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",
    "# plot outputs inline in the jupyter notebook\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib notebook\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision import datasets, transforms, models\n",
    "import torch.nn.functional as F\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "\n",
    "from collections import OrderedDict\n",
    "from PIL import Image\n",
    "\n",
    "import train\n",
    "import predict\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": [
    "# Set directories\n",
    "\n",
    "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],\n",
    "                                                            [0.229, 0.224, 0.225])])\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],\n",
    "                                                           [0.229, 0.224, 0.225])])\n",
    "\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],\n",
    "                                                           [0.229, 0.224, 0.225])]) \n",
    "\n",
    "# TODO: Load the datasets with ImageFolder\n",
    "train_data = datasets.ImageFolder(train_dir, transform=train_transforms)\n",
    "test_data = datasets.ImageFolder(test_dir, transform=test_transforms)\n",
    "validation_data = datasets.ImageFolder(valid_dir, transform=validation_transforms)\n",
    "\n",
    "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
    "trainloader = torch.utils.data.DataLoader(train_data, batch_size=64, shuffle=True)\n",
    "testloader = torch.utils.data.DataLoader(test_data, batch_size=32, shuffle=True)\n",
    "validloader = torch.utils.data.DataLoader(validation_data, batch_size=20, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type <class 'torch.Tensor'>\n",
      "Image Shapes: torch.Size([64, 3, 224, 224])\n",
      "Labels Shapes: torch.Size([64])\n",
      "TrainLoader Length: 103\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(trainloader)\n",
    "images, labels = dataiter.next()\n",
    "print(\"Type\", type(images))\n",
    "print(\"Image Shapes:\", images.shape)\n",
    "print(\"Labels Shapes:\",labels.shape)\n",
    "print(\"TrainLoader Length:\", len(trainloader))"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cat To Name: {'21': 'fire lily', '3': 'canterbury bells', '45': 'bolero deep blue', '1': 'pink primrose', '34': 'mexican aster', '27': 'prince of wales feathers', '7': 'moon orchid', '16': 'globe-flower', '25': 'grape hyacinth', '26': 'corn poppy', '79': 'toad lily', '39': 'siam tulip', '24': 'red ginger', '67': 'spring crocus', '35': 'alpine sea holly', '32': 'garden phlox', '10': 'globe thistle', '6': 'tiger lily', '93': 'ball moss', '33': 'love in the mist', '9': 'monkshood', '102': 'blackberry lily', '14': 'spear thistle', '19': 'balloon flower', '100': 'blanket flower', '13': 'king protea', '49': 'oxeye daisy', '15': 'yellow iris', '61': 'cautleya spicata', '31': 'carnation', '64': 'silverbush', '68': 'bearded iris', '63': 'black-eyed susan', '69': 'windflower', '62': 'japanese anemone', '20': 'giant white arum lily', '38': 'great masterwort', '4': 'sweet pea', '86': 'tree mallow', '101': 'trumpet creeper', '42': 'daffodil', '22': 'pincushion flower', '2': 'hard-leaved pocket orchid', '54': 'sunflower', '66': 'osteospermum', '70': 'tree poppy', '85': 'desert-rose', '99': 'bromelia', '87': 'magnolia', '5': 'english marigold', '92': 'bee balm', '28': 'stemless gentian', '97': 'mallow', '57': 'gaura', '40': 'lenten rose', '47': 'marigold', '59': 'orange dahlia', '48': 'buttercup', '55': 'pelargonium', '36': 'ruby-lipped cattleya', '91': 'hippeastrum', '29': 'artichoke', '71': 'gazania', '90': 'canna lily', '18': 'peruvian lily', '98': 'mexican petunia', '8': 'bird of paradise', '30': 'sweet william', '17': 'purple coneflower', '52': 'wild pansy', '84': 'columbine', '12': \"colt's foot\", '11': 'snapdragon', '96': 'camellia', '23': 'fritillary', '50': 'common dandelion', '44': 'poinsettia', '53': 'primula', '72': 'azalea', '65': 'californian poppy', '80': 'anthurium', '76': 'morning glory', '37': 'cape flower', '56': 'bishop of llandaff', '60': 'pink-yellow dahlia', '82': 'clematis', '58': 'geranium', '75': 'thorn apple', '41': 'barbeton daisy', '95': 'bougainvillea', '43': 'sword lily', '83': 'hibiscus', '78': 'lotus lotus', '88': 'cyclamen', '94': 'foxglove', '81': 'frangipani', '74': 'rose', '89': 'watercress', '73': 'water lily', '46': 'wallflower', '77': 'passion flower', '51': 'petunia'}\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "with open('cat_to_name.json', 'r') as f:\n",
    "    cat_to_name = json.load(f)\n",
    "    \n",
    "print(\"Cat To Name:\", cat_to_name)"
   ]
  },
  {
   "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, 78308012.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device 0111: cuda\n"
     ]
    },
    {
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       "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",
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       "        (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",
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       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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       "        (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",
    "# Load a model\n",
    "# Use GPU if it's available\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# model = models.vgg16(pretrained =True)\n",
    "model = models.densenet121(pretrained=True)\n",
    "\n",
    "print(\"Device 0111:\", device)\n",
    "\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier completed...\n",
      "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): Sequential(\n",
      "    (fc1): Linear(in_features=1024, out_features=1000, bias=True)\n",
      "    (relu): ReLU()\n",
      "    (fc2): Linear(in_features=1000, out_features=102, bias=True)\n",
      "    (output): LogSoftmax()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "\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",
    "from collections import OrderedDict\n",
    "\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# model\n",
    "classifier = nn.Sequential(OrderedDict([                           \n",
    "                          ('fc1', nn.Linear(1024, 1000)),\n",
    "                          ('relu', nn.ReLU()),\n",
    "                          ('fc2', nn.Linear(1000, 102)), # 102 output categories\n",
    "                          ('output', nn.LogSoftmax(dim = 1))\n",
    "                          ]))\n",
    "\n",
    "model.classifier = classifier\n",
    "\n",
    "criterion = nn.NLLLoss()\n",
    "\n",
    "optimizer = optim.Adam(model.classifier.parameters(), lr=0.001)\n",
    "                       \n",
    "model.to(device);\n",
    "\n",
    "print(\"Classifier completed...\")\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Started.......\n",
      "Epoch 1/4.. Train loss: 4.307.. Validation loss: 3.732.. Validation accuracy: 0.263\n",
      "Epoch 1/4.. Train loss: 3.313.. Validation loss: 2.646.. Validation accuracy: 0.423\n",
      "Epoch 1/4.. Train loss: 2.477.. Validation loss: 1.838.. Validation accuracy: 0.568\n",
      "Epoch 1/4.. Train loss: 1.824.. Validation loss: 1.326.. Validation accuracy: 0.668\n",
      "Epoch 1/4.. Train loss: 1.455.. Validation loss: 0.991.. Validation accuracy: 0.774\n",
      "Epoch 2/4.. Train loss: 1.173.. Validation loss: 0.817.. Validation accuracy: 0.820\n",
      "Epoch 2/4.. Train loss: 1.009.. Validation loss: 0.674.. Validation accuracy: 0.838\n",
      "Epoch 2/4.. Train loss: 0.897.. Validation loss: 0.616.. Validation accuracy: 0.834\n",
      "Epoch 2/4.. Train loss: 0.903.. Validation loss: 0.584.. Validation accuracy: 0.851\n",
      "Epoch 2/4.. Train loss: 0.821.. Validation loss: 0.503.. Validation accuracy: 0.885\n",
      "Epoch 3/4.. Train loss: 0.717.. Validation loss: 0.455.. Validation accuracy: 0.899\n",
      "Epoch 3/4.. Train loss: 0.620.. Validation loss: 0.464.. Validation accuracy: 0.874\n",
      "Epoch 3/4.. Train loss: 0.668.. Validation loss: 0.430.. Validation accuracy: 0.904\n",
      "Epoch 3/4.. Train loss: 0.618.. Validation loss: 0.376.. Validation accuracy: 0.905\n",
      "Epoch 3/4.. Train loss: 0.664.. Validation loss: 0.330.. Validation accuracy: 0.920\n",
      "Epoch 4/4.. Train loss: 0.575.. Validation loss: 0.364.. Validation accuracy: 0.914\n",
      "Epoch 4/4.. Train loss: 0.539.. Validation loss: 0.343.. Validation accuracy: 0.915\n",
      "Epoch 4/4.. Train loss: 0.544.. Validation loss: 0.358.. Validation accuracy: 0.903\n",
      "Epoch 4/4.. Train loss: 0.612.. Validation loss: 0.347.. Validation accuracy: 0.904\n",
      "Epoch 4/4.. Train loss: 0.513.. Validation loss: 0.334.. Validation accuracy: 0.912\n",
      "Training completed...\n"
     ]
    }
   ],
   "source": [
    "# Training\n",
    "\n",
    "epochs = 4\n",
    "steps = 0\n",
    "running_loss = 0\n",
    "print_every = 20\n",
    "print(\"Started.......\")\n",
    "for epoch in range(epochs):\n",
    "    for inputs, labels in trainloader:\n",
    "        steps += 1\n",
    "        # print(\"Step:\", steps)\n",
    "        # Move input and label tensors to the default device\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        # print(\"Inputs Shape:\", inputs.shape)\n",
    "        # print(\"Labels Shape:\", labels.shape)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        logps = model.forward(inputs)\n",
    "        \n",
    "        # print(\"logps shape:\", logps.shape)\n",
    "        \n",
    "        loss = criterion(logps, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "        \n",
    "        if steps % print_every == 0:\n",
    "            test_loss = 0\n",
    "            accuracy = 0\n",
    "            #set model to evaluation mode\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                for inputs2, labels2 in validloader:\n",
    "                    inputs2, labels2 = inputs2.to(device), labels2.to(device)\n",
    "                    logps = model.forward(inputs2)\n",
    "                    batch_loss = criterion(logps, labels2)\n",
    "                    \n",
    "                    test_loss += batch_loss.item()\n",
    "                    \n",
    "                    # Calculate accuracy\n",
    "                    ps = torch.exp(logps)\n",
    "                    top_p, top_class = ps.topk(1, dim=1)\n",
    "                    equals = top_class == labels2.view(*top_class.shape)\n",
    "                    accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "                    \n",
    "            print(f\"Epoch {epoch+1}/{epochs}.. \"\n",
    "                  f\"Train loss: {running_loss/print_every:.3f}.. \"\n",
    "                  f\"Validation loss: {test_loss/len(validloader):.3f}.. \"\n",
    "                  f\"Validation accuracy: {accuracy/len(validloader):.3f}\")\n",
    "            running_loss = 0\n",
    "            model.train()\n",
    "        \n",
    "            \n",
    "print(\"Training completed...\")"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type <class 'torch.Tensor'>\n",
      "Image Shapes: torch.Size([32, 3, 224, 224])\n",
      "Labels Shapes: torch.Size([32])\n",
      "TrainLoader Length: 26\n"
     ]
    }
   ],
   "source": [
    "dataiter = iter(testloader)\n",
    "images_test, labels_test = dataiter.next()\n",
    "print(\"Type\", type(images_test))\n",
    "print(\"Image Shapes:\", images_test.shape)\n",
    "print(\"Labels Shapes:\",labels_test.shape)\n",
    "print(\"TrainLoader Length:\", len(testloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy...: 0.895\n"
     ]
    }
   ],
   "source": [
    "# TODO: Do validation on the test set\n",
    "test_loss = 0\n",
    "accuracy = 0\n",
    "steps_test = 0\n",
    "with torch.no_grad():\n",
    "    model.to(device)\n",
    "    model.eval()\n",
    "    for inputs3, labels3 in testloader:\n",
    "        steps_test +=1\n",
    "        inputs3, labels3 = inputs3.to(device), labels3.to(device)\n",
    "        logps = model(inputs3)\n",
    "                    \n",
    "        # Calculate accuracy\n",
    "        ps = torch.exp(logps)\n",
    "        top_p, top_class = ps.topk(1, dim=1)\n",
    "        equals = top_class == labels3.view(*top_class.shape)\n",
    "        accuracy += torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "        \n",
    "\n",
    "print(f\"Test accuracy...: {accuracy/len(testloader):.3f}\")"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checkpoint saved...\n"
     ]
    }
   ],
   "source": [
    "# TODO: Save the checkpoint \n",
    "model.class_to_idx = train_data.class_to_idx\n",
    "model.epochs = epochs\n",
    "checkpoint = {'input_size': [3, 224, 224],\n",
    "                'batch_size': trainloader.batch_size,\n",
    "                'output_size': 102,\n",
    "                'state_dict': model.state_dict(),\n",
    "                'optimizer_dict':optimizer.state_dict(),\n",
    "                'class_to_idx': model.class_to_idx,\n",
    "                'epoch': model.epochs,\n",
    "                'classifier': classifier}\n",
    "\n",
    "torch.save(checkpoint, 'image_checkpoint.pth')\n",
    "\n",
    "print(\"Checkpoint saved...\")"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checkpoint Loaded 0111\n",
      "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): Sequential(\n",
      "    (fc1): Linear(in_features=1024, out_features=1000, bias=True)\n",
      "    (relu): ReLU()\n",
      "    (fc2): Linear(in_features=1000, out_features=102, bias=True)\n",
      "    (output): LogSoftmax()\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "def load_checkpoint(filepath):\n",
    "    checkpoint = torch.load(filepath)\n",
    "    # model = models.densenet121()\n",
    "    # model = models.vgg16()\n",
    "    \n",
    "    model.classifier = checkpoint['classifier']\n",
    "    model.load_state_dict(checkpoint['state_dict'])\n",
    "    model.class_to_idx = checkpoint['class_to_idx']\n",
    "    optimizer = checkpoint['optimizer_dict']\n",
    "    epochs = checkpoint['epoch']\n",
    "    \n",
    "    \n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "   \n",
    "        \n",
    "    return model, checkpoint['class_to_idx']\n",
    "\n",
    "# get index to class mapping\n",
    "loaded_model, class_to_idx = load_checkpoint('image_checkpoint.pth')\n",
    "\n",
    "print(\"Checkpoint Loaded 0111\")\n",
    "print(loaded_model)"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "process image completed... torch.Size([3, 224, 224])\n"
     ]
    }
   ],
   "source": [
    "def process_image(image):\n",
    "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
    "        returns an Numpy array\n",
    "    '''\n",
    "    pil_img = Image.open(image)\n",
    "   \n",
    "    pre_process = transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
    "    ])\n",
    "    \n",
    "    img_t = pre_process(pil_img)\n",
    "    \n",
    "    return img_t\n",
    "\n",
    "# Test\n",
    "\n",
    "img_loc = data_dir + '/test' + '/99/' + 'image_07833.jpg'\n",
    "img1 = process_image(img_loc)\n",
    "print(\"process image completed...\", img1.shape)"
   ]
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imshow completed...\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff2d4039048>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mrSn2Z3sAgJ1DQxCaOTkHRNLqStcm12Bs1xxfNC7BU1cMeXv64lMAgGtEsTYnm+wla+uS79cNIgU39q2N82htrrMlKiJdnnXU68nuX6d9D98ukrBqD/rmxge04Z2w9wxykCxZsmTJkiX707H3FHLwzbeALHS8AXnEnTbBwGt3x5MedGhPjSMNPRjPg3CPn7NoJ8wPzhMHXu+gRU0vjq54O9upUwwQAzxgtvmgWenp2Qtd3Ks9ZZ/grPH+ffl2eSOMrRcSOWIsXex/ZWRUHkvtCx4hDx1zfci5UHyWHSTOhTIkulx57jB02ny1n93iYkoei82xCfNgLlO/YLOx9fK2zm1e+4SefjmluI1zB8xDDGNxCyxWnTH4GjYMFcA6l5NRr03ZfIywa+c+PzWm9md+4DMAgFuHlof+K6/8EQBgFs27mrD916bnAQA/8OxHAQBrMF5DS4/53BXz0s5fMxb+hQ89AwDIp9aWn/vf/y4A4OUbLwKZeWW1BGQY15V4wsHClrtHtt9ZMdoVUm8HzPlTeDBDDsJJ9kBOzQOIQP5dkCcd7VmUrT27a1c+DAB431OGGKxvPMYD+YzKDJExdOXrRwpIOfKV6fsgoTFycvx7Qu6JtCSEIGgscq9u2c+wCejQuw4f0PhHb+kZIlwTyJGRD9BU5n0f1pad8ObyFQDAndyQgxqWrTAiwnJ9ZGN+++IZxHOGFB2+dBsAMN63cT+mWNEGr3X+vHEJvvtx4+p85GlDZd532dCYzdzGZkGOT0uVw2Vj1759y5CCG/eMazArjOPSBmaBtBVaz4jRB1RjKRtsfhCi+gCVwvb+46tnAxQ7DhUhaffjMbxVS8hBsmTJkiVLlqxnCTlYsRAC8qLsPInBzM9lRcUMHeTNr3oppykCHvdk+l6+z/AHmRGdGmN/PjfUQY8IPiEdXmmVqd3fgl7b2oFM8oOXffnkdoVzcFrsTJ59rb4mrdozQIQQMHZeiqXvioB2XMNZ/UxKvUsyjVd0JaRMFqWZMJBTLpX5QCW7vBWqYceJz5AzFuvijW7SLpDsrG0Vi3sDa7gULDZ6obbltDKPaFozrh8GqAPvH1PjGjTkFEQhBhtUTjxrCEG2TqRgU8iB9ZOnTOwB8cjaUzF74+rI1Pj+4vf/eQDAy3fNawv7NwAAI+73sfcZEvDBi4YMbI3NO8vW6NVf3GLbiG4whv3x7zMOwmzP2Or/xz/+edzguY+oiFeppgKbKV37PSIHR1u2PhoP+v5YzvnJ9ihIQnuKd+Ymz7lWDN764dql7wQAPHnNkJb1dUMMWiEGfFRN3mKIBHa8lrjyf7j3qswBT9NgIzK+Px0K0P92CeXQZ2QVWfAWiO7gx/SXfr8ienAZFuad15VxCg6ijaODqXHED3N7/uI95DMbP0t+N4pmjnNTQ9T2+PzP5YZuvf+q6UB85wdM5+7Jp4xbcIGo1Br5HSXfI8ysTaqL0I7sGtd3LHPmq3dMT+9ea+hGTSRLCrV1qNEoI8b1UIQY9D+snartyfYg5GColvutZgk5SJYsWbJkyZL1LCEHqxb63AD9+1hu/kBlbTVmeVrMx7XOm74H/chC2APCfcRqPrJZ7rtqJty/tAoq+XJQS0FLbR8u6xPUuk7jI3izhSR44YO+x1yMmb9MxEDFFpb0LKq5eQaHFPzf43I+J+Nesca28aJWQhMy5c6TxzAd2XKdnvDG2JZr5DusUUNgssLwBlZyxZ3v0UciNkbmIZ0LZ7C9tDj0BpUP11sqGRIJyXSfY55qSoTkjLUlsm3VxJZh3Y4fnaGS4hpf5XWO2bLPsQ5NhuyQfTq2feOu9csHNi3b4Ce+/1MAgF/89X9q/cLb+uC5KwCATeben900D7k6Z+hFPGdeXMW2LytDb8bc/5M/+MMAgMXRPv7Pf/YLAIA3jiwO7V4a+1TF5A6IHOwe2fNcHxExYZuKIMTt/gjCae9uT4t+gBj4vg9ifvPncWH3f+Wcxb2fuGpZHZOR5dZXDZ8ZVS2zNebHtw0ycgb8mjr1AP5zIcisP/ZA7zUc40f1zc9bC5Egd6HIEakS6MwZ8TvESxAnR7UTHDGwZTNf8j7JOYjGYanWLJ5fZxTWoIBGdmTjsORxZVbhKgxR+8j3WN995IJlvDxz1RCDKVGpmvoEGauNVsxuiAvxP2xR8btz+9AQgudvfxUAcCMactVSyyATNyFIf6NxBNSRA4ce+3H/htyEB+vA9O0Y/+PEvfo2uPR9NGveOUvIQbJkyZIlS5asZwk56FkAQhdRdoawqjOqgqKSkkWIV8w55N1scDjt8tmoB/3sGiulU3VV+/9wPtnP714pzGynW2Ef6xdn2w9BCl1bJZY1a1bcLWqpXHIhBCevu7LiCudgWNnyWOzXp88qd0wPmkqALdfFJVgcGdv4gBrsBweMbwpBYB70UihHqzzxrq8az+MGr5Hx/m3DGtn322zDBuP7G5vmpW+vm6d8jloCUyILURkGPP4saw9cHlmZ5e3FGtZb8y4nRCFyeoxxQnebegUggtBqfcOu3azZ8YFtKTatbdjk+GHeN0X5vE6CKi0W4wJYt37IN8lfuMe+pnf+iasfAQDMnjCvfv+mLZ8cW/x3e8OY4qOLlr/fnrPzCDHQo4xEceq5XW+cWVs//amfwN6hPcdf/Ff/BACwjObZKY9fz+1obh7hfEade9ZaEKt/Rfrv/uYeV5/UE0JAG1S0WnoORW+frpRqP78/sv7BRs3nfOZpAMDTzE7IMxsfFd918WWUecIilohtQNMc+1Cg34gBIjDgGnhTXapgUGtiUKNE3w9dpowdV8jPpjLILrrId0nIQcXSzIfmfdcLe0aHzAh4bdc4B8sNe+7rY0OeAo+bskbH1pFxXp4893585CnjpzzxHcYxOAPLkFHGT1sRfRBySJlPnTMwXUlaI/dqU2f84xtfsjbN3wAAzAv7bpS8t6KWMuVK9ot75eozrQ9QqGO+df+bLeXcjhfW55EMq/1iZe14Wfn++O2y3LR+ckbZo1iaHAwsZnnHEfK/qnwROzYUgBXF4qbbzRHmpr/vUP7YizWRBNdK1OJYoRReW4PXU7mGk4SVgToQCnLCnA8sfWBJZoI+zJwEef12TgZcwlR/eLtJgDVFx2nZNUX/7rZxkiBiIr9AfMexz2Itgihn/OGI4YLZghKsChtE/YEXVKu2KSTUrugq9V/miuGFGeHvZsG+J2S5s8/73TN4dDyxD85FFjN66px93DYJs1+dGjT6xNrTAIAzcxZFOoqYsDBSYIphZCgjrIlAyNTFCdenmhwojEApXqUuMozQrnN8SFRIg7T2Gr+2bNCly7G4UcvQRbtr/XKusYnHX/zQjwEA7py9affH+1y/bDB5vMhURsYdcvQnIkG1nfUOqBxzOcKnP/WXAACv3TCy2q996XMAgP3W+rhiGuWCY+2Qk4OaYZngRMsB1HtsMv0gAmIEMtWgZpiFSxcDIhwuqebAFM7Afro6tsnAs2e+w45bULpaeZcT9jMfZeRfpCJXMa0KEUrlZLPcidBkWoRDfR/U5j703d1V6J0uDMq1i/gpqeeyapxWFyV57rNnLhhGyDg5aBkOKDipXPDd3NkzIaPf/T0rjvvKpoUXLn7AJpePkZB5lSHDD00t3PShZz+O9am9QwWdJJVkXu4amXG+a+dCbddqGuu3icaBZJcpHvX8bSMePnfn63ZvU5u45C4m1b9XF6HKghOQFS50PqIXgTPzcINsMNTcEfLQKnfTdzM7+Q96jNHTSU+zTkZ5cM4YV96JR7MUVkiWLFmyZMmS9SwhBwOLsfYZXx76s/eO9MPtmlmztGcTWneWsuzkmd+QxKbZmcsIe73XY0cM1lT7Vp74yl6DMIJfQ6ELXrvRrDmMem2SaFAUnChRFEcKiFqwPOqin9mEpmk73EMaP57aqLbZPyp6ZRUPrhgWWIocyWvWIkHqvOoHB3f6aZWyrrj3cYtkv40kekJP4JAnXfD+yddCfmAe03zPUI3syI7/xFULNzxz3jykS0xpw1wyxDlKFtgpmAZYrJk72VLsKDCEAaZo5RIz2jQ4PdtimuAmvU460Mf4r6oyK9JYLSgY/oA8VVPFfcZ2jYOZeVflWWvjlSsmMJOfIfnxskG9CnlkGb1eFbZqdX62pZI8sb8VOH/GQi0/+RM/CQB4g2I0z738vJ2CY3FOCd57c1vuEkHaUEGmh4RRT09lDMhZBEvjvpFkcVBam1LtKNVb2zM5MzGY/MnzT1lbeJ4Fkadyg6RBT53l3edCC9mCNqLRcxq+30Irffxy3ce7IGohkTwnkUhds9DbL69f44MoQZW3DoEOvdUuZVEsP40lIilEdca87+V1Gz+z5w0FilO+Xwsb2x/7iMluf+SqpcZulIa0bZ45j8BwW7O3DwDYv22hierA1jOmvgaielhyrBEhi2zjy3NDuz77/B8CAOYja8tFlrzOCjtuyf4cn5DWPYT7uzTyPvEwPCDV9bQ07of9/X7WnIA2aPlOBRYScpAsWbJkyZIl61lCDlYsokUbl51b6gFzpqwopjrwTrOsK9ChWWfwlCPNMgfexIBYMoyRHicc0mv3OFgfHnChDvdB4GRHCQy5hyDvnW1x715OBq8lh7BiPyx4/0o30/KQcdDKC6803T48iTwkXyodiK2tXUiJx0G3M0Q5+oQscaRCI7SjXzTGtp0yl+Y5S7ZR9LSa/bJ0koYtpyRHrcM8yO+6aGlWn7xi6VcXonnF4Y61bQLzmMpygkKCQWvmhcd1kgFVYplcg2yDAeotIgdb5Bisk2PAFMW2GNxT3RdgUjg9EL3A0RI4pMe31J0qfVTplGzvJSMeZmtELYheqE5uW4rHQL4HhaeUTys5XaEYSnXLY4Est2t94BlDJf7yZwxB2P0Fiym/eJeckmDX3CW3ZIfXuCRiJ+/AU7oeIHZ00u8iIDZsd5OT71JYP+Uc9wWvuRENOXlmy9pesjjQwdy828nFC+wPjmVBTnwoZc7+zMQjav3bou+GSpELMfD49gAxwOB3BwX1Pam0G/kg4hgMkLTZaOWbNBD/cl6Dih7xfiJJwA3XlQ64uGfrn3jiBwEAV7/TRLOeetqkjh8vjbuytm/9MC+Nw4NFg/bI+nBBxKA5MI8/J2IQyWsI5BqMaxYiO7L+OyLa8/mXjWvwB3cMkTp/3pCos7W9ZyW/hY3zx5yhykX35RgW3gvDOP9DCnC9HZLgO0ksfLuWkINkyZIlS5YsWc8ScjCwGGuPnWMo+uFMYjOXKpWHEdqurDO9qqzoJC9sIRa9rfpEXl78Sty+t6MLMtFDlKes/cUXWIk5dbLGSkEUZ0Deup1rxvX5kpK1ZCHPGFNcSh61luiHpH7tOovYz2JomtbDlMOlFy/RObyN/fvRuT0za8hKj302fpv1sxVWmRpe1GrA5BZ3RM97IRoH2eYlX48JJVk/vGGx5h97//cCAD72mKVdXWMuX7tr5x+JX1ASFRhNACIELQskBXEKNpilQJlkMM1QXAOs8z5dDZlxcXZMTvKB6hdlRBCEHGSK+89qgM81MuMj1vT4eb8bl62AUnbVkIMl0YlMKAczLWrPJhTURP7IPkvy7lNghqfPma7Z1At/oNnEjv3E91gc+vrLJm/7D//FP7JrN0xlJNdgl+jDEb3VjVxoWH/AnIYgnLQeG6Um8v2glK74MVlj7R4xZfHa5vsAANtcX9yzGHtBaeuaHnU7tWWp84hrIISCcfPYtC6KhkE7g3ur6NnwG5QXkgJXITJZ01vIC1TatX8jssz5UZ6SzeeZ+Tvb5xo5D2hk/TOl2Nd3/cinAQDfO2bJahaVag8NFcAdu++wx4Exsf4+fOnVLkOEWSoFx2ZRSfTM9gURJPFA9NK+RHnk33npywCA2yPbb8nU56tEeQqmAjsnLCqTawVBGEi8DwXrumS2kwlND0IMjkvjn7jb/e0Uca8QjpXse9uWkINkyZIlS5YsWc8SctCziIjWPQcPunORh6y3dF/WZ/3ZymxLnryoysqhVn6trSr6u2DwVN75cskZMz0nCZXUXF/wd8kJ79MbaZoOvagH8sYuj8xrVPQQGCJFrXXOzivGputGnkNf6rak/PCYIi9jehKhmPi+FZQzzl4hVXukQkqFaK6UAAAgAElEQVSSk64k0dz22t4oli6kxbMdhKgohqh+7j8zxNipuWifobSzPLpCPBG2kborT0fzpP/dD/8oAODj582D3KSYi7goDZTHTv7ARPyCNcRtQwgk5qN4ftimJyPRo3UJ5vB+pUUhUQ32R1wq955jcS5PkeiODyzGiY+WCHzQserrWKhNJcWNInkPfLzI6AmqYFfj6ATZ/IpvM9e8IHs9p4MYqF1RlDkyykXrOY3Z/s/8yL8JAHjpBZO5/eyf/AYA4F5taMQeUY8jchBqeeMDb+40OyaRHDvESONAMeUxP4vjyp7Z1YnFzC+NDFmZXd9ZvQVUPGV1yOJQ7L8Rn1nO8SSPvFKsPgZHXVa48vbbQFpXiIFnMcjLF7dEaI72z/pZC7qO0B75vKO8y1optbNziHRynpv6BFMiSMVVaRNw7PJ9aal3oGXFkuAZy5IH6j1kRAOyuFxpkTgTLIgkiWUhYxURIx67w+/f//e6IQavVaaLUE1sbB7yfbgxs3E0Wlhbt8j/0HfE/fAYj3ENTrMHZR/IHIka8Boi+ty1duW4PDvZb3dUYsB/OE3C+VEsIQfJkiVLlixZsp4l5GDVIsydUBxbs0rPEJCXSk9Acb7Yxfbl2S7dS6CnQ2WuJb0tef4H/H2fEqSLBWNl3C4EQXF/FTty6WN6EvNCEqcdg1tx/u72+nFX1wxoO2/K7ge99ej9IZU22x7ogY4oXTplHHCyViAnKz0v1M6+2uJCbWBjc3qCeTlSo2ydXmql9Afd1KpeNFZirT7p72KzwRGBfj+It9C2zHAQwEClvDOsrfsj7/seAMAnHv9uAMAmQ6hekEYUcKq6YU28AWoXbG0gUisgiFOwLqVDrtMbkxPWZhpTkodlzFSJAUtxNthP5KCAv4MelzyvuFh6frqXs53aOUeXLN88bJH/MFJ/EYWQVoKkmEdSYWRev6d5sO3UvG4przsiooQpXMmukdAf2eibvJ+f/KF/AwDw4jdeBADM9o21fnSk94RcBLZh7MXEBl7ZKTFZWUREm/U9uBGfezaz9l5g2eDHpk/Z7d3mO0rVvlLS16W4BCwoRO9cXrq8/epIXAOOzTLvvLMBl0DmHqTOwXdO5cHFRdJ3QOhYp8JIr5XvQDPwB0sAZRgoQw7RGMo+lxyr4pBA+iiHvP8D659KHAOW6sbM1oO+gbxO5mjAsuPScGAIScwrZYywnDpLM9dzQwJeOHgVAPDrd75mlyxsfJQcsgt+d16lhsfmvqEd2yyW1Y76HB7E7r7d0+f6sGDd8GENx56Xp1/hg/EHrguJxTE7hkoMs2+OH/KOW0IOkiVLlixZsmQ9S8jBwHrFNLxIkHkpFcQL4CyeM+EDFgHZnR1hRs//iB7b/ox525wJz5by3pQxII+a1xxoCHRBpUGhGcEDcndXsheCa6TrNughD1myXCu0/zBDQrwJDHKwvWQxPSTut6hE+6+wvqFSy7bIBl5J05IjQUU31aLI6I7nLGYkJr1+l8fZtv25s8ood7Pyzv1R+xqfyatNvI+YY9VG9E7OTiwG/x1Pm4b+eqn6B+QYKBNgjffGeHpkXYSwTeTgzLr/u6XCoWosQOWTBZhk4okQAiBSlC3pZSwUryUXQ/Frxo2dTyAvlfyRrGk7T0U58VtCNsyLqnntnP0lJUXUIhWo83UtenlECDIiBhmv3e4zO2LU5Yl75ghRiyXvr2SGw4euWgbIj//ApwAAr372FQDA4XKXS75fNZUSOX4yzwIacAtow+0hBNSEOnKSB4razrFWGcrz+JohBsUuPeQDY75vTqwDD6N5ow05LXmu/u1n3tQLIjD+vrAb2xrdsO3Hvr1YmHNy+toJ/p2S8p90UJTEg057xfYreueXcmKJiOaQHr/QJvETiM6U6zZOco5dXRO75r3HI3IU9ua97aMjvm8zIq7kCbT8KBTULKiXNUDOVEuFVKE4BW8oI7+nZTGvJb+rf3TDOCovj+2aM5IwJuzzAyJxt9i/O9T6aMbkNhH1K72QV+xQHKnYDhKlXBgxC739hmPM0QgdLi6Y6+SI+3Q8u+E05Ou0pSxbKRz1qJaQg2TJkiVLlixZzxJysGIRwCIAR4z3z5Z97/9woXXzIFQxbp/lRGf1AgrPSqe8cm6AvFddS14rZ6fMV1f+rdj9mnxm7u5pPicvhF4PhA607kXJ8dO66zd09ZJtVWWg6QFlYm9zeHR794+Tdy6NBbkpi0UDsKLdFj1maaeDfSXkRP3k+vZyVpXlMOQYyGvxGrVck4Kifu3N2uUJ94OpYpEXkFdlPxTs/LNjy1I4v3WJB5iH3ZT0oKlBEEbsLyoNgmqIUcjB9tS5BtlE9Qy4HHlj7BiHSITC0AMUcjDnuBG3wLM5WNr4SKVtperZcVSE+BTrQjGYTVGKeyHtBPTaEBeqsiivlbZUZgS9WulhsA2t2OiquVAATaHnKVa+WUuPLlIZ75MfNC2J333x9wEAv/mNXwMAHNDLnW3bs9gSL6jtj5Ohet3x6oURcF4Ht1X2/C5MHwMAbLIS5OwVqyC5TsSgHtv7Lz0MjcmSz1ScA9QqXc3cfCJLjuTFiDDqj1vdh0puq++U6ZDL69T3wQezshC4v74LHJNCE5sjeyYLtgk7MyyobwKiddmaIQQbZ2z8F0KYWBa84TsceI56l5yCQ+o7cEzmM7b1kFwVwWP6BlZUzmyi81W8KqNuUKkgbG7LmgrX963U9wu71wEAdzeoUcF+mPC4kgjEjN+buweG9twjb+ICa5aMhEDFCLGxjpfH0beqn+3klS8HRIA87