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",
    "from collections import OrderedDict\n",
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from PIL import Image\n",
    "\n",
    "import seaborn as sns\n",
    "import time\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import torch\n",
    "from torch.autograd import Variable\n",
    "import torch.utils.data as data\n",
    "\n",
    "from torchvision import datasets, transforms, models\n",
    "import torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Assume that we are on a CUDA machine, then this should print a CUDA device:\n",
    "\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data\n",
    "\n",
    "Here you'll use `torchvision` to load the data ([documentation](http://pytorch.org/docs/0.3.0/torchvision/index.html)). The data should be included alongside this notebook, otherwise you can [download it here](https://s3.amazonaws.com/content.udacity-data.com/nd089/flower_data.tar.gz). The dataset is split into three parts, training, validation, and testing. For the training, you'll want to apply transformations such as random scaling, cropping, and flipping. This will help the network generalize leading to better performance. You'll also need to make sure the input data is resized to 224x224 pixels as required by the pre-trained networks.\n",
    "\n",
    "The validation and testing sets are used to measure the model's performance on data it hasn't seen yet. For this you don't want any scaling or rotation transformations, but you'll need to resize then crop the images to the appropriate size.\n",
    "\n",
    "The pre-trained networks you'll use were trained on the ImageNet dataset where each color channel was normalized separately. For all three sets you'll need to normalize the means and standard deviations of the images to what the network expects. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`, calculated from the ImageNet images.  These values will shift each color channel to be centered at 0 and range from -1 to 1.\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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.RandomVerticalFlip(),\n",
    "                                      transforms.ToTensor(),\n",
    "                                      transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n",
    "test_transforms = transforms.Compose([transforms.Resize(256),\n",
    "                                     transforms.CenterCrop(224),\n",
    "                                     transforms.ToTensor(),\n",
    "                                     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n",
    "valid_transforms = transforms.Compose([transforms.Resize(256),\n",
    "                                       transforms.CenterCrop(224),\n",
    "                                       transforms.ToTensor(),\n",
    "                                       transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n",
    "data_transforms = [train_transforms, test_transforms, valid_transforms]\n",
    "\n",
    "# TODO: Load the datasets with ImageFolder\n",
    "train_set = datasets.ImageFolder(root = \"flowers/train\", transform=train_transforms)\n",
    "test_set = datasets.ImageFolder(root = \"flowers/test\", transform=test_transforms)\n",
    "valid_set = datasets.ImageFolder(root = \"flowers/valid\", transform=valid_transforms)\n",
    "# image_datasets = [train_set, test_set, valid_set]\n",
    "image_datasets = {\"train\":train_set, \"test\": test_set, \"valid\": valid_set}\n",
    "\n",
    "# TODO: Using the image datasets and the trainforms, define the dataloaders\n",
    "train_loader = data.DataLoader(train_set, batch_size = 4, shuffle=True)\n",
    "test_loader = data.DataLoader(test_set, batch_size = 4, shuffle=True)\n",
    "valid_loader = data.DataLoader(valid_set, batch_size = 4, shuffle=True)\n",
    "# dataloaders = [train_loader, test_loader, valid_loader]\n",
    "dataloaders = {\"train\":train_loader, \"test\": test_loader, \"valid\": valid_loader}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label mapping\n",
    "\n",
    "You'll also need to load in a mapping from category label to category name. You can find this in the file `cat_to_name.json`. It's a JSON object which you can read in with the [`json` module](https://docs.python.org/2/library/json.html). This will give you a dictionary mapping the integer encoded categories to the actual names of the flowers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('cat_to_name.json', 'r') as f:\n",
    "    cat_to_name = json.load(f)"
   ]
  },
  {
   "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. If you want to talk through it with someone, chat with your fellow students! You can also ask questions on the forums or join the instructors in office hours.\n",
    "\n",
    "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.torch/models/vgg16-397923af.pth\n",
      "100%|██████████| 553433881/553433881 [00:12<00:00, 45648449.43it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VGG(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU(inplace)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace)\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace)\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU(inplace)\n",
       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU(inplace)\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU(inplace)\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU(inplace)\n",
       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (25): ReLU(inplace)\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU(inplace)\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU(inplace)\n",
       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Dropout(p=0.5)\n",
       "    (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    (4): ReLU(inplace)\n",
       "    (5): Dropout(p=0.5)\n",
       "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO: Build and train your network\n",
    "model = models.vgg16(pretrained=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# freezing the model's parameters so that we don't propagate through them\n",
    "for param in model.parameters():\n",
    "    param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = nn.Sequential(OrderedDict([\n",
    "    ('fc1', nn.Linear(25088, 4096)),\n",
    "    ('relu1', nn.ReLU()),\n",
    "    ('fc6', nn.Linear(4096, 102)),\n",
    "    ('output', nn.LogSoftmax(dim=1))\n",
    "]))\n",
    "model.classifier = classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU(inplace)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace)\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace)\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU(inplace)\n",
       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU(inplace)\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU(inplace)\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU(inplace)\n",
       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (25): ReLU(inplace)\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU(inplace)\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU(inplace)\n",
       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (fc1): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (relu1): ReLU()\n",
       "    (fc6): Linear(in_features=4096, out_features=102, bias=True)\n",
       "    (output): LogSoftmax()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.classifier.parameters(), lr = 0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def validation(model, valid_loader, criterion):\n",
    "    valid_loss = 0\n",
    "    accuracy = 0\n",
    "    for i, data in enumerate(valid_loader, 0):\n",
    "        inputs, labels = data\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "        \n",
    "        output = model.forward(inputs)\n",
    "        \n",
    "        valid_loss += criterion(output, labels).item()\n",
    "        \n",
    "        ps = torch.exp(output)\n",
    "        equality = (labels.data == ps.max(dim=1)[1])\n",
    "        accuracy += equality.type(torch.FloatTensor).mean()\n",
    "    return valid_loss, accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "50\n",
      "51\n",
      "52\n",
      "53\n",
      "54\n",
      "55\n",
      "56\n",
      "57\n",
      "58\n",
      "59\n",
      "60\n",
      "61\n",
      "62\n",
      "63\n",
      "64\n",
      "65\n",
      "66\n",
      "67\n",
      "68\n",
      "69\n",
      "70\n",
      "71\n",
      "72\n",
      "73\n",
      "74\n",
      "75\n",
      "76\n",
      "77\n",
      "78\n",
      "79\n",
      "80\n",
      "81\n",
      "82\n",
      "83\n",
      "84\n",
      "85\n",
      "86\n",
      "87\n",
      "88\n",
      "89\n",
      "90\n",
      "91\n",
      "92\n",
      "93\n",
      "94\n",
      "95\n",
      "96\n",
      "97\n",
      "98\n",
      "99\n",
      "Epoch:1/2 Training Loss: 1.648 Validation Loss: 1.163 Validation Accuracy: 0.667\n",
      "100\n",
      "101\n",
      "102\n",
      "103\n",
      "104\n",
      "105\n",
      "106\n",
      "107\n",
      "108\n",
      "109\n",
      "110\n",
      "111\n",
      "112\n",
      "113\n",
      "114\n",
      "115\n",
      "116\n",
      "117\n",
      "118\n",
      "119\n",
      "120\n",
      "121\n",
      "122\n",
      "123\n",
      "124\n",
      "125\n",
      "126\n",
      "127\n",
      "128\n",
      "129\n",
      "130\n",
      "131\n",
      "132\n",
      "133\n",
      "134\n",
      "135\n",
      "136\n",
      "137\n",
      "138\n",
      "139\n",
      "140\n",
      "141\n",
      "142\n",
      "143\n",
      "144\n",
      "145\n",
      "146\n",
      "147\n",
      "148\n",
      "149\n",
      "150\n",
      "151\n",
      "152\n",
      "153\n",
      "154\n",
      "155\n",
      "156\n",
      "157\n",
      "158\n",
      "159\n",
      "160\n",
      "161\n",
      "162\n",
      "163\n",
      "164\n",
      "165\n",
      "166\n",
      "167\n",
      "168\n",
      "169\n",
      "170\n",
      "171\n",
      "172\n",
      "173\n",
      "174\n",
      "175\n",
      "176\n",
      "177\n",
      "178\n",
      "179\n",
      "180\n",
      "181\n",
      "182\n",
      "183\n",
      "184\n",
      "185\n",
      "186\n",
      "187\n",
      "188\n",
      "189\n",
      "190\n",
      "191\n",
      "192\n",
      "193\n",
      "194\n",
      "195\n",
      "196\n",
      "197\n",
      "198\n",
      "199\n",
      "Epoch:1/2 Training Loss: 1.524 Validation Loss: 1.150 Validation Accuracy: 0.679\n",
      "200\n",
      "201\n",
      "202\n",
      "203\n",
      "204\n",
      "205\n",
      "206\n",
      "207\n",
      "208\n",
      "209\n",
      "210\n",
      "211\n",
      "212\n",
      "213\n",
      "214\n",
      "215\n",
      "216\n",
      "217\n",
      "218\n",
      "219\n",
      "220\n",
      "221\n",
      "222\n",
      "223\n",
      "224\n",
      "225\n",
      "226\n",
      "227\n",
      "228\n",
      "229\n",
      "230\n",
      "231\n",
      "232\n",
      "233\n",
      "234\n",
      "235\n",
      "236\n",
      "237\n",
      "238\n",
      "239\n",
      "240\n",
      "241\n",
      "242\n",
      "243\n",
      "244\n",
      "245\n",
      "246\n",
      "247\n",
      "248\n",
      "249\n",
      "250\n",
      "251\n",
      "252\n",
      "253\n",
      "254\n",
      "255\n",
      "256\n",
      "257\n",
      "258\n",
      "259\n",
      "260\n",
      "261\n",
      "262\n",
      "263\n",
      "264\n",
      "265\n",
      "266\n",
      "267\n",
      "268\n",
      "269\n",
      "270\n",
      "271\n",
      "272\n",
      "273\n",
      "274\n",
      "275\n",
      "276\n",
      "277\n",
      "278\n",
      "279\n",
      "280\n",
      "281\n",
      "282\n",
      "283\n",
      "284\n",
      "285\n",
      "286\n",
      "287\n",
      "288\n",
      "289\n",
      "290\n",
      "291\n",
      "292\n",
      "293\n",
      "294\n",
      "295\n",
      "296\n",
      "297\n",
      "298\n",
      "299\n",
      "Epoch:1/2 Training Loss: 1.637 Validation Loss: 1.040 Validation Accuracy: 0.726\n",
      "300\n",
      "301\n",
      "302\n",
      "303\n",
      "304\n",
      "305\n",
      "306\n",
      "307\n",
      "308\n",
      "309\n",
      "310\n",
      "311\n",
      "312\n",
      "313\n",
      "314\n",
      "315\n",
      "316\n",
      "317\n",
      "318\n",
      "319\n",
      "320\n",
      "321\n",
      "322\n",
      "323\n",
      "324\n",
      "325\n",
      "326\n",
      "327\n",
      "328\n",
      "329\n",
      "330\n",
      "331\n",
      "332\n",
      "333\n",
      "334\n",
      "335\n",
      "336\n",
      "337\n",
      "338\n",
      "339\n",
      "340\n",
      "341\n",
      "342\n",
      "343\n",
      "344\n",
      "345\n",
      "346\n",
      "347\n",
      "348\n",
      "349\n",
      "350\n",
      "351\n",
      "352\n",
      "353\n",
      "354\n",
      "355\n",
      "356\n",
      "357\n",
      "358\n",
      "359\n",
      "360\n",
      "361\n",
      "362\n",
      "363\n",
      "364\n",
      "365\n",
      "366\n",
      "367\n",
      "368\n",
      "369\n",
      "370\n",
      "371\n",
      "372\n",
      "373\n",
      "374\n",
      "375\n",
      "376\n",
      "377\n",
      "378\n",
      "379\n",
      "380\n",
      "381\n",
      "382\n",
      "383\n",
      "384\n",
      "385\n",
      "386\n",
      "387\n",
      "388\n",
      "389\n",
      "390\n",
      "391\n",
      "392\n",
      "393\n",
      "394\n",
      "395\n",
      "396\n",
      "397\n",
      "398\n",
      "399\n",
      "Epoch:1/2 Training Loss: 1.379 Validation Loss: 1.047 Validation Accuracy: 0.716\n",
      "400\n",
      "401\n",
      "402\n",
      "403\n",
      "404\n",
      "405\n",
      "406\n",
      "407\n",
      "408\n",
      "409\n",
      "410\n",
      "411\n",
      "412\n",
      "413\n",
      "414\n",
      "415\n",
      "416\n",
      "417\n",
      "418\n",
      "419\n",
      "420\n",
      "421\n",
      "422\n",
      "423\n",
      "424\n",
      "425\n",
      "426\n",
      "427\n",
      "428\n",
      "429\n",
      "430\n",
      "431\n",
      "432\n",
      "433\n",
      "434\n",
      "435\n",
      "436\n",
      "437\n",
      "438\n",
      "439\n",
      "440\n",
      "441\n",
      "442\n",
      "443\n",
      "444\n",
      "445\n",
      "446\n",
      "447\n",
      "448\n",
      "449\n",
      "450\n",
      "451\n",
      "452\n",
      "453\n",
      "454\n",
      "455\n",
      "456\n",
      "457\n",
      "458\n",
      "459\n",
      "460\n",
      "461\n",
      "462\n",
      "463\n",
      "464\n",
      "465\n",
      "466\n",
      "467\n",
      "468\n",
      "469\n",
      "470\n",
      "471\n",
      "472\n",
      "473\n",
      "474\n",
      "475\n",
      "476\n",
      "477\n",
      "478\n",
      "479\n",
      "480\n",
      "481\n",
      "482\n",
      "483\n",
      "484\n",
      "485\n",
      "486\n",
      "487\n",
      "488\n",
      "489\n",
      "490\n",
      "491\n",
      "492\n",
      "493\n",
      "494\n",
      "495\n",
      "496\n",
      "497\n",
      "498\n",
      "499\n",
      "Epoch:1/2 Training Loss: 1.316 Validation Loss: 0.855 Validation Accuracy: 0.770\n",
      "500\n",
      "501\n",
      "502\n",
      "503\n",
      "504\n",
      "505\n",
      "506\n",
      "507\n",
      "508\n",
      "509\n",
      "510\n",
      "511\n",
      "512\n",
      "513\n",
      "514\n",
      "515\n",
      "516\n",
      "517\n",
      "518\n",
      "519\n",
      "520\n",
      "521\n",
      "522\n",
      "523\n",
      "524\n",
      "525\n",
      "526\n",
      "527\n",
      "528\n",
      "529\n",
      "530\n",
      "531\n",
      "532\n",
      "533\n",
      "534\n",
      "535\n",
      "536\n",
      "537\n",
      "538\n",
      "539\n",
      "540\n",
      "541\n",
      "542\n",
      "543\n",
      "544\n",
      "545\n",
      "546\n",
      "547\n",
      "548\n",
      "549\n",
      "550\n",
      "551\n",
      "552\n",
      "553\n",
      "554\n",
      "555\n",
      "556\n",
      "557\n",
      "558\n",
      "559\n",
      "560\n",
      "561\n",
      "562\n",
      "563\n",
      "564\n",
      "565\n",
      "566\n",
      "567\n",
      "568\n",
      "569\n",
      "570\n",
      "571\n",
      "572\n",
      "573\n",
      "574\n",
      "575\n",
      "576\n",
      "577\n",
      "578\n",
      "579\n",
      "580\n",
      "581\n",
      "582\n",
      "583\n",
      "584\n",
      "585\n",
      "586\n",
      "587\n",
      "588\n",
      "589\n",
      "590\n",
      "591\n",
      "592\n",
      "593\n",
      "594\n",
      "595\n",
      "596\n",
      "597\n",
      "598\n",
      "599\n",
      "Epoch:1/2 Training Loss: 1.369 Validation Loss: 0.999 Validation Accuracy: 0.709\n",
      "600\n",
      "601\n",
      "602\n",
      "603\n",
      "604\n",
      "605\n",
      "606\n",
      "607\n",
      "608\n",
      "609\n",
      "610\n",
      "611\n",
      "612\n",
      "613\n",
      "614\n",
      "615\n",
      "616\n",
      "617\n",
      "618\n",
      "619\n",
      "620\n",
      "621\n",
      "622\n",
      "623\n",
      "624\n",
      "625\n",
      "626\n",
      "627\n",
      "628\n",
      "629\n",
      "630\n",
      "631\n",
      "632\n",
      "633\n",
      "634\n",
      "635\n",
      "636\n",
      "637\n",
      "638\n",
      "639\n",
      "640\n",
      "641\n",
      "642\n",
      "643\n",
      "644\n",
      "645\n",
      "646\n",
      "647\n",
      "648\n",
      "649\n",
      "650\n",
      "651\n",
      "652\n",
      "653\n",
      "654\n",
      "655\n",
      "656\n",
      "657\n",
      "658\n",
      "659\n",
      "660\n",
      "661\n",
      "662\n",
      "663\n",
      "664\n",
      "665\n",
      "666\n",
      "667\n",
      "668\n",
      "669\n",
      "670\n",
      "671\n",
      "672\n",
      "673\n",
      "674\n",
      "675\n",
      "676\n",
      "677\n",
      "678\n",
      "679\n",
      "680\n",
      "681\n",
      "682\n",
      "683\n",
      "684\n",
      "685\n",
      "686\n",
      "687\n",
      "688\n",
      "689\n",
      "690\n",
      "691\n",
      "692\n",
      "693\n",
      "694\n",
      "695\n",
      "696\n",
      "697\n",
      "698\n",
      "699\n",
      "Epoch:1/2 Training Loss: 1.223 Validation Loss: 0.807 Validation Accuracy: 0.760\n",
      "700\n",
      "701\n",
      "702\n",
      "703\n",
      "704\n",
      "705\n",
      "706\n",
      "707\n",
      "708\n",
      "709\n",
      "710\n",
      "711\n",
      "712\n",
      "713\n",
      "714\n",
      "715\n",
      "716\n",
      "717\n",
      "718\n",
      "719\n",
      "720\n",
      "721\n",
      "722\n",
      "723\n",
      "724\n",
      "725\n",
      "726\n",
      "727\n",
      "728\n",
      "729\n",
      "730\n",
      "731\n",
      "732\n",
      "733\n",
      "734\n",
      "735\n",
      "736\n",
      "737\n",
      "738\n",
      "739\n",
      "740\n",
      "741\n",
      "742\n",
      "743\n",
      "744\n",
      "745\n",
      "746\n",
      "747\n",
      "748\n",
      "749\n",
      "750\n",
      "751\n",
      "752\n",
      "753\n",
      "754\n",
      "755\n",
      "756\n",
      "757\n",
      "758\n",
      "759\n",
      "760\n",
      "761\n",
      "762\n",
      "763\n",
      "764\n",
      "765\n",
      "766\n",
      "767\n",
      "768\n",
      "769\n",
      "770\n",
      "771\n",
      "772\n",
      "773\n",
      "774\n",
      "775\n",
      "776\n",
      "777\n",
      "778\n",
      "779\n",
      "780\n",
      "781\n",
      "782\n",
      "783\n",
      "784\n",
      "785\n",
      "786\n",
      "787\n",
      "788\n",
      "789\n",
      "790\n",
      "791\n",
      "792\n",
      "793\n",
      "794\n",
      "795\n",
      "796\n",
      "797\n",
      "798\n",
      "799\n",
      "Epoch:1/2 Training Loss: 1.280 Validation Loss: 0.749 Validation Accuracy: 0.795\n",
      "800\n",
      "801\n",
      "802\n",
      "803\n",
      "804\n",
      "805\n",
      "806\n",
      "807\n",
      "808\n",
      "809\n",
      "810\n",
      "811\n",
      "812\n",
      "813\n",
      "814\n",
      "815\n",
      "816\n",
      "817\n",
      "818\n",
      "819\n",
      "820\n",
      "821\n",
      "822\n",
      "823\n",
      "824\n",
      "825\n",
      "826\n",
      "827\n",
      "828\n",
      "829\n",
      "830\n",
      "831\n",
      "832\n",
      "833\n",
      "834\n",
      "835\n",
      "836\n",
      "837\n",
      "838\n",
      "839\n",
      "840\n",
      "841\n",
      "842\n",
      "843\n",
      "844\n",
      "845\n",
      "846\n",
      "847\n",
      "848\n",
      "849\n",
      "850\n",
      "851\n",
      "852\n",
      "853\n",
      "854\n",
      "855\n",
      "856\n",
      "857\n",
      "858\n",
      "859\n",
      "860\n",
      "861\n",
      "862\n",
      "863\n",
      "864\n",
      "865\n",
      "866\n",
      "867\n",
      "868\n",
      "869\n",
      "870\n",
      "871\n",
      "872\n",
      "873\n",
      "874\n",
      "875\n",
      "876\n",
      "877\n",
      "878\n",
      "879\n",
      "880\n",
      "881\n",
      "882\n",
      "883\n",
      "884\n",
      "885\n",
      "886\n",
      "887\n",
      "888\n",
      "889\n",
      "890\n",
      "891\n",
      "892\n",
      "893\n",
      "894\n",
      "895\n",
      "896\n",
      "897\n",
      "898\n",
      "899\n",
      "Epoch:1/2 Training Loss: 1.311 Validation Loss: 0.815 Validation Accuracy: 0.768\n",
      "900\n",
      "901\n",
      "902\n",
      "903\n",
      "904\n",
      "905\n",
      "906\n",
      "907\n",
      "908\n",
      "909\n",
      "910\n",
      "911\n",
      "912\n",
      "913\n",
      "914\n",
      "915\n",
      "916\n",
      "917\n",
      "918\n",
      "919\n",
      "920\n",
      "921\n",
      "922\n",
      "923\n",
      "924\n",
      "925\n",
      "926\n",
      "927\n",
      "928\n",
      "929\n",
      "930\n",
      "931\n",
      "932\n",
      "933\n",
      "934\n",
      "935\n",
      "936\n",
      "937\n",
      "938\n",
      "939\n",
      "940\n",
      "941\n",
      "942\n",
      "943\n",
      "944\n",
      "945\n",
      "946\n",
      "947\n",
      "948\n",
      "949\n",
      "950\n",
      "951\n",
      "952\n",
      "953\n",
      "954\n",
      "955\n",
      "956\n",
      "957\n",
      "958\n",
      "959\n",
      "960\n",
      "961\n",
      "962\n",
      "963\n",
      "964\n",
      "965\n",
      "966\n",
      "967\n",
      "968\n",
      "969\n",
      "970\n",
      "971\n",
      "972\n",
      "973\n",
      "974\n",
      "975\n",
      "976\n",
      "977\n",
      "978\n",
      "979\n",
      "980\n",
      "981\n",
      "982\n",
      "983\n",
      "984\n",
      "985\n",
      "986\n",
      "987\n",
      "988\n",
      "989\n",
      "990\n",
      "991\n",
      "992\n",
      "993\n",
      "994\n",
      "995\n",
      "996\n",
      "997\n",
      "998\n",
      "999\n",
      "Epoch:1/2 Training Loss: 1.104 Validation Loss: 0.815 Validation Accuracy: 0.782\n",
      "1000\n",
      "1001\n",
      "1002\n",
      "1003\n",
      "1004\n",
      "1005\n",
      "1006\n",
      "1007\n",
      "1008\n",
      "1009\n",
      "1010\n",
      "1011\n",
      "1012\n",
      "1013\n",
      "1014\n",
      "1015\n",
      "1016\n",
      "1017\n",
      "1018\n",
      "1019\n",
      "1020\n",
      "1021\n",
      "1022\n",
      "1023\n",
      "1024\n",
      "1025\n",
      "1026\n",
      "1027\n",
      "1028\n",
      "1029\n",
      "1030\n",
      "1031\n",
      "1032\n",
      "1033\n",
      "1034\n",
      "1035\n",
      "1036\n",
      "1037\n",
      "1038\n",
      "1039\n",
      "1040\n",
      "1041\n",
      "1042\n",
      "1043\n",
      "1044\n",
      "1045\n",
      "1046\n",
      "1047\n",
      "1048\n",
      "1049\n",
      "1050\n",
      "1051\n",
      "1052\n",
      "1053\n",
      "1054\n",
      "1055\n",
      "1056\n",
      "1057\n",
      "1058\n",
      "1059\n",
      "1060\n",
      "1061\n",
      "1062\n",
      "1063\n",
      "1064\n",
      "1065\n",
      "1066\n",
      "1067\n",
      "1068\n",
      "1069\n",
      "1070\n",
      "1071\n",
      "1072\n",
      "1073\n",
      "1074\n",
      "1075\n",
      "1076\n",
      "1077\n",
      "1078\n",
      "1079\n",
      "1080\n",
      "1081\n",
      "1082\n",
      "1083\n",
      "1084\n",
      "1085\n",
      "1086\n",
      "1087\n",
      "1088\n",
      "1089\n",
      "1090\n",
      "1091\n",
      "1092\n",
      "1093\n",
      "1094\n",
      "1095\n",
      "1096\n",
      "1097\n",
      "1098\n",
      "1099\n",
      "Epoch:1/2 Training Loss: 1.157 Validation Loss: 0.789 Validation Accuracy: 0.782\n",
      "1100\n",
      "1101\n",
      "1102\n",
      "1103\n",
      "1104\n",
      "1105\n",
      "1106\n",
      "1107\n",
      "1108\n",
      "1109\n",
      "1110\n",
      "1111\n",
      "1112\n",
      "1113\n",
      "1114\n",
      "1115\n",
      "1116\n",
      "1117\n",
      "1118\n",
      "1119\n",
      "1120\n",
      "1121\n",
      "1122\n",
      "1123\n",
      "1124\n",
      "1125\n",
      "1126\n",
      "1127\n",
      "1128\n",
      "1129\n",
      "1130\n",
      "1131\n",
      "1132\n",
      "1133\n",
      "1134\n",
      "1135\n",
      "1136\n",
      "1137\n",
      "1138\n",
      "1139\n",
      "1140\n",
      "1141\n",
      "1142\n",
      "1143\n",
      "1144\n",
      "1145\n",
      "1146\n",
      "1147\n",
      "1148\n",
      "1149\n",
      "1150\n",
      "1151\n",
      "1152\n",
      "1153\n",
      "1154\n",
      "1155\n",
      "1156\n",
      "1157\n",
      "1158\n",
      "1159\n",
      "1160\n",
      "1161\n",
      "1162\n",
      "1163\n",
      "1164\n",
      "1165\n",
      "1166\n",
      "1167\n",
      "1168\n",
      "1169\n",
      "1170\n",
      "1171\n",
      "1172\n",
      "1173\n",
      "1174\n",
      "1175\n",
      "1176\n",
      "1177\n",
      "1178\n",
      "1179\n",
      "1180\n",
      "1181\n",
      "1182\n",
      "1183\n",
      "1184\n",
      "1185\n",
      "1186\n",
      "1187\n",
      "1188\n",
      "1189\n",
      "1190\n",
      "1191\n",
      "1192\n",
      "1193\n",
      "1194\n",
      "1195\n",
      "1196\n",
      "1197\n",
      "1198\n",
      "1199\n",
      "Epoch:1/2 Training Loss: 1.154 Validation Loss: 0.725 Validation Accuracy: 0.816\n",
      "1200\n",
      "1201\n",
      "1202\n",
      "1203\n",
      "1204\n",
      "1205\n",
      "1206\n",
      "1207\n",
      "1208\n",
      "1209\n",
      "1210\n",
      "1211\n",
      "1212\n",
      "1213\n",
      "1214\n",
      "1215\n",
      "1216\n",
      "1217\n",
      "1218\n",
      "1219\n",
      "1220\n",
      "1221\n",
      "1222\n",
      "1223\n",
      "1224\n",
      "1225\n",
      "1226\n",
      "1227\n",
      "1228\n",
      "1229\n",
      "1230\n",
      "1231\n",
      "1232\n",
      "1233\n",
      "1234\n",
      "1235\n",
      "1236\n",
      "1237\n",
      "1238\n",
      "1239\n",
      "1240\n",
      "1241\n",
      "1242\n",
      "1243\n",
      "1244\n",
      "1245\n",
      "1246\n",
      "1247\n",
      "1248\n",
      "1249\n",
      "1250\n",
      "1251\n",
      "1252\n",
      "1253\n",
      "1254\n",
      "1255\n",
      "1256\n",
      "1257\n",
      "1258\n",
      "1259\n",
      "1260\n",
      "1261\n",
      "1262\n",
      "1263\n",
      "1264\n",
      "1265\n",
      "1266\n",
      "1267\n",
      "1268\n",
      "1269\n",
      "1270\n",
      "1271\n",
      "1272\n",
      "1273\n",
      "1274\n",
      "1275\n",
      "1276\n",
      "1277\n",
      "1278\n",
      "1279\n",
      "1280\n",
      "1281\n",
      "1282\n",
      "1283\n",
      "1284\n",
      "1285\n",
      "1286\n",
      "1287\n",
      "1288\n",
      "1289\n",
      "1290\n",
      "1291\n",
      "1292\n",
      "1293\n",
      "1294\n",
      "1295\n",
      "1296\n",
      "1297\n",
      "1298\n",
      "1299\n",
      "Epoch:1/2 Training Loss: 1.109 Validation Loss: 0.702 Validation Accuracy: 0.816\n",
      "1300\n",
      "1301\n",
      "1302\n",
      "1303\n",
      "1304\n",
      "1305\n",
