gradcam.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bde017ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/gupta.37/work/crisisMMD/Multimodal-Categorization-of-Crisis-Events-in-Social-Media-main_change\n"
     ]
    }
   ],
   "source": [
    "# to know the current path \n",
    "!pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebc7a292",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.10.9\n"
     ]
    }
   ],
   "source": [
    "# to know the current python version\n",
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "963cd896",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the cuda visible device at GPU 0\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0c4feeb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/gupta.37/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# necessary liberaries\n",
    "\n",
    "from os import path as osp\n",
    "import os\n",
    "import logging\n",
    "from PIL import Image\n",
    "from torch.serialization import save\n",
    "from args import get_args\n",
    "from trainer import Trainer\n",
    "from crisismmd_dataset import CrisisMMDataset#, CrisisMMDatasetWithSSE\n",
    "from mm_models import DenseNetBertMMModel, ImageOnlyModel, TextOnlyModel\n",
    "import os\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.nn.modules import activation\n",
    "from torch.utils.data import DataLoader\n",
    "from torch import nn\n",
    "from torch import optim\n",
    "import time\n",
    "\n",
    "import nltk\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bed7d783",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import torch related liberaries\n",
    "\n",
    "import torchvision\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from torchvision import transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "57fb4cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load model weight file\n",
    "\n",
    "model_to_load = './output/experiments/full_task3/best.pt'\n",
    "# model_to_load = './output/full_task2_merged/best.pt'\n",
    "#image_model_to_load = './output/image_only_task1/best.pt'\n",
    "#text_model_to_load = './output/text_only_task1/best.pt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18ab9d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda'\n",
    "num_workers = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9cf9c4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "EVAL = True\n",
    "USE_TENSORBOARD = False\n",
    "SAVE_DIR = './output'\n",
    "model_name = \"task3\"\n",
    "MODEL_NAME = model_name if model_name else str(int(time.time()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8dbb4136",
   "metadata": {},
   "outputs": [],
   "source": [
    "MODE = 'both'\n",
    "TASK = 'task3'\n",
    "MAX_ITER = 10\n",
    "OUTPUT_SIZE = None \n",
    "if TASK == 'task1':\n",
    "    OUTPUT_SIZE = 2\n",
    "elif TASK == 'task2':\n",
    "    OUTPUT_SIZE = 8\n",
    "elif TASK == 'task2_merged':\n",
    "    OUTPUT_SIZE = 6\n",
    "elif TASK == 'task3':\n",
    "    OUTPUT_SIZE = 3   \n",
    "else:\n",
    "    raise NotImplemented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cbd70627",
   "metadata": {},
   "outputs": [],
   "source": [
    "WITH_SSE = False\n",
    "pv = 1000 # How many times more likely do we transit to the same class\n",
    "pt = 1000 \n",
    "pv0 = 0.3  # Probability of not doing a transition\n",
    "pt0 = 0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bfc778bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 2e-3\n",
    "batch_size = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "15bcd3a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create folder for saving\n",
    "save_dir = osp.join(SAVE_DIR, MODEL_NAME)\n",
    "if not osp.exists(SAVE_DIR):\n",
    "    os.mkdir(SAVE_DIR)\n",
    "if not osp.exists(save_dir):\n",
    "    os.mkdir(save_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d81b4f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(filename=osp.join(save_dir, 'output_{}.log'.format(int(time.time()))), level=logging.INFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a4613b7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#code from view.py\n",
    "class Args:\n",
    "    pass\n",
    "opt = Args()\n",
    "opt.load_size = 228\n",
    "opt.crop_size = 224\n",
    "opt.max_dataset_size = 2147483648\n",
    "opt.debug = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "292cc06f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader, dev_loader = None, None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f361310",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33m[*] 2468 samples loaded.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "train_set = CrisisMMDataset()\n",
    "train_set.initialize(opt, phase='train', cat='all',task=TASK)\n",
    "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=num_workers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ada43c5f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# for i in train_loader:\n",
    "#     print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c66b907",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33m[*] 529 samples loaded.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "test_set = CrisisMMDataset()\n",
    "test_set.initialize(opt, phase='test', cat='all', task=TASK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7ac7264d",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loader = DataLoader(\n",
    "    test_set, batch_size=batch_size, shuffle=False, num_workers=num_workers\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0a63727",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /home/gupta.37/.cache/torch/hub/pytorch_vision_v0.8.0\n",
      "/home/gupta.37/.local/lib/python3.8/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/gupta.37/.local/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=DenseNet201_Weights.IMAGENET1K_V1`. You can also use `weights=DenseNet201_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n",
      "Some weights of the model checkpoint at google/electra-base-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense.weight', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense_prediction.bias', 'discriminator_predictions.dense.bias']\n",
      "- This IS expected if you are initializing ElectraModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing ElectraModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "loss_fn = nn.CrossEntropyLoss()\n",
    "if MODE == 'text_only':\n",
    "    model = TextOnlyModel(num_class=OUTPUT_SIZE, save_dir=save_dir).to(device)\n",
    "elif MODE == 'image_only':\n",
    "    model = ImageOnlyModel(num_class=OUTPUT_SIZE, save_dir=save_dir).to(device)\n",
    "elif MODE == 'both':\n",
    "    model = DenseNetBertMMModel(num_class=OUTPUT_SIZE, save_dir=save_dir).to(device)\n",
    "else:\n",
    "    raise NotImplemented"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c4fb96bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model = nn.DataParallel(model)\n",
    "# The authors did not mention configurations of SGD. We assume they did not use momentum or weight decay.\n",
    "optimizer = optim.SGD(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1c31c33f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The authors used factor=0.1, but did not mention other configs.\n",
    "scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.1, patience=5, cooldown=0, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e183d4a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainer = Trainer(train_loader, dev_loader, test_loader, model, loss_fn, optimizer, scheduler, eval=EVAL, device=device, tensorboard=USE_TENSORBOARD, mode=MODE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e3b7aad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n"
     ]
    }
   ],
   "source": [
    "if model_to_load:\n",
    "    model.load(model_to_load)\n",
    "    print(\"yes\")\n",
    "    logging.info(\"\\n***********************\")\n",
    "    logging.info(\"Model Loaded!\")\n",
    "    logging.info(\"***********************\\n\")\n",
    "# if text_model_to_load:\n",
    "#     print(\"yes\")\n",
    "#     model.module.load(text_model_to_load)\n",
    "# if image_model_to_load:\n",
    "#     print(\"yes\")\n",
    "#     model.module.load(image_model_to_load)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "65e3bae3",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DenseNetBertMMModel(\n",
       "  (imageEncoder): DenseNet(\n",
       "    (features): Sequential(\n",
       "      (conv0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "      (norm0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu0): ReLU(inplace=True)\n",
       "      (pool0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "      (denseblock1): _DenseBlock(\n",
       "        (denselayer1): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer2): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(96, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer3): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer4): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer5): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer6): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (transition1): _Transition(\n",
       "        (norm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "      )\n",
       "      (denseblock2): _DenseBlock(\n",
       "        (denselayer1): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer2): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(160, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer3): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(192, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer4): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(224, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(224, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer5): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer6): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer7): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer8): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer9): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer10): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer11): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer12): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (transition2): _Transition(\n",
       "        (norm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "      )\n",
       "      (denseblock3): _DenseBlock(\n",
       "        (denselayer1): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer2): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(288, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer3): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(320, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer4): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(352, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(352, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer5): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer6): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(416, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(416, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer7): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(448, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(448, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer8): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(480, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer9): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer10): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(544, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(544, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer11): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer12): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(608, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(608, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer13): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(640, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(640, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer14): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(672, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer15): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(704, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(704, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer16): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(736, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(736, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer17): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(768, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer18): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(800, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer19): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(832, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(832, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer20): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(864, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer21): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer22): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer23): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer24): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer25): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer26): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer27): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer28): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer29): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1152, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer30): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer31): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer32): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1248, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer33): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer34): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer35): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1344, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer36): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer37): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer38): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1440, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer39): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer40): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer41): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1536, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer42): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer43): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer44): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1632, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer45): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer46): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer47): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1728, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer48): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (transition3): _Transition(\n",
       "        (norm): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (relu): ReLU(inplace=True)\n",
       "        (conv): Conv2d(1792, 896, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (pool): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
       "      )\n",
       "      (denseblock4): _DenseBlock(\n",
       "        (denselayer1): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(896, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(896, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer2): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(928, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(928, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer3): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(960, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer4): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(992, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(992, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer5): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1024, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer6): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1056, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer7): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1088, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1088, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer8): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1120, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1120, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer9): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1152, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer10): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1184, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1184, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer11): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1216, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1216, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer12): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1248, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer13): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1280, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer14): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1312, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1312, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer15): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1344, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer16): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1376, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1376, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer17): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1408, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1408, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer18): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1440, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer19): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1472, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1472, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer20): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1504, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1504, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer21): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1536, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer22): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1568, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1568, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer23): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1600, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1600, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer24): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1632, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer25): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1664, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1664, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer26): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1696, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1696, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer27): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1728, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer28): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1760, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1760, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer29): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1792, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1792, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer30): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1824, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer31): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1856, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1856, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "        (denselayer32): _DenseLayer(\n",
       "          (norm1): BatchNorm2d(1888, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu1): ReLU(inplace=True)\n",
       "          (conv1): Conv2d(1888, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (norm2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu2): ReLU(inplace=True)\n",
       "          (conv2): Conv2d(128, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (norm5): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    )\n",
       "    (classifier): Linear(in_features=1920, out_features=1000, bias=True)\n",
       "  )\n",
       "  (textEncoder): ElectraModel(\n",
       "    (embeddings): ElectraEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 768)\n",
       "      (token_type_embeddings): Embedding(2, 768)\n",
       "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): ElectraEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0-11): 12 x ElectraLayer(\n",
       "          (attention): ElectraAttention(\n",
       "            (self): ElectraSelfAttention(\n",
       "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): ElectraSelfOutput(\n",
       "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): ElectraIntermediate(\n",
       "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "          )\n",
       "          (output): ElectraOutput(\n",
       "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.5, inplace=False)\n",
       "  (flatten_vis): Flatten(start_dim=1, end_dim=-1)\n",
       "  (proj_visual): Linear(in_features=1000, out_features=100, bias=True)\n",
       "  (proj_text): Linear(in_features=768, out_features=100, bias=True)\n",
       "  (proj_visual_bn): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (proj_text_bn): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (layer_attn_visual): Linear(in_features=1000, out_features=100, bias=True)\n",
       "  (layer_attn_text): Linear(in_features=768, out_features=100, bias=True)\n",
       "  (fc_as_self_attn): Linear(in_features=200, out_features=200, bias=True)\n",
       "  (self_attn_bn): BatchNorm1d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (cls_layer): Linear(in_features=200, out_features=3, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "310e968f",
   "metadata": {},
   "outputs": [],
   "source": [
    "heatmap_layer = model.imageEncoder.features.denseblock4.denselayer32.conv2\n",
    "\n",
    "#heatmap_layer = model.module.proj_visual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "07f2f472",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import torch\n",
    "import numpy as np\n",
    "\n",
    "class InfoHolder():\n",
    "\n",
    "    def __init__(self, heatmap_layer):\n",
    "        self.gradient = None\n",
    "        self.activation = None\n",
    "        self.heatmap_layer = heatmap_layer\n",
    "\n",
    "    def get_gradient(self, grad):\n",
    "        self.gradient = grad\n",
    "\n",
    "    def hook(self, model, input, output):\n",
    "        output.register_hook(self.get_gradient)\n",
    "        self.activation = output.detach()\n",
    "\n",
    "def generate_heatmap(weighted_activation):\n",
    "    raw_heatmap = torch.mean(weighted_activation, 0)\n",
    "    heatmap = np.maximum(raw_heatmap.detach().cpu(), 0)\n",
    "    heatmap /= torch.max(heatmap) + 1e-10\n",
    "    return heatmap.numpy()\n",
    "\n",
    "def superimpose(input_img, heatmap):\n",
    "    img = cv2.cvtColor(input_img,cv2.COLOR_BGR2RGB)\n",
    "    heatmap = cv2.resize(heatmap, (img.shape[0], img.shape[1]))\n",
    "    heatmap = np.uint8(255 * heatmap)\n",
    "    #plt.imshow(heatmap)\n",
    "    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)\n",
    "    superimposed_img = np.uint8(heatmap * 0.6 + img * 0.4)\n",
    "    pil_img = cv2.cvtColor(superimposed_img,cv2.COLOR_BGR2RGB)\n",
    "    return pil_img\n",
    "\n",
    "def to_RGB(tensor):\n",
    "    tensor = (tensor - tensor.min())\n",
    "    tensor = tensor/(tensor.max() + 1e-10)\n",
    "    #print(tensor.cpu().numpy().shape) \n",
    "    image_binary = np.transpose(tensor.cpu().numpy(), (1, 2, 0))\n",
    "    image = np.uint8(255 * image_binary)\n",
    "    return image\n",
    "\n",
    "def grad_cam(model, input_tensor, heatmap_layer, img_path, truelabel=None):\n",
    "    info = InfoHolder(heatmap_layer)\n",
    "    heatmap_layer.register_forward_hook(info.hook)\n",
    "    #print(input_tensor)\n",
    "    #output = model(input_tensor.unsqueeze(0))[0]\n",
    "    output = model(input_tensor)\n",
    "    \n",
    "    truelabel = torch.argmax(output, dim=1).to(device)\n",
    "    #print(\"truelabel is\", output[truelabel])\n",
    "    output[0][truelabel].mean().backward()\n",
    "\n",
    "    weights = torch.mean(info.gradient, [0, 2, 3])\n",
    "    \n",
    "    #print(\"shape of info gradient\", info.gradient.shape)\n",
    "    #weights = torch.mean(info.gradient, dim=1)\n",
    "    \n",
    "    activation = info.activation.squeeze(0)\n",
    "\n",
    "    weighted_activation = torch.zeros(activation.shape)\n",
    "    for idx, (weight, activation) in enumerate(zip(weights, activation)):\n",
    "        weighted_activation[idx] = weight * activation\n",
    "\n",
    "    heatmap = generate_heatmap(weighted_activation)\n",
    "    #print(heatmap)\n",
    "    \n",
    "#     transform = transforms.Compose([\n",
    "#         transforms.Resize(240),\n",
    "#         transforms.CenterCrop(224),\n",
    "#         transforms.ToTensor(),\n",
    "#         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "#     ])\n",
    "    \n",
    "#     image = Image.open(img_path)\n",
    "#     input_tensor = transform(image)\n",
    "    \n",
    "    input_image = to_RGB(input_tensor[0][0])\n",
    "    \n",
    "    #plt.imshow(input_image)\n",
    "    \n",
    "    #print(input_image)\n",
    "    #print(size(input_image))\n",
    "\n",
    "    return superimpose(input_image, heatmap)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5ab8117b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_3583322/2371582665.py:23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  pred_label = torch.tensor(indices).item()\n",
      "/tmp/ipykernel_3583322/2371582665.py:24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  ground_label = torch.tensor(y).item()\n"
     ]
    },
    {
     "data": {
      "image/png": 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FnueDGGmc/4zFYkFRZMN6LBYLvv7662B4kHF7ezfooJTKQl+XLBYLvvnmGy/etdB23alD8zmdSL9QpJCmlyGEk8DZgbVwOBxRKvOUcVUilT/AfXdkt9tTFAoli8lz05ZPANMk+3nEMO1/nuU4JRHC+UPmRlntWHs0J1UqQwiJCRZWzjnyfBRNnZ4J/1UGG3MnJcaOZqSPP+Cst7l//+59yPKgtSg8lZhlXr8ihcIYy319T991iCD2EkLQtv5we04jQ+uc1nQDG1fkWUB2AudadKJjdUC1qFBZxldf9WRZzv39NpgXpw57DGITKYRHkEiWywVSStquC5ZZBiElUilAQirDT4FzIk6TQrC5uODVq2u6rgNrQEh67fU3Xdcnehrf3vHodQ5lWZIXxUQcttls0FpzMIbeJmNItkQU/3377TesViu01tze3vHu3TuatkZKyXKx5Fe/+hWvXr/im2+/oShL3rx5G2T3iuVyhZRejPm73/2B29s7drsHlFKUZcF/+2//N5vNBTc3N2itubu7ZXOx4fr6im+/+YaH7QPv37/n3dt3tG0LDi4uL7i8vOTrr7+iKHL6frQu8roP5cWHWe7Fo5livV7z619/j8oE1hp++OGPgxK6KArKsuKf/ukfWC5XvHr1moeHB7pO004sl365SuSXpM9I4WOTGClyhA9FcDzWZFkeLGFWKLUBFNrseHh44PrqAqryEf6ZioXm32fJCR6HzQjPBGLeOS+H9cgg+inMDoAYAUcUX2RZ5ilga4O+ZJQlZ4GLSDqSfHUYawYqzlo9e9sUnXnfC0vfBVm+cEHvkSPwIpq6bjxSc1CUBWVVetPNTCEH0Y3E2oKiKLEW+nDgPeXtKeyo05n3Rgfgu9/vaZpmVvp47j1C8FxOXR+9OG0Q/QROL5jkesTiRoSQkr6AkJKy8BSx1n1cBW9I7MAYcPGZ0QkGFxTrxjgEHkG17REpFVnuTYOf2DlUVUm1qFhvVigp+eMff2S326J17x3nELRdx93dHcYavv/+ey42G9brTeLrAodDze9+9wd+/PEndrs9xvQIIajrhoeHB28QsSgoihylBF+8vuHy6oqHhwd++uknfv/7P9A0DSaIGg/HmtvbOxaLBZvNmuVyMev5dL+vVms2mzUXF2v+8Mc/8OHDLW/evPeWW8bQNB1K1Vhn+eqrr/nnf7rg8vICYwz7/f6RKPFzOp0+I4UTJoOnKAmpZFD6BnFIsJbwXphusL0vyxVFkdN1GmsMXdtDtDQhPheVsmoAYvMkHIiPVZAFhzApAOmipH0yPpFQ8tFpTAqJjDAsiHVkzE85hZlCc0RC50VMk+dC+3lRTAoi4LHGojtNdHjzFkrepj9yCipYvniRjgoKb0FUAHtZvHiEFCJ+9Ca9vTetbduZA9tYPzrX6V4PojXnWhAy6CFcAOajSO48ARpFUl5WHsV1QkZEmTw/IBJ3qomE4xPDeKPJ84mZBwjUfOlFNHgEF/PGhfH72Ro7iOu88tYO89H3Pfv9bpg7byknMMbSdT193w/GBlJKyqqiKkvquuF4rDkcDokJLUHsozkejxRFfgIppMOP+yEnyzLapmO323nFvIkm1g5jLLvdjsuLS5zzXGhVFWFWRku5z+l8+oUihVRpK5Lv59Oiqri4uGCxKMhzbwmU5ZKyzCnLIjhTPVBVOXmuqI81TdPQ9zp4qC4HwCSVYLNZYUzP+/e3E52k50RmXX3JcIhOX32gYKGsshMNjMHWpJQo6QFrlglU5sgyD6m8hZH08lwBOJsKggbzxOOhZrGskOWcg4l9c8TDqDJJVZV8+823wRRSBI/b3ltzGUNdt1jnvDNfVVL1FavVEhUQxKtXN4MsuizLoBPxHtfaGHa7fZC5V1NgHwBt09Q8PDzw9u1b2rYbRGnz1Peaum549/aDV2wrOVCaKsvJspws9yKPvu/9up1aq0SiJKViuVqxWq9Yr4M5sTaYrvMWYFmGVBkWgzfH91xHFNvkeYYxPVrDxcUFu92e9+8+0NR1YgoaEcy4x1WmBqe4LMv5+ptvscbOKOdRRFcfG+q69kjL+ras1XRdy26/o9f98B4X7HWt9U6PQmShv359sjxn9+4ddVOj9dQYIRIVh8MhKJavZ9Zg01QUBVmWYZ3ziu1DHURt4zi8ArujaVratiXLsoD85hz5Z8RwLv1CkcIcgM0ps5l4hqCgXHrnK5UpCNR+URRUiwpnHYfDkb43WOuo65qu7Ylxk8qyHHSR3u9g4WXKfAgKvKjwDdS8EBjrxTNCCKzz0VVV4FikkoNpY7TAiaaTxliEcBSlChInl5yBhHoWwct58CS2Qa7r/RmiWCgVU6ezZJ2j63tynZPlbirGCECp197sEBxVWWJX3rLKOUHXG2/x5KagObYT9R3ejNED5cPxQJ7l5EUeqNlgvSX8fNR1E0RZY2/T41+WJZvNiq+++oLd/sDt7QPGWUZls09etu+wxnmFe3B6A5BKhU/G8egR/2MBjnsEe4w1HA5HcN5HZL1eDo8URY61zlse9YKoHhd48U9RFKMpsJLUdTNYC5mJ5dfjPR096I32n7dv39F3PX0/dWrzFL6iKHPf/rH2BIMa7yVwT0k4EyTsnOf8nHXkWT7uo5k+LXJ10X/htLFCnL+IyAQqU8HzO8z1IIwjOFOqQHi4MD/xTH8WIT2XfqFIIU1xo0zl3/OUFzmLiBSUDymhlCIvBFVV0TQt+/2Bvteegj7WtCEERFF4bmLQQQrBolrQLboBqEekoKQAPCIw1gbxDGhjUSqw9QRroSDisNGsUoSDYzxgX7gqnIGRK/IK2CBEkmIEeFhMkLcDlFWJkGLwkzgB8gbFca8NeQRMAwLyfeq6Dq07PFIokMIrlHvtnzXWBhEMAY4G8BryvOVMEebDy4bLsmQplggRLYQUQgZP5YAUvDdwItoJf8uyRCrJN99+TfHhju3DPgDNKbDoe03fG5q6nW6JVHk8ALATXEKc92R7GWPY7ffUzZE8U/ym/C4AXBeAvvSmqREBOYcUPgxEkeej/kdJ9ru9N/081qRmucOrk70cxWA6mLq++ektx+MxiM/GPRkd2qpFidFev7NYVJTl2PZJmCqmPzxx4sdrjAkUfj4EvPNtyEFMVeQ5RV6caHg679HDO/Y1L4qZnwbBnDUL5zSbeX3Hzn7mEp5Kv1CkcIpTeDrpvqepG+7vdxOb6qjgOh4bjocD79/f0XWad+8+UB99/KD97jCIaPyJsOx2Bw6Hmrbt2G73GAOrVYVSHim0bUvTNuz3tX9GvPeexVWOwLPrDw9btrsdbVNT7csQbE7T9623/ReSvuvoOou1/t37wzGYqpqAwAz7/QHrDEZ31M1xmJO+72hbjXNelOBDZrR0vWa3P+Kc43BosVbQdQYpvfOVdxT2Ibjff7il1xqpMg/YlOLufsdhf+D9+w+hTPL23Xv6LsxVrQLC9WKRvu8Q7z/grOOHH96wWFQhxLSnrg/7I8Y4jseGu7ttEB+98V6t2rA/HOm15qef3gE+9lTT9Og+ysvPrXpAUalIwzsYhL+MiOEcsLQJl+YcVoA2MX5WNsjki6JgsViQKRUcHnuiQVRVeoujPMsw1tJrjXV2QHon+z9gcRk8fLc0TUtRFPz619/z9u07bm9v6ft+cDD78ssvub6+pigLHh62/Ov//rfgp3IqzXRGiVd4FAvd3t7hHHz19Zfe27nXPNzfBfGTd05crVZ8/c1XLJfLJ97jEc1+f6AsS47HIzc3N1RViTHW78muI8tyirzku+9/xfX1DVmm2G533N3dzRzYUinAZwQxT79QpPBcerxRvEKs5u72YYhPBJ4yPwb9gQfwO7Q27PdHus6bLW53+yDDJ8ieLYeDp/Sikk6II0JE+bimbRua1osmEC7EfbHe2kdEb8+aru3oA1UnpBdR6L7DOUN+aAJQDVQzjrpuEcIjpq7XOOt9KXywuy6YfHrkET1rjelBjM56fW+ojw3WOfpeI2SHsQ6lxOCYBf7ItV3H4XDk9vbeA70so++8cnG32+Hw4pi7uwe01jR1i2pHqyhjLUZrTO+pRM+N9XRdT1HmEERGXa/JMq/QFEJwd/fggwdaS103gxmmEAIc9NrrDJ62SHlCTpKa5QxVk32TMqBumuccGOsVtwQnPSUlSgZnvywjy/Ohb3kWypUKYVUcmfLKdzd5WdrFKJaLTpGWw/6AWzlWqxWXly3O2RDQ0HsIX15esF6vgn4icmujE6BHQqcmJHCSLn3GcTwevX+EfU1ZltzcXCGFC+LE6CC49ObcyfsmYwhK9KjobpqG7Xbro8suV1xfX9G2XdAfFBSFH0ee54OpatM0M6TwGRk8lX7hSGF+ak8lX/7wsGe7PfLTj+9GPyXAOeGtUfCH4re//d0g+gkSTv713/4TKaN8JEQodV4xF+PF1E3Dw/YhvNGXWTfemewtP/aBOPVtWGtw4Z6G9x/uGMlDTwFHLsMldz6/fX/rvzvjeXw8pQ0WN9wBDdvdPmkruQsiyHkOh2aYwWPdDI52E6JRCLpO03Vb7h92XpHJePhtQkXf3d6NSzL8mQtCCOEh0syp3iCGx/nw4XZoyIVQ4T/++Haysh6IPbH+YkYNn0vnKPUziMFZHxTx/bsPbDYrvv76S9q2oe97yiBGQsohuqkzFhlCnUTxSblaBdv7af8jAO+GEB5RFNbz7//xW66ur/iXf/lnLi83SPmP3tciiGO09pFIf/vb37Ld7ui6jjz3Fkhd36PNNPjeOE+eYIjWRFGG/+7dB/b7A4uFjwX2X/7Lvwx+MH7u/fiapqZpaqqqGqzxokVf0zbBwsrQdS13d7f8z//1P/mH3/yGV69e8d//+/8P5wiBFbOBs7m783GT3r1778PZPxJ+zhfqxLhmesVfSvoFIgWXfOKix9vG5mncJNY5hAtOTgEYRXNPG/wHHJ4CDMToANic8wrLwRLHWQ+onEuPc5BIRHPHVIovhnIn3GCgFI1Op5tWTOqnVijD8Jk+4k48O/ydi0cSajd9bHjD5HyJkZB24NLwzQ5vkx/HHt9xihQ9MU/nxxB+P+IAQhtzc89hiZP7JmYy8kFU9FEpiJ3mQu/QpgN63dO2HYdjzX6/p+s6Li825FlOUfhYRwCH3X4IWX48HoKXt/QRXl26MKB7zeFw5Ic//ogLxES8v2K/P2CMRUlFURYUIeREbKJtvcXO3d39YHIaQ1n/7j9/57nhJpqATtP9/UOI5aXY7XaD53PTNPzww488PGy5vb0bghRa64Mf+o/3MymKgoeHh2DeDdvtjv/4j99ye3sbbqBz9L1hv9vzww8/st3uWK7WSKnCWfR6trZtOeyP3N09BHHmc0TfU79/eekXiBRgAiSfpRLG7x5mpRAxAk0xVBvgWqKIdAHQkz47mHGI5FWn2PPH5OYAQCMQj4BOzMeSjjHWYQRWscOxTvq8mP0du3ieuTolY0/qPgpM9sjSJDSe1hvGma7ZqRe/4ODPQ4qItFycyZ+nj6Ea3ay6mPyJoS0Ox2MIVd6SZzll6VBSkpcFUkl6rWmblqZu2R/29F2PNiHelJu27UWFNT+ZNzjHJJjc4VDTNB117SPfVlUZfBG8GLBpapq2pW3a4RmPVAx//OMPGGNo2+7R3QzO4S/CORzw4qo+cMEWaw1v3rylLL0TYhXuGLHWeRFp42MfgQ9REqOtWmvY7w2///3v6boe3eugq9AcDpq+9+LAzeZyMLU1wfJut9vRd9qbHJ+a/0fpMyJIk3AvDCEoxP/zl+7LmRScvh4lcSL/VN68HQHk4bdiAgyGEyYZb8pK2vbeXcnv8PVR6GWRdCVwIQNCcEHGEUUy0Qs2RRbpnc7Tazx9m7NncYz3QJPUjzeyJe2I5NlJf2J9w3Qukv4MoYsjgmPs0/BJkcKMO5lzNDOuYhh/2nbalotjn831kM4g+iT20PhY2pf5ndszJ6dH8uhZmgHmk5zZwJSO7xJSBJv+zAfxs5YixG7KMumVvkXB+w8f6Np+oNLj5UjTNA4uvcluHjIDxMQbW4R5iPoAG7nhOf8ovJXUtL2xxkgD+Qioc8Wuf+f0hj0X3hXreqZqDL/i25Qn2oMYqNHrI2K7o1n2YwW8fOJ7ul/EiXruRFn6+1wbYvauedm5en+55Nz/+2ydXxinIM580nJO5MPjsAViWmUAYinVOms7OjhNmp41MgDIRxXH36c4gqeInVNDPNnPdHzpeGYU9pwTSZHBU/XcvIp4XPWUnXraVZdeK3qqYvozeG3L8XAPI/YeeT7HjcAycmuTWQjs39DOsNajKfCjwIZuvOY0HXoafM9Zh7HeCTBCsK7vERpkL8jyA0Xe0TRduInPJAhznsa1nCps52vsAhWfEBpDuXg0hREXP4WE4jy4ZF7mjfh3PtXvx5xk1Ds8bi/6jLhBKe7TOQB8qs8v+f7LTL8wpAAjVj6FmU9hfiCKZmIsiPnGSanaCQCcA9NT70obSd/vZnkw+gCcAtIzAHzqNZNnXfL4KWQ4h+DguSQ7fdalfTyFGM6kFH+m/ZgjmFTMNsCxAQLxaN6Tr1KK4IzlbeCN0UMTeVHgnEWbfqhf5OUgC49RXGF0CMzzPFwsFIGuxQZIJ6UPMy4QSOGBme57r1yXMjheWYy2I2oa4LEb5tQT4gJj4cPtHcNd02ELugHpzO7NnvxNKdxTxMV8bU+t36SDs7JTe/kRxufj06l2zr0zzRcnvqdtzPflZ8D/VPo7QAqnqO6PSaeeE/GEJVXiZpFj+dAFkZTPKUJGAJWKkYQYf0/ahynQj5RQciCG+ilVnNZxjOKPOfeQpKHK7CAk/RGJItRNzKpOHbSkDwMumlPnycvnyHJS9QRgGT2pQpXZ79i+i/07QTUn3a2qkuVywatXr5BSeFPWICNfbzY4HFr39CEy6Xq9oet7Dvs9y+WSIvfxk9q25XA4cnm5QamM/e6AyvzlSE1Tg3XkZUmM2VQWOdYattstVVVRlv7OibpuePPmnQ9PPtebpPsxHU4aPmOYBjE+9+z5OJfvZt+TNud762R7Z55/VPYSwH6q/eeo/JfAhac4gFP9OlXn5yC3+OxLuJFPD0l9BFL4uZPzp6SXLv5zbZwCXGKWHRFBqjNwszKRUNYioVhdAFKz4HHxPfGgDUgiAe6ngO4AtGd/XWhjHh9pUjUF2oIxZ3oIRDovIrz70Rh5DHjiOJ6DQRO4cAranXn+FKCPSaZ94rF6YQLQoCwL1usVX7y+QSnpLWlCnKDL62sEBGDtbxq7vLykblpwlpvr63B1qeR4OKKk5PWrGx9awUFZ+gBuh8MeZy1ltSDO3Wq1HDzR1+s1y+WSvu/Zbve8e//hI4Lozdd5XjnuqaeA2lNp/tyMg3lROx8DF14CZJ/r81NA9DkAew7hxHSKs/q5iGFOQH5GCn+G9KdO1KnJjoA7ioccUwV0CtgTIP6oPObLsQ3BSNnCjLpNnz/FIaQK43l5fEeiAHYhvKZI6qUhF5gqnV3y3deX418BbtIPJs+O9ZPvafjwc/viUWC/9JmAVFy6RqnsODmY54jVOS59VMhwL/D+sAtWPQVlniOFYHt3R57nrFZLbJ7RC7i/u/PhSnY71uslSknu7+4TS5yOrmv58Yc/8uWXX/Dq1XXwqrZ0rXfS2+8POHeNEF5cVddHb2XUdsO9CKfHNCMMgMGoISLtdD4G7uLEXJ1gwobkHn35C6anFu/ntneCWDr5vpcgl7kieV7+ku8f886/j/R3ID6K6edwDKcWOv0keRFwD3v21OJHSlo8Lkqpa+bfk+6f3Mypn0QAziIB3Jy6PEWAs4xch0jKUvHUDIAPACYehDmrH2Xp8QrOx5t+LD8lu0nqDkDKTedjzo08SingPAVEk3WK4zkh7vIOdSKRQsnB4kfIeKF8DB8uwhWk3mZehjzvQGZwjsH5Tik1BBEsitzf8tbroEwdTUCNGYMY9tpHU7XRkuYULBHpjxNjTud3sufm8/6E+Obk/p6nc88/1a9zmGj+/CkA/FLu4TlE8NSzT9VP4UHal3PzcHbxZm09159PE4n8nSCFuOmeMtuaA9knyl2CBFKlcwqI5tSnkEn9WbPpYT63B6PViAOswJvDplwBTMc5o+gBb04aEIRzoU+R5ZcJp6IYbgEbuAkxa3tEFB68T4OqTfUZNskbZTYxBPeECzh55lKKNpknB48P3qlDdebwCMdU4TwHOP4KzyzLUVk+KoOl8Nd+KhXCRXul9PXFlQ+PUNf+/ug8G4LrdV3rOY2i5ObG31FsraUoKgQMV2amlj9d17G52LDerPlwe0/bdd70dA7cTw9u/DoMK+zL1GFtyH8JIA+TLsLcpC8Y1uIvzUHMkdi5vFPPpX9PpTlAT6UA8dnZuM8Sjh+T5gjhOQQCj2HUp4MgPmGk8DHUyqk657B5cpDmCzNBFulGFaOoIy0WTKldmQK9BEjGAztIhCLSiX8DAI7+C8IwFfGkQCQihFT8NCKEGGxi1CEkSGVAQAInFILR+Ukkc+FSzkTMn51zIUlx/DvxCxCzs+emeS7lkJIhpbqTlLIV6YvSJE7AFoFAsliuEAIOdY013gnq4mKNwN/E1ht/n7OoWw51w/7Q8ubte6qq4vLikr7rqOuDd7jqWn87XJGH+FMNAm/OmpqlGuNjXgkp0VpzdXVFURZs99472cZwznMxWEpgDMhWnhCRhbkYtqiY/Z7N6SNOO+z1OP+PTE5Ppfncp+fKncg/9/ypdJKaONPGS/PTvTW31konff6uc+OYw5SXAP85MpsjqZcgu79u+kR1Co9OyZ/Y1onFDIfmERwZgP+pDRF0B/5atOleiyarMckTmy4e2sgtBJGHxxtu6JOn5qZ3BkfALoQvc4NJovBcwoQbYCxjVuaEB/Qu1ph5xQ7K5lR3ECtE5BPiN5G8akBwyViHn7HNhFsYfDbE462VIoY0DcM7QTCkCnA3nbks8wD8eKxDDCFJpvxlLbrrw70Vjq7raVsfbG+3O9D3hq++/DLEHDLB89ffsKeDp3DX9ielYMY5H8LiUCOE5LvvroYrTq21yVhOjH+cgGS8J5DrhCiJnOHjqTnxI5m3SGQk/TkLOOffz9U/lc7Bj/kzL4Ez6fvmfXsKIYkzdV6CrM7AkbP153lpO58ulwAfhRReQkn8OdK5yf45z86Bauq5Ovdinbchpj/TZubNp4/M9dYRuMW9aEVCcLuAHNI+OUZP61Fc418VuQPNlApyDKIkF72YTyEWAZgAr31HPUeR3m6fvHdOuU4OY0J5DWftHHQXszbid+vHKiBcNTZ9dP5j0h/H1BAgqTdkSyw+MFtdH/nDH3/gi9evuLxY8/7Drb/OM88Hh+/O9FhjUEqyWq1YLCratglUvUQbMNrS9RZtHM4JHrYHnPORR40VOJRfXilZbzaUpRdDWWewTg+3mw0I+NSUAROqHxcQqkzm+Ry1/rFphrxTruxcvZPp577/T0lzAJ9ytZz4nnK6c+A8F5++JM3rngIOL33200mfqPjoFKXy1CQ+hc05k5e+6RR0h9GZKgIbd7rqUO/UJ0Eu6bsTMX1CfCeNz/QJQYzgPTxThCYYRUmJpVF8X9QtDAA0fWHSp3n/4vdIzcPQxlR8nT5/SryQTNgA6Nz02aiIfsQFzPoyFCeUc1o2I9mt81d0+tDKPbo36N7Q9QZt/KVGJT60tL+32F8yUxT+8vk8z3HOX3DU9Zq8tDghKKsKKRVdr2naDuccWV7QaxPuO/AhK4SUZHlOUVZoY5MQ5iSINJmDNJ2Er09RxSfW4UkYnT4viNzjpMlhrk8hoVN9Ss/rKY7iVB9eUu+JKsNkngLo5+brTwXI5+HJ02VPtfPppE8QKcSN8pS5WJqem9gZAH3krZxQDKk+ISKDCWB3IwKQkUI/0YX0NSLJHJpPqGvnpuGNTiAEMWzulFtLzUQtgxmqIMlL6sRb4gEm7Z2j4hOF9COKPEkuzkWa8QQAmVDACfJ1c7PeU20kh/yZkF0O6I2h6Tq0VrSdBhRdb6lrfzezUgptwFmNwLFcrUEIul5TVRVFUeCvDdXsD0eKqvR3CV/fIATsjzWHusZZr8Opm5ZOG461DytuHRTlgvX6grZtODYd2lhsXOdHDpRJ5+eEt0jyJ8j3Y6nbtM2wtgKm1lvh77Aec+Canqm/MncwEC/zyZnDi1Oc1Dy9lPhM5/ichIFZ/px6TJ9N018n5tHHpE8QKfyc9BS2jr+TxRmc1OZkfchOKVkBSPe4mTmuIdQbnhnLhoAJEQbLaLXDdF+asJmdABcv8km5gPnmHxoc6oxOcqkIKHyPcXiEZdCdOMloCSXGZ2KnJo5qJ+Z1HmxuwoVMCsa+DgrkpL1UNj6hUNNmxLSpR4EI035B3bT0ukcih4iix+ORtvNhoUW4yCdaC93tfGjpvuvY7g4+TIaQaGPou566rRFSoLsx1Ifu/aVC+8PR341hNF3fAT5iqTYfuH/YYY0O14+CSx0dT3kPvxjGzwF5mv8csI7znBJCdlaWlttp3Rf37SXpFOA+038HUzHluTk415d0vp9Cqk/1/xx8OVU+n7NT7/uZSP0vlP4OFM3p75/TxpkycWqxmG64AfCHDZQC+4n4MCADmQDQebSMCFwH6osRrk8MesTQGxd+u4kteqonmFMhJ5DEZD7cdOwDwI8IRE6fcUm92fSNf2Od9F1pO2L2PQIYmIqlTu2vBBkT+zo7yKceC+1q7YH0YJWLoOt76HsQ4bIeMepWZKDwnbWICBjja5yj6erQ/YBcw8u99Ksdvvfx5jIEvTGIYw0u3pMRxjsgvmRvDf0f/hsH+Ajuz8ofBSQ88+ypYzFBAOcQTDwfYpr/aO0+Bk6cAaRx/Oe4wTR8zJP4bz7YJzjYyZzNpQnPpecQ5VNI49NLnyBSiBP2cxQ/83bOYfRU6Rw2wADsk2dU+piYVJ8wGsGUVYhA4Iv47Im+R1gtBcTLP1I3gDmh7VLlYmxgBrwfmd6RlKcNzrDQIFJK67pZPWbPpqaws1elHZ/c7SuS//1Ap7vpBFcwoWI5PS6RTtSJ5DxCG6KcAs5GrmlOKHsLpKH91EBoGN6p9QxcXGxMiMTJWCZwLR2jTZ6NdRNAl85DfPSjj8GcsDqXTgH0M8jhUTDIl77j56ZzSOpj0nOE5VPcwvy9zwH2jwH8ny6i+ATFR09tgqcW+Km8c4uekPNiVk3wOC/mSxceFZOyIWrDRJ8wA54Rtg570SV+C27gFh4Rby545869n+ccz4TajxxAKB8osKRsqAePHebsrOzcpJxYl8HnIy1xk99jqOXk2cFE8hylKJgozyfdiGXzrrnHb3+EdB6naUtPUZcngNeTgHxWf153PrWPmg8ZYvbAk8/FuXqKWnbjwgzPusfVIyFEUv6iNHtm0r94OJL9Ffs7eYVLyk6NY9LJEx0/16efU35q/8zrnys/BWw+jfQJIgU4f6IGMpvHk39OYTMHZBEJ+O9CCA9H03U5pW6YlAXgM69HLBu75KKOIp6DWagipxyDt/OAAFygSpPN78AhEKGzLjY0yKcdg6liGm76kaxeMpqqxrLAEkVg+8ibOUUMJL/FLH+6JoMuJb4q5UomwCAZa+SKhi0Qx5W0P8y1mgKMAVmkHM+kwmzccU5mdeL6PgnwEiQ2pDj/cbzPcDKpWHAIBSLGOXjUfvqeM8AmhYXuxPOp+GfgVNKxumkbj15/EkslZc+liMxSpD/7OyFi5Om1mCCmYYONbU+68xTieAoYv6T8KUXxvPwckvh0EAJ8skjhT0kvWUQJQjKh9E8hgURUNOxjMW3mqWd8uzOqaEAubjQamnc96iZSuEz8HTd8orA96WkcWZK0YyHPhbEHebAI9cdj55I2RehkOuD5IZ2LsxgRQlI/huYWE5HV7FkheCy7SX5MxFJpvTlAShGNnc7/I9gV5zTZD8O8fax/TgKQUpHfXKkuCOLD2X6NgNwJRlPjU+/4mPxp1yavnIuwXDL/wFl9z2Q/RwD/zPtPrtE55DJHGPNBzPuQliVK/GFvvBTwPlfvJe2cqpPmfXoWR2n6BHUK8HhS59TeU889Uy5EACxyZmk0a2L4hAMczEiFED7G/SlEMHwXybonfU/1uZaAGGabPNaLyshUkuNiELoEMQxsh0iQhmUa7iKdl5SKitR8ilQShfWjwzaBJk+UzbOTtlL4mB7YoX4yFsFpoCSSiUqV9vFLSh0PQBYmHEicqzkXkgI6GDubIvd0rE/FHJrrawaKYjbm6QDCMjy3l1+SxOTPk9+HurP3ulN9naXBrPVPgBEDchkaDfmn5vfEmXmEYBIEhzhRPmvnRfkvKT+1bvPz96eu6182fYJIYT5hqUXNSydzDqWfeM0jyn7ME5FiT5GDCogB90hXPTw7/x2nLhLchgRQMQJ/DY+uW8ySckMSFTvptJNAHx4IgF04pndQG6ZU7zwsRnh2cjjnlGrKjZw6ZLFfge9IEOl0HQJ7JByDf8KMqxFDW25sa8ZRiOhM98gENu1j7Hd4xwDUvYYGzIlRROSYLqBFhPdM6g8I7hTCjKa+Z7iNifPeHMnGfqec3/zF87xnzuip4zPR3cjZ34hgz7R7Ag6ftyQ7lc5wIMO7n0EIw+9zZYznDPj5VkU/B0mcgjuPjS+ebuNvkz5B8dF5QPPxaY6dE6pPOPzt5YR4Qmk1Mf0r3SzfBRHPnGMQHh4PeW5KHEpwVoCK1G2yYSU+P3IOER4HU8qhH4gAUwPlGX0QnGKwDhKRQ4h15kB55Ah8i6GTJ61Lnjl0J9OcYkubcFMAmlJ6yfujztidjEEV+xvngRmAEpNmRwV0vKjeBXjjLxuyCJwTCDECfSFsQDwE/8IYRNCNeCW+MihmxZAxzteEsxMklHfC4QgYEcBsnyZjGpH2jKuYzOOZNHRpwGIjAD85x0l52n+Sfk8HnczzS5JjukinxiCeH1fS3NkUF2zOqT0LzOdnYd7oU+UvyZvDp08jfYKcwjz9uSYrAXxBpTAA8glgh2heigz7KAD7CTxSYnpLp5y1RZixgehKNr5NKgT85CygRKJrcIGjCM+nnIxL2ouFKaSamDfFvxFb8Uis4cLfiBzc0PE5651aLKXvG0Y7DEogAgeTVo0igUilg5sprYVwQ97kKkpADBZY3nfDj8wiBynS2B+Z3Ccdo0cpATL6J6TUsZU4IZAicg0SKS1CeKRpncA5hQumtCpwOgKBCdyHSAL8ebNXAkwT4+yE8nQORUAI4zBF6H/K/wRgK+DR/M+RRLomKbA/C3AFE7GPmJVPmJyAQNL7IIZtNiLecbAfkR4hrSTzHAgYJ3Xan+HxeVspYpjlT76/BEifqnMq7yUcw6eDEOCTDIj3l1fCiAQ5+L9yPHSpxEXAYGWUShMS5DFFCjxCHpN9HSQ4qUxdRAlDFCvNz3caAy8SkxmJkjoC37RzUVR0RgH96B6CeUqRhki+x47GwUxndWqdNB88AyLwBLn04F1GoBrAvQBrJc75uwmK3FHloBRY69jXFussFsf1WlLmgmVR0mlL31u0tmSZ4PJC0naCtocPdy1KCq42BRcrqArHsXUDbtwdDL12XGwWWONoGsvVpSIvBE3v2B0Mtw89QkKRCb55nYMTWCvZHzucg9Va4UK01YetwVooK4nWDtM7nPLIVmtHJiVSQK8t1oK2BJGk8Egr7o0JsohrkiL6tGy2FGfwxMk0rz8ve0lDj9r4CECXwuSTgDamlyKaOaBP9rJI52+OKOIZOtWPl+a9FFm8BHn8bdInKj76UzHnmUUQ49/RMi8IBOaAXJCIjZjuGZG0kcLiiXgp+R2HlRhFTGG1S0RFs3cRyiWBw3Bj2cSPLL4obTixOBoatnHYyTMiAOG04fn3U5s4PaTpwfOIVwoQUgYpmUNJD/iVGpGFUiBk5FJ8X40RWCvQxlHlkouFV+5r4zg0NvTMsaoEi0JQ5YLSSPoC2gbyHC5Wkr0UWMdwi1qWSVYLwXoJDn+bmpICrS1SOjYrhdYOYWG9zChLgT0YMmURApR05JngciUxVtB1AqM9l3axVFjnsNbRNQbrYL2WdK2jkw6Un1+tIFegJDSdoDcO01qPFAWUhZ8bF8ZvHcR7Giz+JrhU8hL5sskSDEt7hgOYV55YXbkZHD1xFt0TdSe3yj31LOn2m+WdOrspQuQEXkwJEDGKt1IE8KjtgBAmYoH4vnRO5+Km9O/PRRQvaedvkz5BpPCnpFOkekr6yyAKivKjhCIeALgbjJNGcZAbmz1naTRHECnncMqyMKgABgOJuPkm3szJ35Q7SA+ixZs2Du1HMZFi5BjiA3NsNH9JOoCh8SR/XtcDcTkcWBVKLUXmKBQesDqH1o7NSlKVkmXpJ8tYwXKZk2ege4W2ll5b6tbR9Zb7rebVVcH3X1XcbVsOteVuBxKDFI6vrhaUueB+e2C1VCyqnIcHH2JCOkF91Nw9GHotMALuHxquVwXSKXbbliyXvLopKFsLwrJaKnRnsa0hkwJnBW/eNtSdQxtBmUGlYF1K7vaee9hscspcssgdmZIoqRCmR0r46suK46HneOwh87e+SZFR5JZMOfa15OGg+c8fD5SZo8wF//BtgRCCtnPc73razrBYeCTZG8f+6MN2S+k5D2vBGHBOYFPjhWEP/VwxDo/37FAnecG8/RmxMUnDtnkKWZ3pz8kOzrInoqH4N4YlcCfyYDykKkGkCfwQMA3rfoqiP4vdnhvMmfb+tukTRAqRslXPVXxBOod9xWTdB8A/fE/K5bjXJvra+DtFBpLRXHVQOLvHRHUcZppvE8QzI4pG/7GQEfdo2p9gsjodY+p4FhFBuHEtAvtIUQ228SCSA+QQpF7SYsBaDikEZS6oShUoXBGo+Y6rVcaqlGS5DBfYaIpckmWCZeWB3q42VJWfYK0NJgCLTDlsVLDj+1M3hkNtMXbEw37aHMfGUhRqhEcDdT3qRyyOPohqtIPeCITydfLMEw65FFgJSIeUhI9EhICITghcuNMZ50VF/tY16Ho/n1I4pJAoBWUOrhBgFJ3zeyHLBLq3tI2hcwXGxbuiPddU5BJtHHWrKXIoc0VVqoFbUBI6bdksM89gOjjUlq537Oq4SimxMycCTiRx6seMC0jLJoBdTJ9306pPUsAnCeQT2OhkZF2m+30+vJPIScxERS45xGc6OMYsmTd+ZjBzxPDSZz+d9HegaH5p+ghzsxnwF3MxUWwncgIn9AyDOX3yrFBi2g2B5zbi6Y2EB2LkCNRYFcIejFxBymGk05+K7lO1QfJiMcE6ArDB8kZAEJ2McojgryCiYiMZ7GAymhBSoY5SjtVCcr3JuFhnOAR1a+BO8/qq4HqTgXAY42hbQW/82BcldNqwPRjWS8iUoO1sGIJASS9icURq2HKoNbujRVuFEnhxDBZjYF87lovE+U746zHB/3XCi248UhBoC50RBHUGeS7JlCBXoCW4INKS0ouclB7Fgak1lHNRaQ1t731DVPCWlxLKzCFKiXRgGo0TgkzBcW84HDpsldNpQCiksmSZI1OCTlu2x55XFznLSlHmGZkU5Jkfc6st37yq/FawcHvfs6sN+9bg7EnoOKbnjvFTwPzRs4JHZqPz8CSn2vuzw8VTD88o/mETR+rtqY6k9cVHgL5TSOE5RfOnlz5BpBAndR6Q7VyK7N9T7YlxfSL1HovmCmIVysPhF/N9FeoN1qCJuChFEBMxUhyTCe2mAH9O9U9MUBkJpxmsfiRKSusNGz4xU0UkxN+ERTkxZ1FklPR9oDbHjS+lYFEJFqWiUhIhLJlz6KWiykFg+XDnRSmLSvJwsLS9ZbXMaXvHsRM0rSKT/opKh/8UuQziEU+FZzm8fr2kWjn++LbBGYFF4pz0nJkMi+EUwmmUgtUyY7MWdNZQ6w4TY9AN4U0sQkgElrLwdznnGXS9H7NUgjyXrJY52hmOnRdZKQFFLlgtC66vC8rCkitHWWXkmSTLBfvG38rWdRIpHIsSHhoXQmv3ZIVkIQre3Lc0HRibIYRGKYfDYq2l1y70U3D30FAUkvUyQwKFFAih6TrL8WgpS4VQkvf3PdoJTBo762w6QZFPymJyZ74/0e6gW5ixDXN4+cgSaP6uhKJ/lOLhsEl7J8aQbt9nsVE8dAmXFW+8O82KnEkv5Q7m7/400icoPkrTCzfhs/nJBklhXaCSRw9lx2COOkEMYih/ZKgwQTaPv4twGY9zYiRQ4ugC3hOxQ5Fwd2IMmxMMo+ZT4RjzJtEuHsHwSPGP8xEtXR5TUukgYufc0LexC2Nb/s4BgZReVaykp/xlGKs2FoVASIGx0Gk/F9Z5nYKxEmODMtoRbpYLEWcZjQCUkijlgkc54/xEMdFQdxyHIxKtAWKkcX+S7/6SHN+vOC++j4z3HwhB5BC9gFMMSmAhIJMCpcI8iGBpZCCXXtEdFcd9a5EIVCaRwoT1lkM7MnyUlMOcttoipBeVASGQr8U5hzGWvFAUmaAqJG0PTnv64+T5EWd/TNOTR889+egofRRJPTEvTNYqzT8jrhGhzowxiXvzcXuzPS2SvOFcpPVm7xu+zvt4pt4k7ynkc+q9557526RPHCn8KSmF0EmWIvgDzIoHZfIoKjh1aY4QQcQ+5zBEALYqFUUlmzH97ew0CN+w58T4e8IJhPzotJYObe6o7NJPqOhSO1gGQDudp/jw/GDGxuamUd6k0/RBzy3AItE4DAKLAqlQmUYpgVJZALwOhSQToJBYp9BWIiUY67BGIkSGlJ7S9EpqeNj27A6OrvemrAoXEIFfIycEVkgsEmPhYW/5cN/z9l5jUAEGJFhTKpxUGCru90f6TpOrnL73E9t1kraXvLnt6QwYJE76e5COjeDuQfPDu55ff7VAVYLWaJACIRVYr8M4tppcQiYcvVHsa8vbe80X14rLteLrVyXbg2V/7PwqSElZeATx+sor5JUCYz0S9eG6HFjnEYvw1lNKCFQu+dW3G+63LR/uGpwOoZX+nBx+3BpnC18C2MQT359CDOc6cA6wihOfWCROcCkf+3ve/3l6SiH90jb+NukTRAqndl0ESi9VPs+xMifWU5ym9MN+Gc1N3QQhDMA9DZ8dRUoqiCfS+tH8coCrzufFazjTvkZRUrqf0rxI9VoQ1g3638FJdq50DiIqkYiRXEBSXkU6t8Dy3+MFM26CEMKBnNjhekpaSUlZKIzRWON/KylQUrBaZEgJWQBoNs49AYmGy2ekFAgrcdYrsEX4gMK5DG8B4jznNRCMXjyzqiRFppBC4vB3GFjnEYQfjRy3QDC9jdyXDb8tAuPk4OOtlO//Zplx7ODYeTNY6TzCM85irbf4MQh0a/17glWQA7RzWA2d8/oHJWFZZQhn0Z1G5QH5Oc8BSaCuoW4dD3tDWWQslIzQPfhyeM42z7Kg1PaK9k5bDp2lbb2PxM8PQ3QC6IqQf1baFA/O+MiUuBZJvaeSePo98zbS900ipqaizvhHPH7mbB+SAZwSST2LLJ4qe2ne3y59gjqFP8e7T0xympWuuwwmqCKJZyQYKHlxwufAcwtiRBoRRs4V0grPOTACISKiiGJ+59uK524QkabEe5oXz00QO0UR1FBXgEAkRHHoCBHIhwEEhDBGSPWexPE3wkczdUNwvHTy5NCeNiOF27dgpBhEH0rCcpGRhoaYmIIHkYh1jkxEO3x/BaYToIREBG4CIRHShqsnRADu3glsuVBeD4EcxD6el1A4YRhjEDmvh4idCGIs67x+YpDHO1BCBJ+EHITXhQTVNdYpLCZEQxJoB03rORuVeUQDLiAFgdNhjEqwWiiU6zCdJstVmG+LxFsYHRuvOL/baa4vchalHJzs5BCWRZCrjCKDsrA8HBr2tWHfCJrOEW4efTqdqjCHdacKZ17mZwmvRwjgBNBzJ36kADzNO9nfkDkYQ8zf8YT4bOCiwt5+NKZZH06WPZU3f2H6e85FnMr726VP0KM5TnCEah9jmnoGGaTfI/Cei29mVP+kOQFenyAmZs0D16DcI1ESmX9m+O3ExNdAIKaWRvGdEu+9HBEU4Z2pUjmV5sTndVIWkU9S3xnBGB8pDi9+twxYK5jTCGf8eF062PEFAuuvnrRQ97Cv4fbBO5YtqwyQNB3c3jXkmeTyosK6fsBiPt6UHGztlZT0wtAbR54rykLxzWtHWQi0hpuLglWV0VpN00HbOT48GBYlVGXJoYWHY8/DzltWFXXPsXWMynaP3m53hmMrOTSKunO0tqZrNdY4WtNjraPX0OmWPBO02puxWqDTHon/cNtxrA04we6gaRroW+g1NNrSNH52e+GHa63/qxRcrBWlWpBJuDu0NJ1nBaXyyNRbR3kBmRCeW1kUkjKXZMIjQe0s+4Me9CsPR8v2YDk2UdQ0WCz8hdIpKH0OiP650rn3xTKR/E3zz4miQhuRy3kkUnqOcj9V/lLEEPM+LWQQ0ycoPorpqcV8Kp14Rpz4zJFCVDATKOpAhUTud9gzEiYxjqSb/BYp53ByjyVUybDP3fAnuhZMFM2RG0jF+kHyM3AUURJkAxJJg+0NwH58eJylFIOkVJ8fsJgOfNaJUfziIChkGSyHtBG0HcE72XMBzgo67f0EQNIbgeq9b0LXQ2eg6TyH4vB1rfMcQm+i57VHS8fWBn2GoNOOTjvqzpf3xqJNelOd72PTC4x19EYirMM2Fq3BWYFtbEBSgl0DmbLelFV78Y8OMasOjaXrwSFpO4eWDqMFVji082XRyCyKxwicgsq9QjuTgmPrvGJYeCV8b4DeBeTjFdPaOPJcopTvt3VemtT1buAc4/08UgY0H8OfpEH3HsHqNEOMWU+JeCI1ftJ2/4nmY8YEb8yBd8xyZ5o+h4hO1U/GM3mHmFWZAYUBAKRn5UlZ1gvznsp/ruyvnz5hpPCnphmQG4C0COYdYqyWljumSmSZNCcJHEBSFr4PonYlvGlqRB5z/UAKWx2ewk+p+rT7VjyG2Tb0wYbDOcSyZ6wbr/ccuIpgzfMoSiQMVkbOMQZ1C2IjIRNAkXAV4bsTYI33BfCilmwEXEZhrKQzJQbJvlH0vURrwYeHCJwVuxqODexqD5zbXtH+1CIl9MaFt/n+RWsb6wQ4ye0BxMHh7uIkShw5kZOJkyaCna4Djj3Qg0D5MRqLc54bbcwoXjtoPz/SmWEajYVOwFH7wQsk22OwARZA60g1/2LoR2zWIB4cmXQo4YG/sV73cWwd2hiE8HGYLPBw1FgM15sCcBw7TaMF2mTIzgVLLx/uI88Uq5Vgf7Q87CIVka7bnzGdgOcngecJvDMpTPf8i+CiOPF9ds4f5aWYaE4ZpvXPUOxC4m/4OzWPz717nj49ruBU+gSRQpSNpO7ppyiT+cKn5alJ0IlqMlAFUTwzMUlN641/B2uh+MiAFMQUgYT7FqKIKlLyUS48uZfYxvLQcPSRGF7CY09lF/sf5sSCiOG4I2EYp3DCZYSw0cMdBDIIVDyGEgHweoQQD6wYBhCfmlB5zsuv6xYeDo4+xPKpGxOAqGBfe2DY9JZD4+h6xe3Ooq3DGF9HC4E2ngLWDhrtne9i9A436DfcoA+I/7tkvUWy5tET29MBQX8iE0LAmcDhSKSQQWXkRTmTKwFcsJoaxu9m9/qEd0ZHttAfv9wO4VTkqXwrxuGsQ4to7uq735txvNYJLI597UVZdeNlg865gWup84AUgqmvw1s+icFsdtLJP29KZfDDmGcvmr/3qWN88oFZpflzE27jFJAXCaUGo7JwhiQmiusEZjzq1jlAP0c+T/UrzTvV508jfeKK5pN8b5JOIYJ5/ixbEixb5GiCKpg5nokEIbjBNDV1ZBOh3sQ8NRvLPXIgURiHYxotjhxBiZ2ML1LlgVqPw3dxeI7B+kZE5ZoZHhxjINmkHzL9LWbTOSnER+VMvOQiYhjYp8iqe2TicBjrYxXtDl723vWw3Vms80rdzjhwNkQ39U5n3XFc1+gXoKPyWgo6E0uiNn2CURmV414xLsPVqnF4Dhfs9P3CSyFDGAk5wAUv63c4a1EqCz4BoQ+JMtUGEy8vBjI4ZzEmQRLp5hqCwQWPcukvAvLNBRNal5qKBqQmvGjK2Ol1QXXrRWm7w2hOFG+Rla0Jnt9eIS6lF0lNzI2fpNT/RCAk0i9zBBSJllkf4u/nXj1/Rpz48Qhwp/kR+J+AByffnR5qpkTY0w9Onz+Zd64tOSv7jBSeSE9Nzku9nE+kZA0GYB3WxpughvsRpBsV0Umdwcw09XEInIDLpr89ghAjUgGiqGi4ZdExFV3OCJhBCpGLqc+CdV50FMVPw4EI7zERYTEqoR0TJbeX+8SDmyIKx6iYlcldyiIpg1FGLzAObneO+4PDimDJo3PfpHM4Edis5C7geAGQwHpg7t+GVJIsk8isQEqJUhKtNX3f0xuDG2wtva5jtahYVAVfvb5GSYmQkmPXcKxrfnzzFuv8m758dcN6teSLm+vQFcf2sKdtGvaHLV+9/pLNeuWdzCJfEPQANnjVWcD0HW3b8u9/+B1t39P380BpHiNLCXmWcXV1gRISKUTQcWjqpqFrO7TukVIhpC93TnvHPeMXLJq1+hZHslxEs2Y7Yn0Z7aCEwbggbnuWoPpzp7hHnqOo5yllOf7U/s6B7Knffyrwfer5c7Dpqfd+Osggpk9QfBTTS0iKU+kMl5D8HH3KxKhDjQBSJnGNEoQ+AFrpqXU3QwwpZT54QMsU8IvhkrToICcs433NgfMYLECjqGMU+Y+ioEi4pxzBnDPgRF5UQA+4VQSOZUpZiWHwyVFNqCg31PPUrw4IzEbldAhPIQJ1PkxjGFOvNdZ5j1wRKFslJVVVsF4vyXKFlJIsUzRty/FYszsc0NaOCBWBUoqiKLhYr8jyDKUUVVuSZxl39w90fY8xhixTlEXOarlASIF1lqZpMEqhpKLMcxZlSVnkGGvpdY+zNnAHYRYEiCKnyPx7pI53o44poCvv4ZxlXKxWFFlOpnwE2LbrvbrHWIwxVGVJnmcsyhLjNNYajwB7Td20owgIgZTSm6QGRG0dg/lt5Dziekw3/glSfVxUPi49V/+Z8rNUeujXowruRNkjuRWPPZmfS3NqbNaHtImTeOqpd53L+9hn/nbpE0cKH0s5zDDyfK4H6CSCsplEVBQo52ALjkhDaPt6QobImlL4mUvETykXMjVZTd6f4+FI1Is6gdBBXDEgEjEqpJ0D7ZGBCP0f9F3RjTj2IU5VSswPoqvQsVG6ARacjX4Joe1B9hQH4H+Pzm+xjg/LHYU58RXpcuV5TpYpqkJ5