/ve/n6tVLsBOu7BarZDV2Jc5z6ZcxAHKG8Mx3Z925aQg2TJkiVLlixZzxJysGIHywq/+cqr2Ds0z+CIyMCMHpKk0hVqbj1mTc8I+bEYkCuZKd49SKGVN5eRXdxVDqBH1J9kopszNr1VxT1DFrxKZPQYl2LGcjcUYxfCQK+tLxrWVTo8ZQ7pdQ6ybHWBJlaYk1WeH9rGrfOsO0DN/JBLhY5sfM3CXQFSRRP6CdzH85K9h7mb6kcojrey88AVaDTbLvpKZWOe69y6tfncmnmSeWkIQhPM+8CUaAc9xmxKz4goQZTK4VruioDSL8AwJqzbpYdULojaEClQhUfXL5DegbxeclkyevlZK00DxnJDwJJx53xCBUfWc2i92p4aQ10DDvT2UDoAdr8jVqOMyqCht6/iljrRojaPslynfgIWjmzk5AwUjA3nB3bOxQ071yZ5HT/+jOn0v/KK5bEfUIP/gM/5svrRa5qIpT9Q4mTLArpxVrZUimwNQZkW9pzPwnQNites787skjdED/vwHL8HY+u/goULco6vQmOUDHldu1YFUoFgk4krXEpFseG7GmulczAmzvej4DVqagUcjqjJICCKz71kNctcfIIda0t7jyqWRGrWl1OMN5gBcIl8mDOGmORnpeKp7BTeF1GsuKfUrMg2cvBWe7wHu1ZgP4uzUtDznvNeR4sWOUu7LtY49vgxKTSeDzimeH9/WL8JAHg5smojUd2FUFN67QQUMGYNht0NO89XauuP8ZKIHpGDCWoUns0k/RIipHn/Y+wIax9QPe7l+9i09dyzF7jO702e58eO0bg+xkFA36J/y/GOWUIOkiVLlixZsmQ9S8jBis2XFZ5/+Zar83X5/tTt7zJgAQBBegfOVs272CdtWIdgRaCtZ5Hi+MpDFqvblQBPiyMNmLRZhhUvu58J4Rrvsa9XoPvTTNG9UWmuD6bGccBZaIK8YA/eInKGfzRnRbqZeSVj1hKQKqEqPRZFf1aeD9QM1Q/DmbQ0GJRxkGf9LIdVJTz0H03HPu7SNgAAo0CFwDXznHLWQ0DBPG/2lLgHWdlHDrKBdkFb5g6rhGFtDa/cZ4uM3llYykvj76quKMRAlfKUScBlWPazHJTm0YSuJkLJcdvy3AseqwyRyACv9Oxbr6rIfpJTuzTvtFooBk2vl8cJDVrsmyeZzUvkVFusicqVZJ/LnW5VS+HWLQDA09uWl/7x808DAH57548BADN6lEvWfSh9bBOh8/KU7G+vagpfD9Ge02ZjY/LxwqouTlgZstnXO8gMEsE7RFoKVfXkMx1RUlI1CfQdiSIiCbnieGmzzMfrWM/fFo5AVtH6fka9kJpx/5L3tcXjCsb7M+pCVG8YT2LvulU5XO7as1ob272ubRmfoD0zQjhjSNLo4qZd+zKzcpid4OjlXJwS87pLPqtS9Q8OiU7ss+bEcoVTAHTlHjjeFuQuYFFjTK2FqEwwIl4jjnvVrdhr7D5e27H72iVaUXmdFMX1zRzdFQ2E593fs37a4Tu7Tg5CDBliEOdKfJCTP76Zv9P9bIXT7FjGzH335jV0H6ecS6ZavOH+TXhLlpCDZMmSJUuWLFnPEnKwYm0MmNe5uxdi+XuoWoRormbOPhVDuPv/MPw0LODVSQ70BQ2Gx0l98UHzzOC1BeCKbDqnIxucust7av3+hgqC8s5PnhGH4UyabcykFNkskYd+znxFdvE2lQG3yJjfZacuOaNX1kIYIAfqpnwIlXhaRz+bwxGEkKFwlIG7dlRfrP7Q6EGz7sOC3uiC8V2vmEh+CFh1zq+lwO9Y+eCM4Y5CFwPXc5VynbQhdJ8LcQsU16cHLO+Tv3ttBa9zLzVD8Uv6rPYmABnvSx5BzXOPS2Wl0HuXqiJra4AeojJCpPO/pAdZHVhsOScKJNVCqYOKrBPq1usx+Lu0w3i+6ncQUZjs2/bqnqEOn9g0zYEX770EAJjN+WzWmHEi5KDRuNPDVsVF3rOjXRlG0bgQl8prAIAr+6yJcf2A92PPb0mdhloaHHo2jL2PB7UJpP8AZieoKiqUu+8VSYPDVnr3SpFShByIg6B4tDJieMryttV5mL/8hvXL10wxsLlh9QwKIjKbE0MDxtvU4GB+//KJDJOrVO98zFCa+qxdoxFUdKQKiVwqS4X8kWaHSogcBxnHTebfyz56Uw980qIs/AFVzAwTUhBZEbVmpsydhY2HV+d2f0e8j4aol8vBqN6JZ8X0Wf9zol23mWmxtcn+WRs7byn4d+8Y8Yunejg3/bR6Bw+DHJx2Df9OduSrh2rLW7GEHCRLlixZsmTJepaQgxXLAKwBXR48Z51KrZeSGp0UFJzN6/eATqlO8ajc6zCIEX//pexB2vAyecG1tArQOtNfqIOyK+b8x+FSKnyKpVLBzD3q/jWGNeX7GAmwTpr65tjidqPRFFN60WPGmKfr5p2sb5m3Nl6z9ZtUE3z9jsWYVVNCmRCV11YX/4H96i4C47dR+vASPsi7/ZzoQa9RCnCOzhBZKBSXtjbPeNgBg+yR+d/yurzKoyKcitlL3dEhp+wYF8WT6/VwpF4oJGEpZUQhBvRGhRiIizCjOqGrF7KtQ485ACPG76MqhB4xX59IQaY8d9WiIHcg0psP8oSl8rlrXlygFzZnDvkRvb+Kx00L5pKXY1eGbJlvn+0wXs0+ruWNH1ibxnvWxmfOWNs/uv0MAOAPdm/YcZvsPo//8tm6EL4yd4SO0cOMGdajcQwuw2ooTG+zquSBshHYhyLrsy8n0sMggjRhHRHpPMScFPly3OuvQEZ8IBKRNx0K1/CjsiRKFUpxaOwaW0Ij7lKD5XVDCt74wgsAgPlr9v5sUpVwgyqfk3U+c/JlVPcju2qZOKMnthGumNdcb9s1ZyONITvXmGPJOSV8JrjL6pTMiGj2mSEhZEHIo4NFQjetv1SZNWsaLA+lv2DnyoiQ1VTdXBLFuFnbNW/Cxtohh3LthU76CF3jn03F+7md+90hT2KbY3d9PPJvjWctnOLin+rV+zcru+9+9zNXZ8z631rZclAAJzh6PVT4ePuWkINkyZIlS5YsWc8ScrBiZQAuj4DJyGbXY3pSE87ip4z3jelZKVY7Zmy5yHOU3FaIuUwPtisi2KfISy0txPs/itOQA6/AqNoFbdN5jZyxLkkyuEmG7tdefd3Wd1nxbsAxCEP+g3Mr0Nue05N8ctvQgGcfuwIA2JiOMWZlPzHjXUWMXkNgP61PyRJmTPHmjsUS52Lb06OS1yGHu/CkYSmbjdRa20+OY8hRqEIk960Y91ZccqxzFUJfbP8549a7NT0jhnszCb05ckDPsdeCDnGJK+Ux/d9Nn1vgcXnXO+A/pGQ3U/xeXAWJbvD3mjwBIQfeb/RA8gyBnqz0CVp6fBp5OZGftpbCIfUcyDqPrCWgTIN2z+K1YqWP6CHvE1FQZkWV8fe1iJpaAdUe9ff3dXFbFMzWmEi3IiMPoLFWfmh0FQBwmx5mS7VSPUJpfDREIjxazHeg4Hs2Cmu4mpluxXiXyod8PzSSAnX6G2kpCJVgP44n9MLF6dGzdTiPHjRVIUtlN7AtZRY7/ocQHup/FBz/Y1GOXmeM/U9eBADsfs24F6DGxJnWWr3GfhpRyyKwMmhzzd7R7Bnrv+ZJQxTClTVgm+PXNTiIbHAM5tQIED8k7NnzDNSoaGfKSugrAAa+0/5tCgPtFmZ9LBczVByTqu8wqVUkwo45yOzaL84NIblJBdZ53s9mEqdFGVZCIJuhLgzbesim3GWGxbmNNYzXpDkj7pnt032D42C9b8PshWM1FgaowLCK4/DfJ5n+rkgJUyhoQHtilce3Ywk5SJYsWbJkyZL17F1BDkII5wH8WwD+AoCPAHgMpp79RQB/H8Dfjx3lHiGEpwG8dJ9T/sMY47/3qO1aG4/wfc88gTV6BEIGRkQQxuIReBYAVbwUJwzBNcwxmGXG9pTZJpGDLJZ4FHOxwtixbKUASPExRzhu3bIc4Zu3LYOg8toCissqJr+ahdF5xIrJrdE7uXrOvJDHzlnu9Pq4QIiKO3Z64XYOrgsZKKklcMlQh5L98/rOXbad6McgSUGx9NyRGfafq1Uqvzlz1EFIx2Rknl9Dz0Xs+6yQ5oI9//nM1u8yT19ZHoUqSeb03r3/+h6Cz/4b+9WuNUAOGGNXFoK8TcWvs4U4B0QOyAR3/QPmigstik4QkV6GvJTC8/IX9JLavaNeW9r8iP1Er+3IvFJ5gOqneEh/a8f6ZUmW+pKqmA2XJZG2jChaHgIqIgfzHYsdl0f07PQcSXxpPOZs93fAa14hb+Jj5eMAgOfnX7fzjMknIW9kGZRBQ8SAnuiotmd7pjyPx2o7V3PL4IuKnu0hY+5trloS5LuITzQ1vkzBrBTXxm+kTskxyEcx5juu/s857jLUrueRMdtGmQ4ZVVp3n7P72/+D5639rzHrh4NRSoBTIWgc2+GMkTHay4YYNE+ctXt80raPn7Tt4eIEzbifzTTiy1Zw/OMe0RkqRYZ9olNz1dywRV5QEZRjUv0R2K8Nl5VQLipINkcz1KzD0Hj2Tt+rvlPZWPx6bfoNO8wUasUPUPVGcXecZiTUBz3TN2HO9+bOvo2Bc+sTbIzsOybFS3+nBH6+Rdd8iCQMUYGT1h/IUxDHxrUTVPnznbN3K6zwVwD8zwBuAPgcgFcAXAbwbwP4uwB+PITwV+LxHvojAP/3Cef70jexrcmSJUuWLNmfKXu3JgcvAPhLAP7ZACH4zwH8LoB/BzZR+MeD474QY/zpb1ajRmWJp65cQa5ZmatOyYPuq7D5LE0E2bCStzys1OWzzz7nQKb6BsfMZ6unRYBEue9UvRSHEpxQ0CubrtnMfmObec1v8hRVtnqmY7NPj7lxB/XPJrUKzp43L6SY0vvJGteXxwA5UM0D3c6U6wXjuOGiMcdb9s+NfcuhVoxQ1eqaYYXFXOxuchPkpUmDYGXnnOz5ktyRirrsgZyDjL9n9Hx3iRxUjOeXCs4qYcIzUYggeBpEd1VHDLSsB3oGqq7IrIRIxr/rHCgrQciBKiF6sYU+e1l8EynEtSF4zv/BPbLM983D22S786KvzgnF81kToKnEVTCPL5I3EBmrbyrVKujH3hfMeljOZh5/rZnhkC2pLkgoaEpCR0X0Yd4qm4G/79r602Mbe28y++GQtQTiungO7A55tYzFTysb+5fHF7F2g+qDZMQfuUwh4/2ZuAHUMSAnI+O4b0pxTfo+jDxLNcGVFBtlO/F7UoZOK4VKgNUrloVw9wt/AgDY/7rpFkzI5leijNRZx7m1SfUyym0qH56zDIQFq1dmF+y+x5dtiTOsVlhEsLQISvFdDujx36WC4Q7HHPu+3eX7wqGXM2uj0TNwdU6OaWo1VFSBnYnLQp0MzJeInlZA9U6+DwvWXrlV2zt4nbUUZqU8Zo4fVVLU0JOmgut88H1zYKKPWKni7t29fZzfMHSp5NiSzIl/0off/wfYaZlopyEI90cOiBgN9A38b9Q7VZIR7xLnIMb4KzHGX1qdGHD7GwD+F67+uT/1hiVLlixZsmTJviWzFbzu3Am/XQsh/EcAzgO4A+C3Y4x//E5ePMsKj1t3AnoufNDbNwz+ZQ7CULlKrn8/a8H38x1PQQ5o7XGxAZ6Fnrfiemg7vQJtk2dLQYbNsxZ3XGfNgHs7/XiVai+EwTWl8zAd2b2cZ1xzuk5+N2P2TWwBeps+2xb6oA30unN6BkpIPku+x5NEEGp6bTcZE5xFxrVjd79A92iCcvXlvMSIXNkKrprIGHChLBN6qy3z/tV/5Jzc3jdP+4Aez7gw7ytzVIKzdx8fHA8rU2+vT++Mdv6gyofkEIhr0Eh7YC5muGooNL1lRfctlOK/9LND6loKcUDLYxrqG+Cwn4+fkWvR0NNXlkJNBKFZznvr7RG5BzyftBaknS9vXg9nPJ2iYbXB2YwV+5bM7iEao3EiBcmabm2kCzipiADAxu6FwngDi6U9m+WEnAMOgCLjvTGb5fzItA0251OA9QZyep0Hqk466rPuN8iZCMrmIGIgZ1fDpXBNk77iqD4k0h8pOK6QRTSMux991RCCe79nEdLmJdNxOKNsJlVtHDOzgm2a5Kz3sWFjsiHTHmep+Pf4BbvURUMUXGeiUBnHDFEfDPJbwj4zYJiVgH2OvQOOC5Wt4HsTa76TGpuuI0KOAdGuOSshzmeGNIHjp6gigjhXTV8bYU604fbckIPblR1Tj8QHYVu07IN2x2vYcOnZTDxDzTbvHh5i79D6bn3U55yJi+XI2ANca/f+XaH2lDadgiScclbuy7aoTY7StN0frke0b6nJQTBc9j/g6r84YZc/z/9Wj/lVAH81xvjKQ17j86f89OGHbGayZMmSJUv2bW3fUpMDAP8tgO8G8P/EGH95ZfsRgP8aRkZ8kds+CuCnAXwKwGdDCN8bYzzEI1hAVx/AN6CLISuO7bQBZSY4E33l36G/r9swVuTa6m/3UWgK3OX7ti4SxvgUZ7ojzjKvrNm1nmDVtUPG1Gf0zipmereMResexrn1zXl6J9dY532bgb6RKgMGIKPHoxmtz/AHmuBtbl65LjKuzcO5XFBJkR7QNFp+82s7FpM9aBnn9uqV6j/GdTPVCyj86spoCMpkJ8N5RMb3NuGMBeP5c3IQXoIxxF9e2PLs2LzVYsFrrtPj5DVbenk1vfeshXMNcg0vIQaOHDDbwOsQMF9bNRPE/ObvkfoGi4J54RNWjmQ/NkQMjuj9b0zXcPCaeadgxb5Cri91Pdqanr7itYes8KesBXIWKnq70lhoiP6oloK4B6pKOKJKZhaAJdGXoPoNtf22ZHU9wNCJMZ/RBp9R3Ra8L6FhdtzluSn9zUpr4wLkPSibpTFv/0J2EQBwdWGe9PrtgCMFzYl4SflQKEVLNGZJb33E9RHRjyhUgwiU3uWW3q54RNJLUJ5/SWXA/M4uDv/oqwCAG198ztrCDJANr+baV3YcEwkZE+UZMQNHGQf1OaJhjxmqV1+2cVGc5XbWB8l4L5Mm6+p5HPB+DnlNqi1KxwC8r7xUHQ+O2YWNG6GXLb+LNcd0I97MXLoJ/FZpLMcWObMUpksiCHTtb1Jt8sVo3KPDwGvpO80hXPMbPZSL0bcrj303X9wE99qJAh40wJvk+6xtEX3hSaa1aozIW+f7PkBHHZ0YePmOIKhNJ4PBJ1oYctWCnkk/G66J4R3TSPyWmRyEEH4KwN8E8DyAf3/1txjjTQD/5eCQXwshfAbAbwD4QQB/DcD/8KDrxBg/fsr1Px+Bj50E7cQBie6dTBjxcz/qeQRdrpwoDganxtUGoftL5+zD+to9G2jLQ37sXYq5XyhmjR+UixQ9OrtmJCgVVwreT2GFd9l/Kf1lVDlbCaN4mwmjU+Rka2Jw6ZOXDA6u+fF/naSoGeHAqtHEhB/wUkWlgnMAh+l+SoMS+XFE2DgSktY7fcjSxK/fMqj3o+uWRhfnYrvpGWrC15chRuhKVel5Rxdi6i9VwrlV6WX+IZIojv5A66ZKTmSwyyJIhIjnEklSVAsj7L30mv2bRY1GDCfMR4SL92Kvf+acBIy8lDVTIRlGaEjiUuqiQgYqi+sy3s7My9CKUFkrBEHoGf2iUGUmglU/DJNxItdwsjTep4T5BUoAT+3aJUmyVzILTz2xb8WV8pcZdpjPEUb9cKGEczQuXGhGgmYMG8QV8i93tONCN4oBoOSzHvEZleyIeMMmma//wZdx+IIBng1LLW/yGir/rHCUCpF1YTlbLknMyzcoSXyOKYzbLMPMEKLEkDDSpJ3Pell7+W94eXC+g1pnXyvtUlB2w1BO3bC0d8PnzjBDVWlccPK47P9By6ES4cHFulQWW5LVN2vz915fcnKQ6zsnIl7vlA/9Ie1CPvo42KKqG+xRSvlgy/rs3JQT04EEc3zAtfTzcA7wME08TWCp6zut98/a9gjYj2bfEiJIIYS/DvvD/hyAT8UY7z7McTHGGpb6CAA/+k1qXrJkyZIlS/Znyt515CCE8DcA/AxMq+DHiBK8FbvF5fqjtybiOF0Ep2y73/a3c+W3dy6H5++zj8u7cpZcUjDlHAVltrZsVr5DolmuktVs04ie5Pa6eSGXzxj0v1H2vb372ZB00yEmEi8S1EAiY26elDzMMxSeefbqZR5nxwwBX+cAACAASURBVL+2S7IgyXBydoJm0HnhU32J1eQqLe3eikSS+qlrqji8ZJGgG2+Y7PTB1e8EABRMw8v2eS+EsDOwyM145dacQETPR15Io1RGtkketcokyxsXpOlIAxEFCTlRWEZksJHCGCRT1TfvonrTwgnrlXLXGAYYCR5VriohSoYAjpROqDLCTEHTs8EQ1XC4mOTDfYkjzd37VGqna0KxSdXAq8xi3tvPYXaOn40Dg8vPPGmywBc/+JjteMXg9KeOSL79EsmU+yZDPMozJ/l50R6lGErsrBRyIDKx4nXsH78HCX6RHKzURaIiJYmcYc+u/ebzFkrYe/k6JhTB2iS5r5RUM8WilE7rqb4McVRCPTaJGDBlsTxLgSYiJ3GDaZee2qlS3gw/tY2HFUR+7QY+0Th/SBybJJpWC/PqhRg0WkpuvBaSwIJWRd5fJyyfIfj70FDMZ5chnzdbu8YtkoUPmW5a8X0qBuPi7ZoQmqatcERpbiEIs3XrW4niZUOxswchCE5+9Is9spUSOeOYqx05KLyc86Pau4ochBD+U9jE4AswxOCtTgwA4Ie4fPG+eyVLlixZsmTJHsreNeQghPC3APxXAD4P4DP3CyWEEH4QwB/GGJeD7Z8G8J9w9X97J9p1rLTu/fd+5Ot1BZke0WI3PfXwm8RYQn+WKfljIQFXLpq06t0dQxD2Z+5+AwDWRzZzvnzWEINLRBzWVNr3Pv1wWpqOYqeBhLTMG01vBSLH8ZFTqvXc1NpaXLa4f8M2fmPH5pVLkeGcLJqhVAVll7ZmXD/Kc2R812OrFAUib6Ele+n6XSNDvrlnYNW0tLaM71FWtup7kLo3FPmxGKdIopkTDiX3209/agdeW5QQEeP640bXYhy8Vdoc48JKHZwv0FKCuF0yxk4Oo8f/FTOmx6+YMQbkr0zkSN6SxKGEFDSMObc+3jQ2sy5m6pViWDZ8WKSGz2aUK82yL45TEFnYXphnfGnDxmb2lBEP98g9yHfM+1sQNRlJnnhUonVGGJ8Fx3NBJKUQMjYokCNPMBuQBQVAjCSbzBLHy5v2Xu298JL1z3WTLz8Tg0tMu2Q5l4HcgFwcAQ4lpSOHddtQnyXB8KK9y/lFIgYTEhX5bFqmiorTonTE0NYABZj0jgUtawkL8R2mR90oDZVjMHdeCFEwSYM7SVpVsWx9LJRkRTK7Ic/niAWWjohe3SF6tasUZhEPnU8FmnhVD+fvdur2KonM9w0Zlnyndokc7G7Zi7JWbmD1ovmDLqUhruVg+/3bd7Jwkq6tctJNVLuJEOaj9zYhMYTwV2ETgwbArwP4qRMIGN+IMf4s//3fAfgupi2+xm0fBfBp/vtvxRh/65vZ5mTJkiVLluzPir1byMH7uMwB/I1T9vl/Afws//3zsEJNPwDgx2E1ct8E8AsA/scY46+/Yy3L4rHyyh0fYChqcXwK2E1y+sIXp5Zcjv1/DI/v6iafMt3003bT2GO1nfwfKnvM1C3G0K6Q0XyTM+TFbJf7mWewOTVv5NJZW25MmE7FFK3aPQQ2tY0uYoOVbXYbg/s4NruW16FiP4qp8/4WNps/O7I2v/+ycRAWrXk1b96zuK683hhyNBKl8WIl/TbJLWu8/jURBMkoUxZ6jzLB37hlDPPL15gmxkwBIQeZPCfKVWeTEVD0+97JEUuJG/Ga7JZyZNee0dt3YSJ6a2LxN+izk1UUTFkgEqY5OtxHxdh3q7TAOc+tuKUQAJUPZpaGBK20XXK7im0uVYa5UoleCXqJX6H4dgVnVavvRTZQNoIyPSRUxfRTjRpxKDLG88fkeYS7dp/ru9ZvI5Z6jjdsLMc7lB/WvYYFmkHp3IxlxuWth6L/IrUuC8xnSWGqMQsOjciPGFNM6Oi2jcXdFy2FtL1h6MUZZS3kmXMBGo3zsdpA3osyjVTKWdpFZNCPzps3WzJLQemVQpZQEYHgqo9NvWfV0rNuwiFRO461wCyUSLEiIU+R2wu9y3zu8mazIA5LPysIEonyAUS0JIvS4cKC2QkHS2YpzFnCXVwkR236z25oDwq7e2r1UI445GiJlB0SKbnHQmVbUxtTEhwLGIjHecq0OF6Db90AlR5yA1b/RpwqiDRIlXAhNvV5OQUekX8he1cmB6yP8NNvYf+/B+DvfbPakyxZsmTJkiXr7F3PVvjWsgigRYwDr/20vY95+6tnEhv/rcV/unOuNOmt2jDnV7PtAQBS8CLnpxa3fPy8xdD375mHLO2TTUqubpG16+n7WrYDlCQLp8+etc8QfQn97WHAlRVjOtYqBsS2j61NH7psbHUVprnO4kLLat7F4AQUKDisjAmV5hU3I0gGmh4RS10vKYr0tVsvAwC+4/ITAIBNZjPUZHsXimsvmVu+3ninucfS9PUMauWAsyBRS++sdc0Acg3I9FZpaxfdpldbV0JzKFikojfLBTwuS4+nmkvPgiiD4t6qNKN4taSweSkhRXWtokiUOua1XEdC2QvNauxZefbsB/W5vEuVyeWXSXnvubcdvMbA43vD5LXXvmrL9Ws2lt/4uo3l8YLogPgDxRKQUJY7X/0XpZFOQ5SGhl1TrPtA9CKj2M+UbZ3vGafgFosmLe8ZerEugSpqcFVFxJxq8Spa1pBbsJD+N9tLagIyZikU6+ZqlywCpaJaDYtiZRyz2YxtF+ihfvQCYEu0lNGO3BdHQhAoVEVdC4lh+fvPp7GUJLhE4lg22svRS4tBpc750GbML9saTb0YVdwzXs/1142fcb2mbDpRSpVR7qrd4e2Zfws1qlu/BalJz4gE7hzY/W+vMZuL+gdF3v/+e8sG/IBjn/K3qMlwkmnc1wLgKKx10AwLx799+5bQOUiWLFmyZMmSfetYQg5WLRrLu/NqzVyVzO1+saWT9z11mngK5+B+qMSJ51/dbYAQdC4zPR3N8Ll9k57ikxdMMXHnrs3Wb9w071tqdaoXE3IP2nO9z/g9sc0Db8PvM2sHu+W9ZTfJpqeoQk3BvJhyaXtcppJifsUQBHmWr965i2Vr3pQcnLE8GXmRUiVUzJSe4ZKeQyUUiLPz1/fNM7y+bzHkx2uL+0oXoPG4rjIyWihlQn0lboDnnRM5UPlo1zPwXPq+N+4lZ8ned+I9ry2EIbbKbqg8f12/LSshBmatVAnp8VXknLQqqORVkYgYsI1egIiIQa38drZVpaDzPO9i4mLCDzgzLlUO5W9LH0JjltcU+sV+2Dikl/8nVod8/Q1rw5k3KPlL7YmmYNGo0CISd2mzfoxciFEUn6OmbkEhGWg9Q0oQk2uwvE29EJZdru7Y+6MQe0uJ4z0iUMs8oOJ4mFLSfDolrCC9g0ZZHLYcDTgJKoKlGLx4MuHQEChXP2QWAAbllLGcA3NpanCcL/soRH1AxAAaHzxU8f51ScwQGXHmPy/NwmeZFEhVOp1KrShGzjFavmntfPUNa/8blE/e5wD3d3GYATCwB9Ue6sQsBzyA2H1zKvb5wczassPshemYJbwpI10QEfFn4NQ0vRjc0PYb9Sj1kSLHoLK1Kj6L3dmiK2f/iJaQg2TJkiVLlixZzxJyMLAYW5/Rec6sVLiyvlc/tIDQcQ0eEHPvLjhYnnL8gxGE+11Dnr4tVvIaAHTe1zl6AM88ZipzqsAaGL8WKz2yyE87mL6vogJehOYU3YjM9eiVI69flDMubXmhOHoI8nNVe4AeZ2P7n2Xbnrlm93DYtHhz14rZKINBbRurAE7d90a91LWcK4+dk51MPsDLb5pi4sfb99t+9CykauhFpuoGUciB4JdcMXOyBqTXP/AyPC899n9XbYGSmRaZSjgTeVCMfkqveJnnOKrVfqIPrvPA2+NzztinS/EcPHOE3bMSnwWAQGRga8OyWXZ2TLJEBXdqxcOXc4yoiJk7EiT0wmts24L94xkmQhZa5Xfb7hUrWU1UuOpl896zm9YPW2zzgrH6BVGjNjbuAme+VO0ANYX78rnnnUvYu4eju3bNg+uGJEWWgt4aSdXQnsFC5aQnvPdxiW2qjc4PbIy+sctsGzLlNyYW3z67ZajeeM28bWWe1Ad8zornH0q9kOOGmgKZf8vE4WA/18tuoKuoFY+tmJ0AIk1CK6J0L8hBKsdEAJiNoMJLLdtUrhuyljEDB2Nb5psbbAsQWcb5tdtWgOqlyvr0bkmdDrD5KsXdyks/2b+N8f6R96jjhplWsRvXcsAXfJ/3yb3YYOG5Md/Z8UjIqtRebeF/XI9laMUTN6/ag773qvOgWgozfifu7R/6+/aolpCDZMmSJUuWLFnPEnLQsxYhLqA5U2DuqEr7KgaZ0RuLDLZ2SmARcqNdb1tKgEIdhpd0PXvursOcON33UlxYboA0LOlR5yszTs+AbxUrVNy2PyutSZEe8+SPMWtBymd3dm0WPzsic5hM6Xqk/G4yiUM3Y+34GsMNWpUiXJ+D4GqLKnM7OGPe0dV5PGOuVPfLl4Z+XClZje7yk/jC4hsAgBsH5pUdlaxCyZj6BntdOfUKy6rxUh+cV8wk4LP80v7XAQCfPGsoxVnGf6eM9y6IbmRYd1W4oBK7a4zrB+nPE20gutHp3HMpBIYx6gW3V6ruSO+uoYioXuyGKScHt2+jZr66vEjlo0vRrok6h5ATxql5v7UGtaoVCs1gvv8+OQ0V8+TFOSgLlVuuoVGZ04uUSmGQUp3Gt+o2yGeURKC/KFQI5G4F9Q50v8raiCwrvhSPgDyLtXbinVTy2WBJlTk52Xp36QGrBsOSlUFLNm3O7J4JdS2KTRuDCyIqMz7rhpoEFz/0tO2f5Xjtt38fAPDyc18GAJzNrC0XyKHZJOI0ZdfXrDFQs6/XSnEMlHFg6EWuqo7+AnIpnoBvblfAOL6TRGFGgmdGrM/A0tsiH4V11RBh/ylDQlwCfieyc3bf7URqj3x3d1Q6HXh1bgqnv/zGFwEAX8/sPg5ZklwlnUeVUkts8bDRdc9EkkIpn6UjKkLBYruCFBN1oxjFvYVdezKncmze5yiB34mRxk87+Oih//0d6UOjLBbuFbOOl6HvpHRj9A2vchsfc/KD7u5Tk+FghqZ52F65vyXkIFmyZMmSJUvWs4QcrFiTjXGw9qzHIOWedzyCPkFAs9AqI0tdjOCVY47zaZX7ynh/VBU9sbeFFJCV7EgBPWf3mF0YgGcdMgk6dGHoLAxVGYeKWvJGrl0wBGGLnkFFVb9wYLPU9Q2rseCFFe8bRDttczj552OF2ocnCr3dHLUgGlCyxvyFzSnef80yGJY3zNu6OTcEpKFnvwj2/Fqq6jmawddDz0I69TPO+G/sGTP+1dK8nvPkO+T79KCZH93mEaCKXpyINU+ugIQZXJ++6S+lX8BshobZB43XXFDCM/kBbZ/D0BKJaJdLjxXrkIY59l7Zjef2nO9B9cqmI+P0jsvZ1oWq+XGMF4xBaxlj125lOjhidCxGLM5Kf73WfhwGOeg504PS6FBMVnUfhBLV4moEICpOT084JyJSOnjFTBCetCDCsiSSRmcNm2v2HuT0rGeqg8EMhAkVSLffZ7oY5YatX//il/Dm16xe3MUt42tcmJiXvUZUZ0RejOpfLGc2VgsiC+2RNeKIaFWW9+tCDCG8nONOFScxyhCVpaAhxwyHnBoiLXVOWiIjObUVyjPWhsAxHaa2XWM8UwVOHp+r9sZNQ/BqVjO9N9/DL37u/wIAfPFlQ1COiM40A40NqTB6cs4j1rcZVlTsr+q9t7UFs5cOiE7NRtKgYP+1GvdCi/tn1Xcl+vszaExPEKGftSPTfVdE0A7ID9nZNfRqvqi8psmjWkIOkiVLlixZsmQ9S8jBqo2maJ/8LrSQF3bEHxhrbmx9uTTPsyJ7fxZt1lbFI9Ty4MQMlxfiFRL7s0hpvU+1v3TetZRn5UBBP26lnPQyrPn+mvF1+/S97GGlxILxt9zRDDvniJ7PhdE2u0E59Yy9kXOxVO76qjY4l5rF+rWHM/VHSMIAOj0E8Soisx9yPruyafEEK9a1hXluzUumXLfDWPmyoAuY9/kdwbMWONNX7QE+4z1Wr/tasFpgH9p4FgCwfmDPcsz+qsMcTLPvPNpSCAFvRJURWWshyvukpyytADSMXysDQJwEHq/sBq+0yPh/U1Xuhru3pWsr5inUQVoCQQqKfc0JaU00URoEKxkAK6cV52U50DsAunGgGhBe0U+59A53CRnjUtkNUe8X+wvSf5CqIb18jr+K+yk7JmQZan395ERLB4QxdxxY3y32DAFa0qMWB6UlGrS/xv6Y0Ns/a0jChB7zdI3lL3fsu/Hq14yrcvTyq7g2NaRA4owac0zWwULZLAu7ZplRfZDI2FIqn4qpM/7dCEnh9oIcBHE9oqpBxlH3/BUDlx7Blg3aSMQAG6wtwCyDbMveK/D+Irkby6pf3TG/a+9iZFZH2Dfuy/WRfTf/+ec/h9/80m9an5T8xhLpEXrV4YXKMugjSI9q3fc165K7BmiWvu2H1H84IqLqWU+5sn/6zyQ4CuBpXdxPZ+5nTPRW4oDDxsG6ZHbW7oH15S6RI5f7fAcsIQfJkiVLlixZsp4l5GDFmnaOu/MXUFWMQddctqxK5ttZnYyz+lHbKaV5loG8cE5JS87o8kGOtBcCzAc51lp6fFjeWV8xT7nqgWz9LMs6pIDel2K+w8wAzxjwTAfGJYkMiGVeKBbLeGWhqnJZHxVYpRB7sgaXrXMmuD6c8b9dD0D375IL9FZUF6GtMOU+j22ap1NdehwA8PwrhiDcY72Gubx0OkplofsTo1nePrkH9DC/smPIwcc3rULkJvtrekg+SR09dr6g56osBVX+Uy69PH4tFYvPVTxehAEhB400CAacBdU9OKLK3WzeaSiEvjeuWgGOGAwY0rV0IOilyvt0LgE9KsX3NWi9GqYyboqJj99soACva0qFrw1CK/qIghQT/T517qh7YPaOuBo+3ly0w66fA2OOzpJeeUWPcMHsg5aIgTxgjft1cksCUwhaxqKLTfO0N4kc5NQkGLHN11/4KgBgdtdi7pvLBut6Ju7xs715n9ehCojNEcdNq+dODYIBMqD7VqaFiBTyvDt8cYGWY6vV+75h7W7XyTXYJBdnixwD1XOYkNfAfllSEXKxY9/HnHoaM3JvCvFmqKD52dc/BwD4lS/9KvZhfb6kbkU7yNV3vpQUQvvUo3fM7NvIMeioTP9ZLIgYH85sfUKkJWMWhmrNeOKVV5DkNdhfjSoqeuaWvjcrWW9attKQsGd0yFftHvlfUjst1stH08RZsYQcJEuWLFmyZMl6lpCDVWtmyO/8sfMA5N1oflf4dsXyWTserNIVRx0yEE9eFl7XgJek973I+3En+VWu1iXFQWnPq3Kc4rxkvTdt26EKbd/LdK6BrkEv7khZFi4aJq+tz9rXdnknIxZhv7S0A0etvPjYxQiH2RXyPnX/us8HVG9c2WDncy5GP46XO7mB3l7TIs7Nk1mjN/rMWcvCWNKrfu72KwCART1nm/puSc6cenEOWnprGh+vzExL/8XFLQDAY8VFAMCUyEKoI6qC3mnOinf0HuR9KS5ZH2P80ysjt6BRDQZ660IW1DYx7aOQCCks1rXzFDKy8535rAwCcSr4S5Mpu0FeuJ4v9QCkIKh+QT8OHDpSAwBgUc0dQWrl6ZOHoGPbKLRhKJChM/VRLynkyeOWd6asjixKcVNZIdyviZiyHkNDNcIlY+KV4rdEadbI2h9RrXCTNUjmytrZNk5OdsaQqSNmFMyZFSNvV/UONjguxssKI2YNSNVOmRMVkUDXYNBzJU9GN6xMikgehOp6KGvBdSOkTeFxcPg9ukdLHkKrZ8KaK5HKiFI4FCLZ7tt3o7pDZcg3rP/WiJgFoiE1kbhXK0NMfueFLwAAfnffNA0Oxkc4ooaIn1tZCqH/LWtdlrOvW/DA6q/HaiiId9XPmnGuC45/e3QJIWkHfBZrzBjQs/IKojygVNbGoBKtsiA8OW6VV6bnzY9yy2+QshTusXLmPSJJFU8yPlYH6O1bQg6SJUuWLFmyZD1LyMGKlS1w5QjImLgvryNjcflcS0j3n8xRTs+rIrqn3Fk/dqSQscfk6QhscOnzW3lAQyar2NtyxOXlUUmsja3nxg4zx8UM1+9an0kBj9vnnE1rOSNzeC6vT3FLMmaXc5sxbzJYPylHGKGveCePNneaulw4uS0nz3iP1ZaIw3sTEtH21rN21fuXd2CxT5U3eP8V8/gOW0MSXrtlNQEOG6rQicXN+1I+uFAJeR23GtPF//KOcRg+sml6+duKF9fAkscy3IoxwZqa2QkY6VWUF96PvTfSt6c3V1V9DgrIPWgY/1a9iKzpcvU9jboVEuBSkLZOBrxnHQhR8PHC7BSF7xkHRVRWAz0mPtNcCoMrY9W5A+5FOXZk7Xf9il7Stze+yPpsbB/LzsXRxehB850thXYwR71oAyb0+I7IngdrIiiRJJ/Yc7/IWiNzDua78uo5dGdUnnQnf0q9+yMbF4qxF9Li5/lHZe4QmjJFVGuiIL+n5fhQrQQVMS2hSoB8FzMp/6G3XQhbUch7VcepPkbmqpth1EdzpJUQWFUxaAyzDkR1x5CR+qYt1xZ67tYRDV/6GzND1H7p+d8AAPzhgXF0DkZ23FFWoSnESeL7K6SVqqUolAnTH2uyt1qLxrO+Bhovq8cNf5Mp62DGd1AIwpgokGgeQ7TKv0OO/iqjRBfsxroQElVdbDm4Dsh/uU3UcyZOkp5VkT16ChgtIQfJkiVLlixZsp4l5GDFQswwqjY8dz74shgs5b2IQcpc8rDsaphz8lYN1jtmv5m8j0nV3+4hskEsrat8yPMqLl6JJ5Ct+ND9Ywdic44+jJlU3bBxFeN8CzK8Z4Q3DukBzejNLKj7cJvx3XtL87zWFiW2RmRul1RPC/2a5/JChjyI0+w0BEGlAmsXiFfMWspy8CyDIrf2NdGW26wN8cFLl2w7vbOX75F1XUuVUJcUB0NeF70Z8kW+unsdAPDm2vsAAI+PjbWeVQGVPGTGikdUm1Rly8DgsVj7SyobHok7Qa8ky8X3KHr9sMc2S91uSg9UWQ9oWkcfskEmgMxRKHqxqiXQoVT6h7Q4hKQMNAp4PqkmoKf1LmRHUJI4JF0rAKASZ8A94f64D17PnteSOp1oD+K/LG37SAgV6wdUR0vMyVeYs5pmJCRSclycv2DckTEV//YXzFLSs+D7Ly37nHyR+YLZDsxuUqKJ+lsA1FFoHF0RM34k3RLuU1V9/QshSQJQcj4r8WI0jnJmwfgHxrkdQtHA3wvnGsDz9Tm2yGNQ3Yb2nt1/tSekxd6PqfqcbZoXdv/PvfESAOCfvmAaBl+Mpih6d10oF2uQNHVXz8DBuUEmlABGL63QRyYHiTgPtKFuzCpnQWMrFzA24DWooqMQtBmRgwXRv1GmLC/7XTwbKYxmw5oLoX/zEdGfc83xMecxt1h1897hAX+3Q0djjosivFPAQUIOkiVLlixZsmR9S8jBirUA5iEihL5SXGBO+qCcgbs1OWd1kzqsTLf6LHrZ0ENWTGk2emtT3uizUNtc1oo9BveyFI+Vkl0XT6O3xsZMNP0UPyKTN85ceW5fUtKr4sx5weXr9GIPyKg/mM9xOLcZ7mJkTO+zuS1V476E2MZ25dOQgyFiMFxvIYY9Y+wtGdVNh/6ETKp49L6oeJkzdny2NOTg6Yu2PGC8/g1V+Is6vv+6yKNWP715aGzsF+8YgvA9RA5CHb0mwEheCT38KnqlejtnOYgNO0ParrEgE37vnl1L9RsO7xpyUBL92GCsnURxhLb1zAfXdveYaJ/RrfHSiP8gzzf0x0kYfD68miMzJfITGOTuoSmLRS6wExP6XIrWeTIck8o9F3rRUnuDY7FQ/3pFVY5V1iBQxcg4n+MoF1/F9tk4a1yRKVEvefN37xgXZfOiZSnMgljoRNqO6M1Rv2DCaoTrjNU3HJsVx1FLTf551qAko39S2LidMMZek40OcUjEzs+FVtrPjbx7tqnI+kiBKgY6FSOqhgvHWZ4hE7qQ9ceBlCCbAyrCcqxlVP4ckXMEohQ7pfXt7998AQDwr772uwCAr1X2PuxP7BnMyW1xtc/YcYSk5KhaJHVgBlF/qHaevxAiBxQfhEHe32KMHSrrqI6+VX3tBVVEPVoQUWVtjTX2p78P6LdZXC9x2Lq/Jx0XTPs0/HF/rqqLrKHAtohrIATunUINgIQcJEuWLFmyZMkGlpCDFQsA8tD2NwBYSTq1hWanqnxHryVHjtBoBtef4nplx0G4yb3PYSxt4OU7kVXem3JkGe8s1ZY2unKhl3BXoI4zdSkgumJiqQpoih3LMyT9miL0Bf3Q4Oe35bRUfNyOWzQZFvRgD8gEr0aMjXMaPWXO+JpU2fya/Ti24tWiFCjOq6UT5tUt6va8Uxh01rBXnyt69z9mVcbzU7vfxy9cAADs37BqizW9N9dqcHY+8+SJSMy4/sKh6R68sv0UAOByXWC8kDdNTyAseG56iIxbZtTOd69drHJVE+RDPTuxnPo7B2LK0xOfmtf7xj3zdtv5kveYYcJj13mNMfte3AqZkAXFSpWDr77OgrQaqJTIZeaZJH2PaYU44GNMGS9i3YvPEHnfJT1gZdBkvH/F5DOx1vloq0ZqfeRTcCDUh/TuK1bIJFqEPADKRrhyxc7NNi04ZqWlL6XQmrUR/N2slEGgSpgV20w0g56kxnDe8N2mR56VBTIfwLZYeBXNprddFSOj6qAoG4EcFC1LcnzyXFUZuV/T/x5l+jjkscsAIK8hq7Leeqw8PcXuj8hHMWO+Pyso/vobzwEAfukbhhi8Fm0MLkmoEmoxqajl4ChGdH2SzbG1b0Lve2dmfbtPNGoOQyBV3TMbfJsdBeNql5nV9JbKRBPq28Tj3/0obo4oGDyptCSk96GBvkd+y3TM7wu5Kcq8keijuAglq8JmeddaAGgyoCIicMjndZOo7E1lGhENKbx3kwAAIABJREFULak9IYXMFgUwhKvfpiXkIFmyZMmSJUvWs4QcDCy0cWV2LW+2P4fqqtC5pCAAoIotcunJ+879GNgwJtbFlgfMVfTP0y2VB9w//Zwx1li0aFWZzmsf0Gt174znVrXB2PfewDzlLqbMeKbrvNtWVyOk5zAakYVctlhM7N9zVvA7XFrM7C5V48rWjrlcstY9vdlS8U9HDuRx8/5Vo0H58orJi1qsflJsGk2Huji3wmnIvA9rW2Ss+QlWnTuk1/3SbVOAO1I1Qo8HiovAtpAq/9LsNgDgK4fGzj6TX8HFtq9XcBTkTdE7k6fILs/ENvdKkH09gJye0wXGyQv22/7MvN5y25CZo5wqmIsKkSiFaiGMS3od5Iz8/+y9aextaXbetd49nuE/3anGrurq2RPdiR0T2zE2sUSYBCgiRpYIAiRIQOIL8AUJghzBFyQkpkhMMfmQSDgoSBGK7ShW4njACY6ntrvd6XZP1V1DV91b997/cKY9vXxYv+c9/32qqqvdt1q34t5Lqtr3f4Y9vnufdz3reZ4VUFDsxy6cGtHthcAcZGPywU9ZWrpd9miWH1S+5zPIrTFdJxu9roum65gJORI7X2gY17QjGxcIEjvxKlgPK1CNv8v6dK+2nJcOHkhHDbnGayC5TJKtax1Ce1r1tyjH/IiGfW06H+tY76exW4Z87z7aj/tYpPs99Unxv4ukRgCdCHP+xs/hwJsg+aoIaZAC65oaahBKwz4MO/wettzvOvdLX6c0WReZ3zf/8MvudPh3X/8dMzN7Jfj9sirbtA0zs0xulsqkNbaHPh3wvAYZ4/jyLffNufp7SHWhVet86fmZpCG8rvFk48jGqO+1Fe476UpEcMD/ScApzwMhQsbzoue+Suf8cNuscCck5uCZHkJhA8/kBjRidQGihHnncebPqkXvy3mPS28xs9zGz8JvNibkYIoppphiiimmGMWEHFyPGG0Yhj0T/oA3kB1QQZMqIIEB8R3Zsm9i3x/4bKc+XaqNpW9qX8a1SM3CO9QAw/UpsrJveXynXZMvvX92vnKHwLL02WcofDZa1L7MqJ3qePOoWbwcv9DiR1wah8bmRgfLTG6DdLwLjhxsyHAjnfCaGVnZDA4CUIG81UOQ4516CuhYVNeV7p/TlM7BtRcPNBFyhBCyYmSOd0rvPteSfTdkUl98eM4x+MflPV/LSZB66eu9T++/sHLOwseXN2zYyX2O7FIIAYNHdf9BroJzqS7IyuW5oN4DqnuaeB94r3flaJ+XCz+GbXtpHfV8MQykJphLhQGaU5F1isUvzkHq40E62qvkippnX7VN8ga+J7Rjn8nu+Qny0JdvAddZ57TTeeK49bf4NK2fp4hbnZFpJd17ctCUksLHz6wsUnbd0SNByFop5zp5J5TS//vrW5AFHUxRqt4vSI3PJZ8MdU5UnV/KomsnLfVBsVHEg/GsLova5gy0q+RvrVt+GdqXwLZjcq3ck57EKejEndjpekn5oTHnb28vnDH/86990szMfv3eF8zM7NXMnRPX+YHKhbEq2oMS7SDEKfZWsF8V99SS50F+6ijepoGlj4qjSQm/0KkDdOtQWSMExsTd8HcFJirLD9FSvxKhVRoXQlukmFHdX58X6tNw/hqsNhPKY1JYSf3AuOHvivervrQSDk156cd7cuWfPSkcKbx17B4ct2/58g5Kq+OTM/u9X/uUXZxf2KPGNDm4FtFsNDlQpB/yNzX74ccy299ob7vu1HCGH73rG732jz79APN5If3al/Rx/VgyoDo1VgkJYsswAsp4ihdAVTMmEsvMIf1btRPnTm55y+H61i0zMytv+6TBuEFtrvavPPSB7JotDxcsPXerc9uunIy0vnBofXPhFqrbS143l969LjmQDJj4Max5CFaVJImCKMdtgfUjkmt2pR9unaYwpAfI4bRNkGOhNrFsmx5WdpMfjw/c8PN0icnTS0jXZHGa8Qu2A26+W/mPwuc2fuyvFU/as9Gtmvmd2MvHtJ9IFYeGdYqgKpIopYA+kWFVEuHPg1bPPT9geb5fn56fqbUyrYp3yPu0R7LurZk01BjsqF1yyzbW/Pg1haYb3B8q8aQmOprJ7smuGtd91AnRo2hcihDBVwTDHKmfZGAa24PqS5K06pbk7w5oP2NyNsur/Q9FF0ffkUWzzIASeU/GUow1NR4rjpiYQ0zdRk3kIPgVKg1o8qCEoLe4d3Hyz2hcJ7cfnVMmBWp6NvdtV0iDJTNViSMRPvleMtvSeZHpVtOaQR7mN90KkX5l4ezzD7vb+g/Ob77oxMNf2n3GzMxeYVJwyfGLEJ1zP+lmlM+bJgeanGfBLGN/CrD8mslMNfd9WB/7ca4hbG7UkjslWTY+TtM2RGBViVUy3YOnQa5SRxTfOE1ANVlWMzNJdS9mTCa5sWQapudKfvDckYy54Gd30fq4mTFJOOZaHs+ObFb5M/fZpX/2u5/xSf584ZODI2TSNZLPWOzvq+JdKghMZYUppphiiimmmGIUE3LwFvFOpYE3NfX4+h83s2vZ0wGCcAh/2cFsM5EilSH2w+hziaAXkLTF2sqeFrO9w+Nl9Fno3Hy5zH32eXzkkFR2y5GD6okxcmC3fHY6MGsfFmQzszT1NzOzG2uyuo3IdTsbyLLVLrlb+XJ75dD86tyRg1cfOvR+99yXF/wde4cRc7WoNTUFEoKQtJz+p8hxOn9qbhJiglj7g/KCCJuCWkWwlMQIR1K7BXrxvFryIvG6gIBUKLtn9r6u/e+Xtn6sX9rds48tTjkef0/Zw77RkG9rgBwXREjUPtL8qhFSkiVc1T+WYFnIhezT6hIL3xASrN2L/GhCSnReyCY3OtciJEJ2I5NUO2UhCU2SFZL1JzxW10Jwqu3TsdRqWuRGnQCulbL5TqQuz67mlL4EfQs5yPi+9mlvjqUmWWT7ZFpVXloghRWakVANtn20XLAujrOTVTHSRpCiUMqYSOiOiKpkxgm65vyK+Nf1iaSnMpFQikIWxipxaAnxUARE3YOSXUr6mskESXXKA2aziKFtN1jFuaw7NQLiPJz4Z15s/V79xVc/bWZmn+kdEXul9nv0vu71ZI1urO8AxlOb+ZRh7xFYlWzSPchnClDKm0cOX6y4fy6v1DxM9/mYyJ0sj4PkhPm1d23fTnwYPwssC8mYLpM8UOgM11/E1VbXRMCYKr4N12jn+3xS+XP0tPDxdHvpz4InCjfVWtSSn8rWPLdOJT3KRR3jf6AeKZlpq9KM1LTZ/tgfNSbkYIoppphiiimmGMWEHFyLYD572zfYGJMH3y6+EcvOPXnx8LMHZLkknxlnNSKBpRmw6mDUyrZkaUVfWVX7jDRvHSGoOmxhC0cEjheOGJycOYmle8JNf2y5ZBcgOa2oqdHsw8heO2rUfePLyy0ZExlnv22sk6yHzF92sEWqhfq2nm582081Pjvf9D7L3u4ecL7Q7lCxHCQnpDC4Lp3geFFcsO8yJtpnpPp3l4xQJD1EUiTuQBzXSHPWtSDzlcRxC0Ly1a3zJ2SAnIugCSnsIWTM39u9Zh+Z+XE+Ezx7mO2U2bHMRN4jOxFBMfEsyZTIhGXwXcRxtifZYQ2CsFlrXPUpM5E5S46ZTUUdvzl3dKeCY1DIoEdkPxH0yJjmktHR0roD1dlg9dqBIAS1Mh76ROjQfaAsPd8TInwJ9yIHGajZRoaBzrBV/Xcsr0zNo4QwKQOlqVABT8Bibjn1/pw6fgbxcI3s1grJT/3P9VaSXcii2CRHELXNBqvfXCTLMRqQi2hxPaMWd4gMV/XtTBwMZbGliIjwHAqZPun7IClkuxpXSZaXWr2LN8FBddFCp4yf92pfvrR1SeLfhnj4q93LZmb24AjCncZoQjHVHpvzKlRLRkOq+yc2caJfJ9Kj7K91L5bJHMnXfevI759zZLctz5xW/BHW+CZOgULnQ8+hZBnO98I1oCPTswruDOdpAcJyvPFrMed4b0Dkfl/tyMD7F/68fWoOTwAUoIY8LfpUu/bz2MrAqa5smHFvwWfJZektHgw8qZC4S9xXIb5rKf+EHEwxxRRTTDHFFKN4bMhBCOHLZvb+t3n7tRjjU2/xnR8ys//CzH7AzGZm9nkz+z/M7H+Kh10xvrl9StnV14ssjOdUh22Yr4ekiqlR0gGDOxlkJERBHAPNoFWjlSmSmNN6Xc07sCOubtiN2rkDp2Sry+BIwtI8W4+0d1697Nl2/LKb9mxVC6WtbWBZI3Uqdj5Lz8kMQ+Mz3jUZuJoLtW2XTGlElmhgRMukRfPS9sLrmYXMTEg3ctw+ikzatfH5SUgMiWBD9laqfrcgG5zlqaVySzFUJkaSMW1KZV+w7jF36TEqimSCp2R1L5x6ZtCv/Xx85eLB6Dz1ZNaUHu33tw/sU71nYUfUvOdbNr4kyyKbb9oxQiJpWkbtNZLidJIVpjbJGleoPbAGrlagBf1ePig1wWzp56oiC22Ql/ZbmbswDoQgCBmhnj3LMWIhm5e0q8ZE6wreyEaIUtEnRY9sgFUzVo0cNaVVPRyDDN5Mz4Xe7uVeZvvWzglxMRlViR8gRAVeAOep77Ok9OhAtxLDnTEqfkfDcUeQhIJzW9xxDsq9ld8/64HGREGGS6xPrb5Fn+lUX88tsH8FyEGeVBu8zr5UCz8PeS0L8zFXIR/fHm9SNR3+LXvmPBbpWdOhSngRJdHfeflTZmb2q7sXzczstYXfH1vUOTPZQCcTNT8GNWrTY1Kqpk48oEzj6ropmc6J7NT9s9W1BlFmZifYg99eorThubLWWA1jBCEd9oEdvT6h86ixkEWziuOqUVLd4FzNUX8tg1+LJ0EIbiwcUTzh2XMsXot8zR4yqLk/dmxsN+fepCV4nCHLnc0sO/ZtBI43Cq2SJLUUcixCCwhcll0jmjxaPO6ywrmZ/fdv8frV4QshhH/NzP5vM9ua2V83s/tm9q+Y2X9nZn/CzH78W7ebU0wxxRRTTPHtE497cvAwxviT7/ShEMKJmf3v5vTqfzbG+Ou8/hfM7O+Z2Z8JIfxEjPGnH3WHgoW9Ycahr4E+c+CHeb1X02Ec8hcSgqCZ60Gjoah6XbLeVKYtFjoZMvWtZen1rDvlR83M7MgWVl1yWS/Jzh64AuB8RbtaMv6BjGkm0xOylkqGKm/yDR0rJcR2n2HhKXOQ2GxTKlMsqEPONI0GXYBeO2NWXazG69z3lxLDW+eJj/H6UeY1OfkiqBdKvOaNNKBtV624QDsdsGqNS2qite/LlsNflWjGMTNpUTGcsJ5nbrkPxBtwMbbnl+w0WQ2z+leLrX2mdX7CxzI4FqA3O9AYWS93yWBKWZmY8AeIQRiPJ13LSqoFauxz9rW5WtmWmrlO4uXG1RQ617OkCZfmW54b47qmrJzFE5jRDAe5ttW0Hxb/IcJ2v+p2qT6tcrPscFua/eSgEDU11lkgS1fzL97PUoMd+A1wDNQ+W2oVcQ2qmtotzcOqrE7NnNQeWgqAU1oz3wPV2sBBWGAodUJjrntwSu6u73N+/PzWyfRJHgz+rhoaaRmKLCEbyt6lNlDjJPkayPRIGbRCaqV9+229wUJI6DB+SMlHJe+z1Ejp872Ph7/1mtsh/9rgDcQeLOUDgiEPDZektBEa2ophrxr9gQIFmo01oIPyZgmWW27ylABBOLB8l3Jmxnm5MffMel3DbcJjRSoMPT8PH82yoklMr2QUwjNiMDsFEbsD9+rZ3JGBpzBHOyt9LBVwc5JKR14kPMNWehbJRA5kRpBSO9M44LcAcKxcllYtMEHi+akmXiXIkVU+FjP5WMhfq6iS6dKjxj8pnIM/Y2Z3zOynNTEwM4sxbs3LDGZm/+Hj2LEppphiiimm+MMWjxs5qEMIf9bMnjezlZn9jpn90lvwB36M5d9+i3X8kpmtzeyHQgh1jHH3Fp/5A8bY8TBNxvXyQQOalDKFwmImbfe4UU7yJWCKpyRcFrStZn4wWasOR6zBl8cgBEdoZqXvrtDSLl+kUcfq0rZyTQQJmFOHK5ltVrRL1ow+qskJGUBGtjWQdUg7rjqxmPMtbGXVpJVRZn1pAh8GZscFWboSme09r9My+bZSTojUy4bUeGns/6BTLySh5H3ZjSaLX74Vs72roBJg2T2HdLzM4EEUFsdeS5zBRl8u/fMrEIaNJu8cy/mJXxvbeCbZk2legQbct2gv7pxz8EbwilnLdd7C3yChTccta2ohA8rS5Nmga1IPsvoVNZzPkTmXHEPXNpZ1okdLETB27JNLYWL8w4iWi59UClGpMFn7IFSLa1CgLDhS4yLQr6HJbAMPQWqCNmWIHnNIJEc0kinVZryXVwI8AG7zRk2z5O1r8pFgzDPAarUyFhIXsqReEYdI3gKb8zXnyc/LHOb47IYvH3SOFLzyysucH7/uGtvysBhaZZRwXXTva3R2ZkaNXU/iSE05V7Yp+2Oy8EKOgIxdPT9ylAY9D5aG71XKpFEPpco7/IkmG+z3Gx+bP/O6cwx+c3Ck8UGFEoCxWKlRW/LoEFoh9GP8rBO3Z98HaIxE6VkZrEuvFWqsBFLUBHlKgFbQHn7Jcd3CvHXFs+eKfWx1fkAE6jh2M5xhSnA6gETgn3EnX9oTOMieyluD8ZGLrwEvqKllKcm24F5F2qd3PCDEixoq0EBs1vNaNvW+vXLmyESxOLH62J8p9dwPUM/sAs+FUICYso6ykv9FtbfSfsR43JODp8zsrx689qUQwr8bY/zFa699jOXnDlcQY+xCCF8ys+82sw+a2We+JXs6xRRTTDHFFN8m8TgnB3/FzH7ZzD5tZpfmP+z/kZn9OTP7uRDCD8YYP8lnT1mev8269PrZO200hPAbb/PWd8QYre3aay2amQmK5a0WyMrymcUX1KYyq2wgK02S5oQgMPsk+1J2UVJTninramGANz6LnDfOiK4aaqZQimfU2ivqY7Olf+7k6aVlR8wuad6jOlsl5rsY7/IrSI11eD2pFjgWamliyov1rq44alxUJOSgsFYIylz6bD++E2qGQ0tjJhQCqe6WWMPUM1UT1L7JpS6Xt7qaCWlfBS1oX4uku856HT+ZDiYCYQUH4SGvO6hhde2z9d3Cj//ohIwB57jbS3EZcD6jadSntl6r3UXPKFd5tHMy5pcaz8o+rMY5w7h+r8xQKoS+V1tb0I0DO7asHyNUbWKGk62CimSL2vIdnxUbOyFf8BaasQeBFCKqzWvgiyej1r0928pRw+S9ZzsZXIQjwUNVbjmF56udK2Va3ipALZaG6mYHV0BKieTG6ONlR72/HVbsE7sYxdVhqYwaR7mCa9TF1nocDWvuoZIsdXvl25C+fXbbj+ti8Gv48sVLZma22fnfpRo2FZLOqEZvbEtjW6oHfBCyKjUzEl0+V8tl6tLJ6VDcBDWkkpKCa3GEUkaqFt1/QW2FG7Hcfbnjvvzs+ev2s1/zVsu/HkAMqOPLOXPJ/dJyPi5LZfljTkFx4AuzRxbGRIic+25ToHrqd+mZmpqgCV1I/gXyjsBrAiThxC+NHbPOixXPsk4+Gb6eUyCW26BaT5GBPwsR4Bbj7rSpjMO3hn3Zzvw41gtQO5ABuZgKkezlJcE9F2v1ouEZONezWsoT/1nT82MGYrk8uWX1EcgBbor1whHjEi5OoeZoiYOyf16EAzXdNxuPbXIQY/yLBy99ysz+gxDClZn9p2b2k2b2p7/B1R0w56aYYooppphiim82HndZ4a3ifzGfHPzItdeEDJy++eNmZnZy8Lm3jRjj973V6yGE34hm3zsMMfluJyY4KYBcxwoyjOR3nrTEOwtkrGoRm6kmLCc7as0FdPqjjf99cpc6XkOmRLJWklHcecptH249/7yZmc2Xnp0FXPviUrr+0gb2sw3KBJnxyq9AHf3IIEv1LyALjbyeupB1cg5U1ipkQVNsX2a7vbufegFsw9g1UN3ULulsWMlnPZd6YzzPSzyI1A+B2bkY4KoXcy1SW1idwNgnBYiuo1wnA9lHJIMs1fGPNtAF7Y/LlWcZrdoBX5JJkShSMrTnS3ecfCa6A+Vvrr0m/eX+gWWgL1+1V/21G54BfHRHz4VOzG/Og9LOXugMPJJCGSEOcTpbadiIc6HeAnIIrGzBNrf3HbVZr9R6+BB18UUJmqMZ9w4uhVp9y61N2vJc57wXj0BLf//IFhaKE17D/4LvnKIdnxvjWG2hhWqBwDUmrsL++noc6tfH6hcx7XcbeVpkaR8Kmmi0dLKUi2ANX6NFhfDavVfMzGzV0w5X1wh1hjxN5FWh8ygHyih0TGPYYjq+IlfbZ2rH2RgxSPdcVE8Bzgf3Zur2ybaqdqzjlyJhR5+UT24cJfi5Vz9pnwbNegDuKjVBIWdMtazXn4cur6kdsv4cSwIS/+dNyq29C+SQi+cCmqlxy7lU/4eS5+YWNGrLc+MGz+RczySQp1uB7oZ0ObyJB0et545oEYy37bC1LWjNruI17u/2iIwc5KCOUmXwOuOggKNiKAsyeAEZaEUOf2BB2+WjU1c9nZ65o+Ly5IbNQQpCGlviA3n0oLZya91zsvqkeHnUeC+qFV5nubz22mdZfvTwwyGEwsw+YP5z+sVv7a5NMcUUU0wxxR/+eC8iBz/I8voP/d8zs3/TzP4FM/s/Dz7/I2a2MFc5PJJSIYRgRVGl7G3fB4FZKRlSdtDvvbnWbaxgNlm3Uh3Qu7shK6WD4e6BZ84DOv+2oVPiqS9vPe3mkae3nzEzs/Kma6/DmWea7YJOiWTkJRlIt9ulLnitsm3VpeVoJn0yGaD0x2mGL420us6JzT8uRVuPVt92nBemc1Xfp9phTgZXksE09xzcKam/FdTxjKytT5phMiOhNikjJOugVq1sLQlMpMBInQbjPqfshPCQ+VIrb6SNR3WSDeiY4ZJUg7++FLeEjfc7X88Z5PaBrPZJoIT341D5+XjXvrLxTnbtpWedd6lXfyj49c7YN7nHpa5yyTnSt6GxaShFlHnLva8VR0G+/mJt931i/s/nzk/JSG0b9mXbjX0QevYpTydX6E02+n5EeaC6cFB9uC+vr84Ky+wIhKBGybDjltW5rZRNcv3ann4erHOHOqHBSrFOJgJJisJyjEgVoi5wXuussIptBvT26olQCIWDCf/yG18xM7PzrZNRBqzvcrJ9Zeet1CBkoWLeS0GQzp96DcQ+8ZaEEGhZCDHQ/SD1Dt/VOJiVupelWgFJo3uhuAWrEz/fn1w5H+bnv+peBp/OXreHJ348G9AZqeTFLUn3y9jexXTTvQkxOEAODhEDxXIL/6orrBYiBqegycZ+HjnjIh+ELHHvccpPIuoWUKAjFAfHjME5KEjZiasEdwPlQCcV1Tzajuu7QV0ADczinHEvp0yMCXJ5UOB/kMNV6uE35CgOpII6BiE4Pn3a9/XIUYKa50bIC+ta+ZegxhnGXKSdukmmfhWWlt9Ir59vJB4LchBC+O4Q8PQdv/5+M/tL/PnXrr31N8zsnpn9RAjhj137/MzM/mv+/J+/Rbs7xRRTTDHFFN9W8biQgx83s/8shPALZvYlc7XCh8zsXzbvmfCzZvbf6sMxxosQwr9vPkn4+yGEnza3T/5XzWWOf8PcUvldiH3Vu1ASkljaSj9g0PYHTmBZZuWgmh9qhDeYqd5bs4QJrFZ+BZ0Qb+Gljb61x2/7nFrpsPaMu2BWO5AhlNSxZnIYK4qktpAXgLLoVCtWEtKNmf7KwuUupkwgptTbF1I1mNy5FmyPj2VDTA5s8kRQnXLIPUtdUGerqAFG9P4tnR5bZsy9yuKpBj/2mNAcuRvG2uqY0JBow6BsWscn73dlXUJYlI3reqq+66/nZLmFTAmoe2bUyWecqMUa5n3nGfozi6W9ceSckde6u74tsapnErizD0EoFBrphGqQlSujNqEeHEs+9gHoQYvKTEz6wlbo98tGPBiuL6c29QA4qN9rXEhLPVN/e7bRUf/MUBLkLCMKhBxEpgghOeEpK91bmiibxumwF3qHQoTlztSFT9uiZq+1KOHWeVCXTs6fOk123c5KIUFk3zn9OQK15ZfPnTPyxtqvmTwVdLvXGehdOg/siwgg0scfsMd1bfIss6ISf4nrJ/6HLO+GMeoQUsYIMjRD3VBJKSFUBzRv7tno7144X+L/+fJvm5nZi9CzLpZb6zj3M54bQQjjIM8M9lu8gKRikQXgaHGtj8NYDbN/GWSF5iZVlyU/BvG3tBJ1qwxk6QFVwRk9NpZyd8UopmBn1c1TaKm4ODt4Rh3HMnDtO3xYNpVZA1LQosoIfEbjv0h9DbgPCnELQA5mft8vlv6MOz5zboGUB7MFCrSZv2+gaBuhBdtdujfzA3fexCbg2SsnVClr8rJIirhHjcc1OfgF8x/1P2peRlia2UMz+xVz34O/Gg+wkRjj3wwh/KiZ/edm9q/bvvHSf2Jm/+Ph56eYYooppphiim8uHsvkAIOjX3zHD775e/+vmf1L7/4eHWzHDme8qv8mTMHM9m5rZzucCPvCuiuf+V+95hnyw7sOERSwZ2uyqBmObWK45tSQ7dzdynZ4DCxRJRT4upeXPkst8d62I39/d+Sz0LKqLBx0dquZ2YojoQ5wRqY0Pzje7LBGqKx9GL8uB73NMO5qZzGkfuxKS7eoEyL+9bMjnz0PrfztpRxQDY19EXwjJcXBsh9SqujHz/q7tt1/ToZ+In7LNS4qy0jt83xVqBc22Zi9Pkhzzmw9KGPI/doso/+9gIuxIJuZnRd2mnld8dnMj7tNPSTEf5AGXq/7+dpxfnZyo1TGo74FSWEwZl8rj9ujJmVCF4TmKKMtVTtX/ZpsRT4Y4j90rKuRJCSxoskwye6HnHOvjppBmvWYEJ2C67TCe+Fq7X0pZrofVHPneJtMXRfH40WcFPXgKLk2sZLnfDZ6v+V81nm1753CZ+Pc/3750hUlr1w6crCO7qWghKzi/m/FjNf5SGQdQY4spEVPCBX3Rl6BLpkjAAAgAElEQVS8yVVwn4ZznYWUHSqGlFknN1NeFoeB3gMvXXrH0J//oiMGXyy8X8QFToOha2yWibckZHGMHEq/r66K6TBT7xntmhjzIAwHf6szov6+qDTeBltycktQuRMQgSO6l5Y4wQb4TUsZi0pBlNQtYx7RGoXIqhTHgGNTpq0OrXp/FsxqubUypjg/uu5CTHr6XohjMMObYHHsFfPZwpdzEAODB9GDmGxxrWxSS809x0Mv6T5Xvxv1Ssn4W66eWbn3OzjsBfTNxntRrTDFFFNMMcUUUzzGeC+qFR5jBP47nM2PHRNzas2z2rP2Gxuc9L52aa+/6L7r8gbPgmeMO/TLOzKmc3kmkPkdrT076bbiGJBB0kmxzL1G2DFDbJjd5vSW70rfl/niyMqS+iN1xw1IQklWllHjUtZhR3odP3bNiOE9qENcAiTEmJYbXxjPsEvLrAdNEIdAdTplNMOarKJSE4Z+tA8Zn+9QMcjP3OTWqDo3WY605MpIm61n3s1ut6+FHnSwG7oxF0MZdYfqoFe9Xx0TszSdZ6lOcn7t2uDnq8lAEnCxXMS51WQLyyBPfL9GOzHapfe/4wqHrXkm/dJd/CJ2Ql50MDrX8s+QQiXBI/75/toYzlV/pf7Od2fUSmWgmPgLhbwEOFzO/XYnKMYjV3+AoDFPRs15beBsWJGlBgSLE7ziBz9n9y88Wxd3ZsG4Tp4A4mJE9SlAB69+D1z3gYyqTT0J6CEgnsA1bwapcbrS37y/8Sz71Y0z+i/lwggisqATqhQBSZ0g337uk33mxr4rkzZ5clzjdoSDrDHRPYT8yHtELpQch/qjwHtoU/dJv7/UIfVzr75oZmZf2Dhv4u4JvAku2azvU5aaOiCyle4AOdD9Uxx0TLQEjIDSHPAkdK3sIKPdMb6qwayEo3V64WPxicY/e4q6q0wtHX2hcS7fkz2nwLe1BTFY41WwBhnYVVKxoCrT+AE1sypYXuosS6UlXgjPxUI9ERwFXIIMzBeOHMxqbHd4JjUoR3KOt9+TVnwr8jIJe5Qgh0NS45VQ65mcCdUR2ukLIbBd3yek71FjQg6mmGKKKaaYYopRTMjB14swru8pQ+jJxrYbn8ZezZ40M7Obn/iYPf8+/PRf98yvv0S/v5PTHUzgVGfyqEAjSmanFXXssqKHAtn+An+Dcjbuez9nFlvPFhbIYBJHQik/r7fsv+q1zY7aMnW+Hb3RpZFXJlCQnagTWF5Ik04mrtq8DaZMqNS2cUqs5Ba2FCvf396u2Tfq3HJfFD9iyOSJT/26H3Ojxd5OLHeyn6KobLADl0UpQJJOP3I+lAkqM+bzktLDQs/kkCk2esE+k3CsSmWkaplXWUUml9ALtrDYnbAvuLJ91bPX7Ng/cRz9+rcoItpWXgRkr6mLnTJp9jzxY6QfH0yXqxVjews6dfAYaEB7QqoRK2sHMTpgkifFiPQqPcoTcRsYu4NlqUuekbk1Mzp8Lny53q74rCNhM861PEfkvaDopRySskbcEzLrGZm0rbnmjNWQlZbhAXA1+Hh/6dzRi6uZ38NbOCfiPyjblJuKngNihx8iBmkf1QdDKqBE8k/sppRVp3o9SEFM3xV/SAgZiCGuBHGx5Dz4/fXwdUcKvvLAlw8DSFrJc0j3QJZZyQ518sTQdRVXgsOpOd5Ctinyd9F9ouXBGE/creTp4Ot9lh4WTwy1fQwuzgdX9DjAybLs5Jzoq5AjIu1QbKt9kBOkFEY8bwY+PwdxXXBMPTeKFAnJJiPsr1Nk7EX6LxgqhGIGSktPhAL0puf5uWWA5HRl1LM6/dwm10s/fvVWKEFL86K0Qv0YSu0D+8uaIhwk9ZqR6icL2T/ZPgdTTDHFFFNMMcV7NybkYBTRLMTEbFZmlMq28gFI+m+frd2lxjocz+yJJ9zRsLrhaoXLL+MGfd//rlEh1MqEcB9cgQhE1UgzMeKVhVLPPfJZbKQmG6lNNWmW31hVSvM65hgIOVA9v+a7x3LhS8Zm41ppmp1KraBMkqym7WTsvneQVK10gx9/RzaqpFy9A8RnKNBjqytdD3LQbnAIK0ESOB99q3oejmr4InSq0aorX5anjFkqDIEOUkIEas4VWUqDXr1r5PjHeBCLW53/lFLhrDfUmmujya98nx8MFzbjulagCVIKHLGN5FYJ52TH+ZrBpD+JnrXssHJThjm0vlxt6ZfQeua9o7fF3r6usIrvVp1qyGRR0ueDWqUTJK8AcRCSc6i6Co59EnTNm8TVkXOiqPSF7ZKKwl97eOWOkeUSvgYZ0Yaleg20uzGaI7+EDK8CZXGnN7z+q14kSzzqDV+J81dcDVT0uW25b78GYrAOfg4b1CptoKMh95FQi9iOVRp7Fj4KmjA+P4mrlFj+8k3J9j1XhEp2WgfjmOu8KNStEV8QsdThPSl73XG/3KPr5b3eEcxNCVKg7J/nxS7PbEjcAt+/RKlhf9V1UvdBwRtYrshawTayFkjfZ6zzvFhw/xyDgn1P52jH87awOwPPIrLvwDNFrWYua1QtcAfWIALqPinkcAjKoI3jBEFkHwXYdql7JyiJIKc+7L0VUm8EPGhQmHWD1DxjBUlWSJ0Bt0DqHe6DCgRhcbxgvTX7pn0J+31mP3c73ccowpKqA/6KbnDGR55lk1phiimmmGKKKab41sSEHByEz/qp8wxyc5PbuNjIMIPpvrUgy4+v37WLgno9+tw8H2cTfUM9kxl+4h+jNc+oUw2wbYc5LmxiYfP9fEZPcNj/xfGeczDjs+IaKCtVvwKDpW9krfIXz8nSNAvV8Yo5q+wm9aBnsl1Qz1QNv+8GM9y+5vm4I5m6VvbMmncPXYWxZRauemU6D2Sz6sY24Mde0jEx6DzT56BvmK1zbNZ1Flpm0nJ+ExNc+nxV8lRLF5qhrEJa+iD3QZopEK2cFqnjFjvel/dAXliHgqQfb8pqMbYZUz1p25wsZRF8jM22/v2rlZ+PzcYRgnLj2zpBH/4QVcdVcnsUr2CwnPGwYKz1bHOn2rl8H7juSUFxC2Y82dtL97zXgOrhOfvcU6MX9ySSrkE8t6u+swzEa8X4uLzvuvtl6ds4xq9jd+X3x0aZJPuesjJSqydz15LfmTliUNBh9OL3Pm9mZveRKZwdOy+oLJxRXpYL+3LzO2Zmds+878W2AK2S66J6i2ic9EJCjPcZg4lBPuYm6CpnyvJTDXuvepLHRmxTmu5/qzcGmW8GhyLKlZG6d6yog+N+2TaOGLw6ONfga/PL0b6UeG+oV0EImdWM81qcIbLoFnWLJZ8DqZLkTqisFR4AmXPLeuZk2M90PpY/0vhY/gB9EJ7cqQvqkIgM6xokcOnXYoe/xS4fK6NiQmcOVB6m84d6R9wWkQrIvKXUqVGVxcQzyCznHGctCgHRZKQqSOsSJwuUByVEMRfXgH4hS57VeNVEqcWijh/PAt1Hmz75NUg5Vqmjo57JcqmUKiff5/nhXcr5J+RgiimmmGKKKaYYxYQcXIsQzcouT7U2zfjz3GfpVeHdtMrctehV7llIpE5uWUh00poC461TZp9ka7ucLCU4ByGn0FdeUmN74DP/ShQCsq/y2Pchp4RqsP3bhmyWTHlXXNoVfgaasRr7V85hxYJ4ZLkyXjG4yVI0VU6Gb6qZqnAnHTA1MtP52tcmlbnLDW7Yqcc9nv/MhEvUFkd0nRQbf3XuGeX63JGFXjVo1QPVva4cuzsWZP1dThbbtIkZPzRy7vOvSLcvjsXAhc8KOUmi3tip0+PosBO/QTX7xHMgg0r67ry0tRwOTYiSb+ONQnprX56CAO1AUtYP/Tz0KGNK8tZjIQ10WFR9cwasI9WHnPW6vrVBboMcRkwdCzVGuf7S7ZNdrd7Ag8M8m5dvvbKbZEGZOkf6sg9SkJBR7nY2R31z/w33A1kPKBu4jjc4/uUZx4XKwNbwXtZkpYybs6WyLmrRF/AGuC8qPreFg1FkcIGac3u9cT+DTU6nR9WvGcdy2xRPppcvhqmWThaqcSMEycb3S8lSdX4hd7tua608ILhcRT1WZwhtKLqx90hCNdRJU2oY8YIY60uOv2z8PlpHOUrqIpnVJi4A+82w3fKPthh7CtBg1j53xHngEt3Ai+ADSAneDy/oOTLwJ0H7lq2yeiDIKia3VfE8tix3qA9aMmWhEl3qZ8MzJ3UKBeU56ICper68GfT3mzpGZnsllJDCBJEGuZeyrP1zNSqugmtTgorK96IScsQ4yVm/OCh9B69KfjR1ZXNxsHiWi6di6RGs3yYQVvUm6Xu7phN5pJiQgymmmGKKKaaYYhQTcnAtQjSbNSF5rIcFrNylz+LOjl2JMC8/YGZmw4Y0no6C1bC1gmyqpD5ZM+OtznyGtzvyU77tHHUIlzB57/n7D15+yczMVne9d3wlRvQ5XIWZ1xAbkIT5GX7eN+hyuFgaDcySDftQw1cgUy56sWd9pltrdqq+80mLfugU6ZEQAs1mh7He3UKwjHVrhm5zZd9i2ZO1r1EZbOR05lGdOZJwdsdrxd2lZ3w7kITtA1/GVj71ZNJk0JIoZCEk9rVqvalXBPXsjHUkXwd5wl87nuvHl5apF4VIGKqHq88BWVrfJXfKkKWispmZNdRrZ9TiL3F27FfUHMkmZvTSOOG8tnANtqgfdqhCotAgtr0ha18NjXUc15aMSKqDslBGjAOgfBw4rDk674aMTzr/FiRhB88mZVxKSqU9By1aLpYJtlFGt5Fznfn17dinM7qVZuxEDhJwkvvrT1SMdz6/5f4YQOJmld+b+UxIkn/uonW04Hx3bhe1b7Op4N4E8RvENfHjyKTuwJSj5AarhzGnR2qW7EDt0YHVtD1+GMO1+4zr1eieFKKmcYKz5jr34zuu/Vwfn6KRX/pxzrnH5RB6E27F85WjnK/CgwlrzjN9DVZHmfU8Yxp1LQUx2DEuhOoc+r0cDb7NG8gTPrL1bX8HniXPwg9aynlT3AXdj3BY2rK3Fr7KhnO15Xo14jFk4gfxXTt4BokzIF6U7k0+r6w9S8oLj75X7xGuXR4SIpbF9G3/vzhcSSAwdmUtcznRqhsp+yqX1/SoYh84tkrrqXQNy8Rz0XEcKl0SDyQpza7xxt4ltcI0ObgWIUbL29ZsgRVlji1u5TfuJRdfLX0D5YUT7vXZqjE4TVbxiyTJ1aAfh7n/qNcLSIMnam/rEiu6wyZoM24hweBtG4C4wxY4DF5jc4nN8umZzW97+cOY3Gz04yfHVTUUEpw+V+th43XOh6oHxZ6sY2YWC92YwsVE4BQ0l6UBKrhPkLZWHkpZknJjyBypG5cf0jDHHnTOTi05j+3KH3abCzcPWl1hPqU21Xm2Pw7ZH/eS4CFVk4brwPRHE5lkfiOPnwOTERlbpYkPH2x5yBRFtEZERA7oxqnD5rdo69psmBbxuRlw8Cy1hebmX/pFDMDPAfjRIChmTPBqlQxkBmOdrYCwG50HJgkbfjh6Jmzzyn+AS0hrIktqn+rel5c7L3nEXPcLD3JJGPUQncn6tTZ5OOmH96L1bctSeF748VwizSyxEV/ww3Nz4cTDW4UvQ+tluExyOfa9Wvj4uOIGeWPr42Nd+qRyVZzbdsbkJj1gOV4Rj5kMZSzrKJi44m/ube4vLdUmOnItkpQz2QrzQxf6VOKSZe/xCURDCJsdsslaxFxNxNXyXbAyn9d4+ODxB319z3/IzMy+s/dr9fKFJx0vPnD55m+1L6fGabs5E5FKTYoYQ9y0p5A7T3n2fOiKSYD5Np9Gd5gMjNTKmonIeqYlu07poLcmTQoaCLoiQx50hd6be+nEyfp8GJcJNLOXoZEs0lV2SdbO0qnrx9diuk6RlejaKIkSQdsOWnGLuCxSdOQ5w+1hM575cyasc8q8KhHoOZPl+X6CyvNTx69kK00GDkq8w7tUUjCbygpTTDHFFFNMMcVBTMjB9YjRsmZjHdPytoKoVHpWf699xczM+uYfm5lZOThycBY8+zuuFrYk87mZOSx+NHiGk7f+et0gPYwOB2bHyF7+iK9rfsfhwFd+83fNzGz9JZ/hlyAGdQCBEMQJUWvYeea42Q7WgzYsn7xjZmanIAnl3LcZIH0NEGn6GikmCIGgql7EmXoPuZmZlVVibPoStKTDqCbP9tCbQg2V0uxabxT5wQeZAbegG0ByBSRK23d58X3BXlitb3OguYZGRbvVpa3IqjuMkpQZZDbOGjQ/16w8oYqCjdXmNcq6WJbY411L5kFkKUPXJMjg5MTHytmtJ8zMbOG7ZnMhA8eSqB6NjlNZzABi0KiBzKmPl2Mkfbuv+Xp2niBbB1uszDqztWfZglKzhAwpkwVGNjWCETIE+ZPTVQGr31z4uLoEXr8YHMVJ6lwQA9lSd11nGVm54PNTYHHJb5dkoTXbrMiIn2Ds3pnd5H1/fY1Vc1ki+aT8sgKRuL9z4mOz8M+tKYGs8k1qlRsxIhJSUgOXFyBiQgHnNNypOEAZFiXoV6Zaezcx/1wibnrsrb67/Yc4t6+9zvEgb1vmx6yLbBzELVntInkNyj5BGOqN79vtB74vt8/8+fLDH/t+MzPbYAD12e5l+82XPmtmZr/x4EtmZvYyZOktyNqTHO9zIAbPXfk6P3qB3FZZOyhVD2FvA/KwZeDo71amZJBRO2sTObGjlNHLcCv1g+b+iOMxGQ9atktuvUcQuMYiF6ZMXNbpfE8lpdysrHVuQWvUUEuScJlgcW8NvN71Kl2AhkqODEKwwOJaduKSOqd9SbBqZpnQ2WyPJpiZFZKEq6IrtDIZ8717MSEHU0wxxRRTTDHFKCbkYBTRzFrrmVU32N+uqE2u1NI387pdPrgs8T7IgQ3HdtT7LPFW5xnO0/3TZmb27Oz9ZmZ2p3MkYQkrZ0dRbcXM/+h9Tnr8MLPNB0+86Nv4nBvPBEh0S8xxRIJJdeSiSASjNWS/5iEWtXzkmDp2fpMGSsxoTdbMIAWDkAQTaYeZf5pSghQM4hyoscweNlDGe/0V//ugjavqbWqkE8ecjSxJlljLwVLGRqqPilRoXWe28Bn+hpp6Q429bUUUkvWqEBGyi27cHlgZhc6DHdhG6yjbZOEs7kJv8wV12ede8HVVR+w3yMiSzA+pX0imNxxgKe4G29755zedIyQzERef8Ww+uwkJFaLrbJ5ZfZ8xd+GwgrINdX8VmVGZXyubXxV+G+E9tF0uPautQG/mMwxsbvh6v3L5spmZrbDCzqyymVrl0rL6rISsGPx8nHF/HA9+/Ddp7XwLtGKGkdVuCwpSKOPz9TxcO8p3CXLUQ4TNTrALluTRBhsYYxVZugynllHmZsjJNMaSBE1kCjUDE/F0T5fzL+h+4FXGqAiaMQ77RlnJgYv91W1BkinZ3EC2mhUisYBycgwiR9Y0YkuNz2iNncETqNjA9x9/yD7+vd9pZmY/3LrV+yfvfcHMzC6QP5b3nK9x9Lq/f/uhP1dmssvGqKjhAdPAKehpl6znR4AsXMNdaEp1yQp7ubFIvWNajGVSy/YHyIEoBGbjvwfV4BWcv4PnjlAeoaK5ZYmIGLQOkZ7jGHELcGvE/xGXYAnfpV76UqiO7OYt0/MBs63UXlxjIKQBILOvXiRpoX4clYzNcva/a8co1aPEhBxMMcUUU0wxxRSjmJCDaxGCWVEG25EhSkZzxQzxghnfQBOTPDozvouyjb1vF4NnG1/bOFfgSxvP+J/qXKL44dnHzMzsg2fOJj6hVjyXswiz8fkNzwAXn/Ba68ktbyRz/wtf9c/RSOaUz4UT324fsmR6pCxJ6oqG7GQDm7YEWehpXVvTGCSbk2WkttCwc0ulM75QjSysmOVesr6hS1mWkmwRtYMUALIijRqC45l+nh3+Q/IvpVpqWCOLU9XcVOcXF2ObsqcM3oLaJzfsy7ZXG2Q2qSxG6MVBYx1lgMoIFB2GNFJ1tMz26/ncXvjwR8zM7OSW80Aa4Jf2iOumdZHG57DuxaEIB2hEuRR7HXY3mfLDBpb/Lc9aTm/S8vnFzDplkdSlL9fOYN8OtJiV1XKpxkDjGnLKJTplyv73PHq2X85832Qh/hBDog3ZYFkVe9tXNZ7i+p0Nnuk+ZT7On8r9PB331G8ximkb5xBEstBkzQyS0IAGCVGo4HDYEhRsK8lsZUUnroGP7znIwTHtg2s4B6m5TVBWRk2d50IzSJ0h7oEyQaFYylol9RSq1V9rq64MVrVlxh5uaLkc0cnG8xnnFC6FrK5lu25Y9WZwFAa+12AOljPuQr20khr6dx67RPsjz3zYzMzuI536wq//IzMzezA4QUZo4MUMlQsIgCSPagmu15M9uZqtaVyBvPRZSHwX8XuSWRFjrEDSmUtOq+7qxqrfZqnnSjz4O/UCS02PEoSTECDZZQdMq6oZplZcjGUlnovfazJ0k7Ko4z7JM0mlQSt0jVnKtl5N9mIeEvqo93QdkwEdz7Bto2euGtkVU8vmKaaYYoopppjiWxMTcnA9gtfbI4YiTa76JE2ComrKoiH7ooQZncXBNjnGKsFnkW+Uji68RgvVF99wnsLv3f+cmZl9x+yjZmb28cU/ZWZmS/Tvqon1YnV/0DkLizNHCu7/vnMR7q48YzxF/338zLMWMMqZh3E23irjlenGAo7BktkoM2PKl2l0RNU9Z6qhHmTzRMYXwm4wQzERt9L8sg5piJlVK4lKzUwO1pmSNi3luSBvArUNVn03WUAzOy9KC+iTs2zHUvV9Ca5pzkKb5E62ymkGTu1USosodrHsVGHWwyvYIuaXfe4z7/+g3XzqGXYXA505zY+epm5/w1UHzUMfP+0Kbw2ur25UoTgZDHqdtZa66IrGXCe838iDoazs9tPPmpnZjWNHq1657+qb+5eeIe5AHzpT1jJeR6k2wmy04drWtN5tr/z7l2TxYtyvKBh3tm9qNEd1c6P0fXnCXL1xp0EBtHPEbCH7B5jtO7L0BgOmVevbbGg0Jc8FjeEKNZBY8PumR7XNoc0vgu4XrIm7sTrDkk22EIJxZizfD0VqPGVio++5J2b7cZQN0QIprCyWy9ROHcVEMshRMx98L8S1CH4e1JjKaPE90OI8w8DN8DBoazgbnBc7PTGJj4xzW/Kce+q2P3Nu/im/Nq+939HOz/+jXzMzs9Xr3tzK4HsMma6Nj3/5XQyyE1czKc6XQI5h6FPmqxtdqgQpgwoQQ13e5CNlirdh64f0gPGFDNxSsyyhZHsvE10v7Wce1AwNhKl2VKaS2RGbUmv7nHFS1aAzsk9OyB3PrPzwGbbnPJWVGvFh4sU+tRju6TlagCjMQEXzvEhmbo8aE3IwxRRTTDHFFFOMYkIOrkWXR3vjqLEN1qQPK6+xyUGto0UpCZN1zFtLWpKGLrdOslsyt5KiaKARTrZyBvDFha/7U7JzfdIVEB+tv8fMzJ48fc7MzBbBM6gehm9xwxGCO5/wGvb5a84gvjhHy7+5sNNnvV4b7nhWNsApSLVOMrlAllEqgVatWXWtUk09lCGNncPSrJ1Z7kAmNdxvLNtybkxtb8cuhIm3oGY3ysaT7YFqgfyZBMmqTYrdbKPX+0Rv3nsxRLVexunPaCMtNrKOdwbCsF1z3bdwSaKQBBzdsnGWGsgYugGuCi1f7zzn7nSnz3/EdihCFmRyUga0z/k1MppkPezkFUBmgKvaGrtoeRUcw4Q+OvHvRZrezDnGcA87YY7hONbWy1r5tu/DM8/6WKof+D688aLzWZqVo13iTKhV8w5lQcV521K/Xq88m6mvyDx73/YTp67iKZ/2zPNV3AzNzG5mjpS8sPFs9Aacg+OW7ExtxUEMtpkf/73B75Pz4KoE2Q7vUHGs1AL7hIZex7T6lh8CGdc8RrsNP2gOmiMAIOKqqI7Eum9iqezWF4eWvrq95BSaegOJ9c6zoBDj3jKL0tDj71CglKh67NGzJefDz898gIuC+iB2vu/yKIk0N4py3JS6gXtUTos8wqzNOwtHvs4BuEVqpYCj4YzmXs8969fx+GMvmJnZy7/iXITPf/aTZma26e5yXtTgTA6hatDEenlINulzQzp3sqDWZ1IjKZ4PjZ49IE8DzwGhebIuH5JPAt/nfspAOXLukyKqffJecVVkcgaVzwHjBH5MxvVr4KBEOAWVeCLibFUiiqAokw8CA6ZjrM61PRo25UWZ1CtbHGCFYorXUB35NRFXYtw2/MBk5puMCTmYYooppphiiilGMSEH1yPPLNxc2FUm5rcvNyIX73xm2HZjR7lUT++Ctap9yf2KngglaUiLUqDATVBtUFeDZ22fe8lnig/PXSP+9E3PrG4vvAHRrEQfT2OZGyfOQTi6cmTi8uLcHr7hLPQTNO/VTbzyIW4bvSKUpXc4eskfQfVNReqZoFn5QTvUktl+oT4SeWGDGMmqM6oOduBHvu+frE0ohdD748/JbE4ZgQzS9mkaC3EPimzPkUji8bHrWhHUQAekhAYZvWqEUnuo34OyWljeC67tFedleRvXwqff5+u7edvspr+2A42RNl5M/g1clDlcBOnQK40fsgol80fUolcoRIoZ2uozMtFLH7tRLYH73gLbFmLU03L3+NgRhKeWjjjdf83RrLsPHJXa0O54IN9U4qvW1E0l3wjfl3tbH3+vUlRe3nGFzp/4sX/eTu/4No5cOm83fpkGUr/v24xbIUS+2IEcXGwczVjDmG/CWCHQcU13pP9SKfS4NLY0ZipA9Kqysio1WFJNnONKhHCUAZnue/XQGPs9JEVOEtnrGTB2PxQCt2etF3s+DFlmSY+RSkz4yuvbM+rcauGr+0jeHL36noidr4xbNWoUWAEFkngFlWXJ2z+heSgkIjyFAaXHAF/h9OOuZjh5wZUl5a87T+Sz//BXzMxsddefXbHRifRr1YmrwbiQA2Ue8mstlG18zrjH+kYqJz1HpAQhe+fGSE6HslAADVUPguRCmDwrpJ5htXmeeB+q58unQkIpNU7SPam24FIWSYVghyuRSsQAACAASURBVMeU7DFQgen7IA3iLu12u/TYE5pQJd4U+xTGyGn6vbF3LybkYIoppphiiimmGMWEHFyLmAVrj0q7YBZ6CaO+VY/RltlnOz5tUQ6BfZFcsFKXtFbsfOpUW9VlcYrDua0v6MoYHTl46dz13A9XPgu/cfS8mZndOfU69q0Tz0qXM7rQURe7cXxsO7TuV192j4Ua5nf9nGeIqRYmV7CW+iWpU2iFDDAbT1k9s23+zMULIJsPO+ngM+skCZcfgTob8m29XVKAC0zLlRmn1qRyKeySebr/nUwJx0xweREkDkMwy5Qxy46OgmtU1sU1a3Hy69lmScYozwQxpqtEjMCNjl1rUBK0R36e1/AC5s8+aWvVCNmFDf0e5FaozGZ527OwAub/6tzruLNatnNkDMqAb8Er2ThyVC04jxndCks8HJrGkkuc/NiVuRW+30tqzbMnfFtnNz3Lv+p8PL1+19UNa9pmd5k8BXx9MzLMBXXygGrhxV93ZU6VHdsf/cQf8/dWKEQe+LqVlqndrQChklaix7Qg7vBkUCZ5gQpojbZ+i/Jmeeb70MizhJRqRl297DOLB258Wa5ue8r40JKDiCjUYyMNViWjQiCkyOltHFz8Am/9ajZPiMAcpGABUrCs4BiUYsaP22kPYtcX8kHgmcS6U7c+DTihf4zlrGG5WSfVjS05fjwSotRLxyBNdHW1BePmaV9++Ok/aWZmZ884uvmZv/MLZmZ2//Of4zSh5gKRbWjGUuDUGoZgA2hcB1KWMmHdqzIjNamR1B9FsK2cV3kmqQavLpbifsk/gfNTypNCaE6RJe5VhMcSQFILeYug8tI1SD1U5CBa6pkG7wq0RgjBjPMrJK+BjybVVF3Pk1ohqa8O2oin3xm2LTRq36Xx0WNCDqaYYooppphiilFMyMG16C3ag2Fn5+jcN8w6lWGVPbpVkAS51hks9dgXe+euhBgoWx3PXGdz9O6w0Qc0+H2OJ/yA5pwa68W5F2m/duV14JtXjiQ8fctdzZ6sfdY+L2dWyqlNXv+ve3a1uvQZ/HDMDPYMJ7hy7ytuZhbJoAOzeJMneK/6XCo6++fzMWM2a83K5GR22ENhPNNNlAGW+x4Koo5nB59QtqZMYexFkOq9QhD6PqEIiY8g9jTZ6o4ZvxADbamgFtsq25LmGuRAHI0V2vP2xBGDB2SrFcqS5vTEspMTdt/3Xy6MN7cgAXSf3L7mWXkgcyqWnjFLKz3/AEjBioybbQ+XuNeFcU3SHnqt3jaZDfJxaOXTQFbSi+nsY7JkTM4KeC0z35cbt/14zl93fsAK1CPSAXAJJyff+Hl5bguvApjn6ItLO/mKc2tKWOK52PiD2PqMId9ryxkvp5UjKjW112Ljx3lOJ8iL3s/HA+63Do6COi/mOCce8ciru6Db1mIileMFwPXebhwx2HfuI+u0cRG5G9RlkXGkTFL0GbL5Gt37cgkqcHRkc7pJSkO/LDyrPOL1EnVBgF0fdY+y7V7IgJwUE7/mOnvdLELWiTy7YtdwrCuLZK4xA51CnZKR4ZYZ3VwPunc2SAxqlCFP/tPe8XFx7KqGz/3SL5uZ2Suf+i3ftxUoWOPXZgsc0A6DDfQEGHjGpDEq1FJgTeqJcIBiRr3O+BFBgC/m3BfFgQ+KngWDzleRW463QFTny7lMM+CepEeRkEccFEHeCro5FlxvIQZCd1J/BxDanOdFCfJYVpVlIGiJgiGvjfQMFtKG2kLX2d69mJCDKaaYYooppphiFBNycC2GGG2z62zL7KyJyqzood3Lz0BCZ7y3mZXGWNhALa+HdV1pRss8rGb2eDoX+5jMTzUlVeNzucqhV+f1Bvb5+tI5CXc3XzIzszsLRxKeOnnSbleebS4DGUrj2xrIttr7zFxfRc997NlXuWSf5BEuYKSR8oJulaAi8g5X8qIeBLllFoK6Kpre5B9SCkgqodRAnc/GHe5io5o52TsZhTLr1K9d2Zq6lwktGHobUvdFwTraln+2Opilq9WCjlvZSEwtIHXN/M+rY7K8Jz27fepDzub+wCe+y9d/eiPVrzPOoXa/uvLXNzgiiouxkVf6nHWTXYjHvHzWmeKGO6dUCasLv5Y5HgSZskDrLdRiUQv5IksDdVA/DDlpri7x5Nj6WNs0D9mmf37G/bEwPAUKOcjR7wIVTND90ywsrKVPR/tPXTvvxfwfK0TSIwpej4HiyXNkFf1ireE/3IOd/+pLvo934H08j7rnGBfEMs+trUU2AAGQlp7x0smDQ9naQTausdskxEBqBMY+aE8OKlDRaXMOcrBcHNmCHhpH3HMLPDL23RQ5T8WY5xLITpURD4lHBD8ANCyDMDQMep7AwcBRchPNTs4cdaxvuC9Fh8OngRy2bzjyGOBaqa8HNCfLpH4hw158j4//7zzzc14t/dhe+WVXM2TnOCnKLyS21rZyXhj7Ftib3P7g/3AvllJaCabhdXUrzBjrRZCSgDHJJUweFdzjeVFaDWLSsu3U51AcLHGSUI5VGMXIgyBdb5QGSd0lB0XWO1f/C54/WeJlmQ08WzueUXJpFKdAKG1S6/TX0dGpt8IUU0wxxRRTTPEtiAk5uBYhBiuG2vKWWbhmYwPOYR2z9GZcCJM/uvXBrH1rjkFNbfiY2eWsknNgUrSO1pnEtbhwZZk+t2JTZEzRs7uvnntWd+9ibndqz2CfWbqi4Vblvv7L4L4Hs8FnvAPoxiXrbLaedVZkqzlZV8HMPzsCORGTGPc9o/teC2N+yLLUyU5sWnUc00xe2vB9pzshAaLjylQfdEaZhRCEbtzdcd/IfYzUFCFLbwmN0AxejGid2izpvbvre5S4Bcre9kxxf/3mh/w8L257BnZEVn/xVTpofuUlMxCQUt0od/RfuJIihgwPBnNxQqe3hRj0ICa5Tpz820mBYEi3eO1vOs/2dhvP+rvzc9swRlr2JZP6RO6bOzHaGRegEveoFWudZfDrfiPgrNc6F6EnlWxzFDRBfu/iEWyTz37OPVPrfPAoUk154Dg7fPq7zI/rIQqIu1d+XFeFI2u7ws/H4hRKPWN2gxNpT4auunEMuWU6Z1zplrp3a+NlqnPvC8C+znbMxUm+B0IOQJgq+AKl6t4cazmUVoOgyX8hrTv1sWAcy6cAh83s8NmDy6ES7h61i5CGjKw+wCcpbztKkD97avUZPBahnmvGKtvu+/E9l6lTJj4GmDXaDtnOIKfAJ329H/nBHzIzsxNoMi/+giMIJY6ag4XkX6BTKQfDTlm3fAzEa1JvEamhUhMWX2Sc6xKkQG+IsyFX1ByeSZY6I9ZJYaWOmQPoTZZQCc4hz/KaroxCDHqeK03qsYADJ4haAC1O/WSEYnCeu64f9YbhgEbHOSQnWB2PlDDFHtl6xJiQgymmmGKKKaaYYhSPBTkIIfw7ZvZX3uFjQ4w+hQshvGBmX/o6n/3rMcafeOT9isHqXWk1NdKazKqnzonEOikRlMWJfdq3Mbnm5XLfY0Y3K+WKpdohbnOSN6QMWhmEWOfy/lbXMen3JQDejf7eWWFfo0Z8vnvVzMxOgiMJt+fPsvTM9gh/+/KKWTh1zGELz+HKkYQB3/+Mzl+ZXLuWykbwScD10brOuitq4ThCGnXMiBJE2XrRHSAGqs0rb6f2ZqwnkN0kVEd05kEIAzXJfu/mWInJG1RbNvYhps/4ptEll2OteHPl2WrDPpR0X3wKXfe9S7wpXvpdMzPb/n/uNb+/vaJFiAw5113ObAXZRwFTuYD3cYyT4LLxbQTO+fqen/PXQB66CzLoe/jaoyDo13iyb9VToEv15ywxvX05Y4zJr1/Zyhwnu4prtFGfA9jmD+mh0FK/Fht/nvm1X+JRUA/iIFTWk8mpVn5jGHNuWvZxDZt+jUa+ASm737ta57XMHSVXue/TUPn3zm55PT9HDXTB8RfSrnNJujKzPoixTw2cPg2NmOGp05+Y8AeOd8ms74BPo+xXLoQc85yeHnN4BUd5bQuy0Cr5PCi7VD8UVi3OQU19X6hP6nY6RrvCXFIMZfc8T/AyqJ/y8WWz0nav+DkduH/rOe6SV3B1UntK2PlxzS7h2jnDxfOMTx3p+UhW/uTTZmb23A/8sJmZAf5Yj6Ni3u2siTr3oBfKphkP6mTZyEAlIY9ymyS713mSvcOhHIrncE4WHxkXrThO7WAtXjRB6gE97/CcmNNrIodDkhwmtS+Zsn62lY+z++STQQytulqy61mR7sHELdCH5WfAOCjUvVHIQVZc63D7aPG4ygq/bWZ/8W3e+2fM7MfM7Ofe4r1PmtnffIvXP/Uu7dcUU0wxxRRTfNvHY5kcxBh/23yC8KYIIfwD/vm/vcXbvx1j/Mlv1X6FGKxqC6sQPhdiIUuzf5CsyqdbzPiuDaneVMF+XTCzO5qp8xu1VDLdgrl+MHUA9M9ldGeL6ldwkL0kD3H1Hg9k+1mwNbPkde7Z42XvteYHK8+2vrbxKf6tyjkIHxrcK6E88jqkZsxBXejEsZhRgy1xjMO3vifbNVCRUBdW1kd8l5k+WuphjXZ8Jw4BtXRm630nRETIwYFKoelGr0v/26ljIh4N0vRnXZtQipoZfsiUZTXjdYhNLEaw6n4zz/iOqWPP4AO88rK7V25wDKyoMZ9Qe4/iNgyttdTOd+jye7Z9DiNeCoEk3vh97YM6Saomj8OkfBAYo6qsVmRQczKimr+LbO8ZYCkzprZOFt+DXuVBbnx8F1a/ujHu2PZVf8UxyPWT46fD4E2QiDM6LS6sTFyDnuU9MmXhZmvO2WWvbowgCKAUD1tHStalb3uHKiPxQkDithf+/Yx7sxTHBy16FwZrqfGqfivjy8iA6VhX0pYnb4Z8dH6S0yj3ah7FkMebAnfD07mPmyUuiPO8tlmm5wLZZZQyRlno2Hsh66UQ4polXpTGtGroIBCMn4zuna0cRu85WpCfV8kRtVanQtQ3hVAWODgGMrRBzbFb+5JLYDPOW1hLcUUGKydOHDef+5EfNTOzZ1pf3/qLL+7vd76THFDlYJjr+vrntpkUD1w7ruGm8+u+2jrX5Aq32IbnbeqoKfWG+mVI3ZBVNjA21c9gJhdaXCwrEJMheSVw3uRqOJNqgfFSiOcC8qIGMXB98kL8CCENRVJWZYypGahtBeLRgwQVbEPIQQhh7xHziPGe4hyEEL7HzH7AzF42s595zLszxRRTTDHFFN+W8V5TK/x5lj8V45ucyc3Mngkh/Hkzu2Vmb5jZP4gx/s67tfEQvC4kXfZAFquaourliR7ATLshm2sGs4ra+Qkz9qeoU52REdRyyDPNVLX1g2wk7DXAZvuaU0yIQdprltSeolmpl1SPxH1xS727iY4kPNy+ZGZmd3v3zL+ROyfhZulIws0cFr6QFJzvIqoEQx8ftkISYOsWufVSJygLRwucL2CNK4utcYLcoXhYeT272+DHDhJQgATkYvnjFyACgZjUJndHatZh6C1Ir54s6/gOyoBh0Ln2t5VBBobgQn7/ZHebe44ULFF7zGtqtEkFQQdAJU6hs3IYX8eWLHwmfbV4Kwyuhsy5Uw1Wjpsqb+bygieToAbNlUkfHMheuzy3qhjr9HshCAIUzLc5F2LAuufU71dwB4bCM+AdnBShQh09CLqdH/8WPOMBY3OWV0mfrgJr1rFO9r9h0G5Ad664Nuf4fTyUPz+dRXP5YuQgEIyD9ZXvywnnYZ6P0cAQo205H414P9lY8dGxTLVk4TM4SUb1BgBRyDg/M1wfT2rX+Z+VLIOftyPup2C1Gf4Lg9L01LERBEFIpLaxZtvqKSKUQ1QcnBIzeEAZWa9Qzpz7I9uQSXflHo1gTMWHjs5EUJZ+oR4AjG+UD2U5znjtivuM8ZCR1RqCm6iuhc84cll/3yfMzGz3YGfD55wfdcRtLYRD/R0qoTBcg92C8XIsdQ+eAU86H2A49ePvjunKWfmxPdiBKJzDI3royys68K7izlrGXCbQjuNUt0WhU8Oc5wJKNPVKqObiM/j3d5DVIudZSMEpJ6bM5f3B9rJgJa6K4jvUPFuTiyfr7sRBy4QG2jVZzaPFe2ZyEEKYm9mfNefU/OW3+dg/x3/Xv/f3zezfjjF+5Rvczm+8zVvf8Y3t6RRTTDHFFFP84Y73zOTAzP4NMzszs5+JMX714L21mf1X5mTEL/Lax83sJ83sT5rZ3w0h/JEY4+pRdiDGaH1sUx0vDvIlZxZLliqHRGn21VPd4pC6yh1TIzpi5lfKPfBAjy+nuCwcsFLFOz4oH4UD7kHa92tM6TdNHA+UDn1aevbwMHOv/KuVZ4KvrzxzeGLp/RyePH7BzMxuLb1mqO6EyeavobMgGWY2r/aZUKkhpj4MLNFGq2taMUMrDMt6WJEhrfB/KNajY2nlAAfD3JJngYqKbLbrk/IhU3dFeSSQnQ3JpZJsW1pz1i0eSdS2OL/qlNjD1m+VgcvHPshhMVgAjlCWIIdIOV+qZpyIzLkUFHLfG3edFHIgHfzevU+HL9RLJIbChE6p22SCrcjmB45zR+akwSeNfUG6kjWgXmRMWxQicoFsE/pBd1POW9Xv0vXRvZWycSLKc4DvrlFIbBJPpBsd9wVeCgP1cPkZXJFJqXdBI/c66eVjbzk69FKqJN4T1yCdhUL1XboVDkIKyMZZaqQfUe8/wwX1lGfBgvujknd/nVt2BOLHc0J9TcSdSegOSFmghi5XvUANWnwfoWFSNXTU4NVxNDvyfepBHMrMbOD6ruh0qC6MqQshGf9C3U2REgygWUODlwT+CPJBCTkoH8ed04G2gqsUn3PO080f/V7b5p/2/fuicyGO9id/dB4GHm7lmrG8RYH0gJ40r3MvLnFOBEEobzui8Nxt32bxwgd9fSfwg3judNVg2w50YeXPte1DR1qbK0c19fuw0/ngfqoquW+yriD0lJHBecxRO50n3wM6iTJO5nWVeqnI6VH3ou7/cgUCnVQbcGkykyXKI8d7aXLw51j+r4dvxBhfN7P/8uDlXwoh/Ckz+xUz++Nm9u+Z2f/wThuJMX7fW70OovC9f5AdnmKKKaaYYoo/jPGemByEEL7LzH7IzF4ys5/9Rr8XY+xCCH/ZfHLwI/YNTA7eYYUWm8Zip57iqBHUhkvuh718rUWpJSMdOlvgknUsxEDMZtW7D0M9AbKvXyfKDpECdSWUo1x8+++/PXeVTA9VQ0dtrCVba6jLPUTl8NSpz7afOPU+DnOc8Go4B4l7cJlbTs0sKRpgi+8K6c2ZbZNB5/24G2P6vmbd1PW17Co8GPAYMFQQA/uufu9Fv0cMpHQoOzGiOffM9MUZUA2+oEvdttN58dd1ruXCJr5AljgGfgytapMWNbFPGb86QmYCiFKGLy8COTxKSbD3bTDb16I7oRnyZ1dGydp61tfEbl/UZKxJh61xoPqlkCexsaUVX+CQeX7fM6lM2Sls9HZDuscJVBIbQGq2fZsUP8oA5V+QVDeJXMPxgYB0wxhJEcqxrY7YJL4G54wD0L07RyAKjCOpWoZ+sBneExmvXYLebDjHTS7WOugE4z3yPMhAjCoeozdgs6ufgxCDmXpN1ELNyCyLwWLj6Fy2lbAfNCIpIXwftlzHSJa/kFMix7nu1EHSPz/HAKEgax/gR4U7njkHXm+2rfXsT3ET/gO9EqT9F0JQCzjDGbXb8NxjtOVJ1SGOln++W9MNdg1Sc8M5B91tkInj52wBStH86j/2z37e0cxKPTe4b65KvDm28qJRB0TG8pwxuxj3nAhAldmW5xAeDlvWV4M41Ytjmx+7L8zNuRA20Bsd9wp+wqU/H1v+blBcaTkzdYKkPwrjqNfvBx0456gbFtwb+RuNRRRQ7bmjFesHoBYb31a+2aOSZmYZnIT6aGnhKjGPHineK2qFdyIifr24y3L5Lu7PFFNMMcUUU3zbxmNHDkIIMzP7t8xTpp/6JlbxAyy/+HU/9Y1EjGZtl5zN1B9BzohaKtsT8Vpud3UW7ASPgLk09Zp99+OamXwKUpH47ZAFYjj0y0695ceubG8V4W2wg71PgzJfP0BxEWLF34PPVjf3Pct5YwOScNMRhOdyz0bUWc82nf9nZkFaX9jEcQ7bVjV19