Image_Classifier_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Developing an AI application\n",
    "\n",
    "Going forward, AI algorithms will be incorporated into more and more everyday applications. For example, you might want to include an image classifier in a smart phone app. To do this, you'd use a deep learning model trained on hundreds of thousands of images as part of the overall application architecture. A large part of software development in the future will be using these types of models as common parts of applications. \n",
    "\n",
    "In this project, you'll train an image classifier to recognize different species of flowers. You can imagine using something like this in a phone app that tells you the name of the flower your camera is looking at. In practice you'd train this classifier, then export it for use in your application. We'll be using [this dataset](http://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) of 102 flower categories, you can see a few examples below. \n",
    "\n",
    "<img src='assets/Flowers.png' width=500px>\n",
    "\n",
    "The project is broken down into multiple steps:\n",
    "\n",
    "* Load and preprocess the image dataset\n",
    "* Train the image classifier on your dataset\n",
    "* Use the trained classifier to predict image content\n",
    "\n",
    "We'll lead you through each part which you'll implement in Python.\n",
    "\n",
    "When you've completed this project, you'll have an application that can be trained on any set of labeled images. Here your network will be learning about flowers and end up as a command line application. But, what you do with your new skills depends on your imagination and effort in building a dataset. For example, imagine an app where you take a picture of a car, it tells you what the make and model is, then looks up information about it. Go build your own dataset and make something new.\n",
    "\n",
    "First up is importing the packages you'll need. It's good practice to keep all the imports at the beginning of your code. As you work through this notebook and find you need to import a package, make sure to add the import up here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports here\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "from torchvision import datasets, transforms, models\n",
    "from torch import nn, optim\n",
    "import torch.nn.functional as F\n",
    "import time\n",
    "import pandas as pd\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data\n",
    "\n",
    "Here you'll use `torchvision` to load the data ([documentation](http://pytorch.org/docs/master/torchvision/transforms.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",
    "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]`. This converts the values of each color channel to be between -1 and 1 instead of 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = 'flowers'\n",
    "train_dir = data_dir + '/train'\n",
    "valid_dir = data_dir + '/valid'\n",
    "test_dir = data_dir + '/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Define your transforms for the training, validation, and testing sets\n",
    "train_transforms = transforms.Compose([transforms.RandomRotation(30),\n",
    "                                  transforms.RandomResizedCrop(224),\n",
    "                                 transforms.RandomHorizontalFlip(),\n",
    "                                 transforms.RandomVerticalFlip(),\n",
    "                               transforms.ToTensor(),\n",
    "                               transforms.Normalize([0.485, 0.456, 0.406],\n",
    "                                                    [0.229, 0.224, 0.225])])\n",
    "valid_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",
    "# TODO: Load the datasets with ImageFolder\n",
    "train_data = datasets.ImageFolder(train_dir, transform=train_transforms)\n",
    "valid_data = datasets.ImageFolder(valid_dir, transform=valid_test_transforms)\n",
    "test_data = datasets.ImageFolder(test_dir, transform=valid_test_transforms)\n",
    "\n",
    "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
    "train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True)\n",
    "valid_loader = torch.utils.data.DataLoader(valid_data, batch_size=32)\n",
    "test_loader = torch.utils.data.DataLoader(test_data, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label mapping\n",
    "\n",
    "You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open('cat_to_name.json', 'r') as f:\n",
    "    cat_to_name = json.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'21': 'fire lily',\n",
       " '3': 'canterbury bells',\n",
       " '45': 'bolero deep blue',\n",
       " '1': 'pink primrose',\n",
       " '34': 'mexican aster',\n",
       " '27': 'prince of wales feathers',\n",
       " '7': 'moon orchid',\n",
       " '16': 'globe-flower',\n",
       " '25': 'grape hyacinth',\n",
       " '26': 'corn poppy',\n",
       " '79': 'toad lily',\n",
       " '39': 'siam tulip',\n",
       " '24': 'red ginger',\n",
       " '67': 'spring crocus',\n",
       " '35': 'alpine sea holly',\n",
       " '32': 'garden phlox',\n",
       " '10': 'globe thistle',\n",
       " '6': 'tiger lily',\n",
       " '93': 'ball moss',\n",
       " '33': 'love in the mist',\n",
       " '9': 'monkshood',\n",
       " '102': 'blackberry lily',\n",
       " '14': 'spear thistle',\n",
       " '19': 'balloon flower',\n",
       " '100': 'blanket flower',\n",
       " '13': 'king protea',\n",
       " '49': 'oxeye daisy',\n",
       " '15': 'yellow iris',\n",
       " '61': 'cautleya spicata',\n",
       " '31': 'carnation',\n",
       " '64': 'silverbush',\n",
       " '68': 'bearded iris',\n",
       " '63': 'black-eyed susan',\n",
       " '69': 'windflower',\n",
       " '62': 'japanese anemone',\n",
       " '20': 'giant white arum lily',\n",
       " '38': 'great masterwort',\n",
       " '4': 'sweet pea',\n",
       " '86': 'tree mallow',\n",
       " '101': 'trumpet creeper',\n",
       " '42': 'daffodil',\n",
       " '22': 'pincushion flower',\n",
       " '2': 'hard-leaved pocket orchid',\n",
       " '54': 'sunflower',\n",
       " '66': 'osteospermum',\n",
       " '70': 'tree poppy',\n",
       " '85': 'desert-rose',\n",
       " '99': 'bromelia',\n",
       " '87': 'magnolia',\n",
       " '5': 'english marigold',\n",
       " '92': 'bee balm',\n",
       " '28': 'stemless gentian',\n",
       " '97': 'mallow',\n",
       " '57': 'gaura',\n",
       " '40': 'lenten rose',\n",
       " '47': 'marigold',\n",
       " '59': 'orange dahlia',\n",
       " '48': 'buttercup',\n",
       " '55': 'pelargonium',\n",
       " '36': 'ruby-lipped cattleya',\n",
       " '91': 'hippeastrum',\n",
       " '29': 'artichoke',\n",
       " '71': 'gazania',\n",
       " '90': 'canna lily',\n",
       " '18': 'peruvian lily',\n",
       " '98': 'mexican petunia',\n",
       " '8': 'bird of paradise',\n",
       " '30': 'sweet william',\n",
       " '17': 'purple coneflower',\n",
       " '52': 'wild pansy',\n",
       " '84': 'columbine',\n",
       " '12': \"colt's foot\",\n",
       " '11': 'snapdragon',\n",
       " '96': 'camellia',\n",
       " '23': 'fritillary',\n",
       " '50': 'common dandelion',\n",
       " '44': 'poinsettia',\n",
       " '53': 'primula',\n",
       " '72': 'azalea',\n",
       " '65': 'californian poppy',\n",
       " '80': 'anthurium',\n",
       " '76': 'morning glory',\n",
       " '37': 'cape flower',\n",
       " '56': 'bishop of llandaff',\n",
       " '60': 'pink-yellow dahlia',\n",
       " '82': 'clematis',\n",
       " '58': 'geranium',\n",
       " '75': 'thorn apple',\n",
       " '41': 'barbeton daisy',\n",
       " '95': 'bougainvillea',\n",
       " '43': 'sword lily',\n",
       " '83': 'hibiscus',\n",
       " '78': 'lotus lotus',\n",
       " '88': 'cyclamen',\n",
       " '94': 'foxglove',\n",
       " '81': 'frangipani',\n",
       " '74': 'rose',\n",
       " '89': 'watercress',\n",
       " '73': 'water lily',\n",
       " '46': 'wallflower',\n",
       " '77': 'passion flower',\n",
       " '51': 'petunia'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_to_name"
   ]
  },
  {
   "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",
    "<font color='red'>**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.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Build and train your network\n",
    "import signal\n",
    "\n",
    "from contextlib import contextmanager\n",
    "\n",
    "import requests\n",
    "\n",
    "\n",
    "DELAY = INTERVAL = 4 * 60  # interval time in seconds\n",
    "MIN_DELAY = MIN_INTERVAL = 2 * 60\n",
    "KEEPALIVE_URL = \"https://nebula.udacity.com/api/v1/remote/keep-alive\"\n",
    "TOKEN_URL = \"http://metadata.google.internal/computeMetadata/v1/instance/attributes/keep_alive_token\"\n",
    "TOKEN_HEADERS = {\"Metadata-Flavor\":\"Google\"}\n",
    "\n",
    "\n",
    "def _request_handler(headers):\n",
    "    def _handler(signum, frame):\n",
    "        requests.request(\"POST\", KEEPALIVE_URL, headers=headers)\n",
    "    return _handler\n",
    "\n",
    "\n",
    "@contextmanager\n",
    "def active_session(delay=DELAY, interval=INTERVAL):\n",
    "    \"\"\"\n",
    "    Example:\n",
    "\n",
    "    from workspace_utils import active session\n",
    "\n",
    "    with active_session():\n",
    "        # do long-running work here\n",
    "    \"\"\"\n",
    "    token = requests.request(\"GET\", TOKEN_URL, headers=TOKEN_HEADERS).text\n",
    "    headers = {'Authorization': \"STAR \" + token}\n",
    "    delay = max(delay, MIN_DELAY)\n",
    "    interval = max(interval, MIN_INTERVAL)\n",
    "    original_handler = signal.getsignal(signal.SIGALRM)\n",
    "    try:\n",
    "        signal.signal(signal.SIGALRM, _request_handler(headers))\n",
    "        signal.setitimer(signal.ITIMER_REAL, delay, interval)\n",
    "        yield\n",
    "    finally:\n",
    "        signal.signal(signal.SIGALRM, original_handler)\n",
    "        signal.setitimer(signal.ITIMER_REAL, 0)\n",
    "\n",
    "\n",
    "def keep_awake(iterable, delay=DELAY, interval=INTERVAL):\n",
    "    \"\"\"\n",
    "    Example:\n",
    "\n",
    "    from workspace_utils import keep_awake\n",
    "\n",
    "    for i in keep_awake(range(5)):\n",
    "        # do iteration with lots of work here\n",
    "    \"\"\"\n",
    "    with active_session(delay, interval): yield from iterable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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/densenet201-c1103571.pth\" to /root/.torch/models/densenet201-c1103571.pth\n",
      "100%|██████████| 81131730/81131730 [00:00<00:00, 128748829.83it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DenseNet(\n",
       "  (features): Sequential(\n",
       "    (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "    (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (relu0): ReLU(inplace)\n",
       "    (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "    (denseblock1): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition1): _Transition(\n",
       "      (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock2): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "    )\n",
       "    (transition2): _Transition(\n",
       "      (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "    )\n",
       "    (denseblock3): _DenseBlock(\n",
       "      (denselayer1): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer2): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer3): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer4): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer6): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer7): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer8): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer9): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer10): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer11): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer12): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer13): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer14): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer15): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer16): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer17): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer18): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer19): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer20): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer21): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer22): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer23): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer24): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer25): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer26): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer27): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer28): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer29): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1152, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer30): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer31): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer32): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1248, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer33): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer34): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer35): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1344, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer36): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer37): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer38): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1440, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer39): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer40): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer41): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1536, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer42): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer43): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer44): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1632, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer45): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer46): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer47): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1728, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer48): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace)\n",
       "      (conv): Conv2d(1792, 896, 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(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",
       "      (denselayer2): _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",
       "      (denselayer3): _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",
       "      (denselayer4): _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",
       "      (denselayer5): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1152, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1248, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1344, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1440, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1536, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1632, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer25): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer26): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer27): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1728, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer28): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer29): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1792, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer30): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1824, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer31): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1856, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1856, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu2): ReLU(inplace)\n",
       "        (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      )\n",
       "      (denselayer32): _DenseLayer(\n",
       "        (norm1): BatchNorm2d(1888, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu1): ReLU(inplace)\n",
       "        (conv1): Conv2d(1888, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  )\n",
       "  (classifier): Linear(in_features=1920, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = models.densenet201(pretrained=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Fc_network(nn.Module):\n",
    "    \n",
    "    '''Create a fully connected network to use in the transfered model'''\n",
    "    \n",
    "    def __init__(self, input_size, output_size, hidden_layers, drop_p=0.5):\n",
    "        \n",
    "        super().__init__()\n",
    "        self.hidden_layers = nn.ModuleList([nn.Linear(input_size, hidden_layers[0])])\n",
    "        \n",
    "        layer_sizes = zip(hidden_layers[:-1], hidden_layers[1:])\n",
    "        self.hidden_layers.extend([nn.Linear(h1, h2) for h1, h2 in layer_sizes])\n",
    "        \n",
    "        self.output = nn.Linear(hidden_layers[-1], output_size)\n",
    "        \n",
    "        self.dropout = nn.Dropout(p=drop_p)\n",
    "        \n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        for each in self.hidden_layers:\n",
    "            x = F.relu(each(x))\n",
    "            x = self.dropout(x)\n",
    "        x = self.output(x)\n",
    "        \n",
    "        return F.log_softmax(x, dim=1)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, epochs, criterion, optimizer, train_loader = train_loader, valid_loader = valid_loader, manual_seed=42):\n",
    "    \n",
    "    '''Train the model'''\n",
    "    \n",
    "    performance_list = []\n",
    "    torch.manual_seed(manual_seed)\n",
    "    \n",
    "    start = time.time()\n",
    "    \n",
    "    with active_session():\n",
    "        \n",
    "        for e in range(epochs):\n",
    "    \n",
    "            train_total_loss = 0\n",
    "            valid_total_loss = 0 \n",
    "    \n",
    "            correct_sum = 0\n",
    "        \n",
    "            equals_len = 0\n",
    "    \n",
    "        \n",
    "            for images, labels in train_loader:\n",
    "        \n",
    "                    \n",
    "                images = images.to(device)\n",
    "                labels = labels.to(device)\n",
    "        \n",
    "            \n",
    "                log_ps = model(images)\n",
    "                loss = criterion(log_ps, labels)\n",
    "                train_total_loss += loss.item()\n",
    "        \n",
    "                optimizer.zero_grad()\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "            \n",
    "            model.eval()\n",
    "\n",
    "            accuracy = 0\n",
    "            with torch.no_grad():\n",
    "                for images, labels in valid_loader:\n",
    "        \n",
    "                    images = images.to(device)\n",
    "                    labels = labels.to(device)\n",
    "        \n",
    "        \n",
    "                    log_ps = model.forward(images)\n",
    "                    loss = criterion(log_ps, labels)\n",
    "                    valid_total_loss += loss.item()\n",
    "        \n",
    "                    top_p, top_class = log_ps.topk(1)\n",
    "                    equals = top_class == labels.view(*top_class.shape)\n",
    "                    accuracy += 100 * torch.mean(equals.type(torch.FloatTensor)).item()\n",
    "            \n",
    "                accuracy = accuracy / len(valid_loader)\n",
    "                train_loss = train_total_loss / len(train_loader)\n",
    "                valid_loss = valid_total_loss / len(valid_loader) \n",
    "        \n",
    "                performance_list.append((e + 1, train_loss, valid_loss, accuracy, model.state_dict()))\n",
    "        \n",
    "                end = time.time()\n",
    "                \n",
    "                print('Epoch: {}, Train loss: {}, Valid loss: {}, Accuracy: {}%, time: {}s'.format(e + 1, train_loss, valid_loss, accuracy, end - start))\n",
    "\n",
    "                \n",
    "            model.train()\n",
    "            \n",
    "    \n",
    "    return performance_list\n",
    "    \n",
    "def validation(model, criterion, test_loader, device):\n",
    "    \n",
