{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7edf7168",
   "metadata": {},
   "source": [
    "# TD2: Deep learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbb8c8df",
   "metadata": {},
   "source": [
    "In this TD, you must modify this notebook to answer the questions. To do this,\n",
    "\n",
    "1. Fork this repository\n",
    "2. Clone your forked repository on your local computer\n",
    "3. Answer the questions\n",
    "4. Commit and push regularly\n",
    "\n",
    "The last commit is due on Sunday, December 1, 11:59 PM. Later commits will not be taken into account."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d167a29",
   "metadata": {},
   "source": [
    "Install and test PyTorch from  https://pytorch.org/get-started/locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "330a42f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install torch torchvision"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0882a636",
   "metadata": {},
   "source": [
    "\n",
    "To test run the following code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b1950f0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-1.0448,  1.3495, -0.4073, -0.0601,  2.2316, -0.0556, -0.2806,  0.0239,\n",
      "          1.0922, -2.3037],\n",
      "        [ 2.3668,  0.8428,  0.8087, -0.6397, -0.5295, -0.5003,  0.4682,  1.2924,\n",
      "         -0.9323,  1.5477],\n",
      "        [ 0.6414, -0.0156, -2.9382,  1.3994, -1.0829,  0.7118, -0.2166,  2.0800,\n",
      "         -1.7264,  1.4773],\n",
      "        [ 0.3116, -0.6278,  0.2307,  0.1855,  0.2212,  1.1517, -0.7703,  0.2716,\n",
      "         -0.0741, -0.9202],\n",
      "        [ 0.9318, -1.3372, -0.1808, -0.6472,  0.8863, -0.6137, -0.3358,  0.8464,\n",
      "          0.4691, -0.5010],\n",
      "        [-2.0975, -1.5045, -0.8705, -0.0784, -0.3256, -1.2220,  0.0242, -0.0983,\n",
      "         -0.1793, -0.1118],\n",
      "        [ 1.3658,  0.9157,  0.5391, -0.3203, -0.4633,  0.5243,  2.1155, -1.4273,\n",
      "         -2.0246,  0.8297],\n",
      "        [ 1.1095,  2.1074,  1.0350, -0.3642, -0.2564,  1.0509, -0.6252, -0.8104,\n",
      "         -0.0073,  0.1813],\n",
      "        [-1.0443, -0.6137, -0.2780,  0.0810, -0.1280, -0.3410, -0.1668,  0.7191,\n",
      "         -0.3391,  0.7248],\n",
      "        [-1.1858, -2.0161,  0.2793, -0.0883, -0.4491,  1.6840, -1.2464,  0.0537,\n",
      "         -0.4151,  1.0929],\n",
      "        [ 1.2974,  1.0021,  1.0576,  0.1399,  0.6689, -1.2015, -0.6912, -0.2348,\n",
      "         -0.2107,  0.3246],\n",
      "        [-0.2461,  0.6464, -0.2248,  0.2059,  1.6744, -0.9191, -0.7644,  1.0758,\n",
      "         -0.3607,  0.9457],\n",
      "        [-0.6860,  1.7952, -0.7642,  3.7019, -0.5513, -0.3981, -2.6012, -1.3775,\n",
      "          0.2801, -0.0433],\n",
      "        [-0.5314,  0.6825, -0.7547,  0.5450, -1.4802, -0.6766,  1.3518, -0.1668,\n",
      "         -0.7739,  0.5061]])\n",
      "AlexNet(\n",
      "  (features): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
      "    (1): ReLU(inplace=True)\n",
      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
      "    (4): ReLU(inplace=True)\n",
      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (7): ReLU(inplace=True)\n",
      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (9): ReLU(inplace=True)\n",
      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace=True)\n",
      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
      "  (classifier): Sequential(\n",
      "    (0): Dropout(p=0.5, inplace=False)\n",
      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
      "    (2): ReLU(inplace=True)\n",
      "    (3): Dropout(p=0.5, inplace=False)\n",
      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
      "    (5): ReLU(inplace=True)\n",
      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import numpy as np\n",
    "from torchvision import datasets, transforms\n",
    "from torch.utils.data.sampler import SubsetRandomSampler\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import torch.quantization\n",
    "import json\n",
    "from PIL import Image\n",
    "import copy\n",
    "import time\n",
    "from torch.optim import lr_scheduler\n",
    "\n",
    "N, D = 14, 10\n",
    "x = torch.randn(N, D).type(torch.FloatTensor)\n",
    "print(x)\n",
    "\n",
    "from torchvision import models\n",
    "\n",
    "alexnet = models.alexnet()\n",
    "print(alexnet)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23f266da",
   "metadata": {},
   "source": [
    "## Exercise 1: CNN on CIFAR10\n",
    "\n",
    "The goal is to apply a Convolutional Neural Net (CNN) model on the CIFAR10 image dataset and test the accuracy of the model on the basis of image classification. Compare the Accuracy VS the neural network implemented during TD1.\n",
    "\n",
    "Have a look at the following documentation to be familiar with PyTorch.\n",
    "\n",
    "https://pytorch.org/tutorials/beginner/pytorch_with_examples.html\n",
    "\n",
    "https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ba1c82d",
   "metadata": {},
   "source": [
    "You can test if GPU is available on your machine and thus train on it to speed up the process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e18f2fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CUDA is not available.  Training on CPU ...\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# check if CUDA is available\n",
    "train_on_gpu = torch.cuda.is_available()\n",
    "\n",
    "if not train_on_gpu:\n",
    "    print(\"CUDA is not available.  Training on CPU ...\")\n",
    "else:\n",
    "    print(\"CUDA is available!  Training on GPU ...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cf214eb",
   "metadata": {},
   "source": [
    "Next we load the CIFAR10 dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "462666a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# number of subprocesses to use for data loading\n",
    "num_workers = 0\n",
    "# how many samples per batch to load\n",
    "batch_size = 20\n",
    "# percentage of training set to use as validation\n",
    "valid_size = 0.2\n",
    "\n",
    "# convert data to a normalized torch.FloatTensor\n",
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n",
    ")\n",
    "\n",
    "# choose the training and test datasets\n",
    "train_data = datasets.CIFAR10(\"data\", train=True, download=True, transform=transform)\n",
    "test_data = datasets.CIFAR10(\"data\", train=False, download=True, transform=transform)\n",
    "\n",
    "# obtain training indices that will be used for validation\n",
    "num_train = len(train_data)\n",
    "indices = list(range(num_train))\n",
    "np.random.shuffle(indices)\n",
    "split = int(np.floor(valid_size * num_train))\n",
    "train_idx, valid_idx = indices[split:], indices[:split]\n",
    "\n",
    "# define samplers for obtaining training and validation batches\n",
    "train_sampler = SubsetRandomSampler(train_idx)\n",
    "valid_sampler = SubsetRandomSampler(valid_idx)\n",
    "\n",
    "# prepare data loaders (combine dataset and sampler)\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers\n",
    ")\n",
    "valid_loader = torch.utils.data.DataLoader(\n",
    "    train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers\n",
    ")\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "    test_data, batch_size=batch_size, num_workers=num_workers\n",
    ")\n",
    "\n",
    "# specify the image classes\n",
    "classes = [\n",
    "    \"airplane\",\n",
    "    \"automobile\",\n",
    "    \"bird\",\n",
    "    \"cat\",\n",
    "    \"deer\",\n",
    "    \"dog\",\n",
    "    \"frog\",\n",
    "    \"horse\",\n",
    "    \"ship\",\n",
    "    \"truck\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ec3903",
   "metadata": {},
   "source": [
    "CNN definition (this one is an example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "317bf070",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
      "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
      "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
      "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# define the CNN architecture\n",
    "\n",
    "\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
    "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
    "        self.fc2 = nn.Linear(120, 84)\n",
    "        self.fc3 = nn.Linear(84, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 16 * 5 * 5)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "# create a complete CNN\n",
    "model_1 = Net()\n",
    "print(model_1)\n",
    "# move tensors to GPU if CUDA is available\n",
    "if train_on_gpu:\n",
    "    model_1.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2dc4974",
   "metadata": {},
   "source": [
    "Loss function and training using SGD (Stochastic Gradient Descent) optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b53f229",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 42.706410 \tValidation Loss: 37.549365\n",
      "Validation loss decreased (inf --> 37.549365).  Saving model ...\n",
      "Epoch: 1 \tTraining Loss: 34.682203 \tValidation Loss: 32.756616\n",
      "Validation loss decreased (37.549365 --> 32.756616).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 30.741905 \tValidation Loss: 29.590071\n",
      "Validation loss decreased (32.756616 --> 29.590071).  Saving model ...\n",
      "Epoch: 3 \tTraining Loss: 28.491642 \tValidation Loss: 27.348559\n",
      "Validation loss decreased (29.590071 --> 27.348559).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 26.764905 \tValidation Loss: 25.580812\n",
      "Validation loss decreased (27.348559 --> 25.580812).  Saving model ...\n",
      "Epoch: 5 \tTraining Loss: 25.264787 \tValidation Loss: 24.726740\n",
      "Validation loss decreased (25.580812 --> 24.726740).  Saving model ...\n",
      "Epoch: 6 \tTraining Loss: 23.990562 \tValidation Loss: 24.224369\n",
      "Validation loss decreased (24.726740 --> 24.224369).  Saving model ...\n",
      "Epoch: 7 \tTraining Loss: 22.904920 \tValidation Loss: 23.848258\n",
      "Validation loss decreased (24.224369 --> 23.848258).  Saving model ...\n",
      "Epoch: 8 \tTraining Loss: 21.983295 \tValidation Loss: 23.657920\n",
      "Validation loss decreased (23.848258 --> 23.657920).  Saving model ...\n",
      "Epoch: 9 \tTraining Loss: 21.100526 \tValidation Loss: 22.080895\n",
      "Validation loss decreased (23.657920 --> 22.080895).  Saving model ...\n",
      "Epoch: 10 \tTraining Loss: 20.297763 \tValidation Loss: 22.566959\n",
      "Epoch: 11 \tTraining Loss: 19.580086 \tValidation Loss: 22.426025\n",
      "Epoch: 12 \tTraining Loss: 18.895442 \tValidation Loss: 21.998238\n",
      "Validation loss decreased (22.080895 --> 21.998238).  Saving model ...\n",
      "Epoch: 13 \tTraining Loss: 18.266996 \tValidation Loss: 21.645298\n",
      "Validation loss decreased (21.998238 --> 21.645298).  Saving model ...\n",
      "Epoch: 14 \tTraining Loss: 17.614121 \tValidation Loss: 21.425315\n",
      "Validation loss decreased (21.645298 --> 21.425315).  Saving model ...\n",
      "Epoch: 15 \tTraining Loss: 17.013795 \tValidation Loss: 21.336770\n",
      "Validation loss decreased (21.425315 --> 21.336770).  Saving model ...\n",
      "Epoch: 16 \tTraining Loss: 16.433492 \tValidation Loss: 21.738642\n",
      "Epoch: 17 \tTraining Loss: 15.871810 \tValidation Loss: 22.068780\n",
      "Epoch: 18 \tTraining Loss: 15.372662 \tValidation Loss: 22.312414\n",
      "Epoch: 19 \tTraining Loss: 14.806503 \tValidation Loss: 22.614040\n",
      "Epoch: 20 \tTraining Loss: 14.257983 \tValidation Loss: 22.820819\n",
      "Epoch: 21 \tTraining Loss: 13.838574 \tValidation Loss: 22.394366\n",
      "Epoch: 22 \tTraining Loss: 13.313069 \tValidation Loss: 22.948898\n",
      "Epoch: 23 \tTraining Loss: 12.879593 \tValidation Loss: 23.263349\n",
      "Epoch: 24 \tTraining Loss: 12.414456 \tValidation Loss: 23.998298\n",
      "Epoch: 25 \tTraining Loss: 12.041669 \tValidation Loss: 25.065187\n",
      "Epoch: 26 \tTraining Loss: 11.523059 \tValidation Loss: 25.274171\n",
      "Epoch: 27 \tTraining Loss: 11.059043 \tValidation Loss: 25.448349\n",
      "Epoch: 28 \tTraining Loss: 10.749217 \tValidation Loss: 25.841604\n",
      "Epoch: 29 \tTraining Loss: 10.308294 \tValidation Loss: 26.855672\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
    "optimizer = optim.SGD(model_1.parameters(), lr=0.01)  # specify optimizer\n",
    "\n",
    "n_epochs = 30  # number of epochs to train the model\n",
    "train_loss_list = []  # list to store loss to visualize\n",
    "valid_loss_list = []\n",
    "valid_loss_min = np.Inf  # track change in validation loss\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    # Keep track of training and validation loss\n",
    "    train_loss = 0.0\n",
    "    valid_loss = 0.0\n",
    "\n",
    "    # Train the model\n",
    "    model_1.train()\n",
    "    for data, target in train_loader:\n",
    "        # Move tensors to GPU if CUDA is available\n",
    "        if train_on_gpu:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        # Clear the gradients of all optimized variables\n",
    "        optimizer.zero_grad()\n",
    "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
    "        output = model_1(data)\n",
    "        # Calculate the batch loss\n",
    "        loss = criterion(output, target)\n",
    "        # Backward pass: compute gradient of the loss with respect to model parameters\n",
    "        loss.backward()\n",
    "        # Perform a single optimization step (parameter update)\n",
    "        optimizer.step()\n",
    "        # Update training loss\n",
    "        train_loss += loss.item() * data.size(0)\n",
    "\n",
    "    # Validate the model\n",
    "    model_1.eval()\n",
    "    for data, target in valid_loader:\n",
    "        # Move tensors to GPU if CUDA is available\n",
    "        if train_on_gpu:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
    "        output = model_1(data)\n",
    "        # Calculate the batch loss\n",
    "        loss = criterion(output, target)\n",
    "        # Update average validation loss\n",
    "        valid_loss += loss.item() * data.size(0)\n",
    "\n",
    "    # Calculate average losses\n",
    "    train_loss = train_loss / len(train_loader)\n",
    "    valid_loss = valid_loss / len(valid_loader)\n",
    "    train_loss_list.append(train_loss)\n",
    "    valid_loss_list.append(valid_loss)\n",
    "\n",
    "    # Print training/validation statistics\n",
    "    print(\n",
    "        \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n",
    "            epoch, train_loss, valid_loss\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # Save model if validation loss has decreased\n",
    "    if valid_loss <= valid_loss_min:\n",
    "        print(\n",
    "            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n",
    "                valid_loss_min, valid_loss\n",
    "            )\n",
    "        )\n",
    "        torch.save(model_1.state_dict(), \"model_1_cifar.pt\")\n",
    "        valid_loss_min = valid_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e1df74",
   "metadata": {},
   "source": [
    "Does overfit occur? If so, do an early stopping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d39df818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I01mMqx2NJyIiIlLBaAyNiIiIVHoKNCIiIlLpKdCIiIhIpadAIyIiIpWeAo2IiIhUego0IiIiUulV+XVorFYrKSkp+Pv7X/US6SIiIuIchmFw8uRJoqKibBv+XkqVDzQpKSkVYqM6ERERuXrJycm2RTUvpcoHmsLN3pKTkwkICHByNSIiInIlsrKyiI6Ottu09VKqfKAp7GYKCAhQoBEREalkrnS4iAYFi4iISKWnQCMiIiKVngKNiIiIVHpVfgyNiIhUPQUFBZw9e9bZZcg1cHd3x9XV1WH3U6AREZFKwzAM0tLSyMjIcHYp4gBBQUFEREQ4ZJ04BRoREak0CsNMWFgYPj4+WjC1kjIMg1OnTnHkyBEAIiMjr/meCjQiIlIpFBQU2MJMSEiIs8uRa+Tt7Q3AkSNHCAsLu+buJw0KFhGRSqFwzIyPj4+TKxFHKfy3dMR4KAUaERGpVNTNVHU48t9SgUZEREQqPQUaERGRSqR27dr85z//cci9FixYgMViqRKzxjQoWEREpIzdcsstNG/e3CFBZPXq1fj6+l57UVWMWmhKqcBqsCc9m6PZuc4uRUREKjnDMMjPz7+ia0NDQzUwugQKNKX052/X0fmdhczcmOLsUkREpAIbOnQoCxcu5L333sNisWCxWJgwYQIWi4VZs2bRqlUrPD09WbJkCbt376Zv376Eh4fj5+dHmzZtmDNnjt39inY5WSwWPv/8c+644w58fHyoX78+M2bMKHW9P/zwA40bN8bT05PatWvzzjvv2J3/+OOPqV+/Pl5eXoSHhzNw4EDbuSlTptC0aVO8vb0JCQmha9eu5OTklLqWq6Eup1KqF+YPpLEtNcvZpYiIXJcMw+D02QKnfG9vd9crnqHz3nvvsWPHDpo0acLo0aMB+OOPPwD45z//ydtvv02dOnWoVq0aycnJ9OrVi9deew1PT0++/vprevfuTVJSErVq1bro9xg1ahRvvvkmb731Fh988AGDBw9m//79BAcHX9XPtXbtWu666y5GjhzJ3XffzbJly3jyyScJCQlh6NChrFmzhr/85S/897//pV27dhw/fpzFixcDkJqayr333subb77JHXfcwcmTJ1m8eDGGYVxVDaWlQFNKjSIDANiqQCMi4hSnzxbQ6KXfnPK9t47ujo/Hlf0KDQwMxMPDAx8fHyIiIgDYvn07AKNHj+bWW2+1XRscHExCQoLt81deeYVp06YxY8YMnnrqqYt+j6FDh3LvvfcC8O9//5v333+fVatW0aNHj6v6ud599126dOnCiy++CECDBg3YunUrb731FkOHDuXAgQP4+vpy++234+/vT0xMDC1atADMQJOfn0///v2JiYkBoGnTplf1/a+FupxKqXGUGWh2pGVztsDq5GpERKQyat26td3n2dnZDB8+nPj4eIKCgvDz82Pbtm0cOHDgkvdp1qyZ7bmvry8BAQG2bQWuxrZt22jfvr3dsfbt27Nz504KCgq49dZbiYmJoU6dOtx///188803nDp1CoCEhAS6dOlC06ZNufPOO/nss884ceLEVddQWmqhKaWa1bzx93TjZG4+u9OzaRgR4OySRESuK97urmwd3d1p39sRis5WGj58OLNnz+btt9+mXr16eHt7M3DgQPLy8i55H3d3d7vPLRYLVqvj/9j29/dn3bp1LFiwgN9//52XXnqJkSNHsnr1aoKCgpg9ezbLli3j999/54MPPuD5559n5cqVxMbGOryWotRCU0oWi4X4wm6nFHU7iYiUN4vFgo+Hm1MeV7vCrYeHBwUFlx/vs3TpUoYOHcodd9xB06ZNiYiIYN++faV8ha5efHw8S5cuLVZTgwYNbHstubm50bVrV9588002bdrEvn37mDdvHmD+m7Rv355Ro0axfv16PDw8mDZtWrnUrhaaa9AoKoBV+45rYLCIiFxS7dq1WblyJfv27cPPz++irSf169dn6tSp9O7dG4vFwosvvlgmLS0X89xzz9GmTRteeeUV7r77bpYvX86HH37Ixx9/DMBPP/3Enj176NChA9WqVeOXX37BarUSFxfHypUrmTt3Lt26dSMsLIyVK1eSnp5OfHx8udSuFpproIHBIiJyJYYPH46rqyuNGjUiNDT0omNi3n33XapVq0a7du3o3bs33bt3p2XLluVWZ8uWLfn++++ZNGkSTZo04aWXXmL06NEMHToUgKCgIKZOnUrnzp2Jj4/nk08+4dtvv6Vx48YEBASwaNEievXqRYMGDXjhhRd455136NmzZ7nUbjHKaz6Vk2RlZREYGEhmZiYBAY4d57LlUCa3f7CEaj7urHvxVm2YJiJShs6cOcPevXuJjY3Fy8vL2eWIA1zq3/Rqf3+rheYa1Avzw9XFwolTZ0nLOuPsckRERK5bCjTXwMvdlXqhfoAGBouISMXz+OOP4+fnV+Lj8ccfd3Z5DqVBwdeoUVQASYdPsjUliy7x4c4uR0RExGb06NEMHz68xHOOHobhbAo016hRZADT1h9iW5paaEREpGIJCwsjLCzM2WWUC3U5XaNGUVqLRkRExNkUaK5R4eJ6+46dIjv3yrZ+FxEREcdSoLlGwb4eRASYU822az0aERERp1CgcQBbt5MCjYiIiFMo0DhAI+3pJCIi4lQKNA5Q2EKjPZ1ERKQs1K5dm//85z+2zy0WC9OnT7/o9fv27cNisbBhw4bL3nvBggVYLBYyMjKuuU5n0rRtByhsodmedpL8AitursqJIiJSdlJTU6lWrZqzy6hQ9JvXAWoF++Dr4UpuvpW9R3OcXY6IiFRxEREReHp6OruMCkWBxgFcXCw01M7bIiJSgk8//ZSoqCisVqvd8b59+/LQQw+xe/du+vbtS3h4OH5+frRp04Y5c+Zc8p5Fu5xWrVpFixYt8PLyonXr1qxfv/6aav7hhx9o3Lgxnp6e1K5dm3feecfu/Mcff0z9+vXx8vIiPDycgQMH2s5NmTKFpk2b4u3tTUhICF27diUnp+z/2FegcRANDBYRKWeGAXk5znkYxhWXeeedd3Ls2DHmz59vO3b8+HF+/fVXBg8eTHZ2Nr169WLu3LmsX7+eHj160Lt3bw4cOHBF98/Ozub222+nUaNGrF27lpEjR150u4MrsXbtWu666y7uueceNm/ezMiRI3nxxReZMGECAGvWrOEvf/kLo0ePJikpiV9//ZUOHToAZlfYvffey0MPPcS2bdtYsGAB/fv3x7iK16u0NIbGQTR1W0SknJ09Bf+Ocs73/lcKePhe0aXVqlWjZ8+eTJw4kS5dugBmK0b16tXp1KkTLi4uJCQk2K5/5ZVXmDZtGjNmzOCpp5667P0nTpyI1Wrliy++wMvLi8aNG3Pw4EGeeOKJUv1o7777Ll26dOHFF18EoEGDBmzdupW33nqLoUOHcuDAAXx9fbn99tvx9/cnJiaGFi1aAGagyc/Pp3///sTExADQtGnTUtVxtdRC4yAXttCURxIVEZHKY/Dgwfzwww/k5uYC8M0333DPPffg4uJCdnY2w4cPJz4+nqCgIPz8/Ni2bdsVt9Bs27aNZs2a4eXlZTuWmJhY6lq3bdtG+/bt7Y61b9+enTt3UlBQwK233kpMTAx16tTh/vvv55tvvuHUqVMAJCQk0KVLF5o2bcqdd97JZ599xokTJ0pdy9WoMC00r7/+OiNGjOCvf/2rbWramTNneO6555g0aRK5ubl0796djz/+mPDwirerdVyEPy4WOJaTR/rJXMICvC7/RSIiUnruPmZLibO+91Xo3bs3hmHw888/06ZNGxYvXszYsWMBGD58OLNnz+btt9+mXr16eHt7M3DgQPLy8sqi8mvm7+/PunXrWLBgAb///jsvvfQSI0eOZPXq1QQFBTF79myWLVvG77//zgcffMDzzz/PypUriY2NLdO6KkQLzerVq/m///s/mjVrZnf8mWeeYebMmUyePJmFCxeSkpJC//79nVTlpXm5u1I31A+AP9TtJCJS9iwWs9vHGQ+L5apK9fLyon///nzzzTd8++23xMXF0bJlSwCWLl3K0KFDueOOO2jatCkRERHs27fviu8dHx/Ppk2bOHPmjO3YihUrrqq+ovdbunSp3bGlS5fSoEEDXF1dAXBzc6Nr1668+eabbNq0iX379jFv3jzAHLDcvn17Ro0axfr16/Hw8GDatGmlrudKOT3QZGdnM3jwYD777DO7OfWZmZl88cUXvPvuu3Tu3JlWrVoxfvx4li1bdk3/UGUpXgODRUTkIgYPHszPP//Ml19+yeDBg23H69evz9SpU9mwYQMbN25k0KBBxWZEXcqgQYOwWCw88sgjbN26lV9++YW333671HU+99xzzJ07l1deeYUdO3bw1Vdf8eGHH9oGGv/000+8//77bNiwgf379/P1119jtVqJi4tj5cqV/Pvf/2bNmjUcOHCAqVOnkp6eTnx8fKnruVJODzTDhg3jtttuo2vXrnbH165dy9mzZ+2ON2zYkFq1arF8+fKL3i83N5esrCy7R3nRwGAREbmYzp07ExwcTFJSEoMGDbIdf/fdd6lWrRrt2rWjd+/edO/e3dZ6cyX8/PyYOXMmmzdvpkWLFjz//PO88cYbpa6zZcuWfP/990yaNIkmTZrw0ksvMXr0aIYOHQpAUFAQU6dOpXPnzsTHx/PJJ5/w7bff0rhxYwICAli0aBG9evWiQYMGvPDCC7zzzjv07Nmz1PVcKaeOoZk0aRLr1q1j9erVxc6lpaXh4eFBUFCQ3fHw8HDS0tIues8xY8YwatQoR5d6RQoHBm9TC42IiBTh4uJCSkrxMT+1a9e2ddcUGjZsmN3nRbugik4+ufHGG4ttc3ClE1RuueWWYtcOGDCAAQMGlHj9TTfdxIIFC0o8Fx8fz6+//npF39fRnNZCk5yczF//+le++eYbu5HZ12rEiBFkZmbaHsnJyQ679+UUdjntPZbDqbz8cvu+IiIi1zunBZq1a9dy5MgRWrZsiZubG25ubixcuJD3338fNzc3wsPDycvLK7ZZ1uHDh4mIiLjofT09PQkICLB7lJdQf0/C/D0xDHNfJxEREWd7/PHH8fPzK/Hx+OOPO7s8h3Fal1OXLl3YvHmz3bEHH3yQhg0b8o9//IPo6Gjc3d2ZO3eurdkrKSmJAwcOXNP8+rIWHxnAkZPpbE3JomUtbRwmIiLONXr06IuuHFyef/SXNacFGn9/f5o0aWJ3zNfXl5CQENvxhx9+mGeffZbg4GACAgL485//TGJiIjfeeKMzSr4ijaICWLgjXQODRUSkQggLCyMsLMzZZZS5CrOwXknGjh2Li4sLAwYMsFtYryLTnk4iIiLlr0IFmqKjpr28vPjoo4/46KOPnFNQKRRO3d6elkWB1cDV5eoWXxIRkUu7mjVapGJz5L9lhQo0VUHtEF+83V05fbaAfcdybKsHi4jItfHw8LBNfQ4NDcXDwwPLVa7YKxWDYRjk5eWRnp6Oi4sLHh4e13xPBRoHc3Wx0DDSn/UHMtiakqVAIyLiIC4uLsTGxpKamlriei5S+fj4+FCrVi1cXK590rUCTRmIjwwwA01qFr0TnLS1vYhIFeTh4UGtWrXIz8+noKDA2eXINXB1dcXNzc1hrWwKNGVAA4NFRMqOxWLB3d0dd3d3Z5ciFYjT93KqirSnk4iISPlSoCkDDSP8sVgg/WQu6SdznV2OiIhIladAUwZ8PNyIre4LwDa10oiIiJQ5BZoyYhtHo0AjIiJS5hRoyki8BgaLiIiUGwWaMqKBwSIiIuVHgaaMND7XQrMnPZvTeVorQUREpCwp0JSRUH9Pqvt5YDUg6fBJZ5cjIiJSpSnQlBGLxWIbR6OZTiIiImVLgaYM2cbRaGCwiIhImVKgKUOaui0iIlI+FGjKUKMLupysVsPJ1YiIiFRdCjRlKLa6L55uLpzKK2D/8VPOLkdERKTKUqAprc1T4Ju7YN/Si17i5upCwwh/QAODRUREypICTWntmQ87f4OtP17yMg0MFhERKXsKNKXV8Hbz4/afwbj4+Jh4DQwWEREpcwo0pVXnFnD3gayDkLrxopc10p5OIiIiZU6BprTcvaFeF/P59p8velnDc4EmLesMx7Jzy6MyERGR644CzbW4sNvpIvw83agd4gPAtlRtgSAiIlIWFGiuRf1uYHGFI3/A8T0XvaxwYLBmOomIiJQNBZpr4RMMtdubz7f/ctHLtGKwiIhI2VKguVZX0O0Ur4HBIiIiZUqB5lrF9TI/Jq+A7PQSLynsctqVns2ZswXlVZmIiMh1Q4HmWgVFQ2QCGFbY8WuJl0QEeFHNx50Cq8HOw9nlXKCIiEjVp0DjCA17mx8v0u1ksVjOrxicmlleVYmIiFw3FGgcoeFt5sfd8yC35BaY8ztva+q2iIiIoynQOEJYPFSLhYJcM9SUQHs6iYiIlB0FGkewWM630lyk2+nCPZ2s1ovv/SQiIiJXT4HGUQqnb++YBQVni52uG+qHh6sL2bn5HDxxupyLExERqdoUaBwl+gbwqQ5nMmH/0mKn3V1daBDhB2hgsIiIiKMp0DiKiyvE9TSfX6TbSTtvi4iIlA0FGke6cNVgo/g4mfNbIGimk4iIiCMp0DhSnY7g7gtZhyB1Q7HTjaICAW1SKSIi4mgKNI7k7g31upjPS+h2ahjpD8ChjNNknMorz8pERESqNAUaR7vEZpUBXu5EB3sD2nlbRETEkRRoHK1BN7C4wpGtcGx3sdMaGCwiIuJ4CjSO5l0Nat9kPk/6pdjpRpHmOBq10IiIiDiOUwPNuHHjaNasGQEBAQQEBJCYmMisWbNs52+55RYsFovd4/HHH3dixVco/uKbVRZugaA9nURERBzHqYGmZs2avP7666xdu5Y1a9bQuXNn+vbtyx9//GG75pFHHiE1NdX2ePPNN51Y8RUqXI/mwArITrc7FX9uYPCuIyfJy7eWd2UiIiJVklMDTe/evenVqxf169enQYMGvPbaa/j5+bFixQrbNT4+PkRERNgeAQEBTqz4CgXWhKgWgGFuhXCBGkHeBHi5cbbAYOcRtdKIiIg4QoUZQ1NQUMCkSZPIyckhMTHRdvybb76hevXqNGnShBEjRnDq1KlL3ic3N5esrCy7h1NcZLNKi8WinbdFREQczM3ZBWzevJnExETOnDmDn58f06ZNo1GjRgAMGjSImJgYoqKi2LRpE//4xz9ISkpi6tSpF73fmDFjGDVqVHmVf3ENb4d5r8Lu+ZCbDZ5+tlONIgNZsee4BgaLiIg4iMUwSlijvxzl5eVx4MABMjMzmTJlCp9//jkLFy60hZoLzZs3jy5durBr1y7q1q1b4v1yc3PJzc21fZ6VlUV0dDSZmZnl211lGPBBSzi+B+78Chr3s52asvYgwydvpG1sMN89lnjxe4iIiFynsrKyCAwMvOLf307vcvLw8KBevXq0atWKMWPGkJCQwHvvvVfitW3btgVg165dF72fp6enbdZU4cMpLJaLdjsVrkWzLTULq9WpeVJERKRKcHqgKcpqtdq1sFxow4YNAERGRpZjRdegcNXgHb9BwVnb4Xphfvh5upF1Jp9V+447qTgREZGqw6mBZsSIESxatIh9+/axefNmRowYwYIFCxg8eDC7d+/mlVdeYe3atezbt48ZM2bwwAMP0KFDB5o1a+bMsq9czTbgGwq5mbBvie2wh5sLtzczQ9n3a5KdVZ2IiEiV4dRAc+TIER544AHi4uLo0qULq1ev5rfffuPWW2/Fw8ODOXPm0K1bNxo2bMhzzz3HgAEDmDlzpjNLvjourufXpCnS7XRn62gAZm1OIzs3v7wrExERqVKcPii4rF3toCKH2/EbTLwL/KPg2a3m2BrAMAy6vLuQPek5vDGgKXe3qVX+tYmIiFRQlW5QcJUX2xE8/OBkCqSstx22WCzcda6V5vs1B51VnYiISJWgQFPW3L2gXlfzeZFup/4tauDqYmHt/hPsTs92QnEiIiJVgwJNeSic7VQk0IQFeNGxQSgAk9VKIyIiUmoKNOWh/q3g4gbp2+DYbrtTd7WuCcDUdQfJL9BmlSIiIqWhQFMevIOg9s3m8yKtNJ0bhhPs68GRk7ks3nm0/GsTERGpAhRoystFVg32cHOhX/MagNakERERKS0FmvIS18v8mLwSso/YnbrzXLfTnG2HOZ6TV96ViYiIVHoKNOUlsAZEtQQMSJpldyo+MoAmNQI4W2Awff0h59QnIiJSiSnQlCdbt9NPxU4Vrkkzea1mO4mIiFwtBZryVDh9e88CyD1pd6pPQhQeri5sS81iy6HM8q9NRESkElOgKU+hcRBcFwryYNccu1NBPh50axwOwGQNDhYREbkqCjTlyWK56GwnOL9h5fQNKZw5W1CelYmIiFRqCjTlrbDbacfvkG8/o+mmetWJDPQi8/RZ5mw77ITiREREKicFmvJWsw34hkFuJuxfYnfK1cXCgJbmFG5thSAiInLlFGjKm4sLNDy3Jk0J3U4DW5mBZvHOdFIzT5dnZSIiIpWWAo0zXLhZpdV+rEzt6r7cEBuM1YCp67QmjYiIyJVQoHGG2A7gFQQnU2HbjGKn72xV2O2UjGEY5VyciIhI5aNA4wxuntD2MfP54nehSGjp1TQSHw9X9h07xep9J5xQoIiISOWiQOMsbR8Hdx9I2wS75tqd8vV04/ZmkYDWpBEREbkSCjTO4hMMrR40ny95t9jpwjVpft6cSk5ufnlWJiIiUuko0DhT4jBwcYf9S+HASrtTrWOqEVvdl1N5Bfy8OdVJBYqIiFQOCjTOFFgDEu4xnxdppbFYLLYp3FO0Jo2IiMglKdA4W/unAQvs+BXSttidGtCyJi4WWLXvOHuP5jilPBERkcpAgcbZqteDxv3M50vG2p2KCPSiQ4NQAKas1eBgERGRi1GgqQhuesb8+MdUOL7H7tSdrczBwT+sPUSBVWvSiIiIlESBpiKITIB6XcGwwtL37U51bRRGkI87aVlnWLwz3UkFioiIVGwKNBXFTc+aHzd8AyfTbIc93Vzp17wGAJPXanCwiIhISRRoKoqYdhDdFgryYPlHdqcKZzvN/uMwGafynFGdiIhIhaZAU1FYLHDzc+bzNV/C6fNbHjSpEUijyADyCqz8uCHFSQWKiIhUXAo0FUn9bhDeBPKyYdVndqfubH1uw0rNdhIRESlGgaYisVjOz3haMQ7yzq890695DTxcXdhyKIutKVlOKlBERKRiUqCpaBr1g2q14fRxWPe17XA1Xw+6NgoD1EojIiJSlAJNRePqdm71YGDZB5B/fhBw4Zo009cfIi/f6oTiREREKiYFmoqo+SDwi4CsQ7DpO9vhm+tXJzzAkxOnzjJ322EnFigiIlKxKNBURG6e5k7cAEv/A9YC87CrC/1bFg4O1po0IiIihRRoKqrWD4JXEBzbBdtm2g7feW5NmgVJRzicdcZJxYmIiFQsCjQVlac/tH3MfL7kXTDMfZzqhPrROqYaVgMmrjzgxAJFREQqDgWaiqzt4+DuA6kbYfc82+EH2tUGYNzC3ew6ctJJxYmIiFQcCjQVmU8wtBpqPl/8ru1w72aR3BIXSl6+leGTN2kXbhERue4p0FR0iU+BizvsXwLJqwCwWCyM6d8Ufy83NiRn8PniPU4uUkRExLmcGmjGjRtHs2bNCAgIICAggMTERGbNmmU7f+bMGYYNG0ZISAh+fn4MGDCAw4evs+nKgTUg4W7z+QWtNJGB3rx4eyMA3pm9Q11PIiJyXXNqoKlZsyavv/46a9euZc2aNXTu3Jm+ffvyxx9/APDMM88wc+ZMJk+ezMKFC0lJSaF///7OLNk52j8DWGDHLDi81Xb4zlY11fUkIiICWAzDqFC/BYODg3nrrbcYOHAgoaGhTJw4kYEDBwKwfft24uPjWb58OTfeeOMV3S8rK4vAwEAyMzMJCAgoy9LL1vdDYOt0aHoXDDi/cWVq5mm6vbuIk7n5jOjZkMc61nVejSIiIg5ytb+/K8wYmoKCAiZNmkROTg6JiYmsXbuWs2fP0rVrV9s1DRs2pFatWixfvtyJlTrJzc+aH7f8AMf32g4X73rKdkZ1IiIiTuX0QLN582b8/Pzw9PTk8ccfZ9q0aTRq1Ii0tDQ8PDwICgqyuz48PJy0tLSL3i83N5esrCy7R5UQmQB1u4BRAMvetzt1Z+uadGxgdj39bcpGdT2JiMh1x+mBJi4ujg0bNrBy5UqeeOIJhgwZwtatWy//hRcxZswYAgMDbY/o6GgHVutkNz9nflz/DZw8PzjaYrHw+oCm+Hu6sf5ABl8s0awnERG5vjg90Hh4eFCvXj1atWrFmDFjSEhI4L333iMiIoK8vDwyMjLsrj98+DAREREXvd+IESPIzMy0PZKTk8v4JyhHMe0gui0U5MKKj+xOXdj19Pbv6noSEZHri9MDTVFWq5Xc3FxatWqFu7s7c+fOtZ1LSkriwIEDJCYmXvTrPT09bdPACx9VhsUCN50bS7P6SzidYXdaXU8iInK9cmqgGTFiBIsWLWLfvn1s3ryZESNGsGDBAgYPHkxgYCAPP/wwzz77LPPnz2ft2rU8+OCDJCYmXvEMpyqpQXcIawx5J2HZB3an1PUkIiLXK6cGmiNHjvDAAw8QFxdHly5dWL16Nb/99hu33norAGPHjuX2229nwIABdOjQgYiICKZOnerMkp3PYoFb/mk+X/ofSN1kd1pdTyIicj2qcOvQOFqVWYfmQoYB398P22ZCeFN4ZB64eVxw2mDo+NUs3JFOi1pBTHm8Ha4uFicWLCIicnUq7To0chUsFrjtXfAOhsObYfE7RU6f2+tJXU8iInKdUKCprPzC4La3zeeL34bUjXano4K8eeH2eEBdTyIiUvUp0FRmjftDo75gzYfpT0J+nt3pu1pH00GznkRE5DqgQFOZWSzQ6x3wCYHDW2DRW0VOW3hdXU8iInIdUKCp7PxC4bZzY2gWvwMpG+xOq+tJRESuBwo0VUHjO6BRP3Ofp+lPQn6u3ekLu57+rq4nERGpghRoqorb3gGf6nDkD1j4pt2pwq4nP0831qnrSUREqiAFmqrCtzrc/q75fMlYOLTO7nRUkDcv3KauJxERqZoUaKqSRn3NmU8X6Xq6u000N9evbpv1dLbA6qRCRUREHEuBpqrp9Tb4hkL6Nlj4ht0pc6+nZvidm/X0wrQtVPGFokVE5DqhQFPV+IaYqwjDua6ntXanawR58949zXGxwHdrkvl4wW4nFCkiIuJYCjRVUaM+0GQgGFaz6+nsGbvTXeLDGdWnMQBv/ZbEjxsOOaNKERERh1Ggqap6vQW+YZC+HRa+Xuz0/Ym1eeTmWAD+NnkTK/YcK+8KRUREHEaBpqryCYbbx5rPl74HB9cWu2REz3h6Nokgr8DKY/9dq5lPIiJSaSnQVGXxt0PTO891PT1RrOvJxcXC2Lub06JWEJmnz/LghFUczc69yM1EREQqLgWaqq7nm2bX09EkWPDvYqe93F35/IHW1Ar2Ifn4aR7+ag2n8wqcUKiIiEjpKdBUdT7B0Ps/5vNlH0Dy6mKXhPh5MuHBNgT5uLMxOYOnv1uv7RFERKRSUaC5HjS8DZrdbXY9/Vh81hNAnVA/Pr2/NR6uLvz2x2H+/cs2JxQqIiJSOqUKNMnJyRw8eND2+apVq3j66af59NNPHVaYOFiP18EvHI7ugPmvlXjJDbHBvH1XAgBfLNnLhKV7y7NCERGRUitVoBk0aBDz588HIC0tjVtvvZVVq1bx/PPPM3r0aIcWKA7iEwy93zOfL/sAkleVeFmfhCj+3iMOgFE/beX3P9LKq0IREZFSK1Wg2bJlCzfccAMA33//PU2aNGHZsmV88803TJgwwZH1iSPF9YRm9wCGOesp92SJlz3RsS733hCNYcBfJq1nY3JGuZYpIiJytUoVaM6ePYunpycAc+bMoU+fPgA0bNiQ1NRUx1UnjtfzdfCPhGO7YMrDYC0+o8lisTC6bxM6NAjlzFkrD3+1huTjp5xQrIiIyJUpVaBp3Lgxn3zyCYsXL2b27Nn06NEDgJSUFEJCQhxaoDiYdzW45xtw84adv8Fvz5d4mburCx8Pbkl8ZABHs3N5cMJqMk+dLediRURErkypAs0bb7zB//3f/3HLLbdw7733kpBgDiSdMWOGrStKKrAareCOT8znK8fB6s9LvMzP040vh7YmIsCLXUeyeex/a8jLt5ZjoSIiIlfGYhhGqRYcKSgoICsri2rVqtmO7du3Dx8fH8LCwhxW4LXKysoiMDCQzMxMAgICnF1OxbL4HZg7GiyuMPh7qNe1xMu2pmRx5yfLyMkroH+LGrxzVwIWi6WcixURkQopPw/cPBx+26v9/V2qFprTp0+Tm5trCzP79+/nP//5D0lJSRUqzMhl3PQsJAwCowAmPwhHSl57plFUAB/f1wpXFwtT1x9i7Jyd5VyoiIhUOIYB67+B91tAxgFnV1O6QNO3b1++/vprADIyMmjbti3vvPMO/fr1Y9y4cQ4tUMqQxWJO5Y5pD7lZMPEuyE4v8dKODUJ5tV8TAN6fu5NJq5z/5hURESc5eRi+vddcrDXrICz/2NkVlS7QrFu3jptvvhmAKVOmEB4ezv79+/n66695//33HVqglDE3D7j7fxBcx0zYkwaVuJIwwL031OLJW+oCMGLaZiavSS7PSkVExNkMAzZPgY/bwo5Z4OIOXV6Gbq86u7LSBZpTp07h7+8PwO+//07//v1xcXHhxhtvZP/+/Q4tUMqBTzAM+h68AuHgKvhxmPmmLcHfusdx3421MAz4+w+bFGpERK4XOUdh8hD44WE4fQIimsFjC+HmZ8HVzdnVlS7Q1KtXj+nTp5OcnMxvv/1Gt27dADhy5IgG3lZW1eubLTUubrBlCix4vcTLLBYLr/RtolAjInI92TYTPr4Rtv5o/p7o+E94ZB6EN3Z2ZTalCjQvvfQSw4cPp3bt2txwww0kJiYCZmtNixYtHFqglKPYDnD7WPP5wtdh0+QSL1OoERG5Tpw+AT88At/dBznpEBoPf5oLnUaAq7uzq7NT6mnbaWlppKamkpCQgIuLmYtWrVpFQEAADRs2dGiR10LTtkvh9xdh2fvg6gFDfoJabUu8zDAMXvxxC/9bcQCLBd4amMDAVjXLuVgRESkTO36HGX+G7DSwuED7v8ItI8DNs1y+/dX+/i51oClUuOt2zZoV8xeZAk0pWK3w/f2w/SfwqQ6PzIVqtUu8VKFGRKSKOZMFv/0L1v/X/DykHvT7BKLblGsZ5bIOjdVqZfTo0QQGBhITE0NMTAxBQUG88sorWK1aSbbSc3GB/p9CZAKcOgoT74YzmSVeWrT76W9TNjJl7cFyLlhERBxizwIY1+5cmLHAjU/CY4vLPcyURqmGJT///PN88cUXvP7667Rv3x6AJUuWMHLkSM6cOcNrr73m0CLFCTx84d5J8FlnSN8Ok4fCoMkljmQvDDUA/1txgL9N2QiglhoRkcoiNxvmvHx+K5ygGOg3Dmq3d25dV6FUXU5RUVF88skntl22C/344488+eSTHDp0yGEFXit1OV2jlA0wviecPQWtH4bb3jEX5CuBup9ERCoZw4C9i2DmX+DEPvNY64fh1tHg6efU0q7293epWmiOHz9e4sDfhg0bcvz48dLcUiqqqOYw4HOYNBjWfGFO777xiRIvtVgsjO6jlhoRkQrtxH7YuxD2LDQ/5pxbIT6gJvT9AOp2dm59pVSqQJOQkMCHH35YbFXgDz/8kGbNmjmkMKlAGt5mpvXZL5oDxYLrQIPuJV7q4lI81FiAAQo1IiLOkXPUbIXZs8AMMIUtMYXcvKHZXdDtFXOB1UqqVIHmzTff5LbbbmPOnDm2NWiWL19OcnIyv/zyi0MLlAqi3Z/h2C5Y9xVMeQjun37RQWJFQ83wcy01CjUiIuUgNxv2LzvfCnN4s/15iyvUaAV1OkKdW6Bmm3Kbil2WSj1tOyUlhY8++ojt27cDEB8fz6OPPsqrr77Kp59+6tAir4XG0DhQwVn43wDzPxIs0OI+6Pwi+IeXeLnVavDSjPNjat4emKBQIyLiaGcy4fAf57uQDq4Ga779NWGNzQAT2xFi2oFXxf99WO7r0Fxo48aNtGzZkoKCAkfd8pop0DjY6Qz4+TlzewQADz+4+Tlzap+7V7HLFWpERK5Bfh6cTIHMQ5B5EDKTz308CFnnjuVmFf+6oFpmeKlzi7kKvF9YuZd+rcplULCjjBkzhqlTp7J9+3a8vb1p164db7zxBnFxcbZrbrnlFhYuXGj3dY899hiffPJJeZcrAN5BMPALuOFR+G0EHFoLc0fB2vFw6yvQqK/dLCh1P4mIXELeKXNMy4m9kJF8PrAUhpWTacAVtDv4hkHtm863wgTHlnXlFY5TA83ChQsZNmwYbdq0IT8/n3/9619069aNrVu34uvra7vukUceYfTo0bbPfXx8nFGuXKhWW3h4jtlSM/tlyDhg7sJaqx30+DdEnd/Tq6RQczwnjz/dHIvlIlPARUSqjDOZcHwPHN97/uOJc89Ppl7+6109IbAGBNaEwGgIKHx+7vPAGubaYdc5pwaaX3/91e7zCRMmEBYWxtq1a+nQoYPtuI+PDxEREeVdnlyOi4s5Mr7hbbDsA1jyHziwDD7tBM0HmeNrAiLPXWqGGjcXFyYs28drv2xjz9EcRvdtjLtrqRasFhGpOPJzIXXjBaHl3OPEXjh17NJf6xUI1WKhWkzJgcW3+kXX/5LzrmoMTf/+/S95PiMjg4ULF5Z6DM2uXbuoX78+mzdvpkkT8y/6W265hT/++APDMIiIiKB37968+OKLV9xKozE05SjzkNn9tOk783N3X7jpGWj3FLh7A+bie18u3cerP2/FMOCmetX5aHBLAr0r1q6tIiJXbP9ymPooZB64+DW+YeaSF8Gx5sdqsec/9wkuv1orkTIdFPzggw9e0XXjx4+/0lvaWK1W+vTpQ0ZGBkuWLLEd//TTT4mJiSEqKopNmzbxj3/8gxtuuIGpU6eWeJ/c3Fxyc3Ntn2dlZREdHa1AU54OroVf/wkHV5mfB0ZD15HQZIDtr4w5Ww/zl0nrOZVXQL0wP74c0oZaIepKFJFKpOAsLHwTFr8NhhW8gyG8sX1wCa5jbu7r6e/saisdp85yuhZPPPEEs2bNYsmSJZfcuXvevHl06dKFXbt2Ubdu3WLnR44cyahRo4odV6ApZ4YBW34wx9dkndusMrotdB8DNVsB8EdKJg9PWENa1hmCfT349P5WtK6tv1REpBI4vgd+eAQOrTE/TxgEPd+oFNOhK4tKGWieeuopfvzxRxYtWkRs7KVHZufk5ODn58evv/5K9+7FV6tVC00Fc/Y0LPsQlrxr7gcFZktN88EQ24HDOQX86as1bD6UiYerC2/d2Yy+zWs4t2YRkYsxDNgwEWb9HfKywTMQeo81/78mDlWpAo1hGPz5z39m2rRpLFiwgPr161/2a5YuXcpNN93Exo0br2ibBY2hqSCyUmHuaNg48fwx72CI782ZuD48vcKfX7cdBeDprvX5a5f6mgElIhXL6RPw0zPwxzTz85j2cMf/QVC0c+uqoipVoHnyySeZOHEiP/74o93aM4GBgXh7e7N7924mTpxIr169CAkJYdOmTTzzzDPUrFmz2No0F6NAU8GkbDC3T9g6A04dtR02fELY4Hczbx1sxEprPLc3j+aNAc3wcnd1Xq0iIoX2LoZpj5nrw7i4Qad/QfunwUX/jyorlSrQXOwv8PHjxzN06FCSk5O577772LJlCzk5OURHR3PHHXfwwgsvXHE4UaCpoAryYf9S8y+dbTPspjUeMwL4taANO0K78pcHhxASoPUVRMRJ8vNgwb/NZSkwILguDPjM3AtJylSlCjTlQYGmEijIh32LYet0s+Xm9HHbqeME4tK4L0Gt7zSbd/XXkIiUl6O74IeHIXWD+XnLB8yJDZ5+Ti3reqFAU4QCTSVTcBb2LSZzzWTYNpNATp4/5xsGjfpA/e4Qk6hpkCJSNgwD1n1tLj9x9hR4BUGf982tXaTcKNAUoUBTeR3PyuGj8V9SP30OPVxXE2TJOX/S4gpRzc29S2rfDLVuVMARkWt36jjM+DNs/8n8PLYD9PvE3F5AypUCTREKNJVbbn4B//xhMzPX76e9yx88HbWN5gWbsZzYa3+hxdXcP8ou4KhZWESuwu75MP0Jc38lF3fo8hIkPmVu8yLlToGmCAWays8wDD6Yt4t3Z+8AoGODUMb2CCE4fbU59mbvYsjYb/9FFleo0fJcwLkJohVwROQiUjfBorfMCQoAIfVhwOdmK7A4jQJNEQo0VceMjSn8bfJGcvOtVPfz5N27EujQINQ8mXEA9i2FfUtg3yLz8wu5uEFUS3OX8OA6EBRz7hENbp7l/8OIiPMdWmcGmaRfzh9r/RB0ew08tBWLsynQFKFAU7UkpZ3kz9+uY8fhbAAe61CH57rF4eFWpEn4xH5zWvjexWYrTmbyRe5oAf9ICKpl7nQbVOtc0Dn3eUANcNXGmSJVSvIqcw+mXbPPHbCYK/12GA5h8U4tTc5ToClCgabqOXO2gFd/3sr/VpitMM1qBvLePS2IrX6J9WpO7Ddbb1I3mM8zDpjdVIXbMVyMxdUMNYUBp9ldUOcWh/0sIlKO9i2FhW/A3nMLs1pczf+mb34Oql9+pXopXwo0RSjQVF2//ZHGP37YRMaps/h4uPJK3yb0b1njyrdMMAxzQb8T+81wk3Eu6NgCzwEoyLX/Gosr3PUVxPd2/A8kIo5nGGaAWfgW7F9iHnNxg4R74eZnzS5oqZAUaIpQoKnaUjNP8/SkDazcay7G17d5FK/2a4K/lwO6iaxWyD58vjVn2wzYNtOc/TBoEtTreu3fQ0TKhmHArrmw6E1IXmkec/WAFvfBTc+Yra5SoSnQFKFAU/UVWA3GLdjF2Dk7KbAa1Ar24b17mtOiVjUHf6N8+OEh2PojuHnDfT9A7faO/R4icm0MA3b8ao6RSVlnHnPzgpZDoP1ftZ5MJaJAU4QCzfVj7f4T/HXSeg6eOI2bi4VnuzXg8Q51cXFx4K7d+Xnw3WDY+Tt4+MOQH7Wni4izWQsgPQkOroLVn0PaZvO4u485a6ndX8A/3Lk1ylVToClCgeb6knXmLM9P28LMjSkAtKsbwti7mxMe4OW4b3L2NHxzpzl7yisIHvwFwhs77v4icnGGYc5aPLT23GMdpGyAsxesJO7hB23+ZC6K5xfqtFLl2ijQFKFAc/0xDIMpaw/y8ow/OJVXQDUfd94amEDXRg78Cy33JHzdDw6tMfeYenAWVK/nuPuLiCnnmNl1dGjd+RBz6mjx6zz8ILI51Olohhmf4HIvVRxLgaYIBZrr1570bP4yaT1bDmUBMCQxhhG94vFyd9CO3adPwITecHgzBNSEh2ZpoKHItbAWwMHVcHCNGVxS1sGJfcWvc3GD8CZmd2+NlubH6g3AxUH/bUuFoEBThALN9S03v4C3f0vis8Xm3k8NI/x5Y0AzEqKDHPMNstNhQi84ugOqxcJDv4J/hGPuLXK9OLINNkyETd9Ddlrx8yH1zweXGq3MMOPuwG5kqZAUaIpQoBGABUlHGD55I0ez87BY4P4bY3iuWxyB3g6Y3p15CMb3MKd3h8bD0J/BN+Ta7ytSlgrXYTq+F47vgRN7zecn9prHa94A9btC3c7g7eAZgwA5R2HzFNg4EVI3nj/uXQ1i2pubzdZoZX70DnL895cKT4GmCAUaKXQ0O5d//7yNqesPAVDdz5MXb4+nT0LUlS/GdzHH98L4nuYuvZEJMGQmeAU6oGqRa2C1Qtah82HlwuByfC/knbz8PSwuULMN1LsV6nUxx6mUdvfp/FzY8Rts/NacKWjNN4+7uEOD7uZid/W7gZtH6e4vVYoCTREKNFLUst1HeWH6Fvakm7Mi2tcLYXTfJtQNvcbduNOTzFBz6pi5u/f9U8HjEtsxXCnDgGO7IDcL/MLNQcj6H74UlX3EnO2TusH8eGynuep10dWuiwqoYa6WW602BMeaXacefubqurvmQPp2++t9Q6FuF6h/q9l6c7nBt4ZhDujdOBG2/GCOPSsU1QISBpn7KKlVU4pQoClCgUZKkptfwGeL9vDBvF3k5lvxcHXh8Y51eLJTvWsbNJy6Cb66Hc5kQp1OcO+k0vX1G4b5i2nrDHOF4mO77M97VzPDjV/YuY8XPr/gmHdw6f+aloor56gZWlLWnwsw682WmJK4uJuD1YNjzwWX2PPPg2Iu//7MOGCuuLtrDuxZAHnZ589ZXMxuoXq3mitnR7U4/37LPASbJsHGSeYYs0L+kdDsbrM1JqzhNbwIUtUp0BShQCOXcuDYKV6asYUFSekAxIT4MKpPY26JCyv9TZNXw9d9zXUx4nrBXV9f2Y7dVqs5w2PbuRCTceD8OVcP8y/j7CNgPXvltVhczYDjHwkx7cy/qmslgpvn1f9c4hw5xyB1/QUBZuNFdo+3mBssRrUwu4XC4s3gElATXN0cU0t+HiSvMMPNzjlw5A/78z4hZutNzhHYsxA49+vFzdvc/6z5vRDbUbOR5Ioo0BShQCOXYxgGv25JY9TMraRlnQHgtqaRvHh7IyICSzmTYs9Cc/G9glxoMhD6f1ry/8QL8uHAMrMlZvtP5hicQu4+5l+9jfqa4wq8AsyWm9MnzD2msg+bAafY83MfTx0ruTZ3X3OtjnpdzYCjqeYVQ34uHNttdvEc3QGHt0DKRsg8UPL1IfUhqvn5ABPZDDz9y7NisxVm15zzrTe5WfbnY26ChHvM97CX/v8rV0eBpggFGrlS2bn5jJ29gwnL9lFgNfDzdOPZWxvwQGIMbq6l6LbZ8RtMGmQOfGz5APR+HywW86/cvYtg24+w/Rf7RcI8/CGuB8T3MQOHh0/pf6CCs5CTDifTzMGgu+fBztnmX88Xqh5nBhu13pSPvBwzsKTvOB9e0rebg3SNgpK/JriuGVwKA0xEs4oXEArOQvIq2DMf3L3NcTHVaju7KqnEFGiKUKCRq7U1JYvnp29m/YEMABpFBvDaHU1Kt9nlH9NgykNgWM3BjxiQ9Is5xqaQdzWIuw0a9YE6t5RtoLBaIW0T7JptdhkcXGXWVkitN45zJtMcKJ6+/dzHJDiaZN+VWJRnAITGmSEzrKE5Yy4yQTPm5LqkQFOEAo2UhtVqMGl1Mm/8up3M02exWGDQDbX4e/eGBPpc5do167+BH5+0P+YbBvG3my0xtW+6sjE2ZeH0Cdg9/3y3QfZh+/OFrTd1O5nrklS0VoGKwlpgBpeDq80xVAdXm+HlYnyqm8ElNA5CG5qr3IY2NBdlvNYlBESqCAWaIhRo5Foczc7l379sY+o6cwZJsK8Hf+8ex12to69uF+91X8PKT83w0qgPRLeteAMjrVZzG4eds81wk7zKvgvE4mKu0BrTDmrdaHZPXa+rIuccM/fxOrjafJ0OrSt5TRf/qPOhJfRcaKkepynKIldAgaYIBRpxhBV7jvHC9C3sOmJOWW1WM5CRfRrTsjTdUJXF6RPmQM+dc8ydxTP2F7+mWqwZbAoDTvX6Va+FoSDfnM1zYevL8d3Fr/PwM5fnr9nGbM2q2Rp8q5d/vSJVhAJNEQo04ihnC6x8tWwf783Zyclcc4XTAS1r8o+ecYT5Xwf7ymSlwIEVcGC5+Ujbgm1abiGfEPuAE5ngvO600srPM1tfds+H/cvMDRLPnip+XfUG58LLuUdYfMVrdROpxBRoilCgEUdLP5nLG79uZ8ragwD4ebrxdNf6DGlXG/fSzIaqrM5kmq0V+5ebQefQGsg/Y3+Nm7fZUhHWCELqmo/guuZg44ryy98wzPEvu+ebLVL7lphrCF3IMxBqtjrX8tLGbIm53Aq5InJNFGiKUKCRsrL+wAlGzviDjQfNGUv1wvx4uXcjbq4f6uTKnCQ/z1z07cCy8y05Fy5zfyFXD3NKb0g9c8XakHrnAk89cxHAsu62yko1l/YvDDFFd3j2qW7OOIvtYI53qt5AKy6LlDMFmiIUaKQsWa0Gk9cm8+avSRzLyQOge+NwXritEdHB17CGTFVgtZr7CSWvMj8e220+ju+59P5C7j5mK07IuaATGG3OrvIMND96BZrTm70CzGuvJPzkZsP+pecDTPo2+/NuXuZg5zqdzCAT3kQBRsTJFGiKUKCR8pB5+ixjZ+/gvyv2U2A18HRz4fGOdXnilrrXtjdUVWS1QtbBcwFnlxlwju0yPz+x7+KLy5XE4nou7JwLOF5B5597BpibeB5ca663U7izs/mF5iJ1hQEmum3p9twSkTKjQFOEAo2Up6S0k4yc8QfL95jbDtQI8uaF2+Lp0SQCS1Wb/VMWCs6aO0Qf330+5JxMhTNZkJtpjts5k2UusX/hgoBXIijGXE+nTiezK0ljYEQqNAWaIhRopLwZhsEvm9N47eetpGSag2Tb1wthZO/G1A8v5712qirDMLcQyM06H3DOnAs8Fx7LyzHXganTydyoUUQqDQWaIhRoxFlO5xUwbsEuPlm0h7x8K64uFu5pE81fu9QnLEDdGyIil6JAU4QCjTjbgWOneOXnrczeam4r4O3uykM31eaxjnUJ8Kpka7SIiJQTBZoiFGikoli55xiv/7rdtullkI87w26px/2JMRo4LCJShAJNEQo0UpEYhsHvWw/z1m9Jtm0UogK9ePrWBgxoWRPXq9kfSkSkClOgKUKBRiqi/AIrU9cdYuycHaSeGzhcP8yPv3WP49ZG4ZoRJSLXPQWaIhRopCI7c7aAr5fv46P5u8k8fRaAVjHV+EePhtwQq2nFInL9UqApQoFGKoPM02f5v4W7+XLpXs6cNddX6dwwjL/3iKNhhN63InL9UaApQoFGKpPDWWd4b+5OvludTIHVwGKBO5rX4JlbG2grBRG5rlzt72+nblYyZswY2rRpg7+/P2FhYfTr14+kpCS7a86cOcOwYcMICQnBz8+PAQMGcPjwYSdVLFK2wgO8+PcdTZn9TAduaxqJYcDU9Yfo8s5CRs74g5SM084uUUSkQnJqoFm4cCHDhg1jxYoVzJ49m7Nnz9KtWzdycnJs1zzzzDPMnDmTyZMns3DhQlJSUujfv78TqxYpe3VC/fhocEtmPNWe9vVCyCuwMmHZPjq+NZ9/TNnEvqM5l7+JiMh1pEJ1OaWnpxMWFsbChQvp0KEDmZmZhIaGMnHiRAYOHAjA9u3biY+PZ/ny5dx4442Xvae6nKQqWLrrKB/M28mKPccBcLHAbc2iGNaprsbYiEiVdLW/v93KoaYrlpmZCUBwsDm7Y+3atZw9e5auXbvarmnYsCG1atW6aKDJzc0lNzfX9nlWVlYZVy1S9trXq077etVZu/84H83fzbztR5i5MYWZG1PoGh/GsE71aFGrmrPLFBFxGqd2OV3IarXy9NNP0759e5o0aQJAWloaHh4eBAUF2V0bHh5OWlpaifcZM2YMgYGBtkd0dHRZly5SblrFBPPl0Db8/JebuK1ZJBYLzNl2hDs+Xsagz1awbNdRKlCjq4hIuakwgWbYsGFs2bKFSZMmXdN9RowYQWZmpu2RnJzsoApFKo7GUYF8NKglc57tyJ2tauLmYmHZ7mMM+nwl/cctY87Wwwo2InJdqRCB5qmnnuKnn35i/vz51KxZ03Y8IiKCvLw8MjIy7K4/fPgwERERJd7L09OTgIAAu4dIVVU31I+37kxgwd9uYUhiDJ5uLqw/kMGfvl5Dz/cWM2NjCgVWBRsRqfqcGmgMw+Cpp55i2rRpzJs3j9jYWLvzrVq1wt3dnblz59qOJSUlceDAARITE8u7XJEKq2Y1H0b1bcKSf3Tm8Y518fN0Y3vaSf7y7Xq6vLOA71YfIDe/wNllioiUGafOcnryySeZOHEiP/74I3FxcbbjgYGBeHt7A/DEE0/wyy+/MGHCBAICAvjzn/8MwLJly67oe2iWk1yPMk+d5avl+/hy6V4yTplbKoQHeDK0XSyD2tYi0NvdyRWKiFxapVop+GIb8I0fP56hQ4cC5sJ6zz33HN9++y25ubl0796djz/++KJdTkUp0Mj1LCc3n29XHeCzxXs4nGXO/vPzdOOeNtE8dFMsUUHeTq5QRKRklSrQlAcFGhHIy7fy44ZDfLZ4DzsOZwPg5mKhd0IUj9xch0ZR+m9DRCoWBZoiFGhEzjMMgwVJ6fzfot22RfoAbq5fncc61KV9vZCLtpyKiJQnBZoiFGhESrbpYAafLtrDL5tTKZwI1SgygMc61qFX00jcXSvEJEgRuU4p0BShQCNyacnHT/HFkr18tzqZ02fNmVA1grx5sH1t7rmhFn6eFWpBcRG5TijQFKFAI3JlTuTk8b8V+/lq+T6OZucBEODlxuAbY3iwXW3CArycXKGIXE8UaIpQoBG5OmfOFjB13SE+X7yHPed29XZ3tdCjSSQPJMbQOqaaxtmISJlToClCgUakdKxWg9nbDvPZoj2s2X/Cdjw+MoAHEmPo2zwKHw91R4lI2VCgKUKBRuTabTmUydfL9/HjhhRy862A2R11Z+to7r8xhtrVfZ1coYhUNQo0RSjQiDhOxqk8Jq85yH9X7OfA8VO24x0bhPJAYgy3xIXh6qLuKBG5dgo0RSjQiDie1WqwcEc6Xy/fx4Id6RT+XyQ62Jv72sZwV+toqvl6OLdIEanUFGiKUKARKVv7j+XwzcoDfLc6mczT5r5Rnm4u9E6I4oHEGJrVDHJugSJSKSnQFKFAI1I+TucVMHNjCl+v2MeWQ1m2482jgxjarja9mkbi4abF+kTkyijQFKFAI1K+DMNgfXIGXy/bxy+b08grMAcRh/l7cv+NMQxqW4sQP08nVykiFZ0CTREKNCLOczQ7l29XHuC/K/Zz5KS527eHmwv9mkfxYPtY4iP136SIlEyBpggFGhHny8u3MmtLKl8s2cumg5m244l1QniwfW26xIdrdpSI2FGgKUKBRqTiMAyDdQdO8OXSffy6JY2Cc7ti1gr2YUi72tzVuib+Xu5OrlJEKgIFmiIUaEQqppSM03y9fD/frjpgmx3l5+nGwFY1GdquthbrE7nOKdAUoUAjUrGdystn2vpDjF+6j11HsgGwWKBLwzAebB9Lu7oh2jtK5DqkQFOEAo1I5WAYBot3HmX80r3MT0q3HY8L92dQ21r0a1GDQG91R4lcLxRoilCgEal8dqdn89WyfUxZe5BTeQUAeLm7cHuzKO69oRYtawWp1UakilOgKUKBRqTyyjx9lunrDzFx5QGSDp+0HW8Y4c+9N6jVRqQqU6ApQoFGpPIzZ0dlMHHlAX7adH7H78JWm0Fta9EiWq02IlWJAk0RCjQiVUvmqbNMW3+QiasOsONwtu14w4jzY20CNPVbpNJToClCgUakaipc02biyuRirTa9z7XaNFerjUilpUBThAKNSNV3qVabe2+oRb/mNQj0UauNSGWiQFOEAo3I9aOw1eablQf4eVOqrdXGw82FHo0juLtNNIl1QnDRNgsiFZ4CTREKNCLXp8JWm+/WHGRbapbteHSwN3e1imZg65pEBno7sUIRuRQFmiIUaESub4ZhsOVQFpNWH2DGhhRO5uYD4GKBDg1Cubt1NF3iw/Fwc3FypSJyIQWaIhRoRKTQ6bwCZm1J5bvVyazce9x2PMTXgzta1ODuNtHUD/d3YoUiUkiBpggFGhEpyb6jOXy/Jpkpaw9y5GSu7XiLWkHc3Tqa2xOi8PN0c2KFItc3BZoiFGhE5FLyC6ws3JHOd6uTmbv9CAVW83+JPh6u3NY0kjtbR9OmdjVN/xYpZwo0RSjQiMiVOnLyDFPXHeL71cnsOZpjOx4T4sOAljXp37IGNav5OLFCkeuHAk0RCjQicrUMw2DN/hNMWXOQnzalkHNug0yAdnVDGNiqJj2aRODjoS4pkbKiQFOEAo2IXItTefn89kcaU9YeZOmuY7bjvh6u3NYskgEta3JDbLC6pEQcTIGmCAUaEXGUgydOMW3dIaasO8j+Y6dsx2sFn++Sig5Wl5SIIyjQFKFAIyKOdqkuqcQ6ZpdUz6bqkhK5Fgo0RSjQiEhZurBLatnuYxT+H9XXw5XujSPo1TSSmxtUx9PN1bmFilQyCjRFKNCISHm5WJeUv6cbXRuFm+GmfnW83BVuRC5HgaYIBRoRKW+GYbB2/wl+2pTKrC2pHM46v3Cfn6cbtyrciFyWAk0RCjQi4kxWq7kD+M+bU/llc/Fw0zU+jNuaRSnciBShQFOEAo2IVBQXhptZm9NIyzpjO1cYbno1jaRDg1CFG7nuKdAUoUAjIhWR1WqwPvlct9RFwk3/ljVpX686ri5a40auP1f7+9ulHGq6qEWLFtG7d2+ioqKwWCxMnz7d7vzQoUOxWCx2jx49ejinWBERB3JxsdAqJpiXezdm2T8788MTiTzUPpaIAC+yc/OZviGFB75cxU1vzOPt35LYe8FWDCJSnFMXScjJySEhIYGHHnqI/v37l3hNjx49GD9+vO1zT0/P8ipPRKRcFIabVjHBvHBbPOuTTzB9fQo/bjhEauYZPpy/iw/n76JN7Wrc2SqaXs0itRO4SBFO/S+iZ8+e9OzZ85LXeHp6EhERUU4ViYg414Xh5vnb4pmz7TCT1xxk8c50Vu87wep9J3h5xh/0bBrBna2iaRsbjIu6pEScG2iuxIIFCwgLC6NatWp07tyZV199lZCQkIten5ubS27u+VkEWVlZ5VGmiIjDebm7cnuzKG5vFkVa5hmmrj/IlDUH2XM0h6nrDjF13SGig70Z0LImA1rW1LYLcl2rMIOCLRYL06ZNo1+/frZjkyZNwsfHh9jYWHbv3s2//vUv/Pz8WL58Oa6uJc8AGDlyJKNGjSp2XIOCRaQqMAyDdQcymLI2mZkbU8nOzbeda1c3hDtb16RH40i8PTRLSiq3SjvLqaRAU9SePXuoW7cuc+bMoUuXLiVeU1ILTXR0tAKNiFQ5p/MK+O2PNCavTbbbdsHP043bmkbSv2UN2tRWl5RUTlcbaCp8l9OF6tSpQ/Xq1dm1a9dFA42np6cGDovIdcHbw5V+LWrQr0UNDp44xdR1h5iy9iAHjp/iuzXJfLcmmZrVvLmjRQ3uaFGDOqF+zi5ZpMxUqkBz8OBBjh07RmRkpLNLERGpUGpW8+EvXerz5871WLX3OFPXHeKXzakcPHGaD+bt4oN5u2hRK4j+LWpwe7Moqvl6OLtkEYdyapdTdnY2u3btAqBFixa8++67dOrUieDgYIKDgxk1ahQDBgwgIiKC3bt38/e//52TJ0+yefPmK26F0cJ6InK9OnO2gNlbDzN13UEW7TxKgdX83727q4XODcO4o0VNOjUM1U7gUiFVqjE0CxYsoFOnTsWODxkyhHHjxtGvXz/Wr19PRkYGUVFRdOvWjVdeeYXw8PAr/h4KNCIikH4ylxkbU5i67iB/pJyf/Rnk487tzSLp37ImLaKDsFg03kYqhkoVaMqDAo2IiL2ktJNMXX+Q6esP2W2WGVvd1zbeRlPAxdkUaIpQoBERKVmB1WDZ7qNMW3eIWVvSOH22wHauYYQ/HRuE0qFBKK1rV1O3lJQ7BZoiFGhERC4vJzefX7ekMW39IZbuPsqFvxm83V1JrBtCxwahdGwQSu3qvs4rVK4bCjRFKNCIiFyd4zl5LNl1lIVJ6SzamU76yVy787WCfWytN4l1Q7SvlJQJBZoiFGhERErPMAy2pZ5k4Y50Fu1IZ83+45wtOP9rw93VQuuYYDqca72Jj/TXwGJxCAWaIhRoREQcJyc3n+W7j7FwRzoLd6Rz4Pgpu/Oh/p50aRhGn4Qo2tYJwVWrFEspKdAUoUAjIlJ29h3NsbXeLNt9zG5gcZi/J7c3i6Jv8yia1QxUy41cFQWaIhRoRETKR25+Aav3nuDnzan8sjmVzNNnbedqh/jQJyGKPs2jqBfm78QqpbJQoClCgUZEpPzl5VtZtCOdHzemMHtrGmfOWm3nGkcF0Cchit4JUUQFeTuxSqnIFGiKUKAREXGunNx85mw7zI8bUli0I5186/lfOzfEBtMnIYpeTSMJ1v5ScgEFmiIUaEREKo7jOXnM2pLKjxtSWLX3uO24m4uFDg1C6ZMQRaeGYQR6uzuxSqkIFGiKUKAREamYUjJOM3NjCj9uSGFr6vn9pVxdLLSKqUanuDA6NQwlLlxTwa9HCjRFKNCIiFR8u46cZMaGFH7enMru9By7c5GBXtwSF0anuFDa16uOrxbyuy4o0BShQCMiUrkcOHaKBTuOMH/7EZbtPkZu/vkBxR6uLtwQG8wtcaF0ahhGneq+ar2pohRoilCgERGpvM6cLWD5nmMs2H6EeUlHSD5+2u58rWAfOp0LNzfWCcHLXZtoVhUKNEUo0IiIVA2GYbDnaA7ztx9hQVI6K/ces9uGwcvdhfZ1q3Nro3C6xIcT6u/pxGrlWinQFKFAIyJSNWXn5rNs11HmJ6WzIOkIqZlnbOcsFmgRHUS3xhHc2iicuqF+TqxUSkOBpggFGhGRqs8wDLannWTutsP8vvUwmw5m2p2vE+pLt0ZmuGkRHYSL9piq8BRoilCgERG5/qRmnmbOVjPcrNhj3zUV6u9J1/gwbm0UTru61TXupoJSoClCgUZE5PqWdeYsC5LSmb31MAu2H+Fkbr7tnI+HKx0bhNKtcTid48IJ9NGCfhWFAk0RCjQiIlIoL9/Kij3HmL31MLO3HiYt6/y4G1cXC81qBnJjnRDaxgbTunYwflrzxmkUaIpQoBERkZIYhsHmQ5nM3nqY3/84TNLhk3bnXV0sNK0RSNs6wdxYJ4TWMdXw91ILTnlRoClCgUZERK7EwROnWLHnOCv2HGPl3mPF1rxxdbHQJCqAG+uEmAGntgJOWVKgKUKBRkRESuPgiVOs3HOclXuPsWLPcQ4cP2V33sUCTWqc76JqExtMgAKOwyjQFKFAIyIijpCScdoMN7uPs2LvMfYfsw847q4WOjYIpXdCFF3jw7Xn1DVSoClCgUZERMpCauZpVp7rolqx5xj7Lgg4Xu4udIkPp09CFB0bhGpqeCko0BShQCMiIuVh5+GTzNiYwsyNKXbhxt/Tje5NIuidEEX7uiG4ubo4scrKQ4GmCAUaEREpT4Wzp2ZuTGHmxlS7qeEhvh70bBpBn4QatI6pphWLL0GBpggFGhERcRar1WDN/hPM2HiIXzancTwnz3YuMtCL25tF0jshiqY1ArFYFG4upEBThAKNiIhUBPkFVpbuPsbMjSn8tiXNbsXi2iE+9Gwaya2NwmleU3tNgQJNMQo0IiJS0Zw5W8DCHenM2JjC3G2HOXPWajtn7jUVTrdG4STWDbluBxQr0BShQCMiIhVZTm4+c7cf4fc/0liQlE72BS03vh6udIwL5dZG199eUwo0RSjQiIhIZZGbX8CKPceZvTWNOVuPFNtr6obawXRrHM6tjcKpWc3HiZWWPQWaIhRoRESkMrJaz+81NXtr8b2m4iMDuLWR2TXVOCqgyg0qVqApQoFGRESqgv3HcsyNNLceZs2+41gv+O0dHuBJi+hqNIsOJKFmEE1rBlb6bRgUaIpQoBERkarmeE4ec7eZLTeLdqbbDSouVKe6L81qBtKsZhAJ0YE0igzE26PyDDBWoClCgUZERKqyM2cLWH8gg00HM9h0MJNNhzKK7RQO5hicBuH+JJwLOc1qBhIX4Y97BV25WIGmCAUaERG53hzPyTsfcA5msPFgJuknc4td5+HmQqPIANrWCaZzXBitYqpVmK0ZFGiKUKAREZHrnWEYpGWdYWNypl3QyTqTb3ddgJcbHePC6NwwlI4Nwgj29XBSxQo0xSjQiIiIFGcYBvuPnWJ98gkW7TjKgqQjnDh11nbexQLNo4PoEh9Op7gw4iP9y3UmlQJNEQo0IiIil1dgNdiQfIJ5248wb3s621Kz7M5HBnpxS1wYXRqG0a5eCD4ebmVajwJNEQo0IiIiVy8l4zTzk44wf/sRluw6ajeTysPNhcQ6IXRuGEbnhmFEBzt+kb+r/f3t1JE/ixYtonfv3kRFRWGxWJg+fbrdecMweOmll4iMjMTb25uuXbuyc+dO5xQrIiJyHYkK8mZw2xg+H9KGDS91Y/yDbXggMYaa1bzJy7eycEc6L8/4g5vfnM8Hc53/u9mpgSYnJ4eEhAQ++uijEs+/+eabvP/++3zyySesXLkSX19funfvzpkzZ0q8XkRERBzPy92VTnFhjO7bhMV/78Tvz3Tgnz0bckNsMK4uFhKig5xdYsXpcrJYLEybNo1+/foBZutMVFQUzz33HMOHDwcgMzOT8PBwJkyYwD333HNF91WXk4iISNnJPHUWH09Xh69nU6m6nC5l7969pKWl0bVrV9uxwMBA2rZty/Llyy/6dbm5uWRlZdk9REREpGwE+rhXiMX5nF/BRaSlpQEQHh5udzw8PNx2riRjxowhMDDQ9oiOji7TOkVERMT5KmygKa0RI0aQmZlpeyQnJzu7JBERESljFTbQREREAHD48GG744cPH7adK4mnpycBAQF2DxEREanaKmygiY2NJSIigrlz59qOZWVlsXLlShITE51YmYiIiFQ0ZbvM32VkZ2eza9cu2+d79+5lw4YNBAcHU6tWLZ5++mleffVV6tevT2xsLC+++CJRUVG2mVAiIiIi4ORAs2bNGjp16mT7/NlnnwVgyJAhTJgwgb///e/k5OTw6KOPkpGRwU033cSvv/6Kl5eXs0oWERGRCqjCrENTVrQOjYiISOVTZdahEREREblSCjQiIiJS6SnQiIiISKWnQCMiIiKVngKNiIiIVHoKNCIiIlLpOXUdmvJQOCtdu26LiIhUHoW/t690dZkqH2hOnjwJoF23RUREKqGTJ08SGBh42euq/MJ6VquVlJQU/P39sVgsDrtvVlYW0dHRJCcna8G+q6DXrXT0upWOXrerp9esdPS6lc6lXjfDMDh58iRRUVG4uFx+hEyVb6FxcXGhZs2aZXZ/7ehdOnrdSkevW+nodbt6es1KR69b6VzsdbuSlplCGhQsIiIilZ4CjYiIiFR6CjSl5Onpycsvv4ynp6ezS6lU9LqVjl630tHrdvX0mpWOXrfSceTrVuUHBYuIiEjVpxYaERERqfQUaERERKTSU6ARERGRSk+BRkRERCo9BZpS+uijj6hduzZeXl60bduWVatWObukCm3kyJFYLBa7R8OGDZ1dVoWzaNEievfuTVRUFBaLhenTp9udNwyDl156icjISLy9venatSs7d+50TrEVxOVes6FDhxZ77/Xo0cM5xVYgY8aMoU2bNvj7+xMWFka/fv1ISkqyu+bMmTMMGzaMkJAQ/Pz8GDBgAIcPH3ZSxc53Ja/ZLbfcUuz99vjjjzup4oph3LhxNGvWzLZ4XmJiIrNmzbKdd9T7TIGmFL777jueffZZXn75ZdatW0dCQgLdu3fnyJEjzi6tQmvcuDGpqam2x5IlS5xdUoWTk5NDQkICH330UYnn33zzTd5//30++eQTVq5cia+vL927d+fMmTPlXGnFcbnXDKBHjx52771vv/22HCusmBYuXMiwYcNYsWIFs2fP5uzZs3Tr1o2cnBzbNc888wwzZ85k8uTJLFy4kJSUFPr37+/Eqp3rSl4zgEceecTu/fbmm286qeKKoWbNmrz++uusXbuWNWvW0LlzZ/r27csff/wBOPB9ZshVu+GGG4xhw4bZPi8oKDCioqKMMWPGOLGqiu3ll182EhISnF1GpQIY06ZNs31utVqNiIgI46233rIdy8jIMDw9PY1vv/3WCRVWPEVfM8MwjCFDhhh9+/Z1Sj2VyZEjRwzAWLhwoWEY5nvL3d3dmDx5su2abdu2GYCxfPlyZ5VZoRR9zQzDMDp27Gj89a9/dV5RlUS1atWMzz//3KHvM7XQXKW8vDzWrl1L165dbcdcXFzo2rUry5cvd2JlFd/OnTuJioqiTp06DB48mAMHDji7pEpl7969pKWl2b33AgMDadu2rd57l7FgwQLCwsKIi4vjiSee4NixY84uqcLJzMwEIDg4GIC1a9dy9uxZu/dbw4YNqVWrlt5v5xR9zQp98803VK9enSZNmjBixAhOnTrljPIqpIKCAiZNmkROTg6JiYkOfZ9V+c0pHe3o0aMUFBQQHh5udzw8PJzt27c7qaqKr23btkyYMIG4uDhSU1MZNWoUN998M1u2bMHf39/Z5VUKaWlpACW+9wrPSXE9evSgf//+xMbGsnv3bv71r3/Rs2dPli9fjqurq7PLqxCsVitPP/007du3p0mTJoD5fvPw8CAoKMjuWr3fTCW9ZgCDBg0iJiaGqKgoNm3axD/+8Q+SkpKYOnWqE6t1vs2bN5OYmMiZM2fw8/Nj2rRpNGrUiA0bNjjsfaZAI+WiZ8+etufNmjWjbdu2xMTE8P333/Pwww87sTKp6u655x7b86ZNm9KsWTPq1q3LggUL6NKlixMrqziGDRvGli1bNK7tKlzsNXv00Udtz5s2bUpkZCRdunRh9+7d1K1bt7zLrDDi4uLYsGEDmZmZTJkyhSFDhrBw4UKHfg91OV2l6tWr4+rqWmwE9uHDh4mIiHBSVZVPUFAQDRo0YNeuXc4updIofH/pvXdt6tSpQ/Xq1fXeO+epp57ip59+Yv78+dSsWdN2PCIigry8PDIyMuyu1/vt4q9ZSdq2bQtw3b/fPDw8qFevHq1atWLMmDEkJCTw3nvvOfR9pkBzlTw8PGjVqhVz5861HbNarcydO5fExEQnVla5ZGdns3v3biIjI51dSqURGxtLRESE3XsvKyuLlStX6r13FQ4ePMixY8eu+/eeYRg89dRTTJs2jXnz5hEbG2t3vlWrVri7u9u935KSkjhw4MB1+3673GtWkg0bNgBc9++3oqxWK7m5uY59nzl23PL1YdKkSYanp6cxYcIEY+vWrcajjz5qBAUFGWlpac4urcJ67rnnjAULFhh79+41li5danTt2tWoXr26ceTIEWeXVqGcPHnSWL9+vbF+/XoDMN59911j/fr1xv79+w3DMIzXX3/dCAoKMn788Udj06ZNRt++fY3Y2Fjj9OnTTq7ceS71mp08edIYPny4sXz5cmPv3r3GnDlzjJYtWxr169c3zpw54+zSneqJJ54wAgMDjQULFhipqam2x6lTp2zXPP7440atWrWMefPmGWvWrDESExONxMREJ1btXJd7zXbt2mWMHj3aWLNmjbF3717jxx9/NOrUqWN06NDByZU71z//+U9j4cKFxt69e41NmzYZ//znPw2LxWL8/vvvhmE47n2mQFNKH3zwgVGrVi3Dw8PDuOGGG4wVK1Y4u6QK7e677zYiIyMNDw8Po0aNGsbdd99t7Nq1y9llVTjz5883gGKPIUOGGIZhTt1+8cUXjfDwcMPT09Po0qWLkZSU5NyinexSr9mpU6eMbt26GaGhoYa7u7sRExNjPPLII/rjwzBKfM0AY/z48bZrTp8+bTz55JNGtWrVDB8fH+OOO+4wUlNTnVe0k13uNTtw4IDRoUMHIzg42PD09DTq1atn/O1vfzMyMzOdW7iTPfTQQ0ZMTIzh4eFhhIaGGl26dLGFGcNw3PvMYhiGUcoWIxEREZEKQWNoREREpNJToBEREZFKT4FGREREKj0FGhEREan0FGhERESk0lOgERERkUpPgUZEREQqPQUaEbnuWCwWpk+f7uwyRMSBFGhEpFwNHToUi8VS7NGjRw9nlyYilZibswsQketPjx49GD9+vN0xT09PJ1UjIlWBWmhEpNx5enoSERFh96hWrRpgdgeNGzeOnj174u3tTZ06dZgyZYrd12/evJnOnTvj7e1NSEgIjz76KNnZ2XbXfPnllzRu3BhPT08iIyN56qmn7M4fPXqUO+64Ax8fH+rXr8+MGTPK9ocWkTKlQCMiFc6LL77IgAED2LhxI4MHD+aee+5h27ZtAOTk5NC9e3eqVavG6tWrmTx5MnPmzLELLOPGjWPYsGE8+uijbN68mRkzZlCvXj277zFq1CjuuusuNm3aRK9evRg8eDDHjx8v159TRBzIcftpiohc3pAhQwxXV1fD19fX7vHaa68ZhmHuaPz444/bfU3btm2NJ554wjAMw/j000+NatWqGdnZ2bbzP//8s+Hi4mLbRTsqKsp4/vnnL1oDYLzwwgu2z7Ozsw3AmDVrlsN+ThEpXxpDIyLlrlOnTowbN87uWHBwsO15YmKi3bnExEQ2bNgAwLZt20hISMDX19d2vn379litVpKSkrBYLKSkpNClS5dL1tCsWTPbc19fXwICAjhy5EhpfyQRcTIFGhEpd76+vsW6gBzF29v7iq5zd3e3+9xisWC1WsuiJBEpBxpDIyIVzooVK4p9Hh8fD0B8fDwbN24kJyfHdn7p0qW4uLgQFxeHv78/tWvXZu7cueVas4g4l1poRKTc5ebmkpaWZnfMzc2N6tWrAzB58mRat27NTTfdxDfffMOqVav44osvABg8eDAvv/wyQ4YMYeTIkaSnp/PnP/+Z+++/n/DwcABGjhzJ448/TlhYGD179uTkyZMsXbqUP//5z+X7g4pIuVGgEZFy9+uvvxIZGWl3LC4uju3btwPmDKRJkybx5JNPEhkZybfffkujRo0A8PHx4bfffuOvf/0rbdq0wcfHhwEDBvDuu+/a7jVkyBDOnDnD2LFjGT58ONWrV2fgwIHl9wOKSLmzGIZhOLsIEZFCFouFadOm0a9fP2eXIiKViMbQiIiISKWnQCMiIiKVnsbQiEiFol5wESkNtdCIiIhIpadAIyIiIpWeAo2IiIhUego0IiIiUukp0IiIiEilp0AjIiIilZ4CjYiIiFR6CjQiIiJS6SnQiIiISKX3/1gxK/TGyAJmAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig_1,ax_1 = plt.subplots()\n",
    "ax_1.plot(range(n_epochs), train_loss_list, label = 'train_loss')\n",
    "ax_1.legend()\n",
    "ax_1.plot(range(n_epochs), valid_loss_list, label = 'valid_loss')\n",
    "ax_1.legend()\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title(\"Performance of Model 1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now loading the model with the lowest validation loss value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 21.184199\n",
      "\n",
      "Test Accuracy of airplane: 65% (651/1000)\n",
      "Test Accuracy of automobile: 75% (754/1000)\n",
      "Test Accuracy of  bird: 55% (552/1000)\n",
      "Test Accuracy of   cat: 39% (394/1000)\n",
      "Test Accuracy of  deer: 56% (568/1000)\n",
      "Test Accuracy of   dog: 56% (562/1000)\n",
      "Test Accuracy of  frog: 69% (695/1000)\n",
      "Test Accuracy of horse: 66% (661/1000)\n",
      "Test Accuracy of  ship: 80% (805/1000)\n",
      "Test Accuracy of truck: 71% (718/1000)\n",
      "\n",
      "Test Accuracy of model 1 (Overall): 63.600000% (6360.000000/10000.000000)\n"
     ]
    }
   ],
   "source": [
    " model_1.load_state_dict(torch.load(\"./model_1_cifar.pt\"))\n",
    "\n",
    "# track test loss\n",
    "test_loss = 0.0\n",
    "class_correct = list(0.0 for i in range(10))\n",
    "class_total = list(0.0 for i in range(10))\n",
    "accuracy_per_class_m1 =  list(0.0 for i in range(10))\n",
    "model_1.eval()\n",
    "# iterate over test data\n",
    "for data, target in test_loader:\n",
    "    # move tensors to GPU if CUDA is available\n",
    "    if train_on_gpu:\n",
    "        data, target = data.cuda(), target.cuda()\n",
    "    # forward pass: compute predicted outputs by passing inputs to the model\n",
    "    output = model_1(data)\n",
    "    # calculate the batch loss\n",