oJxlXnyD4/b+jq7vafoY0kJQliVfvn7FP/7me6oyQ2UKpST3D1vevf/Af/zuDxy08aAvzLnKBWWZcXGxYbEoKYsCawzb3Z79/sDdw5ZDXWOMxVlHoZT3nHbQNg1t3WJ6R9/16K7ner2maRv2xyO9NkPAOykleZ6xWa8p8oxc5XRCzzZWBFzecW9RVnwTOJDVosI5x6Gu+d2Pf8RaS9trXt9cc3mx5ldfvUZbjyh2hz13D1t+94cf0MZirUNJRVFkLKoCa7RHXH2HMY7eBIfEAVnPRFqCUfyWAtm5ldEjmPwsZEyK/hxU/p8jPUWRx32dWo+kaTZvZ5FqTE9xBU/1by42eslzf930CSOFP0NKKeUhI80PYZGFtxgaPZDDoQlWRC4ij4BMXKp4TrkJSaKDCGUqeXf0oxJi9CsIiumJ/iuakwauYtD3xn07XM3IVAwqk/MPQZEdKkRLpoHQcsPF9wTZ/KicDC8XENkbEWzkpZRIlYX4RgIpPTXfdR3WWXKlWK0W3FxfsF4vKXPvaaykQio/wKvLDQ+7PT+9+0DX996BzVi6ruOw31NmF6gsw1lLkWdcbtYURUHT9mg3ys2aRrOl5qe373h1c831lcJqg1KKr754jTaWttNsjw1CKnbHBqUk1jk+3G9p2hbda6qHHdY5Lq8uODQtP727pW67YA0EeaaoygxjPRLTxg4hudNJdXjRlLGSuu358d07PyeZous7dvsDd/db2rZHAFr3WG8PS5llkGUcjkeGmwHxe2NRVVxerPn6y1d+nZyj1z37w5H7hx3bw56+78d9JvyK+pDcEmejjscOG2TK/Y1H43lqfvZVhB9zpHKCoJ9QDuJUnVMA2J2rfOJlSZ1H1U7J8E9R8CFvUDo7Ht8yOG/jXP5T5fGvnOX97dPfmU7hZzY3WRsRkEDYOIFCT3UMw3MB+IvBezneyxyfTYC/dAF5MOgRBk4jvjwC/ECpC+tCCOiEoEuthpK9ORBkCV4bb6MaGIEpnBpE+aHOIDpyQz/E0Fr0bo5sRCqH8s5mWSYockWeScoyQyqJVAq2oLVBWM8hXGzWfPHqmqurDZmSiODLoAL3tF4uyfKc+4e9NzHV2juNdZq6PmI3ay82t4ZMStarBUVWoFSLjrIqHG2nwcGHuwcW1YKLzRqjNQK4vrzkfrvnYXegblsy1XJsWvI8xzrHdn+g7Tqchd2xRiiFto6m67l72HOoG3qtEVJQ5JJllVFVFUp55GAdiIBK/dpGYCeCH4Hm9v6e9XLB5WZN3TQc6prt7kDXa08jaI3W3qEvUxIp1agAj4plIcjznPVqyZevX5FnPjZS32tu7x+w1nFsanodORf/nA81IskzLxIz1mBNEIlZG7aiS1wOzpxvcQIgTxDAGYQwIItTFLd79PMRvJ48k3A2HwWG5rBkDqxH5DupHznr4eA91f5T2HRCkX7ks3+79AkihUHAPnv3xyiZT9RLOEcH08ijQTw02ONHZXLkENIgd4PYiYGTmJiuBu9mkYqPVHhR1BkYEDmeetduRBIksDgQJyIT4BwunvkUaYXnvAVvcjgHTkFMHcMjUgo+DkKN75UyQ0pFkSukEOA0fd/R9i3gKPKMb75+xavrDTdXG/LMh7s+Ni2/+8Mf6LqWoqi4vLjgv/7zP2JNT3vs+MP793R9T9dryjKnqkr+4fvvWK/WXKzXdH1PrzVd31M3LdvdkcuLHovkw917NusVV1eXbDYbem3ptvfDBFjnnbva3tFq/3n79h1KKb75+mu+uL5GAr/94x/BWR8X0EVFrsRZ6eVxzu8HKRR5VrBYLHFCYqxhUZVkmSIrFD++fU/bdhzbNrm3IGLukZq0xqKdoz529L23ujo2PYe649hpf6UmjrvtFofjdX1DZ/yC/vZ3P3BojmhjQuuOtm3Ybbe8+eknvvnyC1bLBUWWYY3Bas3d/T1t2yWqJMer60su1iu+/eYrtDH0uud4qOl7zaGuabvei/CaFmNMglRekMIem4L7GZU+gIsUAcSCU7Dk5wLGU+KYUxzBU+94Il/Ed5zrdyx/Kn1aQP+59H+A+OgMixYpgPmeSD9pFNQU6McyKbx4aeLBHIhsEQLSJTetTT6CURktPFWWUiLDPc1SjFyBxFv8hLLBqi9VDFvGeEyOSb+m+FSMxkVJW0pJhPLxhbJC+oilmUIpQa6kF/dIwBnqpuZQH6jrA5Fq8reKecVtr3vaxlPvznkuoSwLqqrk4b5mvz/wsDt4pKA1VZfT9Zqm7eh1epFECNAnJVmWo42l6Tp2+5o899dTLsqCRVUidnLwEIYxPPZ4C1mLkoKmbcmD6KnMczKpmDj7EZciUowghaAoCi4vNlRVgXWWqizD0B335p6m64Z7kqfUbgSGbpCSjIHpBJlSFEXOcrEISmyLcJosU8H6yGGdDVZNetI/bS3aev2CNnrgYKQQVFVFpnwbMbQ2EMKZS8q8oCrBUVGojL7XVGVJp3VQaDc0bcd2tx1XQ/rwI54jssGfIwXucfDRpDbJHpIYuadn0ymg7qbZbv7l51DZ8dCdeyzlHmZcw7Njea4ffz+I4f9cpAAjETErFgMXED+JnPNRmSCNlOo/HqpPQgMlf8Usb9hOkeOIHYreyZErEAEBpPtej6/1wEaMSuh048oJeTZy/QKPdKx/rCgyijxnUZQsywVlnrNZrymzjCrLKAqFkp7yvX944Pbujt/+4ff0uuVwPLCoMspCkas1dd3wu9//kd3hiNaOsqioyhKlBHcPW3786R33uz3GGBxetl4VDe+u7+i6jk6b8dpNociLis3mAm0NzaHl3e0DeZHzpTZcrFdY6/jx/R3OJfHCXbygxwPj7a7GOUNZ3XJ9ecGXm9f8+OaN5zWHORs18C6BA0opLtYriu++xYaFyHKvM9nuD/yIoNeGaBU6bKsgt/O+gYETidMvvHPcarlEZQpjrb961BhM37BclpRl7m0KeoO2Orx73Ce9sRgnUFlB3QRdhwSpMparJUVRoJTCBGpfhH52vUcgm9WS1XLBqqiw1vrw3oFoOjYND7sd//bv/0GM1lqUBdpajnVL3/doY9AmMSxwKW8w3XdDgZvWPX1Gn8o7iWlm9V4qOXiqDzH/uf69BKg/16czAOkpGPY3SJ8wUkiC/zwSOp5Kz0xsqjOI9+wiRgp+bmIarJJcoisQgYMQQQk96BciEkitmZQLUVJnQ0qpd5LbkAUI49tz0bM4eiFHvUfCBaRODk4CegyG7S9wkeRIjHb0rcH1DikUr15fcrFa8+rikkJmSARdr7HacqwbdvseIWC9LMjznNevXvHTh/fog2F3aMmyI1Io1qsN1ira3gexM05gLQHIS3+JjguUeAiXYa2j7Xv++MNbrLXUdUtvLP46SklRlKxWK/b7A8e6oe166qbjWNdUVcna+mB5fe+v4RzZhOgk5i/Mabuet29vyVVGmRd88fq1N01V0psTB4DlIuYMC2CtN1V98+Ytbd/jnGO5XAxmqtpMTbpSqYjA90U46SOzRoQs/H3PmRJUmeJyuUgoURNMZZfUTYdte5xxWDvudyEEWaZYVCUXmwu2u3v0rmexKFkulmzWC8oio8gVne4GeLw/HHDOcjjUXvyVZfz+hx9o286HJclz8iLnYrNhvVzwm+9+Ra81zjouLy8GvcWxaTi2LX/44Uf6TqONQykVCBgf/dXZCes1/fLIYXJ2Tp+FhU8B5pRVfqqxOcsRf08oPfyJHOs/Rkdp3aeAv5h9zvUntvN3ixT+WjqF9H0fo0t4waQm6+RGZ4UEWTBdo7lIaCiLgDoRHU3CXbhBLyGCuCnhuEcdVsB3LiIcF6h6NV6nmESa8HVd+B2AYPSa9ZIsgXQEW3pJKTN0b6lFR+8MEslqteDycs3rV1fkSKyxfPhwT6c19bGlaWpPNcu1BzzLBXlRIGpJ0/Qc644ib/1dAk5ghvAMMngIAy4JYS0i8JVYZ3DGcv+wG8Qr4ZYElFLkWU5Vlmx3+yBeElhj6XvNYrFkUUGW+XsbjEnEF5EqFf637g0P3Z7rq0suNobNeoMxGt01wSdjFBuJBLA452jbjtv7ew+krWW9Xg8WXM46hJSjr0kiQ3KhtYmIauBCHCI40uVSIpRCyiCyy/24G9Hjgu/GXDyfBz+LRVXy7kPP4XgAoCxKMqXIs4wsC0qj8GzbdSgpvc6m95T+7d09h+MRh2RRllRVyWa1pMxzXt1cUzctfa95dXWFyjxo2B4O7A9H3rx5h5EWYYx/n/IcmrHWW2NZG/Qs/jOPJjHxEn6SKHenMhPG4cw5F4++nKhwokyMZcKNiMGHVnkKMTz1rrTec/35tBACfLKhs1/KFs5TtA3lyXl2iKk/QcIZTAB75CoG5XIA9gIPvaP5aSpuije7pSKl0JdBPyHxJqlhSqNVkEu9jv3W9App5+u7yIEYgXSSIs8oi5wiz9hUa6q8ZLNcUuUluczIZUbX9ux2R373nz+y3x643T5grUHiuFpdYI3lf/32txwPHc2xx2pHlkmaruOrL65YLisymaFkDmQYK4JtvPDALc8Qvbevtdbh/cukj8MjVTIBbhwTGQiHUDYougWLMmdZlawWC26urlkt11xfX7IsC5ZVyXKxQMmMq80FD+wGE0yPfGIQCRAojBXUdcPbd3cYbfiHf/jOA7HOK4mPdZNQhJJoAmqdoNWWh2PHMVgfPRya4XazvMy5zHP2+2MSksIMPMMIrwTe3Nn/tULw5vaB3XbHj2/eBC9m6UOKX274l3/8nq7tscYjVWc9ssf5azivL9aslxUAu8OR+4ctzjkWVUWmJItqxWrR8bDdD1ZLsU8eWUCvDbu6Ybs70mlv1ZUrxXLhrbauLi94//6W93e34Cyb9ZrXr29gb+m7DmO86WyRSX7z3bdcX12Sq4yu69gfj3z4cOu5u7bFWhP0EQlITbfAs+mcuOfnAM+fAXzFCEceMTon235pH34uXPvrpk9YfPRzkxj/nPoMZWImUpp9lyOlP+EOJONl8YOoiQQhuKkISkWqFDzk90jApfvDhrrJbyFdkGyI4FOn/HcEoociy7labVgUBUWeUamSTGYUWYawYJxBWpBKcnGxYrksaduWpm05NIr9sWK9WAFe/txrTW/CncFIlMoQwlP/I0HsA73Z4SYwkFL6OFLBFNMFxVy0kR+oMOGRhRRwebH2eM5o9scj1hrKvCDPfPAoYwxaa6yxtG0HzpDnBSBYLhY0TcM+9MV7Tsc7joW/yzkoSpumDaanPaoqKMuSVuuBVRN4hK+yjExl/nkpA+WdDaoaJf3NbpeXG4QQNHULmGBFFDdUIi6JWynGy0KgtaHVmrrththbeSapqnJQSI8WTQwAVArBerlkUZZICYuypF8sBm4QISiKnKIoEvDkOaC4PjLMSVVWdJXGx+t2wzrFuz26vqOuG45NM7Sn+94DehOc+aRACY9QLtYrel2SZRlt0yCFYLVcjErz4OvR997iyseMOgdiwyYbyPMZsJ3EhHkunYLmES6cft4vU8J9DrnPSUgmQOWJOvP+f3ocQkz/ByKFkCYAngRZR8o/5QzctK6aPXvqM9MhiMgxqFl5JgchxXB5TuiKsyAG7+aYG/oT60l/A1mVFWQqRwkFveNiueKff/1rjxRURtf09G1PfWg4HI50bY8ENss1X968ZnO5ouk6Hrbvcc6SScWr6xvyrPC+cmFOcqkoQ+iIPMvRvcX5gDqACM5ZXugjhSCTGVJ6FsfGaniEoKRCiHh3g/eDyDPF9999gwOOxwb94xuapma9XFIUOcZYjscj+8ORtush3PhcFBVFUXB9eUHTtNw/7LDWIgMgl8K/q8gz8tzHFDrUDW3f8c3DHsmGi1cXHLrWx2MS+NvhlKQsCq8gl4IiU6wXJVJKuuATUWYZy6rgu199i5SSu9t76ralt4+dmmKkVn+HsggRWIMFkbF0FqIdsTWCrrc4J70+JokPBQzK75vrK9aLBZmUfHF9xXpRAc5bRgGLqmK5WHgAH6yfhPD7PMskeabI84zX19eURcnuUKN7H8qjKEpUlqG1pmlaDocju/2BIi+C3qdhv9/RGx3i/Qm00Zio/HeO5aKi61qqsuTi8jLo6xz3Dw/UdcP9/Zau62m7zvulvBjQzn+LWd4zQHUWvmWsPn2/SPLmPMrLhOYfy41EIJK+6dNBEP/nIoWnkoiUgf/iKflosM8JURIj8pjU8fVkFC0F8Y6TeB+AlMsAyNxgNhpxgEMM1kGeUPHUVKF8nJ2vrm9YFBXrconVDq0tb358i5WWY1eTZz6MxIf7O3a7Ix/e3dM2BqMthVR8cW15dXVNpiRlkYHy4gztHCKTlFXGt998gem9wnBRFOQqY5lXNPWRHx7esT80dJ2PveGsxNqRFVaqQMgeJ3x+tCZSKiPLc4QIli7OopTXd6yXy6Cg9LemCRxXl1eektWau+2B++2OpvFmpYsqxzpBluW8vr5BKcVyUXF3e49Sku++/Yrryw1VUfDtN9+wWKw4NL9H655eW96++4DWmov1kl7bYN0jyDMfa+nL16+5ubogV4rNasU//frX9MaGgHo2XC0qKKuStusDehOjbmJynh1FlrFclHz/7ZfcXF5SZTnXmw1Yx4fbezrdYYxhsahYLgryIqPTnupXmUJZz4VUZcly4UVn1jrevf9A0/UY68gzj6C9iWnBalEFb20vUhPCGxZkKgtIEy4vVqyWFd9+neGs32fr5RJjDG8/3LI/NmhjqZvWO/Y5R1HkLBYLpJBom3JHcAgxqX56+5b9/ohzjqqq2KxWXGzWXK5XaGO8FVPX0bYdu8Oetu3YH47eAq3vMTZFE0+JWGYgWjjGkAHyyao+L65VPJixok3y5PT5IQDaS3WqqajoOSRxioP426dPUNH8EpbtZzab/hBiQAoiiJOChGQkLqRI7k1gBPDihOgoqTfoK9K8ZEwuyiXAh3V2AhluTHMWtNOoXFKVBa9uLllXS9bVmqbpaOqWt0pgnOHYNiyrioKcQ1uzO+z5cP9AcJKlynM2XY8NsmmZeV8AJ4WPXCRB5YrLzdpzLUhW1ZJMSkxr2e123N3vaDuNsQ7wCmSvTPYiEKkyhPCirRiczQGZyiiLkjzLkcKLpYoMyiqnLAqMtSjZUhUFUgpWqxVFnmEdtL2mbjoOdcPCleRFFuIQCcqyYLNagXO0tRdZXKw3FEWOcLBYLGk6jVIKbfw1m9vdgSzLONatF2n0nuqVUlIE34qyLNDaX3G5Wi2HKKXjndUObb0p6eAXMSyo31MiWDIpJSjzjMvNBYuyQAhYlAVN6UV9xvrgd4uFV/Yq5X0/siwjz/NwmZBhWVWsl95suGla9vsDvQ2cAMqL/PqeLPiN+Kir+OBOyCG8iADPVYW7tDfrjVeYg3dga1setjtPyVvng/JpjXUOlWUURRnETD6pEJyvDuasb95/wGhDphRd18FqyaIqqSix1lGVHVprdHBgrJsGJSX749HrgrSJt014j+vhkJxKcT1SwP4RopjUBvlkGUxPa2x/np4D9vGvPJH/MW399dMniBSiB1b8+yemUyKf1HR0+Egm/gqCx57N0nlqP/gpDN7NabkS3gw1H3ULIjQazeSF8gBJCcEyK1mWFauqAiPomo4//PEtWaHIq4zlcsFyUVFkkve3W968/cD9YY/Vjt3u6GXgRQ5KYaWkxyFyRSYUl5sNy80SlMBIhxbeW9sJ0M7ghEUoR7XIaeqO5thwsVqipGBXH7jfbnn34Z5W+7sLwFuYuHD7WQzBkCmFEhJj/H3Kuje8urriYr1mXXnAkGU5y0UZvJqX7PZ7dtsdX3/5BYvFgi9uNiGCqfOy6r4LwEOQZ97pqj7W9NY7d+neeKc56/hwe0eAcbR9H0JURHGWYHto6LSh67tgc6/R2iCFZb878p+/+wNvfsqIAaacc8FfMcjd8WM1Tg9A1BiTmJUlW9V50VG0cjoeO/rO4oRE5TlZlpHpDBz86ttvuLrcsChzyjxjs15Q10f6zkd3ffXqmvVqSVWWIVLskff3W9q2Jc8cX7y6QTjH5cUFy+WCRVngXIs12juvCT93TafZHhr+9d//HSkE//KPv2G9XFIWBf/+H//B/XbH+7sHtHWDLqYJSuMiL9is12RSITJBVZVcX11xeXnJf/7+99zeP3C/PQAOJSXZ27fkmeTL16+4u7+nblr2hyNVWbCsKr64uUZlCpFlvH33nnfv3tP2PSbEvmoaH0er73Vi2xVTmuNO5J1KaVmCPAaltwySghTJzOpOUsoJvDQ9J1r6CKT2V0ifuPjopQs/S8/Or0jWKYbBHpXHsUzMzUwj1Z/UCzdDBuQhBkXzYKUkY3MCWchBl7bISsos52ZzwaIoqfKCet9ywCuHTQi1vD3sAUd5WdI7S6s12gZbdmc8S4+lLAvWax+ILs+8COhqsWJRlN7WvGk5tn2YG4eN/5wPVbE/1Oy2NUXuzULLqqBaVFRVha6PwQTUkWUZRZGjhERlkqvNmr43GGPR2gPMD3cPVGVBnikuLy4ClR/uSpCC7e7Adrdnu69ReYkTktt7TyEaazkeW/re39GsjaNpO97fPlDkGdrqoIj2YgnnHB/utt6fA0dvTBBJmEFGb62j6zTb/RHrrHd2c26IUbQ/WJomAgc/P/5+Ahgs14XAOo2xxnsXu6ksOt2exliatuPdh1svvlESIfGBA/sucBuOw7H2bVgb2nc+sKCxQYEfTGxDfakU1vorSYvccxZlWZLnOUJKbm6uyXZ7dvsjRaaoKq8Etn2PMZq268A59vvDwCFZ54JZqRm4H//bI0ApBUVA/JnK2KxXXglvLbvDgbpphnsrRu7JI8W6adntD9zeP3gxVFmyWS+pqorrmxvyLDhSBn0IBITUtD50R+/1EL69iTaOx4DUTX/PccEjub9IxElPAe2n4M7P4FQe9WOuW/jbp08cKUByEcDLH0nFN5P8Gds4F/OkAH3OSagI/JPQF1HHMCiXheckEp1DvOBNSUFZ5B45OMHN6oLNcsk/fPsrMqFQCH7443u0NqhM0vWartP8+OE9Td9xdXM9IoUgpsGF8AfOstl4MUOVl6wXngpcyIz62HH7Ycvt/sDDofbycMmAFLQ13G933N7ueP9hi7WW682Gf/zVd1w1HYdDQ2d6us6vQ1kWLJeL4Y6Cr794DUGZ+ubdO/aHI7/7wxu+/uoV15cbXt3cAND3PpZS33f8+OYdu/2B9/c7NIKqbnj/wfr7BLThcDzS9T1OKNreoncNh8MbBGAwwSbe4qz3sr3f1firaEbFrxPB5h+BcQ6jDe32yKi88fNnuo6mm5lbD5EJBxCHN/I0DPFJEtnRWMvXbPueTvccjsdB0SwFOGfpTT8glLdvP3CfZ7wvC69/kYLDYUemFKvlkl57QC7wJp5F4RXgCEFVLVku12w2F1RlgXWGX3//K969/wDiPWXmg+gVeeajsRof1sJoze3dPcvlIoiRvOlwNHwSeKuwTmuatmW58EC8yHKyIuP1qxuyTNH3HQ8PW45NmxwvQaYypMpAKvZ1ze32gR/evg9liuuLJZebDReXVwg8J/Pq5pqqqliv1rRtS900/K9//Ve2+z1d3wZwKYMOJ/YyBcSRaHwJcJ6Jj9wcWKRw5jlidA7gX5JSruRjn/3rpL8DpJCmF4qUzq7lrMALMYMVnJ1xpmLUQalThnRudDSykeKI8iH/pywyVquS64sL75G6WgYLoZbm4Cmi3eHIIi+o8oIP9w/c3m89Gx/eGAOXAd5RKc8RosGDddDW0BvDqipRKLpj600h8Ye063veB2es3vrY+9aBNjZEz3TkRYHKM5CS/aH2oiBrfNygfyi52KzYH/a8efuGq8sLvvzyGoS/LEf7C4LDBTCSvjfcbvd0fc/bd7eUhQqhng3GeIo1hk8wxvGwO7A71Eii45ajT0I9RMp1/O3tN4UdIAQ2cAmJ1ubE+s8FESN4mVKfsa5M8uJ9bCNVF6XObog0KIZWnQshLxyIaI8lQish4qFzsD803mdB1UGv5bBaI6Xkfnfk3e0dWSbJlMIYQ9N2NK2/fEdry25/4Mef3lJVXi9jHWQqZ1Es+OqLVx4pFBlCLsjyjJurK+q68cjBeJPVL16/QmUZ9/dbtDGew7IO3ffUx5rlYklZZmwuVlRVxetXrzjsDxwOR5qu9xcfxXHjyAt/j4Y3hd7ggLb1znPWWlRWoC389PYD2+0D2+2Wum5Zr1f85vsKJTMW5QIhFFIoqnLBzfU169WK47Gh7aKSWofwKSmQnf89B4DPlZP8TvdF+nnuHU+Vp1zBvN6nkz5BnQKcn6SnMPcc2J+oGvL8heppPff4GecviRcufVaMFWYEhcDfLxAV1Q7nKb5FxdXVhs1yyWZRcTw02N5x2NZoY6jrBoWkUDlN29K03ahsw4sOtA6AV3gAMYAu50UJxhhUIdHCh7DuZA8OqoUPE20GSxoYfAiirb7AO8AVRfAWNnRdjzWWLMvYLHO6rgUs75X01jFZNlindAFpmRAbx1iHaTusMdR1TaZiNFOLtRrrvKjJOU/bd128B8AO4hg3TLogWmN5SVBcQBfmfJyHUQwwlxu4R9tpEBOFZ0adZqwvhjUNPWJO0U2FFWLIiyUu9iv2e4Az0XPW0enR7MwNvt2B5xAaJW3wLlc4HDYExvP7wnKsO+7ut5RVEO8UxeAJngf9hTZeNi8DQZEG20MIz5H0msVi4UVBnQ1mxz5ciHPeaGC19BxDVS24u71nfziEeyVGhIjwVmdSKW8UUJQsF5rNZoPWHogXufd/edh6Mdf+2AwBFfvee0srmQ33VWQqZ7PecH11xaJqqOsGkOzdkcZ1IcTGCeAr5nmzem6ed4pyj/spEgiOlyGApxBGTJ8ut/CJejTH952aKDcrm8dIOlE1Vks+IbzQWMeISNr5PMUYXkLjZ0oA4W6A0RHNkUko8ozFakEvLAbL0dSURe6Dsl1dsaxK+mPL4eHAjz++4+H+iDVgW8e3X37BerFGigyJwhkxSC90Z9GdwXUOhaLMCw+srA+nrVtNV3e0ruHufsf//Pc/UISYP//9X/6Jsqj4za9+BfZHHtyO1miWZcXVas2qKFnmJdXXX1FkpY8bdGhRUtG1Gqc7+k6zP+yp6yOd7njz/oMP+Wy9nXvfd2htBzl07Hive7T2h8jhgnWJL4sWPYOceBJg7SV7Iy6vxE0WeV43QS6IYd/4Vs5dUTd//JRYwg17YXwutp3muRDiQySPOxAhYuuQH98bTTP9xGjt93QXN6oYy/bHlmPd8v7u3hszBH8Nf4Woo24bH1dpUZLnBVmesd8fcA4uLq8oixIpFJebS8piAbLgd7//I2/fvac3ml7Dbt/xpRFkWcX3v/o+vFtytz3y9sMWY1XgnnyvpcgoiiWZqsApsqxgtZT88z/ckClP0Dxsd2y3O/7H//63wJk4jpmht4oP2z3rxZIizznWmrZzFMWS9eqSm+vXrL9bobXhYXvgf//7f/D23Xua1oTzeOqTAuhTwPmRaeFsL8xhSgAML+YKnkIEs/JPByd86uKjFGqfYuXO1RfjYzJpwk4fT4nPgROYvUKEfJeGpXaAk1SF8tdBXi4oFwXLzZJdc6TuW5p9Q99qttsDl4sVwoByAqMdbdN7pzALXafRvUUYhxLKW3kEYtjfe+wwxmG0RTpBLpJy55WaujdQ+XB4zjo6o3EG6mPDoii5WK346uaaZV7w/t2WReHDSTjjY/3oWtM2LaY3/lJ5p/nxp/e4oDw+NgearvWUYedFQM4anPNcSrxv2cUJS0Q/I6CLl0VGKt0j4WjGOdYfk4eXj9d5uJLzUUHcIydOWBq6PNTxv+SEAZw2l95GzeSb78PY1rDnJv2cHfjQ5+j17fsQkYMjBsNys36O9dPGguWpxYvShLcui6XHuqPtNG3bk4V7pY91h0Cwz2re3z7Q9YayLAPH2oYrSP07jIW20/Ta4axkuVjTdh3bXU3d9LS99eOLGxEQKHJVIIXCGLh/2HM8NiiVs1xUrJZLut7SaYvWIYgi/o7vvrccDy1VsWBRZuR5iTYuiJEypMg4Hlq6rme/r+k7E/xlUj+FGXAXc+Ab12kuxnmKmp/Dm+e4glQcdIobiERCQiwMcdg+jfQJio/msxMh+xzCq1m9BIE46QXnQjxmJGaI4REyGPCPz3PxtrKU2AQffqBasrlY8N2vXrNYViw2C97cvud+v+XD9oHm2PLW3LHMSmxvud5cYLSjOfbo3mOlrvPeoRjIpQpx/wkX5AgMBKRgEA5ymYVYSb5/Rlt0r32ANal8ELRWY3rHcV9TbnKublZUX2ccLxpMYymywjstacOxP3J4qNnvDnRNy/HYYHpLvW2DqKfHuH4IJW1MErY6UsNEhBCX0CZUNEMwODcA4nj5p0CcpPL9M8NumCANkbB4BAI6BaLpjpgC18kCjm8iuT8tqRMA8oDEYLoR0vdFqjJ9d9LHoQm/wVw6qLSt4WKNtB9pikhm2l9/14EDZwaG4lB3kxFFBCuFoNeWQ91QfLhnvVoghKTXhqbpMDHKrYW67X1EWitYLjZ0/Zbbuzcc6o6un1LSgsAp5CVSZGjtePv2jvuHB8B7Zb++gcOxo2kM1srgJB+srnrHbtdwuYEsK1hUK6yVGGMRIgeXcXvr4ys97PYcjh3GRMB6juI/BaTldFMREcvsuYH7SNeYWb05l3DqYvYTfTvFMHxC6RNECi9NKfkPMzLe/7Y8RvZzacAcMZDAnEGBHLZHwj1IYF0tebW54Gq5RmYC2/Ts7nfcbR/G4GYYjseOMmt5tVEomaGyDIG3sdetwXQG2xsKmVGpfAgBjQVnHKaz9G2PxF+aIsM/JSWFzCiktzoSG8E/ff8rfvrpA8dji5QZvTbc3T9w2Nc0dcvhULPTRz68f0Aa337X9PSdoe16dOv7ZVwPzuCIyjwPpMQw7+c4NUh3vTiBQKZPPAbKc9eh9NS45P+xvkxKouI3Us1icNTyecoDVRsBdwzMkbxBRF+DgFiCXkNYOYzAwxVJvMlt3CQw2ZMi9nGcgylAcsksKFJUNpQPQCyljNNLih7PU7rJU9RtAdtbWtMi657tviEill4bHD5kiTGCutG8fX+L1oZFVbHd7Xnz9pam0aRIUOA9o1fLJev1JVle0WtH21nqxpvwdmbLdt8FKzSNE1mYKz93xgi2u5rjUdNvBK9ffcX1lfFnRQjefXjgt//5R5q2o+utN1ywWeDGUiSQjj3ujYDcxTRPEMyQnZy1E+qmt1OdRQpwGsonXGLkBE5Jraa0xCeRPkHx0RyCw3kA9MQnUlunmhuaSNi2gBjiHhCRS0ie9VZKSSMDfHRgvUij73SIO+SjZErlL64XEZQLD8y9B7DDav9xxpKFeEG4AEQcwz0BOJAIFDJQZd78r8hyCpUhnPc0XVYVucoQrvV3AVvom47jvqFtOtq2945fvcV1Pq6R7i3WOKxxxABGZrjIJg4+UrrjRJ4PbuYPVRzD6TVjhJmTJxO5vAsyeeG853eI54OweMGDP1FDdKlAaTtng9ewVx4VZemNAHBBCuPFZiZE8xSP4jx75XuWqYBQPEDVvVfA98Z7TKssY5TlhW3ngtLYuTHGj/NXdMZ2hiB0w0OGgYua4LsEPQ6K04hIppyGCDMxHYf/DGhM+B4Z5/w1sFi6PuVGRihlneco9vsagaAuvS/L8dhg9KiH8XsRFtWS9WpNWS7IsxyQKJWjsoK+M3S9wZomLKsL4iET4mh5PV0b6vXasVquwxEW7PZ7Dvsj231N2+lgPisDEj1vaSRCXoyCOwD7kCdQAQaIx0ghxqwfrz1k5AhTaC5mnwSpcIYr+IwUPjbNOYC5UgCmpH5aL343TK88mzbvYw25CScwvDqWI0bxeMLZC+sPmTXw9t0du90eITTrzZLN1QZ/969iXRYsViVXlyteX1+yWvjrEHOZUYgc4Tov/7eGtvOfLMsp84LhbmUEVVGyLEsfoKw1WGMQzpEryeVqydXFhquLC88JNB3HXUPXeh+H//jtDx5IGYtuLUY7bB8Av8EDhqh4H6ZWgHTplcMzZPAIsw4rMtaeAn4v3phyF6mrWNCUeqdyAQIVKNcYzRMWiyLcErcMsYgY4vpkWYaSBE9xRd9r7h+2HI9H+q7jn//5H7wjl/J7yRjNv/3H79gdj2z3u2Qsaohz9Ovvvuf68oLVsgQczlnefPjAbn/g7bv3XF1e8NUXr8gCJWuMwZrg3KZ773NhTLAOs9w/bL2FlsiHMBHG2BBmuhsii0Y1/DhV0/kWyd4f5zmhWsW4mAIQUvo4U84xchnn5BUjovFRU+Gn9w+8/bBFSh9rSet4A5uHbFJ6BfLXX33L65sbbq5vQqgrx3fffs/l5ob3dw8hQm/DzfUVeZZjrON+u+N+uwUUxgnqzrE7duwODd9+/TXaWt68e8+bt/e8e39L04FzEWRNgbg4CW09MYZIuL6QJ4Qn0lxAvCqKjJwcoM04Jamo+hmu4CW653lXP7H0CSKFeXpKTPHCx+cfG4D8nBsYuPkxT8QdIkjwksBJR68dojMcm46iKslVxvXFBpVJVJmRB0U0xtA3HS5foJygULnnJIJnrNHewqjKCszCcrVaefGQUFwtl6yqCmkcTd2y3R4Gyr6pO+7udpjGWyB1reawb6nrFq0d1vibtJyN3Igb9YLDOF0yL8lcxGMh0kkMYw8TO5dsxwkfpB0pdBOEe4O9GWSmJIVSnpOSPhCcj1rqj7cxhruHrTfTbWrWiwXr1YIvX79CKW9Sa4ONvBSBIBCOPM+8/4TW6L5H99o7DuYZq+XCt2fNJMz3SHgHCjOapAq8Qh6H1jq8x0eAzbOMRVmyXq9QMsZZMhijB8V7BPLWemCqtaYoC9bLBVVZDEhBGx89dLiq03rdTds19LqnaTpS9XOyMIxQJs5+BJLeS9rfeT1S5AMHPV+fCck6rrV1HkHEQHve4itZ9WCNt9vXCPFAdEjLsoyqWKAuc6pq5e/p7js26zVCCHb7A8e6w4OgcCmTFUOoFOcUfaf5cLvlcOzpQ94wZjFC2glHMIG4KSfhgof4FHHE0OajDsGDxOhh7WbEzIQriMp/GTd4Upzqv59DFJ9Y+gR1Cj8XdZ5BHgGIT62PwiYYLIoSYDjy68PvwfIoIgZ8ntZeSnusO1YrHxDs9dUVF+sVWaWwGIzVNE1D02nc4gLpJKXKUE6C8QfOaEvXaVZliULy6mJNrnJKVfD68oJcKoSxHA81d3c7dOeRwlE3vGstD2JLU7e+ndZgjO+fCeIgZ/GU/yDeF9O5AHAOaYP4IxWrDTAzEVmkYrSJFdAITJiADv+/lII8U7y6vvSxcIrCh8wIl9orJcgyX7vvNb//IeP+/h7dNmyWC64vL/j+26+GEBB1ffR3HVuHtQbnDIsq9/cTd4bD/gjUCByZFGxWS4zWHI132hvubB5CLAdgIATWGqyxLCt/r3EbqGdrbbgLwZv9Xl9cUOR58NXQaGOQMt4nIZBS4RDc3d7T9T2Xl2u+eHXN5WYdQlYY+r71SMMY7xCmvdXQw37LsT7Stn5Bx3WI3ME4xxOl+iAD9X4D1nkHwClUciPCdzBCsSkAdC7cnDcsbbQ4GveSMYK7+x113dI1HYtqwXrlxUmrVU6el9hgqVYUPvCgtW/I8yOjrXecX4ExYI2gaQzv3j+wP3ZoKxltxKOIJoqBAkcQxhwFraCCs6Afl79cS3qRLB5BxNrD2ITCOpmIFIdQfclufoFY6BRHcO73J5Y+QaSQkuUwFQul4qOpAvOxyClacqQAXwRqOH7C4htG88NEMe3iPcApMRb/mghQoKk1XaPBgGk1bdPw45stre459B3WOcoi53J9Aw6WRUkmpO+lgcPe3xL23ZdfUmYl//yb77C9xfaWrm7Ztz13d0du73fc7xu08eKdXhv2betFWiYGq0v6bRmp/0FkRmpc4z+GwaHKj3Gk+qeHIciUogNWYgU03x1ikutflGUZVVXwzVevWC0rNouFvwJSa5q2xjaeci7LkixT/MOvv+XHMqfuWu53e5re3028qCrKouBf/+O37I81fe9wQQfy1RfXZFnmYyi1Hfu65fc/vON403Nxec37uwfevnvPdn+k1xqiYlokO8w43nx44Nh0fPHFF3R9y8PDA+/eP7DbH+k1fLg70Pd/pFosqUrLm7dv2e4P7PZHpPLcRFUoXr+64ery0kcfDTb2y2pBVVQsSu8bUjedvwxHZmxWhUeSec6Hu1setjvutwe0nu/7+frE3wHAhuVu2yjuyWf1Rgr68eaer2SK5MWkzIbm9seOY63Z7ttwRai/PrTIczarNVVZUlUVy5V3yNzuWto2WhH661hXi4r18pLl4gptFV0PTQfGzsVeLiAEiUwsfqJPiBggrxy4u1jmCMjFidHzHwLiEzhyYhBEr1cwOBlFC/a8svgUgniuLOUoPqH0dyI+OvVJy8/5MYQN/yjbL7iYI4wgSnGT5sT0lQ5iaAwCS216L6/H+Aihbdez3R1p+p66732UUkswr5NURUlVeAVc0/d0Xc9uX7Nf1tjS34NgtaXvevaHI03dc789cKg7+t6M4nnrPVtFCugHsVjad/d4LPMxJWlK57uBoPTUVhQPJQhBpNKISLMmL4kseuxiuBynKAuOTUPX+9AFWhuMNiyXhqoq+WKzoSwLf7NX12OBY934CLNKsdsf2e4PdNr6+wOkYLk/kmcZTgi6XqOtpW7b4J3r6Hp/+5kPu+AQSJyISmDfRwt0XU+dKYz1ISXarqftNF3vlcJ9rzkEb1yTGY51w25/5G67Hy7vWZUZVVWR50UQH1m0bmk7H6XV5N6Ld7evUcqHhihyH+RuucjI8yKENUmtYEYOIWXSHpNswWkwcn4nAXtK7czzY6tyxjyIE885T6hg6bVFSoOUmrbrybMM3TmqRc+iM/QGjDV+L2sHqBDNtWCzvmC5XFFVi9B/iRCZD3YoTvRLBGXxgAjFwCmkyEFE6BsA/6BnQPq2na/tBi4j4R6dGyUJKYfwUqTwUqTxCaW/A6TwXPKXv5zlKCJXEHTPI0KQXgydnJdBzJLswRH4R0Ab80ZOo+ssfecjdx6OLQ+7I29uj96yRUhU6cgzR98aSpWzur7GtPBQHvjtH37kcGiody37h5pSKRZZTtdo2qajqTVGW/oerxMIYiAR+hp1IylCOPt7/jnxbFCj81i5PK88tVoZ6VNwQ92Y/DOdtpij5t9/+598+foVi6ri7ftbbu/v+fHth3BdI2yWFZvNkptXNwjpo6tuD9oHy2s6iqKkqhytsbTah8JeLkoWVcHucEQbS9toOuNl8do6jBsBv0PihAzcYTQHTXgbNxqVgae4tQXjBCaM1ThBb/xMWefl4G1vqTvj4yIqSS4lH94/sH048rD1Qf463XOxuWS11FhXc/+w5X/8638Os5UrydXlBb/5/lvatqHvLT4Wuxv6/wjpToDlFD1Mf80Bupvlz5+MpGxCcA0K63k7ctgBxvqP58Q0D7t+QJRF7sVFbedFeIicLM/YrDf8l3/+v7jarFktFwGh9GxWVzh3QEfLpaHHQWEc+uIRyGMoPJqf+u92eC4Z9xDyJf5OhufEMHrgeQCflqfPvISD+ETS3wFSeAqiwWn+60T9lHCNgB0xjYGUyE4jdTyuVVo3Xnvo65vem3XqzqKcpJA5EhFAq2NVLrhYLChVRoYA48iE8CIkJ7xi0XoxVC8srfA3p2ntzfOcCUgpiIMmfY7JRqQ1j+tEECG581PpHlOaEcALxKBsiyDfTSZprPtoFQYWI5SHMM1N24eLe3xMJoc3k/RqWYkIdwp7f5BEWi68/4aU/gKir1+/4mK9CroEf1fD+/e3HOuWvem86WK8+W0CBlIgkAwkGYePPzTe/SwenXTw0WElWZZ7q5qiYLVakUnvJJaH0BIPuwNtb0L4cS++kFLFKfFK1qAzUMJ7Et8/7KmbmrZrBxPM6Ty7SbfHy4Diep5ak3S8EeiLKRXuTtWdvIhp3CBO1JvOpw2WPc6IYAUFxophXNZJeu14eNiDcd44ICtRKuf66pquNzRtPwlSMkBTp4a18l7mCbU/QQ7+txSRe/BtuLC+w15OhjZIVLNkXj5WVCSeqD9HJJ9I+gR1CunsROCfCPoffU7174Q4KYhOxOwTt9ncd3TYeu5UbrI5HVhtMZ1Dd5ZMZJSqIJPSK9dwrIrSIwWp/KbXBmFjH3y/nIHWGIQz1AEBOEdQEIsQFdQjo0EMFJXewzB9WaR2z07XyXyXDFZMYE70Px6HP0UKY5ZLHJlF0rGkFedoe+1v9goxpAfAK/2dxnkWrvIcA/QT5bzOEe5Vzvny9XWwCoKy9KKW4/7oxTPWBh+HCDwiwh+BvBhs1H3fxfgqjxAGqlIEijEClwBQhUQIRZblXF9dslwuubrsUCHchEVQ163XQ8QpE3IIV+0v8QvKaCvAWYSUaG152B28krnrvFVuhFbD4ibAcUYleHGeGwDwuE4p9EnWOkUIYpY3r/OovXk6ZcUksYFDN6npc2jMOUnXO27vdj7UStdzeXmDwMdmenjYITng0n4EvcDgzTwonU9A34lvQuRoxFgmSYBCHK6IkVimTPJziODn6BRO0Bt/y/QJBsQDP0NRLHROExNXK0UAp7iHCDnVUF2k/8JiywiEg/xQWECO1LWwARxIGX4HUQ544GUtptdsFgtWy4pD33F/OPB2u6Vpeh448h/9T/St5nhs2O9qH/dIu9FHIJInQVYlLJ5Kt3jFthFPA/hUqTwgifh9Xpa2M0eu/hOl7KOzd0QMc4DxFLKe1RUe/Mb2Xl1esFlWXF+sg6goY1GVKCm5u99y97Bnf+i8/D9jQCzOOeqmpWnb4HFbUpQFjdb+/mkx72Pq7BQvxwjzLEZacuSDpgjBT2GklOUQk0fgQ0LsDrW/RKdtWVYFUkqczDBOYlFDrCYBPtqtlKzXS8qqIiuqYD5sKYuCru/Z7vbs9jV1o0m9tEdLqRSox5Q4l+AGuDsu8RzyzLH6XPksZ/VOIYt5miOFeTsRSCe9NpKmMfz47o4PdzuKLGe5vEUg6I1mf2hw5MPzU8CvPLV/hjRPvZdTLkAIsDJ88YsSyuxQ7qQ/9yen6BSQf84i6TkE8YmkT1B8lFJBad4pgPNUefw+HiARskdOQQz5nhqDwZv4UfMzsc1AtUOVFxRZ5iWcgcIos9zHKULQa0Pd9Ijex4Fvji1N3WG0Vxo7y6j0ZnyXG7gCl3AFU8QQx5L6H3ggEMYeGYAzUzjC+NPzOhVDxJeemJ85fJk2Pqk44CvnfOx9UWCM8TeJVaW/I9pYbvcPHOsmhGiO3fQIwVpL3TQc6oauM3TaULS995y1DqWU1yO4szzgwBsoMV5daayl05qpmCIC4jiCCJw8sHPWcWxa6mNN07QYbVBKgcq9Ynpi0snAGYEI4qcMJ71mY7lYIBt/r4XD6yuGngfqeMKNTf6GfZ6Gs45jFWnNVBAlxr0yUSKf+DiYmsI+hxSSPHGqXAx51gm63mA09NLhI4B7sVo/XAc74wYGpXKwKJqsVUQEybiSRwYmYw6kI4cQyp3HO+P0/qmWRp+Rwp8rDaRw+J06DpCUiVl5ihg8KxCRwWCbHP4OW33gIsf2hzDbQbnrHIPJtBKSL2+uub5csVAFbdfSmR7XGjAO5QTHY8fBddyZGmfAasC4EcAPpqIBog8mpSKIFdwwTEEs93VFwh24pN5k2ubf02m1zCpEwB+BQGSv5g2d4i7ilE6BVOx3RAnescuFOx78v7435HnOarGgPtYcj0d++4cfaDpNZyJS90BCa2/Z8uP7O+63e7reoZT3RI7E36IsafuO3iROX7E7QgRPVg+c8yynzDJe31xxbFre3fk7n1NK25A4Ps0Ae28M7z/ccr898LA9Uub+Cs6s8PthVA77uRFSIaXkWLccm4af3n0gzxRFpijLhVdoWzxHEiCSe7Qu6UKne3myqLPy+fdzv9OzlYqc5mdu3kaEck+1L2ffpyIti6K3gr4OXI+UQDbskwC3kcmzLkUOM+Qm4j3rEoabEgNXELmBOVLwf91gu+LFlmFuU6A+Z04+hitQs2c/I4WXpKc4g1TPEL/Pn0kRR9LGsK6PN3DKI6TUdeAoPYURmwqvFQ7aumMnBNI5jn1Dq3vum5pGazChUgDyEb46J8aIBGmXXcwT4d0OZ0ViZeQGTmXgCuIIJ8MXs8LZFKWTETJc2oBzMy5/5BCEGxt0IgkQN0HKSdPhS9qcwyudu7albTt+eveB5XKBsY7loqIoK/KyoLPgjF/LGJLA6wrCNZdSIpOQ0ZuVj8dfViXv7x7Y7vfkKvOXEwnvK1EWBVVVYK1BAKuqZFGWvHp9Tbbdcbt9IMsVWeHvPpYyI1M5mczJZB+U3Yo8U0H3IBBC+Y9UGOuXR1rLZrXm5jrn9u7eX3jjvDmuEJK269kfWz7c7Xy4DiVpOovRhv3h6C2PUHjtRLI3RVwrMejFApsb1ukcNS9G3E6yfyZwPrYFA0k9LHwAkJNtMS1/hBTcPC9tP4XKsYEUkob2A7WfWg0NjmixfqTSBDg5tuOUCNyBAynG2zdPAu6AECLyiM1GqZybPftSBPASpPAJpU9U0exOfNJ+nPvE8tTJLTkg8cyMNMfszY9XZxLmIv4Z4KY/VW3T44yl7zr2nUcKtTVYEcwdQzC2wewzAPXJRT+xzUcyfzHov0jESW7oQzp8MQ7fJv136XsTBMK0AwP6DB1zyZymYSxmcGac2/gz5A1SjBlVGXGbtZam6zjWNe/vHli1HVJJFouKoigoq5LOWETfe+ARkIG/+FqiVEamMlxmPPUoBOvVkkVVsd4sOTQt+2NNnufkWT5BCstF6b2TcWyWC5aLiqvLDdpqpPKWQ0VR+JDkwRkrz3IylaOdIZMZeZZ772opUTIfAsB5eaAHYev1isuLDcdjQ9M2WNMP9zZ3Qaz4sDuGnSpoWg3Oe3T7+wQeEy9RkTwi3Ag4/cT66Y+IUo6B+YZ970ZgO2wiSK20hmsuhwUd0YFw85Dic+p/nuRQdaIsHhDEDBEAg18BEHU7sfdTuU9EFd7E2Ak8dyAY50Qw6gxmgPmRxCwgDhGUz5OQUXEQcy7h54iK0rxPLH2CSCFC4LnCypzIj2XpZ45UEpGT8E5OXsGcWJWkMYAmTQYKWszyU8Wwhe22RiiByAQGixUOGzeFwoeoBqwFEXwNRNAj2MgFDBFK3WjxkDhSDh0xETGEDk2QSDxECXBPpyl1chsqTpHq8OwjX4NYMk7GcNFN/JsCqbR9MbYK8XxanLP+es88B5VT95p3t/e8vrnmcr3iv/z6O+62O97e3tO0LXmW8dUXr7naLFlVBf/1H7/3YZjDPQFSCBaLCuccx6ZmWSg2i5x//s13XKyX5Jnimy9f8/rmyjsB4rkhFcQ51lqUyCjygu+++RU3lxesqiVVXrEsF/Rac7/dsd3t+PL1Dd99+zWXmw1KSv75H35N22maXmO0BhxKClaLBUVR8ubNOySOqlpzsV5T/f/Z+7MFSY4kSww9Iqpm5u4RkRtQ1cvMnSH//4cuXy4vOdPT1VUAMjMi3N0WVRU+iIiqmkckgCKH3YFCGeDpHraq6SK7HJmOmOesmb/jAVvSEN3ni9ZBaCVZe4O2MmQ1kZkkDNSw3n3Xh0bryKZGzeSHal0CQEo71o11m0ka6tl08e68al9rIrRftysw1IWwqpazF5GJPDy3P8+ZU2MIbExqB4PdS/XVVNSFmVqJXJC0sgts/XlDpMWuqWYlJlAxbelWzvwWgQ83+27Pf8O+BN/esPmo33pi1O/7az8AQBX7hNwhVfF/Xn7kxf5GAD0yoWSp0ru4xs12Xk0Ka47pXqonuycJNfps5qO+lgy5z6BfvC+Ed6ptplfeR3bn9qL9Tn2wte6aws8JAx2x39GUb8x0Umn+NE04GJTFJCNEgMNhUrwh619iwvEwYd02PNwdIaLQ4mNU+3LaUqtX3Qm6IprlvS4bspmdsgjWlPF0vhhOkn78wlQyAI2Fn+cVUqAgdrng+XwBRLNwxeojt3BVUph0g0IfYgRRQIka2B4YiqIK1QJi1CStaTogBM1cVpygOzxdLsjLZrH73qHU9atOlvrnixDVl4zB79OGo5P8xb+oH0X7Td1fbqIBPCzXIWEa4Tdbquyv6xqLFh3UtQWw8GD3BXTXAlVL8uiw5s+x97fvSsRd8g9O3J2DYu+PZhNU/Nt4JTkTuWUUff7ea+ajW2L/2rFvMYXftqbwH7E5hQyv7PMJxr9yX3EZBCwMFu6ka3JRyqL+bIkkqoMnnhSWfTKhEdzidN52cnu0EntdhCweRaPEXQQaCmvps+S4TDWHwe+v7dIi5S7Rd0Thlm670/r2+G0JS0JbyLUOsDs2YL6DW6fl7QPp1Z9tn1Fq0hYHDviHP/4B3314wMPpCCI1lfz05QnzsmDZVoSgdSXiGHB/dwchQk4KUxGIsc4rlssVazaHtdVFUHRRdURfLlc8Py9YV+BPf/mKIT5rtLFDmTgzNuOKiGBNBddlxrpl/PjTF1zOF/wQWwLbT4/PmNcNuRCu1xWff3rE8+O5ZusyBTM3ab5FIGBbVlxTRi6EaTzin/7hn3B/OiCGER/ejRit7OR/+9c/Y1kfWxf3nM6+fF56zkgvo9xuPksEXsmDduPjudwgv0eBl6kkmBkGbIByPbFvN/HiRlIdbC3lcXdNNYFV0QXNpNQxAQDSJZe5F4GptV06oltNPztCTC0IgNCAUrvjlfAH61NnFvUZbd2Tw9j3msK3CP1rRJ/gQLB/Zwr/9zd55ftW2r/dR6/sf22fji65eXo3crL7JgEK2wKB4ynaGV1YqNPNKuSQ0VWr1aChhtYSV0MLqdnZpP96Z2dSlSDvj5HtqQwj1xVdiV3Lc6Ad8YPf59aJ8QLltFdPuv07BnF7zesbAZUhtBYIIBnrOuPL41dDCU2Wvbth3lb8y59/wE9fnyBZw0PnddOi8wDyv/xJs8klI1n/NajqpgWkLWFNCSkX/PT1yZy71h5XjPp3Fc0qTzljK4KvlyvOy2pCp3bEsm1aGrUIvp6vWFJGcERUI0QMjXhh0lBX952c5xVhy/jv//oXHMYB06C+imXb8OXpjGVN3QTa9ytR84CJ5bAU1jYLUKPlBPWkOnjkRNac2wDM5WHOWlEGUIpTWZdnlGL1ZLyKIiZISVVBPNhDZ+jObyC3eQV2n4p42j2H3IfSzEe6htAYA8GIugVCGNEnRiX2TuBVU4AKcWF/fb228ixzQtcENgKC5gohoYV9/1pG8C3Nwb9vzUxvaHuDPoX+eb+W2P8cw9jvc3CtppK6+uzTn+p1lUVI+/iBnfBtE6Y6pV1TYKqBRwIx3wVVSV7E96Et5D5P4oZuV2G+tolqvP8+qZUqk2g4TfaMSnCaM6K9cf8yPzcugMuaRuZ3I9NObc9rPQ2UkrGsC74+aX3pbUtY1gXLpqGmP37+qnkGXZGaLSVLWNvgRXuKvbu+anOM94nUAuDpcu36tM3jnSPd+82k5OuyAdis33tgaiWmaV5xXtZKP/rQBQ+N72XhtRQwF/zlp68Yg0YaDTEomuu2aSlM6sVGevlbgJp3YRFOjRM4TZfdNQ2qg40Qk6FfuymoNGLn4yheM+Fle6jud8bgBijuGtQxBXTCV/d+7kfoz63EHLSzdPUEtWoAzuRdCjfp3mMReuYgTDvHL/l9uLuP+R2qWQqwa0iTJn25vMYA/hqnsn93Pse3tr3BjOZ+QvWEHd/Y1+t1PiH7fZ4Z3eLtyaSMHiGlE64gQh5AYhJLaVaV+Fq5eemilBrHEDGprP52JmE+hOL+CBizMKZx+6p2P/Zn9QTDGI1IueGH1PmKe0LRO5CdsAu8XtruJh0tAlC1l9vjt09of9ANEQaSFPzLX36seQClFJXsC2rhm+18cfGt04D0Hmr/75lY2+jmj92r+9HaJm67nXnVOXAjfPRcehepI7X3dtutEAEzXhbBdl13DARA1XJ+zpbggsWe1Zjppnt8nbOi5zBC3ad4S1SlXkUZCTrlijN4MyvVzvNII6ljQnQjvPg55LPIYQb9aE+Ru5+VANs3a2Eg4iZAgJXe1/N3ZiDsnMduTlK4K6rMgggQL9vA0NSHSrCp7neHvN5H31c1BV+baIS9u9+O+P8aDeI15vCGtjdoPvJNuu/+Q6/s+7lPd74teCbUD9ki6dWA/gltT9shtuB2Tey/b35XbEuxiwtV6V6bpMfqQurzFHavI2hS/8uZ1Gg2VVrXXsSJ2asNRE/apDunCX/+FkpUK++j/Wvv6On+gfXwlovFfpeacFfQ+qDUSLBbgYBe2deesaf/9HpbqjmLbi9vf9IrO2V3EI1I3t7E2JW0fbVdYnKDO9N3t6fdHuqO7H9SPYGcmO7abmNErRY0geo76e++ZJLNM2oAjz05F7uHn0/gihmF7jy/V8tQoSp4ozcVdT+bw5gaETYpvmkE7Vy6cQKD6ZtMAX6++wduNIUdgbaPwLQo6zshMXRlW4g9U3jNJPQaU/glJvFLFVL/A7Y3zBRuk9J6b0+HBfPiHNzs8+nsxd4FgQSxExT1e7/AKzsRaAIaC8CosANkCoiwqG0/yP6RWSdfCXpv9x04PLf6E1Bd4o5+Wn0Ugj30RX1FqVnVdaEKIKL4+c7fqoZS37IeRTvSM0u6eVDrCXHMeUuKcgJA9DoW526r3KM9pyH0qI2gN8/4U19SO29WTwipSe4341fPu2Weuz+9w+nmWE/g+2O/ZuVaj3cMo2kM5tim3kRYSfOLob5hKXaj257im7Pc0dr5oyqTbFRpz8Jop9S1FvWro/3dmGaogkyfFNfA5tptVMkQ1EJoAIh7PCJjJCyV+Nc3DdjtI9vnEUfVOlUT1VCZxs5yZYxi56AOUGTyDgyPmOrflG2tO8rAa0S+1xp+yWx0yxT6329ke4NMwYn7TXz2C6J1owXs9vcMgrq/9cNUwCjGO3xk/GyVxvUuHcFxAigdIbTbitVp8ON907gG8xAka4RRI88K2+BRR1LsfDRfA/WvIw4G1+9rbaECSBbVREAt0smlVwKcOeww1eAg3y8l1j3PaP3RDGgCZxWv0k3rC+qe0EwgfjtqD+opYx3ezonaEwuxa13K7dpLXXt247W7uZ/b+qe+vTt0iV680muvaYOMnmnWO++c+dxmr7gE/1Jz0PbI7gV6nxYqmRYoQqi/vL65w330ZzbC7lnYpqERTGjRfTk7k7DIH1i9Ar9XbWhsbKMGW1DjP+2VG0MLbe67WauP/KlOYCfiHWOpZiNC1RLoVgPotAcKqOYh6ZjDS41DNCCk2yfWZmJS15JboH9JU7h1IP81voc3sr1hR7M/8/a5t/t6RvBzTEL/rlZT8y3sMzcbgdjTJyVoRIpXRJ7piBv/wu3jYIQaQJ+NTGJY8gKwO/U6KIsK1OcE2c51Z/bL57U8B31ec3D7jby8TG2eE+KOcTShvjMYEIwRdi/WTeD2c+/rkJv9r9H7W3K7h+fe319ud7xoxO0d2xNrH7ZXbuffEuX6QHUq7dFW90/umRMZB2/2eBixboR9Px6tjbeVEto7F3Sn7dtCjSm4M7nGDbnZiKCBFaLeBees1FHdZg4U+Ewgi5pqeQJ2fdeY/h72qjqfjaASd0JVR/RrYnptn13r7/YNqZr6Y0TGDGiflcwd0a9MAUrweybjPgcChKVzOttQBddW7MGh/93O+1mfwV/rfP47U/i5zXvnW+aj233c/fbefe08y/AkUbXVVNd2lkuvPTnrdQU9Eit5rCJpdVRL8Zpj3WLgPhVIOo2AqqnItYPXNrZKa6Un/JU5KKVp0BhG6ASViVXCb4SlQH0q6hO3EN0uEol3b92GpL6D3PZIT37314kvfDtUqxr6dR7a+OKtO1Zgq5lrv3asurNVK0PrpXT79ozqSjPJ+kPqI+r51M0ZhybxGXLDD3fszxjn7qDszwJJZdRk1zGZ3R23EWC+Bb3OCb0LC6Tv7ncPQY8zpDnmd/zD+tCS7Aq1dyJXj0xrISE1iYqCMfbaRXdHALD7dbsqwUWNShKVfOoYsNvq+2u4XVu3nUTeJqDzIif41ZdQw0tp7ysIus6lQ01vmoISf3eRiDMc8jYrncBAe7mzJ/6/hjn8GqbQd+9/8PYGzUc/t8nNBzff3/rsr7ex13WP/YSoNgSgSlBil6owRQZBYES92ttRtQdPTIMAjq54i3dUrD2v5ZNVPtbTmVvK6e0xm3gfltqEYosu6qiRawVsRKrfBxhy6T6+tfWgM5fumEvCPTGWel/Uc6iKae3KKpF2+/Z90e7rbu7qtO+O9ue357a4qh1B8efYH41RtWtetED206JJCrfUV4mSFC82xHU8qR9MNO3JpWadSp0ZzGk1msOYyZmCawOwd/C/pV6z784OtM/uc8s+GZZkSVT76VYzAbwfrJ9723/HFNzJXceqri80BuG3JDTTTuia7L8r0Wz91Yh7d6zLQ9gxDNcoTIvYMYVbbaQNSDV/EcgxCZuM8NcwhddMSn9nCv8zt95PAOwNfT0hc62h/62jquo2qs3VP066qsTt0pJ01cQqLRQU9yUo5TcGovj3tSUCNGgK5QQuyLEfaxSm/VvQJiEAkJkY7F6uCbgtHyKAlzrsCFBPlEEqRaqLXiBW+q1nNiIA3yS37bSGnjDuSClA1YhMHdrBnktxfzVRZR7KTHvNrrtv7RcnMqbRVSpj5/ZtM6JTbmz24J6OGwGEz4pbFtgkc29LbR9pZ/UE3J3HRDAARDtQqGNGe4/N7g36eWbv4KTbcZ0YzQ7vxJuw76dbQUjlGEaF5jAGo3WqATYflZpBNYOZ2ccEaOKLtclMPTsICe9bzw6ug9GFqJKYWck0BWcIkEoY/Tj5u/XLvBJuJ/DSzE5MoMDt+KDHcatNEIE75zNYtSLqtQxo0qpblEsi0ABQhmrkr2kAt4zgltjfagvfuu6NbG+QKfikfs0s1J9zez668z06yXMUunuRSslMsHhonzB+B8/U9GVmRgvZkww2YifFJAmHpzAqS4YaIG6fl+ZgdoUCQiil9E2z7GjqngtjBOK8p5kIChkEhkc3afRGXdB9TKr5UDQ4vTdXoPaPRqw4N2qGsOaC7vq6M2W46aH2kJlFvFN7YreT7p2mkvbjLWGuUGjUsmwryfbnAuijoLz+AO3Gz1WXjmH1DMUTuaBVoh0QxcMxm7ff2tYJ4rvw3fZmrX+p8sS2t2NoriHJjoFS5VuAaQLWD8xciaZdae/gf7V21nu/Qom81GgdWSfi3tmwTGc/1l1e1ws1gu4Emru/PSxXLC+omnEqN5NKiEGys/VXLaR/Lrp9NZPZmUI7lyI6ZmHXh3ZtH5Jaccq4zVXqfBDMBKzYy5+3xP01BhFe+c03n5t+fSvbG/QpAC8ZgH+o+317bC8hvc44bAlQnTP6NJ+wu8fbwpRGovtl7+Gju+uAJnkLmtXG9pXuRKpNbDdoplaq1+n6NKYiTp+MsAgqXIaTmN6807GWl127M3D7zpd9RngZ6NvfiG4ms9G0Os9FesZAN1f3F3X9Ub+clN4aivp1RLs2VJBDEBpWkBF5MLrhedGa3ggmu9XazR+hl+/sfS7tabcvevu8/RvRzTdqx7qEq19UYbftZeGOYt1M+CFnPH7Pb4inxKrh9V0u5puoa8PYDtvzqP/GjsgRAI8eUkdzN4z2kRu46rp+iOCYYkTUGAS3818wBTcF7UxDbZ9GItlkjO151LVBCBURtXa/MwkXGp2ou4x5++6v+Q1eO49+5vy/M4Wf27x3+hrNvih7B7JrEL1G4dffHrN9ns3MqkbuwvaMgOS6YLtYbk/WquuPKgUXEZVyTeqomEV9k5zwe4U16RatS31SMScrOS4QMzNpxjKZtkGFK0wGm7mKOztQZQy1jd5fjWAXeUnQva1U+81MTtpAy/onu75jkdY5Pfvp7feVOfUxlV18fmXC5EysUVL3HzC6LsPexu4WenVsOzOvNwYhGs3Wv4s1UBnvnk342zf4xLZiWRtk7+LeFKnMu5Jhe0+vY7BnCN1LS6iMxNtAJA1TiFofVZPKzgi3Z2a1XTtHTaNKNoL6hv4Mdgj5zggbVWdq2dIEROsNEZWeCeqQJgAsLeegYxK1pX4+SWUWO99C1S46nACP+OmYQg1D5e63nxegGoLt01BXgqbOA5otbc+J1DG5Zs5yrYFcoyQbg5EsiQ0/T/R/jVPZz/Njv22Yi7e03WoOv7SvKsn6bfZIXWRKrMgWVJMlqd6pCdW9em4mG6IaBYTuSvJ93aFbSVvQzQnqrjHGQWjagXiavWsKIGUYdqe9GWNPjGqPiIBIlzuj1yKaVtC/g0r9zQzB+mX37Pt7z4r3b9n1Xc+kqO/h/Xm+z803u1ra1Eh3t7cyhpdaBZmA7+TdiX53RuNM9dvDRHsSHFkhI3LJ5htqQb7ceaKdRHP/HrI3L7XzvFV1JNt7dm1WouV+ryaU7F+CTN6QNjTSMTa/vlaLq5TPzpWdvOKMmnenkd/qpVnEfA6uKTTGY9eaP6EGdbhvoRJNrq8i/f0qgW1aiGMeaSSh/TZi30xPVB3NfR4CxTqJ66ev4dRHIxHIFim9JPC3TKEn+H/NeW9se8Oagj+z1w5e2yc33+iO78R1uCSmAkTLVaiWamrSuktZvnDdfFRJTgEk6D4WkyyKS08CR0lFXWS26Heqvd3bFqQ7tiFQs5DASnYKWLQIjGsqGpZKAJV6O2cFbnLq1naLlIL5DYw57KTULjTVR6IRaifGpfZjx4fga6de2SsF3ROU0aBpXB0lpW5fz2489LS9jxVcsS3XtrEPo53cnNlUx1PvEiCo9r9udTamI6aptOeOQwQRYdsssdz72GwgrUhM97bU/t6FOHeZcmSEvok0zcRZi80IWTa9a5YEZ+TtqUrZHPm0SQVK7QRs5qAOHA+tHQJPqrF7E1SIqF2kHEJvabH8HQIp7c7zR1M15WiNkSaFV02DbND6DGQbkmoe6pYMBVLNJSjRZ/dlMLp7mKbgzMMciJWBWL+IO8er9O5av81kIlC083sp61t5CL/WqRxQzWU7kvcGtjcIiAe8ZKG3Vm0fodv9/d89s/AkIF2kFRCPuzGpUqxRdyKQzRSx20ghSHDSq0ScfF+V0DQPoQe6EqdMhmnkBB8CzUAGKlFQpd2Zk+ziqJw8t1QB6b4a0WGTxiqAnTEEaWpIewcAnuhA3rW139p5zSJvkrExyo56VyKKjhjuDncmJg1HbW+m8es+Gk3TqSxJ9qyJjHIIsGPkVZJFT2RhRI9q7SMxW3qNjgHgyWbaXXtNBFBGoM/i1l9146pxtNa3t3dC39v7/XoPdWbse93f3TVUH72aCS3tXe2HajasQ+oZ0bvoI/vUp3cx+94m5yNCBCGGRxWJE3g/oRI/E4QIHdQE1Uf05pxahKpqDP39es3B3q2TqBuj4OpP4KDaBIfuus6p7Nd5HYbaPoJmV1dGIJXY+3wpRiMw2Lv2PoVvmYp+jVOZb+7zxrY3aD7qlzLd7L+hQLv9/XW3+/rzUCWRHqe+TuD+vtR/UXf3XgJs7as2/+4u9VSjn064vAqb74O4rXtPl6lzKlP/2l37XpU0/D5drgX1l7/Sf7ex9G1/f9t2rxdKz2uN+Na+am4xAtmbMnZnUver0wbqgCmZczt6Yx2MPqGskVip/zoZrF1IbZxVfujbpKYV90fsWmpEff/O1HFvVAZWk+b8TPF9jfy/fPvWht39ATTRGqilZs0m3hiwMgXufQp+Fw8R9edQJxzsmIC90q00X4mc9b9rEzWKAzWapzmQbfg73uT38UinXZGcygz8myqEhYeYqj/Brumcz9VkVJPXqGphEvbv1oi1LloiXdPUU0k/75f8C988T3bn13Kib2h7g0yh36pshJfEvXcm3557azpq+wgFvf+poUr2Y9MIokfNVGdeIYAFpUa2+GNdizCprnSLxia4h7vB7lFxlEQ1Bg879eVfRBDMpFSAlgVtxLsVV5Pafqc5Ku208NJGbBi9C/TW0u2Jaz1uUi/7uu+j7aN6jK2vdvZ6baC1kXu+3I1NI/otrUvqe7rkpu93yzh6rUEAcIviIjYyytYLBVzDYvq3au1ybYhZKZQgwM1sWaQ6psXtI7U1fT/eGIoFcEm6agzeN30b6hj4cW69Q9o2Btq8IQ3B0TwGWKh1YwoQIItpClDNt0rsrjl2KJ1mBYM7u4VQv5upQ5ppyCRz6RzNPTJpTbJjtCJHwQk/7e4Hd7DbOvHQ1hKkMgiK7X4UqWoLZNoBdc/QaCS/tzMjvY+bmiSK+TikMjEPnxWoQ50YwICfJ/i3TKE/97XQVALY1qR/027+/Mdub5ApODG/BcTrJP2dxCTfuAYvrvHO78yOzadATpj6lvTx/B3LsASyykiqxExwI/aO+RtRcNuxawq1OLslQhUphoFULF+Bu/s1ib/JktK4QCfVy+65+96luptuDjslonofP9uJ9C7foDNd9E5cANUBrufpbRwaG4RqLtk9t96hb7BSqh4b6EZGb2vN+t9DNgmKL6W2fx3LLI0hQKjCffR33nfcK+257a76p+zOk9traa+xvdxu3ssJar9XmjanQHYMQmj01R2xO9Md1f6oiWHB7kqCFm3jQpCgeIyz3tS6S1DzCpQzGVMQ1EggZwpOxH1f8GejEsYKeU3WDmMu7uTt8wSq49hMRcQdQzA/gj9Dz0P1L/hzpDMb1TBVh8RmaUwPPpkEYI34q86y14h+T+xfYxDBBDb3X3Z+zJ4p8L+ref7ntzfoaP7Ws53w8yvHescydX/fagqA28YDaSSOT3o/yyeG3kmt+05Axe3aAkU2dUnHj5sdd7/orT1d3DgXI/Y1Ic0KyWu2m8aOi4ANNa9K9eROXlj2Meox5UyamNciYXsC37Wo1ybqeX2X+qos9Vj3lnA8fSdEbZXDIrLEmAA5/QCgCpI/rxHMnuxRfYrv05Hjm7a29jKACUr8mIAhmv2cA1IGShGsOanEjOZK32Mq9WPV9VOdF5VC7i1C+17sGK5Pi860s2Pg+66uV3dcl5mNZlJ7jrTnUaW63NEqwn4yN+3NNQTPRK7mHG8sNam5ambWJI+EapnI/o1qzvHchRYKimoqomhNDXzjG4CZT0xzCV3b/WNmot7hTNVnYPsCQJGbuaiDtnDGQ56n4GYou97fWf0LxiDhDIJBhZXhAC+T17yNsWvrTd+AAaYCoj0DoPq7MYe3sr1BpuAL4+ee54zAz701IfErx+0YtZBU5nY/j6bRkD+PxOGOJNvtpLNFe7UqlioR17P75CFqcTkw4uVeNosGrwRFo1Gly7LNKlVQaQu20zKq5uBhiqKRNe3c1qvaIu2DFlLpLK+ZmFo+hvWH9Y13V0/Caxih94lxLIaaHSpKLLVz2HbsawF0zN4JvxRz+HJlKARChE7cU2QMAA6dYOvJrIEZGALAjLUIkgjmDKwZ2IrgmjOSAEn8XXuG5M/qw3t1cF2wbSzS3q3THD2u4Lbnd5oc3SpyRrDtGVyJPTWr225AuTJkp0Po7tsgRJQJNJOMCg5KiKlJ7qHreqbaxuImHdZsb48gqnKAM4kOT6iXE6r5pTO3NO0BTWq3az1kVeo9qGMg+pst6qiahTrNAQTQYN8d8e4d1gh6H7GoA6railRtRUAoZJXgRgI2UkfzbcU17j635iHaawMB+YXZqJmPfpNM4d97k5vfvoT63/0x6a65ZSj7c6rZiNpxF5peLtUmJTcZzSNnXjbT7bJ9cprewJmN3mF3jS12m5fd8Y6id/JzlWSk37fvN+o1BrR11xMtZwgWvd69a/M6tOf7+idUZ6mbCfrn2A3qs7s21vj42ovumO/fzr6pO0vcfKYgFiOACYITAQMIE0l13HMpuj5Z9xEEMRCyMCIBKxG2ogRkE8FaCLmYE7nczi7tif0btmOqeexbfnNq1+sAxBnwy3N7Q5Gbjrjrqxfnc9+Ldvtv3Nv/3kf7AF44yhmBO7wd/sUZiZhnlggGFKcTvYLK3ppNjJj3iWZOiKvk3kFTVOXPnuU4SfB2VcJONWGtmZFsAvbZzfasyhQYNddCGEBsbdlpBn6+9RcTqUXAAfGAV/IQZMccmnmomA+o+QyCCXiM8nem8P98cxka2BGO3cy/1R58pBqZI9IPsyAE2CLwTN+qgOvdqlTWOYGcuINM0ja3ZDEG4yB66Iim39MzmS3vQAHsjAkUdUy3yD4n/Sa5QNRnjAJBgRdbdxlWpTpF2+shF3qTRYOK1hBDcbztnr5TYxpNhtd76JtYeKItoiZDU3uevSpbHsS2rupziBEhmLmDbHykkbQ9G7f9japgREYUwQMIBwgOEAwla7uSaK1nKSjZdB5eLVKGEccRMUScxiNkCBBiLHREArAx42neMG8Z53lFEUHWuE9jV7pEaoBQJzMIgBa42jQdlTS7OStAg/1s5rxWGsdF/c7+T3uC7wyiRkg4I7c5Ke1BaM5s0blGrn5AtYA+ImZnD9cX9IzlAqmEVgKjhZFaR9zYz/fE3e7HZJoCNcLeSfjkkn8X1SQEDdqw31Ujcb9A6HwN7q+IqITZnc4gtCxnV6d6TSG4tu3rRDpNRYBAqnFO9vzU9ddNVFFP+HmnEey1Av1k+95rEm9le4NM4VbafyF64XXN4DV/Q39Mf5Opz57EdiuE9YurUYGeyBuRFaqEuaa0md2lwhS47b3ewswR0q5RxkBqJpFi7Sg1TLC2zQmN5x50URL1VJIK3OdPq2/lzKLbqDuzMgQ4c7glMPve7RJ4jeYISsooOWPdFhCrQTmtK0CsEvwUwDF0UjFV5tJe1/dTfWYAMBVgkIKpCIJobefklxSC2D6pvoPcCHVK4AIIVnAcQCFiJEEkRgQQpgnbSDgOE9acsawb1lKQiiD3jNPGU9A1uCpz3TtVwt8xvGoSe21Gi3cFPIfAGafJ7nDxovkB+ifYHKM+Qc6Cozup1p2x0nhtZ1/HLnJI50FzLHscv4eMSr0voUUkyY5QVl9A1QiomnHcGawE3bKSLdOY6jO6XIbOfERWBIfMwVy1A/c/mGnJGUh1RvfEnIFiiXjuRK9+FVj0FVtVxIh9ClTVsKT6CjTxVYzg77UC1wTCjin83dH8f2P7FpH3rWcKvaP5W+c0RkI+92+N5PazJrrWqzWyyQm2iy/qJO45C90QC19FcNWjMpEiqL4DFCWaTvMDGfaM2f5NJFVntKY4V8mn2a+ltVucaDdNR+x4zWp2BlId4zqpqz7mxLrrFw/X7BkWzJxEApS8Ia0rlssTOI5gjkjrCgKj5II4jgg86P2MwNaA4yqZu7inTx0gGFFwh4JBCmJOQC4opSBV6Ajz2ZhErmOXTf0HZMsgFuQsGCcNCIlEAAdEIZxOR2AYcM2C67rh6XLF87oBuUBKhreMrG0C1GidOn3qP03+J7DVp+hm1012dh9P5s8gUOcmayPAbrJxOz+18dYxI7tj0aAH2+dZu+jCN2soqBFrZniEa00u48qAtNkV/qHzBYgTSWcg3fnu8HUG0MNWq/OZNJQ1KHGn0ZkCNQJdGZEdC6KhqT2TcObjuQoWftprH5UJ2Xc1jcGZkEMI6Fop3r+F1EdhyLH1fiQIlHdEv5f+I1JH9JvWEJArw+iPd6EU/+HbG8xo7ogsgCbp78nT3sh3u5Wb80yeIgXFYtZPeOVSqf/c6Cxmn24+g9IQQKW/kUtzvmKpLnwppcJdc1GpVoqZcXySGmNgM/FU3iIAs0YpiaiMzBQ64mQLSVxGfEUrsPt0fAaAgB3Ez9ohqJYwPYPMpu8hkRBsaUVOG9bLFcwRRAHz9aJQ4BCkbQFowzQdUErBPF8wno4ACljR1TpiCgP202XFUMI9oWDIGUPOiNsGKgU5lWriac52ac5NAgAGSVDthJQ4oxQQZSQRhLAihtFi5wPSskI4YDqeMDLj4e6E+cRYC/B4vWJJCfO2qlMagNeOeJnlAbQIIFLAwls5ocn2O4agNK9pAq5ROn9o0rMJLv0SQdZ7OuEEoXAzADrB8+gjDtjZ+GvUUGjEX7iFiOpS8tDV+nqQ2H475LBrHsKN6bDnFxBq7gD3YaqDMiol9m2/+xY8yaz5FqRFT7lT2c1ihB0Gkvsf6kAwKrS2w1no/Anw8NRiApWKmgyKBM4CFq3vfkvQg0n+vf/ATUSuHdxqBc4YegbxVrY3aD7qt47g1b/R7ZNXzv3Web4kTVNwqQluv6bGEG5vDZfIBB4i2Byk7jzsqdL+UytqeUbRTnsw1dWIALlQ8uJ9pLajEcCmBfQN3jd/f2ynMfV9YofcZ+CtV4tYgUhBKRnupyhpRd42bMsVcTwgRELOSVFVQ4CHhcYhIm0ZUmZAsvYht3ayvZSPMAOIohFUkxSEkhHyBsoJUlRDKEWTshpToGrmqE47S/QT7tz6RBBsiiFVCIEYgQSCDBBDOIBjRBxGcGCMgVHyiIEZTMCSC7IIcle/oolKsntOG3v7uZsv/vZtrGpOAndn2UHdXzurCgqqHQpy3gBRdsocwCGA2UmN6MVUKrFt0NJoTGHnGLZ5uMv4pZbk1Q9WH3nUEe7d/WsWMTVTUugYgEv+/T4j+hI6BuNz3+/X+Qf6d6qMICgMBuwaXfhSAfEqA/R3hB5nEssvJXBSTZhZwHxL4D2qyLWEW6bwesQRVaagTOPvTOF/+va6VtC+/bcNIhXEAESPocb+dK950Mej92ypmimqNOgE3r6rGOU6KgBhy0om8zXbciXPPSgVilqkg9AmfT+F5y6m8hJEInri3hcAqgRDXIa+ZQiwtrjpSJoJwiRCoKj2YPdY0oKcEtb5qhoWA9u6IKcN23bGeJwwnSas24pSNGv4eLzHOB7AHLHMV1yez5CcIWkDTRHVOe0MwemKAEcUDCg4SkJOK9KyYssFUgTJNDQRQq6ZvVCfTCEASdm8hU56tTGQJrZlFhAlbEgIHDHFEcFqDc/XGWGIGA4Txrs7HIcJd3cTkhxUa1gXXFPCl3lGLkDKqq1pp5adX0bQO4x93lh7q8RPHb9u9v/ewVynphNb6ygCICUjpw3n58/Ytg0hMA7HE45394jjpKYZn2dAJaS9prDHAuqYj9FQIWj2flfu0u392l7ah5rW49TMOLEdq9pAIGsD1UxlBG0Pm9YAaveucbc9s+naCoI+x01Rdi5bboQD46k5i+s9pfOX2GoDsdIJFGDIgjFkCGUAqZqFbiV/l/5fy1SOSC/2B9v3lsxGvr1BpuDmIu7+vu2417SC22t8H9Bmk1QJm30S+y09SsccyH5gL3Gj6u+uH9R99Tn7VsIk2nZUGmCd0QQR0n0o1fzjvKfCXnDHBqR7gjTnGAmUyYibgZq/wO/nmgk7w6hts3sYk0nbYm9YMJ8fkdIGlIIsGasUDMNgUBCGq0OEcRyQtoR1njELkNYVwzghp4xxHAApyGnDOJ3MnFApnS4qEUQUxG0B54SUE3LK2HI2JUOhJoon/ZkF1/GdXMbW1yxq1mJvn9oLOURwUHMXcwDiAB4GBA6I1h1JBCNFECuERCS2aJcjTlIwxBHzlnDdEraUNZTVCgw3s1AbcZ8Vnvwo3DHingFUpmCz1QfaJHUPQ/VdKWVs2wwOjIio2pwooxDDWZFA4BgQhlCJKHfSdS819wRX0xukJmOWKo1LTfJq5xOaaad3bru5ppPmXVMw5tB8Dma+cgZTmQejOnarFkItkS6iMQzHQ9qFrjbmV5mXM7iqKUh9L66LS+d+oYyIhIwEqVFDrzuLG1PYawChu66FqjZN4q1tb5ApAHuif4uEeruvTzO8ZRD9vmamYUbnaKZ6Xo3WqIllt+yoUtYb8u8aQ/9pxNsSk3WhE5T4sx8noBI0MZOImHlCOoehy/3qi0BHMOp1Jvk3LaE1ordR7/vYGYYxYymAZORtgS+M6/kJOW2YhoiUNqSUMH74oOF6trIIhHEYgFJwSStyzmBaMOUMIsYwDgAEJak/pGJOWYOCQJ3KIuB1AdKKLWXkItgKqjko27t6nFYbvTbynoGLwOAQQBwBM6uEcUIMAxCiZg3HiDAeEeOAQIS0JsyXBQgjKAzmhFXGcggRBYRxyHheVvB1xkUWJBRkn59EqOi07fUA+H7L7KY2j9jHxyXeF1qE7mdus05hUjK2dQbHAA6M1cas5A0prXrewBh4UsboEnK16bNJ8fQCCrra82FSvzEI7PwI/t0cvy3bGJUpNAdwYwpcI42MaDuTcUnfmAIHNg2iYwpEKvwQIBGNQfR+hNAxP6LGFKIzBT1W47VCqeNhUSAgKShcECkjI0OqdP8yrLT5CtJu/16DeD13oT73jWxvlCn8v7WZOs8qJTJ36YnVQ+YW9kboW05AUy30iEk6qFZ5k+4EYitqF8lkEq39BY8AqkGEhlsh0tVI8HP684yZOBQ1ASjFghnFwllFdLpRJz+TStlVlhWgSMF8eca2LliXGRzE1wukaIjpwIRhGjEOA2IMyDlX+z6o4Pz8GfPlEQ/vv8N0POCPp/+EkhJKSnh8fEKII+4e3sOLErGFi1LnwD5IQUgJYVmQlitKSkhFKnZRcc2pLicjnoHNpxERwoDp7oQwDBgOR0yHE8bxAAoBzIzAESEOCByVkDmkNMfqX5IiyLkghoiK9Ck2rsaYBim4LwXf5YKvT2dc1w1fnq9IpSDlXOfCCzHGxiJQRxAJUJRXddA3xFK00Ga0c129lJyRU0LaVrx7+IRhHLBtGyQXSM7Ylll9JxtjyxuGvGI6HsCRzRQUrPpgaEloVaZRO3phZehChJpR78k0/oJ2DbudP3C1+7tJCX0OQRcx5H3gBW484klf29aWMQaOVBmJrgSGkKAE04gqI7I29AyiahS6Zh1UrzE91fTqzGJodBwExKH1fVu58DJVL8VGJfncAqbhsUl6Rbk5f2dzeBPb3zBTeN3sVCMtuJt8aE5nJ7L9N175DXQSnc0ZsfDMnXkHqItoP/itfU1P+cbzSLNt3TTRkfnKkLgjJhDs8gj0ceos1mgnVO2EpGBbr0jbipxXlKwOyTEOKFlNERxYoztQ9HegmiwWghb/yVnUjBEHcByQiTTG37QNIigjESWAZPu4WFRH2oBtg6QVJSfkUlCKLqFah8GM4swBxAHDOCEMAw7HOyX2MWK6u0McBsTpiGk6IA6Tagt2HXOskn/z2HdF7MUwzKomoztFBJL1N4PBAgwiyKVgGCKSQPMbtg0p5Z1PCs5kd/OnOV99POVmuvbzsAohNndS3iClgEj7PA4jAEYqC7a8VuKXi455yRnbuoKSSsxxHBAlIgxGHXfzxdeBP9XIJX0jBrEjtlwFpe4l6OZkwDLjqduDtt9BI2/+q4WVqFSNEGB4uVzs2qz/1op4NqZK8xuz77rUfnO9FyhAKKIgwD0HrXf8MtpJ/kVZfP1u63Q/uH3C2t8dzf8uW88QXAyyyBkIgpqXdxOpSfOoEj5Iv0s/cdCu4z4UCK5RqARf6uqRSqmDSzmWZVpr6XatVf8uQyTv9jXCwcZkSqcxMIQ7QmSin07LbO+TIDlhm2cQB4RhALMAkvD8+AWBCeMYMS8JyMBxGpBSwbqtuD8+gAg4P3/F8XjE6XjEly+fISI4HSekpBL98XRECANSLsi6/hAjgamgpBnrmiGFMB6OGoUYIibZwCkDlyeULaFsSa8vUhVrAUFYPaQ0DjgcTjie7vDw8TuMxyMePrwDh0HNQ2GEezU95p92YcvODLrMau+2OrY7KmaDoFKnjkULBhinB5QiePf+Ac/nC56ez/jydEZK2awQTmBd01OHd5OIdfSLMEpXh9tt+ZVwCoAMBFYf1Xo+Q4rgMJ0wDgcMw4TIGc/Lhuu84v7uDkSE83JWk3sWPH/+ilIyhAum0wHT8YBh+E61B3/n4FOWQCVUCV6C5lwY6qPu19T1OtcqQe+AI2td8l6wLo3Q74ZGBFxszBwKuyiAZMiMSFxNTJbbr6HdaPk8FcfR/xNq69aex5lqmyoqLGAJa4QGkheROWGlA1Zs2JDQRxz1ZqNmOtJ9CfGbpqZbmAvU77ex/YaZgtnAq18B2Cvsryxso+HMaifmWq5JF6k7hVvgDdUoETcpOZ4/OURz1TDIzDEWwVA5TqcNiDqDwajEvVcne0ZU8wlM6gcZ0+n8Bc4ERJrfAmDLh8hIaUEpGTlvWC5npG3Bts4YDwfcD++VOSDicDjAxGCM4whAkFOGSKlmAfdbNPQ1lc5ijEh5Q04F2epU5lywXK9Y5xmlQCN6xgnDqMR4HAdEZkRJwDpDtg1lXavpI4moVhCHGiJ6fHiHOI4YjyeM04RxOmA6qYYwTFOV/iuP3s2ALlzU50xnxuvnEFE/bzpznd/TH+AOeptThzGAcMA4MDgQ5mXD80XfX01fHo+ENpu6AAXtmQbASDb3qkzhzxaB5IJt3hCGiMPxhEAByIK0rEjrhrRtyCmDmVC2rNFVUe8ZKCCOIyCk+RlulyuqyUku4MimSXf6jSjIIYpL671PDjUZ0ZkDvxYr0qEIE6zuRaFmpuu63ZlDz0CpuAYSAFKvksN81+AMAajmoZlO0AWHVD3C5rAAZiKzOQ6qvjmB3kMKI0vAVgYQFQsKeZmA5tqB+wpaQhvvfAmv4SH9XVP4n7LJK9+3Bp6XGwFWlIQQajQH14lSCbJrDB1DsNkHZQrUMYWOoDtiZw9TwX6v1gjXKnYkyCd1Pd4zJ1LtoWMEuq8RJ2VKBEiGlIRtvSKb7flyfsa6XLGtM+4guH/3AOIBRMB0mJC3FTmtiFGnRE6bEgJuIl6LhvG+JnAIAJKakLbVTBWCtCxYl0Xt9RwxDCNCHKrjN4gg5gzZFsi6oKSk9nyRmjzF04hhmjAej3j//fc4HI843t8jDiOGQeEqmvexG3Xp5kMVWW/njrz6t9h8eLkJlBjdXqdEZhwIMYw4TAOKCIZhxrqt2LKancgGfW+YaVslVDa/9P8bk4PoBJNckNYNMQ6YxgksrExh3ZC3ZHAjScvH5mJOGWU6xMA0TNjyZvkjJmUXoOSEIgkkERQCqEuAc6h4Nqezt7kSXfuLTYvgTkKvQ0Fo87XXKmpGnBNmgE2b4GxO7EJG7E0zISugRFSjh4oJXWSR6RWXikmZD7X3qAyWNEHU8xWkvY4GMxSGCKOUgCzq8WYpcF+AEnn1vRQ0JqH9w8YqXg9hfUuMoN9+o0yhE93+2utMwgmBEKrQY0k5Fv8u5mxujma391IlQtX3ZAvZCTp3QqiY+UAf4r4G/aZKuNobtQxpdwQTYDkJIlnt8TATElSVL0UtmMgFKSds64Ll+oxtmfH16SuGYcS7dw94//4BOR/x049/RhwChsEA6sB4uH+Hp8cveHr8gmD+g8ABwzDgNBwxLxcUKTgeDwCAZV4wDFqOKqVsGgPj8+efEOOAu4cHPHx4j/fxe11EzBjiAILiHlER0LqpyWheICkhlaJMLgY8fPc9Dqc7PHz3CePhiPF0RBwG9QmQhpk2B/otHEpBi4m8FRSondP1vIOXvyTVt1v+xnEj9AxEBj6+O+H+NOI4Rjw+XfF8XrCkUnmVh8/6dQWE6tPuMfRumBOBUHKGJEHJBct1BvAV4ziBAJzPZxRJOI4Trs9nEBGO9yfkUjBfrjgcjwADy3VBnEacphNCiZCl4Dqfcb0+Yd1mDIcR0+GAd58+WvSP2dKte8hA+WotHjLCXlTqJkOhpY4B1KGoUg1ZXgkhmFYgrEmFYsxHNQVLIPPr3TzktRm8twrAUhyRprUvmDeZmznJNQ9hDQjRGeDMQRmMhw2HzKCNgA36IaAQoxDDS2lqlvM+DDUhGANooREtYa2Fs6Y3SILfXot+cftrGMKeKLiq31eoom6/S/pCTnx7yd1XBNV7vdYe/6U8hmyS79UEglTTALorXUNwcgfoPUwkAotHy4lRD0FKq+YQSEHKG9blqlKiqHORBsE4DiAakHOybGNCKRkBA5gJcRgRgoZn1HwHUSz5OAyQWTGA4nRAzglryqZFqBluHEcMA5CTIMQBcRiqZgArAckOmy0A5wykDbJukJxQpIDHCI4DwnjA/Yd3ONzd4+7hHnGaMEwT4OY67+GdBuDa0n5eSB0b7//+d3+t7pcqBL+U4PZayIuh0y/bqWiwEafDhJws+fBakLNnhbexBtz3Y+/A1M0o5xRo7yeMYH0OIhQzuYkAOSWEwIhjRNo0uzxyRMkrypaRhwQwoWQBU8A4TGBoJvo2L5BcQAKkZUPggLwmJOOv43GCA9y5dqBSftMKqg3fxrlV5/O+6ya9ODPhxlD8ZHKm0n0KWbxx63NSdQCuedUxKWIMo4n/ZKoAixW5YhjSQA1KtdtaOLi48EZAtk+iLgzXaAKLMohKF9zh3HKPnCm4U1q/b7WKt6M1/MaYQt9xv4YxmF68I9pmOiJGcCwYahKZUAt/BFzadxHFRZD2fNkREZX4RFC/fR43p3C7edU+OqKgnAlVY6ltgDIYCJDzakQbOD99xfl8xjRF5LzhfHnG4XDAeBgxXAIOhxEPD/cAEbaU1OwCwTpfMQ4KUBeOE8braAlpykhzzuBAGKcR5atGsBymEU9PK87PZ8QYME4jxsOIw3TCMBwAsfA900CaJV/fsTre1jPKsiAtCzYUIDDu3r/H6eEBD58+4uHDJwzTpEzlxVj3OSi+X25+e1/eGrVvt5vrq+P/lxfoC54OJ00qN4RAuLubMI4B9/cT/u1HYFlXzIvsLFDuiNbf6IioUp7bZwSOiJHx4eMnbOuGddlABrtRUsFhPODh7h7bmlGkINKANa/IS8IlP4MCI4wjxjjhdLoDAchbwvnpCcfjAcfTOzw+P2G7blgvM+ZlRi4F3/3jHxDGaCYY8zkIq7nITDZkIdUNPqZ1vTOIypALaulUX6IagNF8fupobsIbUjekcIc2NcXQtcNsayyiMi9kbQOD23oXQKig2JyqId+2agWMkIJGbLmm0DGFyhxIzbvFYc1ZtYdb6IvX8hR+4yip/5FbT9x/jhn0kBd4cS5B4PUUOGgUUq2XSh6SqJZIh2F2WuERP466JXYOkUaO6D1a6J6Upk24KUhM4vckBN0vAHlmrk1GaU7lXDI0fLTgfHnC5fyM8/kRADBNI+brjJxWrLIiF80fGMcR4zhAimCeZ3z+6UdMxyOYCafTCTknXM/PuLs7GkwDYxwnnO7ukbYZIgVxUII8z1fEIYIQEWLE3f09pmnCMKlGcDrdIYRBcz7MNrw3jrQxo5xBKaEsV0AEw90Bd/f3iIcJDx8/YTwcMJ1OCFO0OsK96afX+Hw+9Meauc/RQl/CnpSmKNR9nchOZD4ZeXWa3ZqX9vfhmz16jxgJzBHffbjHvGx4fDpjS6mGrXq0k5gvxZ9DRlGl3t9vr+bDw/GIcTrgcNSEQynFiBFh2zYMMaKUgnVZESjgdDxhzStCjHh4/wnHwwmRIkrJQFbY88AB03jA+3cBBQXbsmJbFuSSQbkgzyuu64LpOCEOUfMegOZf6yRy6ua/MgVonoeBQRGZ/6EwxHJrhBStFcV8CRbxRaC9LFBs3Zg2QGyhsNW9ZHOj6JhSIYBNSxB1+7ryXoyxeIa//hcA0yI4B9BGjTHcJPmpA+V2n0CYkCmgkIf7WtY9vcYU3hbcxW+IKQA/zxDkld8vzyeCZlQSwCzdAtQJ5jShJg0L2mI1WuMaAOD0w+3Y3X3Y5i1LF1XkBKctcmkP73wXyhCKaOQQoGiby3zB09MXPD9+BRGQTndKDCRjWzeDfxCV4scRQsC2rXh6etTcg3HENB2wzBfM12eUkipDjEPE8XTA5TmhlKQagmSkpA7NEBghRsRhAOGEcRrBMWCIE5rtrUm+Tlf7aB0qBcgaGsshIB6PuP/0AdPdHd59/KjZxXHoDDuaBvS6hijdB+gFAY/Q2kva0hhz/ze4MQV0be/pMJquc8sUWo3qFpPepH0Lf2bGw90RY4xI24brrD6iXJpOIGVvQGilYn0+1G5UgjppBBEBSFtCyRkcGGnbGlPIBeu6IQ4B4zigzAWRB9zfPSAOAwICRHJVZxmMwAGn04gtrXh+/oq0KWSG5IJUMi5PzxqvJ4AMGRI0x6OaCHemJH076oapuKPYTDkEQnYNian5I9zRXNETpSmEfRKOmZ3YtRMRiNcvNS1DTUUMbSmDRdurqAF662ICRF9xsEDU36GwR+pS8jZ4wNqN1mCTogmUHaPQafJSQ/BKbW9l+w0whd408Np2OzKvXe+SoHZ8sMijwFwlMr+PmCjg+QJ6tMm+xeO0SycN18dSJUNSQ0jtYxpAJZD2WrcuT5GCVDZ8/fIZX758xvn8CGbGh/cf8fXrZ5yfn/Dx00eEEDR5LOpM/Lf/8S/qJxgCAhOmacT96YhtW5C3BWMMuDsdcTiM+CoZX75syNuGklYM44hpimB6wPXyDAjhw4ePyEXxh6bDiBACAgdzsCvV1N9tgVZFyBcGCIwAWLJaXmdg3fDwx+9xuLvD3afvME0TwhBV0yAVA/cxWbfan0d+3KaDvTQRibgGIWZz14Q4KVIzslPKGvmUC9KW7XjRqCsnHB5lZmOuyXP6jsNhQogRw3CoWD7MQe3voYmQQwgIhwHx+3d4vlxxuS44n2dLLkuQ0DQsIpX495FmWk/Do9pUQtZf0zTaeRNyTho5VgTbtuGnz18RhwnTNGFdE2IYcBwnmJqMMQ6QccLD8QFpSfi6fcV3f/geWxI8f3lCHNU/dHk8Y91WPD1+xRQGBDD+7fMXcCAMU8SHj58wHY4YJvVNkZew9GGy35yhUjuTKW6ihL8qYcpUWLhGETVmoJ1DAJB0OSsTEUVEHQBitrwL7zxzLpupKyCoVmEOpIKCjGL/Ne8CQYNMo0RwYtBKwILWzlfMSC81BjSrs/19a2rqUYnfyvYbYArf2l6THr91js4olVz3H2An5KPaF4i6+dhpAWYTrf7f3fP3e/yphcx56wwDagIREUUBTSqRS1FRhFiwzFdcL+fKVa6XM0pOCGZWIQKmUYm1E8RSMpCAlBJyUmIOZKS0gplqZFG00pg5KyzDSBpthGHA8XRCLhnjOKJIRCwJwxibjbe+ZxdQS/7OvbRr9l7v9yIIg4Y6Ht89YDqdMB0PiDFa2CvdDOOtOn1L8G/NiUr4S0naZUVzLVIuyCmj5IJtSyhZib5/Ui6QlBXsLxmuUinGAKkyOjLDNck+8mw4HBCHiMPxTiNvmI3JsYV1BgPlU3NcDIxp1N85ZTUlbeXGh2q+KZuAxeYk984IeBZvi1xSZ3zQGhICMDPu7k4IISLGAeMw6thTQCmCjIwiGgo8DCPWdUHecg1tlVwqr52vM3JJiBwgpWjoazYmKxnrslq2OFWJP8TYmCmrek1mHqLSOdRJCbR2MVVCrmYjqcNsltbGGFyFr8qiaRcmzHlyqY5c/1+3nu2/jGZC8hUvzIgUVGtxbaE3YfEr368xBbH3opt9ZmYC6MVs/4/cfsNMAfhlLeL2uIW4sZmO6KXT0m3B+kep0RSAmYNITJaVlnzUz80+3NCdcGaf9LBUMTtUKRnz9Yzn52c8PT5CJCMExrv373A+P+P56RF//OMfAQCfP/+IGAMOhwnbOoNowvsP78AhQIqAA0E2rYl8vZwRI+Ph/ojAguv1rEwhBgQQxmnA4TApHPay4v7+TjWBQPju++8AEQxDbO+NbETY39JtGf3rm2RtfdBSlNRsxCg4PNxjnEa8+/77mrPQ7nMr9ffj1pmK6mN7v5FG1JQi2NYFJRfkreD5+RnXywXzZcG2bViuM0pSCO4GSe5CHkHLjqmGEZirtuCSLZNBfqekuCNSEMYRcRzx7t07q2UQMRwO4KgMMIwjwjAiDAdQUFC642HA4TCoSXBZ8fys0Oml64LKfi3SbBfe7KdVOqORYiDVfiUOYCJM0CzzbctIKUHKSc12FLCkBWlbsWwLhASH6agIt9uC6/MFW94AYZXms+Dp/BVxjLi7u4MkwSYrIgfkkjBfF1zOF5QsyDmBA4Ej43i6U6Lqg8bSmICFdwqR1U7oktp8PZJrSdLWlYd8m1lIijazTp+imloIGqUFY+iqWzLCTZivQDOag2OGEWqJTxAw82gmJNo7mn9JO+iP519xzRvafuNM4XarMwOtx/tjBahOHt2a09JHx3JPnVA5wRcPI3THNNVKacpKWmIS/F8yu7Wo6r+uM7z4DLNASsJPP/0F8zxjnq8oORvhH1GKagWHw6g2XckYxwOOxyPOl2cs84w//ev/QDSpdJomhBCQtg3LcoF83fDxwwdABKUkfP7pB1wuz7i/vwNB8N333ysjiNGwgSzqIwzVbm0UErvcCACyk7UACLeFD+z8JEOIiMw4xGBYRKPCOJPLR9Kpar0I1saT6rgoESgikJKxzitySliuM+Z50c/lqqGfWRmkZvc6dlHxV4KzduaohXOAyvj0aZqkRKyEMREQWE0dgUlDb1nt4TlnnM9nY3QR5XIBEWkm9zRhGEcMo4b9hknxmjgETJEQKIL5hHXTXA1tK+COzurXEqj/yH0Y8G5ro1FLrMJgw22OhkAoJWAcI4gYgRiUBGVJeH581DDZqET0NB6xXNW5PPCggkZawWAMPOLucIfrMmPbNnz87iOWbcb6tACpIC0rnh4f1UwUCB8/fsR0OOB4OoGjMsTmEO5DvsWYAdCHkOoSNNsZwa2Lupl6FDx0tZASbkAjjYgQJFgynfWP6Dkk3gZzboPUIWx9ql0oSJKBVZQZrHjpaH6NyPtUfm3/zzmp39D2G2YKr0mT/fbasZd25/0xvY52zMD29kB3hOoT6OPN983wHypll5KxrguYDTSOA0QKluWKdZmxbUtd0NumeQchKI5/scL0ITCGcURcA7Yt43K5IgRFkRxGdQZDCkpOWJes5iQo8uS8XJHyhmmKGMcRx9MDPBdBzRtSZanmC7HsJOmiYcRUbuV6tnCpol+4EOcS2jBEjEPE8TBhNDTTNjQ7Y/HNmLb+9raoaUeJcM4Z8/MF27ri/PyM8/MFl8sV8+WCnLVGcTETkSfNMbUVKpAKjVFRaM0kxRYmKcRNsSdSzZIIQxwQo77XlpKWB/XcERGs6wKBIGStcYCSgLyhhKg+qZKBYUAMCnlNhwHMwJYyVqgmU7KyQSFo1A4EUpWjXvhw+AxBdcbCrBXmc4CZ54aoCYceKaTQGBtSTqAAnE4njHHEfF40nJUHpLxqXkNkRA4YwoBLviClhCFq7guJahOFMq7ni9YlYMJhnIACxBARZVD/ErX5U8UoI/oNqK7THgtV4QrmeqgyX2kO6b4+tBbSs2gj1w1M9qsmTzPp6JRo81vnmxmRzLksSVTav3U09zLMrQz6GlOQ7vcb1hZ+o0zhW+YF/sa5+Mb5/X1cBNnXW27nW9arcQElqDbyHkctmrYidr912XA5X7EsC9ZtwZfPP2GaJtzd3+H7P3yPaYz47tMnfPn6Bes2493DA5gZ1+sTmIGHh3v89NMP2NKKdZ2xrCOmNOC//i//FczA9XrBn//0r/j6+AXvHr5DSgnLfMa7d+8wTQc8Pz8jRMY//fM/g0iTqj68f6/lGkNArd1QU7ABgKvEqdFQzXZNpP6LZkrShV0FfouGAak9OYaI9+8eEGPEEIJJd3LTr6//VpolyJvCQ5+fLljnBY9fH7GuK9ZlxXq5ql1+TRW1FT4Oxp20jgAAEuRaV1sdrBBo4h+ckWkcvhCw5g1SVKOMUZ2tcVSG+vH773A8KSifS6xS5x9hWdeK8hqgcHxl1up125cv2IaIYNFXHAfVoMaIPEbMS0bORWtJZNg7dXhcTVSuc74mQBGBgmlXHaCeF+cp3CjR8e6I8aBlR1PakEqyeguEkUfV8qaIlCcF0aOCgIBtXpHmDSlt+PrDFwgJJj4gzRtKyPju/Sc4ThZnxnK+4vMPP+H9h/f4+N13UKRdwuF0UGez5TvU+VMAsDjuHhyIXAigbOPEqCirDAtJFVjOBCuqgAl2NMoOFK923Y2pp6YqcScmJoEsolqCf5ws0Mt71PvilX3GrHZt+DtT+PfeGiH/NkN4TbvY76uhiU1gbJqBCDxEohRB3gou16tK+r4vFXz58gXLMhsBUonu46cPiHHE8XjE8/kROSXMyxVMhJS2mmtwPj8h54TT6QAC6n2YoY7BnAARdSJTxDRNOB5POB6PoECIIeBwnCxkkDCMoy5GYhRRBNU+DM8NNqiLsTfY6hk93lM17RB2PpYY1FwRo/oqVLtyDew17aBJaTmpJnA5X7EuC5brjOvzBeuy4nw+I6WEtGUDdNOaza3ehPkBCMrEXAI0k0xvq28zQ9tAXRtENBaFPM6cCcMYMB0GHE4TeAhaXCdlQw91MxhrvoiIRZrAwP0YZSuYlwVDTlavISiAIJEWBCLGYCUsizAUvgTmTyUNfhI34bU2++x2aVeZgYqw9f2oc0gbFSRWn0MpkzqNYWVh70zLmyLOl2esa8EwKKNYr5r3QJGwXBcMowYnbNnB9QjjMGIcR6SsBZnKltWXkwWXy1nNS6xOeTBhPEzqAPYGGnBev/7gioBFgrk70EHyXFvQY6waRi/dV9nQ7lto7yB2JasaCZpJihKpkznhdcm//3b/wWtMoXeD9RbufX7tf/j2xpnCrUbw1+7Dzf6fMx/dntdvPbPoCKiLM9BY8cvzBX/6t3/D8/MZAmCaDrg73eFf//VfcLlc8Mc/fo9lSbhez/jHf/wjTocRDw93+PIlYF1nzMsFRIojNE0fcDiMWJYrAOAf//EfcL484/n5EU9P9xARPD1+wTxfAQiGqEyB6T3ev3+P490J7/HBoo24e0MlYKW4u9yT7ouhQ3baQc22vQ3/dE6pX4YE3hF8c2ZPI2JwBNpcDR16G7ZbdPc13P/lsmCeZ/z5f/wPXJ4veH56RpoXlJyRiq46qiIdKXHtxkebYQ5MAIoAa0vd3rGUXBllu07DVYsUV5qsyAuBA3A8jjjdH3G8P2LNGc/zuTIpAIroGQICq30+xAiJEUIBYQooJeF8PWMMEWPQIjclbQpEOE3gqJpIsCRArEASVBOXwo/0/ivU/qTd3FaqQ9XG2WDCGxyDGlaG4R6a78BYlw3blnB//wAOhDhEpHXDcl1wOtxh3VZ8/foVdw93iDHiLz/8gIcP7/Dh4SO+Pn3BmlaUtWA6HvDdp+/w9ctnXPO1aQCp4PMPPyGLJsrBEAUiR/BIDYDP6HUNLiLX5LDLYVAmgQpdQZYnoYV+jOm4c/hWYu+J+wtTj653LgTOvNcUfs7R3DGw3f5f8kO8se0NM4XbbNRv7fslJtGz5J6wF+w1CenO3zs62/MU/kGdzoynp0dcr1eUQljXDefzBcuygChjWTbzBxyqBPf162e9qwDz9YzjYcA4Djidjnj//qEmXXFQM/A8XxVmOga8e/eAnDds64JaoIYZDw8PCIHx8dN3iCFAoPDXIWhWqhNI+G8j+gygeF3IzsGqzjcAtW5D50vp+tVrQVSjnd2GmBDDgHHQTyO6en1xgdqIWMkqRV6eL5gvVzw/PuHiWsHzM9KWNDxSStPW9C260WzzItjzip9PPnJd4hqpecLrRHvBnZTdlOS+BDItR7CVDZflik0yzuuC87zg6XzBum0gAHfHk5mUTpivs/YxE+ZlwbKsCikCQmQFXU4CzMsVIUUMZUCxhL4BdyBmjMygyIgMrEnBEMmipfaBk/sZ+vK3OaepmAJFlcCq6c8JLGOaRsNUkqqdnA4PQNFAzkCCw3jAcTohxoDT4YS76YT74x2GGLGsC/705z9h4Ijt7h5PXx6xLAuO4xF5zfj8w2dEGhAo4MuPXxQzaxrw5+u/IQ4R7z++R4ih1pRuGp+Po2sIXPMgWIz5Mwygz6R7G1OHqofXZb6V2Pt95vitcRIOhudawmswF9/69OelV655zQ/xRrY3zBSAXyb4L4n2z9/Lv299Ct/SHpqpyJ2c8zybA3XCPF/x/PyMbUvYtoxlWer9VOosGqLHSlzWdamJVMsyY9tWHI8TpmnA3d0R66rmpRg1h2DbFgxDMMC5iGFQJhKCRvtM44Rx1P2n49EikTpyYIHunsRVTUJQU4RLj7ebKQm7v/e94jrHPuIKRAhEGGJADGworO0acUag4VgQUSiFtG54fvyK89MZX3/6guv5gm1VqdXByWhXM7Jvkzg3AnVjKRBD6Sw1BFiZuYYZq8mHrRSkcrdSEryWseeV6CF1/s7rii0n5MsF5+sVj2d1ajMzAilOVGDGZoEFIoTL5Yynp2