    "    '''Test the performance of the model in a test dataset.'''\n",
    "    \n",
    "    model.eval()\n",
    "    model.to(device)\n",
    "    accuracy = 0\n",
    "       \n",
    "    with torch.no_grad():\n",
    "        test_loss = 0\n",
    "        for images, labels in test_loader:\n",
    "    \n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "                \n",
    "            log_ps = model.forward(images)\n",
    "                \n",
    "            test_loss += criterion(log_ps, labels).item()\n",
    "                \n",
    "            top_p, top_class = log_ps.topk(1, dim=1)\n",
    "            equals = top_class == labels.view(*top_class.shape)\n",
    "            accuracy += 100 * (torch.mean(equals.type(torch.FloatTensor)).item())\n",
    "    \n",
    "        test_loss = test_loss / len(valid_loader)\n",
    "        accuracy = accuracy / len(valid_loader)\n",
    "        print('Accuracy: {}%\\nTest loss: {}'.format(accuracy, test_loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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/densenet201-c1103571.pth\" to /root/.torch/models/densenet201-c1103571.pth\n",
      "100%|██████████| 81131730/81131730 [00:01<00:00, 74408909.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1, Train loss: 3.397693967819214, Valid loss: 1.6742035838273854, Accuracy: 53.55235040187836%, time: 163.05111169815063s\n",
      "Epoch: 2, Train loss: 2.0952400219149707, Valid loss: 1.0780048565222666, Accuracy: 70.52617531556349%, time: 314.07100009918213s\n",
      "Epoch: 3, Train loss: 1.6590859980117985, Valid loss: 0.8524710882741672, Accuracy: 74.6661323767442%, time: 465.1348662376404s\n",
      "Epoch: 4, Train loss: 1.5355570005207526, Valid loss: 0.6927699842132055, Accuracy: 81.39690160751343%, time: 616.4980046749115s\n",
      "Epoch: 5, Train loss: 1.3975846238252594, Valid loss: 0.6731866727081629, Accuracy: 80.96955120563507%, time: 767.3763742446899s\n"
     ]
    }
   ],
   "source": [
    "model = models.densenet201(pretrained=True)\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "classifier = Fc_network(1920, 102, [512, 256], drop_p=0.2)\n",
    "model.classifier = classifier\n",
    "model.to(device)\n",
    "optimizer = optim.Adam(model.classifier.parameters(), lr=0.003)\n",
    "model_list = train(model = model, epochs= 5, optimizer=optimizer,criterion=nn.NLLLoss())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model_list, \"checkpoint_nb.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(model_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={0:'epoch', 1:'train_loss', 2:'valid_loss', 3:'valid_accuracy', 4:'state_dict'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_without_statedict = df.iloc[:, :4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>valid_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.397694</td>\n",
       "      <td>1.674204</td>\n",
       "      <td>53.552350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.095240</td>\n",
       "      <td>1.078005</td>\n",
       "      <td>70.526175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1.659086</td>\n",
       "      <td>0.852471</td>\n",
       "      <td>74.666132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1.535557</td>\n",
       "      <td>0.692770</td>\n",
       "      <td>81.396902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1.397585</td>\n",
       "      <td>0.673187</td>\n",
       "      <td>80.969551</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   epoch  train_loss  valid_loss  valid_accuracy\n",
       "0      1    3.397694    1.674204       53.552350\n",
       "1      2    2.095240    1.078005       70.526175\n",
       "2      3    1.659086    0.852471       74.666132\n",
       "3      4    1.535557    0.692770       81.396902\n",
       "4      5    1.397585    0.673187       80.969551"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_without_statedict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b4b40d160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot('epoch', 'valid_accuracy', data=df_without_statedict);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n"
     ]
    }
   ],
   "source": [
    "model_imported = models.densenet201()\n",
    "classifier = Fc_network(1920, 102, [512, 256], drop_p=0.2)\n",
    "model_imported.classifier = classifier\n",
    "model_imported.load_state_dict(df['state_dict'][3])"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 77.78972662412204%\n",
      "Test loss: 0.7606391196067517\n"
     ]
    }
   ],
   "source": [
    "validation(model, nn.NLLLoss(), test_loader, 'cuda')"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Save the checkpoint \n",
    "checkpoint = {'input_size' : 1920,\n",
    "             'output_size' : 102,\n",
    "             'hidden_layers' : [each.out_features for each in model.classifier.hidden_layers],\n",
    "             'model_data' : pd.DataFrame(model_list),\n",
    "             'class_to_idx' : test_data.class_to_idx}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(checkpoint, 'checkpoint_nb_2.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the checkpoint\n",
    "\n",
    "At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "def load_checkpoint(filepath, idx, test_data):\n",
    "    checkpoint = torch.load(filepath)\n",
    "    \n",
    "    input_size = checkpoint['input_size']\n",
    "    output_size = checkpoint['output_size']\n",
    "    hidden_layers = checkpoint['hidden_layers']\n",
    "    state_dict = checkpoint['model_data'].iloc[idx, 4]\n",
    "    class_to_idx = checkpoint['class_to_idx']\n",
    "    \n",
    "    model = models.densenet201()\n",
    "    classifier = Fc_network(input_size, output_size, hidden_layers, drop_p=0.2)\n",
    "    model.classifier = classifier\n",
    "    model.load_state_dict(state_dict)\n",
    "    model.class_to_idx = class_to_idx\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/densenet.py:212: UserWarning: nn.init.kaiming_normal is now deprecated in favor of nn.init.kaiming_normal_.\n"
     ]
    }
   ],
   "source": [
    "model = load_checkpoint('checkpoint_nb_2.pth', idx=3, test_data=test_data)"
   ]
  },
  {
   "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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_image(image):\n",
    "    ''' Scales, crops, and normalizes a PIL image for a PyTorch model,\n",
    "        returns an Numpy array\n",
    "    '''\n",
    "    \n",
    "    # TODO: Process a PIL image for use in a PyTorch model\n",
    "    preprocess = 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",
    "    return preprocess(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check your work, the function below converts a PyTorch tensor and displays it in the notebook. If your `process_image` function works, running the output through this function should return the original image (except for the cropped out portions)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imshow(image, ax=None, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    # PyTorch tensors assume the color channel is the first dimension\n",
    "    # but matplotlib assumes is the third dimension\n",
    "    image = image.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",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b4b408d68>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with Image.open('flowers/train/1/image_06734.jpg') as im:\n",
    "    image = process_image(im)\n",
    "    imshow(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class Prediction\n",
    "\n",
    "Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.\n",
    "\n",
    "To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.\n",
    "\n",
    "Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n",
    "\n",
    "```python\n",
    "probs, classes = predict(image_path, model)\n",
    "print(probs)\n",
    "print(classes)\n",
    "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
    "> ['70', '3', '45', '62', '55']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "    with Image.open(image_path) as im:\n",
    "        image = process_image(im)\n",
    "    image.unsqueeze_(0)\n",
    "    model.eval()\n",
    "    class_to_idx = model.class_to_idx\n",
    "    idx_to_class = {idx : class_ for class_, idx in model.class_to_idx.items()}\n",
    "    with torch.no_grad():\n",
    "        log_ps = model(image)\n",
    "        ps = torch.exp(log_ps)\n",
    "        probs, idxs = ps.topk(topk)\n",
    "    idxs = idxs[0].tolist()\n",
    "    classes = [idx_to_class[idx] for idx in idxs]\n",
    "        \n",
    "    print('Probabilities: {}\\nClasses: {}'.format(probs, classes))\n",
    "    return probs, 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": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "with Image.open('flowers/test/10/image_07090.jpg') as im:\n",
    "    image = process_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probabilities: tensor([[ 0.9366,  0.0295,  0.0063,  0.0053,  0.0049]])\n",
      "Classes: ['30', '82', '92', '100', '31']\n"
     ]
    }
   ],
   "source": [
    "probs, classes = predict('flowers/test/30/image_03466.jpg', model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [cat_to_name[class_] for class_ in classes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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weAlSKpRX+BLKVDBPPWSRbjdBrGUkKqcbhmA1pm8gjwTpyEWOiRkzkxKUAZNhfKArUgobSaNjeXODiXUoLXHRohJDmhfU8ykGQRFzMlKCdpR+xsxOIZE0riaWghAkiTSovYZYzXhsfYvw9M9RT2te3XmDSESJgAsRubASbneMnxHCh8EDRAr/riHe5ZdLnASpFZ28YGV1jZs7O9Q0bVpLibYn37frKpmgSkNP9Ol0esR5QKQFja1hHkhlwmY25D/88l/n9/7N7/HKrVdw0qOQ0Cg2llb4jb/2t5jfOEK8XtK7qPiTb/4hf/HdF5hjORjtUpAwDw1ex4/HFA5wrMl5u8filBJTFDgXoAFXSVziaazDese5rTXSTh8z0STOEHSkESWJSRgdjYk+sr69RWepoPO6Z1Y5siDxTU0pHE0R+O63vskzzzxDJ88x+QpFR/OXt26SpR2yYoAxmsZWKB0YDjaoRUNdWWpfIWpQNnJrZiltxWE1xqwU/Ke/9neRPcP//NX/ndffeRPnF6bCSbbng1rSz/Bu/BiTwl0gWj/+8mNP8OVf/DK//dXf5vr1622JslCkScKwv4wIATd3mCaBaaTcm2O8oZPk0ASasiRYTxo1iZdsFevcSHcY2wkiKFKdM5ADrr74FmYcmc0ce391hVk1RQWB1oKClJSEijZi/3FV5MUYjk+1DfLJ0FKDkIjYVmE56xENeBvwIeCJyERT+wZrHUmSolKJwqGMRmlLVVf4umEpL2h6PfTREa6pKfKcwcYaNjh8U+J9hZIJUga8b8izDCUllXU01pGmmpXVVUazMXVpqas2o9FJUoQNVLMpk9mUeT0lCY7ZjSMeGj7Kl3/miyipeOvqO1R1tRBlacvDF/mIU5fvwa15eRDwAJHC3b6kj5qivHP908G9u0EKhdKSfqdPN8kxQSEWg7QMkccfvczf+Rt/h2Za8/Xf+wMKm5JWCcF6ZCMIs4bUKxJyjiaHdJc6lLbil37mK0y+Oeev3vg2MkgublzgF579OWZXDnGzOYVLidZia0tSFPQ7giIZ4EaS+bjGS39yQ99OYfL9JHEPsce4eFhipC2fXmwrqDZmImhdJjv3NKmjri0+BpRWCK1weKKMJJ0MIyQyRrrLGXMxpZpOEUWCm1eIEBguLbG6fY7O5gZ7syNyKZG1Q1oHzqNpazNC8JTVHCkF3f4yK6trNKFh1kyJwYOLyCDwtSXaQCYMulgi63RwE4sbVfz8s5/jyccu89V/9k954eVvU3uHCx7wSKnaJrDgaS3ElmF/jGXZ3hc/MCkIIS4A/wewSXt7/laM8TeFEP8N8J8DtxaL/qOFtsIPAae/9HswIQM88fjjfObZT1GPZygfkQE0ihCgYwr6aYebO1O6Lmcp9ujaHBpLPa6ZHU5ZG66SmIyaKUmREzxkSwWXti9xbf8609GMju6yMdhgfGBg0LCZLePrmp39G9CRLF9aQa4luJf/LdemOyfqjSdV1cfE8APkjkJsCS6KRemEOH4/EkNELdKJqIirHVVZMZ6O8YklNRk6UyijiQSkkkgfyLShsZ44mZEWGU1T40VkbXOd8xcuMImRWFl6PmXvzR32kl3yfhdpNFVTIZDotAuAShR5N6eYZcSRRyqBShTOWmzdIKOgyLskRUZa5NSjktdefJWLYZvhWp9f/fmv8Mwzz/Lt732Hb7zwLWyMp/pOjrUrz2IM74ePYik44L+KMX5LCNED/lwI8fXFZ/9DjPG//eiH9+8SAoXk4tZ5Hr/wCG+8+hoX1jc5PDpgVM0RSHxtGe0esn9llx4FSa1h4ujGjFA3HN0YUzYGazTKg288vV6f2kd+4vGnuHF0g2//1XcRTlHPHCv9dTIpWTNLJEqxtnEOl3l6233MuYyXb71+IjLiibctHfEui+HD4FiVLNypghwAGQPe+zbuIKCpPU1jmVcl1jYs5zkiaJKQoIQgeItB0MznzA8OYVbiBzUuOmo8ZIYsz7g13mf3ravouaCaTplHx/pFQzEwzJsZ/d4S3eU+3aJgedgn7yRIDU0zR8qAEJ5ZWSN8JNUZSui2eCxIDJLD/REvf/OQ5c0hm5cfZu3JDQ7GR3zrr/4CG8JJgefpGNIZL7w3fmBSiDHuADuL/ydCiJdopd1/JCGBSw89wsXzF7FVAw621rZ45+o15k1Np9Pj/MYW/aKDWD9HZ54Rm8Cta7ss6T6yDhiv8LMGkS1apW0kNoFUJ1zceohza9u8ql4nOkdmUlaKFbpFxkD2SKRimK4T04jtONCQyASNOnl421LejyG9tsiunqaU40If6RcWiYfGeuqmbn10IE1T/DzgmxpjUlxwmLTDrDykOpogqpK9nV2c80SjaYg0IdCUNbvXbzCoJcYkFEnCUm9IPigwiaFxDd1ezqVLl0iMYnS0z2Q8QgmBFFDXFc4HulmHIisQQFNbZGpQJkEHTXV4xFhKRHqd3C/jm+Z2M+vi9/LKECUV+/v7gF8Qw4fTvPhxwMcSUxBCPAw8B/wp8LPAPxRC/CfAN2mticMPtcH38v0/yrMg4/e74MdpwgBKKz797HM8+8TTTHZHCKvpmQHnBucoki6PXHqYTz/3KdbPbTDIhqz21gh7jtH+mNg0ZDGh1+mSdwpUbnCNAKuIo0jhDFUn4eHBY1xZfZvgGpSsMJ1If9BnSa9Rzud4NydPckRVMt+ds6KGbCydYzZ7C/BIe9yKrRaSROJ2qvIeGklPn/9dO7k9hCAXbkqgiYFJXTKzFdakNCpBujHB1jRCQa+HTHvIOCLMqzYl6xuWhmukOpCsbBC6faqbNxFITGZI15cRwy7FuXUGgy5JJ+NgdIBvKvJ+QUgUB1ffphzNya2kcYE6NuRpikkUQkVcdPggUZWjnre6a72VdaRRTOY1071d5pNDVPAkGIq0x/r5TS4/dRklBS++8Bdcv34d21iQqpWRCZ6A+1C31CcVH5kUhBBd4HeA/zLGOBZC/E/AP6al3X8M/HfAf3aX9X6AeR8+SOTkA+IG8s51w6khWACdrCBPC47sIZnMWOmt8qmnnqO/MmB1Y5Wi1yHVKTJTDNeHqAx2Dt9hXpWsr63SqIogImhFN+8xcxVaSKg9cRT4iQuPM3v6iG9860/YG49Y6WxC1hb/JD7Fq4AWmqoB6xse336MnWaX69/cwccKuTh7KSGIjykdcYw7Lk0rc+QsVLOG+XTObDbDFZZBnpEkGSFNCDqhowxl4/B1gxIKqRMuPfE4pY4kSx1QEq01RhnSJOP89jaD7U1qZwnzGuXadudZ3VDVDcNiyFo+oBQpB80IgaDT7VHkOSJAaWuc82iTInzrQnkfsJXD5AnCKvau7nO4d8DaygqChK31bb70S19hfWuVej7l4sYaX/va17hy/RpN24t9prdwCh+JFIQQhpYQ/s8Y4z8BiDHePPX5/wL83t3W/cHnfbg/6SQhIHpBbCCVGVppchPpDnpkWU55UOPLQI8uzdhigkJZTXQBby1pnpGkKWVdofKEpMgQpcJIiW0a0hqSkLGSDbFO8NJrb3Ju9RIYg/MOYxKM0eACnaTTGrUSLgy2eGjpPG8eXKHBngQX74duyB3NlYvOQ2c9h/tjdnd22VzeZH1pg7ViwKSsKOclzXhEmJRIB45IYlKWVpapqhGVrchcSmoMidJUVcO1d66RD5ZwtuHtV15Fz6Gf9ti8cIGNtU3EtCGZBLouYRY0/W4f10uw0VHbmrIqaVxDJjwNNYhWst45SzNvX1+9eYXOoODv/cavkGc9RNQsLw9xZY2dVmwO1/jVX/4V/sXX/4C3rl3FnbgSZ4CPln0QwP8KvBRj/O9Pvb+1iDcA/E3gxY92iKdwOgT/AxHDuy2J2yWBUrQdijIqEpGATFBaEUIklKB9RCKoDiriPOB8RU6XlcGQaT2lbmrSPGt7FFSbFZ+NJwx7A6g9ioCXJSvZCk89+gyvXXmTeWURUi7UlUCiITqMyki0whjJ4+ce5ebopzj48332/OTkNIRoHQf5MdU13RWhDZZWs4ZYBQqdkZocLQyqnjO7usfsqCTujkjQbQG4gL2jAw7smKSbsqqGGKXRQuKco5t2yWKCtgE9j/RCzta5C6ysbzLdnzD63jVmb99CTaGrenhhaVzEhoDztOKzAibzMS468iyhk3SJUYJ1TPdLsJGNpQ02emsUnQFN6fFTh61rmkkJwrI5XOULn/sc1f/bcPXGjTNL4RQ+iqXws8B/DHxbCPGXi/f+EfD3hRCfon1q3wL+i3vZ2Imc2fsts/h1r6wu5ULk71iWB074REpJiAEpZNvFKAR1XbdNNQ6aec0gW0IKhfOBVGeI6GES6JkOuoZEaborG6hScPPWTc5f3G4j+96BayvqYohoqZGNI5SKjf46T2xdJliJtIqmcshEoJE4F8EHVAStNB1dsN5dYbO/TiYSBIt2ali4QvG4PucHFqaSsn3IgrgdlZMs+q8WxYF27jBWYUc1O+Md6qyLKD2zq4dwOMfMLAkJItNMrOOt61dJNroMh12UUcgYidaTJQXrS2v4SUMWDY+tPUxZznn44SepVMK1169Sv7FLMvVIZ1Amb5Wc7byVk0eipKJ2kbqpQUdIMoQBi6eczDmYHpF1C/qyR32rwpQpRqVoaTAe6nrCzJYYaXloa5ul/oB3rt8444NT+CjZhz/m7sn/B2quh5ZI2g4+pTQxRM5tbbG9vc10OuW1118j+ICMkus3rnH9xnVynzIaTVCVZtBZQmMQQSCtp5lVuMRQ6A6pSkiUJkkSro5GpHspSytDpNYEIMsLtDZonRFcGxiUtWe7s0nn4YJEG5IgSUzS9iZEv5jPwNFUlipUkAp6eRctWkEXKRd9TCd6CbF9iCVtp+KHSFO+VzHXcQW0jG2iNnqBmgtmOxP2xzOq/jLn+ivoqUCUEuMNYPCLRqu822H14jmW1vqoOqIj6CjJTEaRdqnGJXbuSKIgN1363RWyLKHxh2gy8twwbmZgFJ1ORuOhno8gBJRQJDLBKkuSG3rdAT54xuWY2loSmZGSI2eKZKYpVIq3gayTY6Jg0hjiHKyyzJUlS3OSNKGqm490nz34uHfr+gGqaLwPEALnPZ1ul631DQqdEULgueee49M//WmuXruGryzXrl3BWc/Lr36XjeEazz/+WTqdgvl4Tq47pEmOqx3BeUIZGB0cIVJH0tcok2GUZrg8ZDIe0SkyllZXsTHikwBSEZAkeYZ1DllVrGcDht0Os3pOIQ3aaHy0SAJKtBZAXddUbobpGfr9HlK1Yq7HX6uUAcRtA0FKsbCA7o0Y7iCEk8VvqzO1UpMCEdrrKEpQFjqqw0p/nWE+oGafsm4QCzk67x1Fr8ujT12m8/AyLjb4akpwDiMUIkhSlRCi5cbODaQNdDaGIAzr3WVkf8qenpOajFmiWFpdweYV1UHNdD4lhoDQmjwr8MdB46Db3pMipXINKmhELYiTSH2zYnJwRDmb0x0OMd2UOBPYqaO2jir1ZKYgTXMq25y5Dwt8YklBSgneI4G1tTV+/dd+nQur55DH8yk4WBus8LPPf4F/8fv/nCM74mCyz9HokKJXkKym1KImMQlGG8rpHD+vWV9aJi00+1dv4icN62ubpHnO+e1thG/Y299DJwn9lRUa7aitRaEwJm8nN7GS1ClMCJRTR5VMqdKc1CStJaE4FlGnURqUIgpBYDGfIoA81bsQfWswCEAoQvD3RgynGke/vzoyHEswtEVOUiEagZEJy8vLPLx9CXvziGBBL9QUvIwgPEprim6PvMi5uXuIn87QSqOFQiLxpaWalq17VnQZrG3RGyzTjObM98ZU05qQGQYXtug9tsKNoyvowx06aU5iNI33TKs5VdVgg0WbnCce/wlWHlnnretXePt7b7du4DxQ3poxKUc0Zc3B7oTu+gASj68CtbdUruHWzX1m03JR0fUgiAP/8PGAkcL738jhRGjkvRY4pZwcQis/hiKjw3KxCo0k2IgJgmo6p67nmJrFqBgRUYAMyERSDFMK3UX7FF+2cflep2DYH+BGDbnMKI/m7Ns91rc2yIoMn2aIpkKkaTvNu22YTiYMV1bwxhKUJzEGhULHhPVhgVYKOQmExCKEQguNEIHUCIKRoCMdndMROZlUNKFop8b+AAAgAElEQVRtzrILHYR2SrVItLF9fWruiXA8ccRpgRha4nl333VbvLSQbEMSokUjUSSkGBo8Lnp0A2F/zq0ru0wqh04TkghkCp8q8qJAjAL9Q82Vl/bZfeMqfZGRVQapJXHcoGeRzCeksqDar1FHkaO9Aw7eHKFCB9PP2Xp6jbLvmB1WIJcorMCFmpVBh2tulxkegyCtctSRpuO6rGVrzPolcyq2Vra5uLzN+OoB09GYrF8gZUrtG1LTZeYdo3FJVZWI0CxspGMT+5PYMHXv5/OAkcK94F6jjO0IOOwO2F7fZj4tKd0U7SSpV7h5w2g6wtmGJy88zstvv8JoOsWHiCeQZynRAvPW6ljqDyhUO51pnmWYlTX2mr12NuMYSRJDowWDwQAVwXtPlmUIrcg7OUJLpDREF4muDVLmUlOWJZODMTrVdAd9lNKwIIVoAlWo0Gg2hxtcr3fxoSEKBdGfaKScfN0Lrcfj5/041nDXixNvk8Ft2bLFlrQA2UqkK6noFT2k0iih8XPLlZffopzMiCGSZAUdqSBT9M/1KJZyJjtHzG4dsP/2HpPrI5owxYQE0USOru0ia0EaFH5uqaLnxitXmEx2caWjuzRg5cI6MpNEPOfXtzCTlP1DR90IcpvxyPJFClUwqiasrq6zsrbBsLdCcJ5RcoBJBBuDJdaHQ47e3sUFCyrS7WV0dE6cOSaTI2TjEb61uII8FmD4JBICfHJjCh/UAHSKL0IAjeDC+W2eefppXF3jyjkX17ehCrxz9SZHh0foRHNhsM2OuMkBU8aTGVeuXefyRoFRCVIv5jEUEqUUVV3THJbk3tDtdfH+eKeCVBmcNIgQUUFg0hSTZ4tJWzVKtqIhQgmkluADidZYaQjWgw14b1EIdNJaDfW0QgbFZz/1Wd7+/65zc/8WzSKFeTvM237hJ9OnhTvfb6+dQB67FDEs1J3b2EQ4tibCcUcoINpMh9aajfV1ls0SfdEnazT10RgVFSYYtFcMegPyXs7cBepbc8bViMnsiKauUbWBAL2kwM5nNOM5WUjJUMQ6YJRgfH2Pxpdoo6ikYyosWnZZ7q4y8A4nKprYIUPBGFa2Vrjw0Cb7bkK2PuDSU0+CdJSjimo8w47G7Ma3CbOSkFjkUJGspOSDDI1gPgExLkmJaC9OFb3FB2jSoY8Tgna2zXur2PwRI4UPYLrjuSGOG36kopt1WFtaZv/mTZp5RZ6kJMYw7A+wkwZbWrTSPLn5JDMFV9/Z4cXBS1xYuUgn7SK9oAxzbNXgSEhVgm0a6smMIu3Q7XaRSuG8I/MJsfZ4a2lMxGtBVBJhNKloW5KVUsTQagkoNInSeGkoa0czrZEJ6EQjokRGga8Dg3SJpx97hj/48z/iQI6woYbF3BCgWhVjsRjdQ1iUPrcWgwyCIBckctIluAhU8q707kKYxB1P2R4EQkqSNCVXGaqWiDqQ+Pb4Em3avTcRN3eU+xMOJ0eEVDDoLtGIirENpMqQq4yiZ6COxKr9rpRJEQRCY8mTHJ8r5kUkFore0go6Rm5eeQe/OyOtE/KY0M8L5ExikpTe8oDOxTWGK6vcunmD2eGcycGE6dE+2IblzSGrm+vULtIbdljqdTi6fovxlRuEgyndZcPlc4+w3rvA9fEub73+ejt5zyfRWpCKVgn4g/HAkEIIIPXxTXusDiRuzye5aOI5HTaQaqHeu/CdT7T4Fjd6DAsf2oMOkkwYyvGMrDfkwvYFurrHrat7zMdzPnXpWfxSzl++8C32b+0RnENm7YCaZxnKCYiBNEtYWhoymu3TVBWx6JAYQ4yRalpiy5r5bE5iNFm/nRxFagNRIqVog2VNTVNWQKCaNGAFCYrZ4ZhsuIw0Bhlp5dO7Q+aiJNgjLg4vsH9rn8rVyAhKKXzwCBQC2hbhYyI4jicAUhzXgMSTIORdJ9c5HZxc1Cv44FGqTYdWkzlJKUk94APdfh+0ZDqZcLi/T1SRRBnwipV0lSrOMR1Dog1NPSPpFkQVqbwlWtpZqO0UaTLObawxyy2yqNl46ByJSLj16lVuvXydZndCrnI6WYdhp8dkfMToyhG5XGLZ9OmLHF8sU5guqc5w/Q7bjz/EI888js471B7K0YjZ/iEH7+xweOUWeEd3ucdzTz3M2mcf5s2Dq/yzf/q7vPH6ayezfX+i8CFS1Q8MKQAL8/X2wYc2o9dCgvSKIssZLi3jvGVvfx+Cb2cmPvkijxmkbTo2UpMpQyFz6hAoTAetUoLxLC+voBvNXrNPmHqevvAEedA0dYkREiVAKYnKDUlQWFsTYyRNE9IsZXI4YXR0hEk0MijKWdWWPTuHUBqFIDMp1gWi8Mi07SxEQlqkNOOK0eERy8Uyvc6AWHukjyRSkeoEJwNZzLDeYxrNL3/2S0zGI8Zvj3G+1W08yRDwHtHzRaqyrYs+Jllx4j4EH5B3aRY7fkNLRafoYGqDRpAojRGRGDzBWkAjZKtR4ELASANOsXf9EIhsbpwjL1KuXnuTeTkhNBEfBFIbVJYhYk2iJd2lFWIxh6EjSyXXXn6Lmy9eJezX9JIe3dUVup0uzbSmaQKJSmHiaK4ckS8tU80CYmQpdMpjT/0UF564iM4SpJT4ScX+mzvsvfIW1cGc3BcYA1rkdJMOwkWefOwy/td+jd/8H3/zw8vnP9A4zlidEq38ADxQpPBucVIpaSsOAYQgNzlf+tlf5Ctf/jLXr1/nt//vr3J0dEhdN0DE4ltLQbURdBHbwGCiDIXOcLbG1xE6svXrk0hWFFx6qMfR4QitC7afeZ65nYHzeGdJTYIJBuckQkTwAm00vX4f6QXT6Yy9W7cYrCwhtaFqKmIUJElKJykIlSPGgBVt92FUDp0KOmlOR2WIWWSyO8bPHVJJbl69znze4/wj59GZJpUJKjUIKTiY7rLZX2e1O6SeL6rwAhxPtBq0bAs4ZVi4UndCCnkiLRcWsRApueP17YU5zkziQ0CISCTgmgrjJYkxJEZDqnBRIjK92JYkTQvqaQ1EpqM589kMEQShdngbUbS1GdWsRFqPEorX3rnC8k+ucu7COrd2b3LrtWuEA4tuNLqXkBQK000wWc6oLlHekgnBrRff4vDVN5g3lma2j4gVSVD0iz4xwmRnxJUX32T8zk30PDBkQK0CUxqqOrKsEmLjOdo74PDwEG0Uzn5SUpO3dUiD8j+apHAaUorFzMsBkyQopfjS81/kc5/6DL72ZCbnZz7zM8QQ+PaLL/L2lbexxyV/IrRzIYaFRl+U5DonJpGrV3ZYaRrOn99u3Y9ZwJYVS0vDRfWhYn11uTWFtUYFQSS2k7VkGcoLEqvpDDN0kK2ro1vfe1qWZGkKIZIkKb62NM6ijAEjcdYhZTtsN7bGzS11VTGfzKCOFN2cg719bt7aYTQ75PxjF+isDVBRkaoU7QQ/+9PPMxUTdr97kyhVm0ZdpBgcYlHNLW/HDDhOLMSF4pBYWAenMg/fr4t+cgM5b5nPZ23zEQHvPMELIpKymuODIEbH+vYmvbVlDm+NiBZs5QnO0dgG4SPRBVLbui4CBTYQbUOhNU0dsJnGydgqMXnPI+vbHO4fMplNcC5wODsipIr19W0204TR7i1m+7fwkwm2mQKCDg25doRJiZ2UxCYwfeMm/u0jijFcOPcIyJQ3b+4wXFqm//gKybBg6ua8/J3v8G/+5I8J3n/C3IeFH6g82Htb48EhBdk2HLGojpPtxOcs9wd86ee+SDfvcHl9m17IOHh1j9nhnGE5YGltyPJPrfAN/oxvX3kBETxRQojtfG228fiJoyu6JEXOtes30Urz0IWHUFJT9LqMZh6JIfGCrNb0qwzVySCkRNdmCVKZEANEEfAyknQTctFBFgItFmVFQtHpdZmMRozdhL7pMpmNKfIuheygspSow2LOBYG0gnjUoA4tasPTfajHQ0uXeO2lV7n++jW0lzyiU4qtHlYaNta2uTHdYTlf5lxng+uTG0glcbKVXVFCAW1K9dhQaOuSjmMux6rUt3XYTjhBHv9z7K+1nyulyDsFHZfQSxPyDKgaRJQEH7FVg9YBcEThSIxkNJq282zqhUCMCEQSFCkBjzCSICMxCJTukCDwQeB3GoJOGJo+s9kEX9V0Q4qeG8qBo+h2WF5f4tx2zst2yu6NGYkxmGTI1E6pfCQJGvfWmL3yNQiS6a05aiKxpOxVJbITSB/q0390BbPdYVZUWBEYHU25ce0G4ViWqtXdXsSvAu5HLvB4W4vybpz/fnhwSAEWtf2KpeUhW5vbDDpLnFvd4uc/8wV0lEyu3eDW9RuUBzXTgzmzSUUeMx5++CLyJ2HGmOs3rlDaGlTrZ4sowEUylZJ1DcP+Eqkx2KppXQghWV5ewc8dnaigtBwdHpD1u4g8x4VIR2dtZWEQNM4RvEMWEt1LyTOJCAF8oGNatyRf6hBczeH+AZ00J/qArxu6/QIrYDqfoZMMHRWhtMRpjV0xJEsJT176SVztufnqFcrdMYe9mySDjKJfsDY8xytvvcrD6xcp/ZT9b+3hfU0kEpVoucaHVrR9cUfL2DZNLfgC4PtHwlN1DbebBSMBQZJkXNzeZmmvB7sBoxKCaqeEjwLSNKHXT8mkZLx3QDWuKUclCSlZmhOip25s29ohWzJYXlvGETk6miCShEQm1HaCPfSUWnHkZhzdvIWykq7uoZ2haSJUgfJwzJwRk/EBNliEFGSdLoNOwcCssndjh+n+iNF4Hx8hCEMTIp2VIclyhk0C2bKhuz2g6bcEn3U6mCS9TZMnNnf750dRIP4O6bmTX/eGB4YUZCs8iDSKRx5+hM9/9gtsr2xRiIz57hH1eE55NOHwxiH12OJrSFRKrCKTvSkXVy/wi5/9RV749rd45Z1XmdgaJRRatnMwOtcw6Pd5+OJDlG5OdAEpBXVlyWIOXmCnHjdpKMczxnslpleQ97pUwqGzgM477UxKUVBVDUlmMEWKDYGqnOMbR6gdg0GfQXeVl67vo2SHWFvmZUPRzeh2+kwmRxwcjFnWfaQU2OjIiNjpnFKN2FrbQBw2jG7ssvP6Dk5Llh/ZJF/OeGj1Iv3QYX+yRz/pUzcHiMS3mQevFlHmcEe3oxAtYRzP8fB+CKeGFSUVCYosJgQXmVUlug700wKCoHQNAuh3enR7HaqDPerJFB0FeEuQCpQgOkfE0VnqgIhkvZzGOeIEVjaXmc4q9BS0kpTzGbv7N0i1obe6jJoqwjyiZp79166z//ZNmtgwq6ZE5zCdgsFSj2w5wbmaem/aukm1IuqIz2CwPuDcpQv0VgZc2b/KTrVH0szIdIc0TbkxucXB0T6eRWpLtcGUwCfIjfgQzPbAkALyOI4g6OQdCpNipxWHkxFx6qjGc8qjilBCM3P40pP1CzKRkViNnAsurz3GxS+e4+v/9l/z7ddfomoaZABvHfPZlK7usdJbog45zntc5UhV2ioxH5Q0N2tMHShUAl6Qd3JW8iGT/T1m44r+UJIlCUV3wKwp8Y2HxOAkNBLQ7RTyc1uSNJqnHnucF//0BToywwZPUeSsrgxZTnrs7I4JqSU1GmMkOkZE5dkd30A0EukhJaWcVlz9y7eYjWoe/cxlnli/zDe+d0g8CFxae5TpjZKGaauxINuqhdB2IyyihJzUd8TT3sE9wCCRDYxuHlFPl5FeEGIg7WTIqKinHu8b6rICJZhMZjgf6C0tUc8ammBbiTcFvf6A4VpKYy2HR4fMqwqhFFsPbfLW21dg4hAkxCjoLy0x6PXJVZcyznDWtq3bdY2TDU5YBAGjFMsrQ7bObfHG1ZfZu3mLMIEi9HEI0NC/0GPlyU16yz1m8xl780PmVCxrgTcQVOS7r77Md773/1P3Xk+2rnd95+cJb16p4969e8eTdCQhFBAIIYRQYQMCg6vsMlM1F8PF/BHj67nyvzCXc+OaYeSywYbCYCwQEhkJHaWT9jlnx87dK73piXPx9tknIISSXYfnZvVa3SvU6uf5vb/wDd/CWjN8P5cB9B2p0z/14PBPsXyQUiKkJATPCy98jSKmfPDW88SlJSx72vMV+JRUZohUDYy4KLGNpcoKaEA3mtF4i1/99OdQMuXbd19mnI8ZJSWm6zh/eMZoa0SxUdK3Ky4WF8yqTapiymh3Qm1b7PkKZSERmu3pLq7pWZ3NaZZL5o/O2d7ZYXbzBgJJR0C0niQVlGlJSEBOBH1d8/jogDvZLtvjDS4OzymKgsXjE16+WHHzqZvc2txncXTO8uSC4Cy2NcTaMS6n3L/3EHvRcGW2Q7Ce1w4esLp/wdn4gN3ru+y4Dd4/ex9ZXnC6uqCr38CL7hLyPDQZvRj0It5cgiFo+EttCSnBvWOjD7ySYaobLi+Ygjxq0k4gDUgUSaKRanDJzkRO30PTW2prWPeGYjpi7/ZNTs/OOTk6JgYGZOcop3WGxfyctm4RUiOVYtksWLYLjOtwtUHohHIyRmQpPoAVkTb0lF4yySdQSJa2JpjVIOnmAuu65uL4DLPqKZiQJBUAyWbG1p09jvtT5os1RZ5z5elrTLSBSmOw9FgOTw45Pj15khVI+ZaYy1u0VJ5UFuHtkPD36ArvqBm+v8/6ngkKIQZkHMxI6tWK05NT6o01cWEJi55+2aIQJGXBdDxjVASC9agoKHVJoQswIFvBxnSDX/r0L/KJj36S7XRG6jPODo+hkdi6pyoKJtmIk/aEuZ0zvbFFmucIozBAe7KgqWvSxYLJ9oSrN/Y5fhCoHy44XvfY+ZqiKiDTyHFBuTMjzRNWOJIsYVRsclw33L93D+0E3nraviF6T5c2HHaXepAyHXgYUZA4TVwMdGjbWGzvsMqQqZQbO9eRueLsxUdc3D0iGWXsj/fY2tthujnj83/5n3htcY+OQRNg0HJ8G6Pyzb1xeRUU4q3g8fb0+N0XwwQYqZydYpOiSZHWYX3P2i1J8pxsPMLLiLEO0zusDSTARbtivDujlw6iYFxNODs5hramrjtc78mTFKk1r9+9S+8tXnhUppnMphjhWXcdhYg4HF56vBB4CVVZUZRT4ukBzUXDxemc+fkcuw7okIFSpOMSEsFCNKyPHvKwP2D35g5XZim6yMgSic0DS9Pw3778x3zl63+H9Q6JJNV6aJICQgpcCAgh6Gz/xC/in8Z04gcPWj8K4dY3gBVDoupijB8XQmwC/y9wm0F96Te+q6KzGGbol/iaYby3WnN2es4oFkQ75L3eBqyxTCcpm7szurbHWz+AhGTK2fyMLJlCH7m+c51nblToGhavHtKddiRtSuNruAmzrQ1G5TGrdQM6EIQjjjwb+YxECh688YjDixP0LGW2OWIvvUmbrVlfLGjbjvV6hc5TqjCjqErysiIkEpWmZEqytb3N698+IrOaGzdu052sOD86RfWRxeqcPqu5dnWPrWoTaQTd0nB274L5RU/0gs2NbTbHm/jOsr6YDyPWC0vTrKimU9LNHEnOfrXH09VTLBcrHnM4wJcuqQ1CcInV+PsbRAyABt4p6zyMIeSlYIsOCh0kuofCJ8Nn8J7ONcRUsbu7RWEmnB2cgPOkaLSPaAGTcYlzY0xrUDD4Wly9yWP/iNbWpCpHyoRVuyApEtIyJRsVqDwF35OojERkhNwTCodPIjUO0zckOkWlGUU+oalrFBHtc2KIdNKhMoPYyNFFit7MuJncIttMCflAMgspdNHwzbvf5m9f+Arn8/MnE6/PfuLn2d3cZFyNsHbQhVw1NX/013/CcrF68h39faDYu4v2H3Um8UO+/ndCsP4D60eVKXw2xnj6tvv/FvijGOO/E0L828v7/8c/9GQBg8NzGBiHWknOL845KI758O0P0jY1FodC4JyjNz1pnlIUOX3TU6/XJEoPcGQxmITY2iCsgl6jraaMBRhJFgYw0iir2L2yS1DHkESccITKk8iETbmFl2AItFj8ek6V52ze2md6ZRdlHauLC5bLBet1jT89xYiA3CqRiaAzgSLP2b9xg+54zXRjg3Qt6OIKukAiFBjPwp+T5Mkwhag9q26NWTakk5JsNqKaTFjZCxaLOYmUjEhJkYS5hxBo+pamWnNzdINH+WOO7SnW92/pI0SewFvfLUEY34RCvl1sJSgGP4SIkoJMp8zyCd28wbeGjAQlBpq59ZbatGxu79L3ltNlTeEVsvek1jM/OKReLXEm0nlBnmYkOkOjSVVOkYwQSlImlqAgnwwBoTUdpJo8zVEG0iRBVwWtt9gYMcHSX6zQSlIUI1KZYNsO5QMyk8jSsxRrlLbMrl9hY28HEsla1djS4GRAZRLTrvjq1/+O0/MT3uR5bG3u8Euf+WcsT84I1mGUJWSB3Y1tXjt5ipdeepGua3mCuX/bIXs3CvJHm0m80wz5B3vt710v4n9U+fAvgZ+//Pn/Bv6Y7xIUgIHCGgcVABTUfUNtWqabG4h1JCwsQmkEmuglfeORIpLrkj4YsJEbz12n7wzOgegTcBBMQEZFVpREH8iKHKkUOkuZXd2l15EkH0RALAn1qkV7ye7NPZIyw4SW1eKY3ixQOqOY5IyylM39bS5Ozjg5OsIsWx6dvUG1v8mV/auUeUm0nut39uk362GaEZbEdLgaSS+hc7SmoVVDsyvLyuGLiJE8JPSrjuPmiH5Roz0oB4lMkUlK0zXYriNLQHeeq+mUD1fPs6jnPPAHOHocHkK87JUJxBNmSHyi5/b2zTXsubfBnQOM8zF3tm9RdhlJEyijBpngYsD1gpOHJ+RJSZ6nZIkmNQHXey4enOOCw7jBjFagaVc1j9c1bd2SJDlJmeHEsEl1qQdVJCWIwZImiiJPafs1QVlkAdpIjDF4BSJN8NZTZBlVVXF4+Bg8JHnC5pUZSdnTjwPpRoFJAhaPlQIvJCKFrJQI61jW54TgkCgmsxk/+6mf4/r4Dn/2F2/w4te/wrpfkmSKG889yyef/wTL0wWvH742fIdvi7ISkFoR3JuPvzNoSN7invxI1vctMvum4Ob3tn4UQSECf3Ap0/5/XUq3X3lT0TnGeCCE2P17H/Ntvg9aa27s7rNeLVk3DT5EIh6PpzdDQ3GkSmyISJWSJxXCJ/StIS0l43xCkWSYricLIzKGgBBxaCXwWU45GxFKiwuGdbtiI3qS6YhKWFI5dNltEzi5d0hcBfb3bzDemJCnmslI0q9XXBw0LJtz1nnG7uYm+9f32J1u8ODlN7h//wFtF9DJNhQG6x1qr2C6PyHfVpimp+96wkVPbCMK0EGQlTlRS6wWZDrBEanKEc2q5ri+IPGQew0+UnuLUAKdJyQ59MGS17BXTtmdfoh13mAPA4ZDTGgHcKd7M/G8ZE0iCP4SUn6JcBquQvGSMz38rUJRyRH71R6zZkLWKYo0I0gJJqCVJnaR49cfMtkYsTGb0vUrgofYgSChUAVapBChNQ2ha9BCkI0zDI4+9qgqoRhloARN3xFFJFcJ46LALWtWpgXrwAiCdaiiZDKZEWtDrjVRBpJRSaoEHk++PWHn5gZ1YWhzz9LWrNqaTljyzYyqSAjScTI/xLmOq5s7FOWUD3/kJ/jcP/8cy6/3PDt6jjo94u7RHPKImDve/+PP8bfV3/K6em04NQ60u1TNTSRaKcazKdPplK5tOTo6xlozjNqfHJMfZInLxue7nv9ufsY76PLvfr//+Y3GT8UYH18e/D8UQrz4vTzp7b4PN65dj//Lv/wNXvjGC3zrlZdYrtckMiWTCeuLBamRRDtQWvM0JU8SqrxgWk7AexQJELj/wmtsjK6ytb2HUhovB0JVtTllnIzoFjUnp4fMzy6oTk7Z3LhGVRWkOEzX0Jws6Y8uSDvFyh4zUgW+FKgskidTxmrN+fEBK2dYPjrmudtPM81GpEExTgtkDccv3EVmGTFRjPOcM7Ni68omN2/dQSwkq/6UBHCuwwvB5s4OjevAO4rJhMVyiTMGbyy+twQfyKUmyggqIojMZhMCnnZ+wogMbyJmHbi9eYPT6YqL8xqHxwWDuNSAD7wp9goxxHdcuUIQl5qN8nITRzQSHSXKK2TUhCBo2x5QeCTSWjzgrWFlHVmiscrRucGuLc9LlIQYDFmWk+Vj5vMOqTQiDnDnoixRpQAl8M6TuoSu76j7JXFhMHWLXRp8byiqiitXrtAjWF3UxLqntx4fHbONKbtPX2PlauZuTrc8J81SbNsyPz9k2axItktiMsNIwd27r/Cffvf3KNMxv/Frv84zN59Fq4TNfJuvv/5N9Kqj8CPGbKBEQuWmRAPSCWQYmLk6SBIyqqpAJ4qqHPHTn/wkn/qZT/Lg0QN+6/O/xcPHD7Dukq78P7wx+aODWP3QQSHG+Pjy9lgI8R+BnwKO3vR/EELsAcff7TWkkFzfuEb64YJnn/kAzns0CVNRkbeKNiw4PT5ma3uTze0dXBs4Pzllc7rB5saMRCsQcPrqY+anZ6hOU4YJ6ZZCpHroumeCzhuUlDhjaOdrYmdAOqIORBkxXYvvenSX4vsVx+Y+rY4EFdjZ3mJ3NOWp/Vvcv/8G9XLNG6++zvWdfUbFiHWyhC4gvaJdNjjgvDrkbHXO4tE517euU1UjbNkiwqAT6J3n6PwcGx3xkrnojcU03YDUSzN83z+hRAft2dndRYXI8eEpIxKycsyqqVkuW7bLDa4n13hdPab1PR2eiCNKiJcuWJFLguTbSwc50Lrf5FAoAkmALEroPHlI0CLFmAHwlaUJiUqGkWdw0HmkTihnFe1pixCKgAUUiU4J3mLbngioJEHJgYDle4/KFDIK6ARx7QidJR9l7O/scVIfEbxF6JyoBC54jPGYuiWuDJmXICP9sufR4RErUTO3F9QXK9SpYOvGJtOdEVXMsSPwZeCFl77OF//yS2zPrvK5z/4yT199iqQbgl/WZORtxvLxBWqp2ZV7pEnJjrzG+nCFX7nBXFcNLM3t0Q6f+smf4dln7qCEYLYxI3cJN7au8b/9xv/K53/nP/DS3VcI3v/IY8J3UuL+fpGL/ymQiIMAACAASURBVND6YR2iKkBeGsxWwC8C/yfwO8BvAv/u8va3v9vruN7RnKyZTbbYneyhtQIbURbkOtJ2ORf3zijKnK3tLZZnKx6+8QjfG2azyaCcHAKzbIptIqKPKBcxfU8fGlxSMMpHqEIRF2EQQln2iNaTlholI2Is2XjqGspI/L0FdB4zr7HWEUNkdR4Z3UnYvDKMLw8PHpMKRd12qD5y7foN3NJwcnBKaCPSR+pXTpEYurOe46UaxqZC4HwArZBSsVzXGN+TpOlA9U4SrBks4hKtyYuK6XRCWmU8PHmdtluTXrot4zyxtqS9YETKdBF4//bTdGVg/WDNiWkJMuJlIIoBexDjMGqTxHcarIY39XkCCZGNvGRvsk0hUrQV4AVJqhFCkGjFuCqpVyu4ZKmICA6PTCQ3b9ygXtY0qzUhWKyxtF1HkmVUZYWHQSzVSJyPFEWKbwOiiyRWEb3l4sEZ/aojI0MIWLQregLIlFTrwYBXSYwMdNawPl4SqsDKL0iuJmze2iLZSdEbBVE53jh4jW9+80Xu3X9EpSr+xc/+Gs9tP8Pxtx4zv3fMrZs32fnAHrdGNzhMLIJ6KCtdQbLMWB+sUZ2kIOPa/k0+8vzH2M13eN/Np9nd3kISWTcrlkdL0lLx4898AH7N8Vu//R945bW7yPAd6Onf43qijMV3DgbDL3iT6PK2J/5g7/fDZgpXgP84mEWhgX8fY/x9IcRfA78lhPjfgfvAv/luLxKcZ/V4SdknUEBMNTgPUaK9phqNuXH7FjJPQUI1Ktnc3CDTKTpLsDg6a5hszli4mqIoKIucUASMMvjgkYVm+/ou/WJJe7HEnDacvXpEtjdmvFuhkwxuTQfi02iBvAg0JzX+xJJ0kdR0HOnH6Cxl/851qvGEZrnm9N4h/fkaPU2JPpKVJRvVBmbZ0s7npNEjU0EnVzCGRCVYepRK0GlCUZasm8FhKjhHNR6zPZmyXCxp1zXkGqk1EAnWcTZfM85yEsAaR49FDs4UjOoUvV3y4evv50yd8Vf3lqzt6p06jlyK2LxrwwQ8l1arTPKUp67ucXt2hWwhkHYgq5VlSZoo+q7BugZraqTQSKHoa0NvLGWWsXttn2P3kHa5InpLjJFRWWJCxFpHCJCIBKkUwVnc2iG9JIspOgr6dc/J6gilFDLRWO/whSMtM4TSBOEgG7AEjsDSdYSkp9oqubG5R3VnQnazYpHUvLY44Fuvv8zLr3yb+3fvcnv7KX79536V2+kNjv7yAad3D9jIJ8y6Ee0rc9S5YxJKrtx6nlW94MHhCW7uIPX81DM/wbPlc2xd3+WDT32IvEuhtsxfOqVdr2lMQz7N0Fs5D5b3yGXKjz/3ftaLOQ+OTvnB1ptS9lyifuVbBkdP/neXDeTv1r74PgKEiPFH1BH9IdbVajf+xq1fA5kznoyZbU4ZTUvyLCfPcoSX4AVOBZRUmFWPXRlmkxnVbILTYHVg0ic8evmA2DtuPn+D8f4IJ4dDE0OEGIgPa9avnDA/XtAIT3JlzI3330KMBH7sGaU5icyIThGaQDxuWX37Ec0bJyR+QOatbEs2Krl96xapUxy8eI/ubIW0A5uy0CUySlyu2XvuFr30PH54n/XFcjArkQWZysCD1hJje9br1VD0h0CVZk/6AF5GGmfQWjIqNK4bVJsUcbhqJwmdMwQX0AEelSvqD2m6ZwX//o//Hw5WB3S+ZdGssUSCHFALUUi85wkgBxnJpODKuODDT9/huY19No5H3Dy8xf7ZbYpQYaVDqkAIBmKgKEq8BecYhN6DQKkBqRgCg7iMBCEUkUjvDUqlpElBUVYopVmtzoGIiQEpoMoLiNB0LV5EYqKIUiBSi6pSOgKd6WmaNQ0d/VQQtnLK95XsPb+LniQct4f80V/+d373j/4za9Ngg2d36xq/+eu/yT+7/UkOvnCXcLfh+vgaEcXeUze4WM/5+t98lYINJpOSp9+/T+tbXnvwmN5FWpZkTxXkz0wIuWZ90qAXkpksePWb38a3HpFKQuGZ3ppx6k/41sGLmKznwekDvvLayz8c0/It7SDkJZHvyRIQxNsmS/DO28ib8ox/G2P8+D/2Vu8JRGOIAd86gu+wMiPZ0GQiQ0aFaR3WOPI0R6aKLE3JRMrKLDk+O2UmA9UlojBEz+b+lG7doUr1hCkphcaLQJom+JFFlprJxpg0BDobiXUg1SnBNzSJIykdSVFg00i+lbH13D5FktG/vqRfdkPq7Q1HrzymCBrVRSqZkco4YCmMxSHo08jJ/ILRzgbbG9vEtSV4QaL1JeEmYo2DAGmWEaPD9Za6aVBSMZvMkFlKXC3Iy4Kt6YiLszNCP9CvnTeYrr3EcAxXcxkDoTZoU/DxD3yMv3nlb3j96I1LZaYAQV7uk7cDlSQ6CeRK8dxTN7l5bZ+0TcnJyWSOEsM2EcRBqEZaqknFreee5vjolMOHJxQxJUVBDBg3jDuFuiRaiECmE4KMpFmO0unQgBMCiSBNE1KpMM6wXC+Jl7JxCIVUCi8FLlhM3bHsarpoIYtMrm0wfm6H7MYUcyNiC8PLj17hD770X/mLr3yJdbuCCJuTGf/m1/41n/mpn6P9xoL1/QXFhWS9XpMUBa994zVa2zJSI27v3eL8/JCXv/5tWt/SC8nW1auMdErMJKmqUHnJzpWrJKXk8Yuv0V001OuWq9evkhYpr7/8OmfhnPPmHDkVdG331gTo7+X332OgeAfIZAgC33X9EH3H90RQ8CFgWosKgdgYzKrDZhWq0kgEygVSrchUOugPRkGW5ixDTW86ctMhE02WZ8QNic0EciSJCEznSdIBpGOA7EqFthVioSms5PjgnPZsyay4iWscTlvyPEN7gQyWYAO9jmQ3ttkY7XL2+Jz16RrZKZRLMb3FdYHohgOnEo3NBPnGhGmWUR/P8Wcr8FBQYrCDArSSOOGxxqCQpEUGaUZ22UjznaOrO3TnUU4gZKBPPSEmGKcQCmQqkRhi1w7TBhlQCrqjNXoEn/2JT/HowX1e7F6mlwEV8stE0xOCfKJ9KWWgEnBlqyRNJBcnC8Zxnyv5bbQdE71CINHhEhtZKMpbm4x+7DoPzRJzYKmiBuTAJxABWeUE74jWIiNEq0iTMUWRI4qcpu9oXEeiBcE4CB4Te6wOVOOSXGls51i0LZ2IpLlG65QiCUP7dBpR+znZnZzRzYLzZMnx4oDf//Lv88WvfpFVt0Ai2du8xr/65X/FL/zEL7Dtp8yXNbpOUCEHRgSnaRdLjDWU1ZTYdPh5izEtfbCoVFPHM9ROxk55DaVGmFXEzQNn94949PJ92romFJFiu0BkkuNXzliwwKeRhwePOW6P3woHUhN4k81qAY++DNH/eIYv3vmj4El2IN/VThj+ZmgtB3gyRv1e1nsiKIQY6WJHSUmIkb7rccbiE8u67aiXS9qyYjvfQSeaGIdDtL2zhbiUShc+YmVAZSljpZFC4q0bujTWI9JBfFRlKRv7e3T5Crvo2Q0pi8M5NlsRfI+hZd4bnHbs3bxOzBjUgxINXsJcInOFtgKNQOmUID1eRLyMBBGIKgEhqFcrTD8gDIWQ6DRjMp2is4zVak2wnki8lEsUeGdREbI0IwaJ6xzWOtIkQSJom5Yiz7G9QSeKokpZrk6IacaNG/t0bcOr5w/JbEQsOsYNfOKpH+PR+SO+dfY6nfTDFnlzmnEpvJJKzWickRU5y9UKrGNDVjRqSek1gXzQlsSBNFRJwVZZYc/m+PmaxEUInjQvubK7w9H8lN3r+6zWNY/u34fgycZTsrKCPKVxPSZ4dKII1uGDQxgBMjKZbbCxPaVdLlmZGhscQanL0kqic83W9g7txJJdGSFmCWvVsmzP+YMv/B5f/bu/pF2u0MC17Sv8+s//Cr/wkZ9hM044feWEeGZ4+trTHL90MmRWQqG1pusaBIL1ckXf9wQiSg7y/tOiYjrehHVg9cohq7pldbQmNJbru1dIMsc6NyztksODY1rXoSpNZ3rmqwV16IABJWqDAZm8A4D0PZ7Vt85LeDOT4lKU9y3RnCfrbW2By2nz99xWeE8EBectRjlGIRkUfWygqztEEAghSHXGcr4imeZsbm8TRSTNE/IiwzqHcz1mHci2MhKlkYlAKYk1jnpVo2nZ3NlCKIWLnmxSUqU5VjcE11L3FyzeOEYnGWkh6eo1p+aC+rzm1rPPMNqeYRpL3bbU2hCVBSVIRE6apBjr8ICNljRPUWnCar1mdXHBOC1I05y8LBFJMqgpezcoQ8kBuEUEaw0m9mgvyIRCKY1IBdY4fAikl2aymU4p8+GQBueJ9tIJK0A1HjOzY5bLBcx7zr7xBs/evsannv1JamN5bfVgQDoqwA9ox0wLNiYjJtMS5xyd6dGt46w/4ogR06ApRhNc64bxYLD4LnBxcMjRowOas5ocibjsrvd9SzQW09V40+DpCRJiIiHVGAImOGI6/O8DkSRNLiXiEhKVomVCXbfUfYeuClSZobzDhJYex/bulPzGFHdVYCaBk/6E3/vC7/CFP/9vXCznQ0DY2edXfvaX+cWPfoaryTbyJNDeXeNeXzIRu1yZ7tK2Btv3bG9ssDEdeBSx67l25Spt6KHQFNMRPhFsbV3h8dl9Dh/fxzY93bKnHFWMb17hURM5Mxe4LtAZw9XreyzjirqrUZ1G9DAZFyglOZ83eOy7oMrfP8ApxDiMci8DwzuKku/wMt/PIOI9ERSUUuhU4RqH7S2ZTsikxvUD8GVjOmOlNEJoIgxGKpflqtISbyPO9DjCJco0kGpIsxS5iJyfnxGiZ7IzJZuM8FoiE4nQKU19TuY1Yd6BluQ+Q6Qae1rz9ZfuYjrLMz/+QfKygplHTxKahSfWjpAWGCfosfhEomXCaHsD5z1YRZlkmFU9HF6GbM4Fh/MRqSSpSLHBDMAsEcnSjARFaHu89zwBF/mA6XvSIsV7RwiRru5ol/1lORA4fHDAaHuMjoLcQX/e0HQrum7NbjXhVrHPo4vHGDm4JQkFWsIoT9mejkjKjPViTrMy0CgaU9MmNY1c0qZrQiIQmaKSKX3XsHpU00dBKnIyUkgSlnbNxdE5QsLijTOikNjgUFmGzDNq12MJqFxRlhlKKPrO4jp3KfoaWdUrLIam65CJJJsW2ELRLWuMMqitlHS/pHxmynm54sHyPl984c/43T/+PZaroWS4tnOFf/6zv8TnPvU5tuQGYz9idVjDI4N53DD3Z2yMdwgaurZDIhmVOe1yTde27GxvIqJCjjJ0VXC6vEB3LVFqZNTEpmWcjinHYxZ9jckEtvdU44rrkxsIralpuFrtMVls8GjxgLwUpInk4OSMKCX3Hh6wWNQDRXuI0j/Y4fke4sn3oe4OvEeCQlWO2BpvUS9bet8RXSRXKc4PV0KDYTqaorIMbzxRDs2poeMSSLMMJTXOOTKV4pylD5BlOdV4TNcZ5hdzRCpIq4ED0baGdr7ErjqUg1IXOBvpTpf0qiMuOlRj+fpffAVrLdfv3CRPCrb3NznrO/q4Ztmv0SrDTwTOePBi6JhLwebmJldmW7z6zRepF0vMco6XUJQV+XhEGLJlREwIQiC0IqtSMpmyDnM62w61uBqEVo2z+M7jfCD6gBISqVKIAucgGMP52QVG9hRJTioDdb+mfrQk3S65k93g0fSQVxb3cNITpCdPYFyl5An0rh94H40nMyk+CGyAVWg59CcInXPz5lPsjjd4/MZ9utWKDk9HR4JFRYuMiiQBYS2DytNQXlXjCbooMS24aDHWkMqEcVXBwrCue0KweAVt56ltQ5pKkizDlZEus7Q4ZJWw+6HrlE9POBZnfPPeS/zJ177In/zNF6lXDYqUq5t7/MpnPsdnP/pzlGbMZrVD2knODs7gzFHaFGkiS3NBUhWoKFmezum0xK4b8JGDR49wOiKaFL9QrKLBb1dcvb1PR8+95avMJhvs3Nrjje4B42tbPKsLxvmYXFQcHByzv3udrWe3+Nj0oxy3x1ws7+NDh4uOrd1N/vuffpkv/9XXuFi2INWl18k/FhjeOUoOvFVG/L2Dfzm2fPL49yFb/54ICvjImBExFTSrhqODI1QUbG5tkyQpQYJAIDxEG4iJwDiD0pI0S1BRQTJg34N3iDAg+EBQjkq0TnHBkBYK1/UIF6kokCLhrHODt2NZkjhHve5pLpa4xZocMF3LS3/5FV76uxfYv7bHJ376p3n6I8/iWku/6KGTdBc9xw+OCGeOk9MzEqVYLZZc2dwhqwq6vsNezuvXbY2TkKbZJbcZVKqRmcYJ8M4QtURohZYagaRuG4ge2/V4Y0l1TpEWA5rQDxqRNjiCNSSjlO2rm3jR8vDoPsb1PL/1DB+4ukFyWPD4zz9P7Vqc9BSjlK2NMXkqWNcNXdeTdoLBmqbECY3LUhoiapwxet91treucmJXrO8vEUWBylKMD4TawdqQS0GKR/mId5KsnDIab2LMAGsWQdDaNavVHNc0qHawsLf0mBBQZU5WZNhg6FWPwUKVk96YUu2UFM9ucMg5//lL/4Uvfe3LPD59SN+vmVCyPd7lVz/9L/jMj3+arE6hhvJKwcW9Y0qborMpq2ghBrSSTGdT0i5ldXY6qEc5TyIU3ruBX1KWtMIzrjJuPH+D6caYRXtGdjjBK8W8rTm+OGVjY8Y4GyM6QSIUI1dx8eoFeZojrkCeZexW2/RmiQk1lYDPfuJjdHXNl//2m/TOvadQ0O+JoKCiIutSRpVkOp6yPdukyDLadY0A8nFFiBFTd6hUUSQZSg4egC4OPgZJkpBqSdu2zOcLsixnZyslzTKSUpEyuBSdHh9CH7gyuYqqIzIODcDGW2azHJF6LuYe7QNZiFQI2mWHUZFHqzV/3rd85Gc/wdb1PcS0IjMJs2uafJJy/rUT1qdLAgIRA2+88Tp112AvXZ7zLGW2sUFWjVgsF9TLNcJDqhNilAQEwXuyLKUQGoUiRtDR420P0eKcRwSLjALNZc8wSDQaiaTuO3JKbj59h8nuJi+9epeLe2fslZt8/M7H+K9f+RN8f4SVkTxJGVc54zKFIqdZGmzTk8iCPJ2wffUGH3r2JxjrMWJzxuzmdaJISfa3SPyKZz7yQa49/zRCRB6+8Aqv/dk3sOuaNAG3MgQSQoC2c0idDcIxvgcVid5hncfWAnwkpoEkT9i6tsPWzjYHx484684otgqmz+3Bfo6caQ7cOZ//w8/z5a/8KcfzxxAcI53y/OhpPv3hz/DTt3+G9EjRHC4YZxvU81Mu7h2TrCIYhxxkbhGSS8dpBsxIEORlQoyeznUY72mXSxjn7F25ytbGoNwdkkhMBYvlmot6wWiWM5YZ2kfmJ3POz+bYdcQTucjOCYuA2IByVzAuKtb1mte//S1EptiaFOxtT3h0shqKwPeIcMt7IigIBPub1xDjlDRNyJKEGALGWIpRicoyDk+OWdc1+SgDNSNXkGfl5UhNkugELSFkGYnWrFZLEpUQ42CUOhoVyERi+h4zb9lJtihlCTYwmW4xLira9gzjHWmeU+gcYSKSSILCeIvDMT8+4c/+7Ets3Nnj2ec/wI2t6yivmbgpzb2avumG5p/UnF+cP3l/xaWwiYDg7RMdQBc9wkuEH0oIHz1eSdIyJUHjbCBxGcRBmDVqiC5irCEMRHIkikhCIjUEODk9JRbxsm7XNOdL4qLn1vtv8+mPfIo/ffFPObeH5KlGKdjcnDDSJe3SsqpXbKU77I1vMp1cYbx5la3ZJmJrSshTmj6wdecGandCsT3lxfsvoxLJ7v4G+8/f5PWvvcCqHqDYGg0x0veehEDfN9jYgrZs78yosoLT81OklugsQWSapmtwp0f0GJJphtoqGO1N4FbBq8d3+a3f///48gt/yvn8hFwKSgpuTq7xmduf5Jn0Ju7uGlN7ZCfxec95d0x7sqA3MNEls8mUejWwMfumvVTa7tFEtNLUbYsXgRgHDIk2CaH3rM7POb53xqOH9wkykuSKRKXs3dxGjQT3791lfnBO6kqqZEwxmyBzkKVAFpDkHTLvwBXMygKnAz/2vmdoveTky1+ht+bJaXhrxXeckh+Keh3/54us/FArhjAI6FqJzFNkliESyLQi1SkEQUbKyrREo8EoXBPwwpBmCcH2RCReDlDYGRv4EFldLKB2zKopSVXBuGB7/zp1sqIPksRGCIpoPVmR0HQpMeSMN65gXCSu5mifkrkeE3o65fCdYP7qYxaHp7ijJc3tp7l27TajfEz59AQ/CsyPL3CNIw0B5WFjc0qWl3Rtx8XpOYlSA7DHe1SioUgQ0aGMIQhBL+KgRh0UOSlSKop8RNQpXd8RhcV4S2TABsjL0CDomWQTQhJYHF5wgqG3hkwLmpMz4hszPvXsxzm9uM/LpyuSJKDVZfnC0HjMlWYz3eQq+4yabdwqx27lSBMJzhKBpCjZqgoaU7M8vkBmmqt3dpndmNG8cEb0J4xDjiYB7wlNi7WDCzhCYIcJJg5wFagYEapACYVvDItuBZsKea0gvVOwnta88ehb/PYX/wt/+JU/ZGXWg+hMmPD+8hk+uf1xnuqfJbuXg4zkMiMXCZKA7Ry5KQjGEEuFT1NsElFeIGyAtkX2DVYGWp2wc2cPHwMX5xeE3pCsFd3dFQ/bnsP2DMqE6VaOb1tyqRAhsDpuaB6ekfvIrWeuU21sEnKFzyyxcIjc08oabzvAs7O9RY9FVVOeuSN49e4Rdx88xDqLQyKlHkhww65GInBPFGXflk5cfo9SKhQDRz6+re0QLrEMMgpkULh/Sq7TPnhOT06ovaPcGHHt9jVG5Qikx0uD0pqNvSnFqBrMSUY5JvaYZUMyqmiXNY1fkF0dkTtJmlZkqeH4+AgTAxkloxYm44pre1dosobjVx+zPD4jVZJ6tYDeYrUkHVcoURGlIC8K2npF3zRI0+DsAmcNmZS0i5ZXv/5tTh8fc3TjgL1rN7mze4urs6skY83qaIFaAL0nGeUURYWxjhjBGk+iIFUpQmlkVMQYcMYg84TWWFSWUeYl0kaMM2gEWVowUJd7TGwHByYGFKWSEkRKUeWMbo1pVMvj+QHr8xMCguXFEdlLjq2PXuVDVz5I19Yc1A9YrSyvPTxEktA1DcIHsI6IJXpH7APCCmIX8DEQYsRL0KlCypSnn3ofSZGR5ZJFXLHyF3h3gWZGJgdp1OhrlIAkGSF1ipKCVdNTm56QKZTQDAoaAZFLZJGirmrU9ZL77UNe/9pDvvrga3zhr7/Aql8RiRSU3Bjv86HtD3Izv4Y5ahlVFdPxeDgVJmBNj4qCIi8wiSYi6NsOHSFB4MwwZkUKhNLgNc+970McHT5mcV6jfESIhNXZilW7oMkDN288w2ij5OTxA/q64XR5RusMxfaYre1drt+6gy4qnAwY0XM8f0Tf1rS0dHGFTiLhcizcdTUb04L3PXeTR0cHWGcHIZzwnRqO7woITx67zCyieLNFBVxyoy6DwmD2+b2fx/dEUAg+0NYdCkV/tqYdLRhVOaQRmUjQgWKcsTHeQEkNOtJ2krp2CBNZnq5YnS94ZuuDGGNJqoJQCxJfYDvHwYM5sRMkBkaznNIm5DHl9HxO7obRYWNqQkwpR5tP8Pc6alTI0KEmkSXaCGqzRoRLA3gF5nzJt8+/ysPX7tJ+5CPs7F5hc2eDyc4+7cOGozeOebA8o1qsoPdIrclUgkYSY8QZj/AO1CBJZ60HAlvbM67s7rK6WPBgdY7xEF1FDPFSCVlfXrkdnoBHYoTE+B4hK7bff4syucr6G19jsVgiPagclJPcnNziUXXI6fqC+ck5y5MjRmnCZj4dVJvaBhEdMnpkP7AXJRLvHMZaogykRUpMBGQaYwLOd7SuRpWSVjp6PAZPyuBDqQSD+1IIRBRSJahEMNoYFKfado3BkE4T1JamnnQ8rl/hd7/2B/zVa19lHdas+zWJVBQh4/nZ03zi6ke53m3T31szTbaodM4kr/C9pbY1IgS0GqwAowhYH8iUphylmGVLbQ0hBFSWoBKNTitWyxWPD4+ou44kSSGqwQfT9Mx2pqRa4EMHCRhpSXLN5myH8ewGVTHCSYWNBqEGp/HzkwW9rUlGILIUmQjavh7Yo84gomB7ljCuNHX3ptSaZyg4w9vO8rtwzm8uAVIMVnzi8v7g8eEHfoS4tLj5PqT93xNBQSlFojSbagvjLVwE/MiQTlLwERMNeZYhKoHO9ZAiJiNkmmCaHh8gRMVEjIhR0B+3zBhR5RmrtsY2lkW3wF7UHGZHVEWFM5a8qoiNISZq0DZsOvJNRaUzVk1AO81WuUXtMkxsGWvFRdSkPqWjp3c9vejBG1pzwV/89RcRieRjH/44H3j2x0i3C8RcYxYdcd2Sa0UqJcYavJcDgChKlI/YGLAi4AhkVYbpW46OD3C9ISp/qbegcM4hwtCnGPCQjojBBosXGq8UXQiYqLjy9HVuzp5lvapJhEQoME1EnGpG6Yj9rT0mecqSBOE9O+WU9dmCvIEcSGxEtp6wckgrscbSW4MuNEFLfIzEGDCtQytDlY65ef1ZXjut8auUKDOES0jQJHIombreYDFUs4Kiyrn29DXOl+d0TUOSZ4hNTV21fPn1v+GPX/kSL81f5cIOgqlSQRFTrmdX+Jk7P8lz4hbuYcu2mzLKJ6ROMEpyrBc0fjmMs20g1RofAl3bcWWyxe54xqPF6wgC1aQiZpraW2LquXdyn7ldkG/lFHlJvWpoztYkOrC3vcnFes5qvqIcFeTTCq8F2WaFmuQ0PhJs9/8z9yYxlmVnft/vDPfc6c0vIiMjsyKzsuYmm+wm3ZQHCd4YNiDAMCwZENQLG7ANwwt755W98EY7w4aXXnhrwCtDgGWZUls9stnsgSK7m81iFVlZlXNmTG++45m8uFEkmyKlotwWeBYRiPsiLmI63z3f//sPaGnQQRCcJ1EZfdsPJ8zRiBBrjJnQ+z2EhiJV3L015o37p+yqxxzqM3LCFwAAIABJREFU7oa78LP6/591Xf7oVTEIB4WSCPmpVf1n93T4hSgKiU4wSUrcgQqa1va8rF4hC0mQAZVo5vM57d2ImWQkmcHkCVk6QZuek3uKpFhz9egFwgu6fYcOhjIZMbYaSUqaGfrQs3615iqsuHP3Dm+++0u4umF1fsF+V5E3cHh5hU8O+E2NtJAkmoka0QhBF3uWxS2m0bJtNxz6PRpBghjuva/psHznT7/Jk08e8rf/5q9z+uCIZtdxeLllpDKUUKwuV4TaoaUiFYaIJioAMTA6nac51NT7w834TOFFBAXaaOgZnmBS4on0oaOmog0dy9t3mbw7o132XOorxB3FqCsxQtMVgvZFxWJ6xDvlm4SnBy67hpHoCEJwPJqjLxwjZyikQlsQ3SAYI0ZC50m0pjAjfIh45/HWEbnBRtoC+hIZpyRmRCIWYDMEKTJouq66Gbl6msOWXtbog8Abh7mdsYsHvvXo23zz2Z/y4fojHu+fUomaoCENmjwaTvWSv3Hvr3HaLeHSMncTjrIjjCmIUdAfWqSQlGlGHRqSJOH2nROurzfsVnt2bLC7PbWtUblicXJEHTy73TX5UU4yT0il4ezePZaLIx49/ITrwwt8Z/nk0Se4uWZ6d0E5LbHBMzqao8YlCHBNS2t70kSSmhTQZCbHu4CLFcJGpII01fg4qFJNIjATwefefYuHTy+o6m4Q3P2lM8LPsGITP2oRgvA//GwUICVCfYpwM5igbj/bfvyFKApSSowxCK3J0xR0xKqepqpp+5ZUG2pr2HQNepoyXk6ZLaeUE0NWFGRpyXy55PrDh1y9OiccPFlMQXYYmZPqDJCYXJFYSde2tJsGN7WMyynJSUJ1cYC64lCvaZRGoZFR0rmAEpokNQRfkorB67BIx+ybNdt2TeX2g0dClLT0NFd7nq/WfO1r/xgVDHeWd5mdzFnOFxAEddKwvd5iO0/nLKk0GJGiSFDEIci2D0TrbnQvEhkCfegwQoEU6KiJxtz0/pLO9qz8JVkyYX6s6MeONu6QQRJ7x9gUiGJGdirodgHTJyRXIA4Og6BXgb5r0DKSiEj0Fo3AiATpFH1t8c5S5GMSndC0B6KKw1MpBIIT7K5b9jvP/Te/iN9E3DqhC8Pc37uaGotIJBhoVYdKPN2kwyxSzuNL/vD9b/K7f/F1Hu4esQ81TrmhjbOCScg5TY74yu1f5b47QZ17dKtYjI6YTZegJdb1HHZ7ANLMsDha0DQ1VVfTtx0aONQH9t4itcSkGY0YdCupyYgEDvUBkxmEilxcvWB3WLO8teDi+YGLq0tO77xBVhQ0Xc9oOqaczogmQTtJVzdsL1YcHR0RjOfF82dcX18xGpWMRIkIkc4rXBAU+RFSj/DsaVyPUQotxT/jivUvXAJQgSDjDSt0uBykRySg9HBPIT+7xuJfuigIId5lyHb4dL0B/PfADPgvgMub6/9djPH//ufdK8bIaDLGTMeUo4IoPb1vMa0hq2tC5+mairaLsJX4XYPqLNoDeUaSGCZ5TvLefbJxQfVyC/uA8SnKCep6T3XYMSpySqnQIqd+ueLRtuFoccQoLyhCQq8GXoBMNASBShOmswXW9sQIEzlne309OAdrMwBsKqe2O+p+T+5SOhoO8kATGt7/zp8g0WyWZyxPbjM7HLG8dUJxb4Q+SsGCPfT0h5Z60yBbPSgmlUEHhfOR0A+9YaITOmnxPhK9RwTQKoEkRYUUyeA1sPEtcxkwkzE70dMfGkwjSZuI6C3j4xI5dpTtiKPTE/btmu3aY8OOTXuFaD0xemLwJEohgyL0g7+BLiRKCpy1BOcGZuVNVmRre3Ztx/TOKW+9+Tq7i4qP//Qply/3jHSKEQKbSEyRYMYp2SKjPC3ZTQ989Pw7/NPLP+XbD7/DR7snNLLHCY+MkAVNScpbyWt8fvkeb4ozisuEWT9mnizIsxFOD9oL33vaviXK4UQ1n4yZL2e8OH9JtTsgbcRpiEWCnpbkecl6s8c3nlE5pt17dqsdWWl4tnvKbrMl2CG4JiCZL485Ob1LvhzRuZYgAi9evGR5+xQOkctPXnGodoxMTn7joJWkivF0hFEe7z3RC169uqIOWyZLjUp79vsd1b7CWccPQ73kp2/CX6Yr/jgzUYLUoBIFiUOboQBEAVFEdCLQZnjohhhoP+Pe/pcuCjHGD4FfBRBCKOA58PeB/xT4n2OM/+NnvVcIkWJcEgtNLCWBiI4pM1KUm9IfOtp9xSJkRHEDvxx6duECFz1SSo6Wx0zfO+XB8YL90Zr1J5foRuAOns460iiZ6JSu6xEu0rtIs99ytenYCEWqE2SWEb1Hj0qmkwlV15Iej4lNQ9/1JGQok+ObHtdFFIZSTSiSlMYUuK5k263RgJYBH1oEkavLJzxbPUflOSend7nz2j1Gozn37pyhvabeHGhMQ/eyxTYWFSUyaHRQ9DaiVcJ0PmXbb4iVp3dD8pLHExWDi1OSEoMBkzKaLbDjgqSrIDFkncDtPLyoScsRZjJDn0WidlRVTXPwyNDQ9zW5SkBErO/xItB3PaHu2Ns9s2yMTjQuBJy1ONeRKkl0jvVuzY6as7duo49GzCcTjh2o0SWpFyRSUOQjxtMJ6SRFTRPkQvAbf/7b/P6ffY2H1UfsaWh0R88QaW2Coggpd0enfGH8S7w7foC6DEz6giMzZ14uQChq25Kjsc4PnJBEE0PAekc+KSgnY9qrGms7RKqZ3zliee+U6CLb3Ud0bYuJKUpmLLIl28sVTV/hnSO6wEH2mFnJ6dtvcOv0LnKSsKu2PH/1gg5POW65/viK3fmayWKC9BG85+TkmN729M5ia4fDUXUd1y92VGGPNhOyCG0biUEgxc9IZ5A3rtE/0UZIKTCJQReKUIBJFVJJovBIHVBaoZPBv9T2ji39T975p66/qvbh3wEexhgf31iz/XxLgss1DR1tAJUm5HmByTNSpRnbgOt6TEgGExkVcDJwdXXF5mpNv2/oryyzu7cImSGfjGhGe9rtBttYRmXBvbMzMpXw5NET+k2DyQuKMsH1Q9GQSqKzSNwHjFCUxZRD47i82KAIxK5n5XuE1uhCoYzHNy2+9aigGVOg0jGpL8ndlDLsEeEFTrY4OrCC3vc8+/hDHj36Lvm45Fe+8GWOZrfJ9IjxyZhRmtPsG+p9h2s8ykucEBihUSIhkYIeh0kiuDAAliKgZE6qJqQxZ5QUZFlCF2uC6BiXOVQe2wXyXuOuIt4JVDGiSJdM5scUs2u61ZpEQT5NUWlBv420oaXrDggpCMHhSUBq2mZHcxhSn9NsRL3p2b1YE4oA9xL2bSQrDfd++YzbZ0sO+wYfBKPxCaNJRjSWQ1jx8eoD/tF3/wEX9oKrrKZtHd57pAIZI4VXvJbc5ivjL/EV/UuI5x7dShbJnLGeUIh0cOTqLUjIRYJIMqy1iF5gDz1rf00+SikWJdvOkxhDbibM8lvsr1ekISWIHt+1GBEZmzFtLbBtREiPyiTlyYL+zpjl8gSzExxWO56vXnLVbnj7C+/hmp6KlvHZktnxjHKcIUQkHCyby2uqfUU+L8nGBfvVNQuz4K07rzO6rWn1GjLYXhmELIg0P9oXP+QqDB4Mkv7m1HBTPRSEsidZKkSu0CmQWNARpcEkoNWNZUD92QXaf1VF4e8C//uPffxfCyH+E+CbwH/zz42MY3BzTlKDNhlOxMEGXCe4GME7EiHAKJAakyYkerAFzyYFx0fHvPjoGVfnl1RPL4nLOfWuQqMgCnaHA/N5wXg2Q9rBbEWZBJkosrxACol1jqLIaKstfWzZbfccqkdYQKeasTFkKsGpwd47ykCW58QswXcptuup9gdSIUlNic5y8jAmdI7W72nDgZaADp4+eDrZ0+06vvOtP0CKDC0z3rz3OT73zpdglEDmiDX4A0Cg6ypebRo6