      "1306\n",
      "1307\n",
      "1308\n",
      "1309\n",
      "1310\n",
      "1311\n",
      "1312\n",
      "1313\n",
      "1314\n",
      "1315\n",
      "1316\n",
      "1317\n",
      "1318\n",
      "1319\n",
      "1320\n",
      "1321\n",
      "1322\n",
      "1323\n",
      "1324\n",
      "1325\n",
      "1326\n",
      "1327\n",
      "1328\n",
      "1329\n",
      "1330\n",
      "1331\n",
      "1332\n",
      "1333\n",
      "1334\n",
      "1335\n",
      "1336\n",
      "1337\n",
      "1338\n",
      "1339\n",
      "1340\n",
      "1341\n",
      "1342\n",
      "1343\n",
      "1344\n",
      "1345\n",
      "1346\n",
      "1347\n",
      "1348\n",
      "1349\n",
      "1350\n",
      "1351\n",
      "1352\n",
      "1353\n",
      "1354\n",
      "1355\n",
      "1356\n",
      "1357\n",
      "1358\n",
      "1359\n",
      "1360\n",
      "1361\n",
      "1362\n",
      "1363\n",
      "1364\n",
      "1365\n",
      "1366\n",
      "1367\n",
      "1368\n",
      "1369\n",
      "1370\n",
      "1371\n",
      "1372\n",
      "1373\n",
      "1374\n",
      "1375\n",
      "1376\n",
      "1377\n",
      "1378\n",
      "1379\n",
      "1380\n",
      "1381\n",
      "1382\n",
      "1383\n",
      "1384\n",
      "1385\n",
      "1386\n",
      "1387\n",
      "1388\n",
      "1389\n",
      "1390\n",
      "1391\n",
      "1392\n",
      "1393\n",
      "1394\n",
      "1395\n",
      "1396\n",
      "1397\n",
      "1398\n",
      "1399\n",
      "Epoch:1/2 Training Loss: 1.068 Validation Loss: 0.667 Validation Accuracy: 0.821\n",
      "1400\n",
      "1401\n",
      "1402\n",
      "1403\n",
      "1404\n",
      "1405\n",
      "1406\n",
      "1407\n",
      "1408\n",
      "1409\n",
      "1410\n",
      "1411\n",
      "1412\n",
      "1413\n",
      "1414\n",
      "1415\n",
      "1416\n",
      "1417\n",
      "1418\n",
      "1419\n",
      "1420\n",
      "1421\n",
      "1422\n",
      "1423\n",
      "1424\n",
      "1425\n",
      "1426\n",
      "1427\n",
      "1428\n",
      "1429\n",
      "1430\n",
      "1431\n",
      "1432\n",
      "1433\n",
      "1434\n",
      "1435\n",
      "1436\n",
      "1437\n",
      "1438\n",
      "1439\n",
      "1440\n",
      "1441\n",
      "1442\n",
      "1443\n",
      "1444\n",
      "1445\n",
      "1446\n",
      "1447\n",
      "1448\n",
      "1449\n",
      "1450\n",
      "1451\n",
      "1452\n",
      "1453\n",
      "1454\n",
      "1455\n",
      "1456\n",
      "1457\n",
      "1458\n",
      "1459\n",
      "1460\n",
      "1461\n",
      "1462\n",
      "1463\n",
      "1464\n",
      "1465\n",
      "1466\n",
      "1467\n",
      "1468\n",
      "1469\n",
      "1470\n",
      "1471\n",
      "1472\n",
      "1473\n",
      "1474\n",
      "1475\n",
      "1476\n",
      "1477\n",
      "1478\n",
      "1479\n",
      "1480\n",
      "1481\n",
      "1482\n",
      "1483\n",
      "1484\n",
      "1485\n",
      "1486\n",
      "1487\n",
      "1488\n",
      "1489\n",
      "1490\n",
      "1491\n",
      "1492\n",
      "1493\n",
      "1494\n",
      "1495\n",
      "1496\n",
      "1497\n",
      "1498\n",
      "1499\n",
      "Epoch:1/2 Training Loss: 1.076 Validation Loss: 0.687 Validation Accuracy: 0.811\n",
      "1500\n",
      "1501\n",
      "1502\n",
      "1503\n",
      "1504\n",
      "1505\n",
      "1506\n",
      "1507\n",
      "1508\n",
      "1509\n",
      "1510\n",
      "1511\n",
      "1512\n",
      "1513\n",
      "1514\n",
      "1515\n",
      "1516\n",
      "1517\n",
      "1518\n",
      "1519\n",
      "1520\n",
      "1521\n",
      "1522\n",
      "1523\n",
      "1524\n",
      "1525\n",
      "1526\n",
      "1527\n",
      "1528\n",
      "1529\n",
      "1530\n",
      "1531\n",
      "1532\n",
      "1533\n",
      "1534\n",
      "1535\n",
      "1536\n",
      "1537\n",
      "1538\n",
      "1539\n",
      "1540\n",
      "1541\n",
      "1542\n",
      "1543\n",
      "1544\n",
      "1545\n",
      "1546\n",
      "1547\n",
      "1548\n",
      "1549\n",
      "1550\n",
      "1551\n",
      "1552\n",
      "1553\n",
      "1554\n",
      "1555\n",
      "1556\n",
      "1557\n",
      "1558\n",
      "1559\n",
      "1560\n",
      "1561\n",
      "1562\n",
      "1563\n",
      "1564\n",
      "1565\n",
      "1566\n",
      "1567\n",
      "1568\n",
      "1569\n",
      "1570\n",
      "1571\n",
      "1572\n",
      "1573\n",
      "1574\n",
      "1575\n",
      "1576\n",
      "1577\n",
      "1578\n",
      "1579\n",
      "1580\n",
      "1581\n",
      "1582\n",
      "1583\n",
      "1584\n",
      "1585\n",
      "1586\n",
      "1587\n",
      "1588\n",
      "1589\n",
      "1590\n",
      "1591\n",
      "1592\n",
      "1593\n",
      "1594\n",
      "1595\n",
      "1596\n",
      "1597\n",
      "1598\n",
      "1599\n",
      "Epoch:1/2 Training Loss: 1.168 Validation Loss: 0.682 Validation Accuracy: 0.822\n",
      "1600\n",
      "1601\n",
      "1602\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1603\n",
      "1604\n",
      "1605\n",
      "1606\n",
      "1607\n",
      "1608\n",
      "1609\n",
      "1610\n",
      "1611\n",
      "1612\n",
      "1613\n",
      "1614\n",
      "1615\n",
      "1616\n",
      "1617\n",
      "1618\n",
      "1619\n",
      "1620\n",
      "1621\n",
      "1622\n",
      "1623\n",
      "1624\n",
      "1625\n",
      "1626\n",
      "1627\n",
      "1628\n",
      "1629\n",
      "1630\n",
      "1631\n",
      "1632\n",
      "1633\n",
      "1634\n",
      "1635\n",
      "1636\n",
      "1637\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "14\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n",
      "27\n",
      "28\n",
      "29\n",
      "30\n",
      "31\n",
      "32\n",
      "33\n",
      "34\n",
      "35\n",
      "36\n",
      "37\n",
      "38\n",
      "39\n",
      "40\n",
      "41\n",
      "42\n",
      "43\n",
      "44\n",
      "45\n",
      "46\n",
      "47\n",
      "48\n",
      "49\n",
      "50\n",
      "51\n",
      "52\n",
      "53\n",
      "54\n",
      "55\n",
      "56\n",
      "57\n",
      "58\n",
      "59\n",
      "60\n",
      "61\n",
      "Epoch:2/2 Training Loss: 0.624 Validation Loss: 0.585 Validation Accuracy: 0.839\n",
      "62\n",
      "63\n",
      "64\n",
      "65\n",
      "66\n",
      "67\n",
      "68\n",
      "69\n",
      "70\n",
      "71\n",
      "72\n",
      "73\n",
      "74\n",
      "75\n",
      "76\n",
      "77\n",
      "78\n",
      "79\n",
      "80\n",
      "81\n",
      "82\n",
      "83\n",
      "84\n",
      "85\n",
      "86\n",
      "87\n",
      "88\n",
      "89\n",
      "90\n",
      "91\n",
      "92\n",
      "93\n",
      "94\n",
      "95\n",
      "96\n",
      "97\n",
      "98\n",
      "99\n",
      "100\n",
      "101\n",
      "102\n",
      "103\n",
      "104\n",
      "105\n",
      "106\n",
      "107\n",
      "108\n",
      "109\n",
      "110\n",
      "111\n",
      "112\n",
      "113\n",
      "114\n",
      "115\n",
      "116\n",
      "117\n",
      "118\n",
      "119\n",
      "120\n",
      "121\n",
      "122\n",
      "123\n",
      "124\n",
      "125\n",
      "126\n",
      "127\n",
      "128\n",
      "129\n",
      "130\n",
      "131\n",
      "132\n",
      "133\n",
      "134\n",
      "135\n",
      "136\n",
      "137\n",
      "138\n",
      "139\n",
      "140\n",
      "141\n",
      "142\n",
      "143\n",
      "144\n",
      "145\n",
      "146\n",
      "147\n",
      "148\n",
      "149\n",
      "150\n",
      "151\n",
      "152\n",
      "153\n",
      "154\n",
      "155\n",
      "156\n",
      "157\n",
      "158\n",
      "159\n",
      "160\n",
      "161\n",
      "Epoch:2/2 Training Loss: 0.860 Validation Loss: 0.699 Validation Accuracy: 0.807\n",
      "162\n",
      "163\n",
      "164\n",
      "165\n",
      "166\n",
      "167\n",
      "168\n",
      "169\n",
      "170\n",
      "171\n",
      "172\n",
      "173\n",
      "174\n",
      "175\n",
      "176\n",
      "177\n",
      "178\n",
      "179\n",
      "180\n",
      "181\n",
      "182\n",
      "183\n",
      "184\n",
      "185\n",
      "186\n",
      "187\n",
      "188\n",
      "189\n",
      "190\n",
      "191\n",
      "192\n",
      "193\n",
      "194\n",
      "195\n",
      "196\n",
      "197\n",
      "198\n",
      "199\n",
      "200\n",
      "201\n",
      "202\n",
      "203\n",
      "204\n",
      "205\n",
      "206\n",
      "207\n",
      "208\n",
      "209\n",
      "210\n",
      "211\n",
      "212\n",
      "213\n",
      "214\n",
      "215\n",
      "216\n",
      "217\n",
      "218\n",
      "219\n",
      "220\n",
      "221\n",
      "222\n",
      "223\n",
      "224\n",
      "225\n",
      "226\n",
      "227\n",
      "228\n",
      "229\n",
      "230\n",
      "231\n",
      "232\n",
      "233\n",
      "234\n",
      "235\n",
      "236\n",
      "237\n",
      "238\n",
      "239\n",
      "240\n",
      "241\n",
      "242\n",
      "243\n",
      "244\n",
      "245\n",
      "246\n",
      "247\n",
      "248\n",
      "249\n",
      "250\n",
      "251\n",
      "252\n",
      "253\n",
      "254\n",
      "255\n",
      "256\n",
      "257\n",
      "258\n",
      "259\n",
      "260\n",
      "261\n",
      "Epoch:2/2 Training Loss: 0.944 Validation Loss: 0.588 Validation Accuracy: 0.826\n",
      "262\n",
      "263\n",
      "264\n",
      "265\n",
      "266\n",
      "267\n",
      "268\n",
      "269\n",
      "270\n",
      "271\n",
      "272\n",
      "273\n",
      "274\n",
      "275\n",
      "276\n",
      "277\n",
      "278\n",
      "279\n",
      "280\n",
      "281\n",
      "282\n",
      "283\n",
      "284\n",
      "285\n",
      "286\n",
      "287\n",
      "288\n",
      "289\n",
      "290\n",
      "291\n",
      "292\n",
      "293\n",
      "294\n",
      "295\n",
      "296\n",
      "297\n",
      "298\n",
      "299\n",
      "300\n",
      "301\n",
      "302\n",
      "303\n",
      "304\n",
      "305\n",
      "306\n",
      "307\n",
      "308\n",
      "309\n",
      "310\n",
      "311\n",
      "312\n",
      "313\n",
      "314\n",
      "315\n",
      "316\n",
      "317\n",
      "318\n",
      "319\n",
      "320\n",
      "321\n",
      "322\n",
      "323\n",
      "324\n",
      "325\n",
      "326\n",
      "327\n",
      "328\n",
      "329\n",
      "330\n",
      "331\n",
      "332\n",
      "333\n",
      "334\n",
      "335\n",
      "336\n",
      "337\n",
      "338\n",
      "339\n",
      "340\n",
      "341\n",
      "342\n",
      "343\n",
      "344\n",
      "345\n",
      "346\n",
      "347\n",
      "348\n",
      "349\n",
      "350\n",
      "351\n",
      "352\n",
      "353\n",
      "354\n",
      "355\n",
      "356\n",
      "357\n",
      "358\n",
      "359\n",
      "360\n",
      "361\n",
      "Epoch:2/2 Training Loss: 1.015 Validation Loss: 0.577 Validation Accuracy: 0.835\n",
      "362\n",
      "363\n",
      "364\n",
      "365\n",
      "366\n",
      "367\n",
      "368\n",
      "369\n",
      "370\n",
      "371\n",
      "372\n",
      "373\n",
      "374\n",
      "375\n",
      "376\n",
      "377\n",
      "378\n",
      "379\n",
      "380\n",
      "381\n",
      "382\n",
      "383\n",
      "384\n",
      "385\n",
      "386\n",
      "387\n",
      "388\n",
      "389\n",
      "390\n",
      "391\n",
      "392\n",
      "393\n",
      "394\n",
      "395\n",
      "396\n",
      "397\n",
      "398\n",
      "399\n",
      "400\n",
      "401\n",
      "402\n",
      "403\n",
      "404\n",
      "405\n",
      "406\n",
      "407\n",
      "408\n",
      "409\n",
      "410\n",
      "411\n",
      "412\n",
      "413\n",
      "414\n",
      "415\n",
      "416\n",
      "417\n",
      "418\n",
      "419\n",
      "420\n",
      "421\n",
      "422\n",
      "423\n",
      "424\n",
      "425\n",
      "426\n",
      "427\n",
      "428\n",
      "429\n",
      "430\n",
      "431\n",
      "432\n",
      "433\n",
      "434\n",
      "435\n",
      "436\n",
      "437\n",
      "438\n",
      "439\n",
      "440\n",
      "441\n",
      "442\n",
      "443\n",
      "444\n",
      "445\n",
      "446\n",
      "447\n",
      "448\n",
      "449\n",
      "450\n",
      "451\n",
      "452\n",
      "453\n",
      "454\n",
      "455\n",
      "456\n",
      "457\n",
      "458\n",
      "459\n",
      "460\n",
      "461\n",
      "Epoch:2/2 Training Loss: 0.928 Validation Loss: 0.608 Validation Accuracy: 0.828\n",
      "462\n",
      "463\n",
      "464\n",
      "465\n",
      "466\n",
      "467\n",
      "468\n",
      "469\n",
      "470\n",
      "471\n",
      "472\n",
      "473\n",
      "474\n",
      "475\n",
      "476\n",
      "477\n",
      "478\n",
      "479\n",
      "480\n",
      "481\n",
      "482\n",
      "483\n",
      "484\n",
      "485\n",
      "486\n",
      "487\n",
      "488\n",
      "489\n",
      "490\n",
      "491\n",
      "492\n",
      "493\n",
      "494\n",
      "495\n",
      "496\n",
      "497\n",
      "498\n",
      "499\n",
      "500\n",
      "501\n",
      "502\n",
      "503\n",
      "504\n",
      "505\n",
      "506\n",
      "507\n",
      "508\n",
      "509\n",
      "510\n",
      "511\n",
      "512\n",
      "513\n",
      "514\n",
      "515\n",
      "516\n",
      "517\n",
      "518\n",
      "519\n",
      "520\n",
      "521\n",
      "522\n",
      "523\n",
      "524\n",
      "525\n",
      "526\n",
      "527\n",
      "528\n",
      "529\n",
      "530\n",
      "531\n",
      "532\n",
      "533\n",
      "534\n",
      "535\n",
      "536\n",
      "537\n",
      "538\n",
      "539\n",
      "540\n",
      "541\n",
      "542\n",
      "543\n",
      "544\n",
      "545\n",
      "546\n",
      "547\n",
      "548\n",
      "549\n",
      "550\n",
      "551\n",
      "552\n",
      "553\n",
      "554\n",
      "555\n",
      "556\n",
      "557\n",
      "558\n",
      "559\n",
      "560\n",
      "561\n",
      "Epoch:2/2 Training Loss: 1.047 Validation Loss: 0.605 Validation Accuracy: 0.833\n",
      "562\n",
      "563\n",
      "564\n",
      "565\n",
      "566\n",
      "567\n",
      "568\n",
      "569\n",
      "570\n",
      "571\n",
      "572\n",
      "573\n",
      "574\n",
      "575\n",
      "576\n",
      "577\n",
      "578\n",
      "579\n",
      "580\n",
      "581\n",
      "582\n",
      "583\n",
      "584\n",
      "585\n",
      "586\n",
      "587\n",
      "588\n",
      "589\n",
      "590\n",
      "591\n",
      "592\n",
      "593\n",
      "594\n",
      "595\n",
      "596\n",
      "597\n",
      "598\n",
      "599\n",
      "600\n",
      "601\n",
      "602\n",
      "603\n",
      "604\n",
      "605\n",
      "606\n",
      "607\n",
      "608\n",
      "609\n",
      "610\n",
      "611\n",
      "612\n",
      "613\n",
      "614\n",
      "615\n",
      "616\n",
      "617\n",
      "618\n",
      "619\n",
      "620\n",
      "621\n",
      "622\n",
      "623\n",
      "624\n",
      "625\n",
      "626\n",
      "627\n",
      "628\n",
      "629\n",
      "630\n",
      "631\n",
      "632\n",
      "633\n",
      "634\n",
      "635\n",
      "636\n",
      "637\n",
      "638\n",
      "639\n",
      "640\n",
      "641\n",
      "642\n",
      "643\n",
      "644\n",
      "645\n",
      "646\n",
      "647\n",
      "648\n",
      "649\n",
      "650\n",
      "651\n",
      "652\n",
      "653\n",
      "654\n",
      "655\n",
      "656\n",
      "657\n",
      "658\n",
      "659\n",
      "660\n",
      "661\n",
      "Epoch:2/2 Training Loss: 1.040 Validation Loss: 0.642 Validation Accuracy: 0.828\n",
      "662\n",
      "663\n",
      "664\n",
      "665\n",
      "666\n",
      "667\n",
      "668\n",
      "669\n",
      "670\n",
      "671\n",
      "672\n",
      "673\n",
      "674\n",
      "675\n",
      "676\n",
      "677\n",
      "678\n",
      "679\n",
      "680\n",
      "681\n",
      "682\n",
      "683\n",
      "684\n",
      "685\n",
      "686\n",
      "687\n",
      "688\n",
      "689\n",
      "690\n",
      "691\n",
      "692\n",
      "693\n",
      "694\n",
      "695\n",
      "696\n",
      "697\n",
      "698\n",
      "699\n",
      "700\n",
      "701\n",
      "702\n",
      "703\n",
      "704\n",
      "705\n",
      "706\n",
      "707\n",
      "708\n",
      "709\n",
      "710\n",
      "711\n",
      "712\n",
      "713\n",
      "714\n",
      "715\n",
      "716\n",
      "717\n",
      "718\n",
      "719\n",
      "720\n",
      "721\n",
      "722\n",
      "723\n",
      "724\n",
      "725\n",
      "726\n",
      "727\n",
      "728\n",
      "729\n",
      "730\n",
      "731\n",
      "732\n",
      "733\n",
      "734\n",
      "735\n",
      "736\n",
      "737\n",
      "738\n",
      "739\n",
      "740\n",
      "741\n",
      "742\n",
      "743\n",
      "744\n",
      "745\n",
      "746\n",
      "747\n",
      "748\n",
      "749\n",
      "750\n",
      "751\n",
      "752\n",
      "753\n",
      "754\n",
      "755\n",
      "756\n",
      "757\n",
      "758\n",
      "759\n",
      "760\n",
      "761\n",
      "Epoch:2/2 Training Loss: 0.912 Validation Loss: 0.552 Validation Accuracy: 0.846\n",
      "762\n",
      "763\n",
      "764\n",
      "765\n",
      "766\n",
      "767\n",
      "768\n",
      "769\n",
      "770\n",
      "771\n",
      "772\n",
      "773\n",
      "774\n",
      "775\n",
      "776\n",
      "777\n",
      "778\n",
      "779\n",
      "780\n",
      "781\n",
      "782\n",
      "783\n",
      "784\n",
      "785\n",
      "786\n",
      "787\n",
      "788\n",
      "789\n",
      "790\n",
      "791\n",
      "792\n",
      "793\n",
      "794\n",
      "795\n",
      "796\n",
      "797\n",
      "798\n",
      "799\n",
      "800\n",
      "801\n",
      "802\n",
      "803\n",
      "804\n",
      "805\n",
      "806\n",
      "807\n",
      "808\n",
      "809\n",
      "810\n",
      "811\n",
      "812\n",
      "813\n",
      "814\n",
      "815\n",
      "816\n",
      "817\n",
      "818\n",
      "819\n",
      "820\n",
      "821\n",
      "822\n",
      "823\n",
      "824\n",
      "825\n",
      "826\n",
      "827\n",
      "828\n",
      "829\n",
      "830\n",
      "831\n",
      "832\n",
      "833\n",
      "834\n",
      "835\n",
      "836\n",
      "837\n",
      "838\n",
      "839\n",
      "840\n",
      "841\n",
      "842\n",
      "843\n",
      "844\n",
      "845\n",
      "846\n",
      "847\n",
      "848\n",
      "849\n",
      "850\n",
      "851\n",
      "852\n",
      "853\n",
      "854\n",
      "855\n",
      "856\n",
      "857\n",
      "858\n",
      "859\n",
      "860\n",
      "861\n",
      "Epoch:2/2 Training Loss: 0.881 Validation Loss: 0.597 Validation Accuracy: 0.848\n",
      "862\n",
      "863\n",
      "864\n",
      "865\n",
      "866\n",
      "867\n",
      "868\n",
      "869\n",
      "870\n",
      "871\n",
      "872\n",
      "873\n",
      "874\n",
      "875\n",
      "876\n",
      "877\n",
      "878\n",
      "879\n",
      "880\n",
      "881\n",
      "882\n",
      "883\n",
      "884\n",
      "885\n",
      "886\n",
      "887\n",
      "888\n",
      "889\n",
      "890\n",
      "891\n",
      "892\n",
      "893\n",
      "894\n",
      "895\n",
      "896\n",
      "897\n",
      "898\n",
      "899\n",
      "900\n",
      "901\n",
      "902\n",
      "903\n",
      "904\n",
      "905\n",
      "906\n",
      "907\n",
      "908\n",
      "909\n",
      "910\n",
      "911\n",
      "912\n",
      "913\n",
      "914\n",
      "915\n",
      "916\n",
      "917\n",
      "918\n",
      "919\n",
      "920\n",
      "921\n",
      "922\n",
      "923\n",
      "924\n",
      "925\n",
      "926\n",
      "927\n",
      "928\n",
      "929\n",
      "930\n",
      "931\n",
      "932\n",
      "933\n",
      "934\n",
      "935\n",
      "936\n",
      "937\n",
      "938\n",
      "939\n",
      "940\n",
      "941\n",
      "942\n",
      "943\n",
      "944\n",
      "945\n",
      "946\n",
      "947\n",
      "948\n",
      "949\n",
      "950\n",
      "951\n",
      "952\n",
      "953\n",
      "954\n",
      "955\n",
      "956\n",
      "957\n",
      "958\n",
      "959\n",
      "960\n",
      "961\n",
      "Epoch:2/2 Training Loss: 0.762 Validation Loss: 0.540 Validation Accuracy: 0.861\n",
      "962\n",
      "963\n",
      "964\n",
      "965\n",
      "966\n",
      "967\n",
      "968\n",
      "969\n",
      "970\n",
      "971\n",
      "972\n",
      "973\n",
      "974\n",
      "975\n",
      "976\n",
      "977\n",
      "978\n",
      "979\n",
      "980\n",
      "981\n",
      "982\n",
      "983\n",
      "984\n",
      "985\n",
      "986\n",
      "987\n",
      "988\n",
      "989\n",
      "990\n",
      "991\n",
      "992\n",
      "993\n",
      "994\n",
      "995\n",
      "996\n",
      "997\n",
      "998\n",
      "999\n",
      "1000\n",
      "1001\n",
      "1002\n",
      "1003\n",
      "1004\n",
      "1005\n",
      "1006\n",
      "1007\n",
      "1008\n",
      "1009\n",
      "1010\n",
      "1011\n",
      "1012\n",
      "1013\n",
      "1014\n",
      "1015\n",
      "1016\n",
      "1017\n",
      "1018\n",
      "1019\n",
      "1020\n",
      "1021\n",
      "1022\n",
      "1023\n",
      "1024\n",
      "1025\n",
      "1026\n",
      "1027\n",
      "1028\n",
      "1029\n",
      "1030\n",
      "1031\n",
      "1032\n",
      "1033\n",
      "1034\n",
      "1035\n",
      "1036\n",
      "1037\n",
      "1038\n",
      "1039\n",
      "1040\n",
      "1041\n",
      "1042\n",
      "1043\n",
      "1044\n",
      "1045\n",
      "1046\n",
      "1047\n",
      "1048\n",
      "1049\n",
      "1050\n",
      "1051\n",
      "1052\n",
      "1053\n",
      "1054\n",
      "1055\n",
      "1056\n",
      "1057\n",
      "1058\n",
      "1059\n",
      "1060\n",
      "1061\n",
      "Epoch:2/2 Training Loss: 0.827 Validation Loss: 0.522 Validation Accuracy: 0.865\n",
      "1062\n",
      "1063\n",
      "1064\n",
      "1065\n",
      "1066\n",
      "1067\n",
      "1068\n",
      "1069\n",
      "1070\n",
      "1071\n",
      "1072\n",
      "1073\n",
      "1074\n",
      "1075\n",
      "1076\n",
      "1077\n",
      "1078\n",
      "1079\n",
      "1080\n",
      "1081\n",
      "1082\n",
      "1083\n",
      "1084\n",
      "1085\n",
      "1086\n",
      "1087\n",
      "1088\n",
      "1089\n",
      "1090\n",
      "1091\n",
      "1092\n",
      "1093\n",
      "1094\n",
      "1095\n",
      "1096\n",
      "1097\n",
      "1098\n",
      "1099\n",
      "1100\n",
      "1101\n",
      "1102\n",
      "1103\n",
      "1104\n",
      "1105\n",
      "1106\n",
      "1107\n",
      "1108\n",
      "1109\n",
      "1110\n",
      "1111\n",
      "1112\n",
      "1113\n",
      "1114\n",
      "1115\n",
      "1116\n",
      "1117\n",
      "1118\n",
      "1119\n",
      "1120\n",
      "1121\n",
      "1122\n",
      "1123\n",
      "1124\n",
      "1125\n",
      "1126\n",
      "1127\n",
      "1128\n",
      "1129\n",
      "1130\n",
      "1131\n",
      "1132\n",
      "1133\n",
      "1134\n",
      "1135\n",
      "1136\n",
      "1137\n",
      "1138\n",
      "1139\n",
      "1140\n",
      "1141\n",
      "1142\n",
      "1143\n",
      "1144\n",
      "1145\n",
      "1146\n",
      "1147\n",
      "1148\n",
      "1149\n",
      "1150\n",
      "1151\n",
      "1152\n",
      "1153\n",
      "1154\n",
      "1155\n",
      "1156\n",
      "1157\n",
      "1158\n",
      "1159\n",
      "1160\n",
      "1161\n",
      "Epoch:2/2 Training Loss: 0.921 Validation Loss: 0.594 Validation Accuracy: 0.839\n",
      "1162\n",
      "1163\n",
      "1164\n",
      "1165\n",
      "1166\n",
      "1167\n",
      "1168\n",
      "1169\n",
      "1170\n",
      "1171\n",
      "1172\n",
      "1173\n",
      "1174\n",
      "1175\n",
      "1176\n",
      "1177\n",
      "1178\n",
      "1179\n",
      "1180\n",
      "1181\n",
      "1182\n",
      "1183\n",
      "1184\n",
      "1185\n",
      "1186\n",
      "1187\n",
      "1188\n",
      "1189\n",
      "1190\n",
      "1191\n",
      "1192\n",
      "1193\n",
      "1194\n",
      "1195\n",
      "1196\n",
      "1197\n",
      "1198\n",
      "1199\n",
      "1200\n",
      "1201\n",
      "1202\n",
      "1203\n",
      "1204\n",
      "1205\n",
      "1206\n",
      "1207\n",
      "1208\n",
      "1209\n",
      "1210\n",
      "1211\n",
      "1212\n",
      "1213\n",
      "1214\n",
      "1215\n",
      "1216\n",
      "1217\n",
      "1218\n",
      "1219\n",
      "1220\n",
      "1221\n",
      "1222\n",
      "1223\n",
      "1224\n",
      "1225\n",
      "1226\n",
      "1227\n",
      "1228\n",
      "1229\n",
      "1230\n",
      "1231\n",
      "1232\n",
      "1233\n",
      "1234\n",
      "1235\n",
      "1236\n",
      "1237\n",
      "1238\n",
      "1239\n",
      "1240\n",
      "1241\n",
      "1242\n",
      "1243\n",
      "1244\n",
      "1245\n",
      "1246\n",
      "1247\n",
      "1248\n",
      "1249\n",
      "1250\n",
      "1251\n",
      "1252\n",
      "1253\n",
      "1254\n",
      "1255\n",
      "1256\n",
      "1257\n",
      "1258\n",
      "1259\n",
      "1260\n",
      "1261\n",
      "Epoch:2/2 Training Loss: 0.768 Validation Loss: 0.543 Validation Accuracy: 0.856\n",
      "1262\n",
      "1263\n",
      "1264\n",
      "1265\n",
      "1266\n",
      "1267\n",
      "1268\n",
      "1269\n",
      "1270\n",
      "1271\n",
      "1272\n",
      "1273\n",
      "1274\n",
      "1275\n",
      "1276\n",
      "1277\n",
      "1278\n",
      "1279\n",
      "1280\n",
      "1281\n",
      "1282\n",
      "1283\n",
      "1284\n",
      "1285\n",
      "1286\n",
      "1287\n",
      "1288\n",
      "1289\n",
      "1290\n",
      "1291\n",
      "1292\n",
      "1293\n",
      "1294\n",
      "1295\n",
      "1296\n",
      "1297\n",
      "1298\n",
      "1299\n",
      "1300\n",
      "1301\n",
      "1302\n",
      "1303\n",
      "1304\n",
      "1305\n",
      "1306\n",
      "1307\n",
      "1308\n",
      "1309\n",
      "1310\n",
      "1311\n",
      "1312\n",
      "1313\n",
      "1314\n",
      "1315\n",
      "1316\n",
      "1317\n",
      "1318\n",
      "1319\n",
      "1320\n",
      "1321\n",
      "1322\n",
      "1323\n",
      "1324\n",
      "1325\n",
      "1326\n",
      "1327\n",
      "1328\n",
      "1329\n",
      "1330\n",
      "1331\n",
      "1332\n",
      "1333\n",
      "1334\n",
      "1335\n",
      "1336\n",
      "1337\n",
      "1338\n",
      "1339\n",
      "1340\n",
      "1341\n",
      "1342\n",
      "1343\n",
      "1344\n",
      "1345\n",
      "1346\n",
      "1347\n",
      "1348\n",
      "1349\n",
      "1350\n",
      "1351\n",
      "1352\n",
      "1353\n",
      "1354\n",
      "1355\n",
      "1356\n",
      "1357\n",
      "1358\n",
      "1359\n",
      "1360\n",
      "1361\n",
      "Epoch:2/2 Training Loss: 0.860 Validation Loss: 0.578 Validation Accuracy: 0.849\n",
      "1362\n",
      "1363\n",
      "1364\n",
      "1365\n",
      "1366\n",
      "1367\n",
      "1368\n",
      "1369\n",
      "1370\n",
      "1371\n",
      "1372\n",
      "1373\n",
      "1374\n",
      "1375\n",
      "1376\n",
      "1377\n",
      "1378\n",
      "1379\n",
      "1380\n",
      "1381\n",
      "1382\n",
      "1383\n",
      "1384\n",
      "1385\n",
      "1386\n",
      "1387\n",
      "1388\n",
      "1389\n",
      "1390\n",
      "1391\n",
      "1392\n",
      "1393\n",
      "1394\n",
      "1395\n",
      "1396\n",
      "1397\n",
      "1398\n",
      "1399\n",
      "1400\n",
      "1401\n",
      "1402\n",
      "1403\n",
      "1404\n",
      "1405\n",
      "1406\n",
      "1407\n",
      "1408\n",
      "1409\n",
      "1410\n",
      "1411\n",
      "1412\n",
      "1413\n",
      "1414\n",
      "1415\n",
      "1416\n",
      "1417\n",
      "1418\n",
      "1419\n",
      "1420\n",
      "1421\n",
      "1422\n",
      "1423\n",
      "1424\n",
      "1425\n",
      "1426\n",
      "1427\n",
      "1428\n",
      "1429\n",
      "1430\n",
      "1431\n",
      "1432\n",
      "1433\n",
      "1434\n",
      "1435\n",
      "1436\n",
      "1437\n",
      "1438\n",
      "1439\n",
      "1440\n",