c1qcQB7w9HlKDIqCknYx6wYARG3EYgamhrZDXVZcuEL54BLYRd2U4bHH5hz0Dt8wrTQu6UpaoNAk43LXGTzIyXBdM46dy3Yk3TdEQMAwIxpmHCEAaQAIdRwfXSsmnUl2kYy7qoKWlNWOYF0+EAAeNyvoAECBzwfH7WXI/poMmXMeJABwMHlFZa0xkaembhGoNpEOTwGdQlv1m/uTO6JyGEBlh5eyy/8vHjvex4S+jp5jy/39+Zwv/b28+Zh3pJ/7Xj8gvX3GoXgpQylmXF//a//X8RwoB//Md/wtPTI56fz/jxxx8xjhM+fvyEZVlQiuAPf/gO25bw9PQFp9OE43HE09NXCz+d8eXLjwgBePfuDnd3R/zxj9/j//g//k/knPD+/R/w+PgV5/MT3r9/j2EYMM9nfPjwDn/8h+9xsPDTEIYqdYeghEIk19dwG3/L7PUDMNnQmZ0Zknr6vvuH4c51vR+qb0XQmAsFRowBd0cFbXPNQzc25EnBul2xzgvOj8/4/Je/4PL8jMvjM9KWkbdkz4IRc0OJKRkEILJjT4lBE9i9AQABWTrDb6fpuM+BEMDkrlnNYs1Z/9qKAgYOU8S6rGqu0uagiOAyX5GlYE1aHS7lbDkiAddxwHfffcJ3nz5iWQ4QAOM0If+fgsevTzg/PmJlBp8WvLt/wHhQcxIkI20FkUYEjlgvz1r8KETwNCHEADoE5FywpgwpN2OhBngdh95n09fzII9C46oF1THj0vahnyeMEIAP8T2KvDMYFa139vnLT7heZ7y7f4ecMv7b/++/4dP3n8CBESSgLAWXxwvW84x1XfA1qXCUSsZ/+v/8Z3BgPM5fgWFCnBhff/wMEGEaR2xbQi4Z//yf/hlxiMiSMU6jQqdHUse+oGIrsaDC3jOZRlPYI06bqehFOKh3zs2+jgyQEGgl1RZ685Fvrzma6Vfc+/baN7b9hpnC7fYak3jt+Gv7v6VlmNRpZh0iYF1XiKz48ccfkLPum+erSijMyDlj2xJS2rCuKy6XMx4e3iEExvmskATDELBtCy6XZ4WmCIzD4QAiQUobnp+fIFJwOh3w/v07MyEpaN0wjohRCRtzy2YmMpmQqJp2Wtao+gz6t7S6I3DCgGrW6fSCzt6uxL/hxdRndP3tWdOtclffhxnLPGNdVjw/PWK+zLg8PePp6yOWecYyL1Yx7uUoarOoa5lDXbRxUmlRGo6OS5BSmuHMBUUB3L2uZVN19fp7ZYMeb5DHlgUNJTSRNd4+ktaqBpFVtVuwrrMyRGbEyDgdD/jw7gHLs47CFFjj28QDFIA5bZicj2lnaVZ1ZIAFgTWMN7JL8XuMrOra6u1rdbTVZ6SVyqjOFT2jMQT2xC3Akuish13yHgadBQTcnx4wxhHDFHG9XnE5X/D05UlDo8MAKoT5MmMajxjiiEKCwgGxKNOXwpjiCMnAfJmR1wwQcH26WHg04/Gz4nWlvOF4OmI6TLh7d68Jj0wGHaFMQsONGVw04bBWXoNgh5PUS/H95OqdvbeW41tN4SUO40uknZ4xvHbv/prb89/A9htiCr+G6N9qBR2B22kIrg14iGJ/vtnSjXD4RqRqfs4Z1+sVT09P+PjxE8ZxwvV6qRnB67pinhdM04jrdcbz8xM+fHiP43GyZLQDDocDrtcznp4EOW8qXd+dwExIacVPP/2Ad+8e8OHDe/zxj99jmsbqQ+CuGhaktCxY+Dxlc4BDQ/sq0ivZ+tCcBzFHgOy65rYv2lctOFSySqd1rVFVz2NQQhhisEgXlzw1d+L8+BXPj0/4y//4V8zXGZfnMzaDSgBU3m8AaC0aCASEfkVX044R845/9aYRanGcStyol7I17yFJqdoDESAlY1sXV6gQjNhmyaqPEGOMI5p5sCBLwbzOuF6f8Xx+xKdPHxCHCAjw7uEOEcAlMCRnBIJGGIkCK6SccL1ezamtyVgoGbkUUCQEKohsUHwMK89iY2eRVwXGHGrEjvaTuNWoC7EhD622fxketWN9SFZ/umJs6IEwDrVvYxiUGaJAMpDXH/Hj+UeAgA8fP6CUgsvTBR8+fECMEdd1Nh9PwTavICbcTeq4fr48I2+q3T59ecL93QOOxwk//NsPSGnDllbcPdzh7v5OGdE4AMzgGDTzOOp7RA4IErViW6qv2/ijM87XJHb2aewXuCCI5kvwj9+P9tfu7vctreGXNIk3sv1GmMIvMYTb7TUHsi0UZDTYq/7+ZL+asZAglVgyqRT86dNHfP4M/Mu//AuYSW2sUePbL5dnAOoTuFzOyLngeJxUgs0JQME4Dnj//r2Fk2Zcr2dM04RxHPFf/st/xrb9ESEETNNk4aWHzk8gqABtZife1e+Bgb9Vxub5BhVqDZ4QpTWilTCiUCUC7sJENTUJgICavMf2QA+NdMkcGuUUgkvcale/ntWB/PWnz3j68hnX8wXX54vWdE6lSp8qe7dWQgRFHJJECaA+RUtiulRPUFgDQUGRDLH76D7U96D69p5v0q1rJ4g+A0qquDkCgy5nYBiimp6KAFIgVBAHRhFCKhvm6wU//vAD3r9/wDBEpJIxHQeMwzuk8xPSsmgiGwrIykCSgRfmtOF6yaCSEDggckTeFohkBBIgBDBHDRlF638mgIKPA9u7sfPOXSW3GkvG0vrAHKsqa9xofwIrFIMukgmqoYr276ePn3AYD5iXGaUUDNOA5/MzPn/9gsOga+OHv/yA4+mI+4d7/Pj5RwDAu7v32JaE9bqCi2q8eS7ABAXDy8AYRnz6+AnzPOP56wU/Dp/VnMaCT999h/v7OwwhVn8DskaPwUD3xPIu6FYy/wZT8NcuRWtRFMlIlw1lKZqvsNyc22sDrxH4X8sI/q4p/M/abjWB/jfwkiHYsZaua3H2TZyorKLGgusCIcCw3hmn09EIfqqOZa9jvCwLmIEYA7ZNnZaHw0ElUq+KFSPGcTBkS2lZx0x4eNAs4xijfUJHrGASblP//dVMmDVbP2HfH92JfX+QRm6INEn/VQPb7jZ+39u+1i2whXiKRmmllHB+fMTl+YwvP/6I89Mj1lnNBVpBznqX8Or92ghUI9T+HLPnqDPYx9WhIFzjMI0B3kmmZeiBylyruaQXGEiFBO1Xae9XjJlBEA3ddMwRTNAMX7jGIgr4FwnDNKpml1ClXDHzY4hBk+9yQs4bCFbNqyQgi0bBkCg6ax3JLqkLLuWbMENUu6kiVlVna/sAjSk4WmcTra0+iHUP7aRZnzMEng4Y4oDDckDOGRQIOWVc4hVDGC3sVjXXwKEio9bazP4NBbLThG9BQECMEQ93DyhJEWyv5ytiDIgDg7IgwPOVqSn/MJORTxVHKN0j2bQZ5dexzzDF0Co5I5eMsmbIJi+hs3uC/q2cg78zhX/P7bWw0ZdagdlQuvN8pWSgZHVkelbyi0zRPaPxxJgPH94jpQ13d3da6eys9QZECp6fH/Hx4ydM0wGPj484HI748OEjfvrpJ8zzig8f3mnIHzL+83/+Z40aOp0q8b+7O9y8554INpt/aY7CG3LpBMxajR08R0dSCtRp6YRZxIHiqErv7Rq/t4U1GtFRZ7BLnQ7fQch5w9efvuD89Qn/8v//b+ZLmCHZI6Gspz0bWJzhwda3le4kAKJ4QOTlOFkXvUgjaHvNr1QbO1msYim2I3g/UctF8HeHWO1e+5iEuaXV3i9AckYhhbr29g9RMY7ef/qI8e6E6e4Oh8OEcRi0OJH5Nt798ZMhjypUd0kZ8+UCCDAgQETDiXO27OySEWWAhABBVuC9GDByRCHCojgYcAjxnfnM+pEICBamLOLzxaOPdFSZ3eNia8CZhs0V/65jZf8qXSWEyIhgTEfNEJcC3B3v8IdPf8CyzFjWBQ/HB1ABro8XnMYjiggePz+BmfDx/iP+PP8biAif3n/EljY8fX7Ex+8+4ng64sP9R5zGOyzrjP/+L/8d0zTh+3/+JxzjARFBiTXUBElBG+/fcPjuXrJ/kR9gc53cdyQoWSHj123G8jgjnxNkLj/vaP65ff68W7CE231vZPuNMgXgdWn4F64QHfRcstYrBiAcOsnbnbRUF4qqzrZACBYiOuB4PFjOArooICUUKkwqAfU8hGka8eHDOwsrHSuA3TAEu2bPhJx4+jfd2It3TmGXfl90h5O9/i9rJ1gLl1u8vhDXUERrAVwCc6m4+Z1pd1NNAgOkKDz15fkJX374Cc9fH7FcLtjShpLzjSLQiuE4wfL9O2n9tvVUXxguLxc4imi7yrOSiVwTQIPBIBgD9DubSYkdl0kze80CZ5sSakXZJUtqYxzvDhinA+7ev0cYJ4RpQikJ69bGi4g0OIAJJIP6ZbJG8uQtoazJYDH07xgDAh8NBlzfQYhQ1hU0QKN8SLVX1xjgxB71FdH/W8UcAnrt2CsPtrz2Jlw1qAzUfvTqwjoMHtHVkuKE1Lw0RI1eGscIkGDdFizriiGMyDnj8fERx5P61yI0J2GMoxYM2lRCzzEjLVobIkgAF8JAAafpiIAAJGlikdN2ny7VZ2YHbI1VxHW0rPUWySYWjZdVq9ukaQm3Gc3+DEaLcPJ9dHOe/77VInzfG9t+w0wB+LaW4MduCIoosc4p1yzUGgPvk9qJJEgXwX6WWbaxEnplCoIQIkpRE5GiLWtoKJu9+N27BwDA999/rKYhr0+wnxVt+fb4QgDQMIhkr+6LL3ZfyKhSfGV21IwjjdyTVvvSmD44CFhLEnP8IGn73CxBrlHoc7SKHUFywnK54Mc//Rs+/+VHPD8+Iq2phoPuV0unNZD2L+3GseuV+lI+jO4bQNUaKoiZUQZ1hMIpGgBFqmXmqitVSdijy9juLepA9XHXx2ueCkhB/jTBSsuTnu5O+PDdRwgHFGZs2wrZ9LGufYQYEGmwcbf7oWC9XnGZL1ZTo2CeZxzGCcdpQk4ahhssmSwzY2BlXgOHGgzTiLJ1ZuMNcKbbWZSglQK5+lXIYjdrUSnxfAbVRLRPc9UwGNzmRn2SCVes1yhyb4SI4OHhDl+/PuLzly8YxgnbtuLH+S+4P51wdzgpGivUhzCLwpunJWGjFcs4a58JEBEw8oC76aSmprUxXoYHHBgjFKpEWEhAoRM02KxNFdojI4uuZTgTIWUI9eNMwV+31zpu96Hb961chn7fG9t+w0zhNfPQawyh6Y9FNF563Vas26YEjb3Klt4np1QjbWDhccEgCVSa1MzPT58+4OvjV2zrhv/6X/8LRATn87kioH748L9gmg44nU64vz+CiDCOQ9UkWvtvRQXdtzf7dAT+xbnY80RPUMKevBK6jGBnLmz6qzTCr9FLLbYf0BrUEEEQL6rS8HbUzVEgOeGHH/6Cy9MzfvjTXzAvs2oIOw3DW8SN8VjrWp8okQOhmqmKZHh4pDrs21vVPnRaWCNvcu0HnyPFbM7VUW/91d4l1WtSSZBSEIIjiwIgAYeI+3f3uMwzvjw+4v7dHWglzH/+Ey7Lgsu8ICVFn52msUJtN2LVTFhDYEguKKt6MF2Kz3nD9fqMIQ4IUW0gIhnICWm5gpKB6TEjB+60yV5TaNTGTT278bf+bWPjerFUuBPVBBoQpCb/udbIO+FJ+9Gfahn3ZhoJgfHx4wPuH45ISUO2Y1Cz3javeDg9IOeM56/PGELEp4cPWJeEOc2gTMimMX24/4D7+ztECUb0SU06BDMlmonRNNk69+y3dO+nvoNsJW81aAAuXDgTToKyiDIfh872afeaJuBT7bVj3/q8we03yBR+rdnolohSJX45W6axhSjqJFcIBjcJierBFiKpCV5MBLBGOxyOB1yvF0gR3N3dAdDJ787eu7s7xDhaxIplWgYjDDcEvv/7W4ygJ5r785qG0Mwn0guM6KXFdo14l3TnevsUzM8hi3f11FxZ8P4s6pBDSbg8PuLy/IzlekHKBhRHbcREmtlHNZG+UfWu1hIl3qUzUagzubRr/HY306F1g13ZMdgKj22uo9qk/kICLNCz1tTVF9d9ISrQXkqbBhuQYM0Zl+sV5+uMnAtCiLi/v8cQo8KPONppAdZtQ8oFx2lUC8RWEFgjm1RbKUhp05wPMZymQppxnaxNpsowdWlnPSGqX90A+7BXptDPiTaXbLC6m2k9DZ1jBY33aCQYqGfvPk+a6QlgMEcMY9Sa0TFAyjvMy4xlWXB3OqnZ8XpGGEcwB6RVAzMwCkpSSf7jhyOO46E6pEGuQTdfSZsaUucfOvMQKtFXjKtStCqfuNZTBMWYgmTTEDKghTN2XdK2X+tn6K/9O1P4n7XtZd9fd37TGDRssaDkDclqFuQQESRWlT4liz4oAiItyUhbi+PmMBkjOCLnd9jWVKEm3r17MHgDlaaZQ5MSXVLcaQl009bu7aijUfT6OW2f3BwX3PZVI4zllfu0B1WBh5WgSo0SsdoEbBj1BShlQ74+YblcsV6v+Prnv2hGd95MC1e13owT9hSHnzA9r+sCqivFNJWSO42iJV955MvOhORvLf6+Qa8vAobG2IegTmUIwBQ0m1nUMU2AwYkX5KK5AWDP9NXn5qxJVp6JzAT85Ye/QCC4rhtSsSxnIRyPR/zhD3/Aw8M9TqcT5vmidSCS4OlP/4bPnz9jYLICRCNOhwOmYcAQdEnmnJCzChRLviKEgDFtSjSHASVHMA+IFJBr5FDoRrPzNbQORjUemuZTGb5xlWqlJ+1XRZq1EGciAOr4JgvF6Qv8tPGz51Ob72ThS4dpAKYBd6ejQqCvK0SA8/mC//1/f8L1ckZORZnF3R0+/NN7XOcrUk749O4jpmlSmJI1a4LfYCGpbvoyYp+yljp1v0dBau3p4M6L1emo6UoFyGIVm0VQrgJZAFkAzPi2VnC7D93f3/r9mqbxBrbfAFPoJZZfOv4a05Cbc50Aegx/cyCq1Nhi59305KZGgFDECR5wmCaMwwgpGSWrVuHkqYZIulRWRbSbkFL4c5rED7jU38w6zM0x+/L9X9t/2z9N8xBp+yvzIScf/b2svfDwR6rOTZQEyRvWyxnr+Yz1ekVOmxa26Z7tQqf3yr69HXKSAJoTwDvxX6wvmFvBG/cfVD+Q/237yPuU2jvUw51EW59gpxR3PJas9ZXJ760vUCBAyTg/P2Ez/9G2bchSkLZNndOARbCsmOcrHh7uME0jUl61RORhaNFJJBhCwGEcMY0Dhhg11r42tygBJgZEtQeVjjPyrEmK6sBmhTivzmAj1NLGsxn89O2VQBqVqhJ1MwDVgSGBFxdyjYPqmY5Y2+Z3zxh6LUSFEu72MWIkEEUz1x7wD//wB1wuWkXvcBgRYsTl/KwO/WnCEFSbQoHWOnBNlERLsJakJuKSa/3x5hJUkx4zAMthcSFCXM7QSdBoANAymd2n8Bph/zkt4Jd+vzGGAPymmYLP2tvj/f7bffa7hmxKhS/wcnwejaDnF7cuVakZOdVbHQ4TmNhMUgkehdPUVEYtxlM1BKC6BytzeM2xjMq0ALeHo3Mgv2QO+wgm6xXCC2Zya4pqjMj399Kj2poFAmIPb9Ts1JJWbOfnxhQMHqK2px+fylgBh1ygCmHZybai+/om1vGot/Mb9ZDZ7YkeKSO1FKPfSPbOSIgBgChBK5K7PlcTUZEmPLg56+n5UX0FIeC6bNisEpw7liEZkhjXyxmlfMI4jViWAAoBx+MJp9MR52lEQMEYIg7TiGnSSmzbsmpzC+BpuMzKFHJKIBRICUiBEAko4wAeR6PZ7iC2fu00Mse2ckbheRp1bpDzFEJLXjCfQtVyVYsgasyjzbYAqkmfXNdY63/uhBJtYwhkUC2EEA74p3/6B3z9qnhi25aszOkT3r17h8M0InDQmZFFPwWdD6Bg21aFmcmb+r5K0agjJgRLgizMQNAeKT7nBfCQVGTHyLA5k24+v0bavyX0P3def783sv0GmMJr20vi98vHdfR1jhZAVP2EJdQEi04onazPLl8JqR+iCBzjiFnNEwVFa7kSI20rPKIDzOCQ7cnmsHZD9jcle3nxrQB3SuC8JKX7DjQzlastVTWK23u5lNZrInsn9v55DdyFqgbTNAW/SVoW5HlGMg0hQ1QtL/1dNWKnkfyGJtRIeO7YOkOJS+uf6hw0k1HpGk7IJrE6wF1zLBKUENy+s5oMAAuTUUIO7Z9skicxIUsGsmkOoozjOKoQkLesocmD5pzkorWc1XfEWNcVWYDz8yOu12cs8xUx6rwJgfDdx/eYYsB6ftLQVEOMLVa5L5dskUcJUgJEBqvRENU0VxJCJGTWmHxmgIcBFELXl+po32NEuZjvmoIgV2HDzCUCMEc716u6UZMcqC9GcIsc4FTutQyuHjSoMRnfQgCmKeDTp3d4//7OappveHp6wuFwwjRNEFmRs/rtlAEkuOmwuBAFqclysPcREDJpUukQCSBFzi28bxGJJs4Zt1GGshTQAtBrjuZv/f7WsV86/41sv1GmcLv1RLbXDvDKPnVAlSLq+HVJw86rMQrSZzg3ggyQxri76QJaQ5lIATRqmJ+dmzlBEDTKiWIn5dMNYdb27WP3+/dywiFWJlKJmEMW+EfDJpuTb++47n+71PZaH0rXbVJL5LaIFEBSQtk2lJwqUathrX4H/9uIsSdylfr+PaPa9wNorxS4CadvLYnrEPscjf3tzHQiQBtf2mk0lQHtvO6llnysDm57Drvih5blHEKAFnYhQCK2XHBNCiO9rAumMZhQUDCMEafTAdhm5JSQV08K1BwEEUGmZKGSAjbkJ7b8CQa0z0tCSRskJ0hQhFB8sx/azprrYmNS+9bmjWpynszmPoYW5Vc1AqOkVKvzvaR8DVvMt9eyuuzdCRgGxc/S8GEgpQkxMpg1KkvvCeTsc88Uq0L18VoBDlU71WE102f1JxAk1PAFn+oGieZ0QXMVajZzX07zW8T/NgT19vVfYxxvbPsbYQrAPsvZt73GoAiYGeuasKWEQxjreSIONAZ4neXsDkhkAzBT6uh1Cpgd4iK4LI0arplZmY+YFBkYZKUwa5kBopv2ZSP8SrDUxNLartS5AJKqWQMxaIWpLlrEE6Yc02cXw1775fWNasZWZ6dnakjMIqCSsc0XbPMFJanKXnIxqdcA82ALr9JyQQiaNZyq3bwRE7Z2kvWhM+m2KYFiYhj0kMqjJBDPHnLiZEQgl+wswfqSawBqsazk0iWxqQmJIAkIlkcSOSIXjcZK2wbhjCFEiCQsy1aJWaTSomDGCE4J13XF9fqEr19/wj/+wz8gRMKaZoQIHE8T0jUiQUN9h0HrT/B0QkoJ16tl1paEkUcICra8IliWtprvEmiZUaaomd6T5npI1R5rXJIRxpsER4EyMtrtglTq2PoNZGHBNVsL3fztBbJeSKkTqJ9h3bF2vUh/D9i6EZzuJuSUkLYL0pY7/5rlFRTVHApga5HUL1AAzbswQU1Y4UmyoOLfGn6TJl76y9taKsCyzVgvG8pc1NG8Yr95d7z2mrcO52+9+hvcfsNM4TVN4Dan/fY83V1IkS23nMAUrBiHSUXM6igsAhS3vep1DKe9mhVNFFzuhHurHCOGqZfa9Hh2yYYNx4ackN2+k4o/qsp3Upmhe+pE70Lx0KRxkVyZAqgxiLbwm6nptm+qI5daryqRMQIhFq2TVUvISZ2s2ZJ/sgX+9U4KJzKKIeTmDPc2lPZc8ed0PobOgaxMW2qBnCJSgwM0z0QJmTML1zaotsOJlbGJausWtOgwQwil5tQuZr4LwRiXEUwPSvBrPdSVAEzTgGGMKAE4Hg6IQXF8QmQUKeDAwAAMh1E1A0MgJdeeSGtul6xO71I2EDQ8VQEJTWomAQWgbJsWpMmKIErcEjIr2+3Gv0KGks8147KVMffEHdjHYrZ8F6eCIsUItc+zW2rXz7M23/0+Ls3nnNXcWLR+SXEUWjOxaUQgzJRq3r+SlIYbQa/1w6FaMwfu1kNBluQrVqPplIcgb8pkiAyHSkr9NkAmIPepy94dHZX/FsG/3f+GGQLwm2IKe0mi7budwD+zj5wMFGRkbElNAWyLmogQR83ClGKOOQjA6vpk0oxnlVQzStBwTQ+XdKnMpXPPAHWbdk7JoCVEIX+Zb+aG67ylLpSdiUkEkKwMDKhwz/V9pUUrqRDH1QfifefO6FvTkhLRaqtpG/U9LooZldRsUXJChiAXtYNXBM/+5hWt1QUwN1F0TMgYGgy5dcdMrRHOVCoRAypjIB9XcX8BoAlUbM5hJ9iutpj0XM8Ltcnkzbfn56KZ6RW+moDboADAzJGs95wmte+HacDpeNBIoxiV0JesyZAgDIdRcx4ExgAK8rbu5oxA61azVap3OPCck87LQig5gRJDcgKZg1zrDFjvvSBArj04kRcoUp/iNniipp/r+SWuc9W7SD+XnCDvJgxu/ri51hi/qCamYbgFOYvCXeSCUrZacU7Ppzp22hWpmosas+eqoRIGy//wle9w86Ye2Brd0mIm5aFiT+k6N7NoUT/kDqyozqUmyLz6ur+kQbwxDvEbYQpGLF911b/GLHqNoTtPMtTBnFBKQmZNCGJki79nlKzXqBBvjMQ8uEwqraKYVJwt5j048e/zGXzyNqKYpICLqLoqUguKtPdz6VjNMSWXKukws7bZksKYGXGMzckMXZgMJ8gNFgOVoGLn3Ns7nW80ql2/d3tygiwz8vWKPC/qjC2OFZRVdTfRlJkUHttukUvTDAhU6yn7k5xMM3UM3AXZSqYtosT5PoCcS2XCmosACw/1KCe9PsOwi3bCgjvzlZg4Y43DADBh2xzwRuPzVSjQBLYYBy2paRLsIUYcjwd8/8fvcDgdMR1PoDiAg2oJBOAwNZysD9990P4QNviVhM8//GjaSUYIhEDBGD0BpAxYRKEvYARV8gbJhLItmpPBlguygzYV7Aga1c5D2TZcn58RhxFhGDBORzTCKzZmzWv/cg41YePlPNqLxiJs8otrQUXxKU1zz7mos9dNtyQAqwZak02LzQbx6ChPMtRJk0t7tpp4AxDEoo08kx+AaRxFBEIJQlrkyhllzgm5XFBkRktp3tpcee09XywjMkXLrrlRNt4aQwB+M0zh12y3zOElQdPdFoaaCwpUAiukELweLeP38ISbFqJmYZ91njfpoGkHLpNK90gn+C3ZPhcCiaJU1rDASvxKtdNnLzvIjJI1DhvusC3FCAA6Ac1/9CGv6BjErRaFSmCd7LY2SxOf7ZiajlZ18uWGa9TwjeSm1/t+6PaRalyvrQ+TP61tqoMZQkV3d/IW7ca2JlrtPOxSX8WtJoIWjfRyWSqxIWlmJGUepfaRZtWWqvUUiErukRAHVibAmuCXjJH62JBrSSZZk0GLlFKQ8mYSc7Z7m2mq4nO51mXSbElqQikMKRkorBqXw6ET1556uR60dkROK9b5auMikGGshHNH5qmbO+Sjsp9PDpHSM469ycja72uwZEsWVehwnRoCIWrtppbwqLsaNAmZ1uzQ1238/IRsTbO56YtFYAKMZSWY9KEWAtcOEqSsaPGovae59yjj5vftvp4m4WYY3l5M6t8YU7hd3r2a71JGAdIG2Ra18QdN/mGKENJFDBgxCoOaIFgdbUDxSEawwHCM7MPqT/DkHa2TcNs+s30SgO45UidlC9vLFvuuGdIeiqrfh9MJALBuq0rDJDX6hUNQSdsnY5XsXHPowwW/rR3swl+dz4ggrTPS9Yy8zch5Q0ZBNl9DEel6XJmbm7ogfT6C3ktL4sA0oU7CrxDe6vCNMSCLakiqnRHkVup1ptb1e/Fs7KpJWWlOot3UMDg1iIVxav+rIzq4z8dwcpxh5bwBJSOOA0CsTm0GJABZEpblgp++/oTrsmJZV5TsyVMRbFE+HBTsX1FUI4gZ16dnSMrAWjAMASEw0pZU2xgHPSai8zQXYCvGgAqkRDWlFIHw2AkFPt4tnNRYObb1jGW+4vL8FaUkjHLEOA3qayM2M5RGIlUk1Npzra+9RGgLl27mpBaBZM8tgpxEmUKd4yqsMTMocNWIFGbFBCfJTQsGANMMdXx0aatcoD9UgGOAM6o3TmCZ8vZ8n+M+MSujymoyyjNIei2h326tEb7vG1rEq9ur9qT/0O03xhRc2vlWJ/dE7va4hRMGxjCMGIYJWRqIV7SQoC1tdXJTFjUbMaGwOhoD6UJmi0sUFKSygYQQ+slP2rV9FJBKfaqNkLtD2VXpFo4KoEI76/lSJUOgYF3nGhOvC0Qq8mqIbE5sjXhic543+G9Cj7i677MbGd+kV7W/FGWky6yfrAXWvU2uIan0KTVDVEw8r9AJ9TF7M0TpFrubSpTZaipJ2qSDu3bsn04yeznc1ZTnbDB08Ab+rrfysOs72tyqVqjk6n9KAXNEHKIxCoXGzjnhfH7GDz/q3Hh8PmNNCSlnBGaM04hPn77DtqxIW8JxGJFyxtPjUzXhBQFYCKEY1IgwtpIU+8iFh1Ksfr0AVCA5oiQgL1cdi0DKcOo4Ux1/Xz/+vFIyckpYthm8MXgABHc3fdtCiwEyk55DTHt/st23ReW5pi2l0xRM03Efikfb6dy3pEEfH/fJQY13kqUmqrXQbKprxHNiqmuDgFQWZRiu0ULXVg1bBQAmhBABBtYtmckK4BgxZNaaE9TDpN5ut/TodS3p9WveFkMAfpNMAbhVWV+es1N6699qhiHEqI4/SVYlC1Qlwg2myhZRIZcJwkAhczQHxzTiOsk8m7Wiq4pew36NlQt023+RYklB0rIyxRlBI0owKcaZlGcSp21tr2USb4xZYRQyKbgaM+IQIazx62yhjGAHNqsPedG/e9XfmVaBbCvyuiCvq0p4NTehY9aujvt72X1fLhmz7Xti3c6UZIzE60wYASyd2eBV4eDFsFcqDkC1juyRAt0p7X4du3AC6FSms0WrXVorpqVNo1/GMCLnDcvmUAsFX56etU6CCKZxwD3dYZwGpHVDLsrIU854Pp8VJqMUPByOGDiAOKiDVaz+h7A5zPXjMM9qhtlQGMhb0GimHIESu65h7Jyh9cVVcy4lI5fNPhEirsM5o29zFBaeqxFCq/aZAErM9Z4hMCQ4U2BIvmEK9X661lwYapqNZ1Hrs9mbakEIwloZTU18sLaUqvxxMHh0UubjeUl1jG3pEUxggmanazxCrs8cBkZMxhQq1sVr288R/l9iCm9v+40xBd9ey0not5/RGIjAQaVPygVZAMmCkBXsLQwDOFh2Z3aMdYKUgiSCvK52jwCH1haTxJIRwW9m/gABAABJREFUXgIjlGxZzQFUCGA1HZQiKNRqCVebKKAMyMDnoJeAzZxcCoFCAISQvPJXcOdnwbomM2Vx9W9U7cHKgIbAGAbF9OcQOtPSvs8ESvRqupIxs2wJa9s8KwqqOdq1cJEgSVaiWwkFVaICNNNCi9TKNiROzT3iSyXtLEWx9c3WzHCNAk0NMOnZYoMq4yBileKr44IguetrWHAs7TNw2f5xRpzNgakRav4uikMkoiY+WLuWtOG6rfjpy1eNyMqtbCfyhNNhwv3piCEoM3n37h2u1xlPz8/4+vkztmVBPJ0wRMLI6mQlKhiHAELBPJ8xclAHfY1Q4wrUJwtQmCCRIQMDPIAwWD/7mnGWrH1xOI6IAyFOEcHyaQCFMHENttj4eoioM9lSsmmkA4iz9RNQwCAEzQcQgM1f1+ZFEzxqQIdNQUdX1b+VAbIlBUpJAAsYBWww7tnMqwWl1UQwO5FYRBNEEE1QUiagIH9xCGoOJkFOi+ElXdXEFwJCDAihALTqBzNe0p3XzEffOvb2/Aevbb9RpgD8LOH/xrnuEHUcdWLUOsVkRBggIChVSNWmTpUyuq9AC7EoMVFhkgEKVfIgs8UKCQrpbyo9qJ0YbIRrA6XaZdGdQybpelKZv8NeLBaL/mnRT0RAzl52MpgQb5FMgVWu6wgr6rt6n5VKFFQjya2UZLaoETQprFTzV6UZuzHa/+qyaOEO8U6dt9/9CO+OeSNFUJWzquW09leRX2AmkD4E2N9VncqV4LfusMtN2ustVKSExKuieZ62I8m6WYTgDAoaoZXVVBMCI4bR6nkz7k9HrNcLUJJWLGM2KBWTJ5mrDVzBV+psNpt4gRT1Y0lOkKRZ5iQZgtAx5r1GBwgoEAKC1Qy3ueRzvKgPR4ogFR/n7N2m422JZOTrxDQSNQvVO7YW75hCl5xZx8XhF705jbFrTkIzO+lHNVYVAEoFJfQbkkcnBZ0DFTSQSmWATSMp4CD6TaThsHmFSIJUDO3eJ/fa9i0NoFPr3/j2G2cKgl/mvm26lVKQUsKyLBiGiDgEXTKkmChEZKHLfRIMAJA63kBaVUu6kLWcNeqDGMSjSSMEMFA4QEqTUDISiAOYDKOmqtAaQeKmJ32wSplKFKAMqCbSuGqsxygA2DQDoDI4hoXbMgoRUgIKlxbOGnKXv+DONQsZ9TlNaFJySsjzFXldkVJCKoJUiiWvSUVH3RHyziTTth2wQGNGthlJaddY9EyWznFPrIzOzU7seQxqInLmrRW5PN1P39OhNvq54UTQk+EaRqYu8MAAkdq6VXhwKVe6kGGAMxCIcBwHmx9b9eeklLGtCx6/fsG79+9xujth3VaECPzDHz9hZMH5ecRAoZodNeCIMMUBOWVsJQOUDWgR1YRURM0oOQs4EcJGCGnQzhwi2hrpk9C07z04Yhhii3hLi/ZDAXLS6CA3+XifqU8rGkNWWHrtHN23i+ixfmyIF2R+NX22C0JKbnvHq2oKOatWUHLLGchFa1nnUix8OyNn826YT4PJoesZYTRkVEtUZSaE6JouQIGtCqHB2EjCtsxY52fkNEOkBz9q7/HLDOLXagdvR4v4DTMF33pS9POD5M5aJwwqtWlVtbQsegdiRDOxDDG2u1s2bhxilewdNkByy16umfY5m703K+FgBhV1AnMIilMjtqBMwmRrl2dKi5TqAHeFvwgQgjIwjh0EcLE2Cmp4usLri5qYRAsFuSOwlGRQ32RRV9kKjuS6+J0GBBJFh71ekNYFKSfLYi5VfW9L31pr9Rv6kXHC/MK+XceRKsYSdVdlk0KdMKlUWhrj9uQ0gjLoOt5VXOxMElVBAUGRUat5yclbkZqjEohAyEgefgrS4i+mtUVozQOvwXB3GJGd2UvTTK6iWEVPT19xd39ECHfI8wYmxvF4wDgGrKbBKafz5DvBlpauhGxQhFbXenIBshEfDpCckLcVvC3afvMrgXsJnCxD2ExcApRUjBha0IPJ90JalYxMo1PlwH97tJaGy6opSKVwN99phzv57xiDxwYTWWKB6PmWx+AaUMnqqM+502ZNSwW0Pnog1nYObIKRIIaAYRjgYcRVEDGsKhUY9N5pSzWIgS3rPCebp5RRyvIzTOGv+bvfeibwS5aOf9/tb4Qp/PpzvXKUVhdTuz9B6yuLACFESFCYY5ekFVYBcFx/n2KOreKw9S6lAIRkUreQqvxUCCQM4WKkx2sFSzPTcCeZm3rsmPdVM89idaCVORTP3gzmQCt7ExI1tmHSZTYpkIwpoEJ855zVVu7quTmLIwooJ/C6IKWtahWutnv2hZZCRH3WzlyxG6uGE9RrCf257nVwaOS6iMxkRJaJDMDMC2KM281IPe4S4AymN1/Ii/bZ86A+CqpmItR5A5CFzPqYGSEqBSFqRFJKVlLIa0NIwZp0TC+Xs1b9Y1IGFoBhDIhRw0/93vrROZRSMnt3UhMnCwK1d0LW50nU7OaSSaEviMB5gqlTO7OMj6FCSLhQ4VE5LR9D38NMTTYPmwtAHV7KBCyTuubxqPlM57f7rnphgGr7NThZa0eIZS+XojhaOW9IyX0ZMO3Q8uKJMAyDaoo1dFyTGWNgTGNQ3CoRra0Ai8pjEwEkQ/KKnNaaADnF0UxSGUwjQAVlZz761vZLWsPt9vZCUX37G2AK/fbSgNE23v3mMOBwPFYpXVd8wbYtUHNKqvH+QoRl2bCljGkcEAIZDG9ADAEcYpVuihX4cCKJwBpjHszpbBj1bLboAl2gKSdIKt46+CIUWc0a2WLLU0lKNNkIUQyWJQ3Ngrbw2WJorprlrG8eQqjaSTHpl9jLXjqhL2CY7yVlMAk4b8jGFLZiyXWlj/OWyiBe1wL26jFBiYtYSFZ1IIuPokmDBLP353qvmlcg7qh2S1JRKR6mUdj4uTOYmZtWI+4nIIiVYI1BUxh1rSqxTO4MB1W8KtVIlKgQIgjQsFGwuqPKpm88RARiMAIOBgNyPj/hOl+wbQs4mNZBwHgYcLw/YLlcK6PUBMGCZBXKLpcrPn36hGE4IJdkBC5gyxp2GRERSwCwKT9Kas7kaQSNg40VIFmappebMa/6QjwggAJiJAARJSsgpEKF6DyNMQBswgBZcAJbhFwq1q+Gqirah563QsReEkExhUTgYcg6VjoDKBeMA0GGqPe0DGgl0gDzqE7haagwYiU5E1KmsuWM67xgiBHTOKoWIGpy2rYNaV0tvyeAEDCNjGkKWOYEwgqiDUQbXiLi3W7fIvKv7X+bDAH4m2MKP7eZemhFN0AaGuoheS4RangegTJqohOx1dgVnegFhOSOTNWZ1VxgajChmImHwdHQL81x2MnA0L+8bVLbqaF2RqSTPkOohcCqM83KQ0Lg0AEAavSGS3WuFfVZ1f68ZkwAWrEZFXCLqesQAVk/5d7JLF0pQ1f16+/OdANrvxOUXxif/t/KIXrbz+6D7qC/32vPqGSvfqTLUXHzkudSMByH32Ghq26IWozFcjMcksMrflFOVVsRsYi2wLgfTgrdHBiHaUIIjGmazKlMGA8TRERDnY3JbtcZeV2xmvNaxzwhpRXRAgTc+QuoD6Bk0ZwFC1vO22yZlmIaoYVvSkHuHexwA6VrKJ401jCiGlRuqeNaFRb4+kHNUBbxPAYPbwUc4gQ+1wBo0mA/TvqbINUc62n7UrQ2AmWYcOMYSVojnW2ua60TMn+daS2i4IJiEUkqEG4Q8cg9Qi4rGBoSrNnQGUR96bV++7WE/e0ygNe23xVTYHYnlP69pdVnMWLQIuS5JC2JCEHOSlBjHEACRJASxaQQGY5JREGl7m3bMA7BtAjVDMIwWEQSNcIDMsesMwZpEZbOJnICcm7TkIOGG6KZnMq2Im163WBIm8rvdCWps5LMbKuLIFvp0QqrDQ22ggi4aIx5LkDeSmUIjALkjG1ZsW0JKZeay6GEwBx+zkfQiHNf5ayPvhDx2Bwj54TKUPrrACiujf2tofsO+ueRLtIWdW5hrn1yH5w5etvEvPXMCA4sSH4eLNxW1AEpjo+jDF2TptSp7Rm1qSTkrSCVhCFo/YNSNlAkDHHAu+8+YZwmDIcD7t89YDqMOHKLDLp7uMfxdFLbvrXv6aefcH1+xuX5GQRgGgas8xV5W/Du7h5SBJtre0TIyQgpAcAVkjeT3I9gOoDigCKaoOXw7MHqkGufGdMrjfGVqsX5eHJlDiLJTGSOOio1O9kHUnxw7U0DQ5mJqPlSaxw0bVOk3r76rX3eRCZFNC2MkhxRdUHKBVvOGIfRmCoMNqMl7w0RAJIi+6ZNx5ZgJTyTmp5YsG2rBoMENYdxSGD+lqbw15qMfhvb74gpmJQsGdt6wTYQKChBCIHAkUBFM4JFRNXKpGalnLdmKrVJUFAwjhNCjKppGPEkoYpWUcy5WETtlcdpNJNoA40Ow4BgzMUl7ZJ1sQlb7j4AkQzWjDj1ZkuD95YiWGbNclZTkierka2nJqMTWNF+mYwYEsABJGI1z8ysFZpUTWuCZK0GVnJjBk7m65mNFjiH857f6Qj0Yo8THmqEG83odJtMZ1dUv4UWnrEcje7Obl5yvBsnVmSMBG4edKOXv1QVHCzcEupwdunTdY4iBfM6g5lwfzxgy2oGLMaACoDT/QnfffiAuw8PiMOAMEwKoZ1XlKLMoxZwEoACrGQmgJFRBsJ1vYIFmIZRi/owYZ2vWtyHFTlVk+kIkKTEkDUjv6wEKymGcDpoJI7BQBSnwKTRdc5gPTBBQ2y1XdlyZyoku40NimneVYmwugtGjJUv63FHRIWbjaDJlEwBHkjgvioxPwmkmBlXMC+r6rekuFOwOQMRMDJYAEJACAFJElJZa6SYa6/qRFZtLgQCGIgjMIwaipoooSBVDSylC9TJ/Jqj2Wfz39b2u2IKbjZI24K0BUOUDFpa052UpLbVnKWiekIU0bNhCrnlSIl5TlqbmJ1ziBOmAq3qaBWjBtaI0iLIApVSMagWYbGTTZ6mKpXp4nCTD9WFCSlK8ERNSWIaQE1gtQWDSuqpzmEpZLhpXOPJaw1eKOGxVG59jsWr18+uV2nPEOCE/OfMRZ1B51WTzyvnAQ5zpM+tWFHiL1XPbwQO1UzRSnn25idjCWYugvk4gvkOSrYELTazBGSncax5Q5CA+8OEYrb0ztiEYRhwujtimhQtlYJWDduSmabE3sNNWGyYTkKKxEuCNW0YSE2QgXWMcto0W52izr1CcEZZCsBFmX9JySRzAQ8BiBry6tnRSlZVs2zghh5o4DWrpfrdBJoF7FpTTRQBKqx3Hf+uWpt/V3NVnc8ChKZJFxPcSmkmHi9NkrdFnx+HqpKSmbkY6hdCUZgPogyRDW3CG1Mo6i/0ceTAhmHmdUhSBepTB/8VwGYCxs85mv92tt8VU5Cimajn8xNSXkGkWZzBJofSfM1SZmIMQUHK4hBsXkuVnAikiJZlw3K9gJkxDiPGISDEiHlZoVDOrGUrS8Z8vVZCuxYFV9u2ZP4GMmhgoFHpYBDctnyNGTAHI2KW7Rk0zlwgmp3t2Zvw0MJs9tTGXHqTSxzH2kfJEp/0WQVgIG+bQlsUqRgyCjUeMMSoWd4l18JnrcN3XxUvCqXzewAQIg07tfayHXEtJIvUcF3XHkBk2dP2DLuZmsTczo+GIdXpSq45qTDcMnSdMjOjRY9BEBmIMWLbLDKLuTKFVNRhO1vNYBFRBywR5m3BlldctwWPf37ClhIu1xkpaTDCOFotZJGa38LVh0GWQKVmxlwy5uVazUND1FyIsm6IQV88balCg5MIJASkIqAtIfGs83oaEY8HZGv/ljSQgS0MuojW/XAu71oMWbw/cTSTk+cp+5iZdlQjl3oJuoo5cMmnZh4DKNB+S5tB2pdkNaqByAEpOb5WAVPAEBjJgjnGMRjGZcayXJBzwfF0MiGtmP9QhboAxoiANUG1vpJQ8oa8FazX5/ru6lfUnKZ5XpG21RjMbyP57P/p9rtiCsUkhXleLBZds0cjKwwEEZm0bVJZ0DoLpdwU1iBfvB6mqBqCJlFZ6r2fLoDGmqrmQSAEDuq8hSgUtTEKchtz6dKt7LwCq7/AjJStbkEuhiPvwGDqOyAj6mxqveZTK26F0kBqGog1UuzbHckglfzYVfmb+H+LflWCAdOapDEuzx0AnFCjLtBiDsh63M6pznD/V2p3w92TaFd0PoOeADnjZHuP1pev2YDNaGXtpGr28nAA/6ualqBMubhfA2YOyZ7B6wVeBFKAy2XGT5+/YlkWbCnhuswWkAB8+vQRgTXHwQEOt5SwbQnzvGAaBjUJMldkaCfcWmAIykCLKFBcLhrNRWwRcHqcWIljSZpMx5N4lKlFHMHyGEyIkOJT2kxFfd5IGy9NaivtHlCNQoPFipoliaz2g2lE9h5VZHEzlYhlLOucrgWkyCLjSl80SXZtrf4IqDmsZjcX0x6oZdALVPDRKMEEr+kg5hzf0qo5IVkZ97qqQNfAGP/2t98VU3DgrOfnK2Jc1UEbI8YhYhzHGqWj6Jxc6/T63/odrM5yiyryGsxgwlYSUFwSNbwdm+DbuiIG1sQak6py3ozgEji4+aJJaG5bFWgiEhFhXZeqsbBoVAoXzWZOBRAwihCG0Ya3kGo9BKy2kMoNcYQxzLRtyMYAIlkkTkooyezzUHkpG4xB1QLQCCqYNFEOJgH7bgu/XLMyTQGsJL3J7u5MdodzZ9ZqJrlGyN1e7ITD/oCHanINkaV6zDc3AdVnADUvpdeilMKThkOKZcdyBJUCQTIgRbLQTgI4aKIVlAH/9OURP359xLpuSDljS5prEGPAP/7TP1UNZBwncAiYvz7ifF3xw19+xP004TBE3E0HhSURQdaEdmwiGEAY1MZl9nI1P0mg6v+haKilgZDXRaXmdDQLI2PLjcBqd0h1QqtvKuyDicUj7FRL8joIUnw/6rxg1lyfMAR4RJCOD0DqQYYno6ldNVUBI0bPlgZS2ZBFMES9JqVk/FmwJdfoYGYjjTgqZhXwGZMtVFyztrW/Uras6Nyga5Zlwbpu2LZU8xtSao7538P2u2IKNcTHo3hsQqwwAg5gXVd4+MM4DOCguQitvKbDGlBn9jFGEUKV5tngLHRfZyqRgpTWqgEwtTBOzV9TppGLZVaCm5PbCFDgACGtOxBIw/AUh0iJcwyau0CWg5GSOooBIAssTJIMDprMWa7nhhABUinbNYWSS8Oe72xCGs1UDIHSNw+TLJW4OiFwKAy7XNsDJ9Vme3LpG01L6Il5jeEvmo/hTFT3qR9eYZmTZRZ3zKNjYjYYNi3MMt29h/uPssFUO/MHgJSVpYYQ4WV4SmnEdTVGnopgKwmbRWsJoOoVASgMIGAcD7i/f1cTJD9+HBA44vnrowUNEGBJjEOIYFaNc9s2UGAda8PZclu/hiybVmb4XCBCWleIADyMwDhiGAZsdr73Z9MJLDqoKAYYbCwDMzxuLJgm2nRINcOimFZMAJFUsxqKVqwj4oogxKSmHHhSae1PJfxCbYwU1l5MCypN+i+lQs74OGQj5hVNFi2HKJv5btu8pomaB0WAdcuK81SFCwJzUu0b/Tz/291+X0wBqFI/M6u6LQ0FUm2sLc4cANjgAJqtumUYa70Chd2tJpSg8MUxDIYxFMGBqk1cQxtdDBZb9O7MU5cDoDWDK1UkBe6jjiG5dYZZ7aWOW+SRIeoENHW+CJKUar8HzFRFaJqQX2tMRmvEFwUMLM1p66YR+P2rwxA1s/t2ewmU1xH53SXU7fU/CRDejYnfZ2/eQSX6AjHTdSMoAL16j/4uld95H4Hgzgo3xcDNXKQmOWLN4q7YTAIrEKNPy7mYTVzbGVxCEFSIiTgMek4RDMcR27ridDhgCIxohOklsVYzjQSz6FNrZwHgtWfEalsIASVlECeUlMDDAE/E06F0Bro3sJl1B5rNb49xh3I31q5n9NUHnRcXw+syW0415/hb7QtM6fOzFdTReiMNa8yjgoo5w4sxXM20F9MA8o4pONCjQ3ukpNdtm4FispsijbmLtpE4dFFtN5r13/D2u2IKCvMbcTicMAxBIzMc02gcAAASYiV0OW06eRzHqCvqkXJGDApNMI0KJVBKNu1BcVcUsjrUDGPXHEKMJuWiScbU1PUiudqGySqqBQ51tYag6Ku5bCiGd3SdVwUZIwLSghRoh92UbSG7Ci4pAYYhn8zRp5uaowaOiCgIHtZp2oyR2WpjdzOOAqD5UZPYgQayZ/HjdlQ1BycEcmvpb1tP+J2oihFmgTQTVrUeuQlKr9G/Qr1bpWk1Qct0OAsKULjsglJMiwpDvbYiUFS2K2Yqkh3LIQsEIBEMATamghgYp3HCsq1IOeHHv/wF56cnvHt3jzhojY+Hh3d4/3CPw//6v1q4KXB9fsJ6nTE/PWmRJwJGy68QAbaSsQEYgtb5CBxqAnkuzc9QcgElrclM44BQgo2TmggdU76WB01ZNRBiKzOshNIhqTcDy8tmvwd1/iUWKLooKvQ1IRhisClLcKJtRF+kCiA5O/BkMbNPwbpt1dC1baoZrPOmDGDbzPckuFxn5KJ+ATYtv+XsEFToijicjja5tLYFRFAs0lDnQ0BKCddlBdK3ain87W2/K6YAABCx+O6AMLhdmQz+ARjqiYRssAhFMnJS2AWye/jEEZDa4MVU5lwAysiWCKZFOuyO9pw4DJgOY9NW9HGNwEqGg4xxiCBiSHDwO7GCIJZox2yOcZfyCCEyYi/ydQRzVz9BBd4a4eL1aQGTnqTTODrTkRNhJ/IvsphNwieYAEjUJPNOyt9Lmk0W21uMXFpv17V7Ue03MWrfZ+hWzUFaX3hf8w6CWp8hwlWydG3BhEwdA3uuV3CrBytjhJkp3NzC8CpeZH/DrF2lCK6X2WBJgMPhgHEcsQwXnQchaNIkEQ4GV52WRe1/Is3fBRjBdY2z6NzL3I01TINQMMFSCtjKTaog4jDU3MbQzDJkUVEBprEwlFGIEo9c1H6ULSFU26LXFzO5iGOwmDPchaGqJxoz2IrlwWTFQSpFYSjcP+IRYWDCsqzqlF9aslxVrpm1ooPl6rjQRcyIgUE0aKGsGOu8JovWGzDqfDAtumIu/Y623x9TAKojMkSrFuVmGliqvJWzzCnWKIWN1A6pm1gkj8E/ZIFnvWbHe9kyYmAMMXSah2oX4ziA6Z1W3jI7aQ3kI8KQNL5aJVUNL00hVJu+aiZAShkUtYzgEDWzlACMo2ItJbPte00qgjtTBQrmR5ZsV6pdWVFILUeO1CbseEgCM38VaQBqQDW/wbuRepMNmiZAHcidtagxAOoWX2fSQrdb9g5l/5co1D7s+ZIDeaiET20vQQsUGSMXUZs8ioF9mPkAICPanvjhrfaSkS2b2JmTahRaFEj9Pd5GNa14JbYiwPl8wbquSGnDw0NGPmSgFMXomY6gGLQ40qB1i9O6YbvMhkcVavRRsCgfFI+6ERTmaiIUMwcWAahY8ahsPiQKKJ1fa1fLoGgSHDOBasi2+VeIwENASBlpA0JXSKealzy72RhwEYdObKanHn5lM8RSzQ3RkO/z5VLhuwGqQR2Xeca6rsibBjMMw1D9D8M41XfRSEDdH0PAOETEqAIZWHM8HCaEoPPCZ17OBUy/j9yEfvtdMQWfAOt8BvKi2aPkUUVc7cRiCW0EhTJgRAxRJYhaTYtKtWuqbm3O4tIIqBOmGjbHhCzqzLpeV8SogF07qwSAeZ7VzGQx2swBp9NRfRNMuFwsGU4IOYXdRCYoDAAx7Ra5GKHNuUVUBVOnhbxAT6g4+FvZgFLAkpUeBlgEki6wIpqAl2XvgBallR13aGKWS7VuhiqdBP9SddAebEqOS339KS0L1sl1LZQkgoa21KsJJrRWHKpgNIxUojVo6j6Rr4igpIZ1RB1gIQc1TW1GyAIYEZpUqMm8es9A2mfLorUoshWkTyljWVYMw6rO5BjBrDUQXPpP84JtXrAtq2FSaf8qnyOL/KFWIlYEZcsKKMcEzhZiPSlSatlW5FWDIobTvQod5VADGVxrzKRmtpI3yKbZ2ts6Yzwcagh3KSoIVdyrmvch8FDTZdlM4DD8JGegNh4xKljfvM61dsIYB5TCyBkoCAbvLlrFkKBFiqaIPBTz3YWdU9ph5H2uMbuvo1Z3AMqqCXoWcuqmLJcsci7Y1k2d3tLNzb/x7XfFFACVTNK2AVIM+92Shkza0vrLBYWzpf7rpg5dTxpTWIEitCNsBE2oqosDqNS+2ttdayjKTNxx28wiXgYUQCBsWwJxwbBFDIhgYtMuYBNfwNKSwTSaw4i9iuyo0AQ7pmDQ0CyVgCqxMaKfNaO1z5modH4HBU+VWEoNb9S27bKaazvsRlL1hHbOi412v+n2ClITVx922rnZ6x7ZXWVMsnLhro39PexUZgsdlmImp3Zvd5UqY9KkN8WVUuLsVhMh1LyWHt8JUEahyWy5Rc+IM0G0sbCMcm9nEdEUDJfevc+NHle8Iq+lauYgkOJ3Scpao1xQzalewwBwO7+ackAwzKCEddGwVvefeaSZO9fd1KjmM+3oLUl18jr6ShspK1MrFlXUaYI1KEQcn9Ug16s5SxAsgVOZs74oeb+XNs4qjFiknHjkkmKBZTNbwQU6aQJeSrkxud/J9rtiCm4vvs6L2tJzrqipMbSKV2x2ejFiykwY4ohgE49YMYYCqVNKHBG1zXg4VVHHtBOihiqqJijCNB7q2YslNgUryiMghEHxki7LhpOZvETMybZtGGREKAHrsiEw4TAN9TmBzdFsaJ1EhBVzI+5QBjEMgzFHbgjXWTWDkjIUxNIKDAUBSsFsBGWMEcM4IAwBl+erVhhLXR0IdojqjvS6xC+VXtmBhjHfy/giqFnOvd7hBIQNGkKkEXSB2/Bpx9wd4TVlk/Qrc7RQXzMBEilRn8ZJ0U9Tqjb6lHMbS2O8ORukd2CMHhRATUsc42AaZAG2DZKa9lMEVplNJWqIxfgj6vsUzey9Ox6xXGdsacOyrloHuhTcHw9W93mrc7IVIGrJaE7gODDKsiCXgjAeUKws5bJcsW5rHRgpMEaVUXKqmt62qcnLE8myhXaLAOMw1ogrjmaGqUzBKtUBgBTEqOCRzuBDHHVfCMgpgwvj/v4eAlatOKu2FgPXHAM2zshmWgKAbbtapboNklqEYcoFW0qANEKvfZi1jSKWhKiiDXP4HbGCtv3umELJRWPjO2mCYWYQMyU7U6iRLQSkkLVKGylRWz2SwaQZlb5Dzdp1W7MyCjUFBO5UfjsO+PMIOQQU6iV5QrSs3WQmglxgETGMYFKlS1iuLxTDV3KpyQkPRCoOUwUqMzu37mxhhGQSZnDCkNQcoYtItQEOjOlwwDApU0ib4uxsucV/K/E24u/akhXEqfL2q7GstPtVzWBuVjLmTUTgyLW/WBweOu3a6pqKmniaD6cYIxEAVDGdDIgKaJnBzliqOUzPyVkA0sgZMqdkMKerwoKoxD0MA+Iw4OP9AY9Pz3i+XCGSOw1RmUTkoYU4s/qU7t6/R1oWzEHhUzSIIRiCacMnCt2cdGtcnz9RSlDY6a1BRZd1hcQACqj1BTwbXSVtnVs1sQ9AsoirhpUkLTfCiDMZ8q74eDMjECrhJiO6/jdgDBotjqsxACs5qioQUt6UKUgxLCjAa1eLFMzztYWkGjPKpSAZs6omJEaNbPKESY0RsJwf0yj2Gszf/vb7YwqlIEOqKcPr8iZpZpLAmlSWPI9BCtYKiaHEqUirzeBxzhryylUq9/DUYJmd0bKmQb1JQ/0WMQSUPBgapTrAiRhTVEwjMmCzXATjMBokMmlMdnWUachg2bR8IZHaWzlwlYDytqnkTk3S3ijVvnELj8bgR0QOyCkhpYwkGgGSDIgvhIDj6YjhMCDEiG1dVeNZt8oIfNNSoO1v8jHY6QS9okU33+1+zqxjiOp7GVWDCiGAEVBywbqsWJbVoJGVkQSySDDRpzYsJ+27JFIlTm9GsopcyhQ80MCZGNX8lgKdQwr7odrRVlq50gMDwzTi+++/QxFg2RLWVfSe5oRm1oijwBGAWDZwxN3dezXbDCM+f/6ClDOmYdAQzmQ+FaKa8Q6gwl+rKVMFhlw8WGAzkx+QlhlUBvA0GFNYzf4PeCgvERDGaERTzO7fMoEdUZaZatRWCC5slMpQav1ln2PR8oWsVx0NtWSDbiFllCltKMkKS5WCbV1sXAuK+dMIZGasjOfzWc1xScNkPf8oZQ0hduYZg2p4KVnOhwcXWI6Lh3G/TND8295+V0zBN5fs1WFMZqrxQRcUI9octHs8LDObjValhwwNMnGJ3DGTzKFldmQNLTSn8baAgya7iSWTlTiihP+LvT9vkiVLsvywn97FzN1jee9lZVVXdfdsgoEMBlwg/P4fgxQIhLNgMD3dVbm+JSLc3ewuyj9Ur5nHy6yeISmgEBVtKZEvwne35arq0aPnGLuojAlM114KEpB0sGb3FDetmRCUECPTdKLW4WFbHY6IpDyTXN7AhnMCpZgQWwx5+8xb49apsUObaTTkOrbYdMF0/Lpz2n3audfG+eWFWQ/kw8RSG6sHn4G447iubpnkOAijUrB9vpVkQ85beIWhD6pj75bl1965OxxsHsTZOSFE5nwAVaa1EF/OLNeFtdwwSJStqS5h9GC6w0230N8+24B/7y3YC9tcRnDNIGl1nCmsPvFMwGHIgEok5Yl379/xcrmyrIWPxVhKIQYOxxN393fkORHTYIhVtMNaE41GmBJVYOmdUDt0YY55Yx+13vbBTGy/t95MIh4gjErQZB9KgbCavHQ6HshpYpo767pugcQxOWrXUUbvA12jOoth90DwgIt4brM15UdwH8daoXVaH5Wj90Ecqswp07tyXS4211ErMSZabVwv563/kvO8HZfr1Wiqg9G3VeMiEOKmIBx9P3TtaMBmgW6OvN4kTLeV3FvZ3lRQGIuNSU+IY5Kj5B0n7ZAFGPCOuDWli2b5ddJ1aPSoN9XURMnULzyFEDphDOMEq0RSj/b4sTTrirZAcwG3sTZqiBACrUaIkZgnxKWRTanTZRaiNd1uJYmDNymHqdC4aDYNJ1/QNsaSmEG8ZXSWz5m0w8CSZPsZMNBoipdSCCVBDFsGOV5b+SoQsENKIyBsoVh3SO+WX2/H5aZpPWA/1Hy0oymShmC2qDHl8a2c3qjUuvhxktsGBqNx3X1RGnz2UTvYLbe8+pvvMXbLtg/3NrgXJnZujca92gCh+nca7DbFehk5Z6ZpQrGFvFRgUWqLlD4ksf18c8jPRiDGfrTMNuj4rLJ9yAEhjoom4L0VwXoFLSGKze7ERAhtrzLG/tb9O48DOfSubL/5nMAmnOhPQPeAgS/8aoFgNH3H40bFpgjaI33oDlUXaVRLfsbEsnZISbeJ61IrpdrU8379sgWILblw+fHhHR2CbnnJpnvl55yq0jZRjrcRHd5cUEgxcjweiSFQmttLts6UbGyttV3h1FgkYxFUv2C9rI2R4JDLWFD2iUlQOs2F8cpSEYFaO1NKzNmkrgW4XK8MxDy621NXh31CYLlcmaaZ3/zmG4j2eZ6fnlGBeZ63RunwGSZ012IyKe9xER+nmRaNOmjVkWwyCwLEnMmuyikizDEytU5q3XVhFGWlj7LaL5j1utoFWQrluhqkcAPJ7JLMTkf8KkiwBRhryNvCMnR2fAI8Rpc42Ps+o6roCL1UJERSTjYtjk2GC4GcZ8r6kVp3zvx4L7bFyveh32AicAPrlq2PNILRJu0huyjgK6ZaHBIiLmMiwvm60FT5459+4OnphbXYeRZD4JAnjocT8+HE88vzRpPsN/DllCfu5iNUZY6z2Wyq98P82At9Yz/FIbMyD00pWzQDNovTtdN7ALmSVUmnExFhzvPmoNZ7teRn9KdwaMzLv95NmqMFGDIVxrjSLZCAJS8b687ZTGNauNW2FY1W1SaCRF7WC611ltW9mImsa3H8P/g5CNN0oLVmkuRDqdZ1yHKMrKU4bdav25EgbRUxJqSHfc9rLYQYyF6BjfVh7OO3sL2poDC2wzSRcia3SlkLtRSy4/6Sp23AyKQZ3JA+Rp/QHBi4bsGi1s7g/adouG4pxUXyEq3vwly9d2qrm5hbHdOvqE0ve4bX1YTdWq+ggevlaiyfmEg5GZui1q2+Lc4GMSZOMUjAjeFDCCbVrS6kJrLx6wV1mMbfF+uF6A3nfcgKj4BCkH0SG6NMshSb2h2Zvt4SSHf64bYCjAXW79+3vSoRGX0gNejKdZgEM5sxXNuPiVdQRqE0eCjGjGYh5RmlUFfzLjV4wKvBkdaPTV/9szmdiQTDyF89POyVpT9pu1tNYmLcVWqj9YUffvzIsq6sq8mSxGyDV+MnpQyYz8flemVZV758eWaKiXZYodwyn1xSqFuFkMZUMWpBRRWJe3AzaATHyU1fSGpDYqOuK5KTkSGcbVRrcZjmRpdIZJPKHj2oEAKDzVNhSwrQkYgZI6737ji9J03Nmr9DXDL60CiIy1uP6XqHFJ3IEEIi+gEKwfwU8B4eAjmnjUWoCkE6ccwhOaljr1Xt91orTTpJI3mamKZMdCnzy+W6Vb5vYXtTQcHKX2GeZ+bJFv+rXFlUydEuxDxlYyR0tcUDk7kYpfvQulG5nS2wCeQYhDxly47V2BV5ypQqnlWZkFfrjdpdctlfV9SkkO2iMCiptw61o71wvZhJSoyJlDKt2dCT+kXbujFKYkxbCX+YMwYuRYSOqDXpJARidwVXwfoRN1ASGjwI6jbpbPDZQCZk++4iskkTDEghbNTS8ZoDotrSfHQLRq+GHhhDW6PCGTpTYybK5KHFG/MdaCZn4BBCbz6wFSb3Tw5MPuFa1oH7++fQgXaP0BS2z6X+uceCZUFhZMzju6lVP2Oh0wGXe9+ptk38cK0VLXC5LAM4NDaORHKeyCmT8kSarPqb58zaFS2NL09nsgi6rBxSIrnkxqYV2ixDj6OyRX0quCOSBs617WPFIUhVm9gNlbSuEE3ryVzHCmVd3H3sNYSmN2q3QcSp2nZCjDbQWEBNVdVpt7eJRO+06tfTBuPk8TJO8uhbohO8pxREkDRsTNX7eOqPMSvQwzzvp5QKMXRUE2PGQVS8YhrCk8rFy4CcEodp4nA82ndblw12fiMx4Y0FhWBN3sM8cZhnyloIhyM5ZeNd47ISUclehkoINhvQbcG5LosthjEgLk52vS4M/+eBfc/zbNlPjBwOdpIul7NVBarU4iP/A/8NOH1SYejYp0Tvq4mc5bxjyQ7xbENDvXuGOa58+1zF6ZIhjB6I3addaezj/9fLdYOrNt/mbgqpWXEowd6H7tXAKC4EVAd9cV8MxrYHzpH9O0tFd1jOtkHF9WepODRjzXXVSFehKkwpMx9mtqlfXGqjGpTRu7Lq6gu68O7De9a1EMNnlrVQasOIqfbauHm9qtlwpmisrq5q+7BZlbXLnQ9J5UhCUOnbdPc2pAUESUZ1xuRvVUcT3nZHjubJEZM1oed8IL4/MCbvHx/fMc8HfvruBx9QjC4Hbp/NFYLoqE00hGy9LZQmpsW0LIuxxkb2LuB8UUBJl4vJP1zOnB7vmU5HlrI6bMIWjLdhOh3UZ6+Xlc0/YkCoG8Q2AuX2HHveqP4UkBA5HOetGhieBylFJCSmnD0pUe7uH2m9sXpzfCin2vWWOBwyEkygcgyQrsHgzXnKW2LSvEexzw0ZRBpi4pQzOSdySiYxIkaL7V3eiO/aWwsKjIlT3GTElESD+yDgjbUgpjYpN0Ght04X9yZAXOk02gXmC2erxRYZ7CIKIZJiYpqnbaGl95uLQom688iHKcnm3YuXzuF2aGtfTEMIN32BsDXLm/OrLWgEew9fjAbzyfB5dUxfN9wYH9qyuQRxzVTdMRF/WPBqYUdQdmG8bWcPDIHbCWNufvP7vpLCsO/46u02vaBd5WEPLAP3HtO1YLt6aFtNOZMR5sPBYJO2v5PtO6sorE8wmDWKNZll20eDHqujpzF+dFeN1XF8/DOO+8e323ePMKoWq8jsudM0mRTL2qwnhGn2iOtujWaxbPvXG8j+mYxia2pXXYUC9GDEiuBeBjKmf1WRbo9de0fWlQLUVrbqdQvoN0dsP3TeFB5xTvbGNw7xxegMJdnd8DycuHBd2PS9FFMKCIDEUYXslWPKcWMdjaQIDzdpUx+OHkisWmqxbYt+dIp4C1axWxXUqK2Tsl2jOWcXl7TKP35V4b6F7W0FBT85tTY0VKQ35vlAno+svVNK5fnpM3fHzPEwb2J26+ZbrC6FHZnygZjMw/kwZ0pZuV6eGUM0IomcMnM+cHd3IsbI5XDayvZRURjTpNh0cpw2zLbUYtVLnpBoPYrJoYPzekVVOByO9H7xstxkOFJOlh0206pBjPLYnWJ4dzxSitlC1tWpmF62D1evIBB8yK/ajjPaaochrZ2ynTpDbM9kj8HzxV+pF3aaXxsr+03PYTxqwC/jBrstuF6QsnSQ1gm1Eknb4JsJzilFKjgbqyv0CCkrMSce338AvoBeTPV2w/zDBintjXSr1va5FGU4elm260GpqVcUzbT/t2oOM7+R4BTYiqDWkPaKoyOU2nk+L9ydz+R55jf399RauFwax/nIFLPNJLCC9k1lNEVjWYkqva2b5lbVRumNSytoCOTTifuHe+4f7nl8fOdDdNMGv1zXK8uy8PnTZ85Pz1y//MSyLqSUuL+7I7lnCP7415vvt+24WdVoCyocD7NNKIeAqjGblmWxYCTmIxFTIrq3Q++NQw7um24Bfl1XcsqEmEj54NPmkVIWaqtoW7cE7HA4bcqyrVXr20TrOQSxwDDlTDge7LsvC+fLlaVW3r17bwkiyrouNnPjooHmp/B2tjcVFMAhpGQsFW3d/JljBM9WSjmACKVWa05huUiICQlpK0FTcgtOEXqxRcAsPW2htcEkW5BHVhZEIFr22ps1iedpIuXENB/cYlOh94F8W9YSA1O2JjPAPB02QbqcrWTWZh4MyQ3dY23WRO2GbYchfYxBKsNNTrds3j+mGOYa1c3YEaeU7A1gw91HMGigbl2Kbm5urQ09nA1o2BbS0eAbbys3/1ePSqPhPoJHp2/Tpqpm90lv/vnC1ngc38caz0Y3rdX1cYikaWZucH65eH0mG8zBqD6G4iv2WXTcjy3yAyIbzxECIepGUR4yHjoCFpDEptU7o+KwAKFduFxXzpeV+bhsEJt2tzIN7grmNpLBlU+r9u14DOXbc6tmlSrC4zffMB2PvP/mA4fjgcPxyGGebZgy2nCcAsd6pNXGw90D5/OZ6/XCzz/9SCmF8/XKIU/kFMkpQ7CdKu7gIyEzZKbHVHzO2QpOTDo+uoJpVyV09v6U2rURRTYZcAsglqE3n/vYYEdGJWqy4VUgiBKniNUWyVC+1pmmhKiL5KWE6i5Dbj08Y6KtxTyrD8cj0zybD0UtGwNv8z6RW9LEX/72poLCgAZiMkMTLcVNScKmGz/NM80z9ykZjt91COVZK9JwcYdmHTYRhGmaCGLl6rqujKnMsUIN3FQksPqATU6JbAMFrMtitpkdBPNUHsNuU563SczJm+RtLZ5tZa6XC4ymn05EabS6It2yWUluEYruZbkM/fwd8e2+SKamO2If7LaN4+0yGhaY+sboCHH0DQZr62aKWdjMtW7Bor0qv3msL7YD+x6L8gAL1K3ENvhA9qnYPZn1RaRDLY0YQdzLAhXO5wWV/ov3VYdgrLeoW1B7DaPs2DhmlmDSFiOzDOMxe7iLwSjMTbsHPXuuqrKsheuycl1We5aMRdfppSnTarWFMthcRnU/4yjBzJK0c20NYkBi4uHDBx7fveOvfv+7TWJjcO9/salVQtfFqgal8/TlieeXF4NPEKYcNmqu+L8hjqAg7ljoSYq9oFGKgxlNGcTHbsjj14yIQ7p+nqfgw4E+Lc02JBc2BdbhHaKi5Byt39RN0K+3TvZ9GIKpzqpCTJNVUq0TvAoptRBTZp4OpDwRPFkbfa+YErE1hkbVW9neVFCw5U9JKZCnRJIJBHpbua4Or5yOnF8al7XxfDXZhrV1DjkwJTNN6dpZ1gGumDpoSonT8UBZX2ht5XQ8gERnG1kJenc8bDhzXW3CeC2VNGVyjIQwYJhGzsJhmohpNs51nlDMy7Z33aqK6IyKdH9H752Xy4UcTKwveXNaYiBPliVq79vQVykuKgaOm/dtViHXTuxKbEpSY7BojMhaUCpt3eWEW2vU0TVwradOcjhFfSEPbq0IslNUkD0ajaV2y8rMwcxwbxuPNYhoPCkmm/UYQpaWmaetJwOW9de6W67GkEhzYj4sxFKMkvwKO79B/WVo4YzAsA/vBYcUSjce/ZQSIZlJ0+HORA6v1+u2gJlmkQdqq9e28zJIoqyV6/myDf/ZZ2+odOYp08rKUhsNSxSmw2xeCkApna6CTBPffPsNv/2r3/Htb3/LPM9Mfg5s14DetIllD3NB4DAbFfNf/ct/ycvLC/d39/z8089czhdOpztSTsw5kqLtm9YNKqu9cTqa9LZBZmPuJjD8sYMEQvJkoQe01W0AlM0Fj01aJoTIFBOH451Bsoo5r/njjb4bsaHLSEgzy2I9guvlOk4IkliAX+rKWhtLaeQNurW5obvj0WREupJFmbLN+MSUXMrj7QQEeGNBYWRhY7JXRWja6LVTGyCBpAazpBTdK8AF4ZKX/C4kN7yLd7nh6GWnsZJiHJnu0IBpbgK/+zVvTU3EplNDQHug6i6bEcPQUHKa6vZlbhRdg8lp1GZBZmRyxgYxC9Lg8wrdFzR1ZpSIK732bthrGJhuIpSK1rLBKdHfayi4mkl8oFv9gTqdFkn+tzjDQ1AZvgU31RMwJmFNB5oN0hlwwcgogxhfqEnfGC8hjmbtYLfEcZTxF9t6FL1bgLJ9fVO+YE3lMSksAyby1xiaN2MGfVe8vWkk+3sYkyhwOp1QoJTVv5sfW92ZS3KDygUP7ga7WOIR3YZVRDgejvTaKMuyyYT3EUK91yJBON6dOB0OnFImt0ZcV7qb09+EWkgWbBULuGPJGwEvt8pB4WGeOE8TtVRqq+QcTQAx2XdonvmX1mzGAAE1z40hyW3Tw2GDxYJXgH1Q4lS3SuB2QNA+n/3dtHmVYXpHeAVlMFz3Kt7gy9Gv2WRnxIcm/VqtzaVAxGC5MM597xta0a47FXkbSH07geFtBQVvSBEjGgIFZSmFda2ANY21V2uyPTzy6csztTcaDYnRBo2wDPYYJtZlpZTCda30AL1nyyC9CTggkLIW1r6ylpUcI1MyKCqGREyzXSgdpjBRY2BpV4aIqRm0N4yG6PLDIRJVISX3iY5IysRWqb1tSyNqwSJ71iMSdh4/kTy5RARWrrdakGhzFvM009oLtVxZ10KXznw3U4P7K0ugaqCJ0MJEl0xncOIj4IGB7l8k3LCMxBYDp1aKKsiKaCc45myrQXWdncgUzKCodmi1c+0rmmeX4hCQhMRM1+gLuy2eNkjnU8xVWU1BjnVtu2ENwj5HsTOoFKP1BocTuHldwAOsefu23pnmxOk089vf/o7WGi9PX4za6qJ6prI7m+BigJyN399R5mliSpkYMsjwEbdz49vf/pbT8cgUE8+XF9ZaWGpDW0V9EHI+zPz13/w1d8D8/Ez58omq/llHH0eBlODh3mZZWkWvi/ewbjZVCIF3OdPfPXI4Hfn+x++JU+Dh/SOHw+zT98HnDcyutrXOenVdolZdU8gqojG5somiSjQTIJSYsu8bseSgK61Wei/060JpbRPzC+49brM3WI8DEO1IK9AqeTJ4OKeJ3hdab0QFaZWm9nsMkceHB2qpXJ6evNfQWXtlmhLTbB7rpGj089txmr/w7U0FheaWf1+uC0tX6mKOUlo7KTnrRmwqOeXJ5DDqysv1gooZrK2tErv1Aoy/38wRTXjl4DQagDElWmk0bSxLQXMmhmnrD9i0ZSRE2RqXeTI9+boUDnI0iKjWDVs3/wa/3Fziu68rXa1xfssRFP8u0TWCenD/5+SYtgeeGAM5BWrfMzscP9beaHQua+faI4scWGKnB0FSYk4zMU3MhyMx2cDeuipr6Xz89GR2pYPB4hlrniKnecIB7c1Lovtix7jIUSLDAQ9mHJtWZS3mexBjwD1SEDFRvxsJK9waD7pP4/bu7CNF+95IHlWMuDSHlwP4B98WV+th7DMH4iVJa51SGp8/P2EUYx806zbfMnog96cTd6eZ6GJsMWfuHh453p04HA9WWcSweQ+8T5HTwz13jw989/13nC9nTjGyLFfOLy/c399xdzry/u6e8Pkz+sOPaFsZ8wG717bAZGqo1GJm9M8vyI20C4JRUVNCT3fcf/jA9HDP588fabXy8eefef/uHcfDwR3/cIaSNYjLMiaJbc5AwLJwPxZDp6i2ikgke4IkWGJiUuDd+hfOeOvdmHgpJ6O86q4mQG+2v7owDIKMJWZ/12rX3jj/gsDhMBuTL03mGSEdumkmldrpQalBuerC5bqYHM4/qaT+ZW6qSu2Nl2VlVWBpxGbYOXFvto6R+3meQJTLevGEy9RREUjBGmZd21g/N7qpMxr9Qhk+vUopq8kIkL2EtZJXvFE51vKUTBdmKYU0zTZIZvgH2zStOOaOPa9WX0DFG7KKNemC9Q+GrMWtWB5iJiINm2gOMaDVWUI3uePoi9SirESKRFqy5+d54jAfmKeZx/t78pSYjxPnc+F8Wfn05WyyCf65B6so54mHhwfr6aiiLw5TLAOyaqgGBKuSBhNsAgomr2xeBrb6DymMEHbm0Y6ld1ek84Wnd3rz+Qbdv+eIJDJksj3A2LZDSsYOGhn4FilsUSmV5+cXRsM9isNsPkchQTkcDjy+e0DoxJiYjiceP7zneHdHnidrpkZzfRCUdDxwqEcOd0eeLy90gTjPyEvguiyc7u95uL/n7nCk/fyR8ukzWp3CqvugGoBOM22akVqQssLnL9aUub1OUDRPaBeOv/stx4dHm59olS+fP3OcJnKMxN63BvGAiW7hWZO/GCJ4RlwY10fvw2RnyH37tTWCgkODVglaFj/EHdkCgm7T+0PZ1OZzmvUtxnmrinr5HILNrcw523XhGlnjnGlN0aZmA9rWzczon/wU/mI3G0iSXggaSIdMUiUqdgLRaKv1EUox7YAkgfvjiehlZMwmM/10Pvt0qZ/0vRNqY5oGdl+tKarK4RCYpmx8+ZSJUzLNfIXL5UruiU7eJod7N0ZPnmdCNArhYT55Fu3a7jGQ52yVSeuEqvui5mJvpjxhDm3JG4y1FN8Plm2PYafg4nMhWAWStLDSqQhnDlxRzmsmzTPpMPPPv3ngeDzw4fGByaGPnNMGvX56upCfFkL4jMmAGJShHpDu7g78s7/9Hfcn8/u9LiuXa+H5eeGnn584n68mDtcqvVegIdqJbWFQgqvLbnSFsja0Lz65Kl41gA1RYXGhdWrpPvA3Ap9HdEtVPZpHcHbPlJKLq5omkKgSJdCoaFOiN1071iQtpaDX4lRSx7lRRNxLO8F8d+T+8cEkQUIgTRmhU1cz3kHV4JdgAadK4Hpd+PL0RGlKmg68e/cOVfjy+Qu/+fa3fHj3aJP3IxDeKL+OzgvsbfQRJ4Lqfmz8rlsoKcVMmg/81e//ms+fPvHdn/6B6FXKNNlwptGio+9wx+WdEaVqWl8xRSQl+trNn9ofr3RatTmLy3UhBq8K44xEo5Ue44G5J6aU0dZYF3cPdDZqa8pSGotPPgeBVgpl7SSHkmqAnO18QC14r2WlVvsJQUgB0jSjMaIxsJQFemUglG9le2NBwTahEzBqmjW0fAhLXVunVjfRCa6bb/izuqlAd8bFuIgU3TSNYoJpEm9w2aI85CzSZLCM+gU7AkrtDa0uRNaNNqdiE6ijsWuVgfhi1rcMVfFmZYjg7BibNrZx/tb71vAcZid4EByTt+qZWO9sWDGWWLN0oUiiR2E+HDme7OfD+3uOh4mHO5MJsYBiGPOyrJTVLCMVRX9xQY1mrUE/OUdSOroG0AQEjocDEgN1WSjXK70XpDfADGJGI3OTDHdlz1ZHw9KrKZRw8wHGEqg6ptUz0zwRQuTy/Myg9U7HgzVT22qN/Jy94rORreW6cLmePbPV7RibbWQzKWpX30Tc6hM1NlZ3854OSGOtlbAsSBRnmXUPCgPFC7aIrYvpablNZBDX6jnMzIcDog1SQqYJrgUZsNkWEDwi5+QnwS7md5sHq4hRW3O2vkAMHE8nzuczrXUu5wv0zjxHpjxxODilMwSXAhlzGj55LzdzBn74x9+tN0SF4RWiOujM5j0eUmeKtl9zUPqo6N3xtNb9Gupqkh9syUJDHA5tLlNDb1Qv7rrv56Zth0zB9ktnE5F8vXf+8rc3FRQEm7+JXYm9IxSGxyzs8M1SKhS/WKyLxWhajuVlh+0Ham1Sw98cM48Pgbuj0UetQeYBRGYuV+HppVGL4+CCTWY6y2c4oKWcyDkRsw3VNYepCDAsGMONnG+eDhtWO9QiS6/UtrKsjbvTiSnZdPQ41+1ysd6CediaUY8Zv0WWHvlUhItMxHnir//mW96/e+D9uwcO88CCgzcT7ZVKbfz88Qvf/fTEpy8XSqvodlGNxmeklM6Xpxdysszt4f6e4yHw/lH49pv3LEvlH77/yM8fv/DDj58o1yv0QpSCMVw6Acs2o0N7tGoGN+KfyZvH4sq14z9LCyCHzOl4z7d/9TvmeeY//Yf/aH2ZHPndH37P4TDz3Z/+yHyYeXh8x/F0MEexWvj488/8+N33XC8vtFrZTeWDMXrUhv+CC8LF5DAcnWUpfP58RovRVNdaTBpcO/PR+izDqhNMqTXnzHw8cry7I8bE+fxMb43jYeZ0uuN4PNHXMxwO8PgIdd2FizwYIRFSItydYC1ISvDxM7TGLgwo9CgwTfBwD1MmhMjDwyOXywUR4fOXLzx9UeYpcTqdrMdwJ6SUNh0kSyws80nR3ANrbeZHYhmOu6PtC+7w2u4qrLWRUueQYMqdFBX6apX+A/gcJ09foGpjfVoNohQjJAwecV1MG0lh0+8q/WaZ90KxsEOFMqJ/b5aIjKbTG9neVFBorbOuhU+fnpzjvC3x43TYB4echjYYM+P2bdrRF979Pqs3Pn8p1CosC55ZBiR4e1WU6wLXC6xLojfDnff3tws4oNArrXQKSguB2qJjqCaEZuqkbWt+Nh8Ya9ukK6gYbXMI93U64tWE5ZCum6S6XxylsKyVL0X4fC58rsp0PHB3d+Db3zxymCdbhLtNVKfoS4l0lIiEyHQ4InKmNsfyt4TUF2QVzpfK9z98Zs4TKWbuXVSvD2psDPz2wyPa4XypfKlC7RFig1ZAV5OPAPYBpzHY5rtSrEqprrYZxfCGIOL7MBBC4nh64O7ununwJ3pvxBQ53d1zOB1JHz9yvH/gm9/+luVicwT3j+9pHdbVPI1bM4G9QcE0ZQxTAe3B3tuqSaVp4+PnJ55ezvRqgcAG0dwmMuwiecV5+fO99Rqm+cDxeEQk8PHjT6jC6XS/VWoyzeR3j0hr1JcntBYvff0sT5lwODJ98w2sFV0Wyvc/otWgudGr0jwT7u45/NXvSHcnJEUyyjRlpsPM5eWFWlZWt7hEDYqcphlx2rO5omWnfFombxWwnedjEA2f3MaZR6Ph35sZJAVR6lIRGrU2QoQp27WnCi8vyuXSWJfVKhyRbWJaMf0j9etmeFNsfg+3i4PfMCoZwKv2Zuy7/lbk8N5YUOi9U1Q5ny+b+QmMEt1zSJ/OvR34GY8BF9MLgZzTXpS7gJrSeX6plCrU4oJaQTZ2EjSuKyxXWK42k5Dz8G1mb2apukJl214/xGDzAl0ZnG913FscmzbBuM4Q8JLgfG2XJejd2PZDfG2U99ItOwpRqKVTmvJ0Vp7WyrnDHM0Z7HQwiKD1ipbhP41/95FlBfI0OcXQM/UNs5YN4qq183I2q8yRjPVu7J3uGkrHw8xhng1SCYUWQEI16Mj58EN+I3hAH1PUIzYi0DCZC0teX09R24T4gcPRKKCtVWIKTPORaT4Qki3Gd/cPXC5m/JKnmflw4nB374Y6gzAwGGUW5k2qATTsEhi1d9bL1adv29bLyjGQY+CudvcCjyxaaF1J00TKxvDK07wF7xACh/ngjV4LcPF4hPeNlpMxbmRMBotJrORMvrtHp0JPiTUmNAihDShR0JQI88z07hGZZ5MRiTaZnHKiq+mBVc/IYxDyPKNYtTAWUwty0XW4GsWnhQWcSLD16LFkoW9N5NYaoSkLBhmqVtZiVfDhkL2ihsulsyzWy0HCtt8HLFpW16tqHiS628xul5pfxU55vRV13I5Zua12//K3NxUUwBaf4sYs4+hvA1WMk3Q4cdkT+na/ODYv7gbWXbHRXxv46aewTbGKQ09xVBdxewfoVnVM02QXW4wby2HMnI4+QJBA8qzLtJDiVq0kD2KVsT7unPvALte8OsPEsmOjyo4eRllXYxAdT6h21tb48fmJtUFV4dPzmaUW5r+LlNIoa2U+zNzdHfkXf/N7UgokDzw5Bh7vjvztX/+Ox4cH/pd/93dcrpWljEzUpnAf7mZ+/7t3/P6vvuHh/o7a4ePnZ/74px+5XE3g7/HhgS9fzvz8s32WLpEWT8ShyqRm7GN4vhoW131H3KR8qtAw1y87JIMCqVwXW3g1RPJ8JPZGyjaMVysun2DSyT99/xPnlxekd8owOHLoLLpkNCh5Mse7sTjZm1of6no1lV1V81R2JJ2UEvM0c5wyp9OR9+8f+OHjJ9ba+O1v/4Bq57pcyclee10rh3nmcDjsvSJscFJIKBFVMWbd2CWKewn48FuwzzW0cEUHFIrzs+Ou++OKv4f5wGd16RAR1lK5LAvz1QJdzmm7Hs6XZ1TVfD98gY1OYx2Ty6hucCeKT573re/VanHdK993qjdJBlRnktXWrfeGGeZYgifUYv1AlzHc+hriX1E9sOlY9hXG0Omw43w74cC2NxYUhoKjj9Js+vayEVC26UXBF5ybZ/tjRlY7jHdutXd6M7qkhCGE1zfKaow+hbxNwggqlaa6me3AMCqxjGVMQNfWtilLE+myyePqMr+bfYnTIIOEbfZh0/DHgkKMkeST3NpNv77UYs3TBS5ro/TxOUxEbC2Vj5/P1NqopTGtldo6Xx7P3J8OhEPeq6lgkgmq8P7dPTFeKV+ujAr8eJx4uDvw4d09h8k0Zz6/XPnydOHT05VlscDYeuByWSl1BEqfq5AMoSHSgNFXkdEAYo/osMtJuJLTgIdlr57GcUw5oxpJ2UULu2eNupvslFL49PETvVvmazTKfcbEoCKv3tywZjRf98MgbMbF/pnFjyd+DsaYmOcDITXmeTbJ9uvV3PbYM9zNAGbAc3JbCflb/NpfN0nQ6/tHZXf7muPXPeEwFGgkVWw+CMHnb0SGzpFBtuMaGwML/eZ81+HepK8l0NWHNZua6VXx89UuDNtXrTeG1/Ltd1SArlsl4qgVigtTsn9OHLIapIhbNQDrVXU/d99GeHhTQcHUQRNTTo7JVzazD/xkcGaHZZ42MNa5GdH3bT8vxQxw/DnmlWxKjqqdup389lhChLgbdtTaiL2TYt+yKNXmVch4lj0/Bp/ojHlXu5T99X2Z2JQnh+fB9v1wKGsowzLWBuF6vXK9rDytwrVC2SBUCw7XtfHH7z5v3yOEKy/nhTlN/P53H5jyIz4fhwKnQ2aeE3/717/jp5+feD5/R61mpfmbD3f89pv3/OF335pXdm18//3P/PjphR9+utDVLvgvT4t/grB9A5WIutQ3svgFvS+suCaRyuitDNljk8LW5jnhDUOrtEZtyjzb4Ng0m9Bb2aAtAVfIrbXxx7/7e8/vbZp3wFUp2n7vrUOAPM1Ge2wGdbU+ltxXqYZ7So8gPqJW4HS6Q13F83q9AnA+Xzbm2B4MtrOLfW7CfrqIZ8KeKaOba+D2ZD/Hw83fG4X1Fmrz/0ZCgg5RO2saF5dYGcKPaymm77UY7BNj3Hos9jYj+bKVWfvueV18n4VgTOF20w8A6zWYR4klWzFmv26B28eNWQnGjAquNOvnTAjb57fAsEvRhORQ2Yrbg/5TUPjL29QYIDEEphQ5nI6becsGQG9iauq4p/Gsx+mQpwkQk0i4ubgNr9zlAsyS07KnTbFI2FVZfYis6ZBaNszbPkUgO9S0yZcJjCI4BDMVTyl5ltUdDRMPVvYqQx+HV5/UBnVqby4nLS7vbe9VFcOKPfPeX+F1+tk7LEvjT99/RBBaU7795r0NI/miEBE+vLsnhsCyVlMm7crf/uFb7u+OxBh4erry/LLwpx8+83KtN59WfvEzclgl0IkI5k2g3mfpI/uVMYAmm8fDWOaGKPle8SllbZS1kacjIQh5zrS6UtaV9XqlrCu9dv7qD3/D4+M7fvz+Oy7nZ87PT9sxy8mHpVoHTKBOy7rZWQ58OkpEezUdrG363ALQiy4cciI5JHO4uydN00YFNf+MSi3V1XCtt6DbYrVHiV3fadw0juKYcN736niODjs9+2O/aBgDjI212OS8ysCj7PwxX2MbalPtW28rpsjs2kQhBm75yWYYpH6+AtppzSqFMAlg7D3T9o0cDyaKGFTd+TD4PMprqEfnzFZxe5ZSyspoJkc3G9qvedn8xVHdyCRLWSm1sr6RYDC2NxUUxqGNXjGcDgdbKBQf+hnNUCy76tWDgonjqTMsQFz3xi6hbUp2O6Gsr6CDOz8yNL/fpDRcAllvFkADfYkStsd1z6ZUrFTuvRNjNp36lL0Z3bfvp32U+3ITCF5npq/CxAZ5jECyecex1xHbr/ZMf2qtnafnK6fDmZwSjw93ptgadkz2eJjoHb75sDK5g927d3fM2eCll8vC56czX14W1tr37PHmMwh70BqfeZPQ3j7YzeKmGKTnSfegGm4N9nGMsYWpVmO2xDgRUyClTFkWylJoa6EuheWycDzdkaeJZV1QVa6Xq0stq3sbG2MG91wYdqndM2AY8yTOGhMxYBvdAkepNrew1sopJaZ55nK9mL7RPLNcu2PzdumWUrxBe3uI9fafsctenQO/tr0OyK/vGXa0tfr7jUd6fy35T4yR3sV7bTbNP3wVopMlBtS2zSKMC0ltuLTR/fgZnTuokDBhRxGTPdmGOHQ/n7e5iOgMKO/PGcRrB39ce7bv9wp0k1IcxwW2YdKbnfMmtjcVFEKwISJJEyHNxHQk5UzKCQmmtxLUBb5a47woXSM5HCjd8OPVjU7mw8GyuhBYzi9OFcXF5yI5uR+CmPeyyW0XWq3UsnKYZ7MITJNp3ITAuq4OPUXXYcJNZwJhyu4vXJjyTAqROWeGLEBzu0YzEukbLmuKr8kDipqbW3CTIG7wYYdewlrN+vG/4SLoGMz0w8cnvrxcSDnx7vGeb7/5MBIwYhDu72bm+bfUaoNbx+PMslZ++vTMf/qHn/j54wvXsjNCRvP21xevYU4PjWihwuhTrx4/oAQLHK7qyk3wG0Y6ApfLwny+8v7de2KKxBT48ukj5/OFy+XK93/8jufPX/jNH37H3cM9//K/++/5+NOPTPPMD9/9A60WTqcTrZnnwenOPIKfn79QqrOpRnoOpBCNBOCGTF2rS5N0LstKSIm1dkLM5jb2cmaaZj58eMf1emBZrlyXM+ty5enpiVqK+ziE7bvfblYoGGS59eJhY9qM5P0G1PnFVsrKsly5XC4s60pplTlPzIcDDw/3zPNs3iA5W4+hw+V6pbVGjtFdz5KfpzfNZT8Iw/+gVRMQXNfFgnVbOM4zMSVm11hyRIiuVmHVai5rQ1pmOh6orbOUQlkXN96ZMJvdCK0RgDsX9gsxjHFQc8MrlVJsjkO0G7FD+yah9Ze+vbGgEMhT5ng4Mk8TISXTbFF1MxFDr8dMZggu9zzkrUW2yV0Y1ahukJI1cZNz1fcmVwgCPRDjfsHFlEgpczieXJ7XoKVh/zf0ksQhLY0RMk6XxRddIbjoWMo+7+B0vj5gKcdyW7dAp6gHK6M93lJxkUAOnVUGw+PXska4bZIqWCNYOz9/fqGrcHe6M1mPlGw/ik3eRrFsvbbO+bLy48dnXs4ryxYQ9qx/B0S+rgRuVzCvJzaMejz0Bi5yOM2gNbkJHkPnKLj8RTPhwGS+FhZAm0tiFHhR4sdP1Fr58M17myx2GuZoQA/v69vgthnEyD4ZG3xA0BZmV5F1NkKMdl7kPG/exYbUuDaQn8fH45FaC+u6Ukql1kaebqU8b+HNAc+oDaotVxOpWwek4u+/fXB9ddjNCGjluiyWuHS7Xk6HA6fDwWjDKZlTWTQc3mifBm0GT3JQtSpSxOdnZKuWxvXWgiU4tVUiMOnk+8HP1RsVAmCTf99rY/UkzB4xhBTHeSJOTQ6+T60/sScLY1I+xMjheERi5Hxdt+vnLWxvKiiklDgeDrxzvZ7uLu69VXIwzRx1QSztSogTg1s+FpA5OZShajx0DZwOR2I0GWxrAjbKcmUYmuAtvHGRx5TNBjRn7u4fAAVtzDlvNpfqipo5mkbS2pSYhSklztfr5q0QYzb9mcmE1KRXm7DtnXyYt8WmO8tomLgEIMdk8sDsvgxTbKxBMZLrbdYpX/22Bw3T1Vf++N0nLtfCw/2Jd4/3pJRwFNvmNRwDfj5f+fT5wt/9/U88nxulqu/fGwjDm4/2boEbc1DGHPXA1Yajgj06Mhg9O1wUN3TOuyg0PKhKolbz556mAzFHb/Qr61qpZqhBrZXLH7/j+csTv//9X9F7J08HINBq53K5eKBWSpk2JpYJEg74yD5ljLZQjX6WELZp3DnPHOYjd3d2jg7j+96b+xtbI/Th/oHr5cLleuV6XVjXQp7ynz33TS1VkVppz89orea1sFWFY3//skTsqrycz7ycL1yvK6LKFCMfHu45nY7cH09bth9TgtYs+PSKaiUF03bSjps9WaDclGAHYwmftenKWhbEr5EhPgjOTHKfEwRyPpCAabIZk94aL8uywb0pJiSqOx1aPyomIzXXXpAu1qMYocNhr5QC8+nEtK48X67UZtXDW9jeVFAYmOPxMHOYZi6Xs2nKLOuGfYoE84kR4Thbz6HWFXSy18CHrJpbCvrCbxh/pFVjmehogAWzRwShlhUw5H40Cj99+tkqlK2ycOzUM+wefERfmzM4AvPk2VirVtGq2okue9KH4BOzSvGmpgB3x5P1OVwuuakyu4uVBc1C7Z1wVr5S2f+Vbdxvi8myKl+eFv7T3/3IP/+bQIqZKbtPgVqzcq2Nv//jT/z86YWX88qefMlXP2y9hNsfw32bSR74AJtl3DcCdx4Qhmon3mweIWULFP4+ZSlcLwv4EJi2ZnLntXM63nM4zjw83PHl6bPh4gioq9w67NY622zKcjVLy8v1sk3ED40mO9bRAoMTpGLMljmHwOl45HQ6cfdw70OARk1VbVwuF2YfDMzzwSivTfn08ROgHE/zqEVMNVRuay6FUtHnF9b/8L/i4+PIsm6zHtZnNgOckc602qja+eH773n68pkUAw/37zjME/f3d0w576w9EdZ1pbZCKat5JZO3CiKlTPJhzd5cQ6zVzWOhN6VVY+zFEDdW0OgfxOD6YLWNGGHXSRwmO9CUTV7EgqzN9xx6ZUif5GyN6Ov5BQ0JQnLYqrFeL6R5ZjrMHA6z+Zr8k0rqX/CmVgFEjLYZANMnMnMaFUgheJLqfsfgC4BN6FoZ6UNmt65V/nfvbqqCU/diJKRsonWtme4QbucosCxXNvnsaFPCSYYUgy2kA/8dMxZj0K21vrm6dZcFvfWCGcJiQ3VVQmAKkdZtsRysKpuGtSahmbRHb5SPFja3fembzNI3/7V1ZVkbn57O/HYp1KZMadwvG7zy9Hzm5bxQarMK4FUX9CuY6OtAYVHZprlvKjFkLPrOcHFmzOhBjNfam9MjQJiAXd38KmTj3fdmUg2n0x3vP/zGOfGK8arGjvbKsWN2o4jRF9Wmes3xThzC2vcjYr2eGDBl3WiU1Dxl8jQxz/MGjYTgOkEO/9lxHjCmcD6fN7l18UyXFNEUkVK3Ayja0XVFP31h42G5VtE2ECZiwkLeyG6tspbC88sL67oyTxN3pyPHw8FkLGL46hxo22edHFIaSr/BmWl2vYwGfNvO1VbbZp2appkgak3nYMcxJmtiB630djOB3D0S+OdIYajxBt/HgdTD1mdKKW77ESdztN7prTlslYnCvkZsGlJvY3tTQcFOvMrlyxd0nsBPnp4jL65AeZrnHWYp5tFc1sLxYJl0dfXJOUKezRfhshSWZaW2Z3pvCPD4+MA8zxyPJ+tNYLMLdV1Zlys5Jq8EzBK0aUM0IQSmaLpGvVX3ABYIyeQEUqYPhzKqZWWtIEG3hV1bRxTmaGY+kwh5MomKtaxIb8Se0OvVGq4+pNNb4e5gmdXnc+G6di71v+ViGE0Oqzyua2UtzYaN5tE0tgW3N7heC2vxgGD38HXv4s/VKNIr1EIvCxHrqQxXOZGIqim1rov7Z+ugpeKLy21FEYBEqRCWRrlWerJz5LoUrmvheDxwPD3w7e/+wHS6Q3vjcDhS15W6Nq9QgvHouxFaXP0aiF4tJSQorVfOl7OZtmjj/TePzHPmdMo+O6C0LszzgcPxnmESc7ksWHM2OnAmdDVJ50POfPzpZ87PL3zzm284nWaOpxn55oNVSZfv2UO7i765edGIa8OEp8VIy5n029+SPryHmPn88098/vyZ54+fySnyt3/4A6fDRIqB6/VqizvqCrag3sSdPMEIMTLPJ1QCqsJlKUarXc/kmMgpmXdBV2qtzgw0tlLrSumQJmtgz/MMdFqZuFyvrOvK0/MwNHK13Ri4P81edTRTz8WCe1kLy7I4hdm1jTCRvvNqkGyaJ/O/aI3r5y9cr95/eUO01DcXFLq7r6XWSTGbv0EQejGo5ep+A5a9WBa9C3v1vfncd29ZyzhsKKipZWZiokDG+nHYKqeIaEbUnNvAJTBCIkmiYe95XdetGbz514bdHzmljPS20VmHNIAl8JYlB+TGX5bNh0Fvsh4J0YeAgi9IVrGkJDycMiKV2qv7FnzVtHy9Z0GsiRijkDetp61DsG9e7eQYmHOk+gTrDg/dZvI7C0k80w2toN0sNWNOJr1wOiISt8DQm3JJK2W1Bqxur2+vF8Q0kgzGSSZlXSpPX55NBbUVF0Ez3f3z5cqnT59pvYIoT0/PPD898/L8Yn0pZ/ZY89KkzUc/oXfTCbKRhEElNU/lpj7Bq56MAvgxjjGyrlbBjGZzjC562JtpEcVIjon1emVdrvz44w+8f/9I+vBIeHg0OOXzE31doZRfHDkFxgQ2AMcD4XQiv3+Ew8zL5cynz5/49OkTKUZzK7uRcLGekV9XXkWNnkfIRjQILsVuw22NdV2Mgbe+0KYJzRPB7WtTjBu4N75z8so1xuBaUupFoMFtzZMmM5Hyc0gCIXT6Bt+Gm2t5skTQBR1bb+hqgUFSJOa0Q2GtUpy08ZbELt5cUGi9szSbIjaqXERJNK7U2nhZrzYYFiIdIYuZ2QwfBVtEgjUgfZGxPoQzl6pT7pJ5Eq+r9xEEaxyGwBQNNtDeWAVSysSUuKw2/fq8XJjnicM8G7tIvBkXjDaR40TohvVKMPZLbQ3pioorA/mFKzjppBaPdUaDVDEjFNTxVzVtHhElR/jwcACurKXSyy55MaroX4YGZc6BFANTNi2kXf9pPMYW/Zwic050DVyWSlG1Ya9XQUC231FrDUtvhLairaDaSOnAPB94fPeeTdMpZlpT8vPC89MLrV3YZVrdo5vgwoaREBOldspS+PnHnwlB6L2wXFdaU67XlS+fv6DA3f2JGAMvX77w8vSFL58+U0tj9DD2wSzZPk/thV7NdGe4fo3zoVZlDZ1U3JcaJc0Z8WDV+4VSzDM8OI24tUKvbBTQKSXO2lmXwj/8/X+ht99xPE6cPryH44H68yf0+dnlsX3Z1B0BHHMhKgL3J8KHD0zffsOiysdPn/nhxx/49Okj74/3zFP2wGeBME+zExgqtZiSaBAh5cw0zTbp7yJ1tVQu5zOXy4VaC3U9c5hn2nzgeLonJQs6NuzXSDGgEphFidloo5frIG8YzGXnfvUKeTgZirO9sGsyWZ+I1p0MkrmuK02tH1drpbbGNDuVfJ7N/VmVVRtLb37FvJ3tbQUFEboIV0tsOfgFkUNA54mafDgsJIJESjEWz9YEZnCZMW6Oeis0mlqoVmtiI7bg197odWQ9QinFyuDpQFtX602EhIREjJmcIIRuDU8JJtwnzrRpHZVK1I6KG6Y3d0xLAaYBjQRfAJSlu7xAiK4CuUtijJ6HYduZUlZK7bRWUGBKmQ/3JtD2w6ezQ0m3e3PTw96a9P/8b37L3enA6Xjk4f7EYZ78IbYchSBMU+Jf/YvfU6pSG/znv/+RT18uPJ/rqwtv/B68CX+INuhUl5WuzY5BtSPx9PziC3zgOJ8IIfHw8EApyrJ2Wl+3yq93XxhjpKuwLNWHqZQ//cMfvb2hrOuyiR2en88s1ysffxr0UIP2WinWPCUZzOdNgzFMJqKbd7Rqt8YyI+s0L2G5wpdnu18C/P4Pf0DVzhWroEZPKJJTtmf3xnJdWC8Xyvllq5yu5zPf/+k7zi9P/NUf/sBpnrn7l/8Czlf0coWtfQx7pFYnawk83NOPR777+SPPLy98/92fQDuPxyP39zaBvsMuMOXZjJ+kE6pNZ5tia4QYrS8QrVpaloVWV+pydsmIzrpWVBemw5GowQOIGfVcq5sbxWh+E+tCq+P8tYMoQTidTvZtBJdwiZgill2jxixShza7SZ5XmyiXkLY05DCbMGX2z1tVuHZlUd38nd/K9qaCAljZ2dmtKAO+qAVBu/jg2Bih71SU2kwF07Jv70Oo6fSHvjc5g52Z7lfrJaprqohDSYRAZqg7Oggthn3bjIMaLq5DcG9ffkc2aabx7Ob0Ip4B79IWorqJ5AW5nXm2bfDpTR6gb9DaBrTIyPwjL+eAdKW07s3vfX/qzf67Ox14uD/xcH9HTmmTcbht9IYg3N2d6N3YkIefnsjnFaTedLX9ldWaoUHUYAtVk7LwuYOxf9e1EGMn9EiOlZQCUzboQjzAbqKA/gWHr4RpX1lmeLlcQTbezfb41psxx8Z8hritK7o1g+XVcj+weh8Oc/BesWb8GB8r1bMTbZv8ypAhKWthiC1259aGoYirynI+s14u9rkcEtTazfOhFY739zY4djoRTkdCnsapxka/VHEJFjv2dZqowOenJ16en7m8PHM6Hpl9wQy3cCQGx6AQ1CrZoIOCGy1IJ/t37Df1Zq626s38xvAF6THuFGKHmzo26GeCjRVVP5/UPC8MDko3wpR+5Bwa6shWvHWUpnYO1822cxcijLKTTzom49382MnYcW9ke1NBwQa2klm7qrKWwkQgiaBrQVrnEBJ2YQauzRpVRSpzCkwxcJoCVKUsjb5Ydns6zK6bgusJBVJMVK1G/3Tt+LU1ltZZarVmMMI8zZRmblx3dydyDCStlrmX4l1A8/gVrJdRXDbBsOhOQMnsg2r5MCEhmBCbsiuo4uqVrVHKugWZWsxRK4pwOh0QoKyLBwbh93eBa+okGpcK12pTDNs4VhCzRwxsfQzVoYQ5HK2CT+0KiDXem3YkZEKabUAMACV6PaZtNVZWgBwO1n85HqilIaVRq7FXQrFp7dCVqyyk2oFoDfkYsQ+nHqAMjqqv+gzqzmTjEwC6+yvfSnHbferBUJ27buY5YGvH2C9GCBtTuP4+1n2w8zFa4lGcYqmYR3BT+PnnT9w/nMh54tOXzw4dBvNUXld+/Pf/geeXJ8rlMnz9SAGojbU3/tN/+PeklHj/4RseHx55eHzk7v5ESom0iccppazWsP3yhc8//MjLywvnpy8kEd4dZ06nE/kwsbRKlGSqtmJV5vBO6F2JIaOYr3jKmeTUWQkBVYMlZQQg1GinfsQXn7uxeRKjcJv7YSFEYwT13vyYQJBODpmYIq1YH2JMqDdVrsvC6DPEaAyo5XxhrY2lNEqzgH6IkSjBfCacMSazDTOWtRM1ksT6VjUEXhXKf8HbmwoKDBqp4whrKfQOtZhiqiDMebJFhGCnrze7BN1oipZ07n2EFJM32iqlNJBGiiu9NWNAOLCenE8d1KUvUMuAui/2rRPUZhK6N8a2LFtkw1txQa9kHo+gpktjUBYur2zj+ta0M9tNEYz/7eYnwTPkMb8BnVrDtrCJsGVjAI+lMkVlTsrL2mndXIClg1b44acvPL8sfDqcicGUXI0xY4uvqjV4a1NaMynkz59fuF5WwlYVuEWoNkq7usy4yR9ITBxO95aZq/D05dksHl2cULxpbguR7bYhkwye9bHrJplVp+wcdJ+n2HDr8UwJQNzE79Qb/GPue1CBYexLNqhOwDNs69u0UQ3JeJw3akdHxSfmbz9TSolaKh9//In28kS7nOnnK7qWLSDIqHbFHPa0NWpXXlwG4/n8wvFghjwmpGjJQi0mvbJcrpR1oZXCJMIUI4dkMi6R15P83aHIWtYb0oU4GcJ7FGre4KqgrdHd42DbP+qS7r07VGdSJVuyoya0N8fDNpVswqndfccLrZu3hyJOwsg+8V8YU842C2oVSgAOOXmAguM02X09UMrqrEOlNHVJlu7053+aU/iL3dRPwuAHey2FslqmHYNJMcxT9OZuRLWiAjknylqopdEmpx6KKTYarTtDxeUOvA+BYUytNcQvqDlNtmghNOdXF90z1rU0JNhEtARvBFt6DYiJpa2rXSjRpj1bbS7c1QwWQSnFZinWZSUETOfIL9gYRzZmBip4Nj90/83dymECh9GmnEgCcgxca2epahmpQtVdVuCP3/9MdOw7xonovZLBKDKUQykNWlNqsWlmmx0xgcEcrIpTbbR6RUJCZHJZ5si7+wfmaWaaJlr/E9ezSX4LODc9EYK54hmDCwYrxcE0ttkFEWIU6LuUuQrQraKxUOI3hOgLnvoiMfx8XDbEhe56t1mUAYXYK1hfyFgv7qbnNcOYJfGwZpDXgBT9c+c8cX76wk8//4h8+YwsV+RyofdqEIeMHpHs9rFqgo7npyeen57oYslLFBuAHO/dfAgzKEzJ4MJjTswpcUjJYVMhh2SfyQkXbIWVjC+5M+Sw71lHNdo6rRRLXmBj8wwPkeV6paaKisM7rRPSRM4zh8PR4NwgFlz8M69FoQjLUmy2J0dCtLmJsJjkeghWiWkXejXIyUyADHe9O84+V1G5uldFvxaHnKyiVG2mlvxPlNS/zM3ReOb7O6Zp4rqaJMTaGjRlVWjP1y2jV8eKa++2+AGX62IZzJx4fn5mWRfLiLoZ3xvQ33l5ed4mPSOKaECvO04dnK6X52zVSlNKb+YKphAcTyjFSuEpq9PzTkwHY7CUuqDY4NOUJ1IcInlXF/2yKc4xKAewrsUXIMUJ4jtM4psxhJLzxSOaZMNWg1ZCW7iLjWO04FJ7o2qjAb0H6mWhEqkSkJhvGryDY2/BSHonucxCioGcDJ5YlkLtjYGva290Kr1ZIMvZNKN+89vfcXm58Kd/+N56FKvy/HQhxJUYM+tSKasJ0g12k2lJjSoIu/bDdlj2RnywPWEzDgb9OL2d0VFoalr/IdjQmbp2d1QlaADp5rEQDTTp3fD38SqbocyIYL4Q5Tzx8HjHlM0vo16vrE9fuHz/He9qJbbGz61y1k7t4hVSZ2nWh+naEfFhx3HsXV/ISDl7h0lFNugw50SOwainMY6yyIc0TdV01cbQAaul+Tkum6ij2Vea5PQ4p4JH2nDDSLMBUA9MXUldIabtGKzXK63ULdgKCuqv3ytahvZwhN5ZlspSC0GEtSzG3jveUVbz0O7+XYIPqap2Pp/PZhVaK0tVercTITkrMedMWBb6p4+8Jf7R2woKaphjbY3QunvHulEKShArFYcEsPgZPE7i7rV/dNpna4Wy2gJkeH2ku2qpSSoHIoEQvPF7cyFEx4gtczcOfu/q4nyjqeVqntqp0sjxZqHGBoaMWSGbT0MMQhIzV4nBFCWjQyTD19Ze2t5zQDYOmW9a8mFbQgbGjv9tnyeIN4CTkNSaxmvre+XQK6pCSNbRaM3nLgbfw4NCGBCLDlUiww+07+ymMQWu/WYh7SYPEVMjxOQTqZ21NEJl85tuTa2PoXu1MMg2IqOXcDOFoaNRj2WKvn8Z8JAHCdufBjGKOjwk0SxXxcUJpbsxEg4XdaSKnSN9iJSH7XtaEmDGNMHfo7fOcr6yXhaaM6KCGiumeDN1gIw7NONNVP/GO1VzfEcPfNtZtk/vjn2zvR47ZDb8QcwMyogTw3DH1T48+HWaO8SNeZvNGGic1/451YUHVSHEsl1ztXkfbl1QnwaMsnWbtrmI4FpkiLgemCkBpxS812D+0K1UqkCtgeqEClyuxtRYff5hnO9e+dS2zxm9le1NBYXeG+sKf/ru+w0fBdkoiWCLQvRKobbiC2l3U5vAt795h/n3KpfLC+tS+PD+NwYhxcTTl59Zr8VgKhFbFIKpQho+aYtaXVeSdh7yPdJs4VtWm6DO87RJI4BJa5Trgk6GdU9iFNDjgI9aJZCM/aHKFANpSiQMuoqHifP5Ql3NkEW99p+drjq5llLr6gNEQO/G0BKFtRomXE0ssHYovRNj4HCYbKHXwPN5paj1VNbWzc0sdGNEKQSNHiiNWmkZrdMcvWdzXczPuVZs0XVoJyWhNbheFnoTlmujd8tWcz7S6kpr687uERNgs9ew4yzB4B4ViMmgliYVomwLf0Asm7ciioJlxhGhjeRXIh1voGJTza2Zv0VOgTwFfznrjUBjmiYPDI3z5cx1uVJaoVGpwTWcOvz03Y+068ohJCQKrRb++HffUdczQuLFCZfPqlSFhmCoP4h0RLoFQe9p1G5YeoyR5NCOOi93C44eHkor5oWNkkJDe2ZKmRTtc3dnD03Z9Iyul0KKShYbopNgLnp12JTi8F2yBXst9XVPwcXtalG0VF6uizXCnbnWgtJenhi+BtHdBtM8e4KlhNCZ5om7h0d6qdS18PzlI0Eb6xRccbXx088fXfW2gRhjLCVTD67NVQN8PRjBVGR87sLmq/AGtjcVFMAOdgyyqU+mlIkxsSzOgOj4ZG50Tvpwzeou6FYI3Rq0vY8Tp0EzzBN3nGLgq1vWb9WBuBBZDMZeWa8Xehda1U3Odzm/kHyKc71cndYKWislrUjopvsf2XyW19rREIk12efonejro9bOcl5Yrovj6JE8ueieVxwOrJC9UuhjOE8i3fPRqJFEIkmjiC2416sLjSGUYo1UUVcqVW/MixBl3CYmeQCoRMPy1dU1CfQeaM0W2b5poQaCTASZEI30BkU7pXbTyiEz7DE30xUdbyds2ncudKgo6ppMal9xM9zrvmgZMD6y7K2t44Fm+1TkOPl0cXBbSDvm2+CeBuzN/LuEyJQVJBLWhRa90qkFtKHXQuWFi36PBqVqozg7R+LEWVebwNZIw87NIQto8JXLQ9vqb4HX4bvRlNbOLtcuYYM5GcCimtMZrq8U/Lw1JpWJ1nXja9qgXUgG7XTrFYGJOg5BxlqNcKHtphchtq9rG4N73gDq3RKQAWt1c3JDlUonJUVSdGjMD2pXalks6aKBGBtsWVebWh5IQDRpe3W4LkWHBftQCY7M84FWXWmYMf3/dqoEeJNBwbx0k+uyHGbTg/+ilVIqDTV8Oyd6d00gVWpdab1aULBosXGje2/eOLMF3/qERkWUsMMRKUZrMKPkYPDFcj07VmrGPNo7L5dnes5MKVGu181ApxannaYxFRxvGBIKMZD7ZH0ChRhnWwyKQRDn84WQEvM0MaVkJb8q0rqzNoTkF2NDfLWMqJhOUSQSJZJCIgRTpFyWyhChM18FQUgIZmhvIpfBRAG3BdetMkW2+YEcZ4Y2UuvNZDtkzIB4UGBCSPQW6E25LsawaSS6NDOt8etX1XsFASRalUCwxVNE/D61gBHF7uuDWWYrl3SIyZlMHfqoPgrbbMiYUg/BPLRvXR6tye3m7/7BQhByFkLMWBBsJM2sy5VeC1wrbX1heblSQqfQqeuCxoBMExUbHCtEhqRGc5ind2PpyPBBFnE4x2E4HTCTz9UEn2+RcBMU7DNDZJcO8Va5mGfySFLE4UBr7MtNUDDP5FabD3H2V0EBbP8Nb43mYo4pJpOp96zcgsLow1kQ6qpk9+pIMVqvq3daWRzh69vUtbnScSPfkkhpNkYgBqsaM0m8Xzfx+HDP9WoJVPeBxJHWvZXtTQUFE5QLvDtNHKaJGDOHw8w8zxxkoZaVWgshKCE0cjKZ30hgLYFaC9Ns7GrVRs+WXh6yUycVNHqmJUpgLBaughocm5yFmDCRNBkYpnA6zoBymVfPNJV3d/NGXRwTn7WupBi5OxzdHCjA9n4CMmF8npl1Wfn09MxSbIpzuS4mF75WTsfMnBMfHh82XaWNZhlM4781jG3UIclETIljOhLVmCIvl8U0pEIiBCdWpoykSqzWwDclUhM1SzExzfcoQm22KHRVd7tTalEkHEnZ7jN4T1AyqonlqsQUiGkyH4fWbcwse6BqOw1XTOqSkIP3cNWnoS0YSBAkybaIi45KQNGoSDdZinG7LGLvl/qmVivDShIcJhO0WtKwquNNajOKQZQQfaSawJSOWNu6UfPBFENfngmqHFuwBRUoJGNWIXRJVuEGHGuvG0XUFEstWzZ7WR9gdBQrufIpA7rp0GonBmXKO6VUHR4rClQlyrBoHdpR6s3rgDhsOpz+jPlmSsFBDOK5LCtlLaxLo3ZoQ9LE+zAEt6hNaQtcA64pPv1M3/WQ6rpwPwVOUzIV3qAQXG1VOtOdyXmbVYj1Zu7jRIqZnI3JNgYfW470eSZNEzll7u4mrqlzTY2mgWWF9RpYq1Lb26gY3lRQELHs4DgFjgdrkE0ZptTRWWgpOE/fTr6cbUgnOP5o2ke2AnQV1GkrOVuTWmAbolFvUAcZkLVfSJ4qhaiOYQ+cW4mTKaxOzTIYFBus88+/5SyrEqMSskFOQy5btsdY8FC1bFwxeCZ16OtKV1hrI60GGpVmg0ThpsFt064uI92tAkguQJejTWWX1iEtm3qnnhcIkXy8I6RCXAvlsqASSXHmeDiZlr2Yhv0xTFyXlbVU1uUZiExTJk5WNTw9PXv2LmCkVYenbr6riGfnPnnqYLlEQVLwf2VTId224NBR2nrQbn26HSLjnCZ//w40fx9cbHAgYjjnfjTGHTRvbc8yg9pxCqp7JRF8EVewzBxUEg2lGNeZEJTYFI1itMs8gyrX5exQqCca3icZH7T3snmHp5Q2q0xBNqZc66ZZpBJJs80wBAn0WmzRDomQzKGw1dX3uOx9CIdcJETEz9fmLnalrEzZqo9SGmtprNUn4kfiMZhL6tWIhK13sRMjnLLrQcIGL6vRR7PRsoP3ivDGtRwGVHdzuFNymM9go9Y6rQnaA11NVypGXwu6Ij5TI7qrGbyV7U0FhRSEQxa+eVDuTp0gzbIqrqS7gaFP2+CLd1xposwiiORNTGzAoMpoCOPlrTEgCHFjgIQx7uyLv6ERzReMgAS7bxHPLO+HXwMOn4zNyt58MkG3Flf69tY20xAa9CbQfZWTxN3dA8fpnlYbT89PlFJY1oWXpXCtHeJCzpmcjW8/yDZjEGx1Z7TD4yOnx/ecHt6bNr9C7UJZV8qy8sMfvyPlmW9//894fnrhfD7z8/c/kPLMu2/+wOP79+R55k9/+hOHwx3ffvsHfvzxZz5/eeKHH564v7vjr/7q93zzm9+iKvw//u//swmt0YBMJ1LVzN9bq2jskJQ4RbT5gNtgjE0QklUL5nDWadStv6CoBYYU4KsLXhC0KL0pC2MQCsiKRgijHyE+KAaWyToVdbBVWjXbyuhkAVBLBhyeMpijUeviA4UQwsxLgBqEhw/vmObM4/NPTsVNvPv2PTFHfvjuj8xT5v7+ziUf9oAAjZ9+/J7L+YXPnz/x4cMHPnzzG07Ho/P9K18+f+L56TOffv5Inia+/f3fbmrAf/q7vwMRptMd9+/fkw8HfvrhT/RmBIoYh2pvIsRMzDPSO0qlrJ3r5crl/MLp7oRI4OVSWNbCslarZm6q45R2dVPU1GpvHc5EgktxYJBiUYeoOktduf/wQMxCSF4RKUyvVnC7UE+avcHdSYORJonR9ba42hCeOc7KcbbbUhSmKVCa8DZ8195YUOgCTeASPGuTPlprmzTBK+pZt+G1Mcm6Z6i6DQkZbOMZogxv2vFYNzLB4KOhsaOecXk3E+lqAPZ4JRnYtrUzt+xMPFOVoZy6212Wq9Cr0FZM/71V0Og6T8LpcGSeE9PhyLJceXl5YXVf4etSKaUTY2XKw+1rsuuluamM2CIQ40RMM9eXl02aIKUDOZ+I+ck/U2Ka71AyyEdCnDmeHonpgBJYlk7OgZyPpHwkp0JKB6b5yOH4wHx8sCZ+yN73sHJLAzRp3jQWuvRN06aHZh7WybJTDZ2e1LJ53XUue/CD6D0GogWH21E1WyAE4ugzOGPJ9X3GPEJTO2tEjfo7CAltNd0eDXulYCxVoTfZCQiO1cdokh+9dbpAzjN379/x+M07co58fPqEAkkix9Mj82Hm08dPFgwkMR+OBs1NkVYX1vVKTBN57vz2dwfef/iG9x8+bDTRnAMxJQ7HE+cXSwiOdw8WFAB1SZIQJw6ne453dzx/+UwpgVZWBqTnKzvBqaLWR3PdoxhtFgGDXWozJtQQp1NtBsGlvFUGQwZl9ErEIbAwErQRbHvnshSadn74HDkeE6eHxBgB3NVq1V/LrixVGypsOibPZev1+GXnJffovSgXmqn4/r+xzvwffXtTQUGxPGrxhTqyuzc1h2n6MFLH6GjiEM9mcM/43xYhdp44HjDgxhWse6XgT1R7P7n5VNtiP4LFDYoz5gXiiApBCCF7pHI4QIWyQl2VclFjfTRF1SQAugROxwem+cRdTlyvFyCgL2dKKaxrR+gEqeghkHNgngYXvyNEv9g9M4zZ/ItrsynkhxPT4UhIM2O1zfmIhBlCtsXleEcIybyPl047CDEeSHHefnI6Ms0npvlEa92DglvxBFtkK93wcml0sTqvaLWFPQ1IxlgtRIU4ArgvGV6pBe83GG7GdizVGzheJEIdQcH3v+6Lh3YLSEZjjVsjtJZuk85xNNI74swg8xt2iRGHTAJCa6vLb4OkA3cfvuX+/SMpQdeIMYki8+Ge092RlGabn+lCyjPzYeZ0d+R6Odu+E2uqPj4+8v7Dex7fPfLp42dAOZwO5MmOyZ/+/jtiiszHO5f1HskLhJCZ5xOn0z3TdDA69Y0V5k7Mke3HkgdjJDXvM7RmC3lTZfdfVW/QJ3ovdt3ouIb8EU7kGMFh3KfauRZl7ZXpS2DRCY4RiY4t7nirL+YD1rN5mmGIdHNVjjfcY4TY+y9qopi7fthf/vamgsLg4p9Lp0VzeTKYBKovCJvhSR9ZOWbUMeQMPIMJcV/WZSRN2wUC3na05/jwUlebbBaiaRCNpsJ2QUVvkCXPJI0yuL+qB58+vAeEulpz9tMPK6ImpdGbNdG+PD1TamOplct54XQ88c1v3oMIeTrxLh1sNuByMV35UhHJqAaeny+3X4qUE4QZkQwaOX8+c70u9B4J8cQ0TyATtXY+f77w+O4Dp9OB0903zIcTx9MDHz898/x85tOnCylXVDNoQjVSVqUU6C2igxFzsJ4APRjzkE7pFoiqdG+Gd9a2Wr9jisxHazL2lmj+X6U5ls1W9nVnJcXsx1JMrVM79KrWiO6gaeCFBl+IGvHAihVBuodtdTpWNXhPO6hLeJTWbVzBIceoSuhCb5HBh1cPKia1HQ2qlAlUOF820qlRdXvgcq68PH/h5fkzf/jbv+Hd+3f8s3/2B37+6SN/93f/G59+/omcE7/97V/TW+Dp6cK/+3/+e7R3/vaf/w13d3fM04mYZw+kiaFCymAciVV9XROLy5AvV7tuogh0uOpC7Z0pTfuFpvZ6ZbXJ9Nrbq8U5BDFvBh+2vBYTjbRqyR3c2IcnLU+yAbhtKVfQKnz+uLIsRoW+f0zkecibWz/CJFscuvXrzORJvMa+DTqjd3HLPBi/f40x/gVvbyoowMg0rIyUvsvtbjLVI4tQNehGxWAguZkI9ZNlQBLbRDA7DMGAfDYA1d8YcwczSGakp1YjdBwqYUBFgHTGw0aV4jmnDVetSlmco67GjqnVp7Vro1RrzF0uC6gwHWbzAEjZ+wdKSh3V4ANjQ4Zi7C3Lvq1xHRhGOL0p3f10tVtACzEjrZpImQZCmkn5SEoHYpwopXO5rNRm3HDVuP00z+rUzYREOjGLMVWawT6KwX/qPRgJfVM3lSSEKSDZ+fcpOGvlJvt02Cl4UMDWro2eqgErJa0QYRs47mLHZfMztuxeRKwC2PaXVXsqYpAggjb7fIN6a41etnNNtnPm5nwwniy4XmxzuGqQG1QjtcGyNl7OC8u1UNaGarB9fF5Y17r5EYAYhfey0Ls5oFnSM4Qfg59Ro/E9SAbjx7wrSvHnxkAXsWqsNqRUpmw9snUtFFewNbrpwOvFKwiDz1JKjgj5ftL9Ghx7alxHA9LZRQL9skFppVOWxuWlEJMlFWkaeC/7621VyD4892fXCB3PvaEo/1Ol8Je63ZS5yOYLYCeJ7M3d7WLVm3J0zzIMJujbS+pNwMAXC2Mt+ayCP9fcvoJjr4MaseOkA2q4rTJsOhenqLpksYBoggKXp85yaUz5ZJIIy8KymPaMmeYoaOB6WSlr47w07u7u+PDhPXO2bG2eI7CaBIAvgMZLd8nnECFkqga6mlOdaCJg06HBs9p5ukfblcv5QtdITEem+R15PhDCicu58enTGe0ZmIBM00ztiQWlBmASZLZht3RMVCnUYj4K1h+BHo0yGpL1A5I/Nh+TjVYIBg0VzDUOW/AlBEIOzkoZvQmlhb7pH6EQmphDS/Mg7Yu+draJ94AQOpsf86gEpAMFtHkV2HDZjgbB/DrEv0wrHnSBKNmmpnuwCoGMZe/Qekbo1qNhAs3UGihFWNZBlw5ANsXPpSMaCJJ2qNHhLm3qZjWmKtq7i/XdBIbWrBIqdVQucL6sXC8L68WmjoMI12KTy7UVg4IEm4T3KeLmpAvTzUocUuA4pa3ZPBKWkMzsRlv/arH2q1A7w//jpnhlDFmul0YtF5ZzYZoSj98eSbOQZ9OnEr9+RzKobQQfTygGfW+/IhmS4JvN7Rva3lZQ8AZTrR0pntMPutnNkd+nkHd4aRhySHhd1gIbFDRMZW5LXFPLdPMaUaQ5pXHTYR7AACMKoIhLSDvujFf1RNBA1wktwnpurNdOq6DJjHmWpbAshvfX6mW4WyL2DutSCOFKSi9wOrmb1xC8iy7tLNY/cEEbm3vOhDAjMhlefbijS3aJ4wntkVqhFLXFpIFq4O7+HSnZ3ETz6iKnmZwykoI1cxOQBE2gETsrA8Q5bdIYiqm4qnaIgR47Fh3t8T02qgop2m2NRgmNliygaPAAMhu1M0rYqMDKsFy0prGYFh+DfSAeELTawtrjyKbZ9LBksM0CW2O6Y68TeiA7cy0Q7HWabmqjKoL2hnRrXluykhiXp1Vo0JpVa/tgWdoUfcd0nlUS3mCRbOcMwTPx4Eys8Thhn7azDyuCGxPZgJ0477Z3KFW5LJWjmAZXqX2jDFe3odVuU9TN/1UGBGoyJmttBJetsMTFoKPex/SwJSQ3l8OvZvUbquPZfG9Qlo62yuW5MHfzW94ej3/3rrTuCaBfwxs5cMCzY8iv6zbQ+I9VFn9p25sKCgYdWfSXNrBg3fj5Iuz0UrtlIA4Mz90wuPA3WxDjd4cQTWYXW7xGZtJ16LRsNaxz6r0yHkwUualGwo552uNkw2q1JOoK17Nn0X49taasXuLX2lwZ1DMhDBZqtSFLIcYrc8yWCYfsLI3gJbw9NkqwVVqDL85p+8mHExoyveFKqMEghiE90UB74Hh6QEJENdBcMjt5s3pHSdSbvoJG3Zq/YQqEHk1hVttWyUmyx/SgPoBm08aVikhCg1J6pYVubDCfJ0BAJiHOPqTl4aD1W6xa0WBZ/sjodTQkxmIf1eEhe1/UzhsZ8yHds02nqBLFdYcsI23OUOvjfBBQN+lpOvywx84ZVWxwFU83DcKDwmCijUxfhe7aHXuwiA6b3Ar5Dfxsx8sN1rTGtYgQc94gpqHku5bOPIlBSG3MSIgFgeE94N99QGa7IGOnuldma8XhzfoK1tmuvC0gqOdO+/9vH6Dj/z65r025nisShXlAgY67jolrxaE4x5RkEEFuUaIBHd2cGm9le1NBwUro/SI3TZebhR/X6PFNbhrRW3Ww4cGYUJcq2tetYdX1Nr30ARhvUsfoVcaoArCLKbgd4K2Gs6ovZOofoCe6N2bLGeraTRa6Wbb28uULa2m8XFbqgI3EcVziFsfEL/BlWXmSC9dUCXGi1Mp1XRzrh66m/xRiRDvkaUbyPdPxPQ9h5v1vfu9S0olSOs/PVz5/vnA9Xylr5eVp5XBYefzwLaV2nr68cL4Wllo53SdDjyKQFTLopGhWelJ6Nippzya/cZgmMpGmjUu7usqRzQzg07YiBrNd6grBhPxCDAZFZZv4bnTa1FlDsaoEG+LqSXfnNbU+gOlbGVykTa1pLE4/jbLBMZLEYSOvANXOAeuB3JALosFQWpQmjRa6PVdxyT1f3hLo2CemuY1q9iQjbBCSVWzjJ4Nkv89gpyATIUzesLbZk5gPhN6ZpiMhTKgHekToPTLNR46HI//2//J/QwTmKRNzordhDhTI+UDOE1OOnA4HrsvCy+VslS+yJSgKrjyqtLoypnOWYie4eVJ0D/ajWt67CbuXOL6w7653jH9vAoeCax+JmTYlmJdEynYdq1Z/H9mqgpv1f9CUNptWwAgbb4mL6tvbCgoMKMhlpuMts4et2bVtuk9wOs7jC7Xu0r9DdAyrJrpz4oMvCrbQe1bZR/MZG5oQXyi69zRlpI3Bs5nBwQgEEtoivQVagVYNG+5tZ0y0Ouh/hr0H75tINx49jMXM4J1S7MI0c8ZATrN7D1hjcXy2tTS6Vta1mSOVBpbVXKpiNBgihEQMGZFCq9b4XNdKiAla4eKqoOa70OlhUEaxs9ATYw1qNFKfSejSrXoQ73XcUIP7gH8E+kYt7WMXQlRjfiXHjvHs3JvK23zKGIQeZtbiicNYLLZk2u+PvHqcQS7cSGSMrHY0ov286gZhadwrRvZTa/tdfOhuzDbsnfHxJR0qctjPvszN/RJumsQmAhdC5MOHb1FtnE535DwBwad2xzSNVzpbBnEjoSLRjaUCtRn5wU3/zCfC9+GAWsZ9o6G+LcGDHuz821+DZcZNthdvgsOr/bRn93L7PDXBvlo7rXQ3/dkhoM2Te68x7P+bPtT+5rp9h7dVKrypoDAatVNOzFP2DEJ97F8dgumblswtu+hVsBiVw1aa+uPCjqPayTpSGfu3d3Hc2SShLbMcDBPLbs3wJKC9m6pls6Z0lAN9UfoKtUCrQq3GvujNDExqNVnr2mFIcMg42Rk6qMb8kSYsqzUEQ4R5PnCYjx4wOi/ni0MA8LwUSi2cL5W1KLXB9z/8TCmVaTry/t03nI4PnO7f01rg6fOVy7Xw/LLyWwKlNT6/fOZczqy6EjSyUtGkyGyNZZmEkeiq2VOwaKFRIHQInsHFQRMeTlyOcPlMgpV9WJ8hWTUhqfvCrvSotLF+jtUkyq5hpNhsQscaxMXmDSQHJHZrJINVjI0NgkAwWfHakUmQHs1cxiuKenGH3+Tnhg/AiIvTaWXre8gkhFkgexWSAoxhPgZNNNK66Uc1b/6D9REg2X0VtAs5zZzuTvybf/M/oijznFynqFCb+HeKtGYJwM8//YyI8Pj4wP39HSEGcp6JYUX1zPli3tkBrK+AMOTRFTe9b2MwdF/Yt6RkC4jjYPX9/lchwc7cMTvyi+32kpQBUUEvnbJ01ks32rHXBa3d+omM68IjmENvMGYjxjDiP/UU/qI3My1R0+JXvCnInvWDn7QDK92hI+WmpPVy9maezE6i7hktVp7KOJlk/9cw407CFvv5cDAudyuesaotDgME91Q6yITSUDWrzdIaS/EhtWaaOqUbfVG8YRjDjhlvM0POKBKCeSVrQ88LrQmoqU+qm6fTjIFxOJwI0fFrImjk+cuFy+WKxCvTdM/xFIhpJsSZrpHLshLOz1YZaOWynqlUelRb7LIMpGP/mfwngdIpVNZu6rSSbKK5ujQyAj35IJU3pk0a1naZJCFkIaQhiGcVxUi0uwcQcw0bz/ceQuxIs99DtAV5nB/aMIc82Rd2nLkmTtQaInT2IdVYTFPwjxepodGLe3er2sKfrTroqzGrNKmt8SoGMzXxQGZfwPD/seMiOqoErxhGMxkseKxr4+ePn1FVHh/vXV4ieLNaELH+0LoWfvrxIyNGTvPMHA90Fy+0qtFNb0KwobTONqxp19M4d8ciP/bHvrjf5P/oJjg1fl7l/v7b7ar8deDY/xpv00rncl6J8+y9FavkxtCaMrTIcBr4zWupM6a4Seze0PamgsLIUkppvkDYwd8TkXHGOhPlZrDFbwZGUDAZ4a0ZrDfPF9lH5m9e2zyiBZo5rEmAfJhQbMhHnX5ql8VYwTwokHw8v7l7nDucNXyy1qsNZefQj/6I9zasT2ERrPtCRu/0VhASKTb3RDYKob14IyVTRw0+X4EGrteVl5cLKqvLZ0dCyEjIqFcHS13pdBqd0laatL2RnPDmsu4w0g2UpECTTtFG6cWpoQYZbUiOS1aIaxEhgDMwSYpkh2Ly+N7q6oR27PEBNhnS2YKruvoxVrvPfHJ0byDTzClO/L26JQRhQFDtZhFs9q8kqwgi0aSnh4lzH+eXM5eares9qMFeKs6y0v0zjmRB9nmGHVLamUXdz6HeDA58ejJpkpwnjsfZA0P0sy1apVwbL08vSIDT3YnmMGLvllDVpl4FmCfIcPPbWJ0yzvn9e1mg6q8y+1f3f3Xrf/Ui/tWbX99uHuWVVidTJBa7DtDb8CJ7cPd3vp04siG+1zNKb2F7U0GhY0YjpXe0iWfyui3cxj7as5KBJ1q56xCRBD+31OWJoaIbVirBh5QJWxDSYKV0EKGujXJpHIKSExyymm8Ak5mbAyKRGDMhJHKYCCQCkVZXlrV5g1mBRG2mW9TVTc29vDEZ7eQBYc94mqtO1tZ2m0ZVsxatL5yqklNmPsygDW2NsqyoKikdrEFJprbAWnBaYaDpBJKQGIzhc4zkYzZaffGsNwni2bskGZR7+5kUnXSML9j+zy6TIG1buEfFwLC4FIzSGtkUUTf4KCoEEzQ0VZBgTM1oMhchCtOUaWJDcLV3aygH69eI+n7UgLRAX6pNO3dQl0NRd2jTqkR1+9UY0aaUpY2TgXQwwcJlXY1Om+3zW+IgG3NGBVpU+06TV5wzdl8e+8fkrZVocyPqvF41WKlqoDb7aT2wlk6pC3/3n/8LrTVEEt988577+0yp3ftCNlluFSRIh7X0Tea6tm77B7wKEZ8+1p0Ugc1w2PXhOM2gdzo087qdDL9c5P+x2/cgEnwhV91hqtugoq1TFmW5WIM5TZ4MIMxxcsfBbNa6YQRUaL1ur9NVWVh5iVe6dNoGc/1lb28qKLzOSfSVBPNGgdv+B3t6sNe8t4+7rQBELaAMvbWy2qKBGGVSwhicgV4hHhIpZoMOQiAFISRbpKNYEBCNaMXkHap5IKxrsWZyU2dHuDSHjgvP31PGLISHg0EDRxgT1KpjYEkcT1bSas3nlAGXDVYt28T0yEg3euoY+xWhaqf0zqqVgxgMIq4qGrIPjlVx7NyqA8kG78QpbI8hebAeiaxirxWcsnrbE/AA8KrSCHjDevRn1R3WvPEcBU3WEO7RgoCiO4Q01p6xrnWHjoYdgVcNXQf04Nn9AJ+le7bft2A8zpUuasykYL0KdfhIw8139vuHBIokH4Z0KXAdSNGW4Y6m85hVGJXg/gM4K61vtOPbymJcIZYjmaTE5m+N3JxjO3Fiu6LEjISsoevnl95cbSMwvIKU5NXr7I++vf3r276uLG4fqb/4/+j70IbqgD13ShNTztwdT2bxKWbW03vnsl6cVdidrh5efbq3sL2poAC+pIlyO47wavRAbxbPbdvhocEQGsNJ1qiyEjPibrkdnp9NTTLEwHSMiDt4dZ98PcwnDvOMdiHEaPabU9zM7XtRejGtGe02Xnt5uXC5LhhODGvt3mgcn3cEBeOnh5i2CmhUQ+PiUB9o2vRgmk9/SqO2QIrdvBNSRHUxRlO3QCJEQpiJsaK9WUUiwtorl75ybiuzVI7JMuIwB6ZTJp0jsUdr9kZr+obZppHTMZNPmXRKMGNQTVL7co6WvA4AHlSCLdYGA7H3FoJn2zdzD5o65GC3JYNoamx7g1fEZS5sgE3GkFOzeQTVbsHMZ0NoYkIUhko4nKc2uyBWIQ54ccz22uCbRRsVb2ijWwNdwSS6w7gtEKYEDfKckSw+8De+l7i96Wg2G7xn3tRjh/lMjM8pOG7GKJuG58ZYyK0SsInm3l06Ur1XoMEhlu64ndGwVIf74FimAzuF6/Z6+roSuL3dX/cf3fz6U1499pZRNK5XUbbjmIPBr1WVu8OJu+OJbx+/MUVXiagqpRZ++vwTS1lZy+qvMYbp3k5geHNBAdgXfs/8RxMY9oBgleYodGXHSp3H7LmjQU5Rtou/VqVXmywWTC8+xQCtc10aOOuolNVyvJgIdEQba7Fsc5MpICLdsN7azaSk1O4Wg9h76cjOcAzUh5aIlGaffrugRcjRTFNiStRig1tlrYyhq7WaO5lIIcZOihHEhs2s35CJceKf/bN/yVIKSuf9b94TZ6GyUvtKpVF7pbRiEtZBqTRkCkRNaOoUCudy5u7didP7I+khcTgdePfNO0qvnJczNVRa6q/pquGXULpsgUKsyhgwtg/FqcthSBI0W4aOBwxNg9aqxgprVpFQcXG8vjE+R9O5xe4Vn3O6uhDWnbHUXQhPu6ujNl/EgkNmIg4p6gZphJseRg+N5+WFb+JvuLs78G/+T/8GOuSQOdwdqLWwtpVOI+XgjeLgMweJECebdA5pazYDPptQ7fzwwCHu5AZDh8rOMTvDvYrQgHZLeVJMECP4ImrVQx/40bi8YGse3zaRbzd5dd/XoNLroPH1c/nFbaPiv71XMXZUqspxujNornfWdaXVxrqszHlmmiamlEGEkxtBlTpzuVwoUn/94/8Fb28uKCgjIKipjQr7RKPfN9hG5nMwEAV7TPeKoeuQvOYGmxd67dTSqbUxXLp6NXygrobjp5CotQEFW6+UKJHSTUTseilEElEyUxxZW7Np4W6NvhEYRhCzjyVbpTGmUJWR4dlFGIJ5LKc0AQ1pnVJ0C5S9WzZ1pRETpAjibBebqrWp0Lv7B45qC3Y6RFovlLZSejVJa/XA0Cu1FyrNDWrEgoQWXq4vvLt7x3yY+c38G+IUmQ4TL08vnNczjeb2plhPYnDhb4PCxjbS/fbAq6Cx9RyS/z6y7AEjOY64ieuJ2O/Nz5fRfA4WAGwK3OUzdGSlsukc9fGcBFodn+66z6joeF075wS2mYQQrMF+WS+UtqKivP/News8BLQqS12ovaC0bdamqwnW2chK2IbJWlMkDKaNHcdxDGttG7w0jGtUu2f76o8xJzU7l+z8saVcKaajsvlL3EJDhjqN0ufrLPvP9Q3+3GO/fpy+CgB/7pGDYl6bEiTZlLzo5txWamWdCoc206aZ6D4QIURi9P34hiqEsb2poDBmElrtvwj+e6YiN//aIhFuONA6OB3iFn5ijJIxZLZeVpbr6vaBQtPG9bpaNqeyoRtP5zNCYJqOzPPEQYW1FErtXC6VoZt6msyg/eVypbTmCqPccKf3vsAoc3vbWRVDo0c9mJVqryGlMU0Hckr0OVifoiq4P+91VWQ12CqniVw76fOV7+KPXJdig2O++K51Za0Lf/zhT1zXKyELhcLL+sJ/+of/xFJXPl0/s+pCS42QhC/nz/wv//5/5sOXbzjeHYlzNLxdOj/8/CMvlxcWlr2H4GqmMtCQ2wCQX9+mQbfGNhEXwTOZbA1OTR3Q0vD4FR8WVFv4syaTTrhWW7xd9wg1W9JWO31t9KpIF5NhrwY1DUYSUYYRmqmldkWLMFhmIvhwW9gE9YIKlYUfP3+P/ufG3eGeQzoQfMjs+eMTl+czTy9f6K1CVl7KM/rU+d/+t8Cnjx+pa0d7oJbOH//4/WYhu65GJf3y+YVlqeQp8fx8QQT+/u//5P0jpaxWxXz+/MJ/+S9/5KfDR87nK6WYKJQ6k8iG9b4OCLfX1W2V4JDZaBL9ozDRwAv/W+Ckf3xbS6HT+f6nn0g5klNiyrMNVYpyWa48vTxb4gaEFInRKqLgfbINmX0j25sKCmCBoftwgiUxr/ML1Z2/DLZIaIBgaQZRhnqoEKP9Gwi2gFSlNzMuvzUg7+5/IMhGK60jM6O4taFpB/Vmkt2WuatrGOHCYcO7dkBYowK4aTaq3Fyfo3E48OJgcwxirx1at8UGo1eakqgJBanIFnhqV2iNa1n4/PSFta6GbQdooVN7pbbKUq5UrZBsnmBpKz99/pnWK2tbadiEsgSxmYXSkaePPK/PhMlYXV06T+dnlrrszdax4Ie9SpBbuDz67UFfVQrW6LZGdkgmd9GdadSjL8zBDObBtPdtmtxgFEQgQ0zBPl/xSdzibYvkXHajtW3L3zbjcouAiOKlBTsW6ZWqjsPljxHLaM/XF8paSJK2YHX9cqZcDDqy4CesdUUvFpAuLxdq61v2//nz01bR1mK9tOtlNctLEV/o98dZhWBB4Xpd+PTpM+f8wrpWk1RR60gNaucO+sirkPCaIToSlttF/vVK+/q3/xas5gbzvbnl9Q12vHrr1Fqtp6M2VxFCcE9rGP7e6qy81htrKQQRylqpre1sqjewvamgME7idgOBDm8Eu99OdlG24R3wTDBFUowcUrZA4A9QgI75F6yFWhqtNocIzJ6z9qH7CKgFCSvHxSACh3dM5lpIklwawCifrSurWyn6JbmxQnTDZi3odN11lUZA6FtFYaJ0WyxZGiGo9RkkQAzbbIOCm6Ybo6i1CtcXXpZn+AjpYJlWkep+BFC0bFl4pdBq5Y/fX2wBz2yPkxhArKx/+fhsk9tfQT18tej/stHMa6go/fnb0sGYOxYUzMZT6QQ6iYrQCEYDo0ukitJcRU8mSCExp0y5Fnoxaqblsn6SdGMaEWyOAj8nxqER3+ebP4Ovh168mYYS++0hCFMKnM/PlLWyXivSMUnv1WCtYfQkIlzWC+d25ufyCWn+uC6Urvzww8ftzE8BUgycX87UalPqrVdUlR8vH2/6ZIpqp5YLLy8vCGrqs3vYA/omZfF6gR9l0m0tHvy1+1ePA3lVCYzn/rduN8ncn0nlb9GBpkZFR813e54ih3lmng5k99+4Los1mmuxIFGa76d/Cgp/mZtn34NfHTaHKYjRvA9MF8krABmzDPa3BBvK6tWkMEzmWLeGZLlW1lo3dUh7T8vgWjdbS91AcaeMYveV2nZ6oypDlbW3oXQat+a4Nf9GMNiDw5jCVA2o2HuMquL2cbhKZuujkWg7R7VbxuSX6oCIxjBVZRi3BJpWw90D1jQ1TM2w9qjGvBHZWEESxtwAdNf2CTkQ1YSEmvRfDwDjtszroPBrQSP+8jabDu5osqw/0AjaiH1BaEStROleZwlNo90uNtndJSApIDmTQ6aXTmsr0kBzQMd8waJbELAhOIfUvQILXp1RPfANwLJb0FABuhg+7zBZnK1iq9703pLogEtvG/zUfeIa11TaFmrH+oNPsU/JzvVWDV4Kkqz53jtrLeN0dd4+Lu64Jxjj/i2YbQnJzQXmkc/6cl8Hh//fYTA3xT7alXWphCkQp8iYP1oWU2q9XBeHiwIpZo7TkdN8ZC0rK4VFFjMd+m+qYP6Pv72toODbyGui8/mDCMn18M031jFiT+d6GyWxUlqld2skfx0UWnHV0q/eazhs6aamxqspyq7W0BsN644JeQUZeZQFq1E9MC7M24X+q+AwZhH46me7TcOmV3PLpXdxVxeb42bht3mJIe3QhghQGAuf3sSo/bnBxr732QEPMNEHzYxl5ZTPscZ83Sz2xf5ryOhXq4dfCRTqQ2wEIahhPUJFtBEoJIxqHFQIuIl8AJVIVQjBqKghCUggTDa0JoMOLJgpj8M0twjR2Dz22Wdyjf5tDkI9nPs+U4fJgjfmt8F0Dwg3JwDgcxa6Hyebo7TjRAsbPGmNU6V1x7oIBFHXZhR2hMSXVGes7ZDQrh20BYQb2Wl5FQBgh5R4ddvXj/mvwUW/du/X4eXXwo2hYbava+2kKB431UkVplVWW6PFRgw2tZ/cIS6FZGq22xXyNrY3FRSG89lxmsg5kXP26mBrK2+68CYuN36vWzbW1eAmwS+6oZezd3N/+b6upa/bRWa6RKa/jw+iNUSc821XMyDEOCMSyDFT1PDO24DwCo/w1XB30bLb7duN1fIWvxA6Q5zPDYRi8AZ13eWkvTtuQ1fNlUZxTJ59UXYvXDY6KPRgU73D94AktNggKTEHUgw2ad7Fgsntwj9tX2lvMP+5oDA+w1dNZwsK/l1a96qgk8KK9IpoIVsXAUvwbd6kc0Al0UOmaacslTgdIUbSQ6QXpa3uJ9z85KpA8Z7UoMhWrw4qVhVEP2eiQZc6+hHepF5oJvstpt0UQySUYNVpcTrZWNOdgmZjIpbJmsyGbJRaO0Ymod7U/A5K1R0CdSFIiFg/Wr0nNs4ZP/YYRbWPM0o6OZm6n/kkFHOXwyFZxuve9hG4Of+Gk9G4Zv53hGc61GKaWZaLuTSNGJSMqE0ya+V8uW7J4el4RIGYIs0Gdf73+4z/f7S9qaCALX22IEugO7VzYKnWf3buf1dvCqs1Wm9E0Zy36k1BtQvLfQ1+mdZY6mg3B8eOhdFzMHjHrvRBI9QBQKs5jgXBKwX88b+sAPaLbe8vjMcpJl9wGxBu/94y0FEZfB1rxt+3i68/PUxhqwBGgro1fm+awoNFJA6naFCadLsvWMVgftRqWj8RnK/7i+pBv64KXLpiu03w6WWTt0jeM4i9EYMFhkxFpCJayRYaN7c70UATN3+RQFXj0UtfQCIxJlA1qq+vbSOtGNAEMqpBP79G9eBJBPjnvlGDGGeJSbE08nD6S34Qmo5C08UX9/05JLYVNpIDvD6evdni3gcM2ffqQIjsCo7dE5YbpF7Hh5St8kjJmrOtVcSd7G4ezB4Q5Ob2m+viF3/Lq8f+Y/WD3jzy11/79ha/fl0FIKS9gtlkbsbUtYKK0FW5rqupBniD/a1sbyooDJrmgGlKMaZQG2Y5enMiyn5i7c5bdpdR05Xgf/fSzIbQtWFuT1ZhcNQVNurqjk/a+/atHzCu4AE4BW2IwzCt27Tp/sq/DAy+rDECkH5VPdxWFPvK75dgcH7+0OrYY4wtTq7kOSQkQgykOZlpDO6MJrpban4F54xgYNPEnSKFGKP1FyQ48abvTel/rCpIX91/AyFZz7wTgxKlEykE7SQKsStJOtNoMMtK1EZAicHouDEITaIvckJxpbrWOiqRmI8gQvXYLXVAPwax2YKL05JvTginpo7e0Xgc4qeWB+XWG2s1qewYA2Sv9urN45qfjwP+GfsBLDm5WV/HcN4ICF0tGGznJuaHITZo4QGhe5KiNwmMvXkIkRSFnE3cr3d7zqgQYBTNt/2G8YFvIaYRGMYOGo///zYj/2XIoHcLgs3OTxHdAuirZE5MyRigXa/mae3f8a1sbyoo9G48/efzleAUtBEMNuhnk3vsbpkpbghyc5p5dtFHdTFUL7dtPyH15ilgF2bYaCjcMDjseWZqgl+Ie7ZvxuW3F8+vQUd7pSFeIfzy/pvnjr7GyO5Dp4ewL7Q3T9Vg8NFAk22Z6BTE9Y0CUQMddVcxbLGf7fW79G3WYMP5I/TY3a/AKrgYzH9ZxaQwBk5O5pc9gxuYSRyDH0yiFDqHCFNUojV90GYBINKZpFnTmUKkItoJLaAhkUNm7VfsEYUokUkytRW6RJtsVgvcLSrdKY0SIcZIC83WwMpm3GNBFw8AN4v2kDTHv1NlYyuZ7Hi3nkFSmASKz1l0tmE+BH7BmOz7azea6zfpXsnp+Bl9AWcIqdws6J0xQGEOglYhxCBIML2szVoZMRVdsYCi2hkup3pLxdquilFFBF7vhP1a2M/120Dy//lmVU0zJzz/LNYj08305xfPQV8TR97A9qaCgmUFNqEpoQ9kh+EDi3g57wv4yOd3n1huV/B9oO0r2Og2R3l9Ot1CSduLvHqGvcee/etII710//UK4fXjb6GjX8eBRjAYP2zxQoNuzeSR/W6wz5j89W8gnhkbI8n7Epgr15CkDslgBZEbMbux0HugkYA1njeVU1u82m1R8+eoqEN0L0IIeNbfSdLJAaYAGcuUW2sEKkE7USoR+ztS7XObKh0qwXyUe/CFNGEKFwHRjvRi0tk6hAfFs/RdfNBOGNu30rDqaRzgW5Tl5pQSX+ztOHoW725tqH9fT7hH83mcrwK7yZ9PXI91V1Gam35uQf5VD2xEFg8EX2H9t8lIGH00HI7qewWxfQcdFNSbcuUX5+otnWq7um42+TO//7nH3OzQ7Xd5dfsGAQ24aJu4Zj9eXz9eb295G9ubCgpjG6qXXzMr9vPTy8fR1BWnhQ7F0XGi+EWB/tpJ/fV2s3BvF9EtFDRIka8fu92nI0f/qlm8/b2bCuj2+9eAvKeJ3Px6W3TcNnRHpYD9bcFhz3bHRHCLzSmpNgwUQtjgJQkQp+iVQadKM9bSaAAnu12iECZXA52FkJItZIP/fyNq96qnMfl7BIOEEp1JFqL/PiPMIpxSh96ovdHbClrIvRDpZBqRxj56FQ0q0EgkUHohEAlqtnCdaG5sGhCNTDrqjQlNyQT3EFu8o2f9IxEevdWANaVHkjyORd+DSg+Ae0WPhIXu37l7hlv9ZAz2PkLwgNv3ymFUoiOoWNmGtKE35M2gG6jHmtY+gazWgreBTaNua/eBruEBcnPum/TK19fCbbDZIt/Ndhs8/txmJ+lGsB7Xom/91yhff+Z1BmRGH/vgdWx4ldTdBLy3sr3NoIDu06bbLf/YE+yE2zLkm2xvIDAjm9ouYMYJNp4nr85Xua0EfpHJj9eW/e1vwsXtgv+6V/D163wdPPyDBXl9103j2PoHume24/6vH/dV1q5BzTYyYDTUIUntEtFhiq4wa49V9wwYYnZddEt7R6ViFFCh3U4qb+wiJQYlSCeLVwYeDCKNRLU+dQepDaExSUVDg95IHhSSNJLPKQAb7q4kjKBqZo4b2i3dncXGQpfsuWJq+52I2VjsU+eC7p/79hwYlZhimkodQhfL7v1Qqdjw5CbJsR1D2wdbfwI86Fhys+ng6eufr5bWTZLDehviWb4v4NtCG3xux87bjsunvLpoXmf2Gzy13Xe7YO9L737LLxfeX96ir34dw/vjsfrVb7/6dK/qX5FGvnqGshVD9vc/wUd/+dtWrn998mxnmGwnwiupgqGrD8Mffnu8jf7z1bVhF1MbynT+gF3b3VYF2S6SG9N0xooxnmer+OtKYrCMfm3x/zo4jBWfX+0ZWDBgk5/e4AmRV2zWbR5hE5mTbcGWDATrDWwTvrGbNPiUHP1qdEtrjV3k8wcqShOf5AjRqoYYBqkGjaZWOqoDCULyLH+WRhYlBQsIQRtZK0k7oXe0F6IoWXxSTBpCseChzdS0xftDJiCBETCtR9K9mQuCSrclU0eQG4pTlYpaqzZFm3MYC9/IakdQEFyJVbccYASEgPgwm8tt31QaGzlovNY4V7bbPPNVtsJRwfoPNxVDlICKVXeiVjlT8WG3Ia648VktdZHgwnq2Gm9eDdsJ5LDhtih/DWF+ncm/zsnVv9TX1+Q/BiqN++3QjMHN/SLcr56b9zGKoSdy/gq63cmWf5nS5V5HvKHA8CaDwq8Wg4OaFm5vGgX2bdqw/yNfPffVq8vINsTkpxVau60QrFG7C4TBXmLfAtC3qfmfW+zlq8fdQkU+DzF6B7cvcwvJ3ASLkAPzaaZjwz3xkExuGqhaqVrRCDEHTu+OxBwJKbC0KwQhnRKNTpfG0lfCbI9b+kLRwupMDhXdGE3pmMmHzOF+RiarFGporK2wlAWNBk/dvzsaq4hOvz4RtHNIyQOE0tZKCp13x4lWrvRaoF4ROjFYVRBDQ3RFtBK08ZATc47k+UBVYWlK0USXSI8nmgprh0sRc7ZLmdLsce/mAxoyi0Yua+eyNmqwhnX88Lj5YtSlQrXJZy0KTYk90Urj+rQQNZA0cv/uDlHz07g+XWlr5fRwdPe3yPqy0monk005tQnry0qvndAT0SXX+7XR1sbl6UKrNoUf1OYejvPRBrQkcHm5QleSpI2VV9fRKK7UuqK9Ms0ZEZt17+qwqUe0UQnb/NxeXYiMfkPj9Xl7q390+/e4bTRPbq+0rwLKVxfxFoq+LhRu8yoFrebkRxcY2lWvggYeHAwi3KSG39D2JoOCbfo6+G+JjP7itttsZNy/5/Py6pxV2RVKh4TGsA60oTCXltiwV331avt2e9vXv//az+sgIdtt471vHvYrBcS4LU2JNGce378z9dOycHw8EWJgbQXWK612Qham48Tjh0dCjmZveQ0QIR6TsZWkUy6VfMjcv39AFoECpRS76JANgopT4HB34N03j/Roz732hb4qKysxCdOU+Ob9gw2gaaWmgvTKHJUsjaiNc3thioEPj3dcXyrLZaFWW/yjNpJY/0HEICXRymmauD9m5tOR0pSXtdLDhIYMkwWF0uDTuVCakE9HrqUT12b7Js1cWmC6rsTLSg0TkmamhztaFVpVrs8LWjvM0NaGlk5S01NazgspJKaQeXj/SNBALxbOy7Vwf3iwyqQFXjjTSuNuujORvCqseaGXjlYhh8wcZtaXxRR7L4tRnhUCgZwzD+8fyCERJZjBTFOiBKY8k2Lk8rx4X6WzLBdaLRyOE4LSe2NdF6Bwy44b56j1b+XmvBvlyp4M/TIt+3ol/69tr19jZPO39cEtILxdo+PxQ2tqfC75tdcDeZXsvZ3tTQaFX5yW+tW/N9s4XyT4Cf5VKSkiNtmp5ic8cp55mrDA021YLgRCmKlVWUunl80y7fbdeA08367Yt7d93SH+tarh5t/BjvmqqbzBQiM5S8LjN+94fP+Of/1v/zU/ffyJ73/4nn/+r/45IQW++/E7fv70M8uXldPjiXffvOPf/l//rePsnafrF6pWmjSmuxlJwr/7X/8djx8e+Tf/0//A3//D3/nzFw+gnZ6st5DvJ37z19/y3/0P/x3n5cy1XPnh0/foS+fKhbtD5N3jHf/T//ivCVrQemV5foRWSLowOQT0X/7XJ05z5n/6P//3/PE//0d++NOF52UBrUw0ZiCJEqIFFLTw19/+jt/95j2Pj488X1a++/kLp3ffkuYTzA+Yn2jiP/7nP3FeG9/+4W/5/HLlp89n/uZf/C3T4Y6XVfj49MLPX86QT8TpwMOHb+lhohH57k+faKvRXpfzwnotZDIvn595fnri7uGOx7tH/vX/i70/25IcSdJ1sU9UFYCZu8eQmdXTnsizFi/IfcE34dPyaXhx1uIhm91dlVkZEe5uZgB0EF6IKgCDm0dEdmVXRe9MjWXhMMVoZoAMv4j88r/9XwjSoVH54z//G5enC//0w3+BIuQp89O//USeEv/jn/6H8R7NhTIraU48/vmRd3fv+O7Nd/z5X3/i8edPPH76ZJUHzjF0A+/evuN//s//aXGKBJ8+fSLNiTQmfvjue+7vH/i3f/k3Sip0ruPx8SPjOPLu7R2lZKbpwjSNjOO0jbSwCmNQbcHx5pYqzRtoPvKLAMvLJ+9KsL8Y+yw+rp/p5Yl5AenWSWUlKmxe9E45/XYqE67Hb1Ip3FALmyHLfbHcJyx2j8EolY5XW8VzqnmCqoQuWLWnd+RiPXG9r/nbxfrj5lrXcB1s3o5tXODW3N7s31zoPsBcsdEXkNEeZarFZYli6YvBgbditozFAKwVpFJcwdWeypnMeTwzziPDfY9zjklnhvuBcAgc3xw5PBw4Phxwg7fgcme9ASx1KNdCNLEMJU389Oknns/PPI9PxDgSJNE5oSPjysQ8PjM+P/IwOIIHl5Rj7xmC4+Oh424IvBk8H73SE+mICJFeMx0Y1xERISEyM7hEL4nx8WfOpwtPP3/k7nhH6AOfPvyRMNxxeHhH7zLFFw6+MPvCwWV0vhBVOZ9mpDjeHHs0eIoUzo8fCId7fH9n1dNBGI4H4+wfIpePZ2aNaAA3ePwhMKYZ0sz8PDHFyWpDAoznkQ8/f+T5+cni8R7mOPP8fOJheEA8fHr6RHCeh/t7Pp0+8en0yZob1fiDUYg7xMHlcmE6T/jg0aR8ePzA8e5Ifxj4+PgRh+P7d9+bMSMO51prV7tp1uSHdh+256pSf2Ah6XZvbu/1RqO9xiO28NLrXkOLV9QI3ouxDRALlb6mpl2bsmqeOVeQr2V7XyMEC2XIZpffyviNKoWNe7mJBay0BOv/i5up5i34WpyTU17SWJOmehMap1HXGT5bijU/74IdJxVrf5ly4jq9ZztancFe4F/DQS9f++22imG3qmUNbea0BpezJqKm2ozGgqy5PuhSKSaKK0jnoBNiSTxdnnk6PfFf3v8XfCfEnOnvBob7nuObI8eHI/1dj+v8EpxufSrsS5OlZ3LUyJ8//pmPjx9IRIJkepfpRAiScHlkPj3y9OFHvvvH7zkE631w15syeDgGjn3gvhcGn+m2SkEynSpeFM9s8BGRXhKByPPTE6fHE88//8w//N3f43PP84efOTy843A40LkMXjm4zOQKg8uU+UyKM6dPJ7rjGx7u31LEMafM46dH7lC896hGxAWG+x7fefwQ+PThI3OZIWDZWb1njBNpjDx9eCRH69SnHvOc/vwj82mmdx0qWPOip48chwPihE+PHxm6nu/efcenp0c+PT+SdOWecsEZK6ool/HM4+MTP/zwAwXlw6cPvHv/juP9kY+fPtKFju++/w5aYZ5zSGXXXeXkrXuQ5aZSLVV8O5BmBDnWfNm9QbSj1tgdcX06bnsRC+xTlYGvpJLNzivFOgIuowWcpVJ/sMuB2laW/oYUw29PKWhDNbcgIy/xpA0XirQMkfpqPDUtEO3FEbxn6CstgSqXaVpK41srzRitl0KjNGaxeNYgcctlWe2lvaDfQ0WvKAJkndrxFb3wGlq/d69c0oUwB6LOxDIz54lLvNBJWDiJ1OuSOUQPGpQSijWc7wBR3ODojh3v//49x/uj7dfWDxU6qA138OAGIRw9w5uBf/gvP3D3JvDpw58gZVxODDgGIj0zAxMDI71EejKqEwfnuOs8//j+yBCEQS8MTPTMHBgRIgcp9EHoHEieEQw+OjBxJzOHh0DQjukSuO8yRzfz4DODT9y5yEEiIkonkUFmji5ydBOZxMBIR8fAQNRCzhmmEy4eCOVApxO4Qt8r3TDQ54HL/35hzJf6HRY0KHfvjpwpPM/PTKcJh+O//7f/zuHNwPf/+B3PPz+Zl9UrxWXLeaq0JNkXoy0P7TeC7hDMqy11rhYLDvcH3kjt91Ehv4URt/4VJ9y/uefuzhrdj+OF0+mMXuW7Nh+6wUGemthb7++WveTxzri8WtW0aqrMvy3YrJt7ee8RmDJwzrLTjOG0EVTWW9t5I/ljjettH/H1ybd5ez4rq3GFhnWbi+qavPgNaQR+i0oBVvewmRAtoLxYBZsAU5POtRq6YDzzrSLSAcF7QvBW2KNGpNdItBSDi0CuKAHWsYd/1uX1/8+/5LV1t5KTXtMv9VUqfLTy5dgDXhYKbV28DfEWmB6OPUc91EwiC+Rp7Y9wvD/SH3syVqQWhsC9vyeTyZIZ04UsmeKKeSl55jB0iB7R+UC8JNI54XEEMr2DY+fIh86I7jSjGnEa8Op5OAQ6D1JmnCYC2V5eeTj2HHtP7yFdRkqyAsWORCcJ33vmwXHXOwZf6FzhzV1H13t6V/Al4oouqbABO76I4GtltCehODqn3A2eQ+cYvOI1Gt9QibjgwbuayZXBm7fZutNJ57h7czRvNCsShG4I3D0cmS+T9X0OjtAHDscB8UJJugjzK8XvMCVShWfzzEIf6EtPiZYFdyskJSJ0fYdD6PueFOMN2Oi65HKrCFabf5dMUVmGVa0CHuC2f7B9Hur1s0maaPE+qD0jVm9/qTQHe3Yb+4CuEBPtMW+W3sYUqwLhqqr5tzJ+o0rBbgBLO6vWSCMR46XTABijalZU0+JFeO8IznF/f2+UD6UwzpPFGJqzoS31FPZppdIeDtYmJuu4hfN8zmPYvN9RV2wckevl1+ioq8B/QS/B5n0H/uB58/6B7k3gXXoHnRJLRIsSicw68/YPbxEvXPIFf/Q8yAP/9N0/UaQw68w///GfeT4/MZWZx/Mn/vSnf+YP7+55f/+G7+/hw58KP55+pC+ZQTwPg+NNeODvHhzp9AmdZpxGXCzIPPPDQ4eXQh6f8OnMwEhxE/fHgf/zf/sH3t8PHDrHj/8SuTwXnuaZg0wcZebu7h4pntPRcR8yx67w3X/7OxKBqBmfTsiczUthwutEL9avuiPW+MWMc47u0PHmzd/jhyMSOv74rxdiGSmXI+GQca4jlYkkETp4nk/MKfJw/8Cbuwf+r//3/xv/3//3/4/z05lwCHRdwDtHnCbSZBldb/xbhsOBMmbmeKHUBkcEqw8prvImNcnX2cv1nkEGuq7j6dOT8VI5gwaXznf172E40Pve+KBStOemtdC7ytDYgu8tqNw8BhZmVqFa8OIQZ1QirzMCtJu4LO+gkfqtqsY7VzOp7N4vFZ4S5ypnmNHTNwFfUFoB0lU6avuv6ZLfaKT5t6kU4Pp+Xub0xZS0ia11Up3lLni60JnrXgrznMi5rJ2wYGOK2GtbnLb0V9CtwN/+vaUIXpuvykPc1VSjXtZaYXzNG8RLhbGtVN4zkrJZrl7DrDOX+cJlunDsDwtskavle3AH1KlZwjozppHLzxebk8SUJ4orBDJxeuLTnyMHfYseOu4HoZeE01hpjiK+XBjPj5w+/Znx8We8KH/33T0uJ5hn/MHjUSSN+DLSEUk6w5x4/vmP6MkzBCWfP+HSyF3IHFwi6MTp45nnpxOnjz/z3Xfvcbnn/CmC76G7Q+IZlwqdmgLoiQSdKRQ65vqKRr2dEpdxos8JP2Q0jZQMcXyy79H3OBJOCimY9xmJ/PTxR2KaGYYDrhf6+wH1yjiOfPz5A0/nJ8hCJjPNE58+feT88cx4sv7YV95CpSdv8IcLFmhOmjidT5yfz9wNR7qhW2hJTF4b4Z3zwuVy5lxOHPs7pmli4ctqXoKAkw7USv7QFfbRzT0vXKeAtxqeFj+zR+ulFH5RQ0Cln6mWv2s09PUZbAFla+60egjtKVRYoCXbxjyPpf95+7a2j+/vnsJvYGyoAb7m915uDF2tC+dkgY2UqhRirJbJprIUuBLmuguuLQGuWwL/Fix0WzEsgekKEcgtj0DWOX1BbcEiGFrg+YVy4Pp4Kibsx/nCeTzRv+vsWAESGckzPT0AiUQskSlPPD0/GTwVlLli7U4SOUYuTyNjX+j0wHeHO6s/IOFV8BpxeSKeP/H04U+cPvyZoXP80/sOSVX4dAMiBUkXfJkr3UWEmDl9mskVFhp0wpE5+sIgiY6Zp6dPXB6fuTx9QOMZl+8Yn2Zcf6S/A8kjLusSvO4kEogUbC6Q6DQCjpJhOs2LgNU0GQ3zeLKmS94+l3PFsrzU+mE/nh4R53gfL0gn9NKhThnTxIfHD5zHC55A0cIYRz4+feTjnz8ynSeK5gW2M09PloQBxWAoCVYpfRpPfHr8yP0/3hO6YLGdekst6dcOxulCHCPcCXOaNwqhkR96nGtecWFt/8rmhrNn4oU/sNz7L+sdXn8O11gfm60XxuN6hoW2gwYVbxQCWNtd75dmO62a/Ro9+u1BR/BbVQq/YLzwGmDp3hRTWjKKFq6XZm1c+Z6bYrKrJ0NvvD6Hre7HHjZi1T1N0Dt9CSftHY2dxyBbRdBVSGHvKdRg5lxmzunCc3zmrX9nKbsKU5kYx5Ef/+VHhruB7//he7JYYPQczwZZAMFlOq+8uwt8/+4t//QPfyCUuVJQzPQ6cWCkoxDU49KF+PyB55/+lTI9I8eege9hjoyXmfNPzxw6xz/94S0DF44ykpghzzBmsph17jqjvehd4sDIQODAhYHRXjrSlwvl8kgniaM78qYv9E45yMSoI30eufcm1Oe73uitdQKEnDPT8ye6IOAdnU6Uorh0IeQe58S8DYlk53h4eMvx8MAPb35gCAN9GEgxWUewDsIhcHh/R8zF6hNCZswXPp4+8TyfyCXhu0DNueXu3T3iHQ8PDzx9fDKYCIOTWqpw1ozrHcysMGIlOsTr0pa1SCE7Iz7EY9g9zhRCtbpLyQtp47WAV0yBCFR2qNVlkJrial7H1rmWq+CzDV+DyEXLFyIQJuVTSutT4txCoteW3ZKqusYJDYKy4+Tay/23phd+Vwpf85PL1jJZbxJjSM0bbqNamtM0RNt5GxG7Ou+tW/s1a2k7bxJdjDSItYhIrhVDs/xa962NwtiHIhYlIvYYzGkmlUSRQirJWhqWtMlQsayVKU7EHIk5MacZJ45YkgWSS+bx8shRjjykB8tyIdd+zLlabAWP4jXTOeXQOUIRpChpPFHiiKda1WUmzydKvKBpsmByBo0TJY21oO1n6D3lbUDSZJATCTAYymnGScYVxblivRbyhMaA5GnxLiRP6HxB5zMleHQ+4TUSgDyd0TihabJjiucQYCoz85gMz4+FEkfyfCHNg11vBk0TJY6Y2EuVkE+sR7SH4W6g8x1Sanpwifb9exiOB8aDBZrnPDPnSCyRUlt4egdZM3OeES/43uPFcz6foa5LORFzxHlHf+iJaWaOM4h1UIspUrTYcdJMSpGU0/LXGklpDeS2+67e+4uXsPdur297XXeh2lcbCLVBTuuRluMvz83mrJtaBCdY4Zw0/qYNjLuefl1evAtWmEmqUqnK7qq24TcyfqNKYSuQXwtwvdxnW9tQVGt/281NI4KWcuOGfu08r40NznPTvPfVbfeo+hr447blL9UHaYrhVpOaJY4gtYAt8+n5E6fpRNLEeT7j1ROLCSE8RBJTGm278cSUJh7Pj0gQIjNJ8lKIdsxHDu8PXOYzUWc0GLFa55TBJTop6DxRJk8Zj/SDQyTx40//yvz80aCZknExc/74R9L5I6GMOCK+FObnD2i8UOKZ55//SBk8051SxkdcHhlkBol0arCP14zPiaB2DeX8iVlHyuWMixMHmcnnD0wk0ukEaeJERuaMU8/p44+MYyJfJmT+juCUhz4wfnzi8eMT/vieuQhlnJi8UHIij0+WjqyFSQuEC53O9KIUJ2i+MM1u6UutsXCJZy7jhbvjkeA73v/wnhwz03ni0+mR0/hM0lh7Z1tCw5hGPjx/IJFwndFahOcAAeY8c5kuPD490oWOu4cjP/3bT5yeTqhTztMF//iROZtieHp6ZBxHckwgkGNhmkez1J0gNS21aKEsPRRaALrdq2ANRpudZF7G2sNkfXZEPSvl5K5rHVw9a00ZuNpT3LwO1mI1vS4wbftfUeTTOh+uqSBSwAePq0HqbVrrb2WIfiUvrMj/4z/6Wv7K4xcohQXyaY5xtZIWamtzie2WvUZU10rOfZpPk9rbdI+wm9unCK380YJHXFiUwlIIdr3ZmkW0mVviBd3m0M7SSekgdJ63f3jHnGemNNLfDQsWPeeZmCP9fUcYAse3d8w6kzRxeHswGmyfF+6jS75YGur7B+Y8kTSS8oiXTJDEoZLUHSRzHDxv7zp6b21I4/MH8wDmM8dQGILw3UNPHJ+J52cLJDt4f9/jy4TkmTw90znlhzeH1aNIJ6QkfJkImugk89AZbXYviSH0BOeZY2QumXNMHB6+R8LA06RoDTRfsiWiavfAmJTLrHQP78H3TNpzmjPnKVPCHUkdTxPQHdFw5GnMJDzZH8APFOl4umSSepI/ksS4lo5vfsCJh+yNEG9KdPSQHETh8jSSY6bLgek8cfl0Jo8ZzeCz4xAOHPs70ilBAlccl9OF8XTBq6fzHW/v31m2jgrTaSLNifE8ceyPdL7j08+fcOq4649oUiiKK46SCnEypZRi7S6HoprR0jhiE7aivQoQ6wNkc1JhJBFFpCzbqSZ0oYVNiBSLudTttcQ1mN2Ugtt4I/WPrwSUCxVGJfArWmpr3OsnfysCRWSJqSzxwf+FGFJV/59f3OZ3pfDVFrxcLbmdUmhWzP5oblEKzUzfVoxtTfZbiqBJ7u1cUwr21ypM/YtCNBwLzfT+tHrz0II0zn4HDFL30WX98r4dO2AtItvxh81xna5tNOt21juh0JHwtYp4cEZSN9S2mKH+dVrometcoq9Qy8E1GCgSyoQn02skiG1ncFGmdyb8O7FAstOE5IvVJFB4OyheMr7MlEnXpvYCxQvFDRQJNZ/I/iZ6sgRmeqJ65uKJdCT1jAxLpUJkIOHqvj2Jjqm280kykAgUCRQ5oK5HuwcmdSS1fYqaByjJQYLpKVKikqcCETQBlW1VZzWZm8FFgQySBR1ZWoJqNq+WjPVtUGcM4ir4lhJaZbNmJU3JqLzV4dXjVMhjsu8oKWTr8Uxq1ncGTTV7KLH2Am3LTVHYstRYgVTFYCREtk6LbSuSq1KoykQLRedFSLeYwBpMrv8LBB/sad08jKlSy2QtXK/Sqyf7FkT1v9L4GqXwG4WPfq2ha3z4xrjteG5jDVtoaL/nFjJ6uX1L41uCyPv00q13sA8q79ffhJR0rVlYKplteQlEbxSLeqwpjocSdAlYy+AsaBkUJwlPoa+9DDoSQy0E6yulhWULzTjJDEScJhzGTRSk7iurUnFaA9JkE7tiimKoxw9kDq4s4tqymQqdJiRXgTqDJEMyvIfQg/gEvtAjzLUvQ0TJakLN0SESrLdzhVFaqZxzjoAJU4/1WgYl47GQTCaTTWSWQs6OzvUE1yHqiMCojTpOSb5SqmxjtCsHnd0drqZYWvjE5KxbYUPrS+Gq7C2or4KdYoCOGKSjRfFdvc4i1vmtSO11Yb0mlppgJ1YwUFYfevtqfZAbjKSb5QYp1bSlNTwtFpvwLRjs1mw+h5j+KpY8oU0xAFLTUFscIBdd4R9hUQbbJ+x/XdH/l43/xErhL8H65MbyrTyjz51rDbLp1RO6FfrbbcUs+hdFAnspfeu13642QNjVJFwJ9rqp7k+373W8vPTqfRjC8nK9N14kKbhecJ2QsUwUfzTyN3UFfxcoUpjKtNQr3L27o5CY49lgCM0cO6H3gUMIHILh+j5fzKr3issXnCaOvocSIU0cQ08nhU5HggSCZJgL5BkX4W7ouOsGSEKQzDEorpincd+D14TLrnoTGZ+jCc4W7ewqHu1BO4XO4itTESQLKRYOxx58z1g6knQk6RmLJxXHWKqak45juKNIYCyBuThicQTtiUXQudD3AQkHsj9SpCP5A8UNqOs4MBDV0xdvtNtZiGFGkiDZoaMapDNDGiMTs5ErinA83NlnSuAHj2bIUyRNmRwTd/09FJhOIw6jVJEseByd78lTIsdilCZF0bng8UgRUiooqTYFscykpevgcr9vFcItJfEy+KyYMtbNIyNL2usLzQdaEHFrcNnVGIa2/bjy2ltq+B4iqhvWY2GNhvhdUcAvUgruy5v8Vcd/5PXcst63oxXkbNzWm0ql/a2FZTelcfeFuVdgJqmW+3bT1xCn7XINZO5hpu0xXOe4/+6eu7d3PLx7YLgbwAtRI2Hw+MEzpgkJwuHNgbnUmMK7A3Oa+OnTT8ak2jn+6//2X0jzyM9/+mfm0wWNke8ejjwcOt7fD7y5G+g8pPMnDp3wcAiU8REpkXdHb9k74zPvjoHgCjo90Tuld4Xp2ZOnC/Np5u+/f8sf3r9hOj8SpPDuGCjzGU0j7+87PBniBZdnJCempxGy4LKjI+DF0/c96qzvdCSSNPEcz5ymTNbE3//9d/THB54jqD9Q/JFLdsQMzzMGcrkef3yHSuCShVMULlG4ZMdlSpQPz9y/u+f45jvo36CuI0pHcgeydBDuieq4FMeUAykJ8Tzj1NNJRzlnylxghscPT/z0bz8RSyRo4A8//AGnDpeFQQ6Q4fTJitTG08h/+6f/jqjwx3/5I0OwLKcyFzrf8e7+LZfTyDzOPBwe0FQYny/0zpr5zCkxnkeDYYQlnmDC2LJ+Vgtl/0zcCj6vcyu2vwaQm+Au2nqTWmBbxFEkW3tQJwSpwe7KhaSqV2cu2+AyO99bBO8tqJxzppSC2+3/Wxy/QCl0/3FXcXN8yRP4kuD+3DG+pFCurZnb591SU+w7R8niCi+++6vYTXdjeSfNRcAL1ApM0zErlt8CxNtdFjx/m3++gYqu9tleQqtNOEJ403H44bgwawYJZBJTmQn3AfXKWc+EY2DoB9zBoTNEHxl6T98LXb5AOhHSCS0jSOQuHHgYhPf3HUFHXM7cH7CiMplxvYUufBkZfMbfdwxSeyofAveD527wPOuJ2UXG5OlIaDzR6UQQxaM4EkhCUsaJBZbnOZKnyN1wR5BAR2/YfDLc3SALUxCZzOP5GU3J4C6nBFGodA/eeQZ/IHiPOkfEExViNpI8zZ677sDxcOBxtvthCML9EHg49swiJCxryNe+2NFV69ib0HRAd380EsUsHMIBrw6JjpwLP//5Z+OcQhgeBlwxCKlMhZSsqrzVJoRDwOG4e3PH3XDHsT9wfrosjLX9oafrOjwOdcJwd4RUyDFbjYMoLlRYa5+LkavLoM0A2j4Pe+95P7emA6laD2hX24FaZl0LDlcASjyNtyxXhVFUVqVQYw7eC7Tq50XUWymbqwrB2ozK5rcXmpdRclkgvF8+/vOqlV+gFPx/3FXcHF8S3K8phT1s80v23R7jS0rBblatWUk2lK3SaMpBaz3BbdP9VlC5SugWJd5nFW12UV83u+UB7HD/V0+3T03t7Hju6OgeuoU62wVHjEqKCd951CtTnvCdJ9wF1FfQOyg+FLogeJ3QMhF0RiuF9eAKxwB3vaNMEcmR40EIzqCkgOJUkTTTezgEh89qwWfvrF/CsaMMHp8dGjA4KVvMoHMQai0EFESLYc4oGq1NZTf0DGFgcANRI7lkK1aq1moXghEDpgy54Ck4LTgKjoxqRkoidIbVD2IxBFcgpmQZaUXpgpEATqrMMRMEhiAcOr9wY6VcrP+xr1W1WG/q4CqvZ9ejGcqs9L43+CwFnj49GR22N8u6GzoLMjslztHqEkoCh9F1dx6HYzgOHI9H7oYjKRrpnkqNJQSxmIQooRdLeW29pB2Il8qtpxunQFa7aOlnu/cU2C3X5+dqbu2IZnCP2/Q5EKDg0KU3NrpyIClS+ZVWJSPiMKL7VSEsCkgEcaGG5FbPxLlVzmlJ/846hf/cvsYvUAr9f9xV3BxbwX1LSH9p7tb614K6XzrOtXJRWo72OrMef38utwFM9xjPDuRv1ZRBXsYHrpOPXoYmvqQA/I3j7fVSy04KkA6JcizoURfGVAJ00hEIlgsfE/QQukDoPTmeCD7zf/rHt5w+/kR8eubw7u8hRDgKlxTJ8cK9O3AUOBJIOiLMHHDcBc/DMdS+whOhjAxOOCIcDw4vgqQzfYkwwsCESGQuE8fgeHf0HJzBUfeHjun5wlwSfTFhQhHzCqISSiCUYERv80yeMofjwdJux5kk1ot6fB6N5E8T4+kjUPj+7Q9MWRhTBI0A+JqrX9S4l4o6PNYmtPfGrOpKROMFV1q2lEdVCRSCK7hgLYHnkomz0VngAu5Qtf/gKGOx4rPgrII6YJ6CSK3/MGHZ1mnQxRiXTgg+cP/+nj70OO/pjoESCyQrWCypMIQDRQtTqq08nfL2+7ecn898jB/XGMBq4Nso9T7XZgSx2aDsltu6cjXXAta5WC2Lq8R5CymeKN4b3GO9STaB6Bp4Dl1YaDdQY2zdUqOu1c2+evTg3Cb1tBJVWk/qLTvBa4L+1vzXwtvfHuveN+gptB/P7d7vhfdr7z+nGFYBL5u51zufXe+zPgWN9ncbMNs/BPXBkO01baSv1GCxq+62uAoR7byCWzGD14T9K1DRi/W39nHV8/cgQaGzHgk5ZMs4ckLxxQJ7DpjBqXC47+h7IfhMUEW04DWTXMK5xMFns4IPHhkhq3IMyiDWRW1w2ZrelIjkhM6zVRbrzKH2XnaqaDLGTV9MUOVU0DhC3baTnj5Ykm6goDFBnpES7bFTcMXV9EwoqSwVvmmOxDlaYyQKUSNOhCyZoRvQZJQHnRM6L3hROgfZC9Q8ey/Osumd4mJCijNYJkXyPEHOlkbrsR7TGAmgiqdzDjRT0lzJFD0Oq0ZGlFIiWrOUSik149MK1o53R8pccLiKNFq2kIFSZck+EmThQEolIUkouVhWjjMvgZQWKoxWZ+KDGSrizSMx6nRdneL2WDRIU9l4DXsoabvTa97D9ZxzbU81aEnFqh1K64EjzS+wpjquVvqjRlXeENxWLEel6Fie40apLUtFs4VKtsR4+2d7qwRuKYQvwdrbfb9227/e+AZjCnvLfCvYP+c93LLS99utx9ab617zDm4de8/x8vI4SsNEG5QUKkrs11Pcygbax5mF2wqgxRNeUxqvQUo3vAzdHEcD6FEpQyaHzPAwLDaBpaQKPpkw++G7B4IUvER6r5ALabqgPlL6wpvOhP4DgW4SZoE3Axx8xM+Ru956D+h8QcfEdImIRoIUHg4BKQlJI3G+kMmEYL0HcomU6RmdL/h8pmfg6AsdBiPNpxN5uiDzTFZnBX5FIWLZRJNV6ZaSzRuYI/N5Qp3RTrvswMObuzf4OVAuhWPfceg9qglcMPghW2opLuBQpCiuWGaToKTpbMHZCC4Xjr2nc2oKoma+iAuMeSbGxJwEdT2+fwBnyuMynYjZMSaHK5YxdBkvFCm8++EdaU6UVGqrTbNyixRrwNNYUxVjpiVyHk8WkFaPxxNcYOgDc4zkWHtpVEZd7wNeHfpoXfiwuO4K82weQWkCVcQC0VqbRkkTwG3LVj/cuqBthWObC7WTYc16LVR+JYPbyiKspYbcDPKzRj4WoM4LFYfdlwDOWd5Vc1IK1CI1WT6DqgWtTTncQhK2mu/W+v22r3kYX9r3bzO+QfjoNSHdpOjn1u8FtLux3f4Yr81vFcP2GL4urYpC90ps2VzaLizB4so++ZpwxmEZRRvL/VXBvp17LYb9mkdx47xLkNorsUs8xifmTzNhMmplJeM9eA9xfMZL4XH+M1ZsluhdQUqEeKbEESmRj3+84ClomsiXR3yeGT9MRFEuJC4dBFE0XvCaCGIlYEGU1GPHyzOuFqqdXLH0Uk1IHiFHdH7mk47k88+EMiGakBQpc0KjVfu2TKMSC5rh9FMVjMURL5Gcck1JVesjPVjqbf92IKaZNCU+/vQT/vGR6DuKdGTXM2ZHUs8sByuzU89zFGJxTPRk11OkYyqBuQh5dnz6+Ueen5+JMtRiuAOzemJxXIpHpUPDkVl6EoFZO2JxzMWjJaDFkcaML55AIBLJOfOvP/4rkgVJwvw0k+dsEF9FX/71x38BFS6PZ8jmUbhifQh63zNf7HMOfkCzEsdIJ1Y7cXp6Js7R7pVNCM347Oo93eR5qyPIpjVctWwEZwVozRy/ega3PRMEqZ3gjA67mfuLGY/QWneuQWNoHoQRVJYGZ7F6AapC3HCTNY9AwGIQzpphoeULwM7n4KG9Irgl+L/dmMMvUAp/7ZKGvUDeC+lbQvw1BfCluWaKs5vbnUOo28niKl95HLKu2umRRfjK5/D+nQKQ17bbzeluOwm7ffeK5BYMtZvLPjPpSJpmvFbIiELnleAUl0eEzDwXnEZEE+oKogmXrM5AKIxPRmgnOSJlxJVIukxkzcTanzhIgUp814ll+gQpSFfMU8gzgRlPIVVyPMPoZyO4yyMjM8QTPo8WEC5mnZOUHEstsDUhSBF0MjPRFSFPxQRYuw06kBFjEO2FmBM5Zi7PZwgj0XmK68iuY8pWFjdxWHqxjdkT8cwMJCYT/GqVz8rAdH6mjNNGKQxEDUT1TBoo0lHcxFjL76zpqGdWTy49pTjyWOhcx+AGg8E08XR6siygbNlHJRbzjmrB8OP5ETLEKVpVdK1+djiCBNKUKLEwy1RrHDIey/SZ55mSS1UAVaBVg3ntbd4eCFnWteejCfu1N3m14Jfl9a8R0tkBth7BqhgW1VH/GWOrNdfR2uFQF+ufCj3ZZYkVv61XXB/vFkeo0NNfTG2xV3q31n2b4xv0FNrY3jxslveCfTvXRnu/3w6ubrDX9m9BqVunw6oqxZvVcsVA+tqltuXXrPRbHsAtYf6lAPJrSmYLQ7nd3C2PwykaMirRcjdiXjBwR0Ek10rjjCPWvzZvgr0Ka801FpDxlb7Ck5Fk+3gyOhlraqczXopRG1R6hDzPeLVjO2YsWTEhVcp5ZhyFTjJMmXk0ojsp2Csb1JJbhk0EkglNonXdcyoLTUQmV6Uq1kt6ginPtUFRIeVo3t4gRmkhHUmNvqIQ7T0dpbYvEyJaa62VhOIRkm1LR5KZLJ7CQJaeQkcpodZeT8yVViOJUW5Y14ZCVkdRIU6RczpZdlABXZQfm/ilLMI6Twmp/cEXgsQqwOc0NTYJYo6LIpEiVbjranQgSOOrq5lHG1m93GNO6rrMWscgAVG3EYtrnK55Exb4Dcs640MC53qz4Eum1XtDtf6Xhog7TIuWjGoV3C3bqamxptCsgtrW2TYtML4X4F8r7LfC4JbPsd//21EU32CgeXu+vQDfSt9bEnt7V263q3OL0S+bvzvz/jV9sd3UiVE61MMsxozb3I/7fb8AGd203L9WKTSoSD5zbL879nI+RXzNA3dqQWFRC4aKpWIG0kYpZKsIrkK9owl4o47wGLeRW/Yx5dFVagujlIs4Cq3Psdc6p8Uyceo+xmNU8FKuz6f1OrDzeTJSiuWkVwEpRvdq/aKjmidQKSBMcNYA45aup3r96qp1LWKFYkHt+20FW0lr85zEWqebEF3s1irOXFWYUpfB1TnBGcylIJLqPJbWCsseBpGEasV6PNnkuPMWDKasZTCOVZa5KiS3MYViykC297fTVag3yLKsx7Lta4AZls+IVNFahT6qyzchjT24Pnq6aYuw0G1rawy1fUY9SrZ1S3vc2qFQhRZ1brh/Q/ypFr7SlE/mWujeEu4s26wV1DTWjRuJI228Biq9Bhftg8m3gsvfjkKAbzLQ3MYtqfqatN57BTfmXpm+qUc+exrj/vEdi3JJjY3RiT2EN/ZdyhS2weH2EG5eLzqfNSjoa2Am4XWF0gTADSXjKlulVAF7pQCwxve+zvk6HyqxWRPWC38RSkfGVYu+eQdNKbh6vKYwmgIITcDrVtFYbKGTslzDohTUjuvFzqelrJ6AVqWQTRlIVQQaMWK4RREYEZ5Tt+DuS4KMYG+2yEZbTuBCwQtkrKbAm69EpiU6ap1L2Deba5KOMR95xPaFypFk/cdSVQSBxkqkFEz5qCbEeTJCDrXOwQG+BkVrEBilBnpZbbm2LDux5F6uX+ResM+qm+9BsXiBcw7nxbyA1u+41C5zYoC+0XlXYsR6XUsTG/WVWqK9ZLkMyOQim/nana00PiVnNSgV4rGYQiDmbCm0N617fTFDO/qmN8QKOzlejq1Xsz/2fn5/ljauGwdtruLG+f424xtWClvp9jnTfQf/XHUZk5ebfekw2+Xwcnvx5inQrQ7sEldw7fx6DdPI5nh7T2APAW232277GqT0tV5I3VecWucxKXgxN9wEvLFWbgW2W5SBiarmMciybBa+32znKQTNO09hVQChKoDVU1iVTCsM62r8IJDxWgjFCO2ur8vWWQq7CX5rYoNlleQaYC5m2WtmgYloJJ71pcWgJMMFdV0Ppjy290AVxCKKd5aKCtCo4ux+cPUOzovFWXDL345Yhf065zXVfUKF44WApbVmqUV1Cqg15DmEQAKiOrJWcrltOn27N5rncMvwbcv7gvyWoBDrcdr6+p0UMbdDiq/W+dpXRMRX+mmHzgVNLI1s0BrEvbJ62thm87Qve70wRTAmVrtPl+/YWXDYicNVTibVBj3pZu91+eVoTXlMz7yecVSv/cX8Le/hlkLYK4+XyupbGN8gfNS+sCZNt4phL7nrdoK931t1t/SK7A79OYURdvP1mNrSNlv5fPMEtpflNh7Ba0ph4z3ctuw3+39OKchuzm/wX7cqKIOFcoWFmtVfroT9dm7rBTTvwfhZVyHdBPhWYIcbx956C4tXUJWJF9vOGEfTsr7beiI1tuA3ysM3QZkxGoiqIDQrJepq/Tfhn1lZmtvf+tIKf2jBoCZXHcG2vv4Vqfu6qvvFsmOkXROCw+Zc/V7a51TK1ZzbvNdlffMiyrqPZvMaNCMVGvFSKK5WMzsxAkK/+Wz1+nAsAu/KK9jLOLcY3uujvpV1230rNq+OFSaS5hE0E0ks/gIWf1BqnQS7h2V7YVuBu53TzXZLOxw7i/jqsK8Pn+nuVVivLGW3BfC1Emiewi+x3L/GQ3gtFvGfWin8NVNSt5J7ewO1zB9eIkd+t+lesO8F/16obk/7meNpqIU9Wy9EMGhmK+z3Ou2Wh7D3FLaKoq37UoD4hlKwStDbAmi1yCsWX63+Jnyb0L+25tMyvwq51XL3y/x27lopbBVFt5kLS9wi4sR6JQQKXoz0zleh6Cps5ClVyMgSC5AiSy8AqYFknVel0DJtrl4b2n/N5im0lPZtEdP+GTbStvW97zPiC4UWSYBcv682Z7BQqgZ8FZQofQ2UGxePwyEUIkm1qmg7YtAJyBQsDlIolGRpUtL1loYq5vEssrBdew26L7Gu14uJr+fafdW8q812mo1im9LiFVUpCaSUjHfIedxgDKuFYn2eqcV0BcjbB2x7EWW3rLvXuo8RpTpUIWVFMS4jlRZ1tm/0CoaSxS+ozbEsXdVVhKF1h1tvgK2HsfcIbnkP13DYtdC/NbdVet/G+AZTUm9I4mYJiLDkRO+F+NYj2ArkW97C1vq+5YzcUgovBLu+vo/Dirzq/u2BWda/Fvjdrr+lFK48Cq1OUqUDcK1hCQYLyWr1772C1dp/ac3fUgpt/vp46eoY/hcoha2nENSgrDWAbE1wTHloVTgZimHINVOxFRJfeQJNOSyvFgS91eslb+YKFTeoaYmOmmtPhZXsfKWmd6pfzy/Ve/CSUZEF9oIVPmrfExhU1LoHrN9bvVARKkkGgVJtYgOmtH5vBY81nDHoSsnWGwGBuHqIS0wh1OuEVVbtZRJcAwFbr2Abk2jb1WVVXZ6RK+O/WACcUr8/B/haad3SQUvbybG/CLvM/cPXlHEV9lecSA3CqixHC6tq+x9aTyypLXPNI6hfiNo+gq7e0k1PYQ8dvQYb3RL0n1MS39b49pWCq8rgNQv/lmC/ZZHfUiS3FMotpbANAgtrXcAtz6NuI92N9dtj78990yvQZU4cV8Fn05EVDvoKYX8dC/i6uT3EsfUy1vP9+5TClZehO0VRj9dtvJOcy2o4lmoVpyp0kkJtBtMUQmsfeeUVRFZDNO3+Lji8NZbRXFal0KCjlhGzgZ2olcs+5BqTsJ4NYIm4RsNn3pFgaa9NKTSlERreg+DIeBGCFAoWas6URXDlGuTwmmtwNFFCj4qQfVllUYslbB+pPTzUlmXdbIGaNnMm6DfirMnSxYjWK0WiYDUNWtN+vV+U+XJdChRnynd5+LZjhZYED9vqZCx1ucFGjedISyO/sw8om8/QCIatCU++6rnQFIfWj3JNe7P1ZG4J8a0nsVcIW0/oSx7Et6Mgvs1As2B45C2BuhXme6VwS9But/0l+/6S891QQlr/SkO8ZJ17sX073o2Yg/MmVpyYgBQpONli+yvufz13DR+tAn4V4rIT9q/NuTq/VTx7RdJgoRVbvz7OS0Vxfa0twN3SXaUFiNUShlq2kG5gH2sLadstFn2SRdi77Gp9gm5ST9t63RyH5Rk1IWiZLjR20PosS6zn2xiMjdrB+yqoyLQgaGJNjayUfNUTcJh6aLjMKhQDlockkkA8WTxOhFQZWEspSHV/mrHcicN5R+5kg9tzde2LzGnXLpvXPtDMjbmWkruDkpb327l6jBZzK0thoP1Gy/fdzp+b+7HJp73SVmbFU5UtrLEE5xyoUkqiqCBqcZh2IeZJQ3DerqVkcoUIW6BfVSHvv6D2gbYQ0VaQbz/A7oO/EPDb9brb5tuBjdr4eqUg7svb/Bpja51/TtjvhbS/sc9rc68piBvKQG4J7p0HcQvbV681AHhj3UZZtKCwVF4ZcSCtv7FQA8O6EfLXGP8W0nEY33+QYmmmpWYNSVMaO6WgebO+rXupWIRcie6i8dG8gKaqAtDVk1g8ClmFvavrO7FUUodaBhGFIGr01mqNTkRXK10bDKGYBd/iCEVqYNkef9FKrVDXS61iLc3M3QQDFlxZqlCoVu2Sh78hWtsiAY1m2QrCdDmmYHKl0f4A9CLWsl4VK2up9M5VUOWmX6Rh/kIuFRurvRuMotrMbxWp/oaNXO9JrbSlrsJIWr+7JUZSqbHbhapqTR91LNlW7f4u1X7OLH0JlhjO5tnQth3t+6sXtRr4C6SktDqY6/XLBRWxAy4X0ZpvQkvWtQurF9h+iaXgoth+V5H19pMLrUdGWQomyiZ20LZdBfoqul+z3veCfb+89wi+dJxva3y9Uvhr8+FtreavndsL+F8avN0ri18y186zmVOnL+aulZnWjpqrtS1otbhNIFynYtrcFsa5EsBOuBsChz4QnGO+nAkOjr09KNYjvSmUpjQKxz6YUMzG/+/EGS8QWAAuWkP1Ml8YukDfBVxpXgV0zhgqrUq40Ilh5KLKEConZbY+DFoyD4On847eY2ymWjj0A2mOjOeE04DH0XfDwmbadz1kGMcLThy+CyZwai/egMeJ5zJfahAUhtATfGBmRooQ6MhzNvI4tyqAMY+kORGCFYk5hCV2VbPD1CvSOaQTunvrMRE10h86JAjZZTRACUqnnuIC2g2cozBmh/RHMp5LxDqtScegvan42pIzScfHUySL8S95ip1HlN47CAf6VHmVXE+RQJaOS/bEIiQC1lwzELKHAvliTXJSTNZrIStpTBz8gd71pDFZSm9tO0oRa8uZMmlMeDHSPF+CBayTVqoQmC+ROEXiJb7wOLShOM1ryHa/X8nILQxXHGjLf23xAFetMnNRSlUcllJtQeWcI6pala2vnoGvCivjnZHkBW89qJ3L6AxIpqijZSltYaR6xVx7B82TaUJq60r53Xa3FMHeo/h2PYZvTynAbUH6tUrhaxTFrTqAzf4vmEdf8zy+VC9Q6xWcq/i0N6HupFSM03KuLVNoi+dvsfytJ2AgxC1PQVDuup4/vLvn/u5AHzzzueCdVKGfzTPQQxXmisbJlMKhg5zQPBMcOArkieAdwQmnTxNpLuSivHvoefvmDnJEsHTS1pEszwFHofeK5gQlc9c70IzGC0+PRlH999+9o/eO4BxpGtBSGELPWc/EHDl0B7rQceyPll6aCr3rocDkjngxZs+WOZSmROcCHs+n8kicI2lMPBzecHc4cpIToo5DOFCS8RxJrtXHKnxIHxl15HAYrBnNweEPAekEBlMIxRfcweM7R/9mIEsmMtPf90hwZJcoXilBieJR10F/zznCJQru8JaM4zxD8QPFDVyK8SFFOib1TFl4vkS64Dm8fUNUA92Sv0fCAenuuSQhqWN2B+vvLD2X7JiL0F0K3g+E7mhQV1Km59k8haoMcsyMjLy5f8PD8YF0SWhWXBHilMhzxj840px4/njibjjSh55OevKcmE4Th+6Aw/Hzjx8oWowob+MpiGepDG5zy2jPz1aGtvV5dcFbAsUKHSmiFmB23m+8vvXgZvzXEzcl4TziXGVaVXJRihp7sb4QzNvlveDfnutL1n9TJtvjf857+HYUAnyrSgG+TO9wS9j73T6vKY99kVjD//fCf388tzuvh6tU1O129bwS1mDpGqTN1epfcfpWHbzP8tli+M3Cv6UUHIW3neO/fjfw9u09h75jOplSOPQGCIteexx5OiGauTsOaI6mJERBM2WGvgsMfccfp5+ZNJNQ/u5tz9//4S2ajIuoc0aQ50XJc49DOXRCijOaE28OAS2ROHpkemTMmX/87o15Gzjmyx0lFzo63OR4yk/c3d1xGA7cH++X+EDnOkSF2EdTCrJRCnMiiPVZLpMyMXGZL7y5e8O7N+/w2byIh8PDUm9AhaRcEeZTRJNyf3wgDIHuGBjuByPEG6C4QnaZcAz43nN4cyS5RJJI/2BKobhMdpnsCzl4cMGUwqxcUlMKnnNUCEfwB55TIKpjVs9pVs5z4scPT3R94Ifv3jEVR1JH6d8i4YDvHxiLsbImN5ClI8rAmD1TVvzjSBgeGI5vKbOSp8zJn2i9Gc5PZ6bLRMmFt9+/5Yf3PxDPsQbmhdPjiekycTfcM10mUsy8ffOO++Mdgx+YzhOf+MS7h/cEF3h6eialtBrKe2G/n2M314zvJcPJPBBTCMGMJmcBa0PqsmV6eU8tLqkaSDaQkR20wXxOAiLWqjOXQoxVKSyCu0JPy8XtBf/Wrbll1e+XtwphrwRuKZr9ef724+uVwl+7TOFrKnRf8wC+tO/+OPu5vVJ4TVFsPJNtlbCrgl2WZiprcdiqGK49gRU+uvYKtoHfBil1XNcNNEXyRuDBnemnCZ8c3/UO1UweP1mswVlHskMfuBs6nh4LcZphfFrOE8eRkiNuHunvBu77I0P6COnMnVOG/IRc4OnDjwTvePuH73HZiqqOHoY+8P2bI6fnmekS6fLMeLnw6cc/kZ5OSMwwKnFMpDHRhc4a0s+FdEqkk3VzExFijng8Th2n6YSocD/co6kw5xlX4wZePSkmpjjiGhPqDMwKUdFZSTnxPD4zdNawvmHtKRWj1VaHy45Oeu4Pd9abQIRcLFisYMfNwnyZkN7RDR1lVkgZGcDhsLRSI5j78c8/o/4AYbBf0/f0fiAcPL7v8GVgSvA0Zs5lRuOMLyMdA4cAve8prmNGSJqZpzM+HCrvUrG+DhmkeDyOd/cH1AlFJwqeqJGnyzPv377n7du3PD49EovRXxcpJE3Wtc0LLlvl9JwjD8FZ050Arne4wayr4pSokeIy6h2pRMuGakJ9K+90NyeswZYWf2/PUVsP5gGo3elWNd76lmRKTf81emsLKJfSijAaZLQCQQ4xKu2iFM31714hNI221VLbC9qOrQJwn9l2qwy+Vjl8O+PrlYL/8ia/ylg9yGvru83tM4o+1zNgD/FsFMAXvYKbnoKu+1byOBw4X15UCXcL7m8VvsLWW1DW6uCXXoHfWPMt+Ou/QikMdAzMhAJBhIMbKDkx50uFeIQe5eCUu+BILuOdtZQUNYWkOhpOWy50Cj2eoDNFJ4JYIZXLgTw94b1ncG9pwWivSo/QO2XWQiqJkjI6z7gMkhRJCtGCk3nKdHQWkE81S2jWlbiutCClUCYLFrsg5Ixtq4DWwq1UyHO+Tk3dZBdpUksHrQRvorJi0a2eoZhyCM5iC8om+FxpMCzm4KtSwXoaw2JILqhGKczjGdeDF4evwePixLqtitJ7s2B9yyyrzLJOMsGBBkGds/oI1QV6L87iVaEoWZQxR1DPsT8wF2EuCcQykeY8L/2Xc7GezeKqIgjOcPqG+4spi3bvi5flBTa3NO1xrG1at9D7PqDMZnn7crvlevzGwCraYCMArc2qfF22E1m3NPMSrpmuZf1MLZO5dVj7IlRkFyZ1Xl9suzvPzbFVBHvvYu86fXvj24OPbsE9tyz3W03nb9FE7GGocGNu6zXs4artuWp84DrN85rLpykFq1bdpoYqoXLeuEWgr8J+K+C3XsHey9jPbbd7QHngiQ5HUOGNzhRNzJwtBFngkM4MsaPvjtzpMwcfebgP5FSI88xpvJCYKJx5EOWNEx7cyOhGApl7ddwV+Dh9ousD3w1/T+cdXjynx2f8NJOfID+fyeeR548nvPP8w9t/IH1MnMYTjPbQM7EIBRcdMgsyCjJW4RAatygw1X0CpjCiBSKX7KBJYBZkBpltPVEgWdMZSZXMoPa/DmLKyPUOIsRThCM18cetQr4e3+IPjkDg7cNbzvOZp6cn3t+9x/eecz6bkinWAY1kjYNwDgmBH94cIBx4nKGUmTJB13sKEKTQS6F3lq0VmiEhxSi7C/Rdx9vDgUvpiGqk4b0LHKXj8fkjMcMfvvuBp8vM5fmC9G+RAHP9FzUyppEpT3Te4LH79/c8fXiipGLYe1+/j5Z+Guzel5Z80Dmkd+Y9OGcFmkGuDcYm7P3mfXuut3JyazQLKxW3gBTBZ2eKsGCNdrT+3pV3qRRqO85msW+1S22yg1hBWy1SM1HsN9tvcaytFtvCSnvNtvUObu2zFf5b6AleKojt3Lczvl4p/LVr17ZW/x6yCVwL+b0wf817eE0pvOIVSDBvoFULizNG+2uvYFUK21z8bkMP7Vkhpev4wuop7K3+LymF7fu2z5HAnVw4hJ4ueHqM7Gfo1hTQrihudqRypssjlEw5G5F+KIlBLwRmChMH9QzqGLigakrhKEceOvjD/R3BO8plIiLGFDta9e15OllWyyx0pcPj8ckjyVmjm2RE0T09Lpm1Op0n4imSx4zOikphvIx0vqP3Pb30lgqaQIojVCGNJZng8YgTYoqVLRXSmJjDzHyJOITj4YhXb+04S1oKqkosZpnWxjLj80g4BlxnAlCw9pZk2/b8fDYYpsA8zrhszKeNf0iz4vG8e/MOQo90gTid0ZgQBrRU0gp1FHV4dThSTRGu94vOpFnIknDuiKaZ6fJMdkdavXMhEYmUNCLF2po6MiVN+M7uP5VCIZOo/ZerZZ9KZIoThXIVA8OD7zyhBPq73mAkbx6bOkW8QVkUmPNsEJTfiLlt3QJcQ/btPddzre+CgGVACQuteckVPqpBZqVQ6+I2bKZbAWwCXutis/lXyGhvxTcykW3MoF30fm4roNq5tjCQ273fxyj2rtE2GP3tjG9XKdyw0l9VCnvB3noTb5XGrbnlGHrTy5BgmUJbYd/4ahr521YZrPTQL7mDVu/gOmPotbmtUrgFFW3ft/UHAgcuPHjoQ4fm2daFuo8WKNH6KI+Z3tt+83jBCwQHfblQiBRGehW64hh0QplwJTOQuPOgd0dQSOfJehhks+S1qDVsqc9L0IAvHqIgUcyCn8F5R6cdGpWSrUPbfJopY0Yno0kYxwntlNAHutDhxFmTenF4/BIwLjHjcXjnVxgqWbbNxMR8meh8T3fX49QZlBQzmmt3smh8SpqVPJtSGGQgEAhdsIA0jpQSqSSSJoozmGW6TEgUii+Gz2OfUbzw9uENRYTsHNPlhLoZ7YwLKJPISS2NVAZLD9ZW4Z2QMpNSITHjDoGcM9M0UTpFXW8qQR1TFjSOIAFfa1NKnAiDeaVITWstsSoFUwKxRC7zhawZQfBi/Rnsvnd4DfSH3vqGuEohUXs3p5IoZOYSyVobEzXZVqFegVXeVa9Bt/Jw411YLUF7/KsQd+YJbDuvKa5CRVth3YR9g4BWgd3qlXURLNuitj3ufytecAtq2mu4vdvT5nW3js/MfVvj24OPmuK/1XR+K9i38NHeo9g3vm/7fgYWsuCm0UYE0qIMmoBvgn9tGtOUwsoK2m1opBt76MoT9NID2Av7vaLYxhpurd8qEkHp54nLnyLhbqB0nvPzo6WkDj2UhGqmxMnYNjXTOXAUcopWMS2QUzRhOWd0GEiHAzxP+Lkwniee5kf0yfr3llzbWJqvbgHCYimkUgx2GXxv8YNUePrwxDzN/PH/+CNd6AguMJ0nUkykKTKNE0zw/OdnRj+SYuYsZ57cEw/HBxyO89OZEIIFiysjapwjXa1J+PDTB+IUrSbiopz8ifE80oUOmYU8Z3KyWgUtpjwu5wspJj6VT9U7EPwnj+sd4a4zb6EXJiaKFLRXg046We+rwe4n6QQNiusc/dueqImoiVm91RX4I5HO0k2l1im4gSg9qp5AIk9nPvzpX5lLbTwanqy9p3qy+0CRwMhQ+zY7nmelSMf/8b//vzjNmecxcShKVk+QzOX8xJwik05kyWia+PnTB87nc1Xmjt51pDlTXOGnDz/WILz9JlKgd73VO5D49PyRnLIpkSCrRmiZXdseN3vD2W/WVTm9tEKtvRm0xkygHrp4tDiKFRgsB1kt/DbW4rSXWNVWceyDHHt35jVBvT1fO55ndY9em2tje97Pzf1tx7cXaG6xgM8FjW8pg71HsYeKtus7LGjcsQaLXcG5Wwrgeq7BQoG1aUwLKndEVtrnVSmsnsIq4G95Bl/rPVyvb3GFQpcT5VKI2oF3xPMTxQk+WR0CWshxrtXJ1rFLsJqCRgegVdCXWJApo5dEGiM5JsolM+mEzI48m6WdU948X0JrXNM8hxwSWiDFRDxHSiqcH88EZ4J9ukzkmCkpk+aEJCFdzBLPsSAiJJLFHBAuz7Zv8KFWO9eU1BBNyZzq8XJhjjNJkvUk9spZzqQ5Uep63exfcrEityRIpciQTvDZ6hXc4JhlNhipN+EvnVhg3GM8TEGgY1EKySUSphSSOIp4kkuLUohYAVpyM9lZ8ZpopuSZ6XJa6hSig4Rn1kB2kUxgZK7prIGUnQFETx8ZE8QEcn4mS4CciIzEnCmaUSlkMOgo55p5JUQJFmzPEFO0BkVRzTjIyiyztSyNuf5ehW1Xt0WmbmXrdq7J3RvIyVrM3JIH2nuQWnRmon1rwe/xfHjpPWyXy+79tedwvU+bey0o/dr59p6A7o6xvdbt/t+OQoBv1VPYCvGt1f8lT2HvFdya23ocgUVor8I+Xb2asPcvvIF1uUFKbbk1j7G/kaW38S4GsA0U7yuVb3kH69w2RtGOVyyz56kwPikT1k1NUc6UWoGsizGVN0RvlM3DWC1+Mozlss5VGXD69MxJT7UFI4ju92W1FpVlu+1xnp6ergw1KY1KgVopO1uQsa1X4VLO1xWwytoboTorNJqLuk8qqR5TiTpz+XBez7vwHUlLTyFOm3uvw7Dy83of6dYrCKYY6PTKU6AqBjzoky7HohOKOGbOxNqjLjJg3au7epcE4ECRxJzyMjfqbEVsdESsJ/QkHVG7um9vxxnnetyOyzSbj1p7SGccWQNqkVxySkxZFk4par8Jts2IGvtsa17UyAXb9+5pX6KNraewnduP7Xqh0nCwGJ4mjgVRZ5BhZYWltAd4e1M1RbEPDN/C+7fr2nJTMvs4wVZ73dr/1rH1xva38nTbp2xzewX0tx3ftlJ4LX5QsyIW5bDfrj282/U9tH7EfpNCGmRVAE3Q772CrSLYxhTa67aiiFdKoaWkvi7sGyHdmpnU4hAs29ly287iBMYZJJV/RmjvK5q6zFG5hPRaSKtuPF25frC3Qr4K761w33oHL5SCQuMmakJ64chpCgXbRvbnaMvba1zmZKGx0MLVvqhulIva+dt1bI6xXL99aKgpp8sj2xRlMyYzRruzybNvPa0XOCTLCn167MIyS7KLFF3gySLGdFrqATMOXwWDp9VFNBoGM1rs5/EmHAGv1s/Y7ppKy60JlXqHqUnYllzbLrQ4M1+gfiZYMn+WsTNw61cLrAaAwCL3dCPMrzyFNpoht5WZW2Pbb+a2crNSmLQ4yFqkthfCTajq7rX9BNvMoL2HILt1t7yQrbLYQ1C3PIbtNWwF/q0g87ejEOBbDDTfgo/2XkHHbU+iLS8ehV5tJ15xvrxQANsmMz3z8r5Z/c2beF0BrJ5CmxuYrvbbp5BeewpNyG+UhlbCuFq2b123moLYxCKaQsgsFf5boS+1u4qJHTFCOa2kX1kXRdB4gBrWboJbKrFcRQoWwjkjLGsWegvSrsK5WXpSKZQrAlyVgib76+o2bK+rCX016gXjNlpZUD3W1L210NSmANp5t8qqKaEmTFSXBjELX7/YdVQgrU7V5SoEdVGy9eFtCgushkBYMqFsX/ssiwfVvg9hye0XMkVMubQWA1aDUpW5YAJRjUZbKk9WO3frP1awSl1FCI7l/slqc13lCXIixFL1XXCUDCXX3Jya8bPQszdZCUvP8FKP15TrwgW4h8TdRsRu5WnTS02Gbtdt5KSwrjeW1bKcQ0r7/rdZPluYZqv1WQ4sLy5kCwXdCg7zynq3+7vffju/3387tl/Atze+PU9hL+BvCf2tUuh3c1voqeVZO6WTtRWkCf7bUNBWUWyF/jb76LXtmjPfznPtKVwrgtdiBQ0eEliEnFTB1yCSmqB92zrfzYUQOB7u+OG77zkMB54fTwDGGtkYR1XovGX4/PjHH5nGCUUZhoHDcKCTam+mwunpmfPzmR9++ANd1+OcVRPnmJkuswl757k/HOlCz+l0MgEvzsjtYubnH3/Ge8/93T1v7t/Q+Y6nxye6EDgMRzt/Ue6Odzx+euLDn3/m7nhH3/U83L8hReNQOgwHSlFOj8+0srIcjcTtMBztulLGu6pIYuHTp0+Ml5E3928tYB06hm7AO8+YRlzvGe4HIjMJi6FMZeKSzrx9/47+2BMOzviQQuHD+SPqlPd/9x3D3UB/1zExkUjMOuN6h+8d99/fEzXx0+OfiWrxhe/+8b+SCHwaywIVabhHfU/u7vjXHz/xfJlxhwf6uzfcv/sDny6FMSqnWZe778fHkUTgh3/8r2SxO+7/88ePTAke3n1Hd/eGcHjg6TxRxHN4eM/pMnEZI4M/IMWh0Yr/SixWY5GskHDoDngcf/yXPzFdRsbn6UoWLvGAdu8FriGk5pVtA8y3PJM2V4PUrSXq4kVs982yuidXD8B+bu8hbK195aVGe207uO09tOXt+32geZ+myma+feBvS0F8e57CtpjstRqDPaR0a84bVNQCyFth3e+E/eeUwt472CqFzymSFojuNLJQUOsmhqDFDDK1G8a8g7LAP4s12u6nphyUBT75GqXg1DEcBtq/EhTnPF3oKmupCQDvrDBrYEBRcsn09NyFOw7dgENIMTE/R0gXfAmWbqqB4AKlK8hs1nLwgYM/WIZQbxKkc4GcMtFFPpaPBGfb9NLj8Rz8geACveuZ8wwFBn8gyAVNRmDngmfwA149Xm1ZHehBcRj+PBZjUT32Ry7pgmrh4fiAIOQ5cXm+MOtMH/oliym4gHeeY3/E9Y6+7wkSyJKNWTQW5uQ59AeOhwP9fY/6QvaFp/FEJuGwSug+DIgTMtmK3XqPHzxDN5gwy0oIgusCxz5QXEd2slEKPcX1JN9x6D1zdITeOKjuDz0ZJXTge2HOwpQdvRcccHfsKWJ3YOfNPr479oQ+4DtHOvSoeIahAxFC6AhuMK9mVnQwGnJX6zHiJRoRoYoFgZtn0GCjLRKyDzhvPdaN53H191aQWl+ZW/bdQJhUT/fqwLes/f2Btppsv90+oLy39G95Gfvj6G7dfrvPrfvbj2+P+6jdLNtgcbP8hxtz2+36dc5JIUi6Kbi3nsJr1v5+7pai2HsS197FVPdNeM2fFdzL3DZ+1tZtvOQGoZArFFNeOc5mPtwF7h7ucBcPSXjn39L3A8fDHXGaSSkxxpE8WVbJIR/wahTUd8Mdb/1b3j+8xYnjcp646MjHM5SnTO4yeGHoB7rOhCtA57uaxSO8CW8IoeM4HMkxMY8Tf0x/pHc9D90D+ZLJJTO4AclCORXOP5/JJfP++J1lUz1FpjThDg6OSsATxCPR/IP3x3d0ocM7z4/jT1CUno7n8YnL6cL/+Kf/Qec75nHm6c9PnPOZwff4SmeRxkQm8+bdGyTYd3s3DAY/zlCKMYve93e8uX/Dm+/fWHaUJD58+pnznJnPE10X4DAwHAfU2zX4g8cfzCsrY2Y8jTy8f+B47OidQnCE4bgEkGd6kjrGnLnrHOUQ8EOg7x2dFB6OPUc63vkjz2Pkw9OFg1cycH8I5imo5xgUL8L7hyOxKHF65q5/AGcEhW8PPdzfMxUxKCkove/pXEfnOtIUefzzE3nOxGlmSrMV7G0hIDb353ZuX8AWNnNbj6GNvezV3XbCGvjeZzRV+pEVr/qctb/VUPuHcK809oHmNvZZSvvz7bffK5svbfe3H98efNQwzX38YB9cfqEUFOmt6ti7GjN4RSkMVYhvcf9bsNDeK3i5ndl3V9tptIYyqSCl4FrTl73Qh5eK4HNWP9RyfTZeg6zeQ9Lb+wrIRQidWeGSgKyUnHHZGe/+2VJQ82y5/TorjKBB0U7JtRGQjx4ZHeWspC4Z7UFX4xCdwlgfuoDVIfhAGiPqCiUXs5xTMBoKgImleC1Lout6jsORoJ4SC+ViabBlLCaEyZRLxjmPiJgnAuiseHH0viOdjca5u+s4+AMlZMumUeikwxUrXpvPMyEoIRhbqyDMF0tJ9epIAkUKH3/+wKwzSoVUauGb6xwhdKagY6FMmTJl8pTxncO7wOH+QJFMToVhGEhdpnMdTh1SYAj2/c1k6/Sm0LmCc9YvIlCQXI0Mr9wfAvNpoqQZfz9AicTpjGjEO2HwQq68Tl4KHsVpJk0Tp/NEd5cJwx0P7++JBWKZ6d1AESHhQJWsmWN/sDhKEHrfE4KnO3TkkhfI8Upw7+7pFzbvHj1p8P82AP3aXHtO2vk2njNujfEYpFqtd91a67p7bS9o60Xsg8+3ICX9zHbbWMP+mNvj3BrfjkKAbxE+al7ZHj5qimH/6qD1MpagC1QUXlju88banxdhfksB3IoprMl/10rBa16UTCDRlYjXspK6tfS+vdBvY+8dfEZRLLeVsgZ/a7B2qReoWTlLkDOAToqPHl/jCJrUsHacNVOfCjrVgrWxWAP4qX63E2RfjEBNHcygo5J7q1Fw2Vm1azarGhSKLvTWcbLc/lIy3nskO/tOqNvXauiCEdWFIViNQIIyFXQulSivUjbMBfHOrseZMI8xGj9PcZTZKnSNp8jTSYfGgmrB4S0TKUOaMlKsUtk5Y0SNUzSB7QPqTEA+Pz6hTo0pNGMWfyyIE3zwC313STZvmDy4zjGEgalM5FLwEgjO48UtWWDBC86BloLVCFc6Egc+OItBlRqbEqXzDrLVenRkyJE0j1ASzgfrhSGgZU1XdljB4nw5Ib4jeM8QPGXOxJwJvqeIo3hrXZo1I8EhSVCneOerkvLG6dSyqepPCKzCegv7bNfn3VyT102u+k2AeW+gt2Pqbt0LuS0bA2offN4+cHs86ksK4rVA9C1FUrgW8PvAM5vtvx24aD++Pfio/Q4HXnoFB3bKwK5LnBLCmkXUhH0T5vu5fjfnyRwYX527hoqmRaEsXohGJCvSGD4zVeBt/t4yOPaWPTfm9oqkeQbavqpKGlfbJ5bGDVD3K0XRXokl4gezsGctTGUmOGuorhe1rmlR4GyWt17UjifK+XQ2agnxxA8JfbamM6VTtFn7fUEnu57irfGNemV8Hq1DVsiEEIzq+CJIccgEgxgVdPCBPGc+PX/i/OFMiplyp5TRPJciRltgyyaVkk8WAC/KnCPlonT0UJTp8cLzz888Pz/z0D1YMDl4ayoz2zUvMq03wXc+nQkp0PlgpRpamE4zEoShdpbTqXB5uuAm40UyriYWL0JTIU6JlDOXeMZ1Vhn99OGJ83Tm8mnEu0Df95R5Mg0u1snOqzdqZ7HGN8QTxAuuvCWNJ55+/hNk6602P39gPl/I0zNSnMVZ0gWVhJRA0ERWxWuk98px8AwdBFfI85k0RdKccSKoC2jx5CIUFT6dPpn3F5TTeCKORqaXJeMHb6m4CZSyBpq3cncP2Qeu5eWt+rG23bYauq3fQklt/Q1P+ur5AdYgSMOf2miuSMOj9g/fazUN7aLkle32F7HHxL72HH/b8e1VNG+sh9sBZFavoaaYtj4Gnwv8NsFur2tPYasMXsYe9vGDybwDnQ0mKgWXi8EyTQFsl2fWnH/45Uqhvd8/dAC6JE6ysHlWy3W13JR8yczMMFkQWItaNbKz7PgczWr2xaOjmoU+KtkXkk8WcMWYMtMlwqyU0TyF3GWSZlxuXoNx7uPBOUcaayqth+yzpVlGS8tMl8zQB7z3S3cwEkh2SC7kKVPmYp6Fg+KUNFrP6kZS13rwlrkQJS6V1GU2qg2PJ44RTeCdM4isWfYYn09x9jeO0Y6Hs89WrNLZObfwIqWpfh/ZrOkS8worpUKasxWwFSg54w/WEU4Gm/P4hVhvHid8LpSQa4GWr9B5IkmBbNXxUiJS+1uTFS1CLiMlzeYlqENKIs0XsmSSeijRUnbTROfg/thbumjJxPFESQpZSXFE8WQN1tBHHVmtijmXvBDo+c7KJV1wqCuoU2Ke13v2ViLPzsKXG3NXz/ytWPDeO9g+Q9vzvfAwWl7rXhNttcaXso/2AeH9+1sewB662q+/NceN+b/d+Pbgo2318jaAvIWQehZPoaWaNmhnDwUNTFeCfespHBg3cYbpBcxkc2nZr6vHdloIOa3Q0MhaCbqdawqiKYWtItjDjXvvAK5v/Btzlp5fc9S9BxUkFXP9s91kJSvRR07PJyY3MXSD5X/nmvUkQic1NTMMlMePxEu0TKE0M86Xq/t1/jShZyVrQgIWyxkEOjge7sg5c3k+cdELghhkhOHXrQOWjgZfjTLSv+2RXricLjjn6EJHT48UYXqcSM/Jgr1ayDkzP03VO2BpLN913Vq7IGIwGcWYVXthPE0Ili47n2c0FtLFGswEDczJru38dLZuXeqY54k5zmYRi1KSMp0ne4ZnUA/qrVmQUWVk4hiZw7yQxGWX8SkRkueuv6f3PcfugGQhniPPH57wQ8AfrE9zVusnbZxGHvJE7wqSLgTtOXaO0+ViwV8SZU5IjkhxkB3j8ydSDTSTJqQIeXzmePeGt2/f8HiaiXni/PgTRXpUOqaUKOpIOHLlYpqTeaK+GAQ2uJ7hfqBLxv6qkynsFCOK0mDKFzHVPUy6ndMbc+0Y28LJbZB6f+ytR7JdbqhNK1rUfQrrdsNbO+fd+rbfa+mlv8ZwX97krzS+vUDzlr+oKYB+sxwUevAu46UKcWmWvSmAwyaAvIWFmmIYKgS0VRhNkQxMS7ygxQp6Jjq1LCIXC9Is2uYNTKzxg+YZtLmtp7C39vUvnKv3q2JWdCOI01wre0VIc+Lp8rQEU73zZkCV2rIQwYurzKOO8+lCSslaF7rI2V9YqqtUmceZMmUu4wURhxPB+zPOWeBVizJP83KpTpxx5KtZ9ChGky0z8XHm+c9POPGklEwge8d4vpBz4eIvxDmio5HeFVf4efyZJhla4N2Lr4VOWj0aIYgn51I7dLF8UeNoRHuXdGGSiYu3FFaAKU3EaJTSSRKZZOR+CrPMfPjxA/6TRw+K0crCmEdKZSJNMTFeLshg2Lt2BakEe+HpI4XCJV6MNynAlEek9igozpPFMdGTNDBrxxghFiHHwuUy8unpzPOkzBlm6ZmSoFGBnpwLP//5jywVMlOhqOPx5z/B014qEmAAAChxSURBVBOEA5dotNMJvyiA1vEjqZk/WQJzEethrI6gASlwns6Wrlpq+moyxtQFQdga2bcyk+AaLtoiD3ukZbvvdtwyprdGf9vmKnlIrjzrlxu+trNArSS/HXy+BQFt3ZsvXtgrH/JvP749+MjvXguEpMuyeKML2Fca30olXedWS7/fwUfb7bZKoWcmaM0uKhlfyirkmwKIrApgqyBuxRT+QgWwOJ/bQPQmAcOsL10UkDgoFLOOW8FbFcxaFNeqnaVVLQslWyxBixJJoBNrxbBW6EmZXVxO3Cp9nTPhXNL6AQyOaX/Nvc45m4Jh5ox5FEVXKEizVVVPtNRb65pWMAt/HU1Z1c9UlUKLtbTVZdOWS7M1XZmTKUU2qb7qCylHpjSiXs247GrHM0mcn80DYGLhONJgwjGLdTWbEzBL9XbVtgvgZgfeYjFkLDlC52rsiHVYc8IsPYnArL2lqapnTkqeIuk8MWVPUs8kvcFEGizGKoX5+XF5IlQNax3Pz2Q3UaRjKo6iYspAQkuYpmC8SLEqiaWyplhgXopYB71W6V69X93XFexl7i2oaLuP7tbfqlPYbrcNTu8Rn/3+7f2VwqmewwuXht3c9sL3HsKX9t2vv3U+2c3vP8jfdnx7gebmKbSgcsfqKRww70CmejuvEFDPvMA9w0bYt/UDI+0xGBaP4ho+an9NWZhScKUGkGvjFi5cK4XEChXdUg4Tn/cU4OVD0ua+RmFUvgXnTLDluB5HalaNNafRJZZgxxdSWVOaBHAqi6IwRVDX1765y2coJoDbaMulpUg1nqPNEJF1y7ye86a1dCthgxpo3n5fN8bWK7gpODZblqtPYfEYyca3sxgjm2MV3VjGDdZoMKdQlWndfhvLzFDIKyxa2rGroaNAsJoPcdA69mUKSsBp3T9nhB6pc+bbFYIaDYo6h9J6eoOo9SSmZJCIVyv5L2RcdW3zkrGRkPqhhYDUwF5RZ+myTUi3wG9b3sPo2/n9XOvmtpeF+9oFZQ1Os5sv2HPV5MStoPN2rgWmrzKg9vGB16z+dpJ0Y337UOzWwcvj/Oca366ncFWxbA+Pl1TbFV4zma6W/uopbLOP1m1WqOiWh7D8LYmgyQLINZNI9pZ/8xKa4G9KonkSIy89ha2guhU3+AqlIPv1UsWlM09Ak26MGqtCFTGBt2URXYrfdvey9SWuBlVZnQuqwGv7fdau0Y3wZr3Gq89ya7ftwudO8JVGlX7FdttNfNWD0pJSWsHUPuMwb95vSPIWAdmWCy+FUtnM+805HCCKk2wFxDhLJ7Wwd43DuqowBCHjEBoHlpJxmqqctH3M1vXNycPh6twqFJf1dR/LLjWpWn0p1Ln62WUVxMI1dPQ1qaRlN9f+7r2LrcW/9Q5ueRRtv3ZP7aEo2W3fjJsrj+E1T+BWmumt9Zsb/UWg+XMexbfjHWzHt+cptB9/F1OQoJVwePUEtsK8rxZ+t/EAmvfQPAVTCPEq0NxVxXHQVSm4UnBJrwX7PoA8ce0pbJVC5Dr43ILNbXzJY2jLrwWjb+zbmDK32xWxx1pxBpNgVcaLRbvZX+q+yyU0R2HrFWyu70tyez+2j81+bv8Rf63xS65RMIUrW6UAK6zc7sutUmhZcltFsF3eQtLNg9unV4LdM3XOe4PWVISMx6jzmrB2hHogXyWyKQ0T9l5nClqVhUNR/OZC/QbHyfUb8ovZbe+l+hqNRC76biOgq4Ww/ZG2BveexmcrT5uxfcsr2MrOW8ZTu7w9f9I+PrxHFtu+Wxm9KI/q/W6tK+AlZHQrRrC9uK3Gu3W37fe9FUCRG3N/u/FtZh85rqqYvVv5idYMovkKAuqZFmXxJaXQAs2HOtdpZNAJpwU3174EW2HfPIEtVLRXAG1u2m3X9tsFh194Cuz+brf7CqVRthJ9u41QOWqMUK9kXQ2lus16m27s+c36vXC9dclw/VxvTn81bs3t1/+S8bnHaa+IvnRsE6zgtkpha+lvLVU2y00oyRfmtgpjby1vFIlzineZIKYASj1h49MF6LB2m41NqwlzeyUzBGjGgv1iXfUsWI7mrhRAG4FU1dC6VfK1FwNqQfY2bv3oy5e5+azbsYV32tgm/ITNXNntsx9bBdVkx/ZYW0W1leHNk1O4JnHaQ0n7D/VaAFk2r61i2T+U2y9h72l8G+PbhY889oR6lqDyLRrrPRfRLX6iwNrWZJ9e2mndpiSjpGhppdsAclMA+0Dy9MrcVpF8JvvoSgDvLazt8k5AL/vcwlx397JRLlcKaJQWRtifYn86eWX9LQXRxt7YY7fu1rZfM7607S0H/d8zFqe+fkGyh4ras99guy28VK6XJd/Y59bcZp00SEaMMde64xk7rydTcBUqsr4LtV55mbNKglJBp0JrF9t+D0/r37BmZDUl0Y5h61eF4MmIKFnqXCXHA7mWe1uIZ/serpXfdm4PJb22z1a7y25ue1PdksVlty9cn6elrV6tZLdh+cz6/V29304/s+2t7f7249uDj1os4QDOFzqJN7yCebHyzQPYewXTIvyPXF54Codt/CAnfM4r3NNgoa1SmDbzt7yCrQJo3kX72zyFW0KcayG/5xGDzwvktv6Wd7zdSfMqBG7BM1u989r6167h1uP0udt7+zx+Dkq6tf3XjC9t+5rnsjVeHeBbf4pblvBWKN0SXnvoSTdz8+ZE/sZ2dc45K8i0aeuoVrAQsOmRhCIEjMyizTUWXl3UgcUNpD7qQqmxArd4IE0JXHsb7uWcOFKoImMbS4GX9QT772I71xTjFg7aKoatAbWf+1ygeX/+FtDf38DbvwvktxXoW49hCyHd8hhg5fnYaqRbGNveA/k2x7cHH7VKZXfdHW3vBazeQroZYN56ClvvwJYngmZCTkZct80kmnjpFYxcewDb7eJuXVseoVjCCGUDHe1viT0Usxe4n3sPtxMuXnN8t+v22/8SpbC//q+11LePxpe23e7zuePv3+/Dhe0aXzvereNkzBhucZerwPMWAtoGjXdzzYsQ2cxvYxGvHSfVa/GmGBAT2o1+3VODyvjqPSSsT1/zHmTxLOyzCytQZN/ONo7Q5lr8Yju39R4cdi2OYt3dnFwjKH63XDZ/3WZ5r0y3P8oelqvfw5W23u67VzKvKaDXEoeWG1I+E3zexxS2JtDek9jCQfvtth92e7xvb3y9qB/+A69iOyzXbilOeynYTah3tTK5KYCBafEQtp7CizmNHLkYaV3Esoq2ln0T6tu5kZdB5cuNuRF0M7ctdn4Nrnlt7mvQoc/Nfc2+t9DMrz3Hdl0b7THYI437x2G/fVt+zdN4bR+3e3/rPLdgrC95Ey0mKloVwlaow2LhiqvCH66zlFrKbXw5dwWhhM369kGa1+DBicFAvgrsUBWCnW4V+qWub8pBcYTqNaxKwV6y+dWbIrHT5fr96LLc2oBuG0QBJBeM/kKpWo/bQrd94XvPiN2y+8L6W57FFoa7dSNtPQ02+772oDk2weftxd8KLN9SEHsP4UtB5dfu/r/9+OYqmkWsl22z9Ld1AwYPWaXxYSfsm1JoMNMWPlphJlMubi7X1v4e9mmK4sI1fLRVFBdW5dGWJ0jZ6sf2matfgoG+5BHA50NSX6NUbh3/axXA56653dJ3xyMhBA5dT0yRUgp934MqKUac8zhnLTqdCME7I+xTJaVE8I6+65jnmaLKYRgopRjNt6twhirzPDNNE/fHI957gvdM48Q8Tzjva1e3O1KM5JzpO7t5S6viFuhCIKXE5VypLZzj7d0dThWNkYeuo3MCjYG1d8ggFFGe8zPOCc57jg8HpHPMOiM9SC9kl1EPbnDWT6F3lM74gjQY46rrhOgi6pTSKW5wuCBXNTnqQR1EcRAGCEeeozAlyOOqZZRIq1Bov1PrzVwqtOTqr7X1Kq7vnxUu6rDe5JFuCTpvlUIRZ96Cl2shvb0ptjdHE+js5mS3vB1NiKcb+7a/LeNo70Gk3fa62W6Pde6DzzV1+zaU1DbYBy/agZqHsFcI2wyl/di7PX/78fVKwf11LlpEK2y09k32C1x0DR3taxX2kNHyasFkTQTNSMQI7LZppFsp/lpMYZtpVN9r3U6TLbfEpa3DMfPydvgagb0fn1MKX+sJfOkcXzp/20Z2yw6g73F9jx8GdA7kkgnDYG65DzjvESfoHI3nqAuUotb/NyaC93R9R3YOp4X+eKTkQqpKQVQpKaE5U0Rww0AXAiEEYiloSkgI+K6jPx4R75GU6IfBZE8r3BNh6AIyz0zzXOk1PP3hgBSDR/phoHcOnJHALUqBwuVi1BghBA79EekEspHG+cEzEw0C7b11Xus9ucurUug9rrOqg+KsrafvbI4epDPlosGUQhBFQofrB6MzkYIfE1Z+t4WU9rUL22wjljTW/fot9GTbNW/kOgNpWRbrHZ1dxXZ0ByU1qKcJc7ebh+vtt9ttkZg253b7bhXPVj5vjfT9Tbt3MfdexhWqI3xd5fN2bj+/9Qj0xvJ2329HIcAvUAqdxP/I61iGq4rgGgKaa3B5DRCvgeZpgYna3IFxmVsCzTpdewWtOrl5C02Kbz2A8ytzO0WR5tU7GHebbUscXhtfYyd8TiHst/tL13/uPPtne1EKIry/v8cPA/Q93R306AIvDKqIE0opPP35Z4bguXv3jlDsio4Aal7D4XhEwEj+gF4VcY6cEj//9BNzKUTnGN684TAMIIJXxRVTJH3X4Y9H3DDQqzJ0HVIfdHs0xYzBbqKv53ci+GHAqUIIuK7Dec/DwwPeCd4JGgpJE5fpQtd1DMeBoR9wnaMXI4073B8YuaBejQMpgASBgy7UGFkyWTKHcKD4Qg6Z7q7D9x4GrIHPISCDoF6Z1HorF98hodCFzKfnS+V7ammqSk+rWBBKzW4IVQXkuuw2Znez/Ld9wvNmffMStsFm+83NC0GwPgyVOuXFzfWaTLSDr4L5tcBUw11vQT/tfTvOvuh4jytue0dvA9Rs/u6Vi+4FPVwHSrYfehtjaPN7IHOrib49ZdDGVysFkb/OB1hzH8zR3aaiyjKfl1e48ijWlNX9HLniw+21ZTDdvr/1akVoLZOoLms0D6Hy411ls+7ff04Y75XCa9DRl8bnoJ/PHXt/ni9ts30U2vNsxblSX6YLRAzFFpQighcB13h+qu1ZuZGc1KK6nBdFglSxVeEd6kvbvnVZABWh2EnJqlymiZIrNh7C0kgHzAh0ru5Trx8R8yyKBRJyqWLVm0SqV0njebLeFcUM5aKkkhh0sCY2fqBIIS04fcWqS60VKUbbjQPxYr0enEPEqMdFrXFS8AEJYlCcKiUn4hyJc6rPgSkBK1PTRbi3Z6fNrYFmreLdUltbiitcew/rMQrbQHX7KxjE2/qJq2iVc3It8z732kPz+/mvPc4vPd42vrOH8q/2kc2DcKueYCvUt5ptP/faeM1z+NuPr1YK7oui4tcZvrrDbnNjWhephoaWq3mDmdILpdAUg6PgWie0Wwrga5RCe8XrZU2g+VpnxN1m2zKFr/kGPxcL2G/3tZ7D1/5yvxTZ3Btl9jUqrlJai0JwJlRyyQsXk3pvgl1WjqQleaUK7iboFdBSbVQR8H5RFtvPVsCOJ0Iuhel8NqWgyuHujiBrIgtiSggRUlG8M6Yf5z0ihZKElLPBVH1fH1tpFwjFoKicrO8AAjFGtPZeGLqBTCbly1VmkYL1adBsgWJnbUsP3cGgJQq50YgUJbiACw6Jghbr7zBfLkxTrl33WCx8qcphDTVvBXxjkF2TUS3svN23XC03BdAUx35+gZSkxiP2tQZfI8D3AvmWAN9CT7eE+Bau38vW9v6WorkF/yu3r+3KBdmeeB9V25/8c8L+1gf+NsZfK9H09/FbGUqNAyS0FN69eYtqIaeEVPhmnmecszaY53Ekp8Td4YB3htPnauF7H0jZupgB5JSY53lZ3wLErnkSW+FhW1h/hRqMlsryClRGV2orz4CKY55mg49yXpXQRhY0csEUkzGt6kx3tN7XD/cPhNCRU0EC1imuFLx4XPCoV+Y88+HpA4e3B4a7AekFHHbeeq32HSo5Z+I8I1XqOefovOftu54+Fp7+9Knm1uvy+hoP7/fx+/jS+F0p/D5+1VFUiTGSUkLVejNTIROtGUQmwyx+MM8zKcaK+68wkh3LKLRLKWiFVvYsd7oj31MUEYcPASdl2b7t25rCFDUK7YXNtQpiRY0Z17sl46ldK8qyj24UjIgQgvk6KVnzpeLK1XZtORfrYue9R2uL0ZQSvvN4dcs5KDYvzuz1ptx6FyhSCMESRmUXrPpd8P8+/tLxu1L4ffxqI+VEzonz6YR3Du89p9PJ8Pz6z4lwOBiGPo8jT4+PTNPE0PdEESgm8JwIp9ma9TQL2onjcDgyKqSYmOdoXn8uzHE2xDxngve8ff+eHCMlZeZpIgKhpbUC85iZaxaUYhlQKc5WJlMKbw4PHPve0mVzRpO15kyacN6yjLq7jrvjPWEIpBLJ08SURqbTBEEY3gyk2dp1uiIQ4P2793SHDu88sURyyUSidY/zvqIJChnr6pYUOnB9Z15U8XR43r17hztH5ufxFwIPvxQk/H381sbvSuGvOG4lRvyvMARAlfFyAbD6AHE4l3iukIsg9FXYT/OEAGmemWerJTg9n3Bix/HO15iE1oC10HkPzbOoXsj5fGKePBSbjzEiRUnJgtWaMyU1qAk8sn75pZBStmNlIYsgKRsuXwq9CGma6Eq2zCa1dNIiFksosVCmwvPzE272JIm1MY+SfIIgROalWY9EIID2EFLATY5ItOO5TFBvjXgmrGtbAO3tnHTguoCfZ6biiUWYJuxz/kbHXq39r/ZM/S3H70rhbzC+NuB8a/w1bv6vtSP313KuhWCarbxXRMgxLkohVPx/Qb9LIaVEKcrp+bmG27RmLVGVggnyULOMAIN5VHl+fl66xpVS0FLIMeK8I6aI5lzbjlIDrNsPaRZzKcYn6sQC1Q7wpeByZnSOLptS8Ip1Wmvd00SJCOUpgxeypEWAa6e1Vac3ReB17dY2gJscEoTsU61dAJelNpSSq97kGhTtFAkBd7lY/b46xjJgOmEbPf38r/Ufde98Kc+mjb/EP/nStf+aBtdv3Y/6XSn8FcfX3Lh/a+f+33tuBVLOizBf5ssqjBJi3gAsNQMN048pXiWTsFs2+bcGYlGl1GO4egFKTfnUQs55CWwDL469/Aa6KWqt8hhV6+Vc4xtNRgOWGYQu1bTjWGo/Zos5iIKK5f3kOV9X8y5ZLoKkuk/Nwc9borzWjW0jbbVktCRj8FJPRMjqgHAzxPzyPvpyIPovGV+rGH4f3/74XSn8SuM6605qet/LDLpby68pi/2D9u+xglqC59eIhG223tcd93o0AS/Xk8vxDLt/uf9WOWzPvV02qF2vMhNv7bNcx422a5/zvnS3wRIkbtMtAL7dBkuXRbA+z1x/1k1i0PWr6HV/Y90ckBvbw1V9gyWXrueSq3/Xc+sdsFWP8uKf1WBwY6/tcV/e0VI9QoWV/A9WpojNaVtX1xdf/q0HRa7f7reTOrlv+/r7+MvH70rhVxpOxIKrSLU8a76+VmJjkdXQrVW0LcURdoZhE64iV03nr9IjgVyb3e+HyPXT00TnIuS2GTmA99cUdldCtaV7UiGaK8FfA8C2mUE5AJusHLe5vFb6VYp5FG7z+dEK47Qj67WkdO1zVbgIdDnfavWvFcuthaisH2PnNZhAKVpz98Wqlj3gau3C0hlWbLnFBxptc6kspgj44K96gOCo3EWlFndJpdOwKmWtxI9LTr8DcWINbByolM0c1n9ZpFYN+PrXgTqcN755Xyt1nPqlrM3jEDHqbFdcrS9weGc8FKoexeHE4zTUY3sUj+DxYn9FPaL1gtUu2jtPEdtWlauyfYPl7EvXtGZ9if3wV2zTEsQ8MC3GTJvBtUY+eUH6cMFB7aHtWpVGsnuy8Lty+LXG70rhVxgCdF3H4f6eg/ckEfqcGUtBUrKCK+fwlextvIwmRJ3nzZs3lGJ5/d57VJVxngmN0ydGFPDOEaNl91guf+E8Xuj7nq7rFktWS8F7b1k1CuIdPnTEaPn9OecqrB3n8xlV5e3btwt2n6rgz5u4QNcFiirPT0+4mlXkvK/1AZ7avJmht6YbS0wBCN4t1b/BewT49PEj3nvuDgdC1+FEmMcR7zx9F8yStjzW9UsupgD7vjelUxViUwpNYVl8QOw7SqlmI9XbXGvfMhF674kxcjqd6HywjKV7I8QjRo4hEEQIMdJ7z7HrLDDslHAXyJKJEhnziATh/ffvjMdIMu7OIzWofIkXxjQyHAfCIXB4fyARyZKNxsKB+oIMgnRCuO/AQ3EZ7UEC+KMnCcRSGIsnqad3R8YEZS48vHmLdAOX5JnVM2tg0s5KO12PSqBIID9NOBzh8IDrjhAGzrNSVCi+Z8pCLEKWDnUdxR9Q6SjieJ4gFpiL8DxltAjD/T2u63HhQJ6tP7gmhaRoVAIBCkzPM1IEKYJOBQp4DWgsaFQ616NZmc8T02kmaeLt3Rtjh52hpIImpdeOkgrzGOlDjxPHPM7M48z58fQ7hvUrjd+Vwq8yTAjd3d9z6HuK9/gYCSmh84xUHp3+eOR8OpFTNqbOruP7778npcQ0TfR9T1HFn88MQ88wDIzjiKrSdR2XcWSeZ0vfTIkpRQ7HI8fDAXFuYRvtum5RMD4E+sOBy+Vi9QMx4sQEe4zGZPr27VuzSIFcCkXV0j2rlzAMPTlnxotx/tirx3srNmtB3rvKWZRTpVyuWUNaCjGlpRbh+fmZvut4ePOGYbD01PPzM10IHIZhzesvq+lZUsKJcDweG5ayfvtV6TSl4J2j63riPFkNxEKItxa9HbqOcRyXdNi+7/nuu+8gZ/I48jAMdM6hlwuHruPN8ciYR4oUhjcDs86MeSRfEtIJ79+/J0smkQgPnZHkdSDPkM6Rw/HAcD/w9ru3THkiasQfPeqUJMlYUnvH8HYAjzGt1oB1uA8kVcaU8Ali8aRwj46JS554ePOG7nBHN8FUlUKvwWr7XV8Fu+dxzIg67u/v6Y5vCMMd4TSTVdAwcEnKnKG4AVyH9PemHPBwjsxJ8Um55JGclOPdHf1wpD8+EMeakTWXhfelcz0UOMsZVxxOHWW0OEsnHWUulKgcwgHNyjPPxhKQzFAJEtAIebbK8YMM5Ji5PI8chyPBBy6nCyc5cX4687tG+HXG70rhVxiKElPifLmgpaAh4EIwIefcQs2QUmKaZy7jxTJzcq4plpE5zsalU0db1wUT8Ifjga7rLO1SBMaRnDMhBIbDgaHSTMfK+qmwKBTnvaWJpsQwDEs1cbuGdj2IMBwOdM5bBlCFdmK0SuJ5nvGVmvrt2zdWT+A9cZ7JMTFNM945hq5roD+n5+fqoRT0aFXLRpPt8ZXkrlRhL/VzmzIS+v5ICJ4QApdKW5GqcnAizNNk31kpdF1P1wW8CFoK58uZznuOxyMPDw/2OaZp8VpOz89M00QuBeftHKfTCamZRzkEXCk8fvzEm+ORQxfsN5KCT445TVzixaqzcUzzVFveJNydNwFYO975EOiHHuccp9OpUmsrb+/f4jpHcZnss3kapSDejAzpQZ3y/HwC7wjDQO8CHk90Pec5k1Nkno0w7zIp0t3RDz1UT0G04zJnLtNEjAmceUVaCnGOdH2Hx5FwDM7j1VdPIVB8YEqFVCnQ1SlRU1WwhcvlTFaBMKDFvLXT+UTnOo7hSIoJChz6A8EFet9T+mK1I+e5YklmyOQ5MU6j0aTXbLWcM6fHM4fuQBc65smONxwOxjcVIyGEF/Dn7+MvG78rhV9pLJBLtZpbBWrnPQWhVEx7JWWz7BlVs8xLKYatCuZqiwlu5+vLObquQ6pQbccA8M6vkFEpFpCs11FE6rJt24Vu8QqQGvwta2zCvAiHSFiCraVssP+KjRu8ZccqzlMkLwJXxCqbdXPcdk1bqAdsO1HFhWAZRcs+FYN3JrC9cwYryRpLaN9bA51bjKZgyqXzfvF2BNAKx5VS1kwm1tiO1mu5mitlqazOOVOkbMItjZvJrqPIJvyr6+/TPr9Th5OVVC/4gA+OEjwQUUmLsnbOUdTObSGp+r37gIilpQr22xZtv7nixY5bLBcWKQ6RFWpTMA6qSi4oyBJ3ceLw4q16vPZMcDUu5HDW9GeJgSklr/cZNX7QoEnnpLIOWlzEe48PHt85kgo5X0i13sMHv8TUWkS6BfHHaaT3BhVZO7z1ORKt98+NuNrv498/flcKv/JogsA5R+89ruu4TDOqhUPfM0/TYvGvisH26Qe7+QsjwzAwDAM5JxN0pRC6jq7veXx8NJhnE7D2NRisXcdU+YRayibYQywIh+NhUWBNEGoxWmsEvK/Kp3o2MUYOh2GJJbTgt/cW89BaYFalIAA5lyWGcXc8VtqL5xff0c3lRkchunhCTSA65+j6HrQswfp9BtI2iK7L9eSFH2nboEdEeHperyuEYMkBKdF3PZ1z9H1vMQk1+o5M5kGM56h3GTfXb7jmter23Bs043K5MMjA9z98TywzmXpN3hN6R05GB3IYDhZT8IVxHkkauXt3hzohIQzDgLqOeYLNj3tlIPT9gNDhVJhn6PseCQP+aaRUD7AfBrrDPR+fLuSsRsLou6X6vLHX9n1HEE8cb2Vzrb+5YPfg3fHOQt7i6X2ADPOzea9ePNIJmuFyGZnOE/N55u++/zt8sOvOUyFjBkZMiefnZ479kbvjHcfDgCZluswVdvScn87/wcm2v73xu1L4FUeTby3gm4rhwBmTD63q1oqvXu4f5wgi5JRJPi5ZP05YrTLYWGf1fNXap3onbZsWhN1yCV0ul+XBXwTyxtDKOUNTWGXlJ5qm2SCgYoompkjLmVo6mrkmvG0uxQQHruAqYGPZyZKBtFj5IshiTRu0MI5rzCDVeoZtdtS2HoGNApH6/c0xWnZRVXSpKkwwRdC2885XS9tTtDBHU4oprPCEqjJNE9Jb0Fuc9Ty7TqGsn6cq2ia0SynEGCEIIXSknCmzWbpZMyq6eIE4i80kbfEZh3fCHCNJM0UH9hh688LmODOrtd1RDaSUGadELrla79WrnSM5F0rBejLUmgmtjY8KhVxSzexZPZht1tx6D1bLfjTL3geP5rRQ/pZSiGmGKPYdtPtSjZQwSKDvO2Y/1XveEXzgeDxaX43622vGWECSeW2p0o9w417+ffz7xu9K4Vce7d40nhxlygnpOnCOlDMpG1f/9U72StkEQClGpZCctbDEGb7aSNxKrei18+lq+Vdrfsv1z1ZRqTJP1rIy+HBtpdeUQSOFM2K3BmHFmOyB1BX2STGaghBBs8EmW8FvGVVrP4Gm4NZzNrjKFEP74uwYqzJLKdlnq99ZSmnJMNp+6dt0f7A02+UYNQ6jzi0ZWFphh7YdGKziReiqUtOUSDktyrh9hylaBphBOe1rvi4saNlcVrGiy2+SUlpw8JRStcpZ4gxNKTjvyKWQtSyQiXOOOEXmIpTQWXbZ1fdQf78YiWqd09R5ck7EOBuEKPV+SZmCcS/lIqi46qVVpUCxGIla6rPS14y5qmwXGKndOwZfxRgtnuIsY4gCToWSMzErOhtvVbPuGyGgeGEI/QL1idhvOAwDznu0qMVECoia4QT2eVc48HeP4dcYvyuFX2mYYE7kaA9AyoVZC1MpkBJSs4/6ruN4ODDVIOl4uZCzCYvD8YBzjjxOzHFmmqcVPkEYp8myj4aeOUZQZRpHnpzlnTtn7KBjzTQCLGhailFOF3uAXN2+ZeOkui0iyDjWLmd5sQz7YVjSQUXM4/n55w84J3gfaKnnd8c7QLmMo2X4HCyTqGVXae3NbN+XQUyHwTKnnp6e6YLn0PdGQFeUkmIVxJZRJEAueakJmadpAyOZ9eir4Or7njjPjM0zqp6CqtJ3/WpVz7MVxeXMu7s78wTGmUOwQPjd3R3eOcbzhZIz4oW745Ekicv5YsFkp0zjRJZClsyQB8vZ9ya0csoEZ3Bb3/fMaWaMF8qoFGfcR929dV4r0WJLEsW8kc5I+WKcmcbMJQmxOGLw9TcWI/ZTxxTFiiOKZ6Yni6c4xbnA/cMDny6ZpMI4jeS5UGSkSEfGMZdMmQtZJmJ2FPHkkIjFkVXIvoDvGIYDQ9czJ60wYWQcRyR7pAj393cGGc0T8RLRpPgSKKlQYqZMlraa5rTci4fhgMNxfj4zTTMpJ2KKeAIP9w9oMg83XRJSBKfeDBFV62dS7+PfU1J/nfG7UuD2fSRfWP/iGKVUd1/JzhFzZkaZKmW0OG8tO+d5sfRVjU6hlELO5no75wyqKbZNw/Gp+8ZqsTdrN8aIXC4Gwzi3ZgMt6ZfmocRkAdaSV2GfKyQ1bzKWSrU+S7FaAwt4KqUqlMZVJFJpKZxfgpEttJCSKSwRQSvpXIzRag0qbJVztuyoYoHjy+VC8p6S8spjlJN5AU0pVBjNuRUKatxGJWdS9FYXVT2CVGm5F6VQf1kz4nXpzZCwtNbz+VzrFBIarKNxSYkowpgLs0YoVAqMxKRz9SIMUiquUJwyni+47CGx/BY5JWJ0XC4X5jKTNFKCoq6QfSG7hCuekqpSyJbX7zqxlp0oU8lM2RHVE5MnxrwoDArE5CjZodlhUYtAdgquQ323eAXTOJGkkAmoyxQcc3HWJhRPLM7qGjxWt6CO5EF8QpLW710pZPMYxxHJDimOgLd6hVlJk7G8+myfK8eMTlgBWlSrP8gVjlOx77Xep5fLBU8wGvJoAek8ZvtOiqtZZwrFrUbN7wrhVxm/K4VfaeRSmMZx6dHc/k5YB7aCdQVTXcEGgI8fP9Hu5vPpZMe62gKuIoqYUGrjcrks7KTrtltYCJSXNBeCLFWgp/N5OUPhtPtkX1aPLXbx8ePHZXm/ri03/qF5mvgwVWrsTdWy23zW7XEcrRxue5x1v3YG1/arkNR67lqV/GIfyDFay+3TCa/GSxcw/roDVqQcgFK5ii7xbDUEHfby8PTpEQ224zmfEC8LoR0ezs8JxjOP4yeoc/Rm2NNhN0slwcMDnUCvSAB6oThBvTNCPAIjMwlPwVJpMxORQGQmMpqngCdidQZZPFOtWL7Mn2pvwkCmqw1u/TLX+hgmLrTGnzN9PU5g1kDGkSiUmCiXGY1YC8vWzTCBYU9AFKtdyMDMUrXcWhT+6Xlcu9PVuZ9++rNl4bUG5xmY6zGKVIVQb4H2d48h/j7+XeM3qxT+I+6d/X15/V6vKSuWba4zhODWvb2+kxczr2/b3t2iANgrCd39fe14v3RsVdRevWw/r9vN3b4K3b3fL2/21mul8vqRN9dTYxOLrNH1t9hGgUqNv7TxArXQzdl2J12ykjYv3b2vF/P/b+/elhwEYQAMY9//mTd7UQ0QwqHKeuj+38W2qxCRIhmlM41x9RtWtRFiP+UYaPsr8pPFiGW37a9kz1br/VXTJMoax4xsWUexc24haX/eEU73OH0l8l5YLuLZkBMv6E+fEnybV78IRtTGaH2yHYtz1CfxZhzbm672Hntmn420yyZ0r97hhtmJr1U9OXicB9NJvUwRUmwtF+HLEmlcKbbVaoSsTOXEmh3Z2CfuW5zg394p4FqLeQVwDyQFBxPVMXZNwSONfb26iJaQr9u0ytn/nty/e+4eRh8LPblfZuDxUcfMW1dvsH3zABxNDr0+Hpn0rnJVu/b2Sf57Cde7SzsQcadwstGL4OkXy0hCODPOUX917DPOKX1Ut5h3d3CfliAEkkJXa8CyAIYn2e4RmITRsgi/ZwcAWLGmAABQJAUAgCIpAAAUSQEAoEgKAABFUgAAKJICAECRFAAAiqQAAFC/p4yzHtHZKO4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for data in test_loader:\n",
    "    #print(data)\n",
    "    #print(data['image'])\n",
    "    \n",
    "    x = (data['image'].to(device),\n",
    "         {k: v.to(device) for k, v in data['text_tokens'].items()})\n",
    "    img_path = data['path_image'][0]\n",
    "    #print(img_path)\n",
    "    y = data['label'].to(device)\n",
    "    #print(\"y in the prediction function\",y)\n",
    "    #input_tensor = data['image'].to(device)\n",
    "    #image = grad_cam(model, x, heatmap_layer,y)\n",
    "    #plt.savefig('./images/input_grad-cam_v1')\n",
    "    #logits = model(x)\n",
    "    with torch.no_grad():\n",
    "        logits = model(x)\n",
    "    # indices is a tensor of predictions\n",
    "    org_img = data['image'].to(device)\n",
    "    indices = torch.argmax(logits, dim=1).to(dtype=torch.int32)\n",
    "    \n",
    "    #print(indices)\n",
    "    \n",
    "    pred_label = torch.tensor(indices).item()\n",
    "    ground_label = torch.tensor(y).item()\n",
    "    \n",
    "    image = grad_cam(model, x, heatmap_layer,img_path)\n",
    "    plt.imshow(image)\n",
    "    \n",
    "    filename = data['path_image'][0].split('/')[-1].split('.')[0]\n",
    "    if pred_label == ground_label :\n",
    "        #print(\"yes\") \n",
    "        plt.axis('off')\n",
    "        plt.savefig(f'./images/correct_prediction/{filename}_{pred_label}_{ground_label}.jpg')    \n",
    "    else :\n",
    "        plt.axis('off')\n",
    "        plt.savefig(f'./images/incorrect_prediction/{filename}_{pred_label}_{ground_label}.jpg') \n",
    "        \n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4b8690fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.transforms as transforms\n",
    "from base_dataset import expand2square\n",
    "from preprocess import clean_text\n",
    "from transformers import BertTokenizer, ElectraTokenizer\n",
    "tokenizer = ElectraTokenizer.from_pretrained('google/electra-base-discriminator')#BertTokenizer.from_pretrained('bert-base-uncased')\n",
    "transforms = transforms.Compose([\n",
    "            # transforms.Lambda(lambda img: __scale_shortside(img, opt.load_size, opt.crop_size, Image.BICUBIC)),\n",
    "            # transforms.Lambda(lambda img: scale_shortside(\n",
    "            #     img, opt.load_size, opt.crop_size, Image.BICUBIC)),\n",
    "            transforms.Lambda(lambda img: expand2square(img)),\n",
    "            transforms.Resize((opt.load_size, opt.load_size)),\n",
    "            transforms.RandomHorizontalFlip(0.2),\n",
    "            transforms.RandomCrop((opt.crop_size, opt.crop_size)),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "        ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d52d257e",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = '/home/gupta.37/work/crisisMMD/Multimodal-Categorization-of-Crisis-Events-in-Social-Media-main/datasets/CrisisMMD_v2.0/data_image/california_wildfires/10_10_2017/917793137925459968_2.jpg'\n",
    "text = 'RT @KAKEnews: California wildfires destroy more than 50 structures: https://t.co/IkSrNpmT83 #KAKEnews https://t.co/8risKwbBqr'\n",
    "target=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "38848875",
   "metadata": {},
   "outputs": [],
   "source": [
    "with Image.open(image).convert('RGB') as img:\n",
    "            image = transforms(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "c949bf2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "image=image.unsqueeze(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "32ef0385",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize(sentence):\n",
    "    ids = tokenizer(clean_text(\n",
    "        sentence), padding='max_length', max_length=512, truncation=True).items()\n",
    "    return {k: torch.tensor([v]) for k, v in ids}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "83b0ef4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_tokens = tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "fbbe9d31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0], device='cuda:0', dtype=torch.int32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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HxHval/7HK9P+Et0cNt/D+p9JMDOG4JMczaG+DtG9+NzJ98MFmSMHXRUBEaMlwPDI4Igkz71B/B0Hg7IjMkFTF6AhyZFTjEuii7Z1xFUJXk8tOoIZe0Tmjlqtr40ayGxN1n7E+QgMwzAJpxHBdPwe5+WQPFFiv+JM9s22Xk/2SJgzJ5rjvMY31tqZ77+Oe49woJCzQnPoDfrnNRwkiNebAPALDM2POap/X3hEEH51I39K3X8xdIjqKDAdy/8ZfQjfP7vkjkS5T/vwShvOrcZkaB6HkBKQIeptxG3zOpKpnLYA7j3k9TnzSEQgz2o3BCRUImC3XUVS5PW5LtKS5kUkOo6drS+JALYGfczpxbZegRHxF7RhcxhUJAYzZqITOoaXVpH8tGH0a3mYjBgE9miOz9m4x2UewieZ7mh8njEMn4EfJgpRYXBMSPVaV4jOQ+7/NPBNFBH2y8i77+sZpwDgyO39BAatnNcL9gP/8lmkPHJOj0oexfeTORj5su65mC78ibQzrfv8Yscxo+csOybTVDA07iaQ3ZOhwpYKO/baVR9Fmj7aEdy4Nc82rQoVghjr7OtrrqHQ98AYVEjx6xlDSjpuof65yikTaYrv8KLdi6pII2Ra10C5Dk1O1sYJR8ZRwqFjkX5PNRltnlBQwj54sPsmmHNEsNpaH1V99vwhUR6O7zrawryvh3voidKD7rY+OoMBehrb8Ah+QlKQ47dxD/2Cuj/91BMEP6Cpadljgq+xjIy3+++Bs5k986hfz2/2nXsdmT8G51YP1z9J8Ofz4UqEWeqO84OaWptP+mRlZpG2FAsMlJpIz0iYtdNd7n4YqjYk6fEFxTZhDoRB0Dj6bnOaB00jHFqtEpxiXGrADscpANOAeMKPwxsiHBanE5ruvQcunV11ZBPgMRlFSOdswItn0BmEHeLzAUFWu8JYdyUC7bfGhQQSal/6d+59mOCq2YURwQ9dncZLxGUVHmzT3dbLiNBnjhIjMdG1EO4N44trM8axtJiGz5OFHycKw8L4V8MrQx4Jwq/sfq3zk5X+XB9+jZQV52NCD39R/W1TP9iir7X5o2tP2g7u8OSkeocabUyoiFFw7Hl0P/X7I1J51mXnxNUgLA+fHafA23q+Ih5PXjUue+miBKEUgNx9ypD3s7Xut3n4PWt+oIFdf8YKq4xAVN15X2EI/d75HB0p8IEwfK7C1h/qkX9s8WkVL7Rx+sgPPPsDaS4CfHJzztJfv5oS+xHEBTHbjJG6tk/qfoea+v49aLerM7bxI4Th4TOTeQtfpo9GbuuFRTHbfNOWz/p5smvGea9i+An1OR3LK2MYi508R4OymAILGDdSj9ydtMl03VMgNr3EPOlAZNPh7VDl+NWLybGIqwIaDxmnOnKxMcaiSWTHSThzbHWjct0bIjUI7wAT7jh+xp9TaWHy+OwVj+uy298Tg/WzdsaMs/q+J+JYkKIoPOft1bae4sA+AXjc7FF6mI1/VDPNYAzC82u1zrrGH/Wxh7/2PIU/GeKLni0SwpP3N5R/Bbu/ygX+q+Cz/foBxuK0ns/OzVO6+COE4VUYGo8qIIH5r0uTFiriJwKzolE9arNPY/EEX7TmCQcuveHzNpsHhgcRySthCGi+KyUAhEb3S65dIfvF5LW0AMKUApKa9CE259cZbTwjYZgSgAe/ZWi71jG88Nke/5n1PJvvH67zlEk5ShG/Yg8+aPIhvE4UjsrlTzb158LD3hzYxzN831YujddeFM4/R3Rer2zen6GKk/o+6yHW9e+E83/+cP/zjCMaifcBaRCBpFcPzJt1vlxempOxkxK+u5tmy22k9QoiYQjrqGKpyPPH6tu1Hn3XIdbHHXFPUWSQQMa5PCLbMPNd9Fhf75F4TVhPUa+o3iWSoAk4+vLNU2jA1OgJ3jCyl+CQ+8f7Q+jm3FdCFOpeXv4D8YrzPl2DAyI5umwfpZAaaBfeexdXNZT3bkznSeZroDUrnxi8wuvqo6Hi19uR6VettFtlk0ZfbuQTL95Ezvk2qGWaH3bHl/w0fGJItfSIOE8rDavjjIt7obnH1+TY3NjeGcd3IAh1A56jtPj7nLtsx0menVRAscEHIDApIewlsVXgZyPEXrqyRYLrkUfF6sNtJcWssrUG6rtV8yFZJByFCW9nBIS4eWpqpzhYQlzjjTA01sZr6G0XdRIIIGJINKrAiaN/5/acPaN9LNP11wcpdtUOhLR/fy3Yr81BF9UeEaPXF6hBRxiOS+3Qp/ijP2PhMZD914/kQYPhufgODgSf2vWeezm2cGjlB9DWL1cfzXicPxto+JyKti/c68s8L/lXj7PBOfKsXMIv6Bw9eZvjvLcePPcoGp999L4ewTjU86z9UB10wEwP+xh3pWGTSBDcj57DalHiQY3Tl4g+j/1m1mbck6jNXCM2ighbYj3HOpEItGf5iBgIocb2RGsraa8NaVfEZm6pYPU4KgXqrUXaCmOUfaRKCt7zLhYjUI9RwXV8Dz1hj2krpgjUiWAkCF7TQDRiC/6lIlsaPg+FW6M/vL3OuJtwe1b3qcTzoL4pkXgBPpkQr+/LDEYO4FfBq54OGBfEBF7Hmc+ndG4o/xHpp99iurIft1/nOkioz5AsAIsPif2kA4fT+MmhppOf82dnN5vWuz17HKdM/PXOJYXn1+q9SBhqvedrW7rd6NhHyzOCWqjuZqp/ujZKZ8juG4nz7iqyQA78vQosFcNx5fY44fhyKlKW1g5A1QVWizkClw4r1ZH4eolBd117DUU7UWjPjmMW9GsgzJ+PpWPprV60+3HM/X4J1wIxmKpPu+ot9YytNTXsH9eI9+35KesRXKQZBhBuxb0nVQqMYw55koamxfsO1HU5MgWfJWG/+DyFfw2M7f5l/XiML//Kpn/g6cnKrPAa2ZyXIODAF8/KPCB6IyKZdTHCC+zQIyZmSmykceOayyj02BCkbuZ2GjIRQBwIXsCxcYP7jwKA6442Y7VNnQjUHRTKqfcEQzvo8kTvnTQbsYQ+dCbkrjQzkCBIFEp5m+LIfCQEPnbRP0izJ0wnva2NQ56rJzDDq/679QTdS+2Q6YnE8KgHB0+evwie9WsG5+VfZ4OBX5Hm4tm1EfcMt37VPD8c8vTmOaZ5pa7KKfUMRV+UPreQnr22cTs++j5H/Oe19vWco/v59SNBmTIo4+9Jt2Qc5Gw9U2g1MP5n45yhsUO7z367eygaQq28rmEOqsi3cf0+oAOKpv4NcfceCLkoFxjHG0YdvjUJova5lj/2NtYjoRQBJkX1JZs6y+aZGAKpKTPmczrmbzoj/u0p53El6mZkKCdQYnimS2n0Uou/svzPqopN4ucJQu0K4aAEeEaoZgQw3qzRyxHfHBb564ThJ4gCTb79yMydb1E6XuoeeRWJvlrmHMEiqAee19/V8yKBboeIv1p+0sijvAbjnUk7NHx5SvDDlYMKiI71dd+747vsUv3/qM45is79GolIbtbxfiZ61UVsTbOY9jxs22eWIDkshpo07tC1xlmPcz12j2s5qtxsPMvZkS8DIKFuxqdSTkdufPb68xArku/6E2JIiFC8HmnjBAgipabIaOToJEWeIfIRGr4fsEdI0NcR+0bjOqRZRxqCB4GGfF+VDPo6ToiA9F/mzJOEb9S8il5GjU3EOdC0B4x1t0eGctL/9xR+SlJ4EX/9chgNqf+qfvx7wYRqnZR6tbYjnKmFHiy2R5Tz9LEjFWnH0vR3Zujv1S6M990EGpFt6agDVSTWRf2KSRJmWGY0o/TIjasuX2UKjQmwcwpC/7MRJu/cTGkz/pqwUcOVOcvT+hbPAwjo3ZBwYs2VJCiqzioE4igtzMQ+r2TyZqgV6R95uDDqHI5nJnR1yuGSSW5tPO1m68jIVMRAt2rMPpNSHsC0+Cfr+Az8bNWfJwp08jbjvRe7NM3bob/6eiYveZSGHm38Z4ahA6cUrz989FU+5DGc+W+PNR9YB7tYL7/oWXPqnTRwjtNXMnlgRE31ntfXcZWTOmc7u4MHO/HA7Z4WedCCoTVH5MdlZ60PKPkgbhFchcREXQbTSEwqKib/RmDS53Kozd+nE6sZ8q33Dos1komhn96Xuo7EkKLX5XJfcKe1+osQyrBePeXEw8SITmFCd47LsH/HUWJoa7wh55m068i/kxLCOjsYakOZiFt8OuvKi8/Wjsuhon4pR9nsON7oLXWQjmQyE2GKj2tzmCPp3+FnsNJPSQpnuOWvgD+j3Tkv9Rn4taT/tA/1xjOD7o+0Gc8H+9yT5wj98VPx+4zMHus/8IjTOh7QvSkUtFTYXrpqiiwwraJJx4MBy7BhEmatKwexoyLGQMS4/lGVFgRAsYxzbYQhHqAbu5OUYxRzJCAz5qaxEsVSa1AlClm8dcFCMMNzI5hCbL8L+gaevZ8j0fpZOHufI9I8b/Uci3U0gHrJ4UfgZ579kboPNOxF+ImI5oeF8XxRfA4ebfAJE/JCU2cCd7+JIvV91reHTVL8OpZ80JdppQFVviAdTA/GiazQvOaT2uKdiIpovIkeefXXKPRrXLzH7p5vWv3rkdAj4n7gnKkhu1IsYtnmptfPckN7gats7qOq8EkcuNnSpA8YAaHaXlAJMXUH50SCQFAPJUJLk930+QQM3kvte2Ntuzmokx0PsKTq4VSPDiWAE9e1UgWBKAFViaOl4e4IUG14jOiW1sdWZBjFsCeG5UXjDTjdpq7d+v8ZZrTrsz3Scd/x/Zxh4HDtYKgeeitAx/X3oz5pY6zgpAsEtPfxiEM6gX/r3EezKMe43h4j41fQ2/H+uOlfruPBy+rrOdZ0OhbfCIdHCLPxnXFH50blyUBPys/gaTGbTF+okZg8f3/t5nH4+jQFjna6T8PF/h0bshgGmotJAtXLyMpPNrt+JxA1awAZZnc9eyYNZGvEC81TqXosxVxDCKOxPovZHsQkAkso5ITM5/gYaNhUWTMoAU2JEQUnFARBYmrps8lzIPnRlWLqjbbyxpYrszKJt6k2EgLcd+pI0ltMxGFoh7KE7lSjWsP4JSDriCwHzP1QKqjEsb/c0m3MYcRkI6GY4v+BMEHCMx11aX2g/tIpo/UI/ksamv8s+NeN55XX9qtURT87yrhDhw3/oOq4UKMyZFR8PBJ1D7NUL6ji5VE08+vgu9t6UTTJgiL01q6h6hoESBSCm4zzR3iGwqij14+qjbgh3oDcqKhfEhe2WGUfJU7cLVsHo+G4v+1kx2deiZqAkEvWU+Q4vqHcWG1jkwVkqrbSOGeEQTmXWpEk1zk6Br+R1daPwCn6+M4jUR7PITiLRYjPvbo+Du1+4tmfAQJ6u8kPtPuzfX2dKJy6O7YFOt3McvpjqOkco3QM3aQK6coc6znv+VB6No5HOPSUi300zvn3Uf0SEVzMngMcF+tDoPHrgydephcTvufw7GR3UhtDG0nPJT/swgzHDbNCQ7mpBBdZr64W64cc63Fs1q5TW/gPsFCgI/op8Z4Gh7V5iXPmq2Ikwn3HWq9juf4+gC6COYgYiAk4CvRcaULM3UTTqfPT6mSYKO9Fky6o/Q5UcTRy98ufENlxJ7gxPXm350fcJMNXr/uMYEyuxRq69iphHOro1lTr1iME7eM5ELfajFRp0sdxNobYB6XhYdDt4yX4ufMUHt74yXr/RHiKfH4YHhC2k8/n9QXi+4leHLfpj8Arz84kmGNPCZPaqhN33BKPRzn2pv2OTorndYzEWIOluJ5hIPFxk0LqhuSGqlviOvtzLMq9PcDRPXeNK3feedXUJ6y8VDoEsfxDrolRUtH6xXC8eMJ2zJAFHN2ZLQFSXWHdDbeIoBAjAZrwj0RtJtUewYE2xiR4HmvdX9MxW6uRjzBsK93G9HWl0kh3UM9IP9uA6vUiR6nxjCA8WnEjQXgl7iC+/w7JP2rkyT2yiv8KqeHHiQJJ4yZGHSyAo2VWDpWcLOH+mWf9eOHOtNiE/akvkGa1PXNsjdikY3smNc10vRN0R7Prx2KjOjUio0GMelZV+OazQSdlwq/uRQa1hcSSJ0uTxlmbvaDJpcB1arMxW8xkK4y5cbru61gdyUekVUYOwt9JITNOKzLL1W9fE8kVtOiChgDpwHk7p11Xj91MNVjMLxJqVlLxNeorqRdtxpXa5tdbaShW4ynECIH2T6UAQSmEQoSdfPr0ZZGnTjUOXliJlO+Bpjqzl0ti+Z+kYtf+9dk6qwb0RpXH86elvtoRQ/ro5Cg4dBPupdu9KvV01Q0m73Etn2yp7lyEQLwqMelLz+sIjx/6T7Xah9g+2k4cp70KLxOFsaOt9XafxsIn8Gr/puUmCPsT430wkHb7QOAetjcin+cd6JH3A3HwtAb08x4qamghotgHLUxf6JEYxLZHpNo/HRBTWBDzA9zHGZigs1fZHIojDgTaEf+seftTq0Fri6Rx6mV8Lky+iHLXIlS9idzDiKGGWt/RbaQEj+Z2hGeo1DyVjAcXqys+HGenvggdxZx026zXPjRC396jnb9cWpJEr7EIkI0QcJgbJ0dt/Rz7RYbBlQYEoiWhIGlrrQoJc9zeYscsTM7WcKnNSUhkzyLEdOg9ZQ6zNqzpaYI66rsbv0btcdybFa80uvjy0q4Q6qbhd9cH/EDdAX7Y0Dw2+inE/C+GP62vJxVHIvB62+elZ0RLEZJ+e5U49zV8Biis/ueNaAtRzVROysyunmDzF+ARQY+kL4FAQlhZUIoieyqq709AIzhinDkRsgi24hw8GgcnAAkfek/MIDHVDNwM3Eh2sXt+dgCDkMCmdw4H/HTkdWQv4siGUUsjjEIWkwGg5GYgd6fbJKSSVxFsEOyGTT2CWf9cieVv1kY0XRdtJup5CHaNELnYkVCi1jyChE9/IkaRvwzTwtR//RkMG2rrGM5fUG+t/FfUE+B1SYGO7U+W3imc3Xu1jmO5OW/Uw5x0/RhRmI1cjpdO6p9t1Q6xTyZiVs9Mgml1G6IhzKOWDxX2vXptfTlBQENYPVM+QVXxbY2y2By11V8jSzY+NNyj4VtFodTfjyM/HBLWKGxnHPUnIjcaxfJ2jsK4PqW9Fyb4ATq1/87pVi4cJm20URS7VryXraPdqKUeztNd7ghzETGiIJrnyZBpzfdKqgoSKXaWQ3sNXNSu4F0gUEtH3gWehHd8EGU93bPLSD6vjQBWpC/DaonzHcvhCLNrL63xOHk1Gy4q8az9DVbfDiP4WhukASeA9XpYk+Me6p6j2PYvpwEH+JSkMMMzfzWcIdyze/8l4NDx191P3aRXqzkhJscGBxQ8IE0AQZf5qEbnin2DH584Pu3tPz+MJyJjhRhFO0C3W0zj3uPm3sCJtkFVhaLXuLpkNk42shM1HYYfQuxt17gFGOeNkFTO0lCzHlFT8yVFhE4AoUkZyonb8wTzDOIhnUlPrNpQhxTVBLgLbYEgI2OXnpABastgYqi9QLAXhpRiXdD5XG1SGuGKRKoe1db3qBJa7YsTOe3rMA/+aV+K3R9I6UFasFCLc8TZmn8qIJzCr8DKT+oYuxFn58hS/Xr4gdxHk2uvDnDOuE+rfYr8h8RJ48EtczjhOF+Y4b7eZxLCpC8PO3bE5GfzQY9KzTDxFLG37RVdeWuOIudYZyzaZBydIbX+f0T41DUoh/In1XfcuL7pyfzLrB/S/dKmpXumEGHPwF4EGaYy4lC5hA+bFz+nWeska0nqvVSJR5vT1vtQdeiKq2LAAIkSAx1PfPPxcBwOyehMgSNtZrUBqkTNdfEFml8pg5HrGct+DoLO2F5KJSRKALmOcL5XSgjeii+kxY80jNwWVTtboqH4w57pTwM6EIxOapOT7sXqZhdHmKRI7RyfK3Fpkdy1XOzDbK9IX0e9HngLoLnsSn1DrRc6m+3hsbdSn/wx+DxRqGLOOZab3zm+kml072Sintf9eZgh7Gfcw6O2n/Vrej9wSs/m83xOA8jkcsRCEWWO9KReHATeEyJw7B91v2M9cwIf6g7YZqy74yFHpDN0oqHPse25BOOcZRZBLqpSUcTcIyICNa+XEApLQIvcrS9TEZ9USkId4UU3T2HhUZP5IjGoUkEJowjEr3kgOQEQk3jIkDxVz6wCQTGvwSIEIa6GdKamtilih02Lv6c4GYR6GLWTio57iOPzOIxoQfAiI/8b3l8lmNqemAh3QH6zDfsqJoxND884Uj1GvIVxTCkMunXojMRnkLOf0hdXmQRnhVclhQNhkvjjMXyKKPwqhPzz9Uba+Zc0+FNNvN4cTX9NEdqnevOqOurnJ2YkDe33sx4zZicERFe+2Tyctx9/zdsuIOwC7CLYfLexP2nxAgQQJ4AX7Pdcg7tUzWK5iGpHFeHuNowoC3XkYMKuVoWVNLsQiGtxIaqcv4+sWw++6atYZdHW5PHCaj9QdN5SfLsk4z1wycARoMcVuKasnc9M2IuAmLCAAHJC0cbUsitFwjKDJuk3FqMRAj0O9MHzP8oSP4Pg5hwN4J+Fx6vw0Ci6XfNo3H8SvB6nUCUEDF8el28XTspNyp8jlnCVxlIKHQJ5hEVnHPC8iw8LHp6x9T/O08vzFqI2Z4SlcQs9R3FefavptT7QVEI7L31yvXs/k4VN87KVnzyhqq90bbpeZiAIBl7Aj55sB2y2A3CYCBlhJqVxtDGwuZI1yxNUEfk4no6VMymBlCiAUCOdm6uqcs3OZPtTbnOpz8ZGqj2mBHdTJ3bUqTK83jGDmtsNPBlGtSFA6ty5xxRZ+UYE2moFYt2z9zM6I0wLAZ57yfpccWbATxOae6i2uzR56KCc+STHX6sNDY3NTPta26ROFDovO/b35+EvTogXhkZxETx+4tHvX//gWWUvTPtPtfG82vbpmXB6DfsB+XxKQvjM26Dh18DhT6uaz+GIK8nMs+etKoxZcWZNvrQEbLMyAUSM1CFBIEGlgsSMnVqG0DbXTpz1nIFsnwJHwdYGe3EjFtWVyAy2RpBa0lNuRMZ0/X6gfHurjE52oJjJNbW+CeComyBISJVQS5N1oEfoGEIigEhqMr7kc2Srz+0EaqMoYCFLHe4I26UjQvOZGldrlKXqAA9L6cj4GFEK73Cs8UzNEkJHWrv2Rc4eml/6KRjI5Wn9L2KeXwo/n+bik2XqImi04VP1xYns7tOk3I8i6UcVn/SrclvPysV7NLn25LnuzN9ZumJgGPhZbY8nhybfDr+NsA+21IpwpnXUr0c9f+Mwj6qkWLalcxu2jIQPrcakkOGcrg73OPKSdr4BE7hoV5akBl0WwcIELMBuIoOqtlrQVyNpkTsemB+TMNylic3bp5srW0uOSp3AgKiLgVBKQ1XZIyWehcFVAutPyBCLSNZGStDX64lmrP238fnpcGRjYXJpgcGW9oIpaZ3i4xVUScGpVLAttLkJv+m4ZqRbR0583eA9EAZ/isaAvW7q/ZX0WVK9BRpSbfyAdBDBl8VA50Jfh/KOtKTNt0hgf54Qq2lff2AAP+x9dJq2AG1AoThmM9zbp55h8F6gO5Q+shOvVvcEe8uDQsHDpscDk3qOX2dI8xFhiAmnnSkLlwbC8ExKeIz2afg2BYmIvd9+M6J3bHMmOUy3sf0vJ70e3pH51nebyydM0IzGdo8sDQOJ6sjrmcikB8wwoyJAgabYDlSh61GHiqzI4YQ2J+yOMw1LNUbG66W2mKrraIhFAdX3XNNQeH3dG9RynvK6us3aAvL/pY5J7QMeUe1ETJPa6fV2lGhzSo5Q37+fRhQJPLVS1TsrfoxIeXjV46qRB7/OIL4SiY2Htuv9vosPcd/Y2QNBONtOcZraMjZ7yrx8/dpP79NePYN/7XkKU+z+7wkvdVPwlLZF/N0HmJ0/+Dq9e4LEJ08fCcJrar347LF8RDqPN9DjDeYc/rxd/xXz+qM05Hx67KjfByExobA+wQAWBm4Lg5IivpUJy5Lwdrsh//MdORfLc0QgSsjiPvyNQLApWHIdY1MjRYLRkL4agfVny2+UgqIG0LMZmEM9VRLwaDKleBk6+OrmOkxeVL15Um7rAQR20pobmBmGuMUIKIGRkQhYSdVKKRhkGz0i9DELhkyDS3IjW3OsJ/XbEcqDez8KB+Qafv/Kdn4FeNfqHL1AGF6FT5y8NnyJOgMZisSLsypm0IX0/VpK8dnazsq/im7bVFH4LYdyfpk+M+aOyx1vRK7yeU/pcKW/c6zl/MqhyQfeJl47kaNmOd6ncdYE86V3ZO2cFsSU7rWU4067QbBjNEmR4ZoYb+uCsmclMKSEIyWLSaCApL3n/g6JwBL6VNnsYGwWNDfTyhEG91Gfm9rr9mbqXfK2w1tzrh5UBy5edxy/PY/o3VM700tiVEepE8VVSjBpipxgIDxlT1JfM8VEiVVV5P2Ufl3P1CSGH8TLx2Jh7bndpbcdTBHU8MwPAIUqI/HoOvZJPGjzNkoHWlNc7e3brO80fPvM+P5aSaExUz+MeKdlJnP/tJ5P4OBX7vX7fHwlx3oI8+cft9Yjk2MLz3r7uF80VQT0dRwPqweoMwxHBYq32XQ5fXvUPUc0myevKSCsSQdrOueIIHD87qiP9CEABQkCJmBdGG9rwu9fVrx/K8glg8FIRFiYoSqYgqbXboinGqDZTKsSkHSIQfBNX8Ixms7Tx/TQTG3EEa237y0JhhOMNkNc1Wcae6EEWJTKWSk2JBMRtBPekPHUaq5nSRNAxGAqRiTC/AYpQH+SvdPefnR4j0HyEWlnwvVfjukvRnDCS3a/7835uj62hxYrYfWVWSVHvhgHo8S03eFKGMxA7h70+lhiividOH+CKvylREGG7y/i5Q5eeuZHKp48evb97JkZ4v9rYIJNx/uTX0cS8ej5V2bgkXTQU+4Z8n9FV3teRu+XoJt3JMsDwXakIWLlrcpEAElB3u6a2sGSwqkJgVGEkIWQnRqUAjfzqh8/VRN55bAPyMR6Qah5imIqayckDD+NoEkIWq96HFX1iSfpC/mU3L0VBFBVp7UZN7KktVMz3HrOIxOIQqvu56TjTESWCqR0NVIdd3jfdaO3+04AAOn88KN7sPe6e9vO8JPFF54wglVS7Go6hxHPj6v50IdfhF9GGPv9SoOvjfNznf4LiEJk3WZy4VjqXKd9irClqQpigc++uxlyn38PyI1m5ZosOTLWP76ezmeFZp3oihw5hVcIQzQIPiUKB++R2TsdVA003n+O9B1pHXgr818fZZZWb4eq6v0SUik5V1tyrhhHE8JZxLMREFXLaA1senbPC0Sh3Thr1d7qY63/NVkozkuVLSTclzAOJypCoZ7+rfqY/HslDEHF0rriY6BKFPSe1PZcXeRl+tG2d1F/D4cL6DPjm2tFq0G3E/mGNROu0rwI3GhOFMriBGTOlz+SSLrCU5R2zvzMrs1m8FjiQTcaujkQuFrmE0ev/WsNzZ+AM0T9V7X50yzCw7qflZyUnhDB00KnBOERSfiRM6FbX8cEeX0r843eUNlxAT8jSwLP66PPJqDlwqmvLrimGqeZDW+5h39V2bAi2S0D+/uO79sfeN8K9iJooVxaOYOwWNUCaKoCaX32gK86TrcfdNMxrHDSVNvdTFT7hH4yEXJfAJV/rzYGb0Oq15HUcLM6UzADSuxQ7b3aA8S8sSxOQaQ/Tq5GbPZ1tMyv8X6PifU8itjuEWLVc476HF4p6/XPYhheR6ev9eXZvc+MaxzbSOB+pO9/HVGgeYebmGr/T8j2I4IwQ3ANJlPyBL+2jfvkBYabZ/07/d7V/QzdxeeGchNOqGPg7MqME4omzf7eq+Q3lK4G47G/s9Z70ZCGB6bxB4d+j6FrwKhqGBKQVlGyMtYH7krsyEmAloSUCMyEPRdkEZSt6H2EOUbMWdXWr/hYZJzBOAqq4WStB6iIP86cRzQLhZzVILh7ansT9i24pIKguY/smT54jFDdtbo2NQK64XwCUUF/VoKlnuicKbz+UWIws7Zliq2UuiLh5+grcv3+pT5F7beX0a5K+31S76mUMGGkfoY49O0f1/YjaSXulRZ934huxaVDm1Ga+ky2jH+ZpDAO4hkqmhGD2SR0yObIlMzrPiE+sY2emYsIMCCKUNdpvwNBqBspPDRdvJEA9Vi0crbdfEpDFXJ4xlESnazyOZFq6oLjmyJYpBfqBzrOOCLxirDkRKSNSGbYPJVTLg251Zcnxx1CqEjI56EiWoLOOwNStLa8ZyRawMRIywIB4eO+IeeCLWcIlDtPHFPC6ZdSqL5bH7dHKVd//zAzvmrEDteJzJGAugN19M8N3XUidVYpOgfEdxp7MiSU8zgFEngGVDXymo7fT0yDYCEL3mMCk8cvqPSQRc3VyRIjiaXbdmLYH0xv9dbrY66k+J6nl9v9MFSf//H+sz0/g1nKjNOysz6BqndbK/MCdXlCcR7d9rmIBMNbjSR81ucz+DdRHz1Bii/AdMAvzsIjAhWvycn9lwnCSd3Txh6Wfn2WjhskoOvDauNY8BPtHXibQJwbeepL+fjK+Mi05v63Y/zJ7nKH7WjApPicEYCi2UQZ7fSwNTFWBhIEVLLtqII1Jay3L/jYduyloGQY90vVoN1yAgXZSGDGZ56MjfoAO6VgJg00W4dmxDB1VaOsSiTsWY2IpobgPSFTXZDUzX1dXvbb+6HfFel79IJfExtvoma7UVdeZ2rq8T9K1AqQklQSDiHkTFiXOEOTdTNZF/FapPnjkxieeHT/z4FHMkm49QphmCB6AIOrLdxe/3Scn5mHfxlReEQGfpQw/Ep4iKAqcyrHe8PvGdfzcHyP1lTHCAaO+skbf2Us3WlgYfHO3xPFLtRrTW/c97F/emTDeq+jQ7/G/lN7JiLfcZTR2F+fsw1VIPXAehJFbgsxbgtjXTT/kR8ATwJwYvC6IosAO2EvxZClc76x39SxZ40IR8lmJPhO5NrEN+6O6jOVG63f4otSn6V6UtDwTGtJVUFGsQIxsDEbkWzqN/1SbcbUFHxxgcRU4Z5dVOswflmoEhch6no0YsPR835UGY6EYRiidygwA0foke0LJORFpERAi495JK6E31X6i2XCcpHx+6RcV+dPUsJ/CVGg4fOXVfiTjz6h88/h0xUcUGXdqz9Wi/6aP/6oUp5zL0+f60u1BNCle8dNeaLYsjUVkdbx3ObDKqcHd6UvRuN9b0VU7RG59C+3hC9rwv/+H1/UD18E94+MbdcYBoK6q/72dsO+Fux/vOuGL2HMsbGgAG+ePDz0qs0LgTvCUiUpiqes+bOjNEfdHzNbhHEKlen8VgJGnvJbIGaaL7VulwPimxOgxmdrFLSnAiGnnBb3UOrLKE2NBIaIoBRBNikrJa7lasUnHO+jbVWR8J8sEvy1Esdj6Ib7gpTwWfiBhHjn3PHD8vZmxyU+ggxtPGqnzxQ8mRrqPoZ7/dUZAZ5yJJ+AkRf0b7377LH2ar+r0kDHN0IPTumfpuHXU8JAQ7kDW3VCugcOp6EO/y52JazWrqrG9bWEaLOedlgWs3XXXCxbW9SVEoxrqapLan+BhQgLETy4C4nBWbAIcEuCjIJt2/Q8Akd0ohG+bWFze0tGGBoxHNZaFfkaMWhlzUOoPhbJbZjIeo9BtS2tp4sYqH6eXPcIdUSYullG5eJ1bJ45lcXdUt3NSmp9LI2713Qd2i+BuvaSGMmhxi4kWyN+sJoInSL32eVRjXJ8KKgru4O05w9J/HtAoGKfumdbY4d7z2BE8rMCBJdM+8NLY18llB+r+gwO++ksqQF3VTh7WdHJMSKTscIRl8zgx5D1iPhOpbaOIIxlIuKpCHvEt5PvemH+quh4qcde4dZ5X85m5My9NCCaY0fRLdeug31uo4j6PDGbI30v5QuFjJM8okpDNtMEhPOd4sZyCkZLN4jL5L3UOANH7ERgUYKwMKGUHeAETglpYbAQbovgfRfc73cUixQmGfKuEmp665h+mZqYUDviCLCEWfP/O5tEt57sn3BAAEqImDjEQltiP2pp6qTORWnzLvYOpcC9oHyV6x2NWNY03z6PkegYu++SUIfWbFw1AV+pEhkxI9fjNUujV4iZYFs9bcbCSmj80bgc5tcRXoEMUkXgV+MfJp+zpoBIDCYFh7LnVyescPhB4aLzbjFJ3yFGa/L8Z6SJfxND878PfJ7Q/GRl00XFh9vt12d6OCcG9ODeeRvH6w2lTVagc7LBL7M9PaSyPrQxuz5b1kF6sKZm8kQsGSNm3fMnpQVrWiBZsOeC/fs7UiFQMYOv4jjkIsiA2SMCIQx1F9CwucV06I1980C3mJaicwCotXL7DP3Wu8prcz1TwX2yzHtJKRVa0ghVEToBcCSshK7UWSJR5K0GZD+FwbKjAtgByyjLmjXWKGzteTU869yRaHwDWL2wSpGQM+IRzNbHL4LJchq3YV3GdM7kTi9/FgOf9OEJrWnPWHu/cqZeJgqvcO8On9WJ/yx0BtifAuM2z1t6+efAID6HM4Z9rHfS6PgMAR0XdLgXkdShjTGyYFI79XcpPmUrWoOzhtZ9l3UXYtUzyavtsjEHY+OWju+NjCBFrZhMcvALDMkbU6t67wIIKfJjhvsZFoEFi1Ftr75m0TyjNMzfbJhxLC099YwodC3UDz8bzt1dGa4q0gIx9YV0GzdIUAIr14+nJ/B9BlohqseRapoJIxwkSId1opPaULsn8TvOv7/j6oA7yV8hk3mccvFOe89Y/Nm7CP1oy05aPVGAGTpQp3fA4hT+O8oBvfQ1I1D9uj32NY5zMvOHefgMTv6/vKQwbsPZvc/U87nnAsv5Qtvzeh+3FgmmlvwRCeGV0nEmS7gZkMrhmTOJYQbHsqoKcrfJ895GxCdu9ISrRgh/vG/I9x2/feFqy7nv+uCX334DSwH2DTlnZAg4qR6dhAwZUgv2AoJqSLn4dj6aXvezy+K9dnolwQ8GksO7ImiLTbIgEJhSnY9sSKgUd3lt2KXYHBYpVY9SQkSznrTWn9pBFtGsUoOqgvxsPI8bYbtIEKBkoJQ6t35KoOeZykUgHuNg4+ydDMb18IBV/4UwQ6y/qq5PCQ9WuCL/SX9ifbHc7Nr47Cvw40ThqejwfJpjKtjz5yZ3X0SixzJzDPzo2e6eWFbIcEjKWKb7TsfrMi05aZN6njOGP8XHKdyNNyMTcuzHGTqnJjLXO8MTND7Tty4hHXYNrLNN7aK484V19XsNke0Kz7SO9gjC7QMdFzlIE90las8oUVCfmnvOYGH8xit4SUi3Ffd/vCPvgtuygAl4WxfIKihFCUK+Z+SPTdUtpoYChRmrwyN9l9Ij+DoslzyqpNDK9TmA/B7V8gAFFXw7vrOIq7GaXl5Mhy+ApeFolgad2rjaWlK7SN4ghsCJ9UAif8OiqjWiFl8B8XensRfVxZeoXa+jG1yM64h/FK3NOeNz2tI4ChrKnSHlh72qe2Qyqq7uIEP4MzYpXRym//Y9He7B78d+xfvOE8jrs/i58xQ+9W7OCz9D4K8shpeiHieE6+FBauFyRIJdXYJOLXIo/6RvVAsMSP5QrieYdXvOCOJInILladbCjCxWX3BC5WKbJ8pYsr2fTlkiMH0yIUastmygTgraZmh3WhpmGPKozwfkpvWF9qxuRXr9enECUActTVVUoJzrBmATc+DkBLq9YfntK/Z/7rjLht+XBZwS3hZGcvd+YXz88R3ftx2UtWeKLqkGs1UEbMFrMSag62N9OqBgaQizhYTV5NXo1o5hjTYPThRiSowCAVdOH+J2BKl5khTpq3GZbP1wAYjdOG1ZlYRBXKoNxwPJHemLUEj+p3vF7Sw+H3ovQdxgbWOcOW0ME9XBIxRwqqWk45qY2Q0iBqJ4IdZ31saEgtT66GGxKaIXO/9aAAsubIg/9nGUFLouzlHHFP4vrz5yiCiaJte6gsevP9jisyvP2jnzN5pJRMfRHKSPJzBqvdu3wPEY4uv2WJQOqBEFJbZkC3ZQL8WV3nVxaG+8OuwM70uNyhVU9ZHieAEtC5gTPraMDXeIAO95xyaCb1vBwgvWZcX73/+B/b6DwSjb3veBqBKAmFC6iJhahgKm8nk3NRBRTYkBNAmhKXJS/e6G36q2AmEvKqlsAuxQI7OISg4JrqZyTF2qlKBzX+AuVWrXSCAjKQCA4lgtnDxt/avxBzljM/tFApAYWAhY1wQQ8LHtKAXImfB2S0gsAGU4EeMOjfncfUa1+CIEIuDzHLlwEjes9zAi3VgdJtdnu+8XdLsnAqHfvs7P+vNZeN0lVY4Nng38TEx7zN3P+ddQ60sT/RpnrPW1e0dkd0CXkeITDuXjvedwnKBaX9111F1v945Pdoj0tAv9iFyEP9qC5yxFe5pCoFVEYuHgmVnzlTBE7suf15u9t25Y6XJcdc1VshUZN66gcewCVW2MaIZIE98xE3Ip2PcdGYQtF2QB7vcdwgxaEvb7hn3bwEiQvYTo0rAaiCzSnbp+1P50s+mEwlU91N+riL8fezUAGyEp5uLqny3raMzGapw4VYtAtwN8srsT0oBWrhuSzrRGLTcDvDsVMemxoczq8VQlNke+YU5G7zVnHqi+tx9AcQd+aIK8uuJUb5OVHw20s/X1I33y7X1aD/VLHwDEPL8OKqRKGCZ7YhzyJzr+X0ZSiOvx4Ab5i+o+JQgn5ab9exmOiD+eqHvWh+cwPEnPRvP8eotUPumXISFXIbjKo6qknLMXW6zEJiBE7rPFKRzJ4cxIfey1cp09inGPlyJSFVqG/uxgHOAtMW5M2LcN9zvwDXfkoiP9x3/+A8sfCeuyYN83VbtQshbZksDpHJDVm8CKpGFjRR8LEJFxJVim/iFyVRNh6hRgMQjF3wsxQAkQQi7QQ4DE2nPblwee1YhlqfPtXlptnvs8qi3hYrKeF31r1GI+ikkoiswLbqSn2BElnfeSABSkpdSAvz0LmKWmFOne7c9s7Vc2zQMBpCdY/Y0fIQxn5R/WEwjD7JmejJ/XE8t9Bj5FFM44wB9ufQI9Qgjbh8Kdkxd/YBBO7xxf+OP+tB+xfxPnw/OqDjeo4/x969EwA/NywBOxCzHNduUkZh0KnMec7NHxkvcySAhKFFp+G0+lwM4Je6RnfbUSFjU1Dm3SA9fY9xKobQevxOwf0QXeSFGNK2j12v/2PhP0nIBirFfJqoN3QlekYC97GLmjsWBMr/VLV39s1U4l0N/BrhS3kJ+14JU2kkrHP2Fk81Iq5obajO/WU+PihQSJNapZYwkKgIJcY4xdgrAz38jpRtyDULdUI/7ev1IYOUR3i4/VDyaCncXAbSaKGaarc5TEMZ4jkl/m7j5rZnZazas4bcTUD7DPITcSDetT0AXZTbtCgWjLvHkZhvQqfDpOoX+Js9728NToOv0uh4ttf80bm6Gz9qNf2Mc2X5uyirDPZpr69fSoppfaqx0+EorzOvrrTdXTlxmRGQCI8DA/hMp7S7vWJBqpRMB93sX+ExgxEjcgDtia+q/tDQ2SwjDHzpVbh4dxE+DhWu5hYykYJBhv65yQ+9yTGUAVMZYiaOFc5sJZFKkS8+D7362ubnNT27no4r4pEIWQ5jzOvXSz5tID2ztyUkZVwnBnUkFztdUU3KLqLAESUzMiV+JjhJgIoHagTt0scb8RUOOjgyquCCNLaZMLWw+iyN+rT0wgM3ZnzyhC0bIV1pu5NY8rHz6vdW4me3dAknFyCUogIajql4iAx9pewQwdfaHWv8ndfjTDHgB03io1HyuOozEi2uGb4HLYHp+IHE/ghyUFso6NiNZ/niHH59fmvOIZnJZ68nhEqw/4klpiSnQCPMrIOEfis9I8uR6Pcx+vad8Oz1JfLva9hHI1yAlUEayY2qSm3K8IaDYmbcORpGY00A3trpqK/3Q1dJvBEEwX4FbTYvhib6kZ1NiqjUgsb5ipqqjIN4SPXbRjaIoRz26rzRX88QF8J1XI7CUpi2zpqncwknn+MFmaCXEEmuBkslROF4q8bZAeceyxCZ54zshRJbxkBKL4sxQC1JjRAs00mE7bZpUWRNNH5N3UUJY22yU3JkBYCZ27kqoyrVTCQNWJVUCmKlK/IQ+N811dqufSXkr14gKZaZgzsgAfOwDzLmIiLEmwLADJrgGCZVGDs42puWf6C3WfpYLmyeYxFZEMn6/MCG3NuG2lRav75u1sY0NlEiuKDXQ3Rgx5KPRSfyuR7jkmjOTGJYG+1eYGq/zZSKCew+dcUmuPn9+c+fCPpfvv44seZn9CeE5uHfrwDHrKTeE6dVzHgWsZ6pehzVjTMdtPzx/pt16H7G6GnRnc+yjjBFOnMtLrPVHQGU11+SrSCP0SU4fUQAXvX4Lznv5sPbzGuNAYPCaH/poZdaCsblQfI5+l/hf97Ns3aQWsQadKfs8Qrf+WjF5vXskiAEEuLZFbQ+42DzDuHGzEgGr9EE/r1pR+2r4hdGqfFN5lVSG5BGCI3gmF1hNdT2O8sCXA41SJE0RP/XGzjPe9vgtbLsURfwneRyCoSslTWZgFRBQxd4fhmM7I3X+LNKLQkgOq11OphLn587T/W47WuA76v1I/aXItrqNx8TS1bnvLvgYblhk5cTTX1Em95B5bEVl07qEjUegxWFclHYpGvB/G8ZjMTKp5ePXXE4WhgU+cA/0vhrMI3hkcJ62fzIjYPzsBvuH9F08WzDGCtb/2SJIY++dX4jUKz0SLSEy0BkPy2j/NbJkqQvfUyN1MiG+MtlhdQ1LP9wWc+T0OEdZoN6djOgTnY50whChYanYD3/S9kbt1MBI3h+z2DvFU1dUMDYT3JObg2QbiCNsQr7Rr7hlU6yGo5FQJQyMAWt7eo8SYYotLKAnswouPLSnyzSJISfu4Z3c3Lai2cO+9CDIYKALeAUgCSpMBTe7w+GMIFWSLj3bllCPkIqUa7nXei0qJJEDyU/Hi325rRFCT6dVU3gKChY8jh2dLvdYWyUAUJqBDLh3P4G+vMivk8lkfDa+3ct0Lz6Cud/sinTPyow6Gh19sw/t9eCYyRS8i/WfwOZuCc80yXA8XYrd+TRefwfNWjjq+8Oswz0NZOl593G5chkCTmBqCb+vCOTW/1ic+a8jNYcSqI8KnOia9ckYwvO6I3seJ0GeL6MlefqAMgKaSsEG60bG2TahBSp46wU/pGqYozFT82n5X8wHIPJvMmN0RJleJ9GN1CYTrdj1ycvqOzDgOqtJAS0URUko49+4nodWMpLD2GiGJKiCXBBqS9rnk9meGDgGUMAWxipgANlfPpO6eYNI8TAJkqzrV90BuclCiriYR1eMLASY4kWiCOmfwCgR7UClmC0SDB/cZYlcJoRmRBQKVNqBnW5OrphyJLxCBehxRhoh6ITUJcbf3p0RI37mpntyXjZwRcO7cnx2wpCHJtq/nuCkSAl3LUhH76PvvjIQ7QjQDcdgTUAnrzB2/X/MHilVxe1eM0LmheoeqO6r15VefJ/EJotD4m0douHKK9juO5zHQ4esr7eCkR9NnA+fU9XHQM7V7EQm/OuuP+sORzthnGkp5/45ERFcEo4WMRgdMrk917dUSwZYA3xTNN17q000F4kStmG1ATCUDBC+fgcH3Ka5EwTiJajSn9ph+eXFeZXywfdWTxOxK3W9OGBShqP2r3R9RSttXZB5A3AgCWVRyVPtEYgpXjTXVj0sUNbtRVAXW1xwimZn6KnthpM5numlMBakvJyQLihvMxYi8oAofTFDVUgbseAPLcqqf7Iy7tGhvWD1SVC1VpxdOmJtdoamBjChwMqKQYclEAGS1O4hYR1TdFG0YakMolUi7LYEC903knaVAUAapwRFnd8EtJ22stU44bitGt6VDxFUCtiA//d7mg7p2ziWY+gR1HWhPO6J3wtCoUbdIx3KvEoRXtxnwA95HgDE18soU/BnwjCzhxfuPygypBGZv5/R5Gr6PBIfQL6Y5UegxxHjvaHx2bja2MT6ry5baAnHOV9nToe723LG+uBq4clbOGEdGNzratKfdXfIVVkExpBMyzzHk7yP+PKYG0eeTEaY8tFeHbR495FSwziYhkZqZyfX3AWvrAfZOSNs9ctfagOiriskRfsxCgXCtVmObrE69mBmjQBKBb4xkePa+C7JIDT4GAcviNqaCJACBkY0oIEPVPQWQTPU9OFG4a7YLJCcIAjV0+zNCkJKQNZ8GRIC0MJbkA9KK2j9PnwHsksEkWFdg4QJCVqJOLin4W3VbQqBa3V98i2jXqb8l3f3xmfBcXVHx0x0UeqBKCCPMkb1CeXCvf67uGwlXaYLUKzqiShzIxvsJ/D+Fl4lCEen4UrQ+1Y49IlnP0HSPcB8BDZ+zIp8gCpOi0cDbkQPHPuFltOfP+tXaaii7/90Q8hkRcEziwJFdObiudoi883un3nUU0QDqY+eGsAMS9HJWqrVjcmyH/Dui4BJDm8hm8Its+/F9zMbV7oXYh/aGGoGy7xGnetHDKjWvIo8RIL8GUx/ZGHyAfj4CVVY8SFxhGOJ6/fGVtti3ds9faZ1y6Yhs9ywDdEPDg8mQs9XTPFbV22ghRmJGKaou2u4CKaL41k/tDJKCpUACV8nCPHWM0VeiIJoCwxhkWlWCaXvEEa1oxlYCCgOUNeBPj2woOofk3HyzR/g50hS8j8SITQ2P9GU9EgZgeMkj4o9uC+hWY6+GNLfY6EVR656xxGWCegRjSOXs+/hcZXaAZvzuLvo1ar/R5uJnCMPrRME++aTF2N8/DyLSG6+fIflHhua4Y4GI7Bq6cUThIzyrL+7cCIKZNECHZ8Znz4iCEZFadOTsefJdy8WgqJ4QpR5hO8MlzgnP+tP6ygNBaCryIL+0/1ofOujntVfp+cYaZCGlXmFMgKpsAr4NzLb6wnBXa2gRLvVopteWfE69fdA4eQCVi++Fh6r/V+KjZQ60LYW6ZkvA59LtzxTmmK3dN2UlpSizxo4rCcGgryNcV8bXdQWDse8Ff//nHWW385K9lM2fa3lQgGL2X9XmGBEwacEnjwTgAvBqtFWk2j+kmHSxJ2TzBEtG8DiZramtSpMoHEkfJYXmShuNz2fSQ5t7GZB408fPJAGjiIEOiBmuaSwXWlWVTsYRRuIhk2sNImYo6CXfOaJvDiM9OZhh49dIxaeJQt17fb9ea/+nqAYdPulwz4G7TTFH1lZOYB4nEso5Z+xXIxIeEfesf3OEBqDjuKljAfvnZIrsTxBzdS20OYmukx7cJLatPCWFIVBH3QgSg26agNIdq8X0z5VbDr2vdtiGuil8IkgprQ+11TZdRHUMZAZMFPWk965EWlWDkKTZPyx4txI4w0Xdu6pVULjCALGgijvxNTgy94cGguCEgBK1M5bZ5yoQCbapDHZrFzqytFdHieo916ELAYUEeZX6mtVGQCALulOJQRFKIqBwwZY2JGFkCMqXosceZKlIn0QN1QmoBmzsqOohNQWIpd22dVykziutOm8LGykVIO+aalwWrScXP4UNoJs/27QMarOyJVAsPqK4KAM1NJMAEjnyGILX0CPZuzDsr68seqC5l1bYqo3ooK9zYNtrLiK0eHNdaGmC91pcjRrLpfavDix8AMqOiGGAgra+rdE2SkEfhyBDRfB1N15/DJ8iCq2B5/BT+N/nibyuGVKPmGHSbkC+LaycumrE3Q27QaXuPXXo4xAbwMdrQvU4wlZ3QNINRdo9J2A9YTiOOaZNDu1338OzQRKoDoWCitg7f3pvq0YfQ/XlcL201UOtPfe64Th8ckLHPbGQfiw9Xzd7t61fRObpVF08Q3FHir5ZnKs1ohC5vRpoOYL3028yGvIn1OUgQHDzxJHTD69CUqNE5ETGL0Vbtat6WOsmQjPq+rXkS2dQbKzSCIup/OCMqthrtkEXEhQqSjQIkDclCKUAZJ5IXMK7h0oDsigxEDdU20R4VtP6LBFoERATeLF3YHET9dnSroEFWJ2geZ/JiDjV5zspsHbMpYBSX4VAqj5d7zmmagi+VSQNz1PujcoACDlsfEXi0ftHOnfXJjKJ/+4WWS8VNIcI/w3U9C0VR7Wh+tqMxuVhJP38xLr9FmG+7h/AD6mPZvGtfy3MVDizoT+6NiIjOlxrqJ0H6eiIsB2qZBGuUXft/Nl231s70y8cn21nePUShvMypWhdpXIXTjgMq1aFthiiNcTeSTPcxkhtVLVH1AjcqOBTTG01lb75YaIqoqdElnFTqsoiar04LkRRXOOcVUT2WfomIkPV8RWOZIMtYIgh65fPRAVUBbRYn70Sn+LaZLAjOIKnEq+pNMBmwHU1Flm/sgA7BJk0LK2UJuFwModaKm72qJiENqgfaxbIpog7CdT2ALIT0oCtACVDPZxMlSTiiIYhe67vKq2MlDSVRSmCvAk2Fn0eVKURXvVsZ17CWQpBDVE1N3472pkd5wpBDTbtuYiPG/cs1f3WF49m43BOP6qAGoJ3Q7IjZpESdmWBuzADIQBvIACta00aIZdgqg93394Mwkj68T7gzunx7afwCUOzNuZmn54g9q6qn6VMHfiiqBOJusmO9fvVXt3TSlGdIYJym57NsnrARDZhQigaFz10pmKAsYMR25klgpzaR32/P9sQbW95dKQ7PhMRbhtjQ8+mpbU+io0VcNXRWI/528f5ekIUAAQVkf0moOXG0Asjaai0p/RTN35WNZTp1IkJ5qDSI3FH0i4hoOneNembNEQGVPe9w/qMryL8VSlkuObPiPeNgOgpVNPXOCHxaQlTWO+H+oUDc8xtHiphYrEYBisLQ05eZ7a2jbBFA7+gGZKZGMkkheJYOBBqJ8CU9Q+FVDOi/qgopg6jZCuUCMsNWBc1Nsvux50q7hZonSwA3/S5koJqQ1DzM9W041maxOD98ZcYkaK/z+iV7CouEOBSml8rfl8Q1aHiUrIH4flrtgluEkWTHtTRQWLnwqIKHbROVpdo6gcg5hDgw5vFHshYfYRGsdAQqN/4PPyQpOBEIa7pDp3Gzv8UhWgI7ZzgzBoI1zyvCQA3nPr8SSUmsQ4OLdJQI3XfeiQOffd0ROy6oBxbRIIxEJYD8o0pFIDoOx/727fn5jt/RuusPuiGgQaTrRECqn+OEY92jzAEb/U45PER/eDaVEfko8BChKBHRzPwtr1VafKQvw9EeriLq148z/+e2/qt5Sks08DNex85HfvDYZzK2TcbTOUtCFW17IizSjRke8eRuCOaSIzqWkWnxhJSqUEsyylS2JOs5Jd3K5dE1Ti25kQ0jjibDeG2JkgWSCm4L4q8SxG4lsfMB8CuSJQKILshUzvQByJYTPu9MPDlDXhbdDxlI2QilMU8j4TqgQv0VUBJzRVgbmYbUcSI4p/tnVfD9iygOXp7yvGvMhNe1rQ/akynikPJz7R2AoRAHIEqsTTUFrH0rGONHdPPyK4eJZRIBAr1bdXPgFdpuNe3G0t8Hj6V5mJEkd6JOFxHdzKWdSpWn+qntU6+jVzCs3XPdNjIuV4r17lmRY4VwMgdGxfhBlU37ehV7oJTYl4bRt9+/N7sBL0evp8rww4z7pviOGYqpYA5Y3sUUyaEeRHPoGlLsrd8Dn2LtgLtPy/NyNxx552xoHU7Vluvxd+O6Ov2kPha+vKGkNmJgtVR1UMIrxRtWjqHqgXBrx6dpBCf8TaraicSJQDJpRXrF7wKgnYwQe0eRrhEgJJCmc44HfqMpuoiL0cwCUD7zWZoVk2JST5GGDpzlrvFbsqOUwLSqkc4JpMCqzFaAJSEPWfQTpBNkWaqaf+AvagUsW8AZVF13wqwSdmlKPJeoYblNQG4MTIT9k0PJ5IvhIUWFCFsHxkoqkrKJincFkIugizAjetUNia6oCLpeK379JcR7wNge+FRS1PVj6WdNVEKjAjpPYoECK2dat+u/RGzfdhaNsmDOpVWRNetI7oEh05LWFe2RMbhRaABw/5qeP3ktfBZ13bDgx3OnwkKqoukel//5Dh3oTw6PtknIzptUShPXR3uP99Ad7lIqMe+N0TbWnSi5EhMjx50Ltrqqw1w1y/vC1GvQBFwxRatXVc4xzFxmDsO145IvGG0FAis94ErQuzriW05FnQERaoSqSoPsqmT/rHQhSopjDQPbaqc42/63HatW1yxbKCDtYigCVwOkc6Jfa5oQVoSysWdN4zDETMFKaFKDEGd420QC2hpU1gRkBOaaKOQ0GZ7TXo51h/rSVBpxHJMdIQwjjnZF68jAbyqZMMBqzQvJQLt1Azntq7Z1ELI6p2UScAZoKKBbEwqq5ascQ5+9GZaCHJT6WDLUEM7AE4qkW6SqwiSF4AXAm5A2UXtFek49koMSn8N4zUcr1WvM/t0ZO7q0+r9ZlJQDc8X1GC9SJj8xFLU7xRPMW0vMtqZa/8iZYoIyRtqONG/xGHNgfoS0t/pnov1vUhHPkUUfH078xOHNSMEQ9+qerD+SXsfs/Zc7HNnjmLIISIJr1z7QV0NLhVUzCLeV8VeTK2c0/FGBABHtAT1b3cP+NZKj9F6gjXj9oMO82AYlmm5c6IwYJhoHfW/QIAkPNPZD2AT7bdXNATvSDqhYWF7MTTpQvWUOSEYHJsdCUCkjeOQqb1bDyQ6CFRWrtjzvChXqBxcoLmzqY5EzPuTFKmmpd0uTn/jn7O3UUWUwzgW9NKChL1AvrbQpJGldVJCG2ILv26X+M4WrUg+VGLhBCzm3eMeSQLBfd+AQuBC2JJgXwRYARGNiNa8SoyyqXdSvquUwAJ8ZaU9iYGyK5fNYocoEfBB2kqRYiobArFGKKdFqtSGlYBE4FXrkU2w27wua3g/MwIwkxAm19wrCtKcmEo2RiloeMgIHlUDNqqnlFg9QCMIkVhVojP2L/at/tmLzLFcatLHI4jE7yfgVYIAfJIoDCil3oiEIX4CIzGnmhGliGa2PNrrFZxXbqrehtQjevRORIRM3a6NHQmjIJsockOr3ScOL4EqMmxSSlBbdZLAiKTJ+qWjkNgvAWBJ5lyC0ZJjCKzPRBi1sclyGGPUWVSnwfDpCnhq/TYE71RWGC3zRdXZqd99HKZLD9G4SrGrKYwgEgpHfHS8VqUMqyci/eiK7SNypFt1+aPwtITNjP5eJUr+OgZ62xEqQ65RyOg8kljqOKoNtIRXscCQdsvI2am52hILRAZwJqZABnpP1eBZl8TiqbD1eU4AraZ+E7cnmQtrEQ0qM68idzWVIigsTbRImrBuAbCC8LYQEmkOqG3LijDFcyHVN4KF6rJRV+KicrQjR3ezVWJJlsPJMOzqGEaOdoTxu483uOHWvzyUsWtUpO51ElWzoUgvHdiLpqJqQCcuVSoIEoKrlR7aOqJhnFrdVXAYzRDxcxwXndwbn5nd+3OIgqO0ZjcY++Aam7HvAKDH+BF2IwZu1BrH5G2UUE9M3ksAigyJpy3xWGubm74Pjkhi2TYC97yp/RdHoEYMLO7AEWypUb4U4h4otBIQtbmCRAlESxj2df1IrWfk5mN9bmRuieo6YhFIZUfAHI1Grrta90Kz8Y+gKQl8A3eumANlDsi9wlifc95GcwkN8dapts9KlKjfD448XULtELsjZYT1lAChxnh4HZHTP5h+xj4RLChL/9ym0cYocdp13jBI0Atp8JaPR/r2q9rI7T+s74eS6u49RbUSKQrGbtL4BwYKZS1jfeVEalNgXTG7qD4fe+jgrn9irqallMawLYoolxX4kghfmPH7m8XJCEHegW0r9hwsB5K6bd5WUolCCCjq2so2n4lJaT8JZFHkSJksfSsgLt040g3EIOr9/SVUpHpiiI42CcpS7QHOz8CN6MH25IZplxQObQzPVrtDRPDS/0kgUt5e9KiaMi8zpF7az1NigNCPsS8vwg+dp/Csjdn4soiuQ5FujmdlA1od57fuXf3e0HD7PbMJhJLVXxAVG7UEbWjeQ+baorYHXdUxh1DvldPa7nvv/eGhdyMydzjRaVgdFK5LeKaqofyRELHiiLZ1l2pTFSnG+yl8siPX1q1q0I3XAjGJz8JUGSDjXq1Mx4l7P8fAMKeXcEQqrSyUwJSxzzZ099JxxoNTy6pzaCMSy9iuc/6LEsg2xYQuwijcc3VosX4wQ2MNql2gEQXV4asaLiU0PbfVxYtVKIJcDGmyQEzyWo14gIHC5iW/m8GbBZTUbbLY8aFSYETBToPbM5AJtIvSNtGDfEgAKnrtJsCXlfCWVG2US8H9o2C/qR2giIDASMRYSJCgkoV2QSUSyoLkEYUA1lWP4ATULiG5qDGcAV6NdYob/pGh+eS7RERtCJk9XbgjYwSCYPELhqhauyeG5gMC8/sxy0Vpn9Um4W0HG8UDx6W6XqpdBO25M8IgJ2W6BHtP4PUsqZFze9BAt/HsikC5+3owB6Sbgx6q5y8IjSssAbXrNf8XnzRuKyBq6UoFTENAZJ+dRjTJweowbClRx2CIl6g3MJ8RBUfctVzVnRyJQrMz9Jx/Q/zxGQHALf7C1UCNJjR/d38kdq0jFqFqRofYxVNNjNLBQAQ6g3ONMUCVFKKqKHrzAOh08l0b0q53aqTY3xB17OP0XG0CgKp7ZiMsnVQwvgonLIzG5RM6NRp5JywXkccYuOpIyAiqz1+X/srWAhOIzFjtCAhQtcpKVZ+v8QfW9yT6vEkNREC2lBZkgWrEisSVKGhnyFV6A3dFrKkrnG0ha4/ENDw3PSNBBCh7wZ5UohC7xgwsibEg6zKo3jyk6ikzsusxoIT0RqZWknq/JhxcqU2CoItT6AhD6e93On9Hzo7MWZGwOwJESQMF6o1U3IsIpsJQyu42hagi8roPBmlvA63uKEFUqaKEdT0SivB+ZKgHGD5n19C+j+6rcek/g0+lzq7vhB43MPQPAo283NGr+x4DdWPX5UK2jyNfzgF/6GrXvkXuHHDUekTeAYm7xw4MwbqLWef2ybV30vXEOznj9s8w7xHBNyLSE4Vjne0DQKd3dm7+4PlKMJ2xQVSTjN2KSduAY/Ox+04Ahk8w2olhrooKBKJrLyLr2F5c3IHg1U9GM47b9br/rF5JUgmKP3tUmVE3R77W86L9V02gHOaKjYXwvrq0IIAFaZnE4PMMrceTLLIRx6h6JRIgZSy+A3aNKShFD6dJLPi6MhIBTIIPEexFcMeOGwRfAdxIT2srBOzM2EC47y58cPOsSsrccEpIhpzzRmAxpykGNhR8/9A5LF90FpkIv60JSyKsC0O2AuSiEdLGOb7fgZyBsqh0sBBAq2LkfdOJIgHSjZGY0ELdpUc48XO8Nl4vaGk1DMl3CFmAjAQRYNuoqr+Svf4kgSDkY9uUJ2070RrVWkaEJPTlIIUYAa4EAa0tvHDt4bMRXkO6AD5FFGSch24vn/Vh9l57eERe6FBXHwaCqluP5EDqk153w46N6+6xXE84HAsoForx2pqyGfBjG6MqSbviaCKFPkSMp5iYptgw9KszAljfzEXl4AETH0dtoicMwZuoTqA907lb+rXgljmlSYF4VGS/HIlClBSqxLIc27OprJ/+11704VW2590tNJT3SF8JY3cfAoqvos6LwM8/EFIOHlCk7kTAOfNIEH39ke1OgiEIkNUvIBawHR9KVjdbOSZgNfsHoEjbvev83OPEAjvrDATN83oDI0H96IoU88racQNwI+AL6frbIbaGue4fUHNf9iNCFylIdj5ESYqsWQhkIoePKbFF6xDASZAYWFLR/EqZ9LjoAk24B5MmYHFAUpDNeFtEkEiN19V7yl8rEVBkqmIZDbvV2GtIN6qMKnK3TxFCLoR7ZuyZ8M1ca0WgKjIC3hKDioloJv019Q/V/UOuZhr7I30f/fk6uNLWeeUDneAI2pjRfx+RPtl3r3YsW69FeJEwvE4UxOddTFLolTLKHIT89u5tgV4NN6l5co2mJaT+tdbr4j4g2VjXGQbVnV21/R5f4Ag4OpwbMvZYhQJWpptaxDIJV5tE8/ZpffCIZMdI1A29lyxa2dBv52Y9ffJMGAlD7rj/wJVXzsLLOZKOKhwOdUQEPCJEIwqcoBx7agSlEo2AfFVNMNQV/qraJozFuegaihKJ3DBG94YBtJ4SiBMlX6nhOVeCO2GoNhed6wL1wvHyHCULEnWvtGpdH+52BwbArDp1Lrki02K7laCI6Asr1hDRwC9tqtScPZkAoQJhDbwgAF/Fz4Iresa0CBgZbwC+AvhiTMlH0GclcyljTpruWmwoQrgVBlNSBJ7ai6C0AMxq40gEXlJdoUkyEhUsCdgTjEtnlB0qlawqdS9pAZUMKpsapUVPi1sWxrqo7s/TRFdGxJFr5LSDqiUi4MqZOwFwghCIgsasEHJO+P6R8J4J/3Pbdd5E8DsBb4mwrgQWaudNVCRG8NThNeI6SinWzyiV1P55PdzuYbQ/yOQPT74HGDJndATB9/uLNOHzhmYnxECv+Og6CN0YPhc75BNqI6+xYQUa7lSOB70nkWdZ787VtX+WiFfLOsZi81ISQsKg96/WWcc2ljoi0IpinNUi3ipXzqtUrAg4WyyGGYUIKRj2BAyhAUOOyN6JwaBeoXh/glyd64xImAB3jOo47SmRodr9Wl7162ictiFWXkI7RgwciXbG6KXvjxOPkag5XSUAu6lCMjUiQ4SWpdVUNX6IUIa0Q25CtPChvQPRlIqYdO6KcuR2XWmFWCCXICVz/y0ZexEsELzZjlySqiUSAUR7Xb+AJkBboAbdpRQwrH6GUbaWyC6TRjOXUoCs5xkvpfXxKwg3EnyUO74m4Hcw/mNJYCJ8KzvuBNxBFrzGSLzgP3cLSANAxPiaEpgYDMay3jQRIQRb2bAL4S4EloSVzQAigsVUYxr0RkBh7MsbtoXwsQmS6BgWzkjQU+y2TTHWGwkWZhAnbB9Fk/IBuK3ArbN/sCboA0B5wZIF665SkwD4sHwfXAS8KwHMNZhMKlGQzNh2xvvG+P/8seOPu+APACsRVlZl8E6E394WABkMQvGAkwKUHZCdcH9XlnS9qaMAk9RMsupJ5XaZUgmFu0dLVD0FguO2I/MnUGlxkBaoLZ5OkvDlXCZlDlLHi/DpiGZvV+IPBDRe5Zn2ULSe/xjMCIMZhLswTzKC7Oxlw2zRRRPUVD59/qMeK/kh7h17XPnQGTZuR4Z4veTYlCwnkW+gSjphz/mzRpaoIe6IhLsuRm4+dCN6y5gwM0f6My4+SAuRJlLsC6NXGbnbajAuV3VRas9WSWVCFA6nlCH0xb/7K7W22YUxe55Y36cA6IK7ooqoG5NUO4GWM1bDiREBCVmRP0lYBUYoIKpyAVBoVyQhnmHTkD7MDRPq+6l3dAUv9taTNCKzuGeOHU1JAmRKmtOICoh3wJAt1Q3HmloId9xAuBHhK6/qoYXNUkiocZqEkZLgXgQbAbuoeudGCcmIwo3ZiELRvVFU955Sws1cdog0xYVK0GpjKOpCpF1KNk8E9UxiRuKEbLhhMcRfCNi2iv+xLICslT4bYrNFkRkoollz6zySc54AE8jOeBilB8kacf1RgG8o+EME72Q5mogq0d8ZeiYEoXL7UpTQ5GLE1dbzYmtczFdZ9wapNGGODVRguaKsq0YExOr3CH2YkCo+bn+1tjw7/BkQf0W1Ej7t/mi8fhV+IHgNiOcSdJKNM7KBOhX6+SwdHbLsrnvNPdbzw1h6VO9ImMDGMkZiUNAiH+L2j1iYACxFObDEbOoiu0+M7IhfHG+pjOLtVYkBQKEguRhBiJxsfcSNkBPVTdShd/dcmunpVZV06tAcOae+nphmonKvkeMndCondoIQCYE/v/YEAXatmmyc2MH6F/rtiAKM6gXE1PcDBI3KVdwFPxW4e/mBCDGK6fSLzXxBogxGAUNZNQaUCJAoEgRMPeN6AkfgAs4tbYfyr0URH4AVpSL9xsoUZHioohKWlQHTwOPGDIhAStaAMxHsojEHSyKktIOpIK0JJReUPeN903xFXDZ8TYyvnPCVd01BgQ98gPABQmFVb67Ljt29f4p6H/0GwoKEhRjIH8gA7iK478Cedb3fyoLfy02nkwgrJxQBtr3gfSPshQD+jkUYb0S4LQkLEVYBOCXQkrC+6eSkJHjfMr5vwB+pAAvw2xshrUBaBau9B8p6xnQBYTfVS7F8TUq4LYXGLiALjeaobrI4jJwZ7wD+8yPj+yq4i2BLhB0FHwLcpeCdgTcR/C0Bf1uBFQwpgvsH8J2A7wK8G3PwBYTfE/BlMQm5qEE9F8OPO1X33mjzcOnBDzBqus7hu0ElEIMX0siUI/4eJIo/kSg0bbjzuvHg9b4f0l37WXCkHpG71+98VU8ugE4pjR4BE5yL53ov/qm3kbdoxMSNyaRms0VStTsUImQQdkMtAOFmDukUdC7Ke/m8aP+K6T9G3/+KyCOC72lL/2dI+KCW0X3SpmNU54wIO9yvwWbc33MJwdVHo6G5q3fp2xRD4tXrydw2ATThjtv3zrXTEXuYAyHlLFW1JC0OAOqpo1ydEgLn/J0oKAlXvbgTCFcNJUPmK7eVAXjKDOPw7RmHAtWVF1MmL1BvlhR2O0FTSih/L1gIeGNCseiplCvbUAlcgiIYLoIbq5TCxphJKki7YIOgYEcqhJIZ2/0OIQFLVibGIuhJGAs0md0qqupiAr4Ykk/MuJesyRQLkIzTZiTcZMcXk2aIANkZkoG8F1OHahuLcd9vvCARIxVAD69hJNWlgZmQqWAnnX8QYUkJyyJYFjWuM3R9sKgk9H3XRbIV4Au5Wg6qJkrGOJiOUbLUdCOlED6E8J0F3xnIb6wxH3dUW+ndOPV/SsFCjDcmLPbGdhK8A/gnBPdkQvFCyEnXJqOtOyXktkaD3cElguqNZAisJmwcrlc05gid++uR8Y4wqoyqI9efLimQI+OufwDmxmSaXHu93UYKKoKGxwfEKGZ037WkisOKIhSTUFDVeD0gMj7NuPbgbVGN0ICJ1GTBOskkDcadEooQPgTI1dCsC4pE+0DO+lYEbe36wuYwWcZt9+kUhs8zQjCqYcjUSeFlVKOy06tIJIK6R6Nja1cb8o/Pr6H8GvqTWt0dETGiAOqlnygNReIAMluBjzfYEoo9U5KK/bsTBQApSAQJuyHxAoZLBXpvoRzevKmFpCCRllGbuICp1DxdZKd+uc2BqgRgOMCkiyQqcSxhVxBE7R2i8uzKhN8IyCgoosFc7qrqck8GFPFJxldSdQxMNZlY3fs3FOzYATU74J4LhCzVEDMSEXZRHf4iwCqCVQq+QJHrbwSsKSExIX8wcmFQUYTOAixgfCkLfiNVmYGAb3c9fCffC5b0hoVZVTv2/r7wm5YNaamJE8CqXCukmQveSTHy27JiXYF1NXuF7SSxfE13IWX8mUAr40vSudJzHkijswuATY3dbsvMBHwrhD8I+IME5Y0tkWE2pEnYDHn+JwQ3AL8lxpu9gTsJ/kDB30WQE7AyY10Zvy2luRyLxovEIESpUosRih2WmTUgqmCURlD9VCjh2okKaZQaqrpeTso9gU8Zmmv9Mp6I2r5H4oHh2meh4TV3FG019b5PLeFbh+xNSWDbB1Tvx08tmw/YlypBaaZqRmFFMaCELJqe+vsObCL4EEI2uXYjwkqEL8R4IzZ3vzaTg0ap/3OduSHDqvYJHLcj3+r/PiMK4V4sF/X+oIZoo02A4/fwTOX4Xce/BEKwtM9OzRSM0QhEoetr8NX3fhwIoPWZnUtMhjYpm4qm4JYdyRcjAgULdlSDsRGFBLMBAJVQpOryWUxFArxZQBgTUIpUlY5eU+QOUUJRCBYHoMhqQTM0e1Sb9rLAT+5NBbhllS4EqlMnY+2KqAopl1Idz96kYBHz7ye1V7wx487Ahg0ZglwEtwTcSPAbEzYAdyG8gUEl4Xa/Y98EJQtSUYblSzZnCyZ8KYw1E96yua4SsHLCl7Tgf1sE9z1j25XTJjDebgmy75ANwKa5sjgREjYsnLBwMgLA6p1UCPdtR94KsAn+Bo1T+Jvc8aUw3oTMs8/eVC7YN8L/8fcN90LglMCrrik2OxAEQGaUTPgHFrzngj/uGZwWZCL8r487vhfgWxJlEZKy7yyqNZCcUCD4YMEfb+qF5F6n/wnBPxPwfdWA58zA1xtBbglpUSwjIkiVAAgocY2grmktzEPL1Vo1vcagGjp8hu+V6R2JwUl5//tTJAU/nu5R2zMY8d2rfetRfv97LDO2Mqp+qk3CMGOBGsay1SCxDIKtQUJwGjXiUUyy2MHIAD6KBuZtYp4PZsgu9kkmiiYQXBlKk65HFRIN37uYg5nUkPpr0fgc01xQQPYVcQ9xBRRUQePfQfXk0kMgZjVuwKSHKClE4lEN16TXBK2eePIYiEC+iVkzc7oKSVAgnsBHClbaLXq2ICEjOVEwzr+7ZiwBU673WJQwVKLgiN1UPJ7M0eVNMd0AkZiUAJTiRKGYLz4AyUoQhCpRUFu4IBXP26v0UtdHQUaxdB1Z2RoivFE2tZWup8WkVwhww4bdjNaL1fVmhCOBcKNUieMmxVLOLGAhrKK2NhLCCj2WM0muascbEt4guIEh5m6zgMCU7GjQ3fTpRW1twspYICGt+kJFCGVnFCHs9wTJBZyLEg4QvpSEN0m4CUMKlDyLYN8I9zvw/iG4C4PXBe87YUmqekukUddFGFkI30XwrQj+UdRdtoDwBwR3ArakijmdY4vmFtJcX6IS50cCvpEqmoWAdwY2VrfbIoTMwM6CnCyvlRibad5c6hUkvfupM2YuGaC/58IkoSHw0XgceEpXOrTfgQBQvGbtxHqfwaclBUFz/Ryv+6B874Ocw5eu/KvQ48CeBODk16zPSowali2ddBBTS5idocRROBuuUsd3sUUNYM+iXgnSpBTXmXyAsJkr352BhQW3peHNxPBYqWrQrdLDgPiresbCLj1Ctyu39M84sg80rUka9rtKACNRIEXibGU4SBKHuoMaqg0OnYRQ1U3+t/R9Ee+/992kDyZAiOph7wTBKrvl2VEOX6TgXjaoUiYbty9YsFepYEEzJB+kB1Mfqapo15gEMYReBDcj6CsBOdn5w8XcZeCuqm23EUSlRYG1Z+7QspvRWCUOT9vCIriJ2zIAZEX4C4k722CXHYkJt5TwlrLlEipYmHFLjH98z+Ai+B0ZHoj7ey54A/C/3Raba8Ky3kBglJzxlnekLSPhBoDwFaYSJVUhqe22uXIshXHbE5aPO5KSQrzdEjIl7Mj4JxXcIZBdQKQSCXAHFsZye4Pkgu1e8P6hhut7TmAmvDHwZWWsxPiaE97KDWtZ8Z/fCz4K4Y+S8O0d+LYB94+MTYByJ/wf7wl/JMb//h8LfvtC+NtvwD/LGz62Bf/vf+z4vgu+o2CBujl9uzH2pOof7PqOkwCrAEsBclapP1PBP6Tg217wR2JwIuSvwAdRdSDYSfANO/4g9fR6u6lqeSlSTzikRTQ2YiOUpCl+6vkeal45BNrNMFoyAuAIXfy/SBikEZBi9dQgulD+Vfh0mgvvNIff4z2/1iSDz8gIPYwon+r/41+8zsM1rn3qyYt6D5E0wtCMz6pU8DqlqOvdblKCbr5ghxifJ5gSg7AZnRFXV1CL33LkHiN8PXLWEbaQqZJY1O1yDNyKSNq5dkfEXk8kMkEtBGqqIufonSjEPtQ2vJ5HRGFRtZITh3ovShKxfCQKoW71eFTOFqRI/BbtA+LqmzsEutvchbQSBVICwCY5sEkMTaXUCMZCzT6QDEnfoIbeRYDFkGTGHmIZmjXKV1sJcru7pBa1JGCFIpUivspUv5+oSR8EgEQJigiwclGkA8INGQupzn1lxm1J2NMOyQVfsMM0GPidBTcBlryjiNaTKIOIkWTFgh0rClYSEBFuxFixYCF1R80MbKmowVmAt9uKNRXcjMUVARbSjEc7Mnaf9+TSsTJHVPQ86H0vwFaQshm7OSElAifGmtS7aUkEKRvuHwXfv2d8ZOBbJnzfGB+7Si4QoAhDZMVeFmwfgkwE3ID9A7h/7JC8gIoSdsr6FtikPVkAYerVKhZ0xqJHt+rJoYJvZjsSJj17QocOESWY7wT8AcLfboyVBCnnmnncHSMKWQ4oMZxQgOo6ZbYFVzMBDZdGKYDC9SoNmCDSIWGgS/h3uPerJQXVzauI7NKOotqQdsLn2jkMaRvlRyQFf/Z4pf9zO8LxXuD2J/UwGkGIhKHYZ9VOi7mMiupni82HVmuYs/MZ1faE1MhbA6/QpIMMm/yKXBqXXgIX7wi7GrRCuuYxvq5T7YTrXTI6++RwrTMquwvpEF8QbQ5de8EIXJG9RTbPpAPxugeJoRI38jgLdfkEikbDYgMjYyXz8TK9PURActe3IgUrzGWzkxR2jbw1lZFLEkoMdiUwVKpxOlGp07dKAQuZB43qi4vZGRJDeWZRFgLwjadqJd8jScRcs/XfJi5tlzo1C1y9Zc+XgmIT7xHVLII3FKwQfEkqOdyWBR9pQ+GML3SvCOW/AVhAoMyGSAGWu0Yt8xtWyVhQcEMGE+MNhDdasEIlhV0EHyj1jOXfV2BhxkJZ2VEBBKvq/CnjbnNNi7lpm2FbCiNlgPcM2jLWogtvWRLSksALG9OTsKQFWy7Y9g3fv+943wXfdsF7WXAXttYAEfX5ygXY3jXWAF+A7T3j/sFAeVMpAAVinlSJFrhxSHMfKT7TdBoFVNiM1KjcfhaLKidA/KCInQDRXFPfCWAS/N9uizIvuYAtmV5JKhFkBiRTS5RYpJ4tTs5Ze7xCwHCRh64MtksNRpgYg0pIlLDV4LegTvoMS/5D6iPBuaSQhuvj/Veh5/9nEsBAUTsYOX+EZ7m5ldYSja1Wja2qlu72WYx9rfEE3pdZ8jtH8J5Rk3SBFJIOie+kBsi7fSZqhCD5UYoMO4BdEXYJyBnkRAIHjr2qg4LuPtojIiGokgINz6R2vx45Ge0WTySFeuLYAnUpjSqjxfq9tHqEAUgBiSEqFNygv1kyiDbA+NIE9Q5KotnOijt9U0HCplKBBKJAThRMKpBsdgZF5guZBCG5vl2NUzA1H9yt1L3KWmAZk3k5VRdkAEXtBzu1qNdSjFBISwzpQWgrgBsrEVyYlPCUjK1YJmdzpwUEb1RwA/C1EFIhpJ2Q9oy1FPxGWaUJZvz3pCv/HYINyszczG205HdVjUH19iyE38D4nVd8WRJSetP1nwV/fOy474I172Aoh0+WIfXb/Tuy7ZmvhfCVCX/7281YX8Ef74x7Znz8ccdaYIFnjDUl/MffvuBeNrxnwd/vBbsw/vH+jj2TqpeKAEL4YvspWYi+S+YbbRBK4PIb5IOx/Z1wf1cD+G94B6UFKa342DK2IrgVdRDZQdiwagYBIkhiFGKUYodepQQ/tlRyUfpnWQ4pA7wSUAg5E74lQU4F/ysBf2PGbVmAnEFSsGdgZ8KHYwaCEvcCjbL2zLh7IwK+RUdc6QQBtuaim2n7VI/JUlperSytPCb1nsEPnafgDRT7VhuTJjW419VnOjOCTpL/36N/GUr599Erqb/e6nJ/Ir/enBJh6qFGENwG0ROAmWQCXWiOYO2yc9I15oCMg3DO2P64Inp1NXRdOs+as/udsdg/HaEHF9LOcM3oVUoDYq92Bgr1xHKOxP1esCVUomDX5VX1EelMkyHzZLYAdyMFNhBy5eyTqX6AAqHd3poRBclItFf7wUobmm2hVym5W6qqlVSSAOwEMdKkbRq4VtoKrOc0wlxXUd20CYCYq2MyokBoUkIxaUClfE1ktzBwg9oRFlJOtJASnmzP+IJfRPTP0ilABJzN/iEFNya8UcJXVlanFGdRCUu1aiaj24IVhIUYX5nxlQq+0KLRySB1TyWVjbkImBhLUS5FtSBZDcrEuBl79QZpmXEXNSDvewmuwoSVGCsl7FKAnFF2QRbGHQU5a9I6MQZLDd5mgDcdo2oGMgoSkixAScgbawqQUnCTDOaiZ06weqYJCjyYlHREut9Jg049ZxknnStPl1JPVrIoZM9vJLaH7wx8FzXsb4kBKuCigXwfAL6jrYOb7ylD4F3GYlf5nMHo6++I3t6tEw5XQ7mKqePIf7X6yPntWP8+lHEq13BYz9V/hjxQ/X8kBw10vD2ibijfpZnmTuqkIIXT04SU09lJPRe2tgbgIU0D9gp/Qedh7ECNwGXpPH6qaidID+7PTS4lmIroxkBKFkafTH0QkLtYsjbYwfGcTKIw6cK7W3Mf2XV3iT2cqGbePpKGvnKrp0PmI1GIbqdWHzthWPuyxOgS4pEpaSnfgZLBJeMtbVjJVRsZzKo+ghRQUa4/kZiOPAOyqTpJ3NMoq6RASlRuuMNtCskIQ7UzUDa1hLMCHpjmmjDRFBMCeOhhVryNAsIqGfV1QN95Jn2GfCcLao4mIagXUzErgymhb1B1z1oU8wgK3thPKWxHeX4tZhwN6bSXDXjzvEtgvCXGb+ui6O9jwyqELbPq+YlRaMEXCHYIvkCNpf8jLXhLCQsz3rdviuD5hmzquH1ncGakvOj5zADeiFFYZ9aP/sS3gvXG+PJlwW9vN2xrQtkFWwbuAFIpoI3x/j3j2z3jjy3beQbmxGsSgeVVRSHGTdSeIrZgBZo7TJBwk4xUFtzLGxbZ8DsKMhhJEhbcsLJK/CyEXcz5A5u+bUkoRW2Hu6iGQCihkK4UJMUfuXDV/xYRUCFQiIP4/35seL8x1tuCvy2MVQj/2Al/CPA/7d3cCPi/v6n96JahgXUClNUQWUYV+8mZjgjB5tAx2x3il+ppBFEjdTV81weew6eC1wb1VdfBSBB8OzhK17KfkxfOVUORGDTJpCcdjftv7TckXup1rvrJDSpu7YOKyD3Zj8TAnAgDQejUKkG1U7lyN9gGeuIqIef6XZ+eyNYJqQoDicxjiZASQCy1TklKZCggXiJDzEaUIgHqEDShsyPUYLWoKvK/NVyjdi3mPnLV08HTqEojguQRxtCALJixmES59RU7VilYSRF4oh0sLg1sYIsSTsggyWDcjdNX9ZJKDK5mygeikOABbcUMrlmTtzlRIJgS0TeI7iwyE7L7rwNNqeiGabWJmK969SWUaqBUDlhdTTcpdk09YVJGk0nF8yg1F0qNPNYkbjcWjQhGxof1hkSQhJEKW38IN95Rzy2o63/FZsQtF+AmhDdJWDMjgbFktaMV+cCt6Dh2Uokg8WLToRKJJ3MU1rOg30tRt9MtYUkFLIzfIfiAsmyqiSHcPwokA6mIusOCANkqI1ZN/tK+i70VgabkKMLgXMCSAPnAIro/OC0QFGDPuMEkH1KCoDOQoS7lS60/uVxJ+plsNRUhENtqIEKRpOO3bS+iquA7Cb6VjHVRhvC7CL4X4HtDhMis0VKFjSkUJewoxjS4/y83LFaFxCBJNM0Meg8joK4zAmpa8SpF4DX4lPpoJAzel0YQRtR/vPIZiJLPsSfOxc+eiwi8ESrHZG40du8g5UNdVRSfjW4+k7+oM+DJX0Sg9r2LH3AuPdxzm7UL+oCpMhiQxBrBmqSLQAbrYTAuJThyYnNhLUn6fob+uh2hqoyCGuowrqXvfyUUo7pqpioKLqeu61ef9t0Iw2bbMGOFGkHdgyhhRzLvI5jNgMXdSzMW+rB0DkpUWIqpj9w2ca/qo2h8dnvCiqZeIqhUn8jREOuqoFJTGOyV1XA6J/osKbNRzAWEAXgy/poUU1TFoIFTFiVtymGCptYgoEofTlQI6j3zBcAKwi0JdhTcS8GKUjlCLgTObMdDMFZWryzKBasFdxQUZYKg9o2bEN6KBVgKYc3qdp2zShGJgJ01rUuiHS3lS+NAdlak+74XSGbkjfGFBBDG38yTiojxUdT1c8+bcvwgLGDLVdjiw3MgAL1PmHsGWpKRrCosKpbBlRLWRMgl454Ft3TDAk3EV13AkZGFDTtZ3JKtph1LbXfHopmQIXrSnDOJMSIZGpu0MfBdCr7aiXjvKPgO4B1kamFCYapR3JrqhICtJTes6XoiWrM2umA1tGtNXEBDmM6LlLHca/BjEc0vl9dBzojJK21FacR594jse4jYqv3uJYqeMLRkB574IBKDmYTgvONADMbig8QQrws3zt2bqrmJBhsAsXouMYDvEKxUsCQGVtVDL6G87QczTFOLe3BpwglRDCxjQz62tzmhT2A3EJDRmF0lhVG9FM9LWKHI2jh5dg8hQ8huuQFtxvXv+Co7Fiq4udEYGQs2MClRIKsnGSFp9oGM5aAqiuqjaK/YTYrYNCYBQVJAqVnKU5HqHQbbuCU32dcznCbAguakpsPwY5aEgPeSkQuwWyQrRFUJIkDZoPEYIOxZ7RjMjCVEH90Wxte3hJsAnIHtvWDfgY8N2FXzpiucBcQC+dhAifAlJawC7AXgrC9cEvBx3/HxkTX+YVFDMxeV4H5/W824K/iWCR+F8K2oqkWK2gkYhBsDSyIsK+OPnfG9EP5h6V/KTviS7nhjwv/2hfGPjfC/Nsb/LHpfUweayzaaHc/3oX9GqSGjZquC1HIrRBgiCUwLmBO+JkFhxs4EeruhAOB/fsdG6sP2fdfk2Bv22oemWPQVwlixVULhOGJB1ne1qFQhxFjXG0gE3/cNH0sCJcZ93bAXoBRNunengvuyIgN4B3C3FOJ/W8gEcPdXa+ilQ5yjmiZ8VknAiEGUGmriwD+LKLwCUXJoVx5x/Wf1zBH/0cLQSwXxd5/yohEG/+0Lok9bffYXCISriia3DhKCIccqDQS1kozPOoEIRAHUDNGe9G2FZpEsZN4xjKpaqkZlJ0AcpIpIKLiVib9HCQR0vB6lgkjIqsooQRWathrV3TODxeMFHNnvNYKYqiFZkfwixsFTIwqEDKbdDJztWiQASjxykAoU8atk0YjCShsSMm7Yrayo5BLYA5cCWppqXcOllMqgeUpt9hVFmtWU0BKqFQFoLxq8tKP5xtuGcFsmCBrYBAFcBeUrVjSaO0FTQOwbARtBNgI+oB4yIuCk2VRX5R00LqKQee/oecS5FNBd4waIWT2nFqkSDCXz66eCJOrltBbPadCitL+wnc/MalsQIXyxVOQLE75yxheTTvbCeM8JK4q5Y5fKpRNsbQt3RAHwpCNUr7mS0Bk6Nd+bXUMKuCSkksAWiAe5IwtjkXvlvHYAarVAdSaxY7Mqi9jaUKnQfxOcgCdNfkgLiPU8iJ0Ed5MQMxPKAk3OJ/rMzlSDF+8iQBG8JZUsVtIF5WopJwxjJHP9MIQa7wnQpcsmbsQiPv8MfjlR8LZp+P15iIjcQRdKT3QaQYh5XJ2oOLcxv+bJi0esPjMsO9acFOHJXyjXxRPMpIrhnhMGcDtUBkn1liSqn11ET9j6jQlvC+txggwwikY8M1DMNdsJBwh9xlHj6Otvu1+NwVFwcuIQbQV+z/9clbQAzGKpJbIh24xF7mBkrI6cJXgA4UMlCWSsRTNnfmUtr95Cd1MRqX2BBvvBYg6HbFLDiq0SixVZDcqyVeLxhrsRGwuIE0WqlbWQ/u27CG572ZKOkb0yXUF+z8uSqGEyiyBbUNRuRkaIJpnTrJmlUh1/tkArI+j7y0lwXzLWddEEdzkhbQT6AOh9A+0FqWSsC+HrjfEfrDmEVmHcpeBeGHdm7ALse4Z8FLX8ckFOhPtmPDkJ8JGRkmBNBdmkmjcQeGGsa0LioudEf1nBrLYGFvWgKm8LiNT+9f/4mnBjxvsHUCRhx4o/9gwUtQk5Ql5tb2ayVBi22KLE4MShce7JCIdL+wlSzNf54w7mFZxu+Pj2DVnUMO+bTMRVRVv1Z1ut7sZiuFWKkZBrH/TkbP0sklCwYb8LXLn5zwLcUfC+qEuyHpStTMR7SthywfeSse3q0bVCT7p7uynp6XhbBO7fYSQSA7d9yLQa1EivwieC1xrHH2HGz8dyZ/c/D62m0WbQ7vvvaCxuz/TnJDTM3Ae+nRADaPbUTi10LHIkFgHRdzFu8btxopJ6Ll/roM72QKzEIZPaHbIIPnJBYRVD35jsQHaAFlV9JFC1EcgiNaguqn00+hJ9Guwo3Xj/o+eSPRuJArEgpZaNNLlXfsngvIFlR0p75dbZDLwr7mDKSBZDsCBjlXtV66wWvOaI34lCUx/t4HBtNUKhiF+3+RuZuyoK1rKpm2VRgy5bGoK40nT6CRDpk5aFcvW1E+phKvsOaEI7TaqW1eZpXCLqRq6Hr3juHAA125YU28ykHmiJIJmwbZo3ad80NQPvDLwLKKsqKu+qmvgjF2wL4+1G2Arp+QnJvJk2TWaHTYCFwCvhdvPofsH3PzQz6DsR0qKuqWkpSFmwctYTxAj42ItKqYn0bAkA/4M3JBasSfCbMLAD9zvhvhO2O2PJwJsAXKRD9K4eimqjhvCb7U8R8l4594JkziIJSAVECzgximTk7UOPIDXi6P88VJCRkZHMqMyV4CRkIwa++nKVUnaTTBKKEqYQq8RgFZAtOo1SwrIswK7S2T/Ljl0K7sgqSTOwJ8KdCVsCmAl+frevr5nnUMd0h7QWIjWusKmWPquiwSfTXESYIXuZ3O8ll8+TCMW7s/iDaGXoCcB438/Cgi2qSATmkdDjn6XTnhGDiaTQqWQcmRvSj8/W6F675si3Eh5XUwXiIazHM2bSk7NKATYb3Q0qc66JkBaxhGQ2f47cGeoGGfrhhMCJwhi1DJN0qj0kuK5iUeLjhIHMVdTtB8lcSVF2cNkqt58oG9JWzt7tB0x75fJX3M1onMN9VQ8R2bPSCIYmsjOjMqkX0yJOHApuQVJIWZEu78pJkSUn61YR2cYsGlBWuTInChTYEzbpohCw2QYFkLNmLc3ZJAxdkCYhiJMAtUcIkFg89qtuHo8+l6LpFbIIyl2JDhdRVZC5RBXW5G3vWZAXTW29G2FKC9RYvgGyibrc2VJYSjIDcsH+npWYgfDbF8a6Em6WbmQhva7u20WlgkXwZVVCsCQNwrtRBmVVXW0bYdsE2x1YhGx8huwpSgItO1VT4CXLPutEI6Y65I44uPqUWTOrlqwoH6IpPJomQd1WWzIbTUZSEI3PDK5tNHWSzpAa6/WfGuVBCeqz5KopJeQpKdYpGfhWdhTS3FgpaU92Juz2zm7OiCEwJmHNYLhGhCq9wpgNDQKdEIZPwJ+iPvoVEPn+8TPedTc1nR9H8k0yaKqjllxnJAyPWX86Xj6TDqIUESWGicpIxmsEzcvSEQV0XjuVKC2Ax0R8sGZ//E5+PjDwdQfehPC7eWLISppimADiYp5G0gjF0voUzzWuyJ+0XxwkBfFEdzV6WZpayFRDbkeAqH9XMoR+IzUy37BVzx8nBItdS6QG6WTIXdNbuF1iN54uGppdEaBxDGpA1t8rNlAR8A49KS1DI0nVvt1OwfJNZJsNYpHoHrzkKiS0++4kIAzPtAFRL1A9lSybyijqes2ewNTyBJWcIUUDvxhaJnECNTckyPesMQICrEw2loz0IWBTTyEBsmhcwg7gP//+ru8Pgt9WTY2dEoM+SE+W+RCUVPD+8YFlATgRVkpgwzYLGMvO2P+xa2K3ldHSDypyk0VwN5WhUAGSgBdNGvkhwD8/gHc7jGfljJWAlVs8UPM0aiddBKtOVTPFpOg7mgzpBEXjVlYU2bBZwFrCgkJJD+oBYwNb1idVH5Wwclw95ATADcw7cpUkFpMQ3OithKIAwiBa8MYr1pTwlQr2krDJBnBCYca3lCAF4F1wIzXYv2/KpGUp+B92PnWH7KRJDP67g6DWFNH1l/34VG5YcpQ2HsGfRhQ+SZxOYSQOc8mDqkgl9Xf762ugUD7aLGbEgU4vj5z0oVyal43RzQeiECUNgmGOvl7l4inUQ3qKFMGOo1TwEHdNwKaGv8T6SWQcqauKKLQ59m2QUqphOeli1jqiMTkgbvMm8nPpFgqG5Pq5m1Rg0gFt7Rp5uUY8mt1gN0O0a3+b+iiZcTkVjT9IRaUC2pUIVKKQ9ZpruCpHZQi+QlD5xM0pgSiAYMRD1VAihGL2g6gyosjVmeFCjPDU+uARr/a+a5oLn341/ppWDnxXYlcESKsmt/uSkqLZbEnyKgEiDb7aVKLRtWoGTlGD9JoYJALJmhISO/SMY1bV02591rWhqqucpSZ9RAJoFXwU4KMA903PN5ZCdrSrjtu9ush19mQ5BajtW2INKlXi0SQF5eITdrgaKlnq74KcM4qgOpcWZAglVesEbp/sWTbErt/F5IB2X8kV17KtpEspHlhnEdgQ3FhA5oZWD9Iy5pWT5pFKULWSSwolAfBgVQQm2NZctC1UmlHifYJkJ7RK4MmSN0lnmHgMP0QU6HmRnyYK1P3Fb6MXkUNM0O0EwWuKWrpUn+j7OMHgrj9gauqS2V8kAKPU4Nz+A6RbOc2ZETpmD/XrS1D/WOIkVSvpAtyt6B+lYC16aMpXJtxW4Cur/remxLB2/bOMies8WyppWL+fx+xRzJJgHP6OhE2RclDTrNhApLmLVhoIADa82fc33DtCwaSupAvt9ty92h/Y2lqCdNG8j9QgvcgOdoQfpAJ2omA59ODSguWcJpj05AvEObXwO0oV3RJ0QmHxeNsmyMWCEIPqyTNZetoLQTEkqdx1cvUS2xdSVeC6kNqM1KkdW2aUncDfd/BWsIrg6xfCf6SE/44FiQR33rCJnv2x5YScdUroO1AT8yTt04KEGxKSJOylgD8y+L2gsIogOwruKMCixOXrCvPBL9hZz6YWs2l9LMD3rVimU2UNQKSHz3CpuZzEGBQhAbPPA9qnqUSEgKb3XwM3v1aiIHmDFM2Bu1SkbUFuSFgqy7BXSWB0Q+0NzctwTVVJrr5y6Uav6opeSsZCCb+tCVtmvO8CDZTTI0YJegbGbdXYiHfLbMeJkBeVupwX7RDViaRQJQSgpuLwSOu06iQmF2//Gkmhdf0YtnbYL4cyz6CphfpaBVUgqnVHzNmXP/seCcCM/Sf7oI7DPxCDiMDT8Zp781QPnwcqJYyEZ5QyxjoY1WAFS8AnrMjbI/N3CDbJ+Nj1APWPm9oc3lLC2yIqPSylpsFI7hZrCL+EMbihmVmwsPl8WExBEvPukYylEgWLL8AOpk3VQWJcvBGAFZsalXGvKqUoPTiRcFWRqqX2antQm8LdktkVsHm3OKKniPTvaFKBp7vdocRDY7zqYqNuVxphMPWQ56zvFif7K6HaXrm34xkrgek4O7vshIdMny0W62DrrkjRqFwW0EIq9TnlKgR511iHIoK9FNzLjm/fVf+fWNNSgxj3bIFWQsh3gtyVc08L8GbSQX7fcSediDfo80SMnZQ+7TkDi4CZgBvhtjK+3hK+7buqbFICFQIXRn7fsO+q2xdjsDIXjaMwBgZAU1lyIAguGVObB/VoyyC2xIFEyNg0fxGSegMJg3BDIiUEWdwWsJj6J3oVRVWREpANq5GBFJB9JArobBnuFqvEx1KuQ0ClaHZd0UOJGAlfxNJ4FMbHVgAwNpC5jepZLQRCerPUKWZn8jXktlMyJG9HPKmEWNQute86aYUB3FRSIFtorwoLP0QUXFXjO0MmVyKnD/TM1WttNDtBA6r32mcjFKN8EYSs8DyF76NOaLg2u3wmLTz46zKZjvRnlDLOyo1EA2M7Up91zqqY9LAD2O10ryKkroOkxvOVxQ7v0dQaGudgi4qlnoEMlpZum3XGmYpy5K6NFUX6S3EvoB0LeaRyb0D2hHdRaliQzUDsMQTuNhrtB1tVF0VJgZDBpeg5vU4A/NDkDaomcvtBtmumRnJJIqqJwnKrAmOx+qi0e40oUEt25rmx76hqIXhcgoR9EFVRXqXt3EoUYIFntsuWm0pomrjNEMUHIJuGPmmqFsEdGcJAWhmUGCmxlilaX9l0YRADXAjrLUH2jEIFOzRFxltIg202aFVX7IAktcwtRPhyS/jYs0bp2sKTnJDfdwvWM5UQaQqQyjBRY7Y6pitKCmHfecwNp1w95hIVM0KbVGD5qIuoiskJAJlxmavqSRVBjvBVfZRtW6n/UQuxc+uBHunr9TWigPp0ErbT3Ip+guGn0d1Q7HQ4xp7F7CSqCmBhfAgjAfhtNb2HNKcDXRbU2bt8rQigZ0EXs6h6Mr8F6s1Uir6XF+GnsqRy+B4hMsJ+/7PqpF71E6WCUQYZWevZtbGeEyIQ646346lgZ0blNFxbJuVGVdDZ/TNJZPyL9c36GIjDljQ6eiuiroVU8JU0jcF/Y8KaNHXCwqgpu50ALCEKeiHffupdBNrgcQIuKaj+30x64gh+64mCpZe4BQKgKScaUfCoY5Uo7rUcV0nirizJVkzvjUYMtiYhcJAUKBAFuG3BM2/vce3h8IOda9tR09SQ/8cA3LPG1VXeF/dqcuQfOCVqtdQC7qpabAPthSwVsqhKYGF8/f0LyAyK9G3TsbFypmtacCPCUgDJRfXUDCx3Pezm/l6Qi9oxKAnSjbCmZIKHZiDVM4ZvuG8Ze96xlYJMakNaVmBZCH/7smjOpI1A32GJ7zaNhSiE7yXjLkqsiq3TEqXokRkitMDNuC9me8BcoTllMAML75qKggiLGZCd8+9VQdGAvNR7LiksyLVcDuVa2f1gkFbFmh42pKf0LGBKWMScWszIvciOLAl3LEZCVPVEwsiZ8P1jAxuBWKiYYsztEi1Ww2UEtXgoL5OqFKHeWsy6z5mkugu/Cr/A0Cxw4oX2bu3vKD18Tlp4BA2BHxVXs3KjlHAmIQAhScpj2jHj9kfEPeGApgh+lEZG9dKZN9MjKcTuiREst12oMVOLZlFvnBsRNia8Kc7BkgREpIvLkrtRSETHyDU4TQ3KHkOgRuWoidWYA89ptGPhZkheK4K32ARqhKTFGezmTmoEQ6zNYq6YLgHsioglEAVyVdFAKBxhV0Nz0bLjoosrphr1HMkHSUILmJi+i+5Ub6Nl2u4EjGNQEbU6qqRA9YAWEk0rve/ARlmPgi0C+ShqLyHU84qXRaOOyYyPwup9lTaAPtQISiJ69rUActOBEJM+w+r+um0Fe9bznIk171KCqFlrUwPmvQi278C2A/ddVFoRUfdZNORfiUBcr4x2vq+vV9Z3eIzZCX8FXbyMJKiahAUL7RBiFLboYniUsksKbiou2LHALZCllkvwPK12rE+VFmDX3A/Kvxd7u/r6iqb0hrqrise2i6L4BLIjVR3lCyB2BnghUFnArGeGsOkW1SfBDdxi/dAFlAFQYUghoABJ9IQ/ChHxnzkS+SeJQq82AuK76yMLPhlUF1pwk/EZsh/l/Vm5M3ZjgrUjUp4h6nFhj9dfcU+dlT+TPmZ1jEdXxnt+v+s3md6WIIulFGDBbhzE901TRL8x43cGbonw20oWoKz56VO29NKcAc4Q1hhOlh2JNlUdwY/AbMbm1UyBbyh4qzzZR8g75N5C95qcbqUNSXZLRaHPuCpptZQVtAvKRyMA7k0Ev2ZSgRuVydQeVW30jqY+cvsC0NRDPp3OIHhwm6mB6oqrt4PaJ1sfPuaEpkkIDg2h+HIWNPWKKHVQF1eGpuX+foeQoEiBfBMdK4C0qx/C+oWwmPSyibqPLgDKDizfgWRBe+qSrMn19HAn8+AB8O37jk1ULQRSQ/dXZvBewHcBFsHGgu/I+ON7wfddcN8UVRVuwliM0D/sn3HtBgnXrx2IQkIviTP04CZzfkhLBqUM4gxhJQnv5ZjbyCUAlwL6P8Zmxz0luHE6gauRO4VPjbBOKMiSzbC9wg/voipPAM3TgJEpGQYjzTQrQCkELko0FspIrPpHPdDNWGtCJQy78UV3cyIouybQTNCUGmRr1J1oX4GflhTc2NFf6/fBj6iP4lO9ymisKa62eO2RhDAShfD3jH6EoK3DYo6LfCQCsZ4zlVIa2hjF5RmXdSZR8FDfULckRuFiRmWNkL6XgntWN9Y7gK8s+I3VawLQs4KBlt3UkfmtqD/IGxyJ32uW0psJ8G+ym4fRhlRVQVYWGTdS7yMtr9HNTggSdg1eKwW8KUGoHkQZKHdV6ahjvHH/d0PIUSrY0KSKoGaqdgZH1oP+yFmPaiQujU6ofUW59BqhbHXLTtVN1ZPkRTvCSBg0eE2JQ8nS3rco0ckF2MhcP9nRAmH/IJSs5zyTqPpvIZX2dsmWhhtgSpAduG2Mjx3grHYHWexch52ABeDSmC22b2kh3ITxlRPKrvt+I8aHFPxz03MRttL65Hm6PGeXr8eaWPBMQg7fo81hJAruDSiefHFBOw/8BrWTZYBYD9y5JVX7uFE6m8TgkqirlHS1mourKWj0WrZrpVrIWqSzHtG6kB5jWsDIJMhiEoUIWrJFBkQjrwsKEkHdlwVYCsA74f4tY7kBtxshsamQqFTHhKpeFNFzIrJmp0XR7V1yQSnGaDAgCyNxxvpiFNsPEwVfNmcgcFOxpsj4McLgLY0SwPj9rMwn/2Iq7LO/M3rySACZ/Y2b4Fn5WbnZM6NUQoBhBP1MgB0YoIm9WNMCF7OClqKGSoaK4ikBN1L1UakitPFJoqqhRVQ9tFDuIpX9+yJqQF6CN1HzPtrNvrCrkVmaq2nLX6SRySxS2U9SgQXiNoGA7GmUHqL6yD2QtkBIXHrI/fKqkcVoRAB2rSYai8vHnqkEx72anPt39h9A3A31OQHSwip17FLVfNnCo/Xc5tZeRcAbVHVAiixptT9pfwyAiLBkYNnZiKuuFSmArGIGcaq5DCFSjwJNRFiJsGTCbnEHdwK+Z8E/PzLei6qNOJl3oBuWazyDzkW3hp8QhO5+YGhqBDrbTJrR3ZTrWrbYHDMALlgIKFTg3jts3z2O2mMSql7e3o+7nvq1pkYiezqhQGNBmFy9pE9ohhRXOmkyQ5W9dUAMj3/Q97gIwAXY7wXZDjCi1bLwksWbmI1GfGrcJdHWGQF6pnkulZkuRd2A+VcTBTq5PpMSSv0u9ffrBCGywCOifwXjjvfS8DmuQLs+qypKBTMJwa+tofzI7Z9JCl7HC8bi6TMzyeBMCmGg7nTzJIKdtaBSA9rRoCjYi+CPIrhTxjfa8UcSvCXBf6wFX5PgKxd8QcGKjC/F7QU7vohKCAt7fiJVAa20Yy0flSA0058anxdTFfn9m0kcN0+CJ7kaiF0lRDs0XcMOy+OjnxSIAgWkD1PnVKnAVDvi5RyJByJQpQa/ZohfSlvTcZ/VrCS7Eiy6Qw9q9xQXtpTdNuErm2GvgwhfFz2KqyCrPt8Pgoduen+N4l0Wy3Bh6qVsaRPuApSFQbc3LAup44AARKIJB3dSAmkINX+oYRKJ8GYcfSJgX1jPGBcC70qJ7u+a9+jv//jABwTvcEMyIS2GqUntCSVuN3NtxkQCiJ5GU+nAED0z1BC7LOBEuO93SBY9tMZPMYNKvyhhrwg0in9Rm0MxiqKnWisogs+VQOg6cPbWA9l8/vdKKO7gSnwXsjTbQpXQk6a9M5uFqmoX8eypGSA9FS+xnr2wF+DbR0beM9LvjNuiEeBsizGThdj4prWFWpkEI0yqyhSLMZLquPAMPkEUqH6zaa/3RoQ/lviclPAKaz6y6c9Y9if3xxxDIyKOv89UN2fPjuqcV2nZK3/PaOMjqSJGS5MiC8/lAzQOdYfoaVrQg2RKEUgRLKzKdUuM2r1jTTfdEh6THwGF6N7nSUjCWVti5eufLXNpDTTul1APpwnupLVsmfx5HR48JrVbrQ4EYoAe6cPE984m4NyZdQd+35lUQ9Yxy6r3wwWNKkAQINn4uxI4YjcYSkvOYqeyKNdpZUT0rIe8q5cRBBboRoD7sW/qJeQxGbU/ICVgEE13wYQFgsR+apkNrRC2LPjIwEfR/FvZUq4AqqaqgxrgkJxtmOM61z6XJ5yoblcN4mNiFNLsrlMcE4l6/Und7XY3sqHzxgf5DlJXqRFmmLeVvXdvL2ZXqJncSE9147SA04ot7ygi+LDo9CKasgYAvrw5Bm7chRjxyaZ+ykU0VqSgZpslaN6ru0XAvwKfUB855fQpkzpJM6JA4d7rBCE+HZH/zHo7w34jez665IzXE+oRV2dup+NjEbEv6LsS748njv2oJDAr/6pBelZ3N4WNMGiaitKQDvS97SKQXLDlgn3b8c4F7yygpWBPgrRQjXi+oeWfZ+jJYiyWjtkIBBBSn5l3hZ/CpgnvGlGAEQpHsi0vPBkBUOzBHlA2EABxzx+PVwCar/1IRCLRQSvvZytHm0DlAG3pEKEan4shci6aI9+RRVUh1ax4DcQaEhDyZjyqSQgUCFB7pQTmBDbPor1o0r0imiV13zP2TeugAhQmZIs12HdguwNlL8rGJ62ZQEBRwrKmhMR2ywztUvTEub0Q3gvwbReNBSRCTqgUsXju5khZ/au0sYxMay1O/SPdHgi/PSaELVlU4dxXPuJ0CpIajuc06LGiZviV/uCttiL76zFfU4Ei5t1ScuzdM+30OBCBzW2KiHFb35CWG9J6w9+//YF73nGHuhOvDKwfmvjwb1/Y9gk7V6D+fsLYijJw9yz4Y3PlFWMhOzMiC76THpb0CnyaKLT30lLPxTXeiEajmp+DURKYsb0z7Dvz06RJuYBVR8R/RhSWybWzLszKPZIUniHxSCjGIcWhnkkhcUwdnZUWNVqXbQ7FPINky1kJqGj7LsD/FOCPXfCRd/yeCn5LYgnRgDegqiriiRX6p/Xp4TRiB7nE7ecShN6rzIcj8h2wFJ4tsZz1zVUsXs4jkKs04J5AQZoQlxyccAycJVswFAFqvAvEqXKz/llaXSTqBQSoa+g9W8oL5x7DpvH9BGJ85JbKsaYvgEoDuaAeonRLamhMpG6jmnFVgKKnnmFlEDGWlLAX03MvilBKzhrMJFDpogD3+w5KGqCY7WCYTEXdTkF434qeXLYSvuWC79AjPUVIIySTGeTtpDow6sl+HqOA9tFBJXoUptLW5oGhFEBKwb5vINGDbGpenwnx6K6FxiIx0JMKF5BneLQkYl6mL+/EoK1sT8Nj/nkHQuB/BXr2BDEjpRXMK4QYHyLY9x3/LMAuGtewsGArwFcQVlK1ICztexEjzjvjvpsrcC7YJGEjDY4TML7ApXbB90IvB7B9gihQ+EYVcfiukHAP6PeKf38NIkI/+xvLjIRgdm/4O1MZPXhk+jcSgpkw80gqGIWZWV/OaOPs2tn9rq4mIRA1JNx4H0fevm0Aj1vPACBqYNwpZrsv+M2Gk5MePqOZQNFtDa27Ib6GL5pPNYX7dqsi9kocHNkb99lFfvpnMPK2dNRkqpZQ7+w7ENIYW3S9q6vqYra1Ti3yNEogyZ8my2Bq7boNtIKzxGSnokGaPZpa2nhPV+D8jKprzLVUDBl7A6L9XigBKBBRhbx6OOYwXjLPJo1eJ/T9TDYJOWvw40cuuItg83G4ytH65YSys7WEoUakQON9oqYGiut2eFwKkLOaa7E09c2h/AQduPH+UIAaK9Taogd/bcVqTa2OuNkk+NbqMwwhBnECmDXMRgQfe8FWYKf2qbOBkEli5MZ7sRgODdJT24WuLU+NUoiwQ8+YyIED2kTPxn4FPkEUHHs1J4LUzX9MOuHbXIHRLObPwWt5poCfGZCjzsbvjZJEatU8MyAvD8pFddIohMya9e6NSHya8A7j2jpei9x/JC7PJI+wB5o+v55dBYYnBW45IL2cg+WdBETwRwZyyfjADkobPlIB34D/ljTnjtPe5FIBPF60VALhCM6xGaNgEQ2Qqoh9h6akLqoWghuJi9I4MkmArHzdDgIku+cJ8iSkq+BCFsiFqjJyVZHWo0F8OoWqIrhbdwluANTvbq+unkRQpCpMKJw1KnmMW7B2i0tF3GZiseR3K9iMr7aLCnD/npGzaBqJEEyXKGFZuR5+89/+tmg8AxVsKeH7XfC//plBHwIUQcrQBG2JsC7AsqrEx8kO31n1dLV37Nh2wfdNcM+MXUg9k9AYBidEivilZn0NHGOzrxjoUae6SAqjuaE68UD49PtAM0YXaZ5KPp/cP+N/wlEO9eSaxtIUPedAl1Y78Ke3frXzoeurI82rxLxg5TcQnFNfsQtjz736KUPPk/7YAaDgnnfs0Ph/XXfKCm2kWWnvKWFLjMIEFk3W4bnIvjCDFyDtwLei6dORmw4HlmCxEPBRNG7hFfjEITuNgkakT6GE/pZagurnLJr5rIevsOuP9DAzFn6QEEZu/pH54RUJ4dm1s+GMjMWj769cm0kJkRgwlLNj424rR96rb1xYxmD0DVuo/Vlyn0IF96yqoD/I0lUnwW01rpZm/Fav1VWOP/BiwQDcGWgHacA5DgY6e0GFwL2LiNkiGjfPgUMthuyrRkJQuXBXU4gYB+iR3t41ZZ31ujSu2/sWcdxRUrEHSuu62iIIKVFV81SZnG1nVenFWG+fm6zuoN+/7wAJCgl2Fo04zqZuMpsDe3oOR9DepwL17BElxqUI9s1yHLk0I01SGBnRKkEMr8O5ejXnhVlJrUSt0wcV6pZhMjuiED/d2GO/Ccph1yAJYhAlEC0Q4TDxUbxoEkPsXfHrou9CXVDtZDdKEFrhx7Um6BGefrBQLlSJ213abvL0vAVkUqHj0taPQk6CLQV+UkaFE9f37vaWIupYm4Xs/OtfLik0oiA2yyOS7yOPx4TXfVmcdjBi5RHTzq7NMPLMKpwaQRgfGYnC+PhZV5514RGtiutuxtE/4vYfSADT6wx4NlXd+JEQCBIsw2hA/r7029Em/ucpLfzoEysvgg8pkJKVHV4LylLwJYmlRm5rhElaGmwJvJgZmVUlUroT0SLyhygCLmZIptyYaLbrXo7QpIhGGKCuekLQlAMwd1zb+sVVI6TPOhZ3RGk/Sax975oE1ZCzzlY4vqKKbkrrY0To+qRK15wLkuUlklKUCBCgBxMYRLVWNg8kUgPkf2535dpJ1T/3AuxbQcmqcuIC5TCLIzZU5CJQjxZQQdkF+1aw3TWVdjXaknRjFlHJyg3XxD4en9PWbeVpDeERNBW7z2uYwu65kQiM1JZa7caN1OuV6IVNwpyAtGDfmuuM74o6vQeC0LNKpKdWgcBqRKaENa2QotHkC6uP3p6BXFTKsmM3cBe3a6i9AQTzHipqD3M8KmabccJg88SsR6ZSYpB5j/mwS3HzWrOfvAI/YFN4RG2o+x5p7Xm5WR0zjDljfUcsOJYNz4/I9FFVS/gckf0rKqWzciOyf0YQCMckebNnZ0N24kZAZ0PouPOoHuq5ds1y2nP0LQwHcEE8OYcP97oQvKPg77ngo2TADNHlBvCSkJLqaDR7Y8ultMDPQzCvJKAiGoapdNyWEBxcoh7fiQIJVO8aDc3OnhoCVSQrKLuAdtK4AkFzBTWM5BHLrgYTIpTskgIhU64SRna9rdWlqhEGmLBBA888HbKqAuK+UKRUDduiicwWBhYS0w837lQ2O+pzF3M3BRgC2QSZMvYbQZIiGj/MJ31ZkUhQ8vdKjBJD81tBPVtICOtClh0VijRJ8+1UtxHXHzs1o0aUND6S7DAnAlgsstlOawv8YGUYjfvNOZvkZu+cCJut24PDSiQAdPI7fGeG6vKVl7fZZHXDlR2a1UmP5SQ7F9fl6HbkZ//d1UHIKnwQmycQBHveTVXEWHnFwgu2rCe4ERjECwgJUjyYjQFeARD24hmEC26ZsGYnCmZLMIpMwrhvwPsGbEXdhreMSpSTTQLDVJAP8W6DX0wU4vtpksRrXTnW8PxvxIZnZeiIbB/RmBln/4zzH5H7eG1sa5RMzujeGTE4GebxWiAIBxVOGT6jn0SUGtDdb28qCtb6vzL2grsdN/ZNVMX0hQVfSLCA8CVFe1STHhI06pJhyLPWispXdNHE0u65F1FTDVHj7CNhEBXNu3iGrH77NQbC3EDF2nPiU9UjJbTr3fC2hDpxoabT9lciqGkNyP3NfWzUuEKG5bChOM+Bk/fgJ1Pcaz81XTOKPSFUc+qIiB1iLxr9HOaXqSFi5R8cUaP2L1fj/JmUL+0dAXqeAUOpaSVpZ0B1Xv040mQutJmCXoEabY+PwZqp9yq3AFv3gQrB1TxBLVP0fAgx9Q+J3ZN+R5wZnZVGEkAc4gWKRnXXHTJBMvW+1u2hcQKGq1s1a6oGvgmMMNhEEHQNZIsmL+YO6/1ncy+s0tjjl1Dhh9RHM4iar+OiOVlEp+2csb8Re47s/MieW3ln9WZupTNuf5lUPTMqj6onHu7PpIfPSACx3KP7cegPCJ4j+QV7XcrpQAxiMFm7loIKKaqdAlatf1Q1l5qs7T1nSM6QbcN9yfg9ZeCr4CsDvACr6eWTSQgr9C9BqmQw8ziK1/xcZOyW4dP0C/kuyHeBvKsEsYjml0kuUZiEIZ5CO6hznBj4ObdVVRQJCpQAEBTxsJCqqUpb8VKK5v03wrXCzjMoTQKSAscRJn0BNwIW6PGYyYgHk4B50QygYGAvyLIjF3X+JgDLwnj7uuBtTXoWMy9mkBa8vxfcS6mGaT/IJYFw4wUrGIuPQZTtv++aCvv7+46PvVg+ITPSZjIqUtQzhtBSZAANGRW0g5sCj2YyltmlUKXChTTLqwamO2Fta308rMpVVDVbbXg9jmbdlYwArMsNwitY7OChTPhyWyG84F1WyC7IO2q8Qk8Yonxt9ymB0orl7SvuHxtyLlVFVAjY3+/I2HEXlb89DbZIsiyq2t99z4ZHdYCFANzeUFbCe77rGqsciY4nJcbtRsDWvLa2outqTYzEjIUZed9aDMkT+CGX1COSj9zjaGTu75/XEa9PqOopsXjEKjv7Q4+5+Uf2AXpyP/4eCc9or4jIenZtHDJNys2emQ5dkS0IIbo4qoJmEsNzCaLxRtJ9l1DvgoIkrY2Cgk0E73pQL/7zo2BPBVTUywWk5zkkQKWZopGxLhWU8B1RSjCEWnbAY/9Lbkg+q/aqRUAXaN3mseQ0rUoIXj/sOwEkdm6vtKOkmvtr6ycIIQIZ1XALQHXr1ofYloCAIqbf17WaEmMhYGXSU88AtRFYgBpZBx0Rts7acEjfRoZorqR9s+ctbkg0It23n6uyEiUkVnVT3nM9h+NeBFvRM5hr3IS4+ddfQtvfKngJ2HPvmM1BAk/pyEunX3D0Vmxjk3G84R44IH9GjbupZyLHz7pkesOtpwtvFTCI1YDbXKHaahdrPDhMQ0DIIijbjr2omkoo2YE66gG0C5lXkx3Sw3rYDxeYOoiQYIcaWTsbCP95L9gtc+qXL4y3xPBc6gUA2M5fSOqtlxLZu1e34b1oBlyPj3kFfkJSqLwQmgdBf78N74wAzGBGDM50OGfEw367LDpWNSMIIwE4IyJn6qGRKJzZFEakf/b7jHg8JArSXSNS4y0PyD2mlzhKCcd4zRlh8LJNWnDNqFiivAI/xVYg2FDwvWTsJWPJO+6pAGvB+kaghfEVCMboNpRSMZ3r4o2zzqTG0KxcnRuj75bCgXag7KKphC1wp7mtmruroKpSAARdUGM3SSydAtT4rQy0NIJS7Q/2CqK0AVPhgKrrLMPUV9VgrWci1KW0qj5+ZWiKCgje94zdkHMSM8gT296yisVyDUED1XLJymnfi6UmUbWJhPBqnWflTJmS/jHjvmkdOwk+csa9CHI2zrdym37cS6AJ1IgCStZZZPJ8bSjsnjONgNWJQ5szJkfEzv1SRfhNn9cTGj8xUAmEl6EqgYWS9m69bganBOIEsdSrxKzqq71tOScE/kvCpi3CKFmwlw1CVg/p0Zt7JjsjG81FgxgrLyBK4GzzIYSVPaJas+HeQfj/vWd8YwLfCP89MW4rQWRXZoG0/8SMdQFuIKw7VHUlyojBmQAARxw+h5+yKfhLPKdBPqWv0qj4zIidZ8j/TE/jp82HYs+MwDMi8Ux9tAz3PqM+OvNAivcHGjd9dnLfbQcxxsCReVQZdXmHqoDscQrR66gvh+4PQGd260+1bWXsXF6onz9lAZWMkjf8ljLwRfBbItCasBaVKJZSDGk7ly01nYUUGFEwLyS7vpsbJW8E2fXgEeIEycDHXceUhABLv93ZGwpajEMBzOdPVyXpudQkghTiJdxm4Dp8QM/k9RgHAPVMBCGXqZR7vheNUt6M0HDRRHPCqg9WGy+jrIxSCmTb1duECLdV8/ttWXPdOCe4kx0PUYrZQTRKWU9MS9izIH3bldMUnTfZBffvGWXLoETYSlbCwgWyMJAs138u6tnFTmDQxDdDPJJagj690IhAjagNSFrQc/U93nciYoOb8Za+R6BcMiVCZrEzxkWlAMvfRMsbluULtqyEeCPy4HhF8kYYSxa8bwW7NL+8nk3qjc2FfPWT5SLS97CLn/3UiEGGZpj19ZWWtdqZ3tYFie0gHsrK0EnBwsBvvyWsq89YgcvmftBS3kzltQMLGFgWZRRE9Fzt6eTN4RcYmh9JAhT+/0w7ZyqiMyw6Eoq4wkKRWdGf/ZuplmYCzgvI/NFwTglEOPMWaNLByOXPzWS9xvSRFHH25xxnIxIj4cChvIjqkzcpeN8VoXxLinBvDKyWhJ+F4QiBbOM0rG3qDJcc/M+a9SA2mI1BsqAUTeCXQYHYSOx6G4ld8/2kpin3HydVWxVoQBR5ABQpUTKEmHw5i6tuGvdbyDyWIBWJCimXvRWVuDzvUmaq2VA9tQUD4BgjAfVm0lPPbA6sDgL0k4P6yrrWiKESXCKdp2KfNoU1FXZ9q/FH97pbvEeHGpxQUCwb1y2awZuU6x9TPbsk4N5U/pwTZt/27bqqhjxojaDag5JNqhJbNraGVO2mhHqWzO64ewYFq7RlWOuXvrxLjnsRk2K00wJSW5HpyYiBxIwvVPAlAW8r64E7JLUOAVVrnk+tnr6nb3cHodSgntdZ8x9UH0U0H9/wCE4URq3hIzjDsjMj84keZmb4HdU8D4SM7m9E7uvwe6xnHdqI3yOR+gyBeEI8OhUQHaWDQzbSwPMcM7ocJYQxeG3uxuq2BdVnx6R2xicjiRu3VdFRAHwU84X/tuGPVPDxkbER4ysYf5OCpQhSycNuQyMAdk0ssncxLMbQFAK5AO/vuUoVjmgX+PHtaGmzQxbJbOcff7zvWBfgbSUkUg+QXRh7gXLdxGAzHP6xF/zzI4MTkJjw20KVid5zsa4SMqkJZIPqmjcRJCIwa8rrvQjuJSPvRjzsjAVF1hmFC24LqUutIR4fArJA7hm/rdDztgmGDgnFYhjcu4oB+66+7stK4ARAVAddsurK9zLgfmlhjir+FQt6s+yrrFx6fT+s5fRYUtIDckQ0aFAvgaCG9VtivCWqx8YmUsILIiy3hMKCnYqe01DdtnQ/qSdOgSSAmZGWBCyAsGUJlQLsu53mQciSsYsS3O19ryozP4mNWAPO5P/P3b9tOY4ka5rgJ6IKkDRzj8jcldVdc7iZfv8X6ouZWTPVq6qmdmZGhLuZkQRURfpCVAEQRjM3j4jMjD1Yi0YaTgQVCjn8IvJLi46HPrtN5g5SujYe0pQyTYlvnprwINcKn1Idt4rryidwnR2RiuMcR+U0Zv5vP2Y+ZecHpuXZmhswi2t4Igp5dEjOZ4WraXihjWF1+3R+ZPmOiua0fF5L0bYI4T0fYq88PrLc8xDumc7b9RtzfMtp9FYMYA8X7d/fCyq/5xW8BUe9dekfUQRvDUezlgRfitFexwg+bue8HzvYhti2d3flL+rO/i1q7Lv9tlxJ63YnhNtcjRevqDmX9lCd3HlovWexyM2ngltY/53yIvB9aaX+rJXQ1eMBbM0HQtiEBSUmaKvqlcgrbOmiwlSE0pSEGyRfG0XV6lSLytRzCQxccgpOIIsgsW6sxt4YpePt1uRZt0aXlp+E5+AbQW80ptr27bVx4UT/Y29W5uYJbK5AxE1lk7JoGBbjZ5Hu2SuSW7kySmDpk4XQulw9Cq6UoMhe4LWGuXn7vjYdaVlGdxcHuhLwZreLIimqyVOCh0E5DMqQhGvD9YImpP00VRaKd+mWNmRVxiEjKe7vrEbP3e1j7WjcW+t8it4qfYXiusRjKgFzqipuIXzN06un6TXhXfc8mhLQ1LzUeIi3TF/9c4ebOsNq7YqyDeuIc6AyShhrtdfYeHi63j0ab2JPo8bEStz31OZaZv19H1m+k/toI9qb9yh9Ym/ufX+/9SE+qhTuScp73kLfZyN9V1/y2wL8LUXwkaDyve17pfCWgtkL+w95B/7aURPWB+RGEbyerrexha2tc+/Y1wpkTUHdXsL28eiXdKsI9kpnZZJZvYjOimUeXPAvxSizMVh3p6MpiVZtMQNpVcwS3kEJTH0pOtuwnQbE49RiC422eQiZaJAOZkrqikFYHtDrDFMVVAXUGMw31mgEXktV5iogypCV2YXZVnbUyqa3rgT0Uyzet0pBUwL11nN5JesLvBqmHhjWyAgygdmsFYI1SGVjNIt4dGCT8I6qh4dgXlu1d59AHpWzTlj7LaheTZiL8HJxLIMlmHMop2gW1JRUu06lnzO+f9n2aumqqyVdqiCqqBqDCg9DJmclJbjUqCxWpY1N/NaoBVwVRVJhSImHMePqUW2NRLGcWaPoiGszWBhju9JdeY5aUrWHMg1Yp5VntpiB+20y9l5BVFbfu2cyua+Ukv2hFlmfyNKNh3aNKjA05TmKM0phJGIL1Vlgz2X+tPkUSiFK8FpIjtT36Wmvb2rs2+VXpaR227Auum8LEcly8/vgRVjkYxd0KyHvpQJtlcEOQtrDOfeciXtC+x7cs4ePMq8VQe+4Nuz2U2DcfUcX6t+Agt5SEEsGhfBK2K6C/nbdXsgDrL1iV4G+r0O4fV+f7tcpqt4ehSWUdqOUdPO4sLHFajtXPJJleXezgHsqXKszzZUvBj+bkD0Iv+wKOkdmiF8FCugUQdpUYZylkdwFvCIOpxzeowowwbU4Xp0scBKwosgMmp2rw0uF56pMRTgelIRQPfpuiQSmXyVs94trQE0vM5iRMgwa+XjX4ouhMldbisCqh/AZBgkuHl3xbeltNQmfK2Imvs4BTbgKl2oLVUKnL1AqKcEwwOlhYEiCz871UilNiSwIXE+F1HanXZkmYarOSzWm6pi2jKO68wA6LNQf9QokXxlIt7BR30e24IWDa8CGVnkYlDFpUEQTWTNfvTC7Y64L91SdC5KFYYTTmEk5snJSFkaipgGcPCRmr8H8apH1VGhQGMbcLOdCI3GRXYVNu1CTLtB7v4VVMaw/sxO+tNkuAUrOto51bI8Gs2gm6QCeWh1ECs/NIMi4Y75+zvCnkzCmijRYylrWnbQK9s5jsfQTITzqhDMonFJ8fm7ZZ7begHeXX1mnQLhGd7bvw45s3j/+Pa+wkg+s23gIb1nm7637yOstj+FbHsg+0+hDHoJvPAKWmoMYoS7A68baf11/8FZV8vcHk+Muvl53zzvY7nP/HOu1rCwy3bzfBo6vm+yibOEdMBGpggWk9WHWIiQTUg0KCjUhbYLOuVle7lHEFQ1oYr7U1GCiKmiyoApoEEP3Gjpu333iDpFVvAWAfeESEiLQq7AIGWB5qDt84bD0RkBpWVCydmBr4xVCthlfvvxZ5O6CFbc5ohKNZ5aMHiUYT1sTH3ehNsVs7Ts7P390bmv8SN7vUjTeWaChvW3ndyCjDh3cQgXrnHm1vsMvPVAeSQhxDda6uUUgPQODKKecGEZFDiukVMQbvXSkYQYv05o4fTsrN2iG3z6csa5dV6OW2FY38+o8d56apoCjqrmT0TUls9BoxAVIOKGMImSBT4PwmIVjap32WhKAWSQz+JJEsb7WOpuYh8mDwdec1nnXl9Tpby2/WinEQ7Ep36c/Ll0x+JvHvr+8hbG8Y/Jv4wh7R+O9QPPeC9h7Cn3duPncjx02n/PuuP26e1DRGqJ5xzu4tdT3AeQ9DLTd73Ww+bUnEV99P4awVS77/Xb1nIuS2UNE2zTVOEc8omnxKPr52v/b9I0mQC9VucyhDKjApeWPV0euhOU0QarRQ/hhjgboo8A4GePsfCKgp/PsvFyN6xU+ywFXYcR5npxpgqwZIyzOKoAEm6hZBJ7daBBO8Mycq1Ms/GWVUC5zcabByVl5GBOlVmqxVsUcrSxVI91UF0nvDUcOn6rPD2tKMvtm1ltF1BBVvEZKapWOg4NkQVNinoPXqWWUchqj6/V1dv5+qdTmlT3WhBflaVYutTCZUSUgmpbVGFBMCSF7Y+B0b4DN075Zx3adrIf1n6gSRG4GTGY8zSFETTus0mJUqpBAc1Q6PyThz8eB02kgnQYmKzxPF84a+f1XK1QxLDlVt9U0vY6/kdBtZmWf3cJGcadVqERMIm2epH1ls9zM/vrqCUgIwcYaeROhoLU6I3BMwg+D8DAof/lh4OHgnLKRG+RZilBMG2wZ2IuqLEy3US/jiHlQxTscNZSKtdjaPQV9b/nNNBdbpX8r+rfh6O/xFrYS8lum/TvB5Y8Ekr9l+e/3v7duDz3dW/ctT+EmRxBE2xRt9A/ryKwewh4y2grvLQTEcsxreKir79eK4bXygPvexfa6tt/PbttWgezXybZ4bJtV1F6y/+zrg0D1qDdoOdqXWVB3rhin2SPmoCHQpwpVEp4Et0ypzss88zQJ08zC0d95gAaF4+DkBKISPQSq8FKdszmXnsUjwmEYwGHGmDxSU7VaS3UMJRItExunkbcCNSXgKGOBCTolRG8IpB5TPDUoqtMnV6FZxeEtVCIAXmocYwJp6OyngTXPDi9mS+xiNMeKUc4wNeiGJC3d1ZEUldVKZE8t3OKwGDYLZPTOQ96nPMRvsR6ZtkgCUG2Hl5gdS6a1tpN7wGgJ4XQYotcElVLityTt0E8X/05uWUtCwEexfZVS256CvWjOdECkdUVrWVeqGXEF0yUj+pZsfjObXRZ+oq0phggqzVKUFIFiQFU55swPw8CjOMfkDBIqRFy5XDVoM6q0+opQ1KpEBprH/3VDcb56EI5YeB9dJH1k+fXw0W6xzR575RDX+FG1sDXz94rhTuS4B5c/Cv98S+i/FSi+Bwl9S+Hs94NbBbD1CvS1R7D3At626m137K1wvz3WX51nHfVbhUDbZ70zr1Nc13PvFQHvfO9eQflrkrpFOfiNQpBlu69B5QauW4k00QUIb9CS5SYQawsGpmCnrKVyvhovk3KpwoyTNKy2k4SVfRhoefMwN9qCl2KcDa44g8f2MSWKVkQiSoI5qXSl2wqZPFJO3cBVyG36Jm2/VeM4h2atx/oeBJcmXJa4QIdLGpWE0GIWNYr4ULAU6ahLANmMswckJLD096VWZjVqGOW4BuNtThppt5v7/0oB+O79DswUynPdZt1DagpU2m+ixoMiLUU3yToVxEPQn8aB3Erb5zm4pTQFVtIrtlWJJAERirSTbC5sC/tAIxoMrC0YTDVHxhqOpBQKoZMLvnqKbv3yqE3QG0K88CYTSCaC1y0zSJQxZx4PB061MEp40mLgVblchblEjKLUlgatkW486BpUrs2jXQrOF1jJg8YE0O09e2f53Qjx+nX099cq5KMQ0h0c5U2zXu5DQPdqBPad1fbB449AQR02euv79mR678FHciswb2GiNVbwLahnPeb9da8t+z6VuTlm9T7ueSivhfp7MNKu7nN5bR+j5fdtUktvvAXvDzrNWoz14uHi932lYak0V7kWOM9QZyedIyB3KY32oRjDpaLVkblyrnB1YRLhgHBMilJIYmQNEKMgfDk71+o8mzATwmYuhaE6p1yoQYBPtSh1LqpRW2CRdRQ57G2UHU6qpCwch3jYa3Vmr5Ex5ZEyCcLQSB0DZonK6FprB76X4qUk4dFogiFr0FfnsI6LCF9eKk9TtNPsSJBHaW10eJPgXcpJQRxXQ1Mmp4za1FhdbZ3Dffz75/0j3gXTZtNhHEhZSIf47SnBpcwUN6Z+EleSpuYdGcch9p1S4eHhwA//6QculyfOZQq6FNEg/3Ml4xzHgfE48PDpyM/Xwnk26jUaRMQU67BOingBHiR10oLPZlQrzJ4jIG3G1Kip625m72GoSKvoykHb+UNuzR4ZUZNDbWR4c3W+zBPleeaHlDgOAlkoL9GH+nwJmK+4tMI+CUrtLAwqrT8DPD9HIkNKsjDMlhoxr65Q/vGB5m8sH4eL9stOcr7yFl5J1rd3fw95+pZHce/ze/+/6VH4zbUtOdbcVwp7If6eUug+2CrY78NI6zFsjrmFjvqy3b5d93u+7tVPLNbnWy/W4JpsPu9fsvr3YS07vEQlUVjFzWQvleCdaZZyXU4hLI1sulXrca5rcc4FrkgTItLksnOt1tId++W21EhvZHbIcoc6wV8PPhdrqahbL9I2fpc0K9r7Dw+cuQcOF/aIVp+gKQrRchJUI7g+mXOejUsJ4SHtfD3VM6WEtr4bXcYXJxSU2UJp4ct18K4iWLZt9nEg5cQwJE4HJSkkdbIE5FZriTqSXmEocX05RcqvacRjIosoUnWnVmeRSihTCJgta4e9tlmRt/7rMr8lYJzgLVqpKtanqz0V3hvwvH5y+76rf90+uyzndDzqS6x7Ps3Kd+fqzhUBUZ4nmIsxTcY0R31IbRBZ0rUOaC6N5sJhKpvB1lAgQaLYWHm9Vzd/e/kHKYXVRbs96iMXdQ972ZvkHTbivgfwlnPxXvrp/uveqmh+q3p5f8wmTVX1Wxb9amGvI7jH9NnsY7t1fne//brXHsnb3kX3FPZCfL2+exb/Ph31dTX1/dCcrYLE3njt6g8W6bs7RjZwU1QlK79Mm+2zo7MHTXELIpRmhecEKo6Ztfz8aPIylwhSf52dS4FJAlLQpMwIszkyzeBduAf8gQpWA8cvrPQWfepOJYrJpslaXUEEWVfJC0h4F4GSeQQWCUbN3lOhGxtQSUnIo3D6pOQsuFdevlZ+vhb+djGu8w750Qj2juPQ7q8x+xwZQDh1mpFa8ARR87sqIe6BB91z2L7aMQKM48jDceQvnwbUJtRm/PHIxQx7OTOrUFqNgTfPZxgyD+OwpLz+9PzCbIUCXIoFSV+9YmNY2Ypj1ZjmmTKXCJITCZ9bqIdmjIgo4+FIlcxcwBqJnSGgiqYcP9V6sHrra2995U1q6maWV4+nqbhR0IhvdCbgVjdSEV6qMLlzeQpYrJRm4dM9QSOJcxI41FB8aNy16Pkck8tRVOA6RTbdZDC37/jI8t3Fax9b3vryjyqFvel/xx34VlzgrcDxPSt/Dynd66ewr1MYuK8oNi9RQ7VNjY6hslr2tx7AvXXvCWxv63YW981+r2ydNyz3VQFw53vvXct6J2zzva89lXsQ1q0XtMGp94J+EfiyiSXwWlFslIS7NxbVeEWP4Sb1NyR4l0qzIRVLshT/COAiXGu0NfECpQjXoqAedQjNerfuGQicm9cQgc7ogVDpQle2enxZqhFZRFkp3oK/bXRSM6kcWtA5lNXYLOBRNazA4mxnTDVnLiXqDIig88vkPE3C1ULBDB5QUwKqCDNwKdGSXnAmIp+/SIAj0tInbZMWfbNsA4p7MbFVDMDlPOFmHJPxaYSHQ+b4OTOLU04esByCWcUwZkr0+c7xkFZ3Xi4z1qguprhLQPfSHKkVLU66GpOnlYiOrQQSnERKmTyMPH56oJB5/vnSOLaIimtR0pipU7DU0jOQ/DYC1xWCs10vTRk1uKpxZCESsShjpXB3Z55rEOm11OpaIwsrMsAiKywRRoU5ZI1WtyptrkNLFDBSq2b3bhwtUubbyz9IKbxePua4wGsFcO8FS3C5r/qI8P8WNLRzRu4Gjd/6fw8ZNestybYwbCXXuQ3o9p/xNvyzX7cqhXsewT6A/L6nsoWYtte33643km3rNG+v77WS6ue+CyP1WMBeGWxfvVvJO57EQrNgDWHpMYqWlSFNKXjj/pkaw+mWhS2IBNvDZQ2rb3QXUwkYIKVG6NcfVu/W4zoareNowEMBjLOkrdC8iP6zAFSZa8Qdkkb/hsQKS5iuY6EKOTnjIJQSnkltwdlAUjyyVUpAF1MRLgUuJTBx980Ub0KqOEipRI1f0JxXcYqwZCFhLHxEN0uHA9YpuVs6ahAbr9OMe+V8ICqYh8ynh5GanOuhBs20g3uieuVcLBRCAiwgkHkq0c9ZWwZWq/jvqbniFvQlxYPnSLYdxfs1xSulxJgzD8cDsyec63LdUfORGFPiqhbJIKbge8G/+sRvreuxBm+e4mDSFPOaODDV1l3NIjtprWmJtGPdJBS4wzBFkVpuGWh4ZCGp0wJGsnjWLnL/9txZ/mExhX7Mau98dOmS/l6BQVqVwX7T3tp/K+h8b9s+wPzRAPI7+2UN2q14dUENK+LYf+2qFPaFaH0E90LXb6bda6Ww/byiqG/HJrbnZrnG23O+/p59UPm29uA2J+M2E2o53z7j6K6XQJvY/obCoHkE6+cN3nL73vbxFliOgqAa9QcCD6OQRXD3EM4OZaqNEkGWNM5opBKvmbAg+0gLYf1pdcp5Rjud8zLO4Z9kiYIyc/g6Va6NkuFPnw5knDSX6IlgxlzrInsHhUOGT8dG70xl8ghGKkZuLs9cobow1yH4fLIhKe5Btk7uFr0apAXFB4+K2lk9uPq1pTgSMIfTlEKfYsZ9CKkvssYutMVApAV0v5aJQzVOZjxaRlL8rtxI/h6OI5LA0pH/+TTz82Xi63QJK3tMJFFSEj6PBwqFS73gSZAsPPwwkg8j6XDg5+eZabKAWaxDObc1BSLC339+YqrCNFeqJ0SE45CDdt0bYCegeYj01KILfYTtnoTXKRYJl0yVhEgiI3waRw4oYxLmYlyL8bXOzObMLe7T01adTjS5JhVUAWZ4HARNgpW4KapgnVLXCcp5A2/9NT6y/MOVAnyPl9CPeQc62nIbvRdMljvbPnLc975epaP6QlCnYhulsIVzfBmZ/vkenAN7IR/bfTnGFntye549tMRm/S3MdH/fdf+3Pm9/w/Z33Akgv1JEd16rLvrYax9T2G+7s34JUrftoSeiO1WnkUhVMNEl/VNorrd3HiNfA8MSLv1kETw028xaj6lQQhYFRcMy3bp4DysuKMQjGO19bgtRhNYoxKW2YzsM02CjWkNot4Ls0HveA8SrhR5pr8FAGnNHVpI+b+qs/Yn5F7Ns++A66xjeruT1sh37ZV4GDNUDv+4WKadZozGSOWeri+I9HXJrrbrxyFqVslQgg6AchoS6UyU1bRx1DGSNwGqvBm9devYzs5pzLc5EZWpUECIB0eXWfGdGCVpyX2yM17P6G/STTquM7okLrZyt/8ZGyeF1S+LXh/N2kJf7TCMsJNJ6oenoNoe03YulO+AHl981JfXt5XsUyl6ibyT3ohDkbThnG2h+q3L5I697sYn98XcCzaoh1HMj6I33+8Hb9Re/hlru77fGEu55EN/ajw8onP1+t8ev13cb/1ihrW16qmxspr0HsXgLm8DwjbB/K3bQzaXC6+PeOMdNYLod2y2voK+OB3WenENyZICOpRcP3qPZLeCahsvPHqmd85KR0yyzNh1cWlzBibaYTTlkWHpEu3cK7VYYJmAerTAZgt3SEeaLLYKiOlxnoUw16g5cg8G0KbKrQ57hkJpaU0OTMGb4NOaopi1Qao3mKx6YdFIhekKvkKO36/L1Jvfp8balt+AbIYjE4rf2gkBtv39wOA6Zh4cjT+fKZS789XqNbxRIh4GUhKm24LjHg13NmMvMmA1y5njI4aWVgkiBBH9+PHKZCz8/P+OzIpZImlCLWbrOWDgX52upFKlY65qWUmqwUvRynk0pXrmaM4m1gG2PZNx6HftUiv40G01RG9RifC0TF1OKDtFDe45CvGoh7NeB3iyyDrHjYEYuChWmlt2kHlX6g8Ch60lfx/4jyz9JKXzPsjfxtxlHxOzaZxzds973kNK9oPJHFcK+TmGviDKgjqqHEpD6hlJ47SlsCXX7so89bAX8FuLZB37hNQR0z1PYnlduttHs2P25b2GobZ7FNpOIzbHbamvdPTZ7L0G2QnsvzN/zAnbCv3sBS6D5nlJpwmo9hVBYm67UalQKx6pRJSuNSM2DWtpVFobSzo8UVvSqFEK4N8u/mZWt1IDu7VZawx68KYSAVzpennOjxvYoVpKQAY3XqAWUcebiLTC69kCcBV5mZ3BlGI+csnBQ4atduE41hCDBFZSanZVaoHxldm2is02D5V7Q5n8fd+FGEWwXVUWzoFmWRIskwqjCjwd4GDPjkPnr1yvPc+HcKNEBfv46kXJkf32dKy+lMlnFNFhOxSpWnJ+fntBBkFYe7jg//fIUPSlmxyWDJKyEB6LLD5C7r5wSwzgy5EMYANWC+iOCP9F6c4Gg5GZWv8U7XBuk5Ogy8Upt3sFUFjbfnml0V+HuxjeG37k2WpKCh4eDk1XXe0ZTChbK+CPLPxg++jXLzjvYYj17VGkPBX0vHPQ9Aeg3t3kLdcQUSLK2ouw4ey9Gi193G9DtNBZbKGZvwd+HePaB39cew1aYr+LrW57Feq7teffxCN2976Gi+4/HtsBthZ/uxRW8C+9vwUQfhZG64tg9WP1u1Caca1MMxQgqhZYXXw1yllaxvPY6iHP4TWGQOy1DpD3J3qZNq753kRVOcoLDpo1cKCVv/Yfj7EvP4sBBiMK4CEwXDLe0kPH1jnBz+80DiSElUlbmyzX4gLw2hyvsZkSasGyU2EJrfypLEoD3+MG9cd6u3ywqPVjbZk9XQCqcsnLISlLlWozzXJkrS+bPy7kiA5RBeJkLl1oo0rB2aTTetfJ8vTKQORyGUApqPJ+vIYw9BX/R0pt6O7tfKwZp15pTIueByzVgnrlaWPmdOnsxBm6pLl6nUmwykjrXdxuz6gTZ42xLcoS1ufNqafdjD+kZwfUU1e000kxpeRmydL/rhtc/gBDvn+Up3JH0ncpi6wHcg4bu9TP4FtndtwLIW5ho6yGM6/eklnY6yLwog0xZXnvqiNtfuw0630I1fft9pRDLW0HpdfvtunSjSPpxe0jp9hy3x7w+dw+vbR1o2Zx7W7sQmTW+hONeUVvsXkuDrd4ZbQ8P9fe6ee3Ps61x8BVF2vo/TgSNhQ4POWog1vvhQjZDCM+ht71R0WbEVTr2W2nX3CAdxRmsjVdrqBPT2RhF+JQHsCZSBo3OZMk45OjqdplqEPMRfaaLs8Qhird+ExrnT4MwnITDKZGS4HpBJJEkMx56I6CpVedGBkx1mFv2UVI4Hg+4wzwXZi+U1s2lJ4B9dLGWHmzFOoNEe5aVYQiWybk45/PEZSpUFQ7jkSEPXOsL1Y2ra3AhqmJidK6GuJ1CEUBCxKcxk5IzXQukTBqPmByorhQvlBufdRXqtMvq66bZmOvMyxwNh14qXMkLbHQLlvZE3i1Aupo/qxmoN/MzitZk4SryrjXd72uGRKuraV5pu5LS5hkxDNQKLpUJGOmRscg8+5dXNP/6ZR8F3iiGvXK/Z8l/rwfxPd7F3ktIfuMhrFk49Sb/YB9TgNUS3wZstwpiHY16c8x2/1h3i+1vIaL992zfY7FX27bX2M+5VzS32197FP0a3/MqumXblcISxNxAPH39q33e8wi6h3FnX7HbVdsxY7PO2rX3auVFiVhccK/uDcOtC4k+8r65pNavcGPgVWv9r1QY8FYwJyvdgjevQyS4doQGQTTuJHOKRUVvdOtqj4aDizNqz71P4dkQcYHJnYlWlOUsdNEGKz10e8wOOWPmzNO8kU8L+HVDO7IM3J0p4i1C7BrX17uLFTNeZkcvBVcni3LMCZTWl7nSA7ulRj2CyXIJ228Ive9OtYqXgCOjxWULIlNDIG4s+dvZyjLGLhKZRq0+oRD0Et2L7H1hXnsD23M3rwNpnmFCXRdnavlu9/aSD43lHVttNxTtSXQ2EqP9WmEpoPvI8gdUCnekdA8wb+MC+/jBRyiy31r/PQHojSehySPDaAMZ9VjC7f+36ZyvFcRrSOfefuu6db/7iuR2Bq2P//ed+711+3Aa3HoF+1Ke/tqSD+/ZT33z/42w38UO7u6z2++VYtjsf6sYujhn8e67EthyCwVZmy8QSKTGC+LGtgO5iSw+gzd/Xbvw8cZF4wUzOApkVbJ2zh1hKqGSjsOa5DsTNAjnatHrwLryaXGAxluRHY6i5GHkcEjkwTEK0wzT7DxXeKnBl9PK0uI8KpADgkoCn8eRayn8UismdttHwTaflVv4aCd1rKccWwhedZhrxWbnfz5PPM9XHp/g0+nI42HgLJWXWrnYhKVQZqVRgy+FcztDoLpBBZlLaO0Ew+PIbMLLtXAliAxnxlXw35gtShXFpD9JStKM5IFSbCEyXDKP2FYt72f66iEkEUgJ0oi1roG9bTWwKPVVwe6Uw27x+o5G6PejnarnYBRWc6X/to8s36EUvmPX37TcMd/3cYStEN96Du8FjN/KTtrvd6+K+ZXScCQT3oHsM40qicLATGrw0XueAsvl7y38+wI5tr1WMnuFskJD2+9a398+91sQ1n6ffTH/VvjfBpnl5ns7rYXfegcfVQL7bffgIt9s223v3cpit27nr1MCYeUUunNafN3fcJKEcBURIl9n7TIoRAA3qeC1CTl87YCm62U60CkKqsOlONcSXsV5MmZrdAUtdz01z8IJr8FpBXkXp3rhMgt5UIbDgefLzPP5yvOzMU9OsbCEBYnKYBqXkxm1GueXC7NZsG62gRGiqK6P25tLH7QmwH2z3hw6k+nkytEddwvF6kIewGvUZZDCg9GN5dvvi+yAhN4eNIjgBDdrGH6/I035Sa9AjxlcfY0NWRt7d8GLgddgSyX6F1TpQeY1qHwvr86bzMrDQMojaTggUxQT6qDUOTwlGi9RXeAjXz3it0f3/rIohdU4Weti1tvy0eUPGFPYPqIdLNtBR/dgoXsw0d6juLftXoD5zYwmX7Z1Yrt9UDkt0NHt+jWm8Hq5H0u4L7Rj2+sylNug8nZ9fyT2696PE3zPutcOdbe+33K22zXtrL6b10a4v4KC7sUUdvvs4ahl/Z3T9JfAIvS3anT/kv1LGgQEK+mYe2vC3hVGPKi3YRFZ2DkjJh130BzmylIXcS5RGxHtKWOf/n3dq+l9h2UOQWvuDAMcPfF8KTxdjOdrwYoj6OLpsDlPwDDGZZojbtLSbW8hpDtz0t/4vBnAvrrXDkQguGU61XiWttegXd5I/9YVnOnrQ5H6giSIBOePty9W1QiWb+/nls7at/+vPmNQpRjBvy2trcU2T/B+UHn1QBTVhGompcygFU1OskRpWqCnQW9jauJ3R/fbi7/9r7+34xvLHxA+2noI+tqivwcRbd/3imDPY6Sbz+9RXe/XbYLOvShtG0yODqwzQyt1GXbew2tLHtgoitcQ0Pcphe1yX4j3Y2+VgW/Wvxc76OfdXt82o2rNtPLN9rX/1G1tQ5P09zyCvUJ4y1vYego7z8HLZltdv843q/prbq+ektodzj5mW6djP2YQ9QriQk663sMWL0kta6h48AlVYOp7ecexE4wj2irR5qm21NDol1zMeCrBwFoNkviqaHytrjaEyeE6O1/mmXGGMSufZiMrjMORc75QvTJbqybHGJ2FWG/yiD1cLkH1YL5qSG+lza8qmrcDtV/69htAPeJJqRZcnCvOVK9YFmZPXE2oktvhzrZawjcsrjezOAk6DBwOiaRw9sJhGDmdHtGrc6kwzxq1Dq7R1pKG/HvLOmqFJKrafH2N2I3F7A4CqtVqvE1DfV2fUCptrCcGzYw5kTxhDoejY/MVMHLOWLWgW++9NO4M5d1lKzQWzS2vt8GHM4/gD+0ptNdbweC9ufatIPF+n4+moO6OUVkrlfXGO1gF4NZ72FY0x6+71ePb0Nd2+349u31WR/G97a/3ef3922Penjn34K+3bCa92b4LMvuGAK+/tpf6non+kVe3uPZwlL32Eu6FJd67jHsj2j0Hw5v13Q1XaRktsW1NY11rmqsHRcZ5rmQz1K3l88viHTvamulIa6jDUiHdA7fAqm4lSO6cyK0/WCVrYki6pMTW5Z60h8fBOp8zkev/ysbcB5f3y7e2tUU7oR/GmJXDIfFixkxQk0dANIGVgHt0jWlIv9z989zWFatN2Eeu/mEcI6OswLkaZkrqcqz1XO4nVRFEI0XWkXW+tDFpONV2JrP1GiLIGJ6JSlCiNLysZbAJFMMrNzUfItr6gEjTdN8hvW+Uwu7YvfDw/bP/9vIHVgqbAHOfBPcK0fZK4Y2g8LLP9xauLedySHCbcnqrFLZB5sy8eA/fqi/ov3r9/L6A7st73sTtfs3Ku7vVX+331rK9rldFaGzovwTUt4/PbfPCvWS+qR3YSetFwN+LO9z7f3PO5eHr3sJm11cZrBvw1daPN1+zHbGtDBQiIygRVmcSWSgpQnALE9Ke+zXeUDz6MPxynjjiDOLRrF0b7JQkerwXWq+FrmLA3Fp2jG8EgmBZKEmYqzHglNa8eRykdSKDqhHkt3aMm1DmBreIxne5od363Jrmyrpu+9E3xuobiwgMqgwqPKrxcBg4fRr5epm5VuNpsoBdklLngien6uohIK1DmoI30kmS4BqxkWsJ8slxCBK7x8MRHyAX52m64ijVeoC9/RiP3xxwT2LUjFvQoZtbQHJEYrE0DyHSJW7LOF00Un81hE301rAGRVXUnTIJmEKRoOzwiNQshFrbwNW3Ftm9L4e9Yyh+UN/8AeGjbhZwqxDuWfr74PA9mGkPBd1TGt+qU0hE32QJSGQLHW0VQPzf19XN+z7Y+tYv71DSx+7ePtj89mj6Zo/9MWuw9SOTcT0fG0Vw+/uSb/tT9dBbXf4XWAtqjKWtprf/ezxg6bK2hZF6vcJWqRSW4pwl26VZZP24gGTe/4UOjWVzPX1HouKqX9vQ96ZpUml9iMFMlmrYVYsEKFIVru74ZEwS5TBlEAaCouBSjak65xq2fQ8OpzY4gkdNQ7t7ual1sTVoOVf4eq1BnucCkjCbl2ufabZXUh7HgdOQGTWK6J4vE6U2Yr4tBNQ9siakF8zfaWm73CgPTRoekAX0lZNyPGaGYUAlk5nJEnBX1+K9zSjim3HVoCHPQpWg1o57bVhxhjGRs/LpdGQ4jIBR5xoKz0E8eKxMWsK4r1lHSZRBlR+PIydJnCQxnYGquI94VeYa1TVIImvGLeGm0OgxYn1CRTmNA8USF1NqaZXyJcW1zjAVpxZnLtdQ+i34/GE/YbVIbv9/Y+m35SPLH18pfOu1y0R4d783IKH3YSNfAssqr1tobAPMXWGsXInrtrcF/cct9feW947dGhXbadct9+9wWGHnnXQhf1vBvC0RWpVHf98KlyXQ1r2C7fuSmbGuY7d/Vxqd9GvJ4DCWis73xmS7i+/WvQcvbc/TqbCBpTsXm3O4dOy6KdQ2vZH4TXOwsFEbX5K7oA1amltdAkIj14vzdHru5a43rCrSZFlwrVJbC0iFHGXSNzOgV8O6QErKmBOnnJhL5TJXaq3r+XYj2Me7+z5dMfhu/37Ny7hLNPZRTSTN66OoffzaGG1gIwFEA+IRjeoL8TaATlCiayblRM4ZbeR/ZoZXJ9Oo+LzP4NXP9u7hpcTpMKKScFHStZJRkIFCizs0LCtJNMsR2QuXUN5DCkbU4gl61pPT6Cx8adNqwWj32h396PIdx3x01z+gUmhS+p41fy8+sN+2s/DvBpL3XsO9gLPeruskd7mFoSLddCbvAs5DWzcwbSqa7wvs9zKSfq9lEcTLf7dTY/USvm9ZPYF4z5vXgLeXMWBkvA1xeFo962irBNha9VvSu7VP5mqhtv0WRWC06uN1v65UOvXz1rDaA6Edzl2+Qm6/ssMiW1hpXZbQ+bLjXKL2VUWbJAdJfju9RBmSRoC3GrNEH+WUE0VDUVxmYy7BsxRT2zlkOKhwIDNV51JsiV3MFSQreUhNoDu/XOfWxlF4lIAqdDMPnCi/uDg8mVFr5SpOLZXnaUJqbcqmoxOtKEtpqaCsFBhtgKUPxY01Gy5YMUeq83UWhk9HxsdP5MsLyQpSy9LjwvM6dtEvGmq7wWKCqXcrp5Eqth7TKXGujhRDslGKIK58Phyxq/E8x4SKOg9rpouTc+Z4PPHj//Kf+XKdOZ8nVCqHlPjLj3/ip5eZdC5cL7Z0oNPlZ4ciLxbeR3JF84ho5mEcA7qrznO5MlXjQg206B4s+gdY/pgxhQ2F8F0r/y2I6Fvr7imQD3gQiXpTsbxnTH+djrr3IO6ni3IjsP9Ry9tBZ1ieq+9e7nkEq9BbCQRuqS9s9RS6Rbl7KPbewPZ/2b13paBNAdxYqV1x2Kr4dBmNXXoj0Juzmd98/Y7C+LXHEHw0cnMf3UOeZVUkJUQVs2AkFTOyRtN1bd8p0tp1uvNSIAsc1DFRNCvZghRvVOHxlDkNSjLHp8oFbz2eI8BcWsWwdYubiBXknCONtTVyWb0ZiR7CIlxrUFdHS0pjtk7icOuF9fH1RSS8NjS2/7h5S+mKAGypxmWqPJ+vpKRMc6GaRUvSBhGZCi6tMHCLBHR1tvEiUBBtAX0zqhvmBbMrtWTcE0PKiHaTvLstYfGnbtmrcJ4LT9eJp/PENIflUYvFb+gz3yVoy13WDmwSNQ40YkGzJsbcWvTfkRZ3GjVz8RmpfltHc384/+nLH08pbIPLb8UQ9oHhjwSQv/vlSyzhdTbRtgbh9f+6KI41++gf7RF8ZLnnFfwWT2GfnZ0JgZZogpquFG45kbaC+xUk1L0FW/fpSmMrlHTxDmTlizdf4g/b2EJAOHFd1pXSncHZhipgVQhd+N9sYw1Yr7kosYhEE52UEilnJCllrnh11I1RE4cUe88egflqkXY61UoSmFU4jiM5J5I7WZyDOj9+PvJ4TIGVnyesFqYSRVCu0VpzKqV5YlGhnHPicBhIDlXqouxENqpdhHOJamIhMPhOcZ2lKd3mAnS6kIbGhAbEX0MgTf6aBfdRsLuC1crLdUa+PjNPFy5cMXVkUNKgSNLlcO0dkHrfyo3nEM9+BONFQyHWUrkgzJMxYQzpREqJYcytaDjiM/RWmxIVzGMOpfDz+czPT1d+epq4Toom43otlGJrl73WHa6wb9jTncWgTBdzpJS4N41WN3l837MVZB8f283Hf9XyB4SPuK8U9jGDe9u+Zf1/JM6wgZKShocQkNBai9Df8wIVbdeVBT7aZh/96+7yrU3chdt2669ZtnUK0NNVt+3P7NVLJXI4OlHarUnOjcfQs4cW638TdF5SPz0UQurQUY2sDmnKYGmoc+eH3lj8vkJG94y2vt9bnv5WsfZ3XYqiHBXn4WGM7lcvxpCEIYWVaQpqjtYQLRZRaYrBdTZKJeitU7hE7jPVjHOpPJfClzmUiSA8jLr8mCGnCPyWaM/5dL4gFkVZnR21ccthZswzIeJ8lfGp35bNumUQuqXO2sXN9wO2HaPmMYi2+4ZHG8zTkXkuFCrXGvTz2vjERIwslaSt2Y2XyNoxyJqRQahaGvzn1FoQFEmHVjQ2IMOBKgN/fb5wrlBTQohA8efjJ07jgYfDCT2eKJr5pVRkyBweEpMLxRNfLzNPFc4GxbfpJPHu0kEkOKRgpR3HRK3CVOA8FXx2vAhe2z1ozXSWCfWvtxmX5Y+nFPaC+8YE422hfm/bW+d6S1nsgsu9JmEPE+kdD+FewHlbu/BWoPntXKTfa+mVA7ff+FtV1D4ddcV7bqX9fp+GACzv771uYg47b2KhAl7WSVixm2MWWgS5nUbbAVjGYaMYfL9td2n9+ZXd9v4usNBpaNtxHCLVcdKpBSflRmXS8+TbBXRL09xApfURCGikmFAs6L1r94wkWnUmj4DtIAoekNBsxrUGiC3NM4kMqxYUd/BOvs+aYXVTmrDck/VXL49lH3PhdqC2Y9OnR/tHJQLbQ85IVdyM4t5+f/wGkVCea31FfGOl/YZwP+ictUEV0VKD2wNtzWo/l8LkCWscGUmV0zDweDzw6TTih5FJEs9TtCUdLBSxF+VajckkKEZE23k3gKjEaCgwDsphyOQkTAbuzlyNWjx+ZzW8ruyo6+v3eCp/n+XjSiF9e5d3l3u4xb3lW17BW4Hhe+t+TaC5QUZRoLZWJe+rl/OSbtqDzvs01dt6hreUQv2NsNzr8rd7y2vA8tbL1w+cY116gHzlYOreQWWlkN7ShjWaj17pu9UdHUK6ifRy4xm8qmpeigx8xYb6+k0PgPas3ixLcFBW4b+c1m+9gb7/cpqeQrNJyE+6fkFvJRnpr87FCioDh5z4dDxgVqnnKzORB/8yV2YPsruskeM+DgdKrbxcr1QLq9nauA1JeL5aUwQZqcKBXl1v/LlOpFFJY6Jq4lqF/36pXAxeDAb35RHqv+nWc4x5suV+WpaNgl3+r7FzVAZv9tke1vbrukQIL2o8KMOg5JzwM1iNiu8kiubMcVRSctJQmbwyuzHk8KwkWbvllYotxIPVAROSJIoJl1K4TmcKymxD1HU07y0Bnw8jn44Dn04D0+OBw3Dg4fRn/udfX6h/feGnp5kZ+FobqZ4rkkbEFTelulKbYlBRsip/+dMjnx+OfP1lolwr12vhfDXK7MjV8Sp48eih3TMcmjUid+7Iv2L5uFL4vUMKb8mgvTJ4q/L4I95AuvP+VhxCWeIIa9VyDyjvuY32FczxvmYn1RuF8p6nsK0S/qhg3iLY2yqD+/tys71fxfbZVjpp2MeW/s3xgG+rlVevoAeb8+bVexerZkyJ4iC8WZB+oxju0mX3eENTKn6zrp2nIVe9MHhvzm+N2K2A2+ud7XjtT9PHQCUasiBCtRrZTh5HBKDmpCFzOBxQTVg1ptbApzhcagj80jwDdcc0UT1orpfQloKnCFprGtEUdAlJjSyCqKASd7HzGaUUD/cwCAOt1Wbt3d5YlGKMx62h8JaHtNyPNkCS2tg0OoxtrRu7c2w9MyOs58t1CoiqVhKRVYUZdZ6ZVMkIMjSPSnxz7wRza1lPvlFGMe5zrRFwJzqBiCqfHj5xrcLL7JTGY1Sbd1JaPMIdhiHz6fGBuQz87eUpMoVqxBJElER4C0m10Vbo4rHEdTlujoqSNBozwRT8Ti22EBDSZjIGCRO3s/Nft/zrlMJbyx7+uRdUfsubuOdV7BXCW+fbFaltoaJ7AeXbIHIlvfIk1vePVijbBwZ5JZL42OS5LclfBXr/2x/gW675jy8x5LfsqNu6z64QBiyG3IQxDdRqzAK4Ya3/Yxf297KIusJY6hc2wWhzX+KQixdxR/tt2YkFGpOERFtEjz7HO8j85hTgq+ATISUlDzngotIvvu3ZzhWpjgeSCLMXrrPxUo2rR+FYv0716FddUmQATQSUkiSyjlwVSZmUD6SckNraviZZGEHnphBEYEgti2nQaNvpEhTTu/vXIaT+m2/v7cYLImZQCLzNI+erl2CbE+gy0/wVHGcOU628XC7U6YqMMGSBnJi8ME/GVYLuInGgyobNlKD6sFYxTHp93XMpEd3TFEyrmvnh0488XwvXOgUEJuGtFaCIMFkYNaec+PRpJGXl//jZeLIr5ToR8JAiRKZRlkQxRSWhXYi4hMdTbal7yDmBF8xscUWtNoW2VQwf7YDzT1g+rhR+D+j7Hgi7X7ZCPPNa0O+F/z2o6B2hfxdKav/3TKM9VLTCQbe9EnpgeWTerO9Q0u3+PSi7cqXc++lBhfUt5WDLINwuzmtvQzZ/99+99RYSSwLju9/d1duiBDbVy9LOE0rAGel1C1G7EPUEQiYepKg9CkFjG1ioVzYvgn//6kqj71/BNgVAvUp6McRkhS8WxeCb0VmKq/wWR+/j7TGF+lf0Y1Cl1HpH0La/2ui0zcjjSMoDroniQXY3N3BfN+23rvO0XIN276pGkdQwV376cuagwpiMay1MMvNMz3YR8uQMtfBnh0GFhyF+bDLnUmI/b3c56huaMdCs9N7VKyqndZm3sdetO5BFyUkZcuKqhSrWoC5FNaAeVwhCju3YhJIbDyM/nEY+PWZkUMoBfjqf+XK5UNyYSuXyfMaS48nRY3hF/ZnuFNot5EDKIzoc4PiZl9m4XOaYX9WolwsUGAUOnz5zePzE/+N/+98YTkfS8chZNO7L5Dw/X/nypfD88sz1WlGljcWASEiF3jsQT3g+BHutO3MRzhfn6UvhcoXzBbw4qdFlBMS4VQYs0OcfRS38a5TC+4jHreW/Xbf34+95Ct96vbVfn2D4nde6/pYL8R6zehfNt9Zzf7gWF//uwKxwTrfs7w/PfdSxO6BvOPDLT72nOG69iNdw0+vlNsB8O5RrnYLcHhLJMe5LHv3yfGy+aE8hvP38atpsnq/97b0R7rK+dbK67SZp37udotv3fXB5+S7rYwE9gtvPs/pnRh4SgwWvj1gPjcoCXfXv8s4TsQnoVvdGM+2UWlGDQRpUJQE9Te34bDHH+jlzgjFBTcJVdlNeZMs8fdPLqttUIcpa3KD//vYDRSOmMibFk1IkeqYtGqcrPRVcwSUa+kjv2ZwEyYqmoKcYDsrLPDFotNk0oqlQt1M0peXCtNUysOVFallKaRhQq3i0s8ccLqUy16gxGIeR4/HE4w8/oscDDOGNUIIh9nItfH2+MM2VavFEstCgh7eABReS0HowNGkw1zCMrAhWDJ9jgsrWTX3rtZnT/8rln9U55/dflojhN5b+9P5O51uDrO9xGEXlrtwoivVytv/f8xpeCbTN8h7q+K25tAiiN7ff3/Z26V1fQvXd812ilWRQAoQEr1wuz3gBKxv8fqtBNOQOKTyFG6WwyxZzbbCFdiEF2vfZKhp6imUIt85pBAGzuDsDq1JpMWt68ZSb3+RaDAiDdUhbuLZgp0mcLwuM7mSvJDE+/XhkuCYe/pq5NPiot1DcBrbXgK2sc6UJ7qQwqDGqkDVHDUELMhvOTBTMnXLm4XDkkAS7TuEXKDydI+Ehtd/cHeieqTUSvEt9HNqtQBHGPqQKNgieQZKQknDMcDhlPEedxRXnhcD0ERhGR3K8yqh4EhBj8sKTwfwyc7DMn3/4RB6UcUhcxIIIr3vxWTicRkhgUiAprk7REoVuSZlJiCUeZUS1gJQlmfzLeQYywsApHRmGBzh+po4HahoxSbhHltFfv3zl//M/fuK5HphdsQqqcd01jcyWmEwwz+DKeY7+z7Nk9OwUcR5kxKVwKVekNEqLGgF1KwS9e6/a306AP8DycaXwe1z0R87xnibtwOd+nb6zf4cl0ub/e+epDV5JayQhN0tjfa3bFGuCMP5vzC/Lfn1dh4LeEsVbv+L++ntDuPVh9tvl7uftuu899t7nTr+8Fu7sOeW3PpLFmElBdjCgKCtVva3v7tzuu0kY8N16USLouVEkolG/kJrVfnvLpSmIUAS9L/LgG2HYXAltg771soJ631uRGkHypkJxYzZdqroHh8EcLYV6uVKnGS81isJYUymF5intFFm31Jef34KXQVuRSOYoSu5BUoGTCo9JQyEZS3vQYsHCqhJCP0mMjYYxv0B+GWcEkgg53fqenhzP4AOQwUdgdDjC6SRIFqRGkVfC8RReg4zOcEgMxwTHgSrOU3lhUsN95ooyuFHnF56ZeBkqRaMvs2eQHPe3RBQGV4s5o1BU8ZTxfGD2jFVhfpm5mmByQPVAlgFsjEY5ljAPkPfqCavBT1Rq4nIx/vq3M+erIMMn3BVzobox5gPjeORKxhCuHtlSRVpE0YRahTNOpXItTpmM62TUpgS8eLzvm3rssxv+xcvHlcI/66L7PNwL7n1e7zZ30N7Z3s9jb5xnp3BqSi2XeyvkW9pZE24QD2MEnhKRGtcyE1hBpa4YvrV4s6bf2vaWBV9vbNePHxs/91uxg/tpqn1dKENvoXZrvzs16OI2Lyv21UATOhacAnpxXYV/Vw5em7LYJwF426cr+q4MWrBRvR2T4nh1lsrq2qzyKgFv9cKsDEsXrAGWX+dNKQRSskJ+TkA1AaNY4OoaOffFoFDJ3rPkYTBDS6VeLpR5xmvQKHf4x9ikDSxTZTUH+nliFCWEuUSevaKoK8kCkB4GOCXlMQujh1I0Qlkls0UZjJthDRgplGP24KgagaxwGjQoI3BmjSpiGx0fwnL30fGDw9E5HpWco/9zdWNwR5Jh6mh28kk5fjoynA7MGF+/npnEmDyKGpMr19mYqMy5UFO7l80jkQTzohT6DxCKDngakHzkMsNcheeXgmmmygFND9EBzTKlSPPfM8UTFxJmibkmpqvw8uz89W8vXGYljZ+gBr2FeUHzgeFwYrKIvl29Uhs9SOdBtgJnM67myLXC7NgENjdl8JZCWJqA/zGWf65S2MqYt2Rl36fP2K2C2L73Qey02XsFoHeOLe1zj3vtPYmOcEgI9UIm4Ib1Ie10FSFY4oGpTTnU5lt4UxUdqoiftSqZe0sX1G9DON8OPr+nft4S8nHsFrt569y32/svKWQyRm2ek7VQ+xp4Lu3ba9ALA5pssYxbky2sbIR+n5VtnVqs74Kzp0VqZWEMbRxtWDtWRtA59tHanAxZz3FtRVwJ4SAaqZ0WQrHQYx63+Qqh7KGKIxIcQp/GxFGUZMEVNJlFta0oKs5BhMGMer5Q5oLXSF1NdG/hdQps/xR3JVJKkwtUZyo1eimkwmyO10quQTp4dOFkhWMxBo3+0dAyddx5bFk8NCgsUob7tayxhKPAaUj86fPIbJVilS9U5uTUHMonZfj8mDielIdPyvEQXowX4TgOHE+Zwkzxypdpwkdj0pkyF6o4wzFRs2MNy/LBKXnGCc8rNSUUebXx3tAaPEHRjEliYqDWTDEWekrkAfNE9UyqI2M68l/+y/+Vr89Xfvr5idOf/gsPf/kL8vgX5hleLpX/1//7/+D56czl6qTDkYdPJ0aZKFPlKDPHh5Hjw8iQHklX5+/XM8iIyMCfS2aqxvM0M1Vnqo6UhFRBDaxEG9QbyGj7+T+sUvi9Ma/3gPHvfd3zFN7bd+9JLPCTBHShctvLtVn+jbB3URNbOrhtyLnDKgGc9AYdW0rl+9b7+n375VaZ3Dt+xf3fUjq/HXK6XRfskrfjEL94v27ZJnGMasAKqutDrg322cJDsnl59xT23gPhKaitHsgizVsWk8jap1cXyz+8g56WCmFFAytVN05uDXP6/hWJlpjtMo4iPKRg6Lw2XqC4qBC8jZ0Cmys2N1K82LxAWFt17zefuschLeU2MmncQ6kqARlpiv1OEgL9qM4okDTOYSq4ClfpJHy+KLpFKcjGK0mCJok0USOymrTBZslJKVJIj4fEOEoo9gymERTXDHkM/qfiwjOCJ2duRZwO6CARaE6CDhLHJI/WmCJY8xQsNWWQwZNEfYtGy85Kpkrw8BoZdETIFB/W3D+LQrOILVRmyVg+YHrgMgvP55lfnq78/ZdnXl6uSBrJpiQTXIO3iiSYREstzQdSdSRVlIx44qBhBM1eKdWoLSOO5hlsXzdwkfEfHD76PZTCR85xDzZ6K5bQrf6Ox+49ie5BbN/3x8rr81hWXIXUagy64F97izmd7AISqeXOrgztElkYQM8A7x7GvRC133x+O2W1b38rbfQ9T2Q997c9jve37xVU9w6cCLd1+Cg1eKnl8VOpbWxyjrBzDIlEmujQblsvKya8CPUQ+gxNIXRPod1HkVAInRTPOgbU5o24kNsxDqRW1ZubEPQG4/Sb0H9daisGXc/tok3XCFnhkJw/KXxKyueHA1+uV37B+GK14erhfWh1ymWmlNp6P6yW+ut7tL9jzVBuHpMUQ9wYBicNSh6iklaBwStjFo45LHdRpZhwVJgy1CtRHWzeknZiW2pxht7LwI+Kj2BaW3W/cBgHoDKZcTwIx1H58w8nLFWuMvGSFE/wXI1BnBPGcehEfsrZjRemuF9ZGY45mhElYRiFPAinByKDSWBKRhFnVqhDZE8VlEJiZqC0CIimIyKZxEjKR4zM+arMKJMnSklgxvmvXygVrj7wuQ7Uq/DTf/+Fv/79C//jrz/z7397oppw+vyZMhvzlzP59JmalKITT/PIxTOfxkeqxtxMc/RY+EGPlGRI46byWqlXg9lhBps8FEJvBl7a58KaNvZ7G92/YfljxhTuxQL2UNA9D6C27dv3vNn/rePurHeLDk1Von6g4/f9fU1J3a8L0VwbCtyzclYv4bXw3s+HDue8PU9+H6t/Ra2329d197f3bZ2TXjfX2z2DDqzdMkVFza1F60J1NPlah6AecYDuMbAGj1PLAXfCY+hwn6ZVaSxpoP1eNq/BiXvpDW7ShpsPGh5ERSLlkzWoHIptN+ICeNzNAecAnEQ5egSTfa7UYkzmXK0d76C1wixMZsw1CtSSQ+8P8/r+7L6XJsDNkWKchswhweOhdTPzGta+wCDOoHBQY5CKiEfxVDFKMcYOffZ4CcKoLU6hkcBpAhwSdRDOuVLcqOJMQ2Ju6bYcFB+UMoRse7bKS4nWlXMOb2LGSM3lkuxLwxxrUJCcWNYNB494yDExCxQFHTU+w5Jy2zOJZgaMEdeB8fAnRDPIwKU4tQbGP6NMJCaJeoLLbBHvqML/+PmZfHau8swvz1d+er7wNAvVE8/PheIxZ5NVgnfqkfPkzGfjl/pCcWVq0KQbPL1cKLMxTZU6RxqqNxoWK77U0rB/bT2G/5BK4Z911R3G+QgE9JZiuJdptPUY3lIMsv3+4LPvQn8V9reB5h5Y7dt6MHqbgQOvRfwep9+P7resfvj1geb1Gr7tWbztlRg9mN7piLcw2i3stlZydPZQaVAEpUtzbpVCE+zaBL7WBvd0iMgCfloCzc2a75DUsh9NkUBQF8ehkYYqaygphG+MRWdOCOaBvWpswVgRTqqMCNmcOhfmUpmrMxPZTOqgwZvMVMJC7wyvGUIA+zrttp+2S7IGOVXjcBx4GJVPp4TXQplKxByI4PUgkUXUPRFVoVDRGsFfhGj+05ZRIxaARuAUBR8TNcMlBedQFaNmwVpRg4+CD6EArmacqzOX8I70mNAkYeV7I+DLDQYU8ENAQXIMT0UEhgOMAxwOoK0hkJ4aNlidOgdX0LzAQiMmI8iAjp9IOiCauNQp+inI0PpiK5MMoUgKRMdLpX55QbRwnuClOM8FLowUV67n2sSBoLWSh8zx8cTL+crzueDzhUAHUkgCc55frtjslAK1rArBK63VJrdw0T3FcAsX/EuXP6BSkNfCfB9o3noNdbNuL+R7cBluAzkdRtrKw769bFZJAhHyEje4hZF6tk3dxAy2WUp9T3/TFFjhnLdG96P0E78l0Hy738cgqNgjSJhrg48qBaXT4lkb5kLUiQ/Nf2gZOhr5+z1YXGuDaPJqQVuvaCc8gEhvb4Vn4gGnSPMOiH21378xplKRgKQEGs124PMqa64+yhLIrb5i604rqncWISutwutBlUfNZMkY8DQVXopxrd4yg4JbZypEBo7LElxWwjO5vV+RgLqfK9K+d/BQDnadqFUo1hSZGUNOJDQgMY+c+KTBf6Qy83mEB0CuMDmU3JrRi5BTjYEZvHWaFKrMkJTxITLMikA6BmSmSXA1rmr8ezUsOfYAs0RMYRicOYMNUbuRknP4lJHk5AQvYniqDEfn4TFzOp0YkkWG0hFSSnjOXPKRWoTyVJglPIVp4ykUH7CaeflyjeY5MpLyIZpb1xRMpFUZT5/JaeB6KVyBSxGezt6CTQcmgwlnloFZE1dbqbFzGjkMBx4/PQY8lWbma1QnHyTBHBDRXOMu5WFArxNSStQiVMdNAkaqxI+4F2j+j6sU/kmLb95/7Wt//D6gvFcu9+IW1gSk9yYkIey3geRVUcQW2ymPrafw1p3uUYj3M4O2Z/j2fvegp57Lcvv//et5b581YL16SbcB5dvgczBI3g9IQzRIwZvX0O7RYuW3oLE381pacDpSWCV4qraB5+5JdOioP2zdYzAPrp6+u0OSsPwdGCRSCPrgOSwFs4lIBY3LDcZXzCMTCaEgdC7ctBSlhcfbuZkWKKyN7EcR2e5ZZCIgnCUoLGKOrte4dKYjUmSHQcldAybhMhau7lwKSxGgtvQqaTEdF6gJcoZhTMwW7T6HQRaCt0mcqk5NFsHf1LwqBU++pJNGkFg4jpCTNFjJIQmnU+LTp4HHTwOiJShmjr6Qy+ThQG5eQ4/019oNkIgpVBLW+ia7CmOoRiQPCBKwjUfaaafQnh2sNrdFdWFOrdKaycpAcQ3SPBcw4TLHs61pQH1eKsrFmiyQjEn0w5YKUgWpwYhKZ/PdegX34KMuf/rE+xcufzylAG9DO/c8he0+9c5+97yH7bq9oujgcoOaTIUieRGza0pqACUhc3pMwRePIW/2iwykt+/0e6movvn7FufRvfN9a/tv9Rp6VlUhkaltNHKzgMMniLmfmxAuC/SWu8IQRZMHAWEV3IMDSQghoBZBx2oeaacI0tJUxQhsul/Q3IR8VwC+ehzu4cbTgrxSI3ArBCTlHkHhlJTZAxIpLYYhIcPICGPzLKoDpTKZcx6jAX1NCTdDXclN2s/S7nrntfE19fOjUHL3Wg4CJ4VPo/I4Cn9+HJmnyvkcRWoijaxOQVU5HkeOx8TDMZFM0aKkyxPnXPj5XKlNmItEG0w9hAemAgzKeMz8+DjiUwUrfD5Fb4Os8JKNSYXrIbUU7K60HR8cyzCPSho1YghHZ8jOKYUC1UH5X/7zAz/8+cjnPx2Y/IqrI0fllye4vMD48AM2w/H5zNmVK9IM88TEGCmpJKaWeVQZUMskyTyePqFXx6eZ6eIUMSoHikl4HJ6pniLWJBmTTOEYGU1yoBhMLthVkbnyMj9xGh44pCPMhVydB4yDjCRNvIzwcpn529MLXJ08C/UaAZFaPNyorhgKyCYzafEY/lkx2w8s36EU/pmR5tDQS0fy/Wtv+fd12/e9Qnlv3b3z14avApZ0EWgfCTQDUeJPD7++TZ0Nr4Xv61hDP/bbqamvj3/b2n/vuLfW9f9X9dQ9gIRR6LBaB76cFMJe0uJHGAmT4PNJGhlLvZhNEqtvlVbh3++rtzgCTigIVqoGHGRkccmTh1Xf40y9JgKJB7N3GVty9CXaVyIwuOC+cjIK0tRbfJkT3dEu04xIoURPzaC44NbmUAmaCAgldJua3lmFYnRXr3H1KA7iHBU+Z/hxgE8H4cejMomTampgORQXqIIVJYsyiDCUQrKEFkhqpGSMo1EVLEUFvyewMWoPjglSdnSoFJ0puVIs6hO0uTuzxuuqQjeGveW2+hAppsMoHA7COAifPmVQw1rDKgblmpSLONkLhx8ecIn+yC/VeCowzJlrgYkoikiSwB2VxDEf0JQRTZzryMWU5znjMuIykHSMwPXpwHSBucLZhYun6ItA1Dg4RzpB/tQK2iaU2SOV1khIVdKkzHOkJ87nQqltXtYLGMxFmGZjuloUqxVpHgNI8x60Ql6CW62Pdujbd+AjuXn6XjGeCexX3Wx7db6PLX9ApdBEwnvB5veyid573VMke+9h6zW095oUabzpHwk0d3gpwq/R+vxblc3brXYTQL5/3EfSS9fzfXu/HiTeq4XX3x62a88tWqEkZYWUIvjcVWIohoSLxecWi3Es0kxlhYwioyhgnl6T4Cnw6o6TuMsSYFZC+GPxoPqu8K0rEsKlawHpkGTS6g2C+4iFEM4kspWQnqpJqwuQZax6OyFKsKR6q4UYGsREU0iB9QvHdlmzLGZPjOZy3rjYriiclftoBI7iPCThU4ZPWXgchGyCZ6GWqA+IgKZCTaRWkJetkqpG2q5EdfFAfDaFeQiPwQYnHYSUhTEHDDRpoSZrUFFAdY5RxJiVoKmQ9vgMTbEPIIOQB+UwCIeDcnoYKESzHAF8UCaFC0ayyul0xFWZfrlwscpLdVJR5ipUHRAdSZ4jnVgT6TAyDgNJEjKP+KQ8V8XlgEtGUyZrarj/TK3O1TQEfuM/dk9YI3QPpZAoKJNH85yAmxT1BCbURsVbrlGHUCuUqUZguShmUGfWqmWjpSNHcaW6cFiKaAxMKD0Y3eXO7qHrT6MQNCaLDlgmz52H1LmdYNvtH1QOf7xAc5fEpqtgvgcLvRdo3u7fI3pdMLD5vEVj7gWf07q+VznvA80hHFah24tzUquGhtuc9Let9P3/Hw0wf2y/fnXfprfo5317P1kUQeC7PUowk9qwlxZyzuhSANJBE3Dv9B8RplYgpdYfODePwaOiNaUQzKVlH/nAAnPoECRxI6zdwrx5eK1iGcBHlodveT4q0duhrE9RcF7GsT0zZkSbV2PMCyQW1v/QU1JFGY8HLrVwrgVavv+hwjEJh6Q8jAfMjC8vz1w8uHOUuHD11VRMbc6EOdGgI5wj8IBwsMqhOMcL5NkYrFIUqsLFIxCcU+ZUlKEIOh4hj5hk/OUZTfAIlETwC7mhSXg4ZU6PBw4PA3pUZownm3m+GNdi/NIoDVWhZLDkSDIkC5olOKkzpCFqDvIoHE7K8SDoQyi+hHJMSsH56fzMzz6SyhH/TyeGYWCWA0/1yt+uE1MBzYnHz3/i8+nEj+OBH3RAUiIPI3VyygzzX43ZhdESVRKaE+nTiSGPHNKR6VNBzsZff5qZq3KtyuQD1jhVzAN+KoxUFPeMekJJjBbX/CCHoLwoxjwbpTgv1xpcRhWYfekRHvEEsJayOlThQROjKidJ2BzpwV+aMVo3nsL2Ke4GyvJM+msZ8fq5vH2G7yqNDyx/QKWw+8r3Xvt9PhBA7g3hF6WxjyvsPYrWMs9VMLmtWDai8Ua3kJVtELZXQPfK5i0Q9H4BG8s+221vQ0XvHft6/bfhp3vb+v8d1Lgdh7Vfbfec6ma7YJhsK557AL4fY+QlgMxN0FjbZ23ra4sQd28BIvbQU1KtG2NZWJqYtICsJQJHboHQDh91hdL7JkTQtgWlZQVzUhuA3OyVIPFsWUUesHHxyPyJa3HGliI6BrJz0/Lj3l3pxY8sPlYENUOxJI5ZGVsFdy9AQ5olKU4SYUzCMCSGQdHRkZwhjehJSUnBLAoDtSXbJTgclcMpMR4THMP4GaySTUgiXPuYJRYqkjxqVCOPih6Fjp2pNqiu0VhMXnCN7CbNKWaGw1SVeoG/fa0choSXxEt7XUwYNfFpPDA8PJIfTuhwiPnuUGqlTBaBaZdgM9Voi1klIXkgHw8c5cg0Ovr03I5TSvMQek8E71yzrpj1Zjopg4dZEgAAbMhJREFUJoRBwWAOpVCLU0qkn/baAymOWFBaqEnMG/PWPyTudZZoh+rNGEgWSkNM2lzbwKaLcbrWy/Q+F28td52DHbz0UdPxj6sUtoJ+//7Wup1XIMLqDWwE/dLZ6170f+uBbKuch9bCbxMr6KK3B5p77v6anhowSWENUMd+b+f/74dgHYqPQUWwt/LfViZvVU7f90pX0RXDGhn+leBArRiVTJEoMupjM5OW0ahUhPbQ4lgLPgvgjaBouTc0TJYm/HIbB+uWk5MG1iyaDst07gpp7nfb7u0Zr0JLF6RjRSRXpAl17Q+jx3fU5Tc7YwJRiSpbi9RP7Xe/zCSrHGrlMQ8kacH86lALqYQHMdAI+ODuHe0zKcY47vpJhc9j5t8ej/zpIByUZmKGhzCZtSKz6J0wjsrjp5HxIWFjheMBDifGeUTPBpPhOQLCQxZIiXxQhgclHZVpDE/o4JkDiXGqXBBqAstxL1JyHj4NDAdlOCYOPxyQQblSKDYz14l5cEyd+XoND2Js7UQ1M+qBpwl+fhIu//WFw1j54XTip5fEL/PIC8pDHvhfjp8Y//xvjD9+Zjg8Ms+F51++8HW+8PRifC1Hvpry1QRSMMmeLXPSkfH0yA8/fEJnIf/9f+DuXCeleCa4zRRnbLGIYD+txaIngyh1ioLE63xGZofizDNUa7GAEjEDpgZ5WnRkSyTMauxYOr5QyR4V7QPwpYIUYu4ZqBtjmxCzeaNU2bYgfXvZirn+3O7DFB9VCPAPjim8dSHf1nkt9cd7pFeWVTcW/laIbykrtgrFWNIe+/+9peMiGLoSgHV0d4oEaxCSRCVpZWVJvaco+rq1DuFWBHTr/9t4/wo7rWs+Dhm9v++3vQzfbet/6+a/iBM0rezagni1KcnUqhkslIE7U1MUKhknqmiTN59LWmtNwppaLr2jiu3+0QSrtSnSMVc1msvAEliWdu+06Qtr93aJVUyO1Eh/rM1q69BTVDKHx3EihPYkyizGVQJ+Kb0lpTg5CZmw2DWlyHypxlTnqJxuRQ/qLGEz2Qzy2uB+9VCkYfxFnNJ4iZ6LcTHnxdb6gpyAkyCP4KeKHwzSHBTQM0ylUrySDkYaYMigoyAHZfzzA+l0hCFzfnnCcIac+PH4wOBHeDkze2WWqE2QQXj88ymgogHyUfDsnOeZSolalFGRUXn8PIKEITQzUD2j+YBWR4tjcuRiAy9f4aeL8DILU8qkWfnpq1MeldM4BI11VYoe0aMymJHtkTwpWYXzfGWanJfpgOUDfhkojFyrQv6Ei1E8SBzDm2VJa106AM5gRCW3XaOfMsVhDo+gFvAq+AxS22Rq7TWtOFebES/IBCwpqmFcPCRtDLeC+oxYBJ/VI7vuoVk2Z7NQDLbDlHYPpGzeU+seqATVN/6aY+8f4Cl8j1JoxTHL/1voZL/s124jwR6D/h6EtIWNNlCR3NlPXBaX0LfKZa9k9jCUQ69yjthCpGN2uGSbfbQGnVf4JEbk/q//qFLYH/NxpfB9XsbrTKjbZVUEYNz2kNhCZ9vahWWsfKW9iCboXbE6VRRXQ1oAVFLAPUv6aa9ZaPUH3oS6S4P3GlTUISXK+llaUNm9BZA9AoDWgsPS01W7Y9GizkL3HGIcRtoDI/G9szjukXJcaQ5Ke7BFHM1KtGAEsxrB4BaF3nY864PsNANmO94CkuK4glMlgtXP7sEn5A21UYkm8SNBZz0YPghIxX3CZqHUykxQX+sQxHVyENKDcvjTgSgoyPgUMEcahNPxQM5wlitTjeK8a3IYhOFhYMhCUkMHMDWsVEwrLo6MQjomDp/GEOZVsJKjOU06ImqoGi4j1RNPE7wU4WqKa2IuytcXg2eoh3aPRUCGgKkOjhyOqETxXkA7lamMyDygU6KkxNUU44CJNVMlt/kZUGZ1XaAgr+EF1mr4DEu62CZlbJtKGsF9WSgtrFaokEoojCX91J05IvzRsKcZEsnDWMkGA+GxTkBtPZ17Aub+gdwqBCEgO5WAvqz1m96hRx9e/gHwkbS+rZlaCu7eWtp99HxdCqcm0dItRLQV1t1D2ASEcW4DzftjYYWFdLNtG7DuMNRGwPRLrmNkE8WUSkuOTc8w6oFmXYbWF8H31tK3fDSb6Ha0vu017HOK3oOO3lpu4SNBGrASfb8isNqhoIFKaQmcHTYL/qhYN1KQBkw73hhpM0JBxAKTbgJem1LISYNtNEUqn2tk+mRbb78QaI23Kumw7uKzV3DxhXwPBZmjOjXl5uG5kNxDyOaw6kYAD48gss8iQOu9YY2179LwAqICuKKqjCmIEWvrL9phsdQEwjr9urV/f+xrIiglsvOlFqQaX8qMIeiQGNwZ1DkNgooz14qlhGfF68x0nbheJgozczKm0ZHmKYxHRT8l5D8fSA+PyDDw4Bem68TLdObzX/7M4Ycjh9OV8+XK80vh7ypcM0yHSsFQm1FzTJyLXSELaVR++F9/5HgaSSlFYPjqkB4wyzxPCVQ4HpWqBzwNDMcT+jyBFsbxEXPlv/0k8Pwz8l9fePi3zzw8HPjzvz1wmZzr7JwlMQ8jmo/opEDl6kfcDng98PXnmafJ+dvXytO1e5cZUeVTEkpx5sm5XqIz2rbSeAkkb+sK5g4ZrR4ChTh2Xm5o1CdUYA5SwsmgPs2RJizK5exYFQYUSsVmZ7YavFQuS+HbK6mxNSBYDYuERl8KUcx6H4ibQz68/M6ewqaFIC37w1kE5evLu3e5/fFuEv4tz+DGin/96hCDNK8g3tdrevO89xTPZn0UvLSgs0QQdbWWb7l+eopqF6lvLb68b/f69fDQa+v+9fa3Asnf+q5+XWuFsjRLWZp3ZBSPgJ8QFlwvclMM8VsPK4k1Qrqen+KI2GICLeUqAq7BnYSCq0dQr6ON7bVt5CMthVW2aOQGhuy03bLxSIT2WRxRIYvibohHQxWDgFDadeUUCaUm3ZBsno62CaRAg65ol+kWDdyn5dmQqNq+cwsc8Bxpo7PAtd3xDpvFbwslVfvzYlDMSQ1rKzWyZqz95tpeFrwbNNIkGAnrfozg8jAMjA+Z8SFzeMhUZs4TpBQFaIwt6DutlNgM2lyXFI1uyFiNqmFTcGnNGIiItbpyMaWKgo4Mx4GHQRiOj0F7/WIUyVRJfHl2LtWwZJAOeM6kzz+iJVGvGRvAreLDEQ4PyOkTdq7UuXApTTATcyhr4vEwRJqsF4oZUnxRCmKKVWsGY3iYXh1rRWi+qVK+ocTevlcHi37NbjAVWbLXSvEWHwuPwM2ZSrdz43vcVk/yLenhsBALLjtv466b/T66/M6egkdja4mZKQSOo8LSqP3b59rtsxfO/X1rze+F+EZheAtWehtw6wKin2cbb4DVK7j3fTQ3UZWSejA1chdWIRmpmJ06u+fjfEQpbJePVi/fW36dx7FCUv1b37ri8BDCjaoETTFNATipKcpeqdChpKhAcwLTpXtWrXJ47g2NOmmbWoNh6BVg4ETj+4bxa11rAkgB76Ve8SzhTksH7Pt5mmmlCpacOrIol9rzyxczzMlZ2zXDFaEA5xpFbjkpo0agebJgSJ2cqLTWwKbHIcU+HrCUEtCMe1iaxSNYv1or6+L9ukelZOGqthTcM/R7FRdsOJdaw0t3mEtFS5gDczEu84xlwbNSklESzCoMo6IHwUdHRkdGQw6VYRDyeOL044HhMTOeMxMJLiCDomNGHwashIVLU9z5ccAkYY1HqJYUwlUUcsJTKzA7RAWyWOLl50KtMD6MnH585OFwIj98prpwulTOs3Ce4f/7P57wF+evz4X/9H/5M48//sjpz/8r88W4/vVMOR4wqfhpQD5/Zvi3/4T+fMHsynM9czFnRniQxDFl/u3hB17qFXhhLjOlBHaTTKNOpkoTsBK9laszzzWCzHPzPpunsMiIPadR8zioMJWAh2YjgtMWaShWI1Yx0Wp34KYi/62lT9MtBmn4CpHvZpR/UJz8/jEFDwKyUINxSWHcxSXWV5Ua+2Vnmi/Wj9y34vfew/bVICHvp9l7GWu0dH3vXx0pQ7Fs4SkTaPh0JcDv0obxXqBZm3jsFuH3LPes+e89x/cc+5ZS2Howq1ES4mnekDZIg9Ocirm0HCQlN5KLilKIAraEM9BYRhHmVtkxoAxao5FNWq9Bqi2QUrM1QqdLy/xocQhJoTwivdVX6obuPTRIsJT4XdUhD0EOd5miKCt4ML1RStfG8BmW5uS+kL+ZGleNlNTaiOjEAFUWyh5xRI0xRbvWLN76/sJ1jufBfDOw20ejWfYlxTleHA45kUSYahha1aNpkeAMSXnIzqfszD6TDPIwY6NRa9BZl+QwKDU7U3ZyikrjoxdsvuA+U/1CVYchM/sZnwsvdebJKz8rXLNSs1J8pvjMhYqoIEnRfMJ1oErmaiPuA59+OEVntOoUOWApM5wOVD9S6pHLly/MRSgX4/h44vD5P5F++BEBDpcX5ueZfCn88G9/4nKF5xfwX4SvVvnhh8zZjIspL5NTZ/h8fKSOj0z6yL8/X/nbz5XLpMwloMfpWjFx/n9PX5ivheulMF9tqV2xGgFmn2KSiUW8wkrrt7yNKxgrbHSzLlxHrR4eiEGtafE4tCmP6ka1MEhWUssNbuTrM3lT0Szr5lAi3egWzHyhn795oD+4/ANiChYC03xjmDX32LcS+73v2b221c1v7PJKKeyVRl/3XlB5f+yb+0kELWXbcKf3C9jCSGuefozM91vw2+V7Asz75fsylvbfe5uO2idoZ4iNcGoEjUP1dTync8aGS9ajLoK20erJerE+EzBCjyv3rDFX0N5vYYGG4ivEWYnxDES0eQTN5G+eqiCLcsDBNHL7LYGlsAxnbX1QJGARw7mqBeOoRkFX8ZY73mCqon4TF4jaufBGSOCtSbSmaFbTaxhohHoLP9M990xjfdUGqUHk4atwabG62dffOLZxGMW5Wolgeq5M6szZmTqBn8LcxjAYWA2dS4xTEa5lxlI8rzpfKVI518LZKxeF0jRescpMZRILuFCErEOQyslAsoR4Ih0eqNWxqVI845pJxwPKA2Inily4muGzMOqIHh7x8YGW0oPMwYh7fByDVuOl8nwVLmrIxXmZnPMcRH9mStERZYSS+PJi/PJSKQWsyBIcru682ESdjTKHQujGhlsgCzY3CMc8rr+0uEOHiBaYaPN5s65nHmlbX9u2pRObhQFtBtV2cb7mJr77xHZDlVAK1g7c1EKyPLTfsfwmpdAFRIR3ujvjy97ePojo5gzf8jhs894kcmMrXAZ8i6xsA8RbCCith9+8bz2Aji8rt8HnrdW29Tr69mbZuciSc/860NzvTGpfcTt+v1VBrOf59d4DdGXxPdlJMTQzjUSuBZVjfW2/WNElSl/bJIsb1OfACibZojI7Jh/5IdGhbbGILAyMpU5BoosYvsvRTmW9SGGhwwhN0ObnbiqLFcSagiDua2ly3aRzV1VK2666TqEr3lKUfaHTsBaPmHOfulHLkTRI5RDBXLhajOPCfgA319aVTBVfYhkXj2hpxSOlts1zkfBirjbzNFWevw6MZ0FSWZJnJiFan05BCJgR6peKXi4M00/IoJCE83zGk6NHJf/8hOSoUZjcuIxQkuEyU6pTvXJNivuIl4S9GBOViwufH0ceZODEI5dqPM2FqQgyHDjpX6jDZyQ9UvLMdS6c5UgaHknjI8///sx0nXl5eebw45Hh8ydOY0YfMpxG/tvfL/z8tyv/+5f/SkG5WgYbSTKQv2T8y8xc/s5//W9f+fp0oc6R4jxW4ZMeyZJwFy42YfPESXMkMlzrwkl0voYSKLWuPRG6V9AHtX/eQkdGpLG2dQu/UamLMrEWxLbWb8FtA23snrf3jOh+1HV/zOtp/uHlN8JHssjPDj7csPwspXm2MfS/5SnspXH3MDartrGAvYXfL7Urgr31/5b30M6nzTwN3Hd37M6L8IZzR6BQWCt5E7ZARn7z65eh2Y7TzW//vuUjgeb3l7eVynvKZh2OiCFEPtZKIb5NyY0Iwxp8T2irXdh2Zuvjth7fYwJLpfPm5c2DkFZroH29bPbrwrbdW2mB5uV8/X5uU1eh9TFumxv5nbZAwxLIXsauv286IQgBMaaNLdPmnrYSaoPgaXLuK4X1wWqOsm/SVdvxK5YX1+oRQJ7VOZszW0BgVeM65hZ4z0mwLNQsFAlq7CSROutmTIR3k1samIi3Mm5BTZHUeJM8ZnnVkSojLlElXCXjMiD5gAyx7WKFr5NxLUIkFJxAH/D8iBw+4WXiuQj6YsxfrtSLUWa4zol6jWKy8xVKFUoayKfEKMaXi3Op8BKc2NHv4u8v4TEU5fw8U6eAJY+qPOREbqyll2sN/qLZ4j47S9c0L3K/t/JeEWwho+YdYKsnYEajwGDB+rfHLHGJLsm3c6Ddh1fL/gGX21XfikV8a/kNSuEWAO3/vRZ19vq3vrvsJXd7ajtxTU/z2CYpbQbe+0O2F+JdCW+yT262KdGsXHPLZ64xGbamaN8XWgaMtjTHUAg9w6YXxbCMx+v2jveWjzbC+fZ5fpv3sD3PPY9GCCgkElMz3YyyJthLiyYEgpKa3q30FN5tvKUuUYjUbo/R6xgMDbx8EzNY6hXafXZABtZ50B2UXrjGKuxp8IBs5wMNzmmZSwCirZ6heX09f1RVlgpqpz98ssxUlveWYpqagmlZS6JR4bv0he5P3wYGWJbNsBvNS9LdThs4rO/Xi+OevZJNGbJEbwON4jdNoKPiB8UHoXpBB0UOQyvuqhQJplM5ZhgqJCedggNqcCLNFufiFg3t9UCREZMEkvEUDW+G4wP5cKSkI8925e+XyssMyTOjP5L1M5p/ID284OXMz7+88PLzlaF84fPwAJa41BP1JWNT4ulcQZ10GDj8+MCffhj46b//TC2Vr+eKlYpb4eefLrhJC3IHvPkwZj6PI386HHj++cp1Lry8XJkno0zORI06pmsX1LZWvm8UgGwUgO+CylIiJhGeQPMAii+07TeKZatUtgbtVli+9whvjYc70+e3LL8BPvKbT77b0itMYxE+ftnbEdqZ6tafWm4F+xYG2nsBGyF+gwN2C7KdTxEGHfjT449cyoVLvTBZWS2/fjldOWzOU4eVCbULvG3wuW/79i9/PQt+Lcz0a6z/j++vLA1loEVNdCPsK9pqEnoHuto8CghAKYaw9772m/HSdtM08B401ZXDut93uJ0Dw2Yd3Fjf0uZG50cS2v6tMq0risalF0VtvZ9Cs6JTCu8RkU2Ia+W06tOsKxGJH0DLskBaOq1mbYymjvSmPg2q8u2DLrvP29uwX7edlw3v9xwcT3IIdlpa3wQZhHxIoUhzu67BYYxCsiRRSEV2LBfyUYPj6KiYO1KNWhOzCVMjkzMOFD1ikol4UqQMVBJzFf79pxf++mz8+1k410zWgWMZyVMmIbxw4irCbJUxPTKMP2AyMlXjr08v1CxUVa41oeORw+HI4fCJMR348w+An3n+5Ymq0UznMhWsCLVVIWdV/vLDZx5z4kETz9czdjVOeYRSKF6Ze6HaJquozrLULiw02FsLfxtorkCVaLzj0oLUcZ9vvAtjKWhb1m0sCoG1Hezmln9Tevx2G3BZfnOTnVfK4M7679NjW6m++/9bvZvfetmd/zcKpcckkwjjMDLbjFZdyfNsc8yd85g3viO5DTB3Kgy450F9dDR+H6u/L789lhHXYi70KMq2NmOFjG470a2vqAC25dhtdfj+laISWeqq8HuyU4eHYK1RSK/X9WME1vqFJOt+7SGVRSLrZrQjOB2B7NVH74aCi99Mh2V4NkK610bsyf7W9EBZ3Zjtom98lnXd3tTq3+EKnh1PoRwWhdGI7CQLNAhIeku3ZJCiSU/ydo5kSFbSoJGG2pgkg3ZaqTVjDOEtNP6gUMoZb41sZlOma+V5El5m5Uwi18RzgWEOWpOrp6C09kS1TPXM5IlLhedJqCWUwkQiacKLki0YUY9p4KiFI8osmSKCuC3BYikEM2waOaowuqDt9TAe8KLMXFsgGWj1Aze9DjaB420weaHKaQohtjXrovoqN5b9Zemv0OWKN9njmykgu9zR+5Ljjtf4Oy2/SSn0h0FZJ/kNPfFuv4+ftY8k3JiH1p6OPQRUN7uy+X+7br/f5lghJo8UQV3x6tQpNrTeHuv3bUjylldhqV3o/BqyBF+/XdH8PUtcyluC/a1+CN/33R8JPnfDvAeZYx5EoLl3Vqs4hdS2reqtE+F1mAlkAc5CdtU2rN5+FWgqgevv40SwCnhpw98euo7/K6xtP3tWxEbJC7485DS/riM1PX4g9HhArLcWc7IWTF7mBKvn0ZWCCVFdrIHvF4vMIUstLUG6R+bcHfa9gtjfZGHpHe39O9UoCikHBYUOCQsGNuYh3skgo8MAJc9I6/U5aMI1isT0YHBQ8uORuThzmfFhQDxRprR4Cq7Ry2Ayo9pAZSTbkVwzU018LcKzCxdGtGb+/edfGI5OHo2fzzPPV+N5zlx+KaTLMwxHZhO+lAHzEfOBOWcoSppeuDwZpzygk3KahX8bH3ixzNWjT4HMlXqpaDFSFh5cGE0YcD4PI5/Skb/85//M3/72xP+cfuGX5wvTVJkvtsYSZ78R6uws/N43YRtT8GpR31D8JoYgBoOFJ6EGpVpLfWUttGXjAG4NkH/y8mGlcE+obZ6Du8uqFHpOzkd+Yn8C7pn83Lf8ndts1/7A6511O2tfNjqoVuPycqZMJQpu0npTb4698R5kCXwaGuRj0oGFtQvZ77m85zl0Mbru8+s9lPeW3kMiAsTS1F5P0U3NH4jPASPZkoa6ehRr287UoKTOHxUB6brs76K4GJp8UdKd/noJFC8WSljAC9Fcmy+9chm48RRoFNkRDW4nares1yjIdjg2xr00S9w3VC7S/yyegmM54CLUWjqsYLWlIOyhod3nHmDukNTWKwkPaPUGpF9oiviHJw8CO3UkOySwSD2KWMEAPoINho4pKpXVEVVyBsmKqzBXmGvUk5iGV+BVqR6WvYQLAkkxMhMjs0bNwmWuTC4U16DwTiMpj4gk3LXVfwg1jSADTmayzFSFZ3MiIp+phUZ1HrGoWhS1hNbEyMjTJQLH6QppgjQ7ak7SoC5JxLGnPOKujFU4mfJIwvLItVSe7BwsqC5RwbwJDG8DzveqmKVCshZrs+atVBZK7UPnwDWPQsnKxor2JrKaYbfQocftvKXO3vqy++d75Y/4tc/+dyuFvagR8RvZuzlgI6DaEf5R4biX5l3yt/PsSfI2XoO098V669u3gea2Tjaeg5tTp8LTL08UCiabnKF9YNs366BBAhLBLe3kx3URbr8n/POR5dbS//1tjU74F0ZBzyUKmCiCx70DHTdKwTZjAuE9dG6kbexhrZZOdIo9Ew2rP7fq8O4RtJ8o3CqIpX+z0OgrZIkp9O2L0BXZeJGy5Fh3gasaEITXFlfYOKxByhdZJutDwaIQuoNrOS6wyuq7RdeyO96B3J5nUUL992ymUw98d09hURRJWmzBIVmQDY6hGC07DBLsq0fBB6iDoceMjI3MMkM6JERCIV9nuFThagM2HEBHvAhmylQyAxmRAckj1TNXG5jTZzwNvNQXLibMKJoOpHzgeHjA9UAleidcXKl5gHzA9cizD1xM+GqgPqAk1IwkTq5hwJVUGVTRKgyeqS9PTC8TeQKfoUytJ0Xy4MkCMDiNB8QSw7VyLPDZM3nIXGphqmeqSRSU9UK1zfuN59BlSwHMkRptUBPCXGtQYVhH5oRHzVQPbqq5SHgSAtIA1YSsSp2QrYtFsNADbydIm4eLpxofHI9EhmWmfd/yXdlHeyMmXP1o5j03d7q3012N6+5PfNRTsN17x3kaZuTdLG+zf1OT8IoZta7bbiz8fkzrw+jtgivG9TLFg9TTG/vlb+sY+jnr63XW8IZCbrk2/Vfc/+2/V73CdvFXd+rXHPf+0iGeKD5bhXfo3d6fOXKR4rNuBH9I5L1SiKtdg85lyUiiAVNRYextvKUR293c8xsPoI3A5tmC5YSvrXLpj5REUxiAJEutgW8IjIQICFpvwpM2z+wmW60X3HlrXRnTI+aupM2I7xTKzedO1brh2N7CWpLW/YICunkjGsHm8SCMR2U4hcKbRVpPZQlai8FhdCzXBisJrkpN0qCbzKUJ6ckBHbE0MjcG0hcTUh1RMmk8MZtydeVchOzC7COFKEx0OTDbwM9PHgFem/nbC5xLYuJItiNqI3+flMmEqSijCdltafijqlxeKtda+epOLcI0CdPXGb82dtPiyNwTIRz7+cy1GuVaOE8Jq8JTSZRLZb4UqEqajQdPuETDHisTc7EIQtfNy3avhsaNDp9y5qCJWZxeUZ+Toib4ZExz8FDhJdAEqS2eFf7zEl+4mZhEnOTeemI+OZtCyOXvP8FTkKbJvGE1SlDOZNGF2ygTBAi4L1rP8fbDP3qRe+m+e70TcBZv1/eNU9ycbuNt1Grr07YPLu8DzvZ6P3dZKKKja1e32197SW+lfP4rlu8NaHfi4W0F97bzWq9l7sHkvk/dBaS39QwdOrp9bfaTYJGiVe5uey8vcyL5qsg7vLSNJ6SmoFP7zU405tk4ojcB5NT+73nsREqpNJNde5ZSs1UcFqWwJefraU+SmtfSt9OUSz/2rlJYBn1VdLo6NVulYOab4HZ8lhwkd8NBWwy0BaFTeEDSAtCuhqsgKS2dBotHy8rZUjS0B2jzu5CZCUipWEIkM3pibn2Oq0cmjvXANAn3CEK/XIViMBk8F+VSA3aqHnDQ1OAqr6nRojvJG71iFeartYrjGatCKYpNNTqkzdLgndY4tjh+Llgx5mvhfK3UIpSr4HOcB0lQPPpetyc21aCfeMVttJUFDXFQj4LAQ462m1li8nU2MBFhNgumB2sR5uUE1nixbsWNsxpqPfHZ2399iqgGm281a3N4K2P/wUoBgk74cDgwXSesFrIbA8qhXbTjHFWZ3Zk8ctkdqNaplT96kdtAc48qd7OfeFLvCGRpmLC0AiFXbgPDfWmfvfjrbJB77tA2cP1GoHnxGBxchZJ7oNmIquZfL/y7YP0jLVGTIYuy8+WzNILAHmwOr6BsJnVqymLN8F/PE3UK3fPIdLNCF+/Cgo9osPU+9LFvU0WId+1C2RfPmg4pRUpqbPB+j0sXzGsKsTViPtUmTNu5IlUV8DhekixcRCSC8iHH5HKPu68KKbHEKQZTtNETFHyBmG8UASwQ0XYRZe3/kCW4h1SjgM1bS1ANCuvhJIwnOD2GUrCpRoA8C3pQZAhvQQeQ7Mh4pKKcDaofqD5w8YGrC1dx5pqoJjzPymQDFzlwrQNuiZFocVkl+hwnTUhSaklcPVHqATyT/EBhYPaBn2fjahFvkGkAErWlgQ5FGIjX4/AY/Y9fCuXZuU6VyWsz6hQ7V2R25kmjmY4LR1dyFfTnEskHRTg/GZfZ4NL6LBfn8XgCE+zcJ4mjE+gcLzMJdKJBRbgvnoOWECMj8JDhkwoMKSqYi0GJ4PPsMb+LVKSnMnmhR+MGdJmz3r6muLEJVy0StE+Hw3hkGEZezi9YUzrrXv9opSCdcCmspq2nMKhzbRbTkMNksgqnY0IErtfCpWVcdNfm/uVuZ/49sz6EjtBcspZa6Bo9vtxpPHwrJLTolLdPd6tcbLNtr3j2MNQ+TlEJadFYMJ3ehey3i/Rfz3kEryTK77AIt8ywW96n2tRFr1jonkB0aOt8UGv3urrxLmqgx5tAc+K2LjxUSuruua+Cn2YVC77El1qKfuCzFp4kHnZFVx49eNuho9UlDy9hkb5N4aSkJHFSs9a7S1DNKGaUvFJ69xiwNkWiDetHFKqvdRP9iXhHKciGSXXxNLqiafCWEPj1kDV4oobwEnQkGuFITNM8CmlQGA3PUAehpIRLRupKhT6TqZKZZGASYZYGB3mko7qMSBoRH6ieOJPw5noUGVAyhei6Zym3bLWBYpm5pZ7O1jwSG4Kx1oAS1Og6h3GpHWuvgtSMukWDmsZeWuYOG0mLdXhwDRHKss7hFqWckPIMV8cmwUqDn6iICV6gWtC5lxlqDa9DGmOqlggGD3TiuTAUtDVw8kaToaXi1aiztf4ZwiBOUQvCRy+orwzKSsynQYSsvR0niNsiftggL4s9ZCXYM7xivhV0v375sFJw79k0RgcOVCFJNPiQlokzDgJtUn06JZLCsxd8br1H6SrBV8we+k/kNqIL+xxEIQJ/Y0oMQ0ISVDFmKcxuEfDbhyNe+2SvhXo/ZpsHv3VW9sqjK5y6OWYJSEvASHizmP61Vv7vDVGteUawdlzrINm2UG1VCsGLGgypa/zAbmIKzWmPRultXejb7me2bCaJQJpouHC9zkRVlgByr4LWFvcJBdFhR1nYVlGlJG8xA8A9Gq8TxxjScvZlSUwYciInIycLIS8CnpgraLWgqO7BaCGsdlhqJCKTqikHB9xWD6ZP++0tS1vBuGgROhyV2vZZaptryjhG207PhFIYBB1YqpvTUTgelKoeTXwGpchAZQjBKkJJmVkyMwNXGbiKRitSHymkVpswIHJAalj4ZxEgI+04JTNTqZrxPFDqQPUBfGCyzNUSs8PsCasZraDF4t1AZ0MlxVDN3rRaQm0guZKqY1OhnmekRLKHzZ3q2toACWVKpMNIGg9IOSNXxyaoJRhQp1pRI+RUMa7FmStU002dgpNqCO7HlKheqbX1xTDHZqd6wSZDvLVhLcZBpMHsziCVQQupBrnLlkIzS7SjOGrzEprVX/o9l0TwZnW1AGYzda5UmzdMqb9t+bBSMCpzrdTzHO6wQ9LEmJXTIfHVK27GoBkag+NxEIbcEJoLzLSgncO1dNihL/sfszXl13dVZczK//2//Bd+/PEHoPB0eeGvTz/xZTpTLVJSbgrPOsTTF2Glw36jdmFZx+Z/WB/Wrhi20EVf336KJQXtIaTffrN+zfKPy3xaS87W+gNb3usi2EORVzp8GP/FGSLLPcR+DzuH8ohblxuq46RND4YY8qB91uYtbH5wLHm1pBVZcPcefBVkCdZ6UxBmgSjpYpqF1WgKlsLKTyKcBmXITs7hpbqDFyOlBgGJU0UahKQMCayWxsvkC2wlLhHPqLvZv/U++/UpjWYjxH7VNhZtLrduseGNqPPjjweGLBRmhkjqYTjBrIaUyuHzyKdPI5fzNfpEyMjESGFEJTyGIgeqjFQfuNrARGKWxLkmZs9YfqCSG7wUQecvlhFNqAy8WIOepDKn5iH4IeITNnC1OGYqSq3hrnnvYlaA1sN5LsZc4FpLy/PPWAGrzstLodbeOzny/uvc+hvXuBdF4ac6MZ6E8SEjZyNdo/NZ5zbKVcgmiBlYnGdqVclq4ZFohRHlKMqfNDM5FDOeFm/BySWo1K2WmKnqzCV6PucUnmTMkhl8NbANp1o8LZrDCk3a6ncaLNSTdqp3IN6xWoJG5cOZnd9eviv7yJ1oCk0HcYIsKwJm3izC4EVJOIMmhgxuwsGUg0HKiWLO/HR+I8bQlcE9096X71RVckq49ai9hAXYdg1seHdo9wD2X7H/3L2ErU7aHie7z9uEqZ1n0qud/1VK4R+1RAvN2yDympwcUq1DRSshXq+C1mb9b72Mvl5vtm+D0z3oDJEmmfqoNqK35d1Z+zMj5DGFgHAiVc9ZYkmhLBzpVO8ekOjicMr6UoWkkJOTU7Sz7BTc7iGgE8LU0kB6V7cIPhBf2L+vJSEgd5z9HXzUA8feMplC2bWLan0nFieiBZjHg3AY4/4Mo5APihwCglOBfGjrJo0KXslNCSdo9BWFgeJ5SROOV2b2xGTRESP2yxSU4sIsCfGMkpg8R5GbKFUyJplCYibiC5Mps6UmnKPat7eh7GykPhOB4BnqXJbiL1yDwfQaRWDV4lhrFNfeupmZQBXn6oYkJ42heCjhYWqrRVAX1CC7UFvjo9GJyv3IHUbMaewgZAu5hgVc1yuTozdyCPJQ/s3EadwZ1Q1bWPDi1eFRc2mvYEdw0SZN2/b2bBkbpeDSYg6/n3z51dxH3aGfzbgWQzwSCdVmsjmDO8chczgkhpyRMZGPiR//7U9M08z//v/8r1B9Y8D3B2eLz/T1q/R1N0ot/PWvf+Xpy1dKuTJZ4VJnitoK9Ww9gW7Nv/VV/XPf1l/9Evbn6Z6HbD53ioW+X0+NpFmnN9VP//EXaaC2k6J/8UZzSosp9LRUQygB/AFOXsCn8Azi0ejlbk4PQ69GQzSIaQ4zaaNOXCqaypqO3OMHzWpG4eH4gJhQzsb1ZWaWGu4qkb4pFnz98YCvaOAS9O1xgRy4b0qFYXAOQxSGISAji2tSrpXJvVFMBw331nMR6XlbrPQXG72xKIXNnDVxitbVY8jSSAlBc9yPJA2Sk8rxU+XxqJyOwuEhMxwzL7XwXAqnMwxH0FGY04EZocqRyhi+WzpQyFw5MFlkE4XgT0wMXMlcSUw2YhLVy1dR5iQUyVhTLM82UCxiEdemJC4+cjXlyYcG3Qg2+SKovQWYdSGjE3wiBP1ExGGKtSxIZX6xVmwGtfdUnmsofwu6bwQuo5OycipDnOcaUJC3mFPPHnp0GBEecuJk4FXIJTFRKRa1B0kMrCJTJc3O0GqUxJwkFRGLd/VWWW6YG6XOzG4RY6GlpG4Uw9yuWUpYIC7K1SvFnZlQXkvEQG7Rxjee0t0eH5NBv5olNZ6/QIRNNgVq0nNmjVIKOhvFIvCac+J0zIvFVY1GPLYF/bef9wGBUAq1Vi6XM2UOHK0SGtjz7e69P/PNKfbOxxY26j9zH6S+pzzgFjLaKhH6581N+P8nnSC0QDqYtuplX3H/IETzNsSteE0sOqSxkgau8FOPKaxqIm7JGrCp9BqI8EMj9hs3JqkjqfsYILW19axhUefjgJqiYsxzxeeCtyYy1qCCToZ3k83c7plIxAI0t0I2cTQ7eQBrgWOlJVg4HCRahq6Z5T0FVVrvZl8e6rAut9kQ3BomwuZYFmUhGrxBSWXJaKqiqAbsMByFwwkeHiEfjHxshYMlk+oMOWNpoGqN9FEJqKeQMR/CI+BAISqRK2Hpo8F1VCRxrlF7UAiXyEVxPURKtiWuza6OoHVuXkZmMqWU4DSyGXxqhnQjsKO2dQY2E4yjRfArLWGnPZBui0dh1vZrpHadYiL46JxzKWBXZFbKuWKTo9VZik7Nqc3jCxzLyQUwIc8RkO6QFUCtFZsjjhCxUQerVO12fKGaUzCK1fAQLGCkySqV2kDSJtfaf5UIkLtH3KmatWSnRcLSP/yjsIff1GSntnQ621TSrfs6ZS6Rn+tgKZEyHA7BNdAfrtvz7qX0a2keblblcr2gooF/qgRDwSZGvdBXKLcUGFuFsKWv6AJ9DwvdFfa77dv97ikP2az/j7psr9+b94O2qEB4CmHDxw4rVtrZT40kkW5q1EZVkNp/NFXQK5pj8LRjPI0Eowei+y2MTJZKlkhT9R5c6NOmvacxkVqAWl4uC/VDr7ZcLC/fKAVZlULPakqDkJTIfMmQhharUCWngBCqwSEFjDlVb/UH3jKOPM7VxkiEFtSOL+vXsSx9Pi+QUVun0jyCYG9NLSU1FETEMcaTMJ6E4wOk1nv5MAzMs6DnBCljulEKHHZw0UCRMaB9EWqDilyaUiBzrhqwkShjzqAJl1AKBWWSoL/oNQ1dKcymlNlDIczgrXVlgPTt85XGVApUidTRRmsdZHPR+MbnUATbbmbMLFlnZk5xOFPw6YK/VPxcI5W0u4Mu1LkpBTF6V7zsICakWShzxDeshoAu1JVaW8AxxKLZbBEHaUrBm1LomWlNKZhX1rSbNbvO3MOIlhZXMzZydS9E/jFq4cNKQZtVHPhp/IDZ4GmauJQIPifgl0kjHawaT9dCqlEFGXrxSvVKKUYpc2QKLctWYu9B+9fRYKO2a0nx3jFij8BipKf6cgrfUtTu4SW4tfrZrNvrrS1s/k4NxE33t/15/6MvEhO1qjdYLCx3RMN11oqQES9hiTtN4MdgFRxvydhL5XOjFenr1pB1TNJ5+Q+ir1s3ROJ8PWs0DyCN5toRzODr5QUxqJfKVOco0ko9FiCLYs/aUmlFooGSOOJzWN8pehCLRr+ZPEQNQPGKi5HGuhRMDWOAZyLCZTaucwSrvRlDvegsko8kWllaxDV61lL7oVEPkdvz0GogECcDQxIOSXj8NKJZeS5wOI08PGYefjTGg6EPBoNEXUbKBPUFlPwA+ZFriurj4kdmOTB5sJUaCZORokpZ4KNIQ5XDSK6Jem7xhVaQpp4xOVFcuJpwIUjylISnTJIReTaYnHoJOMhnYKIphbjR0QozYKRUmtdQaJ6CB8zU+hDrRhHcePkNzun5xta8DrJjl2AqTUkoJdJQL5Mxe9TQ4A3aa1kx86UymVBdKVOwfqXUkhA67OkVs4mze2tcNGPikRlZA8mYangKMx5UOoun0NVCtwPWQuHaLJXuS96HjL6tHL5H/PymfgpGe+CtocEiXKst8zdiDTB7RMerC/L0EgyR1p3r7c/cSth75v3WY4h3a40bfFPlHJaX33ROk73HsIWF3nJMutLS3bq+/1vr9+/9Z/5HXe4ZKG2dtXoMlx4M61Taa1DZmhjv3sMSSJY1jXWh3xZdgvN9XdQupDacvdowLSl93hryWAvq9ViCtfsz1xlqzFNXj1qG6iuxXpszPR3AO8lhs+RTEoYU0IxqCOk0KDkrs1m0+chtUDygJWmVwZXAiq3HYFqTnT6uPdjcWVVXetb+f8QPIKBWbcHmJCzB7jyCprZuCIWlQ8QayBK0FVkwTXjS4DxKA1UyRQeqRB6Ye8YlPAUnR42NpAbyDc2bG0Aj/VRTRj2hnkGj9zKS6R0xguolRwmiJ9QVqbYGkkt/Dw9BCsFM2pSCVglrfPZQHrO3Y33pViat8EIao6k7UctgsW2ZugXoxkIk/oQXV0IxlTms9CtO8jVvwSvMxSkW+sh7Ia4I1Y3qNajYGvtdacJAGtRtam2/aGBU6SHjNdC8yrOmDmSt5+qZfev7x5XCK1v1g3HNj6ekWn21rtIDgnFhak5tjDUZoVwKiFNcFrf8Mk0xqN5d660SgNVnvieRN0qhPdGOL2kivcPWwkfePQW4FeJ93b06ha33APcD1t152Qad76Sk3jg9/9GVwr3r74aYRl3K7M0a9sjTjniBNsGwLUBbC9H641AXoR/WVQjqRM9KUmZAWtRC2ldnAkiJ21faHiIeOHsTEud5RqwJzUNiGIRJoiF8EQkO/erUGpXYiGAtdTQJHLPywxBFWCpwOCUOSTkkjUpkMeQQwgtpQjoL4ylDsgbBhLJxlUZWZiv9BZHOvXSmaq/QI1Fp3NVsavG4cWiUClkYxoiUpgRowBWewjuQg+KDYjlTGKmWGR4USydmHZnUmVQp9RCBZomiM5MEDJH55Urh2CCgAfcBkcTpdCR7InmKWAMJ6rFxBwmTjCAtW6iAToZciddFmqAHJpDqcJVFKfgEtTo+RdyB4m0/0CbUe6HqcvNXGRuGYN0MZ6ERzEU8wVvwthbFq3CdYvxdgsPo4BHsNoOzQfHAOoY2Y6vDZHOYJaEtwCtzjxW0dFO8UqwFmmnxT3paqrcBiIesE11EO9hWi7M8AasY2UrEt7Iad2hv0we/s1LYB5rjK5oL74BEAVD1nlkirSmHY64RmGxCY2vrv8Zn7pnrt+a3L+8AFSFF8dwSf/LAl7fIk7M2xdiq3G1Qmc1XbZXFvYD0HtVit2+//H7v/xFK4Z+laN5SCJGDiSNY6oVqQY6YJAR+bth3VmVwJVM5akJ8jmwNQuAlcRJKEkU9iPQCKalBMbA8AtZyshO9GiLAxNQeoIyKISkC22Gs0OivhSFlhlZQWNSwWiP330CmNjOlLmmeg8JhgIeDBBWDOEmjPkKT83AcsOxYminXSH9MI6TsHA6VQzEOBlcL3iCTlnvuAovXYKswk9cvySHQEgF5JCHgoEFgTOgYzAFprkGPrc17EqGQgnvII23UNJEPD0yMYf1LxUSoHTLyhGv3vDJVE9UjxlAZmmKJfZNGP4VBxojveMbroX1vBJfNBC0WnkAFaYpAJgnhXnz1GiZfMpD6Opu8BZrj2N61rMcPFt6yG6XQvIfK0k9DS6se9zD5vQ95CeFPDU/gCqT/s71vbY4cx7U8IKVMV9X0zH3F3tj//+c2du9rprvKzpRI7AcQFASTktJ297gmhAg7M/nUg8TBgwRZQ2bLxrE7UMzVaQGFLL5NXTIqS1GTLISpTEVUDUYqZ0CvvAdYmA/K2BbOllgX66jWyFiiLS7ccjnmqjdtqZQvGzIP8ot3OZrXQCG3OqOEgK03teBZLlI9Q+/RA4JNs6DwWlOw+eVYlMqoOPPryVWYdYlC0Wf2XnugRhqwXFrL9JRd/qPk67TMN5q+1X5biHgfuf7kqEsCIMc+ZqISOTRDdxBSCBgHYGTCiISncQBSlA1frKAARBJQiMxiOCIAicruZYAQkFOqBicxQ+nO57LAj8pDH1SlZ4AXc8J4ibjGUWz4SJhuSZZ2MonzEmUSRTHTjBG4Xghfr2WjE4AQBRAoAk9fB/AICZNAYkaNI8qiiozLDFxSMY2UOEmsW/mtdqkR8eyYhYzXMLCesimO7kBirhoJfCHQRWLmhCzLIBFysWcHCUfBAYkjZgQwRcTLEyiPyDnIuQi6tBixfIZikotIxbEsoCB7FxJHZA4IMSJiQCZxUIMHgMZyAiEVYRAIxWRE5S8URqy+Az0Cs4JDWqfZ0NUhmd9lnq18CnZuz6iBAymhbBTkWi4x1WiqgAyVmcvqnyzWjATGFJV5p9oJZ/WSqUYgY56L2UhBQZYfL0ajZUpm017pv+SXBU71t2rXvcn4WltYh2pB4ZB0kB+88zhOvUlCCR1ZJTLJFcmRYkBOurRqfbOvyXJnnSU9gz0KV6I6+ZEB3CFLDgestYFyqaQgHk1Tdq+BSh5YLoG0DTVpGxMTRRTpVjbTZcgGmqrnFfu0eWwrUht4uTyEECX6YQjirwmEy+WCaZoxz+KgYpaYL+MQEUPZDWqkCwYwZ79VFtAwzAxe8lh71g9TiZfkQEAcggR6CzLh4gDQyKAR+Hq54hoJ/3QhBMxgnvDj5a8YA+PrJeIrz3iijH/6l2/gdMP8/DeEfEfgCSE/l61RCSE9C7e4/0AYxEH5NQKBZ9y/y3tmlr0fCUkMSxRAVGzcyIhhBmEChRmXccSAgKcwYiARWS4cQYExTYQvccBAAWlECSgXygoS4C9fIv7564h//+WK//frM6ac8e3rF4yRMEZC+DqUyKsXIExIxAjXgGEccP3LNzyNN7wMd4zf77InohwBx4yyf0Xefyyb61JJq6uVSJ7xJYhTOZb9DlPZB/EDMs8CBfDTKLupkfFjCnK2x5cvYJQjLmnAHAbQ1wEhXxDzCPq1jG2MYFwK8xeAAEZweEIKsqN54ohnHnFn2ciWSUxKdx6AcEXAiOHyFeOdcZkTxpvs5sY9i5R/Z3wt/oHnW1lZNAH8gsXRrIfa3yBAWrQGngG60cLsE2TZb9mEJhvRytzU+ElJNrlGEGKSB5xLUDtZnUj1IJyRxJiJIKvcEnSZqJpDnX2qmh3ygk6QVUMEhga6qxf3Spi1oVeXOStguvidpAddp2SmKFBE4fYZiQSqAUkzeyawTe/SFNj/YlFz5aKcUEmArsWV0n1YWD08H8OgInKoeWIu47rzkDV8sGFo/p2wvldnrdJD3r2yQgaPfJ1YJuUYBiRKIiWzOdaP9VkUEG3ctrZPJCtXBhL1fM4TiGUdfNDHYa6VMiQmTmkzK0YSgbKGk1j+r7CgfumoGxYQAEQijBQwkO5NAAbWlRIou8wDvgyDuHOYwSECgRGGESML69HrIQoIISCweB0iixchUASRhNEeSFYFXUdxaObhpUh7ZVscAUSDrC+kjJA10lMoa/slNEVEkLABLGaEMBDyGDCMkh4BcTqwTMxQ6oaBEIeAeAkYRlmpok7sFBhhCAhjwHUcwfGGFG+I1wHxOmL45U8YOGLIQJxmhFmlxQWA5bkV53HZgQuSUBw6iUIUZ/dlIITi40plDwaKVK6bCQGAgmgEmQbkcC27lWU1UQ5X2bnGF1AaEcYMmrlqBplEO2CKIIxAvIJLND3OAXkm6O7mDDUtDeCqZYiAGMC4hAsuQZbIIhWbPMvGVkrK9KmGtKiAoOYjDXdRypBqEUXjYI3up77DrHNVfnPisgS4xGbLVPelCA/g6peQJb5Fw0VegQKVY9aqJlDUEy7mIl1FpGEfdfnEYtMuQLH6UzCwgLEYfIgXPqNpZhbrCNdtK1gzFRkPat5Xg/5RepdPYWH9hsFXJmFE36o+Lduz+6S3vZxH4Dk0I5XHIVxaVCPl9gv6d3zUy60UbUAldZEuqG4QqiuYim0SAXVAgYpqyoRLGDEMEdfxggl3JE64cSrgVMCq8mSzqakgRAUvkgNExhgxhgFDGMC5bHO/5xrmV0COhDGWs2CjPm/myiDmEvbDbpgFGXnH461+0pK2LI0nXEPENYwYw4DbfJdJwCxRQzlIiAgQnmjAPc+4JwlfPIaAMFwwcsAlz3j+9VdwmoqoOIOQEUMUiSwxBkQJKc2EgQMuIPzy9A0DMYZ5wnwPmG9cAWQYBoxBLN4DEiIDYJHKY4wYBgEb5Fx3r45jAHHA/WlAZGEkHHNdxRbHQQLKhYQ0MOYRGC/yYF7mqTwbwl8u3zB8+4Jf/uXPiC8/gOfvCF+uGK9XfPnLv+L5y//gcvmfct2ThGHQDZtl3IUAjKBqy84EJJ0+JKFhxiHgaSSEGEqo71zfdSD1zJCYdMYIuozAZUAafkGmiDkMmOMVabgif/tnII/AHBG/PoF4xvzjjkQjUhgx0xcwjYi4gi+/gIYnRIygOYHTHQkRE0dwHkvAvAvuWcxKssqLMGLEt+tf8PU64JonzPSC+/QDV2Ikzgh3IN8BTAy6S5gL3Fn+zDJVmgnhLoBA9zJHFTiKkC7Lz0UgEqE911VJXJfDiFl5zlw2oHERPDJSkeQJslgr0YwZc9n3smgETLKclLQuC2hMZW26brwM4CIclyAwRctYm3nUdLSAgnLT6t5krBi6F6PFaGs1B/m/diqr4ek4MHwAKDTSWMoPcZC1tlAEtrfVu0Qx3KtaNMSyjpsiZjnhQ0wkRWRWjYOKiC/qEqpmFoZQLqlwvczLsQxQNRLVvERA0TgIulOXAmEIETEG2bQCcTTJoGNM9wkpJdnlGLKswLnI60xJ19+ro0n7KfhvzD1EouVMeUaaGCFMmGdZ5DbfM3JZxVAwCaEOISrOrdJH0CcptuaBCmiqia3QXKLKZl7rbapZFaFVJG0ixByQ7xnTPMm+lRBkNzHL0sE5J2DK+K+ZkfINmSdMKYMS8Dd+QeCEiROI7wh5BuUJmWeJLx8zAidEZllJwwDnQUIHc8Y8yWqdCFk2iTgWNpiAdAM4gELE5fIFA2WMPCPyBYFnIMn22EATvnz5gqdhxFOQoGq/fGPgZQKmhPQ0YM6Me2JwYb48i0PxNk+48YwJCfeU64ap+3//hvHHDb/dJsx5QkoTvpWTa1J6xvVLwD/FP2HKd/x4fsGvv34vPgUqEVYhiy/iKCeTPU9lFZeRTXRZKMn4Fvkn1rGa8wBwOUsBETNF8DwiUsRvv6EcqBMwjSNyvgJPV7ykgPsUcA8j0mVA+PMVQ7yAwwXX8AWZLmD6hmH8CopXMQ9NCXx5QbhJgLpEIwYeQDnKmQOJkTlhThJMjn/8QJ4HhIEwZBYQnoFpYlzKOQVxgiw5LY5mnkhAQTWFqaxSKktH1QegAJD0gCTjXxgyRFhh4DoQLhH4eh2Q7hPuLxOYslm9uMR9BriEwk5gEgdxNSmU+RWQK1gkyOKaBTQS6pmvrCGxl7oLDwReg8JCFhyUw1numU25CPHHLWc408JbCn/Rdjr2gFf0Tkdzj7EXFTKUg3bKGyMyHvVuXVWTZDngJQY5qIMikEwIvXKHWTUEs/lJ/QQaOLDG0Gc24ZOxfsqBV46q6qkv8WUixWI2kReeGXXz3TxlUM6YWU7boEAYx7hI3uByctdyz6RHLFrHdUABErVpQsAEwHqdLKqpiYpJS8DCaCBqWisMXTW4oIsbiRCybLa/ZydF6CMldW7K6U6BAzhnpDkjXqJsxELRzGbgjow8M/6WiuePZ4nnEhg/csYIifk+IiFyQiw7OyOSxBVCxsBZzhEGgBxAhBLbPhf8EwGBaKg7gvN8r87SGAWoIslSyZgncJ7q5L5eI749XfEUBiCLAzkNL0i3CXwJmFPGbZqRgkzhlyzT/54m3DljQsZzLsaFBNx+e0Z8ueN5TjX0xPXpihgjbvcX0BDw9G3Etz8/AZHxcvtRXAVUNqWVxz0OSDTi10mCu8lqkWIAKOckp7rZBiXMiErAAxiExAwqjrF5ikCOSIlBA4CRMKUByBHhPuBlFix85oA5BvDXARSviPGCMX5BDk+Ywy9AfAKFCxCuApwUwGFGnDMmjEAOyCkiZMimVSQgA/PM4JcX5DSArhfEKUvAucQYE3ApPoBUHM5cTEOyM5nEnFQ0BZ5ZViJVU5I4g3Ox0FTBTk2DqZ4qKkuHx4Cv14AbA/NtRlanInTfTGG9zCLYqAnYmIVQNAR1Kq8t/RoWMlXrBpVjNoNaO8rqpOWNKygsJl5l57qiSDUGy6YYa04QS8wrrmxfRUat+RggAO8EhVZHZie+mANQDpAg1M1EzGsGuSbh6EMkfPvyhP/1b/8b0112QUfckbMscQUGMAfcJjXGBHAJskwcxN5IEkWRIhCutFi6dF+BFY3VzjgV8ND0LGmZkgzEKCs7kCDn8mpIZvPmOMouSA4sDu+wLMBd7vK15pWzTHMEXSnA1UdQ4/+j8gWJja+AmFFBSOMpaujMO8p2i0D4+nTBZRxxvVzwcr/jPs/47+/fi/ZlLob10QR8uX7BEEUynWfRXua7hgqQyJQ8AHNgzIGRcyo7d0t0zJTwgzOGEJFDwLfrN8x5KhuWZCfv9yQRkSIDT0RyADqNCHlCzDO+//aCMQJPA+RgdY4CIJwRWKLwPgVGuing3HGhhJEShpkxhiAH0X+7IH69YnqeaiRN0IQwzLg+DUiJEO+MW5aVKF8uF2TO+M/pjh/IuAfGc1ATuEzRwBnxloQBEOP//MckDvn/kjDIYZAl2SllfKehrIoCQhLBYS6RPRMl/DZH4Yck2ukQInC9Yg7ADwD3WcZhSsKKUiZZasok0UhRlr7+VcbphIyZWKJHhO/gMCFdIUHuOCDjgjBc8eWXr4hff0H48idc//Qv4PECjF8lrAVH3F4YU5pwD0C+TOCYMfMAngkxA0NZFZTuAfGey3kFN9xww1/n7xhywJgJ43PElzni3yIjDWIFwxXAQKAhYCY54CbN4rwbxkHulWQsUiDQGHC/z5hYzmnWgHEv04yUM67EslqXgH//85/x9DTg5cdfkdIzBr5hkPgWSDkjMmOgsg+BRUtADfCo1ghl3Iu0r/F/F1leNIYhyRqugWQ8hGKeImJZDrta+mgkWDv1SE3EKCs5pfdVYAw1yJD4zgSslqlb/1SLIDoMDO9afWTt1JXlcZEuY8AwSIS6lKYidcqSwh4cWNK1wPf7HdOUME8ZKaey30GRGguzpYK0hLKufWHUXKT6RfrXB8Yy2vS9MIrTihbnVSmXEgOUinmGa1gNtWuudL4MOeBD+ygMt3XfFtMJOiBoAU29h/XDgaqGoiFQ1UiqZlIaz6ZxzsA0JRAHEM8S531eAMXitDq6MnEtQ5DJOqeiQhcnt+5sTwNXMCOINqRrI26Z8azXlQiBRUqgLLb9wGXfMweklBFJDn0PHDBwQEoJY2ZMnIv9WE6/CkwIXFZ9pRJCjwkDiwViJDn28gLZr/BbYoQ5IbKYEFJKSPMMzDOuLOa+25RKaBZZDZRyxpQZL5C4NrfAi4ynZx0gVylvTjMCk+yojQEUCRQicgZuKSy7cbkw88zixAXhJWvUWMKcIyICkAgxiw4wlQ1VqYZ4Jsyl7owCCry4N+8ImCmWGEWETMA0ZQl0BwknQgNwoYQwzwgvd4zpJuEgRsJMMxIPeLkx5inhdktISYD5liGB4u6E8B3AHUgvjOmZcXthWVqcgXiHLDLIAfws1ry5SP65mIWIRcDgImSkojlQEq0jT5DoqFmWLOeU64Y/sdvLKiHmVE+7AwEpz5hmFj6SZglbXSaVuv1l1Z6mm2WnK8aNVZqNbmr9nrr4HsSgwBiirNZLAMLLi+ydsYyp1l1Ydt3LVVdxyuRk5ldXxqTnmi9AUbsogm9u9LFF71ySKhOfqBzyUJhViBGXywVPT18AAPffpuWAaZZY431NQWhOGT9enjH9x/8VFZMBwlWC4IURAMAl7DKRLOFUh1tGWetrDHE8C+PSOPu1ezUb6fMqlWt+DYEtEhqGoh0Ai3letQws7XDK6/fdeR+rOppG+XX5hktHh2VrVyOb/yqXEBhpuiHQHTGEEruFSyyi1/UVdL9/f6lqb/WLGADUJepihgOmAaBRZibxgJlSCfQ1YUTC91wmcQZijlW7lJ3wjB95khVYCBhkoSPGLGe2DVOCHoZ+gawqGjDgJSXEBFxBkHUzcixPBPCEERfOeL4lzL/d8evM+NMgzOdlmvHy/IJ0nzBCdtHep4QUZRNXKvsu6k5UyuUIWJRlQ1SdL1xe9g/OxQYeKnNeDBB6XoR6zlT+DKtPMBXhRo6klMgbAYyh1kkUSjC7Ai4o8fgrmyLMkBPQprLxbMaAl0kAZC5bBBOA+ddnJEpI9B358iIhK+IFM66yaS3H0rYEQWQE5DmXMBEB4QYx8TwnOeTmxhKtNgF0A2JiDIkR71Q3oOVbRr4DfBcz4IUi5hdgemZMPwSVY06YnxnzncF3Wfp9QcQ834XnkGrGDN2swChyHRH+9v1XhGfC7eU7cjFX2tGduITErmnyxmxcIn1zat1fPvMqv2ytLKZsBsWA8Trg6dsXcCT8+p8z8jzL2c2mL6ECJyXUeiyoloqmkLOMv8RcF7MKWARksmYw1C0vgWRs5myBYZ/eDQp6kIWlOWe83KdyiDRwT6nefjoACEB59JmR5wRwALNMicS6RKyU4whwBnEq+yNC2VVNxXOLyqwq417dANagYJ+dAXJ275Ato8/m0xr8zD6FFZP334+9qy7tVV/JOkU1nVXrgTCtxV62HjzMKKszHKmmpBpQBVmDX1ni/7AY/yGehoicZkRmRL4gggqb0aWOsptWvDcaXDuWtIRUPovshVDSUmFvGXK284xYTgAQO/gNCfc84/YM/Pd9wldKchDKPIMxgWOSWDcE5Ag5PIcyEqm1eFlsyKAixS2/bUCjUJ5rKks0NR7UwvT1ndgFjMrIl4OG9GRvBtXQLcwlnwQAkmlb9gAtZ+EtoCDB7G4kexBuGDDzUPJkSekdF8w8IvGA+S63kwgSCoIYE0tAuMSx+FUZaWIJaT1l2Q2eIJFMZ4DvDExl0969bFhLJcyFbkorDuV8g0QjTZPsYC4B8+TsBCDryrs7QMyIXABBT7YBl9hqYmBJReAIzPjBM4gYKU01PtEiMC1CzgIKZuwbho/G3xpIgGVZKxA5Y0zAPKdycJMsE2Zd886W6aj0LxaCKUtspUBZt6rIUlosGioXXpY5lyXp5a5ouUKdtnZv2BF6FyhU3umYvKwISPWUtjmXfX+MBy5N2k9ZObp5ccaALxvkuABDMdmo7Ey0fo/2om0nSxfr7x4U7F92nxZcLGD0Pm3/fwBpN7I5m1+lowLD64rdS/RaEGGRolX1JQIHgMriuUwJIQvrHqCRIUVlD9Dd6YAsO1YXGUEjp4qyFqAbI2MdG+pTEqDIRcoayt3OiEhMuN8FUH5ozE+ehY2qxlgAXEJnq5FAmb++5iU8WS7xmRY34fI4pgoKC6NugcKStgCNniqxGrpsNA0OZgguW6PL7o0KVhrddKrgEOWTBBimoi3c6wlqEVOW51y2/2nYoWLSIqCEuk4lTAXrMtIkUv+yAU2AAgUIaGZQ2ahGE9WQFqzLT++phrBYdjRniXuk+xh0VYNj2DXAHJVFqHWd6oxlcqpNX7mQnbx2UJvfZNMawFB4n84pYvErBcgCCU4Z18wYq89PAF6dwlYm1KuaWQCNdEryGrLyqg7KAoMloYICM9az/Rg9AAotjvE6TSQkLCu59PeKc7Cp6y9Z7RJevPa1Fu4r6arWGfOGJ2XaKsraUBb6tI2vYHXJXisIWAfCM5Lzqo5v66Opw8i3yr/KNsvZDoMWYw2eVlvS30MR5AJhHkdkEnkfACRMXiyyqsTfl4j7c9EGxFYakBDLMZHyXdmcaAMBCZeiHQQxdBSNYkDEiCiBGSDReyZEFl1kwljCu4kRReIlqQxJyOalCzsJ0IXS1mCXLGMvWoWGPM08FOmPDONeQEUBRdq0As0aFAC1XFM13Swh0rheU8YasAQUouwjwIg7X5GKOUnPN7gXk9INF0x65kF5YjMG3EvZOy7gRDUGERLqUlHM9Cp+Uf0rpiKNZ1T2gZWT1ghUzkl+tXlNfX26WW2mRTyug063w1pGTmiLngrcMOW936DH/NFIy64dLUXlukSgne4JL//5X7ILPatZOoDM+CLXC8ptw95uLbkm7bk1Td8qb74TFCyz9+kLirZfUK/dbe7JmyWsaL/Rjn/fvbytPwsm+tvGRvJaw+8FCmi0/ehoEBFGbsfuIN9qR0EhuN/6guw16Xr8SMgUMIWhmIek4loiVpNWLmyz7NhFVp2wMkjpUl5CBNWVGip5Wyk/sqTFAiASyqCYpSgYY42VP5ffiwkorPLZggJTPSeiMn1eNAQlazZa8pTJaR9xYVdFyskFFOBAIRcByoLC2nw0Yi5/yuwTrPlIfQvR5Eepx6I95BQKg+ZyTCbWjF/PVtbYQsrQ5yWvMv+6RN99X15a3RrASXwT8jC4jtXjzHs7f/EVdMrX/nqfjXbZjmMxBwEMPa3Qt+MZ/2P8sjdJH+Orlj7EfNRLfytSCfU5aZuXWw5tXs4jUrotrwqLT/Mg4IHC7HiuF/p7AMIj92TLb4Hh0Tdmn49qXvrdLJ2tITmKtJcvAYiMFC5FchcmpqeppcKalXEzSuTUwuTlGHPVHiIu5fuMWORZCcdYznZDLJrEWPZCqM4RitkxIONeWGCoL00fh2UVqjEsjmFl6kv54gdgNWcpk17X0QfIq/QFaKwWoW8kYQGWso+7tpOx1lx8n6pXzSsNYKxp95oWi6Yg5zALGFwwlYB6y2YyFHMOyq7jYk7SE9NSWKT+CUsYixn13ATJ5xLmgsXHYPdyGcCo4TASYTnsXt/QIk4s7FUn4rJ89PUkzfVpazuvNQVPR+bG8gb1dwawtj0D1O3D9tOb4Ee56tu577s1hR49tl3C96McZquNlgSwUdSajiwz30pfFgYsfzbNm6Oo0YaamH5PTcHT1phDP++hYaT3qdEp9R4TZFRlyCqt0ZQ19TIF3MMFA4nphqHBsBeTkgCEHtAZ6meJ3wmGOp0FDGYMGMvCzKHkiX9hgjiz1cykoJGLEUvhSFmLNTKsJf3l9xoUrBah5dari2iVZ0EBJn9Z2LpoRdb3sORZQNFr8NeqK4zUVDQWDeBS/Qd3XBppRZPgiJwgmwksKNjvhcHjXkChnIewaBHsNAosJiXzHVb7sGkZYnHQeDN1UnnLuk44YM3s10Ch33lV11JLY7Cg0aI1L+J6Dcu12GttObXbbf596HcDhbeTt7cc6bf1Ys3XFn603jnQj5fU+2uBib0ECxh/FG2N348mXW2lc9YK3CLer9PKuZkSIVRMONUOS+poXjYMLYxyCTcmgKDOZ7uCR0PhLUw0QIOBpAIyoRpmBBREE5HaqbaltGbIPm0Zo3nF5FugsDBv3mxzy/ewDKLUTAur61dNYe2JidXhvE4r4FEioCYO5UwBrBj56vfqj1+XyTt/vkyrDgOLz+tVhhloWz6B14CxniD++9YfGvUtbTEaf62fk969JPX3JQsQwf29g8tm9+ktTz7fmpH82AuujE2zobj95WZ8LKX9Iofo6HXpKxjKXzTp6rC3z8Gm6fdMSDEiUcQ8DAjImIuZR8w9c2XWM8ZicrpDD4icatpUpX1Ni8VqHjEXvSEXs5N4Ei4Yyu+McvxMMTesGbMy6tcMfNEOtFxPq9jXFF6DiPSzNi/ZNnM3Ta97uWNrQsoI1bmcjKZwN87nNJc4I6oRZCznKHtNYXZpdzh/AtZAMbs/TesL9VibX7xjGBt5e1K+LWf3L2yRb/sR+uhJ//vQJ9YUfoe+9V1aELB5On4a7oma3vIvbAkmPUfzkfF39J6At423Vv9712TnYYCucRXSZ2I1hYTXzxmmXD2BjJAlmJJoALRoBaGaWPTs5lSkfVQGrEDCkP0OY4lnqYxbTE5iZtK4UgoIapKymsIeE16batZlU0VIW1/NQoumsAaAVpo3ES2Asw886yWps1mHZUHCahIpy+E5wtSpz9g9g/eagS+XG2W2tAOvENQB02L6wPYk7A2+1mTdKtcjfx0/Pz0ACmG/yIfQRy7V4fLOac3sW6RmoBbT974D7/Jo+RQsMzz66N4CFO8Bl7dosVp+QHu+Wa1KNQOb5rWuAagH/wRCigQOEtVV/QiRUgECUYeErS/+BQUEdSYH5KWuSZO9z2lVR1mj1xT2pHirKRyR4pW8iQe1ndfmpLyq27+u5RUsq6SW/pbNaxMuxctSnM28OJ8njBK9VXasrTUBqwFYIJjcp2X+qj3sgcQWmKQyf1c7R1vmIzTSW2V65Vqg4Ns4AgxHJuPnB45PbD7aAwflwD2yaoGjlh8gYv1e7VjpKS9WIrbjSuL1rTXTR1wjj0j9j4KCbXtvnG+lWxC188ymJZMWsZiZNI3d91IuxyCnosURM4npRxm3BQBNG4pzOSJjrquPFqeylpM1NrmmeVCw46XF4PsM+BgoWAe1z29rDIB1QntTla9vtYclbudQjWb3AgoKBBkRE49ybKauHrKmIv3cMiVZEMguLTXKec1C547NW2FAizl7BuzLtJh5T8uAacOX8/mtdE8/PzB8clCwn638BzUKixOt8bVnEuqNScsg/TLWRzXLI+PO0qMAcqT80XHty3hNTJ9pMPm+HGPtj6jhHKgcVZnBRZMI5eLVtGTTVGcQhpgrk9Rd0qI9EJbwYYRUISJULUSpJ70vt/6Y+Ui/90BByTuQF43ktXaxd63ZgEJGWO0/yFzWcHEU7cCs9umairakfm8i6mkALYG/95uXp90HBjTSegx9T6L37bXSgXbdVjs/J31Cn4LvcwmXdpyUI7skbbIl5dtlk15TUNu5mjuANfOHyfOSuDJGcmlHbuGIxrpHH9Wfb886mi0T0FdlncrBlFEtypaLLm1eynEg2fQWAxBQNYeWpiDfF+1grSmkqmFY57KmaZSlFpNebl2Zvt+fcFRTeG1yAtYM/mP7660+GpejLFXCby0Hndx3rwlYp/Lk6prT0faWnK4czSstoqVGeIlfn+wrNKlPt68heO3C017e1qT5eYHhk4KC59zv6HsL3Lc0BJuvjM2n2cvyTle4vKPX5K9tK/+9Ur8XhraEp1abes+2nDUL6VmB9n79aq1eXa0XyZimWJzRATWkRCDVBHSpKRXmjpWmQMbstITQWMBFF7UCbSncMtq3MmkApe7icO71Z+tyLd83Pflr1M9qJOOiMehZIwlrDcGvDPJA4c0/VhDYXKpqyra0haYjmss46GkJnsF7KQ4u74h24dM87U2iR+p8bvqE5iPVDj4QhPy7sUzdawz6u7X3oLcfQZ3QlpnZtC3GunXNe2NqbxnqXhtHtIMe8Pi5FNynzW8t1/Ub2vRT57Y+x2TSA4EHQoqhAkTgxamcSfcv6P6DVP0LfkmqpllHtFrxj5hmllt/Cyis6+6Bgj7iPS3jVV0WAFPPiawsote+gJYU39MeWhJ+q1zP5NQyQbXAogsIW6DQ0gS2/AutCdLTAHzZIxKVL/dz0CdcfbRFe2ChHKxjavJg4IHArh7S5iyDt2n2Xau5I5pyNm2LeW8xXfvZu5de3lHA2Rv7vXRrPmqZiux8VI1iMN+tqSi6tNgop+/JpOUYZMUSRYQgp1uJ1J9XWsGyY3ltXvJmqOVWX0verU1lktdfqWTbWupuA4pvw4OC0Ovra10rg6pmwJnajN2uKmo5hr1zecuB7M1LvTR/Dfpn05po0mLYLdCAK9srj0ZZuHQ/0Vpptu2fnz6p+eg9/XpVsFO/pTV6U4dnnDp4vcbgzU8+betS98bRFuPv5W212QIDP2da6a063nzUul8FVOtMjmg/f5sezF9Lo1CHdCYgAHr6LUjW6yhztLukJd07n0M1JVmyjF1/L9/boOBXEEn++0BhedxyfoLepF8JxSCAG/3lgLrnwPoPWtJ+K82a9L2Zp6cBePNQz8nshYeqIQBtraA1yHp570nzbaORdzTtaL3PQ5/UfESN74+SUwusRuB9BC1Gr2SZEly+aha6jM67QDwu9cbClhnnLdpAr73euH+rxmEdzcrs/ZJU61xWn4t1MGtdu57AOp/J9OOd1JPLi8AcB4CAKULCYYNX+xD8klSNmErg1Qqk9o7h17uSbfmtuvo9W4a9uUqp3Tbb6L/2PfcEYsvwPbO3eXbJqd1fMKEPCjfXjgcS73y29e01WEWgmo20oJ2UFlnsTR/RBo5qCFtag6dH0nz659UqPiEoKH0kOJQXwrRIrXYsWQ3BS/lWO7D51meg7SlP8b4KC0D2Ezvp2t8jgNETetBI37qePY2id48WYG2alf61jn22FlDsvLSmPttuwlqTCEUaNu1kCiBizGGQg9NJDlGX0wtCBQcLCltO27WU3weF7pJUNlI8rxm8Svq1js3zQGDaWI2BlpXDO4l7oGAdzFubzrzZ51F/gbcE1Xwu928Hhv++JeXvmXhadT1Q9NJ8G9hJ8/S5NQNPn9inYP0H7wWIwl1aQODfvWf6yqj0kpQZeT+EZWL+91EhoSdQHAWFR6X+R7UNr0HZZ7iYu1+DgjUh+XIeRPxz30obXJ7TGhAITIQ0iGMaEQgs8r7dDR2Qy2lZi9lpueVQn9V7NYXlGdLrZ9wSbO33VrktUND8loTfMgu1HM2tOi1QmMxvvxLJm5L004MCA0to7FdeZ/fnmX0+mNYCkyNp/qH2pLre75+LPqmmsMf09/IbL0UZtH63JiD73Z685p2nqhVkrEHBO5WtSaTnaG4x70ck/17akXaP1t3q1zJhfR6WOXvns02zUn5opOlSVjU3WU1iMr9nk2bzLTg4oND4SjmIFoGwmJl09VHPl7CW3oF6kJN7TqwH5bae4R6Qb/ExbzWx37f660n7PQ1gS6N4Szt74MLAOpSFZf4WSebGDaZOmn2I+iDsQ/EaQCut9X0r7ZH8z0ufEBQ+wmzUEdH92GkJB62yrfHRmogWXMik0YH6rU9LPYa9p4U8yuz32rfXSI0/D74WLGx91RxsH9YJ7TUxBSJrMrJpuZHvzUsGoHiQHdNgSKwlcjfKeplhnWaBoMfMt/jPe0Chx/w9WPj+vFbQ0xq8mWmrnAcNn+e/txSADLw+Ta2lIbS++4e0xcRbE6z1YHvI2mrTpx0Fic8PFp949ZGKedZW8wjZF03dpNVYs6Yizdc061Tu+Qssc7MCTM/R/FaG7evu5b+n7a1yFgTsd8ukze7klaPZagXe0eydy9ZU5MsHvHZIe+2CTDmvtQSASxC+FW2Zdvbyt0DBlz8KLnv9ttrRfG+26dn9t/wDVkhvOZX3TFMWONildTts2Z5aA39rMvRQ1j60HnNvgQ820o4Cw+emTwoKvb62ACJ38j1nxhoE9LuVWn2eZ3Tanb9cLWvBwua3hIWedNe6BZ/2KANp9b11PVtzwDuAez4F75PxdfSZWpOR3Qlt27P5wbSXXHvK9K35qWeiCqhnSL8SFPZ4yxHeswewPr/1vnw/vXfor0vb7knuPcbekvYVFPbqtlYVdbWF7Bq1DfkbspOzh35HH8jRulvt9KhV9+eiT2o+6jm1e+akrYfvuHzP3qtMx2sMoVEOWI8tvSzLlLZoj+m3pMs9s8MeE9+TWFt9b7VpHcDmXOaaZhm/vk6L2Rlrxq7P337PeA0k1KjbYvZWe/BaidcotJz1J209pz0f0XtAodXfezUPL7lbR2+LsW8BidUYetpF7vzp6Wz1HjzKeJXGggCbz5YU03pwto5N26v7aB/o/P6HB4Ueo/4j6K2+BUuF01iM8NkWEGx3Ps0CAJsy9nK1zd6YOMJ8e5pAS2DZ0wq2JEyb5kGvV8czUusY9vsPbFlrgrNpM5bnOjfqWtNPi8m3zEwtk1KrDQse/t4feS+ttJYQ0eM5e3yn9X628i1fZazNOfxAmgcP354FHMvnm4HuPLP3HdnGPRBYcFA6isZbzLr3vYXWvX7/ceiTmo+0P+90PkK9F2RFWazHTutTv29NUCUdH17T8Je+x6S3hJM9rWAPZHz9R6VX3wbwWrJnl2bb9L4Xz4R7DmQ7BDwAsPttzUwZr6/P1/f+iJZW2Hr3j4CCkl8Q8+j7a13X1vhpCddbANBj9i0LTy/NaxmrNC7XZy++pQVkl+7L7TH0vZe3V65VtlWnV+7np09qPvJ6/aOAoIbnHWrZooHXEmOvnNUY7OVnvGZS9vK0TU9HhJ7eGD4iPNn83pw4el36Wuy9+nv26S1Hs9UQetK/r6tt2+WnLVNQS1PYAgVPjz6brbS0k98yC+21fdSU1LPO9MxBrTR2bfj2rBO7BS71gj2qtH5v2aC2GHpvYG9NEPvgWg/R521pCP8YwPCJNYX3kg4EK6JC0thwgVYx/e3NQLac1Qi8DwKmjJKVnm2bW+PtkTG9NR9affj+j0qhNr3HaDWvle+BxP55Cd+me9NTCzBsmgUF73C2ZSdT1lLrft/CuJVaS+dbz7gnpLbaPlLHMmrPzJVxe4bvGbwHkha49LSFuv+AXaN7F9YDg63J8Kj63UPUrYnRq3u0nc9P/8CgoGTAwHL61ju3xfz7t8zKN23rAm37tK3Tm+RHJfjWmDzazlHwOCIVe3OZfW5eYrfPj11+DxRio473CQBtZm81gC1QsNdgqfeMPwIU3lPuLddlea8CQYsvt8rxRh3rD17xd17yVgyesUaSFrL48h4Q3sro7W+f3yt/BAh6zH6rz89Nn9h89AlIGZQly9BaDKYnOdv6RwSdR8DDlt8a071yrf56IOTHt3cGe8bechpbJq3mI+/w3TIHeSDZev5H2rZttZ5F63m1ym2l75l6tsodBR5tszVWvAbQYvY9gd0CRAs8fDsVDHzBjLYD2e9J2EKcrQnUSnvUVNQq03vYrbI/P31yUGh9fw9gNAaA3aGq3Wgxb/IB1qaPFqP0aRZY9hh3z7Rj8/R7j/H35sNWuW4+u/vjBsOi8lpoYfaMNdO1z9NK/foJl79nclKtZA8AfN0jQN5j/nsa317eo8x+a0ztfW+9Sy+I90DBA4AHlCYo8Pr7ipmzK9zSAPZMRX7i9G4ejfxemb3f/qHvAcdW/3t1Phd94iWpdlZ7Ue49wKAcBQsgaLL+tD4CcnmWwdh0y7D8ZVrGp59bDNvOhVb+Wxj/WzSS5kU7Ds9B/rTuEWndm3+A16+59eqPlDmqvfWGVouOSvm2vC/7KKg8Un6vv5Zkr79nvB5zLY3BlvFaAdsL9F5q+93vP7AN+gvzCOTH36MPaquOzbfkJ9eROkdA4XMDw0/oUziqMejL92BGr4uxKabVegqK9RV4RcbwytV325fts8f0e3/2+vz1t763gKDmm4J1jPYa0k9v8HYcVh34iYrmQMszsHsTWsy5BRZHTT49xq90xHzUG04t4dQ/oiPpLWA50s5b+9XvntF7ntzSFPSzx8Mzy/hZaQUeCHKjoV6DNs0jUUtT2FKpgTbTPwIeH5HmX8LnBoAWfULz0RE6Cgr2c6scrSeRBQhNV8ZxtFzrMt8j1R+R+pt5vO5v9TiOXoSd8JY8+oUCDOH1Y7eMOkJAo8WkM9rP8C0agFLL0XxEU/CPxOcdZdh6DUfbUTpqctq6HstTLU/WP2vOb/Jrbqd1G2ztRm7ZrXzdlonJ59tyWzeORtoj5Vrpe2l+Lv289Ik1hfeYq/Sl9Ga6X/SO5Z32TEAtU5FOdMuUtJw1pfjuH2X6e2X9uGwy/1bnXvLqNaiUXNoWd3a2ISYglXyrQVgNrMfYPRP35Xz91u9HNYWeZK95LfpoUND8t/TXGzee57a0AmvNqRplco21GLW92BaTP1K3iUyNi2zd6JGH8wiSHwWGo+aon4M+MSjYPj+qb5Vq7W/XH29k27RWOR1flln58j0ebMfmW0EB7CayvSjfeatDn+/LJZcHLN5Zcp/ajuXomm80ieropzWYEq1xZcvk1Po7AgpotNd6B0p7guAek+6Npz3+8Za6/vX2eO7qdxk/3d3HRxj3Vr6V8o+CRu+3v9neQ+g9lCPlt9o+0t7Rdj4X/QTmo57G8ChQ6Evx9hwnKnohxGsHe1qEkpc+/di2afZ7j29rP1vmolqp1cmeNvDIhfn+vK1nSwUw75OtP6KxkIDwuumWz6BlAmqZi+DaaLWzJWDuzesjPKcn2PbSbf6j/Mm+tta4ecVnjzB232BL6OiVadmptspsXuzBm2/R1sN+BBgeAYW9fj8PfXJNwfbd6r+l83um1ErTl2nLWGZVNAZlEsB6KSTw2nHqm9TvLanfjqXWfoDWX81ntDWBI+jSmrxbHfuyNg3Y5rZKftmRrWc5uRfZgVVEWyrp2QCGlu2ZmeC+225svr+VniD6e4LCXh+afpSfvXq1dtxkk99j6K20FlCsOumUa+W3yqGT3xub9mZ7AstbGPqRF9kr8yhQfD765KDQmrF71+E5s2+LO79tOpukUs5iiwUK34S3lHjFxF6mH8s+X9X4VwzDS1G+Ia20tQOtxeh7YNHTIoBtjqvle5zZt+PLOYB4pVEUsre+00w1S5Grs0VH5/luOT6mEezlP3ot7BN79sst++R7yvm+j5RrAQvwul97b3sPokctkNkq03y4D7T1uekTm49aM9WLd3tkAaJhKnqVD6wC6XGjHz8eVRvwALC3oqU3n2w51i9HCrYAoJW21/HRNEt6s1Z9snm+XCsNru6WxqH5Ybk1zUutukYLOdKV0oeBgS17tMEjpoYjHT/ybo8IDnvCxEf3tyeU2Lpb9CgwtPK3gOetdT8f/SSawl7ao7T1gnRwuX6U8dhs71+w33vjoGuKZSeMsKngK2+pHF7K8mm2497F+Im4Zc/VfM/o7fXZ7/7BeUTtaYUtM6C3GwHtdqzvAks+uyotDfNDmb1t8BFmvlX2iFSrn3uCQU9a7/3ZskfHUqs8Nspt9b33HN5Sxva9V/coGPw8gAB86h3NSj0xbo+UAbW+27btS7NOAJVENS9gOWQcxcbduDy73t7n6bjPJmE1bt4jOXXRBq8ZTCtti1nsSWreVtNi1lpuT1T3msRb29kpx6YP9vlHpHRPR+062uEjjOKRtrX8Vn+PmAiP5Ptx6q9hS2Ow7dk29q6xRUee6xFQfi8obM2Vz0+f2HxE7vsjwNByAChZAPDMTPN7zMEylVKO18krTPGX7AV3AOtdoTbzCAC00h6RArc0iS1NwQOrmo30d8+g7397yV+pZXvrAc/Rdnu/fRrw2ER+jzR4tB6774/21WLiSntgYMcAGt+PAErrGfX8Gr4NO0aPmIje8u4eKf97tf156BODAtCesO8hy8HtSzvCDHyeARw/Z0FrJUMpo1HHM2gtqOmP+Ar2QOGjwMXeWIs5e9Cwn/q9J91bZLUUXRk/Hh7VNnrlHqVHpXhLb2Ecb+nvCEPeKvee+nttb5VrAcHeHH0Pk39k/r+nrc9Nn9yn8JHkAQDY1ig8WTu4kpNoFRCA/g7WWvAo8/1oAHiL02/PfKS0tafkvUx6a+GBpd6Jex8NCu/REDy9xVzVos1Bh/b19pjzXp2t8XCEsbfq9LSRrbQWved5vud9ftR7/PvSTwAKR01HLam/Z9j31HEu71ILKHomEe27xXBtnmfgPea+VbflL/CxZmDKtcxDe/0cfR5AH3RbmuAWsPTatunpYDmlIyvaWtf+UYDAne9vaadXn91nr05ulGt9bznBW+V642UrrdfeW57371X+o97956RPbj4C9k0FSryR1nuBmmcBpbettWUP2rq+FnNrSeY9xu+lNp++Bxpe6vKMf8+p3NM2euaLLfDek8iPAP8jWshb2t4DhT9KCvw9GM7W9R9l2m9pZ8vUtdXHnhbxCO0B5VvpBAX8/VYfWWrZp/fIDqZHtIat/i1ZMGr5K3zb3lTTYvJHGL5n2r12fD9vLQdX1gLpHm05jY/SFuPuaQZb+UfaUPojGcBeX2+5lq06R30ULS2i19eRMlvzrKUl/B70j2Hu+Wj6CcxHnvauY49Z9aTcXt5WPZvX0lI8eSl+i9l/BChsmabeUu6toGCp50jeoz1pvlX+H5E+mkkeBYWj/R4tc2QM/J6AcFKPfgLzEXCcGRwBBD/IjmgPwOLEO+rj0LIaNMlPvr1Y9EfLHQWPjy73R9M/KpP/e9PfQ1o+Cgon/T3oJwEF4HGJtCW998r5AbrF+I8wRO/sbjHanlTur2evnNcYeu17baBVvnWtW7//SDolxt+Hzud60pp+AlB4RDJXetS0sWVSOlq+V6/nMN4DBjTKvAVAtpzHXkPogU7rWv5oOpnXSSf9EXTq5CeddNJJJ1U6QeGkk0466aRKJyicdNJJJ51U6QSFk0466aSTKp2gcNJJJ510UqUTFE466aSTTqp0gsJJJ5100kmVTlA46aSTTjqp0gkKJ5100kknVTpB4aSTTjrppEonKJx00kknnVTpBIWTTjrppJMqnaBw0kknnXRSpRMUTjrppJNOqnSCwkknnXTSSZVOUDjppJNOOqnSCQonnXTSSSdVOkHhpJNOOumkSiconHTSSSedVOkEhZNOOumkkyqdoHDSSSeddFKlExROOumkk06qdILCSSeddNJJlU5QOOmkk046qdIJCieddNJJJ1UiZua/90WcdNJJJ530OejUFE466aSTTqp0gsJJJ5100kmVTlA46aSTTjqp0gkKJ5100kknVTpB4aSTTjrppEonKJx00kknnVTpBIWTTjrppJMqnaBw0kknnXRSpRMUTjrppJNOqvT/ARMhEZsvNuFQAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = (image.to(device),\n",
    "     {k: v.to(device) for k, v in text_tokens.items()})\n",
    "img_path = data['path_image'][0]\n",
    "#print(img_path)\n",
    "y = torch.tensor([target]).to(device)\n",
    "#print(\"y in the prediction function\",y)\n",
    "#input_tensor = data['image'].to(device)\n",
    "#image = grad_cam(model, x, heatmap_layer,y)\n",
    "#plt.savefig('./images/input_grad-cam_v1')\n",
    "model.eval()\n",
    "#with torch.no_grad():\n",
    "#    logits = model(x)\n",
    "\n",
    "logits = model(x)\n",
    "# indices is a tensor of predictions\n",
    "org_img = image.to(device)\n",
    "indices = torch.argmax(logits, dim=1).to(dtype=torch.int32)\n",
    "\n",
    "print(indices)\n",
    "\n",
    "image = grad_cam(model, x, heatmap_layer,img_path)\n",
    "\n",
    "# Remove the axes\n",
    "plt.axis('off')\n",
    "\n",
    "plt.imshow(image)\n",
    "\n",
    "# Remove the axes\n",
    "plt.axis('off')\n",
    "\n",
    "plt.savefig('./images/input_grad-cam_v2_new_2',bbox_inches='tight', pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6393d6da",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "x=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c85b9a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4e74e08",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "for data in train_loader:\n",
    "\n",
    "    #print(data['image'])\n",
    "    #if data['label'] == 4 and data['path_image'][0]=='./datasets/CrisisMMD_v2.0/data_image/california_wildfires/10_10_2017/917815040962695168_1.jpg':\n",
    "    x = (data['image'].to(device),\n",
    "         {k: v.to(device) for k, v in data['text_tokens'].items()})\n",
    "    img_path = data['path_image'][0]\n",
    "    #print(img_path)\n",
    "    print(x)\n",
    "    y = data['label'].to(device)\n",
    "    #print(\"y in the prediction function\",y)\n",
    "    #input_tensor = data['image'].to(device)\n",
    "    #image = grad_cam(model, x, heatmap_layer,y)\n",
    "    #plt.savefig('./images/input_grad-cam_v1')\n",
    "\n",
    "    logits = model(x)\n",
    "    # indices is a tensor of predictions\n",
    "    org_img = data['image'].to(device)\n",
    "    indices = torch.argmax(logits, dim=1).to(dtype=torch.int32)\n",
    "\n",
    "    print(indices)\n",
    "\n",
    "    image = grad_cam(model, x, heatmap_layer,img_path)\n",
    "    plt.imshow(image)\n",