WWOkBvzQOkqJRiGEIteBUbZAJzlt02MTi+taOhvJ+5SxykkTzeXzK6gU6a2EMptwenyXw36L73fUEZRXnN27x93mPvajgT3gbIdKB2OQel+xP2zpmoZRXqDRA09kVzEeTzFy8ILoeot1HpSgmE2IUqESSSNrqnbD0/VH/PHDb7Dx19i0J1iHjOGGVAQjmXEnWfLl2ed5U91FXUNmUwpdMiuXjMwIEDhn8XEQzmllECIiHPS2BR/IFjlVtWeUTzDHBav9lpdPX7JebQneY61FFym+c1jbcXFZ0fUNJMPDaXl3yf1fepNdJnn5yWMOqxUtPTvdkywLdBeJPnL/jQfk4wKERyWw329ZbdacX1yymMyZLZcoY3jjQU7janzS0tmaNjjQCTobQorip+3Bz5A2fvqyu/kckysm8xF6pBHaE1QHxqMTidEpWhoI0CngX9VJQQhhgP8A+G9vLv0vwN9jOOv8PeB/Av6zn/J1PwyDWaRzqt2e8WJCmqeYPEMZDWpItokMvL0gBN4I0AKlBOPxmGk5RnSeyyg4f/iM0b5hu90RDhZZBZyPrDZ7Pv7kKW+e3mE+X9A2Ldb1ZKOU0zu32W63aK1JlMRue4zJODm9w7PzV3g7sAx1mqJzg7WOpqqorR107JkEpdAYUrJBgdnWlCblFo7eHaj6HTU1fWhoGP40NrhBVi17Dm7Ld2zL968fMx3N+NIvf4WlmbN/vqNPhqde3/dU3tL3AVTABIUSEiUUeImUCeloSlZMiX1C4jVFEIRtDxtLZhNMTIn1jtY1A9fgSDHOFtxa3sX3FR/v97R1x/LNI15bnvHoo1cIrQZJulBoqdBIcpUiTSRPUurdgd1mg7eORCck2gCKrunxfY/yYFINKuLElkOz4fvP3ucb3/s63zt/n0NSU9MRnUdbQR4MEwruZif86skv8556k+I6p2wzcpWTiRztNJYAkoGjIQIZA34UvB9CdYKgWlc0TU0xLzCJwfYN3b7DyoDznkBgfjwjSRLW59d0fUPrerzy6JFB5ZJYaK72a66f7qk2W1KlWBzf5qiIbEJNt9ljkoSYB6LzBBWINgx7Wmnu3jtjPl0gEmhtT5CRICNdcDS2pfI9V/WOr/3xNzhfX+HxP9r54cff/UTrgCTgEUKgE0WWJQgtB3A2gSQRJFrcbCEBhfnMe/qv4qTwN4FvxRjPAT59DyCE+F+B/+unfdGPh8HcH92L1X6PD47JYoZEEGNAJIMUWWqFyQxohcUPSGqM0AVMTJgfz8nTjN2rNRaBzlIO64qw79A2gc7y4uFT4r5hNCm5d3aP1nV0tuVQ70BYfIxMpgtEB/urLYdqT2I0d05v43cV3W7PaDpicbzk8ePHdE1DnhdkWYaQktyOGQdNtdoRo4IYSRKJE2NKP6b2W3rZ0IaKKuxpQztYswGOyL6puOjWbK5z8C2ZLpnnR5yd3WdixggUenPA7i1u1dOve2QfSeIg9HGJYDRdMluckiUTgu1Q+57Di4r6RYOUM+azE8ZiRKgP9Jc9xuQUoxHLyQmH/RV9H6m2NQ9/8AghDYSUKME6R6JSUq3xncVWPVpJQuNYnV/Q1Q2jcYnJU7q+J3QCr8KQx5EIeuUhcez7l3zn4Z/xRx/8Id97+V0OyYEq6eiGyCrSYFjIBe9N3+Kv3f8i981dzDNFWhtyMow3ZEmBDJpgISZgZSQqgQiSWHsIYrAm84IkKmIX6bcd17tL+iZCH1CGQXI/Mtx/502uN1fsXjwmCRafAEYzvjVHjhNiErAScKBkwtHJkvSoYB1qkt6zvl5j257sMGF6ssBiaWzNbD7lzmuvkcgE7yNNfaDrBtVnS0uXWK63W2zWc6g7nr04x3r7o0TrvzSB+GfZjJ8a7voAwXuECph0ABqD9EitkHow0hEiQvLZ2/q/iqLw6/xY6/BpCMzNh38L+It/0Q2UkmRFju17fNvTC0HsW2SWoFI9jJuCx1oHRHQcKmBPgBhQWqOLlPGDu4h8yItqyxE7d46/7FFO0TYdnxw+YXm04N69M5JEU7c9zx8/JTEapTQFHuxAyFnt1kiT4IKF6HHWsd9uCQKsHYqIUJLxbIqUksvzC/b7jhgFUqQE55EuQ1rIUGiZg/a0oSbrt1jZErJIj6W2PZnfMA47+r7n1fe/TwMcH91l2+2YThe8dnqf8s6EQhRcfnLBzm2glUhhCJ1DKs90NmdczomdAieRrca0ms35nhe7HeGuYH73Dl3bsm0b3CglzzNyOUKS4p2i2tU8rp+SyIz7yTvY4PBSkWWGruvpmpqmaUiTBGc79psKnWgWt5Zki+kQANs4smmGSBRIR68c2+aKb3/wO/zRd/+Yh5cP2csDpAMqjhPIXjFTC94b/xL/+t0v8+5kABX9zjEWC8ZZhm8thhQc9Dhc9EyOJ8xuzTg8XtMdKqKH3g8c38zkWGGptxW98JT5iFFeULkWJRV5XqDkIE3f13um2jCeLsjGJbpISCYpIoPOWrxQNLZn71p0NmG2PMG4GS8ePyNNS8aTCe2u5vnVC4QRHC0WZFmO7T3euSHSHkHvHfu+oukq2mC53l7x+MVjjiZT3nrjTeaLY37w0cc8fvYca+2gnvyJliLAEH2GHzCxricPg1vXgBx4YozEOGhNIj8yJfos6/9rGEwB/LvAf/ljl/8HIcSvMpS3Rz/x2k+/j5SMZpMB+VcK727CUbVEpholBD7GGwZgHFKRtRqCVSI4H/AhIKclutQYLZkZw8wbduGa5rwDJF5I9rsDTx49YVQWmGQgqnS7lqqr2DY1isholJPmGVXoefbyKWkvyLxit9lyfb0mEkm0xrU9fdUSvGf16hLTRhIGeitRUowmZPmS9fUFok8xica4Fh0yrOxAQy8dqWyY9AbvDK0M7PAcZGS33fF89S10VnDv1XPu33rAe299jundOUol9OsWd+gGoYsG3/a0uxaV56hMksuSdCJokjWr7SuCe8Lx4jamloTO0uYVejRCmYQ0G3P//lv0VzX2MMy9nRpUnDIzqETTdC277Q6FREfFZr2naVsmszHj+RQxyWlTEDlEE3DGgum5Olzwte/8Ht/8i9/mfH9OnfT0ukcIha4VooOFPObz08/zleW/xj1xl/5xj9h4SltQJAVGJXQ2EJ1DqkHAFXtHLg0jnePLlsLkrK83NFXLtJihs4Su3iKVphyNGBUjqqamD93QZ+8qvv/n32Xf1IzSktu3b2P0MOGqmhaZJuRZyqvLS9bXFSF6nr56iV1ozl5/i7EsWNY9Y5mC8Ow3azIU49kMJfTQQjnP4VBzfX1JFzpmJ1Ma2SAT0ALOr5/jD5Yvv/0lTs/OuHP7LqfFbT4YfUTrOz74+Hvs+oaf6pISwVlo2x7nFAj9Qy5kCAHvA0I4JPpfnclKjLEGlj9x7T/+ue9DBC0pRwUCAWFA1oN1BKsgGBIkSilEHPIbcMPTIEqFkAqRGSQBF3sO1iKURC8y9HGJ6yIphkIJ9odqCFhtA673jIuS2HliE/FeorzD9z1mkjKfLOgODdX5mrrrMGlJPilwXT/YuO97rnYvEEDuBxkyXhAFuOBJ5yXL20es7ZbuanCUEojhH89DUzdY4VBao9QUqQuMAi09i7GhCpZNu2ffVDx/8ojL5xf0bceb995BjRXRMWQ+iMEhun55oBEdJ8UZSg2yaakNy5NbdNc1zfWO5nqHiB4aR3XZwNRjbqdMxgu++MtfZvfsmmq3GlScMQ7j4VTTY3E+DqcykRAj9L3FhQBKYGUg0iPyDFVKrO6IpmfbX/CND77Gb337q9Rhix/F4XtWID3kXc5CL/nirS/yK/MvcLs6Rr3wJG0kCQk5yZCtmUWSIsF1g1ZEK9BJyur5OdurS0bTKbdP7tA6S9U1OOURSqHTZDA6TRWNbwcA0jnatsPHgFcRlabMJ1OWx8dcvVyxWW+G3j4MUy+365hnEzwdjappqgOvXr5kNp8jHbx4+gSw5KOUxZ37JOOEvrMcqoreebabHedXF2TjlFsn7zDXM1zScX71nAcnr/HuvXtkWcFuV7N/tOZzp2/zN770b/Ho8gkvXj7l0Dd/ieH4wxUg+IjtPbb3g3vTjQAqhCGgWYghWu9nBUv9tPULwWgMMbCvqwE70AlllpGmhqqpwAV870ApcmVIlEaIiA+eEANxaCPxEUY+EKWiJbJzB7r6wCE0hFnCrWLBDEPkgsNqx2a3HdKITDvQT5EkiUKrQVoyGo24/eAe11fX9Jua5rBnbHLeePA2+9Wal0+e0FQdSZoxynKi8NTVgRADQhukFpxvztnHPS4HWQq6w+CgJGJAEEjkAFL2wlEHxcEGgoLprSN++cufw4xSnr54wotnT7i4vmTTdrz/wV/w/gcfcrY8442zNxjdnpAIwX69Q7YKEWF7tSLTM7TRKCkYzaZMby1Zb2sunjwjn04wSlI3Pfv1juXJMVlacHm1xlqP0YYkMSQ6QemBQ7+rDkihGZUjkqio9hV124G8SXHCg/LoVCJTAWmkCXv+4Nu/wz/+w39In3SIBLwIBBGxXmAcHKfH/PV3/zq/Nv8Co1WBuoqkB40JcojzCwGtB1/HNE2p1hX4ONC2mwYpAq7tuPaWy9UVRmckWcK22qGdIckNQkm21RZre3CB2A0O0loq5uMFffC0h56PP35Mv+/41Aa1P7TYtqOQOfPRnKrf8drdExbvnODGmpevXvHsw4dMRM5r9++wa3c8+cFD8uUI8oQnz5/RWctifszZ2RnjowlZnuGCJdpAkeYcTxYoERHRoHWK857EGULtaLc1s3LCRb2htz8xOfg07Z5BLh0BZz3+htoshLhpHyRCqIHm/BnXL0RRANjsV+zWW5SQnN6+zfJ4ySRL8dGBlEPab99go0RJNdhzI/DKoTMxUKNNJNYWrCWxivHRCYsiUh0ss3LGcTalzwO97/A+Qhvo9jUhQqYNOUOeY+gF1WXLIT3Q7zqE1WiRETYdL95/SGsHxx6TDrFvaVawW+9IgE5EamlBaYwUnL5+ir5VcvHhR5x/9Iiu7VA6ufETkJhiwv2zU7rU8+2P/4LtdoNwGpGlzJYnBJkynpxwtFrx4sUzzs8vabqei+sL9vWW0bjg7PW7TF6fIihQJiWfT/EhYA8tfW8h9GS3CybtLTafbFFozDQh6cB3HjxIlSJEyp079+i7DWIvEKnEaUfUlsZ3YAOKlr6P7DbVYPs+MoSjjHAkMUtJyC1dCnux5zf+6Kt8/Vtfpct2JOngmZEETxYgs4rcjvn3v/gf8YXbn2fyTNK+OGB6gQoBEQaEPSJYnh5xev8uVVWz3w+kIhs8EU/0FplIMlVQtR0uBIIApyJSeLq2guCx7Z7g7QBih0iiDT54DvV+IM8dTSiXU15dPgYbQSgICWY+RkhBdXVFTEC0sHm6Zd0deHV1gSbl5OyM66srPnnykH1XwfU1ukyYLMbc/5U3KEcll/s9XgjWqz29q+maA82u4XDesLq4Aoa87zw3qFSwKSLjheA//zu/zv/2W3+fD773EO8DzvkfRdNLCTYgrEHcuHOHGAkhIIjoG91DjMPv5LOuX4iioJOE49u3uHx2iQDqrkXv9mSpQWmFbXvyXCIidG2D6wO5yUiLDC0VRgxAilcC+ojxBhEEvvOsV1u886RasbYehONoOUPn0F0e2F/vcc4Rg8XGFkWCVCmxjVw9PuewO9B3LUYk0AcO/RbLIB+2bqAgOxfonSNFYNKUW6+f4ULk1Q++z8WzFxyPzsimJbHU6Lzk/uuvU5QlL14+5+L6EjkqePDuHcbvHPHq1QXXlysur65I0pLMjJGTnNnkFuN8wsliw2a35Xp9zcX2ko3bYEvLUi85fnCPs9dfQ8mUZ4+f44InhsF8w5Q5+dxy/eE5u+2GkRnT25Z2HchWBzhJGY+nHB+fcH1u8ftIFGIAVn1P27dDqK0U9K6l6vbERJHOc7LjgvRWARMgjzRxy1e//g/42p/9P7TsKUoI+IFC7gYMYRTGvL38PK9PzpAHOWgriHgDKoihPWRIpdrXG8R5oKu7IbJOQAwR5xwmSzBpQrSRJChcjLhoQQ7OSc62iBAh9EgZAUU5HrFYHLHZ7Gg6C0LQ7CtaOryzg3JVJkihid0gBGvbCi8j248qghGYMuN0PKXvO5pnl6xfvaSMmnfe+gKMNDt3YH52i+M7JzSuJW8sh2rPdr0hRgfOEXaeuIrovcF5R1XtaGQgSRWMA0lnGC0Lfv0//Nt888H7/Pl3v8vHjx8PpwYVBowtKKRXBDdMcIRUSPFpuvjQcishscF+9v34/8cm/3nXEJG2wN8OjMYjZuMpmU7p6o7V1TVt1TIuSsbTETEIXOc4dDVSalKV4jqPFppcaLQYSDL73Y7V1QqBYjqdg4tcXLyg3xzIXYLyBqUl4zIn9nE4eoVA17akUqAbRd80uKoZ0p2Fx6EIwuOiZTwbc7qY0xxarl5dkesM6wMBjz20FGWJsoHt41f0dU2dWKxWCCFY9RW+TChvL6Hb8/GLp8jbBbffvkc6miHUU7qq59XTcxJpGI8mjMYjFuWcWTGjO+lZHdYc11dcVpec19dcXuw4LD2i0iTSsI57jMlIZEJiNdGCyROCdFRtRdyDLQPdoWf1cstktiRRijTNSEyCF4OTNDfAb7gBfq13bHdrNvUBM5uTH5UURwXJPMdNPNeHl/zGN/8hv/9nv0kTVoyXhkjAeYWXCjrFu/ff5V76gPvFAxb5hNB6snslt986RR8i8brFdFCvdrSHiv3hkrq9RETQwSCcQniFIqKCIPpI7HtSOSRWR9vhpSNNU2bzOdZamgYa26IkICS9dcPEKdXEAF0cgodibuj7wf9BiUBfHej7HiscJIK2avDRIscForXorqOqKwoJi9PbLMsF677CqRzVKx59+ITzzRXCh2HSYzQxCqyLSK8pzZhyURKDpy9n7LerwdS276m7Pdtqw7/3X/0tzt58my9+6Yv8/h/+Ib/1O78zuH0rMUxvokegiFEMqeIolB6mep8SjKX87EjjL0RRAIEuvsy3AAAgAElEQVRJC45fO2E8HjEpJxgSNhdrcNcYEgwpeTpCa8XWbtlv90S/ZRLGCCOgjYjGMyknlFlOtT6wX1dM53PGownSQf1yRdUNkWBeFuRpgnSaNE0wSU7ddWw2W9q6JvQ9Rhu0jyRCE+LwZELFIchFCIwxOG3RYiDNCAe963n1+DnjskS5iAnQP1+zTlqOHpyyPL1F1VdcbK4Hn0DfQ4R922Gqlt5F8nJKrqFaH9hcb6muKrIsR+tIPirJi4I7d0qW5hbHbsPj9ROerJ7ypHrK9vGOshiTyZKRmjLRM6RKCN6jU4hppO4rbGURSYqtHfZiz+SNOTIfZHZtGEQ4doCAiT6Q3Zjl7qoV62ZLpyPF3FCcFGQnGeY446o75ze/9U/4zT/5Kge5Yn5iSHOwrcZaiJ3m7uR1/o23/23eGr1D3hYsZieAZ1QWzNIpYu3oRnviqgEbCbbDdS3WdeioiCFF+wzNoHx1zhKdIwkBkSikd5jBwwkjBFmWgoDOJ9xaThEori+vaeoOIw1Gp4N+IEnIpxNEWfDs2XPauke7hBxNcAGrJAbNdF7S9BX73Yp6d41WgkggnU2og2X95BHrek+2GKPSnENT0217skyhU8msnCEU7PY7Yi/IF6NBxeuH1O/rywvOXz1jVV/SHSy9r7DbjrNfe4t79+5z5+w1mrbmm9/6Y6q2HmICfogtDH8/KRVC/ojsFAf232devxBFQQrBZDQlZD2JVjjrcNYigOVigZEpi+mCdJrT2w5nI8EPP7RJDKjI5rBl9fAVR/MFk9GEw3ZPsI7tdkuUiqP5Ap9p5CRDC42UKb6GtqsR0TAaFXilSJoG1/WE3tF3DhmH8FejErx2ePwgHa4qHv3gIcEGMmkQYWhFY4iI6NivVwTXYoTAeMiioIwpr91+DTXSrFbn/ODRQxCBe/cfcPLaa7RSEIQiK8YgA7ksKeWIqxevuF5dopQgK2pG0zHZcYmcaY5v3Wb24Bajyzkv7VO88LRJOwB6eKQUFMkUlUm62hJz6Kue3lpE19OriGoisXMIA0VZEqTk0B5oCkvdttgYUCbFhciu3lGHlnQxJTsp0McaeaRYuSt+909/j9/4xldZ9SvmdyXlxJCoHB1gLHPyfsSvvf1v8tb8TRbiFpPxknxeEqRFRM+23dO3DcE1iNiy8xsO/hpvK4Tv0GjSkFMyuqH+p7gAIPBY8MNALpGAFnjXs76+GgJ0lGQyWxADXF+tBvsyqQaDVuuJISKdYjyZMl22+KIjtQLRefLEoJWmnJSUs4JVK+myHj0xOGkhlTCbsEdQHyo6pQnBMWp7JqpkthwTxhBNRKSGJNUUWpMWkxtNS0bsWpztQGlIE+Q6I1avsH6Nrz0mCtq+J5cJn3/3PT744H2qph6wRg8hDCcFrZIheyT0RATxxnAl/hwe778QRYE4tEiJGByJY4iD9LMoMCrFSEOa58REINHMjuZMZlNkjGS5ARkRG7jmgu3+gPeBECJJaohS0HYVVZcSU8X4dMlkVKK9Yvd8RbcO+END3G+JLiCVoigK6B1909BbT3DDeCtKiUgk5aQkywz1/kDXNjcEnEGVl2iJFhGnI7UMBA+pjxQxZf3oFd9tKpYPjpndXfLmO29yXN0mS0uCEIPKT0jyIgMRiNGRjBX5Wcp2vWV1cUm/79j1gRgDMmREoSnyCe/c+xzTMGdVXbBp1zTdgd41RB2JKpJlOclY02pLqxqCDYheIfKERBpi71AYTm/f5eLWJYdPOizQWkfXd8jOIYRnd9jTCcvi1oj8doGfRDas+Pqf/wH/5z/5P1jVF6RTRZYaEpFTioHoc//odY79Ebcmr7HMj5inC/J0gjeBvrNDsGsdqXZ7fGeZj3IOumMVVnhRI5VjkowIbSTBYMIgBBbSEBF43MBshMEy3g1hNCE4ohjo0M+fPENKRapTlDKMijGu8xwOB2xwCOnY2yec3r3D4sGc1csLzp88xxhFgmA+nXKIDa3qOf7cPc5+9U1a4wiFIo4yfO/oNzX1as/zD5/wydPH6D5yNDsiKcdoY8BIQiLJTYFLIziJcJKoFL4HlefM9C1iJti8rDicn3P+9Bzz/QIXIv/0W9/kN776j1hfrtB6yNiMkRu3ZoVSkiDsAJTe2NlHPD9P8MMvRFGIIYCziEaA/dQlIuIjRC0IRtBJR297og+DpXsyTCA635FlhqOTI+x7gf1qM2RFKI1IFCYd6NHeWoxOWBzPKMcF0kPiBWHV0+9r2roGBEYljLIcUwq2UtK2Ld57fPQQQVhHUw+aDGcd1lmC8ygUQXoGvMcyPZ4jRiPa1Z5u20A7jFFXl1dcdteIS4OeFhzduUs6yWiCpe7qASjNFUrEQehDxJiExXxOQkLftGyu12zaFYWbQqdxHsavLTia3yWdFiituKClEzWHeA3OMk3mkBbEsaRdD/N66SV5KIkhoTsciFmOD5Z8POLW3TuIepjxxzDkfUYsQguyMieZpTDRyKnkB+cf8Cff/Qbr/Ut0Igf/ytYg0xF377zF22fvMU8WzDliPj5mPJqTxJTgeoyV+NriVx2H64qXTy4H5entE07fus/JgxmX50/o+5azo9e4+v4r7M7jvEP7nhse70DjVeDtoLRUSBTyR1kKUWFtx2yxoJiWbNY7mq4bbO2UJDEZaWY47GvEoWXbXFBVe4rZCAHsNxterV7RJhY3CcSRpDUd6dmU3kQ6YfE2kBUF+digBawuztltKx48eJ353RNsEojihksQoNtXVOuahITMaOrdHgR44fH+Bkw3Od/47a/ze3/yu+zqiov9ht31BuUVLvYEwDmIYZjHSTm4OwcxcBtDCEOeyM/RP/xCFAVrLevra0ZZQZJoRKJJMoM2eiAHJpJeuBv76ojr+8FnQNw8CRJHWqScnN0hGxX0dUNmEopRSTkeIZRif7Xl+sk5r548wRRDPJvYOYR3pEiEC3QABOoYQZuBIiolQUTiDaMyOMfhYOn7FuGGQqEQxBCGotD3RFrGesqbX/ki/frAk299QPeqIkaJxaHKlPJ0iVlOMEcz+sTQ1fVgauodTV0h+0Ht563DNRYjDeVojI4KZ1q2uw3r7gpdF8imQ8SEhAnj2ZwwaenUjtWuxvYV19WBQ71l7u9yCDWN6LDUSA+hd3Sbhs3DljfG70ICeTlivtD0tcd6j0AQgqe1LVFF8lGBzhPMKCGk8INnH/H9J99Dyh6dGLQ0vLZ8i7ffeJd7izNujY8p5JjZ/B55llJOp5Ta4DYt4aM9T7/zCesnl4hoKEdzTt44ZTYfU04EaXbM3fdOkElC6hP2q4pmv6X1zWChj2c8m3P77ftIKXnx6Cmb82sUEiFBhmGkr8KAj3R1TbDhRqNhkdpQTiaYNKFtK1IXac/XNN7iDZjZmH1b00gHiebOG3fx48BFc8XDR3vm8hYug6gdZVZSmgmmNPhZxmhRkqUp5fGEskixcojMc72lb3ra1Zr1syvswTKZlCAC5XhEURoox/RuyraactlWdL5FeTidnHDr5C7vP/4+5/U1VliCVzenBYUQ3LQLingjvfbeE8Rnz4z4hSgKUkjKpKAwBUIqeuvovEV5QZLpG9MIEGEAUrwA51s8kOYDSNQ7R4xDFJiazZBKIrWkrRvqzY5+U2M3e2zfoytBFz1JJ0ljRjD9jctwJPpA6zravhuKQIhIL24ivz2BiO17gutugmIkvbQDwh4gYgnScbW9RDz6mNhF9n2FpyMdpRydHTN6a0F2b4aajsBktG1E2ATtIjIF+ghiiC730d0YdfRkpHhvScuMEWMOfYPbt9imISHBeEd6OyE/KjhOTwiFZxvXvDi84NXTDyndM5ZJRp8GbOvBWWxjCV2gbyvuvv06yTjDYmlVhdMOHUFFQ8DT9DU2WNJEIXNN1I4nz57w0Q8+oNoemE6mPHjjbabzI+7de4O37r3NUXmEdoJRMWV8dMKkKCmVQVeOl6/O+fiP32f/asNUjVncuk25PGJ564jUKGTiIXVEOUZnGUkLd15/hxeXHxLtEK7rcBgVaHxHsI6u2yNoEFhiEAQ0NtGU5QhnI3VVI+oWiQEEKoEsS9BG46wgBKgPB0IqiTKh147RyQQ9ijhvqdoaoQVpmrGczBDeoGxAC8Ekpvhtw8tXL3B1ZD6fwVRRhY7u5TkqVUgl8DHQVh0JmlFSsu02uKpnuhyTJSnWB7K0ZLn8f6l7k1jd1vS+6/e2q/m63e99ulu3qe66XHbsBDxgQAQzhJRRkBgBYkjmZMY0UyQkZgjCgEaABAMGSBEkCJPYroqrs8tV91bde/qzu69d3dsyePd1Sk5iX8tOqCzpaJ+zzne+fc531vuuZz3P////XXFwHXdvbplphRUZW81YPTrn0aMn5Lnkd370e3x2/QkqgM4JLSRZ5AcPZUbkkvv5r1yjUWvNYnVU/AQhMfVFdFOHjNUWJRJKCHICkUuGABRHWKUNxljCNPH2zUuWpmYxKxfR1DvuX7/j8OoaGwVXZ4+ozYzxEHCHQJ4i5EiuIOOoEiQ08QGyoaMk4hEkVBZkHYlaljBTPDF5JhIi5YdsPUHA45Jj2Pe8+4evAYkUxXB19OSYR7/yFHnZMDaCKQdk8Cg0ylTgJOREJKKkQMkSlDGJiX7omaYRoxSm0tSyIXnB6CfiNLF5eYNyG2bjHDVZ9EnDSf0IkSz7duBH65/w03ev+Dff/y1oNLlXpMmTXSgXjE5kl4gh47NDtpHqmGK9dpoQPZOYmIg0ZJIGFwe++53f4bNPfsbV0SUff/xNvv3rv8H5xSPms2MaM2NuZsxMS2MbZk3LSbXg+o9e8fKHP+f+Z9esP7/n6uSKrz39kPnZEaPMZamHTPSRECS9kLAPXNqW4+Mn3NcvmVIBqyQE3djx888/xU8D9B1VSgQEEYnXFfXylHrZMu5GRCeQKIzUhbmBpD/saeY1s9ripHpI207Udcvx2QnYjA879ncd2883zE+XPPvmB7z35Kt0euTt+h3j9Y77dE+/7Xjz4g2zdoG2NcM4sVlvcWFCKJgfzzm9vGC2WODTRJoL9JFGNLA8P8E2Nff7DWN0VO2C0/PHDH/4+8Q8FaVisFRnml/58OtcfO0RUgkO/+89YtqhE8ic0BJCygiRkFkjchEwfen1+C9mmf85DylIdSaEyDANjG5EIDHGoLUkpEB0EbJEConIxUuvjCaJjJACYRQ5OEYfmfY9Qmq0NqgJZqKhsYbjoxNkNnTrO1w/4nuPjAWmMl/MIWSccyA1MYIWmqRDUY3FTM6BlA1KKELyxGQIwZVHmJR5SEoAHJMfcWosEdsJnNYs2wvOFgqsZgy5qA19AZboIFBTiXyPw0R0ApsMYUrIbJHZM0wdXiZW2iJkSWNSKmFrGLNjv96wj3usq5C9pn7UslTHPD3KfPRozf7mh4QcmS0XqDHS+0BOCas0pm4AQQoJoyzVXJEGj+95yIXMSFXivXRlCCLx9uaaXXfg5OyUb378DX7zr/wGJyfnWNPQVnNk0FinqIVhuNsRP9uz7Z7zB7/zA9784QuOzYqvnX2Vo8UKmRT9wUFrUUqTYvl8gpeIWiFEZhx6rCi+4oDHypKknacBHzNTGFGimLm+0AErJWmNJfUTfnAYqalNgxS2KAR9IHhXYvNryxeb+BQCVtcsmgVd6EtlFSU6a3TQaCfxtz2HsOX158/ZfnpHmDzGFBq6nwec7qHWaG2Zxn3Re4QKqyWztmEMAo4yldJ4FWFmkW1NJRe4bkOIgeVyydXpBbfvXhMjWK/IfaK767hubnh28R5//d/4t/h893187tGZsoFkgRSqKHRJD9fnlztEzn8BPt1f0vHtp7+a/4e/9d/iNo7oEmSFMhZrK7RRZVQjShajVPIBwU0JlYgRRMZYUwwwU6bf9ESXkcLi73t2z2/w247GWkRSSK9IUyb7ACmjraGyBiM00XsO3UBlaubtgqEb6Q89cQqYUPoY8CAdffC5pxxJIZByIOIZ8oBXI73dMoUD/bhjY3t8VbE4ecbR2XuoXMPgMNPITGQqawhFdkL0AoKApGl0S9ssEEIwuolpHAp9OgaiAlUp2qMltrVMHNj2O+66NYNw2JMZwSTq4xmqVrx1L/nu//OP+OjR+xzpObcv3zFuOlrToM9bTt6/4OjZGaY2yCHib0e27zq8V8Qp4PyEXBqOn51SnTe8696xOJ9zenWCyIJKtNS6RjqwXuN2I9u3G+7e3rF5dUe9V5ytTrlcnHFUrahki3AKow2yEQSroNFYa5GxLH7RWmKlyAzEbs/bn33K7U9+CpsDTRI0KBQKJxPRalTboKqamCTTrsd6iQ2CSs7RcgYPofwkDVKSUyIkjzGaqDQ5F56Fl4koI9VRw+WzK+ZHc9a399y+ucaPA2N0TGIiV4KkElVoMVpDlWnO55x9dMn86RFpJnAmUUmFUorB+cIiiYnaNoWBmuXDplaalWFyRSsTRtrG8I9/++/z6Q9+SACOri745r/269SPVvg5xFZSn1n0xcTndz/l73///+A+vSGYjimVLpmkmPS++19238k5/7U/az3+UlQK3pe7nMQQUmQaA3VskaqMVbTRSNRDnr2AHJFWI7UkR0EMnhADUhb13RQjQhnqZoZOGrcZwCVm9QzpBNM0ImJC62IJRgm2+w3ZhQd5dCAqRy0NJ6sV87plv9kR9z08yGvFg+2sJOZJtLKQagIBlyxJt7z37Clj2NGPW752VaNmNcIsUNUR63cHru/u6NYbXE5IrfhCiCoomC+jDH1Vc7AVSj14bVPBq0WVQEoiJdcgjgHVKBb1Aj9FmDqG67GM0A6OalVBkzi5OiU1EGxGHlui6/EiIXSgCx11mqOkwqWRMR04sCcZU2AzOtDOZ8TG4ZSgnlUoocgjtPUcGyvMJPGHiZu3t2zfbOjWHXnKXCyuODs75XR+RKMahIMwZtKDCtRMD/Z4CSFm9IPmI3rPITrQgbqSzI+O2J4ecXA9widkSlQIlIbjiyOefvQ16tUJ69str37yGX7dIUhoWcrpGMPDmC4hkyKnhKZ4BUiRdjbjMA4lXt9mwtDz5rPn6GWDHyeGQ1cmHDmRSBhb8d5XvsLp1QU36xteXr8A1TOqgbaa4wwMYiKICi0swSjINdl5JpVxwgMlEyTExDD1hMmjpMCYUs0oqTGmorKWq8vHXJxdEmtNhyufz22PVpGaJSoWxoY0BoErpkHx50PT/lJsCillQoLGlju/UOBzoO8PSClpK0vdVMgH9HwUILQg5IdSURZfWyVqMo5uOtANOxrTslouifM9092WaXBlNDg4GCNJSHQViTrjQyCRSvmVIjJk+vUW5VLx/edMlpkYPUoVbp9UGihQFCFLuH6MGYHm5PiUb/3Vb9H3t1xfv6BeVsyOFpjFkqQr7mZbGiTT/Jh0mBi6A2Ho8MGRCKTk6fHIroyclVZIrRFZYpTFmBptWyQV/ZQgKkSQBTebNXPRQoRIpNt0dOOecXFgebxCGskoPXklcIdA8hFVGUZGovTIRhJ6zz7uGO2IywM+O7QR5JVhsh0uOebLJa1tWagZVayZ7kc2tzuGdcfh7Q6bDZezC5qTGUerE2ayxqAQUyaM/sHlWhhXVdJYW5ONJsVAcp4UI1ELyiOZwMxb2ictdlbj3n/CeHNL/+aGfntACI/2Bzb3N9je0W9H4jCRQkCQCckjmUoAqq5LFHosIJuUMiJmEooQJsgFdSdyIk2Rrt8z7u9ZzOdcPLoiucD+sCNXS6ItKoCtOJCWgqU5JsjA69t3uDbTnC/JRhB1JsaAEJowRqbRUdW2NB7jRIiCHDLdYc/YjWgh8dNAt9uw73tkW9HM5syPl0zBgxdIoyB4Jjcx3Hh6NVLrOXVs6JmQQpFFfvBBfPn1+KU2BSHEfwX8u8B1zvlXH86dULgP71PCVP69nPNalDjn/xz4d4Ae+A9zzt/9095faY2azyBGqtrSNAtSgKkfGYeB5B1aa7SSDONAlJm6miOUQCmND56h67Gxwu073M2O/tCxDRK7OCKNPeP+QNdllBfU2ZLHogEYHai55eLqAqkVw3qP23TUGFLv2d7cllwCpSAHpCgBLyllRC4ZeW3bUjU1292GKTiyCDRtjfOF+txPE5tXI/pmZH45Mbs4pj1uefbNDwj7RHfb49Z7pv2WftjRj2uGacMUBlzoiL44FPNDfI6iRpsWY2aYdomuZ6A12esyKckCJSQL02KtIYfMIfU0uqZ91JYcP5FolhbdCMSU0VbjVCTqCDYhqoxoQEvJ6D1eDKhGk+cBMUvUreb4aMGyOsIfEvevb9m92XK42WGCoqXmfHXOxfnjEqiKAO/x44TxEpECOP/gW5EMSWGtICeFdxM4R3QeO69pli3YCiENScH88oLF+49w6zXPf/AHPP/RH5HDQHfTc3t9h6VFpxqZBApJRjKlgpuXukFWAiEFbgrFK0EiJhCyYr/fkiQEEsSI1JnVvOHobIESmsVyQQyBqIFKEW3Cy4zPE7OTBaezSw79npu7G1InoJKYxpJNJhLKJuUSlRfFY6PBucAQIjFmVIboHNvdATcNEAPH56eszor4Sc8qtv0BKSwIzcTIGEayyIQqsZqdcBhv6NMXOKj8QJL6y9cp/NfAfwH83V8497eBv5dz/jtCiL/98Ov/lJLZ+LWHH79FCXL9rT/13SUgBeHBjqwfNNxBwOHQM8aEDGBazX44EIjMvMfWBqM0h+2W3f0Gf+gZ1ltcPxCBXeeZ5C1qjBghaE4aUsjkIJCDIA9FqWirltOLKwSRd/sB2bQsZMPke1ycyLF0xEUNddUWvuU4EcZyB6ubFmUVPg241KOVZn97xw//wVs6t8WlgRwto8w0w4pnC8vRvIKkkULQqoqmbpDHK3Jy7Pd37PbXTOOOQ3fHodsQwvQgRCmOQ+cHOj/CuKWdLxHakjBUsqYyLUJoVGWQNRwvjjiZH6OOKw52S88BXQnMac3x2Zw4lvn5buzBZpKIRZI7z3R+yy7vGPJI1guW7RGz44bT43OqXOEPjs2rDXcv14RDppY1p0dHnDRHNKpG50QcO/pDh+86REjYJEndxLQbiFMopmEpELcabFnGyQdyjFSLGXW/QOiWbCRqaVGNwC0qjpua2cmCPvfEsKZSBcqqkyoRZBiSFCAT4mEioZXC5wH8VO64qegwAEgZQVNyHiVIa2gWFWePL9Cnc969ueblq89LOV9VzJoZdtUgZxZWirOLM+azhu12i02WITiqUaOyJqiA60f6fqRtWmZNi44CN43gEzoLYsxoYUpPRQmqxtI0c47OWnQjcN7TTyNOJLp0YHt74LbfsBsPhFlALAN+3hFTSS4TX2gTcvrLlznnnP+BEOL9P3H6bwB//eHn/w3wf1E2hb8B/N1cOpj/UAhx9CdyG//p90+JGDqCsSAyyXny4Jh2B3I/IpLgMG1JMpFEJCbHeHOP0ZrKVoTJEwfHMEAKAZkSKmZM8MQYcFFy8eiMJ996TC8Fm82B8eUG99kW1QumzvPJTz5nZoqBQSaNtA1mVmAskx9IopTYx+cLTJSsX90wxUhGcH9/x80mE/SEqDXKSeTWI9eJmWxohcXJiJjBYnXC/OycYICYkDGRqoBvS9imSi2WzJmZweTpxJaD3zDljpE9E45JBiY5IfJAiD37w335HNUMr1fE+Tm6XWLnNfJcs3q2QC2h20X+4Ac/gNbRHCuS9AxDj/Oe5dkZs9Ml1cqQdSbUiZ3a8bPtj9j0b+m7zNPq6zxefYX2eEFGcbjvGd/0+NuJOljq5TGrdkkri5Fs6A4M4w1x6Hjz+hXd4YDKUD3I8FTKD4kJAp00gkyWxZGaKMQDcWvQ0qBlg7ANcj7HHC3YLSr2S8n+7jV9WhPSDR5FVgGhJCl+USPIh8UuUHhUnMh+R0I9oJglX0AUFApDxsoKUVvqVcvsZE4QmesXLwk+kmOgH3rq1DKPCyoM++sD+1c78tWEenJFnBzb1/cMbmC2aKnbGmLN4WbHZr9mOllyfHEMJrM77AjZU7ULyBKXRqRUHF0eM4WO129f8LNtx+rpCbZSdP7AGEduuzVv7255ffOG5zevWXNPe6q4/GjO7CKjZpEHPCmgkOLLhzT+RXoKl18s9JzzGyHExcP5J8CLX3jdy4dz/9xNwUXP2/0dq8UJWWVSFvg4klRmeXFEW88gCybvUEZhreTQ77Fas1os0Shu397iX020pyeEPHG/WeNCRKfiblNHFWFWobXBTpFsRpI40CiNjtAdeg4mgAcbDXlIqFx6HDElco6sZMvKtExTDz4gfEQ9QF7nixmLJ1c451m/u2faT1ipMCkRU8QuNBcfPeXi4w9I85rdsMW7CekyMRXMmVUSQiaMhTfoOk9yMLMrrKppRIsTgUl5vPIE5XGxZ7O9Y3KOKARdPMAUmS8mvnK14OyDc7q45eUnn7G+WSNij3Mdd6/u2Qxr7ncbDl2PrRd8+1u/wdnJJaoqmQfd1HOzueMwbKiqFeePzri4OIcE6/s16T6TdoEqKWbtkkbNkT6z3W/o73cc7u6I44DKkWHoSClTSY3IAhnKpScQD43apkx0kiPLsiUIJDkqgiwx88GPuCmQ+g5MprKB4fCOfkrYB4gtyTxUCV+QV//JeDIiH1R+ZSMoFYJEoUFqNKZwNHSJohv7oQTR4sgq89FHH+GHkRefP2c4jLz97BXq7TUuRqIK7G7WvPzkM6JMeBFRVjB0PTFHgpNMkyuUqHHP/fqarBKHqWO+mLE6g8M4cLu7Y2IiVZkp9txvbjnEHbd5y/36hpdvXzDEEU9gO+7Z+54hlszLuIlUdwEaS1sJknpQc2r4/9v78M/67v9U8/MXuQ9nizOSkbhcJgg5Z0SlmLctlTBIaRBCYFODMqpAMxvNYjHnbHXCuOm5u94QpgNvbt6SZoqzr15xeXaO33fc/Pwlt90d7356KJ35LqHWAT1MZKdQaB5dnHyeM3oAACAASURBVLK8POKw7nj5yQtCiKgIhBJjJbKgWx94NTwnOE9y/sFGnbDacLo84ujikrdvXxO9Q8hIDh4hM5rElDymNZhZRZ/Kc+wXxGBtDMZqdMiEwRNixLnINCXCKBBeEJPGm4okNUJZtIpUraSRHt0sij9DGwbpuHYbNtWGr15KxrDnxU8+4/rtNTnsqY8ywzRwGDbcHK7Zjj2HKZC6ifH3f4/Lq6csvroiG0HSmcM04ICPv/4+H370jExg6HqSU2QvUFkhs8IdRoZuYOodvg8In7Gipp3VGC159KgmC4nOAhkSNoGVprxXiKhgMEpS1YaqsUit8SExDCUhW1lF0hJvNWJRMz+eMZ9J3jz/CT/+wUTTV8yloaLFphZNVRY7iVwSF//YA6AwKGkAg0SjdLnGciiiOB88OQhyLKniUSSizLx7/g4/TrjOQ3yQ2w+OTEZYga4t0Y+M+NJviAo0hBwY+pEQPLq2pBCxusbOCs0rpIj3I4tVy+JijpkbmuMWx8S7u3eoGrIR7Pc7Nrs1SSVEI/ndH36X3/7eP2LIDpEheugPDr2LmGWDbnUR56WHHNQvefxFNoV3XzwWCCEeAdcP518Cz37hdU+B13/yD/8i9+EbT76RT5dHhQClDCl5jJQoa0lZEFMkQ3FQilS61csZQcDt/T23L665f3fLN559xN56wpHi4sMrVssZ/e09MQ3gYLsrYZ2rxYJE4L67Zhd6chqJThPWGRzMFwv8wWO0QlYl/SflyDQN3G62xFzYE/KhgROR3HcHDi8895s7PAGhEyFNiIcSeYiRl9cvSTcNzdOjIspSgikFiOCTJ40TcQjsuwNxcGSXEEIXh2aSOBeQlS56ixaiTQTlWczm5UOVFZN1NO0J9WVNs6h5/elr7j9fEzeJqALz8zld7Nl3jvW2Z1CZCait5oOPPuLo5AhpFNIWXPp82bA8Pebp+4/ZjVu2255Hqw8wuqKbBlSQ1LpCZ02VJZUyiFZRqYrGNtRaU1tbHH1Kg4swBWyWWGVww0QMCTmUQBBbG2xtUA+bwjh6tNJgUwmHrQ32ZMHqZM6sVeAdbz57wRxPGzUWi0oGndVDsnFRiGah8MiH25NCCVP4HA+UVi1rpFGkKEDAbLXCWMtufyDFSAD6ddnAZvWSZlaTyQxjX/QbraKeNwQiIo6oWYWaWeYny3LeT6QUaeYt2/0W21Q0swZlFf3Qs3Ud89MF7fECbzK5ysh6RXPUooRk3DlWueP94w+xswoawbI94+mTDwkm8Nndp/yfv/33GA4jzdQQPVgMQoRirEv/cjIa/zfgPwD+zsPX//UXzv8tIcR/T2kwbv+0fgKUQu7IzAlakWMCLctCTIksJMrqEk4ZSgdXaUvWkv3+wO3nbxnebpFDRF9YlquWcKToh4Fdt2ZYb3CuY0XLKlms1DSiYccATcX8eI6tDLvbO3YvXtCYllY0GKXQaIjFdppjxKOgqpktKk6uTnDes353R7efmPZb/GEkEygG94iQAUlGI5GVLGDVWoFIkAumzPtASqpUH8MErlQVKIFpLcYaVNSYGCBafHIIqQpoRXqmXEarKIlIlkDg9PyC8w/O+NnPPuHdyxtUqDiplwyVRpjAfJZ5bDz32bO+u8ZnzbJt+Cu/8Zs8efoMIyxzsWSxXCGV5fLqEbYxvHz+OdI3PFq9hzGaJCKoTFSRtq2YqzkKW3oyQVMpQ6UrjJQPzVqJsrlMN1wiJYluLDrxQAPL6FojTREQxZwQddlMZJ0xKkNraWYNQkpklpzMj3l69gw/3FOlh8olZuRDNN7yaInWirvDnt0w0rZz5rMjrK25eXfHNAVSkoScMVmWz11rmrpGCEWlDFJ/ATbO0AiqWc1sNUcqwe3dDZVsOHp2zPxkRe977nb3eJU5ujrl+OIU21aQAil46qal3c2RRlFVlkgm7SRuTNi2kNH2/Y5u32Nag9CCOCTCbcTvAsmAaCKhjjybPePxbzxldjXn5fBz0J7f/v7/zTRkphFEHcsKlyVa78seX3Yk+d9RmopnQoiXwH/2sBn8j0KI/xh4DvzNh5f/75Rx5CeUkeR/9Gd+gwR5KB8KKRXZsC/ZgkKB1BJlJDn4Eg4iIkZYyBAnx3QYWKmWn/78U0YTUCemzI9FoDEGETLD7g5zF6lGBaPiMDlYtlx99B4Xj075o+98j/v7ieA9fYQ0JFpdQUyIh/BLiQZd2IinTx+RkscHz/14R5xKBE4WGWEUVVszdDvc5BFZEOsaUUmiDAVj7h0xxodgzaK1KBSsDFEiGklNTZ4y43YgR02TWmIfmKaRbDU+B5wMpFwenT2OpBOXxxcgBJ999pz9oeOkPcHqBjXTdM09q+OK5eqMtDIcfv877LqeFBJaWho7RyRJZWoenT+isS1+TKzXaw7DjoU2pautM6qWkDPZBGjAyQTBIV1ExVLdoRWJ4jTVWZNiLDqAnMhCo6Qm5lT+3QKkSchKEXMhP0VfFIeitCER0eH2HrmP9EKgh4mZqNkFSXYUyXzKQIYsaNslVV2xd4HUOwSG1dEJ1jZs1gNCRBaLYwhwuD+gbMkk2O62EMomg1KYWiGswqtMHyeSlxhtCZVAVwoua+zlnBQN7SHjCMyuVuRGsg9d4VaGibjZYquaujak2nA47Nn7AVlbtKlAKipbg5KF3TB65rLB6QptFX5y9F1HnmWssci5Jk6OxWzBNz76Br/7g9/jsLmnOhJQaUybyjr6cyiXv+z