    "    loss = criterion(output, target)\n",
    "    # update test loss\n",
    "    test_loss += loss.item() * data.size(0)\n",
    "    # convert output probabilities to predicted class\n",
    "    _, pred = torch.max(output, 1)\n",
    "    # compare predictions to true label\n",
    "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
    "    correct = (\n",
    "        np.squeeze(correct_tensor.numpy())\n",
    "        if not train_on_gpu\n",
    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
    "    )\n",
    "    # calculate test accuracy for each object class\n",
    "    for i in range(batch_size):\n",
    "        label = target.data[i]\n",
    "        class_correct[label] += correct[i].item()\n",
    "        class_total[label] += 1\n",
    "\n",
    "# average test loss\n",
    "test_loss = test_loss / len(test_loader)\n",
    "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
    "\n",
    "for i in range(10):\n",
    "    if class_total[i] > 0:\n",
    "        accuracy_per_class_m1 = 100 * class_correct[i] / class_total[i]\n",
    "        print(\n",
    "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
    "            % (\n",
    "                classes[i],\n",
    "                accuracy_per_class_m1,\n",
    "                np.sum(class_correct[i]),\n",
    "                np.sum(class_total[i]),\n",
    "            )\n",
    "        )\n",
    "        \n",
    "    else:\n",
    "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
    "\n",
    "print(\n",
    "    \"\\nTest Accuracy of model 1 (Overall): %2f%% (%2f/%2f)\"\n",
    "    % (\n",
    "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
    "        np.sum(class_correct),\n",
    "        np.sum(class_total),\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second Network :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Second_Net(\n",
      "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
      "  (fc2): Linear(in_features=512, out_features=64, bias=True)\n",
      "  (fc3): Linear(in_features=64, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# define the CNN architecture\n",
    "\n",
    "\n",
    "class Second_Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Second_Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)\n",
    "        # H_out(conv1) = W_out(conv1) = (H_in + 2*padding - Kernel)/stride + 1 = (32 + 2 - 3)/1 + 1 = 32\n",
    "        # Size(conv1_out) = 32*32*16\n",
    "        self.pool = nn.MaxPool2d(2, 2)  # After Maxpooling2D new size of image equal =  16*16\n",
    "        # H_out(conv2) = W_out(conv2) = (H_in + 2*padding - Kernel)/stride + 1 = (16 + 2 - 3)/1 + 1 = 16\n",
    "                                        # After Maxpooling2D new size of image equal =  8*8\n",
    "        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n",
    "        # H_out(conv3) = W_out(conv3) = (H_in + 2*padding - Kernel)/stride + 1 = (16 + 2 - 3)/1 + 1 = 8\n",
    "                                        # After Maxpooling2D new size of image equal =  4*4\n",
    "        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)  \n",
    "        self.fc1 = nn.Linear(64 * 4 * 4, 512)   \n",
    "        self.fc2 = nn.Linear(512, 64)\n",
    "        self.fc3 = nn.Linear(64, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = self.pool(F.relu(self.conv3(x)))\n",
    "        x = x.view(-1, 64 * 4 * 4)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "# create a complete CNN\n",
    "model_2 = Second_Net()\n",
    "print(model_2)\n",
    "# move tensors to GPU if CUDA is available\n",
    "# if train_on_gpu:\n",
    "  #  model.cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Training the new model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 \tTraining Loss: 45.198202 \tValidation Loss: 40.966207\n",
      "Validation loss decreased (inf --> 40.966207).  Saving model ...\n",
      "Epoch: 1 \tTraining Loss: 37.330688 \tValidation Loss: 33.521531\n",
      "Validation loss decreased (40.966207 --> 33.521531).  Saving model ...\n",
      "Epoch: 2 \tTraining Loss: 31.562392 \tValidation Loss: 29.229370\n",
      "Validation loss decreased (33.521531 --> 29.229370).  Saving model ...\n",
      "Epoch: 3 \tTraining Loss: 28.058459 \tValidation Loss: 26.177679\n",
      "Validation loss decreased (29.229370 --> 26.177679).  Saving model ...\n",
      "Epoch: 4 \tTraining Loss: 25.456967 \tValidation Loss: 23.716301\n",
      "Validation loss decreased (26.177679 --> 23.716301).  Saving model ...\n",
      "Epoch: 5 \tTraining Loss: 23.113733 \tValidation Loss: 22.240193\n",
      "Validation loss decreased (23.716301 --> 22.240193).  Saving model ...\n",
      "Epoch: 6 \tTraining Loss: 21.204408 \tValidation Loss: 20.603154\n",
      "Validation loss decreased (22.240193 --> 20.603154).  Saving model ...\n",
      "Epoch: 7 \tTraining Loss: 19.409736 \tValidation Loss: 19.268198\n",
      "Validation loss decreased (20.603154 --> 19.268198).  Saving model ...\n",
      "Epoch: 8 \tTraining Loss: 17.837116 \tValidation Loss: 18.357212\n",
      "Validation loss decreased (19.268198 --> 18.357212).  Saving model ...\n",
      "Epoch: 9 \tTraining Loss: 16.416749 \tValidation Loss: 18.906651\n",
      "Epoch: 10 \tTraining Loss: 15.053826 \tValidation Loss: 17.337641\n",
      "Validation loss decreased (18.357212 --> 17.337641).  Saving model ...\n",
      "Epoch: 11 \tTraining Loss: 13.805687 \tValidation Loss: 16.633078\n",
      "Validation loss decreased (17.337641 --> 16.633078).  Saving model ...\n",
      "Epoch: 12 \tTraining Loss: 12.541472 \tValidation Loss: 17.184920\n",
      "Epoch: 13 \tTraining Loss: 11.335604 \tValidation Loss: 17.543164\n",
      "Epoch: 14 \tTraining Loss: 10.047115 \tValidation Loss: 17.556785\n",
      "Epoch: 15 \tTraining Loss: 8.794780 \tValidation Loss: 17.938292\n",
      "Epoch: 16 \tTraining Loss: 7.607265 \tValidation Loss: 19.072760\n",
      "Epoch: 17 \tTraining Loss: 6.449549 \tValidation Loss: 19.529105\n",
      "Epoch: 18 \tTraining Loss: 5.425620 \tValidation Loss: 21.515781\n",
      "Epoch: 19 \tTraining Loss: 4.283669 \tValidation Loss: 22.043078\n",
      "Epoch: 20 \tTraining Loss: 3.533014 \tValidation Loss: 24.846188\n",
      "Epoch: 21 \tTraining Loss: 2.861788 \tValidation Loss: 25.897939\n",
      "Epoch: 22 \tTraining Loss: 2.286396 \tValidation Loss: 27.009543\n",
      "Epoch: 23 \tTraining Loss: 2.219529 \tValidation Loss: 29.655110\n",
      "Epoch: 24 \tTraining Loss: 1.721309 \tValidation Loss: 30.484672\n",
      "Epoch: 25 \tTraining Loss: 1.391777 \tValidation Loss: 32.229886\n",
      "Epoch: 26 \tTraining Loss: 1.279259 \tValidation Loss: 33.819620\n",
      "Epoch: 27 \tTraining Loss: 1.252377 \tValidation Loss: 32.575949\n",
      "Epoch: 28 \tTraining Loss: 1.323867 \tValidation Loss: 34.618063\n",
      "Epoch: 29 \tTraining Loss: 1.130607 \tValidation Loss: 35.113301\n"
     ]
    }
   ],
   "source": [
    "\n",
    "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
    "optimizer = optim.SGD(model_2.parameters(), lr=0.01)  # specify optimizer\n",
    "train_loss_list_2 = []  # list to store loss to visualize\n",
    "valid_loss_list_2 = []\n",
    "valid_loss_min = np.Inf  # track change in validation loss\n",
    "\n",
    "for epoch in range(n_epochs):\n",
    "    # Keep track of training and validation loss\n",
    "    train_loss = 0.0\n",
    "    valid_loss = 0.0\n",
    "\n",
    "    # Train the model\n",
    "    model_2.train()\n",
    "    for data, target in train_loader:\n",
    "        # Move tensors to GPU if CUDA is available\n",
    "        if train_on_gpu:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        # Clear the gradients of all optimized variables\n",
    "        optimizer.zero_grad()\n",
    "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
    "        output = model_2(data)\n",
    "        # Calculate the batch loss\n",
    "        loss = criterion(output, target)\n",
    "        # Backward pass: compute gradient of the loss with respect to model parameters\n",
    "        loss.backward()\n",
    "        # Perform a single optimization step (parameter update)\n",
    "        optimizer.step()\n",
    "        # Update training loss\n",
    "        train_loss += loss.item() * data.size(0)\n",
    "\n",
    "    # Validate the model\n",
    "    model_2.eval()\n",
    "    for data, target in valid_loader:\n",
    "        # Move tensors to GPU if CUDA is available\n",
    "        if train_on_gpu:\n",
    "            data, target = data.cuda(), target.cuda()\n",
    "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
    "        output = model_2(data)\n",
    "        # Calculate the batch loss\n",
    "        loss = criterion(output, target)\n",
    "        # Update average validation loss\n",
    "        valid_loss += loss.item() * data.size(0)\n",
    "\n",
    "    # Calculate average losses\n",
    "    train_loss = train_loss / len(train_loader)\n",
    "    valid_loss = valid_loss / len(valid_loader)\n",
    "    train_loss_list_2.append(train_loss)\n",
    "    valid_loss_list_2.append(valid_loss)\n",
    "\n",
    "    # Print training/validation statistics\n",
    "    print(\n",
    "        \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n",
    "            epoch, train_loss, valid_loss\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # Save model if validation loss has decreased\n",
    "    if valid_loss <= valid_loss_min:\n",
    "        print(\n",
    "            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n",
    "                valid_loss_min, valid_loss\n",
    "            )\n",
    "        )\n",
    "        torch.save(model_2.state_dict(), \"model_2_cifar.pt\")\n",
    "        valid_loss_min = valid_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot Loss in function epoch for the new model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig_2,ax_2 = plt.subplots()\n",
    "ax_2.plot(range(n_epochs), train_loss_list_2, label = 'train_loss')\n",
    "ax_2.legend()\n",
    "ax_2.plot(range(n_epochs), valid_loss_list_2, label = 'valid_loss')\n",
    "ax_2.legend()\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title(\"Performance of Model 2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the second model with the lowest validation loss value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of airplane: 74% (743/1000)\n",
      "Test Accuracy of automobile: 83% (839/1000)\n",
      "Test Accuracy of  bird: 62% (624/1000)\n",
      "Test Accuracy of   cat: 45% (453/1000)\n",
      "Test Accuracy of  deer: 58% (586/1000)\n",
      "Test Accuracy of   dog: 70% (704/1000)\n",
      "Test Accuracy of  frog: 76% (768/1000)\n",
      "Test Accuracy of horse: 71% (719/1000)\n",
      "Test Accuracy of  ship: 79% (798/1000)\n",
      "Test Accuracy of truck: 79% (795/1000)\n",
      "\n",
      "Test Accuracy (Overall): 70.290000% (7029.000000/10000.000000)\n"
     ]
    }
   ],
   "source": [
    "model_2.load_state_dict(torch.load(\"./model_2_cifar.pt\"))\n",
    "\n",
    "class_correct2 = list(0.0 for i in range(10))\n",
    "class_total2 = list(0.0 for i in range(10))\n",
    "accuracy_per_class_m2 = list(0.0 for i in range(10))\n",
    "model_2.eval()\n",
    "# iterate over test data\n",
    "for data, target in test_loader:\n",
    "    # move tensors to GPU if CUDA is available\n",
    "    if train_on_gpu:\n",
    "        data, target = data.cuda(), target.cuda()\n",
    "    # forward pass: compute predicted outputs by passing inputs to the model\n",
    "    output = model_2(data)\n",
    "\n",
    "    # convert output probabilities to predicted class\n",
    "    _, pred = torch.max(output, 1)\n",
    "    # compare predictions to true label\n",
    "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
    "    correct2 = (\n",
    "        np.squeeze(correct_tensor.numpy())\n",
    "        if not train_on_gpu\n",
    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
    "    )\n",
    "    # calculate test accuracy for each object class\n",
    "    for i in range(batch_size):\n",
    "        label = target.data[i]\n",
    "        class_correct2[label] += correct2[i].item()\n",
    "        class_total2[label] += 1\n",
    "\n",
    "\n",
    "for i in range(10):\n",
    "    if class_total2[i] > 0:\n",
    "        accuracy_per_class_m2 =  100 * class_correct2[i] / class_total2[i]\n",
    "        print(\n",
    "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
    "            % (\n",
    "                classes[i],\n",
    "                accuracy_per_class_m2,\n",
    "                np.sum(class_correct2[i]),\n",
    "                np.sum(class_total2[i]),\n",
    "            )\n",
    "        )\n",
    "       \n",
    "    else:\n",
    "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
    "\n",
    "print(\n",
    "    \"\\nTest Accuracy (Overall): %2f%% (%2f/%2f)\"\n",
    "    % (\n",
    "        100.0 * np.sum(class_correct2) / np.sum(class_total2),\n",
    "        np.sum(class_correct2),\n",
    "        np.sum(class_total2),\n",
    "    )\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this new network our accuracy increased up to 70.290%"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "944991a2",
   "metadata": {},
   "source": [
    "Build a new network with the following structure.\n",
    "\n",
    "- It has 3 convolutional layers of kernel size 3 and padding of 1.\n",
    "- The first convolutional layer must output 16 channels, the second 32 and the third 64.\n",
    "- At each convolutional layer output, we apply a ReLU activation then a MaxPool with kernel size of 2.\n",
    "- Then, three fully connected layers, the first two being followed by a ReLU activation and a dropout whose value you will suggest.\n",
    "- The first fully connected layer will have an output size of 512.\n",
    "- The second fully connected layer will have an output size of 64.\n",
    "\n",
    "Compare the results obtained with this new network to those obtained previously."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Exercise 2: Quantization: try to compress the CNN to save space\n",
    " \n",
    " Quantization doc is available from https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamicThe Exercise is to quantize post training the above CNN model. Compare the size reduction and the impact on the classification accuracy The size of the model is simply the size of the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:  fp32  \t Size (KB): 251.278\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "251278"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "def print_size_of_model(model, label=\"\"):\n",
    "    torch.save(model.state_dict(), \"temp.p\")\n",
    "    size = os.path.getsize(\"temp.p\")\n",
    "    print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n",
    "    os.remove(\"temp.p\")\n",
    "    return size\n",
    "\n",
    "\n",
    "print_size_of_model(model_1, \"fp32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of model 1 before Post training quantization is 251.278KB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " **Post training quantization of model 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:  int8  \t Size (KB): 76.522\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "76522"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "quantized_model_1 = torch.quantization.quantize_dynamic(model_1, dtype=torch.qint8)\n",
    "print_size_of_model(quantized_model_1, \"int8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of model 1 after Post training quantization is 76.522 KB, so The size of Model 1 was reduced by 174,748 KB after post training quantization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Post otraining quantization of model 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:  fp32  \t Size (KB): 2330.946\n",
      "model:  int8  \t Size (KB): 659.806\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "659806"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print_size_of_model(model_2, \"fp32\")\n",
    "quantized_model_2 = torch.quantization.quantize_dynamic(model_2, dtype=torch.qint8)\n",
    "print_size_of_model(quantized_model_2, \"int8\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of Model 2 was reduced by 1671.14 KB after post training quantization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Classification test of quantized model 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of airplane: 64% (647/1000)\n",
      "Test Accuracy of automobile: 75% (755/1000)\n",
      "Test Accuracy of  bird: 55% (551/1000)\n",
      "Test Accuracy of   cat: 38% (384/1000)\n",
      "Test Accuracy of  deer: 58% (581/1000)\n",
      "Test Accuracy of   dog: 55% (558/1000)\n",
      "Test Accuracy of  frog: 69% (696/1000)\n",
      "Test Accuracy of horse: 66% (662/1000)\n",
      "Test Accuracy of  ship: 80% (806/1000)\n",
      "Test Accuracy of truck: 72% (721/1000)\n",
      "\n",
      "Test Accuracy of quantized model 1 (Overall): 63.610000% (6361.000000/10000.000000)\n"
     ]
    }
   ],
   "source": [
    "# quantized_model_1.load_state_dict(torch.load(\"./quantizedmodel_cifar.pt\"))\n",
    "\n",
    "\n",
    "\n",
    "class_correct = list(0.0 for i in range(10))\n",
    "class_total = list(0.0 for i in range(10))\n",
    "accuracy_per_class_q1 = list(0.0 for i in range(10))\n",
    "quantized_model_1.eval()\n",
    "# iterate over test data\n",
    "for data, target in test_loader:\n",
    "    # move tensors to GPU if CUDA is available\n",
    "    if train_on_gpu:\n",
    "        data, target = data.cuda(), target.cuda()\n",
    "    # forward pass: compute predicted outputs by passing inputs to the model\n",
    "    output = quantized_model_1(data)\n",
    "   \n",
    "    \n",
    "    # convert output probabilities to predicted class\n",
    "    _, pred = torch.max(output, 1)\n",
    "    # compare predictions to true label\n",
    "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
    "    correct = (\n",
    "        np.squeeze(correct_tensor.numpy())\n",
    "        if not train_on_gpu\n",
    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
    "    )\n",
    "    # calculate test accuracy for each object class\n",
    "    for i in range(batch_size):\n",
    "        label = target.data[i]\n",
    "        class_correct[label] += correct[i].item()\n",
    "        class_total[label] += 1\n",
    "\n",
    "for i in range(10):\n",
    "    if class_total[i] > 0:\n",
    "        print(\n",
    "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
    "            % (\n",
    "                classes[i],\n",
    "                100 * class_correct[i] / class_total[i],\n",
    "                np.sum(class_correct[i]),\n",
    "                np.sum(class_total[i]),\n",
    "            )\n",
    "        )\n",
    "        accuracy_per_class_q1[i] = (class_correct[i]/class_total[i])*100\n",
    "    else:\n",
    "        print(\"Test Accuracy of quantized model 1 %5s: N/A (no training examples)\" % (classes[i]))\n",
    "\n",
    "print(\n",
    "    \"\\nTest Accuracy of quantized model 1 (Overall): %2f%% (%2f/%2f)\"\n",
    "    % (\n",
    "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
    "        np.sum(class_correct),\n",
    "        np.sum(class_total),\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Classification test of quantized model 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy of airplane: 74% (746/1000)\n",
      "Test Accuracy of automobile: 83% (839/1000)\n",
      "Test Accuracy of  bird: 61% (617/1000)\n",
      "Test Accuracy of   cat: 45% (455/1000)\n",
      "Test Accuracy of  deer: 58% (588/1000)\n",
      "Test Accuracy of   dog: 70% (704/1000)\n",
      "Test Accuracy of  frog: 76% (768/1000)\n",
      "Test Accuracy of horse: 72% (720/1000)\n",
      "Test Accuracy of  ship: 79% (791/1000)\n",
      "Test Accuracy of truck: 79% (791/1000)\n",
      "\n",
      "Test Accuracy of quantized model 2 (Overall): 70.190000% (7019.000000/10000.000000)\n"
     ]
    }
   ],
   "source": [
    "#  quantized_model_2.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
    "\n",
    "\n",
    "\n",
    "class_correct = list(0.0 for i in range(10))\n",
    "class_total = list(0.0 for i in range(10))\n",
    "accuracy_per_class_q2 = list(0.0 for i in range(10))\n",
    "quantized_model_2.eval()\n",
    "# iterate over test data\n",
    "for data, target in test_loader:\n",
    "    # move tensors to GPU if CUDA is available\n",
    "    if train_on_gpu:\n",
    "        data, target = data.cuda(), target.cuda()\n",
    "    # forward pass: compute predicted outputs by passing inputs to the model\n",
    "    output = quantized_model_2(data)\n",
    "   \n",
    "    \n",
    "    # convert output probabilities to predicted class\n",
    "    _, pred = torch.max(output, 1)\n",
    "    # compare predictions to true label\n",
    "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
    "    correct = (\n",
    "        np.squeeze(correct_tensor.numpy())\n",
    "        if not train_on_gpu\n",
    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
    "    )\n",
    "    # calculate test accuracy for each object class\n",
    "    for i in range(batch_size):\n",
    "        label = target.data[i]\n",
    "        class_correct[label] += correct[i].item()\n",
    "        class_total[label] += 1\n",
    "\n",
    "for i in range(10):\n",
    "    if class_total[i] > 0:\n",
    "        print(\n",
    "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
    "            % (\n",
    "                classes[i],\n",
    "                100 * class_correct[i] / class_total[i],\n",
    "                np.sum(class_correct[i]),\n",
    "                np.sum(class_total[i]),\n",
    "            )\n",
    "        )\n",
    "        accuracy_per_class_q2[i] = (class_correct[i]/class_total[i])*100\n",
    "    else:\n",
    "        print(\"Test Accuracy of quantized model 2 %5s: N/A (no training examples)\" % (classes[i]))\n",
    "\n",
    "print(\n",
    "    \"\\nTest Accuracy of quantized model 2 (Overall): %2f%% (%2f/%2f)\"\n",
    "    % (\n",
    "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
    "        np.sum(class_correct),\n",
    "        np.sum(class_total),\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comparison accuracy result between model 1 and quantized model 1**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_4, ax_4 = plt.subplots()\n",
    "w = 0.4\n",
    "x = [\"airpl.\", \"auto.\", \"bird\", \"car\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]\n",
    "bar1 = np.arange(len(x))\n",
    "bar2 = [i+w for i in bar1]\n",
    "ax_4.bar(bar1,accuracy_per_class_m1,w,label=\"Original model\")\n",
    "ax_4.bar(bar2,accuracy_per_class_q1,w,label=\"quantized model\")\n",
    "ax_4.set_xticks(bar1+w/2,x)\n",
    "ax_4.legend()\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Comparison accuracy result between model 2 and quantized model 2**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_5, ax_5 = plt.subplots()\n",
    "w = 0.4\n",
    "x = [\"airpl.\", \"autom.\", \"bird\", \"car\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]\n",
    "bar1 = np.arange(len(x))\n",
    "bar2 = [i+w for i in bar1]\n",
    "ax_5.bar(bar1,accuracy_per_class_m2,w,label=\"Original model\")\n",
    "ax_5.bar(bar2,accuracy_per_class_q2,w,label=\"Quantized_model\")\n",
    "ax_5.set_xticks(bar1+w/2,x)\n",
    "ax_5.legend()\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## Exercise 3: working with pre-trained models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PyTorch offers several pre-trained models https://pytorch.org/vision/0.8/models.htmlWe will use ResNet50 trained on ImageNet dataset (https://www.image-net.org/index.php). Use the following code with the files imagenet-simple-labels.json that contains the imagenet labels and the image dog.png that we will use as test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\LENOVO\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\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",
      "C:\\Users\\LENOVO\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\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=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
      ")\n",
      "Predicted class is: Golden Retriever\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Choose an image to pass through the model\n",
    "test_image = \"dog.PNG\"\n",
    "\n",
    "# Configure matplotlib for pretty inline plots\n",
    "#%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "# Prepare the labels\n",
    "with open(\"imagenet-simple-labels.json\") as f:\n",
    "    labels = json.load(f)\n",
    "\n",
    "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n",
    "data_transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "    ]\n",
    ")\n",
    "# Load the image\n",
    "\n",
    "image = Image.open(test_image)\n",
    "plt.imshow(image), plt.xticks([]), plt.yticks([])\n",
    "\n",
    "# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n",
    "# image = data_transform(image).unsqueeze(0).cuda()\n",
    "image = data_transform(image).unsqueeze(0)\n",
    "\n",
    "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n",
    "model_3 = models.resnet50(pretrained=True)  #  pretrained=True : permet d'obtenir les valeurs des poids après le pré-entrainement.\n",
    "print(model_3)\n",
    "# Send the model to the GPU\n",
    "# model.cuda()\n",
    "# Set layers such as dropout and batchnorm in evaluation mode\n",
    "model_3.eval()\n",
    "\n",
    "# Get the 1000-dimensional model output\n",
    "out = model_3(image)\n",
    "# Find the predicted class\n",
    "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Size of resnet50 model :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:  fp32  \t Size (KB): 102523.238\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "102523238"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print_size_of_model(model_3, \"fp32\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Post training quantization of resnet50 model :** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:  int8  \t Size (KB): 96379.996\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "96379996"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantized_model_3 = torch.quantization.quantize_dynamic(model_3, dtype=torch.qint8)\n",
    "print_size_of_model(quantized_model_3, \"int8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test classification of quantized resnet50 model :** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:  int8  \t Size (KB): 96379.996\n",
      "ResNet(\n",
      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
      "  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (relu): ReLU(inplace=True)\n",
      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
      "  (layer1): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer2): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer3): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (3): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (4): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (5): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (layer4): Sequential(\n",
      "    (0): Bottleneck(\n",
      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "      (downsample): Sequential(\n",
      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      )\n",
      "    )\n",
      "    (1): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "    (2): Bottleneck(\n",
      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "      (relu): ReLU(inplace=True)\n",
      "    )\n",
      "  )\n",
      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
      "  (fc): DynamicQuantizedLinear(in_features=2048, out_features=1000, dtype=torch.qint8, qscheme=torch.per_tensor_affine)\n",
      ")\n",
      "Predicted class is: Golden Retriever\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "from PIL import Image\n",
    "\n",
    "# Choose an image to pass through the model\n",
    "test_image = \"dog.PNG\"\n",
    "\n",
    "# Configure matplotlib for pretty inline plots\n",
    "#%matplotlib inline\n",
    "#%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "# Prepare the labels\n",
    "with open(\"imagenet-simple-labels.json\") as f:\n",
    "    labels = json.load(f)\n",
    "\n",
    "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n",
    "data_transform = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize((224, 224)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "    ]\n",
    ")\n",
    "# Load the image\n",
    "\n",
    "image = Image.open(test_image)\n",
    "plt.imshow(image), plt.xticks([]), plt.yticks([])\n",
    "\n",
    "# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n",
    "# image = data_transform(image).unsqueeze(0).cuda()\n",
    "image = data_transform(image).unsqueeze(0)\n",
    "\n",
    "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n",
    "model_3 = models.resnet50(pretrained=True)  #  pretrained=True : allows you to obtain the weight values after the pre-training.\n",
    "quantized_model_3 = torch.quantization.quantize_dynamic(model_3, dtype=torch.qint8)\n",
    "print_size_of_model(quantized_model_3, \"int8\")\n",
    "print(quantized_model_3)\n",
    "# Send the model to the GPU\n",
    "# model.cuda()\n",
    "# Set layers such as dropout and batchnorm in evaluation mode\n",
    "quantized_model_3.eval()\n",
    "\n",
    "# Get the 1000-dimensional model output\n",
    "out = quantized_model_3(image)\n",
    "# Find the predicted class\n",
    "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now we will test our model resnet50 with other images:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GNzjssMO44oorsCyLp556ioceeojjjz8eCIT9VCrF5z//eVKpFA899BCXXnopQ0NDXHvttVX19fb2ctJJJ3HGGWfw8Y9//B01r8DbCql4U7n99tslIB988EHZ3d0tN23aJH/2s5/JtrY2GY/H5ebNm6WUUp599tkSkF/+8pertn/00UclIO+8886q5ffee2/V8q6uLmlZljzllFOk7/tRua985SsSkGeffXa0bPny5RKQy5cvl1JK6bqunDNnjpw1a5bs7++v2k9lXZ/97GdlvVPojYixHg899JAE5LJly0asq6xv1qxZVXXl83npeV5V+XXr1knbtuUVV1wRLfunf/onuWjRojFjaGpqkp/97GfHLBP272WXXTZmOSmlzGazI5Zdc801UgghN2zYEC277LLLRvQ9IDVNky+//PKIOmr3H56H69ati5bNmjVLAvKRRx6JlnV1dUnbtuW//du/RcuuvPJKmUwm5apVq6r28eUvf1nqui43btwopQz6FJCNjY2yq6tr3LYrFO80ws/RM888M2a5s88+WyaTyRHLH374YQnISy65ZMQ6x3Gi629XV5c0TXOXruee503oev6lL31JAiPKvBEx1mNXr+cHHnigPOaYY6rK/+IXv5CAPProo6NlPT09I+q9+OKLpaZpcmhoSLquKwcGBkaU+Yd/+Ad50EEHRe8zmYxcsWKF7O7uHrM9Ukp59NFHS0Bed9110bJCoSAPOOAA2d7eLovFopRSypdeekkC8oYbbojKua4rDznkkKpjuTN1hufBtGnT5NDQ0Ii+uf7666NlZ599tpw1a1b0fqLHXKFQKBQKheKNQukmSjcZjeeee04C8q677hqzHCAB+Ze//CVatmHDBhmLxeQ///M/R8s++MEPSsuy5Jo1a6JlW7dulQ0NDfKoo46KloXn5BFHHCFd163a17XXXjtCX5FSyu9973sSmNC9QyWrV6+WmqbJf/7nfx5xDCqPWT39aOnSpTKRSMh8Ph8tC+8hbrnllp2KQzESlYn+FlGbEThr1izuvPPOnZ6ILqRywq8zzzxzzEnerr766jFjCyei+973vrdbJqJ7I2KE1zcRXYjneQwMDIw7Ed0//MM/1K2rciK6cCS2lnAiuolQmfWXyWTI5XIcdthhSCl57rnnmDlz5pjbH3300ey9994T2lc99t57b4488sjofXt7O3vuuSdr166Nlt11110ceeSRtLS0VB3fY489ln//93/nkUceiR6hA/jwhz9Me3v7LsekULxbOfroo1m6dCnXXHMNzz//PMcffzymabJ69WruuusubrjhBk477TTa29v5whe+wDXXXMOpp57KySefzHPPPccf//jHEdnWtWiaxg9+8AM+8IEPcMABB3DuuefS2dnJypUrefnll7nvvvuA4PoMsGzZMk444QR0XeeMM854U2IEWLJkCWeddRb/8R//werVqznxxBPxfZ9HH32UJUuWcMEFF9Td7tRTT+WKK67g3HPP5bDDDuPFF1/kzjvvHOFjfvzxxzNlyhQOP/xwOjo6WLFiBTfddBOnnHIKDQ0NDAwMMH36dE477TT2339/UqkUDz74IM888wzXXXddVM/OPFkEgX/5t771LdavX8+CBQv4+c9/zvPPP89//ud/RplFixYt4r3vfS+XXHJJlIn/s5/9DNd1d7nOkNbWVo444gjOPfdcduzYwfXXX8/8+fM577zzRo15osdcoVAoFAqF4o1G6SZKN6klzDS/7777OPnkk8f09T700EOj+xyAmTNn8k//9E/8/ve/jyxP7r//fj74wQ9W3T90dnZy5pln8sMf/pChoSEaGxujdeedd96E55cLz4vf/e53nHvuuXVtP+vx29/+Ft/3ufTSS0dsU3nMKvWj4eFhCoUCRx55JLfeeisrV65k//33j9bbts255547of0rRkeJ6G8RaiI6NRFdPTZu3Mill17K//7v/46wQKj09xqNOXPm7NJ+Q+qJ9C0tLVWxrF69mhdeeGFUYbx20rnXG5NC8W7mlltuYfHixdx666185StfwTAMZs+ezcc//vGqa81VV11FLBbjlltuYfny5RxyyCHcf//9nHLKKePu44QTTmD58uVcfvnlXHfddfi+z7x586qE1A996ENceOGF/OxnP+MnP/kJUkrOOOOMNy1GCKyf9ttvP2677TYuvvhimpqaOOiggzjssMNG3eYrX/kKmUyGn/70p/z85z/nwAMP5O677+bLX/5yVbmlS5dy55138t3vfpd0Os306dNZtmwZX/va1wBIJBKcf/753H///fz617/G933mz5/PzTffPOKmbGdoaWnhv//7v7nwwgv54Q9/SEdHBzfddNMIEfvOO+9k6dKl/Pu//zvNzc188pOfZMmSJRx33HG7XGfYPy+88ALXXHMNw8PDvP/97+fmm28edxKhiR5zhUKhUCgUijcSpZso3aSWOXPm8PnPf57vfve73HnnnRx55JH84z/+Ix//+MerrFxgZJ8BLFiwgGw2S3d3NxDY1e65554jyu211174vs+mTZsia9tw/xPlox/9KD/60Y/41Kc+xZe//GXe//7386EPfYjTTjttTEF9zZo1aJo2boLkyy+/zNe+9jUeeughhoaGqtbV6kfTpk3DsqwJx66ojxLR3yIOPvjgUb2nQsaaiO7OO++su83bIeP37R7j1Vdfzde//nX+5V/+hSuvvJLW1lY0TeOiiy6qOxHdH/7wB+69915+9atfcfPNN3PppZdy+eWXA8FEdEceeSS/+c1vuP/++7n22mv51re+xa9//eud9tD1PI/jjjuOvr4+vvSlL7Fw4UKSySRbtmzhnHPOqYptNF6vf+1oI6qVI8K+73PcccfxxS9+sW7ZBQsW7NaYFIq3K+eccw7nnHPOuOXuuOMO7rjjjlHXn3feeWNmBkOQUX7ppZdy6aWXVi2vnBwYRs/gOPzww6smoalF13X+4z/+g//4j/94w2McK4YvfOELfOELXxi1TG1dtm3zne98h+985ztVy2snfPrXf/1X/vVf/3XUei3L4tvf/jbf/va3x4xxZ54sqozhiSeeGLPs3LlzeeCBB0Ysr93XROusjPOMM84YM0tptHNzIsdcoVAoFAqF4o1E6SZvHW9X3QTguuuu45xzzuF3v/sd999/P8uWLeOaa67hz3/+84gBld3Nzugb8XicRx55hOXLl3P33Xdz77338vOf/5z3ve993H///RPOaK/HwMAARx99NI2NjVxxxRXMmzePWCzGs88+y5e+9KUR+pHSZXYPSkR/h6Emoqvez7tpIroXX3yRVatW8d///d984hOfiJbXE1beSubNm0c6nR7z3FAoFAqFQqFQKBQKheKtQOkm1ft5N+kmIfvuuy/77rsvX/va13jiiSc4/PDDueWWW7jqqquiMqtXrx6x3apVq0gkEtEgRSKR4NVXXx1RbuXKlWiaxowZM8aNZSxbHE3TeP/738/73/9+vvvd73L11Vfz1a9+leXLl4963sybNw/f93nllVc44IAD6pZ5+OGH6e3t5de//jVHHXVUtHzdunXjxqvYdSZmyKN423D66afjeR5XXnnliHWu6zIwMAAE3mGmaXLjjTdWZbJdf/314+7jwAMPZM6cOVx//fVRfSGVdSWTSYARZd6MGCHw2pZSRqObo8VZi67rI9bfddddbNmypWpZb29v1XvLsth7772RUuI4Dp7njXhEZvLkyUydOpVCoRAty2azrFy5ssrnbLS4amOXUnLDDTeMud2bzemnn86TTz4ZeSlXMjAwMKqPr0KhUCgUCoVCoVAoFG80Sjcp827TTYaGhkZoDvvuuy+aplXVB/Dkk09W+bdv2rSJ3/3udxx//PHouo6u6xx//PH87ne/q3ridceOHfz0pz/liCOOqPJDH43RjnFfX9+IsqEoXhtrJR/84AfRNI0rrrhiREZ5eEzq6UfFYpGbb7553HgVu47KRH+HoSaiK/Num4hu4cKFzJs3jy984Qts2bKFxsZGfvWrX01odPnN5OKLL+Z///d/OfXUUznnnHNYvHgxmUyGF198kV/+8pesX79+QsdPoVAoFAqFQqFQKBSK3Y3STcq823SThx56iAsuuICPfOQjLFiwANd1+fGPf4yu63z4wx+uKrvPPvtwwgknsGzZMmzbjgTmygGFq666igceeIAjjjiC888/H8MwuPXWWykUCuNaPYaEx/irX/0qZ5xxBqZp8oEPfIArrriCRx55hFNOOYVZs2bR1dXFzTffzPTp0zniiCNGrW/+/Pl89atf5corr+TII4/kQx/6ELZt88wzzzB16lSuueYaDjvsMFpaWjj77LNZtmwZQgh+/OMfT9h6UrGLSMWbyu233y4B+cwzz4xZ7uyzz5bJZHLU9f/5n/8pFy9eLOPxuGxoaJD77ruv/OIXvyi3bt0alfE8T15++eWys7NTxuNxecwxx8iXXnpJzpo1S5599tlRueXLl0tALl++vGofjz32mDzuuONkQ0ODTCaTcr/99pM33nhjtN51XXnhhRfK9vZ2KYSQtafT7oxxNFzXlddee61cuHChtCxLtre3y5NOOkn+9a9/jcrU1pXP5+W//du/Rfs8/PDD5ZNPPimPPvpoefTRR0flbr31VnnUUUfJtrY2adu2nDdvnrz44ovl4OCglFLKQqEgL774Yrn//vtHfbT//vvLm2++uSrGsH8vu+yycdvzyiuvyGOPPVamUik5adIked5558m//e1vEpC33357VO6yyy4b0d+A/OxnP1u33tr9h+fhunXrqvrplFNOGbFtbb9IKeXw8LC85JJL5Pz586VlWXLSpEnysMMOk9/5zndksViUUkq5bt06Cchrr7123HYrFAqFQqFQKBQKhUIhpdJNlG4yOmvXrpX/8i//IufNmydjsZhsbW2VS5YskQ8++GBVuVAf+clPfiL32GMPadu2fM973jPi+Ekp5bPPPitPOOEEmUqlZCKRkEuWLJFPPPFEVZnxzskrr7xSTps2TWqaFmkt/+///T/5T//0T3Lq1KnSsiw5depU+bGPfUyuWrVqzDaG/Nd//Zd8z3veI23bli0tLfLoo4+WDzzwQLT+8ccfl+9973tlPB6XU6dOlV/84hflfffdN+I8Pfroo+WiRYsmtE/F2Agp1TCFQqFQKBQKhUKhUCgUCoVCoXjnI4Tgs5/9LDfddNNbHYriXYTyRFcoFAqFQqFQKBQKhUKhUCgUCoViFJSIrlAoFAqFQqFQKBQKhUKhUCgUCsUoqIlFFQqFQvGOQQjxVoegUCjeRihXQoVCoVAoFAqFQvFmoER0hUKhUCgUCoVCoVAoFAqFQvGuQCVaKN4IlJ2LQqFQKBQKhUKhULwOZs+ezTnnnBO9f/jhhxFC8PDDD79lMdVSG+Pbpa43mmOOOYZ99tlnt9YphOAb3/jGuOW+8Y1vvO2fojvmmGM45phj3uowFAqFQqF426NEdIVCoVAoFAqFQvGO5Y477kAIEf3EYjEWLFjABRdcwI4dO97q8HaKe+65Z0LirEKhUCgUCoXizUXZuSgUCoVCoVAoFIp3PFdccQVz5swhn8/z2GOP8YMf/IB77rmHl156iUQi8abGctRRR5HL5bAsa6e2u+eee/j+97+vhHSFQqFQKBSKtxkTFtE/fM3zVe99QNbksWvSZ7yH1XxA7sITbUKOnzYvS/VXBlFvO6+mDKUylYvqxanJms1G8Vh6qx/Z29W43q6eUbvSnxPpg7dre18Pu9q+t/qc3W1ImNiVovrDLCW7dmFCBheZqkUCxr0SjsSXXim2ifHbr/7DTu9DoVAoFIp3MyeddBIHHXQQAJ/61Kdoa2vju9/9Lr/73e/42Mc+VnebTCZDMpnc7bFomkYsFtvt9SoUincXv//ltaCB6xZxink0zSCejNPc3EEy0YppxTBMG00zEEILbjOEQPoeUkqkBKFp0ZM4wa2ORIrgHu+tuOfdmXvLMMbK3xGl0KMlghG3S0KAQEalSt0TbaRVLEeAQATVCFm6CRSRjhRupiFASIRW3Q7pS3xk2MVj3rrV7wOJqL0HRVBdVI6x/Xj1V6+v7M9oX+USpV+y/F4G/RJoYxoIUeqdsLhfu5eauCv6unRPHtxmB31dLq0hEFFtwatw+/AIleoKwwRkGH+4TMggxtq+EFRuVFo/ij5Uty3hEjHObX3pXIpilxVLx6P0WS2dawClD+3oEQoxYhmUdR8ZtVFWtFZUNX1ndR8pw+MQXkvCzi8f3+r1Ff1dUbz04aP8iatpR8WBFZSuB1H/1qfy3A5rkoRdONaWUcnaGgFINU4dYztl56JQKBQKhUKhUCjehbzvfe8DYN26dQCcc845pFIp1qxZw8knn0xDQwP/5//8HwB83+f6669n0aJFxGIxOjo6WLp0Kf39/VV1Sim56qqrmD59OolEgiVLlvDyyy+P2PdonuhPPfUUJ598Mi0tLSSTSfbbbz9uuOGGKL7vf//7AFX2NCG7O8bR8H2fG264gX333ZdYLEZ7ezsnnngif/nLX0bdpq+vjy984Qvsu+++pFIpGhsbOemkk/jb3/42ouyNN97IokWLSCQStLS0cNBBB/HTn/40Wj88PMxFF13E7NmzsW2byZMnc9xxx/Hss89GZbLZLCtXrqSnp2fC7frrX//KYYcdRjweZ86cOdxyyy1V64vFIpdeeimLFy+mqamJZDLJkUceyfLlyydU/2OPPcY//MM/EIvFmDdvHrfeeuuEY4Pg3DjxxBNpamoikUhw9NFH8/jjj1eV2bBhA+effz577rkn8XictrY2PvKRj7B+/foR9b3wwgscffTRxONxpk+fzlVXXcXtt9+OEKJu+UoKhQKXXXYZ8+fPx7ZtZsyYwRe/+EUKhcJOtUkxPq2tHbS2TqG5aTINDa3E7AQaJk6xSDY7SD4/jOPk8TwX3/dASnzPw3NdnEKefG6IbKaPXKafQn4Y1y3i+9XJjW9UwlRtvbXXrAltXyH01hVDa3XaevWUVopSslSlmKeVfqIBBsJxBhlJjQKBjkAr/SCoKg+hCF0jxo/R1okNXpRFwKj+cfqwLByOXf/Y6yslx/LvaPAhWi3xw/+kjy/LsUpZ0Y+lwZzgR+JL8PFLfRwOOMiKmGQUhUb13ztNBOK4ED6aJoPXURkfES4TYTKbROJDOLxR53ypjK/cwKqGRj9ChAMzMEJjreyy6K0csSJcNu4ZIKhOyKt72GXFT/1C0ecnar+obmVNouWuDKzVHrvwlRDlIYPqc7fOgICU0YBJ2M+aAE0T6EKgC9BL78PBseBYV58jtZ+RyvO2vLh2wKeWev1YZwyjDsrORaFQKBQKhUKhULzrWLNmDQBtbW3RMtd1OeGEEzjiiCP4zne+E9m8LF26lDvuuINzzz2XZcuWsW7dOm666Saee+45Hn/8cUzTBODSSy/lqquu4uSTT+bkk0/m2Wef5fjjj6dYLI4bzwMPPMCpp55KZ2cnn/vc55gyZQorVqzgD3/4A5/73OdYunQpW7du5YEHHuDHP/7xiO3fjBgBPvnJT3LHHXdw0kkn8alPfQrXdXn00Uf585//HGX617J27Vp++9vf8pGPfIQ5c+awY8cObr31Vo4++mheeeUVpk4NMrt++MMfsmzZMk477TQ+97nPkc/neeGFF3jqqac488wzAfj0pz/NL3/5Sy644AL23ntvent7eeyxx1ixYgUHHnggAE8//TRLlizhsssum5D1TX9/PyeffDKnn346H/vYx/jFL37BZz7zGSzL4l/+5V8AGBoa4kc/+hEf+9jHOO+88xgeHua2227jhBNO4Omnn+aAAw4Ytf4XX3yR448/nvb2dr7xjW/gui6XXXYZHR0dE+rzhx56iJNOOonFixdz2WWXoWkat99+O+973/t49NFHOfjggwF45plneOKJJzjjjDOYPn0669ev5wc/+AHHHHMMr7zySnQ+b9myhSVLliCE4JJLLiGZTPKjH/0I27bHjcX3ff7xH/+Rxx57jH/9139lr7324sUXX+R73/seq1at4re//e2E2qSYGJpuYBgmMTtBKtWM4xYoFLIUCjkymUEM06KhoZVEvAnTjmHoJhKB77t4XgGnmCGXGyafT6PrFvFkI4l4E5adQtdL2euV2mKgqgFUZYDXMjKztFqYHVX4Hg1ZzhqOMmelHFu0kiOlrkC0K+cwV4qGFUp0KTs1yBwPy4qKNpQk00DIFeW6wzVBBm5FtqqsEChLAnw9LbJuRn15bVWbhajOhh2vK3fbkwXCD9o+QhAuR+MLn5oeGFFGlvqkXI+sKiVLK0TFcSmX9MslpShlvpeKlf6R+CUxVSd8AlxSv2/liDOlorlRZnRZjC5Lv5Utqjg+orI9FYJ71W5qj0V15vQYEY2IvkrQL6dTV5eqjF9AZU60qHz8AoEsDVBVr6fqs1cV0agnX3i+isqPF7JCPKdyX2MOAGkIXMBFCJNaP5DglV8+llGXhNeaMPZ6x2LE3sZaWRPXxMuCEtEVCoVCoVAodivvGoust5B3o+Wa4o1ncHCQnp4e8vk8jz/+OFdccQXxeJxTTz01KlMoFPjIRz7CNddcEy177LHH+NGPfsSdd94ZCbkAS5Ys4cQTT+Suu+7izDPPpLu7m29/+9uccsop/P73v48+61/96le5+uqrx4zN8zyWLl1KZ2cnzz//PM3NzdG68Hw/9NBDWbBgAQ888AAf//jHq7Z/M2IEWL58OXfccQfLli2LMuQB/u3f/m3Mz+W+++7LqlWr0LTyTf1ZZ53FwoULue222/j6178OwN13382iRYu46667Rq3r7rvv5rzzzuO6666Lln3xi18cN/ax2Lp1K9dddx2f//zngWBA4pBDDuGSSy7hrLPOwjRNWlpaWL9+fZWP/XnnncfChQu58cYbue2220at/9JLL0VKyaOPPsrMmTMB+PCHP8y+++47bmxSSj796U+zZMkS/vjHP0bHbOnSpSxatIivfe1r3H///QCccsopnHbaaVXbf+ADH+DQQw/lV7/6FWeddRYA3/rWt+jv7+fZZ5+NxP9zzz2XPfbYY9x4fvrTn/Lggw/ypz/9iSOOOCJavs8++/DpT3+aJ554gsMOO2zcehQTw/Hy+LhoIrC4kNID4eE4GQaH+8hmB0kkGmlrnUY83oBhWCUxOLiuFIrZIBs9N0ihkCeeSJBINNHYOJl4oolYrAHTsBHCqJPBWRbCRxd+qSo/qvXKWNtV/Luz21WKkqUKIu1choJjPUoZ0UHcVGQpi7LwW7K9iaKTobgrESVrDVFp1uETCcN1IovaV/u+ur2V4m215jhe31Tbs4z9PWnEUwKlPP1qfbK6DZFuLEJxuyyglnYaDFBUtKJWiC8PbERRR7uplNBHbAOAX6pQVFUjhR82iiqLlSiQstxfrw+lDLPZa+KqirG0bcXgSGVfl+uUgahb9zDJKjG/dgCCEWvqDVFQjkXUlqnIApe1NkAAWtVAQbTF6/hOLSoOfXWUNQNAIyIsVxCeMUJIpJfBd9NoehzdbEYKvby97+C5g3ieg242oOkxEJWS9fjDExNrU/Vg1M5ck+B12LkIZPChqviZ2HbB52Jnf4DSQxrln3p7rK1/QjHsZNsrkXV+FArFW0m9T+VEPqHjbffG1qUkN4VCoVAoXh/HHnss7e3tzJgxgzPOOINUKsVvfvMbpk2bVlXuM5/5TNX7u+66i6amJo477jh6enqin8WLF5NKpSI7jwcffJBisciFF15YdcN10UUXjRvbc889x7p167jooouqBHSY2MDbmxEjwK9+9SuEEFx22WUj1o0Vp23bkYDueR69vb2kUin23HPPKhuW5uZmNm/ezDPPPDNqXc3NzTz11FNs3bp11DLHHHMMUsoJT8BqGAZLly6N3luWxdKlS+nq6uKvf/0rALquRwK67/v09fXhui4HHXRQVRtq8TyP++67jw9+8IORgA6w1157ccIJJ4wb2/PPP8/q1as588wz6e3tjY5tJpPh/e9/P4888gi+H4hI8Xg82s5xHHp7e5k/fz7Nzc1VMd57770ceuihVdnzra2tkX3RWNx1113stddeLFy4sOpcC+2RJmpvo5gYA33bGR7qJZMeoH9gO11dm9i86TXWrn+JV197nnUbVvPa2lfYuHkVXTs20Nu3lf6BHQwN9TA02EtPz1a271jP9h0b6e3bxpata9m48RU2b36J9eueZcuml+jr30Q210/RyeF5LtL3CTw3RmaqVlLPWqRWeJ8wJRVzIte7UK8pv6/YJwTiN2PoOCOE7FIdfvBG+GGZQC0OrEpkZFniyyDX3S8J8VKCJ0s6lB/8TMxOJRShw7IlG5IqMbTafmY8dqZs1Taidt+SUkeU3gd2KLI0QhNo0yU1vW4mefl91JrKjO/Kn5rYkQIhNYTU0NCCuKIJCOsJ1DKqN3gCQFY0I2yTiMpFfV+hGgbNCHy26/1Ue7aX4w0tYKJ9ymgEprLldeKtK4/XLClnjpd/V2Zdj7cPGcUZtjs0KYq6aHcJlKUPZXQ+yyLIAkgHIT2kCM6dcgQ+UEBIh9BmCT8H0sVzi/heDukH2wbB+kg/Ty69jZUv3svWDX/GdwYQ0gssX4DQcEkTRMu08AiGTz1UDhLWzHlXbxBxZy2o4HVkogeHd+ePSPm02DnqTfRZK5JPtO7dZgQvqkfz6sX0VrCrGXDvpsy5Cf1xfhe1tx7v9vbVpepLyk5stqsXpvJOI+Qu/qUS4ci6QqFQKBSKXeL73/8+CxYswDAMOjo62HPPPasyoyEQU6dPn161bPXq1QwODjJ58uS69XZ1dQGBHzUwIpu3vb2dlpaWMWMLrWX22WefiTfoTY4xjHPq1Km0trbuVHyhj/rNN9/MunXr8DwvWldpp/OlL32JBx98kIMPPpj58+dz/PHHc+aZZ3L44YdHZb797W9z9tlnM2PGDBYvXszJJ5/MJz7xCebOnbtTMVUyderUERPILliwAID169fz3ve+F4D//u//5rrrrmPlypU4jhOVnTNnzqh1d3d3k8vl6mZ577nnntxzzz1jxrZ69WoAzj777FHLDA4O0tLSQi6X45prruH2229ny5YtVULe4OBg9HrDhg0ceuihI+qZP3/+mLGE8axYsYL29va668NzTbF7sGMJnEKRdGGIfCGH6zoMp3sZTg8HrzPDbNy6hd6BXia1TCIRb8C24yTjKWwriRCCouORy+dwnAICKFo2lmmhaWly2QGy+X6aGztJNUwiFmvEMANbGCFenzIysfvNcnbueKVFScwMy1bIhBVlKsT0CdQZRREJuKWIRIV1hySS/qLyIxTVXb1dHFPuf8OoEvnH7/igfYLIx7xsXxNVWJvKPXIwYxRboKBCLXL9iLLJI4FaRPsPAwqtXEbEGRwsqp4siETzitCCnRMK4OUBmPKgTOUG1Wdp/fv56AyRZduWkR8BWV22Zo0YEeXoB0fUraVi60gll2WP+rAdI07g0ZmYbFTEdzP4vgtCRwCel8P3PQwjjm6mEFqM0HpHSA/fz+B5eXSzEaHFARe3OITnFylmMpAbJJFsRzdS+LKAWxhmaLCLbDZN22QN181gmE1hZ4wWfenpgWpxPOw4Icu2QpUZ569HK1N2LgqFQqFQKBS7idfzpaye16hCoZg4Bx988Kie3SGVGdMhvu8zefJk7rzzzrrbjCYmvpm83WO8+uqr+frXv86//Mu/cOWVV9La2oqmaVx00UVRFjUE2dmvvvoqf/jDH7j33nv51a9+xc0338yll17K5ZdfDsDpp5/OkUceyW9+8xvuv/9+rr32Wr71rW/x61//mpNOOukNa8NPfvITzjnnHD74wQ9y8cUXM3nyZHRd55prrokGQd4Iwv659tprR/VdT6VSAFx44YXcfvvtXHTRRRx66KE0NTUhhOCMM86o6ufXG8++++7Ld7/73brrZ8yYsVv2owjQMAEXxykyPDRINpdhYLCXolsANPAtCnmPHTu6KRaLxKwYpmlgGjbxWALD0Ont76VQyGOYBqZhoOfy5ItFGhuaiNsx0Aw0GWxj6CbgI0ig61bJj7qUYDsRqxAZOInX0+hG2rwE4laU5xT6oociaGhGHum19fcbTUAq/TB5PBBwxa6nIEVxyrJQKWr1zdJ+KhftXAY4pYGKMEt2gjHVod6xiTzJR+mF8tMCYhy5tiLgcFuoeziiROzKZaPWLSrE3LJNiqScJSzDc6JiG4lESD0Q0isF9fDpAar7SaAjpV/KrK+wOQmz1GXpPAxF9crAIciwliJyaZE1QvpIwVyWH2SoHKsYzw4pbNuYOeoi2llFDj2VgwEjKed/l2usHGYaedDEGO/CZVF3Cx/pFchlusime0HTkFLS27ORdGaImTP3JpFqxbJSaLqNacZwvRyF3ABIgS19DNPBdTP09qxnYGAHvu8xPNzPpPZZdEyZj1MYZsumV9m+bT0SsLdvwDBs2jsa0c1GQC/1YDlWWdVGWRV75ctIVN/ZkbdRUCK6QqFQKBQKhUKh+Ltl3rx5PPjggxx++OFVdhm1zJo1CwgydSuzoru7u+nv7x93HwAvvfQSxx577KjlRrsJfzNiDPdz33330dfXt1PZ6L/85S9ZsmTJCN/wgYEBJk2aVLUsmUzy0Y9+lI9+9KMUi0U+9KEP8c1vfpNLLrmEWCwGQGdnJ+effz7nn38+XV1dHHjggXzzm9/cZRF969atZDKZqmz0VatWATB79uyoDXPnzuXXv/511XGoZ21TSXt7O/F4PMoor+TVV18dN7bw3GhsbBzz3AhjPPvss6v84vP5PAMDA1XlZs2axWuvvTZi+3rL6sXzt7/9jfe///1qQPdNwLQsNF2nUCziODtwHRfLjOE4Hrl8HqfgY+kJBoeHEHqWpkYdx/MQFMhkhzF0g6F0lr6BISQSXRPEbRtd0zF0jaamRia1teN3QDzeiGFYSOkjhIbQBLoMJiqt9GGu9T6HakG2ltHPExFlelencpfV1EAbrc5yrudZDGXngaqM8WhPO0/95AU5qiq8M0+7V0+aKkaU2dm5X8aarHQCW48oF4nCYXNr+7y29jpZ6BNMYaYyzz8cuKiqXVZmXPsINCR+SYAPs8mh0n6mfE4IAlMPQGrR3kLBNSolSssrT8NS9aFli5C10jkVgzZlcbrs8j1Ol9QUklEN5X9HCuoSSWky4FD4lhXbjrDzqbC4CVZUhV9uUCnqET7r4braDSrPXx9PuhQLw6SHe0k1tuEU8jjFPBs2vEoi0UCHbjDQu4FMZpCWlqnkssNs3PgqnVPnMmXKHAqFYQYH+/nbi0+RTg/ieh5DQ0PEYi9w+KFHkc0Msn7DKqZOmYmua3hugYG+LTQ2dZIwElDyTh85CFFeFo6zyDrHoN52u8puczZRKBQKhUKhUOw6u+rNp1AoXh+nn346nudx5ZVXjljnum4kUB577LGYpsmNN95YJTZcf/314+7jwAMPZM6cOVx//fUjBM/KukKRt7bMmxEjBJNhSimjrPDR4qxF1/UR6++66y62bNlStay3t7fqvWVZ7L333kgpcRwHz/OqbEkAJk+ezNSpUykUCtGybDbLypUr6enpmVC7XNfl1ltvjd4Xi0VuvfVW2tvbWbx4cdSG2nY+9dRTPPnkk2PWres6J5xwAr/97W/ZuHFjtHzFihXcd99948a2ePFi5s2bx3e+8x3S6fSI9d3d3VX7qu3nG2+8sco+B+CEE07gySef5Pnnn4+W9fX1jfokQyWnn346W7Zs4Yc//OGIdblcjkwmM24diokzNNzHwGA3/YPbyRVy+HhYtkUsZmPbFolknHg8hm4Y9Pdn2Ly5hy3b+ujrHyKbzeO6PqZhousGnitB6miajiZ0ikWPYsHD1GPErARaaZI+DQ3hg5R+Kcs3kKBGm3Q0el0hQ402B11db/XKN1HmcUVdE/RJr8xCr2XnJOl6VKaqjlJi3Mk8y/WEr4Uo/4xWz0RF9RFC+jhZ69H7OhYlovb1ePsv+ZuX51WtzASv3lflf+WlOgKtpHBqgVBcWYOQgFcSyj3CWRDL4juEnthBPKW6pFbhV05pmSgloJdiJngtfRl50IfbSF+WJ5mtWl8Wzyu7Ltx/tdd91Enl/Yab1hSRpfXlrPxqI6EqmV2Kko/7SII6vOCcQEcIPeqfqp/w3iIaMQiPBxU/Muhv4Zc/2JGmXsQp9uK6mWCi4niKfC5NX98WNmxaw1B6mI2bVpPLDZJJD7Jt6yaKxRzZzBB//vNTvLziBTZtWsWWLWv4/T2/5MFHHuf5V15j3ZatrFy7hSf/+iI//9UvePKvT7KtewcFp0h7eyctrZNpaGxD12smFo183yuHO6rPRSHDWTSpWybq4vEm3ByFnchE3z2Ph4WxVhKYxI9N0Blj11V5Irxe6u2vHloYyJtCvQv77mrx69nf+H9wdj3OicTwdu0XhUKhUCgUCsXbnaOPPpqlS5dyzTXX8Pzzz3P88cdjmiarV6/mrrvu4oYbbuC0006jvb2dL3zhC1xzzTWceuqpnHzyyTz33HP88Y9/HJFtXYumafzgBz/gAx/4AAcccADnnnsunZ2drFy5kpdffjkSW0NBd9myZZxwwgnous4ZZ5zxpsQIsGTJEs466yz+4z/+g9WrV3PiiSfi+z6PPvooS5Ys4YILLqi73amnnsoVV1zBueeey2GHHcaLL77InXfeOcLH/Pjjj2fKlCkcfvjhdHR0sGLFCm666SZOOeUUGhoaGBgYYPr06Zx22mnsv//+pFIpHnzwQZ555pmq7Ounn36aJUuWcNlll01octGpU6fyrW99i/Xr17NgwQJ+/vOf8/zzz/Of//mfmKYZteHXv/41//zP/8wpp5zCunXruOWWW9h7773rituVXH755dx7770ceeSRnH/++biuy4033siiRYt44YUXxtxW0zR+9KMfcdJJJ7Fo0SLOPfdcpk2bxpYtW1i+fDmNjY38/ve/j2L88Y9/TFNTE3vvvTdPPvkkDz74YJXvPMAXv/hFfvKTn3Dcccdx4YUXkkwm+dGPfsTMmTPp6+sbU7Q866yz+MUvfsGnP/1pli9fzuGHH47neaxcuZJf/OIX3HfffePaJikmzrMvPIXrOgyl+8kXcvi+AF/gOg5F18XzPArFIrqu0dzUSC6XJ5MugO/h+z6FohfcnfoS13FxHR/P8zA0g5huMqm5lWlTZjOpbToNja3YsSSGYWMYFrpW8kXf1VvZ0CphHMp5x6K8HzEyqzwqP0JkrikY2rtQnVM7Int6JwiziGutwMu7nEA7S9nD5bITj2Ss+utl5kcDG+NkhFfGMqJ/ZM1vRj8mZUrqcjSwMk7ZsEYRZJYHInTpfSnjumrnVGYN+6UayoK7rDxAQlDXvL7uEknoFy7D+EOxuyKLW1ScUPXaFk6aKcIs+RHabE2dMEJIH7m8oter6qs8GmP3dDk7fmQatih9gMoPktQc5TpVCymRsoDv58imdzA81Muk9rm0tE1nx7bV9PV3sXHzFrb19JPJSSZNmk5jQwPPv/g8A8MZpk2ZQiKV4vEnnmT1mjUUnRwbt/QxnHbwB4ogPKTQMDSd7T2DdA8MouuQdzzi8SQL9tiPRKoF3YgRXaCqJpEN21wedqgazAkHH0T1sdgd2egTF9F3kz4YNmTkwp3fvV+zYrQL3q4wkXreHpJp7enyVuxvIqMIb4c43037U7xd2ZlZ1RUKhUKhULw9uOWWW1i8eDG33norX/nKVzAMg9mzZ/Pxj3+8atLLq666ilgsxi233MLy5cs55JBDuP/++znllFPG3ccJJ5zA8uXLufzyy7nuuuvwfZ958+Zx3nnnRWU+9KEPceGFF/Kzn/2Mn/zkJ0gpOeOMM960GAFuv/129ttvP2677TYuvvhimpqaOOiggzjssMNG3eYrX/kKmUyGn/70p/z85z/nwAMP5O677+bLX/5yVbmlS5dy55138t3vfpd0Os306dNZtmwZX/va1wBIJBKcf/753H///fz617/G933mz5/PzTffzGc+85kJxV+PlpYW/vu//5sLL7yQH/7wh3R0dHDTTTdV9f0555zD9u3bufXWW7nvvvvYe++9+clPfsJdd93Fww8/PGb9++23H/fddx+f//znufTSS5k+fTqXX34527ZtG1dEBzjmmGN48sknufLKK7nppptIp9NMmTKFQw45hKVLl0blbrjhBnRd58477ySfz3P44Yfz4IMPcsIJJ1TVN2PGDJYvX86yZcu4+uqraW9v57Of/SzJZJJly5ZFtjn10DSN3/72t3zve9/jf/7nf/jNb35DIpFg7ty5fO5zn4smZFXsHoqFAj39fWzcsgEpJLYdI2bF8DxJvuBQyBUoFIoUCw5CCJqbG0glYjQ2JBFouJ6D70vwJFrJRzlf8EjEDNomtdPQ0I6um2i6gaYZGLqJbtrohoEmDKQWGSEAZQEzuluJhNZ6SmttxjMjpIGy3lpZ1q/U0kcIW35J+BNRuq6s2q0AZB0Rd1fvxsPwxtKjR9pojF5bOOFj2dol3IscUX+1/cvIukd7OqAcd22FgfQcThJaK8BHdiqjtKNC9h6xrDyh6+j3siNVkfAYltoZium1nhtSgNSjNpSbU9oOWc5ylXqwfEReY5g+roVbjAyqYpsRwmv1P+XiI46ZVu5TOWKT+siy3l8ZSqBhypqF1UegnPVe7/yAMLW47OQiSm+qW1d7fpUnIa0zFiFcnHwf3Ttew/McfOlRdLIYhkk2PcTGDRvp7R1i/cYdrNm4jZVr1rHXHnuytTvNUOZV0sMDuNJHGCabtnXR0zdEvuAF1yzXw5cedsxE0yWFgoOf93FcB89ziRlP0pBqZOasGL5XRBoeZSuaisGPqvOk3KbwfTT8EHqih4X80mst2minLh5CTlDN+fC/PzvxWsfAZ+QB0nZR/PagekM5saz2dy6jZXy/Ua48E93fRJ9S2JU4dzUTfVf3t6sxvZH7U7zb2B0i+q8vWbwbInnnoWwuFG931Dm6e5jodVINSioUCsXEueiii7j11ltJp9ORfY3ireWTnzyEofQgO7p7KLiFwKscHcPQsEyThBUrWRqIYGJkodHS1MCklmYM0wAJnu9RKOTp7u0jm8szlB0mFY+zz8KF7LnH/kzumEEq1YJtJzFNC82w0XUNIYzy5J6+DCanlS4QCIVCCNBK54kMs4Arfc6hUsusJ6LXxy9lupZ0hHBiyUj4C+6rtZIUJqkp9zqo9XuvSsydYOXjTiBZ6ojRRPS6wvYYsUYWOHXKjRV22KeVnvI1QVaXHaPeqC2M3V1jHSFZU2bkMEid+oQg0Jvqay2i8gCKmqhK4mmUsSxkKRt+5MBDVeSCsmAvtKrqqjaZCLLqV/l17UhFaVnwCdCiQqP56ldtVuVzLoKnSyYUWJ1s9HCH0qWY72L1K4+xZds6Jk2eRmfnHjQ0tNKzYz2/+vVPWbl2Kz1DaQqFIp7noxsaQgSTvLY2J9Etg8GBDLZt4zgOhtDxPPA9n0KxCJrAMgyk9PGkj2UJJrUkeM++e/KefQ5k1qy9aOuYix1rR4hE+VhUPDlQPh9ldD6FnviyctSicrRAVi6r6I5SPanGaWP2nJpYVKFQKBQKxVtOpR94vR9N09B1vfzdTpZu9gg8DAF8Cb7vV/kdRr6HKMFVoVAoFH8/5HK5qkloe3t7+fGPf8wRRxyhBPS3Edu7esjlsziuj5QavgfS9zGESTwVo6khRUOygdbmRjRNx/d9PN9DGKHXsY8vwDItpkzqoG9ggHjMpGPSJKZ2TqehoRnLtDENA8PU0UwTU9NB6CDC70glgVd6eJ6D5xbw8dE0A10zELqBpploJRFL1iiCYwmKVZOTlgTx4Duch5QlL/+SYC/8QODUdAiV+tGtMCaaGV4/ptAOJmzPxKsJhfHRMserBTwRJgVXCJYjJ2utX1/tZJGjCddVyyvEWRk2bmSQ9RKzq+oSUXxUOJ9U2MhUV1izTNbUFB7/WnG3MopQCA+zsymJpBXnZ2UDSxnYMhTAQzE9zEaPBmbK514ktEfialh3KXO9cpBG1PZQZV+UT5ixTpvaz0nYNinCsCoHWqrT5YVGTZxyZC/LyoGV8rZSelXHrhpRti2qHRWoTIgXoGsW8XiSvv5uuvp6GBoaZtGiQ7CsGPPmzGHd5m50oeF5Prm8B9JDN3yEJtiyfRAATRPkCw6armFZMHlSM4WcS2+vi+t5uMLD9QILqqZUIwfsvYg9580nkWgg2dCKaSYQwiwdT790PkhAJ5woNuymIMFclrP7w26X4fkjy4n+lAZXKiaorbIKGoM3XUQX1P8cv9V1vbOp1wnjHf23ouPe7H2O1y+jxbOr49u7chwUCoXi74dIEBcCXdcxDINkMkk8HqelpYVUKkVrayuTJk0ikUjQ0NBAMpkklUwSi8exbQshwPN8HKdIsVjE931c10VKKOSLZHM5Bvr7GRgcYGhoiGw2y9DQEAODg9F7x3HwfT/6UeK6QqFQKN5tHHrooRxzzDHstdde7Nixg9tuu42hoSG+/vWvv9WhKSrI5fMgBPF4nHwxjxAatmFhajq2YWNbFolEDNM0ScSSmJYFuPi+h67peJ6kf7CfQiGHEBaNDSkS8Xba2ibR0txOMtFELJbEMG103UTXdISmBXK27yF9PxIsPb+I5zkUnTzFYhbHCeKJxxuIJZoxjRhCM8uWIFCl3gZCcHlhrdAtIEh8kB6+V8R18kjpIzQjsJzRTDSh11YbiHuiUjystikZTdCuRVQKo7uSVRxuy2iTwMuqMuPWNEaSR0mKj/YlpQyy92sGAMqip5jYkwATHC2ozhYvVVy1baXIHHqYV2Q3C6rfS0EweaUEqVWUh2By27A2WcqkFgQ+6uW4o2zjymM3IqRIoS7VRrDf0cKHCjE9OC8qJfsy4bEK34aK+jhdXrEy1MkFMhDSw9WyPGAhS/GEQrCoORJVR6X2aYKazql3qEX9TUf2T8njI5lqRcPi+b/9jdWrN5NIttDR3o7r+8RiFnvNn81r67awOduHlGAbOr4vcT3wpQ++pJAP6td1h2LOIx6LIYSBqWt4rhvsSWhoQtCQaqKhoZlEorGU1e7hyyKa0JHSwffzOMVhhBbDsFoAo2SVHgyQlLtbRMdQIkc8pBAcj4qBrZ3QKt+STPTdZXoRjmv9fTHWxXqsMvV4I0WDicT5RjORftlV4Xt3HgeFQqF49yGEQC9lj1uWRaohxdTOTqZOnUpLSwudU6bQ0tJCW2srmq6hazq6oQdZTgJ0TceyLUBg6DqO6+A6LpZt4TgOmTRYhoEmNDzfQ9eNQKDXdaSUWKaJZdtYlomuG/jSJ51O09XVTXd3N5s2bWTbtm1s2ryF3t4+0uk0juPgeR6e5ylhXaFQKBTvaE4++WR++ctf8p//+Z8IITjwwAO57bbbOOqoo97q0BQVDGeymKaOYWpYuokudHRNK2WL+uTzedIlywPHc9CyAssyaG5spaGhCd/zSCZTFAt5YnaS1tYOkg3NxGMJYokEtp3CNGMYhoUm9CgrE1kW0QO9UuJ5DoVClnx+CLeYJZMdJJtPE4+naGnpJNXQjmWlAsG7UhgP5V4h0EqTQcpRhGaBwJeBaCWlj+c54BWQno1uStANNKkBpex0qCMWirq/6xSt2XeoRYkqld6XcqdEtDJlj/NyVnBQj7YLopcsHQcI21QejCgLriPl3ZE2N2Un8XA91FcnQv/7UKAWNSJ02dakXDJYU95nmJ1dK3BX76iif4VfJXZWKrjVYnxNLBVtoWr7cLVfsUzW7CMUwEXpf1FRV7VAPfJcGJmIGR4LWbIHqbasHnkuRatL6rmQYVvCFPXgraB87MXIrateh9nWoQBfuUyO2K60+9JAl8QvlS0/lRQOOEgZDHZoukFDYzsH7P9etu/oYePWHTz8yP20tbSyYfN2Vq3bhmXbECSh40kfIQS2ZVAseLieh6ZpaJrA0DR83yedzuF5QbmWxgSGnmJwaBjPd0lnM/zpiSfJZgeYPXMGAK3FNELopJqmY9kpivk+0sM7yGczNLXOIp6aAZpdbXsTZeEH/vuy3FNjfsrDzPbxeEsy0d+Odb0zeDsI0zvDyIvNu4N32nFQvN1Qk48q3o2EorlpmjQ0NDB16lTmzJ7DzBkz6JgyhWQqgVby3dREMFmUYRggfQq5Arqul9YLpPSJxWL4pcmyHMehWAx