FpaBxh8gpYI3oI83Orty0tsHJfbsuKqUv8rqcP29EzZrI0TSYNY41oNZxTjRHrQGPxwtRqaqlpHXFPlvImhAFxUOLYCrkNJLEm8+JKpdxm823EePkFVd+Zs+eAf2BXcfpfIaq5Gd5HTLZAkuyKVSPx9KTjFzETEZ8D2lc5oPM+CCasTWWz2SNLsMqon78e1XV/l54+TgfQmr9CvH41OfsWf7zv/DzAJaBdkI9FcctxFIIpWSKYid7l3EFPIXB0npc7elKyJli5kX2/cODDr6qi4O6rl/A6ox86/f9E/zB+AjtaEc72tGOdrTjyxxfC22Fr5lRVxWyxdRXDFFPcaFcXfJqyaCnpAQqSu3v61EXk5H9bsKemEiAg44IdVYJ/KOf/AcAgEf/m38IADiza5XydGR/lymHYxXr+/m8ZQXJTjL7UZ+v2+kj3bSVuWxPy1oBJ+xfK1iI5LaYPIaNjhERVZ2f0E7ZB9OwtIpoRHO8bwjFZGjbHZBP0et3ENL0qGblNxMLhy+DNduXpJ5Dknz2jwztuPWGVZ3iInQYJ12yGlf0agJFMzdjUS9csAr1xo0buMrKVdVWmogkqphbmSQ1ST6Bj4slwUpQCauZXKt53gtdt5xVCIEKVHXm+/N5TkvhQr1mkeC4U1Xx5Ap4VpcTJ0HIgPrkWltLuijrZsUG65Bzv43SIwg6QJIjQ7tXtYJlKOXMCs6XUFUpfz/XsbE327F7ee2NVwEAr155CQCwRtncYNDxqNXJ0ObWnXuUfXF6n2XA0vYu+S5EXESGjYQYjI3AeEKuwt623W8RdgsvQyMxNveQgW23Wr6n8rHG4LGIDGaVos/CUgXJ73QRqGHNfXCOexqI0D/Oj05EhI3zr5hnWLA3fu+2nc+tmzcBAF320h952IiF4mDIVnke2bEtWEmOMeW+lwFCwFLCJ95DSn6ESNd1Vfv7J+6F58zKZlyIComG4uKI/yJksZrbzwvGLo+JGMSSk54zXlB3nQTp3OZAjhIZuVcKVNK0jr3RHLk2Qit4DAsiARP2++e8HlGv29iO+CAyOtPny0yokZ1kt9tBRROkbC67a6G09pnp1M7roQtmH//ed78bAPCJz8p8l7wocSzit0MH7SX05lKch4FDIOMtbVE8Dn1UzyyRZUWmdQEeFCXxa0XK2I52tKMd7WhHO75GRoscnB6uNntiVkjq5y3YI1KNlpJJHHe4+haCEIaIWbXHNGOZs0qbycKYJh03bluP/dd+/V8AAP7ynzerh2Pax06oOBArPZe8TkFLgQJoFA5D1nddYo1VeQeU7bCikeJB0jKtLiX/GXHfsaKquSzdWueKvysWrp3vgpX2lKz0cmhIwmg0xkFoveCQPeQu41+7CsTh8W50DRE4S/toxcQeMT76zm0G8yzsup45b0iC5EFabdcycJGlK8cjDz+CV161eOc33rA+7uOPXbbzWyZR8WeZtojV34xJlvHKMvyKDOmJVRKdDVYWQ6tmhf5UZQFQxaIWYl7J1IiVkFOYT3PfUEiSb5Y37YG9wICfqwLxKFRpSEZVoZQVq6SZ3hzK7knIXrqQhayw8ypLmy9BLE7Fovl7ntSde4b2vPCiZaFtEj166kmzDO8NugCrbbHJt3eMezFgpbe9vc1tnpKBYqlaGVNhI6XNNgOZBgzWmbNXHQnme1M7ffkL2f9KjaPqNEDTkjwvxfuR+RV4bHYvpoyyPjq278/Ohp3T1oBR6dyj+DMjWkfv376LMS3HxZB/9zNfZ59dN+REt7skIjDjschwqkqIlPC+S1aYz4m4pIooJjokFECSwDrwUdNu5WLp50DSTtlJO1W1RK8yoqXHdk8nRA4Cfl+kTEl6RNbAYDMhU1W9tCJnP1/7EidAyGnAiz8ZCrUUh8COfGvNrn1IyWLl5apB4/wnIzsG8U3EkSrKwqvPdC31HimhFP506ZIpzn74R34EAPDsC8/aPrktoROFZMgrDXTxCvQMk4ICODUXFTQWR43fL4OXHoRo8a1Hixy0ox3taEc72tGOxmiRg1MjCAJ0Bn3fg5MutSzIwuf7ikhLvqaFbR2EcGXTvCNhooismL01MzkIH/roRwAA3/O93w8AOLdlK9+jfas+w7rPz7O3zmNSCIiY4gWPOVwMUaf8/0R2sDRhYdRoxIpOzFdxCDpTsq3Z35ozxyrZYf9unav6ULpn6vnZi+xMyGqeZ5jJG4Er9OENW+mrlyovhUNWBD1va8pX/n23sgrgzvNWlRa37bpcuGSs7XUiDcGafW7ESlMR0PP5Ao8+Yu99+RVjxt+6aZrxxx593LbpERTpjKVT1zVWFUskiX1KF8s3gaZS0ruHVtXmpSqwKSpWD2lMBYAMg+gHseA1nLO8KMg6L9nfjoQgVEI5eC9oJ+vUH6+lxBDTnJVTnCGWrS/VBDWRJa/zh1W0EWSHzBCkyu5hzP5/Rd5EzPM/OTGeyKsvf8Y+T+H8hYdNJdIhejSvFt4ytgrtmp1/2M4/oSZ+kFIBUVsVmhJpW8ztmsof48yu3dNB1zgKmVd1yLhGzWZ5kZA3QmQhDHJEZPgX4p4QOUmF1vEeFYx9lv10yO9Lltk+rr9oyNSd+8Yb2Pymb7VzWrf3iRchFExI0/p6H+dogra9YYiJtO4n9FaRMZdshfl48XE84pYkRB7oU4UenzsJ5D0i/oBUHM5vp1vI7Il8Ht6/TJQU/hwtaNBWUPU0tZ+PhnZP7k7s/Bx9VTbP2jlVikCXR7GTqsO+u2EcIgnWdEa2L6EU/O2MCqzZkN8tco5SWRLv2OfTDu21Q/F/FO3M2GkqKVLO+S7Dj4Qc5LPcx0MLbRHqoFAnxT+LqwSqweTZIQAmotW3XMaTWAojony8HOmqTXVdoOT9CmuptBSHzuMUcqiPRm759wckYGiRg3a0ox3taEc72tEYLXJwatSwPqOY70v9qb0Wcgbjz6uOamWZoWYvWT3EMG3qzjuMUw5ZAty6Z9yDj37cEITv/hbzdSo9E95WmZtbViHJgnUSWPVR0HGwmzJ2NSsxn1ils5FY31KrzlzHqao1EsOdlUGPbmKyYi4V4Wz7iDvUGHOVDqIhJZn4cWSIxGBzDRvc5w77keOxVQ+BGnJkBE9OrKo6ZBxwOLVqteIxdmg/LQ25gmUO6I/QI3Kw85AFMPXPs79JhOLk+MCjC+9659MAgM8/9xwA4MYtu/aPXzIOwlC9UvEZhIyIfe6dzETnZi+yUiWuqp7Ma1Z/cVh4NMJ5+16F3JCXAPUbxZnQ++Xm2IxsXn515X/RdLETYuCp5yiW2+Z9c3JT5PGKi5DLYpeoReh3JYTFSiExy597znqtM/IAnnna2Ns72+ybE80JXOC5F6m8Q/iaSunBaxlzPkvdccT5sb5h2+z1idKIWyE7aqE8PG25zCrwSX30rMgQEgHTtgrOyZxulLpeSsFWgBeokR+So3P9qiFR8hQYs8q9URjadXJsP6v3/vjlx3ksJUL1kisF7dAzQa582jePaHl/yRPg90nVrfckkGSibjpErpggwiHwVttLtY6qa6KdCidCk2NzfI+qhAU5NuQLbO4a+tlZt+eHLqC/JwR50pz8KUSI5UMhRIM8jgl9C4ZUI4S1bLMZwU2OV9lrhkB5zxI+RyUs0f3vSEqkOGnux1XL56XcR9XXT1L5pPDZTA7CwX1DzhCcqt6xDH8SX0CKA0XdC4CWPbl4E2EUe6WUd9cMm/NDI/JEhgfPPWiRg3a0ox3taEc72tEYLXJwargACJIA8Ix39qAjQQVkiIowXi7X8wAQxpFf0VcBGc5aTWqVyA93u+qR2S24Szc55xQMIg25vV9Vili7odf9soIgezWNut6/YHJMVYKOl0tVsZO7vaY//0wVHlfnPa54R+zzZff5OVbzUV8uhVwZU+UxrkrUXA2ndIbcGFj/UUV3TD3/2XP0d2ClELKk7Cj0xMcks0LkuUlTfsis35sM3Onk1jd/lMEzWT7zngIhq4Snn34HAODZz36ucS3P713ksRD9icWp4FCQivr/Tv7sdF8jx0AVqaJdXVIhlk2FwozkAFd1Gj+Xfm8srwKVvuz3qwcpdQOPpabWvAT96tkfd74iy/3x+0jeFZRC1UpeqHK28+p1iX6x114SHXuVHI67d63X/MSTdl33GAss1n9A1CQMI+RU7WifPs8hbla+ugxDIkvSqw/Yx89r8QCkLGBPXcFdlSpIomOV7g13j3pZGRIpmWf2HYvoB0FAwSNuopsniTI3GIVOd84+VQri0/SZMXF2zzwYUva3F1QLzRc5IpbTEdEIVakKHtOu9XtlZC3FFzxvNEfpn0PimfCsJZnQA8nVKOgV4uh0mEhvz763eAsz9vnv3DP0LpvYnOxv2nlun5eqyS7cQrkI5GYoVjngMzCjv0SZlT4DZEy/kxEVP/r+96hy6pBToGfOiNc+mykHgxHPfEZ1qLDSVJfL4ThXoJn8MIQsBR6FWTqAkoNDZEgci73eWQDA4dGhne9cke3kAwnlkQtjKOUQOJp3zXHeRGHo/1932ufC8DnvvydyvPQyFDyw0SIH7WhHO9rRjna0ozFa5GB1BJXvMdfwy3YAS4dEJYOpitWiLYqAiIQEv8JX61CJW1zBBvSnn1DzfHvfkIP9Q1uFSrddzLkS5kJSVZ+80uVzIC1uWQBdrq7FQtfKVu5g8oifje33Op2azmUB+/2brBi6XMVmVB5U9EyP+4p6tr8vEvlDRP54JiVX05F6pTYK+hwkgjXogJfTTSwlVyOiVwDIvp+zyouoez9/xnrQF/n5jD1qaYvhCqT0oxjTnVKM5qeest7vc59/0a4Ll927Z+nqyEoylkOmnOGCZq8+pFudXCpjUp+nZNwXeYUklqqAFTORAiUlFoWUAbxATj7sRDFU8S3DAXgMKhWYkyCFhBrlPmshX0YQQ8evGGNyR3wy3Unj/LpEDpSqd+36VQDAq6/adbtw3pzinnzicmP7GVECCjIQR8BkzphnVjyK+VVug/TtUrkocnd3+yF+jnyOjH79/LnMZfhA7o0QAx2REhj52u10kJNTMZ0P+R5yTujCGHEOyh8kYHVfcE6PxuTJ8Lt96WHTvV961FQaZaXPk19DJEnPlRKV77HLc8E7fXpnP1W0/PntWssr3AIfP8576BEaPZf4nXYRUBRMVaXaJhXHgMz+ekJkZZ/R7dzIGaqA+ltEO+XJUOrZxEpY6acL5iCc2L2dEdmczWdYOHkB2LY7RGfW1uS6Sh8LxWB7wRhRW/5CCgO5XirtU26FcdCMl9b11dwvi2rZ5w+aeQ5Khu2SWzEnAvTiiy/Ywfi4dD7rTisIsPSoKWt958X9YtS9ns9BtHSCJaLqkSM/AXi+ogudRoredpJ8eaNFDtrRjna0ox3taEdjtMjB6eFquKRYpnFJM07FgFinsffklzufvT2Klu564iV0uuo/cTBFrGIFef6i9a0++/ynAAC/uvbrAIC/9Of/AgBguK8qTr0nbp/9L+U+yHM8y0q/Ih2wslfvW6vODpn8YkprpX+UU6/PPt+ILnYRHRTFUp6ROd5j/1f6b63666D2vTC58g1ntsrOxkQS6OO/3Te2ca1EO72fKgzvqS6nM/Em5KAXyF3Mjlkkb7H6+/0OMq7wlVMxox/7YGC8j0fomXDrlrHOo5SpcetNR0BAlSNRD634qc2fzO2YO8wYCAN5DkxR1d3GcZW5XCrtGPLMKpyypCImbM5BJbYFPoRBlZbc65TWKWfAppoByLwiQs6IFdEGVUqzhV2XsiKKQd5DSC+F0dh6ra+88nkAwEAOiERgOuSLzIgORJGcB7lfFAjDjMepSlcVsx2l+rwnE2OAb6wZw1+ZCgspCUKbwzkRA3ENVmLv/ZyUL74qrNl8ilKeB/Rr6BBRi4MmL0TqFc2xmzfNz2DI70FXzHm6Gqp6zcSXkchBIQ0eVQwRxsrtUD9aXhtyyFPvWc8N/rRynr5yrMXBULJg0/XQO+zJWbSofK6HFFaxdkKX1sm+zQfRYB46axyK9TN2/6WzWRTyA+F3ktydEbNZFkMibGP7RFrZ/Ot1U5/Q2OvbNlPyNXRDC86LPp+5cmet2IMXlylnUuqE6ighqx1xvKj20vVbEO1TtV+6yiM9uuYZn3+a15tn7fnw/KvPAwA+9rGPAgASca94jBHnU+EzWeSf00QMekJ6JeTKl4oEx/ulRE/dt1Nv4Lb43EDIDbXZCu1oRzva0Y52tOMBjxY5OD0CIEzdqRWTfLntJ/kCSI8q/4Ol0rRAHKsqp0Mfe6bSbW9Q+52Q+R2yGj04Mcb3rRPT3odrRCeObSdzsmu1qg89C11VDZPAyhLzY+MSFEQxAq5Qx+yR1uxnn1nf5bbsmCVErumABrfiCZ7ZMd24wcREsm+3zxv6kbPiKpOlLj+EGP7cBJnrw7Hplqf0SJcP+9qGrfDFuRDjXRrhmExolTsd/hxRJ398fI/7k5th5j3wVdkqV17pamd27TqUvFevsiJ4x9PMexjQMY8sfvElglK+EHZIgUpEMq9FlxhlM8+WF/ckSZSqSK4I+93e96DUa7NnHLGKlWudrq/ajBXnk9IZnVNK4wIVVRUBq+4AYniT+R/IZZBzjZX/mPfqxZdM3RFQifPMM+8BAKzTx17+B+JJJInULOxZz6a+1y3H0MpzIoQY2HwY0FWxN6Cao2YV7lvnqgCVnGm/L5hEGqqiClW92bnKWTSOQ6ytrXPbxzxvXR+iFPTcUO88TayqPTiw63Hzln1n86yJWuRC9UKdP5U3+n4puyFwKJRzIT1+qOthQ+ikckBWEQP/7FG1yn06vx2+g9tP5dWhL3VZwlE5lMozY2rbOr5Pl1ZyJy5eMIQtoMJqMrT7PMvl92GfU27GdGb3UtkkPc6TzS2rvDtU1iCsQNHOcl445Vo0FQNSpehnubwGi6aHi3IflIcSekSNHA6n7yH5UkQ5AOe/S7oHQp26od3/PhGzD3/4dwEAJ0SQ9Cwq5NYpXwdxVri9SKgyFRWriEFRFP53q/QBzSndP6F+QpDlJfEgRosctKMd7WhHO9rRjsZokYNTwwEIomW16p0DwxWNNPu7SbSyCo26yBes4OVsyJS5TbqjiXNwj33LMZPc5qx4Pvbc7wEAPvXCxwAA3/T0NwIAjm5YtdLtWbUTsoQYHlnVk7BMTbuRrzIKVTBMbuuwCpMe92Ron+2zz8foBQTyGCDLvODqusOqdXTPPn/jtat2/lNbGW8+Zqz1uhNiwfMpQ+WoUzO9af34ekD3NaoxFtQfH96xauyE57Czbc6QnZ4duzzQZzNWiGvMVuAK+5hudLt0aQtcuERXWHYqfXHO5LoR7DN7e3avstIqmytXjI3/+GVzX9zeNk+GMd0Z81zIkqpUafdZOac82GGOopBbolW8Qg5UZdeqjNnnTti3VgVUEEEQGhT6mnEl550/V2oQS+9fL/zfVJ1JOcF2PWYLstGpAAhi+/unP/ph+zvz7S9ffgoAcGaX7nRUhkREoFSNyQdC87GsZ4iZERIlRM6UsUEEKSf6sM3vi1CaSlkJdEIsvJsfz2UleTSM6awZNLXmG3TUA0osyIMpxCkJpAwhu16VvvwNuE1lrmRz3W+qecgsX37/1GNuenRUtTz0l5l6Tg8XlZXe6bGJNgS1fFPEpLcR+/Q+qmKiJuck4qPeUYGQiN3vAo94jA/o+HfPkMdKh818iwMqSBb7hgwsqEapI7suMXkCMVn522cMeVvb6PPY6MRIJYE8B8qyRBALGRFvgZdB5Sv/p+Bndb0G9I5w5P2M6YshHkUcyO2Q95S/l6/KZE7uj9xvg9DzPIS6yBm1w+yNu/vmqfLhj9j3Qjvz1gSs3oVqaD651VKc5yo+RelTUuFhKN1fZZJ47pVTdoqN0s//olUrtKMd7WhHO9rRjj+c0SIHp4YLHJKut7LzblRaxXtnQK7KVYHn1NAuygqd1CrZhy9YHz7q2PZu374JALh/x1bl8wmrLcoaOuzH3R3eAgD8r//7/wIA2Psr/yUA4OKu6bwXR6xAySBXlV/51WIpAiu69AJQv7WmTlsr3CnZt+p7b3bFlCdfgpXlgq/JulUAlwaPAgDeeOUqAODw2m3bICvG9Yu76HXZp3eqDngMPMqIvcCINnQbPeMa1Gv2eu2abfuV501DfOasVe3nHrbrkAysYojYsz2hl4MUCGrORnHsPQMy8hZ6rHBUXAmF6LJCuPyo6dU//9IVAMDrr73I9xOBSZt9bPV75YwoxYoq8zhyy76j9yGgUoSM9pxIy5xKikDhA7UyA9C4fiqtcp6b0AD1HmtVEtL/uxJhLE8M5RFIvQAeA+cW7+PNm1cBAPfu2Zx8/PITAIAzZ7Z4ftTDx/IWUPVi59TvylOe86zKkHBOqtI/PDS0YkFkZJMug0JIlF4ZBuL78BxYlS+VFzYSmiqkqXz7WWFx7s/IM5lMhp653+nzmqnPK+28ECFWnyIbKYOhqprIoXrPqvZDae69Q17TeyLC8lmijAQN9dYjzsk4FpJEFNNvUxVmk8Vecx8676gUR4WKDF6wbJhhfmRz7tYNq4jn3NTaplX+mdQMPHz13HeU3cJ7CvJ+AgoNaDmBjJV1XtNJlPOv6IofEEL/FOk+VivFrHYSAAAgAElEQVSOgLo8UdJERHLe//nUEAD17WPezGyu549QIKI3PJeUDopCKgIEKDOpTDgHyUeIeF6/++Hfset16wbPX46ZRGq9QIQ8gDdV8uQNFXpO8Lvrs1veXLN79YocE/lAWJTNY62r014IX9lokYN2tKMd7WhHO9rRGC1ycGoEQYBupwe26oHIVqPq58y58gUr7pL9u02m/p0/fxkx8+Xv3jXVwZ3XrwJYeoYrLS+iqkF+5iXXzE9ctr79aGrV2q//1i8BAD7w/X/Rdk1WbsKlrtz75Fk/Ocp9f7JLV7EuUYkOtcQdZttPT+xEF3QOPM6pEKBTl2NWeslVq1jXPfb5zp83vfPJLeNPHNzeBwD0d7cB9h0XYgMTQUgUw8eKoMuK1ydGsjI486ghBOl929fJPdv2gl4LSql0Z/dsn1tUgXheBSuJqkbIPp2Qg9qnLepeKFPA0IcOq/Wnn3wnAOCFVz4BALh525CEJ95hGQJ36RhXV+R7hEIDpO9n9ZcmmPF4HKsMZU+oR7ygx8B4wmyJaIvvU//TqtKIczIWo54eDoEjQUTzoaKbZUAdd1gD7Pf7moDXfDI1/4I4YlVNV7nr1+VnYOd39pzNzQ570DVVDY58knLG+cJzUx/55Mg8C1xQIXR2f6R9H47smq9v2X1ULkhVyFGUko/aqtUsa7qSyuehwwo8pYfHnKjehPMF1NTH5A2sD9a8S2NIt9JsIZWBfUQOmWw144T97DmRQrktpvQyOZrYuQyn4vLQw0NlKqvZ0Dvv1Yi8/CLhvnl+0rdLzSAHRLmyqspkJZmQ91DwmaS0vliOrDnnxdh+ce+uzaf923dRLWybg3VDbXY2DX3T8yId2JyU06WGEKiCc7FiDz1f8WTIWLULiK2pNKnIvwmyBcKc94n8hZCKGvXphaSIM6E0V3G2dP5r4j0QJat75H0QiZL6ofDIGrkr9BcpqwJxn0on5jV0iIzc3jf09//45/+nHQM9WwYDOw8pyuTa6N1L5f+iTBP+HES2XSlMPLpaVTitgQOA3KPY4qs0VUrOq5OcIaUPAD1okYN2tKMd7WhHO9rRGC1ysDrqpRudX635NDuusLtWxeyetz54h/3Qe3cOcO/QXPaGVALUhVaXXCXKx559yQ7d1SJWExGrMOWWv3blJQDAMZPAeolVWAH90B1X73Kr67kICzkaKjWO1VMc2HvUtxXDWW3pA+Y6BKz6EyIOeS1HQGYLdOjwxRXvOfIr7pxQ/33lGh5+p/nsqzqX/4P620JKZmSd59J+q7fGCunCI5aueO6MnXc2oR8+89dfeelVO3+qE/oXjeXeZS8xCAKI2J7wGHz/katrVZDypHD0ku9SUfEO9tqff/GTdp63rwIAtretJzscqsKU05l6zryncY3ZMZEjZUd01JhlDzi0408jO88yV26FVVLHI7v/eUGGuPz5lSoXyXnQjqWi+sFnLAQRcr5X7pRi6+d0xuyt2d+vXXkdADAaW8W/t2f3oE/HTfU3E/JpSrL9Vd2mydKVEQACqh+SJMJoYnNk/769bm4wyY8o14I9Yhn1B5yzPlXRKwmInJHXUBNR2z8wFKvi9yOJDVHq0DdD7oUuqE/xQOghIbIOuROq/CJ+H4RCKP9A6Yy6zxOiP6OJva6tEf3hpI/ozhecoq1L2aDqUv4MGpXv9+u8+XbvgMjtLMifkA+KeEMnNqeP7trz6D7RvYJKnb3dPaw/bHMuJVLI4h2lshL4jJllVEzw/layJVQlLA2+3ArJC0nlvEivAY+C8NxK51AnUmEQ3SJyJAJEPaM3yQldXPmM207sviacP+LcZEwOlVuj+DBCEKpaXA1W8xLYRA4Fn3chFUUdem787E/+LADgxRcNUROCdjKmn0Ooe0ikoGpW70JSlJNSS82ifBUiB6hqr1bwBpeV3rvikChPhurULx4M5aBFDtrRjna0ox3taEdztMjBqVGWFcbDmf9ZTOKESMHGhvUQK66gx8e2Or11aCvH/XsjvyoWE77Ts88O2JdUXSClQ589+Zi9eFUCk5FVHy/eNbe+u3dNEfD1l60iZ3Ce75OKxdrtpr7SUYUiJvzwwKpXrbqLxUwnbsfA96tfP5nQ7186Xlb1U6EBRD96ZETnZM4eHY6wuWar6vOPmauaGMtTViFzKSYCMcObLGqxk4UoJHQf26FO/SyP4f6hVdQ3eX3uvmy8gDWqHra2t7C1s8l9kHvA4xQjeplQZ8es3IspK8UOHeEuPWTncuU1U1CUdPNbI2ojrwU5pOWsUpIYSGI775x8lTTe4N/s2M7tnOeJk20f2fEfHhiDfDS2e7G+ycqH1A2vqIlsvuSFVYhVLR8B249zqffYqNkjnmU2iYLI5uJwaFXl3buGfm1uEo3qEaWqm1wKX654LwU7hpS8Cjkydogw5FmOw2MlGdo2u511nD4hnylRKYdAc08eFdKU299PiKjMRuT/DKwK7g22uD3bfl3Q24L69jBausspEdPJ74AlrVcnCVkSj4W9ZX+ovN8p+Qxx2EQ3uuRBSNWxTNKs/Be+Ipci8CoFIiehJhU/ojY2b3tI9QupJVgwD+H2HUNQ7t2x+SNEYoe+K7u7RD17faDTrOR9u1p+DHJvVMqgEERxaqTaWAl+CCulfPJV2Qsr71+gwozoa0SfmJiv1YSKKfpg1EyjldtpTMRhHvNZJn4YOQyVz0mQysV+EkKRsO/vXQ2DU26VvE937hr/6+Of+H07RnKvQJdSeUiIH5ITWQtWVQe6HorszZvzTb4aQRR4VdMKTvCm4d1rRWv5Iu//ckaLHLSjHe1oRzva0Y7GaJGDUyMMImwMNpdLpibJGONjW43eu2O92Nzav8jplBbWAbpM2FLKWJer8lix9UwGLDOl7dErgY6KvTWrJNXPPWSv8JXXXgYAvOP8ewEA1Zyrd65Cp9Tq5/PCM76lAVaFJy6FXsVwVf+731fGua0/x6xyM/VmeR3kBBGwag1YEaz36H6IGrdfYQXPCm/jglUqmfqUZD5nyhrgxlNpzKVTlx8Af5+x3Cj5/vUzVgl1NqxCVMrakH3A2WTmq9G1davGQ5/pvmSNA0DBnqjcHeNIuna7jmc2WI0yx/7F5z4NALj4kFX9va45KYZOVYhSHWskhETmc9mnsfJx7IknZ/l3++x4JM28IQZb21R15FblzuhKKY+FIte9YvXeMbXHRkLfB4SIqUOfz6lkAFMYS7tWL79q59Prs1fclbMb57eSML0rI4+JPgaqsGtWqYfH9/k5x3NawJGpvslrGQXivSg9UWx1Nr6VFSEXxxlVDkfk0XCenDtrSoo4tPMtFrynrDRVOYt5XtX5UhvuER71/5sG9bOpfUf1fn2/gsjeF9Pt8fye3f/dLZuTic+R4HdVHgXKSaiWaBaItggRkG1lJJa+KkShFWTSj49snpwc2D2UokK6+MeesMTMwbo8CfQgUrZC5RNOHVHM2kdk2ks3UniIvag6LfSd5HOm4DEtqOaoPFLAXQm54r3WZZ7VFUoiST2qWSJyDMSZSPiZSChdh4iLMhnW6sY2pYoKhE7w+7LgduVAmvLB3OnzOYMKJTlHQkI/+BP/CABw48Z1O4aUypBKbqXwnwXwpmTQtxviPvl/bpSs6EKvRtBYRXM8J8G/Y7lT5x6MSWKLHLSjHe1oRzva0Y7GaJGDUyNwDmmYImP/acS+v1bI8vWeTViNlNLsS4FQoqLrnOvbSrhgJah+NGIJU7mKZldJ6Xtz9trW6PS3d5Y610o++EQDlFNPfkGovleeL6ulQJwDuoSx8tmUJwCrCDHeQ2YDxOyxh2SCj5lOpx5tJ25W3kEtpzxbxq+N13HvDfN5uPKiqS2eZoXXYxb6wver5YCnVTOrGDGfI2mA7RinfH+Hq2z1bzOutOXA9xCRBDiHOb0AFnQ+02ekpFDxFoj5Xiu5TqUS2ehEI9bJp3jicUMQ3rj+Ot9m12lrVw6CvD5xx+v2JyM7/j6Z4UGwxvOzYzo+vsvzsG2FqWVNTCZWtXS7TcRpyATO4cQ+VzH1cn3d3tfpmKKi1x/44mKeW3WZl/Z6eGQcg/nMOAeXH34MAHCb/eozazu8LlQlSGvtneCoXuG8UHWfLWz7SxZ2jI01JvKxb+tWtd9eE878AqISo7Hdw4qOopvrdkwxM0WqTCl+VJwEBk3Vcrkr7PNSDNR1jpzcm4iVc8rvg7gFchlU1a5MDikeej3xi+wYNpnyKB8QqWOqUr74RBCUqBrWS906q+xI81pwJUGafEbVwb5xLIZHpvaIxJehkuTSM8ZJWiNfZBnewB+dL0F1Ul5d48tsFq3FQu573ATPPyuW6YGA/wp7ToG3CBRiQtQmqXX9hEjZuW5WDgnb7yf7dAo9sTmUyOGR+Qw55RhFn88FGjnEKY+V10tKLOSCnOwPKZ+XEY9F5zoZE5HLptjdtbn1L37tQwCA/+eXfw4A0O9x7pKLMpwIedP1470Lm9BBXZaNv/v0W/494hz2yYr18riWfh78WUgSPyuMy0dQ1A8sWqFdHJweZVHi+GCImjDqmDe/v25fvAEfbHNGlcqqc8Av5ubaOiqSlUA5jKQ0JUk4kq94a03uu/DtAZly8B8BvuHGTfvHYYMGJWPC55OR/YPVFSxZ1/7h06e8pyQMdnSswCWDIte5SIg7NCUhTFrwVXDhgueih3wh8yAfi8t/ZEt7kPc3+9gujRg2es2u4SuvvgIAeHLdYn57CrFR3C0fkomCRSTd0kzn7+d8SIqw2KNkUUZPHsrU4iuovZyvS3KoZE2V14UpAIUmOFpwsG1SUOLYTQQ728sWWxr79+3e3L77mp1L8igAoNOzv6eui27H2kX1pv2Dsb5hrYhu135/48ZVAMBgza7xwZGRHu8ffgYAsNbjQ5D/4Iy5UDmidE9x0guZvOQM0+qbcUvSWYNifsZTfca28foV29f5M3K5seswndk84fPUtzQiEjcLHwHNlgnNdIZjmUPJHtYuWK/fR5rq/iqKmNA8Wx4KKZLkccZ/oNb69sDu85pmnDdDPtQ7stfVU9KTvLj4YDsjO9VK0EI78m0EtkkKSRX5jzu3MRlnjfetDQaN96ckHCb6h0kGRXHz56XErfIPcgXq1GwnjFmYnNBeesbQI33gzDlrQ23s2EK0EizOvy+UpsWFr9YavmuhmOW8gOP8zhcKAePRKqCLC7Yevz8JoXgvhfbyay3wmpC31iM+4l6LD1k4n+SY3rCF5IgLsx6vbbxm+yzYZaq45kFf15RzkBuP+Q+zyJIKMvOEbW43onGZwrNimdOlA7zwOSOBH9y1tthf/MBfBgD86r/8FQDAmERktY0KFXiQfTpbo7xu5cqioPQSalmeNxObyrJ406JAY/Vnjeqtf/0Vjbat0I52tKMd7WhHOxqjRQ5OjbKsMRnO0dmQhMkqgXXK4i5eNBhZEN/dGSFgVlbO5V5TlLMKDbhMrmRrqqoilIUoSW/isOW002VVJs/RF1+08J+XXrFV7YVNg357NOioSKKrXI1A9qWs+AWb9wmHHw6tKruxbxKdDUr94n4z7KUmghD0JCujBavgaREVtXKmZKm3u4kYhko8FJvV8M3r1mZ47nNmIPLObzAEYU+wMI9R8j+/cpbcjFV+l6ZRWp3PaeTTpW1qwFCkhUJQECLqSsZGyFm2ubECT0Qwsn0WtP91vHdJKAmk4EO2Qvj3i5cfBQA8/5LJKa+9cRUAcOkSSZh5gvVNI4adv2BEwSC0CuY2pYqb232ej7UR7h7YfQ6ju43zmYwYUV2pjaTgGZke2byaTqzqv3vPiKFnzp3BkJbFi8KqtCtXDM1RsNI7n/h6AMB4zHhwtpciSbYiollOsLuhFTmNvrhLzBf2e0dUoN+370+SOJQV71fHfqd49OOpffhoaMcmg5mzlNzVjGoeT61qq2irnKYkXMq4rLRzFMlUREQhUbJ2ruoSUayyWiE/CloiKY5ytxv37J4cHY75fnvfhLLBwLfERDgkQlCu2OgKEg6WCEJOaaVMvSYMEFJrMCZJcu9hQ5oGjFcPkyYsLqOiXHI4NFE9bzykFqkQtqzwoU8RSaBJIqtptix9343nUaklyuvm46OJhpZCbXjWMhxSJ3FhB318RNLw/jE2+E/R4Dwj6YlSlB3bt54P7DLAyQxIIVg8HwVrOa/LtJdQ5nO8rt5uWGFSkX2vrl59DZ/+2KcAAI9dsmfsB37ohwAA25RR/3f/40/a9eKzycdKr1T1QhJEDhWYo+ukMC3//nKJdvnWp67p24kU9WtvTf3gxIwtctCOdrSjHe1oRzsao0UOTg3nHIIwQYe9xh6r1N0dI3Vt0gRp94z1P4eHlBHRKnkxG2FtjX1IVtUFIQEZ6cSRbXMp1bJRsvKjohFRT5ol2849Vpif/KRZ+D75p98FAJjRTjRldb+Y5cgzq0YmM4YdReqnUc5FtGFOks54odATe0lFAhJhr9NEDtBsoSHpM/aUfeB4Y82bF8kQ5zFKkK69Yn35mzQreudjT9kmWa0csRpXH7cUJzBsEjCX8h971aK9dDJNsnMoytyjEXJmFddAvT8RKhPJLD2CQJJoodAo9a9JKJIhD2WnFx6xKve1VwxBuHrV+v1PPPEMOp2L/IzNoduH1vdPWCGtE/F47jmTrOa1za1sfsBjIHmOZlD92D7XoVnStJaxk5ATq0T7rKRfv/Z579pTkM/yGpGDNaI3IpiNGD2cdmyeKLI2DMU9sZ9nhfVkM8oo5yOiN7wuin6WgZFzJfqM6A54T44oxTvO7EZuMSZ40OV5kUyaFwpFst/H5D1kuapSEg5DyQaFnjXjs8UzCMMacSIDoqbETlV2RpLjCclxMvPxQUqsPvu8h11KGkVc1N/LrEngE1/EiJtCsex81skpkv235LTiDHh0K5cxkapT23Y3lpkSz19fUhJiE1axOlbEqYAQX3MKKBBHwAcmFaq6bXhJK69L7GgCtaLlKwmCHu/zeXk45Pvse7OzuYHeFsO8OD+GPBiFODkiQyGj7vsicPKYpjz/oCAKwqpcskzdMoXlyTq717Xn9euvGqn4kx/9FJ6+bM+kcw8Zb+rmNfsef9+/+6cAAM9+2nhAH/7ohwEA6YDIpPhmHAHEA2natYuzoYhmkdFFwnWB88hp5b40JGCJGHyNIQfOuR90zn3QOfc7zrmhc652zv3MF/nMtzvn/l/n3KFzbuqc+6xz7j93bsVcvPmZ73POfcg5d+KcGzvnPuqc+w8fxDm0ox3taEc72tEOGw8KOfgxAF8PYAzgBoCnv9CbnXN/GsAvApgD+HkAhwC+H8A/BPB+AD/0Fp/5qwA+COAAwM/AKNU/COCnnHPvruv6r3+lJ1HXFfJyjoDxqRubhhCE4dL+FQD2xFK/c50f1LK+RJRalRGwSuhSCZBSegNWX2yhYsaqYs5leiomOFeT2Zh9YvbKfufZ3wYAfOd3/3F7v3pnGXkSSc8bKkmCGMmimDGmsg/us2KcsZ9djBTMw56jDFNKKQlk8SxqOF+4LJ+Etqov0gIzsYi5/LxwwVbh5xgL+9mPWAzyyx+nkdB7n7R9P06JGsuVTNV9KIMmx2OWDe7SggRYSteCkj15FyJllVSLucwqQz1xXwnLBpcVb0efy8nKliyV+xACMxzaPer1HgEAbG1Z9XtyZOeyufEMkmiT7z3kpbPPbNBo6OTEKpfjY+NmFBUjmgMiT4wVLkNDqeaFqlv2mBkzvSAfJk7lMmWf3795A+cuGXpxcGyoxSHlcOuX7DgPJ4ZSjOZU6bAnXzm71ovc3p9xLk5m9r4puQwy5EqJpMjQqEMEotvvIuN9Ozq280hjq9z29ox9r2p7xG3H5GYkjPCtCsVvi1mvfRFZUj+/1t+JqPDnTqSKvIfx2I4lpUWxi8h3oUaz2zElwKOPSBZ51a7DgrJRcnp6A9v32paMuGxfo5EhKlOqHIRICBXY3NlZWi4HTSRRwxsIie/C6e7Dkfj7mMe8LDSbKiDv1FOd/qu9St6o3nhOJEXOzXqtKcmkWhuh1Bis56SEnA/tDYdEhRRYJUm10J01Wjmnm12MaqJPjEGuBV/IapkoDij1nmWyfJes1BC5IKXCSNwSIq++Z08kMyXn5WMf/TgA4IXPPQsA+Pr3fAPOM5r84MC+qz42nJybH/yzPwIAeP554wUdzmwuR7Riljzd69fJj+AjDGmniW4VK4ZHAZb8BP87r2UkIqQ/iMfgeSxfogPTlzAeFOfgrwF4EsA6gP/0C73RObcO4H+GPc2/o67r/6iu678B4L0APgLgB51zH1j5zKMA/gFsEfHNdV3/lbqu/xqA9wB4DcB/4Zz7tgd0Lu1oRzva0Y52/Bs9HghyUNf1b+r/3dsJMZfjBwGcAfDTdV1/4tQ25s65HwPwG7AFxs+d+sxfApAC+PG6rq+e+syRc+7vA/gnAP4T2OLiDzxcACRp5Q1TpMmWOYVW2J0ee0aJrfh2dtUHz7yRkAKXNjatDz0v1Ndn35YrQHl0KB5U1rbFiHp+WpGqp/TitecAAL/xkX8FAPi+b/teAEA24kq5XPbKc1YVsggVM1xsY630O6HMjmyfGez8+9vLig8AclaMPoJWdryskBZkr487CWbUaSdEYW7ftWprh73ky89YX++FT34WAHDjY7aCPz81dcNTTxmS0CEjOKtkRUz9sgJrPIFa7HQxxokcVJHPnhXDP0kVHEM9P9nTC/bGF+yxx85ee+z79mhMNSNX4+iAGvueoQIKlNmi2uWRC88AANJ4HVlmfdYsN+Z7v2fH++pLVn2kXRqq1Fat5OSLiOKdEUGoOH+mrOKHQ1a7RG86vNcbW+TFDO2e9Da3sc7Y4kOqNcTriGksc0yVwox93A0hR+RiTOdWIU3JqF8wTjfPNc8YdSwEgddZVrbHx8cYjWzbXfoWSGWiqO4FfQ1CZ3OuYAmlvqyvwqQQ8DG57Nf6Kkwx4zQ9UgUtP5Fpif3bds33duw9g44MZqRasOPe3Fjj+RjiosZnyedE0iXDntd+yu9wRLb/2fN2roGo9p6JXi8rQlbjSw8E8XukFCH3QJbcmu+qTrmdZcEpo6Xm5hSipstZOiDk/Uu4j0gIwkykA3uRrt8rJOb2/hkD3Eb0nBgTSdLzMmbo3PqOVffyJJHKI6sLhLxWG3zWOgVKKYCICMBCnAG9j3b1Fa+tnn2yrHacuwnRwAW5Cv/6d38LAHCLRl/f+v73AQDO7pzDwT0iZ11Djkp6yYxG9v145ol3AgD+3J/7DwAAH/yffgIAkHKuKSOrdkItqOLqyBqc128hG3Ib+pezrpdAz5v+htX/4Y9ChB4c5eCrolb4Tr7+2lv87bcBTAF8u3PuNM72hT7zqyvvaUc72tGOdrSjHV/B+GqoFZ7i68urf6jrunDOXQHwLgCPA3jhS/jMbefcBMBF51yvrtm8epvhnPvk2/zp6TAMsLUzwHhqFdLxyCqFvb4FqkxZze3tbjdeb94wNuvudookUb/S+s4XLppWNlZYB6waXeSK97TfX3nV9nWdr0oQ7bCPNWDVGtIh78orZnkb/1HyA1j1zBbLyr5A0ya4r9AS9tDF7B/QyjehxfFoZFXuCeN15d6W9hhvytLBVwzssUZzBZYMl7G1ZFN7NjH7tIOzVm0/+p3fCgC4f8sY/vfeuGHbPLQKeveC9aLPXzJ/gD57hWko/wMeC7X2EFub5V2F2rPla4U2kalcVvQ9oELCsRIMAlWpcgpkhZiQMZ3Spo2chDm5DC6zfW5t2L1fV4UUHeHePeMUhB1773PPm2oj5P2tnPqzZmE8pRdFypKyQ75IGkjXzXnEax+zJ99nZHFCPkmqnnQSYsrK7oi91EjVJJGTERUOCc+rw2puTj3/ZKwob3sNiQp1WL3J4yOIFdlMRcKxnct4VGBry/T625t2nPL3mNMZtNu3KjxXcFIpN0uFfxHNC2RNzXsri3BWaeIJSYt/TEXR8IR8gSJGHLHipwJGEdN1TTdFsc/Vk/dVvKp0avPputfjvAgVVKTHq7fjbvod1HXlK13Zn8unw8vXi9UwHyIpK+FQ+cq2V191HSpvU710MUxycglmdeOTkdQW3OeQaOYxuQQLoVs8ONmxb+41UaGEzz7vCK24afmvBKG3PZYKQ2FnWS5kjK/cV0yfB8fvBe1hkPMcIrnEdg2tkNvt733KwOqMx/JHv8u4W10+u+7fP0HK+zgil6is5OapiG/bxze+95ttH4okrzVflk6Hdp6MuJedts6t0HzSP8N8Djn3JfsVrCIGVVU9MPTgq4EcbPD15G3+rt9v/gE+s/E2f29HO9rRjna0ox1f4vha9Dl4U3vlQX6mrutvessNOPdJuPobEc0RdWxld//AEIGCvec1Vt51bavQwaZVCI93rLp1deG9EcYM2OlSb3/hYWPA3j+0Cnmd1WV/YKjEzau/AQAY3bf+1lbfVpnyae9w1amqNSdrdzS09284q6jzPPOrbjmcyQ0t42cUc1vLhZGXLeXKOCTzd6LQG+4jlyuZmOGsTh9iUMm5xCqFbhCi4Mq+ZjUxpGd6FTUro3TL9nWZCpB3v9OELrfuWYV35br5Idy+bz+fu2iV5+55qzwH9JXosFpzclST3r/M4Vj5iUMi5vqC+nxHBUkaKXLW7q8Y74pbLspm9VrL5RJ0lgt4/h2ysFlQX73ySXz+xd+x89g3hcsZxvu+8xnj0d4+MCRolhlysGB4Uc0eeUzG/5QC8JDHGtCt0ZFS3mNs9garWVnAdQdd3L5lqMxrLxtqsbNuxy/XuAWRk8Fgnfu2bQpJmlJjnpMpLk6BYqNpDwLHyOqTE0MMktS2t721hw6RnwWRgojqAceesYLHqkI6fdumXPpkKqf4aHkviCE+ZYUoDw/xQ6YT8mQYyLS+NsA6g5IUq668hyiRPz+19ERCBnQYje5TMZFL9UL+i5wA+Spvfayos6UwCMNl/LNXI2bZJDgAACAASURBVKxQtoK4+Uh7O917+TZdav3Wx0XXp/9qGUmBmuT0jJhR8TIe2/fj+Nju/5xqrTSV2sLqt/6GXRcph3xQFXcu3kiWq6Lm84jVer0A3Fy+JEIfCSXwfHXtOwpH48SQI+SC0EPIZ5MUMvfuGhL7iY8bYrC2aTyCZy4bt0moYK35GHf981IImvwpxF+RA+bOpj33v+E95vb6oY+Zkqzf43O3VOYGVQzeiVNBdwqmslOt6lMBzCvI0NsNTTHRycIwQFE8GO7BVwM5+GJV/vrK+76czwy/guNqRzva0Y52tKMd+OogBy8B+GaY9LHR/3fORQAegxE+X1/5zC4/85GVz5wH0Adw44vxDb7YcK5CGC6wSZb+4bFVb+OpOcGVtf28v28r68ceserv8hOXbANV7rXdz37GoopnGaN32XtfV8Ij/eYzMr4P7liVVbFyWkDVnO3zhP3iXFriE/bDWZEP6EWfFwt0GFss3XlJ9v2UveN+rPQ9bmth++iACYfsIddktR+PbG3WZULaI48ajyLp2bnmSp4k+3ZS1Z6PsOC25VDW5b67iimVSxh7zDkrqZ1HjWOw8Yitzg+OTIM/Y499n1HFh+Mhj9U+32e0a5+96zSJvVNfTe+FQpWu18SLD8HqIVVcMPX9dJBUddqlNn+eySnRtr9HX4w+2dk3r1uOxI03PoZsYdN5MrLX3V1yAphfcevWq7bPhV3risqKKePBA3JTwKq9N7DP8bYjFieBygBVvTV5FePhHLfeMBQm4bVaI9ek4JwTz186dOVWDPk9yDNVt6p41FOlgyR/P5sLYbF73Sea0en0vaIjCFcrZilFyPQnitVJunxlBod/H5nx9BA4vmfz4+RI2QxCBex7tkVf/J1tQ542BruoK0aWM2tCeSgg58A3dFmWzedML/QZxvbS69qcS4mc6Q9V1Yx8FufAxzK72leTUSx3RuUWyOGxmfAHr7po7AqJjxdvJiKuJiNqlOQLTKYTFCdEXUa833RwlIIk5vNka9uq7o0tQwxUxRc+CZJKLPXUiTTk5bKXDizj14XEJWWKNfJXel0lfioZVox+6fupVvAupazO+azr0ADimImKn/qI/RNzgUjd+Qvm9TE8sHMUgrLIlQ9Tev6Hqm85GpYQsjLl9TCE8Ef/sx+1bf49m4PPvmCKssGmzd2IKoX5TMozG8r7qFZ8Dt5q1CtQgM+tEDh1Kq/jQY2vBnLwr/n6J9/ib/82gB6A36sVHv/FP/M9K+9pRzva0Y52tKMdX8H4aiAHvwDgxwF8wDn3QXkdOOc6AP4e3/Pfr3zmnwL4mwD+qnPun8rrwDm3BeBv8T3/w1d6YGEYYG0rRUL/+gW1tVmuNDr2JMkovnffVooVV7fveMclr0qY57YKv3HTqrXtHatg1qiZVhb6jdu2wj28bxVjwFV6IQ94rjMnc4Uu2MvVG9aj/vTnzef7j3/LH7NjqQvPrvWsY66ms0xOZVyNx6qM2JOXl36mfjYT4c5Y9X72rFVdynWfsC9ecuXrWfuRQ84V/ZyVviqeBVfdKbfdIy8CvOYgGuH7+jyLHabS+cx01rkLptppNT+jBl/udAEcevRr39hktU7gRCxsVa9yPpRPvdwsQzoEVlJpcB8REwF3to1PMmCv+nDfevrjsd37ND7G2V372wmrtKDk8dHBbyQGuAjPhXrv9nNBVEb+Dhn9ACQU6A+U2UF1yOSY19E+d/2NawjplbC71eF5KwGTPVXmc9Ss7KRqkIdCQDe60/1qe7X/mUyHPF+7Lhvs7/aIkjmEHilSH3/pLkpvDfJhhPaUdFS8TT36wQGzJtSv5uf7iaET589ZhbhFNUSvS/4HMygCJ2VC7R0ynS+rpUogd8CJH2M/T6nSkAtdwjyEhOiGI3JS+SpPPfgmkWD5c+1RhXr1b+It6PesjCMfbMK3+XyQlQ0pU4DzSL/W9+/wvj13xqMREl6bDr8Y5x6y+dwhfyoSf0jVqTwVghW3Ph1bqbyIsPEqvxWpn3SZwjIAJnT85IQPiIT1qZDS6WV8Jpf+ntk+N3jsx/dt3n/q94xjcGnPkILzZ+2cJlQgxFVTxREQmXIx4Mg5qPk6J4oxo39D5fT9oKKI8+Bv/c3/GgDwd//+3wUAXL13jdtBYzjJNFZtDk9dSf8XOSK+CRDgc1DnIT5DWb4JZfiDjgeyOHDO/QCAH+CP5/j6bc65n+L/35e9cV3XQ+fcfwxbJHzIOfdzMOfDfw8mWfwFmKWyH3VdX3HO/Q0A/xjAJ5xzP4+lffJFAP9tXddfkQFSO9rRjna0ox3tsPGgkIP3AlgNQHqc/wHANQA++6Cu6//bOffHAPxtAP8+gA6AVwH8KIB/XL/F0qeu6w86565yO38BtmR8HsCP1XX9zx7EScRxhAsX9jCj+PqQ6XS6SBsb1mtL2C8dn1A7PbbX51+YYW/PHBF79N0/ppf+xz/x+/ZZMlcffcRctg736Qw3l3MctbALua+xP8lKoGRlOKMz3stXjNvw3f/OdwEAwkmAishByv5d6HXWRCEmVuGVwYzHSp0uV6M9pk922d89e97We3IjHB7SKY+9eLGXo3jpJKl8gogcg0DObPT6r+S26J3dmDEwol6diEDKvnifVUyH/buKS2mpOXqbViFWSt8jQ3o+n2G+sP7iHfYhEYz5ascf0nWwH9rPMX9OEyIHoVbvZL7z2MQolrb6jUNLOVQVPFijj0INHBwwJZFV1O6mzaWcnJHJmH1a+xEJL3ZMropeI/q7K1LC+/RvWI9dYXsFr8+c3gV5Pvafkd5aOv2JVCys7CpVTFNSeDh/AlbaIRGEkDT3XImZVC/0B+IYMGlPthGu9p73BatLVas9KUkqO97Du6bquXXLEIMRnR5Vpe/u2Pdsd9deBz177XeN9+GQ8LyXfH1gyQiv68rnMCyIoEgZIY8OBYNIGVQqv4DXrUtuSSehiol7UjXnVmEB/X1Z3vvquS5XUAser8ccfFHpGj9zSiLj90rV7YSqjSJv9uz9PaLKZ+/8HqJ00NjnEq5o7luHsGqpoCI+llrBp3Gi8ap5MJtrrivFMPBpk+q/i3sQE40Tv0dcFVDNosTZwxN7zj77+c8BAB565GEAwIVd4y6JVyPkSj4g3idFqBFq39BXom5N/le3K9Sry2OioywTRTf7hrC+5+vM/+D1X7WcFCEFQq00KiIvnnLwFhW/p5i8CWVovlZvBh2+4vGg7JP/DoC/82V+5sMA/tSX+ZlfBvDLX85n2tGOdrSjHe1ox5c3vhZ9Dr5qI4pD7O5t4I4c5Lhq7ZBtvbZu1Vmh6pefWyNb+eTkBDOyrR869ygAYIcVzoRa8aNDQyP+1b80TexsRCYsUwRjJnZJUbDBfnnFynE4Z7VL/bNLVbWp0qjR4apYPvLS6Wqpv+DxL1ilebbxgg5/1Jy/4wkDfgpWVtMpe25aWbMCyOd2bmt9+1xSBchZToS8hjUZ2gGPzYVMPORVjLiq3uIxTLmang+tArpz1/T//b5VFLvbVnlvkf9QsqqdsdqLWGH0uh1UtX0mK2wfcqcUL2RCdnY2Ywqbo/8BrKcYOENKHHv2cpJ0dCWcj4mO9Ni7l1sblQeduEKPPfb1vh1vQgc/IUaoxAPgj/aChNVKNxHDnVwDzpMBVRk6X59CyKTJ4dDQko31CGDVlPtMezveNFHuvPToWeNYSs4fudLFNDToMHG0Q/RCZH3xRXI6L+ZUfSRxB72e9bclUpgyffH4yDInDtgLn5NjsbNl3IELT1gluLVBhEDnTRSjAtU60paTCZ4oKY+8CakgaudQ5OLmMAsilgqDvXH6WOzfs/khbwY5Q2rbPSojJMmpiALIk2G1IDztlLikIxA5YUWce4SN3zVWzhlVBvMpPSd4nRb0LCnZs094bwZUGPW8TwDnKgkjZZ37ql38nUzJiJCnxopro3etlHuhTpQIJd+pORl6BUVTceGzGuIAjlkjrpYro5xP6d5JFEsuhX1+72/fuwUAeJ4ZJds75h+zfYa+Msw/KRdEHvh8cERXxe2a0w2zKnKfZAqfrEvHUDqezoj2SeVVETmQp8bagMmysR2j/DO806LIOrzXpU8OxRcYq38kL4LPeCXlmkLGvcX7v/zx1VArtKMd7WhHO9rRjq/h0SIHjREgcAOATPI0tJXf2hpzDbjMn5L1X7Ph1+lRU9/bxH2yZY9PrAJ65OHL9nrRWLNXXjeXutvXTdceMMVxsMYemBjk1KnnYLXKXvWA/eIOkYOTzPZ3d2Ys7rWtDaBQr9Q+m7pmE3FrwBRB9konua2uZ6x4Hj9r1VrGpazUDetETjLyISL23EfSRVNzHgcdOOq25R+esSlWcBUdp9Ljs4LJ6OtPB7vNTUNrAvoWTMf0mCCCcO3AqvIzrBT2LliloOqvYOUchTXySqoD2+da36rPlGjNGnvkLrPXsrRtF/QWYCGN6cSQhfusbodMaUu79r7uGitoVvlrSn+sZuiwgsO+XasZlR7nz9tn16nWmNLuSxwNcQiUf+F4DwesYvp944eEiTTnrPpds0fbSzoIPHJgfxvzmm6vm6pglIkXYhVkl9tckMfRo9unfDAizkU9RoqF/CNYQZOTsL5uf09jYDQ2hODmLXMfPTiwa6n5LaTgMr00tjfO8zqoL66+rW/W23nKgyJcImgAfM6Id5JT/CEC7wWhKI28aPI71P8+Gdl3K9ec8l4SfHxynwV9NJSEWL6pec/9UGlRFAufrbAYy8U04ys5OfKFcEqhtG0kdALtky+0RvQiZk6E/C6iVJV481Byn3Lq9Q8+ryKJm6ie914olAjJTRFx8L4F9DdQhTxl5R0RJZMjZUxUw3tcRPXy2vEZFBK9zOjr0dHzgoqQ69euAgBu3zZOyoUzxi3oMfX1gHywDvMdaiqPhBwUCnPgMy6ir0iW595GMpK6gvuWP4ryH9bo/DkuNMfsun3Pd36/nQPnx0//wk8BABIq0ZTBUPGYCqEauhMBTmUmfGEygXPN+j4M3f+vHRLb0Y52tKMd7WjH1/BokYNTIwxSrPUvocfKYXJs1U2VWbW3uWXVH0n52Nig2x9X79mi9P3bgqjCzVumdZUvvVLozp21vtSYutv7+3R+XtjKt2L/NuN2Kq5040h9X3u9eucqAOCQ/IG9c5cwottiWpD5zFf1+DobZHQP6BU+Y57DGava1rft77Op/V6chfmRVc5z/h6VD6G3c2Tfs7+RImCfcs6ecpWIAS5ttBQB4lTYn4uoya7Warq/aav03oApa8fkItyySvR4bKv3Mxfs2Nc2WUEGC6/HDxxZ5azKIyePfFZXzNCoeAwJkzV7/NzWllUnWWbXYTIxNvL+voWF3r1mVUxeGLI02CAysdH1/enumqE2C7rtFbndqycfYUrh0OZcyN7xBgvlHpGYtNN02Iwi+1lulmHCSnTE+ZKy15wmqOkAGHJOpRvqidpxjqmAefiCVetbZ+xYX3rFFDGp+C2s8hNyD1JWrf0e5xf9MlR5379jrpA3bt7AdGr3bX3d7uM7HjNEbXPLFDHbW4YAoeR5FYROcuaBsMcqMMwnaBLdcLVcDptsf1VYBSvxMIxQ0pejZE+8qNQzpwsjuTTj+RF3ZtdNiEl/zc6hqJV4aPM/4ves8OUf+8I6UlWg5TJBr0eVUocKiMD7Qdh5R0SjAj6bAqfzVgKrqn3ukv+XeTkET0EZC6HUIUDOqh3MmIgRN94rDwXZf4DVvapuHwPA96l3PiOSUChLgr17b4dQiCeTI5QnRCx/CyIGej4QQXj9FUNcR+QiPfmwIUxKmh2RDxATISiFvPYV9ECOk9AQZTRIDeEiVFQEFeIxyVuBHKug4DOYSGQc6zzJUaBvxA9/7w8DAD79CXNp/Pyrn7Zz5LN9ToRa6qG45r8jReZRlVXkQPNYSbt/mKNFDtrRjna0ox3taEdjtMjBqbFYzPHaa69iSM3sjN7yYzKDR/QzSNnvk7f4Pern8zzDGlm026y+5Xz4/PMvAgC6dHK79MhTAICKfe/PzKz6vH1Ef4NarGXq2nmnOl5bbSvq+7etqvnIb5sH1NM/8BQSnzvOioA9P7mPOb6GlZ3HOVaKO9SMZ7TpK+mNPqcjYMY+eYdVTMie3Gho55iR/e+Oj9HborKDPI25kA+qLwpvpsZMBPbjlGinJLdAjGlpqvk/vS1bnT/GrIoJ3euOWZlK3t7pASl7nIF3vOMKXx4JrHyzUqoNsfnZ/6X5gGNlEDk7h62BIQsxVQyDge37LhnUt+8YanT79jE2BnZ+Z3atUi5ZwUyYd/Hk45YStxgZp+IeEaG1dfIZunb9BuQ1rK33eV2oGGBPPqTL4Zy9/W5XKXUR5rx/CfvQC1ZLc97vgo7lScfmwd5ZO7/9Q0u2q52dwwYTRdPODq8nXUFZYb9B9847d2/yOsLOfW8H5x8yLfgW/fl7XfWfmcvBDJFlNgBd9tiD9h4LfHRVrHLxplRCDhZe4hfokVfWy+MWGuf9HPieY2YN5HP1we38o9DuwUPnHrHrQKdM34v3qIWqvCaKoewNVZA2hAQ0z8Pr1xWdwB8LbU3Vuqp4fU/QfPUAggcSlg57SUTnyxWjvlpsem5bqgPxXoQg6BCLQt4izCRRUiK335GXgKpfiMVfA6VQBNtWzHktdcb1q8bVKsmLefxR+77II2BCfweZKnSJamU8BiVASmlWk0flnSXF6YgiBDyjjAoGRx5Tj26Ni4LPRz0PiJIqn2HGfVV8Dv/J7zbX/xeuWtaKlBiDxJ47i5nUb0uUIAyX3wC7Lrp2K7GdK0M+GQ9itMhBO9rRjna0ox3taIwWOTg1RuMJfvu3f1fGaEjINt/ett5izf5VSq1+lxXpiMmAcZR6hqoS/WKuOvvyK2BL9GRoffskEkOeB8Fqfj6Ryb60sfbjnD21jhzz6DD3S7/4fwEAHu6fx3f8EctZGFFVIG9zta+ODw0Z2e5an/thujpKe1/IAZCHFHFVPaBXfkQ2b8GVdYdeDlNyF+7dvYcNrnTXya3IqfBQbzFRjgMrRq26dQxaAVd10ydBenUlvg2IHMRUA3QLoTxWOYeVQyT/cfVKPQohnbu04qxomA3Q6bAyIvO7mlt1klEZMp3YzQzIUu50bHvSWtds0t544w4efcxUK3vbxlv49V8xn4sosLl1ccsqoV1mJEwoBOjTO0EoVdpdYa+Dff/E7k3pG8BWlkppU9fZUm/tS1w7r1qZC7t2LFvb9plDJmHKhTDkXBVH4/6+IWZ3+TqeGMIg58DHHn/UrhvvcbfX8b31PhEOEuOR0PeiQw+JiFV6TW+KAMp1YPYGvQYCXynRvwDiDYj53cxHiCQxh0PIyg5ERJSEqXTFmhyamt/JkpVxl/koPoVRioKVqi1kJbhUToDbkyuo8/yGOlTyp5L69ErlB6+51Cc1mq+R5yCsjBVlQb1yLKEDAs77cLXo9JGO/CyfXdlC/ga2zwmR1QlTB+WL0O1TvdPhPFP1W8vtk6+IfYaIoI/p2K7HbTpkrnVsbg6YUXNyTIUUH5xxRA8TIgNzurcqvdH38PmcCZ2cEsll0QM2AEreb+c9U/j8pEohINKk6r6kWmGdz6Blmqkhqt/8DeaY+O3f8n4AwK//1q8AANK1Jk9EypUoinxejXfOFGKgFEaPIDT5LNUXUTd8OaNFDtrRjna0ox3taEdjtMjBqeFgDmrSDkszrDwEsZqltY0j9SBttdrvpahKWz0fMLErZiUsroBjpT/iyvj1l4zJfXLE/hUGPBoyhn01Ij00mbQhc+/nShCzFeY/+5mfxrufeheApaNjzveoWg/pEb9DdYI4CUobVP5BsVCfjooJHov0v+rTZUzO26RvArqbmB3btoKubWOdaEtEN7WwZL+Xy2ZxK6pYq+emllw91Yiln2O1M2aFIMQGrFp29ujVsBgh44o/VR+Pq/IKTca34zaUyyBvhphVaofVbqDc+oUhRveYA3D9/uvcLhEFyDExxK0bVl3v37b7dnRkqEvgLMHxXJcIEZUOW+tMJ4ypMFDCHb3k5fIYEbVwRApOmOWharhLVcNsdoI4YW+Tuupu3/Zx9oI08nZ+ctE7JrrluQlD49zM52S8E0k4y2u9xya6Uve67NHmnB9BWCOm6kKpi1L39FLNe1ZwtTwHlGyoJLumG5+UAJXPIlCaJy+Dq5uvfvvOo1Ihmtt0K9tWj1w+/5tUzshbQt4JZc2+t1cv0KvCyXmRx6YSvY58JV/WQgpVGvJY+N2MPALCyyCPAVkF+DJPSgJ7EQoaxq7xOa9eCOHpDspp8K6MrLIzunjOpnaMi7k8Cex98vDYIA9IuQVJIkTFtitOgs6BwiUURYWE/xTt79sckwfHma2zPBYqokb0miAHZUoFwWROg5BKHhXyCeF3lxwEx2OSN8MiEz+CiZvO+apdnIJA+S9ETHQ/hSgIQYiIKOT8fZfPeiWq/sif/QAA4I07xp/43AuWqDug6i1ngmqeFwgUhBI2kYBlUiiHV76IJ/XgUhlb5KAd7WhHO9rRjnY0RoscNIYDEHh2blCsVLE+M12rNPtRZltBECH1Hvj2O7HSe5GtpkfHtvo8ouf3cKTVKftYPJJiJWYrFqNYK0kdCo9RjOirN67iZ3/REq//9l//MQDA/i2rWmeZ7evipUsAgPV1Y4wf7lu1GRIxkO94zRNUj3A0tEo5m9rOU5YrFXXAGSvS3c46DofUhlN9sbVn3IOU1WU2p+8+V8JlLM979neJJISJeqTsHXJV32d1K814yeulnlueKYUt9n76qgTLImtsKwxVhXMbPumM2RFKkFQ127Uqdys1lQfYs5/Aqpo3bl4FsEzIy+YlDu5a71TWEBtrVBUwz+HlF6zy+bonTe+/TgfETFGYZMg7aqjrrMdjZfqgqj9WVH36KaiCipIu1lnxxlHVuA5K8RyO7BjGY3s9GdkcDeXRQGdNcXCCqMvtKKuClbFcCeXmSPQiTmIkiapovtZUKxAJ8rwQHpO8A0InhMReBQIJCdD29MUQ0lA3frv8TYBAu8CSitLkJ6yt23H31+x6zOlN0etr50TaakN7suweD91usmNp7IhA5rxOIe9ZHHW9vl4N/8I7BJJ7EfW4D26THBMHIQqshMmQT7gvOY+KzV/7eD/wWG1uL7I5yrm+7/zusEKek3ukrIiUiqKNTaa2DtgzD5vXWkOohlcerSgpNBfy8QIH9/k8ZBJuzWft0QHRKimnlC3BY8vlwshbksZCXO0XQjvmTkmqSo7lMdZSXCh7Ifa8Jw2vzqCbbaU8C/KbxGdQMqZUHz7Hg8+mRx8yT4Z3P/luAMDLL7zI7dNvpaTzbBwtDUDF0wia11jeM8o9cf79D45z0C4OTg3ngCQMMWC4T+n/gSZJkFKwBaVtk4mIKbLZLT35SDB3TTlczhXEjA+Yw/uEsUQMEizKSR6tGIoQ4VqSY3zOJ9sKOtQ0xG/+/ocBAH/mdZNHbvaMrOZjWs/aP0CyEJW5h/6xFyks0NNT+6SN6JhSz5ODIffNh8uUckUX+MXRrTdM1rdO0tpAEjZCkAoz6ZCIlxCKy2RqI5iYT5Zc//DwARzrXwnBqLwMemikvdSTGPWQq1dManJKkxJGOAd86CtoqWY4S1VKlkk4lfDpzgVbbG1fMOLe45efBgA895yZnlx//SXE5K5FetDIqle22Pz74YERKTu8Th1GEReS0aXn+Ht7QPdSW3RNcjt2WRaXChPiPxadOEGXBFv9o3V8xEUjCWXZnLbbnGvdjsVg92S4xLjkkCxCLWCLSmFQ/IfL6R/opmFLp9P18K4W3Hp10vW5JvFQ/wj6L0LVDPuptaDz/zJp31XjZ5FDFbccuBBhrIWV2oSSvMooyDa6u8v2XElr4h7bSjVtldkKqmr7B04LeMd9SiKrf/CKXKTaFP4RXDRbD2VFQy6SIjvpxsr5iWTM50ak541gds5dxSTzH8kZY5JF5HNBhDTU+XNhuW73YjPa5L6a7RXB6L4AUn/Ax03bUKGj887mWoTb52/esut37bVrWNAkTgXLdK7zVyuTpFFuU8+NgWSoWvSpdtKDlRND7QPN2YDFSEpjJt3rGo7hRafJnpxDNMHyJ07i4pQyan9Neb5aZKjdcMiWyZ/4ju8GADz7GYuXvnLfTMa8VHJe+PPRvvxioNRCrjh92stjeoCjbSu0ox3taEc72tGOxmiRg1OjqmpMJjk29mwVdumimZwsKAm8dt2gcoWf1IT4trdo9VovozK1WlSYyXzGYJiurXjXtxTRSghvrG2u2Af7YA0iBCvrORGt/OfiDo5o1vTRZ82280+8/7sAAA89YrG3gh6nIyOcraWSZNl5FoTRZX9bs5QUzK7Kauphcxo3cTvjrECfVXV/0yoBGaEcHdo1vH1gZj+OaESvZ1XpNivmLklO69tWvYY9waiEkQnZCT9RSExGiC9lFYg6QCFolW0FD4MGIlbaS7mwSgYByZSRVV+6l2CEr4KLpoUCU9jaIETc69kxf927vtE2XxS4+vpLvA62qfe//30AgKtXrto2hoYYCGpdEPY9e87u2YASyOMh5bRdQ4PeuGFkyM++YihFFtpE0nV84h1PAADO7G6jltUuq6jZXCY1uvYmwVSlH/CeCXbPWXUpFljmLx0af4lsGKwS8HjdwyCF6pEyU5VJkmyYND4rsyMv1fIhR6zi1EYiglRVTSjYl5D6usjCWIFUzvYOLEnD8C0aohO1QtFIouW9cRHhZGcQuKZRXcjCWj0/EfvYGkHzeqIAwkBVtaEP6wNDaXStFyTcdljpVr5iZCuHqN9iSlMtEvlmM/tcQrO0lIhNn8+flG25OIq9VFktOU9oU8uC3ykf/U4ELSHyEtBuWLLrioZGpSSelGWqoj68b4jjtddt7nbSHtbZqot4vBvbNr8D3ptc6At0rDwU/o+six1/VticLOMzPtOmNPxaiHSoDlG1usIjwwAAIABJREFURKBEbtX8FYBcedIf5wcT2SKSPdVGUOy2JI46Rhk47e0Y+vft7zNp43O/9CyA01LSAIu8KSf3yLA0n6tAQd1EGh7EaJGDdrSjHe1oRzva0RgtcnB61EbsGA9pm9y3VWYoFIB9UHjCnq3SZLebL2Z+ddxhBG+qqFH1zheUubCwHXpOgRCDtzEz0V+5inehiFU2ZNhRVJWXHqq/eOFhqz5VGRwfGbKwoGlJxuCVgvBFh6Y/IVfdEwYtDUkyHDOqWGYnZ2m/HPRoUBMn6PCarfX7PE6iDayQClZwc5J4FpRRHtKgaUZ5YEoJaMJ+3PqOVcQ7jJVOeZ295CtWKcBzK/MlIVH9XPUQee1ESmKh7INiPImLSJH62AISFkQOMl5HVS0iQ/Zoq/u+970fezuGJrzweavwD+8fcF80CKK0r0deyLSyPndZyJDJyEwxr1/at7+/+NpHAQAvvWrIw0OX7fP39m37sgC/dOkiHn6IsdaK9SURIibprRML5VlGxwLAPLN5FAci1NEUSiiZU4wyCbyUV8qgSyiAc4k3M/LVmCf3ChkQ50CPprcOUNJ8F4LkZFwjGaI3jRFpVh3w0za9kr2Re8PKOCeHQqZRNeXJMv+pKOVzvB4puRheAjum5C1qIigBEaueOBtB5OWTa7TXLkSC1TUnwW48tu+cEEjHaz4jzyeglHWTtuW7e8ZRiEJyPLhPSWI16rzyhFtJe0XI9NbVnA+KLl52/pumYqpeK1+F85nE6yor+eGJzcmLlwyZnYxGngwsPktMVGIhAgy37SOXZXZEFEyx0Cn5UgpYmhL1LRQrrWNfeQZERBqLovIIgc7Tz1EhAZJdK6pZPDMZcMm4jfsU4VfPQJ3LWn/QODbk5JmV1ZKQuBJN7i89kTQPFFRf+N+NP8hokYN2tKMd7WhHO9rRGC1ysDpKYG+D4Thzria5WusEVv0dH1tVFrGXNl7QkCgtfD9WfXmFIA0Y7jNjX/+IzNXpCVfMNStgJxvYVfOL5mH6VamY1fx9jNgbhKSs4Nb71r+bD63qlAwoJjKgPmbM2NNQvXn2XOspV90zyQPtuqzv2DmlDAMqU0rj0hwF432P2L/XiAY0vSH7eK1uGohIYjSd2LEuJFlipTA6sX7l8X1DGHbJSVg7Y6z+gPyP1Esjc7/qztkjTBNWfGRZz3L1H5c8BTt/Bi/xcwEla06Rx6wsFf284PvVRxZLezEr8dD5R+042et9mQYos6nNnZ09q9pnlf19sGHBXAjsdTwy++W0Y0jCa9dNBpVFdv77E87Fq7KTzXl9bJ6FKSDvqssPGw+hoPT06JDXZUCb7DXbVkT76LSnuSlDITXZmwhT6SWhS6QAwJL97WJUQdOaVjKxKFAKkeb/ckbj/2PvTWNtyc7rsLVrOvM5d3xjv4E9qMkmxVEkNViipCS0lMiyLTuI4wGKgQBOEBi2kQFI4igCjCAwLGS0AiGxYcGx4yT+ESWRLCMJLZIaSapFUiSb3ezpve43vzufe4aa8+Nba59bh91Uk/0iMkltoLvePUPVrl276uxvfetbC0BZN48pcyQhCRA6tC6fTB6BSgNzSQUjQM17J6cFd1mTO0PkLGV+WkJMSWwoWJbatsppaMYoP5fccsEy1EA2zOK22P3z/IsvAgBee/UBuj27h86dt+1V8oJK9lviP+MxI38KWZUlRZPIfwBFlELaLkv8RxG4WP85UTEZUblOiSiRvLEQIHDMmtF1VarU15ovTcybqI3usynn9oKlf0ILNx4zbssR5YWrHuA4Zypv1MYxJaohc6iVzjzRGSKovUglrfbuKa+dqsMU/cdEJhLxLFSGWKgEOkPJY8poShVGsa9eYPmtKgnyZpmo+AGhEBhWd6QBEVo+055+p92H773+DADgxRt2T4dxhJz3mu6pqtT9QHSh8GSJsy8/0tYiB21rW9va1ra2ta3RWuTgTKtKYHpSeFGjD33w/QBW7PRf+WUzzDhgpKUctuyCBwNgwiqESElVfncZkCXLhX63Q6lh0CBEtqih9A3OsqrPklFVY9yUJl05b6zO5+KlC/yscqsS+hBXgtEG9xV3ZD0q8RPm9RilxF1GlgOLcpFIQIS5N7K5w06Aih0TG1i5QuVQK5mWiEXOlb+0A5Ke8rgrM5KzrWAuUudwytwiZtanbqVadSDx+VbWlxcWTQvlqaSqLCtrX6Ct17l6l+ASo5qIYkJJR1UQtp3TiEv133FYevWjjU277h/9XlYr3DAtitN9G+vH3/Eh9tXQq/H2+wAAy9wQgpu37/Fvi16efML288v/zLgHd5nP3TawCL0Bqx8Khzt3TaRnc2zaCGOiWWPyHeqY1tvkQchXO+n2eL6K+puiNpV3zaFZknK0QtEYBVd17Xkc3jZG8xiSSdZ11kVo2imDEVTluQOsegh1vzT1DcQT8eIwEsJyzpcNSINE8sgzRrz7rKgZjcicp9x00iXKwRz88tg+v3dAMyDeP1NK/Y6IrD12xdAtH73HMXLmrR88sLHv8JqcTinN/dDEs65csfmwTRZ/QtO3Mfum+/7wmJoL5E1IhddHt055bT0/wpXpkQ8ViTIsxbZv5s5VpRCuPS9OeezZnM80jnWP1SwDfu/giEZ1RCArFCtJZqJymheK+KV7IMMkoVVCPcRNmFHyeykekHgxvpJAegG69tIwWeXsQyc0M2x81893cQ8g1NcTaBp9d2GzsqLLSoxpatf62hXjXPzJn/yTAIC//d/+5zYu3Q6WnPfLnM818j5kBy6+m3gytefPwFclvd3WIgdta1vb2ta2trWt0Vrk4EyrKyCdAc9/+VUAwGxqq7EBa3Cnx2T1skb76AFzaQy06swBrHWmhwZOD2VmZCtaksdxOtVyvZlLwppphladWrWu28J6+2EvF1t7VKHPmmZVTpzs24o15Ge1Mhbz95QIQcFoPOBqdEm1tmVm1QzxSBEkEQgqxNWZWNmxlzXuMGpQxOIUykgOlJFfyX3NtJKvmrk0ZUNlPBQyYpDE7VB6EsxF5uUp918BkrMVa1z8Bk7/IqWMMo8hvoeiUKW1vUkOORo55VMlNxslzfywGOZZtkQnUuTGVT2j7yvXTL/gNuWB6+S7AAAXrppGwmxpKM3DhzZ/BmMzojk3tij07l1DHoZjQ4lKoiKDDTu3KQ1p7j48QbVpEex9RqPxBTuxPudHb0hZWEnVMne8PKBcNhEEySkrAu4n5J507T5RjlWsbtlIx3HoGewa7YrXJFxTpfOcgTpsbj2SoKoGGTLx2gjF8pCbl8yzLedJ6AIUsk7m3Cw5d/apHHlMHZDxxKJzVdrEvMe7XSkdMnI+oYkPJX7Fl7h/3zhKA1YknNu1azU9LlHmyvfbNwrm7wcDQ3f6fE48uG99uXeHBkOcP5dZgSJEYe+hvX9E7YF+z66lZJg3Ng2Biruc+1UNj7KgOWTiWnlUk1yTmSyJH9g9VlCNUdwboX4d6p6owkpVPPCBNrlARe51HTQOUlHUgy+mpbcUNYUczogoLE8XjWP0KPXc15zlfPJ94HzoxeIZ8eTfMHfP5xyvcy5kjdF8oooYcpcqr2WurzfndJ/Ik4ytvvcj3wsA+NRvGEr4mS98BiNp56hCji3NdT1tHGQKWBFZK6sCy0X1SMyXWuSgbW1rW9va1ra2NVqLHKy1AMBsaivh579gUdmKnsvcEyOnIlNux1Z36RQoyOw/ZZS+vhIlsdnn+QLWIctYo1KFwHrHPEJgW78w9IYbys13kDLi+83f/HUAwE/80T9mvWQ+PyhV10/bU0brmUw+OlJAY1TDPi6PLXqZ55ZjVW4uoEJYmCkfuGIJd1TjzgWwNO4RiaVOe+xusy9SPEuofS5kQXm+mHW+uWqzGXk41nEnvEYISr9Cz7nyl7VuTR6IosnAr5VVS47GVshBpyMbaapbUo1OhkUxxyNw8jlY+GhcPA4fPzOq7m9cBwAMdx7neVmd+l0iBt2Rnc/uOYs6bt76KgDgtdfN8jmSgmBpnzs5kbWtIqQMibNjz89ZlNnp2XfuPrhhfdpjhcOO6VYUuSImx/NjZExvkVAmP/RzGPVs7Dcm1ndxF1JaWyedno9gE5lNkGNT1k0DGeWKna8+AI8lzg1RIFUzcKwrrN0YqmeXJr3uryDyDHXdL7PTfb5n+z53waJyx7m0pKKk/Bx6JKcEsSoDDMWan8qgyRAV5eCnHL+nnzJ0KM+6eHDf5sx8pqoDm+9bm4YMDfrbPA2ilUeGRh2f0CTrxM7r+NBsgKdTVtQ4mzdBSLSQ1RrXrl8HAIypXJqmSyRUT+wyohXqorE8pQbJMU2RNLTyJeixXl+qpHWl+WL3mYy5ZNksfoGUBOvA+ehaGhzyq1l5athGlVaZKiAyoXbkYNCbQc+HkghDCFXg6D7hblX15f1TKs9Bkf6AF7TkMymrmu+XKrHyyAHYB1XW8G8ilrJGP5nZeE6obfJTf9y4By+98hKO+B4BE6TeYEp+PjKR4zPGV8nZlH8Urs0tctC2trWtbW1rW9sarUUOzjQHh9jFnmlfM08sdqrXdxfD3mepmbsvy5VzHwMBx6hqFX1aU6RXyTXOe6vKotP+XKcYeLMxka+1R+X4u8BkZBHLxz72QwBWbGx5RCRk/C4YESjJ2B8xF5aJGWuvD5nXL0qqG7KeX9+LuLxNuIIeuo5XEdSxuDBHurCIJhcvgupysrMN+taHfp9RyNq4KccsdzX/OlfStVb95El0OhE6dAvsM1JekmNwmknRkA52Tra4RA4i5WKVc10xvIFVxUXf0U65tgh5PqftLPsQhiVqhhOyca5ZxpGT3zHcNj2DPrUj9pSnZnVD0rVr9dWXzHHzq899FgCwf9f4Aw/vWRXDXMhM17Y720R16hpdr4wYNvrJYgV0OhbBVAWj8cw+vzEyJKGq7DrfOrFj1qmdwybz3bo2pzO5FDKq0XaZQzbHUURtEEblqmiIqc3huA2JdkgHoRYPhKGkrIyrULofvGdLRW1y3pRSIvklEZDl2oe9Js2BhPfBklySW7eMg5Dy761N29eCLoP7yvMfUlmRSOIx+UbjkdX17xKRmc0z/3eva+/duW2KoKrWGNIee3urw77IpZARNq/VYGjbw4P7/L7uJwBYIRJLzT9G3juJtChW/gVzIkKn5FqkKW2gqZkgvwO5NyqS5iMOBe9hoYbzmRwgwb7bVjd1RxoMzgGsJJKTodQIVXUgV0k9JvX87HsVTu2bXguZiGC20b1cMrpXX70tO8/d+0dg5awrVUl5RETku8i1N+a+6pj7FKKsLsiTRFUORBwC/o4c7Rsa9J53vgcA8NEPfwS/8n9ZZVzM52HN6++tpr3duFxKC3Uaj6q1yEHb2ta2trWtbW1rtBY5ONPq2pSnal8jba/HoWqrRbO1jTwXCq4Io/qMJrxPfYqFnftjAEDpVKddNT7vqxLc+rqtqZjo9Q2kmMWobb6Yo0+VtN1zu9y13PSUI6sbx05iKZsxwuPqWvlw+ZlLv1wKX6oxjuSUpvxXnn8d6Vf5NjlCisG88GxjRpKssNjatoiqT3dGv2KupRjGs5eHvHdtY2QpbYI8966AOVfZijI2R5YbV61zTmQly+bcEv6B8uFiodO1kf7uisTHzB3Gob1+xPr36fQAKc9Tl7vMrU+n5HFksdXUT44YQYYWOUpd7/Y9U1QMwxsAgI2RRbOvv2Buj4PQ+rZDlvPG2PY/GtgBB70MfXJIHMOv2YlVNigcKxlJn54YAhLFVhlx8bzVYw/HFvlGkSkKHrIWf3PDxnHSJ18E1pevvWhuc/cevA4A6I96Pq/d6xkaFSvfHVsEOGCePvaKicrFc+yJxig6r6umc57nGNSaZ+SZVFIKJDpUll47wVct0NnxhPX6hwd2/fJclRPW14d7dEokOiNnyT4rc/JMOiJ27BWviIqSRNqqKsRgYO8Nx3YtprwPMlbCCBHzXhK8jyLm4jc2jJOwuTnhPpnf5/11fGIVBRmj2e0deyZk0jDJUszTI35Geg/Wz+3ztm9F+FIK1X0h3RR5CkjDYpXvXnuOEnFQ1J9wHIqq9B4IhzNqhBRS2yRCwHmiSitxTryfQy7lSLsmHVYr6Dkk5GXJygo9s51XM5W3Q3SG19J8iulPr3worQWKN4Zew4XaNtIo4HjIkXdKr5peQiVNcRmIjnzgvR/Ab37W+GJS3+0S/cyIjAqJ7IgXxp+dqvkz8bZaixy0rW1ta1vb2ta2RmuRgzPNwSFEhEIOYMo9e2orI09PBFD+01oQhN4drl5TJZSy1deF1GvKhivEoKn0plZ6Ja81Oqp07aMYk4lFZVrpKs9dSDNcBvQ6gvL6ZVN/HF75zKKPwwO6Oab293BiK99Bz6L8JfPdpygQ000xpfZ5wZnW6SnvzS0HYMT87fzQ+np4z3KoxdwiyR6j8ogMabF1NT6rcaka51QVBUqotp2RM7+b07VSsvw9qtP1pIBXy9fBzlv5T7nz5YX1dca8t6ssdzjoE0kYq+4/Q8oa+fv3DCEQsz2lXwXT2F51URFTtrR9xs6i9aMT01//6AeNSf/MFUMYnv2Cjdenf/sV6/qpjcdwbPvZGCToUGNheWrXbwZVhnAcVH3CsSsy1pbD9v3YVYsktzYNSZhsWF+/9BVzmrz6mEX/m1u27Q84P4a2n4Pju6gOBfkwAibDXaz0LnPIAX0JFK0nEfkAsW27CZGHqMeBY723OC4Vt2T/wzF3WynX3kdABn9BR0jxEjJ6TvS7NrYXL9pY93tSNeUu5TVBWOvkwObH3p6Nb48I3rXrFzlu1sfZwqLjIOp6vs7O7gaHwa7JYjnlMVVJMGBfba7pXoyjpj6EIuelcuhylCwsYr7/8La9roqDTg9dRrTjDaI2DIVV139CbpJXLeSzJ6/kZtl064zIURiGTa+SishUwftnvphxO0eWy13SPuv5MZHu96jxfuXz+NQ1EBeJ99GU6JcUFqXZkfMZ54gGS6VRVVFhECAQGlF6oQf7jtBIvV5JByZrnF+V1Y3vBZU4ajY+4oSlRIeOWVkUJHYuH/6e78E7PmFVS8+99Jwdm+eVJPIUUUUZUahCOjfAYp7hDerdvunWIgdta1vb2ta2trWt0R4JcuCc+9MAPgbg/QDeB2AE4B/Wdf3n3+CzTwH4KQB/FMBTAM4DOATwOwD+i7quf+0NvvOvAfh736AL/2Zd17/wNk+Dxwo8E1qrr3rdK1uOWYVy0MpBBb6O2inx5k0RuLoW071xBKyWaT5ht57vanIU5MHg02Lcb7rM0Kei4+6uRfRSK1QtrONqWUpfOVnFWk2T2IuU9dnZ/JSnwGMyUpqxpnoyZu3+2PY7qzJErNroDJgb5k6V6+xETd31MVXVNjtcVbNvi6VFLQcPGHEzkhIXIWaUonGRNL/U2sqqlJjcmevIvGQitjTz0uyzopJQ3ghEObqJRc4pWfqLBTXoczpIzmz/+wcW7c/mR/591XR3iErs751wLJtOdosZVfkYtRZUpXzla1+273cMQdh/QC16pjWvXqC738RDJtZn/bks4MgtWTCvLWRsc9OicDn1lYVqqO27jnluxyhfLH7pZIg38lu/bRyDS5et73nNWm1W/fT7HYScB1LPE+cg5nyOGCkG5BQEATknlXLI2hIZKKVbsKpTt2bHlC+EIkTtr9ubICYXoIbdL2lGTg05AxOqUI76O2f2ePbOLBuvdKhvMRip6oWo2FjnQo6PV8iLEHL+hnREddxHzbk3S20+7B/TOZLR6v6xzTHl6iN5svD8xdFRpc7G5gb7wFp7KuslcRc5752cYzlfCmls1tCrBfI3EB+C26Bu5uIDcbXI2p8JKWA1i7QKXA2M+0P2R1wS9omfyahnIEVVPSa9iivvH1WU1OIisW9dzoOe5rL4Nxy3kJywqqpRZk2/hZrjIE5V5f/m+/Ia4fUWZyHgMYQc6LniuUx85nfJj+gO7IXj+QkOWcHg/Uxiia7YRnNRSNFKF+QNJR6/pfao0gp/HbYoOAVwC8A7v8Fn/waAfwXAcwD+CYADAE8D+EkAP+mc+yt1Xf9Xb/Ld/xXAF97g9d/9Fvvdtra1rW1ta1vb1tqjWhz8Ndii4CUYgvB10f+Z9k8B/M26rj9/9kXn3McA/J8A/pZz7h/XdX33Db77S3Vd/+Kj6fIbNFejDjOfKxIz3qGZo1eNrP+aovoQXqBfjP9qDXXw0SvRh2iNEat8XsAVoJTCVsjB+spQSoOs4a9zxMzbysegN2AOcSULBgCIqZjouQZ0WcvmqvmVjrftuzO2qCuitvyUedG7x1aTf3FokdZG0vNqcuMR2bSVXMakjcDIj4voRcTVOPvaYQ45KamodyrlP4tGDw/MYXBMpnzct3MuAmmOk2G9gF9mh2T0CykI5EwX9rmVMiJzqVCFAZnQync6O2anz4iwohpdLOMM6iJQg3483PK18g9uU/GQvBDwO2Vsx3juZXNXPLf5kr2tfPaRnW9d2PedNBRiVhwEFnm9//12jZT/jt0qEnNLOdzRO2EptUn7jJjz3Yntq+T82HiMboKsVhGvRn4h53ftmF99yaoSXr1r6/cLF+0c+0PlkUN0Itu3K2zfHXIIhuQQCFFIiCB1e4bWBFRUdIz2daOlvDah1PZUeVBLjW7IzzM3W/KaudC7S8pbIVYlTdZkws+InKFuZmHlflf5ChpG1ETFSvbt6MgiZlRr6GGZ4vTYEKS8tmh6cWrbzcmY58PImax1jY8QkyUZ7tevP2nHZDQrpCQM5B/CKJ3eDSFz1JjVCFX6RD5GJ1BlBxUkWXVReedYcnj4XFBkrCh/pdoH9p0cnYW9n3D+yLOmE4UISkXlQgyU5w85PtQm4TFyPQf5CxbGrLRgtN6lFkF3jbMgHYyCKE7JypogYJVE1sGS+h4QmlPxeaFns7wU9AwrVUHQ1KoRDypcE6spWRUiSZKAPIIDVpa4KMIT101F8/Sr9K/gXFpUnItxszLEVapqAR4F3wB4RJyDuq5/ra7rF+u34PZQ1/Uvri8M+PqnAHwS5rHz/Y+iX21rW9va1ra2te2bb99p1QrMTqF4k/ff75z7qzDj+NsAfq2u61uPuhNyPiz9QlAM+KCx9Xkv5bfOrBBXzF42T0GQAiKjDeWppL4n98Kv0zkAX5dWvMJhHY98gbDA1qZFWwPm81e5rmYuLdTqmnk+1VZLO1zIQo/5yZrM54qR5yi0/Z8uWM1w3/Kily9dhHwUM1YfdJhTHNPTPEyJeDAa6Y1Y81xK6VD6D8zTcpV+ftdq76WlLz5FLbe9RJGhfT1JYoShVvxkISu6FFeE7Gyl9XJvoNjUh6jEdPa19GRfk2ldUpWvTz181XEPkgE26Z1w/bqNx28/a3XMg76N7Z07LwIAbtwgItKlKuXSznNITfhubNfg3l0b640tMseJAkyPea0XRKa61OAII88OrzgeEdGa27dNXXE0tr5skuEfsg+Hh3Z9L1/scTxsfIYDy2OfHE157H2Op+13enLAc6Rz5mATo2Gf/WElBK9JDqpvsh5/RtXKkBUiILoDKScSGVLdf5/vh5x3FSPLSnO+arpBRlElU72Vfgnzzx26VILaCF67RE6SdbMyBqXQPXmLcC7y8yURKfVAeiFlWfhccScRIsA5x3EYECm4sHPOXufcDS7Zsfb2Dhtd8dwKuVtyHIQOOd5HkXL3IRCQOuXyJiIarjkiSo0vW4r5T0SAc3QhZcxKqChz7nw29eSrIcSS55LO58ipvirVQA1xWdaNbYd96cjVlc+/kHyaWF4cvvpLz1M5rMr9kZVIROAKPsOyxRIoxuw/55p4ChoX/Q7wPikLeUXIdZLH8NUKQrXkzUL0ixyfOiDaQ4Q66UX4+D//cQDAy6/Zc+HegVWZuMR6UZRNRDrwly54RLjBd9DiwDl3DcA/B2AO4NNv8rG/svZ36Zz7OwD+al3Xy7d4nGff5K1vxJNoW9va1ra2te3/N+07YnHgnOsA+IewxOC/V9f14dpHXgXwlwH8HzBuwwTAHwHwnwL4SwDGAP7so+hLXdd+5bvqn1io3zwT1KtsSV0r+Mba16HYqas9vOHn4k6TB+Fd7VyIdzz+DgBAn9H6KSO/dVUxRb7iRaRk4coBbMTa38GQ+Xyurk+nlueWg97OiDXazHce3z/G5sR02EfUgA/o11AtlONTvpv1zQWrDpj39YpwYkT7fK9U+OR/YK/KIS73bmXKf5Y+9+eoaFh5FUXVMzN/C+Yd6ZFQlPybOgCSIVNuMc+nHEfjDcy8E6Id52DPUICj/SO8++nvtvGgop1yoNMpne4Ybb/jqkVXJ0RchKAcHdm4TUZyI2Q+nMPS7zFa5SVOJe7IfOj2dhddeiPk5FLQvgKln2J2rIcPjR/xjqeeAQCc37nCt+0LkVMe2/Zz8cJlAMC7v8vy3i+/YloM+3QcrBlZRQ7Y2rB5scEKCV+/nli05qhvUJaaB4PG3zmRJTnhCf1ZMOp1yv9CEaJQnmaOtkblqwlqXi/NyVBmAczz1jxvIQeqFOokQhDt9SyXMyrvYaEXlbQrWIvPKqeqqrxap78v1CfOWVWCnNIJVQHjMe/p116jbgGRuM3Jlk4QwBl/g0JznTl38gCyPEe1FBqpqFTPEtfY6j4qvcOl1AmJoGVyBqQCIhE5eW74rbxp+DzKkWEBIozkO4yImAh16nfkGMkoXpUQqrSgg2iwJhyT8UYoOOdT3qs1+UeBZDZKacB00CV/CZW0FeSUq8nDZxKPFBMhETiVy4HVu1FyvMomGrqao/aylGbzZY4ePWdK+sCoMgicW16Ncq2Gxrk3+8X45tu3fXHg7BfrvwfwAwD+JwA/t/4Z8hE+dealOYB/7Jz7HQBfBPCvOuf+Zl3XX/yDjlfX9YfepB/PAvjgN38GbWtb29rWtrb9f6t9WxcHXBj8AwD/MoD/GcCffyukRrW6rl93zv0TAH8OwA85SVDgAAAgAElEQVTBFgrfcqvr+ozS3goxWKnxrT53tvkcdFWvOAX67hp1QPlr5R0jecIr4q3X6RbNlaFnvrq1932XKlx+7DHbN+vSUzJ8Q0Y4YtvGgXKl9s0ONQQKcg6EIByxQiCnJrh8EZJQuUSLSPusJT45muLkvoE/IRnyipqOqQEg17Sa+fp8wBw7NeKHqv/mDM1KVlCQaxD2OX50WgyIpMRUH1OuMQxDSUB49ngkIQeOZcrzVd6zrOUqKMTA3u+xT12OU8L8X0VEQV4UDx+8ZuNBN8tev4f7h4Yi7B/b9ul3XrPzqiwvL9XJ2Zxjz4gvnau6hVEWI6SIUdiUioMjqvdtbrKS5NCi/5UnvcOIio2V96kg0sEx7pL/sD9VBKgqBov2s6VFb3L5jJJmDfkT158CAFy9bAqKr9ywfOnLr9j2pRceIgzk7GcR7oQqmyWIJHhPBV1/Xk/O5Yivg9GaIqdIqI7XqpBugtwciUzp+2XkUYXKUfmQFR8gd6SuxbWRC59UTzlfWP+v6DWV86MiQ7HWGTnmdDlUNBsEAZbUEtlitLpM5euxaJzfgp974gkb4xMqAD73FdO/OD6w9y9eMDVHXeQ0k6qromAp7K2qn5RD7/D6d6Qk6l1Im2qkqgiQB0OHnCWH5ufEmyjzJm9AVQ0Kteuwxiiy+96DNkRS5jzv5UxQmPhdit6lDKl5zmtENdAFdSLqyL4/2LA+D/j8SPrcX2Xjv5iGXumxLuSdIM0Uoi5CSuTGSCRIFSRy+fS0MVXFlHr2S+uGr2dCveC/N+rbvBfH6vD1Aw4Mx3ZNUXd1iUKO0f+LFRKdsXf+EYA/A+B/APBn6/rrfhnfSnvI7eAbfqptbWtb29rWtra9pfZtQQ6ccwkMKfjjAP4+gL9Ye3vCb7p9lNtXHkXfirLwCl/hWvLG59jWOAnhmQ86H7G+ccXD6juM3qXUxWWaXwGv2Wv5SgjRBohAKPemHtTLGj2xYdm6zPv7GhByC+TCplrqhBrrvY4U3OjLvrDV9+EeV6+Fvb5BLkKcMg/GfHFV1Dg4sMh1/4FtfTUGh04ujSMqI/ZHzPNtWF/FdwhUMw5GmAwtSikh0rUw7DInJ6fIQNr98YorQq97+ByiBoR5bmfnVTJi1DZQLbRysNKW9xxYRl5EFC5esehtmZE6U+YIlbelX8Wc7nMJ2dKXLhiScHxs1+LVV34PAFAxihG3Iicz3tdQsw58f98irGuPG2r0gQ/b/r7wecv/n6YluvQ46EqVkLXSOSOflNuYWgSDvkVz4hrIAVEVBinP7/DI1ufPf9Vuwe1t00X47md+AADw5BOWybtx8zU8uEu9hspyzFeukYNw0dj4VcmxZ743Dg1hEDO8KHQtm5FjUVmkLc6FiOO67RSl6f2qrP19rsEtC6nlCWETr4esex+dEaXQvCAiFw8H2jmPzciS49znXJdDZxzFqOgZ0pdOyQM5HlpfLl0yXwbxFuQg+q5n3gUAiFi1cXpofbl2za77YGD7zXIhB0ReIp2LvCeAImIk36Qk+aom72NQNRERabDoPMU58FG9FDaJHCTibOga8tp0On2voliTC+EG9p3s1MYqZ4VUKlfFVHwpVd7oWtpG6KfUC8cbrJih18jJqc3Z4XCT42Ovn+YpKuoaZIzoK6IWFa935bVm4jP/X52Q0As9s6V7EQiRkXePqjhg8yIkOHK0OMR4ZP394R/8EQDA83//qwCApfg7fO6pcmaFLz86hcQ/dOSA5MP/BbYw+Lt4CwsD59wPvsFrzjn37wP4PgB7MHGltrWtbW1rW9va9jbbo/JW+BMA/gT/vMDt9znnfpH/3qvr+t/hv38BwL8I+0G/DeBn1n2zAXyyrutPnvn70865rwH4HL8zgREY3wMjJ/65uibF/O22ehXlKyf5ZnoHb1S9oNW0Wlk21z1S7tNWXAPvjc58uKIYNRUaeAXFUGxtqbTZ+zvntrF7jp7tXF3Lw1xqYR1GjplcJsl/QCF1Mq2Qxb5mrp0ue4upvX8i73lGQkf7dgnSZeoj/gG/M2T+fURlwAFXxnKQBNUXU6qFhXJAU7Tii4y5VZF6hznUQJwNMoXFyi4rn+vU9dJYlevoTDjiscRsF/dCTO8m0qJrkKsaglkxn0tk5BmEq/V8TY/3Hlf+c3IM7t62iPrOnSnHzSK/MLbtwz2rEDk5tu93uMchr+WMkdXDA/v+D/7Q9wEAnnzmewAAX/n9L+DwwCRB+kJlyNzudMVnEANcAgBCpdac4MjveLhvqJDMCi9esuj/5k3TTdjbt3mxuWXVDO965kfxTkZNN19/nfs234JRYpUOSzL+Bz36PTjyYHgNur7mvFkJoIoLBZCVCt1r1b3z3LyhXr2K7OWgKg18hWEqtq+b2iOqmVcxfeXr2KmQyfZ1T4dS7oXsqwsA3pNzOoQKvZvRxXPOihBVMU2nds/duW+R7/nLhiyEgc2Puw/vAACu9Iz3ESXSDyFaJq6Bov2yRgbrd0UuyUq/pekVoCofz8uS4Kpy7FGTk8AhR8b7SDyrhVwMfRVR6BEeqVYmnJMJpGdhfYiIEOrOVaWVF3mU9gjnbI9oZn9gf89nhn6KwzCk8uZLr9ywU8r7CMlP8SgV0cpMOi+SxZDGjCqqPMIiLxsiCNquefUIgSGwhISI1CAZ+LBdDpl+/g/oziknWI6D/wl9VCIHeHRphfcD+Om11x7nfwBwE4AWB+/gdgfAz3yDfX7yzL9/DsBHAPwogC3Y3HgNwM8D+M/qun4kKYW2ta1tbWtb29r2iBYHdV3/LICffYuf/eFvYf//7jf7nUfZ3izrsVLKayrnASs/Ar1WMFcoxKBLtzStdDO6iq04BE2PBUXGfomoFaJWzEQHkjDyOV8dM2QEm2dNt8E+FRRLRrPZqa1GpT4WdaWqptwjldEyi2aW1DuYU3u+xyh2a3cb2+etDwPm+jJScWvKEBbMf5+QBuA6rEd28jNgTn0tt+z17Wsl4ZvDgkD5Xn6sdmdq3fXdZt01fG05V+lCe1gLLTZ27myclgur3ihlDMEVf5bZyUirH1JmDAsvChF1bHt6KHdF29fxkf0dxpZj3zpnegg371gUPitmHKdGl32Nfoe+CA9Y/36TSMSVaxaRJ6MtBHSJTPpMBpdUTCylVikfDPt7Rt3+LG9GRAHP6+jY+hbFFsVGPfv8tScNPMxS1qaTLzIaX8R4YuzrS1fex7HS6dhn5Gy4iq9t203ItRBnWdtInAMy7Zk7DjvK/xIVSYUkgX2KsKr04XeY+5UeSA0NdlNTxHk3RmslFIVTEW+N5a972Mt2OrkV5p73UOqeI3IwCemoyvvmmNUJQikW5P1M6aA42bb77JVX7Vq8+vqrAIDHrhoHIWZFiXLWXmA1ilbaCtLnF3IgbgWjVukTxOy/5xY45dCl788xV1WLeABU8RTVJyYPYjGb+2qmHp+LYu6L4a9nmTwWpGq6OharEoTqhkLt7ByExOj5cWnXkJV8Zn1fzm0cz5+7hMWclTJyCmWVUp/qnEKWcvEkqOYpBGpdMVf6Bl71VQiCEGp5vEg/oQJK3nN6rcfrl5dZYysVW12Dda7a22nfdp2D77jmVgIT5dpDQC1ar09UOwPpaHEgiC0jVK/yn4QTzpf16PdEcLkXLLKNJpwm+8pxwzYFJ/K5C+dx7dpVHtuO0enwYSQYlAuR6dR+SAL+wkbEAX2ZoW7EjuB4EpF4Tr0hDXpISNwYG4FtsrMJR9h8IRMjlv1RywYL/vAKok14M8c8fZVsSkjE1RKgCRrvezEcpUA85KsHdOhhwDwvzr51Ru5aD2s93Oy8agqnKDUU6ylHSeI5RaFk+9rtMJ3DH/KilgHPEgWJiItj2+eMIkfd2ASk+pQmHm/Zj2fNdMm90/u2D3r3CDYfEiYNud8u0zSH921c7zwwKeNFQeLnaIzNymD/bP46j00zGv7Qas7qx21GOdyYcy6hoddiYQuPl18lSSp9GQCwe8G+l6X2uQ+9/8ft9YnJh6R1hAeHthha8H6Q/PWARkuhfpA8tG/7HDPNIvlbF1FYiA9F/ZgIuvZrC/7wRZzDy1QPz/CMuI/KIiXaI9lfWTorJSEIWyk9BQVKCTZLg1cEVraq+SOaFqXHxyX7XfDHW6ZpfZY4xoTTl0zhjSY2b7yIFM9/c9sIdienNjdv3zeRpHMXLP2QkGx81tI8ptFQp1bAwudf2vxh1niJ/KfzKJiezAoJCVlf9KPnWBKYSK6dUtfeRC4pEcQsPeSPdCYb+UJjyGN2RdDVsQmvcz7IolkBW7FG3I65ACwze3//yObj9tY2v1ciYWqzlOCWk7CW0gFNUmzl884MaNgHLXjUJ+7OL5IqT1zluXG/URij5jG3KJq2NbKg4fWDmzZkLDut1hYDdfXoMgvftlLGtrWtbW1rW9va9p3ZWuSg0RyiIEFRSjZVrwsebkavqzUaV45hgEppBJLXRCiRyYZKFlVFJdMNjxhIutTLYapcSMo1OlTc2H/Bz9+9+wAvv2KQ4offb9FCVljJWSLoUo6kXPEnIhJFLLWi1fD9oz12ksgCyVFdL//J1TvTCdEWvz9ymBGCD2gE0+mzPJL7ink+saA5kf4oG+pLPLVI57WIJL0qcpdW3xIWodVzcEby1aMKPjXDTc2BUOkW9LrSLda5RaZoTqQxiv8wmj2dk9w0tB1PKNwzndn354sUpycGe0oAZ2PbSg4Hg+t2jIXBxku+n89s/CYUBeruyljLjj07tf1tnLNIUQZEMcmBn/vS5+z7tP798Id+GFevfRgAcLJvx5zPjLw2TylYFRvyE/UYZfUNkk5ZXpisEdR2t+zzr7xM4Z5Ti2YTMqw+9Yl/BAA4f+53AQCXLr0XnYGhFwEn8oypjvSYBExCzYGTXLKdz93bNpaTLUNILpB0e0KUplLagHOxw4EISGiTEI8IakWxgqwFNoDRZ8Z78DRjKod9UTkqiNa4guWTQmCIOOhY3uSJ93AcyQqZ5LLuEKWeE3I/CpTiYkqG98eAczWiqZcEhiRZXhJB6UzscyfOkIOHRJCWhV2jwbaNi5DMPMsxy+yzgrnzUiJXsnxnqZ1SO75kz8ZD6OAysr50fEkkUcEe+y7YnIhCzrJFpCXSY1oS89mjx10swrFIjkqjMp3m07Wh5o8YqappBftsfZvSjj5l+imJV7bRgJV3i6AtpCBQ+TAl3lMKMwlh1vQJ+XMaeIEma0IsK6UrlRkjlJAKLSXa0UOIYmr394jPTyHOQpRlRCeTvPoRljCqtchB29rWtra1rW1ta7QWOTjTnHOIotiXoql0TaSXYK1exOeslTcMgpUsqV9dM1/Nla+kNdNUOXfmrbgCLv2yEjymSGAkw/gck2w+KWwTK2dZ+9LLyuc4RcBrmrr0WYMWUq2kZs5dq9TRhkWGjnnQlMSbXHlBvo4OV8CltE9z1IGImIyIlioTYxTPoL2rXCtDBRnChLKm1Src56C5Wud4hpQ29SVaa3nAqiz9d71V7prhlHLBIvWUXhaXh0STeDpn9DGaWN8niaI5iyj2mFc/oYVxXS0xHLKEc4fCQo7CQwv7TkRUYrG0iOErX34OAHBwQJGkixbh7OwYUlBzbvZZKpoQcRGPZEQEQTK7r7z6NVzYNSRgm/a/OzsyNaJxFG2er3UtKt/cMqneGUlvYWzH6HB+XL5shkz37th+jvcPOI6UviX6cfDQzunWawd47Pp7rA8XjFsxGlKsilHZnKTYFa/F9q0x39s3vsOtu5Z77dDKuhKXgFHp5qYR+rYmtu11OCCeWxh40msSkHjJnHhCyeWQZW5F1bx/cm8wRG4CUZuc6FdOTk+6lJU3uTpCAUtxFwKUhQS2iEbwM2PKgJ8cEVGaZzyPHntvfZHIkdAOH82zNPKYiNXpTPNLAkx8bnT7iHqCM8nnYe5bZmbiR+nZJVRUXBXxHrZ61mc9gAIiUCC5TkJGmhfiW4UIvShTl4TMIJYXM3cpsSPyAXRTp7p3JUQVNKWcJYIUEd3JI0k68xnAc56n4pc5j6pI2jrPmiJoQgDCNZK4nisyWnJeNE+Ebslvq7SRw1NqPpHzleYoljYfzu3avfrE41b495WbxvPpk9AtMTnxoqooeGRCSC1y0La2ta1tbWtb2xqtRQ7OtnqVR/rGjVEt/yqDM2usN7F1VuVAccbYCQCCSCVaTVMSCY4ohzYcaEXNXBOTsRWRBuWq3/3EO3H96nUAZhUMAHXpl9+2D6IPKhNUWVMxl51pwGNatFVLHIdR6JznINndpNdkzmZp4fkZzrOGZfKj3Lkds06bDOCIpTliyFeKhFLr24J97PBzE6IbsaxMicB4QytEflWt0qpqTaxE11xMeOUafVkkI4JuR9K+RFJYHqVx6LIMT1GaIy9ke2uCgEIqOQ2Wul2W7jGnupxbRAz28colKwc8t63STl5nVkgw/Yux7GUZdXQYcY3oX9SnL/Nk2IGj3PMp85mobV+drp3/Jk2vjmeGfGSFXe8BkYRQJXuMnCdDY1CPRxadi09x+/Y9jpcd+9IFi34WC4eDPeOxhEQf4qVt+/x7e9eOFTACFK9F5jcbOxadHrIU9ObrNwAAR3vW5498j6mpj4f8HI2+YkacEed8kiRefSYv7T6ZzWmfHVDSe3CJ3zXURyWfMdGukPwFiWFFRLv6XUZxhUSDaDPOe7ZUSVsF5Bkj2kxS3hIcYnUFkYElr//s1Pqox5TkkGNyLfT0GfaM/zHctq0qC+KkKaPciTvIWaqsKLsS0UPVFirNk5lVLiSN1y4kmjG311PyIYTE6Rro/MWf6vIe7vT7CDrNKhUvVa/nJe8LlVHqWVaoIkJQq0qmi+Y1CIny9DknF5SGTxesLOIv4VlxrCAWd4DPokrVWv7DAFZl6LWsmVXVpbJKXznVrF7Rs7GW1D6rxFxVISQisH9sc/Pw0Maws1YCv16tELrwDdS3vrXWIgdta1vb2ta2trWt0Vrk4EyrUfv8DwDEcce/A6wkOiMf7UtGlBFzVfkBDSPl6VSH3cxvR50mYiBJUm09gCGDIa1WlfdaKk9KEQ8yjp984klv27p/z6IML17CqCFm/XZJe9iC0EHMaLtPM5iMRiOykfVWraokYMTQZfQqYZK8KDzzNgkkrEPmP2vnHbkFnQ3JojLUlbQxow9FmIralTNdcrXe0fVSjTmvxcmUojF15aMkrca9UIgXr9Jgk2vg5ZHtVXEzotiOvX9gQjMpGeDegGdo53LunI2/jKvKfL6KRpjP1nVephLHsjF8wCqEKW2yL12yfYl7MadtdsJz2tiQnbL1YaR6eCr55kRHhsMEg4HY9GSLM2o9PDIthTusiXeRcSju3rdxeOZdFvnHjKgVCUtQJl3Y586fNyTh5MTQgXu3LerpJZKE3sbFK1alceEK+Qr7dwEAD/dtTE9nQobsWKOJnZ80FsT/OX/BkJXtXUMtKtatjwaGGMyXqiSwPn7leXNzH5H7MR6PcN6jFDa3hj2736fM7x8d29iNN6yvVc0KgpDGZmSSL1nNokqjlQ6CECiy0PvkDellF6Es7LwiR3RCks72EZy/JCEyIgz6stj8RDFcrdyzonZwP3q+CGG0cRGvJnUVCq8dIQEhHsJLTjf1HQIeUwehCjeWNEmacex1DdTH7pBVLxT0Sfg8QhR4KXNJt3uJYS8gpBFhBQTvZVVUCGGSPfaKb0UEZiapd7tWerZLSn4Z6RkfoJSuw1oIrr4IlfT6D0vrS5E1Lbl1TuI/SPDKrelfeJSY41GmS88V6mxZP598ysTMfv/Vr/AYNtZedp/P5OARCh20yEHb2ta2trWtbW1rtBY5WGuBc4hpLOPz3mTlKkftm6JcoQJnuAcyClEOTavMyDObyWQuJBPM1SijkUCMX67fUkYn2p+sW5O1K/jyiy/i9Rs3AAAbw132jyiFeAqqp+WqOZRsKqNYWcoeL8kxmFq+K2DU7/OAEgxkVF+TV5EXy1X0xJxfIuSD0ZRUGGU402MtecA8ptQcxUUQe1mKYVI8q1RhwJxtLNOTM+MlOWPVZ4tdvqpSsPfDMzwFe8M+fzylWcuBbROeb78rFUz7eKfLPoRiI5Nx3+2jyCRVvOT5U12RdtFixGeZKiLI62Cde8IIKSKDPmT0qtx8wvHpUgFP8tx1LqnWYiXvSpRlxLx0p2