04d//5/zWv/3PeG0G/pMv/TegxEU5D/WQERlkEJAUKUVSSEQkGkNKkb4/kCZYqhXL+QxxeYXdZaabA/dxy/xkhvUCHyOL5ZzHz54ymy+5fXXNdf8COkeVFU09Q81nJW7dOer8cANPDxXKQ8daCIUxGqkEau8ZQ+b+zTU3m7ecnB6hkLTaEEbPJAT1fMbJo2POn57zve/9LuPeYYXCq8irty+5NR1n710yaxqEKEzM8UFubY0hSzBLiwgSK2pcPzF0W8ZxwLqMDxNJhpKIFANTFSErHJGN3vHBB+/z/q+9z932Bm88ox3Z245oM9Jm7JElzhzReB49PuVb/qt8/0c/Ih4y0+CJLtHWLVJkFu2Kr330DTbdO969eVMeJ05m6Krg4yYxomtFdVYzP1qgREUYMqmHNCpCSIxxYvQRmaGhIRMRIqFk2fF1pVGJB2VjwuOJOaCUBKMRRhB8BClJBNw0sLvbcPvqJWGz40RYzFRm/xlJSuGhqVUet168fFVi9HOmqhqUNlxf33DYF39AM1vy6MlTZFL8fPcJ1aymaiz73YFpmEix+GwyAa0qZKWJEtbjDrJE14pRTGz7N9zuO2aLGeqiYtEs0MuKKXkm75BS4lJhSGIyyMjoRoKC6miB0QrbVCDB2hr5cP2TLTYraCICi9YwHXqCjAibCtaOzA+/933+5//pf2G938ASht5hnUA3pZkb0r986vRf6BBSIquK5BIi5xKQIRVJyFJCJoF4kP3OmhnZSrRWWKt59OiK49zyNr1k6x0f/+rHtIuafurIUtC2R2WO/0gjnaKXN8TrgbZuqZqWw7tbppev2Lx+S4iJ1fyYmW7p1j3TUGLZ1Fj61CZIrE54kQjdxNrfoqNCjpQyc264fP+Kqw8fo1cVw/cji8sVj64eI49qOhsYbCLkRDeO2KAgKpIPBB+ZMoVx4TN46MKBsZs4+AORIn2NeIapRyqBw5WcCasIFtzCsfrKivay5Sevb5i0Qy4FYiYQFSirELNMMoE+7JE6cHo24/xsztod2G3v2WzWVGctddOwmB1xvDhlHHf41JFiQAiJNILkIl54qrlldtJglhZlW1os4RAZ7kfcrjTiIKKFQmdJyhGZS4R6FJLeDSilqVqL0ZoYC5XK50COgYhAVwZpFUmIwgVpNfGw49D1xH7CFCwIAv+AYxYkqUsaU8zgI1jDo8dPefT4Kevbe/ruOdFnwjjx2SefFi2B88xNy+WTK6p6w6vuFTJCTB43JayBo8uiCbkft5imAl3kyCfvn3D+lSuygsPQEYh0TAiVSkWowM5qZscrQoxIbQBB6ENJiNICVLneu26g6/ekGKisxSlJJCFNye2Yn7WENsJKMNqePo6s91t2+wPRJ6QHP2aiz+XpV0bCv2ouSSHA6AIG/WNmhS4WaSMF4mGqlGLp0qtG4GLH65tXjOsO9pmRDpEDnoFQVdzt9mzWW+TLN8z0jEoaLIokoRMj3aZjtrUs2gWGCukbMCPN8Zzz00uSfsfti3c0RlNhSdOEE7vS84hQCU2eSs5+yIIJT+8nhps3vO7uCQSOLs94/JVLFquaF5tbPn31CrNoufzKe9i6JbtMig6ZPDkFep9Rg0f2HgbPNDq6yeFyxOHoxUDB0pZRpVOeMY7MFzPOnzxi9cEVj7/1AeqkpdOeJ19/ipGaxhTjDQbEccDJAxMrhrDj9HzB0VHDi+fP+cPPf4f58Zyj0zNUrGmbY37lw1/j/UePue1uuNlvOVmeoowhV3B0saIxM3TTIuqGVBmyltRLgzmShL1FTQomybSbmHYTY+qJk8dGSS0aCAYtDEY4lJRIUxq7OSSSy4BGGYW0Ea3AO4fVkm99/WvoD95nuLvj9uUr7l8+x/hI/TBVmFQgSI2SFSZDyIm31/eMgyOOE9pPaE8BAamEyAqT4P6wZngdiN6TjEeJjJWWKZaR4na3Qa8s86YBbQgJpsEz3fV09QHZVJh2BipTGUlrJV7s6bwjUVy+IcY/BiUnU6qmaME35fo3QdGKGmk1uja8ePWaRhiauuUQeqY40FQVymamMPDi9nN++OkfMHgHUcGUyb0m9YI4zwhZ6Gdf9vil2BRyygy7A3UqaHWtFPiAf4Byaq3RUhXqdAjs73e41FNpja0rKm1hgvvXb/n8s89p7++YhonhbkfaOcao0UGhqUg+kocALtPjSf2IqSqcN0jv2d8ekKNk6ka00ChlyCEyholmueTi0RXT6Li5uaUbRpTUpS8QI3HM7G52uOCZkuPkcYvIiVd54np/w832nvOnT1i+XyGzxUXHYQgMh4nRjZATfnNA9A53ODCME2MOTBIGMXLIW45WRzx97z2OL89wJjAwkOrE7HLFwSQO6UCbGkQluHh0Sa0ralmjkHgRGPSWmAbGYaQbO3ycMNpyevaEH/3od/jd736XeX3KR+99jXnTcnx6xXE+YdVd8Xjqi1JSWLIRNAuBVhVCVaUH4EdkVmhjmS9agnBUNDw6fUr0if1mQ3e/J2xH3HZEDhD6srEOckLIXDwlZJQt/98a6MOAeOA65Pig1ouJFAJ13TJr59wrhVMRoRXLs3MWJyds+4nxMHF59pjd9Z7N/Y5pv8cIUbIHSmZWGeelRJACJxJKZFRtEW0gTRB8MeVlLZjcRLcdmfBIa6mbGSl6uucjssvoyiJqVaTUJzPqk5IjoU9mbHYdu80Wqy1W1UQSympkrYg6IW0ZfcsEyiiqpsI2FafxGB0EVlWYRnMYNKICN/Wg4Gef/pwf/Oj7hQchKY/cPuJHsB6UgX+SS/dnH78cm0LOxG7ERwFKI4wheM9mt8WNE1VTc7RcEawtltXYYxvF8viYhZ0hRghD5IqnWKs53O/w3VR6FL1AjgkdJOPYE1xEomhsAyj2O482QDLMc4vbet7dvcMag8zyIVEpgVbkuiHZGucCDkESBbahpS5imugL6DYLZM7cv7zh7uVAwuPEAVSgf2t485Of0hyfsusmNusdh12HNYpZY7i5fUXoOqJ3qFoTDTSnSz78+Fv4ueP47JTTy3OoFNf7W25uNvR5QB63KG3oc8d21MhaorImhUzX96QpApH7dMOPn/+A57c/I0mH1AnnJ6bYstk5bm9+zJMnj7h6dEISAz7MMcKysOcc1YLoIy5lshYMk2OYHHEYqVXG1gIfFESH0CUhWRtNn3qGMLJlSy8OeAZ8GomTg0FgjS3jwUQJlxGAFuhKU7cNbooc1gOTT+A9UwxsX7/FrTestIXeMYWSNNRFx9HFERff/AZzH+lv98gBxF1HJeWDG7LIxeuqJvoSNWerGUIrutyzOjni9Oyc/XrLm09fcpgm5tZgm4qkElkE2qpBtxXb3RYhJbNDTRo6hrgrvQ/p2ZwY1GmFPamRJ8U70W13ZGmojwWyUYw54EhEFVHKo4QkGV8SI8SI9xOXz86ZdiOuLzgAZFkzznmSlYy9Y7fblh5EkgU27CMhQAoKm79Ic/5yfYVfik1BZLCoMv8nI6XAi0wkM5GQD19DLOSftp3TzmrqqkUIxb7fMUTHtz76Os6PfN6NuDhibYNeSKQGo2vkFCBAHBwyKaypiN0AAozSGKfIaSoMRanIOpK9L0EkVUUXejYvfopRBsdIeIgml6kg7JQoz8HJT4jk0IyAJ+BQssfHgeu3G95sXnH85Bmzs0u8gqlymLZFzSziyGIXgnZWcfrklOZsycUHj/nKt7/B7f4tw9hz3+14df2Wu35Nlwem7Lla1pyenmK0YXfY4r1n6iZyn4iHSBwiOTjuwi0///HP+enLH5OrxOnFMReXZ2TZ8vjRI2LoEWrP9d1PkKHi/Og9GntCoGLZzqjrOZUSBDKqCTD0ZdM0AqGKpDglweg8Gs1A5PP7l2zWW/abA+u3d0zrnsprRJ9gHbHCUokKLRWNtdTWgo2ouuSxkkThT5BBlunUOB1Y375jiALzYJ9PJKKW3B32qM09pl4gteHF55+Sbw+YLLGVpWkblNDgBS56lCiBPEiBcJFhu0ecnmGlKVWJlCQjytRFFt7H6vyY+cmS+MIzjiPZS4btCJPHWomtIVSRvdxzv35Dvqv56P0P8G7k9fUb1tdvac6XTDOBPmmYzWqwBS/vtKMbOwjFq5FEwh0c0+AJg6efBrTQTMLz6uYVb29uiH8colKYHTkX16WIoIX88wgafzk2hZyLpBUrSv6YNdRthV0tCvKsqVFaQ4aUAyEFtFbECL2f2Bx2TNkz+oEXL57TuYHF2QkzM8ftA8N+QjQNKXtqNLnzWC+pUUz+QJwmEJaQFJMbkVohTKFZu+kh+TgIxjyVKLR5zTCODO5AkEWoG1MsuXip2HEFEUMEGSA5JgYSPTElDmNP2im++q9/TH16Sjc5osvkMfLe+YzFouHs4pjTZ6eIuWWQnj969Qn/+He/w/XdNbI12HnNB9/8Kr/y699m3x/Y9wcOd2tiCOVuMyRSFxGDwDhN7AJTX2TiH1x9DecdXe749re+xV/9a7/JEDIuDBz2b9jvrvnZJ99HxZbVxysYJTebkZOjEx4/eYxVNVopTF1Rt3WxQKcMofQCtChyYYRG6YZm2VC1Ky6awC039PaAcRI1wL6/Y9h0+Pse5yPTQ9BM29bU85Zp2aNrjZtJbGuYzw3niyPi9ob9m4RMgrauaZoZPnr2h57Nqzd0vSv8jC7CZsCGQJYSaSoWx0tizNy/2yKkgghD1wECpRPbV9eM20OBwAyBupmBhClMaGPJqcBcxExx/uycXbdndz0hKlUAQznRHjXUT1eMpwrh1qiV4fzygnfbnq4/8ObtBrNtMJczpKu5sCcc6yURwWHY8+76HVpq3nv8Hq+fv4CsMNJiTcWiWTExgpB894ff4zvf+y7e/wIWLkOOFA5GABElWv4ljyT/RR9CCIw2CCMwdYUymhADAdDWoGcNIQaim1DiwUEZIjkmvHOgJNWi4edvP2PwPaeXJxyvztjfd7x7s4akUPWMKGC3O1BpxWI2J+5GIg50CVx1MRAtTNExjmU2noIvHXMghcgUAnfjxDRNZCI+CeTDfSySEVqhkiA+lM6ICEljqYgkKpkIMlPVlsVqxuLiCDEMTH3AHwKztuLodMnsZMHr7R0/+eGnvFlf451HOclsdszTD5/yjV/7mJPzM5z33N7e8fbz1/T7DdYajldHnMgjQhXo9x3jfiJtIkyCxfkxzdWMs6sLqiPD5XuX2KpCiQFhDUud+OTmhmndczxbMhOW7e2Gz3/+lvQ08/j8MePQM/QDSuviXBCyaDEmcN2EigXVV7WaOCb6w4A1lpme05xVjNVE2E+kfaA5MTg5YFQkDoFx17Pf7ugOB/rNgF0caI9nIDQCSZQVeiWpKk9lPTIkzi7Oee+jD4ku8MmPf8r1u3uuLmqePX2P2xc3bO66cqOUmf1woH8zEQJEl2nkoqgrUahEkZqLhE4ZYSwaw2y5QM8s6/X64Xo0dExsxh2L1ZLj0zPM3DFte9Lg2G236JSZBYsfFZeXF5y+f8a8acnvOZq24e7umi717HPHi59+xiefR977yhNm8znb7YHgIifLE7ptx8XqDJ8oFVMGLxKjc4Qqse637MY9X8T9fHHkUNgXBMgB8r9qm4JSivl8SSSijSZS2I4pBpwPJYhWSnxwaKWQSISQaG3Q2mCqmvVmjT6TfHj8HnVW3L64Zv32DpOgkhWin9Ay0a87xsEz5R1WaOxsjkQyThNZBxQao2rmswUyS8Zdz2G9I00eExUxRYwymHqGD45MIqVITIGYM0qXkJD8/1H3Jr+abemd1rPW2mu3X/+dNrrbZabtQqWyRCMxqZJgABMEg5ojGNW/YMGgVBIDxD/BhDmCCVBILmSaorCdZTB5M2/euPfGjYjTf/1uV8tgn7SyKNuZLgopvaUYxJG+830nTqy1137f3/s8zhC8HxH1ARKmZGRIJdCJoErXZD5HOY1w/UiGSnOUkPz8y6+oTcOu3nEcakgl08WCi1cXXLy84PL1Befn5+wPB/74n/wR+8c9qUy5nF8wKSfEPkAIuL3BbQzN3ZEEzao8x2uH05azi2umV1Na37K537CaliihaHcCt0sIhxwRc0SjaR4OhGNkrc8pzYT337yjO7VMspJCpVjn6U4N3anHNJZm3zKZzFhenqPmOYO06CJltZqzmC8oihQfJTY6mJixBa08tjEEBWUCTVfThp4eR2sM4qFDScf3/Y6vpCc0Na5pyUzC/pRRdgtm1YwkgSQYhsdHnoKkrXuiM8/gPIUXgs72SKEJUiGcBRRajeKgsfIy9v7X6yXWRpquY6VzyqqgD46+s8SJIl9OWX96TTbNWF867r+/5eN3H+hSgwgeHSeE3jOXGevJnMEbstmUfFYxfbXi0O04tFtkBdv9E7u7DWItuTq7ZrlYUaYl0jLG3WXAx4C1Do9jCAP/3T/67/mTr/4plmf/xZ85LsaTQrABvCQ6kPrXX4+/EZtCCJHODCQxYMz4C5QxkD7P2fumHylHydjDVgTkM4kXCYkUIy+/zEhFxubdHY83t8SjYyom0A9wtFxeXZHryGa7IYSA1UCWjLq4PCNXlt5YiBJValxrMN5h46j9TmNJpiSJTsZgFR3IcdzGRzcWyVSCdx0RiWcgupHk+HyfG4lyiSDpMk43DchqzNDvGj4+vqPvO7b7J9CCfF7yYnVFNZ+SzUqKiynnLy542j7x0599SXM60ewblpMly2LJOl0RB8/xdKRtGvpTh3aaZbkkYdwATT6gFop8mXOsj+ybmjyfoH1KezoxbCIVF1yUmkmyQJmC2AmykLPM1sR9ZPPVEwUpuhzBqMoCXYo49XD09FuLTxvsUJBeJug04NKGm27H3Z1GCo2KCcpKwnGgP3b41uIaQ+g8ITg62SFKxcVnZ+gqYbi/4f7mI9vdR3AthYyUpEDOdhvZ/HygLKbIY0tCZDgduG1aIEUpzXK1YnCO09CxOjvjxavPqPc9mw87QgODH3Upssx4+eaauu+4P25JdMFgB8qhZ3W2Ztec2HYHhJDEhDEjsN8Ttg0+Wl798BV5NcX5wHb7xP6wo759IhSBXX/CCbh8dcVktUINmqzLmK+nNPVLurYjLypWq3Mm5RTfe07HPcF4YjpOdsoo6IaWP/jDP+B//j/+gIfmETTIqAghMLYfRjOZd6MMBu9R2V8zQ5QPDucMMUmeY8wC9Yw3I4wBV+csbnBjjlvpsdIdzRhOkZAXGndquPnugcN3W0wdkR4OtkVbTakKEivJvGKicoKIGAI9Lfl8yvJ8CYPHPe0YTh2HzYZ6XyOiJCZi7D4gMM5hnMU7j840aaLomvZ5Gl+Cd0jfE6iBE1GOQg6UQQqPjALtEzhaNj+9o7ltCUrxsH/i5vEGMli9WLF+uSafl6RVQVoWJIXGuIGbn77n5tsbmm3PenrGD1efMc+WiCGQPrXUfcvQdXR2RISnkwxST3QBKy36IiW7yrndfeR4OHA1v2ApFzzePRJ7SdVO0U4xS0qqfErhM6SHfFGSTzPCsUHeHZiXZ0xiMp7YRMZClIhiQRN7atWxq0/4NpBbzdlsRUgj+1BjBovzY/TW9IaH9zf0TYexA8I40iBIiwy5SEmmOdOrOZdn5+wQfHz3jiwUKAJ5COQoNBIXLP39LR335KTkMiWM5wIiFmSO147V1Tlp1zIYC8GQ65wsmeKUQqKJvsdhUWlOdAPH7Z5EdyiZYFxAJJK80OQovPKYtsF0Jcf2SP3hnklZcfXyFeuXr0BIsq8U3dOB3dsHbh5vqUWPnKUkhWSdn0OaouWSqEpSueByOnayoo2wB1tb+seBPNMkQoyAFdsj4sBXf/p/UjcHpILL1684m6z48mc/GduSKIIDM0QGA7kA+VdY6b8RmwKAjRYhNVEJ+n4A4ki6lQopx4EkLxwogZKRRMpnTqJESEHbNhzf3bB/eyDsAkWaAZEgHEma4YPlw8MHTDsQbUAgUVqzWC8olxOUkpyahmZfE81oZQrWM53NyMuKoR9wrcUGi+l7pBBMiwkxBnx0QMAHA9ESQkdkQPIc50WivCLyCwKOwLWWp+9vceoBC1gdmc2mnL86Z3G9IJ3lBB2RSiMcDLuGzfaBelOThZIvqteclefIPmHY13SnE76rR7GqiqQ6YzFZEEKgMy1BRELuma1nPB2fuL25ZVHNWRYLjncHaARn1SUte/ZdQMWEZbYmo0QYzfWbC0J03H3/HntqSPIzNBLvadnjXwAAIABJREFUn7lFQaDJWM0mlLkFNIMb6Dc1hdTMLuacXZ7jQ2AwjgC0h4aT3pLkCTZL0QgKrSlmE5JpAYlA+Ug8dsx0xZvzV2x9IDSKnIgO4nlbSBh1VxERfjEeLZEyQZDgh8DTZsNkuWY6qbj96mccng7M8nMSW5HpKUWaMQyB1rV8/+23eBFYTZdMZzOCi/St4e7mFl1qEjXapyZVxbKaIwATtpw2NR/evmfoHFmeE70nSRIOux1ORlbXC5JpjjQeYRxaZUgnsa3HNBaZjDU037ux8Bkimc7ouw7pI/vdnn7oyc8qXp+/Ri01O3XkR7/725yfXXCs93z/4T3WeHDgDAy9xzsQIv211+JvxqYgBbvuiB4GEpUwDAPBe6q8QCQaqTWJkOiiRKQKKccWSwiBJEmIMdA2LRkpCRKvIpfXa2Qq6I819jAyHHtrmRUTprMZ3gZ0mrJeLjkNDd998y3dU4fvLYlMyPVY/U6SdKw4I3GDJc1SvLMYM9A0R4QAGww+jCcEcDg6IgY9/tckBEjJAT1iPeQYaUVrlE4o85zJ2ZzybIqejHc50xlEUERraZojze5IqAdezS44n1+ivcbcDvT1nqHv8cFxsAeK+YTlxRpyBU7ggifNNCYE5FrydHzk2w/fcrE+49X5Kx7ePXK42/HFi9/mrFhxaHM6DFJHVrMrBh9wgyIl5dsvv+L9P/lTZq2mL1tsZkhUMlJ9nKR3njTRTPMZi1drvBu4vfvI008/IGvD2XrN4Bz+mRkhe8HvfP43EDHihGEYBlxwiFzjFWRKETYH3v3kS+ZZzu+8+gFvjy1tH5HejzcMciQpWTKSmgnhmaCUIoIeKeBIool89/O3I+Q3ghSevtmRMVKijbNYImdnC1xwnJqGDM317AoQ/Pz4NcfDgWJWoGYp1XzKNJ9gmx45RLTTbJ92HLcNT/cbVJ5grSHNNBevL5m+nPHyR5+gqpSOHhvH1GrsLGltCSYg1cjXNG3P/nik1BlaazbbE0kv8L1DOEHqNP/Bv/Xv45dwylru6nvePn7Hi5cvub27wbtIIIynhd7jBjXOj/ya12/EphCJ6Cof48ZICqVQUo39VR/GxS8VEcYorJLPIIxA13c4ZymrknSasVFH8lnG7MUaEsdxOGC0RS8yUllyNl1xOb/ANgMPdw+8/flX7OoDXdtRMWFWzUiTFC01qIS67eiMJc0LdKo57HYY26OUAOlH845w4wlBjTIS5TwhgJLpyB8c+TeAeL4bCLJFyeLlNVaAzFLSKufQ7rl5/5FyPWG2nmNbz+bpCdNZVsWMVXXJKl8iGkm76+gPLc46ZAwUmUbPLiimFYUuIVEY6YhqvPsl04TiTc7v/y+/zw8+/YKX5y+5fXvDzdcfWU9XTNIJVVbhK0uaFWNMdzajafaEVOF7S6wN2gsSH9k9PqJ8ymJxRZGVSBJ8CKiowUg0CdrDjJK+3rH5+Q1JkjJZzMbUn5ToHoiC6XRK1IEns8GEQJ4V4yxDZ2jujwx3O/ZR0n14xHeWkmrkJgUFZCAzCIFEhNEHEkCh0UlKGIXgJHIMaY2A/BFpH5G4UNPgiSEBVfLJxUvKyYT7u3u2D3ve/exbhBRY11PmBT4EumNN63tq0zC7XjOZTZnmC9w0EpNINi1wiUdkkrPLFS9fv6K8miCnKcfuxKE9Yp61hKlXVCqlmFWIVEOEyWxKUZQjDyLCm+IzUgmmNTR1i0gVy5nilLakWc4f/8mP+W//p/+GwdRYa55XlSIYz9BE+iYiJ3/NBqKUVBRZgYoJRIGFsbOAwAZDFGLk53gHTuAHz3a34VgfyYuc86sL5vM5X//pV7hEMj9fU5zPMLFjJa7IXxWE2lNvdgih2O32NLsjm+2G/XFPYzt0kpKIhEiAGMc7RzfQGYPUGqwkGQwEh1IwqQouz1fUTU3X7IjBkmaBy8tzjocTh8OBGCRBCoQci5legtOBZJGzeH3B2asXCJ0xGMv9/R13N7eoUpMmGc2hJfrIQs/J04xZOqUMGcokHLcnTpsa241pz/PLSxyWIfUoqVFh/HcUPuKU5WQP5GmBKgQ/+uEPeTG75vD+wOH9npmaUogSnUqyeU5KTqye8YUzifWO1ZslecwwpuGiWlMlguE40ByOlHLCkCkKPSOVObEPDL5HpJFca5blAjG3PO0euPvqA5evX7K8PEfnKc457u/vMJOWxdmcs2pJ6Q2tsxxODbLtMbsW2cfR0hwMEkUq9YjBCwrIIaRE50e6txwRbCObUSNVQpooRBLJGOjdgI2GEX8LQVqccQhSirzk4fGeojkxtANpohFecDociVng4up6TBAKy+xyQTrPeX/7gafbJ/KYk5Y5s9WMclUSssjD6YmDa+juv0OFiunFjKAig3SgxTPKTyKURiuNc5Fh6AkhjlF/wDjL9x+/x7Y9fd0RfWS+Xo5CozmQRLqmY7vb41w3ztDLZw6bh9CBaQPa/EWr75+/fiM2hRgjGoUSCmstIkSctZgQxmTesw5+/LSjil4owWQyoZpOWCwWeO+xEUSSkE9KVJGhgke2Ke1hoL0/0TzsqP0R0w70dUtapLz67FNO9oSzjmW6pspKTtsjx/0RkKTp2CK1pid0PdKPU37R9NT7LUPfgu9RRIKzdHVD8BFBipcJKpGoRDIkElGkLC4nLF+dUS4qdJqx3Rx4utvgveWHX3zBZD3HCE9nDMoL5tmcihyGSLNvub+/5bA5UeQF1WpOVuboRQnRYO2AipLoIqE3NBx5dPcklymTlxMO9sjV4or2pmX37ZbKlRRZSa5HuIfIBdIpRDkOJsXM07gjn/7wFTd/9J6nbzasvCSNkgD09YG9U6hJIJ9kyDQbk3Qehs6Mk61JwmK9Jikzts2Rp5tHrI9cvLxmklfcO8/99x9oNjuur69ZLaZMkoI80xjXML24pu6g2bdonYMdxhy/HLtPAY2SOSQK7ywgxyJ1AB8EUmmKyYQkVwyuJTpDwkgjytMUrQRt3eC6cZrxcfOA2I1l4zwpScWYptQ6QepxY0+ylCQbTyGZLlCJGwvQ0SG8xAyONMt489ufMSjHoTlyTA3et8wXC1KhGdp+5IHmGcInDI0Z53xEghMO5z0uOFwMpFWGkJDmOUoq8iqn1wNdbLh7uOfD44dfrKRfWlXP6dLBY1rQ5q9ZTiE4z+l+j84znPeoRIEQtH3HsalJtGY6nyGCI3j3Z0xDJEwmE7TWHI8HJiLFJRJlA0/vb2n6I91TjXsKhJ3DHwa6wY3KteiZLZd88Vs/4P70wNN2Q5VPES5S2xoTDGdn56RZydNmy3F/IO89Ko5G6L7paA4bJI5EBlKdYILk6WkHMgWZEpRCZAlZkZEtSubXK1bXC7Jpyv644+PP33H/7T30gss3V1xeXDI9X9BYgwuBxCvSXsHRUW8ObG/v6ZqO+WTCdL6gmk+ImcCnHhcCIgBE+qbmZI/s2HPrb/j0h18QC8/j4xOVeMP+/Z6kTShVjm09aEjEs1KPBIEiSzXToqLSOW8uXvDkH2g3A1kc7RNKQLADzbAj94pKViifIWRCmuWY4Hk6bAnCM5uV5Ks5F/MJT7sN26ctJJKrV9e8vr7mHkG7OfJweIc9W1Gt55yvZoj1lFAu2amCp/cbusOJGMdOlHfxuWLzfFdEk5clwTi8Cyg56gWDGCG4ISpckpOV05GlGEbhTp5npBPD6emAOVqmq+mYUxgcfRiQmSab5uTTFJEojB/wXrA71XS2I80zqqIgySxKS1CRgzlgdh7OUmbXa8pKEujIJznTxZQYPb1KkCZSxpRh3/P08IgfHEVZUMwqEq0w3oKEqzcvIUa6pqera7JpNlq2s8CPf/pP+ZOv/wT3/MgqAfmcVwgIghEMjScdfv31+Cs3hb9ABPNfAP8eo1H1LfAfxRj3zxj4L4GfPb/8H8cY/96veg/vPIe7sTiTlgXVdIrKklESk2qysmBxcUbiPYkUSCU5HY+c2hM61ZxOgrZpR5vT4GkfdtRiYLfbQBOZ+wWyUZhBo4MClWHigFSKumtGy280bNsD7f5I3R9ZLpesL1YMvcX7garKmaaKZr8nBE+eKCwC7zx5kpAI8DEhlRobJIGEYjknrTTrixXTqwXzsymNPfHdNz/j6eGB229vyE3KdXWN7CxNfWJ6vqDIc6wNBOvpjx3t7YFud0J2Ay+WZ6wvL0mylEF6jLQMwmFCTwwR6wJtc+Rmd8cx2bP+GyvOXqw4xCNpltPcd4TaU8YC+pExmGaaXJTklFgcqc8oYsb1/Jp44Th+2JMazSxb4A5PDDiKRJEIkDhMe2JIaspkBdHhnKNua+62D7S2o+oKlssF58sVn//oB3x/84HdYUdappwtZ/zw88+5Md9z/+4DflfTPE2ZvbpATHOm8xnnb15RpCv2j0+Y5kASDP3+RNcOz92diA+RUueIRGB6Q/ABrRVCj0VW0/aoPBklOlWFIrLbb5Fo0jKnXGry2FFNK7zwKKUIXqKiHDUC0YMUKKnpo4EYKcqKYRiQiWP1xZrlxQrnDJvDhl174P3DR0TzyCA9qYazy3NmsxIlBCkCMxhOTYc7mbHjNVhMkjCR8pkgFRjsQFJp2qHjafeIGTrWsxWhDOzaHTeHj2zNbjSIuV9Qp8aGLCiIHm9g6P/lnhT+S/55Ecw/BH4vxuiEEP858HuMzgeAtzHG3/21PwFj4k+lBV4JZJ5DpYmVYnaxILOT8Rm92TPNS1yQhMFgoyQvpiilsb1h6GqSYU/bGlwrOFtdsc4K2rZhcJ5mOEGUrBdz8lQz1DXDtuP9H3+LFxHTtORLQeFTlFiwys6JjWB7+wB9z+tX11Qx4af1PV20LF+9IMbA/uER03QjrUmKUW+uEvRkyvSzS8qLCeVZhdYDj7vvePuTt9x+d8uECZ+o1xQqI60TqAroC7QvCcJj+nF46PD9Fr91zOdLqh9cMJtN8TEyBDNGXf2YmzDWgotIH3B9T2827NWGf/3f/DdYfHpJ4da8vIA//kf/K6tuxkIsObYnlFEon6GlIpGCRAt8EhgSQGXIU8rDH36NvINVuuagaozdEsxAgiKlxJNhQg/Bk8gJXe3YHTbsT++pQ8uTzXlsDvRuYHZ9zotP38BHydC23O8eOJ/Pkb4ljSPjwD8MbDd7QpqiPvuc5fk5Wam4+vSc6BZMs4J3X39D/e49XkRisIQYMV6RFTOkzrHWo8qCtMzobQuHGjqBV47WDvhUklUz0lkCiSWZFKSLCcfNlhAjV2dXICS9s8RUo4xhP5wgz6nKKUKBkRY/9yQv50zfTEhnGtdaqmrOIjvneDqwO+4JdUt3MBx9wtlkSVKkmH3H08dHhkPHtJgxvT4jKTLKTI880L4jlYHToYYwEKSB2JHOFHHqiGnP+/svMekNl68D23tPdwRCgmMcrBr/AAOwSxnv4b/6+pWbwp8ngokx/g+/9Nd/DPzdX+vd/oJLJYrXb97QJQ4rhrF2lDhq1+G9p1rkZLogDh4hnkNNcQS+JkFi3cBwqtnbAypJWZ9fklPQDy0MnhA801VFVkyZlSW26cBKYus5bY7jIlaK6WwCTvLx4Y4P+/dopbCmJy8TtreP3DU1ZjCkZUpRlCRK0exqGgxBgpcaXRSoRJEuC37rd79A5fDt+2/48u1P2d8+4E6RZbpila5JbYLyggSoNzXtl9+RBc/Fp1f0hxM3Hz4greTy5TWTyZxkmhKUIDhPNwwMg8EMBiUUmUiJsaeuT+z293RJzfr1nNX1nO1pDyKjjAVJJylchnQBd+wJwZL+gmERw3g0j55UK05Ny9ufvkXWkVU2JyklsZ/TNA3eDSgZ8GGgDyeO/SNFO2U2zYlC4Z/xagHLYAPDKQKOYjHl/OKcyWIKLuX9//0133/3JanzpD4hiSk6ZgQbsSZw9/VbHj5+IGSeclpxsT5DVROKScXFi2vcYKnrevRlYMH3CJWTFClRQVSQJRki+GdVoMd7R7CRfFZy+foSqzru7+7BK2bXF7R1w7v7O7KqpFrMxlFNLbFJQpplpGWJiBZjW7w3zNcTFldLUHCzveftt98ymU74/LPPefH6Babv6V3ABQ9B0NUdMgqKoiAlJZc5Jlqarme3H2tDRZJR5Dn4yMPNPeurBddXl2zNgdocOLElpC3zM8WFLbBtTddGRuqaZNwQ4jhEaAXmaP+yJfjPXP8yagr/MaNT8hfXZ0KIHwNH4D+NMf7Bn/eiX/Y+XFQXGNNRTHLSNMdg8MH/Qt+BxNM1BxKjECpn6Az77Z6+bSnzlPOzJVfLS4bFOUWXsnYzHn7ykc2HDxDh7HLN7OKMxhrq0xHvBqww6EmGrQM+eoKK3B7vCR10oSf1Go1CR4lvBurnWYaUHAbB5t0jUoJ1Dj2pCFJhg4ZJQbHWnL1ecDrd8PCTD7z/+TcMJ0fJhDQpmGVLqqTCDP2o+FKCxFns/Z4725BpTx96qmnB5HLBxeoF8+mcujtSn2qCH4tiph8INpDlGToR9F1DXT/wePiIeC352//O3+HqzRVJ09AePbv/a4OuJZlOkRaE8ehEkAn5PP49EpJTpanSgkIXDO3AUmesJwsqXZJJxyOe5iSwocbRIWnwpkENGjWdMJm9YBZXnPyevn+2PCuw1rHdbylnFXmu0blm5/Zs64+s8EzVlFwsAf1nSVbb9xgz6v38qabfHHic3JFnOVcvr6mqKbv9jpuPjyPXM8pxFl+B9QbhoKoK8mo1ZlWOAyqOgBUZHb3paGVDHx02Jnzy+Q+4u73DJJIQIzFRUGTs25aQK+ZnC5KoaLcnYnCkmeTxw0dsabh8fU0+q5isZkghaVxPbCOpSkYiuR+7Cc5bvHUgBdY5ZOKpipI8CbRKEAyorCArKpY+UhYF2bykFwMhiRg50Pg9ZIZyrZnbgv2jIdk5nPGM7e/nx4Xnw4L/9feE/2+bghDiP2EkN/xXz1+6Bd7EGDdCiH8V+K+FEP9KjPH4/37tL3sffnT2w+icIQ2aNIxkHxccmEAMAaEgdg4fAifTczq2+N6SSUUaE8QQqYp8JAs9ttze7Wk3+1GblUtipVi+WRN2G3aHB5brBctPXnF82rPvRsTaZDVj+mbGTM853dRsvn1EtmHEynuPQiFkggwRaUdbUUzApxJZ5ahJjoyCxfWK9ZsZ6TTy4//x95Fbx2TImPkleTalzCryrCJYj4sjWg0RsKHFuRblFT46ytkEvZyRphVCS6yyRGkQKhDdWEnSUqHLnExrhubEcXPHYX9P648UxYzlqzUPmwcGC3dvH/nwv33gUq1JB0W/b1AuMq1KMpGMA2guEm0AE0ijIokKFSVpklJmGVlIiLNzcKBEyrH+SO/ugZqBE2FQiCZHliXzsyVBf0rRTmj9iWbosEGOwNlgyZTGCs/LH73hZB6o9+8QYSBLUlRIyfSE+XRKE2p2zZbEZmDANIb+UJNOSjprmS4tq7M1b6ZLeuN5eHii7w1ZPn72zlmcCZR6itAZUjowo+gnGQITXTF4h+0DDJb7x0favicA1WRCnuYcD0e8tJy/eMnZ+TnN9oDBUy6mTF5MeTBbbu7u2HdjTejFZ6+QiUJKhRIJxjhsP4xNUDmi4mWimM5mTPI50QSk8OPszGxKYguMdbTDKJppmhpXSHzhEEXAJS3H/oEhOaAnnnKpmC5T8scOM0Cwo/rmF1fwjNzPX/P6F94UhBD/IWMB8t9+JjgTYxwYn2CIMf6REOIt8CPgD/+y7xWCp+sb2nc1xjog4mOEZ/Z/ZHzEmKwmED3Se5TSqCgwdcfjqSE0HZu2pn+3Jz9K0kFCKjG5YJhKmCfkJsELR2tapmKGkY50mjKYnvWrNRdfXCMbQXvfIEPAOQfWjwlKFUFIQhjZeolOCSrSKEsxy7j47CXkMD+vCEnP9++/oXsauHIXnOsLZJriQwADiVQEAVpKgvRY33MKLWEiufz8BcX5klBohPcQwJiWoTkhR7sZwkCmU9JC4HpLe9xz2j9xOjzS9UdcYoh5pDUNxXzGcOw4fLeh/9AyLS5JbMR2PUFEijRHIHHGE0xgOHWYeiCa0auZyhTJiGYPzqPQlMUa68GFnqHeY8KBEAzOPhDqlJhkXF38FrP1CjXRyBr6OOCjIibjrIpQghgjr774jEPzyMPPPtL3PYaBFIvGk+mEqlwhpGXYe4ITICXGR0xj6IYnNpsTg/VMz6+YXS6Rk4LhmXTd9j2m7zj1Pb5RVLEkFTnO9vjBYRJL/zSAVmijUd7zdHtLohNECMgYCH2PNJbFtED2ltNmixkMJBKXKcI0Y714RdbteTw8MkjH6/WnFJNqxOajSZIAscf5EbNWb48QIKtSFvM5tvcM/XhzyrIcET3BtJhhwHUtLvEE1RPUQC137MMdB3ePEQdkbkkrKOcJxUxRN44Qx8g3z+PWvwgy/Tmitj/3+hfaFIQQ/y5jYfHvxBjbX/r6ObCNMXohxOeM5ulvftX38yHwtH9kOAx0pw7TW4IfQ03lpEIlCcuzJemFBqVxQwAT8YNnaAdc3xN7Q/1wIO0U03SFDZ7WjnCV4myBJdAcTzhr2Rx37LcnlJRkk5Qiz7j69BKi4N27d+xunxj6DuUgSSVZmeMYTU56khG8xQnHIB2q0px9es6LL67p/Z7DaawQ6zbwonjNfFiTuilpquhon7H1noAhuoHoeoJrsPToyYLlyyuKxYKjGzDGEYwhcSO3X4ix/x6FIFWK3vfUhy3H3QPdaY8dujGTv5yzur5kcJ6XkzX7r94xvG9YiOmIpatPdH2PmmWU0ylplhN8xAwW01mSIBFWYFuDDHIc9ZJyNF9JRTWZEyUo7ZCZYXuw1GZD7xp8c8Ngx5zFdPYaVDK2WEVAFZrJakZWFmNnSSl0FvjBb/9Nlnnk4euP9Bso5OgXPZ5OrPIlhZ7jqEetvFSsVitqZzm0DYnWHO8P7I8DF69ekJcZItdoJMUspzIld/eP2GY8aSopIB3lNIMx3L6/QZQa4QMxGF68fkXfd5yONV1zZBCC2WxKVebc3t8zxAeyoiREO5Kqh4zXF59xnZ6T32YcY4sXI+l6sAbX1CRBUyhNmecYNzyTISPBe0zf0x47Tsc947xPT4wCLQVaS7pCMVlWNLqjYcvGfWAT3tOrHTHpUN6iEosuI1kFyeidGWsLv4xl/PUDjb9WS/LPE8H8HpAB/1CM5plftB7/NvAPhBDPmiT+Xoxx+yvfQwpiKknnmqA8qktGFFWiWSyXTJczsklOFA7TG2IMpDqj1Ip5UbF/eqKrT2ijmZVLinKJDzVllrI8O2OuCk63G55unhBOUWUlwUc6Pz7T/+DTT0mnKQ/fbzg9HfC9xVtDmmYsz5YUy4JDc0AGwfJszc3TDZ236HnJizdXnL++oB62bO7v+PZnX/Pp5Rv+5su/xVd/+hUKhQ2GgAAF2bMtqGkbrGkIrkXSUUlB7hNUbVFDJFcZQYvnvrh7VqEKlEwIwVHXNd3xyGn/QHt4wpmRFFTOJqw+Oefi808gJhzuTrz/8Xccv95yLV5h+p5DfeA01KzzCZPVkmRRjsj1wSGDoMoqhIscNwdEECQqRciEKD1pkZJmBSIR5FVCXiiCiwxGYHiiC0eGfqA3nvzUklfrkV85m5HOpkyXC4qqpNQZiRRMKs356pLPX3zKN9MveffjL3EHhybQGoO5P+KiQJI/G95Sri9es9kfqI89FRUTPWF3OPF0/IYoPaJQnF2dsbq6YMgkYr4gFJCS4mLAOoUPjr7vOdodaZ8iFXS+Zf3JJe/efUffGpSWyBDpfUcwATUp0T5g+g5relIhUd0EtzvBvOT19QtMGRi0H6HKQ8f2YYdpPKus4vLyHJlIVrMFeZ6PtYl9x+PtPY8PDxRZThZTfDegMkW6zghTT5O2HOOGnb3lyb/jKO+x+ogUFkUkTSQ6B108Q1oTkFFAjITw3Jr8K2jjRPwrmGP+/7o+v/gs/md/9+9j/ICWmjKrSEUKURIYDUFBeHKbk6UFMQSOhyPH/QHh4MX6kuuLK77937/Fd56JnlCIDA10zZFTs0fFSFVMMEoS04xyNWe+mIE37Db3HJ62UAt86/GtR3lJkZfM1ktiEmlMDVkLVUKjHK9/6zPWFxd0u5oPP/mGw92GGefkMUcbQWoj0lqiDAgNkhQXPO1wpO/3hNCRJiBDQETQfpx3N5kmns1oJgmzz15y8fkryDVd1+GPA/40oBpP87DhcHtDfXyg7w+IRLBYv2b+6RnqTYl8naMKzfsv3+O+M6zdikpXtB+e2G2fUHnG4vOXTD69JFlPEdqTJmBsx6k+IJQCLzje7rmeXjDN5nSnHu8cZVmBj7SHIyIG+tOJm7tv2NZv6frN6NCUFTI/I1+84OyTT1henY2DXyIjM4Ld+wd2d/eoDCbLGTpLEdbSbZ7Yvv8AgyMJGVpPSERGpiqE0MQosNaDUEglR8+jdyMpSUaiGpmYJIyWJRRFNUMtCuR5SbmqSOcF5bwCwLeWQuWkaUYXHKJMiN4hoscHTx8sloALEplk2N4Sjh3xWFPXB4x2hBLM3KPWOavffsnFD16ik4TYO2ITaHY13d0ON4w3tIf7B5QQrBZnYAP3908gBdNiStoqTnd7jvWe/E3B8m8teTv7kqfhLUf7iEtPOH3CiY4QHd4HzOB4fGh49/bE+7cdpy0Eq/B9JLgwZtZDhOD/KMb4r/2q9fgbkWh0wbK1dyiZjxpv51EiQUmFVskoA5EJyaDJs5yQRPLUEqvIvJpxtbxink1BfE3fniAOTNYXhBA42Zoh8aRRMnQdgxQsVgvOL84w7cD9d/e0D3uSoEZVnRcUVQVInPXsTnt8jGPYqRw4v7zi4mJFNZ/z4Zv3bL99JOtT5u6MqZqRyYTAANFSTkqMGXvD1ji6oaH3J2wyQBJYnJ/ja0O7b/E+EkPAOMfx6YngS9bqJVmZMSTj6K4ZeqQ1FM7j2xPtcYczA0pnzFZLlq9eo+b50nXyAAAgAElEQVSKzvekjUCHBHWMYDWJzkeyUt+CD2Q6IcsStFaoTI+PRH6E12Rpgel6hmMHxiMECCVJshSV6THuO1iMHZjojCA1ZVIh8ldEfc7+tCFISTVfI6c5xrSkOqWICVOZsftww/7n77B1S58Y+scnNID3BGdJnISgkDjwRzwJnoQyL4iMswyIBBv8OMfgwchImmuurs9wvufp4Q472JG/2DbYXYva1PSzjHSWYdZTiqpi6AwnB2makkxy9CylzDQ4R1FqpssJDRaZaYTS2N4jVzOkOaPuTtRxQC0y5MLSFR4WBTaJCOnROuKUo1jnTKsrfO8Y2o6kzDgdjtS2JzqPnuUslxNEENxvH7nfPVAtKi5++JKwttwevqFWN7ikxlMDBoR99lsKghSkWqEz0NkYqA2BZ+otI4bJqX/2ceIvuX4jNgUbBm6336KSAiVTsjQnVTkyasp8wlRMqcoZMqYcuwZSwWS5HB8NyjnTpKLbNygLqUpIgsDYDp1rFlcLRC5Hj+HWIZqWZAj0Nwf2Tzu23z+QkqCLYtRIawEyjoirQtC5ns72TFYTLn7wCevzNaf9kY8f3vH04Qm3dxRlSao0MtWEKBicQwRPCBFn7HhUHXq60NEnDjXNef3pNa+vXnD/5bf0m5okCBwgfCRN4OzykovZAvlcuY6DQ4ZAtI62aambhsGMjyWL5YLLFy+YrNYwk0h9YrfdcwwtqSzHISuyMYxjO4KK6Coj0QpEJFESE+QYIlOgpabpag5Pe6QRxIkjJg6hnn9hIhBkwIrxmCxSSTGdoCej+ry2nsEPZOWMcrmmExKcY2g6TvbE4fYOc2pJI8QgYQhI48FFFOMAmXjutccQAEtwAy7045yDkM8ta4FWmhghKIHKNUEotC7I8ynOnNBSkaUFxIA5GUJb0+9q7NOJblLSNB0qyaiqCisjvfbkOkGLSDrNKS5XpOuSqkpoTEPdNNhmQNlAWqSsLlZUL+akc0mrDa0efw7fNzTHmiwrSfKckEhEkaAnKcvlDH2Y0R4b8AGdpFRFjgqCvvY8bDewiKgzwcbfc/RPWHUkiJ4gB2J0xGexcBQKxDg5nGcJaQ5SjVF8KQUhAcIYff51zQ+/EZuC84an9j2eBGsDMUjKbMY0XzJNlzRtx8VaITxsHh5RecJ0NqHMK7RIOGz2HB726CDQaUF0nqav0UpzdnXB+tUZYRA8mCcmIsHsW55+dou0kDqJFIqoJWQSh2MQhmxZMDufI0yDjjnn15fML8+pdyfef/0Bvx0oQ0GVrRAmRcoUlWbjs1uiiM7R1jVKSmzX43xPSBzlcsaL33nFm89e0W33dEOPCgGNHEfIgag0crDsb+6J7YFkOaVQCp8k7LuW9mnDaX/AB09RFly+fMHi/BwnFSEKUlni2yNN0zKTMzJdkPgEpRihsdOccj2DVNIOHWLoiHEs7AY7zuwrB751SCcJfcAVnpgqhJAEJRFCkU8LvA+ks4JZfk5jWqL3rBDcPdxSd4a5zpgu5wzHE/2HJzbHHlVbCiQ6BJwDqQRJSMfauBQkWmLsgMc+V8wiETNmWQqNtQPGgJApOtFkqcZqgSewP9SkUhKCIkGjRIKWmtRHcj/yF3ESYVPmyYLJZILS4zTl4XjAxh6RKAbnYNuQngyTdknX7TDA6dQwnFqCcZSzilWZYDYQaotPI2qicX5gc3PHfrujXCxYvrwmn5UoKWhtR70/4TpDMSkpdIYzjoNt0EKRnOec/84Z/D/UvUuMZlt6pvWs277997jn7WSdU1Wn7HK5220bd6uFgAEDZogZTJggxADEhBGMkFo94zJkgJAYIcQQISRo5i2hxjJ22V116pw8JzMjMyMj4r/v+7oxWP+pMmrjKjduqbyllDIif0X8GbHX2t/6vvd9n8rSVlu24Y5ObfHicKoOLAFPjCnwOApAJeNeWRUURY/SI0IkYrtW6tuEtl/5+rXYFHyw1P4RLzKG4DjuGhQlq+kNi6zlrLTkeoJxDjV6plXJVBjKILHHlrufvaVZ15znBUF6mrHGB4sMoAtNPsm5P+zY1gcqDLiAsh6DRuuMMULrR7rQQQaTywlX33/K8uaMlR/ohx7nAw8PG5q7HaUrKM2EMpSUcoKTit76k54hUk0mjN7j+5FpVTCKdKOLouDq+TO+870fsN8/8uWPf4K93zBB4UnIe0mEYeTDq29obgPLT5/x2d/5LWSmOdqGoWk4HPf044DKMibnZ5w/e0o2mbBrHV0/YL2jMnPmZ2eIXiCioCiTk9JlsLiYU5xN8XliCgxdgxAamWWnMygYnVEUJTTu9JQJCKPI8pTgY8eAmmTYtiMvCvJJTluDDJFFbuii43DcY8eei+ICt9+yubuD1jOjwERFhkxRFC6cGpkCmedMlgtyHHW9ZehrBB5rR0QYESKitES6SAieGDVGaSyeECNFNSOMlrGvEeI0tm6TG0iEiBAaERWajEm5IJOBXXPAtQ2h80gjWS5WhGFMTIhBsvnmgfG25uzqmlkxYaYqvBjpDyMfv7inlgOd7jh7ck4xL7l794a7N28Z+oFQZDz9vOHl73zC1bNL9Cg41lua45Gri0uyIuKVY2BgkOAWPdNFAZVgo9/z0L2hl1siR4SIxG/VSKifTxiFEMn6XiiqqaYoLEObKFrRq0Tzlv5vVqUwupE399+giylKGlwGfW9p7nsa3WEuC/SNYpoVrM5KpstFKrePPe+/umV7u4YuUF1eIn2Kfz+2jovFGRfnl3gXWN8/0LYN4xgZt0dKY5guZlghGJuGAUtXRRaXc66+/5Tqco7VCYS6/Vhz//qOYswog0G2QJfAosEmHqSI0GzSZnRezZhkJYd+R+1SPqLKBIvlkrPpkvXrO7768gua+z25hRDkqU5wiZ+AJ3pLjIFKaaYmo+4H+qGlGzr6oUdlGZdX55zdXBDzjAaHzwRG5giZo6NHK5PsxFmkmJR4G5GFplhNkRODVzFp+O1ArgS2678FBpDlhrOzFbU/4ERERYc+LT4fPU74BLc1AlFptNKYmKeOdx94+vIF3Dq6ZocO5xTCkQXP6EYiEkEOp0NCIG06QSrQmmJ1xsXNNd+8+oLm/esTcFhiXZraSFkRokKqBBlGeEKwSCU4Ho7p5y0ToVkGR3AOGwNRQmY0USp2x47j6zcMWJwGHSNicLQ+4NwMO/TYcaTUkuhGQNDYhjELuH4k9BaZK3wl6UKLnzkmT6f4LvCzH7+iPx5xo6VYLNjebpHzQFlpJlXJ1fWSZqqYTjXKOEbZMLgapxzeDIyhoQmPfNh+xYf+G2xWI+gTUxWBSIesn98rgqRfyQrBZJJRVCPN3jEKkmYhSqT4G2ad9sGzqY8YO6JMRllOEYUh+sD+uOWY78AHJssp07xEFxl1U9PsWzbrLURBVU7ZdDX22OOGHh/AWTg8HAlZpJIla/vIeruDEBikIptMWZ6v2D/UuCEdGc6enTNdTRmHgW/+5BW+8ZheMetz5qpgqit23R7XB459je9d4jkUOUWekqOk9YTRIklPQa0UI4F6d6T78Zc4AsPxQBY05hTs4gFHKg1TNp9CTyZMyhI3DEka6yxNU+MJnN9cs7i6pLpcMmaaEUcsBEJlFCZDCUnXdLg4oGRkiD1DX1OuphSrKRRJORlFJM8NBTKh17GJAi7BTDJ0lxFzQdARlGAkGbBijPTRInUaJ6Mk2TSRm2fllCrTrB9fs3l4x9svW/YfHikHQyYVMXiijLhTXyD5HBNyzw4jm8c9/RDoaoehQoQ021emoCgm9L3DWofW0I0NEFG5oSwmeB8ZfVIPWucR0aOkx0aPyXKy6QQfIBwtwXkwgk9ePkcbye3rr8mnBWpukMoT9yNd1yCUoCwSVao/drh2JAtgpEZYwUQZwsQwmZaUN3N+L/v7PL57D6NjsTzj2Pd8eP8eJwd++KPfYHI2IT9TdPbIrn1g1zzyZvOao90T4ogTHU4cqeOaVmyJekjhP0Lyc0ysAEGAU+qnVFDkJm0KpcRkKbgVIVJFodJP+Ve5fi02hQgMzuMZkS4V0tNySV4YZmbO5599xuVqRbmcI5RiWx9p+o5qUvHke58RD45xP3JYbyjLAt+NeBdZf9yyXe8IOlIqk87ls4IhWGaXZxRXc7bdnjUbzp6cs7q+QRvJ4+072t2R+s0D+iAp45wy5EjVMSqP6kGeZve5MYjoybQgkxpCwLYDtutThqCSaKNwPqQytvUIIZi5LEWDhYCX8kQaSKQsTcR6ix9HPn644xAG8uWMwVpQsLhYcf30htlqhVmUhFIhDEnvPzq8TWfOdOcIgg4c+yPH7ZrJco4qNGQK1/cM/YjRCucdwY54EdKRq6jwJlDXDTETiFz94sbSaSkX8woVYAwOJxzCRGQIaKOomy318ZHHh9e0u2+QfSSTL8i0IYwwOJsYG0iEDMhgMQhG56k3hxSqEqHwWWocy0iMEu88UkjyXBOIjEOL1oJcJoZltECMGKWRmULJQDXJqfuGvrd0Q582bgRaKiyW9rhHV5ryZspv/M6PONyv2b+uEZmgMobp2YLZkwsc8O7NHVYE8pjed1losvmM6nsV06cl/cxxdnbN7NMZZZAUKqepWxZuySBHOkaiiWAC/9cf/SF//NM/QueCoBuC6IjSIfSIMCNWNEidPDfJBCiIKTEh/R5iOK35QJCgVDpCTCY5eenpak8UyYb9V6DG/XpsCimWOYE+oxCMg2UQFi1hcXbOZ59/l5vLG2ZqTtf0tG2PlpqrqytKXbJ/OPDg7slUxfXqmu3bR7bvHhOgdhxSEMbNFcuXl3Rrh1Kayx89AyV4+OId2UVBtZpy3O3Zb7YoD+7Yk7eawmZMZE4pMrztcNaiTU5UgmpWUhUFfuiwXYdv2iSf7fpkglEGBMwmC8bRkxUlN+dX2G7g/t07xt4BIsmf5Un+i0ScqgXXtxzuarbNPWpaQFmSlxlPr55z/eQFWVEyhJHbD++ZLCZUZwtyLendSN+1eOcI0iNiZIyWLnjmRRIeuTAiTnJjH6H2LVJJjNIsygW5yTn2e+zYYHOYZBVkgiBEOqf6kBD3AY77A31zJMsleVlC8Dx8eMdhc0+wB1rbMpUVhA4fJEqXhJBo4/pUDiuZAQoTUlksACMFIkjEKfglBsk4JjisyTRSylPEv2K0Q+q2u0AcHSbPEVohM0V1PmOZnbHd7umaDi0zKq2wziKAzf0GvTBcfu+GaD3tsaYZBgqtubi+YXa2QMwFtXVMLyfYTGL6gHcjQUWCdPRixMSeevTYIlKeFWTC4DpLbkouTYWXnjF2jD5AnqaEbRjx/cB0IZgvpkiV5OM2dsTok5IWIPp0bEhJOsQQTg+R9LGMBqHB5JqyysiyDmkEMQqCk4nW/StevxabghACUyjEyfYpg2FoI2erGS8/+5zp5TnZcoKqNcO2xW87siwnVA3MJX048uDvef6DFwipmegpj+1H2r4lmxap1H52DVWAaU65NLgXadZurisKm3HctTy8ekS0cDm5YDJMUAGMFsxWMzKjGWoJo8XJgCkk03mBEpqu8TgbiIdDSv4hEjNNQKclHhSNCkxWFeKqYlyP9EVMm4FLFGQRUgJvJGCDAxwaS25b2m1LvXPYsmS+uKb67HuU84rDrmZ7f8/7n/5TLq8ukJ9+wmQxwxAYSERpT6I/G6FReZ4Wkwj40YEQ5FmBUhm9GSmUYcoMWUua7Zrt5h377TeMK83ioiCbzBiDYxgdWIceLFNV4bzmuG3YtQfOn9wwv76CzpINljmCnjF1/UOHCwKvBKurC6bFgvXtfUowTlGv6RkeBIpADAK0JkqDdkVyFUrL6FpGZxFCIbUhKyaMccCH07TCD/Rdi3SGEBSx1VxNzskmhg6LUAY9KPzeoYMmGAMIgpd07zcc77bYwTNZzrCZZtMd6bodg9aY6RwjNP7xSIyK3nvq0aI6Qzko5nmBdQ7femrf4WxAoPCjx+MIGoKIqCj44Xd/hzzL+ertz8hzT5WbJDcPI+14IJIRlcSZhshIiCEBAoQEeUqvJkCUiCBBiLQxFKByQESi8KkUj4q/UdRpRDKKCKmIPgFGNTnPrj/hd3/4e5xPzpFBsV5vuft4z2azRgmBE4EzLI/1hlGMFJOCsbPIuUFeZMiQs7q+4uL5E7wMDGLg/OwKPctRKok5JvmU+m6HbCJPFucUy4LS5ygjUDZSasNsMmEYenpSdzwMQwoPDQrbdLhDjwqk0k5LhFQIlZ5iEdKoS/XU68BtM9C3HcOYkoXz3CCipOv9afdPN4+Xlih7rG8Yw4ERi7cdzRBo79+zdZLbr9+z/nCH6zrqfgTnaRYTxlyQrabk0wJhPb63dO0R1z0ymDMyrRl9wMbAZF5QFBoxZJyXZ/hNz+1Pv2bz7pau37Af7uBx5OzmKbPLa3KjGEdBc2zx24H5wnBVLOnlhg9vv4Ih8mxxyVm5pCnPaGwLzhFCoJcBqTwhDry4WvDdT3+T9w8fiYMjP/38MqVJ1VOif2VZRpQwnrLGIgGdawY3Mo4902IG0TIpCno74PCoKgcpGcaRoe7p+oH6YZ2AzFKhtaWIGu0FeZ4jRWDf1Kwf77j8wQ9QWwHCYyvLJqxpmwYrLNliwaJMaU4+DIwO5GLKYlUwv5oyNRUhgAiC3kfGrseHSGYUUTj6oSWoQC41aImWkkqXnE0WaDyi9YzW4buecRiwaoQyojODlR5iSMdCkUbCApnumRhPDUWBlJI8U2SZQhuHG9L6EvJv2qYAp4xBAUHhRs/zJ0/5rc9+xKJYorxGekldN+zbBowiKwv0vGCUDnK4eHrBYAeEhqg8Z79xzfx7Z6gso9UW6waqZY7IFT4GusayeXvPmz/8mku/4g9+8/ewdcvD+weUFMxmE5QTSBcY+pFxsJAVVGWJqFuafce4HRCjh94mOlSEoppSlBU2JrAqSKwd0AjGY4eykdlkSnc8ILRkdX2BdSPNhx3epQ0hYglpHoKjIdBiCGQBdNdw/9M/46DfY5uA6UemSiP3LV3/hkcdGGc5T//W95idlUQpeLy/5/ZnX7L/+I7nL/8283LB2KW25sV5TmUMcQ1TaXj96jW7b95j7IgkYfqGrqH+cEt79YTy+oqoJJvtnv52z6rNmZ8V5K1EtxG/6bGPNQs142CWBA4oo2hsy0DyNHgsm8Mjz0KDqgyHvqUsJhgyohMIofAuAVN7Z0+8ThBSEhVU0xLjNW3jcWODyDMWizPc3tOOXdJtLJc8frzneL9GuECsLS5GMBmIERs8WUwTJO/TSb07NHzz1Vdkuebi2UvK5TRtaq0gRkm1WhCMoI0D8jLn7OyK8mqBnubIrmd998B+OJLNc+Y3Z0zVhGPfMNgBM4lMcoPINFFGmrrm7d0r7ne3id7kFZv7PfvDkf1xS2PXiOlAdRXQecTpACKmk7b8hU7hF9kJ31YOCqEgzxXa/KK56P8K3odfm00B0i4ug0GLku9+53N+97d/nypO0VbT1i3N0LG8Ome5nDHaHoyiixaZKawfqZuafJqjJwqtM5RKfAEhFMFDMEk9CYpXb17x5T/5gqf+kqmasHm3pvM9MjOoomCwHoVES8HQDLjoiVJTmoyXL6/Zf1yz+/CAtBERJTJANDmz+YLZaknTddR1gwSEFmRRwxjwfmTXj2Qm5+b5DSpXrD9sGKVPJSH2VCWMWNcTg0vNOAQZikwq2B8IMlCEAhmSaScSCN7RMFJUhrnJUM5S7x75+PoLHt9/hRxqXH1g8+GRPmhmixnLfIrvWw6vPuLDgfr+kaIPGCFwQjC61OW4f/UNXhZ8wm8zO7tmkZX07Zq79RsO6pGxa5mGjNB6vv7xlxQADRRhhpQGZJFm7DaQK8Hh/iNf/PhPUKTpx3c++5TSTHn75WuGdsSfxpVlWRAJDH3PYHuc83jl+fzz72PHnq+//Iqm3yM3ksF7skmFJfB42DNaTzWZkXuJ7TuasWe0IY0vCT/HcfhKUhgDlHx69Rm6VJhpzn6s8WPkk5tPmE/mWDxv7m7pxwYzK9j6A8fjyHl1iaw7Ht/e8XjcUi4rgoNyMSEXBpEpjLYgFO0w8u7Ne96t33J/+MDAAVNE9ruan/3kax7XG1y0xGwgW3rmXnO1gJg5lApIHRPp+zSGCCGmDRNFjB6ET5BdE0/KRoA0lvxVr1+LTUEgyKQhUKDVhD/4/b/P3/+X/hXm0zPoBPiAyCKLixlEkeLIe0VUgmgDRitCkKzvPpItc64/e0JxVtEPXULCCyh0TtseePPuFoVhXs35e7//d5nvC3jn6dqexfMrPvnkBTIIPt6+Z/3hAWEFmdEoUyKioG9b7puBcd8QnMM7h4mCKCVCKY5tTzM+4DixJo2hq2t8HxFBEoPHBU8k8rB5wEtHMzagT7u9c4Qw4oMlBshkgZAFhEAeNDIYcp1hwreQGYnRGutGkKlxl6sk7BrXO9599QXrd69hqMmBdruj795zfvUJ1/NzOFrevX5Nd7vDjXsYLZkDSURIxVzO6YPkeLA8vnrNdHHG5fKGZxfXNPqB4XgkDB0QU4Kyh3p7wEYofCQLFWCQsiC4I0CqqtqB/e07pJkjbGC92TAtUhoRCLTWTKdTrHcslktyo3jz5jXeecZh5OFhjc4kKtN09ZHY1jx59oJ8PmNX1+z2e4yXGAxKaWKRJNw6gpQG7QJYz4hltJ7BRYyrePhmjTDghWNUHq/Bri3jvGawHfVxzfTJgqtnTxhywb7ZI/qeTAnOz84oqhIyxUyXaGFOzAlJdzjw5Vc/4+vbWwbhOfiaJuwIWUO72XK/2/Jqfcv6MSlVo4lkfWAuMuRlyWTpKSqVNBMiVQ18ewfIBBpS5jSdUaBNQOv0f3b221f+atevyaYAwgeEzvi9v/N3+df/5X+Dz64/J7YRYUTaAaWnMiXdsaXpa6y3SC8Ig+O42bK+u+f9+4+40vNZ/xnf+9HnzOZz4hChDezXO5quYeIrZosFn958h7LPWf/xHaEe6ENPjND3AyEGGtvT+5HSVCiRM9Q9wjmUCzRtj/ES7yw+WoQpkFqiRUoWioDMFH1fU4WC0Y1kJkMJzRB7nG8Z7UC93bO6WlJOMurBAiNSBmw4RbaTn1B4GSmUMo3xtMwBhXeAhI6A1ZLzJ5dkMbDpGu6/ektvDxzu3yP6lgxPzhw3WghHChmZCMX29T2bnz2QHx3RhjSqQxLRQE4mlyehUcexsazffGB/veZm9ZTz5YL1fZfAvDiEUaxWC7Iio35YE48dkowMg8Sexq4BFdJYLdjUVFU64+HjPQe1JxcF0UeUBu8c4zjQNQ2qyBEhYqQhKsXjeos0KTLNZ5rRO/quQwiJGCzZmPIKekijVCMhy1iullyeX7F9f0e73zO4HqaaaVWB09y+e5cWlZLoKsMT2LsNh8LgXY/TI2aaEW0SbjW7PeHQcLY8g5MQbF4tk3jtsOfN3Wvu9490uzXvP9zRhcDq6SXL+YzLsxkH7vnZ61uOYYstanwx0DaW0EcaDx0jxXlg2UeWq5y8BJ1HsixpE9LNBsRIjB5jBNZETCYxmUCpiAOklCc1zC+/fm02heA8psj50W/9LV588h2U00QdCdFhjcXJnmhhpKfzPU3bMtYDw65lc3tPf2y5Pj9HLBSFzIi9Y/Athw87ju8PdPuWvCz4zg++y/OXL8mk5uH9PdvNmsJpXLCM+4ZdfMC5keNuS64UpTbIIZALDVggYIwmiAC5ZHl5hcoMj+st0afUg6wwjMGCEHRdgxQCbbKUguMkIQRMrji/eMKLz55yt/7A7uE1yiXRjQgRiSajImeCcoYUy9KCVLiYmnFmWpJPpnQnVNj80+d4G7j/v/+Ux1fvsP6IZMSEgNAaE5ZEqdGZpDDQbNZs321RrSKLDnWqPLTJkCJndJoQUsNWEanCSLs+8u7NLU/PnnF+eUlzu6VpDgQCMjcsrldcXV3x2o4cjh0KRQwniZIsIKSwQE9IceSFZoyCiVQE5xldj5GJ+DS0HT54tps1DRGFQRhNkBoKgzSCMUi878EHNo+PFPJIpg06yOSkFIFGuNSJLwRynicVovH4AkBhljnnz68wpqD54x193VBgmBUThJY83D+yqztyLRBFYLfZ0bz+mqEUuH7gfnvkw+wO7z2b+zXTasr0/ZQhdLy9f8PPvv6C5mHHdLrg5Q++z9Nn30GfG4pnklatYFJzeLVlOsLYA0EiZZoU+Wj58KqlPhiag2C6iMxWMJ0pMpMcalEA8ls3q8JkkBeSLAelRZI4/BXMD/+83If/HPj3gYfTy/6zGOP/evq3/xT490ghK/9xjPF/++VvQxAdiJDgHs56LB4E+GhBpVFdlIHedjw+3nP/7pFm01CFDBM1nz55yY9+77eZXc+IhWCIjsd3D7z/6S3rbx6YqinFZYldDxyrLYMd2D1sQUW6sUN5qIJmKgt6IsZKcpNjojqNTHOQgiG0DG7EjiNn5+e8+PRTmr7n9vEj0abjQu8sMpMspisOj2u0TN1yNzpcsCAFWVZwdXWJd5bjfof3AyGMpPZaAqRmeoqhgqBQRpJX06Rf8CPdODJZrXjxvc949fptErLkJW5sAAjWokNESkiODNBkRJOzXJ1R5TmuGbC7Dj0mv72QAoJAK4OUBusi4kRgMqHDeQu9Z9jWjHVPjEk047XAC4GXgc5bsrIgywukNMggk6cBgVcFILCMiVGB5emzZ3SdxT4eEztUyqRTkBCDh+AR3+oVpCD4AJlgsphRLgrascaKAV9btNLgI6F3gCYqT9TqpBP1FJMCWWi2xz1eRnwuESYjX03QqwIzLTnbn9Ptc8JoyVclRVliTcT2kTD2kDnIcsbeEo3m6vKKbYBNt0FrDTryxZf/lL7rePL8mtmy4OXTJ/TZEikN12fXvHj2CdWzimFyIC4rbNnw04c/4fy8ZGJyfJcxqy6YTRfUfcM//qM/pD5YDlvL/ExyVgtWlxmTmbz5Oo4AACAASURBVMLkIvUQILlYoz2NaQ15kWEyT68i3v71Hh/+e/5Z7gPAfx1j/C/+X0tbiB8C/zbwW8BT4P8QQnweY/xL65YQwYWMrLes7z6yfbpmVkqMLH6OYo+DoD623L655es//Rr70HE5ueTlzXPOVhdIKfn48T3HTUGWl+SzBdkw47x4wvRihhkEqsvYvHlkUz9SLHIuz6+4uHzB408fOL7a4XYj6+MjVkQEBeV0wayc0O6PHHZHqB3DyXAklEJNKno3slk/Ep0jSkXMJVEJZhdzumMNWYbtHTH0iYuJAAl9N/Dqq69xsaHutnhGohwgZBgmZHKGJMOKiJMj1WTC93/wQ5rDntu7t4wS3Nkcc/OE4fUdtJbhY8PQHwFHMGlmH2wgCyU6SApp2HrFfHlDFue02xo9pDN36uykBRhHkeLSQ1Jp2uDwp+NEaTXqsef+J2/YH1oGAb7KIAc1ETw8PnBWLLEtQEkgYhAYmaVQWiR9GHCyx0vJi08+IYSM13zF4X5DqZL2zocRiAQZkJlEhTJtxgJctFxd3iTOQm+4Pn/K9s2G0IK3Kf9bonDRY7FpgiEVs+mCm+trCpHxIbzl42ZATTRnT68oLxY8DjXL71zzRDxjs9uxaw50s4wnP/g+ZYy8+eYNdd0yWy2Zny0pLydMLieE7z7FSYv3jtdff00zbDBtpFwYlvMpn336CYqKzW5PvqioliXFvCRWkUaCU3OiKLi6uKa6mTPRCxbTa4yZ0o6Wn375mvvHB44t9LtAu1fs957FjWV2pphMFWWWobAIGYgMYAS6BJUpUJ4g/xq9D38R9+Evuf5N4H88Bbh+LYT4EvgD4B//ku+BkNDUR/7RP/rfaWrP3/v9f5WLxTXKCXw70B6OvPrZK95++YZ2V1OJgsvFOYvZktViiXWOD+/XdLZlHB3OaKLSjO1IbmEymVMuJ8RpQC4l50/PuVhesGBBaGA4WMYTSsz6kLT2MoL3pwRnn3INRkueZZhc0dYth8OBwyGNF6XIyPIMIR11c6DeHWAMCPft1zMEGdNRxfUMbsDTgvbJbBQkAoORGUZlKa48BoSSKG24uL7GDgN2sJRFwbKaUaCQvaM/1HwYG0IcyfMMHT1DNxKDRJOT6QwhEtJtOllgG0dXN2ilUMIQZTJhGWUQIeCsTd0FpYkBtMyTOEgbxs7y6ouv8BEuLm8wUlMtK66fX/Hjf/LHfPWTL1FDOuoqqRCSNCsPkp8LbgMoBG+/fs33P//bzOcLdh83CJk4jvHbxC1gtVgyVTPe3r3HnUIfTJEzmc2QpQElyETB+1e3tH2LCooqq5gtZjgZoK9p+pb7V6/xu5r5bIEdRnQhGcaWD6+/YT5eIFcTPm4f+eTlJ0yrFceHAVUZzESzfdjQhBGroVxOWV1dIKcSnWVQSqRMnpHv/vA3yeZTvvjiC/qxZywNblJwNr8gW0354s2XDPeBm7OnqFnSGrjBkbFkMr3i6flzFpNzptUZUhfUXcf5+RPWmz1uGBhCispvWkdTj6yuDMvzCrcQVJOCLDOAR2lPUQnyqUPVntRc+dWu/z89hf9ICPHvkpKa/5MY4xZ4RoLDfHvdnj73z1x/nvugFcToTiNEgRCC5ljT70bsoafZbhmbhnp9oBAFz1/e8HT2hOv5FVho90fKquR3fvC7tIcj7+/u6KVgfnWOFpLCCy6uL4hLhTeOWARMZfAOvn73hseHB6weMXOB7VNwqSFjt11z7AOyj4RuxPcWiaBQCXNe5CVjsPTFQNe39L7Hd55yUbJanbH5cE+GoShKvE15/zEkkZKUiU0gBPh4yhUIBYoCrXKU1GAj8TR7GtqBL774gsP9A3FMWLNuvWfNO1TnyCwMvme6KDm/uaLt97y77fAYtDQoYSAaLs6vIArqQ03f90yzOWVR0dYpOdqoHKkEUtiT/FoiZIRgEGScLa+Yn5/x5vYWZx1xGFDBUynD3FRUsqBrW0ww6J+7DGIq+9MwDYMhkmTN2w+P3IpXHOsUzOqtJ5xsP1obLI66bRhxSCUpiww1L7m7vyduH5idL/jk05ecX18zRsfD7Uf6bYP3R2bVjKuLCz47+x6Pd+/5+O49h80j6/0GVWlWlyumeYWIjlKC0hnHRcmDOzKZT7hZPSdXisxkZOOURfQ478lWE+ow8OGbO47DAZFJpssp55cXVNOK5eUNn5n0AEAJtrsdH+pHrq6veDH9lJBHnPTEALdv3/KTP/mClXnCsjpjUVwwL1ZUxQwP9AKqYorRmqFPuLrgoR/BNZF+azkuO6pLxXKVsTpXlEWGEFBNPNWsIzuAbQTuX7Ah6r8B/gGp9/kPgP+SBIX5i3wXf+E7+fPchyIXUZyaQc5agk1cPTcGbNtz2Bxpj3tmlFw/veHTyxecF+fUDwceHu4ZhoGX3/2UPM/pVcvN8xv02ZTJ5TJtMoOlKDP6ImBHi/UOBgGYhLg3keyi4uL5ikoW2H3P8d2G43pN3A/kTsEQcKNPsmAfiWPAD57z63NmZ3Pe3r1jaiZ0XY/3lro+cnFxga0tsU8NvBAigSRNVYTUDY4J7BKDQFFR6Aods1N/JRJCQCnFaB1vb9+hhkRzlqPn8P6B4aGG2pKFpCuYFCXToqJt9yk3QJYprchrPIInT1/iRhjqLiHPtWI6ndDWLQiwLiKFIEaFCyeXXdBoMjyWxWTFzeVT7j88EIZAuzsiQ6RF8mGIqCFSygx1SoEWEhLpIHVLBAZFjiYSAzAEPr55hycio8QTcXzLtdCIGKmbhkxZqskUUWY8/+5nrNsjX9++oekHynLG+bMls+sVWZljjz3Nes/2uEYXMiVyK01Z5LTWM7meM39yho0WJwLn0zOmsxneKC4/uabHoyYZZZ6hfMD3Fisj5WpOUZVIo+jGDmk0sZcc1gfGQ5eIX1cSpyKzYk42K1NytJDUzQ61yLkoJ3Shw2rH/YePvPnZa8IxcDm7YqaW5GNFlJIxeoIKuGGgrxvc6H4R2X7CzLsBmiMMO89mP3JYjrSHnOUyoygNREVRKMoSRm1SduWvcP1zbQoxxo/f/l0I8d8C/8vpw1vgxZ976XPg/S/7evIUZ20bz8PHOzaPG+yLEeNLFpMFahVw+YSFnnG1uGKazam3Rx7vHrGjYzabIxF88+Yr2ubIzafPKS8L/NQl2Ioe6enwVrF5eEShOZ+vKIsCWUmG2cjYjszPljy/eEL9YcP29R3ej0gCLgRiDAid5uJSa2IU1HWLmeQwkQgjyaYZres41gfW24FnqxvGaHE+Lf4kWY+pVJfhlJ2XxoCSktzMWE4WjJ3Fe05TCAgSiixn9eyafr2he9wiLPjgGNoWEyQGhSAwNh2bB892s8GNAU2JokKS4XEsJmd82H2k7wYKpYkx0Nsh8SyEwAew4y9unihP46ygyKRmaDp29xsYQQWJby1CCOrhQP1YI1Eol870MUQCkSxPCUi27wFFQKNCBghkSNFqycknkoZBKrSQhBhAiESWlqRFPHi6fkBJgyZj2Pc8vL1H6ogTjvl8TjQFw+HI+rjH9QObuweKoiAawerJBTe//V1W37nim/ff8HD3nmiPHHYd7eCYqmsunl+jpKA5HvFNmmwMVpAVOabM6MaOpm+ZTadcX11zPB7p7neMDy33hwE1ycgXFX4MqFIzy6bk0wxZZljhGP3I4bDn49sPqFrycvIUhYI24ocuWcPznmA8Tb0mDC0ySHSSXBHCKYwVCF4yesA6trXD1p7j0jJfGHQmGYYkXPoX7pIUQjyJMX44ffhvAT8+/f1/Bv4HIcR/RWo0fh/4P3/Z11NaUeSGMCTNthTQ1A3aeorKsKjm5NMVq2wFo+D2zXsOd1vkAN95+Snnl+c0fcNhe0hhdErgpaOLI1ZashIqkSMagRwEdA7X9NjCpNSe/cjD3T1yHzFNpHtI9GklJabQWDGiswyjc6Q4nfVDKow32y3Hx5qBHussQ9djtGSeTbj/+BHhFAqDEikxIWIJYSAESxrQpDM/5JR6QZWvEGNNO7QIZKIGCcl0seTpk2e8O7ZYeRpF2eQmzE6FukHRHmqOjaW3Xao2ZI5igiTH65H62HHYHsl1xjSviC7wsH1AC4WSKuU1ek8M/qSXj3ji6QiRxn673Z7gvv2+Cn2y8roQ0VLDqdEXZMAGS5ZpFmdL2O05HjcQsiRSCqDQKJINOuWGSYRWCG0QBER0aC0hFzRDz9A7fvJnf4bOCmQQaA/i6Gg+1vSuhemUsWnYfljDkMjdXgfaccCsCi4WK6aTEiHh6uaSwfe8+fobhr4nzwsm7yxaCjKl2T1u8M3AfLFgeX2NyiTj2DOMHV19oN5G1Dlczy9wruL1mzfcvfuAnmacv7ik9FPGPjX9xnnA9x1eeJq2Zf3+gXCwrJijRoHzHWO0WBFRk+QgDaMjdpYnywuGPrDZP9IOHaMbE5dSnpo1PsKQKF+HLtLsRuqpJSskUcDQBexfZ0/h/4P78K8JIX6HdDT4BvgPAGKMfyqE+J+APyO5L/7DXzZ5gPQkyoyil3B+dkaWZfRdxwQDHjQaFWXy2j8eebxf4xtLoXJ0luFCwIZAGSqsH7G1Z+IURmUIIzEyYkKGiJp8MDx+fcdm95GzxTnL8zPsesRueh4+vuf41T2ic0x1wex8Qnds0JOcyWSKcCmn3/Ye5SNIwTg6hAalFDZYJrOKp9fXtPsjzUOdxDY+njwNI4GRgOVbc4rCoKQESrxVHPctfrRwEhG5EMmKglwXvHv3juNuj5EJzpJ6Ezm4U+hqSEZaf7IEKwxaFihRARllWXDcNYyj5+nqgqnIWD+uE60pREJwRKkRKuKD+3kqUiRgTq+JIaSehtQIdPrdoIkh4HE/X9jIFLwScTgpyKYVF9WE4Xaga91pPxTptSfDON8+CV1A5oqqquj6FiUi0+s5Z1rx5s1bur5HY6iyChEFftOzPjZgwBcdVaaZ5zOaBsQoUU7hPAyHnsc3d3SuoXqcMX264sX1E0II7Nuaqxc3xE3H45e32HZgaAaUMEirkdmealGRV4bMVIRhYP1xw8dv3jPOOubTM6rpkrxtGdzAbntEVgXGZByPNce2YXY1R2YCHRWr6QpTR9rNgXgcicJjCoXJky5ERoOLjqlyfP/l58zPrnjYfOR+95HH3QP7tk4WbDGm5OugEgQmeIKFQxNRmUfqhKe341+j9yHG+O/8BZ/+7/6S1/9D4B/+yu8AQCTb8Dh4jq7mWB/xK0vUkRhjYvIhCS6itGEynTI7m3I+XTJbLri9fcvHh3tm9xVjbJO+vdDkcomZG0wG0irsfqD7eKR+t8ftRvyjZf9hS9CC69kluZUMdYfJS+Z5lQJBYmS+WqK0ZvNxS8gFVT6h2dR0x5YhdMyvZjx9/oRtfeR8sYIYOez3zOdzSjNhtzkyjCNJ/JR+OfIXUR+nPwVKpIzEVDCnZaJlit7qm57tvoZ+ZJLlyQyjoZos6OsB1/WARAVxckpIjMwxqgAM8RRdb8fEUmyODaNrKcqSp9eXvP36Da4b0VIgtMSoHG0Mwzj8PPVTS8UYPPJbKfJpQ9BoPB5FQCJT7DgQfFLTDYPlcKhZLFZIZfBIlDInY7lKDc0Q0SYdzQICZy2jlcQITd9RuAnPXzxntz9i7RbXDRTFglzlHJsducqYZBPGsSNThmp6gd8nB4W1njA4vIjobKD5YHl8+IC8Lfjkt77HiyfPOcPiJ4KZrBgf9tjWorzEZJru2PHw059STAtunl+TlxnYwPl8hWstXd3SOfAiks0mlOUCNdVs9juMyyimFUUOznoMksLk5IXEh5p6bxFtSu4SmUaKVDVGL3G2Q/mcs8kFZIZyopmf50x2Ge8f71jvjwzOw+BhTKlMYUy26oAg9Cmj4690duDXRNEYAmyHFh9K8CNNd2CkJ2hL61uEBxsNZTT0Xcc41FBlyJVgyC0He+Sw3VDeS1QmyErBeNdBpoljQTbNcAfL4c+2HL54QGw6VITBtQQbmOqKmc5RPlJOl2Sy5LitOfqOi6sVN995ivU9j/WGrmlZTCao2sFhoAgSU0fiR0shM9qmxjU9fTPy7AefJaVdvWXswylIJFnDxenpqDHIoEGWEA1SWqSMWJcqCSENYdS40aMChKBPoqKAEImI7a3HIZHkQPyWkgBqhlYTgoZONTSZYrWHWR0R0jKGiAqS44cNwib6UquhmM64vLlCRXj/+i0cG4QscbEghh3gTz2MVJ0IGVDS4/8f6t5sx7YsO8/7Zrva3UR/+syTWVmNKBUpi7ZhyzIMSX4BX9jwG+ghfOV7P4IB3wm+86VhGLBhAoZhiTTJoshqMiszTxt97G61s/PFjCzSlAwVJUIoLiAQ+0TsaM6KteeaY4z///4IRmt8lMQoISYMCjnDcDPB/gHRKdqwIuARRLTMBYrDU1YVZVEwdCNCSryfCT4Qg6D7sOft+I65S5RUqJQQu56JHT6OFLHCjRERNL2PxDAwJ4k0CYqUFaEiYhCUYsk09HTXE9/IN5Tnd5ijiuJoSbtuOf/xS9rtin4/UKgCrTTiJrte3/z0G0BQVCUXF+fURw2xOHCIB4RWFI3BlJaiKRk3PcPYIwqZS9qDx1iDUBK6iNwLzKixsUAqhdAKLbNTdN47vPfMzlNMilVhKVYtej3i1y1jW+OuZ3YPA1OAqB34vAbImH71uoo8LgzqX/XK+1cfvxGLgnOBzSZShkBKM2/ffU0MgtfPv8/3nn2BEgKpxGOC78zDzYb5fiKMgqcvX/H0/CVm0hzeP8DsiV2i9ivS6Ll/d8374QAHz/BuADdQLC2eiF42nD9/SmEs/jAy+0ihLPM4s9nkoJWoZkbVE4XnfnhA2IRdWIpdgetmZJAEl9g9HJCNJc4OdxgQEbbbLcE55jlbqJG59s8x63l5EBgkmhy/nj3vESCKjNaTmTjkoweZZTmzT2giUsLgBtzj1lt8t9//rniQFrRF6IjWlmJZEXYDyXm8BKE1wzgwHA5YW6LLgtkN6MKyXK1wQ0aXaSGziSj5x7FWyJ4Ul3AyIpXITcFsGkBFSfR5N2SiyL0KFP1hyFBVbYjf0ZiVzBqNEHDeIaUikR6rD8UUZmKM+OC5u7tjTFDZkrY0uMOeKfTMeJxPtJVFC4EbZ6JPSCGJKqBrw9OXTxiDY/dwoA8OWZRYrfBOYFWFNXU+51oj64JKrWmXRxTC4J1HKsU8esLkebjfMI8T/TDgRKBsS6p6QRKCYRzo3YiJisWixbgxd1xcwvkATjC4nv3thnGzRwudmQ4ml1tK5nMRncdNE/M8IYRAR4mVhsZWrPWCOZ2QIggP9/2QuQky/cr39Cv94nfmyL9CENxvxKLgXWS/FRido9avb99x//DAze0N3bCn0g2vX3xGe9ywvFjSXdZs3j2QZkOpVjx/9YzqteV2Z5n7jsH3bLYPBL9l029w48iyXVCdtRy9yLNpJyJq1XJ8cQ4hcPnL94z7kcNhoA0F60XN1fWOdx83iF6xuFhRtAZhBc5EyqMFeMW0GZgnn9v0LiCFoDCGODneffsGQqRE0NaLPHefR+KsEDGPKSHDFeXjPs/5XEp9t+8TQqG1yY29ssA7QXQT4XGv4eV3bvo8p0rSZ4koeZIgIviYKFcNR2dr9u/2eOmQUqJ0Tn6q64qmXBCVYNpMzPuOuw9XzOOIHyekkBj9eHXJ3DtwwpOMpm3qPAXpJowx6DaXQWE74GePkDld26AyGs5aIGZ4CooYpsfy0DD0E9PoscYSkbTrJVJrttsNuil5+vwZby4v89AmBTAabSpmJlyrGWxCHHoaVdFWNQfXExqNPanRpzWFcBAH7h+2GFnT1C1mWVE3a6pFS6ohScmhG/HzTKMrJIJp8kQJF0+e0FYtN9e3vH//jofNBrfxPH1+wao9wRPphgPTPFLUhrYqkSIy+xkxgXCRxMB+u2G/2aK8pCorKlWSVCLEQBwDITmCDySf0ClnnwoVsnQ9aWpZclQuicvIvJs46Am0wsf5cTrBny8GPD7+m5b7ECMEDzMzSkFZWYax46u3f8qby28oi5a/81s/5uaTa87SCoqRndgx7gPFrkYeJCk57JOSOhTUY81dt2F7O6Cs5sn6nIuXF9RPj2mkQUtJMIpQaoKWSC/gQTNfBoZ5j1VwfLxgM2h0vaR4umT98oxKKrYfH+i6kbPVCdprDpueOQTULB671o+avQQxRLSUFMZQFhUJiSM8zu7h0R9KJHf+pRR4LwlRYox5hGjkJwsJxuZdQ846UPgY8CESVRZAOT+BzOEpCklMmf8YSCzahqK23IYRiESR78ZlXbNaLalUxW67RyaJHycerm9znY9EG4012YcrvSCKnKchdMnidE0/9uzmA+iEWVQsqhXjdM00TxgUKQRcilkaLgU+OOqywEdP388IIVGmwKBwLuBcRBEyck1JbFFQLRqKtsaWJfM4EXwiqYSTUDQtnDVM/ci87TFJoIQg4IilpX1+SmcOJBOoX6w4qATCMBryePDDexZuweln5/l3WdbETtB1M1MIMPtHUPCMmidMYVksVwzTQGEs/TAwXl3lEiVMRHyO9BsLnPPM44QZBFLI/Pl+pEyGsiofdSkqTx5Gz+xHZjw+ZaFcZUqEzVdJmCfm2VDpCqFAloK+6tjYPUFmfuavyoW/uCh8dxn9TYqNy7goSCLDzpXJHP4pToxxpht3/N4f3fB7f/x/UM6KV/KUTxcvWdZnLOoWQmRz/4B4L3jSrDlqWl68fsGnTUVSMkNKagW1ICnBYRrpZ8ccBWWV5cLnLy+wO8OHQ8/7j++5F5rlyYrPfvuH+KXmw8M1/X5EW4X2GoRgDi4bnWSWaaMlRVEgZGByE4UqsVqzqCrmIdD1HUSJRiOVQiWQSpIxOqCVYvAJtGJ5ekL0gcN2R4oO7yemwwgR1usjtNE87HeoSqK0QKZAnCJ+9MTHq0BJhTKGYGZMa3BhYprz1lapfOJDivT9gIue/WFP9D5PQ3xAJKjKEi1zLFkKGqlzClVZ19h6RXq0JaumoO8mej9hokPYXEqkBCEFjDSYssR7lzF2IRF9wIVAoQ11WTFrRwhdZlyGwP7QwaNW4W7zwNV+izKWsiwok2TsAsZKqvM1+tkSrTTX8S0P7x/o3EAoBKW1rF+e8uLZJyTt2W1H6rM9w+i5vL3KtbhJ7LYfuPrqmr/zH/4OstIYWyOKCH3Ei8T+oSPeXFPbmrqoaJcLbG1ZH6/YHjZcPzwQRWS1XuLjzJdf/hwjNZWpkUFgU4PRGaSKKMEKiqLEaIufIswR38+E6LPyVUZ0kUtAgcD7mbEvKGONsoK6rjHCMNaOfTMwbPaER5R7DOlfXgB+fcbKb8aioJVkuTTYEJASIpkeUxSCOQUSAS8cHYneQFPOrE8UoZr5Oliqxe/w9PSC1fkJZgykwSEKiW4s0lisVjgVGb0n+ZjzE8YB99h8SbpEx8Q09tzv73GpIxRFrilP6yyAUiCEwo0T46ZHG42PHlUZhM+juml2+HHIzMHKcn5yQWEMlVDcX23ynVfljns2RkmW6yXBObpuS4gzMzP1csHJ8ycc9jsetg+PToAZ/1hzn56f41NiN40cXZxgaos0kt3tPQ/XH5n7EaUsxpakQjPriaQD3qXc/NMGYwoQBpzAec922JNioKxqIgnv/aNRClCSyXlG53KiuVK57u0ntvuJJ88uqNuWL7/8ku1+ixsTyUfqtkWlxHDY/Yp0lETWISRifh/FI3DUYqXBlZF5HhFCUJYlMeW5Pgpss6BdrRi3HeM44qPj6PSU17/1Q67ZIYSkOl0w9iPD6EgWVicNutIILZkf/17Fcc1+u6GtV4wMzLGn2++ZDgOD+oJq0SKsIDzGvBd1y8qfMHZjjvkTihgifX9AGIGxlmV7RMBTlJrZS6qixHUOP86ZJyFy8KuVCqRFKEmhcrJ6mCfG/cB4GECB1io3N4VCJsV0GIljQPWaiorCaoSQaF0RSk2/CByat1xPM3MIj6Pef/M0+d+IRQEytDMQMNqilSJET2Ggrip0XdC7ieQGmkJRL2BuN4SmYKtvSNZzvD7BxgYYABCFZNaBJB2T8Bl1Pae8mn6XOCAVepbMdz2VV5ysljxcHHMpHdXZESdfvEAuCxayQFlFuBu57285hB3bboscecw2DOwO2ftvo0CmSAqe1mq6aeTQDYx9phPlnUEeOYYU6MeBFDOAxCfHxEzyAx/urzLIo4B5nEi4xwUTbm7vmEIEbbB1xcRMXZagLcMcsKqkKBqUNYwi0BxVLI5zkrQVmkKXaFngQu5bKGNxoUNrRRSQQqRtWo5Pjum6jr7vmX3A+Yh/vAPtt13+fZKieFlgKo3SGqUk7bohjZF5mBmnGVnlNKhpmpBKotTjbStTSIkxMc8eZTTWlsQUmZ1jnCdOz04om4rr+xtSihTG4KRkdBPBzzxsNxwf9px/doqPgd3thmJckYaRMUxshx1//JM/JKgZVRnOX76iPTvmaHXCZCZUm/hw94Zvf3bP9c0lf/r2lNOLc47qNUoKphApkmF1sma1FAy7nuAiIQb2hz27/Ybnn7ygLBb04555DOjS8vrTz/n4zQeG6wOgiepR6q1BaoGIWY8RnKfvBuZuJE4BU1msLVGFIqhESIk0JsxgaF1LSCXOOXwM1LXlSBmeLiL9mSO5wO1+wxRCln+QtXzwV2op/GYsCiFGum5Gp4hRAUyurwtlaBY10UgG31MqxaKpKBeaWE6oVeTVs2cs2hb6xM3dLUe6YrVaoGqLKzWjzOzC5EAmhUiPeDclEDExjR3awbCb8ENEW83Tz17w7PULnjx/SoyJbrOnNBXHT16wEku+2X7F7uEeOYKVBmEStpIkLE9PLvD7jn13YH84sN/tULPDBIHRRTaq1A2LxZLdwz2Hbvc40vO4kOvJae6YN4FnT59w1pzz9sufo2TCKIUSis1mw4zANCVXtzf0YcDsLGHnkaqgfiVDegAAIABJREFUNCWVrXAIpIInzy9o14Z3Nw+IlMuK5EFrjdUlmow+izJhlcn+E7LvYbleMcwTwoP02QiGFIxjxOOwxrC726EKUEJgjYGU1Rj9NBGdY7FosxDLZ3txeLyLKQ3S6IwTS4IUBEIqpNJY0mMiVkdMmadBjJTWEgtLlIpZCPb7HT/5yR9zMT/l/NkTmqMlvU8wFAg/cre9gd6jjMCrRO8Dny0r2udHlG0BrefZxRM6eU8UI//L7/3PvHr9mr//u/8xF9UJUzfg5pHKy6wg1QoZBTEE1kfHIEJOy+onAhKNIKXIsl1TvLS82X1Df9vhqmxii8T8/4mBacqMjbkfSCGf+9JWlEUJVhFEYIwTpSgpjSXEgPOe0U/0w4wSI01jOG0iB7/nsN+z77vs7YlZnPBd1RD/CuOH34hFIUbox0ihYHCBwuQVThuNEIZ9N3AYAqlJqDaStKQ1J3yy+IK/tf7brOZz4r5A+REqS6wrhLVIqSiA0Y24ecKKSB89SUREkNhkKbyluz5w/+6Gqe9ZfnrE9/7u55w+eQKD5MPP3nP95iOFKZieRVpRMg4Th92WQmrq02NkYznsRwohSa1nTomkS7qHgWooaHwBMpFS3j5LZRDW4rTGPbogNZ4oJ5SRtE+XrM9PefnpJzxc3jC/EyxkgY0Gos45igFin5iGgRAHghyxZctaHyO9QlUtvZoRRwJ7ZJinkfixoyhqgtQEn7BWIYxmCpGkNKaUrE/X9EPH1eU1w63j/OwpiAL0DFja8glVU/Dh/TtwggpFuBsYU6SghaDoDju01OgADsUUIpMUzDLnHoqYKAqNMYrB5UmsIb93AsCgo0CnSH/o6OcepRQ2StKYFRmmqZGUzPOBw82BN8MvGe47Tp5fYI4k6VlLXR7hv/X4ux3LRYta1iQreLj+yP14A0vJ0aszjp8+44dfVCzkET+9/Yovv/1jjpaa8OwHFI91fHTQ9xOLZpkDi4KjrRoMObkpiogKGrfxDO5Aer5g8XzFcbrg8PO3bG4fOJIrrCuRwjIGzxRGCDNV8IRCQmlzKViqrAhNAuNz0mawGTrkxoBSOlv5YoEaHMdyyayfMTUz+yKjCofgiVJk8krisZz4G4Rjy4fEx4gPjzBKBT4ExnFmf+gZXaBdg9UKKyqW9oKXq+9RuiUMEpkEbd1SlxUgOey77LvXluRjtiHr7DoUSqCFwiYJc+D2wzXTvufs7IRXn71kUVXsLm/p33V8+we/ZPPhDilhu37Pul5y9fU7XD9DXWNWFc35iv3djBnh8vojJpYoLPiAtTZHrsuA0IqoJNtuy971xOA5Olnjp4l+v8cazfmzC179nR+yfHLONA7sdztAooqSNGd7b5rTd8oUjFI5akgm1u0S4QSHhwMER7CBclkRZOD++oapm1hUS1JUKK2w1mCkpHczLk7I0mKsRYYR2xRoZdlst6SUzUBh9izWLYtlw83VDaSIlRoRwSiLTpI4zaQkECpneXgB8zwzx4ASkrLIrMqqLFBSwD7DXmPKiPJoBMWiYqEtru+JQ4elJIVIt+tx4zvaRYNQgnq1pCiWxPsbKGB32JPuBYtXp5x8cs7i7IjjkyXf/OGfUjcNn//tH4ARfHX5LW8+vGX7puNieMFrPqNdLHh68Yr//D/7h/zzP/pn/OIXP8fdTbxaf8rKHNMmMitDC4ahZ3pE4E2HgcqUrJZrpu3E9buPJOMpzwylNDz93jN8Slz2v2Sz37EUwKyYfGBMA1JMVLWmKAtSrZGFBaVIUpJCFqgpoUgpkoTC2KxriY9J4NMwkmSkKRY8PXnGzh3YvR8Ywh6+ay1Exa+7IMBv1KKQF7QQ85sxGucm+t2BbsiNvloajDO0cs2Jfclp9QlFWCC9wsgctWmUZhwGrj5+RErDar3OeYox4LQEmbDKUktLd7fl2598xXTb8Vvf/xEvv/eKIfZ8+ad/xv3bO4Zve7hNLKMhhRm/+ciDuaVUlupkSTCSTRoQomJ5fkS680x3EzUWEQyCwBQDSUVkqTh/fo4LgvfvPyKD4+T4mFfPnnDYbnjTX2NkxcnqnErWHK53/OLnP+P2/RWVrBj3E8FHVosSXVp8dIjZZ/6j1KAERV0hveBh88A4bQmNYb06YXKOh+0WSBREBILSljnbQhj6vscYzTyOvH/3DltZnlw8oTQVt1f32MLSLFru7rNwZ5pHYkiIBI6IjB5tFUpKxsmjpUJbgZQCbRRJCKwp8MGxPl4RnGfY95TGUJoaow0+5BwKpMJay+npOfeXl+wOe0xZIUMelSYX2G02OOP54vtPWLw8ZbpU6ELh/UyME7ZQ1JVBGUF5VNM8W1PUNZ2eKJuKJ6+fI1aWy80Vu7sN128vUc+eYZLBUvP3f/cfcHt1TXfT0fcDki0AZVWTYiY/CR0Z3B6fZrYPN5Bm/CEQfM/ps1MWqxqpE5MfOH9xhpoiX//zn6P2O1QnCUkidUTVGiELhLDIx+i8kFMDiSHnZ6bHBKgUAiIoiJnFkB4xhkllgE9dtRwfn7PYXbEPA3PeZDwev/74QaT0b96l/Os6hBQJLZABUImmVCzbAqUSu8OArTTrkzXn65aXp6/47Vf/ET84+/dQu5paNNgEyU909z1K5Ro1pxJnwElKgEyo0uST6SIGxcPHa7796Zc0ynD65IL5vKBeGk7WK9qhYPMnd2y/vKe/33H0ZM3p7zylOTtiEpI3Hz9iy5LXn76iMpo3v/wlt1/t8DcD4n4gHEbKp2va45b9/oEXL14yz54Pbz8++gUkcnTEcSCOA8IJRDRQGJyIOSLNeV6/+oRPXrzi9//Z/83eD5Sm4Ec//CHRR376J39KDNmZ2CyXrF6e0fc73t28Jawk8qzgk996ze7+mqtv33E0nfLFi+8zH0b2dztESFlyHQXOjYzBoyuLMLlpaKzBjSPeeebZMU0zbdlQlSXbu3umcaQsSgprQQnG5PE+8OPf/jE3Nzfc3twyjSNN0/Dqs094+/5bqqYlxMR+s0MExdo07O83uDRi1i1m2SCExO864uSQSiKLAjFLpnEgmYn2vOY+bhmbxPf+gx/TPj3l9u6ecBiYD1uubt5zEDPP/tbnvPre9xi7gWkckN+BTmNAGoMtLElkqOl2t6cbegYz8NDd8/zlU+rCcv/xmtDPiE0Odw0iEmTCR49IiYuzcwpjcVNk2E7MQ6BcWcy5Ra0kVd2yMCvGb/f8/H/9A9Qve1ZdiREls4GpClADpQSTmdfJClSZiVJCCvrdjuQSIWRPQyAwxpk+9uzcgYGBeAr2uGAsRn728Wf8wc//mJvbG+JjMxkJeP/7KaXf/de9Hn8zdgrp8U2qjOWeA9YHSqUoa8tytWDZLBGT5mLxitdPviAeEtqDECmbbETIgA7vqApFUVXIlAUjiNxXiDJRmBJpLTLC8uSY1z/6HlrkVOWj751z8ewUKyTTh45NeMuV2yAXcPRqgft0xb5USGlZledM3cDHh0twM5M7sDhv+LjZYNqIbirCApbP14wf9igrGA8jtir47NVnXL15n2Wv5BGpiAZtLEIZUvSkmN2jUzdx+/EaKy2GLGjpu455nnHBIaRGZGwv+67jMO6Z5ER7vOL4e08xC8NpeUEhDOM3DlMWHHYH+jCBB42ksBYfE6v1mjl4DtOEEIkyClSliWkkOIctLUiYxhlrKqqyYfIjgxuQ0hBFIurEhGeIM7LUNLbBTTNvv/mGcRzouxFpNYv1kkKX7D/cMfoBpSFGjwvucVeRMzDXJ0f03tFv92ASYmmpPjliIQu6/Q1vPrxh5Xq0MJTWIoylahtE0TA5x4d373l+8YyVbRj6gf1hj08RWypSyvQray1Pzi5wRK77W7ppwLlIqgW61QzzAVUalJaEFHPpoEAiuN3fcn5yRnW2RpU1c+9IJuGkI5DQ8XESMCfmw0QdJdYUaFGAhaDn3ECTitm7jOjzAoVHFxqhc78CmakbUuQRrkWQJKQg0CjG0uWSsNCsj5ZUVfEoio1Z/Sz+hjUavzviIzgipMAwzUhtWS0aFu2C5AOFPGZZnqMpmQ8DFssUBqL0xJgo2waBIIX8h/suyg0gkcc7s/Ok4ClsSXNyxOrsGCkTwkKoBD7MuNlxc3PFx8Mt6sRy/uKC5tMTfCsIRJSfmPoD15eXJO9YrlvqdY3oBO2TilWxZBxnquMl1aohXAUuLy9xQ6CpFnkUlxLGaISxBKUIIWGkRGqFDlBUJclHDpsdw75DCYEhOwvvbm6Z5okoBUprdFkiy4qQHFOYGOJADJJnxyXVuqYRFjEEJnHHw/0DnogsNLrSHK+OEMDlu/d4kZhjxEmRA3asYrGqCcpRVBVhgukwoqNAKJ17BnOHLC2rkzXbbs9288Av33zNPM9cnJ9Tm4J3X39Lvz88shZyO8TUdc5IVI5Ugneeqiyolgu6fZfzlGPEuRw9nwjMyRGsYn1sOX56THoo+fD1W+7eXbKoV5ydnvHi02ecvHzC3jgGApubOy4PbzmyS9wUIQRsaXIStxvwBHRlWRyvUEbRNEsunrzAthJdxoyVC4qkBS7m60xXlhQhTJ4oBB9uL1H3DyzsitYsM0AnzDAFUpwJ80DaToTdjAgFLoHQkqATUoFIkcctC1JAkgnEI3hGSmxlsj0+ZZVfFAkpHFIppHgkUtQ9wQSG4BAyoVTKUSGkP1c0/prHb9SikI8cWuFjFgvZMjP/pmHghz/6XS6OXzIexgwtTzPBRyY3ETCcrBtEEvR9TwgBHz3eeawtsFUBhWIePOPo8EiKpsIXkpCmvKh2mYTU7fZ8+8uvmP3IFz/4IU8+f4ZcKqIVMEaG3Zb5/oGw26May/LJCbI23P7ZW85fHXN6fMG7y2uevv6E7Zt3TFPHtBVY1TAPI99+9TVh8lys1wht2LiZzk+kR2ORMorj4yPGbuCm2wOaqm4zuyEkDrs9SSZsaRG6IBpNsVgQxEA4BGShqJYVUQdkKbm9uuP28gopDP0wUFc1VVNhbcHp83Occ9ze3TDM4+MFq1GlpVm1qErBpBBOoqMgWZsViZOnHwdc8BwvlpRtRTf3SK243z6glKJZttS6wBiNCmAoiEbgjaAfeoZZ0J4smHaCaddTNRVt27J52CCVxMWUk57qirKyeBUZbKCzgcXZgucnLcYJ3t79gu3bG6a7PU1b8fzHn1M0kt5PVElz9+VbLh92JK+oFxXLiwqhJWOYCDIQxUTf98hKowrLsjhC1wlZzpg20B4VPFx1XF5e4UJivVxjVUkcAioJ3n79huGh43xxjjnKmrCBPaJIyGJmM3XM3wwUXiNjlp+PyRFSjhGUj54FCkNRGIIBUShUoTIPQYtMqX7sIyASRhlkobNqVUiSDfSiy6rVsSMmhy6+k4LkmDk//jUxGv9/ch/+R+AHj09ZA5uU0u88Up//DPjZ4+f+r5TSP/n1FoNEnrPlh9YayqogxkB3OCBi4tWz5zw/f8544ylVCUOCOSHGRJpy/mBKMHVdHtMKAdZi68cFAY+uNeu2RguNDw7tdFYnjjMMDic8QzdjbMXTL844enUKlWQOHgaJHz1Ja05ePKFat0QlaWzD/WbLfhi5uCgRK0sdanAjX/3sF4Te8/nTT9DCcnX7QO9GFqbg7OwUGTz7fkewDnVhiEPChAJbVfgxYLHUStOagjkOJAJTikQBprEoWz7KjDVxFIwEyidLXv/dH6CWGjdNXL2/Ynu35Zl5jvKah9stxmhOz8+RlWY/bbC1xc0BK3PD0Jp8Bxrue4b7nrlzVKahLnNZ5gmokAlLzs8c7h+Qc+B0ecTDfo8Igv7+gDM5iF5YTc2CGYcWCT/O6EKxPD7KbTyjcMFxd3kJIZIKhTY1QuaGpVWao7alPJMgPJvdLW1bc/zJGVoLPnz5kevLK376s5/iK7j47BlaJ2ylWL8+41DtuH1zx6HrsaFhYVYUCEScSS4Qu4kkE0KDFpEUIsElpKmpqhqaistwx353j1Kao6UBEdncbbm/ukHeJ/rNA3ebnGo2MKGMIhWeuJsIX3vMoIghMcSREPPWP4oAKmsXdKURhUEUAllpsDm9nDHj7lPy+JQIKk+zogx463HKMwjHPnaM7JnFlnI5s0oQkiDn0Erutn99I8n/gb+U+5BS+q++eyyE+O/gsT2bj69SSr/za/30f+lIjxKs9Kj3DvR9IAlYrBasVhWImL36qWAeB9TkCENk83GDe5CcnJ2hkyAJEDYDLoSW2ZegS4xQGVOWIMyOECJhTIRxotvtAUFR17x6/QWLVUuSMMwTqExZGvqepqmpmwa7aImT47DZc7h54Oj4DMqSMcw0dU3YjfjDTG0WLNYrYkro0SCjI7jAzc0lpTWMzJhnNS/+/dds3m3pPkxc7h+QU0RbSyAxTlM+30ZBkKhaYtc1pl0yxcQh9hBGYgl6XbJ4skZYOFztiL2npMTIgjAFZNIs2jUg+XBzyW7cIVTEC4/SgqosgEi33eC6kbj3xDkSRFblRZWwtSEGgaoa5nliPhwoTYFQCmsLwug53O/QRpKUJJWabvKPdKYsZzfaMI4zwxyo2haTBN39HojItuT565f0hz1Xb9/hAihhOD45ZfXyjLkJDH7CLCueHn+P9uQU/4dw2O/4xU//jMOw5ZMfvMYWCvvkiKptqFdLok8UtqKfR4J3VE2JLTSjH5k6R2ElWimmyeN9QlmVsytSxcXxM1LKCV9umlFBMPcDu7sNxaZEnkgqYVHKYCggCcJDwN0N8JCQcxYU+TgTQyK4lAeFWuFVokqJwhpUpUmFJD5O1LxPRO8zhk1EUImgAsF4vA147QlyhuQQfkLFmbMXJcVplTNGhUJYxd3P+1/rVfhvlfsgso3vvwT+4a/10/4Kx+w8XZcorKBpKqqmRlYSaogmcuh24EbkMHPz9oard/f8+Mc/YnIzk3eYuqQpCoSQzM5hZA42USInEAXn8VOOKbv+eMV+s6WsLM9fvKBdLpBKonWOf0skgo+4ELORiZyDyBzw08yw3dNvdzw/P0VJwf3dhtYUXH97A0GBMry9/sD5q6c8/f4r3nz5LdP1js1hA96z3W84/uw5599/RbMc+Bd3P+Gw6dFRkVTEJJiiI5UKx4wzgqPzM84/fc4sJG8+fqDzE4WZWTxd0jxrkUpS2gJdSQ52Sy8kJkqkhHpZU1QFu/2Bh+EBU1uaowXDMCIR2MriR0e37yEGpIoIBVElhjBjkqQoLKa0rFcLNg8PDGrPom3pvOd4uWR385AZC0iOTo5YPT3j8uaGseuRRqK0xpFw3YBA0m8HTMrNu0QgiJmJAVkLynVJf5jo557x5oZ0ZljVJ0StGEMEI6hPV7z44tWjSnXg7u6GP/ujP+XixTOOz09Ympb6pGSeA303MU8TKaWcRpXAaotzHndwVEpjlclp4lJmo54UlHXLCecgIo0tKLHU3vCt/ho3R6ypaOolRVmSlGCeJsbugJ8jMThiyPJ2fMg7kRDwMSELjbQK7SU2ydxPSQlCxMWMxxdCIJXCGEksEzQSWUtkFZBWgimwqSLOlqE0MFe0KTCHGakUUQv+hL+mReFfc/wD4Cql9Iu/8LHXQoj/B9gB/01K6fd+/W/3XTy4yN3fFIkR6rYCKbg+XPMs7tDLJWM3gXbAyO3mim7sOTo9YXfY0zzeFaTRuX8jwAhFmgJTcsQQGLoOoqBUBX7K3f56uaBdZbfdHGdGN+emjxL42RETWGMIPuKDQ/qI8nlzM+wP+OAIweFnR8CyudthbU1wiTu356h9yvnzc+bouBwnTus11+/e0Y8dp1ohlMh1pJVUR22OV0uRsm4Zux7vZ4bJIUtNu1qxPj7mcntPEJ6jpyfM4Z76pGH96TGTn9CTwvUeGywkTximbEBSFp88c3AYk4NiZi2ojlsKm8/HPE2YQmOMoXdzRscZCTpbp0MKaKUQSqGtRRcWLyOxUKyPVwz9QBgnUKCUxJQSzhMrvUYpjYiCNCTmeUKjGTcjs5spFwVJRvrQEbcfOT0/5cnnT7m779lffeDh/p7pX/Qc73esn53jC839uGdpNdVJi0ax5ghTWN5+/S1fb75ke/zAq89fU7QV0btMrkYwTiMxzKRFTdU0VBSM44QXHqzAWJnzK7VgVJKmXqALQwoBlSAeJu6vH4hzYrFaUTdt9p2IMofaCIUUMwnDJCa8n/AhZYJWyKwLjUC4QJohjdkpGVVmXIbHAFml1SPxvCBZQawiaZGITSA1kWA8jTEMSeK7HYNpmfsdJI3NvJ1feSF+nePfdlH4r4F/+hf+/RF4lVK6E0L8PeB/EkL8Vkpp95e/8C+GwfxLR5LEkOvm0cHo5qxEe/8VbhJ8svgBR+aEMXRo4WnXNZVtkEbnNCVrci6iECid1Xtu9kzjjNYCFDg88zyja8Pq5JiziwsWxy1lk12CIhiklgzzRHhkAViRxSLTMNPtt8TZ09YVUmmaRZPRYiFQWUtTNhhrEQvJul0zNj3yqMQViWCyAGaODkfk+PyUVbPk6hdvOdwcWNUNwUUOcqJcLTleHXHl84JjtKIwBX5wXL15z22/QYvE2cWKD+MW30R0a4hE/BzpHzpstEiZiH5kVhCY8h/eSkpVAbAb96zbY5IS9A8HBJGj9Yo0e6b9geXJAl01SJk9I0PX48dsAlOPxKWDnyjXa2Yio59hnikKzewm3l2+o/rRkmfPn3N3v6HbdqhCUY8Gv50ZI7gYMQRko2lOC9RRyawzDbo9bngmz3Cux4WJ+68+cHjYc/zqOXq55OAOCBnoh562aHn64gV4wU/+4I/p7zrKsuLkxQUxghSSqijoD3sG5ymKnIQlRUFdlsyjx0+eqBK6TJRNiakeIcBaM88j4+7A/cdbrj/cUJqKo+aYsqqRSaOCJkSQQZAet/4uTEwhi690fBwvopBoFIIQIDlPDI4kFMIalBaQJKawCKMwpUVXilAmUhuJi0hqIlEHgvCUseIQe8y4Ifo7ZidRxhJDIOl/B1H0QggN/BfA3/vVaznHxU2Pj39fCPEV8H1yitT/93X/F8JghPhLQ9SYbcUxASkyOo8tC3bDhv/9//zf+NGTLf/ot/8xm37DUpScvnyCjRXDNDJ5h9tuWJJoFwskGu8Dh8Oe2TnqRYmtil+RjkQtOVmd5qxDI0DlmlJJk0dQUiCVRksFIeFC4LDr+PDtB6xQ8OSMZlXxyRefY5oiayOUpm1anIycXKz55PPP6PQBbxIfbi95++YbZPR8vLyk2+958fmnLIolH37ylt3tjrPFGdN2ghBYrOpspy0MSpTMo8Bay3zoubm+pJMjxVHDMOwYzIhUkmjAmgr/4HLilYdSW3o7Up8voNAMPkfEaWWZ9j1TcDjn6Dd7pt2BddmipaYbOwKwPl6h6pJpGBFJkIZI1w0452mqmikFghG0xyu2231O3Q4ziJIQPNvNnuPmCevzYx76Pd39AF5wuljip4kZTxKBWII9Llm8Pqc4WXB/d8fbm4/UsaBpG5pygRCJN+/f8uGbD2hd8MnymF1MGTQD9H6moGZ5csqLTz4hjZ67mzuCSRwdnWVehBAURcE8g/eBeZwQ3tOoBVYrRIJpmpgGn0V1wqBMDreZ3Mj93Y7rqztOTs85WZyhBk0KkXmciCngU8T7iXkcGOaBnT/gwoSIUKAxmEy/Uln1qbTONzIj0I1CLlVuNCZFQYPWBlMYZKmhglhFYhPzrkHFbHcfQYYlaWzpNoaHDmwJ0irSv6Odwj8GfppSevfdB4QQZ8B9SikIIT4j5z788tf/ltlKC0AUxEeIzBwiSQpcnLm+u2QlPzBHx24aKEvL0dESnWrmzrM77GkXC4wxGJsToPrDgWme8CKwD4Hu7goXPSfHpyzO1tSiJU0BP05Ms0OZTCZy0eUtvZJ5ZhzJF8voiD6xOD1idXqKqiXSCmRwMENhLNM8EWzg5PUp4kQQRtjs9jzc3TN7x7psiIeOkCTbfUdx0yEOhvHe8eHuIyTJannEiONu3xFlrlOSUaA1SQhc8gQCtjFcba8QnyhOXlxQNDUyCrpxT5g9YXSEWRBqePW3XrH3Pd31DdpaVsWSbQzULu9OwhQQQRCmQDd3HPYDEckUHCoIHD5rQVSm/EQS4zwxR4dZlniVEIWhWbbMMRJSolCKMHouv77ieH2OUQVJCHrf00rNpAaiSZl3WCbsymJWBXZdU8eZzd2Gu801nSt5tXzF+uSEFRM304FD1+H7gWJZ0Eco6gIpFAc3YUvDqx98jo6CX3z5Uzb3DyzbNVXZMjtHYYrMaYwzU9ejhWXsJRiFrSxJaObgcH3GyulKk3Si2w90+x6pLU8uXlKbmsP7O7qPG/aThxlcillQNx0Y08SgZpx2WckqMro3KZljAa1ElZpoBbIUqIWGpUKVBikMNpR5BKk0yiqSznaGSCIGjwseHyfiKMAVhLHgcBd52MzoIqILibR/jd6Hf1XuQ0rpvyenS//Tv/T0/xT4b4UQucUM/ySldP9r/zYR/pwlJSCF7yaUhBQ47Du6w4Q7cuy6A0lJphQ4uInGVCzrmifPntE0DVVdA4K+O7DZbEgKRC35eHvJT7/9KYuTJf/Jp09RjeGw77Oph/xjfQxMbkQalSXTIZC8w0b1SCZSnJ6esz45pWhbRCNwckLuHZAY+p6ry2suPn1CeV4z6AE3eWKIrJYrnvy/1L1ZrK1pet/1e6dvXPMeztlnqrmqB1dXd5e7207bjm2ZWLZDEkwcIoSCBRfAHRIX5AKuEFJASCg3IBBEgBAkjhwjhBRiu+1ujz3Q7a6udtdc59SZzx7X+A3v9w5cvLvalnBEAQlqr5slbe291tE+63328z7P///7v7CPOuk43dxHqoaoDMQc6SFTI7SSqTsYl7hC07iGKCJDZ8kyk4xv0UNhqCYFN198mke7J4ixZjSdILRilNXIaeRhXNI2W/LBYItAdTRhtezYnXVkOkfkgtl8QthK3ODRpsSprCeDAAAgAElEQVRrQSYMSkh8EIQgWK5XjIop+4cHaCRPBs/QD1RZmWLstEaVhov1kvF4xmg6Zr1pGI9rZgcLHr9zytntEz7IbzO7eUBeFGijqSiJg0WONL4JuCyiRoZ8PkLWGaWfMtlr2PiOYlQh9yqaQiAPJ2TbKV3TsdmsKKsZeVYgjQYl8c7TD2mbYu1APZ9ACPRdl1pybcjzjCyrsEPHdr1GiIgQKfA4DJ6+b3ExUpqM3cUG2RtMbcAFJpMZ1w5vUKmCLCpCtWMbHNvVljgkdqYoFVKFpH8YMgYRiEPKggwI0BIhUxESucRUGbrWyFoSSvBlREoJgwQhkzpXXob0ehCXYUACT4w9wQ5gI76P2F1gc9bisCgTMdVHPoX/r3MfiDH+8p/xtV8FfvWjv/2feoQP4fQfPl9WtgAyKMq84PRsje09d+/f42vf+TqfvPEJtpsO7bdkoxFOD4ymE0b5CCJ0TctqvaIZdpja0Jmer7/1VarJmFc++xlG4wneRZz16KAwpUoDs+iwbWq7pBA4SzrUziMR1JOa8qBCG5OoTFFgffJaSGC9WbNtt7z09Mew0mHDgFSR0aigWEw4zPfoRhfce/82re55+tYe8/mCt5+8C6XkmY8/T8gEjbO0bQ9OUqic4HrKakRd1NhoaazH7GfMnj9gs+zo5g49yiizGt9Enjw4Zrlak2WKmCnMXkE/WNpVA41n1244ayLCRtwgyUSWgmB9imwbgmPwNvEtKoGeGuq9EQwCXReIriNoibMd4/mE6sqU+w8fQhxQmWAQKQgmr0fk1ZhyUrE72TGqSsDh88jBM9eoRyXNeU/zZEkQliFaBulwDHgdWFxdMJ0ZhNGIUc6gBVlWk43LpGGoinSYgcEPyCgvWYUhXRUCzPcXaCFYLdecX5wxHk/QSuGCRJuMrKiIMeIGB16glSI3JSJ4ggcVFXQetEZbRRY1xeWQNgyByd6cbtVw0jxGRIHRAl1dSqOdYpgbxK5Ft46sj6guafujisgMKEHXqWOIku+H6Xid6GMMSeEpok6Q4DRuIBASEi9C9BEtFeNiRKFr+l1gvbMprKj4cyhzlnBp8UyEySA98kMfuIdSV/i+wA7wxJ7w5v03efmTnyJalwxOIdDElpGaQgBvA6vlimV7gRzBrmj4yhu/S3VtzI99/ovcuvEMbAW+jcgGjJJQC8gCIVwSkXXEO1IakhI4mzDkmEDMBoLy2ODBanRpyMoKu+1obJdks0oRhgE7BAgdaDC1og2Wh5tjzv0aP47kVwwhc5zbE3QO23zN+NoemVCs7+0QW8c4K2miAxw6l/Q+onNNeVQTZoFqOkKMHKGUZCrHLy2bkx0uSPJRSTSa8dGE07tPaB6vKBt92Qk4ooi4QYALuE2H6FKupA0Wpzz1omLv2X2oNfdO7yN9Rjkf46Sn73aIWsJcsB7WWNeAqNk7nLJe1Vx0Dep8hc5r1GiEOz0h21rKieKJWyIOKqZCYN65iz/vkJmhnpSQC6z2WNFRmkhejgkIeueIBKRQZHmG3TUM3qOVwAd7qeBLcnmtktpPCwM+FfjxfIJ1Z5yePmE0GlPEOileqxrnHf1qR57l6GhQSuF8z9A7tBYpCHhtKUmWc9l7Bt+CEOhKoa5W6FiiZbL+Ky2QxmC9JHM5qq0wrSXrBkQ3JAy+MYQyXWN8FoilA51dztPAi4BSnugVDk8K4SE5f7nM2lQaGUZkJlDnHbN6wt5kQV1MWF40OAsfMVs2/d7+GZ7r/0+PFELk+f5cIaRQ0eA9Q+e5f++E9XJH8Anu2rmOJjSYQnP7yQdEAs8dPQ8qsnU7OtdxES9QC0krGl574zWkUPzkj/8Uzz/1ErFNbdh2uWT7ZMP+Yg+Rl8loFIcUo+YC0SfPgUIxuG3SzhuTJrqQeP1SUOYFakjZitFFiqJgdbEhaFhenKOywGg0wnWW08cX3P7uG8jBc3hwQFaUnO/WuEpweG2f0/aCUbnPYr6g23WIxjG0Dtc6etfiRIF3LaO9gptXbxKFoqwmFCNBRUFoPBePzxlai770EMwXC6aLGe995x20lSymewhI3MJMcbo6xzrB3nxCbAZs21GXFUZljK7MmO/tc7FZc/LoBC1ynn/meabzm9y//z5FrTF1wfnJGUGmGdBkMmM8OufeyX2KrCIvC+wwMDjHerOlUxD1QCY1zjtcCHgJ1XzKlVs36OucoAJKK5wcEDEwWH+51VDkRcF8OiP2LvEalwGVa3SepZZbCoT40CGoEmh3GNBFwXQxx7mA1AY/eLqhIctzijynjzvapkFKicgN3g7JkOQdkRTSo3JDXRb4S3x70NDkFuaRkarxtkcpcZmKFxGDoB4ioZRIUQA5PgT6wSNlljYESoPweONRtYMsYKQni5DJDIyAIFIGh1Jp5fnhLTtEQohkIqPKcjIt2DsYce3GlI07obUeNyiGP3+QlQ/bm8CHcTbhcrlqO8/bb9xBOIlUmhA9y2bFt9/+I6pQcHL7lGyc8enZZxm8ox0adqIhLiI7drz78F02YcfP/ewv8PTTz4GX4AI4aDcbus0aX9bYnSJKjxfJRCJU6lKklOw2O5r1mulsSpkXWNejtCKvy6QyjOA7R7dtCC7Qtza1okGxPt8wWhRkpkgrvtWGwgMo5pMpIsvou569Z69w84WnuH98D2s8aqxZXFuQDYHTtx6jo0YP4DctMfToeUaZlXQRhiAprEJFSXO65fTBMaFLxU0WgvFiAlLRt5Yqm5BJgzIG6S1dGBCFREdJOS0ZZMQCepKhq5JqP9md200HLiHeQvBMD2ecNiXZSJHVFcPyDAqNF4K2t1g7IKTAGIUpM3QZkX3J6fkZTeMYP7NPITQXnaUfLE4KxKRCT2qsieA9QklknhGHQLy8WcYQMEJSLxaUJqfdtWw3K2o5Am1ARYRI7EeIqJhi7GyIeOHIx2OumAJvHbvlhug8MkpkTOve5W5J8BuEkbgYqeoK9X23rcP5DkRKlFZa4WLPYAYCHttZBtdhPuT4B3BhANfjhaPNBL3yWCU4vHLElYNriKAS0FUEHh4/oh9atCYRvmMCC7lMXsrKDUboNFsQguBC6iDiZUR98GQZLPYyrj89odUjVust22XGyfn/P+Klfw6PD33U4nI1qUAohs5D8GijCNGzbs/5w+/8PrNiyo35TSbPzIhZpO1b1mrNxXBBO7R8680/4mR9wr/9b/xbHM1v4nqPAmSUtNsNuTHMjo4oswIXBFIaknZEptVbSGTizemSvt1yZf8AYlJESiUxKrEHvHd463j88DFVXTKZjFHa4IPHSIVWOUpocm2Yjcboq0fcb+4wuIAPgvH+gmI+IpQCOdYMZqAJLaIS6FrjpWdvbx+sw3ctQ+gYdoFHJ48o53tIlRF7OD89p3u8RbpIZjTFpGQ0m9BJS3uZpG27nofnD5kuZkz251gbuXbrOrvVkidnj8F6jFYsuxVCG8p8kkxpzUBdjlBC0dmO9XpJH3vyekS2KIiP0vxlejin6Tu2XQManPZMZwV6v+AsNlw86ChMzc3ZEX7Vob0gMxmqzOg1rFyHd8kcJoTE4dAClNFI7wg+ptAdrXEmIxQBGCNFSuAUUVwKhyTeOUKMBBEQMqHwkIogfIKixpRtEXpP17VkmWFUjkDCttnSdi3CB/YmEzyBftMSM1CFwvYeMpEOr1bstgOnT87RAbLLNG6JZHAO17c0vqHNI9ObVxiVBbeuP8OLz75INqT5gfUWGSVny7M0T+hcChA2OVELghfJBSDSnCTGFCDjwsCQ0mYQylHVklrCQSyI1T7Llebi1HDy3p/bovDhQ/7Js/8w8mbAxQA6JvnrRDGZjbj13E0OntmnaVrOmzMebh/yvfff4J2773F6fsqLL72IlDlhSCYfSNJnlGQynVDGyxgzZBLniA/XogERBBcnp6xOL5hOKgpt6N2QzFaXvCsZYLCO7XqDkDDbmzHbW7BttlhvqUYluUrJ2UZniDJnwGHKnNVmQ77ecfP6s3Sh4d4H72GlRRcKkQnszrHpd5jK8NTRLU6ePGK53NGHAW88veqYVBpyTXOy5cm9R3QnG6pgkJmkWFQcPnOVVbtj+WjLfH9Bd7KlH1qaLiNzFWQCmUk2zZYYHVWRMVhL4yylToe1Xa0IwXPlyhVyk3F6ccr7t8/ZiDXjWxP0NP++1n5+tGD7aEVHD7VEzwxqrpjfmrFqT2FlWOzvkznJW1/7LtF5jNRcf+Zp6qt7BHP5/xN8ithTlxwBlxD18TJgRaEIMSRtSghoDzok30xwnuAdwTlylYRowadkbatAIDAmI2YFobX0m4b1quHg2iFlVaWsy21kc7Gk22zJPEnNaQeClEkVqRRRJ+l49Aq7GdieNuReEoLGoMmExrYDq37D2m4oD6f8+L/4E+xdu8LbX32dX//ffoU6Zugyh9rw8c+/wg/9+CscPznle3/8JlEXiLpCSovyCfnurP8TfF10RB0QeY/EIn2Ptw0ys4wmhpCPGS9ybt4Y863ffPSRTt4PaFEIfJiHSPBJR6zS9ULmoArB/MqIq1ePOJwdUs0K2tjS5jveX93my1//bd774DZt1/HpT7zKv/wLf5N5dYAWGWVeJJKxEZTzHL8ZcENMxCYPHk9wA9vtBhElhSrwg2UyrtmbzYjO451LnYQUODcwWMv56Qnnj864duMms8M5Ho+Tjm27RmnJOKvxreO0P2d5fEbrGq6/9Bz3Hz7iyYPHjPanFKMMEWF/PqcsSkIMlFWJnYw47R9x59F76a/UjRKCozgsWTx3gCw89+/fZvW4YXexgs5SVoZ8VLK4ucdGN2yyHYdPX+Wgq7i9fQeTS4bYs9xdMDqY8ejJIzbrNS/eeh7fDdy9cwdRGYSWNH2Hw+FV2ljYzmCMpp4u2K4SQTgPE2Lp0COJ045iXjK6MuH0/JSuchTXa9SBQJwZ8u2Idhg4u3/K8f0nQGQ8LpnfPCBfzFNeZQgoF3HOE4VAyIRXy0xGP6SVoZMDymjKqibLcxARFyN0Ns0VnMe1PZ6OsiyoioLeu5RcpQ1GaoIybNot2/UO6WUqND5SlAWL6QzfdRRZxrQe07geN3jIRXq/UuNNcveutmv6Xce4GCF3Htl57KbFDgHbD5x2FxzcPORnf+Yv8+1f/zrnZ2fYdUd3uqGWJTJXqElG27Z8rshQkywNtaXixrM3mV8dwSBplh3vvf0eUTqUjLRdw7Wnr3Dj408jC8/GnvKl33/E3btbkAGdGQ4Wh/zUX/x5/rv/7Ksf6fT9ABaFPyFCyMuVC8ojNehMMV6UjPdL9q7MmU0nROu5fec9FvUB+VjylW99mdfff52utZfMwys8f+3jlLJEiTTAtH2PEgJVFOzWLYp4Gc+SzDjrZsPp2QlGKA6mB/R9ijcf13VyVkafqDikO12z27I6PycrDdW4wIaeZujofIfFMsoq6qxgu2k43yyh1MyevsbR4VNsrOXegwesjy+Y1Fc5WuwjS0Wwjigkk3IMsxk+DhxvHvP8Zz9OUw4YBNdeuk59raZpN2w3Z6wbj8k1ykcsA6PJiHK/ImYD/fkAux3D6Yp2aNClQWWa8d6E6ZUFjx89JM9KlNJsdmsG56nNiOlsznS2x27b4Rg4XZ2gneLo+jWuXb/OSm5BJlFNNlKEVqNqxWS+R338gHvrB/gx1DfGhNoxe3qBKQrWt1fYJy25L+ldC2XAqKS8W27WTIqcTKjE1oweK2Lyr8R0LTJSpzVcjGl9SkiBssEzDENKBfMR31lcP1CiqIoaIaDtWwYfqUYFqqzYiiXBOjQlm/UWlUn63uCHjioryPOc1XLDql2z7NcYVZAPI0xIOSJBRGxokVlkNC3wvsO1A8622G2PbQfW3ZK9q3MOrx9x7x/9Kq+9/jo/+5d+ji/+0i9x/OCYb37jq7z8hU9zvD3lD7/xu3zi1Zd57qWbnJ2fE0TPnXvLtIUzJbeev8Hd+x+w67bc+thN9CjwR+/9LtbtiLrnuY8/xVac8p03v8PNp27y8iuv8ODeR+sS4AeuKCj+RKeQ8FzIgNSQlxnT/ZLZYkQ1LYkx8PjxCd26pzm3+B6eHFznzQ++R9M3SKmZjKZM6glDYymKHESg2XXsdg2ZzpGlJqokCum3SXbb25Z1s0IWgvlsgQxJSDQaT8jLku12S6YNZVaicpOCS2TH3t4B5bggK3X6oDpPOS6RKlKZAttaTh4d83h1xtUb17ly8xpRaZxKg6zRqMIYQ28FXdOBMGih6OlpNs2lAg4W1/awakmRC+a39tC5phAF14+OmE8Mygvirse2DdmowmWBfFpR+hFhM/DoyQOiH5jN5+xdPWR+9ZBBRsqiRCvB40dPcK1lNJmAFngRqaclxTLn4iRpGHVeUNcVzW6Hd55pPUNpTT6uyEVBPi5oVluW2zVRQzGvkCNFry1mksOyS4GqIlKNK/pVx2g+ZraYcdpbludnVIeHyLxM8l/viNZjfUgGIalBqD8Bl146CZXRKKXTaw8Ou92xO11C58hkRlWPIQbczjK4htyrxGmYTcmiYnu85fTkMfOrC7rNlna3YVKO0EKy2Tbshh1mlFHPa8hgEMlPM1xmdXRuh+t7ZPQ4enrf0dqGtknw1s98/tN868tfYXl+wU/99M/w3Msv82i3glHG53/uJ3jp0x/j69/4Ku+/8RqH62t84tWXWbXnrFZnvH3nHt4NXDk45LOf+zTxgWfXLlnZgntvv8vvvPaPaNoNo1nOL/+b/xrT2YzVcsMnPjHjYHGVX/kH/81HPoU/QEVBAPlljxAJcgDpkZdDnWKsKMY9QQTWFx3NLrJdt7Rtj5CB4eF3WLYfUCygijnDNvLis8/xmVd+iKh6+j6ghGKz3LLZ9lT5lFyMEF4wdAPrizVKZqhSYGY5s+mMq3tX2R5vmXHA3uiAkGlipymFZkwJfWTV9eRmxOTaIYNJgE3jNIXPLqf3GdJl3L/3gPv3H+KalphPMNegET1tMTC5PqE+HNOaQCchyrRXdx10Tcew6dBWkIccZy1mT1NWHu96tpuWi+MN23XLwZWK6d6cPiR6sw2OrfKMAlQ+A5Gxq0rM/ph8f4Y5nDMIOL7zkGtXrnLx6IzzzRItFcWsoDOWs+GEmVyQ7Rv0qWbXdkxLQ1EUfHDvLqv+gsnRBB8EmIpsJmhFx5OHD1mdrdIMRzk61TEgQCn6oWcbt4hxID8siaeS7OkZo72azbKhHMD3A50xhEyhg0IPKRyGKPE+FZQsMwipyDAo3UMe8Dk44em7nrZvWIcd0kZmEToHofOIladbrliueoppyd7RPkIHNmfnmEVkqFqiT6nP7dDhfSCqiMo1xfWK4qhAjUTywTxe0zcdbb9mt93SNz3aCRAOmzmcsnRhy3Of/RQvfPEV/ov/6D/l1pXn+JFXfpp3333AV373t/nhVz/GX/l3/jrn2xX9ec8sTsnjCOslvRIc7u8xkDHQMJnUSAX9Zkuz3fD1b7+LLXe8/MrL9ENHXijKakpnPT7C/YcPufrgDi9+4tpHPok/QEUBZPJSpe5ARaSBotZkRXKM9RvPtm9o2w19d6ltEJFyrCmKjCBaDq5MkGLH0vU899LzvPTJT5L5Aj1I/ACuC9AHMiPRg6PpLW23Y+O3FGWdZhcmsDhcEEKkqkd4mwwuMTh0YRi6geXmgtXFks5ZxvsLXHSo2iOCou8t1ieeXz8MFELQ9A1CSMajMUIqbD+g84rF/h5ZAJ1pbAwUZYnSgtYPgMAYk0JjnEMJzZPjE8aHB5SjiuZsy713PuA73/xjLlY7Xv3iZ/js536YiCCXBqkzSlnQX3RcPF5iN5HJ0YL9owPIU4r0+fE5F8slRwdHrNwZ3jkGMUAQ1PtT4kwSc5jUU1YXS9arNa3saWVP4zuEkejM4FwSdpVFweAszluUSZwCoQQuBrxIWRNFXTKajzETxa1bt4h3BKbKaJ0FISjygnbXIIymqCqkBJ2TQCODAxlBSGzokWi0Mck5qhzCBGxIlndZGRZP7eMvLGacMzibwnNiGtQttxv2Jjk2OnoRYFpw9ekDLtoVhMCoHtMve7bbPhnipEgMhijQKk8I/87SNT1DPxD6mLyPmcZkOVluWKvAyq5BCkJhiEczfuaX/hr3vvIW7//WN7mSK6SBruv49h9+g+2TU6bViPwS2++kZHLzkGJvD4dFysD5ySnL5ZJm0yJLzfPPvMgLL1/B+YEgBsbjObb3OBd46513WO/W/OJf+0Xgf/lI5/AHpihICTJciiskSA3KCLRURB/o2gG6lJBjHWkHLQADSoJWoHJLlB2mgFvPXufo5hFZXZCFEuMzYhPIy5aSisVojJaStW+wqqcrLCu35vXvvI7tLf/63/xbmMERG9DR8GEqvFCJ4vTo8WM25xdMZ9Pv312NyVDCpKKAxQc4XZ1zML7CeDwivyqptSEvcoKAwTvGsyl6iOl1xWUbrBXBJXFUyANt12G94+DgCirPMSonFxnr0zMuPjhm+fAcFxKoc+jTMEyiqKsRsoc7b9/n7PiUWBbceOlFdJ2z61viAApJbTLuv/M+m+U6yWZNpFjUXH3uOnLPoGYZwXlmN2dYbxnWnr5whEqwv3eV2cE+G9fQdS3jIgMd2b92wG7ZsOt35HXB4AaCUfTOEbRgdDBhUk8oD2pqN2HYeTb9liCgLEpscGTSkGuDdS4pXHNIeZwKPwSGYMm1QhcSLRUhhDSYBLq+ow8tB0eHxGmCp3QXDUM7UJicajoCnYJnnBToccXoOqi5QZt0yPEGOQgMimgt1g34RqCakrwGgkIGRSYMdTknljPQoIxEKvDeoqea87iG3GAl2DpD1yVy8Iy8p9WCUIEzoESCuozKklFeEruAVDlv3rnDyQf3cLEH7/Abz2a5Zrvd8sUf/yLn7pi/85/85/gw8MzzN/kb/8ov0lvPcrmhqDKWF1v+/v/4ax/5LP7AFAUAIQWRJDyRQqGkwg1g+5QnIO2lPQz4UP0oJUkJFi1ROXQe2T+a8xe/8Jf4/GdeJRsZjDPEThC8xtQ5uYHge843G4IeWPXn/Pr/8SXeffgWV2ZX+JHPfCFBNTtLc94y1hPK0QihBDF4goipM8gNRVWiL1dmsQ/0Q8dysyWMIBMSz8Bms6TZdJQY6rpOf1mDZ7taYpTCZEXqLBTkRUbwLrVBIr1uWaWQ3XJvwsGtGxTzEWerY07vn2CC4friCn3w1HmOdymjIQwC3wa2Z2u6i4ZaV5QHM4pC8/jhfe49esj+dI8XDm4Rhebew/uXk3UoFjWTa3OK/Qo5M+zo2GzOyXLF9Zdu0q8HVDSEpWC0N6Eajdju2iRLj0lxOt+fcTY94/zhBcPgCAGcDwgXkrJOA5VkJbaYPcMQLRYHSqFMRkmRMhtdGkBa6ZKbUGqMVMhKE5yHCM5bmrVliJ6oPXIsybOC1XrJ3ZN77M33qMqc/rTF45nU4wTuySSyNMhc4QlkdYZluIz48wxDTANNoxFGQ5C0zrI+W9MPCUVfRIMuCnKl0UZBLnBZgCww+JYhc6iLApsn7XMZdILd5BJ/OCJMI2GWQaHwpSbWGU9//EUm4ym3v/MuOhcMpWW9WtK7hoP5ghdeepE3Vt9lt9tRZDXtyvL2W3c4ONznR3/kJ5kvrtI0juOTCz77w6/wUz/5E/yT//U3PvI5/IEqChGV9IxSXcadR3wYcP7DDiJ1B4EAMhlJlEkR3x6LF2AqTaEL9q/PGR0U9N2WbbfGd4pSjKCODGJIASFZx2bY8Otf+yf8wet/yLI55+atm7z88qeScs8rqqzGqCzthL0nBg/B4aJnspgxXcxRUiCUwg2ebdvT2g55GVeuM8mw7ZAxYjJDZgxZUWBjpB8sUWRY57BxwBFQWY63lkwZVJ5je8tqu6H1A2fDiuvTFxFaslluqLOK6y9cZVzP6KNnsbeHksmZp5VBeoE2qXARI6Myo92tefL4PquzM47GC0ok7fEF2+0KVRSJ6nQ0ZXJ9RswFjW15cPyAYdcwykt8AdVkhAgKWRsG4emHHttZ7Nqi54oYUp5CJMlvhUgmHikUQorkKZAOFx298/hMUCxqhJO4JqACEAVDO+C9RxdpuwEKT8TaAa1Sq26HAXxMaL3B4qSj1DnT0YTTeMJX/+jrfPJTL/Ps7BbFosY1QwKjKs0