8QjVNx/M8TNPE94MbSt/30TQXXddxXTdY7ji4rktTUxPJVBLdMDEMg+nTpjFtaicHHbS4VIckly+wbetWVr+2mnVr17Fm7Vq2b99ONpvFdd2dEtXHy4Ya9VFdhUKhUCh2I1dffTVXX331Wx2GYhw0dIo5ByENkg0JGpJJ4raF53kUHZdcziWbGcD1uonFbTraW5g7ezYd7TNpm9RBItGAZcVKT9b56LqFYdgYRpDdrRsWuhFascjSU3gevufheUU814kEUMctUMhn8Fwn0PWkT3q4j+3bXyOT6aVzyp40Nk7GthsQhh18pxGiSgusvC2uzFWukiulh+86eF6xbKEQ6dqSsp/HzlPjgFKOpVJorkiQrhJ361GZdV9uQcXrimzWUAidqDeMqBSxqTtgUFN8zNjK/e0TZkkTZrGPqDvsqFpRPhSv/epjWduf0e/qpw0qOnaCCfmjZ+OXM6urn3CojGKEZUpFG2oHFioLhiJ45flbneVfO0VlaZ2oaFfV/ip3XbmyYvsKobecEa7V6dtqcb/8aqz7i1FXjUBKD88bxilmMIw4uhEHJL5XxHPz5HODDA31EE804HsOVizBor3359U1v+evL6zB0NaRyRcpFD3y+RxCQjwWYziTp1D0MeImpmlQKLo4jo+mQUNLksZUjHQmS2tLK5pw0DWwYwaub6KZBlM7m4kbCebO2ZOEpTM02EeqsQmvWCCTGaRj2l54bpF1q/+GbsSQCCw7gWm3IzFL/VY6IwVlT/ToHKs3QDKiJ8ftP+WJrlAoFAqFYqcRQiA0gWEYNDY0Mmf2bPaYN4/p06fR1NhIIpEgFoshhCCXy5NLpxEiKG/bduCD53olAVxDSkoe50Hd2ewAnueVrFoknudhWRahP3qwTbWXYOVkUbquk0qlkFKSHk6XRHqtdFOp4zkujl7ENAxihs6cmTOYP2c2mmngOA59fX2sXv0aL7+ygpUrV9Ld1UU6k8F13Simen0ykWUTWa/EdYVCoVAo3v3MnDoZfEnRcdENgakb6JpGKpnEtswgcVVI0sM5Ck6BpqYmUg3NpBqaSCabSCSbsewYhm6VrFr0QLaTMvI9FlqQ1S0BIYOMc9dz8dwCxWKeQj5bynofZmi4H6RA0yCfGyKbyZAeHiYZHyTbOIRtJ9E0HQTouoUe2q9UiNHlLOogB1RUiI3lbE9JyfgZpI/nuxj4aJodxTsW4yUthJnxhBnWkVhcyhiviDc0kAl9qUPf6gplu7TP0cXKyvZP1K89EnIJEkwCNX3kTurWUuHLIWqXVW1YzhCPBMSKYvVDrJeRG7qTi5LteEXGc6WAHu2iJGZKn8ALXSOyc5kgZXugsJuqBzOCpyrCfpCl7OrSUwwVWcd1vp1TztWX5ZZVWBRFZ0ulUF46PlHLRRBDxXMCkdd51Oki3F/5fBBRQUH59KoYcYiy1SeujFfdN5Qqrf6MhPX7+H6WQq6f3p61uJ5k0qSZgGRoYAf5fBqkRrGYZ6BvG22TpqEJg5hlsWDubLY//SK9aQfT1JkxbQqe69LbN8xwOo+uCTzXx5c+cdvGdcDzfRoSNq7r4GMxc8YkUrEYjalGcl6BjVu3kkglKBTzDAymaZgao7mlmZnT96CvZwsDfd20tE6me8dGBoe6aGudjg8YInhqOVTMKwcZgvOzciDDp95ZEHVXhTXURFAiukKhUCgUigkRCti2bdHa0sqs2bOYM3s2UyZPJh6LYegGpmUGIrUXTGpTKBRwnCDTyHVdIJjszDTNyDolnOBMKz37qmka2WwW3/cpFApYlkUsFqNYLJLJZCIBPYwpFMbD+OLxeJDBVSximmYkvvu+j2ma0baaELiui2kG/p6u61LI5WhobGDatGlM7ZzK+5a8j3Q6Tf/AAK+teY0XXniBF194ke07dlAsFiNBvfZGaVcmuqq3vRLTFQqFQqF497Jg7jxs00Y3LDRdp5DPMzQ0QKGQI5fPo+kahqFjWgYSj4G+fvLZHIVcDiFANwxM00QzNDTdKGW2RjndJcuJyh+fSPATGprQkNKnkM9SyOfIZdIMDw+Qy2dw3CKFfA5NE3huMCdNyYCYKGM8RMoKGbA2E1iW/i9n5AZz5Gj4JYFL07RSPPqIHOJ6jPnUX2nvQoS/y8K0KAmWZX/qsrwYCKKlXNZIwCzbnexKdvxYYn9oXTIyv7pCYA8HP6KV9eoKU6lllIVbvboqbbr6Za1mH1ZVU40sZWJXSPGEPR0U9qGqfHBco8ztcKvRle26jJ60Ei4P65XRQEEwd6hPIJ6GAn5VpaXBqVJGuCjXGx6Rcn+HGfla+RyiotOqMvkFombO5ooxmJqBpjB2UfFTWlbPwHsMRvSRHLku2F8goOczPWzfvopn/rKcbKHApNYOJk/qoKd7G4Zu09Exg9a2DhKJRvK5IYQG02bOZfuO7bQ1rqXoZMkVCmzb3su0zlaSyRhDmRyO62MIQaHgYOuCmGEDkkV7zkY34bUN6xnKDLNozzk0JRt58unn6O4aQuqSZNzCc3X6e7NomkVDcwe2HWfbllVs37aewYFeNm3ewOTJG0gkEui6Ri43hONkMWN+RcN9kC4Ik3DC2JouGaf/xr/vUiL6W8LuvCGuV9dE6n89MUx02507Gd94diXusdjZv6Kj1TtePbu6nUKhULx+QmE6Fosxa9Ys5s2dQ2NjQymjKcj6GBwcwCnGaWxsQrgCx3EAGBoaikb3QwFdK3ni2bYdCeVh/QDFYjESyctZ6pJ0Oo3v+wwODpJIJKrE83w+H4nohmHg+35Un+/7ZDIZpAxsYWKxGA0NDRilm07btnFdN8pSjyfiZDKZoFwqhYagpbmRpsYGOjs7OOrIIygUCqx8dRUrVqzk8ccfZ/PmzWSz2aoJoiY6ydVE+r8WJawrFAqFQvHuYMqUacTseCCAS7/0ZN40stlhunt3kM5kGB4eolDI09TcQEtTC00NTTQ1tAA+TjFPsZCJJuTUDRMhtED0DMVGP/A/93wHX/r4rotXsswDgWnGkCWtMJlwcIpFevu62dGzA8MUWKZJfLif1vwwRaeA5fkYfjhJZPCdxPdcfOlVZb9rJUFflvzXfd/F84u4ThHPLQKyJPzrgXgudEp+KLvUl0G2bynrvCSSlvOMAyonhpTh7I7U3HHLsvRWKZ4HGf5jZ7TuzLLIZqUqsNL3vIp+qKqhSvUui8nlf0W1CC7Ka+vGjAApSqJzuWhUc+mFKL2RlUJw5UBKpR5ceh96pUP4tCilTHHJWDGNRVXWsPDqtIdSsH5F28sxRBnkokLUrlDSRdguKvpYVPdyxRDVCMrPO4TrNcCLlmii0oKmNktcVi8TEim13aL6SOkhZQ7XyTA4uIOHHvoDL69eR9H3aEysZ1JzM7Zh0NjQhOsV0XQd07IYHuqnv7+PxqZW5i/Ym607uuh78kV8KfF8ycBQjkltDSCgu3cITejYVmDRqQuDmG3S19eLaRgINAaH8zz1l5fYe8F89t5rD1wp2Nbdg3QlwtLZf9EBzJq9J/FES2BTtfFVnnn6cZLJRnxfsnr1q1iWxR7z90LTumlsnkIs2Y6ma0g/j/QKFHL9eL5PonEWQrORUhtVOtuV+7UJi+i708N3on6gtTMwv7t4o9vzRtb/Ror0lVfesJ7x6qrdZqztdlfsu3opq1f3ROra1e0UozGRRwQVir9XwhsP0zSZPHkye++9Nx2TJyOlpLenh97uHiwr8D6fNGkSiUQC3/Mi8TwUlEMblrCuwIczEMYzmQyZTCYSrtPZTGSBqWsajY2NFAoFXNelWCyW4go+t4VCASllJLA7joNlWVHWeSjaJxIJoCzMh9nv/f39xGIxpJTEYjFs20YrTYKq6TpCgKkb5HN59JJ1jK7r2JaNRGJZFgfstx+L9tqLU048kfUb1vPEn5/k8cefoKen9w2fmFRlqSteXb3qrQ7hdbE7BpjeCCYSlx/dmAdUfwrLt99lnaW8rLxR6akbfESkz2gVldV8F47ehpl14XK/lO0HCB8/ygQMs+7qfUcOFaPKdeXyQgqE9BGMyN17l+GPX2Q38lZcr9/efyPGP7ve/PgnJuyF1nOV7L1w0S7vddvWrTSmGkk1NmHoOj4S6Tm4nhdM4KkZ6LqB60Iu49LaqNHa2sbkydNoae0gHk9h28lg4lBNI/QFDu1bKkVAKWVJQ9QCn3Tfx/OCtks/+M5mmBaWFcMwLYpFh1zBpbGhAdeTSC/MZveQvocvnEgwlwQe677nlPYfZO5qWmCn5/kurlOgkE+THu4lmx3EsmIkU23EYgk0K4HQ6l2zxqcy2zdq7Ah5oLywcgLBsk1IzXerwBMCWZHdXM6ArhQ/K3dR8xdhgpZ95RrFxJsfbVRlrFKtxUaeNBOprP6SUGeO8s/DP25CjuzmmgWV7Sz36+u79o6Z2V+TKR6cF4GxS2ThI0tWLlVVVOSSy+pFslSvGLHv+lplpcAeDCK4+F4e33PQDBuhxyvcb0odFh2zGp20qraq/PgJUHMwhEB6LgO9G1i35m9s2NrNxu2D6IZACJO4XaCxvZHJ7VPIZTOsX7uK5uZJNDVPYuvmDeSzwT2b9AvkC3nyBR+pQW9/lv6BYYQAx/EoFAvYtoYdN0jEYzS3NjGjs43u3gH6Mj65gkM608+mrX8hEbcxLYO2SY0YQQezefMG1q99ESEFlm3R09vD9u29NLYWiNs2yWQjlm7QvWMb2cww02ftFwyI+Xny6c2kh3vRNQNP+sTiTehWO6BXdUflcay0Ap3o3xyVia5QKBQKhQIoC+dtba3MnTuHGdNnEI/H6O3tZcuWzUAgLsdjMVKpFE1NTThFh8HiYOmRyCALPZ/PYxgGmqaRzmTwS5Yt5WwkPxK0c7kcUoBbEp4ty8LUdLq7u7EsKxDZ43FisRiZTIZisUgsFsM0TYaHh3EcJxK5Hcchm82SyWRIpVI0NjaWZoJP09HRgZSSQqEQTRgai8Voa2vDsixc142y0w0jENBN08Q0TeLxOJZlgSaIxePR5F0aYFsmM6ZN40Mf/GdOPPFEXlu9mieeeJK/vfAi6eFhfCWmKxSK3UqtKFLymY3W7ZoIpVAo3hoaUo1oGjiFHJ7QyBfydHV1kclnERrouqCQLxKLWXR2TGLWzBl0TJ5BKtWMZcUwrRiGYQaCe+m7FqUM4fBqIZFI6SJ9F9cLrOhCaw5NNzBtG90wkUjyBRPXd9F0jVjcYjjtoOsapm2BXrJGKW3r+z4IB3yJ6xbw3CDJQWg6mjCQ0sWjlA3u+xQLWYbTPfT3badYzJNqaCYWb4KSjYum6YE3+Ijr3NhEuqcQaGH6tKwUy8sDmbJCkqTcVaPm8VUlV8uKbOaJxDWe4BsJyyLq0zDeMIs+NBipCrHiyceyLUi53WVRtjLOcm3ULI3aPNqfjqoxA1lVrkJTrygzSjW7+oRBHcvEsMsqxdDKcCtbWnXMRNiE6tHw8LQpLysJ8aK2nkpKk3F6BQA03ULT7LKATyCiF/MDDPRtJZ5spqF5JroeIxTFA5scv/S50qqDEOXjXz06Mh71ygmEZmJZCdavXcu6DVspFB3cgk+h0EMmm6OtZRINTS1MnTaHoYEeCoUsiZYpzNtjEYVCHiklc4fSzJm6g1Xrt5Mtuug6JBJxMtkMmhDouoaPhq9BupBl/bYiUni0T2qiOZ8i7Th40qVQ8CkMZoknLMyYRtIyScVjTJ3SgW3F2LppBV1dW9m2YwfdvUNkCkUaEjH23nsvZs6YG9lMBU/geLiFYXq61jE01Mf0mfvQ2NCBbqaiMyG4BlbfO9UK6fWP8UiUiK5QKBQKxd85mqaRTCbYc8EC5s6dS3NTU+DJOTxEX28PAJZlYds20vNxHIehoSHyhQK6YeA4xSjL3HEcTNMM7FSEYGhoCF3TMQ0DyzSjfYZZ5lJKHNeNMqRc16XoBCJ8Op2O/MoLhQKGYRCLxYjFYgwPD0fZ7q7r0tfXVyUmd3d3093dTSwWw3XdqK7Qf900zWiS0MBbT8cwDIQQ2LZNPp8nHo9H2/i+D7pGJpsNrF90Hd+tmPjUdTF1nXlz5rJw4UJ6+/p46s9/5skn/8y27dsDH9E3iJ2dEEehULwexBgPCJbllnL23hgZt7Iyy6xqRWWl9XZUKlNddzQpWu12VVnnIfXimlgmrkKh2H20tLTQ3NRMLJZE4uK5RaZPn8nQ0ACZbIZcLkMqEaehoZHJkzuwbYt8Ps3wsIZuaFiWhW8YCD/wNg8+6qWJ9ARI38dzHVzXwfMdPLdIoZDHcfLk8xkADN1CCD2YZLSQoVDIkMtn0DQdyzKDJw5dh/RQL43JZnTNQCIxDRtdGkigWMzhecEcOKZho5smmhbMQyOReL6P6zlkMwP09ezAsExs28IpZpF+A0LoUfZ6JJBSTtCd2NccSTR0ICntu7ZExStZqrskRIvRru8lKrNW6xeo2MFYYpyovsqLysaJSPKribd6fbht9c5Hf1vJeFJs1TBBvaT72iRndlUgDwcBKgdOKifbrN55mEEuwkk5o8zz8Dcj+j36KxvWN955VNX42tT68JcE6YEQeG6erm0ryOYyTJuxN7F4C5oeK1cnNDzPpa93M2JgOzPNOMlUB0Izq/YRnFehpU5ZTA/P6MrWjM/IckHfCTTNIJh4VccrBtnjwtIYHMzT3deP50PH1DlM7phBPpfBdV1aJ88MLJicAqlUM4lkgocfeYJX1m4lnS4wb2YnMdtk1dotDAxncKSDW5RI4eMUPdZu7qK3fwjL0pje2UI+49LVm6ZQdNAtyLsOuinZY/pM9lq4D62TOinm0qxfv4rBwX46O9ro608zf/+9mDVrHjE7TkvbZHQNioVhskPbyWV7eeLxe2mbPJ058w8OBio0uzTuM/Kgj/tZHgMloisUCoVC8XeKrus0NKRYuHAvFu65B57n0dPTw/DQILIk+moI9NJkoY7j4Hsepm6UhG9Juj/I6vY8D9/30XUdy7LIVHiE27YdCd5TpkzBMAxc12VgcBDp+ziui+O5eK6LrhvEkimGhoaiST97e3sxTZPm5mYSiQSu6yKECB47LtXV0NBAOp2OvhBFAn3JYib0Tg9F8lDwHxoaorW1lalTp9Lb24sQgsbGRorFIoZhkMvlIqG9pa0Nz3XJZjJ4rgdSUiwGN4yGYeA4Drlcjnw+T8y2OeqoozjqyKN49vnnefjhP7Fp06aqiUh3JutBoVC8XRhPeqjO9Ku49R+/rhGF6tzti9oXpZt8qVXkWo7MQBs93vpL1VVJoXhzaGlupqmpLXjSTXp4roOm6XRO7UQ3DIQPuUKGouNgm3Es28KOxYnFkpimjWHYpe8jmdLTfhJNoyRGa/i+S7GQo+gUkL6PQFB0ChQLWXy3NAm8k8V1HfKFDP39/Wzbvom+/n5yTgHfd3GcPKlUJy0tHZh2HN/3KBayOMVc6UlDHcfJggTLjqNpRvTdRtOMkgWOU8qO19E0H6309OLgwHZ834FWCVoLpg6aFgjqVVrmGFQmY3vSLy2rviKGediVed2RRXZUU4UthwizvUOxdgLz3dRcgse7jo5YX06vHlFOhuvr1iKr3o52ZQ915Mp5K2u2HpnvHL6pzTivrK9+ayrqlFWhywoxW4jS++jvZLmPa8cWynXUiOtVu648hrUCeHnwuMqCJcwcr+mIWgFWhGeQdCnk+8jlhjF0E8cp4BTyuE6ePAMYZhzTSgX78R1AYlkJHNehmB/GMGPYsWYQgaCNdEshaghC/24/iqNsyzOxv8yRQByNEgX1Sd8FBG2TJtPRPonBoa0UPBfPleDrrNuwjXsLDzI0lGbunLnE44nSxMUxkg3teE6WQi7D3PkL2bxlCznXAamzeUsPpm3S1tqErgmkDgU3T6aQB0PiCYfBnI+eF8QLLqmG4AlfT3rYKQPp+wxni6xYs4Yp7a20tk4iZsdJxBuYP28B2XSazVu388rLL9M30MekSS3ELIMZM+Yxc/YUPN9hx461bNm6mUyuwF57b0XXTWRhANNuRdNT0TlW2T+7ihLRR+WNzsSo/QCorI+AifT72yFLZnfGWZudONFbl/G2q7f/idS9q9spFIp3AuFEoW2trRx00IF0dk5lcHCAjRs34nleNMFmYFkiq7zMDSP42uALMG2LQqEQ+ZVXTqoZepCHgrppmpiGGYnphWKBTC4HmgChkbST0RXGcRySySTNzc0Ui0Xi8XhkM9Pa2kpvby+FQiESw8OJSkO7l3w+H3hveh7xkvUKBJnvnudhWYFnqOM4eJ6Hbduk02nWrFlDPB4nHo9HWe8DAwNIKcnlcoFVTC4bZLLLsi972KeFQgHHCbxBi/l85P0uNI3999uX/fbdh7Vr1/LQQ8t57bU1pX7bfddVZe2iULx5lHMABcH3Me11fjV9PRtXZvDVqB0j9lFvP3UytBhd9lcoFLsXy45hmAaaLsCFouuQd7NouiAeT2JoFol4I80tCWw7EQjnpomuGQhNw9ANNE3H8z08z8X3XDzfLU30GSQ5SMDQDaRWSjTwHFwnmEBUEnihO26BfCFLNpcm7xQxLIO4rhOPxejsmEZb61SS8SZM3cLQrNIEpgLPcygUhinkhjFMG03X0XWLQAwNxXQfz3NxXQff97DsZJCk4PsU8gWkBMtOoWkG0nIxzBi6biNKk6WOR2R2EXlylL+flb8XiUo1esz6KrXX8PVEEh7CTOgq8T+cR7OyjKRiYa0ILqqWRvuv2qaSsqVLJE6HoYpqA5folVYjUFPT5tHaVnoRZYRHgZVF7bL4Xt2JIwYqIiuN2n2I6rZHYwojo9r5JBRZ3d1C1P9bJ2pey0ph38eXLq5bZPvWVdE9ytZtazFMm0SyAdO0aWjqRNeDLHQpJclkG0ND3fT0bCCeHaCpZSrJZAeaboN08LwCmmah6fERQ/GRZQ5U9+toraw8uFUvJZl0Hy++8Dd2dHXT2JSiUPTwPJ9cvki+kGdgOEdv3xCHHLgfs2ZOpblpEjPnLKRBCHzPpad7I9u2rafoF+mY0gquRz6fZ1tvhsH0QClpKUdDg43rSdK5LLqp4fgOUgpyeYf+oSymaaCbwcTDsbhJbrhAerDA8FCaNatfwrYTOL5DY2MbzU2trHx1Lb0Dg6zZvAXD1pkyuZG53Vvo799GQ0MTGzeupej6+L7Dyy89TueUzTQ1tTK5cyF23EBo8XLfiJFC+s6cS7tVRN9dHke7WmYiE5SOVe7NRwnpijebiY9g1vL3lim5KxMg12NXrzd/b/2teHPQNI2GVAP77bcf8+bNIZNJs2HD+ihDW9f1yDdc1/XItzycxDMUjkNh3XEcdF2PMsPDH13Xg4k4bTuySJFSRhN9IiGVSAaZ7dJnUtsknGKRtra2yHrF8zyam5txHAfbtquy3Q3DiCYv9YIZsRgeHo4mDxVCBI83V0xkGor9YR1hfel0GiDKbDdNE8dxouxygFgshqZpUR+GInyYBV+ZiR4ORGha8Ii1ZVnki4GAP7Wzk7M/8Qm279jBw8sfZsWKFeQKhd16jJWYrlC8MVTmolUoFAQKyejJBhOYPrFU1UQtn0T5Z6c+5pJqf9w6MVTtQ6FQvBkkEylM0wIEUvoIoWMYoGkC3/PRTEEslsKOJYnFkiX/cyMSALWSTZ1fEvo0PZiI1NcD4dqXeYQfZLp6vo/jFMnm0rieg2lZpeQAydCwx8DAANt3bGdbVxeO69Da0khL82Ta2zppa+mkoakV205G4rbnOniui+96JUtvQbGQKz1JaGHbDRiGhS9dpPTKli2+hnRlMFGzBqYZQwgd13WCLHbNRAgPXS9dQUU4sWOloCpL33VEReYtVN3vyvKvesJ4Pao9xkuJvLLaT3nENqUtpKiRZIUMMtkZ7apaI6BHlNpVGbyouIZLrU7pIDclrFaI8j7Dfquc4lqK6qjKmfeVsZWXU1W6WqSt7bOg8GhZ+6LiOFXWWzOQEPq9jyKgB7HuhB1HZYwjNqloWXkUAPDwnDyF3DC6aWPbjQjNREqfXGaQbCZNU8skAHLZHI8+djcLF+7P7Nl74XsFCtksvT2bMO0kqYY2mnVYt/YF8sU17LmnDr4XzEUgfTy3QLGQo7l1FqbdhEAvfS8IR2FEybvfj4KscF4f0Y6KXop+CWGQSDQxd848Vq7bitBM2ptTDA1nyRUdDF3DsAzS6RzPv/QK3d3bmTNrJm2TOsD3cYp5+vt38Oqrr7Bx6zaEELxn0b54jmDt+u0M5xx0U0fTNVwvi+9rCN/AzfkIXceXHlIKDFNG33nyOQen4GIbJs3xGEP9A/Q1D5LJbmXlqrVM7+ykIRUnny/QOXkSC/aYyfaeLrr7B3DcdbhugWQsxo6ePrp7h0mnC2zd1g3iKdonNXH0Mf/E/AVHYJgOYKBpCaQUkZC+K6hMdIVCoVAo3uVomkZzczN7zJtPZ2cn6XSaV199lfAGJPQJDyfpDMXj8H3t61C8DjPCm5qaogx0IBLRwyxw27axTYumpiY0TSOVSpHJZKKMb6QkmUxGk5KGwnQmk4lsYBzHwXGcqix03/fxPI/GxkY0TSOTyVRNNJrJZMhkMtHNVRh3Pp+PssYrM+gHBwfJZrMUi4HHezgIEIvFIkE/mUxG/vB9fX0YhoFt22QymaifwgGDbDZLPp+P+gMhyabTNDc0cMbpH6F/YIDf330PK199Fdd1Rkx4A68vQUEJ6QrF7kVGonklo8sjE621/ut6jJkfWPO+njDuj7JOoVC8VTQ3TSKTHSI7PEShEMwJ09DURENDK5YZKyUnmBimjW7opYH6IAs9UjClRNc0BAa+9AkzboUw0LQYrqHhFosUi4MMDm6nq3srvufR0jyZVLIRO5agSTcpFou4XpHh9DDpTJ58rkBXVxdNja20NE3CcQoYhoVpliYwNXRMP4ZIlsVMz3PxPIdiIUMuOwxALpfB9z2SyRaamzuxzARDw/34vosgSHYo5NN4xSI0gmHYJfE2EBFHOpzUfkeSkdYaitaRZUsoHU8gl6xSDB7ve1RlpnS48YhngWQgXNf9Lle1cZjdXSPWh/pnKBZHbaiYFLH0j6isNuiIaGEorteNo0ZXFqJmxSiMqKumf3fl++vOWB2OtX7i34FHnhRCBkt9L08h38/g4A66d2ygddI0JncuxLIC6zTTspD4rF27gtbWDhLJRmLDA5i6ha6ZgMCwEsTjjaxd9xKeFFimSXdvD739fbR3zETXTTKZ3uA+qKGVYj5DT9cqJk3ZE8tsQvouQhjlcyWafDSMvrqN4SBJeObXTiUrhIWdaGb+/D14v1MkX3SYPLmD9PAwGzduprt3EMsKRP3e/gGGhvNM6ZxBKtVCItnE8GA3ruvS3NyE1dXHHnvswYyZc+icMg1N1+jpSbNp6w629w+SyXu4rsAwBboRPDWMBGEIPFfi66DpGnHbxHOK2JbOwj1m0JpKsea1taQzBbq7h4nHkjQ0xmhoStDS0opl6aQzGbZ29bN1+yCFgk9LQ5xkMsHeey3Ecxy6unbQ1TXA8HCGqVOeo6W5lVisMRiITHQi9MbyMxPhrMI7cboqEV2hUCgUinchob9fKpVi3vx57LHHfIYGh9ixYxuu5wFBdnn4RTP0MweiTHTDMKKs67BsMpmsmtAzZtnMmjGDTCYb2a+4bpDxlEwmsW0LTdOjDPIwKzzMEA/3G2a6F4vFqqxz0zTRNC3KkI/FYpEAHrYzFM9Dgb2xsZFEIkE+H9yMep4XCdlhe0ORP8yUD7Pq88VCKQ6HbC5HvLS/YrEYZeqH8bS2tuK6LsPDwyQSCSzLolgsRjG1tLRQLBajDPXQTgYgm81iWxbnnHM2q1at4p4//pFtW7fh+btvAlKVla5QvFHsjDhQ8ueteIo8krNFeX0geIzzWY1univSDUc82h1KOH71IiTVvqq1/gIKheKtwI7HMGyThsYmHKeI6xYCAUz6ICSmGcey4+iGHn1sfd9BerJGcAw+98H3Jw/f80oTffpIKfCkj1/6vieEZEf3NtK5DFM6ptNux0gkG5g7fxEzZy9g/rwdDA52k80O0NvXg/RdstmhiqSLkm2fZuB5eRzXwbRjyJKNjJRQcHJkMj2k04P0D/SAFMyYOZ/Wlk7a2qfTMmkKvufjOgUcJ4fj5Cm6RayihWPaaJooWcYYaCVRsko3ldUSqKR0nQ31Rmpmiah8gKgOos71t/K75oh1tb9rs6LHEoNF9QshJLJ0zHzfLT1RYKIJo6TvifLvcDNRfwg3lFHL8Y0Rhyz/naiWW+tri2MK22MJ9aNtUq9fxxHHg9jG/ltZOffQuE+NBxtU7UOUfjuFHBvWvMDg4AAtLdPBB88tkk/3MzTUhxAmr7y8goL/AgvnL2DhwgNpa+vEtBLksgNkM8Ps6NrEo088wZauXkxTD+aUkpKmxmacmXnsWBzTsnCKBQwrgecWKeaH0dFx3Ryu5xBPtKFpcaKnESTUO5Grh+RlnRIGdnwSM+fux0B/H4ZtY9kxUskkM2fOIZ/L0t21HU0XZLN5stkCc+ctorG5g2IhzfZta1mx8iVyxQKr121i/eYu1q/fRCpl0zypiX0WLSRfyHH/Q3/mtU1dIEUw55Xw0LSKc1Nq5NIu0pfkjSKNDTGSdozZM2Yxf85cssNp1q5dy7zZDolEnFRDAh2doYF+NEOwddsOevvSuJ4kn3cZGMxiGUMMDORZMG8mixbtjee/RE//AL0DA8G1FK98PkSDZcEnqGIsbiJuOe90EX20R18qEaOUm0hdbzTqplYxGm/kuVGv7l25exotxjfqTuzN3p9C8c7GtiwWLNiD+fPnk81m2LB+PUIIDMOgWLr5CUXsUBgOrVtCAT2cTBTA9yWGIaJM8UKhwJSOKTQ3NhKzY7iOSzKRIJVKkc1mI3/xUFAPbVdkdAMXCNeTJk2KROlcLkcul4vsVjRNw3XdSJgOCT3Is9lsFKeUEtu2icfjtLS0YFkWqVQK13Xp6emJRPB4PE4sFgOIrGKy2WzUpmx/ruTNaSFKcefzeYrFIrZtI4QgkUjgOA7Dw8PYth34pWezVZYvtm1TKBSwLCuyjAn3GQ4mCCHIZTPMnTuHiz63jBdeeJEHHvx/bN++vSpDPmyzQqF466n3YHvlmnISn6yQcPwRgkvpuZ4okyy4Y/FLuWPVBOvC7EqtQiCqyLIcwVgZ6fWWKxSKtwIhwLZC27gwm1kiNA2tJKZGSculj6/nu4GVilcMLFt8H103gifuXA+Jj+MWSA8P0D/YQ1/fdgbTQ/i+T6GQJ5MZZsv27RhC57U1q5nUOpnZs+bSMXk6pmFiWQapVIp8rh/L1EC4pLN9aDrYsTia0NA1GwR4rkchn8P3XZASy7QQGiUhWMPQLfK5AtlsBs+X5HIFJrdPJRaLY9lJYvEGPK9APjtENjtIsZjHsvIYoYCuy0DsFRpV190aLUxQMRYZCenlstXXvKoU8rr2LmMJsKFgX5VNPqogXL7mlgU8qq/1JTtEzy+Qy/TjOAUSyVZisQZ0w66THR6K74y4lEcCc9U5Iyp0YhllKVf2RvB0ajmeiQvotf06MXble22leD6en3WlkD72/qr7NPqbrFkkGjqYPmMfYvZmYrGG6Hu+6+ZZveI5Vq5ezatrN5NMNjB3hk881kAi1RxYEzkZNm9ayYpVLzMwlGNHzxDJVAzD0BkayvDIk8+SL7hMnTwZISRGKWlo0qROEok0+dwAW7esoljMs8ee76WxaRYCs07so+mdlXnpYfsEApPG1hksWLiYF55/jNnzF2FoBi+88BzTZ81jn/0W07VjMyBpbp5EY1M7pt2I0HSQGps3b2Xj9j4yeZdMf5ahTA58H8PQOXDRXsyY3oEUUHRcEok4lmaQLeYx9GBOrulTWpGOz7Yd/QznHHxPkMkUMfQsXT39HPwPnUyZYpBqaKa/v5tiIY9lx7FMm8bGRvK5LMl4D0IOgO/j+5JM1iGPh2Vkef7FV9E0Qb6QB6C7p5dsZhjTsMmlt2CYcey4iRCxYJihcvBsggnp70IRfSJl3o43ouqLrCJkoufsRB/53ZVzayKDT2/2Z+ud8llWKN5adF2jY3I7B77nQEzTpKtrR5XNSphlDkQZ2qEfeOhnbpomuq6XssI9PDfw+c7nC3iei6YJYrEYEhllf4cZ3WH9mUyGbDaLZVm4rktjY2OURZ7NZqN99fT0RKJ2KJ6Hoj4QZaVLKaMs7nDS0VDsNwyDRCKBlJJ8Ps/27dux7eDLVmdnJ9OnT2dwcDDKVs/lcpimGX3BNozgZtEwDGLxGMNDw2Sy2aBPIBK9Qx/4wcFBkskk+Xwez/OIxWIMDg4yPDyMVfIXzWazNDU1kUgkSKfTaJpGLpcDiAYNYrEYyWSS4eFhvKLDvvssYt999+GRRx7h4Yf/xHA6s1vOCZWR/u5jdx/LN3uQZqLxvx0Hj3zPD3xvRSBwiZKKEfgBS/zgBboIrBaElEj8ICus4l421FKEHthPaUKAJtGFQAqJ57o4joPreYG/sQRdE6UncXR03cSXoCHIF4tV808EhANxXvU+oz4NVaaa1E4o2wa8SezM+fx2PCd25+fxnX6dnlj8E01ye3MJQh8/rt19jOLxBhACTdMDr3MZfG4DSwm3lEnu4UuB7wZzyXhukUIhRy43TDY7TKGYQwgNp5jHcV1czyVXSDM42MdwephNW7ewvbsXKWTpmqIRs00aEynQPDL5Prp7dDwvRyKZxDTj6EJjUvtUmls7GB7qJp0ZwDRM8vk0umaAZaPrJsmGFux4gnwuQ2a4n1wuTV9/F5lMP1K4CKETT9g0NjSQzeXp69uO8F0aGlqIJxqw7RiOk2Owr4v+/i4amlowDRPDsNE0I/B8F0GGaN2HdWovCTWXuCrdfYQYXf16ot+XgjmAKpdIPM9BShekQNcstNL36zAWIbTStqVrs+/ju05pmYbQdDShIaXHUP8mioU0bW2z0HUj2nZEY+uEWf47ELaltj/KgnqVa0yllrgzXuOheUiNx/pYjGZduLvmIKtX/+iiu6y4JFUMSgsN3UoxZfpCYvEEQtOQMjhmjpPFNHW6ursZHE6z/377sGDhQtqnzCCZasV3PQb7tzIwGFiidHS0k8m79A4OUiw46EJjOJ3hqb88x6EH74OBxqp1a3E8jz3mzGXPPfoQGmzdspbW1inowijNl0B13KXf5acPRv49r+4nSl88TBpbp6DrJr///a+YN38+dtxk/drV+I5Ha1sz+XwWxynQ17O5NIgFnlekra2ZbT3DDA3myRdcTMMmFY9hWZDNB3NnzZ4+hbWbdjCcyaIJiSgNvsViNov2nMf8mTNZt2YjDz3+AlLTKDo50sMF/vT4X2hMJFn8ngPwfIdkKkU8lmDb1s34vs+kyR14nsfGLduIx2JojoPr+UhfgiZpbk4xZVIT23b0MjCcobEhRmZ4kBUr/sbCBfswNNyFJ2HarAY0w4qe8Aj6xkdID+T4c1W9w0X0aib6OY98oiJ2X4brrn/wR4mgYrvX+wd7d9a1u+qeyHa7c+LGN6qu19OfE5/4drxzVoz8gzBiu5FM5NxT7Hp/jDYyrlC8EcRiMfbbbx9mz5pFMZ8nm81EIjRQygDXIq/yUEAPlwshIvuWQqEYbCeDSZECkcgvZbCLSLx2PS+YQMYN9qHpOvFYLBK5C4UCvu9H3uCFQoEdO3YwNDyE70t8zwuyrUqZ2WbJj92yLUwzmJhTEwLHdWlobMB1Pfr7+ykUi+iGQaEYxJnJZJAQZK1D5EdeKBZoa23D87zIeoVSuXASUSEExWKRYrFIU2MTlmkxefJkenp6yGQyUWa567qlLK7A8iWRSEQe80IIcrlcldd6WC6ZTEYTobqui+u6WJYV2cQYhhEI7fkcyVSK4449jsWLF/Pr3/yWl19+Ba/0aHTI670eqWuQQrHr+NJHoqGJSuGglFPuB4/kg0ToJfFcBtdOZJB1rgkR2BRoOmgCwzIwTRPT0EEDQwNf+BQLIHABH9vSKRZdDB2cYvCUjqFrOI5XEt1klNkuhCwNEFJaUvrMl3QCvdJHecR3yxqVf3f01260qFK8W6hRNt9GTOTv4+7+G5pINUX2EUIIpC9xXQfHLeD5LtL3cB3wfQ/Pd4L5YFyHQj5LX38X27dtJJNNU3QchoYGAju6Qp5sIUffwBBFx6PgO8F3N1NHCHBcl4JbpOi6OF6KTD5HppBnR28PlqVj6BqmrjOlYzrJeBOxeAPNdpzGxknE7CSGYWEYFpZlBxMj+nFM08a2YxQLWTxPkskMMtDfR9F1mTSpk9bWyUwxY5imhaGZaLpJLF6aKNUw0Np1rFislNjhUixm0XQNXTeRmjGBSZrLAnV5QeW68bcPkyvqFi3PzokIX5dUSd93cJ0s2Uw3mcwAydQkGhs7MK0gaz8IoByb73k4To5iMY2UPpaVjCZhjcVSmGaMrh1r0DSNJukGfSAlumFjmDaaMEbPki/FJpGRaF7ZH2EY5aT48O9B+XM5Rv59ScytV6J2cGz0Dq/9DL0R30tHE9Br15UJ2x6s80MrJCS6blAs5vGlxNBjmFaKPfd+L5OnzOPe++5hy+ZNvJy06eraQktrB1OmzELoGrlclq1dvWze3kfcjoEPUhPE4xYzOttIJeL0dPfSkEqB0MgM51i/YSNNDUkMTWOgvx/TTFJ0iiQI5fJaqTwUz8fq71rdygfpkYjb5LJZHn38SebOms0h7z2U1rZJ+L6P3w8rXnyO7p4u5s3N0tzajtAMfE8jnyuSiCeY1BID6eIWixi6wcuvrqF/aIDZs2bS1pLC60tTLDg4js+gm8NxfJ7668ts2thNKp7A9T1cxyMWj+P6BXTNJJvPk8vliMVspB9ncsdUJrdPY9u2TWSzQ3T3dbG9t49c3sPzwXF8pPBJJWLBAKLrUSi4pDNFWltSxGIWTz/zNBvWrydmG3R0bCbVNImWthiIBEgNKSQCH9/ppVgcItmy15jn1rtKRFcoFAqF4u8RXdfp7OzkgAMOIBG3ARgcHmZ4aBjQqPTRDb5b65G4E4rJphFkjLuOj+97pSxLveo7mZQS13NJ2PFIlBZC0D84iGVawZdzXUPmAsuWdDqNYRgkk0kSiQS5XI4NGzbQP9CP47rRF3shBJZlBcKzE1i46Fmd9kmTiCcTDA0N4bouXT09FAoFMplMJD6Hk4sahhFZzUAg3MRiMQpOkWwuTyIeRy9NatrX18fQ0BCxWAxd16NJTcO6wsGEtrY2Wltbo/3HSoMD4Y1uLpdj/fr1mKaJbdtVWf0A+Xw+yo73fT+ysQkz2sNs/JBivoCQ4OQDgf688z7F008/w733/pGenl6kVAK4QvFWI4RWkdUNMvIe95F4SDyE9PF8Ab4E6ZU+tzIQsXSTWCwYIPRKA5OGoWGYGrou8KWL7xTRhU8ybpJKBtmeg4NDIF1sM7Cw0jVJ0XMRukVDIo7jOkgRxiJABI9X67oFMsg+Da6PoQd7qVy5ZdXv354ap0LxrkPTtJIxkxZ8LTJA0w00Q0fTBIV8hlwuzfBQL0Ppfjw/8Dgv5HIMDvWTzWUxLJuElUBoJpn0MJ4UDA5lyaQdhnMZfOERSxj4UkO3dHRdwxDB9WYwPQQIzGyOhJ1Bk2AaGrZhYBkx4lMbaUi1BIkTxSKOZhGL66Wn6oKnc0JLGU03S7EbeL5POp3Fjtlk0kNk04MkGxpobGwlHkti2wmyWQ+nWMBzg0nZ7VicRKIJo/REpKaZUZLHRMyKd2Xsb0zv7FAsL5UrC+fBunCZKMmYbjHH8GAX6aEeNDQamjsxDAuQSN+HUsliMUt6cAcbNr3Auo2rmD1rL/aYfxC2ncL3XXTDwjLjSOlTLKQRQgu8470iTU1TiMWbSz7xozUKkJXZ8jLSyMNm1KTx1fwOBeaSN3+l1UvUNfX7rCyy754/Iq/H9mUiCZSVA+FleVoifYfMcA/FYhbPKdLbuwXNiNPc2olpJykU8rS1T+UDH/gQjz1yH2vWrOXlVa/S0phi3uxZeJ7Phs2b0TWdTL5IPh8MsPvSp+i4bO3uY0ZnB5Zl0N3bT0/PIAfsszdTO6czc9YsTN2iqWED2UKOwYEtmHaMRHIymmaHz4wxsQHJ2mMhEMLGtJtonzKdQw46mP6BPjRDY83qlWxYb5JMJTB0gZ2MsWPHNjqnTsdI6whNY+bM6aSzeXIr15NKxthv0Tx0obFx4wa6+gd5dc0mnl+xHs+T+H7wufU9gStheLhAruDS1ZshGbcw4ya5oRyZ/ix77TGT9+wzm0JmiKeefhorZrFor/2ZlWimsWkyQoP161bS0zPMth1phoYKIEA3BYauEbMNcrkCm7f30jeYo1j02LZjgHS6QMwwSSWygEUuX0RIie9mEboELQ5SBySabuMUxn8KWInoCoVCoVC8g4nH4+yzaB/22GM+g0ND5PMF+vv7yGQygICSSANENim+H0xWFficB48OF4vFaL0QWuSNHnqYh6K0Yeh0TJkcTZYZTtxZkIVgP1KScdxIHA6FYtd12bp1K8PDwzhFB7cUVygqh194w0xtz/PY0dWFJMiwl1LS3d0dxRlOTNrc3Bz5mcfjcXK5HMViMfJ4T5cE92IiEPJt22bTpk3kcrnIOqa5uZnZs2fj+z6ZTAbDMIjH41FMkycH7e3v76+agFVKGbXfdd2onZZlkclkiMViuK4b2dqEdjeVXvSV9ZmmGXnCF4tFPN/joMUH8p4D9ufnv7iLv/zlr5FA/3pQGekKxa4i0XWB1EQp0w+CjK6S/YJ0AQ9NgGWaOIUijldAFxq2ZRKL2diWFUywJV1ipoHrOhTzOZyCxLR0wMdxCkhZmnRZGBSyWWIG6LqBU/QCuU3T0WKxkh9oGqELhE7p2u5HA36xWAxDN9EQGJoI4h0va+2N70iFQlFCQy8JsZQ+lsFAlyb0kp2JGTxxIiA9PER39zYyuWHSmWAulqamScSNGEJIdD1BLGbS3NJCU3MTVizGQGaIXDFDwc0TPlXo+z66qSN00ETwlFzR8XGKBRK2TUOyhY62Via1dtDa0kEq1YJpxQObFdPGMgI/dKeYw3WDyeJ96eA4DvncMJlMP46bRwqdeLwBUzfIF7MMDQ2Sy6XRdYNEvAnLMPF8L7iMSh/LsmlozBGLJ0kmmzCsGJIY5SdlysJwrXwYug0Eg5slmVHWWpVU2JyUXo0QgyMBtmIbISpeU7aBkKXjJUFoOnasAdNKkBkeZHioBzvWiIgLfN8lnxvAdQpYdgqtNIDR272D19asYfv27XjFIrNnLcSykwg0kokmNE0PMqB9F6SHZSXRNCMamK2256psaHVGfdRn4VNKtUnjdQj3MfLpdlnn+6OsKB92YaWYLiv6eayn68sDFhOZFHQ8JuJ0ICvGngMhPTgzNN3GNG0yw4Gn9vatG+nv62fGrD0oFtL0dG3DshO4joNTDJ62HeodwHfhPfu30ZhqoqdvgKf/top8waW1McYec6aSy+fZvKOboXSBtRt3MH9GJ0J4dLS309bSRi49xNo1K5g/fxHNrR0Utq/n+b/9iUWLHKbPNIjFJyGEWQpZlI5QrZVLZScAwit1tVY6GTQMu4W5C49icudctmx8jaHhbgp5h/XrN7J+3XrmzpuBhsD3PFateBHDNhkYSlN0ikF+lu/x6rpN5AoF9lowg85pk7HiNkPpPF19vfieKCUdBdZ3wfEoPU0XM8gWCmiOg+M7CBOyTgbdMJi/x3weePAJ+tMZPM+hsamR6TPm0dQymcaeLoYGhmiIx8llXbJFJ7puFopF+gcFuaxDLucwY0Yb7S1JGpIxJre1MqWzI3iCp1hg06aVTPMdfN+jqWU6pt0BMkdmeAuGnRz3vPo7FdHVV0PFO5HxztvXc15PZNtdqX9ntpnIKGq9+ur/8VUo3u1omsa0adPYd5998T2P11a/BgJ8360SWrUK+5aydUvwxUbTg8+L67n4PtEjp5Wieyj0QuANPnlyO7quk8/naWlpKfmmu2ULk2IRHYHrusTj8ci2pKuri56entKNVlkED0XkcBJRKWXkge55Hl1dXZGo7nleNCGqEIJsNksul4v2k0gkyOfzUdtnzZqFlJLt27bju8EEoYVCgdbW1qjecILT0Bs9HDgIRfl4PE5rayu9vb0kk8lI5A995kN/80KhULW9pmkMDg5GHunxeDyyuAm3qSwbfqEPhXcpA2uI7HCa5uZmzvvUp5gxYyb33ntvNFHp673BqH3EVU1iqlCMTzi3XeBzTpCNWHo8WguFGw10DfS4QVIYGFrge27ogQ1MkL0usXUT33EpZofJ57MYpiAet9F0ie97OIVAME/EUziuxC/m0KSOLjQ0guuhJyVx0yTn5fB9D00HTRdYtggss3wHXwsEeEPXcB03bEltyypeq3slhWI06tlDvL4K/ciOQUrwpRdkdnte5LMtBFh2nPZJU9F0g66erWiajvR9XCdPNhM8feL5Poah4boS13Hp7x9ge38Ppq2hG2AYgZ2UlOC7PlKAZpZEeilojiWY3DaZ6Z3TmNY5i+amtkDMNmyEbqLrFqZpoxtW+fuh9PG9AkUny8BAD93dm+nu7UJogrb2SaQSDRi6RdxN4TpFTMsklWwkmWwKvqOW5n2Q0i8JfEGbPDeOW8zh6jqakOi6FQwqaFp50CHswtCvqiwbAxVJ42EmtSwvq7oCVpiEV/qYh5nolSYnsvSfkEGWtusWcZ0cRSdDwckTizcQjzVQKGbp7n6NpuZObDuJ77rksgO4boHGpg4SyWZmTt+DpsY22lqD96YZBylJNbRSsGyKxRxFL4ttJ0ik2rBjjVhWqtpvHSgPjlKytAnum8tx///s/emTJEma3on9VNVuvz3OjLyzsrKqurq6+pyebmAwOGUJIRZccAmAssL9wCW5oMhShLIU/CUgP5ECkFjhzoJcriyBJYgFsRjMgZ6Znu6urvvMOzPu8PDbblXlB3PziMzKqsruzq7OHsQjEpkR7uZqamZq6mrP+7zPu/j7ca79c/FpovyLUT+vP8lW9rNU009Sv396ffs099xpkcjnrWVPFx1d3nuLvlR2OIBw8BtrdBDMpu9zODik1bG8GLXodNcBRdho4Xkhruezu/eAq7Mph4cDilzjOB7XLl/mT998j8EoJ5kXrHT7nD+3ysrNu9zd2SfLDR/desjaapPf+MbXuXDxCo6U3PzkfQ72dtk6f4XzF65T3it4840f0GiusLHZQjkKUMs+fx6sMFgzJ4uHIF2CcA2EB1R2mkGjR9Rssv3wNkpJ1tb7WEqMtoR+RP/qGsYY3nrnTd764C7TOCcKA7LCEPguO/tHHBwNaTaDZS2twHVJdEXcS0cgRTXnWGMX9a9ywoZLI/Qx1pIlObu7I/71v/0J1y5ucenyBfTtbYaDhE8+eJ/jg0MuXLrKxsXL/IW/8NvYP/wB3V6b+9v7zLMcXVrm85IsM5jSIBV4jqIRNDi/uU6v28Z1PSRVQsuHH/yYDz/4IVevvcrrvQsIYdFlws0P/i1379/nP/pP/8rnntM/gyT6k6JbT0O8PZu2n6Vv8s+zDfxsE8zTfO5nPaZf5jl4lp/79e/D538JPf3+nnbLn3ex+LRfuE+zzdPc288/+XPmk36Gnxf1yPF9n9dee40LFy5yPBgQx/FSLQ5USnI+/aBXE9DGGJQj8TyXLM0xZpFgunh4qEn4mkS3tiqeGUYBFoMQ1UNMlmVLAlrK6kEoz3KiMCSKoqWCPM9z9g72l57ixpqlP3hNKFe8fUUc1X3WWjOfxWhdWcxUx6CWnuwIuyh4mi49x+HkHhsMBpXtgaoI+jRNyfOcMAzZ3NzEGIPv+0sFuzGGRqNBo9FYepk3m01c16Xb7S5J/brPp1X2UFnIuK6LXNjGLIuY+j4HR4cIW127ugDgysoKs9mMJElwHAff9wmCgE6nQ5IkzGczPM8jjmNC4Ld/68/z6isv8zu/8zvcun2nouGegVLnDGc4w88AURX+rNmYmhAHg1R2+ThubUkYBTSCqniVLgv0olCo40gC30cpi6sEnhLkVpMnGZIcz3MWnsAZeV4wGw3JM43vN+n31oiCCHCI84x0noKSKKEpqdqXUi59c402lDqnLHOEkKilBcBpkqNKuX5UzWi+tCXV2Rx2hl83PMsxWxQZQoCUDohK+amLgrxIybOEJJ4yng4YT47RZU6azojjKdpqtCnxPAmq5PDwgMPhCOlIsIIkzUnLEsdxyIsCW2iUC66SKEehFOS5pSwNnnLotVpc2brAtauvsLl+iV53lTBooZRfFbeUdUFKgTWGoswpsoSizCjLar0zHB1xf+cOR8MBxlbEdb/dYXNtC+U4+E5E4IXVPGQtUdTEc/0FeS2Xa1ClJEHUxHMClOOjlEBJiZISIau56vErcPpJqlboIuyyz0Kc0uvaU2r12oxFPNZKbXl1attTO1iss+UiEFBSZgl5MiPPYqw2WAyzdE4azyrveKXI0lkVMFWVQn/rwg3WigzH9QmCJtoUzMaHpGkMEqQVYCxGGwQK1w1QTk2g10T5KWX4afX8p0jrX/xZ87PH/aNit5Pnj0eV8Z/TMnUQ6UTN/rP393FhyOcT6QASu5Tpa8oyJk+nIBRB1ENKD9eLaDS75FnJ3s42O9t3uXDhBaJGg93de/hBA8dzMbpkdWWNV175GufOXaHUBbt722gtcKRL6DdoRg0aUYPV1R4f3L5frfubDUxpOTjc59rVa6ycu8KrX22ys32LweEu/ZUN1tcuMRq/izUau7yu9mScL8/yyfm2JkPrHNCYMmM83AElcb0Qx+mC1RTZmMP92+RFwle/+X2KNGc2GbK+dg7H9UiTOePxEMdxWO2v0Az2OBrGxMkUpMD3XFrNiGboMxrPiJOSRhhweavH8WjOJKnGsVLVnKSNwejqfEu1EDEpgee7pHHBw+0RB/sTzq12+fprL7C1uUa716PVaTMdH5MfpTx48ID5bM7ewZCyFNhSYkxJaSxGWxxH4HqKvYNjkiRlNJzw577/bZRUHA2PGY0nSEdwcHSA47Z45dUYNwDhtmm1N7l363e/cJz9GSTRz3CGM5zhDGf4swdBtSjt9/p85zvfQQjBzvY2ZVnSbreZz+dLP29rWaqb6wWV67rLxaWU1aI/yzK0Np9aZ3uet7Qb0VqDELTaLTyvIl/KsqTZbJIkyZJAt9aSxDHtVpsgCJYe47Vy2xiz8PGsemRtrQqqiP8kSStllNG4rkuaZouCmtXDTSUGEotgQbX49f2qP7Wau95PrfCO45hz586xevkKw8GgKt65KJw6nU6rgqELor/2OK/Pm9aaJElQSi09z2vFe+1xHkURcqEwrQMOeZ5TFMUyYKGUwmLR2hAtPNjrYqu+75PnOa1Wa2kHUxQFnufh+z5RFJFlGWmaLs/nSr/P3//7f5//9p/+M373d393qYj/ucfVU6p2fhacKdrP8GcZVT2JqtgYdlFwmUp9LhFIURXv9D23KhKqy4X/rEFJqsKioipElxcZpixRwuBKSJOUWT5FObJSouc5aZ6hC43rRphCc2xg5sxwVIDnRQSui3UEnmfJjCUvNdpojM1R0kcoi7CGUhuwAuUrqlzsUxP/kkA/TaI/Rb7/M8TTzBdngoMz/JmEtWijyfMUrQ2CqoCx1iXGVsKDPJ0zPN5nHk9J8zl7hwfkWpOkCcpRuJ5LmqbMshyTgiurgsOuKwhtgGddrC3RukQXBqM11hFIabFK0O11efmFF+h3+jjKEM8HOK4AqWg1Q5TjLtaX1dxnzcLaynUpipSyyJjPR+zvP+T2/QdMkwQpBe1GRLvdpd3u4Xsh7Vblh66UQjkurlvVbKgFFEJKPMdbcsCizuIRDlKpkyKdn4JYWrPAKTLxMzlfe2p6O61i/yyptvjUr/VHHNcjkn08LyBq9CmLBF3kDIc7zOZjBoNduv1VLl34CjpqkyZzsniGKTOMNaRpjJCSPGthrWF35w537n2IHwRsbVyi1ztHs9klDBuLbEZxopyvu/Sp+fOEZH/y8Twdvmhe/jRZvtjbI2vKZz9vf9Ga9fOyRU7WvZx81S2a0mXK0cEdPL8qlCs9tbBqMzSbDfJSs7tzl9APaDRbzMZDfvBvf49LV69z5fILmKIk8BsIoSjzOW+//Q4Hh2OsVWRlxo/fepf27Yidg0OOJzGeE9PwAy6dW2O1t0q3u8nK2hUcx2NldZPDw4dIUYmFiqzAcargOKfK7J4OJ9W/WzRGpxztfcDxcJ9G1CHPE7r98wgkiEXmgoVkPmZ3+z5CuJw7f531zSuMjrc52H9Iu9OralONR1y9cg3HcTE//ClHo5iiKEmSjNkswVnt0uu2ydIReV7S3miwvtrm3Zv30KrqkTEWiQSjKItFxrHVmFKDBs9zF4+EBm0N9+5tMxoNuX33Pl//+tfodTrs7e1wNByystajsJqHu8fMtcFxXKyCsiwAQasRIaRhNEko8pKbN+/yzW9+lXPnzjM4Pubew4PqubQoydIpjWaGEIprL/0Wf+NvfvH4OyPRz3CGM5zhDGf4NYDjOFy+dJnXX3+d4XDIbDZbEq/j8Xhp0wInC8TalgV4xHe8JpxLXVAVspKL4nbOsjhmva3jKHr9Ho6jHinkGcfxUlXt+z7Hx8cUCxuSuvCnlHLpMd7r9Tk8OjyxaimrBZwRAmns0pPQWkuWpsvtam/L032qiXttDEqdWMLUr7uuu9x+OBxWqcKLoIGUkqzIUY5DHqfEDx+wsrJCEAT0VlcQxjKZTHBdl0ajgbWW8Xi89Enf2toiTVMmkwmdTqfyg88yZrMZQoglkQ/QbDYZT8ZIK8nyDLtQ+Od5/sg5LIrKRzQIAprNJrXXulIKrTVhGNJoVB594/EYgL/39/4er7/+Ov/oH/0jDg8Pf4kj7wxnOEOFav4xC/W5rUx8F6nflUWCEgIlBa5SRGFAWWSkaYISFmeRFSOo5t88zbCFBjS6LDBlXqlO52NKW1bKxrIgLwqKrKDdXSWbHzGb3SJNNZ4TsHXhCiudNWTo4HcU1q3SmIUEawqMXRSIVhVLoLU9xRzwKLdhP00SneEMZ/g0nnWA2MJCdFCQJjF5npKmc4oyQykHYzR+0GBj4xJHg13G2xNG0znj6YyszLBC4LouWIPWpiKaXQ+JxJpKZW6RYH1KCjKqApW6UmcgLMzzjNu7DxjOh7QaDRphk8aoRbe9Qq+zjhQOUio8L8BxA6Rw0GVBnqVoXWAWhFhhCqSS5GVBXmjaUUToB3iOSxQ0CIMId2Fzp3XBdDzBInA9HyHAFQG+H+E4Ho7j43o+SnkIqRCfQaBba7Hi02E/a+1CRX6KVK/V5dWhI6REPEklbQVGLMjapZK9bmKxvaWyyzIFpc4xusSanCydMJ9PKE1JWVTv7R88xHMDzp17gV6jQ5rMGA/3GU32QUjCsEGRp/hhA8/zObdxedFZieN6KMeDRZFVayyLTlWkqZVLxf1pH/da1f2zDtefZXw/VWDT1qr9+gXz6NvLNh7LJXhiP06eA56G4K//f1ShztLoZmnZsyDTpXBwnQAhXZAu1mqydApC8/Kr32A6mnJ4sMPO9jZXr93g2gtfZTye8cZP32Jr4yKbm1scHT7g8HCHVrvD9Rde4odv3WQ4zcjyOUmW43lj0izDdapimH7gcPXaRb73vb/K+rkrSOWQFzFZFpMmMwZHe+RFzvUbr+K6HsZWxctP+8ufziurjrFE6wwpHJJ4zGh4QKvVResCrZMqq1gKDJo8m+O4LqtrF/GCFkq6dFe2MKZgNDxCl4YkibEWur02r9w4j8VlZWWV27e3+enbH+J7Dr1ui/k0Z2dwzNsf3eHcep+V1Q7TdE5pLTaHPKnmBWMNxljIq4LrrnIQSFzfIfQUjdDj+vXz+J7L7bu7/NN//q9oN5uMJlP2Dkdc3FrjL/7Wt/mOFvzxD9/m/Y938HyX0PNJi5KDoyntRlDVmZEwmAwYjgZsbmxy/tw5BkdjNjY2uHzpOr7XAFuSxbs8vPcBKxuXP3dcwRmJfoYznOEMZzjDcw/P9fjWt77JlctX2N/fX5LUcGK58vhisibFgSXxfaJUP1lUW2txXadKD6Va5JZlWQkUbG13EpBlGdPpdOllXhcVjeOYdrvN6uoqs+mU8WRCnmWUi/1prfE8D+JKKV8URbVeXSzu6z6VpX6iKrru7+PHWBQFQkMQ+EsluOu6S/K9DiCUZcl0OsUaS7Gwj5ELCxaLpSgK9vf3lwR2p9Xi0qVLSCk5Ojqi3W4jhFja0wghmEwmeJ6H4zgMh0O01kvV/Wmf8zzPUVIt7BuqB9DFQaEcRbpQpJdliZCSJE0pypIgqFTvArEMDhhj6Ha7XL58mVJrXMfhL/zWb3H1yhX+wT/4B7z/wQePPCT8vFZsZ0rPM5zh81DZWxkWQUm7sG8RFikVSgkcKRdBR0lZVN6fdqFEX1TjIo7nJLM5apF9UxY5WRaTzGeMR0Nm8YQsSyjLgrLUlIVmPBrz4N4et2/fZ2f7gDyDa9de4oWrN1g7v8LqxR4rmz16qz3arSrAVxYZyvVwXRejHMrSgNWc0E32MTLh0dTwM5zhDL98FEUKFhzHo9n0SNM5eZZwPDhkMNgnjueUGJRyGI2PGAyP8RwH3/UpCkjzjCytSK7A9fCjSkjgKAfPh7KoRAulthTakqYaY8FoKHONdKA0MyazmH3fxfMcosij1Qi5sL5JdHQXTFVXwXX9yt/ca4IxjCdjjIGyKBlPpjzc3WEwmpEkOdoYpFSkWcJ4WgktDNBsdPCDEFMaDFXmjtUlhS4ACIIFie76SKmoixMiREUgn0JNji9tZhZs6HIrUdsVLv+ktq8StS1XTUTa5TS4tHYRgiVhLmrHl0VxRiGr2bPUmiwec3hwj+l0xP7BLkk6ozAWXWocB+J4RpblWCyNqEUYdvD8AFd57B08ICtyms02IGlELTa3LhM1OuiywGrNcLiLH48Jw1aVyRg2cd0Qx/EQQi3696jyW4iTI/5Z8POsA7/IqtQuM58+bfnyuEhm8e5n7IfPff9JePx4PvPorMTxWnRXLizuSQ0iwA/bqOmIVsthdfUSKysbTMZDGq0erc4q34taOK7Du+/8hL2ddYazEcPRgMsXL1Dokl63xXheYIwlz82iBpVTjX8NniuQwhI2mhhTsn3nPf7gD/8VZVmyvrpOmsSkacL5Cxdpt27juD6NlkCpxuJ4TgVUFudYihDHrYRNs9mE49ERR8e7zJMZSTzCDyKU4xH4LbIi497DD7nywtdotlawtsSaaq1z+/ZN2u0V0jTlzbfeI9OGMBL0OyGNwOWrL12i0/b55PYeviu5cX0T65aMZjH7w2MEVf2tsizxVICnXMqiqDJtSoPjCVb7PUJfYXQlqPJdQbcXMRyO6XSaXLmwwfyjlE9u7xKnGVHks7newxXQ6DTZWGvzwSc7JFnBlYsbFHnOaDyn3W6y0gtIswSrYTabcj+LuXzpBW68/C3WNy/T65/HDToI6SPkhDyZ89E7f8L5658vR/8ZSHTzxZt8yrP4Sfh0usenP/ekbZ4Wv8hnf5Vtn+EMfxbwtPfIL1Pi9Phc9TTz0hnO8Pyi3Wzxve9/j8AP2NvbYzweL73Ea2X4adQ2IrVtCfBIYVE4WcxK4SCVRCqJtVWBmVIXy1RdANdzlvYltT2J1pXlSm2PUr+eFwWzeE5ZlhRFsfRWz8tiqRqpClHZiqyp024Xi+jThUNri5jT79XHoJRaKO9Zfr4mzWuV/aMBg5PP+77HtWvX+Pjjjx85Z7WdS5kXKKnY3NwkiiKGwyGNRgNjDEmSLIt6+r6/tLWZTCbLQqpKKYIgIE3TZb9cWdm05Cav+u9UJL5QEj8KybIMjK7O27hETiWrq6sIawn8gDiOiaKIuohXs9lkOhri+z7NMOA//8//9/yD/+P/iXfffe9T4+EMZ3g6PI/r2y/XTuRp91fZKywjgSzqjOJgcRZKdIxmNh7RjHxa/R66zInjOePxmGQeo02BEgIrBFmakMRzkmROXmTkZcF0NmM6GZEt5l7XcRgOjonjmNVexHyi+PDhXd5582OkhK+8foGo5/K1b3yF733/N/jm118n9DySYo7SDkI4oCUUAj/sYoSHxscIp+q5lRWZLpyFxdbC5/1LDKo9Tdzv6bvzPI7nMzyKL/caPc/x4Sydo8tiUfPFQesCqRSNVgchYTQ84pObH3E4nDCZTau1nRDkpWYWz0jyDIHF81xaYYMwCGgEIXmmSZOCLC9AGopCk+QLJaixKGmQUiD1Yq1lJElpSeKc2TRmGsXESYbnOISej+u6eI5D4Ab0O2tYo0EorBEUZQFYWo0Gs3lKmmqEKonTnN2DQybTGWF4TDTaJ/TbSCmYTif4fkC/u4breYRBSBh1UNJdBPdqr++q8Kp4VM58OgRYFfmsf69fF0+2ukA87iMtllaDAJgTe5eKOD8h08Xia8LYuvBrjtYlynHxgwbT6ZDR6IjReMRgPGN774iwGbDSbfBg74Cbd27T7/fZ2rjE2uoGvt9gZeUcf/Anv4cjHcLAQ1jB5MqIrXNXCPyAJJnjuFVBV2s1xpSkyYiySIiiPo7n12Y2y3H+WfPpyX1w6ryIX/zm+Dx7FWs1aTwizeY0Gj1cv7mw5TkV3PjUZ59sp2OXGVOfJuO/sI+n2nm8/aVwRzq4fgspqqK91mgcp8nauZeqDIPJQZUx4XmkyaQSE82H3Lt7n/c+vEMQPmA4nSIkaG1pNxs0wgBrDLpc8CRW4bsOjiNxpeTcWo80mbO/e4dud4UP3nuTO3fv0Wo1CX2fKGoShBGtZqcKYjk+UrgLP/QCo3O0TsiyOVhLEPZRToRUXmXfYhW3bt6mt9LB90PKIuXo6IDBYEq70+f+g7uMp2OE+u947dXfoN9fY3fnFju7d/g3f/gjRpOU1dUO97ePGI9Tup2A73y9g+83qnVLVrDabfPJnQekZUmJxXUcEpHjuR7WWLKyZBInOK5ASIHyBI7v4ihJkif4ToTnuczjhOsXL9Jvh5R5ydHxmPPnzvM/+ff/PfZ3D/jRT9/heDQhTzPyrLJgMbqqk2W1YXA8ptUKKHTJzv4RxrZ46cVNXrh8gXOb55jNJ/TXtjh//gaNzgbKaYFwAYUfbXLlxW+TxIMvHEtnSvQ/g3geimOe4QyfjTNS+0k4u//O8DiEEKyvr/Hnvv99ZtMZDx8+rNTT1i4KayocR2GMXKqvAZSq1M6V4vvRRe2TCo1WChu7UJ7bR953XRdjDFmW4brusu1mswnAwcEBrutydHTEZDJZFhuti5yetlip++I4DmVRYiyPEOXW2qWKuybs6/frwp11G8tCplYvP1urwU8fwwlpXynxy7JECMH29vaSsF9awyw+77seo8mYZrtFs9FELM7TbDarinPlOcHC2zzP8yWpXZPmYRjSbrcxxjCfzym1rvwHebTwaB0MmM1mS/uWug8A+3v7uI4iDKoirXXAAiBJErrd7jJY4SjF/+4/+8/4J//k/8Ef/MEfohdFWD8PT+Nb/vN6m5/NZ79ueF7ZpROF4PO0P2sWykgJwhqkLZGAqxSNwCcMPDxXYfKcIs+Ip+NFfQdDGHiEgbe410uGw2NmeUxSJOQ6w+gSIxVB1CIMG7hK4rsOwpYIDB++/z4zEqSOCd2M737bR+c53d4M6RmY3WR4p+S+vc/lrXVIpkzjlG53lW5vA6e1wqwYo6ItchSpkSS5JisA5eA5LuPRkChycB27UM//YpDy2V1DJXmq4Wqeckg/q5EvF0rVL97fF2/06XKJP39bzxrPdo/PsrWnOWfPb4A5iWckyYzaj2M2n3Lv/i22dx/i+TW5HNAoMgpT4GkXYyyu1kznMUlcIoQlzTSFtlgkQoPjeXTaEb1ui7LQDIZTsqys9mMshdZobZEKnBwKV6IcjRDgeQqrC/JkjBQSRwmEEriOJPQDdsMhSgrW++t0On3a3RX6Ky79eE6z1WL74Ijt3X2GxwnpfA/lSILAJQg8mo0GvutTFgX9To/5bIwxmmarhbUazw1Qng9U6nelnCfeF1Ux0FNeIaf8TIQ9eQmxsAa0dpltU5HmlpNSowt2vCahlz9iaZ3yqGrdkOczZpMjJpNDvKCB6wWsrF3gBSO4eesDrPQwVvDw8IDAd+l1WjiOTxbn3L3zCR9+8D6lLoiLkns7O6x2O9y4fJEL5y5hy5Lthx+TFhlFkROGDV56KaLZ7uP6IWWRwdJLXiCQteHYon+PWqOcBInFqdvF/szfsKfFKU+3ramIf10wPLrPdLzH6sYLuG7ls1/5ez/h2tbXbqmql4vLeyqI8piK/XHYkw3rVllGQZ545GZxPkuMLbFFieMGC+sThetFRK0VsixlONwnz5tVkXGheOXVr3Hx8nXu3LrN7MNPKLTh8GjEcDhjNk3ZWlsjSVIms5S80GjAkYrrly5x/dIF2u2Qg/37KCXZ2rrIn/M8yiJHCJBC0e2t0Gq3q6K61i6sjRY1o0zGbLTNwd5tjkeH9FcusLZxGdcNkEKysXGJjbU7DKdDDo8OsauGJE2493Cbu3/yFsZY1lbb3L93j8lwQKvdJPQbpHnGyy+9yJ27e9zb3iPXlvWNPuv9Jt12E0zJbDLjrXdv8Vf/8m/z2muv8sMfvsn7t++SlSUX1lYZT2doBIHrYhbPsI5DFWQoDNKCVpLD4RRdGDzXYXvniDhukswzdnYOef/jbd79+BavXL/MV1+9xM7ugPF4xp0Hu/R7XaZxTJrmWCEptWX/YESWlbie4mg4YXPSQAjN+++/Q55ritxSlCntdp/1jRcJGutIGSGEJGquYu0XP0OdkehnOMMZznCGMzxnEEJw7dpVvvrVV9nb22U6nWENS8W366nKIgUo8kptvlQ9u+6S0D1NENeoCeqajK62qQvn2EcWyLUlyeM2KsYYGo0Gvu8v9z0ajZYkP/AIQV0fU/13EIaUhV6S2zVxfFptXpPqtde7Umr5d92+Xai360Kop8npmnivC4PWBDrA0dHR0v6lPie1Fcw8iRGp4N333iPwA65ducLq6upJwGGxfRiGSy/zPM+JoojpdLr0p68DG45SsPBtD4IAx3GW9iyO4zwSdKjbrm1wihzieUxRFLTbbQ4PD5nNZpw7d65SrwPtdpv9wwO6vR7/8f/iPyYMQv7Vv/4fllkIP8/Yq6/paSud0++d4dcHX3zNnlcC/fmFkmrJQ9RFQj1l8ZSDkhWVYcuSssgxZY4pM/I8r+olaF2dcSmQCoIooLQ5hc7JckkpwPdDPNcFU/meokvSJKbTbnDl8hb376XcuLHOxQsRs+kAJWDz3CpR06HXi+g3SvR8h6MH+xxt3+P46Ii19S0uXH6R9QvXEd4qRipwNZIWSvo4jofGgCMImw2UKIAvfpB8WjyzucOeKBc/cxP7lMTQM5zPnrqlZ5SsWR3jlzsfPyYC/uztfgWS72eZxfCr+J6rAt+SsiwoyozxeMDu9i4f37qD4zgEoYfFkGclUNm0SFeSmAJdsCBQDcIK8twwnsXkRYG7qMPgOz5h6NNuNFHCpRlWa6c8y8mKnCTNyJKSLNVIVeK6EmPAUW61L0eiFhGsIrcUecLxaF4JDKYpgb9PEIR4jo/RFtCUeYawEIYujqrOaVkYEluQxEOaUUS/26bTbhMGFane7a/R663h+S6ShfXUY6IIIRbho1qRbGVFhde1Hhav10R59Uet0D6t1q6I1JP7aLEfWSvZq21rlfbjghSlVOUNLxXHg32Ox2POX7hKI2zS766wvrqBdFziPMMcwTCOcX1FGAVcuXJ1YcWjuXfvDu+9+S6TeYbv5nS7m2ydv4qjJHE8YXv3LgeHh2i7w87eDhcvXqTf3aDd6tLvnccLWtXxLexnjCkxpspWFdJBCPmYa/bPb9Xy1GvBensqkt/oEisganSYzYZkyWxRcFviuhGu31h0q57fT2eqilOE+hOsWX7W+caaz50wdJmQzI8XwRaJU2S4vgNCgTWURYrnh2xdvM5oeMj23Y84Gu4zGBzzF/7iv89v/6W/zp1b73L//i2yLGX74TZS+GhrKYqM41HK4dGYNM3xlOIrL73Mt779LYLQZzw8YnfnPo1Gk8uXrzKfT0njORcuvUi7s8JsekyWzrGmAAwIs7CTkzSaq2xuuaRpwnvv/DHqwx+TJAVpVtVtGg5H+IFgNBkzT2POb2zR67S4de8Izwv4xquv0e+0iaKANJ+zsrJFr7/K/bt3KIqS9kqLN976CF2WbK6usr6+SpLMyUxOZ7XJ+x99yPe/+x3+p3/rf8TKH/wBb350h/Ew5tzGKoHnMJnMGYxixvOYMrNIWa0v0lRjbb7IMBAYbTg+nqFLjQA213vkRcnuzjFFnnPlwhplUTKapgwn23TaQ4x1aLUD5klOUeb4QfV87LgOngfzeUqr3cfzfd548wNu3/9j/vQnP+bixQ1+6/t/iSvXvkand5E8myOVQ6O9+YXD6IxEP8MZznCGM5zheYEAKRUvv/QS11+4xvbDh6RJRlX4syKgPc+j2ax88I6Pjxfq6+rjtfoZToqK1uS6MaZS40iQEoS0ICyOI3EcSZYXjxCmNRF9muCuLVLq9puNJsPhiKIsyfOKQK9tYKSsvCmXViqWBVlf2SHUJHetNq/7CtUC/bQlixBV0azTxHr1AYHRFqPrts1S7X5aAV8UBb7vk6YpAJ1Oh/F4vDw3tZd5/WOtrRTnHZ84jjk4OKAsy2Wxz1o57jjOsvgogOd5zOdz5vP5MqDheV7VVuQRRhGu65LnOa1Gg/F4gjAV+VZqjRUCFmS90RpUlU56PKoKya6urqK1Zm9v7xH/97WVVcbjMXle8Df/5t9gZW2V/+a/+W+I45SfV333tOmxZzjDv2sQtXUUFiWqIqKeC67rIBGYUmOsJstSbFmgS01ZFORFTrkIFDqui3QcyrJSESrHxfdDHNchUD6OFBhdoIscU+Q0GwFJPKHX6zI8bmJ0huto8sLl0vktosih03EJA4EiJ09TjBvRX+nSCH06nT6tho+0Ocl8gCklMhJIz+KKSk1njUAXErUgkRBfPlH7rPA0ROjTksJPBfvFbT3L+fRXMT8/rbnS8xpsfV77BZXSNIpalGVJEk+JwjZXrlyj2WlxeDhgMBwym2XkC7s5z3NpNkOKPCfyfay1JFmCFVVAfjKbM50LHKXwXEWn1SRouDiews4rYbUSgjDwkUJSLdEklrJyFbcSayW6tBRCo7XBugLPd3E9hdYGYSxGWOI8wQ080ixjHmeYssRzqjVNFHk0mh7GGhylaEURnuOgdeXv3ogqCzzHcYmiiEajRRQ18bwGjhugHLdSoQt1sk48bdpy2nObU+8vrVlqor2+P08rqE9mt4qIPBnhp2vyPAkWg9ZVQMPzG3S767z93jv86I0f85VXXmW9v0qWZ/zpj9/iaDxilmcYaTk8GrHSnnB0NKbf7dBsthjO5uRFiVCCyTzm/Vsfs76+wfrKGp4bIVCY0uI6PkdHx4xGx5Un9No5vvWNv0S7s75Q2hu0zknjMfP5MUWR0u5sEkW9qijpz4DH1d5PlblYbVx/qFJ0mwKtM+L5MWk6I4nH7B/cJiumrKxcIQq7COEgHQ8p3WqOOZ1BsGxbLNs91cnHOvCovcvn9JJlFdonHLdSAY4KSeIxrh9hTIEuM6SySMfFdX32Ht4mLzK6KxuMBof8we/9EV4Q8upXd1ldWaMoZuwf7HDlyotkWcpb77/BcJKhHJDSIYp81vttfvv73+Y7v/E9mq0WeTYjz2Jmsymj0THd3hrdzgofbt9HG8tLr7xOGLVZXb9E1FxBKnfRa4VSHtN4h8ODezTbPW7ceI2iKLh37y5vv/smQRjQCCOUlOzu7ZOXlutXX+X739si8DpMpjPWVjbY2FhjPDzk1q0HHB/P8IObHAyG/P4fv4MRijjOuHCuSbvbotXq4LouN2/fIZmnxLOUN956k04r5Oa9BxwOZqz3+3zrtVd44cp5wiDg7r37/OGfvMn24YisLNFWV4GW0pDnGmMtxlp63Sbf/cbLYEuUkhhrmU4LDo+OmU0zstxwfDzn+tVzdNsN7jw4pNAGL3QwpkT5grVOC8cBxxGsrnZ58OAeUgpGkynDSUqhA87bKqM5mY/p9C6jnIj5dBfPj4l6nznUgV8Kif7zfqGfPaid4efEZw2d53etdIaz+/0MZ3gilHL46lde5cXr19nb2yHLssUDhFhalVREeUySJE/0Oq9tUE57kJ+kXpqK6DnlOy6lpNSPEtZ1W7XlSW2popSi2WyilFp6s1cefycK95p8rxfCRVEsLGPEgjCyy2N5vJhQ3cZpUr3eLs/zT3mlW8tSkW+MQVTcVmVxsiDSy7J8hLA3xpz0aYGaSD+tEj9NqodhyOHhIa5bPehprTk8PCQIgqU/fBzHSCmXCvW6jZrodl2XlX6/2n7hZe+5XlX01NpKWS4EhS5J0xS1KApbGoOQEoPl4OCA1dVV2u02e3t7bG1tMZ/P8X2ffr+/tI/5zre/RRgE/M7v/FfM5rPluXxcSfU0eJ6JhzOc4VeHygJLSIFSEtdVlRf6IkBodJWhIoxGm+rhsBLaiRMVo+MuC+8FQYSrPPI8wxUOjpJYrSiA0lpcx+PocJfVfouV1RWE0IyFplN22dg6R5GOaYYVie5KjSMsjusRNkI63T5h0MD1PPIsYT6bolWJV0icpsXxq2MxFtLMLPx1Lb+uJUaf1wDgr/tc+kyDDs8Yz+P1/lkwmRzjuC5KOrieT6dd3bMrK+t0mveJ/AY7+wcMRxPKUpPnBYOjEULAaq/PuiOr9Y8yZHnB3mDIfD6rrPO0oShHZHmB57i0o+aCvBZYA0mSs380ZDSdUJp6zQVGW7QGz1G4Si4si6rif7q0SFVlOWZFyXQWE3gBURAhHB9hwXM0ZVmQpClCCjzfAWkQ0hD6PnmekxZz8rJBQzSwwiKFWhYSrdexp2lyAGtOK8dPmxqdCvOIE4MWUacN1YpyTgh2cbL5KVuXU/v6TNsSi7EaXaZYYwiiNteu3ODegz3eff89XrnxEmsr6/z53/wOb7/7Ifd2d5hkCUJIxrOY6SxBsEczDEiyjLI05FmBLSwPHuzzXu894kuXWe2tEPgNXn7xNcLQpyhLsjTm4GiHPCs5Pj5kZeUiftDCGE2ajDg6vMPd+x/Q723QaPYpywStM6ytCFzleMsi908q7Fkf95PwqddFPS+c2FlZayjKlDydL7JFc8qyQKrKxuxosMvx5AgL9HsXaDTXcEyIlC51dsCn9vOk/tREuP15hCKnv99OXXNbqff9RhtjC44O7uG6Ddq99UVmboyUinMXr/HJBz/mD373n9No9djY3OB4MOD+3Y/otDs4jk88m/ODH/whcVJirGVlpcF0Mmc8mXD5/Bb/8//wr3Pp8hU2ti7h+w2yZFr5phvL4cEe8+mU8xeucOXKiwwGB2TpnKjpLLJz1UkmBiCUT6u7xWw84I9++C85t3WVfneFC1tbTCZD7j7cxvcV/U6fwPf48PZtfv8Hv89XbrzMSy9do9dZo7uyiqNcZpMR8/mcdrdNqSWHB0Om8xzXc4mCgAf392kGAa1G5cM/jzMuX9oiizMePtxj2GxhrIMSgtVORDNwaTRaGF3SajY4t9HGKsPBYM40zui2IjpNn/k8Zv9ojJBVdl+cxpxbW+HBg33macZwNOV4nJIXBXlWkJUlDw9GOIGPcisyXOeGlZUGzaaH5zi4nkAozd7giLQoaHgurXaDeVpgMFy6cJ7NzWv0Vy9UYjWlabRWETL4whH0jEn0MwL9DL8aPPnr7QzPJ37ZV+Y5XeGf4Qyfg1o9/frrr3NuY5P9vT2ms2nlKY58ZEGZ5/mnlCGn1eF1W6d9xutCpJ7v4fs+WZYtP1+TyjU5Xf9/2gKmfqBRSjGZVIV0gOoBzTymdq/7JKv0X7tY4ApkpWZ6gqKlVpeftm+pfzzPW5LSp61cTvez7s/jKaenvdLrtmsLlieR6PV2dZ+m0ymr/ZVHPlOWJb7v0+120VqT5/lS6T6dThFCLIuAni66WpYlo9GIjY2Npbd6rVKvgwTannjB10p813WrvhpLqUsODg6W5+TOnTtcuHBh6c3uOA5JkhD4Ll/9yiv8r/6T/yX/+L/4LxhPJr/4ID3DGc4AsCimJ5ZETJ0lZLGLwGKOLnNMWUJtk6UkrvRwLQgl8VwX5Tgo5eI4VZ2EsiiZzabkaTU/Cw3GQKmruhRxmpJmPv2VVZQSSAndbptWu83cxCgMkevSiAIc10WXJfPMELgeNtck+YyymDBPNKWdEWaaqCgJOuBHDkZJ4rwAFp7vz+h8nf6OeibtfcH7T+vX+1SN/Qz4d2L1+Rwe5NNe7+eZaL/74BYYSxQ2q/WWUsTxnOPjEY1moyo07kB/pUuR5Xi+T1EUpEVGHCfM5jFZllKagjwvKJISYR1cR9FsVl6/rqOw2jCcTJjO5/iBh+d6VXHD0KFJg3kyJS8KhJA4TmUFI1AoHKQFoSui2hSWkhLHq+aKaTxnOJngCA9hFb7yEEiyPMdIi+MIdDkncVOMrdZZUkpWbQcph2Cr7JxCZ+R5iuN4OLa2CwSsWaqRTyvRT/9aW7DU5PkjqROPi5YXlicgkVKhpETWntvwmdGik/WrREkPTUqaTjg8uE/UCPn211/j9v07pGlMViSs9lf43ne+hfOWw52dPcaTmHES47gKR8jKwqbdQTmSyXSGEg5REPLJ7XvcuXeXaxcvcuXiRXq9lcriS0pGw6qw9eHggPLDHxFFIeexuH7IfDrgvfd/zB//5E/49je/TauzijYlRueMhnuEUYfV1av4QQshTqwWfxbUJjjVeVycYlnZ6Fi7qENUFqTJiLzIUFIxHu9xfHxAmiccDEYoZWlFXSKvTVnkxPGARmuDMOyhpPekS/ZpiPoZ5ecRhzx+RPUxGazJKdI5WPD9iMH+LsPBflVINJ0wHg1YXb+I4/js7hyizRHf/PbX+frXX6dIY0ajIy5ceZl/L2jzztt/ysc3bzMYT4kixfmN8wxHM3qdFqPRIRcvnmc82KPVXUEXOZPJMT9+44esb5wniJoEUYcLl5u0On2UcoinE8o8I2r28VQAizFbXQiDpeT+/R0Gx1NuvHADY3M83+HSxS32dgcIXDqdFu1Gk1v3d9g7GPPilQv85d/6ayghkUpx7cWv4Pket299ROCFrKysYfVthHG4fnkLYSTj8YyPbt5ltd8iyVJuf3yfqxcvc+3aJW7efMgnd484v7lCGLocHR/xYGeXyTxnNEnwPYGwiqLQVWaeNXz3W6/T70T89M0P+en7H/Nw7wikZXg8Zm2lR5zk5Gmx8JSPcRyHMHKZJzFvvPsxQlR2VlHDx/McGmGA5wj2B2OM1JTacDTawfdcAt/FSgFKYJAox8VYTZoMCaI+OktQTvmFY+jMzuUMf2Zwej58fpdJZ/jl4zlc4Z/hDF8Ax3H41je/ydraGvu7eyglabfaJGmK61SEaa0UP63irnG6gGZNZp8o0itfOCmq32tLk9MFPU8rsOvX6nae5JPuuh7z+WxBatck0ol9Su01rhy5SAu2CCTG2KW6+3Qx1KpdsVCvP0qE1z7rp/vgOM5iX2ZhGQPGWhzHXZLcdeHP0z7mtWodHiXia4uW+phrBb4G7ty5w2uvvcb6+jqHh4dYa2k0GkRRVCnGF0r1msSeTqeUZUkQBDSbTebz+fK9NE3Z2dnB87wT4vwUyZ9nBSBQUiIcB0cpjDlJUVZOlUa9vb1Np9PB8zzG4zFSSnzfp9GobH7yNMNRiiuXLvKfLIj0weB42c7T0mNPCkr8uqspz3CGXxRWm6qYmKpZGonVJRqNzjPKPMPoolJ6AlYKHOkgpUIKBVIssnsMgd9AOVXgLidDigylDIHv4QqB1SFFEaDLHN8V7O7ex+oMozOCqEHguxhheeH6C+zfe4/93UOUBOUFOF7IyvoGrXYPU5TMZ1PmoylllpFnkM0T8nlMu9R0PJ92s0GiLV6gsMVCkfpruKZ6WtJ+aZH8LGC/uKnnmcR9ajynw+Fpzu3z/N31//sf/g3NZsiL165TFjnzJGN3/4Dj0ZiV/gpR4OF6znJtpY1ekKMaqw2+52KthkIySlLSrAABvi/RpcZzFUoo3MBFOnaZ/TYYjciKjFKXS4W24wocR6KcKsumNCVJZlCqItaxhjQrKU2JSTJcd1GnJ1AoZbClYJ7MiOOCIq381auikBo/UChX0mk3ycqcg0FJGmd0W1067XWisInWOePxPm2riRoVeWhtuSjObCvbD+kgKw+WxbqxVhYvKN7TPi1LiAXRq9G6oMhnaG1wlIvrR7hOsAiO2s8gEqr3rNELm8ISawVB1KHbXePO7Y+ZzsZEoU+n26XR6hBPRuzv7XN4cMzB3pDSCJTj0W81mE5jjgcT4nmCH7jkZYHF0m4HtBoNmlHE0eCIwFf0+is0mi3G4wH7+zsMJzMGwymrq+vs7t3DGE0YNBmNjhiPRzSjFmWes7P9Ma5zl7IssFYThm1cL6QrXTwvgEXR+yfhkXtKyBOl+fIsn5ymk+wAFtfaMB0fMZ4ckGYJ1lrieMrDnQdMJ1NeuHaNdmsFi2UyOeL2vXdYWT3PjRd/k173Mko9udjo6Uv5JH+pul+PbvuoFcwXzQNSevhhGyEV7W71XDQdH5OnMWHQont1Ha1zpmnCtatXyIsSRwqajSaq1Wb7wU0+/PBNvvrV7/CNb/8mvh8yTxOGwzmyZXnxyhbrG+usrq6SZVnlEz8dEs9G/PEP/y3v3bzD/tGIKAzRRUqWzknSOStrG/h+QKPVqZTojxyHAqFwvZBvvP51RqMR1oI2ljhJmc4SDo9n3H14RL/bwvcdGl6IRdLvbtBqtdFGMzp8wNHRLlvnr3A+u8Lv/t7vszuc4gUu7TDEEy4XLm6yq3a5+clDPgLOn1vhN7/5CvG8ZDaOuXDuHA/3pxxPZ7Cj2T0akWWG6TxjliS0o5Be38PxBLKAo9GU3/23f8o3X7vBhQtb3N3Z43gSMxzG9JstZpOYS+c3ePmlFzk8GLCze8D23oDCFPS7EdN4TpwV5JkhjtNlKM33FdN5QqELgsglans4riSJc/JY47mSd957hxevvUp/5SK6SCjSEV7YQ0r/c8cInJHoZzjDGc5whjP8ylCn9X/z69+gEUXs7e0QNSrf7CRJaDYb6NJ+qrhnrao+rSav26utVxzHwfUcyrI4efDSGn+RRltv6yysRVzXXZL1tZ967btdQ2uNQJLEKZUYU2HRKEctFr3VElYq8Yh3udZVYaHq4ebEzkApRVFUZJM2YPWj6aSP+1JWFgjOiVJeVEWftCkfOSe1mr1uo1aXnybuTyvna8V3o9FgNpst1d9SVP7GxUKl3mq1kFIynU6J4xhrLa1Wi9lshrUWz6uU/jVp7vt+5XGaJEu1fJ7nxHG8DFzU510phbNIYw48b6n4F6pSv1sh0RiU6yAQJAsCfzQakWUZq6urAHS7XfI8J8sytNZcu3qV//R/87/m//x/+YcMBgN+ERbkeSYhznCGLwtVkFLgIFBSLfxbLWVZUBYFZVmArYJ2WleBxmWR5UWwsigKsrxSO1krF499Ds1mG89ZIfRc8ixlNh1RpjGu62GlYmNzC2szhNUYnZOkM/J0zs2bd/DKFHRJbg1lJtgfHrPzg3cw1rLe7XD1/BZXzp8jG00p0owiK5jPYgot8KIuvc4mUlTFA620VTmFZ8D7PlMV+lMYc/8sasRnRWv/LP16VmT6l21b87Tn68vv19Pt73m1+QEYxymzNGNtfcL1ay/yzjvvcufBNkmeczQZEwYurnKIogZr/R4b7VWiRkSeJZSlJp0WFKUhTrJKaKAU2lTzkavUkkQtyooYx9rKX10oJBJHSjwvQOuSUmtCt/JKL/JFnRuhEaISRkhZrWPysgBhyHOBSgWup1CeQkmFdS2tvosrImxezZNSCKTUBKFPt93CmJwg9HAdD9938fwqK8eakiKPmc0GaKtxHJ9SZ0zGBxR5Rm9li3ZrE88NF/ZY8lQR0IpUr+0Egcql4zFTmNq+xdiiUt4rhVhYydTr3xqP+IMbQ1Ek6DJfKrmTeMp0MqXd7qJcxb3799nd2WNjbZOV3hqtRpvd/QGj2YzpvAAsq90OX3v5OlIK7tx9wGSW4imPJEvZHwxJkgyxZmhEPvM84f72baTj0mj1uf7iV9ne/V2KUnM4OGJr4zy+10A5Pp4X0u322RscsNrfIPQa5GXK4HiPOw/ucfniVdY3r6F1jjEuUp6ssU+LWj5tp/KoqL8m0nnstfov12uwsnYFqXwmk33efe9H7B8NCPwAV7nEsym377yPtVAUOcPxPrN4SKfVIwjaNKI+QnwGTXl6ej8tJjq5uo/1/XOO64ntC4Tw8AIBVqOLKvOj21tDOS5Ro4vj+RQbc7bOX2Uw2CMIW6yfu0xZpFgDR2+/wb/4//6/6XS7FHnJZJKysbFBGDi4roPVmmazw8rqJo1mlySe8uDBfd557zbTNGdzVZJnU0qT0Wi3EFLiexEra5dot9dQTrS0s6lPgVQuUaPHYDBge+eQV5pdXMfDUYrNjQ363VU+vnmPTrfJxQtbXNhcZzAcsba2jh82sSbn4fYtHNcnSWOiKOLChQvc3H4Lo6HXjbh25Sp/+a/8ZXq9Fe7f+YR/+H/7x8xmBd/99ktsrHX55OOP+OOffgiOJS9y5pkkCEOiJmQ6p6lchNDM5xnWQOj6TLI5O3sjhsdv0Gz4JEWG4yiaTY/rL1zgz3//t4mCgKPDHaLA5auvvUSz2eTtt9/lhz99uyoErAzSEaRzQz5KmM8y2l2fZi9AKA8hwQ8cXK8KDurS4Dke/e5qdd+qykpodPSAqBXT7Fz+wmFyRqKf4QxnOMMZzvArglKKr3/967TbbQaDI3r9LkEQMJvNlsR2PJ8uyeDTSvHH/dBrSClxHAfP8/A8F+t7xHG8VHArpfAXhahqsrcsy6Vlymni+lHVd61cFxhjFw8aFiEd6oJMpx8+ah/yJURFE50uTroksZfHIlHq5PXTxyeEIAiCR6xd6kVxXcSzDgCcJtRrpXdNpj9JTV2WJZ5XnaeaqFdKYbRGWNjb3eXVV18lTVOOj4+ZTCY0m00A5vN55We+6GNVHKtBkiQURUGz2URrzWQyWRL3SqllYKNGrYiv993v95dK97IsMVTqsyzLlp6BjuOgtSZNU4wxHB4eAlVmQ91+lqb0ez3+zt/+2/zf/8v/kul0+nSD85FLd/Lw8TQFps5whj+rEIAjBcqpfNA9R+EogTUaYyyW2o5KnppzqrnSmKqocvUDSAVIrKiIG1dV9i5KCLKyYDqbMzwekWcx165eYjgaoJSLzgvieEaSzpBoorDB8YHmYO+QZDYC5RD1zuG1tljxzjEejtk+Pmb34BM++vg+W+0AXwh8L6AUCaVs0lyb0NUGbQS6KBfk2rOzYXlm84V9hDf5hfb5TD2+n6Jfz3ru/NIzg55yV7+KjKWntXN5Xr+3JpMYYyx/+sa7vP/RLUajGZN5TGlKRGoxNsDzPcZHc0bTIQ92Hi4y+ySu49MMm3SDPsaMGRyPyfIMbUvyTKJLQxQFhKrKxHMdBwy4kYMxFoFBiIAwChlPJygpiYIQx6mIpTQtmM9Tcl2gFFVResyiSL1aZgKmaYHM9aLujsANXdbXumytn0MYURVaXtaHMFhTrbGa7RZR1Ki8u22lxtbGcHS8iz66j5BVps/R4R697jqt1iq6LDGOXZLntSHHI8zuI3zpCaNuTEmRZ2RpTJpOydKYIGzRaq/ieQ1cN1xaIy4bEqDLkrLMyLOY6XRAEs9otlZot1dptlYYDXe5f/8T1jc2sEKgXJcyLwkbbb7x9dfor3TYOdjnwe4+x+MZYeBx6eIm3/z6SxwOJty884A0y2gEIZ12iyTLGY7GJHlK4N/lp++8z2q3y1de+gqvvfoaSRaTpnOEEMzmI7w8YDYfcXfnAVlZ4gYRQdTBLUPuzm4yGU0pNkpMqRdRBrlkvz/TD732PLfmU6L+03JwW5PXdvGalAhZ1f3odDd55eVvs/1v/nt2hgdsrKzQiDooKWl3VnA9j7v3JZPJETu7t1ldu0YQtHFUrbZepPnYU9fj9EUWi+4sPdnr+/zT9/pn3f+PZPaeGidZPGV0fEiSzuivbtLrbVQBKQzGWIoyZzw6RikXawy+H7K6vsmLL75It9Pm41u3GE6nZIVme+eQ1X6ba5cv8bXXv0Wr1aIoYnYeHrK/u80P//RPkEoSOlXR2IuXXqTV7KCkoNXqA5Io6uCFHVj4x1trsKZcBIBc/LDN9Rde4fh4wMHhIZcuXWZj4wKD4wOazSarq23u7+6yPTjg5ReukJuCf/4v/yU/evMnfO0r1xkdH5LmKZPpFGMs23vbzJMqe9n1Pbq9iGa7Qxh12bpwmb/4W9/nH/+T/w//r//2X/Mb336N9fUGhS6wlDiuwlhNoRM6nSa9/ibHwzGr3Ta60MyTgslsjik1juvhewpXQLsRkuQZAsFsFpNnCb1uB6NzfvjDHzOdl/zGd1/n3LlVzu23mT9MkVagU0OpDQKJ60ms0MSJxg1FZVVjNEJb/MhhxZX0ohZb5zc4OnyAVA6+57K/c5frL609VT2YMxL9DM83PmsM/7LWQD/v/r7sfp7hc/AsVSZPYxL0ZV7k51NBc4afD67j8PVvfIN2u83x8TFhFBHHVcHQmsCeTmdLr+7HC3/CowQzsFR311YhjquWtiu1hUpN1Bpjlt7op8n5eh+P28MACy/ucklKS6lAmEc+4/s+juMsbUyWbZtaHXSixlv6hgux8MY78Wist6tV9XDiBw8gVUVMSXnS15okr4lzOPGJr/cFLAlmz/OWti9lWeK67vLzGxtVoaBCG6bTKTdv3ly2VVvMdDod5vM5URQhhCBN0yWhbYwhSRJ83ycIAhzHWSr9tdYkSbK0X/F9f3neao/1MAyBqkhqr9cjbETMFoT98fExrVZrSdDXbTuOw2AwoNPp0Ov1MMYQhiHz+ZzLFy/xd/723+F3/qvfIUmSn3m8nlb2n+EM/y6iHvlSShypcJWDqxRKgjEaFonEUgok4pH51NjqgddUNDvS8QhcRZ4XSwWkEgKxkH87UtJudfE9jzSdM57O6PVWGQz2SJKceZKTxBlZNmd39wHp+AivTEjnOXGeUg72mekBiXbQxpLPpzhlytCTjALJervy4tWyQDbiyv5BOSBV5RPKk0mIXxc8zTz1q1Ki/7rimQYdniGeV3X5z4I8L9DakBc5w+EUbSzamipgLgVFrsmzpFJvOi43Xr5GGASUpWEynTKdxNx/uMPxZEKeFwAIqvVQnORQ6c1BWPLcoKTA8xRISW5KEBZpAVXZlYzjMaEf0WyEOK7E8xTCgC4LtNEIWdnoCSVQjsJ1qrWVUoui9gh6zRbSwmw6ptlo0Wq38L0Q11VgNUVeUOYFYRCxvnqRNJnz5r3fY+dwHy+IaEUBxqTM5hOiqMXayiU2t67jeQ200RRljlLeQhEuFsTX6Rtx6dx9auBKlHSxro+rQ/I8xXNDHMdfznafZQdV1aYx5Nmco/0HHBzukucFK/11+isbZFnC/Qe3+PDWTR4cHNFutem2mqz1e7jKJWr63GhdpNH0+On7H/HRvSkP9vZoNwOwEqvBVS6zSULk+WysdmkFPlmekxcleWbINSy+XYiCAGHL6jsEyNI59+/f5v79bbbOrfFg+xPGwwOE8gjCJq+9/Bqrq+dwHW+57q6Lrj7pHhJCLGt3fopUPB1gWJzrR0TgFpCCeTzi/Xf/hFmW0Ww1CCOfaxevsr5+jjxL+eTWBxwNRrQ7ISu9NYosZvvBe0Rhn6jRq67YojbUsgrsF86z9UaPXsO6YO7jrz/pmQpALWxdmq0uXhDQanVRrl95aJuSZD7C2JJ2t4vnhwipEFLhhy2uvvAq3e4aw+Mx7773gLwouHxpnVdfucHLL79SrctnY4bDfbTRzOdTfM/l8tYGcRJz+dJlfC8ijVNc12Fj9RKt9no1TkWV0VZ5t484HtwDFP21yziuT7e3iuc4xPMxP/zjH2Cl4MKFDaSUrK/1cAPF2x/d5s0PPiIKQoTjcu/BPoKC0HdRjuTdDz5kMk2ZJzmeWz0D3rm/TSsIuHbtMo2wya1PbvLOu+/x3e+8zHgy53f/+I+QLuTGIDyL1FVtp/2jYwajKVZDaQzHxxNW2h1G4xjPdWg1QkxRsNJpcv3qFufPrTOeTtje28NxBffv38Z1Je1en29+53Vu33vAj956iyDySbIE13Mw84Iyrwh0IQSlNqCqeSGPNYaSVEl8X+K4JUoJJmrOj975Effu3WJjZZ1up0PUaLIVj/GjFl9UWvSMRD/Dc4/H3bp+nhrMX8b+vux+nuGz8MweiZ786qfN1h7d+2cs5h9JB/yFFvy//g8LZwAlJd/4xjdZWelxdHRI1AiJ47gqbAT4nk8jChmNxp+ybfE8b0kQ1+rlmhiHSlziKEWpNbPptFI2nCJka6LWWkuz2URKyXw+XyrHa9L8tDWMtQLXcRHIqgCTElhbKZHsKRVLTd6f7q/WZqF0Yammr/dz4qHuUxQl9fg+rSSvSW2lFMaa6sFtsc0jKbqnSP+6jfqY6/NU9/E0kW+MWVqwKClxlIMA4umsUpK6lX3MZDbDcRSNRgOlFHEcL8+Z7/tLi5Za0V4r/GvC2vM8ms0m3W6XNE05ODigKAq63S5CiOr6L0j6mlBvtVrLtubTGfP5HCGgEYYkeUKTikQPggBjDFEUkSQJ4/EYx3HIsox+v8/x8TGu6/L1r32VovgP+X/+1/81aZadTSf/DuBpv2+eR3IMfnkE2S9CaColqzR4ayhLgxSKsiiWmry67Wrekkgl0YtifUoKyqJYZJIofKEW85bC93y0LkmTBEdJvCDA9Vwcx+HgYE671WAymZPEMaXWGBRFCaNxjCglUdCj6bTxrKLwOgQiJBcRRVkwGw7IJ8ek2ZRxnNEMFF4usVKSlZLSKKxwKSxIFPZJhrO/AJ6VZ/VzOkyfGs9Shf5l49mOiGeLpz2vzyvhnqUZ0pE40sX3K4WwNoaiqOz0qrqB1XooyUpyrbl+/gK+G5DnOYdHR4ynb+ClDkJZrNUIWa3BLJYg8un2mkymi3WLo4ii5kK4kWOsIc1zpLBIVZHPuU6IC4O2GhEIXGvxhYuUHtYa9CJwaC1LEcPSDsXAeDZjFie4wwm+GlTbOIp2s8nW+ipr/XWaqz1msxFplhKGTdbWL7E/OOKNt9+h32nR7URETY95Nmd87220SVntn6PVXMH3mjRbazQaKzhOVWfGGhZZjyeo/6oJY4RCKhfPDdB+iHY9XDfEdcOlrcNpEvZkyEikcvH9ilgdT48xWITyMNYSRA2uXnmRNM94eDDg1sOHOI7DarfLCxfPsxms4Xk+6yvrvHQl5972PkfDMZN5TDNo4Lsu7mLdGAUB/V6TlW4fzw+4dfs2h0dD7t/f4eLWRV6+/gJSGG7ePmQwPKLT7tMIW1w4d5nRdMjthzsYYBbG7O8fUWrLn/vud7lw8TpRo7uwXzx1jsSnn2WtPfFweaKo/3GckoMLoMhm3Lr5JgeDAyazGQdHUzzH5eqFF2g0unS6lbf9dPpDpFSUpcUYQVnmGJNT5jPSZIrj+oRRf3Ft6v5YhP2ie94+tq6p/zDLvx/JNsBWWWOL8SOkImh0uXz9GyTzcZV1IQVGV4Ib7YXkWYoUTpW9IByCqEPYXMWUOUHY5rvfF0TNDu1OBykNs/GU48EhK6sbJKYkiRNc18F1HC5euMAL17+CEJYsSyiKnEbUJE9TjCkX91yBsAppncV4N0yG+xzs7fCiFLRba6xtXuav/83/iMO9HR7c+4Tb9++SJAWOyohnCXmS0fA9do9HbB+McD3Fy5cv8xd+8zeJ4xk//ulbzLOSRhDRa7dY77fYP5gtAlY5n3zyASu9FR48vM/tBztM4gKhHFK9mHOsIQg9VtsdpBEcHY+ZzDKErTJo13p9XriyReQHCAparRbddpO9/R0m0xmu7/Dixgs0Ih+hJCUF77z/Jp4f8HD3Pg8G+wyzGSIXlMaSZjlFWaB8iRuqxTOsJs80UtZ2VJZCaopMIZ0S15GUmUa0QNoJZWk5Oj6k1WwRBC3Oz454ZfU3P3d0nZHoZzjDGc5whjN8iZBS8uqrX2VzY4PtnYc0GhXxGQQBWZoD1QPJaDQmTdNHrDSklEuP8tMK69Nkbl6kFEWO67oop7JuqQtfep63VC3Xnzttd+K67lL5fmLfAVgw2lTv2cob03VdoihiPp8v+3y6GGitxq5QFXp63JOw7kuSpFTFiOySWF8qOBdtOI5TeViaEiElQgqsOdmuKvR0Unz1SR6Pp9uvSe6lVzGgHAffrVQ6ZVlWCnmo0okXlL3WmtAPCIKAsiyXqvQLFy5wcHBAmqZ4nsf58+cZDodLe5k8z+l0OkvVe7vdZjweY61lfX2de/fuLftbe7efJvin0ynGGBqNRpVlsDgfdYHROI6rY1hkImRZxmQyoSgKOp0OH374IecvnOf6Cy/wN/7Hf4N/+s/+GUVRLK7xowrzX6Zly5kdzPOG55NU+jLx9GNxEeQTAiGq+bPQJUr4ZGlKGDiwnGcERi+CekKitcH1XJRySLJiWcQvCHyyLEcKie8HaF0Qz+dYJSr7FwMIRRg1EFKSpAVxWi7mPBekx8raOaTVrIQBYRDhNXoE/S2a6xeRQY/RaMzh9gOOdu4xO3hAw0xZbbiErsssyVF+B+W3KG1VpBROByi/XG/rp9ruGe3PPsE+9xdp68uc0T5LLfs4lmTYU7b5uW3xdMG2p+3X0+Bpgy9Ps8/TdnVfRr9+FjjBwhZPGHJtq9oEEhy3UlOa0mJFtcbKcs2bb3/E9vYhVy9eoN/tMBofY8XJOqX656TYe5EXTGYz4iSjLAyOKwmSnDw3FGkldlBulYxSOWEIyrKg0CXIKvOvEjAIpBYIS0UoGovRBmsr4qosLcJasJWlFZrKbsMIsJZ2M2S9t8r5zctsnb+CFC5Kuezs3GMynyCVYmVljVeuWbQ2DCfHzOKUrMxRUmLMLeJkTqd9iOcEnN98BVd5qKiLVC5VgdFaXb2o57Moarq0EhSVCMRYyLKE0WifZquHkJJALgpaWoEVdkm/1/O6sZayTBmPB+ztPSROC1wnwnMVzVYbz484f26L119N+N0f/YSkyNg+OiRNMtJ5xtdefpkLmyustLustNp8dPseR5MJzYZHKwqZz3Nmc02aZhwOhhRlWdXDEJZmp8Hu0TFvvf8+xiY0whBjFa1Wnyhs4ro+fhARBCHHwzFpWRD53kLlbrh57yau6/CiH+EHzUXg40mFRev14OIZYPn6SXCiGmNP8LBaTKoCieeG+F7InYe7KFfRakUEjsssmaBcj0azR5rM6XXahI0Ax60CzJ32Or7fQEoXR/ko5VVZr8tdfHFG5KPk+OIa8hRrzzrOstykImCNzShzjeOGFFmM1A5hs4MVgtlsQlHOkLK2cavGW1EkGF3Q7zXwg4DuSo9j74CiSDnYv4vj+WxdvEIYNolnc/zdbTrdPq7rMBru89HH73Ll8g2u33idsNHFmoIsG6PdAD9aQcoq+OO5DdI0IZ6NkUi2H3wECC5eeYWVtU2ywvCDH/6QtY0+K90mVy5f5cLWRX781ru8e+se06xgniQcj49YXenRaIVM4pT2Sourly6SZRlp/BHGWF596SU21jdRUrKy2uXC1hpvfXiPwXCKcARKW6QQWCVJkxJHqGr8GUFWlghgEs8IAsnXXruBsIb33n2fS5cucuPGDd577x3++Edv0u026bUaHB2N2R+OKLVGSUVJyThLUI4i9H2wGqEkfuQgUk2pNRqNVALXk0jpkMUlZamRjsAqi9SgjKWwhtQtmdiEJC3QmQazx7vv3eOlG9d45dv/288dY2ck+hnOcIYznOEMXxKEENy4cYNXX/0Kd27fQSnFbDaj2+0uiGpLHMefSW7WD0T1e6eV18DCL/tku5por9XOx8fHzOfzpXd4kiTUBTFrqxHXdfE8b6lmrgjdE8sUIUVVGMdWBU9P+5fX29TkdKWOFxR5uTyW0/YxdT8+izw//aBaliVSikcsW2oyvN53/d7pfdTv1fuoyXPP85bnRwixDDBEUUSe5wRBQJpny/br85ymGd12h83NTcbjMXt7e4xGoypooRRBEDAajRgMBoRhSJZlNJtNiqJgNpsRBAFSSlqt1jLYsLu7u7RdKcuSLMuWAY4gCMiybBngyLIMRznL/tYEve/7S6/12ie9tvBxHIdGo8Gd23e4ceMG3/vN75KmGf/iv/8Xj3jUn+EMZ/g8CMBQ6gJXKMTCTmppfGIqz6rqb7EMaDmOg+O6CCWxApSj8Lzqfk3TBCmrYmNlWQXYapunPMvRukQqj61zl8iLmCvXbrC9fZ/x6JjpZM7geEin08N3FJsbW7RaPYJmn3Bli87GFZr9TZRysHmKSWfIbEJkppBOUFbzRz/8EdqN6G9eITeSXn+FaZzBQmX6pZ7dL5lEf9aNPY+z6POqvP5V4HlWqytHYYyu9LDGUOgca8BxJMpVOL5CLgotVkpxwfFkRPpJgus4xHnKbJ5WXuh6sfYpxFJNnNqc2azAGoO1ICXMZrsYbTG2WktJXTkgiIozRjkC5QmkAuFURlRKVtmIYrGRMQZrQMqq2HLgenSaLTzlUuQlea7JtUbrEiuh123z8osvsXX+Giv9TVwvoN3u4Qchw/d/wse37iCk5NqlK6ys9GiNQnb2HtL0Q0prSdOc3f1dhuND+t1V+t0toKzm4trugzqDMaco4mrf1iCEosgT0iwmijq4boQfNpGTAYeHDzk4fECns0qruUKj2cP1GjjKW5wPg9Z55T+NpNNZASvJi5KsyDBIsrxgeHzIrbu3uXv/IUVe4DrV3N7uNrjx4lUaUYSrXFIE3Xabi1vnWOl3QRhcpapsAyzzJCVMXDpNySSO2dk7JAoD+u0u80nM3XvbIKqAhVQejUaL1f46xpQcHh6jlEcSl6x0OvRaDfK8YDA84o9+8ofM5hO++fW/RH/18oJEXxrZwBOCp6cdUCre3J7MdcvooUCXOWWRIpWLlA7Wguf5uI7PrTt7rPQ6rPXb7B3s8dHHP+XKpRtYa+n1V4iTKUqG+G6AEpWZmJQOUgmkcp86Ve60YOazfNEffa6qsw4W88MjH6m846VQ+EGLLJlxfLjNeHgA0rB57gXAosuC/d37eH6TDT/A9UKwhiydo3VOq93hj37wx2gk3/vNb9Jo9nEcyd7uQ8bjCZcvXaO/usFLr7xeea3rHKUUX3vtN+itbOL5AVJU2W/KaaOcECndZX+V4+L5Ps1mn3ZnnbDRocxTsiwmno9YX1/lm9/4CvsHh+wfDPD9gG63y1/4/nfx/QZvfnyL+w+PSOIfsbHe5eHeIUVpSXNNFLXot0O+9fVXCIMGjUbIfDbFmirLrt0IWFvvksoBSNDaYLSh0CWD6RRhJHlai40M0pPsDUf889//EX/69odc2FhhOol579ZDXvvKi4SBQ16U7O4d044abJ3bAEexc3BMUpQcjaYYqXF9RVlojIEsL6mcluyilphCOQJb6RcQjkDnAp1bXFcQBC4bvTbzeMZ0kjIlodtuoKyDKcFVBb//gzf4P3zBWDsj0c9whjOc4Qxn+BIggKtXr/L6669zcHBQKWEWSmNrK+VR7VH+uCr49GtLMluIpUWJchRy8bdyTshox3GW7a+vrxPHMVpriqJYKqJ931+qsesiovP5fKl6r/alcBYEteNUxGwURUt7GNd1FyR87YFeP6jJ5THWCvrH1eGnfz+tPD993MvjXxzj0vN8YSWjtQGqFMvaJqYm0U+r0uv91edtWdhUa8IwJM9zkjStjt3oZeHVmlTP8ypTYDQeAdBoNDDGcHx8TGVL49Jut+n1ektv8zRNl8czm82WKvQsy5jNZsv+OI6zJOxhERBZ2OOc/ltrjR/4+J4PVOc4juPlsdaFSOsAged57O7ucu7cOeI4Zm9vjwsXL/Ld73yHNE34N7/3byo/+sUDlPgcKujxTIIznOHfPVSFNx1Z2bGoRWE9IU8Ve17cS0uLLOVirSDPSrQ1aGuxQpHrEm017TDCddyqngLQXGSbWGuRSuIoRbPZIEldzm1dJAgbHBzssre7TWkkvX6HdqvD1oVrNBo9hNsAv0leuBwezpFK4SuBL318r4+ggbE+ju8SrOxjHB+v0WOeaaS/IBlgSZR9KWf1LJD3S8HTztlfvof82XfJadT2eEoJPN9FyGq9UhSVSlxbi7FyEcZbZM9ZQVxkmDwhKwrK8qTYfLUe0Ghtq3gYcuG2UbWghcBSBfFZkKJGChZNIAQ4uhJ2CFUR92CxjkZLg6OqoqTVHCUWHtsS1/FY7/dwHYUrHayRTJOSJJlRmgLX9Wi0GqTJjKPBNutrFwnCCG00YdTk8sVLvPHOB+T5LdZHLVqNkI21TaIwJM1SLJbR9Jg4MRwNhhSpxnVCzm0FhKK5IG8NRZGSxCOOjh6wu3uXRqvL+a3ruF6AEh7T8RFFmWKsRaMpdcF0esxsNmGlNyeJx3T654iizmLQG3S5sBwUhlZnla+9/j3eeusnvPf+u9wMbrJ17hy+79Bf6ePvHmFyqkCC1My9lKPjIZc2LxIFLVzXI05Tzp/brIKpyZxpPAchCYICaQWrvRaehEa7SeAqNrbO4TsunutwNDjm7Q8/YjJPwBrWV3scHuwxT2JuPdjncDhBKcXGaheEotv0GIw1u/tHZFdKiiJH6wJrg8VYqhTXyzvylL28WCj3yzIlz+aV9ZjfQioFj7mlZ+kUXRa4fsjg6C4PHj4gTjKUchiNE268cJlmQ/HBR+9y9/4dzq2vE/g+ruNS5AVeo4HFkuVzhFQYW1IkxwjA9Rqnsk6enEZ04nteHwRP3O7J9+DJZ60xlMUcXeZI5SClQ9Ts02yv0OmuMZ+NCMIm1ho63T5xPOX9t/6IweE2V659Bcd1Ua5Db3UTfzan1e7wcHePdmeFZqvBrY/e4+H2HtZ4uF5AliYUeUGj2SbLEvor5+n01vGDJsCiHx6OF1UZaFSZFhZdefQf7bK7dxeEIJmP2d29g0UwHh/x7vvvsXc0wVEepdZM5jlJdshkNuXje/codEmJ4OFwxMFkgtWmKg5s4ebtO/TbTb71zW/x8stfBWPY37lHs9MjDENcN+Dmwx2KvEC5ssqacAxgUIECbfF8jyJTzOZpNc85klIYdo/HTJOcXivCcyx/9Mbb1TNp4NFph7x3+wG6NMyzDOFI8rLAStBlNV8ap0RrQ5EZEOD5ClcshFRGkBeGzGiMFtU8JkQ1R3kuUcPjwvkL3Ly7Q15qprOUKIgos5JGp4Pr5V84Xs5I9F8nPCFVcJGt9cv53C+7rafE03iLn8QRTyAee8EK89hW4tTPL47H+3nKGuykD8s3fgn4FVybP/t4mgX+04zQJ12EJ42Yp7lYZxf01xWrK6v8ue99j/sP7pHnOa7n4NjKY9v3fWbTGUVRLNWLpwuJOk6l/K5JmdOEuuMowICQCFkptmtSVkpJEAT0ej1c1122m+c5Qoil4lEIQRAESzV0rdquVcwAjnuiaq5V3GmaPkJU61JjTlusUC3Oa3K8Luh5WiV+muCuye362B5XSddEf12cEysXhH21j/r81O2cVsbXr9WFSiv/TrkssFqT7/PkxJvcGENRlkRRRJZlJGmK57rM4xhrLEdHR7iuS6PRYDqd0uv1lkEIrTW+7y8J87q92WxGkiRLS50oipakd/25mkCr7W7qdieTycJmRtJutpc2PTXZHobh0oO9toGZzWZYa7l37x4vvvgi4/GYVrNJWRT81b/6l5nNp/zJD3+0uB5n88sZ/uyjnlMeJw6fjshdPIxJgecqXCWxxuAICeiF1YtYblfoyvLFYClMiTYW5Xgo16FM6qLJllIXla+nVMvgm5IOvu8hhSBOUzwnwPWabG61cLwQjUS4PsIaLl59iY2NKwRBG1SAli6FViRFNY+W2lbEnLCkeQGlwik0axdvIIMQFbWRVnI8OCAKm0hRraM/L6j2645nSwrz3DojPbXC/xnZuTwNfhaLmS+bbP9VKNaVIxbzChitq3tPKBxlEUJV1k7akOdVgWIwj9RfWPR8YeZSvSalAmtRAkBW2Y7GIoSznAsqIvzkugopFsSppXbQsBpMndVnwHEESHBVtT7BCHRRZSkGjofnemxtbNJstJlMpjjDOdIagkhR6JwHu/fYWt/Ay0JG0yPyIuftd9/h4e4e3U4PR7loLRhNUiySMARtBFoXzOIZ20eHuJ7DtUvn8AKPLIvRZYo2AVI6y+KCUHlL7+0+YPunP2Rl7SdsbV1kc/0SWMtoeEQcT9jZv0dhDFub5+n6TSyWyXiA1oakMcT3WyjpEM9HICRh1MFqwFrWVlfI0gl3d7bZPdgjCFwKXXA4mKCkxJMuWIEygvlkyuFgn37XUOqSyXjMLE546cUXKUIfz/U5OhiTpTnf+41vcO3KNawpmc2n/OTNn/KjN96gLDWdTgdHKeI0IwxCXEeijUT6EWtrHXp7Q4bjDKUkd+8P2N45xncUWMOVi1u8cOUG7dYqnhshTmWyiuXQsVhRh4IrGF0wHe2y/eBD+mtbrK1fx1XNyunGnIgwpJTM4jFJOsXxXIKgwWqnR54Z9o/GvPXBR7xwZQNrquwH3wtoNnq029UxaW0IgiZZMiFLJugyr8QyxhDYAs9rVgU8F9fY1gWXeHyuOH1fPHqfikcma/Ho6wsuxUqBcnyM1lhTvZfMh5VYxvfxiupZxvMbtLsbgMCRD3l49xae67K6vkUSTxgeDxgOR2zv7pPnGXfv3WZjfZN5HPPJJ3cJ/Cbr6z1WVrrs7T7k/IUrXLz8Ms1WF9cLqt5JgeN4S0shREmRT0njMY7yaXfXuXTpRYaDXR7c/ZjbDx5y+942q/0+nXaTd24+ZHd/TllU9j33d49oNQNyrRnPU4LAozQlaVZQYgldD2EkGytdXnv5Bp1mk8n4iDfe+DG97hq9TpvNratEUYPpdES7FRAGlViryAylKSnLAscTOI6kEUZYAb6VlJmgmJdVrRhHMo9THCnpdxuc31ohns/ZP5xzeDxFKMtsmqG1pd0NcVyqou2LAGKRV6KtMq8CQNIRuI7CGoGpkgiqH00lMnMljl9l/BwOJvi+Q6fTZDCZgoGo4dFZ7yOMwA2+mCI/I9F/DfFEovSX+LlfVjs/186edpsnEsqPvfgsCYOn6MMvewn4OAX7nK7lnxv8slRPT9fss7r7zvDrgna7zV/5q3+Fo8FgSeJGUQTAaDSi1WoxHo8fsTsBlkTv6eKYp61JTvy9T5TVta1Ho9Gg2+0ymUzY29tbqJqKJUEMLMl53/fJ85w4jiu7kIXlyWmrmNP7j6KI6XT6yENkURQYa5bqJM/zlurz2valtm85rS6vyeXaO7w+1tqj/bQaXWtNkpxYxejyJBhVL4xP97nu7+OWMa7r0u12efjw4bIw62kFfK3+rvta+bYnaGPQUuJ73iMkv9aaVqvF2toa3qn3ms0meZ6zv7+/tFnxPG+ZRSClXAY0ajW553lL0j/Pq7TOLMvodDqsra2xu7u7/BxAmqbLwqt10dmiKJb/W2uXhWUHg8HSxkdKickK/tZ/8LcYHo/46OOPHzuXj2YNnH7vFyEPzhSnZ/hZ8Mv7rhZf+PtnfBKl5HLekEqSL+y3jDYoWX2+CsAVi+KABXoxr2hrCZRbkWLW4EhBmsULX2FwHYd5MieOEwI/REgFSqI1SN/DcUOsNYRRm253g9ksI89S1tbOY4RHUrJI8bcILJ60lS3xMuteEbZ7YJukyZwwbIKjKIVAORJhM0K/hS1MFZt9ztYdT0tWn/4e+CxY8QxXVU+wB34STtuffRl4lvfPs1aiPw2exv/4afG0ivxn5SH/s0AIkMriOmpBTFaqWEMVpDOlRRcWU1TzhKUmiU4KsEtVZccYYxBIpFjYGgBY0KVBCIM1tioKelptJurxa5e/C7mwq1rYRghhEVLguBLP8/CUi+8EWKvBVip6ISTbu4ckccK3v/lNZrMJFy5uMXj7kDTL2Tp3jqPBAbP5iKuXr/LJrY9BKu7u7DCdpUzjDE96JEXCNNeM0wmNwEcg0KUhLwvirMQpNDfvPUBYuHz+BtaUYA3GFAgqOxBrLWk852hwzP3tAfe2B7z3wS0unt/k8qVLtBpdCm0wwiHP5wwGhyTxnNJqep1V0jQmL1Oy9GFVnDkec3Q0oNNeZWvrAq1Wn3PnL5GZmFJZHu4ckqUxmS6JWiGv9LqkcUmaFviuQ14ajgYDJII4iWm0IvzQxeiSfn+FRtTi+HiE53lc2LqwWDMa0izFcR16vTZCKAbHI3zX4eK5NRw34HAw5JOb9xgMx0gliNMMhMTzHaKGjxAFroIbl67zzde+Tbe7XolKrAFjQZ7c24vwMpIqL7GeIJVyCMMmgR+SJzFlmeH6DazRZPMRxpoF0TxicPQAow0Iw0sv3WB9vcsf/OBPGE1SZtOC+w8HvHz9PL12m0bU5fz5F9BlZT+yvnmJTm+TskgZHN5HOQpHhRTFnGQwwBjL6vp1PL9NlV3xKNlSObjYhRq5IveldJZvPkG7/pi1S30GBEJ5+A2v+tsKpFRMRvukyRSjS2bTIY0WNFpdPN/D2BxNSau7iuP4CBRvv/UGP337YwoLF7c2+eCDj3j/3Y/xfMXmuQ2Oh2PeeuctXn75OpcuXmNz6zKNVoeg0cZxQ9CaLJ1grSWIKt9+qwuyeMSdT97E8yOUciiNIS9yHu4e8Ht/8ja5tuwNpriu4miYkBUGoy2FLdg5KHCOpziBg3IE2hryXFMkhmYU8srVSzQDl8l0ys0797h8fh0JHDy4T5YZoiii984bbJ7bpNftcG59lZv3jhgnKVI5WG0pUkvDD+h3GggFqulDr8Fat0s8SdFFVZT94HjMaDIlKVMKCs6t9lkTLuN7e1hjiTo+Ra7JsgJEldEHtfBqMXHJKoO2srup6s/IZUDEodAWjVk8M4PjScLQI/A8PF8xnMxJspyj4ZTROKbh+2RF9oVz9hmJfoYznOEMZzjDLxFBEPDX/tpfI4mTpdd5bTtyeHi4fK0oy0WqLUsV+emin3BCCj9uT1IUxdLLHCCKIjzPIwxDXn7lFW5+8gk3b94kDMNH2q6V5bXlSG1BUrfrOM6S4D5NwB8cHGKMXhLfaZou1eWnf057dgPL12pCvyahfd9DLOxsarK9tpipfgQgMfYkXRnA9TwEJ4VEEWapLq+J5Rq1Ut33fdbW1paq7vq81oR+bRdTn+PaLiVNU4SUrK+tocuSIKiKi85mM8IwJAxDut0uOzs7NJtN+v0++/v7GGOYz+dLq5coih4huLMsWxLvRVFQFAVBUHkW7u3tURQF8/mcJEloNptLix4hBEmSLM99s9nk+PiYOI5ZX19ftlP7qPu+z3w+x/M8tre3WVlZqRSusxl/9+/+Xf7hP/pHbD98+BlJso/iLBX/DF8GfhkE+mepzz9Lnf6EFioSXYoFoWvIiwyrS4wuQVW2CYUuyPJ8GQiss1q0sbh+ZdEglUKgKcscozVKeRir0UZjqUh3KRS+HyCVQ1lalPJIs3ShYFMkSUEcT3HcBqgQhItQDlJUxU8dwIhKyVffsbk2KMejdEoMi0weo1EONCKFI8qF3cPTFWx8VngqS5Gn1L88Vb+f4aE9bVO/zkHEZ61Ef5pz8Sy/Z57nc5+nVeG7sihxVGXPJx0WFimyqregJJWZC1QFDMWidkplEwWg9UJtjqmCREIhhUIqhTEFEom2Gqv10qnJslCdL0h1KQQaA1QFApUC1xU4nlqeQ60LUl2iUEiqgKKSkjhJQRgKm/PT935KGudsH+1xcDzE9SxpMWNtvU+SaSbxiIPRMRZBUqQIBXmZ44Yu165c4t2PPiQuSxCaXquN77uYmSH0fDxfgNB02n3CqLmwFrEUxXwxthSN1io3Xv4urhfR673L7bu36fdX8AOXwWDC/sFRVYcCTVaUHDx8gOcq9o72CbyQjfVzNBstRsMjBsMBk8mM6bwgy9/GdRVhGCAV7OwcYYSg1WjTbDYZ7e4znM0JgpB+p0Wv10VaxXg6RQrL6kqPTreD6/is9NZwXY+yzBgc3sZYy/2Hu2RlzoXzm7SaTe48uM/D/T3SvODVl27wwuVLpNmc3f0j7j3cxgt9XF/ieNV1FgvRSpZmFGVCEEo2L66xttGh3enTXdkkiLpI5VTBm9oT/PSAFBKxtDcRVfA16nPxymvMJkccH96lVcQ0GyuAJomH+EELISzD0QFh1MFRgg8/eoskSWi3moTBIZ52MbpSGK92Kis0PwgIwzUElQ3P+HiHg717SKXor5zHcRyGx9tMpkf0eluVX3rd2cX3nF1aEgis0RT5jCJPyPM5ftAhCDso5cEiPHAap+eiZSDJ1vOFWJDvFi9o0u0rjg/vcrD/EK2hm6bkyQwv8HFdH2sz7tx8k4uXXkJKh1e/+jqTecknd26zf7CPNYYb16/R6bRJkxn0IuL5nFu3b3LlylUcRzEbHzEdH9JbvYDnhQghSJMJCEUQddBlyv7OTY6PD+h0V5hNx/zJT37EcDLj8HjELC3JioIkK3CUpCyqwJlUApTA85wqyCQs1lgKq9Gm+mLN85LxJKHdCDi3vsJ4GuNKl96ibleSpSAFD/Yf8sHtWwjg5Rsv8pf+/Lf48bsfsns8quYEZZjGOXlRIoXF8xRba2v0mm2ubW3R9hvkZcqD7UN+8sEtUnL2R2MOR1OkUBi1ePY1giBwyGNDOssRDtU8YC1Ii7Hgh6LykjcWpMHz5DITuuH52IbD8fFskZVsSLIMx4F5PsN1FI2Gix84FIlGF5bRdIbjf/F3xRmJfoYznOEMZzjDLwmOcvj+934Tz3UYj47J83xphXJ4eEiRl0jlkMQpURghpSAvcqQQpFmGNY9anJxWpNeK5UoxVNmxaK1pNBp4nkcQBLzyla/wG9/5Do5S3Lt37xEF+GnVd6Vszqq0XsTSksXoanFaLvzHTS1ponrdaIM1AqzAGlDSqQovCYHWJUqJym9bWJqtBmVZpfkJwPMdsiyjtjIQQmBsrRypiPC6SIzreZUlCieBg+r/ygtPObUq/0TJf1rxflrBX5YlR0dHjyyca6LrdOAiiiIajQZFUXB8fFz5wJ8ioo21XLp4kU8++YS1tTXCMGR3d5fRaLQsDjsej/F9n62tLY6OjhiNRkhZqbhq7/Jabe66LvuHB5Xix2jiNCEMw0dU+JPJBM/zcF23sgBaeKwjBchKEX/hwgUcpzq3dUHSOrjS7/cZDod4nsfR0RFra2scHx/TX1nhf/a3/gP+r//4HzOdTM8ymc7w3OB5U6JXiidVEQ/GUBpDksQoayqPWafy5M3yjDRL6PV6laLUWMqiJC81yitQqiCMXHSRo03ltWvRzOczHOXjui7JPEVrUxUO8zzSvEBRpSu7rk/HDzm/ZZnOxmgrcR0foVwkEilAWYMUFiM0Rlq0tWgL87TACwOM45KkCQiNH0iktbQiF1MkCOsjRFUY+svCU9uOPMv2vkQi/Vmqqp8Wvwqy+lniV3G+fhXIkgJjLcaA66qqoOii7oKlWotpoxfWK6IqACqrjDwhJMpU9ix5VmA0i7VUuZyvaksYax4tug4ngREpZGXjIkWl5pYLElVUs5PFLAscOkosxA2VnUeuS6QWeEohlcAIw2A6xhUKYy1+S6KUILcle0cDmlHA7JNbHI+npEWBsZUc2mCZzOc83N0BYcnzkuFkhjbQbobMijnGQJkrhDK898n7DMcHvP7yt+m21nCUR793jijqErgBQdji0qUbGJOTpEPiLEejSNLKwm9wPOR4MmE4mZLrAtd1aDWmdFoRg+mY1W6XTrNLq9mmKA1W5hBbBscThrOEZujTajXJS8N0Nmc6ndLtt5GBy+FgwmRnTrsRcW51k16vQ5Zm/Pjt93EdxQtXL1HqknazQ6+zwubGJrP5vFrnGs27739AUVaFYgPXx2rQRYHnOUTRCtZI3v7wNtPdlCBycYSL53h4roPjuaR5hnQqT4uDgyHx7D3Gx0O+Njvixo3foNVep/LXfjyXnYUNI0AVTAUQUuG4IcpxmRwMGI32uHDxqzQaPbL0AR9//GMc1ycvUwb3j4gaLaxV7OweYSy8cv1KlXmJZqXXpRFF9PorJPGEfFGEsygyGs0+W+dvgDAk6ZT97Ts83LnFcLhHq90lLzNeuPZd/KCzzJSquf7K6shidMlkvMt8PsTzI1ZXrxE1VxFSLj/x+Nz46O8Lz3FbHXftCy+lQ6uzxnpZkGcZ7e5aVfhTKvygjev4vPf2jzjc32N94xwP7t/lT370Bp1ek29+41WUAiUFWTZnHmeMxjNcD3qdLkZrdFng+yGz6THDowf0+uexpmQ6OWJwvEO7s0EUNRHSIZ7PiZOCg/09Pr79kOEsIS+r5x3fWhpRiC41Ao3AUloNEqQCz1EUuhIbBZ6HQ0mJxpEek2nKcRTz+leucv16l/PnL7DSW2c8GvGvf/df8eHdOwxnMWlh6EQhnnOXvCzBasrCoMt6PhFYIUnSgumsZD7bwxjLd157iUtXLmGMZp5qHHmPLE5RvkA4EPmKXrtFnGTMk7SqCdMOMAVM4xhlJeH/n70/b5IkSe8zwUdV7fTb447Is+6qru7qRp9gAyBIDgFSODvLJUf4DVZGdvaLUFb2M8zO7sjIznJEdkByOCSHSwIg7kbfdVdmVt4ZGaff5naq6v6hZp6RWVld2dXZjW4gXpGUjPAwVzvdXO33/t7nDQOyMidLM8ASRK4fmNFNRbG7L+nCIKyPwbomylqAL8h1wZ2HI7DgewJfeWhra1SQoCyrz7xnn4vov4Lxeb/ef/r3Pf0d9hPTxE8bWfx0yzx1kecM+v60dXzaNn36m55pG37BqzsXPn6p41nOzvkZ/OsUQsBrr73KhQsXmM+d67kRd6fTaY3xUBhtCMOITrdLWeYUZVFzrd3EthGTz07wznLIpRR4vpvkxXHM9vb2yrFc5Dl/9Ed/xNtvv71yhTvhmpWQ3qBMmgadTYmcrIGYZ3EyDcv/yaafzrFeubLe+u8IlxBwAq7F953rparK2hX1aKym8l5KAfYRj9xaS6vdqh3bduVEfySSs9qXs2iVJs462pvfjTHMZrPaAR+ukhTNOqMoIggCBoMBrVaL5XLJaDRaOc+LoiDPc5ZJUj9oODfYYrFYYXqaBrFNU1djDGtra2RZ9hjOpcG3GGOI49hx0ZcJUrljHnquYWun02EwGJAkyapBbHMcrLXoSnM6GjHo9pjP56sEgnOpOYRMg5Zp1rdcLldu/Ml4zJXLl/kn/+T/xP/0P/0LyqJ8rq7D8ziPX6b4SQL6s4joQjgUS1kaqrJkPp8TSJc4DAMfYyqSZcJ8MWcwGLhEZFmyXKYss5zSgNHQ7mywzHMkBiEgTZccPDzk8uUXCcKYg4NjsALPDykrDUhCz8cPItJ0idYV2zu7tJMuBkVRVQgDSkh8CcpqhKlQGCQCKRRCKhQKPA/PC/CERUhNEAA6w1eWxSJFKYkQrnHcr2L8MrqOf1n53j+VuP8cD+uvsnD/vLddCIemUNKukFDWQJEbjG0EP1b3Hixo41zpbiolXfM862G1piyd6O2EP4Ogcs/v9WYL7KqRKUIhZY09rfdNCof0kEoglRPXrYFSO3RL5dtHYn8gkFYhpUX5juuO0KRFRt7MY62hqiyldMiXJCuxFoqiIkkLqsrSinzXuwfB8WiK5wm0NixyzXI5YTSZ12I+CAmBL9G6Io4itDGk+RytS3K9ZG/7NcKoD0Kg/IC41UeqgPeufciyKAiCkLIqCerKTOVFFGnFIskYTTKUHBOGHp3I5/LeNu0oJM9Ljk+njOcZiywFIdhdG9JphfieRLYCDk8mnC5mAHhSoTyPZJFybXqHKAzY217H9xQnpzOq4hbB6wGB8jguc4yFvZ2L9Ps9yjLnwQEOgbOzxaDbY5mm5HnO9Ws30MZVnL586RKj2YzD0YhMF+RZ5bj1fomUGgRUtZkl9AO63S6eJ0jT6Yr1zkpYPjvnE64xbVU44Vl5WF1RVfkKiVhWMJ+f4vk+QRAznZ6wf/QAawWhihBKsjbYYO3XNjk5OeHkaETc8nnhpau0O22ODvZJZhNsVRFGcY2Ycddeb20XIQyn1+/xF9//Q/YPT9leG3L54hax38boEmtLBD66zNC6QHo+ZblkfHqPssypypzbt99DKo92a42oNcQTska9VBhrXdNQ5eEc6mAxiDpxUBUJy2SCH7aJ28NaFAbPj9nYeoGqyCirHM8LHHYNy9rWRd74kub6R28zn0Vsbmxz5eIO3UGPtY0hpio4PjqhLAzLNOdkNOfNN15l2I24f/8GSMve7ossFhNuvfM9tnYvIIEPP3yXw9MRvU4bzwtpxTFFnrG7e5nNzV0u7Ryh9x8yW+aEviQrBMZahsMOVVmRZRVpmbMscpZJRq4k2rhj/eVXXuGNly4xnsz54Y+vUerKYXqygpE+5sH+Ha5eeYHNtS3W1gaoOwpjBEoq2q2IXq9FEAYsiwJhZxR53cvKCEyp8aW7TwSBR1rkfO/H13jvo9v4XsjDw1P6/QEbOwMOJ6cs8oy8qsiKkjiOyIuK+WxJLn06YRtfehRVxSKfo6tHycAiM/gBCGEfYT6tQFMRBIIgVuhSYoymKut7aejuRVmuKYSuq1kMQaSQRJ95zz4X0X/F4hcnoLsQT2l++AkRXaxqaM7E01iET471yWWeNm2xT7xPPHXsz46nl4A+Q3PHZ96/x+Npx/zp+/fTL/Os6zuPX5b4PGfn/Iz+KoeQgt2dHb75za+zWCyYzWYsFguiKFqhQUAghXsQiqIIay1Jkjj0icBVFD7hqj4bj5p8+ni+E4I9z+P09JR79+4B8M4777jtEY4v3qBbGjQLPGq06dzoZS3as0K1NMJzw9ZummSeZZqfFaqbZaVwZZO+71NVFfP5/LFmqfCIW+vW7ZIM1jxil7v9C0mSxeq1Zn/O/t84yOGRwP8kc73ZxrO/N+7zZrsbDnzjTJ/P58RxTLvdJs9zptMpvu+jlCIKwhXPvCiKx7jkQgjyPKcoCvb394njeCWYF0XxmNO+Oabz+Zxet0cYRc7t7vssFovV9p6cnKyaqgKkaUq/32c+n9MKQxbJgmW6dKWfWbbivrtqBZdcyPOcvb09Tk9Pmc1m3L17l93dXcfDT1Nee+01fvu3/za//5/+YFUefh6/nPHs3N6f84b8nONxx+TPvjOf24Eu6psyAmsFurJU1qGSkkVKESgwGoO7j5yOp9y6dYuw1cX3Q+bzhMOTUxZJQhzH9AdDPF+yXEzptEKkFBwfnfDOO+/R720wHPocHx+jtSEIQkBw4cIFhBC0ohbvv/8eN25c53d/93cJgoD+YMB4nmIsKOUQEJ4VUDX3QwFSIqVHFAWUwGKZEsUBnrJYk2M1LMsczw/A/uoK6Kv4zIQIz3UXn8WJ/isfz7QLn48p/rMcn+d5bJ+Vnf48w81JPLxA4nnu0hUNnoJaZBc1qs4YrBUIKzEW57A0zmRgDVgrwXqrykFjaKyZ9cbX/9VsdU+5akLncLcI4dzLuqq3qxbHlQKhJCDQpUBJiW4QMMqiAomst90hMAQYiecpiqJa9Y5wKCxDUVbkpRP8q9y5rzvtgCDwUKpimeaYCqrSOeCrUtfnxuFnVE+B8ljkCz68+yN0pUmzDCHgy69+ldde+HUGgwsInKC9XOSkmeZkMkfIhCgKacUQ+z6h77He61CUmtliSV6WaF2xzDRZ8ZDdjTVaYYTvh+TFjKJ0lQMn0ym+N8Q3krVBHyEUpS5ZZplLTmiB1rBYFJSlpswrFIL1QZe97S02N9eJwgiQlFXGbDkDq5nP50wXM4yxhFHM7u5FlOcxGh+zSKZoazgcjUmLgl63g1Ies3nGcpkjPEFZ5mir6bRaRErRawf0OgGLxZQHD+6iZEAYtvF8r24w2lS8NmKJ+74r8jmVLmi1N0AIsnTCyfEdxpNjtNFMZ6cs5icIqTDGMhpNkUqytR7ieT5x3CKOI9bX1njpBc3Bw9vcv3+TtY0NOt0+WzsX6faGSATaaLIsQajaBe4ah9AJOww7pUtYLyZYDGWZInOFpwKsNSzmhyyXU7JswfVrP2CxmKM89x3W728gZI0wsRV5NiNJThHSI4oHRFEfYS3GVlRVQZbNCMI2gRdRFUvS5YzAjxBhG+kFSOWD1XhBiJ6NmI6P6A62CKOYssgoixzPkwgpiOOIL7z+Ateu3+btH73LW29+gddeeY3JZMIHH9xgd2eDr3z1q+xub3Gwf4uTk0M67QFFUfL9H/6Q4rvfZ3NjQBCEhEHM/fuHJMslIDHALCl56803+fpX3iJNc4wd0+6GSAn7x2NGiymBHyB8gawkgR9iPY0FG/CluAABAABJREFUpDHoyjCZJUjp88rVy2Rpwu39A4JQ0e13EWg+uH6HaVLy299e46UXX+T9Gx8zSXOKUjOaLTl55ybWSCaLBWle1U09LVEc8+qL21zYGJLlOQ+OxhyMxkxI3PVeapeomWiCKEAqQZVDRUWxXCAE+L5EBR6dVsS3vvQmncjjoxt3+ODOPouicM+LngMSOXFcYLTjvzc3uiI3hKFHFIaUpaEsK6raaxWELpFYZG5bpOeeeeUzTAzORfRfpfi839c/4/f8Y37yp471hFv8p3KUf8b6PuV9n2eXPv9heJb9e4YV2k++/LN4+D9zfb9i8cvoGvp5xYox9xPir8Oz1t/kaLVi/uE//AcUeU5ZlOR5gZSKIi/I8pymlNAJnK5JZLJM6lJHVgK6kNI1hJOCuo63dkKKlTCqtWOFKylJcU0mpXAuhKYplDEGz/OcuzkM0VrXgrFzhOd5gTHaMeXc89MKMwKPHHRlWT7m/m545VVVIqVYlRpba1c3L6MNURhRlOUjBqd9vDFq1cxocPt01hnXNNw8K76fRbTAI+G+STg0y8Aj3rkxZrU/TVPPbrdLnucroRlcUsH3ffI8X4ndrVaLoiiIomi132VVMptXtFot1jfWCXzXSLXhmKdZRpqljMZj5HRSXwOSra0tLl68gC7MihXfJDnysiTLslWiQwBBEDCZTun3e0RxjLGWtbU1jo+Pa9yNT7fTJs8z8jTDNfhy57UZp9VqrY7XbD7npZdeWr3/8PCQvb09lknC+uYm3/72t7l+/Qa3b9+GJ5yTT4qOfy1EoV/ZeJYkx1OMAb8E8by/7z+Nc3725590HZ+NJ6/pp23rIlky6PewVpHNl3T661gM/+L/8//mG9/4BoPBgAdHE/70Oz/i9//oO/y3/9f/lv/u//k/8vbb71CWJcPhkCRJ+Gf/7L/m619/iz94522KPOdb3/oWw8EW//yf/9/4h//wH62ScH/6J3/C1772DYoi54033mA6m1Dqkulixn/33/8/+G/+m/+G8WREEPfRVjrnqvJY5EvGpzPW19coqxIMxK2QSjsMVssLoLSY0gIe2DaCCHfNSJ7XtfNcz/YzDSaeablf9CfjbLL5ecTzalL6tGv8Z7q3f97npM+57r8O30NC+GAFVQllbmpB3M2LlCeRwkOIuhKvmV8JDVY4Id1RLDAWtKG2rItagq+/r4WDX0jRCPONVNo06asb9AmBkBa8eg4qLZ5yIroXeHWVousNgXDvsdaiS+P6VNaoFz+QNd7FxwoBUlAUJUVV4dcYGiHBojEC8tKgF0sCz0cI0BWUhXPAo5yL3pqmaSTkmWGpKjApHxV3QAkEBk/Cg+N7bK+/4JYHTkcHvPLKa+yfnmIQHB5PqIqc9f4a3/jKm0hRMZnOufbxfaxNEUIhrKAqNLMqRYkZb7yyyebmNsPBOh/fvcfRZMwyL7GepN1r0+44ET4Z5RTLkot7Wwx6HawxfPTxPncfHPP6y1fZ21ljd3ODPFswmZwgpaUoNUfHpwjPZ9AfkFtLaTTzacLHNz+m323j+x7pcs50ukQLiIKIdFlw4+Y9Sl0R+AHLrEJrgzYVvu8c5C+/eJk4DNgYDBj0OuxsXWVn50Va8QBPhfU9aeVFf3RNSs9VQRVLymJJ2OoRxwOstRwdPUBbjakqTk4OmM3nTJIpB6MZgfJoR23W11y1gtGWVqtFq+XE3ePRmPFoTOtCm05njW53k8noPkeHt0mShO3dy1TFkrJI8cOIb33jtzk+fsit2x9iTMXNWz9kMjnhpZe/SqezjrWGNJ1ycHSdm7c+JFAxr3/hG7SjLqen++65QAiMzkiTBR9+9Ge8+8EPGPSHvPXF32Rr6ypaF0zGD8izJelySre3xeb2C/hhC883Z46Mq+Cw1kMKSae/SRCEnB7fQyrJxvaL7F54nTxb8t2//DM8P6Tf6fI7f//vUhY5nW6f3qCHF/io6zf5+OY9/rd/9y/pdVpUZcmLL14hTROyLKPfG3D99kO0XvDaywOiKGRrfchymaE8n8OjU65du83tew/o9zucLqbkpqBYVGyt97h6eYfjk4kbO4cZBUejKUZoKqtXz0SH41P+4C++QzsKUJ5ibb1LVZV8/0fvEHgehydTrt87YDJfsD7ocngyZTTLCH1FXhTkmbsPBZ6HNIayBOXBoNem147ZHHbICp/5comxPR4ejdnd2uTS7iZB5HHnwQH3Dk5YLDLXHDRSKF/grWhyllznnE5O2H39BV5/5RKH0wm5Likz6e57urkvunujMe7aQwhs04+m0mhNbUwQlIVFCocMlb4CbevnT4ESn/09fS6in8d5nMd5nMd5PMdQSvIPfud3iMIQXVXkeUGRlysHshSKSldY6xzUYRiumkNi3cONtdQ2JOOaPgmBkK5RimjKEWshuqpcg08jnNtQSlk7kJyo7QRuuRKFVy5uBGXhxGO3TZpG3G/GboTrRmQ2xqA86UqE62XLqnSN9jxFWTiOXCOOCyHR2lKWbpKFFbULXiCkfkzgklKuXOiPCbS1ON64x5v3NIK57/ur5qxnHenuXCg6nc5KTO52u4Rh6Pa/dmy7Y1g99rrD6ThW+mg0oizLldtcCLFCogghWN/c4GQ0ohXFBLXb/96D+5RV5XAqRmO1JS8LrLU8eLjPYpmwsbFBEASPOei3tjZBuP1I0xQhBWme4XkeyyxD+T7TB/fxPY9ep7vCtRwfHdPpdGjHLdI0XR3/RqRP05TxeLy6Bo+Pj+n3+0gpGY1GTKdT4jhmPBrhBT6/87t/n//hf/gfH6s6OI9fvWjK/5/feM9vsJ8VmfCThPOzv3/aej5LaP9JEUVx3egTEJKsKLFWUxnBf/qDP0JrzWQy4aMPP2I2n/HP//n/nQ8+uEZVlXQ6LZJkyWg04V/8T/8z//Zf/UvC0MNTij/+gz9huDbg+OiE/+1f/2s2NzZJlgknJyeki4R/+2+m/O3f+m1+62//JjvbG3z0geL3fu//SzKf8n/+v/y3eH4boXyoK5z6gzW0gbwydLp9hJQkSeoqWqz7DnA7LrFIsBaL1/jtn6vILJ7TaM9KdHxWHNXz2q5n2qbnyET/efLVz6Lazsazru1ZPkXOS3L+3dJE4Evy3FAWBpBUFauKQFEBVM4xDlTa4CknQle6qY5x6JZHSL6mkrGu+JOPRPPakovWFs8TCCsQ0uH0pBB4EqRXY/aEY7QHLeX+7qka9SSxBoSUlGXl1qUtVeHMEsqTVKHChB5BILASrIQgDhDW4HuO0e7nFhUIyqpEGIGvAoLa1ZxUBRiJsAolFEKCEQatK6x1FZNWWAyWXFdIK/CUSyTceXibXnvA5d1XyZYZP/zhD5gvKw6OjlkkiXPAasvduwekScr2Vp/+oIvnS7K8RFcN7gYGvS7tOOLj63fqGblj1fvKI0lz3r9+FyEskR/Q77bwZUCv2yUvMo5PM6QnKW1ORcFf/OBt2qHPV774Mq+9coXADyjylI/v3OHmnWOm82yFBFsfdvjGV94kDmMCPyAMY3w/5OWXCh48OCQrSwI/JPR8WnELJQSSnLwsWeYlmpKjkxHLdM7mWpff+tavcXHvJXb3XqXT2cALIpTyz1yFzuVvz9wVpR8hvYCiSPHDFkEQMxhs0466zBYTykIznp6yWCZMkxnbG0N2NrbZ3thEKncdteMOAoVUEisk1oKurEvySIUftBluXMLzItJ0RtTqIKTAr7dvOj1mMFjjjdd+jfnsFE94eMrDGoMBiiLh1u0f8f13vkNeafqtDot3Fvh+SK/TRyFJkjGe53Fyepf/8Pv/ngcHI168tMVLL7xFVaQgLLPxMUWZY4xlNjthMjlECI+trRdodTfOHKb6+NSs9CDusrF9ldnkIdPRPu3uGtu7LxCGb2ORvPaFL7O2sU2WLjg8usPDw5tMphNefOUKW1tbPDy6R6cTEYd91tbWEVJg0MyTnDv352yuS976Yp8rly9w4/qHHB0fU1aglI8f+oSxx8HkmMPFmHbcoqoq7jw4oixLsrwiTUu++MplXrzU5c++9x55ZZHWI0lSdKmZ25SqNJzKJVWh6XYC4thHWscHTwvNMq9459pdPClQKFpBSFlVrvF5pOi1W/z6V79Itkj5yx99zPF4TicOGAzaVFKgLexsrnNpb5cre0uWy5LNjQGttkdeLKm0ZrFIMQZSXaCNIV86jGgrCljvtQk9wcHBIXlZYUSFqJsh69JV4wgBynM9vZpqQeW7Z+eq1AgUprIrBJa1grJyvSZkfeV7vutpky01nxXnIvp5nMd5nMd5nMdzCiEEX/7yV3j55Zc5ODhgsVgwnU5XIm/jmD7b0LLhjT/Z7AkMQjxyVdvGWc4jkfrsw27jmm6Wr4fAGL3CtTQIGGhEhkeu9ga7clZAP4s7ObtOpZRrwiLcA0scx06EHU+Ax1EqQogVPmaFppEC18BKEkURWZatHOhnhWUpJdqY1d88z1vxGJvj3fzfOK2fFMcaJEqr1VrhT5qGm51Op3biu9+bpEaTNGiqBarqUXKgOb6e5+H7PkniXCP+5ha2doZL4bj3DeKl4co3Y5yenjpH6mBAt9PBUx5VVTGdTlf72Kz7EWqnoCgKyrKkLArKvKDVagGsWPdKKdrt9kqEr6pqVX3QHPuyLKmqis3NTdcEazBguXQYGFHv/4WdHX7nd/4+//bf/ruf6HY8F9j/6uJvUvUWfLoo/rTj8FkC+k9ax9Mc64/Gcf97vr/CP7mqnZKqKuj3B3z3uz/k6PAAcH0HpFK8/957GANh4KOEpCodi3c2nZBOLZsbQ8LAZ5qMKbMloR+RLubY/oB2ELIMAu58/DGT2Rid5STzKZsbGxTLJYNOix9973v8b//yX/J3f/cfcenKi4RxTJHnzKdTojBkOp1SVSVBEOJ7jt36yGclVqLnI9ncJXDP4zzO4xcTunKOSWOMa9heY5mkdMq3rUXvBuditPsUa2c7p0GcuDmXdO8xwonMxmIr50CXEjxpUcpD+DiOuZQIYZCywbKAHzgnqMA1BA1D4VAugtoEUbvErVlVjQshEEa5/dAGrRVaQ1noRy77osT3JZ6yjmluNNKCwaCUxPMEYeA5J4kRCOsEdSGtQ2jpR/elKjcslyVGQqBdjxyFRSnIixl/9P0/Y71/jVjELOclt++MOBpP6fZ6lDnIUKE8wcHRmGkyI4wCKq3xfYXAYLUFA+my4OLuBp3tiKLU3Ll/wmiywA9CPFVSlBVFacizjEpbIt81dTfGkBcVWrvt9oQ7X8us4HQ84eNbGmssaZrz8Z1D8sqSpq4C9ML2OlGoSJI5EkueRa7KVAmiKMT3A4KoxfbWNhd3t0iWS0bjGVJKHhwcU1WadivghcvbYCs8BWhLuz3AC0Is2nHBqfsfCYE1FUWRkKbTVeVrms4YneyDlaxvXmK4foHh+kXe+OI3ePjwYx48uM1kmRBEIS0TEUceZZlhrUFYxcnxQ/JuQbvV4dYHNzg9PeF0MuLLX36LyxdeQklBsjiiKJYs0ylxqwfWMBs/4ODwFh989Davv/ZrxO0eg7VdWu0OWTZnc/MiQRCTp2MO9q9xeHyfWZZyOnPNNQ2QnCzRlcFTIfMsYa2/SbKccjyaY40gXbqETFkWIHHVsgjSdMl8PmY6O6KsNLdvfcjexetcvPQK6+sXieIByvMRKBASqXyCqM3G1gvk2YLZ9JiiKHjzS1+mKAr6w02qquDu7fd578MfM1rM2R8ds7425KWLl9jc6TKejEkyw/7JPZalRuJzcDrDWEizknfe/RBfGi7sXWK6SPjw3ZtkRW0Uyn2SMiNLK5JkQuB5hL6Pkh6eJ5gtMn70/m0Cz8PzFZcvXaKqSu7cP2I0SdCVJUtLPE8hkMxmBfNFicXWaDhFVYvUG5s9MA1KNObkdMJ4njBJ5/z42vtsD4YMhhEn0xmT2ZIbdx6QLFOm84xht8uw18May3yR8f7128gAsiInDkPW+j3XA2GckleFq242lkG7w+svXuHS3g7aGD68dofxKEVXjvmvK8AKvFDi+YKyNJjCUtX3JqmcJcAYgxLu+dMYS1lWrmrHuopp99ly9zH92X1Fz0X0X0w8ydT+ZMnMs73veYZ4CpvF1s7Hz9qmT06sn74n9ik/Pfn3x4/LJ8Z5WkVmA3H7xJqeOMaf2D3xxO49hQsjnrax4tMRNU+89SfGamzxiZd/YfEs/JjnHJ9CtTmP8/hrGevr6/zmb/4G+/v7FEVBlmUrjIeUsmahuwiCYCVqNiIxnBVqH3eEPcn3hkeoEre8E3YbrrcQjjEIj8T7s2K6G9fdz5vfm0aYZ/EmTnh35bthGCIkZFmK1g4RMxgMuXTpIuPxGCkkVjwSvaWSSC1d2S81q9cPMEY7B0HN6W5Y4VpXK854GIaEYUhZPXJDW2tpt9usr6+v3nvWTe4eNB81E2321xizaiLaoG0ad37ThPWs8z6OYjzfI0tTfN9fMdLPJgbOvifPcyf8W8uDBw+YL+ar89MI6Z7nPXZMF4sFeZ6TJkvW19fxfZ80y0A6pM3ZxEuTVEiSxF0DWhO0/NV52tra4vT0dIWmiWPXpKkoitUxWSwWxHFMq9UiCAKOj49Xx0MpV5YplMRXHt1Oh1/78le4efMW77333t84wfY8fv7xvPnmP824Pwnn8pPe3/zJU6rGXz3qV7FcLtna2mI8nnDv3n06nZgwDGnFEdN0Sa/TWSUiJRZfSeKwTawkkecReh5Rr0eWpcQqoBO28Jt78mDArdNbRJ7Hgzt3+ONlwt7eHq24zcXtba5fu86f//Efsb2zRxRF7O5dxJOC8XTM1tY2oVIUWQrWEoWRq0qiEc3tal4mVjZV6meE8xnbeZzHLyK0sWgtaseu++y5RF3dSrNGtRht3WtWA4+S+1K5eYlFo02BpxwqxVNOMNJVwzx3vW6EdJ9+3xdEsYdSzrBg0XiBIYwUcUvhK+dGNdZQGeMEeWNBGIy1brutaxwfhB6B8KkKV9UohKue1Aa0rjDGCVqVtsxmJaAxCrJSY3XdMFXnWGNRuCpGa0BrsKXBWO0q+2ytfGkwCMqqwPMVYajwBEgJeaEJQtjcEPQ6LbY2Omxu7/DR9VscncyJowiwtOIIYQWerwiUQqExQhPWc3YhBHmRc/vuQ7q9Fp5S+JEkm6ZYTc1Yrs+JsRTKsNaNefXFPQKlmMyWLBY5eVlxeW+XPK84PR1xOk4wWC7srhFZQafdRqYFsR+yudGh0w5YH/bZ27mEEJbT8TGj6YRWu4vn+ezu7XBycszR4SHTRUJeaIpCc3FvE89TbG5k+L7k5asX6PVaKCXodvouCVILvw4vbxAoLAatc9LliJOjO5RFzmw+oigy0mVKt7furoMyQ3kBZVEglUcYxuwfH5KXhrVhnySvMLak21tjbbjJYj7i7t2bBHGH9Y1tqkownSYMelusbVwgz+Z85y/+FD8I2dzYwfd9Aj9mmaeMRid04jaj04fkacpiPkOpkKsvvIKnPKaTfU5P73P3/gfcfviAg/GcbGkpl3MCEdOKWozSKSfTMacz14iy3YoY9jsUYcV8kfPn3/kTtjbeYz6bcToaM+i32dvZQwiJryKUEhweHvPhjf8fG+vf4+/+3f8DW5uXCaIWvh8RhD3HW5eKskzRJqfTWyOZT7l94zrjyZjx6Ajf98iKlJ2dK9z98fc4Oc3YP77HPEnZWGuTm5wyt+hKU1YWhSUKfK7sDdlYW2PYC+l2e3R6fbY3NvDlPe6djOl2WxgMyyJ1DuvSkuQFVeRMWkWlKStDaTSh7xH7EUWVsbk2QFtLkpcUWQUCjHaObCUF2bJGTdatUYzWFLJkMvNZ67Y5Op4yTY5QyiVh8qLg7v4pRycJwko0muPplJPZxA0gJMvslNkywReCzeEaF/fWODgec2VnB6Uko8mMeZJRWVM3MwYhJCejOT96/w437uyzSJb1WAVVoRFSre592+t9Xnpxk4OjEff2JxR5ha7cMcXdkZCee3YzVJR5XVmNS5YJcMfCSorss00EfyUi+udtJPJZE+Ofxg31PMf67DCPK4nW8iyNKT/xvucZlqdA8z/7ghGc9aycfV08vtATy1hwPLTHXnyiVFQ84rY9NtST2ykeLevGtk/ZoqeUOWrxxFi2Xu7sS0+rfX7yXLlkg3hiqM86o/ZTBPQnUwdP25vnGU9u5/P2Gj123J9yXAz8gjMHv5pxLlr9aoUQAk8p/vZv/SbzyYSyRncs5glSSoIgIM/zlbO7cT034utZV/kKSfLYbdVhYKxpSnftiu991sXued7qZ9/3oRayG+G9LEva7TYAxuQgDGXlRGdPum26cuUKH330IUK6yZHneav7krEVOtcr8V4KQRA4MdcYg+f7SGlW+BWlBFEcrFAoURghlUQXjokJbqJnjWPauf2zSKUIQx+poMqq1TGWUq4wJI0o3YjnVVWhlFr9832fIAjwfX8ldKdp+pgg73ne6jiumo5W1WqboyjCAnme0263ybLsMbe753msra0xmUyYLeaMRyOWyyV5UWCMIQiCFUe9mVecdfqXZclimZDmOcO1IRiL73l0Wm1MWTFfJqttba4ZpRS51pS6Im63sNowW8wRSqGLgrR2QDUif9O0tkk6TKfT1bXYcO7zPKfX63F6ckqw49MVHayt+Mf/+L/i6OiQk5PTGvFwHr9KcRZ/9LzG+6uKZ+GeP+3vPwvO5enraJKV7h4cRRHtdpt79+6wtrZGuxXRabcYDPpu6aqi2+3W7yldkq4VsZgvGPTWCLHkSYIyMZubm9z8+AipLYNel+npKVEUEYahQwq02ySLBXle8PDuPfwgoCpL0JpuFPGXf/qnzKczvvq1r/OFN79AFEgCJdhcGzCeTqmKjHYcUdSfZoHFcLbXBDSODyFkPd9/DnPScy3+PM7jJ8Zi0ZgpHt23jXk0t2tMFQ3316H73D9wYlPTA0cqge8717kfglRmVVEmhOufY4zAVG6uJSV4IUSeB1ZhbOVEVWPxpJt7GGuxZUVZNwitdImmcaELylKRlw7rJ5XC8xWedAK81i4ZUBQVunTjrpqk1hgat3FglMSWFcK46kI/8PAbF5o1VKbEWE1ZaoqqwmhDmQmqwlJkFVGoCCKFkhIfwel0hq5KNnolx6MpyzyhrDSeH2K0ZjZfkGYFkQ3wPcGw10ZrS1ZUWGOI45C+DVEKRrM5aZaTlxpLPcf2JKYCbUrAzUOPTiYIa9kYtGm1Q9bXYx4cjjk8OSIMA/xAUS0071+7x+HJiGG/Q7sTAWC0IPB8hoMu3XabxXJGXjqhO801N27foNuO2d1aByvQuqAsCsaTBFBMZgu2t4e084L9gwe889E1djbX2F7vM59OGPTWieIuYdRB+QFK1FKg1a6xZpHh+T4IwcDbwvdjwrADFlqdPmHYcpx8Jbl//xofXv+QLC94cDTi6HTMoNsm8nw+7lwnCtv0eutcvKj54Y9+wEkw5sLODr/+rd/AV4qb13/IdDbmwcP7vPWlrzEcbrFYTPjo2h8xmU7Z2tpiZ+cqy+WCJEnodvts715lMNjE6IqDh9c5OLnP0XjCjXtHzNKMIjPMbYEnfb7yxosM+n0+vHWLabJ0zXA9xYWtTQadPh/ffMCNm3f54NpNWlFAtx2ztXmRS5dfoSoKTk8eUlaafq/H0cmEBw9OmY1PCZSHlIJOb4u1jRaiVpr9sIMQkmR+RLI4pT/sgoLNrR3CMGY6O+LevZsskoy0rMjLght3DjkZtcBWdNstdy1VMGiH7G0OWX9ljTgI6HQH7F28TK83xGjDsHeDy3uSl67s8vLLFzgdn/D9t9/ncJqQa01RavcZ1+47XGOorGZZLrm9v+TW/SOH8KwrULWuUVCmQgoQdQWtxT2f+p6H1ZrjkznzucOtGGsdJkU+WteizJ0+Vut0fqiIA59uq0WgFC9d3qbXislzzXS+YHPQp9IFUeSxs97l4dGY0SJhURVoNN1WhG88Tkdz9o9LEBBHIaEXUGY5ldZIIQHD4cmYoszp9Vr4SlDVfbqMcVU4rnmyg6SaugGz0YAQeFKyuznAWIMQkCzzz7xnnzvRz+M8zuM8zuM8nkO88cYbrK8Nmc+mVFVFskzRNd7DWrviSzcIl0du8MfxJ4/QIY1wI/A8f/U3IR4JOg0m5awo2wjY4FAuxgqM0SuXefOeMAwpS1fCh30kxt++fds52IWk3W4TBAHz+XyFTGmY4A0+JU1TJpOJ25faNd0I3sDKhd0gRRqepWtS6pz1VakfOwZSCqSSKyf1WRf+ZDKh1Wq5B7szrvxGALt48SKj0WglPHueR7vdZrFYOLxC3VC0wbiMx2OUUoRh6IR5C/P5nF6vR5pn7oFhtV3O8d6cy52dHVqtFpPJhOVySdxqMZvPV+e0SZIEQbB6f+MwbxqYamNAOOd5ulzSipyDdTgcIjzFfD5fifFVVa3EfXcMAoLYY54smNfYGikl68Mho9GIdrtNu91enROt9eqYNsewSawURUHg+9y/fx/f9xkM++RlxT/9p/+E//6//385vvqniJXnSJdffPxVCNp/1YL880C5fNoYzya2AzxK3DV/D4KAG9ev0263aUUhG2tDPE+xWMypypJ+v09Z5mAtYRjRCiMCJfGAMkvZWltjfThEKcXFnR329vZIkoQiTTFFgQ5DulHEi1euYIzh3r17JElCv9vli1/8Itd3d9k/OiK5f5vp5JTJ6RGd2OfNL3+Zh/sPGfSHDDotykqTLxOXkG0EdBpcxFkTieBJ28nPFOf3h/M4j58YSgmsVRRFWc/HeFRUXX8QpZRILMYadOkwLQKJ9KRjmavaXCDBmMLRrYXA8y2iRsO4BvBO8DKVM0KEEUjfopSbA/pSojxXQWiw5GUJCIwVVFpSVYKqEg6151QotG9QWhKEFllZTGEphUUKiTGWojIUuaveQQiUsg7rZxy72Do9D+NrjCfxpaIxnEsFUaTwPY+8EGSFQ6hIJfFDV92jjUZXkKGpDPi+oSpKirwgWy6xlUUohYok23ttbCmYTEuiuE23G3Nhb5s0XZCmOb1OTMuEVKWmHYd86fVX2dvZI1nOORlNuHb7Nu9du42SHrrKsUbXRhOL1gYV+Wyu9+l1O+TpktF4ysnJFM9zxo4sK8nTEokkzzWzWYo/9AhCHyUVyyxnPJmCMRgso8mMw+MJrTjCakGyyEnaOWEg8TxJtx8zWOviyYA0L7h2/Q5lVdJqSy5f3OaFy1fpxC2kgGQxJ0tnZOmMEMCLV88PnooI4w55vkSIik6nTbuzRtweYk1FWaaU+ZIkmYIQvPjyVzg5mXLz7n9GG2fImc0zMlVw+95dptMZ7TCi3Ynp97tsbV+iFbVZX99ie+cFiiLhh9/7fRZJwmIxxWxqfC8gCkKssQwHW/S6PcAZkoIgZj47YT47Jmr1WGZL3v7wQz68s888y2kQkVVZsX80wheSKxc2EEaymGdYI+hEMb4XcPXqC+xu7vGX3/8Rh+MpcdwmiiLGp2Nef73D9vY63f4ai/kE/yhg1J8ivBA/cHz46x+/S1kWfPFLf4urL3wV3w9XBpl2d4OqMih5j/HomAuXLrO1c4nhxiZSwuHJiLtHI8rSUBlLUc3ptwOGClCCpa5IipShNwQ03X6Pi5cuEYYR89mE6WzK1Ss79PpzXnn5IhcvbrO21ubDmzdhmqBL/RhZQZcWXVYYpVGexFMecRQgS0klNWXuGPXWgjUGz5NISd1nweGbNoY9hn0nfo/HCxACqzXS88iKnCRPKbVGawFIrHYGrqAVsLs+oB37hF7AN7/6VV595TU2NnbRumJ08pD79z+mMu4eNp/NefuDj/jxx7cYJzmlKtje7rGx1mH/cMw8TYlbPl4rxlrBbLFcJeGy1PAwnXJwOHN9G+oEoxKSjbUORVmS5qVLFmiDxfVuUL5k0Gvzza+9gZKW67fu0dftz7xnn4vo53Ee53Ee53EeP2MMhwP+1t/6FrPZBGsNeVEwn88p8hKlvJVw2bjMG6G8ET2fxuEF++iB5wzrvBHNGwG5eW8jjjeCbyPWesLDGoHR7mGkEbMbVnaDmHHlts493Wl3WKYJSZKsmmwK4bjiSZKQ5/kKC9I03fR9nziOyTLncnbcuWrlvG+2vRH6m5+DICAIQ1cS2LDJFatkwFkOuxBixQVv2O4Nwgacq30ymRDHsROo62OdJAmtVmv1/iRJVs1Bz4raTWKg0247nnhROg5ofewbPrrWmlarhVKK999/nyRJVutsXPnNOWuY9825apzvTTQu8SRJiIJwJXZHUcTGxgadTofRaEQURfi+j1KqxgTlmJ6hUIqqqmi1Wo9471KxsbFBUTi+ZrfbXbHYG4e653krRn5zfprXJpMJ/X4XT0guX7zEb3z72/zhf/7Pz+8Dcx6/kPirFr1/meJpyZ7Pt0+CLM+R4tE9yvc9jo+Pefvtt1kmCdYa0jRHCsHm5rqrMJGucTHWksxmXLywx6DTxqtK+q0WnU6HMAzZ3th0yVJjiYZrGGOYLxYsZjPee/sddnd32VrfIO90KcqCw/2HlFlOr9UiarcRSjA7PeJP/vA/cWF3Ex/LZHxCu9PFq+952nXWQNa1nE1daK1Z8dgT+Hmcx3n83CPLNcJaPM85wx0Cz9ZzFEtZaJf2Es5NKWT9+RQWIQxSSERdJe0pBV6IsRopDZ4SKK/GpVBXTgYKEUIQeHjq0TxUycaQIFaV3pW2K8emMY6FXlWWqnIOc6xFSIMfSkwlCELHWBfCoqSq+8I0uBmgxjV4nkSUAltZKgy6sjWagToxANY4trup0e+erxB1I1OEmxu6MZ1zXxg3vqkEZWFd5SSSolshsXT6AVlWgDIElSFZJHjCJytSxrMZ0/mSNMsJAg9jBFcvXWK4tk6r22VtY5O49ZD3rn2AMRpbQVVqqtI1hJUCkIb5NOf2vSNefTFie3OTzTWoLCySgm43powCipZGyjZrgy7WapJFynyeoTzJhb11gsAHBPNJwmy6JM9LEJKLuxvsbW+SpQnzJCUrSkbjGcZYrly4wOW9DYaDkMl0ihUlcagQ2JWRAzSz6QnGGuJWlzjuEoVdoqiPlD5xa508Tzi5+xFxpwdSEERtfC9CVwXz2QkP7t4gSRa89NpX+Fvf/jsUZcnv/e//gdk05erlIZvDNu04ZDpfMJ2OuRzs0G71uXnzA8bjMV/60lfY2r5I3OqxNtxhY/0eZZGRpgu63SHbWxdoxx2sLpiMTphOxgRhzOWrbxBFLU6O7vCf/uDf8XB0yv2DESenGUla4oeCuK3oRjHKSpZpzmi8oN/usIgLbt4/QpQBsTph2D1kY2OT1197GXHtY9IsRwnF5tYOSiq0doYlqXy2di5xfHzC+9duce36e2xtbnL33n3u7N+l0oZud51+fwspJEoFKM+n29vg4uUXeO/99zg6OkApSLMlP3r3B1y7dRuFR5XnoAwyUhgsy7Ig9BV+AC9c3OabX/4WO1uXUMpjNj1hPDpivhhzfHqIFxte2bmINSVvv/sDbj88Ii1L9vbWOB7Nmc1TkoVDI2lt6upUgRd5CGnIqhxPKaLAsd0Xs5KqrHsXVBZhqrrKxdBuxaz3O7RDiclKIt9DeT5SWoqiIGzFhH5ImhUsFgV57rBQkS/p+DHrvTbdVkxVVcynUzrdAd3+ujNYKUGyGHHx6ht0On0O7t9kPDnm1v7D+vNrkFYR+dCOfNIsZzHN8b2SKPbIcklZmjoJVN8DZM3NMLZ+bg35ypsvE0YBP3r/OseTOYb6GVq6BOYyS/mT771NuxOQJDmlPm8seh7ncR7ncR7n8XMNKSXf+uY3CUMnkljr8B+NqL1YLFYYjoZv3Yi1Zxt4GtNwxF1jJqwT0F0Jr1mJ424d9rGfzzYWPetMf9T0yblkfN+J3k+y2QHKwom/WZbi+x6tVmvV8LMRh9M0XTmYm31ohPCGad7tdihqnEmWpyvXdsMRb8Tls2z2IPDRuqrRMxqvFnWbY9WI6Y2A3Yi9zT43DTSb7ex0Oiip8JSH79frtJZet8tsNl8J+01DzYZXH4Yh3U5ntb2+72FwD51pmq7Oqed5ZFnGrVu3yLKMVqvFdDpdiXR6NQETdeOvR5z6Rrw/u2xzXMqqxPc9tDVM5zP8MxigNHXHsnnYdTgbOB2dsrG5hfIUy+WSoigYjcdktQt+a3OzTm647UzTlF6vd+Z6tSRJQrvdptvtEoYhSZIwGo1dk1Vt+N2///d5+523GY1GPM2feu5G/8XHswrAv+ridxM/yTH+LKiXpzUdftrYP7mxqIuwFiSsMdj6nntxd4/333kXrCEKA0rh/NxVUXJyfESn3aIVx6sHdFNVpMuE7X6foiqYzMZ0O12CMODw4JD19XV6wx6e8uj0OlTVBtPpFCMMKAhbISEh7V6bdreNNobD40OUEvjKcHq0z0fvv8tXv/4tKl07JasKXynQFoNFn92vx/mK5+7x8ziPX2BYYzEWbGXqFJZGSMfZ1sZitEUISxAJWm2PMPBx9DuFUq6KxOJEa4TA1ggDKVzSXimBJ90cyqzmjAqjDQiF9IVzjddCfcNGlkqglHTCWO2Ql8I1LBTSEoQWIR1nW3kSL3CIvKLSaGOpdImqkQuB7+FwhKams9QyvXCsd2scHmJpLHnh6mQkColCG4VFoXyLFwki5VPV80dTOryNsLht8QVR4OE1jValYbacEwYBZWmZjDMW8wIQCOtRYvjg49vORa5A5uBpSeRHHJ8ecfRnDynKiijymcwWHI+nKF+gfAHKw4o6KaB84jBA64o4ijk+HrNYzGh3IjSaQmeczgqqyiAR9NoRYShpxW0C36eqHNpmPJqxNejx4pVLxG90mEymvH/9Gtdv7nPr7j66LLDWsMxL7uwfsjbo8/pLl4m9CIms+29YTmanJEWGVIJkmVDkC6QCS8VkeuDm06Zka/MyFy6+QRi2UMpDKR8/iDg93uf05CF5ltDrb9PprrO1+wrtzhrHh7cZndzHCtjYWOPNV17i++9f42Q2YWu7y/b2Bqcnx0wXFVJ69HtDx2InYDqe8+EHP2I46LP/4BYnxxPa7QHaaIIwYnPnMtbeJklmDIc77O69QBS3sbbk9HSfu3evM5pOePWlV7l6MeOd927xow/vkM011sBaO+DKzjq9OGKZZCxmBe0w5sLmFst5hicDTk6O+fF776KCgJ31NdbUkKoqWcxnHB0+IPA9jo/vc//+A7JSUxQVoe/x4P4+k/Epp+NTskxzfHrK+PQBwhrCqEO7s4GQHtamJMmC0XiGtjcYnz7k7v19Dk5njBYpyyKvzUKWqjRMZw4TFHqKfjum225x/8E1bt++TrezRr/bw+iS+w/ucnh8wp2Hx6RlRacTEfqK8TzlcDoH4fBCZWWQnkushZ5f4yAFRZlTGU2ZG4So8JQk8Hw6vZDlomCZ5FjrqlqE56pIfARff+st3njlBcajY/7sez/i9v0jBt0Br75wlVbL496Dh9y4dQBViqBE24pXX9zjN7/xBRazmfuc2orR6Jg//s//nksXX+DS5StYoymLkvt3r9HpDphOT5nMx3hSIqwlzywHh3OuXhiy1umQLEtG44RJXrG51qPfajFdpFR1hYuQuM91/VwnhWBve4Nuq8UymSOsxNaNSJtSH+UJPF8wT3Mm82SFyvqseGYR/cl+k0/BO//Vh4BPEp4Fn2yg+TnHsk+hXovHl3kC183TtujMCs78Zj/BGrfikz6Qz1Ne+fR5sMXK50PDfnJ7Po2g+Ilr6Gn7Z38yk/Kn2aZP9EgVn812tMJieSL79InzLpB8srnpUwbj8aNjscJ+YsOe1sf0k2N9csmnHffPHEo8O/n+eX3Anzdz/TzO45cpBHD54iUuXrzIZDIBJHlZkaYZWEFRO6Yb8fyscN6IyI0I3pSWOeHbYE3DphU1n9zdixrhGFiJvQ0mpnEen+VvKyXwfSeUN4Jtw2hvkCpKKaI4RGvtXM5FvhLuAYqiWDnHz7LIm4adDd7AWEOyXKxc8kEQrJZr9lUptcKrWGtrNIxrIuP76rF9WfHheeTab/a3wc80gnTj4F65Q5WH0Zq0HsfzPLwwciJSza70fX/VjFNrzXQyoSoKup0ucRzT7fVIs4wsy1YNOJuEyHw+XzVBbY5j444Hate6rm/O+jH0zKrxa/0Q2Djpi6IgWS5XbvWG755l2ep4NQkMKSVFWSI9j6Pjo8cTFVjmycIhYOp1lGWJUopWq0VZlnS7XZIkWYnyjWM+CAI8z2O5TAnDiNHpKa1Wi3/4D/4B/+Jf/M+Oo8hfH3H2PH7xcTYh+JPiLDLl08b5rPV81uvPIso/+brneVhr0PVn2vM8tre3XRKrLOqeP8JxZmu++fraGoHvu4fGssSUBf31Ad1+j047Rlcl08kUIyBsRWgMpa7ww4C402KZJMjAYzafMZnNEALXY8JTrK+tIyUY1tDa3aPjVszNax+yvbXDzu4uylN1Y0CDwN1vJQp9xnvu/n/007kb/TzO4xcTzoFeNx9f9QjDuaqNQevSzSeAItOOZ+4pPE/i+QpV93cxxq7G0boEKqcvWPfZVlJhy4pKVxg0lQTjWUReoby6n0zg+pgVla3nWZYir5vUa1zjUyNQno/ywfcd+kVJifI91xhUKioNRVVSlhopDHEroBV7VNqSpjl57u6PVWkw2rnQpZJIpRBSgnZObykd8k4IhS0MRlQY6djF1jgsgxAg6oasVWkobIWMfLo9nzAQBJGHNhUGg++DlBat3TwNW8+x3dM9y9TNhabkjKdzojBESInyBItFznKZuflojc8xdaPVqqqwQUAraiGlzzIv8QOPRVIwWSxIq6LmToPVgkG/w9bGBlmSUhUVF7bWWd/o0WlFRKFPURbIPAVMzcGXzGYlJ3FCO3ZmDCU8PKnotiI21jbxvZBbd25z98Exk+UEeTpm0O9z9cJV+v0LjEcPUcqn2xnS6QwZjw5I04TRyX085dPq9Aj8mMFgG88LOT6+y0cffhflKV588csM13fxw5D1zQv4fsTR0T3efffH3LhzF6RFW83HDw5AKDaHXbZaPrPFnNHkfdqtmNPRgg+u3+aHb3/A3/6Nb3Lpysv0hkM++Oht7ty/z5VLF7l84RJGGyfmhy16/XXS5YQ7969z+87HrK9t88arr2OqAi+O+MLrL3Dv6JT9wxlFClVh2RiuEQc+Dw4mfHz3BF95bKx3efHKRfrdFnHUphf1OB7P2c9GCCFptVv0OobFcsHuzh697hpJcpdkmbO5tUY71nQ6EcZWYAStqEVVZJwc76OkRIhD5vNjhsMLKOljjWE0mjJPS1QQ0x8O2T+dMV9kgMQaqErrPh8S8qXF9yrytOL7733Ixe01POnRmU/JsnWiMAIR4kcdlsUBSVownadkpSErSvJCo7XrNyCRqFCiAonnC5BOZJZCgFF1w2LjElnC4HuKMBAuWWUE0npEQUSr5aEw/PGffZ/9+/vs7qzXZixLWRj6/TavvnyFV1+6TJ7+AQ8ezijLijj28KXl/v2HeNIDqXjj9ZcRaD6+cYPFckkQeqxvbDLc2GSZLJhPx7z37tscnizw/cA9A1vL6emMIsvp9YK6WkYihKTXjnj9xV2u3XzArQdjzKrXi/vIuBsEjMdTTkdT2m2fIi/w67lQk8jT1SPzGZ6iKl3D4M+8Zz/rzf1Tdb1nee/nnOT+tCE+8QOs7qw/9Vj2UzTEJwf/5EufuTrR/Pf4Gz8piorHx7KfV9p8WtvNpw30Oc/Dp10IT9n2x98mnljGfs7GZeIxZtynre+ZBxZP+/WT5/2zh3vK2RKfdl19eriStSdf/BQR/bMvz89ev336Wfhcn9NzjeU8/pqH7/t8+9vfJlksKKtyxdmuqoogDACxEmDPCtvASnheuR1hJY7D498ljWDcuLKrqlp96TciaPPaY+upheeG953XTSdbrRae5zGbzVBKrZpONg5tYCUQR1G0QqYEgZvENI1Cm8Z6y1r4bZzajUDdiOhn3fKe5xFF0QrL0gjizXgNbuTs/p5tltqI6Y3I3ghyDe/b932yLCMOI7CPGnM1InIQBNhKQI1AaUTs5hgni4RlsmQ4HNKrmwOmaeoYxP0+QgiWy+XqXDWM9LP71wjS1tjVPbwRv6nPc7PtZysUgMcSAU2VwFmkTfMvDENXSlnjedI0fSzBclaIb/ZvOp3SbrfxPI/5fA5AFEUkScJkMmFzc5PhcIgxZoV+OT09RXkeX/zim7z77hd4+513z13nvwTxi05iPKvw/azxLNfQz8Yvf/znz1pfs8xnudXdttdJQUDV99zt7W3CICBdJmAMgvqeIwUba2u1iO4+i+lySZZmrK8P2d7eIQx8ZrMZ1XiCrkqskORaM10sWKQpZVWxmM3Yu3iRyXTKInENhwPfJyly5ouE4VofXWauokdA4Clu37qJ50f8+rd/g42tbaTyKaoKqQLnAD0zv3T3P9dMWilFzW/4icfsafHLdG/47G159kTBL/Lz9jyP4ecd62nve67bha0bif8C1/kMY/1VXb9N5aDTdhoXpUOeeJ7jpQuMc3v7TRLe1BgTgdSg7SOUAY2GICRSWCfAVxZrJLr0KAs3dxESggikJ0BopNB4vnNiKuW84Fi5qozUxjXzlMrhBj2l8GsEjXNjK1SgiEJFlmnarZBSl2hTUlYlge8TRwrlh8ynGWVpUACqnrtaaqOCxFiLV+NIlCdAWow2dTNkSdyW+L6kyCoWkxKdVeALPE+CdHPCZSIoSwmJa/wpcHOiVjtiuagotXMvC+Gcr1JSs+Rd88GishRVhtEaIaxz+Dtui2uSWjlHuzXCOYqnSxYqw5stCCOFkIa97XVevXqF2XzO6ThhulhS2oqj0wkCy8ZgQKfbdVgdIUHAdJFy7dYduu0W1sLx6Zx5kjFfZMzmGVsbHdaHbbY3+iRpyjsf3ODlqwVSeBwcHTNNFizSjBDFx3fv0Y5CfCGI4pjlckpZZCyXCb4Xsrt7hX5vmzDuIICiyBBC4QcBYJh//B6Hh0e0W2sYWxF4AZPxCUifqNPjrS+9RWEMf/neh6RpzjzJGJ3OuLS9xt/99tfQheGjazc5Gc0JfB9toB1H9PtDorhDVRVgQlqhxBMeUdSmKHKmkxHVwzuMjvcxtuLh4QNOR2N2di+xtbnHeHLMD95+mx99eIfZPHVmkdywnFfs75+g84p798dkmaEQJZ5I+Z3fepUXrl5BSMHDB3e4/vFdHh6Nmc1Tbt0ZcfPmATvbHTbWe7z+6mu89vrr3Pr4Np7qcPnFS/S6LZLFmOOTCYaUi3tX0KXm9PQQrQtmsym7F66yvraHlZY00xyMT5jOx3i+5cHhKeNJisGhl0wlXI8DC5U0VNJdR8bMmIxTlCfpxG2GvRG+L9GFIc0qrAZPKAI/YJ4sSJcGrS3KlyjfVZAIadHWUqV14gaLBNeLQLpmqBiBttDrxWwMBiwWS5JFxsbakCp3iKXdnXW2t7rofMm9ew+wCDb6XcazOX/4J9/hnfffJQgCTuZL2u2I0qT4SnLp4jZr/Ygf/vBjDk4WHB2NuLg7JM0KxrfuYzV84Quvo3XG0dEprc6A3d1LvHftPkcnC4rSPRd6vqLTi2m1fLQR5L6mqlyvBuX7vPHKCxydZqRFuTI3+75HuxVjtGG+zPng9n26rQDpS9peSJ5pFknqnjM98FAoKfB8HyEN5eI5iujncR7ncR7ncR7n8SiEgC984Qt0Oh0WyWyFxUiSBGMMg8GAo8PjTzDPgZUrucGSuAEfIQfcAxWPCaKNUAw8JsA2bu1GmG0wJ82DSOMUbwTuppkkQKfj0Cue5622O45i2u02WZatsCUNr7cRttM0Xe1TI8w3Du1G5G4c5wBJkqz2PYoivvzlL3Pt2jXu3r2LUmrliM6yjDiOPyGkN8fkrOv8LAqn2X+ttXN3A9Opa0AKEMeOx7dcLh3G5IzDPY5j8jxfic2L2ZwwDDk+OaGoSsIoYtDvs7GxwWAwYDqdUpYlcRw7XnEtRjfHvBHUhRD4gQ/WorzHj3vz9yYh4q6nR1UFzTk8u2wj8jdJjMZVHgQB/X6fg4MDtNYr5nyTyGjWI6VkfX2dLMtW2xmGIXEcE0URi8WCg4MDfN9ne3t7lQza3t4mzTKstfyjf/RfcuPjmyyXy890CZ/Hzzea6+OvezyLqP205T9LcP+sdX3aeMDqM6akc4Aao9nb3WNnZ5dkPqOoKpR0gpInJe1Wi8BTRGEAoY+0hnYcsb62xt6FPfYPDzidTTFSoq1hMZ8TRCGnkxmz2YzJdEyR5bzxlS9z59598H2EtWRac3jvHh9dv85rr75CmS1oRQHDwRAQHB2fcHA0otXp8NWvfZP1jS1kXersjCvOMW+sQ0boyiD9ul9CaX4mQfETVaWfe6Tns/6fJZ6b2esZx2mSpr+o+CsRji01cfsXuMpfogTPk2HRLoHVNP01mqoqKataALPUwjb4gcD3a1c51rHEhXAMcW0wpkTg1ZXhFq2bCsg6eWGNYwcLBdZ97nXlRD0hbY1NcTXyUhkwTjS21rm1hXTba3HHVFu9el3Ue+P5lo7n4wXOQV0hKQqNEq7u3jU/9UkQlEWJLZ15tKoqJ4QJDym92mUPfiABjcksVhukEBht8CJB0PXodn3SZUmea8LIww8dXkYXGmMNynO8eawALQgChWgplIA8rygqDbjj4nr6NJUA9VzbVxij0VY/+hyLM8YXi2O819XrzphimcwWxLGiXYSMZ0tGk4Rk6XCKEsHx6YTDo1MEgk4ccfXiBp7yyCvD/f1j2nHA+rozb2wMuvRbLTxf8sLlLXrdmLzMeXh4gpCKuBWytb7J+lqPdz78iNF8SscPGQ56XNi9TKc1YNBb4+joDqPRIe9+8B5hGPGVL32dTncTzwvx/JioBUWxZDk/IYo7XH3hTaS8zunRQ9J0SrvTxxjNrds3+fO//DE7O9vs7e7w1iuG77/7IVmWU5iKfTvlw4/vc2l7mzCM6PoRZZlR5jnR2oDlYsRw2CduxXzpzTexCIIw4O7924znE/Jsyeb6JpEfsVjMSJI5yyTjxs2P2VyfMRrPyHJDutRobfA9SRi4c+qriO29DtNE4PlLtje7rA97ZMuEk6MjiiLj5OSIteG6c3fLMa0wotSaj+8cuzm3EcRRSKsV8L0fvsPdu3d460uvcPnyZV66eoUfvfMeVSnww5jxyREn41Pu7x9y/8EBQdDi9HTOyWTGl774Em9+4UWs1ty+e5cPb9xjskiYzBNmRV67ngUCi1aA9SiXkJYWI0oOTw4R9ohhp82rL1xk2As4HS8wVUUYSwadGGMhzQqUL5GBu1cYY7HaUqUaayTSE1gPrAZZJ6aEkBgNx6Mpc9/HV4rtjT5ffO0qVV5w//4pVZGxu/UiF/c2KQqH58yzjP2HB9y8d5/KCPKk4v6DKbN5jrGa0O8wGs+pipwgCFgbtlFKcuXyBcLQY39/n6J0RqqyLHn3nQ84Hs15+aVLRJEkDCWV8djbGmCFawTajWPWu11mvZST0wXjacqffvcaAkOWFwR+gJAGKQXddguEIK0yKm04Ppox8RUqECAkVa7RlUF4LllmjUtClGUFCNQzzK/PRfTzOI/zOI/zOI/PEWEY8pWvfJmiyGi3YucAlJIidwLrcplQlPlj4jY8wpJYa9GmwlKX2IvHRVNqp4IQajVpP8sTP4svOYszaQRshwph1ayzWaaqKqIoqsfTQMB8tsBoMAaSZEm326XVaq3E2kbcbuIsgqTZv8apfrZpZdPksxHCwQnq3//+95nVnLxmH4AV+qYRjRuH/dnml2exOE8K6M34eZ4z6PfxqgolXWlw47BPkgQv8Fcu7zRNV81TlVIgBYWu0HlGGEUIC73NDdrtDsYYZrPZap8mk8kKPfMk632VBPAUUsjHKg8aYbvZ17N4nyY5chb9cpZ173keZVGAsQz7ffK8IF0uV+ciCALSNH2UeLGGsBXTEpIsTRkMBiwWDrlTFAVpmrrmrjXGJUkS1tfXVwmDwWDAwcEB+TKlFQZ8/de+wh/9yZ8+z4/SeZzHr1R4nk9VlWijKQpD6Hns7V7g5ZdeYjYecXx0SOB7vPLyF8jThGyZEIc+o9MTsizjxRdewJOKq1eucDQ65Xg8IilytIBOt4uRih/+4PscHC4IAp+NjZgKy//+n/4jZVGxvbPNpUuX6HQ63Lt3l8U8Qfo+naDP1voQU2l+9OO3+bWvfpXZPOVf/+v/lTDu8Bu/tUnciimMxRcKIT0qY6HSaF2BNc7tKsEI+1Nj0Z+stvrlj2evf/3V2afz+FWMwVCRZ6ae29ROciRSeWCh0saJ0RXoyjjchSfwPIXVrjG8p1TtQrUI4eZ/RV43Oa/xfFLhEvtKoLzajCBd1aIxdXVAJUC6OYquLL7nXOnS9wAnOlWVqR20Gmmda1xaUJ6bR0gUZeUKWiptyIvSOed9zwn7xoIELwCtBWWmKQrHUTeBwxoq5ZATZWWppNsvqwxN+bmtBNnSIKQTxeN2SKsDZakd1gaL70uktPihIow9BIJsburmh0CdVKjKkqqsnKNeSnzPd3cH6xjTUjlTiuc5d7ab44EuXUWAQyhLhHTJBKwT5KyQlNpirKSojMPrKYfA2d4ZcnFnjcDzKPOS09GMB4dTpIQwcAzr6WKJkJJ2q0O33V5VN6112/ihoteJ8KTl8GTGR9ducdg7IYpD2nHM5rBHFPnMpjO+9/b32du5wM76NmkyZzRZcPv+AcN+m6LMcKBox7aXUhIGbeholsspDw9uITzLsL9Bli659tE7TOcLSg1XL19gNkv47vffJisqBt0OURBSWYOQhtv3HnJ8fEqZl1za2eKVqxf4xq/9GgiLxGO5SIhbMe1uh1u3PuLB4UMm8yX3D4/R2vDqC5f49td/nZ3tCyyWc0r9Pm+/d53h4IBWFDOdzl0FAYKy0vi+R6/bJk1zRtoy6IQEnmIyysjTkquX9pBKEccxFy9cIUmWXL1yFSx89/s/4r1r9+h3O/h+xHBtg26ny9HRIWvrXb7w5stEcUCr2+XNL32V8WTOd7//A45PD1jrdVkuM7JUc/3mAWlWMBpn5JlGGIktodvtc/XSZZJFxrDocTyZc+3mQyotqCqNlIIo8Hn9xcu8enWP49Mxtx8c4kufOAooi4Lj0xlKCpbLlCiK2Bj0CdIlSZZTaen6EGQglUF6gLEr8oUpLbp0ySHpGawW7npHobVlluVIBLHnc/XiZS7ubfHjd97jO997l2s3rnFyvM+FC3u88cYXaLe7RMF77nq4dcC9/TGzReHmE1YwX6TcfzDipStbXLq4wdqwT6c9ZGtrnX6vx/raOnfv3sIYw+UrLxP4Lf7sz/+C/cMTKmPI8pzxNMEaQxQEdOIIXRryquDS3iavXL3AtRsPeHAyZb7M0Vh0kRMEPu1WTFE5NKnB9YMpSk1ZWbxSuYoSW99HjEBXliI3eIETz3XTSPkz4plF9OdZwPX0eAqH4nmv4meOZxv42Vf/GWzsZxjsWY7m546f9Tg+y/ufWOZn2u4nmetPWeSZxv/EG5+h/O9TXv15Om+e6fTYR0SjxpFg6xc+e9vOruFZ9uRTtuhpPQkee+nnehWfx3n8XEII+NIXv+gmI0KuXLtGm5qLLZjP5zWS5XFMQCMON25A4DG3+lmXi2g+vPBUJEzjRK+q6jHH5Fknc9MY9Kyr26FQRM32dZn3pjmoMYb5Yu7K4drtlYO6EWjjOCaOY5IkWaFMGlRJp9OhaYJ5Frvi1uMc1FVVMZ1OEUIwGAzI6waYDTKmEZibfWnc1I3A3LxmjKEsy5V43uBemp+zPKfVbhPFEX7go0vnRK+qilJX6Nqtba0ljmMWi4UT6oGqdu1Pp1OKMGRzcxPf80jzbLU94BIpi8VidZzPuvDPHu9KV4+dmwa1cvb3s/vcOPGFEI+5+BvRXXlejY5wovjh8RHgkDFNQqC5pkajEevr6+iyWqEnsixbuembRrP9fn8l7J+cnNA0L20SKIvFgs3NTf7Ob/8dfvjjt5nVDvzzOI+/aaGUj0Q69IrWSKXoD3q8cOUK+/fugtHEUcilvQs83L/P+OSQ4zJzD4PDNTCar37117h19zajRcKDowMsgt2dPZbpgv/wH/+QLLe8cHWP7e11PF9xejJmkaZgBXlZUmpD1Orwxhe/yHg05b13fkw+m/CD738fIQRXrlzlez98m29+89fZvXiZ/+X3fo//+Id/yD/9r/8ZX/jil0iyDIsgjNpEUUTcapHlzhVW5Om5aHwe5/ELjMHAR3qOk6wNYB0b3FWgwXKhMVo516TVKE8St8HzoKosy7mmLDSVqQUinBhvreONG+vwTVI6VIsQBqUFXuCqZZRymAfnerfo0jHKPV8+QrvUibWmWb0AhLBgDFqAh0dlKoRRjjGuIAgkLS9EG0WRV6RZia6cCF9WFRjXC6cKLJ4FZRySQirhHLXBo6rMsjTYUtcNRAUEiqpyjHZrDMYIfF+hK01VavKlcYgGJagKia00Qezc7Z6CCksYQxAGeL5gMW+qjDRCCjzfQwiH0dMNI1lUSM+hcqR083MpfKwVVNqAqHsc1SJdnlXcv39MGDu8o9auMqAsKm7ePuDw6JRBr0WolGsKiwTc+drZWiMKIpLlkqOTCbqyDPotuu0QKxXT2ZJ2p0UQRFzcCQHJwckpR+MRUeSxttYlLwuWWcH49gNu3XvIpZ1N+u0BpirodGIuX77EhQuvErcHeH6EMQVZuiDNZiySU46O7rBMpxwcPSQKWqytbdDtDDFGMZ3O8T3B1vqAoiiY7B9jhEUpn6rQgEVJQ1akaG1BKXYvXmGtv0Eyn/Lw4T1gnSiOWSwm3Lp7l/kypSwtgQzIdMXxyZT7+/e4euUKy+UUoTQvXNnBV4rBoMfGeg9h4d7+iCR3DV19T7E+6PLi1at02jEPD4/4wz/5gIfHC/78L9/jm197mS+8/hqD4RYSSZ7lxO0ecauD0X+EtYZOp83tW7fo9bpIKWjFIdeu36Lfi5nPE65cvcqLL15mkUzotrsM1zZBeCRJRXY05fgwpTTwra+/yTe+9hVeefUNojhmNh0Rt3r8xff+ktliilSWIPCRwsMaA0YynS+49/DA9R2QFl1qtGeJwpjpPCVJU9Z6XXY3hyRJSjJfMp2lLpkTuGdCq13TUq0tVWGw9lEvKfd5dvcaY12iCgR+IFjrd3nllT2OTh6wf/8uR+MZ/bUWw2GHVhxy//4+YdjirS9/mTe++Gts7eyyd/EGeW746Ppt7u+fcvP+Mb7nsT7ssLUx4K03X2Nra4vNnUt4SrFczBgM1tja2SMMYwSCMqu4fuMB87xgbdjm4tY6nSCmsoZ2p83FCy6Bc3Q85/47t3jh8g6lLsmznCKvExA1QnWZZkghKUrXnFlJSRz5NdpKru6pxrqGzVXu5jq6AvcI+dn4P/grEdHhk60Gn2aZ/ySL5qkM6E/Ez7O81X7qWp9c6rPjk/v35PueVUL8PO87o8v85OWecRs+z/uep0T6tLE+7/H85HKf77wLYVcC2U+1Ac8S4vGhm+EfW4UQWHyMNauJlLYaK13Hdse5E4jaefTY2E8tsnyWHfhkY98n5fqnJzc++b6fbwriPM7jZ4vhcMhrr7/muOHWskyXRHGLLKtL3XLHQfd9nyh0TRobEbwRTBtm+add6Y8YxJ9EGDxVkOcR1/eRs1shBI+J6k0TzX6/x+7uLtevf/wJnjeCFV6laUTajHEWBeP7PovFAq01w+FwJag3YnIjMFdVRRzHjwnqjbt8sVishHGtNZ1OZyWOn23CenZfm8SBWj1kPnL7N1x1ay2TyYQ8zxkOBiuMge/7aNz+ttvt1XiNmNzr9VYCerfb5dKFC1RVxYMHDyiqijSr2YtneO7N+Wy49M02NMe6Ob5nXfaPNyn8JBqlcdmfRfU0PPZet0sQBPi+j+/7bG1tcXB4uFqP7/uPNYDd399n0OvT7/VW11WWZYRhiLWWdrv9WMVCmqb0+32stYzHY7rd7qrscjDo81/8F/8F//Jf/atzoe08/kaGANfITwi0dPfDoiiwGOIw4uLuHv1em7LImE1GYCyL2Yy1ixe4sLeHJwXJfMbtm7copGS5zJnMF1y7cZv9BxPiVsCXvvw6m1ubSCmZz+dYOeP1177A3Xv3mC5S/vw736UsS/YuXGB7e5sobtEOI7qdHnmWMU9yrIW333uP7Z1dhhubtLs93vvgQ3rDdbqDIUEYU5UFZVmhfI8wipEC5osEr25U+KzxaS50a5vZ3K/unO5JVv55nMfzDt8XBKFrcKkNOOCCQFcCYwVCehSZoSzc93cQKpQyCGnxAxisKyzgeZayAKOlE72Fq3hMk4Isqag0+ErS0Arc3KKZO4pa/HVoiSByPHbRGDqEc2iWhWs2aqzBFo4VrnyB1uAHhlRYfKVpRR62ZrwIBVY4cTgvK7KinttoHGdcSXxfYLRrSiqFRFgoC1chY22NrEEiFDSCtBACqy1F6ZjwVeUE6rJwTvwwUFSALQ3KB1kZPCnxA4upBHle1UYMi+97LklgXH8IUzXHBmRQ429QrkZANaYWS2WpMx/mEYpJuCSEEK5ZaVG4+azFYrVrXFjkzgErkMS+hxSgtWFjrcfrr1xhb3sTKSxlZbh3/5g79w6ptKHUlsUyIVlkHJ2O8UJF7Pt0220CX9JpR6R5SpovSfKCvK6aCkKf/P5DfHnMoBuhjeHB4UNGk302ty4BmqrK2N9/j+99/z+S5gVS+ly58ho7ey+xf+8uYdDh0qU9wiBkNh1zcHSfZLkk7rYYz1OWecGrL13GVBWzxYL1YQvPE9zbP+bB4QEf375Jsjnn/oP7LJdzXm3FJInk+PiAPC0wJQRK0O+0CMsSIeDdjz7izv5dpBCcnsxZ6/XpDSIwhksXL7K9ucu9uw+ZJSmeUiTJktlsztvvvEuv20Fry/p6iD01TCcJBwcjXn6potsbEoYtyiKv2T0lb7x21SELkwW3bt5nOGwThCGTyYLJbMHxsWQymXFweIBSkhdefIVXXv4CRpcMh2N2dnZZLBbYP3mX8bzg6199i5defolkfsr1D+4gg5CiSqmMay4aej7tKKYTxxRlxXgyZTSes0xTvvDKJf7em19jfDrhe+98xMPDKVEYMOwN+Dvf/jpXLu9yfHTEf/ijP0VYMBZk/X3rrk2HeGoaDrtHTpeUqkqD0O5bWSrrehv4Hgo4PZ6itGU6W/Leh/dotVocnyzY3lxDWEMc77Ozs8XGxjqT6YgsTdnZ3eXFq1d4552PmM6/y7e+9gW2NgYEvqEsUlepIiBudVHSZ7mc041ijC45PT2krFK+9OZVhuub7OxuMegNWMzn/OV3f8Db12/zx3/xEG0MfuDTi9s8eHhKpSsG/Q69NuxtD4ijkLv3j0nSCm0NWnkMBzGXL2xgLNy+e0yaVS4h11Sh2frU1/ehojRU5tlQds+Oc3nGucNnTzLss6rhnzCnP/VtTzWw/5wmOp9TGX4Wz+9T4yn7/EzrfFJcfco5+Wkee3/arf/8YrX4XKfuUwX0Z73OPmOsz70hz14l+pOHfdr5s5+8OJriv7Orz4Wh7VuK5Rzht1BehKlKsBaBReEmNYjH3+fW+8S+PPMGP/6+z/lxP4/z+KUNKQTf/MY3HHe6LJFC0u31AMF0OsP3faQSq0abznDunEHN1X0WR/Loen8kNTgmY904VKjHGNpnBfRGNG7EDie6VkilHLfSGjzl1+54Vo7vIAjY3NxkMpm6Mn4slS4RQhLHEWCR6hGCREpJr9ejLEuiKHrMOS6lXLHHsZY8y4jjFrZ+8GnQLs2kJAxDtNb0er1VM9MoilaYmQbNEgTBCm1yVohu3NvN2OCE6KIoHhOrlzXiJEkSdFWhqwqlPDY3N2jV++D7rlHqcpnQbrXQVUW306GqKl5/9TX6/T6j09PVsc6ydJUIKYrCna8z56XB3zQVAk0y4/GGseKxf01CoNn2s1z5pjKgeTBrnOKe72OwBDWnftAfUJYl8/mcqqwoq5KqqnjhhRfIs5z5fMZ8NkMAe3t7q/PW6/VWzWXb7fZjSYBGuG+qEaIoWrHjf+M3vs0f/OEfMJlOV191z9K88TzO469F1AlQ6kokYFXdIYSg1++xNuxz8HDf3QeNJgoDVF2uvbO9xQcfvE8U+FihqMoZy8WSbJkx6Ld466032NjYWCXrAi/AVAYlPZYLh9sK/JD5LOGjD65xdHBE6Pt0WxHbGxvs7HXJsoLbt29zcHxKZRW9fkFeGqTy+fGPf8xbv/ZVdve6lKWmyHPyPFslUp3r9BnqFZ8imD/t9V/WcJv52dv6i763/aocv/N4ftFq+655JjXaT4ka3YLDg3QkRQ5VYSlyS6tdzyEBpRxSxBrXWDQIZT0/UoDFoOitKbKFR1noWgx3LHRbcwv8QLnGhZWro/Y8QRArPL+ee2qwVpKnFSEKi8BoN+/0fenW4kmU8LFG4Qe+45YbTV5qiqJimVRkacEyLzEGtLZY49juUkqUD1YBwvVpsJWtm4k6hbDU2jnnQ4lUEqM1VaVX6AWXGKjnStrNm1KhUR4EkQLjBHDjScrCoi34oSKIBEhNnjuXvDUOD+N7AildVWClNVUzD3XtGQHXxNQaS2VKhKgRfbLRMyTWGMpKo5QTK5UnCSN3jy0LjTaGxSKjCjw85RqlTucJ7310k7IsqaoMYyXTSYbRkBcl62sdlFL4kcfROMUvfbyuxA98vnj5ddJ0weloxJ39I4zRlKYkLSsKaygqjSckaZYRBz6+kpyeHjEaPSRZZMxnC27ePOCDa3MeHo0xxvJn39mn1+3TCttoXRIG1+m0IxCWPE85Ph0hPEPgx3gqQmnYXBuy3u1iMGxuDhi01xnPEk6OFyymBUr4bKxdod/bZW1ji7W1i+zsvEJZWISQVJWh0iXaVGRlvrLkqRcVg94A3xfE7RbbW3t4QcAX3xyzWExXFa9HB0ecno5JFimLRcqlnS12NzS9bptOyyNPFmTLBGEVy2TCZDrC9yPWhptcvOCeSwb9PvcfHOD5ETs7O2xtl8ShT6fbYTabkyZLxqczsgsF6+tbBF6LIrvDyfGENC954dL2KkHl+SG3bt/inevXWZYlp+MZURjw0pVNJB6LRUmaFvjKwxOCqjB8+NE9ysywt73B3uYmoR9ireHqpR263R7WSk5GE47HiXt2CwMEtk4gWYQSSKWQgRPNq8o1C2/MWStDpRFU2qKEAKMoCzg+mZHnOVHLx0hLqS0f3z1gmWQcnEwpKs2lvXXSZc54NEcIH88b8eP3bxAFMZf21nn55ZfQZU6vP2Bn9zKBH7JMpijlk6VzPvrgxwyGG2A127t7rG9uMzo9YTGZ4ElF1GoxGPSZz3Oy3FWA2KIiVRlShoSBz0uX9+h3Wwz7MQaBNBVZaen3W+w/PCVZppyejDFWURQaISWeFJSmROD6GBiDw9zUpAZtJYLP5rmcM9HP4zz+GoeVFqkXXNxsU7V3OM0EOl9CWWLLHK3LT4jv53Ee5/GTYzgcsLu749iEtcDreT77+/v4vkerFTObzVZC7yJZOMcOThg/K6g6LeaRyxqcwxFqdEddXfU0QbYRmBuxuSgKJ8xKNzE469JuBJHGIe15Hvv7D1fuaYSty2E1xlRsbW0RBAF5nlMUBWEYIoRge3ubIAgYDAbMZjOWy+UKP5IkiXNDaNc8qqwMYRiSZa6JUtOMtBHisyxbNbf0fZ+qqh6J8VA/QFSr49Is0whVDc6l2bZGAKqqaoWFaUT38WRCu93G6Io0zxFCoqSkzHPnwhaSTqvNhd29mme/dGMHAVEU4fs+s9kMXwd0u12EEEyn09Vyj86nXe1jI6ifTYCcbZLaVAY0IvmjZrBqtXxTzdC40c+ey1a7zcbmBsl8QVWWDHp9kvkCT0mkcomKg4MDNtbWiYKQ+XyO1pr19XWGwyFVVXHh4kXu3rmz4qDneU6WZStGujFm1UC13W5zenpKlmV0B33+3t/7e/wvv/d7br/5pJB+7t48j7+20STy6kYSUimqskKJGn0gJVhIFgsC32MymrG7vUav06HbaRMGHlm6xPND8mXOdOIaprXjNsPhgPXhBlnTfE5KfOWjhEeRFZR5hdf3WB9uoITHtevXSRcpvh8yHHYQ0kP6Ef3egPXtXY5PTkjSHM2MRZpxMh4zWyQIz8dYwXA4JAwD504rCozWhL6PabANnzE/fBYhXXxOk8zPP56d+35+LzuPn2ckixLhWdc41PfwlSQIBTL2ELJuOB4rqsqSpw7VABa0xeC4x1pr55MWArBUReka7XkKXwlUz0NribVVPff0nMHDWjzfuc6tkSglaLVcc86wVTfoLC2LmQFjyK3DP6ggBKxjhfsQRgonzvmAB6LE8wUIiUURVhZrHPrEGHd3KSuQykciXFNVY9GVxoAzAxQOLQMgm3mtNlRGoyuNrpyzW0iL8Gt7va3Z3qqeXxlLVRqKQiCXTtA2WlIUjvmupEPntFoBVV6QJiUFIETgHOumJMsLysI5zJVQeL4CC3lVz/+Ec52DRhtdz+fACwQCRdPbSEqJsI6Lbj0LletFBIrA9+pzAIsk58HhCXmaoq3AaI8kyUnTnM3NIR/fOWQ0m1HqklYccOnCJtP5jCgIWFvrITYUk0XGJFkihcTDQ1pJK4jZ2ehhjaEqKzbXL/L2u3f4d//+XSajhNl8SZoVGDRK+LSigG7XJ5KCtu/Taw+JopA4juh0u/T7fbq9Pt1eh1anQxTGhGHomjwK6bA4nl8fG+Hc/HUFbnOdgqtWeIm6iv8TlkBx5ufG+Ncs4555NnYeLWepSQDWPdNY485J0/fD1QM43I4Qkri9RhivoStNt7vJcP0CyWLG1tacN9/8EtKTJPMlN27c5u79h8Rd+Ma3fpNWa+ASQFGXTMd4rXUuvHiR9b2v8qWvFUih8TwYTwqODg+ZLTw81cekI6RVmNJSZgW6SjFIAl+RZwVGW3qdFpcurFMWOVme8ObrL6A8j/sP9jk8PuF//lf/hk43JMsrlOcz6HaRwnH7l1mBRbsqFAxloesmol79ebFYUTvXpcXzJKHvg7UUZUm3u84rL1xCVwXvfnSTG7ePKPKK7e0hG+s9JqOEGzfvE4YCKRUFlvv7hxwcTXj32gOUVHznLz8iSQxXLm/T7hoOHt7l4OE9fvzOh8StLv1+l9l0yqVLltdef43+YEAUtRAY9u/e5A//6Pc5Hk8wxtJqhUyXKeu9LsNhh8D3We912VjrMui4Z8YHD0+4/vEdvvaVt/j2t7/F2nCTe3euc+P6NQ5Oprx3/T4YjUCQlxXWalACWVfllNrR02WNeniWacG5iH4e5/HXODxj6IeWN1+7xMdFm1nVoiUqbJ5RJlPK+RybpHUZ2nmcx3l8Vkgp+cpXvrJqqlkUxUpkjKIIpRRpmiKlpFM7mpfL5RnmeT1BPCM2nv25wXA0wvOTDUPPOq0bwbUZvxmnEcrPLtM4GhtBtnEUN47pRvy11q7c4A0WpCgKkiRZMbTDMFw5mBtRu9PpONFYSkzp2OnWWpR51CizGbNhqzf7VFWuvK7BjzTYkLMue6XUarmzTPKzHPVOp8N8Pl+J7M1+NevNsoydnR3a7TatMFolHxo2exRFXLlyhRs3bqyaqY7HY6SUXLhwAc/zyI8OmUwmK5TN2STF06oFypqtfvbvzTlq9v3JhrPN+xvGelVVdDodptPp6vzPZjOiKCJN0xXz3vM8wjCkLEvavS69Xo+iKGhFMWl9jWRZxmw2W2F7JuMxrVZrlYRpHPxRFK2SMr7vE4YheZ6vuPknR0f81m/+Jr//+3+w2i7ORabz+BsSrs+BRlclEvB8hQTa7TZKSfI8Yz6zZMslgR9weHDKhd1Ntrc2WV8bMptOWOv3OTidcHo8ZjZZYI2g2+7RbnUo0pKDo0N8L6Db6eAHPnHYIs8LQi/CaEsURKwN1wmDu6RJhq4qpjIhSW5zcjrh1Vdeo9sbMJnOqawlWaYkp6fMF0sm8wWn4zHT6ZxvfPObXL16lThuMZ1OSdOcfn9AXpZo48SyZ41fNSf6T1P2+CuzT+fxKxlJUqGUwAslZaXpeIoodI5lW/PNfd+5061xzTgBJ6IbJ6r7vqTMNZUpHqHvPFcSXKfsEcLU2BZdC+mu6lBJxw+XviIMFVHsHNrWWopUOyHbOkZ6WVp0ZR1eQXpIoYhiQdQStNo+UigmJ5o8d3MgIR1W1FOSKAyw2mmcVe3g1rqk0hZPucafFkPgeyjfR2tJIQqMqcU+T4GUaGPI8wpTGoRXu2prXKlLYrr5tq4rhrCWsjAoDMZ3aBoEbp3CceJb7RBfeSRxRVkYp8NaixDg+R6m3vBGrBRCYAQURYmunElCCR90fbwNCM+51V0ipDbEqHq+LRRSCPxQsbu9xvqww2g0dvPgyrB/cEqpNZU2eDKg025RZZqDkzFJlpHlBWnunOoffnyf7Y0hrTzjzvsPsUJjDVzY3eBkPGWiU8IwwlawmBp63TZZbnn7xyM8FdBp9XjppUsMBgPW19bY2t5mc2ubtbU12p0uYRShlI/DDLkwjfnH2prV7xIWFqianw1Q2jpx8yhp6YqAHr+nrr5pxBkAmHDXqEvENhWcTkJf/SyokxSNtG7PVHt67p+CwHOieZPwEEiEgKht6a03xWXyTLWvS5Ib68xF3/7tnD/78+/w7//gh/zJd++zNhgz7MVEoWteS71+JSVKOVxlEPjErTZr26/yu1fewvcVAkNRLFnMJpTFgof7d7l+4zr39w+4tLVOK/ZZpjmbG0N0WRJHIQ/uH3EymTNNlgzW2qxvdpkulpzOZ/Q6bcelT0GbivVhi2S5JCkKFmnhzpNxOCLPExTaUhW2FtAh8AXddky3HVHkOQdHp850U1VMFxlxFDBb5OzfH/PGqxd48WqHo5MR3/nBRyzTEqlCLm6uUxQVcRzSafV46ytv0Ao9fvij95j8wYx2t0WSLOgNegzCGGstQaj44Y++zzvvvk2v2+Ktt95CCcG9OzdZ31jHj1quYrbf58btQw6OTsjSnH6nxe7OBtub6zzcf8Db733McLjGP/6v/hFf/erXuXj5VaJWn6svf4mLF3/Ev/93/5oszdhaH/LySzscn8x498Zt8toA5XnO/KariixvrtPniXN5CrP4yWjyPz+veLaRn41f/XOLnwF78fOKTx366XDqZ3//T37bM43xPB65f6r9e57jP4/1fU62ydOOm65fl1A3ThCEUvDS7hphGHEwSkiiiNj38KRPt7NOdGGD02t3yCaf1SDO8jSO/2dH07DiMzYe8dhuup8/a33iif/P4zx+/rG+vsaFCw6H0Yi5jSO9wZI0ruwGr9EIovC4YN5M3N0v7nPbsKqbvwOPianN643D+aywnGUZURSBMI8122xE26Y56CPWukOrxHHM6empw9Cc4aInSUKapuR5vnKbN4z0mzdvkiTJKnmQJAmtVss50JUTc6M4phydrsTcRkjP85woila89Ab30ojIWZatWN+NaNu4vZvj4Ps+g8Fg9XtVVczn87ph6iMcyVlEilKK6XTqzptxx6XVajmHujH0+31OTk7odDoIIUjTdOX+ns1mWCxVWZGm6Sqx0Aj4zXqaY94I91Iq16TqzLlsxPsm0dEkE86K/meTJ8DqWDV/z/OcNE1ZLpcMev3VeQuCgHadTAiCgBdeeIHpeMzly5dXSYGqqvA8byXCN+M15yEIgtUx9jxvJaqHYbhKEC2XKRubir/9W7/F//pv/s25S/M8/mZEPVERyLppl6FG/iKUZDgYEoYhJ0djlvMpQeBR5BlZJWi3O2xtbdHrdblz6ybtdpvR9ZvMJnO2tzbxg5jFfMGPf/Q2/9V/+X9kd2uP09MR6TLDUz69Tp/Qj9jd2ePo5Ijjw48p8gJpJZvrm1y8fJGDwwNG4zE3b9/l+o07BIFie3uHuNMiz3PKskIqj3t3H/Dhh9d4eHBElueUZclLL73kmg7i0FWPBI7nM5l+ToTDv5I4r6o5j593lHlFKUDmID3nrJYS4kjh+RIlazFdWBAagcNeWEXtnqR2xbqqmCJ3TS6Fcv2vqtJgrEEI6/pjGVO7tZvGouJRHwQDRWGIYh8/EoiWIkuNE+o9Dxtp8rzG2SlB1PJptSXKtxgqlG/ob0rKNKCqPCcwSkuRV1hdojVUlZsXeQIKY6hKDZXF893yxlZYYRAKvFBhK0HoO+SJ45DneL5ECycQNrcpqSR+6ARVow02NzXyxjUX9D0PbcFad8yqeu6pjMQKJ1gLIeoEAihP1NvqqgSsqWV+q1FK4McCLwywRoAVWCNqZ3zTaFSgjQZtkEpgtNsv94xrQLpE5SKZY3S+crD7vkJ4ASbLKYxmni6ZJglCKsrJFGs1plbr51mCVVBoy3SZcHQ6IVkmBKHnxNFWj43hkN2ti1y+cJVLl15ke3uXKOgQxjFBELpm2VI6h7KpKyG1m58mmWGephiT1IIs9dy4ma+6hIOpm9paa+uGlXZV6dB4iFbiZI2lffRr3TtjZch49P0jamRakygR0l3zUroqBynF/5+9//6y7ErPNLFn733M9Tdsmkif8B7li2VJFln0ItlNtRalHq0Wu3u1WmaN+g/QPyD9ol6akUZm1iz1tEzPSDNNsllNWzRFEmVQRKEKJoEEEukzw8f1x2yjH/bZJ24kEpVZqEQRRca3FpARN84959zj7t7v937PhxSqapor6ySHVP53X2ngx+P1MhIksmqyW/VeEn59XpTfRy4iYkDxjW99g3/3//tjdseGleWcRE7RmcHqKhFUzcestdgKQCNVNf+QEUrFJHFCu92m2++ztLjE0tIqT3/0ET7xmV+gLGZMRrtsrF/lnXcuceGNt9jY2OHo0SWaiaIRJ+yYCTdv74KomghXPIFut0HUTbh85TadXo/HHz3Fq29dYnQzQypwwvcbcNZjj3zBs//8rbTJ6bVjnDzW49rVLbb3RjQbDZwzZGVJFEmiSDGbFmxuD7HGMp3l5NqQZQWLPUWhCxYWesgbO6xvb/CVP/oLzq2tcPb0EZ55+mOMJyNe/t7r5PmMvb0dsmlGr9/n7NnTZNmEpNHAuJI4bnHy7CN865t/zfZwTKvdZnV5kVYak2Wa4XjC9u6IGzc26LUSPvbcE/zKL/40R48c5dz5x1hcOQ44imyMKTM63RYPP3KWW9tDLl6+we31DZqNJisLHXaGE8pSU2QaLUMjZYGwAuy9e2z+AEz0OwdR7x5QiDv4x7Wc/SDGHeI+hnEibPVvN+512N9L5v+gW6LeeXLu3N67JMv7Oebc5dJ4r324k+H9AE/V/Xy+B7bu91jo/W7vnueBQF6785WDYRxQuRhNxcVrMebMyUf59u2Ske0gCg0oJuMJM5fT76SUVr/r3Nx9Pw+eMHv33XjXjrt7nWgHrubLza39nu+rSsAO4zB+RCEEPPvsM17MdtBKG7RSjyUxlZN6nmE9jxTZbwwpQVaMa4RPeGERldNIV2A2UQ0u51nY847meRe3FzU9/7ssSxrNpB7IhQGrz7S3DrjBQ2PPIJbGcez5u0lSC+RKKZaXl2vmulKKyWRSc9Fns1ktrhZFgQTiKAZgaWmR0WRci9hJkpBlWS3oA7Vg7Zyj0+mQZRntdptmswn4hqQBL1KWZS1Wh8leaII5m83q4x1en0ephCaknU6HVqtFr+2FZqUUx44fZ2tzk+FwSLPZRErJxsYGQM0lv337dnURULu286zwpcCRQklBUZSV4wkguDxiBAqw9TEM53Q2m9WD9HlROywTYh71EgbBRps68aGN4cypU9y4cYPl5WV2dnZY6PfZ3Nzk5rXrdLvd2q1+8uRJxuMxk8mEEydOsLm5eYA9D9T7kCRJ/TPAsWPHGI1GdZJne2uLL37us/z5n/0Zw0rkP3i/HIpOh/F3NXxJeCQFSklvDUXT6TRpJDFFNkNiefj8Ob794rc4c6LNo4+cJ8sybty4yZFjR/mdf//brKyd4vKtXaJC0+mkJEsJN2/e5ObNm/5ZJCSbG1tcv3aD5557jq2tLQDazTbOOFrNFs8//zzvvPMO/f4CWZ6zsLRMUZbcunkTIQR5XjDNchYXF2k22gyHQ3YnO7TbbW5ev8Hv/vbvcPGNN/nMZz7DT/3UT3H86DE2tjZBRSA9U/mHDfdhpbkcxmF8SCKOE4zRCDyeZDYpMaVlGgkazZhGI0LGXvSKIu+ALkuDb3PlRWSpoNFUKJVgrENrSzYpKWYa3wnLIZRnJQuhqkoT6x3ejZhGK0ZKQVEYZrkm39JEiSRJI6RUeLC3F8pF7EVHhaAsDXu7GSiDdZokjUgbKb4+x/OtIyVx1o8hFxabZFlJWVooLUr6dp3GAsIA3mmuna2MBg7pJEUJpREI6dCucqBLCdYjKsqZw8ba42kiz4ZvNBs4W7nHcbU+q7Wl1B7foKTAOi8KeyENdBnQfBWKUViUEljh0KXBOIcTEqliIhWjLZSFpdTai7NziRCswVq/TSGcdzjj0TBx4qe7WZ7XaEUpFNY5ojTC4TDVflm8gKydAfx+OQFIR4lmZzxkd+JZ71Ha5/jRUzz35Ed45snnOHHiDK1Wp+KqZ8yynK3BjGxjjyzLPa4mzylK3zvD6JKy1Gij0cZiKlHdi+tgnXe6e8F8fr4hDlS2+n5QlZtc7IvcvueTQAk8t7sSvpUURCpGqcgz9pVEqsj/KxVKRZXwHXCN++5xISvhWvpzGowsSkkiJVDKJ2EipYgihZLVv7EiUhFRpIgj/5qnwVi01uRFQZEX3F7fpdlKiRLN8dUEZUsi2aTZW6DZapDECSqKfALHel5/UWTk2YzpdMYsm7E3GbG1eQtTIZHiOCZOGrQ7HRb7ixw5epTjxx/mpx/9BD/1Zc3e9gbbG9dJEku7FfPGm2/w1y9+h9vbI6SE5W6X8XjGlasbPHL+JAvLPd65ss6t9R1meclsrBEyYEsExro6qeGsI7eGwWDGjZvb9DsdVlZXaHd6jKd74Cpfv3NEyvc+2N4bUpbOXxPGgoPSaDZ3xrxzdZtZlnHiWJ/zp1f5+HNP8dxzz9Bstdm8dZPh7hZ74xGf/ORzLPRWeO3113jr7bfZ3h4ilKDMc/LZjBs3N1hY6NJppWT5jO0tSyOSrCx2Gc9SIiV4+OxRHn/kDEo60jhCAq++/C2ctRw7cYrV1TWkUty4cZkXvvltrIOjSz1ub+wxzjcxDtrNBrQFg9EMU0JpfL8IIalNUd8vDnEuh3EYf0ciFcI3R5AK5wyJKnliKWco27w5ycijmAYjXNRjcXGJVFliacijbTSze2/gMA7j73n0ej3OnD7tES6NBkeOHKm509PJjCj24nFwUmdZRrPZRClVO9LnywyBWnQNgm+j4bEZ/k/7rvUgoIefAyM7MNkhdGCvSijnRNjw3m63S1mWzGYz35QojrHW1mJyXg3i8zyv3e2NRqMWuFutFmVZ1vvXbrdr7nvgiDfThseltFpMJ1M6nQ6z2ax2sQc0SDg2zjni6rgFfAxQC8wBzRKSBSEBEJpcBvY4eFc9UK+z3+8zHA6x1rK8vIwQgvPnzzOdTsnyrGab37p5s8bWxHFMt9s90OyzKArabS8+mQq/4lnoBeDZx2YO4WKsqctWVVW265Mirnafh/M1/7nCcZ1H6oTEga0G886JumlVWZZsb2/7zzOd0e/1GAwGGGPQWrO8vOwrAqpzCJDnOQsLC2it6+qA2WxGo9GoP6OUktlsRr/fr/8uhGA0GtUDyyRJmE4mtJpNPvvZz/CV3/8Df82Kg6iiwziMHya+XyLmAHN7brn3Qorcz6To3uFtMNPpmDSOSJMEjEFnE1aWl5iN9jiy3EPPjjKbTIilo8gmPHz+HNoUJGlKp9NlMBiwfPwEV67fJEpSVo4cYWl5lVk2I202MM4ilKTQJXlZMJqMeenl77C8vEyz2WQwHJIXOf1+n1anTZTEHD16lOvXr9cJ3Xazye5gwLNPPc3Ft99ia2ODtNGg1+2Sz2boomAyHtNIEt65dImN27f5+gsv8E9/67d4+NFHKZ3nEhtjGI9G9Pp9JIq9waB215VlSZz45+4BY8Xdzpu7lxx/fxxS75c6lOTv9xn7oJerDVY1luHd99s8A/+9mPn3G/M9ZD5M8aD3qyhLhJQIIOTlilyjSy++ZnlJpARxrJCRqBLf3ohhjCXPDOW0JMs0KhK+oSF+GZHECOG5v