ZQYxnVvoTY6LvE7DG6C6r924llIROUwyEv/oc+8h9Um9VnI4Iz04o5wTebjJnvJKU/McdGOc85j28spT9pz/1jyoDDxCeSg2e/7xbOUa1o9SNeudJ19JFOU+5o6b8S0j9RWRa5QR0AhSrnSdpQJ1lsjRQT7pXXmoBI5vSIKDf8CJEBDFGQhh/l9+dDP+U7XqpQDnM6POt3hbdBFi432wWOt/U+QgsaF9Oe8LX0+J6ARCgJqNzRbtgFixOfn8B9KxnAepWLZWo+74hYlyRXXfQ180eNeWHNPD+pBwWmp6S1xA1XCE0v3CTmrvb49U9vCQrwZzxBbOC5dPiQ9e0XGr22l3iogbRWZvh3jyneEj8jK79FazPnbvi/Fmfe7ULdDIQ+LM7gN+B4MsyL1esjTvQYnACRQjoFGrdxwBqw6R+1CI46Wv617NpkSsv5+B5/kniNPPZyd/OzIsu9yF8VKDpAcz/Cqj7EMBLI4EjLGvqAcGRi0UhDYUh4scFoWqvfjAgqvYnEsbhTPpcosx20zF/QlhA30szz2ddx6yp97EV+HHt7D7i6KK1Cs7j78HxRIc/gx5ZfockofjB50oHvjoRkOsxX+LrX4Xeq1gYrDQJKi+A+zSwvykHnEeOyTAzP7cTO7YWZ/6y2IiGZmFkL442b2W1F9afev/5iZ/cf8+dceeU+iWWgHy4UBMznoaUgz7PTQY9hjL1vAADlZ1nZWyg6VhzRkrfQ7o988YTapPPAOu3ZQRtDHE8zM3/7+uAnJ288cVJLghzZoAoNhUXTYrAsYiWQ+eLdbnyScv+YCkauZCz3ed/acmZkdlwvLNkCMwF+C1opOsDrEshyb5YS760dUzU/GDVl0LInkQ/VBEJ0eIjlwcrxcmVF66JHedVt+kNUMSWWHNE/jB4ffq5LrXaoZDHsiqLtnn0UWivqhFjKcFWZqlJIkaJIwelT8KMiae2+XyvEPar0sUyNJFYHJuXbpRzPpsNhOmacJRwec3lN6mTGB07BO5RFmPdoXPftlm23n/NBjKy3TmOxg4qpSUVGEfeOb9KPoa9+mpkdAuOxDlxoxjdvnygRqlwHt8v4a4t0pzcOGAvMfNdrR+sPOemb5a5ikOxGQ1RQKqVnNztT8uC2RIt5euLX1jaX/SBxR8krtgmWvCzExnPoPN1UtK0NvZSFCMj967EPHOR1UXtMPMw2Wtux7RsOpjgZdxU2fFOQf+ZAfJ22344Wfp5Wgclab5dE6oOrNVx2EXeq4Mfup2KaG8JDkc/L7lcxWMlWel5JtU0YIMk+iAVj5Ve79XWf9k35usg894Z/lPtl92p8xStgKjrOFSNjORejk/DEJyLCJjkg78eeyATJojt5bc+g0CS0Ki7wXizRb4rgJlUBVClJplBLHcuB5yu9GPFfra3/+tJeeXLVMAAyyaZlkqzHZw+sRHmVypPbYJ+zbqWyxuaB1mSY6jxrvhcmBiIhv5Yio+G/M7LuRLb7Eax83t1k2M/sLMcZf/dbs3hRTTDHFFFN8e8VjnRyEEL7TzH7Y3pmI+FfN7E+b2feb2b9o7hb7mpn9X2b2l2KMv/yu7FA0C222J4OpYYyy9tTkh5mxZnrMPk/y3Go+m5MZ5cM4a1d2Jsg5EUoecdfT+uwaCvGOXhjMgJFmJXlZGMvGZK0zYFnaqxlO7wjCZy/9c69s3cjkhdMP2DOVN3daAv91r7k8UjKbXEQhYfrqE1McQHmS3R10UFFJRFhob8qw+fpCLVgXFjBCKXbQUmgs1a0wcdKSrHPWYfpz6UhJUPttMqHUBFbXUhCwxocaHCljzjILQmWSRHV8HJWSLkHQyZhIBDUZzoiQmBxTRtuUfLIZZA0NqS6EZKAi9KrR9QRGPwJy7XTKIWLq87LsVWOqozOHtpsdBEWOrS5lyIWUa0gnKh2PEKISJKk5VMeqtKFu6EIS5KqtClaFDW4i7vl5nul9NteC+iQkKoQ9irXjOyAIS+6HOTbSNTtxxE48deqtiE+P3TSsKH1cLSEJqnS449osJJcj6+vUdjwG63VugId1fFtl5ZDV1Fa5fp9vs1+ppOP3XEHb4EZdxV7x7FT3y9ETfj/WT57yKufh7oVFEJGCbXSM40ukifbwDT8OxmCN/bNamAc1dWKLMREZgdtXjlrkmaSMoBwXIHq7+7aj6VN5yxGE+mnfl0pPxhXlIAjftuT+men5oSWlLfZJ0s6CUlgOUTyodiZHOyETFvb3plA8DUqOp9uCWl1SPnyIgv6eL2vKC/lD9pnyQnXuz82S8xqFnqq5nKDK0FuPdT1cX4u0ZldZpbvBeaG6lBh3R0VqJf+o8VgnBzHGz9jXI9PvP/dT9s3xEaaYYooppphiij9gvBfKCu+ZCBYsH0orqGcvMFAJpE5rzD5kt6sGGgvIISdluUcOQAz2hMExNyD9fcBFeKc45B5olpuIjjFaQgTegcgQJJtD/pfq+SlJx+wlwRrK+qg1R0hgpVd6BvMM8jP37trD6gUzM3th+QEzM7uJhWwJqSte+Ow6IC1KM35m+pHaYJBhCkjCAfUgoThFHGfkqu33WW9ByTjZ2XDCNjskjBu1qoaAhfVurJjJQ8gamPGnlqtIW0XgUuvjDOvmhsw7hOEap0SIAbugZSpvUluk7p1FmSNJPivymAh7oDyyU07rh/yV5IltMmDSulo4Bz2NdZqa8YChTgt5MB601S6QftZq0HMhKRemN5z7SiZTquX30bpBRkPIaCtxCcYNpYZk0MRxkdXnssuGyFntuEfJCNVe/az38fb80fvNzOx9jdeyxR+xYLbQOZa0U/a3aoYkrskC3oZstwVqiVhI3f9clKijvRmYmVlbeIY8gx9S1mrglFkUeRMEBViy2AAAIABJREFUTeO+PnNyY3nsxxFBNxpadOeQABeQIWPlJMhMttn3Nxwn2T4NrIYLRwGiUK2HazNIfDlIkJ1CVoTM2CMTzi4h3MHh6YB71PynhDeTM34k3RzUbl33KufN4GDYem32pXv+77s8F25LcukoRScTsUp2wkJWxi2a1QQrvSCigB5xjWzMGWe6A8Xha4f9vxvBTiAhW7gWIAcBFXXJ8yE+RF4NQpBBFk0EQRlZychMDZjiwTIb9k3SQEaGI541J1xPOFUBebEsrYeieMfn/jca7xUp4xRTTDHFFFNM8R6JCTm4FsEyK0Ntc5oEHatZS0kWRua4o8GMGvrMkeTUWWZyIFXWIYtV2dvKeVjLJCd8Bylj2se3MUMawv79cPDe266LbRdGzT3NtpVqq+GQsvZxVpoEaYWznLsMaY7dsleQMu5gQn9o9oKZmT2Re80zY1au2bdq55LkyNI1qoESf8tqNDWoEU9C3I4kEbX0ebGrW+RyyqZbWhHHpWyBNTvHLvYWhTwygd19ZFXbcSag2mRHY66YpI7alZCMYdL14+8cvoIkejlZuZCQlrp4ahMeDsYTf+cHDXvE1ZCxUTt0e5Y1vIZA1g753vqCbRbUihOvQ2NZzcFoIwsCM7tSW2WkgJgKqfW32s72bZ+ugTgFazWz8j+tA83RKP7/2XvXWF+y9LzrXXX73/Y+177NTI+nPc7cZHvsxAlgI2KEhLgITARG5AMiCIRERCRiIHwAf4gEQrIAgewPRELIBgUpEYkUFClEQmAHBDGSIYEgW+Px2DOeW19O9zn77L3/l7qsxYf1/N76V3Wf6Zk+RxpL1Ct11/nfq1atqr3e532e56VrOFK/NaiejuETQ7b03jU5096s8/blTUYKXpPM9uJWbH+NZz8MVlFvZlzGkm8eF4PPkK/72zLv5bXl+fC4/R0zM3uPNtIbqT9ez/XyQlkdGWej+fVyk6+BVbV29ny4n7PoTlLFzTb/9l2Nz05GRe17umCe5tcvKDYLvaDNOnbt3SHX+4crHaskkSfdu542ZitJdVdCiEqdz1KNl4Ky8/jxvN91kdUH9Vtqlib58Ek8iFqcE1Rfbu3t/CFxGkSw35Q7uzwJZfi6JMjvCukQR+KErEdoT1lwjwKBRepHozdMgvSx2V86t8TnnIt3YYdo4VaZ/QECCK+p2Ze2xZ4mbxBlxCUAIcCGXxPKeQRC6jBPY+P3iKo0072puqvjlA20SUaZxHtJO0nEuV82xcjXes5YkIMlllhiiSWWWGISC3JwFimYDU2wpsqr1bvbnIWY2geXhep9ypDuq9b2irgJayvGer2zrGca2RlXwG1jwwev05yVzwqZh54po5M+e1+YZqnPLkGh058pADyFAnlgBTxDLVBI0JvUDXyuvA77CJ7Gda513tQfNzOzlxtlfEO2Ki3FPahU3yu1GqcRjSlzMum+o2rMKALCLMt1E5k4+KCRfADSJNdrCwFQybW7K7Y57kbUw49qFyt70uOb+dg+9lW14u3EpI7KUqUoGLrOjjTAKVETCG2o9dulUAe16h7SdMwjPXkjlr/yCYj8JtpsHVwzPZcxDtahfIgw9pVlYVKk3zr104zG0Q7te1Tmg6/BRkhLIdJ2q8ypFIKwgqUfRtXBoPa/K7HU1+IK4C1wIa34XTcayjX1yzJv79R5zB+WGSHYrHI2W0odQ9v1svWUM4+P0IIi9hY4Pzqe23Ue2+uUs/NDl7PYU5cf36qt71O1/X0qjs1RBe1qxW8q27umD5z4FXrUdvlz4RQt3uo1Md+Pmr/D7+Z53qjl8sUuIws7KQUuLvJ1c09W5pcFY573oVd7dZM3C3Xv4jo/XivjrO4HqzXGhVQaRo8z99IROlnImppOzDJUqoS8pYPm+K22e/EHhKw1BQoi+SfQmCylUX2g5l6teA3dV/M5qKVSKFbyEBAnwWgLzfXP/J/2FBvvn8rmrZ2iAg6yRrPUwkPQvQi+i+yTnYsgRMiEGKJmCG7K4tK0yS7ydF8i3eLaRcFVOepUCCko78orAzvwS6kuhBjQ4C6EcEYae75YkIMlllhiiSWWWGISC3JwHsHMysJKsZXvX+TVaYcMlZWhVoovyy71QqvayoZRmDlbvL1PZeBv+86mBI4oPOP7nFeQxufnu/As+2Te0VO/41lqX14zF7rBMcw+n5IyZedTROtlxXxU7e9dpSHwNZ6knD29oVTyoeqYztZXtgFLnX7INL0pd8p+t3nl3ClTLhusi1XfG2z0SlBEZc7Uv29KNVRREt6JCd0LrcCaNQSc4mTN+om8mt+/krNZUxvZ9mvZ/6FU3XTXrqxWNhll1Utb5Fr1+5Pq28cetYKyFlnZRakSqNUPnDsaMuHpjVtdABVh3hVW6nLHf8A5JI4c5eiFHIBWeBOgYjoXC3kLrOQpsca9rpMPBi2RaVw0JDse8rzoT3nbiIXfgBgIzbgrc+f7cta8o3HYOcKASke16ZYsXeMmD4ce90opBYYk/kjaG1qRvZozf+uY0S3m5r5VfV+OkjD82yr/xkHqBJwnN9s8D0oQGhQmygwjfAt9LobWte74E7TiPwCYXd/kx2/lTsbukWBlHo8g/sLdi3wvev2VjCR87KU3zMzs4WV2cYSr0rXwBOR+2pV2UtOhZidOgSzJi2ZqAV+I6T+2Fc+bVDNnhQjQll1qn6SGXBFWfyf/BJqHBXOrwnCJU6SaP307n4uoBlWlUFpvaCcbab+/ljirkq5P+T88H9zVUHwYb8eeDA+Ogf2UiqnwNvFcAEiM9FPeXRlekR5qXwv5ZhRcm9pXWp/TpM4uNmZyeLQNiKlUW6uZggxeAwqIIT27yd/3GAtysMQSSyyxxBJLTGJBDs4is8pLW4nteV+r0iu0wVpB3pE64cEur7jdbOs7+Dk9S3s677/0rJgrDz5MifDdvJfMERc9VtksiH3fqNnPy/pkrzQa4g022KBMLdW48OWMpZdz5F513ajP3lS58co9+SHAwl7JQwAHRHoINEIk0DmTOY/ZLj4Thfc6GMuSQiF03BeCDE5yKmuQHcNaly47CGFJanp0Etci/nDe98uHr+dj/ErWbL/zt/6vfKzfeGx3axot4cKXH29UY0WtctIleZBnwrWyDZrjPFHmeyuPia7OmVUv9IOGTj6f4KQUpQX6LaCQwJGtp98A/BXmBeiUxq90KEm/KXb7Fu113tdbtebt1JCGTHk4DTbABB98B/NvUZcXz2Mf8vHBj0g6v624GfugfgAxM+QbNeAB1SloUew+INoX8QNOw8GeqHfIO2L0X6mm3ohDcRz03ShptK/HiHOe5osUB5sL/XYDR4NDxBdEyhFlqSl1NghJOupcnEAXvGmX6bN5S4YYuX5i5kEdD7ktzaO3f8vMzH6rEFdB6o1XHmSuz0svfUyP83a7/rglQaOHK83rQ0ZhqsuMhFSqd5c2G9NaDn8R7oDmj9QKTqPS47TWvtM3Quz+rj3aSWNS67hr8Riae2qb/m6e51eHfG1t3xVqFTP3AsQxhWlPlqDHBZ4W3Ec07jGhMNJFH8xKtdjmvknvEMA5muC5t4grIvSbIHO0rmaOI7Ty3htwDIQYrEELmhEx0H1jqHCAla/JrRRF2u++51wkG3BcfM5YkIMlllhiiSWWWGISC3JwFimZpaGwtfwNVqoV3b6XkYNSXcVef0XaaTFEq/fRUd8frLbnXAEv5z5jnebvf1Yd6TsgCN8tulB4/4cpp2Bs6Tx1c4ywYf2YpoqMaOlMf6zfoFyNc6EyhUdl9oA/ist9FbOK4ePbXDt95V7OcEy11sNtrj12Xc5umr32/UnO+tonOZPq5K/Qt0dr26mX+QC6oIxp3eIVgZ+5HtdADcpmtIqvcMpTJnB6RZnnZS4MNzfSmssfYehai7rUWlAYDchKaARnfy1viZ1QiYuQs7ZVoJ4rxnxSR0llL9f9lb5hCvsk75gXrJfOui+n7zH3UsivV0LOHEFIdHMU5wbFAT0kNiAHcoq8UUc8MeRbOcX1+8Hbepd03VTfDmrOPX0JxAOgFwUcjXVN6+W8byuxzmtQEX1/0Hjj+zDA6aD9eLe3t+s8Zkdpyi9fzdf11THPrU7OgFvQK4AWVEDiHtyTR0GpduQoRoZEdqrPSWHSicNw7A520r87jX1XoAzQdZKm1z2o3eg1gsemFCHi8ESpNdou37uu9980M7NvvZMRhQf35APx6hfstVffMDOz+y9l5KsUdyTIfSJqPtuguXpJ62YX6Of3o37RuR1c5aQLRc6JhTLzKqkGf2ysVb+T416eCbofhJ3miVQnR8bwOiMID5RRr6XqMFdOOWxh0+Aep+sDrxZq9iFZFB8mOmdGx0U7eW6X9IPhJjdMH7snC9yChs6K2jcdG94T1lTjlnbRzbRHBK62KCNAPFqRVLq2/XAY+ruMBTlYYoklllhiiSUmsSAHkwiWUuGudfhw7x/nbPWVO7kG90C91StYvLD0w7O5Bf4Ls9eLDyUb8Lli8niUJ+h1nBJjPPM3mKIV79sXVAjKyqgt47YIy3rMZvR93gkQFztllJB3h2CVWMA1He6k/W+UfZS9MmQxgJtjHuNKrmOHIWdtb1tGFqJcyQ77XHs89bnmWqoYK9L6WDs866RZeGfDaU2dFX6rzBkNfuKqoAOiMuSew1YmUCkzqL4shKB8M++bsBe3GihqR1COnCdlAEdlzjVIgj5bGb3hR+6EmdlK2dYDcTqeajwfd5kfc+ilFBCy0Er/flq31jdSZYhU0YhrUdFjgh7yjB011JkPBn0RCmWtlZz0GiEHcA8O6tbZCnGzwaxSZuedGtWdkxnaKTPE2bESmnOU+qCmD4j2YaVzV9HwIE7P7SAVgDtFinvQhd6uGvWCIBMWQ/zq3TwXt0oN+47vlIoFPotM60r53acth8lxSzGhGjDjcNK2HU7Wqvbdu9d/jp7acpxeW45Rei8R7QsOgZrEjjho/Mg4W6mADvIVuXr3S2ZyeDw+zejCxWVG69YXGcXbXGQ3vtrU0ZEeEnI1LCpQmmEyTn6dcd/QvuPEyrxabbbWaB50Yu4frzMad5C3xCB0qhCXgt4RTx+p++RlVlg0lRCESKdIoSCJmjwcFF3b1VRFFsKIetZzJRmeKgVde/UqiMLMw8b7xWjfAwoEdYzEDiXOXF9jGfwvc6DbJF4IPnaoLLSLtKhZN75/zxsLcrDEEkssscQSS0xiQQ4mEczK2ruMHW7kFa5V+cMH8kTHax7XOmpMRXgmcjD3ORjf9519DkYv/unr81+ht8D57zur+Bl8hVGFAHJAPU6rVJqJo9dGlaDshA57sPtXYulWQ2nVSX7tJ2WnBxXoJB2P6r3Q3+asxfQ4tTn9uu5yTXFPdov/fUmNWZ/Xyrpn9T1DB4rCvRN9ZQ9lIgqNOWmF70PsLHPOleqeLKULsjNlrTruTl9cYs5YnGd1ZBXT/byWHwT+7CKA2zqCJGhM9Vt3BWtcCsa4kCfHXcuo1kF9Mq7E4n/a5Qzspru2vU7UfBzcBYLaOGxn+jaQGXpnyPxyK9UGNeZKrm3NjfTwQhRiSfZqTlgAfYmzLpUgKvOuku734QZ4ICyqxXZu56fPg3JNUaGeWn4R7Kjc6K7cFw97/Xaned/pvIMcOLKk374U4rLN33mshMwok2yF7rRCEjpdZ6Ajp9g5wjNmnarXD3CNqGfD/5kqb2DGo+YpZh4llVC+SuPXaLxW+r2mPFqji7LQNYt6JSp7PwkR62s5KcpjIW4y16KQM2S9lYuftoXY93SvJPxOduYTwGXRiN9RrTIStroD10CqJrlLmsb6sJcKSvt8sZHzZpn3le6lBWoW9/gQUgMKFs7vwyCGM3kWyIHzwPBWyC9zHbivBdn/RpwDIQimx/iplCB0jsCUjtKlaorS+p1c18+gMWzPuBRzJ9uPGgtysMQSSyyxxBJLTGJBDs4imVkXgxWoFPZ5RX0p9u5uRZ1fdU/6cave+UGl/WdzEN6X+3/HfQvP8Mse25hrZVnE794DgbqsslEY4LUEucWgPu3a4loG47xQKtUcldVI1z7cdGZkYfJb7+WMp0TZih52Ol4J1HOlGNAbKxjRiawVZYG2pL3qJEgHONQARVmOfA3PSk3Hmz9T99PxIvuizk9GMLKxqQ3mz12tlGmpflkKzSgBXlKyUhlg6Um5+ByVOnySjWtfroWkrITebJTNrpUCw2egJ0GljOtC2cdOiMJGfILtsbFrOVlS++7RqQNOMab0fYCDwFiSOWlncQZEtbCRW2U1Uy8kdbw7xr13rkT7j6rAFTGmfSFzIkMeZp75WC/MWOegXrC7PfHWPOlL5luynm6RevHmSkihuAZRahZUBq2u9yRfg8sHOTsdxDW4EU8G/kArLg++Bi2adHkZ9BZda4CnBLp1zkEQggSyhO8D88fr+yAFmqO1jg0Hzo3mx2Wdd/ae0JI7zYVdysHyUp/ZCaUoW3lNcG76XOcHpOkHIQj4PzDhxR8oQRRg49f4QOg+ovdZESxQn8clUAfEH6hdL0+Afqvf0r1G3gknnauiF4dH6oWKucv9FbAChEXISjnhC6BK0HXNvJfKoPCuh/BmNLlwUhW6WciDxXRdsE16P+on/2nkTyGMKCXh810hRKEWIsKfJovRvU6eNxbkYIklllhiiSWWmMSCHJxFSmbdEG3QSvkgze2qwcVOrHRlBPQaID2JVry/d8JMMfA+zoG//UOQg2e8Tg3qvHvA+3kN3zm6QjVCIQMNWarc+zbiCzQ3yt6vxLC+lhb9qX5XfgFVX1kT8PFXJucMd+0bzPcBb3T6rysDpPc5PRKo4xUgDqqlanVPN0f8/k0chJhKrxU6oKJh6ZS9koXQfx4NPlkXXujmdV2E/hy3fpLf0ff3MKGrYC01Y0rjQgjW4k7Qg72Xu14rj4WjNNdHqTV2J3EN3LXxzBPezErVVNdtft/9kDPEi7Sze+IIHITiPLoQr2N7q+Oczkm8NTpxa/Czd5WGfD5ADni82qmj4IVQER3i0HfW307r8qWgH/fQYJw0TMHrvWRZcBby073R90DH70hS3rj6QftOb4pTHBwpe3KTEcJeHSJ3GsNCbo29OmGe1EGzusxZaVInwYO4BSAMcHHcw2DA30BuoWf8gc7/reN3BryuGwai09zUnPOsjrHXM6UQk5V8H7bq9HdPmfTddb7WQRDW4a41gj5QrdCMtKapzCkjBia0r5TipPHJzLwRsiKFTJQnR+HzZNYnBqShLC0JTRiENkRl1TbjEtVCsw56+eTQUn79pIvy2IsPo+9NjiDQEGJ6j0x+q4zjjYLP4EvAdynzT3gPYJErxDCJa0NHRVcnbOm1oXPl9pdCKnAkHXpLLYjYbGL4/TNMXz/zpPGOvc8ZC3KwxBJLLLHEEktMYkEOzqKNg31tf2P9Tc6oblu81afe85VW5fWsLlbEeGZONXWhG5MyeAuwZHG+mvpyF+IBuHOi14VxHRv9CM3MBpQERRgZrqwyxbpGVbCJ+JLrOI7SM6sXfC3/++HdnFFGea4PT6WdP+A1nr//oqPnwMgH8Bqfpz6Mw1SfDXua2io9EkASQFxYDBdxxl53b4bpSttRnRDfhxg485cae8H51T7CAC5gOutjaOlRJejrNpxSan1krRJCd2W0oeTferPGauWNKtqzUTJnSDer/GVHIQqn2/w9T6X/Xivl8b4X+N/TgVHD3cTSatWItzrvJuTnWvPjKB+EmzJn/Idyr+MSNKLsmxIr7OqIrhoZt3oTrJ+IfX2QEmO1slYqC6frSHcPYjBwrRm/Ac9jem7mGROntvfrpJ88pntlR20/Dtbp2usO+Tipy0fxIjq59HU6/mOTH19cqsYunkuE3Y/LoX7jpMyaYwHt4nvjYBZ7fPj1pullbVFcgyCkifuDS+lB0DQ3Gx3TRp1id0IItqU4KFWeAyALlRWOYnk2qg6oUR4k5YDrIPCOUB9cLD2rxQEwvx+lAAPgtfDABYKHQzLOYJKio5AjInV+hgck8qKil0b+8qc32e0yKTuPO3ES4NXQnKJn3HSdwGVyFDi6P0EQJyKsZ4gBaAZcAkcMND5SI6RLOBj6jRqkRI9xVMQnhokSSxs6FGTs1hQFVssa76OQUBj1g/suPG8syMESSyyxxBJLLDGJBTk4i2PX25e+/Y5962muQVbKJNeqKe1Uc9qqp/ZaHuEXyiSbInndudDSv9DysESvje5Wvzn2Negnz4MkjMIDoQCusdeK2GB9iwlrpaUe9EH7J6+AhzFnEXeV6dcZGLHq7ezfEKVPj3u5ku21AlbmcOqmHAuc4kLllPL8sg3escxZt67PlvuYjoN+BmSCw6yfQ6SjHRlhMR0vz1q9GyEQA8XZOJZEHTmYaptL7+g21dizLyBGJm4CdfAO1r58MZI6RdKTIfK4SjYohR/iVBFwUJEctjSZUFDW1lL/lWLGVM/sTvmFnfgedQ97GX4A2RuZV+FoDkjIvVPmIzRyunuyEqfmQrwH9Q5olTlTS+1XqDHQc+fnj4y5COdBbG3q4E3V2AluBX0Z6K2gO5F3sHOtOeoV0/Oa9wnUDiSqmHzez11+1ev89DAYYrTeuShca/jsiw+j7z7ouCIZoTrm3YoZ3yvLGwY4Gvoaprz2rde+UamPQ+ET2zNbXFdnjTBnFhu+rdzvATQLdKfRVmhgsZo+1v2iKQKJrzXMQbLSgawUNA7XQbwCuN5n0NwAfwjEIU0+h1tjKPV6FTyNjuqVEYQMpDRVhBGuwikvtW/5HHzrW9kxcb/PN7dPfOpT+dhq2RH6/RQEUk+n8eWC3hJyzoT/EOEWCBkJQhLwMVAD1RG90GNXYoASOsIIPCQkgptc13vnRn/v1N7C7Cj0pgWN4e9A/11zzT4sFuRgiSWWWGKJJZaYxIIcnEWMya5vetvf5HQMd7FSy7aVakZbaXE3cgBb7/IKc71Z2a5mRZtXbxstSTcQXsmgSSu0TC8KdUAjw1Tfee9n4LV5dO15WUoNstFKuglru1Nk57KHIfdyvxAbuX6sn34k7sB7mVOQ1M8+uuOjVqXeBwBkBIWAas06hkJ1Yuq7hQVnsI91/ilyMMw4BcGPkwxPKMZM4DsaxPE5UBJq0fqeM8bu+9pSKNzHfkrRGN/5PibzlCnMY/zxzfu3x8nnQlWMGmjcCWeKB7LWgXp0lbcneC0BHwdlLTuyUWUp12LGy3NirfR9JTe7YIX1+mzH8SpbrZS5VEey0Bwby/O6XEnIL+b7AGu7xtdBun5qqRvxI1bow8U0r8x2dHiEmV3CIs8BB6cUugPz3/kgZxme2ahe4FxRbvVEXL+HKoDHfd+7W2KpLG0QqtC68x3IUf6uek0zhbyPqBGYi22Ee8DOycWup6/BtNdCHJIrOfxywd/AEQM4BZrnoD+zceD4cbNcrcQvkVvhbpdVQeuV+sJI7VLH0pUOvuNCCBN+BicGVcogOmDClyrhhQD/cK9y68DJ89wo8HYohuiufnyXd3Vln6D2e78G9lmqnIt8r4u6jr70lS/ln7rJ99XP/OiP5uPVPduvVdALvwcEs604VDgainNQrOAYcNxCK9eaICAFehxROdAZFGSKyYkni1sswr+JjragvsJjhONGzTUiyyDJ5RlE+nyxIAdLLLHEEkssscQkFuTgPEKwsmpGhzgty9pZ976bW2X7B60Eb9WdrjnZhXS697RCvaf61H2tOi/QgiuTbLxrmjrASSM8wBA3sju0xdIn9zkT2BR5+2qTV84vre/Z5SH/dvlImt9vZA7F8K7q0fJQ36X8mc5yz/djpGMdbH3tAm5cOCWSatNtrkSJQG3S3CsiOieCOmT+aBxmK980y9JpVOZFVlbOH8xWd4SBDMQ7qI0cAU/HwvS7YkWmz+vsg76jmnISyBgKbekdgEPiUFEnh2cQz3TVZAL5s+tAbRXVhT6zUrYJt0Qe/GTKZYnbWk5Xqkvt2xOdwxt1AFSf9yqtXVY+FChhpoqZlc7v+kZ6d+nZBzHfozTyfQO/JWdltfTsgzgGrlkXmztsaL5hVsiXAe5NP+tjQYaEGoVtN4xcgTwe+WNknC4Z986RuBtOVQre+a4InpVyPiGEwzVw//1qqmtHS49/AbV3vrtnTjLHo02eb/sxg/TeECSZICeuzuHA4Oa4dCa/rnmwbuRyuMv8ocsLbbcZRdys1GsgwEWhD0phsc3HcXvIcwX+SiP5SYm/R+EnKz+2GUIAT8q9XXgWNE9jn6b3CyuTjx1zcUT54Cvouuf8jV0G8ue0zw/XuTvjZ177ATMz+62vfjn/hK7hT//I5/MHxGlgPHweFTZ2uFQ2XtIbAndNIYWVnB8rnBDVKyHBPXDARPMLCIrxg2rQwbeQOuZ4dKAE/xa6L8LvcGVDN3UGtRekVDBbkIMlllhiiSWWWGIWC3JwFiEEq9ZVrvnYmMV6PZvMIs2yV5zE2pMdb3OGdy1k4G2tWC+kV78UP2ErZOFS2di9+qGZmZXSTlfSENda1V9YXvm/FLInwesvfcbMzB6EnCEUj3P21v7ulfVwCZ7m71ipDh1YuVcoAgY/brPRyYy6HxmRe4wrQyhbsmCNQ5pl3B9Q8mJBW7tzIfaCs/WpE3pn3vi8nyimWQiJoec20JCL8D7EwPkNlP3L2f7PXg/TxMi3SYhD9GU+KgV9H6z9urBE1u41d7IFZQZCCDpl1oNqiige0vwYQp5nT9b5vDe7vJPbrer7b6kz4rv585t+YxsxukuKo7CvYf5rJ++mjEaVQquSOmser/Lre3ktnJS9lVv1DpCzZvlynuO16t7dVprzXWuHa/2buWdTJIWtnz9t8MgnS4UfcBxm3ATQPpAG7SM9DEBcLCTTJTa65BXwOEB+OJ/5fYXGqwNhc92BvlsTBbSi986KpsegGSA2ZmOnP/2G+51o30BKUADAQYDTpL4Id4UY3BNicEeIwVZ8kUJyD7wYgCbzMYtYAAAgAElEQVRijBblDFl2zIfSX8v7Mq2Zg/5VcXqunHuAUknjAmLApe7eLXF8XzrzJTEb0ToGIqHicT5QMd1G+C95rr788DUzMzvpuvrdt75uZmbrl7O64ROf+qF8DLrngRwMVbBipfscCiFxzMKGLpM6MHESUCe44qaaIlLPasDLHObYmONVWTnPLczva9q6P4ikMWyHtrc0zO6VHzGWxcFZFGVh291ubJDiUJ7g4ogsEZmdbqq6R5QxjYQ62rbqAruWHOjNg5q7CJq9t86LgoeSHr1ykRcBr60z5P/GZX79h+68nl9PL+XfvM0TtX8ny89Ob+VtfWisPmm/+EO8EQFR0HSPhAaL0l6SHZnh0DAGAlUlPPrhw/zbV99+Ox8jjKWIxGlcbAAPNrrIGbvulImWY/MSblqUB6YLLy8zgMHN/mjw9xro0zdA/8W4wPCFh0uKdEOB9DNrzQrEP5Y6Zq87/Dwtbbzv0iyL8Y+d5gOtd3vhxifNuRPGMkg8ZwsSlzxqp25WSGAFeYo0uKV9rM759du3dtnlhcSdkP/4D5K1lZK5rTQOW8HJmzr/ccdY6aQyGg2GDiLaYT/dqn1uUcg+mT8ymj+nVbA9Tar0h0aOxK7Y4hx4OUnB+GG8peHyRYFD+SwmvfKl5/n8Gfdu5J8ie4T8plIGfyvdiCk/7nR9sHigFMKChDns51jXfvRW2OafiyL/ueLX92n2x0E/vtZYbpv8R//OOp/LS5UNNiKg1hr7cuBzlAg0fzR+7aG3pAQE4ywWN1GDXLiRmxZ/LNRcTgo5eJ4kTEt/fk3b5GkLlsaShV/veui+8BoProPaLwg9D/SvjVo2v/rJXF54z/Lc/OpXv2pmZvdezouH1Uv39Xl9z7qyiN0z931ZMVckRbrG2nIqPz+XQ+bny8m21Of8EBPXOAsf7svRWM6SVPk9hoWlXih0DQ5ontv+/czrjxhLWWGJJZZYYokllpjEghycRVWt7KXX3rCupdWqGq6odenhmOH6TvaqIAi1Msw6jrDm4FCcvlwrY17vxPa73ucV30YZ0NYyoeYnPvWHzczscxe5jFCAELydtwdlaZVO4XqTM4jQrK2I4KXKQtViugzIA9XMR41gOhEsaWcL+fFCBKy+ze/f32TUo0c2IxOQiraxENwsGiv99TajEkc1sSoFxQ2STbqMTONEtuUZM9lHjVGNEBkaq2AfG6aZB9BmCGdoXjFdUvMQySayKJbehadtMy2kVvMQl2JB5jUdBzLIYUhWeP9mflxZQzOFTaOyM4ExFmqQAtfm5cdKFNYyqsLk5+TERpUX7qokcHOy9lbE1B5bXJEZkSoie8SYSeWSSvMGSHcDrCwyZRUzVHuQnPLwzTxP0jq/v3GFXOEw7gHbV2Xb7gtTTjN8zh7nEISgI7Oisqfxo6GTE2I5dZgkUa4qBjcsizNiohPPyPBXUyLiCWi+mMLuo0qZMtwHy3XZqZTSON9pKKXjLL31eH680vncSooIUnCnydtLIQkXQnt2OpdbnaMSY6MWfaKkj33jJjzJ03WIhSqzcAnqfkADpRJkzb27pxi4lwr0PGUZ7oWBPtpFMaJyIAWlX2Tnu3TWFnmYvV8lCsiyun5WKuP+oTs/YmZmf/c3/46ZmT1+mu2WH7zxh/L3YZm8biysMTXjt4rJVkDauOXWw3f4YXG+Z+MxI2widTWVQKp149bNHJ7forDTxqlK5F+QaDt1bpT3vLEgB0ssscQSSyyxxCQW5OAsyqK2i+0nLG5VA3ICjiR7fc7Wj4ecgbXKqA/HbCLU9yev5VWSrTSq+VVaZpazhh9vvPpJMzP7Rz/7U2Zm9uOf+KyZme0eqQHJ7+m7r/IXr5UZxAeSo+kM0gQqlObpR5Sta4kcrsv7W6oN8FoISL3NxxUParG7V1Z+zO8jUerV6nfNepZWpN6yWlldHCwoM65EEPrED4iv8PiJ9k1oRUdb3FmdFhmZu96AyEBiEhnIZWJTLsJZpxbzcDnb1IBo3szGmziFKYrh0kebvh5FIsUUCEIr2XxqzIIIQ2VN3VaGRBXmRjlTqLQTrbMeMaRSFoO0Sa2Pq7fEJxER9lGXz+FTISv8zmYo7e6Qf7vT+borAuVWYygXZeuVnR1L6rxCIQA/NA51lCWv5LUXkjSexKtJt+LBaLpc9oV1Os5eXIjTrMbM+QKVKypIXbxBYzuTtjpBsYXEgHWzxlu8gQIDm1T6dxFB6A7KvIisTr9FDZo20M6phR8yQw5cXeu19WkuFtNoI17MEJJCc7LWb29qWaFvMlJwAedAjZU22CLTsM15Dpp3kIlhZkISTIXzc5ywQepbTsce7gUoWEXTL4iIs/bKxYwHRIOz6OTCs3shybMTDCE7grLwVefnb7wPYOE9iAQ86DwXQjc3uwdmZvbpH/qcmZldCQ3uZNRVyyrZNiuLNFoSElA2slP2luxCimu6ONnkOEE93I2b+eDAio4btIN263aGUIC+OCdL3+WICo3VxD0Qb8SO/YhwPGe8kG8JIfxsCOGXQgj/awjhaQghhRD+4od85qdCCH8jhPBeCGEfQvh/Qgh/NoSRgvIBn/mnQgi/FkK4CiHchBD+jxDCn3oRx7DEEkssscQSS+R4UcjBz5vZj5nZjZl9w8w+/53eHEL4Z8zsr5rZ0cz+spm9Z2b/tJn9Z2b2D5rZP/8Bn/kzZvZLZvaumf1Fy0KknzWzXwkh/GhK6d953oMoysou77xiUVKlFJU5i5W/lg3xnW1ehZIhnmKupx+GvbUyEmnVxKiXQVLTTk06/r4f/QkzM/vjP/L3m5nZa8e85D29KetmycXqbZYmre5l7kGJD/OKmqr2lSJ0EWxQlkp705Uy5gbZixCATpld276Xn1/lzK+ohSCodS8Wo722MM1HsxOtbjWOXexd7lXJTjp2mNqg79NjZdsB62ZfZbPsFhMeeY4rDmRxSi3Ou9wg2xwfugGTM9bnjHjqvdDTURRMs7mZq7JLPWF5v88eFRvayixVMJOn9e1byfxqcTFWMP2V5Rf6Lp1mu34nIy9vfSMrRi7eJkPUPml4EyXIRtlaGV3wEQudV6ELPfObqVVxrlBz5AM+YXalY2nQagpB4Ji22okgdGjo+L3ekZFa2eYh0haZfZtua0cO8mPOkcvoTM/30zp2IiureTy1+i2sdDUKSBJpyUC7Z84Z3BvVuU9pihSM5kDad/5xpkowO3MAdkOswscWsyNvsa3rYysDnjsrEANJocUpWKvNOgZWjbJ6TI4Yr76nvbQyTVp6hzQy9WcoTQIZcKVQ3lTU1iMqDpQx+fVRlaAMGsTOTy7XxYgspDmhJ07vLa5q8gZTjCEKE30n4IWy/6D7TyFDrtc+m5GD7SHfswc1z6svhBxcrs3WqGz00zpHoE/OyXGOxkylMVNlFBFOkq4Lms5hpsQ+ij8Q296SI6vTpl5wajCuCkg4kdMfO0fRnjdeFOfg58zss2Z2x8z+9Hd6Ywjhjpn9l5ZB3X84pfSvppT+nJn9uJn9bTP72RDCn5x95g0z+08sLyL+aErp30gp/ZyZfdHMvmJm/3YI4Sdf0LEsscQSSyyxxP+v44UgBymlX+Xf30W7yJ81s5fN7L9JKf3G2XccQwg/b2b/k+UFxl86+8y/YmYrM/uFlNJXzz7zOITwH5nZf2Vm/7rlxcVHjiIUtmrWztIOYvqCJEQ00+IRDKrrVTFn4Ot0siiGtqntLR4Ir2+yX8FPf/EfMDOzT9//uJmZlU+k+7/Ob1xt1IL0jpatsl2mAYfXu+EYFNMMKqVkNWtWcQrgHpykGOiVIsZLZRm3+vRGiMGlmOBCFuw2IwrVSaz1o+q66LYTrO/8sImDe0SwipXww9ZVRkJgEx9RUFAjp8U1aZmOZYWefVD7YM4FvVz8/cqMvPvNMCIHaMdnK3sY8HgIYEbi7X9hlPM+/7wyCs0Htj1s7rETlaeNpWc4eXtA6XGbH19oYDZqq51u8rm4eZx5LrXO1f2DrK+T+CIyT8LC4kIp1AlVR2N2XGnsgAiU4e/3+bN3NFfvoann/DaMh/ghKlynFZbOe+3L9JzVysD6Ls/hth3spHnck3UGVAY075JCwm0wpt4SZLXUnLFJZj6t0eor74HTgMplOImrMZgFNVKqGhyqhKRI5XOSJ0O3EWKgi3mQyqeJefywxR2zXwIkSaqeHh4STdJGrTuowkbHdVfeE3erjFZeBtmmq6i+UT/gnZCmWlwOavJju/FZFumcF+2TBTd9Agl0/w+y9YRqJb/sbbOFGEXq/nF6TyrnTZRmRgAn/57eatX1SzWKinBH0vQ7Ha105IAs3nT8M9MwUA6x+YuNTKPuZJOoVEwJA6kYUbco5Ii/kgWPGReUA/Ah4EFw2sn+j0JR/R6ga9vVTPn9gTb0XW/DUTbjra4loZDYKfd63fQbBudgf7DQvRgL5e+HWuEf0fZvfsBr/4uZ7c3spwJ/mT/8M//D7D1LLLHEEkssscRzxPdDrfA5bX97/kJKqQ8h/J6Z/bCZfdrMfuu7+My3Qwi3ZvZ6CGGbklKpZ0QI4f98xkufT0ErR69Ji5Wrup9bvYpt6vUwNTCyobcozsH9y7wC/uHXs03nj336C2ZmthKXIF2rJbNSpPTJ/P5ViQph2oAkuY2qak1kp1DIWdTG6KvuXh4KhRQSdch1y7Xl36JeZbK57WXFO+zztpfmvhIz2rRaRdXgq1aNVydOg3XdaCbodX2YvjaJSllaCSJAxk950t0qtapGQ67sjWwFe1nenyJZXu+qkwSXBATB+Qt5C2cgjdTo/D5nDlMPnaE3DW+YchWof8bUOw+k0RyqtX0gp8sVY60M4fQ4M/5Pt3mse415qfHZkL2gyTf7wH32dK9Mzvwv0UZLSUKmeHWT0Yn+mH/jUu57m4TmesrB8OwMdjb7MLOpXq1ztlbE2rr8E3YYpkhRK1QqzVQJOOGNrnxTZQxNwCrUMWTOWMsJ3WvFI6A0W9Urq4TKlar5JiEAg+aJuyzi04A/gcG9yc8Xs1bnPiwD/Ij8uNYHajLxvrBaYwviARJwIcRgW6rduurVJQYYmrStrh8yaxBGrJwr8YS4h3l7YLwZhmLkcdAULOAD7OQaHe/U76NDUcJxe/MkoXrukDj52Nn8GO8NNKmyw0HP6DNwR3AWdbCSuThFBb0hU5pmz6WjgXCStNU9bXgCOlxZIdSuoEN3PVWGje2d4Wi4G0H+jhPt0zUv4KbJxr5uph4dfJ4xiIONY+58jflxoe7SLrlld/8BCNZHi+8HcnBX26tnvM7z9z7CZ+4+4/UlllhiiSWWWOK7jD+IPgdzpfoL/UxK6Sc+8AsyovBHYpGcdV1LWwzbNlHgFuOeFfZKKeKmLOyTr2Y9/xflvPXqZa5t2bV8C2B8P1C9UvXfYTdl48axR6eZmZX4c1PQUl3JW9hSZxpGD+9aGWKgfqe0KenxoGy0KMkgcfwT4rBmbMTKVzvpUCImlj/+Dsa0srO2HVnkZAc41pEBoM8VA7whW1PGD7t6GHheGab0ye0J3TarcPo8kFlqm86mBFCBs69V81MDnej0czwjdE7mHvG0jVZ9/AiqwfeJcu5NhEKwUllXqf1z5KDN2TmIwc1VXueiclm7llyKALlddjpuV3toD1GSdPhEcCxh5AwMRs+N/Jlmp94JTT6/V+Ka0Jm7K3L22ih1Wuv8g7iQaLqd38y7AMRiVda2k7cCDXE67e8hopxQlgWSRNtonTNsKzDXI7Os9fqOn9Y12ej7biIcjzNfjcB3kVXmz+AD0enxQddW75lefn3PccpJr4A/orldU8e26edg7ddt5SqDXUlPjCmHIIwmG/m7hQJWqqEH3T9wccSBEi19ARkDx1YHOaj/B2toSRymbpscT/B26i7kz4fhF7ne7w0ydO35dQM/IEwew/6vdheWxAWJ4kmhgPD7h7sU6sTDIVADO1wNg5ok0SwJzpY3cnJPBx0+CNORG210bwT3VGDQfM7pvMJNAL2YfefKjUHwR4GMMH1fiSeD/t7EckSfBngIapqGs+wYup50Tz/ub0ZfheeM7wdy8GFZ/p3Z+76Xzzx9jv1aYoklllhiiSXs+4McfMnM/qhl6eOk/h9y268ftFxC/d3ZZ17SZ/727DMfs5wwfOPD+AYfFsmSDWHwuhZe17WQAtwN0Qjz+J5cC7/4xqftsx/P3RM3ZK6qaQUhA6ZVadJKPzXTGisKCJdgzz3RT27Dlt/HzqsltHW9Dc5WRdNMfRHdOVm8ao10cKtzKpkwz9fxlWLphl7e+7LUCzgAVuh4tWKOm5FVLMY7Tm0BRITMV99RUyN1DGjqWx/o6kgXO9Acz6jwzNdqnPFM5Yj8jPn1ZIvePdqMc8BEoCudM+e1i24oR8Y8YwnjKR+D13NrCfo34tvuDnluHZ5mJYk6MbuHQEsLZyEG8BuqanbpeklSWS4KG68bpzFT0Tm4ibm+u9vlfDuJ73KQD8Kg7oudMp9t0PxQNtfQv6IhE4RrIR4E/Q6EPNVNaQ3e/+K/HOUl8rSX7lzznyz9iEJCxwOD3l3lVNdfofKgvu8nSYiTkKVW5+HQHu3U5+PfiBNRi3vQK6Ptdd73tMfFtTDgITDtsSJQxCrN6UZI3UoeDI0OCjRjZYnE1hpxK6oSDgVbfVZo4GollE4Z81pZZyXWvfcogf+D4gTFhKtEhCqGsy6i0FM4PucgcL5Vey8dKsobEBUuXm+nzGbKQTlrW5gfdifze9XMQ2RsUa6HcBCEBKRyhgh4k45q8ngYi/j6Hh0LnBbQn1VpQVycgEoH8wk4BvwUyggfdB1WBEnScfJYyIATX7inieO1WmeErmpW3nOE/bQGNIamK6fJcTEOz3QQ/Ajx/UAO/mdt//EPeO2Pm9nWzP73RK/ZD//MPzF7zxJLLLHEEkss8Rzx/UAO/oqZ/YKZ/ckQwi/hdRBCWJvZf6j3/Bezz/yymf27ZvZnQgi/jNdBCOG+mf17es9feN4dCyGvTGuY44ktq+78PnwEXn4pVzP+2OezmOKNew8sKDt3NYHq9pFFo+q87Yr+23m7PeC+RcatfWIF6SRVCqVa1Xo/e5ixg69MyxY+wrTWVfh3zRzR3ElQq2r5jtMBDWc8XxF73/eDnqCmmrxHPB4BY6c6ZYawhdtpNtt18CO0RZ2hz9ctqIXepuNHKYFrJWx2G8ZEhSw6+v7DOUABMmNnkxHg9z973T3kvYsd35eDfghVKm0jlvkd9ZmvTtLhX+U1MJQJ/ObxQYiko7hUajwBh7zOzS75OZ1mdUM/jDV2TZXWqd/iw6hGequ52Spbp29FVFpXlVP3OdouIn7w3wHkQs+9So7w1HL2q9t8gZQnaebVbdRLq8r86VwHaz/QH0LjtdFxr3RMBQ6K7lEB4oCrZWGdPCCO4nm4Ll8++8CQtzjcVdTa83altN9VLJpPNSqFDo260A1l7ZtKHROb0lYFfIz8no0QQHopbMQ9WOGiRw8Beknsybipe0sf36P/F0cBZM37Hoxz1+9V7h5ZTLbznhB+D4JzoqepvXs3StQMcG/i7PoZRlSLffBOhv5WYIkpesdc9O6rY1tP7dvUq4TjHdlDQha9G+SZmgrUhawdSgaWCIpxVHStDlOk1pUGXAjcs3ms4N6OG6JFs8I/ozfB1dK+JSHJsZ36Hayr2ooP9xr6ruKFLA5CCH/CzP6EHr6m7U+GEH5F/36EvXFK6WkI4V+zvEj4tRDCX7LsfPgzliWLf8WypbJHSun3Qgh/zsx+0cx+I4Twl220T37dzP7TlNJzGSAtscQSSyyxxBI5XhRy8ONm9qdmz31a/5mZfc3MvPdBSumvhRB+2sz+fTP758xsbWa/Y2b/lpn9YkrnFHP/zC+FEL6q7/mXLC/cftPMfj6l9F+/iINIKbNBa2VppVZ+a618L+Xi9YOfyLyCH/7sZ8zM7MFW2U+I5uvJBn9uaadx8qvQAGuVSW39pMy6dVF1fp4UkcySL+J9dF1zjWwca1rKfIMyGOcI9KzkYYDrsxqHUvW6Qj3FTbrdqH0koS5Z7btOF8+BwTq4BY4c6EOqgcJF8OyajFI16TV9HKida+U8yFse1cJRq21qs65ygLVfFK7Ppm7d9XAQhAgoNYAJzDiQxXBcrVbnnglVOMhNuQizZMSaobS7RXa+3Ihj0L8nLwkhR6gKPIPxrTJFOsIpA2qUWQY6QFLvVg2/CbNMKZWWQG/a/NpRuut+LY8A1a8PUqvcCGLY4WOAg6RX/OCqVPx4ftygqZ8qb/rQ+7wotzrfQg7qQ762UnfQZ1CraOKLGs45XAvVuxvpQij0I059H44agZZMkfGwcJZNhskYck6OGvNbPBh0EW6V+T/Fa3+Vz9FWn0cxsVGmOdDt0VTLrvIcvl+t7GXdgjdSK1yqy+JW7wG1NAA1+qLIbbGWYyLdG0dXQpQ22pKRzzotmkW/xnrddp2jNOtnEA3PCaEzKEiekanSB4DrZdzq+0CWiujX71yV8L6vdsHDzCuB4ybbR+3A50b4UL/J9abnP6jA7hcVzodTflicqTSYexanKMecmwGSAF8g6Prh5fawt4KJCvlGc/B0o/vGbebodNq2V5mLn24P1oN8PGe8KPvkP29mf/57/Mz/Zmb/5Pf4mb9uZn/9e/nMEkssscQSSyzxvcUfRJ+D72sURWEFK2it8LZaAf7wD7xhZmZf+MFP6Xlqc1oCbxrX/iIvdlstZcq11AYrakonsnityr1MRY1J3zNDFOj4h31b2eWMooxn/ARqeqw+WVD6Y7TOygRYyc6Yv9TDkVwHavXJd3b6fVZYA+MX9MJX6rgVUlObuhe6r4H0zliGj/pnlCKqWbtrn9ABOcaRYRYhWsc+GMdBHXN0aMu7DxKgTNl7MUxrsCMngaPNT3SBz4HEoMQIVuO/8J6cAW/0W/XUM6H0DnDUgfOWzJK5eezpa0A/+5zNos3vE9kgCozoHu6gU4O03cNBmb3m8Um/cduKSyL1SiM056aXu6fmxapX1l9N57AjCrpOTl1rJ83TWt3wqm3e7u5mv4enB3UKPTEvpogTfJkLKWt2QkGGljmZX2/1xpOu3db7BGiX0nhemQbFjKVPp8v2OOU9uD5f82MriOCustlLddS8430S8rm5LPM+XwhbeClt7GEQUoYvgYYcxID7QsIfQzcWrgOUVIWy2krfU9KdEZdXd17VluvIzHuN0OEQDo1fHc5RQrWRHw7tlKtz7uuR3w+SyOeo/8N/cGKJcVFVM5XJaJYBH0ova27FDlY/94cpfBfoAjtDTII7q4KCwK8IztvxXWA3wxRBSfUw+W7mDx0VmS+jAEvXm7a4NpZNngO4eq6KyoqZ4ox7+WrLeGg+y6PiaHhzFP69zxvfD7XCEkssscQSSyzxBzgW5OAsUop22h/chfDVy+x2+Ic/n/sifO71jBjsGpQF+pwYxkMdfLk1UM/Et0A1w0rl2qJjlamsRHWijo6HzsrXahsG9KxTWlRGVR1wCBz17C4V9kYFNtmOC/wpI5j+Dea931Wb93omKgVlDrDXHVqIZzW+qVuX1xDnzmc+cFqV91OGt49XRDkBGz1/rNYxtWGqf7cU3TsBb/zCMwBW8soyahzi5NKIUsTbrk2PHyY8yotCmZEbqtHdsGqsU/fN3TFnMo2oz6HIE6LlFNGVkLosY+6s7alOvYVhrnp/LOkDL6+CiNPgWY1X36gk3qJ6cFSgOsoulQDbSRneXl4LpXsMsNNorJX9eg8BzSeynCJ5Ruf8lo30+km1dvWa8Nr6EVQnb7dSOdzX+xv517eaL73G7aTJfwwoL6Z8mTCMvRMwxqQOzfWDB8eFXAwrQyGRM/83tK+vDRkRuFeAGOT33dE52ilTXul22wgFCtXK9fheG+eaJW1zcA+ESQgJ58TdPTUHyeK5JmcZM0iUd04szGpdt8zJWp4KPtfguXhfEPFgvOYu7gloBP0cHL2YcRNoB4NXQVE6r8G9Qzzd1ke4ZzEO4oUM3FgdYZqeO+6fKCWC+8poHOBy+SQYzM9Gzf7ZNHSzGfCQ0EdH80XxGugP0oDWTHlCxfjHYvp8jN5JN+7puqg3nXTPbfP1Qc+VQZyEIiabtbT4yLEgB0ssscQSSyyxxCQW5OAsimS27sw+eZHrn//Q53/EzMx+SOqEGj/zjTywGxjlaKcHRwrKmUuW16+MGiiprlaXt/ocjQ470l2xu1ldokTAcRDd61lnONAE6mlVM+0fTk2c7L33FEpP4yI2y1pGj4VRLZzfqOzWWb1x5CEobffVLBmN1zFHbW9+H54CqttrRd9QB2bfKPfBEyjoPZG//+Sd4krr+Qx1fX3XSdlGV9Fl00kEeVOiBZ9mAqPPgYZDbP5GGeZaTpL3orLbJ8nijdz06F9Rqq+F0gas33tvFem0bH0O1CNnq4107ycdE5kxPvgUzBnHG+v8tDEVhz5/R6tOoZ34D3ZHWdUuzxu4FAdlq5Wy0AqXvX7K1q6UIQ0iH6B+KZvKkpzokow/YiHXxYvcrnHzIP/W7XXey6cnvR6z2uOzMaN5n35S6fXsUQC34FSxr/lQWkeBqHsr87bBnRshk280hy7lBLnT+bxUpn+PjonaPtRxX7j74FRTX/r8yd8//iNH6DoLqk8fNVYH1dZvNWdvRfg54veAr4fm4mG8seRIU34N/IHpbBr3pajM7ugc3JUHR1Hh8T/lKaxAhnRfeXOTGfJHuCpq1poGECQ8G/J