      "1441\n",
      "1442\n",
      "1443\n",
      "1444\n",
      "1445\n",
      "1446\n",
      "1447\n",
      "1448\n",
      "1449\n",
      "1450\n",
      "1451\n",
      "1452\n",
      "1453\n",
      "1454\n",
      "1455\n",
      "1456\n",
      "1457\n",
      "1458\n",
      "1459\n",
      "1460\n",
      "1461\n",
      "Epoch:2/2 Training Loss: 0.904 Validation Loss: 0.549 Validation Accuracy: 0.843\n",
      "1462\n",
      "1463\n",
      "1464\n",
      "1465\n",
      "1466\n",
      "1467\n",
      "1468\n",
      "1469\n",
      "1470\n",
      "1471\n",
      "1472\n",
      "1473\n",
      "1474\n",
      "1475\n",
      "1476\n",
      "1477\n",
      "1478\n",
      "1479\n",
      "1480\n",
      "1481\n",
      "1482\n",
      "1483\n",
      "1484\n",
      "1485\n",
      "1486\n",
      "1487\n",
      "1488\n",
      "1489\n",
      "1490\n",
      "1491\n",
      "1492\n",
      "1493\n",
      "1494\n",
      "1495\n",
      "1496\n",
      "1497\n",
      "1498\n",
      "1499\n",
      "1500\n",
      "1501\n",
      "1502\n",
      "1503\n",
      "1504\n",
      "1505\n",
      "1506\n",
      "1507\n",
      "1508\n",
      "1509\n",
      "1510\n",
      "1511\n",
      "1512\n",
      "1513\n",
      "1514\n",
      "1515\n",
      "1516\n",
      "1517\n",
      "1518\n",
      "1519\n",
      "1520\n",
      "1521\n",
      "1522\n",
      "1523\n",
      "1524\n",
      "1525\n",
      "1526\n",
      "1527\n",
      "1528\n",
      "1529\n",
      "1530\n",
      "1531\n",
      "1532\n",
      "1533\n",
      "1534\n",
      "1535\n",
      "1536\n",
      "1537\n",
      "1538\n",
      "1539\n",
      "1540\n",
      "1541\n",
      "1542\n",
      "1543\n",
      "1544\n",
      "1545\n",
      "1546\n",
      "1547\n",
      "1548\n",
      "1549\n",
      "1550\n",
      "1551\n",
      "1552\n",
      "1553\n",
      "1554\n",
      "1555\n",
      "1556\n",
      "1557\n",
      "1558\n",
      "1559\n",
      "1560\n",
      "1561\n",
      "Epoch:2/2 Training Loss: 0.686 Validation Loss: 0.503 Validation Accuracy: 0.866\n",
      "1562\n",
      "1563\n",
      "1564\n",
      "1565\n",
      "1566\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1567\n",
      "1568\n",
      "1569\n",
      "1570\n",
      "1571\n",
      "1572\n",
      "1573\n",
      "1574\n",
      "1575\n",
      "1576\n",
      "1577\n",
      "1578\n",
      "1579\n",
      "1580\n",
      "1581\n",
      "1582\n",
      "1583\n",
      "1584\n",
      "1585\n",
      "1586\n",
      "1587\n",
      "1588\n",
      "1589\n",
      "1590\n",
      "1591\n",
      "1592\n",
      "1593\n",
      "1594\n",
      "1595\n",
      "1596\n",
      "1597\n",
      "1598\n",
      "1599\n",
      "1600\n",
      "1601\n",
      "1602\n",
      "1603\n",
      "1604\n",
      "1605\n",
      "1606\n",
      "1607\n",
      "1608\n",
      "1609\n",
      "1610\n",
      "1611\n",
      "1612\n",
      "1613\n",
      "1614\n",
      "1615\n",
      "1616\n",
      "1617\n",
      "1618\n",
      "1619\n",
      "1620\n",
      "1621\n",
      "1622\n",
      "1623\n",
      "1624\n",
      "1625\n",
      "1626\n",
      "1627\n",
      "1628\n",
      "1629\n",
      "1630\n",
      "1631\n",
      "1632\n",
      "1633\n",
      "1634\n",
      "1635\n",
      "1636\n",
      "1637\n",
      "Finished Training\n",
      "seconds it took:1449.0\n"
     ]
    }
   ],
   "source": [
    "epochs = 2\n",
    "steps = 0\n",
    "running_loss = 0\n",
    "print_every = 100\n",
    "st = time.time()\n",
    "for epoch in range(epochs):  # loop over the dataset 2 times\n",
    "\n",
    "#     running_loss = 0.0\n",
    "    for i, data in enumerate(train_loader, 0):\n",
    "        steps += 1\n",
    "        print(i)\n",
    "        # get the inputs\n",
    "        inputs, labels = data\n",
    "        inputs, labels = inputs.to(device), labels.to(device)\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "#         print(\"optimizer zeroed\")\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = model.forward(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "#         print(\"optimizer stepped\")\n",
    "        # print statistics\n",
    "        running_loss += loss.item()\n",
    "        if steps % print_every == 0:\n",
    "            with torch.no_grad():\n",
    "                valid_loss, accuracy = validation(model, valid_loader, criterion)\n",
    "            print(\"Epoch:{}/{}\".format(epoch + 1, epochs),\n",
    "                 \"Training Loss: {:.3f}\".format(running_loss/print_every),\n",
    "                 \"Validation Loss: {:.3f}\".format(valid_loss/len(valid_loader)),\n",
    "                 \"Validation Accuracy: {:.3f}\".format(accuracy/len(valid_loader)))\n",
    "#         if i % 40 == 39:    # print every 40 step\n",
    "#             print('[%d, %5d] loss: %.3f' %\n",
    "#                   (epoch + 1, i + 1, running_loss / 2000))\n",
    "            running_loss = 0.0\n",
    "\n",
    "print('Finished Training')\n",
    "print('seconds it took:{}'.format(round(time.time() - st, 0)))"
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 10000 test images: 86 %\n"
     ]
    }
   ],
   "source": [
    "# TODO: Do validation on the test set\n",
    "correct = 0\n",
    "total = 0\n",
    "model.eval()\n",
    "with torch.no_grad():\n",
    "    for data in test_loader:\n",
    "        images, labels = data\n",
    "        images, labels = images.to(device), labels.to(device)\n",
    "        outputs = model.forward(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "print('Accuracy of the network on the 10000 test images: %d %%' % (\n",
    "    100 * correct / total))"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Save the checkpoint \n",
    "model.class_to_idx = train_set.class_to_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint = {\n",
    "    'arch': 'vgg16',\n",
    "    'class_to_idx': model.class_to_idx, \n",
    "    'state_dict': model.state_dict(),\n",
    "    'classifier': model.classifier\n",
    "}\n",
    "\n",
    "torch.save(checkpoint, 'checkpoint.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the checkpoint\n",
    "\n",
    "At this point it's good to write a function that can load a checkpoint and rebuild the model. That way you can come back to this project and keep working on it without having to retrain the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Write a function that loads a checkpoint and rebuilds the model\n",
    "def load_checkpoint(filepath):\n",
    "    ckp = torch.load(filepath)\n",
    "    model = models.vgg16(pretrained=True)\n",
    "    \n",
    "    for param in model.parameters():\n",
    "        param.requires_grad = False\n",
    "        \n",
    "    model.classifier = ckp[\"classifier\"]\n",
    "    model.load_state_dict(ckp[\"state_dict\"])\n",
    "    model.class_to_idx = ckp['class_to_idx']\n",
    "    model.to(device)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VGG(\n",
       "  (features): Sequential(\n",
       "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (1): ReLU(inplace)\n",
       "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (3): ReLU(inplace)\n",
       "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (6): ReLU(inplace)\n",
       "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (8): ReLU(inplace)\n",
       "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (11): ReLU(inplace)\n",
       "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (13): ReLU(inplace)\n",
       "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (15): ReLU(inplace)\n",
       "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (18): ReLU(inplace)\n",
       "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (20): ReLU(inplace)\n",
       "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (22): ReLU(inplace)\n",
       "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (25): ReLU(inplace)\n",
       "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (27): ReLU(inplace)\n",
       "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "    (29): ReLU(inplace)\n",
       "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (classifier): Sequential(\n",
       "    (fc1): Linear(in_features=25088, out_features=4096, bias=True)\n",
       "    (relu1): ReLU()\n",
       "    (fc6): Linear(in_features=4096, out_features=102, bias=True)\n",
       "    (output): LogSoftmax()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "load_checkpoint('checkpoint.pth')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inference for classification\n",
    "\n",
    "Now you'll write a function to use a trained network for inference. That is, you'll pass an image into the network and predict the class of the flower in the image. Write a function called `predict` that takes an image and a model, then returns the top $K$ most likely classes along with the probabilities. It should look like \n",
    "\n",
    "```python\n",
    "probs, classes = predict(image_path, model)\n",
    "print(probs)\n",
    "print(classes)\n",
    "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
    "> ['70', '3', '45', '62', '55']\n",
    "```\n",
    "\n",
    "First you'll need to handle processing the input image such that it can be used in your network. \n",
    "\n",
    "## Image Preprocessing\n",
    "\n",
    "You'll want to use `PIL` to load the image ([documentation](https://pillow.readthedocs.io/en/latest/reference/Image.html)). It's best to write a function that preprocesses the image so it can be used as input for the model. This function should process the images in the same manner used for training. \n",
    "\n",
    "First, resize the images where the shortest side is 256 pixels, keeping the aspect ratio. This can be done with the [`thumbnail`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) or [`resize`](http://pillow.readthedocs.io/en/3.1.x/reference/Image.html#PIL.Image.Image.thumbnail) methods. Then you'll need to crop out the center 224x224 portion of the image.\n",
    "\n",
    "Color channels of images are typically encoded as integers 0-255, but the model expected floats 0-1. You'll need to convert the values. It's easiest with a Numpy array, which you can get from a PIL image like so `np_image = np.array(pil_image)`.\n",
    "\n",
    "As before, the network expects the images to be normalized in a specific way. For the means, it's `[0.485, 0.456, 0.406]` and for the standard deviations `[0.229, 0.224, 0.225]`. You'll want to subtract the means from each color channel, then divide by the standard deviation. \n",
    "\n",
    "And finally, PyTorch expects the color channel to be the first dimension but it's the third dimension in the PIL image and Numpy array. You can reorder dimensions using [`ndarray.transpose`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.transpose.html). The color channel needs to be first and retain the order of the other two dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_image(image_path):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "    1. image_path: Complete image path of the image file which will be converted to numpy array\n",
    "    \n",
    "    Processes and converts image file converted to numpy array\n",
    "    \"\"\"\n",
    "    # TODO: Process a PIL image for use in a PyTorch model\n",
    "    image_transforms = transforms.Compose([\n",
    "        transforms.Resize(256), \n",
    "        transforms.CenterCrop(224), \n",
    "        transforms.ToTensor()])\n",
    "    \n",
    "    pil = Image.open(image_path)\n",
    "    pil = image_transforms(pil).float()\n",
    "    \n",
    "    np_ = np.array(pil)    \n",
    "    \n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    np_ = (np.transpose(np_, (1, 2, 0)) - mean)/std    \n",
    "    np_ = np.transpose(np_, (2, 0, 1))\n",
    "            \n",
    "    return np_"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imshow(image, ax=None, title=None):\n",
    "    if ax is None:\n",
    "        fig, ax = plt.subplots()\n",
    "    \n",
    "    # PyTorch tensors assume the color channel is the first dimension\n",
    "    # but matplotlib assumes is the third dimension\n",
    "    image = image.transpose((1, 2, 0))\n",
    "    \n",
    "    # Undo preprocessing\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    image = std * image + mean\n",
    "    \n",
    "    # Image needs to be clipped between 0 and 1 or it looks like noise when displayed\n",
    "    image = np.clip(image, 0, 1)\n",
    "    \n",
    "    ax.imshow(image)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc92b03e9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(process_image('flowers/test/1/image_06743.jpg'));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class Prediction\n",
    "\n",
    "Once you can get images in the correct format, it's time to write a function for making predictions with your model. A common practice is to predict the top 5 or so (usually called top-$K$) most probable classes. You'll want to calculate the class probabilities then find the $K$ largest values.\n",
    "\n",
    "To get the top $K$ largest values in a tensor use [`x.topk(k)`](http://pytorch.org/docs/master/torch.html#torch.topk). This method returns both the highest `k` probabilities and the indices of those probabilities corresponding to the classes. You need to convert from these indices to the actual class labels using `class_to_idx` which hopefully you added to the model or from an `ImageFolder` you used to load the data ([see here](#Save-the-checkpoint)). Make sure to invert the dictionary so you get a mapping from index to class as well.\n",
    "\n",
    "Again, this method should take a path to an image and a model checkpoint, then return the probabilities and classes.\n",
    "\n",
    "```python\n",
    "probs, classes = predict(image_path, model)\n",
    "print(probs)\n",
    "print(classes)\n",
    "> [ 0.01558163  0.01541934  0.01452626  0.01443549  0.01407339]\n",
    "> ['70', '3', '45', '62', '55']\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(image_path, model, top_num=5):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "    1. image_path: Complete path to the image file for which we want to predict the class\n",
    "    2. model: Name of the trained model\n",
    "    3. top_num: Number of top probabilities to display in the output\n",
    "    \n",
    "    Outputs:\n",
    "    1. top_probs: Class probabilities for top predictions\n",
    "    2. top_labels: Top labels predicted\n",
    "    3. top_flowers: Name of flower(extracted using JSON file)\n",
    "    \"\"\"\n",
    "    # Process image\n",
    "    img = process_image(image_path)\n",
    "    \n",
    "    # Numpy -> Tensor\n",
    "    image_tensor = torch.from_numpy(img).type(torch.FloatTensor)\n",
    "\n",
    "    # Add batch of size 1 to image\n",
    "    model_input = image_tensor.unsqueeze(0)\n",
    "    model_input = model_input.cuda()\n",
    "    \n",
    "    # Probs\n",
    "    probs = torch.exp(model.forward(model_input))\n",
    "    \n",
    "    # Top probs\n",
    "    top_probs, top_labs = probs.topk(top_num)\n",
    "    top_probs = top_probs.detach().cpu().numpy().tolist()[0] \n",
    "    top_labs = top_labs.detach().cpu().numpy().tolist()[0]\n",
    "\n",
    "    \n",
    "    # Convert indices to classes\n",
    "    idx_to_class = {val: key for key, val in    \n",
    "                                      model.class_to_idx.items()}\n",
    "    top_labels = [idx_to_class[lab] for lab in top_labs]\n",
    "    top_flowers = [cat_to_name[idx_to_class[lab]] for lab in top_labs]\n",
    "    return top_probs, top_labels, top_flowers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.9902989864349365,\n",
       "  0.002102087251842022,\n",
       "  0.0020295947324484587,\n",
       "  0.001994472462683916,\n",
       "  0.0013319903519004583],\n",
       " ['1', '76', '51', '83', '86'],\n",
       " ['pink primrose', 'morning glory', 'petunia', 'hibiscus', 'tree mallow'])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict(\"flowers/test/1/image_06743.jpg\", model = model, top_num=5)"
   ]
  },
  {
   "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": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Display an image along with the top 5 classes\n",
    "def plot_solution(image_path, model):\n",
    "    \"\"\"\n",
    "    Args:\n",
    "    1. image_path: Complete path to the image file\n",
    "    2. model: Name of trained model\n",
    "    \n",
    "    Displays the image and shows the top 5 classes that the model predicted for it.\n",
    "    \"\"\"\n",
    "\n",
    "    # Set up plot\n",
    "    plt.figure(figsize = (6,10))\n",
    "    ax = plt.subplot(2,1,1)\n",
    "\n",
    "    # Set up title\n",
    "    flower_num = image_path.split('/')[-2]\n",
    "    title_ = cat_to_name[flower_num]\n",
    "\n",
    "    # Plot flower\n",
    "    img = process_image(image_path)\n",
    "    imshow(img, ax, title = title_);\n",
    "\n",
    "    # Make prediction\n",
    "    probs, labs, flowers = predict(image_path, model) \n",
    "\n",
    "    # Plot bar chart\n",
    "    plt.subplot(2,1,2)\n",
    "    sns.barplot(x=probs, y=flowers, color=sns.color_palette()[0]);\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa0AAAJCCAYAAABzrJj9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzsvVusbVt2nvW11i9jzDnXWnvvU6fq1NV1oapS5Vuq4irsxHaMDY4tguARyAMIHiwhJQLEC49IeQXxiGQEL4CEAsSywLmCEsmgKJiQKNhOKokTIHbdy+fsvdeac4zRL42H1udcuxyDLGSpdHbNVtq19zprzDFH72OM3npr7f//JmbG1a52tatd7WrvBtPv9AVc7WpXu9rVrvZ7tavTutrVrna1q71r7Oq0rna1q13tau8auzqtq13tale72rvGrk7rale72tWu9q6xq9O62tWudrWrvWvs6rReQxORnxWRL4nIPxCRf/87fT1Xu9rVrvb7ZXLlab1eJiIB+HvATwO/CfwK8K+a2a9/Ry/sale72tV+H+waab1+9k8D/8DM/qGZbcB/DfxL3+FrutrVrna13xeL3+kLuNrvu30I+Mev/PybwA//vx0cY7Q0JcwMDJDH35nZ4x/65RgRQRDOUfq3Res2fhYZx8H512aGABoECfiWSUBMocGzJ28A8OTmGYKgoq+c2y4/G4/fF2Kg907rHRVBBKwbYIjqZTitdTDQoIgCKljvSBe6dfo4ZwjK+fRWOyKCqtIFNCoiivV2GSsimICY+EANWmv+uRT8uDHOYEpvr1yrKqKCiO8d/do71jq9d8AIOSEqGBBEaLVdxq5B6b1jBioKGLU0zPrlRooIqPo8i4+5t05QH0trfr6OEWMA8WGd510RpJvPcanjnEqIERMB9WdBDHrvdHzOAERlXIaBKNaN3rrPs4Zve9Zab2MuxmfHL60bvbfLz4LQ1K8vjHkz6z5+gdb9/Crq96378ztO67folWdIgG7d78GrB4ngs25jPv2+vv38bV68eE7vHQlCjIqI0fvjd4gYKudbIPRmjGHDtyW25PK+nO8jIqj4h199rwy7/Czj3cJsfIZvNxuf8HP/zt++6+3qtF4/+90e0m9/VUR+Dvg5AFVld7sHGC9Ov3yg1UatFQ2gKYFBECWooihyXtx7o5shYtTaKKUioghCjOmyKJd1QyvkWyU/C6RbP1esM2nZ8W/9638KgM9/9IeJJKY8UdZtvJxGCpHeGj00NPk5b+5uOW0r67YwxYD2zno6YdKZb3b04td4ui+Ywf5mJu0DYYaHly/Ro7JRaMHH/ezNZ1htaBPuv/6cSGDezdghk5/dgMLpxUsAoigWBaZItAQV2lJ4eH5Pl857PvgmAOFuoobOzZK5/8Zz7reF3d0N892evN+Rp4zfC6G8fKC885LTO/fU2njjox8g3u3o0qCsHJ+/AODpkzfIu4n744nnz19w2B24mfZ886tfp5XKafGBy5RI+xliJIRAPa5sL08c8o7aCi9PDz6Pz56wf3rDUje6GLVVrHf2DTgVtoeF+3d83LvdDTfPnrGqwJwJa2Am8/L0Dsf6wO7pzudnH6k0Co1IQjbl/vkDWSf28544nPpSVl4cn3O4OxCmCWsNOljrlNPCtqzs8uTjUeV5aMSUmEKkrRsiQgiBZVsvjmc/3RE1siwLtVZEhN47qmdn5g54qxu1NvK0w0xoBoZgXVDbkHLP1la/j7Py1W99mV/5W3+dX//7v8qXv/FlHk7v0Ppzeld0vDspVqa5MiXFauTF88KpBVKaKFtlfDV0oAvbWlmWFbOAaCDG6BuAfvZ07jBLKf4epETO+eLgv825mdGtYVSWh/K7LAXvfrs6rdfPfhP4yCs/fxj48qsHmNnPAz8PoEHteP+Ail5egleOI2oghIBv1UEMrBmdziWOEUXFMGuklFAN9NbpY4ceYxiHKc0qvXsUJQQCgbZ1PvTGW3zyY58GYMoTdan0XglR2daVFBNgdOu+C44js60gQdjtJqIIdVmw1pDkO9bzzjvEQCuN1joZXxhEha4NMG7uDgDM+4n1tKJidO3U0jHL43ilWIURQEkUSEoLvjNHjF7M36rOZT5ziHTboBsdyPNEvtmhu0w6TJfFu2wblY0QIQShFaAC5rv5UhuiPu6YI6UUNAgxBvKU0KjsDjMPLx7YhrfOEjziQdAuSIPQhO1h4dgX5sMMwO5mhwXBum9eVHyu/ecKymWjkOaERPXIEENEqLVRWyXPifngTpgklN6xriABFY+AgipTzpdFeTkeEfNIWkyotdNq9QjPOnnKTDu/ztYaMcI8Z6QZFnxz1FqjtnY5LsaAopdMwdlZ5ezXtq0eYQYiMWXo7rCCKGZKB6QHpCfiOcrr8P73foSf+LEbfuBzn+P/+sf/iL/2K7/Ml/7B34QObXgjAWLpNFVaaR6FoWCC2SsRlIE/zkqMCbMAw7mejznP0XkM4BtNjx6FnP3ZPFvvnbWulN7/iff5dbGr03r97FeAT4nIx4HfAv4V4E/8f31AAbHuaZ5Xd29mpJCJIdHMPHVFH+kTMHlMscBjGiaEgIrSXnFc8JjW6OafEhOEQF07H3r/R3jvs/f5cYvQpVENYoh0acg5UhipvCCPqTcRQxFiUAiBMF58dyTjMFXf9ZeNmYSqpwmrdMIUODy78QOT0LdOKw0ilNrQGIlTxmKgtwJj8Y5zpieh0tyZmqFJiFNiW9ZLJBo0EHuglEYzI+1n4n5Cp4hk9SgKMGmEKBAg5wRNUQu+oGIsZeMwFmVR6K152mg481oLKSc6nWZjAQ1KTAlD6aXTt0ZdCn3bCE8yuzsfd9hlVqtUOkEFaYK0hnSPoBGjqzzOkXREgt/TICzLQqFy2E8+BqCqAYpKJIUJLY2owe+TGdvmEcy2ru7g1g2p0FqF8SzF5C6jnsOTYExTJCZ35NKUtjVqLUw5M42IzAy2stF7J6VEjPGy8K/rSqv+TPZqmFWfUJXzX+7omwCZoL5MdmtQhJvpDXLa8+H3f4y33vwA/+Wfec5v/eaXKdvi1zh1Wm+sWwVLmCqB6O9Cs5G+BrpeNnciOrLNPndnx/VqCv78/pyjxnPkFUK4OGMRoa+d1jcfyGtoV6f1mpmZVRH5k8BfxGOC/9zMfu07fFlXu9rVrvb7Ylen9Rqamf054M/9Hg9GrA0UALxaEhPfn/vf5il4MQZo4REOcSlqi3gNTAMpJlRtFML9OEVoAUy86DzlmRQy803is3/ge8nRd8k1NAidhhGDIhEkQZoizZSlFuoAQ9TeLpGGGUwpUWNgbW3saH08MUR67GzVd/fWOyawUdlNe9LOv3utK1U7qBEPE9t2T1fBQqCJ0RV0Tj6eXaJrp/WKbYUcE2nOSBO2ZbnMY1SlEXgoD1gMpP1uRGlgahdwRRBAjWIFUb9GuhElsrXV64+H3ZjzjsaAlYKJ0TG6dKZ5Ik+ZtPk1hhSQINB8195KYzst7NLE7tkdYYylR6+tdGvkEKF0pBopKDUECuWCNe5AtY6OFCvBaNrI+8R0SJfIsXUDU1QSUTPIQlAhhkitldPpBMA8TeTDRBdDNJJTGiCejvXGcjpSxn2b9ztSUKQ3ggiiQjNPVe+nHTIi8G3dWJfN5z9GWmuYGbVWB1Gc8QndIR4heIjVrdNaobZCLR1p6lEvoARqg17hsHvKclr4yJv/FD/9R/84v/ALv8g3Hr7i50ywLA/kndB7xWJCLbJt47vHNfr70tm2Qu+GhkgI3x5N/U7wxTkSq7VexuKvsR+XUkKCkvPESbbf9ZV/t9vVaX2X20hG4BC4b0csAY7casWLxHhaUIFu5p8DEBupxei1H1HayKmHEF5BqDly0EToHZ7ePuOf+aM/ySfe+jSfeO+nGXVsTA2iQDdMPeWmSYg5oC1A36jNazatFk9j5uQOrjZCitALrT8i7WJSrAdKc+dZa7sgr0JK2Mg2ltqoGDEFpsOehxdHau+EWmnVsABpLGLEUaMTCFHJcSK3wLIZ3R5TPUEDSqP0RtrN5N1MmBIaAIzeRuqrNTAHC4SoLFYY+D22dWOaZx8bUEshxsyybgPp5qCXFCPzYSYsDrCQAd9UCQjQSqVuldtnb9KnhI1UZ7VGs+4IPozeGkEgTzNmC40+amMQciBNiZAnauuclhOVwuEmE3NgGfem9g6WCSEgJo7gBIIqrbaBkoP97S3TzUSnU7vSeyOIElW4v39JqQUNo54ThK6GmJ9HQ6CIMKWJFCNb9bksZaNbI6WMYWzbRoz+fIYYaOeHrbXLc9x7w8QQ6agaMSpoRsK5iAlBE4HE6X6h1g6S+KEf/CNo3/Pn/8IvAvC13/4NQpjofcMxoIb1xrZt9PYK6rH7nPTWYaTTRRoxpgsy9IKmvKAKHSHpv1NUA9u2UcczlFIizIG8Txc04+tmV6f1XW7GGbmtdJMBnR6BlzVaL4gaEswdG16Hcsj0GWlYHVnYlT7g5N3jH1QMDWOxM1iXjRQm3nf4AJ//xI/yY5/9Y7zvjbeoS6XYQLx1ZQoztW1+TpQQEy0KNSlaO7b5d88kqhhxFyixsp5WbApYzXSDEH23GcLmu+q1IRJY1w1pgb0emOPMNhbUFhTmhLTIZDPTvKFVSFsnJ2GzThgLvRfYIzdxQrKiZHoTQgRIBE1jLgXpldUW5sOeNCspCBqUZT1ixZ2rIPSuxJiJSQm6gHW2srK1ymG+oY7FG5lZlkK0zGyGrYZMgYWG3s3wctRCWkVOjVlmylIpp4LuM/I0se03NA+IuAWCNaI4vKZFRx7WnCllo1mjjZ17yx07BJoJ0pS6bUBD044inaIDRGCG9MYhBmzbaKUSEXqtrFsh7nx+4l2m76BKo9QjoUW0ZaIl2tppzQgHH3ebwXJDLSASsWaEODOlGetQR3S19ZUwB4qt/mzOEY1Kp9ObXZ41jeECg9dwRhYGzBSNgqAe8eK1tgv9o1XonWmaCOGGP/z5n+BjH/wYAH/+L/1Z/tav/jK0B7qcaBRWoNPQkOlt1MiK0FtC8RofsVPaRlfzzZ8JNmpvAaGXhg5wh0gcdbBAp7KOSHSVlR2JyPTINXnN7Oq0vtvtlZSDpx8GQiwHYszUtlJrJQYhpsnRT74xfCwaW8e6YcEjMdSLxkEDmHGYPKUVgPe98YzP/eAP8cXP/wif+eT38+TwjPV+JWmgrANa3BI5ZqptnN1k751SC4YRU4SxkKTgUD0JigXQHNBdoiwj5TYcpgaPklTxYrj43ykkYkoXlKHRCTEQQyIxEXOmvlyJIaAh0KRTx24+pkAMkRAiXbvzvjR4isagLn6cnRrraWHKmcPNgd1+B8G5SR4FPXKSzIlwtNZo3V1/rYXdYUdIkX6OCsxo1pnT5OlU8QW4BRAJl8jEihEIJA2cthPrunL3xi1hjhDbBUyjjm8jR0clppxBYdkc9ag8cqccvTY4Ts15YXGKI1XXLgAAHehQeofWkW5sy4olmHczYT/SblOkaqVKx8KYgw6lVBqGhsA0OwCFiHPBzvww9WheNbCWjdPiKUeCo1zNjHmamaYZM/Fz1u1xPZfHZ1XGvJdSL6Ci/kq0DoKOsTnkPNG70UqnW+f97/sgAD/z038cCZW/9xt/k/vjN5jz5OlU8ffkHAD5ubvfg95f+e+dfnaifWwiO46GHeCboAqqxJzQnFjLSCNqI01p3M3XM9S6Oq3vchN8kbsQVMdzHoJye7fHbOblw8vxIjdCyBe4+WOkJZg5V6sPNJmgjpoz5WY4rU994pP88Od+iC/+0A9ze3gDqlJOhUwgmFxQVVYrqDsmsw7iC1nrDVPIU7qk1M4EYsOorRFCIKZIG+jHfn5vVSAoJsK2rcQ5UBXSPJGmTGUd5xOCKFkzk0ykFDltLzjTUVOKNPXv1ugLh4lB8HRXnjJahdiF3DytlGqkvr0xPcmkKROiUnql9uow5gu82Z1PzgmyO6xSNjBlmnc+D+PYc+oz5cQiJ3daQVE1xMaGAZxInNzdLMsJ1Njf7dEcvq1GgjnqM6VEaw2NQqmFti7MokiHOGoxc5xIKSMWeHG6x1pl2u2IOSDJNzEAZfXxlVrRCnUp1K0wzTvmmx0y+/laNKo4KlQ1IE08VVgLqDHliZjcwW1tdSpGDkhXfw5EOC1HjqfTBTWZJJI0E1MmpQSmnI7LqB/1C7Kz0wc1ojmBurVB04ic6cXnOTpnDPq4b6qeqit1I0ikjmj9ox/5JH/iX/43+LW/+zn++1/6b3j7+ddROxFDpzXhnI5sfRubFANpmPnWoZ9h+sMZA07jEIfMizjK0aST58Dhbkftjh5sthKy38N+jbSu9lracDJnEYXzThIxTAwNsDtMpJa4f3lCamOad6SYBn8LT9PQ2dp6ybNjijTlbr7hj/7IjwPwz/3ET/Gh93wQsUC791SUndqoW8ULsXkrRpXNHWFXTzMGwdR3+xoC6LmehjsnMxqdlCZCj5i4M5VzxBFBCHSMZV15Mh8cOLGf0BTBHDgRo9dfcorkkIgpUlqllI3QjTztmM81jqhstXrUESsalRwCskASJZ0JXaeGHRu79+088pOBsLYBdbGRHlTnX01hohcHPnQx5ikRomJSL/fHqkPRQ9Sh6MBlwyHCBaZd2wJm1G1jWY5M+4m4z5To9zcM7o81I4bo9bQQcHWNAr0TQ0S6XZxWCpkogW2tlG0h5sDudkeYAgSQMmovrRNzhOZKHKfjiobA/maPToEynH+1TqX6+FG/d9LZ2oKFzv7m8FhH7EalYh3KADD0Dsu6UFphf7O/XGPQiGqg1cZyOnE6LfTeqbVdYPkhKBIfIe1GJ+Yw6m5nOPqZ1PEomuHncec1T9lBLmMjUZbOLr/B577/R3n5zsr/+Ff+Ag/v/AaTzrTY2bZR421liG+Ig1DGv63794ZHWQ3nzl3UPBwwEqISs9Cpl/fhvFnZ1kpvV6d1tdfUbMQqokoYROCYA6JGswLSmHaZUhsP9wvloZLixH7viLsQHCThBEmIkqAKSTMfePoWX/jsHwLge974CP1kqEaSJNQ8jVXKRu0b8zx2i9LotaLigAOieDRz5l2dpYkAVFEVmpQBYAhIdaJ0TIEBSMSkEEJG4zZqUUKMEZ0dATjIY6QQqaV6kV+FkCMobK1wm3YezYy3xlSoraIheLpUwUrDGijOHwI4tRPSA7vdjpACJkbrnqKUIMgIB0092gkaWNuR2io3OTLvZ1r0cZ/TnVIFNfX01iBYI+apWYQ0FuK1G9YaZe2s68KT9z+DLFRt7hweNbaIaXKJqUEClyH/5Mg7CGGk8ySwroWH+wdqLdy98YSQFQudavXCqYrR5Z6osNXGVjv7uz1xnyHZK9wrkH7mCfompUunUohzZN5Pj9G9CYmGmLL1Sm+dUirdOjd3B9JZXcQCvXZOpyPrWlylBd9QTVMcdUdIk6tLrMvCsp48LfhY6L2g+M5z5JsGl3Zy/lfAqPQGcTwY69ZgE5AdX/xDPwk28d/+0n/G8fQSlQDDudkAU/ifEXCLerrYRqp+3J524XcFzyr0yn5/IGah9oU6yOQmZ6f2eqYG4eq0vutNxLf95klzX/wYxGH1nXgrDqY43O7YSuX+xTqIsr7ohCDc6h5NAihJE/O040m64wuf+QKfePPjftxRsSbUZgMW7RGCdLkgEAFyimzrRsqR2psX0K1Tu6FTJkalDzTvGURiQEgRDQO5qEpIkZgfEVeiEU2Rtnbo3XfWUSm9PaLTFBSjlo0qrlYhKrTukQBilzkqrVLqxhRmzpp9zZweYNilvrKKK07kefLamxi1Ffog8qIXTgAhulTVspwcfj55StFCRWNER0pWgpBj9oVtXFft1VGXQE75cYK6cToeURXmw0xPUGMn50QcYJEzETfgeoTnzUvIgb50Smvc7G/9v8VME1jLRp4SN0/2WBIqha2ul8gxxkxQpavwsCxUUebbGyRHaiwXgENQJUtERbDWB7qvIlmY55mQw6P2oEFHieoSUevq0f3N3Q15lykDubidVk4PJ7a1EFN2BQ2NqKg79AtYpLGuC8flSIiBaZq8ZmpOPK481jA1KCkFem/U6jqXrTVa26il0/pjLW89laHmccMXPv+jvP3yK/ylv/xLnJZ7WhngF1XO0oCiYaBnddw09T921jSXi1NDOhqUEIVmG40ykKjQMHptQLwqYlztNbax4H6bGK0A0glRkA6tF6Z5z93dDWaBslVOp3MdyFjXlZvb2aOnFPiej30PP/b5H+V7P/IZdtWL6P1Fp6UABDR6Ebv2QrcK/TFlM6UdRZxbladEG+nDLsNRSkLHolpbQ0JAVEnJd9J1eLQQg0stAb146jPPE8t6opWKTEKcInVIQ8FIw6iMYE4cSp+iw52jO7CBVXHdxBxd0sgEleg75BiQoNyfdf1u9tw8uyVMEQtC7W0IxBp2WaQYqTmlbxXDSFN2x5ICLXQ0can7haBMKaPNoy0NLrdl6Ywyi+M2Cr02jvcPxBhIU6QmxWIjxHhRFtGhFdlKZVkWdvvZoecKkrweGIbqQkMgBpa6MU2zK4BoY1lXlnJCRxgjOqTBurGVynRzS7698RqWPd5vsU5EXBB4pCe7dTQpFozWq0co52fUjNYa9/cPtNZ5+vQOCcrxdOTlcu/3cetETRyeHsh5whzrMIR0A+e6Utk21rqScuJwOJCSi0ev63pB603TUNnA03ZO4bABWjJyjKSgDD1htmLs5kiIkdoKId7yx37qX+CbX/8W//P/8lexNlKTkuhmvonrDrw4C+H+Tl1B8xeULmCi5CxoVIxCt8I5jSjqUX+vj7qFr5tdndbVPCoRcbDDqCuJel1I1NNs61aJ1tkd9sS8o5TON7/xTQBq2RyEUTq9dj7xiU/wsz/1s3zvRz+LvOyEUeOg+XdpSohEpikiOnNaX7Ks96wDPShNyDESg0dLta6outp6w1/wM3jBaiVlX7AlMFJaDrkPMRLHwrh1J4/maWLTlW3d2B8OWBCv/4z0SlChNweAGGMhCV6QR+WCtgOPUlNKngJTI4SESiDEyP72wNdeONn07s0bdk/9uwrD0QqoBqcVDB7bNJziWhZqr+QpEXO6SPiAXY6VQay1Zp7SwwEoBSeKn/UeU4xs28bpdOSN979JmhKWlT7Sqec9ijEkjto6om9IKaNU15tUufCVJAYeTke2Wji8971IVI7LS47bA8THFOaUJ6JF1nIEEfZP75AcsVhH3WaMxRpJ3IGaniPjOkjT3fljg9l8ll96+523qaVyd/cUgNNyovTCNFLM6ZAJ8gqQYeAdgroY7TaetdYau92OaZpc6qvWMV8n2tYJPb0SsThkUoSR0hvYVhuK+wNoKHRCTK7LiFC2Sg47fvgLP8pXvvIV/uH/+SUAXhyfe8pXPVOgaq9QUB7raOcBnB22iBJTJAQo1nzTcq51XnKb7XUFD177aV3tale72tXePXaNtK7mHB0J0A0501J6wHqioThYb8H6giTjZrdnzgfyKI5/7be+TkDY68QHP/Bh/viP/4t87uM/hKwg2mHUi3owBzzUQikL4bD3WpI0Qg7E6Qxr7swx0XpDu6IGMgrkGoMrjp+Dt1ahZ5J6Xy3tQrLAfiDc+kiTpcMT+laZU+PEwrY1bsItOtIyNl4FjzJcsQNzFFsPndgboXfupdNHxHFoSrTIFiKVSsHQCDnCLitBPF+Ub2e2nSBZMWmO+gviiMguDCoOpkJpBUOpqkiOxGlG+uhppcKpe4SQzzJHpk5VMMA6ZgWkOiIS0C6UFycoxu5wR5hnuqyo4Krul1RVpG2NLIk0B3SK9NQJzVGgVidy8JqWmvLy4R3m20y+2VHWjXI60hPE/XRBGU4hYGthKUd6quRDR8JGDK6ofkG3SaSY129CdfCDnYRYI1ECKUZS9Aiq9MI733qbctrY7w7O32sFjXAzZcIZZChK2UYdlAw9oAq1OmeujdpXii6yq6I83D9QSnHF+No91W3JVVfwaConT7/VVqE5zUIIg9M1UrcKYg4SaWXDaiPqLZ/6+Of4d//UJ/kzv/BfAPBLf/EXqW1x/l0riHktLaqSVLBWncTMiKACdCkj7Zqw7tcnKOGiDO0q/r0ax9cTPHh1Wt/t9mqbBEEe027N6Ca+KGpAQ/Q6jBU6hZCFD3zA+0X1tUI15hD4vs98P5/6+KfIwbXk4izEAcdd14VaNkd20ahlQWJySSCUeSiY9+aIt23dHK0nQtTAagWxTpBwqSs1zqDCce2j0WRQ/5yOVUxypJTmDlKNrRfylIlBqK1Rh/5fCtHrQxovta2YI5EhX/VKbTx2l1vaeHR2BEFCR9TOvprdfoakTjBV51b1PlCPDVS9ZqIKfdmGAn0g5OxOOulQqa+cQWHCKOSPexdCGMX87g0a47lOFnh5fySEyO5wQHKis6IizuMZ3KLeGsvDwu18IOXM2gsEpS4V3UB6IIovF9u6UuvKm2+9iQTldFpG24+IhEg8Q/1Lo542jg/35DwRs5CnQNcK1pln5+/V3jgt3oYmotRSaaURQ2TOM1HjpQHli3ee8+LFc25u7jjcHvwZFdDUkdgRPeseiiuSWKSsRqmd02llWRZEjGnU50IQtrJ6ynA796vKTJOiPWCrMnAYXJo1dqO1gogw5R1haCmWcnaEjjiMOTHlRGud41KY844wJf7Apz4LwF/95f+Jl8eKWb88r+c6YG8u+7Rtj/qB3prECMHrftYfVWrO/knNNyqvptBfN7s6rat9mz3m7x2vJaq+EM071uK1pa7GaXvgycHrCW996D288413qA9GSjMxRtI0mtQNHhOAxETbVoK4oK5htFaRyGjRMFQucibIxIv1HbZtYd7vXPWglVFXeuTLiOJabVGpbDTzRbAPcuZZq69Hj6i6dSdthgHzH4XvNna00Rxdps1rfCJD6Le2QTwVbxfCcByiyEBB2kCVmTk/KER3mHme0BRoeD8svVz/oxiq/2RgXPpA5Tm/0uHZKKW8omeYENUBnJGxsVCorhRxzvunnFj7xn5/Sz5kVulOI0gg1S5w++V0ZHk4crvfOwcoBI7LidygnFyxxMbq/fDyJYfdxM1h721OrBGnhEzezTiLjzuZ0rYNWzZubp/6vVPGPdCdrnGtAAAgAElEQVTLxkM6nIUne4BSGl0gTRPTPKHIpX56//Ilu7sbbp49GYRrpfTi6Fd57DcWirG1RttWtqVTV9f4m1NCxC69r5ZlGxs3ZZrSBda/rhvL6YSW8xyDiNGrIQHiEPWNyTdDpRYnMQ/rYwNlGL26Yga907fKhz/0PQB84mOf5G//2t+8KJ+c7Qw0ebUJpAxdUFEnn3sPLQeGfPtnuXTHfj1d1tVpXe132DnyqrXSWvT0UAALgW1tKJEunUblOJBah8Oe8LbwyU99lj/4g58fkjmu9XRWqoCzyK7LEnlaJmPBtfVQoRRPfcWcSTlxc3vD87d/m2k3Ya2A9SHr5EKpwFiojJQjjYTUs5wNrh13ViYfBesQA9Nu4uX9Peu2oO3gnLSxoe29oSnRt0oVR2UJzqlRcwd2WQ0Mai3O04oR2zq1bYgmYg7k3YCdB3e0MUVEffy9n8EYconyEJeVEjFqb4QpIlFcQFg8+szDCUcio4XXAHQM4rUGtlKdK8eQ2NLO/ukBmYM79t5IFojmzToBttNCTIGQguvzde9ZNoXI/baRQ2QdaMiWNt7zxlM0QKO7AknqhBC9824dnKpq1OOGNmOeJtJ+pjSHxMf8CHAIwUhRPXrRhCloCkz7GQ3Ky3eeczz6d9/e3jC/+ZSYIvXcN0x901FaucDtUxPa2mjVgT0uJVWdFvHKhiFkj6pbc35a2TaWZWPbCjRzPuFF79H/T1SHMK+xbNuFIHxGYriMlyvKmxmlNabsPcDW1vnY93wCgJ/48Z/iN/7RP+S4vsSsodFT3Ou6XhzWufHjq52XXZR6QOAZjTsvslQMUnR/bYEYV6d1tccGczzKA7XSSDWSSWiITPPMqW7Y4ET11rBz88JmvPXet/jpn/xZPv3xT2Ob8bDdM0lGOpcGdTF6/ee4nKi1UKuQ84R2YS0b23A4YeywDzc7Xj7HZZd6IkZla41KRxi72kHQ7b3RpWKtO9FSzFXJz7Wv3ii9MqsQ4iOJ2lM8mfvTZTYwa54y2jravdOute2itH2WKcLM01ZzIIVA0ebR1+BihdGRWIJQpZFDprVyaaWuKhc4NUBthahpLOxGmCIkIPQhoeVtPWCQZ8toxMhIB8nQmzsTZMEbHgbYPTtQQ2Oz6lJXoiSLrNvRr9GMeTfTaWxbYeuVvJuJLXhX3lZZVh/34dl+KDE00i4j1TAtdHTUgc66hxWrnd08O4Q+R7b7B2IE5DG6PUc5QgRTqkDKkTxF7l8+8M6Ld7i59c7ST549pcyJao1aN7YyHMxogXNWacnmEa0NiSbMeWk5RaYpUYdg7tbK0PPrHI8Lp+NGKY3dbs/usCMOjcOztdZY121IemVvcWMe65w3Z/4U8aiYMc/QO82aK4+MRpV/8Af+EJ/+5Gf49S/9H5h0jqd7lyp7pfHj+d/nzsshhpEWPrctkSHD9pjmbc1e18wgcHVaV2MIdIr3JHps8z3gtSPHPk0zOU2IdpRIlIiaO45JZ77ve3+QT37kk+yCL3xzTOzzjrKWx35RMTNNE7VV4hTZyopWJaXEWtdLyq+0wrYt5HnHbr9j3VZK2djf3KK94Uyhc+HZF4PejR48fdKtI0EIMdAHibSWShs77TBnpjmjKQzy8KN81Rla3q2xbo192rsen51cZgfhXFgSw9OGtaHNFxmvo4mThwcRWHOgR9eUOx2PF5KqiFwWI4AkidAia3O5ozhFSHLuBYOaXxsw0oFDARgdPB4nd59VNQCWbUWSkm4zJ9soeHfjgLjeYz13lfYW9ikF1rI6wVgiZkozYd027g7uOA5PdhR1Mdsp74gjOioMvtpYVnqvLLUy390QdpnNKl0NUydhn6OiMARrQwhID/RQsKA8f/Gcd95+h9u7G5489VR0TIlVGmUrLNuRsm1EDU430Hjhc4Xmkc/xdKK15pH7YU9QGf2yPLQu1duFmHlLlGmeuLvbkfNwWFY5nVzi6yykG1PgcLjBzDidToPgLJfNWbdHaoKqEkOgl0YtlTnvOHPE3vvG+/kjP/LjfOnvf4n7h+dsZcOl1PSVND2X5zLG6NG6MFqTgJOO9eKkLulC6a9roHV1Wt/t5vnzDlSC6GWnitqICBq1en4+n1uW90DUTOy+Y/zQez/KP/vjP8P757fQZrSwOSqvdW6mmePiab/leBqE2o6IkVL0vlAa2O3mSwTT6JTeKK2QcuT+4QVhi8zt4FiH9KiQcAEmqDupoMIWHeyRcqKckXnjf9M8Yc05TqVsvuabp7jAnVcYRN0qlZgCc564H4rhegZc8FiY723MUe+oehO+av2iexhzpCdXumjdFR9CiBdHeUkBqSI9eCuOlIhTcvSiVDqupn6pD6qiIdFo1OZq79YZUkjhUtValoW8z4RdYpVKyBFrnaiRbImX27ktiosBmwwAAQElgAildao1bp85elCyQhKXsbowi1xmqBucea3bVji1ws3dm1gOVMoQGfZF9VIrUgbYRgkdNGWev3zB17/1TZ48e8aTN5+dO7dxskLtHaORUiCnHTkkv6cWkVFvPC1HHo4nrMN8ODClzFY3rLle4mn1dGNKDryhC4ebA1EnQsj0LqzribI9XNK3QYNz1zReFE+24mChGNNFib7WQh/iaAzASwwRmYTSGts6NAp75Xs/8wP84Pf9Qf7Xv/HXiD16NPZKpHW2syakBvEoq3O5z9bbuekB1sWRQr7Dei3t6rS+y83MG/6pylBpePxdax5FSPAIwfOCjDVKec8b7wXg89//Bd56+n6mNXlunkjdVpb1gbC/ZRopLWvGaT2y1ZXaC2mKWAAzb2B4buxIDExzRvA6kKpy/+IF+ye36JxcNaKeW3R4VJVShDBkocSVLFKOWBopxyqQhop5qKQcKXXzFE5KF62+PtIu53oT5orftVV6a4g9Ftsd8DB6kA3F4XPTxbNzBVzNA3OVdbzmoUPbUOQxpeXkYdfSOzeCNCrN6iD/vpIe9JDSayZb4XCzd3XwoYt3roks28KzN55e6kRE9XSaRO9iPDosxxAu4rsxBu82HCONzv3pyJPbG6aDL8qSQbK3zyitIdaoy0oLE1NMWPXV8rQVJGXCfqINEYqYI0JzHZB+ls7yFFwQRXrn9HDPN7/xDW7ubnjy9I6uXEjvNhQ2YgwXmH/QQR1oxsO9O6OX9y+JMXN4ckOUSKuNtVZKOVG2E9M0yNcp0LvLZ2HelmVZikdebUMw9nsft2trKstpZVnKAGK4aslZSQPA6jkScp14UUXNhmPxfnTgKfgnd0/5mZ/5Wf7ul36V02+/vLyTZ4vjuTzXsXrvXFoX2LnWZf9EpKWviO2+bnZ1WlejG74zNLu0V+g06ErHVSusOctexNuTPLt5kx//4k8C8IXPfpFUIrJmtHdooNbptbr+3ogk4hSJFdAhCrtsvogFL/6fF2S6EA0kKHE/cXjPE15+9ats68JNjkRNHMWjt5o6LRvEQLBEr56WC1kcoDGioilEFlVOtdJNCGGiLAUrUGelD6HVbVtc+Tt5fWULkJOyhc6KkXogtqHOYNBUmS2wAs3KiB4277zLWWBWMXOtwcN+z26aaG2jOVbdIxqAzVt4SK3E4G1P+lABMR1cunPLEUC7QfEbKDlStYOCVL+ffo2V3W3C8kqYM6VVVJRgkWU7cazutA5PDtQwFBm6L3opKMvDkW1Zufvoh7DduM7sO/leC1BBOkup7HRmOjc0BEot5JsdaZr8+dLhTFUQSQ6QACKB0AVplePDA1/72leJKXL75Bai0KTTRprXxPX1gimRSKiGrFA32B421qOn/SbZcTPdElC2ZWFdF5b1gVo39vuZJ0/vAHh4OKLigrSlnFjX4n3WUmbe7Ynx5qLcsZVCqSvH7USns9vNRBVSFIRGWUeTzFZIKaDogJ4797AU5271kToMPRJj4DA9wXqmd1dEcfSmb2jS2PjE6JqHzTq9yYjOu3MJsUuWwuPXdkGcvo52VcR4F5qIfERE/oqI/B0R+TUR+bfHf/8PROS3RORvjT///Hf6Wq92tatd7ffTrpHWu9Mq8O+Z2f8uIrfA3xCRvzx+9x+b2X/4ez3RWRBUjaF8fa4euCiodXNIbYNuFQkd1cAnPvopvvj5PwzA7fyE8mLldL+RRF0HTyd6OvdG8h3fw+kBmhNLc5rIc2JrhW3deEXzk2nO1BhhNGgMU2Z/d8vzF8857HaAXhBY2iptEEqVQG0Fow+4u0ePALuc6a3STSFG5t0NL198A9s64BwqgLU1Zo3kPNGt0AdfyqbI2iuTBc4SFqVVZIjOtuotOSx4nytM6Oc0Th+E0zmTNV36JHmlqpMGaTdYQK1jrRFyRFTponSCa9N55z8ATEev4ToUElLEovcak1eUyac5cft0R8gdixWaC8kGUdZWL2hRiV6H62bMcUar0LbC/dsvmKbM7Rt36N6/u7zSsJCh1CAaiCihGctIOZa68eb7nnlH5V4IcXCZTTAR0pnHRiAKrA9Hvv71b4DA+z/4fvJuBzlQrFMfhf0IGuhrpzcjVqEcK/cvHti2Th7PxTzPtLXwcHoYCv1GDoGb3Z2LMJ85uy1gZmyrg0/mnB1dGSMhZkqB4xCGdnoD7A+z1z6jp5u1C6fTwrI6ErNsjRwzSMAaiEYI0SPNrdHLuY7obWlSmHjPs/fx9be//ErjRtdwPNc7jU63SmuGShxx9GPt6xxpGXbmG7+2dnVa70Izs68AXxn/fikifwf40P+fc4UQeM/TJ1iDbVkvDPyOeF+5GqEpXbwtuKKkkHjrrQ+67AxgTYdUxGjfoMGdXHV497mV/bIuRAXUuF/vOdzeEHOC7rWptQxE13HDxNipEXfO53ny9I77ly94OD7wnid3pAFZzzGOdvUOkvCOxuYgCJELIhGDVqv3y2rtUYi2doLhuT5gFxNRlF4q0rqL6YqhMbBtG+dutuCcrhi8zXyvzVOqMYLqIP4OE1eslzkRukPSVYM70dYvmlSvEp2ntPOWLOMUKuKkZB77SoHRakWDDIXwszKH8nB6AUCaArtDJs6RB22oes1KakFqIY/vTqpEVW/cKEqrje1+4f7hnmdvPmN3mDnhNbnWzi1QXHIpxMR+ryQy0oXT6sdpUHaH3QUUIEPJnXMtcIyttkbZKl/96tfYmvGRj36ckBOiASSC1UfgTTNk68Qq9K3y8mHh4cUD27Y5mfvcNfnh3oEL0bl1MaYLgKZs7YKuTDq5qn0+XDhQ4ACW4/0LttIvSL4pZ3J2Um/rFal4L7jWOR1P1KHa4QjG0RVaPU0qKheB5XP9tLbCw3bPs6fP+OIXv8jf+7//tpOXB48sxvzIM+ydWjtdvE+Zmr7S5+v8PPjfJudzvJ6e6+q03uUmIh8DPg/8deBHgT8pIv8a8L/h0djbv8tnfg74OfAWF9uy0EpHTQk8toCgQi8CTZHkC6eYUkvn4cXC7VDEKA+NHHYwK21dUdNBAhZUhW3sLM9Q991uz3E9caoLU5rIu0xtjY2h8daF03ZifbFhyftFicKTJ3e8eP6csm2kIfk05+wKF+KabQ7TF1ChvdLioWyV48MRmYQwxAs1BmwrhAZhRIPzNKO18dvvPEc1Me1c5SHmTKl1gCwe0X4dbxch4p2I/R9+7sfOzgrWODc5pDd3WL1hFxDGWc2g0s1Ig3wr8liaCCKXxeks4dN6Q5OScqAHUO3O+VkckBDnSJoCcQ6EVsnJyGbYcqQuJ9IYyy4lr5d50QgpxsM7L9nKytP3PHXdu3ZuE19RSWOowet3MRC6R4JnOaPbJ7eEpPQwuEzdNz1BdDitgR4EvvyVr1Ja44Mf+ShxmtEYvVlmHYi4sT6XtdDvN7ZlpSwLZVuJIbC/m4k5XUjnOUykEC9NFuvWOR0X1rXQWme/84gsx5m4P3PlHNhyPJ54eDjSWiekRB4RYQwB6Q59790BIFvZWKtH67uhMB9D4sxfdgK4P4cd17Q89xGrtaLiEdunPvUp3nzzTb769d/EunmkFx75V9bNofmv+KEL8GPUzcYIRhPL19NhwdVpvatNRG6A/w74d8zshYj8J8Cfxh/hPw38R8C/+Ts/Z2Y/D/w8QIhqx4cHcpxcx20sDjparlsTYkioeHfYM8I5xsTzt10R404PvogFLzi3bSMNJBoIu51rzBGM0k9svTAfZkrdeFge2IU9MSf2wXlA2jyyMRW6NUTUF8/bW3hhvHzxDm/tPwBAC6664aJL3TWBnK7kWbzzgHzgWO+kPEPx1iVl2UgDag2+K5dmtLUgUZC9Sw/lKbPWMpQxxsKYs/ONBKJ62lDEU19xymj0tFLvhlVvZjnkExzpNxzs2bGKCLX7bjpE7+F1dloiIzK5hHke8a1lQaO3a2f83qpx3DxFNx9mdPIu1NI7SmM93bN+656H3z4yHbw9fVSlwWjEaESNrA8L05y5ebKnUamj6WeTTpDRm0pcIeRMwV2W4wUF+vT2KaaG6YgILaIYSVyJI45o5+vf+BrHZeVDH/owh7tbupmDc1qllsrxdGRZfTynhyOcnMQ9z5m7p7ekKXrXaOmEM80A5371avQ6eqOlRK9CCkLUIcBb2iU9eBbLXdaVVisxR8yGYgtQqw5H1BGDirmm4y47GvQc2ZhRto1t6EiqOkevN1dVOTsiVRe2NeDDH/4wn/70J/ntd752aVszTuVz3lxqCgw645yPCNqzA5Oh2PKaYjCAq9N615qIJNxh/Vdm9mcBzOxrr/z+PwX+h9/jyVyzrT0+7aK4XJMValuRsmC6kdOO97zxjE9/8tOXBoKtGlUaicB+v2c7Henday21Vc7bZF80CmUrxBSZ5x319MDDw5GpTxxubwAIAxFFUubDzhdk6WgM7A973v7WN3ly5+ivOE/eLRg8cSeMOot43WegzmIM5OSt1YOEEc1k6kj56Tl6Ml+4Uwyu9YcjjOf9jvtvHd3RjGn7f9h7l1/Zsi0/6xvzsdaK5977vDLPzfss2zIghAG5sPAVwuWSaJgGcgOEkRANJPMnYNGk5RZ9LNGgYwkLXIAEFEalsiVARRVIVNlQCJer6ua9N/M899mPiFiP+aIx5lqxs+raZffIk3umTp7XPrEjYq2YY44xfuP7WWMpRoOWNzpsPau/fNuoPBuI4wgrqTNeMJeOTOU6zqUvaw3BCGIN1vtFycc8tMw5ZglCDhN9f6S72GkVTZQlmDL0tdT65HKtbskl03pDiYnb22u++N0fc3x/4o/9038SAOMMgQzWUZJ6jJ0OJ55+9imu1YHn2e3Xznw/o1lMSUXLbblwe3u7YI9c4zXYodmfQXDGITGzcp63b5QneHd3z7e/+13Wmy1BMjEGwqC9zsXbqs5KNd6zuliz2qxpuqaWrXVuS8q5HCxF74hUSp1pMhrocybHwv2oB64pJkQ0O9Q5PINvG9YbPUiFHB74fukw9pI9YXC2wzStlsFrHzHFSAzpzBvMolNbWcvCUp0H+qHX8qo1dVD5SNc1OOcYhjMoF6hyeYPUjFU4Z92SZbkx1LHgYy0M6noMWl/DJXo0/8+A3y6l/CcP/vxl7XcB/EXg7/1jPBbFGDXaM0KNQ2RJxDJCiRSxNL6w2q7Z7y/Zth3WCk1TiRippYxJe17VnDBMCd8YUoz1lAjr3RrrNgzjQH8YcK2na9YUU+iHAak9k/1eoa1aKssYpwR4MfDk6RNCP/L+zRsAXn7vO+ocLLrpi+gHd7YwmXcxZx3eeRrvdUNLGfFWy0wxqDsvVBivwTctp/5OT/2ijschBOIUcO2ZfKD4N8FgwCjlIuSEd81CRRjHkbXZ1MxJZ79E6syVmPPmI0IumuW6ZqZKaGY2l1uXzajAeBoIKXK56bBOiJVFl7OQ6mM23ZqY1Bm41JieQ+T1l19wuD7xrR8ovDXmSHLai8NZ4pAZh4mLqz3YSnh4MMhN3RoFQymJQqncvoFdPXyIBZxowHIWlxymgHee96/fcvvhBoDvffe7rLZb7k8nTlENOmctQdNYGrepfUtYrVaIsYQcGZgQp/bzUgRimeMqUiy5qJ19yonpNHG8OxDGiBQh1TKiaRqatsXnZhn2Bj2QYIWSTR33QMvKCUrMTMNInAIhJhi11Jtr0Col4Zyj8R6F2mZyUXPROUuCeojLCdNY/v5v/Q6ff/4j5ujTNI5pioTZDrmAYCkoCoo6SA48vCv0PhQ9wH2s2dZj0Pp6rh8C/y7wd0Xk/6x/9h8Bf0lE/nn0zv994D/4ox7I+4Zus2E8DUjJUG1ErANrAZ8Y44l9u+bF8ytCyIQ44qwsDWVJuhvmlBHKgqHJWWnheSZq9z3eWVbtmkxmjAGS0LUdgtWZLuBgDrhWbezHMNaTZMHZFjGWFy9e8OqLVwDcXF/z5JMXSx2l5IKtDXXNdCqmCCoKRxl3OQXEWmUSpgSNfvDVfqVoQDamikg082mahuPhQLfVMmbOGVxFKaXKWxcqncMtm3xKqZZz6ptey33WWP4gbCdrWrCUh4w1tdpZ67JzFTAridw4Q7vqamCbh1sz1mtg7VZrBRRj1dJdDNvVmjiqSOb+qAOtp6HH7DcgSj45nG7Y7fasN2ulqHOmiT8sZxrRLCKnzBSCUsi7qux0hlTLgwal45sM79++4+2rN3z2nW8D0HYrYtLA5p3DNgYv6hAlWf/trAKlZEYSNBpgU1GCuq3PwS0buSVFiGOmP/Sc7g7kkGmsZ7NaU2r/aTJ62IkxqlIWzWKyKSQySQpil2it15FUyR9FA0jIet/Ua2iqq7HCkHO9/k7dAwqLCGQta2w2iCt8/vmPmILCjEl6oEkpn2HKRQ9k6oxwJsCf3YwfiHmq6OUjjVmPQevruEop/zM/uwLw3/+TPpZxwu65xx4Cks+FBd84mm5DKomuM+wvtuQMq3bLy6vP8NaQU631h8ymWakfkYYtkhEymW7VzfGAMQaGcSIcBtpVy2q9ZpxGhvsR3zXsKxV96m8V2Ltu8Z1VbI/RDSbkiaZp2D5XL683b9/g1hsury71FFwEa9W2IxsWekBGjfOcU8O/FBPOQyBS4oTJ+r2L6EZYfINxXjMdW2hbz/5yTz/2lFouyjnhmhUpZ2IKWCfkKJiuwXQNpdPNKRBrL1DLr0W0tGRE3y3mXhGFWCYyUQ8PYmt2VokbuVDmPSxnpjDRrlYYr95KRrRfEvNEW4dSfdOQJBNSwXkhh0TXeTAqwx+qYOM0HtnLGiNCI5bT8cT+2VNk5ZnKQCxpGV2YbzwF9WrYNVXwYKzFV9m5tY5UJqSAtwaf4MP1e96+fc0nn31Kt9V+WsiJQK4+ZQ5vBZuhxKjlMMqioos5E0SHbxEN0CZrT27K534RuRCOI4f7e6YhYDCsd2vapqVxDakC+HOaKGSch5hKLRNSZSJFD3JVnJRTIieBqO+/FTVfTDkgldoP1KxQ6f1hqjQTW9QOJ55BwbOEPuTIv/4X/gKH8ppf/lv/HTGMWGdx3hArhywnVU4WUTHGDFye6S1LWX8ujcuDXu5Hth6D1jd8ZSIXLy3buCZPZ0RM4xqccVCEy6sNn7x8yrMnn+LNGh/WNMbRzKQLW20yjDanxVia7YppGjj0gyJ30NmZpllxPJ5qCShoJhYyx9P9QjxfW0/oR0gj3XZPnxLBF7CGqUSmnNhcXQCwCROf//gnrLs1bdvSNdrjqtU6lUxTEzHbkorOv7SdpemEk0QkBxrR752tU1JFUmoGWaBRHuJ6uybfpcVNVij4AiVFxrFn43bk6qcVXSF3upFM6IHAGJWL1HQMU5SJtTAXTSKUAVxWE0dblYnUnkhZWv1a8kqBi4sL3a+yzodlEcY4sGnOiKSpCCWMrH3CmYhzhdW2w6dEjJrd3t5es3/2jLX3uDEz9gO7F5cMkrTPVDJuKZ9pGVg9JNWU06LKQ+dbfAXHlqJZUmM8a+c5vLvh7ZtXPPvkObunF4TZUaAqMDWUeyVGhAhVoScWYt2EJxKJgimqdvVJX3sZA02xxElFIKf7W073J6YQWG+2NG2LGJ1jU2ivPl7jpfI3C5mMnSkWItgCJsrcUNISZJFqWxJprdOyrGgmdWYGqnCiZLBWy45FknrHFVl8RErJ9ENPcYn1ZsXF5ZXOrzUNqUStUJS5nza7j2ppdsmu5OFspf61zvelJTP+2NYjEeNxPa7H9bge19dmPWZa3/AlItjGYpyQbF4ObSklulXHy5cv+d53PuPZ0yeQHRJavvPsj/PZJ9/DJc1OnGsUQVf7WCIqB++6hrHvF1Ds8Xik9S2briV7p+6xqWi/JiVilTVHk8k5UiKE+xOJSB97OmPYrlrGfGYKPn/yhNPNHa+//ILvff/7mr2I9tsCeSnZzCy2jJpTNqtWafHm3Ec6vym1X2NVcm+LBwqr1Yrj/WFRifmuw4qQY4XpVjFIJleQby3t5MQ/TM/1B/sRRYTVZo2xtmZXWv5Z/nX92nEYsc7QrdcsRG/RIeMQAr6KRYx1pDEjKet8UO3XXT295H0/cOyV4sD793z2vYRtDH3fE1JgtV1rJ6s+x7MhoT5XMaKwY7GUoKW1ruvwVaBjnGC8objE8Xji+v6WqxdPefbpc2JOpDBL3kQhv6UgJVJyVKscW1Q5amSGkCDG0IgKIlRUqr8mQR4jhxt9Pf1xxDnH5bMnquSs1yQRyAbyQupHVeRZ87ymgoxzViWmyaJOzyi1PkYdCG+7lq7tQMCj98MsmhjHQIxJRwKsxRpDKknnvspZqRpCUMFRTJzCkS+/eKUKR1OYxsg4jEs5eLZcOavq63WZ5yI4Z3lSs+5HIcbj+jiXQMzqeaW1d/1w7C52/NwPfo5vf/vb7NYbVrZDQkPXXfLZJz+gtVtCNU60pTaeRZvROUfEqGdUSWnpM8QQmPoeay3dqqXxbW18gzVnKXXnIOWg5cYx0K4b7u9vObz5wPZih9tuKXWD6Kzj5773XT7/0ecc7+7othtCTqQYKZ6FuyBSKRVyrrI47+hWrRgwrc8AACAASURBVG4cy8yLVAyONuBjirR4hFKpCqjvEdBtVlAKcQoUzhSMXFTk0bQzOX4eCv3DgauU8hV3WoywWm00gDFL+c9zN/N7OY4D3WqFb/2DoKWE9yIFV8UQIWdKfdGl6FCzb1uePL/i/atXapgJHI8H4jhBkzkejrQrpWiMJLVqEcGYc/N//lmqKWjKiZgj++1e59FAbTRs5jD0vLt+z2a34+rJU8acmGJYZstKKRX0WrA5Q44qLLE6WF5qf1S/VnDZkFPC1v/iFDh8OHC4PWJrOXh/+URdiZ1QrJZgi6RqiwJLn6qIKvqcw0Qg6lB9Cpk8FvJgdKgcCDFSTGa17mhXHcadpfJTCFo2pJpBit5HpagLtbEGa1WePget1aqj8y33/S1rv+blJy/Z7/acjvfkmBY35fm6F3NWbqptEMsA+hkMr8T3jzReAY9B6xu/Sp1tEquy7flDcvX8ku3lhjEPNNHSmRZvG0xsWbk9ZXJL/yumpHV6q8fWnDPTOEJxC9sQFBkVi5IAjocD3js2mxVt2yAkpnqiDc4ipsV4Q8oB5yztakURuD8cYRzZ7NTbyW53OCM8e3rFq1df8t2f+z6+8RQFUZ1lzGIQK+pXZByxJLrtirzfVYfjuoGKBpHijNrITwFvDSlFjBO6zYqpqiE1EBXiNOLWKprQXE6FFO1KM46xsutKHSz+6jobb5asQ7W29ZW6P2OPWHalOcubwsT+Yj9fQXSbUhWb0jj8cm2scRjRXpwRfU+3F2vs2hJqZh1jpD+eKOvC0J9Yb9ckp8FcVaJp4RTmrE7Kqmgz9XWp0KBbtyo4AJy39GnkcDqw2W+43D5lGEdVRNozxinlfB4QL4mUg75PxjE7M88VgJJBYsFlgy1C6EfevXrPeJh4cvGE/VZ7nb1P4Ir6kZGwzkDNnFMKjFMNRFOhxILDY5OhTJkyFeKYKCPIqPYsAO2qpVl5iitMcdJ5rDpw/jAdlhl0X7NEawxiMqXMdjL1xYiQktI1iilc7C6wqKjGWlu93ub78qvfI9f5L2v/8EHoPMP1s7P7r/t6DFrf8DXP/3jvWa1WPH36FNB5qEjkw90H3n75JRfNJV254nknuJ9rWPktsXIKlToxl9jUrK6kpOWNfM60FOVTreyzSudzjqS8AgquntDHnKu/UUa8YwKk6/CNJebIOA7c3iidKk+B3W7Hfr+nnwZev33Dp9/5DOusenXlmT5Q/aJEnXNjSbi2IeWEM5yVcVKWeTXfOtI0qnQbIVtoth03NzpfFCXhna/qs053K0l1g0m0VQ3Z9z2llk5NneWa332BZZ4r56QlS+/OA6K1BFSqOGCs73ksEd81UJV1ygMpZJK6NtcSHcViUQfeHBPOGcI0YVeWbtdxutdyWo6B0/0RnhT6U8/z716RTK60BfPALJR6sj8PWZf6nlmv82WzoWYqkbv7O3LJXOx3amsv+jwra2h5z0uM6gBdh3eNt1jXYOvwtcSK+IoRM8Hp0JNjovUdzjouXl6yW++XUQG7EsYcyEWtaKaoyKeYgpaey+xL5rHeYaLipRBLEbA2EyUhztJWNSSuEFPUH0wUo++99VadpNPMmmJRIBpTVY7VMPQhrSLlRDE6mB3SxMtPXtK1K+5uP4CocGmc7U7KHNQL53Gy8kDyXu8okWrGWb7iy/Uxrceg9Q1fIobL7SW7/Z62a5ah1nEYefX6Fe+vrxkPB5ro6NJTfuFPPycHGOMI1fW2cYJtXOWqJUSszjV5SwqBvvZN+r7HGaHtNFiUkpnSRDkluq7D1s2ONJGDMvVyzJiuwYuvczwN7apdPImgEHOk8x1PXjzjR1/8hPb+lgt/gTF2mSULIVZllZ5axRrylLi9u+HZdlXJHXqaTlVm7ZzVmRwpZItKrdcN+Vh7ElJoTMF3LTJjl2ZSvlFnZlCL+FzUDFDLOXN2JV/ZWFJKGOew1SRS9/RSSQ/aZ5kJ6k3j67VSpaRmP5XxR9ahW6gw4wKlGiXmTCLRrBzriw23o2aBYUj090eON/cYEdabDSeJIKYOx6Yla5V6OJn5dkaELLBer2lav5Ra7+7vyBR2+62G1KImoznPANzz7JVFh26N03EF45yW+lKBWGhSvY595P76ntPpSIyRZ8+fc3G1Z7VZE8LIWEt0YAmpAptL0v6pAec6nQObzTRxSBbKBHnU+S6xhiH1DCUgtizlTqzm0WKEdbvGNopnmoaBaRoXQru1rl5b6kyVml6SZwVifThjsV6YiiGHzJvXbznen3C2YQwnfOOI9dCVpkAqiZnqOQerPzg7p4cLpXzIY6b1uD7G1biGi/UlZBj7iev3msFc314zDD2b3YbVqqOJhh88/x4//y/+abxx5CktwNPZPt2YRhlrOZBTwlmhaZrlFKjE98Cq08wqxKl+2DNDmpDaX7GxkOJAdoLtPMa7KmHXU2m0ZWn2u2ojMaaAX3U8/fQ5bz+8Y3u1wxSzZDHeN4zjuPSvnDWIszjvOI09253yETNn+oNxagkypgmsZarZWVMdfAOJbMCvGpLVr8XMpRy1rQewzhBCpKniCOAcuMqZkKBYp1kSX8s81E1ODDkHQu2nbXbbc0mzHt9FhDjFWSlR/w5y0qxIXCU0loRrPeuLDeXdtT5HI4zDyPu379htd4gVsskYzLnh/2AT1Odfg5hR0UrXrUHgdNLZr1gi+/0O0wj9OGCyug1bqtChbvIOUZqIteSkQ9gpqq28KxYXYbjRx7x5955pGOk2K/x6S9e1RMncT/dMcVr4iHb0UERtRnyDmA7nFagbU4LZJTtGcgJJFiuOKSYON/eEMeIbT9OdwccYHc6WCnCewsgwDsQUKYWlDN40HdMUGIepGjo6TO1dmloiBDUNjTmTU8Z7T3/qGfqBlALGaL9snk9zlZCR4lk0pIzCOoeYy/JnioZyIHMA/7jWY9B6XMQpcXd/x2k84et8z4unz9ntN/jG0R/vGD4csday6lZLdjDPXyFR7T5Mq2qp7AjTxOFwxDt3ns0XYUyROGQuLvbYxmOcI1eBwHxa9DkznI5MacJ1npICfu3x0mkZypRlVmpKEXEGax2hFC6ePOUQJl69fcPLFy9YV0FCTomm8ZzGnlQKDm2It+sNMSQ6p4Eoxdnzd7YDEcI00a5XCscteSm9TSmCM/hqx15KXk63SubQX1ujYoFz0CrnH8KCKHLWknLNxOrXLY/HLCbQR2i7TpWaVhZQhogQkgatWbigZaJUs7CMdRaxyuDb7vYLT7ikRAoTx/7Ak+dPtcwosmR7xoiKFmBm/p6Haa3RH8Zw7E9LdrDbX1BcYQwjVs6UfGssFCW+63tVQcwCJhstD6q6g+PhyN27G9JJM8JNu+Ly4oJiIJvClAPFC6YxdLbTsjIgE2rzYYxanBQVXcRQBQ41UbfFUBKEMTKeBvrDRBwzq3ZDu/ZkH8hS/dpMvR9SIMVMygVjHZ3zajVTX3d/6gGpSDNVqOowucEW7VnpvRYogPWOtm15cvWErusYxkSxooKeOZMiY6yQUq5l2mp10rjqeZeXa6F9xnNG97Gtx6D1DV/GwGq74uW3P+HZs6vZzZ1xPHF/uOXNm1e8e/0aMza0Tzu8bTFiSSKL0tCYXE+1HqXRWZzxDKcjYx+q4gkt/fiOU39ifPeB3W5L164pORPDdDbA2wqWTBsnSg7cH+6RCVxs6fKadXO2i6BtGMdI26xwtiEE4dNn3+GLNz/l5vbA1il1obEW42HCkwQdHi6W9f6Su/cfaOqhNIlCcKc44bKwMZ4wBtq1ZUwJk6GtiKRjfyKJqPRRdEO0CJILJcuihvRYYj8iu3Xt74mSC6R8hXcHBWfONHepkUFLQUoUmVWBxjtmWryexrVfN9NDCml5VGMzYiKlBB1atQWypW23eFPl6ZI4HK7Zbjf4y0bt7aNSNEolpQiz1YoG/Fwyzhkar9nu4XhkypH1fltfjVGKemqrOadK1nOhAm2rarIKeky2mOiIfc/h7o5Tf6LkRNc2rK+e1MvdYBASmdIIpvMEU0jEWo6tA935pP0iLELlTRaLLfo6bKxD51NhOA30x5GpV3LG1W5H23QEGUkuLvitIUZCzvTDqIIZ57DG4keWUiRwLs/NYwIZRpkqG1GtXPTzoPSPOEXGEDj2vZbNrTCUqBDjSYO1NZbWe0ISNRtdJO8aBGfDUWPU1DKX/LHGrMeg9U1fYuDqmQc58cWba/peyzB397fc3d0SY8SXhk2355/9p/4UXbNhPGRWZo2rsyOmChCUdJ30NC1C161Izi3WDoKwWa9ZrzoO93fc3t4ytR3bzZqubRe33SLaJ1q1W4wpxDxy199x6E/cjUe21lUHY9hcXoJzBI7YDN41eGf4/ouX/O6Pfoe7eos/f/4cbx1tU5iiWlCkIpqxlKIUDWpvyKCzQSnTtC3jaawikqyeVl43+tNJ1XzOqYWFEUHQco+KLszyesZJIbDnsl1Z+lBLPwLd1PXLzr0vnX0TYoyLzYt6LZU6Y6TlzJxzLSnNQvn5XdeDRMqFPAYwRvt9vlmk0sYajqd7XGPwrWeiYJ1dME1n8QjL85ufh3GW0zgwxUDXrRbLkTEkSkzkqD0dZ3y9P/gKGcIZR5gC98d7Tne99u0MbLYbtrst3ao9OyzXQ4E1hVT7jJFELoFSPcoAcgrqQiBS2YVqHZNjIY2Z8aSnlPube+7vDzjr6LqOVbemGOGUThRXCDkvwSijJcCm6dRmxFoNhjmRKyWkfiC0/FltSYytnMSQkKSke+oVT1mNRq21/OD7P2C73XF9N+GdJ+TxLCwRzU6t1SCVUiJXNuF8r8A8jnAWaHyM6zFofcOXCByHd9zf31c4Z928S6TtPC4ZXOxYuz2NWXO8HWnCRjE6XjfYxpsqcNDMwRqLsxYjBSsbplC9kIae0zhgjND4Bm8dJSfGvifUUhzAJIlpGkEym22H4Ni1F/jcMaYJHxLDoIHw/tUrcI5mvWa727Hb7jBNx2q/5uXVM97+9AsANk2L329w3jCWskjffdN85QOeKmvOGFOVYQ5KlZqLbhbWaaZlrdV5oaahZPWZKlmz12WQGZ0HG4fhKwrk+Xs+DG482PhEzs12EdHva+3SN5mXqQO+80AzKD28gvxU6i+K1qJaZChXrw4H14ws5cgUR8QUQhjIriFbr6DbBz23+efZkt57z7HvGePEar2iaTpSnaGzZc7PDDllyqT2JGQoqXDqddDv/uZeFZYFmrZld7Vnd7HXvppkgtMyHKBw4yRgIEkhGn2fS8l1nqy+bGexOJw4JKkJpERhOkVuru+4/XA3fwLYbres1itcY/W6ScH5Rt/TLDRz4DAWZ52WHavdSUyJIJFsZTHJlJxxddC75Kww3op1eiCaRMdD9LBXcuJP/PE/wZ/5+T/DL//KLy+cyeXeKNqbfOiuPH9e58AGZ0HOR1sb5BHj9Lge1+N6XI/ra7QeM61v+Aox8erNO/p+0LLNXNLKMAwj4zjiYyCW93zxxWv++AuHFIvBE8faoBZoO6cN/pTJOTGliJFZAKG32W63o40tfX/C5Ky2HCKUHJmmc+8rOT0x3h7uwQvdxuO8o8nC2m9onac/qYw+SiEbIaP4p+N4IpOYbnqu9num7haAt69e8dn+j2GdYKLSN+YhTtf4Zf7Jt2uSkTpjQy0vQY4R452+ntqTaJuGGAK5UeO+khOpaEYkotkLgGs9pmaGmkHVclPOXynhlFIWA8WHa8ZjNU1zVrI9+Lt5+DZnFVo466iVVi2nLfJngzU6LFtSwThHt1IRyWG8o5TAGHuG0NO0LTEmnLFYWDI+YHHjFRHGceT27pbd5QVN0+qwbZxZYCClYIoOu6ao3mKHw5HT8bT4rDVNw7OnL9hs1pjWkkFnoJzR0QQpCzA3VfpHkbzQTQSDJLOAg0EFHVasKkinwnQM3L47cLobkGLZb3QIuV23tOsW5y1JEplELIkhjxhraXy7+J3llEkh6+vJKqSYQmCaxRKzMCnpsHYKk1rblKKUe+t0Tq8+XkraCzYIU9JM+XjsiSFgOwdpWry8YCZpFKiZ7s8qAc7zdCKFrxZ0P571GLS+4avvB37n//2pSqNzfjB7o+ICay1tydh84vXb1yQSY5xwpj3DHUTJC6VuGqUOQsaoZOtQe1pNo8Fn3XXQtkzTyDQMYBzYvAgX8Ba323J7uOX+eECaLeu2o7GeMI3chRGpM1CmbXCN1Q1D0Ca91RJKzJGnz3VY+u3bd9zcXLPfvKi0dcUbGdvQdt2ieHPawKmDumgPCyGFRLvqiIVlRswZ9QBbdZ2WQ8mknMk1YMVUy0quoekaBRVlHcSd+xJa3plRPdWL7MH81sNekvf+K7+XSlqfr0GYQiVeJBaRg9H3pFSKRc5RmR2pYKSjXWm58frdgOsMIfVkAsYbHA6Clt1i7d3BuTwYY+R4PLLuVqzbTpFd6WGPTi3mj4cjw6mnxBqk2obNbot3lV3pPSLKbMTWQXWD7k7VyiVVCF8SnRfL9fFLKlgEWzwuFHJFfJjiMcWSE1y//sDbV9eY4nmyf8p2tcO0em3UVzkTin7PVBKFjPVG5f5nPQv1m5JTZhxGpjGoy3Trv3IAsUb914IIxKi9UGNpbKuPWYN6STp3VUSVhTHBTz7/MVIMYQzai5vLnchyeFGp+9mzbu65AcvvfxYp42NZj0Hrm74KxOiU+2b8UjAWI/VUr32XyMS7mzccx3va7HE4St1s8xRxRekCMWiG1Xhfg5BdjOyGfqCc1ChSRM0aS8oYAW/sg56JxYrlycUlH4433N7eEuPIbr/GIQxTXE6RTevVX4hEqby3LJpBTZJpqzz98tkT3t7fIscNTdeSh0kRQdbQtO3ywVf34Fyl0qVmKep91AGUzFQpBU3bcIy6UVMFC85ZUgrkbEi14W6Nznz9rKPvwyD0MHg9hOg+VKP9zEtYBRlTmBAjpBjPgo55GJVEShPFThQRrBOlStSsbwoDu82WYTqC6KYtURWiUg8vc5bnnCPnzDAMWGu52O2QIkwxMPUjQz/7rM3iCUOzXuGr4s4YOw8Z6f1TagACtfB4CAkWkJKrjQtLlqqoIlUe2mKwEZpskKD9xpQVOvvm9Vvu747s1nueXD5DxKirtKkzgb4Cf+drn1XwklKipKQorfq6Y1SV4Diqg7UgtNZjxFDMV3t/IsI0TQxl0AH6XNRUJecleyqpkitEEWqC8K1Pv8Xv/fj3QDLTODD3ppzoHBn1fjTG4L2vuKfzPKL+XoNdeGhZ8hGtx6D1DV9iDF23qjM8D052pmAtytzLGcmZwEBgZLu2TKeeefLReuW6kZVyTikKrBWWUyFAkbpB5MQw9Nz3aqjoncUZQ6wn1ZXpSExIZ1l3K8qYuLu5oYSRi4sdW9sSZwv0oG7LKWfEW6y3WGsWt11jq4Pvfs3l2vLu+h0vPnlBYw2m6EbTtM0SiEr1vJq9iqxzOOs4jifNIinMwO2cVTEYQ6RrGw3GdZNR4cQszatN+QfBCL763uiXzdC6+e/l/H3qRvgzr6EIKSaGYWC9WessVoXbStLniGRMLlhXcU9ZeP/+mlM1gdxu17StV6ir0SCdkir7Zun2w+c6jiMxRvb7PU50Dm08nhjGaTmkWN/QrVZYU2fxclS6iChpfy6fJimV1SeYkvTQkAUrs3AjIVUVaFGaSdFzAhJRDuFkaGmhlqyvb2758vVrUk48f/Kc7cUFYqyOCFhqRgdZQh3+1WBP1tktU+qEXEY5migwF6kilLUO0htjsBUcLPMkcCk1ExtVXYogWZSxmM+qyZzP90PJmd1mx5/9l/8sv/a//xpTSMQpkWuql03G2wrHrXxM7z2+0lNC5XYaY8iVNvOx5lqPQesbvkQE462il6QskmFItRSR1ZY+Jr58/SV/77d/kx/+qRdY3xBSpZ2LJ6TCyq60zJNTJZvOqJ55yNaQU2QcRxChbRusUShsKZlQ+0qHw4F20+EbQ+scrtnReuFwf0MKA5e7K3w9/UrMFKtIU2MLthTtlxnDlBM9+mEu2bDabNh74c2bN3z72QuNuQbW281y6i/z3FPRk7+xlm614jQNy7DxHDtKzqxWK6ZpAlkjiCq8pCKOltKOop1U3q7Z60wKVODFTJaA8+Bx/ZdfYcs97F/MW5J+/RQnHTb1vqoW0/LXVurcTlG7jBAnPny44+bDzZK5bPc7SorsL/eKD4oRY1rd0GvAnMuD/dAzhYntbquzRONEDgErhlXXIfXapFLLrEURWsXWVyDKSJylfkpKUQm3Q+8ZMVLRU1qOLYvkHUgFKQaPw+KRIMgonE49N68r0eVwi289z56/YLXdUEwh5ICxhikPpJkWYbLa4xSLqyXxXAoxJMI01QFlfT1t02KdJ4uQciHEUHtWmZLLcv9Oo5IwjLEVlquPq2258zU0RmcbrRHiFDgejxwORx1fKOAbT41F+vg5aAZq5SvZ70OngJTSRy13h8eg9bVdIvL7wD26O8VSyp8WkSfAfwF8H/h94N8qpXz4Rz1OQd16m+JpioMq2y05UCQiNmFNQ8oNP/npK/7mL/2XrMqWf+Xn/zzxVAkWdsPUD2STsXUzHSos11u3bHZYYSqZISudYrPf4JyeaHOOuDpI+eH+nhwnZBI6sTgK7bqjay+5vv3AF+9fsd1rI3212iLWEqWQpokOx6ppNbswVvsKaIBZU9itNsRx4u54x5Orp0QSdDp4CqgNfdFeUTFCFEuz3tCNAzFMuMYsgb0gNL6lHwKIIRdDQedpcmHJmkQyUh2SjdENO5uJ7EeK6NwRANlQpMVIi1pn6POw1qAa63DuddHppZcEJdH3x7qJeeU2zoG1BHIaCNMRIsTBQPGMfaBrWnylwQ9xIJdMs9sh1uPEY6XV0qfRsucwVV+0/kjTefAwphHvlTphjGY/c8acUIsOVUgIFdiESCGHiVxRSpITJuvgtrMdtvHKeiQTspbU5o3K4WjHFZK1nBbHxHAYuLu+5cPba2Z2fPt0R7vpCE0hhHtimpQbWC/zTBYRY/HW440W6HKEqU/EMVJywbWWdq2zcaZxRAphGpmmkRgiKQQVnwT1LAPN1BrfIjgtG1pLKToQnFOdZUSPLqYisLJRAcVqtyGVrBBfkcUGp4hASvjGfaUkuMyQPRhsLgVKET7WsPUYtL7e6xdKKe8e/P6vAL9SSvmrIvJX6u//w3/UA5QM/W0iYSiixADQ/pRxDmvASUNKieE08sWHL/iv/utf4lvPv8+f+O6fBFQFtdnskVzIddjRGFMhqyylKigkk5HGMhGRPNLZhrZtMGIpg36Y29wxnk7cH+8odDQri2TlDV5cXXJz6nl/1DkbH3qeffKc1WZFHwZCmrAIpml0HmlW6pG0ub1qeHL1hHfv3tD3J7abPWAWlmFKs01GDVxSsI2j26wYYk9jPIlzecdKi/U6QK2GfGo9kXMkpTkb8hiTtJYFCBFFgU8UZNlwShElzhtB8GjQqlnYnI0tJagH17AUDod7tttqVVLykr3l2VsMaNoV49RzdzxSimW33XB7q+XBIQRWqy273ROM1Q0350LTOqzXQH1/f6+vpm1YVc5gkkwqai+faonPLhHzPPCqz1sFITmlxTgTNMN0rmb7xisRP2XIBVMzKjffl1mwyTIOigk73B+4u7uDIqyfbLm4uNSv2zRgRfs7BpBO565SLbnV97HU0p0pCmceh0AYEt54mnWH6Vjgw2Ma6aeJYRpJIdTZKxXTeGfwlZTSWE8pEHLBGKdoMzEkSYR4JmcUCjGoAGm+Tz/55AXb7ZZ3t2+ZQr98rXO+/rALX/AflVU9ZlqP6+uy/g3gz9Vf/+fA3+aPCFphjLz+B9c4sTgsZe5HGBThI9r3apuWGAoWw5evX/E//I+/zPN/+1MAWmkpndDZVk94RctoMasHk+VMO1/ZhpgNMUemNBHGib44bGOQRjemdt1QcqQ/DpyGI6ZZV3q86GNceUolQwy3d3z44jVcXLDeb5HWEWtZiaLiDAByIQj4xmJEuLy85P2795QMFxcXS3lFN4k5QOgvkxHsqsWMAfGWXPXkqRQimaZrmcJI23hEtDRXeIClEg9m1CxMEhCBkcwwhxZAN9Cy7PcJ1ciZWlJUSvsStJaopWzBYThycbknl0ghLoQP7c+pGCKmSMoQQmG7ueRwl0nVEjhMwvPnT9msr/BuBXTk7PCNZwo9h8NhESSs1xvmRFLEEGMhY0j1e83P0JS6eWbUjfiBmMI7BzUDzzJrASHkpCU5hMY4HI4GS6m9qvHY8/7dNXf394xholk1XDy7ZHe1p+naxS3aOLvYcyhCMZNTVDl4scs1BoMpFpNtdS/OGDJkVYCOtbcHEFXug20szhmc1cNUUxydbwjVo+t0OBJTxrctTac+cFYKyURKLEuwLhTGpOMe7aqhMPLrv/Eb3NzeEMOEMSo0AnUuNkWWvuh8v8YY/5Bw5+HPH+N6DFpf31WAvyUiBfhPSyl/DfiklPIlQCnlSxF58Uc+ShaavNHTXkjEaW5QF9KosuyUEwczYI2jdQ1TCXzx5qfc3quv1GfPv8U0jYgYnBVyiYiAtTBOA1OVIbvJ0jYdXddSvCc3hjFNDP2JPGa6GohWVs0hySOnw4GhN/jO4cVhrMN6x6pSKdbiKPcnws2RwzBh92vcxRrvlVG3YIrqvNVw6unWK4w17Pd7Pny4xnv/h0gTD92AsxVELM42laRRH7SoqWTXrhj7I2AQkyglQA7qlAsULEVGbZBnTzaRXEZKGZdTsz7HWWxREDT4IBaRBnDaxF+CVlUhEEl5AgkYm8l5QCzEKqu2ksklEMKIc/rIm+2e1XrH8e49VF+p9fqSp89e0nU7EM2y2q6hlMD9/S05Zy52V/p6irrmppI1iM5YoqwBdKbPC5p5p1Bl2E77YqHSIWbg8jxuob2tjDMWVywmGlwShkPP9ZdaULi7uSUV2F5sefbJ3hgRSQAAIABJREFUM9aXW8zKE00GqxBdoEJ4dcQgZR29SFV+rmilM+XCFkeeTM2Oc3UoLoi42oeroh/rsLbKzlPGihpRtsVRyAuubEqBpm1p1u0ylzdNYx0HOL/umCJiYBx7xHV8/vmP+NVf/ZXq/C0q4Hlw6JoZkMb4f2hQejgq8bGGrceg9fVdPyylfFED0/8kIv/PP+4/FJG/DPxlgAeOco/rcT2ux/X/+/UYtL6mq5TyRf35jYj8EvAvAa9F5GXNsl4Cb/4h//avAX8NwHlfnm6fYawwhZEwKicwhAkpBUsh5IGUIwUd8pwYkaZw+Uxp3rYRJUNkTwSMKVAyzlmca1U9BqQwEe4OlH5itW1pW09jW1qB03giHZRyEUyLFWHTbSghcrw70sUO69T4sDENUtVxXefo3Io8TZymgcPdAUkBLmHVdksVyHtPqbYOk1HF5G63JcbAze310gtxtR+RkpZcCsqiE6c0iVyBwMt7mVMtv2XNME2CPIEMqixDeXnGZnKcwLQUmchlJKWeUs40eMFV0UYGJnIxiGi2I7SInG1ecikUAiKZMdwhNmDsSJZJxRAzjNZbhtOJw/0Hrq4uEGPYbXb0Y6g2Lfo+Xj654urqKWINqSSapgWJ9MMRY4SLi8tFFZiLZjKCgWKYnY0VqRJJ9XpL0Xk/5zU7QGb4L8sQNdQ+lXiKyWQRTAaThOHuxOu3NxxvDkjtDz55+oz98z2m9djGgReizcQcCQ/J9rMkw1B7fPpjJtTbSqw3STQTDDD2SrgwXuhWHtMaTHPmGWZqdok+/xIzzjXYrI7SY2VsijOYxpEo5DRp1pfVmVkNPfXxrNUSYyMt/XDi13/913j79g3GivbAclx8ssiljpScna9/1gD6XCqUubb9Ea7HoPU1XCKyAUwp5b7++l8D/mPgvwX+PeCv1p//mz/qsUouhLHHeYt1GVP7DC4JJWTCGEjJkJM29bMEsI7Pv/hd/u+//1sA/PBf+CETE93qQuW7aYIsuofJ2YrBGYeTwjCO3F1P2MZiG4P1hrVpKbOhXSjEKSgxIBlMtBw+nMhJuKDFGaXIA/gqonBdx0p2rEvgdjhy9/aasFmzv1CVoSmFlAvWmaUnMIWJy8s9799fc32t5aenT57hfVsb3bOoAcw8G+NUCQZVLR0jpmigKWi5rhDATDqoC3Q54owOrlIihYlSRpCBRXcPlFnaRpXBi75CaChlAjylaI9D7RkzhYmQDogdETNSyDqcWwPMGDPv3n/J9fu3WJd58vQ5vvX89NUrTsM9642WWV9+9oLNbkUsga71iIkc+gNj3/P06inWurO4o8xqQKWZ5yoIELL27KrgRP2nHGFKhJJ0HGIB25qlLadBS9WFMUSG08Dd+xuOt0dMEZ69eMrlEyWbWO+YfNTZLqu4sJiT2rEIc3tw/qTU/xuEgsFpSQ+DrbQSky3jlBlOE6d+ADKbixXdxjOFQMlm6cWFnHQIH4NFcNYhsTAOJ47DUGHLgLNMOZD6ESNWDxoxQFFp/fy8jBVKULHS5z/+EX/77/xqLb1miuis1XzwIc2QXvnK3J619g9RMT7mfhY8Bq2v6/oE+KV6czrgr5dSfllEfgP4GyLy7wOfA//mH/VApWTG4UAWi7GCzHeEADnhGrDSMvSZPkZimnDecH33mr/xN/86AN9/+W0+e/IZMfSARSRjva28uaBDm1QqtW+xzpFSIJbEOE7kQdE8ztdGv1thgONpZBxGyILBcfjQM5zesto1tCvdbGXdEVYtUQSxhk23ZbVeczyduD/dc/tenXlT3tNtV1AsYQqq8qszNk+eXPHu3XsA3r59y+XlFavVuvYvzDKoqQR7R6lDsZlMDpk0BawVUhqxUmqACYRYHXwTKhAoBtUlTBRGkAlElmBQiqWIVUHGTH4Qp0GOCYqnlGokaVuyZGI+EdIBYyewgzb5TaFkDW6vXv+UV69/zKptaFce31hO/T03d9dYn7m80qB+9WSH9XUezEaOw4F+GrjYXeGceTBjNjMTlRQfY6LEjKUgLiISwdaMR4SYkvIhMeSofTsjhpzS4uXlRQjDxDiM3Ly/5eb6Gm89L549Zbe/gMZCFen0ZaKXkSIFihBTxFqHNxqQZoNF7RWaub2GFDAoj1CDVp1rGgrTKTCcJpzzbC9XdDtDyCdCCITBqrEmGg+dU7m5E4MtMPUnhsM9U06YavJZrJCNkl0EIYeMoWb7sRDr45E1E00h8uTJFT/4/vd5e/2GVCYdFrY6W6f3Rm0VVpn7jNL6g+rBh84AH+t6DFpfw1VK+V3gT/2MP38P/OI/yWOJACaSSyLO9ATAWVX06fS9w9ktLk1MBGJRO/Cf/PRHAPwv/+vf4S/9xX+HNI2EQSW8zhi888swLagaq6ew3qyw0tWTZCDFiVLS4pk09hMmC1IMu/VOMUuSGXNkCBPp1C/A3Ls7MKuGdtPRdh2r0LDpOi63O9qu4XCsNu3vP9CNAxdPr7CNI0d1y63WhDx7pif5L798zfv373nxwtI0rZaBahYh1EZ6HTZNWU0b4zTQbCyH08DKO0rNtkyV+sc8YIrDiFvEExDqD2HJtEpWEQd22ayQpBlciZTcUCdUMeJIZSTEEykPWJsoZSAXLSm9efcagB//+HdZtx3f/s5L1psWsZkpjuz2G1rvubzUoNV0AhIQC8NwRz8MbDdb1l1HzgnBPuAcnstTpc5RGQFTS6S5Dp0nMikJIQmnPnB/7LnaX9JsLpbxCIDTfc/t9Qf6Y4/F8t1vf4fdeoNtLNkZhhKZRB8z2kIwiqnKU8R6i6vBYZau6zq7SM/PV0TnvEwR8qjv4+mupx8mjHVs9zuajSXbkSlPxJKxplsYibMwwomBmJQAcuwxAtvdFlvhw9lZSkWUjf3IPD4+I5yWQeAqECkl8/LlS/78L/4iv/Xbv0kcJqy1tRT51eCTcyHGAKglia2czdlMNKUHEOaPNG49Bq1v+BIjFGeJqVCSWUp0YnXqX6DOCnka7/CVx5djXjoI/9tv/h/8q3/uF/jW7jkhjJjSIrSqxAqBkHWINJMx4QhRZ5+yVeKEk46kumgAfC/kkAhlwO9bZNthGkGYsLnB9YKp8mKbE4d+5PSup3Eebxyd93RdS9M2NLOGPDhu3r4lvT/x9NPnrHYb3KahlwJWZ3QAnj+74ssvv+TDh3e8ePEJzjRaHmVEJFSgqj5mSAlsJJWBYh1Z7hUvVQYkD3inr3uaAq5dM+HwAomogcZEKGBENzuKICVgyEi2aG6QwGQtHcpquW6LyDwfIJ+wNlByj5HMh9v3/PTH/6BeX3j24jnr/YpkEklGij3y7BNPUxrq3CxFBqzNTGHgcHvg4uI5m3arpScsJQmlyuP11wEjCWsmkq0zflYgRFLU3o7FIMUT+syXv/dj3t/dsPmT/wxmdclpmLipWfDQ96xXHVdPnrHb7DHWMeZEFlVqBsp5Nq4oNcMWwZoGj0ey2ssnzOIy3BYoJjO6QnSQLPjR4ieLnOBUZ85uhhtohM2TPWwnotdMN6c1xgmNbJAZhIsG5vF4ZDweieNI0zpkv8avOspMAkmZGCIlZhqxiDdYm0lTVD6kzMaO6jXWNi3GCN22Y4qRGAtOOs1M6+exSAZTxxeEimoqgKkmpPOdoaZdD5FbH9t6DFrf8FWoxoelIGXBopEpWpMoqCtsUUCotcpY6zqPqRbfX779Kf/Xb/9dnvxzPyQlpRs0XYt3nkEMcagWHc5CSoRhUt6gdaSiJ0Nr7YLLcSsDPtPFhjCONGtH220ozlKINC20C/rIsY+JoZ/Utj0kxmPP7d29Qmrn4czK/ru5ueG+P9Bs11x9+ozu6R7vhcPxAMBms+Kzzz7liy++5Pr6LZ+8eKkwXNH+EbOhIjo0XdBNOhWHMYEQJqxMiDzMtLQ/kUsg4wEtowk6YzPXZJdTdd2MpOKYZqb5QsBAZdgpT+SkwcZLIcWBU9/z+7//O0yVKfidb3+fJ1c7rC31exoaX3DOqMVKxRlZa4kpcjz2rNYbus7X72cpWTQrqFWtuXWTcsSYguusStvzBJKwMsvOExTL8f6OL37yU4YYdN6Ld/T9xGajQp4Xn36CkYLzDitOOZLFaMSVefJAH9MUNa80qFhBis62nY045vewQnDJeLSUZ4thOPZMHwb6oJl603k2V1va7QrjLbkIORZKVCNNx5m4H8f/j703ibVsS/O7ft/q9t6nuW1EvHjv5cvMyirJncoyLoysEgZsU1gMQLIsZDwwkpEwE2aMYMDEMwRiiGRGIIExCFlCCMsYsLBsuSSqdVWWszLzddG96G57ztnd6hisdc6NTKfLwMgZ7y4pdF/cd+Oce+7dZ317fd////tPzONAv72FlOgax3K9JHXlZLX3xklKSMqoVEDQknPhFibQ+k6uHilz3kQJiry+uS1sxNp2VWIOni5t9q3ZcjrfUzH2wox3W4RFQPT+rvui9TVfIqAs7N2i+8s9CwUvJOVuT4Csc6ULCUkizpUTQmMyX715Skp76oEw9CPJhNqS2fuaMlo5tDKM/UyWufDnFNjGHoyhE6FsNkYTN1v8daJpLXplUUYTon5n5pMwC8d61aGSYLMCn5iDR6zB7RVvMRKiJ+liEr2det7eXmHjlscfPmKum1iXFUZpPnh8zrOnLxCBs9NzcpoRPaMkk/dJtjqUgs5EDB6tE9PQk1XA2nzY3WOcSLElxpliXZ4orcFIrgIFAFEBsHXjKpt2xkOulI6DMZm6sfWgZtpWiFkzzTuePP2C68sLPvrGRwA8eHCKdaBUQClNSEOZOyGV9FDFFSLshoGEsFock6j4oFxAr7EqL8vnVFGIHu76Z3wYUEy0LiF7E7GUWJd5HhnHgXa5xGpL27Wcnj48eONCLD4ySQkkEXMx8Zb5Xz4Urv0Fu79Kk+R3crUSKHVIHojJIkljYyxA3SwM/cTN1Q1+jLTrcu0ena9pFo5ERqIUoUQQ/BQJPhLiyFATlvvdhhBmnLO4tsUYwzBPhJBIOaNtndEZRwqZOHmmkMl78kcuRv0DcilFfPZsxw12Ybm4vMD7Paor1VTq/YxOsVeZvFusgH+CEOP9LVv3Ret+oUw5VeWcC1UbCoonl4tfa8qpQYMyBYujUiztLWDyG75684w3l9c8OntImkvOUdl6EgcvbkpkNMYZJAmb7Qa0oI1mHsa7EMiFoENiCbgMcdOTjpbYhSUqy+xC2cyAlCM+zzSqziqy4IxBjAGrUHUjURgkGubscdbw0K0RK8Q01TlSVbzpzDjvMMby8FFpFUpOnJxbcuoRndgD7EQlFAGlpkJAMBDjAKk8R2ZftGZyCoSwK+IPiaQ4l42/Pm99wMKoo25StWgVVWICHMJ+npbwYYNoj7XCbux5/uIpL1485ezsAd/85GMArFXkNKJsQQFN81Qk1UqDjAdqR9+PTCFwtH6AMYmQhqIWjZmcHCkb7jbCooAzRhNTYJ57MiPGJLQFqroyJ800juz6LVoLHz7+kI8++hCtG3JWzFWgk3IikEk5oilzm1KsamvsnWt1367OUluGqly3WRWxy/7GJ0aHU5Ymg5kjcTsTrnuc0pw+PqM9WpTHM0LOAZWLijHNEb/xTLcTkx+Z8YeK6Yxi0a1wXVPy4ySTUvl3SoQ815lsv2P2AZ2LpJ1KC4kpY/b9PcrbqxQ7zTD2fPbZDyvXsqoD1d38NKVYc7J0nWGVzoH3/keKlhT8B/dF6369v0uBbijJtzEdUD22Ov/JhWwBEdGCaUxpz2Q53FEvlyu+//n3+Ae//sv8uX/jzxJr2yvGSE7hAHGQqiZLKdE0DdM0FUirLv6rvWfoNmxJwTNMCR09Uz9gbjd0K4dSrlCz95VQIjF5dvNIGGYaHKtmiWhd3ujzXn6tyonRqEJVcopELCQhibUdBiFM5ByY50DTNJydHXN99RplHMujiEgks7+bD5XyEJjigHMWpWZSGsudfn3dMZTCGMKGmKSy5kIlJGRgn2pcfs4561IYRQFzEXbkiGR7sA9435PyFuIMCm5v3vL82ad0bcc3v/khy2VbX88MKqO0Bwko5WtB1IR3Nrt+2tB1Rxib8Xkk05Bi4h3AxV1GV9ZAmVem7BGdcFYwaq92i/V7DLx5c8vt7Q3OOdbrFc45QoSQAqi9xD8julAqUsxV8LCnoxeyxrtbsKJS8qXMZIuMvp5K3jnVmKyxEfLOM12P6Cg8PD1ncbIiVvPVHAMhCn6amIcJv5sJu4CJhsYZ2oU9AHNd2yBKESUTUsTHSIoRFTN+mhmHOsvLwsp2WGMLmsoXsQU5EdOd2i/EQIieZtHw6Q9+yD/8rX8IlN+JMfswzz0Qt6gk27acTvf5WeX//ehJS6EKo/DH3+vvyXp/p3X3637dr/t1v967dX/S+povkZLeCsVouW9HKCVlDg9AxliLMoWWYI1m2S7RlNOJBOHt60uevP2CYD3SQJiKmLzE1dc7yxDwKRF2gS53oGAeR1CR5eoEXaXFkiM5GMgTpEyaBl6/fsWJzhw9OkfbTHL7778ATrNEUp7ZDCPDbofWlm65oK3eGXLx8BhtQYTGGbRr2NxeojT4UIOLJGFtSVsOMXByckz2E69fP+GRcSzXthinKSctksLYhM8zQsI1MGxHck6o2kZ0Vsi5nMBydqRkISVCihidD4QNUakGTVpyTalNeSIljxKHiCftqe8yITIieuZ2c82Tp58iMvHtb/0sx0dLpLYdU5yxTqHUhA/FFyaiyRQKf1/nNW3raDtDksIyFAwZhdKJ4Ge0mAO9PYZECB5thK7piBIQNSEk/DwcDsFGW66vbnn9+i0hFPFEjAmlLEbpO9GP5Kr+KyGbSqna4Uolm+wdjl5pD5aMN2pOFTljtUEXVVG9li0uKULv2V3vUFE4OznHNIZpmBjrScWnwDgPTPOAAG3XsFotcThEQ26K3wrKjC7kSEhFORsosywJe7tIFRJpXQC3qBJ8ajV+nkB4RwcJxhqcdSgHv/mbv87N9VUV+pSfQ0p3rWOlFM7ZA929eOXuTm37DskhZ8so9if4923dF62v+xJQ9g4AW0cCKKHQIwCjXXmTkFgfrVmuVhAzV28KMLe/6en7id/+9Nf5R1/8Fn/4Z/8wOSeapkUnGCqeSVuLaYrqKaeI1oLD4MPMbtjQVWBuq0FZg+8y0SicWrK7vOL1k+cwBtbfeISy5dL1aSaohBjBrRfkTogT+Mlze3nBtu5269WKrmtQThPmiEodKmd2u5710WIP/SHnTAjFSDtNAdLMycmKkB2vXz/jA7Xm+OisvO5hB0HK7EyXFp6zmQFfwiBrarJzQggTKU2EOGF1wTekULBYxuy3sVgp756cJ3JWBazLPkojHDb62W9QypPywPe+9xtMPvDRR59wfn5UEVf7SAtqWzCT0oA2Fm3K3KPve3zNtFqt1yQSOXrA1taUgZxIacaYBqnVSGlYNAsg4sNEwKPUjD2YYfdCFUsIkWn0ZGwJy6yFKcTI/mKLZGItUKJVEVTsc6LISL4DxwqgTYn7UNoge8NwAJPlEK0jEbZXAzcXG5LPNLZlO874zS2zH0m6kl/aBmsd7WlDu2ywtnj4whSKitHIoR+VciZFyKokRStdvHxiEn3cHTxiFlMM1BnGeWaYS26aQqGUvmt1qowRw8u3L/jVX/8VfJwPiKeUQiW31LdpkVD+SIbWHte0VxLuP4fs0VXv51zrvmh9zZcowbaWGAKSwdVcKa000RcydsoR55acPThBWc3FxVuu3l4x7uosJmiMs7y8ecH//Lf+Buf/1gnffvhtYh9IWR02p0Q9GWnBNvZAb1Aexmk8RIuvG0G0kEh4nXFHKx53Ha+fvOD2xRswiqOPCnG8WdSsohzKjMMarLas2gWxbXn9sphsn198yfHJMQ8/fESzWqBzpN/0tG1H8brsd4ciP3bOoiQzzzOuEz58/JAvnz/j1ctnBym71a4o6hJoVdh7OQeMgRj9Xp2OUo7gJ2Y/Mo8W5QySXXEUJA5KyJg8gqoFpsTTIxGtMyn5IsqoVcv7EdtEXr16xtXVKz76+BMePDjBWoXR5i6oMgWcU4QwkNOM1ZacPNttz5wSy+W6/m7K6Ukpi5IaDR8jWjzWtlhrigydEpMxTRPjuEPpjO0qd5AiINE1WDJHRfCFCC8C2+2mpBNrVYIt39mRRQvaGGRfdCr1wWhd5O3vbMrq0ALQKDHoKOgEes7M20pav+558+qSKUK3OiaKgBK6RcuJWaLqdS7OIK2AAy+JSGCWQFQRSYIWfYi3SbEoG1Mq32tOEGJkHkZCCOi09+95YkhFSKPANg2pEi1SCIUMQ7lGhtDzq7/2qzx/8Zycc6VdAKg7gznV4hBjoWxUDNlPiiEpN12hmpZ/7/f+T+u6L1pf86VEMKqpEQyReS+pjh7J5YRydv4R2q25vHzN22cv6Hc7SBQIKqVQCMLDB2sub57wy7/xdzj/l/8syi+wwWDqeyrEgXFTwLuua2gXLc3C4qIwbjzT7gaANDdF9dcaAmC1ZdkuOX8ws3t7ze3ba7qTIwBy0zCbFbPMBdCbMy6rsrlYx9FHDwDQl4b+ZsPLz5+xPl6Tz89oj5bIoiFET9hz8LQw++JNU0qDzsS0oTGZjz/+hOdfDVxdl0L4+MMPmHYDKRts1gUiKxllLePYsz9xSJxQSuH9QN8vsOoYlSw53aDdiJY95mrG0ILKRPrCeVSAWEChc2Qcip9MqS273RVfvfwhTaN48OARi8UStAfbk8bKKEweEc0wXBcYL4p+GNn1O9rVSTEEU5R+pIacWkIqPERRGasUzlj8PDEOVQafDMY4losFSmeMNngf0JKY5+Fwct/teqZ5RrLCOMvNcMNmvGVtNRlzaLNqERQZnRKILXgiTfVZFTzX/sZHEGzS5JSLwTgDU8ZvPddvN1y+LUHdeVZ0ixXH50uaZUOztJhGEJWIOZD0Xupf2pMBCDETIkQsWVkkebSfyTV6xahiAo5JIET62y3jtsckzbJt0e2ewJGJJOYahZJDhFRIkboTfKVxiBJePXnD3/rf/jbX1xu00SVMM6ZqKtaH9p8SwZgGrVS9ocm1KOVDGCQUY/Odz+/9XPdF636VzKFYnPT7O9q27ThZnmDQXLy+5vL2K3b9LTnMNI0pPfM6P2iaFqM1TasJaeTTz36X63/uklO3QJQ5ENkXbkErwpwCc/RMw4iWFktRi1GpFDe7G8RaTOcI1a8l9TnOTo+5HHa8efEVAB+0n6CVxToHCQKBlEOZjyhQTbnEP/j4Q/L5A8JYeIbPnz7HrRd0j89o2wa138SyYGs7NJFRKjP5GTUkXOd4/MFjnj3/AQBXFy85OjouG7U4RDKiKWrCeYfUtp9rFoDG6lOsOSf5FUpppvGKEHvSYu/azYidASnBiEqB0oSYcEozTwMhlKKl7cTrN0/ohyvOzh6wWC6IKaFVIuWCDgIwNoPyKBXRBrb9Lbe3PYtuTdO0h9wtKltQRBcVX0rlNBES1zfXpOhwZn34fSttUIqqgiwQWYhFWVqhyyHsiDFgrEYUDNNAP+w4Oj4jx4SuLbrS2qrXgGh0nQ3llFG6gHX3RnaFoD0QMorMeDtye3HLzcU1aYqcVFr/6vE5rnVop8BAzKEkCRsKt1DdFcycKUrOXGC6KVd/GhRqi9S5aFCkMZGGmXGzQ2LkyLSYrkUZIeRyyospElMgHhSOpeiUYhYxbXl9/TzyN//23+S7v/NdfAjMfqpjugJrLpfE/qMihkSSdKBhvEt7v0u/zgfP5f1J6369lyvFjJ8D2hia1tEtin8lzZntzcTupufmuidE0DrTtQ1d69ACfi533p3TWGsRlemHHV98+Rlv3r7mg29/Bz1bgi9FSwGucYUKgCKEmd31FkIgzCNSh+NKFH6eGIYtQTJKYNIaGzJOFD54tpuyQay6hoefPCYpzaAjk4ZR1zvTBGlPjlcKu2xZHR+hRLi5veHN5SWXT56zXC44rgy+9fFR2UTrZixoRDlinpmmiDWOh6dlpvXq5ZfE/pbVYoFeLEgEUopYHTFmJsxlc2rMEc6e8OjkI3Jq0dKxvb2gH7YsVh5n9htOxKoNPt6QpMG4IySZOttKzH5LpJAudtvXXF6+YLk0LBbl96EVkCIciPNgrSZlj7EwzzPbzYgSS9ssEFXCJcuT72PcC/1DK8VuuMH3itXijKZbY1RtJSZN8JGsCyElRo/gIUca5w4RbfM8EcJcZ2GJ1rVYY5CcaWyDMdVDpxSQS1syqEM7sO7zAEjY0yYyMmbmfuTq4pKriysUwvH6mJNPTmm7cv2qVhfT8d6gnDJYQawqBbfWaolSWq6B8ppiIVmkVIQhk2h0vX7NGEk7j9/2SPA4qzEtzG4i58hco0lSTuQEMYASi9PFzpEkIzkfwiK/evWCf/hbv0HIM9oqUiyg5NIGvJux7j8W43E6BIe+W7R+lIgBcBeB8r6t+6L1NV9KadardVFwAbtaDG7ebvBDZB4CRKF1DctFQ9sqnBWUiiS/v0ufCSHS6hXaKnbXW548f8J3Pvh5miiHDWIKgTCnQraORbiQYiwqMQyurYSNxoAkej8yB19ixxFUTOiUMeJgV96kV8++YqEURx8+IDQwtTBrsEph36EUSOWzBV02xOXZCXa1ZA5FQTcNFWekeharZT1JCCKJKIbJT9hcAB4n69KalPmUF88+RR2taN052lRGnCScToxjnUlEh5VjGnfOOEyM45aUbum6QI47xqH8zHOcmYaBmD2ufcjadCi1RCvN7HtC2jDOpf31/Pn3cI1HW4t1VMpFofSlWDx1UIy6u90tOUfGISA0rJYrjHGkrDE1AVqJAyknrHnqGcYJZRynpw+xuiNFOcwcRQzWOlL2xBggR5wpAoAETPVmJoRQ6P1GYZ3j4dkDTo+OaYxFK3PYmJUoMtU4KyUdQCXu8E05E2prst/u2Fz37LY7lAheeKiQAAAgAElEQVQffvIRy9US6xzK3MWdZJ0Qow4JxkaZcroSCClBvXbjFJgnTwqJFEuOGknIWfA5k5SlqcR83XvSZsZmQXUtqlPkVlDLYnW3vm6nITMNnjRFtFiscThXTkm7YctmU9rgf/1/+Gt8+eQzRFHnl1A81T+52sQYD1lke3Pxu3/K70Z+RG35Pq77ovU1X1KxS+M4EHI6tIuGfgKvyKFuIDGhcib5QJIMuuYnUVpEKSa8z3RthzlyzL6o3rLku4wuFDlEUggokUJbiIX63jSO/Y6jdMS4kqPVjwNGK5zWOKVQFLLDYlfuqC9fveb10+cFrfPBCTYrdConLV2VVQBoVaTKOeCk4KByjLTGsVyvDjQOHzyhcuKUVoguxlehGH7L8L0UmfWq4/HDE96+eU5WW07OTlDOonRTaBBVcbxatoisCAH64Rq4JKkXhPkljZuI9Q59e3tFP16SSKzWE9aeslyvQDKTLwXr6uYZAP3wlg8/fsjF5TXOPSrxJanMRETFInQAxrFnGrcVwWRZdmsaZ1FAFnWYfGQi3g8MvSdlw2KxLhaElKsEXlfuXSmEWtnSho0zRkWM0YWvJ3ezFJECc+06jbUNR6s1y7ajbRpI5h2Zdilae9ylqies7CPTMLK5vqHf1LaoUqh2wQff/pjVegFKHXzYNY2+rlSJFbmYe5Og0PgQiTES68ktxVwg0dYgVmGzRqsi/lBR0GOmvy2/n3k7YpRBL1pCB2mhiC6i3UwKHDLM8pxQk+CsxSSDpMw4DIQcyCR+57u/DcA/+Pt/Dz/PNNYyTvEQ+Lg/RZXAzLvTFIC1d8V+L3vf/5t3P77P675ofc1Xiol+OzBMA23XsartwXnrmWbPomtZd0tIlZWXAiGWjeWgqqIMp8cxkWPCROHlq69wnS2tnEoKyEpKYKISutYRg7DZefqhBwOuKz6tbBJBZ2LOBIkkYlG3KY1RCqUC3WmBra6j581XX/H8xXM+XDhauyJRaABOl7t4qOVQK2bvSVFotcIt2iooKREsQAHKGlNaPBSydsoRkUwIkcRMV31t2iiOT9fcbDQXV8/ABBarBzh3RONOOD4u7bTV+gNcu+Ty4jXG9lze/C7b7fdp3VjIFNUrdXv9lnHeEpLgQ0vTPaTpliQU2/4Nw3DJ868+A+B0rSqgd4fRiRQHxEBOhXwRxvI99v2WEAsFo2tbmkbQqszqssqHAtwPM3NIdO0RTbMgBhjGCac0h9jCPXQhO3zw9eQVSjuyzmG0tqQqox/6ABi61iJK01iHNQaNqjcy72zI+9NCLl6uYduzu90w9UNp/52XluzyaE3uTHm+eqrY45BCDIW0ATBPRD/XuY5Ca4NrCi9Qa0Ww+5NonadR5mY5Qo4QfSbuZsbXO6axvB7btZhlS2oFFgKdIsuMyoWxuc/wDBLQomjaFp0cwWcshuA9z54+pb8pLd5/7U/9En/37/9fvH77EoMQavF+txC92x4sJ6zSxg0VgfUTi1TmR9qF79u6L1pf85VSQfVY3XB0dMR5TYhNc+BivMBUOniWQvFOEomxKMaoM4msDCFmVNBIKnLbzz77lC+efMa3HvwcTVe+jhBINVAx5YDS0HSGTT/y5uaCBUsA7KJAa7NO0JXZRBBhzh6lNcRAu6qiADnmzAqXby949uQpn5hvc7peglJ4Fe+G0UoRBaxzCOBDQDmFWMHHO9SUNpo5TKAq0V6rwsJLgUzEKmGqs7csEWUU548fsH36ildvXvJITgmxY330M3TLxwCI7rjebVmsLLe3F1xvPiWlp8js0WmJhGranT1z78nK0G9vuLp8ytHpEVOM9MMVz559zsVFUS5+4/G3GPoNzim0iigdgJ6YBmKcqCJDpmlE6UzXLXAWEI8xiZw8w7itab1gbcvx0XH1p43k1GDMEnIkpqGEKMrdyWjv29O6zlSSRsQCmqur4t/b3A7ECNGHIsWv4YnU4rRf5bRfsFDjEJimmRgi2hpOP3hA27bYfZtXBG9ilYOru6DFVOZWaa7KvKTQymGsxVpbPGC1/ZYyByq8iCrUqTJ2g5CJY2Bz2+NvtizQNA8qp7DriK0hGcoJPEVMSKSYmcfpQKLPMSO5/IwUxX+mk+Liq7c8+fQLPnn8CQB/7Bd+gdPjY/67//6/ZbO95e7ugMNrfdeTtYfk/rgQ45+03te6dY9xul/3637dr/v1U7PuT1pf+yU42+Jaw9nJKSfV/9RvT9jd3NLf7ohhZNE1hDiTUsAqjdXuQHzIWRFSIE8Z5YQweV7vXvPd3/ltvv0nf5Zcqd/GKaIkwjzTTzPWKLRVmNYw+pl+Lm0yDLTKFae/q+ZOXeXorSMnwRyXu9+gMp3TfLDsuHr5husXr/jGBx/jzhf0VpWgRoqKULTCWItQebtKSCqjD3DS0trBFhVXkIzkhNYJIZNi8d7sCRaSE0aD7hrOHp3w7Ok1r19v+Oa3TmiaDxFdfpbX/S2mcTQ58PLlE8b5ihAuCBLRTtFWVZ6VFpUnQoApDYzTLS9e/gCxjn7oefr8S7pqeosxsN1tcE2HDxM6jsSUGP01cxiZdqXVqgS0NmhV1J+N0+Q0s9319KFhsSjP3bYdPnpCAKUWiGoIAVKe0Mogyh1+jyK+nLKUFLpGLBimnBXBZ4ZdEU14D6RyEurajtY1KClzyeDng8l27HvmeSRnQduWxXJB0zRl5lUPZvEA7c0oAkbVY1NOSFJlXmody2psrgr5Gu9RT4OHQLBItcbhqlg/xUwYI7ubHf1tjxbNalU8XnlRfpaDgJeMxIQePG2ImBgYJSFR1e+pqAdVFaXkGuvy1dOvePr5Ux4/eMzpg5N6/WR+8Y/9It//R7/L3//lv0dWibAPK+NHW3xaa3S1IuxPWf/k01a+bw/er/d3ZTJaZVbrJda6QxDh2ekxF2tLjiOSPMYmkmScddhWo1yuLSmY5ozPkZxnfMj4oWcII7/6vf+bX/gX/nkedWUeIbFFdFGdTdOAkYwymZA8zhSDKkDYRZwYXOuq+kyjtFQJtDCKZ67PPTJjrHD+6JyzbsXz73/OV9//jLPvfIT55JTGViq2COhSAOfkiwcqR4gBlQx7I7CxhRghqtAoYpqJ2WO14LQhB02u32dMCdNY/Kzp2o84OnrINDxivfwORq8ZxioeyDucCLvdc25unjPPN0ie0DYS8oap9ju8z+SoSdFjGiFNE9dvXnP88Jx+d8X1zVvco1IIb4Zr+rFnbQ2RidnfMIdAP+wYh+HQqmqcK4GPjcM4yxRG+n7A6AXro9MDuX32HiUtWgw5qYOdweg92ms6FK2cJ6xpUdIw9R6rE6ILQaKxaz54WG5mxuEZ0xgIydMtNYt1xxw9Y98zDoFcW4RN07A8OsIah1GuiEOUkFWZVYnk/a+HnBMm76XwVblR2YVwN2eVmrCdU5XJZ8BrShC0oPOBzcQ0Tmw2O4ZhxPuAUYbuaIVbNYwqMIeCIUsIpJKqLWNk8hGrDE2zRHS8E6pkX26IROED/ODzH/Dy6Zd8/NHHnJ49PNDgvfesm1P+1L/yr/LpZ5/yxasnJeJECkszSzrwHp3TKCnqxr0NYK+UlMpf3L+fS+v1/RVk3Betn8IlIr8P+OvvfOo7wH8CnAD/HvCmfv4/zjn/r7/3Y0FkxJiSlZUre6hdGNquxIEXxV5iJQuW6zMCHkwBhwJknRnnzLwbib0nTzOC4tPnP+TXvvsr/Olf+JMA6FDUbcGXu0/r1F6FjEaQajaNoycyo1RDt2hJMUFMBbDrJwIToyuy4bkfSWK4mRMr03H+8WOef/aEtz/8lBP/mI+++Q0A3KIpKjGtSRJJQuXdCUp0iQMBSBqVE6bmhk2+UELmMGKZaZRgmsJI9PNMv/Nou0KrNafHLd0H36Ft1oSwYxzL/MnamefPXqDUFbO/LOKCFDHOE2QmxFsAdtuCPHLWsliumSZP1yxojUFIxfOka3xLf0uMiWNb0pyH8Ya+n5inSAgJZ/enIo21GqWEzWbDNAlNc4xtmjLnS4ehH7HG05ASSkqOmhYqfDcezMApJWJITPPM5mbm6MiwcIJkg1aO9boyJNsLlFHF09QJxllmP6Ot43R5hNonNtdCo5RCxyJb3yvplIKs7jBRSK4hkHVjFg5CjJTvtCIqJqzSqAxhzBBLZEj2mTDDNJfCMQwDwziSBbSzLI5WNF2DNoYoRSXZ1UtDkiABoni8JLLWiHNkbQtrspIzlJTi0W8HvvfDz7i6vuFbP/stzo8fMPaJxizr9bMjR83Pfef38Uu/9Gf4b/7af03IniiZpEpBvsOqCSnEoqqEH5HGy91P5x0Rxn3Rul//DK2c8+8CfwRARDTwHPgbwF8C/ouc83/2//axtBZSHtkN17iFZpzKHfZ61XJ8vOBy3rBcGBpnadszHj3+hKZz+DztARbMs+KzH77kxc0lac60qqVddCyaBc+fvYBfKHe1zjqmVFo2CYUSQ9NYVDZESRhXLkdrNOM00m93WGNoFg2JxBRmRh8wIZIvy6bjlCJbxSQZ1UDz+JxH6wU3lxdsLi/5vCrz1mfHnD1+wOJ4jbaOBMxpBkORc1dLlZYSQJhTIOWpRNTHCYUn55k5TiXBGRDbobD4GFFBc7w+Z7lYg7rh4uLLgxH42eUrlDZk9ZpheMP29gaTPLkBnSL7J5/ngNENXXdM13YwB6xRjEPPzdV1aWNVo+vudkPbdhitmYaRoZ8ZhgkRW8jhtbiJTsRYYu7nSVguH7BcHkM2zAGs3acHCykW2oKSgDIKbRJEX6XXmlC/z+1mQ79N5OSweonW+7DKkmKcsj1cW9oIGUXXdXTtAms7BAvZIAdhx17MkUgiSDUWp1zNWuSDujOT8bI/SZQ/ifKfku/8STZrmIvnigDRJ3bDzG7T4304WByM1SxPjmjaplx/IoUTqErBNgF89XSFEEkhkVOqdggBqwlaiJka+Fi4nbc3N3z/yy/wCn72D/x+VhnGPiDKMo7lPVasAwanGv7QH/h5jtcrLm/ekrInkdBaHQzYKSRiyJU5WF7j7yXGuG8P3q9/ltefBj7NOX/5/8ujIRkxE9ebV0Q1slzWWVFsWKwtbfMArSKNaxlH6BYNjz/6gG1/Q7csFAklRzz7/IZhM7JQgrIapx1GWfrdQL8rhWPRdiWaPBVfT0qZGPbFKqDqxuS0RTvFGGZ22x1RwLQW13Wszk450462er+S1kSr6IMvacVKsTo94vjRKWbyXN4UJduri1dcf3rLycMzTh6c0q0WRcmmNYI+nBqVAqMUkRK1rsRjtSfMO2IaUdofZNVKaZJYkgbHgq49RhvP8+ff44ef/yqvL4un6vjkAT/zM3+QV5fPGKZLpqEnxoRLDUPO6NrqRAJKOxaLBavFAmVmlNW8efOar56+YNGYwrEDxjCyWq7JKbHdbhkHj58jziqyTYfWbWZm1ycad8RicU7TLEhR0MoAijnsQwZL600bjdEZJRFkIoWZYRjwc2SuyrxpyGjVsazfZ9ep4q3DHniFAE3rMFoRQq6erOJ3U1JDLg+S91QRWHIHjJW701WWfCCWF5HfPjyyxJ2ULxck50NbVCYhTYlhmOj7gc22x4eEtY7VekVXQzKVKYo80RBzJoZYUqGnQPagJlMiaCihjZlMu2iwjSPlYsYIgLLqUAjfvH7D559+hmlaPn78CLIQpghiSCHf4avEozEoLTw4e8zP/6E/yN/9B38HlROQsdYeXndKGZECyc383srB992rdV+0fvrXvw38tXf+/h+IyL8D/ArwH+acr378H4jIXwb+MoBthNVxx2Y3sBuv8amcDm43iccPTvnw44dYIyhl+Pyzl8y+R+nMcrUopwqg32puLjf4MTALhH5k10+IukGnhptNaX8d2zUppxIXng05eKbJ01oLKh8yrZxqsM6QlTBMI8uTYz54/CG2c4UHGCJzbR9NyTPPngQ0qmCLrDFkI6jWcbIuJ4nF4xM2uy0+eW76LRs/lA0olBPAvrWjNSXXKkeoSbwmB4yJhDARQl/8W4DVLSFplHYsl0c4p7l48ymvX/0mw+777DbPAVgtM1Z53r59yjTcEEIgDKBLhC+LRSlETZdxTuGsQetMThO7zZa3r1+gsqdzjlT9OaiMVsI0jOx2M34uMFwlFhN0OaUA3keMXtI0HU2zQDCQi4wfiQcxhFIGaxxWQcwT07xjnjzbqw3DbsTZBc4Vb9zR+oij9UNWy1OUWFLqqyUgHQpQeW5fKQ6KpulwtiuQ5Vy8UXeGWIVIrkWzkh7ejdUoe/j+2qVYo8scR+W9hyzhx4lhrCil28Q4TOz6gQRo61gfrViuVzStI9WRVojlZ+AnXw3Ypf0ZYsRGg8PiXJnRRSwhFU9WqvO2nDOWjMrC5au3ADz5wRccdSuOT84JUyoIrjmSQ0KhDy/GGI3kjE+R85MH/Pk/9+fZbq/5td/6NbCCtZZ5viO9i9L15/Kjp6j3+VT1k9Z90fopXiLigH8T+I/qp/5L4K9Qruq/AvznwL/74/8u5/xXgb8KsDox+fikBPnNISJyB1C9vrnG6MTjxw8xFqbQ8/rtC9bHLeuj4zL/AN6+vuT6coM2tho6M/M0g/Y8e/mcH3z+fQAenzyALKSsUJiSt+SnQiwwAWpLy1hDimCtY7FecXZ2zqJdMIaJlDNjmst8iqL2m4KHnPCbAad0KYLOQmuR/dwExfG6qzORjA8z0zQyjRNDPx420La1LNcG2wTIJRBRpRlkRtSMlplUBQnBD2i74mi9otGR7e1Ldv0TjFxysvZsNlXMwARxYnuzYRxnciEfEXwgag5sxm6hC0fQZOZ5S84jF2+/gjhwtNQk8qEYKWvIMXF7fVMI7VmVzb/8gpl9Od02zZLlckXXLQvKSPSBliFppjH70M+i6Htzs2Vzu2X2ERA6veTs9Iyj9QO6tqjeGrNGq7ZSQiDkQjVXB5JDvaEYZ3ISrG1pXIeIQSgBlO+eBqQaqETKhVuEBeVP7Q7eeY5SRlL5mEIosfM546eJzc0NQ19uuoxaYm3D2cMzbNuW2BOjSutRy922X59UozFaY4yqJHVQQcEkh1O41gqrm9JxzKGIRBQ0IlxdXPH0B18AcNodc3pyVuJJUirtRmtBF7JMqkU91kIbCcQcOD9+xF/8C3+J3TDy2fPPyTGXC4XqJzvMq+6K1I9zB+/+/v6etu6L1k/3+teBX8s5vwLYfwQQkf8K+F/+aQ9grGG57tiMO9BS4i0AI8Lc79hsbvF+5Oj4CNsobrfX/M73vkvrOs5PvwnA5RuYpoBpLCKBRit8TgRJ7OYdT14+BSAb0KnAUa21VcGVGYYtSY80deLtTWa5WrMwipMHZ4gSht3AMA8YaxCbD3fzxiiUtogUxFROocykEIY+EOrpzVpN0zoaZ4GMMo7WuULbSJl5rjlMvufq8pKstqB6tJpo1A6jJqwesWYipnI3T4xImvGD5yJ4su9pmi3rVWSeZpZNKQ7Hy47oPeMuMI9gYlHlGZ1RuggeALquZb1clNDI5EmpJ4Qd1iScdmQUUz0ZaWOIMZa4kGxKGrCGpCMphkOLrm0WuKarp6ti6k55xBiHEQ4nrevbHZeXN/S7CWMajo9OOT09ZdE+YNGdoKUj53Jq1cqW4pOL0TOnTE7FUiCiDqeDGDJCmYc2bkHJiJKiUH3ncJCpcsBcsEtS1RWS69e9o3mPIRHmkhAwjQMpFmafsZpm0XJ8VlrW2i6K8ViVuVfmTlSXJR94mFZUsW9INa2mMluMsyemQnrPNfzSOYdxiskXIryoMoe7vLniyfOnrE9Kxtvp+hyVhJQCjdWEnKoisHx9zvuU7EjTKFANs5/JueXR6Tf4/T/3h/ni2QtC8ofrHGqbNL9bwe9OWT+5cL2f675o/XSvv8A7rUER+TDn/FX9658Ffvuf9gDGlM3cOkPTWhaL0gKK3jMNO5QxbHY9wzRwevKA9bplnkZurq/5we8WkWJ/a4gebOtobMeyNWST2c0zOluCqjOgBiSCFVMpBgkQvPd4PxLqGy0kh+uWfPTocVHwzVNhAvoZP84sOoVtyk4fVSYZIRtKuOK+zSeaRdBM9S553g0M1ze0znK0XrHsOkQrJqXIGbq2toCSw0dhjoHZ9wxDzzxdI2wRuUa4RaQUOGUUWVmmvkWaiJGEpC3OBDqnWVXiuFOWMHuIlhQ0OSecU7ROYYynrc+9WCyw1gCelEaG4YZFp8iiiXX+hy2vOyAEH0ron/c4V1h+zlmapjn8fLQyRemXPNZGrBVC9Ixjz25zxWazAUobcbU84fyjBxytT1ksjnC2ARbkrFDKIFVgIVkdpkkFSFt4kKoSMbaVEzhNRXjgXEfTLCGX1p/S/zitIZc0TCQVjFPykeTLjMlPgbmilLyf8dGXNrM1dMu25LJ1DqXVoa0oWTPNAT8WT9keMBvTPqp+r6EvyK+S6VXShcmlmNEIqDs5fZLEmBLjPAEKpTWXV1c8ef4lXbdguS5Fq+9DIXNI8WKFHJjDTPSe7OdDmkHjDE3jmPxMDDNhEmaveHD2EavFCZv5hrQ3qJGLDD6nn1i0fvzv7/Nc675o/ZQuEVkAvwT8++98+j8VkT9CuT/94sf+3/26X/frfv3Ur/ui9VO6cs49cP5jn/uL/18fR0QjtFjVFdnvnnaePKIy3cLhOmG72TKMPY8eHvPw4QO0vOXls6KOyynSNI6YIllnkk7YRrG0DoVl68vd/E3Y8GD1AQRNnCNWCovuaHlENB1jKnOYqDTLkxOS0qQ445qmtmI049CTcsZIVX85xRwTgUxQGWMVWSlymotnaFU8QyxapmHg9vKGN5vXHK2POX1wjmksCY9R+ztUEGlwzQlzVCy6BmZHSk1JlQ2RoS+zqu3NNf1wizJgO40zioUTdJ6wrTuc+nyYOGuEVdeyuxF0zjRKWLYGaxSrZWnJLrojtLNESvaS0rBYtIgSvC+twK4pp7edF0iJrrFMOdI0mrZtcK5FKcd+NElSpBBou4iWkd3mlovLK243txilOK6ng48++Ijjo0e0bo3WLSDFb5QpwY7ciSNEyqylCFLKiSIlQXQmRs+mJlD7MGGMLvlpWhMllCTi/SPVQ0IInhBmUojMQ2IeQ5lzxsrcU4Jxpd3ZdZaFW2GswTqDsUWwI6rkZh3CEAFRqgRZ7k8nMR9oKKmqJvdqwRJKqVl0yzJTTZmkEkkF0j7LC00KCa2FGBJvXr7l4uKS9eKYrlky7A3ZzpJEUCJFcRjLjKlI+HMJpgSULcKKHKExHTlkRCx//I/+i4QQ+J/+5v+4d1HjGckq1sb3HWg4pX+8DbhXGb6v675ofc2XEoPhiHH7ijgNLFelVaVJLBcW1xlCSFhriMlzefmWaQjEAA/Oy/xgaycu3o7EQUgK5pQgFPSMUpqXVy8BeDvf8ujht4i3HgkRSWC1Zrk8gc4w5PKmX52esjo5Ka0g78nDDj/tyDGQo2cKgW5ZIbwizN4Tc4mYSJTCJTqTdCQecD6axdGapl3Rb3uuL2/px7ccPTxieaQQqXlaklFqQVYNiCGIJmaPEGhUQnLLal3uFfx0Rd+/4PLqCVevbvF+oOlguWywymJre9D7iRQ2fOuDU+L2FWmKrBth2SjarjuoMK1dIlqjbWLaeYxbYI0ip4iVGaeEXL1N0whnD045Pl3x5NkzbFtaTa7pcM2yzEooURYpBS7fPuPt27dM08hiseDxg4ccHz/k5PiD+rqPUCzQuYOgS4KuyggRyb620fa+KiHlgChNyoGMFM+eJKZ5Rz8WtWiWgLENi+WarITAjNb7CPk7T1WMkRgTOZeQUGMdIgZtNdoolMmoQ2pjIuEOwNvEQb1R0oErTjUwIxqsMyWfKxdzMDEV7NT+wkglX8uHRCSjG4soRYrFrCwozCGbrLRb/Tzz6vlL+u3Ah6ePkKiZQ2CuxvwkEdUKhIhOsRSrkIuJ3SRivaOYieQ5k4NgcoPREURxtjrnl/7En+G73/1tvvvpb9avzQQpwGiVNDGWWeyPt1nf52K1X/dF62u+pnnm8vKSvu9JybMrxBpco3FOMfsbcoq0rmW5XLHbjnz55ZdYteTRw48BOD/RjLunvL4eiTERfGS20DYNxhhev7wA4Dd//bf4mePv0ElTTmU1Sl5SwhrN+VnZQE9OTogxMnnPPIz4eSwxI0oxDZ4hjuTbqvY7XhFUZo4RZR2p4n8aaxEplAcow36lFapRLPSaZrlknjy7eSLuEm3eD9vLpiU50xoLzQqfIrNPpBBIIRxmEtYsOF6dY5SnbZdcXL7i7cVLLi6uWXULzk4K5X2eA372/Mwn3yDsLri8eMZ6AYtFZrmyLFeVbq/bIktvhGmEttVYK8zTgFAMrH7ee7oyi4XjwcMzNv2GLA2LxTGuOSZnxVgLx6vXN1xdXRBC4Ph4zen5iqP1muXSonQm1BiRLHMxUEuom78pUfRVPFFWVS4ehNeZXGM/cjZkFH0/Mk/168Th7JIH549xdgmikKxQVKVjfVjdOGiqXELFIonPJfYeVaJH9jaDkh3yo9q4gzZBDt8iVpny+KmyEUs2J5JVSSren/JiKr+fFFHGFEKLApUyOWUk3gVLqiTMw8ybVxfEOfPBg8dIVszjBDljq3cwpiIWibMnh4iUF4I2hpQzMe2FGIVW4r0nx4zWCh9DQVVpy5/4xX+Jz5//EAA/DaDbH4kk+UkF6t3Pv68F7L5ofc1Xv+v5wQ8+RRtYr7sDScFaS9MYxrFHKTnAOrXSGKvJPjPUxF1SSwmwFSbvmcaINjDZjKvqc4D/43//P3ncPOQX/+gfJ4eIoiTE7nZbTo5b1qelVdXYhrHvIZUI9vViidG5tAqNZhw0t9dlU44ZurMjYs5o0RjniPw/7L1JrK1tdt/1W0/zvu9uTnfPbb6mOsp2NY6dxAiEhBQlERlAJhkxQEJCyFImMCczphkjJKQMEGQCYgZSEBJEQblLjUoAACAASURBVGEACsQyjhOX7Wq+qq++9rbnnL332zzNYrCevc+tsiMnAxT71lnS1bl33/fsvd92PWutf6PkUgnuvk1SRQwm7xwFZdhs6Eqlq4lpfn00ssXXtipH24OykDXgwoYuFjQ76mw7lKfMMoLUnq7r2Zyds6SJV69e8/jxU64ungDwj7//e0Q55/ob73G16UkH2KwLq7VnWJkNC0CQFV24JFFwbmGz2aB1IWdtAIZ6apNdrjouLjaM456rqytwK3KK3N7c8vL1DYfxlR3LPvDkySOUwmrVs14PbDaRfnD0XW/0AMBLRGvA4RE6nEQcDhWDyTfHKcCe4SYIK82upENdxLmBWhNaj1bxhl4chk3jkDVoOxXHfeJQGlJQFJhRcVQ8VR21+mYSeTSkELzVV8dfbgBDbd5oTRJKnZmXVizptpZgWkzV4tjKm5eEOEe/6uiaIgtqiE6nHs3h1HI87CZev7qhdyvOLx8xHkZqSTh11JwMbAMtMR15WUIIEe8cOKFUhyuuHXMT83Xe48QAKOv1wDxNTOPCn/9zv85f+L2/CMA//Ef/wAR/1Xhk1rJ1zern5+kDx914NxGED0nrIcz1tzP5H1qLw4vHuw7vCkPvUS3c3d3h8Ax9R/U9u7smU/STT3jzckbrgGgw1n6pzKmQ3ch8FLfdLfy9/+Xv8a99+HW+8uQr5CTmBBsc2/OrE8F3nmdySpSUqbmcIM2lmLL1WVwhwd4z7RK+L2xXEV8jrgYypm1IUNyJh+TtYevtQTGVBec8zgfOVhfkJoqaCy3ZAaXNbZyjVjMdDNLR9fZgWEoiTbfc3HzO52++YE57Si448bx4fsObF5bUX7+8gfIRV2HC14mzdSB0CR/MFbmhzum0Awam+QCyol9FUtoTawER1rFjvbWWow+efujZHSaWVJnTgZQ8IiuuLi+4uLyXUuqHDiiEIPRDpOtNPmvoVi0ZWYSmDiIajJ+A8b9O7oYtRMSQg+oa5yoi2B+HOznrxug4O98So0MkU+oBqFSU6CwZWfhTUqo6t78HxMXmrCzUepzjmLzSMVuJmC6f1vtkCq0gK4a4zKmQpsw8LcYfVIjNcHS1WdP1kdhZS9RabtZ2K1lxWbhtpo2H3ci6P6PmyjwmpHqWeSEtk0HZj8cxBPrQ4bxQSiGVbOajjWdXm9q8E4FUCWrkfY/De09yjr4bCOr4d//aXwfgsy8+5Z/+4e/iu9g87UrrCLjTObHjo42CcK8W/67FQ9L6RQ+953V4Hzi6IapiBn5FcK4zew4xnox3Hfup8OaN3cw3b0ZUfRs8A2qVmYoambj16FJd+MFHP+Dv/x//gN/8D3+TOiulOp48e4/t2dkpCbplRksxyaJcOcwTaQTvzF5CUuXMGcBiP+7hZmLrL4h9pIyQnaAxAoHSSigXgOZGvOr7thquBiUoSmg2K6bQKpSyUMXUy50viBa0ZHK5VyiI/Ybz8Ax1C3sd2X02cdgtjIfE6+fPj7xQhj4wHl7zyU/u+MaHl2zWA1kcEgZc2EK1fal0LEUo6uiGNTj7jO3ZFu8rJS8o9sDb7fbc3ozc7g/4MDCszrg43yK+p1Ql5eOtbZbtIXq6GOn6SPABJz1ooB7h3LWiknDkZlxIm2GZ/NLbiesEpMDsSKx96BHnWa/XrLcGklmWxGotiJupOrHkV6gmUwuM8d5eXiNeemvltrGTdx3OD43TNOC4n2HWlC3BmkChffcKKRs/DkCXghYlLdkUU4o5FGzPtgzrDg3+9H7HKEV/JglO08z+9cxdg/CXosxjZsmVaZqZpkQpGedsNuvbvTPnxWakzVVZtVKpiAum1tL0Hp0CUtCUyMUUOVKyejaImWp+44NvAPCdX/ouP/roI2YOpGqzrVOFKUJtVVUpBS0F6rvL1XpIWr/oIcJqtWquqIFpssRhyC9HjIGUzG7eew/VHgTznLi5teqkFNqsQk+IM61W3UC1f2Ar4bEs/KPf+S3+8k//Gh8++QqX23Ourx8bh2ZuvlsIMXa4mukc1OwY9zfc7u+oOcFcCMdWlVbmL19w+/oN64szhost3WYgbsxa/bjeFMyWpKjiqomR0oistVRCvPcG06KoODIVykyQPeL2iMyoy6c5WVEldgNXzz5k+/iC99//Gj/8wff45Cc/IOn+nsDqbI4SfWJ395r+fEM3XENcEYanRG9KEzGcMaaFJAvBOfpgEkJ95xCp3N3enB7KJQVyFlb9FcNqg4s9IfaWhLUwDKvj6TUPKx/o+4Gu64khEnzEuRWo7be43pJUbcjAluC1kYhNKaWdR8kIAScmYVVLwIVoEkud8OiRcf1yXRjWmcwrik5UvUMo4DKpGikZQHNA6KDGUysyy4SvMzGetYqsne/aKq9s1YsWSy7Lci8ZBcYFjMGzWg+cnZ9hslFWyYiTk+gxzcLEpKCENFdyUV69fMNnP/2c+W6ib8cy5UJVQVxEVeg3A955lIrXE9DP7pGUyFpx3lrWvQfFtYqxtQdxqFssuRVHnaxio9pMzjvPbrQ2+L/9b/4lfu/3fp/vf/49YteR0n0rspZyamHWUkyD8UER4yHe1dBqyejx48ektPDFFyaqoWTW64HVat0kgqq1MJwRSvvBsVnbXbocKtNOUXJj8B/NEtqfo0K3FLLzfPH6Jf/s+7/Ps6cf8vjZU/q+Z7/bsXJtNV1nailoNYh5zZlaKof9nrzM+NCjTcR01XV0mw04z5ubW5Y3r3F9YFgPnJ+fs2ogh+3FGWHobFbRFBIQR6rVJH5axVFKQ6R5jzrTiavpDbgDzi/44FrrDEp2zE3IVDVyfv6UX/kVj5TCpx//EI+VWr/+q9/iy88/Ix5e4EOlVsfF+QesLj5gWgTXrCp++unn/OFPfp/kD1xcbPn6177G4+srlEAtiXnJuAb1P99e4XxAfKCiFMxqJXgx6kK0/fHuqKZu9vPg0eJR6VA6nFjScmIqF86bkK1WNT1BjfZv0VPr2K4HmzfKCYjhTZfPFTZnTRA2OFyYqbKcFC9csEqtlAXXWlslF2qpprCfLeFWVZY8m5+UZmgqEvNcWQ6F8TAyzzMhdHRdz9AN9EOP90crD2eJV49w86YzWCvRR5w7Iv2qqXkUB1UoWbm72fPZJy/wEji/PDsp4ftoNIbcrpGqUKqaMn5thGQgiCOlZAsQqqFaTwuBe3V7VUMk2vxrwTnBYTD4kislZY4iic8ev8evfffP8/uf/DPmYi3OnFMDobxNOD7SCd7NKgsektZDAPOYWKZsN1N7IGtVSlKWqVDyhCKsho6uj4hCmZS+O6LJElrTSRfv2HGxgfH9CN106ipLThymkUfXj8E57u52pGmyVT5Q5wNdNNO75TBxd/eGw/4G5x2PnjzBrTp8Q3f0IbIeBpwIc5opKHNOpGnm7uaWFy9NxDQMHd1qYHt1zuX1I7qht5ZODLbyb3MGfxxwiztVZj5Ua7toBdxJWsc5JZVKnheoFVHYbi759V/7Da6vLvjRH/5TAA77O2pZCJ1nWK+o/gz0itXqa2QU1yDVP/70n/CDH3/O+ePIYZoYp5GvfPgeH7z3jNUw0IUVwVvS6vyZJVsPS54R1NTBxZyKj+cg+I4QBwNWSIcQqSrtgdkhbaGgYkK6Kk3twjmceKp67DEhb7WOTSH+KGyrdaHURKkZ1Yw26kJOhZoWxFVCiNSs5CUTgkNcj28AFB8jCY8QiL3NY1JacN4zHm5I6Qatbd45VYSB7dkZV9fnhDDgfdc6Bfet6FwytZp+onghNE4gom93BJHGdSrFrvebN3sOu4nrJ08YDwe86MkexMeOnAvizGeuLJlUE8EFUzJp3K95WUg5U7nvMHgfKEVN4aL1jYtCzaWpf5gqsFMPKjYjK+2mwarly/MLnNq8+F7OqWW+0w4Z+EhqfWfbg+5P3uQhHuIhHuIhHuJPRzxUWr/ooXA2PMHlDaUooRph+O7uNZI9dZnoemP1e4TORVZhxVhm9m9spjXtMpQAElBKU5Vob1+VenSmFSFqYJCeIazZrs8pWUnziFdT1gAI2eOqoLVQx0KZMt5HNpdrurOB2me0byvqXrlzycAGLtL7yBpvq3gxJCLAsiyUlBl3e/Y3t5yfn7N99Ah3uaEL7sQDUpSaEyEWolPmXKjSgQNV0z8UNeUO0URsvKaktfGCHLUG3nv6FTYNQv3pxz9gSYXL8wtqOGe1/WVc+DbT9C2Ejs+efwTAWC/44sbx5ZgZhsLjccEFJQbHV9/7CmUWDpMdo24dWG/N+yoOPb3DVNQl4Fww51vMcsRJpGqgEjBUXlPZ04HjI0DVo0S0HpGAvnGpGhhDPDQwhEhHrfa6oqRyizLjvHGQugZyCH5FTitKdqRpYSkTtXQU6RmGSG6o0h/98Ed8+slr+m7D9eMz3n//fftetbLeelyslNxEin0CJnynhD7QxWDIThRxBfFNkV09Xq1CdE6a3uDRh0rR3FB32ZHnhcM4Micjow+Ptuz3I3Vt1VtuFeZSDDDhXTAhECl0Tum8DXLH5oY8z0sDNkWCOLRUUjaNyJoLvrWigwhBAYmUBnISFbS5LIuDVFrVqoVf+uY3+NbXfpnvff93yVLJzrCY1ezH7TwWeZcF3oGHpPULH2bEKHz4wddJqfLjj0xv9/b1Ql7M46pfwfmFQ0pBl4KXhWlUvLdeP1obufNnEUtHouPPcxzFCY+urvDeUQxuSMrpZODngzKnhVoW5jLjOmG72uA6Z4PzLph5FhiZuBGPQ4zgTbTV+UAMka6ZWpZccM7ZA6xUUk5MJaHTSFLwbTAfYoePQiozRQtVgFpo4y1z123tnVpmvCuEACqY6jiZ4ApOlKurcwDOz77NZ5/8hPkOLh5/l67/BpuLP8eYznn16oYl23d8+uw77Kff5vbmhssLYVgLu7HwxfMXnG/PuVhfsunaPMTfkkqFArE3e3jnbH6otZxmX2YFc1QCpz3QDMwghHtUHh4htsTl0QZ3F6cGwtCfdcxVNX6VqpJrwrnMfpy4vb1l00SX12tPjBt8CEy3I6/f7EnzyDQV1uuOJ89sgVRx+BiZl8LL13tCd8s4Hri9fcP19SMuLy/eEhW+RHAc9plaFxyFqmbAKMgJbl/rkWQrLMmACqUUQgh4706AlsNuYppmKkLsekIM7A+jUSQqBvY4ohybyHPKC168AVyCsKTMOE4szZEYjDJSKWjNlFxOUHQncrKG8c7EequYvYqjNKkpc2+uqnRNpLjUha9++FX+6l/+q3zx4jP2y57EwpwXmuJvuxWFWk0to76jsPeHpPULHrXARz/4hHFX6ULP3WtbLS6TkJdic4jJIUlgUXZxpOQ9876yu7WHt6qhu/SIElTeqrbuk9YRpeWcEDvTjsvZTPXEOXJDRB3yREkTTitLnZnygeC3iFOWktHF0HAABSUOKwMQOAfeDAKrd8zUe1i1d4QQEO8RgZVbM3jP4iqljpQjzrAWe2h4e2iXXJsCRMVJbRp3R/hgNmPIlHE+sxqEaZpYykSUQmir/ug8z95/xsu4JW6+zfWTX2PMj9jtFobtNetLQ6ctnx4Y1tfspeIHYTce+PL1DieVF69e0Mdwenj3K0epamTuGyF0NtupKvTdGX1n7+mcI+eKSGnHzNBrSgNctGW56DFR2QzLFN39/aBf7lGglgsEcULJjUxbFm7vbtiPI6uVAUumeaFGT/ArlpTY72bGg4JGxvGG1doWPefnj7i7qcxALULNgVV/ydzBzeuF168+P47TeHx9zdnZGbc3B7xbGIZE7NZcXFzS9ZF7UXSTdVKF1DQjRYTdYWI8jJTZrjUnnth17dhhztrFIPSlFHMe0GPVauT04D3OBUrKLMvMPC6UVE5mkUPX2Vw0VbRWXOj+ufOlJSVSzeAN5q4cldztnjiiIfvYU1zhN/7Cb/Dbv/Nb/D//7/+NbzSG2vzMjuc7iqc2qad3MR6S1kNw93rk7tWPbACsRwmYrjm5epYCN1Nh9/qAc9ZmSQuU5bhtb4RiaC2YpkLQUIQnuwS1Gz+lhR98//uM44FVGFoicydX3uwt6wUX6FzHtIMlj0081eOzwzUxjiUnorRSh4xWwbmI88LSlAnAREyLE6ozPpqKUAXEe0KISAMFlFwpJZPTRBUzmzz5OrlGZm0Pet95SnUs08Iy3pk+ni6ojix5YT6SSLWCCqvz91hdfo3kLnizS4R1YL2BTz79CIDPP/+YWoVaO/b7iZoqQ1dYx8o4HXCh8Pq1VcJymzk/f0QunlwC9ZBYlhnnIyEqm+ZNttlsKSWTciHEgRAHYlgbAEPTaUFhqd4hGDncKhZvdhgcMWkNKFOLJbhqba85LUzLHnWes4tHaAN3jEui0rEOgPM4t6aUmS4OTIeZqbU6P/zwA7Suefn8jnFagIHLyyu228eUUtjt7jg0c8dxFPa7V+z2I1r3OH9HDCs++DBzfnFBaahSH3q62CEilFrZjyOHvblVxxjpBqtEowSqwjhNRkTOlXk2Xpe2lVfsjohEb3SIoixLIjWYvQ+B9bA+ATYo1SgZDihW8dSam8aiCfTSjmYumaSZUgtdFeNYFYPDC0Jo0lDBO8Z04Orsir/yl/4yP/7pj/js5aeoU4qWU4Xp5NgKLX+kw/GuxEPS+gUPQXA1ntoX3h2lbFwjl9qKsWRHpj3kWlK6h+4eyVlqbZpTlfWzgp6lVmIQzs62fPdXvwPYrMlXocwL2tpu3aqnFsFpYRXW5DJSdAEqwUVCFdZiD4jghEiADHk5UEWoYcDnggwr+q61yZxlHFXT8KM5GGouFF3omv+UrZaFJSemBVLORoZtEyGhHjsxrfXpkKFDFKZ5R80jkKhlZhmb9XtRSob1pRA2A7v9nm4zULnh40+/x/e//9sAfPrJx9y8+pKpzPhVJXYQVOi8J3iY5z2uia1GH6nJU8uK7eY9qvZ8uXuNIsR4Bq2tNI0LucwUTcxpRFwk+BUh9HjpCeFo7DjgZCC4DU6Mo0X1pnSOIQaP5yfnBdWEc5WUKqVWQlzT95Fpynz53Mi4eSlsz4T9oXD7Zs/udkFLxyFVNutLzs9NeLhWz6NHz+jCBS9evMaHjs320rym5gXnO/reWo7D0LPb3yBuYFkq05SZ5pE5JVxwxvUCxAuHeWIcR1JOdDGyPtva4qgWXFPYKFmZ58Q0TuTUNApViCHa9eKk8Q0N6ZfmxDxZy9CLpw/9KeFrQy42Tj1V7djUkslpbghUcK096LwnhoiLJhUVS0VDpS4VTdWI0+09a81QIE+Jb/3St/i17/waz//PL8kuo7me7jM7V/Veq/EdjIek9RBQHE4UY8M2e28UcYFSq6nOVU/RagKmlMZzOiptG9zb/cz8ygYo97wtUCpLmlEtXF1dglTruxezbQjtZi6ltJvVmlhOPClXuuDxOCjlVJXZXN24ZqHr0FpZ0kJ6M+PizPmFzZVWGxNsrVhyRgQqVApopS6tigg2GwlO2K7X5LqlLCOpGRKqVlLjDIkuOJcJzugATjp2ux2Hwy3LdCClNvtKwjQWUnjF+fQa5zzBFV69+QPgh2wGU8EnfcxFt7CNkbONcH5WORsKq+ihFsbd3QkuvUyeg0RC2HJ9+R6bs/eIYc/deGC97jjbHlu1Mz/++A94/vJTQufphxXDaksMPV0cWK1MYb4LK5ysSHLA6QqRHtTbzFB844q1+UrpUO0RKWj15AqlOqZpZne3kNMRlBy4vS10XQJZMazXLJNQpoXYrQnhSICOiItsth3jPDJNMykvJ/WOGLtTB+Dy6hFXj0xQeUmZ3X4kZeXi4ozcuGwAc1rItRBD5OxiS2hOz7lB0Y8mkHpU1FBO5GPvPV4CuZjh46ER7ms5JqoO36gBqELOVC2mKNPunnlZWJaFXI5mkNV4a+Luib9iCdI3FQ4nzua82NysqjJ0tqgYU6WLPalkAp5f/fZ3+a3f/ce8uHtBCP6eXFwLMQa0CnMzznzX4iFp/cKHNCfa0paIx2TgAEMzOfFII+TWI9teOM1DbK6v7XfuEVp2I92v+MQZ6XOz2dB1kWVZcAmCBpuXyHGIrngX0TRzGEd2b+6Y057VZmC1WpGlMLaKw/UdGoQahdhHQox0OFazknYzhyasW5dMtxoIQ296hCKmHiDGI6tNAFBxiGuSVLngnSP0W/quA51IWU4D95QqOY2gC1Jv6brC0HfkEhkPlZcvX9t+a0/JwrkoQqYLMM83lPwly/gR3/1lS6wfXv4KTx+94rf/6SdIWjgLgYsh0APL4cDelRPBV8Th8NSyow93eP8+lxcfMmwWPvroD0/ouPVWCDEQOrjbv+DNXTGSeOxwCF20xBH8gOhA8GfEsKWLZwTfQxioeNCIat9O9wrVAedWiERKjaTiyIvQd5c8fmwV1NCvT5U3Vdi9Wbh5M7HZCO+/f83VVZN7yhOOnhgCl1dnvH5TSXkCWdENHUMayK3VKs6UVkqt5JpRClULt3evUPHE9pDvhp5Nt8Y5T86Zubk8K5Wi97NOhzfwje/QIpRkpN5pnpjnuVXadp13PhB9h3feZlZV0VIImDXLODWFGFXmnMlVbQYWIl1v15SonioyqlVTFJt9LZrQrLaAKnq69wBW/YqlLBxms1z57ne+y/vvvcfNdEPWdOL6iTiiD40jOf1LPAf+7MRD0nqIUx1k2nzH16qhzsQgwxS74VBFjwlK3noDh7UH3wJgqOqp1w4gYnp/q9Wa7dkW1DyLEKXUTJnazewMgr6ME8thtPl/hakhtOQsMLVZlauZLgxULyQt9BIY+o7N0BN7PZGGl2Vhf3uLGyP9eiD2HSrWRjo+BO0wKLVkHIUYPVUCqo5aGoAkdHQbSzJLhHmGnPYsh8Lt7R3j+IaUR3KqiNjttduNOOmY55HxcIvrt8yHN3z28R/i5DmHcNSiUz54OvDpTz0pzaxcxmWB7Jj3I47KxYUhDQ3Rb4oceI9KJBVwRPaHA599+iMAtucelTtUCrFz9N4e7F23sofwUQfPWWsQXSGyOimIqxS0mn5hjJZkgg84P9B1W6IfqKxZlkjJns36Edv1VTvfBnoxlzPoI2w2RvjdbHv8CZ6u1GJAGR894qGQOUy7tjCo3NyZseRhGumasaMihC5yvlpbIvY9sclxZYzcnlMi53wCJRzndU6OQrMO8fdovZQT8ziTl4IobPoVsalsaFVyKpSUyA2VKWrGlykVjg4uznmGfkC9R4LtjyMbYX8pp4SpqaDZRKFrLqhom2QJLjTaRwO/5JRQlC4OSPDcTnfkVIza4eyeBGtLltpoJ3/Sjf9nNB6S1i98KOrMw+dt+4naQFPOxOuoYqiqY4/dMslxtSon4SbhHmr8874+BiN2xH5F13d0K0+VglRTTDhmTOfNP6iUTAieGNa4pIz5wKv9LY9WF6wHe9iWRVlXB8WTiwn14oUUQNaRztl2sawIY+RwOLDf3bDSNd16RXUBH1aUI1pZFR8yRUxmyMkK5zLIQtWEqunuAeDPiEOAEFG3R8eCmye2/ZYXN7cc7gwtIt6hMbHInh/85Le43HwEOfHm5WdouaUm2/HYTdD1fPObjv2NECXYkZ0qh7SwLOUkU7RZ9wznPaWLrB9fUmJEcJSyZ7v2/MEPPwZg/OQVV9cd661j6Dxd3BBZM7gt635D363bZ5/R9RdIWOP8AC6QSsFXaXp/kdDkpoQBZcA53wA4jq7rqTlaK7ehQGkKHVCa6oTHd8a/Oxx2jPt7VF7JCzAy14VlnjnbrKk1UXK2eVFbVKQ58+jisbXynFmp1Ky44nDVI0dwEELRSiRQaySldOoSmO5ka0UvmZJNyikvhTJnYvUMcSA6h9SCtvNTsiLVRKHHnEkKBHA+E32ga2oyXTCFjuogS6FKAnbU0lFzQFoVbFwvNfUR35vJqRpK82gDdITb11ysQvOOw5xxruPDZ1/jy1fP2S03LNWuNfUm6STFvbOKGA9J6yEa2u8IVZefef0IbXbq7q3Mj2jAn3sT5342UUlDMt0nLQcucHl5xbBaoyrkUii5EDQQ3JGTksi1NqHRiNaFOibU20zs8GbPcN0clp2HXTLn3tARBEKt9Gsj0h6loVyF3nf0Fx3VFZtteYfrOkMEtnmEayZMekRSim9zOUOA1ZJOQ2/RRkauSq2REM/ZnnkG37FZvc+Tp18H4J987x/jg/Lllz/i+fM7Br+mLoVIpYsTL5uFyfasUtJMmtTs4L3ig2+ctYz6zIvnzwEYtyvOWdGtrxmnzGYzoNXTd2uWeWEe7wCY5x03b5Su29CfXXJ21tPFnhAcSCEdJZeWA0t14DK4GQkdznsGN+BO2oJHvlcBCp5qyvui4IXgO7OmOQrtqOnB37OczLZE1FNLPuk9Bu9xPuMEVs5RU4IKwXmG7WBuAy0P3t3uqVkJwSPqodq16dQjVU7XpqYmWYh1BaJ27VrLzCWRmzjzPM7WjiYgeII3MWGyEXtzmU6LKaqpv1cVokSC9y1peQL+pKXoBBwVlQrMlDKT04E8LWje4hs3zgdblLgoiIamsZlNwikXnHICVIQQUalUyQSJbMKGv/gXf4Pv//j3uR1fn25GberujnczYcGDjNOf6hCR/1pEvhSR333rtUci8r+KyB+2n1ftdRGR/0JEvi8ivyMi//q/um/+EA/xEA/x/088VFp/uuO/Af5L4O++9drfAv6+qv5tEflb7d//GfDvAb/S/vxbwH/Vfv6Jcaqc/hgLbxH5mf93zv2RWdXp/xr67zi4FjEH5GPUWun7wAcffMB6taFkRdUdBcBPn7OUQqqVVdcxDIE8g+6UoI5h2OBUmBsJervdsvYdfoJaJpwkXIjUrZD7ntgkhbxr1ZKrJMkQxGDjoif+DNg8gJxRqbiilJJR3VN0Ty47ap05DS9qJecDpUzMh0ytmbs3t8z7kfeffYXNmSk+eB/IeUQZub7yHG5vyaUwT5nNCkpDGToVhr7J9ywVdZnzqiG6ugAAIABJREFUrjOpIinEoGiTZ9KaefPmhq9errm4eIyTAZGA1sr1oyd8/SsfAPDxxyP7u9e8JBOd4+Lsgu3Ziq4bkG6Lc0PblR5xa0rtyTVQEVQcc8bafLWcvLzM/HFp4ho2a3FOzIBRCxxbVZixYogO1NkMxt8Lwvp2raVUOPrZBBKijpIKMzNjHdHquL0xQM3nn71gGZXL80tEHPPYOFXFjuMRsamLs6/dRHNtHwulic3WJm5bUwEcwVl71eEbMMmRJTEzGQ8QWMWeVbeiCwPiQ5u9KmU5MhKbYG5JVGaym6k+4YJCzTiJxL43k03APLQbB1A9vs2DbX5lHC7N90AmFUXUJJ5yqVxsznFiSEd19bQN+rNUk3ctHpLWn+JQ1X8oIt/4uZf/BvBX2t//W+B/x5LW3wD+rtqT//8SkUsReV9VP/sTPuOkyfZ2Ivr5fvjPJzZtMHPg5FF0lDcyXKGpa9+bDDW1Ge94+vQpMfakQwI1G3bv7tXTg4/kkkilsDssLIcdJRdicKxCR+97xp3p/+3HW2RTCOI47A5m56BK3W4p52es+tZGDJDrTHYFN3jCKiB9JKw3J3knMDpX1YILQq3GUTOe00TVBcj0DZAQuw4fjH8mJG5uDvSbNSEG7qY7Dou16L7y1acUepY8cjgk0mFhxryZlgW0IcQOGVbXA9utYz5U8mzos/VqZZwymU93bOwi+6UQ/IpVfwF1oJSKOMcwrPjwva8BcHV+wRfPP+XzLz7hi89G+rini48IVwPeXYBvsHPXE/wZTjsCZqCpmJo5Vajq8GLbqnRAZ2rv6qmyBjagPehwUmSv1VqJpdlypOIprQ9dSyGn5j49LzgRm2V1gXmeWJYRxBY6j66u2e8Mdv7xT37C3esDTx4/I/jIsmScWGvP3JTbtSaOro8GMRdpwIuA954Yg7UAocHWoWZTea9FKanYHM8PrNwGye1Nl8I8L6Qp2xXezluttWmmHOH2B4iJ4czRd0K38kjdMu9bS7MceVpG9Si52OKoZLQWM5ppbVc9omSbTBUZpAiuOrarLU8ePeGL15+faBi2/0fJrXcTivGQtP7sxbNjIlLVz0TkaXv9Q+Djt7b7aXvtjyQtEfmbwN88/rvWak7DbyWqt5PUMakdk9S903ETRg0BcUKq9TT3kiN8Sd6ak7W/r1YrRDwx2meKFoM0H9UMOpu71DwzTiO3N7foMrJddUTn6GJHbnyc3d2Om9c3eDE9hxgC69VAXHVwtmLVABveC5WO4go1Cn4I+D4iXcQ5f5ppiTOFDyUB2fhJXJPVUzUgLCdLd1BLZBKIw8BluCJlj5YFh1CbyOs82TG+Hp7x5vXEJz+9RSWiTklF7jk2wN2u8Oy9Sy6+9QE//ckXFFF85+l6xXslt2O0aMX7wDBsEe1RIiKJJY3s9zc8/+IlAMMw8M2v/Xnef/ZtXr5+ze52z+efAgxcxMeorts5jUR3gaenqjfoP6B1MjmkopSjNUnTxtNqP8EQlqi3BCLHYyntgBZqFfphOA1P1Xu6hvRbnCeEwGq1olNlXhYQz8XFGd6bM/KHH37Yjrlj3itPnj5hszmjFMW5aKAM509JK4d8Gn4YD6ue+Fjeu9OcquQ2c81AAo83xGYVujjYJbk05GFS0n4hz4llztScKVXx3ugghTZ488p6O9APSko7us0WVWGqlZIWctuskqi6NB3HQFQaB1LBWefiWNRLtTPSa4eUSsozF+tzfu27v873fvg98mKEbhfDvczYOxoPSevdiT9uWfXHNglU9e8AfwdARPSYhP7IG/4MKOMeWAHW9ju2/pwzeLTz7n7s7o5crrdG8c5xeXXBo6traoG8VIJ6vDeQhzsmxVwNDqzgQyQOPWMa2R8OpHnijh352FILQlwPrDdr1mdrNtst27Mz5GxN7sP9QWneT3jQ4JA+IF4oFGq5tySq2VpdVV2zovfgzq0K1EquO7ShAmpdTtWAVjU+k+9woVn5qbXJut5x2M+8fnnDl8/39P0KFwM3dcdhNxPbk8mFjnEu7OfCr3/7W3z9l3+dn3z0Q27efErowLkZ157EOQtUhza1dRBidNzevSZ0mc2ZVUUvnt+w28P5xTO++c3voAivb9/g5IpV/AalPQJiWOHdQClCwBTyVZWKmP28VGiE3KreDKmt42rqD9Vh4rz3hoQn+oMYWMUyQOP5qZ5AMtEHgvMgJonlu56yzGStlKqM48zSFinXTx9z+/rA3eGWbt3TDSv7ENdMGdtnJ59MsFbV2tZi8PFSCgU5VTB0gveBmivZFUQ9Cxkt7XqswtGb1HuHEHFBcJ2nqnUXvFOKKqWdG/EdXS+kxWDpkcgXL59T0gqqJ7dKC8lkXTCuuwFGpFZraR4VZjiCXyzxehU6ieTQgyibYYNWiM4WALZ3cupavIvxkLT+7MUXx7afiLwPfNle/ynw1be2+wrw6b/om76N8gNLRD+ftI5Ge6UUYoz0vVUxqkrKS3s43G9//P0TokuVrotcXl62bTwlm8FF13Xo0YbhkAk4XNebKkeeWOaReW+V17ofOL+0edHF9RWr8y01gPSOuh4Y