    "    plt.savefig('./images/input_grad-cam_v2')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca3a870e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74efa684",
   "metadata": {},
   "source": [
    "# humanitarian\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7bde4aeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8154c8a8",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>event_name</th>\n",
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       "      <th>tweet_text</th>\n",
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       "      <th>label_text</th>\n",
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       "      <th>label_text_image</th>\n",
       "      <th>final_text</th>\n",
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       "      <th>0</th>\n",
       "      <td>california_wildfires</td>\n",
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       "      <td>RT @KAKEnews: California wildfires destroy mor...</td>\n",
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       "      <td>RT KAKEnews California wildfires destroy 50 st...</td>\n",
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       "      <td>infrastructure_and_utility_damage</td>\n",
       "      <td>infrastructure_and_utility_damage</td>\n",
       "      <td>Positive</td>\n",
       "      <td>RT KAKEnews California wildfires destroy 50 st...</td>\n",
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       "    <tr>\n",
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       "      <td>917815040962695168</td>\n",
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       "      <td>917828283047260161</td>\n",
       "      <td>917828283047260161_0</td>\n",
       "      <td>Why California's #wildfires are worse in the f...</td>\n",
       "      <td>data_image/california_wildfires/10_10_2017/917...</td>\n",
       "      <td>other_relevant_information</td>\n",
       "      <td>other_relevant_information</td>\n",
       "      <td>other_relevant_information</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Californias wildfires worse fall california_wi...</td>\n",
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       "      <td>rescue_volunteering_or_donation_effort</td>\n",
       "      <td>rescue_volunteering_or_donation_effort</td>\n",
       "      <td>Positive</td>\n",
       "      <td>needing recovery assistance Hurricane Irma PBC...</td>\n",
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       "      <th>2870</th>\n",
       "      <td>hurricane_harvey</td>\n",
       "      <td>904997956486496258</td>\n",
       "      <td>904997956486496258_0</td>\n",
       "      <td>Fossil fuel didnt cause Hurricane Harvey Its s...</td>\n",
       "      <td>data_image/hurricane_harvey/5_9_2017/904997956...</td>\n",
       "      <td>other_relevant_information</td>\n",
       "      <td>other_relevant_information</td>\n",
       "      <td>other_relevant_information</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Fossil fuel didnt cause Hurricane Harvey savin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2871</th>\n",
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       "      <td>908365073176985600</td>\n",
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       "      <td>data_image/hurricane_harvey/3_9_2017/904277366...</td>\n",
       "      <td>rescue_volunteering_or_donation_effort</td>\n",
       "      <td>rescue_volunteering_or_donation_effort</td>\n",
       "      <td>rescue_volunteering_or_donation_effort</td>\n",
       "      <td>Positive</td>\n",
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       "      <td>Atlantic Hrcne Season 2017 Major Hrcne Irma Ca...</td>\n",
       "      <td>data_image/hurricane_harvey/10_9_2017/90679032...</td>\n",
       "      <td>other_relevant_information</td>\n",
       "      <td>other_relevant_information</td>\n",
       "      <td>other_relevant_information</td>\n",
       "      <td>Positive</td>\n",
       "      <td>Atlantic Hrcne Season 2017 Major Hrcne Irma Ca...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2874 rows × 10 columns</p>\n",
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      ],
      "text/plain": [
       "                event_name            tweet_id              image_id  \\\n",
       "0     california_wildfires  917793137925459968  917793137925459968_0   \n",
       "1     california_wildfires  917793137925459968  917793137925459968_1   \n",
       "2     california_wildfires  917793137925459968  917793137925459968_2   \n",
       "3     california_wildfires  917815040962695168  917815040962695168_2   \n",
       "4     california_wildfires  917828283047260161  917828283047260161_0   \n",
       "...                    ...                 ...                   ...   \n",
       "2869        hurricane_irma  910250204812632064  910250204812632064_0   \n",
       "2870      hurricane_harvey  904997956486496258  904997956486496258_0   \n",
       "2871      hurricane_harvey  908365073176985600  908365073176985600_0   \n",
       "2872      hurricane_harvey  904277366851198982  904277366851198982_0   \n",
       "2873      hurricane_harvey  906790326215614466  906790326215614466_0   \n",
       "\n",
       "                                             tweet_text  \\\n",
       "0     RT @KAKEnews: California wildfires destroy mor...   \n",
       "1     RT @KAKEnews: California wildfires destroy mor...   \n",
       "2     RT @KAKEnews: California wildfires destroy mor...   \n",
       "3     RT @TheAtlantic: Photos of California's destru...   \n",
       "4     Why California's #wildfires are worse in the f...   \n",
       "...                                                 ...   \n",
       "2869  For those needing recovery assistance from Hur...   \n",
       "2870  Fossil fuel didnt cause Hurricane Harvey Its s...   \n",
       "2871  Help us lift up our Texan friends as they pick...   \n",
       "2872  RT @MIGOP: President Trump and @FLOTUS serving...   \n",
       "2873  Atlantic Hrcne Season 2017 Major Hrcne Irma Ca...   \n",
       "\n",
       "                                                  image  \\\n",
       "0     data_image/california_wildfires/10_10_2017/917...   \n",
       "1     data_image/california_wildfires/10_10_2017/917...   \n",
       "2     data_image/california_wildfires/10_10_2017/917...   \n",
       "3     data_image/california_wildfires/10_10_2017/917...   \n",
       "4     data_image/california_wildfires/10_10_2017/917...   \n",
       "...                                                 ...   \n",
       "2869  data_image/hurricane_irma/19_9_2017/9102502048...   \n",
       "2870  data_image/hurricane_harvey/5_9_2017/904997956...   \n",
       "2871  data_image/hurricane_harvey/14_9_2017/90836507...   \n",
       "2872  data_image/hurricane_harvey/3_9_2017/904277366...   \n",
       "2873  data_image/hurricane_harvey/10_9_2017/90679032...   \n",
       "\n",
       "                                       label  \\\n",
       "0          infrastructure_and_utility_damage   \n",
       "1          infrastructure_and_utility_damage   \n",
       "2          infrastructure_and_utility_damage   \n",
       "3          infrastructure_and_utility_damage   \n",
       "4                 other_relevant_information   \n",
       "...                                      ...   \n",
       "2869  rescue_volunteering_or_donation_effort   \n",
       "2870              other_relevant_information   \n",
       "2871  rescue_volunteering_or_donation_effort   \n",
       "2872  rescue_volunteering_or_donation_effort   \n",
       "2873              other_relevant_information   \n",
       "\n",
       "                                  label_text  \\\n",
       "0          infrastructure_and_utility_damage   \n",
       "1          infrastructure_and_utility_damage   \n",
       "2          infrastructure_and_utility_damage   \n",
       "3          infrastructure_and_utility_damage   \n",
       "4                 other_relevant_information   \n",
       "...                                      ...   \n",
       "2869  rescue_volunteering_or_donation_effort   \n",
       "2870              other_relevant_information   \n",
       "2871  rescue_volunteering_or_donation_effort   \n",
       "2872  rescue_volunteering_or_donation_effort   \n",
       "2873              other_relevant_information   \n",
       "\n",
       "                                 label_image label_text_image  \\\n",
       "0          infrastructure_and_utility_damage         Positive   \n",
       "1          infrastructure_and_utility_damage         Positive   \n",
       "2          infrastructure_and_utility_damage         Positive   \n",
       "3          infrastructure_and_utility_damage         Positive   \n",
       "4                 other_relevant_information         Positive   \n",
       "...                                      ...              ...   \n",
       "2869  rescue_volunteering_or_donation_effort         Positive   \n",
       "2870              other_relevant_information         Positive   \n",
       "2871  rescue_volunteering_or_donation_effort         Positive   \n",
       "2872  rescue_volunteering_or_donation_effort         Positive   \n",
       "2873              other_relevant_information         Positive   \n",
       "\n",
       "                                             final_text  \n",
       "0     RT KAKEnews California wildfires destroy 50 st...  \n",
       "1     RT KAKEnews California wildfires destroy 50 st...  \n",
       "2     RT KAKEnews California wildfires destroy 50 st...  \n",
       "3     RT TheAtlantic Photos Californias destructive ...  \n",
       "4     Californias wildfires worse fall california_wi...  \n",
       "...                                                 ...  \n",
       "2869  needing recovery assistance Hurricane Irma PBC...  \n",
       "2870  Fossil fuel didnt cause Hurricane Harvey savin...  \n",
       "2871  Help us lift Texan friends pick pieces Harvey ...  \n",
       "2872  RT MIGOP President Trump FLOTUS serving meals ...  \n",
       "2873  Atlantic Hrcne Season 2017 Major Hrcne Irma Ca...  \n",
       "\n",
       "[2874 rows x 10 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humanitarian_train_df = pd.read_csv('./datasets/CrisisMMD_v2.0/crisismmd_datasplit_all/wiki_settingA/task_humanitarian_text_img_train.tsv', sep = '\\t')\n",
    "\n",
    "humanitarian_train_df=humanitarian_train_df[humanitarian_train_df.label_text == humanitarian_train_df.label_image]\n",
    "\n",
    "humanitarian_train_df#.label_image.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a45c889",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Positive</td>\n",
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       "      <th>9595</th>\n",
       "      <td>california_wildfires</td>\n",
       "      <td>921051431368515584</td>\n",
       "      <td>921051431368515584_0</td>\n",
       "      <td>California Wildfires Climate Change Fire Depen...</td>\n",
       "      <td>data_image/california_wildfires/19_10_2017/921...</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9596</th>\n",
       "      <td>hurricane_harvey</td>\n",
       "      <td>905114243871240192</td>\n",
       "      <td>905114243871240192_0</td>\n",
       "      <td>Siri rescues sick teen from Hurricane Harvey -...</td>\n",
       "      <td>data_image/hurricane_harvey/5_9_2017/905114243...</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9597</th>\n",
       "      <td>california_wildfires</td>\n",
       "      <td>919938318795788288</td>\n",
       "      <td>919938318795788288_0</td>\n",
       "      <td>Wells Fargo ATMs are now accepting donations f...</td>\n",
       "      <td>data_image/california_wildfires/16_10_2017/919...</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9598</th>\n",
       "      <td>hurricane_maria</td>\n",
       "      <td>911512002954067968</td>\n",
       "      <td>911512002954067968_0</td>\n",
       "      <td>San Juan flights still on hold in wake of Hurr...</td>\n",
       "      <td>data_image/hurricane_maria/23_9_2017/911512002...</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9599 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                event_name            tweet_id              image_id  \\\n",
       "0     california_wildfires  917791291823591425  917791291823591425_0   \n",
       "1     california_wildfires  917793137925459968  917793137925459968_0   \n",
       "2     california_wildfires  917793137925459968  917793137925459968_1   \n",
       "3     california_wildfires  917793137925459968  917793137925459968_2   \n",
       "4     california_wildfires  917815040962695168  917815040962695168_0   \n",
       "...                    ...                 ...                   ...   \n",
       "9594        hurricane_irma  910172979526594561  910172979526594561_0   \n",
       "9595  california_wildfires  921051431368515584  921051431368515584_0   \n",
       "9596      hurricane_harvey  905114243871240192  905114243871240192_0   \n",
       "9597  california_wildfires  919938318795788288  919938318795788288_0   \n",
       "9598       hurricane_maria  911512002954067968  911512002954067968_0   \n",
       "\n",
       "                                             tweet_text  \\\n",
       "0     RT @Cal_OES: PLS SHARE: Weâ€™re capturing wild...   \n",
       "1     RT @KAKEnews: California wildfires destroy mor...   \n",
       "2     RT @KAKEnews: California wildfires destroy mor...   \n",
       "3     RT @KAKEnews: California wildfires destroy mor...   \n",
       "4     RT @TheAtlantic: Photos of California's destru...   \n",
       "...                                                 ...   \n",
       "9594  On the Cone of Concern graphic, I plotted Irma...   \n",
       "9595  California Wildfires Climate Change Fire Depen...   \n",
       "9596  Siri rescues sick teen from Hurricane Harvey -...   \n",
       "9597  Wells Fargo ATMs are now accepting donations f...   \n",
       "9598  San Juan flights still on hold in wake of Hurr...   \n",
       "\n",
       "                                                  image        label  \\\n",
       "0     data_image/california_wildfires/10_10_2017/917...  informative   \n",
       "1     data_image/california_wildfires/10_10_2017/917...  informative   \n",
       "2     data_image/california_wildfires/10_10_2017/917...  informative   \n",
       "3     data_image/california_wildfires/10_10_2017/917...  informative   \n",
       "4     data_image/california_wildfires/10_10_2017/917...  informative   \n",
       "...                                                 ...          ...   \n",
       "9594  data_image/hurricane_irma/19_9_2017/9101729795...  informative   \n",
       "9595  data_image/california_wildfires/19_10_2017/921...  informative   \n",
       "9596  data_image/hurricane_harvey/5_9_2017/905114243...  informative   \n",
       "9597  data_image/california_wildfires/16_10_2017/919...  informative   \n",
       "9598  data_image/hurricane_maria/23_9_2017/911512002...  informative   \n",
       "\n",
       "       label_text  label_image label_text_image  \n",
       "0     informative  informative         Positive  \n",
       "1     informative  informative         Positive  \n",
       "2     informative  informative         Positive  \n",
       "3     informative  informative         Positive  \n",
       "4     informative  informative         Positive  \n",
       "...           ...          ...              ...  \n",
       "9594  informative  informative         Positive  \n",
       "9595  informative  informative         Positive  \n",
       "9596  informative  informative         Positive  \n",
       "9597  informative  informative         Positive  \n",
       "9598  informative  informative         Positive  \n",
       "\n",
       "[9599 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inf_train_df = pd.read_csv('./datasets/CrisisMMD_v2.0/crisismmd_datasplit_all/settingA/task_informative_text_img_train.tsv', sep = '\\t')\n",
    "\n",
    "inf_train_df=inf_train_df[inf_train_df.label_text == inf_train_df.label_image]\n",
    "\n",
    "inf_train_df#.label_image.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8b0aa156",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_name</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>image_id</th>\n",
       "      <th>tweet_text</th>\n",
       "      <th>image</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hurricane_harvey</td>\n",
       "      <td>905960092822003712</td>\n",
       "      <td>905960092822003712_0</td>\n",
       "      <td>RT @tveitdal: Where Harvey is hitting hardest,...</td>\n",
       "      <td>data_image/hurricane_harvey/8_9_2017/905960092...</td>\n",
       "      <td>severe_damage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>california_wildfires</td>\n",
       "      <td>918008272363368448</td>\n",
       "      <td>918008272363368448_0</td>\n",
       "      <td>#NAPA #California #wildfires HORRIBLE, NO WORD...</td>\n",
       "      <td>data_image/california_wildfires/11_10_2017/918...</td>\n",
       "      <td>severe_damage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hurricane_irma</td>\n",
       "      <td>909396901254090752</td>\n",
       "      <td>909396901254090752_0</td>\n",
       "      <td>RT @business: Caribbean tax haven begins to bo...</td>\n",
       "      <td>data_image/hurricane_irma/17_9_2017/9093969012...</td>\n",
       "      <td>severe_damage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hurricane_maria</td>\n",
       "      <td>912097936200355841</td>\n",
       "      <td>912097936200355841_0</td>\n",
       "      <td>@StormReportWX Steve, have you seen this from ...</td>\n",
       "      <td>data_image/hurricane_maria/24_9_2017/912097936...</td>\n",
       "      <td>severe_damage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hurricane_maria</td>\n",
       "      <td>922610290281402368</td>\n",
       "      <td>922610290281402368_1</td>\n",
       "      <td>Thanks for following! please share! https://t....</td>\n",
       "      <td>data_image/hurricane_maria/23_10_2017/92261029...</td>\n",
       "      <td>little_or_no_damage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2463</th>\n",
       "      <td>hurricane_irma</td>\n",
       "      <td>905636943630884865</td>\n",
       "      <td>905636943630884865_0</td>\n",
       "      <td>RT @LoopJamaica: Barbuda declared uninhabitabl...</td>\n",
       "      <td>data_image/hurricane_irma/7_9_2017/90563694363...</td>\n",
       "      <td>severe_damage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2464</th>\n",
       "      <td>iraq_iran_earthquake</td>\n",
       "      <td>930068973206622213</td>\n",
       "      <td>930068973206622213_1</td>\n",
       "      <td>RT @TruTawar: Earthquake Kurdistan Iran https:...</td>\n",
       "      <td>data_image/iraq_iran_earthquake/13_11_2017/930...</td>\n",
       "      <td>mild_damage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2465</th>\n",
       "      <td>mexico_earthquake</td>\n",
       "      <td>910691589034409984</td>\n",
       "      <td>910691589034409984_0</td>\n",
       "      <td>Mexico Earthquake: 2 Cathedrals Sustain Damage...</td>\n",
       "      <td>data_image/mexico_earthquake/21_9_2017/9106915...</td>\n",
       "      <td>little_or_no_damage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2466</th>\n",
       "      <td>mexico_earthquake</td>\n",
       "      <td>910537563974307842</td>\n",
       "      <td>910537563974307842_0</td>\n",
       "      <td>Dramatic video shows moment of gas explosion a...</td>\n",
       "      <td>data_image/mexico_earthquake/20_9_2017/9105375...</td>\n",
       "      <td>severe_damage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2467</th>\n",
       "      <td>srilanka_floods</td>\n",
       "      <td>870230068156448769</td>\n",
       "      <td>870230068156448769_0</td>\n",
       "      <td>Meanwhile Sri Lanka Flood. I found it from my ...</td>\n",
       "      <td>data_image/srilanka_floods/1_6_2017/8702300681...</td>\n",
       "      <td>severe_damage</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2468 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                event_name            tweet_id              image_id  \\\n",
       "0         hurricane_harvey  905960092822003712  905960092822003712_0   \n",
       "1     california_wildfires  918008272363368448  918008272363368448_0   \n",
       "2           hurricane_irma  909396901254090752  909396901254090752_0   \n",
       "3          hurricane_maria  912097936200355841  912097936200355841_0   \n",
       "4          hurricane_maria  922610290281402368  922610290281402368_1   \n",
       "...                    ...                 ...                   ...   \n",
       "2463        hurricane_irma  905636943630884865  905636943630884865_0   \n",
       "2464  iraq_iran_earthquake  930068973206622213  930068973206622213_1   \n",
       "2465     mexico_earthquake  910691589034409984  910691589034409984_0   \n",
       "2466     mexico_earthquake  910537563974307842  910537563974307842_0   \n",
       "2467       srilanka_floods  870230068156448769  870230068156448769_0   \n",
       "\n",
       "                                             tweet_text  \\\n",
       "0     RT @tveitdal: Where Harvey is hitting hardest,...   \n",
       "1     #NAPA #California #wildfires HORRIBLE, NO WORD...   \n",
       "2     RT @business: Caribbean tax haven begins to bo...   \n",
       "3     @StormReportWX Steve, have you seen this from ...   \n",
       "4     Thanks for following! please share! https://t....   \n",
       "...                                                 ...   \n",
       "2463  RT @LoopJamaica: Barbuda declared uninhabitabl...   \n",
       "2464  RT @TruTawar: Earthquake Kurdistan Iran https:...   \n",
       "2465  Mexico Earthquake: 2 Cathedrals Sustain Damage...   \n",
       "2466  Dramatic video shows moment of gas explosion a...   \n",
       "2467  Meanwhile Sri Lanka Flood. I found it from my ...   \n",
       "\n",
       "                                                  image                label  \n",
       "0     data_image/hurricane_harvey/8_9_2017/905960092...        severe_damage  \n",
       "1     data_image/california_wildfires/11_10_2017/918...        severe_damage  \n",
       "2     data_image/hurricane_irma/17_9_2017/9093969012...        severe_damage  \n",
       "3     data_image/hurricane_maria/24_9_2017/912097936...        severe_damage  \n",
       "4     data_image/hurricane_maria/23_10_2017/92261029...  little_or_no_damage  \n",
       "...                                                 ...                  ...  \n",
       "2463  data_image/hurricane_irma/7_9_2017/90563694363...        severe_damage  \n",
       "2464  data_image/iraq_iran_earthquake/13_11_2017/930...          mild_damage  \n",
       "2465  data_image/mexico_earthquake/21_9_2017/9106915...  little_or_no_damage  \n",
       "2466  data_image/mexico_earthquake/20_9_2017/9105375...        severe_damage  \n",
       "2467  data_image/srilanka_floods/1_6_2017/8702300681...        severe_damage  \n",
       "\n",
       "[2468 rows x 6 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task3_train_df = pd.read_csv('./datasets/CrisisMMD_v2.0/crisismmd_datasplit_all/settingA/task_damage_text_img_train.tsv', sep = '\\t')\n",
    "\n",
    "#task3_train_df=task3_train_df[task3_train_df.label_text == task3_train_df.label_image]\n",
    "\n",
    "task3_train_df#.label_image.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0adead4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_task2_merged = {\n",
    "    'infrastructure_and_utility_damage': 0,\n",
    "    'not_humanitarian': 1,\n",
    "    'other_relevant_information': 2,\n",
    "    'rescue_volunteering_or_donation_effort': 3,\n",
    "    'vehicle_damage': 4,\n",
    "    'affected_individuals': 5,\n",
    "    'injured_or_dead_people': 5,\n",
    "    'missing_or_found_people': 5,\n",
    "}\n",
    "\n",
    "labels_task1 = {\n",
    "    'informative': 1,\n",
    "    'not_informative': 0\n",
    "}\n",
    "\n",
    "\n",
    "labels_task3 = {\n",
    "    'little_or_no_damage': 0,\n",
    "    'mild_damage': 1,\n",
    "    'severe_damage' : 2\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8b4b7010",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column 'value' and map the labels to their corresponding values\n",
    "humanitarian_train_df['label_value'] = humanitarian_train_df['label'].map(labels_task2_merged)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "4da2402d",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>event_name</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>image_id</th>\n",
       "      <th>tweet_text</th>\n",
       "      <th>image</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>california_wildfires</td>\n",
       "      <td>917793137925459968</td>\n",
       "      <td>917793137925459968_0</td>\n",
       "      <td>RT @KAKEnews: California wildfires destroy mor...</td>\n",
       "      <td>data_image/california_wildfires/10_10_2017/917...</td>\n",
       "      <td>infrastructure_and_utility_damage</td>\n",
       "      <td>infrastructure_and_utility_damage</td>\n",
       "      <td>infrastructure_and_utility_damage</td>\n",
       "      <td>Positive</td>\n",
       "      <td>RT KAKEnews California wildfires destroy 50 st...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <td>california_wildfires</td>\n",
       "      <td>917793137925459968</td>\n",
       "      <td>917793137925459968_1</td>\n",
       "      <td>RT @KAKEnews: California wildfires destroy mor...</td>\n",
       "      <td>data_image/california_wildfires/10_10_2017/917...</td>\n",
       "      <td>infrastructure_and_utility_damage</td>\n",
       "      <td>infrastructure_and_utility_damage</td>\n",
       "      <td>infrastructure_and_utility_damage</td>\n",
       "      <td>Positive</td>\n",
       "      <td>RT KAKEnews California wildfires destroy 50 st...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             event_name            tweet_id              image_id  \\\n",
       "0  california_wildfires  917793137925459968  917793137925459968_0   \n",
       "1  california_wildfires  917793137925459968  917793137925459968_1   \n",
       "\n",
       "                                          tweet_text  \\\n",
       "0  RT @KAKEnews: California wildfires destroy mor...   \n",
       "1  RT @KAKEnews: California wildfires destroy mor...   \n",
       "\n",
       "                                               image  \\\n",
       "0  data_image/california_wildfires/10_10_2017/917...   \n",
       "1  data_image/california_wildfires/10_10_2017/917...   \n",
       "\n",
       "                               label                         label_text  \\\n",
       "0  infrastructure_and_utility_damage  infrastructure_and_utility_damage   \n",
       "1  infrastructure_and_utility_damage  infrastructure_and_utility_damage   \n",
       "\n",
       "                         label_image label_text_image  \\\n",
       "0  infrastructure_and_utility_damage         Positive   \n",
       "1  infrastructure_and_utility_damage         Positive   \n",
       "\n",
       "                                          final_text  label_value  \n",
       "0  RT KAKEnews California wildfires destroy 50 st...            0  \n",
       "1  RT KAKEnews California wildfires destroy 50 st...            0  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humanitarian_train_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5c02c5d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column 'value' and map the labels to their corresponding values\n",
    "inf_train_df['label_value'] = inf_train_df['label'].map(labels_task1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ebccfbf3",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>RT @Cal_OES: PLS SHARE: Weâ€™re capturing wild...</td>\n",
       "      <td>data_image/california_wildfires/10_10_2017/917...</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
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       "      <td>917793137925459968</td>\n",
       "      <td>917793137925459968_0</td>\n",
       "      <td>RT @KAKEnews: California wildfires destroy mor...</td>\n",
       "      <td>data_image/california_wildfires/10_10_2017/917...</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
       "      <td>informative</td>\n",
       "      <td>Positive</td>\n",
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       "             event_name            tweet_id              image_id  \\\n",
       "0  california_wildfires  917791291823591425  917791291823591425_0   \n",
       "1  california_wildfires  917793137925459968  917793137925459968_0   \n",
       "\n",
       "                                          tweet_text  \\\n",
       "0  RT @Cal_OES: PLS SHARE: Weâ€™re capturing wild...   \n",
       "1  RT @KAKEnews: California wildfires destroy mor...   \n",
       "\n",
       "                                               image        label  \\\n",
       "0  data_image/california_wildfires/10_10_2017/917...  informative   \n",
       "1  data_image/california_wildfires/10_10_2017/917...  informative   \n",
       "\n",
       "    label_text  label_image label_text_image  label_value  \n",
       "0  informative  informative         Positive            1  \n",
       "1  informative  informative         Positive            1  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inf_train_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ad10a7da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column 'value' and map the labels to their corresponding values\n",
    "task3_train_df['label_value'] = task3_train_df['label'].map(labels_task3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d7c39f28",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>event_name</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>image_id</th>\n",
       "      <th>tweet_text</th>\n",
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       "      <th>label</th>\n",
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       "      <td>hurricane_harvey</td>\n",
       "      <td>905960092822003712</td>\n",
       "      <td>905960092822003712_0</td>\n",
       "      <td>RT @tveitdal: Where Harvey is hitting hardest,...</td>\n",
       "      <td>data_image/hurricane_harvey/8_9_2017/905960092...</td>\n",
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      "text/plain": [
       "             event_name            tweet_id              image_id  \\\n",
       "0      hurricane_harvey  905960092822003712  905960092822003712_0   \n",
       "1  california_wildfires  918008272363368448  918008272363368448_0   \n",
       "\n",
       "                                          tweet_text  \\\n",
       "0  RT @tveitdal: Where Harvey is hitting hardest,...   \n",
       "1  #NAPA #California #wildfires HORRIBLE, NO WORD...   \n",
       "\n",
       "                                               image          label  \\\n",
       "0  data_image/hurricane_harvey/8_9_2017/905960092...  severe_damage   \n",
       "1  data_image/california_wildfires/11_10_2017/918...  severe_damage   \n",
       "\n",
       "   label_value  \n",
       "0            2  \n",
       "1            2  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task3_train_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7dd4f98f",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_folder_path = '/home/gupta.37/work/crisisMMD/Multimodal-Categorization-of-Crisis-Events-in-Social-Media-main/datasets/CrisisMMD_v2.0/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "066dc037",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[42], line 49\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;66;03m# Remove the axes\u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;66;03m#plt.axis('off')\u001b[39;00m\n\u001b[1;32m     48\u001b[0m filename \u001b[38;5;241m=\u001b[39m input_image\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m---> 49\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m./gradcam_images/task3/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mfilename\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m.jpg\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mbbox_inches\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtight\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpad_inches\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/pyplot.py:959\u001b[0m, in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    956\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Figure\u001b[38;5;241m.\u001b[39msavefig)\n\u001b[1;32m    957\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msavefig\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    958\u001b[0m     fig \u001b[38;5;241m=\u001b[39m gcf()\n\u001b[0;32m--> 959\u001b[0m     res \u001b[38;5;241m=\u001b[39m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    960\u001b[0m     fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mdraw_idle()  \u001b[38;5;66;03m# Need this if 'transparent=True', to reset colors.\u001b[39;00m\n\u001b[1;32m    961\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m res\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/figure.py:3285\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3281\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[1;32m   3282\u001b[0m         stack\u001b[38;5;241m.\u001b[39menter_context(\n\u001b[1;32m   3283\u001b[0m             ax\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39m_cm_set(facecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m, edgecolor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnone\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[0;32m-> 3285\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/backend_bases.py:2314\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2308\u001b[0m     renderer \u001b[38;5;241m=\u001b[39m _get_renderer(\n\u001b[1;32m   2309\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure,\n\u001b[1;32m   2310\u001b[0m         functools\u001b[38;5;241m.\u001b[39mpartial(\n\u001b[1;32m   2311\u001b[0m             print_method, orientation\u001b[38;5;241m=\u001b[39morientation)\n\u001b[1;32m   2312\u001b[0m     )\n\u001b[1;32m   2313\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(renderer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_draw_disabled\u001b[39m\u001b[38;5;124m\"\u001b[39m, nullcontext)():\n\u001b[0;32m-> 2314\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2316\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2317\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/artist.py:74\u001b[0m, in \u001b[0;36m_finalize_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(draw)\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdraw_wrapper\u001b[39m(artist, renderer, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 74\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     75\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m renderer\u001b[38;5;241m.\u001b[39m_rasterizing:\n\u001b[1;32m     76\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/artist.py:51\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     49\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/figure.py:3082\u001b[0m, in \u001b[0;36mFigure.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3079\u001b[0m         \u001b[38;5;66;03m# ValueError can occur when resizing a window.\u001b[39;00m\n\u001b[1;32m   3081\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[0;32m-> 3082\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3083\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3085\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sfig \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubfigs:\n\u001b[1;32m   3086\u001b[0m     sfig\u001b[38;5;241m.\u001b[39mdraw(renderer)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/artist.py:51\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     49\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axes/_base.py:3100\u001b[0m, in \u001b[0;36m_AxesBase.draw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   3097\u001b[0m         a\u001b[38;5;241m.\u001b[39mdraw(renderer)\n\u001b[1;32m   3098\u001b[0m     renderer\u001b[38;5;241m.\u001b[39mstop_rasterizing()\n\u001b[0;32m-> 3100\u001b[0m \u001b[43mmimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_draw_list_compositing_images\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3101\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43martists\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msuppressComposite\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3103\u001b[0m renderer\u001b[38;5;241m.\u001b[39mclose_group(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maxes\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m   3104\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstale \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/image.py:131\u001b[0m, in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m not_composite \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_images:\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m artists:\n\u001b[0;32m--> 131\u001b[0m         \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    132\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    133\u001b[0m     \u001b[38;5;66;03m# Composite any adjacent images together\u001b[39;00m\n\u001b[1;32m    134\u001b[0m     image_group \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/artist.py:51\u001b[0m, in \u001b[0;36mallow_rasterization.<locals>.draw_wrapper\u001b[0;34m(artist, renderer)\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     49\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mstart_filter()\n\u001b[0;32m---> 51\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdraw\u001b[49m\u001b[43m(\u001b[49m\u001b[43martist\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     52\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m artist\u001b[38;5;241m.\u001b[39mget_agg_filter() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/image.py:641\u001b[0m, in \u001b[0;36m_ImageBase.draw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m    639\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mdraw_image(gc, l, b, im, trans)\n\u001b[1;32m    640\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 641\u001b[0m     im, l, b, trans \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_image\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    642\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_image_magnification\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    643\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m im \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    644\u001b[0m         renderer\u001b[38;5;241m.\u001b[39mdraw_image(gc, l, b, im)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/image.py:949\u001b[0m, in \u001b[0;36mAxesImage.make_image\u001b[0;34m(self, renderer, magnification, unsampled)\u001b[0m\n\u001b[1;32m    946\u001b[0m transformed_bbox \u001b[38;5;241m=\u001b[39m TransformedBbox(bbox, trans)\n\u001b[1;32m    947\u001b[0m clip \u001b[38;5;241m=\u001b[39m ((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_clip_box() \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mbbox) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_clip_on()\n\u001b[1;32m    948\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure\u001b[38;5;241m.\u001b[39mbbox)\n\u001b[0;32m--> 949\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_make_image\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_A\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransformed_bbox\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclip\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    950\u001b[0m \u001b[43m                        \u001b[49m\u001b[43mmagnification\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munsampled\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munsampled\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/image.py:555\u001b[0m, in \u001b[0;36m_ImageBase._make_image\u001b[0;34m(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)\u001b[0m\n\u001b[1;32m    552\u001b[0m     alpha \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_scalar_alpha()\n\u001b[1;32m    553\u001b[0m     output_alpha \u001b[38;5;241m=\u001b[39m _resample(  \u001b[38;5;66;03m# resample alpha channel\u001b[39;00m\n\u001b[1;32m    554\u001b[0m         \u001b[38;5;28mself\u001b[39m, A[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m3\u001b[39m], out_shape, t, alpha\u001b[38;5;241m=\u001b[39malpha)\n\u001b[0;32m--> 555\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43m_resample\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# resample rgb channels\u001b[39;49;00m\n\u001b[1;32m    556\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_rgb_to_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mA\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_shape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malpha\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    557\u001b[0m     output[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m, \u001b[38;5;241m3\u001b[39m] \u001b[38;5;241m=\u001b[39m output_alpha  \u001b[38;5;66;03m# recombine rgb and alpha\u001b[39;00m\n\u001b[1;32m    559\u001b[0m \u001b[38;5;66;03m# output is now either a 2D array of normed (int or float) data\u001b[39;00m\n\u001b[1;32m    560\u001b[0m \u001b[38;5;66;03m# or an RGBA array of re-sampled input\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/image.py:207\u001b[0m, in \u001b[0;36m_resample\u001b[0;34m(image_obj, data, out_shape, transform, resample, alpha)\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m resample \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    206\u001b[0m     resample \u001b[38;5;241m=\u001b[39m image_obj\u001b[38;5;241m.\u001b[39mget_resample()\n\u001b[0;32m--> 207\u001b[0m \u001b[43m_image\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    208\u001b[0m \u001b[43m                \u001b[49m\u001b[43m_interpd_\u001b[49m\u001b[43m[\u001b[49m\u001b[43minterpolation\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    209\u001b[0m \u001b[43m                \u001b[49m\u001b[43mresample\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    210\u001b[0m \u001b[43m                \u001b[49m\u001b[43malpha\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    211\u001b[0m \u001b[43m                \u001b[49m\u001b[43mimage_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_filternorm\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    212\u001b[0m \u001b[43m                \u001b[49m\u001b[43mimage_obj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_filterrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    213\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for index,row in task3_train_df.iterrows():\n",
    "    input_text = row['tweet_text']\n",
    "    input_image_path = row['image']\n",
    "    input_image = os.path.join(base_folder_path,input_image_path)\n",
    "    #print(input_image)\n",
    "    target =  int(row['label_value'])\n",
    "    #print(target)\n",
    "    with Image.open(input_image).convert('RGB') as img:\n",
    "            image = transforms(img)\n",
    "    \n",
    "    image=image.unsqueeze(0)\n",
    "    \n",
    "    text_tokens = tokenize(input_text)\n",
    "    \n",
    "    x = (image.to(device),\n",
    "     {k: v.to(device) for k, v in text_tokens.items()})\n",
    "    \n",
    "    #img_path = data['path_image'][0]\n",
    "    #print(img_path)\n",
    "    \n",
    "    y = torch.tensor([target]).to(device)\n",
    "    \n",
    "    #print(\"y in the prediction function\",y)\n",
    "    #input_tensor = data['image'].to(device)\n",
    "    #image = grad_cam(model, x, heatmap_layer,y)\n",
    "    #plt.savefig('./images/input_grad-cam_v1')\n",
    "    model.eval()\n",
    "    #with torch.no_grad():\n",
    "    #    logits = model(x)\n",
    "\n",
    "    logits = model(x)\n",
    "    # indices is a tensor of predictions\n",
    "    org_img = image.to(device)\n",
    "    indices = torch.argmax(logits, dim=1).to(dtype=torch.int32)\n",
    "\n",
    "    #print(indices)\n",
    "\n",
    "    grad_image = grad_cam(model, x, heatmap_layer,input_image)\n",
    "\n",
    "    # Remove the axes\n",
    "    plt.axis('off')\n",
    "\n",
    "    plt.imshow(grad_image)\n",
    "\n",
    "    # Remove the axes\n",
    "    #plt.axis('off')\n",
    "    \n",
    "    filename = input_image.split('/')[-1].split('.')[0]\n",
    "    plt.savefig(f'./gradcam_images/task3/{filename}.jpg',bbox_inches='tight', pad_inches=0)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3bdd88d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9aff920",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "    filename = data['path_image'][0].split('/')[-1].split('.')[0]\n",
    "    if pred_label == ground_label :\n",
    "        #print(\"yes\") \n",
    "        plt.axis('off')\n",
    "        plt.savefig(f'./images/correct_prediction/{filename}_{pred_label}_{ground_label}.jpg')    \n",
    "    else :\n",
    "        plt.axis('off')\n",
    "        plt.savefig(f'./images/incorrect_prediction/{filename}_{pred_label}_{ground_label}.jpg') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a6708898",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "args.py                            main.ipynb            preprocess.py\n",
      "base_dataset.py                    main.py               \u001b[0m\u001b[01;34m__pycache__\u001b[0m/\n",
      "calc_metric.ipynb                  mm_models_old.py      README.md\n",
      "crisismmd_dataset.py               mm_models.py          requirements.txt\n",
      "\u001b[01;34mdatasets\u001b[0m/                          optimization.py       setup.sh\n",
      "ghostnet.ipynb                     \u001b[01;34moutput\u001b[0m/               train_debug.sh\n",
      "\u001b[01;34mgradcam_images\u001b[0m/                    paths.py              trainer.py\n",
      "grad_cam_original_img_study.ipynb  prediction.csv        train.sh\n",
      "gradcam_w_change_v1.ipynb          prediction-task1.csv  view_predictions.ipynb\n",
      "\u001b[01;34mimages\u001b[0m/                            prediction-task2.csv  \u001b[01;34mwikinet_tagme\u001b[0m/\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b0fb63fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/gupta.37/work/crisisMMD/Multimodal-Categorization-of-Crisis-Events-in-Social-Media-main_change/gradcam_images\n"
     ]
    }
   ],
   "source": [
    "cd gradcam_images/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "00ef5e49",
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir task3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bdd80fe7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/gupta.37/work/crisisMMD/Multimodal-Categorization-of-Crisis-Events-in-Social-Media-main_change\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6da15921",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = '/home/gupta.37/work/crisisMMD/Multimodal-Categorization-of-Crisis-Events-in-Social-Media-main/datasets/CrisisMMD_v2.0/data_image/california_wildfires/10_10_2017/917793137925459968_2.jpg'\n",
    "text = 'RT @KAKEnews: California wildfires destroy more than 50 structures: https://t.co/IkSrNpmT83 #KAKEnews https://t.co/8risKwbBqr'\n",
    "target=4"
   ]
  }
 ],
 "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}