1QE8OPByq+oGZme22d22n+XxHLoF3VIe/lPPjVtyBFdwb9s05Szoub6U5TLfaN5wGw5qTLnRkLcVAHceOnltUTaAKkK/ypuJ2UXP/g7PBb6C80McCKJ7OYSuERueUFhc2mFUghkCiIMYgK6jWcIIUh2cY+vfNs48aC3KwxBJLLLHEEktMYkEOzqIpa/uhex+3n/ziHzEzs0+/8jEzM6vRM9fUydG55iRTzI0AACAASURBVIdeY4qDd0f0jmZ4mVOecrUBq1G0xSwJ0QLjMqa3offuQQqmPgJkacGCJTS+sG4L31H9hrI3rZBZsAZ7xopz1mvAlQbeY5wOatRPoyWXWZC5zJjLZPM9XInZdwsIwenOnLWtrFXbrpiu2pP3R+AYg5EvueqA2qj3deCzQhJ8lU6922GN6bB4kg97PW/v1TkD2+Rk2IbbozXeXS+dD5n7YPBdlTKoIcCi1sdwhGMftK3gWJSgI9NzTvYWrDSTB8Je2Wjyjp7KplXfpN+Hkjbr8ArQGLe6a+zJWlUPTULBGnl5NDhsMt5F7VwK7Ob4ip2yqNtVRu1uL/SCEILPqOfaG3V+/XD9nt6f9/mgcTpKIN6K+NEPUxWMt8sws9f1m3fkX3JX331Z5MdbPAh0fjdCChqUJ9SeK3gANn75+eOZxwCZ46FI9hRkR10ob6UyudXA7Omk6dln3lKDX3fjdW82ektwP6k8bdU9SvePtXo0NFVjxx0cCPk16N6y5VLUmH5jm9Pvb9Z5X99aZWXJjXxQuk0er+Mhf/B4BPVUlq9LvRSPpGpHDsZa+70VIvBglZGDT1zeMzOzh+t8Td3f5nP0cJtRh3vbPC8uL/L7mnVGmPDsKKvR8SLvhK4rVAry1yjFVbCm8Iwfl1pNBytwBAX98h4zwT+b3wgfanY/dXRvSv7iFgak19vgD8qGexFIqsaOc6ZdgnPUptaedRv/XmNBDpZYYoklllhiiUksyMFZXK539se/8Mfs1cv7ZnbGeF9NqcN48Xew1UEB0qgmcHYsJUF8CsQqDf0UQfCEGe6AWKkJ9QJIQUsnOGXQMMFL6v3JiQ6e6OIKhtB9IGOECdtP3sfOoAwwd1+c1tySd4acZicWzzI0qBDDdB/I5EpxNLzx2UxRATwTHDEQ98AVFnqfIwe4jY3Zvo81CI/bMczOK70X3OiM863vnhKdvb/9KqkmXdFpUZnSrbqtDZVr4ztnauu4VWgcxt54eVyMbnw6HtAqmx6LOVISJ8dUCuVaqT6+DlvbCDk4iOexH/JjujPuNQ9ON/I9UF+QgTq4NzwQwrCiS+mInJmNHhODUAvXdYfocyXowuhEsqkL1Bs5A6Sm/KlNrof/RPFyfv69J2Zm9q0iwzI3asOoq8SOdLrTdTYIwaJHCX4Ru3plnxroECk1gTLcNXQZHW9Nb4WZY+Be49K6sYM4PJS93WMhv37S95103R1CMrVUsZOQgpOu3d6dVbVFfANixKkgWQUp4Py7yyGOpNS18/P1RR7XYr0dETKdVwmD7LrJv/Woyc//vtCcN1c6v7dS2uiCKtfqJKpr+9bRGnmOlNPrrfV6+ugZsdb2jg74nTYjBnfFwVo/zZ/dqKtnIZ+IppHaZZPnz2aV7+HrJiMLd6Q8e3j/FTMzu7/L8+nhOm9X+p4qlVac4H8JIUBKpLGLqyn/ic6OBfCOHycyBt3DNPegExSlOAjcj3ULqMraOu8hoe/mFluDVuCMK+QDl971dkQ4njMW5GCJJZZYYokllpjEghycRRNK+2R938pOOl1psL2Dl7KSVoqBjjq5Z/HNqF3Fv9/1qVrxKcWh/jYIUcDfAMcv7wymrKVGpyzlAdkw9avQ45fQjXwEkme9tz9K2E4mjc+98xe0z7gzxulvsOLHAc1LrDMkIcXodezxNVCFaUE24I3PZx3d0Oed12B6n34TZrhzNuj2l1+vHfQpbNBKvlN+CWrRkvCRnfGdcBBwHyyoKWsc3bVMvyXWPwiCPc2/sxFMsK5q6wK1Y51vkABPCRgn1e0bEAU9D1CC+kP73KDfJ1vHEVEOgp2gm1Mx2AX/FvJ17IUYaHuV8vaxPBJK8nFN6YM8E26lwa7VcrSq8mM6ZNZcJ5r7jeq862JrlRACfPnp2tlT34850/tiyJnfZy9z59PmKnMMvtVn5OB6J695DZ+PE455qndvNQ8uVnk8tpUUJEXtapTOT6eua5C3s8zWbGSdu8pHdPVW86JjnHRMNxqngxA31DFgPuu+so3q7vRGgJvDZTLvNTAXDLGvY64qlAaOga5SOkhu10IMNL9iKGztHS/zfl6t8/Zb4hg82okjoYsrtvm4Vjf58VboVKWLbtB5x+N/8F4DHIPQEfxSQvJRiZzJNN5LzM5aJwgJ2ltWqbQoY055exJq91TqrydXckztpbhY5/l0t8kIw2sXGTl45eG4fXAn8xnu38lz8OHdvL13J39mpda6lem46ROjk1KiXqBTJlysmmsXjpYTj/R2xqlwvg8cNvgguPGGwmVt+X1SMVVh474zzxsLcrDEEkssscQSS0xiQQ7OIiSzpjULYllT56fm2jmFPr9es/IL9E4vvecBiEEv/3o0sbwe5exGP24y4HLGqKd/e9HDE2CLakErx9NBjwdHEcaytOqU7Uzp4LUuPNKnygL2yR0U3V5u6ozmrgdOAwheS/dEQByKkudnemw3/ne3MFCM/Bj3QRCDBAGAcQOZSDgpwk3oLCb+TTauvg1ant+EnCHByq7chVCIkH77RJMJN5fPm0s55DViYRfarkr1fygrSwVjnDMZMn/qsPj448JXe3MO5pjex28LvVkLScAPYkhka+KJ0FGxDqPTobgGJzobyvP/gY7/JY3PE8tz6jrk9z8epGtXL4ai4jeUOQHX0EHQXed0rvtkJkQDvToKj5PG8GPyM/hCkTP88vEjMzP77VPevnUpxYX6PcBB4foIymorZZz8Dj0IUBpUMVmH26CbdQphcv+D/N6jstUelELP9/g/CBnY67evpTw4Uu9HrSBnVZwz77bBNirwj+cdlQ3QgJAQegvovHc677frqToBvpCr4+VZ0Ag5QHEVIoqEaKnM8+JRna+Db0uF8M5WSJMQg1po6FqQW5LKo95lBUFUP5WT+mPsda/qvc8MF7fuHy3eJGdqpghKx/VsGjPdw/AFgUsi/kyrbq43Gr/HmuNvay7fiLM07DPiUB2+bWZmqydfyuPzZh6fTb22HX07msxruFzl7b3LjGrd22UE4cHuoZmZPXwp8xnu3hW/4V5GGi4u87ispLzYXsjvQSjWuoCDoHHAA8YKK2fopPe7ce8Z/qBMuSVDWfj9+HljWRycR8pED1oaM/7eAwOyIcSROfAS49kfDqwxdXM+Acnpxkr7U8w8mOyQ3zD30B/0eJRNqkoD2C3TqrnQH/wQo98g+A1gLSbQyMiz6ev8ppcspjee5FinTY4xCKLk/pTazoq19D9Aa/iURgxidLNCVik70IiFKPJIZ//NSJLIE9N0EeKlD5pJxXLUJHGzFgS3Yp/0RwHiIeWDlYx6joLTT9oedfOnWctLQ/6D1mmBtmnyTaFQWaKoSyu1OPBGUfqDClRf0SBHC846VJPnHcrmJsoozKygnYBWAk9zzGaNFjeNxrQF2udGrJvUy/ojeKvWzNciMt5WapNtLC5k3SqCXXmYLnT441qvVXapa2v0G1v90V4nteC2PIafSvlGe+dpLh98/cmbZmb26EIlkEtIgSr57EUO1NgftOh29akRU0ObIabRK4YtC0+MqDT2+wQhU2UHXRe3Grc3r/IfnF6k2AHimk6d+9kU/IETVF4nb39Mi20kiqXb/05lxG55ri9vWBz5sWCLLOtulSG9TbI34tLb49G+scpj9/XLfD5vtCjgD9RWf8S5h63WeX7feZjJfYMWme/JyheCIi2J3UoeSFy7OnqvjfdR7/mmN90Kor+lxKPj2qlMu6aZEaVCLaZuu7xAQRJ6q7mO7DyoFHar8YcQu6oOdiWy60olrEpjzfxeSdt4EfIf+0qLokZjvdnoed3jaj2/XoscrIXMRcjj2Gjf1momtS5L28nU6FL30Y3usfP22IEysxZqx36wm8ONvYhYygpLLLHEEkssscQkFuRgEslSHG1mcTCOheNiZjY2UCm0esVYxEJ0PI/sCfg/DBBKWMFixqEfE0IwCJocDpK5CI6niU3JAhrYfUZMLIrCUxXKAVGZ3ZhV6jsoi2CCI1QD6LpUhoSsLvRTqM+hbs9aIVeerNIK3le6yCl1fCNcKGLUMC0XIBPkuBzsIOWZd2jxBjPO7MvbobM00CgLExIdn7LzSzc7EjSncapVXljrfQcN3E7lAs8AZKQiuqfD60moUF8Hz1jKOC3dRMkfMa/hN4GeK5eJCvJ2olkO+JruoTVrGz1yU5MFZUe1vrMV5AwpMGnCR2WIGxEsL0w2yio/qPeRdQXjxc5oSylJx1S2zJ/aSl07a20f9Bmy3TUZMehv8nz/7asM+/7+nZy9PdkJMcMmWe/bPxUCc5szxdiC0EAmFeKiYWw51iH5jo6SX200tzpJ2G40ljca2+tTnsNPdD081eOmEdkPG3GyUp0bKUN9/rVhsH01zWhFabUqgigICaAZFvJAXdNIAHkdq/QKy2p+06ZxUCnhm9u9vbXOGf/VWuMh4x1Vk3xeWBI8Lhngg1WG199+lMmi7ZPb/NsyQVr1GDHpR4vpvrSQdEOyZMiIJftE7kjph5KO3064bwgF0zgdhOodVG7l9lq4hHa6D9wvCnzui2iJMrLIfjRD495zUrO0fS2pMuUVlUzd2rinzKjxvOEepb8fml/9Pn9PFPrVxN5eltT04TZvNxrEiutf982T5vetnn9nv7dHTx/Zi4gFOVhiiSWWWGKJJSaxIAeTSLlWW04z4m5AHpTf1VMnjtO1VTf0LkWkdlz62p3QahmyozLpKBLPoCyEBksQlcYGToIkIEQgp6TdbIiOGLgkEZtO7J8biIRaySJtBFGI03zDe7u4vHDKWeiHnHl0QlCKZOPSnFayQl9iwhiFLxVi4m2hp05DCRIo8kk3e0LCBwGEjFxvBzmoSodx0oy3EBK2x9SCGUMRsJQ5Y2W8xRKauigNh7DMlrSvEREJImuMrRX8BkRSxp4xpy0unAO3op7WwcMMOXCiEhQXkeNaZdAkPUUKI4pT0ChIXwKXhEzHla2qMWsfBli1nknp9iGyH5MUgydSxlKmLGVRe2OstTKf107KumTm8v++8y0zM/t78R0zM/vm5XX+iSFvi8f5u/f5oV3LRWjouUYhC+bXyU6pYTtHNwzW0qJ6DjZhhqVM8Epz7Z02z/PHp3yt9tJRNrIiNhoWpem+FH496nf8H+XorKw9q5QaNxrqRm91Wxvdc3okrHCOMAnTnKv8zq7f1rk56nq6EnLwpQcnK3VP2oDSSV7da5480WfvCDm6FGJwo7n86CYjBgdMsApMlUADzvdkzNbXWMFbsGFmSLbSTafRNVZrLq3cLl58IF0nN7pHX+v+eMB0DY5Tmt5HC7+v0o5b10QorXS7de0vctFZy+n9Omf6XS20SvvkqKZztkAM2MoQbcD46ka/k+fVRTHYrhJiILSP9vHMk068qRvN+3ePeW6+3bbWRjDM54sFOVhiiSWWWGKJJSaxIAdnkUKwWJdj9n9LRshyUytKZUK9wYzGmKK0FRJE74+rupXsXCPmRv30sde1SmpL8AG0ipfk0ZEFMnLtOzbFMQ02JF+j5++q1tonlAE0cdK79mLXkuGMFe38m95WWbJLrVaLSNaibEWPq7IekQ7UFaPgMW+oHctCFKY3tfjgK3xlYbC5ad3sbjCTrxvr36hEYvJRotY3MtfFyqaZidJN1Bs1v00jIX066Jx0YqknneNthSyN+rAyqVhaUMXZjZUijYHy+CSY/WpVG7BNdia90AyOFz6IzknX5e856tyipCnErO6H5G2hAX4KZW7NLAsdm0LZZB/YJyZdpex/rVp0gemN5iiNaNIgOWG5trVsanfyZt4c8vb3UkYKfv32K2Zm9tXqXTMze6pW18Ne8+JW4wE0MJMl9NTzNb6FzkWAGQ/536Jn+L2z6/OXoOLY6xp9LAXElYzKWqEftSR+YYUZmlQb8Gl0DxiRmLwdaDNejoZapxl3xneYeQwagaRPh70T4gK62Q2oX8RR0Tl4vFFmeZH38brWPBkKRBnWVrNMn4kCj0NIC+f5bTXquhLSMLj6Rfc0VyvokDhJ2jcMrFIyt3Dmfjb2IqLGnp/phZCUnDuNxxNxMB6JN3WMSJrhTel7xBfg+0oQOqClclT6IEGNzkvQ7iNDdrRB93wQthkUNXireF0PGJZ1GXk4mtQFRX58WZW20UW4QX2j/T/qHH27yef5PaFZV1J73bbJuVPPGwtysMQSSyyxxBJLTGJBDs4jJRu63tssswCLyghMOlTaLhdrMghpia001slRK7pBiEHqaLKBIkCrazwJZp4C86ZJs2TeCq9fmd6vVX0qLLjoGU4BagR5KQgZSSdt4TFgtESjFKxLlZW4pbP4EiAmJxjXeBakwQINprxGTj1fe6Z67rrjeGcac4xznGUcJlvnQ0hLnLSixnvAMIXqOl91Y1ABOtFK+nGlLjYNhlTuvCTWeqQenH/rMmakxWg9K/WCN5ahXTfZXgxnJwrrVB0G5jYrsc1lmBJdl87Rah+cEa1MsaO+KF17Pf18zzlMIx4ENwSjHaZaDTKgsSTjmW+95bUyphJfA21rmbtUBqOcfa6t0XOlrrFW8/bLb/++mZl99fptMzN7b5VRmRsxubEjr3qhEBrjwlvZ5re1mD8xZ91vGw7LmV11mtbv0edfa0yvxQfa67d7rlGUAd4mXV/Jte0pl667gFW0DsVRjnF/vYESWepMjOOPQX1o5oNfin4Ry+LuJo9br33tNGVvhALeCnq4jMnbhs/z9lI72ksxVWzy63v5GTx5Ku8JtWymuRiNhObKgDHIyPE/GG2eR1Qvh3smgLo4H0Aoj8Z8f5IHBwirq4Jm+a/TAabXuD9/9tv+Lx8eUB2hEWH2G3NFlaKc3bv4bfecGFDYyFeiaGxVTP8GYXqFcuZKiOyVzsVe9/JT976f/8ixIAdLLLHEEkssscQkFuTgPJJZ2UWLozg2b8iMtYpdCTEoxE5lpdyfeuuEEKAmcDPLgmwSroCczZwprtU2SIKnGVPkoBQjuvSaJNmwDqHvLYBe6Dv6OXNd38m+wtqmjS6eCmssi2kGxYCg43ffB2VgzkXoPYMvhba454LX7RiPKQ/AV/LUx7WltuiuyXrbrdrK7htW+WReakRSrY2U9ul11sy/9VbW0L/3XtZnF6KE78QtKGVxXAjdWcecjb9efszMzC7U5KVULT3U6Nqn+z7yrwevGbsnRKBerf2UIxpNUxxYKKZIA8xnb4olhUQdqM3qfT7PRgc+FCIttVHafes9ZH7Md2/ixJiKtEAraxA0zm0lNjv1bqPVbcAhcWOlzgvZzVsxcwu+8t7XzczsUZfP0VWSekcZrnttaIwBd0qycs1hYXzWa5wGtyVXkCEWweRsbnvp8k/K4I6a761ryoUQ1dPxQRvvXAKuWTgH8GBK2P+gHXreog3sJ74UqHbgwXCOuA3M5n9f8H7aTmuegVjKQbK6UH18wFFRjPnV4GPDvQgVjgwwHTGrV1mFc626/pObLBmhBTU8gM77tZPlM07T7Zl+YYREUIpoOwzwnnhZ46X9P+redit0o4/Tc3ImkZg8MSoHpll9KM5QPv/o9D2OTjIXae0+O74w+x4C5UGHK664LbQOvwiNn4uD9vdG941HfUYMngohOarBWMfEH8pz6OO5YkEOllhiiSWWWGKJSSzIwXnEZHZoxxqcPOHrmqYlyoIpwave02uVbmnUwpLh+OrRm9DMVpN4B4AcDFMEwfsbKKPEEdF7LMgZrtJK8nQ8nbkF4pSYf6og7dbjZpfd6Zo3cqOQ45uZMd6++djMzEqpG7ypDStlWiDrUFb7vHpds7yvCk+beK/bpDkSkL/z6R2pFtTsKikrs60y6a005Dtt10Jr5NnwVI1nHiv7b8X1iKqtFtWo/T51OfN56838G4++JgTld3PWejxlJGG7zlnIHXkqfGzIDVU+vc2F2/RU2Wyd+wHAyh+7RKFjpyYZbfR7GFnR+cfEV9Ac88ZKSnFpVOVKC9WUqTkbWV7PHIQaT8YtJCKZDZEMT58p0UwrOwPt0pxrhFK5vhtFDOoG/VbEz0AIAeztgEZdiolyWFuSbnsvlcpvXn3NzMy+2eU5d6ANMmx0zamVm+67Pkf/RymQw+v72mVvx6x9gopwbHu7OjIv8ApRtqqT08uTo5gpZbwtO/wG/bbzabyXAGx9tjgCnjfTmnJLGFP3WgAR4jtnSALNo1AH4c9P86vjQXwaqTwudvmckmG31WABjwS5DZa6sFshKusy3ydAqR5f5S+DaxBnrqTO1vfUk3sg8MeMq2FpVDL4czk4/95iAtWCxufakQO1DZ9Of78UvX+ZN1Wb/NwHcJreHyAGc+SAiG5XOn0/wfXVCrHt+mmTsEu5pW5SYb0m6knX6CO99y0hB/zJ6XSuEg68qbEXlfMvyMESSyyxxBJLLDGJBTk4D6kVEP7STYs4ijnd0+KYNqtSKzRNY4GVvWvHVfvDhZC2ySoS4a5XgyCw9D3ocwfp4FVTG4QYuF/CMF2Fh36wIAZ/hXObp096Lz0VdJynr+esbXicUYhmr4yvg9dATV3KANzFyOKaWUvbuvA2rYeVsgkhAcNWaIy8w29eydv+jhjuO/n3C7VpxW84ySLupCyl834OWjnjONnl7zkd87G03d56ab+jVuH1G6+amdkPfCL7+Zdf+LSZmX39rS+bmdlb387b/ZNcU32tyohBo9atZKXlOj8fV6Q18k1gXDAKOPbOOShQtuj44pqueaAMTqfOj11BokySojMZUImfv1ock3kqqyscgYrjb3BaUUrQ56NG+01HTLIS7UOPwiaf3BUoEFlvxFNAXS+FPATmUarcI/9KvI4vH75hZmZP1/K7aKiJa67pOjkxh0FGdPwVKXM1zaBQBNCum4/v5VWwPxxtL++J3i0Fpioc0BrGa8wLuZ7YJfl78H4uRrJVpx2BsOj1wkZEyVU6U9QBjUlBF1PjN6lvT68DVEwN2nvNm+NNfv/9rd4n3tTjdWeFxqzR+ZP1hvUIYXTtXu+lUrjJuvy50oJ9xjNgioHZ+9L180wb/lKaqZbge/RCu+jkeNB98lo9aETWtyFOB585HEbSjh7DG4EHMqICXrLnOp8hBiHMjm/GRUizc+p/EzSXoyvWhBzo/bty5JO1Ou/vCRF8JCT0ivNOi5pheq8pimqGwXz0WJCDJZZYYoklllhiEgtycB5FsHLVWNjkFVwrnfPhyIovr+YaZX9b9dou6xFhGFpYs8r8qePija0VLvX8IHcxE3cgHWEZCyG4lc7bl8YIX6XnpV6qlXC92Vj5sdfyc2R0b72j/deKFW28MoC6U294McmLbuYoiDTceQPaKqt/86Hql8pK7M7G7GGux6f7OdtO95RlKwvxZJJ6tzJA+B4dXgTKbr2rnlbUsLprpYhr1TE3Wn2vduJFtKWduvzhg1z24CVQ1y7u5H27d+9zefvpj+fv/mbmIHzqNnMy4td1nnH+wwlxi5eAslZl7a4kqRpHaUx+Bqa5M36XYtbHAomJ94PA7VJcBLpcjlma9gXeifMfondl9IKs4SIoh0tlpwX9K0At4BakaQZV8RtkjOprX8rl0dopzyQVZnaZf+Mrb33JzMy+2r1lZmaHrRAPKCpGnwLq36rX4iwqZU1Tg4xQS4ZPIR8F1Wb3yjCPur66OHItYNejOyeLrOC/uHSEniQ5BkcHUY5IOQBCoPc3Goat9mmt1xsrnK5Sh/E5M7O6n94v8MxwXwN9ZxVAObRvsyx2q4wyitUe3pMSSeduWJc+j5NUCQfde05AQvQD6Z+amdm+k4cLqM0sk35WTPUC73+c/y3eh6YogKv7HGg+7OmlgEeFqxlADGbIwawlJKhnqXPC43RmEhAcaZsiBswPd050Q4cPztldqRWn3J4gBHrlHVmFkliya6GQj4U83+j+BmDMtVkkurjShbMcD/I5Y0EOllhiiSWWWGKJSSzIwSxiGeyozPKIP/sqr6g36q29pjZEUibq6LmjdeWrzfw44Ep3q6xSmUySJ3bci06smijIgWlbzDoiFr6apyuh9ONtZ/23so6/oyujVtmFtwDUajp52pLfr8yp3chlTVnZoPpkwt/hUn3d72cWf/sD4g1cah/WtR2VEnVSIRy1yu5nXIE1bpNkqXGKTuBTn/S+lTv+TX0D9rO8hVptrEuzMiMDZA+FMtwoLsLtbXbjO4r5ux4y6vHqRUYMXi1yF7rQZ25GtVMGrqsnXeicSHEwXGnc4CBY6fX80OizKF/qKYPbkSH6HKSpx4az25XVqWzpbPXgtedp500roo+QoxD03nC3PWUuXt9n8rLP4hDAWZl1iiybPA9MXBdcQEv039Vgj0NGwn7z3a+amdmNPOKP1VQB4S591Nzdr16bAtb6dLxAPRjGgxC3W5RFQhKiFTYYqAu6dB1WMd2SpnuF3Fn5+SHZLU0r3Q5EyMNG+4qOfeVHOHh2VunHap2DVUl/D82tYTrvHWFwlr2+qD6HaczWA0iEPEmu8jmBBd/eqa0VCnUtFO5Wfv44Q57k+T/M7ifJWz8yPjrPqBHc5+CDw3lSYUQPPHOHc+AeApqbcEcECbFlykaQFTg7+MmAbnBf0Txx5MA9P87nQ5h8tnQkYYp5xGdwDAj3QaDXCr4QA2oQkOf8vtuhtUfqdPuu7vMHjX1ClDTrjOu//aJMDmxBDpZYYoklllhiiVm8EOQghPCzZvbTZvbjZvZjZnZpZv9tSulf/ID3fsbM/lkz+8fM7DNm9qqZPTazXzez/zyl9Ksf8Jl/2cx++Tvswp9OKf2F5zwM6+Ngj2+eel243OQMebXJGVGJmxl2VNR9tPKu6tq763VCBAa84cX0rW9ztjJcqyf8ISMGSR26CnEISlwNhTjQCXD0QVDNnfqXO+kV1uszZQXbWtlUh2tjDhQBjy/1ujLi/u4DMzM7XSq7FV8g3M05T9jk992IWb4v8MlHN5+8s59r4fXba1jCEdc4onlejwAAIABJREFUZSfrYnJcaMhhepc8L6Sk6qeOcp61eC8HfX/Xj6xofBtUWyVbLwo5+8n3v7sWWnOSD0SXkYS1UsNio1X6VtnYJd4TSuPR/a/z/ClD4Z08HTFwvwOKytSOYWvbZNyYY6NVIpmOq+wnn/MklkQy9eNzZEswtKmt81WRur5eFxehVjaL5p75RB51mtlXolZgb8La7GuPv2ZmZt+4zT0U9vfyvKdm7JMTtMu/k8Mk05NaATdHatTKzm7F1WlbzjEOlMyFwoKQA7ItOjgyHPPUCXI+mWLfT51ICfpfrIW0oOqgkyIchLIIno3ivw8C5IgCx8nzjMvsuwY8SLgfyM0ySB5yqQx6p8c3QhCe3BR2u5LvxyGP2UHz+BDwPTj5/pqdzSlP/fXY0Z3vLnsdbQLG948qDSEFuAnqnnbUXKK/w0GZdc8Nx4EHTuLUv2DihPgBzxdhzL2LGedghGfC5Hj9/gIawbu4lkFcnHMg7wl9vub+qedvupNdaW7egpAK+amEbnK+w3iB67cGsxeEHryossLPW14U3JjZN8zs89/hvf+Bmf0LZvabZvY3zOw9M/ucmf2Mmf1MCOHfTCn94jM++9+b2d/9gOd/4yPu9xJLLLHEEkssMYsXtTj4OcuLgt+xjCC8L/s/i79pZr+QUvo750+GEH7azP5HM/uPQwj/XUrp2x/w2b+WUvqVF7PL748iBWv6YGshBmWRt2hJ8RYgiamUGVGT71Jn4UaOheqKVsiXIJBV7lXnFPpATTGgmZajWXHD+/LzeMR3ZKDyAzDVsMmw+lVprRjh7aVUFztlqxfy8de2UGa7v5R+X4xd5PlR9cu+kMcCdXzfqtas8aC+HWJyNm1pntrk7/K+9Pkx6oMu0tpvmvriRueudGQUCVUDCAIa/OmqebDk2Qf+FPSU6CTobuXWd9S+7ZQ63kl5XFZP83c2IDEXqoeW1Bw5edqHZlqjtKJwXkevFobot+l8B4egcBdKZcjN6HBoNtYaIx01dQWDDrkj3DDN4mIfLZGFC/Fxn35Pu/HB0DzBf76cucrhdx/oViruQpCTXpe3aOxjnd93e/fGfv1r/7eZmX19k5EDMW18PBzkYf81uaqdmP4Jzw29bzZfeKHT/Bv6KQeDTNoGszLIU0PEDUfa+A3vyjrlJETNkwMufCgNdK7urvPn7xb5uDc6Z+x7g7IolX4DJnvkMTwN1CdnzTa0ExxPvtYbm35+BRpSCHnSfWIl/shGyOSDr9X2917On/ntmNVL8aTrWihLp1vOoH1c96hW8vNzzsbYW8B0DDw/e93oZjseb6FrCHUG82Kvydrr296TfKkTgwPeiwuq1GkVBAJkdWT1oziYchtCsDMnWO2vj73q/vR5mSEk0REoXtfzcDQ05to1d9xsdZ89aM5eJ7Mj3jKoTYTGdcWUm1LMVDqD3wCeP14I5yCl9KsppS+n+Wh98Ht/Zb4w0PN/y8x+zcwaM/upF7FfSyyxxBJLLLHE9x5/0NQKY1O1D44fDyH8WTNbm9k3zexXU0rfeFE/HkKwdVV7ljaoR7hFeixo1VlPa9bhSc5/6uPRojTAbi/WTZEC0pEBLoFUC9WTjDh0ckSkO18vVKIXUtB84pX803dzdtaKH9FRS92tLAoxOG7lgKj+A70UBAPsay+lTTsnwmYHIxnr+NoO05XzUJA583y0WYM6X33j7HYiy9b4RE45XuqzOu7oZz9jMYNiCHlAex6FEgxD58/hZX7See2FHHRRvBCc4tR18aVt7sWwCkJzKhQGWsXTzbJV1q7OiqlFQqC6YFmOVxpgQjmt59MRk6w8ORqBa19+6LVS3Dnl4pkiyAnKFL2funFZWSDjQTmiIa5WeW55Pwp30tR3wAT3fgc4QepckBr6b+vzvH4nj8/X27fsK/vfMzOzp6vsPskFX9BNb574OGCEekXXD68jrJh/jEwKHgz+CEmKkjQef5IHRfBzoedxSvQx1/NIIZTp7XYZgfv/2HvTmNmu7DpsnTtV1fd9b36cmk02yW6S3a2WuiVFUhQnHpQ4iIMAUYAE8ZBAMBDEARIhg50AGaw0EASBYcFAEBsxEAu2Y2dyAsRIjAxGZLVackvqST03u0k250fy8Y3fVFV3OCc/9lr71rmPlNh8z2Ynuhsg76uqW7fuPXf4zl57rbXP0u2SwB32uA9LXn8Lol015DAZvO6sW0iZ79gpkRkxX0tZIlRH+6h+DjWz0YbqGL0Wn6YpLdNu+ENNv8AHT21snhvMx+A6t7UhMhakjOLJ2JbaR/lcgMscKrhTpZCyZdz5pvNStAavIbmRnvJ5uebYr3upGDQO/L7fX1xOFAal33e5Z8UOWQHZbgEO23kPiYmaYeqHsAMxAYBzwKRS0L0aeA7W5K5s1B+nT2gly4hSvWnbE37QVCkS7rwX3mv8wEwOQggfAvBPAjgF8Nl3WO3fnrweQgh/BcC/k5IajP6uv/Old/jod+JJzDHHHHPMMcfvmfiBmByEEBYA/juYBPg/SCndnKzyAoCfB/B3YdyGcwD+cQD/BYA/BeAsgD9+t/uRUkTbbd11bHHWMkc5ICZloy17zXOmV5LdXm07RNZhe+SzRGWTAxECHPM7mzwzTHuWhZxQEbC9z5jy8QOX7PsPmvb+lD0KItdrF9KSF+jdZdBC2TUmzFxl+guJ5Se90FVL3BF42/YmfcsTXbxU7x/S4HU2r1NPegcow5XPgxi8QlT8tf94nocUkwxa2Zz7/zML7rsWKam/Bdnp9DcIQnXWUifYtvaGswCAC+qYqN9esSujavbkD9RSpGxYx9wSYdD36tp3v9TE31t7ip2dsuNHzMdPxxvlByFvgkq1Z9VxuV6bj0dRBpRCGbgPHc9b7zVWsdGVjSkbUSbEY9jxoQdGTbmoC7EmGkSPi3jell//1tdwtKAvP7Xz6NQl0F7KbVB6dWfCI9uFnUtSznYxe7+kNwl434l34mqOAtioTl1LATHZtjg1ujf9urExvcDv3U9U7wIVAiuO64KZt3wNKm2PMMECJRZCH4mIyPmwEVKi2jLXk+9Hob4YpWrtRAJ4EAvW81fkRTVEPxfkUS0W5FX1DT7CjPaUmfEN7ssJLN+SoCaReyQPk1Wi3wveW8Sdc+Z1fKEO8jXg+yfqS8DnRdvLBwTZUjG+nztITrsuTgQICNhBzPzDERHdeekdMnd+FcDIDxOq07mbrZ5Ntraezxteoy3v2b6ProzRb8lt0/u2OKp757P9TsTmvcX7PjkIdtb+BoDfB+B/AvCL03XIR/jVnbdOAfzPIYTfBPBVAH8shPDnUkpf/d1+L6X04++wH18C8GPf/xHMMcccc8wxx/+/4n2dHHBi8DcB/EsA/haAf+XdkBoVKaVXQgj/B4A/AeD3wyYKdxcpYsHacZAule6Fsc27aqVOHgSaxUYkZaMbfkdZJZGCkiqEisl6Txbx8QOWlfb0FNjcb9lr95AhBUdnLf9Ysx87k1OfWdeVlqO3dhhU6xRHgtlILo1Hq+bvqmM6t4BfkxufesxrrCb1Yc1i+35wN7BCBA6x7Lmuz7mVKXLZcizVm6JnFuZ97qe1RbGO4zS3lO/9FgMzviGOaAIAtOSFaNtykTtbk98hz3nVXs/k/RBiynsouFPawX62byh26pjqoidnTDGclRF4y76RVW9fYzYirbn6eRBpSqdkmitTInM+yYu9DEhVno3L0bA4MIQM1I4XzGCwJQKgTqLcM/FhUpJShqxzZXtit9sw4NXTKwCAr934Ho4Xdj5bHaXUOIPOL/fX5Tv5NRedAs6FS2vyLK4iP6RmmwcheYOywA4oWPN1nweGewmo50TKs/YFEaTLdEy9sGIXSmbWC7kT8rqSv4FObenb632bMjYUUqD3xUGQZ4QrYOTrXwjdEHJARUGw9fepIGmi7WMd6b3Abp5NsYeLfK4VvO5fJdp51Jui5GjB+0bKB8/u/YrAe4lR9RQQp5Xy/FJFr6zb77XJ6u7BoAxa7+dOiFre0Q/COTppx/sg/5Hpnya99G1OHBPVmVd8qDS519XVslePDnHdUtpxdiQ65+oEbtuVItzvcodTcY+gg/fNITEYC+5/APBHAfz3AP54SnqUfV/xFpf792rf5phjjjnmmOP3crwvyEEIoYEhBf88gP8WwJ9M6Q6e8ruNn+Lye3e7X0UI2G9qVxi06ncgJz3VKJNmcfyYM8N2fYJAf4OSfgeByEHHGW/LrKu8TG+BD5gb4dHjxilI7BB4SrfCEzoHbulipsxHs1XNiPY1em30WSdYd1RPgZEAL28BIiPiVPB4+l5cAs1oVUPjrLXT+6qL5soBRLjZubID8TU08++1LbHxJxpy+bh37kcubwKhADmDeswEVMMXmtNBaaZ+q+O+bMhBSDyRS07HD85Zzwj1Ekj7Gkd1LeTHA28f9eBgth6YSao2jTiu4xR5OVrqDlTaUOdZiGuuVZQXx4Lj1R8dcvXdzh4AiBKU7CORht4zGfE5OnV04xhL+RDJqQnehbDP9llMb/RELwa7ZtFw7OUbQR7M5575GgDg5c0hbnBbG2W4frTkcUDnf5KF5QDUSC53BCH/XMzxupaDoq4n1Y8Hr8fLC0C1dWXxanuxR3fFA3Zr3WfviAOqWJZEoOSEKI5BTVSj9I6KFlJ51DsiFq8pT5ADva4bqQzUWVNup+xOyWtzQRRHaOGqsSfEPuRBQb5AsHO2Fw5QsqtowXvrD3DMjuil8cV4DQDQcr+Xzm9heNY94TQxphn32HNhPJlOa8npUP7sEbKg58YwwSDFfymmHRSLt/8c09/bUSBM0Ycp/1/KgTBBK/wZpH3kfSa1QihyRU4vp81B39MzK+w4ZvKeEyopBJGQUpjwhFDg/7vIAcmH/ytsYvBLeBcTgxDCP/E274UQwn8I4KcBXIOZK80xxxxzzDHHHHcZ96q3ws8C+Fm+fJDLnw4h/DX++1pK6c/w338ZwD8L+4P+GoBfmM40AXwmpfSZndefDSF8F8AX+J1zMALjJ2DkxD+RUjq82+NIw4Du9u1xTqo+28r2le0xi+vp3z6cMtPabhH5b7nwDcz4t5csG11TbbB90Dr9tZeMW3DCToeJXgo9Z4ZxMuteDJq902s7aga5u1ZeT5Nrlmo28kSQbjd0+UxeXIXAmWwt5ECzd2mS5cHPDmIYpHqIwKD2d0QMxMIXgsD3hV707j/ObfOSqFT3Uzs9jmvB1y1n0q0zhIdsOylEZ6F7owbug/qpi79QdTb2F87YOeqYhdX7E5dGZgAV3emS8BtuTx0zwVouNq03D5D/gnsg1OJ5qNguWzZxMXIEQfdKVDfLQkoBvs9jXJwzlYsQm6Ld7vQryOv5pzdvAQD2iDKEVvtPPwNlterK6CoWIgu0aYzyi2Dq/K1XngEA/Oo3zd38zf1jHIqvo8xnggTIxdHVCxqHCYLgJG2d54nuW8iKNykkkiLFShkSKuScAHU4VBfFs0urwe8VzMK9U6Ktt+B41qyG1rw3F7xOHBXgzel9FNz1M6Amf6UmGlO5e2J+zTW+Dwtuy7bd8BhKqhGW0Z4jS16TK/AYuKxrqRcavr9Ed8Dj3dj1+sPR1r1RGyLyUmt8lk1D91ciLXIIFfL2TgjBO8XY02LklEy7CsYJMuSclAnnYIpalDxXrqgpswfkqLRyhA5cP3gPiVEpM+FWTBGFCbqRJkilcl/ncolrIGsTR2p1X4U7EI7xF3PExMdY+3qvpAq4d2WFTwH4ucl7T/A/AHgJgCYHj3N5GcAv/A7b/MzOv38RwE8C+BkAF2HX0ssA/hKAv5BSuuuSwhxzzDHHHHPMYXFPJgcppU8D+PS7XPcPvoft//vf73feU8SIeLr22bW7aUnHv6G/ATssxrU41zab28Qtjukj391H3/aHjVPQPkzVwUVjhp/sWwbQezZGVAITXoMyCdVDOUWsNFXkBPNUNTVE79Og/ZLboLp+9RPnw6XUFsrq3Lcg9yCQZ8NA34DN1jLFfmugTbux17Hv0PdiunOseqEKuVtY28nfQM6GqqVK363ioKbZY7YBjJlkecfkfpxZ39FdjbGn8j/rtZdrO0cP75nZfL21czScyXXwQfyBTgb/dhsN6negOqFm8zE6s1uozCgOzz0EwsTQ3XeZ2ZnX2InSaFjG/ge5Jtsz76F3Vz05PdbKcNlPvlGvkK0yGyIN6iQpjoqLsPnb3iLQ9unVt14GAHz2G/8PAOBa+yYA4LBqHb2SSmE7yXRcleD0FalRlI1xeNwfgF/wc8nrQ7cHj7/ROHDfq6LAWdZrF0QK9so6ey0+xILfrXkNqxOq+qLoVHrXRaka1OVRyAHPaUWiSYFRObQgr6Fh7wN1Y9R3aqIzS6oOllQ5eS8BojiLZHu9gnEKqpb7wOtqr1ryt0crwYbPoIpowgGfB//I0vK4F07NzfL09EUAwPEZ3qvKuvHeQv0TQhqRwml/BvGj3BdlorQaVQmjE6gtc3XClIvgXCjI1VPrhXEfwhSemBBdnCeT82OkvHFVm5AD5/xw2Qrl1DgIwR2fU74HjmbscAt2lqHY/Xtwb+CD993n4AcpAuyPrwyLZLQBwcRre6IFtQ3myTw8Y8vbl/ZxfMn+wBzeTzMjtjnuVnwgE+dc8mTuR5LYkBuKjCBYDl3pb8eWF6oahwyCeAegUtMjLiveALUesIQiO8KhqTfYsCexarsljEg5ZtvZcr22h8SWhLWTU04KTm/bUpOnrvUJRq/JAdu+Bv5mS5mcmpHIPla4IbleCGpIRMhWY66Hplo5q9VzmNw1RahczrRjSmvjEShBS1biefDDDwAAznacuKn5CW1xxb/TTRM2MiAi6U/3pBu2cL2q9P0rnDGnEzmxTS7zSaEr+qRpVCmo1h8YQZo5dBkm20FT71hQ67rQGLE8ss1bkdcVywy6xkQO1cMusLlYYQd69fprAICvv/plAMAmXAcALM5yXNC65K7g2G6DSI/6I5B2h+MOK1g3lpJcV+ddMkHBynrNY1ywrHD2YPzje55bWrrhEP948zgbnlBZHKu5UTORkemPvSYVZS8jIk3CRDaUpTHt2JGc9CiTopoT1TpoYlFl7zvxMNjzRSZYFScPC9Z0FoOeJ5rosESmB4jaBFcJZ1RW4f4OtId/gATbP1Q/CQB45Zbd579ZWhkqFJpo5QZD3qp4YpY2DUnFw+5ZTYW/l21j6sc+3ZaXEzgeVT5JKCaJgf8h9r+5Izx/x09MJgluvOaTBGT76mUDlj6HmEvgRa5OfV5aG5OYUY7uRG03HuN3JMPVNVeNk4bfpZrzruN9kzLOMcccc8wxxxw/mDEjBzsxxIijk2PUbERSizymrGZls7QbhE9fOTSJz+HCyF/Nw4+ifvAyAGBNKeJxLdKOYH/OJgUtiXhHwhEmEhy1bBUxTU1xUin5Eb/fW4YeYkQh6FrZKsFcF4VIBkho+qSzTKDtLPM/PTFEYM1ywbY15OB0fTt7vd3a+j2Rh1amQEXvlsVDQeSg2JH1AYiVShwiLgrt4CYmmaCa+Qx9nkGo3FJ1IjYKunO9EJqekCwJhytmVXtRMi97/74nrORTEM0p2PRqaDjW0qgpw+Ib5YTYOAgr37GplnEQZCDELD3o3KgMMuTlkzix/kgTW2Umkm6fK3eUaUOesln59TC03MZW22SJohShUFk5US1lobQGj5Wd95tLQwZeHJ4DADx39dsAgCvVmxwP285jhcl0L4azOCrsmjoe7LfktyQr2ZaH0QWhEyLxSdIFHpdF4+2iSdzjOFSSMjrSpBJA8O3sqamRsvdSmT3Pfye0h8Rcoh5LIS1q/+u/nVvc1lFkQmb/IjQ6KlA4etM4MkCInxn+gmZGS/0Wz0nFPsolSyFVqXNFtEOlItqr17wHykEST8pymwR13kZSq25brvic+DjNv/5QS0njjW8CAG7wOhn4jBu8tbOkefZbHZcta0laqgHcEHdNkCbloomluyScQqDUWKrUWHupjPbyzKgJ5qB30qAQJiJXu+3qhQh4GSEnLY7eZnqmS5LIZ9qWDd22Ml2blE4nJbDR2hm+HBFAvie7bMkouaymsss7LJ3fe8zIwRxzzDHHHHPMkcWMHOxGPwBXb6FulDnatO2tZJnys6c2c/6Nq98FADxzYhlSvTG04ODkflx+6BEAwEMf+jAA4NIDDwMAVvualUtyRrtgN32hvXIhAhZn+JLZ+T6yidDGvY1twSYpEWnHIIRRyECEXAOoqZFm8KqN8f1GRBqfvnORS3U0y48iy1VjnTByttyraC5ghLVmz0pZY3TyjkrrqscpcVYmPeQknoGIgaSCBVOEsmXd9wQ4Q7LA+cHqtGc6Np/pmfnT1ObSfQ/ZPpCgFWuiGTUlfTIo8uk8KWtLNQQlmZJZy+BGJQVSKztpkpg8KWH2IWRB3IONEBVu2l2AmEFMCUpJVr/KLPR28qXqtlHZln7LzzvRhiWzSnIpsFWmbOud1oYcfIeIwVdrQwxuPmT3x+mK/JI37XcunRg5975iiZM98nQo8W3JldnyQNe85kT11RWoer+ku+JAOiGP64ksKKJf4ZkmsmUAvNmREkQhCMrSa5k+yfxMzY1khuU1Yu2LZInM7sVtcY6Lvd+UY9Zf+rpl9pl4CrLwdqKiSI1J3xN6UWYDUnnrZn5PxEQiCCIhllWP5IZc3C+Rayhx3iMa8RPn7Zm2fstMaZ85fMNeL+1aPamELPH6J1G3J3qz4f3U0o75mN7WHcJotOZZOJ9VvXhRedYtREDoQ3KitkU94Rq002eYowATSSACdggQ/la2dGm0kA8eFxGDDQnrPdHBngTPKA7bBP2aeCghIe4QLPPPnIDpqIPe531Rlr+rhPTdxowczDHHHHPMMcccWczIwU6UKeKga3GTmeDXD18HAPzWLbNR+NqhSbTejMba71hzW96+AQBoTl7Et1/+OgDg3DOWhX7wkY8CAD78IVs+9rBZP5w7Y1zphtDAoBa2MljSDJnZ3WknOSHrn+IwSMKmYyhLr0OqsYzXr8Q16JSt23eKVoYhzC7If+iV4fB1xYygZg161N/R8EmmKAPQQWz6XIIUlPkJMWCKmLzqTARAMlL+lGroccgZ9wNbMyvLOVjnKMHZocGZDSVaGxrFqDjKrGpzns1pLp3nUUk5wPGR3LSRYZFm6TwmZpJM7gDWgytl/2WHouC/xZKWVk/zc6W0/QT10Xopz2LC2NvWPhbvQRnkZDuhCDvb4HdqKRyU+hIpoPVu5PUQV7beYWOZ0VduGGLw9fZFAMDVy0QeLvBkrVQ3Zib5hl3bq5MKZ09sm5dr43ccVnYRbrhPW14o4h64VFXZt5AWJ5fzXPA4NYyVowG8hifZWkhAJxmbHIX4Y1IZLCntVF1XjaUcteAFoHp/Jaa8UA7+mssRpVZQ1h6qO4yR9N0FzY7EY6gLqRhKDQh3OWb7slhSxaB9HSb7ooyay1TVrpSSIkgSGbeHp6Lm8gV7pj15/w8BAE6/ZxykQ97Eh1StDOKLCJni8W+JPG2JxB029pxtQwm/rSuta683HOutsnO+PuG123MnZV0t0r5uRalBpOBSlt9z/AY9RwUlpB25pOzD1eRKvCDkz4Ht1o5jzftdSqxBEnGhm8NEITFejfpBfr4jvybatIsMACNaJbWKmocVdXXP5AozcjDHHHPMMcccc2QxIwc7cRxbfG7zPL7wxgsAgC9vTLd9pTLOwWHFNrtsLLPk9L3nzDumFi29AQ4PDV1463Wrz73+Xdvmax80zfBHn/gYAOCRJz4AAFhQbNy7Fla9dm1RK4uXvp2NWGpHDsRaDiNjVbNxtzvONcRSQshARxassslVe9dE9vLgtjDKuDSbJb+AM+M4FCiYXnWqo4lT4JAI67ie8rEWX6kuTgY0P05EHJKyG3ISpOdecbvnaFx0+cjq3Gdv19gnmqB6brFidtIwfThnyEFFXkgX1dxJHhTUubs5CpcyB1qoRstzoYzhRONTjHXACXLgmb5UCr04BRwm2Um7IZfab0+MAJh6KVtBfgnYetKKi+fCTkOlM5/p10AeAIcYxwu77r+1sWv4t24ZOnZlYYjZwOtf6Xl1wGzuMseBfBCsO+wd2TXktfUDXieFWOUlX/OwJk2/Clfg5LVmvRYvwA2HUu6Pofp/KAI6seo5KrLNVfbudX9pyR2VkVGXPpeJlFAKoQD8LbVK5+tlKZO12sva4znIeQniL8g+ua70zNG4WbYq9KKRbTDHI047DHGck/MlFt5gy82skpQ0RDzEQWjt3H3k8scBAC/fehYAcIP+B0IOtU/aB12b4ngUzKT3eK0uMKDjWRB+SFDPPSWU4csHZBihBhs/PcN0f1EdJd7PVt4uQje1vqMBXvD3MZtaEUdfl8NBj5Y1W51vNvz7wGeR+FJpEDqBfFw8/dfv7SIHRCOLfB0/V438DoQk8btlOe7gXcaMHMwxxxxzzDHHHFnMyMFOvNUd4b9+47N4q7es/7CkYyAdrgZm8zVrcqUK4rQ0jaHxbLyQimBjHgI3rrIuddN4DCc3jL/Qxx8GADz42P0AgKax2XnJ7KJqlM3btFMtaKtGFr1q2MK6eFm6i5arCrwpj70sxAdQcY2cg6h5u3S3hdbjPrAGWyYpJOS4uNCG+TvBx0Gab2VbymzHhkKyCyZLuRCXgOu7SyVrqzHPEC6d2r5dukok4bpt58yhvX/QNmjIvm/ZmvZ4ST8HZg/nzln9u9wztEFpS3T+g72tuyXUQjmYrQuBobolSbwAuf+NxxfkU6HMX9masivVRr0RE8+rPBZUhNc4buUHMOEkxDzLTd3gCJJaFEfVluWUSCZ7Inmip5/H7cIyw29dN1XC4YFxCM5cNmvwNff1+qGtV6+YMrEf1el5OundiujX1NATAVnRTXLJ60SZnhAjuTEKUXP7W7HO3Qcj5yQo+3dnZ7H3dU2HgFDq/rVF6bp1KR5C9h0hAYmkm9fYAAAgAElEQVTZpzgIjbwICt67jhjk/Jkq5MqCRbGATph8GZpGfARxK6SAEL+B157utSWPX2meWvu6c54sfvVMINdHF/VQucLBbXzFX5E3AhGlfmvfOXPWfF0e+Iihn9/8jj3TbgW7r9bkHojnETrZb8vbhePTSYEUnRflzao6jb02kiMfK51XHTdJB8ruN/zeidrJS0klrgZ5EYUrVnSvFjvt422Zivy3dS2uT+0+2JwSaZRphyMGuWeDkCZtzttF++Z3eAWT5k8jciCu2YhK7r4fijCiIHcZM3IwxxxzzDHHHHNkMSMHO7EJPZ4p3kKiZZjq5JHeAmKrt8ziOjJm5bC31y9RqV8BfQdKOgQumJQ+9AHLtp78qDHjLz5ITTWTmMWe6k6cITJLrZnl1OQajAgDs3pq0ocYR2a/Sn4+BbZFKc93nf3VHrfFDIcKii3d5wq1dGYqIB9vZ86ypqwafSxLb5HbK2tgHbKUpWNS7ZCIgdqbQr0WbKmWzUsWwPdZ/2y2tv37rtrywkucpdP/QU10sAKOVzbDv71v5+Jwj6l9xQyI7Y2bFTMkzsqbhRjA0MDZ8TFL6XzGz4xLYAAUGuBi9GPX4XtWwmtLNVQhC47KINu2slE5uiUqRbz1gmjY4qx4k6ToK6nU6cx12ce5DJu9RYiwvHpsap0byRwRKzrmtURzXv+ecXNut4YcnHuITcfYc2RY8tyeBZoTXkMU58i5TzvlBpEaS2VMQl68Zi6PjZxtruxeOn81KJNfgPoalEWJRoiYmu4IMfBsPtfKqy+BfO01XnIxXJVsi6xrzz0qbFFN+ACLeuE8BXcvVZv4SuPCcdjNKgE07PeRiCB27F2ipZDFkqoHHUMcc3Pwhz1LjXLbG3Lehq6pyh8oNvaP32f8qYevmnrl+k07/4n1/jYKqUI2EN7bhUjbMCQ/4fKKKPkbfi4czeHeu3Mgl3zuFlRW7FGlseI4nK91z9p22RYFp3wuj02TIjr921EWPsuIwnVUJwzkGgytWtYLFeTheqtmcRVyl0OpyRSFEKkCDnm5QyhPW+FGq+JaTFQoRXGvKAczcjDHHHPMMcccc+QxIwe7kSKG4dT97YN3FbOPRfbumSlEZggr6aJDCTU7KDkrvnTJstKnnjZ3scc+/DgA4OCstaoThyCSfaqMUG11K2rPK7qJFaqTMp2NSUoDdTiDT6dLeYJzPxOzBdXzlSGkJPexfGZfR3mDq+iu7I7ZPuvjkfXtOCiLKRCl6VWRT/V7L+DzXa+Rk+FMpu+SWYu6Vp6jS9+ZmxyfN2zf9l+1fThYM1tbEt2Ra9tyg/XK/r3d53gw85f//IIojLKXYcPWvBxzzfhdCs1zpbJ+oTSXETXLl71lDSS6bqaVfB+YGSkF0m/TVW1gtubtXvXjURkzx41kBG0nMpuRykHtCVNIo7GjLmjVNT1H4LYb24ebpakRbjS2XD5kCNPLVw0peOMNc0S89rrY6hoBIgtEQ1olwauAPfbJ3uN5XtETQsiKVAUDkQGRsYUEdBwP59XoENQ7wVUJzM6LvFeBkIOmqMdOiOql4GjDBL2T4sF7JIiTQ0SB7y94Lhres0IkIhGWupFphy1KBCxZz1cXTd2DOiWOKDDkqFjU6gSad+d0kCtpXDn4Vc4bUo+SKkVHDJQplw4p5n8eCvfUMNjnoWitzX/ioR8FAFy59qKtyKYdW3KYNH7etZDb68QvCqMzrJQj4+Wu7+RZt9wWBzZXWfL8ju2yqTCiMUJBbo562pwuhCi5HMw+L0sMfAa3vIakmDlu6YB4Yr1kkjrNqgePTGsFfwn1Uj8H+Sbo+VDkzwChAUVVOBdLnLPlkuo0IseLhT2b1CND3hlNVeGZ+ju4FzEjB3PMMcccc8wxRxYzcrATCQkp9t7BK4oZLek4s7nlKZes8x+ww2C9WuP8BaNoP/qoIQRPPP6orXPROAbF0rKvgTPBzv0L6EdeCzGQ6x4zC9e9irZOVzNprZlihTDOsjXjjZzZumZ8UtdNzmTOa49i/orp/E7LFJWlCB0YfNA8sXMZce74pxp6zTS85uslS+nnj208Dq7Y9vZfsMz47HWOk9z8iAqc0Nf/5sJUIqfLLYoFkZ3CCoxnOjsHi3TOlpyNa0DcJsKzc46DBACl3Nns9UIDKWWG2P+VVAwVwiAmeD4g4nG4o6EutiZ33XOqhtQHUXwYbscZ0EIFJkYHIe3Iqic1VS/g2vun9DV4dTAW+jHVCaetZYI3blvmdOvITtIG4p7YNXl0nZwV1ZbPEokZArbMqhPZ4ysSeOToJz8LqTUGd3OUq2WZHefQkymuHgpMOReT3gS1uh6G8bpRpi89fykEgPeed0p1trmUA9pm3h9EvIeSqaEQhKZRdmfLjvr42A/omdEuFvxN9vmIUkiV4lrYPqjYLiRJ578MyiypbnH1Cs8NUTL1PVDWGuLaL56oZwyRUSGNY62c/Bi5Mm6t2+aHHzLVwqNvfBkA8NZ1Q5R0v7hzash5NNWwo1AYbQNtXT2DuW7vPhhcQfwocU78BuBvxtxnZUQmqBThSV2s+WwkVyHWJWKTo7Itf2uljVHUtSGyWgzkqLnbKQ+Fv1k1Up4RBa7kwMlnnvgD4pmUo0OinA9LJx3wPmHPiZaqpZYct1PsKLzuMmbkYI455phjjjnmyGJGDrJI6KsOrYqn1JirFll2ynZsZnaRDNn9y7beQ49dwJNPGrfg8gM2q26UCUjHrRowsxS5EY5G/apnyvGQs3nOHEufzefRDe34IuRZY/TuirkzVwiTuaGzq1Uj5EvviKitSK/LrmtLZhLkJqywdEezEx5nC9bn2LlMY7imjZhc9M6f2nhcuGb7sHqZ679ux7d3bL+xx0v3lNyCm3vmTHlKv/Zj1s3DqsbevvE+VtEQg4Oe6gwK8VVLTnIXZKbjum/WaVHl9W6Nns6tVCBTtCQUwYvBaSHGN8dY22ZWHtlNMtW5WiW06kYpGEvsdd7CE45BWNr7Q08mdRGd6a2IRHyKhY1HV5q/x5vJuo2+fGiOiFejcQ5utoYYlPSNCPvKZpjln/LQOkMahmNmxby017HAbWafZxa2vw9SO7+SD4EAFCJIa0Etcgxckl7Oi7Pjj/YSl0svT27BkvfVkq+lLChS6ciAkANl/g1RuQWz7VFIQO6BuCpUAui8y5NgISdO+RqIYq50WFyFZsfvwtEbXkPKJmXUIEWMHk3+AOC9qHq10CvnLvC1EAMpKcQD6TtHRsKkPWB0Dwnxh8jnSGLv8/nXmE/Ixz7ySQDAtw9N3XIUTd2ypXrB95nXaiPPkrDj6jdBSKatC5WNl2r2GnVOxOoXQmAH1fJ+kBWr0IyKSN6iy/1ByjB2p43kK9REdfapEDnbmOLsDJ8nR6d2DQpJHYj6uasjz02cOnIGIbpCAeSXkEYOzcTvwr9M2NIf7dDHCX3f4V7EjBzMMcccc8wxxxxZzMjBboSAoixQu1acs+xeH3NGRvb73sM2c3z6E48BAD70xOPYP2OzaJH0T6VjlwqBGUATpC7gzDfkWYgiunKC/QtYW1KPAWUKnScKhU8no5y6nPGesmVPLkJPlzpxEyI18oMvmRl2EwRBdc9SqIVtx4jEtn9b/taJvNLFJtbslse3v7VZ+QNX6Hj4Tfv87BXWbzkux/uGJLx+9iYA4KixLLWlK1sUwhIsw9wrz2JV8pxw5n6sOh6n4Q8tVXvOXejkPx+VcktzLnc2vtEVqtEyhrxOXJQJyQXaqm3SQVKZsgjP1D4Xizyrdec79XsQz0G9KpRCCJliRqo+EhhKDJ2c63jNEUHZsmZ6+6IhBN++9Q0AwNXeOAebE3JquKmDc1SQyJvgNrXjtSELciNcyu2P+7yOCa/W9lvHhWVbB52xrh9mJnTQCZUSl8K+u6U8I66ZzXHsy8HOc6cszO3peA7c9ZDIBLfXhNrRCPUxUA8EuXAuJTtBjuop+67JYfEOikQiCkc/+DUhkVI7qIUghpEA5M0wxAmQekH1bD5H3ExDTHrV8Sf+CBByxOsgB5x2avsrBKmTanlnqA+KnDR5v9BjpNDtTkSgPLWNfeKhTwAAvnHWem/cvkF2f3XMfeR1xPFcu+qj9L4WtTJ4PS/5UwIAdF2oFu9ugN6fIOfglOpMW+U+GS2P9YQunuUOGugcC1+SW8D7ZslnzBO1IZLDypRnW+7rbd5nNzf2bDpmt0b1j1BfDPV/0HPJvV5C4f4O4neI7yKvCVd1TDswhjtR5fcaM3IwxxxzzDHHHHNkMSMHO1GEEsvqAo6OrfaKxBkj07vlvs2cn/roRwAAP/LDTwMALt1net9UV1h7UzxpXOVcJa93zniTFMmsdzPTaVt6C7TqIqY+DurrkNv1uTNWUk1ydOO7cw45KVD5zDhln6s7m74tx8RajHJXKdjnB6wbKwO9UXYYos2jm7Utl0dSUHDGzn4Uj16x5bmX2F3uVcsywi1mgoQaTiobl+OFZZybirVmqSKYnS3U5lFOclXth71YqOugsgQVXnVccjTj26p7anTkysfXkxK+Z4qjZkHXQOEQQRTfQ2k4G9cPvRwNc+2zei2MtWO+39TZ+sr2VKsfeC5qZVLNAgNRrNE10cZ029uY325N4XHt1Njm8sg/JSu7ZSap3iL3kauwIgpyLHDkwDLqwPHeUB9+fLIeuyn2dF+kl8SSNfGGnT/P8vpohMLIt14pMDPojTJM5+QIKaE7n8QxPHfdjtNgI6TA+xeQ5xPze7UIOYs/JKkUcu6JGOaj/aXcLrnLQgnFEylrd2EdBqF7O9cM4N35nK4Q8vVKXbv6rS53wYw8Zz2Rx9r7PcjtsRzdOyvV7VUjz70FnKuk35Zch0jC2ZXV4j/x1KcAAN/4vHFW1jxn286eq7q2lQUXIez8Bocujs+z3e+MT7ScP+UZtMZDKg9xMISoCYJwt1eNR+Fblc9LlAJGfDHvrGr71iZDBoTaVOTDXGgMSdhLtrx5YuvdOjZe1Ia8q34rbgd3ifyjVIz8j8LPr63T+/l3O1Meds4LuRcxIwdzzDHHHHPMMUcWM3KwE3W1wAfvfxKvp5cAALePrgIA7rvfPAp+5EetpvYRuhyuOFPcMBMYUIz+6aqhyQEt0G2P9elA+rpY+hv1YvAMSJ3MlGmqR0HuqV25DpYz7FCOjGX5EUS58sX8NddSB7vk/eqZOTAb7zXDFxNe2ZzYud58YORHjK5g8jGwbV7YMnu/ZVnsw1+xfVm9QM6Aamykra/3bNa9JpP+qLSMs2U2Iq5GTSRG2vJe5yG6+hgVlSEduRSebYpzUMtJksMQhdrkPvdxJxcEdqwEfKlZP8c1DmObQc9cqVKQM2KbM4yVvaVB2vLcs8ITrZXULDKvIJOc41iL3X12HwU/S71lMLHjNbe0bV49JcegoU9/pQ6aTL/pHBmIBCzJQVHNvjYgAaccri15AHL/HFpg2JCZzQznWjDU4lyy++NiZfcaOisGL3h/NFTjDJX9dixtXDaecBI5kgOiWP3SfIuKoC59xYgqQKz0WlwUcRAmLqXksTjj35G3PFsLlRCHyZXhmSffToVnzFIluItgzHkr7mughFGZdZFnvsndOvl8KOVa2mlD/B43XFfjb3BbQhA8656qGIRuOf+Fv82+GU898kMAgMe+9zUAwObwOY4LOQriT+wotFys4NwCKRm0c5OM2PdNyqsJ50DeCkIK6hEpAYCC13aqe+0Ctzf6FXjXxJHwYK/Ve0EcHl4Puib1fC153Tx4vyEqD9xnCrYt74Hb1w4BADdvmcPols+lPiREnQs//JxrMD5QcoTh3nVWmJGDOeaYY4455phjEjNysBNV1eDSpYex2COrePVBAMATTz4EADh/ybIa9Vg4Zp1Ync6KonRGsmZwykKShpracgx5N7qSM9tGHtnMLKvaspVKjOggly1+7qzd0RVL2ZGUDsrG5ed/Z0w4B3wlv3fXFmspljqZwK2OjbXYRarQcv+2bFuwiPaPy6/YPq2+aLPlC8+z+yITnlNaI4rNflKzR3xpGaPqfgVr0mKK77E/QiF0gBnTsDOT7jnTD0XONRjEWyjeadadcw2mESSx14WhxY7XuvdfUDriWaXYyAzXu+fZmnoNiJ0ulzXv+6AUg1nfYsXxkD/C7SP0x1K6EEEq2aUy0MegN65Bt7AMqJevAzPDhkiS0K5hENs6b5Ent0IhDiW/fzCUAJ3oVqIQkNG96XNEKPD+aNQZUNp6DpMc8/bkbkjVxoLnsBl57hwfXstRfgqlOxyq70LJpa6pij4nQvsK3staBjHgvWWmavcjCx8Y77vkTVqEsAWA97k6ParHSFR3V2X6mGbQMiGZ1KAnfR+EFMg5UWiZPwmKgLBU3wW+K94OpNPn9e+8Fl4X6vxJ7kG0RBgXzlim/KmP/BgA4JXffsM2q+6cyZCr3pn3xQiuyGVVKIW4FLr5pux8770wQfOEIMjfwIeP6+sR4D1t7HUsR98SISetrmcdv84jx6X0W4/f47529BgZ2Cl2Udmz/NyeLR987AkAwIao4ZvX37LljWs4JTonjtJ4vvja+SAalh3k4B6BBzNyMMccc8wxxxxzZDEjBztRlgXOXjjA/R+kRv4s329s5reRHwAzh0q1uZ0sRSXO8R36GpS537qcDyv2Tlgu2AteTmbKapxFywxSHb8gbwJmkuWYUcQJQiB1gbIOzfy1l0GujBMddxUt25An/MBsvY7KAGw7B0wgTpmRH4fK1RnnT+w7Z58zpKD6vLnvXXpJdX/77pobOVlYdn9YUyMcWGvmvjawcVoGacylIc/7SXgWl6J7jXPod5jPHBdl/LIOUB8EpSsSDMhDnzut3hOFfCCkLFB9W46LXRxd9nRqlPGxjq/ssvEeGjxX4m7wa0m1UyIkqafPA/kD241l4Gd4HXW3LUvDpr/jOOOe/eP1kysAgFuwcxRZh1UCqdp8Td5LvWeoxLpi3V8iCB6/+mS4Hp4SnmYdsNjYfpPmgPNcd8Us8qSz/W0X1vdinx0ea2bYNV3q1lSVn6FBhOvUNcBS70zcQKVAaLoCZSFFi7oo8p7jUt0WlYrJOa9wpFB1bt3teTbv7P6pJl/KhD6OPRAclyKqoJ4SKd+WnBDdtZQIg7hKrrDxzptF/r4vd9QvutaIXoiwUfg2hVrlboKJXiXiZnRbOaXag/NjT/84AOCLz1nPhVePjrN9GD0Xwk4tPUcMdM95h8PpUs6Jk2xZz0Chp/IsCc49YAZOpY28BlKR/K+ixmNUUujA5TnCcXEEhMdfyElVkhEqzxIVVlsq0njUB/vmw/L0U+au+3j3GF6/any3V94w1GVD1LMTb8yVMPzNeted9d5ABzNyMMccc8wxxxxzZDEjBztRlAF750tUdDDrWUzue9Xc1e9A/RCkNR9Z/M6Ml/5a2fiEVSudsXfbklc863iBs03VmpURNd6VMWfQOu+hLJ0bMJKkJ1poZiM9Z9WVd1lkBqyOf665Fo9Cs1O591nmsGR9bOu/u8L+bdvWud82Bvyl3zbE4OAatcFk2V9fWDZxUoshb5vYqHBHn/uGWdyqXPF13u9gcNsI1Ul3sjZmboN3XWQtXPVHekuov4E8BJRBTufhyvKdi84afNrK9RH5sotIRBe8F4IsLTupLnJVivwLPL1yvTdr1HJxFFjB7oRyyHPPdaEXbYeChdeOv320MrThSme1zk3BcyNNvIMw3CdmUp065BFR2qOj6IK/NZza97sT236kAULT1qjZRbCJQoIs1iUVE0Qv1EWx3jMPEfkeyCGyGHKOgU5Sq+tAPhITTX5DLkPdl35diw8kJYA8AcpJZ0wVk2Oy4wpJHVOFXin7FKKQ7dp4TsUFigVA74mR7kJIhdwLbXq82Pg8UQasph0TJY1q687Oly+G+wLwZV06GiVvAJ1n9SHQtaYOkKOfg5AxPid6+41+bcd038PG1fph+h5c/4ohVJ17CdL9cQfoTJ77qrY+4ejoPsHkHvTmCOA+2nJQ8q7nL5cDb87BOR1CIkYkRc9cV3HpWSyASE6jtbhYeY8NIWl63kb/LXraUKF2i8+do62RNs7uX8DTTxiK8JHHzFPn+ZdeAQC89ro9T0/pGaHjdZ7THdqZ9x4zcjDHHHPMMcccc2QxIwe7UUSE1Rpd1EyQ3vBFnjFHeQ+olzpnbzUCaumLfZnX+bfMsiU77qm3baXrlY+B9Nqc8HttXUmcuraVOYO6YH8Ie+/t9dXqqeAzXK93ql65Q90FMAzSWDMjiqrB2vubRrVGe/3AGxEH37DeB+e/ajPcC7fsO6fc9Kv7Vlu+RTWC2NY1GeI1s7I9jYdSATUjqMkErlSL1UngvqpgXpco3QuCGRAzN9XpB9b9RRgJtTIE/tYECUhdPp4FmfKVzPtyEz8gjgqBpP4VW/2m6tgTvbayS/9pss7JNZArm7IS+Vw09GzALUNkiigfhYhuTXSB6NPrx6ZSuJHsHLXM3rWP7og5BURGOQsAYMVsPm7stzq6W5b0skCr67HybqPKVteQ02GelfdUTuzxnK3KB+z1QM5CbXXasrLr6DYM9egPyHF5xNYXyrN50xCJ4YjM8VCNWbf7fORZtbtO5uT98X6KeW6VuJ3B3evyTNPvRzdQjIj0sXAyivwqorwAuE/u6snss8m7tapbZ5wgTVqO7+d+AKkoRlRTHUObCbog5dUeeS5EyAI5J8MRO66G/P6q+Nz46MetW+MXvvUZAMBJR26LVBzjkGBHtoBsJ4QkuBOgxjZXWI2tKnLdv28/eoptC6dRBF8Gf2bbZ26q6AirxpAqD3EzJiiW1CyDI7a6p9mRdrBrttGzO8lR9AQ3rtt3zu5dAAB88inz2PnYYx8DALz45vcAAC9fMUThdEs0q67u7Lb7HmOeHOxEQkIXt6gIXcMfwIJwJ8QU3VS6sCL8SSCi4OCPVP5RKPSgtHASl1rK8gatKc0TYVFSRlm8qizhkslilDDdeWloEiAioX5cS8ll2ILZHzg5plmk8Qay9QUz23oHN+xi3//iG7j4TXsAnLltx70l8efWGfvOjQVLEbwDF04GswfQHgFn7YGsTJPsc1Vd4UMyUuKp1yXHrahqHyvBxtGJg7xZW0nLeLQuRcwJhr5UyJqXcHqhBl2cLOj9NIxlBTeW6fNtRCeU6rdZXtIDSeUStmIO3J6XKyqVuAjpk5gI/sGO2/Hx2xHKvnLzNQDAMSdosnUVvOzXiUyvvLTBfSB8vL3GycWhbacgAVHywkK2sE2FSKi+VcMwPshaGuQk7v+N1h52rxFqPQcjuZ0pTCa3F1ccF/4h0vgdmHnSAS3N91Z2HdzgjCZubF+LoRwnwRPSnh6uyScDuVRR46A/bk6C9FNor6dlu5H4y9VL+EzS7ZE19u6Lm5OHVTYQGVLmPlpd5yapPCVzJBH3JmRDlDVCrYkCf8qNgpQk6Ljr7HOVrPqNnatCdsOcXA9MhB544GEAwOOPGET+7Wev84hINg7pDtvfCfdxlOxGlWv5visYU7Z0+2nNx1Tqka2y/vBrUr4rmXUOqJ5zqiNMyipKHr3swN8Kk3MkO+bJRGXoRWjMy7n2ALL3TreU+F5jmfaMTRY+8SSNph55HADw3AsvAABeef0K3u4vwHuJe7KVEMK/GEL4r0IIvxZCOAwhpBDC33yHdR/j5+/03//4O/zOz4UQPh9COA4h3A4hfCaE8M/di2OYY4455phjjjks7hVy8J8A+CSAYwCvAvjou/jOVwH87bd5/xtvt3II4RcB/Glu/7+BcZn+KID/PYTw8ymlv/ge9jv/jVSgjvsjGUaZNlsSu/FEIVth+15ihh1TcKKU5G4imlWUh6k8EHxGz5m+Mko1VnLjlSZ7rZKGWvzKPjUwFS1S4cYZmuD6NFwSRC71sUhap5xOb7mvLY+r5vb2mG1UUftsy0e/TYvWLxnpMDz/BrC2bOLaGfut23u2PG5kEWq/veoNIVlJquiGOjLqEabHS7UhQsBMeSBCMNBgpFGb4FpllgJJZlWSgfrx8xw4QUzjxSxzghgEH3NBvSlbT52r1SQonfK6KUtx0DxTKSX/4+u2EWGKkL6IhDxJmxXHYWm/ubxFZOCE19mBjWO/fZPjxGPiasV2H31jpYY3gmUZ1wZb9sFQnrEzNVEXR8w4DC2zezbTGk4NKRpaWToTedrn90jsLc/wmt8DOtpfnxK1WUfzXF6ytNOQkHuysu88xwZeaWvlg3McyIs03EkbO+7z+4YsHJP8eOvrBrd2NVuC07K26ilbTEu0peSvPC6R/Rqhc7z3vEyXkxxbWYOLkMZrVS2xXdfM0oE/P3bkt0lS3UkrZkwMyEQKlZnTwFqPVzZEdG6EavLadBth7usyz/5jik5ahOzDda8tZNAkBIH7uM978JQlwIEn44jXg8Axtrxf7T0KAPjY078PAPDKs68CALr6NR5D688oHwYfjpAtvZmVaxd1TvjSEQZm6W1eXnALaI5vLzMkl0yWju6O5aWJOZTLRXlOKhERef6dQ6nrQc8dHqMQqFplaq4vwnAaByAWel7a83SzNWnj3pvWLvr8nhnz/eTHfhIA8MjFt/C1L38Xh4fHuNu4V5ODfxf2R/s5AH8AwK+8i+98JaX06Xez8RDCPwabGDwP4CdSSjf5/p8H8CUAvxhC+DsppRe//12fY4455phjjjl2455MDlJKPhkIUzeKexP/Bpf/uSYG/N0XQwh/CcCfBfAnAfynd/tDKe1kmCHPCCQF7NVel4c6cGbZDaOQRAYqyZVGsvOVHJLv+3jl9S3VoWTIUzB9HZSdiHPgqsXxfW+Q5AiIDGFymdsg6SIzqAVrYGBDnRMOw4aZ0UD73D2yCofnjdB29AVrrJJetKy1Wm+RmMHcJmnpiHXIrpRpE62WRcRUTVgWzZUzL+23RCrcY+OmSbaz2rdas1s7OwFw8La13haZUQulcItWNdLJw8lOWk+QkRvCTlwAACAASURBVFAcbxtNRII8gHZjWW+1bFCKPSHLYTV/4vlveJw9maqSO+laXLG1sYiuYrQq81kyy6u2IjQK3RjZlD0T4ddv2Hlat8z0xOdwkhbPBTPeLQloG2b3W5IpB2ZGy/vNsEjSv9sb2uOSXBeXzH6XCa1IsbKN1q2Wl86dDrBlu+i3OrvtX+ksczq39yAA4MLaxqUmErNQbV7Hv5FszsLr6KF0ct+yMfRC56CWW5Y4Nsr0nINkH8vCXJbpnrzqHlXNWkY8GmfVpMvSM1ZJE6ultjUx61bLdxI6dc7EtRAa4UZF3hQs5N/np2mHR+JSzDK/5ySrDUIQdE3KwEtkYW2T133PRlYFTZIiuSkfespa3B9ctAZE109e577t/BkqkYU4Fo6QuIx4wvfyb+T8CdGmdD2NclOuPeFfpSGO/A+ZZNUif+ZGUpKPazzcwpnXS+1/P+rs2GRkJ2t3NYSrZYCGamwL7nLqvE34wLG+fmz3xQkJiRcuXnQC9t3G+yll/EAI4U+FEP4jLn/kd1j3Z7j8v97ms/9zss4cc8wxxxxzzHEX8X6qFf4w//MIIXwGwM+llF7eeW8fwMMAjlNKr7/Ndp7l8ql386MhhC+9w0cfTSmh7/uxla+MZrxkP2m84Vk/Z8ohuWpA0y6XpnFNMVI7SdhkdlJM2uNKZSVTEzfFyY2MJDOs6nHu7E1Kijz7CF4yY3bN7ENMcTVSaTrZRQ+7P4mKaV/7HTsN/ee+a+s/Z69Lsf+bgMOF/fuQyVXH+vNqQdUF103bvN5bkVMQqNaAOARLdXBiFsPlasE6KQv/w6RlbTd0rsErpIggOqGGO2JVp46SLMnFXI1i4QoCIQwTo6HgxkZqSEOuQ1c6c9nPr5rvqK4tNMJbN+d+y+KqDDIWYqaQaGWs2nKgIVE4dacW+17Z4ziSc3BsMsGeBV7VeQfJ/jgOG7WQ5b52uhYX3BciSsdUDMheOZIfo2ZKibK8OhYuqfP+U9oHDoOGtKeiQvfPEWxbL8CMdM5yPM7FRwAAJUkse95hR+MiWjqvTVniVsEbQlV1zuAvOaYucd0x6QF2slSXjOl+ym23C2/1y9V0Y+qGKsNoDyxjMkcldb/n1+DIyuc4Sq3CjFL2ykGcHFdKCEmC/7at3yDqOSY+FO+1yPNb8F7z35SpT5VbPfvz4xYlwmqWdWzn7sJjJi/94EftUf3yl+z5UaYwKqlkPFSNMkc7DnEn7LV6RI3ZbY6YTJEDnXdXK/j1l3MRAgqElMMNhSsZlNnz7wIzfUnB/TKZyDClsJCSpCbC1iE3R5JFfFXWqIkUStKubfU83zpnFdUnLVGaG7fewiB57F3G+4EcnAL4zwD8OIAL/E88hT8I4Jc5IVCc4/L2O2xP75+/53s6xxxzzDHHHL8H4x86cpBSugrgFyZvfzaE8E8D+HUAPwXgXwPwX36/m36Xv//jb/c+EYUfSymMtfooLgGzUrU+9swyt0YuqnEmqtqfMtly4n+Qinx3p6aX0dvqvtP6mo1yX3c+Hq2auW4xnQMSKdC0uthhyWLMNhase+0f2euTb1jWdvjZbwEA9l62epdsdjcLchWWCccr7tee1XNXZPo2ZI0viUKUSzOzUdZSqrXsyuaHsZYL1IrvM6sj0rDuLcstlZHrHCmbj/B6v0xMlKX3nG1vBsuot6eW4SzOiV0sDgLHU0mdA0bK9vlbrUgabA0tNCD0Yxam5FEZnbJLIgYl6/uurZZNLmvEHVnIpVCJA6I55CA04hwcMfPmel0VcXVrNsm3mYWLM9BzP0/oc9AOuaJEHgvRW/dyX7hs2Zo2DGqSpfUESYhokJxbMIJazHwXKvQi+w3QmEktu99iEvtcZ7yJxzuz6L3Q2/WybMm94L27JjrWUUqiY6nqAnWSbTKvB+n0aUWtfSgn9uOY3k+jLIj/l3eBFAfIvj+4RXKBQiokXlxyqvYauTJ+qZkmvx3V/KgQwkaPlgknKalOrutQ91W9GH0OiOoJnUvMcBPvOWfruwGReELcNd0vvLcroahCPXnOn/zUjwIAnnnmNwAA2+0RIq2Ue6o3evo/9BpLHq9UCvpNXUb+/BTy4L4G9vYgMECIrFReaj8t06QURst72WvL7M6beOVqNXnPTM+Nt+x2b2v9beBvsYlY7eoH/d0JfsuIe1U6D2S8l4DxEZSk7ur78dlxl/F+cg6ySCn1AP4KX/7+nY+EDJzD28fvhizMMcccc8wxxxzfR/ygOSS+xaWXFVJKJyGE1wA8HEJ46G14B09y+d17sQMpjXW/Xnr4SdMOsVJdgu+z9OB1pSpII57P4ooqz/jHGS8XmsFKWz1p2KS0Vb/pNspq+lEUdyAGYuGP3Al5I3Cf+K81962nS+P+MTPDr74EANj8va8BAFZEDBbcztGKmScTjPVBjX4lVQb3/8QygkVr+322tJVbcgbiipnPipmPmOCqAxM56LmvrZABdzEUK9+iVsvrAqNjIc/r4JwAW7Ql2wSzjr+SBlzojdwNlRkpgaLCIsiTQI2X5AfAXsYxAYulIShJagQ1gFEzFyIq4i8MlXTr6kTFBj3kHMhOeqt6P8vjBcc30bJY+vl21eHlK6YrPyksCz+iWmFDN0KhD16fVYMl7qM4Cp4R2086auNWztKmK/WQIqGNTogXdbtNaoDD/S/zbFvmC5EchENmTi+v7Rp8aWuKmQam+17eJkIVq/z7Qj+ooglNQKXHn9ofc3lKW+29s1apFDO+o69DOfUi8CyfmaHzg7hgYqn3dR+WZXDVjd/3S56/Mj8HYvQH+npEcVPYJnnwNI/XqBpwVbKr5gq6tokclMulo1Mao0D0LrKe7a6u1YS1r8yYnJKBiEs4QwRizfHhcbcnNn6PPm5OiQ9dNOfEq6+96ChWy4tFre51LblTqu5/rj9MsuTSeR78XIgt0RGpQnRORvdLEbIq5yZ5ozkep+r/UgKNHgx55u+Isj+T+T21/pZKTLwrocBcr140flH1vCaltJJCppIXBw9j2MqWvcNOF6+7ih8Y5IDxj3L5vcn7f4/Lf+ZtvvNHJuvMMcccc8wxxxx3Ef/QkYMQwk8B+O2UUjt5/2dgZkoAMLVe/ssA/lUA/3EI4W/vmCA9BuDfBLAF8Ffvdt9SArpu2MnSbeEsZGmHvd2yGm/IMz2MWviQz2i9DqSS0SDNvQ2DZpeq/zlPQG6H/A0xwpVpODufNcuUylGXLVax0jJ5KKjvQxordgBQs6FQc2TvH33V/AsOP/tle/81AjvM9o7o1nd0YPvYHtistlvW6JU9ndh+7bHl7t6eIQAD97FjpuSua3I2lEqBGdOW9XB3E5N9WZczc+Vd4H72fXRFxKBsSwiAaoDMoNuNLV1lwAyhdM8ANSaSfp3nWqz2VhwE1o9dmZBGBYPOu7PHJ+oFV6soO7VFOmbWSnfCgozyRjX7UyoubnKcWCftWcu/2t3AlbXV6W9Hc7Rb0xlQHJNBPQO8bptrzKUwUH1TNBjtqhjUabKUKmbYOVdCxnSNdsyiKnECvA6bDRdaIgc3kx3ntxbme3BQWc+Fs4NxWJb04ljusepIB8m4JNKyrJHUalvIgXgf8sqgC6Ez/heqISvL5HFzX+R6WhMNG5hRa9yEAumu61Nw1CGonl9NEKUFv6MVhTzK94L7pLHvmDlXROIinxfFMr+/hEikZuEcA/1myXVVE5dDZFDWrW5w/PMx7O/xeIlqqYGZK634HOI1evZhUy089fQPAwBef+UVdHxe9DK+UPvoid/DaOOQX2P6LfcxmKA6zu0RIuONqRz24etqREbUC0N/JtUunL9Re5+c3L1SzolFJH+qyBEXcZ+S+omIR6FHQZ/Q8Lwu+Bzs5XvT56qOhueu1HVTDmOPjLuMezI5CCH8LICf5csHufzpEMJf47+vpZT+DP/95wD8EGWLr/K9H8HoU/BnU0qf291+SulzIYS/AODfA/C1EML/ArNP/pcBXATw87M74hxzzDHHHHPcm7hXyMGnAPzc5L0n+B8AvARAk4O/AeBfAPATsJJADeBNAH8LwF9MKf3a2/1ASulPhxC+BuDfAvCvw6aUXwbw51NKf+ceHQeA4Jl39FabnFEXvob+wc8xrs9szGXq/FIvRzxmIyVnhnIKFHfAO36JherZP1nXnbr1WUgbO3IOhlE9Mak9FXeoFjgDZi3tzKlt+9aXjGNw8pmv22pv3gIAtLxa1nt2wFv3++fslnXNLiZgY/t7prSs4oAudFvpcsUxWKh+KT2+6pjUpdONT2z9Qo6R4k14/ZazfPIL1Acgbluv45dCM7zGbOeiC1t+RygOM0A6lyl9jSnnBYizEMkHKCX30G8LqShLJHEiRHHmqejVHlocil6ojlbgdUPkIBAFCTacqMVhYDvmgdyOnuOw3bdz9Pwbr+BGz/PI3hlR/vONxlzXtbTUuVLGRRrSZ/P11MsjThjlUjUMSH6+KvfA5/mSh4QQMilMshaGQMfLZdPY957dtw5/59ampHlk37LSs1HdOjm+qpc3Qv0KDDq9PLLVGaM6qZV5Tw6JODmqIcvFsnIFkow05KnAG4XHP3hSqnt0rEmLQ5DU58GTcv4mlQKdrv9GPgbMStm/QP0POt03vL90bwZm9+ITqP6dwgKBSIEn0VIMcak26Dog9dDw/i9UJPVUwWgY3DF0y/UFlrJT6BOf/DEAwC9/7ldweHLED4VuEgErcp6LuBeuZnGFiF4JauJCX5O/hrYv1HTiUIswPt+D+xRIrcJnT8yvTXEPopvbCHHgUm6HST07hA7narcoD5sQHM3q+cypG3XntW20RDNP1iIbqd18wKTB5XuOe2Wf/GkAn36X6/4SgF96j7/z1wH89ffy3TnmmGOOOeaY493FD5pa4X2PAgWGPp9VOsdAtaMgNrutJkZ4GcLobc5JZO1ui9JX67X0q3k27729Q15r81qalAd8W6x17whWFO7SGNwJURkNUQbNjOWex1r58bdfAQBc/2XjGJQvXAUA9OQzHO5TO34g/TO9x+Wprv71ZYUze5aF7bPDXyA60e9btnHEzn0Vx6selG0zA+a2aqE2XY4YVD6rt0Prt3R7VIaurH0YJJX3sdPxR+ncg2ri4hTEbP3YKdOWGgEcP+6DFAZUHMhboCdPoqgrzzqCK0eESol7kHsr6AR3VFCU3FY1Ua1AvQPYAVLcBmWYh0QLrhxdRVeJ+Z/9BNwiwrMrvg75coTK+D2tyevN5exCCkQLgdQOQNBYU4DOXRrrrbwWhWrIzXKsQau2btu52pjS5Pnbxj34OAxJuLy0Lo26XnoiUOpyhxTclbOmvr9wPo/uKe6bril3BtTNzXua2aw4Fbp31XXP0Q+pmuQkGUpXEYh1r7p+InKgsYNUK41c+cSD4DOJ2X+t2jTXr+QLQmRhek6rshq19OzY2E34UnpWeXY+rderFwVW+obtk56jVD3VulmZ7V562NQK9z/+OK4/a6oTgQud+lAIOUC+4xPbF78dovPDpALJFQRpcv9E5yzwPgzFjvxEz+aQvRa3QEv91vjcnXT1FKobJvvEEfU+EX6IYVSaaTxadQbW8dh5XZB7dLwxHlHbb3wM7jZ+0NQKc8wxxxxzzDHH+xwzcrATRSiwrFZYr5mtcZY+6lZdHAsAGNwvgDPAMjovoWFGUDfyNbD31W1N3RY71oYdQVCnMzliQW5dzFLUOU7a27G9IwBgmQJqzmi33ORangnMWBas+e3T0e/2d14AALz4uV8HAJy89RLXtzr3tiHfYS/vOVGuc/27Uq29vT33Bt9opi/NPNddbHI+R6usXXpegTdRSAHHSVpxdTZr1XFRGSezd2XmSO5XofPk/S8UHLttUL1fvRLErmc27h0VmTnIpUz6bnEWiCAkZqvF/gqgk13iuiWzimatjn1ktjNTaniO0il9Cogg9NTpV1yfbd4xEEGIlWXS3cKcFK/ceh4AcAu30Daq/SvjUbrFbMuHJc9aRtdCIS45IiXmtHfQS+pBQrSH41pHAK2uVy74o01SEw6eA/5oX+X8HkdGtupsZ+u/Fgwh+dzWWq2cP2/qhSfWplZYnZAvQNikCxELeQsM8nfg+a79JgYAlCvyAqRuIRdhy6y9OWcoRXubdXM5CMqLhNuZ9qhAHbxzpXfyY52/lt8Hs3Zlo0kqlyT9PrPSxgkU/B7PMbk+6pg5+iZIBVWNaoRKGS/PAf1OIv1LEn/LWw8s1P+Ch9BMXE2THDP5PF3zN2/TzfOyjdvHfuiTeOHZbwAAtuwNEsnCl4+H6DBlzPdBJBZ93k+6AqvrZeldDYkWiTehbN47KFYo1GVTDpDclo8dn8kdt60+DxW3IfVO4V4NeQdNCScS9yV6op/82IRAC+WTi4vurUCFTMkV9sgtKbb92InzLmNGDuaYY4455phjjixm5GAaKY0+2xJXq96vToh9XgdTbb8qa0cMNIFV7Tz43I+ZsPuu58tiwoCVHryuxZDNOQZeDXPNbbHzroVmsuoauOLy6EVzzHvxM78JALjx3Iu2b+zK17I+21Ir37kvuS2U9MjvYZ+M6KZpPAtRtqUOf+rgJtRFfStU1y3lP6++7XII00xanR9bZeKq1fMYfVauczTW3wr3hhCvgRk+bNZ9fHLM9znT96K8iun8DRXpXbDOzLMfeQ4A0LHbYxOjqy4cGfF+DTnfQaV1p5zwe4NQCGnlN4QMjuz76zetZtseWQaNfdvAlUPjjXTF4He7xtR72uv69ssmZ4Dr8Md6teqzQgyQrT8pB2fvpzRkb8rzX0hZy2sP7h7HzE4sfN5H6jwnjs5xZ0z5Z4/smv5OY+qF+1bmnHjQCnERcz4h8AJWfde7lRb5kTjjnSjdHlUsrXpLiBcihYDuXakTmHFXg1AijlhTOaLovA/V94UYiJ2vZ434HUIrhYKJy6TupXID5ZHoWRakpPBeDaUrHYRaBT7DSio7hgnHwoU03NSiklvrUr/GgyILny6OXsGXLQjRsY9/7JP4/K/83wCAt24Z8jWQ5yR+h3wLKlcEiDMgpEnnjL/JV34tRqGGHMcw4X5J7VSUvg33UMg+Axw5FvfAOUxEUp0XJofUfG+80aK73+Zo0BAjorw21N9HfBchqNwVqZIqnpumbkafnruMGTmYY4455phjjjmymJGDnUgpoeu6MYNgduLOV+qAyPUrZnFyLSyLwjNc7wDoxbI8058iBj7X9XZ1d8x9J5Fvz123QuFe6JpdikO84kx+c8WyyRd+/bcAANe++R37afZdV81dPRY2vEpUk9fMtGFmcdBYxlBQs41QeasD+TK4M2SvrD3PzqL0/HpXnfLEJZCzoGrOQhaU1Lu/P2fnO6XVKC29nA/dZlGXv+3TdmPHLwVIqf4YzL/kNKn+COq5ICZxIFKgeriQg6Is3FNBfg2CBpJYyFQjKCt3JQk7RRZy7+MedyfU2r/F8bjN7/O6ubY29vL11vqR9dWAKI8N1kCVjQ3IORjKSuVB4P3mo9zZtB6y1+PFnbO79UFCj8FtFh2XsOOAxtpCnJzRS4G/yX2Q57yQhmEj7omt+BtX7Zp+4LH7AQBPn7sEANij33/TlRgKy/hHBriyUvk85JwM7auz+xeqV4tjQJ6EOv3x8mqZQep54TyiVeMHJuRIWvlB16B6L5TiFlD94wgCryOtJw6SkEi9T05C8O5+UkwE774YVQznZ+6YKfRSHCzyQCJlQOrwmEgUSrxnS3bIEZUDRCLT2j7fnti5u3j+ATz5+NMAgFtfMdVJ4KOkXJBLQx6EUDvHYQv1UMmzfEe3hCTosnMUeMzSgRFBCEXpfAzd/462+HXMwwn5fTPN2MUx0fNH95VQAPe7EJdjR60wjeDPOW2T+6AeLb0Q1t7v17uNGTmYY4455phjjjmymJGDnUgpYRgGVwb4dE0zRs34KtVJ9fE4m1XGJ6SgKJQBaR1mxMyABvn1q2+5aqtlXjN3D/5J7wU5LDZyTCsK50BoXrtQFs669LO/au7Ub3z9GfsO1Rmq33U8ro7M+E5aYxYbG86Y9xrDJJo6rzW2fRw7GqrOqtmz6nGedtJFjetLZaAuZOqHIC5CMdnuWKzNWe1lGhUlnvFqbD0TlCbajr9jjwWvh3tvBO4z/Qzke5DcCZHIAREFaeqdtV4UI4og7bfAC63L/gzus07opTs6ysZFHIuBHe6am7wm6YzYrmyfXrltfRQOYet15eBl5prnseNgiWWtuq2yKjfGS/k56yc66tGVLteQ6+4ZXemAYcJTEJohnodqv+r/0bbi7PC3uF7bqhMgPSX4/prH8q21cQ/uu24IwqVHrKfbQ3TrKzcBQZ4BOh7xW+QDIWWMd+6Txt7JNva5S5SIJHK7rSNVPDYyynVvDGHkXBS8fz3L5DNGqgU5IgrlGNUJuTLET5I8G9wZcoIcyPeghHcAdfdEKqzCQr9FRMQNAHMnQHVUVKaN/SZ7vyCjPkbeX0JLt0JZG3z4wx8HAHzr2+axciTvGPJCCvYhCP48zd0aRySW+yCegI7fO+py1wUteh8NoWXRPQ+CP8NzRYM/4uWdgPE+t/Xy69+VJf5a/AFJLPrs81AXO+CbOBL5vSUUNOSbMNLSjBzMMcccc8wxxxz/IGJGDu6I5IxqIQR3uBWqNuSsVadeOyNeGWAf89feZTFJdy02co4geFarTFgd0VytMEEOuOyLNNbMleFes7rzdz9nHINXv/5NAEB7ZHVpcFZOibH3UOjIGNbhL3ksezzupTgYzAY3kXyCrtvpDClfAmU2eXbq3Qg1u1ZNrcsRA0w+l9OgZ5STzpniKpAjz38pw3VoAABQ0Jf88PZNO+611fmrJdkaOgc6NaoPiyfh6gXtA7fvJfeEgUqGnu5wdaF+DzFbpiFHTIRK1NpYR9UCPw+S5jPLOWF2duXEOmi2lXz/B1TuyMZMz/vL5+GcAmnEpxQCriDf9+gQAY+hyMfbuzI6fgYfU0cr9FqImrJ4Ige6fvRbiWls7NVTgMND/shQmvLkS9e+CwD42OXHAACX9h4HADTb4OcP5Cmolq7EuFfWJi8B1fnVAZHHW5854Po8NCEStZwjyXlx/b+yVABEBOpKSIC09fwNZsDNUl4L/K5zCbhNOWyKi0QUwNUK7jkh6QWX1YhSOBlfqgQx4qFgpu+b4DkQsibEQeoGIo/hDB8ogx1Dlzb8mMe0GfDIox8BAFy8aL0xTomMJV7gPRGmwvt9CCGY8EUmyIF2Nk6QBXV7LR150MVd3KHSEYqnTY/P3twzwnsyOHeF91ccr//d7XqG78oqvuyiPwfcj0LniOs4U82VESPCnGbkYI455phjjjnm+AcRM3JwRyTAs09m3qpzkeUde82QORsX0xXDjmac9SjR6V37bN+ppDnXtpV1S79dKSsTU55ZqWbnk5pZx8/rpnF3wXpt2/jOF74CAHj2t74IANjcNiRB7GKpElovBTI7Z5a+zyLsAafONWuIqq13nRjk3OdhcD/5aoogyMEw5lmsew5I76/vxXFGDIw8CjHD4Yx6fjDRpucxnVFzzNRt8sSQlMNb5hWwunBZv5YdgzzjE53y5E5YqpbsdXB+3m48syuURcqnYMjzdkcY5IioHhE8wFp6bdUpyWXo2Gnxem/HcJQM/QCRgzoAIeo4pHShRtoz/NzfYBwtXrvuJqfaO0fHEQTxaXLkoPR6bxr7FSjTmSofBCjx9ZbIQa/eGmL6q9495F08db1EIghvba8BAL74ijnwffTpDwIAFqsFSjK80eqGGbslAkDFe01a+8AeDGKvK7vHwhCmitn9mveyWOgK8QVqdX0cotfthSCoju0KG/FC3L2QSgKOUEGXxpJe+7qPUOZurtpOMcn+h1A4itAJrXROiRATvvQMmVv2fgWTzFe1evk+6KbVOLO1Zh2Ibpysce4+QwweeezDAIAXv/Gq7bdcBN2PQLvC4yvybrZpshydBfn88GuYX/dre/SBcG6BUJwpX4rb7JJUKORziGMhtZuUWJiiFrmHQYz5fRfKMPZeET8q5g80OeU6ysG+L1L53IuYkYM55phjjjnmmCOLGTmYRErJ63qDeACadapPghQDmjFzBplCGPuka7qsmpZroC0j3HT8Dbr1pWii4IpufcvS3AZV6E71pGbP/fWMWRPLtneP/1e+9FUAwDN///MAgPV1q6l7LbERLZvIAevZHWehZziDPmDWovL1mtntKb9fqf7tbfniTnrBhXclnNSQJ53+PEmZuIqNzpH6CTkjTpjyIwsBY6TsE83wS++mZp/L5+DWNXb2e+hD9jnvkrSVSkHZPTXYfN0RSVkfmctblH6869Cpq2InrwRqnpXpSQNNJGDgOezlssid6Nb2uj+166ji9XPU2HaubQz16Bux/8H1Cvfr0C+Kda/Mv5ici5Fcw2vVC536VGxu8l1Sfr+k/OsoMCZAOo9dMX62u80Qdd/wOuHYuz+GLjXeLyX9Mxp32mQPC2Zr37thHUe/e2TOiRcufxx7N3T8crgUP2ZyTcmlj7V4ZesVa+YdkbXF0u7ZRWX38lDmA+CupuoGGeNILHevBSoeOA5bolQdV1wQvQCvG/EiGnVnHNS7gr8hzwLX2jOUeRZxRAbkDOjZqpZCnLia2PraGMelkz+E38R8Fup64fOiWPLc8oIKZXBE46FH7Z6rnltx/3gf0Csi+QWkw+Q2pc6Qw6SrEHRsughzjoKUCV7bLwonF8iNVveeqw7ELZIKTF4l8oPQfaLrRo9Z7YqeR3IH5SkbkbfozzV5ZoSYIwLFBK1wjpf75tx9zMjBHHPMMcccc8yRxYwc7ERKCX3f7biL8QOfnWGyzHXQKezUl1RT9U51+pItBnX08rqeZn72ubvUcTtyGtxGOuHlQILXoGIPvPn89wAAX/n1v2/fed0cEaWx75Y2Vd2yeUC9lnuffb7gAS6YfZRuTmj/OGZ3v0MiCudYD24GMaLHHgFi3bq3Mhc1GQAAIABJREFUgGf84mtwBqzaa1DtWJwEvu2IwUTPrW5qGjjPjeSQFh1cKCZEhNJn2fabDWvOh7dv8SdyTwLPmJhZiE8h1YM0+B25BouSWV5Koy65V7Yh/grHnvuyVS8F+SLwR91tTVkKx/Uk2HpvboxHcqM3lr47zHHfKxSeRfWqr07UC5hkWaq9qp7b6Th3Ol7ufs9Rs5DnHKqHAiOiFKVomAI++s1Ilr609t6vQvcDx4Uw2Ir16zM8hwsnsxORG+xaffaaIQhPPfCU90jAhlkpr+PYi99AhIkdAjvdwlI1sJdAFHLCrLZamXrBeQGTbqDO+UhxRGXE+Fcaz2xV3KQo1ZMrKOhKSNdBT6iJLMg3Ap6dugTFvu89W4rRr8F9QGzV8SGj1HeiPFJ468AcOfDrSlk+EYaKKIdITsWiQN+bOuHRJ4xzcP83PwAA2Jzm3Cs973S8jkimPMNWF8pFLX8HeRKEbAOhz6/ViDDyD8SHUvYuREHdNguhMbZseU8KgQ16tgvVKEK2vpACP6Yd/xApXHydEQKx99cT7w3ui12b9wY9mCcHO5EAdLHYMb/QDckTNTFFGg04+MBGdIhVN58afBQkDFWVQY9VabDZ+IDhzc6HXlESqh5kUKTmKNzuwIeHWj5zz4pXr+Ll/+1XAADr518CAJzQCCed0QOHNynlggMv5kYkQv1R4/PleFLKCLQH3qcJ0JJPA+1DKsIo65L+x0lw+g1Cdu6Go4Y8jllra9mymECXMtXpCxGtctJUkXalhrTq5T41/keesOFghkObmy8CADr+wS3O3mdf5zlDZeWHVLKsIIlSn5Mqa588RP9D2roJEImpp3YONiua+0QjFDYyOQp2fdziH4uWf2gvntpvb5Ktf60306N+Yd+LtRpTcWYXIwpJE0Xa47UjeFdzJcnEqjLfV59M6EGkUyV4WNItEa4K/QG39bq0Q3rUw1xzRan8ZP4VNNG0be2lXLIbuO2Wf2BWlV2zZ3htuzCwV4te295rJy8CAD5/40v4I5d/0o7zGkl9JPGV3FavP+a8H6qFxikn8BW0EY+cNEgSHBYkCWqCJwJfPx6bExB5L2kG53+AXG6qZxAHnROWUfLIe1ryybE+Z/vimmhk2+mrwic7bifuf/QY/Efh3aW1T5NJAu/xSn94dR2oNLRn4zvwXPQscNWIqI5sxy7s27326OUnAABX37B7cnCLam1D7a95703Kibrf9Mj2JEyERElBWQMY7ZTH39I50OxGzZD0x3wkhXOyWMpUSwRfkmlbSaY1Y+XXxBxXec6rOcktuiuRNjVxVclb5HgnrEsSvrODdxlzWWGOOeaYY4455shiRg52IwGhDyOpw7kqYfelS9qUkZaF5EQFSpe92bqypq0825K1LjcW8+xNM/+SDWWCZy8+5QcwNmpZ01739MgyxRd++bO49l0zfmm3BjF3B5Jc8TeJFFSEsIVyNCIpcQYraaLDxoLnuVxB2Q3HRbahRRjhYkHxKlGogYwkecoQJUFSG1RllpOSjmbZ3vLEs9U8A/AsNwWXppXIm7QUHMtamQAPrDu1skK3sfFbXjKIs2+FfjBr7Q3VEYRbBP02M0ieo3bbouOYRy8nEDkQYCJbYBLpUrfmNuz9E2Ylga8TS0HXV3b+b58lSXRp2z2igdXQqcQRUUShD4Tq2YSoTJJeSbKbZ/wjP1EEMkHfzGIkhbwjy9UGBPMMo/UwP3KrYpXPJlbMJbdRK5NiGUHjttD94S2dLdT4S8TWtjW055Skyc9/7yv4xPmnAACPXb5gv33NUJiVQ812w2w4EIsDZutOULZz0jMLVUOdSs+HRtlqTswbu4KVnq1XQg4nDchEjpMxWy/5LbdZE0HwNvK6Fkt/Wtn7TnrLs1oUzVgmFeESeYwqOqE+eSmk8BbXRbYMXlYYS3zAaBYlW+Z62aCkrbVsxB9+8BEAwFfe/LatQ4v2YqHnAQmn0+NU+UUHLDmintWOPOUls3BHeXLkOkcnPXOh5laDSOaT0qk/s1RGyomMKjEOvJcrNe4rx/LDFJ0oZCyli8h3jteFbrU4opR3GzNyMMccc8wxxxxzZDEjBzsRElD0acxWxaqbzJBrzhwXqnMXaqNao/L6LJcionFTqn2qnrskR2GfU8WKhLOW9e4j1pSPaJqzYWteHLLd7CEJbFcsg0xv3QDW9u/IfkiSDnXMSgvOWCWXKkTMYshIRbNWL63xc6EjTrByI5+Rk6FaqWbNatak30wTEt+I0uQz+ZH8xpraxMREKMDoJ8pzovEsw8jHcCMUSZSYuXAXapK8emb5pzRDOvsBZnNOQCUywN9Sy2a4YVFuAR373s2OZG6U1vZ6seW2SCzbEm3o3fTINqmGS0tHGmy9wxXlp5d4lMxWSxbxRUIN2x5JLlfiGjiri9npIMQgJ1pFh464umRiQlpkpuWrKQPlMeh7afCWzc7B8KZeto5b1ibtqkh9+W/Xyhg9u7Ox3joBU9cZtxvy372+vY6/+6qZg/2xT/5TAICzN7hNonDlOSMWLg5suaWx0nK1l21T9soVs3iZKQn1UyOm3mWYI8IkKV1RKxvlueA96lmqxk0SRi7rKDSD5068Gm5Pr8WPct2ijMyGiLrMiYaFmjM5gKp6NxgxX0bJtvn2CDVhN0SX8MZ2ImFGeEasZmYPP2RmVWeeMULpcWsy7L4SJKtMX88DGTIV2WuNbxzy68ENjbQ5b4BVjNyACcdA90WaIAlNkyNKLe9h2SZ7a2Zv1MTD5/iJhKzW8VVZo/Jnck5MjG7ylbdPxxQRugcxIwdzzDHHHHPMMUcWM3IwiZDCSNuW/KcUU5YvZSwjVYNTrkd5nNfUIWY8a8Y9ZU+nbPbTm2HOIaVrccMMkqYva27n+MRqpusbhiQsud6KWc59BhYgtRsEMr976rk6NR9h5tLwABZq3esNQqZ1OWZGnPGXtVQJMksRCSGvVQYEb+ojeWQjJvNEtSCGd1Hm3II4aa/sJkgu+cxfexlcqIY4BzE6R0CGsrJmFjCkBlJCIVbMBPsjShqJ/ii76DzLZfZLNKDjOUpUc6ge3G+3rmBILpujWQ/r2qqhe0MYZqsw2gPqU54Ettdek+8Ql7bPDff5QsU22p4xMwNJvZ8fSeyGFVGLWvJANreZyE+9cZKyFmUnXu8kSuNZm84Js1PnMkT0QU1oxNOwTXWeIXF3XW5i/ziVUZEnjqznO9+B2f6E6+IteWuhPUR3QoNfu2ncnA9f/xAA4A+fszo33iQ6d5tjfN7GtN9X/Z7GQuQLxMm16QpYnuPFcpl97ilnjF63l0IquD1yyt4Xy8Y5K8wkG57VmojRhlwlfV/e4K5e0I1SqeYex/0WUqDngZjwfi7yz/VADM4pGI8LAFKfnwxdD4XklUH3QOEbF7fq4sE5AMAHLpit8s0b/y97bxq0W3aVhz37DO/0fd/97tRzS2oJELIxGIMKM8SAcSq2k3hIwLETjJ1KlZO44jgkxk7FIVWUK5OrnKRiKoHEToCUUwXBpGQgTAYJECAGCQshWlLTtLpbPd/5m973PdPOj/U867x733tb6r5XhWzOquo+9z3jPvvsc769nvWsZ5kw2fFOGSgAiH061krJTDt3QFwn8SDStip7QYWNDJlKn5OyFvJUaGWdeGE2nnteSXgqLS8+iukp88YObCjDrhTIZrtBzWPzglx+bS/WlKXMD/1UeGmyySabbLLJJvvs2IQc7FhENOZp1Kyas07mMSt46sxfze5ZJrfa+b+KM7WNLbcqC8t87SIyZ75I85U1A9xwVi1JUhV1uXiROgnXDEFoXjU52MC8d9Rw2d6W7ZXnNisytr5nCCC9X82UvY4uPcNMxAWuBwH2g65TIqiGLgWBRGjW7LpSLLWUmEsaWxu9LFlM1gs5kKTviCBIB0JJ2cOIGLCdMzGTVQhHDHC6VzMiJM1Ni3P2LG1d0gMUKpLHTiV5jDblHKyPj7Gl99Cc2FgJRH6qxn5vCvNST4OhD508ww3FXE4V77RrXI/GSfn4jVcAALcYtyxYJldLz/qIATPenwreFDPFmOmlCBmiYJByp8V36H3JttOLm/EhdMrnZ1xYkdQN89y7Emg4ZlRgqpJ36iI3vAa9NJWN1rg4Y5uEQNX+LvJZOGtfg5qrpS8hqkLZ4yTYc/2554178CVfYDn2jyyJnKmw1k2Db2arc3YOeZAS8/HsJqIUmfxwyzbXMwoUsV8iRqEg1+nwykLkKQjN00vG74eezZaI43zfvic1R7ty8kvyaLYuRMXTS9ugGzzzSdyJNsv88GP8H0Vy2/G26lm26LNSxzuvJI+z5aZpsJBEMftD9/FWZgo9c+MZAMBpwe9cKW4XkuM6El/UtlqaLuQFeMl7xf89u0FITeWolHNvcoRU3w3pW7ANnUvDExEhgrAQkiC+iPqXY3um7wqfVbNtsCEaueU3Rdec1SrAJZQqRQ5QhBxkeNM2IQeTTTbZZJNNNlliE3KwY0URsL+qcRpt1lZkMdRxiihtAsXS6Cn1WzSkSw+tvGxj284r8zr25lYGeD4/b/txBrsN9LAXygCgx6DpNmOJK6rz3XjWFPFKeaSU0Q2zCj3P0csTlPJhzNi38oClPeCxNS7FLq5S7oHiYF7alF6JuAxFHxG7bXJubavlGTkrnWxqpB6SK006AzrLfxdy4FWApGvAn2KKF+7fY8ZzL71oCTeweNFsUBYK16+ND9K+ZvLT5SV7diq0JFU7ZTeoGM5Aj0llmYcioKEH0LJgUsnY8VaFcliQKwTyF6SZQLngGT3iE1ibfjtcBwB8CFcAALfoQdbblEktjxkhuBy21AYX9DqqIe07R0YGLdP10jmoqJtQEzlQm1251x1qaVaUrvDp+eQsmJS7qV5QTPQGInEEokZtBRYmc+0FIQ0eixUSwfNot9Aj8l36xJF5pe+/Zjn1f+rxr7T7e8VQnfKU3hvVLNs9noQKiO5Y0oOe8/3YqtgRn7WUAmM5apcI4REK6XodpVj2kg9OtROk5SxUQllR1YJSz9IyEQdHXIaYdXRReNeneoCjeW5Clq3gHjXHicsmO9s/PY9H8vWOk5EfZz0iUuSAyTt4cP8SAOD83L6fN3vqf7g+irgDaavH4SP0k98XSV9n41Dy9kVReKaDpAU0dpSlpL8HUpksnUNBxEFS35JGzxQUhR6LVyV0T2jAYln4t3hLZLHh+93wu1oLMhs13a2t0XOL7tkm5GCyySabbLLJJktsQg52rIgRy3aLQYxY5qBXLA4kz6oW69Q9cLLYh4WXCPCUgWCxwKaT7jYL5nBGOMZAFdf3CDk32GKP3kh7wzgGw7WrAIAVPa/1kjuuCmAhVS16zypiE5W/TC+iSpW5VLxJs2l5QuIaLKRSJiU8acTPbL2XFd10zs8QUCBNfMXOpCpYq01ehIReprsZMV3SCkcQNINOA5lCS2oEz1yoXbls5O3bBu6rGKDHTG2Wfusl6/O6tpjxQL2IniqFXbNhE3gdebFSRNtu0TDLoN8wZkr0qVX2BhX8EDkuBCiQt6AEgZuVIQfP79v+L563/ddEjhZNGidteJ4mRM+gmRONWHKYV2J8sy2llCPFUYjpUryRmvvNhaRlhZzEtJ9XGkcFFqwI1fDTc8ZzlFSbC0ShpFPRRGkwKMZOHoC0JsSEFytdBbyKlKvgRRxcxXJAubUUnyN6gD999UkAwNve8gUAgC/aOwAAHFKTIpxR5TKyVDP7s5qJoyFtijtrMagp1U6sWlkbYB950SqifoNKu3MciBfixeE4UIQQRc+DN+tUTG2muHeqsGdeftxt5g5iiqThuTaAStaPPqYTQJLVg9eLEFcn5SxhNhsrr5fS6bCDH9gzhPXB1UUAwGsbe2brUhwKPk8v6Z6hvc6LYD+q8BnSOitS4CyqyjkUoq2UWY0Q5yfwGagg3VgEiQjl0r79Wz67plPGzW7fw7U/hp0sIaG1C9b1EKqwIRrZ9mL0mAkfHayBuB82IQeTTTbZZJNNNlliE3KwY+UQsb9pnF27YQaAimdJ7U+ewXlmEISFsZw3WKHp7Zg2prHOll7XuhUbnR6kZpHcr2L2guv6Kdd8S234554GABycGdP6gAdeOzDvPS4qhHnm6dErq4hmlIy5l0REZh5/0yza9psRrahUFwHKBaZnWSjvnzNnxtrj0Hr8TcqInhnh1dLYlxLBd5XFLCYqLoFnL6TbS0cDpIfAe+X2IkbMFRMUm54zebGzB+3MOG63tmezpWd5unkRAHB48UEex3ugFyIUZCCKM6jAntb3ld/fqNZo59iyhkK5MUQgsgonZmlfyys5K5mtcJ7ewb7dy4Iox7xL+6eTPkCIY9CXFe1OOA46Vazr5MXbbp69oaqb4hKoDgiXSy5n3L6g56VlzX6fxYil3gd2RCsvWpkuTrr2QK/tp7LhRGCiIwBEoMhREe5WZcjbqO4opc0B5/iOHpd2zieblwEA//Q5y15452N/1PY9oUbFDVYIfIhIwcr4RLWrNqZZHtIe6FQvRGNcHmldejaCsgsEtXl9F/8O6P0QKsjVQvFcGY/v1SzlHgx8p2u+j17SeeeaegYjiqcd9AFMEQW9047S+O7KPNKy452kSqz6FsSq8FLcM/Gb2Gd7rFp7npki+4N546r82TlniU10vRR58XfOevKaBbl2QQw725JDRp0PfZOH1EPX7uJ1qOqpI7ga+32fHD9Kcozoh+tfKEthniombqlzom+ZK4l6Jcl7twk5mGyyySabbLLJErsvyEEI4ZsAfB2ALwXwBwEcAPi/Y4x/8Q77fi+Av/xpTvneGOMf2znm3wXwPa+z/1+NMX73G2z2bVYgYjX0mClFnzO/kkxRedZzyR6QN9CdWuZAKGdYKs+eM/VYS02NsUHF3IUscBa5Yay1E+OVbVgwtLb5lMW9i5uW3w5WNGv3WGqRudmzxRw1eQsFPbr5nCp8VOOriU4Ipai53r1uzlIrHhfblA+xYOy9pxfXaoZcj5yG2R7bo3r1ypl3Zjf/wRiytPBV4UwUg1mdeU7iJNgvV1pcSN9esWU+mzA0qAvlFYsZzmML80rEyTg8ti1H5BI8Bevrmyy+MC/s92Ozc9g11a9vutS7E2q0HTqA3kPlHiJjn9I12IqVrrPSA5IgHOOV/T6VNVmFsWTuvCMMC/IhxFbn8cthQCEEICpOTQ9R/eMxYlv2DLpuuUFVK/XVoKPsyILU+ubsjwWRCNWuqPox88Grlm60TZkURJh4zoaNmZGS0VNZdE2EDj0RAzpWxSjgb+cRs17ZAI4gFCCVwPuhIyrzkRc/astLbwcAfOWDb7NTso5JeZMe3j7bKr4P30GpPVYcy9Ugng15JlKHrCpn6bu+B/vWFSL1zGplKaTxbmXreBKC14UhYgdlKRDNUNaCi7qWjggq5q2H5JUd3ZNOUanWZRdTZEGZIl6lVd9TecrSJOG9DP2AJsuAWHhtERtklxfGPdg/Np2Xda04P1EdcShcIVVtTjMJcsTA2ygUY+gdrMlRO2V+iOfSe92KVHPAkYOY/g7On0qRCdVskeJq1w+OhHqinFepJW+I3+iSKRWurtg2900h8X6FFb4dNik4AfACgHe9zr7vAfDsXbZ9C4B3APjxu2z/JwA+fIf1H/yMWjnZZJNNNtlkk31au1+Tg/8UNil4GoYgvO9uO8YY3wObICQWQjgP4G/BhNW+9y6HvyfGeLdt92wBAbOioB89eqeeK6yJH9161ZqPjOvGIcAr3DE7ITZSAGQ8UjN5z6+l10KVumPmu2u/8pj5rVdMW7ym91HsW7yzZY35+ZKe12KF0Kf6BTNWjZyT+Trn75LXWMDieIVUxJgr7UX7WEN9uTC9/0jWtli8y5VylKk0WNWjzj77cMm2DAweb8naD8wV14x+powHeVJiwKtSpFO/Gcem9zJX/BPUlq98yo1GlTHpnYoxH3vmWbd27C0iIU9ThfDXVvb7pFL9ihcAAA/t29zX87UVH+SziZz1uz5+XXlMWTP8uCU3hfoVtWLolarpMdau/mLctp3buZsF70mZNIoly6Ns5J2xX5pRk17s6ECvq/L4vVnMvO7oCIM8Jtve8hmJN75RFgORiVnGWaiG4JkQWi4a8V3EQbFzKXNipowK8mVmCxv3awoeNMruERCleg/K/5e36564PO6IM8WSFbfuqCFCBOjHnvx5AMDjX/2NAICHqFmyf4NM8UPWQyFisJUIw0JtUvyc3wv2Z0NEoYjlGCMWiqEMCBeJEMNfkEK3e+qx6iBvr/eHqBg00QyhAvJIuR79+LydQ5CZ4wPeX/T4iyHZwyVJVGjV6xmIlc/zSC+lUWx+8CqTkc/Pv8FUWr2wsFoL54na3QqmWjk4R0GwlzKtiBioVgnHiTKzRuKEvuHy3gfnFjiq4OgCkvWV11AYknNERw5SHkif1YvR+Uqvc8H3pBzRpziqTNg+vF9loeg2pLVQDwMyGsWbtvsyOYgx+mQgvPmWfQuAJYDvjzFevR/tmmyyySabbLLJ3rh9LmUr/BUu//fX2edLQwjfCmAB4EUA74sxvnC/GhACUM+KUX/bvV8x7MH1ab7uGN8qxhoJyl1V/IczfsU+tZ9m8rETo1vntt+vvWIKeDPqvC9U4W1FdbalzYjne9TnDrUzuecV6zDIk5uZ5yPuQMU43Lw05KCiF9972rJtP1jacZH1AFhCHss5NRwGqiFWYm1XzjUQJ6Cj9r8U0JZSR8v1vDQj5qy78voVcq2EIJDNLC0GeuIbzbRn8lZmqOmFlWtu2nIbL3mFaMNLS9vh1/esrSwljznv+/nXjPdxtbKc69lG2QjsFw/k0kOUljrqkdEtxIT1F4o+9XhaepXioqjy35o0/lMSYuQxefVJafPzGfTy1kqhGcHZ04PqWEid0ccoO8TdbAVf0+1SsxxV6Ybk/uXVxqz+BQLQF4oNm23n9JTJT5AGQ72V+hy3M79/f2FjbhltrG6EZij7g21oeukhEIlRSx1Qitik9A+Uqh3BZ/Dx65al8tPPWCTzz7zja6yN1w1Rqo8MaagOiESt2A90e2OlZyovUCgYO3LboRSBiSYPeiAiJC0AVx/MOAqVK+OltRNkoRLqmSKVc/KGYtGPWQaqYNlnaEsGKHg1Vs8IIfeiTB1Djbc8Aj7wzVvzm7BtevR8H4IQDnnhfJ7SXNkj6rnQR0jfYiJu3jTPVsjUX4sUOXB0VHc0jHogrmAoLoHeSfWP13nhwQ6d8JwxfT/0YoyvGzkoQrn0jSvHKpLj/QiV0MsljoS4CLZ6Np/di4Oe2OfE5CCE8FUAvhjAU7soxB3sP8l+9yGEfwjgW2Ok5vGnv9aH7rLp9XgSk0022WSTTfZ7xj4nJgcA/n0u/8Fdtn8SwH8M4Kdg3IZDAP8SgP8OwH8A4ByAf+deGxEQMAvViAxkcfOQMVzFNi0HVTEsfGevbZ7n0ZJNP6Zfp7PSJTO1N7cspxo3Tc9gn5oK1aGptrUHjDWvbL0qew3dgAVzg6soZICz7ZlxBsasA9tvRYVDb5Nya+mleaU7VttbHS6S9XNmPcyoFBcQEDjl7VXLnNtmRCfG2ghOHwc7wu5rEHIg6rxypekhq++lOBjIzpcn3jHmOERn6avegioDHjED4OmSqoMHdo3nLlq/3WJS/uGJXfMWtfWvnVi1xkeolDd6FuKeyFvn7L8vRjSBNRZqqQpSd79pla1AD5DPoKHPuyl4bSohygNfKK7L8/VyX8S5oKcUQ3D3uZBHR15C5LXlpSkmXCBFfzQ+xG/QCcvbFPVsIVa+1x4IYQeE4POneMK8EtJh22cUFxHnIHj4NkXzqjG1wvZXI1mLofN7YZsc/QCKWrwG8h1IJejYlptsw3tf+C0AwLse+XwAwFfOHrJLHtk7Wpyz92R2zqCmhshAp9R0PiPF4D1U3/WIfK7K9NEHecxeSJECRxIyoKBkm8Ut8Gel7dI9oLKe3rth5/Pk6qU+noXipUiq+8MulTAkC89OQJqhJI0JfetiIw2X3is4VkQGXZ9BCqHkzCwpkLEkgjBISrRI0YtYqX6F+Ff8RqtggrgI4ogJLeg6RwzCoDo36Tdb6K8qp47pTzG5plRaXTS3F7cnXYqL0O7UZnHNA2WMiAfU7wxgYHzphCTct8oKnwOTgxDCIYB/C69DRIwx/hyAn9tZdQbgB0MIvwzgNwD82yGEvxtj/I1Pd70Y45ffpR0fAvBlb6z1k0022WSTTfYvnv2uTw4A/EUAK7wJImKM8VMhhB8D8M0AvhY2UXjTFmA5ybk+ty+Vey6nDPJId5KTsxhWXiNgjEJJGU25v9zO2NvJNctOkEcQzhl/oL9oy26f2gTkHsjlqucllnNDCNBJA8COEedgSaRgybije3Ocfc6pCa7qZB1n7aWqMUo/gZkSUpT0WW7fY32mAD+V2YhsSFVRXkrps2fGmuX4uvshrX1yDejtNxtWKyO9PeyxTWt7FvuMWYdNwJoe0Ia6Da+xAuYzWzvHx5YWkVpr5t/xHCxMoLjtVbqWN3pjSj/QpqqOeX63xzWHzmvDt2K0k4UtNrZyp1t54xJyp17GNWrJv9gaynG0trbPFLOXEqULAypurJoCBYLCrcqqULya7Wzd6chU55BCAl6uQNvpCruGg3uQQjN6v0dH3YRKyGOTN6b28+RbEQI8tirvVedRXDjl/8CZ8GxDJsQ5xIAoSkVWRbLlNU9q6/MXBtO3+NnftozpL/qiPw4AOFjzmsf2TKpTInPUHtEjHOZktbO/ZopVDwFDL00BLuuUAa8ceUf1vPYC70fjqFLHaSyqG2w8zepU20RjtShLV9cb/ChxDxTv177Znws9Z4+LZ5uFPKS7e52I2IpvNWBgBlRzwuqkrCFQsW0z/qnap65FcZZqMUShWVXKLfBMDKkU1lWy/6D3Ru9q1bnqYE9PXkkcLvsg7RVlzCgDSYhAkf6dCP73RB0lrpIJNzSEAAAgAElEQVR6JPX6+77zehSO9klbRDUg1H5lFEkzIg73DT34XFBIFBHxf3uTx1/hcu8+tGWyySabbLLJfs/b7ypyEEL4wzDxpKdijD/7Jk/zh7l85j60CAizMX4lZjO3ejUupDE4qZGNtFV47QCvVLfD2DYTY56zSB7bUZ3vjLPX4tC8kf4il+cZ16T2QK0cWVKkF/MlCtczoAYCsxaWRA7m5BqM7Fm7wxW14j13WjnXkNqarRdHoXLIwBaKGzabDUqeS9UYVWOhJpLQNWmlMlVMrImg1PJsFIdjbQnVrtClB06pW2ZMzORBNtIgAEqiFq9RLvDD/U0AwDP7dszLS7sWf+KAXsmSTv4J2efXWDHxCpGDJzp53orf0vvnPUlCcNsBrWb4UZ4uEQL2sTgFPbUHZvK62W9HrOdwk/3TbD2wy2vpksyVDmJzM5AeBh97Za6Jr/Vsf6N4thACecAKb2YVMuVRIRvjoyO5k+2j90L57C7L2CW349VKK/WDzi1VOvu5IAok76zjjr2O4+17bQbeW9/3HkvX+G91/0IQXFXR9vvNV34HAPCrD1t9k697+A+mJz1hnYwLxgtSVoJQoUExfdU/qEvvJEedvC9T/QL9FiIknkd0pIhcHHrGXZdyD3Qe5eZL+6NAdCTI0aY8gUiqjEOKBIzfCb6ruavpKoxCnHjiXlwccQ6GUYdDGT5jgRIAwJw6F/M134OXzCc8pY7M/kXTQahroZnMhhKgwPeoco4Cv7+C0zzrJyDUykKQpgDv18P/5KooI000J9ZWkW7GMJJ3EgtVmu3W+fvBZzgrHQFS5kyj7Ca+N0UtDoUUIO3cQ9PiNgjnTdrvNnIgIuLrpS8ihPBH7rAuhBD+CwBfBeAqgJ+4/82bbLLJJptsst97dr9qK/xZAH+WPx/m8qtYRwEArsYYvy075hyAPw8jIn7fp7nEz4cQngLwazB9g0MAXwPgD8DIid8cYzy61/uIZYnh8BCLlXnaJ6cWSzw+NU9RM35nAKuOgmLVofcZveKvpWshIDk2Z7Rrv1u37Da23LOiEuJ2n/oGe6yHIM+J3mlNNKCoZiiJHNTMWphVxiGYL6h7ADF07RxzZhDU1OlvqakwCqDZMFnW1ENw3XZ6aZrlcvd6tRxjnKoyJwWyLT1n6fJTI7yWwpf0+6kcOKha4a63BaCX5vyaXovK/LHNZ4r7Ha7wzMb69NePzdt4lkjBNWoGSERww05d9GKvs5Iin69yZa/3hmKcBOu3jtTxLd1UxcU7efmxR+sMcNunYLyy4jhYV6rPTuSAAcuOXIsjnutMBQ1IhRdjfMP9CrL/Z8y0qD3W3COOvGlbCPFxRIFIERGUrk1jo4Uya0Lqj8ozLB1qQHJ+afeXKMa8bGUwyPt0Z8d95dR4o8pymRGuqIjqKJa8ofaE1BsLV8xj0xTn7aPrGkjJbisFQ6kLakxxHFxv7CQ/+vFfBgB83oOfBwB4qDCvdTiy78TeEas18p1tFJTn2N2qP0PYqZGQmqM7QmEynYOx71P0A87FSDMPhEx0Mf3d94OPVyEEcIdXnBkiIMogYraTK2j24k9lN6HTdalqpVeE5Pjq1w3iqb1dPbkGBflADX/PCP3MCescvWacrE9eNxTw/MOWQXLpLY/a7+KS7b8k3yqo3gybpn718q1jRyuryVEcAcODsnakOSApSFts2H/RQRB5/4Ie9HdCGgzKakivN2BwBEV/H8RJk2Kq0lWckwaM57g/wMF9Cyt8KW4vpvQO/gcAzwH4tmz7N8N4Ap8JEfHvAfgKAN8A4CJs+D4P4H8B8D/GGO9DSGGyySabbLLJJgPun3zydwD4jjd4zHcB+K7PcN+/+cZb9cbtdLvFLz79CcyYEyyFuzXV96QlVnBmKeaovN1ZCCi5TTH2GT29JZn+C8bC6iqNxc/onRzfYjVBev19zRi9Vx20th7wuHM8b5ypSleFubxFMt0r7qM4labNM64X6iBd9yBGLCe88yW1FFQfQrFDzlojY64VEYtZWe+gC2b9hoxexoJrqiuqSpyQlUDPuiNyoFm6zjcwA6NlzHTJGOSSWQo3ObPuL5k394kbr+KD158DALzMegS36CqXLRUgCR3cYN2CMybLH2ylvZDO8I87e0Y3C9aYYJul9y/UREjCNkT3zqOHPOkR8Ji2lHdBj6FVfJoeseKafDaB1erc83blPPO0GmoyVHNW45vtoDhCDHw80wvnWO04yKRa13LZtcoYkfuVxsHlKRZcrzioyhuUgL8fClD3bJPn1NPTLbWkl7nH9+Y8Ub19euXxgGgNz3PMWiRXG0OLpNUfojwsIXW9KyMKEdqKp6BKkKQSVEQQtqy58Rs3bTz9zJOmp/aNX/INAICabW2JIFSXrA5AtU/uiejrs1G1scry2MVb0fhwxDHLnHKRPsJ7Hhfn/qrxot+d+hNCmKQD0TuaI7tNMFNUAVVfpf6DjvNqjjIlmIhTsINS2JK8AqKI2LZOdBmomhg6cXKkiWC/D2tDZQ6pwXL0mvFAbhzb8375uiEKDz3+FgDAY48+DgC4eHABALAoxEnQu61sD6IkZfBt0VUVxRdSd6RZO7U62bNfiFZ10p5okvuOuWKmC+aM6KcQ6EEIqZQe+c1tWClY3JLU/sXJVphssskmm2yyyT6H7HNB5+BzxjZti6deefU2JSzVR5Byln577EhV3frOk1vHugs205vTQ57TW5/Rq9+jiuEDzDaYccZ4cJne/ILT0wVn0Jy111DFRbKTiQosV0uUQRXIiCDMlNusHHFuJ4tf6fquPaDlQrnzNfej17+Q9Bu9et6Ljq+L0r3FjoqA4hjMVRtCKoyejk8lQHENyEqfUXteXkvL/P6SSAsLJqIhMjE7bx7FJ06tDsJvnz2PzcGareU+qqrZ2jOqODuXmuNWCmfMqZ5Rx70Mdu6b1JR4ldkPDyoJmSjPmkHHRvHdIoy17IUgSPNeoeVCiADvh/11OmMGSD3mpdtx5pX2jD0Gr1nAA1kvYkvORt92WC6oBcG8+6ryQLy1iZyLnm3rZkIMGItvyHTXgPEyezFZil8ijXq/qWHAIJ0LV19kH6uWBt8fVdlckpNyQI2KfaFYc9t+Ri0Ceadzrl+xUmizyRQU1X99cJXJkSrB587VQhZU52MrJbvCnu8//R2rufCuJ0x5/SsuvdVOd2LKie2RxcPb80IKyGkhwoZ+cGKP6xsg6zMhLZnJ2XSmgcaVfjtloUjWa/8ao1fct8rnZxvcw03Rh+i1QtJzq6l+DXFVlJkjrgERRukICKlEuwW21DfoiBi2/E0PuSYaVfNbdLGipgTf3WtE85prVg/jypFFql95xUrvPPqIPZsnLhFJODRkseK7IK2LoSowSEVR2Sqqc8B3UN//mlVdhyLNoIleCVR6IlJSFfJKXRDyKpyD4UjeqEEiZHDk7/CbpYqQ+tu0y+e4P8DBNDnYtQHAZhg72lNRBCdJRtPFK8yUutIN3ZjWxm0uEKJCKQ5r2kM+YGnnvjLY67FHbdkv7A9Zy+9IS7GfWCs9ihCw5EFVeCTAS4xKtEQvs9oyX1JqWX/UnNxkpvKfBT/MHdIvURRTUYRGftgUIoh978VVFEYoOYHwv4aC0Db6YJCQ51mhDB9sBV2SJKmS1RS5OebHA+ft/C8eGeT7WvMyAGB1cIpHKSD0gE/y7NwbPt+jzj5EK5KftpxorCXEw8lBM1iK2i0OiKtLu8eL/ixElhzHA2CwfOf5gpxY8aPUMTWz4h/eOSeJZ3M79iYnNps5RaVEAq332S8nXKsUN5bA5nmqwmD42J1ie8YPLwfEYmH3MecksCQ+qklCq2JQnExVfM6SG5aEb0HIV2lXmrD0XkRHEG6BqJADz73P8In+IM/Zh6p67MOG43/NlM9Tvk9rTj5bVzki/DrjBGipNokEyCbFwkWKCg6hhdIge73DhHB5TDfWVQYAPF0ahP3DnzCC4hd8pZHgHi7sGWxPTDyp3VCGnDdVgtB2EdwB8VLKSi/NQj4yjS2tHoW3eBp9q1QuOpO+vj1Needb5TLahP+79Pl1TiRUGqQmGNwuCXCfXGSy3EzxlSRyy1hI2TZjOLFZcx9NFvgODZI7tnY/urTiZ+c4YX+m4rtLobLY2nleuGITtVdPXgMAXH3VJg9PXHoMAPD4Q7Y8ODxv/VHPXYgLnAyAk2SNHU3gesUKXeqYIWJ9V8Ud9LkxJ88cX3O+J51SoNnv3dArKuQEbBGTh1GL2hZs6igONm67V5vCCpNNNtlkk002WWITcpBbCO55j+QfFd5IoWFNCV3cYwieUuSAgbNYNNvkDHAQkYjn3qfnXHGWzfBBRxGbwS8qGJVyo73NmEtCXKErUNZyt8BrpMV8XKZTnpDK/SoNUx6OCIe6vzL1RjS7Lek5ezYVSgQRqkQ45LHyPgVlboYUtZBn05yYO9cwtTOeMJ10TY9CECdlk5+9bgkrp4Pt3xItqCugDiI9iijHNhA+PKUmFFZ2zi2hzVM+91N6AN0ZvVpKN1drg4373iD+LUMiLYlrDYsoxVBgEAFMAikxE72Rt8LtG4r7bAhhnw12zTU97i0h39qJZulpnByoXK1i5WGmYRDRluljCuGwMQo7LEjQrCRhXQnl4bjhMJOcbNMLMeFviecQHahjiYrS1DOuW2xTUlaZVW/aQFA2006FysjLJWqhsBKBBCwIO5cUgyoJ/Qr0KwugJAlUjp6nBeq9TqMOkKLtCCjaxT7y9EcBAL/0wBMAgD/xJV9tO2w53m6w/4jUDIc80axykp689cD3w7OIPYdRqIbSSZG0McZ0/5CJJrkgkYN+9P776KJE6lOZF1bq045QBp/OMbAxHVE8L2EuISMVBSIKoP30DeibBsXW82d5rJAgogx8zgXPdX5hxMQDFZljG/uZ7luIEd+njaUfP7825OD4qhVPe/HaqwCAxx8zAuNDlx/Cam+P7RJhkuOkEZHbllsfN1w6wJoiCjO2ucrC0yoOVYoM2SuldOuooxfcIrpQOnlRhGaGNBwsuE8xBUzIwWSTTTbZZJNNltmEHGQ2hDiSxjI5WE3PXD5WCINSG2OB6FLDabqKpuyajWm7Su7OZzw3GXobxtCaTOo2OJLAGJ3SzwaKh8QCAwkwbeRsHCKgMcWO6W4zSexqL5L/xGtw0ZwiRQyU0qd4qDxDj2uWO2k64hC4oIrd35YcgwLS6mWc/6bFaTfXrttqClHFhglnjNM1PWOKR+YJ3ALJXyxpHIjAhKH3QS5S01zek7gZnLlX3FNlg88RvWlIciuWJJNubL9LREP2KCe9aaytzYYIhDzpIo7SynIvlD4o4pjIgOzzhumUp0yXXDPhTqQ2OXkzrx6ke+LNipswyOOoUZaESAqJ2lB6mkWyzrZ20tXStq9WlJwlQrBaiJilXEh6vSJ4knDVsAOrlqm0W6IcJwWKU7bnjGiFF5hi++nhNyRBCjloGJNv2D89f8+YZti29mzOjg1hagqiOyo05C81eycUCCp25HK2RDqyWLG8dXFVxJsIfI+uM4b+I7/5AQDAE0/8PgDA779sMi+zI74Lh/RiD5kSWhYjByDjHUr8ppG4FftUKZtK0RRxbegczrD9kN4DXISLHnYrEvWAcux83hfvuxEy4C8MT0WugFIUu5RjMhAhEOjRCqFiP6lsuZCmsusdCQyZbDKcz0USIJuy78RECrxtJciWCXz50o4XR0X9cfOqte0Ky7A/8OplvOVh4yE8cN54Das9u0ZJEayWY2/NeuKdUmD5YRH52tOwHZkV6kPU1DPLJQQmfkiBIkNz1Hcj8bRIfrtY1n0SQAIm5GCyySabbLLJJstsQg52LISAcla5xKqyFlT2U4WFvCSvYvDdKDNa5DM3pT0qxpz9VupWrTQZsrK38t5VYKNUIRUiByWFNRhzC4xN90XrPAR58nOypzt6mSpCIllX8QMktdsrluizU26XNKvSbAgLxD6VAw1lcNGWmZjvjPlthQTQQ+hODSlY3zDmd3vN2MU4tv0LzvQrZkgcbWz/a8eWqtQUdr6CaYUSJFEGSQC8/KnSfrZyp4QcyGNSipFSEEmhF4u/kNgV00pLohfHa94D+73JUxlRoOW1VUJX8/KZC++k5WHP6JUebQwR2dTGpRjIIVABlkhhql7pp+JTjBV8ABji0Mjz5e1X0TIZQiSCxMIxEmZqiCQsPD2QbGyW+taYBMesnF8hMwKuKr1AQ4fulCJf9MI6plfqS9TznWo968cp4jwH3yNnjCtDhqcpjWuwXYv3QaEqoWRePWpwb8vHs48Lojheypgmko1nuvEajB1/7JahWD/2YUMQHv06k/K9LDTxmB70Rd7srHJhsVxGWe+ccxLYH+LsyITuiRakd15S11422R1xjhsiM0VfjPfHg7tOWVnsY4kVqc87FQOyZ1mwbfJy9duTVIQUkGuwZTqyshXqYQCUrcQshZ77Fi60xLHLpuxxnD+8ML7PHrN9tkqh8WJXbAPfZYJbLq/dBrteyzTm01ePcXTLvkUPX3wAAPDWBw1JuEwhpeVqkdzvtlB/sB+YnKLiX8o0iBwv4mpIZEny80IXQxkcvRFXKzpqQ/TBM4PEj9mFjO4P72BCDiabbLLJJptsssQm5GDHyqrE4YXDUZqSMbfIGW7reb+KQXLWpjBWCRTaJmaqcnnl8UFyrnbMXCx+Pgk5cf1cvxmvZJwqcDlwttox7zuSc1DGYqQyiwEuwRmJmvTSWqA+AQsydfIE1R88rkLqhbrUq3KxnRHvSh1eOrghYrA5Nu8a1BLo6T2cXTWREqxtv4Elq5dVWqrqtevGKr56bEum8yMQUVjSA1NMvlOmCQZnuqvKsbwwCQdpdh78GSnTgh6QNKuzAF+nRGbyAjpyFKTNoN7s+2EUjnE+i3Kbxc7mdmW7MPa+WDBzgN5Aw9LVqkXcE72Rh6RMlYKlsVXyOdQLFJXi7xJ9IleGmggl4/Me521UPlz6Dyx+Q0b4nFktS3IxpFGgMt0SQdpsjQdwfP0M8YyFpVjk62wlHQy2k0PJhbyC0Bf2B5ECZ6GLy6KMFKFXPE5tjkR1BAMWoUQ9TwP9o/etglP0oCW9LBldvj+R2SiNMogqezYf+KTJKr/7874QAPBHPv/LrC0s4BZO+XIv5hhq6R6LU5B6iDJlHYgHIJMscvSMAnn96U2Vkrrm+kbnGXaUDgQpSeyN3nybZSG4SBKzcxxI8QwsMe1z8aNtcl5xVdqmcc6BCq65/gfb32z4rWF2y5LHnmMxuBUzcY75LqpQkRCDrLr8+A1nxkqrjokdrrNvuiv2jTo5NvTuwUNDEh570Io87a0oAc93VAyvM+k9UMBuqyyfQploRIHUTxpn+XPAjjCdshNUUExI05BmQOS6GPdiE3Iw2WSTTTbZZJMlNiEHuxYCqnk9SlXuKP4BQNGlSELvnATN3sa8VGkDqFBQ6NIZnxQNJRM7U1EkxnfbOY+TMm9KSndltUbUasIXVaygGxhL5UprgGzywea4C3qAogD3UF4+r5XpIggZEHPYS7rSowruYQ84OyNr/NRi5SW1Afoj4xRsb9hsHGf06DhxX64shniTrPMPfvifARhj73sXqfzHftjfk+w0ZVDp/SomXSIihtTj8dxhL9NKhETPRvF6L8CCpF/k/ktBr2dmREdtgtZzrImedJ17peJ3KM4dyC0RoiAVuT1KVJ970FT3DvnsTpX3zW47alk+mlKsWxYcalV+V0zoaoaBXlYh+V7RqqnYF4Jo1vJSOF4UwuzS2OoZPayzjf0+UOYN0xtqFn86eoUSzsdbzMkuL4k6FHOOJSoalkzf8EdQiKMjCn36LEHvHVshBLwjFSwjqrfdCg0YY/EU2URNefFC8F2hjBd680J7XDmVKJ4knyspilJmeWn3+/7nTDnxLU88AgB4OBgHYXbdntV8toduXwQQXiNK6TTPFOJ2R+v0joubwLEtr1VIFLtncPo+NzCHv+m7sfCSBqkyZojytexEvVOFiyuoL9kf0j9xlIMxdj0Eju1SJd49Q6H3sdUxK6kncuBF3theAqWYc+yt2HF7LB63oEKiMo7EG+ocRTWTNHatzBtXie1dsfCEW6S+eXrL2nS9sW/R5T1TTH3g0mUAwME5k2Q+4BgWX0oo51o8Mj5rjZ+ZZyBQP6Hv/Tvg6smVspuog6EiaJm+gfFBJs7BZJNNNtlkk032WbAJOdixGAdsm/UYv0PqYcrrV2EOsXJVTyEOw+hejhRnnlynlHdqP1XHoCqVY09v1Ev7pt6sZnOdX0BxcynItT4Dde+C8fjKi/KkHn+7U0oFAKqYecaD9N6VAaB8XLZA03H2y3pzimbDeTeX25uWRxxvmGcbTs3jLcEZP5nATz73PADg//3JnwAAPH/VaiRI/2HOGPX583bcxUPjS1w8d8Clzd73WBJ6WVUQwbtWFga9TVcrdOVE5uN7+WP2dsYE10uj+HYxV9xf7GQiCRxHsY+jch3zy3vPt9bYsZ+BzOY5vfjaGcx2jQsqu8y29kROmsFU3c5YJ+KImgsn9EBO+wanayk4Ep1SgSUJGcDOUZZLXmPOaxNBkw4EvdqGaoeN6l1Qs2BG7+/4ij3z7U1ry6KeoWS9jpqM7wPGfFV7IzB+q/oOwYvgEKVTjj29znOM9M5Yo2PLzIkzusw9hfKF9m1ZiKltGpxSx39Flc0FywAra0cO9ZZ5+l4el+/NnPoWBxx79T7LsrNWxbPbZwEAv/LybwAA/vgli1Uvjom07bXOLaqE2jhap/hzqtqY6keOiqKK66vokSqAC5FT0TQvn00EoW07RwaGPj1IXnyr4mkujchztWlhJcXFPYYulb8uzdVv1VZlfzUd+i7lI7T8XYf0W6NjWiJFNckE8yr9U6bicVpdu1ohv7f6fCoLTB/kCg4vSCNBmUbKSrgWbDzfZFGzKy8Zn+rxI+MkPHpgaN9FjvGGrT9mn29mqVaH0ETP7ohwlV4p6KqwWHBOjf4miZPAcV5EjKPk3mxCDiabbLLJJptsssQm5GDHYrTKWJ4LPIyeH7DLXt5BCjDmzw8xjp7gKHYOYIxfjuxh6RuQ2T2Xx8T4pVjbzqRPvVhlQ0hRr5LmAFpnsHZRZWAZKybDvaxVrVGxcXqxmnBqNtpL1VE55Yz7uUqbmPV24OmZ8Qm65gTt2hCChohBf8Nm13tUMttfmIffr2z53g/8KgDgh3/2fQCAF8kQPo3SVqeiIvPXi+u23KOXtqJHerg07/finuW7X94/h0v7+9y24DFk54vZr/LJfB3kjRV8Rp7EobLI4lxIKY88EWmkqxKwj5MQPL5ac53ijJtO2S2MPzJGKo9nJpTGESctUz6M0I8DerOHM2pZSJESBU6Z235KL/sGKzqeDuYBSSmxGeSFUENjRp7ATG6uCDAa0yxFu2U9iBvkbXNZc/tsb4FA7zos+dzENSDHolyyTsdc9S94CSifW6p81Fag3n1NtvqCTPBZqTg4y3X3hlQ1jF2HEmio0th1tk01I1YrG0NzchGEVsi5rJkJcjCzsbsUZ2FFL57XPmntHfjwJw05eNeldwIALl/6crunkw5xqRx3PuccOZCHK70D/hzLvco757epSWtIjOqVGkf0glnL4HSzRidOgEptCxGVF9+m2QrOUVDtAaE5Qj1VH8F5ISnHQO+yaiugaz0jwmspdCmTv5PWApuq90IluPcKIS/0uKV6GmKyX8kPq9BRtVnATaiAku+OJ4xEqS+yCUT3Oq7fklelejBHr9k378FzVunxwgO2PODYXxIdO5unCGOnzIpQ7qh2Eo1RhVdxbnY0EQCgEtwx7Ojw3KNNyMFkk0022WSTTZbYhBzsWAgBdVmjk4qa2OqK90CKgKnXLtGCgAGl4nHy8BW38joFtqiZxaDKgM0y8xg1s6UXV7pXLxa6aqqrRgFXo/f2Rs/93/hWYEfZUL+FHBRksyuPmzFnmRj2aMWktePP5FkwBofNTcRjUxkrqWdQU6FuvmfM3qY1r/T73vseAMBP/dr7eC56rzV5AWzbEOQ50Rtnf54xhhy4rI9s/1lhs/ll+ZozmVf0fM/vW0bE4Z6tv7Bn/XThwGLHB4wV7s1t/VyZJVHIQKpa11NBcblkvjMfVsfuOg0RHdu7R4+olpoaXRblY3eV3T9WVC+UZ0DKs5QPO6EdjmIopcIWCyEUdCH3Q8AleiPdUvwUQ1Qa9vEp+Qq3+AyOO0OCTraG4qw3HLPMnFD2y4y8kYJi8b3qJpC7MNsjInNQYLsiY32P7xTRm8DMmWqmzBGqcsqBlhen7Asp5sGembTzO3FTmFM+q5jdQkRuYM55c7PBgu+WEIMttTZAL23FPPYVdRwKZsLMqRA5r4QKUrMiSK2QWg58JOuTVwAAv/6RXwIAvPUrrALgA8WDrufR0Nuj0B9YMgIV38VKdADPPhCqR69cdQu2ZMh3+mbtBLIxald0eof7DgPfX0dMC6kRSs+Ax3j2gRqRIqpS7fNyjsrqYBuVDdQ3RHE47noM6JltVSs7RTcqtDIKASPngJyU2drWv5VVaZ9cWQeu7fXBBQISNRGDtSrW8t2s+KzEQdiPc7zjgbcBAI7Ji3r+pZcAAGfM8hIHQRlBt9gvLxFJmXFsnr9q78XbrhgH4Z2XqbR4aIjT7Jw95C0/s7fIl4lzoJ8ry0iIsca7stTYT+zqmn+z6mH823OvNiEHk0022WSTTTZZYhNysGNFEbDcW3oNhbEsm2Luip2lLF3VKIj9gKLp/d8APGdWROCxAqCUERnf9tre9HCUpaAYpEMDKVvVK0VKSxwDykJcCCk7qqKXKjgSdaAOedVL41uKb6zwqAwJkdl5E6pLDsaw5XlFogTdrVNUa7aPCMHegVU4u85c4R/4wR8EAPzaCx8BAGw4K++j8nfpGQkyYQcoNu8hV4/v0xuWKhs3N0PASWBmBPd95eprAIAZ490MIWNBZGCnlXcAACAASURBVOHcnrkdFw7Ms75waN7p+X2LRZ+jR7kkh2FFhGHB46Bc61PG3MsSBc/dipMiVT2PJSumHpL788wZxSNjiqD0mZsgVrMjLOWOjgLHipjcqqI4p4+w4H0cBLu/NT3AU74HJ4wRH5/Z/W2oZomNIQxty3guEaeCmRTzQ+tHLCM2wy22oUzud8wMoNaGdOilrS+2vjQmVMd+LENp98+OU5FCxZjP7R/yvEL/gJNe6qLguZXdY7/rSplEKackZCqMRaGaK3ZcVPU9Ppoti0z81iefBAA8um8Kil//h74W81MiRzNlCPEdYyVAIQStj3O21fuFvICNvH++k0QIijSpyfttUGx/u3FEoHcFWOm4KO7PGLuyDNT3LnfAd1LfTaTXEJLSZnwRZb1sm82oJSP9D2Vhiegh1EGhdY0Hrl8xw2TBzJoTz8DKOm4Qz4Tvj2L5yiKKHa4xo+rkxJDQq9eMQ3BCXkQjLoUKSOr+ldzGbjglOnx23ZC3G1cMTX3skpQWDVFYXRKSyWypNmLN9jQsVLLVSZMaCmOGVOSDbsodPZZ7tAk5mGyyySabbLLJEpuQg8xCGFXaxBQWt8Dj/vLWg1AABbAKlNom9jBn4ZqFVYytSpirdMU86beLCZ7OED12GO68HHyGPIzrOKPtC1VZlIY3Y4h96ik581m1AtjqrXu3YsCn6EiQfj0V8YpNBDbmNR7uGWLwNPUL/tF7fggA8NTLnwIAnIIZDuROCO3w6pZxx/Pd6QfF2OMoKMH9Mk+yiJ4rLPd0rYqN9E4jA7zxxBCG6oZ5CmKhzxmv3l8YUiBk4YBIwuG+IQsPl+ad7reqtKcaAyPLXLUAzuQQDVK8s9+zQmqWrHMQNH7Eak/z3QdHjIRIgMuYLBGjoysyvfyF1xuwfWtmQiwZnz3PMakKimvmaQs5uEZOwoly1ZkN0yyZxcKKmV23HTUxMikRoU/yKtssJ77P8vzlOY78GCFLytRRBpHtLy9e9VIq1NhfjQxvO7nQCfaLVPX4fig5A45q8aRV7qoJBbLtm2D9sumsn37uSava+MTjT+ALHjb+QTxizLgQiielP96X3mlp6ytDQPF/vaPMMBAa4BSlQQp6yv4hatK1rjopjYFe2Qte8Y9jz7MM+J4j+844UiCeQKooqXdby87rBACuvaJ1Gr9SnxUClnG6XElTvzWOqHPRUAZT38BmK0VJ6Tyw/3hc1QE3rn7M9tH3QtUkhZhoXHTKLmB3IV1u2cgbrOuxProCALhyat+8q6/Z70ceNv2LBx62b+XqwgrLPWrG8O/IEb/ZFVUW53y3Gt7/phInY3Au1L3ahBxMNtlkk0022WSJTcjBjkVYlStn3ypfd0g9zzHgLY9aWvRxx8PTVJazS8IOpfTHtdl1DNw15jJFJ5ApheWFEl1XATvetDP8RXjok99CECRPP8grVzybcSxVSBM7XTHIgpyD/oTZECec1bYr7C8tK+FDv25x1v/nJ38YAPDMLWP+HhcbtpteCjUYNCvvhnz2m3qOI0KQVitTP8hTjIArnsWsYpmHYxmkUzZKKyUzaSqc2e+bJ9SJuGYxST3TGWnpDxSGILx99TAA4Ilz5hHMUKOjB9CxDRseu1ScUvFt6jUUVHgEq/YVQhQUe/TGi82ceXkeB3XywpifLjQmU+90oUvpPIhzIr0M9tNBpbiune/Whv2k+C0F8LfBnvGZOC7FgHPkXnQkBaiqoHg88uSkRtg0KffAkRGhVkO1e0uj/ofrgJDdLe3+HWb9iATw/uWFuo6DtlMrwIdcUEfxAMWD5UJKe4LeKb1fMe2fvWWo2a987IN4UIqe0eLONXlBzZrtpR5E40OX9804vpCDPlMh1DvcSktA3r1XSFTcfBg9f8XUqYyoehbO5/Ht5NJI539IOQn9IN6Inju7yTuQ6A6RhrIq0WlfRwqV+cG+dwRW/A7xZlTvhv2htA5pKkgngRkErTg9un3xJNqR8xPZN17OwzVG0qqlapNoL15XJ0jvwjZseP+kIOCsY7VHZla9sDa9kUduXQUAPPbQZZy/ZKjk4ry9L3vzlLu20d8ZfqO9BkkV75dA4oQcTDbZZJNNNtlkqU3IwY6FUKCsF/BSZvLyle8bVPEsZaeOvIBiVNHzOK625XRj/hSDXEqJzj3I97Olz6QL1TuguUpZ5TG/0pGDrP2QDjvzsZUjDsUOmc+ri1MnoCAbXZXR1qc2Ay4thIaCsfbV/gHe/4u/AgD40Z/6GQDAC0eWIbCu6H2UzIiglyEnbhjp2NbmDEEYY8787fFhIQjgcoyvj1Ulk1Oh4FWDawToWkJ3FOcUmqFYKdtA74YOAM7krZzatQ8WhiQ8ODvwBosz0Bcp8qEsgyN64b/z0qsAgNUhVdWoFbBQhoWWkWqPGc8kOptfiMvgHp4vPU6vWLu8shQZk/qahmRPWOaYLOzNlgqD9JQar06pO6QeQihwSmU+xYh7KURmCELn3IMuWR+dpe+NsfXiXjjngOfTmI4p8oBiVKn0+iaqwqg8f09jUP9Ix146FrpGRmzI6p60dFPX5NUct8Zp+cDHP4S3sN7Cu7/gS6xZ0idgJkxHsYxmJo+avBAIecn6x7N51E873AIArXgBykDYdo6QuucvtMZrKaTPJHp105RDoEwj8SXUFj2qsfjjkBwf+97PLd5Cyb6r9Oo6F0lILLU2lI4RNVb9Q2ltzrM9Bn3ThRamNTxiFxGlb+PVZ9PKjfpelPpoQd9khyiT++yEJOj7woF3wposN8jVuXXLvpFXTm/g8jVDDh5+yBDYw0uW8TPfN0RR+hczgRnMuBqq6O/pvdqEHEw22WSTTTbZZIlNyMGOhVCgLlcoVNGtUDxcsTTF79LZulcpHAIqr+So2fGQ7KOYmuqJS61QVfcENTjbXJ4gA6RCEG6LyLtKY4Rmsq6ilsUrVUtBM+K+l343kjaoVvxWccyBanOsj9Af2Yy3WLM63Xlj2773F34BP/pTPw0AuH5G/f5gKEMrRUcF/cR/0HRXjoCaLOTFg81pPyLb744/UxBmrGinZ6O2ZNoJ6pDMkd7JkEgzRdYkb7zW2D1/6pZ5//PzBWaeGk1vTJXfpIxHj/HGmfEZXn3e4o8EBrA4xz6m57DPPPgLc8uc2GMsfy7NhVmm7ljNUHjRB3rRGheOUil2nsapO68XYquP1wYVvXpsmgUnJZnhTMHZetyf74d08mPllT5VW2RQ5Up5en36bnks3Znzu2QSIGTeqyMIethlmg2ilyGUwTVGZpIl5T6e38/7r+tUgyRHmKLnzCfdNmpSCFgggnfaWf780zc+hZ+hauKj581DfOu5R+zMGQO+aVWFkO9wJQU9ZpIMQgOJ10gvZMjQQKohSgdhaAdvoPQImj6rpRDTZ6H1g6qX+jhJOQcaV7oXfT/1vmgMhL5zRMM1WaIyZ/Qtkhc/JOdY6hpF+l1wRVU+ss7fbX0nkbRNNpTxtkyfGNPvgPhOUboXUbVZdA0XIUh++veCY3DLDAMWN3V9nXXb4NoVvmMn9o5dvmpVax960MbJ6rJxVRaHrJyq2hCzbkIOJptssskmm2yyz45NyMGOhVCgni9R9mTld6wQV6ZKX6pTrplw1yu+V3j8fgywKa5G9ThuLx1hSGenip0rh7r3/Gbl1Coemh3He4iIo6pcFjMdXLlMMTKy0PvxWOsHKcGRCc7T9Bve2ylZ+tTS36us6tjP/swvAgB+/Bfei1e2Flddk1swlGL42slqab0Xuk+11W+EbZFiWhojjLfVWkjNPYO4Q/PIdnKVQZV6u4spo2SUhEuvUtNn6OilnvJ8nzq2GOK5osIDpcUQRw+HNRLEZGZw9SZVJp+/dh0AcCYewHV5r7b/nByVVUVOApEDqTUuWXlyj3UB9pZz7FGnYbXUvratlvYGPaEZlyU9amXYrM/sWb5w07gG18i6PiYsIiSic66OGOZ2naYf0HvFy1QzXmPWswmyCqgjqCXRAVsMjvqkvJLC6x7USdv0LMuqHItLOl+F70krpT9etVahg5i0Ve+NuDm9w3risoizkt6T7vFmPMWHX/o4AOBdH30UAPDIu/9laz/fsUaqrOzjUt8Fplac8iZ6Vy9UzD6N0TtyIFRA3v0QnL/hGghEFr1ao7x1ZWE4ApBmFDi/Q5kGglD4rgodkLs+V/XPoXdkyNGmLHlL3ArpHEhBtpL+QaZJIajI9SFc7RbsH2WJ2W/XBojRUcwRMUhRTZkQgJFiwu+qeAxZNhvS3VAIztBYVnZDKL3g5RmVL281hiRePbV38PIN4/k8+KB9e/cvEClc9gh9/jV8czYhB5NNNtlkk002WWITcpBZCIXzAJwq22Uzx3xK5TGlEjNPek2Rg4JxuqC8VJ9Fc6ZMzkGuDOgoQHkXzkGW/YAYfRbsev3O5FYsVfvaP6T85RX+IDY22y4lsFNrw2xjHugC5oH+4vt/GQDw0x/4BQDA1e1NnAaLq3ZVqpZGsTxPDXf2/meo6pWn8A55f9xhv10UYdc8NJiHkn2H7NxFevXAmOJ4L+RwcHzcaq0Prhxfw7kDqg6q0qUX2xBvwex4a57BCbdv2D8kNCNulZXCJcj7KCxGKZBjRo9SKEBdAjPGqYUY7HG5JNqg6oOH54zHsM+qlWrr2andz9FNi4Nueb4z8Ws4fiq+P6WC8F4ltHflQnef6NLHrIZCL4p7pvMRMq0KZ6srl178gJl4BEKm6GGK2lMWDgiMMXduY/vd2SwyD1mIIRGCLquIKZ0IVVSVbn/biPVv+22LFte21pe/+vEPAwDexYqAb3/b5yfn1DPoiGJu6SJvSVcXLyBkfCNXP6SOSD+k/Klu23mfe8zcPX/pFaSZQ16zRSqLmZqrvjuN0AnpGuhbWClDB7zOqOrngJKyDMR3GZRZxfFSploCGvjKGJDybOvaHro3aRHkX5Jd5MnTKnbW3Q4c9hqaGoNDup9QLr+0xjBXLFzN0X434kcUAS0Hqn/feMyWlSJPT6xvb90wFO+By4ZMnr88R+/I173ZhBxMNtlkk0022WSJ3TNyEEK4BODfAPCvAfhiAI8BaAD8JoDvAfA9MRd1t+O+GsC3A/hKAAsATwP4PwF8Z9Q0+PZj/nUA3wbgD8Emur8F4H+NMX7fvd4HYLPTzem12/T6d3YAMHpI8nbGxkasFWeM6THKN3e+AtmlLWe6Dd3YOW99Tua79N07qfdxKjkr5ZEzFglp8cexzgLZ46HmORhDrOThciZ/izPahxgAXzZk3890f3btBYvNL1lx8YMf+WcAgH/ykfcDAK5Ei5O3Zees4krl8aLisqmHoLhdyFCOMXFA/Igi+e0KlCl1Y7SdWbvna3s1PXdPbPWQexF3sTwjIg1/O6Nao33LcfJKe4aDwWb8D/HGl20a55buwTGZ7BsiLsrPHuP37Mes3oXaIl+t6eRJjvFzVRGczcl/obKjfh/smS7DHmtkLArzaovO0IyaOhm1lDXn4g/UbBvb6oqTdfp7GD1clwYYxicNjJwbvVX+2wEEcnZcc9/aUjM7oxIcQKSt84wiMylRhlAAIc3K8Dx2IWZazVoTHR9G16ZcHT0bN411Vr2M0ZAYvX+qHFq0ERt69r8RXwYA/OjTpg/y5w/tmEfnh7xfO+aKCSnidGFtWW1svAzyxr0iK98TtU1whbgc0pEoO0ShmOISUOcArbIWNAiZncB3eMvtznsiSiVErmGtBq8MKm9faqH8xlXzCq3UNdkW6cKMYpX2W3VdQI7FKZ/ZlryZJUfh3Kuf2vY5b7/h9pb7B9Xm4HlDBErep9d10Dc9e99jUIZMusF1ElLx0vG7Q2tcsTbNoKgQxj/Mjsbwe8C+POYjOT6zZ/Day9Yfh8d7WG/v8vfrDdr9CCv8OQDfBeBlAO8D8DyAhwD8mwD+IYA/GUL4c3FH3zeE8GcA/BCADYAfAHAdwJ8C8D8B+BqeM7EQwl8D8J0ArgH4R7AJyDcB+N4QwhfHGL/tPtzLZJNNNtlkk/2et/sxOXgKwJ8G8P/tIgQhhL8N4FcBfCNsovBDXH8OwD+AuQZfH2P8INf/VwDeC+CbQgh/Icb4/TvnegLA34NNIt4dY3yW6/8OgF8D8DdCCD8UY/zAvdxIhLGjpQDnld9US1xxPzGDMzU/FNHjVHk1NFdPU9zNaydIY0CzR7LZ3bvNOAXKrY7pzHhUaRy9LcW4XI8hV4njoQu2TeiFmNA1g2BzqvYNR7bfrz/1DADgpz78QQDAze6Mx41tVO7v6NFncUknB/e7Tbktzu/ZCVkdgDF5WCvuNluOO9tSr9Tj2G8yujbmSqe59E7pYH+cDQ2unBknYL5ScJyaEbwPSru7Jzm+SGy5nzxFWnT/ObtbTHEhB1UdUDH7YM5MhpJcA9WI2LJioXLgT+jFLemtnGMVw1olReUwiaMjlKdM475J4/z58T1xRCC9AeXAlw4EpOqE8kKLmek95Aqjg/QwesXJefpR1g5R27K+dBtdRNtPCphZgsSouYB0hVctJOIgtMczmBoo0n88mDbGk688bcsXHwMA7D/6TttXfSwuhWLylTgG7GtViMxY+964LOMAxcj76ZyTRIRD2glDeq6xt4RSqOqmWqbzOfTCS8kTT7kobb91lUkdK4RV38fKvXC2n5kVtWeAkNOV1cFQ5oCGrMZB692SahWEYvy2jjQEjTl9L/QuKpNMyGSegaPbj8n6EcYqks0JNypDDPTey/TM9OelU+XIm/1YhfYe7Z4nBzHG995l/SshhO8G8N8A+HpwcgDz9h8A8H9pYsD9NyGEbwfwMwD+KoDv3zndvwdgDuDvamLAY26EEP5bAP8HgP8QwD1NDqqqxqVLDzsUNw44wfIiFAmG08dFoh+dj74xM4YjRDKxkiAVCcjTTlRAZgSpbT/98SScOtaN5v5sys73rRS8x40t4T1Ji2qciTC2v7X9zgS1MYVxcUwS0DVrw3PPW9Gk9/3WRwEAL/eUzfUmC7Id+0bm3wOfoMSk/dHTf5AuPZWTK+7yHY+3sQnvvN+uhWzC8mbNU9b8pU9P2A3ADQojLZUeeGB/mA/5x+2stb7cCIrVqSSSdRsZVn94BU0SEve/IQoZ2PkXi8r/wGreKUEijR0vcyxol7BnSeJiSbnXXul0ImKVPpUxq1JJ2mKHROgFlHxOK6KhJsX60PI+GPqoak0WtD+StqmfNAwk3DR4NylsIdEkwCdWSD/aBTtRkwZPs+yUwqino5TF9PZ3VG8AjO+flu5UYECkFnlkgaWzuYVwPnrVJuAPszDTI4cs4rXmkTzFesHj9YdKITJNnpA+U3RKUyQxMQSH9wOXPZeNyyDrjztvT4Rll0tO4XH1m5ZLjkGfAKg/FXItA9T5rSZOLrGsCYguzm+yIqf+DG05Y6nz8/uW4ieBIZyJBcqUzk5tBo8fZ/YSh3OiZRZ2dGKhxlj2h9vlk317/rHSae88fgqMfx+iM2jTY90p9HRKjtk+vt4n7w3ZZ5uQqBBot7PuG7j8iTvs//MAzgB8dQiidX/aY34822eyySabbLLJJrsH+6ylMoYQKgB/iT93/6h/IZdP5cfEGLsQwicBfBGAdwD42GdwzMshhFMAj4cQVjHGs0/Trg/dZdO7ilBgudofZ6tdmsI0v430pkbswGS3ERI1TdQMXucmLLY2T6EczCVwQaIMERBxS6hoGNU7eNyYR1NEoQ4Sa1IoQ0REkSTTWXpk+Vt5J/MrdvyNF8zr/eXfMcjz2dYgcuo6oRT5T9DnUIziNYQVopM40/tzD9JTsVKYz2fZgmTdSw/pCV4HGh7JLmk4wUttD3eG4W7Ldsq3322K7o+GQkcBOKHH9lrD8EJphVTOrWwOvL7B9EBBlF7Km6d0iJILIfv02mfETRUymJGgV+4IOLmwkMOOSoOjB+PlcEkGo+e/YBuZuYqhIPws79THJJLjamHBux3pHi5vkwcrtKGBwbo6mJP0WEketkiRJ13cyWMZEc0Lc+UPa4hOEHOgx3PQ6AHnRaEyGenxXOnhuc/lUtAuy8x7LwvM9ihec86exWAZafjU1qS3n7rxPADgYGXjZT/u876s0WuGFeQJt4pPKbVPWZYiGeZFlkJExzCCvPLOC2cxrOAQvd49Ik5CkLL0ybzoVUO5bclPa7/eQ0bVWA6a74kkrYV4uIS3iKq831LS1byvgu+BSIUiQGt81XWatunfwh3IX8iBwgi5xPc4puTVp6RrPX8JLY3jJh04Ifvs7H5uPJ1e4WO1U6FiIYa6tL6zCPeMhHob7s9p7mj/PYA/AODHYow/ubP+kMtbdzlO68+/iWMO77J9sskmm2yyySb7DO2zghyEEP46gL8B4OMAvuWNHs7lGwmdfMbHxBi//I4nCOFDIYQvK+t6J0dJ6S5ZfMqJbPwdSYZCdVv8cuQeyJPmuTiDnZUGdBQnr3JHpSDRQ1I81wmKeVQ+PT+wS4BKyUcitYVsv1POQmeURz53lTP7Fw3V+PgLlmb1270VBdocWFsOT2z/rVpUj3HfkThELoWTsXRtQSJCCsr0vt2bZ7xTE+mQephj9SDNzjPS5c6+0REDrX/9lJ+70RhyEx9kEKfD44J81qHwN+0oWm9d3Roac7gyV/GEsrbyppxjkhVqEtlvSW9+ubKUt0qxR4UgxZsZFDfeuZmQesTRnU0SK5kmu0cxpBnLRZcivzlUQO9N3AInIsqjogdVjB5UqSI1KqjjfATFn+1aNTNzxTVwoRm2UUJLbfZ+KW1urPaT8kqG3dRJeX6ZbHieeS3Caqn9HeVQ4aEMvdJ5vFgUvV0RfcntWSwXmB+Qz7Fvz7NjH18nwvSbJCheqC3N9Pdderudg1HXetjxGDGOmzVVs1SuXdwDiZH1QgfiMJZwJyLUCRkiUtS1Sj203wKEmKm6gxzEZCmvvBG5UNQseeQaL7vn4AdTKY0qD+2phry0ELHGi0XxfsgWXa9t3G8cabDzb1x0LSNy+2ck7Hzf+e2J6X3mH3dJgY+vmMYTUa9S21OOy1Dn6MC49K+7vocZjDmSn/VQ9J0sR1T1Hu2+IwchhP8IwP8M4EkAfzRGJr+P9um8/HPZfm/kmKM30NTJJptssskmm+wOdl+RgxDCt8K0Cj4K4I/FGF+7w26fAPBuAO8EkMT/yVN4O4zA+Ex2zGUe84HsmEcA7AF44dPxDT6DG0BZljtMWa52oR6/ZrIsBjKmMdtJI+TOTjkQWzomGxZio5+ZDOYwbHm8ih5p5sijbvNm06yFsiydwOolpjUrV7EjTxeit8Y54rk1Gb/XbBb+0ss2r3ueZYTXZFTXvOZSKZ0jzVs36wxe6uNAoXTxOLw4SFqbxb0rF2/JuAojcKBg224vADGm890Yd/YNOyt37a59m18zXe9Ikqf0xTvuP3ix3vG+rvN5X1oZk/uEnJM2SFZbcX/r84OF7bdcMnWPUscuiyv2uZfZfZ178ipE8mzUMda2BZ/zuXPmOi6YftmXQg5s78L5AiliUO6kC/JCth7ROydHDNQWB0A8JY9jday5zLYIjRDylHJwnLKSpaXduWMyT8vdyPQ+xve+zLbHO+4vU6xdaI9zexYLlCRT6L04Y+x9s6G3TpGjc3TjH1hYtPVyfQkAUBG2Uwy6WohJT6896h2VsFPKK+mH3tGlQTLYM3rv7eD7ADvp2UR3hIK2klvOSoG70Bnf9daLJzHbgZlbZSwhwEeP2ZHGmKISLoettNlBCIiyUISa8pqSzeb6RsiBMj/1yDIOFzB68i7NrewCRyDTb2/MOCm5A69CdzMWSXM0WSXOVRK760d+kNri6ehqkv7O8BwCM8Kwc9S92X1DDkII/zlsYvBhGGJwp4kBYFoGAPAn7rDtawGsAPxSjHG7s/71jvmT2T6TTTbZZJNNNtk92H1BDihg9HdgSMC/codQwq79YwB/F8BfCCF8544I0gLAf819vis75nsA/C0Afy2E8D07IkgXAPxt7vPd93ofERHd0DoT2j3wTJnH8189R5ux2jDsiNNo3/HsAFDUKWdAmQUhsEy0kAN5RF6D9M6z1LyATQhjCVZ5C/JoVfhEZZPV1opeSTiyWfiVl82rfW5jcfE1Y2PnnBHOedseZ+8xRUviEF1gJg5CAnj/ZNX2XEqi1utUSUhFEqQ6j3MRUiQBeTcAyQpDfYpknT8h91ZSmGdwZnAqJjXyGpCaZwRokTYqRuykF9iioYd/Y2ux5TU1I2oKDe3vG0Jw8cAiZksORnlfZyzqlAt2Oek6qJBXOm52zcVXvIw4OScHNh4ODll4qVKwmB6OC+xImMg+I2PxsNwTH7kujqw5DyFlYY9aEVqKv0APsEjj/IN7UOmzcu0Jj/OmY6Afbh9D/j57HFqxYqEb5F6IMZ4d53YbWmjbhQK1JEpsmx5R8uJBXAAiP0LYqI3w9GufAgC8/YJlL6z2WAJ8LU0JDWaOUZVPluyuuCfyuD2Hv0VHXEveeOccBGUVsA/lzfJcepYxG9MeWxcKqG7J6FL6jnZ9N2YCUIArZBlEo+iP+DLiIEgsSZwFxvO9cFHaL16oyVHftC0hBucEjAWl1OCkSTv8lRQ59Uv6+LHf4ubUM8lQp9yUXfzO9V1UmM95LBxTPv7Vhjtwre7R7kdthb8Mmxj0AN4P4K/fgRDxbIzxewEgxngUQvgrsEnCz4YQvh+mfPinYSmL/xgmqewWY/xkCOFvAvj7AD4YQvgBjPLJjwP4H+5VHXGyySabbLLJJjO7H8jB27ksAXzrXfb5OQDfqx8xxveEEL4OwH8Jk1dW4aX/DMDfj3eQu4sxfmcI4VlY4aW/BPMtngTw7fer8BJixDB0PuMfY0IZq90nlPIgjNUf++3o0eSs+YyL4LfIIhlLzULlQUtpUWpzmXfinmJU3q6Y8oNrBIxaWens0vPY6U0pS6G5aXHvV1ma92qpfGXb74GOniFz6W/tMU5Mb19oR4zRY2SKnQ2+JELSPh+ieAAAIABJREFUywNI71sIgW+/G3Ig59y5DEjX7zwzxUx1rD9PlVx2udPUC99hFfh9JdszaWuP+0l2ezdql41o9cvNU0NpVhcNITh38UE7lsqAQYqa1MPomNUwiMUQ0ri/XG7dfxgkEIDbTGNK5Z8XRIgOz1su/Wpln4dW7HVxFJwZzSydIJXCO4/VUQ0zutZGyLzIkJfDdlp6hgh4vFcx2YzvItVCz0kXgiQugp7dcBtikC+LjFNQuDeXxaed55Ky2f0ZcLdKxaAUq++GkSOjuL2KWvGaHZGTNSG2Z66aSukDh5cBAI9degDAqIjpCpuF4vyMyQc9Q6F9RIvigIa8lU5lnB1tILKoMcbnKy0C5elLY8WLJjn3JCXfuJJiSjcC+mEcMdKCkC5DjjIIOejEg1AsXkiJUFPtH3dP4+WRxWGRaqj6JSAkmgd2Tr1LGnsczz52bSndA0ceMpRXxbBavvsV3x9lIvg7HMfCYn2h9qXfsNK/VX26HfHT6rN8pnY/5JO/A8B3vInjfhHAv/oGj/kRAD/yRq812WSTTTbZZJN95vZZU0j859GKELA3m3sc1D1Oj0WnM8AxDmTH94jjFNULesgzTr1NeRVtME+w3GOiBVnrKzKaVZhncF6D7Vb2VLdjpkTDGfA8FiiZlxwqxXUVn7NzzNlIyrLj8DoZ9FepY0BFsyVL914kEz7Wmo3b732WbN36vZBJXhSIWandMcCtZZopMSIsmun3yf7y+EbkQMn5aY69ZtK7GuSFIwbJJZzvMOsstu5xTb4VjnbIU5DkgLQnMsU0IQeqOSCvJRbjbct7vHDhAgDg8iOmJaC6BVux1tc2DpRj7qgHCzaFgoiAQy5I+iV6SgHYxuBxeY+Bwu67Ynv3Duyah5eYlbJS2XDqV+hh1rpvxeBZwGmnhgIwjn1vez94/F19W2TAhtAZZa2oWFSOuMm7E/olL39Us+P+febdy12N1c4PrhrcTbVzdYq10xPOUC2Xt1ccPy+Cxn5VrLmc8ZsgXs3QebaFa6vwudXs88WC5Z7Zt880xvNeXDGx2IuMX4eZoT1bef2u8qqBp+8IL7NT7adXXQPdHz1+L/4mXovOxSyGYbNNzlXWKj+v8std8ns7CPVKkUvEYaemjNBKfh+ki6ECSqydsGVWR+DzLaQMqZd6oRQDjiP28x6/eay6vSNqOHr7GuchK0E9qrtKWTPnpGQcJecepAhkJ4RF6C+3jqKfwZFQP+dYSYpt0XdOHx9Pz8H9gg4+mwqJk0022WSTTTbZP4c2IQe7FoLJf/XpTG9kdduySB2OHSW8fmQ6e7g/9S7HMqBmIhmX9E5iw1lpL7Y+43551oJ7RlxWYoSHUS0uSiXPZqqVFL04/SwlL3dkJzu5YVwDqYvJsao1e5UHJG++FYtdtRsY3+wjGsX0lAvvCIFm2WJVmw3pZHvMkb5LeFez99hnxynLIY4epJ9LDl/m8QeoKp1iyzoXY4M6d9r0sbImK6IJKRC/RPdazGpcuHgRAHD5ouWnr5asqTAY5+CUKEwrzXshAK6lnrKsvSR25X6HOibpD2SoFzBqDJSsrVDx+V28aL/3maUQCmuTFO1CNeftpVkf0rF3rkGWKeAM69moA+Dlfd3JSdvpGgqFvDTxQtjn8sZ0AufqCDnhWYf0HU5KceTMJs8A0rsXk6X291oiPOy2yhwaBmzagtoUAxGWDTNU0HbIwAbUKqtNiUjFsTfkBTRrGx+feOWTAIBHyPd4fPaEXZpluMXeR0UkoVdGgt5Vxb8HNJ24BuJLpS9VoTLh3oeM7xNRVLlsWcux7DUFiCgU/H4IyfTqtv0w8nPER8iKrygrRXwh1VoQiKNvdsyHRaaKKsSp8Eetb4G+G2EHKdYd8ZhKHBuOxVYKtCkfZkQOhIJmnrxrMehZZMehGEu0e+YE0nZ6jZI8myHjx92DTcjBZJNNNtlkk02W2IQc7FiMEdumcSZ5GKnv/E1vOEMUetUYR3sb79kZ7mLvq9Kj/+as/dRm2yt6V3WhitU6t/wTzqAzdn+h2W0ABrZn8GMUE7VzV2LpN9a2zS3bf7Uw7/bzv/Addm7GOwMr/FWMmRbSYO8sRn0K00PYSOd86G+r1X2bKGGfzYDVl14PYkh+qx/ds+7S/RQPlba616vverTqYy69z+WF8NqdV82jV+K0hyyzgtYLOOmYgcGH3nLWvzw0PfwLDz6A5Z55j6qGd7w2lKbphRhQ6VCeMz3GUkqBQqfinb0QX5ttdk8b4baMAHExlqx8eHjRcufrJa8YpaBo9xdUKU85+ar/EFKvL6+fMbL1C18nBnyE6tCn+f5Sk0NMvXR5aXo20nmAWNuurKd3VB5Wxhvqox/jlsmQ9s5VSe/TdQ6y8TD6WlrPd5kqiHO+21XFSqyxc+hLCNtM+yizQaqCXhfF1r/GyqgfvfUCAGB5ZGPt0tIU5oUG9Svrjy1RPX0bZvJe24hta+0ZPGMi7RfVPQlDOn5yHQO9c7nmhjILxE1wborqiAy7z7VLzqFjdW3PYmIX12xqJZRUH2VluXAYjdojbFQhzpOQJnnvAFw7I4Mtxzu3S5SpYuJtGTV3k1zN+EHZajs3s0+UXDNkKITzIQZxrQZfhnB/fP4JOZhssskmm2yyyRKbkIMdCxGou4A5u6VWhTjOxGp6O6o1XjqLm2z+0Hs8t/CccHoP9L7qUt4Dz00PqeJU+OzWFQDAS6/8DgBgK8+5Eys/5R7IvNY6eudADKRFS9dAiEdJSsFwLL11cxW/+mu+DgCw/+gjdm0iBsoJVmW3Gb2ZkrP8TWmeR++qbOMxYx660+bZTp1TFS2FIMjDE5NXcWt6+fIMM0RBccyeyMHWEYbO0YaGDOftlvr1zNc+ozqh17hviCww13q7teV6a/e50fFcf7a2tp8x1lpSQ39B5GDTNjjZrpNje/JA3MMtxfznc3RdDPVbkey/w7LgftwtFxDQr7C7zUzj+mCf9RsuSBHR7rMixyBKvXPnXMA45pSvjezZhS4m62OI3qyKceoRARDWpPtM/RZ5/E3TJktEL2PJpbxTjl1HA9SUnTZm/J9cFsRrp6jvM5RrfBZ31jnQiZpWFQP1rIgKheAIQenxbN2v0D8zB1aECJCT8NvrawCAyy+bguLBJe7Hyo+D8uX5iEqhA2oTgiMFHSs5IstWELoloKUYxCMSKgUenyJ0DrxKrVCKi1KF7ISqDjtxeTH6+b4zrq8qnP594N5NHD1mYEfnwHUt0iwN987Fo8oeWYxhp3ptitLmdjfE4DbuQW5qhFexTbk5MY5co5iDUZlSonMR+HehLIr7pnMwIQeTTTbZZJNNNlliE3KwY6v5Au9+4vdjf2Gx1z3GiZe1eVQzef2eWy2vWCp1haMJUgcr8n3dw+eMVvXLo3mh1181D+DmNctnXrdSHaMnoRoFznLmLHUnl1xF48asBftdKWd4q0qQNis/PP8YAODhRz8fANCfs/supAUvRUVee86ptOKWsyyTICCMecNe5yFj0WtfxfruJOF3JxvS8ygjYPB8f3f7/DKDx1fp2Xh82vq2g3nKwXPkd1T0MHolDd3chvFNsbxfOrGK4k89Zwzyl64Z+nPzzOLCp83Gmcle4Y2ojEQV1KaYaWg4UzrPqdbWDEgqbqsHAf8tj0iZAAt6lRcv2/he7JMhT4a74p4dyDno5SkSkVLZA6m49aPKnB2fxvAxxLHaonMB1EJVISVvhR5jUab3MXQ6Lj93+tPde89iSGPORSh2Uhe0azpmb+cQpDYiCLYUH0BkJDHJxSeR6F/LfwQUkh9wL1WecpshB/rG6Bpbfg9acgiev2lj7i1z4xwcHBhqVW70LHmeUroZvA4GtMyEUJaCviXiC3X+DmuQCYlkP2yJ4jjaJ3RPbddxvH/PdpC2w8hBkenavRNYNP7ZNp1Dz9P1EXQc94spuhEz5KG8wzP2Ohy3KV06PKeT4U6Wcw1G7k36LiPjJvg3MkZEorJ6V/NKkI5O5BcPO+27R5uQg8kmm2yyySabLLEJOdixvfkKX/HOL0epKmruldv2sZa2ZoCa+dHrMU647Qvtm5pXH+PvFpohmvd68dC09Q/3LHh47fotXlveSOp5uQaD2P9hQJTCH68h5ncgYjBrZvxtx1x85G22357p+/dCP0I6qy5LxXMVGNU9Sq1Ps/jg6l+6zwFpTNBDaLcxvj+dxeyXGOWpgqIsFME9mkoxUq/wZ79rxnHFyVClPDHo1fY1/ZITsruvHpnO/dMvPg8AeO6VFwEA108MMWiFaoQxR9rn9Hqe7MuafSjUos+QhKKm9z4KaFibq0yJ0m8/RVDKshyrxHEPIQD7RIrqhSrZ0W8jy76gHoKc+IEKmcH7UWOyT5aO6vSjRznqzmdut5AEOZPiDogRL5W+LCNC76pXp+PCMwkyHoEq4Vne/Igu7bYBt43JzBNzb1Vx39ve8uTWNmtxVejNcnyFohzZ91zXEN1q3QtPmf2uqqfxQXTnxa2pm37s+isAgHct7NmVrNo4Ez9ozlh+xeuFzvkvLTk4kvj3XCfl9WdKmVJB1ftROCoK/gbvSbUb2DvSZClVnbAf1URdhVZLniSvc8L9WqIWgf1WMsurcl6YHVXGO54Ghb6jrqOx+w3RtlRZ1Ft0F4RAdjfOwRDTQTxmGozjZsyu4HuQnUuKsCM6nC7vh03IwWSTTTbZZJNNltiEHOxYEUrs1+du1yoI6YxQPIJdVS3AZvnhbhO3frjj6iJDKYrSNNIvEUF47rrlMY/CBlwo71Ux+17yjcWoHuhC3GRCsyJiXDODorX9Hnzb2wEAJbkWo4ed5jG7Zjx/egxSedC6hyGOOvM7SoXA6K27Mlk1HvN6lk/8x9l96iEjd6SHEQGIGeog3Qex0P//9r411pLsOutbVeec++rumZ5msMfYytjGBskk+REEZCwSTxAhEQ4JEEN+EKyQSAkCk/CQQBGBIB7iB4FAjAgKIpZiCUdyRFCQQ0CJ7QQiAiFkGHvG9tienvbYPenpd9/3PVWbH/v7VtXe595+3OnuO913faM71adOPXbt2lVnr7W+9S3P25enhdewSwv7EqtVPnc2Z5I8+/nnAABfupp5Ihs7yhdnGybin7Qjb9RIAx3ALlvqeg7qe6/QRitTlqM7c2S1qmPFJ1jkGmgp60O6D0Zzam0tW1ut6hxIY4NZCkis50DXQUuehFcQlAXkVUzV9JJX0HejcK1rIdBCdIW72u6U1SZdC78ynqviCVTZCcOytv6H7/wYA5Wdn1PxvTxLrmjnVmlFeOD28l7sMPtlT/r/GCzwLknnQZUa8xZDOJ5jV5oaXvGQ69mGV5CzYfqr2XNw6tSjAICvWsncg5OqKcC2buVHHbtpD508BvQEud6Huoqs+l7WqjwEPLeUFVuvRqruoBfMtGQbVLMiKWOlHzghFe/HOQSuPcNjSydGVSi5n7LBlulpk+bIEnfUa1jDQe8A8Qw6dM5ncM+SPG5u2d+Zdb7oQeC40u9H9e5CMn9QvEKwS2nKk1Zp8RzEQXgNCM9BIBAIBAKBAuE5GMMMaCa1kT6w8FWkzDcvzdSE3vkJVWhrRO3VCk2BpXPAr6mVfuZ09hzMzs24HXOQU2VhiQeRpD1uI09GGccFvQvdFlnqp3It+LUzuTY8mDu9NJhUeaHaBDzMXHFg5QM7v0Cz/86n6LK+rS0rQ2pbj1OWZOV9oDaJ6FB6JBya3Wva2wKt8qurKmjqFpWYEP8hMQZ7nZ6Cly/nzJFnzr4AAPjs+cwxePX6ZQDA9l622sSsV866jfQwJrQ+ZpWGvHsz+L2zN9i3Ut9MTv12ey7/X0pwuhdWWrdDhdEe80o7YmU1ZymsnchmZDslnX6aj7kzV/0KVUZ0zjcX8hgoZgqeS/eKi8qDkI9JS25eMdcJZUo416Dmkrj2QMkPmUxmxbndQ+EerJFXIFXWViq3XfA+VDFop9644Vcmzctynlc6B279peQ6D/O5rHadUhYieH16fqSlT48Dh9PGSr5H27uZ73KWGTNveTR7fVamsozzeNqcsm3NHnox4zVm5Dlw91szXjgfwCuA6nq5ubI0xJvpJUUhT4O/kJihMe/8ua1r0vhoV0fJi+NDkNvx/m9vZM2SjXWqoao6ozK1pLfSlOtdVyUteoTr97w4Ne5RqDwJhZbGaDthQcPFvZ9c2PBhKOrL96VXp+1H247G8N0SOUB4DgKBQCAQCFQIz0GNpsViCTd+5NKtFllpY02DRrNGzfR13JLxmur8Xd6Kntb72mM5Zrg0Y+W8bc0cZRmXOdWDZZnckl+i5TfbYxxui1ZVnlzj8bdlrsF0tjxuCmrFN6lvjYxxLjULLz0NgKH3fcq9msrybTrVjpAnQVPhMtfYjdHKSvW87UZl/sZ75X9ZZfENWgGlRbvDGPp55ow/98XPAQA++6UXAQBfungeALBBJbnOuRlttaQ3iCp9bduM+BuKkTNTgoztJWfd06JTQXd3U2mcVIxoEgW0Xiz3vbpcJYasDTG0T57K2SlLS9lz0ElVENpVuv68l6oxIHPVFeSkNsfv3XFQWjnNKO4/V2x5rmdHo0reqLKfXHuik2ekLbpnMHMK82uUO+4XNSw926YcY6OSfsVOnZcE1fZVGpNaUEku7u1SF2NHz+xwrV53wJU/uWeVfdPtinMhT5OyFfJHFnXFDs957mL2dp2b5PooT5ykd2iHtRWojro36dDz/bVZZUSJY+IVDPVuY4aM7aieAT0jXmOGkKdB1v184GYBQK+B0jQA6zL4GFF/yOvS+YMAAFjxoi35WNfZH1++kL15V1j4sjnFdxt1RZZW8jtwtrLMU+sdRk0Py3/AwEHqvMKl3hfie8nyH7ds5Dmz8nprT9RCxk6B8vn1PlWWTqU0K6SuP+B4d47wHAQCgUAgECgQnoMRDFlFbmDEl3OnoTJalXOq2WffDwpWTRlE77t6JsgYYBXe7BWbXs6556urOXuh2biYt+OM2ShL6DnIVPtLfedWyIQWwDJjxu1uPvaEke0zZ96UPzM2nup4VVvmxetffmULzPEhnlfn5UqHX6x0eRBSW1lnrhMhT0OpMKn4nBQozZRzvzNuyijdPw1a6Wyv1ArV3J3N7Er5HLMQfusznwIAfPErLwEALm1mrYlNcgvkSZlKD58W94y6AO1k8BgAuV+9PaqZoFoKHvssvS/ucfFxpLhnaa2ozr1Y2Lr3XWWmtEieETLjvVldXi7a0piqLtIyMrLYTdwCDj4PttPacouxel609Nh+M7Ke5BkoGeFD7RBdTyq2Vz/4sd1RcFDWQjm+/LgwVxkd9OpRHGMxc6KMrTvvw3fUgv1XPRfuDHKOTuMcAnEw9J6oDunjo2bxtzy2eUZOxmVWbTzLWi10DsJ4/Anb0q616Ka5nTtePCF/1o9D7xkEqp2ie8Y2s16DPAeDh0jvJnn7ytj9HIO+iDgVrbw18qgws0bcJB97e+qvvLzKmiUXLmbPwXXVl6F2x5ScjGV6DtbW8nt1ZSV7VJaphjtZnmF5acZT0AvnLiZej173u7WbUlls+lxxFgh/h1XvqkEnYRhhBzIIxAM5gO9wNxCeg0AgEAgEAgXCczBC0n/7qexhsATcWmlKKwZNM9IjqLZtpaJYz+w4K9fskjPC6TSzjE9QtRAXSna24qGuTofB+qnDzeaWS973kZP5mGcezxkRE8at+wOmqa6+hdJ6uTnIKlZmg+LVUs1j/8zb8thDrQjFjKt4nR+9jFW3VPHz6mu6h+3gKeg9jzt/9eql7I159oVPAwB++/m8fOmVrC1xYzd7CuZyOfDYk0bXRquE92o2ozrbVPc6o0s9jNs2Xm2xZLSLvzAYqzIrPPm52N4VJxmr7V1akEpxnjZDZnXfY8oGrTD+enIlx6P9/itGzH2ld+FV9vpqrFUW4oK1noYxmduYhmelUqYbvA7lM+WeOK738eQ8htLKH3gD9MBo7FcZB03qR9kXNvp/PusYgyBotZ3rIGhsl9ck6N7wVjl3obHWuTYDx0DN1zNWeS0GlxG/5XXLS8XGbtGyfvlartr4ODkHZ5ihkjZZT2GS0ClrgMqYDTMb5h5T55hrlfmiSrGlN08eNbgXhDohc7k7ZXmX19SNPAcuB+PZA9W7uHqvSvdhZ4uVUrdZx4Lbz/mwb2+xgioryN5os7dwRk6XPAfLa6tYPUVvAvtsNlNJS3kCOaZEIamqMOr5cW6COBYV12lhnB2Q/bAvXJtD59o/M+K1ICYHI5jIa9UPzG0XBcI+EwYiHSCCJLT+Bsr7L7PY08m1XEhlQkGauUr91vEICat0/UB61LPqkrT5HKceOQ0AWFnNk4R5KyGQA65J1yCZT5fhdeYZAKCppH3zuesHopo0tJoE6NvqZeCThXISoZd/68fVUOaLycMUyV3s6zv5hfDSubMAgGeefzYvv5CXF69kl+QmCYdK1WynZVldTQqWRRad5R9ZFUmpC6u0ZkPqlPOtVMSpdk2WPwJD4ZnyJeAvGvmqVTTHyh9NSwoxdVhiux9hOOHU6lrRbnebyt3LMIMTsg4QKPKyydU9Hn7wxxOV0tXaNtVzUaUe1uf0Mum9UjzLtrWuM1yO0eFHWBOXdrhPfo5yQoVq8jPMw/kjoHNWEz65i5s6tKjJBH+wUgtYqsMI5Y/i8EyW/VBPJhRn6PXDznt9hT+Wr1y/CgA4NX1DPopSKDd3sad9l9Rw9UZpZXSNSK+aHEhmnS10saByEjHMaKz4vhgLlVyyHUDWa1L53tPEY3M7P7PUc/IUX09HVFpuqwlJPsAuy65320yFvL6NdbIZVzk5WNGSE4gllmRXaHcQIFNoTOdQP+hSUrFdXZBJMLNbThD862rs9WnR/DwsIqwQCAQCgUCgQHgOxrCSEHUrYR4nmiwo8QwYbGm55Pd3IwpOvKPVdnItpzS2xtmqu5HKdBlNxq1p3GJzj4dcq/QcnDj1WD4mLd95rTlcX6f+4alN1Wy+klXtu34hBNHX+4iIprZqFq2ywhU3xz974Sl+rBKJfDO2dbvbxSuv/g4A4LMvZhGjZ5/LhMMXv3wWAHBxM4cXlP7nMsoU1Gnohp9SknWJ62ezFbY53xtZBLIk5WlAY9jryhTFvlbWcstWqW0lgVM3eE7ftCyFmYtKZSiNSoRNpVW2Zlhmu9aWcnuXl5TONSmaMrjgdey22E4Phqe8OYFRITJaTEnFcGa8tt6JqiKxKTzSexntQYwmd536sPZAqf8k0FSG20SK6yVYpLGp58Qwejh1zvJeaFmXzxb8WlCB6ycj+ex8/N1y+64bPAcyiN3KLA61IO2sx3/u42MofwwAHa3aLapqvbx+BQBwZjnLKZ8mQ9F29oYoEN99nRfU0ik5FlWQTGWQrX7PKB2TY9zFjiootXEcI/F1eU1V223wEIpw2yt8kp+Hje3tYvuGP21OjhRxs+rXIcAxPH8SedpSOHKXZOfdTGJs0wkeg/d5KpnxVBxTvyUam150TN83ko/WGEjFcj+4R6kKZdjIo3S3dJDCcxAIBAKBQKBAeA72wRBapNXR1FOxAz73aSgdzG8OIu8Nx+asstrMGMc9sZo9B7M2W6lbXmiksji8DY0TrRryF9ok6ypbcCcfzZ4DtCywozYcMOPsq5hhffnm8dGBmOTiLYxbLvARuJj04kpwtRdgcR8AALj07zArp/XKxsyb8dbAxnaWPv7Cl8/imU8/AwD49OeeBwBcuJw9BdvzbbbR3S4ABo5BU3kIlmltzVqVT6ZnoU5lpCUhy2Gvmw8iVQuxdMq8ipDVDQRCAGhVxIf9MlGhGB5n5jH2/HmF3qB2UsbR+709//fSMomIvL6+vqFuwcgCrDkGQypeXpZtGAaSLHKNrwY1+c8Jmp4GVlpP4up4qWKJZVUcDVlnLtkrC5Rt6yrTvEnNYMmp2QvR2lJ4Zx8zfl8MVl1TfS6OVshJO4+5eg8MBacqkqNIcezjiZ6nrjwH9c9wcTOTa89fz6Wdp49wjE6ACcfYLs+hktKtBJbEB3DrWrwG8SX07uFYkxxzNa4G/lFlIff9yFqu+1jEUh0k/0Mek+tbmUe0ztLNqm3VOQ2mJDQPPqDKo8lFay0aEXJFOCZpekpC5kTETaWdtyXPRcs9H1jiGvBUXjyptP579w5K4WkRA+Fwf7s+pZrmeHiE5yAQCAQCgUCB8BxUSH0/muIpHlrOoYZsEcXNOfNrBmu99iAcBJ/JVvQFycMqW2GVpVcvb+aZP6aKd6byAH0/MPzbcoa6pAwIZit0taTzAa29dbZGX22XfKqvGX9Xl0FtS1NfbZX1tgel6NFq4eR8j1bJfDdb/dv0pGzs5bjg5haLJTEd8f9++hm8eO6LAIArNzJje4fZCEORojLLYDLNnoLpSl62lLRWyqJS/6a0vFt6dcSkl8Wo+H83B4bUPcZCZVV3+TrcWpClbOXYmgySNFzmY68tZS/AGlPUjGz1TWZm7LJfmhYuh7t88iSPJHa5uAI8sidIlJa1M7+dLyEuQsksH+KieXXjFnPyMdI5E15FbOjdqo7RVXFrjTBluwxUA3F1tKVizFpWHJ9+OFrvgjK1l+82g7dVsSt1R7PgQVjMZFqo9lxJ8qYFEacybs3sSEyZATHjrWm6sjDTdfbzS3wGTjFT6eSkh83kaRQvQ4SG0vvnBZc4hue+nvyWKrXT+UI+sPa/p0jJ99GrtUv1O4Vg26Q/dHkje0S2yMWRBPjcs1SUxaP3pDKueLxW3s6BhNDMJJzE9+ZqtVwSJ0PcC2b30FvXcv8l3m+lUosfMd+T52FaLIW2bd2D2FcpneKvNHTrdO6t8R8O3Pa4vQXCcxAIBAKBQKBAeA72gfMElI/alTEgxdxaj3tzc5jPSOVBcCPL5U3LbAXF6dw+6NzsAACsLmdm7GOP5rLKZ185l7/3tP7KuusWORMSFFphrHmFwkpdeYgDvRzW3mIO2ZUJCfiYAAARpElEQVSWo5kNVkO1r8eEOTPeYXxza5ciJeQKXLuRPSTXKV18bSN/vrqeP9/YWM/7UTZV1unWDmOrF74CAHj5/MvY2MnbzhM1ImhFiDuwRAGlhlyBGXULWnIMGnoOJvS8LFPsSJ4EI6ejr7UIpJPRTFyPoKPM9SCnzTimZKKlrcD+0r105ruPi9yWM2vyAuTj3dhc5/G5v3Lt2xYTirlMqG8gi8/v0EJefylWI26B4vsq/iKWu3sOnDegWDT379NI/In9UOV+L5S7VbnffjS2Rv0yV8K+wtdeNE0eg2pwK4uhT27Zp+q7WwdtS45OUzHHvb+UedOUn32wdmnENSg9BgfqjKl/aokGWvkTWbHKjmGRpF1ud5HCXpfJwD85baWbhY7ehuReOr6zvE/VNnoAeC55BlwXxnUOtN6K/Uaa0DxgGq7L/CQABqnvkSsFALC1y7LqzFLYkxdsUvVxK46KxmxeTdkYGPlFU/bT8soUM0our6zxfbDEzm7zOXf1zmYSQzPXM8YxtysZcv0Y8B7Qu6NCXF1Hbx+3UzZU0zQLWgg1rPrtUb+0NolshUAgEAgEAvcG4TkYI6EgIqfK2q/XL0hW2uJcy1nZXsRILGw/JYBFeVeTUmKbZ5dv+l1PAACeZbxvhzF3zGS9qa1DPE+W25QxrZOMT6+S0T6USS4tIUmwDnzhKg66sCzznbvUYUccgO1sqaxvZNWxK1ezCuGly1nW9eJ6LgzzKmVer1LJ7To9Axu0hDfISt5mbHGFlvPJEzmbQ7HlK1dzJsL1G/k823tbnm8/5B1LyYyW9Cxb0kssdiX9B2vJNZhkD4FKG0tyVQqCA51C/5DniRbC3g76uQpjSdFQfS6VOfIdeATFbxWNXKJVcYIqbTN+3qa83dYuuRgQC51WoCxJ6zGllbQ8XeV3tE4aWYgyq8TBKMeHLEjnEEjfwMuTi09A9UJZzOr3IVlh5FIrre7OORepWKpNdZ73oFqotqrMdGWt7osqC0FN88+y/GpTTOtp7ap//LD0/qgwl9js4tfoPBh7DEp9h9puW9BBUExdSofK6knK7+cYr4jv6+S2nN/Knrg3rD7q7zMmH/jYUzZC31b94eWSx1kow3V1g4uBe6mfy3suq7ez5O+/1pfk2ijjSvwHnvvGJpUR9Tj5eOdzs5zbvrya16+u5jG/vJSfj8kSvWB8wOShstacz9JW7rvdlN9pdFp4fyXnrmg8yFMk677kobm3qKPrgZirGNtkgslUWUfKfCozIuaVcmg5RINzEAgEAoFA4B4gPAcjJKQFfgEwzIzH2+UlP2ta3y7O2Wq7xQsGtcq/zutFNpU12rjXIi+feMObAQCPn34cAPDS5S/l47nkG3PSW0OS8lbPmWjKs9BTyznzQbF2uOcgo46VY6Jc6ow9xsx2GDffpp75+nrmCVy5lvkAly69igtXskfg4tW8vHwtW/JXbmSlNnkSNrpLPGb2DOyQQyA1NrVFbPbTp7NGw+RE9hzc2MnnvH4l77+5nY+bOMvvsTfEfml9SrdghRkg7SzzOpTYrewElXOVUqJ0AZokK7byRChu7rwCMqjnc3hBJPcqKG5blm42XvcyrTWpGc5YzEnja4uFZjbJA9ijSqM2GLgL0kuYY4XHWGY2hhuCnkSuGLwC2uI5sFiPG9olOz2Rha3r32X9jyGboSy8k/uInpKq/sBcuv1qm3MNFMcu49bSAzHPNNjl9rJS6TWqnARNYyPHQcl3sLocbl3MZkFJsXzq9Yx7RoXXIuFevfgQnY/Nujy8n8qzN1AsrfJC7Po9rDyTslJZiEkehovk9lydr+IU8v2diisgbQWKJ+y2fjPydc2V3VDyQoZ6FhmdvD9aoQwmtVo6IBhY96oKvsJtljhOkjxj9CDc2Mqe0y2+QFvWhXjkdH4OTp0hP+gEMwdWaYnTe9az1Htv5dhM/cAH8xp3zoPhdaiUc1Xfps7q0RcDr6Qvtm/6nWI/ZZ7sNi0a1XGh13JGNdMp+U4gL8oLusl7k9Jd8huE5yAQCAQCgUCF8BzcBg5ijAqubtinUQnljNpzUJeD9nO4tgIX/pHcgxM5w+Atb307AOALl3Iev6ab0zmtu2SwXqzyPMvsLX83WWaWAvPy5xDLnixcek22dvKsfIOaAZeuZWv/VSoLXiRf4MKlC3l57RUAwFXG+W+sX8c2WdE7e/lYO7vZst/jueaV/rorCKqf2AErLCv8xBvfVHy+di3HTDdv5OPvUiFNnpbGmeJTt/iXyRlY4XJCDwJaKSFqqdn6Co8hC1qPS5UhorLLndjIVGuj5wBpOEYdr/b8ao6xqax7WgqKW6sSnlvOtChUm6KsSTmOySc/3dIKFR6ZhSHryTk1Yp17hTd2j+LbVQU4t5+lf8B+mbYlS13Lpm3cwun2xIUoA7tJcW550hrVYOD2LufhgVu2obyGQXtAlqLGxeh8srbdIlQmgDxAlcaCrMthTXEN7pVQLZMyxL5ISbLRd2rCQqUG9+sVGy7oIqguSCfuhvqFXIQ92aW5rVv0+l1ZX8fayTzORfSfSPlP3hruOa90IJRZoLYre6UWzKyvxN+Q3GCC5FwBpzfUPC+6FHb4XtnYzR7DtdP5ZI89nr2ij5wht+CEOAXy1NEboCb6zaAHV14C9F7meehbtqGy/OsaCJWo4yjjpNUB+Ll61/flekMH8D7OqYPSkSsyJ5er53q9H6b0ejZNu9jxh4TdVu3oYwAzu7SysvLYO97xzsMf4za2Oai3/aVep/nINcWBpR/s9c0bxUldCtbGL2+5lpmawx+F1ZUTRYuH9MryXC7nyReNftAl2KOlfgQ7us77rh9EoHQsF4UqzzV0wGIABhh+kETA02cXCfFCPQf3rLv95XrzH7kqVc/DD+WPoJPmDrjDwy0rry0VQlgHtK/0QC7KClff32r8LBzXPyYPXSzNlMfFllXbDj+0+x97oRfql2R1nPG90b69i9zc9FCLxzjo+2pcebel8h9pfFFWHqM+5q1R+/q5qFIid3YkfqMf7HHYovwBusWZDh4AThYtnxt/f1Sl0/VuWJ60Q8hKv1magPGYLjDlYZdyRjNEi8rtbjXkMeqmSmnYi7u5gJLSJ/lOkuiR2jZhCLSdlCnBQ173QU/hPj/wqdrWL+d2b1KN8j3r75uF4w5PQz0RteodlSoi77js89b1LaQuXU4pnbl5g2+OmBwQZvYigLcA2AHwmSNuznHG7+cy7sHRIe7B0SPuwdHjQb0HTwK4nlJ662s5SEwORjCz/wMAKaWvO+q2HFfEPTh6xD04esQ9OHoc93sQhMRAIBAIBAIFYnIQCAQCgUCgQEwOAoFAIBAIFIjJQSAQCAQCgQIxOQgEAoFAIFAgshUCgUAgEAgUCM9BIBAIBAKBAjE5CAQCgUAgUCAmB4FAIBAIBArE5CAQCAQCgUCBmBwEAoFAIBAoEJODQCAQCAQCBWJyEAgEAoFAoEBMDgCY2ZvN7N+b2VfMbMfMzprZj5vZ6aNu28ME9ms64O+VA/Z5ysw+ZmaXzWzTzP6fmf2QmbX3u/0PCszsO83sJ8zs18zsOvv3w7fY54772czea2afMLNrZrZuZr9hZu+/+1f04OFO7oGZPXmT5yKZ2Uducp73m9n/Yv9f4/147727sgcDZnbGzL7PzP6jmX3ezLbYP//dzL7XzPb97YvnYMDkqBtw1DCztwP4dQC/G8B/Qq7d/YcA/CCAbzGzd6eULh1hEx82XAPw4/usX69XmNm3A/g5ANsAfhbAZQDfBuBfAHg3gPfdu2Y+0Pi7AL4WuU9fxlCXfl8cpp/N7K8C+AkAlwB8GMAugO8E8CEz++qU0t+6WxfzgOKO7gHxDICf32f9p/bb2Mz+GYC/yeP/FIAZgO8C8Atm9oGU0gcP0e6HBe8D8G8AnAfwcQDnALwBwJ8B8O8AfKuZvS+NVADjOaiQUjrWfwB+CUAC8IFq/T/n+p886jY+LH8AzgI4e5vbngJwAcAOgD84Wr+MPJlLAL7rqK/p9fgH4GkA7wBgAN7Dvvrw3epnAE8iv0AvAXhytP40gM9zn68/6n54gO7Bk/z+Q3dw/Ke4z+cBnK6OdYn358nXcg0P8h+Ab0L+YW+q9W9EnigkAH92tD6eg+rvWIcVzOxtAL4Z+UfrX1df/30AGwC+28zW7nPTAnn2/TiAj6SUflMrU0rbyFYZAPzlo2jY6x0ppY+nlF5IfFPdAofp578EYAnAB1NKZ0f7XAHwT/jxBw7Z/IcCd3gPDgP17z9mv+u8Z5HfZUsAvucenft1j5TSr6SUfiGl1FfrXwHwk/z4ntFX8RxUONaTA+TZJQD8130G0Q0A/wPAKoA/cr8b9hBjycz+gpn9sJn9oJk9fUA8T/fmv+zz3a8C2ATwlJkt3bOWHg8cpp9vts8vVtsEbh9vMrPv57Px/Wb2NTfZNu7B4bHH5Xy0Lp6DCsedc/D7uPzcAd+/gOxZeCeAX74vLXr48UYAP1Ote9HMviel9MnRugPvTUppbmYvAngXgLcBeP6etPR44DD9fLN9zpvZBoA3m9lqSmnzHrT5YcUf55/DzD4B4P0ppXOjdWsAfg+A9ZTS+X2O8wKX77xH7XxgYWYTAH+RH8c/6vEcVDjunoNHuLx2wPda/+h9aMtxwE8D+GPIE4Q1AF8N4N8ix+5+0cy+drRt3Jv7g8P08+3u88gB3wdKbAL4hwC+DjlefRrANyIT6d4D4Jer0GY8G4fHPwXwBwB8LKX0S6P18RxUOO6Tg1vBuIy61ncBKaV/wFjg76SUNlNKn0op/QAy+XMFwI/eweHi3twfHKaf497cAVJKF1JKfy+l9Fsppav8+1Vkr+VvAPi9AL7vMIe+qw19wGFmfw05u+MzAL77Tnfn8tg8B8d9cnCrmd2parvAvYEIQt8wWhf35v7gMP18u/tcfw3tOvZIKc2R0+6AO3s2bmXRHjuY2V8B8C8BPAfg6ZTS5WqTeA4qHPfJwWe5PCg29w4uD+IkBO4OLnA5dp0eeG8YN3wrMqHoi/e2aQ89DtPPN9vnCeT7+PKDGGd9HeJVLv3ZSCltAPgygBPs7xrx3hrBzH4IwAeR9SKeZsZCjXgOKhz3ycHHufzmWjHLzE4iC19sAfif97thxwxfz+X4wfsVLr9ln+2/ATmL5NdTSjv3smHHAIfp55vt863VNoHXBmVK1ZPguAe3ATP728giRr+NPDG4cMCm8RzUOGqhhaP+Q4gg3a9+fheAx/ZZ/1XI7OoE4IdH608hW00hgvTa+v09uLUI0h31M7IV9dCKvxzBPfjDAGb7rP8m9nMC8FT1XYgg3brff4R99Jv7vXuqbeM5qP6MF3NssY988vPID+vTyG65p1LIJ79mmNmPAvg7yN6aFwHcAPB2AH8S+QH8GIA/nVLaHe3zHQA+ivwAfgRZzvRPIacQfRTAn0vHfQDvA/bbd/DjGwH8CWTL89e47mIayboepp/N7AMA/hXyi/FnMcjGvhnAj6UHWTb2LuBO7gHTFd8F4BPIUsgA8DUYcuR/JKX0j/Y5x48B+Bvc56PI8sl/HsAZZGPn2Mons7bBhwB0yPLG+/EvzqaUPjTaJ56DMY56dvJ6+APwFuQ0u/PIN/clZPLKTWeb8XdHffyNAP4DMlP4KrIQyasA/hty3rEdsN+7kScOV5BDPM8C+OsA2qO+ptfrH3LWR7rJ39m70c/I8rSfRJ7obQD438g5+UfeB0f9dyf3AMD3AvjPyEqt68jW6znkH5s/eovzvJ/9vsH78EkA7z3q6z/qv9vo/wTgE/vsF88B/4695yAQCAQCgUCJ405IDAQCgUAgUCEmB4FAIBAIBArE5CAQCAQCgUCBmBwEAoFAIBAoEJODQCAQCAQCBWJyEAgEAoFAoEBMDgKBQCAQCBSIyUEgEAgEAoECMTkIBAKBQCBQICYHgUAgEAgECsTkIBAIBAKBQIGYHAQCgUAgECgQk4NAIBAIBAIFYnIQCAQCgUCgQEwOAoFAIBAIFIjJQSAQCAQCgQIxOQgEAoFAIFDg/wOC6p5slIUzBAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff2d4317668>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 251,
       "width": 259
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def imshow(image, ax=None, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    \n",
    "    # PyTorch tensors assume the color channel is the first dimension\n",
    "    # but matplotlib assumes is the third dimension\n",
    "    image = image.numpy().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\n",
    "\n",
    "print(\"Imshow completed...\")\n",
    "imshow(process_image(\"flowers/test/99/image_07833.jpg\"))"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict completed...\n"
     ]
    }
   ],
   "source": [
    "# model.class_to_idx = train_data.class_to_idx\n",
    "\n",
    "def predict(image_path, model, topk=5):\n",
    "    ''' Predict the class (or classes) of an image using a trained deep learning model.\n",
    "    '''\n",
    "    \n",
    "    # TODO: Implement the code to predict the class from an image file\n",
    "    model.to(device)\n",
    "    model.eval()\n",
    "    img_t = process_image(image_path)\n",
    "    # print(type(img_t), img_t.shape)\n",
    "    \n",
    "    \n",
    "    img_t = img_t.unsqueeze_(0)\n",
    "    \n",
    "    \n",
    "    img_t = img_t.to(device)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        output = loaded_model.forward(img_t)\n",
    "        ps2 = torch.exp(output)        \n",
    "        top_p, top_class = ps2.topk(topk)\n",
    "        class_to_idx = model.class_to_idx\n",
    "        \n",
    "    idx_to_class = { v : k for k,v in class_to_idx.items()}\n",
    "    # print (\"IDX_to_Class:\", idx_to_class)\n",
    "    top_class = top_class.cpu().numpy()\n",
    "    top_class1 = [idx_to_class[x] for x in top_class[0]]\n",
    "    \n",
    "    return top_p[0], top_class1\n",
    "        \n",
    "\n",
    "print(\"Predict completed...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probs: tensor([ 0.8481,  0.0479,  0.0222,  0.0188,  0.0119], device='cuda:0')\n",
      "Classes...: ['99', '81', '78', '39', '88']\n"
     ]
    }
   ],
   "source": [
    "# testing\n",
    "\n",
    "img_loc1 = data_dir + '/test' + '/99/' + 'image_07833.jpg'\n",
    "probs, classes = predict(img_loc1, loaded_model)\n",
    "print(\"Probs:\", probs)\n",
    "print(\"Classes...:\", 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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probs1 array: [ 0.84806722  0.04788331  0.02218257  0.01875591  0.01193537]\n",
      "classes array: ['99' '81' '78' '39' '88']\n",
      "completed...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff2d41b1be0>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 481,
       "width": 521
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "\n",
    "img_locTest = 'flowers/test/99/image_07833.jpg'\n",
    "# print(\"Image to test:\", img_locTest)\n",
    "\n",
    "# Image number\n",
    "img_number = img_locTest.split('/')[-2]\n",
    "# print(\"img number:\", img_number)\n",
    "\n",
    "# Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
    "# returns an Numpy array\n",
    "img_display = process_image(img_locTest)\n",
    "# print(\"return from Process Image:\", img_display)\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(figsize=(8, 8), ncols=1, nrows=2)\n",
    "actual_name = cat_to_name[str(img_number)]\n",
    "# print(\"Image Name:\", actual_name)\n",
    "\n",
    "ax1.set_title(actual_name)\n",
    "ax1 = imshow(img_display, ax = ax1, title = actual_name)\n",
    "ax1.axis('off')\n",
    "\n",
    "\n",
    "probs1, classes1 = predict(img_locTest, loaded_model)\n",
    "\n",
    "ar1 = np.array(probs1)\n",
    "print(\"probs1 array:\", ar1)\n",
    "arc1 = np.array(classes1)\n",
    "print(\"classes array:\", arc1)\n",
    "\n",
    "\n",
    "ax2.set_xticks([0.2,0.4,0.6,0.8,1,1.2])\n",
    "tickLocations = np.arange(len(arc1))\n",
    "ax2.set_yticks(ticks = tickLocations)\n",
    "ax2.set_yticklabels([cat_to_name[str(x)] for x in arc1])\n",
    "ax2.barh(tickLocations, ar1, 0.6 , linewidth=4.0, align = 'center')\n",
    "\n",
    "print(\"completed...\")"
   ]
  },
  {
   "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
}