2EckSSKuKGQyrtSpVKoWPptu4iiNL5Zae7QucUZWzVot+R5gbYaFSl6Cw0iBVESI1WMKUtGo4LdXY+NazVS4kiiRSVSSwGG/aanVlEY48c11oGTEKrynEEIiVK+macf33pxE+mrgOJU4gyYwqC1o8wtRluiWKBLQaMR0WwmJHGMNaZyklOxsi2FNQhN1WQV71oWCleN2Yz2+22cxVY/ezSGQDuHwFcDOCfQ2mJKR2ktykiU8p9TSomQzovpxoAQHg0DKOFxL0kSUWnQaOfF+Jkusc4nEYT0InrtdLYVpkcJlBOUhUaKGEGTbvskTz/5KZ5+/FHi2HFjfcab77zkxeDqOBlHVSkeLHKVmU3gHfXCs9kRfr88gt/3TjLakBU5RtuqN4jD4uce1rpKcPfiuneW+6p05xzOGl8hEPAvFQ5GSOnZ5dX6XIUgca4EUfm653jpSvqKjUhVDu8oIY4VSRKRpglpmhAnKUmSkFb/xnFlHIqiSkT332NxJEmUqt4TVYYVw3g0JptN/X3gBFHc5vOf/RxSQqOZkiS+uWWkFKo6z76RqP/XV8DKCkvjxX3noCxyppMJg8GA3Z0ttre22N3d4dLGOhdef4Uojmg0WiwuLnL8+BonTp7j2LEV2s2YheXTaBPz7e+8wuJiiyS2vPLmdYwR3Li5SZaXWByjae7nHtKfR/+0cjVuJo4kzUZCs5UyHMy4dnOLoihptiJOnzhBHDe5eWsTYwXNZpM8NyhlcYVhOiuZ5Zo0UkgHm5tjtnemICyLvZSzJ1Z5+MwxVlf6ZLMp/cUVHn78OXoLi1x6+02y6QTTbvPRjzzH2TMneP31V2m3e3z045+mkTQZDHZRUqEiRxylRHHC+u3b/Nv/97/j1NoyD509w8rKCidOrJFnM3a2ttjZ2qbTa7O0tEp/YRXnYG9ng1dffQNjYj72kWc4srrMa6+8xTe/811ubA4YjzOOHV+klUYMhznDcY4uNWdOH3/QOJfDOIzD+DCHElBog4gaRNYQ2ylPnz/L718uGegmoixIO4psptFZxnK/gSl8pvkwDuMwvn9IKXnuuedqgTC4hgPuJIriWmCeTCYURUFRFDV/PLCyg5ATOOXBvR5E2+DQ9sKrL0Oc52zPoz6AWkgPbmXn/HLtVps8z2tMShBv5x3dQRwPrPQsy5BS1n8HarE+CMzBdT6dTun1eu9itSdJUjO30wptE/YvvD84uYNonuc5zWaz3rdwfOaZ7s65WnwPTvywb+HzhQalgeu9s7NDv99ndXWVLMvqJMB0OiWSCqNN7U4PbPL5SoLg8A84lyzLmBX5PoJFKXS5j2qZR8eExMY8Hz0sM//f/rkW9ecJ7vf55WpOurM47SASB183ukaxLC0t1U78cF4Dsx4ONmgdjUZorVlYWKi3eeTIEYqiOCC0h/W1Wq3anW6tZTAY8NGPfpQ/+4uvHXCtH8ZhPKh4kBUNP/S6qrcrJatSdAPOP4vKIgMMzmrvunSGt99+iyybIBcWUMo/E4WS3NrY4O1LlxlMprT6q6SNBp1uh26vW6OXOp0Oq6ur5HnOxsYGrVarrtTpdDskRVL3uFhYWOD69eskSQKANhpnHbFSLC0tcbo4zfb2NpPxmOHegH63R6lLzp46jZCSwc4uWxsbbG1s8n//v/7f+KVf+WWe/ejzJI2UvMhpthrk2RQpY9rtBlle1N8JB6Tx+vgeRPSJ+2C5/CDa6gO7Ig6rZYAfUNgOZXLh0N3lra4Saw7j/qMsdYWOsMhIEDlRN4gHgTFecDVVI0rvdnVQtcOTUhGYGUaDduAwXv20PvmuKgeusaZCbTiUVLhKIJ6O/LgMB9IJjPNjQCmEdwVHEmsFQsXMphZdGLJpgUo1sgSwtYjonGA6LShLTaOZ+LEs3r3uQhZGSuI0RkYSYxxlbrDGoY0hn8MPOhxJkqBUNe4VVCKwx4YI6VnyMgKE8rgK6VExCoGqRNOoGVHmFmEs1gkvgJfaC9CBLeJEhX+JkAKksPtGBuETC7r0AFVrQReGsuJ969LhrCCKI+JY1kKtA8pCewHaaC8KS5/gUFLU4q61xjPWM43WXni2FbLDGotx+056qAR0qYjTFlK2Ge+VHFk9z8986ddYWV311xUgG45WAk3rkT7WOe+ktxZzB5rFWo+0wXqnsQkO/WosarRmPN5jNsv9sTBlJTJXWJ0Kv2Kcd66H7KkL27a+JiI8HtK0TbvTRSnvuneVU7reJ2cq4T043n0iwlQJEFeUXrw3OdYYrPWJB1dd42ARWFTlXI+jiCSJaKUpjTStTD8NOq0mzWaTVruFksKbZowhVp7lH0eKJE290K4k4+EYbfa80776fvMJgX1kpqpc71JKojiqBP2EKPZYl6PHTnLm7HmSJEZIQZ7P2N3eZv32LW7evMnm5gY3rl3jm9/4Js1Wk5WVJU6eOs2zH/sZPvfFX2Uy3mZ38xqx+iYXL18hjb2DvsjHaGdRStJI4hrnBBIVV0kR7cgyTRwp2u0EjGN9YxfjYGd3QruZ0m83OLKyTJ4VXB1sYa2k0WgynVmOtrs8+9gap06scOv2Ft997QqjrOCnPvMxnnvmYSQefbm3t02zkSKWBGsnz3Ns7QyjwR6727dRKubokeM0Gw0mkzHdTp/l1eOcOvc4QkqKbMJosM3O9m2SRPHko+c5deY0n/yJL9Du9LC6xDlLnmdkswlZPgWnGA2H3Lh5lW+8+CJvvHWNY6tLDPf26LWb9Nspx1Z6LPQbaO1Y396j0Wzx9OPHGI+mXL5+G5zl0YdP3fOZfSiiH8Zh/B0JjcUoiUQQ6YJTS10KeZRLtyeYuEEcT8h1h1g5Sm3QpUGBL8c5jMM4jO8bSZxw9uwZxuMRx44dR0rJoMJ/JGnK7t4uZcXDa7dbTCYTjDE00gZFXuKH+34wGcUxiwuLxEnMaDSsBfcoir24zn5pZJjowL4ANC+mh+aWSinSNPVCrNvnjpdlSbvdpt/vUxRFjV/xDS8FWTabE+llhUTxZX9huYWFBaSUvtFMNaFa7C/gnKNf8c6xjrRqFhoE2kazQavZpNloVM7tkiRJ/QDZWIpyn3UeXNjNpm/uEieJd1GnDUaTcY1rgX2BHahF88BNP3HiBMvLy1y5coUoimrX9eLiIuPxmNu3b9f77aqqgOl0WruxG40Ge3t7jEajWtCezKZIIcnLgjzPa3a8UoqyKAHpHTeIer/mHdmh9DQ4z+f56AFNEz5XCF9GXL6LoR8pP9m0xl8b2Syn0WhSas10NgUhKAeD2k0fkjShKWhA9gQcT2gAG5IxURSR53m9/XB+kiSh0Wiws7NTH/fZbMZoNKLT6/HJT36CP//zPyeU8h7GYXwY436vzTsTX3Vj4Oo1GYSnwOIVHuekosgLzGVJURZcuXLFCxJSenapkBTasL65zcbWLo12m+XlZUpdsr6+TpIknDhxgu3tbW7fvs3y8jKdjmepa63rngS9Xq/uATEej1leXubq1at1YjK2Mb1eD/BIrHarhZKSUfV8GwxGHDmyQrfb9clgrclmGePRmOl0StpskJU5jz72KIvLSzht0GVJo+WfF3EUVaIHtRgi3JwQfqifHsZh/IDhm0wG/ISUXtD1wx6HtM43v/JQD49kcSCkrNzKBt8w1C/vqFyoVRNRaSVU+AwVecScc6CNJU4qRIgFnEXh3e0CiS4NhS2qMY1ECkFRGnTV8DLLJJF1JA0/NvHCL2hrKUvf/HM6CxV3ECVeSI+U8hgOQFfoC629i9w6UTelDONmJy1CKqQSCGdBgEoiL15asNoghG9g6sVLiarGYVYbCmkxQhLFilgJEH4u7LDICJTwFYFKeCc6TnhhPzNeAAeP0kkEIlbVM04QRwrhPH/dJI4yd/68ONCFw0mLxVUVil5U9m565xnfsRfZta0MK6Wm1Mazu1WEdWC0I881TkiccB4JIwRx3CZNl9nZmpFNCr7wkz/LT/30l0nTpq9k1AZdarSzHstiDbb6V2svWBvrBWlrbeUsn+N6O1s1DPXXlNaWnZ0tZtOpF+Ir84nD88rjOKHZ9k1Id7Y2mE4nPi9RjTEDm1YgiJKUdrtH2mhSOkdZ6mq+w37VkhNAVH1/OoStiEKxX8Cb8W19jVjnKwysq1A3NojulahuNLk1ZJllMC5wZopzm/4CEo5ERfS7DRYXu3TbLSIlSeKYNE1oNROs88lyKQRWl0yziT8v1X3o5wWyqv5VVc8B4fsY4Pc9jj2qJpKVKz6RxEmDRiOl2UxptTo8+cxH+NgnfwIpYDwacuvmTa5cfpvrN67zjb/6a77+11+n2+1w4tRJHjr/EL/2G/+M2XSPy+9c4Hsvv8Zg73WG0zECP9+UUlBGgnxWUOYF2vo7L4oEWVbQbqYcWV2gKEqms4LJJGN7d8qZk8s81G2zvNgmL3Ou3x4wGI1JIkWzqUgbEQ+dX2Oxl3Dpyk20szzy8Gmee/6jtFvdap6p/L1e3V9xkiKFIk4UcdSk0emymk/Z3d5gNhkwbbVIyhyERJcZe7vbvPjNFzhx6gyf/8mfpNdfQBc525u3uX71HS5dusztzW1u3dwgTiKKrMA5y9bOLkZaTp5c5ePPPc+5s+dYWTlC4yNNPvX5L7C7s8FwsMsbF9/iq3/5Mq9sXWZltcfScp9rNzZZ396+5xP7gYro1XV/IO7ElM8PuPbDvuulD3QK5t69C3ffr3u/725xtxasd77vQeLbHyRb/EDc5+e93/jA9pMPFof/ftd9t/vhg9xebifEchXNmEhEPHW2xwtXFONJgkkBYig0SZqilCSTkmQ6gaK8r3PzwKT2u15X9s5F7vK+O++kcIR/8D171/Y9VO7d+/CAboD7cb8dCk8f3hDA6dOniKOIuNOuz1WhS+zMkZeF5zdqXTWp9ANggLwoCMKib44jKn6fZ93aysXYaDT8JKJyNisV7QvpdziVgdr9HYTl4C5uNptEsazd8u12mziOabVaaK1rMd0LxL6zvTGa6XSK0V6c9W4oVzuL/UBI0u12vSib5xitSZKExcVFtre3kQis8c7upBK2rbHg/D61l1tsbm4inHcUlUWBVP6eCwJ4URReyG42iaRPCCZx7EtIK/d5r9cjiqJa1Moyj2XRWtcieJ7nnDp1qnaWZ1nGbDbzLpDKCZ+kKdPplCRO6uM8HA4Bapd7WZY4/CA0yzJa7Taj8bhG2FhrPKfSVQ5ysT+kComNwGQU0tXn7k4MRWjkOt+cNFQQ3InlccLV50NUZdw4f71IFQar+1UPoUnovjNGHahECMuFBrNxHHP79u26aW7g1Od5jlKKfr/PeDyuG49Op1NGwyEf++hHeOGFFyiK8l37/YPEnQmjwziM+4n7ud5+kOaQd96jtZBOKCb3LkSomvmpiNJY0rTJdJYxnkyYzmZs7uzS73dAKmZ5yWg6wxjL5vY2pbas9Pr0en2G4xnr65sAnDt3DoD19fX6njtx4gTr6+uUZVn3TxiPxwyHQ9I0ZWVlpX7+h74OaZpirWVvbw8hBK1Wq6r20Vy9epOjR1frhFlIRmZZxt7eHi+//DKT2YTZbMonPvnJauLvmzNnWebL5JVPuNlwz7LfEuLdxnN3X07z+zmPomI7/6jjR/lMeuAIlsP40IdHSFiMFmjtiGOBiiXCOkoLSSJxQoAT3s1rBVHk9T9njDd2K4FUosJkCLS3a1djugrtp4IRo7pnA0u64oxrbVACnPbjgywvMU5UzGrvbvWUDy9Uy1jghCMvNNb5DldFYcimGl1UPbqE9cJ5onxXQ2HJS13Ndz0CxJX+PxBEUiBjhdb+b7Yyk8jKuY31SEM90wjPrfGOb+nHrlEsqgak3q2fl5okUggr0PjPWGqDdrpCkHgRW6Cr4+UNBdJ5Qd4o0KVBG+dZ50ogY0kUC3wxjkASIywUmaPMJJGUWEfl3jeUpcBaCSY0kHfgvLPfGguRpCwdRguUjPaxKtbzp50TWOsrAhqNLnHSZ3drytXN25w8dY7f/M3f5PzDD2OtZTLLKHRBUVhMadCmrBnW2hq09XieWiw3+8gaE5qDWu+ut05W+6DZ2dliOhr7321ZCe3+umq3O0SNNsZ5F/fe3g62QgeFbwOHI6qE9kazDUKSVfMR8G71A1x1XO1c93hLf724Sjd0iKqB6Zw9P3w7VOsRIgLlkFFcV8cIfAIKa9BFTjbew+ocIy1FkWNtm1k2I6nGy0JYkhjiSDCbjhkMBv7ektIniaZTsswbhYwpqwS79XstRM15bzRi2u0urU6XXqdFu92i2UhQ0axK+uCb1cYNms2UbqdFu93hxKkzPPr4UyglGA32uHrlMm9dfIOrly7y+isvkzSarK2d5rHHH+U3fvOT/MJoj9dffYnX3niNvJwwmky5tTElc/54KCmJFPQ7LRqpYGt7hFIRx44ssjcY+3t5VnLz1oBEXePIcoft7QnjcUakJA+fW0PJiBu3tnnpe2+hYgGJQkWSb3z7JbTJ+eTHPsXK6hqtThelYowpMbpAlzl5MUGqmDhtEMcNlIxod3KsM15klxGz6R7Xr17iwuvfxTrBsWOnWFhaYjDY5rXvfZcXXniZK9fWSRtJNT+2nD55nOeee47llR5xJGh2WginKfOMJI4oy4xbNy5xY32Ls+fOs3biFNksY3XxIrvDCZcu3cY43wC4323d85n9YJ3o76GE30sgv5s09kHG+93e3cTwO+OuwtsdG3igwvQDXNcHuY0Pcj8/6GPwftf/fkXY97u9SHQQIiMhZqFT0Ftc4tUXx2S5IkoU2kRE2lEKDbGktCDzAnEfTvQHKijf56refV/e7Z32B9+v93gu3Xv7P1zcawJ2OBn68IZSiqefehqtNWmaoJRid3eXoiiYTqcHGkO6SiRuNr0onmceoTF//rXWXlCuuIj7zUdLBLJmZAfhZl5YnceABEd2cEYHdIy1im6vWwsfxpialR2EXY/pkLRazapM1rugwnqFhCSJ62WTJKHX6zGdTpEVq7zRaFSD5za7u7v19mazGc45JpNJ/XmXl5frz1MUBdoYZKRqQThNUyaTCWVZ0mq1vIOjYnMHIck5x8bGRr2/SZLUXPZwfLXWjMdjWq0W46rZZRCPg5AURPOQlAhi8mg0AqiP53Q6pdPtIqoE2/b2No1GA9h3iiulajTNvEgdthcSHUqJyq1O/X4pJc1mc64hk28AG1js4b/58zzvTg/NYUejEQhHp9shThJ0heeJooiAwZFS1k1d5xvShqaiAUXUbDZZWlqqefOhIW2WZR4x0e2yurrK9vZ2fa5ctd9nzpzh4sW3HvwNeBiH8YDiB/2ePSCgB/wSFc3CVQ3RIlX9zdBsd5jMMja3d8hnU5CKRrONRbC1s4MByqJgY2OTTrdFu9Nld3dAnDRoNpvcuHGDa9eu0el06gRoWWENzp8/z+3bt4miqK4wGQ6HtFotP6nXmpWVFV/NYwyTyYQoitjc3CSKIrrdLtZaZrMp/X6ba9eukWUZ/X6fdrtNt9v1vPXBAIBvfvOb7A72GE8mfOlLX6Lf75NVzzajNdb6Z8usajR88EDPH8SD9I/vd27uKxnCYZIN7v94weHx+nGISEqcVBS5pcw02QxULEhjhZQOk+8L3ipypKkXtpECGUU1HzxJA2rKUeSWbObNGaUucYWtnLGVf9daZCkQuRd7RSUW44xvvqh8w9G8NJS59SgXIVCRlzW9AO2dyMZ6zEmslEdmdCOPlSktuvSNKT3yxCAU+2Ov0uMwlNtvBonzzGtjXIXq8AK/ikuiRkQriihLn2zAOc8xl8F/b70b3mlipTBYSm0pRiVJ4k0NxniXfFk4CE0pEThtMWWFXKFCvECF8PMNNWUEUQpIi4t8008BWCsRxnuyZOLFe2McAl9ZoEREqX1DUS+werd0UWhspHBCI5A0GimmcvEbY8lzL94bA41mm/7CEXZ2ct556yZRFPHFn/4FfvpnfpG00WQyySjKklxrysrRbrTF6hJjDaXZZ5UfGP86n0gI6BSc865uB7Zyphvjm9PGjTZWa4ppSZ4XFcIEsskM2ADwFQNCIlTqzUPOM8+jJCZOfMPsoqh6ABEwKK7eFzgoqFMtI6DGp3iBnWod1XPQhQRrsLJ7o1DAS+03H63mBShklPom2maG0QWt1gpaG28wKg3Nhse3xBKuXL3MLMf3rirGDMZjEAmNTpvewomqMjkmiSNE1UjXWIvVBl1VshZ5xo2NXV544dt0uw2PJMHR7XVZWlpidWWZxcVFep02u3sJURSRxBGNRpNOp0m/v8D5R5/gmec/hrUFt29c540Lr/HWW5f4/a+8SdJIOXHiJE88/gQf/9QXGQ83uH71In/1wou89tZNcu3nOHFs0aZglkmS1GPhbq1vY50gy0uSRGGBS1e3mc1KFvpt+g7ysmB3uMOx1UXi1PLGO1cZTTSDQYGKNDfWt+hevEgSwROPP8uRo2u0Wj2StEWSdpAy8fPcKCFJ2/5+Vwn9hWM4Z4iSmCKbsnH7Onu725w+8xBnzj9KHCW8c/EC/91//x/YHU555OFzPP/805w+c4LjayfRuuTG9Utcu3GN19+4wvHjp5lcucHuzoDbm5usri6xdmyVyXjEa69fQoi/ptvvVI1jLa1GQrvVZDbNME6js/Lez+wf6ol/GIdxGB+aiFyPglu0yw6PPNLju1c0O0NwSYVucCVJLLECnHQ47TBlOVd/exiHcRh3i6PHjrG8vESWT2sBIzg4gmjeaDSYTaeV+894btw0I4pjrC1qUdwYj0sJTUa9EOqbeoZpbhDLg9AJ+wz1IKoHp3BwBEeR71gvpcLh39vtdqsSy5i1tTVGwyF5xbFdXFwkjiOybMZkMq1cMY52u12hP2LOnTvLcDhkMpmgtXdbOOfodLuAR7Bsbm7WjnXvZtoXeLXWnDhxgizL2N3dPdDMNE0TdOBdVkJxu932QnezSZHnRHFEkRdMpzO6vS7GGJrNZo1aAeY++76rPTQxnRfKAwKh0+kgpeT27dv+M81mdNrtmnO+s7NDu9NhYWGBPM8Zjcf0Fvo1r308HtNoNGr0ydmzZ3nllVeqUlVbC+l+f7yLRim171afa24Y1jHPGA9Cd13+yr6Ddl9o95PAfS6+YTgYkK0sk0QxvW63bgqqlKoZ/eH6mWe4N5tNFhYWuH37Nnmes7q6yu7ubuWo8U7+VqtFHMdsbW3VgnpoxFonCsqST3/qU7z11tuHCcHD+JHHg3ai3/O9QiJUhFDK83mFxzpFjQaTLGdzZw9rNKvHjrHQ79Pr9xlNp1y/dYvRaMz2YMjC0jJbO3s8/5GPMRpN2NjcrBN5AaX1zjvvkCQJR44c4ejRoxhj2NjYYHl5ua4OOnLkSI2pCn0twndEo9Hg5s2b9bMrJEXPnTtHmqZcv369xsJIKWm1WvzET/wEX/urvyQrMr7x9a9z5coVptMpv/Vbv0UzihgOR0ihfF8JY2reeTgDdzrSf5BzdF/nwq/sgazrfuJvS4A+fI7+/QoVKag4yvlMeAG0tORWVw0QHVEsSWOPYRDC4zWixI+7okgihBeMhQIpHSp2KG2xQBonnqctochLL5w6zz5XwjfFdLpKzFSCammMZ4c7fOWbhmmFTVGJR3hYEwRy3yvC4XEvUWWEiFREJAUOS4lGu9LjNVzFMo8FzgikE0i8+Gqsd1AbDc75hoxRKhESSq1xQvsepMKLo14UdxhtMNYhE0iairQhaDVThHAUucEZR6ktKhJEsSJJHWUB+czV+4RwSCFr17dUIFDoWkzer+LTRuJElWy1Flc5/1XkPDrHSaSpXOzaUORe3K6by0feOS+kQxiPAAkdmoUEnXvEjZApR4+eZDp1XHjtJtks4/SZ8/zyr/0GDz30GIW27A0nlGWB1iWF1uhKQNfGYo3GWO1d5tbV2EefkISKNEYSSaz1yeGy1FhjkQ1JNrPkhUHJBjIFGxnG4xG6yP3cwVYVBElC2uqQNFq+OSuuQthQC99Ga4yoGvNWWndgqQM1/mW+Z1D9vA/mkjted/OZWjc/ZqZm6gshKqNQNQ4GjDA4U5DPxphyxNqRFaj2ucg1aZLQTGPiyPDGW2+Rtnq0uwoN9JfPcLTVQkVR5bb3Ir+UVN/BVUKo2lbAOvrlFFIozp85jRKOrc110maTnZ1tXnv9IuPJCF0aGo2E1dUVTqytcfTIEfr9Nu3mNo1GSrvZotvtsdBf5ae+/Cv8zM9Ltjdvc+HC97jw2gX+4Cu/R9JocObMOR5/8mn+yf/sE3znpRf5yh/+KbuDka8ocTCaZCglWV5cwOQF07zg4XNHaLdSOu02Wxu7XLq6zo3NPY9jiiTGRKhoRJpIBuMxw5FmPNI0Gh5DJaRme3uXP//TP+LRxx7n4UeepN9fxrqSJGkRp839ZEo2ZjzaIYoSmq0eINDGUGrHZJZhENy6dZ3trU2++91X6PUX+Yf/6H/EQ48+gkQw2FvnyuWLrG9sY3RBnk+YTEbcunWbxx97BCkku6MdlLLcvHUNXRqOHVsmjSNGkxGTIqeZJjz7xDmKsiBVEUePLtNuN+/5zD4U0Q/jMP6OhDUZViQsd2acWz3Nf/lHjqxIq46jJU0hEEaCMWCEL+U6bCp6GIfxfUMIweOPPUqpC6x1xHHC3t4e0+m0QrdUYrqxJFGMLT0PPMsyL4QU+0J4LeiqqnS3GkQa7Won8534joBxudN1FhjW3gWvaLUbFEVZO+U7nU69/wv9PomKSCLfxDMIJkE4ttYQxz4x0F/ocvv2FOcUN2/erN3rQgiyynGYxDFJmjKcjGuut7WWOIqrY+Sd3P1+H2NKlJI0m12cgMl0SpblSKmQ7AsUYT1JkvjyTCGY5Tmz2YzhaIiQgn6/T1mWlbPbC815npPneZ2kCO5ooHZMB2FpNBodEJJHoxHdbpedwR7gGZ7aaGSeIUZDOr0ut27dZmNjg06nUycGdnd3Cbicmzdv7p9LoN/vV1gY5ydgvv4UZzlwjqMoqhuazidkQuXAvDud+jhV2AQpwGOWSZKodu9keU6n3SZJEoqiYGlpiSzLSJKkPkYhMROOYcAztNvturlqQEUANRIinHtf6WBrt7yvYPCTo7NnTrOyvMzm1taDuwEP4zDuER+k2Hh34V14AV0qz7rVmqLUtOKYaVaSFZq8NAgcsVAYJHHapCUUWZbjRAZC0er0uXTlNUDQbDbp9/vMZjO2t7fZ3Nzk5MmT9f15/fp1NiuRvdPp0O36SqN2u0273ebmzZvEccze3l6dbAzPpHC/AnS7XY4ePcra2hovvfQSzzzzDJcvX66bkn7uc5/j5ZdfZn19nSiJSJsNNjY2+M//8/8cpRT/+B//T0mTFBVFSKHqXhZ34bfceSDv61jf51m5x8YebPwwyZfDOIz7jTyvGiI6j2gpC4+Vy6xHoSjl8SHaKJJGTKNynQspiSp8oCmh0Nq7mK0GLE4ITOBaC/aZ64AzoeLNC9HGWEzpcMYjNJTy45VYRai4qrqxeHb5rOoJgcMJhxNeYItTQRx7rF8sIrBQzvB9IorQrDI0KwWVSEQsEUahnEIJRZIIXOld9KUwlLb0YrPyYzejvaPdN64UJLFCKHAaTGkpC+9+LwuDGnkRvmKB0EgjlPRu+jiCZlOSRQKdW2ZTg3YeqSNcaNzqkxZxEmGwCGMqURbPSxcWFXn3vxACEeH3VYA1kGUlpvCJCYdDKI+e8YKwbzAqqyajUvjzqUtNMbNIYlZWT2JMk3eu3GSwu0cjbfOlL/8Sn/3Cl0mbDUaTGXlRUpQarTVal5RaVwkFj2epx5QBg+IcFn/8bO0Ar4wZ1XEKzULjWDGZFmRTXQncAq1LsukEU5QgFFHSIG22SRreee4clD4D4q/Bav3Bte6cr4IAsS+I1+7xfVH9gJDO3NeI83iwfQd61dyz6hfwru+HUGkQTCTV/SKcoZjukk13WVnooiKBtYY8N0RK0EpbtFqCN956h5XVVRaPLJHbFFQCwvcT0CY/sCkZGuhKhVL7iEYvovt5ilSWZqfFzs4uR4+u0u60WV5ZZe34cUI9RVloRqMxW9ubvPHGW3zjmy/isCwvLnLq9CnOnD7J0ZVlOu0m7VaTbrfDwsIyn/ncl/nil36Bnc3bvP7K93jllVf4nX//Ku32Ak89+xT/8n/xP+dP//hP+PqLL5JrXSPqxqMxjz90gizL2RmMaCWCTzz7EEePHOHP/+ybvPDyW2wPM5xxmNhhjWR7Z8pwpJnNfGWadQ5d9TJOGilnH32EteOnyWcztvIbFOWM/uIK/f4RpIywwleOaJP7JI8zTCdj3nz9e3ztL1/ACclCf4GtzSFvvXOVj378aR5+5BhbGzcoi5E3iCpJURpe+d5F3njnMktLfTq9BuNsg2tfvU6sFI8+8hBnTp1gZ3uT4XCEimIiKXjyyUdJ04TLl67x+psXkVHEE888Rq+dkqYftIh+n+Od+1nsrsvcjfvyfjZ0v2OfH3Ac/p6L32M997ub717O/cD7eGDl7j4OxKHx4cHG+zie9/uWO8+mSqZI0+PU2hY7t+DKZowWMUIbZGRxRlJkpXcnSDClwBUlwno3wAPbsXvt6PuOu+zA3dZ9r+eGuMsy97u9H+HE7TA+HNHpdDh95jQCR7fbYTqdcuvWLVZXV0mSpG622UxTqFwPo9HIl9IWBQ5qJvW+g8KidXAlz5UqznGrjTEAB4SQwD3fd7WbWkgOTmchqLEyQSQd7A0q5qElTVOPUpG+nDKIqab6++bmJkkS1+s1xtDpdA6IM1p7DmXY53a7XQvYaZKQxAnLy8u0222ybEqSpHQ6XbZ3d9jc2qoRAsLsC/NBrA/HKjS0jOO4RsF47qAXhaWUNTYlNDO11tY84LIsa0d1EEACaiaEUqp2lud5TqvtsQihIWcURbQ77Xo7w+EQpRSNRuOA6B149jiPqgnnMxxzIQ82FQ3JgtCcMyxPda3MNyedvzY8tqYajFcPMldVD/T7faIoYjga0e/2DiBfgsNfVxz7+YRMwPIMBgOUUoxGowNYnfkGtQHXE1z+ga2+sLjA7t4uEsHzzz/HH/7RH7/v++1QrDqMDyp+ELH9vRqLUnHRsaIqJzeYsvSl4lHEcDShKDUqihE4BsMJxkLaaJKkDRrNNs1WgaGyhKK4ceMmi4tLtNvt+p7zSLBm7SZ3zjEajVheXq77F4RqkdFoVFff9Hq9ug/C7u4uo9GIXq9XP9O01uR5zuLiIlmWceXKlbpSqSgKXnvtNa5fv04cxZSmJLGWKEnI8px/82/+DaurR/nMZz6DimKcg063y7jCdt1taOWZx4dTiw8iDp3qf7dCa0ujFflmiMZWFYsKXfPOJSoCqRxlWQC+ws2gSBNFHCukhDSNAU05KymKKpkf+eaG1nmxXgg8g1lFOCmrhpLOoz/qppe+Y51QXiRXiW+aKIRAlo58WoDxHG2kdzIHlEZZaow15EhioZBSkcSAUAjjKCqh3paOotQVasMQqQinLNJJtLZopynKshKzDdbu90MImJdICXLjXfpxQ6ISQZl7R7rOHIWxCK9xIxUUsxJwNBqKVkuStiXdjsI0Y+LYUeSld17PjBfr8S53qUBEDhswohUuBAs6M57rDpVIvI8NsaUX04NnWinhz6VUOKdw1lGWlTEm0khjsAaajQWaraNcub7J5voVjLOcPf8YP/eLv87JU+fQ2jAcTfyxLksKXWK0b/ZqtEHbyjnv/JzD77lP7gZUitFVY1HjXenW7rvsA4drOoE8r5IylfnbWkurv4TVtqpAVQAVT730mrYT4PyxB3zlg/EKa+CdB0E9CMdUFbGuEpv9ToUjFwRzCA1a9/Et9u5fMgHHVgnZAeMSmuTqfMx0uEOzoWi1moDDWI0zlk6/S6upeOfydRYXl1k5foy9rMpw6bxOmjj2q0IDFsi73g1GedOLFBIhfDWHQyC0pbe8zNU33qbZbhNHMTvbOywsLvjPJSBOEo4cWebY8VXE009jrEe0bW5uceP6dV7+7qvgLMePHeHxxx7i3NkzLC/u0m416XW7LCwu8pkv/gyf/+kvs37rBq+8/BKvvfY6F777Ml/4/Bd54vHH+G/+v/8tg3HGsdU+Tz5yisHekFu3dxhlGe12A2MM25vrPPnEaRaWurz82iXeemcTowXnTp0AZ7j4zg1ulSOcsyRxxNbmiFe0Zm9QsLZ2krWTj5CkKVvr73D5lde4ceMaJ06d5ejRkzSbHVSU0m4vsr11mytXXuZb336J6ze3GeyNObW2RqPRZvVIjFSCdy5eYbA94Oz5E7x64XXeuPgOUZzQ63bYG0xwImJ3b8Isy0kiRa/TYGc44uatm5w+eYyzZ89VCTTFeDSg1e4TSYXVBafOnKAsLZ1Oh+MnTtDtLt7zmX3fIrq89yJ3jfcroN+PNna3eJDvu594vzLb/bzvQUp49/O+w6nrg40HeU3dz/pLAcuZ5slHTvJvfxuy3EJUEmuFKBRGgbKCQhtEqkgzg8v9F6d6H9u733hQw3wp7kJEv1NwceJd2/My5fyrAifueKLd5cOpu+z5u1sgv3siIxEHlxL3+Qy6i3h0OEn6249z586C80114jipS+I9W3ZWC6xKRSRxzPb29gFh11UIj7opaGUHCgPV0IwyCJvzwumdsd/Qch+D4tnaohbDgzMwcLBLY0kq8RPg2NpxdipxJTjRGw0vyCRJUjPeQ1PJIEyHZp5BiLXWkiQJsD8oTZKEbrdHq9mk1+vR63XxeF0vtOeVMBvwLuFzBMFaa12L0M1msxZ1gZo1boyh1WrVIntwsINHuQRhO+BnlpeX2d7erjEv4TNYa5lMJiRJwu7u7oFjHDAm4ZiGfZnNZpW73p/bsiyZzWY13ieJ05q5PpvNsLZi3Rs/AZzH8URRxLhqUhpem0+MhGtg/jwL4QDfpMpY4xtXVVx0Yy29dhtVXWvtdpvBYECn06mF8sDMD9eQtZayLNnZ2aHZbNYNXuM4ZmFhoUbYhM8a8C9FUdTO9jzP2dndJU5iprMZTzzxJH/y1T+tk0CHcRg/jhHuwXd9L1fPb20M2KpKyFjSJCaKY8bTKaUx3umkJJPJFG0sk2lGnDZptNq0S+2ZsVHE4tIS165dp9XyjvI0TVldXWVtbY3ZbFbfwwsLC2xubtJut+t7NDw3pZScPHmSa9eusbm5WTcYDdUiIel4JyJqaWmJ6XRaf2c45w6gpWz1LFJK0UhTdnd2+IPf/32iKOKpp55meWWVIp9z4fkcQ+0WnD909+0xv48xj+Aug8G/p3Ffx+swMfljEc56PIpMI9/YE8/Bdi7BmOrewvdXkVKgYkGUeHe6k4LJrAQhiGM/Hmo1GsSRFxtV5PFyuhKey0IzyzRSlkSNmCjy7nRrLFI6UJ4GHiWSpBkRJ6LimGucoeKyKzzopBI8q4mkdZYo9vtYTjW5LhAoIimIKte8lZJZVuCqYmglBWApnCZNY9I0RkYCtBevG02FVBKLo9QG6/z4Iopl9V5Xsde9SN1oRDhH3ShTVtiYKJGV09/inGVaWEpXEimHsxIrFHEaoSJLnEryiaMsDeANKj7JUDWMlFRVpQLjKl3dOowVFZLFN4mUkSBKldfbjfN9VaVPimA9h70sLVp7B3XU6NDvHWH99ow33rpYVQp2+NwXf45PfPrzqDhhMplS6JKi8O7zUhuMqZzoJjjMgwHHeda5s1j853bOs9H30TSVU7xyE3unuK+gLMuK4V67w20ltnsGjHMOq/14PKyrFsDZf1670Mm2Es2lBCwY53DWVNurRHT0Add5WHf1As4drNq9+3PQf1eHdXgBnUrkljhbMBvvYGxBv3fEm9OdoMhLWs0GzUbM1s4OyIi1M8dZH5bMD2tFhW2pUk0+0VUjWwAlkdUcQFSoR4868ddg2myRlSXj8ZR+r8V4MiBtNaGaD5TGGx6TyDcCFUrS63dYXOjzxOOPYLRjNBpy48YNvv2dV/jqn/0VS4t9nnrycZ549BGOrC55NGW/T6+3yM/+4q/z5V/6ddZv3eDN117i6Sef4Pj/8l/yb//r/wcoXz3yxOPnWd/YY3tvipIRu4M9nnnycZaWVmk13yaJLKuLfb716ju88Dev024m/piVmkgpVlaa9HopZam5cn2DP/nTv2Lt+GnOnX+MOGmyunqMzY0tppNhVZUdMx3v8Op3v8kL3/o2b12+xZUbmxgrefT8CZ555jEef/xJllePYMuCq5cv8tLLL3L96jvESYOzZ09w4/Y2125tcO7MGo88fJxYRTinefihh2k1WwyHA0ajAUoJmq02nXaPRrPJ0WOShaVjSCE4cuwmw8EOabPF6tEztLvLKBXf65F9/yK6eB8jlvqSvtdb30M9Fu9+6b7iB33fDz28uGOD972++9nRO5ZxvM/9dQcHtPfa1GH88PFDHc/3UXWh8pjzj87Y2DzOm+/kONcGHKb0/DoTCWwscIa6m7twd5OFf/Dd+QF284eKOydld06mXHAG3LEXB1xl+2u79/YOrCX8ftChdvf9vNv27rGtO27QQwH9bz+EEDz26KMYYyhLTbejiFVEr9slqVzhkVIoqWi2vLiIEKg4YjyeeleNM7WA4UVZz8qr3cbSD7wdFZ8SWYsfIQLjdl48B2rxNooiVCRrxEkQgmezGWmSoq0hK3KiKOL2+nq9bY+caTCdzphMJgjh9ysI2SEZEBqFBsd5nvt1Bb55JL3jPY68+Lq0uFgJ1hKk9MmHyYS04R2U84iaNE1rgRy8iB143tZaGo1GLe6Gzx+WnXdbB6F5OBzW+7mzs01R5PX7hGgzm82YzWY1nmYymdRs8iAsBwd7FPkh0ng8nmuaOqUsNXt7A9oVOiU4tYvCo2u08U6n4NgJ5z8MsEPDz/nPBPdzz+97PYOjBrxb3xqDLkuoBPXpdEqSJGRZxrlz59jb26MsSwaDQd3MNbj/p9NpnWAQFUO/1WpV171HE4XkQ0iuBB66tRZbWnq9HmVe0Ot0WF1dZX19/YDj/VDIOYz3G/fLO79XzD87f9jr0ZfDBzOiRKoIhMQ676w0ztGIYhqNJlIpjAOpIlqtNkVRorUhTlLanTa3bt5mMBjU912v12NxcZH19XWSJKHVarG8vEyr1WJnZ4eFhYX6GR36PMRxTJqmbGxs1AJ5eNb0ej0Gg8EBjNfe3h6dTofZbMZ4PK7FdqUUvV7Po68QYB1SCNLKIX/h9QtVA9IWnU4XIf1njqIIbW1doh+aVEup0Lr046cf82fAj3JM9iC39UDX5Vd4YL3vtf57iVx/G2PcD/O4emG5RRSBTLzYZivOeJE7Iufdvs6qiuftxeVIKYT0Am8UVwIoHiGhlCJtRNV4E7Q2SGMphcYI4ftjaYfOvcM4CL5OOO/cFl4oNsZiMi+Oam1QMkIJ5ccziW/oGSn/HLDCUljvgHfOIfGYFasN09x6tnqFtUjTBCtCrxhRudxldXlZolgSaYHRAiUl7XaCsZBlBdMiYBQNRoCgwqPgcJGAikntxVJDXmjKElQpK2a1nzsKoMwNUliM9TgcrP8vkh6bkzZiz2guS3ThGfFeOKYWRSvqtXdIu9orj3Gev05qkZFEObDaYQqw2iKFqmeUkVIsrZxgOk353qs3GI8mYC3nH36CL//iP+Do8ROURUk2Hns0TllgtKWsxvva6Mqss9+U3laNX60Lzer3G7ruC+heGTbWHhDAvcjuq2arEtp9oX2/o6c/5s5V4nzlDq+E8vq/YNFnnwtuS1sdJ7v//VCZzpyxVaKjssC5+tu2WrdPOIRts796qP8yF07W83f/3WQosiHT2ZiVpR6R8ueuLA1SSJpJDJRs7gz5zE88z8YwJy9s0P+rsTKVG70S5tm/tgGksdiqT5QMPa2cRdgKLeOgs9Bjd2+POJKoKPbzl04XYyxSSLQUWOOb/ioriIzESEtkI5SSLC4tsLKyxPPPP8Msm3Hz1i0uXnybF77+IktLfZ5/9mmeevJxVpaX6XY69Ps9lleP8bO/8ptIFeNMSbPR4o9+//d447W3uBhHzApDkqZcu7nLxvYekxk88WhJmnbY3M55452bxIkkm2bsjkpPNbASGQva7QadToM8L2g2Y2bljL/6679mPBqzsnKE7sIqi8snEFKxs73L1StXuXrlGt988SW++/olIqU4d/IUUloiYfnud7/D7s4GC71llpYWaLebPPXks7Tal3n9wjvMsoLhYMLqYp9nn3iEY8ePoZTAlCXdfpelxWM0m21/jwpotHokaRtnLdl0hDE5SavHwtJRjCnQ2jAZ7yKEoLtw9J7P7EMm+mEcxt+R6CvHYw8t8NtfKRiVTbQxRMSISOCsACtA+wYuaIdxJZHTlbvxx3tycxiH8UFEt9ul0/GNNrXWDPYGXlCIE7AOXZTIalBWo1usdzE7C7nebyg67zIP7mPwZYlCitrlMe8YDKJmmqa16BrWM8/hlkoBrnYgFkXBbDbbdyDiSNIU6Ry64owHDE1Yd7fbPeD6DiKz1rp2PAYxtdfrVU5r71J3zgstZVGwfvs2RZ5z7NgxnHP0+gu1sztRqhbN4ziuB7/znO5ms1k3rpx3Rga0TEgazJdPBvEbvKAcBFylJJPJpBKwFcZs1ziT8N5wbgIPPPw+nU7rZYLbU0pJlmXkeYGo9qnV8g1IRcUs1abkyJFVtra2KfJ9d45SohbqhRA1WzyKotphH8T1MMmZb0Lqz7ep0TthoB6SGsHhL4RgOp3y6COPsLu7WycKGo1GXWUQcDLh/UmS1M1HFxYWWFlZodFoMBwOKYqiZqYH1rrWmsXFRba2tmpXfzb1Lv0sz3nm6adZX19/0LfjYdwRH1ZBaL557Q8bD1pMvBORFOJOfMu9QkQKhagEFH/f6tLQarYp8pLZdEYrTel2u2itaTSapGmDVqvNdJoxHk/qXgXdTodbt24xGo04efIkq6urXLx4ESF8H4hms4kxhuPHj/Ptb3+bL37xi7z22msopVhbWyNJEi5fvgxQs9Bns1mdHFxbW2N9fZ3JZFInA69evcpkMjlQCROepUtLS34d+dSn7YwljWJ0WRJFiu++/F0ajSZCSD7ykY+QV4gtW5QYa/yzSVUVN0qRzWZeRFD3YVz4kArtd14vP8x6HmT8qI/XfGL0Xsvd7/p+lPFhfWYCaKsxhSCWgigCY/y+SuXdxCryDQmFBIRFSUGSKuLENwwti5JplleoQHDOj0mC0CeVQBvvSo9RCA1W+vNpSu371EQCKwVSOXDWu+GtIDRpdNZSlCUS4xntSngx1lqk8gK4cJKiKLHGIbAo4QVMpaR3LzsASRzFHjXqTOUO3j8Wsmr82Yq8sF/MDEVRjZudg9LPZXVwTRuLEA6lwGrvmFeJ//wqFshI+cah1ovHBkGgKbrKZKi1q8XcqDKklNqP5UMTVqFAWk8YETis8/Ppeow/P5cW1f3pwBYOPfNMl3ANyqrZqkTRbS/SaR/jnSvbbK5fp9QFzUabz37hy3zi058nihWz6ZSs1JRFgS4spSkxRmONQ+sSbYxn0wex3Jjage5clWCpmsB6fKOrRWiPYbFzTnAwFV++fs35+Yq1pt6GwB+b8PdgHpkXzh1VYmFOCJ83hBw0jAcR3zP5vSPegtjfr7DueaN6pctX+ziXKK/+KITFVcwhn0DImIx3SBNJs5kArqrMLem227RbEVeu3uDZZx9jNzPM8vC597fpES2iTjj5E125zUXFX3cCicBaPwcRMgjtAuugt7zE5dffpN/p0urFjIZ7pM1WJdTb6p5xSCeJDaBUXd2qIkVkJUYJ4kjSajZ59OGHeezhR8nyjFs313n9wkW+9pff4sSJ43zyEx/l8cceYnE0ot3ept9fpNNb4rNf+h/yyc/9Em9feJHLb73Eq69+j2++9Dpbg5ws13z1a9/mG99+hSI3DCeaQpcIYSC47oXACUuhHTdujtjby+h2m2xujFDRlOvXXuT3/+gbNNOU40dX6DTjal4yw+HYG03YmxQsrS5z4mifJx89zcmTJxiPhhS64GMf/SwnTz+CkorZZMDO9g3WTp7k5Mk1Gu0OOxtbPP+xL7Cw2CefjRnsbLC7u4XODcPBDs45Ot0+zlry2ZQ4bpI2OkgsG7ffYTLaZWn1BGunn8FazWS0wWD3FsPhJg8vfvz7PrMPRfTDOIy/I3FiaYqcPcLbV3P2yhEL0YoXhbQD5XAarBY4jWfRuQJhyztQJ4dxGIcR4uzZM7XjNpS1W2NqFnoQH7TWjMfeae2ZswVKKYqiqDEqsD/hnRdy5t3lfszn78fwvuDIDiJviPnGk0We0+m2D0wQtdasrKwwGvnmK+12m6Ioald3URS1uB243sHVKKVkOp2yt7fHwsICCwsLTCYTRqNRLcYGJq8QgkhIxqNRvc/b29uev95IaXe7ZFlWo1bmXdDhM5RlWTvU57ErAUkQsCfzonsQmcNxmRfTA35EqYgkSUiSBKMNsuGF57D/Yfkg2M8nJ4KjPyBvwj53Om3KUpM2Upz1n3VhYaF254dzdvLkCW7evEVZFpW7Z59vH85j2EbYdmj+B9QCetgn/3NtAqyvlXB9hERAo9Gg3+9z/cYNBnt7rKyskOd5fQ5C09WwreDeDwJ/WZYcO3aMnZ0dyrKsEyth3eG6HgwGOOcdqKGSIIoiUuDxxx/nq3/6pweSRYfx9ys+zGLV3SLcg3fu9/zrnn/qwycXY5/AtN4JSCxpNBq0qh4EWZ7RbraI44hutwvA4uIizWaLfr/PYDBgdXWVty6+jdGG4XDIlStXOHbsWP0saTabrKysEEURL730Es45vvvd79Jut+l2u0ynUy5cuADAsWPHWFxcrJ/xQZAI3wGhv8HKygqTimO+tLRUP8fj2Jcvp2mKEILxZOSRA8CtW7f47Gc/y9f+6gVOnDjBK997BWd9I+Vnn32W3cGAJEk8WspY8rzwLPUs85/lQT4HKvfjjyoOn2GH8aOIUBnmxVUHVI07hXeHz6YlpvRc8mY7qpAhFmO8IzpJJd24wWzmGxs7Z/xcz4oKQeKRJ85YsN4VLgOvWUrfSLNwaGcw2o9RVCxRkUJFVGKkd+xqa/F1kw4VHbwfhfOmLZN70djKIBp7tVRWLl5rHMIZEB7XGUWV4OwsZW7Ji9xjZiJJpx+jpEQbV4+BbMVw9xutUDiRTzoA6MKhnfbt4ETAb4QxlMMa/3t4xgmBr9TGe6Ol8hx46xy60Bjjj18ZhmnCfyZZ4TkIvWqCKB+c3SX7byCIqP7fSCWsLJxmsAMvvfRO1W/GcubMQ3zp53+dtZOnMVozDc1DK2RLWWqMLiuOvcEJP5Y11VgwjAlt/bOtxrveTW5t4KKHxqIV9sVWArXFr9dU6/AL+O/HOae7P5L7ynJg4jvrl/cHI4js+07yOuEgwldD+G6thH1bLVftZ923rUoI1Dx1wlwqbH/fEug3J/x+S1FtwVXNK3cwJqffWybkdsuyJE1jGo2YzZ1tlleWSbsL3N7zlbjWhM9X293rJEn9DVHNQ5RSdRNTJ0AKP04XCESVfXAI0lYTjWOaZ0SZBCdrE5F39FuUivzxV/5CVtUhNTislURW4qy/f6T0iaQ0TXno4XM8/PB51jfX+eqffY3f/cof8gd/KPnc536CT3/qY5RFzmQ0pN3p0Okt8fhzP8njz32Bj/3EG5z7k9/hL//ya9za3GFSlLRabSYyI9MWbQXGeKqBx005pJA46xgOC0bjnI3NiW+e2lAMbcbKQp+FhS6DwQ5nzzzFZz/3Gcoi48Kr3+Nb330Nm0xxaDa39/iTv9hC22/R7bZY6vWYzSTnb21y9NgaiwsLlGXJjZtXuHr9MsiEdtrgrYuvsLy8QJombG1e5+Tpx4mjmDyb0Gi0SNI2adJERREqThA4yjJjNhtzZO0RWt0jOAc6H5M2e+Bgb2/jvR/WVXw4RPT3GAu96+W7LXc/Y5sPeqw1t/77G2q5A//Ur965n+Ldy/ww8X4nNYcDyB9tCO5yLdwRFoMkAV1AEpPYkmcfX+ZvXpyxOxY0WlDONCIyuEh64bx02NjirPCDKGfuvaEfIO62pvt97c4r7L2uOHvn/oo7lhZ338LdX7kPN8093ne3/fTO/nusO4zq3vXm7/urf+uPlzbxYxtSSs6dO3dAQMnzvGZFa2Pq5jztdptCl4zHo5q1HdzJdw4cYV9PuBPjIatB17xAHv4Nrx0UVf36ZdUgs9fzDSWDw/H27dsIITh16hTLy8t1c9A8z2scQFhfaPA5Go2QUtLtdlFKkSQJJ06c4OLFizSbzVooCUzeJEm8I19KSq3Z3vZu77zI6XZ7GOeI45itrS12d3dx1e+Bax6OQVmWdDqd+jjnlWNeSlknAII7PIjaWutaAA/rCrzxPC/QZU5ZaPKoIIo8k7LT6dSNRIMALeaOO+xjbIILPSBtQsO+NE2QMvLNgHa3vdOhYo9LKWvH58JCn52dHYTYd8oHMT6cw5B4mE8MzDvk77x2QrWCEd7Zs7KyQpZllGXJ8ePHKYqC8XgMxmNkZrMZe3t7ZHmGkvvYmvnqhvF4zPLycn1M33jjDaIoYnNzsy4LD/sRRRF5UWCtrbEujUaD3d1ddnd36fV6HF87ztLSEpubmw/mZjyMH6t4UK7dD2t4956de25LVKzqhFKv16HX7yKcYzqZ0Gw22dneojSaTrdDp9PlyJEVrly5wvnz52k0GoxHHqnS7XZZXFxkOp1y7NgxlpeXiaKIvb093n77bcbjcZ3MDNUxnU6HyWTCysqKF7/HY4qiII5jNjc3WV9fJ4oiGo0Gi4uLrK2t8bWvfQ1jDKdOnaqTlqFhaeiJsL29yc7ODsZaHnvsMS5cuMDzzz7LrfV1NjY2yLJvs727w7/6V/+K06dPk2eF7wkTRYDw34/WEieJF3ce0IRmXij5UcTf9ev5MD4ckTSUb46ZKISUvgG8tR7Doir+t4oQTlDmFhKJNhmq8G7zuJAeZWA9fsNai1DU4zWHI1IWXVbGDGMptXcJSwkqFhjrMCWViOlFZoP1Qrzx6DZn8Y2TZaistFjh/H+Vq9laiynBlq4Wr6XyYy0DRNJV4yK/vBSgJQglPFYm9u514RzaWFwEKpEHMB1CeEHeO6G9qO4soLxILaXAVE1SBdVnEh5bE+bZpkKWhMZ/UgkcAiehsNY7mE3ALbpKOPf6sDV+m0o5pPLYDiox2qM+at90hfrwn09WyYJeZ4l2fJyLb66ztbmLKTWNZotPfean+NRnvkicJOSzjKIsKUtNqTWlLtGlRmuPb6mxLc5ULPTAN6/Y5cZhcT6hgq8kqA0sc01E/TrAP7f3ky3zzvCwvBey2X89zItr9ZpqGTP3eyXaU4nulXDvCI786vhLf95t9d0RkgKRVNW02jtJbHWNh0oC6k2Hoz2nswl8JX7lFi/yEbPpkMXFDkkcH6jqTeOISDo2h1Oee+5pbu3lFfbG1hUJAn+uDf6ycfPVXNU+hDG+H8sLnPTVIcJViRTlr2QhoNXrMJxMSNKURrPJ3t4ey0srQSnHWeGvL3yyRlVCuYSQPai0ET8XdAhU9X2lpOTokSN88uMfwaGIJPzFX7zAX7/wDX7jH/46zz/zFKUumWUzWq09Ot0VVk8+wz/4J8/w0790hb954Y/5sz/5fS5euYJSmk9/9BxXrm6wOZgxHM78te78Peava39txKmk3Yw5f+YYy0sthuMhn/n4s5w9scaf/8VXufjGa/T6XS5duc6VG7eJkpTllZRWo8lomCFLf33t7A34i699g//4h3/G6kqPJx89Q6/b49Lly1x46woGQStNEVby5Z/9LHk+4NSpMwip6PWXmUYR6zeuUuRvs3TkCGunHkWJNiKK6Syd5nx7CSEUQsVgDLPxNhde/RZvX3qLazfXeerT/6vv+8z+UIjod8pJtQR1x4t3Nje172PdDzLe97rd/b1PHIplfy/jfq4rKw3GRig0MpGcoCSJl/jLt7aYiWXizKGlJRYa6RKElQhXDayMAKMR+OYodxSg/VDxrvv2biHqr7c6JPcu/zbOvut94o59F3VW+y77NfeLqPPX7x222tfv/77K3nBwT9+l6999WwdfvfN5Fjp8H9iFesByGB90tFtNVpaX6uaRQgi0NURJjMGhooiycvYVRQHSD/xGoxFFJTKGCAPRIJzKORSHww80RTUZkBxMXL4XgmC+ZDHPc5rN9ACGJTSQBO+8DizwLCsYDsZVeaEgacQH9jWIx3mekyQJcRxz/fp1gFo0D4JMcIvPAKEkk8EAozUiUgyGI7Z3dmhutmrhOLgfm81mLQAFN3XAm4AXukOzu4A6cc7Vwu/8soE57gVxP/g3xoATlQO8ckMLUFrXE4iiKEjTFKBmuwMkSVIjZOaPc3BaF4Wm0+nW4n3P9OrERMDfxHFci/qtVovxeLyf8KiORfh53jEajn8QrYO4XwvoTmKryW0SxxirGQ6HdLtdWq1W3VzQaFM1u/XNUEvjcTlCSvq9PtYYxuNx3Xx2YcEjd9qdDlmRMxyNauZ/q9UiTVOscwwnYyazKYPhgH6vz2w2o9FoMJlM6qQLgC4Lzp07eyiiH8aHLuafmwfKvrkz0fnuZ7Bzrh5g+P4TFqM9jzhJInCG6XiMcBArha1QR+12k+FwRKlLbly/zsLCAkdXj/Kdly7Q6235CaiUtFotjhw5ghCCwWDAyZMnGY/HbG1tsb6+TrvdrhEtUsq6iigsF3oYbG1tsbe3B8Du7i6dToe1tTW2t7fJ85ytrS36fX//djodRqNR/R0Rkr+el+6rV2yVbAtu9lajwWA45saNG4zHY3773/82/+n/5j8FoCwKojihWYkBSSWg75fQfOCn+AOLH7fqisN4f/G3dZ5V5CU4oy0qrhzVkcLifMNPBU44P1kQYJ1HwMUVtkRWorYFnNXYimetC0scKZQChPSMZgRpFKOUpSi8s1lr36y0EScoaSh1idUOjEVFniUuKpFOWIWzfowVxbJyRxvKQnt3uPOzMxXLuoLOlF7d8RCTMPOs+OICokQgBVgsUgmPh6kcybowCFcJxMYghCOOBHEcVWJstW4JBGZ35QKGffe6MwJtK3G7wuXY4G6vnMXzZ9+L4QKrbb1MFPn9tobK0e3F+TAVDC57W7naPe7Db88VDikjlpbWyEYxF966RJYVOKs5efo8X/ryL3Pq9ENoo8mmGWWpycucwhh0qbGlRhuNrURsY/V+5WTVNNZUDnNjvRBua9Hb4qgc62a+iSh10sWjcRxOzwns7Df9rA0ewWVeX3H7v9oK/0K1zfr41y5y6u8Dvy5Rfy9YY+pZveev74/z/bH110X4jM7ezep20NkqnCC41K3zGJc4UnQ7Ld+U1oHWJa1mk1Yz4tb6Bk88/ih7mSUvy2pXPbrHWRCY+vr2Dnf2P4tzIPbnaT6ZU40VrK+4EEL6a6/SQvpLC9y8dI1et0uaNinLglJrP8bAYYXBk/OtX5f0x1NW7nSLJXExJQZVnUwnFU5USSMrOHvmDN979TXOnjnLP/j1X+fl777Mf/F/+S/5+Z//OX7tl38BO5tijaEsClrZiHbvCEtHHuFLv/oQn/7ir/L1P/0P/Df/7X9Fq9Hi0594lpe+9yaXS4fW+3OxVjMG4cgzjS41raUuQgrevnKzSnZkrBxZZXX5CN988W/Y2ZvS6SQ899TDjCdjNrZ3GQ8GtJpdlhfbPPbQSR566Ax7ewO++mdf58zZ45w6vcajjzzJkWNLdHsd3rl6m1MnVmkkDdJUcPPGHiqKuHXzKp/53M9z9NhpFhaPkc3GICBp9ojiNlZPmY63ieKGN31NdzFlSWk0Tkh6vUXKK9ffdW3dGR8KEf0wDuMwfrCIHGiXoRsdonydjzy1yqtvTbg+0JTCEgmJczmw33ij/rK3novmsIeJmsM4jPeIEydO1OLJPJolCKJFWSDFfoPMytvim3mmKXlW1IOqOydlgYXtsR77LMh5x29wRs874UPj0LocsnYs21rEDCzcsJ4kSeqmdbu7u+RZ+S6BPwjRgatdliVxHNfCcCjzjythFajd1kFADqiAsI95nlOUJaISloNoHoThsizrRpWB0xvWGQTz8Xhcb9+7y/Ma1RJY3AeTCv44a62rJq6qFuqTahsBYxPEbmttzRIHaoF+3jke1hOSBisrK6ytrZHnObu7u3XTzn6/z/a2d6YH8fzIkSO02202NzfrYxrc+GE784LdPAc9VBVIKav3cSB5EBoEZlnGqVOnuHjxIs45ms0mURwzHo1otVoIIZhW14Xn/HfqfZ5Op7Tb7ToBAHDj5g2Orh6pUTbhPIXkjEcc+ZLRKIpot9t14sEYQ14WPPvM03zrWy8eCk8fYHxYj+3dsCg/7PoeRLxXEvK9hPP57dfvdQ6HF3uCI11bS1Q6jPMissRRZDl5PqPf69JuNYiUpCg11mgmkzFpI0FKQ57NiJMIpdo0Gk2iKKqF7+XlZW7dusVwOKwFjuXlZay19Pv9ul9BlmUcPXqUmzdv1hUxeZ7XVTlRFNHv9+vXw/tV1aNiPB7Xz8x5sWK+D8Xuzg6tZovr16/TbDYZDoYYY1lcXOQbX/86r77yM5w+faZ6lsv6+EVRRFlVOT0wpIs7IJP8WMahs/0w7ow814BESIMqQEYOKVQtcnot0DunnfPjBu+G9k1Fo6higAtJrCSu6oeD827xsnSoyPcs8H1RYuLIoybAf3cb7TBSI6Sg1UoRwmM9SusQ0uEkVUNSiylFJdr6ClzvXJcIJbBl0FHdPk5FefFeICo+Nz4JiUAq/7PvcelwRiJSL9orKTDGVkx0h4odURJ2xI8njXW1sBzMFAFhg6sEbukwct9sFZAqUnlx1JQOo6kMDp7LLoRPXMioWka7ilIS3JAeuWErbI6ompYKKagoMbVrWyBIkjZpusq1SwP29sZYXZKmDT72yZ/m05/9SRppg1kW3OcGY0r/s9YYrTHGN4G11fnSzo/nnfbnwVTiua0Z5xUTnTBm9I1EXeUSm29CGgR0U9paBPffB3MNR8PxnDMAhQjJkhr3Un94e2AbIebNRf471rL/5+q7NojtYfvhmOKTA/tL333MIQiOb4F1htl0gNY5K0eWUdVcxGhNpBRprJjNMtKkyfLqIjd3C8/Qr5z01cih3k8plT8RIXnlbC2oz6M7w5DeWYsVAiHtfsJBCNqdLqXWvllsHpHGKZPJmE67A0IgpUVY8B8+7AX4TliSSECJQeLRQ0I4cJKaNqAkSkoeOneOi2+/zTNPP8XHP/4xVlaW+Mp//H1GwzH/5D/5R+T51D8DjEHrgm5vhaS1RHvpBJ/78m/wxqsv8OL3XuF7o7eZ5pq80ChlPUoqjjl3ZpWFfgfpYDrOsVjG45zZVNNpNRlPJsxmM7q9HoPRlGmWs7jY5MjyAp/86NO0213KQrO9tcWNWxtsb+2R5zO6vSaf+uSzbG1tMhpPSBptbt+6xduX36HVatNoRnz8I88SScfa8eOsrB5nMhrQbDTJ8zFpo8vikbMImfjEmtNk011u37hIq7NAt7/KdLTHjWtvsr29zd98528AQaPRuMtT+mAciuiHcRg/jmEVQjrQJStyzOmTD/Pv/vg6M5Pg3AwpUmRscE5VnbT9RMi70MFpA1jkfVnHD+Mw/v7FyZMna0xFiOD+C802F7q9mjWdZRmzzAuJRVHUjmM4OKCax7PUTUerQZ6r3DPh77AvcodywRohMNeAVMp9waPRaFQ8cFUL1u2K0VsUZS3ihhL+IFrHcUyv16uRKUH4nke3pGlaizBxHJMkCdvb27VgnSTJAaE6SZLakT0ej2k0GrUQHwRa5zyzNwymoyii2WzWDUgDtz0cv+BQD+LOvFtbCEmeZxhjDjQubbfbdDptHLY+d0EQnhfIDpyTO14DagRM4BGvrKzUg869vT3yPK8F6lElYM9mMx5//HFOnTrFZDLhrbfe8u7wsqw/0/x257E9fsJsanHbb6uaPFvvuur3+1hr6fV6TCYTOp0O7VaLqDr/4/GYOE0YVI51YwxZntUseaUUWmv29vawztLqdOpzFEpSy7IkbTRQZcHOzg6rq6tMpzMaFTs5JBoajQbtdpuo9CiJZrPBbJZ9aMXeH/f4MB/XD6tIeDf3efh9Hp8U4gAPvX7dYc2+U8xZg9aWRMVVg68Gzhpm0wlrx46As/R7Xc+0LQrybEapDcvLPTrtNiCZzfIaGzUejzl79iwLCwvcuHGD0MBZa02366tgOp1OjW2ZTCY89NBDXLx40Tu/q2cjUD/7syyrv4/Csz0kPOeTZeEzB7xXkiT++SQkzlqm02nV9yNDyghnHTdu3OBPv/pVfvXXfp1jx48jlSKb6xHBnIP/xzUOkS5/v+Jv49kqKjGRIJQLgRMGKXyDXoHESAPC4ZxASoGS0gvIRRBrbeVK99g2ow1RJIlTj4LQ2lCWBu8I9/iYtBEhhPO4kNJQOlO52I1HDFaNDI0xWOcbtkuFFxaFqFjRvtumdQ7hBEkzIk4UzlqyTNfscqWkf5Y4sMK7lPfFc+++l8or3KW2iEAEcY5ISYSQvirZecez0cFl7cXrgPRUyvPbw3vB+iag1XJxQ3hHeSX8WgMkjrJwFJkXy52FKAGh8Ov1fSmr9YWS6ipJUSFchAVXMeCrpfz7hKTZXGEyjrl+5RZlrrHGcHztJF/62V/l9NmH6+d/UVpynaO1wxS+kaOxZeUo9y5yYyuHufHPbuMqF3rV+NMX/8w1FZ0Ty22FJglOdNz+9W5LUzUSrRIgVBgWWzmcq4MdBPJwl8wjCH3RuxfPwzGybl84vvN71rPCw9/C3eCq/avGuwGfFkzvc874aukD6zz4/e73rSxnTKdDOp2UZiNBSVl9TkOjmdBoKm5e3+RTn/kI25OyOlZV0sH5cyj2d8/vg/CNZp30iQUhRE3NqGZ2FQLGJ1+k9NUVobmooKrwbaTkeUkaJ7SbTfYGO7Rb7fpcBJMW7CcahPR6jhH+bxKFQyI1GKn9/SIB4w9ss9VkdXWZty+9w2OPPML5c+f5tV/7Zf797/wepTH8i3/6jymLWcXMr6qCnSZtrRCnLf7xv/zf8uRf/zZ//dd/wXdev0xeGqa5pSw01mkGwz2G4yFpHHHi6Arrm7s0WzGn+0ucOLbG6tIKf/zHf8LGxh7NVoOf+InnkaKkkbRYWTnOqdNnSOKY9VtXeeSR82hdcuHNV7n4zhW6vUWc0eTFhG9+66ssLPY5urLI0WNH6bSarJ08iykzrHP0F1Y5cfpxwLF5+21UFFOWU5QsMLrE2oLNjSu89DffZO3EaRqNq1y/cZlSl9y6eQttHf1+j0jeWyI/FNEP4zB+LKOJQNM1Mz59fpXvvj3i0jWNsEXFwkoonUah6ky8Z4xJ31bcWJz0LKu74VUO4zD+Pkeappw8eYLRaIhzruZhW2tZXFpiZ3ubRprSbLeYZRkqjhjvTtjZ2QEE7VabNPEu4TCY2xd8Dw44ww3oB/wHhZ0QQSgOwkcQQ+YZ3nlecPToUabTKXEc14708+fPI6Xkxo0bzGbTCuMicVgazRaNRqMWwW/fvl3jRYKwGxzTQXTXWtPr9ej3++R5XovvwU0yGAxqx/NoNKoFnVarRWhcGVzUQURXStUiT6vVqoX48HmDg3uehT7vpg8JC2NMzSX23Pqyel0znfrGr2Gd4fPsO10gOHNEVXJpKyakFT7p4KxjOp1y8+ZNFhYWPHu8itFoxGg0Ik3T2g06Ho9JquZ6zz3zLFJJyrzgytUrlJVg5QVrL6h7h1nlRq+e2aXbbwBq5ioTkiQhir3wfvToUfr9PpPJxPPt85zZxDvMA6YhuM43NzdpN1tY5xMh4X1CSmbjKdvbO5xYW6MsS7IsI01Tn0RRCqzDlJrd7R2OrK4ipaTZbNbu9MBgT9KEtNHg5MmTvPnmxQd5ax7Gj0k8KBHqgxIu30sUvXO/5wX0OhmKxZQGJVOUqAyR1ey+kcQs9PukaYrRGiUFw+GAZrNROcMsRZaxNxxy7swZer0Fsrzg+vVbdQPQNE3p9/t1hU5AaE0mExrVfbW5ucl4PKYsS1qtVp3Amk++lWVJt9tlNpvVz/ZQEQReTJ9OpzV+LDRljqKI6XRKq9Wk0UjrxOHbb7/tMTDTKWmSAILtzU1a3Q5f+cpXePKpp1lcXqbRbDKbTml3OpRVY254cOPM2uH4I4xDAf0wPugQLvRoqa43ixdHRRCzPcoirhzpSimUlEipMNoihSCKvFBtnEEqQZxE4HwzUCH9eEMIuT8G04Ky0EynJXnux35RQ6ASEFagtYWKG77fwNyipG/2KCqetaAS8U3FGdclWlccaCFwDkxhIZbEkauSAl70p2pK6CqhEcBFHsPhKgHWAUmkiJIIZcEUBms0Qol6P3XpRU9Z4S5qsbFyjAtHLTS7TBD6fdpKjI0TQZwqogh06dCl9o9sQy3AVyQdL2pKgZIKLXyj1lDaLYLzvmJoS5uQpEvcvpUxHu6hi5JIJnzkk5/hc1/4Mq1Wi6LwlZtaG4rCUJgCXfrEpjYaJzzGxswxzesmocaL5rYWycFUPXZcEKOtrz6vG4kaahSKg1oot8bW3G7rKkHdBuHczztcEN6p3NlzFbGuFuQrB7q9g1I+7y6fd6Xbak/Cd60T9f7PL1vjW4KT+45Kznp984lwwBrNdLqHlJaFhS4q8g16jTakaUoSRwwHI46vHSNqdhkM85otHxIQYTNSeESRdf5YWemvQVcJ6BYLwQ/lKld4uGaqpMJ+Pt7fP91+j9nuhGYjxViFscYjXWTkt09wnlfHRFqcASUcIMEpiDwKR+OQWBAWaRTeLSlRCI4eOcqFN9/k9uYWx4+scvLESX79f/DL/Pe//bvEKuaf/9b/mGw2xlUVIWEuFKcd+ivn+NRP/iZbW7d48/JVGk0otERLgXUwyw1R5EijmJWVBc6fP8nbly+D0DQbEW9cvMZgMOba+pBTJ5eJY0k2LclmA7I33iDPZywudrl86RKPPvEszbTJq699j+2dCYNRSa/TZmVlldcvvMZTTz7PJz7+EaRKeOLJj9FspFy7dgupEhaXFCpq4Iym1V5iOhly+dL3iFTKeLjL1s4Wne4is1zzjRe/yWOPPMZ4OOGtS5dY3x4xzTXTyVXOnjz2ruvqzvjARfQqKfh9w91lkbu9793M4HuvG6hLLeY3eOct966B83vsk7jHUu59OnvvPuG42148qNhHCNwr7j1WlXc8xO7v6B3G3eO9Jp8HSv6txqkMJx0fefIY/7v/5xvskVKWBamyaBlhyxQbx4hQf2e8eC5MgtQKGZUVL07yfq+z+X0SUGe132uZ9wp3h5B/t6vlbuvxjTXuc2fnt8e9t3f/azL3XOp+3ieQVCfkwFLu4EK+VOswPtBYXl6i1Wqxu7tNWXoRMwi3169dq9y+bfYGA4yzDIdDylIjhBc1S10ynWQHnONC+CaVZVkQuIT+mpZVKeK+WD6PcoF9xEeY+AS34f6yDoFkd8e7oZutlNXVVYbDITdv3mQymdTCu5T7TZdgnzkeyv+DgzuIomHiFITtNE3RWtdNPwNXO7ieg8sxiOVB1AmCexBsvDPEC0DBER2c7FrrevsB/RLHMVmW1TzzKIpwztUIGYDJeIKTsLy45J3UNq7XF3A4IQkRUDTBpWPN/vFWUSglpS6LldLbkZxzrK+vE7ApaZrW58VaWwtbYbuz2YyiKLh54wbGGHrdLkePHGU0GZPnOYuLi9y+fYuyLCq31n7VQvhX1g41KueUd8dEUVJfY7du3WJpaQljDM1mk+2Nzfo6stbVjVmLoiA9cYI0TT2+J8+5eesW7Xab0WTM8sJinSzJsozt7W36/T6j0QghBOfOnmVvb6/G7/R6vVqsbzabbG9vo5QkjiKeevLJQxH972F8WF27d3Op3e3nH2R9IlTrCEc+m7K5tUFZFrRbTdqtFuPhiFLnbG36Z0cUJ75xHpYzZ85hnEDICGNcjUc6evRo3Vw0fG9IKTl58qTnqR89ypUrVyjLktXVVR566CGiKOLEiRN1gtAYUzf67fV6daNRU/VDCM/P8XhMluc18zw846fTKSeOH+PIkSN0Oh20NXS7XRqNFqvLK0xnGVtb26yvr3OikXLp0iV+7/d+j263y6OPPgbWEQnJJMtJGr6nwoMbufztjIEeRGLow3hfHMaHI0SkUM6Lc8ZYzwA31ZjACVQUGgcGUdSBVCglSCLpUSwWEBZbJey0tt5BKwRRrGpHe3DZCiBKIhJtccLihMe2gE8MSuW37Zwfq3rnd5UyrEwGUkrf9FRIiAQosMLhhOd0OyvASW/wMg5tLVa6ylEPUSSR0rvHg1telwYpBTIRyNgLjRovEqqoSh4IRZZZisJiCv/8FpEkTnzDUW0sujQHnvuqwsqIahzlDFgNzjjKmSOKXT02Bq8f43xiwHqNt8a0eCyNJVL+2Hox2tFMWqwdXWNt7TTWCP7iG9/j2uURRTHDas3yyhG+8NO/zCOPPoVzjumsoNQlpS4oy4pjX5aUuvQCqHAYW1UHGO0TGth9k4cNOJlKILf7P9sgZLuqWqB6DgfciqvG4jiw2u0vGxzkoZ+Ff5O/LuaEcif2X6/d60EoD65153DhufceIrqfAHHg9bp5t7y7XrXvlH+v727/PoslLycU+ZSV5T5pHFcuY58ESpMYJQWj8YznPvoMe5P9OQquqlKo5lqIqpFthTAijMuD2B8+A9TzOldVjToX2uKKKt/i6uuo1++ze3MTrTsUpSFNG4zHY7rdPjg/v3PCVaKtBVPNGSt8kLUWtMQpj3CJAYdA4zX8KPZvk1Jx7uxZXr/wJt12mw5NTqyd4Fd/5Zf497/ze6Rpwj/5T/4hk/GI2UxXmoOkIyCRMa3eMT7/07/J37z0Cls7OUo60iaVyccgraDTbvP6xasMxzPiWJBEiqtX32B3d0S7mfL5Tz/D6VOrJFHEG6+/wztXbzHLHedPXeL40UV6vTYLS5s8+9zH+NhHP8WNWztcvXWLWCmOHj3DdDzhyuUrfOlLP0ueTRjsrHNttMPeYIur16/x3HMZTzyekKYNrrzzCnuDHdK0TRo3ufjmG7x56Qrj8ZS98Yy9ccb2XsbHP/I0J06eoTQ3cdtDigjevLx+l2vqYHygIvr9DhXepTuH7OH3W6Z68Z7bELyrjPBOwew9B0Z32d6PNj7IDYbc4I8qftTb+7sdhXJEOJ486Vg5cpJPnbnFhdffYJseJB3Gswn9XoPSaZxTPrNsLVif8fdlR6XPnLv3d16CgBfCubs365T3mDTUQvHcYu6u9/YdX6R3eU7cT9R31T23dx/xfi/pu7zvTm38bvsJdz/Gh/Fg4+TJkxRFUTv6wnU+GA7RWtNutxkOBwilaud2QJ0ATKczytKL7gGrAl6ID26MELKyrcxjSe6MeTekdwLpA2J7cGvPZjOsNeQZtcA8Ho9r3Md4PCagXILbMGBagjgdBPDg+g4RGo2maVqX/g8GA4wxnDlzpmaBBwFnOp0ynU7p9Xq1gN5sNmtROQg2Yd/mP/+8I7LRaNTHJDhwgiifVjiRVqtVJQ+aNc8vbaRoLevGryE5ED5vEJQ9A16TlXk1IRRIF+1PZKtjHMSlKN7HpISGmo1Gg+Xl5ZrVHsdxvd9JkrCxsYHO9xuZdrtdnIBGo0Ge5xw7dpTJZIbWBqN9c9r5JErAIhzEzPjExuLi4gFG/Gw2Q8wdK6WUF+ir3z2KIaff7wMwHo9RSnHlyhVWlpdZWFioqxCE8FzAkCzZ2dmpHa/T6dQ72IWg3+8zGAxYWFioKgn8ZOP8+fMHKicO4zA+bPF+Bf+kanJsjCVWgihOuHH9Kt/61re5df0aWZbRaDQYTaasHTvKeDxmYWGBfr9PFEWsLC3TarW4dOky23tDOt0ey8tL3L69zt7eHmfOnGE0GjGZTFBRxOLiAkYbkIJvfftFZnnG2fPnOH78ONvb22zv7nDhwgXiOGZlZYXzDz+EMYZ+v8+FCxfY2Njg/PnzHF87znA04q233vIi+nSyn/BUEhkpOr0uxlkms4zBaIS2lqIo6S0s+gm9lCRpwuLiIlJJbt++zdmzZ/kPv/u7rK2tsbS0xPHjxymKCiEzJ6D8fY8Pa4LpMD4c4fCCm6oaa1otcFqircPkGiE1MjTrBApdkhclEoXE4yaUAhVb0kZEo6XQGibjEl06VOSIIlVVuzkvNGuLthqjPUtcKItMvKitlEQpgTVVTwfrBWtdGqiQICoCnMej+CK6qnGl82gPISoBPeAlwietOOY4g62QJ1L69RhbITgKi3QChMMicKUjSRyRlEQxRFbgrCBJBMbs40S0cz6poL1Jguq4yEpAl6oaTwvvtnXK89B1aX3CwPmmpJVuSqSETy44iXPCT6etQwq/XKQUJ44f53Of/BmefebTOBL+6oUX+IM/+iNuXt8mzwoipXjq+U/y+S/+Ap1el6LwQnlZWkpt0MZgSk2piznnv8VU58aP38wBhzSVe9+YIIAHId3uO9NtaOi5b3Lz8xC7L3prVyO0IYw12RfDQzahclGHlGhwrYf1+NWG5dz8CufW66qkTACe+HPhEzb7AnpoiCrY35f5pHc9Hq5W/+7nqj9OxhRMZ0PSNKLbaaOkQgqJMZpWMyVNYrZ39jh77jSFiCl1ViNNvInGu88dsjoW+/eqqqV8URt7Q5IiNAMQSqAiWTXW3XekO6rrC2i0WmhXYdVKQyNtsre3Q7vdnTvOFuPAucpwVzUVNZWg7g+dxOAQUiOc9M1tnU8WIB0CSyNNWTt+nLcvXebxxx9FCMHp06f55V/6eX7nd79Cq9XgH/3GrzAc7EGVyJdCVI70BY6eepJ/8k//1/wf//X/njcvbzDVBQJLsxHT67aZTKZM85xxVrDY75EVsLU1YmW5Q6OhyLIx589+GiUFFy68TaHhxMmjfPELn2Kh3+Ol73wTrUs2N6/R6/c5stxnOBrxyU89TxRBo5Fwe2uDN9++wGMPP876zWts72yxtb2Bs/Dnf/pHbG9ucPz4KV566dtcvX6bn/nSL3H0+Gm+993v0e8u8Phjj3Pjxi3eunyNq1dv8vY711hdXOIjzzxCu5Hy8mtX0Pre39OHOJfDOIwfw7A4Fqzk08/0+MMbgsd/7gn+J5Hjv/rt77CVdWi21tCzPWj2AVU96n1Jmu9aXiKkrr70JAf7kR/GYfz9DT+gOFU3sJwXUYeDAYuLi14grfAh4/EYKWXt9iuKgiRJaDXbDIYDfHf6fTZjXbEBc6K5vw/nHRp3Mnvv/PnO3wMjV0rpHSzCi/nLy0vs7e3VSBrPNHdkWU4UqbpZZxB9g5gdsCihqWgQUpMkqcXnoijodDoMBgPA83et9c58rXUtcgcBvygKlpeX2d7e9mKvEEyn01qwDdsKPF6AnZ0dZrMZcRRXbvKoxr/MM9IDWiSwMI8cWWU4HNcomsB715VLfHFxkdlsRqvVYjqZoktDWRaArFEx84mEgJ+JYi/Mh0REQDAEd3sQlYXwTUF9lULJaG/AsePHkHLfoe6co9looCLFwkLKaDhic7Bd4Xb8KDtcI3Ec0+126yaD4Tjt7O7irG/wF6oAhpVrfHNzk6WlpTqBEATtsixZWFhAa83Vq1drp3kcJzXyIbj/J5NJnTgyxtBsNQGIkxgh/bG4ffs2xhg2NjZYWFgAnHfBdrv0ul129/be3814GN83HhQy5QeJByUA/iD7fj/Lzif83ivutu93GgG+37bmn8vZbEa73UZJSZnnlEXB6tE13nrnCq0kIUpbmMmMTqcDRPT7i5iyZGdzs8IuKKTRRDiaSUK71URbR6vdpL/YZ5ZNuXHrOnESISLBJJty/ux53nr7bY4dP87y6irGWC6+fYnbt9dZWV3h8aefpt/tUhYFg8GAveEQpGT16FGyPGc0HtNst1leXWVza4t2p+MTfHNMdOccjVYL6xyNtEFpDHvDIY20BQgWen1GkwkOQdxI6atFZBRx/fp18qLgP/zu79BuNfln/+yfsbm5SZo2aCYxWal9k7u58zkvhHy/c3TXc/aAdOjgNrznNu+saP5ht/sgVrKvOf3IYl4su6/lv8+Y5YFOO5zD3scK72ff/zaeqwDWapzTOAxC4XENkUImEiwVe9w7qaNI1lWp1giwHikhpcAYXbmmLUkS0W5L0iSmyL3gJqDigUvK0mPkvNPWC8pSCCLpK5RNaWsHaxRJfOU56MJ5R3dAEFpHWfjxkRBBaBRYXaFOlKwaLHpBzxqPP5Giao5aOsrcECXelS59cSGBcmgtOO0AizUFAoG0Am3AWFOPlZSSfq5rHAiHimTlmK/WUYnJXljcr8hE+eamurQYUzUZNRXjWkGS+gpFoUQtTFrthfRG2uRnf/oXeOLRJ7jw5jv8x6++wPdeeZ0Lr7/KZDpB64KF/gqf/8lf4LGnngcgy0vKUlPoAhNEdF16ZrPz4q92umr2WqF3rGegW2OxULPHa9d5SFw4f514DIjnzu/PMypDo6NmrDsrCD06D7LNTT0OdVSeO3+298VrMXe/BCU3NBL1W3oPY5AlrM2ynwzwzmex/z1Rz4MgON2rNdT/BDGauW3tfw5Lno8xOufI6gpJEhGrqDbUJHEKzlEUmnYnYTor6kSMqRIl9ed1FhlFWKf3xf5KPq+3H46TdR6ZK+YqG6qkDc5Xh9QmXyeIopi44cffOhFYIiw+aaKk8s8DWwN1agOkscEW7F8TzmN6jfGCv1QOKYwXhFV1bKRgdXWF7e1tNja3OHZsFWkkD59/iJ/72Z/kv/vt36HT7fCLP/eTDAcDqDCRQgpaQNzo8+jTX+Sf/4uc/+xf/x945+YmKhacP3ucSDguXroNThHLlM31IdYaOu2Uk8c6dLptPvaRZ5hNRkSx4vjaMlt7E5rNFGs1e4N1mq0mWzs3GAxvce7so3z+819iceklHj7/MDqf0EgbTCdjXnnlFU6vnaXbXaDV7tLudrl25TrPPvMoG5s3+NM/+0tu7w6YzAoK+wc8eu4kt9c3GIzHHF87xic+/lGOHzvKt7/9Kq+8dR2rY/aGY7KiZGcwYWcwe9d1e2cciuiHcRg/htFyJeeXHKp/mq9dLFkwET/7Mx/jJzaGfPWFTYoiRsddBAWSiFCOF5qMIkr/nzt8BBzGYcxHt9tlpXIVW+2qhi++AeXy8nLNA5/NZoxGI2azGSdPnqxFUSklCwt9ut0ug+FuNV71fLraLWG9aOw7x9tqgu4nMta4AxiYEHcKDwEfEkTvgBgJaBRrPCIom+VEKibPSpTyLhsVRxhT1G75sN8BqTKPcAmCepIktbgaRVG9TBzHZJOpL/k3GpSi2WyytbXF8vIyk8kE8CLXeDyunf3hvfOs8/F4TL/fn2M+2gorUlAqg672t9FogJOUhakmnq7e7ziOaLc7DIe+kelkMiFJEpYWlzClRgL9bo+9wV59HJM0ob/QZXd3jzRNKkHJ1FiTwFz3CATfbC/gbIL7OyQAAuJlNpvR7/dxzjP1Z2XJsMIoCAFJElMUJVleEFvvbhqNR8SJxzG0Wi1GozFZltdO7iRJaiHPWks+h9xpt9sHxHmlFIPxCCug1WrV10m73abdbnsUxJEjNC5dAus5ht12u+Yvh8RImqYsLi5y4Y0LLK+uIqPINzMcjynKkslsSrfTpcgysiwjqv6u84Jeu8Pa8eOHIvphvO+4X+fu/Qhfd1bP/TCxj9FytZiZJCnHj68xHY/Z3d1lNJ7QbXfr56WonKK6KBjt7aGWl5lNJ1gr2d7eJk5TTp5a84nKsmA0GqGiiHa3Q6/XwwnB9s4OpfGNAU3lIjXGMBiNcEIwGo3Q2lemCCkYDIdIJRFKkpclk+mEbr/HwvISy0tLWBx7u3t1I+GNjQ32BnuUpabVbiOloiwKxtMJw9GI3dYuRZWIDM8eKSVra2vs7OywtbXF3/zN3/Diiy/y8Y9/nN3dPY+W2JdUfuhj/8Dd3Pe5qg+bg9yFQ3rP5T4cJpm77ceDOqIfjk/4w4UxJdaWXvzCO9GNqbjUApCe47x/HVZisHI10s/fG15wK3LD2FmiyDthhRQoIdGld3gL5ZEUDlOJcg4pPZM9TiRxUlGYjfNIC+NwEt9EMfJzSol3gIvKLW5Ki4iq3RUg4+oNiFr0nzcHAKgKweJc1WTUuVrQdxZsaalR2GWoyrTI0MLRgR8iWVS0n2Tz4rMjSqVH9FlXCcHCY6Sdw+q5K0cIVCxREdjI4ZzcrxyVIKpjL5RX5GUM7bTLL3/5H3L98g3+9R/9n1CNI+zt5Gyu36AoMpwTPPboR/niz/wyCwvLlWjum4WWhaYwnoOutUFb7R3Y1njkjbXegW6onOe2Zpc7LNYIL6xXTTttJWLX4nhIUFZuev+8mO+NBiBA+6RHfSSq9Xih3FXr99eaq1TiIHCLOgk5v9w+Mqb6y4Eb1M39v36lEsgDEvNdIvwdN7jDi8F+H/cNR3c+o0udkWcjOp0GnXaLKFIoKSi1odFokCQRG+sbdLpNvvH17/KJz3/WJ0jmEEbhuvXXixfQ/VbkgR0SggOYliCkl4XBOoMSsrq/5htu+88hgHanTTbKaaQNtLY00gaz2Yx2u1PP9aQUPungBMK4KosRkmC+KsIKi6AyRFkwQvmElQPhLA7fj+Ds2TO89voFer0eSnrz0VNPPMVsOuO//n/9f+h0unzhJz7CcDhgOlFIEeGbqwqixhLPfOLn+ef/Iue/+D//Z9za3uPGjQ3iSDHJNIV2OKtREfSbDdKGYGdvxPJyj8FgByUVO7c32doesDsYkDYTXvzO9yh1zmQyZaHfRjjDQv8Ijzx2iieffIa9wR69fp8onvD5z36By5feor+4QplNuXHzMsPhlFary4WLF7l1c52iFOzulhSlZWdnwvV4g36/y/XbW2xu7XLu3CM8/tiT3LiyyWtv3ibPDP9/9v702bYkPe/Dfpm5pj3vM587V9WtuasK1QMaaKDRABoUJhFkUDYthhQ2RUcwZMuhkBUO64Ns6U/wJ8shi4NFUSaoIE1SBCmSAkg0KTSABnru6uqaq+58z7znvabM9IfMXGef29Vdt6pvV1cD942oumdPa8iVK1fm8z7v83z5q68xX+RIJcnaKe8VHx0E7Yf5BPyg236P333vecn3/+EH/d27b+v+f3Pv4PJBJ1bvVjLz7ptaffODT57fe+L6vbb73cd5f9/7IPHD3DbhSdIor8T1nBc+fomXb0Vce/k2t9R5Lnym5pf+3M9w49pv8ertY2ZZh0zUuCelAasaIB1RI4QGq07XNj+keLeWedfd3U/3eKDMlQ9nf01mHLGSyhdnVXT8g/e72uWjtWb7UxEbG+sIKZnPZg2I2mq1GnPMLMuYTqccHh4yHo8bOZcoimi1Wg1Lez6fNyDt2TFslfkIQjpmj5P6EBR52fSZwDgOQHZgOzflmX7SuMrCXAXZAZbL/AzL27Es3H/a1MRx1Oyv40HU1f3fy/YOwHA4v5OTE3rtTqNLbnCAfKfToSxLlFLN9rTWTCaTBrA3xjhpkxU24mKxaLTRg26v0dpJGfgoiqIxBpVeezxofAcJkhDBiPT27VtYbUjTlLIoSLOMyLMvA+M+TZOVhALUQjfHHQAjrZ1WuLWW6XTaGPWtsumttU1lgBCiYYMXReE100v6/QGLxZIiL9nYXGdvb598WSClA7p3dnZI05T9vcOGHR40kgOIrrUmLwpiD3q32+0G0A8SOkFfPlyfIN0zmUwovBYyFtbW1hqme+hnrVarkX/Z2NhgfX399NpZS7vdZjQa0cpajW7z/v5+09eMMTx+9Srf/s53PtC9+DAexoOOBwUoKp9YIyzcpUSpiMeuXuX1V15lNl8wmc65dCEmSVMm4zGJkmRZi0oIDg/22d7cJC9yjEpZlBWiLBFKsb3d4datWxRFQS9zJqNra+vs7x245F2kfLKMZtwripzj44qqLAHn17C+vs54PHZjoNauwkdAmmXN2FEUBVVdkWUZ7Xab8WRM7sd7JxklUVFMXRTM5/MmoRzGaK01w+GQzc1N6rpmNpvxzjvv8Hu/93u8+OKLzrxN6++z7vnxiY8KGP0w/mSGiiNnRml0AygrJbBKIK0CcTpvQlhnVF46U0m0Ax8jJZFKISUYW5OXNaKEJJWkaeRkSQSUZY2ua7RxxunGag/COja3sDSAdqQgzWJirSjyCqMrVAJpmrikYG2ovXQKwhJFHtAz1rG6tWMeB2kURGCD45OQpjGMjGIIzF8HHOOJKE7uw2o/L5UOCJeRQMSCSOLHYomKgua7oK6MI5QIULH0shRhnmycZrv1Mi6eoR0GK6dAIwna7QBSgkSiMShifvGzv8JX//hbfOOl7yDjNov9PebTKVVR0Ol0+cznfpUXfuLTSKUoy5yyqqjq2jHPq9r9rbVjfVvjjUONNwx1ILqTZnGfN7Iqxr2PPZ3TOwBdn1aLWP8dH/ZMxas/IS1c1cAp1dtnLpwm/6kMjLsQru1OEXd7ZpEcwGaX5ICAWZxKt6zi6c14KpxWuPBt/93bPKV5NzIyXgLmFHw/C6Q7QlFFWUwR1rI+HBArRawUxhrSJCFLYowuMEZwcuR8fnS1xMYdtDEY6+F5CcViyXy2wBpDEiuSKCFJYtI0Jsli4ihGRadroFq7+8nUDiiXjQmvaNohANI+H0F/MODW4XW07lJrTZK0mE7HtDsdd//gKk7c/eGkkYSwGCsRRlHjEmIRyvdX4fejkQKMUAjh1jNKKlqtjN3dbd65doOnn3jUuXPFCZ/6xKc4mcz4a3/jbzHs93jumceaKmfnqSXIhCDO1vjUZ/88//tizt/67/4meydz5nmJlE5OJmlJ0pbk3OYan3j2Ke7evcvrb94kTVocHs9458Y1rJTMy4JX3r5NsSyJVcSLz19lc5gSRzHf+va3+b0/+BI//3N/hr29I/6n3/5t0jSh1+6RRPDqq9/mzde/w+tv3fD+NCl7h8dEUZt+J+LKhW2sgYsXtplPp9y+e4201eL1N66zv3/I+toGlTU89cRFzl88R6+b8KUvfZOamMmN/fccsz8SIPoHxonus7zug27//rd+78Tqw0YDf9SxMiD/QN8Bvkux/v0cw7tt6/1+5weJe7f/gMqesdTK0CtqFrEFKbnYTun01nln75gnPnWBt/54ydduJgxekHz+157l+t9+B1NOUXGCjEpEFGFFRK1T2lRkYkms21ihQdScyaj+APFdCQ9xf61yPy31IK/Uh7k//2gHK5Gy8g/NNpWtnR628GzklfK2B3oAD+N9xdraGsYv+qUQVF5rGmiA1Pl8ztHRUcPeC0zDIJVxauhoVhgEK5Iu/j4RniVc1xWdToeT4xOiKG7K6sPvkiRpdGuNMY2m9yqAC5zRsl4FfqWUZ4xCw/Fi4fz58xwdHTGfz1kul/R6ToMvmNCVZQk46ZDAwM7znDh2pp1YS6fr2M1FUbBYLpt9zGYz0tSBQLPZDKABdANAG9okJCICq1xK6fXKK3eP+GuwypB352rJWq3m3NI0bQDgwOivqgpd18Qqat7vDQfcvXuXYFwaRRGdTofxeOxZloIkiVll94fjDNc4vNftdinLspFFCcmBNE0bYDrI4RweHgKWxcK1d5FX3LlzlyIvfemo01sfj8dnkiihn4XEjtYaGbkEAdbp5J+cnDQJgcFgwBtvvEEcx00FgRCC4+Njqqpie3sbpRTr6xuURYFSTtqn1+sxHo+J45jFYsH29jb7+/tUVU1d1xwdHbGzs9NIHI3HY6fBXtUkSdLsLzDjH3n0kTOss4fx4x33y/j+KF7vB8lgFlK6qj5rnFmfH48uXrzIO2+9xXyRM58XpFnqJI/u3EG2Mtrra9gs5cat67S7Xccktw7lmUxn7O0fMBgMuePNfoOOuhBw8+ZNQgVPGDvTNKHVcoy1fFmwXLoS5DAOhDErANzB2yH4Oty+fRutNbu7u/T7fXq9XlNlU5QlQsjGRyLIVUVRdGbb3W63qXDRWjMajfjyl7/MK6+8wtNPP02tayxel+HHNB44+/1hPIx7wljHXAU8+9tJbVBbpMAxpAvjZEi0oC4daC2FRCjAePkXZQHjVNIlIKxjWCtBHLuqNyEhLzSlLqmN9hQeGVBuN7+R4GBBxzSPFCSdjHYrpfam7G6cl2hTIyJQeKkqKbzho6HR/RBgagdcSxGknHBAH57JiwMcVaSQ0jOahaXWK9rXUjSAXjg/7eezUhhPqnfyPsYz6esa3Ijp5FkCoB9AfVchSgPmC792Ndo4GRhjodIeEHXPuCcee5JXX77Ga29cx5AwPnLjq9WaS1ce55d+9d9he/c8dVmxLArqWjsGeuWkIIuywGjbsMmN0WirMbVGW+PZ/x5Qt7aRZ7HWgmekG3s6f7YBZzGBQW4br61G6sjrwIQkhtVOkMRxvByr2QRWuQ/hYa5VKZUVKlDzmQ0guA04TkDMV4BvPLh/z8YDzC54d2+owLIW1ovP+vFYyFOQIcDtIUFSVQuqsmDY79LJWi7BhMBYaGUprTjmxq1DWu0Be/t7nD+/RbVcIGXLH6OgKJfcvXmT/dsHFMvylAUuBEq6pFUUK9IkotPK6HW6DIY91jY26fX71FhKW5O2I5SUzOe1k3QJ5Wsr59bp9yh1jbaGqq6JopbzKvDMd2nBCkHQONfCSTgJGWRnDEJKtHH3vhbuPVdxIBGEamjjK0UU53Z32d//NsejMZvrQ4SQyDji85/7LJOTEf/V//uv85//Z/8p53fXWeYLpIqQIlSWCKJ0jc98/i+RL5f85t/7TfaOxkiJG2eMoVjW3Nk/4sv1yyymOVYoXn3zBu12i+OTGfNFiYoiirJGW4VE8OqbbzPd2eLc9i5vvn0LQ8Vvf+FfspyXHBxO2N4ekOdHSCH41j/4R5zb2uT2/ohWmtLvWFpZm7evH9JupWxv9ah1ye07h9zdGzHPS7rdlERJ8sIyndZcuLDF+nqX115/A4HiZLZkupgiPPHo+8WPN4gO/gb8PmE/2Pbf32++i1L6HvHRW1w8jI9uCCAzBh31qHVBN9/jhRee4JvXFK/dbPPYzy/59C9nHN7O+fq+4qeeeYaPX7nJ779twFYIqxA+Oy2sQaDdxOYB98N3W2CEZOh3v3n2/O5r+x/gmH6Q7Tyo/VmgNhCpBG16HB/PkLKg3bVNSTSh3u7drsn7HV4exgcOIQS7uzvOHDKJkVJQrch2hFLa8XjMcpF7JoDg9u3bZ5jXgYkb2L/3yghY66a0UsUe0HCmoypSnvFhTlkb1jqjIaOxGC/j4UyfpJRkWUIUx+54xKkxJ9AYS55hrAvH0FDKGUXdunmbWmuUlFjrAJBgJhkA7aD1Hhj5AaCPoogkTZBKkpcFi+XCT/4iJpMJURQ1TOYwKQ5ATGCahwRB2Kb2WvNF4Qw/hbO1bxYK0jP3k9SB/AEQCp+dO3cOYwyXL18mjmNu377NcrlkPpthPPjc6XRopSlxFDHo9ZnP55RV6RIC7TZJmlKWFXVVucWBcUaj4ZzDuaRZhhSCVqvFoN8nLwo6nQ5ZlrFYLBgMBh7Qz/j2t19mbW2N0WiMEC5Z45hminyZNwkBB3gZ7t7ZAyGoq7pJHASz29COtanR2pAlGbPpjFrXrK+vM5vNvMZ5HHocSknyvHQLubLka1/7Gt1ul3O75+h0uwhoWKadToflckmr1WKxWLiy19Tpu5+cnFAUBTdv3iRJEvI8p9/rM55OuHL5ClkrAwttn6wYDofNdh7Gj3f8IJWPP+x4kOD+/cnHOKkAIVeZfQYVxWht0LUDpqRSiChiNBph6w6XL12i02nT6w5YW9/EWsEyz6mNpSxLlsslN27c4KmnnsIYw+bWNsvlkrfffoe7d++SZRnGGLrdLnEckyRJU31jjCZJEuq69jJYLvHZ7XaZTqdNEs76KpJwL7darabCKSTPACaTCWVa0ul0iOOYbrd7xvshVGEJIcjzvDmm+XzOzZs3+a3f+i0+/elPs3944BiJ8r0XpfcbP4okzUcxMQT3218/msf+ME5D69rN+WoL0o8d/tIaL+EUWLdCOKBaKsfIDnil1s6DRxs3j3RjFBgcG1VKLwVXGYpSU+SeBW696SbSg/CgazfPjBOJFA7ErbRGeoC91payrihrg/GSLlYKJyfi9aRrbdDaaZ87cBynUW7cVFeqwNL1/dg6hn2aKeLEEVfKuiYvNVa7al5jAG/uqSsP5uOWSkbgxl7/hrEWIQWxcBIWWoOpveSJd4gM+5YyALSAcG3RMKjBgdoAkeXpJ59ld/MSX/z9l5hMC5aLnLoqiaTi2Z/4WT77i79Op9WhKtyYXhuLiCIkUCxLal157XrjKi21ccC5NQ70D8agJuiWB6NR25hdmlBVCg0g2wDSITnhMxMykMytA1AJ7HUgGHcKDNYnIUTDvrdNezTC6T4h4qlAmOb9AAqHYwns8ObLTaLku8P6DIl/ToPrhx5EcKQmmrVA2LwK83EbpF2cZG1RTqmqOZESrK8NiGNJEkXo2pClCUmqKOoSVMrR8V16fSe7VpUlra5bi48O93nj1deYHE+pw8W3gTkunHa/qImUZQFMENwVzvTTIFFRRn99jY89/xTbz1xiWdUOf0E0OunufnbtkmYZUglqo1EmoqrdfKKuKuI4weB07KUQKOESXAaBNBYrDFaqJnkksL6SAIwRaOESWrFwlS5GOikeKSWPPnKZ1996m17vY0ipkEqQxAn/9q//Mn/nN/9//D//6/+W/9v/9T+mlSUsFwuUcNJQQkhaQqHSIb/wa3+ZfLHgH/3Tf8TRdMY8r8nSNnHkZCqPThYksWt/VbtzFWLGbFZiqClL/NwCZnPD3uGMr730th8jBEcnmvNbW5zb3aEocsrK0umkHI2WzJf7bh0aKUSkuHPjiLo2zGYVuh4jhaDXlxxPc07GOfJoSbsV8fTVHlevXqbXTjl38Rm233yHl197CxnFzJcLP4Z+//hIgOgP42E8jPeI2pLHitRGbCWGC1fP8dYdSanmLEZdTATDXsJrU8HNVsrnPvcYX37nVWrrJCIEKYIUbI0VVcNUCA+Dh/HDCyEESaQoypq33lwymS25/GhCTyVn2ckfNOP3MB5YxHHM5sZmM1kVnuUdwOAsyyjLkpOTE7R25Y4hFotFowcdAIlut8tisfDl7KffPV0IwXg8ApyUyqqGofRgh8Wide2BfGdSEyRYhBAkaXJGAubdyhoDkC6lJI6UB7ZzjLGUtmq0vqWQ1HXVbCuYbwYmo7W2AVharZbfpvOnH41H5LljNHc6HXZ3dx0zfbFopEXCMeR5jta6ac/AIA8AUFmWRFHEYuEkC6IoxhiXoGi3WwgJSvmVI26h1e12yfO8Ae8PDg4advVisaCua7rtFuvra5SlkzCJpCJOI4o8R9eSNE5opRlWCrrdLqPRCHyCI7A/A0s+JFYWy6WTXolilzDz36mqivX1dY6OjrxWuOLo6MhfG5wUUK2pygpgRaIHwJyRrwlM9HCu+P24ybKkLCuM0aRZ2ly7IHkTGOPGWIo8J4oThIDZbEZd12xvb5O2ehSLZXMPBJmaAJwdHh4ync04Hp0wGo2aPhCO6fDokDRNGU8nPPXM01x7++1TGR8Lm5ubXL9+/Qe6Nx/Gw/hB40GyieuqRgYJK1M3ycW9vT2nZa4EWSY5Pjqi22lRFAVzKZxhZ9YibbURyi2Uy0qzKEqskKxvbvPaG28xXN9kOBxy/fpN9vf3mU4nDIY96rrmnbffZGdnh+3tbdotl0RMh30ODy1H+Qlaa+bzOTdu3GBtbY1+v8/u7i4bGxtNZRO4MfzKlStsb28jhODw8LCp/rHWIoVqPB/COB7GFbXC0rp+/ToXLlxgc3OT8XjcJE2//vWv8+qrr7K9u4O2Av1jjOM+ZKK7eAjG//DCatMk3YTgVLPcentIDxYrpZzOt5JEsXRAqrZUuUFXFlk71FTgGdQeJF/qmrqyaKOp/bija9OArSqGOBYI6RnPwiCVY6e7eadLymtt3XYKTV04ZrkDWBVaO5lA4UlBVlhELLxwgANKpQVtaRjh7uRtYxDqJPxAiAgl3VwYf47W4JmwOMmNAHQFFF1yitRaz1y2Fi00yicdpHQmo8bIkPv0BqYe3LQe+PUArgMrJcJ4Vn0tONw7wVZ9xidTqjwHU7C+tsmnPvNvce7Co8zGE8ZHJxRV0RBCagN1lbNcFg5A1wZttAM1zYrmudVN5aVp2iRonptmjbCqNd6wsJv3xLuT2HylgbHGZRxW8e5GY/S08jNs2+EIfvsiXLLTvwNRaFUqxvoDaEw0w28agpDfb8Mfs831wvdz26xnwpEYf6irTG6Jd61FlyXLxQRjC3Rdsbk2pJUl3ogXBJJO2iZLEq5fO6DV7nLn9oKLF3eIIkFdVkhhuXX9Om985xWWiwJtVsY96w9WGqywKKlR1iCt+8waqI2gtgotNIuyZjHPkRguXL3UJB7CpWhaXLhKkSTLnCxS5BIeSZz6yt/EXwvrSVjSp3ccY95gEcZi/LrS+B4gMAihML7/GGvR1iK1dsmSCPq9Hu1Wm1u37/LIpUtOM15At9PlL/z5X+dv/52/x3/9N/42/5f/5D9E2QV5vlypghZkQhKlA/6tv/BXKes5/+Jf/iuOjhfkuaa/NcBqwc07x2idO/KBtbzx5hFKWeJEYmxNux2RpTGdTszaoEev2+Wpxx7j0StP89WvfJk//ONv8fVvve4Mk9GoKEJJN86lqWWxXKJrS5bGzflOJxO2ttZotyLXe21NlrjjbqUJvW6XJ5+8yvpwwK0bNxj0urTjjGp5gFLivszEH4LoD+NhfNTDgolSinrCpqh57NFdvnZnyjfekrRbKcsDwes3jnn8sU0GMubmyYSdqxd48vJbvHJzAUYhbI2wBoMGSqQK+mTfMy38MB5QWGuRdUkrisgnBYN+n41N7cqwVgDPh/Gjj8HAGYKWZcF0Om20tYNutjGmARK0NkggiiL6/X4D/joWddF89+LFi1y7dg04y4QMwHwAGwPDL2hfAw3oHACaVaA8sAbLssRYJ58SJF+ARtYlMNMbNr0v0HWgsG4WJGFyGwCScPzhu+AkXcLxhmRBkjjQtShKyrJqNHp7vR7T6bRhTwdd78DWr6qqOd88zxt2emjfLAtavxW6Nqfsx7p2upQe5A1AcWBMB9mTcC1WWfPaGI6Pj0nTrLkmcRzT7/cBmM/nTjJHuPYIbRHaNUgahG0GZn6SJCipSLzETehHgQ1eliW9XpejoxOyLPPM+xrtzUtXY3UseDcJoCiKiKKIPM/d4lIJLzfjtOuVUg3AFc7f6ZG6thNCYiLZnNPx8TGbG5vE3W4DfmWtlpMyqpxBYbvdZv/ggNFo1AD62uvJSykbffxer8etW7eoqpr5fM5gMMDYiosXLz4E0f+UxYNifD+ofT3I/cFpItSBLqeVMvP5nNFkDFLS6Q44GU/YXBa02h1UpDBCYoTTLR5PZkRpQrFYkLUyLILRaMxiseRLX/oS29vbTbXN9vYOo/Ex4CTANjY2WFtbo9VqNVVG4RhCdc5yuWw8PNI0JU3TZuy+c+cO0+mUXq/XJAbjOGZjY6P5Thh/yrJsxu/GuNpXOUkpGY/HHB8fO5PpPCdN04a1/tf+2l/j//5f/hfEUYz2CcMfuO0f8HzpTzo4fiq78TA+yiGlqxSJI4UVljRTRDGURU1dG6pKO0BceCNB6SoaLU57WkbCSzfYU3BaOKBJRZIkcVrpSgvQGivBSIupHFvc1hpTAhhkrLHCUgmoI0mWRCRJIDsIkn6buk6Yzkomi5JiUWFrxxI3GhDWayODwVB7TDZosjtc1Y2fMnLsXWm9aaa1FLl2euD4bQRvYuvY7Vb7l4T/uW0qD9yGikUIfd8bQYtAWhJE0hmjKhnQdyd6IUzzEotBK4trLAFWEMUR1bLk7ddeoxtbko6gOzjHcy/+Ap3uGraYumspBUSSRKVYk1DVmqKwtGKFMa4qstaOOa21N+XEgehCaLdmt14W0nrw1LPSA6vc6qCj7tYQxjPXT1+f3v+mMRv1BB1rPGNdNG0UEjWN7MopJu4a2OL6n9e1EUI4OSGv7x1AcxGqq4VASIXwEihurPWGtY7yTmjpALaLU1TdfWJxrGpcYtcKl2QQfv+uT9RUZU5eTHDa8Zo0Vqyv94gjRx4ytabb7pClEVWRI+OY/f191tYHpGmEkhZda6698Qavfus1Cu99ZM0paxwrm0p+KQ2RsqjA+tY4s1ctnFerVCA10/mSP/7yd+h2W6yf32Y0LWjKRxCcSgpJ2t021axCp27Nl6Qxi+kM0R+c6esY29z32uCMc4U35bUSYSAY6BKAd1+9ITQY4Y5Z+mq6Ry9f5hvffImdrW1UJJFaoqVmZ2eHP/vrf4Z/8I/+Ob/5d/8Rf+Uv/0VmkxF54cw3hddIT6Uka63xa3/h/4SuCr7whT/gnbsnvHVtn6p2FTZS4tpSuPs3imO2tnroOkcbTRxLWqnCmpqD/QM+9RMf46knL7PeN0Ryzh9943WiKEHKiOWi5u7BiKLULHNDrZ2HwGJpEBhqo4lkxDJfslhOKYo2WZZS1jkCKCvNG29d45vfbPP0k49ycHDIpUuX+Oznfpp5lWNvHDDNi/ccs39sQPR3m+KExOb7/d0Hj3fb2dn3THOjN37Bnq3oskVWSCwCYWtO62GaFB1Bh+veQcvfavgnZHPyjY7XGRarwJ7u3ZXFhHIdv31xZien8cMsC7z/ierZ7d/f/u79jgC+uxTj3Q/h3X77/T7/Xt+7n23f8+n3uA5nty3QGtKWQo5u89Rzn+P3xzEqTnn92xM++2uSZ3obfO1rMy4MJOtPZtxW8KmPb/HGrVsUdYSI2ihRI2VMpKyXdLEuu34fZ/EDxQfoLj9Ii3+QeJD7s0KDASkitDZEkWQ9EeyPNItFxe7lCMsJxvQI9/D3vTXs9335MB5wOJ1C3YAQwWhTeg3cACQYP4nr9Xpcffxx7t6507B7A/tcKeU1sF2sjmXa7yMA3KGMPjC+3ULAlfM6jeku0+lkBYh3kxf3fbxh0mlSJgDz90rJCA+aOyNIxaA/JPJyA51Oi5PRSQOmhvL/AKgUXq4kANdBazuwyQMzebFYcPHiRfb29pp9AY00TJIkTbIhy7JGIiUA33VdUZaubDJJYtIkZblcNIZLARQOxxc00sN1CccUkgBV5aRZ2sMBcZycAfCDhEkURXS7XadTXlcNAB4+CxrEde1YVnEck7VaPPLII+74a+2Z4U6CZTafMZ/P6Xa7WGtotdrACUZritwZiN4LmIdzCEz2AIyFaxqufVmWTkYCSxxFVLai1s5QKM8L2q0W4MAv9xvTPGuquiY2UcNCXS6XjMdjzqEWVQcAAQAASURBVO3uNv2x026TJAlHR0csFguUUpycnDTAWUjUhCRGOCalFLPZjJOjI5RyRqNpq8WVy5f5/d///Qdwdz6MH4f4KLN2HxS4r4RACj/TXmHhBU3xuq5ot1poXTGbzbBIirImzwuqqsYKydHJCa12h+LuPqiEVrvNYDhEW0u/3+VkdML21jadToc8XzKdTul0Olx99DE2NzeRUlAXBb1+r0lkxUlMFMXNmJgkCaPRiCRJzpiCgqt+yfOc8XhMmqZNgu74+Nj5e5Q1Sqrm3Jz5tRvHw3ivtW7kmo6OjkiShHa73Twjfu/3fo+Xv/0ylx99jDjNvneD/hjERxGI/qjeZw/j/YeUChVJ2u3YyTt0JCpygHC+rBzQKnAgl39WO3ayB86ExBAkQTzDHLC1wNQagSWKHYM9y2Jk6cxKC12iq6A7XiOkdYC2xw1sAsZW4CVdpFTkuZunVmXtQPjaUJcWox2DUynHyjUaau2kXayBSjizVBXhDEiDrrmVGOPWQtpaf37WyVYIiYyc7EtdO41yIwWmBmukR1c8GxoPzguBUu5va8FqGqmQIIWYRhFZnNBtdRj2BwyH62ysr7M23KDX75HGEXfvvsPdvbu0sjadbp/BYI1Bf412p0esUhAxRZ6zWOZMpnPG4zHT6THTZUlVC6RMsSJCqoy01SZtraFUhJJZI29lPVCutSFYR3T7CYNhhopVo2UfpxFxolCRWJFMAWsckO5kYWpqrb3Zq/Noqko3H67KkqKoKIuasiwoiooiL8n9f8tFSb7IWeY509mS2XjJYl5Re9a8Mavgu5+XQgMyC1Z02QPF3NKw35u1iBBI4ebuDgiXKzr3igAsiwDAo1xSJopIW+voaka5mGOtodZOarLIFxjtPMdUJKl1yeb2Bq00sNAFSEHWyojjiFt37pCmHRaLBY9s7ZDGCiks+wf7vH1tn3xZYa0zDj9l6LszthYn5eIrRfAMb2sEtbFUVnj9fZ/w0BXT+YI/+INv8uf+3OfothLmuSO03Dt+d/t99kdOytE1