vnf8LrXNOAanqq+U8m/SohbH3mHBa6cfGSjdf9B6afcNCxc7x338bt+3/gj2B76xz7b/2+cMGi8slEVSZUZ2Qu+NYdU3NckouzJOP9wnkzczp33qoehrSXzoiCRdz/qzR4mhORWTDierj/AHfuGVJyYdf0QC5dMNuXESPcew8N+bh/x445mNgxu9QacEKkGPEFRVNO2BfYVM0cvFQgi7xEzvtD94tK4X3wp+eC7jlxdThnj4mQxUQ/EubxpdFReRM5majJbnrFrPfse88xUP5fsr7ayrKbPBneu/fu27xxhfQ/DBkY9Dm/hCzqGcAce1Eryi/8nBLSI+6WEA/pQUiNsSc9k0AVIuIYEEnlMyzncyaQ5gSRSFXcKOKupR5b117Su8r1zGbEr62kp6VfwfGQAZO/djonFdLwWR+sy+1LZ+WMJfaAfK+Syp4XL9oc7XQkUiN+Q7P6LQxDjxa93dYiB21rW9va1ra2ta3RWuTgTAuCAP1ez0f9iozWo34VJIifoBrcMFjV/Kr+VE2rxvWmCFiRntS5ZjPp9ltf/EqYEYFXa4vEUrfjffHzn8OnPvUJAMBP/PhPAVjZAncVderY7GPC6FMsdKmULZgzzJhLi8SL4Iq6IGvf5zmVk3MRanIG5qz9D2nZ3GHVhRi6C47hnDn2bqg6dyU+VXPNSEFVCd6LQpbNZEpTe6ETxf5z6yt5P3Tclyo9TqlHPyUjvNenhSrz1IpSCBwgYEiQZRyvGWvSWZmg/HevG2DppGxox0pYdx/SY+Jgn5yBkeXSJ4xSZXmtWvOjY4vSFEn1GMUWnB9JxyLkbp+eC6Gi2y46PfoTcAakRC9kC/34FYtO7jxkFcM2qxFkH83zl0/EybFxC+Td8cLLNwAAD2gvvUvzrYcPbZwODhe4eNWiyYrKhaG39rax7hB92g7su+cZ+S0WNj637lhVx97+McfD+jIZ21ifp37C9sT6/vgTT9vnHxqn4YXnTT+izAvs7HLsSntvOrN+Xr9q+g7nqSnx4kv3ff8B4InvIvfCJ+f5XIhltibekiJDmQ1wIwZ9lCAKpXwqFT3eS6tw0vbN54CeTVLtPNizvm8M7VyGQow4/3Vv1rVy8+oEKwYSh1xeMryvpeSountpk6jKQn4F0pYY0GNFCIrnR8mOXugelRQ9asr7MShrIBVSak16BbpHaz0POLQyuVKlkUeWFvJDkelc1OiTqjZUDSJ+UVGrgqlacZKc5ruuJ//OhGIt2RmiVUSOxUmIOT+8hRiRWelcyMp5yT5k0qCoKv97MCef6YtfNM7MMasX+kS35lRnXWb6XQjeYsXdH9xa5KBtbWtb29rWtrY1WoscnGnOOSTdjmfyqr5daIBy0lnejJjVyrL0K9aqlB+DPBJU88pjcV2mwKHW8jLX3qihHcsmdL2G1j6VSuedfz/99NNeVbEqlHdvugj2uSpNc+UdqdZIxruqEbYGFo2krEGWzoGUwE6ZF98/MCXAkqzd4eYEfTKSK+rVz6gE2GPuXBayJVnCMZ3xQube06WYv6xOiPh9fq8r9bYuV+f0h5DimSKkGvBqjeIczGas8w+keGfHUu51g1HncNTleNjHi2NGPMz35YzegtBQgJWbG/XbU0WBJSrqqI+Glhs/PbWoW5UeSwqs7+3ZeV65Ys6anb6hFl0y6U8XIrEwlxrRD4GM8F6P9o3MsQ/GFv0OJ9uoY3pgqD5f/h0F/QgSY/5vb1p0XzvbSlmy5lxbUsVxQV+EopR6oc2vku5+s1R5XossDw6XOJna/THcHHPfqtu2scoYhel6djpCTmwctnas2uGE7pbTKbenNjfvPzB+w2Jh+5uM7Pyfoo30aGDn+LUXv+bdOnsDVi1QZfL4xM7nIv0bLl00nsSXvmzuk7dv2vbSNVOt63RtnDJGhkGoSFPMez0TwL9tnqTzJVIiIlVxJv+OlfqeKgVyjr3Pf4vXIK+Ays7/RB4kcnXs2bhJZ0U8As/EdwF6ch/UM4a8FymiLhasxuDU63SpndGzuVcMiAyo6oGfy6SkWDU5W5pHei4Bobcs1DUpOYYhH4zi+QSJng9ElGgrLq6BnrMxP+/8c1IcL6KeZbNPXlGx/nrlVG0TrxFBRJH37qr6RNwMDheR2FxcA+mrrFWueZRZ1Rt5jpo8tznVaO/cvt04tlQcC52Xfnfg/G/E220tctC2trWtbW1rW9sarUUO1lpRVH5FK66BmlZnQg6Uv0pi5bsLFFJFq5VDk1OXVuzN9ZiicC2ilWtS3sr5K6QoX3lOrni5qB+OLTrrxhFy6hJsbIzZh7TRl5BRg6IRbQfKkalmtseDs15/ye/FzN9pFXuyYCRJtKRAjc45WwVvbloUfsR8fFppNc0Vr5AE7qti3tcrIFJzwedixeYWC5n50USuapCznFz/5jjh6rvge50e0QeyqvvU709CRVe6/syR5tJWl9Y8OSlEO2TfqSvrGPWWdAxcphkCVQowrJAngpQTHdn158mYn2xSlVBe8PKaCO17kWdXk6sSix9h492dWk4+IrdhY+cCKqrEFbU8AWxfAzGgSxvrmJMq0+Oh1vnYuBwfW3Q+I/px7XE6Qw7MN+H5r34ZAHD5kqEfw75VJJw//wGEiZ1Xyn2mnDOuUqWHtCgYvfIaKBztB4qIiTwQOdnZMa5BXlrfp/QU2D8wdCNh5Hzp8nXbXriONLN5PWeELKXQlBwdKV1uEn05t2NI2q1b5usgXYCr158BAFRy+fRaBPLoENOekbUsBxGuODPyXchX1e/2gv5Ufl7KqRorH5IAACAASURBVOQM8AFxsmfX5JjIiZDLAXkvKySy29jWcEiIHIWsrhEaGRGV61IBVe6KcmcsvH8J0Us5zBL16Iqr4MeDSqPSifFKqxUypfm9fwErp3RtvAsldQuILArFi6BnV9T4vPooN1yv9hgJuSVPQEqLDnDyzJBfi66bOBKe7sUofs3/odZEEmrMarBQSojkMOh+Eu9M/Js6cP41IUviRfnzk2cNm+YRao7dIwAPWuSgbW1rW9va1ra2NVqLHJxpVVUhXc69q5ZXS/RCWcqRcQVJxED4QhhFPtfnlQz9N6QV7kkF9rrPDykioH5BTyt71UQ3a24VlXQZ3W/Spz0eObz0+gsAgOOZRRE50YZBbPlH5cJqMnwHjNKlb66Vfj6XexpX6cwpalsxCh6TrdzluWXHcyxDi1yH1FIYJdY/x7z8nKp6YB86AxuvUcTz4ApZDnHKyarvc2ouvP78KwCAaW5/pxOt0rmNIwyJoGxsWwQof3qxy4Uy5FlTCc4XmDCSkCOeZy0Xy0ZfUUj3fsk+eJ6yZ0VnRKN6zAXfun3DXqfC3YULlhOPyK1Y0r9hxLrnsEO/e0ZUCORBYVGd62zxc8ZiV3VLvxuhx7FPF+KvUEMDitpZK90h16CwYxbkQ5zMjFsy2rBjnM6fAADMljZ/Nie236eetHP47nd/BACwWFBRsx6jrDRWvIcY8S3o85CVqsdnJB1ItdK+Nk8Vpdr41Iyc09RQGiFIF3cNrZjP7fv71Cy4d5d8gt3LYOocoO+HELVFh94Sue1zxrD2wmWb53vHNrZ790xDYXfTdB4mG9e5Qz03mJNmRF3F0v9nfj1u1rbbP2wjbQGhDKEHFOTRwXuTvJfRLr0UBoakzGbkh/BaZ6wsEM/ohEqby+USBe9BcQXERVGt/aQm+jBtVgj4Cgr1sZbColAu7i9oxqBSMwyJtOQ1ILuJnIz9NF2x7xv74OcSIQDk4mgeSMMllK6BVBy9yq18Dqhpwt2G4iIUpVdXFH9BfB5pJkjV1GtHCORRBYVgYI8okpMhlJioL2J+UdOFmiRFXXt0oiDfI59KhpKIig5KxKDL8y2qEiXKR6J10CIHbWtb29rWtra1rdFa5OBMq+sKebaEl/FWjinU6lOMVjkiWlO+J4nCVWQfaLXY5C1oX1rhCoyQYllW0jOAEaHzan3K86+t5+QcyDKHsBPgS1/5EgDgxs1XAQAXdyyPvbdvOeIhdfy9O6P3cWD+n8dyzMMVqgDwaIjGhTlJRqSO+dIkSVAQbTh6aNHmLn3Je+QzJMzrzumZXifMQSc2XseMEA+OLIqbs9rB65UzaunR/33AmvytcxaRTybkW8QhOr0mMjJLpd+gnLBUKpuqltqKJyIKihABx2i9LMRSbqoVintQ1CliMuHzysbl7n3zFNg/tPN73/u+r3HQu/Q56CR2HjXzwVUpR0D6ZBANKpnnrAIiMdQ0yEsbtyiIz5yYbSLyNbzvBZGyLtEe5FTbkzgfx1xjv3P+mn2PuhhFaZF2HBlC87nPPm/n9l47t7IKPLvaBzacY1I0THMiI7wxokSOeKpDZ0WAWP2sdvBeJrwps4zRb5+VJ9foN7JvOhG37h2g12dk3LWxqskt6XPsakc9/sL6tL1jc3iXUfprN8x98t6dWwCAXs90InpdObDyHOV+KmStFIdlNZekHSGEJOL1Vp2+KmlcJdSO+yZ6OafPx7hn5zuIbd4MN4goeWdB+1rhI+UCBeekmPxS/FM9/+xoyj5yzAWl6tmVMtfO+8Mz571eAI/Ja69KAammVlEM11PFDKtuqD7a4XVVlZIAFFVd6HmQan4UTY8aVSsIBcjzZiUa/L1NrlKWI/fqg+B3ysY+VOnhbyeBwd6zRRUQHA8eSxUm674XusGkf7DIlhgSzXV89pZEVHSNKtdEtQVWrPtBvJ3WIgdta1vb2ta2trWt0VrkYK0VZ/wTxCDWCkqqWqqb1+tyCIs7EULv8CfkQLoEzMupTpnHEYPe+dUj++EUGWnLHBqjUTkNTudU5VtIi7z0mvGf/d3fAQD81L9kyIHz7nCsGZYjJCMmx0h6pfTGfJ/UxOShXlSN97V8Dbj6T8vSs6pLIQTUXw8Si8KWZJMvjq3/e8zjLZi3jHpEGEYW3fU2bTs4Z5HR5MI2j0l2OyPnRFGx6rqD2tdbl1QTyxl1KiqN6RxZ8fy1sheXYBVBqD47bHxu3bUu9yp3cpYMPcVZLOzbd+waFYzsHR3/IlYQBLAcfEJVS1VrJJEPue3/OT8filFt34u5n5V5fBeBU4WEzofOmLHmg0Uryk9LrXC+tAja6RjKoWPOY5OrQLfPTWoY/N7v/abtj5HVu9/9IQzHlhM/PDIOQOSjLPlQWIR4j/4M0tofUh9Cuv9CcWLeBwnz47lqy3OeZNn0A9i9YGhHsL/vnTtBnYZuKF8G7pvRWzexcTuZ2tiOR4bWzGYvAQBepe7B7qV3Wl96hpzkNe8r5bd574Z8clT1Cn10mq9isiv/L/4LI+IokBcD8/ZEw05m5EkU0iRYRcIAEDo5k9K1kxVIp6dTOObAt7as33Eohz87/40NVgwVaw6J8thQNC62/trr8nfQ80MRsyouahciisXn4X2SqzKAiCnvUY+AcF+5Vy8kKqG5zb4Ivag84qTvES3jtpY3Q1V9nU6Aqi/iWFUFYeP9KFMVgqqcODy813W+sXhQnIthqYkmJJaIU5V65My70pLXBWrNeAHNunmejzLcb5GDtrWtbW1rW9va1mgtcvCNmjTB62buTBoE0us+q0Ugb+711adQB+lp+9rZdYVEIge5V/TiMSPpltvfQjgUGfT6Vj8+PT32eu2f+exvAQA+/iM/DgAY9Td5EK3ceR5UE5uTGTtlNF9kchC0CHHJCoEllRYDqdrxeKF8ExBgRGW/kCHQIeuw9xYPdAIAVqjLaGJIwPldY7qPtiz6jDcYAdPnoOwzR8jV9oxMc89CzlmBwMjIOCDM23Fwg1Xi0vYlhnzQ9NQQ+1jeErpWyr1LSdLrYdTSolfFBCONMEbE6PzufVY0cEo9RiXErJIfvXzq6TJItTrlYqNEURlzkLXqmzlXGY10Ovb9+bEi6QgR7JrkTnOPLH2qcAa5cuXMrbKefUHkYDgkakUvhaMTqjFy3DboB+F4Q7zvve8HANy5a+z+/cP76KhWnvM45jGWSqFy7Gaca/cfmq/B9evmZ7+9TcU/IiOVxq3S+Aw5DowQC0acPMcst+ONN84j7tgcW1AT4mRmaIYj32WnM+R4SLuEPAZGcZcfM47BV56zqoXnXjCuz3ve8y4AQJ/3AANF3ycnDlNVee2DUs+BtSonr5jYlJzwOfNaSFKgagS7pgPybEpfDdF0jl2khtw92LuDrR3rZxUY+nLKyg8hR4FHOrivQJUmuiFs4/VPQlU9NZ+B6rM8GvT1PC09R0nKqOlcFTREb6jXEfGZLBdGoRPOI2lN/wLdF/o782qoJfdPHRo68QZhiEjcGo+6NdGHlVotX1clmrhongBAjgGE6rLv6gO/L42FWlyHMEBG1OmYaq4Zn0me9yAkml1U5UjluRRvv7XIQdva1ra2ta1tbWu0FjlYay4Izvivrzy+7W/7TIerzEhe4GiuhIEzzlurcBPAKkoVB8GvzuTbjiYLtYK0wJm34jeyuUXv3a5FUEPmXO+8fsPnJ2/csJzoF7/4uwCAP/ZjfwoAMNu3qGzK2tk+6/edaqt5rA7Du/GGIQ5L5ugXiiiJICxZM+24mo+SDgrmkAs5vjFfVzJ/O9oyxveY2gODif3dowOiFCYL5be59e51fD+WUqQc5BQJcCWNKFhdn7qJ6vgFvveOB5tqoan0RtVKRQ7OVzU0c6y16qIZpa5y9BVKOlgqcr167bsAAFtbdr4p85aZnC6liChDP/5Dym5lrigOHA+iGRERG16r0xlRETdBDeZ1fUSjnVuf8kI19awcUP27z8Gr5tz+Tnq8ppyyM3I65guLwM+dJ7v/gkXYCEIsqUfhNSCkmkfdB3EyJmObD6/ftqqOmzeNozEcmBJivyfnQNsulxp7IhOcuxnncspxV547iiKPMvTs1vF8B+lUqEqhH9HfZE0L/+l3ESEYWp8Whc1tKUguibzpHh2PDKmQ42iQF56T5H0HeB2918JKIpF94/ODc0s8Bl8JQYdQVVwEofRVeM15X2SF3bt7B7cx2iQPI5b3QdOpUGimHFGl95EQzZQTqlCLTNYBuvcVMYuDoGcZr1VVVF7RUGcrtGVKXshdok+qahEPROMkx1B9P10KOZRCbRPtC6gK6selJApcnYn8oQoZKaXaeRal1An5nKTmhhQOHfkR68qKQhDk/7IUWlII7SDvKk+RU9U2JALUGVD3hkaQQq+FWj66GoVVa5GDtrWtbW1rW9va1mgtcrDWXOA8O73ySmDNSoM4UXTSVNJzZ5wT3izzI8TArauGKQktLwafW1ROkTWwy1O+YSvG7Q2Lzvb2D9iXzOd15Sb47O99BgDwAx/5QQBAh6x1raIVdZeMFBNG313Wa3cSRquMhA4WYu3b/geMiFTt4Grg9MTyuIo++ruGEGxeNG6EmzDK4gycM2rpBwuOE8craCpLJhzrDlRJwr5pvMjeVrQLt+po7REgRRkKceR70WQh6/y82iKjUdVxq/5ZUbBnYZN1jFA11ynu3bXr8+CBoSyPP3GF52XjUIgB7lXZqADIImpflU3SSTO2WbG6a1AxjvOpk1gEXlY9AIommxr/K1kPXj/ptzOCHNA7fllYxUkUZ9wyAmKe+PTU3r/DSoyYTOsnnnoKAJAuCtTkEvTXOCn9WK581qfdbXNRvHTRxuv5560i4M7kPl8fcz92Tqp2qAqNAxnhvqJGXBRV3OTo8D4OArsGnP4YUP8gywwBOWBlRcWIsUsEpcvo8+pV41pkOZEZ6fjz2Cd0jJyx2mFry3g13W4P4NySSqF8GfSs0XUUEhmETSRBc1g596qQEyR5MkQDE3+v23FOqPIYuAKjPjknQsY4D8RziTmf17UmxPTPi6YPgqoaSu9Ma8cWUlDy/pueGrfh8OEhTvZsjI7Jxs+XfN7NOdeIED35hFWEDEa8P8jjUBWCKkN6RPt85QT7WHruQdk4p8Dn9M/wOcQpkICDryCRuqKQAnFI+HwJGh/38J5+T6q1/VRhE5nuxX0s6TbZo7eMqll8E+KhQ/g33r4yolqLHLStbW1rW9va1rZGa5GDMy0IAwyHfSy4olW9uqL4TpdcA1GHtWpX/i+IgHJtSH1Y1vRc8Ok8/UO0BV+1oNXkyp0BWLFzB32LdjJGCkeMboaDGGVq/VK9+Uc++iHrH5nNlerbO7aPEKq64Eq/Vm0xnQ5TW8WqhlwrXql1Kdc+ZLXCoJtgSidEOd5tbjJqZZ56Qc18uTI6OdhJM50RlRjTioCUD10wp6iIKCLKkcoNkxFo5epV9CUtdEVlPo9LpUiVnfhKk7UaYmlSuCYnwSMMUZMxLN+ANKtxdGSIyNamVSdMhhZtVt5LQLlgeWgQUWCO3leYMOeKmugNpGega2LjIzfG7e0Nfr+Pag1VCHT+jIh9fpv9T0sb49FAteVi0jcn6yk9OEiw9siVPCwODk2Z8+7dB5gQ6UpOGRl1qWQZWD+Fygk62t4ypGnQtyqX126a/kGe2nk/doXOmoz2hQIp6hPbXXwSqX4GYeDz1Rrrkm6VR6xemAwN7YpGFoUuyKUQJ+n4aMZxoYLilu1HkaQ0+HfGdo4ZI+rTkyNuT5Awf9+b8FisBJnz/hH6IBVDCaSKmyE3y0iqpZrL3u2Vc9a+hoyo387E7sfHr30AXXGOeKxYmhEcn+n8iN9uakZI6VAlFHHQRAZU5TIlGnB0aFupVB5SHTRdLD0Upuj6HNUor12/DgDY5PjsbtvregZL/0BdWSypWdGll4vEHDkBpKDovRaE5PJaFXmBlNUqqX9tpX1j++R3+GwuwPnvFRI5x7yiIrde1bF5rqpi8SqZUY2CY/g7z34OAHDzjlXEuNiT0QCsPCRWeEGrkNi2trWtbW1rW9v+H2otcnCmOTgEUYh+z1bSswXruLXK9J9sKn3577sQ5dpnzkjUATgTwXhyghJUEgFntArqr3uHQFvFbmzSfY211kIMtP8sTeEK1X7bd3Z3qC/PyH7/tq3gO0QAOqpPVp0+o08pCc5ZlSAlwDHzYHVFFn8tPoTlh/P50nMBJuznBqMTucIVqkuW17nc0QJGseJzdFe+8wAwo8eC8pUdXquNWGx9ngPO6qMzUpbGe9WsGfbObxzJZpywais9dkUO2qrvRBJ8Hph67mmJmm6EF88ZYhCF1u+Mzo4h3Sgr/U2+SMDISL0KA6Eg9KSoB/wco7fExk9RSE4uQ1nk2NqkZ0KtKgTpzFNFT8x38RyYa1W9uvgfOmEx7ItM+WGbV10iB489ZlGeECo4h/7Q9CwWM9vX/QfGxSAVAVevGhcjYT7//PmL3McrAIAHRzYX7959wPMzZOAd122ejcc2HjXnVZqKWU4OgqwCq9LfH0Up9cAhj2UfWdARc0hUotu1sd8c2rV74fnnAACf//znAQCXzz8GAHjnM8/YOZBRX3A/8sPYGhqSUFXA4amdz4zRY59eIQmjc/l0xKqcoqJmRv6PfDEyIg3dTRvfEKoQyNgXziPmty9f2OU5RTiZ0sGRyKCiayGEHeqgiFug6DwVori0vs2m9vc++U8PH5ivyvExHTdTIZY2nrs7VuUxvDzCzo7xMLa3tnje5HUJSpVKKaP6SjBVpCowIYX2ciEEUYiavQy3pm4onoC4GlmaIhP/Q+6UfaEQjNLXOGmOzyxVjEhBUp+X4qSe8fJ0UQVa0hFJxcZxWaU4ntqz9Lc+Y3wxIaVDPg+FPpS+soI8CBfAOfd1OjvfSmsXB2ebM7Ka80ZChPRk6as0A39sJDbklDKoS/8DU/tFActaijXBHT6IE8LqEu8Qu1HERQnybFE+eIOiQA/2jJi1WNKSl5LIs9kS53bs365jE+oTv/krAIBr1w3SVtlgQIEdsBSp4A1XeDKTvV35BYsIaCrp448ux0Glb4siRc3vdMeUBc5tsg9GhI/5fsEHRpioVI+lZny4B970hA9LTtk+ZYUjmgSFAUVw/O+XFl21N2kRhCjpVZUoBaEMYGRyBbamWYsMtkT+W7eiVYlThzdwhxDxnbt3MBwYwa6T2A9mybkVBQaXZmuQpGR/HcV9Qo51qAdSaQ/ciCRDJxnikJa3fIBHnF95nnvDnNLf9iSiKZ0EzUV7V0RLlUmmKeesJ3sxtcFxTVg+t02TrShmeWrXtrs7fQihPX/VfpzixEoVjw9JauQPjASIRAJ1EjtSyVvHxuPeLTM9GiT2o9KhkI8Mubx1usxveE0Xywy+mjKR2Jmdj9KHB3v2d59lliEXdNO5SKK0H+ffD/ZJFn7xNTuHczYOlx6zRYOIaVMGHVEQYosGYSnv+yWFmI4LWXJb3yZckMcybtOij6VtvQ4FrphSvLlvfdDz5tIl60PCH/o9Qvp7ew/Q4wJ+d/c8u8nnHRdWs0O7d+/espTOwb71MePCYzklQXehoOlMmhXAJLG+dUgi3Nmxa3/xoplh9XpDT1bUnMuWClDYF46PxOF07ymNBi7wokDkSf0A833eVhITSrm4UsBTkehd5IUfIxEyJT0dOC3UeR95kyel+Jqlm55cykVWIWlvjqvzBlVaZKgkusTvP2cLzrsPbH6HvI+zqpl+9elqyUQDj2RhADyitIJz7k875/5r59yvO+dOnHO1c+4fvMlnr/P9N/vvf/wGx/lp59xnnXOnzrlj59wnnXM/8SjOoW1ta1vb2ta2tll7VMjBXwfwPgCnAG4BeOdb+M4XAfzSG7z+5Tf6sHPu5wD829z/fwcgAfBnAPzvzrm/XNf13/4W+v11LQhXkH9IuH21aqXwjtc01opSJJF6lTeom3BYutRKlt/lSlDwn1OJUtEss9uY2Cp2OLRjPdy709jPcGiRkqDx0QYwInIQUWL4uVdtFfr7L3wWAPATP2JiSOUJzZsYIVUBTVsiRmfE6EKS/nIeM6AoTJ4pAiKkz+h9WWSoKXOckqx5d2oQo5Dt4ZjiR4zGSoo9qewp9MiBfV5EqxGPMWa0E1G4xpMDZSIloSLUfiWtkkShOlrgh7xWqiZURKBopkdDKbfSU7X9eMlikt8YcXS5nTIKLPIQlx8z0SPJYGs+SHAIjpLUTnCooEh+Tr4qMt6pGUm6EXvE8w4Vzdv+BAlnywCpN+Fplnl5sqcToVKGMUqjULo7NARgycg3lccPHyODEW2zNyxKDSPaVFOWudcZIKOoTcDrfuXSOwAAF3fsM7knjumesu13v9tItbOTZ3k+Ohfr8707dl+MekSmzqns2M5VYlw6pzhOvF14mVkU3R0SjWLZ3GRiY3uL+37soqE/y6Wd+IApksHQIuHHrj8NALj+xHUAwM3bRiL7zO99AQCwu2ufu3qZMtNljRnJjwPC/kmXaCXTSTNKl5/MLI0SlLLwlVywDZPsw2/dss8dH1u0f+3x6xxGimItbJ48+wV7zL52+w6+5yM2toOxXfd9Sp3feOlVOzZRhuUp5xYfcb0u7ZVpDz3ZsjnZ5etJ0kQDJEikMs0stXOr8tyn3UQG1DNXaJ0geklZC2BLNN9TIURENRX2rm+dUMMmgsvhQ1lVWFKSfckyylCmT0q7ilCYi6BsKJ03Q+I1EllwLpSC5OxQyIoQ6UKGd0Iq53j285ZOOFrYc7MzoBCXiOnrRfNrplePoj2qxcFfg/1ovwTgYwB+7S185wt1Xf/sW9m5c+77YQuDlwF8uK7rQ77+twA8C+DnnHO/XNf1jW++621rW9va1ra2te1seySLg7qu/WJgFV090vZvcPufaGHA495wzv08gP8IwF8E8B+/vcPUKOvK5yUlm6soXcSrbKmQqUlIcwhWBEO2gjky5cBEIIopwdzlivCUEUSPfIcuy4m6tC4+Oj5SD9k35sO8EJOtSgcbCUpKby65Ih11LUofb9sK/4jEMSy4wifhpl7UPD+uhJmLF19CcspbY4sQsswireWC9svM0fWiEDVX4vWKBGCfPbG+9UiGS0QQYl6uq2hDPAGfg1eUKwIjCTjkTcjQShG3Fzqq61Vloi8PlQwwUQdGJalKEFVypJImsuUqnkNMspwsfcWZ6gthIIrx8KGV8I1HW5gMLao+ndnYirCJSrcgoyrQPllcFYg8SRTHEzdl7TxY+77Ngzgk6e2MJLI4AyNGyDWJq04cE5E5SYoU+hUwrAooCy2hmukJzXEo2TyheVZCAmJdCXESWbSHIQV30rntW8TahDKxIsFqDioKO79rRMV3P2PX6NnfNZMjjfXpzNAMWT2PmcsXYdWtWdvGSYKEeeopxYliXr+ioFAT7/8uEZDFqfUpob1wn/fmhz/6A3a+Pjq1zfVrhorMd61vd26ZONRXvmhR++XzF3GREtM1yQMzEhQFHDkhaqU4OjYnZzS9mh7ZvVcSDbpy5QkAwLveTWSpKzKhXeOHd4zj8XDf5mZZ5nh4y/hL92/YeyoxXMwVEdsJnTtnyMnmJvkdnP/9LjkFFO6SeFrC58F60/1Xeil058ma/UIiRRKFEh+GqB9Ri5U5mv62bSbCM/P7us36nPOSvhc/QI8GEYLzXo48lekdP0P0qsz1rCG/h88gR36Q+tIhOpjIgpm8Kjk06/t6tOWV7jd74eat1/Dya0bAlSW5mic9C2FbN/irzjzv3mb7dpYyXnLO/SXn3H/A7Xu/wWd/lNt/+gbv/eraZ9rWtra1rW1ta9vbaN/OaoV/gf/55pz7JICfruv6tTOvDQBcBnBa1/XdN9jPi9x+11s5qHPu2Td56501TOAlYF4q4Sozpg2wkAPvOEoWu4ijJjMqUw0a6MhoiSs+EeIDp7IhMVVlpGOr1m5Pwiwsk4HEbRRpM9/NVWdM2eCgD2RO1QZc+xWUoH35qwCAp669h/3lvpi4rDKafXg2vm0zvi6LVkX1kUJmnsOcObo6DjxnQNH6OTK3JZyEpc63aSijHJpY1pKVTSneosqBYl3MJFSOWWVpsksOPLoihEcGKl6ERKWY/hpQ0pd8jg7FfDpijLO8LEqa1SzKcx4cWJ5QlY+7uxexXDIKKZuGL3Wt8ihBLUI3KFiUS6JVlRISjRKDfsxzkBCLJHBZgZLLjrqPPCWqomg9JAHEqXSLeXzyOpwXf2K0RWOdEGYHPehKDIrj1d3i51UyqXI07g99j4ypnKsuZQbF+0OID1GK5YL5WpaqXbp4HQCQPmP7+Tzz+cOJXauDA4uIj04MYOx0he6oEoXXrEi9xfDWmOZXtCrf2DSkrWLEtz2287p37x4AYEIjsn6f4zAkwhba/M9LiWrZ97tEL55+2jgJe3csUr93+w7uv268hCeeMp7GmOJEC9ome2EdbhcLu849Ru3nHrf76u6BHXs651xmdVAYUdCNyMHde8ZJkLFXGEa497r1p5/YGG5NrKxw5wnjSKiaZ3PbkKENboWoiu+j8sOVHDfvp7V8uGykQ48KAgEfjAnnXkhuhBBBCSzVqhAQMiBuUrcpzLTM9Lxgnp8RuDgqpXg1egbwoR4FkaeLRSoPDiUeZq9LiE7fyecqG6V9vJ67OcdYfQw8HAQASCU+xvsnpkDcC6+8gFlKGXneY0KeO5LBlrCSBJTw6Nu3AzmYA/gbAD4EYJP/iafwwwA+wQWB2oTb4zfZn17feJP329a2trWtbW1r2zfR/tCRg7quHwD4mbWXP+2c+ziA3wDwUQD/OoD/8pvd9Vs8/ofe6HXn3LNw+KALV7mk0uelGJ2S2SrL1lgiOGfyYYoelfNWNKZ9KlhPcwos8fXzF7Z5LFt9LpYWxUgvQPXq60YrhZfVBf8uPItYRNbpsa2fvvyC5To//rGftPNjBL1kbfl2SIljyaQy8Zl7lMPOmpVesQAAIABJREFU5YDypxVz1bVqjXnyizRHzKhgwPyzz79JvlaWxBym/fsW8T18XYIiEklSzTQjy1DjSIEWchQUAWTeKnulOKFVtxezIpKgKgQhKHGfuWRG1j0K6oSKSiSdwE4XjMaSrtAAO87hoY1Pl2Iv/d7E22MrmirW6rWVc11JVKtaQaJHTYviMKRoUqXXOS6SdnYyw7Ec9nBjx1cbuJp2t6zPln6FcukxKwly8mM6gY1TSpvfHsWAJH0svktQ8/vUXqiJTEi2OXQDoJZxFK8zKxnE0hdyQoABKcO4JZEjVZA8+aQBhRLYufm6gY2K0o+PbD5tUwDMWzXHsrqtfPXNiHMoIlJ4tGff7bASZjJhFQa3t24ZgrCzyzk+5vgQ5VOkLREhRf8yqNratsh8d2MLd4gcvPaaVQZ88IMf4DAQSaJ2QE7Gu5fg5TyX7a9soW++Zvvb23vI86R+xMzGaXqqahEb7yefegLPXDbr6R6Rg4oI2miD8yVWjb3EsyTuI54IeQISFJJceSUUUFbNzUoBb1zkHHLOIZfXje96nyl9l0iYcu7dyMY+SSTk1uRgLVgVlKusg81XXkkyOlYlhfOonqqWpC0gJXs9a1YIAvkPMljK7IMZkQSJyqkiTeNb8zkU8Nif/uyvAwA+8RufQMz7IZN1szRVVIVVNfvvLdBh/ItHwTv4jpFPrs0S7u/wzx8685aQgQneuP1ByELb2ta2trWtbW37Jtp3mkLiQ259WqGu65lz7jaAy865i2/AO3iK26+97aPXtiqsZbVbK6JiDTLzVonkMCVpoFWacz4Kk+Ww2PpxR6qC9vqI+gWTiZTg7HOKLlwkYgO4P+We7e8Czf1RnRhhN/TSwaoNTsiq/v0vGcP7f/vlXwYA/Kkf+wvWt0QGQsp/S61QpkaSz2W+u7a+Hx1Y/jLNZAbFcylSzKg4Nif7+nCP0RSHSmZPHUYfJ4dSXbPV9ph53x2yuYeskIjIKZB5kqo2VDJQMOJQTbUhB7K7tVckNexzpERnxCb2NrrkIlRrRkxeNpmfF1JzuG9cAymkbW+b4txikfsqijwjA9xrJdA4R1GUf3c1UsCqxlqIQsworxT/IxJaRHQrlhIc9xKOkOXScyD6oDGiQmZQCzFitFpRZZFZvjrWmGog7RhFIQtvIWmyUWblhddVSBARhUgiVQbIvKdpTOathonWFaHkYe1vqde9610mqXJwTMleVohIjleStsppK5KOogQulOQ4eTuMPtMj1rmzLn00tvhjOLDzUlXKnVsWpT/9LnsE9WMhLoqQhfpw1FT9owg0iHCB6okVDIUJWQkTst/KmU9GtDynUMghVQs/z8qHBweULi+aHCcdO+axd1hRIq2GOgWm1ETYukquwXnjGmS8bxasXkk4xzIfKetE6sbf3sJ4LXr1qKq4BmdarShcX+I95KN4qQ0KpeUxslKVBfyaLJkLySwLOSDSqhy9tEliIXbiKEw9l0C91Fiqiini89+fr5AUPpMiVtgIFvUihlSenFLfoeYz/tOf/RQA4Of/7n9jr7vCV6vImE+/QSqJKKWguWYz/yjbdwxywPa93L6y9vo/4/bH3uA7P772mba1rW1ta1vb2vY22h86cuCc+yiAz9e1nDP86z8KE1MCgHXp5V8A8BcA/IfOuV86I4J0HcC/BSAF8PfefueM/Bp4+gKZ9MwRhaWiF0ZzYsFzGKM4QlYYl0CmLhv0PNg5b1HHcMg670zcAkbY1GeXgpvY9gGj92KtTlvGGwIxYhLEg8D5SNlbjZIhny7sGL/6q1YRemn85P/N3nsG25amd33PCjuHk+89N3TfTjPd6ukJjASaKYRlkEnlclk2LlOACB9sKLtMGdvYVa6ysb5QNraME7KItrCRwQaDwBiwJITEKI8m9PR0T+e+3Tffk8NOa6/gD8/v/+6793SPNDNHpSlYb1X3vmefvVde67zP//kHMzP77k//Tv/YRPpbzZmZtYvXQOUYUZ2K75AxbU9wbSuLeYiMzWAdHx6oQlRiCBUg1YhmxruXvXoZ0Ctur/txyzkHhipD+Rcl38vo4YvTkKaq2spQbYproJJAXIpwtgNYw34oO0JVvXqEIeQINGC+HDizueH7ICRlMslCVa3GpdwrS/VxFeakvm0hfwPlWkgpo7Ak+bDT54QRPc+oXhpe7acJlVWRBlQho4/d6zWX9svKRUaIDyEnso9T7gecBW5hheMUpXwRBPwJ1cHdrUot5t9JQ6oTVZ/iTKRL+9Um56CB14DUOxleBNvbXlE/9ZTnhhwf+zk4wRdkButbyhPFKFtklqBHF2JUgRj1Nly9cH5Kn37kHUtVzjced4fDs9OXzczsrTdeZRtu+KKFfsS6XpKlnxV5XZZV0LoLxRHy2ACFqAKiggtlz3kAvTVHpazp19gvf+4XzMzs9i334m9x7fXJu+i2/fUyXgVXdz3XYJ4VdkRWwiuvupqpcfMtMzPbIZxpsO6ohlA7XRch+hs/i0p6/+AXwr5EyjsA0QzXFwhDUVg5k78BKq9sOf5MboR6fAgZDFkbfF9IlFxLSxPXBDfb4MCKwoBtanD/dFo9S7vL1bjoY3qOpFwncxAUxdILKdI9KzfThGCl8zHX0QDnXZ4/P/mzP+Xbzs+dftsmszP2M15ad8jgCWiNwtEs7PdF+RxcyOQgiqLvNbPv5cddXj8dRdEP8+/9qqr+JP/+M2b2EWSLt3nvY7bwKfjPq6r6uUeXX1XVz0VR9GfN7D80sy9FUfS3zA0vf6+ZbZrZH6/dEetRj3rUox71uJhxUcjBJ8zsD6+89xT/mZm9a2aaHPzvZvavmdlvNG8JNMzsgZn9X2b256qq+sz7raCqqv8oiqIvmdm/Z2Z/1Lys/7yZ/TdVVf39C9oPZzWLQi+Zt7SwVG3zuaocqhsqijQymxJ32wMx2NxRwpcvajrz2eNIMchMkCFMh6S3ENKnaGNVWPGyt4LiZYNlgXq4ZkZRGjzRB0N6il3v40+VLqdZecgephKULjkWQsLyJooVlh+8Vzkl0bRxPrc2PIZBz6uwzkCVkC/zlKpMeuVN+rqKpE6I/YXcbuNSKW34QNCz3iClspyhAz+XCmChKBA/IY6XK//AiJYColKFoxMvjbXcF/1dIRAyd9wnIbNJJSrkQCmVcZza2blXAjkoQ4eqvZL7ZvBZd+RJzGYlQOo6UMpcpQhf8SAqEhZRCFR4FywqikWffgrK0ovkRyDkIH10twPPQchYhOJEDw15TSh2W/74Jb4IianSVFxyGpQ/Up0UqdQIsO67y3r0cG7E/Ocq1HGa0b9dW5fngCME777rnckPPeuqhi2SAJWCmmXzsNsNHRcqwfYAh0dO+NnIGf7b645mjcauRnnhI468vfTil3y/4Qs18cWYSCUTOCxCgais09hSbuA5CMeYzIheU+fTXw8OfRvuPfT7Zvf6E2Zmdv26oxVXL/t99tJLvi1np35chj1HVgSPVDhmtmhqb2/v2LXr7j55TO7FET4dR6Awcp1MQOt2rxC1PPT9TEF1jHtSHA55K6hnrypebH/xS+IqsghIoJBrZ4hF59glSsaVCkfZJNyLnNdYyGKwrU2W1l0q07mUR4Gir/3tyWgSHnTthjJFhBxyDLXsfNmrRRwlqRhi8Rr4fpNr+5zY7Z/4zE+Ymdlrb71mZmZrm/7sOxkfmzWETgq1Y5XKYXiE5+a/vrhMBY2Lsk/+fjP7/l/lZ/+Kmf2Vb3A9f9XM/uo38t161KMe9ahHPerxqxvfamqFX+dRWVEVIQFPDPBIbP6Zynlma1RWao/l8aKKajDzm9MTUvqaGlgiiutVM2VpgqtQvC73yTVk+NZmpiwmdGGFBYZ76H2pH+fV1qXLPkP9xMfdsTqVI14iH/5lZETKgpjZtnwAWszWZ3OvHPJzemvHmUXiJ7BdpVAWEJV1oJQzIQgtX/cEBrz2v0HFiFGgTfFWyDkX0ZmY0VQhrWUmcVFWi3x5VRmqQtVrlivffNGP5jf8TN8vVLEcaz4+OvVt2rnsvegi9AMtrFdOj1p0GX6WbhuUZi7/Cirpnrzgl/0bAooh/wdlLpg8OsRuhrMyL63flR8D128ptzXThrJx6nPjSVCqil92wCsK5US02G+Wj6OoFAIWEIg4pCOKPR+q9hGacM6BqlRdB0JIhCB0kOdMMznH+eevUtXq/B8euADq8u6VpfW1Wi2bcg5kX59LpUEV38CNtJn7fu2TR7C16Yz/CYmJzzzjCML9W67eGW75/dVBiaSg1sKW1QtFvvDBV66FnPEacsDj9fZt78B+5Y23WLajGM9/7KNmZvaxp72b+x2f/KSZmd176Pt9goqh1/ZtKjNf3rs3fXnXdhNrD0FKuAZ3OVbXb/j1PBr7PbqHOknPsoOHLhxL8M3YWndEch1/hC4eJ0KYcpNbp2/DDCWGFZG15IyIp0jwihDCRBUuNcYE34vFPQqKpQwKKuwy8ACWFQRSOYXEVd9Fm89mIa3zjP0Wa7/DM0vflb9B2tBzhWddsuzzULFts9zP7WDL0a2vvPmKr9NQMOGqm6Zl4BbJpTYna0IqNa1LWxccdotycUy+yfGtplaoRz3qUY961KMev86jRg6WRmRmSZhdisSt8mUWNNn6OOxvek1JZdZmdtmE8Vo16SnL4UoMcFEIpNnQMmkxadqm2XxABlbyHDSDjEPFGAefAgspgv7jOsl3z36ba6sv7Xr1UYyipVWLIT7P5GOufl+xtG6VXHMqqBnbNikK2xzgsw+6MEF3HNzE1EMH1ZileKJ3QWmYfQe9cqpkPyoMqt9qvKxXXuiaOU5lGfpzYbZdLLOKlbWQc6xbnMNUfux4C8QV1S5pjg+pznpt5xh0u87qVqJc8A1IY5vTS+5Q4cZzORmy3UGt4BV0gctaY0CVJS8CVfVSM+TysvBtn2Qjlss5ldthViwUDkKpcCdswJWIIl9H0KmbHP50TlZiB1EeNIOHgK4H9htmeClUxyygElIjyBmyTUUvBnizCXcioBnaJvXrQcxIVhyAZu3ubNujQwzy+/e9yn3iCa/y50UZrpkJPBDd8IFMzxDaJfWCrnNdJwkIS7Pl2/DOO+7WeAMFxdaO3wtn0+nScZnOi3BsoiYqlqD8AEHhkI+p1vO5b8ODPec9ZC85T+D0oasPPvSMey7sXPGfOz2/Jg/35bjqC9zYdITl7r09u5w56tDCD2XOPTnGXVOV8JVr/jmlDJ4ese4Hrna486ZbzTygTz5cJ6WTcxtxH7Vwc0xBtaI4WlyvTSEFOEByzOZKNgwKCV0PPua6l7n3MlAJIQdavjgsOd4NwdMggzdRVgtVUkgpJTNDHhkBeYNTMRNCtsypiHlm6Z400NFXcKp98cXP8zW2Rc/rtGHGfV1m4lgIKVHwwzLvQby4i6Qe1MhBPepRj3rUox71WBo1crAy4igJroOqkNTHErM89Hds+ec4rWyw4bPiwaZXG6OZ9ymTaLkciVZ6xGX4BdW51q1WmRwXNXGUFFcTSFCNpNWwEtb4lIoo5UPf+anvMjOz3/f7vs+37f6ys1nBXHEM61o55pG2sdRs3ntkymYoArtf/a846JCb5MmXKCNi5a2zvdGE3qB4DmFRpKdlShkEOQEZkBNi3AJZkXOaFBZoj8uqspTqPJX3eaigVYUYP/tQJd2W7aS00hx0eVSc4lN/43Hp25fn2qpayrJcVECkM17Bz0FOZ1VIY+S7OieFKh/xSKisxJRO5cWwzNIOzoCoHvIsD9drQeWTK9FT3AJVsSxrhoKgS0aEnO1yZS7g0phTpQVXx1C9qBLnOERV0G0Hp8JoOU0zZ52LxD+x7EHc0mWmewqb3+AHdAd+TQ45zlLSHB77uRrBbeh0ekH5IdSmAcTWpMKbc86EbnQ6rgg42vdK+dKO99iV+nkZvsOcivL11z0w9iOt53359LeFPKSPqDfkbxB8+1E9qWqdTbz67ICcFezfE5ccBdwY+PV0947zAiaknl675kqEHbb1aM+Pg+k+rKZ25967ZmZ25aojAwnnSOmuusaUdCgUs7vhCpGNdVeCjMi5OCBbZMrxnY5BBZW0aPKL8eV0uh3rdslgaQrFUpIhnBqUUQuljJ4jvg5xk4LvS/z+da/uM/EgIimS2uLFROGhW+IPk6HOMqlzgmULz0VBk8pNKXWfSWGF583AUZyf+JEfNzOz/UOQx3U9Z+BkWCM8m8Zat7x0uDcX6Fa19N3wd+QCRo0c1KMe9ahHPepRj6VRIwePjCiKLE6iwEbXpKxUQiBVawAB+FirTW57O7VOn859Qgof/aRquVW8cMQOVdayn7daSKIzlyu9JKU8iu2czeQl3rKSaWWGa1gKu/a117wn+Jmf/RkzM/v407+NZWkZ6nvS/6OSCr4A7HeK14IUE9LWlyocy9wKKpM5KESZMUOXHpnZ+VwKEHTMHOKwDWUu/bMfOLk0hhx3qmCdE812m+qXR5U1xcHIVIWvOD42pPRASRJLl798jFXF3r3rqXyDvlcCQhpU7arqSYMfulm361X2GC25hvwNBA2p3x+c38TzCP1P+plSAPCqxLs0QEr+vZb4I3kW+thyiZNjWwfORJkvn/c5CXkTnDXX6KlH8oegosxN/hGgPcVy/VI9kk2R6wRLKRLyKpbTNkekCPb6Xq0rx2Jxv0hBAscHjX2/r5vT96GLz8burn/+Var555573lqgFfJKKMIFzPUeSYXhPw+Hzh2YnuP6uecIwpXLXnErF+XxG44kneN+erDvfgHblx1ZUJJeGjdskonvoB0EZeE4dWDvP/2EIwA3//FPm9kiW6NDdfvEjWfNzGwMwiAexcHhMcvx6nTnkvMAjg/8OhxOunZ43yv9ew9cwbAZ+X42B/gWyOclEqoBglj6M6cp10/UMMOUJEwQKfnAzLmOxiSUnsBZOD87s9NT/7eQL/F9hCQkXLM6dgvVjhBGXArlkCikKaSgLjuLLlQMcnv19ZdFZSXLyLPlPIfgBCokOZeCwq+fRkNcGxwQQdSGW46wvHPLvTd+4Rfd408qh5xtV67GaJItFB5sQwvlxyJ6gntQfzGCQuriSAc1clCPetSjHvWoRz2WRo0crIwkjkLFWIYeU770mTbW8a0efudD+uud1CzxamFKlSqfg3hR0pqZWaRFLiLa+FGVz3JvVRPClYDARY+dr82zKigcVF0Mh7jGvee9xb//D91Q8sN/9NO+SfQ35WinHmzQGMMUnpHkplTKFj3UOZXXjG1qNtp2dgJDNzj5wYAuhRAAoczkz49HAmlz21te4agXGSqsYqUHSVUi9nJqK86CVWnxCqoQrCNWUuSEoKgiUIpaCqv95Ngr7dNTbaP3cYMGW1WxqtpI7oWLfvZc6XNFkLzwsvwaEv3EdxDnQP4PVMwJyIGQBuVBqC8cceG1WrllHMMu7pNnI3JA8k2WCdIhJQGV32wEU3rgFZB68IW8F8ScnosvoKQ8eTMIHYgCdyBt6PwsI2PyoDjjukhjv7Y6PZwww/0h5IT7huPT7/l1kyZ+zc9A1MSYf/zaE2Zmdu/uA7t2xfX8fRCRMdsvv/2gMQ/5D76ROu+nVOWnx34c5XCn6v3JJ90g9vDIHQfPTvz6uXTZlQTTyTxUpeKByH0zeJTAGXnyCecWXCOl9N2b7so5OeLeGwnN8eO0Roqp3B33SQwdj7xqf2z38bA+Va7377miI3vgx+xyihM+91rUoBfPNTrHkyPjmpPiJMFrREhkjm9ADDKztubX0RAlxXQ2DQmYOl/jsW/nEfwFPSYDKidkgXuzlSzfHzqu4m7Ng0pq2WdDaF8ZiT8WBXdVPbN1TTbhtwR1Dw+SbC40gr8XrKPFc1fJkX/v//17ZmZ2wPXQGfrzc4KjZAHyZJVZKm8Z7dfq83+Z5vWIgC66MMFCjRzUox71qEc96lGPpVEjB4+OSL1NqROogNT/AynYvep9zOEGLHjIplFShN6ZFTBzpUqgumoxC83pwUu3Xq2kLWqmHFAMpoPKnE8SpfFRidFrnldxYEMPUQqor9bu+cy3hyPalF6ZwYvoKLEsJMQpU4JqX0xfvPkrtPdCDqb4lEdVFXwXGi3fhi5a8fG5V0/ToAVWv9rXPTr2qjxiNt7pqmKkrwl/Yo4vQhlPOE5Ur6ISy+ohjq3ThYXeXnb4U/KlMgfkfS9kRAddOQCnMN6vXfUqTjp5KSdUpevcZY+490UryhfxWlSxlNEyYqT9Ve9RuQ1NKqYZiEHFcWrh538WUge5niqxspOAeLRRH+iaGXP+ht011u3LHPS8+jza8555AYdFFaTKmcJUQY05DtKgy5ODfnocLY5DULgwpPHmmLbx5Djhemm05OYoRYwY7zDrBbTJx77D/aD+MFXe+tCr+9HZyO7f9Up5+5JzAVpCZ5rq/cIXWunjNslDGOIEeLDv1W0Mh0GVZ1OujbvuNPjSSy+xbr9mn3vuo3aiXrspawOFBIqJDMfENtX78x9x5cNLL7pT4j3UCU+jBmpxj5fqVSv3hFLz3kNHHKQceOzqdXvmuSd9G1L/zjs3b/mOch634VQk8DhiOQJyyymVdTSRuyOVNgiT/FHOD/w4HYP2bAiJShtB3aRnVYvnhlRN4nOIO6DnYZYJ7VkurVVxNzhuPXwwlPOgZ1wY8GfKogg+DhmvCdsbXDzFAzKlM/o2jGeOGImDM9j0vxNfftV9DX7xC79kZp66aGZWKfyGcyPX136vF5xlJ6Mxx0GJqDz/td0LchrbdHGKhRo5qEc96lGPetSjHkujRg4eGZGZJUllRYFjHmxctYV7zMr7fbTV5ADMZuQDRFUQzYfWKNMv+e2rd6SeuRywQuYCPUdVPGWoWsgewBdAfvANpolNWM1RVgXv8gT27ITKMKY6PTxxfe0486ps65L3H+dHuPiRGa8kMECQ0IPLqBDKzF/l69+mr57nc9tc8+p0kXAH6lAsOxhK8SBEoMH+N/A1GB/6sVUfN6GqkyNaxcpbARUA1QiGYWVg+qv67JPcJz6DFAOqNjQbH5IQefvWHd9vvPi3Sfg7Z1avyjJpBgo+xysJyw/LZmTKcYArobA49Zy1zLn8GkA3gnqDylB9zzhSpoQUEis5EVVhRb7MT1HvdAT3QHkFsXT9VG979IG1LS0+J1WMXDulXmjjbpgX4pNw/JNO4NboxigXG8gyS9bh+zsaj5Ze+7hQ5qhhUrY1avj3s3KZw6LetNQ8Tare7a0Nu3vX0wYb4p6AxoxAwELSqdAOzknGfdTAUbBHhoLY9z0SSMtSigO/jq5ddQThC59/0bdhY9cGQ68uJZSY49ynY1fJQZRrsk81unPV+RJvvO1ujFfedCTho594wZcX/BN8JByHHVCAybnfu/f29mx32yv4pz78BOv2b71306/7hHvxMq6LShst5UrI81JcHk6N5di/yl9EqqeTQ5JpT/2cDodDS2HqC0HVNkgBY8GfYNn/YoB3gBQ0Y55N4i5MQH/ko9Hmuip4X9dZCX8iTZKw7jH3hTgHGT4PQhJ1jUmlUIxwLeU5c2/Pkakf/pH/zczMbt27xTZzTc4nS/uchmdEEbgSC0Rg2c9gUdfr55XwnQsYNXJQj3rUox71qEc9lkaNHDwyKiutKGePNHTELPefVNWHChrP7CrFY8DSoNeu5IkgFjp9bCEGYtUq+zzM0lZI7JpNluHzvE8F1KcyVxUXF5V1mvLMV1VJH5Yq+95DrzZeevkLZmZ2efMZ1uEV3xSkodOgN8bstEOl1GmgIDhVsqDva5OqNauigHicnnmfW2mKM9CGUJaReKn9bFBdJOJYUI50Y8k8/JIdst9NUAA1QJvo+qX6yPJ5yMpQVTpR6tq5nz+lrG1uOmu/1/Flijdx+NDZxbvXvfITghCHc6OKHNUCaFBDSXNRFLgCC4dDVSq++VW03CvVMVdPNbgMclzkXqgUx81d2M2xkhc5XOrNx2ZJgmMdevPewPfz5NR7wVvbUlvwHWCdNqjMlOPWRv+u6lSoUJKgJSejQfdNqeyBqGvhkUOVvqiV5J0vNQPpi3APzs4cQeq08VqQ7GfV/2NF/96kIp2j1MhzufyZXb3qXIN9EISrl8gYYKNGU9/fBKVEJoVNSGX1H5uoP0pQvQnV66DvCIKY9xvrfn09ds05K19+8Uv2yU9+h293k55yQy6cE7Yfrwm25ZhlXb7uvgf7hzfNzOyzn/2srwMU4MqNK2wjzwWWm5Cc2cSVbzqe2T3SFS+hEHrhBec1xHP/zs23vOKtkCNt4u4ZHmIgJCU3bejRc+3Kk6Pf8XPX3PLrYf+hc1kePHxogzVHAAYDR0bEQVDGgip85ZbomAYEoY8Coq0kSF3rft7lUDqROyXLH2XiTUlhM7cGvJU+PIWM7w6GfsxmEz2zud9xiu10yVzhfvrrf+dvmZnZiy990czM2lwPM7g5wT9GN3WQVVULhY/uqaBu0p/si/RCfP9RIwf1qEc96lGPetRjadTIwaOjqqwo5kHPWjG7bLVAATha6mfN8Ad4tNUsroBayqG0YQYsyXQc/AuolOPlfrUqSrHWlWPeG/ov1ujpa/Y+HcshMQkKgRI2rOznpUtP8RH/6Z/7x2ZmduMxd1f7zR/93WZmlo1844+OnJswVy+NZmJD/Uxm55GOQ8o2lWZjmL1RY1m/LX2++nSZ8hnEvRDzvVD1KY6FVxub2xt+HNa9UpjIv1x+EmF9MKstDez5FKXHWuTLEOtYVURXLPQ1r15u3faeq5wFN7f8e2KQR6BBRSjT/UXXB5EOlja7pnm4nB4Dz0F+DOy9eqzybSiDU6J6yBXrgKXNea9COhu9fIk25PJYxKGnPpv4dzY3VVX7ss5PHOXpbZKlwCJVEU3Zb8EXgfltcoZT/oOUNPLW9/X12utmK73UIgi4xdVZXnav6+fiBP8LHepmayVhk6UqJ0LLT6hmpcGfwF2JqsK6VJl3SFt8fe88coTJAAAgAElEQVQ1MzN75uPet9+kGj1TImZA9TgHXJsF+9+CPzA+xAEQ1GwXP5AClc/TTz7t2zKa2Ruvumvph57z93RdNKkyq8iXcYSzZquHooT7aZ1rUhyMF7/oVaq8Jy5dw5WR4ylNvVj8rU7LCu7fA7JCIjhVz3/kOTMz63X8OL34+S+ZmdmcZWxvO4KwtgayIi+TcxwnxfqHuyVBzhBn0Z1d//7R0bEdHzqKcIoXxNaW/67T8eMgr4kBjplyHD05IRnyzN0dxaMZDLzq7/W77L8fj/NzP9dSN7RRdDVBqIp8vrhH58XSd9sdud/6tnRApfb2fJk//8u/aGZmf/v/cT+Dl9961b/Xl/KCZ6Ix2IbofUv0D3ArELogksqvAddAo54cPDKiKLF2sx9IX/lMBBRkJWf+EBWRpD2UfE7wUGHT2SoRTMvWhaaHuCYDgqYhFIZrQjJC2ID8QrGqss+Uqc4EslmrmVqzAVEsAipjiRl/aPoEyJzPXNb0f/+jv2BmZienPhn4DS+4OZJB4hlAqDq+h8Uo8rkBlp7ZhIeBrH0ttR76Tk2CSpmNyIq0AOacS6IDIRP511kBEY0/+i1uzEkbKBt7akkAFVQzLc9ZDkSjJAp9oDiYmRBiwiRJkKsMUiZzX/es9HVsXXI4WH/s0lSmSYL2ON6lrJ1lKwxxK5mGIBVJF08JqbmySQyuZJRMFtuZDHg4h7IF7hKfzXVRAZdXnAMRU+cE70REQzeixJJKEw6WSfuoxx/Os33/eXugIC62hYnoyT3/Q5URvNVsouGdaYJLi4s/PKMzf+Af3vXX3d41s4R7qAXpi2dcZ8XOtiobbLef9/We/5E7PvDjtnuVPxqER8VzPbilHcaaln1uMsERETYbjwMc/KEPf8TMzD7zE/5wb3zF2wzPPOvxx2qXjZEZKj48oyWWyYAMiWcDu9yT+26zfY4V9Dp/FEXKfPLJp+y1r7id8+lt/8z2dSZmWibhRzH32joBS4M1v1ffyb0lsDH0de5sEA99z//IphNf19XHvJUh6fAshwzXLC1P/A+pyH53R36+tiDsPv4c9tBjh8vffdXN1MZI7xpzN2YaMmHvMNk+o+2aY41+PvLrRyZZXa6r/lrHYoqfYyYHDzFk6nHMhmv+DNKkZs5kqI1UU+FXmpgfEWqkiUiH45dgpy2y4fTUr3nJmdvNlsnzTscjK9Um9P06Ln0/fuRH/66ZmX3+c37dvPxllywqmK6LvLhBHPckXyYTVop+5u1WS7btqekPSMyfaLVEF5MCHwsTvJWc8QsYdVuhHvWoRz3qUY96LI0aOVgalZlVVpQyP/J3e01mnYmsaQXtIG2aUP0myQIqShSEs2r2I5iXVVL5K8xElVIEhinTlxYzYwXTBBlZiLb1xTXbaYBc86Cp8890sfPM2d4uPtDnGM38gx/7Ud8fDEf+ld/+e8zM7PA2xEvZpELYa0NMLAOkbWxzFYhmguQUDKQYU1lTz2WoJEg6FmkQu2SFW6UixdHSoWIWmVDIgo6LIlzzamFKJPRiDjTXwmK3UhUConIAUWrQoWpB0jil9VGxTx0qAxlXqX2RUFFWgbCUWQtzHxFID+66ec21La+IVQHEVF1CM4pM7RXZTrN/VFDjHMInrYFIklbZxs4VWJWE1pXeG0Mw3AEefnDHq62MloWkjClVVQui6znkwM1tSfaoDKkUe02HurtAwPem3p4ZnZ5ZD+KZTK4UsFXFy3HaUZBw+egDD99/4NX4vVt+XK5dW0ZeQnStunkrvrMJ56GMZnbGtbSOzfh3fMe3m5nZZ37mM779EBGvPO5E1D6o1gn7qRAxoV0TLNNbbZmN+XLPJiKgcS/zPOkMenbtca/o33vlpu93x6+HTc5jXsoEy5d5jJHVcMOPcVOwO8jLLrHL6wNHEN563cN+dH1ceczliDHXTz7PrdGFDKyIba5b2WaPICJ+5GOOsGxv+vVy+20nKh6e+bVsU0cW+pAKRXjNuGZnoGAjyITzR1oFa5AhKxlzcf9LwqprUsRDXd96HsYtWRtDNOQaPed45S3fhuHQr78+7apzEAS1G/JWbkkkFELPoGUZ+i/+3M+bmdkP/c8/xDpBQ3noDVjHhP38wCwkPS8iEYAXLYJqpa0QiO0rYxFUJQJnaY46fPMmyjVyUI961KMe9ahHPZZGjRysjKoqglxGXAPZzCaRJIL+WSEIGRBDFEWh91Wa5G7G7/xVxiia8ckEKFLkZqmwmjhsj5lZQg9dLLF5MV/6fahimpVFkLAkZczpbR1DalKEagUnQXawMXbKL7/mPbTv/PbfyLZ5pdPuIu0Z0R8c+2w7oT9cBennPNg7y1JY0jvNxlP6s1PCnAK9hu+JgKRecQtjnbipwBSR6HwbJjSvmxiMFInCkyzM0BX/Kh6EgmBEfpsTxbt/b9/MzDaoqIfrkvT5NozYhxF2wuJRNBPJ8GQRrN9XwdW5ISdh0Is5krUSO2idqyZVu3qoc5m/5DJeAUlZRRa4dhU0kz5iEytkQxXsTFGx9FIVRnOw5/v/+A03xxIZckBgzhEhPuusS1a3M0lc2ZYEtKfDdfPgzgN7ioqtsWILrrAjEXBLW+aoqDq7RmX8xitf8W0Axeoh4ZtLVqedVjkniSMoSKNbWTlVlUq0LnbRzz7rBN2vvPIK+4d5FsFKQj0GIEdHMgVT/5e1dyHFVS0IbqVs2WXUFNlgw7d7a8uX/fabzkFoDh5jvwj9OvcKuEHvvIERVSZSrVAJbMo3t70Sn2GrvI8c9703vdrXuR321m0MQVUyygi5o8Fr2dtzRKlY88+tXfLKOKuca7C/78uWNfj9PV9HF8OqnUsuq9xmmybcNydU9Seno4CExVzXknDKaEiyYpEhc669viK9BZ1yjDfX2X+eL2eQToWKrsNhEP9oChIxm0ytB9dCoU6jmSOrWcZzgoCuFEJyH4njFLKrZJNJMDV6/ypefxPSVPJbuEwWBU6Zxiq3QMtetZ02yy3PvwZa8XWMGjmoRz3qUY961KMeS6NGDh4ZVVVZnuch7laz1Rkz3YIqryKviEm8FdiEFlVl3Q6yuL6CYiTRgnUcDJYkY1jZBlPIkVj4y+8rDlQVkr7fBjloddKFoYzKVaEPrFs9UjHnp5nPqrvs0O0Hb5qZ2V/6X37QzMz+0O/5d/33sRjfmuHK0pdtaClGOQoIiCreWaZtkj0uoVWyJlWFzLFuUpWtDb13qlm6Zs5STATJHjPtU0Jw8spfm92ODanOhLo0MCSCihHslO/d9H52To/18g5GMuxvzrlMMIfKgqKE/rgo5iuOVkmSWAP0aW3dq6l9AnOOiW9df9x7zRPkgonOESXAjGpFfBL9XsdXttQyulL1ovc3NtcXcj8ZcVFtnJx49HCT8KtjoojnVHyqTjpI/+5nsPDhLKj6jQ0EQUYz9FI3Nt1u+taXXrNrLLOx7suKBB30FlbTZouI7xhzoAxVS58o4k167jfpqT8//KgvT4KR4CoryaQtLSdKGxajmJDkbsT9ff2GV+2HB46gvPhFl/B912/5LjMzWwcJOUM5IdtsKZF0z+s+i6SGofde5o+YR7F912849+Bg7EqJmzddEfCxHZcTxpXM03xZqtbFJeiwrowQoDnqnY0dv3+aDb+fbmGJ/O4bIAiPPWEtIufV8hZiWnJv7k38Wt0r/J6yHYKFdqjaATUVTKRgoSPkp7fvuMxwe9v5Dl24PVFf25zbWAiaWPexgrhQrbAta6gPjtj/vfuuamiCIPR7qD3ksQZ61eaeFd9Gst0oqALYh9nM2sFELmF7uVa5F59+ymWn62viLfj9Eq3wq0S8CiZHXzUku46X9tnMrJK0NyjhFcy3rErQPRz4Tknisc0XAB3UyEE96lGPetSjHvVYGjVy8OioKsuz3IwebLC45ecm/coRQUvVVKZC/vUkjkIfKfSI8kXf2cxCpa8WWfBI4lWmLTJRkhmSYnLFxl+dF0oNUUVFQAxUfSkQRcExmj3LfKMj4yBdDbz/U59x1vYnn/9uMzP71Avf49sEGz8fL8fCdvpEGOfTYCSSjbySadOXbtKHVYUrTwDxPDosaw2FQItqtYiW97iKVUGLT4CHAb4R07lsh83GJ94LHKNp7rKOHRjS5wTmvPP2O2Zm9uSH3E5aVsey7s2YzQ+2vRpTP3RONTo3WbOGksGXE0chAKgJotSB5zGBLd2nMggmqaucDfYnKujjS0GAwY5CYXr0SWUec3DoFdZsNrNmW1bEVE2UimJ239i+YWZmd295pXd65BXR1o5zLyJKoXZTpkhUrVR7kayrFZNMuT6g2kui1A7uef/6av8JlkmVjfa/jMRF4P6pxNZHvcExvXLNFQQn93z/7r3nFeTVJ7zqV4Wl+NzwIuOmJLYc2CnDe2EeTLF8Xc9/7NvMzOznf/bnzMzs5ZcdQfjEJ13VEHGTNrk358EcyljHcohWJlMyroWyKkKfv2z6Z6+CINwDvQtIGuV5QTa1jNo2Bussy/dhCnIgw7OM49eimr/xlJ/jm6+7hfrbb7xru9f8em4Rcz2gIo5UObf857OJ995fe8vRmisYLF2GS3B/4udAVexVos3v3vdz9O67vs61NUeSNtf9td/rWgTycXrs66iK5VAvPZvESemjhJA9u4yXQlgc6gTFKodgpcAPAuVMxGFaBC/NQTEL+D0VAXuyGNjBoGljzdGr4xNHmISwyU49it6/eg9VfizTsOXf52Xuf4dsEcr0q1UflFV5IaiBWY0c1KMe9ahHPepRj5VRIwdLIzKz1HL62V1F2GJlm1Nxt3EpbFEFDtZ9Vt7qpKHHO8t8JivWdZgcBrdY+lMrs7yQr8G0TdW9emjaFg0KRWvI26DKQzxpriAkaeAJAmqyX614uW89xfHsEPvX41N/ffU1Z1B/+uO/3T+HnWwbyCSwdXEGy+f5I8FHVJVSYXBsdYxlA6zjIUtiMd2bUmk0pAIRJwMtNhrybLx8vIVANHud0J8XS1/fbZqv673b3t+ds+ytS14ZFIGrQYWNMqJDIEuqmOhiFebRufUf53lhI7lq4qK4sebV1v5tmOBy7tM1JofHgPZwHXEuhX4okOaU6n8om216r3Kjy7IsKFpUscgBLiqofGFyy9L57Te8QtwMyIGPdQKE9h56LxpTvtBrjYQCUPSkcBk217ds/4Hv7wDb38FVrx5lE71qUZzNUbfAhylh1ivE5grOf7duOtqxNvDj2hpimwuiNpYNd1MIXG4RHJQWve8RPXIx4gegVs+/4OqFz/3y58xsEQYlVcM5PJGm0J5gLy4ETx4WcBNkL50v7u9Z6vu/Bkcgt93wGTOzVqrgId+fZ59098a778IHOHK3U6l9Gh1dP74vGWFbLUKzbnzI1QoPbu3Z/kM/J02UQEcHjqQNNkB88GXY2uCaMvwPRv65vcqr/fUtP5d34dNQrNsluDtx5J87OqTaP/PlbF3asZjzIiRkxrNkNMc2uq2wL7mP+sIv7Tp68fCe8yFK2bDjoh28JUBrFMSUFToefpzWUeKcHB8HDxZxD45xuGyB8pwew8kB7VwdUmoF74EVnkCyIJ7572Wlzr1eFgu1UZEvcw/Cd7TMXOsUCnFxdso1clCPetSjHvWoRz2WRo0cLA1HDpJYmmt/V5W3FdLW+oySNnp4TZtm+VQBQu/vdR3kqyIdrOhZQ+kk5rCSafU5/V7KA2b7C6ZsFZzIKs1gYerK0z0Sp4K54YTZ9MGZb/vhA+U5+Od+7Md/yszMPv2J32FmZk9eetLM3K/fP+frU6iLVVWYHcdBZ86vFPPLwW2AFET0OzGZs0MY8xS11l/3al0VtCqLMjguKqDKqxE5rDWnmXVhuHeppns4Q86ppk4OvKK5/pRXU3GLkCQFVTUVs+zH5ejQe4xb+CDQLg6Vknr5lTzUS7MZkbB9dPmDnldId6fep81gzMesK+fzDVQdYzTUZWAx42UBciBP+sshVthfOjCts9Pj8B1xD87HXsGtd3xbFN60DRTw2useCjQajdl2P44dqjghJOe42MU6l7rOdGmDCq2vbVhO5X+P3IF43Y9HiiOg+vdZtMz4P5949dZv4LqHN8H2FWfAH971CvO915yF/+wnnC8QHDZhvWeohub5xJJYAUr+XgM/ixGVbZPt3rjsVeWnPu2+H1968SU/XpRtzz3vQU0noDcRF4S8SApUDCGOO1r0wZUNEndh7pOJ0aPvLw+KEed3/6Hv59Wrfhxa7KDCr6TEOePcBo5TF54QyNRgi4yBycQMxEheIuc4+z28506hzS5hRiAHzR4KAhxGz05wLR3Jrc/ffwDXYA2UrEfctqGOODn2bTw9PAkcggHKofbAUYtYCJKiyzlnisnOcLmU6qmHV4VKbXkS9LsKhVv4GZiZTVBYxG35aaRWyimSZYjfU5Lz8uUvv8z++TUcrSQnqee/2vvXM3Hhf+BDqKEQ3LKoFqqssIjofdcVkOcV/4OLGDVyUI961KMe9ahHPZZGjRw8Mip7RB9tFtjMZbHce5aroeETMKKqS4tGcNuTSx4T9RDVLIc8zWCVyqgZfpIqEsxfJH2t5KyobQiM6GW//KLMQ7xxEuMlQDWRTek/K2XMxKWAa3DgFQNFufX73hc+OfLK8fOf/7yZmX3i93/CzMwm9GhPzvF/UEVULPqXqmzTSP7kSrycaof9/VicA3EL/HMnsJdPiZMVpyDP1B/XLF0HTP4Q9L+T1Ar62XNY6SVwzMGeV0ZiLF99jN6oqldS+9o4pu1cgpX9wHuq5dyrmwbnoODcRxLbV4uKQFwRuRJ2QRBU0ciF7toTXglXMKblBX80VqId+5eLW+HHV5W4HOHERZCGOkmToEpQ/1a8jCK4clL5qcKlKHl425fdf+YZe/QXXY6LGOObPUcc4kqVFxUzy200G7a9433p+zC879915GT3+hUWTeYCfiGzkB0CL0LcAVXMfP4JtOdf/KUvmJnZO6+78uT6s87OD26QHSrEJLYZ2nqhcFMq6IaSLan8dZ9sXfbz//FPuKfCG6+7ouDtNxxheeJJPz4ZSNo8RDrzLMiVQOrry6aZNanW5/BblKkiBZC4O3peTEbwgF52h8iCbVYGi16FdknlIr5DSO9roKzoJlaiKlHV3UVdcoyr4BE+GDOeMcMNXBqpttNYbp7iV/n7m/hb6DkkZYacZqMKd8Lp1E4O/T5XrHUfVZMimENCopRWcEkUT9/ryYnVj0+ffclxeTw782e0uAVCLHU/BefBOAm8JlX0TThcM56bb73p531CsmUblY62cXXoHhTXJPACglKtWtoWqx7hJXwAclCtIM7R6rIvYNTIQT3qUY961KMe9VgaF4IcRFH0b5jZd5vZJ8zs42Y2MLMfqarq+97nsz9sZn/4V1jkT1ZV9T2PfOePmNn/+jU+/+9UVfXnv87N/qoRlZHFWTv0ljRtU3WawCiWhj5NNYvlc/PMMAkMQ/O7NFT6cgaUZ/iym2E1W/58qiqU3qT82dX2TdmmoNNNG9ZBh95tewUwV656wex53XvMRwdevZ+PqCrG6NhhRotzEbf9Hz/z4k+bmdn3/A73Pbi+41rzA7LjGyYXr7nNx9Lj+zbkuWb27KB06HAE1Fvvk9onSsaI/IZc+m1lSpBdoV6jwSyuUJA0xVLudEJ1kbKOKX35+2S+X7p8id/755RmqXyGnHN0+YpXQg/uuYvd6b4jD49dcW19hY59Qo5EBHoyzubWJyPilOoqxZ1v47pX2+/cfMuPw7avI6g46Ll31r1HLDSrwgGxi7ylc07yHb336IajPnnD3+90Ysvp0xva+caOV8KH9LMLOBhDtqF7yZfx9js3zczsxjWvwhOUMW1eH4LubIz83HXpB8+5ZmcxuvG1wk7mjkJd2vV13LvtyMEpSX5tVAbhnuL0yiFO96Z8/Ce831/3dT7+YWf5v/oVV9hsbm/w+zW+j+6/0w/JqVOUM73Kr/sp98msZLvZjzHoTRs+wO7jzlF58RcdUeuk/v7gql9PReLV6wS3QoN7kHL9NRuxNWD+R2MQk6Z4Cf46AhmYUF3eeOopMzO7866rE44P/Bo+QUnTyOi9F369pTgqVnO5eYJ2zv3z8VpqFY6Ns8qvHSmF+kM8Adp+7E6P/TvjffxCyMlIfVXBnXLhCCi+AB4ccFZinlmW+La0ey2LMzIkyFsQF2ATv5MBrooTVD9yJW2gGGoO8EmAm2KomDa4rkb4iRweOw+iA8dBqgYdtyg2y3SN4W9QUY0fnTva9dobX/b9FjehWOYBhYwEZbioqo+Xq3opEuRlocMWR4mVfEcJwUISUrltlr/2df1FtRX+M/NJwbmZ3Taz577GZ3/UzG5+wO/+oJk9ZWb/8AN+/3fN7Ivv8/4v/6q2sh71qEc96lGPevyK46ImB/+B+aTgTXME4Z980AerqvpR8wnC0oiiaN3M/hMzy8zshz/g6z9aVdUH/e6bHmVV2XQ6CVWpfLcjKkfNh+VvoN5+Eanv94jXP0c2DY6IK25YwbGNnyVb5bVN37eB3n1OYmIolFtyyJMvAA6BjY6V+bJTl6qFwANQkpdOf5ix6hXlAP29lG2499Ar5j/1p/+UmZn9kd/7B83M7IVnna1tc7n8FV/Vx07Eg5jhDsc2SVNfcUAm9Ay1LeJSxLzRx2NA+6SebELfb95Y9hpvJWlAaRK6aPN8kYpntuhDanY+mvmxFiNa+6Ie9GWQhltvu0/9sOMVcxsGuPwRcqV1WmVTfC9S4JgxTneDbd+f5JZv2z168I9dd1RmxvXRhRl+dOZ67hnrUDJg0pSO249fzxyROKYS22x3LIXfMD7zbZEvhfrR4wyFB8f2iWe8j/+Fn/GUzhP87OXX3yNjQEjVEQjCdtMRCem9VeXHSRzWuTX0z+xc8mN5G1fGZz/qdYWyNpIWFZOuE5alTIE2SZAZveXdJ/24Zejd33nV+QAffs6X2+jjF1Dl1oPPkeVUmysKIPXz5V0Sc7+IozOkcv7QM66M+OIXnAfwEXrsA5CZjhxKYyW10ntv2sJFs1AGBOgb5/eMSliujWskQ7ZwSHzA9fLW2+4+ePsdf7162RGUQJjn+x0QtkmmZME0JCGenjhy0MdrIE2kyvLvylPg/NSr8HO4RrmuOTwDOqiBppxDPeSUG6LnplQQk/PM2nhKlNzvIxJkjzg+vT5JiaROxq1lzw6ltXbgwYyO/FrssK4hCILyQMa8KruhjQdMFFUL5ZcOHudib8+5RvcfOGrTA76VK2US0nuXfQ40QnKqOAZwFPR8CYhLUoV1fpDb4eqyg3dGVVqWZRfiknghk4OqqsJk4JsgRPxBM+uY2d+oqmr/IrarHvWoRz3qUY96fP3jW0mt8G/z+he/xmc+EUXRnzCztpndMbN/UlXV7YvbhMqqam6F8talLFCVH6p5UAByEIKpW7JQNMTBBataWohYprnkCyKoVlIhSIsuLbHS6kAGgsudqjKJ6/k5Lq2UbhZPgArOxPa2V2mT8ynLpgc/8tk6bW5rt6QYYAitYJvees/7ub/44s+bmdm//K/+TjMzu0Wfd1aMLWG/c/q2cbziUsjPjUh8AKqySLNoX7sY9eo153JcjOOl90vcynJY3c2G0J3I2mqGcszPYUbLc6BL1SKdsaoJoRhi/h8fedW+c8krqOM9r26UOvd03x3zuuiixwXVb1kF0b96ylOQoIgK4OlnP2xmC8fE0Idk0+V6GVH5zTg+56gz1unhn517VbcjBCoCUZhmNsAZcw5yMAFVuHTNlQJ7h75/J1NfxgBEZJ0Mipvv+X5u4IOQQLC5esWr9TvwB2ZU3B1QLVUxcZzaxqZ/d//AUYjr+BTsT/xY3mEdl/HtL1f8DqRBF0dHfv+9TRAE7t3Hn/LlHt3xbXrzS94nfuHbP+nb3mza9FxZCEIAScTkmMtdUSmKuRIdYffLp//GY+5WeLznn3vrJXfcfOGjaPdJoMwSECV8NPI4D0hBIpQvF7rg27B3zDW34edglsM1AQHZBK0acp8c3Peq9vY77vewddmPY9oRox5EDb+I0iorW34sj+e+rlOY/VscUz27hOJ118Q18Hv3+ND3a/+Bqxrabb/uu6BaTdat+6vgySJXxLiZhOyDAcignh8n3HNyI+ywDb01MlpW0D35WaSgQhrncA50b4e004BucB6SKKCUynfI4RQo8TJfybeJIIgp/0OIs0bI2VlRGgXE4GtU+avFttAFebAIOZH/yUXlKph9i0wOoij6tJl91MxefxSFeJ/x76/8XERR9JfN7E9UFT6hv/K6PvcBv/paPIl61KMe9ahHPf65Gd8SkwMz+6O8/qUP+P07ZvbHzezHzLkNa2b2XWb2X5rZHzOzoZn9/m92I6LILG3EIemuC3u9g3tZ3NCszWebmsXJKLAszCpV9JoV5uo729JrmOAV8rmniknEc6APPJYfgLZRGnL/Oc80i1V/LLIo1ux4OZ0xzC5NPXjc52Dtx6XPtlM0wHN8HOZUYzOulg5OcrcPXEt++9Bfrz3tyMTD9x7a8b5vN5LmkDJYzFl3Jf4DqoaeH+uotay1z+iDHxx4v29ED7apSpL+uGbzTZYzl2NiFVte6ZzwHn4Pl654X1bpgmnmy2x1UWsw08+Cp8KKy5yY0KQ97t/xqm0HVYOiFpoWhcQ+XSvStZ8WVILmy3pw38+F/N23rsB8V9VKVSPN9SlpfOtbzpu4f89VDyUOi0MY4tPZxOZySMSFTsjBOT4FTXz3p3w3Zb+HqFve+GV3Bnz6CXfIHDbof4OsbAwdFThnuc02oQuBV1IGNYqY71OqqCdY5ksvOd+4j5ul/P3FTldOhpAlVaMTjkMDNM9QZjz/0efNzOz1L75iZmavfcn34Znf8PFQTc+V+CeVkrwWUnFvlNgnt0I/3z1o+mr/Pv+8+398/ud/wczM3vyi8x1e+KijQinmfRXbOElLK/i3EGT4FlgAACAASURBVJFIiY2gfdduPGFmZi317VGpyJU0A4HaIaviyeuuoJiADt0aOS9mE7Srz7lUkmiUxNbv+rUzG/pz7YBqXRyELtd5uXL9K+11fc3Pc0qWywT/CDkg9pRl0ln23pgjzUobsTUSuARSY8mVEL8CIaUVFqrHe45SnCV+HXW7y5+bgZrqWd6CNyXPBvE+RPnKUVQUaWwlnLMRCNpwy5fdAgmRlGosxRA8h3LFe0AIwVchB6BeQc0Qy1dk2RfEP7TseBjyUOR3wPXzqBLiotCDX/fJQRRFa2b2b9rXICJWVfXTZvbTj7w1NrO/GUXRL5jZi2b2+6Io+jNVVb34K62vqqpv/4Dt+JyZffLr2/p61KMe9ahHPf7ZG7/ukwMz+z4z69o3QESsqupWFEX/wMz+gJn9C+YThW94RBZZI01C/0kzPDOvbsU1EBehUA8Rl8M4LgPTX7KECsfDlaIkeAjoH1I5NBvL31d+fVONUKbr6oM16PuqyjFbaLkTNMD6Tc5GqFqfoYGOc/pWgV0vZQUzWqk3UjaajX35DXej+8t/7QfNzOyZy1617K5fsY8853OwMS5rJ7x2KZ/KoN9ts59UUGRTzOgSyefgnLx2ISRyJxO3oKlePLP5nJl1UphV9CvltqfkOlW8I6qGViYWOfwOfl8EDwkqx9H50s9yqVTi4BZ99TbsbYtLm5mqJNz3YNcXop7InRCPiQlMcMtUtdIr5vdTHCbP6VGvk5ynrIlzKqut3Ut8r2tj+tU5VWpLuQ2n3rf329CsST+6oEpJuQ7kNPnOm44UPdPxTpz610qCVBZDBgLR6flyz8+OQx5HF535Abrzx695uuJleDEP7ror4zrMeRmKJvR3Ox2qOPXDObdijHNpWwLT/pnnnQ/ypRfdF/+t1962D3/UVQYNltluyOlvmZMzp0qPlGYJEjeCfR9xTYsn8G0v+HJf/qz7H7z6kqMWV59xbke/vUBc5JmidckZdC5HR1j0er/NfqdiBHFPrm358dyFq9FsebV/813nXCgpcZtUxz55I1FSWsnB3Vx3XkPGeT45deRAiqkypI2CQIGsJvBaer5K6xiqGJxjT079WjTumw4VuDhb/c66NXjG6oaIS1I15WcxXlY6NKjedQ5O5r6OpskHANQPx1Cd2234M0JHhAZ1cRqdzWfhObm2ARq37yqtf/LT3vGe4BvT6urZxblYQQjkzbHqZriKFGis5iY8OsqQ14C6JVr+eVW9cBHjW8EhUUTEv/ANfn+P194FbEs96lGPetSjHv/cj19X5CCKou80N096vaqqn/oGF/OdvL59IdtkSTA/l9e6emz90me8vQ3NmKni8oW/eRPXtWZblT6zSVjlhQIemTQquS5OVIVSCTNbjxtio+KYV0ohIWTBv58KUcgzS1tUfiFVkGWjZ67o+0/O4BKcA1uwcXGPvh+z9PHMZ8qhT4lee33bl/fK6581M7M3X3ckoZP07Te856fju7/zd5mZ2e71J3xZuDKqatf2l6g0piOf6U+m8nXAAZB0QSlJKErD+6r256TatendxlEUXBnHIARr6PQjFACCdR6QmXCt6Uz33pDjCLozOvfKJwEpSrFXW9/wfu/Nh15Rn1FxXRp6pdiwRjBukDd+qcTGSMHzfiw3trxyPrzjvWK5GsrlsQtCMpYnA05wWSqFgF+je+QhbFB5F53USo5JgWOffAh6Rj+WPrUSMttce02q2ktU9TffcTb+9lXfv8tUoU2ql80tT6s8Pj9k2+GXNFsBjWjDezg4dFb9GdkZN+ixn3/RuQH3b3nVdv2GuzOK39Ea6ryDxIgnAbJS4L53jL/EEE7GjRdcWfCVl9+y9BU/Vjc+5stW4p8yF8SmV1Umtn4LpcAZPfVGpGwR8+O5hVvjs+6cefjAz8XeQwdGH5w6WnL56Ss23PByW+oTAQId0JgcxYsqxXkh9Y//vHPVkYJhz495d9P3qZH48XnsCUfzDg78Hn54z3kxOTyb/qBnsyl5E6BaW3BHRvgZKK0zpHAKOWv4fgfOAZwC8YSUSaLjeuuWezAkoEelqvuzQ9vacJ5Oi2tUroTiHih9UYqjNbgTHZRG+6d+rQlR63G/NHiunnNPhmRWtk2JtOIwTabn4bw/2Pdj9QP/4581M7MXv+LPtwb5HLOQUQMSsKJGCP4Y8mK4gNyDD0IXHuUiXJRg4dcbORAR8WvJFy2Kot/yPu9FURT9p2b2aTPbN7N/dPGbV4961KMe9ajHP3/jorIVvtfMvpcfd3n9NDkKZmb7VVX9yZXvDM3s95oTEf/qr7CKfxpF0etm9llzf4M1M/vNZvaCOTnxD1QVxuDfxCiryiaTzJr0KROTNthnreen9IFTJcepehfdObcMnb4q2UWmgr/qo2rfpzidVZVS07w6OYPxHeH4V8kQIRVUgC+CyWlQft5VqK6n0v4ye85ZdoyH/IhKsaQPqf7enFl6d81n35UyByqvGPoDPy5r6/wefkDJ7Ps0m9lP/tKPmZnZO+965fdbP+UIwvNPfszMzPaP/HTtXvLLJbhRht47ygE4FarmVJX0cPvr8ipXw4zZ+gD2ciNOLMVTIVNWBgiKjo+BIMzJFhAjvDug7xkvz6HVU4w4Z9qWtU2vTvfQ8G+jNKhKszwEVeDI1qIPDyozpc87WPOK6ISe+xjP/J1N3PxQN3TgjVRUVmcoDrQtB/dQPSgpr92yMjCbyfdgP9qJGPLyEEAZIMdMft4hZW9EGufhAbp2/O+31n3dLa6DNF32yx8Me5aKCyLnQ5CQwz2vqoc938+nnnR3xldfec33F2/9/qbyQpRX4Ns2TEGWJjgMsvFjbrSzqfeY13b8HF29cdX2b/q1Wb3ix2jnKb8WWwmJiMB6M+P6JhshpnKspr7u89I5G1Ul7wrfls5VrsHMj4v4E0Kgbr1409a3/L3eNa/8myA/BQ6acer3WIkL6xx3xjnb1Bn6cVnbXDg/mplNZmQn4CC5s+3LXx86yvXgriNT49Mj21nz9zrc5yGFdM3783tHfj33SErss41yUD0b+72ctqVGWOaBNLlGH7/qCI1UEMpaiNLEjk+UzwL/g/skKcW18evkjGdW1vJ1t/u+zWschxFojlwKm9zrykcRqtdf534CTTyB+5GXefAiee21V83M7KVXnMrWgWNQQo+YwT1IgIHFMQhctYuzHAjP96/Ob+B+ShbPqDy3C0EPLqqt8An76jClp/jPzOxdM/uTK7//A+Y8gV8NEfEHzOw3mdlvM7NNc/DtPTP7QTP7s1VVXUhLoR71qEc96lGPelycffL3m9n3f53f+SEz+6Ff5Wf/469/q77+UVVeNcllTrPWkI0OgjDF9W+4Cfegu0hOTNPlWEbpT4MMVTM6rO+kYpBT13iq2ba/P6A6V39O3vLyQwgGiVp8uWCLG9VZJzjAMeukOTo5Q69ckBrG+2Mc0oSgdNG/UygbZmxWVco98G0+V7WStm3IzPz193z2/fKXPAP9v/ovfsDMzK5e876+vATm+DzIlXA29+OxcP5aZuU2qAiENMxgd5eVkJaFhjiWNn6q3jCcC6rPCnRGiodDKv9Wp7X0KjZ+DsKQnc+Xtk1+AMoJ2CO1cbixZogzQsZECe9DZYi8J7r4NAwHXuHuP3QexNZVr/xaPXgAQjPgblS6bjjnuXgWJ34uuxvDkO+QhLue1Dy8NOTrYTC7QwEE16QPv6NFlsI93Pg6W6gzqFI7fT+eA6q505F/fzKZWKe9fD2vkZZ4jr/D2aEfs7WhoxSPXfO+/XtwD57ow9YHUdK5TWSBBkAzo8qvelKc+L4enJEgeWnbGhPfzrfhUBg8ocscayWgiksglGLOTdsDKZFff6fLcwJ0JxI6sgZKRFrnNdJMq96O7R16XfTOmfNVhpsbvErxgv9HotTAhGOJ1wpcpFxcJ+n3OZfz8+W01zYV+GNXfRvOz45t/55fY10cQ3tr8BqU14JPRca9Kb7IBtfoCDXHiMTNDryPBnyQckZyJnKGEl7Q9AwH0n4n8KIikJEpXABxDGLkJ4GDoHRXHqxSpYhzU8CpUC9euTIV/JopKobFc1MOiYv3xItqpFId+cs8k9eEv/AIDlk2IasmuTiugcbqsoQY6O/MIhHzAtZ1YUuqRz3qUY961KMe/0yMbwWfg2+hUVllhUEbWKRyqTUNazufklN/imY4xqc8aYTeViLycUu+8lSycieUw2FwOuTzUwm6vTJqpSScaXkoBRKY5PKSV1VSWWw51XgHxnafarXLSttwBhpreMY32MECnTd9uHgCq3rL32/2ikc3LRwfAvAsjbxyjuZNu/PQv3v8AJ5C2/uX/9/POG/03/pDf8zMzIYwo/ff8MpQuRDdJmxj+uOG+1olSIUS8fjce4wFjoKdCk8KVABVHNkJeus8XvYlj+gzYuNgXVzaJigAilM+Z6rGpLBglk6lWZjY3pxjjs/Bvfvse89moQoF+cGLosOxg3piWenV1PC6V5D3DlynfgQCsNHx49gCtSlBEKYtEvKkhwbtun/bj+tzly7ZgFJnhvfGHOfIwIMxX4eFlDnQGdCKgkyAxja+Dvd9/7Jjr/IzEJZGA/VCH25D5dXc2fjMYsqqlO3s0DMetdHWww1Q/3odrfkpnIq3XnbXwQ+R3tiiEszp94uLMw+pgyAr9MeVzTHOz6x72X+3Wzrj/85rrpzossyNbTIkYNePC/rdSmHlnJ0Xfo3PQbtagazhL5uXHUW797Yfrwfnvo9Xt6/aZRQxjZFX7/dQNowOScDc9PO9fdnRjE7br4syV+YKHh74fhiIQUhW5Z4vU9+2HMRGmRv93rrl2769D0GpRuR6SAkgBYicL0v4P9EQPoCQRZ5xqurzRIoczgk3WpPjMT71a+Dw6Mwuxf7saABLns6dG0D0ikW6X3iWSW0xAQ3sRb5M5WRMeD4UuXhhvi5xGoqR70uL4zdV7z6NrcAZ9s6eI4AlHiWJvBgy+X/4jyFBUc+VUHKjUFu2MwjJvElIf5Qz4rIb4qNjlWsQ1sDCk1+DOr+eHDw6KjMrHj1JnAgeoiK5ZBjPyHCnW0hml9uYi60NfCoyWwl0r4eTXFqy6XIcrAiMFSd9xoUoOZCgPgUP8TfCJsguo7hpbcGZTA4yDESatDIU5vPYYxDMTngw39MkoWBdSBohYPH32mb88ciAC2MeHmVJQNH+yM4g0iWJzIn8Bvtr/8dfMzOzL3zOpWrf+73/upmZfc93/FYzMzuvZCDix74HQa3Qw50bTZbGIRqamyOSza6CqIoyQIiSheoz+VShK76uDhB0xrJmo+nSPsSQBie57GMVR82EhO8NgU/n50zczsYWDfyhlweJFn84ZZbV4HyWmsD5fvcgKD6AYNhl2Qo9SphsNIBbYx5I65eYdL3rf2zu37lj3Sv+h0bGUbEkepDfCtoOIaKa9llViBTmF8AWy96CiFhgrnR66n/0TJMIqTRlNNPrhTAw2dzqOh6yrJN9JzmucYz1YL36mJskTXn/zde9TfXhb3NpoibLgpG7tDimih23ZSZwZLk1mNztEjx1dur7+8arHiB248knzMxsgNywS2ywWhdTSfa4Pk4ISaqY6LRiQsNoT65t+nLGx34/nucj6wKDr7f448h5PTr243BMi2s89ombJHx9LI0jnk2yeM64QYrSz2Ub0p8mnxX3bDaXNDqyBKnpJqTFyamuA0yKVoLJFA51fOTb2Mh84V0Ii81EJGKeSakClggP47GqtpON5zaCoDzEBlryYIVfHdNuypBy6l6VTfRMUe+B+S3yrf9Ycl9NOY7ymmtiFHdypmCm1DLkkF95zc2rZjzTS4WhVcvtg6pSY2F5rJICP6i7EIzxws+L5X0tY6Rf61G3FepRj3rUox71qMfSqJGDpRGZmw1TESqqGSRhrooRcpNmr622z/lOxlOrMMaYzsVKCT7J/n9g5FgweaWZPMYZVAAiQU1FPAIFiErNXiHwyGCD5SdVaSVoA2AFoJhZF5g4pRL40DNedU3O/P13Zm5S0u971TpYo6LuAgtWkvBRCTC3nE9os0BsG53klhNilMp4ie1tE/v8yqtfMTOzd/+H/87MzMbf5zP33/6pf8mPB1X9CVK/ZiSLatY5X44sVVBTAVlMzqRlVVmTskk2x3OOaQ+CnSyaZQ4k6PWEkJYQ1d2lEgRZmSlYJcJciRbIWgsyIWSzo/1j26LSkyG3WhG6tjIQJVVdGeTXrU2XQ95/V9Izr5A6Gw5HxwR3qTrT9RaikTFDuvvwju0MZUbk+61o6SYExCmhNuOxTG78920gfoWA6b649phL/1572eWGM8hkCaTKDiSx9ppva1RGdg4cPJd0lQu93/eKeALJ8z7SxmtX3MRH1dXjmPq89KrbIL/3nrcCnn7cZXKRCLu2fI3GtFlMLZV5Yefcc32qxxtPOfnxrbfeZhuccPnOHScL7l73/V3f9apW8tvNgf/cA4k5JBJ8KktwEJm1gZ+TnPtz/3jPdpve0hDZMcbSukfkcINjf37m5+ThPW8zZRD2epADW6AX8nCelTI4g0Qoq2O1XUD/qiSyJmiFWnNCL/QcyUEZNiBLlhi6nY282j88cMRkDNLWxS5bJLmE50VDrVUuoKaeR72hjc4cGRidIHuNiYWm1SekZEw7YAIq0SRYqRzzbOLaTAXZF8vP0xZhW+Mx0knIloqVHmdj2z/z9sqbbzuCJNZ4Ua7C/uxf/AGQQBi6198/yjlZKdHzR0iFvxKXsQqE7WUp+EWMGjmoRz3qUY961KMeS6NGDlZGWUWW0CNdnYSpehElYUCwznAd0lM6CeS8hfWy/wxXyyhWrB16Y1iSYmWcKhSHikHRxnP6XhSUwWAkbSkMSj3XImx3W7HGmYKVqOJLnyW//rrH+956z0lrra6vQ4YqnT6EO6RZIS2qUrgJFr5IJyfHzNbztkXS7hVqdoKc0ETuMuMfTb0S+J/+0g+y/b6M3/U9v8PMzMYHkKAkwyyFtEBuor+tU6Xe4jlGM1GcWBuSkyJjJ0SxypBH8q8Zy2xQAZ5hdTxtw9loK/5VclUIfiAI85GfwxbHNy4hC55NbcrvqoGiqKuldamKmFfiJGDuQrV5EHnveXrqyxlQrXbUc+Z6qFLxQHw9a0hKp0VuM/Z7eqIgIa/G2g2FGAkZIb5WRDK4FpKNtWjYvveeV9TvYpW9vem8gMEmxFRQogYR2HEcWQuUIlIssuzB4e9sbDkP5vZ7jpScUil24Z7Mgd6efspNkm7dcdLY7Xf8Gr565Qqfo6JaCVFbBDPFNpr6NWI5nAiMlmRpLIQsGft3boFSnI59m4bbvk3dtngAfk4MU7EZ0eCnWIGP+Z6Mex7c37M7B5hfwSVR9b6IjYYPABI0Hfk5PIOTIN5M2nQEQddoH0ThlOo+q5ANtmQKhBy3yi1JiKWJZLfOs4QKdjwT2VVGbP7S7cN/YRtP4SBMMCJSVV8RUCeLd0U/J01ffquKrQKlyjhfE4LWdL5lMJbCzTkmOGkyF1nQRysWOsazSyiqzj9GYBUIyinPnynPp6Sd2FvvOGJw/6GjbuKPaRm6Vxsh7EgyyfeXEa7K2YPicUV2KLnto2PV/OiDhmSU8QWW+zVyUI961KMe9ahHPZZGjRx81agsZmabJupbinzgL4VJTsgMuMEMuJ1aWqgKXa4AKg51yfslc9057GGZIYlDIGdfAAOLGrJoJtwj0/ZqxovsLDdL6F+W8xbL9N+NTn0l999zpvfNt3z2LUOhnR36101VWaqQU44Ds3HklrMRaMYEfgHBmFW+MGcS40KzUE2WZzDcW1SXOl5/7of/vJmZDda9Kvndv9URhMPbBMZQCcQwzbNSbHRm2JIPmXrPkVUc6xYV8nwuFYFXVVtI1rSsc6SM2uYcgyr1eSWzE5KiPnfBuRQ7vRmh1JhnIfil1/UqU3JH9XNlMCUWeRcNl0ydui0sa8c65ki74EFMqM7m6rkmso+Fw1DklmeK3uYaPUN6h7FQE3Z9v8v+VMs9Z6E2ZVCG+Lo217yq293xqr+HEuOYoCExyAebGyFyWteBKiPFHav/2hn6cTqS5IxKWKoEXbOXd5wHsH/Tr4+Hpas6NjEyasOQzwJfRnHDnUXA2ikRzU2x7gnzeeBozZUrbtg1RFb5DkiJQq/0EG03ll1xylxSaAKdShA4drbZadjZOdbLHMs+RlviSlQgCTpOQ7gIUtyMUSJlSPqkEupPXTnQ6S7HjsvZKk7UqzaruEv12TFIYS7lVKnQp+VAoTlIQx9kTtbMkgLnKLGmyCxz5NZCOYXQNioLspQO6F6eLpuinZ1wH6AYCcjIMZJH9iGD39FGWVPxAFVw1Qzpd8G2z+AZHWGOdb53av/0Z3/KzMzSliSpIMlco3oupJGkzBrB4c4eHdVK1S+eVBVJmbXyuWqBNnyQhPGDRlEUX7W+b3TUyEE96lGPetSjHvVYGjVysDQqq6o8aOwbMGWlodfUUez1OX3S6UQM+fmCoStbyxW75DhWNDM2nyABsqrVjFZVPpNt66/x/ZZsROWkQX9wRo+ySENlktCHnNCnvnfkVdXJ6bKZ0Ra9005vyjb55+XCLJ3/fBLzvn//8CGfn/v6+l1YzmkV4m1LKjZpynOxa6kUpABRJPGYCui//u//tO8v/P7f9S/+TjMzO973ai6GizEWSxm2frul/inHOW5ZQj8zYj9kn3xOVTpc8yorML8xOxrBOZjDsJfXwjyoFJatmjN00NJSt1lv0wo7Q/mQo2zZxXPA6JmKm1Jy7SEcsCbb2oKDMKVSHAddtu9TsyWkQTa60trDs6hm1gCeyOE7qJ+7f+LXxYDKr9FQ8BhKAtCJTJwFmPMtEKndbe/zx5zTkuM1pfd8oFZ1lFqP6lvXQc53ZFYkDb0JKSuWEaIIVKYLItAnJCi65EjBKeu8e8s5C4897eqGGPRoxLmJm7FFXEMpffhRhv03PfcWlfTenh+f3V1HKZ770LeZmdlbN52z85WX3Jjp2hVXHojV38YMynqgFzLd4tx2W+2g0z+B8a/IYiEIupd13gUYrm/79RMde8U7BsVTWNwYw6IMA7BW4H34dZJyfCMrwvY0tN+Km+f9CUFsYv43uR5yGZFRvcu4bI0gpym20ufwJMpciiopjvBLmc+tERRimJ+lMnkTrwkjMwzaZMPdAaWKGyAHinxnvxsKXZNxE89PWWDLCv/du86f+bGf/HH75Rd/YemzLZ61UnqkghBExZEqxYTG8PNKAS/OgZ758jTJc90L7++X4Mv6+hCEixg1clCPetSjHvWoRz2WRo0crIzKiqBzr7DLjJh9PpLAYmZm05nP1vvS8Hfbj/Tl5KIIRyA443k1MQESmNJDblFlqrc8JYK4QM8bUwk3mL2GbRRXQU5aeTuwhw8ejFiXv9I6N0miN9DKMwkPM9q8EEtdVT77Amt7ciZmrO9Lyax/QnXTbjRDmFHoT2p2LLLEygQ4m8nRjXho2P3/7f/0Axwnr+q+57vdSbFSX5/K+eSIQJ0eTHnsYfMisi5+BgXHKKI6k4OdLGu3MuceKBQK6kao0uVOp+PEJlh/6NVwxMz/DB+FVpdo6PE8ID0nB16FNnCfuARqkQQrN/gu+GAIUqp4bVExh+AY7KMTISa6pfHZaMB7mI8yS7DczeaKNfbXBsfjnCpUoVlduBUFEpsG13kM6lVybWbnvi0P9lyDrz55wjU/piq+X5S2gYpn87LzE4S06d6SNXXaFNrDbjVxGOV+GQNrtYC/InEs0KvnoCLv3fRQpe3L7hehYJ4szy3PpOygila+DsdJSNLeuXMn9h746yYclcfwYFB40oM9P34TULwrVx1p6Pf8+piCNEbiw8Sx5SgaHp76Mk4I62ryzOmBjCS4DyqgbQri1KBH3yyisEwzs1aDnjwKAoVriT+VtBdhbDl20HnIlwc5FV9jJhSTdRrHSagGgUtCA6WU6fZxTORcjoUgSLFE1V8kaXA8lfqgxX0dgpS4zidCzBR5r+cI2yz1y3ymcCR4JRz7XNfy0J8z+/f8eP+ff/NvmJnZ23ffDryWOaodHmsBzTDuF3kkRKxb4VDiPQW+WSnbdpxZpUiRxfNKYNOjQz4Ger7rNV7xVigvMHBJo0YO6lGPetSjHvWox9KokYPVEVmYwUXQsRUnqpaQ3NemzHbVg27FqZlcxqRfFzcALbVaqhMqYy2jB6IgRUBLAUNUTEIOxDqWJj+hT9hoKnClZaMznyUfHPksW/LkNtHS60TNrhMlm8NOLuhJq781p/qcw6mYwoPIM9+2NvroPFWFQZVi46DPlvBWzo8amucWIApp2z83JvZ5iIY6o7/9Q3/RfRDu3nNd+6c+9Z1mZnZ51/u8l3HrS+A/nBBU1O8M7BTuwGBIkIw83hMxmP2zR4TdSAv/8Y9/zMzM7tNzvrfnzmnbl7wKVSUlpnV3yDmEnd7keDYbiU2pQoa9ztJrl4pXmRHyjGiATvRLYB2uqy7V7IjlHR77NndgVktbros1pdovJrkVsMfF75AbYX+T8xj7dXCmjAAqv5ATUulccl1QxVdU4KooR4fkIxDlnFDdHz08CN7/ul67ICehjQtyIGfANr4IJce6CT9gduLb2mStHVAa8Wi0zvfwQbh/19GhHa6XVrcTXDcnc68epdIpy2WtvKrXE5wP5aWxfcnRj+vX3J3RQKDOTv1zb77pqMWlLecH9Ach69zXF0W2jhvptAf3AFXLGeojBZG1hSA0l/kLDY5Hk9trzP3Sge+wTUbBhP6//Ff0/JiXpUUJ4UMNBQvRa+f+FioT8gmowtsoB3RNCmHbJw9iHWVFCydEqVYquRZWUnm1wzqEiExAxoQkylsk5niUKG8S0A6pDhqxsmd8V844nlKWzVjnl774eTMz+9t//0fNzOzOfeeorA0GQV2heyjj+T7LxQ9DQdIUz4f9Crkw1fv+LP6VxARyqfkZtgAAIABJREFUVpR6QShHaVWINNdYdWGUE+JFKRPeb9TIQT3qUY961KMe9VgaNXKwOsok9HNCr6jCMU4xuUgQKnqTs1OcEgdrgel+PvPqK4pF1WaGJ4tERdcG1yxUClTfbXzp+zCd5xU9NPqZLaUOUrxWaKrH08gODkZsr/9OaYoU49bvK0rUtzGHCZ4QrZvPcGVErZBNxPgVYxYGvCpnsgWU/FbM81AJixWdgiBMpsszYs3wC6rPpiJXcTiUTr3A4e2v/50fNjOzf/DjPuN//Lr74T/+2BNmZvYbP/6bzMzst3zXb2M5mcVKVYyU88CB4fy1E+cSnBOle7RPH99cO6/KIR+B2ogZTkU5Z459eOQVk2jKkarfTjvEfffodeqYjzjIqs6Tri/rjN6zeB0ymlQkb7P0de/Rq7a7vs1p25c3Yh9z9rm/ednGVPrDFogAGQhTjnV3o7m0/Ydsw86OIyUp50YOmxGOiiJQd6l2hRJNp76c7sDfb6Vzm1Jdvvu6ewVcvnrN10EkcVvad6rWMoazQmaEuDkGQjQGJRPTWxkCSr28dskRpcMD35e777jL4frmtnXxUkgi1qVk00I3JfulDI4GTqA4Hu7DHxnARdmkSpf75wNyEG6jnNjEOXKNxM0kKa1Lzsl62xUfxdj3f3aqpEyePfmyWkkeJDN4T7ni2EEFT/Dk6Hf9XHe7fnxLjv8YXsx8Ogs8l7ihfjYIUXCx9HU1m8sJq+G8w3fpoo4SX2oMYlei0FFGx5Tn55Tqv7TYSpBDJbzO4AxUcCU6PFfLSDwoX3clP4gKLhPPEzmMVl3xgMhs2FCCrm/jW29+2T8/JLslWcTh6Fpr5OJWsA4erJk8FIxtUdqr0itXqn+NheAAnxw4C+HTj2Y8h0tRWTK29FlV9/qbVZaxLcHf38SokYN61KMe9ahHPeqxNGrkYGXEUWRGb3UCM1gMaLnaKWJA/fKzY/p/7alBlg891VTab6kY+G5ID5NDnFIbSY3rrymNzF/P4SjkhZTOcBqo5nNQDfn8my18tjeoCAfrsGzJI9D2ax6a0ZOejGH4jqWL90/Jm0FeDWN04ZXRg4zFe2guNLwzOUSGqf7SUO8sCWxrFCJcmdov8TzUcz6f+Lpfe8N90D//5ZfMzOyzn/0lMzN77ttci35190kbn8ANyZQqKB8L+vGsM8VVrSTO8h6ujGJfd1CSnMJn6FFRyctCFbPy4BswwpM4WrjGwe+Y4FevL19BI38FPsMM175DcuyThlc8CqLvwkZv43LYpoppo0Q4o3fbhNtwadC303Pvv8p17hKIwOzY9+cUtUKfilqVkxCEAcmJnZZvizToZ3Bb2m2viNdxGHxw4JVzjuyj0+2ENL0x2viHhVQV/p21TfFCSNmUmAHkRczuFq580vXfv+98kLmc9qg0h/AmhFzt7Ts6dHZ6HlIoW3AB5F8ipryEEsohaOj3nWXk7BCuSn/Nj48SQq/gi/DwgW/b+dkyB6gsChv0WTaoXbft8N6MEBWluM5xBpUTYkyvvQXiFFH1G1yfGZVxcc46+8nSNiYp6Z6dwgo4Rbo3uyBiwRhWvXA5ZPJagFpIrdACtRFaIaWSKunxSiVdBLR0bA3QljbnTV4CmVQG5HvE9PdnXMN6jjbaSmHEPwVUIlZODvfwBAfFm+SC5CE3ZoEWx8GN1l9iUJq2tjuVa+Wy02O5wjVYCAiUrRB8Yvm9LgRZLz7CK1gp25XbsHh8lo/83wL6c5GjRg7qUY961KMe9ajH0qiRg5Xh/WicvNTfYebbpBIUE38yVe/MZ597e5mNSWS7csV7fEr/mmUkAcqxS65jkfp6vq71Na8IG6AVmr1LexvFmr+K+Ut/C5Z3YiPru6zael0ltKEZp3ovqZjmVH5FRrUxwt9gSh8PZYVmttJIS/cel3IGU4mx0OsuGLvkEmRKRVvNRKc/z3ERmqFJtlQaZchKwAdA6X5UjjtANlNS1m7e9srgwx9+3ka40DWoSuLAIvZtm81xxouVqZEu7WcDhECzdOUVzOiHtqi0eh2vnCf0wau53DKbtv/A+87Nln93fctPUhNOxfjct2ncQr2BB8Nk5JXuBl4KpdI5I6+cekrOZJ1pj8o7kxIDnsFaz4b0q/dQXZyBZgxY9sNDTzY8h/Hepzc+pzI8P/NjWzRg0MtxUlUN3hYtVCHr5mqFvQNn8aetRuACzEGpRvhTPEj8+Exn/p3uup/fAV4cFbCdfERU5TdQKaxteb//FKXFMb4YG/9/e28abVtaVQnOb+99mnvufW28Fz0QICopoAiUDT0olGTS2FH6I5WRTY00R6pppVZlDUurrDTTMsfQylSs1BxmSqjkEEwYoEDQRwRIExD0bRABRNBE+9rbnXuavfdXP9aca5+9772vixvxiHjfHOPFiXPOPnt/u73fWmuuOfdbJL7Us9cDZIYP5jXUQLPJZSUa0qfC34jZCfB1wldlkgp2RBzjNjNmWqSOKs2BI1Qz3FQUz+i23+9jjZ0NeWWvFev86k6IjCY36Oh4iB0gq/QF2eQ1qO6eHs9Br5KCqG0zJycpLLObiscv1JU/g+TToN56dWnoeaf7Y0laJGTtS/mwYppD9e9+X39eGO1P2x0nvZ54SBNEZiHmc42P0borZbYZ//L70DN6PuGYyeGYlPJ1IC9K6+Fz9It3mL/MJtd7oBCpp0Gl56UrF0rFUXofHZtFQs+Lnru3GsR1qqqOSqwUFat2BudcoEWd9xDr81vBGZAyBwkJCQkJCQktpMxBB7GuFmTZqPA2lmubHS55pkvxCnPV8EtMx4qe5emu2ad9PJCmeTHnuuyL5RWbZY72Sc9A9TxGitKg94hZEQKjem62GE699ieNdPkcqMRVllJAtPfjLbJv17nXUueLmrWzR5jrEZdBWuPiaMiDABEIQZoJ4G94jKQRzvF7vy7DckVb7oQGft5RVGzoEiJxUEmQ8f073/NeAMAzn/Fc952veC7kfCl+g7wjAmnKBfuyS7ptzhl1VWRKHz5k0duYNefJqkVcvT63w+xRObXXfaMRDh8wTsGp01a/r6esqfJcyKHtvql9P2Drx3DIbgVGL3NGQnN5DWQaI2uz1EWQOt0Juv6N67lnn0YHbJynGbUvr9jYxKI/tWoXwtKSRe0ry7b8hNkuOeHV5B7sJ0dhzvVPKOaxfMDWt75l25lNplim/4a6EkrGJ9JW0P5sbTE7MyGP4YhtQ50AE0aKHmkzYzLjtufk5mwxGlPXTJ98iNnGBEs8xgUzKmP6X5ymTsOs11ZhLDqv4h5MeW7WqCUgjRL5JPTEeu/b2GbsFBgOl7wTYmvTtqlunYL6JnKKjLz21pgxOHrEztnp0/a71eP2+UC6B9zfAbtV1k/Z91OqFEp5cTjs+fNCnBnxY6RauEyfh4IZE92zVaVsoNT7YGOVbwbvG41J3Qpl53fDwQBzdorMeN5qqjZKlVD6BVXQOvl8qaW9Qs5Vxu4OPgc2mdUds8Pi5AN2nc3l4qrODGZBjOevrKf8X6T4yY6KXM8uPdPa3rPiImR51lqPFgtBHWz8O6Nrtmzcf5sxYEc0HRJobWMvkTIHCQkJCQkJCS2kzEEHIQuWPYCL0vk0bcrZaeGRAdn5PIzzaozIcJyBHIZklU/qttpWwah7mV0JGXUQXM9AqlnMQIgxG5rGV3svtj/rY4NRo2tQcRCRs+tAxzMalGHMLMfWmCubiV2vOj9nvtIskOoh1HnBjAOPSyRTvggF8rxdDdMsO3L/66AeX37O6Kvyup5SDjxu3AfNpMXdGHSY0UP2Wt/66c8AAL5y5zfwPU/8Pi7DmiczBtoPHbvAOr58G6SyF52tzeidWvNyXTxF/fqgXm1mXGZigR8osDywuv6JOZet+61tZQP7zQa5A8NDtl8DRucTKSd2rgNnW/OC2KBmwcqSRdp98k5msUbB3wxZSx6v2bLSvD+wz8Y4lbsko61DHEtOhU35G8yYKTpylCp80vJgyLHBevdopH3Y9PMvtzxFTfKpqFiXDmTEr86oxbFlYzp4uWVt5Ieg26RmXVsqlWM6bm4xEh0UctazczarKpTkUlxOfYJldimsMRqfTdpZGiUKiyU5q9q52E8lwDH3TZm6FWZexCuRD8jxmXE+1tbWcRUVG0PG50at7JVtQ4qZ6koomcU8dcqOyxWX2++nPRvrKsc+XqNICa+Lcq6OCzqr0qtgOi1AeQqP0gOzDdq2onNlEFQ7V019PhU5SZ4rTSYVAMpSWS5mLOVEylaUYa/v7rKFlnEPCdvfWhlXqUtS/0KeLOqOOsEMyQZ/f/BK68i5lx4Kr/nP/wkAcNfdd9lYmZmZ8looQuYKqdvAazQLne9FkIrqQpAjbztj0CgkxsXVoSh6rdeqrhfceju6MJ0Mqm9ajQ97qJiYMgcJCQkJCQkJLaTMQQctreoOFXSDbGPVuw/SvW5pmZHVNLjj3Qa136+42iIddTSoa6Hv0Qf7t9kaMNdsU7V09q/2GO1OnT9gU8V93HafUWw2WHdHOu8BViaAXQeVaoub1EiQVwJr7a4+ViuiZvSv3lqv81m0E+eq1+lyCg0jtzOTDd1Ztz7P1TMsRq/N/NWfrpq8uiCqoDFQnZGz/YrKeqdPW2R0+9dux/c/xXwYtlYZpZJTUU3ZGRGpTphJOZLe8OytXtpn66zE/1AtlRFhzlrheNUicUV5PTpDTjen3gtdyCVR6oFcVtoZ0pDQOVC3hpzt1O0hvovOjXt5zMRBoN8Ha/TzuTIMTfZBHIMt1rFrLqvPT1D3QP35y+Q/rIyopb8qNz5GjKwDKzKsPQOn6wiYswNkwAh+S+NcsW1PN6hCyIzZoUPW9bNF1c8x76sjVx3l/pE3Qm8JqXz0h6rVk4kvNU8JB2TBvFAAjNkzv8SM4MHLjnAsts3Zlu5ddrfIG0BGKXxArNAnQd4KWq+UBZd53OYH7XenTpxyXYbA+1fbHJFDoYzIeJOKh4zeT5H3cfy4KWRePrKOiJrbEHXH/QvkgMj7Rhmpsq7cK0GZkB6j9GXxOHif63wqM6CuBGX/lCkIvGblkDjls0/L61XOtbEonGuke0/8hCmfp5OZOin84WTfV8qO0q2T1/kadUTu+Kz5W3zo1lsAAHd9867WcaCwpI8l9HsuP+jj7YTrygBI16B0AhSdIYu2g6J4UMo46BFYdrICyhygnLvaoh6puse2tSd4fK9tBOwV+yBlDhISEhISEhJaSJmDc0K7bj4h2/h0JKubrmTDwQBDTjs31m3menCsWIYZAOmUD6XKxxpbpZ5YFgD5s+kWFeUYvagWu8Ja8tEjxiTXzHltEp2PEOWex9qe6rbjTbqMjdVTTPW9qB5ye6uLQ9GeOic0ey3Ve8z19zVjRo7oMRyhel3HXazRoXParW1bjRKw/WdSA6R5QG3JQ2lPsL+7LKknwT76z37pw3jly15m41umNgR5ITXEZOe54crnHX7IgMqIivIyp0PQfVFKacwC9AfSlTBsrG9gH6NxZYC2Nuz6GFL5MLJer8yA9OvLTnSqhJICiB67V3JyFnIeR/WUL63Y2KtJ5ZFgRa5AyVdlluaMEHNmKaR0t0p9A2VCpFs/BlntHGu+pI4BRmUiBHCf6jzHnPsz4rr7ZPxPXL2SGRBmdeZUgFTULU+A+zbNI2FAf4ijj7HIuWA3RMbjXKrOy86T6URKg1McYufC2qZlfNTXvtQXT4OZD0XhOjeMMmtmjJT1kjKisnjqj19lBqaixsCAx3d5eYS6VtbFxiLu0dqG7eeBA8YDmbDTZsbsxX5yK9ZWLYNwaqYMgY19mc8k8R+m1NxYWrLnhWrTWV0jBKqQdjIBHiHr1XUMyD1RRki6/soWFrxPmC3LpH4qD4GOauxsPsdS365TaYdIUTVKx4DPpkrdDPy+t2zbWF21Y3zoSsvUfvSmTwEA3vjWvwUA3HPc1E7V9dF3CQJxndgdUNXOMUKno0poMgbqJLPPpY/SfcR1KAme/QwdxUR1JAVkzfOvlu8Cj0fVzsRiz/IE25EyBwkJCQkJCQktpMxBFzuSPcUuDa1FJlQUk3tZNeshy9r99qcZNQxW2tNJeSRIIz1wVh0r8QPkCKk+XDmAiSmsDgmbac+mm9vGH2O7s0FR55SZg16uWjsjPjpCKqJWeW9GToFq7U3ZS1G/CmPiBwCxakdVC6PqvFedTt0ZrENy4weolHfN41hTZR9zzBmdcoot9nJJ98Yrr2ZtduNO3HfsdgDAdVc8CQCwcdzOidjZcmHrMWuTiXshFTlGBHLCU8RUsR7ay6X4Jl0JG6MirKquMaEmvur9iqCn7I1X9mlCJUl5yYu1HDoBgrocpE4XeI7ihFGtfO3l3ZHlHslm0shnVKlslHgKBaO4JSkDMns1YfaqoH7GMqN/ZdIGjNp7iv55HamzoKpyTNnPPuf5HTCbsblq7Ps+j32fvfebJy1rMcrs+AyoiHnyNB0wud8nBnZd7T9qkXHGjFLoiREvZVEen2n0DIr4IFN1CvBGUeeM+txVyK95X9Xif9SKOm0x6RrU4rDwOK9SP0IKm1nWQzkX+17Ohzb+TXVb8Hzuo2rjxvop7gc5K8yczJiJ8qyfOnJy1c3ZgcDjL20KVDW2xuYuqexTj+ffMwjKpDGzpEyBIt2CGaUew3Hdu/JUULZQWVNdkzPqw9RVw86fuS6FOhuYMWDkXPKaWttUt4U99+644wsAgM9/2bxWPv3FLwIA7j5u+6ZwXl1QukcpJ+FuuotOinVH+bDJfLSfg0WuZ5g6aNp/Vj1joA6ESllCLuBCi9RTKArkfLbWpTI81JKh+6S2naO9EstGBOzyh+y8kDIHCQkJCQkJCS2kzEEHYWG61G0ZbTS2uaymfmS1b1UVCurOa0J3330WjV95jdUOFSHOqedeUyNgxkh7HqUMRzECzhSHdKmTdjpyi+LWN9os3pAVmKpeO1XnA73NwY4GuoqJQlDNpYjGGhr3pyP85dYJmpTqWGlGrTphXS7W3TpZl66014L3HdDoFawcpC79FWQ+9y1CqAJr8d4HrAwFB9VjX/uyHcfTp+7Cp267GQBwzWOsJ7yS6VxNrfyarPUNKvt59wkjBNZzC6pRBmVFGPXJUdJZ+9xJufvNy9Jd5NwRkpGxmNz7xlQjDOQIKOpyJTdqx4+oVsja85is7Iw8in1U8ZzzuKtfPKsjJuwEuIys+v4BW/eJB8z7INJTI18mt4bZjZwR4UwqdowgR7nq+lQjpOOdtPflk6FOjH5/5DyXTfbhH2RNfYmRs/ggqpXPS9u/06tWM1Y0K6vQYWDGhf4Z8hxYOWDn8NDRy7gP1MWIzA4NAqabtu6CEa00BpTd6On8UYNADPK+Omu6/exSzuMNM9FxUpcCMxUT1tWLvEBQNw49SHQvrSzZ+OfM1qiz4uAyPSTYEdHj8yIwc6JMgfwSFOVn4vJw7Bs8TkujZYzIS1Enh9Ql1e4kTxl9XpLfJNVJaQxkrnZat5b37gS3pAX3WXyImffzu3cC76ENZgYq8Dru8zlJDZE3vePNAIAPfexmAMCEmaS+cy/a2TzXquBzp+jwCQLyRtXVP+P4db8zK9fwArRfbZdbPS7rKP4Vf0+emXd3MNOgvzexrDyt4M6x4kaUnfe89nTNdZVkHwxS5iAhISEhISGhhQedOQghXAbgJwD8AwBPBXANgBmAzwF4LYDXxh2En0MIzwLwGwB+CGaV/RUAfwbgNdGbWbf95mUAfg3A98NCzi8A+E8xxj9/sPvh28gW6jVtAn3zP6EdKVZi+dcRkXXGgr7jIpdurZJ1zRltpBPi5qrNjHtUU6vVrcBovseiGIMxjEZtRuycTHElNWJYyAgo0oV67FnXZH1Nanqq622DMgad2WihPuaiU1vz4xMXUgfiYHQpvHxRXVadAyN7f/SKldbr1uwk96nxb/AdBlDL/50BQj5kdDPK8I73W3SxSiXA5/zA3wcAHFy51hYmk3uZ0WWgc6LU6DwTwHPT60svguptXqtn5wSjvJ5HBPVC779dVCOq6lWMwuan7XyvDKj4KKU4blvucqpn9ulfXyiDkjEK5j7MJravgT3sK6Mld1vUdStmu/av5LUkp8+ckWFRtzUVJA1fKbMgLQFmEHSd6RTx8GDfaAlzZtYm1O9X9D3gNTWvbR3qXy+G7DKorMY8dPlPG8T6mtXgB0MqJ/K+W33AImvQ52L/Qauxq7a8PBj5zbmhTiCmwpS9asQCVK9mf7+eZlOpnbJHn0mwENs8mxkVBMWnmBeSswuNuirPr5xQh+x8yd17wiL9A8z67BuKm0BlP3bvKILUs0t8AfEccp68Dd4Loeq7xL/cAbXbcllU9mFAToIyjIqw9XjXs04ZhDq0HSClUKrnScipxDmeoM8uDV4G7oUwWLFjduhyUzr8+Kc+BgB457vfAQD4wK0fsjHRq+Ygz/OUWZqK6ykKHRBew9xJXX+TeXN/5R0tltKVHpmVVDeSe0zwpNVSQNRz19Ok3GEu5iIm/LaSF4W9r1Aj5/HQNaOMDwb0uZkqg8ofBenK7A3fANibssKrAPwxgHsB3ATgGwCuAPCTAP4LgJeGEF4VF9RwQgivBPAmABMAbwBwEsDLAfwHAM/mOlsIIfwigNcAOAHgdbAJyE8DuD6E8NQY46/twb4kJCQkJCRc8tiLycHtAF4B4O2LGYIQwq8D+BiAn4JNFN7Ez/cD+FNYYeYFMcaP8/PfBHAjgJ8OIfxsjPH1C+u6DsDvwSYRz4wx3sXP/w2AWwH8agjhTTHGjzz43Ylnn3h5hMzZWmhmigx8UCyrpmXvtxiFjsfshVdvPaPN4VBa8/Rf5yCWluz7pX7bfTGWUmlrqx7OULrCl06vZvwKuoW8kJsiP2iKbNwI3yoLwPUGubB1+puFRS2DTrKl0RUXi17ujZx1Dxn57l+xCEBOcXM6HMrfXit2ZUFOrPvkZGywP37/aAU1Z/Y33vQuAI1S4ot/5JUAgAPLpog3PGCZhAnr3lOGcerBVlQmtzXlW+RFkeXaN/tcjOuY5diii6ArsfFYL43Y53+K/d2MUodkocv5Tup80hQopO8gVzn1mHNMijSmjIpXDux3DYUJ+Q+B4Wbt57vtBBfk6yEfDPbn1+xeUL1zyoiqoiBAxUi54EnJFjJQ8hkQpUYMbmWh1H9f8MRqXRrDkJoTy8ygbZJrIE7HcL9l4GZT2+/T9CAYM1Oh6+/AwYM4RE8F78qYtjtlFH2qS0W3hzJt6hSRbwF4T7t7I4+vM+6li9EXJ6Pyi0XXlvsRcP97VDacMgO0PrYMyj5yT/T80ING9e9atejOfaf19TjmKkYMyB0Rp6RkJkheEFLyKzKmJwq5D/L5UTPr4RkEe1WmReqHunUn5Ml490yRYZ3KsQN2WQRyC976rrfb53wOvvem9wAAPv35TwAA9tP5s8+MmjrI9EzK/BmlixAt6PgMpDBbFAu6BM4a4CvH1nl2db0WPHvTflQtoK1v0H1GLlb75evR944oqrvyGpXT7EOBBz05iDHeuMvn94UQ/gTAvwPwAnByAIv2jwL4C00MuPwkhPAbAN4H4J8DeP3C6v4xgAGAf6+JAX9zKoTwOwD+K4BfALAHk4MzwE+eHnq8SBb+qtaQQIaWleiGljUsUTDmMAlGkkGdMx28vI+EFG5T7WGzLRG1VEeQ3K4GV7kMp2RM9WCYM62nljulLqeTeWs5Xesi3vT4sFP62dOJyo5KyCVrREOi//Fusxi7Ikh6l7t1Kq1WN9g2xZznbNZOzSn9mHk/kMhh9o7qqxiPNzEa2jJL+2w/PvG5DwIALqe09fd+z//A740cJ8nisCTGpUhjHAsnDRknbEoLxgUREztO+qMw98lLz62sad7Egc5IelO6cMhJgx5Qmf8RMRR8gskWmH9r/boY8A/eFlu+6nmNvoxd+MdqxpmsT26YYp2pHTJvk570YNJEruunrcmA/ljKClmfT7emTlbTZz7ZUVupJp6S5FV7F7cx5cRtqLQwr2WR/8oJr32V/GTYJYtoTraO3/+Al4OWD9AOmgZRsiw+zTLM/n37OAbuXy3iLe8rtZv6YeE+yqKY51Atz7qvapQunNNTFCGjJE64JJLWY6lLf1BnKkO4gI5skHlNettde2Kiv9C67uZliUgBNv0BUqARZGWuiUqjTGbbEBFRk0ido2m7LKU0uu59CRGpPbs/GqB/yI7x1pYd849+4qMAgL98/esAAKc3rHU143WyREtyTWx1D+ZMw+sajR1pY2/568gQa+xlVS047umQiYAoiWeuO7SJhjq2vmp/ZOnznaNON8/bgU0Y651/o9JlyRZujemRZLwkebfFovaL+PrOHZb/AIAxgGeFEAbn+Jt3dJZJSEhISEhIeBB4yFoZg4WtP8+3i3/Uv5uvt3d/E2MsQwh3AngygCcA+NI5/ObeEMImgGtDCKMY47i7TGdcn9jlqyc169x5gcLFLbpzqmzh/xhdTGU6w2hhRUIgMtJhxMNMwZSvVNnFMtNoM4riiPMSa7VydaL4WkZO+YIsJxcVEShTW6BmwhIe4oIdAmZNVxK1Dw0pL6xtdq1ahRjjNgGRrCucUnQvPft+c4OR3TEjYPWWLLOSB9r+lhZZBEbebvXE6FZRnLIdVVViOmfKcllSvRZnf+Jz7wcALLHdafAEa3s7cvBKWzfXsTW247ePAjWVR0YkLoos6CUTcNsSF5p7OjfIsrpqW/MqKpswcyBBIqWJFX0oqiszkaDURsaohuFK7PwuVpVHrCFT+lckMGYOFOHOZSxTtfazYAQ15fnuK5pVFpXrEYFLKXKlfLcmY7fcVaZIts9DyvrK7Go277TTkQRWeuSs8oxssrmfpUSjuC8qS/A4jGQ7DGCT5lwnaHMsEyjZIB+6TKUtZutkuMT9zZTR17kr2+JhA5YZJM3btO82JTK19npKWbc3t6lylO5Vpb+VAVFH+ASJAAAgAElEQVTLp0TBZDSVKVXlqW2aibGGqGs7ZMHX0eu01OlenbvhmIyVSJad615WlofZPpbXZJamyFDXU8XMwf4jdnw/cMuHcPMtHwYAnDplxOOPf8KIhyp9DkYSsWJGUvLJhcqSJBZOZDPdzrg1FHdmnPhaZm2Bt7IsPYJ3USNZWcfOskrtK2shmXBtyQmI6MBTbp33u0PnIu+Iyvk29zBjIDyUmYPfBfAUADfEGN+18PkBvq7u8jt9fvACfnNgl+8TEhISEhISzhEPSeYghPDLAH4VwG0Afu58f87X85kKnfNvYozP2HEFllF4emslHaKIInJZllZVJ9QOTfOKogxyenDosEUsyyKasaZ46oRxDfbtt4hgH8V/tsaMVii8ISnW+TxvbVKnUOS3CvNtBBcRwtwwhNGDRGqaWmrZ2m/V2nqs48kWuedyyfaiepcizh06V31mL6idLkq8SBElZ9Ora0bA6j9g0d2V19hcUXK4kxnlZcdqI+Q+9yRZrLppU3dd27SoRNKxx059CwDw5rcbvaV6sZ2jH3zm87ifFs0WNEfKuG4/y26NrdZOEZXaoi8BuR/D0qMtivEwm+OW1ZJgnilalfAMo3HW1mfMnHiUksnymuQy1aBFlo0ZcueUtGVsld0akpMgeWCR/FYk96soTBeYBHfcSKfdNifZ4Jw3QQAwY2ZEZk+qw9YdAp63C3vLa1twSLe8hHgk8Qxm2vqSrmZlM+eKJJkc8syzCVUpkyu75t7znre3xvLil/yoHYcVtg+OLRO1WYlbYfduMVPWhvcqz11vKMM2ETcbgauK+ynKgQiGIhR6tC2dHY9GRWYTAVHmSFwfs4NDWlqP1Zar4y4yNSqUWke5WP1tolVF+ptsf9TzTwZSMxISpzO1X7PuT/a0nk2iqojw2yfB8TNf/AxueOcbbf9yHsuiLRsvyW5/piliXpA7tuPB41M39559XrV+X/Pc5x2b5cU/icGvd2Wzytb7BrJo7krF8/fbUgdqp2z9HDiHDIJvMWib7WcOsHdWTHueOQgh/AsAfwDgiwBeGGM82VnkbFH+/s5y5/ObtfMYakJCQkJCQsIO2NPMQQjhV2BaBZ8H8CMxxgd2WOzLAJ4J4LsAtOr/5Ck8Hlam+lrnN0f4m490fnMVgGUA3zob3+Cc96PzP27ZmbXfqw6oGlRdlej6DF12yFi1V1111FaRy+bTXo8cse9XDljUur5p9c/pTPWtxu4WAFTml+gRg8DG0Gc2W5A1lnwtWdWc+c7L0FqHolm1JuaMOpbZUaHptjIP2qZmr27VukPGQOh+p66OuhMJ5uIKMIh54H7jGKiEevRqi95WRrbtNbKYtZpyS4ZM9n5pVHj9XnyE1XU7xktDO/bTqW3j5g+9DQBw2RGbgx4ePQ4AcGhoLY5Luc1Bc3YtTMlFiJQ0lUSv6v1qhev1ClSyzZ5rb9l9MlVEI7Er2kNzQUVAsfMqprzOrToJ1FmhDIwio1DXqL3jgTXjUhEusxlDuwZLZSUYhTey2cw0KRJU9Nmpk4+W2CkxawtWDfp9bK6t81gpK2WvmzSgWqEVsQy1arUXylIX6kJgDZaCSwqtZ4z++2Tje1VXPa8uOFN6VkX3yf6DNu6P3PIBAMAN77wBAPDn/+3JAICf+ImfBAC8/JWvAAAcvcq6XY6fpEAX2euKZmVgpgg773PsZdPCpmOta0idRpl3a7QzAzJBW6L8tuSkZ5W2yeyg3vP6UNZP8MwDgDKIK1HtuKw6Rool+3yLGSUV02soG2Mfj+f2vThJQ0qC69p8YNXu2d/+vf8HAHD7HbehIJdCz66MF13Fjpl+57gIbvKk6LwTfHuHgCJtcQ6ydoeNd3fU0e8xYcbrejKZtj7vSyTM+VPtjohqm71ye3DbH5dnSHxLmKrqZi245qCOqr2zcN6zzEEI4V/DJgafhmUMdpoYAKZlAAA/tsN3zwMwAvDhKLHxs//mpZ1lEhISEhISEh4E9iRzQAGjfwPLBLxkh1LCIt4I4N8D+NkQwmsWRJCGAP4tl/njzm9eC+B/A/CLIYTXLoggHQLw61zmT/ZiXxAXakGd2rsXc2SLqkK3m4WUyMmiVaR7hIzcw4csGi366kM3Fv54y0RaTnM27WU/dnKqzj2hKc6cEajY3CrZV9IkCD0oiIyst1aVTF3y9rLqQmCUJeEdCehIcKZk/VuGMTouEuZRZB5deGX7nNPL1OoFVm2wUg/9kPsvVq4toIjovvssugfldy+nrPLSiGMUe90DRFtuPC7BMjz6fVkwM8siWWCZZD3wFQDA575wKwDgyU8gS58tJEsVz2HNGvpcdUtuoFafP7sXoA6SoWdfeoU6GPgTp5O3GdJzt7vNWsdHGgWqpap+3TScqOdczHPxB2YYMKJXB8SU6+pRlEdRa02ei0e8khWWZS+HPJeGQKYskJjy4uYww0IBn2F/4F0Z6lqQtO4q69XZROdI4k7q+mlHRKrr9/y4UlyJIjixkFwws1sSDfMIq/KMSbZiY7nlo6Z/8ZGPWOag37Ox3P5lswP+nd+11xvebRmml7/8ZQCA73/GMwEAh49Yl0NvJNGktkCZhBAKF0sq3AZ7QtGmvkvwKlMi7oHrIQMAqAnmQkWZ1+iVQVHmqS06Vrn+g9YHZOQz9QbSKRA/oSP6xXX2JNjG23w0tAyjOAejPm24aZo0ZXbky7RT/ou//HMAwOe+ZLbKKKegTptf/xrfcl8d7dLs7kbXfOa4JbNEWuwlVrG1nDgXWcfSXLwb4we0I/x5x4hOz3bdo72iXf8XNKYu5yBeQGdB6c/Y9vn060QCVPXOmYULwV54K7waNjGoAPwdgF/uHiQAd8UYrweAGONaCOF/hk0Sbg4hvB6mfPgKWMviG2GSyo4Y450hhP8VwB8C+HgI4Q1o5JOvBfD7e6OOmJCQkJCQkLAXmYPH8zUH8Cu7LPN+ANfrTYzxLSGE5wP4P2DyyjJe+lcA/jDuMLWKMb4mhHAXzHjp52Hz1i8C+I29NF7KYt6En427D8fQUc4qFP42THvNVAOjhH0rFpUdPWzRZ8b+9BMn7wUAbKyJQ0ldA0VKpV7t24osZEUClZt7qJ9ZNTmgdllOKXNxNs7hbpExXrPOubTfIspeaDOlXfGOGQjNgL2rgYcplxJjWFTpakd627MJzrPnutvHVpNt9RwPaEk7oULk6qplElb2s/9ZkZMiMEYI06pELbdWjlu/mbNjRH3MkoG97/6vAwB+8GkvAADsO0DG96adq6K03w/Itpb9ctD1wOzOcGARVJ5l3isuy+Y5VfhK7p9sgGvpQWRtxTudC3UxlK6QyZcgZjhflTnggZxMJt7j70YyHJMkjUlj8Pqtsg8uySsbYC+AMhoT41tBAaPXHiPP6RZNdIqeZyPKmYtP2zj5drpJZUBmzKSH4LoNmdQnxcFgNE7NhfmGcRqKUtLHUvUj74bXftHvNR0fXMfffcR0L05tHG+NQaGOOEaf/qz14N9Cm+ArrzVuyvc+1TIIT3va9wEAnv70pwMAvvM7nmjHY0mdSJbdmM6iR4AHDht/QYqRsmQOzqbnceJ9s75G7Y6BXZuDJd3j5IXw+tfjQbXq/pByzLKPnk+Q8b69jJkP4VvfuhsA8JEPmgbBMu3mn/uc5wAA3vKWvwEA3H233S9XXm2///o3vgGg0YkYUyv7PTea9PHxY8cAAEOuLx8E1JLg5j0qBdnomSNd6J1ncK7sXTtj4N550puHND7Iu5HNvDrQFjJTjc4BM2e5xsBtBt0XUp+UFkVbVlqQvfJ21diz41w5BLVnEPZO72Av5JN/C8BvXcDvPgTg75/nb94K4K3nu62EhISEhISEc8dDppD4SERAMCY6o3JnY0vjS7NSlxi32a4cWud1dJvSa6+2WfO115jVaE2VvlPkFoidPR3bNiZu12lRvIxjZp3e406LeSM/xlntNM6b+nvQsqyJS7d+IMtVRvqBmQGGjjJikYb81obN/CvXHG+XjbwmzZl0FmpUndlxoZl6V0RCNdVaano69m09ckVY+n6TOgiatcugasDsR1hQf9ycyXKVm8yossia8kDKkTyvX/2q1UI/+Vmzg33WM61D4sDwKgDAxrodj/7IIqU1msYU3MCSDGvYcpFVGeY83/2Bapz2G5lCFYcsclRffuS6PAIUGPlMJ2LCky/ACEkqjYqog1jf1dxto3V9ZJ0oTOqK/srP1dUgbkpQ7znbFPS9s7zJZdD3la6PaeU8jNqzF/aTXpQngjQiWM+lEY/TgFwWg9wN2mZHZhQmMgFSF8hWWzFQXQyrm2sY0Evhg7feAgD40Cc/ysFwG7QBLuWmxlHI9GplZJHv6XXLNLznRlNyv/H9Jgi7b589Ax73uMcCAJ703Sb0Krvk9bU1bG7YdfGjz7M46eUvMx7DoaPGb5nKM4LHQ9moPnVRpD0w43WztCxeCblNJCDdc889AIAvfdxEZx/7WBvTcLiEEbM5736/RfaKgL/whc8DAK7/i+sBAAfoMfH6N18DAPjKHcbRmfIcSP11izoQ8m6RxbMyVAcPrGARsc4QuKxH/JU8I1ToVz9/O3OkS67YZn6kDErb5KjJVOots19qb5qX3sVVOA+CWV1xR7zj4cx/Putuh1bd5gf4WHfgaJ0vGr5X2LPswUPtrZCQkJCQkJDwCEPYyxrFIxmmkBiebkp25BDkKqrbi/dY8ze5q9uxZjudo1fYt097mkUJT3is6RucPGZqfJOJRa3rm/bqpmG5zZw3pjZfO02996LfaKADwFxsYy+A2eBGS2Szh6mPUPVZZ4hzLjidqn5PO2B6BQxgEcT+Zercs095/bTV2uumwG0vPhbNiBmah3qb1ndvm5dCG+JOuFKZ22FLZc4+PngZuxqo4y42u0jNoyV2XkiLPUSMp8zSqM4t2QoOaUD2NWkNoM0Fpmv2+WOv/HsAgJe95GcAAM946rNsv9m8MWMUv29ox20/7XSnzDDU09J7oldWLPLbGK+2xpANpBhoELte0Ua/o7A45zkbLTMKUz2TjpGKvORiGNHU0HVupMYoHYMB1emmM9lHS0GTvA+5DJZtF085SU75+cH9crdkZoGRY0Dw3nd1vgzI16gy8Vo4bkbMuuaWuZ+enHAFUrs/otQ5xermgrJqdsa9vCd6OU6tGz/ht//gtwEAt339cwCAFUXfse3voMxYwWhcDoDqSsjcK048j3amRjh4kJ4NZYnV03Yd5LX99ilPfSqApq6/j6qM99xrkf8115jmxuWXMyPJ/TxyhB4kzKx981v2vBlSvfHtbzfVx/e+730AgMOH2EV1+Aj2H7QMyBe+9AWOm90l5DMo4lf0rU6pfft4TsSP4vWga13qjnDH1fZx0DVbVqXrGnQdDLvRdzdz4LynrJ05aPQA2vGvu5u6Zk3b36AsS+cHdTUFXFuD2+q+V6dF43cgXoyyd2fXg7lQLHYpcHuf3E0N+FyRMgcJCQkJCQkJLSTOQQuRjHvVSu3TTLNYKSZ2etNRSdYLuPJKqx0vMRq9515j/FbkHEgdLSuoPjiXbrlFJafXqOY36/TtUnw9X9BU4ArtxQebOdVfXAiP+BUiSumQ0+ceZ75DhudaXr3X+YAz5am2pSitq3oo9noDaURsr6u1dcsVKUjnXfT1ft9+N2SdN8pNjbNwHR7pBpxetWhtOK/8d4O+RUbK6mxS4U3951vMKNDY0nvHR1SvvPverwIA3vp267C94vIjAIDHXv0EGwsYOUdbwSbPXc19mM5mWF45yvF3u0vsvTJKcqFTB4g4CZUfL0bzHCtpE5gyOp+5FgFrtPIkyIJHNorKlVnyem2HD6IIyHvtOz343V7rrKPz3rCzdV1kzWdRSoWMvvqS/OxwJtRrr3tSXQu1PCPsXFU6ftx2Tqb5jIqkysyMp2L5D/D+d5uewZ13mRjrysoKx912zuypCT+Xx4S9VSSoDoG+uEi8vwp2rwypeqmOg6EcV6cV+gPpk9j+ffozt/L1Fv6GmUNeN0vM3ihbo2zQNddcDaDJSB673zToFNXOxG3hOd2gEuvWdANf+7rd58vMmAyoHKprT/oHupe1P1MqIbpKJe/liQhUhBxZ51Lc9CCf9f+q9Gdr1pWYPSt0vdi77mNGWa8mQ9Cu++v5o+xpjLHxlHDejjgGHV7DHnAFzoaHwm3xXJEyBwkJCQkJCQktpMzBIjITJlREXLEVQAGyNyuofi7VMUYzw36GK66y2l9BvsIa+65VHFN/cVHY7FyR0wPHbCY/3pSCIvuRt6R7L+U7rk7lf7UruOtY4ZGeGtdLr50pWmP0xmyD6raKWjc2LLqazNWf3m9ttKraevfBa65cDBmy0LBnebDaA9eIdGwVUHO6qpl9n4zyIfUi5szAKAIdMsrXPs84259M5SNRYThUJKcae8HfiiEtBzeLhGbsuZ5MTb1yhfXZY+vfBAD89VuuBwA8jX3t1z3WpD4ed9VTAADjOXvtM3ZQrPQxJw+kmknJzDY921LniKLPtgOislRVqTpo3drvwChXLp/O8peToqvc5f6Zq+NBegZSEezUd1UrViTlNdP28fRteIYKHJN+RyXCItvGpPb6a1T9ll0pvBZryH1P/BZdV1JAzFvva2XSeLzFg5jWdm4nVGV/81v+Fm97l3knLO2jwl+10RrDiPwQOUnmPdmbtnYBUdskJ6HsHB/V1jc36EjJj2eTTVRUGVXn0HCozhlyMdR1IC2KUl0J3H/eOPfca9oCcoyUM6bOcV/9/W6Uoe6HHpaWmDFRhpTf9XttroB+2zDjbU3T0ngdBTtLtC/af12Tvm2pmJLTEWN0jQzdB64V0NUE8OD9bBF1N+7ls9D1Y9oOi85HQvPMOlcuwaOVt5cyBwkJCQkJCQktpMzBAvI8w/KBIepZ2wFN6lwi0mvWCWehsu539WHs22+z5zkZv3N6xQf2fNcsbG+wU+DUaVtujVwDOf6VE26b6lxLZJJ7ca0dpDUT6TJ3zQBXE1PNT/3olfgMFl2ok0DuaxpzTp6Du7KxqCr9g4qzbVcpU12vAqrYnnd21SWbjAHreZ0uBVEoChXVu3ZrnN1PpdKXqU7OKLanmnuOKRny8rRXRJCzoV31SB1CtUaXqqHOLaLcf8iyQl/9prG6b7vd2O0HDxkH4ZUv/YcAgBc958W2PZ7bXrGMLWohoGzXQLXNPl3p+owY5QQnJ0zv8xdnpd+u63cRPBJrvledunaHQnuRNoe6T9zJT06g0y1tHUBTO+/KpGss5Vy6Eu1zXuQFyo7yo16lsqjL2xnh5ORUHn3KCZTeGezzXwqK6mwMs5oZA3ZBHLzCuEB//da/BgD81Vv+G3qs/ed8DA6USZK7orwE1BHhApDsJCnbGYPYdcisVIOnRCdP4lzvQ4aKmR89iKtKx46cArlTShBTXCMoStc54af0D6k72b2GPyIvCnVaRXeI3eYBQA0AqW0qk6hzow4qVxLgIJVB1Dnqapo079vcFe4QlxS/Rb9UqlTr6PBalAWr21ymqtLzQ8qiyp4q0xZb+xJC1jwf+CwRB8mzGtzjOlPGsX0fdDssdkOXs7DYxXCuXIOdNBMiql2WPj+kzEFCQkJCQkJCCylzsIAQgP4wA6iB3lcXAmehim5mE5vVbaxbZHgZI8prrrkca2v3AwBqRk9en1L2oWf1vRNrFo3ed99pbkOtEC58YMuzET732jO/ZW0aZLWXM62/cQP0ENitCtVvrpk/N1W1uRVO7K0V1YsZTs1xtQaQja2oP2O2ICJ6V4XQsUhvMga5olDOlNmdUCiDwh9OZlu+btttRnOKEOQHwbEo0szy3LUiqq22b8VEQgU+KHtRTdiTMVF9/FZb3XfIatFzSgx861vWzfCGN/8FAGBp2bIAT/8e4yScOPYAeqV9Fpk5WB6Z10ZPAg1REaLUBGPrVXVwaVU0QZmUBsU56bo76pwvxG8KQxXhyTuhbNeSxRlwzoqcAPvkeXSiG3EPGlVLtMZUlmWTtUBofVcz4ssZpc5mW63lFDorSpsxm1FpDBzTBp0BZ5lFhnefMn2Aj33l4wCAmz52o62uN2+yL+7Ix2iaWSjPFLATpOrsb91RyhPnwln5yn7pXPC+kY4AEND375iN0HOC98WM6oMhs6xGL293nAz67bp+1VFUFZQ1HLDzwrtktiYoqDURO/wW79Ig9PxzNn8nQM3CbtFum6vSXd+ZIA+EvPP8GDDT1tzDvIaVaYjahsaqLO+i/wu2JSTzLPdspTII55oJ2A3d/W6eee2NV54dWtied8ztcqx26AazLpsHz4NImYOEhISEhISEFlLmYBEhAmGGjLV29SDndN9TX/wymfPqof2O666zz5czPHDMMgIq+3j0wHrmxqbNdO+/lxkDNdf3pC3fru8KzSxdLG6DeAEqRZaxwoiRqxIHk6kYzHLq46a9vicd83amQMF/U4/TNFaz2DZzXHXMOkR0Axh5xnvPsHwe+F61VPWpZ86E1z7Q1bBg77lm97m8FBgZMMr3duksNroOmerbBkVyXo+V+h5LwmyVR4/XQaQHxRY5CJsbdi4PX2GZo40N6254yw3/HQBwgH3zT7zq76EfyYgnt0TRtRT8Do1MVXDCbpX5XDVWsfDFNeF+Ug+gayCqnnK/fBb0DrxuD0Xr9