1x7pK9XNb6EHDb1m7NQ2RygVOc4CjktaGmpQfYP192h2FLUZU/CB0qxJcr6lqmn0lbxqjseKcwmoiDsi1JTb1wf2uwPOr7m8vuCT5y+ZdKLGcnK8FZ+Rrufl7YEvX93x9NkFw/kVh2VH7PeITpS2P76a5fs4LpSixoXymTe3XyDyHNdZlXf9bIvqOV1/zuXVV9lsH/H+ifS6eYsgHhD1dD40blAxtBoD0Qc85aQikbIlTWc9S0QCjgHo2p+jFJgYaR2gSRoJ0tB17tQ6Foe1NkVQF+hWazZeqFKY5pFcM1O2a6OjsD5f8fzLF3zx8nOunzxlWG0oNVHBhI7bZ1ObYoU7wr+FILFxDN/CNIojU6gIJZtyvsOzLLOZRx7nsrmgoekHUhEfcOIp9YAiJxFn58R0IPGcrx+hM+R5Zr2+pOSIP7YHQ6C60IR6BVKyuVVrBSqVowKwotSWtJwaYpJqavJ97BmLXZMlJ3x0rU5+N+Mhaf3Zi/8J+I+Av91+/o9vvf6fish/jwEwbv6kedYx3k4qP5+o3q7AQvCIGOclxkjXhERTygZ596aIAVZVOWdtmGOlpRUuL6948vQpXdebbXmtaE5M04RrMwFXLTFWBYn22yEEZLWiBs/mbMvmkRF8/aYn91A7oXTC1GW8F6KveC/4pjEnjRx9BAxotXmBa+yye0sLyCmf4P4QqCWAJNAZLdUs4IEQEstyQMUTwpaqE0mFXA7UNDPfWUU23WW6cE7/KDJsL0lO+HL3CvZK8I5V196PQjdEbneZz56/ZCqe7WbN2dU1Zc54GZrDrQnCuqVjXhLORWLsOBxe8dlnP2I//oDNmX22d2d88xu/wtPH36bqGXfjzDgnSi30MeGOrTw8osIw9HgPIbTVf1WcOjrnbYZFIzargjoQT62htScjlrSOEkkVEW3Jzo5xm8w0dYh2nSFQFnvwR0+tSugiMfaEppyy2TQ7D286k5vzNfv9yMvXL7hyQtevjafWFF1CDvgG+Kha23dx9wuvo45iLdbKLYIrHlcDnWuzMWcLj2lui5QxQbE5mAytUq+mJCNyDzKvVU0gWHtqruzGmc3m3FRMorXSwSxJ3k4ttVZE7fryYpXncU6WG4dLq8OpEMTj8Zxvzzk/u+DV4ZXtd4yolgcgxkP8qwkR+e8w0MVjEfkp8J9jyep/EJHfBH4C/Ptt8/8Z+OvA94ED8B//C34KNEkakXtUoP00KQpxtjItWghOEE9TEThKADTLeDX7B2krcIrgJJwER5MWXr+548vnL/jqe9+kajI3kWCryNC6Jp6OZZl5s79B64JMI12pBozoesLlQN7axikuSDAjxEohVKMa1aq40CPhOHA39pBH8Ea2QZ1QfYfQ2lpg5OL2fY+D/aoVYUXOZ8x5RrxVMT564rBiXhJD7wl+zf4wmz3JYWpeYbA9f8bQP2NedqaPtx/Z+ki8PDMDyfZQ3GzWiAvoPPF7P/gnbJ9f8ue+82/wwbPvMh8+ZD68YKpv7LPDhig9cfUhSXs8BiY526x482oiNSQnYc/v/vb/xvWjH3L9+FfoVtcUcRzmA9PdCi/NE00ipc3X1tuBq6tzUpnQRRn6lbUP27ainr4GUzyvBSJUr6RaTFuyJUInloykLtZCxiznrc5yhJM4M4QwUGpikIw6ReggK1IDLmzNph6ouVJU8cM1fdpz82bHtBwYLg2IcpTj2qeRqib1FKKnX/X4aKaVqjC3CjCJXXcGEvL4ItCQiJoKeckn762K4kOAEKwQF1PaCG5oxpD3M79CZpwXEp7otgQnVk3iTfUfkOIoxVPmGa1KrIrgDJSEkFM+kfm1GMl7EfCiRHWEKbPxPX3XnVwKnChVlXc4Zz0krT/Noar/wT/nv/6dP2ZbBf6Tf9nPONp9mPTL25D3t2HulqBUFcSqHlUzUjxGLYWqualoGLDBVpwV7+4V2Wu1aqaUatDiJqjbdT3+1Eoy8du1bNDkmceRPCdSnpDBmUtwWylrVSSDzoZUDNoRQ6QWs6Q4buelJaBSwYdmmuhQbJ5W742YTrD+Wk1cVzCLk6raSMe2ZUmJ1abH+S21TNze7djd3aA6sx7W9OeGMnRSyMtIrguHw46ffPRjbm5uubzccn6+Zd+UIbbbNc5FDuPIYUzc3b7i888/4dmTr3J5+QS5WJPyYwCW5CE+4urRV1nSwtCZ5caTJ8/48vOB3e2LdnwW5jHx6vktn37yimcffIvLJ0+IQ8AL5GRzt+AdXT8wzwtvbl7y/OWniAddlOgjZ5tLLs5Mnqnv1gb7PiWniaqB6Ne4VaS0NiIIJTlErLrQUkkpsyzmhH2UUtJScc5R8oLIghPHalhx2B1ISyH6eCIs51JIxUAV4zizPxyYpsSSM9F39EPzEeuaXJNGuj4Su0jFklitZicCMHRrqNZh0FTJKbMcZkoqrXUYTnqGzpRtqYAPHkSZ2/UeXMfR+0qr4oOjOKWkikMoSUGNOL9M8+n6QfUkk3WkfdRic0Ft8Pzje9rvV5tzJZN/enR5yXvP3uN7H/+BnUdxeHFNHu3dbBE+JK2H+GPjj3McPkYI4WdcUb33ptu2ZKoq3lVr7QQzoqv1yO+B95+9z7Mnz5gOM64EXAFfwQdHbu9ZlgTeUIY1CDVGxp0pdEfpzXm4DeZFHaIVqTYod1KRxR4CrnOE9mBVzPdpnmZi17E9OyM6B03F/piszdCwtKG9Db9pVh0ilsB8q96qRpZ5NC+k4BinGXGR880a5zLa5gzBC0SHHp5DyVydbzlb2SCdkuhbibldDSjCqnds1x4lshoc6Mw8J6gHQjxWb5Fxnqh6YIj2vWpVzreXbNbneGe8pufPX+LwbLcD1MzdzWt8H+jywBA7zs5svuh8j0jAR8f2YsXN3S2fffYJ493Ed7/9q2zWW3aH23ZxKD4owUVTJa83TNOOKmv61RNCd3Y6PiIdTnqcM2v5fqjUVKml2PwIq55QWBbXKiWThlpKYcqJ3TyfKgfvIj52SHCsh57VxZkZH8aOEO7BQdmbhFQpxdp5XpAq+OLwKvS0RDSb87BWZX+3N4CPQowd3TDgYrivWto1IaWYX1ZVaj1JUZzmp2RrqfbBM6dCSjPFWfVTSzmhGjXYe0jwoGr8rFrsNfvWp+vSe3NxU61NMcSO3eFwYJ7nk+/Wu+yjdYwHRYyHeIiHeIiH+DMTD5XWL3gc2w8hBEMOu/uVnWql1IKWNgvyvrmw1tM2cPS/KpTm0irBnwbey5IpJ48ux/vvvc/11TVeI1ocPgSCKqXM1KbIHpzNIBKJaRlN/dt7OtfRDwOueSOBGccHEeq0mPdXKnR4whDMIT4fofFmQTEtC3NOxJIR75BSmabpfp9ObUMAm/MppnYhHlOSaOrkt/tCzjNVM6zh8vIJOc9omUDdySK+VqsSve+Ypom0JK4fXeG8MM8Tpe33+fkFaVnYDB3jKBQVNpvAZh3x4kiLY7c3u5Px1Zeov+TFa/ilbz6ljxu0FBwe1HF1aUCV3d1LXr+4Y+hmzs4Hnj19wvV773NzuOPm9oZxtBZv1w9sNueE2INzXD264vLqEs3CZr1lniZyAzn84KM/YL1ec77d8ujqiuBX9P3AOCX2+8Jw7J/6bRPfjaScoZQ2PKqIQN+qIi2C4On6gcDaFPcdXMUOFUNuHssH0+uLDe1q12+pmZQWtFayt/0pwSgYThvgo1o70kSboU4NGn+YmeaZKS14H+jXK0LXEbsO8c6g7I0EXbOe2naCGlQ/OlSdzdOO86eqpGkiz8kU8bvA4gAnTQCYdj8cv5cBj5g9ZUmkZbG2dOMenrZ1Du9BqiFYp2mi73o+/OADfudHpvS2H29RrSck47sY7+6ePcS/UIgIXd83Emf5GVJxPTqnntQi3Any7r35FIElLef8idzpnCOlhKqScz6BO1Qzn33yU7747DM+ePp1nARK69+bMna7Qdvw+26+Y9zdwOHQ/IGEvutQH1jarEqqQFJ8tBZlWTLTNNNLJKiBBQBqAQmOs825oRydSQWFWsjzzP3o2oivLrjTDM9JI9OKZz8uzI3I6UNH313gvdL1iS4I3k14t6bmkZIb6iwdUI2U4lgWZZkrVQPBBWLniW0wH/yA6wJDNzD0PakYAEYcOB9YxS0hNm7T4Y4qG8bFoSSk8XhKKTy5vma9Nd7Oy5dfcBMO3Nzu+erXrrm8eEzfb3m0WrPZuBPUfxwnvnj+OfOcWXLl0aPHXF1ds92cU1QQH/nyhRGW94c9d/sdX774grPnG55cP+PJ9Qf0/Zrp9oZXB0sc/eYpq81jQhfsISo0NGGTbDpmde/NNVhpZOSGVPeOKooXd7QdZZwnvFO62J1Iw2C8wdr23863JTovHlEQFfOnwjPPM7ev7RxqEWLX0w9b4tAbus8LhWqq6rXiwjFxOJOMqjbjHHxnqvbFnIaPgI2cTQw4xECM0fhejculoiewiHcOCXJyOg7iyccFU604ldOi64i4LFgLG4w4XgI8evSIvs3d5hTIOZ/uv3cxHpLWL3j8f+y9SY9sW5bn9Vu7OedY473f+9po3ovMyMyqEqQAIYRUlSkxgAFIDJgwQjAo1TdAJZCQmCGmjEoCISSEGFQVEhIDagCqVJGBMqAyCCIyo38vXn87b83snLObxWBtM7+BCiXNgIwbvqT37nW/5uZ2ur32WuvfiAhdjOSSDXH3IPdgfJRa8WKVk1Vj8ivzLLAk1fc9gmc37hARpmlqCQtcA2KoVn7y4x/yp//4u1z84SWUROd6k9rxHi9N5X3OTPPENE3M80yotTnOJptRFD2Y3mk2aR23bCoYwbGrhWmzIe6yDcwxCH7Xd+ZvJIIPAe+EVGY67w+JWdvnRA2NN6fENG7Yjff0vdAvFkjXFspOEZ/JZSRNG/KcWSyOWPQwT46cbBELcUCA6xEWy0tWq0tWTe5pu70/LLzD4oySZoZhx2KZGHxH7AfickV0PTmPdG0DcHbyFi+vJ4bFOevFkuAraTey3VzjPHz2hanB39yNrNYnjLue3VgJ/TG4JbFzxODNEgY4PYOLlNmNM8+eXXN1dc/dXeL4eMvF+QXH6yPefsdogM+fP+OTTz6mpExKkPILdqPw9ttfZ70+4ovPbfZ1ezvx9vs94hzed2Zl4po5mnI4bvP/jIQ+wL4ia/5urlEP0mzghdvra1b9muE44J3pC+41zUUM1QqQq1JyJTiTd9pvwLQqZOgXDbARIiEGu/5eKZLJtRiatKjNR/dq9BLwwaPFYPe5JbBpSkzj+FD9i7fkBxRRimZyNn+6PQ3Anq9s4IqcmabJ7muE4B0hGmS/SpsHF5t1TSWR5xmnBrwpecPPf/EL7u5MMkxFyTkxzY9J6zHe0JBWRVB+lRBcSrbk1CDfBqioh7bf668VEbTqodoKIaL68HDuXWdj8Lzz9hOzGikzVGFMhaHrmJstA4CbZ4IPnJycMERHub2l1MxuvOf6+poy37M8sWH/YhhQUcZppnaesIjU4HA1IEWYJ9v155SYvGcYrIqJvccHoQrN66mhERvCcdpNqBNyTmhVjtYnLJcRH5W7zb56m6kZvF8bUVQn0pxxUqjFsViuAdjtrghdZDNW7u8rT55cMqyPiDEwl9hEXUHCMV4TXb/D+S3D0RHLo3OkW+L8EhGPq4ZIXPQnnJ0U1J0RfKCWxDRv2G2vyWXi+s5g+ZUOkYjvjrjfwmYHbtHT+wFz8bX7wHvjaPXDESen77C5n9luE/ebe16+umVOymJphO5vfvOc46MnfPLJJ2itxOEUDQu228TZSTy0Jn/+0Zc8+/xjnr77DVbrc3LVhuacrapvYNGaA/NuYp4KmjKllsalq+RipHVtYJ58v2FKwqZYS7vvFsTQCMO4Q4LpkmtAGkg5WRUk0HWRdb9msWjXsJmX5popNZswcLbPJ+rQYolx/1qn3sAeTpjmzLibqNl4Wj7uUZMwl0QtBokvWgF7Rsw6pEHe1Qw1Qwgs+h7rigpO1eD2OR8AF7UlrRACxAg5UVTZbXc8e/bVIUGlnEjN9PIxaT3GGxsl2wNiica+l3Myn6lgcHhrxTy0Dl8nIZuMEQZbb+K5UCjFo9TDorxeLRl6T0ojq/UCivkvOYVc5mYECAvnzbl1cGieGAViCEzOsZ1GpjqxB231IbI+PmKXRrKqkTBDJEbzVxqaakdcrg7IrDrO1CqEIaLe5nbb0Vp522mkCMS+p18ODMPeubaSSyKXjGvIs9D11DKjUqyFKI5ad6Q5ISKH9tdiuaCWka5bcfFkxfHpsZFkBcTPuKbcjlsS+sJydcaw2xL7JUenl4jrKRJxccWyzSq8Lom+kOkQtXbsZnPLbrzj8skx9N8G4OOPP+LJxdusV+9zcfF79Iu3mfGkAlKTIRvZO78XEGHoeqsEg3J6ekGpmc1mw/29JcIu9jx58j5PLt6j1sqYEqUqKcM8Z45WZhZ5erTi2Ysvrcr9+gJ1CxzmxVVrbpwtjA6INy6Xb9Jb+4W9gqdysLxXx/3LV9zpFaiY6r9EYugJoTsofMTc23nJmdB7Yh+IfcQ7Z6aPjbQrzu75qAWvBah0YhuXVCqpNBI1tpGbUqLmSk12rCXZHNF5d+D6lVpbK9Ra0h6Hq1grEB6g/rUiqo0jFkiqVjk2s0epSqNfYfm0GEHZOVSMpj2nmXE3/qooAA3Rm3+1I/KmxGPS+g0PrZVxnMglNWmbB9kY703yyLmAC96UcdCDYO5+DGRfV4LLxNijGijZ42MAycTOXnh8MuAX8Omzj7m5f8UinNL7JZqNdzLsRWs743iVeaakbPYhXlEPKz8Qa6K2SmJ29yxjx3Iwu4hpzNQp0w+wcgOhcYYkQ00OUmHajXx185Jt2jGuBR+9tYiA5fGao7MTukVn1VO1AbslJIdITxf2/CtlrrMt/PkeGI2YrKBqO14A5zu+evGC4gdOTtf4vqfkTOgiLs62i4YmojsgfcF1V1RVFkOki56c1VTh27A/uIKLx/iypmTPZnvDYn3MMH6DVIWTE6swP/zgXb7+/rcRjnByQtGIFABh0vHwGX0UatMI3KUEeCREStoRfODy/B2mXQOgXG+4L47T43Pw4MmIKhP3pDziGlDl/MnA9f0VNy+/5PzkKWfnpyA9IguqFOreIj4INex5cVYdSW22880WJTcCdqmZ5fmaxWLNPFXmWUmzMs2Vu3HDNFkbMU8ZUZsDDfPAUV3B1m5xe982p20yTs43rlYjHwsBVCkqzHOr1udsr3cmoRRFiH1HdcZbdLon+FriEGcbGRFwORuXsT48N853lNzsVLQQij2PNO1Bh0lf2b1R0FCRpEgxgnXF6CFlOxoMHshU1DmCOFJLzG9aPCat3/CoDSyhGIpJ9wsJNhx23hOCJ3hDw6mlLdsVH5yMjcdUasH5YCoY3uNcqyZcIyFLJWviJ7/4Mb/89GO++e7AnDODdHhRfBt4O++RKGiuJExhoKI4760VVB4kc/Kc2Fzfc3Z2So/Dj9WQbiGzDROpDbLvNvfspqkJ+3q6xcDy9ISTyzWr9erQ2glDRLpAyskQYVrbzl/3GqavhTckWwFPxAcQXylM+NgzJrPTuL2+4u5+w4cf/lWcd1xdvcT7oWkcLtjL5QW/budpouvXVCaCDwYiqIJ38WD/MqvS+R7NEVRsxiee49OnzPOI0FQ7XODnP/uSb3zznBj8Ya4jxRTZH7hxGReNlFpzRij4MOBjjxbQGlgsrYJaDk+5udny8mpH3/cswgLqzNAtwI0H6aHj0zUnpyu++OyKZ19+wWLxFqv1ilJN2qnUB4kixMi7pYGBvDNbEQpmO/0gl0LSxNEiEPrIojYblWoz2Lkt3qXMoEpwnpIK4/2OMmek2rXcC7LXXF9DxFqlXhvQiBBg6InBQC2h74kuMvSDaQMqlKrMtf6KJxtY0irV5KOcMwBHzoWcyqHdWMquvdJkcTUBpZqyDErRehDMNVmx9qzVylwyfi+JmDK1IRxLUFQEKr9Sfb1J8Zi0fuNDqdoQfvJwozsxgEWIni4G7J8tYYkzlQwOCc5QVrWqSRe5bETVYBDjad4CpuIwp5Gr5y/4/v/+Pd5/65v0zjP0HY42bAZKypa4KoABP3K2uQAIrutJe6WLCmWb2OVb8DvmzZZxs+E6VZIE/NAWnKMFi+Mlw8Uxq/MTpA+A0uEavHl/NpQ0T7ZgBEf0vhnx6cG+pey939mjIhUVRy5q5y2abtx+l/z85QtUE86t6cICIZtVO7WZVO4BCT3eK8KGEJYUdYgMzeDRiNphX43KwJwMdTmOW5w39ZEqMxIqoZk7rlZLfv6zHzFOjg8//F3WR6dstjscFRFPaAvyy1fX4GC1OsIHT60QJRD6BSUruXjQ5k3mB04vjrm9vWO73VL6DtIFAAAgAElEQVTTTOeV0ImRfKPdQ1OaWS4XTNPnfP7lp5w++YBuaag/8a7ZmtiSXVvlIMFT0l4pXlGKEcb3CL7OM073zCU1dwEjVjvvmdPE/c5QgeIqpyfHBBdwCEfHCzN8LFgFlR/g6doI5fvkZRs4E1d2vm/3HdSiOEJT6AiUXHE5g2Sb17XrWLU2VftqIIuUzLmg/qqbgnMOH/zBKsU7oaZCzooW84XbG6s653DFoUEJvafoTKojEjxd3x+k0qjzPs39Bc/9r288Jq3HaFFt598SkfOeENsDZUSXA9QWpEka7XeBaiaF1WZh1SnBa9uJ2mAdbBay3W6Y08gPfvA9/sV/7q/z7uXaIN3qDgvTNCXmeUOeR/K8ZdxNTNsdaCY5cMPAvtk/dAO+KLdXd1xPCarShcDy+IzF2RmLU6tewlGPLAO6DJQeUrNw0ASppsOcAbHUbCQvqNUEdksuVk0Ws9ewlwoh+AZYMQ8qm81EnO+4u7NkXatw+eQJpUZS6XDeqo3gBR8M0AFY664qVTu6eMRmhJx7nKxxTiiaHpTEa08qHoJjnHcsVwJeqJsR5zzLpSldqHq+9vVvcne34257Tb/scaESYqAWx95td7sdeXn1kreevsv5xZroB1QDVYcGMoFULGkpPcF3LNcdPq6Zt3dst7e4kjnuIu4g46gs12sun1zw8S9f8PGnvyAOntV6TZDwcP+0vG32HnoAw1Qq3lvqOnQAvNktpjKxiL2hAU3Flt14x8trMz04OV2hfsFUJjofES/NmqapTTTJRQMP7TUmrT1ZWgu2JDX5sKa+Mo8FR6CLA146am73hWZKa//tj7sWmxPXWgnikdg3s1HHwWtVC7X9p00vsGL3mQpNE7OBmeaK1oIrgpZMdUIqhe08kWo5+G6ZdY2+sVUWPCpiPMZjPMZjPMavUTxWWo8BzZ4D6kGYtIvBfITkodHwemvjdf+tB4NIxQz/MkXAq5LTTNe1Fl2I3Lx6wTwVPv3sI37y0x/yzfc/oKbEbjPTtVaVFkVzJbrIcnVMdLATZRo3pHmLbHYH/b9SzcI9Bwf9guH4iPXpMYujY4b1+qBKoV6prqJSIFe8FBBHdc6I1Q1Fd5gbaEWqmrlgaW1JcUiD88NeCb+9/56L5M3OIhdHyvbvb739DU7PVpTsGadMVY+IDcwlePajEBWhisP7FZ2DOTm8HKEsbaDPdEDR5bq3icn46PChgofYO0qC3WgtzGnacnp2ytnlBc53zHnE+wgofT8c6AjL5Yovv3rOF188Z7G4JKwDjo6U/EGXsDQV86SeUiH4jtAFUGsLIjOlToQ9KLAqIXi+8cE3WawvSWXBZnNL0Uzf9ywbJcD7gKsNXNCAGA/E4YJKOfieid8rYIwM/RJpKi24ypTuubmxSmt1/DbiMrUkMtVAGd7RDT3BB1Jr8aY8kyqtNWiCvFoUEU9OhTzlQysx5cq0vWfaFWIYGLoltVScGLJ2nud2PP7gPxdCsBliNTQr+qCcUWom55lSEwJ00uO9IOLR5gX2oJ5RD7OzWgpTTowl4bpAHPpDq9WptOrzzY3HpPWbHiIHIU7vHb4NvLsu4oKnajEIu77+I3vI+0MLwuDye2sGkz6aZyMXLxbG29lsNuQyE4KjauIXH/2E29tXHPdnOO8OCVNLppNA1weCV/K847YNmvthiSv14MU07TYUlOHsmOHJGXKyZNd5UudIobDPRd6B1kyoSoAGOoEaPXg5yAQpNttwbj+rk8PnMnWMB3PM2lpJ5hPlcL5DfCXlHS9e3nJ1ZUi29957gpYFuZoChQsDquYfJb6jNtBEFTGCtfSIVrroUF0hrBqkXg6XodZipPCc6YZA0g1OZ3wQdrvEeGcWJq9eXRO7yFtvv0NJiXGa6YcVISgLf3S4Zmenl5yfb/j448/46suXyFvHHB0fo0RU935Z+8GfJ89QXTESrF+QiiN2C0recLcxcnEplS44oo+8/fQJRVc471GEcbth3NpxHx2fHOgSBfOUct6QdNoscw6kLgpCoZYEmnCHWePMuLshBksIqyHitOCpxCAEF1rLMTPlfEjWKVlSKkWbOogjp8J2e8/Nq1s21/ds7q3Nm+ZCnpToO7723gd0i84g7NVae6+bou5nZDnnBnxpyu36oOJhbcHauJLyoMpyeLTkgSOm2LVyigRHCD1lt4EuoP41CkrlsLF5U+Mxaf2Gh3NCDJ6UEs7JwdjRe9+qrGZwVz2Hxrk6Xk9YNguQQ5ViUj020O5iT24WJrvdhi7YiOzkZMWHH36DrncohRAGPHvnV0AMtlvSRM0F7zxaPc4Li07om6r1OE8krRyfLHHLyBQhdwouUYSDk20M5sxbc2KcEjEEQt+ZXI86pCHeDHJdoM0GzBjTIzzM8A7k0LawAIi34xB1pAlTk1i9BcB69RaiJkeUasH7BaWMiFS8j6jugR09qMlpeec4OjoBlqj2qEo7rw0dp4arnPPEsheKQhVFcezGkXmy4/ZR6IbIZnvL1fUdw+KErpvpuzXKmmEwwEbX9ZydXvDF5y/54ovnlLTknfdWrJZL4wwhh2F/LdIUzaXZiixwITJNW/r+qFVyMI93oIXNZkeadxwfDYbmy4WjxXCoMMfNPdNmh48dcXWE78xPTZyjpIQXDnNRasY3TpXXYgnBVXKaOT9Z8u5bZp8y9AM+eKI3AMtumkFpgJZ6qEZUhTIr9/db7u+33N3ec3NjiWoaZ6Ry2LScHJ9yeXHOydEJZycXdF3Eu0DNMM/zwfIexLQ4q82qRAWZs9mezBPz3ABHuRweIwP8zaA2W7atkjs8c5UGLKyVXDJZCrs084Mf/TlfPP/qMPvayz29obxi4DFp/caHiNB13QE19ZCL2iLdSLOv28qpOl53ORasajnoyjWh2dV6Rd/3bLd7QEIl5UwUQyOu1gNdF5jvZ4J65ECcNe24uWamaUutajYlNVC1MM0bclu8ncBy6Fl1PVUbb0XF2iw+o61tUrqOijBOI9vdjq7vWQdPDMEI0Ien3KSDqhbTqhNbsA/uy/rAZXtdGcQ5aegxxzxX0I6L83MAol9SSsa5jjrPBA8qHULFudywzoAaitF74+5UBNkVOO7sLIsRc8FsWIrmphcphwRaa2WeDbEGsFgOXD45o+8XTbJozTw5rm/umCbPW29bYu0XAxfnF3TdwM31hq/KC0pd8LX31xwfLRGEMu+BBh4nzkALc6H4DvELnId53rEcDBrvBqH38MuX1/z4Rz/m/PQJ3/zgQxbDCs3pINvV9wO1wmaamKqaFqY4gjdBZa2Z0iSxtGTKPBNWAjlTsjIMC8QLF6cnxGjXe5wK03ZnViLi8D4ypcQ0bZmmmTTZsew2EzfXd1xdXXN3uyPNha4bOD09562LNSenpxwfWaegjx3BR6t4SwHN1p4sgooc9BGdCNW1Np+aUWatMynNpkvYnpthsWgJcd9anyg1o9nQi8HFQ6WVagGp+BhRqUxp5sWrV3z/hz/gfrM5AEusTb/fdL2Z8Zi0HoMQPS4LudbDjrZQDg6+aD2IjoJHixr6rd09IXizX6+24KtU1kcDi2Xg7v72wJ2J0ZOLMhwFstvw/R9/l9/6rW/xZPUeLinSKrKqiVxnaFyVgmPO+9aJkObC1BB36pQhKL5sWfgjojMlAKfG+2k8V1yuOBxRHUMY8KHH+46g0mqqFm2XW2tpyu6hIdaAZhjp3B527g5zrVoLXdfjQmQcldXyCUN3YW9ZAk7F5Hx01yzTe6RWHAWpjVPFYF9LIpcdwbvGMxMcoRFz22kIpmLhvG/IPo9zPaWOqDjiwiqok7NLFstThn7NWT2hFOH8bM3Vq2u2Wxh3VmEOi0j0kc4P1HRPksTzz79iEc7o3JLlYm1OxNASuJJSwrYsGecdvgvs7itdtfMT3MCURkp1bDazQfm/eM7JaeHi8vKweKc0E1xgvRhItZJ2d0w1IaIsFj1ezEwRoM4QpGMeM3c3X4F4Li6fEGLg5uaO6xtTwV+s1pydnVOrkcnH3R0vn7/k9uaOeUqkRpTOcxOFRli6BfG45/zsjIvLJyyXa7puODwHpVQ0KalY1STOEbODEqDKgUNXDANJKZk5z9SSqfOOUjI+BGKwDOOdceVUBcmVohN7JmRtEPoD/yplpFirEamkOvHjn/85nz37hCLlYOVVxVqcb3I8Jq3HQILgoken8qCQUD0+eKsy9vwStR57SoWcC/T2cPjG4RLMMHG56uh7x+b+ilIrQ+NKOe+I3rE6i9Ru4hef/zm/+OxHfOOf/wZ+6hhvmkK3z6AZVdtp55LxoSOlihPFS6TUluCCMEoh7245GhzLeMzgIuoU7wKhgQd8BlElSM9qCPiux+ORXJor817yppGoVVqbxlOxys+5YOTmPQBj7zaLEoMndoHN9pZaI08u3iFEO+6cFCGgbHGiODyqjVBNwWM6ipEeISFuBrez1mF1pFTpQmijjtfbstWSqXicwDTdM8+CSGTZXIaPT96miytUO/p+zbib6OMRbz09IufuQfy4BqjKB9/4NnfXle29gQSef/WM1eKYznd0vSXCYqQA4+BNMyVv6ClIDPR9x/WVJY7T4yNKFhaLM46On3Dx5B3efu9dru5u+NEvPuLk2CqYy7NzpGRqTvTBm2p+J4x5YrvbIllJYyMiz5VSPC9f3jLOM0+ePqXg2G0nfvDnP+Wr5wbE+PbvfBsXFtxc3/Dlp19wd3PHvDO9yxg7htCI0uslfdcRYzQLnBDoYsSLkueJPGVSSxw5F8Q5RJzNwIDoZuosVvHutz7e1DCQRk4XhWAgk6qJ3BKwk4yoo5amSq+j3YfFgOtTmtG24ZNakFLIpVBdZbO94c9+/H1u7q8gGE0AQGsjKr8mu/amxWPS+kscIvKfAf8q8ExV/1r73n8M/GvADPwM+LdV9VpEvgn8GfCj9uPfUdW/9X/vF0HXdZTSkhGQJrNXEOfM08cKKUo2pXWbQzVdv84zjRNd5+iHgX7w7MYNlUJsxGQAdYXhyKE+MauQXcb1sJ23dMXjm72Cr6F5L5k6d+cjw3JByh3juEFjomuky9opeLOcmLeZ3u9/p9CFgG+6ca7SUGH2d1cLkm1+ZoP+B/kqG35Yi0ZUmEuxyovCbN4rAITO0HPiHN1gqMLb23uWixPWq1OmaQ8L1DY363CqePEULW3+UHGytM/oBsxuPhHchFZwLMizp48LnGagyVLh0TKDmipHkI7b3T3TToh+zdHaWpNDv0boyTnQ+R66SsmeRb+gj90BkLD/8/Jiybc+hD/74c9Is7K5v+fFi2ccHR3RDf3+dkHVkIE5e/I0IdFDrYTgCK3tN+8ywXesVuf8zu/9U0hwdMPA08XA0W7k6qVZnXxy8wkXJ6ecnxyjJZHLRHUVSJAL85gY7w3UstuMVI2cX77DcrWiG3pyyVy9eMWLV6/YbE3u6dnnz3n1hSmRTNsRRTg7Pef4+MRUPHpLWn23oO97YmxtP31Qe7G2a6RvaL80J1DjQtXSiPRVKBlSeuhSSIV+6Il9d0hkzvfklMlzeU1zUcwyB7svKQLVEVxHcIEylwM/DYEqlUxhrjOfffkZn3z+id1Hr4kCqN1ub2zCgsek9Zc9/nPgPwH+i9e+9w+Av62qWUT+I+BvA/9u+7efqerv/z/7FUbcDJ0jZH9IWrVUSiqIVMRHaKKspVhP3ruKuPbaqqbd5ipd76maqWRcEMTVA4HVB49bQPEJpbKZ7/jo84/43W/9PivXExo6TUo2RXPxVE04BG84MSTDNBdaAYWrDofnZHViwP0ZyLaA1CSU1jepKZOmRM6G6kIE5z2+CwTvD1YnZs1irR3VSqEyeUuMw2IgLgf6lSWZEAQfpM3Klbu7O2qB04tLSm6wZSxZog4lG4yeCMXaS04EJzbzc2KoQmpBSAZCiGty6hFd2TkX23mrJrzMIBlHpOqIl2OGzsjgi/6svecStMdLh5MFffSUrGg1FYXg9gP8wjiPgOfpk/dJHwZ++MMfA8r19Svu7i4ZWsvRtwpSFWJ0ROko40RcLJCsrDuDst/e3tL1A8NizdE6UkOiqjby85Jlq9xuX11x9fIVty9e8Pa7Zxwdr0ilQKloKdR5pmu96PXlE643ynasTHmDv98xziNffPmMaZoPrbz7V/csuhWny1OO3z3Fh8jx2RlHx8fQ4Oj7e1L2MkqNXFxbq1tVcC4SW3Xr5wBFiRKaaG42KHv05FKYZmvzppLx3ttsq1U8iklq4QXv9h2KgBYDZJRcTPA5G9FdsuCrsz4wUJ3dk0UKu7Tjp7/8GffjPVWqPRl7bodz4KoBo97QeExaf4lDVf9hq6Be/95//9qX3wH+jf9Pv8TGVOwND0PznwrB/IoEs2awjZypY3tvAqNVp/YentPzFbRZTMWM81SsXVjars8H40oZt0e5293w049/wtXvX+EWK3S0JHgkPSEEggTKXCk1c393T8lTM7cT5taycQjLoSNItF1yBq2F2+srXubZBtwtnBO8s7ZnjBGackFxD1OtWguIiQX3fSR4T+gj/XLBsB5wfaTuHShEqVqIXaSUzP39hi4OLIYjpimb+CnmlyTicBIpueBipFQQjTjxeL9q1zYiLuKkzTPKlm55hnNHCCu0ZrQ9ssEv6HxhLhNRepJuWXRK1x2hNePF/KKCrEAWqA44WQCRiQwlmOXHHlTibHI2T5kurPja1z7g7m7k088/h2nH7e0Vp+dn7XL7ZhdiLbEhOEoWdnd3hNCxWlvbb9EveXV1TZqV1fHCShCxpFWbEjrA6ckZx4s1N69e8OrqBWfnR4h4ahZysQW5KUCScqGq5/nzF1xfX5PSxG7cstncoyjvvf8eAB8++YCj5TG+i6xOjkwtREwmCvcgsaWoaQSqGTvuHQ2gQcw1cRgYqcl0ZdUGdmhtYQK+CLltUqbUIPXJ9BDFiW2MnKNkDhY8STNSrf1emp0IKmiq5FJNA7Lx06oUiqvs5pHvfv+7/Mn3/oSkGZv4ysOsU5uGqHtz51qPSevXO/4d4L9+7esPROQfA7fAv6+qf/QXvYECOOOo+OgP2nbRRXNUrQINPei8EkTpB+PRLFfWBlqtBkKwh1W1iZ8aqakJztpDH3sIHW0naAi9H/3oz/mHf/RH/Mt//V9n6RvPRTzgwQV839S3i+fueiTnwtANUNoCkXeMmy13wVtSUo/Hs7m5I+fE8th2/cdnJyzWK0IXiV2gi51JNqkh8fZRteCatmKMJvhbvCDBXHTVgbiHhR4PITg2t3fUqhwfn5GTggZKtp232cIXgndk9ryvdowS8E01viJ46UAagjALIityjlTtGjy+VaPq8aJQPd4twUfoPCozpc7ooY24QliiMiDaI64j+tL8ltyDTQYQvaP6Eeeg6xwffvgBN3fXvHj5iu323kRoseRaq+B9QJpenguOeTMxp8yyCesuhoEuRK5evWSxeJtuESlUq373vCOMLhB7z/mTp+BWdMNAmkDLjtXyiPU6cnNj1ehPf/4RX764sTbg/j0UjpfHlFrpZWgXUlit1hyfnaJeyFrAw1Synek9ramhYKvqryBk7ThNA3BPBjZeAbhiJF7ByMk1V3IupHZ+cs0Gaip2rM573JSp2exY9jWQE4O1G52jmllm7wjFIa5SxVFalyLVmW3a8J0//Q5/77/7+7y6eU6RhLraULdtg1Qyr9NR3sR4TFq/piEi/x6Qgf+yfesL4Ouq+lJE/lngvxGRv6qqt/+En/2bwN8EDk6vj/EYj/EYvw7xmLR+DUNE/i0MoPEvaZu4quoETO3v/4uI/Az4NvDd//PPq+rfAf4OgO+87vlZ0jhZYMoPTlzz0nKkMlIx3s/pWc9i+YDgm+eRMWWz/WjK7lW1bUz1YKEQO2vRqdoQu2a4vbvjO9/5n3n//Nv8C7//NwCo6kgPs2okRhCliJJLRWqBtqt1WkjTzO1NPvxA5yLBO87OLlmfm9vucLqiBgedw/cRF6M5vFaHOjnM3UTMlVadySoZLrIpjosNzKUhtcRZlZXzxN3tPevVEcvFipT2SLGGEvNKrQYP11rQmhrzq1Dqa3YwItTqqSXi3QLvCrVGg/zPsFh0D8K+2hSKNWPq60byVTwqgVr3FdnK2oLSoUS86yAYbwh1yB6+j2PoIyH0qGZS2nFxccp7773Dze01t3c3zM3yfrFaUTVDNQmm6o2M3q0WTLuR7WSWLF0YuLw8Ybe949kXn/HWO0/xMVDFkIe081i1mKln8HjfUcXhQkdtleiwOGJYXQJQ/MBUfsC4uyWEyGJYsBxWnJ1c8OTyCa5VmNv7HZ9+/hnh5TNOz08ZVgskONSpIQD3RPZi5o2uCkKkUgyyrkBV8pTJzU+rpCZsm82B2IjFFWYDMO2FdisgLiAx4NQg9aKOGAeGTg4IUNGm9O6EnAtjnswpoRZDlnbCrp1zNwhjmviT/+27XN1fodF4j847xIuBQmjE9/rQ0XwT4zFp/ZqFiPwrGPDiD1R1+9r3nwCvVLWIyIfAbwM//wvfb/+/1h7RPU+rmjVDCK5JHhUWi8DJaUc3QK5bpmbMV6upcQuutVkqRaspRAjErg2yw15Triloz8rQr1kMS+ZdOij1qHeoN327Kg7vYM4FHzt8l9F5i2ufs4+esOjoVwMK7LY7aq6sliuOT4+Iq8a67D0TiSQwRMf6aKDMyjRaO3OPngveGVHYmRajdx5X94rkleAE3xZb5wUnlbvbW7zzHB+fNmSYY5q2h3Za7B0i1mbKeYc4RSSgavyaPdxexejJIh1KojaNvxg75pRZLHvQPXfONWWSABpw0iFezcJFFWrbKdAhRCAiYjqB3oVGYM64hvQztXNP5zyqjly2lJo5PlmzWPY8f/GMzdZsP07Pz/DOWytKwIVAKpVu1YMTxtnuixgdpSpf+9pTPvnlJ9RxJniPOH6lHVeFttmJ5DIxpULnI0jg6uqOZy82nD15G4Cnb7/H2enAxx+dc3+3ZYhLvBnMsO5WHLV5Gm957ncb7u7vuLm75vmrZ4QucHZxxmLR4WXfBu/IpRzQnAUxgX+TfYfqmvYkhtQUR5ZCqvkBlBQEH6K1xNtTpZhkV66VMtq9PU12P3RtbmwzY1BxZu3jGhG5FHJRggff23veTHf88f/6x/zsk5/hB0+aJySACx6qHry8bP9jYKI3NR6T1l/iEJH/CvhD4FJEPgX+Awwt2AP/oMFc99D2vwH8hyKSMdmEv6Wqr/6i32E1RCW4iITXxDarSQOl1sNXNS5XkcrdbgOS2I+CXLCKpOyRVzSTRgHvLbFAswC3ZdkqFoyg/PTpGb/1rW/RhQYe8GaL7tRR8bha6RYLfIDYB9JG2d43/kr0ZuDoPX3f4brAuB3RXph9JTW3Py+eSZS5ZvKkaMCIxNGMFfcoQ5VqMy3vmrSO4g2X3Iz4/IFc7MRRUmXaZo6PLzDysZ2TNM0cfLJm47DVWinZyMN97xu3rUHusff3IvjgKDWAempRq2aqUDU064kHYIxIQHGIRMyXamrcOkP41RxwPiB4+3wYECL4yJzSYbHbH5O24+q6nmlODEPP06dPQOHu7gaAaXrCYnmESLDKN0CuBYriOodryLWbuxtO1scUCm89fcrNq3v6nBmOVoTOkw4mopCrGbuIc+zmGReEWiqfffY5H3/6Gf2RVczf+vZv8633L/jd3/ltrm/u2N5NBDegs+fliyvIdhz9aomXyOX5JZdPzxnnHbe3N+w2d9xezQS1e+1oecpqtTJSuiqumqFnKgW00rue2LdlUjziHLnNsKZ5bpuZQnnN0sTAPwoO+i7Sdz2ys3tiGkd2Y5t1ovRdoO96tGRzwPae4sy9uIoyF6u0fC/85Bc/YpfuSXVCvFXyjmCowrbx8c5oGpTHpPUY/z+Eqv6b/4Rv/6f/F6/9u8Df/X/1i8SqC5SD029R2/knMh6h6yNh4dCYKFVNMqk9F6400V2v5lvUdPZqVVuE3R4U4DCArhlOxr5ydrrgt3/nazx965w9B8kVoXOBXKwVs/9M+EAWIVVlbg/p0fIY6T1JDa1XRclOEV+YBqAJqEpNqBe8eGsJbWuTcMpozSZoCoi6Bh+2g8tlplSliwaE8C6aOgWG/ru7uWEIJwyLtQ3dVa3dM6WDPqJD8a4j14qU0UAtQ5PbqbAX4ZMSCJ15dkkFLx0laVNBgJLNERpAZMboc56q3mDUCsKA90JJTQU/B4NWH5pSHtThXEeM6cAtCvtdhoI6wbmA9x1HqzVPLy5/ZeN+fX3DsDwCEcR55pJRbxseJ0JofLtxOzOmzND3hL7DucSzr15yrsrFk3NrrQHiPKm0Jmy0+quUjFAZN7dsNle8vH8BwKx37G7e5etf+zqn5xcMy2M2tyOpCovFES+eW2I9mx39orP7oSbwcHp5Sk2Fze2WzSurGp+9+iUhRFbrY2LXgwuE0BlHUQLeP/i8lVKN9jFb1eyrp2oliyWx3Ij5pVTEGbS95Nnag6m3itpjpSWGKk25mphzyVA8TrHr7YQ5j5RGK/nJz3/Mj378A+Z8D64YCKZaWzDl8nB5nOz3Sm9sPCat3/AQBNfaRmbP0GZDB0sIQ4atj3tWJ5Gp7EzWSWiyTvYegpgCNUKtNhsTqbjXFKjtYQ6Ao+TEohv4vb/y1/iDP/hDOh2oO3v0iirjnDEylqMUyPNMLlOT87EFBQyaH2PHbq6QKpozMiWqyGvSU5hrrVr7smrGd0rAUGMlpUOryruIVzF7FBRpaK/gA0M3HIz8wERSt7sdT548wftAKcnQkqUyzTO5WVWcn64P8HfZc3equdI6nPG4AGrFi2vmugXnjcNTNYOKKeQ3hGVVAQLztGHoTdy4aiXGhenbtQ3AvvLbCxh7J5RazJU6eOb0gDrbQ9H3IsnBdyyGNevjM+YsuDac3O1mxu3MsFghDf7tfcRFqHPBN3BPjAObux2Bjj4GTs/OyFnFtGwAACAASURBVDXxxeef0w2RbmF8N22wdkWbEolSyGjJ5HmkQw7iuherU4a45uWLG1IWLi6fcHRyzEZHNGVSk8R69vxLzi/PiC6QQ6aGCg6ceBYnR6zXVrnt7nbc328YS2bcFdKUbdYnnqHv6YfhYK2z5+vaPeCtFVvtPhIfHtqDHmBvglpAIXSCaECLUFrnIU8OaZYoqg6HyaMZ9D6RKft9HH/8nf+Jl1cvjdvnHL61l+d5PrR87YLTyOy/6pL8JsVj0vpNDwHnolmDl2oLZAvnEnGoDAvPsHK4WKl1Al/RzIPIbLM8F/0VWV0Tkt37bEFrYwQTzk1K55VPP/2MH/zZn/FPf+stutYmS8kW7y4Gg7s7pbpMBDpvLcqDvUNSsjORUe+ABG4yfhizPrjEVqWkgg/eiMXVIUVswdEHvlKoDpfUZKC8g9ChIsTWsgR3WLxubq7p+4EQuoO/mHcRCgz9gnHTrN/x7by2iqlUtFacD8aB27f8qiUwa08KnQTGOlGrkVW34z19U+Gv1eFdIM0wj5mwGlqy8cB0kJqqVV9TSND2OeRwffbAm3EcGYbhAAW3aiwSY8d6pTi3JMZ9woQvv3rOO++sCLFjyjNaHVUFRZrFBywWR6TtNfc3G/qzHlXl6dtvs0s77m7uOXHNUSB2IIE5z+BMLV8pDH3gnaeXfPrxLwnYa5c68O7Tb1CopJq4u93SdQNHJ2u87Jgn+4xXz2548SpxLMd0Rz21YNwsB3NV4n6WtxpYdgFRR+c68pSZNhOb+y2bzYa7+/vDpsv7QAjBwEniDnB7TRUt1WTYseci53SQP9sn4Vqh5vxQYVbB46mlorlS6oh3YnwuCt2y5ycfm8DN977/p5TSKmMJ5JQaKVkJIbb5ZiP686iI8RhveDgxTcFay2HR6hdCv3Is1o5h6XB+ZMqFzIR4s6Anv/ZgqHGeDmveYaF8UEgvpZLUdtMxDsSu57MvP+cffecf8cG7/wwn0cirgd78ozBbiXlOBIl0fSTNO6qaAjbAdjPCtlIpUAqaZzRbS3N3v+WkGUsOi4E5JyiCC5GIhwweBfUH9KArikNNQqlJNlUHWmpLKHC/2bTfveOtt05QVXJqwraiaDF9u7vWLtqDT3A2GNsbDnbioMphQfZqYr/qTC1BvLNzlmerJscdtVUIKh2I7binbWK1WBnqUUuTpdq3qsprVip7mSI5qDTs0YgpzTgnDP3QiObekrnzrFYBH6aD7Xx0novTJ9ze3HN8fMwiDoy5NM6TI7dNSsBxdHzKeLdhez+yOj5iTiPvvPMud/e3bDeGIzo7X5Cz4tVTijOgii+ETnj/vbf4+KdnfPaZjWfH2x15doRhIPTKOG3Z7m4Yu5HLs8s9EoGSEs++ekZi5NRfEBY9BAO2lJqZxM5JHztcF6mpMuWEU1iv16yGFSklxjQzTTZXGseJ7XZLbZ5VIZheYec8+prHm6hC1gdJKGOnNeSpHp4x7z3RmVCxqJlMlpqpAr6P3E/3/A9/9D8C8PzVc5wX8/5K2e5P8QRxdp32LUHl4CD0psZj0voNDwMXVCqJqpnl0hbQy6dL1qcdcVHBVzbbDeRkw3IRfOepe1x6lgazNdKwc4acCiHgvBxaY947UhVSrnRdsDaLVFyEVHbk2IbOrscFj4q1nhRTDJhLIU0j8zQdkIZOPKHr6fvAuNsw14p4gzTvbrY0HAb9whZj10dcyEwNbMEeAddaWl3f0fUGe5fgTVPQ1P1M0EGE7cbQcefnl8TYW6uuiefmXBC8KTqU/Tn2OKkUrNqqtR5g0N4bGRpAsxD7nl2ZGgLTVqDtdkffLXFO2E3W/gqLiCqsFktur24oyUAQVZQqjlzasN+JtUNdYG/cud9MiDhiQ7LFGNls7+3zeI+ogQ4UkBDoJFBqk5vCsVqtmcaX3F7dcHx6St+g71NKDyK8YgK6CxXub+5hs8MH07k8P7vgo48+Aswe5Otf+4A5J4SIaGmgkpk4OL72za/z8599AcCLr15xf7fjpOvRKoRuIGvlxdVL0pwscQHvf/Nd1GU++/wr9DmcXl4SFwt8sGNKDS26LSMBRyRQU2F3u8UXYbVYHSqrfaXVdb1toqZESlY5pTlRajokqP19PgxLU7533kTXnV3zPCfyvNcoFObdTGow9VISm2kD3uGHyA9/+ud87wffs3MebX6lWqm50veDCRaPpiJ/IGojh57wwRjyDYvHpPUYqM6UMpsVxNJuieWyw/lEKjtTBBA15J+Y9I0iByWJWkzY0zuD+FpLBMaxEKIQY9t5dx7RhpDzQioTvXfc3r/idnvF04tvASDbSr/okAJ5NnRdSjumPOGpdN64SwDDckG/HKiukqsNwyUooe8gOHyDK4/XG8ZxRGKgOqEGoes7kMa92esjxkDsAsvVkmExkEshRIMzBx948fwFuUlIHb99TCqVWoqRtLWph5QmpaMPFSZqahquKcrnnEw5XoU6P2yTZRVAy6ESUvWUPJOzErqebROEHbq+SU11pGkmz4mu6y1xloJvAJQQonGy0GaJsYfKy0G6CyB2gd0I47hjsVhhEA8PTim6h3bbvZHnhHOOo6MV11fX+FLw4phyq/IaJaCgFKf4IdDnge1mR98HQFmuBi7OzLrlJz/+GUfLU45Ojumko9bRLOilgFPWZ0esTk8B+PTzZ5x//Al/5XhNDN4ADyFydHJKmjOvbo1Lf3G84v1vvEsula++eIHTwPllpDhQpwc/NqWSVcgacNU2WLd3WzZ3G5arFaHrDtexVj3MJHPOOBdYrlZIVaZpsvkSMM2VEDyx61CdiTGiYvdEKcrUNh5OhVRMWzOINw84B1OZ+eqzz/l7/+3f56NPP7LrGCu5TM3J2TVIkyBYgn9IT685IL+h8eYKVD3GYzzGYzzGGxePldZvfKg5/JINXdaqJ+exnR2p7eVc288J4n3TnbN3yHOyWUJrk5m1g1BKpus8i0VDX1FxqvRdJAQDGzgHz199wY9/9gPeufwtABY1kMpIJIIU+j7Qy8C0TVArUSKJh8HzZrtjrokpzaaqIIKEgBfB7dHyBXoJiAt0qyVxOUBwdENAtTK3dhFideQ4jtxvtwzDwNnpCcthaVylmztOL5s6QzF1BS3W8sspE6S1LENgX8bUBgVXoQnN2mtrLhQnTKPt0GtR1mdnOOlQ3auSKF4K85SJnWe7awTf0yOgWCuqj8zjjm7VNX1Exft9bxJyVlxVYhgaqrC1I3mYd8UQGIaOzWZH3y9x4tv1BHCIiIkMA3me0FqJ3nF2esT91UtOj07t86UZGtqu1AyqBBF8HwiTcZucF3IqHK3MR+zJxSUf/+IjPvzwWwyrgIuOnDM+FsZpZFdmTp/8H+y9Waxta3qe9fzd6Gaz2t2cU+dUY8sOdgyyETZyMNg4dIqEAncgARdIhAu444orkKLc0dwgISWAICDimNgmCKIAtmgExDFVKceuKk7VqdPtfu/Vr9mMMf6Wi2/Mufc5rtjOXdWp9R8tnb3XHmvNteacY3zj/773fd57AFxcveDb773HwfGCt7/4NrqRzDVtLDhFGGQHdXF7wdFiyY/+6I+go+H5k1eUsbA8PgCrQMnrXZRwEMcQMEVRuwazWLC6WXF5eUXTtcxmwlJUShK767ompcwwjGit6do5XVVTTTPM4D2j94whYoyVlmAI5BgxWuJ7AHIIOFPhvUerhPcDKUdenb/if/rNv8k33/smysr7PKZRzrdSUBOIWoZkmU9r3D/fxmK4K1o/9KtMpchag3avQaLj6MnGY5pMnhJ7Y5yOL4WYX8eGF8Dqqe20U0vlRNtZZvNmD5gdx0ApBuckpkMbRS6BhOV3v/FV3nn4YwD83E/+MionVqsNcRvAR2zOlBRROaJ0s/c2+dFTnCCVnKmmYbWfJO5KTK+IIMFWluXBkvZgQSATySirMLbae6/QYu5VekkpBWctXdPQtDVnFxfknJhP0SQpBVSxe1mzHz04MVMXzd64G2PCakWZIthVFgl+TvJc+omUkEIihyxkjISka06y/+AjGk2aWpP9pqdbysWvaWtGPzDPC4xW2PJalq+UIqfE0G9o7EJ8dBSxCpX8xrzR0NQdq9uenCSqBkRcYLWRV3m6oamqinHoUWQqZ7lc9zy9XPPwS19k2c3Y7ijmUyM5lIzRGte1jKuR9XZD01SY6f3z9ltvM/af8OLpc95+5yFVW7CtIzGwGQZMU6Mn1WS3nFFS5IPvfkBVV5w8PMXVguTSRmMbOa6UyE2/5ait+MqPfpnGNXzw/odEP3JweCjUeSApKT7JB2IqUEHbdCwPl6xWa9arFdtJeLNcHtA0Ql7puk4k/ZsN2+0rrHU0jXxPUWEq+r4nxoCzFXXVkk2ipLBPYVZA3VYUE6Fkbm9v+erXv8b/+n/8b7z34ftk4p6qolRB5SL/V2r6mE5ixV6AwjSv/Dyvu6L1Q740CrKRbCAMfS93qrGMVHOorFzkvM+w22vliHZmT1oX/5OZPFHC2VOq4GpHBvwk8R0TOKuxrlB0ICkxUhoXuVg/5rf+1q8CsNle8w/9xD9CUy1p7Jztmaj+WtsQx4GcEG8Z0FgxzeYUpy4/+4RjbRTbyXfWE6lqx9iAsp5cMlklPBFjKsoOHKw1yhisNjijhVdnMkPsicWzPJrvi7DWBW0kLywlPeVvBbIRL9Rum7dZbTheHlNKxJVMFQ1ljKQ2Umot5legjCNlHHBdTfRF5n/FYPL0+4WBbtrFrK82HLaHKKOo5i3rsw1x9FTU4DVK7y7KMGwCN2crjupTdK3xrjDqQoWIQQDIDqccyxZWN1uOT2qKiujkUMWQ9evJiatriZLvB7RW3L//Dt/+xocoLnnni+9QTQWmz56kFcEWggZvDFrP2FxesO17lu0uiLHi4Vtv8fST52xfbakftui6IaoRU7XU9ZxueS0/pjqn7RqW3Yz+csNtMJw+uE9dO3zxlGmWF21HzomzccWyqnnrK8eE8ZaXj17iQ6aKEpLZLeZUStMrRdCJYdgScqBpG9p5hUqJ1Up2t1fn5zTdjNlsKSkBBeqqRutCCJ71SoREuemoqxpTt4yjJ/Yeax2VseILnG6kQvFo4Ky/5Df/9/+Zb/z+1/nO+99mvV6jtKKxlnHc7ZoMOQlOTTuLUoZdsjbltc0A2MvsP6+y97uidbcmWTRUrn7N1VOCkvFjRE95W0oVjJ1y5vQOJCt3tYmMxUrHwqh9sKIgkqRwKAWuNrjaUFSm6ISrKgjyfS+vzwD4v//2b5FC4E//wp9huNxQtIgQnK5FNZglbwpE1KGQXKycM8QirSUlJ/KO91asGIT94IkpSDxJ7chFCveeXpULJUeSKdSmkZ2Gcqyv19SuZbZY7I8NqVA5J+mzBgmEJJFSQCtF14ln6Pr8ktPlEaUkYRlaiw+D+LVS2lMuhhgJIWCKDP9znAyxWhO8J/jAYiFsvauza2KIVMbinMNVFf12S+UkQbkPcgF1TYurW4I/Z1htsFNbV2th3u2zAlXGYmiaisvLS85fvOLBgxMwZfJNqf2xuYCyjoyn95HWdXzxi1/k/fffp65r7r/9AICuatmkAaeFLDEiApmma+j9sA9DrKuGdj7n6N4x549eoeoFh40lu4JWhsEH2plEzNimRmnDyb0TjhbHnL08J/jE0f1jbGtRaVdYjWy/cyaOgaAyX/mxr3C0PODls1es+isA1nFN1bS4pkZnQyiJcfSkVGjrltlyKWxAoB8G2T35xGw2l82nMSgnHr6hn6Tx254UMl3b0VQNfRoY+p5kHEYrQpD3JFZk9L/3d/8uv/5rv8a2vyWliLWWEMIfOE+VmpLEtdrbF76XQFDUu+VOPXi3Pp9LkDkJrc0ki55mWhogkaMoAaUdkdBKdhiliC8JBIRLltTXKTwKYyVBN+a4V+ZZp7AVaJPBSKpw3/dUugIXQcuJOqYrvvPR7/KTf+InmKv7VPWcsElshhGVDGZS/IE8nFYSChmCR+kihWpSxu0AN1Vby12xs6Qc2a57xmHAdtOMZ7fTmgIiNRpTNDpr/NpzfX7L8ugItXBi5gShKERpnxod0U6UgTuChbU70GoghGFKWy445wjjSEwRnfQ+RTdMLMAcgqggtcIomSdprQkx7McVlXHcXF3TPniAVobZbM7tzTWzOMM6hyqyIwtFgLbONVyeXdE1M6yRIMuo894gXopUXmUMJ8eHPPn4Y+ZVxfz0QHalgmaXB58o7LYthMETRpjN5zx48ICnT56KgRs4PD1GK0VTVayDn56bTDPrUCHuw0GHLMGis4MFfr7mxfNnmO6I+akjR+Eimt08LWecLtRtw8HxETnCzdWKs6fn3Htwn/lCipsfI9oCxUEMXG2uWc465iczgjri+kpwTz4mblbnON+wPDjhYLFkDJlNP7DtPdoYlJvoK7lGK/EAJu+JPtI0NTiDQbOcZnTBB/p+IPlI13bSBs2FkEZMVe+xZj6McpMTI977fSESZaL+1E5pV4TMFCapJtP+zg+5sxnsiC279vDncd0VrR/6JSIBpTQhJHySwjH4jKkz2USKyjSNpeksSmXZSRhkIAx7zxRkKAjhXWcyIpfeLeMqXK0pOkkcBRl0ETiqSsQJwRMNPHv5Eb/213+Ff+Gf+lc4rlpyscKDKwpr9escsJKIMRCj7BatthjlGX0gU6g6mfvMZ0usqci5SCGezJ+b656sVrgpSr5bzClFY5xBJ4XTlsvzS3IoLOcHUAxxMlUr44hlIi0guyKKiDNq62Aus68rztmub+hmx5PAwJKRImUrg5pmRTFnUk44pfDeE1NkNu/2FytKoZ/mK5VxXF9ec3p0QlVV1HVNCIlh9LTVDLTs8mJKaGOZzZdcffSUfnlEaxYC3q31fhecmZBdStF2LYvZnJdPXtDMW1QjKc+7CJWMIky7LapC8hGtM/ce3GO9WfFo8l+1bcvscE6/GtBWobW0bZ2zKGfIE1MyZ0BldGU4eXDC6tErzs7PmR29jSqGkhXrlfzeIUYqY4glgiksjw6wuuLs5SUvH5+h7ktBqJcWVxuMrUloxuGS63TL8mBOd9yhJ3p6v+mpG8d667m8vGA2P6Ju5qjiCCkyRL8H4aLEe1gpw6ybsb6+4ebqClM3tG2HndIMnHZEHUkhMWwGubUrheCDtJ0njFOlK5IeefjwIYv5gour7fR85D9QtOTh1ad2WfKRJ7N6+cxx+nO70/r8luO79cdacqemJpf/67u0ECJ+alEYrSWqo0gEwu4kka9FXPgU0OL910baZVK2RM1WdEE7KCpLy7CkiQNYxF+VA0UJ77DowtZv+Z2vfpXvfPAd+Z7WkKb2UtW22LrG1jXKSnS9rSpmswVt01G5Bq0MVdWyWB6wWB7QtjOsqTHK4pSjNjWNbehcS6UcKoqgbFwPbK43DLc9hMzmes2Lly9p2w6lDf12YLVes1qvpx2okN2jDNoEImtk99S1HV3b0Va1tIyy4Kf0tHvyMex9YNY5XOWkPWgMVVURY5QiZwR9lVKm7wf6fmBWdwzrLaurWyiiWKxczegjKWe0tWhr5YVA0zUzVNHcXtwIdT4gO8npP4CE+KLQcHJ6jxILF8/PUUGwV0TkI0+pu9agjSEbQWnpVvPOF98GlUAlHj/6iNAPqJhwuWBzwe4UqlpEEEkJRDarjDIK01pOHt7b31yY4lBJVH21a5i3MwE5pyi5V6ZQzxvuPbhHDIlnT17y7MlLrl/dsLpY4TeJ7BVWt2z7wJPnL1n1Pd1iTreY46oKWzmOjo4x1nF+dsnl+TUlK5p6RlO3E0dTkE0xxiknrdC0DUoVxu1Av94ybHqGTU8YPCUUdFFYpaiMk7awUgLJ9Z7gPSlGSi4cLJYs5vOJnPK951G783KHPU5JPImf/Zqy3zl/PudZcLfTuluIEK8yspPZwVur2lJ3lu7AUTWGlJNI4EuaVLXqU5xBmPAxSqJKlJ5sjllylQCsE1ySsUIaDzGhtcXaGqftXm3X95Gbq5HLqy3vfee7/PSP/wIpBeqqpmkqQvD70LucMiEnYSdSpsyqCalkHNpIm8zaCmUNGUvWljD2hHEklYirHFUtOy1bVWgjsnmTFOdnF+SSmR8upXDrwrCWO2JbV9hmUjLuWH9lCvQLI9UkVFkuDrl8cTHFrydqY8hM9PoQqCbFm60cm75nlhJt25LSVLSsXLBijDt+KmH0aDRPnzzl4OQYZyyLxZLLy0tmy/mexkHRlCzsxHm3YHV9S3uwYNmeMnl35edWhZg92laQFfWs4/j0lFcvzpi3hzSHc6pmmu1kuTHJJLTJmFbj05aYoZ5Z3npHsq8effyIx48+4t1336WkgnjMC1lFMnm/E1BKSZalLsQK5ieHZNtDEVJFKYov3HsLgBePzrlaX4FW6MqS9VRADlruc5/LlyLYuL26pt84Vjcb2nmHdjWLeUNSI+M4skZ29U27YBhXlGI4WB5D2bJeDYzbyOLgAFMb5q20/aIe2KaMrZzcjhnF8uiQ6/P1HvMFQiGpjMWPgZQzVWeYdx2bUigpTWpM6ONAIbJZrQTNNBWd3U7pzbnUTjGYS6KkQkpicE6p8GZ9KtON5ffaqX1e1l3R+mFfRaEnwnYMUSgEQFsZ6lZRNQrnoMQsbL9JQ1YKe+XZLjoDlVEGmSUgwg2tpViBzJ9SzkRfQBuMkdBApS25KIZeLiSb28j1+UD0ht/+21/j537ql/ixt38cZw2ZwBjGfQ/fKI3Shn2QZc5YYymVpFWtpwKDVTSzlpgD3g/kGEgxErMn9SNxkiG3C0XbWRrX4LeB7XrL8cNTbGPJaiKvTy219e0NB1phFcSpdVmKRLL4mGjs1HJsF5z5M7z3GCdxFtY5+jF+KgzROsdmsybEiHWOuqmJW7mzt9Yy+IGuliKsEiznCx49fkK/2WLmc+q6BhTDMKLsLrlYTTEnlvlsSRoDV1fXmGVHpWqck4ti0MJZHMNAqxtSSByenLI63/Lq0RlvVy3V9Do6owglEZNHqUQ2AdUpxnWPNTVH9w+m3+dLPH30lMePM1/80pexSaabIQfS9HgghPMUA0VbqDUlKLrlQnaj24Gb656mESKGQ2OQuc4YBtAaXWtKzMxOOsZJgFK2slsdxsDV5QpTWdpFRdU6oLBZy2s4KMjZsbrd0jRz2qqjNIbtZmC72tCmhq4TJWbdzVBKkGIhT63donHGUbkKvUsUiEnUhVYRQ8APHlUJUeVNPqezlj4OfPDdD7i4OKfk/AdoFq9hvWaaXUnLPSVpCzLt9t88/s0Z1+dx3bUH79bdult36279wKy7ndbdklaSYN6ZhGwoA4XI6AdSEcabtpCjDM6NFs6eLKEKaF0m0jfkkqTNYWR4DDL3KSVjrMUahzJGyBApEzKME0j09jow9oXadtzcrvn9b36Dn/jKT1JKZDv2kjJc3oiLcBUGkeinkNHa4CpDngjtAOvVhnW/oZRIDH4CskbQSeLe3S7pN5NDJunE7dUNRltO7p9iakMqCVe5iUYBl5fXJO+xVUVWO56fqPzsBAYGAa22bcft1S33Hp4AibbruB17yU6a2qKVtVxOMxFtRKChjcL3vey8XCUcSAAnv3uhcP7ijO5LHcoomqZms9kwb0RFJ1lpRVpWrWN+sOB22HB5fsGxOcFOlwBtNHlS1Ehqr4g/7j98yIff/oAXj59zX0uLLjcFVYvgMqUoKtGSUE4TsuwSAU7uHaGV4uWLVzx9/Ji3v/AuRWtimYQ4U4tXU0g+YC0Uo8UoXtWoHMmpMGwHrs6fAeC3IySB9mpjZA63+9lthZlNCcwp0bYddatZb3vW2zUxRdooYiA1pQSEmClZ03ULCfg0BqMrVDH4ENiutvufs+0arHZkneQ5MgpTVTQt+MHT1LvHzvjR09QN1lpJ9M4JozVlyj0D6UDEIfDJJx8L8V699ljJLNN8yiS+20GlKTVASQ8eUPvjXqtqP58iDLgrWnerKPCOgijHdsbZkIN4iFIiqMQUQwWIR8to+9rTRRICdShYrST2Y5JhVJXCuNex6kpZatdSuZaUBkoa6WYzbq89wzBJv3uNU0IxD2rk77z3//KzP/cP8+WHXyIwolUiT2SImGpq25GyIfggRSdFbG1punp/8g6+JyVPzhFdhMZhraN0DRhDPakHTVOjnGWMgev1LYcnR1RNQ1YiHNC2cNBJQbi+uqS/WXF6fA9tlQRXWsNIRFWOzYT1mVeO+b0F65fP0Mu5PA9GUc9bUpG5FkAdM9U24S826MaSqkzlFLlPZJ8xywXrCbZa/AXNfEl9tOTps+e88+BtXFNRV47rq2u6ragH29aQVCTqRJplhjGwqBbc3NyyahpmevI/WSWKzAS5REKUG5D6wHH69iFPn75gvhAz8OLoAFUUrp7yt0KC4vBlwGdPQX5vUwr1cs5yDHzy3U9wuuXg4X1K3eJLmBBEYHLCWo0loLz8LLko+pjwSnHy4AGvnl7I67i+Zb3uUX2hiY6srdDWc8SrET2bZou+ENQW4xyd1tiqo9+OhNuIqmpUPbXhzESQmEDDtbMYqzDVpAIMls1GnvOxH1kuZjRVI+cHCu0crjNsx5HBS2ty3nYSEDpusHUlwpyoUQoCYkAHUCVyu7nio4+/iypyQ1d4k3jxOkB1N6sKQURQahJliNJQ7W0Tu2M/z+uuaP2Qr5wLfuzRlMk/JZ+vjMEYyQKSRNnXkluN/pSpcffHnMBNPqeQPVpDVTmq6Q7UWIuzDSVL+CEFqsrirEWRXkd5IHeRMWWMyjx+/pj/7C//Jf7sP/fP8w/+iZ9Cl0ye2HFkudvU2qKURRGFFoAixLj/OUvOKKUFw1McuSRMbYlOo42mmrxXlTYYxPellGI+m6ONEem4U+JZmi4KB8slFy8uWbuWzi1xaELOqJLxYdhPJ0YS7WGNPlPcXN5wcnqCsopaNaz6NWHysdW1wzqLH0aaXBj7ginpBQAAIABJREFUkc4Yqqqm70e8H0UMAmzCiFaZe2895IPfe4/VzS2H6oC2bqldzXYtF1pnK4xVRMQbl8nY2rFYLLi6uqJZyLymDAVTa4zS5FQIIaKMJhvFyRfuEWLk/JnsdlxRNIczCpq66Yh6IBvxWqUMYbcbLApdLLN2zv3Tt3n00XPeVjXLe8c0riJNvjytC9oaKAmVIU2Irc3QY51l2S0xRd6YL58959mzV5w/e8VbDx5i6gplNU7X+JywU35acoHNekPXzrCmomlbqqql344MgydOxb9uDFVdk1IBlQkqoIzG1paYo5DaJyzVsN1wkxNt11C1FcY5YXBWRdBWUz5YdJGmrhknlaA2hgo3IZheJwrEFLi6vuTs/AxbOUIQNuHO4rAjyst5mvdqQQl6LPud2ZtrJ3VXk23i87juZlrfx0sp9V8opV4ppb7xxuf+faXUU6XU704ff+aNf/t3lVLfVUp9Wyn1z/7xHkXaZEVC3iUC3kgU+661tytWRmuMVpN/WNoUaS9/l2Osc2ht0EqLSVfrvTS+lDIZVRMheorKLJZLSgajKnyf8X1GPLRKfEEGog68/+jb/N53fpflgxm6ySQ8CU8snlSCRD9oaRk5W4vsXZv9CVxVFdZa8bPssq9CJI8RFTImCVTXFQMhM6y3dHVLU9cSIaI13ov5t+97+r7n4OCAuq65vLwk9p4ai01gJmKHtiJK8QykKvHWO1+gFEUKojSz1qKsYdSCVRosdEcHDOOAivKzpJBRTnBY/ban61q6riVZxTZ6oobZcsmzZy+JPmG14/jwhJIKJRX8GChZEExKG9quox965ss5zlouzy64PLsgjZHiCyopCbxImfVmg9eRYAL3Hx6jYkDFwItPHpFWAyYY8qgErquyeLHMDthaSDERQsKZmof3v0DbHPPyozOGl1u6QdN6+TAokoHRFkaV6XPEl8xNvyGpQlaZbtHQLRqWBx2tdawurzl7dsa4GimjogSDo8GqGqtqqrolxsxm25MFK0JRimbe0S1nU6fAcnu95eZqjR/FuB0mkYVyCu0E6Ny2He1kXxj7gc3thn7dE4eIktOGyjnapqGdYLghhH0BCj7g/binXJSSKCURcyTEQMhxgikLaHm3a9oVqjiZj2OM39O7tTv2D5PMf57W3U7r+3v9l8B/Avzlz3z+Py6l/AdvfkIp9ZPAvwT8SeBt4DeVUj9eymvr7/daSkHTOJq6omksVrpKFP3mXVoBNLlMO5bCp3lnSXZZ2kjLwvsAekfFlguBPBiUEoSuQxLIp4aCxqDpb6aTOreYyhGzJ5aIKhlTweNXH/L8+hPeOjghRnlsv40Um+Uu1sqdvTOOuqtRldpDHLwfuLm9Zhi28jlVZLfVOoh5amlCzJpUMsknDk+ORD4/QU9zznIxmmZ52WaatqVfD9ycXXF8/1Tyw6Rhum+1ppLYlJFKNVxe3lJiYXnvSIqy1m8Q5jOzZcfVJy8oPtB1NTFHkiropiZvt4y95GmZ2jFGARB38zkfv/cRb91/wGFbUVcNs07mbsPY4+qJKGyg7jqG/ppE4fT0lCdPHwNQV46lPhS6/CSXjjmxzSOV0aCyeLCA5x8+49nHj3lov4JdzpDxkBQqo7WYxpHXf+gHUA2V6Xjn3R/lw/e+zYsPntLmd7AH8pxrA8kWomZP6Kdojk8fUGmFjx6ddqmfkePDJfdPTgj9yNnzCxaHhXa5QFn2isSu6vCzA25ub9lsNrTdnMKUzm0M8ykBWheDj55+M5BKxjUVWqZsWKexukZNxl0VE6WOwrr0ifX1mt70ONeIeHU6zhiFs47t0O+NjDmJUjQR8HGi+jvxNaIVYxyxzu5nVzs/5G7eudtxfVYCv1u7fy+l7Hdpn9d1V7S+j1cp5f9USn35j3n4nwV+pZQyAh8ppb4L/Bzwt/6wL5JWhFyktNbYXW/cyQwnFwkwVGgpDFrwMUzyWwCyyLyds1OhEsYeRdobxtjpsYQWXkpGOw0Tv9Boi8oidQbE/FprtNKS9msh5cD7j77Jb/yNX+Er99/h3vI+AF965x+gz2uGMVBRU0rE6JqiJmr6Dpg79PTbgZTlLtc5i3bipck6YXdy5TEQc0I5i6tqfIyk0aOclp1ayfvh3jiM+wvEzeU1B8sD6qoBW+EVqAnjFEsgJpHnG23pNyOLZcZYkUvvZNp9TrTakVKkv1rTuAZcxZA9SWmctQxbaUGVrsK4lpITWmn84Hn25Dmua+iOF3vi+Og9IWbqpmIcexE51BXr7YbjoyMO5sIyvL28Yd4t5PahMlSuogAxR6xWZAvNTHYRD959yCefPOfF4xe8/SNfwlhNZStKSihjSRMoWGlFItOHAdW0mKbhnS++w6P33ufxhx9z8q6EQHbVHOs0xUgYYqaQkkfbBkXGugJqSrWuFUf3ltx/6x5Ns+TVxYrL82sWHtrFDGv3/W0WsyVhjGw2PdZUFK3JRTBaZgIPd/MFdY70w5ZNv2Y7bKnbGmU0bT2jMg41+a90nVEpM/YDuohpexwDYdxgADW1RZPS1K5i3s1Y91scUDtHSB5IGCU/45Pnj/ja17/Gul+jnd3vkHbF6k3Z+q6Y7f785trtsN487s6ndbe+39a/rZT614CvAv9OKeUK+ALw228c82T63B9YSqk/B/w5YFL2ycmXUmRWprgL61CmIkVJripGSVJskbZPKiI4BPZeL60N1jhx/jOddIq9+VFOyCReJVWwVtp5GU2/9oTNdFcZIjQCo+3DSCkRbSK2sXzr/a/x8UffYF7LnfJP/tgTfuYn/hQPDr5ICkkKi4KkCn4YGabZBWScq9BJkbKnaRq01US/xRm7T3bwg8enyLxtUM6ScmB1fc1JfUI37yRra/KTpRRQGGazGa+uX7FZr+mWMyyOogSGKk/PdJopy2xxyOb8gjh4XNvSOEc/7dzGOBJUwDnHzdkli9mSMq9J1pJNxBqNnXaE6+0ItcFZh1aWw+Uhz58+5/DeKc1yIRgpYDFfsh7XWKeFrFESrq25Wl8yG1vunUg22LgZuH51xemDB/jksW2NNhEVpW2VVGSj5QagOplznO7z/MkZ+tFz7n3lPq4zhCi7uR1bL6qIaTT9ekDTo4yiPam59+4pNxcXe/7f9bDi8J0T6kVL0gJAdqajlAgkYhn3MSZH94+ZtUcsThc4O+eeabm57rm5uiUFmTcB2MpijeZgdsCwHckxUVRm8CPBWmaTmMbUlhILtWpIKnN5cQFZcsTSAMvOUhl5Lo3SNK4iDoGSYN7NaKrMzfaG4CPNdHOmlWLsB1xVUTuH90I58TFRyLx49QKAv/rf/VW+/q2/w5CkVW6KJCenJIrSz+6m/rD15nGfd5/WXdH6wVv/KfDnkX7Mnwf+Q+Bf53uH6HzPW61Syl8E/iKAMbrEWCArgk+s19J+SjphW8jaTOF1CVdNs60pXmkX56GNpWRDjJHNZotxwhIkF5TldbtIyzA6pim7yFhSyZAt/XqkTB3J4gtpiLRVRXGWoga6zjJfOEJZs04967XERVx+fc2jJy/52T/5j/MTX/kpdNQcL4/2czQ9FQRjrRhDU6BpWpqmZfAjqRSMq7C7C44W87OyllAy2cguZhgG2lmDegNEKgNxYex18xnXt7cc3jumaInlU0ouds7VxDgQsmI2X9KfX+HXPd3BDJczzXRRxAR8P1C7iu3VijwEVNdKFqO12GRxU0EgiujBJsEFnRwc8Wz1jOvrW7qjw70sv3IOEyx9P9DsxANawLjrmxtm92RH9uD0AU+ePOfW3dIdL0TKrqGjJvsiESe7RM08UJ8uWA6em7NLkoocvnMyRdEkvJrI+hpUoymh0LNGKU9xDc3Djm3uaetpNziMXHz3Fd1yRn1viWsaspY2YVaFGDNqYlg28zmzpSOrwhA8pqk5PDpk3iVuri7xW3lf0HY0dUPV1CybBberlUCTjSUV6L28z7WzKC0BnXVVc3p0Sr/pqaxDFctms2GcTq2qGFQCpyz9OJJ8pm5rZt2MIW/31HpnrGSYDQOuqQghUEIgkQgl8OjRJwB8671v4WNAW43PAbKiTKSLXZvvj1pvqgt3f98hn2KMf9iX/sCuu6L1A7ZKKS93f1ZK/SXgf5z++gR4941D3wGe/dHfTwb8qiiMUfv5k0+BrlS4mZHc4inhVaJHNFAokwkqB4X3kdEHqkrR2UqKURI5bpkAszY7lMqMIaAqTa0sRWUUBT94dqMdnWDY9FS1ol04cIWmBa0TSiWGNKAnICzUfPvDb/Hoo5f8G//qfX70Cz8CThEHiZjYxUC4MpG6rd2nCvsYhFCgDHoqBloZivcUpRlCpDi5CKxuVzRtTd00E3kCUonEPknW12zG+fk519c3LA6WpMyeSpGjRmUn6sXO0bUzVjc3LE4OcM5ST4+tqoo0BrQthNHTr7Z0h0vxe8lTTpoUYY11FFsJOigX2qbBOsf5+QXt4QFNNVEcZpa2a9kMiTEEaudAQTdvGS5u9gDerl1wcnTM2eUVpq2oqhrbWMwoW2pvFXnCTaWciduRxfEBpi+cvTinWM3xw8NJij15i7TcsNTzmn4YcE4RshQyt6wJg7wvTg5OUT6xWq14uXlBs5wzWy5o5g3KGhR27+HLJZNUxJqELoUUEmRwleP0+Jh+I0Xr9vKGwQ1085aiNSorwuip24amqRmnAjxmgdgaK/NY11YQFH4YMWSUM/tZ1ehHiAWVZWYVJzK7rQx1XZH9BB/O4kUsiNiHiW6vzKfVfmVSAMY0xWVOO6S/37nUm8e9WbQ+r+uuaP2ALaXUW6WU59Nf/0Vgpyz8H4D/Vin1HyFCjB8DfueP8z1LmrYlyqKn9lMeCmGtcLqh2EQsU5R8KSJRLgqzxzgpki+kUZO1xupGhA4moqzeh95tRw8Yckm0JtEaR6czutXUC01upplA0YTgsT5wUtXUs4SpJFgxJ4VWM/IoP+fF7Q3rS8+y8/zO7/82D999i9nBAT5syDlRTxJoqwzgKNZSyGz6nhQzVbFYZcnTid+HnmTy3j+lkqI1Dt8H4m2iKhpT72I/FNkZcBqrMuqqcHN2QVO1BKMmwzEYCnVUmEqRdaS9v+Tqw0vWm55ldyjBjUDWilgVciu72Muzc+bHx7S1wttCLEqgvIBmQKWINg5aS9EFd2hY317hV9cME3rIugV2ZtHU9H4LFKwq1F3FsE6svLD6nHMsFjO2256Xj5/xlv4CrW5FWaons6vwuchR8suiSnSnDcsy5/LpOQ2O5fEhcyt+rrFYRuXJNjOUgRQ9xiiKNsyPZlw9l0yr27Dl9OCUw0WH2l5weXXFcCPF/+D4kKqekeMudt7jo6e4CtAknSkmY3OhNpZ2EqDEWLi+uqb3mbqq0NoxbAcIIyorbCevYSmKOAYyiqIUyhTMUUfeFoZVz1J3OCevT5+24mXLWQIdQ2AMnka3aGUZp4KUk0KXIuGdfsDGgFcKZcAXz9n1K/kZCUCCmDBAyWmaRykg82aHTylp5QuP8NNG4s9mZ32eW4NwV7S+r5dS6q8AvwScKqWeAP8e8EtKqZ9GWn8fA/8mQCnlm0qpXwW+hbC4/60/Sjl4t+7W3bpbP2jrrmh9H69Syr/8PT79n/8hx/8F4C/8fT0GIk1W0396umMLKQphItWUksglUNcO0OSUJSl4kp2HkORr0ZQEMSSyBW3NpGaa8EBjRGmonEKrTBxHkqqx2nD/4TG313LczfMJ91RB3UI1RdLHWFDKkaLl9nqC6656SIbb1S2/8dd/nff+v/f55V/8p/n5n/pZCnmfMty4dhpyFwlbzElMzJOIJE/SYu/DdDctaCE0uNqhEozbAWMcccIzjSmSEJySUZqu7kh9YLvq0Qdz8m4WYi0GhXKKojX1fEbVzrm6XFG3M0wru0ajLM7VlFbRHcy5eXXDxatXdA+PcbVF2ZoYJhm9n/BPuUCOOFtxcHLA4cGCFAbWaxE5qEbjXIexlko15CQpxMpYusWc7c16+l1GHJqT01PGl5FnT57xjn2HqjGUlKlNxTjhjCxGFIIm4+aOE3VC30eePHrKfR+ZHchOy85k9jSGAacs2+2aYhKVaikournsBv16pM8jbdOwrA7QzrJar7i9vmEcBmbzGU0n2WSmVKhUJmKHx7lGwjdDJiSPmZ5zW1fMD5b4jSdlqOtafIUxsrpdY8NkJm8bDJqQkmCznKQDd7MOhojfDNi5vD6L2RxlFOv1ilQiVWPwvSeM/lPQWpD0gehHFBlntXQZDKy3az78+AMA+mFLzOOn5Or7xITpffu9WoRv+rA+z9L2v9e6K1p3C4WbWhJKBvBI7lXdOKqm0HtPDgEfJj+UzuT4mvKeEwRfhJSuYBwiuIjKBV3E1CmPAzpLjHzy4BVkZ6mqGe984ZDt7TTbCS+p64rZUtN2ioyHolBF48fMuA1sbuUCGr3B6YqS5MLzjW99kxfPX7L5Z1b8ws/8Au3EwdumgCmi7IpR6BhWizy/pJ1UBKxyGGNIPmKbhnEYyTkSS2KzWqO0oZouGLFkYdCh6YplZjtu/Q3r6zUHiwU7736e2HAhJay2FBLL5RFXL88ZNoHWysVbIwIJZYwwAl9dcX75iqOFoarmtK7GTUa65DPDuBXZvLOM3mOc4vDwkCefPCHuvEVNxawxuKrD6Yoh9kREoNK2c0Ivz+PVzYp7xx3GGu49uM9Hjz/m1ctXfOHBQ5TSmNpgdroeq8EmUgwMJdG0HQ/evs/TJ8+4uLzg5YWMXQ/uHbI4XaCs5nB2QGsMq5s1Iffk0NM20spLMXGzvUJXR5IK0FR0zLDOMvQDV+eXzGaixGy7VtiVWhNyIkY/mdgVkbhnTbrGkpIl+0SOkVIyi+WCrBObYcP6Voq12Y7UTS2sx6wwU7SO0xWm6RjXK/x2Em0YRWVruq5j6LdC4KgcZZT30w5rVqY0BOMU4yi+RO1gG3ouLs/4+PHHAGKwR2J0XisFPz2f+qwqcHfGfta39dn1eZW7w13RulsID1ArNaV7TNw4C93MMV841CBeH+8FYFpV0rd3k5S75MJ2M2DQ6NqKPytO2T85Y6upGGnJ5SIDxpCVJXmL13Dx8jlnZ+cA2MazOGzoZhJFIrAOQ46KfhPoV4k83SmTDDDBbnMGlXj+6im/8t//KiHBL/38PwFArR0uATERYsJYKSkpZ7bbHmPld6lcTVVX+7t2q7UgnBQMfY+ratzk8XHOThSFRBoCNmp00gybkXrr0dMsJAQZrKfJ7+Zcw3x2wIpbwjowm/KaKmsIKlGsxMvPFh1jCoxjT1gXlGGPm1I05DTx73YMP6M5O3vBi5fPOFpKMeoOlpiuwtQV2hmcqYAsdoOimC0k8uO8v2A1bOm6OcXAw4dvcX5xztmzc05OTuW5nQpCThFtNMkohnGgoHFNw3w5I8ZMP9kMzl6esRnWzI7mHB8vOOyW6KAYhsBmGPB+mF5vy9XlLeE60LWzSfhQsM6xsI5xGOjXIhhJY8BULa6qqduKIYzE7HHOEpXfI5K0akHJey8UGPwghm+raNp2L1TYrjf4dY+t5GbFZihG/GZV1ijjCFOnwPcDuWS6ZYeedlxDDFhEwGPNLlNtkpyrQlKZ0Q8MJIY48NHjjzibirp1Zl98rLYTA/p1sfkse1A+2Ccp7/79swXq81yw4K5o3S12iiMJPtxFqmMLzmmsgwaDSYUQMoVMyYYUX0tyQ4jkyTxbopa2odVk9J5oDRIGqTKQCyVUFFWzvom8ePyYl89v9yKQw0OFbRK6cqhkUMUSYmHcBmKvMbl5HXKY0z6jSC4WAW0U19tr/trf+HW6KfL+n/z5X6QMQQpHpYnFS+tQZfIUagkCSa1cQyqJcRhZLOdEleRCnDzRSzsIwKIoJUPMpBFUVBhkQL9drWk7+X2sdaQUJHE3BtyUClW7hjgk8jixB21D0BkPGJOpnMPHSPQBM1rG9YibBARG1XSNZHvlEFFFcEeUwnI243AppuHaaNYXV2ilmR0dUNtqapkW2VHUU5usG1iPPVXXYqx4z3JK3Dy/5ipfsTw9xNZTwnEGdEFNJIjQB7S21LOa8WrF8fExADM/4+XFK/phQOdMWxlBfkUgZ/yk7LQObKVZb1ekVJi1ncjyVUGjcLPZPqxy6Ef82LOlp5211K0TFZ6JKP1GHlXONF1NnwZUEOFIiGJTMMrSTAId02nGcSSOAZ9GYh8w1oiiMMv3qSp5jrKBcRxhA01b0zYNm7ihkAhxoNaTuCMn4RhmT1KRIY+c31xxdvWKr339d4glTO8LRSmiZDV6t4993fp7U1CxMwunlD9VlHYijB+mNuFd0fphX9OFHq1hMggDoBUheKFKl4BSgqehaChGoiGU7CRKyqQovL20GfEJ2oVG1wpj3J4gkSLYZIUJaGq2Q2S1umSzjiilOTiUNlnVRWKO9P2IVo4wFLbrws1FIEVDYy12Kq4pR4oqGCvG2VKKyO214mL1iv/6V/8rAFRJ/PKf+kW0FfrGMAjV3uAAgzXy2MpWoB0FRd/3dLOabtZROcc4jCQfiD5Mj51FdVgUIWT8NmC1xRTFdr0iTWrIpjqCIvOvEhUpg0FTGYcPnmGiiNu6wlYVhYLTUOkaU0ZsNuQxE3LgeiVqv262wDmDUWCbmjj2xL7H4ThaHGGm9mAaRvphIMWMQjM/XAqJpCiUrkiTpwpnKTkSSiKXQmMbZu0MlpnNdsv5qzPuvf1ADjVWLt5WoYpDGRjjgHIKW4u3CeDg4IB363d59vIZn3zwhJPjAw4ODqltC63d2ysUmqaqGDY965tbKm2oupkUxNGjgPmBFGFltgxbiakft1vCKJErStUYZ8SaIe9KrNZUjYUMQ+8nAG5NyoXN5NNqjGM2m+G9p9/2kkBcIJSEjwkLuMlvZ40hlMSw3ZKCp3FODMVaEXvPOE5tRJS0/irwJbIaN3zw6Lv8P7/9f/H1b32dUOT1Nm4CUCuDKtNN19+DHbhjC+7CHz+rGPxhWndF626RsweErFDYtSPUBIjV1G1FzIk8RLxPZCWy9nGQ7c4wRJyraU2NzwM5RRRGOHQ57RseSmlINTlprlcD/TBMrTrNctns49yhIMi/TI4Rv1X0a9ClnWZXfo/rcVak8DF5UooTAS8L3shYrlZnAPyVv/bfEMYt/9g/+vMigZ5Z+s2WRbugZEOe5nO2bijGoGyFVoGLq0sOUuTo+BiNpu9H2m7nGSoUpWi7Gaor9KOIVcKYKBbWNyLprm3iaCYtqZI1OhlBA5WMNWZP415vtmjbULSBaDDFkoaIDoqUC9HnfXpwv1ozm3c0bU1JGVUsCkcOhbgN3N5KcSshgjH0Y+I8FaKPHJyeYKZWVpl6fqaqqayIReKY6Iceh8E4g6sssSTOXgjJYXl8RNVWIoaY5oPKSeuzbmtutysAVjcrZvM5947uc7O65up6wxgUpycntPUMZ6b5aSm0FaQxcrm+Zn19S6UtXddh2pYQA3GXQdU4XJZWdqUN/XZDHKSw6drJrhlAeYH3KjDOosfIGIPglpRinKJtcsh0naapW0FqDQMlZaw1FJOJMezZkLYIeDn1Eb/Z4ppGHAhaYawW4Q5AUZJDlyWX7dmr5/wvv/U3ef/j7zDGYZ9QELOwGp11OO3EA7k7A95AOgGklKa/8xnhxqfbiJ81G38e113RulsUCtqUqccun0sovBdKdz2zqFKw1khbLhsJ5+tlOO59pLEtSsnMxNQWrWXWFaewPADnKkiObT9yeztQSqJuLQfLlvmypZQdBVuTciGN4IfIsIbkK6zu0EYT4oYYp7tVYwS5k7KQN5QQyo3TxDTgjGB9zq5e8Ku/8Su8OnvOz/zMT/Pw4QMW8zlaG/xYqBrZNW5Gz8y2cqFzDX0/cntzgzGGr3z5y1ytbrm8EWXe7GBJLGK6rtsGnQoua6piCATmjbSLZrWlNpByoR96hhBpooNcqGvHMM0Rb1e3OFto6goimGwovpDHhGkq4phwVn6fTKBfrylJYjBM0aQopuriC0y7mO3NmvnBEq0Uq4trYioo45gfLtDW7oM8XV1Tm4oSw//P3pv92nJfd36f9Ruqap/p3kteTS1ZtmxLtibLRndgw27LTrsfAuQtDxn+gUb+iAAB8p63PAQNdCPIS6OBIO50t6F2uw1bkyWKlExapEZKFCWKIkXe6Zyzh6rfsPKwflV730NSkt0IYl2eBZw77LPH2lW/9VtrfQdi9KRxIjhPv+pw0QACr92xmePd1+5w9tgt+tUKrx1jHcEXBDuHQrRl5eLC2n1nN2/QDSv03l3Wl5e89NIr3LxxxvGxVbfeWcVxtjphHEbW6zWX8YJaK/1qQLwjtxPTdZFQbb4kznN2csI0TUxTZsplfz+XmUKHZE+QwYwip8o0TXSrnq4RxKftjgcXFxyvVnQ+4JxbZkax68ALY7bzsqaJSKQLViXWKeOCGV+azJQliu1ui6CM08RzL3ydT/35f+D7L71I7CLqC6lVwblkUp6VY6RVwHux3MOkNesJOic4Fx4iIh/Gm824HrW4TlrXgeApRSkhGfABw0qQA2nbIycnxDCRpgucq0Ci1r1NgkfRWhjLSL/yZjyoCU2FIEItTYw2KdNOOD+fyKXQDcLRsbA6qigXi7it1p46OcatMG090whOOsYymXdWsB0+mIty8B7vpFV1bR4w2cxtnptE77i/fo1//5/+mM984c9597vezXvf8z4+8dHf4vd+5w8WyRsfeiDgdEDVEalsdxc8uLtByz2zitg0CH8s+KOerMLF5gK0QN/TrSLBB/rjmcCamHJl6DpC79hsztHSU2NFhoHQjs/u/jn64y1dN7LTSpWO4fQxUoJV7xmGQB5to9AFR1Hl4sEF7swTfUQ1IjIAlb7NYbRCmoQuejyV7fklr+SXuL19F4/dvk3XACguHlElmzRU75HsmNYTU870sWOIkcdv2qzq4sE59370GkeLNEnrAAAgAElEQVSnN+iPT3ChR4uh7xzK0OSZdusd47jlwbmyOjnh7OSUPkbu3rnDa6++xu7UAChnJyeE4On8wGn3OHUXmNaKlolSAr5zi49YcZUSwB/3bKbMqlvRr1bodiTtxkUxpBSHhOZL5U0JJcbINE44bH4IIGEgpZFpN0Fnos+KMuVMFaH2nsapJqeEjok+RLx0zfG7KVs4MxUFKDGzy1tiD+PunJd/+F2IGddU7N3cbszOkl9VUk049sloVndfrtHZFNKZrc4MVkFADzy6qqqRVx7h1uF10roOUCHnRNeH5odk9J+SlPV5InQjq1OD+xZXqTlRKsv8wIlDpOJi4fh0YDjq2I3FFNWrmOIGZs2ecqJqxUfh7Ebk9DTS9aaaMV94aVdawnKk0eFch+DIZYuS6MUvUkFaoaj1+anW3HTGwMIRELGLX5uT8pQKd+7f4fz8km9+89t8+5vf5EMf+jXe++4P2OeerF3qMOFX812K5FS5+9o9c6FtrcnddqTrIhKVmgvTaPYS3jm2mw25WIJJxVpVtx97jFs3bjBd7Li4uMT7AYmevm8ixdtEzImQlbFkXD9w62hgO25JKbNarSg7W5Q36x3D6ojgAhfnFxwNJ5a4vFLrjjiYIOyw6jlfPyA7JQ4dWivbBxfcyeAZOL1lnCrft3mmj6AQ+wE0cHn3LqUqQ9dxetJcjsVx78El2+2OsUAcIi4IQ4w4Z/5nYOhKcZ6cExfn56xOTgghcOvWLR7cu8+2Qcm1VARhNRzha0cXmyVL8wOrU1kksUIMzbcrMJWJst1yPDiGo2Ocj6Rom4+yy6Aw9MNijLjMhVJmat9NDJGhH6jVqpspTTYXpZKnHWHoiHEWzPWU0dyxXXVMu50pVXSKVqXMWn/Ooc1u5KVXXuJye0FiAjX7Hm2taAmyvC8RcwO/WmE9dJk29KA92FT0tTXE31BbPcLF1qMrUHUd13Ed13Edj1xcV1rX0dp8Rizu4t52fpy27HYT9U5mzI7bt0/xvpIBjzAG21GLV3yE47MVcfA4jwmQ1h6toPOu0YEPIyenwmq1YnXscU6pCUoR0tQsOjZC2jnK5KCYWodq6/2LPrTj3KOqysOeQ64Jnc5tEpXGaXLWDlVPjJ57F3f5wcvf5wO/9EF77ZIJLiJa0JLIZdeIo56UM1E8tcHjXdt50w10YcXucqRmIfjAxQMj/gKcnJkv03ZTGYIi9EzjJce9o9aMawi+fuWoa8X3EV8gUQzkMUQePLjPLk30s0/W5YaNbjk6PmGbRjabDb3riWIzPmntwf7kiCNXOF/fw3eBznlyyawfXPCae5UpG0/r6HTF6sbKdvulosETVoHV2Smbi0suxx0nbQY13DjhNAR2UyFVGKcdMhXC0THHsUM7mxcddQPaPNMerNew3eJ9IIbAjRtnS6VFVXwwUIXkS4oqse9R50hpR3+8Ymwgh3E3MfQ9IYAkM7mUVGFVCM6zYOO9sN1YNdX3/eII3Pe9gRrqrDxhVY6JKCulZkLw+CBMKaFTprgGAhFvAJZq2pQlJ9MDHDO+60mpifCWkexgVyrfe+llNilby/VKiVCX+VsTuG2IwEP34kP4e60mvFtnzcG3KKfsan504zppXQeqJs45TYnYFrvYGf9lShMlF6ZdQYtBpWturbNgF2m3qvRDoD/2qBZSKWb54aNZeTTkl9dK9JnYB7roG/lYmJIyTdAQw4xrjBRTo4EySgEq3hmYIeXMLGFhiYr2cyBtU6Upjs8LwyzwI9QqdmWrIqXwl5/7HJ/4jd8B4KQ/oyQzI3QSca4juIL3gVIN8TUjLFdxIGs1qR9/REmOMjpCP3C6usX9cwNs1FUgVSFPlTpdEuiIcWh26hMz6UylkOtE0UA86hnHHZNUJHj6oxWbyzXDyqDfR0fHXKw31FI5Go6Yxsz6cs0Qe1arY7Txr7JAf7wipjU5JYbO04sn18rm/AGuLfLjdsM0nXBy64yu743q4MzpOJdMqoWxkWxDCMTjFZNM9OIJyTNuL9lcrCl+xLcGTnABFzy+i6SqXI4jMVYkdHTdQGlzyWmc6LoONwxMuzUBR+g84gPslGlKHB+ftPc5sr0Y8S7hneCLY31+SdruWA39wqkKoSMEbxysnMw/zXkDE9Wy38y0819VCSEgLqBacM7RhUhJidzmZFWMbB6cA6fEYHPUkhKTmjMCwGaauEhrXvzx97nYbSkiSM2LZ9t8ns4brX370tLN/P/D+y6bs1rgAJzxpqCLtql7VAEZ10nrOlA1Tb6qlantaH0MxNAj4k0fLSXyJPT9gIrp3vloF9bgO7zXBcoLzjzUmV1U58rEEY8gRqg1UVMFImlUdhv7AdBkCUObMZV3YmrcpZJTQbPi2sJoygayvNYshVO14lQQ2e9KS82ognfBkJKawUe++vWv8/0fmnD+r33gMURdc1lOOIq55io4F8ELZVHztoVufbEBL0h15EmhBlbdKfezQb8lR2oRfB/YXCbOjgdiGMjjRAh1mT/UOqGa2e12DMMxro8krTjniEOP326ZGrH5eHVEypU0ZfrTY5wGyrZQpsLJzTOmrs38REmTGUvmcTRFdOeh2BwmN0NLLZWaK3msnN48w3cd3pun1XB8xG63Yawz1N8hPpC1UqtyenxiZpaXl6TdRK4z9BuCD4TQmedU84pa9Z6+64gNuHD/3j12u5Gbt24g3lRXnA9MUybEQE1G9AY4PT3l2B+zvrxg3O4saeRMEWGz2ZCSLWnRa4P1e8bR7jcMQ6tkvFmagMmStblSqdn83vIECJ03J27nW5JJiSkVtFnz4DJp2lFK5mJ7yaaBfvzRwA9ff4U//vf/D9/+3rdso5NzQ0k+XG7NSWs+YFcdiK8quYcQCCILwvCqnNM+2b3Zlf5oxHXSug6g7cpU2LbFoYpwcnJM363w1SDou/XE8eqUxEiuidDtPaqKVgqltQMVp55UMk7haHaT9YJ0ufGoFCeOkh15rOw2Sp6alJKL7XlMiBc1RYOSsvGC1C1Q7f2OciHoYLtVQaRaYsIQjjjjumRXUDI+CNlV7q/v8PyLzwPw4V/9DWoSqnobuNPjnO1wS610XWeKB1iBFL1HEg2EEQk40i7jXMA1eakyQdd3SEuixn32eG9E69qylnNC8JZQaql0q45NnfDOEbxjNazIu5Zk+sjxcMT5est2s2UVV6z6gXFtCXZB2xUTB1bVRg0oRB+pqgTvmBptgW3ClcAubRgvRk4fu0m/6oiDxztP1/dUGi8vTaCZira2aaLrolnChC2pnUMpTaRa7UA5zzCs2Gw2jGOiCz1Da3WenpiXlqrgQ8+UR1axoxMjMXcxsm0AlPXlmrNwwlE/IAi1ZkoMVvnEuIB5pjQZiKa1BHPTHxQx2Li241NLwXtv2oU5tU2OOWzr4ks1w+0DUy6MyZKgamHSHaUmNnnk9YtzAF767is89ezTPP/C82ynDeIMpCFNQPpqglmqqGyJafbSOiQQe2+STwpvSHxvUMh4pJuD10nrOq7E3OvfbUdUTaet6wJBerbrxHhaUHV4H4n97ArsbJSgnpoh7fKiZHB6NHDj7Kg9e2abbIevRalZSLvCblNJWxBpEHGaMK8oOWVygwWX0hZ8/3D7RLURl9lL2tgEq+L8PE+rxE4QL/gOuk4IwZHyFvGeb73wFQC25b/kuL+BIf+LSfr4nlwLeYLY9dTWLhIVXJVGwu3MHVlht9nQDyuis0pi3Cai7yHaQjWlZPw1rCXURmR2TKPDqZKzPUabXJamhOa8jGxKLvjQseoHLtdb4lEkhsjERJomXIOdl6J48ajzVOfItdD3K0KnTCUtoseaKnU94WpgWu+4c/kqw/HAjXeftecOlJl4DkyjCdH2Xc9uHKlEOh8omMsxQHWueaBlENcMOCO1FNbr9aKsH0KwCl4F/ApxsJsqq9XKCMwlM7R25267ZZ2qof5WPTkHUkn4LiIii3cbms1lO7jW/psrqWAzoYOE4ZzYOaW2cSgFai3kkulCR8lzhVmsctVkZHbNSFQut+fU4HnuO2Zt95df+Ct+8KOX2E1bnFObjy6v5d40aeXcpNAOzB8PZ7T7Cow92rDNvQ4rrn08urD366R1HVdivpCU3WZHKUruPcEVcJnz+5cMJwKeRVfQUVGBqsFIwdVmT75JP5VqO+9SLBHOM6hahVKEaaymNN8qk5Q2qBZKqeRs0jVazWFY8KBusRyxqsK13aW2Vps06H5e2oOnN3puv/sUCRl1BRcEcUquFV9W3Lt8AYDX73+Xs/d8mJIUCQ4pglePC7EpZQR8I59qrWhRk25yjloypUnxOPF42QuoTuPI6mhF1mpKH7UiLdku3LKiSK5oLmj2GFda0FKQqlB0mWeUXAhR6LuecZeYpgkfrapIYyYMrc0rZm5I8NTqyVMmayX0HeNmQ5C5zeoZz7f09Hjg4vwcUkZiYVWO8KuIb8LHXddDzWhQe2/imKo5UBcRI1wD6pwt1mKkWa1CDB2VRC2V9XoDtNmXOEoGlYDzR0zTjpyUYWWK6vNctOsCeZxMViwEJAjORVKuDKujZdOVxvt4ByFYFVW1tErKEoBvS58PHm2VlSUC2/zkbFVpcYXU+IPilFQyRcwA82Jzwf37d/nuC9/g+e+9yLPPW7X+0quvMuVkWoi14Jzi4x5YcVgZHSYm51yr+vYmj3MymhNVO+WX5LcX0r1CMn6Eq63rpHUdNOCSDXDnHnnbqdVUGDVTQkGccvfuxOP+mKOTfmlV5bFQVJhqR5q2UDNdNN8s3LS0HJWK4pimSk5QS0C1w3klJ5iS3S+nkZKL4ROcVTTzZTi7ur7BT+hQIVvstZDC8YktADcfDwyrieomcyUW27WGPhMk82D3EgDPfecLPH77JjGeEcIRrgRkq/Qx4tW0A31pqvUqSFJ8sW6lwyR5csnkNC18khAjOSX8uCJKZwN4jahA16+WAf44jZSSWTmH1kJJEw4l7yZOhoF4dML53bsA1DHjVkJB8TGQp0JOiU4CU02MeUbOxVahOmLoyWNl3I4cH5/Qiac2blHJiSklLi6rVXZ5wpeOzcWay+2a4XjF2a0bgCWtjohEIe3GpiKhpJIM8DLD5No806tV4sVV8I4pWbLNafYwmwzV6QBNiAhD7M0epus57o/Zbi3BRYESASdLherE5qXjbkfXFPj7oQMtFM0gEPtAromi2T6fzue5zXRzNS3KUo37VDWTi6FSy5y0BHZ5R5XCKBN31nf5s8/8KU8/9xSb7Y6GK8HHQhBTCBEF35K3tgpv0ZBuVZMTZ6hNtQ6DUpsm6J4sL9I2ia0yLlrx3lCtKFSZARulEYwfXffi66R1HXtUuM5/zP+3mZBWRSlmvVDVPKBijzbzomka8Z0pSJBMtXvohVXvca4eCMxCVU/OUIsHDTgX8b6S8oZpbDJO86Uq5n+FmoK892EZmu/Zk3Lwc8i8LMRBufW4wa9vPd5TdE0pY0NXOSpCThWJCW3D9s9/+c/ZjBO/9dHf5x23fonoj/Bdj8ZAGROuHgqoCponcjU1cucc0XtA2U3jMvvqYg9aqBejmRa2hWaqpo4emw7gWArV2S465xFSYlj1jLsRqYaImxFoZUxQKi4GQhctaxbQXIDCNMOpdd7hC30YKL6SpglfocPtbTfqhIRKqmsk9Iir7DZrfH9CrXCxu0Cbtu7J6Smx60x1o1PKOCFqAsLSZqNg3500pXRxgjhbwMFkt4K35ae4alUljpwnHIKPEVFh3GxZDQOreNSO0RaJB1VEkxcTqex2o3l9ASE4Si4LRcI5q9JSyqjKMutctP6oiHON3FtBLIkLGWnPWXKmuEJ/3PHNr3+Dz37+M3zxmS+xyef0/YDMliFjNoBNtUraIaRqiER3gHsvpZBzNtSiGBIxU/G0qkp0uR4XEnGdi21zWwgh4F1A4nwtTo2o/+giMa6T1nU8FFdbF+1aQTPm7OpsRjOtEnFuF8UOH9vuPBr8fYgOL1BzWWSccjIofKmyDMlLpumpyXKZXWX9G3LPHSQsHurXX33PM4Kwi4HV6rjdSchJsSZWm4O1RXTcmWI3wPnlPZ548rNsLxO/99t/xO2b78UfvZtUEolMF/vFHdeJUBCT5vGKOMUHMeX3aQ8CcR76ITKdT2iFbuiptZLavM7PgBbnGFWX9k8aJ1bHRyRxpDGBuAVubztxa4eKOLroqCVRtJJLpuRZWT8bRLsdmxA8abS20zzcB5ullGLajbHrGJxn3JkVS1gNTGnk/L5B+HebHeIdx6cnnJycEGNnnmKlUpIJFbevj5IzNReKVjS07z1EcioLWCSEjlrqMossteKrVRK7aWK3GxmaVqAPpmtZG3Jx3qjYvGwvfeQlUGqiZsX7QK2F4DrwDhHIc4sX42zN+oeoMKVs7UJvMPa5MhrThr964vMUMk889QWe/cazBpKNQk6pvZ+Dc1DavzHU32FLbz5vD2+Xum+d76+Dt27zze3D+TlgP996Szj8IxDXihh/j0NE/qWI/FhEnj247V+LyNPt53si8nS7/ZdEZHvwu//9/793fh3XcR3X8f9NXFdaf7/j/wD+N+D/nG9Q1f9u/reI/K/Ag4P7f0dVf/M/5wX3FczeAtxg56DFABY2V5Jl99v1xzw4v4/mHcPgiE4IOPKYKVnR2lpUxaoc73zz7arUkvDB0XUd49jsIortwp3YjEiCIOrecuf4ZpWWocCUXbNPiX0gZev0izMU4axS6D2Li24XHKle8rVvPcljj5/x4Q9lhpsnRNejvUNF0GZWmWtBOodkj9ba5iPFVC48hHlPqIWu68higInQRxusA1K1KYRDdJ5RbV4RQmTKk3HBnDcEm1oLC0AaqKJWqFirTbVSnaLeHgPYzCoYsi5PCa2GsEwpEYNHWmsSZ/wz70x93HuH845xyriQIVfyXDm2ajWPEzVlE81tAIKpFvqmE+jEmT0HRk7WYqATHyO5sPDdnJMFFRqcWdKUarQBH5yBKMrcOjZVCHFixpdUas2mtNEFyoyiU0+QYLQF79Babf4jVp3Mu3URDGjhPFDAQaVQtVA9FKlcrA3K/tXnvsof/8m/4d6DO4x5RESblqA2IdsGmijze2h/yF5j8PBcPQRc7FGw+3a3NbyvACwWsMjh4/bQ+Fld46rg7qMU10nr73Go6mdE5Jfe7HdiZ+l/C/yT//zXmZ9zho7Pf88Xj7U0cjL7j9hFumhyPAA5j+Q0cnwEXWdtJi2KFnMxnmWPtCriayPu2tBYmkpBCH7fCNGmVu1sUehDD5U2j3hYTPQqJHj5u8J2nbl/tw3wO1Mjr1qgZgTFUQ3l6NwybM9lpAuB9ZR49ltfols5hniTd9x6H851hsxrrrc1VYILlJJwreVYqhGvQwgPvccQAj54g7tnaz+hSskFN/swtYSi7XijQKl48VRRWxTdnBCEnKuJ+8aAFyHR7GVQaC26uV0UnCNNE1oqDqGkQt/FZZHPxcApIdptqViCdBQ0FVxVUvOgKir0K+PerR+cc//8nIRydHTUlFRa600U8bqorY+loDUjPtrnae00Lw6VhjFwmKlizmgui2hxbvPBkjPqMh7f2mp2zLVoczumnZOZ4Lx9J6WQ26wPbJ62CEPXAq6iImQMEVqd4vrAs199hi98+Uleu/NjAL7xza+z3lyQa0acGkioJdxxnJZkNXMLwegLVYy8XxaEYvsOmxvx3tzRNoQNd3SFIHw4u52v1z3t4xAm79ps7lGN66T18xu/D7yqqt8+uO0DIvLXwDnwP6nqZ3+WJ5qpuYcXlIgzDlEbG4iIEV4Hz42zFcPQUZv/1Xr9gKPjwOrYPLhyVoooJTtKtYoGwHtHkbS8roiC1IUAuxCGpUC1izp4m325sF+A3hTi+wYFAUHVsb5sat53HY+/85guCFPeMI2jwd6Dackti11NbNOa1XDCj17/Hp/+wgO0rPitjweO422iP8a1Y4QKWjJCwHsDY5iDs4FGZpmi4KORjb3H+0rJhVQSlUpJaUF6qVZzsZ2lhHCNkOzsNtEFE9a52BL5xCpG21gA6sUG9bNpojcNRucNIpqmiSCuVSy6eF+JazMY55YKwYUAYwLJBHELT8spZi7ZKoVcRy7GNU8//RTOO37ztz4BwK1bj2NFqXHFXKqghaSK74Q0NouXmtuia1Wnj75RHsoM71tW8EpFtRgqLziCBMo4NcSoLgToXCsG11HINgMbm62LDwHXqsaUd7jgwVkiqyi7NLHZrnn6uaf5j3/xqaaQ0TYAWghdQJw0pZbaEszDVZSq2kZmdkJwtk2az9X5+UopS1VkXYKHFV72G0qxc1TePHEd/v9Rr7auk9bPb/wPwL86+P+PgPer6h0R+YfAvxGRj6rq+dUHisg/A/7Z8v/97ct9ll1stgvViyIObpwd8c53Pkbs6uK9dXY6sDrqWI+XpFTJxbbMkwHc9u0M5yk5G+HS2UWekpKyox4I63rn8dGZrcTcfmlD7TcnUj4cxmlxCIE02eJ0984WF4Rb71ixGo6pu2RQ56niPXS9JaJSLaHouKaLA/cuXuepZz7NjRunvPvWB3ls9T4GaXYe1VNGheJxwaFVSTnhfKAWRylzG8eTJkXE0/WuqTNY5aSlMHs7q2rzf3K4osbzqop40CbdU5rNS+hNhaSmglMQrTgPKhV1uhBiu1VnrTW1BLbL1vbyzrEbR4ZWMTnvqTotC2Wp7fmKyRqFLi7EZqn2vjWbhp+EStWRL37xMzzx5Jf4hV98PwB/9E//iE9+8g947Pbj7KaRKnVRA4kh4GYNvmxSVkWthTgfA9RagVrqIvkUnJiLNkqupbUVWxI4EJOtZFBrDUKr+jphN+7oUGiVdXXmZZVzppOObRp58Qcv8u/+5N/x7HNfZRzXzJBNEcFJJQbBt+ujall4aSzfIwf3twSijas2f89gnYPUrFBmdZQ5aS0bxpnQveTtPfhkvk5V9S39tx7FuE5aP4chIgH4b4B/ON+mqiMwtn9/WUS+A3wIeOrq41X1nwP/vD2XHs6xDsN7b/BlrYhUuuB47LEzbt06ox8qPhgMebM7Z70+pxRBiSCBKTlSgSkX3Az9do4owURaJTVCp23YfXD0y4ws4p1n6AdyzkxbQ92llJZK600+03IBq1rLryrMmMSUJu68fonEzK3HVibtUzKilVIr42Tv0XlMlcILWY0c+vKr3+Fb3/kb4gdO6G+e0WUTb+1qj2QIBFAjFwuB4DtrtbWCLBdZWmbee3LOVkU6Z2aW80LbZHrUmzstKZPGTD+YOWDKBT97O/mIlkQMEYdbqpKsxb6yA8KqYLp53ntLCNi8aZemxVUaJ5RqZGsRQWsliCW6cbuzOeS8gJba9EZs4pJ0x9e+9gx//fSXuHvvx9y7MIfjb7/wDT79+b/kk3/wST76sY9z87Hb9P1AiEKtCcPDgws2J5umRC2mYGHfczs2hyhB7xEvlJY4Z8TeYZvNPnchBmcw9VoWpKYLjrHumpkp5DIh3rHNIw/OL3n+u8/zqT/9FM9+/Wvstpd4X5dWr9baWsO60Blo2oXO+yWX1Lo3YpyrnnxQic2zrFKStSqdtKQjzEX8VcHct5rnzhqEV6+FRxk9eJ20fj7jnwLfUNWX5htE5B3AXVUtIvLLwAeB7/4sTzaPfEXUZj7YIiLeW3uqATG8d9w4PeLxm8c4nzi/uAPA7nKDqpCyJ+VKztigvZrqxQIewOwZVEpb9IQCTClRaqBbWdKKPkKrOnKbY812EtIMJ/fX4yHJeK9ubURMljzsRHBOGbeJ9RqOjgPBCzWZrXwurV1UAQo+dtRacF6Ytmt+/NpLfOSXR0Jv0Gew2UjveoKP1jNzjs5Ha83kvFQSedxRHEQXUGe7ez8vZoZI2H+UNldyztpDaZoYhgGtBmWfF9CKreXOzZp0ZqRIVWvfzd9ttYqtTAkfTaS2pAwINSvTzqpl54xPxEyA1UqtBR89eZNxxS0cq1orRSviPbkWLnbnPPX0k7x27xWKTLRTiLTe8eRXPs9TT3+Rd7/7vXzwQx/hox/9GB/9yIf5hfe9f+GcjdNkXMAqHA9n4EzkV1WNp0fjYGEKF7EzFQsn5kqccqYWS1hzxeGDMgzHUOC1V17jueeeZVgN/Oqv/ApPPfUUr776MgDHp0fcu3ePbtWz3q558itf5uVX7Hdd9FZRzmocvZloWiuwHWMnODGAx3yuSfsCxLl92/UA2OTbvNG5gkg5aMXrsvkCHko8PwsI6fC2RzVhwXXS+nsdIvKvgD8EbovIS8D/rKr/Avjvebg1CPBJ4H8RkYxNnP9HVb37U18DweNRV1HZz3bUF6rLVD/LEsHj7zzjne+8gdSJi/N7bDa2eE+byq5Arj3TONpQWW2sIsCsuaQFJkmNAAsiEZxDYrbdYlvtfLFZVq2VcTeSczFVDDXViQqLPJPtMhvH5rB1ojbEn1UTcI0PhrBZj9Ra6Ido3lmNnwOwS4mxKCo7XHA2E9LK93/wPN98xzMcDTd4/LjtmDnChZvkmvGp0PsOqZaEyjTiw9z+2lCl4E5PqeLQmo1k2hCZebJFMcaO4LBk135KTuQ8mYpGrczzweIgo1QRUin0oYOScdWqkNnuRHIh0JQYk4kNaxVqFlz1yMzn0kIQ005Ejd5dSiJhJOg6NSkpLFH6GBjzSNLKV559hr/+2jOMOqIh7/X/RPDigcDLr36fH77yMp//wqc5PT3jve/9B/zyL/8yANvtlvV6w7RL/O5/8Ul+73d/j9VqRS2FMWe6rqNhd9hMG2IORhQuNmt67e5rvPji93nH7duLIka3Cnz1W89Qa+FrX3+OP/3TTxFC4PHHH+Pll19mmswHxwfHbrcjxrhULX1DQqqChH65VmwzZDsLcXuwjK/eWtgsH3vhaCm089XmdKos1aFrKFppG6rDuFotvUEB5qfEo5y05FH+cNfx08M5pzFExBnSa4bYKjCUQFkAACAASURBVII4Dy6gObMaHL/2oV/k/e99jM3la+y2l1w27bipKCqRi1FYry+tVdLgzrlVRwB936EuAU2k1HkER0oGTU9j29FKx6pfUXLh8vzSUFlqQJBalVoz+/nBXhn7MEQc3jvEtQXUFboBTm50VJlQLYTOsRoC/TCgrVW1SyNTytCIwjF6YhXy6JB0xPvf9RE++dv/FQC/+dHfJsqKPFaO44rj4Zi8mdCpEr1ndWQL6HZ3iQSQzoOIzZsqRnpNtSHbYOhXCFbt1lqZckJRQjRVdhd8k7SCzkemaQSa2kPfUYol/2maFrTdyfGxmVXmjBZIU2IaJ2KIVGdK5YARaoHVsEKxuVGIEU2mro+WBcigAuodWZQfvPoy//Jf/wu++vzTiK8YbLwl61rxIRrZWD1OAjavqa1SaiaZJyfknFlfbhj8Mb/2oV/jE5/4BGc3znjw4AG3b9/m1q1bgLVXT0+Pcc5x584dcsl87rOf44knnmC1WnF6emrPeXbE9158YTlGVatVimIKGjnPNjr7uRDwUKutlIxIfahVd9U6BLUZ3dXHzveZW4SqkHMhpXTlNcry+rP2oMlM6UPz279LpdUg9o/cYOu60roOkD3KaX+KN9SfmlL244+fMQyee/fvkKetLbSu7UKrst5MXF5WUobYOVxo3K6al1ZVqTPHpyHdsNmINhmkuf0UgxlQjtOICzavqaVQy2Giau9S9m2XObRBHqsWaO2iGIWujyA25BYnpFRJeaLPlb7v2msPeF/Z7XbkUSmTQaCji4QOfvDy83z2i38GwK1bZ7z/H/wKBM+EmDJ8Nc+vW8PNhc+FsxnXOG4IoTknN9i9ooQ2VwoeUlvYbFG3gZ9zzrhrh8m5HVTnHEpZOECu6dxdVQqf1TNUDWpeSsG7snzvaMEEidsscDbhHDMBh/Oe1PzSpHNcpi27mnnqb77Md198obW05uTQEKjOteNtsynnzecMMZj7Ul1IptaJUkZ2ufLXz3yJp//mSQNrOEfsur0ifIw89thNxnHkzt07gDBNI7VUNjvP5foeAPmHqbkBGECj6wwOr1rJ2eSzwFq8KZV2PkkzZmyndTUZqKtWIFfjEHI+fydXEYIp7f2v5hbmnKT+tnEVpfh2i+uk9XYPwdS6tXDoxeN9G7srDEPgsdsnxAiby0u0JhM7bUlrTJU7d7Zst4UYA2ghqGtit0YABRoMOxoqrFTrYGkmF9MCDDMpNTh22x3jNBppVpSipQmKugVJuHyEN2uZCI1AbNH1Hd3QUXWkKvSxPW+pjGMhF6siuq4zYi2xJRhFGEklM6VL+njMy6+Zmvef/sUf8+EP/ga/8N5f4v3v+XVbmJ0jrCKZQk1tplXMiqWoEoPHic3zajF+URdm2aPS2oEZHxoPyZulx5yIlgV0LiCdI5c5abUWqMhCH7DKppl2uisLpFb8kjAdVRvMuiioNy4UnpoTGd3rFE4jr1/e4S+/+Fk++1efo8zJTIShH5iyzcnmeY51Oiu1jE1z0NCBJbXzogRK3qF1Mg8253DO/NiCBMpuwrdzQ3Pm5R+ds9vtlmQxoy5NYLZtfDrHfAIHZwRlabNOcZCaOLMlr9rmSuaQvZ8vVWYD04cO20GSOhibvuE+h5D2tJhO7r/DEMJDVdXbMQH9XeJaxuk6ruM6ruM6fm7iutJ6m4fzwuokmtlerUulVXNp7Sbh1mO3ODmOlLoj5Z2RWqv5YAHcvb/l4jIjBagmnCrSId6QaMtO1DSUDCmmGLE4Z2p1OLH5EVg7J5WEtHZSiKHZeVgV4XijpNPhvME511pDlbnX42NAnLNiT4RcFJxxoJzsPYwMdm076b7rTC4Ia0cWTSTd0Q3WSvzOD77Gt1/4JrduvoM//N3/mn/8O39IIAKe3bSBfKAuIkIMPcFFU8TI1sLUUpDGQdID9XqrIAyUllJq3mWKm/eZZY80K1NGxKrjQ+QZmDKENvK0d57iCq7dT2biLgaBr0WpuYAKpWa2mzW9OIqYCvymmNSVHHm++MwT/MdPf4rttLXPFiNIoWq2art93ab0UO18ohla1rJUYwDjuINaWwvUqv6U0h5qfvB5VAUlgxRmQq9zvs3+wgJ+mVtxIqaK7r2QcmKW+DpsAc5/z23l2b0Y9vJLV8+zpbrXgzmwPvzas4p7bQCaub07twQXJGgzhzx8jTeLqy3wv82c61GK66T1Ng8R6E88vfZ2MbbzPY3KuM2cnZ5w+/YNtuN9ajZCblVBJHK+thbL669fUEo7mdQWQFFBiyKNNAnG5xQ1AAYiKMW0DLWJKzSOTdZq/BcnJs2jshBynXN43CIBdHVQPZvoiWOxa4CmiDCNqFRc8A0Sr3hv8O4Zlu/FU3JCa2Xcmfuy+HY/oJDI2OIdVx1pqvzw1e/wnz77JwxHkd/69X/EuNvic4cme86hP7ZFnUDNjjxBnmxmJS1Z2OculNxcaZ2nLGoLpnwuQBVbZLU9xtpPFRFrzb5xwdqrKhxyeoyL5Ral9ZIMiadNmSQ3wIB0HRfTJZd5w0t3fgjAS3d+wBef+QLn032cA4cBLWgzupnYnBuyU3GImKRRSbVx89wCQBm3zdTROYOtNwX62bTRFOhn4ESbcbZkM9MhYgw4Z5qKAM53aBVDRQaPqiFELaGwDG8f7izrAm9/4+/aqzu3JK0QAtM4LQnk8NiWUt7Q8vPeP6TIPr//nyXebK42v483a4/P+pKPYlwnrbd7iOK6SnS2sMycoaNVZBoK73nXOzk+Dpyf74x7BEBks524d+cCgFIE8R2ujiCWtGY2vxYW9KDNsCrDEG2ukHc2r6ltlrLIGZl4qjbipjSIMWK3e/HLon0o3zTPC6QlRCeyzEIQrHpz2lQKTMWiMLXk0Y6HKt5j4r7NqkPFwCIhCj5AYWqfu7CbCt1Rx92LV/nLz/8Zx/2KD7znQ8TYsagZSGVMI7E7YdqZG7NWT23cqtmeHod5MPk9iMJJm8c01F1ucyXNNo8qC9DE+GELOq09Z2kVM81Daq6mnXNoceQ2VyrZgBLoDBzYknNiVwMX04ZPP/FZPvvlzwBwf3uPi9094kqYxi1OjhACbdi4VOsLIKF9X16FEKQh91xDgcJuNy2zo6uL81yd7KsdG5LNi/Whhf3hAp6Tzam8i3YeqlU7i6bjDDx6g7pKo3g4q/5/Uk7ZJw33EMDiEKZ+VRPwMMH8NGTgIfDm6tzrzRCzVx/7qMZ10roOxCnq1MRUZ/2/UrlxdoyXzG67RtWqrFRg3FXu3Z8WVXbnerwPBNfU2V1ohMvaKpp24dozm5dTq6Ksiig4cUu7pJSygAJgNoLcc5fkIMno3EYTaUaDiw2z7dLDrHtoFYf3sgzoaXiF4B3ezW3RA2QW1ZKb9zhRnNOHpJRMAcRxctqjKfL9H77A//1v/y/ec/N93Fq9g9/77U8C8L73/CLR9ey2O8qUqTUTmgNz8AFd1h5b9Cva3JVNPNUAKBjqb2buKngRcs3GQQueospuHE05o8kX5VZRzjqGCzxUTcx4Liy0wnrcEvtooFFfuXvvDi+9/ipPPP0Uz377Oe5sjEyuoaDOoPVd58ljQSmI1oYIPTyG2HeGIGIVrqotuLNwspKZc4e17qyaskXZMJZzRdjyryU3Md7efgEXE/61bx+turQIGwGtidLO2h7s0ZPM5PQ5KbQ/3qQNbb+fkYa2sTjUEKx1j25dvtl2zj5ckfETw1rTtOe0OrY2xRJT4dg7RC+Jkv119qjGddJ6u4cIKg50BcXjmytvdMqt4xXj7g5lukTDRNHIbozce5DZXNZF1ie6jKOioaeL/bL7dK7iXaHWsb2WLaA5O1TNrZUCaUzEuEfQSzFF7tZYsh8FKQ0s7/JyxUt7F9o+iympwuoo0g0FWjtNHKbu7loSnGWBghCD2++4pRrRukH9nVQcc5LiIZv36ipZK5t6wcoZQfsHr3yfb3zz6wy+5/H32bF8x/tOyXpCTc0UsyaCdwTncfSU+TKsQqJSxFp+PnhKNoi2YlVB15tWoI8R1dzmNMqYRlRhykqtsqACrf1o6vCO1marSlWYpsI4Gsl2IlFDxfXK8z94nue+9Rxf++bXeOHl57m/fmBoP38wu6lK0AgjBBEqmapzK3JWQLHDvEDN24ajaBNEDvuWbM6ZWeF+ZlsglUo29Y25Wlf7Tl2w+dbMgdorPc2V2v67t2P3MFH3ajU0Q94F1zYILNXYGy+Zw+redDNT2m9m9vYi+1iMTrUetCAfvs9VRpVrs9D229lIBxHfBHgtIYtjoQSYgK880onrOmm9zcOkfwKzHUbKlmBu3jpDXEWciZOmqZJyZUrFVAEOdnquLcCEiPNCmgopT4Qg9INfKigRpbra5hnN5qIUplRahdR2oE6Q6sC1CoH9LrWU0lTZ3fwBYFlomnVK9Ayrjtjb+wBQLcucbHlog86LN28vMJ4UQD+EpspRqCkBNvAX5xa9vlIzXpSqiTFfUnGsx5FM4sbtU77y1SfsWN58nPfc/lWGesIQOrJmUilEiYzTRN8Ux2MYQObKz1OzUoolphkqHns7lr44xqkguQBKmraUrJSsIA7f7FMcnlwTNSeKq2YJIiaCvCtbRrX5XPEVPPz4/o/5/Jc+z+e++FkebM6RrqICuWbU74+5FlOAMFyLW1qCbw0OsL9nLb43BxJYi3i/gbBqtxQQ2QvCLiCVGT7evDz+NnYcb8V1uuoW8GZttkPATy3aVGAefn9/l/hJj9tLQHnsfJ9J+3WRG7PwzQXaeHOPYlwnrbd9CCW5RsStnAxW7oSobHYXCFbV+DBQ1IFrKL+jSJ5161QNmUdlmkrT3qvWPlO/8INi50k14ZwpXms1Fe7YCV3v8V1LgpOpPGhDy2mxnTJqKguti2bv3s1EZbOlcFpxtVLVL4kKaK9pCUtmUqtjQaDNQIx5w24gAdtyu2YYiVjSWkABovS9gFRy2rIryqiV01sdN9+1YlvuA/Af/uLf8t53fpAP/sLH+eiHP85Ut2w2ay5fu+Ds5Aa3bty246OZqB3kHp05PrVSciXlBJ0soI3tem1ITop98lwAh3eWrMatvUcfDRhRKZQ0gjSwRC1cpAeLPNKkExcPLvirJ/+KzzzxadZpQw2t3SdQ5aDCrIvpBw7wBzOmQ3WJh5OBnWuHMVcc3jt0BnI0pXbvZst6aYmriS7HSIxxmZl6H6ilNpSe/q0TxiHX6/D/P4lQfDhDzW1DcZVg/HcJmavMN/vdPBPDU6ssRqNWyupCjg/Bs91sZxWvRzKuk9bbPFTBy4BQEJ+Jdu4z5UumtKaPQhcHUlamNJIyiIuIq4tcj9bmY6RuGZqrKjF2rT01ty4sWdXm75TVZmDdEIh9WCqg2sRatUKhoIXF1mF+nvniXl4bBWew6lqUUiZwe5UNlWrdImn9KrGEV4HK3unXede8mJRcMlqVGG0WpMU6nLMRYx8DQy+UOuEGqwiOToXT047qR3yDsr/yox/wjW+/wBP9M3zsGx/n5o0zXn31R7zy8o947NbjfPQj5j/18Y9+gtsn72wzP99IwXWZnUwlcb67BGAz7ag1NYuXalJPxeSttApdU62XBKlOiAfx5keVSyaVxJ3t63znhe8A8KMf/4ivf/vr/Pjuq5yPDyDYTl0anNx5B63SqtNklbm0+Y2ad9TVpPVGPyeDs8+6kfMa3/c9XaekNDHOKEYn4GXZDC1qRH5GAFql4f3DArM/e+J4OLEeAibe7PY5ZvDHnLhSzst7+1le++8KR5+fu5RqaqHeU+oEFMRBP9i51neRnBNp3OuIPmpxnbTe5lFLZbce6Xuh78DH2XXWZgKpQtkJKTnGsZKLtdoUXZJWLTajsAG5tahKqQvjf59hrAGfq9lkFK04EUL0OG88KJjbdg22Xm3+JLSqqszWD/PFX23OpBVBKUVxAZtnuLBcuM6J3Q+s7Ti75LaB+2zsGLpArROCAUVc54hByAmmYqjCoVmonB4f0a2UcbzA+0TnI10oOFF245ZRrdV6sblkKo7Xz1/nM09+BidKLZmSC99/9Qd8/bvfAuC1B6/zj//R73PanVFK4eLiAhHhXe9619JivfPAZIrubS/xwRJmmka2u5F7d+9z7+45H/vIx/jAO94LGMx6l7aICK/feZ0f33mVbujIJfOVp7/EM199BoDNdsNUR4pUilglJd6ZDYnQwBQWIUZT3Fe1lu4MhJFZMmqPijuEZdeam3TR7Ck2CwXPsk9K0oQiOG+yW7ELgC7w7RA8rlp7bPafSimZqLK4n5hsHuY58ab3e8P18RZ6gjMXa+Ec8pMT0rzxuBqHx03Rhzp6V9GGJsFlM6xFJkrNQmaZYboZcHLohPBoxbUixnVcx3Vcx3X83MR1pfU2j1qVzeUGUeHkeIWqtWcQsyWvVVlvEmlyVPF4L1QMvTS33pImpjGRy4R3hVL2CHUnbjFYVHFUb6DcWgtd39nsSk0ZfG4PzuDdxfXV28xJWluPg/nIsoNulVStps4eOjML1EWkr93XQbP6XRCH1iacX1XNjl3M1E9VSbWwGytpJ9TiyQ3qv+ph1a/oPVDXpKKU0T5HdcLFubXydttMrSsIQqoJLwJBF1fmXVOa+NyXPs8L33uRx27cYrfbcffuXfq+52Mf+xi//uu/Tq2VLz71JADfeeUlYnBUndiNZu2RUgH1PPnNJ/nY+z8IwLvf9S7W60tev3uHF7//Iq+//hqr4yOcOO49eI2pteNwmLNwA7yYqn6hJuN5VSdI2LdQHUYWXnhxuodzX1VCn5Um5pnVDC+fYfkzKdg5oe97alcJwdP1ka4zwvQMNHDOLFVMtcTsXWa4uTuoQN5qJvXw++Hhc+hniCsK6m2y99MfWxv8/vB9HXLL3qy1eRXlaP/2i8rIDNpxjfwOVmnVmpv9yaNZal0nrbd7qFJzwrlIjBBm9DWFXNVIthoIoUNFSGpq6z74hdNFMeXwqolSC7WCiCclm7GMU5tTEGBol7kIoUn/lJINMj1TkKq3RZD9ovCQfM7Bxb1c9G2xNTV3s6BQLYtYrxexBClqC3SF0lCQVbUhBCGXYqaOauaUqGm1Ox8YhoHtGu69vgbg8v7Iu95zytlZJGdls57QFOiOjhnXI5f323MmDyXYPKlmEBPi1VrpQs802v0uLi/42vo5k5Zyxlvrx55XvvQjnvj6Fyml8OD8AQBjSUBpavrG6SpFKRl2r93n1Ve+txyjWg3hVottBF4/v4MIpi4/t0+burvZhogpmmg1lkKtiyeafT+GQCy5oiKENgidF/KrYsZL26xh4LUlMLeQYxuvzjli6BBn4IwQvLWEa95zkpgX+v0pbNJIdTlP7LR+eMF+s3bhW/3uJ8WMYLV2pPyt8sKhuvvV1756Xs+vdZWAPBtGllrMRNKZ1UqI87G0c+FRnWfBddK6Dsy24+i4x5BoM0GyUqpSsjAlbH7hlaJGHq1aGt8Jm0k5QxZuN1N7zkDVwpT2swvEtOcQIURnVZvajlsbQABA1TXQRrWRSWnKDgfeXIdh/B675k/PBk5vrChuJNcdM5TdE4z47I30jBaKocNxbk9KrVWNp1VtofDeDPokerwfSNtKbs7Facy88sMHbC8DMRRygqPVCdTA+uKS9cUeqBKdNzAIZtLomtpHyok9Bl9NcT82ZfygiDcw+ebBhlKyVWlAF4RSE6LKlHaYrYg3GLoK66klzClRmyWMF4/JbxjvTTOLYkjNpYFPDHHpxBhyNqy0TcmMunRic4Wci7kni28k77eCu++BFwoH9Kc2E1WT1po3KfMCXpeKpi7IRWnzGpO1mjl6ex3Gh4AUut8gveF8WUhRD8/AfjJQor1aVTum7mfJWHNSMgBS13mGhtA1qaeHFeN/6nM1mgbVQBkqlaMQl0orFyUERw2O1DaLj1pcJ623eYjAzbOOviv4qCi22BUVcnbstorWHicRLRnnqv3IfrBcMACDA0h5UY+odYvvBNe13WJf8Z1DnBKjX8wOXQigJucEtuv32IBbq1JybYoAHsVRm+UJzJSpihNQL7gYCMNgXLApow3ckXJmiM7sRsQxNQHa4CGK7nfBgNZCLooPSuys+pQsUMTEZ+fBexGceqIMBJfphsCqP2K7KVyeV9J0sDD6jalrzDfVpuAhbpFtqnUCPGhruRWxlh2t5XZAdjXgSSXnEUE5Pl6RJ6gucn5/Td3N+PSAqKEA60HBU8WBqweAFhZkp6oBXYxOYHB/7yphbr2JNGNOj0igFAF3oGn4Zoi81nK0TUtrlfkDQVy319H4f9l792jZsqs+75trrb2r6px7+/ZLL/SgNWyGEa8IJDAYQkSc2AYyIttAeBkkgiN7BGJ7DJxYJnYAM5zBY/iBExunMQIpyMaYR1BkzGPICAJEmG4s64EgEJCNgoSQ1N33nnOqau+11swfc+2qOqfP6b63+6pv1+35jXHvOWfXrr3Xrseae835m3M2UX4z5rrzz0avcVrxZGKIVihYmkBoOrc2UYZs8/J23Xhnb362YordjfaJ2FwHdkOAVvus13a9Ulou4XT8SRASNm1iahkIEeaLnkuXDgArzrxaruxGQcX6mMl0vtOJ2jAJWioxBmqNJu6gEvpKmLVyXCsl19Bu/Nw96NyGhCBcvrIg9Rl0YNKI1SIcH4+sjq1HUoxx++VX+29qG14LIJUQI7NZTxWrlhEk0HUdsVW6CJ0SwhILF1leUZCIMEPV+mwB1Dqagl0CRSvjOBCCNQIsVUwduElEtqTkqfLD8dGK0CdmB0LX9eRWhLfUcafnVKRLyVxKQXcvCjZ37Vj+WC8bQyNgydcynVtZzDvm856QLM4zjiNXrx6xXK53pNCB9TAQNbZJjLZyCZsCv2DGwFavoSnz1HputcK5QdiUHRrLGiUTYqXvEn0foFo+lVVK182YpwaNG7MgYtfQlJO2SSzfbtdtZqaLlJLVbDynLiDN7Mmpbaf32RiP9pmwGpOyiWlJa2S5GZ+C6pRAPF3FNuY45YlNx7fXUMi5bIrwBomnXtudXgOn3u3HQiS0Opq7caVpPNdLq8Uosnm5p5coxrAx4ClajcSNKrCeP0JLF2g3OyEgEVIXNu7BuhwYxoK1h7s9fYRutJ7hhCCkOaDaytA0ccVKOXq4MCyFuhg5WPTM5p257UqrqlC3k0xpK4EuWRuRMlqSMdXcVmAxlK63orWl2p231kitiVIiNU+tSSzvavqyxxgpo8U1gkQkQt5UJQdIrbI8DOvM8dGKKpH5IafuksdRkTA142tdY0PZmYys2ry1sjAZ8aYoq9SWB7bNZYsSWBwmUmcxhGEcOHrkmOPjod01T12GzaBaLcMpVjFVCt8G5/u+JzIF5m01VfP0PCsgXFvtQSEzWyRSatUtVtcQOmK3YHHYUQa7oNy68p43CV5U7eHsJC1yJja1+3xlszw5W7T21IpLd4QZ6KnXfLNtY7amlUaLde0IbzYtR3Qbtyklb1bKG0N4auVz3vXqzv8XYWOYqrc84ZXLxv1nq8PcqrTYeCrjaLleIZib9fFibCb3t5uYlKaSXe2GTyulQtWdVJPbDDdaT2NE5IXAG4DnYp/K+1X1u0TkbuCfA/cB7wX+K1V9SOzT/l3AFwAnwKtV9Vcf8xxRqAx2eOlo3ydOjtYMS6hZGJYF6opSEikFQkhtpdRiHJvOsJabxQi5ZAqV9WpNbfUMQ+hIsSN0gmgr/BktNgPbpFPCuk3OEUnmvpvNoxkTiYSQOLra2l8MZvzKCEWscvpqmZEu080iMdkXN86SjamVBIrR7volBHI+XbbHJgVzaeVsrkc7TmU2Y2MQ5l0kdUrVAUtHEwqV1HcgkWHdVqIVkA7Z9IBio7azagp22aVUoppoRIFc1ayyYKuuWjdxpdl8xsFBBzIyjtX6T8WI6kA/i5ueVuMwUOtZdVq7U99xkU1Vyicxxa7SbRrr1hjZMaYg1bTSeiyjpWxf46rbjr6wrei/nWN155zTeNmee8eVV2thHHOLE277icXQnTLAZ43u2Tp/51Gr9XubjBbwmJXVL0QtFmsrTN3EjWOMxGQruZILpa1md0tdPQpRBGulI6p0fSB107HtZqvrYRyFMrjRcp56MvANqvqrInIZeFBEfgZ4NfAWVf02EXkt8FrgrwGfD3xc+/dHge9uPy/EYroZiFAiJ8dmta4+PKJZiJKoubKua0przBeikDqhn1wSrZJ2ihGpocUZrB+SVZxofX2kBzoW0gEjhIxIJsS1fREnwUaopGTZ/X03s9YhKpRSm+wvMKxtcirjiFYlhEQMwerkjQN5VFTDtk9WhKp5M+HaJFaJIRBOxYpo4otJgWjSdFOoJS7dMbNGiUAXBMHUj0Uj66GgKnT9DJG46fk01JFJpj8ZrE1Znk0bl1Y6ahisj1iMyLQvJpSIKbCYWzykn1mybm2u0a5LTcFp8agpGdkmvylBdmsMrMr6rjuqnprkd6uJ2y5nDbvV/KtsV2e7arezmDCCduwmetnpXVWriU6mcQS1lec07u1+Vl7LynK1j0swt+opQ8tWlj61tD89nvaRDDtxN063C6mlbmJ8m2tg+95NK0SRjbe6PT5VeldL/k3JRC0KUNFWa1KBLlkaScmmdp2q23ddd+7qVtWqtdg5Lb7VdQGdqv9jHoHYDdR8/bUY9wk3Wk9jVPX9wPvb79dE5D3A84FXAq9ou70eeCtmtF4JvEHtk/42EblTRJ7XjnPRWay1BYnVCo6v2hcqD5Bij5AYxwFQakkMZUTVViq6sI9P6oSQLIdJagWtlkNUFaRsJoacK8O60PdKSBkJIxKLiSHYTiS1di3mZNXXh/VgzREjSBISM7redl6Fipatq+lUsD1sC7mCxYx222bYpARl5/kmtQ6t9uEkLrBK3vO+4/AAhqaQDO10pVZyDoxZUBIh9iQCtTbDWiuFoRmrVh6pxYNCiBujMgzZFJS10s9mpyrSVyz8Ns1NVNhXqAAAIABJREFUeTSDrGKryRgCQy7UjIk8NqWSbDVnU2Swyb5WSrEbkLPli87+NONlDStj3K7MTq22mFaNZTOhn/qENYO1nfxPHWT6w8QduzG20N4H3dYeNNdg+2wxdROwSf6sC3S3YeM0jo1h3mhkTvfi2m2UuTFsO+66RxkulKl+5dbAnOdGnAKjaiXQgJQU1FbFJdsdykZ0cwEbbwSVEEzdah2rt10K5guldplto7bbC6+IsSeIyH3ApwK/DDxnMkTt57Pbbs8Hfnfnae9r284e6zUi8oCIPFDyxV8Qx3Gcpxu+0toDROQS8CPAX1HVq48RqD3vgUdZJVW9H7gfYH4pKBIZB7h2dc1qZXdsIfSIBqSaD30KiAcJlKrkPLJsVd4XIXEwPyB2B0QV1ixZDiuCVMuFaqO6654Zz33eFfo+cLK6Sjc7oJ/31nG3VJZLq9W3XBVUIedsMbFiooyurYI6qRtBhangTKxgq6oWN9jJDQKLH4SYGMfRBBli7jZz/2yLi1oH3Unx1lxlRchVGcWSp6fqDNJyuUpWxgy1xpbbFMz1Nd1R9yAxUkcr5jvFtUBbdfKtXDxIYBgGcs7M5r11dtbKer3mpBRULUcspJ7ZXOhn1jtsdrBgWK0Yh4zWulHR2eqHjZTaXJJsr3H7mdj8fTamYqKVs110m0ADW9FOrtRdl+OpFY4oolv323SO6XFkt7XIKT1ii12ddnWZ5D9u3rvZfI5W5eTE+oNpzdaYtK0md58/xdg2r/mZ/K4p5ytM3bLbODf1Btuq0lystCahW7HLRco/hKbCbK9zFHpJdF3HejlYaU5OxwMfjUL7rMck9LPEbnnPUiqhEz7mRR3v+/Xbs9S7G62nOSLSYQbrjar6o23z709uPxF5HvDBtv19wAt3nv4C4Pce7xyqwnI5sjwZ0ObSSqEjEIBCjZYvVGu2yUeElCzoD7Bcrq2SRFdIEsjDimEY6WYwXwTufe4lAJ77/CscXhlRGZgPEYkdOcMwKKtlZXVi37xhpBkXk77HGOj6RN/1pBCoQ2UYp35cW6O664IJQUgtJwgsFjJNFlNcCTUjZZPPNpYzSapDnLrcJqJ0LJdrhlXetFrRJkkfc22uwA7FJo4xbxVtRTMhKSn01GzGeHJDWWmpSeE4GZLt5J9zZhyHVmBWLScKYKzkXFmtCsMwEkLPOCrW+ssiie2g7bVphqFdfOrSowzM2X+TEGIrgpgm0sn92NSVzXBMxuHRBqYZtMlFKFZRJbUq+NN5gghFW6mnMuXOWexx19iJBGquzfWpUCEFex0nAUrJFkuaerdNBXc31zaNbaeKxzT2jUEVQLev0fSelVI28buYgglggmwEGxcaren8m4bDlRjtu4RsxSbb1+Sce9BJ/d+MaUyBmOKmHFeM8JznXeG+P9zzgf/3wxeOY59xo/U0pqkBvxd4j6r+3Z2H3gS8Cvi29vPHd7Z/vYj8ICbAeOSx41mACqsT5ejq2poxThNUKKjYhLvpa9XKoldtKr5oXXQt2Tig9ZgBJc7gjjsTl68krtw94/CyJWrFbuBoeY3j9ZrlWsklUnOiDgEdZWo0y3ocyVoInRAXEUkKCYY6oDExrhOj3VCTNBJrMuFDC5x3857DWUfSgLamlloGpLPYWalCGBN5DYEZIVQktuC4WLn6oELSOUnnhHyAyJph+bBNathkm0tgvcqtor2VrJqqiJhtaBMl2STKfbYaDkWpq4qOrTS+thVHNaPZpcB8PkcEVqs141ip1eI3XUjThwMtVn3+eCxEWVo337CySXmS24/TxC5mvwtAJHUzJHSbeFqt2ToKY7FDgqJkUMvVqvV0BXVVbKkZgFrRuu2WOxmYWrYryDLdCIQ2OVsJ/+lSqFjli9CUdgFpnaqFkiuxpU3EEKjYyq2U0d4rEcZxvakg3z7AptrEFKA5T94CW1ltDYs2gyHtJsby7ahtBRa2hmOKeU0GTtWqrudsx42xa3uWTRV7CULVgSDVOoTTk3WS5VeUFSFmghR0V9GhSoxpK6KRtqonUEz1RJxFxi6S48h8bkbr2c8LvOiFypW7VqQnIHTcB9xoPb35bOCrgHeKyNvbtm/EjNUPicjXAv8B+JL22E9gcvffwiTvX/N4J1BV8mAKtPl8tpmQoyTKaF2FrROqJdmGaK6UmITJyoQg9PPQtgl333uZK3cdMD9MDOPxpl5erhmhMGRlPcJYlHFdqCMwQmzfz26e6GeRNAtoX5Fkd6UmrVdOTgbGsU2gOhUvane+XeDKHZe5446eqsesVy2vKVhjwRQjBCEPsFxlKtAfxE15pCpQCsTYU8fEQ48sYT3Sz0Yk5SZrt3OXAkUDkgISA7VA0UrRaVJvcvuYiE2sEmKkI9H1HXkolKGyXppxUzE12azvQJVhsAaDZtjglEtvk69l7UOOj1dcutwzn/dW/mq0c4cklhPXksEFa8oZQsISW9vqoKUwWBFfmk7fVuFgbs9Hu6yaIdjJidptZDityjcVHtpLMqnjznwS2/OB5voLIiYFzzttPZRTYoUQpHkEaCudycjY1LZeD4zjuDG0Uy+u3WuZjm2ruMhuKsBFK55NztmUFhF0c02TcnO7chMkNOUmAWkr9UohkJHQSmS1G4StpF93XJNlmwsuFSUTO5gfVOYHyrPvNaXlxzznEnfdFSEsH7OR5T7jRutpjKr+AufHqQD++Dn7K/B1N3wiCRwcLKB2UFslbyK1mEpwvRwoVjuWUjNaCxWYzZsrsavErnDlrhlXrtyBRFgNJxytBkoZGMq04hBCieQK41AZRvNzaK5IVeYL++LNL3VIBzVURoq5emLHrF+wPFpzdLS2ZGZ7NoRKFOt4W0rmkatXKdLTzypj6/QbEsxrz2xxQKDw8NEJx0eVkEZCUiS1qhQoEiNRZ1w7GnjoD1b0NSB3wOxAqEHIzUU3lkjoOjMMRcm1Mla1iadNgIAVwE1CSNUK90qiCwId6EFg1bdY3smK1KpkjGNmvV630laPVq/FGHaqfVusbLUcuGO+YDafbWI2XVeoZSCXJjkXJXXBXKWaWyNB2IY+ZbMKEUmmrtOEtXlu1fo37tj2jMkrK6fzwTbVKESakrP1z0qJ+XxmxYMxdeU03lpN7RiCtZW31dE2p0wVSivXZLUHZePqLe3mCqz6eWkdjXcn/92Y1dnXdHs9p1MBznOhTn9P11lr3bggJ6OsqqzXa7uZmlY9sk2AJphyN3WRGCHnaacKGuy9D9vlkgh0UQlh5O5n9zzvRQvuvEfp+xX33GnfnbvvntN1ylgTZ7y0tw1utJ7hqFoTR8sj2Sa6phhJISHMCBIZVtZNuNZMmerhtWMcXppx77Mvc3ApsVwvWR9bjKvUQtnZL4VEKAvCqMhYCGOlUokBJFrtQ4DYFWowgUUUM6BJespaOX4ks1zqjttmKqxq7jlEKePI8bXCMIRNmaa+D5ScEOZWHZs1MWSCKjVv41RVlS72DEvl6OGBPkbumM2Z90KpK3NrsalLBTEw1kwZs7mhaguSx63QIEQrX6B1bd2aKeZOUyEQuXRobtaDec96OTAOmWEYmhS/Fa+V0yud3bhRVahrS5yeHXTMFge0PpXkIVvtxjw20Xsr9CrFujvvJLqaG81y3oJY9RHEihdrlY0RtlXkVFmk5UvtCDnO/gSLHU0Fk6fySqI74oe2YlOkVbjvNqujbT2+tl7T0M7X1nqlUsbSXHTbVc5kMCajcqpCx+bzr6f+TaKMi1Ypp+N9O+kULXY2nXs6Rt/3lFra6qhJ7ac2K0msGfQsEjsh5ynWZjU2EaFMqYsSWzxrzXPu7XnJJz+L5zwf0vwaXZc4PLA3XGrm2tGSzFaMc7vhRuuZTnM7BQnmU9+4vsbmlcrEaJn306O5FOYHiXufdQjAwWFEZeTa8RHjmMm12oSLoK3lBcBYQHIkDxVyIFal64XFpUBIIwRbcRTWTL4QESsFpVk4enjN1Q9n6rB1CSHmMjRFlSK1IgV0VNalkNoKKiPokFgfF2IMLPoFosqib2Wj2vd70x4lK3ldWaSZ9Xiqa0KMxLSgDDZ5lxwoubAua8pgXXlDCk1Rt6M+RNFqldUt/sOmekPNmXUTbFj9OevSO6n+drWfcur9KZZQHVtldzKIsjwZWBwUQrI7767rGOJIiGUTxI/JhBRVS8vhAomTYrC5UIm22mmqwFIKqdvWSKxTInNTIgKPMg7TmDerk+YaNIFJ2boRVbdJw9K1hNuuiR4sfyuEyWhtk5KF7eqLZrCm61mvho0QZjrHZGjOVsrY3cdWRfHCG4Rdg7abkH1WiTkMAylZOxtrvTMgKra6DM01GuymLfUdaRZZreyaqlYTt+jOKitYxf2ug+e/8A5e9MLLSP8BiEfcdechsX3OH/rwikeOV6zGvKm7ebvhRsshhNZYLtfNRGCTmhIiVnGiFErNpCjcdc8VDi9FUmeTwmp1RK4DBaXqdOdsddSshJCdRwgULZSqrNfWbn7RzYhdojtMZFpNtgqltX+IKaJ0rI4rRw9lhmNFyjbuoE1dJmIykS4E+qYBHsq2InuMgbqCsY6EeWIeE/18gaTMOm/7RYlanytBmHfC4cGc+bwja0EDzbVp+66Wa8Y6kuuagLWhT11LVmXrVrJyQDaWIB0x2E0CbXKbJsUUM+NQTk22E2fv7LftOKJpITRQS2FYF5bLgflBi0022bf2lXHITWwAIla0dlulQtsKKG5ELfY5qKha366pJNbGNVe3Cr8YTqsRd8e9/ZyFVlGlJVRv3IptGG2lk5L1M8tj3qmdaPuWUqjSVjKTjE6n19PqYk6v+S67q6PHSt6t1VyKk/JwUgtOxzhbxumxjmfxrtwK406tVyqbxqSt55uEYhVbqK1/mFoJr9Dc37TYpUK/EJ7/wivcdfecq8e5Vf5QhpWt8o5XlWvHmdWQeQwR417jRuuZzs6dcAiycdnUUqh1xPzr5lrPZSSkjjuuLOj6yvHJNQCGcUXqA9RWSgiQELeB762+mPXqmEW/4M7FZa5du4aESmWkxEJtLiddb79tpYCOgeNHVpw8UmBMRN3eCVt+lv2LIiSJzBBaKfmNMUo1Uk5G1utCXimHBzPmi55BzZhMk3QMimgmxcilS4mDWTRXZTPIw3pgbCstzUpAmcVADMGKlwY1V5Buq8MHsWaGlATVlHRZC6LNAE6GY6wM48hks3al5rsS9OmxqRJDiMlSDYq5w9arkRDtBiA1d5x0XXMD0+InucXLpkl464JUHSlaiBIIodD3Qt/PNu6mcchMLTumyfSx2LremgCjhemmOFYIgVBDK09k4yilblZNmydgxnUqYzS5g0MzXLmUG3aJnTU4u40sJ7Xg5Pab3JpnV1bn5aXtPj8EQUU3RY83OWKhXYcWYmfviwLUgmqh6NajELC8roM7Infde0CpI8NQiV3k2jW41vLTHr5auHZc2s3jDb0Ue4MbLWcTX8h1K1cWmSZKqyJesyXVzufRxBXHK4bR3HkxBRMiZOvTJJjc2L40Ea1TomtlyIXZLFMZCVHo5okwg3VZQz8p4mwqtTgBrE4Gjo9G8iB0zIhq7kvDYl8STOM2j4lL/YLYdwxsXXRdHxnWx2jNSKloHtEc0ECT808tLYQgldgl+stzyjhSQqTUzDCOrNa6MVqiqcXcoE+Rrgstbl7IpVKmBN8cQCNlTORRKdmaN5rCefNCtd5htArxu0YLJrn2bhmq3WRgEZPya3udp4lWtDXZrJbDZApQoY6VcTRRiB2sNRVsOV4SIHWJ2Qz6PgK6adIZQoujFXu3zksn2h3jbn7XtM0m8K0kXkQ2Nxi7JaHOJgbrJh/vtBTdrjs/aoX1WOOanrv7cxrvbi7WxG7n4bNG61Er4Z3jl1qpmKvaGpE2VWmKhKCELKRZRMNgzU6l0s1iU3I2FadYJ/FuHol9ZT2sqSUxDoW6qvzBQ/ZdfPioUFVJ4bTr83bi9tREOo7jOLclvtJ6pqNCqS1CH0qLdZhbKWgiZytTJEHpIoRUODl+hDyOLGameku149q1JUejqf26HqRTK3AbldnC4isShLvvVMblQKmPcHhnJM4r2gmjKtJyxFJXWwuRRKwd65MVea1IDagEsg6obN1AIkICFrPE5UXHnQcJQmCsbN2DQVhWQCMHi0NSCAzLgcKakiq0ivWjVmKKdLGn5kodFI09q2Xh+HhkHLauxBSF1M3pYqKXSM2FMq4ZiykJh2W7Sx4haKIwUGrZuPnMVbctmGsqs8DUL3gTC5Sd1cjUWFIt0VtU0FIhQlBLKpYimwrfGkCkoDIQOxASuUbGsUNrmfKaqbVYB15Rq96RhINLMDvsTBgwFFr6ESVXW+Calh+wtu9VLbamm2BVS1Cm5VaxVdBZm5Um4CAgdXJNW0WM0ppfTo07NysXbavhjXvSXpOptNRWrn9xP63rWYHsVvbYfd5ZwcVFnH1O1UqumU66TTzWwlYmgJrNOrp+IEZzkV66tODS4QKkpYuEwnq95J4rQDnikZOH0FgYNHF0XLh2MhVdVmIndIvOCvLehrjRcuxLJCMx7OSTaCGoMEs9QXpKWSOagcw4VvJQSS3vZnU0sFpbrtNqaSqpRZcIUUkHPenAPmYxJeYqlLkABQ2VVR2s9buEbRWH9iNJooytkaFaNQSr6F62kutWrS2FwEHfcWnRM0vKelxTx0odp55NkRnWNmURZ3SpQ9fK1eURK1HkoMUO5omovfUjWis6CkdHA8fHltBcipr8GBhLZRxGhpWylDWljox1tOReFFqCb6zmMg29JZTWVrgvpoRVkJ/cYgLaml/CKeXg5r1ix2jJ5M6qhFYZAmlK0KmwQgWixYkkCLXAkFvMqlckTA0OCzEoqRMODjvmi46+j5RYqVmZdz2huVCH1dI6L0+JuDJN4LutTCzmJoTWjh4mS71xp23chWZ4pgRm67WVz7RHmfYFKXVbAV+liSe2ZbHsVOcnBD8eZ/fZbXdy9vHHEmHsjsOUj4EyVmqvWxGRqJX4ksDsYMbhpcywzgjKYtFx5c7FRoAiUTgoHVcuB2odWA5LZCasNXJ1lVnlrRv8yp1z7rzzgBSvPu717iNutByqrul6IQbZdASupZBSz2zWW37WeqRaCQhUIfVxqm7HahxBlD4BvdJ3prybHcwIi0jWbXwlK0g/aSTUpLoxWpypTQB9ivSzjj7MeWS5bPUFW9WJulHpGy2PqU8dlxYHHM564jgQChzOFhtFYlA1pbFgsvNiK4P1IJxkJTULcTjrEe0YVhXN1lzy+GjNcjWaBFxb12VAqJSqBClIsDJIEiF2Fu/JU9aNKH3XQV/RsbTgfHiUEm1Xln0Rm+aFLaAvKq1hZis5ZPPj9hhivZxKNoVaiEqIA6kvpA66rhnWGOm6yGxmCsgYrV9XISNSCKljNm+NJec9J8WuTmVaV+3mVG1jWNL6eWlTLU7sxuw2f7Ot77cbEzr1eqhFtTaxMlVrAlkeLTs/+9peD+fFpc5yXjzrsfYJLW2j5Ewd8lagEwMSpVXCKsQUkbG0vmiVUisny5O280A/EwbgI9eOWWmFLKwGODrJ1PbBODzoWCwih3PZxHNvN9xoPdMR6Gdw+fKCrhNOjs0UjWulT4EQC6WskJitmkMILVs/bno7aWx3pCiXDjsOF3NSF5GijCcDY7tD7xcdY8ggFpiO0Uog1VKppdiXG+hiIoXArJsx7yGFE0YBiS0htcqpbP8gttLqY8cs9M0dBl03Zz0219u4ouuS5SNJoBYlD0qpTYlYW28w5pTBavpRAsvlQM7mVtPa3HMb5aK0nKxgpfRUuXxlTpoFjo9WG512ItDPktVObCKESYk2VWyAGwycT6ssTP0oanfjk0YhTwlnOTKbzQhp3mrgDUhXODiEu+6asVi0upCb/KnMelhbyoFUqysZBKmF1Nm+h4cHDMMJpVixXKvcNIkjtn23VK2I7aQ1seszqf3ZFRlNETiVWLpoRbNdeU15VZOxemwjdV7Vi/M4u99uNfqzicW74o2LjlWniy/tczvWTemsGCuhs9duWFmj0BACFRhLZtTMsomdKiOkxKrCh66t6S9FpCaunQwMWTiYm2v94HDOpcNE1DV1p+Py7YQbrWc4qpY4HJO1TJjupmOwVuPr9UCt2e6Ek901autYG1oc6PK8sxI6NXMwOyBJYnUykGtmXUfY+NY75NA6txa1NvY6KMUEgHTNLyhZyKWSC0SZ0acZQ7Q6fLETIpFx3UoAtWKlIU5JzFa5QWthfWLdlsHK+vRdhBioIZBrtdJG0hGozDvrCBxqTx5HygjDsnB0MlBrt6l+b+xOVtsmgN0sMD/s6ecRpbJu8ZxZmBFUGY7Xm5YWUwLrWfn0eZxVtm1GsCMbn9rVT0m8U1uUsa5JGpGQkZCJKXN4mLjjjsTh5X5rOGoml0KtAxoyBCuwK6L0cUaoHWG0hOUcSlOIVopOlfCbFLxuq6qYjbLXyNxk0+rKUgK2blEr1ItKK99knHXNTUwGtpRKHvPmfI+3Sr0ezkuMPsvj5XpN+0zHqwqhxey0bo2WSEWlVZkhYEpbM7+FakWL28uhAD3MLh8i/YDGwFhgtR7pUmcreUAo1DEzDGue3Cvx9MWN1jMeJUYYhhVrHdmtMl1yplSs1Ey7sy1aLaE3gEYzCEOphCQczi6TpOfaQyfkdeGeZz2Lq8dXeejoYQBSX5CuQNRW1sg6BwcSqQp6YhPBSitahJNyxMlRYXmczTihxFRJIRLaR7dEmHWJ2HUsh4EPDStCLfShY9F3dHP76qZkRUYrhbFU1gWyQtVIjDNm/cKuuwiBRC3K0fFxq+BtD20Lv24nW2l3x6GHg0s9ISm5ZmInmwTfWeg4uTowDGsUNj2edluU2PEee8W1a7Q2U6aYAIEKMVgC61jGTSfbEIWxjnR9ZXEYuHLngit3LYhRGfKK9dru5G0sSlFFkhmrGJN1LNaI5MjYeq0dH684ORkorc6ihEIMEZFkx5lypcQmZpFK6sOmwrs2Sf9Gzr3pVf/oflxnDYSI2HVuqlGYATxrQ857Da/XRfh4x9k93tkKGzClIEzikBZn0+ZKTeaitf22leIpJvAptVqcqybGmknzlhMYMv2lxOzwEI2Jo+Mjrh0NjIPSh0QZWgpIEVLoOOwvIXLthq93H3Cj9Qxn+k5a3se4+fIlq1WKRW4sAdbEbdpcbEptyiZNlcWlOZcvzSgriCuoKuQ6ILFycGiT9+JwRlwkNFTzskm1Ku8D6EoZV60/1xgZR2V5vGRcKzW3+oNdtJgMSt/6Jmln4oosytG45qQMzFNkUQWtK7pWzzAkS/AsKEOFTKSGaJW220oBmthDBdRiWFOjyBBAKe312VaLkCDM5h3dQaCbSWttUlAs1wuwFeu4aoq50xPa2f5Tj79SmFZWO+9hFCTKZrUVY2DqkiGxcHiHcPe9HZev9MxmHTHCejW2ivR27qzWsDMr9J2p2UKIdKFjdVR4+CNHLO3eg5OrhZKhnx20CifD7gC3E7gqtSkSE9LicHUrxphMrygiioRwqorDrltut55hLdZDbBzzuWKV63sdL+aiwrjnrXh3r/c8EchktFQUjcr8IDJbTO1lBkxTFGAIlFw3sd5SC0MZ6Q9sdVtkhAhjKdRSeOTqwPK4MO/mREl0zQV/2EcOZ3NEI1XdaDm3IVbTzFw1us1zxURn0hr32QpDgkAIzX1RNiXUDhaJw8sJDUfUIly6K7HuMserh4idcOedFgvpFwHpI7lmhpLJVdEMOoCMAWlz38m1wnpdyYMAsfXuaiosKYhOCaYmpS5qIoQ466GA9IlxBTKMVqwWICYqVtC2iMUNChYMTylsCuZGCZysT6i1Mp/POFkO0NRqtSXfTmkBNtkG+llktggQC1WzuXy0bkoKjSfjpmnlbmmn642z7LL7DHPZBlIX6Wc9IQUTZwQg2SR277MPePbHdHSzJUWPGYogpaPWjmGojHmrXBShVb9IdDN7cztJjAGuXTthOc2BJdJ3c1KaUUfrFbW7Gg2bDsctUVm2CdDTFey007Jt0z7ETdxvKqM0VZaYXrf1YEWFa7XP6Lmv0zmrqusRWezue72G77zV1vTcadVVJTO/3HPnPQu63vbLap9/qYKOU3I30Nzdilqc0o5IobBar6iaGZtQSJvsv4+bfASuPXJMjAc7FUVuL9xoPcMRAQ2FEExNt/mKiFA1WLO5oICV7qnYCqPr4qa5Yz+LxAQ1L+0L1/cc3NlxKAdUzRRp8SdGSu4ZsnUllhIIpUNPYDhWTq429+BKQQJJrBSUdUdUVKzobGU7oQQVpCpdVOYxUmqkjBWthVkS0sxiVSqRMVdGMjVEcgkMOTOWQtf3m1Yn1x65xvHxkpg6ilpzRLUDmLxewqZgqwrEGaR5JSVBg1CKMGZBS0dtkvf1ekSLzdK7E9x57kARmupL2urPjEnVaqWAWowjdELqEqnrrEdXH+hmgbEOECqX77Y79LueNUO6NUWKXQ+Vmq3yQtXphBBiT4iJkGwVXWj18Iq1hTm8fMDq2gqAWX+ZEGZNaWqrzRAmF12rtk+7DoUQEin2pDh1aZ6UI7ufw4iE2IQ+kwrRbjh2SzrlsVCyAoEgdtNl8vvt5xauxxU4BfOmX3eFHrvvw84wm+DEbvCaDCdY1XzVbX1EpaUdYCW2LF8jc8fdCw4ud6i2DqZVkNoRa2epHK0NjUpFOkE66xQONMWhkEdhvSroKCRRohaiKl2rbFKWlRMRxrxkGG5Po+UVMRzHcZy9wVdaz3BUlTEvsRIHul1pBatMXa3UAkKm1IIEYT5PHB4umM3tbn7MA3nMyJQ021mScq2ZkgdqqxNYVFmWgrVWCkTtKKvI8uGB5VUlryZXXusdJGoFbKW5k5rqrGhoFb5NmNjVyrwoh1Uo7SMdZkqUTG3xp/UAQ63UaFUgighZC6nv6RcLjlrB0Y985CqqSt9cOFozldgyKRhIAAAgAElEQVQ6y089kltic1RmB4EwVwiZUpVSoJYO0Z71ykQOw1KJ1aqxn11pPUo92EQvuxLyqpWQlK6L264W80Df9e1OHrq5MJYTSAN337vgjnube68vjHmEYqKBoliNSTJxcrmCraj7HkQQMtSMaiaPEaRnvphTxVZauclgihZikuZatPwspbS8sWnVYn25YuxN3t1aokhgc24pppwzv6aJO6xgc2YcLXY1KQ1rqYTdxpi6G3s6/bne5ayrbxt2sy5etug8rXK0uKueDiAqTSlp0n1rODxVrJ/WAK2qe4uhiijdYeLwSkcux6SpmksNkCOqwXqbabLjJyXOK2leNm7eJBBV6OMBA5UUMpHC4VxI5E0bk3EIrNaZq0ejN4F0blMUyMnK7WjdukQCQIVcNjJcgPk8cenwwArQjjaJDeMAUi3JVpK1ARFlKNmK97TvfC7WJyiFjtQn6lI4Pjrh6rVMWQXi1FwRoWTL5+q6SN93ptnTDFSC6KYaRy+ReQwsgpBKJpRCP+tJ/YxC3MSVhArB3GNFpXXLhcXiABCuXbOATc6mCByGcaN0MxeltslJiS3O0B10dLMOiZGhjtb4Uk1MUIY1QzNatVZCi7ttXvYLc3vae9KaBooIqRMuXzlgsUjUFgjSZJXjTcs4UOpI7DN33znnWc+9A00WIBzHFSHCONZ2Pc2QhEAKQuxbp+oERZbkUgCli5EygNbA8njgQx88prY3UsK2oOyk+Jtk/+cKGFqHXm3jJYAkITb3X9bacuAsrlpaseFxzOScqUVb7NWoajdXuyq9G48PTv7ErRq0HX0T32PK1N7eym3+iZgLMCahBhOH7L63Vn2/iXekcHhpbu9p60MGrQBvlXattbVdKXRdZD7vmM87iq7aQa0J6zAsse5wWMpGSFQNLPNUxFl45HjN0bKe+rzdTrjReqajoDm13gfF/mECB7MLui2V1CUO5gtijAzDmrGtoLTdodaWuDuqxZRKU8hN80lMict9svbtY+R4HDk5zgxrJSJN8GG5V7Xl7FgV8tAMlvUzSqLM2iR2EBIzAn2FeRRi35NSYlnXjFo2rR1SnCYiRYM1NJEYqbVydHS0aWkRgrSAeGkKN1NP2sLP+nZNfaXmi56UrBdZFmWkWhxGYb1ab0tIhYiWtmJ8nJjLZq4Uu/NPSbh0uefgMBJCIbbrHiWYKCWMhGSS9st3zbly1wKVJbms2/tYQM3QljKJQ5r4RAKpqSs1jgiZLkEngaQ9VWYMyzkPf/hhjh4ZScFuKmopaLHkcFqtRjNi0HURkakCv6kFY1RCVKpMgh4TkGwMUcZiOkXRQjNc1hJk+vzIJv5nTTUtQduOUa1Q5emP9dnX91FGbbufGVvdtUnt5s0S6U+vtFrMDbEmnCGA2M3UNi6m7aajEqIS+8BsHhmGFfNFaHlpbYwqVtm/bF+vlAL9rEdEt2KKtmw6Ob4GamWrhMAwwnx2QJpdspdyyFw7GaiiW8HQbYYbrWc4tSjDSSXNA93cOhADqGYEIYkQEKJEFr3Ja8tQUBG63tyDiOUGVUkgCa2xFXS1CW6+MDHEwWJuCr4BShbyMJAHBQ0ogXFq/RADItGk2zFSSmaSRceY6ENlnuzOskeIuUKtdH3HfNZTcmYch+ZGiZtxaMhowhSQISAhsVoOnByviM3vNo7W1BDL3LUJS6yNhmKrrUnKnvqESrUiuC33JrY6ilL0lB5h4/C7QDa9xVxkQZTUBS5dmnP5yoKqA7msidGKFOdiicLImjuuJO55zmViV1BZs1otkTitmIOVjRJrjFm1kiL0fSAg5GrGTWJhejtjFRgjHQescuDkyCa/qQivkBEiU90/sq2g7aZDNpO81QcMxAQhWS5frZWYIKWwUQRa+5sp30xOG4n2mk0GLohYoeIU6VJHCIFxHB/VkmT6e0opOL2aOp3nZnJ7O2aYhCLNTVnKTjWOHQnAtNIyscjk091RRoZCiFZrcnEwo58FVDIS7HUHKCGiJbRGqdG8FaG25HPrLq3NLVqydbxmZkYtj4W+64EekQOWS/ucf+D9D/PBD2We/4K7ELk9aw/KE0m4c24fROQPgGPgQ7d6LB8l7sWvbR/xa3vyfKyqPuspOM9TihstBxF5QFVffqvH8dHAr20/8WtzLsIl747jOM7e4EbLcRzH2RvcaDkA99/qAXwU8WvbT/zanHPxmJbjOI6zN/hKy3Ecx9kb3Gg5juM4e4MbrWcwIvKnROQ3ROS3ROS1t3o8TxYRea+IvFNE3i4iD7Rtd4vIz4jIb7afd93qcV4vIvI6EfmgiLxrZ9u51yPGP2jv5TtE5NNu3cgfnwuu7ZtF5P9r79/bReQLdh776+3afkNE/uStGfX1ISIvFJGfFZH3iMi7ReQvt+23xXt3q3Gj9QxFRCLwD4HPBz4B+HIR+YRbO6qbwuep6kt38mBeC7xFVT8OeEv7e1/4fuBPndl20fV8PvBx7d9rgO9+isb4RPl+Hn1tAH+vvX8vVdWfAGifyy8DPrE95x+1z+/TlQx8g6q+BPhM4OvaNdwu790txY3WM5fPAH5LVX9bVQfgB4FX3uIxfTR4JfD69vvrgT99C8dyQ6jqzwMfObP5out5JfAGNd4G3Ckiz3tqRnrjXHBtF/FK4AdVda2qvwP8Fvb5fVqiqu9X1V9tv18D3gM8n9vkvbvVuNF65vJ84Hd3/n5f27bPKPDTIvKgiLymbXuOqr4fbDIBnn3LRndzuOh6bpf38+ubi+x1O67cvb02EbkP+FTgl7n937unBDdaz1zO61uw7/kPn62qn4a5W75ORD73Vg/oKeR2eD+/G/hDwEuB9wN/p23fy2sTkUvAjwB/RVUfq3rtXl7frcKN1jOX9wEv3Pn7BcDv3aKx3BRU9ffazw8CP4a5kH5/crW0nx+8dSO8KVx0PXv/fqrq76tqUeuw+D1sXYB7d20i0mEG642q+qNt82373j2VuNF65vIrwMeJyItFpMcC3W+6xWN6wojIoYhcnn4H/gTwLuyaXtV2exXw47dmhDeNi67nTcBXNyXaZwKPTK6ofeFMHOfPYO8f2LV9mYjMROTFmGDh3zzV47texPrPfC/wHlX9uzsP3bbv3VOJ99N6hqKqWUS+HvgpIAKvU9V33+JhPRmeA/xY61eVgH+qqj8pIr8C/JCIfC3wH4AvuYVjvCFE5J8BrwDuFZH3Ad8EfBvnX89PAF+AiRROgK95ygd8A1xwba8QkZdirrH3An8BQFXfLSI/BPwapsz7Op06KT49+Wzgq4B3isjb27Zv5DZ57241XsbJcRzH2RvcPeg4juPsDW60HMdxnL3BjZbjOI6zN7jRchzHcfYGN1qO4zjO3uBGy3Ecx9kb3Gg5juM4e4MbLcdxHGdvcKPlOI7j7A1utBzHcZy9wY2W4ziOsze40XIcx3H2BjdajuM4zt7gRstxHMfZG9xoOY7jOHuDGy3HcRxnb3Cj5TiO4+wNbrQcx3GcvcGNluM4jrM3uNFyHMdx9gY3Wo7jOM7e4EbLcRzH2RvcaDmO4zh7gxstx3EcZ29wo+U4juPsDW60HMdxnL3BjZbjOI6zN7jRchzHcfYGN1qO4zjO3uBGy3Ecx9kb3Gg5juM4e4MbLcdxHGdvcKPlOI7j7A3pVg/gduPee+/V++6771YPw3EcZ6948MEHP6Sqz3q8/dxo3WTuu+8+HnjggVs9DMdxnL1CRP799ezn7kHHcRxnb3Cj5TiO4+wNbrQcx3GcvcFjWjeZ97zvw7zsv3/DrR6G4zjOU8qD3/nVT8l5fKXlOI7j7A1utBzHcZy9wY2W4ziOsze40XIcx3H2BjdajuM4zt7gRstxHMfZG9xoOY7jOHuDGy3HcRxnb3Cj5TiO4+wNbrQcx3GcvcGNluM4jrM3uNFyHMdx9gY3Wo7jOM7e4EbLcRzH2RvcaDmO4zh7w5M2WiLyT0TkEx5nn+8XkS9+sudqx3q5iPyDm3Esx3EcZ7940k0gVfXP34yBXA8iklT1AeCBJ3GMqKrlJg7LcRzHeYq4rpWWiNwnIr8uIq8XkXeIyA+LyEF77K0i8vL2+5GI/G0R+Xci8jYRec45x/rWtvIKZ7a/VUT+voj8koi8S0Q+o23/ZhG5X0R+GniDiLxCRN6889jrReSnReS9IvJnReQ7ROSdIvKTItK1/d4rIv+TiPwC8CUi8tI2vneIyI+JyF1tv78kIr/Wtv9g23YoIq8TkV8RkX8rIq98oi+24ziO8+S4EffgHwHuV9VPAa4C/+05+xwCb1PV/wj4eeC/2X1QRL4DeDbwNapaz3u+qv6xduzX7Wx/GfBKVf2Kc57zh4AvBF4J/ADws6r6ycCybZ9YqernqOoPAm8A/lq7lncC39T2eS3wqW37X2zb/kfgX6vqpwOfB3yniByeua7XiMgDIvJAPrl2zhAdx3Gcm8GNGK3fVdVfbL//APA55+wzAG9uvz8I3Lfz2N8E7lTVv6CqesE5/hmAqv48cIeI3Nm2v0lVlxc851+p6ogZnwj8ZNv+zjPn/+cAInKljePn2vbXA5/bfn8H8EYR+XNAbtv+BPBaEXk78FZgDrxodwCqer+qvlxVX54OLl8wTMdxHOfJciMxrbOG5jzDM+4YpHLm+L8CvExE7lbVj9zgOY4fY1xrAFWtIrJ7/nrm/I91jIkvxAzYfwn8TRH5RECAL1LV37iO5zuO4zgfRW5kpfUiEfms9vuXA79wg+f6SeDbgH8pIhctR74UQEQ+B3hEVR+5wXM8Lu2YD4nIf9w2fRXwcy3G9kJV/VngfwDuBC4BPwX8dyIibWyferPH5DiO41wfN7LSeg/wKhH534DfBL77Rk+mqv+iGaw3icgXnOPye0hEfgm4A/ivb/T4N8CrgH/cxCS/DXwN5lr8geY+FODvqerDIvKtwN8H3tEM13uB/+KjODbHcRznAuTi8NLOTiL3AW9W1U/6qA1E5K3AX22S9r3l8Lkv1o//qm+51cNwHMd5SnnwO7/6ST1fRB5U1Zc/3n5eEcNxHMfZG67LPaiq7wU+aqusdo5XfDSP7ziO4+w/vtJyHMdx9gY3Wo7jOM7e4EbLcRzH2RvcaDmO4zh7gxstx3EcZ29wo+U4juPsDW60HMdxnL3BjZbjOI6zN7jRchzHcfYGN1qO4zjO3uBGy3Ecx9kb3Gg5juM4e8ON9NNyroOXvOAeHniSJfodx3Gc8/GVluM4jrM3uNFyHMdx9gY3Wo7jOM7e4EbLcRzH2RvcaDmO4zh7gxstx3EcZ29wo+U4juPsDW60HMdxnL3BjZbjOI6zN7jRusm8530fvtVDcBzHuW1xo+U4juPsDW60HMdxnL3BjZbjOI6zN7jRchzHcfYGN1qO4zjO3uBGy3Ecx9kb3Gg5juM4e4MbLcdxHGdvcKPlOI7j7A1utBzHcZy9wY2W4ziOsze40XIcx3H2BjdajuM4zt7gRstxHMfZG9xoOY7jOHvD08ZoichPiMidH+VzvFVEXv7RPIfjOI7z0SM9VScSkaSq+aLHVfULnqqxXC8iElW13OpxOI7jOMZjrrRE5D4R+XUR+Sci8i4ReaOI/Gci8osi8psi8hltv7tF5P8QkXeIyNtE5FPa9m8WkftF5KeBN4jIq0XkR0XkJ9vzv2PnXO8VkXvbOd8jIt8jIu8WkZ8WkUXb59PbOf5vEflOEXnXOWMOIvKP2nPf3FZwX3zOfl8uIu9s1/XtO9uPRORvicgvA39DRH5s57H/XER+9MZfZsdxHOdmcD3uwT8MfBfwKcDHA18BfA7wV4FvbPt8C/BvVfVT2rY37Dz/ZcArVfUr2t8vBb4U+GTgS0Xkheec8+OAf6iqnwg8DHxR2/59wF9U1c8CLloB/Vngvnb8Pw981tkdRORjgG8H/tM2nk8XkT/dHj4E3qWqfxT4W8BLRORZ7bGvaWNwHMdxbgHXY7R+R1XfqaoVeDfwFlVV4J2YcQAzYv87gKr+a+AeEbnSHnuTqi53jvcWVX1EVVfArwEfe8E5395+fxC4r8W7LqvqL7Xt//SC8X4O8C9UtarqB4CfPWefTwfeqqp/0FyWbwQ+tz1WgB9p16Ltuv5cO/9nAf/q7MFE5DUi8oCIPJBPrl0wLMdxHOfJcj0xrfXO73Xn77rzfDnnedp+Hj/G8coFYzi7z+KCc5zH9ez3WPuszsSxvg/4P4EVZgwfFZdT1fuB+wEOn/tiPfu44ziOc3O4WerBnwe+EkBEXgF8SFWv3qRjA6CqDwHXROQz26Yvu2DXXwC+qMW2ngO84px9fhn4T1oMLQJfDvzcBef9PeD3gL8BfP8TvwLHcRznyXKz1IPfDHyfiLwDOAFedZOOe5avBb5HRI6BtwKPnLPPjwB/HHgX8P9gBurUfqr6fhH565jrUICfUNUff4zzvhF4lqr+2pO+AsdxHOcJIxa22Q9E5JKqHrXfXws8T1X/8kX7icg9wL8BPrvFt57oef9XTGjyvY+37+FzX6zHH/idJ3oqx3GcZyQi8qCqPm4e7VOWp3WT+MK2QkrAvwdefcF+b27CiR741idpsB7E4nLf8ESP4TiO49wc9mqltQ/4SstxHOfGud6V1tOmjJPjOI7jPB5utBzHcZy9wY2W4ziOsze40XIcx3H2BjdajuM4zt7gRstxHMfZG9xoOY7jOHuDGy3HcRxnb3Cj5TiO4+wNbrQcx3GcvcGNluM4jrM3uNFyHMdx9gY3Wo7jOM7e4EbrJvOSF9xzq4fgOI5z2+JGy3Ecx9kb3Gg5juM4e4MbLcdxHGdvcKPlOI7j7A1utBzHcZy9wY2W4ziOsze40XIcx3H2BjdajuM4zt7gRusm8573ffhWD8FxHOe2xY2W4ziOsze40XIcx3H2BjdajuM4zt7gRstxHMfZG9xoOY7jOHuDGy3HcRxnb3Cj5TiO4+wNbrQcx3GcvcGNluM4jrM3uNFyHMdx9gY3Wo7jOM7e4EbLcRzH2RvcaDmO4zh7gxstx3EcZ29wo+U4juPsDW60HMdxnL3htjNaIvJqEfmYJ3mMX7pZ43Ecx3FuHred0QJeDTwpo6Wqf+zmDMVxHMe5mTztjZaI3Ccivy4irxeRd4jID4vIgYi8TER+TkQeFJGfEpHnicgXAy8H3igibxeRhYi8V0Tubcd6uYi8tf3+zSLyOhF5q4j8toj8pZ1zHrWfl0TkLSLyqyLyThF55S14CRzHcZzG095oNf4IcL+qfgpwFfg64H8BvlhVXwa8DvjbqvrDwAPAV6rqS1V1+TjH/XjgTwKfAXyTiHRnHl8Bf0ZVPw34PODviIicPYiIvEZEHhCRB/LJtSdxmY7jOM5jkW71AK6T31XVX2y//wDwjcAnAT/TbEgE3v8EjvsvVXUNrEXkg8BzgPftPC7A/ywinwtU4Pltnw/sHkRV7wfuBzh87ov1CYzDcRzHuQ72xWidNQTXgHer6mddx3Mz2xXl/Mxj653fC49+Pb4SeBbwMlUdReS95xzDcRzHeYrYF/fgi0RkMlBfDrwNeNa0TUQ6EfnE9vg14PLOc98LvKz9/kU3eN4rwAebwfo84GOfyOAdx3Gcm8O+GK33AK8SkXcAd9PiWcC3i8i/A94OTIq/7wf+8STEAL4F+C4R+b+w1dSN8Ebg5SLyALbq+vUnfSWO4zjOE0ZUn94hGBG5D3izqn7SLR7KdXH43Bfr8Qd+51YPw3EcZ68QkQdV9eWPt9++rLQcx3Ec5+kvxFDV92JKQcdxHOcZjq+0HMdxnL3BjZbjOI6zN7jRchzHcfYGN1qO4zjO3uBGy3Ecx9kb3Gg5juM4e4MbLcdxHGdvcKPlOI7j7A1utBzHcZy9wY2W4ziOsze40XIcx3H2BjdajuM4zt7gRusm85IX3HOrh+A4jnPb4kbLcRzH+f/bu/9Yu+v6juPPl+0Q+TGKVAORjnZaYhuWMagEomMsNI6wDeKsIpE4NgJR54hzYlh0zql/LGsWliUsWDOG+yH+WPzRGDcxCJaQFmwVSltCVinROhHtkAHNcK3v/fH9lpyU23tPL+ee08/t85Hc3M/5fj/3e96fT869r/v5nG/ubYahJUlqhqElSWqGoSVJaoahJUlqhqElSWqGoSVJaoahJUlqhqElSWqGoTViD+/eM+kSJGneMrQkSc0wtCRJzTC0JEnNMLQkSc0wtCRJzTC0JEnNMLQkSc0wtCRJzTC0JEnNMLQkSc0wtCRJzTC0JEnNMLQkSc0wtCRJzTC0JEnNOCJDK8nSJNumOP7RJKv79mNJFk/R551J3jGOOiVJ47Vw0gUcjqr68BB9bhlHLZKk8TsiV1q9BUk+mWR7kjuSvCzJbUnWDPS5Icn9/cdrAJJ8JMn7+/b1SXYk2ZrkM/2xE5L8Y5KH+uNv7o8/c+CiSdYkua1vvyXJtiQPJtkwrsFLkl7oSF5pLQeurKprk3wOePMUff6nqs7rtwP/Fvidg87fCCyrqueSLOqP/TnwVFX9CkCSk2eo48PAb1XVDwauIUmagCN5pbWrqh7o21uApVP0uX3g8wVTnN8K/GuSq4B9/bHVwM0HOlTVkzPUcS9wW5JrgQVTdUhyXZLNSTbv2/v0DJeTJM3WkRxazw209zP1qrAO0T7gt+kC6lxgS5KFQA7Rd/DYsc8frHon8CFgCfBAklNe8IVV66pqVVWtWnjciYcYjiTpxTqSQ2sYVwx83jh4IslLgCVVdRfwAWARcAJwB/CegX4Htgd/lGRF/3VvGjj/6qq6r78J5Cd04SVJmoAj+T2tYbw0yX104XvlQecWAP+S5CS61dVNVfXTJB8Hbu5vqd8P/CXwBbr3v74CfB/YRhdwAGuTLO+vcSfw4ByPSZJ0CKmaaqdMs3X8qcvq2cd3TboMSWpKki1VtWqmfq1vD0qSjiKGliSpGYaWJKkZhpYkqRmGliSpGYaWJKkZhpYkqRmGliSpGYaWJKkZhpYkqRmGliSpGYaWJKkZhpYkqRmGliSpGYbWiK04/QX/2FiSNCKGliSpGYaWJKkZhpYkqRmGliSpGYaWJKkZhpYkqRmGliSpGYaWJKkZhpYkqRmG1og9vHvPpEuQpHnL0JIkNcPQkiQ1w9CSJDXD0JIkNcPQkiQ1w9CSJDXD0JIkNcPQkiQ1w9CSJDXD0JIkNcPQkiQ1w9CSJDXD0JIkNcPQkiQ1w9CSJDXD0JIkNWNkoZVkUZJ3j+p6o5bksSSL+/Yzk65HknT4RrnSWgRMGVpJFozweSRJR6lRhtZfAa9O8kCStUkuSnJXkk8DDwEkuSrJ/X2fTxwIsyRvTLIxybeTfD7JCQdfPMndSW5KsiHJw0lel+QLSf4zyccH+n0pyZYk25NcN13B6axNsi3JQ0mu6I//fZLL+vYXk9zat68ZfC5J0niNMrRuBL5bVWdX1Q39sfOAD1bVyiQrgCuA11fV2cB+4O39lt2HgNVVdQ6wGXjfIZ7jZ1V1IXAL8GXgj4CzgKuTnNL3+cOqOhdYBVw/cHwqvwecDfwqsBpYm+Q0YAPw632fVwEr+/YbgHsOvkiS65JsTrJ5396np3k6SdKLsXCOr39/Ve3q2xcD5wLfSgLwMuAJ4Hy6ULi3P34MsPEQ11vff34I2F5VPwRI8iiwBNhDF1Rv6vstAZb3x6fyBuD2qtoP/CjJN4HX0QXTe5OsBHYAJ/dhdgFw/cEXqap1wDqA409dVtNNiCRp9uY6tJ4daAf4VFX92WCHJL8LfL2qrhzies/1n38+0D7weGGSi+hWTBdU1d4kdwPHTnO9THWwqn6Q5GTgErpV18uBtwLPVJVLKUmakFFuDz4NnDjN+TuBNUleCZDk5UnOADYBr0/ymv74cUnOnGUNJwFP9oH1WrpV3HQ2AFckWZDkFcCFwP39uY3Ae/s+9wDvZ4qtQUnS+IwstKpqD90W37Yka6c4v4Puvas7kmwFvg6cVlU/Bq4Gbu+PbwJeO8sy/oNuxbUV+Fh/rel8EdgKPAh8A/hAVT3en7sHWFhVO4Fv0622DC1JmqBU+RbMKB1/6rJ69vFdM3eUJD0vyZaqWjVTP/8ihiSpGYaWJKkZhpYkqRmGliSpGYaWJKkZhpYkqRmGliSpGYaWJKkZhpYkqRmGliSpGYaWJKkZhpYkqRmGliSpGYaWJKkZhtaIrTj9lEmXIEnzlqElSWqGoSVJaoahJUlqhqElSWpGqmrSNcwrSZ4GHpl0HRO2GPjJpIuYoKN9/OAcOP7DH/8ZVfWKmTotnF09msYjVbVq0kVMUpLNR/McHO3jB+fA8c/d+N0elCQ1w9CSJDXD0Bq9dZMu4AhwtM/B0T5+cA4c/xzxRgxJUjNcaUmSmmFozVKSS5I8kmRnkhunOP/SJJ/tz9+XZOn4q5w7Q4z/fUl2JNma5M4kZ0yizrk00xwM9FuTpJLMq7vJhhl/krf2r4PtST497hrn2hDfB7+U5K4k3+m/Fy6dRJ1zIcmtSZ5Isu0Q55Pk7/q52ZrknJE8cVX5cZgfwALgu8AvA8cADwIrD+rzbuCWvv024LOTrnvM4/9N4Li+/a75NP5h56DvdyKwAdgErJp03WN+DSwHvgOc3D9+5aTrnsAcrAPe1bdXAo9Nuu4Rjv9C4Bxg2yHOXwr8OxDgfOC+UTyvK63ZOQ/YWVWPVtXPgM8Alx/U53LgU33734CLk2SMNc6lGcdfVXdV1d7+4Sbg9DHXONeGeQ0AfAz4a+B/x1ncGAwz/muBm6vqSYCqemLMNc61YeaggF/s2ycB/zXG+uZUVW0A/nuaLpcD/1SdTcCiJKe92Oc1tGbnVcD3Bx7v7o9N2aeq9gFPAfPl/5YMM/5B19D9xjWfzDgHSX4NWFJVXxlnYWMyzGvgTODMJPcm2ZTkkrFVNx7DzMFHgKuS7Aa+CvzxeEo7Ihzuz4mh+BcxZmeqFdPBt2EO06dVQ48tyVXAKuA35rSi8Zt2DpK8BLgJuHpcBY3ZMK+BhXRbhDPN0JsAAAG+SURBVBfRrbTvSXJWVf10jmsbl2Hm4Ergtqr6myQXAP/cz8HP5768iZuTn4GutGZnN7Bk4PHpvHDZ/3yfJAvptgamW0q3ZJjxk2Q18EHgsqp6bky1jctMc3AicBZwd5LH6Pb018+jmzGG/R74clX9X1XtovubnMvHVN84DDMH1wCfA6iqjcCxdH+X72gw1M+Jw2Vozc63gOVJliU5hu5Gi/UH9VkP/H7fXgN8o/p3J+eBGcffb419gi6w5tt7GTDDHFTVU1W1uKqWVtVSuvf1LquqzZMpd+SG+R74Et0NOSRZTLdd+OhYq5xbw8zB94CLAZKsoAutH4+1yslZD7yjv4vwfOCpqvrhi72o24OzUFX7krwH+BrdHUS3VtX2JB8FNlfVeuAf6LYCdtKtsN42uYpHa8jxrwVOAD7f33/yvaq6bGJFj9iQczBvDTn+rwFvTLID2A/cUFV7Jlf1aA05B38KfDLJn9BtjV09X355TXI73dbv4v49u78AfgGgqm6hew/vUmAnsBf4g5E87zyZP0nSUcDtQUlSMwwtSVIzDC1JUjMMLUlSMwwtSVIzDC1JUjMMLUlSMwwtSVIz/h8ZptKGCvWSywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc92c0cb7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_solution(\"flowers/test/1/image_06743.jpg\", model = model)"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}