QPVIrHMkTL0Uguh4ZA8J7jBdkPqQ5howoqVHBYRuPdT3kkVIW5DrHREmMkcEHcJFo0vzElAXTvRnVYkqGxJzu+MY//m0+/onP8dM/+mm6IlBdyRkfTPjUX/gcQ9tDN3Dn9h2WJ6dM9uY89fw1bry6hw0dDJ7N4xUiaHJV8Ae//XvMnpnxH/4H/zG73Zq33rrNtZs3EDIHDO+9d5fDK+/yi7/41/mf/6tvfqRz+ANUFCQO0l1AgPeJVgsesnTnRkZQAUXSGWS5QhvIMhDS4zFEqQgq8LVv/yFvvPEuWSgZ5WOevvocz1x5kazSWHp2bsMHJx/wre9+i6+89mU23RapFFVRMK9njNwIaTSh8OxWW7y21EWOJE3kVaYYzUaMFhNs2yNEAs32rmfAMaoKdsMKEwXtcgetRpY1SqvLwhaT3VhrHAHrU6HxUkGMKCkJznN6fMbF6SnFtKIzjl45VAQbBspxzZWrRzTBk41qykWJ9Y62a8lzwWQ6pR7V3HMdtuuoxgV+6KnnE7K6ZHawx2a75cnjR1hpk+jqqX3qqxNa0WM7T9SS9qKhznLadc/67DGHV4/I84qszvHCc3z8hNOLM5ARHRR4z3K1YrVcY1TOqBijgkHKBM9xziGiRKAY7IDODcWiSni2rkFGAQMM/YCOIq1ZtU5bhpigKPikSmUICYiLJESJcILMKvJQMCtmjPMxb7/9HrOPj7k5vUZW5gQL2y4ZqWToKUVNXhoGLFILpI1I0udMeujbDivS76JQGUYCDlQUZFoiXcADQ3T0YkjdgQN0QAfDrJyxeXLO3ZZHWwkAACAASURBVO++w09+4Yt87cvfIB9f4fq0QAUJ9zveeXSGHBV4Hbhz+w7lAC88/TwXj06xD3eohSHLDJuLHY/ef0RsAyIqXvujbyHva5579UUePr7Pb375H/Pe3dvs+guyrOLJ8Yrf+M3fIWfykU/iD0xRkFIQVAQ5pBuCAmki0ghMqahHGVQKdEASMVpQZAYZA0PfM/SeiEKojCgEr7/xGk/urimk4dlbt2g+dsb+fEJRjTjpz7l7fI/X3vg2v/+136G3DVIqXnjqRT721Ator6lEhZKK7bCh3e1QRjErCkAQhWAym6GKnN5ZbHBJfi0jOpOMqpp6PqJXOwpjGHzPdtdiZZ208M6BTsVPGImWGkni/kklk4ovROzQp4BZIXjhEy/R1g5fgNCQjQr8tmXrekRlKPfHmFzSbnucCAjhcNqjCoPPA0VdoMc5Q++5vvcMKEV7uuPNd25zPjToSc705oKjF2/QyI6LdklByaSecDS/wiwfc+/OXdbrDW7uEKKnHJVkytCtG2SQzOoJwgqiC+xWHd22RUhN6CN2ZxFawRDxvcOgiTYSoscUFRhJHFJcHlIllsbg2Q0WYQSqyAGBihIXJM4Hcqkp8wzbW5QCpQ2DA9FF3NIyNzN++JOf47W3v8tys2Fet0zqMTqTdN2KptlBD1lloNKEAigkKmpkp/CXxWHwDh8cmSzQJgOhCDEiQsRvLQjHIGTq9qRLsBofkdonhF4vefDme/zuW3f5F37+F/j0j32Rt7/xPu9/7T0mk5J8DKLyiL2S6Us3KGcV++WU0MHqbEP/3Q8IhSdomwygrcBuLX3b0TUdX/+tb/L3fu1/IkRLVJ7b925zcDSlHAnyImOz2fLf/r1/+JHP4keBrNwk4d2vkqSG/3WM8e8KIRbAPwCeBu4AfyPGeHFJeP67wM8DDfDLMcZv/d9XhYjMAlJHVCHIKkVWKrJKUY1zyrogVJeFw3s0AiMkrrMMQ5eUbFFjVI7JDFFERDagjMDLLQ9P3+P9R0eYsubdD+7zvbfe5s7t27SiIco0mPv8q5/nL/zIj6Eahd316N4wNJa6LEFGtqs1Rht0nlFXI2KInJ1dEL0nywxKK4pJxWhcoCaGTX9GGUrGhyPO2iW5zPHB411EiZLoAzIKqrJCGEXTN3iX4tozk+LYhZTUdcXRUzdYZw0BT0BSjUf0IdL6DqsDHQNS5KjcUGaSzvYcnz1hMpkwOhgjQkLBVXVJPduj6Xree3yX09MH7N88wGjF+GhCLMDHlE8plYIQmdczyphDGzFBJ9itD+R5RmlyCmmo85ooNXhBcCmEJMtzBhfZXGzQoqdQJVVWUVUVsQv0uw6RK+IAg3N4l7QG2iWvi5KSi4s169U5Czswnc8x2tDHnjA4vJYgNAqFFJHhUsEYrEow1FKyPz3gh1/5POvdBdt+i9GGaTlhPJ+w6xqIEec9PnpEneHNkFrvXU+/tSA00ijycEl4RkEQl4TvmKJKosfhiTIQQpIf+97iQ0/TrFmen2IfX3B3u+Tv25Z/9V/6W8yGCe07p9SlIUwFahyIc8Pesy9QXJ3x3hvv8tUvfQ3dSK7dVBTjDMpACAOb9Yaz1QXvP77N3fv3OD45YWtbwCO1px8i1vcs9guKkUGEDKkyYPPPpigADvj3YozfEkKMgW8KIX4D+GXgSzHGvyOE+NvA3wb+feDnSBi2F4AvAP/l5fM//aFA7QvGI0WWS4pak49yssqgS01WZMhMEEVM+O9BIQJED86DdeCDoDIlMghUAKMhr6GogInlQj/mD+59CS0KVhc9x8tjdttzfPCICEVVMZrOMXlN3Bm6NlAMEbvtqGuDj5bTiyWjasJRvaDf9XS7BgYH0eNNwC8MKstQpcbkOaJViEvb9eHBFTKdQQjYriHvB0ZRYXpPVgSCFOyEZxc9eVGQ5wWlTsWha9dst2s64RkYqCYVRVEiZ5Le9ek+HbvUGeSKobWcnjzGSM1iMWNc7XH6+JTzd04YffY5Bj9wcfyQfnfC/EbNjWeeQXtD1JpN6HBElMwIIdB0HTEDtxnwW0chNKb37IYd2bQmqJz2bEO3a9F1iaBi11oiMLq2AOuIfU88H9DjHKEhn9ZY2dKcrxnXc6JT+POAHwLBpXmR0wN5ppibkvOTUy78I8Z5ji4yskJgYyJz9a5HRkWwnm7XI4RG6iIJ5aPECE0hPcV4zHp7wvG2wxURU2myRc7QDfTCpkOTe1SVoXtJ4zpss8WEisLkxFgiZCqSHkeInhBSRBsxpDySmOZN0ffYfku7W7NdLbk4OWa7vkD3luGtu3z5136Fn/4rf5nnf/aLRJngwiZPeLv337/DG6+9wfG9Y7aP1oRdYDFaoPIatGTlLnjQPuB7T97k3fvv8f6jO6y6DR+6jMMl86zxntg1jMYZWZHhZfuRCsJHKgoxxkckSjMxxo0Q4g3gOvBXSZg2gP8e+PJlUfirwP8QU5z1V4UQMyHE0eXr/Nk1wUiOnj1gPII81xRVgcpT5kEU6UOCBBcc3nsUEinTYM/bIa3vPFRVTpZJfLBoA4vFiLzQSCPx0qWBo92yuejYbta0XQsejM74iR//CT75iR9CSkmQEes6jJR4HTjbnuN8jw2RKKFpGpr1DhkFdVmhtEAZyVb3OJ8GXudP1uTTkriJnF+cM9ULynFN26acRIWCGLDrhuPNDpcDlUFJzdAPbO2WQmeMyort6Tlv/vGbmHHFeDZBC4UaEgpeR4OOCuEuyTxK4KIDE1GZZpADg91w//QBm2aDzg3nZ2f0u4brB1fxPrBarbhx/Sm8C7S2J6oUeBJ9JBDQCPpdoB0sWSZS8K3tiZ3GBMX56SnNcsu1Z57GoKmyks16zbSe0MUdjz64y3RvxkIbsjzH2R4fAgKRkPII3OBp2g4RoTIFQkukg6KuqJsRPkv8ixACWmm8CQQbL6XNgRAHOt8zqsdkeYYLHq0y8qxIoJugODu9YO0H6vkCoSXZrKAIFQOBVnQoIaizihiSgnF9fE5eKqTQyMvPYRCBEB0uDClRSgqijHRxYHBDojb3Dbvtks3FWYoY3Gzx/YBwHt15nrx/j9/7rd+m+t7rRCmY1lNmezMm0zF90+N3CbabS8PWb9P7OZs0MEPHk4sTvvvOH/PB47tsuxb3oT+I+P3o+eCh9RHXW0zuUOVHnxT8P5opXIbCfAb4GnDlw4MeY3wkhDi8/LbrwL0/9WP3L7/2Ty0KWW44vLVAS4s2MgWvystQ2JCcbcH7D2GNKAEKkbYBzqFiCiuRKmKHhrZrkNIzHpffH+wppVBK0FnLerlJwJaBxOtTJZ95+VVefOY5ZJekxZ3osH0LpcNMS6IF1gNBBRrb0LuOcVFTT0aXTlaH1J6iyJBKsVvvGE8M1jpOz8+RVc50vAAUwUOzbdFa4q1ntVoRC8HerStkuWG13tC7FisNQgjyvODi+Iyiszx19SZD6zi+/4h8XJAVhlIXYKFrLL1wuGiZLiYpSl4PrNcX+Cpw+OxVrLc4a8mF5vjBfV7/zncJUvKpz73K9Vu30r1fkAJEvEdEgXeRpm8JWlBNxjgf2O06TFkhDHS7hu16g/SCXBhyrbGiZWh6VicXNOuG6XxGCBGlDDH0KePTZJcW4DSoCzGglMYUOdEFRPRksaKcWKzweBeQMqVkaaMxRQZOYhtLNi7QriHI1GIPnaXd7Qi2IvYdqpTs713l7jvf43//zV/nR7/wOa7M9qjrCiEVbey+n8oklWS13HHy6Dx5WWSgygNZmSMyiScyEPEx/ZuijLSxpxka2mZDs9vQri7ot2voLYqAUTKlSbuI7ByP37nPcO+EKCSzcsz+4R4HR4cs9vaYqBoqh522xOCRBsDjQ9p67Nody+2aZbPFE1Omw4caHy5pxwjCEOl9ZLAe+c+DvCSEGJH4i/9ujHH9fXvxn/Gtf8bX/i+AuD+d+5DXhmyUfjTEwCAGFBCDuzQqgQjJPh0CKAEi+tQhDA4ZI5kRhGDpuhbnLWWVJTZhFEnZV1bEAGfths2mwXae4BUyaLTKqcpxgqcMHllJhmKg7XYUVUHMM1rhUSONqQoKWV4mRws2zYZhsBTjinxSkNUlrRsoRxU+WpSAoizQJolwEBKtM9rdljJmaKXJjYFMUV6KmqIImFKjo2Kz2oCQHB4esmu75PvfNfiNSwo91bF3uIfFEbSl9x3r7ZIBSz2rqScTbBioZyULNWe33XD28DHru4+5/d23uP3BXcrxhE5GfuZwwcHVq0n8FAS6KsjQuLansT3zKwvm4ymny1NsZ9EqeUCyzDCqSoxQNKstvbVkUvL4+JjtesONGzdZHOwRIliblJpaGTINF8enZNMxwiSIaVrrOVDyUlos0XmGHSwhxLSVFmmtqXQKoxl6hygio/mIaGNCj/mQ+BGDYxh6pMzYPzziWrPi3W/+Hr/1+1/iuVs3eeUzrzC/egBOpCFh8GiRoYUmOMF61dLanjrfMdubU06qS99GwAbPEIekc1AQXMSrSJABlSmklkQXiZrEANUC7f7P9t4tRrPsuu/7rb33uXzXunVXT0/PjRoNhxRNmhqTigAHjJHYlmQIkA3YhmHAJgwDebGB5CEPsuUHPyYB4ocAQYAENuA4ihRJpq2JKVmUSFGUeJM44kzPpefW9+6qrvt3P5d99t552OerrhnOkEOLM12T1CJrqvp831e1ztl7r732uvz/YGohqSLqt0k7mEJRHZRMZUrm0ojY7Dx5nsK5AaYjeOUoypLR9IjDySFlU+AINEsP4eQSO4l36iNYkbfvdaW/R6MgIgnRIPxyCOEL7eWd5bFARC4Cu+31O8CjJz7+CLD19t95kvdhsNkJaEu7bcSSUB8ZorSKFN3BB8C34BmxX95WBU1t0Uro5imiwDmLSYQsizu2D0In65BlXWzpsGWgWliamjjxlWF9dYNu3o1gHNoTckedLrgzu810a0Z/MGBjdZ1LGw/T6/bp0Yn4ifsTDnb3UGnCuWGHTpq0ZbgwGA5wnQJfNJzf3KTj+tS2RitF3ski27BSQKC3MiAd5iRZwqIpYgHRsIMOmsnWDOtrfuypj7N1a4t7d7ewZRMDnomOIC21IzWGxBvK0jPZG3Fvf5vuoMOFhy+Sd3ukSUZtCt54+VW23rjBwY0tqvGc1dVV+ufXabSn8jWYyLOJExKVoJ1iPFmQ5xkbG+cJpWUxnTMcrJClOVVdgQ4MVgdoo9nZ3mY0GvPwpUtUZUHd1Aw31hisrzINFWVZYbKUPOsgTc3O1j3WfGCwsUKiFHXjWCwWZCrGXzzEwq7Gk5gUBKy1OO8pqprUZAQTqGyJyQyJ0RGEqKyjl+g9VVHE5idj+PGPPcVOscP/88UvcOPWK4yqAz7xzKdZu3AOo1KKxZRbV3eZjsZ08w7UiqqssLM5qj0qkGvIFIE4V5XRkaIvjbTx/X4HVw4oB13q6YT5eEw9q1DiEdfgK0uoHHnf0Msjq5XznmI65xDIeimkAd1RDFb6SF9hfc20nLA/PuDOzh3G8+kxZuPxmeEt0mK18S4vfx95L9kHAf4lcCWE8C9OvPQs8Hngv2+//+aJ6/9YRH6VGGAcf794QpTAMXqztCm7ENCiaHwsaY60hArVItf6EPHyPIE8M3T7nVgJqRTKRHAaACUGrTOU5DhbUhfQlICPHD1KKYaDAd1uF5IEfOBwf5s/vfqnvPzCS1y9doPPfuY/42ee+hn6vRWMJISFw6SRJZlEM1hfRRJDbWtmi4Js0EPrBJNlLIKl1+uRVElkqapLjBGyQY4SWCzmZHlGb2NIpWtKX2OGBt8PuNpSmApvPMPNNaytuHNjm8nhlF7W41y3x2DQxTWeTtajl8TmrrRJUbNAvVgwrUf4jsXlHeZ6zLf+4BuktbCWdkh7YFa7PP7xJ+heWEdlhlk5J9M5mUnwtWf33j7727s89pHHSRLDvTvbuMZzcWMTZVIOD/eocfQHfZxyjOYjCrvAY8n6KdMaxuWYpOngE4HGoUzkcPQ2olXVZUVoHKlOWuJVRyMW6nhknM3mzOclK+trpFmKrUvKuj4eb1EqFpsFQeuERiy1a+HRBKx2ZEkEkkkyTX+YY/2CW3e3uHdwhzfuXuMvfPazrA1W8TPHH/3OH5HNU57efApRaTyaNlAWJck8ITVdkixyhBptoteS6Ej8Kj1caCBYnD1PNZuyd2+b0Z09qoMpWkXvtbQVmfL0Vnv0uwOCihkDGxoAksSgOgkm15ALddkws3P2JvvsHOxS25pj6MJ3XPRLYKLjlfyDlvqxvBdP4S8Cfw94UUSeb6/9U6Ix+DUR+YfALeBvta/9FjEd+SYxJfkPfuBfCCEeD1ry18ZGwhFPbJUVNKINARex8pS05CigMyHJNCbVuCYgWpMkGmkLoFLTRSTD1cJ4VDOb1DjLMUOUaxx3797i7t07XLzwOEUx5Y9feo7f+O1f43D/ABU0ZJ5sJUcSTbGoqGuHqgO9QZdev4fu5qAVjXc4H5mZEeHe7g4y85hak0lCv58znVqm8zGD1SFBa+rEIYnHJo6FKymlZOWhddJuxmj3ELOekw9yfE9x7tFNRpMZo8MJaafD6sYG+TBlWo+ZL+ZUixrlhDUzZK6G9NIOySKh3C2YFoccyhhVeLpJh37aId9YpfvIOg9/9FGGFzZxjae0FfjIkuXqwM0bN5DKM5/MqGYLDvf3UZJgK4fOPJ1BDxU2SFVCjSXpp6yeX2Vwbkh/c4Xhw+cxnRTpRmzNxjax+rQooagY9AYk2pAQuRia2tLYBp2YCIwbhNQYJo1jPBoz1CuoRJNkCaJUZBT3gqBiPYSvcbbBdFJS6eBp6HT7JN2Ug8M9dsod7u3fpXELXFNQLRpeefEy165eJRVN7lP0XHEh32Rn5y4reshA9xl0hvS6PfI0I1ERaE23noOEaJREGUhSQhJABzwN+Uof18Y+RmUg+AJfO+ZNiXIL1tMGvZaQpQl5yKi9jWn3BFwSsFJT1w2Lasru7JD98T7zckrj61ji78M72ITlUeIkUOuP0CiEEP7o+/zG/+od3h+Af/SeNVgqomLO2TqL0SlKBGsbGufQKu4I3jtEVFsZF+nmlSbSeRkhRC6WFnwDRHQsnmk84+mMne1DxkcLmjKmkXSImA3j8SG/9oVf43d//+uU1Zz9g2229+5A48iSDr2VLr1Bh2pRo12gl6SYJEAW8SNNnuGNYlqPMakhBA8Cv/wr/xc/+5m/zNrqGsF7UqNAeaaLCdkwjzX+qz10mjB3BXWoqLXDpwGXe8bNFJVrNh46z8HsEDWzLOoFaTenM+yRpAkoobAVW9tbLMoFD13YZKWzwnahYnDJOvQsYCrBmZr13gq+jBmbzYsPsfmxx9DnegQN08mMPOmAFcqqQBURtmxjfYNBr8fu9jaL2ZxHH32CbrdHhcMRINERibnT4ZzfJNUGr0Ms5uolkMaMjrcBLwEDGG1IOh1meg4ubgoGhQlCRFqMRt0FGA6H1BZmsxm6k5ANUtIsBRO5Gq2zqDpQFw3z2Zw0Sck6GZIpHGDyBJ953nz9Nb763a9x5/AGdbFAA85b3GJBuShJPHTJGaoeo3IPP60ok3XU6kXOrayRZ1msH1EaQWIQPHgEMMogTgheYUOIxsJkJJ2EzDcMNhaEmaXW09j0psGlwrie0mVImidkaUaqMhrlKX3JvJwxno05WoyYlXNu3bvF3b17WOci3qT3MTukdMQe+f4r+T2fIk5FRaOIIlU5RVOxXNU+xAWvgidQg2hy3YslpqGicgvSxNLJOuTpCkoyJJ+h0pQKQauMRGXUVjjYO+De7UMO7s6o581xFsP5GoXGOsXLr74MvIxXNhKJEElo0qDp6j6570JQpHmCI1AZS5M4lFZIUuM0uLwmHShYhRevXWb35m2STwnVZIZJY719E6A3XEEnCTrTFFWBVo60k2PyLuf7a2ijONjeYzGKkPIHe3fphIzzK5uc+/ELPPTnMurasrW4x0Z3nUG+Qti9Q3+1Q5oqbr9+jXu3btPMK7Cepm6obMVUT3joiUfI1je49OQTPPSRRxmcW6E7HDJWM1KfQohnZOlC6AUe23wSLYq9e2OOigW2bziUOamfYBoNs4J6smBal4zzCZ1hD5t5nCopbQECechJXIlH0IXQ7I1RTqN0Sn9lgJfAbFwyn1QorQgNTBZTisWCTrdLt98j1R18HVioObfuXGdj8xznNi9QhQbrHM61rM65w+cB3wdJhHJecXC0z87WHV678zJ7+28wOrgLvomYHT4Q/AIh9t7UFIx9SU1J42sKVzE5mHNvfsj6aJ21zgbrgw0GKwPSQYLqxZS6JF2ChrqpaEJNcDFN6FouiXIBli4qz9no9NHaMK5mjO5MeGVnQmc9YfOxTXrrfWwSmLsFN/Zu8NIrl3nz5qs0vsY6R9HUzMsyEghDpEF4j9Dt71VOh1GA2Arb2FgAQhvcb9F8lRKU0ijAGENja7zzCJpu3idL4uI3SR7bakMMSEowlKVlfDRhfDClLtoyWh/RgBHwLpxI58h95jp/X7mgAl45rI+ALmluaGpH0BFe23QMSZ6gs4hPeOW1K/z7//DvOX/+QmSPqhpy7XHB0+116a32ccqzqBbRKJDQ1LS8iwEaONofMz6csd5dZbi2ji8tY7fAec9KntMdDsltjF00RUW+2qO/nnO4s8PzV55jtLuNqiuktkiEpMWoIdoaLl18JLIyp5qpLTCqj3QU4oRyPmcyGUET6Gc9dD5AJxm9Cz2SYYK1kWW6ThxVVWGbiqKqONw/ogEefuKRWH5uDHnWZWd/m8uvfpf1tQ2evPQUroK9rR26uoNZ38SKJ+93ODg4oq5qNs9vkiYJCqGuahKTRJanNNYITKZHfPWbX2H93DpPP/00g5VVHn74URLVQVZ6BGmQTJiVY1598xWu37nGzv49rt56g73xPZwr8b6OVGsqDnIE4oMQOxgATUJKRQ1+jq0cha2ZlnOO0hmzcsF6s8Ew9Mklp5f2KNLIwO2kpq5KQmUpy5K6KJmPZ5RHFl87spZpOlEpg84AW8F8dsTh7JCD+ZS1ixsw0Cyk4Pb+Djf2t9hdjKLX5B2ND7jjAOP7I6fCKIQAronY9SK6LRWNrdFag1IJSmK/ORDLWW1A6RRt0vigXBOJN4MgYtCSIE5TLwoW44p67qMxCBLBWY6zOMuAjHBMRuOJUWUEH+LiTzqa4F3MiGhFnmbR28CjDTjtmLopN25f51e+8KtcvvwCv/R3/xm9fp/VbJ1EJRSuioVXiUa0IImilw1AAgtnUZ14HDjcO8DOLYnJSbKcvNcjdAJFtWA8mmILoaf6aBEa32B9SbaSY7qwtX+b29vX8bOCLpoMIVNp28m3SjO3eO/prw1wPcVhNaUSh+SKjs555bUXeemll/G24fzaeZ649Djrq+dwlSfLOpxbOxczGYWlmi9wjWWwsUp/ZZWjo0kLVW4xSrM/3eMr3/oKX//W13jy8Sf5m3/lb/PRxz/KcG3A4mhB5SqSTpfxJKIWZVmG6Gisl0AotrGoStFIoKpqDvcPmBwece2N13j+O3/Mxz/+SX7qp3+aJhiUMfQGXVxleeHlP+HLX/s9dkfbWF8SXMRfUCq69hAI/n4wLi4xh0OjVASFrX0V2cYIVN5R24bC1pSuYubmrDVrDOshXguYEqcbrCuZTkdUizmz0QRfNSgnJKFHlmRkxkAQnAsYkzLIBtSNo5pX7N3c4/BoRLKR4XqeslyQZCmlbyKC+NKDXW5ey+n7I5ZTYRQgplalrU4T0RitIvtDSxIoOrbdusZRFiV17Rj2+wQfWaSUCGISUJEbUIeUqvSUs4rFpMKXARrBh0jw4o+5JeAdn2xLed2IY7wYsTvZQXvDsDvAGoekghGNQZiVMw5He1zdvsqvfOH/5vXXX8M3gceffIK1dIOudDk6GjFZTEnzlF7SZTAcolMHEoNqPZMQUqGuK25cu069iIjDoXZkaUZv2Cft5HQyT5rlFE1Js6gY9oYE4zE94e69q1y98TplVUa6czWgp3t0dZcs6WBEszXb4w+/9YeUK45Pfe6zrKyuorKAJJGvc3e0w3ee/wbTyRSDMOitcfH8JYqiZH19g8/85Gf5cx//FMN8iAwMRkUPR7QiW+lQK0eaGmrT8Hvf/Apf+ubvIHi2Rnf55uVv0et1uHD+AkprSm8ZrHbYvrWLTwLDzVVUV2Ntg60brGpIjMOlAd0k0GgWh3Mya1hRXcTBnStXufPGTY7E0lnp8cgTl9gb3ePVqy8ymh/iqPHKtoFlj/OuHe82INxG8GNyOPIzBqCRQO0bYkuUtN5j3KV92VA1JdNyxjl7DpUp0jyHTCirGQeH++xsbzM9PCLxmrX+CudWOiRJQq7zlhcyblCpzhh2higVUM5zMDli3hR0HurxyPmHWF8fMK3G3N7fpqljsYFfzs/3SU6FUYgdDQHvozsvS04FAOLOrlxkgy5ms8gSLZoQNHXtEIQ0zXAI3gW0EmrrWUxLJkcF5TTy9nEyFvOOqd3wltc9gbqq+a0vfZE//Pof8smPfZrP/73PQ54AChGHtSXPX3mOf/vsb7A7vsfO7j1KV2NIYhNXcMzqGQfFAU48WS9DekJpasiFxKSR3ToNzMoZ165f5cqVl7Fzi9EJ6xtrJJ2M7np08fudPgGhnBRUocJSk3cMSqVce+1Nrr16lUx1WeuvcrF3kb5eQbmE4KFyexxM97g32+faF29ylIz4qz//83gdg7baJDzzqU/wygvf4TvP/TGVh9lsymg0Rrzj7t0bbG3d5ubt6/zMf/mzDPMhCCzKEu88NgkR1t7OmR/NuHN4i3E1pt/PWDDn5Rsv0QTLX/qpv8QTD32EpnSMQ4EMEkIVmIUCFwRHQ500SD/BmoCYBhGN1jH9ZoKQozBGUzvHbDJmxJyd2TbbRzdZ2BnjxQHNcSjUoYhVsHHgXTuz7sfpBYnNrGcSjgAAFsdJREFUTst3OIfDtSbBobxrp49CvOBqhw0Rcq4/yRkO18l1TjftcuH8JkYp7obA/GhM6erYLyGWoDOMpLHwUMcCp36qSBMQasKsYWFqhp0VHnroAskgQfUMX3/lOd547TWa6ocoTfxPlFNhFHwITOoC07r0xpg22xBXrkgb6XUVVZufzrIcQmsE9NKjkGNz31jHfFowHxcx29BIRGaOiU7eocjyhELSEt6Co2FrZwutdjkczZhVBZ/45J/n4sWLHB4d8Oqrr/DKq5d5880YDGrB6QmK2MylE2QR6J3rknZSOoMeYhQNFtNJIInVapjA4e4uX/7932Hn5g7KxiPNpfoxHn/qcSSLhLcBcD4giZB2E7QJ2Lrk63/wNd544Tar6SYXz2/y8HCTlXQFKpgcTBlPxxy6PY7sPjM1Zjya8eyXvkApJT/7cz9PlvUIpUUH6GQa8TV4jXjBVgXax4DvTn2Lb85n3N2+S552yXXO008+zcee+jiSGiw102rGl7/xe7zw5mWcsZQBMqkZhQkv3X6FRz/yGOcuPYTqaKbFEfQFnyrGzZR5WWBMgqTxSLaoC3LVIcs6NHPLPEyY2AMqOyYNJoKuUBCwiILpdErtS2Ifo4/bzfGuuqyH8S2RgMZHTqmWyylei09+aSbiK8lxzr/BxQQolQtMF5rDcYd00KXf7dHpdul1O+SdnF6/y+jwiKqsIjmNNAQT+yW0JC21PCRKo9OGBR0y30GyjOFwheFgjf7GgE+uJMxVTTVfcOvWzQg69P99TwGcFhKvlmisgLRBRhUBR0LELxSBPM/Jsg7OOkQcpg1GNSiUQNN4ZtM5R4dj5tOCk9ma+w7CiUCN+t4HHE+bAsq15LcNe4d7fOXrX+PKtatsnD/PZDLi7t1bFPMxHoci4L2glPDjP/4Ug/UhpkkIwbE+XENnaXRPlcc5IWQxgImGN66/xn/87d/i5SuXUYWQNDEPj4brtx7iY898rCU4jUelNNOIiR2RVy6/yBtvXKWnz3PpsfOs9/psdLrQWOb2iHl9h8P5LnvhkCkjCqY0quLuznV+50v/gdHRESbr4kPD/sEW119/E0Mkuwk+poINmlQJtnGMR4c89/w+HoUxGde2bnDl5mtc2HyIj37iaXanu7xy4xV2Rjto43G2xliN0wnzsuCbL36b0WTKRx59koceeZwbt2+yffsOSgmPXLpEYhK2tu6yvbODc46VlRWyboYrFyATJnLIotnHeI0ygksEo1OMiRDvlILzy0NB3P0VDn1iI5Djr6VJiO9WSzCP4zjT8lU5nhkhNkojHuYl7B0otOTkJiPRhmw1ZXVtjcH6kPUL55hOpvhRha4FT4xRKSWtvrrtnXFolRMaQ+EWJK6haDwd0XQ7Ax6/eInthy+xu7XNwi7ej1DCsZwKoyBArjJEohUP7YlfaRUJXIiYeYTWDTSapq07NybB6YgDLQHEK3zRUB8UlEcVrgDC0i18+6NsJ8n3POEYb/Dct8jRa6kpy5qbN6fcvP16/I1+yZQd88V4T2pS/sInP0026LCYVITM08kTSKD2NSRCowFt0Upx/dZ1vvjV3+Kbz32NFE1mEpqmRKGZH05589aAwn8OpXOaYFHaIOJJUMwmE7au3eZib4OHe4/S1ylJE6gPpyzKEXvjbW4Xt9l1e+yoIyZqgsWigDQo9rfv8MVnfx0v0ASHx9F4dxyRB3f/IOdTEnQk7ELjcLi64sbNK9y8/ToXNx/hcLbDwWif2dEBqYo4CV45JtMxhbVkKuel69/l+tYbfHT7Y/zE+NNcee11bt24SWoSHn/iMbQorl+/yu7ePSTAYNBHxJMnhofObTCVkqlqSBUkypCkCZ2si1caLFS+RtVVhHVjmbJzJ0cc2iiCnLi/QHo8H5cz06v4DueXn17+LoNGaLwwnc3QzR6JMogWzmXnyPIueVfT6Q3prCyoehPKUYkdWyQYjCQYnaBzjTMepTKSpIepM2aHe8z2F7hBg81rkkH0iGI3mBxn0N4vOSVGQVDO40JDCLELUomK2YYQ2uBgiAFFFUEtqqpGJxplNEFFKDfjINSeamqpZxW+cJF/MCwf4g/zIN/+3sAyKBGNxTt8wnuSJOHpp57mk5/4BK9ffx3lDZvnN+n24sC6IDjtkUSomprrV6/y5d/9En/ynW8jKhZiOdvEPg7fRFJTuyB4hzEGRDBas1jMmB0sKPZGbA7Ok6aG9WaIX1SU0wmTwz32Z9vsVrvs2n1GfsSRGlH5GocHr+IO5ZuIPaBchJpr7zsQy8tV62AHIripJgZXI1YU2HhaJnjY3b3L/h9s0/iauqkxEgPEnjhmti4gbWgQ6nLB5atzrt68hrexUC0Ez8Hle7HCFQfKIgilHSPOs5g7Rke7cXdPM7zSNEqjlGnJYzUqGFCxVCcoHzNGbVxBTvCTLuMJS8MXxzWmbkN7vAhtp26DRwjtYvFti16Eu4nZqoZZOWPv8JB0kJOvdJFOgumkZN0OpAYsSGOQRYFUAk7iH9AqNn1pyJMea2GV3XKXrek9JtcmHC72yVY73BvfY3vnHlVjWzbt909OhVEIBBobi4aUxJoEEd3GCAI4T2g7v5TShOBJEkOSpSTGgNI4H7MKVRFbo8ejBVXp2hKEH21xx7uKSsj7Q37ys5/l0o89zm/+xrM886ln2HzkPDM3YzKaUrvoKdS2ZDw65Nln/x3Pf/c5tBe6eS9OV6/QPlZxBt9A5di/s0tnGqndEZgejBnd2ccUwvnhJlmTwAgWoWDuSg7qI3bKQw6aI8Z+wpQFdWNp2qlMu5Dj8wfx9wn7TpRoHJ+24x4Z/6swSJuyVUg8unmwTU1o4pHI41FaInyZBhsstbco7yMOZ2iYVxZvVUwfq2UdY3vKVwrRpp0PgtIaQ4J3sTlOEJCYmmsktlnHmjSHC77N54fW72yDiqr1ftpMZPDtvSsVcVJa4wG6jUU0rTbgWvayZaTBtQYmggB6tG+YlDPy2YjebIjqG3RXg1G4ENBeESx4G8v2gw8RG8RHvIY0Uax0NXQCC73g6MaYGzs3uXbvBqafRvTug4NIiPOWEOmPXk6FUSBAcAHRJsKAiUa1Z0GtYlCnbmLU1doapTTGpCRJikjrUhFobGSNGh0umE0sTX1/L/hARBlW1s/x/Msv8d0XLtNUlv/iL3+Ohcx59aXn+epX/4iqLji/eY4b165SFQsmBwdo58hMhyRoxAlGJUBAeaGjEw63D/j1X/51gsqo65JUG1b7Kzy6fonHzz1Kv7dC2miKtKBWjomfs1MdsVMfMGbMTM0pqGnaOvlloPWk79Rm7tv/LSUuIlHRC2qIDrdRIa5riJO7cbRm4NiLUokiSVKSXB8bncZXLbaFgHc0jaNophiVYiRFK02SJC3zN+DawjVRmBCBW00ieO/wzrcLO+CN0NAQrKWoS6qmwmFZahUI0bj52NUYy12i+Vs+g2gAQxtJCIBtl3sMRLrjKFNswFIYXIy84HygwOJ9yWg+IT3YpUk8TgeyuiRIQE0a6mlNU1q8SZE0zlsXq/RQWtMf5JHnZPgoZcdSmJIX33iF/Z1DbFNECP4PQE6HUWAZDoq7o1GR70CQtidCYbHHPQ0hRKBXgNhq4QkhUCwss0nBfGqxZYgBxh9lREbdNzAnsyNLcQH2DvbZ3r5HXRb0B33+z1//N2Rpyu7eiDu3t3DWsr66xmI2IVeaQZqT9XqIU4iPbMrex+Yg5WPjVrmouPnmNSAj+IgyPOuv0nks5Xy6zlzNsSGh9iVzv2CvOmLH7nPImBlzCl9QUx/vmuHE17FHfcJrCG3sPl72eL9cLh5RsWAr+Kb1G1zrSPu2lTe0izslzzOUVtRNjQoa0xafiYAy0Qu0VUHT1BhJSUxKwMYqRq3Rul2QrsG7COm+bHQLwR2f8xvvEC9456lsgXX18Z7vl/OqPRSIj2MXaBtnWHoLJ7wIlgbStQblpKlUS9/gxGfjB2ssk3IGB0LlLdZahqvDmG2YOdy8RqvYvCeJoDJF0NCEeDxJ05S812VNwSN5zZGfsDXd4/bunfuKvY8ewlJOhVEQINEJQeu4S7SuaTzYtnULPtYMIB5tUpTSuMbFeINSeB9iCnJaxWOD5QTYBHwQD9O7htlksvwXk/mIb3z7G4CPCMAursH6MHIoJGmCaI9u4yexgC5CoMWFF9qshj/hxMedrCkW3Lt7B2Nhuj6lm3ax5YJ7B1vcPrzBQX3AjDklkQDVEd4yraJxaHc/1Sbq/DKMFhe+oI99B49r/YYIdBM9uajpfSMTYlQ9S8myFK1NBDFtdziFiR5A0/ZXSIyxeA/W13jncM0yTqAxKolcEYA0kbQXPD60qUa5fyfSDm/jHNYvvYSTTUByfBxaXlTqflFraLMtS4n5hmVCUmPan6LrH2NY0hY1qRZ0BSyL0hNCg28cyoM0kKYpqW1RBRNF0B6feEgDTjWR3asJhDqQDTqkvYzVdJXN4jwXdzZ5/vLSlH8wciqMAoDRGkx8yLIczhCpvlNtyFJNWcWYQpqmgFA3Nj6q4Knqhvm0YDGrcVVoDcJJNqkPQk7+Hb/8fxvodhHjDyKQCI4Eh04zRCUIaQSmFfAiNAiiAqqJu4i0TyW0QS/xloNxQ1UW7B0c0Mt7+HrOwWyfo/KAuZ9SEenjw3FhjsOr+16C9/FY0PpceAXW+xM7bGgTdss4RFzABtCtHv54T45ZoTRNSdMUJRrnmhhAdPGzhhSRiG5vnIkI3N4dW6TgYg+MVx68RWFJkoREZxgfcHWD9w2xKbkhHJu6GPpDSew/aPU96RWdPIUv7xj/1uvBL0ueY1pSMIgyGGJrt/b62EvQJ5aOCKAdSAyKWyvUZYmdl9RpSWeQkJoUbSI1IBnQAZc4GhUomoqirEjnJYNeE7E6fIPWLVjQMlP/AcmpMArHkzJ4VLgfB/Deo5GIyWcgzzuEEHcsFwIIcfJ5R13XzGcVdRHwbmkQ4kL44ORtf0stjZJ/6zcVsL6msppEICgX28JdXG6hJa5UKpbK6GVUXBEXT3tSrj2MCsesKmORlFtQ+gVz5lQsYkNPuwQ0QkNrCNoYwbLQNwLYRGPQEGiQ+LcA8eF+SpLoRSgiJP7JvUsU6FSTZLHtPULZu4jzCLgmQNAoHYvTEpWgXER78j60RyVhGb48duNtmw4OCu3bnVrRHh1gGRvxuNbTcceeTnx1mS+IS33pHd0/Jt2/t3BcjbAEAVTtpxRJG/wEA96AilkOpUI8PmjQSiE++hMqCCYojIc0GNIkLm4xoBLB6QavhFoFCl9xMBuzWCxYbVboFjlWV4zGIw739t7X9OM7yakwCkGIzl6IJ9cQaNNSIMrgvGde1ri22alp0WmUihF0bx1NaWkm4Es5sTaXNQQ/evnemod3etPST+VENWXAKEg06LZ8N3gieYhz4M19tz20uIU+elC6NQzi4ywMxOh77S3WNyhi2XNDc3zWPz4ioKNRwB9X+N03LxzvrE7FVGRo9T9pEJZfy0LhdvnGfVNrNCni2uOHdy3yNm163+G9oFWCkhQtWTQQCpx7W/DH3w8PO+8iWnNbW6FU7Fd5e5bE4olYS/e9hPjs4zMSf/Kao2l/R2SNEAxCDhgVvdDgVRtjiHfriXSGWhkgQbQBQhtmih6PSBwj8UJwAec81jbUtiYNCWIMaS+ldAV1cK1hd0ztlO3pNjd3b9I76LG2uYrKYGvnLtdv3+T9zja8XU6HUQCciQ1R0VeKXApaxTPpbFFgvaB0gpYYGRaxSCBiNc4d5bjBzzTUy26yHwQ68cNIO0XfXvmovved33tnrXjBtx14WgWyFPJESAHjJHpEoZ2Moa3dQMeouWp3e78crrj3h+N4OYgKBBMRg30LvgHtYlf3d9KT5/+lr+BxWMzx7hkn+VuLwaP/FuMa7tjq3q/2004jSsfdWiK5CgIheELjWOItohIQDdKSxeqG4ELk6OR+4doyBrDUwCmI5lIflyMvfYCg2oLm9r5PHh2OKxNVPB54taQjjKAuwQualA4JK6Rkpo/zAetd62NF89eQoFQS9W8jDARBB4n1JE0VodZ0vD/rA/OqRBVTJBdMMORaWgoCjaWmomJm5+yXB+zM7vLqnStUN0rSPEOlmlkx42hy1KI1/6C59qOTU2EUIPYuaAmIOJSEiKhEwInDB9c2xASQGGQSHwfZlo75tGY2rmksfJAW9YcR33bZKRUiRmPWIU87pD5D6nhe1SoeeYLTqNCGtdSyEBeOjZOPC9e3JkGURDox5SPCURs4o3WaxavWdT5eYm9Z7Cd+8/Hkk7dZvPuYA7BcsMu/oNBxsYb79X4C+OAj9XyzrBpcZowcIcQAcpImWFu1R5rl37qfMwAX788HgmrJa08cCx3RoIRjdVXr0bx9Fd0P0+oWn3Ppk2kcKZqB6rGaDWlqx6xZdlQAyqD9svS5xYQMqs1GRY8geMG16c5gIlT8wpXYqsYVDaY2yECTZZ40SXDBUdYVs2LGzmiHnYOdyGK1WERjre57ch+0SEzpPVgRkT1gDuw/aF3+DHKOD7f+8OG/hw+7/vD+3sPjIYTzP+hNp8IoAIjId0IIn3nQevynyoddf/jw38OHXX84HffwA0/FZ3ImZ/L/LzkzCmdyJmfyFjlNRuF/e9AK/Bnlw64/fPjv4cOuP5yCezg1MYUzOZMzOR1ymjyFMzmTMzkF8sCNgoj8rIi8JiJvisgvPmh93quIyA0ReVFEnheR77TX1kXkd0Xkjfb72oPW86SIyL8SkV0ReenEtXfUWaL8z+24XBaRZx6c5se6vpP+/1xE7rbj8LyI/LUTr/2TVv/XRORnHozW90VEHhWR3xeRKyLysoj8N+310zUGIYQH9kWsQrkK/BiQAi8AP/EgdfohdL8BnHvbtf8R+MX2518E/ocHrefb9Psc8Azw0g/SmcgH+tvEiqOfBr59SvX/58B/9w7v/Yl2PmXAR9p5ph+w/heBZ9qfB8DrrZ6nagwetKfwU8CbIYRrIYQa+FXgFx6wTn8W+QXgX7c//2vgrz9AXb5HQghfAw7fdvnddP4F4P8IUb4FrIrIxQ9G03eWd9H/3eQXgF8NIVQhhOtEwuOfet+Uew8SQtgOIfxp+/MUuAJc4pSNwYM2CpeA2yf+fae99mGQAHxJRJ4Tkf+6vXYhhLANcQIAmw9Mu/cu76bzh2ls/nHrXv+rE0e2U62/iDwB/CTwbU7ZGDxoo/BOWGkflnTIXwwhPAP8HPCPRORzD1qhH7F8WMbmfwWeBD4NbAP/U3v91OovIn3g3wL/bQhh8v3e+g7X3vd7eNBG4Q7w6Il/PwJsPSBdfigJIWy133eBf0d0TXeW7l37fffBafie5d10/lCMTQhhJ4TgQgge+N+5f0Q4lfqLSEI0CL8cQvhCe/lUjcGDNgp/AjwlIh8RkRT4O8CzD1inHygi0hORwfJn4K8CLxF1/3z7ts8Dv/lgNPyh5N10fhb4+20E/KeB8dLFPU3ytjP23yCOA0T9/46IZCLyEeAp4I8/aP1OikQ+xH8JXAkh/IsTL52uMXiQ0dgTEdbXidHhX3rQ+rxHnX+MGNl+AXh5qTewAXwZeKP9vv6gdX2b3r9CdLEtcRf6h++mM9F1/V/acXkR+Mwp1f/ftPpdJi6iiyfe/0ut/q8BP3cK9P/Pie7/ZeD59uuvnbYxOKtoPJMzOZO3yIM+PpzJmZzJKZMzo3AmZ3Imb5Ezo3AmZ3Imb5Ezo3AmZ3Imb5Ezo3AmZ3Imb5Ezo3AmZ3Imb5Ezo3AmZ3Imb5Ezo3AmZ3Imb5H/F5+eoy/TlSsbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b4a5fdc50>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with Image.open('flowers/test/30/image_03466.jpg') as im:\n",
    "    image = process_image(im)\n",
    "    imshow(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "probs = probs.numpy()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 5 artists>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3b4b304828>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(names, probs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>**Reminder for Workspace users:** If your network becomes very large when saved as a checkpoint, there might be issues with saving backups in your workspace. You should reduce the size of your hidden layers and train again. \n",
    "    \n",
    "We strongly encourage you to delete these large interim files and directories before navigating to another page or closing the browser tab.</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO remove .pth files or move it to a temporary `~/opt` directory in this Workspace"
   ]
  },
  {
   "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
}