CcoVWMXy7zdpaBtKmMgzsGUEbDWEzuMcK9hi4tQlk3vONrnJLI2LLdGZTGkRuhWIqzFz9ljLxW+LZ3vJfufd7/vJgDA2258GwBgY24qn1ulve4/vIRVOh+OCjmmeqoDi1D0qsyZFlPGoJ63szvij0hJUI9Z8QmUDVjU5vdrM3Q0AsSc12tH5VLprorKmBqr60Lw+ORuxqL7jiPrFyinbd+BQt1J3ERZt7clp0Q/TFk7Ku/Cg1p5dkihMzQaHNu6EnaBKyR2+ApSqXS/mE42VM+RbZoc0oKJzXOpq2fQjfzPN5Pg3Q95N1Ows2JiCBnyXRIFuyFbeEaXZcPVeDBImYOEhISEhISEFlLmoIs8+pQpcqarvnex+9WJcJT976jlwb4BBIXbnNEzEBhTr/7ESXYxzLUYOQWcrfcZndZSr9P3QaeKbFtFkorAsyaEVK049NrrEHlcEYy6DJyNy6gllozms3ZYmnmo0GYIe9TKbxGCz5K9XB0U6XfY+uIM8Li5lkSvnf0o5LJWKPJRLbrD5pegJGf/vSxD6WG06o2daXnWZnArEupliqzFVbBotZyzhs6tHqZDnrwH7r7nLgDAn/35nwIAHnf5E3FgeBkA4EXPtU6GK4+Ys92QfIjxmDX0qdTZ6GRXqM+frHa5TbpcJ4+HlCOZSpjrwvNruYl4pDrox957vnk8pN/BdShzJNa+Ir8xux/0+7xoR8iiNuS+PXjKS+NVJDib65x4ygcAsLZmmhE9KmT2Bva6tN9U+o4fOwYA+MbX7gQA3PQRUz38xOc+ab+f07tgJC4GFQLXN7CyQi2RDfp36GCpW0Hpv51b5j3zJogv4RGhp/dUF26LlFQRftDlWuocIh5T3bvO7+90kihbo6g8W4zG0WSJ1Emkc6HPjR/R1jEpy24k21VC5Rhc7ZMv2ZlZ8k22o30dZmGbdIRDfBDnr2jczMq4xoaet/Ie6XiTZJ1z2I2s80akZaHTQZ0Mu+3RuYX3u2VzlDFo+A88VwDynqc0NCr+ZueUQPMsjnvANgDHm5CQkJCQkJCwgJQ5WEQAQp6h5iy8gEUnmFO1jFoDgVr6yhysnzYHNBSbyAc2o53WxjGYTO03x0/abzbXuW5OOgc5dRCkoV/YNodkEAuZO0AyomB0U1bqodbce4LpVK5hPY6bp5ndB+q7ZqCLYsg5oiI+qDYtqGOCdTnV8dzkkJkEd2UEAorWdz6bVQiQe5gKAFih4ttgyBk/9y9X5FuLAa66r3dX2385JgX/Pa+9lx5VuXKfNBF4iFXn9bwIAyDZtysb4QmHHjNK1MNYmxvXYDlYL32P6pb3HLsXAPCZz37O13n542w/r378j9s6NslWp3Ji9C4Xux7mzBD0pAsQlrjXiuKp0y+XSv5OKohit1dljXnVdC4ADadgaWh8iJxRueq2M15byqBtzeXWyMyJmmBqja0d/bs/AqPhIvT8PIlTID0LHcsJuxDm6ksnx2R00C7W279+OwDgM1/8DADgs1/4LADgtq9+HgCwOjFOwYiOgeKeeDaN1/Ag6wOUnuhzISmiujcJ9zPU6sZpx7cK9tUpI/8CMcvdJ6Rm5pHaG5FZrbKc+8mIaNe5/SQF3Q/8DTk1mWd3NCapUHIbGqMX1bkd8Qj89suB2HFL7ECZom7Q2lVida0B70TxBfm57ll5KmjksUvzcDS8Bu1X7HwvXQN7r+uoyeq0uRbb1tt5XxQBlf9WGaDdMgTdlOlu+9DmaOlU63nq/LRMvir5grdIext1xxHVn4dczBoV9iZ3kDIHCQkJCQkJCS2kzMECAjL08hFmZERHZxDb91ubVr+89mrzUocY+GTKxyxALcyzUszk9qxTM/5adUiGH9IzKDo+B5OxRR0zOiSKOT9ckga7VP44y80zZxurn7pRcpO/g9jCqg1L2axdv9Ok21nFigh4QBSzSG1MjPOQL/bltjUVag8XGNmyT3tER7j+oN0Lri4GD6ScY4E22kP2bodYRsypX6/59Ghkx6fH4yTt+Io1dG/KUHpHimlkis+kbz+UDoD9bquy+njgtseMtPvLAY+50lwZP/wxq4lffpm56F11xXfauiuNiZkA1lQDz1k+sOh+jZHxFr9fGpjjpKJ7aeq7DrwUBusSc/XrO4OdUfSybTtjBmDC45DN25yT2dw6Kyp5dszUrUJuC8ciFn5V2/5PeX1URXRuSc1QZ86odVLbujdLhvPKAJFrsLpl997NH7QuhBveY74IU2a7Bis29uVlyxg450IdNKrRV1IkDE3tXNe5asLipigSPk92urp98tIp8u0F6mqAaXQAAByVSURBVCaM7ypZSt3Or29pLkiVVNwScXC8gUjrsffKkOjebRITba4OHmQPP7Cgeyi1Qo1JmRfxjOp2BqKbaTgTXL0zl2ojMyn8XDwwqT0KWZds8FBgt7TH2Rbnc7TwGJ0nKVvMNpCfIl6Pt2G1O0TEXSuCLZu6FRISEhISEhL2HClzsIAIoCoD5qzZ14ERDxnk+5epUkZ/87X14wCAoOI7Anquz26HdouUgL4EDffRe2GLbFtFhqp7cso3GdPpjD+c0GshqHWa7m1iH/eHdC2MU2eN5+4KV7eWHZBrMBjZ9z06Ps4mjMpFKuDsUxGmK8lV7WhDEYN04vOYo9I6VFOVQpl8H3J1TOhz+TaIt8CaWlM0Bg9Ya72C9z8zM+ET5zz4/yuimcq3wMt5PD49/xG3ITU5Hp+56n2s1S+z3uldLXSzZGaGJnw4eLDAFY81HYPZpmkk/NXfmJri4x9jTo5PeYKpKf7gM3/YjsManSG3LCK6915T+jt80LoeLiNbf10OkPT3yIJlGCKP/2ymSCq6+t50wqzDEvkLzARsrFp0PmdXhjpgIq9R7b9zCHIpJdoWJpvyIlBU046G18dbDf9DNWRyD1anpvsx4n6Jc7BBp8R3vvudAIB33GSvgTyZYeD9RM6FB8S67sQ839ZHH5tLyHvf0RpbXrd5AFVHKbWL7ufd7JYyd4VrFdg4DO0OBxc3DU00aUNVtxIzIANmnHod1cqOUmbJLJDUHuUPkYewoDXyYLMIbXVXZ9DrOujoGzhH5Uw6Bzop/uzhKz8ueU3KB6dbi384ELb9z5mh66RWUodultKTKKsZiqLRLQCae2gwsPtDOi8b68y0uVRFxoE8+NRBmhwsIgbEeYGBmxzxBuyTwMcH0taE9spTezCPhnailkbLYMYZ67Q91vuMhjpZ2f7jp5u+dDIUb+q+0qBtJtFAf9lFepLZC++WeVn5ja8Hw9QNU+yXA04s+iTOeVegxGzEVoptcSQNsdr1IbJAhtJkwE1ARMapFpZs7J9LbqMnYlC//YdX0y9JtEr0wzv6NIdwYo5a/3rwLlBNrHjM5jNZxtrHw4FSs9y2Wpk6G+lzkMO+DKsocTtUCtAWX2Gn68HDQ8yiTfb6KzYDOXbKWvC+8O43AwBu6n8EAHDr5z8BALjySjNzuutrJs38ta/Y69GjVwIAfugHng0AePrTbVKxP7fJx+aUk0r+AXLp4qrE1sT+6GsiOmH5bHXLruNNykOrjdK1a3jMh/vtGpbUsWZTkzHLEDQTC5wUzSqWRiQqNuwhY7lsi/LIMn0aZ/b+U5+yFsR7HrDJ0Ec/dSsA4PhpO14lZZG9bdLT8G0JY/TaErXVgmQxYERI/el38R61nOlvlv6IK6W/szLxtuPkhkPb/ljYPTBcsgncoF97yWUybUt5OwnWhZnaUuiuN5TrmiVBedYONnTxF7Lf1qxDfaZ7CT02tgkUtcuR0ecMvJers2fmNdHwyU8p2XBNWNXinG//8Xmia0C113AhOLQneroiB70+ZqWuh/bzcrhkz+ylJRq0TVl25nkvsgyzkESQEhISEhISEh4CpMzBAuqqxsbqGDlJXQrSGSCi6DOyZrSjSfhUZh6bU8xnNvvbnMh6tS2s0kTSnZabTjrUjUbd/IYEPrabFZ5m1Og5Oy8KzGXWolKFMgKcnveGSpfbL2dstfLgxHu0GH0p2ur4RGux6IkGRk5Z0+akiKBpQWImwCfL+lxlg/Yl6VGnCHayy2WU5gFS15lFIlJFH4O+jJZmrUVE3PTSDDMHMrGibpWPdTS0lN7hQ/tseWYKNjdPcD224BLbUEd9kSkj1seWHu9z/zZYXlD2Zotkv3e//x32m9DOGAn3nTZjr1s/Y0ZCP/VTPwMAeMWPvgIAMIi27YrX38mTp3iAgMc+5jHcH5Ve7PX4xMZ/csox9dVqx7ZcthdOjltK4P77LYr/5jctun/+s58PAHjS1Y+33zESqjbtdbhix+2+4/fh69/8OgBg30E7hgr0brzp3QCAD9/yIQBNOaRmK2PWkzCXLT9DW3hJCl9+FciWnDfvkOfOBcDigshRKWKcDlU7ZlI5xlet1DXaZQaVxJQul1z5zDNUbGMmYTHr5aDmGaYiQXfKAsOhiMpqDTbkvDgLHpfAe7XoMWvIko9u0vlElsUd62vEsxIuvVyyS3S/mzCP0BA7O+TshRV2Ta18maq9rEo7eh5WzcPnjGM4G5SgLcvcS1FdS2W3qO5yOrXALodB14OXSLW4l1olACfydrawDL9jydMlvjsZWT17y7o86/k4V6TMQUJCQkJCQkILKXOwgLqqMV4fAzIess4s7D9q7VEl60AZI8weyUCa+a2tTTCbcPbX04xf5BzWqwu1INr7OVsVRRITsabfl2Qv+CpFHntRTXljzCyBeGdFQxhTlDQkWVGzz4oRdB1kc8xVM1LWJFxjVIuXGxZp/ZrWb5uphoUaosycJJmqGriiKFtK2QxlCkSCa6RZ22MSMVGcBJcgVfaDs+8qVs6R8BYzHYjOlF/tQuuMeOfywCJZkgkZLC9b1LsysusiMqM0JTlwxuzRhCZbvSJgxPN14riR/jbHimDt3BRs2VNKqPA7U+EJoy+ymHqsW7/35vcCAG6/7Q4AwNVXGCdhbd3Gcs/dd9vy/R6e85znAgCe+QzjKegY/+27rC3wS9+8k/vH7ANbNDfHxp/Z4Dp1KaowfssXPwoAeNaTngYAeNx11wEATp+2rMX9xyzb8aXbb8M93zTBsMOXH+Xu2v4cO25ZCJ0jcTNKzwWQFOl8GF5Hkn6meJRIkFIZm5ck5zqfgPdn3WSjXE7ag0/WgueqczN70cmgeUueWxXHhV8DgfefjrPfT7w5ppOJn+8RFckUCSsjMOTnA5peSQJbWT9vIy3U2ifuBaNV3hCbpTIHdu97ZgHbI9ptyNoRv6LZJtpXtqMtbe7Euz0gx+kerur2Odkr1L7e6qwZAyc7nmOE7pkmXQ9+2HRdiYzOfSznzflz22z7ba+nLAR5U27VbUvlZzuX54GUOUhISEhISEhoIexVfeKRjhDCJxDw9GKp8JrpgUM2u776WrN0LQrN/ChwNOV0jRHUdBPIYFFX5IRvqpmdt0sxQmQ0UY7VskYr2qmWt8UaF1TbxsGDRoEf9FW7tkh0SPYqlmqfVcsIZTQacR0yJWEGhAIrmihPxmx7Ymles9CuzOfmaauPK9shToIXhBEaAxi1SDjnom0ItExDnJXLaAfM9xJuyvuM6lSDl7EVx64Eg6SOMx5eyU9XNVCSqa42U0mNjtVnqo4PckxqRl95bamjLdooH7/favLapcc/3s7FoUNWz57OLLLeWLXjMmdr6KHDhzAnO//+B6zTpZpJpIkiRmqXrJRtUGui2pvseCz37VxubtIsiBrQ4izoOElMSVmj4XDoZjW6htTWtrrJjABktcy6terZMrPKxd5n1ML2OLVCimPQDRQlaFXkBYZ9jpctmopJl8i9cK6KmxhRuAmKVm15zzDpA7/PJBbE+40ZG13kylRgXnpLna4Lz1F4ZNzmFCii1OcdekAzdq7HuQiloj47J8vLds6ns2lj1uTcGXvteTuyhNja9ely3r6PehXltMUp6MgmSzJ96jbSak+uPevQjZgdu2UOPKvV5hfpCJyLuJFwtuyCjmHpMtjctEfhD7Z1sdm+y83rfU9t453j0L0AdkHDi9A5bLev5vyb4cZ49cwzrQUF9sSHOnrU7l3Jyz9wtz1PZjNybEKO6bREjPhkjPEZZxzYWZAyBwkJCQkJCQktJM7BIiJQzSoUA0XpNsOvK9koS1ClbUlbse97Oi+RKzKObXatauJdK1VpESwvs05d2Mx+c6Pd9zxgl4KiugnZx12v0yLPETnnk8WuRHzEulY0olSA5IObyJ8RUimxoHYUMivbIkK9LGu9jzE2XIHQ+qnP9C87bNH2AbLWJ9FkgefUZNBMPoc6CjQ0hVhNjRBoAgevd0r3oKo92iyiGL38LV/FC8kKdaGw7k3jrdmEETIFeSgDgK/fabP2tVX7fMDMEsn92L//EAfRwzrr75tr3A2Ou8dUR2Sng+s3RC/sc/9lmmXXhbI6Wn64wszR0JvI7deMPKpBiWxg6zixddKOA6NP6V6MOJa5bG55jN0MqVRkzDEzY9ArLOUyHWj/JX1tQ9HxRQC2ROTI2xfIhP3aEncJUQZUtphblMvWNpNZUjuSRKezQN0wskRWU8t0XjWiXczm1Xn7nvWMgeq43f53SR6Lja6PZQam2EudRerUkX5Kr1j4jOPkwt7FJI0FZQ7U1++tQtxtZU5ks62h+mLiBymryKxgkTUaArtoHzSSLO0IOe8QgnY1XiIuKEnt+0eOkYa4R2FtN9tRV9GzlepwKb21ypWG+Nt6cYi7QhyTbkdGV9pZjVtVvdDxw00OR3aP6dmtZ3NPnAQ+o+sYbaf2oCKQMgcJCQkJCQkJLaTMwSICkOcRRw7JBEdysKpBUj5XkTVna+OxZpZLqKCWAMojK3jIpGDVSC0DQMU6t6KXAH7AGqHEf1V7Khm9Fpzd5hxbHHL9gxyayw44qyyjRWvKeOR9KUAuyrg2EsR5J/Jx51lJlHpyRGxkMqWdFxA9MNRs24XZtAjZ1/mIFtUM/de3rOdevI45CRBDRpQynnJQ2lqT+h7tlPsqLVfzZixRHSHKGNjHsolWeTqyfqeIKXaseFVr7LFrY5jLHphGSweUDTLVwq2NOdZO8/wpSeNKfpZKKHjdaO9C9HDT3iti7i1ECABKSh2DUr9hni/+zCOuOkSvV8OvqXzx7YL9L7svZsZjUBR78IDVO+ljhQF5DSeO2zXJVn3k7MCoOzXZClgULbZl3RhIEbK+EfNfioDtFFnXBKyxPFbWg+dU2h6ZuhekH5EjpxKq/KVix6rcjZl2U8zztBgjb2UDM95fvB+kWBp0UbokbnQJ3UKqkx0DJr8iPOLvMuX5XlQmjZ0nQ+Zimc71XBegnmk1AnbOjKiLwyPI0DkOPoSdOQbb/KZ2i7EjPGupaNq1VTyLw+tanCNvkOAzqpNByjwl0L5fnA8ROp0jUiTMgGVm4fbR9lt8sPGWde24omY7SefZmGwhh2Tfe/9K6/smacGsoXgmVd8zZZHZyJzPqIyvc/Kg5p4xYPam2jp7KuMckTIHCQkJCQkJCS2kzMECsixguNzDwSNWB0dO4yWFykTkdH993Wbhmxs2i9s3WnEL5XZ1auG3KiXqtVTdklEK+1gHSxZ91NPujNdOmTwWgjIPOpNhzaekldSzZJ3KmrJ660synDOxzytlK7hO6fJz0DNG6QUZ5/VM2uBcvtf05LqZS9U2fdLaV09vcEw0g1phnY/WxHNGrU3tjdtmhKWae68ntcd2xFh3GMJAEwGrhEzyuBtpBS+S20ufY5VCoPMceHKXV8Q+H7S+z3icS4bYJ0+uYVOdEYouGblsseOjp3p+JzHSJUQX3sUgboVsstXVwchLkSbHWs3LhWhUI2lHeoqelCGT4uPyyM73YCjeS5tzE3Jlb5QWYgaqw+62iKYT1mSdMWm/fD94DQZ1CIj30F5OzPn+QJ4S6lLYmUluGvxNBN8aUqc7oYuu3oHGkvvYmVFzYx3xi5QdZKYNc39QdPIA/nzIG7aOrVucFI/yee9u4wsoZWnL97ntMoj1T8OisnI/CmWSiryd4di2TqGTHbzQgDULuXNI6j3WLzg7xN1p87AM6hjQfjMTQA5Cn10uXX6H0KW/7IbuYQ4Bnmr17pWefF3k52Irn1IJtZ7QL6fXBzDHXqQPUuYgISEhISEhoYWUOVhAlgfsOzBEMWjXORunMyrlMQhcPcm6+Ji/ryZYpg2ydAfk8Fd6e3GbKd84lHEGqN5r1gprhuUzRqED8gWUvcgivRZYWO8Ntzz6mrM2FiS6wEyBVNTcJpllSCkniufgWt9BLo/2+zF77LtqjYrIZ7O5146bdUgzwY7lnDPe9Q2589HZcj9/qEwDJ/Lens3Ip+edBazV60pWqX6hftrVQi98Nt6OENW/LatiaefL8U5UDZLzsbJCbQZuW2XjGd0PV0+f5j42mafMUwPke/D8zlnv7gTSzlb2bJA6TniwBzy+ue9L02Vvy6vDpnIWfe7eGRyvbLJpk7zcZUZz/zdoUQ72Zff61s2zb58tvza1a7QsxTMJrTGfGefXp96N9qN3N/B4dGrzVee42uWl9J3q+f4tX8Lii18/QSkBHcDdhl63+9pjt7UowlM7vjdct8ZSdxnufnvYAvO5sjiquXdUCnVNSiGxbnOeekVY4G90OBEPk/1xQJMxcMXLi2C9bNttsipTSqK6zgvP0nSmY0iOSt7mGlzokBcbsrQKPVt0z+oa0rNK3ARdk7EaAHEdQDeTdP5IIkhECOEEAg4Xvcy9tHUxNGIX/FQtfTM9XOwly7OGUBN2W0dc/Nix7Sx0suLRH2Ai7rRbwfy+XvjrEjsknBjbfziif86P611Sej4WksS6D/tuK1es/Z0+a+xa2w9DtVk2r+1tNQ8J7Pi6/U7s7HPd/TOyOKb2KprTrLFT3GoqGdz275VuzJXSdxMU+16Ty6paXHd7FOd9//n+hx1ft/2hiju8aZhQnYX0R12kMP1R645R17hIUEq3qoylMaL1fsfd2ZZS3fnJ2k2jd/c7dkhfuz2gY+t/Ovvvh7A7qO6P9TbuuFj3B/oDv9uzYYdV73p9bzu93fuks4QfH5mi7XaPL6x99z9uu31xbtfwbkstjnm3+2Hv/07pHm//0QUWnkV+Hxh0P+taDJ2H0e633s7P/M5QmrHEhf1lMKlARhN2iUHJJE693AGZyocnY4yX7bLFc0KaHBAhhDsBPAbAFMBtF3k4lzKexNd0Di4e0jm4+Ejn4OLjkXoOrgOwFmN8/INZSZocLCCE8AkAeLCykwkXjnQOLj7SObj4SOfg4uNSPweJkJiQkJCQkJDQQpocJCQkJCQkJLSQJgcJCQkJCQkJLaTJQUJCQkJCQkILaXKQkJCQkJCQ0ELqVkhISEhISEhoIWUOEhISEhISElpIk4OEhISEhISEFtLkICEhISEhIaGFNDlISEhISEhIaCFNDhISEhISEhJaSJODhISEhISEhBbS5CAhISEhISGhhTQ5ABBCuDaE8GchhHtCCNMQwl0hhP8YQjh0scf2aAKPa9zl3327/OZZIYQbQggnQwjjEMJnQwi/EkLIH+7xP1IQQvjpEMJrQgh/F0JY4/F93Vl+c97HOYTwshDCzSGE1RDCRgjhoyGEV+/9Hj3ycD7nIIRw3RnuixhCeP0ZtvPqEMLHePxXeT5e9tDt2SMDIYTLQgj/NITw5hDCV0IIWzw+Hwwh/JMQwo5/+9J90KC42AO42AghfAeADwO4HMDfwLy7fwDAvwTwYyGEZ8cYT1zEIT7asArgP+7w+Ub3gxDCKwG8CcAEwBsAnATwcgD/AcCzAbzqoRvmIxq/AeD7YMf0W2h86XfEhRznEMIvAngNgBMAXgdgBuCnAVwfQnhqjPHX9mpnHqE4r3NAfAbAW3b4/PM7LRxC+D0Av8r1/ymAPoCfBfDWEMIvxRj/6ALG/WjBqwD8MYB7AdwE4BsArgDwkwD+C4CXhhBeFRdUANN90EGM8ZL+B+BdACKAX+p8/v/y8z+52GN8tPwDcBeAu85x2f0AHgAwBfDMhc+HsMlcBPCzF3ufvh3/AXghgO8EEAC8gMfqdXt1nAFcB3uAngBw3cLnhwB8hb/54Yt9HB5B5+A6fn/9eaz/WfzNVwAc6qzrBM/PdQ9mHx7J/wC8CPaHPet8fiVsohAB/NTC5+k+6Py7pMsKIYQnAHgJ7I/W/9f5+v8CsAng50IIyw/z0BJs9n0UwOtjjB/XhzHGCSwqA4B/fjEG9u2OGONNMcY7Ip9UZ8GFHOd/DGAA4I9ijHct/OYUgN/h21+4wOE/KnCe5+BCoOP773jctd27YM+yAYB/9BBt+9seMcYbY4xvjTHWnc/vA/AnfPuCha/SfdDBJT05gM0uAeDdO1xE6wA+BGAE4Ice7oE9ijEIIfzDEMKvhxD+ZQjhhbvU83Ru3rnDdx8AMAbwrBDC4CEb6aWBCznOZ/rNOzrLJJw7rg4h/DPeG/8shPC9Z1g2nYMLx5yv5cJn6T7o4FLnHHw3X2/f5fs7YJmF7wLwvodlRI9+XAngLzuf3RlC+EcxxvcvfLbruYkxliGEOwE8GcATAHzpIRnppYELOc5n+s29IYRNANeGEEYxxvFDMOZHK17Mf44Qws0AXh1j/MbCZ8sArgGwEWO8d4f13MHX73qIxvmIRQihAPDzfLv4Rz3dBx1c6pmDA3xd3eV7fX7wYRjLpYDXAvgR2ARhGcBTAfxnWO3uHSGE71tYNp2bhwcXcpzP9TcHdvk+oY0xgN8G8AxYvfoQgOfDiHQvAPC+Tmkz3RsXjt8F8BQAN8QY37XweboPOrjUJwdnQ+Br8rXeA8QY/2/WAu+PMY5jjJ+PMf4CjPy5BOC3zmN16dw8PLiQ45zOzXkgxvhAjPH/jDF+MsZ4mv8+AMtafhTAEwH80wtZ9Z4O9BGOEMIvw7o7bgPwc+f7c75eMvfBpT45ONvMbn9nuYSHBiIIPW/hs3RuHh5cyHE+19+sPYhxXfKIMZawtjvg/O6Ns0W0lxxCCP8CwB8A+CKAF8YYT3YWSfdBB5f65ODLfN2tNvedfN2Nk5CwN3iAr4up013PDeuGj4cRir720A7tUY8LOc5n+s1VsPP4rUdinfXbEMf46vdGjHETwN0AVni8u0jPrQWEEH4FwB/B9CJeyI6FLtJ90MGlPjm4ia8v6SpmhRD2wYQvtgDc8nAP7BLDD/N18ca7ka8/tsPyz4N1kXw4xjh9KAd2CeBCjvOZfvPSzjIJDw7qlOpOgtM5OAeEEP41TMTo07CJwQO7LJrugy4uttDCxf6HJIL0cB3nJwM4vMPnj4OxqyOAX1/4fD8sakoiSA/uuL8AZxdBOq/jDIuiHrXiLxfhHPwggP4On7+IxzkCeFbnuySCdPbj/ps8Rh/f6dnTWTbdB51/gTtzyWIH+eQvwW7WF8LScs+KST75QSOE8FsA/ndYtuZOAOsAvgPAP4DdgDcA+IkY42zhNz8O4I2wG/D1MDnTV8BaiN4I4H+Kl/oFvAN43H6cb68E8D/CIs+/42fH44Ks64Uc5xDCLwH4Q9iD8Q1oZGOvBfD78ZEsG7sHOJ9zwHbFJwO4GSaFDADfi6ZH/jdjjP92h238PoB/xd+8ESaf/DMALoMFO5esfDK9Da4HUMHkjXfiX9wVY7x+4TfpPljExZ6dfDv8A/AYWJvdvbCT+3UYeeWMs83077yO8fMB/BWMKXwaJkRyDMB7YH3HYZffPRs2cTgFK/F8DsD/AiC/2Pv07foP1vURz/Dvrr04zjB52vfDJnqbAG6F9eRf9GNwsf+dzzkA8E8AvA2m1LoBi16/Aftj89yzbOfVPO6bPA/vB/Cyi73/F/vfORz/CODmHX6X7gP+u+QzBwkJCQkJCQltXOqExISEhISEhIQO0uQgISEhISEhoYU0OUhISEhISEhoIU0OEhISEhISElpIk4OEhISEhISEFtLkICEhISEhIaGFNDlISEhISEhIaCFNDhISEhISEhJaSJODhISEhISEhBbS5CAhISEhISGhhTQ5SEhISEhISGghTQ4SEhISEhISWkiTg4SEhISEhIQW0uQgISEhISEhoYU0OUhISEhISEhoIU0OEhISEhISElpIk4OEhISEhISEFv5/I5MuTC7o6+gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef76466fd0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 259
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "img_path = 'flowers/test/1/image_06743.jpg'\n",
    "\n",
    "with Image.open(img_path) as image: # original\n",
    "    #image.show()\n",
    "    plt.imshow(image)\n",
    "    \n",
    "with Image.open(img_path) as image: #processed with functions\n",
    "    imshow(process_image(image))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class Prediction\n",
    "\n",
    "Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.\n",
    "\n",
    "To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.\n",
    "\n",
    "Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n",
    "\n",
    "```python\n",
    "probs, classes = predict(image_path, model)\n",
    "print(probs)\n",
    "print(classes)\n",
    "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
    "> ['70', '3', '45', '62', '55']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(image_path, model):\n",
    "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
    "    '''\n",
    "    model_class = model.class_to_idx\n",
    "    topk = 5\n",
    "    model.to('cuda')\n",
    "\n",
    "    img = Image.open(image_path)\n",
    "    img = process_image(img)\n",
    "    img = torch.from_numpy(img)\n",
    "    img = img.unsqueeze_(0)\n",
    "    img = img.float()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        output = model.forward(img.cuda())\n",
    "    #ps = F.softmax(output.data,dim=1) # use F\n",
    "    ps = torch.exp(output)\n",
    "\n",
    "    probability = np.array(ps.topk(topk)[0][0])\n",
    "    index_to_classes = {val: key for key, val in model.class_to_idx.items()}\n",
    "    top_classes =  [np.int(index_to_classes[each])  for each in np.array(ps.topk(topk)[1][0])]\n",
    "\n",
    "    return probability, top_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity Checking\n",
    "\n",
    "Now that you can use a trained model for predictions, check to make sure it makes sense. Even if the testing accuracy is high, it's always good to check that there aren't obvious bugs. Use `matplotlib` to plot the probabilities for the top 5 classes as a bar graph, along with the input image. It should look like this:\n",
    "\n",
    "<img src='assets/inference_example.png' width=300px>\n",
    "\n",
    "You can convert from the class integer encoding to actual flower names with the `cat_to_name.json` file (should have been loaded earlier in the notebook). To show a PyTorch tensor as an image, use the `imshow` function defined above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef75145a58>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 209,
       "width": 205
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fef74218cf8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 195,
       "width": 284
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "plt.figure(figsize=(3,3))\n",
    "\n",
    "path = ('flowers/test/1/image_06752.jpg') # pink primrose test\n",
    "    \n",
    "probs, classes = predict(path, model)\n",
    "image = process_image(Image.open(path)) # need to open with PIL first\n",
    "max_index = classes[0]\n",
    "    \n",
    "axs = imshow(image, ax = plt)\n",
    "axs.axis('off')\n",
    "axs.title(cat_to_name[str(max_index)])\n",
    "axs.show()\n",
    "\n",
    "# following graph is adapted from https://matplotlib.org/gallery/lines_bars_and_markers/barh.html\n",
    "plt.figure(figsize=(3,3))\n",
    "#plt.subplot(212)\n",
    "\n",
    "names = [cat_to_name[str(index)] for index in classes]\n",
    "y_pos = np.arange(len(names))\n",
    "performance = np.array(probs)\n",
    "\n",
    "plt.barh(y_pos, performance, align='center',color='blue')\n",
    "plt.yticks(y_pos, names)\n",
    "plt.gca().invert_yaxis()  # labels read top-to-bottom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['[NbConvertApp] Converting notebook Image Classifier Project.ipynb to html',\n",
       " '[NbConvertApp] Writing 1153916 bytes to Image Classifier Project.html',\n",
       " '[NbConvertApp] Converting notebook Image Classifier Project-zh.ipynb to html',\n",
       " '[NbConvertApp] Writing 295106 bytes to Image Classifier Project-zh.html']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!!jupyter nbconvert *.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}