sfdegSbBIEK7rja1DaQrb+oqBc4Q18vPeCa6u3YSXyRKqIJPs4Sd3S3euXadZ59+Ai0MWkUoBM8++wx39/b4Z//z/8y5c7v8+q/+PJPxCUUhUVK5JIockbYE7d4uv/oX/iOmkxnxt19BpRk37xwwGi8cQ94aoliRFxWLomQyWpClkqKqqWvY3hwy6Gbo2pDGCUU+5dKVp/gzv9RiY2ODRb6k31tj2B/yh3/0FbSNeOfGHtfvHCOVpd9vM5ksyIuaQTvj8uVd9g73kJFhOispq9JX40WsrfV5/rmPcf78NnEs2NnZ5itf+Qr7xwckmSDWPyaa6O8W9y78P+x9vXvm/r1eu9BW+JIDz+yTosn0NQOPONXaEr5ER+DSn9KX6TgTZomQEcK6ki2kxFiorcEgUF6oI1QeWWmh1m4clrIZoIKeU7hpLPbDgE8/QDyoSer9wqLvBWDf73fEu7z33npK97dtiI0hr0qeunqOpY74w5fnPHYh5tKjbeYzxcYuPPvJPq//67t0t7sU0SE/8/zTrH/hHfYnJcYsUVEH6pQ0EghbIYWi5nQMfq8IrKT3E+/WKvcb9p4figfVNX7I+3MAn2MtKCHA5HSShLdet5goY7AlUESIKGTbQ5nYPfvn3Xvivcf5MB5c9HtdX07p5EuCxMiq1EZRlI2WoLWWK1eukHgjS2st0+m0ASOVUp4xrNDGnGoucirNYYwhyzK/T401jsnhNPMCy9iVowWWcVVVKBmRL8sGRA7gbjAyDbFaZmmxFEXefN5qpQhpOT45xGjLeFI2bG6gMQYN7PNgBhoA6KOjI86fP0+SZE6epKoaLe3XX3+9AbTDMRRF4Y1U6zOMdmud9vru7q43u1sSx4mXffFSCaZ22pZSIYSbugQj19AuAdANLOl2u90wrpeLJYGNlOc5h4eHbGxsEEVRowEPDnCfz2cN0CSEYDgcNomF8XjcPMcXszl2Q9PpOta5MJZ2u+UMN0cjJtNpAyiV5Yx+v8/R0bE/dnvm2rTbbYJcTgD/Axi5yjJtwrqFY5EXnmWvPcvEUBSl01b0TPGwLfevZjgc0u/3naROknJydMT6cNhICsVxzPHxMYPBgKIouHv3bpNICvdGSPyEfl5VFbdu3WJ7e5vj0YiLFy5ijKGuKs6d2zlThfEwfnzjw2SYP+h4kOC+UmEG7e4L/LbX19ebJF6/16M0mvFkwnS+QNclk+mMwWCIsTAaT0nSDBUl1FqjjaHd6SCVQuua3XPnyNKUsiyZz+dkWUar1eLSpUsYrRmNTtC6Zmtrk+ViQbfTds8n665Bu92m3W43Y/NkMiFJEnZ2dlBKkaYpxhiOjo5otVpNgiwk3wTuGRXG6jBOpmlKVVXked48v4IGe5DtKooCKSXT6ZQvfvGLtHt9zl24+EDa/mE8jD+JISOLkIZS1wgsxZhmIaKNJUkiaq2diaiwDRtZeFAMq0Eaz1bHYZhWuDmlN+BEeGk+IxxYLSFrx6i4pi7rU3wgOl3vJbEkjh1YpmuLpkYgvamlY86TWmrl9MadpIslqCFI6XWfFSAD89bNgaRyczKsoSwsGONYtRaEx1JMbSACLcDoU010rERJ2RBIlAfklYRIOaAsS1K6rTb9bp+1/gb9wRrD4Tqbmztsb++wsblDf7BGu91u5rVal+i6wnr2tFQKpeIG7F2hqbv/hbmNsA2YGeagxTJnPp0ync6YzSZMZzMmkyn5YkxtHHCojaaqDZW2ICJkFMGyxbTMiaIUFUVOWlJJhDz1QxLKMZCjyCVfVBSh4pSs5V674wahZCCFrwDZ/uKKYOrp8WJjMVZTlZrpZM6ta3u89eZtbr69z9HBnKqunDSNrqnrkrquqGvH4Damdte1dsC7NsbpzzfrJ5/usKDJXVs1pHN/QCIA8n5uLARSKISEKM3I8wUYTbGYumpOqwm/FlISSYnRBZ1Wxvqw74w/laKuNZ2sQ5LELJZTJtMp1fEJw7U+aSpJYsVyueT6zRUAvSGnugMNKRrk6Zpda4fc4RNG2jjZMivc/CBUDtR1zWg654u/9w1+489/jnf2piB8tYQMXUrQ6Xap9E2HC4akVawwRjtZSxt8ofy6IJy7FQTzbkfON02brk7ZjK+00I1Xgh8WsJzb3eFr3/gW4+mMjeGgScYJFfMLP/dz3Ll9l//h7/0jdne2+OQnnmE2nVAUketfUiKFJGkpeuuP8O/8+/8pvX/813npO9+m30mxCL8WhOGwTVl5IpEWnJxMGfY3WOQz9g/GbK+vkbYN8/mI0eiIbm+AkDUvvvgCL33r60zGxwihabcjQPELP/s8/+S3v8yiyFHSsrOzRqwks9kCFQu2ttYYjadEkTOBxVqyRHHx/Do7uzsYrTk+OuLZ53+SC+d3wMBiXsBDOZcPP4SwRMLpfVkUSEWFQqBROiehIFFQRYpYQSuCloJICpRyBm1h0esceh2T0BqYzZbMaue5XdsIa2Ono2W1U4mSCoNESg1Wu0x0XQIGFcdugLQrrsof0YXOw7gnBJgsY5Bf5+mrP803bw248mSXV//oiPZAY6otvvz7B/zEZ7Z49tO73BnlXDrf5u6y4urlbRav1yyimNqbYEjpcqTOYRruH0Z/GPcVJkbIiEqXdDJLojSHM8P+Xsza5RwVHUE9hOjBlDY/jAcXATRMkoQ4jom9PEco05cSokh5Rouk2+002tmj0aiRcQlAU9CNw1pfSrrCKuBUBzsA00pKai/TEkURw+GQ27dvU9dOHkNI0UiJtNtt8ryg9maZYXshAlgU2M1KeWMlD3oCDIdDut0eR0dHGG1ptTNeeOEFXn/99YbZGCRNAhM7sA9nsxlKKQ4ODhgOh2fMTAPjsaoq2u12I1ETmOtCiAaI0Vo3wPrt27cbOZaQxAAajd5ut0tVOQ33wDp3euNRY+wapGPCImY4HHpT1IwoctIqo9GokWbpdrskScJgMCDPcxaLBWsb69y6fbtJckw9GC6lZG1trQHJokg1yYGqOtU2N8YwHAxRXlbGydTkaG1oZS2Wy7y55uDY7eGYV/tGYPCH6xiSJHBqWruqCRrO2TFSo6Ytwu8DCH5ycsLa2poDvLTGGsvdu3d59NFHabVaTQVDu93GGMNgMCDZ26Os3basdTr7q8z/oJ18cnLir6dj4HZ7PXZ2dmi1MhZed/1hPJj4UciYPMi4X0D+fr73oLZ173e+12/C/Sd96bpjPFo2NjYaQz0Vx5i84PhkzHKxIF8umUzn5EWJRXB0csLuuXMkWcayKCh8lU631+Po8JDzFzaZjie+wkmztj6k3+vTabW4e/cOk8mYNElIPHC91h+wzAvq2hB509Eg/VRVFYvFokkktHy1ShjLANI0ZblcNlJdcaSIVNR8D2i2GcanMF63Wq3mmRHG5U6nw2Aw4He/8AWeevZjbG7vkKRJkzy9N8EXtvtBr+8H+f6DTu79KO61B3X8D3I8Wd3Wj1vy9EeV8LWywggoawcuRUp5sNqDkEEf3TqQTCqH85jSoCsnhSIVRJFE4vwXGs1z6YwyhXBGfaXWVLUD0VsdSU8lVCaizGvypQPZ68r9thTay0VEqMgxXU2tMTVUlaHSxuk4W0+uFha5wlQWxlVTS3mqg250MD10YF4UQSYFdaWcASruO7q2vhpTICPhmPkGhHGgnRSKLI7pdTrsbG1wbnuX3d0LnDt3ke3tc6xt7NDtDshaLeI4dZU1cmXt24C4tgEhVRR7IF94YDxQIvHemiv93wbb0ZX+DggREccpcdyh01tju8FdzdnfeoNq7eUgi7KkyJfeZLqgqgrKskZrKEvjAHWlnZ67CI6yAp0bDJaakly7RIiTcql9//HVAKEPmVMZGGutA7wDkcfjR8aDrYPOkOiRDoPezFeDWownt1hjXV8w2hOFpAfhS6q6pCwNuqqpqtID7zWVN74Of2ut0cYlX0wwUfXGr9aARoN2eueIibtWHr9YJfc5aRl3725trJNEUQM4R1LSaXdRSnLt+i0W8yUG2NyMUQLiCF65dgdDTNqS/rr6pIjLRrnktAfRhdJIYcBqp0VuDbUFjcDg7hEpLEkcIYTG1HMUloPDfV556Tv0zz1C5TX2AzsfBGkrOwXArZM5SuKEosiJkwR5pvt4MmxIRgh8Lazf5irnBrBGuOsvDdoqhHEmwVK4SuY4jrl08RJvvvU2g594nsgoRyozLoH+5/7sr/HX/+Z/x3//3/99drb/j1w4v8Fi7ipVlZCekT4mziTD7Sf4M7/xH1Cb/4pl8RrLomKxrKlqjYoUFy+sc/fuEXVV8bGnL9Prtjg4GvH2jX1u3z3h0594mvWNIa++9i0W+ZzHH3sWXde8+PGf5PjkiI3NHUZHY2SScufOMWvDLvWxpSwEB4fHbK63OTqZc3yyQJuaNIvIMjdPMtogIos2BW+//Sq379xkPB3x9OEdnn72Bf7in6/4wv/yh9w6HL3nmP0QRH/QYUFgiBFYKtAl6JJ2Ihj2BMNMsdZJ6LQtrVjSTRXdNCKNlSvBDOxxoMZ1OIHCWpjNEuZakhea2aJmviyZV5Zp7dyrl0VOWRtyErRQLh0rpL95ysYowjl+uyzyw/joh8WiqylPrWfI9XX+2e/e4onPrPHkxzp865t3ePGndjg6Wue1L1f8xE9JZnbO3ut92ucNjz99nq9+51VEvE5VFbRbGTKq/XaDt/PDeJChhANCo1Rh6hmXdrb4J7+7j5EbDAcFaWypSwU8BNE/arG2tub1zp2kRtCKnU6nVFXV6HTv7OyAFXR7nYa1F8BlOAXPgxllZTRCuIWQ8uyBIIdyhtmbJGhdNNs6ODhYYY23WCznpGlKURTMZvMzzMoAei6Xy4YdHMDO8D0H5J9WXk0mE8bjsdu/L6+8efMmdV3T6/UoiqIBVILBKNAAvkHDfD6fN0zEbrcL0LCrg8Y5QL/fP8PkDiBMaLcAkIftBhO8IEHQarW8DM2pDner1aKqqkYaZrFYkCQJk8nES6k4lnvkmZfBzBRogJy1tTWEEGRZ5koGCwcALZfL5vwC6B600JfLJUq45IDxTJNQFZBlGUVZMhwOGY/HXks+YrmcAafXILDoAxAd2DqhX6zqDodrFkD60LdCewR2f0iqKKMaHf1V+Z3QzsEgEGOJlGPh3Lhxg2effbZJHoVjG4/HdLsdiqps7o/w3+q2jTFMJhOGwyF5UbA2GDaJE3dtHoLoDzI+bFb4h1khGuJBgln3w0T/XvtblcoCzngPmFp7Ga+afr+PkjFRlJClGYf7ByymMzqtNmU5YVmU1BZkFHPj5m2e/thzSPUWBsiLkrw4Zjgc0Op0efk7ryKwtLPMbVcpHr1yhb27d3nrzTfpdrucu3wZIWB0cszFCxfZ3Ngga3fIMiex8uabb3qjU9VIZx0dHXHhwgWOjo4aNnoYZ5bLpfe6SMjSFkYbZrMZx8fHFEXBxsYG8/m8SeYG35CdnR1effXVJnEaxspLly7xO//q3/CZn/0Wjzz2GJcuX26Swp1OB6B5lrTbbebz+Xddix8moPlRlh66n3hQwPe7V19/sG29n+N6YOEBtPeK75WkWY0fVZ+wuEpEJRzpTWtQAr9Wd+CPERZbaoSyxFYSJZK0o9CVdGBlaZzpcdCvDgCxkSBtw9qOo5g0g6SlyDJJFIOuDFVpWC5rikJjvaa20Q7Y1HWNiiNiJRBxjFQOsKtqy2JeMV9UVJWTJJFSOKA8KFEIB0AbHNAvY+GPxR2kFcIJmmtvXGpAWsciBkEkBLGSJFHCsNtld/scly9c5ZErj3Hp0qNsbe3S7Q2Jk8wxl4UjOIhV81Lw4KgjJt7bQ23DiLanrxtZjXsrhi2nb98zTtlV9q8FzMq2HZAe+NYOoBUImZBmCWkmoO9/J1Y20YRoAF5r7Yo+um4I3dbJqzs9da09SK+pawd013VNbTR15bTYi7KiLGuKsmKZl9TaOrmXsqIoa5SNidoV7ThD15qyrqjrEl2fAvVCgJKSJJIkWUSWJM2c2ZFFLCejKccncxYLt64oq9odkwfXa20cg107CZqqqinrwiVqqopKa4yuqevKJwAc2z1cH4FA64peN6Pf6zhJwUhidEW73SJKIqaTMXt7R1gsm1vrSCowgkgmgGY2PnmX63vah2XIXXjGt/TdxRqLDtJFFrIsptdPm2euknBuZ8hoPOP1t97i6eEOifcuEfh7w2i/5sRXOzsQP44Tijyn3x+ihQbv06pC9YVPpkhrvcGpPAXS8VUgoe+IIO9ikCiXIPJJtkgKtrY2uH3nNuPJmGxrm1Xz0p3tc/zaL/8Z/v4/+Kf87f/v3+M/+Y//Kq12RpEvfZWERIgFUkiiTLG2+ww/90t/CczfJXvtbV55+w61USgp2N874eRkwbIsWVufc/Wx8+xsbfPSy9eZL0pefuVN8nxOt5tw/dpNnnr6RQ72b2Cs5c23XqM/3OLilV3+8EvfwVjN8ckJBwdzOt0OtbGgWly60GVRzJgtC9I0IkkkURJRlppOJ+XihR3myynTuebVN27TG/4BP/dTP8tkcsTWTp/K1LxXPATRH3AI4bWGqgUtWdNvRWx2araHGev9Fv00ot9JaaeuNCdSikgp57Kt5OkNZS2GyPV5JKCwmz2MFdRaU2gHsNfGUNSGorAsCkNRauZ5zbzQjJc1J4uKeWlY1ilVrdF17Uf3hwj6j01YiIpjXvjUM7wxN1z+qU3e+eOc81czLly4wMHegkefjPnWv5lz91qXbq/P4UHOW7Xhp7b7tIdtjqclrbRFZEuEqv08YfUJ/eO7iPiohRTuQR9FCe0oY3a04NqtjFZnwfqwjy3nqLi4L7Gfh/HhhRCws71NWRUI4bTl0jTh5GTUAAZaG0DT6XRI04w0TRqzzWCOGSQuAlNvlT0cJjVAA5ICDYu3Kk91sENJaIiq8iW02no2jDO3abU7REo57dvx2E3Q/T4Dk1lIiGNFrb0wJI79PJ3OgFCyLxsQNsuyhiVflk7iJZjGgTMYDSznJEnOSJAETfhwXoGJHSQ/gh56YE0HHfIASMEpuB5A3/DZcrlEa0232yWO44YdGQDcAIQvFosG8As68512GyEE586dawxP0zRlc3OTTqeD8kByr9ejrErXplHMYKfP+toak+mUNEndhNJYrPZmp+BAcw9KB3kYYy15WTQA93w+RwjpGDl13Vz70wTH6YiwCn4HMGo14dJ8B4MQll6v27BN3TXQSFljtHWTYxVT6tItiP11DZUB3XanAe6rquLg4IALFy40UjdlWdLpdBhPJg3AFq5XSB6Evhx03LNWi727dxHAud1zRFHEYDDg+PiEh/HhxoMGs36cAccfJO4F1sI9I4VEWwcggNNEj6IIXTuAot1uu0QbFqVi8rxkNluQZC1UlDCZzrFCUvpqjlbWoiyrJml4cnTEfDal1hXPPPGEA78P9ul020SxoiidmXArzTg+OWawvoFQcVMds5rsakrqfTVKSHouFovGYFkIwfnz5zHGMJ8tqKuaVqtF24+fQJPcDGB7t9tlb2+Poii4efMm0+mUCxcuNBVHly7t8sUv/i+cv3CeK488wuHhIb1er3kGhMTpuwHoD+K6fb/409qfH8ZHMZwGNDhpjrp0ZqLWamcainDsXCyihqp280ybWFchiaSuLLq01MY0ADYCiDWLeU2hJEJJlAJbQ6kl1sZkWiCVA7aTSIKx1MJSV447rbXFmAopLFEaOSkID+IKKcjaChFbFsua5bJ2pqAa59lmvWGn8OCuxhkjKttgxW7OEmp6BMIKIiT9To9zWztcffQRHrt6lcuXH2dn5yK9/jpx0va8WwlSoIRqziHMc51UjG0QD3tKJ2d1aLD+9Sqf3FqaY15970xYGsZyyFo0oLsN488pKB8EHcMxna4Imi98d5/gnlX6uw5Zqnn7lJytIIpXfuQNZ337nG7GEWwW+YLr129Q24TJdMZssUTlOUVukWiiyICRKNWmlQ7IWhntVotWlpKmCSpULemaonA664tlwWKZUyxz6qomTRRSJJgsQrv8gmedh/a1BL33VYJQo21vLZba9z231tZVyXy5IM8rbty8QbfbptXKkEJihSWvCgSwmI249s475EVBq9NFSphMS+aLmvGsxlhQkUva2JVWa/4fWOMWApLtVCBcMkhJjYycZxNWM53OmU7mzW/v3NlDCMHaRslTn5DN1XVgvEJXFSqOiKJ4JYEMSkYOU1ACEa6zT1RJ4XwFkE4Jo5HtQZxtQ06rL5qqCj9vDzKj7t5XPProo7z15jW2NrZCzsZJKCl48cWP8+brb/CVr36Fv/8PzvNX/vJfwugJZeHkWUKlC1IRJUMuXv0Zfs5aFvO/wcl0xmRaUlSG8bSg1E5L/ubdIybTmascSCTTecE7tw/Iq5IkUTz/1DMc3L3Nm2+/yUuvvM7RaMLXX36LK1d2WdRz2qmk148oTebXdZI7d+4y6LUhhrq2VFVBmsRkWUori1gfdpnnC6TSjOcnnDu/w9tvvcOFnQssygV5XXDu4ta73Whn4iMBor8bpPvecu4/7CP47vdqa322DYyu3OAfKWekYVyWNF/OUGbBZrrg6naXC+sD1ocxrZailSUkUUQcKZI0cZq3QhDHEdZqYuV0r8CbzvkbxJVkuSxibCVxFJMYCdJN3K3RaOM6ubFg6wJtFHmlmCxzSltzMpWM5nP2RlOOJwWLMmJZC0pjMTICFYMpsbZ2ZRl487zYl1sawHo3XzQI1Zikwv1BsB+Fksn7Owb93l8Bzhh73Juy/AHCGouQGm2c4n2kSi6uKRZxj3/wT2Y89+e7jHuSl78155O/uMabr59w6ZzkhZ/u8OU/LHjkiYTLj7V5+9oh5vE+va5mOnMMh0QtSUSNEgnGs2PdYHsW0hXiw70D3y1+2Bro77W/D7q00mhEKyIrBRe2Bf/mjy11kdLdPSFOY+oKokQj7PvXmH+343wYDyYiFdHudDAz7YFPZy4ZwOgAcgZNWLANw/bw8JAois4AxFmWeU1vfQaghlNpjdVxrCgKNwm1ogHhw3eEEB54dbIw4BY84BZPeZ6DcCacVVVh9KmMSxQp4iSm3WkzmUyabQWAPZxfkFAZj8euPXwCQGvNcrn0Znl1oxEfgO0AtIbvJ0nSsKJDgiGw2QO4HkBioGHhhzYO57sKoC+XS/r9fsNODyag8/mc+XxO7DXpg357YHgHoL+uayJv8hrYl0GHPkiXjEajhm3faXdI4qQxHu12ukTKtdFisaDVapGlaZNw6HQ6DejfXDMpGgNTl2CRXkPYNue5uv93k5BYZb1ubGywt7fnjq/ToazK5lF1fHLkJ8invysKl7yQSKdhWWvfhy1ZK6XT6ZxWFHiAXmvN7du3uXz5MhcvXmQ0GrG2tuZldhxbyPXTYIh1mugJgHoURbz99ttsbmzQ7XZdmWtdszZc423eeVC368N4GB9KfC9War6cY40DfsJ9ZGqX4Ns9v8PxyRF5WaCNZbnMiaKYtN1mmeccHp8wHA4oqoo33n6bTqfHPC+YzWYUZc2yOGF9uMZwOARrUFJw/vw5nnjiCfb29nj++edJ0pjFfEE7a5ElMW+98Rrbu+cZbmxR1zVlWZGmaSPJFMrnw3gb7uUsy9jb84t7X4nV6/WYTCYsFzlKugqeTqfTjO1hfAcalvqNGzfo9XqNbnsYX998800Avv6Nb3Dl0cf43C/8PJ1Op6maCuNHGEtWJawexLV7GA9jNT6IpNOHFdZahHQSrcKbOJrAOtZOpiVNE6R08huO122ahLpUkrQrwUSY0lIvLWVZYyqNNRCnEqEEyrp5o1QSoSx1bSiQtFJFpATEAqXcfEzXmuXSzR9kBEkWoWKFsJJl7t5HWFTkMAmpvOxcbTG1cBIzAjTB/POUv6Vr24B9Urnfdlotzm3u8ORjT/PMMx/j6tVn2N6+TLvTQakEY/BzGUtdO2NDZ7TpVuBaOylc68HtVTBytbJlhTTeoOQr2DcN7P5dLPRVIN2eguh+O5537jStV77mj8a/Fmden3263Etqc0i+OPNS3Iu+u3a85zEV5tIigObCG9hL95u6rsjLkul0zNHxEccnY2aznKKsMdpJfSRxRKeX0e2s0et13Fw4cthVVdYs84JFXjFfFpTFjDI8C31GQghBr9Ni0G070mgc+WvmyKNKhn+91JA8PTcnSaP9HLKizJ03ksFJjAihnUZ+bal0RV1rxpMxRVGhtXXSOEXJeDxiPDri6GCP49EJVW3oRTF394+dDr9nmNe6cl4n0omUSyWaREhDObSBiGL8Qfokzcq/oSKgycr48wnGuZu7OyRp7H0LhQfDnXEsQpK2UoytkVZ6gSGJUs4ANPJmtEK6+0pI6z0HpP9bem8A0ST5pRAo4TArV6UBEr/+9JUgTupJEUWCzfV17ty8xdHRMRe9RjjeGDiOYn75V3+Fd278df7VF77AI1cu8iu/8ouMjw8pywohQRTCH49CxQMuXv0ZPvdLIyaLv83+0YzZtCSJJdrWSAXWCg5Olgw6GZGKUKqkKDXXb59ggSy9SZoqvvy1l0izhLqyzPIF+197mZ//mc8ym5yg4oSfXN/k5VffYe9oQnZ+wGKes8hLl3o0BoOm2xdsDzdJRMwXv/RtPvb0FUCyubFBkgq+88arzBYF33n91koy6nvHRwZE/3EgwsZK+pITi1IJSEFpNbGMqJczpuNDZD3hqct9Xnxim0sbHVpxG6Es3a4rbwxO9kpYkiRG6wqprM82gbVuoZvG8ZmJuxaghEUQeUakdIYDyouJNYtngxIxWkNbw3AYIWSNLWPyusPCbDPJJaNpzcFowclkycm0YDyfM0NSGCgMCBljVUpipk7nKMjCoDAycg9w425qIU+zvn964l0edA8s3MAmlEAZ0ItjfvaFKxyuZczNMXsvRZg45dHHWqxt1zzdzvjmv1mw9bM7PPJ4wTtv3+ZTP3We8xc76CSj34s42NfkRqOUJhIGQYyhWjnu775+P0qGzoe95++1v3uZZ/e3MYmoYgadGXXR4rVrOVFkGa5nCFUjpTMJuQ/Pivs+zofxg0fWyuh02hTFotF4hlMQJUmSBjgIjOwwQQ2swfB+ANPDZ/myaMB3ISAvlme2faZs3XIGpAyfrx6PUoooduZzy+WyAXtDaT5o2u2M5WLp2QGu58Rx3BhkhhBC0Ov1aLVaHBwcNOB9kDwJ0h4BZA9s48BcLsuy0TkP5qqpB5ittc3vA3CcpimDwYDxeEwURZycnDTASZqmDfs9gOjtdrsBZM6fP9+wJoPhXXimhm3s7+837weplSzLODk5AWMbbfOgwz4ajZjNZs1+w3EHfeNer0dZlo2MAdCUaAZAaXd3l9u3b7O2tkav1+P69evkRUHuTVxDgiUwuMO53XttV98P5xfaYjQanflNXdVnGO3umogzCYp7xyzjNRhDmy4WC/I859FHHuHg4KBJhrz++utcunSJJEm4desW7Xabra0tTrw0TQg3F9Fnkh9BL39ne5tOtwvGMpvN2NraPNOnH8bD+KjGu7HOw323yo5zeraiGZOVkPT7fX7qp36Ksiy5c/u286KQkm63y6Dfd4xwAVGSEKmEk9GYrd3zpFmbPC+pa03lZZM6nQ5ra+v0uh02N9a5ceMG3/rWN/jf/MX/tbuH05ROu8Xdu7dZLGauMmq5pKhqpIrY3Nx0fga+tN4Y0zyrjDFnPA2KomjGvqqqGI1GmNqdb5CCCd4Xq78LcmDhd3EcM5vNuHHjBp1Ohzt37nDx8iWUlLzxxuv863/9r/mN3/iNhskffCGAporr3mux+u8P41o/jPcXP+5tdj/SNj8qbXtjrdNY1hUgUJHBCldBaIRGa4kVTprFGCfXgbEII5DGOtAMi1KStKuIC0GxlNS10y2PImfmqWuNkLbBAcvSoAtvkiwcQJjnrlJEe2+2OI6I0wgJ6FoTRRDFUTMmVGVNXRqsDkxiuwJEeWa4dWCcUg5TiaSglbbY3dnlxY+9yCdf/AyPXX2W3mADpWIHSmvDsqipS1dhGCURsYoR0pk66so0gLmTMjmV4miqb8Ln9rvHlFWgfVUuJagCnAXUz5Lm7Oraf4XKfu/7p2C9Z6CLgCK4/QUSBJ6aLxD+GE4B9NVnzxlwXYCw1stpnM4F3bmANi5hOpvPGI9HnIxOGJ+Mmc5mlIWrTE8TJxm2NhwwXBvS7/bpdNu+CjVqpIbBMh2PnYZ6C7pd45n4vpJBSoIBqzjTBE433Jja6apr4yuRKmesWrpn0CwvWC4L8uWC5dLNT/Mipygq/3ysqHTt9dOtN8WUgHHtISQWDxZ7ILnTyjg6KFks5pR5RZzELPMFy/ncMayxTvYnXDmv768r4dsx9Gd/ifVKH/OXtfnWCgscaPT3A/c/bWU89tRTbh0gpPMJCOQb5dj27XZGPl1i4giLk3SOVQLWkMSpI1IJ4zwTZJCHPN2Wkk7mRfh/lX9PSIFENmtCJ6fkePcuUeDwvEQIHnv0Cu+8/Sbnd7aw0vsXWFfZsbmxxa//8i/zd//+P+S3/sm/4LGrV3jy8UcZHR1SFQJBiZQKKWcIoZBxn6sv/AqfX4z5xpe/wBtv3QGpyeuKg+Mp80mFlJZ+R5EmLVRek0QRxlomsyWvvnmTYb9DXhpkZPiJZ59mPF/w+lvX+eZ3vs35rU2uXL7AoNdhPp+yvr7Bm2/fIGtFCGWIVMxiuURGLtHQaSuSOGZt2KWTZhRVxcnJlLeu3WEyn6OBVpbwyOXzvFd8JED0H5uoK2IRoa0EK6l1iaFkdHLAbP8m5egmn/+Zj/HZn9hld63rbr5IeiaYy871ej03oY1dNkgp4R5Y1qBk1CxE72UiugV1BLjMkrWR14TSIIQv5XKsPqMNSRqhbI0RoA20lKKtNWtKst2LKQeS+rxgmbdZLCXTecWdWc7hPOdkXjGaV0yXFZUR1LXBCgUiwuAHKKVQGITV313e9DB+oBDWmXkgDDFL+tmCjX7CHy5i1nbWefMPOgw/MeLJJwVf+sIeP/9Lj7B1zvLmGxOeeq7NeDygtgUb220mec36xjpvvb5PFBki5QZaN2kQK0VLD+PBhSIpDVuPWr78h5r5TNHqFfT7bcCgVERVV/ARYPs/jNMIALC1ARw8lUUBGnB4uVySJEkDHAf99NFo1DDpApAZxm2lNEVReMNQ1UiRNACokGcmyAFMDTrbxpiGZR1kY8I+lHNyotXKmM/mbnz2Rp5xHDeZl1A6vyoto5RqTEx3d3eZTCYsFovGYDVonQdpkVWWuPISMnCq7x5Y+KEt4zhq2PpgGY1q6toBwwHcDtsI78XeJC9IwIQ2HQ6HjSTBcrnwk0YnK7C+vk5RFIxGo6ZtgsRLSEgoFSEUDUM9nFMwMA1yNL1ej+l02vy+3W43ZnqtVotut0u73WY2mzWA8iuvvNIASycnJ+7YI0U37p4Bv7XXFg3HENouXPtwfoHdHxIWQS89sPBDciD0gXDO4dqE+UNgoK5eb2stVVlxdHTUaBLv7e2hjTMYDd9ZLBYsl0um0ylZlvlJsWyOMSRZTp8lp3MX7ZMTw+GQJIooS8HG5uaDu1kfBvDhg1l/mpIf71YZsprwXDXFDOMh3nPiueee56233ubNN98ikgoVx7TaHTa2tqn9vVxWmjhNWZQFyzx3ValJgjG5k3LQNbPZjHYrc8m0quTmtWvs7x+xt7fvDUUjqrLkjddfYzgYkKYJZVWSFxVpejpWJ0lCv98HXIK33W4zHo+bez2YjIakZxhLVCTRtW4A9lCFtOrlEOShut1u84ypa5fcrarKV0YZOt0ud+7c5Xd+53f4+Mc/ziOPPPJd1VZh7Lq3/e/t5/fb7+/new+SdfzjfH+8nzb4cQfR3yt+VNdRVzWicok5YZ10nzEWFeMYnkJQGoM0Aqk8gU0KrP+eqQ0C57vj5B1AKItMPDNaOPNGZ+4pMJUFLbDCGRlqUXtGsGOWtzsxadLG4u75stLMZyWmdqB+AyKaYDhvweCNCp3OuQ1kXeMNQpWTjVAoNvobfOLFT/DTn/5ZHr/6NL3+BirKECJCG5xG9zKnLJxXTpqmbp5ZQ11XDQHBWpfQXAXJG/B75TWsfOZB0KYvr4Ci+GTG6csVJnLDPufseyu/Pf3HG0QG9PWe76yyosKf0gPkwktvhE+EEM7n1JvDKkkzB1bK/SsR1LpmOZlwcnLM0dEhh4dHjEZjFsscayxJktLrD9jYGHL1sfOsra3R7/dot1uu+lLJe+iBFqstla69oa1GRQnCGLSvmrcmAOLeYNR7hNTaMbZN7eahZZWzyJcsZnNm8zmz6ZTZbMEyz8nzkqqq/TzaXy/XvT1j2pcwiDDXBISTRXbPDkcmDSx9954giQQKyXp/jb07d9DGEmcJdVX455evLNDOXDRNIi9Rmbtnkdecx546yAm7cun8JVJh/SYJiDnCnj7XpJJ02h1+8tOf5vGLF5HNuiRUr4Y1oOW5Jx6lLivvd6Qo8yWDwUXanYxIJaegvAx9IVS/+tee0X8Krp8C5u76hnYM0j/utetDTl6n12mzmI2pqoJup+W04Aknbnnuhed5/tuvcu3uMf/sn/1LHvuP/iqd3pD5dOLXCqVnwytiEaGiLs/95L9DvpzzzvX/EYszXo29fI4xmv2TMdYYhv0Oly/s8p3X3yLJHAHglddugDDkRcn+0Yg0Tdg7GnPh/AZpK2Yw7HP71l2u39lDigwVSY7GE6JUuX4pHIGx2065eHGHl7/9BpubXSoqlvWCLEu4cHGT6etL0iji4rkdzp/f4L3iIYj+PsIoZxpIZYlEiS6PmN65zsHNm+j5CbvrMT01Z//W20z3EqI4xsQxsYpopafmOzs72+S6ajQFja2cYzSnuq6req8QFsAeXLfG3XhKIqTx7DJ3g6hIIBEo5bK3SdzBmAhT10SJRKCJa0MmBRpBrxXBQFFWEVd1Ql53mOeGybxkNMu5sYg5Hs+ZLmsWhWZR5BhtQCqMlVSWxt33YTyYkEJQGQtKI+opL37sPP/T19f53QNB2s158ZdaDJ7VqNKi5wO+8+WKx5/L+OK/us7l+S7nLnQpl4Y0gePpgvX1IUJfRyUaGcUeRDf+IfTwwj3o0Eaw3ZliFn2+83aBjBJ6g5I4pZnkyYc3zEcuAvjt5DraOCNQJ30S2HLB+DIAiIFlLIRoJEvCtqbTCUJIiqI8w9QLoGj4PchmMry6eLv37yBL0iwGNICgto5duLW1w3z2DkpKal0hBA0AHUr4Aws66HcHUMQYw1tvvUWapgyHw4Z5DrBYLBpd83BMYbsbGxsIITg8PGy+k2UpaRpTliVJknpz0tzroadEUcR8PqcoikYaJQBUeZ5TFEVjJhqkAUISodVq0et1WSzmzGYztNZ0Om3XHtaeMSldBf6DPEFVVahI0ZGdJgkQfhPY6cvlkm632yQ55vM5Sp1KGgQjzwC2h0TH1tZWk6Rwiy7BwkvghGNSSqLvWZyttmsA/UMFQwDP72V7h2sW+sZqAiYAYOG91YRB+N18Medi6wJaV2xs71BVNUrEpGlKlRdNtUIAwQIoprVu+r6THbJYIzwQBdaI5vVisWAymdDKMjY3N2m32mf69MP4weN+QcIHub8/6dfw3jZ9NwAd3AI2vH/qWWDI85wLFy4wGAyZzxf0ej3iJCWKE7KsRY67N6azOWnWYl6WzOZzZOTAdlVXSCWoas3J6ATsgHYrw1rNaDQiSRKOjo44t7ONkpLRbMr+3T0uXbxIksTUFox2wPd0Om3Gr3a73Yzrq4neAICHqpdWq9VUpEihqDjVVQ8AeXjWhcRinueN2fJqojbPc5IkYTaf0W51GE2mfPWrX+OrX/0qly9fbkD08OxbraK591q8X+D2/QDtD6pP/0kHl/8kxEd5/JJSunmdBadEbrxUiQi4HMZa6tK4amXPcg1SC0I4ENsoi5GWWhrHGNYO2RbSrS8FzlizNsY76zhwWxA+x5NIHFirYgnWUlWaqjKNXGATHqjWtQOfpRBESmClAz6dwahFW4hlxOOPPMZnP/NzvPDCp+m2uyzmc/b3D7lx4ybL5Zw067O9c4VaC+I4IUtTDJK8qBwoa4w3lPRyN9q1ljWnjHPjqbMCziTm7IocyykgfirH0ZzSCtu8QVHfpXLbNoA3Z35PYIuz+jPPOvebsMIDsmEDDYt5teLJfSBXnjNR5FjMCDfGnpwcsXf3Lnt7exwdj5gvFlhraWcdBsMhm1sX2NrcZH1jSK/fp5W1iCN5eoyeZbwsCrDWMcWtA7ONB8G19n3J6BUmuXEmoHXVSArp2lBVJctlznQ6YzabMJ6Mmc8XFEVOVflKT4M/h2Au6+REwvNUeIkT4c/TMa5VY4wbuN8ysN49+17KsA1BkkAqQBlF69xFjo+PMKpNXlbcvHWTRWCzA1EkGPQiHr1ymSuPPsc7b7/F29fepCrLhiASwPkmeSGFJ/tExFHwlZJE0q0Rsiyj2+2Qdbp02236vT6D4YCoWqCMQ9uVEX5dJpAeUO9tdJGeGm+B5TICa+i1Yme0K6z3GHD3tPAdxbWXPQXQg8eAcGNDYKu791RTXSCVl3yJnKyOiiIiqbhwbssRZmWE8aa0rXbHXR8s//7/9t9jsShAwNHBHpubG2fWMq6qrkCqKUpKorTDx3/m32U2OeF3/tXvcCIESRSjzdLdi8ZVBMwWjmWurSUWkgsXNrCloNfNuH1nn7euHaFNzck4J81mriJuOqc2NTu723z1G69T1c68PUkUmVIMB1vcOTjm8HjG73/1Wzx1+Qp3Dw5588YB88UUR0RLEcrJVB+Nx7z1ezf4L77fgM37AtHvd3LwXg+od9vOB3uoNZOfpoImlFyczaO9e7z/fdZSuIsagVke8drX/xVHb7+GLkuyCGR3l//P3/xvWM7mpMKXv6TOMFQJycWLF/iJFz9Onud0WhlPPnGVVjtjmc9ZGw55/OpVHrlypVmEr5b0nzLShQdS3GJbWeEn8wYhJHGsXPYXg4piTJ3w6mt7mCzmysUevRYIqVFEaCzK1EgBkdUYE9OqDYOu5dx6G2ssH68LpsuYRSE4mVXcHS3Zm8LxrOB4aVgahXX2zP75IZpMla828ueAGyytbQZuJQW1tj51F75/f/3jQU5WP+ik6oc1YVZCOZMUJRGLGS88/wle2ujys4WgcyHmZAp/8E9jfvKzKU99LOYbf7TP5Y+d5/KVHd58peD8Fbh7XWCvGLSF82sJStRYqREyds/x5iH9/c7hQZ3fve17v2PA/ez/Rz8hDiV31k+GlFBcvljztd8vOZ5HaFuxtd1xZZRWOIf7SD6s4PiIRWB6LxZLut0eQAOkrjKiV5m+AUgIAHUAKqUUrK+vMZ8vyZfFGRBGG02apK7UTikEitls1mwv6M1ub29z9+7dM8yZwCYOJpMBWC3LkuvXrjfsZKlonhnheRL+W9UvdwzxU/mYwKwORqEBzA2gdthmXTuWZKfTaTTBkyTxjGXRgCOrLGunG+6MTIO2bpBWKYqiMQytqoput3tGu3cVUO52Ow3Q47bRbYxCA4s9LJq01l5Cx00q19fX0V4GJUiyBPAmMLaD/EzQ9w5SMkII+v1+AyiNRqOGuRmA7ul02pxvWZZNkgVcVUGv2+PkZOwn5G6x6fbl+uDq9gLjO1yHVSZ5ePaEhHsAzlfbO/S3MH9YlanR2nLz5i3On9/123fXdTabkSjHdO/3+9y8eZPBYMDbb79N6cH0ZkFqxBlgfxXolzJqmGNRFKOkotNuI4U4k0R4GD/8+CjMlX6cY3XsPpPY5LQCJzwfJIL5fE6aZmRZ1owrWZZhtCbPC4qiZLHImc0WtDptosWcoihJhKv2CFrExXLBMl+QxjHLdguBJcsyhLCYwPZezDg5OfZG1zFZElNoB3iFROX+/v6ZapQwfq2yyIPUy2p11eq5BZmtIGkWfDSyLDvzbAwmpUKIJiEZpLTOd3okccJ4NOJ3f/d3+cVf/MUzVUdSSpbL5btqon/QPvxhJ5l+nONhAuBs/Cj6RZzEYAXaWHSpnVSUceAwwkm0NOs263THhccA3LLb+nmlREUS4XFhETlvNaUkVhiM8cxl5QhwtdHUGqzRCOPWMwGctAbK0rGLG3a29TJ02snDWROq6jzb3AO9SrljkRawkrXBFp//3K/y8Rd+msn4gD/6g99lOhuzvnmObmeIqS3nLj1BZRLGs5JWu4M2gvmyRJvTMSswiI01Hn9w52k8a9haRza0Hvd2rwNQ5NrO+vYy1sngugj/BlzJNnAGnPYJ2zByT+MUBw9AuWigjebzFQxeiDCnl438R3NtxQrLPBJEKiJOYk+QKTk8PObWrRvcunWT45MpeVERRxH9Xo/NrW2eenqHjY0NBoM+rVZGpCQIz9IH5suFaxPt2sVVPHiZrgYs96/D97STYnGJVM8Wx7HPi+WSyWzCeDJiOh6zXCypaz8P995/jq0tHUtZCQTSGcHKoIPuK1ylA3SFipAoEAIVq0ZrXwlQvioymGq65ILy+uoRKoqJU0maGBIhwSTUROxcvshv//Y/51vffo12GpNudX0Fg/Md6WYxw7U+vX7Mp37yBX7y08+jpPXG144YG0URcRSRxDFxHBPHCUmcECeKOJHEUUIcxURRTBwpokj66gK5AmpLn7QRvjfZM/P60G9dX3PXYjabkqRt2u2WA8Q9Ec+RWF17IsUZhrr0/56StE4Z+rDSbko1bSoC1rgCwjumuEuOuHEqRSDY2Nxyx+/xyCiKWdvY9n5dpxXLFrCmQIiUtL3BZ37pf8d4cszyD34fpIVpQl7UGFGzNuyQpREHx2O0tlRlziuv3aTbSbkYbWCV5GB/gpCSLEnJc4OwFVJOuX33hPFojrEQJQolIWknPPXIFScNMyqIYsXoZMK16A75sqQocza3BlRVTVm5fjDodhDAoNd9zzH7AYPo9/vQObst1WSV3mdIEFYiUO5GF1UzGH2//X1Q4E3oCKRFM+PW21/h8J1vkOcWXS555PHLHOzdYnx4grCuLArAzpx7dZREbJ6/xNdeucFrb94ikopPvniCsCXXbh1iRMRPfuIJ/sv/7P9MIpzkg7W20YtdW1tbYWm4snClIo6OjhkOhw14p6TCCIuILFWdQyaZmZj/x99S/NXfqPmln3bsytFkyWt3ZnzskQFRx9JCUVeQZhJpKioDpUhJSkU2kETLhOXAcuXRAXVRU84LJrOEvemIW9OS45lhPK+Yl5BXFlTtDUWkZ7O4zGlqltQiYdg2PHdlnS+9dszC9ohMTiU0yT0Glz/I9fpxDaEFtl8gjyI++WiPk3aP+TTn8EbEeWto90umNyz7bwjOfbJi+M0ed1+puPCU5I/eTMm6JVs7Gfu3SrY3B4yTgm4n4iYLtmMZErin+xPv5p7woJJmf7LD4nQKlZDovEXSiRiaBVYVvPzKRWR1SNQSbG0oopBkkmDtn+52+yjGcDBspFpWAdkAEgRwNoDcAdgN4He77RjR4b0AWp4Ci6dl63HiNL273S7LRd5IAwCNMWYwkVzdT4hwXAG0j+O4YQgrz1RYZSKHfQVg2LHCE9bW1hpmfZ7nrK2tUdf1GcmYAGavnkfQ1W21Wo1OekgoKOVWDuEYq6pic3PT64on3Llzt2HAz2YzhsMhcRwzGo3Isox2u92AMIvFgm6360zulks6nQ517bwcWq0Wo9GINJW0Wq3GBBZO2dmrur2hAkD5CWxg4wcpl42NDZbLZSNt0G63GyZlkDwI4Hblgf0kSRqWO+BZ92VTvVDlS1+V4ORhgqa6WzTIJvEwny2c1Iu1Z0z7QnuHuJdpvspIDxEqD1ZjFQh0LBLBYpEzHk9RypmyCiGoqwoR2Ub7WErJ2toa165da3TjlVKOqW5OF35h26cJp7qRgQmlvkVZOk1N827P+IfxQeJ+Z+UPMh3+cGbgIgC/DjCKyfMlWdZqgJFz55wR6PXr193Ya+B4NMZaS16WzGZzLl68xMHJCG0MldaOVaYUhZdCiXwyryxLWpnTQr1795hLF6ZoXWNNDdbyxONPYOqSTqfD7GjCcpkzmc2a8ajdbjf+FaHqZn19vWGQh2qZMJaH5GKtNUqqZrwWQtDtdpukqhvvFevr68zn86ZSJYw/WZbR6/UwuHHNes3Vr3/963zhC1/g53/+55vnUTiGAKI3gMIZitTZv94r7vv+eEAA8p8GQP79tNVHtT3u57h+FMfunuWevW0BKZBe4xsr0JUDrYUSREoSCXFKxglgLTg9aIcmO7UJIVD+GV3XhhLtgHLlydGe+yYljZa11hZT+vmUpNmObSppw9wksLihrh2vPbBjrbGUdU0iM37uM7/IZ37q55hOJvzTf/r3ODy8w2JZoGJYloYXXriEED2KqkOr1aHWgvl8idGOeGL8/MgYC9bpcDuA1zPMbZDmsFirm+82uLlHJgPz2NqVvmyD7vmpMEvQJHdvnM4r/R9NWzetLk6BdAJp8JRgTuCYh/eFCLrrp58GLfNIRSRJ7LXIBbPZiFu3bnHz5g0OD4/I8xIhIzqdLpubu+zunmNza4t+v0ccxUh/7GVVk5dT3zaeWb4KkIe/Q4JCnyYmguqBqQ1VXaLr2h2tMeTlkulkyngyYTGfURQljlru2c6eCBhFiWNFywB8x15SViI8YzuOvMmoJxVFHoyNZOzejyRKxSj/rImkdMB05ExJA3vbsa/DthVS1mAqdFEzmy4QpmB8ss+Fc1ukSczR8SHjyaFPumhXrZVm7O5ssLPZRskIJWMnZWwtYPx102AMVVFRF4q8YXp7PmgoMQjwswjEUekx89WKhpUsS2CWc+qLhAezLZaqLiiLiv5gDaddHmpI/NoSf++KAHsHENw2nzV0Y2spfYVweF4LaZBWNRKfcRQTxTGdbpt2u0232yNJW8RxTJpmdHuDRnIzihJUpBBSEcWpM49VMVJGbr9CIUTsG8nSGVzgF37lP+D48DZfeek1qkphakfuKYqCJBZkqSJwbrc3e6wPO4xP5hwczxHKJRrL2kJZUNSaw3FOPq8wxiULdG1J0oTdtW12tnb50le+AUZw9cplXn3rdVqpr3A2CgPEaYyT1hbUumJ3Z4exX9d9v/jIyLl8kAeWwFLbBVZIIpWhTBtr8/f+4QcMYS3KGkYHt7n2+isoLBd3Nul1Mp5/5nHu3HqbKxd2KYuyKYnRunaDkKl45qknuHHngI1Bm/OPXsZKgyChqiO0Ubz5yg1eeekNPvnxZ9CmQhvDdDql3+9z9+7dBvAILETr2SZKqQb8iKLIMdNN5TSzZMnzH9vkY1855B9+seLFp9s82pIcccw/+K2Irz4f8R/+iiQyhluLBeOJ5PK5PqmoaBtNLWJsDNKWDNOUSLustupCtQGXql2eLzXLvGC6rDga5xyNZtzK20wWFYvCUGhBiqUul9RxlwrLM+e7PLXb51tv7JEvx8RC+4zV2X7wp5EbUSQWlacM9AEfe/E5XhnD/t2Sl74E0+OaX/hzbc5dgde/M+XCC10uPRnz5uszrr7YZnNLsZifcPXJNndvC2ptkElElsaoypLGzoDj9HZbzYC//7i/n90PE/3e772fK//hTHbfrY2MsSCh1po0kYhyzKMXKl75+oA7VUmu4PwgQynD2Sb/aC4u/jTH9s4WZVk0JmvgMvlpmqK1bhi/p8DJqaFSURSksdPNtpnl5OSYtOOAiySNsVivhesmk/1+vwFHiiLH+km6itxkqNYVxtQkaeqYQrpqGCuhH1Z1iRROR1cqgTF1s40oUg1rODAJhRCNBnZgaPf7fdbX17lz586Z8wqgdZhkh/Zo2PRBj12pRp87aItjLP2+lzrx8jWnMivu95PJhDRN6fV6DSh84cIFpJQNsIO1xEoxGU9Ik5i6rpjPZmysryOloCpKsiQlVoq41WZjbZ3JdHIm4RBJV/4ZeSBoZ2cHKUQjdRAAeyEE0+mUKIpot9ucO3eON998s5FVcwxQD8rXNVJJ4jSh0+1SFAV7e3sYYyjriuFw6M5d12jtWKg7Ozvcvn0bhCCKfSWBWyU3bBRXai39IvWUOR6YnyFWWbHhvyRJzkiwBIO+e5MKp/ITLkmxd3ef4+Njtre32dxcR0CjUx8SONY6UP1kNMIYd937vQHj8QgZmPGcZf/HSYyxhuViQRxFvsz24Zj3oON+npIBIHkQYVwO+D3jT8OVLsoaY5zOpqpdFUqapKStjOUiZ/fcOZ599lm+8Y1vOHB7PmU8OSFJUrSuORmdcPnyJWazCXHWoq5KlJK0shRrXGVpK2uRL5ecHI+IhKLVapNlESejMVIptjY36LTbTCZjzl28wv7RMXf39pjOF5SV06pdW1tjfbiGtoaqrBBAHCcMen2MMWRJytFszmKxQHvwe2NtnSSKG5CpLCtOTkYURcH29o6XLRM+GVlSFpqyrDyrPaOuF4xGE65e3aCqCnq9Lnf3jymqEhVFHB0d8Zu/+Zt86lOfaqqPQuJ0FaRdTbfdgxXeR9j7+q7BlcY/jIfxo466rMFKN9BqR7ax1iCU56ueFnJjjPWMz9P3XWWbRCkH7NnaYo2XWJHeCFn770snqWFMAOIciC5FwAwFRjvQCyxCWJJMNRrODqQGXZsGnEUELWYIPO7NtR1+9tN/hnO7F/jWN15i/3CP+WzGfFlhLCQq5fyFpzBiAyVbICLyvECb2gO8AqM1xrrxzFocyBuY52cAdNz3rIGQfAgMbBvA9pWknCclOlDeJTD8O81nQgiCtI6rlD9l537XmlCuAJgiAOuhatB9pRHmafB4NzeKo9j5XKQJSgmOTw64ef0aN2/dZjabUmtNlKT0ewMePf8Y2zu7DAYD0sRVlxsD49EMa51WubGOyFgbp1PumOQOTMezz7Vn6K+2n/WmtkWZo2snQ2isocgXTCfTxuvCna5AKYtUEiVTxzZXwQfKg96RRKmEOFaewZ0QR5GTLlOB0S08Y1s54NWvka3QDuTWYLVG65K81BjrzEjduiIkBXyiwOLUFoRGGo0uLEVt2T884PD4jiOmxjFFsSBSxlcwgK1rZtWEl77xdV556Zu0WxkXL11lY/syWE8IlQ7UF1J6guhpxYZbP3l/K3zFiHI3lPSAuLtHQzWJONMnQuJJyMit9fw9HWRZrLTMxhNaqZdTkeG3sgHIT1nnp3P+Rv887Euussxd35Qr4DuE41BnJIQsCl07gLrWS2ptSdMYIVQzv5fCJzZi4UH4hCiOiKKMKGmh4hZStREyZmP3WT7/K/8exyf/L8pqH1NLrBBEkaDXa6FthVKSvCxZW2tx6fwmF85Zbt0c8ca1O86Q1pbkZZCacufRSjOsNmhT085SCl3z1W++TFVrrDB886WXeeLqozx2ZZevvfxN2qbFdJ4jIneNI5UwyQsOXn2DjidUfb/4yIDoHygsINoUWpAXlkzUzoDjh7Q7JQSizFkc3uG5q4/w7NXPcGF7h3O72wwHbfrdFlhNpE47nhDOQMOiiRLFnb0jvvRHX+OZT73ItTff4ptf/RbLxZT14Rab/SG6LJ2+mRBUlZvQBhOzwPQKpfBCCK5evQq4MvygaRipBK2N00fUOa2o4M9+LuM//28EX/qW5fInNVsbGZ/8bJff/MIev/LsNk9e0CzzlP/+7+zz3E92+LXPz9g1KUWpuH0UcW6zRWaWDEioKoE2kPaWtIuYupWg+4paC6pzkBeasq6ZzEuOpiWH45yTac7RTDAxsKxqnr2wQVUULLWENMViWJaQyvo+gdk/uVEJTTuveepim1snMW/KJVvn2qTScHS9pFiUbD9WcusanFw3bF9VfOclzZ3rEx65usOduwOyjqbViSl9OVW7nSLHNfFDL8sHGlJKtDVIBcoU9OM5WQzfeWmHmSywFnZ3IqQof9SH+jC+TwjhNK2tX2yUpWP1hSx9YAMrpeh2u8xmMxaLRcM4DpPpbqfjQeIxWmtf0p5jrSGKHeieZqljoC+XLJdLx7ZQ0jPKIw9EJH6yWjYMmjiOMNo2xys9qFvXFVHknOixbiK1XC5PWcfCya8cHR015fhBC/z27dscHBw0zOder8e1a9caxnRYdKyy0AEGgwFRFHF8fMxyuWxkWQJTeukBmaIsiDzA60r1Hbjb6/UoiqJJDg8GA8926DbbE8Yym83I0gylgkyC00TX2jEmK9yCan19gySOybKU3DPMoyhCCUmv222SIUZrhFLs7u6uGJ668w46wWmaMhqNGlmXcM6tVou8KOj0vIGeB4sjpRgMBu56LTSL5ZJlnrNcLkiSmLKqmM/mbi6gnPRapCIqU4OF2WwOXsfQJT0irLGNfE1o/8CkX9XODxUCZVmeAdxX5V7CdQxzEiklg2GP+XyBtQ4gm06nbG66NpRSMpvNGI/HlGXJ9vY2rVYL6ZlCVeXYrcJPnN0C+lRuqNFi1O74kjjxCQjdMGsfxgOK+5wrPbDCJ2sx7yq5dzYEnDXf+gHChA2+5z7vd2/vj5ohhP/FPckIGSln8oXAGkur03VMPanQ1jBcG/DY1UdZ21gjL5dYDCqWWGqsrUkSxe0719k9t8mdvUM3tscRRW5ptdokQlJXNSKyoCXzaU6v16XdHqC1oqjgcDRnsizJsoTbRye8+ebbbq3gDU6VlKyvDdjaWOf6jRtMxmPiOKaz1SaJFO1WSlXmZGnMsN9juVDkRc7J8SGtdhttnMb7fDGjKHPiOKMoK4rSgeZlUfnqnARrY7SpsUgib2i9f7BHkkakaQtjaic9gaSuK44Ojvmf/8Vv80uf/zwXLl4kiiPm84UHdZx0DcaNs1iLFTCfzWl3Ou6avCcj+v6yR2Ez7/XVd6/Y/K6tPTBWu9vnR28x9KCP6aNClPootLUwTiPaeY1YBwiusMmFDfrfFquET8o7CQzhgdGq1FRWE8eSJI7IpKTW9SlYLqCqdFNJBiA9EColKA+oBW10Yy26Ycg7CRdde1a49ox1L6+CB/2c7Axsru3ymU/+MoKM27fvcHh4yLV3rqFiNz/bWF/nyadeRGUXWOaSNLWU0xl17djRtQdJ8dItOgDlxsAKABxA4MC8J4DoBLEM9x3HSD+dE/m/3PeFY527VvJAuzmVtLANGzmAkj5R4R8OgV3eAJcBJBUOyHcSOco/T4I0o2Oct9st0jRiNh/z6qvvcPvmTaYLNz+LooRed8j6xhbrGxvOv8InMSYTR9Yw1veXoFlutE8arEjfGJdgcO3W6Ac2TH2jNUWeU5aFB1mdJOF8NqOsiiaxeyotrJxUsYJIRagoIYokUeyA8ihOSRI3/4vj1LHqRZA2sdTakXEW8zm6Lqh15QxJg6SM1afXwSd5lAz+QaGqV/rkkJM0cn3ZGaxKFbkTi53eeX8o2Ds4wKCoa4sUKVK5thHeGcAa12ZCCHY2O1y+tElpMkASSYVQ7pl6agaqzjDgGxDdg+rWnoLmDUs8MMWD1rtYnc+L5rXkdD4NLukl+rZhiLvztqe68tID6uKUhd5I3gjHjpfKf8/P3U/10sWZ9UEgUzkAPLDSIxL/OvLEmCiOiePUGc36/SFOZWPwPHh/BjjkXwMSZMRjH/u3+NXfGLP4H/4GQsywU0ulNSejGVmSsjHMuL1/zJ29EccnEyKlePTieVp7KcuyIEkV1gq0hnbH9UWJRdegTATCcjIe02mlXL68waDXYf/ggBefv8r57Q3eevsNjsaHVKUmFTEGS5RIFqag1pZixZfye8WPN4huFAcHEfsnCYNezYWdGUI4p9cfRuiyYrZ/h51uyief+RSPP3qerW6bVjul1c5Ikog4VUhxVisXDZESxLHl0rltPvHcsxwtZ/SFZT1J+cQLz/D4k0/x3BOP0W9FGFOikox2O+LcuXMcHR01hj+htD9IDuzt7fky+Q36fccsiRKJKSS6tMRRipLw3CMlT53T/LMv1XzmJy2XbMYvfHzCP/ztgt/6ypL/wyOGJ3ZaPPfcY/zdf3qHXCT8uz+XMC8m/Ob/eItf+Lmn+OknImJZktcZtRkQM6aVaiDB1DXKWGIiWi2LrMfs9GMe280oyi6zEqal4fhkweGy5sJum++8epdMWSqTI2tNJjt/OihM7xGytmQcsnbuUf7Fv2nB0zWbl2I6nQWL4wV3bp5w7vF1Xsly7rym2X4Ktnbb7L2l4eoEQcLJgeDcxRZHowKkoNNrE89mKGk+MpPWPxFh3eROKkEkKx7dbfPyOznXT0piVZLGLXrrxf1S9h/GjzAm4wm9XgcpT4HIVQkMKWXDoA4l70BTBt9rd5hOp5w/f57JdILBkOdFM2Zr7aptgnRGAJx7vR7z+bxhYQet7qCJnSQJee4MnSPPDsyyjDhRTRl8YC0HhlCIUJYf2OABEG63243USQBfgwZ2MMu815sj6OCuArnBbDLInBhjSOKoSS5IKSm1O7bZbAaIxmSzLMtG0iWwqUNCwk2WnYRKmqbMZhN6vR6tVpvBYOjfc0BvnuceBN5kvlgwHA4b2YJev0sSJwBNMkEpRa/XY+wBpclkQlEU9Hq9BowOpqvhd4vF4kzyIegNm1ozm04d2z9LqY0D0Z28jiuL3N8/QGtzRpe40+1wfHRCWZaNbmBg99SV05y8NwkSrmvoi+GaBAb4aoVaqBZoJuDi1KQ1JHga3XprKYqKyWTK+mDgdNGThPl83ly3OI7J0syfV8VyuVyZKHvQ1P9tjEGKGG1domN9OGwqEaI4Bl/V8DB+8DAf8nPFLdC+zwxiFTh/QBMNX6jxnhE0Qn/guGczvjL/uz5zAJcDVAwWhKLSGrRbRHe6HXqDPmVZkGUp88XMl0lHGK2pdcnJ6Ihz5y8SSUHt285o7bViFQKNQBHM+XRtkCgsEmMERVVT1DXLqgZhKT3QEcWSNHYyTcP+gKPDQ5aLhWc8RuA1VpWSxK2WGwfqmlJKp4uMK6WPfTm2sYZaG3QNy2WOMYK6Cs9IyWKxpNXKyNI22o/7eW6p6oqsldBqZbSWFcu8RNeGunLSLf/yd/4lLzz/Aju7uyAEUikEThJNW4u2jkWrvIRYXpZoG5blP/j1Dltp2Kc/YNiASD2A+LBB3Qe9v3tlxj6MuP9E2spvVs57Nen8owgpYiedImoPhrp+6ditK8cYyMye8W2scYBYkHjRxrGQtXZsYSGxVmO09dsTTZWH9Kz1QNCwxjHSjbAo6VjIWtP8Nk1dpaMFjLSe2exBQA/gYaGddHnmiU+wmC+4ffNtinzOZDZmuZzTyjK2Njd54cWf5niUEmGpo4r5wnnjaOOr+BvmvGOeW2tcEsE45rixK4ahXhsd3HjVmIZ6cN9Y51nh3jaNtIbwbdfctyv/hLFfnBkh7CnQ6d/S1p6ykhvKbyC1nALuAXxXUpFmGa12QqTg1s23uXHjHU5GJwgLSsTEWZt+f43hcJ1Wu42SirKoOVwe+bk5nnV+OldfZeaHsegMy7zp16eyHnVdki/mgG4qGIsip/Jz4YYMquJGOiVSUbNGiWJFHCUkSUyaOIA3UrEn+dSUZc18tqCuCqqywlo3/1de5z1NUrI0o9cd0G61/BrAi+sYjfYGp2IlYRKqL6y/lnWNA2/xjGjlAWMLUrq+ur7eR4qKV159mdH4xPnDGfeMlUIhlMJKl4zIUgk45rwSKdpn8oP5Z0gihBtRrEwQ/B3WqBYa3POLlWeWZQUA99chgM7hNd4sNdzvUlja7Rb5siJOU4KOfBOhfzWQdZCGWdFL92A6/p7gTEJCIIRCBb33xGm9u3WZl2yJ5al2ugiSkhpbl0Rx7JId4f5ANv3daW/7NhAKr2kLMuaZF3+ZX19M+df/8p/x5o07TPKKJFV88oVnMLWhyAvGiyXTqSaKDF976W10bdG1RkSu/Yq8hjRi0GsjpWU8XhLHDuhPEkFez5ksLJ1+ghU1w8Ean/qpX+b1N97hnbtHTktfnuruW+PUBfLivZ8DDxhEvx+Zhvfz29PfhQE/wlBZgZKCtqwx5ZCiGNDeXZLE81Pji++73ft7QFqss6w2Bmktk4N9kmrJp37iCZ57fJv1tT5rrZSslTpGWhJjbY2S3mxLhsykJI4kdZUTRzFKwE7aZ+P5Z/nEx55BRBAlEZEUmLpEqoiq1ghBo8u7trbWsM5W9WmD1mwAMoQQFEVOHCUYLRBWUCwLxidHfOb5AX/nX8x451hxca3LxU7E5z++wT//eslf+LWIJ7Mxn/u5Pv/wDzL+h3+cEckR/6ufb/Pkpcf4h//6Bi9eeAI1nHDr2jG/+S+m/NJnL/G55yR1AflSkHYjIioiXVLFGdIWJLaNigxpr6Cbl1zdbjPOI2oWnNvt8uvtAaP5gpODI/anMw4KSVEZaisxRFiftcO6Bz+elWktIJWb8FuDCdk+S/PA0lb730mfcdU+A6wQCLQxboC9p898uJNX4VlD7vxqUxNry3pvwt4446vfTnhyfc7V59do9ecsDrrsvyPYeTpisC25c63m2YXg0uMJv//P50S9KcNBn9e/U/LJz6xzMrIYG5F0IzoqAWm4v4LsBxn3OyH9YU1cf5Bx6fuHQBPFklq3aKcjOkmLr70SMakKEluztdkmzgqsjT7QBP9hfHixzJe0WilJEnsprlPJkgB0L5fLRjYjaLg2Wt3eGHM4HLLMl+wf7hO0tI0xzOfzRhpFCMFoNKLb7bK+vs7+/j7tdpuyLJ3ZqHDas0Ejtqoql7AxhkuXLlJVNUJaDg8PkUI2euir4GkAtQMICzTnVNc1GxsbHB25Cfnm5iZSquYY3feD5rYlSeJm0hQSAQFMBwfWhwl4rTXWJwwCgO9AYFguFxjjdO9CYqHdbjeMcK01nU6HbqdLvlhweHhIlmUNKN9qten3+0gvIxMS1WVZNoz67e1t8twtApI4wRin/xmSGFJKRqMRrVareW9ViqQsy8YwL2id93o9VwGma8hzsjRF15qlN0GN45jxxJndRCpq9IGvXHmEJ554ki9+8ffJsqyRvjk+OsaYFfPwRsvzFBhflWsJzKVVUDxUnq0mTkJ7rxqMAk0bheszm819Msax67XW3L51m2KxZGd3h6IoOD52hoVFUdDtdun1exyPTxrQXynpzbusnyyfBSGcXrKTlwuGgQ9ziQ84PuT2vFdu791CNGy+B7bThgn+PffJijbwg4gza9OQNjh7AMFYrZE2sJZK19jKscta7TaD4cBJuXh98qqqaLeyhqm5WMwpy8LfG15OwFisMGhxyq6UMnhqOP11cMkvtKG2mkVRICNBmrVQQpCoiCxNybKMNE05Oj6iqirSNCH10lTjyYTBYEC328JaQ1EoD4BEpFlGMB7MshSlIqSMmc9c0rQqDcbgjyeYpHvTawWOsWmw6DPVT1Vl0LV7VhVFwUsvvcQ3v/lNNjY3uXDxgoMffLXKGbM1P95FK8bbq14RHzjeR585BToe0AbvY38P409XmApkBFjReNs0a188JuHBN6FOjQqNsQ4YMz55YSxYQV2FihTRsNmtDRVsIdnuQHNbW6JEECfCm1kKdH3Wh0f45J1s5imOpR7kYoQAW4Mk5dknP0U+L7h7so+K22SdHjJJsSeH9Lo9Llx+induF8RxRiRKZsscozW6qtHGYI32oOmKzIi17n2sNwpdGSNW5lBnOOj2dPQOACzNO+6l9E820zDSg1aG9M8y47SpAz6Oe1/518Yz1IOMR2hr6f8VuCSvkIIsbdFqJVgqXnv9Dfbu3KYsKz93jMnaGd3OkFarQ6RiqqqmGJ00YHgDmAdQeaXy8CzA7NrAWCejuPqMtNZQVTnL5byRezS6Yj6fOlIYspnnueeAcsC592GKYqdRHidpUwUrrSXPl5STE8qywmjXJnGS0Gm16fX6dDoder0enW6XTqdNK8tIUwfAh+07002vx25dFZLwl0s0bGZ8Napm/+4tXnnlNbr9DXdu5lSyaDadoOuK/f07KKFZ5iXL5RyjXd9S0j2Hh4MBzz/3IoPBgDiKabUcszqOuyzmTqax1sFQ1elmW3t2zuvAV+HwYmND9qW5d85KMIZ78XTevlpxGrZlLKjIJ3iEQgmJwFUeKIJeuv/YOrzxbCrRhoZrjiO8p30xghAgpXVM/8hhURUGcHJAWmvKSiJl4UxfvRa9EE5SxrHdHTSnIkWaZqcJFxV50o+TqHEMdAsiIoioJa1NPv2Lf4Wnn/88X/7i/8gXv/g7HI3G5LMZnU6GArI44ulnr9AfDPnCv/kK7XZKXuXIKKasNa02RIlgPF4gBDz7+KNMJxPuHh1TWUkSS8bTJZP5TcplzVvX3uHJ228w7Hd45Nw6h9MZo1nBeFQ6aVQtMZVAf9/R2sUPgYl+74P/BwfQAQyuDECJmqWJiQRc2ZLcOYqw0yVxckKthQetP8hx3rt7bzQhXAdZjo+IF8d85icu8xNPbnB+p0On06KVZE3pg5sYx1hT+TMQK+UnIGRMbXy5ibUk6UrzW3CSsQnauKxTuPF2d3fPHnkzYFrSNCVN02bRWpYlSZScAhxCMJlN+Jv/7T/kqc/+2zy+VnHn2zWLn+8xGU145umMf/6liNdfNzz23JDz6yN++mMd/vEf5/zjP7D025LPfyzhH3+nxR+9XPL5Tyc8fi5BbfT4J1+M+MTFCtHW/E//yx4vXRc8dmWDpy+0uHIJBm3DMLWOOWNjOsM2RVlyzmQsyOhsWbb6NZgWy0cGjPOSeV5zMFqyNyk4nNZMFpq8EJSVBiI/cGkqi59tQCQMJYZIRlht0aWfgEcCISOMEGgjENIiXSGcA5mUxPyI56nWGAQSaSXaaFQkaFUj/uwvfoJ/8tsblLJmdFeD1PQupxy/0+LoZo6tYeuK4Pormtl1GFzWtAYCVW2xfV7yzjsFi7ygnOZMoxQ1SOhSo6Umkvd32z+oZMK9m3n3tcG7vXnvIumDMlvee3z54FGDzZAarmxVvPyG4OYtiZJdLHP6Wwsk8bsew73t+3DR9KONwAQP2ueBHW2MIU1TyrKk3+83ILhSqtHRrusamToguCgKv0hxUhkBIA4TpSiKvDRJijGG69evn2E9B8PQABZkWdaA8CoSLJZzx0T31U5lWfpyW9uw1wPrPJxD0DQPIHpgf0sp2N3dQcmIN954iyKvAImSMQDW1C5DLyKXhPQTwcPDw+ZYkyRpAHtjDEkUU/kEg/Qgsdaazc0tDg4O0FqTZVkD/Ic2DOefZRmTyZhIqsbgstVyOoBFUTCZTBp2/mKxQGvdaHcDmMpVBeRSUenaLU6kOvO98XjM2toaRVE0Wu7z+bw5p6pybOvV66a1Jo4idF1R1pUrnzaGoq6pypJWloG1VHnBsN8nL1Pa7U5zrYzRXq4mbxayp3ItfmHmF4aO4WG8x4ltmFCrbKN7zUcDayjo4K9OyEN/DjJwUki0qen1ely8dIGjoyPqukLGEdP5nI43yVVKsVwufWWCZThcoywrV8Hgx7RGgsYveKMoIstSAEbjEZcuXcDa+yuNfBjvLz5sg+pVttQPO97P0/DdQO4fJL57GfrdVHRj6jOLY/j/s/efQbZlZ3om9qy1tj0ufeb195ZHoVDwQKMN0UAbtUfTSSRH00OOODERmghN8AdDIf5hhBT6J0XIjEYajUbDoUQz3RPNEdlstkFbmIZrFFAFVBXK37o2feZx26+19GOtvc/JQgF1G30bhl1f3LyZeXKffbZZe5n3e7/3XfSvQRAQpz12zp3n3e95D//m3/wGg0GPsigIA0UU9TqZxpOTE79OUL5CRKOxKNH6XNhujZGmCdr4JW7TIIVyerVVidSCYX+ACRRxFBF4Q+yyrrBCoMIAGQSEcUScJuwd7JP2ewRRhCgKVBiS9FJq3ZD2ezRNQ1k6A+V+v0+SpITBjOl0TlNXjoGK9RJnfQ/El0hlCQJFnIyQ0mJMQ5Y5fw3hJaHSwJlCF0XBb/3Wb7G5tcmFixe6Pm8hGSBpfUfaCqZOj/ctQPR7mU91d9O+dXv7fpD7eDv+/Y5a1whtu/bo/EY8m9oKnCwINMYgtEG3est4hqkHHBcay455rWsny+I2tUgFUSxoGotpnAlf01iXHOsZkn7QsTKNcf4tQqnus7Re0LUDJXCUCwfkrYxGvPddH2Y43GE+r7h05Rwnp0dEcY/DvbtEUcpoZYuTcYQIIrSp0HmB0Q22NbvUBuPX6mCx2stuCOEY9T65CHhDdr829HijY+bTJeJEe+5Yn5dowUUJ1njS+JIUk8eRrDd+lO319WQ3a7sZGZ18i1hirvv7tJAe8SbyUQ9jal557TlODvfRxs3TnSxGQpr0COMIY12CFRZyfl176BjZvp0sHbdLOJ7FiNqEQsu2r8qCvJgThTFxHKFNQzafOiDW61o74FN58NyBpipMCIOQMJCIwCV782xOXVc0dQ24vruX9lhdXWM4GDEY9un3B/T8eYVKEQTeDNRdYOrKeQcJJR2+6vt1lxwwLrHidd21tg4Ab01PraXRcP78ZdomabQblxCGtLeFbjRb29uEYcjJ8TF7e3vUldPcV1IRpSmBFFy8eImNzXUC5aRL+oNVJI6okhclZVGSFwVlWVPXulvjLNj/y9ij7J4Pi6tUaOWY2sSK9X5q7XbtYGS0l3YUblujLTKU3QZhFGAaDYHCCusZ334/S4x4fNKkM7BtvQHQDmu0fj2x1P+0BAjrcU9YVGO7dm59m3cJ9FamRnnZGGMaglDR7/VdNW8cEkUxkdf6j6KYKI69+WiCDGKkDBEiZGXrUT7+S/8r3vHkj/G5P/o1bt55DaEEYaQoxjPGpxNOxyc8+cQDvHrzFkGliMOIauzMh5WCMA3RhZMlyrLcJxINaRIySHucTjKkVLx6/UW+8cI2Qaj4a5/4RV6/dYNf/+0/Rvs+MIwCgsAnMd8ifmDkXNz9tDTWMRUiUdEfnGf/cI6UQwLVdov3aaJjXSMxukFXGUe713nf1RWeeGSNyzsDVoc9oniADEQ32XOTOoNzol1Mqls23Wg06jrEP9ehvQlzermMu412ER0nCVWlKU73eeTigBef/UM+/oER/79/8SmuffCDbK9rvvG1nJ958glMWfHuxxK+9vKERy+P+LVPwpW/dsJHLl3gN75m+LH31KxEir/14QH/6//3CZ99MeUnf/iUhx+7zG99fsJnvmaQSrMymvCBh1b42z+neMeVApv3yRpFKQWDXsWorhgYgx0mzAlJG9jWDXV1SrM5otSKvIZJYdmf1ByezDmalIznNbPKkDWGGotuCqypWA1KhklKOoiIVABSMasM86Ik14paBdTEbjgUFm1dxx0sZRK/N2FQUrjkSqDAFrz76hqXzj/Iy6/fJQrOcXKSU+eWC1cibkQVp4cwPYKN7YggOuVgN2blSp/R1phLV1eIoorVlZjT/ZBG1xzs11warKCiKSoI/+II338JoxYhygzYks8R2SGff0YwKQ1RoAmjmpW1PtbWvuz7e320b8e3ixZ8bN3JrV2YNJ6cLBi4tWcfLy/y4zimql1F0I0bN7A4ML2VTGm11Nv3t0Dy0dER4Pqgoii642gB8tbktNfrdQB5O34MBn2Gw4d5/rnnATpQtQUcrLWsr6+jlDoDorffnZmbO86ynHbSLEAnF7bMfm5NNluGeWvguQxOt8c4GAw6eRShJPMspygW5qRHR0dnmOotAN9K5gz6A4qqYDQakec5RVF05xWGIVmWdcfW6/U6GZiqqjqQJUkSTFHQ6AVz0VrbJUKOjo66JHQ7hraSMu05teavrfGdMSFZNgMgywosUDU1aZyQZ1mXYNF+ov/CCy909yxJEoqicAA2pjMCBzpwu72PrQ6/k1tpQLQVS+IMeN4e4zKTvp0HtPteNhcFfKlmQF0r0p5bSIVhwNbWJtZasixjZTTqgPiWnZ+mKXlZdBJBy2z5dkHbftYDDzzAeDxmOBxSVzWz2YxqqXLh7fjBjLcCJj2G0VUP/nmnGq0eekcu/Fafu8Tq+vPGvc4HF+Z63i9CCKxVaN/fBEHAuXPn+bmf+zn+xT//VS5dPM9hXVNVNVGkSZKYKIo5PDxkc2unk9wyxiIF3QJdCUEQKJIkotdLEdJwMhkzzxrCOCKIAv9eJ3+Qpj2SKKQoCnZ3d6mqiqtXrzKdTrtEYb/f7wyx288Jw7Aj5KRpyng8piwKpFSkaUgchzS1pa4dmCE8mBQEASsrK5yeHpNlM0cESROSJCaMAiaTU8bjMdpIRxCSAevra7z++uusrKzwpS99iQcefIB3PvEEDz/8MFlRuLHA2m58qKrKyUt1jPz7M5nqMKa3iPbT7q2N3b92+INOrPhuHv/9XMd9L9eEDvhyPamSnuhlwGgvFSEcYCQsznxUtJKDjuEqlUR4OQ+tDcbJYEMLfqk2/W07WRchBGHopF2C0I3nTjd6QXqyWOrGrVfj2IGr1kAjnPGmBNIw5W/+8n/AY49+gP/+v/8XvOt9H+XixQf54uf+gL29u1gRcPHSo2jbI68EssmxFqeB3emcmyUGuu2IBS1Q7qRbFiC6cQ6idPIttoW3AWscIdGYjs3s/rYAmYVlUfHu2euWFkT3UjrW/90D5sZqz2xfyJ20ADaI7v45RndEFAVYDHt7r3F6eoBudLeeCMKIMEg6v6AiKzxobtsb58HPtj/Q/posjljQzr2sB1Yd6N9ZK1tLoyvm8ymBCkh7fcBSlnmXBHCA6EKqRMizYDrW0tQVVdHQNJVPijoZw9FwlbTfJ+2l9L0xvQycREZVa5pmBnPhJW9wJFUDVltq7UD4uilpKidNqZuGRrfn7uUKlZcmEThGtoBlyZCFxI5nekuQQXs+AUFlQASsrKxTFTOqunFM6EAxPj3hX/0P/wwlnW795WuP8cEf+jHCMPLtwDUGYyxSKsLIsfPbqt3O58e27QuPIS7p41uB9RUhrQyaNQ4XaNuaxTrD3zYJ47d1pr3ufkRRzHyeE+qQQLYyT6JrJ1IuiA6tFAzW+Su0a6i61ujGoJuG2mhqrdGN9lUlPhknWulIf839ek1JifLfkc7/QCrlgXRLVeesr2qkcrJPgZeHiaLIVR3EIXESEUcxUZwQRSlBlCLDBKkSLj70Q/zyxcf46qd/jU/+8b+msZoL57eQUrF/fExe1QirnPdD7XwDlJBUhSGKDCjL1UsX+NEPvYc7+4d84StfwRrD0ckUISX9NGSWNbzw6uv8ws/8HFjDb/zWb9PUmjSJyAsnDyRQGP3vlSa6QUrhS48ko0iQFxHzoiJIwdoSlELYeyHgv3VYC4FUNKbkcO8Wsp5wfnObrfWIYT8gipQvZxGLbEVXKtRmcJfLbM6C3H+WifqZq7AEwLf7e+M2b5y4RFHEA1cuIYtDHn1whT/9csWd8YCJUdy+9Q2GvfO8ev1Zbu5e4nd+8zNces8Ps7PyKh959CqqWeGffW7Gzz8Bn/nCBq+MZ7zvwhoPb855/Fqf3/nSjB9/co0PPgj/8D+xfPV6yac+L/jq3T7N0yHn+zmP/p0+J9NT/stf20dsPMDV7ZxHLiU8uj1gpCqkmJDGAToTEK4QIggbS0/DxkrD5R1NUY+Y14LJvGY8FxxPa/aOTrn+6g1O92/zzosJl1fPcX5nk37PlX0aEVM0glkl2DuccGuSsz9tmNaWRqZYGXSD1PcqpBQY2yCEJNAZV9YFP/2RR3htv2S/TLG2oigTJsfQX7PEI5gfDDjcrXnwXYLhSsjNl4YU0rCy2ePufk44VAyHAcd3c7YvJbz+yoxwNSRInKkeb/u63beQSiGzIx661OPrL5bcPBzibH9KVtcFac90k7q34/s7nLxF0rF2W8C4NXRsWdJlWXJ6euqy617vvKoqhIXbt2+zsrJC2uuRZRlxHHcM96ZpWFlZQWvN0dERo9GoA+kHg8EZ0LoFUtvJeVEUZxKzQRAwGAw4OjrxE183wVoGhAFOTk4YDAZnQOowdCzzsixRSpLnRceAbPffaoO34cBUN960E8YWeGmahul0yvr6OlVVEfbSDliy1nJ6espwOOL0dOxZ5pNu4tmWkgZBQK/X68DyPMtYW13tWP0tqD2fz5nNZmitGY1GrK2tnRlbl++XY0QnFGXRAc+tlnp7L9v3tGam7fktj9tBEDCbzdje3sZadw3n8znT6QwhBVGSALaToonj2LFLlTPiacs3XbIlQao5ollMcr9JumVpTBcCpFBI72WxzNhvtfjfKDOwvLBb1kxfXCdJv98jz3NWVkYoFZxpb0opRsMRg8FgocfvkwCbm5tMp1Mmk8kZzXVtFsaz1jrzo7W1Nfe5LLNH33a2/vc5WhzB2u+8buzN9vlWw+e9SW3c6+fd275EEPqycC+3EihngFdbtF4kTx988EEefuQBirIijCPqqmY8mZDlIVIFhGHkmI0GnKxvW3ViMMbJWMVJSJJGTGdj0tSVwM/zDFtZLzfqTEgnkwmjcxcwBuaznPk8RzdHPPzQo+jGoqQzS86zEoFfiFbOZFCKwC+EAwTONHU4jIj8mJJlGePxmCxzwFcUR4SBSzpOp2OEEKS9FGPc+FGWuT8X5+WBhiAMSOKUwWDAYDDoJM2+8pWv8Ku/+qv8b/7RP+pkwaqyXAJ1fL/igf4W+L+f8e3g3nudwbWKuPfleO5jm/5exHc7AfCDfr2ARacpFmv/Fgo12q8lJGCd0SICpB/XtdUEoUCGBqnwIKVANB6MN84U1BrPblWLeWJTO1NSB+RJz1YXhKFEaOnlYRzIbbWlLDWNEIRKIYUkcTYLPPLQY/RGm4w2L3P1wXfyhc/+MZev3eGhR9/Hu977cYwxZNmMLz/1DMbWGKu9lnkrQeLB8xbk7rS/XT8irHFgsrGL1zzT1i5lWh3I7vpSI/2cyF/aBYB+NjkmfIKiY6+Ljnf7TQkt22E+IEwLlNoFG91DoIEKiRVk2Zjx3V2apl4wd5XTmQaXeNUeNDZexqY9ru4z/X8W07Hdu3kqXhP+TcJay3w+dR5BgxFSSZqmWujXLz8zPvHdyv4YC7XW1HXpNKjxlbJRQq8fE0cxcRihAifbV5UVTVl3V1kKd62EdfdSG+2Y601DUzdegkd7mWxBKAMnBSIkQikCqZBeMkS0pqQemG71xDsiRwugq7MVRg5Hdu0mSRLe9/4f4tbNbV6//iLz2RiJIAwj6qpEqIir1x7jXe/9kBtTtV4kJOwC0DZ2cW+WxycnRWPPHJfwCLlwF6O77t21dyULtNh3SxiWvs0ZYZF+XmCEJfD+Bm11SsfPWwbs/T21YgGILy6JwNhFRWuXSW6P12+3LDvTfjkddrdfI0BZidWuh9JCY4WgqTVVbYmQNLTPaLNoZ2JJ/923Y4QlEBqpQKiEKNngwXf+MOnn/i0ImOYZRVOBCJx3gxBgFAjB9uYKeZYxzTKayslgf/6rX+XawTne9fjDbKyN2DucICTEiSCvSuIo5ebdW/zuH/82G2vrIBUbK6ucTudo3TCZuHW5sW9N+vnBAdGtRQWC2vWIjNKA45MSTY/ANsShbKuZ7sskRgqXZbVNw/hol/WoBtsgpEKECiKJURYZuOMxXhfMpwy7BasQgu3tbWDBAnyj+du3P237Tb8vM8/b6B6aNwHWlZL84s99jKysmOuQz3/9HP/yd0pmzSa7r+5xeHyRkC9zUh6R1QE3bz/D1Z11bj73BX7lJz/G/+H/+zW+GD9KvJJz47mch8+tEfUqPvaw5b/+ZM2reyFPXNb88EMxH3o04pHRmP/tv6j4W38t4vFNQ1MqNtZ6PPDgVf6r3wCpElJVcG0n5z/6xW1+7F0r1GXGuOkRRoY0CglDjakqsBIj+6SRYM1K6p6mXtVU2tI8vMrsnWtU88eYTccYXVFVE296kZLGmpVBwpaGBzeHZI1ld1zz9Vs5L+1XNPRAfG9BdGOtr6yAi/2An33vDttrgpfGK4ylwoo5pomZHlrObQuiniUXEYe7Rzz63g02tlJefb7hYmK4cHWFz/3uCZs7PYZDxevfqHjkvRXJXUU6CAliaPQP0kP//R+BMWwO99m9DZ/5SsQsnyFFSqMz1rcGIAsEwbdfob0d3/NoSyNBeEaI7cDklind6joXRXGG9S2ldCzdLEMoyWQ2I0xi0jTtQM9lZvmyLvf6+npXqt/KuwRB0IHHnRSY/97v92kaV2J/+/Ydb/oYdAB4uy9rBU1dYy3Mpl6mxDNdhJAkSdyxpg8PD2gaTV27Ujw3NzXdpKodc8IwwpgFWz6O404XHugkxKRyY3HS79Eb9JnMZ5ycHJNlWTc+lWVJGIbddWwNRKuqIssyB6jnBXEcd5ItSil6vd6ZSoCdnR2Ojo46pn3Lum+PLY4i9BJY34LiLZs6SRLquiZNU8Bpn/f6fU5PTjqmfcs2KcvSy/2oTnJAG4PypbqDwYC2ekEp5XQ9raWqKlZWXEIgz3M21tcZT6bMprOubYFj9QhaSZZW294Z/SkZoO1Cf7n9MsYwGAzIsowoDn1iRDAcDsjz3C2IrJvPLKoMYoSQXL16lbIsSRLZmZVba+n1epRl2bFW2+SLbhpvKCq+qV0q5RhLVVV6ZlVAOSsQAgIpCaOQ8cQZGb4d9y++H5mqusV57sNc3HfJ9zR+frcAtJa8rKTA2vb5FeCTXcqz9nRjUEqwsbHFP/gH/4B//I//MWma0uuFjmnnk5HO26DGmYcuV434xWWoOmb37u4uK6sD1qN11DSgbhqXyFcKpRV17ZJqRV64fme0QlVVXL9+HaUU6+vrAOzt7dFW3gBdP7VsNh1FEWEcIaXbbzbPl6qVHAvdWI02zmA6ikPCMEBrqKqGsnRgSRSHJElCr58wGK7QS/uMp5MFg19KDg4O+OIXv8hv/9Zv8Qu/9EuEYcjpeNz1pcsSa21S9Z7u1Vs8Hy04Lu7TunGx1z9//KADwt+PfdOfJb4X1995oTnN8UAIVCAJlQPhmrBxsiu6ZUkvwNuWcWoNNJV1QKs/ByEFmsaDt/68jMPrBQ6E89MwglAQxCADtz/nGyYIIteqtbFoYzGNpSotaP98RoLt7XWuXnmQ67f3GKzc5O/8yn/G3t5tPv2pP+LTn/oteoMtHnn0Ce7c2vVSJTjwfAm7AM84Fwt+W5eYNU3HjO+Y5V70pd2ow0VaY0prQTuvNM3CX8EBlXjMqO3RWYCY/nOt9ebd1kPvtkNl/eat/vnSsQqBFQ7ktVZzMr5LkU0cwLvkbSGEcL5tpsEIr2vPWVzHgfs+2jGhXaqwdI526UK17wXqumI6nZCmKYN+H2satHHXXEjlUn6mZdu7z7JCILTzMKIp3ZxUKILAyZyoIEQpt66pqpKmLrtrYvQClG3vk5un6qWkx5KJpfKsZsfZP8OEd0x9//fAVQg75RJJx78W3i+oA2S95EgL+nogeHHdLEEY8eBDj3Pl6sMcHx+yt/s6kV97Xbr8EGtrm4713t7z9u3fhLn5FtCOSd68tQX03zhPdvNwur+x/Lf2S7pzMUaTFVOKoujIM/3egDRJkDJCBZLKNCiCM6K3i6bZ9g4LAF3g1ocKCJRqcz7OZ9Q/S+1xWuMTNVJ257hI7LmqBlM7n4BAhYQESOHNOYUkzzKC4QCEQGt/nr7ty1oiRY2UzqtBta9L1yaxCoFkZesaF85d5NlXb1MWFdN5DkhmeUVRNiAgjSM+/mMfZO/OHb74zHNoYyhzS9Y03No9ptbPM5vngCCMAvrDAK0NvUFMr6e4fWePo6Mp49kcIwxJHJMXliAwvururfHB7wKettRB+bi3wens+6SwztBChVSVZX0FXrmVoo2lpwr3QNrSlXfcl6O2WCUp6oywmVJbzeS0JMtqyqYm0Apla2rbdA9bO9e3pp3gCc8YMxijaU0srQWtvZNza+9sDVY037ReWGSYXGhtOgDAseLc0RqjPdtN49yPz16HjY0ROyrhpJ6ztvlDfPIP1rCDxwhXNiirmkfXzpEGmq3zqxzrOaNkyPHdgo0NyUr1NH/y9DprGxd5+etf4kPv3uLmrdcpmgRpNnn5xgGPXd5AVxFJGPDhd/Z4cK3h5KTi3R8NWBUFpVR8/AOa3/zjCWxsc2V1yv7rIZ/+kuV970johyWf+f0pf3jd8tilgvc+fI5HzjecG4XkxhDJPrpu6MfQ6AxrA1eeMljB6D5FcwGtG8oypygydFNR+VSqkII4CthcSdjZithZLVh/bcbXbubs5xYtAzegNCVKWIyVrkTOicohjXmTVvWGDO6bhBYCpHADiZeKEMa5OxthMGjH+DENl1dqPvauczx2dZVGRDz11X0aGVPbkEjGlNOCXtkniXJOpeXkbsQzf9Iwnlre9VHB1XeHxCGsbvTZvWl5x7tiomFJGCm2zglEv4ccxsjGsQzeKr4/JvD3Ogn/9tsZoRaTDuG+At2+05deSYulRgmFaQyhCkgbydxWNIElbJzfQSYtkYiIdEXECau9U7L9Qz71xTVujjPHSGOVZKDZ2lhH2LIb1N/kyN7kte+H6/6XM4SQZxjJLTu3LYFTXmO2Nbo8ODhgfX2dpnHa0tPplDiOCcOw09NeBmBbkHJ1dZXxeExRFEwmE/r9Pql3pW8NKYVwMiWtpEeroS6E6ADcKIpYX1/j8PC4kzdpgXEs3XjTSsgoiwdpbAeSCAFZltE0utOrjePI7Q9nGNfKepRl0V2rFtRYjvl8zurqKmnaoywLZrMZw+GQMAw5PDzqpAOWGdItKNwaWbbsiDAI0FHMdDrtgPyWXd6C31VVcfPmTXZ2djqTUMd6H3Zg8Hw+J/Hgeb/f9+fadJ8dBEHHhuz1ely8eJG9vT2AzkC2Bfpbk1FjJL2eMzht28bx0XGnn58kCcPhkFk2x/r9RFHE8fGxk52RbpRXSvkEBYRR2H2WFGrJ9Et3Yz3Q/e6SGq6iwPmxKF9u6Sa6ZVUilQOv67pGN24xrJRidXWFRtfcvHkLoDNGbUHzoiiwxrC6skKWZSjlDGfjJGE1jjo5nIODgy5h74B/94ysrIw6dtlgMHTmsWmP3b19mub+VAq+HW18L8aLt/7M5fnw/YhvXkW84YjEm4+m31l4llS37wVQsswztkbg9G7936yDrqxfVAufUOwPB/z4x3+Czc3/q09COXPQtjy/qrXrW60DENpFZVtV66RcYtJe4o28XKI3qSKnW6wUKgwwGJpqTlVrqtqBJUEUUdUNd3Z3nWHzcOT8NrQhjGKiJEEbS1nVZHm+8DcQztRMCJfQq8q66w9b/wvHnmxoGkUYBYSh8sk0d+ftkhRBXTX0+q6kG0HnRZGmKVmWkaQpk8mE3/iN3+A973sfly5dIggDp20sFubU2ice/iIA2rfeY9uqv3WI+9bif/BB6LfjOwgrPSnU90HSa5srCJVCKYHUxhv+GVjSQ3aJdwt6aWlqLUo4mRalvLyFdBrhtrZgBBJBIPFGnhZTu760bgyNsARKIJTozEO1sAgFvaFESUEoHSP0/LlzfPGLX+KHP/432dp6gOPxjM2dB/hf/P33cPvW6/wX/5f/I//Nf/1/oqwMTV07/EM4GQ4Hmoqu/5BhQCADb1TsvgdeVkQFCiUDv6305pCBe59sE6+egQu0RpRd723BWuPUULzMjbtULUFx8Rp+X2fIi2bJtP3M3xZjhBACjWY2O8ToxgOSwmuru/07DXho0R3jP7NjtbNgv1s8q9v95IHqFjt3wHU7BrVbzecZWTZnbW3V99lNx3621qC1QQgFwnTecK2ki9FOB11KiQpCpHLyLhbhZMO09rI7fpy3C0mZ7vpZ69niopMEEULgFVgcAxkH6LtrpjAIlG//Siovi6Y6z0FnYin9fh1x1XTZFieB4z5rGcS2/pydVr4Uri0rFbK9fYELFy9ycnLMcLhGHMVYzOL22w6if8Nz6hI9C08994xgFsjbMoAu5eK5dkC/XORjutdcm71x6xWyouLg7m0ODw7IyxwVRGxt7fDhD36Ahx5+BIFmOq+xke3cbRdjjwfQPci/yPi0RqQCpaxr/wKkBiUEaANohDWY9v3CtVl3Lpq9u3c5PTnmzp3X3HpISga9AQ8/8igPPfw4YeDkiYqyIOnFBIQgnEZ9UwskNVK4ZJ6sHBFBKe8TKSVCBShRgQwJoiGXLz1OLP8UYyxFqZ0PjRBUjUvclJXl9z/1GQKl2N5cZTrLyedz6tqQ78+ZzQvCSBBFIWVRI5UhiiUHJ2OqKqWfpNy4s4+UgoPjDCVCN+eQpsNU3yq+J6TU7wSgE8K6TJ2QRMKS9nscjd2DoaTT+1lkXf78CwspLNrWKGEI9JymzLl5/VV2H9lkY/0SCkESRVQexF48tK6Dbhe+1ov3u4W4e8CrqiKKesxncwdWSHeT3SRUdYtiWGQZ28cjilQnLRAEqmOxtZ/VAiR1XXYsySAMkGFDmdesD+FjH7nIndsVT11POd57F71G8KGf2uTqeUjee56D7JDnX2ookzWa2vDA5Yd49uU98uMnmPQKJuNDrr+yy3N7M9bX/gpf/fIX+dDD72VtfcSN60fsXNjiiXdIXrk+oSwvYJMJFTHnzw/44XcJnr/d8J//ypBNEXB7d07UCOI04N0/lPDffvEWT30t5V9YePgdgr/5UzmfeHxE1BhEPGWKJFAjhHWMGxNIrI0Ita8G6CUYs+I6eON0f+fzOUfHExqOiUNYH67w4+/bZmP9lM9/45S9Sc5chxghUYFE6borjVJSYIV6gwGpWPrfxZtNdxOhsdrNaGSg0KahFDWSEGkkgZH0QnjoQsyH37nJO6+s0k8j9mpJbzvk/X8l5vCGYXI65uUX+9y+c8T4REJ/jlEFlZZ86CeGnJ5W7L5Uce2xiNV1w2S/xoqQc5eHYGB1TZOVFTJIEI255+fjLwpI/873+529T9l6ic7QZq5ddD8JRa5BBAqhJI2xHFpNE0n6aCKhKQLByJY089v0g4pmts+LXz/kldci9rMKJUFVETbJeezxR4miApBYI0G98bzfXiB9P4UQjk0Xx2HXl7f6263xpJSS6XTaybMsa5VPJpNOv7sF3oUQnXlmHMedVMzu7m7HNG9Z6S1YCXQMxTiOO/a7A7wFg8EAwAOwDhDv9XoopTjx7GkH/hvW19cZj8dL29vumNqJXa+XemZfBUjiOOrA5+UJnjbaafp5EOWNx7/8+vHxUXftWmBcSslwOOTk5OQM+N6C4+317PedgagFZrNZV7rfAu9RFHWSNMaYzgi2jTa5cHp62t3XMAw7c9aqqs5IotR1zblz5zq5naOjI+bzeSc/0m4fhuGZioR2ctoeU5IkBGHIxvo6169f71grW9vbTKdT9vf3uyqDFsiHBShujXWgdxh6WYcWPF8A6W37gLPAedtGqqrq2llZlme1y3FgWHuP8iKjqqqusqH1AGiTMePTMQcHB1y7ds1XO4Rk8zllUzMajRiNRhwfH3fXsZVXSFMn1bC+vu5LtzNWV1eYzeee5Vrdt2f27YDvVxD9z7LZW0UHoH+b/bXww32LpbHaAeii+96GXiAPOCLLwtxOSgf2SOWe7dW1NR5++BGee+7ZM9UcrvqmIcsKQBCoACWdqV8LhkklieKAJPHautbQNDWtsaeUro9rtMGY6cJoWgjqusFYS567/U+mU2cSmqYIr69a1Q5Az7IcKSUrKz1XVVLXRHGMNbaTLHN9mML4Psp4xlqaxkSRq7Zy645WjsZrxlqNtZBlOVU57ipfWr+JXt9Vvzz99NM8++yzrK+vEyiF8R4b0EpZCv5CBKHuoeksM06/1QZvDbO/HW/Ht46g8ztpVyaOBObYzdaBhtp6Yp5n+mqPjSCQCvclHXAu5IKNa7Sl8ZUqGDpZEePf3zTaMeAjgQoEUSRRoURICJSTfCkLTV2D1RbdePPD0DAcDJBS8cBD7+EjH/44g+EaB/sn7O1P2N99ma9+4Y+5s7vLB37kl9jdO2IydXJwTV35qpwKrSuqusbUGl1ojM6xtvGmok3X31ivnd5S1TuAuvtyWsxSKsIg6BjUrgLRvaaCwOlk+3lUq/vdgpltFWhHjIQOuHZ3ZcH+7oBsQedbo01DVc48qLtgTrf4jGMBewkP/7oULgEr/N8QeL17lrbDA+Ze5MdaHMcenDS8QZua8WRKrTXra6tY2nmXIzaYM0xx7QBxbWk88cEB6YFPcuD4grjKIwFIZUE4oDlQEaPhqtcOX4DG7fezELTfmR/MraWrRmrlRQJXKtHdxxYPi8IIFSrCQHUyLghvsNpWi7aGopbOnPQsmO6uYwveu38GY6T3FqqdfJFs77r0571gpHeVDHKR4BD4z+sqIERnHopYOnspll7zSQV/r1tgffdgj6e+9jJJOiSOVti+uk2ShJzf3uSBqxd56IGLPPjIIzz/zNcZv/w6ln7XHj3c7c6vhTXar8WNRAiLUsJLvTgfF6ei4dc0VnR5J+vbpRCCm7d2efGl11nb2GDrwjtZ266I45AL57d4xyMP8I7H38ntm6+zv3/EPHPyRFJopFFY4Q1ilUQbTdO4hFnT1KhGolSNVgrZ1EhZgYhAxjz65I9z4ZO/wf5RhhIKi6FpjMt9adDKUJqavKi5uzvxJGOn7IAEYzVF4UgCYRBS5sa3M8HYlEymNXWjSSLn95JVOboBgUQp0VV4f7v4wVF2sAZtA4Ro2EkUmgGHkxpE0GWn7ufkRQkwdUMShpTzU4LqhNvlCV9/NmU0lKjLF6EXEoYLU9EWRBeizci5DvPo6IjV1dWuLNEtOiviJCAI+968rCabF2ce+nbhHHpRfjd5bbrFfFu23jIT37jgXmS4wBISqJg6m/GuK4J/8J/0+dMXK1557YjLG+f4kfcKRJhx7YFzXDQ9vnEz55YekkQ9fuSDHyHfeI1P/0nEXF2isHMunrvEs9f/lEFvjCksr736Cv3eI/zxpz/NR3/64/SDGxwdwQsv9WnWDti6cgWrZ7zvA4ZPPS35l7894307Qy5djbGRxFQll7czful9l/m137U8/uEp+T78d/805PCnNP/zn6pY1VtE4RwhK4QJOxYcQBguTJ665IWBum5I4gHra9vMqgxdZezv7jKZ3iQi5lraIMqaU0ZkNqRqNI2VRGGMwrUB4zulBTPEugzuW4SxCpc1duxFrKIfRAidsRIbHry0wWNXt3j0Qp+VnkaKhtpE7JUB0fk+Q6vZGPXp9VcxUlKoU4zuoUxA2g+IVwN0qfncb4cEHHDtoZDVzYDD2yfoxhlQnR7BypricD8DIiDjB+mxvx+hpfJZXoW0EmGhVg2d4p01BEIQWwhNQ1VkSARSlQyLjM2TMTLPOSzHiDDgxRc+w+T4JqYQJOEK7/nIx0jX3s/XXuwza26x80DEzoUIoUqwCmMDP3C/Hd/PMZ1N2d7e7Iw/wzDs5FyWzdfaJOZoOGR9fZ08y6jKCm11B44ua2q3uudtUrMdK5IkOdNP53mOlMID5pLxeNwBDS1Y7YD+GMCzuZ0++mg06gAJd+wO2BdiCai1reGkM3Wp6oq6qv34EjMeT6nr2ut5eza0sZ1WthQSbRfXoWWTtxPhpmno9XpMpzP6/T7j8YR+vwfg9cfpjqNNSrTXaT6fd1rmGxsbTpLGs5bTNKWu6y4J0YKzrflmm9hoNcKXJ81pmjKfz7uqglaHvdX7bkFtrTVFUVAURQegO2kUdeaz28lo+73d78bGBqenp7zyyiuLybUQZPM5PS9VMxgMODk56RidUsmOPd7ouhvPan+cyxqvy0kEpVpJl8Uisq4rEAtTUmsX900phXEzQ4LQMe9VILtt2woLYGF8a5whulKKfr/fJTcC3XByfML62hpVWTKeTFhfX+f27dudRj1AFMf0ej0mk4kD9ZYMT9+O73IskTDeKt5yDi3OYAnferOlbf68d7xlTL3ldn+WDxJv+qN/QZzZV8dObNfWPlqWdPv+thy/9ahw+qhQFiUIwxPvepIXXniBLM+7eX2MS5bO57m7T7J9rlugw0nCODaeY6TneYZQiqoqsEKiwhDBwli6KArHzpSSwveTQgjqpuH09BQhBP3BAOE9NCaTCXmeu4oZn4QrisL1Q7XuDEirqvYVMh7clw5kUUqSpkknSdY0ddc3OYapm4/XVcNkMmc6nbK6vka/3+8Mu9v+qq5rvvjFL/Ke97yHldWVM+bGLF3+dhxx9/2716e81WeJe9jmXmP5HH8Q434f+72Qb+7lM7/VdV1+/c9C9Lm/ZCPHsMUnvYV/3qTwoHggUNatXEzX/kFrB6CG0knAAJjGIlrddCOQSniNalwHvQTA1o2hqi2BhVD7/k4KgsDrogsBVkNknRlkBU27fynY2trm6tXHee/7f4bx+ISmUkxOx3z205/k+o1ngYisErz2p5/zxBIPlvoDFFISqAgZJn5t7SQ9WvPMBaHWg5/Wel10R5Zz637t56OeYKcbCl1hKo3WJUbPsbbB6NaI3eBsVW3H0pWqZUBLB8CHDoQPw5AwCIiiEKVcslAFIYFqGfBufo4AqxsaPcfVJTnJK4dHOMDXQkf6dPxzZ7LpWNUu2YhnTbfSNMYua8Y7KRttDNo01EVNVWu0dkoFjTYO90gjJpOJr0JwRrAqDInDgCgOAUXdWGYzjTYhQsU+GaEIZESS9OmlqSOJRClp2qM/SFlfiVkZQJVXfPErz3NwcsCFnc0zUHn7/c0Tj+LMfXX30icMDDjNITzRRzm5kFARJjFRGBAq2QH07fpEa430iWWnmb0MHrcVCZ2AEUjbsfdBE4aKuqlBRJ7xf/YkrL+HrgUuQHUpBKYFxbu3LH7r0idyCSzvcMIFGVNYQFqKsqSqNI2eUigIw4jBsM+hMoQqo5jdJVAe7Bf4vkL5w7Td8brzbRUyRPe9TdYglUOBJKBMRxQVspUYatubRQknz3l6MmE6n1NUJUEg6ff7rKlVZuNj7t4xrAwM/d6qW4cAxjRoI5DGVZsY6YmuWqOloGkkQQC6UQ5cVxVBoLBGIWQFBKyff5QnH3+Sl2/fpbBgkFitEUg3V5KGqikxlcRqd25KgQyd1KbrxiRhKNhY6yNFwPHpKSaWpKHEaMhLjbUKFQhCJEEoqStDkze8UdHjzeIHBk1zjUwhheHcQHE4NkwK1T1oHVPkPn2emxCHyKhPECfU4xOKueWppzSKGtsYLl+6RC8NO5B7YQa2lLH07K5+v9+x4ay1NNp1hFIKwkgRRSGDgVoCgu2SHmtJUWZYa1hfX0Vrw3Q65fDwsNM1baUCWhClZeB1C1cdoa1B2JTT/X22Vlf4hfduYd+TgZ4SklIrQWkaGluzujokERUNkifec5FL77pImZW8fPMRwp7g0rmYxx7Y5MWZZDbb4GCckSYDenGP44NjhmqfLIPrtwu+8YXf4xO/9De4tLPKI9cm2Fjzbz7d49/lERvbOX/jx+Hv/eSQkZX82LsyPv2lHr/4/oSPXMr5/HN9/umnjhgkq/y1j88ZmoDGmE5HbQEy+EIn2+pUWoyGJAgBx1SM6h5YwdrwPHVVMZ1MWFubcbkSTEzM8STndF5xlAuyrCAvwYrQsWpYTNht1/l++yitG2ADUxKLitVBwrlBn0ceuMrDVxLWR5pEQiIURueoMCVrAp56NmN2bsDB3j7XP1+Qppbeesp7f3KVqol4/nM15VRz7T0Nlx8X9NYU8xur5JOQ0WqNEDA9rSmKkumJIu0rppMMqwOkbBCEb33w/x6FxpVOu2KxGolBmRqJJrANympk05DqikBb7u7eYPfOTfLD64jZISuTObKqeLmcMCekbjIn2WRjBoM5L77we/zN//D9vPODD/HHn4vJTYkk6hJerY7ffZ1rvx33Nay1VGXVlY3XdU2/3/dO5nUHNLYs6CRJwMLx4SECQS9JyMuC0WjEdDrt+t72vUmSdKz2yWTCyspKpz3bAgVN03gzzNhVWHn2eMuWafu6pmm4cuUKBwd7Xrt60gG+/X7fM7szqqo5C/p4pqRuDFXVIIQiy3PC0C0OwEm7pGnqgQ1DawLj2OELULddbLbgRstGns/nTh83igmCkKKoPOO9z3g8PmMG1wLf7Xm242dRFPR7PQJ/zVvmfAvGJklCmqYdaJR7GQKnN9zrPmcwGPhjiboEc8tCb+9NywotiqJLRkynUycD44HzZXmftiqgrUIoigJrbcd8X75Pa5FLnEsEoQoQgWJra4vT01P29/eZTqdEkWsT6KVr4hMjCzNO2bFy4iSi10uZTKb0+l4KIUk4PTnt7lObOGivddM0TgbCLwbaRVVbIdBGm9gxxqCNISsLZvMZvV7PJ3gkkQoIlSLPcra2ttjY3GQ+n/Pggw+ilOquTVWWZFnmqizyHOETBlLIt+ma9zHuhZe7YKzdyw6//Y2x5h7341bB3xLobBNE3w6AWixkxdnf70fYdteLIujlYzGWpb7O/71djHbbtd4EfNO5NEbTVBo8yBPHIQ898jAISRglDIYDijxnb2+PixcvU+Q17Qq4rkoCJZEYGl0ThW4eWdclcRyyt7fH5atXKarSgeitD0TaY2Vlldz3CVEckxeFW3QqRRTHTOdzrBD0BgN6/T5pv8+tO3fIyxILqMBJubz0yis88MADTGdT8qygLCtfydJWzEbd+gRgNBr4PjNiNquYzWaMx85suk2w5nlOVTckadL1yYA3pp6R9no8+eSTfPazn+VXfuVXiHJnagp0/e28yL/JL+TMbf0eg87L7MT7ta97ibcCcr8XgPb3IpbnF8vHuPzzcnJ6OZYB9G/WP37zfd3vWEjcAcaiK4NpHLvcPW+ehG29b5u0WAlKuf4nCOkMCYVHAy0t+1j45Feree48GKw/9zB0oLkQDoCvS2f6WVfS719irHuvc29xScRe2uORh97DL/zif8oXvvgp4jTl2TtPceP6Dfb397h5+5jp3KIB3TQO/PaGksafUKuZ3RmLuoM6wwBvdcQRdIlKh/04JrmTulIIEXjTzhAhBwghAbV4b8vCbiv4jfaAocaYGq0brNFUWpNnNcaUGDN3RATrpHMR2rGIhZOQaWVlpGrJnJ4h3Z4LrrpSqoAgkERpQC+JCEOJlAFKhgShOMOQD8KQNIqJkogwUoRhRBRFRFFMFDjZyLppePn6HtNMYoU7d4PCmpBz2yPe8+Q7GAwSBv0eo35Kv58y6KX00h6RJ1T8k3/+m0xK5dngDZKGMBB84q/+MlevXCCOQiKliAJXETWIQ0w95Stf/hxf+frraOPamVDy7DzjDWPiGUkc66BoacXivhvHCndtwRFNi7ykKix5CGnaZzQckgyHpGmClCFYS11XVHVFWQkPqPtxyYglJP8NY7S1HmR3f1OBpKx8lYaQCxJAm7yxokty03HN29P0Eil+od8a07aJcCFdcgbPyhdLgLp/6p3EkhCsryYkMuPk+BTdlJRVRWsgKzFsbKzylaee4oMf+gh1UWJS4e63basVhG/vvp8SXhddOpnI9oSNNyUVwiKlITBgpERa65J0clGxINymrK32aV44YHI8QWI5UjG3pUDXOf1ezMMPPcgP/cjHCaMUIS26bpBC0QjtE3myk6l0j7fBaIHRjatqCSRa10hdI1SFsBEyiHjsnR8k+b3fBQNN7QxVXaWd83AIQ/dsrI0iPz/w6yaB6xulcMC4N4kVUmBqKAFtNLqxzOoSiyVO/FwrhLIEXb/1uPmDA6LjzA2UqlnvK75+lFHZDSKxcLW9n2GMwMgIFSWcu/wA1/e+RlXl3L51A1PXNGXF+9/3Hi5fPkeapp2JnFLODGGZTX7p0iWAMwxHIVozIjfouQm5F9sP/IBlcK1ABJ0rcbuIkVKyu7vLcDjsSt5bM7AkSTrdwpbBaEyJjudMJg3PvvAyH/jAk0RiRmAVMrSUpkYWihhFGBb89PsSfuTxNcI0pZa79IpV/soP19y4dQXECdeurTE6/8PMPxXzhVcLRjuXCIKU7ZU1ZKW5urWFErucTCt6MuToZMLm+iabap0feuAOT7/4p6w/8ihZc5m7t/qMy5pAWx68EDE4f8BXvrLNT7234qM/lrPSi/na84abp4aH1ytUMwCpvb6TC4umlfOx1iUnULbL5lpriQILMkBEAQGKaKhYr1c431iqRlMXIdYYTgrNOLPsnWoOJzV3Z5BVLvGhtekmJK7DFF3b88vFrikmsmQQG66uJ7zz8g7Xzo3Y2ZYkYYDyci4QYIRABSmCmIP9GS++NmH13IBB3MdOE8ZHFbMTRf3DEA5qXn/pLipbZW0t4cojksH6MUcvG6bjgIsbMVLG3L1dsX25z/UXSurKaUnqBpRc0vv6C4pv3v3yK0uZ4e7PYpFF7bZZfmu34j27O7HIrp7d1navCSBpcqSpCCgIKFA0pEogrSYwJcrUIKBBUucZO8EROjjk2ZvPMsnHHEiB0IpmNGQQ94iDh0n7W8yLKZkoWX3nL3Pz7gY/fu2Qf/i3L/Krv5fx/M1DNs/3UEmFUK3U0mKQRdh76LK+1Y36/lzA/KBH3TiGeWtKWRQFaZr6SqGKNE07MDWKIsqiJI7iTgdbho7VHEUR/X6f6XTa9b9lWXZyIi2zvAWerbWdKeWyjFc7fvT7rmRv6kvxAV+FlFAUJWAZj8esrKyQJAlxHBPHEQcHh10LahncrYTJsjRKFEUOJBWCtsKmPb5lsKJ9/7LsTAu8tuxnKaUDcKKok/ZoAeoWVG3B/jdKzZRlyerqapfECJOgA7LbaqsWxJ7P58xms043vr22sAD02+vVatK31zVJkg4sbzXknZb3CrPZ7Ix2+9raGsPRiBMvS2OMYTgckiRJB963iZe1tTXiOOb27dvOlM+byTZNw8rKCla6Sf6FCxcAzlQ5tNe4qirHXvJs/2UzcmMMumlZV4bxeNxVBCRp0mm9twauLdO8vSZtIqhldoZhyHA4BBaGte3coaqcKWBrWuoqCWwnnaOUW3S1LP1Wymhvb4/t7W12d3cRQrC5uUle5uSFq7JrjRPfjrfjW4FZZ7bp/v/BGvMWSXOBMdA0hgcfeJhHHn2UnXM7DAYD/sk/+Sesr69zeHiIFCHzeYY1hsGgR8t329xcJwid3JZUKesba9y8dQOLIe0lzOYZp+NT+v0RgQrZ2t7mxuuvu/4b5+OwtrZG66sRRRFaa27dukWSJEwmEwaDAVpr4jhmZWWlMx09Pj7xsg2LsQMWQF8URZ6pqTrprfl87quKBMPhsBsPZ7OMxu8jDuIumbmQqLIURcHB4SHz+Zxf//Vf5+/+x3+vA+CHw6Hz2BgNKYriDFj6/RTL2Mvb8Xb8WaOuNUo5iSbpkbxOu1sohLSe7exZygji2IHcbnlh0I3p5F0EjkDerq8coOSEH6QAEQi0tqjAAWvGOJmWbv0khJPx056oZp0EjIwh7SusgUjGfPADH6coNUnS5+kvf5qj4ymv3zzidGaom4DJeJ9iduL6ESkcUInAmTELL3XhfSSkMymUDp2mk6mQfr3vQU3rc8PGakxdY23dAfAOJ9Eu4YCTnHKdcgvG+88SEiEDB8pLiRQBUgVO+lRIhPCVoh2FHxx73JsrG6eRrk2NbmrqKqOpS8eQrnOqqkRJRdobkY42OH/tCdbXtvn5n/srvPuJh0ijCCEd6z1spWWUv8ZCEHo8SUrRAcFAp1OtdcXv/OEf8Uef/zqTacVsXlLVmvl4wvFBzs7WD7G1tUYgLYGAQJaYumLeTDgpS377tz/J1555DoOl0ZowCHnn4+/kyfe8G9PMuXvrFUIl+cqXv8jdvX3W1rfY3jlP05ScHp8yLWoC6dpr217aeBMl8S6MMU7qzJNF3VrDEkWGpjri4OiY6ekxReFIPkJCVTaEQcBgOOT8hUtcvHSVi1ceYLQypK5CskJQ5DV1XbkxS8CZGUTnP0hHDmpZ4VJIr13vZFVahrw7D49NSIkwBmsbB3CXJVVVUjcF81lGVRTkxZS6rmiaGrCsrq75r1WSpOfBbOnJK4tkfAuwr632+MVf/Dh5UbC/u8cf/dFnnKGmEezsbPPzP/+zjEYDolgxn51gTYLRAYszFb6ZLtjuLblUs/i9JZka2/pTecwTixCBb2PSPaf+nj7wwDU2N0YcH53w/PNf5bXrr6GNY6q/7wM/yqOPPoK1yklRSreelmohnalkC6AvVDusbWXhwOjA+QSYGmsahGxABKSDdQIZIDAYDbqyjjwQWDBO3qq0NbWxhIFbD1ltyfISEVhUGiEklHWBNRJhnbZ/VRiMdfIwSIhCN2eqqhrdQBxLokH8bXprF/cMon+nWf/7lbUVIgAjScKM9dGI15/pYQOFbSpQGZbSdXL36fOstGBrTN2jt/VO0s2nKW48h24qDo/u8sU/LajKinn+KBcunGd9bY0kTYmjCNn4rPASg7Dr/Pxi2JU7vrG8w5dw+cFw2b1ed+VX7rqnacoTTzzBeDzm8PCwA0c2NjZoS78XrGk3QErVp5ofoYShqTWB0jRYhFa+xMXQMMfqAUGoWQkBk2N0HxVbnjwv+If/0T6ro00aC71RTC8w6HqT3voGNp7ykY98CC1LDrNTLm32mU0CLp5bYXo6RQeaYVEz3IjJdMV/+I7b/ORHH6Wu5jRVwa//5h/yUz/yUX786pgvPF9xpzjPhTDjsXMNX36u5vk7Qx5bNRhqqjwnTWMHStrWzMhdq6quvLascWYJwulKWuWuX2i9hlwcYUKIG+1KUNMh2sKwcVm4Ry80lGXFrIGTWcPeacP+WHMya5jlFXnVUBNAENNYgxY1MYZUaC5srvHYuZR3XF3n8laPhJqeUlSBn/xLi5ECqaEWU4yKWJlLdmcBz18Ped/7JEnfYoKMqglQdUNTaIarkn68TZ5lnJwUKBmwsT7ihtFMThWXgTS1nB6FPPZDklefg2aSksoCa135mW7e7Pn6Tp6b5YFgkdV1mVifERUGdOUSQJ7JpQKfrTe2Y2A20gFwumuzCmlcaaMS1nkUtCWOaJRwVV+mLhyzHIO0DYFtiGyBpCYQDYHUjnGAxjS18wpoGjIEdVVRlyVNXbnMtAWjG6qyQNeS4c4DxFbRX98hXdshHKwTrj5ELx0QJynGGnRTEcYpJyrhd78+5/1X9/nrP73Bl57Z5g++dMLovCCJ51gbuvJEYbGqptIVoXjrDvrNgfS3l2h/EdGyxsfjMaPRCFgAxS0LGdzCv6oqtra2HMvPL/Sn0wlRHFNVVcfchQVY1JqpbW9vUxRFx05vpUTCMKTf79Hv9zg5Oe0+u9W8boHKLMt8dVOP+XzGbDanqqpONuzKlSuMx632uOi01d9YxlxVFf1BCjiQXSrFxYvnmc1mHfDvyvQVRVGS50U3Ji0brbayNx0j0jvdt4B5XdfUdU0cx522e57nHdDbeXcsJ379vloQfTgcdkD5bDbj9PT0zPYtmGytMxC11lJVFdbaDhxvkx0t678FgtpzaH9flknL87wD54ui6PTTi6LorkHLGp9Op5yennaSbct66XlRIPw+lvXt2+NP05TZbNbdCxBdUmJZQ76qK+rTqru2vV6Pzc1Nbty40Z3/Yo5hu+vQVkO059e2y2UpBQd2zVj3uu5KKe7cucu5nR2MMd117fV6CCGodHNGa76VGmrnLF3lnXGfs7Ozgzb67e7r+zbu5cYsJa2/TXwTxLnMSl/e7l4YsvbeZC2+kzlM+452QXf2c896LIk3vH6GdOe/2qozny93Y0cYkFcF1x58gI/9xE/y4APXUErymc98hr3dXWZZwcbakLg2buEpFVGkiAJYX18hTWJ6/YR+P3Za5VXOwcE+6xubLgFp3PM9nU7RWnPx4kX6/b7zMtjbo/AVPGVZIoMAZS1FnjP2ps3Xrl0j7ffp93qEccxkNqNqGmfqbNWSwVvQgdeuKiYmSWLCUDGdzhkM+xwfnfj+xmm4Hx8fI4QbL421RN54u+2vWwKQxVXs7u7uEkYR/+7f/Ts+9hMf54EHHui8Hdpx4oxf1H2IBeXizxcds/F7S4Z/O36AI4pVx2I1AAZ049mtnQeD61uUgjC0hKEj3GltqSsnD6I6zo7tumzTWLSxFHP3XhUIZIAHsJwpqZQC47/rxjGEq1a2SkhUAGHsDsJqwCog5fjkmM9+/p/SlFNefvkGtw4qikpzsn+HbDoGa70paOAYy45O7kE1TVPWqDCiKcqu4sdV8diWa+jxHbqFprVeB1pKpPSAvwwdE106bWsVKs9ad+x1916JMcLt3zbuq3EyE9a0LGLj9289kRHHIJYSKVr2e+C0w2XgjREVUTIijD2cKUHXFcV8TJFNKcoZRTZmf7RBPrnOzY98kEBJqmLewVaNbqi1A/+xNYECg5Oq0bpx8ydjGA42+ZEf/2keefghPvIjH+VDH/xR/uSzf8C//c1/zZ3X7zKZFtyua/73/7unUMogsUgFQnlZHwS6MWRZRV0bhJL00iEf+4VP8Pf/0/+M1dEqQgYcH+/yW7/xr/iTz36Bk3GOChOiOEUq0LpidnyX86sRZ3GA5XHYJ338z6YlB9GO6Y5JvDoKGaQBt268wv7eXVcdYPzaKVQYbRBWU5Y1ShkODywvPP80SdLjsXe+i/e8/yOsrq4wVxlZZqmlcNrZxj0PYvlwBIv7uQSig+n8A8BhDu4p1FRFzu3bt9i9e4e8mNGUJVVdOTlNpaiqxj1H3ty0zfsfHdz1pKuA4XCFjc0dpJRsbe2QpgllmTu2feVIJucuXCSKIuJoyOpwyOraGocHp0ip2NnZYm1t4GRKjCOkzGcTlAro9Xr4hwDhvREXkxQnkyPE8tBk6RQzhO9f/PUxtlnMbYR/jmREICXra+usr21w4cIOL7zwPLP5jP5whXe9813EcYQQrj8RBDQNhIHFoEFIlAAbeV196XwCXbLf4ZPOsFZhTI20DRZnhGuakqY2lIV2gDfGHbvFJQK0qzI2jbt/jW4IlCROQ6xwlS5hECIElBWUhU8qmgYVCaQyJGlEGEmyrKKqnIeUUpKyemsPp+8YRL+fpWb3ElaANJpUCUwwYDzVXX8qxbfLeX3Hn0gYSPKqJhxssPbwh5ns3yEsjqmzCUd1yZe+/BST2SFPPvkkVy5fZmV1hZXRCnEUdIvfNwLpy4D6orKkLXNp3vQ6Ly+I2/2GYchoNKLX63XmZ+0kcxksWQY9jBGsrq7y+OOPkyapy0AJ0wGZ7pha8wM8EKAxxqIkpEnM9naNUBN0FRE0BR9934CL62tc2yiRpWRlNCK3J5wPHqLXP+BgEvB3/+pHGVUpfQQYOLcWocKU2wc5E52zvdWjyULKg4JnX3yKJBhg6lO+8IdjVqo5P/7RJ6mrl3nmTy/xEw/tkIYZQRhSNS1QI2jqpptsJ3FCXde+DJ7u2i2fXzsRlzLwzEfP0sPJ61is027qGzZNxeU1Q31eUVYwz2uOCs3xtGB/UrA/zplmhkE84uErAx69MuDiVshG2iMQAiU1ihCtlzKP/kcZGkwlCcKEKj7lxRsB+3vOwCWKA8IooKwURlfkc8mGUvQHluLEUmUGXSnSviSILJMjjQxLBsMe61uCSBmSVDKftuVrGqO/8yflzZ9je+ZHaTRBu/A0NRowQtGWT4FF14HLjgqDEhqkwci+k0hpSiJpiSQgSrAa21RYo+mLOREVEtcxCqtJEg3WAeS6rtG1pq4MdVMyr3N0XaG19IOOpW7cZMQ2M/KyJqs0tYFaRDSqR5QOGKyeY3B+gwceHxEmfZJ0gJXKs0SixbOIM6lBQC0gszFPvXbE7t0D3vOuC8hgwB99STPZzFkbarScY7VFNH0StY628+/4Xrwd9z+WAc42wjBAiJiicIxljEUJV8zX7/U42N/3JZYRw+GIunEyMC1o3kph9Hq9pT5Jdn12EAQds85pdIeAIAhCX1Eju7790qVLvPbaa51JdZ5n9HoDTk8mYOkMMKuqIsuKDqCtqgKLJu31yLMCWIzfZVlhrWE0GpHNMy5evMjR0VEH0g+HQwaDAdevX6cs3YSiNZUMvOmbSwAE9HopeV50SYY8z0nTlH6/z+npacf6FkKcYVm3oHOapmxubjpQt24Y9HodK3wZSM6yOa2JlLWaunbATsuoL4ocpQIGg0EH6C6z1ttjbjXRJ5MJSZrSHw6Yzef0hwPKouwY21proigCYHd3t2N4B0HAyspKV6kwm80IIjcu13VDnmfMDw8IgxAVBp3UShiGbGxskOdOEuXo6KhjbkM7uccZHQUhCEsQKqrSGdy2IDjAuXPnOHfuHKenp5yengCu3WGFH/t8FYyla2NVVZ1hybf3sWX5L+ujl3VF1dQoHTKdz51ETpYxy+ZeazB08i4+KeLkiBJqWWGFS9TgjQ+XJWbejvsT94RBd/S9e4m32u7eWOHtVvYNry0+xs9R3+IELHCvEjJ/1nVGO0923xdHeLYE/Zvn6e3r3+p4F/vDy3852aw0TfnABz5AmqaUec7HP/5x/uW//Jee8GHp9ft+IemeyeEwQUpnIG0xCOkWuKOVASenx/SHQw8cSYyv5HFzlCmz2YymaVhdXe38IwaDAU3TdBVBeZ4ThiEnJyeuH9OaYjzu1hjGgtCWUCniOEEpQZ5ngCEIA8LQVd66NYZlNpswm0+9/JaTpAjDGKVCx+jEjUdBGJxhIAZBQOP9LKaTCWsbGxwcHPD5z3+ewWDAlStXOtPkLMvOJEzvS9yvxeP3EDz/XsvYvB33J6JEUBWu93SVz65qVQoWCW/PQm8aMFpgW78Az85useKOlW0dy1UpAcrh3g50x7NSja96d6CU9gAjwgHzDocXjvkpBFQWKS1WCp587N088ciH+e1/+z8yWhnx1DOvcppZxkenzCanSIkzhExSpEod+CwExjZI4eZ9TTUHq0gGA7LJCXGSooIArdvqlyUzTiGwKKp8Tl3mWGsIghhrDXVVAjkqdLIVpnFArFSKlr2PZ7SDIy0K6JjxDnhvgfEQJxcjsDZwhDBf6d5KYZjGYGyJZe6lSCqEFARRglQpUoZIFTFauwCrNVk2Jp+OyWcn5OO73LrxMkE0dMaeTUOtaw+QuvslBaggxNgaAWhbueP11fVf+OIz/PW/9Xf4uZ/5Wa5deYCHr/zHfPRHfoz/x//z/86/+o3fIc/mNFWF0/P21c/SjRcLE0pBlIQIFI++47184m/8z8iLksnkBq+9/BL/7jf/DS+8eAsRbRMNzhOmMeX0AFtV6CpHmJpeuuLbGN1g2TKeO/jco7jWk9WEECgUw0HE1uqAMNRMTo+Ynh5hfPVkK/1Rlu68W8mf+bwgywr/tzFf+sKf8MLzX+dDP/RjPP7E+xCqT54VyLqiqbRrwx4Yb8mqS5mlzoNoPN7l5OgGw8GAnZ3zSLWoYJiO93n1la9zcnLi8DAhMNY9c7JF5VmYzAqhnCSM8dJutaGuTzg5PcYawY3XXgFpqcoa7asz4zhm9+4dgjAg7fUYjla9P9SIpqwZn9xmOlGUZUE+L1FByPHJHnEcc/XKVbZ3dojjBKyXLjKmux++RS0mLsJLy2AQ3XXxf/KpA6M9AVFbUBWtLJKSDrR//wc+tJRwkM5doJVpsoaqsp5NLjGRREkHkgOd1n0QBijp5g/GeBBda6xpwNZgXQLFGIGutZ8LuTSbUhKLoSgaqsLQ68e0VSpOPtLdo1BJhBVUpaEsLI22SGVJ+yFWaBotnCltYwnDACkNZdGgVIh+M8bpG+IHRs7FCItCs9mPGBcxk0nps0Zt5yfehP7ynYdA0DQVUkVUIiDeeYKdh25y65k/JBQl2Ibj8THfeL5kPss5OjzhoYceotyuGPQTer1ep/vXTr4X5qOLjuaNDPXlha4xpitjb2PBLF9MIlvwuGV1LJurndGB8tGWby7rUy3HMttjmanmflcIq5A2wWaGiyPN+uMZwlZQ9TGJQQhDIjQPPyg4Pq5YGQ1ZsYIZFSqCJBlQyvfw1I2YF//5jLX1Me++so4c7nB0pHnk0XXyT32GonqEF77yGR577zq1fok7ewMOyzWuJA1Fbqkry3DUx4gFyNGa/jm35cV1eCOIvviSHcNUCIE2FuEXdzaQREiMcGBDaCyDHqwPBefrgmI7JTcrTCvLdJ4TYLm42WdnZUAsNIFpQDoQxqgYEcSINzRSKxpilaKbht1K8NmnC4wd0GhDmgbORdm6ErdsJlEKegPDkbWYMqDKI9JBg4ga5scRyrtDp31BSEWSGo6PNYNYu4yjDe5psXlvC9I3nougkj03+KMRVmOsoBYBEoO0Gokhsg3CaoSpgBJhayJziqkKRJWRKE0vDggClw2WaCwNpW7QtsE0FVWRU5clh0UrfWQpq4pGl1jlZodFWTGbZzT5DGs0jbFU2tIYiJJNBqvnGZ07x8pgFRWlRL0hKhmgVUKpne4f1lI0mkBCFASUXqe4a1eA0YbGVGgVYmWfl2rB7T894NEdy8/+RMrnP3+Fk+mMZKNAxnPSMKApc8QPTO/7lyPqqiZNF8xsay15njMY9FEqIE17SATTyYQkTpjPnIFmURRkWUZv0KfX73WyWk3TdKzsN2qIr6+vn9FEb2VYhBAURdmZh8Zx3DG8p9Mp4EDsra0tbt26SRAoBsMheVF6LfSC69evd0x4IQSj1SF1XdPr9VFSkedF93xrrUnTUSdhc/PmTcqyZDAYsLa2xrlz53jmmWe6Y2r1yVtW4mg08vIxDrx3jPUcoNNWb1nOk8mkY6a/+93v5plnngEc2z8MQ65evUrTNBweHjrjU29UF8exA/mzDGj7JqdNuWzw7KqxKnq9FGvxiY1hx6Ju71Ub7fg89AaxSNHJtPR6PaRwWulxHJ9JdIRhSFmWrKysdFInVVUxGo0I4wiEoG5q4iSh56V/tre3KcuS27dvd2aeFy5cQErJ5uZmp71+48YNAs/QOD4+QQpFEsdIJQmjsEvEtOPzcOju7cWLF1lfXyPLcu7evduVdEupUHZhUjWdTkmSpEtaGGM6eZ/BYECv1+P09LQDuQCmsxkqCEiDlKIsnGZ7GIIxzGczx6jXmtPTU1ZXV5lMJp10nauOW0dKydHR8aKk9u24L/H9DJ4tDLiW+U/L/1ve8vDfjCH+XY5vqaHcUSRbJrp10gdLzLeWjS78ovvipUtMJ1OKPOe973s/n/rUp9HNLVepE7lKHaPdWmE4HJImAbPpKWXpEqdZNufcuR2ef+FlyqJAKKcL23pixHGK0Q1146pk1tbWqeuGumqQ0pExjLHEsTPvE0jG4wlxnGC0oShKgiAkUA5ECsKQOE5I4gRLa/BpOtlKcJq0UgrG47Hr2wJnCGiMYXV1FaOhl/aodNWtZ9r+oQXS2+vbykgppfjC57/AQw89xMWLF7vE6yzPuvn6fbu/93Ff3+3e7btNZns7/mKjtxIRJJqmstjcVUy7JYclitoKd4nWBt1YisJSzLXbxjrt9DgRhKHTSVcBXl6WBbNXOODYWvdaUYgOLJfSEog2/ek0k4NAOr1uJemI7dbJP5weTckvz5Aq5Pf+4CvM8ob5dEpduzWYDSRxGnt2au2kpR2KilAghaWuS4QKAOvBc40KE6Q0ndSfA4EVca9PGPewxpBNTyjnM3qrG0gVkp0eUcwnJL0VVBxTzMaO5a1CorRPXcxoypogkMggIAxjrPHzHF/5b3SJbXJ3IR0i6iQmAIHs2NML/MBVTUqpCJM+QkiauqAqjgnilCDsefKjojfcpj/cpC5nzKcn7N66gVQBUdxDqgRtDHVVe0kLp1XvpFzcmt2gUYHE6BprIH/2ef6b//K/5It/8il++ROf4Ml3PIKSgvPntsBaL2tSe4DfAZUyUCgpQTnPIyEsTupd8urLz/M//uo/44EHLpHNx3z+C09z584RRm6wc+U9RHGMoGJ/PkGgqbJTNkdxhz+x9NWl2pewLvcDYB37fHtjwHCgCAPD5PSU16+/SlGWvl162VwraDy5tJPlwe3DVbs7w9bx6YQ//L3f5ubrr/KjP/6T9Ps9itxQ2toRbZqGuq4o8oIsmzObTJnPJsznE/KioKlrgjCkLDJW19ewtkIKS55XlGXJ+OSIMi8c8Gzd3NrijsHln1zVhvA4tSPEnn223djpqkwKX5naJRuEoNYNWZH5qgtQgXJk0dKtWZxKhUBIl9QeDkfYxjLJJ3wje4G6LtnY3KIua5I0od/rIWXYJTDwVRMWDV5zPsvd+kYQILBk2dSRAuuSvd27pL0+k/EYjFsvhXHIaLRGr9fHWtDWEkcJvf4QiVMScOa2mrJuMALm01Nm00OKfEYSKeI45Ny5i7zj8Se4cPEiYRABGmzj23YAugaPn9X5nKpaGO4K3OMZhgIZhFSVpZE12bwiCBVSOaa7Ui6RoQLBPHf3zAiNDAVhEFDXxpGxlOtPpZREoZNRkqliNq3OYK/fKn5gYByDIcZwbnXI7YOKouohwrZs5s1NZv48YYUrMQgDB4IGYZ+dx36Ug707VAfPIuo5RlimU8vLL7/KbDZnMply6dIFLl7YZnt7uwPSWyCk1cf9VkB6C6hkWcb+/j6DwYDBYHDGrGy5BLteAvSWS7mXgfE3gsftgqAFQd5sEmat7cCebzJXaUJErTCqxhAhm4bTo7sEqsfW9jqNyDG1RKo5f+On+wT1Com8TUmPSkSkUcVITYmF5NXbPZpTRfNqwLPPnfA//cmrXFuRrA4labzF7nSb3brk9ZMaqxXF/CYHk00e3ezz3HMv8bWnX+IXP/GzHBy+DrXh0Ucf7ViDLUD1Rmb+8jm6BY4DClpgQoglYxN/zjYIEX5wdxl+w9CE9IzFINnUmnpFYHRFWcy4s5sRRT16qaI/CEjSCGPdLEaIs5NZY0FZizU1X3ymxws3a0TkTfBChStbcdrp2cwilaE/dGUrpnKvDTZqgsTQ5JKbLwtEUHPnpubiuQGjVc1kz2BCi5AaSYzlrbNr3f1eul7fHG/IWglQonTyLaYmkIZYuEmKsprA1igapNAo4ZItEvelbEkjSiqTIbSBRpDrBmsamrKgKnMyq6jBsc6rEqNrqnpMXhTMi4qi1ti6xs4yitIQxgNW17YINy8RjjYYrG4wWN0kSocYMXR6YAgQjk0ucKx0Yx04p0yDEhYRuEllY3T3LLYgU61dKVyiJNCgG4uODeOmx9duFWzEig9/aMKtG32eu31K/9waDVOUrLB/yQxev9+j0U23uG/DGNsxytM0ZXtzk1dfeZW2ukdrzWDgTNW01uSTSdf/xnHc6YW3+uhtcq/Vom01uuM4Zn19nTiOOTo68uD9gMcee4zXXnuNpmk6ve6Wbe402qHyeuutrre1lrt37xJFMVVVEgQB6+vraG3Ic8dibicH0ut0b2xsdLIrrbZ6Kx3TMrcBer0e/X6/GzvSNF0yXI08kF13x9geZ57nHTsS4Omnn6Zpms60E+D4+Jh+v+/Y2r6CK/BA+srKSicX0o5x7ee6BULDfO4qO5yGrpPj2d/f70xioyji6OiI4XDI6upqVwUQxzHWWgb9AWEYOsPQLGc+m3VAe5vQHo1G3Xm0wNXq6mpnMFr6CoM2GbMsQ9OC+q0UzDIr/NKlSxwfH2OtZXV1hX1f4SCFM6tqQfwWhBoMBly7dq0b6xzjv8fR8TH48sh2juDmHkG32DHGdNddCEGWZR3rvk3YtAlox25193Ntba0D+40x6KomTVKqqmI6dW2zlY+p65rDw0OGwyFlWREErXHV9y/o+3bcnzjLPj87D33jdsa+BfPF3jvIea9rgOW59PLvb3x9efsFY30pIeCBlpZ53gHprfyLnys2HtAu65o4TRlPxjRac/HiJT704R9iPi+5e+uOl6+KieMEa10Cdm1txMnxPnmGZ+HN2d7e5hsvvkJe5ESxm9eXRcFgOKLf71MVJSIW3bgyGU9dyXhZO/1j5aQIAhWiVECRl07TNQy7Zz1JXP/lxjBXPVPVbb9gOxajO7+6qwJK0x5hELmFdGNYXV1jNs1I0x6ikqjAGQu3VUlvXF+0Y836+jpf//rXeOGFF3jyySfZ2dnpfDfuKwu9+/A//y485vZdB9Lfjn9/IgwFKlCYVJAMJFZbT2KrnWxBaSirVhgTB4Y1Drlr1611bdzrElTTaj+DVIIwErQ2BMa2mIOgqb3ZpxRop2ABWMIIZGBRgUUIZ3KqzaJTfu6FF7l9e8rz37hDWWrqSvtn1H9GZZiNM3qDnpdncFK2xlqKWU0QBg7AriuypnbJO6DMZ0gRoIPAg42K3mAFXeXcvfUc1sLWpWv0Bmsc713HWljbvMzK5kWK7BTdNKxtXqHWFVGg0NaitnaYHx0xmxwiTEPaX0PIAMzCDFK0FYBR4hjhQpBnx4RRjJQhZTYhjPqe3W28FKhG65omz8BKwrBHnI7QuqTMTgmCHkGYeBBUIqM+q9s9dFUxG5+STadoPUYo6eVonCQNxnmZNS14LAS6bjriqBUBZdnw2quv8bkvfJbBIGZtZY3D0wllkQPGS462DF5LYCUEFl25dUoUJ4SkWANBqHjfB97LX/25X6Cfpnzl6af5f/23/x++/NUXufv87yNVRBAFqChENzPQE1ZG510ztGeNwpcB9PbveO5zvxezs5WSxg5vqeucvb1dsixD163nnEQFAWGYEEUxKlBY01A3JaUHvdtojTStMbz04gucnJzwEz/9c6yub4Cu2N+9y9NPf9UR6hrtfHna8cMu9NylJ1FOJlO++tWvUlcVpnFywCoQTgbXtDxo9wzIbtT3ULrLEvl5j3sO3TEusQE8tigEnlTp8QQt/Xs9UFxrKEoPgrskDp49bozFIGiqAms0QkiefmbqmdSKOI7opSnCCrTRGK3Z2d5mMFgh7fcZra5y6+Z1rl+/yRNPPsnq6iZFNuPZr32ZLM9Iez10XVPWjZ8z+Oe5TXoj0cYileTChYvs7OwwGm2hZEKtoawNd++8zu1br3BytAvWEocB62tOPePpL/8pn/30H/ChH/owP/nTP8/a2iqmKZ3yRVNjwhppNIia6eQILRqCUFCUXgrbCrS11GWNEIo0jSiKGl0bB+4H7ntVNdSBJFAxSmqM0a4axzrJTKxTKAhDRZKEfv1vqWvnTXkv8QMDolsLsYLNQY8vPVdiWAMKR+uXBmGNr0G6XyEIwghhay9PobDDi1x4/CO8Pr4BeY4x1ukNAnd296iqioODPY6PLvHgQwXb29usjEZoYwgC6XVXXWsMPKAupewWCSoI/MLCsrm9hSBAa0lZ1szmc+azWadvWNUVdVXTNIaqMk4vSxu00UjoAIggDFFSEkauxqvny+v7/R5pEpL2eg6YEAIh2kyfcS7c0nUR7WACrhRHSo0WBVrGIELS3jbWBs791lZIIjSakchIlKIxAQQpQQlKlbz/UcU/+ns7vPK64dlbU24d9Olry6MPXuLJq5rDk4Ltcz/NF76esLb+Yb76WsTd4zXOnRuQoNG1ZmN9nb3dfZqqIs/mPPfM81y5fIXPfe7znDt/nmvXrnF0dMKli+fRTU0QLJyJWwaMlBJjtQcZBBJFbQ1CSppG05qXyKYgFgYCiRWuzM7IBLQmwBIqgVEBWo9IU0NtG0pTURpLMc4IZpY4jOilfYLAA1gOwXfac1Lz0i3Lr//ulHGVEgUlqnESKFpCqGIqPafIGyCk1zcoEVOXUOSazZ4g6luKfcntlw3Xnki58dKUqlKMhpJ+OoPGdfWujMeb2lrXKRtc0ghru8FASeU6TJzOZ7s4FBiwrswntIUDw30WVgqI7ARbVyhhiIR0xihCY+oKmhKamklTMatrTGWwlUVXDYUuvMSLex60rqmqjKaqHZBT1eTTMUWek5c1tTbIIET1tgmjVYYra1y6sE2yuokebRGEKSpIQUYkypl5aBGikWRWoGzudNVdA3egk4BQCYS1WBqsFA7it2C9s3VgnWN9u4hTgQPCdV0SSIECornChBPyIOGOyTm9MeXKaMyHtnb42ku7BGkKYQA4zeb2vH1Pdw9909tA1F9EuMmm7bS4F0lJKIqKICg5PDzsFvEtS7plBa+trZHlOUVRMJ/PCYKA09PTDpxswdQWZO10sK3tMuxRFPHII4/w8ssv0zQNzz33HPP5jIP9Q3bO7dDv9zv97ZaVbOxiUnh6esq1a9fo9Xq89tprHagJDlyez2ddkiAIAjY2NpjNpw5EHgw6gLbVIm9ZySBompr5fN5NmFtZGoCicLIrbQWVEKKTG7t58+YZHdwWKGmviQPgw85s1AHlqgPIZ7MZL774Ygfeuv3L7hl0OsGVT5g64HpjY4PDwyOnA+wTpAcHB50BeL/fZ29vr2Oo9wcDwjhiNBo5U1I5RnugPM9zkiRhdXWV09PT7rPaRO3JyYlLFBR5J4Vw/vz5TnalZd8HQdAZRg0HAxpfDdZLUybTaSeR05r0FUXVJSPwbSMMw06v/YknnqAsSyaTCWmacvPmDW7dvIVSkshXRLhkj2OOtvJySZJ00jLtvtukhAPr3fhYlo61OpvNKIvFdWjBvDROwFqCMGRre4v6rksOPfzww53s0HA4pN/vUZZFB9a/Hfcx7iuNVty/4WeZQW7PvtC9vERW+LYfZ+1bfmQLMtxLdOC5f2Nbiv7Gz2jB8JZZbt/wOkvHJfy1e+ORam3RtZNfbJqGpgGlgi6Z+ou/+IvcvbvH5GTM0cEhdVUSb6yR5TOKImNt7Yobf8rCLR6xRFGIkk5aJQhjkqTHZJp52RbD2oorA0+ShPl83o1TR0dHnS8FLCpYhTcdTdO0q2Z1xthzgjBChYED56rG9yFu3uwWoo4tmSQJSRKxtraKFAF17crowyDCmLnvz1WX2Ctrl1ztzI89uURKSZZlbGxscHJ4yLNf/zrvfe97uXr1KqenpwufhfsNor8db8f3QQwHLgGV5Q115UCfDpCTliAVIAVN7dbqUuCAssb3Y9J25qFBgDccXVTpN7X1sqLLfZolCITz6FJeL91rprfaw3XtNJSNsZjGYox7VotSs7d3i6q2/ni/uU93kKJ2gHDVIJWkKhqn0S2MB0sD0t4qYTRgNj2gKUvHmG1EJ0MiZcPu3dcxjUYFCmEbdJ1RzKYYbemPciIJx3evoxvLYC1j5+qD3Hrxa1RFSRCGbJy7QFWnVFlGPpsQ90dIGQDeyFV4UqYHR7ENVhukCJEixoG7CqFCBJIgrBEq6oB4Ywy2qaiLMUIGhHGKbTTl/AQVx6ggRWhBI928dbi6RX+0RpHNyaanVGUOoiCIYsfGV4GXs9C0MiEW6zV7FOvrW/z9v/f3+Gu//AkCCX/wh7/LFz//JfIsp65KjF4qHcA6TXXtSZe4KncHhsLJyYz/4v/8f+OrX/4CP/Xxj5EkQx5/4gmef3mfphEEYUKUJKiw5u4rL3BuNSVQbt3aFi8sg+n2TILZgcOjQcTOVkocLapJX3/1VQ72D2hqTRimrK9ts7l9gZWVdXr9AVHoCDUqcBKFdVVwdLjPzddf5WB/l7qu2rQ11lr29/f47X/7r/mpn/kZNje3GaR9yrygKku/jbsebvz3bVQ4FjUWdN1QWevAdvd4OGK0X3ssp0ndVTW+6s5Ju7TzBWec6zG0N5IJbAuVL6TfFklY6zGQs0Tb1vLRWmhqjVQNump8UsrpxbtpiJNXEX7fxjjA+NadfcJAoIKIze01qrLm6PCEsirY3j7P8cE+4+mEsqpQ45kzzNUucU2XqBB+rJZEUUgUBdy6dZO93V0uXnqQK1ceY57Nef65p7l763XqpnCseiGo6prTyZg8y5BKcHp6zB/9/ie59fqr/K3/4O9y6eo1dJ27BJWpsbZBkNDbuYBIFLqoXH8kBMZabOWSC0EgqIvGJxxBWElVOB8BpDNTNkI741ChqeuKMIkIpaIxhsDL4RVlTV01FKUmDBUiWFSOf7u4ryD6m0mH/Hn3t/hZ0osMvSjm5t0MjfQC+ppIev0cYlfy8W32c8+fTbugiLpnpraK7Wvvpj66wa2v/zGinmKlpqpLyqpASkuRz8mykrzQTCY558+fZ31jSBiqzmTMlWy6ch3t9VSrWpMVmsl4wtHxMdPJhPk8ZzbPyOZzhJDESUwUxwQeIFdB4EovI2doGkURQoVYq6m0IStzmmaKbjSZN4YrirwDkY12ZZODwYCVlRW2t9bZ3t5ka2uTwcCx8vpJ6MsnhdMqsjVaWKQNkbiMTpqmKCmxtkFXAhk5s09sSi5qMBGiLgikoLEx/R586FrBDz9omJR9TquQ6Wmfq5sJCTVGRMhgQNXArZNrxLe3GNsR17YtF9ZGWKkZ9Xtc3NmiLjJWhwM/ObCk6YC93UPSdMTvfPKP+Dt/+6/z7Ne+ygPXLrG+sUFZOaZl+3AYDHVdooSkzEviJMYIjbGaKErBSrStEbLVZbMoocC6jJZrHAopLSJoCIDICno2AgKq0iVKdKOZTzPCSHW6y86VXHF7D37tk4YX70Qo5TSRTa1QQUiYQF7MUYGlqmYYvUE6NKjQImRNPhOYRrIyjMhOZ1x6aIXRSkUUhWSlJh0pVjYqxnckUlgaGoSXQZDeEV3pGqzGkVMdDUFUBUpYIuXLl5oKYbVz+VYGgSa0jeNSGyexgtXkRUFZ1pRaM641VVNRO8cNBIKm9tlAawmEAdvQVAV5PmGezSnLystjzLG2odESZEwQpiTDTQYXHmZ7/RzxygbxYBWbjojjHmGYYExrivPNyTQ3ZagIwCXFlrsE0U2ZvJv7YgPpv1hmzJ3p5/yCXIUdL78JABsRY9wEFMVrc8NaecADOz1evpmhw4TB0BsKQwcu3Vtf9fYC8i8iqrrqjCGDIOjAUsfuqTk9PSXdOec0bJcY5C0jWQjZAeb9fp/SM8SttR1juJU2WQaglVT00pSmqslmcy5duoTWmvl8TlHkTCYTqqritdeus7a2Sp7nHUB9cLDvjECV6Bjv169f7wDoIAgYjyedpntb5gbunIqy6KorgiDgzp07zgBSa5SSHfvdsSDogO/WELQFh8fjSZd0EEJ0IPzNmzfJ87wzAm0BZSEWx9FWRZ2eni59tsI2utPsBjrplOFw1JnotaWudcdMcYZC+/sOMC+KgtXVVay1rK+vMx6PybKMoihYWVnpQPYkScjLopNDK6uqkz0BJzMwmUwIgoC1tbXuXua5G0+3t7cxxtBY05mUDgYDsixjPp/T6/WYT2cEUpHGSZdIiHt9Xz7pKiB2dnY6yReX+LXddU+ShJWVFdbX17uqhV6vx8nJCdPpFGMsOzs7tPJr+/u7BEHEdDrn0rkL7O3vdVUEKysrxHHMwcEBWutOkqYocu8D4IyntDZgJfO5M0Bq7z0AUlCVTt5ndW2Nvf39DqA/PT1FSsloNGI8npCmiU86vA2i38+w3zTbffN46638fPetxp97BS/fuN3SQrFll1sj7qkywUFI3wpIXzDfpL3Xef4SA922i13ojOvwj123Ow+b+7+3L4s3Y6e3/3vwwO3IooKIxjO5e2mPQEpOTk54//vfzyc+ccAwHfLUl5/i9u2bHBzuI4Xh+vXrbG+vEkZu/nhycsLGxgZHR0fO36JwSfg4SQjympOTMbPZXaSE8+fPc+7cObfeSGIuX71CEIXOKNn3XW3/JpVEW1dRFyUxs9msGxPqpmE+y9BNgza1r65dGH0GoSKKQ2bzSZeEnYwnCCEZDFa4fv06de2Sq9rq7no0RrO6ukqv13N9udN7cPJfxjCZTDBS8vQzz3Dl05/mwx/+sLt2w0En6/V2LOKtkgpvS7nch1juE870D9/mtT9j5FlFrQ1NbbDGLKpapEQCYaCIhyFYKLOGybgmM5owcizzKHIdlPRmgUot+ketLXrJE2thKOp+V4pOC91aPDOdDrxr548oRyBJk5Tx0TGNUQRRQF0V4MlUbT8ohCCIFHXliITbl66RZxOsPaapG+qiQquappZEcUIvTMFqhLTouvTrMU0QDpiNj7C+Gjzp9cnmE/qDASpw1XrWzGlqQxBHWFsxWBkwPtwjjGNqL/95enCHdDTENBohFVUxR6rAz249W1qG1JUnINSuuq5uNEKUBGFMPpugopgodKC6FGClcmOQBIKQIExo6oxqfkwQ9giTHk1T0lQnBFECeJ114QhUQTRgbauHbkrybE6RzSnLMTIMUWGCklE3FxRSEYQJaTrk2qPvJBqO+B/+9b/m6Wee4umnn2P/YE46ukBindyFChKUipBKEagAFQYEUUQYJMRJnygOCZTTgQ8CyZ0Txa/+5lM0TUWWVQxXdxBSOh+MIGZy/DqqmbCycsVVSvr2JYTs2smCkS6xaKwQjAYxO1sJYYCfo1bcuP4Kd28dEkQJF65e5cKFq/R6PVSgkMKTG6WvpJAQRhFpL2VtbYOHH3kH08kJL3zja7z28kt+DeCemPH4lN//3d/hf/KzP8fG5gajYZ+8KHASMK69tziBtdZLx7TscGhqVzHbnoebpyw/4MJPmRYTG4txigPd7x4QFwvZl3a+YbvdWSySrn5tiWjgjmWJfGBbbXeDEspplfvqTmvbOYdPBKA7IpIj6jqZFa3BFrkfQ932hwdHnJ6OXX8j3XFUvqrUf/CZaaEj/QmssZRF6QhRQcyVIOFkcsSXv/RZpqcnrm34KpgwVIAkjlOUEOTTGY7Qanj55Zf55//df8Xf/1/+5+ycv0RTzV3SyNRYM0eGpSN5CulUlqx14gHK4T1FVlPl2kkVhRJrLFXtqlz6/Qg0mMZQWUuRNahYEMcBgVDoyiCVdHI/pZO8iyOQgcJi3LzkLeIHhokeIOinkNcxh5MMDSjrUrHdQ/sXjC3FsqG0MRtP/gTzbMr4G39EXeVdQ5/PJUXZMJvfIMsyb/Z1yrlzO2xubrKyssLJ8ZzJZML45ITjkxNOT09p6tppY4U9er0eq6ur7Gzu0L8WMxj2WFtdJUlShBCESnWJRVeC1IBwAEKnke5Z7oiF6YGQfV8K7rQV6wbyUjObzTg6Oubk5JjDozEvvfI0JycnWGsJg4C19QFbW1tcvHCBy5cvs7GxSqQ0QWCcaQAaaS3ChigRgFIEIqAWC7bIsi78cqhGsRrW9NMCOVIEdYWteuyszPk7vxDw0Y9E3NizzKuA4JFVPvyuhOFw6lmOfT72Ez/K2tqIopJsbG7QNK5M/e7du1x98GHGWcGNuwdkleXpZ1/ioQdyXnvtNT74wQ9y8+ZNHnn0UfJK00sTwjAkSpwzcG1qglDRNIWT9IlC36ktMqx8iwXbGY15KyEOXYdjlXemXmQ0jTFIBFEY8KH3QbopeO3mlNcOLHdfbxBijShIkaHC2pR5tk8lJXKUYpKcel6STwLK0x5VOefae0LWHxAkgUWJAp2HaCUcY96URDReY9y5kqMNCE3f1kRo51Bua7SuqfNThr2USCiE1VjRkDc5dVbRNJoyK8gaRd1YrG7AutKzWocIC4FyLG/b1FRFRllmFFVOXuaIvKTMM4qmpBY1ZVNgmgCCGJUMGaxtsn7p3aSjS/QGIwajdXr9IU26ilWRy8Q2DcYaUqFc+U4jaE1mrb03uZrvVlig0nBQzImVJhxGTMeu9C9NQ4LIuGfYvhHdfzu+m7FgOTuAd319nd3dXdI07XSj9/b2WF9b6wDCoig6dq8xpjOZtNbS7/c7A7d2/1EUdfrTs9mMqqpIwgjltyurkm984xsUReG11WfUdUOjXRni4eEhrSTH3bt3uz6paRr6/T7W2k5uJPTl+eCelzRNOxmPunas9Nl0ypWrVzpm8rVr1zrZFK0dSJz7qqsgWJhRtmX/rYlo2++1yYKrV6+yu7vbMdfbr/ZaLHtttIzIoihcosJf79pP0EajESsrK4RhSJZlrK+vkyQJ4/G4Sz71er3OoLStJmhB/dls1oHWbfLj5OTEyxYkABSFA5dcwrNhMh6fAchamZujoyP6/f43yZ6Mx2OiOGbY63cyPmmaMvdmnFEYUlk6kz+tNRsbG5yenrK/v0866LO6utqx0Nt22EZ7HNvb290+bt682YHvTdOwvb3dXdPpdMra2jpCSObzjHmW0e/32dzc5NatWxwdHXVts/2sVo/YJX7cAtt6dtF8Pu/uEdDJ1bTmrGVZsr6+jjGGp556ivF4zOXLl7l16xbb21sOvKuqe8Zg3457jXtNSrzVuGLx6eJvwone+P1b/Y3u57aird12mYG+2PJeMW/97Sb23Z9a0Pre9ulWwGKBli+z0/HJCbuQZln85LcS33xM7fm1bbwtew6jRVVNHMdgDYK4S7z9/M//PA89+Cif/cxn+f3f/yR/8iefZnttg5OTPV6/cZtzOzs0TcV0lvPQQw/zymuvs7m5zd29A8qqIc9LtDEIGTAYjZhPJ5ycnBBFEZcuXeL09JQLFy5w7tw5bty4wXw+854bhiRJyfO5JyIowDi2X10Shooiz6jrBqM1SRyyvrVOL46otCt7lspV0B4f7bG5vUNRVOwfHtLr9dna2mH3YJe10TpREjGbzxxr0JU2dtVY8/mc2rPRpZTEYch0OmV9dY3TwyOe/9rXuXv7DhsbG0gLCoHmWyVV/vLFvbDyv5Wu/9vx1uHALf/L8qW2Cwbu/Yoyd4l4sAjr5khKSoR0rPIoDolCSRQF9PuW3qAhn9cUeYXBA4HS6yfb1pyRjnXbmY4asMIipftZa9A+wxmGAuGsFtzruUWppaobK/iFn/plMBWvvfSvwEr/Oa6qMOn3EUphvdRlOZ9RVxXBMCUI4jPXsp1ngGF2ckhkxtS5RcURcdpjdWOTo/3bGKu9hIe74EJY6qqgLFwiVgWKMp+TSoUzQQ3QdUmV5wSBcuCgteimRkmJaWrqokAFEUGsPCvbHZjRU6SwxP0hSb+HyJ2WuNVgEASxI2uWZYaUCt0orBBIEfhrW9FUJXVVuErzek6ZT1BRnzBOqasCayxSJbTV8VB6AFoSpn3iJEXXDWWRU+ZzajtDhRFBFLOycY7NK4+SJn3GheBf/eanEFZzejKmsqskvZQgdhrSxoIxDbZuqMuMQucY43TXO+11r40uPOgpFSAsusnRjTumFrQfjtbJZ3e4cn6DUCn/ELQSxcsYSTsYu+RoGsHmWoiz0WiYz6fs7u6xt3vExvZlLj/wCGmv77ErQVVmzCYnzGYnlEVOU9eoQJGkKcPhCltb26ytb7K6vsaP/djHeeyxx/n8n3yKg/0D10YFjMdj/uD3PsnHP/7jvOPxRzk+/RJN46FtYx0+I8ziAfaVcaadCyymBW3Lh3ZmY93vrQwQcLYfEG5+4cgC/r3+Iey2P/N7m9BvXzeA8nOahfG5OwLppGiXWPGLvl1gRbvecobBlhYEt12xodYtX0I4YN04Rrt7sTUgXSIH+L8J1zQwtqHRFoFESMmlyw8wGAz50uf/iPH4FKUCD6A7IpBUIVEUE4QxgRCsrTuSmjYaXVfcvn2Xz37qk/z8J/62W0vrwh2TkcRR7BUKPGVEClQosBrKvKGpNAZnICqlw9OiNOp8oVx3Kql8FUyaSIbD2Jm9Wt+GcVoLdeWqXCwuyRgEby25+wMDoksLwxRu79fM9AChLJIAwcKw8y96gSZsQ2MlJtniwQ/8DLd0xZ3Xv4qoZ4QYqqqmasAoyZ07dxiPx+zu7jIcjAijxc1IkoRLF89zbmeHdz3xhNM9D0Ap7RuK6BiQWhum02MmEz9Frxv/oPlJvZCugRlL7dlrQRAhcGWa0vVsiPCwK2sPQ5eNVKFktCJZ31gjDLawhGhv1qAbB7Bff/2A3d09vv7ss3zmM5+hKhs21ra5ePE8ly6f48rVS5zbGtEYxzILQ0mla++IfdbU840xMwFSCaydE0lJLUNCY4nQPHR+zsMXhxgSynqGEiErPUs5LZHJgMZoLl3e9myYIR/72F+h1+txUZ+jaUrW1kfEscKYisFgyN07U1ZWNzg4+CJN0/Diiy/S7w/4wlPf4KEHH+Dq5QuMTw954NpV6rIiGSZUpkIp68HvpSVUN/DxhtdctICRMZYoCpzDr7VEyjmOt+AOOE3QpFfx7kc0166FZMWI+anipZsnvHTzEJ0nlOEISUljoa40vZ4hCAoa02f/tZLZ4R4Xz43YXg+YPndIvJGzrU7Rd29ghWVjMmfDTgmokNYSBgpLg0UDDaZ2pUuNrqmbElNXzLKa6eSYKs+d7paQaEIkklAGYAXazKibgrrM0XUFpiEb79HUtat6KEvXWxtDbTVWCYI4IU0TprpCJlucv/wEvdEOyeZF0sEqIu5DmKACp1MspMRKSekuLKauwWhCKQiUwpgIsTRoWLtkPvL9EhaMFoTxgLLWhIlgI+ihSwNeM15Kwfffgf/lihYs3NjYoNdbyGHAwoSyLium0ynD4ZAois5IdchAcfHiRebzOSfHJ0glOXfuHAcHB2RZ1oHOSikmk4lL3HkQstUKB8Hu7q6bYGhNnheAM0rGQpbPO5Y6LPqdFqAejUadzIgQojMDXV9fd2zo+Ryw9Ps9/9zAZDJhOBwCkOc5QRB4ZrMbA1wJf4hUgrbkvi3Lb9nc7XG0GtrPPvus12y3nbnobDYD6K4B0CUgqqpySYSyZDQaMZ/NsNp0pp9RFHUSMFLKzmy0NVNt2d+tpEzLhNdaU5ZlJ4PTMjPaxEMURWRZxvHJMVYI+v0+89kM0zh6VitBMxqNMMZ0wFerTd/r9TqWeJIkJL0ea2trAJ32eFVVSKU6ILnX65FlGbdv3+bw8JC8KLjQ73Va6S0zX0pnet1WTW1ubnbVbG1CoSgKZrMZaZp2lRFVVbGyssJw2EepkLpqGE9O2draYm9vrzMt3NvbI8uyLvHhWOZ+vEJ0YHpbNXFycorFse7bNtuasLbVBUVRMB6PSRLHPHfSEa5tOIbqGxcmb8efL954MdtF1Vtv903buBXXGxHx7ns775R+obZgW7WEDvez9Pi0xbC0RO3MbfGLe8dF8MCFB63PEC/8GlxK5aoe231127RcBoGvxF86S/GG7+4aGOvMvTy1xAFJWKxwC8L2RCUKKwzSSv7/7P1ZsK7Zfd6H/dZa7/DNez77DD1PABogQIDEQFIcLIuyRFGyLMmOEjtJJY4vc5FUxcmFK85VkirnRkmlklK5KrGSWLIrclnRUKIocwBIEEKTAIFuAD13n9Nn3PM3vtMacrGG79unG0BDbAKC0Kvr9N77G95xvWv91/N//s/jZITQPXgunSSCzf6SbBjN4S6Nyb5KxqZnWGvtq9tCLN40DUjN4089BSrnYrHg69/8BrOqpj/c4t79M4bDLYz2TPPz8wqtFVeuXuNs2nAxW7CozukNhsxWvupmOPKVg6vlnLpasbO9xTu3bvLYY4+yvTVOC+a8KCjyjEwJurama3MG/YJ+r2A2X2GswQkJ0nlwJZdcOTz0CUapGA4HSCW5mE5B5Iwm2+junLwoabqOuw/ukxX+Oqo8J8sVFj9+F0WBMSZJZJngN2O0oSxK5os5vaLA9nqcnp7ye1/8Iv+9v/k3KVRGg/CSzekWbw4q617g3qvbv0dzeD3o79c+qErrS0SbD2B/7/eYPigprR9HMP7h673ph/DdEgziobFj87upl22g6B8Enm6t8bIE4dhkWMMLKVDSmyjWlaapu8T8neyUDEYZ1UrT1C1No4E1wzyyQYUM52uDdrONfdGfnx/PBNZAY/w5qmxD1kWAQJKpnMODfY7v30V3DqEUtjNYA0Wp0G1Hb9wjG5RgDPVsTlHmobolQ5ATE7YPX1MRgD6v+50lqZWubsmKjCgvY0xHlhe0dYXR3hMHkWFNQ9c0CJklg+K2rtLz7e+39pJ1xmB0HYDtLlwDiTeidPQEZLnE2pzdw8cRIsPojsXFHVaLKdYYuq5Z46MIuiZ6KvkMgd2Q3xbNlLZeUQ4myEzSNisQgf6/Rls961oJMlUy2t5j4DSr+ZSmWtFUDUfvvMzJnVe9xI0S5LnPitRVh+7CpChzisE21nY462NKP1+HvutCojswmL2cqEuYjelqunrl35aC4dYBUmUwvcPuyLE1HvnzFnHdKtZ/u805EITr6JeCtlmiVI/bt97mYnqKszmPP/1Jrly9Qa58AmI5P+Xo7k3Ozu77ZMN7PVAClJSMJ9s88fRzPPvMR7nxyA3+8r/91/jS7/w2b775eiJ/nJ6e8Du/89t85mc/zZUrB9y9dxxOWSYg3JOSvETRZlz0XpjOuq0JBzG9dKk63cXevD7mdP2FF8b192A9dshwCywgkfHw0nYSliY2ZFpCn1lvR1wC+AVB8z3GcEIgYpi3cXAxUnP2clz48Ki4ThgIrLMoFAcHV3niiaf4o69+mfn0HCUDmQkYDbd4/KlnufHoY2xvT+gVJULAajnl9q23uXXrTarVjHrV8kcvfI0rVw75+V/+txAywxkvqSuU9IRM41JSQFpfQYcQEVpHG0Mv9+A8YbwUTqGURGtfCdPrKYb9DOmkl6XSFm0d9cp4o9GwH9F5Il1Wfv+57scGRDdth9MN3755l9o+i3UNmfBSK3FRfFmt6INv2sKgzGm0QU5ucPjZv05b5jx45QW61SkWg5NgGpNAjdlsxvb2FteuXePatWvs7e2xs7PNZOwZcGfnDzg7f+BLekJGz49H4hKwpkLQbcNAJYX0g6gQKCExxuu3lkVJVpoErkvpP2e6NjAsJUr6bJ8UJFACvA52WXrd1UxlbA0yfuaTzyE+9REchDJ3zfHpjLffvsWL3/oWv/vF3yfPCp77yDM899En2NsbMtkaUCIfSnCszUpjK/sGrR2ZHpJLCbID0WB0iXMaKSoyB7kc+sxpJ2jNBL2o6Q1yrGtT8F2WCucM+/u77OxsYRH8jb/6FxkNhlTLCpqKUb+XwInRaMTZ+Tm9/oRXXnuLq1cP+eKXfp8rB4f8d7/5JT71qZ/i6rUDzs9PObx2gLFmY7FESGJ8796WZR4ocmFAnU7PfRARwC2lFFiBs4qxyxhlAjM01P2KKxPF88sRP/1ox0uv3OHF1zrkss/J777BVlbz8aJGPVaTiZYBilHVIN6YMrD7dPcf8KiZUL3zACMVuRG4rqM1hlp7k06tDZ02GG3RNgZ/nqFunUXJHm3bYLsGZzRt13nZoWbFxdkRdbXEuaD32zaYpqbfKynKEbIcYvtb5JMBqhzQH2wx2jqgv7XPYLJHlhc02uBkTm8wQRV9Wm29ZiZxwrIIvN6zM/65ysPELzMFQtF2OiynggRN0M7zWu//6jQhBKXLsG3QtNMNuYSs75lfLkERP36Lk3+dmrOO+XxxCciJwGxd10GGRSRt8+VqyWA4ohfMXIzRdG1HJhV55rVel/MFw34waWlaOt3icu99EX0r8qLAAqumDsnQLoGpuKi97bVnpVAJgIrBnycZCJwVzGYLr5tdlGijefzxxxFCUFUVUfYkzzOUyrh69RpKSc7PL6jrmp2dHU5OTtjf3+fu3bvJw8MHRI6i6NEkp3ibJFiiPFgEfwnnliSzrAezI7NZSsloPKZXlqxWKx599FHOz8+TlnzcZll6+bJNtrUQgsVikQDxLMuSbEr8TATHI+MbPCvFm2h5qZ0I4ETt+ul06nX29Ppet23LYrFgNBp5tnYAk6uqStq9EUAfDAYe/K8r5vM5o9GILMs8yF9VnJ2cBA8Tzfn5OcvVkuXKGx1dTC+omjqxvCM7PyYqYsLh8PCQ0cAnA8aDIbduv4MKEjnRCDZWA8TqgKJQ9Ps92q7H1paXALp58yb379+/BNqvkzjgnR0C68auwb/j4yMefeyRJNcymUyStvy1a9e4uLggSvlEuYcqeAQYY/352bSTD9sH0DYZ3yKAvHFBH6/xJQ3w9J5Ln7l0Kzbo4ZFsvUm6FnGh/dBxrD8TF5TrBWJcPEZNbwtrcD3Et5H95YFyl36CJ9BsLl/dxjFGAMgJD6T7720C6JeBMBdoZPFaxWPz18GlRS4hwejCqlbEoxPr43I2yCYE0EAKgRUCeeloo4Gf9ypC+vOP61o/LkJRwpWrV/n5X/gznJ6e8N/8/f+aTncsVktWVcuw32M43GI6WzEcbeOcxDmZKgHLoWIwGmPTytubAp+fn3Ht2jWOjh5gjGE8HoVkV0VR5CEBV4Y5p0MbL9kynRlPshACpQTGeNPkrutACIbDEf2BT/z5OadEyRwbyDu6adeyMApUJinKtTRmURQMBoOUtO26DmeD6akQFHlBtarold7g76Vvvsi//9//H1CvVl7SQYb7LEhA0Lr/pY7//saZH+Ok3vsFtH9SNOT/ZQH+9wLSv9eW1sPduu99EFe4Xq3nYZlJwhIxEFOlZ3bi1zjW+TG/aQzWWWQGg1FOb5BjtKauDU1t0J0ngkkhUFk8XAcSRDAQdcKbzOe5QCkHeHZ5BN4ggLsCdrcmfPObX+Xtt+7TaZBKYXXrz1964Fk3C0y38GufQYEJ0i2z6UWIz3pBMmPzGRWexKcUUuZ+u2HtrFtN2SuRSnrCV6cp+kPqxRxnHUZ7bfW6qjDaIaTxm5XedDl2/7zM6Zo6MLD9OSkp6Vw0S5a4YMY5PT1FXZwhpKSrl+zu9GlqzWJRU5Z9pFA01QrdaR+L5gqtOz/6Bo0TJfz4j/P3UypBvTxHiJyiP0F3rT8WpGeM27iedeBWiMV6aJNZluZR6ywYz1HrGut1002cKAU4jekatK4RUnjWeQD2/WeiEaYfL1OfwB+8015jGuHXRdZqsB3ohsMrT3pFg4hTCYEM/dJf08QZxrmGdvmAOh/S7+1w5+abnJwco4oRTz7zSSbbu6hM0lQz7tx8hbOTexC8NoTw2tc6VElIvL51nvl9zaYXfPNrX+Xm66/wyU//DM995Hl+9S/8BX7vi7/Dt7/1UujDlrOzKX/89a/z+FNPcHxyig7Gu3YjoUAgPcpQ/RFlxx5u61jKBqxCBEA+RC1uffNTXKDiMyZSfJXsQ+PzFatCnCMq8gcVXP+aEFh3uRpufZXXU50/ahnWhKGfhO+lW+/88x2yHoFMJTbiqkCI2EiOpO3H47cCJRVFL+fKtau8/O1vMj07RimP2UhV8vRTH+XjP/UJhqMhUngCg3UtZVFibcdHnn+en/rpT/P1F77EKy+/wtn5lFe+/SI7u7v81Gc+j9Mdzkqc7dbmr8ofj+ksNhAfbJC9skbQNJqiUOjOUPR6SCE9zuUMWWCzV42hbqsk0dNUGmcEWIHIvL+ECBU7bfP9R/U/EYj+w8xIm+aCB/cX3DyR6HZOJ720SWZyrFNY0yJNh1EyDcpxUBT42qSYdTVEk0mLkH4RIlGXd+gc8qHAQ8gMqzWZA4sknxzy3Of+MsO9R7n/jd9gef91JA4lQGSZNwBxlvl8hRT3aZslpyf36fWH9Pre1HMQjD3zLCNXmTcDVcprfDnrJz6lEIH2ImWegGkhBJkSSOclBLxpmaWzGhcz2CFT6BfbEmugcx4QLlROh04ZbyEsTbX+WymFVPPAcCvIspzxsGB7+yrPPnUVa2GxWHLvwRk3b93mn/zj38AYy7Vr1/jpT32Sxx+/wWRrAKJDWBAmlk4AwmEbr0WeywyHCbpmHUYqnOghhEUCnetozIq6WmDDrCRQPssmHMa0NMuWrd0d6qZCKj+IPHJjn1yWNMMxB3t7CNXwK//mL1CO+2ztHpJnYw6vZdz72k1GwzG6hVYbjqZTXnn9Jnk/55/91h/w63/xF3nlO9/hIx/7GJO9Mcu5YVIW9HolXaeDtrlMZTR+EvWTS9f5hIrWmvliwfHJMskOAAyGBV23pFpq6rpltVqwMBWt9k7cbduya1b83LMZxipoVhRdx2jHL2ywgm5lWExXNLZB65sY1+BsgTYtXWODprfBhMJoiQLrvM5l12FNxWJxQdN4CYm2bejqCq07/3vborsWJa1fUOkW5ww7j36BcrBLVy/ob+2z/9izqGKXrDdA9Udk/RGqKJGy8JlF5wdh6xylBCG8xr6lI8u11zO3IJxE4s1AY7Y22Zq6UP4n8RlP6QKDTiOtwYiCVg5QzpI5TeYsJmtxImrZe1MOL6r1Qxq/wiI6gpEEcwxnA5vkQ0TpX4nmgLqqsUGTNYK2ZVmmJGBRFEghg1wING1DlmXsbm1x/OAIHWROHn3kUe7du5cY0WVRJF3w2XyG3WByt21Lpz1YuqyXCSSOjPLIqjDGp4nj2N/v9zfkUSKjwpKpjO3tbe/Kbi29Xo/ZzGuW7+3tJea8MX5simaR1lquXr3KvXv3EsPeBPNL5yzL5SLNPfFnZOTPZrPE/M6ybMNE1Cb2N8LrPyqlaOoa3XWBOTni1q1bjEYjfy02QPOmbVFKcXZ2lkw1nfNmqfP5nKeffjrtNwa4XrfXH1NZlgyHQ+q6Zj6fUxQFi8WCyWSSZGkWi0Uw0punex6Z1V3XpX33ej12d3eTRM5mkiUavYpMpfsQmeeZUmxNtpIp7XQ6BSGYzedpblgul5ekhKLeedu2aa6YTqesFksOr1zh+PiYpq7pBbb/crlkZ2eHo6Mjdnd3aduW1WrlgfTSm9VeXFwwnc42NPxFAtBTZZTxPJi4ANg8Jl+dYBNrfVMTv65rhsMh9+/f5+rVq8zn80vP1mAwYDqd/VCe45+k5hJ7TWws4hLt6fJniTF7jMHWr8cXfpCZ6LsvLzYA9Id+hvXqpU+GEyAuW50LJckB6HYb55K25h4Grt69x/c8sgAaJCWX8E1BnJvXpdPrrYp18sBt7ikcevjpDdoJuNBaL31tGR5N1tbfinejblvKXp+PfOyjCPFvc/fOO7zwlS9jjGNVNfR7fcqyz/HpOfsHB0yns6CNm9Nqh7GO8WTCcj4PC3OJ7jTn5xc88fgTDIdDqqpiNBqxs5Mzm3lJiNPTE0ajoV+c4tC685IRRe5lXJzxsbYgjZV+3Mj9PrRBd4bJeAvnQGtvLhhloWTQ0i2KPGiymuBlIdna2ko+E0DSaddak2cZbdOQZxkmJB7n87lfl+QZkQC57tNrIPRhlvH7aT9sdvWPgs3948gg/0lrzvp1v7MOawyiEGSZSGta55FzT2IIeHuWS7JCIjKJNgbTeWmkLAObS1rrgn+ZC3IWm/sLyyfrx7+YSxcpgwoRbHVGkGUF4/6E7ckBZxdvoI2XT7DGe9FlWUFe9KiXs5Qk7No1w3pxfg+hvI/DZG8bIRXWGKy2OGuoOs+6X80XZEWBcwprHf1RD61b+pMhzvgRWXcalRfkzpMDnQHdeY1jT4h26LYNYJkgLzOKXsFyukxeHFFvOx5grJTyOLMLCQyDrpaokUVXHaYxUPbS9xKz+b1mz5B4cA6yoiTrDajnM7q6QciKwdYuTTUny/phfNR4Y8UYzzp/kwQIF/pEJIqFdYGfK7k0R0rhSYXp3gVyy3o8dOka+bF0DaJKmUEu0LGDOZB0CLfi0RuH9HtF+M66qlPIOF8meBicQVcnKOHH8+nFKcenp5S9XR5/+qd8JZNwnD54m3u3XqXrKpQSNC2cnNUcX1Qslh2dtphwnJkSjPo5O6Me+3sDhoOCxWLKV37/i5weH/O5L/w8X/i5L3BydJcHD07THHB6esHW5IK93R2OHpwS5driRVOZT0j77Oxm/Vy8x5uyLetku5cmi1V40eNsQ/ZXBACcGGfE3a4B6ngfbYwbgi65FC70HX+swgqMDVV38rIhutxIIsf7mWU5SiqyXNErS6RUzKYLqrpKcY0fA2JQIkP6XawTgzGeEb4zx2SLvxTe9+Tk6D5Hd+/6xIR1DIdjxjv7IDXvvPMyWVZw/OABF2enAPT7PYq8IMsFn/jkZ/jVv/Dr7O9v89WvvMDxyRm/+5v/hGc++in6/TzdIykVOhA+ozpO17VY7YLHJBjrMBqMgiIvcVbRtd721VqLQQSSmg2ELu/55An9AqQnc5hOe211A7b+UwTRf9gTsrVw63bN9BzUtKE/GGPVCTrv6BzorAdCkRMGlzB4WBxGeFmICGxmRvsNYsJPh3SxHDMuSOS7LUpDlmmT9GCzGzz2/B5DXfP2quH04hxEg2sbnPMarCtR8HNf+CxPPHGDl156idnFjMVSUJQLsiwLJdolZZEloCDLgqYQfgL1DA2ZXLG9vnko+jAebPFaquBkBxufAcgys/G730+t60uAvExAfRwcNdAkxl3cb7/fT8c5HvYYPXODp59+hD//q7/C+dmcl7/zOi989Zu88MIfMxoNeOLJx3jqiRtsb/UgwqEChFCoLMMFSQvjBJIci0WoDGcl1hoyKRB5EY5d4aykqTuaVR30dCWdbui6liwvUVlO0zSYtqVFY5RgpSsWRxVVlXH29i12d0qkqdBmwtMfeZxFe8rBIzvMmyVXrl3h+OSYydanuPfghJPzFYtK8p2X3+bJp6/yu7/1Vf7qX/013rp5i6IsODw8ZLlc0emOpmmomwbddXTaUK2qYJS54mI6ZakNxvp7oTuNUhk4P0A4JzDaYdBeWqXraNuOumlwDGjbDq3D61oHwxeRBvNMGCQNxhp05wGhbtXQNBWdrmk7byzbNRVdU9PWK3RTe907s2aS+pK/JgBgKj2AxniX6iwvUVLQnN/iYz/7S+RP/QJC9RnaJcZ6fTkrBS5kT50DTIfywxidLLFIz/ByXraokkPf1331sM+GC4XAorAh82tw0iKcQRo/wTey9Fxu4xlZqjsna76BblqW1YqubtGNB82effZ59q/cYFE53ph978X2B9rCfEmYRD2bZG1sEttPClvoX+VW1VXSeHbOJWmTPM9p25YyzylKL+Oyt7eHCwsaIQRbW1vMZzPOzs42KnxIY2ZZetO2oiloQ6lpfD/urygKxuMx9+7dC8B5BJ/WP+PcG5nLRVEkSZUoU3J+cc54PKaua/b29lJSQErJdDpNbO3hcJikTlarFfP5PEnPaK0TyA4kdjOQSvKVUpcA4GiaGgEUP665dJ4RfI2M9a7ruHXzZpJIiabPZ2dnZFnGeDyi67qkMx6/I6VkZ2cnnXcRkhSDwQAhRDJ1tdayXC6RUpLnXlKtLMukOx5B86gJv729zVEwyIy69k3TsBN08E9PT5OESZZlQVuYBEovFr4S4Pj4OIHP5yenlGWJlDIB2AjBaDTi4uLiUrIimtlGeRqlFAcHBwEk38VqnUD9vd09DA4TgO2TkxN2dnaSzExVVWit2dvbY7Va0rYNs9ksyfHkeX4p+WCMwVifuI+GuMlEFEAI7t27l7TXnXP0+31Go1G6xk3TpPtkreWZZ55hPp/7eTEkfD5sH1x7r3HhPVmVG+/Fv9esqo3Pfp+wPmL0zl3+7LsA7PSLX4X5XUXgOyxI0zFdBvLT98NOImMsgQTOL+42tUQRIknDrIF5+RDgHVnjXJr8L5Wgh5/GveuILl0a4TyzLJnuOQ88CYdneHoKX1rkRua0sCIlPCJ0j7NeW1xJirzgxvUb/O/+t/8p/8v/xf+c1195mcV8jrMGJQWz6ZRHH3ssJR21hdOzKW1VU5Y9hPP6wFjo2pbFfEldt0wmE46Pj+n3e0kOqijyIFMGeVam62OtZWdnj+XyLm3TkWWQqTwl/GLSLnooDIdDJpNJSm5uJtiGw6E3Ny0KmqZLYy5wiYkek5FCCJZLTzQZjUYp+TsYDHj77bd59tlnfaWN9ezb94riHu7rH7afnPZ+cIkPsl948PKD257XB/drJmcsuvX7yPBa58bYqOAQEt4CbQ1K+0HIhucX55myXu4jMFqDkbOzYfQJYF3cngsgnnO+Cn59nfwYK5Vg1OtxeOUKjz3+MZT8w/DZPIznIFWOzHJ6gz6D8TbHd+5cut5atwgjwC79OCwkzq3lLSrtWeAAbd1iWk+AynuKvMjRTYu1oFtPuir7BWXPJ+jq5YK8V1L2t3FOU68WZEXOaHsLhEVJwWq+8tt3Hjgt+6WvVnQugO8ySFuISwO+P1aBtvG6RTkJEk6zKQn1sBQIQNdoHC261TgE/eGELFPUpqNpG4r+BITCWunxJ7uu2fKJFY8g+qnRS3lEFvU6DgBwoVo7gvvgpNuY4/y5CCHAevlTqTwU6GWDci+RE9D/LBMIGg52d9jb3gpVVA7wDF9nZZIOisC/AKSrcK5ie2uLtqo5Oz+hKMc88fQnGAx7CDTvvPUaZ8c3EVia1nH73oJ7R0uqzgSN/jifh6Rz51g2DccXDbeP51zZ7XH9cMSoL3nllW+xquZ84ed/gcOrO8znvooLwGjHvbvHXH8kShEalIzPmgg/Q6W7EIG0atPrPmGwPsckAyWll/uV61jKs7rX0iNWCP+MiSjvEjwPkiWrTWD6mlSXsu2elCsETgqk76oo5eVUhIAsxS1+DSKVTyplKvjFhUpqpTLyXKKNTxj4NZtXofDYp49JonxxJE+Bh0mjbn4YESh7JU29YnoefJ6sYWv7gE995nO8+NIfcf7WHcBite97Xloa5rM5WeFlPF/4F7/HZDjgF37pz6GE41svfYvl7Jxv//EL/Owv/ArOakD66gPhkMpjZH7hrcgLia/K8f01yxTOCbQWWNP5Z7wnAEnXgJA+yYYDq/G67kL5cdc6sjzEq9ZgdQpRv2f7sZFzyUd9ji6O0fYuzz95xNNPXaFqBGfnFqkbshqEnJAF6FsEZp4Rko4MXyKyoZsegPJUXhHKBS5VG4uHNeQeAr0AKxVtq7lzfMLKGIrxNovljHo1Q+gGhWV/f8Jf/vW/zMeef5pf/dVf5Utf+gP+6GsvcHp07EGKft8zzvMsMd7KsvDSLCpLoLaUAim9BEBctDtrwBqKIqfpOrK6RqjMOzGHz8GGdEAAR+q69sz1jX9Jg23j7/haBEmiBq3a2H5eSAbDATZvmIxyfvmXPs2i/ijWOM7P57z+2lv8g29+g6Kf8cQTT3Dt6g329w4Yjgo/cTlfnlkWOc4WOF15s1KRYRwoq72hamT2KMhz/8Bnymttbu1u4ZCcHE95584Rb751xNu3lty/WLE0YxbtAGVhNFLsXyl4/rkdDiaOgx3DE0/t0jRnfPSZTyL0gBvXrrDo9bi4OGFr0uc7r9+mWhi+88p3uLg45uTBKf/oN36Li4sL2q4NbtI5QqpQnuslOpx1tG2XmHtdp6mDPEM05KubFh0GNMIApgjlU85tlE2tGYBaa7q2QrdLmralDozHZjWlC2xyb3rXkUvhFyTDEUJC07S0uqOuVpi2wegGQ4ZSRZr4R6MhmeyzCGxCqSTGkiZ2JxRN16G7u7zwW/8l+w9OeOozv8pqcBAkiCwh/+cTWsbrqAaOIxg/sSkcSvhFrbJN6MutLxtzGmE0uq0w9RLTVjTdEt02tNWKbrVENxWiOadrW+pqRVXXdNZgipy8HJL3J2TliHE5xBrFm7fucVYrZDnE8f0NIz5sP3mtbVqcs57Z4jwzZFNjvGlaus6bdF5cXPhqouGAvd1dbt28leQ+7t+/j1SKvCg4OTmhyHN2d3cZDodooykC+3i5XCa2dDTxjIBklCLZBMUiOzgCoXFMb9ugzSh8pn+5WjAej8nznHv37lGWZQLyT09PE5s6sr8jczAC814f288dm0BrnA8ik92bny6Tkam1NoHVcRtCQFl6yZv5fJZAFOcc5+fnASwfp/F9Z2eH6cUFo+EwlP7ZpDnunDcIbduW8XicjnvTSFRKmfTBz8/PU8Ig6q/3ej2yLGM4HHJ8fEwZZGVGoxH379/HGJMkUmKyOOp+t22byooPDg5YVRV1MGJdLpf0hwOm02nSKJ/P5wjl72UE4JVS1G2TZFciM3M4HF6acweDgdek14ZMZQicB75CQqfrOsp+j+GGfn+mFKvlKkm7RKmE2WzG/fsPMMYG9lWQwDNepkZgEzsrGtJK6eeqeI29RI/lzp27SCG4es0zzsuyTImaCJ4ppRKgH6s42rb9IT3FPznN2mgaFRd77/49gd7+zzUbCtZs3vfr8vk+mgssLIdL1XnwbvDKL1rXjJSAB4R/66XzGix36bhtAAuAtK9NPDVi9u+Ct4KhXjjrxMbzvz/86ffWRyWQbXCe4Rkl/iLLP+YHPO4S5FusX0yvq3jS0gMhLG3deCKDUozHQ0yb8bf/9t/mb/57/y5vv/UGg2Gfxx57nG+99BLGGKbTKY89votz+AoR0zE/P0flXq4xzh1KKd544w0ef+Ix2rZNZsw+4dVx48YN3nzzTYbDIYNBH5V5I+bHHnuM46MTb1hoLU6ux9bow5BlGYPBgP39/ZRAAxgOh2kM2d7eBtYJ38g6j+N9rGTJ8zzJS8UE3cnJCYeHh/R6Pb7xjW8wmUwYj8dUja8WI7D+LmnSxr6wvoMP94B3tYcrC75b20zk/LDaB0lW+1EkFT7I4/9JYNJ3rQeKlPQgrVQe5NbarVdQ1oVqj3UVkpQkyQPvs+R/9UCfRVowAqR0CCUCPhKMQ7Xz+sAOrPFjlsocKhP4fL9/QnJRUGQF+weP8MTTn6RpOkAiVZZkBYWUgfUsw5C9CSz7f97c1KEkCCUZbh3grOX85CgB6PHY66pBSC/ngmtQmTdZdc4fX7PyZEWVKca72zijqRYX6MA27ZqGrAiVLp0GC1mRkRUeTzBdF0A+H+cOJ2PPBmeJFP6a2zBPzZYaY0GoHIsM5pyGvPDAXZ4XNKLzuvbrHHGKa43WICryfoFUBXnZC9/vUS0uMLpG5UNAY51EEoxhCWBsYpOv1wURQBeb058QyNzLAQaNkKDeFg/I/4uYlxACGcZlrMVa7WV/yHBWo5Rge9LjYG9C19VoLb0scCCDOCkRBCliBEIqnFnStuf0exl1UzG9mJJlYx574nl6vZK2XnD3ndeYT49BwL2jmjfembGqtE8EveezHuIFBwZY1JbqfsXFvOXRwzFX9ifcfucWX/rdljIXTCZ9us7QaW+Kuawa7h+d0e976cNIFIm4nnPOey3K+EypFG9c/mkvAengn9F17LKJIoYqAOdwqbI+xAzhWfUb9c+MwSGDOoaQoAL+A17KRopoFmuRmY/bBfE+x35m6VoDdGl+9MdVBQzRg/CERIgIr0Xs3tl0pVOX8VWBXt3CORDKyz5p06G7DmMdRdnn2Y8+z2JxxnIx9/1IeHG7Zd2xXLZ0xtLPFYNRjtGGtqn57/75P8U5zZ/55T9LvVwwX6146Y//kJ/+/C+iMkHXrDzwHe6R0RatPQ5oDEGWWiJQOKPptEMI328LJelaTdNYdCdQmSArwsMSSKu662jqjrLvn2OpfIxmtH4Y8n3P9mMDoju5w+Ejv0hffYuieYBdvMPs7hHNogQFbR8sI8qhX+B6WZQMkZWIfOw1xYOkiZYDrPDmFsJbEWOE2HDd9ZlaycMgehh90p8ORE1WdFTNGYvVKXkxot/r47TBZgWmayl63nzs3r377O/v8xf+rb/AFz73s7zwwgt85Stf4f6D+zhHYr7HoDLPvLxLWRQUZRnYaf79qJkKeMC9LJGyCWyWnCwA8hFsj8DG5msRRI+sSbnxd3xNXAr81yC71jqVmksJ04sL8twzGxdFznCckeUZ168NeeT6Z6jqT3F6XnH/wQm/+9tfZT5fsX0w4ZFHDnnmiUc42J2Qy5pcFgi7QtsG40qsLLHCSxnYkDXVTgd9JbdmzLmMF174Or/zpS/zxpvvMK2WtEoi+nvY7Ala+xjaPELVKvKBJPviA65fKXlue8gnnz/g+ecke1tTyO+zvTVgu9fj1e+8xO72iOOj+9y/e8G9ezdZVqdsT65x/2wRPDMzzo+mWAfGCrqu9dq3xqQsYPznAx/pnYuD3I6UDlwTtGf9BNYaTVNVrFYr2rbxgEk7pWk9w73tOkzdoAIzNP7TBLmTMIhLITBSg8vpcsH+/hXKg6uczxsWswvq1ZLVYoYza3C+KArKskfXGaz0GvKZUF5/SiqG4wFFAMumVY2oF1Tf+IfMBwU7n/l3QHcINMJ1SLQfwo3FGY3uGpzuEHqJ1Q1NtaKtV3RtQ1edo9uGplrSVhWmrbDNkk6bBOS4cAxS5iiZe9bDzg16wwO2rm9zfbxFORyTDXbJigFZb0xWDHEiw0lFZx2nTqDynLyr38fy6sP2k9aE9G7gnW5pmpquW4PD1lqM9MmuwWjIfDEnzzLUcMi9O3cReFb4ZDLx7GCjaXWHCgFtlNcQ+JK8XGUoPPA5CMCDEF6vFoBQphrHuBi4pWRiANp9iT2AZW//ILGg4zFfv36du3fvAiQQxVqbzETjfHJxcYFSKsmhRGD36Ogoge7JfyTowm8yleOcEUHhCLz6cdum84ggTpRsidva2dnh/Pw8ma7ubG0zHAyw1jIcDjk8POT27dtpPBVCJFNSIQS7u7uJ1RjHs16vl0D1yJyO53bnzp2UIFFKJUBfCK8hPxwOEzgez29ra4vZcgHGYIWPFJRSqRogMsnjPSqKguVyyfTsPAHfcS7uuo7xeJyOFzy4FBMxs9kMpRSrxYK9vT16RelBeeH1xq8EWZe4nfl8Tls31KuVL0lUXpf/7OyMk5NTtDYpQSCEB7aF9GZ+qvBm0c6uvUsiAB/7nGe0NBjtr/1isUiSDIOgj7y9vZ005Q8PDzk99Sz8KJXzYftgm2elrf9OrOkN4Hy9GEocp8ufdpe/+yc+pktgXWR5rcvJ1297JPl7gZgOv2hcS6+s/+djZs/Y8gu/zcVr3O4apPdl13Z9von9tbGzzSO/dLE2E5nrz5hI2tmIka3xe5fCL/CkjH5NYey34b3IAAv3RusAyijpWeUU/Mf/8f+K//Pf+r/w6isvY43lkcceC5UyfabTC7S1DAd9VnUDaKyRVE2LC/IKSinu3LnD7t42h4eHLBYLFoslh4dXuHfvHtevX0+Ejl6vx7AcpEqhra1trBW0jU4J07ZtL1XVLBaLS8m6mBCNCbher8f9+/fl+vZkAAEAAElEQVQ5PT1lf/8KOzs7OOe4cuVKijfBVwfVtfeFiEbOUUpra2uLz33uc7z66qs89dRTbErkfC+rTEFUrv3eTfK9++D7bdJ9cM8Q/GSAxu+3vZ9r8a/D9bIBhJAB4LbO+TWiEevkXxgPldpIyNmHkpUubsvPA0JKMhW8GAQIGXp8AO2tcx5Mt5BlAoz/jJeS8QDeoMgpVMGVw0c5PTsPLF8PPEXUWEqwpiXPSj8OSon3q1rPQZsJXaSg6A0xuvmu18RZz7B3FlSWs7W3S16UTE+PmTerkECwNMtpIp0lJq8DF6rrBJCVGUZb2qqjKDNwvtrTeao+y+kZvdGE4WRCptZEA2dBO4ssQXaNNxbVGhHYvs5qjBGMt8Y0bYtuuzWYKvw4rzJfMamKImi455i6Iy8H1MsZuq3JioEHNJ0LjOB4zSKB0APWOOcVXVx8PyaGg7Z9VmJMixCxcsFvwydEAgM59AsRwFkhFTIvPEbWzXHGkheSg91tPvqRpxkOBojA1vf4hUySq+nZEwKBpl0u6UxL1zlaa4CMq9dvMBj2aKopt29+h65eYi28cvOCO/eWmMh6FmtJtXj+scNcAq4BbR3nc01npjRdx6NXd7h39x79XkF/UPLk04/y+qu3QPgqjGrZMp700/H6og2X1FU8yBwkldxGcjzEGH7t5FI/NiHhH7L6wSCdSPZPxy6kSgGZSJ9ZP89syromzoDDWIPBy2CKJAkLxgkyJ5NHgIePbHo/VWOFPXqz9wBEh26ZZ+JSeGTdBgEgJKiRGybKzuKsDAkYiVAZTTX36z2VceXaIUf3b3J87JNhUgmOLubcun3BfNliAlG0yGDQk+xt9biyv4XpNP/0H/8TRqMRf+bP/lle/Pof8eorr3D75hs88cxHqJdLtGkxxqLxibde7hUM4pjVNhrwUkhKeTUEp2Ex94SeTlt0B1ILXPDM07pDCEdeSC83HCoCus6ijUGFMfP7tR8bEN0KS2Vm3L13k+nbt+maKeVAIMXYX4hSk2djFJ6xnSmFlAqVKVTpGdMqyzyYnhcehNv454bjBB4rpXBZQZf12dQHCnmlcERBt8hlSFcyKndo8gnzxQprWgyS/mgLlZd87BM/xe7eHl234tatW8ymK67s7fCrf+7f5FOf+AS/+Zu/yZe/8i+YL5aXtASlkP5GZlkwg1PkpaJXeqa6n0AlZSiXlFKSZzl5ociUJMty8tyXrty5c5snn3yS7e1thBCU4fNSSn+N5BpIjyzzh0H0TbB9E1AXzk+yTrd0TUueZ5yf+ZLyft9Slo68zLh2rcejjzzD537meS4ulrx19z5vvHGLN179Xa7vb3Pj8AqZrZn0Dav5GccXNbOu4KQyzBdL2q5DSYW2OV2HLwVViq2tEXfefJmXvvUSR6fH1G2D6aCfD+hvn7N1UHFlq6LOCo7mB1Rmm9bt8tZ9y6tnc37r9VOuDx0//RHJ53+64OkrLX2lcE7zX/9X/yXfeeV1pBzS6JZf+XO/xGjs6FgnEay1SJVhtAgGWWsHZvAleFp7N3DXTqmryku+aE1bV+jlirarMaah7RqM6QKb3Xc9ay3SGR8M+DSp13EiOG6HidSPkWvDFERwUBawXFW4k1NG2xYtIRtIBmUPUWhW8xW6NYiQNazqBlmO2TqY+GysdXRdg+s6MpVRreqgn/w415/6GMXWAbLoY25/BVs3dM2SZjWlqWZ09ZK2WQb5mIquremaCmM6jO4CgG/opEMikeRIMmTeJx/vUm5vs729TznaZjjapuiNyQfb5KNdsnIUgiaFlR4oB5CuxTmBcRKDRFiJsJZMWgppcN0K54ofNqnoUvuw1PhfzTabzTHBJNPLcUi6ziTgczgcJgmTTCn0yLOhU2VJXVNVFcZajLNsbW158KGqmUwmnJ6eJlZzWZbMF56pPBqPuXLlCsboJJNiAtNosxIoMvbKskyl9WsGpGfvge9fg+C5sVwuE7NYSplAjmjCCSRAPMsyVqsVZVlyeHjIarVid3c3ybxE8D7KgeR5nrTGgUus/bquk7RHlMOJWuvxfOJ3IsgaP/fII4/Q6/US6JznOUdHR4m9eP369VRxExmMAKvVCikl29vbSSptMBgk6ZKmaSjLkouLi8S8z/Ocfr+fkssR/Dk7O/OVAxvM+a3tbSbb27Rdy41HHuH+nbsw8M9yVVXYukoa7FJKiiAdE6Vdkr6+Nmij0zHCWhJms9Kga1sG40nqn/FYF4sFR0dHgNcSns1m1HWdzFT7gz4isNyjOWqUuPHzOMGvY933N+9HNIbdNAMXQtA2bWKTLpdLr0doLdvb29y5cyclZq5cuUJVVamf13UdgugP2wfZbCohj8yoywA6uBSvwUOAe8Q+EJfmwj85FBXlStxGfHK5JfY468VeBNhdZNUl+pcI5mvrg3M2FbpvsMTikXt/mrQy3twvAWwXQd9cOJSTWGERYdHs8CCVFdYbjYkoQegX29LKS3B9jL18ET6IqIMa7kWSigyAuQfXN/RTLYyHPRaLBdZa+lvb5EXG9PyEz3/h5/n3T874B//tf8vXvv41hqMhRw+OeO6557h7756fq1RO11T0BwN01+G0SbF6fJbv3b3PJ37q46xWq1C14iWjqqpiZ2ePi4tzVqtVkrQ6PT1lMpmwWKzQ2oMuUc4lVvQYY1gulzRNw97eHm3bpsqTOE9syrcYo9Mc1HUd5+fn4b5dTkT2+31OT08ZDodpfDk6OuKNN97wx3Tvrmf1Z95jaZNt7jb+3qxz+N7dVbwrgfKn2d4v2PvjzuL+oPf5QQHpH6icywccxztrsYHQF1mfxpAAKA+AOs86N4BbP5se/HJrhnHYZpQdEYGt7vXWhWesauelOIQns0olUvLQGug6/xT1BpK93RxhFdvb+7zx1uvUdYvKMry0iWeD1ss5utP0J1toZ+kNffyn284DWwGw1FqAyMhCFXTbNny3mSeCek4IhJLkua8kREQc3j/xaY3tfD+I5Oosz5FCguvIVE4bEnVCdOtEc5LqsFSLCy/7m3uGvdGRsGKD548gyxVIiTWGtjMI4avHTVcnOZsEkzqfFNHaoTtNvZqRZWVgOluU8jGhbhuKcoVQ5aVJ00t0SJwJhoc2yIh5njwYX5kQr4eQ4ExFUfYCSKwTLuE/EbzpImiKA2cQ1iJcixSCYS9nsr/F4f4Oe7s7FGUR5iyZVB5E/DsmLQLonStDi5eDbdsGKXPGk20GowFCGO7ffoWuXdJ08M1XTjid1oFxv/ZFiee9fr7WviWXX/dxwHzluHNU4Rw8dm2PutEgFIeHOdevX+HuvRNP1kbTNu2a3EPUzF5XpcX4OwHoxCliLU3pNbUlNphyylRW5gHm5Ee4vovpGhHuhb8bcv02EbCWIQGjUnznwkMpiOsvT5YUQaIp+pakg43HQjynaJju/95knxtt1/3DyShm5I8vPK/xGbT4w8+LwieWOg1SUPYHLJdzlvNFAv9fv3nGnftTjHXBGAAQllYL9MrSthV1Zbh6ZYwF/vE/+of8h//Rf8RTTz/Jd779LV782ld4/JmP4CCthxB+rPFxYpA/tprBsEBKQdP4e6bjOYXvKOUHQNNZ2lajlAukt4yyJ5Eqp+la78unbALos/dBRf+xAdGl01ircL0Jo4niwX1oqpLJOKdtKyrjMObMTxLOIrHkSiBlhhWKIssoVOazZyoHmfmsk5AIKVG5QinhTToygXQOZfGZuaKPzEuU9BkLkZWILAeV0WUZPWe5snvIarxL28wx1rFqK5YzjcgzXn39Db74e3/Ez33uZ9jZGmL1MS+/9TbTds5H967w7/+lX2c5n/H/+6f/xAfeKkeqDGcCYyVkjByWXJYB4IEs8wvqqDsopUx66kr5J6TX63F66geQ2WLp5VGUosgzpHTkWY5UQYNVZGut9QDex2BcKRXOfw2ix78floVptUHQBW1drwfc7/eDiaqj13NcPRhxsP80uwPB8f0S4RxNPeftt17iz/2FL/D083torTg+NvzmF1/haAqLTqFcjZMtiIKcjGFf8a1v/Qu++vtfDjqNHiiQQlBZh5yWzM+XPPqk5cqzgse2f5Y/vr3Pg6xkog3WHtDalnuLAfe+VvHl12d89rmCz36k5anDp/jkJz7Ny6/epNNzsB3L+YrBlsM2BmF9NkvrDqMbmmpO2zRUVe2Po15iWm/U2XadLw8JU4RnDsRBOQ64UZNy/b5/D3T4nA2v+yBBB0DdkuUZOK9NJpXPKobcO7rVCGlZreZ0XYMq+pT9AXkxRso+/bJiOVuh1IDeeIQRGlld0FQGbRxWSESI5nQo/ZfWUF884O4rFca21NWCrlv5AdkGYxRrkc5XdBjnzXgRCgeovKQY7dIfb5P3RwyG+5TDbXo7V+ltHZAPdlDlOEzYMvwLzvRsJA4CRc3nD+LSVvlPiWgIZtLK3KLwQjIftg/bu1vbenmS/f3dUO1DKGn1Gtb+M55RPBwMOT09JeppR81xKSW9fg+LZ37XdU2eKfI8Z2tri6qqUkXQyckJJ2dn9AcDer0e1noN8vF4zLmeYo29JK8xCNIda9PRwDUIZZsPg+0RXI1gaFEUyaAtgv7ApeojpVQCox9//HHeeOMNjDGpBFIplUCSCNJE1vMmy9wYw2q1SnNK/OmcS8x28AFSlBnb3t7m4OCAIi9SsOxldJoNbT+fxBiNRkSG/6bcTZSJiXrd/X4/yc5EXfcodxJblKGJyYm4n8ier6qKyWTiz6fI2d7eZm93l9ViydnJSWLwW+Gv5e7ODl3Q2tza2qJergAvvbBYLC71o6IokoxBTGxEw88iyLsJIZhOp4xGo3TPtNbeRDRoqpehWi0mK4ogJRQTAdH01VcPOPLclzoORqN0beM1jEmQzWsU70VRFBRlQa/0oNdkMuHBgwdUVZXkGy4uLhLrf7VacefOnfdGUz9sf6IWMfRN4Fz4lTGR6GE3Vqbr9dUaOHf4+TOaSH2vuxQXUoiHbqdYz8lEAD3tI/zfwZoIHufxh4CTjWN14X+eJRjfT/ytAFBs/J5Wf16v3C+g3Mb+NhakbuOVgLdfOhLLWnddpjVuOs/0XgQurAe+VIjX0sIzsM6EvLx9D9QHggpwMZvT75XeULNr0bolL3rkueJnPvs58qLgiaee5M03X+fNN99EKs+49OO99jFi1/hFoPQEGj+eGHq9HtPpjOVihRQKbQ0XFxcURUnTNIxGI1Yrn2iNz3Dbaoq874k4mWffpfg+yDJteltEsDzLsktja6xgKcsyzFs6zXubHhlASly3bZuY7RFgn8/nvPnmmzx48IAsz6NYYLBCi10jxrwRHIqwxPdu6+99z0994MPXD5NdfYkt+gFs6/22H2d2+Ps5yw98RhNRp1ysq2wCoB1BStQ66eNDDBNkWAjjkU2JQIig+nqs8qxaL99itAuVMx5nCBLJXkPd+NhXKj9WSeEoB3B2fsw779xEG4dUCmsN1hKec0V/OEQCpvWkqf6gwA2KIHnhAWelMqxxtHXLYnFOW63o9QuccZcS95euixBkuUCpjLW8SVg/B5BuXRi5nkQy6eVI48l7EHE9F27sIs1RVjhUnuO6DqW8RnbXeolVIQhref8dFZelzq+7bdehnI/Ju6YFPL60mjc4PJu8Nxwk1rwXQ/Dji25b8n4Rqg5M0NoWSBSqLPyxZ76PGK0xtfZjc6gycNaDo6ZbgfVVhF5VIZA+pURlkiyTQSPba833i4yyLJIMYll4AmYmPfk0mrXKgLzKoL8d9eHjc5BlGl1f0NZtGlGVyrhyeJVcZdy79ZqvNm/h698+4nxe+77mIqxMmigfHmbc5f9datY5FpXl+KwiV+c8cn2Xum64dfMW165eRQVJRSkERmuyPEdJiRGece5CnGGlCIzzgCtI6xNLJgCz1qwPkODfFo5ZSJJ8iUMmEoGfO+zlOWYjPvG4Tnw5JghimUFILARzYEQQcHEu6HiDjJUJXgCIWIkS+6SLCZNA7BSBQGC1xVgbrr8fH6RYky4iqWHNRxCA98kbD7e4OD/BWb/WVFKwms2QQtFow3dev8/R2SoB3zFQjUx5Zx1NB2fzDsGFx4qw/PN/9k/5a3/jr3H98BFm0wVtNQ9qCf78dOflktvGUZQKY8Wlddp6no5JRZmkr1y4d/2yIMuDRFUuMNahCgHdWtlCSH8/s+z7z18/NiC6EIrRYJfnP/YzvH5+xGx5Sv/KPk//zOcZDEra1psmrmYLVss51XIegsoG185ZLlYsjPYZo7wElYWsnR8UlMhSdkZKb86T5YV/GMQMJySZ68gEyCxDqgKhFK0zKOsY5ZLtG8/QIllenJKJBc420FW8/ear/H/+7v+Ll1/5Bvu7E37pF3+e5599GpVbrG7o5xkHB/t0JrA1ui6UbvRwKKzx5RxKZTRqGQByP6BFQCSaiUVwJuqr+tL8kiyXXFxceGO7oiDLFJlcm8PlRUGm1CVDs1jmvynx8rD0yybAHn/fZK9HoDiWfkbjvMlkQmcsW5MxZaYQ1msTbeUG10mapcVaRTVfMu5LHru6hegNGBQSmXf0ygGmNvzBl77I7//2b7FcVQmIiBNHLN89PT3CAuVgl4/+/AK2a77yGjg3QBkQhabSGiW2uKjGfOkbLTdvrzjcWvDiqx1WbuFWD1AS3nn9Jg/urVgs3/EmnwEIsdbAhp6jc17bHHdZhiE+pBFUiguJuODxIIpfaV5i/CuZTOw8AOUCS9aDUS7JsZSe/RcYPEU+QGUqZOc8QKPNCoFgVPYQeYGVAiFLVouK2dl9JB3kfZ/dlB5yFsI7lWujQ9mPwCyPuLi4g7WgVIGzjkz5omVUn3w4RJVDXG/EsDdmsn3AZGufwc4hWX+IKoaoXh+ZleTlEItEO7ChTi17uFh3g1m2DoDExv8vv/bQALLezIdgzoftu7Smbem0puuCh4Z1yRg0Vp5EM7ROdwwGfS5mM07OTtmaeIC8LMtk4Njr93DG0GrD2fk5RRhfo2HltWvXqG6+zd27d1MwcH5+4dnmRU4bnm8hPdMnArDwUD92no3gx1u8jnaWJaZ1lD9ZrVaJ8R0BjlhWH+eRqMt+fn6eAJMInkdAP4IdcRtVVaUFc5SL2ZScia9tnnsTvj/o99nb3WNne5t+vxdkAXJm0ymTyeSS4XGv10v63/EajMfjdE/KsqSqqgSaA8EfQicgeTabJU3epmnStYhzX2ROR4AomoFG41OZecA8Bp5Xrl5lsVxQt62vdFOSTHnNeBWOaTYchAVVAL6ATEifpJcqsTVjdUGcH8qg3x4ZmXVdJ/33fr+Ptoa8LGiblrqpMSHBKrOMRbh+0ZQvXq9Nhrkfz8uwOFr3qXi/wAe+PnHiyx2N7bBWImSZmP1vvvlmYqhvJiJ2d3dZLlfcuXvfa2V/P5T2w/YDtfUYsAaJ18xadxldZ+PXiKvE5yi+9DCY/PD+2Njkxgcv/e02Xtt8/9J935iP33OPceW2cY4uQvJr4ZaokRqWT2E/Iiwa4b262+Yl8JBT3N27r1UEzzePUOA2EhNrxlnUlfegQqxUBaRftApiWsN7xGyCOF1nGPR9jF+3FVI4ssxbse8eXOGzn/85bjz6KP/sN/4pr736KvPFwhML8GuG4aDPdDpFyYys7AM+3vP+Hb1QuXIc/HsyTk5OuXHjOm3bhHGzR9fp5MtR5AVt26XEbl15uYVYqeOP2YPc0Rh7s6opJpS7rqNpvP9D12nApCRtNCON80McZyIYH8efqAH/6quv8uDBA67duO5lBBKI9nCLz4L4Lu//4M257/1crD/4Pj/3YfuB2o+Cvf+juZEuANgOZ0zAIyRZHkBuIVJi0BoHQc/be1Ft9PxAqLAOMC6NvzZkALPcYx1CeIZ0vfKgU9HzWsdeG9jHnEpKeoMxiC2Wiym/9c//PvnoWVRWorXycijOYxSDrT2yoo8Q0DVLlrMlbbWiKJWXAsFhgqwGzgPIs9MjpPSA7GhnSNdoL99qIqAlKMocJTO0bpNhuspzip6ha1q6LsrbPHTzpERmOTrETJs3NSUU3Hp5GBm3HhyXRAU6JS8Tr8TG99zmFp1PICuVeYkcE6rHXTwcQTnIKYdjEAIlFQIRDGU9gzcP4B/CM6Uj29v/dFitqVeNZwEDeS4Zjwdsb3vCiMpkkBGTwcxRBOlYmV6TUvp5Sfq5SAWsIVMqmItm6aeUEiWzkMjwWIYUUe6X1P+csxSy5XwxTZKy1gj6kwHatBzfu89ycU5nHS++fsys1ogsQ0HKOvuwJVak2Q2iIZfiiojxbI7wxjnmK8vpdEWvV3K4N6aqvYnrM08/wcuvvO7NeB1kmaNXFmh9udIyPiM+6eAr4OzGzqOxJhKEC6B1fN8F2R0cFpMAZOHeWywsxhYhDeT/H2IKf61JCV5/nf2Hk3STkKEiL5It1/OhB9JDRiykJ6JuvnMOZwJOJtafiYB7lOP0/cafr8d8/H7KXh8hoxyKREpF1zQoKdEGXnzlHicXS+KA5M9h46HxaD0OqLXhfGkosgW9ouDFb36Lz33hCzz3sWf5yle+yoPbr3F2doQLiQNnCQaiQR0j82z01VKHZ9Wl61b2vCpJnvkE/WoZE2IGlcv0/Ftr6YKRrnOeMCCcIM/V+woffmxAdF+C5MBmVK3AySGjK08xPnwGqQSFsAydZd9Jb17oDLZr0LpBm5quaWlWFW1dUy8v6NqKdsOAUQRDCGNbTNdB5cis78wqAKC+w8UyljV7QSnFWSir6u09jejtYE/u0i5OcM4iM8XR6Qm/8Zu/iXAtX/zSb/NX/+Kv8wu/9HlclnOxnHOxWLCze4hzlmq5wFlDZxus8fq4IrjRGgfGrAfECI60bZsM6SIYcskcrbcG2bMs87IvGyB5luWUZZZA9biNuJ0I+D7MWEz/5Lu11KNu+6ZRWzQYe/DgAcuq5sVvvojThi98/vNgLdrAW7dOwwSSY4zk+o1D9mWOFgKjW6BCYvnat7/Bb/2zf4xuujThpiYEXiZKg4PZ2RHvvPYGH//kDv/WL30S3cHX33ZkrkenGpTK6WcFQi5ZWcUbFzu8fepYuJ/Fjc9o6xWjYsbZ6RuY4zsYMw8dc70kE+6ydrwxLmijby7MXGLXJGPWoLkc7501DXrDGGqtm8kGgLRmBTrnAuNyyGg0ZrlcopTyQJDxwH3XaYz20jLeyGrFciYYjrfIyz6qlFCU0OaYxTnWNQi8KYYNDFMbYzaZIVWOlgX5eIuyt0XZ36Lsj+nvXKU32mK0e0h/skfW6yGzApX3cRR0JmZafQreSa9f1Ti/OJVYVCrU/rB92H64zeuwnrOzve0DBCEYjcesVp5JHNnYMYGptWY0GnkA/CEweTab0TQNvV7Pl9HXNVlgCA8GA9q2ZTgc8uQTT3L7zm3u37+fxoRNtrpnSWeXTJeA9NnIroZ1CX1k6sznc8bjcZKQ6bqOfr/P9evXuXnzZgI+op5tZB875zg7O0tMwU22N5CSglH2Y1PvfFPfO34ujnmwZmA7axkMh+zv7zMejZiMxxRFkRZJZVmm8bHX6yXwJSaM47FH1ryUMn03JlTjd6y19Pt9dnd3ubi4SCBNTB6Mx2OMMZyfn1OWJcYYtre3KYoimb7GOXG8vcVqteKdd97h/Pw8AdBFWSARDAcD+v0+UilOTj0TXGUZdVUnQ9MonZASq/PZJbmcwcBrEw8GA8Ab8/X7fQAGg0FKXmR5zmOPPca9e/dQ2RqMj2avsZ9NJhPOzs7SvYgVAUIIurb1pdAByNqsAABSEsZrvOdErfe4jdu3byfZna7r2N3d5fT0NCUfjLWcnZ2t58EPUaYPvG0yfx/+/eHy5/d8PQHBD8274vIfYmNfDyejL30zLXjdxuub4Ob6MxHwvrS5DfDbCS5pokcm1kObuHTc9l1stnAUGw5sUfrDPZxscOl/cfm5wbUT6ZuboIxYb8kDvGFXBuFZiek/zxeTkJjoFktvMKBuO5rGS6KoImMxn7Gzu0NnHP3BiI88/3Gck7z04re4efMthgPPZmy7hvF4HLTSBbGipeu64CUEUmbcv/+Ap59+irIoOTk+wV13gXTRprmkaTouLqZcuXLAxYX3OnAWVsvqknlzlOSK64CYsExXO8T9MaHp5yUvQ2GtpaoqRqPRpWRoHK/j9uP8B348e+ONN7i4uGBvf48sSGA93Lfjtj7o9l77ec/PXeor33t7/7q3H4Wcyw9677/fWPl9v/8D7e39bM8F4HOdlEvvBVpokoVC4HLQrcVo/15MznlQWnqwLBK5NhJ+Rjs0Dt056hXobr0Pj5s5sswDmcbAc08/y3i4za23v00mZ8jeiuFoQHVWr1ndAoTMkDIDvAoAYT6RmWL34JCurTk7OsUaR557fyqV5xjdoGuLbrTHDnqxCl5hnaVrWqquoigkhVqwmK6w1tEfleRFFljiNhlOKyUpeiUCSdc2COFBednz8rdNU6N1YNlvAOj+pyTLizWxABfOad0EG+cc5wThEhNcZhmmDolHGeUYfUJAZSV50QdryfICpfK0fkAEbyEh/RzmXGKqC8B0LdWywlnHaNRjb2+Lg70dBsMeWQTGU395dxox9Sgp00kIBJmI2uiesOdVGnwfWleCC/96kv4NWtvhHpt2QdNOfdWskBjrKIscicV2DcvlBcY6vvP6KWezFqRE4betpEJmGVlWkAfmuxQCYzRN3VLVS5q6fdecuzFVI4BWw6JyHJ3MGPV7DPo59+7d52c++xneePMmXedJKtZYeuM+TdfRae1541YkjfcIxiJU0hCPF9UD+BuETRfAayHW4DuWKOjrZXLXz2A85PhXTK7H7UmxNgOFgD+GbVq3rr7w1rNuY+wKcVCKjYzfg1CIpGrgZYk2xxSh/DFZGzFuibMR83Thnoukrb+1vcvF2SkAKlPgDEFin++8cZ/js8ofYXw2NhMFIshJsT7eZQ3z3NCbzuj3cv7gS1/iL//bv0b3u7/N+dkxd+/e910WAdYn4RBeC14EYqWQPvnnsUyJNj5h7+M04ZNZ+HHRWIN1PukmhN9m2xgy5eVbs8z3q64zdN33H+F/bEB0pbw2c1M3nFxcoPpDnnzyGfKiR9c1SKXI8hwnc++4bDqyUqF6Q69PLhXjkCnMaRHOl0Br43W0dL2g0w1NU9G2NaatMXVNV1fUqwVdvaK2EuMcwhqw2mtIkWG7BpUJrM2YtRope+Q7j1BO9mhXU5rFFGONL++2jrfevsf/7W//F/z2l3+PX/7MZ/jkJz7Fl1/4GvPFkqI/Rk2uek1ou0LRkdFRz6Ys5hfkxYi27S6xrjcXvvFaxQVsLK3Mc0UWdNX7/T6D/pAiK8mLPMi7FEjlPxeBds9Yz97z3yYInIXAd5O5HgGWoigSw282myV93uVyya3bt/ny7/8B9ari/OyMz/3sZ+mNSrJsgBCege8yMMJibIM2GpllCN3j4uyYL33pSyxWK+rGet3KjSDLOod2FiWkN1CwHbPZTd586zH+g7/ZZ/BvFNz++y3nVUFne5RZwZXJjP09yyu3C+rWkpU7bO18krlR1HJMW/8hefUOg8zRdv0EMBlj1lro1oaJ1V4qd30vcEJrnZINMlQF5HlOpnLq2ps42aDPHBcUeZ5z7do19vZ22NnZRkqZtGm7znBycsrp6WnSp1wDXEHnC18NaHXHcj5DINg/vM5ke5dWZpim5sGbr9FOj0BmuHyAHPTJekPGo13K4YTBlv/ZH+0xHO9QDLeQxQCZ99AUCJXjkHQhiZA5ge4cKIksJMo5iHI2NjBtXbxufmrxpn3vwxr5w/Zh+wBbBI+fevIJ/7z1BwkEqKoqJQTruk7PZyyBL/MigRcxaQgkzeionf7444+zWCxoGg9+DIfDwNAeYa1hNBql93u9XmL1Na6l69p3gddAkv8AUqQc9dHjeN7r9dJ8EEGQxDpgvZAsiiIB8/P5/JJZ5iarPLKxN0HrOL5Za9nd3U0AdrxuUfIl/l0G6Zor+wdkG2xvDwj10j4jiz6eT9QBj6zFOIbGYynLkvF4fKkyanM8jYBQWZZBzmB1SYImMvKjkV7btvT7/XT+XddxenpKXdeenR6A+Hq5YrlcBq14L6UTJVtGwyGr5ZJ+v8/W1laSlzEBzI/JgHhvI4AU9x/PJWrNl70SHYCqOEc0iRiwTo5EObWYDNiME9b662spongfN/WN/fzSpe/0+32WyyWTyYSTk5P0mZgk11qzWq3Cs9Jcku/5sH1w7f0AiJvJESAl+R+OHR/+bmRmpd8DSOzeh7a9ZzwBYRG13o6Khc5pn37ht4FhPwSIgwfS/YKI9XdYf96DNp4tGNn0cTFsNxMDgW2VDslt8Mw2VuRSrY3ANt/bXLQjM0/22LiOEpBqbYjmpe/CxwkLy/C5+BkZrqkKrxvb0WqLzHrMFjVZVnoARil+5vNf4C/9lX+H/+P/4X/P/v4VdNeyOFpwYS4Y9r0h9KqqKMs+/f7QMyKtC/FjxnK5YjgcsLe3x61bt3j22WeZTqesVp6sUxQFs9kcEElSpSgLtra2OD4+Zrlccv36dY6Pj1MScLMaNlbwxDEyMtV99ZInlsQK2ZiY3By3I9M9y7Ik67K7u8v169c5Ojri7bff5pHHHiUrgtzXQz3l4X4c57b43nv9/oGxnB0IYS9JzPxJ2+bx/0naez3f/7LtB7leHySQ/v229Sc5x4cTg3Ec+X5t02/ig2h+yg/VZFGaRHiJz6Sz7DypMKX1pM8y6i4MotJ7lK2ThSKMOwGIEn58siHTKJXDdQ6CrIWSAVzGg+vGWF5/9RZ//W/+Wa4/9hn+6Mt/h2Z1xGDQwx3PkfkozSFxrnGOVO0mA/iaZYX31koaZP5wu67DGoPpggSDlPSUoii8NF27qNfV2UIyHihmU4csSrqmxVpHliuEyAJw5+U4mrpByaAprz1juKmXlP0+RW9C13RYF83OvcFqnmfe38w4VsuK/rDHal5hhWU4HlMtV7SNRkpQmQhjfYinhESowkurCnBYsswz8FWmaBvtAeu8T5H3aJslRVF6QJYgu2ONJ5JFFQTn08ZgqJcLbGc42JtweLjP9tYWRa480B3OwUZMiPU0lUD1jX62nrPWMi2xsikB5MpLqEa/vBh/XlIcIJJnOrrmDN1WASgFZ4WP70djZjMfI755e8a9sxoXjDT9tgqyoqDMC8rS+/vlRU6mMjzz22K0Zj5fcHZ2Esys10kPHjq/ZW0ZlJb7xzOeeHSPpvGx6CM3DnnzrTsBVPXAqX/OPLAs8JI4ZKQ+7Fnf8Squ4xURk1QJLJcpIbGJ8aznAp9wgM3EXXwe/fx/aRzxOHH45roCJc8k1vrqESW970p8nqyzlyoP2Ez0i+i3FBMEcWz2FaZSKIQMVTBh/6HeBWs9uCyEQOU+adU2tb83QuCcX0PevjfnztE8XQdENDndGFfjs++12MJxOy4qw6BnOJ+ueOWVN/nzneHq1QOsscwu5v6cO4Oxbp3cSWoYYZuZ35mQjn6p6NqQLAz3QxtDVvr+3rUGUUr//EqH7QwGrzWvlEAqfw277vvHuj82ILrF0nY1SmiqpuHwxjMcHOyBaSmUwFiN7aBFk0lBFjMkztGT4KxGBF0/LTKcyCADmfvAtegfpj1FB2PrNM5680NnNEZ3mK5FR3PErsN0DmNrmmaJ1b6j1c0Uazqs7Si7mv2uYXZ2gpKa+uIBdnVBow2vvfkOL33tRQ4OrvLO8TG60ch+zejgBkU5RIgJVbWiWU4ZlAN2rj6KblvKYYOkxegGY3wJpnLOM1PynMWqQnc6ld176YEWt/LB9GpVcWJPyTOvoVoUBWVZ0uvnFHkeTEz9orwoCzKVkeWevZ7lijwLzPQ89xIwapOl7ss7EDIt7MteiQCqumK1WvHg6JhXX32Nt9++xXQ6pVnV/Lf/4B/y8iuvsnVll62tPfZ2D+j3h0glKHsFvTKnqitmiwXnxwtm0ylvvHnHD2GZxISMUSwxMlHbSOGddoVAmxkn9y+oLhZ87jPXeezLjvObHdgc6Rw9ueRw5wqvvSYZ5SeUgwnLOejieeRuD1VvIc/+BXb+KsJlOGGwwi+knK1x0dAlLN6kkkk/eHMhK8Mgo2PwkGeX5BSQCqcUKs/Z39vlYH+f7Z1J0ENumc3nnJyecvv2bbTWCRACr+9kIjBiDYKoQScAA9YgnMQ4n+1tVzOO77ZsSRhef5J8uM9VsUXW65H39ygn2wx3d8nLLaTqedMTlWNFRia8ZrQRkk4IWmsZ4IL7hKUg6nBl5JnACU2nazpy/3pypHZBS0xgiE7kFsV7aON92D5sf8rt7OyMLgDenXcqSaaNsbqmLEv6/T7n5+cJ6DbGMAgsYy9/4sdCgWcSTybeIPL4+DiZZi6XS3qDftDtronVJZGNV9d1WqBEkGFzcRrHlcFgkKRecpVjnUkARtu2XFxcJH3wxWLBrVu30neBBNgOBgPm83kCwv18sUoswagVHmVHNkHqTU14pRRnZ2dp/IuBpTEmzUtR+11rTVVXDHreNyMC/rHqJiYINo81ystE8L7X6yXT1+lsxsHBAVmmuDi/SIF/lmXp3GLVVDT1HA6HlGXJyclJupcxEXl2dpaqB4wxnJ+dJUO9LMupVp79PptO2d3e4ezszCdegrV7BO6FVEwmE+q6Znt7O2n9CinpjE59Imp9etBe03YtCG+QVVUrjLUsV0uuTq7hAnPp+vXrnJ2espjPKYsCnF9Q3bx5k8FgwGg0Yn9/n9u3bwOkvgQkw88UKwQWqHO+vFgKz97I83eHi8459vb2OD4+5tFHH2W5XKZ+Favajo6O/lTYoR+299c2GZabzN/N9n6BoA/iPv5Jt/AugB1CAv7ye+49/gY2ROI2v7QGz0RcgBJZb/E9celzJi2uZWKUSUCYCFL4BWQWSqbtxmc2FVIloJxMe5N49npkq9vAOes6qNsF/6P/6f+MF7/9Lb7z0kssplMGwzE72xPefOuNVJ3qfTBiglWlypuq8qbUXid9mippoqeHB767YK7cp8hLv1h2gvF4TNM0qdoqAojxOY9x7mYfefjvuKDeBC032cBSyjSf7uzsJPktgP/sP/vPODg4YDIeg5To75LM+UHZxB9k++Dg1A/bT2Lr90SSEIkGjuCBJiUVZZkHANyhjcXmliwTlKUH0D2pwaE7S1NZ6tqDgyoXKAlCOLI8Sp94MFEpr/0rhH9dqbiWBpX7MeiV79zh7/6d/ye7V56mmlZcObD0eyOc1UgyVO4B7Hp1gRQWITO61nusSCVDZjP3bNfwlBjtwVchM59cVR60ixI2w9EW3cUFUK+TuALq2huC9kdjuosznLOIUE0vhMToltVihVQCiujfE7ABCW1bAwKpFLnIAogosc7R1B1Sai/tYC3zi6VnztoVeZYxGPXC+lST5DICqJcpgTVe5qJtVuSFPx5nDc4I+uMhutU4Y1FZjqsNKh/QVHMPngsX9OU7yv7AJ42NxmqDMy2PHB5w5co+w0Hv0hpgEyyXkd8cp6pLA1J4MbyuYpYm4RIyJTG87Ms6lpcyI8tUkABay+YIJNYZ2mpK1y19RSM+ptza3mWyNcGahrauObmoeOvOHO8z6ZPeWVZSlD3yssegP6AsC48zhXWFZ29DkZf0+z32D3Y4OT7m3r0HSery4aaNo24ty1XDbFGxPR7w1ptvs3+w5wFx55O5CE8abVuLNg4nLQ4FVqQ+aoM8omeg++QC4PtAME7x9yJUkARkOJC7k9hLqioRhGR5/HstZucgoPixWjRiRSFRlwB/Q1u39Ps5OIcxDuts8DmwCaiOa7G4vcjC3yRQWGexkboofP8xzptyyqC8sZa0gcloxGI69Wu/THk8SQmqlebm3Tmq6FNIhVTec9KTGSzWaro2kHvcWl4qXhStBRdLS1FWnE+nfPulb/HMcx/BOcHWZBslj7zZr8rCvclwxksYq0yS574/RdzPBKNaqcAYR6et1/aXwssdBc3zoieDbJW/v13nq0GkVBgsVn//OOLHBkQ3QqKKjPp8hnEljz35LHmpfAkNIMKAULAuISA61roQGKv1uJLGlhjMifUDmYgmMgeZp6uUP3xQkX0cAkNrrTdzoMEaG+RhLMpa2uUJr/zx7zK7OGbQH3DjkQO6VvPa6QVv3L6LM/4B7do5Fye32KVktHsDmfeYVZZ65Zg1OUo6ekVNJhYgHMPtHfrbkNU19WpG0/nyqigREpmCHnhwNKZN7IamaSmKgrpR2Gk0q8wSqB7L5uNrPlgWKGWD/EvhjYekIg+fEQhfSqGKFKzH70kJ2lreuXufl994i2q+QncWg+R0OufLX/0jskwkg7fIzB6Px56JGXTVW625eu0RrJZBxqVJjJt4d4VzZGFAFMKra4tOsTx9m3vv3OeJjz1DXjSocoCyHUJA1U1487Zjb6vh8f0LTha73Hlnjq4LyPdoxRdwQ5DdEVl9gZY1ebFFT/RYzh9gnMTXHfvstBD+mseMvBACZyxdAFWiNm7XNRRFFpg5Q2xWUJQDZN3gBJyen3Lz5pvUja+4WOusrRfFMVMplaTo9UEImmqFMKDyHCu8Wcr2zi7F8Arl4ApFkXN29Bbz03fgyCckrj76PDuPPcfu05/GiCsYobFUCFdeyvx6azrnJwUMykEWM7Wbz4gE0ATJNwR50EDbZAXFha1jPX2lL/9r2f40SlM/bB9Me/DgiLqqyHNFU9eowHzOsiyB1VFHPJo6R9atT4L5MUhby2Q0Yj6fU+Y5u7u73LlzhzzPk2dF27ao3OtmX1xcsLOzw3w+p9/vM5vNElM4sqSFWJtwbrLUItM3gsPOiWAotW5RszqCnJFNHAOq4XDIzs5Oei2ykSPAbq3xCxaRJwZR13Vsb29zceHlOqQIDHk8OyGyvyMYH/+O4HXXtqjRiCz3+u/9vq/wGQ6H6XjzPGe5XLKzs3OJgR8Z85vn98STT3J6fgZ4mRfCefT7/UtAjzEmyQlYazk9PWVnZ4eiKC6x5fv9Pjs7OwBJ614IkaoPImNdB6b62dlZMvcsRJGY/sYYcqUSQ353d5e2bZOxX902rMEkr4fctnWqItra2iYrclztbcN6vR7GWfIs9/4iXYfuOjLpEwyDXp+jU1+JEBMNkTG+qU8vUvkqCTiPbHTY0LoMiZ3Y705PT9nf3/fnGWKD+/fvJykcazV57plE90I55oftg2/2fcJ2NjC3YuwrLv3nwxbxMLAef26Aku97Tgqlv+/eoru0eHpvmJtLNNDIQE9M8BAl+JJnkT7vxHoLNjHcRQLTEwNdCKK/ii95lklXNO5DuBCJuHXJ+Bqs8M+gCWXJiA3vAEDIDSaaEDgViQJ+obr2uAuMMOcXrzYC70GnVipJZiVGxLJvPxbMqob/4H/8P+E//d/8rzHGsb29xbdffoWtyYCuMyiV4ZwMDPQsgB4ZTbPi/Pwc5ywHB/sIITg7vWCyNaEse2SZX2S3raSuGyYTEaqQ/Jyyvb2dvD5iFdKmIXOU0kp3dyPRG+eQ2H/i+LP52fgvkkJmsxnD4ZCiKLi4uODv/b2/x3/+n//nLKtVSgjFkv51z4hauf51mfpguCeBYSc2Xv9g28N9+k+wpfeZ2PpJad9v7Pmg4+X3szXr3o917ftvSj3EgBXEzBO6M4D12ujhLSniOBfWYsqRFX7cKkuFWvrzsNZhtWdl22gCgQfgdEfYBon9HmVOlASjQWt447U7HJ9cMOpbruwJeqUkLxSODiELcA1KWtp6hggSEoPxAG867Cvmuk7TGwyplkuss+Sqz5PP/VmKXgnUyKzGdi3WGax1bIsJvd6Mrq3RXQdCs6g9IN92nlgRZUgQGUiFwANhQr47Bo7MWOs80C+koqsbVFbS1h1GG4pShrVquA5By91ajW41AkleFJiuxTnI8oJ6Wfk4qRTetDL4lTnrWfDWdR7b6PfJ88LHXQTVgGpF7G39QY9+P6NenISEYsHB1T2u7O1RFCp0B8nlrr5OQrpI0d78gBAP9VGR8seIeO1iV1vLtaz97wTRYFYKh9EVXduSZX3yogdaUy9PL0nqqixjNOozGQ+5ffMurXZ8580LOhMY91KR5T2KIqgi9HrkReYlEYWX/Oh0i9HeSNJv06sKHB5eYTwe8dbbN1ktK993w32N57dsHIMenJ4vGQ8HLJcVu7tdYoN7cDwmV6IkS4L0EliuhEqXLF3t8GBusvJl7iVpvE76+jKbGNNLme5J9EeJEiye4Q4EZnk6jzS2RGY33ivBeRkVKcVlYD/+P+KdXJ5DisLQdRqtW2+Aa906JnI+ay+kRLp1VUlMmoQzRkrJfDpNzHRCbPb23RmrxiBljpS+6q1X9hiNJownY/r9DN11nJ4dc//eHZq6STI5PiZyrGpN3Spm85pvv/gd/sa/91e4d+cW8+WS0aBHpiRt8HX1JC6DkoquDcRn4WVPZfB77EKVSV5KdAddY4PXpU9OKOWfTa29FLcnV0naxqC7zps720sP2nu2HxsQXViPtBkkw509Dq5d+8DDnx+0+eDapiATwLoCY0eoQpDFbI+wTE/eZvHOS3TzIwZP/RS/9Muf48VvfpOXXvoWZTkI2TuNs452VTE/f0DRH5OXfYY9ybxeIoSlcZrVbAGmIhOK2pbUXUepekjbMelZtvsDnBNhkd6idRd036M+dpeyUZsllRHUiezHOEBsgtpFkdMr88QyTz8DeGOtpaprsswDRYOgDzsaDsiLnDffusmLL77EYr6MVJ60bxlcxNeMGJcC8EsmZ0VJ02pklmMJKaXv0hnieXjQOadpHW+8+TYfufi4L98SLY30xpiDwR4nD96hMCv2Hx/x9hszRqJHo0oWtsOqbdzo49T2jIH4XWgqcimRIgdyjGl9GUgYeDx4sU5otMF8IRqeSumzmZnKkrZ5VVX0twqfcBCwWK2CI7EO5+nIlfRlYiH7PhgMuHr1kCefeIpnn3mGnf0Dfv+Fr/PWO/c5uX+H8+mMg+uPM75ynSc++rPI4aN0eOBjNT3mjT/+He68+DsU928xl1A+0+fGbsad0xW2KHHaJyX4MKj/sP0EtKZpmC8WbE1GSCHI84K69gv7TWZ4HKNGo1HSfo3Ac1z4J8mnTnP79u0k0bK1vZ2M1RbVisPDQ6rKl9RHJvZwOEysdyEEXesZOE2zNnmLGuiRIR7HuuFoxGq1TGNoBCiKokga7lF3PSb5vIauYWtri67raNuWk5OTBAqL4CS/mC98eWrbJvb0I488ys2btxDC69HF4G1nZ4fFYuFB3gCwRNBVCHHpejprE0gTzzsmgfv9fpq3wI+pi8UCITxDMlYK9Pp9HnnkEd58801viqcNTTDj3N7eTkDwpkyOc47hcJhksSJbPMr4xHP0zPMsAeHL5TKxMoUQTIORp9aayWTiyz0D673f7zOfTpOUzOnpafo9ys/Ea6a1QSl/DfPcp+5jAiBKo0W2fL/fZ39/n/l8zqnyeut1XSNCVVMEv2czLy2z6aOSgnjHJZBrU+LHz2cqybfF6oZYGRCNWXu9nk8WhXsmpQqayCvOzs7+9B/an9D2vmPgACoLQYqz1i0Ajxsxlv/KZY3gH4zhG+H5WA6dDmP9t/Of++5nJdJ3Nn9GGDQB5uGnTO++9xY3U/TpEAIDPO0rEHC80sF6H5egfueTF9qKS1ruUgRQ3QZQmIBniKhoGuD/sG1PLvNHJYWXsBNhBxL/zwiHFCEBInxV39HpOY8+8RSf+zO/yJe/9EXu37tLXpQgJGWv8NKH3ZolF59nH+97X4adnR2GwzHT6Tn9ft9LMgqZnnMPeHUIJFLaNJbEWDaOR1G2LI7lm9VN/lTcBoh+uS9sbge4tA6J/hwxWZplGV/96leZTqc+ARuAn40usdEzREQpEosw9bPY9QWXX/8gmojpnX814+QPQXnfvi8YD9/VlPhPuw37BXXtje3rxnvbeGDdBW1tB9oF/WIXvOIibhrlUNbnkfeEN7jU/gUbCUsuyGKHsQjWgB6Ex0v43mzCF4wB3XXUwm9HiY7hsM90XpMXferFMoCUEfrziUYhBFmRUa+iL4phvDOibRoEOyzOfxq6HsWg4Zd/fUyRt2jd0rUdumtp2hbdtWjdUDUG3TRoXaNNh+6WdG1L22i0aTC6pWsaZNbDmA6HwVkdDFU37agdBAKBM0AWAeho0hqTcetvgL/eQvqqwK4NQGAA9XwCwldpykwgCRIaxlFXHVJJ8rJH2e/jnCHLM7w/WePHfCEQzjA7O2PQG/DYozc4vHKFIlcJ5AUPoscbHIc3ATgR65vW+tubc5v/bjohf37Cz1tIbxYaz18pgQqsXRWkLdpmSrOaIpylHIzJgjljV88wuvbXRnjD29FowGDQ4/zkHtYa3rh9wbzSAfSVqKygKHr0hyPyrKDIvYSLtZqmXVEta3TXhGRKkH4JWFS/XzAcFjz37JO8+cZNpjOfKXLOM8Kdc7SdZx+v6o5V1VIUPdqqpdfzGBBO0GkC410T4xV/LdfxTlqbbMRAUXp382/7HjHSpbnOhXuTgplEUVin79Mcxsa984lgu/64B9HV+j57SqPfgAs3WT401kdyTK9XUteOputCvCERznqNchviEbnWYHdWeJ19IcP404EzYfsGIR2zpebe6QJEFtYKGYPhFnv7V9jd3mZrMqDI/X1//NFrnDxyjW99+2VOT888rhTGWW0dVWNYVi2379ylaTV//PU/5Gw65ZEbVzm8fcZb92ZY4zXtVSZROVgDXeeCpLYFbdHWG4Q6J5AuJp5cWN84DOCQyEz6ihjtB0j/vPrztkbgrOL7tR8bEF0JPwZOdq/y7Md/GpHn64DpR9RiJm+zjNHhcNJtBHgC3Ta8/co3YXmE6uZgas7Pz5nPZhRF6QHWUJZgNeSZoltOWc0e0B/tIG2LcjVdtcJkIpRfDbFaUq0sGkHdWkYIdlTJ7dv3aIRiOBwyGo3olV53K8s8cDKfzxNgEkGeOBi86xw3QOikiSUIcgY9iqL0wEKv9CVmXRfY5zl37twBoNfrsbO9Tdtp3nr7Fhfnc4RSWOfZk5u6clISOv+aMRcB9Hg8OLh241FOz5Ywn+HQ8D4yRtpqUJI33r7JC1/7JhfT5xA2I1eQ5YKTsymN6NNIya3zlrdPLSvVQ2q/BHMyR2ePYnqfhukfoe0pZrWg0g3aRl1ggchFYFF6d+4IUvnr4jOITdOyt7fH448/zt7+Pl/+8pepqsobANYVw1HOYGebTsNiscK2glwKDvd3efyRq0y2Jjz25FN86pOf5MmnnmR3d49ClUigNY5ajql7ryKHewxmF9x4/Bl0fx85egTHEJnlGGA4PuT5n/91Dg+v8fof/CNOTqcMD4+4Nmw5u5A01kv52OYDXnB82D5s/4o25yyr5YrJeEhRlpdKC6NWeGTSlWXJ+fk5w+GQk5MT9vb2kn521CDv9XqYtkufWy6XvHPrFpPJxMt5sDZh7vV6iYkdy/L39vZYLBbM5wuaukEGNvqmvEkESqSS5HkWjDK9OWfUya7rmtFoxHMf+Qgvf+c7zGYziqJguVyyXC4T2300GiWgNFYA+e0HpnsWZVQse7t7SCWTVnue5VhjMVajtfaa3xv+HTEpGwGZaCTaK0s/TzlH0zSJgRjZ2pH5GGVWNiusYjJAa83JyQk7e7vs7+8zGAw4eXDE7s4Op6enTKdTyrLk8ccf5/T0NEmnHB4eJi30KEeyqWkfr0VVVRwcHNDr9Tg7O2OxWCSZmMSMFCJ9D0hm3lIIjDaURZkSB088+STT6ZTZbOYNlIKPyf7+PqPRkHfeeQdrLZPJhKapPXhnDHkAlaJW/Ww24+TkJAFORVGgsgyLT8omk+mwrel0mjTXm6YBJ8iyfMO0mg2QTF5awMWEymg0SnrrWZYlNnzs7z5hIrh7956Xo/mAWYIfNt/eR9iTWpy9bVhgr0Fnl8Bb/+Oyv0xsPwjT0zO515+PWqKXtpf+915HevkNFwD3uJ34XbfxSRc+t/75Hix0RPqeX3TGxejl843XNe53DQD7z1mE/+fWsOn6+sTI34WiJOvL7AOYvgbEPVjjS/C9f09caCsHQvl4WAqCYaDzvqja4WTGL/zSr3D33j1ee/11RqMxSvjiceuidqvFGkfTtvT7vSB30IRx3o8F5+dnyW/BJ8xkSlD6eHSUEo1xjRMTZxFMGI/HSUos3utNAOLhtinvtdnPNok70eciJk5HoxGPP/44L730Ep/97GdRmaIzJvWUzX+b/UsK8a7XNnCkH6i9v/7/7r77YfuTt/c79nyQbPT3u6UPcp9+3rQI6fXRmxY6HSRZlEU5b5dnjMOY8Hx5DHSdFBfrXujk+jmQBWS511N2FmwA461zFCXkhaAoAhvdeiBdSrAbqhlGO7QkaJTX7OxMODt/QFZug1A4BOPtXcr+gOnJfZQILNyY9LIgpEO3K1Q0qaSAXKDyEVt7V3jz5S+yWs752S/8Oe7eepPHnn6Opl55M8jBEKM1xnQY09F2GqNbdKNp2yqQNQy2a2nbmqbzkrdtrWm7Bt16X6Gua9GmQbc1erz0Xg2yQHcrnDDrZMDGgOFcNGwNZvDWa0hH+b2oER0/GwFu6zzAlxclRd6j7G9huhXYmrP7x5h2RVF40LpQgkeefJJHblynLDIP7UoRKhM89IfriE7bvvoTr6nthciT7rWXGwGX6tVs6tMuAItIBVkPKXsI4T3KjHEU+TYqU+SZN1rEWawEORhS9PpkeQ9Bhuk0q8UpzhkcXhfeSYfKHGenD+jqmvNZwzv3FxCqBlSWU5R9+oMxSkqyQpLlEt1ULJcXtE2LcTF5HDXnfRLHOosxOsxfA55+5klefuUNlosqkXzidV/VhrKQnM2WTMYli1XFtWu73Lx5ipAZbdORDcv1gy5IFf6EKxjfE+G9PBjOJtPZOJcBTojgv7I2+I1geHgsU9yxJgi49GbcazR1iVVqEVRHxMSZDRUWIZZxGwfpwr13MWm6/j3O31mW0WqdYj5xyT8hHE6Yi1PyAEtR5Oiw/kwyNUJy+/4MbSVSeD+B4XCb7d0rjCcTVCaZL2botsFaQ5FJhqMhP/uzn+Ybf/xNHtw/SmOLdbBqLZ22XMyX3L1zl8ViybA/4i//9f8Qqf4ef/cf/g4a4Z/7xKR3qMxX4Ti013V363ita2witOIcXcCynLXeUFcE3wQT4jW3xkI3PFi/a/uxAdFxjrIo0dZx48mnsRtstx/ZIfFuWFEgUEE3JgbK3cWbLE9usVytuLo35qcf3+L89IQHDx5QliV13aKUZxRKkSGdw9mKanHO1mSCkwaF9o9jC8ZWWAXWSjKRkQlL3bRkPfiVj3+GL7qX+Nadu+huxny2QEpB2VNpQX9wcJDKLyPjLpbmR9A6Dg6R6SiESK7Lmcro2hWLuTd6E0HTWxvj2daZYncy4vr16/R6PV577TVee/U16rrFWDBhAdIv8nVAG4J+r+O4Dkri/iPQr5Q3j7L4rK4TMgxZ762fvQnGC+lAdTw4PuaFP3yRt2/uUZkS2c+wmS9xycqCXJRIOSMrFFle46wkE2CdAZORFYdosUPbvsNQGpxyKGUR5FhrEvOw6wxa++MajUY88sgjfOLjz/H88x/j8PAqV68esr29i0Pwn/wn/wlf+tKXEEKgu4bF/IKhE4x3Dji89iiTyYi/9lf+Eh9/7mm2BgVlr0AGEw6vbWeQKEohEcKxtXPA3YuXMP0dDrf3KIs+befIVJ9OFHQ2ViNktHmfyUd+js9ce4bXXvgNjm6/hZnf5er+Jzk/6XCsJ5YP24ftX/8mODu/4OBgH2M6bN0k0HxzPIzgqVIqsZhjwqyu62T02HUdSkr6RcHO7i51VSVTxrZtEc4xHgx59MYjnJ2esjUeI6Rke7LFd155ObHX+32vE46wQcdNheqhxv8tRWISlGURvuPB/PPzc7T2wPbF+SllmbO1tcX5+fkl3fKqquj3+5RlyWq1oiiKUI6r6Tq7MSf42e/8fEqWKbTxSdmyiOajfuyuqyaNwZFZnec5o9EobTsmdHGwXC0ZDocpSVDXNf1+PxlvXlxcXDKg29/fT3OYUgqjNffv3MU6x/npKQJvSLqZEHjw4EGqopJScuXKFbIs4+joiOVyydbWFqvViizLmM1myeButVrxzjvvJG35yCSP5qO9Xi8FX1mWYaxNxrHz+TzJ0sTkyv179wAoypLje6copTg8POQjH/kIx8fH3L17j6ZpubiYkecZZdlje2sL3XYcXrmClJLlbI5pO7pg3jkYDbHWcnx8TNd1bG1tcf/+fYzxckSnp6eefSIkAoVAYZ1NOvXOWaSSgVXlq8Ok9NdqE/QSwuvw+/hlff1jZVrbNhRFydHRcZjOPwSX/jTa+yk3hcgqDAskG8ARILJ1pVgvBv3La2bV5s/33yITbtM8dM3Ceggi3zzS9zr6jWOKiIZnjflocS2p6NLnRYgTN4Fw39YRpri0zcuM+4dDnsuSNk4InJDYAL47SHI4zsXzd0HVJkDugYASF6mE50kGOqMJ+qjCCkyUVXCgiN/zC+hB2WO+rPnYT32Kj730El/9ylewuqXIFRfnZ5jOkzmwDq0NbdMF6cWMoihDlYgNicoySWLlRY5SIj3rq9WK8XjrkmGwMSbNaUWxnmOi18TD/eTh1+I8E0H0TVPrh//FRKExhu3tbX7t136Nl19+mc9//vNpfI3X/mEQPW0/XOdLckTxeH6gyor3O3q916rwR99+1GvlH0bbZIx+v899z/c3CaMfxIH9AK3Xz7zUgrE0jcU6Q1X7o7CRNig8+9IGaQcXmJVe2oQEsJoAkGfKm2BGEC6N/WFKFngQKQuUWKUEqiCYQ7LOTBJ0zHNJU1uyrGM43PIa6HgJD2cNMlRERoAwgqDOBrZzJtBdAEdFOJ+2QAhvvCmFoauWOGf4xtf+Of3RX+Hi9AF1XfH8T/88QioyelTLGX/81X+GMS0f+9Qv8NjTH8Nqy9133iDLcw6vPeK99OoaIXLvaacb2s7StQ2mq2naBmNX6NZwdnTEt1/6PZpmhdWhCks41o9OSDIWJTgPI3pW+BpQtcZLqpIum5fNEFKRFX16o10sLdPTm7R1jZKOYV8gVUY5GPHc088yGRVglrjWy9pgbZINEnE+xV0eZRLYSQJeU5Y8zDnrTh1fECGZsgKTBwDYUva2yTJJnkuUAiWcZ4GPJkCM/71USdsu0F0VTC4dbavp9TPapvKG2Theu3WBDtdFqpws69PrDRACsjxDSclqOaeuF+hOo5SkTJVLNszF60qB6AMohGBvZ8JHnnmMl771Bm0Xkgxhz1XrmFjBsqpo247lUnL9xhWK4oKuc2jjj9ePG0ETPlyeCKD7+cl5b8As81kpuDyfxGa9jvilpG16dtaZLeFEekZDzjwQ012K02z8Xfh4LVZApKSZVJcBdL83X1HgSM96fMBNeM6dEDgp0/tROs5aEw4zgu7RuNQG6RsPFWvdIZXy1bLSsVwZjs8bCBUGvf6EwXiHst9HtzXniwV1NQ/mwd57oOyVXDnc4xM/9Una+g85uzhP16jtLNo6FsuO1994E6Eytrf2uP7k8zz2xGP+M60vvcnztXFpTBbgBDpIYWljkdJLtVjnjZe71hOk2k6HpI4HzstC4pyXSaobjcRgnaBtvr8v348NiO6cC1kok4K8H0V7mDmxNhZYx2axVCMOdtXZEWZ1xqDs8+d++Rf5d/7qr/Cf/p/+S0pl+PTHn+Lm7SPu3DtNnRf8A23qFtesMM0KbIOzEmeMZ7MYh3QW6xqEtAgn6LTg9XfuoMoCsF5kPxSGVlXLalUFF2JF0d+iyDO2thz93S0mrgDdsawrltUSnKbrap/tttY7RgsPkCSTNxEYOF1LU1eJUW46x+nZOWdnF+zu7WJdyKbLDJxBWEe/LMLCbTPVE7No6wEqBtRRr9U5h9OGB0cnZKMtnMjInUML4ce3yDwJWbnIKhRCINSAUa9H2xxx/8GTXJydsOxAlX3ysqQ/KMmsoV/CsCzY2VsxyIa4cY4xGc7VzM8X2G6IGz3FoP06Q7lFIwxdl4HS6E4yGA159HCbvYMDHnviKT7x8Y/z1FNPcfXqVXZ3vaGcsSYYe/jM7C/94s9z9OAezzzzDM5ZXvjDP2K5WJDnA/rFAJmXPPbkUxxeu0JPGIxufdatc1jnAZGVWTE1gq9/83VefOVtru706OocuzqhNQ35aExjLDazSfrBGEMmFE5kZDuP8vwv/g0e3PoObz5YcePZnKKrEGpIKzUPt/cbtL7X9z5s/7Jtc6n4YfvTavfv3+epJ59IrMFYxm6tTezuqqoYjUYJoPXyFZ75G80clVJcXFwwmYwYDobMplMGgwGr1cpXnXSeoQ5e5mR3d5fFYuGZ6FojhUhM7ChbEitzZDCeilIcsTVNzenpKVevXkUIwWw2S6Zw8/mM0cgbFB8fHwMB8A0s9Mhqj8B90zQJRInAdZ57/wlnXWC4++VdlilWwfgyBnjWxoXUWgJne3ubK1euXJK86ZU9jDFMJhOefvrpdF0i2N00TQLcgSRzcnZ2RlmWXj4lTMDT6ZS6rnHOsbOzk3TdrbWcn5+n6xuBovv37yeA2TnH8fFxMhKta2+OFYF057xW72KxuNQfIvs6zll1XbO1s+111ssySd/Udc1gMGC5XPLgwQMAlJTs7u5irWV3dzeB+UVRslgs0/1eLs+oVxWT8TglJqbTaWJ/j8Yjrt+4weuvv87Z2VmqTvASNEXoJz5maZomab9HINwboCrKXpHm+KixD+t7CCS5n4ODA/I8T1IudV1T13UA0I+YTacfYug/4rYJWj5MjIhsJRMXZ2F+2WQ+RTAc1oyo774zwue+++12EWh8n/0hghHrc3l3DOHea5Nu89c1E/3hGfRdvycm2uUduI2f1oETnia+fl2uP2cjIO9AW9gE0UVcR6zNvJyTCGETECacQFoQmVdsl8JX4xYqZ1nXUOZAwyd/+tP82l/6df4f//f/K489+QTTixn9/pAi8yzGTV+kmMBUStG2aymy1WoRnn9Dr1+maqG6rmmbliKw75zz3hJR8jFWApVlecmTYzMu3JRoiQWl8f3L3gzritc4n8aKHWstJycnnJ+f85nPfCZIJ8jUn7/XWvCDWid+GLP+6NsP7R64CKS9n8+6DzRnIqWjbYwH0oVFZV7OxQQgzYPf/uBi4lComIRaI/8pt+qCrq8Jb+A1zm2QgQmhGsJ5wF1KSV5KP7wAzkDXOsIGsNYAkra1ZNIf32DQY1nVqLyH7ZZIkWN0lJNinVcS/no5B0p6Ap213rxQ5MazroWP7cbb2wgl0c2Sl198gStXr79LouL+ndeRTvP40x/jm1/9La5ee4K7t17lxa//NlIoPv35P49SOV/8jf+KP/Pn/wb90TZf++p/h1QZH/3Ez/Hokx/j6M479IePsbWzxx9/5Z+i2wa0o2u1B1WVZ6rKTFH0ekgh0W2LtR1bu7tMz6bkZZ/SCNpqSdsalIoSPP7UrbFkeZ+83GYw2mH64CVy2dEbKpz1VQVbWzs8+/QT5CzpFmdJEhbhK8bSvOwiO3kt3RHBUhHud5xf/evS7yP0Uxuzw2lO9SitCskPVQ7p98eUuUAImxjVECX+NkTTnGM1P/GdRAQCqVJeelmAkJKj04qTiybMawqlCnq9QTAW9fjRarmgaysQjrLMk0a5CbIdxvo4FSe9mWXoh1XlOENw4+oWjz9+hTfevIe16z5irKPpLHkuWCxbyiLn9GRGURZ0XYOzzldUQHiOQzVBGGf8POiJIVIpnPWJk81YKrY0D20kif1EHwH5dbVblH7ZjFf8XL2uJIkxj99krHR7d5La3wkXsPKw/YDRiwjOu2DwbT0b27DOp8RjR4oQ1MS+FZPQoSwFXwVbZMr3sRAX3j1Z0GqHzARF2WMwnJAXBV2zYFlPaZrKn3sE6Z1A24479zRXDw74+Mef46t/+DXa1hszRBPQttG8/totBgNQ4oLf/+2/zwtf+yLG2ZCYsrSdX4M6fLLchvsj0jn5Z0hJqCsLAprGIGSUswRnLLJUFD2R+owscl/BV0VS7/duPxQQ/QMJZITPdMXy4n95hsz32sX33tZ7MS0ikLxuzjOeWc+tXVXRLOYMneJrX3uF47M7vPn623z+Zz7Oz33hZ/mjb7zC8cnvUbdrph9h6dK1NV1boXVH1wqiGZGLx2sNCIkTsDKa33v5W2Rl4SdR54i+usZopFRY449KZTkiU9y784CVmDMqYdIb0OuPGQ13kybvyel9los5XVPhrE6LhXWGLZbAkAYZIQQ6BO5HR8ceWLc2DQJKhWWZu8w6D2PQd7238fpL51gu5mTDXX+9hDd6ss6lidanD0jglpSS/cmYYV4iheL8QlHVHUZUmNrR1C1VtaLoF9is47gYoK0jkxNE2fnSDyVRKKQb0rRbVOcOKXKyQtArt8kGPZTq8dlPP89/+D/8Kzz+2COUZT8tQOL5eDfwaGbhmaR//a//Nf7df/dvhMXDkv/v3/9v+C/+zt9lPjtFShju7fAHX/kKj1/7S0CH1R0u6NU65zh+cJ+vvfR1Xn71Ta5cfYKf+cRHuf2P/gGvf/t1BC3b1x4nHzzCpOyhY4RljQ8QXMhECoEoJxw8+Wlm9ghzNENIiW41Ul0uz/bn8eHC4kfS/vUnFf1Im3OOs7OzYB65hQhA9qYudAQYN8HnWN4e5VNimbwxhtlsTtu0SQd9Z2cHay3Xr1/n2Wef5fT0NAG2nl3eJokVFUDZKMURQdXRaJSMLiPYGXW7+/1+0vSOTL7I4vYg8oC9vT0uLi4YDodJjx2g3+8nMHTNLG4T4NE0DVKoFFBa65DK/55lmTfACscUK4BiMtMDOC3vvPMOUsrEJkeA0Yblcsndu3cvXfPlcq3tHpmLbdumxCqQmOM3btzgzp07lyTCDg8POTs7S9d4s9qq6zru3LlDv99PwHi8p3HeqOua2WzGwcEB4/E46fJG9nZZluneR4mV8XjMeDT2x7/wIFXbtnRdx9nZWUpMRE+S0XDEbD7j7OwsydXs7u6yWq3S/qWUFJk3aH3w4EECx+I92d3fp9frcXx8nACpaOba6xU0jZfg6Vqd+lmsllgnZiRVVSXNx9gfVqtVugdRF77X65HnOcfHx0l+JyZe2ralqiqq0Kc/bD/6Fseth00dASI3fKPgm3Vk+91+f+8WGWhBvGTzCL7LN8TGT/fQa1xmhsaF5xov2vh8jP02QHO3Tjs7IptTvutovvfva9mXeKUcwZAMlT4TgSbHGljzx7peqK+Z6GvSTVAfTa8JZ73BFw6pg8yLAJdJ+mUPrTuqpgMjeOKpZ/jzv/aXeOEPX+A7L32TrZ09X18ilQcgTEiCaofuGspekaSvzs7O2N7eRuuO5XIRxu8cgaJXDqirltPTU2yoqBHC+090Xcd8PkdrzWw2o6qqZAKdZVkaQ4GUxAOfUIhSjZv/0h0Mc0aUpIqJZqUU5+fn/K2/9bf4jd/4jWQYrbK1zNrDbPfvtpb7bhIz36+tY/fv990Ps4V/Gu2DYJj/S+33T/j+D9qM1et1lvCjlXMe3PGM8yBXEYYUu5GsE9GoWHqWetQEdsID5v6DQdpFCaxwRP5Flnk5lywXCVQrC0mmJKuliV/15+wEde0Y9EGYFTtbYxbLE1Q2oWu8JJ5SGVLlHn9wG3rr4fFwIhiCugxsCTYjmEngrEOqDJyl3x+zWpyyWg4YDHYQSKzzbGWjGw5vPMWjTz3P6698A5xDm45Hn/wIOMvs4oibb34LbZZY03F69Db9fo/rjzzNajHl7de/xe/84/83n/rsv8HP/MJfROt19kEpSZYXSKmCKaTFtDVOSExQQZhfTH3FpHP0Bj16ZUanu43h3k8+eaHIiwFb+9fplndpVgu0DtX+1nHj2gHPPXWIMBd+dgqa5HGCkG49piR2PyJ6Y9NqQ1VrmsZr6Wvj2bydtnTaYawNYLTDON93/HYkoBBS0S8lN670uHZtRFnkeAQqpJyt7w9R8zxOyKZtaOo5UWZMayh7JU63KCkwRvDGO1NflaoUmczp9QeekS48blvXS3RbpXE/zoGOYLJrBUIorDF0raFqKhASpTypqUJyfCZ47PEbnJzNOD9by4oBVK1hNMiYL2t2twecnc/w9t/+2TDaerTZp3CwTgQJm+z/z95/NduW5dl92G/Oucy2x16fedNn+WqD7mrDbqABkqIAkgiRBKGQaEJgSApBIUYwFNIX0BfQA5/EJ1F8oEIhMUJS0KCFBthoNNG+u9qVzUp3/fHbLTuNHqbZ65x7sjIrK6vQAO7MuHnOWXvtZacd//EfA6UylIrYVtjnmnHgUoYagww4G4H5mNkWPWfCM2Qws7DD40Wyg0htZzjzcDicCGz10Ac4vKa5DRkSDv+efYavl9N0IlzfIEJoAwbl+cBxZuSxOguIQPS0zlLmJdPxmM2mRkmJIOPorPYynzJnNNohy3OMaalWZzjXp6CClF533FiLsJ6EdXRywmsvv8Trr9zn2++86+/JCFrt6Izh5GzBns5pGs2v/tf/Fat+jXEWlQlECJaITPprNTYFMWLQkVCXtI467w6V+T4tywUOjdYWlTusFSghKZRgNMtQOT5bho9fv/wzw0T/Z7k43SOdo2o7vvdowTcfPkK6jJ3dfXTfk+eKssxpuv7S94QAbXqsMzin0+QfQifqvPGXtZ5lYgSsZYatvc6pCIab1voooEVi8Kkgm3ZNZuDwxhvsjg44evpnmG5DYTU2pD+pfMKNG3u8/PI+mRScnx5zdnqS3JLT/Q2YTlcnqdFMzhiLEGrLVjeGmOZ1+Z6fn/w+B6bjWF+ccjiaMJuM2Sx9Qx6miAoHKnwvgkh7OwWr5ZKefZ51azZ6jch7lJyAHGFFhkZhS8vFmebkXGOoKEuvT5dnAowmLxTTfEOlJQ09nV2wM9olz3aZ7R2gRjvMDm4h5NY0KeoCX2WNOnw0NAJOnu1T8rf/9r/D7bt3+b/8Z/9Xjk/PuTg94fGDhzx9eswr927T9JqL8yWPHj7iT/70Tzk5OeWtt17jX/mrv8x8Ouedb3+Xx9/8OrpeUfWCTb7HV750i85opLjc7OM1RnaaygpacY9NZSjyAmuMH8g/w4DVi/Ki/EUuXdfx9OlTptMpZVkkgDQydCM4HcHQCGZG2Y/1es3+/j6TycSnpGtNHiSvIiP64OAAIQSz2YzFYpGA7sj2i+0xSsVEDfFoLCoDgzn+7ZxLwKbWOgER0+kEcIkx2PeaJ0+ecnBwCJAY1OD7qb7v2dnZSYx0byoqEjhirV/sxABe1JuLv0cAt+878iJP1xpB9iEDkTBmNE1LNtAajGBtNMpcr9cpwDw0BJ3P51RVhQ2mpPH4Q83409PTxMyOz2YY/IgyN1G2xvt9jNN1RGPtKFuSZRmr1Yr5fJ6kbyJzfzqdpr68qipMCPhHeZ9bt26x2WzIsoynz54xGo3IsoxNXaXxIcrbOOe4efMmi8WC09NTuq5jbQNwHoIRd27fTtrkbdNw9OwZB/v7zGdzdnZ3ePbsWdLs1NrrqsfnEA0EYWt+Hd/VULIomsLG8TWaoEYgbijxEBmpRVFwfn6RwLQX8dYfTfko4MgiBvtEUkJcKvrFohBeGT3uKV1wnyPO40Qa8v1pRCJwXF/8qj4sywJeIhKhwwV2XFwKEj53zx1jOC+8MudwMn0+tImL+7rnvnf17zCH3jI2tjcY7m27no2L4y2AHre6sELzTHL/pXgvApEu01nQCUTCy7/ILeTveV4qZBP6dyGd8FrCLspgBWKKdlR1T9P23Do8ZLU6R2M4uPsq/9H/9j/hP/mP/g77Bwd0qzWu0zhr0J1mPPWmzNp0zIoJ09kEBByfHnN484C8zBA1WCzaGoSV5GWBDJlF0eA6jklxnhr76ti3eeAhMgVtGicvgQzX/Lv09kNgL/qKxLFtd3eX5XLJf/6f/+f83b/7dxlNRmRChnq5BRLj2wlYU6pdV2tGyqwYBju+b3GBvPWxO6b9t3X5anDo6mfX7+/cRx3ro47//T+LWSc/3LGuPoCP3n/4bq4//jb89HFnBpIB30c9EfsJ4xfuY/bb1iLxMYQVd4lM9lmUtrFJn9cGFqVSzoNCcpv1rkPliORgH5jb6kH77loE8FwgBoRbP+3yAJ8Nki1CgAq/S7G9b5URCFSxy/TfqTsX2Lkd850dxONAyFIF9eqMovTeOHlZgsiQ0QxTeIBaOElXN14ytcw4ePmA9dm5n2e6YETsICtGvPz6F/j9/+G/4+f+8t/k0cP3OD1+zGtvfcnLTMgsjFMSIWF374Cv/+6vIhD8tb/x73P/tS/wB7/136FUjjOW+c4BN26/wh///j9md/eA19/+PDJI6Rq9BSCFEPRtCwLKcYG1BikzVD4iK4RnqDuJ8GkAA4KBpO86cHgme9CuHs0OUPRcnDxJsojOwe3bB3zx7dsI02HFFkPxEvJh3I5oIC4RFi2WTd3y8GjF2YWmNREQd0SiIyIL9xKMOQUIJE6Glyu9SgFWs+7hw2eG6aziYN9ebuoeYSUM4/56HDSbhdfnRgZpOAE2ALZITi5qzlZR6iMjz8cU2QjnDFIqet2hu8pLFUazaOfo+7WX2dEuYU1CSi+JOcppWk3b1pTliMY2iLVjMil4+637/OEffBtttn1O24G2iqrTaAvSRgJnrMueALTtbB2j0fiS5nnYfKkv8H3NEDwP27GpT3Dg5wWBEr4d6wSR3e3CcQVbGTPcFvMSsS06B9ZLChut6fHKHDasE4IoWgDY3bZNxHmK9Jr7PiDnT+/f5xZ8lmlkFF6aboDlSSEYjcZYoDOaUZlxfNZSNxpkRjmakxUjpIC2OsfaHiXw7TuT4DRaw2ZT0bQtZTGi2giOTk544/VXefj4MZvKt7cukImN1jiXs7d7QJY1nNdrut4/sC0E6bZ9YJjndp0hLxV5EfycnGM09m0jzgeK0pHlCoRl77BkNJbszAuyUjIe5xSl97A6vPXxEPkLEP3HUIwDWWYsFjXzyYxWZ4xkxtHRCQd7E5SC0bhkETTGY1GZxDmNED5aJqVvRH5uEhpcYHnnytG0PY2wKJQ3BYlRMwAs1kJeeCZ2rzVd25Kphl/5m/8WRvybXCwfc/HkXZ5+8/dolxf0puPDD1aMyoK9nRmzyZj9/RucnJ1cus6h8eeQBXJdysvzEbznZyAfmxGAo6+WNMsz5rMxfTej79YJ4PGpJiTA6+DggN3dXZq2oW56ZF+z4QyRfYh0GUJOcYyxncIWGfloh7rS1BXgVujKgMi927btyGSDOPoz+srSZy1WrqhWZ6yqBQ7N40eSDx884uCLbyawBkgA0NW7kVKlRYffX5PnJX/lL/9Vdvdu83/7L/5LvvGtd3j65CkPHz7h63/4RywXCz748AG7u3O+8IXP8Uv/0tfIpWRxccZ7zx7yR1//Y84v1pzbDjV9iftvfo18ehODuvo4L6XURtMpp0aowmIwyEwFjbjv+1pelBfln6vy9OkzXn7pPkrJZKQWQeYImK/X66SFHUHmyWSC1prVagWElPaioNM9ne79JGnA6n3w4MEl8DeymouioOk6bt2+TdM0l4DoqOd948YNpJS89957yZTSOZdA3q2xaINzlrZtKcsdZrN5Su0fAqTGGM7Pz7l3717qh6P+bV3X2z5ckJjvQm47Bv+Z11b05lgyAcJlMA6Nz88YwzQEEIQQ3Dg4wFmvyz6fzwES8ztKDESgOYLZQgjG43ECv9frdQJv5vM58/mcp0+fpmOAB4wj2DufzxMwZIyhLEsmk8kluZN4rKZpEmBdFMWlgGgE4GP/HuUQZKbCMy/RiMTaNM6ys7fLZrNhsVqmIEXf9zRNkwIzzjk2m02qF9Z5Fsq62nDz5s0EdBtj0H3P+WnNzsxr6p+e+2yKV199FSEE3/nOd5jNCrquRUioa2+oFetdNI0dF6PETo/3H+UghmNFLLHOn5+fE40J267j/GKRIKzPFGl4UVIZEiuGmWJXwekINKeMfycSmuSBW0cmY9r49lvpNwFxWb899kdNCBxKSmI+8RYqG8KaYVbqbCRlXbn++LsHl5MtS1qHerAgpTCzBb5SGjsON5y0uOev1w3+bZ/TcN58GehDxHmr8CnKIuiPOxcMrewl0NivYWUC6P21RxahD1Q4JJrMS7g4kMKSGQvB6MpFcAxBVdfMZnMuGotVEzrtTUtvvfEV/ubf+d/xD/+//2+UUdi2BauZjGbovvPZjLMJFkPdVZTjEgrByeIENcoo5xPqumbTVBxMDmnqlt29A9arJXXbsVitGY1K6qZlNJ7QdD1Ga7T1vj/laIyxjrzIcMKQK0U5nrBerZAh4yb2HTE7K8pbxXVCHNsmkwlnZ2f+vQ5kh5xz/Nqv/Rq/8As/x8HhHuPRPlVVpeBwnuf0oc+M53F2az46fPvxLbtQLYbAenh5wx/hO5+0D/v4/dylWjf8zuWa+EmO/UmO5VGdzwJEJzAlE8J2zbnF4HzwUf1EZNLGtuHC2DZE1YXzwTIZ2/PgjGn/gK7Hd5mOny7LXX6PA5B4+JyG+0QJjOcJXHFtG/6+9Dp++DGurhx9b+n1di0tgVEuwpzLY41WC7T2WuZ+QhlAdML3EEm32ppwZW7YJ20NBaXyrNeylJSFQOaColQedBMC47aAn7X+ppvGy7pY2zOfFIzLkqptyYqcztQYrRFS0fetlyARWz+zVhuM7j3TVvVeCmKsyDJFXW149vhdyvGMXreA4OVXv8Af/favoXvDydExy8WSxw8eYnqNNi1//vXf9Jn1IuPB+9/izS/8DO1mxcXFKa+9edczdj14ghSKulpQbc74pX/53+Dbfybomh5fR/r0DIUS/tk6gVSBjY5CqTzUg+HY69tqlpdYm9G1rZfAVQqVlUznN9m/eZfTh39MGwKRAAf7O3zl7btIp3HCkxtlSFUKoW4kIvi+uVRndQ8fPl3w6GhBp8HaHB8JMWA7rwhgY1u9PKYOyZdlMcIa2Gw2TGYzRnt3OD5dcf9ljVR5qDMBEI6BeOUbrbWGuln6rDPnme9FXuKM9lmqDj54fIF1wjPPs4ysGGOcQSnhPZS0N6gejUqUymnrNXW9DGQlH0TCBUAYnz2htGcSKylo6oa89IHW09Ml91+6y61bezx+cgoInPNrsl47cmnpjaHMg4yQ20ragvYBICmYTccBN3MMHrmXbou/x4mIC0EJX01SwAJrfUZfAuDDrCniYnJQg8TlvD5ngwxzCjJrtriZzw8gzjMIGWsiZHypjDwvKPKccjxhMpswm84ZjUpG4xnlqERrzfnZOUdPn7Ber1hvVvS99nUmSAEJB0IGLQfrwXSVee89P74a8mLMw6NjHIJMlWTFGKUEfbvC6Ia8yCkySV2vWC4aL+csRJDHkTTdmgLH+UJwsLvL66/d58++8V2cg14bLNBrjcok0+kYgaHvNEpKjJXhOfl3IDOvUKIKhdH+Hc/2JhQjicoEo5En/Swv1lTLlvGkYL6TU5Q+c6UoQfeGTdUwFhnOWayTjMqM+V7+sX32ZwyiXzeI/GiRt+uYOJ9W4uU6VsTH7gOXJurXLReL2RybK9RI0PYbFJa+h/Vqw5e+9CUeH5/y93/9nzx3Lim8DlGaJLkQQvIXh8PLLapMYfuWsVQ4V2DQOKxnCuIbHCjGkzG3776Mcxknx6co0bPs1zypG1793F9hmo15/fPP+HB8l2/89t+jqRY4IaiqhrZuOFUg8M7A25QzgZCSqL/o798GfafQ4PHi/n7hYdN3fF/knqsiH/cehBDkUrA6Pyaf7rF3eMhmYdPCP048puMJ06k3Wru4uKDpe7Jih/pijSieYuQFWhbk2S7IKVIWKDFFGMPTkxMas0uZWaxco7Ix1iqErWnaE/rzD5iQox1MZyWjTtC7Fc8efZOdieXbf/5n3D2Ys7MzJ1NZGsBi4EOIWFv8RCgyN+O2qlrz6PERZ6drXrr3Ku989z0eP3zI2dk5Dx8/5Uuff5uf/5m/RF4oNtWCi5PHrC+WVE3F2briD/7821RyxuFrX+SVz/0i8xtfwDAJUeQrOk+DRYoHZbyumdYtMhP02qFUibOXMyWeL9dN8K/d/CMr/0JIzPwLcIt/Ecr52Tld3zFhlEDXqA0eF/xDiZKoix0XDF3XURQFz549Y29vLwX48qBXe35+nmRIptMp9+/f53vf+x5d1yXN9cMbXnIlggK3b99OsiNZlrFYLBL7PDL4yrJkd3eXyNQ2xtI0DaenZ2itEygb2/tsNsNamxjv0+mUW7ducXFx4U1Rw2Qzgh9CiMDM75PxjlS+34os7CjfIqVkMpmyWi3TsbwZsmedF3meZE3atmM2nTKdTlksFilYYK03wTs/P2c2m9H3PdPpFCkl4/E4SdREgDoCMUIInjx5EtKL1WBi6hn7UQ4FSPcXAxibzSYxLCNzPcrbCCG4efNmeueRgT8EiSIre71ee1kaqWibhp35nCzLaNoWYy3r9Trd/1BbH/ziJoJPsVyVSpBSUpalD0hMp2w2m/SM1us1fd/z9OlTZrMZ+/v7SerGH8eP0/FvKSXWGPJ8nOpqvJdYp+Pz3d/fZ7PZcH5+zmg0oqoq9vb2ODs78xIvR0esVisS2fdFn/WjKZdA4ks/SLCf2EJy8TMbUadQlHMoNwS5LxePGzssz2uKDyhr6UJCLO0KynT5W1vJlcFCP/4u4hrU65RuWaHbxalnxYs0x3ABIHj+eobX+VEVcRAYGADekSmWtNQH9+QZaG57SWmy47aAnl9ZJxA8GgPGa7NCBiZ65hereIEY4cxAnx5wEuug7jTFTOK0X+hJBEWeMZ3v8Jd++a/xh7/zuywffUCWK6SDXnfkpQIFo0nppR2Epe0bpJI0Xc1oNEZlnofWa40zDuH8olypPAU+i6IA6QGpoihonUMbT+qJeso6ZPfEMTIvCrrNJn0eA48xsKq1Tn1qHI9iX52e8WC/Z8+e8Zu/+Zt87nNvsbe3Q55n5Lmi77vwXKP+qvMLKLcFXp9745fw3pBW/xzwOmhNP/Cy8iqYHY87BMivax/bNvD8Z5e3bZnQz7fKq9vcc+e6bv+PO1ZoY04Mtl8NT7grf1/3uz+G2/4ajr29n7g0dEQjzBCsGhxvW0+2QNblu7nmPaY2OHzf2/0jaBjZwul7g34heTYOPx8c5dOWsgzZQQIv+WEj6Aa5EuSFACvQMpAU8CS9CLF5mEAgJbTGeVa79h/EjBYRnkHst6wR6B7axnnmuwDdOYqxIFdbkC8sXTHGenNO7aUGsRUHhzusPzxBllNUXvjseykCU9uCNRhrsIElnBUemBYSdOM4ebciy6HerFmcL+iePOH+q18my0eU4xlvfO4voVSGMxnl4Ru0/Yad+R4P3/82i4tnvP3FryGloKkr7t3/PJv8lM3i3I8d1ptaegDXB3el9NEIY0JGpHNY26cghAxZ/N7zQpBlBQh1qY5s6xap73DO4oJ8iso8qLpz4z7r0+9RrReeoOZgPpvyE196hUzqYerTIFuE9OL9HEqAtNS15XsPLji9WNG2Pb22KcMwkG7TNYoAxochLRzLt1eVSXS/YlQq9g7mzHcOKEYZm7WhaxuPATgbJEAcfevB+XIyCfKBNbptPakjBG2kNMk8crnuOVl4LXQlMspiHPAgr5VvdIPKJKNxSZFnrFcXXja402ifdoHKfQADJELk6L6j1RobCB9Catq2Qcoxm6rlfHHB66++zLPj81TnrfXM5nGe0TSGvZk3xZRCMipHCOmNKoWQTKbjINEWX8FA0z+UOBf3z9UFiRuv7y6lDJhcyANMA8b2Z9xkrEnHstZ4v4NkVrsdC6X0Y4W/34z5fAeJYGdnyu07d5nOdhlNxpTlhHI0oshLsjxHqQKlsoQ52QC6g8AaP552Xc3FxSknx094+vghpyenbKoN1hq8bFKavpDnGThH33VkmaDuDKeLOswFJmRKgOvo+ooiz8lzxeLilLqucCJDSi/hYlpDWWYURU7bbsiV4NnRMS/fv8t3v/c+bavR1s8zkd7LKc8Vo2JKoXKkhN5sM+GkEl4ZYnfMaFLgtKVtNVkpyXLf1p3wpqyjaRaCJT4otXdQsHcwYjyGptHUlaFuDFVjqVvDdOaYTn/sTPTrBpAfLYj+4yzXAnRXI0k8f8d7OzN2Zrepz8/AtuRS0dPxjXff51d/4w9YrVZsqgYrDdZ6s408LynyHKyjbyza2KRvPjyjEgLpBMZJbNDSymVOp3uEUpRFgTUaYRs0kjd++W+z/+pPQrtkuXzG48ePmY33mQtJ03Uw2uP+z//rdErz7d/5b7DrFUqWYBXGVDh6xmqHvtuQFRlqPOXOnZeYznao25bV2TGb8yNq6yPZUlic7bAmmJyGlYg1GickUkYHE98ojN0ytbeT1svPvXWGrneoLMPW50il2JmMceNR+J6fhPR9z2JxHlJCBZqcrqqACmfOUAisnCLyKdaNkcWMkTpg9fiIVTdCZnuILMeZCl2O6MyY3axGr36LV+5O+PIXfpqvfPWrfPUnvkyZl3z44AP+3q/+Kt/482/wZ1//PRbLDU3rU+cdITVJQKY8ECGkRDhvxNp1PXVd09Q1nTY44VPid3Z2uXHjNv/L/8X/jD/4g9+l3jzjl37xyzjnOF0+YbNa09QVdV3TVTWni4Y/fv+MbvfzfOkXfpbJjbdCVDkolCZU41IV3mqhyqAvT+cXfcYHTpxpr6nZ15V/vvuAvxjlxfP8cZSu77m4uKAsC7JMpfRy5xxF4d3ahzIukakdwUwhRNKMPTs749atW8xmM7quA+uZ2RcXF8n0MwLv6/UaKaU3cOvaBCC0bZsMNT1bwzAej1gul0lbPGp+F0WBc477r7zC0bPf59HDJ8nMOMtFkqKJ93Pr1i2ePHmCc47XXnuN8XicjE/btmU8Hicm8jDlNoLlPmi7NXuLk0Avs+IB8RhUGOoyx/vxersz8ixPE6T9/X1WqxVSSo6Pj4kps/v7+yn4AF4PPcoAAAlAj3riQLqeeP7IOo8SBTEgEkHsqqrSvjdv3kwmrJGBXtc14/GYGzdu8O677ybZk6LwppzT6RTrLF1dU5YleZ4znYw52PcSPg8ePUr3bQOYPmRdbjabS7Iucd94z1prz1jvdXrfQggmk4kPEgRde/AT8NPTU8qyTBP3siwRSLQO6bfOm4oOteCHpoA7OzvszOesgxTNxcVFel593ycmfh6CIidBfuZF9tKPtgw1Op+XWolI9BAcHyzshkiSEEQtzC28vd3HLywjgB63DReJl8GyCGpx3Zx5UK76rKTtERz7yO9x7ecRoI6My+0XxLUY+uVN1wB+6flGEHxwZsGlLcOrSuD+EKwTA4BebJ+hRXrWXQQ88Ex1TwILT1k4jPNyjI025HlB27coKSikRGUZb771Jj//l/8lfu/XzuguWpw2rKoN88kBMvf6xNPZBIDVZsVIlWAsrtcI7VBW4KzE1B3jvES3xqd4S/88o8F17Dtjm499wGg0SoFS5xxN01wKwnlQfhvMjP1GkmAMfV/MuIr9TzxPlE777d/+HX7u577GSy+/xI0bN660AYL+6dV3cqUMK1AE0tO72+70w0GiH1U+nS77R5Xrr/L7Hf/77f/JjuW3yOe2b8v36/iHgT+3Bb8H3VUicX7UlQw/F1uI317zhXRnaYnjcIMgwCUg/dLvH30P8Zl7WMp9/8f9A5Su9cC3cFtviQSiggfbgveYVCDUFjxnwDp3zvmsFiCqd/o+zPf1HuD13xPSa6LjHF3rkD047ZDB0rLvL5OurHVo42g7w7TM0Lrm8GCPhw+eIZyhGO+CtRjdgTPEBiaEZ70TwHV/R2ts9me0/QFqUpCPbvGv/M2/gxBQFCMObr6EUjlf/ulfRsmcZ8fPODk/5e69lzg42OPh+9+kXq+wWiGc5Pad1/n67/wDjG74yZ/7l/n67/5Dzk8e8+zx+wghaesN5XLhCYpBG1xIH/SLhp3gPNiNzwRCKKyVAWD1OhguBHRENADFB3ut9qawPoiXMdm5h23OWJ499QELa5mMR/z0V19npLwMooimjvE9iii65scH77En2FSC731wweliSV13nokbihAgs0Fgy3nPCK29TM2lw+Nw1mtLWwdZJnBOY3WHNo7NpmIyHuHwwUhrLfVmBcKRlyUCH+ywgTUNoCR4mRZACj58skIbr0Gt8hyVjYmeIabvkRJG5YgsL1ivz1it17R17yVLRHgPxmGtIcsVIivI1QjXNmjtTbGVlOi+p2s9WH9+sWbn/l0O9nc4Ol6mgEjX+/ayqXukzJDSKzsI4QFsISRZJkP2XNBAHwTnnptjuRhw9aC4De/VJvDcP+hIRImSK8661MHYS2RYl0B4JRVFMWIyGTObzdjZ3WNn54DxeMpoOuFg/wbnF2ccHN6gLCYQAm5x7hAOmv7z1yyJs5coFSSLgqwYM53tc+/e63zxKw2b9YKjZ0949MH3ePr0IXXdJQZ5kWU45xnb48mIh8cVbW/I8hEqK1FS0fcblPT96+L8iPW6xjpJXvj2HtdWddMzHuXkeUbT1qw3S9pmj1uHB3z46MjHWazPzJiMp4zLkl/8y/8Gd+9/k0cX/x8u1obNqgVhGU9K9m5OGM0KL52nNWWfY50nG/VdT1N35JmgHBfs3sgYlYL5POdgH8qxRfeapjecL3uqymA0OCxV2+MYfVyX/SOQc3mxaHruGRSHr7B353X68w9YLToqxkgq6rbj1/7Br/v0H+HZHJ6obZhMxmSZSov8a9H5cLI4KTXGD7g2RJ9y6Xjp1iEnp6dUmw5lBc8eP+D2F/8Kpphye+82t+5/GWsFWjoy2dNbAeMbvPXz/w7Z+Dbf/sf/LxYXjxGyQzqFMwXGWaTK6LVls9zQtR+wf3CTL/7kz1K88Xn6Zs3m/IiT02NOTk9pe5C5Q4UopJ+4+AivjawRvP677xS2LIDrFuHGOqzViF6T5wVlWVL3dQJZYir8Vo/Vb7fC4YKpRCwyWyB0gbM52o5Z93O0meKKXeRoTt9L8nwHpyHvLa/d3uWv/Y2/zGv3/jp37twmz/NkfvTlL3+Zt956i29+85v81m/9FufHT/hbf+vf9an8WiftXq0N1nhHaykzYjOUSpHnGUp1FIWgKHKs9VqU5+c13/rWhD/+46+jlKDMFbprWdc9i1VD1RgWbUZl97j5+a/w0v59WjFGyCw9xO2C8epD3YbTY8cL28FDpMXwJ2zcV6NKL8pnWF50sD+u4nA8fvKYN998PclsDBf1e3t7tG3LZrMB/AAegfA8zxO4e/PmTdbLFYXKkA6yvMAEUzalFGcXF0xnUx48fuRTKycTivGIp++9S1EU3Lp1i8PDQx49eoQxhizLuHXrVgKYo/FbNH8Dkpb5H/3hH3J0fITKBKbzk8nxeMpsNmM0GiU5E611MrJ8+PAhd+/eZX9//5JBagTRPVvMMRqXgcnsAbiu6wO72SZG4ZBZHU1K4++j0Qjddrz+2mteEkcpJsHEMrLBV6tVAuonEw8AHR8fXwLjs6IgzzNWiyVKSdbrJRHYGzLnwY8F4/E4Pa+joyNmsxngmd9aa87PzwESW//p06fpmUewPd7T0dFReu997+V6pFJkZZGAoaIoGI89E8fiWCwWVHXF+fk5TdMk8DlK0fR9T2S3R6b80Ogz7nN0dIS1llfuv8JoOkFbS9e19FpTBMPQ9XrNq6++mljokVnvgarAThFBp1RCJrOUTVEUBVYbbh3e4ODgAJVlqOwsBYgis9TL2PQJYPOSQBcDoOjFIPCjKkPW0nMlAMlD5uslkYgrQ7qfO24XekMwK/7hQd8rcGNMNx8cWCRq9se/+6uA4nMAuvDMtIDkx6vxQBJsGVPp7PG8YSGJN90b4KX+GEJsbzXMceKZY5p2uvdLF7S9zx+k+MsPk9o4nxIABocMbFxvMmpC/CIx14XEKQl57k3/xiVdX9O1NatuhWtzvvD2S/xv/lf/Hln1Ab/3P/xjFmcLbtyekOeWgxuHvPvuu9y5c8hkOsM87BAOTKMxnUAaR2kzMiswq5adGzssuhWZ85IO1toUOLTWMhqNkrRZ7KuG/+K2oYTjECAfZjXFfmSY4RXrQRwvoo+DMYYPP3zA7/3e7/HKq/f52te+hhAi+XZIqTwDLwQc26Z97k1dnZ66QY2JnybJns8Q7B6Wz+qoLqLIP7YyzP744e7CK0r5azfpWIOgHQzqTtw4ZJ9f7TeuAd3d5Z/+2OKaz68ey2JcuEgIIOvQ28Cm7fGqxWdQV9rWerZ2BFeFIFOeIR5jFlIEYFUIVOYocknm/f+wxl0yHe17DxYb7dDGm+xpzSXjPc/O9PrnKhNkKmitO/+9vh2CtSIwzAVt2zMZZWANeWEYjXKqpqIYTRnN9snyEpxB6xbdNWhdY/oO6zQeUHVYvWJx8V8hZUbVZPy3/4+M0WhMMRpRliXleIfRaMZkPKGc7JKXY/Z3JkgsTV3zs7/0P8E54zNmcLz61k9QlCNMr7n90iv86R/8Oju7N3j4/jt88Sd+iW9+/bfp24q3v/Sz/KO/93/nycPvUBRTbt99w6+VXXheRkPoe51xOGnRehvYi3XRS3X58a5vappmw3zvgOX5OcVoznx3yumjP03r/jzL+Ykvvcqs9Hrcl4I4YlvXCFXPy7k4llXPO++fs1hcUDcae6lPdZeGFU9yceRFjkNj9OVsDBEAWz8YWvqu8XI+eYYAqnVNv7NBZQUCx2a1Yr1ckRcF05lGOEtXr3HOIqXAaBeCOf6YXed4dr5J58mLMSC8t5oEnJdeLMqcZrNguVjStHoQQIsqBl6SSDuDNB0qG5GrMT0+Y0pkHiNp2h6VFdRNz9lixUsv3eHkZJmeRa99tlTbeX9BGVQQEoNeSN++BmU4nxpmsA7nDHEGIoQDG9nlBm181kWcSgm/V2hvXoVhlJdkZc5oPGIymbO7s8/O7j6z3V3m013Goykqy4MJq0rzACFgPDY4cu+vEi5DCIc3YomzF5LGfhojHJ5gmkkEDpU+cyhXMCqnHOzf5XOf+wpVdc7Thx/w+7/3OyzXFVkmsc6EtVjG0+MlDonKvFEszmC1pRwX1NWC1aoKgRsfkIKQBYLEWkHbGkYjRac7Wt1wfHrB7Tu3efD4WcqSNb1lNt3hi1/9Gm986Sv80Z/8A7JSMRWKcqxwzqKyzKsldB1dYzC9DW3Xoa1JbVUgGI9gZ1dSlpbxpAcpWK4Nq0XPaq3ZrDVau5DRYeg7QV9dUW24przQRP8xFDO+yctf+Dmak+/RG0HbSJxt0X2Hw4BUXgfJKooiYzweI5Wj61p0rwOLOy4ZrpatSUKaXAiFcAbT9Tx+doxWJWQjxsJy/MG3aeo1+XiPXnv2mRCG3naMMmg6C9kUpw544yt/lXF3yh/83n/L4vwhuZEIkVF3DaNM0RuY799gc/qMk/4Zv/mPfgORZdw63Gc2zZnfuMP85m0ulhcsz8/oN6sQKAhmCNJnXBKirtZJrBGXJzyDiVMEb4yLwLij6yo2m5rJZMJkMiHPc+q6TvILwyJxIHzqjI/wO3JtcaZHGI2mw8oKITOsLnH9DDma03Q7SLnhzZd2+Vd//k3u3/S6XicnJ8l0Drwuk5SSe/fu8df/+l/n6OlTdL3ENJKyLJiPswAwbU3vjHPoeD3WYGxPXzc0Ky8HoHuvO1n3mq989YtcnC+oq5ZFs2G9WnBeSWrm2OIAdeN1xtNDrBxRUSKyEuk+3l34RXlRXpTni3OO4+Njuq6nLIskCwIkgHI6nSbD0b7vkwZ5URRJK10pxd7e3hbkCaBw1Ly2OKqqoqoqJpMJm80G5xyvvvoqDx48YLFYJO3YyB5fLBZeezpIWM1mM9brNZPJhKry3hpHR0fcvn07gfrOefb7wcEBb7/9NpvNJhlPRkA6Gl1GVvP+/j7L5TKZcEaAYzabJRNVz2IuKcuSzaZOAEZkfCulkhll1N+OTMMhGL6zswPAaDRisVgApM+iDIwIwHRkkd+7d4/j0xMchGNB17VkWQ5sgfwoCxP/3mw26X0OZWAiQz4GKeK4E79XVRWj0Sgx3SMjPhqPIiVCuvRO4rNQSqWgStM0bDabFCDvui7JtkTgfMjONMbQdR2TySQ9/zh+zOdzjPWBFZUppFGszn32lZSS0WjEd77zHSaTCbPZLJmaRqZunmfp76izP2SCjicl06kPuggpqdsmAf+TyYTFYhH2FRijkVJxdnbOcrm82pp+ZO30X+SSWEjx+Q5BtYDRWriy8COBy1yaa10BvQNoHr+XYPEBsL6F07eLzgiG/sAg8yWg68rXv8+h/CI1gHIJFPeLRg+g+w+VCCaf4UtxgavwkM5WU3hwOkcASUR6Bp/ooi7d1yCYJGIwYgAMCvw6AEH0k/B/CayMhpDSZ1zu7LLe1LQXJ4xywXyao3RHvznh7FEF1Tn/x//475D/x/9zqvWCb3zjm/zv/w//J16+s89rd2/RrVd0Vc20nNA1HXXd0DtDWZRkeYHEslwsuM1tnPBZk1pY+uD3UNd16rOj0XVkqdd1zXQ6TWagWZaxXC7TONWHfs3r4G710IcgegwolmVJHbJ4YqC4qipmsxl5nvP48RMeP3pC8UtFCtQOgdDYrw7f8/Z9DN6e2IZ6ngNgf6C3/AOUq0Giz+KQn+GxPsnJvh9L+xMfxm1b1NV2FcGd60Fud6k/e/59bhvwpe/FIJm16b3HPuJSg3feHNma7XdjRojYdq0QgnPx78vw/6cr44lIBnlSgFLeEDCBlUPgWwmyHJLySFhPO+uC14kHzZ1z6B5056VDdfhnXTAsjQAdjkxApkI3ZeO/y8++7zRZJqkby85Mk2cKKTru3L3Bu997QL08wdme0XiXPJ9SFnPKcpeot2ydxdoeYzqs6zH9hvXFMQJHUwlWAWhUUobMkmBAHyRYlIxGlQVFMaYcjxmNJmRFyWS6y2Q6YzyZ8uzph7zy5k+QFwV5kZEXE/71v/2/BqMpJzOWZ6e89fmfoqlqypGX9tiOW17WNC/z8Cw0IH02uZOpHzemR+saZ03INBesFwtUnrF/eJfzJ9+mrb10n5SSL3/xFQ52cqx93hsNSAlTXvbVB2aWG8P3Pjzm4mJN3ehLY/DV4I+UAmt9m2jqnixXTGYjXHK69gREa7djkURgtPVGntaxrtdsKsVkuofA0TYNNgHIlrpqMFoHA1qBwXoWt/OA8tH5hqYzCJEhZY7KCqzpkFiM6SmLjFE5pqsblosVVR2kf4LviQipKb5/CGbfRuNcgxAqPa9OW4pcoJSka2qkkKzP17x8/xZlmdM0PQ5PunTOA+/aOIpMIZXcguhSkWVblvmw7xiC6VtSob9vHxjp6I3FaB90ie8vk4qiKBmPp0ymUyaTOZPpjMnE183JbEYxmpAXIzKZEX1TYjZhwOVxVngwnJj9K1HZiLYzvp3C1rg0XGqkNMTrtc6ld5VcZIRMHgW+5Ejn8TFByWQ64+DgNkoV/M7/8JtIIWl7TVbk9NpwvqyRQe7Nz/07ikJhdEtVbTA2Go/HXtEN+hmBsQ7jBFmW07U9y/WG/f3blEVO1xuaziFlzld/8qd5+f5d/smv/T/502/9GdlYMJt4BQutLabXCKGxVlAWknKuKIsRKnOs1zWrVU+16Wkrx1q2KCmxRuJERrdwVLVhcdb5wFTqCx2jUpErSaFeGIv+hSjWwY3Xv8rF6ROWm3/AIads1IRiFFKunfOGnwiyXFIUGV3fYHuDMyKk7YhgBHJlujHoUeOE1DrrFxUyIzt4mTe/+ou898e/jjn+HnJ9QnvxjPF4B+s0fesX0rmC3vVkZeFlW9ZP+aPf+vt890/+MV0nKYt7tKtzlPQ6WJ0BZM7hrZdQu/f5ys/8AnVnWV2c8eThe3z44RFd8xhpKorMMS4UuSg9j0FYkF7SJVOeEdf3OoANW6frCJQbvTVasNYG0JnQSH3PW1UV6/WaPM/Z3d3l8PAwmcNFgzgPnNswyARdJVEG9r9/Udo0XhfOafpO0jGlzHtevZvzi3/pDsKecbEEmbUURUmWZQGMkSglk96vtZbDmzc4Xy6w1iWAxrt8qxCVIzA3TQKLtNbgMoy2tF2H7nuMNazqC5xzLBZr2kazXi0YTaeU+2+gpq/jpvdYuQwjFVJIMgvW1GHG9eOr6y/Ki/LPU2malnfe+S5vv/02eZ4n6ZWoLQ5cYuBFQDamsB8cHPhUdOUBhcj+jiC4C0HFyIIegpyr1SoB55vNhmnQCzfGMJvNWK1WTCYTnj175k1ImyYB5qvVir7vef/99wEv/xIn3I8ePeLs7IydnR1u3bqVmOZVVdF1XZJeefnllzHGJP3rsiwT4zqauUVN8oODfZqmZjKZpWcRdcuPjo4oy5LZbIZzLjHwBV56JRpXrtdrxuNxenZRV7wsPZAb2fIx7T8y4yOw8mj1MLybDiEkXeelBUYjb5Q5m82oqio9993dXRaLBZPJJDHFz8/PL0nzRJZ/1B2PgPYwqGCM4enTpx5A0j1lMLKJjO0ION+9e5ejo6MEqkdAPQLWQLq3CNZHwClO6KPm/WQySfe+u7uLtZZHjx4xm82SHEsMCMTvnpycpOcZs6NixlZZloxGo5SWGut6rryM0c7ODtoYDuUhVVWlANB4PKau63Seuq45OTml7z7OP+NF+SxK8qjCr/1sRM1hy2xzW/B3Kz9i0xdFWLFuWa0urs7SecL6wreJS1NQcWVKGjQoB1q6H1+G8JMbHNNd2kOE9a259N0h/Bna0CCIED+Oh0vAdSKc+P/JK0fyHw7QubBQHXLuI3Pu+5ZrMLXnAUMXWOfgAs/f4jB4QM0JiRMS4aBvW4zT6M2K0U6JwpK7hmnewWZJ3pwj64b5SMH6nPbhe/ybP/8F3nv0hJv7N3n07BRVTnjjrdd58PSYutHUBoTKoCzRSnNxoXlqVvTKUART+cgaX61WHB4epvEkjnWw9WsYejbAtk9zdpullIKOkLKrouxV13VMp1MvbxgkYaLpcgw0P3r8mO+88w6nZxccHh4CEmO9WaISnhHprE2MzWEdfQ5QD8CFHbwdlyrPgJH4mUHV4i/osT5ZSVr/P+TCIra3aJYNQ3Dwmv3d9X9fAtEhGJF6rXARPojbYkDxasAuglaxo7Bu+FQDCz3sd4l9HkCXT9IVfJLy0ktjBA6trQf4wroS57WMRSKeeWDf4hmlUkKWC3Bed9xo6zPTfbyAtrH0nUvBg6b1vmy98VnuUnmjUpUJ8mBi6sKDi/cVmc5KCVCCptE4W/o21m24ebDP2emM8/M1zeqEdn3qwXCVe8mHvCTLRihVILKCLC8RjHDZlK7q6bs1XauxziDQ6Y1G43oZmPlSSM+olQRWfGCHh9+V8v2NUApBRqYUeS7JixF5OaYcTRiVM0bTmWe5T3c4P3vK4vwk9FX+uHmeIUUARoWXoTK6p2v7dE3OeoKFkrnXhcY/t8nOPvXqIXW1QhuDQPD2W/d5+dYORrekKKqz2zV6wu9DfXKOi9rwzgcnLJZrz9ZmC5xvg9WRpewBdGM8oU8Ij7GAoizHZEVB19ZU647RKCcvcoTV2N6Dy3rUoo1huW651WtwhqZu0G0XJIMcfdOyXq+A6H8EUrp031mW8fhojXMClWVk+diPYdZghUHgGI9H4CxVtaTp2tCe/KjtjbR9/5sCVqEvd85e6o2NNvT4zKO+a71vUd/S9j03buzy8OFJCoobZ+mtpDeOSelljuO82M/F3XauMOgcLmXChNdljKYN8+Yol6JURlEUzPe8/O6Nm7fZO7hFMRqTZ0WoP4KrxuWCbVZIHHW2Zh2BICAECN8fWAt5XrDerLHFGN8kJFa4FOiI41k0kU1TOnFZ5swajbGarmtp6w1NtWazXrBcXLBaXrBer6g3VSIkWWspi5zTZU3bWVTuSWLescYH09brNdrokJ28LTEAOQxM9J2hyDOs1UE2tGM2m3J6tuJ8WVOOxzx68CEfvPttMuXoMJQTQ9v0mF4wnhSo3ZzxpAgPEXIF1vZYZ9Fa0PcCayXNxtL3xmv+d4LloqfvHV0vaBpD23opbSEFeS4xCgoFWl+ebV5XfkgQ/cqo9olGkR9kwP8shqWPOt+PEFm8cspSZtS94a2v/Wvs7+/ywR/+Q+yz72F7rw2kHOheI6REG0vd9FgU0gU9JRt1u0A7O4iGuxSti9FoJy0Sh0Igyjlf+fl/lVd/9l/j5dfe4Dv/8L/k2bMT1udP2b39qtcvwuG0BmmxQuKsRnRrvvWn/4TV4iFf+eX/MTcP7tGtl7z7zh9w+vQd1ifPEHh93bUp+al/4z9k/+4bfjDtW75iW0x1TrU84eLZA44fvc/FyRM25yc01RJ0ixKWfDzl/ttfoXeKi7NTdL2mrU4haGspHEpatPVsbOdFzMhV5jsk592JVZaBs2RlQZ5l9G1Ltd54MxAJeZmjCkkhJK3u2M9KbNXysFljXI/MJdI5ZHjWOAXOMJsW3Lh3j3u39rmxX9PXF1w4y2ojGRdV0niMrFOwAVjJg4GJCDrv/llFHWFvQrc1GBI4n3GgA0ijWxyWpnes646+WtPUG9ZVi7Vw++YtvvazP8/uvbf4YFXwzomkcxMsBqs7MryGlcXgUM+xRobWPL4iuU/YLD/JTleOdS1j5dO1yY9Krb02lf0HLv8U+olPXX68i6Z/0cvR0TGvv/4Gee6NPCMI2bbtJdbdaDRK4EIEFiK7utGGLM8x1rBcLRM4DiB0T5EFbwiBn3QHIDWaZgohktlnBJ4jE3A+n7PZbBKgPR6PE/v6jTfe4MMPP0yAP5D6oPPzc/rem4NGjdsoCZMrL71y8+ZNZrMZ7733Hjs7O5wGreu9vT0uLi68Bl1Zsl6vaZqGtu2Yz2bcu3svMOPHZEpxeHDgmet1DXjwfD6fM9/Z8T4M2uIElKOSccgoivJX8Xp3dnZSW5/Op1htaEPQ4PT0NAA0UJajBPoACVCu6zotPHZ2dlgsFgghEggcWeBFUSRtcWNMCpzEDKdodDoE0/NgdlQIONjbZ7VeIRDkRc54NMJow9nZWdKsj8z2pmmSqWg0Jx0yPIc69EPGZp77bKZRWWKNH1/6ruPo6Iiqqjg4OOD27dsURcFyuWQ8HqdsiTzPU9DmqtlfBNTjeebzeZK72dmZc/TuMVEjPTJJs8wvIKpqw2ZTeW19Li/2XpQfTdk+2sgqC0A6YREFxDF5+BqeB30isnRF49sNAfSrANRHAHiOYMj28ePUFggIX70C0A8XkxEIl4AZbhvsGe/dBeArXrcY7Hd1W2SPDQ+WGO3DzVe/97F3d7m44Q3G+04vwSDTiTxgbwV40V1PFjHW0W8qbu7v0LoGaTp0s0aZip1ZxlxaShyrh4+olCB38PrhHu2XvsB3/vx7iOkBb73yOsu641vf+DaHd+9TTnrauqEPCKHJwYwVR/WSQimEKwOA4Re/bdsmCZfYR8SfMfgY+6YIhm82G6IB9fB9R7A9Bvti5lYMuhZFkYKZcay01ptkP3nylO99713effc9ptOZNzh1AqVcAtos4HRCplJ9FERJovB2wxzVhZR4r3t8ubG4lC4/+N6lGvL9tl3zmYvX88Mey+KsSGzEWH988CX8HNy3e+6za/b/vse6XJ+HVxPLD3IX1jmvgezS3aQvxW2x3V9Vq/fAt0vLl5iJ4rP7/DeHbHcdDrTNWNleidueJIxdw32ijIvvUeWgk5KSgblorGufvtw8nOCwtJ1GIAOZzH+mlCeZ+XdiAsPWUOYeOJYClPBZ7V6+xWKNJ2oVhaTXLgBHljLIkLjed36xXYapwLYftlx+H8Iz9E3vqKyh6SyzLLwlc8Hn37rB02cTFhdLdN9incahwdQYDSaueAPbXIgcGQwEJ5O9QPIWQIYzImgba4w2/qfRWKOxQTrDWQfCBwJ87CyA4F6cGSmkB93V1lhVhrkUIctH4Iln1hhUJmibYfPf1lh/XA+4SpUBAusMmcz9Owkmpn3fgF6zWq89COfg/v3bvP3GbWy78c8wVMKYMZVOFQFkK1g2hnc+OOX8fEXT9s/p/W+D3rF+KP9OnR0EHiS5KjxRD7x0Sa7otQEhyDOB1Yaut2jTYp2lqn3/13cNzWaT5gW67ajcGhs8mvI8p+v70Da8fEzVaM4XjScKSkWWF1jb4ZwBLEWRU+Y51WZBXW8QOB98ECIFfFJ/JWV6n1HNLbLhtwauIdtUKfquJ8sUi4uKWzcOePjoJLVnE7Iqeq1xriTPFF3Xg3DBC8gmTGlYUgAYv6bru56YPaykpMhH3Lh1h3uvvMatW/eYznbxJgQC6zyIrU3oT4VLY3s89naeHM3HY38TmOl4/fQoyyIcKKkQiCCrrLbXSWSZh6ZrvWxw17e0TU1Tr6mqFevVkmqzplqvaZsNfdf6thr6GhcmRFk2YJJbb7abFzlHZ0dYoJSFD3pgyJTE2p62bmFYp31rT3UoYPw4PECdKc/C77ue5apiNp9ycrpEG8fF4oLHj59y83AXcu8BUeYwmub0HQgsmVK0VY1EoqQgzzJGs5J13TKd+KB8OdK0446m9bJFOG9YK6QJQUJJrhQ6EGzbylKvDe1EsbeX83HlE4Pozy+EhhPA7eO6usv1B/uYgeZK5/BDl6vn+wxS0b7PyXhueWIhFx74PXj7a+S33+D+e3/C4w++xeL4fejOcW1P31Q+JUg7nJXoEOMRyqcx+TRvuT3DYJEqwiTVWYsUlsoqvvLTv8JLn/95tJsxf+kv8ZN/Y8J3/uR32Nm/iXXeqRblK7cDCiS6b2l7w+ErX+ZLX/tryOwWQlgsLbf+0l/h4Td/nz/91f+CzdG7TEYZb/3sv8KNe59DxB6wmODcGFHssrP3Gnuv/Rwva41u1ujVEYuTRyyPH3D8+H1WneDVX/7bjA9exjYVdnXMcvGQzfKMxfFTVmdH6GpJ2a1o2y6x38JQmTS3hBBo0yKdpsgFO/MZy8UC6BFZRl4WSKUwTYeh5yt3X+Pt/Xv8p7/197G9YDyaJNYhuOAIXJDnmr38AVl/RHWRoauCqvDprKMAUhVFQVHkXi82MBPzPCdT6vKqNRQrSDq3CIE2hl5bnPHZA7rXNMbQLI/pyTlbaeT6hHVnefuN1/ipn/wJXnn9TTam5DtHgvcW0GYzpNAoa8mEb/Q+gKa2M9DLNfLyX3Eu+XHV+uMmiINUqMvn+pi+47Nu75+2/Ij7iY872nOLkWvBp+v63RflR1lOTs/Qxms4AgkciAAqkADHaAiptWZnZ4e6rnnllVc4Pj6mNzoZq0lHAtqN1nzxS1/i8aNHaKPZ1BuqapPYyFGy5fHjx8xms5Q2H1nhRVEwm80usY7n8zmPHz+maZoEynZdl7TALy4uErOwKIqULl/XNYcHB4z39jFac/fuXU5OTnj55Zcpy5KdnR2++c1vcnh4mAw+nzx5Ql3XzOdztF6Ac9SbDfNwjXdv3/FGm9ZycXHB7u4uN27cSAznzWZDbzT7kwkqADB102CM4fDwMIH7eZ4zHo8pygKVK7q2pa4rqlWN1VH7vEz98mKxSPrnUc83gtMxaDAajRJYMwSPd3Z2Eug9lM2J+ubgAyQnJycJFFJKcevWLYwxrBZLH2wpCi7OzplMOxywXC3RAz38yHB3ziUZlxhgibIpMWByVSbnJ776VYTDm5JqzWg0Znl8FAypd6iqiouLC/b29hiNRhwcHPDNb34zfT8+C9gGGvI8T9I23qBVpmBC9AWIwFnMYPB1tGG9XtO2HYuLRWoTL8qPtsiw8PImV1umUWSL+oUnbOnpMi6L2KIj/kcEpIbbXGDFJVNRNzDvcy5oGsc55BZeloNjfGQRIpzveXAuzW3jrnEP4RdkMgCh20W3SOe1AeyK69bh3CXpFl8CxFOy8eA6Igznt+NcukM3OMbHleG9+LH76hrJS0LIwf16sCNqTws8suTn90oqMilwEgoM0wxmmWDqWsz5M549eY+5MFigqVv0ZkOzXPHKnbs8PV6wy5gsH1EWE6wVFOUEpS29sxirkZlkvDdjXa8R+YjemaQOH+97aOQ8NGSOIHuUhQIuAeE+adRd8ioaAvHxeLFEVnoMiI6DWXLfeyboer3h7Ow8pJRL+s4kJibSE1MQim3WRdDHT+/5udrlgUt8/YotRBCZzBInbACVfe0QIdghUzq9SDiJcBIr7OAz0v4RFBbp/PG6ttf3UZ8Bg/oa72ubWeKLxGKDpnKsa2J7H9EScwAW+s+uPpurx4oASdz/+mZ+3Wfums+iWa8Nzya15cAEdwnU3/Zp6f6DCWGUWkos8+EZE/tzK/siwprIpSM5XOowo947Ib3fX07sR5IMVrx6py7f/w855Knc+5MUZRlkVATGmohBefa1ChiAAKt9T6iCFEZvrJd/dUPA0X9mehdY6i5kbTjKclvPpZJIJVAqMLylIJMCKbdsTM/SlvRtD7nkg4drDvdHzKY549KSZ5ZXbk/h9py2N9Stpukqqrqjrnv6YITuTayNlxYxYLVAxxot8OxaZGi/GUrmfg4kRwiRg/T74CTO9lhLOJbPdjG6w1pPTNABbI/gawIaB6A6AoQUgchmkUrQddqbUErlTVyRiEyFOqISMOiEwBnouwZnNZNpSbXeBGAWbt3c58tffAnhuq2nEPH64yC0xXKshbru+d6DU87PvV74tvIPGsGwpjvoe43KMsbTCSpI03RdS9u15EWO1h6cHY9KjLF0bYvuHONSYXpNVW3Ispw21Ptq4zMOEVuTSm16L42jvAh/3xnyzJuuFrniew8v6I1D5QKVGMw9hLc7mYwwpqduqyQzEue8JjQgEfomkZ6LCH9v27EIf3vDTlDSZ89aU1DXG27cvE2eK7rOBL3y0Ab0NvPL+zlt64C2gz5CbJnwve5pOp8Z4SWWFLvzOa+89iavvPYFZjv7ILLg1eezAaxz6dX6MXxAfg3G154cE4Hq2MHE+xwC4hF093tZB3leorVBOIs2PW3TUDVrNqsV1XrBZrOiqtY09Zq2aTG6D5JEJPY87vJ9eo17RRZ0Yqw1fq4VxmjwY/fZeeUNWlXmvRmcJs9ymk3jg0Zyq8UupUr3Fe4+gfTO4WWOhESbnrppmU3GWwxeyJTJ4uiY7Y+ZFSOMha4xdK2mrQ260/Sdz5Dpe83UBC15aUAY6qqm2ngG+3SmmO6PKUcZdaUxnUEHB2YDYfT3PhPjUcZ4FOWDPrq8kHP5MRTrWqTy6RDOZOzu3IcvvMWtzy/BPGOzep/NySP641MW50dUy2Nsv+F81bJeNWjdIYUly0AYsFZgnMAKH5ES1iCMn/jlWYHJcl7/6i/z+r/0t2B+m6z30cTi7k/y+YMvkOHAdJAVl66zQ2Nzicp2uDXdR2uL1NqnN2hHlhfM5vtop8jKsZ8AWesn/IOZROoUA7MuyzLynUP62QHZrTe5peBNZ7HVmvF0l86VCDFjMjogu/tFbgiHdAZMh6lXdMtj2raj61qMsQhpfRpL1E2Tgr5fsDx9yvroA+qj91GjnEZ39E2FqioyBIacPnP8/je+QfalEbYLE6FBR26txtgO5Rxt13J8UrNc5JRFQVEWFHlBORoxKb2ZnTfI8MBNprLksiyiAQmxH9xOfodp9FZbH3HH0ZmOdbWhs4K7OyOqtqK2c4y6yaPFI96W8Or9e3z3tOW755pVN6WRU79oNH6QegGwvigvymdb+r7nnXfe4XNvv+XNyoIbfJQcGWpiRzadDmyNqCd9cnKyBUC1pihHidGXZRlVYOu53jGbzWjbLu0fg4enp6csFouk6R1BZp8FswUooiTH7u4ujx49AkiA6d7eXpJosdayXC4Te74M/Vhde3Z0ZCa3bYtzjqZpkqb6e++9lzTTX3/9db7xjW8kgLVtW24c3mB/f5++75nNZklm5vP7eywDcL+7u8vu3l4KRNy9c4e6rjk/P2c8GjE5OEj6u0VRJEmsrusopAfJj4/92BDHn2g+13XdJX33t956C2NM8rE4PT1N0jqRWR/Hqr7vk5RO1PWNWr2Hh4esVqvE5Pc68Jskq3JxcQF4zXitNWdnZ+zu7nrNxCD3tVqtEvv89PQ0BQe6rkvyBZHxPjRzjaagQghGoxHrzQbddskPpNOamzdv4pyXN2sar1++t7eXgi9RbmzIdB+a5UZphZhhFWV7dnZ2ePL0aTK+3Ww2tG2btO67zkvwLJerEBx6MRb9OIoYrL8IC10ptgRCMfh3eY525UAuaGcO9hlqjEeg8TITffu7X/QMIGj3CYIoW4Q/HMulexhWnuQEJAYnH1xEBNK34ONlUHwIOwwhyMgG32KMLgHm1yXnXQLQh/fwCUsC1AOlPS5MfVAgQFkhGIKM4P0WKJUIMpWB1oyEYypgP1fMncStzqlPH7F48gGj3R1Wm4qzswVni4onT59x8+ZNPnj8DufnS+aHI27cuM151ZDlJZlqaLsagaPMS3b3d1jVSy+tGNKk45gWfSGi4WcEwLMsS+SQKCcVA3/DPizKssS6Efv3mBETx9I4rsY+P/a/SaZKKNqm5fz8AiUzECqx6RAemtXGBnZmbAERi9oC3vHNDqo5HiDegqmERb0TIUAzrNeRTSi24HOQ9A0BLoEd1nGxPVcKklyqI8MQl9iCMNf8TL+L+HN4XfFaBueIYMlg/+19x+cT2ekfc6zBc/uoJnD1s6u/++fmAeytyFQIQgzb7tUm77ZhEeu2bzEC6PG9Xb0uN9gvPYvBMR3D38XWvJEYwBh0teEv666+wR9u0JMANph3Zh7gKsgDQOflXBAeaHfOoqQAK5FBziFeQ5QZyQuF7iw6s5QjT6pqW0PTO/rem5g6HL0BbUP/JjyAbrQFKQfH9dVCZYro4VBVPcY6zpY6rIcNZWYoc0WZC4pSsj8ecWN/B4RKHmB9Z+i7nqrV9F1P13niibNx/mpw6PBOPZmQbnB/4WKCCIgH24VAycxrmec5QpSegIgAkcU35uuPMYHV7n860/mfTnkpMqkQskBmOTIfUZRTinyCzLYG9QBC+sCmVAVNc0p18YC2bbyCgIP9vTk/9eX7FJln7/vL3jKQ0xiQ3htUjeGdB2ecXSxomi3hLlbswRASfnrgFud7rnxSkI8nmLam6xrv1RYC7EIJnFVBwlYihE34fLNpyHKNKwq0sR50DQ0wDu0+wzUSK2ww2PW65No4nhyvQAik8M/ER0gMCEueFeSZoqnXvg9Pc8Qoz6sQUmJ7G4ZFl24dJ5KcT0B/kdLDl14uNyg1aEPbG7SRzKZjztqVB55D5F9rQwyHG+dQLhi8xoCK3M6aOq3pdJfAbiUV+/v7vPm5L/Ly/bcox3OsdWjjMM5i7WUme3xlV9+zszZ5O2333YLnoSrgLFjT0/ctfd/SNBvqzZpqvWSxvKDaVJi+pu86tO7BWW9oOuzvRQg4hL5lq34w7B8jGVXinKA3xgf2hUAqLxXl/UygbnpWVe/blgzZGcJn5HRdk+ZQDhECJFGLPw4poa+3/rqsNajMS1D3XU++O05BBu994PsMhcU0DrTFYRFBD91qn8En8dND0zuWFx1gyXJF2zuUyshzjcoUs3lOJiW2c+jOepUPZzyeiKPvHCqD0Vgw38mYzF5oov/FKMI79/q0oYy+d/RFg9WKTL3E+OarTG5q9JvnHDRL+s0xul7QHn3A+vQJi7NTNstzqs2KWvdgerAGFVqaEIAa4cSIfOeQz33lF3j7F/4Gdu9ltLXkGIQqWDUCke2B2TDKNFfVfnqjKUYlunc4p8iLMdL1gCPPR4BjvnuDe6+9yYM/P8LojvVqSa97Mrk1PY365ZEZaYxBGEMuJUWWY4Sj0z3ZdERja6SyyFygHYxU7huhNRinkOOSyewOk8EqyJu5RHNQ3yFIlXHX9LB+xru//fe4dbDHmR2xOH2Pxek7rM4f0FUG02kurOMff+sbFJQY5YGKvu8DmGTQxqJ17ztOpWjrNkm3+EVBRp5JsjxnFKQd8jwnyzPPFMoyH72LlzzoMJu+A7yuVtf1uF5Dq6mNpRMKUYy4f/c2f/Nf+6uI2W3+wQeOZ+2Y/MM/5Pe/+wdU/78/4vCLv8iF2KfPCz+5CIuXH0SF9EV5UV6UT16ePH7CK/dfTtrZERSOfgsRoOz7PgHQZ2dn3Llzh6OjI8bjMev12oOjxvD47DE7O7s+YwU4Pz/3muZ9R25z1uuKvb29oBXXJoBTCMGdO3eIMiQxIBfB6sh0d84lsDNKtszn8wRM1HXNarVKjGwgpdTv7+2jlOL09JSd3d0ENC8WC15++eUE/sZAQZSUWS29Eed0NmcymSSplyh1kuc5WZEzCozwvb092rZlPp8nkD6asNbrTbreuq6T+abWPbfv3GG5XrJeb4I0ydYYNIIxo5GXdJnv7CQj0KixXhQFk+kUawyr1Yq2bRmPx9y8eZOLi4vEnoza8BFUnkwmnJ2dJUmDCO5EgH+5XHqWVt8nCZrIwK+bhrbzmpNZljGbzjg5PWF3d5flcpnGn6grHO8jyqu0bcvZ2VkCmQ4ODiiLgrbammhr7c/bNA2Hh4eJ9W+MIc9zlsslk8kkSSVEdvnw/QMJ7Irsz8lkwnK14unTp0nKaL1eJ1Z+rGOj0Yjz87PnwYurwOOL8iMqW6BHhoViBJUSZugYsE8vQ8vDLTBgnIe5VvzcXdl/CD75w0Uw/Ips3JVyVcrl+ju69L9wtWERHTZZtotQD2qJbRBBbL8/BMDi3XgQYgtTb/n6QRrmioSFGDzDqLH8ye8nIrIuAfgRohMuSkYE6NR6IMtZzzqPwQGJxdQ1O7liLgVTYVH1irOH71F2Z9yYlbz3/ru8+8EDzhZrsmLGeV3R6J7dW4dUvaDVhlk+Qvcb8rwklwqhDc5qsnHG/mTGQinGUiG0TY8+zumrqroEqsdMp5i1E7dPJhMf8Axm2M661NdE9nr0pxhKb+3s7PDkyZMUxOy6LgVy4/d1bzg/u+DBhw+pqspnU7mg1y8FmZAJZEngcwKPSTVhywX3/7eXAjnbt+YcmFRrnoeGzXBbOH5kVrrtRuKFBNhoWHvSfsP9r267DpR+7vjfd/9rPhPXnzsglddfVwDXrqvt7srP67a5cIwYlIvg/fVAuAhg3eB4jsH+7tKxrfBSTv7nts+SzvsMyOF1u8CMdxEWduF8sW37Y8h0hm0gwUsGicGWKzf9Kcp6vTWc9ESxLSPX4ZIBY2SLRxKbVNGEFLJcIYUgz7w3gO4tzjiy3HckXWfoeufBxs6ijaPtDJ32OvI+OgEar8cukoAWifEMLvl69b1Ga0vbdFSVDFIeecgU99cmRUumBHnmyHNHJhVZnrEzK8mzCQLpjVEzh9GCvvfBuK43dL2m6zRtp8N812C0N/K0WKzT6bnr0L6lCOOfSz0qATzYyrgIf29KSkRWkIvcZzUIkCpHyhIhMwQ5CIGxLbbrQxduiV2Jl4HQNPUZfes97rznzoyf/urr5CoEK64EftMYMAA669bw3oMzzs4vkgZ6GjMGJf4ppUJKFcY9n0nQNhUWg3AOleXoXtNrb5wola/VxnjGbZblSIzvO40HS5VUWOslgdMYGip31OcHTwaUIsrjKNZVx8WyDSTCDCUF1vRhLhkkuZzDmO37ikCz1/L2jHbvM2TTeOrc9t0Ne7mIL/ln6MKY1GNcQVW17OzOOTvz+u0usszTOOPPa50nD2RSYIUP9Pr1lKYPfU6WZezt7vP257/My6++SV5MMRbaTmOs7ysuvx5PZIgyuqH5hnPbLXgufDBHG03fNl6vvlpTb5ZsNhvqakXTrOmaBm36JF8kiIFqDzQnZnd8TPFcg+iiv9cwbwp1X2YydaLeK9Bu20PwXwzdPNoYijzn+HxFryEr8tA/+zphTI/DkGXBmyS0vW0GYZwfbYNBUUIzz1SqF0II8kwFyUvJqCyZTsYsNisePrjASShLhcp8r2uNl2zKc0c+KskyRZZB17Y0dYfRPWXhmEwzhMh9ULF1tK2hbTRWu9S/Owt9Z+l7H0CoRuZSAPGjygsQ/cdQBDkx9QfpG5gwDblUPrXOGaxUuMlNyslNRnuvI4zGvbREtyv6akl1ccLy/JiqOqG6OGV1+oRmcYI1HVaV5PN7vPKlX+S1L/wc09uv4JBktsFYg81KrBCUtqMQjlYYOiG8ksugjPIS02syIXH0WNN7loCLaRmSprUsNi3lZEZzfhL0vuV1t50aCYCQCuOE1xlxmlJaeqOQ5IgeEApkhjNtSoVBSIRSOGv8gCxkaoixgcY1Um8dThbk4z0OXn6dm3duMzv4AsJ1mHbFennG4z/9B3zzt36NVmqqvmWic3RI+YosQMJERSlBlvkIr3A+YuUZodB1ChdMTZRUCVwHr5mWhQVCTHH1rHS/TGuCLIDWvW/8kx3y3XvMbtzn5q3X2bn5Mnk54rut5XY5RgpDkY258frPc+OVn6YzmqNmgnYOZI9wlkz4SHwMKLwoL8qL8tkV5xyr9ZrFcn2JnRu1WiObPMqiHB4esl6v2Ww2fO9732Nvb8+DnoG13HUd5WhM13doI8mLgnfff58bNw5Rec7jx08RAubznSQPFRnIQDLJjGxApVQCTyPzb29vj67zLOWhPnjXdbz00kssl0tOTk6Shjp49vR8MmVnNkMIwWQ2ZbVace/ePZSUZFJyfnLKbDzB7VsWy2XS+H7t/iveSFNKppNJkqGZTqdJDkUFk0oTZFGiFvt8PqeqKlarVTIWzcuCdb1mf/+AvcMDNqsl1abjlVfu8/DRI2Y7u0ymM4o9r6kZAwlxvDk4OPCTa+kXTWeLC4QQ7B0eBLNmy3QyScGP+XzO0dFTuq7HGJvMRtu2Zb1eXzKP3bKvPcAepWayLGO1WmGtTSztmEXQr9cIKcnCWDEaeaPVxWLxHGgewaPJZMJ6vaau661JqLVkUnHn1i2mkymm14lxfvv2bc4vLpBS8ujRI548ecJLL71EnufJMDUabi+Xy0sguM/CsskoNWYUVHVF07Wcnp7S9r7unJ2dJRmapmn8WKckWV5wdHzyT6GFvihbWHg4qTMePJIgZBbWLlEmwW0BLMKaS6mwoIrT1WDm7jxokDhMg6y6tHhLxR/Qs40DBBD2v8q8GoKnw+1bVlY4otHxzAn/dOnzAD8IGIpRyOH9iaHXqiCJY6QFOBjnILAU/aGkN/AbAHnOxcCEn+/aS0ZnXLqf6wkN26CGS1oR/iJzJVEu3qEcyGaAM4CwKOHIBdhqQ0eFHDu65pTFk3d4/K0/Ra2f8vj4KX/y8DEXBjqXUVXHnC9aOpPxxhtfZnW64mS1xORjfuInfoJvf/vb7BYlyIy+q1HnK7qm5e29fY6OTynGMzZtS9107O7uUlUV+/v7lzwiYl8V/TtiwDcGGAE/HtRNCjpHz4mzszPyPKcJ8l1vvfUWAM+ePdu+H0hjRQz8Gut49PgJv/7rv8G//W//LS9ZNRkDjr73BJUszz2jkoT7PPfTv0h7tRI/V+J1fNySWkkxePcpgvNcuWxc+fHlo4/0CWf8aU01+MYlUOrKcQLQEfuM7abnryI9myuBB/lclsnz+1jngVubOpLL5xqC3cMn4Ab/c/EPQcgg2HL4B0hSEmjqjB1cx3C/7c8okXUVOI/fiODSVWDvh0XRlYBIM3aA0Tb1z1IKr68sfTZ63EdKh+69OWieSTIZWZzemFQKC9Z63fRgnDe2DmMyrIW+t3Ta0bQWHbolYxx9500vhTRAMA924NUevMdXlodj1J4c5te7IGXjwTq1lV31WdrKy9EIBaIPIKxnROeZv0+lQAWzxkwpiiJjOs39vQUtcoHEOIdx4Vp1jzEdXWdpG+OB475HG0uvTTI1drgooZ4GsOuMOtP4EnZMuKe4UvdSPQBht/vePNzjJ750n5HycjXO9QkniASFdN4wpja95nsPLjg6W9C1kYVPGKuuqSxCIFXGaOSJPdVmQ1N3IUshBFxQA3JJTtsEj4lRwWhSUOQlfXVBIJTjnNe8d3arIS8CMpsMXPHzAaMdeZ6R5V4R4ehsgzEOmUky5bOQrPFa6FJKJuMSaztUJv15bLjGWOejcWwCx8PTCZhP7E+kjH8HkqbtsdZ/r9eenVzXNXs7s8tzFUKmrvMZdzEDKr5HozVN23npTiHIMsnO3i5vvPlFXn/zy5Tl1AefugBIuwCgh3FdSpl6nziug8Pqnr7vaNuGulrTVGuqek1drairDU1TYWImRAT7XSAuxOw4MfAUie8+MLZTnUwRiVA3XeyrYmBJepkxKcF6Y9YoARjftX8WXifLP1sHztJ3mjzLOD7bMMwCwIV+qW+Rwss/CRHUMoxFynhwQhDchHrvgj65n9/4jGuLNpayzKjrjizLsb0hyxTaaJpG01tD3SqU8oSHvjOhHUhUpRmPc2bzgqJQjMoxfZ/hnGG9qdlsOjaVwWjIVYaUXvfd9o66NnStRWsvTyQsSOtw5kcNoj+nGXwdihenjT/wwT/l964HdD/NsZ/f4/mpsXPycoRRPPcAfO8qQ8cTOtBclOEMFid6QCLtwMFX5rjRIYwOKPcck3uOG85iuoquqWg3K/pm49M4RMZk55CdgzuIfELvHFJ6J2k/gfRgsMQkt1ln5fNRltCAvYygC52Cd3l2wtIbR17mmM2C5uwEg6TvG4wQ9HVFpltEPsEWc6xrEOQIXVNmik7kYE2IgiksEomPiNlgGAoWK8P9h5YtABFcr60zSSfu0lJFgMszplrS5RO+9PYb3MTwdSnp9Rg5npMvLnj/z/+UarVKA0LjXxhN03odRQKTqZiBKCho0Ahvtuq2HQ7W4oT1A3icaApAOaJJiRAKnPbTuaxEFhNUMYLRTeYHt7h96x6zg9uMZjcoZ3fIyilWFmgr6JB8Y7Hhu2cNLisppEO7EitHuAyccGTCxuUiAvmc6Yivm9fX8Wubrbv05w+Fxj//1e/PSNue9cok+WoLvKZtiSv7xWHg+b0+wQ19bJfwSfuk68/3w02vhwd6ESr5cZeo531wsJdAzyGgWtd1Ytc55xiNRmjtAc6u61iv157lvb/vTTCd164EaLuOTbUhW2S89tpriUEc0+MjMB71YeNENqa3x5T6J0+epHT3pmm4desWL730UgLum6bh6dOnZFnGyy+/zMnJSQKQ4zmbTcVms+Htz32O9WadQOAvf+UrfPdb306GlLPZDOtc0hHfBAB8Z2eH+XzOcrnkIJiJelbOLqPRiHI8Ji+8seXOzg7r9ZrlcgnArVu30FozmUw4Pj5mPp+m+4kSJqenp0mbezQaUZYlt25nnJ2epgXK/v4+k8mExWLBfHeHyXTKdDpNjPYY7HDGJvkXrfsQRBXpeSilqOuapmmYTCZJxme9XieJmizLOD4+ZjabsbOzk95/BJUi01tKST4IiHS6v6TJHqVgYiAgsvwjcB/7cotgPpvRtR1lUSbw/fT0lGfHR4zGY1arVZJgiUD548eP2dvbY2dnJwUroixQzABQSiX9+Ki7f3p2lupAWZacnHiQPN5bURShngree+99z1K7bg70A5QXhqSfTfFQw3ZdZd3zY1BalA0wpSiD4r4P0PeZjWWfQRGQNIsHPng/UIms8kvQWwSxP+l1DAMDLm67FnfcljjZcjI9ez/H9Ax0f19h0WktznSMBfyjv/f3+MP//r/m/q7iZ966w6g55e7U8fDdd1h0hg8WjosKikKgZEFtLLXuycqC5mLFn/7pH3NxccH9l16mXq8pd/fou4KmWqFXS+7f/zz9pkajkNMZRWmSL8RyuUyyT+A9GYBkmBzlW2IfETN5VJahQl8SGeUxYBIzVpumSUB8DEzGPhOinECO05au6zk6OuIb3/gmf+Wv/QpCKPq+8aQfpWj6jkxlzwPoz73TrS4s6Xlf6XtiA/qYMtxDXDMT/bTlhz2Ou+Z3ceX3T/Kd667juv0g9OMDQPgSGD84q2WbJRLUkHFp2/Ypbq91C1UNVxbD37/f87JDcfWPKBHAHJ5ve8WkvyIL9LPqETO5rTPGeVNCaxxSRVauTaaG6aEJhxIClQlMLskzgbKO3jgyFWSFLPTGJbkKa322jgGMlIgchBVI4wL+Z8gKidae95bqRwA/x+Mch/cfyLKMni4ELT2zU4TApOn8+TxT3gPeSoUgqQgQX2SsS+WBWiVwQiGF94BD+uCUxw78vauB3ESeC5QUZJliPsk52PHSG57a7s0psaCtQxuDsdaD7J2h63uMtrRdj9bBxDRIyjgbIMUgpbOtAFdbue8ypJRMpyNeuneDl+/skLkeY8J61IA1nmCXyG4RmEfQast7DxYcnZzTtv0WnB9WuvgKIgAsFGU5CSoBIGSDSMEOi0CFgIYiU97nzc9hLSpXoR6EzKBQnXxQwptsCul7ypidIaUHxZvOMBoVRFPOuNw9OlnjYlBEZuE4Lu0npaDtOqRQPjgUgHKpvK+AkoRn7Q0eY/fh60iQG/F3R9REj5XSE0AkaIc1lqZpuXkwR0nhMbfw1mwwj/RZHC7IB3kAt6rrdD2HN27x1ue+ykv3X6MYzTAGms5nFKT+I4FPBqN72q6nbSvqekmzXrHZLNlsVrT1xuvPax3WfB6oDtWfqB8O2zkEEQOLDzf1nUNfliQCtw0kukEAMXzfhc7MiOAl4yslYLE2zBCFQKqcshwxnkzZ2d1n/+AGh4cHTCYTTs+OefLwIb/9J499BofXncLLz2V0rQ+iKSGCUaujhaR7j4Oua7fXSWDQOxH2UaFdOoqypG06iiJkWAjBaBTWug6EVOSjDK2Nz7KxjiwTHOyMGU8yiknmvUecIS8UTd+g14Kq0lRrA04ics3ubuH7m0AO9nMXb05ejjLKMktGy9+v/BAg+scNux+xy6c99GdZPvXxU6tOfw6m3H7TNeya2GD85pgYFp+VJFoPbwHA7SJShpbmm56Ecoei3KHYvbO9pMHlOKd9NBuBi+mMsRMRMkm4XO/tGia01v9ujSVTAluf8+yDb/Hww/eQtsHUS6QzdEimpeLB13+V9//8j+gWZ5Szfb78tV/hxue/xsYY5mWB61uEGiOFZltH4sTVIlW8en05DhNZU8Mw8JX5j8NPwvpOgyuw2RhX3gC9Tswe3bd89+u/weLZhyH9JUwUpHcc7oLjsicu+gj4nXuv0S2esREZh7du0NfrxHbUxhs6SCGDgYFEqBxZzCiKEaNyTFGMyMZzismc8c4+091DysmcSXkDp3KMzDEiDxqL3hQHCxkOaXusKGijZn2oZ3LwcLY6h+JKDfz+5RNP9X6Qg37cwT/uONd2JddP18Vz7e/y1911FfsT3vQ1sYWP39H9AN/7FNfwafZ4UT774pzjgw8+4MaNA2bTKYeHhywWiwTCAomV2zQN8/k87RPlXrTWSSYkSqzEdPXDw0OyLOPRo0eJyRyBitu3b3NxcZEA+cViQd/3Sce767qkyf348WNeeeUVRLiOqD8rhDfL7Pue4+NjxuMxX/ziF9lsNindvgnX5pzjww8+QGUZSMHe3h4ffPABUkratmUymfDg0UN6rbl9+zbj0Qjb62TOuV6vU6o++HTI3d1dr/VtfGAhaoZHqRWlFB988AFd13FwcIAQnjXvjU9L8izDhudx48YNtPV9QVmWzKczdN/z7Nkz9oLG+rNnz7xUjXNJAz0CxZFhXm+q9A4eP37Mzs6cum4SO3K5XBIlDKbTKcvlEmste3t7NE3DdDpNEgZ5nrNerxmPxwlMivUi6gVHcCmCbPfv308M/VgvomZwkkETA/NsrcmDlMvu7m4C6M/Oznj69CnFqKQMOu9SSvb29i5p+LdtmySHbt++zdnZGV23ZZBFsCq+467rEngV5XEicz3Oc6I+Ps7x4Ycf8ukGjhfl0xYxZEOH+VLc4kTUMhV44gBX9UeIyRp+zJJbwItoDiW2oKIbvNsBIDkMZUcWlorHvDQXDtSHAZvqubmyGHBIAzNKCOGN668OvNvbYDgxTHtFwMORrBQ95uXCVD6mOcfdB/Or9Fwju2x4FzYdfnvu7dx/mKL/8VJG8dn5RWVMg5YChAUhPGCAs+RKMJ9M+M/+z/8pf/KbvwrLZ4zv77HYE9y4MebZo3d58sBw3DkObu3x8r05fW/54IPH3Dm8A30L2jIfF2S39qiWZ5zkktVqyeHBLsUoR5uM1abl6PSE0WjCsvUguMy3BqHDLCwhRMpkif04kIJ/WuuBeagP+EXPjti3xmfW9z2bjTfUjhJUsc8e7q9UFiQBDKvVml//jX/MX/6VX6FtewLHxmcWiKhtHjMIBlBoqst+v2hOKYXA4tPaI2Dha4a3w3TxfcQ66kQwG5U44bM2hAMrHNKJ9NOFuiXCNqyXDxBbx9H0mUxmmv5MH3es4f6+DV3ZXwRAyvm1kkKmz2L7EOCza52/f8LaybG9rsTgDU8uGoQaSP2MdS4FpLwG77ame9mUYODmIpTkQe2Br2fsSHxf4AKw7hi8je22q5/F9zJsW1eL3/cq2U5c+hmBKSfENr/FxSXoNgPag1aD71+/sP7EZTTKQ7/stZal8gCftd6EzxvnWjqjg+xqWBMFfEGIIOmS+WxqpaQ3xUwBPsBts7ml8j2j1yfeZn4I5XXWhRF0rUlNJhoxNm0LeENHmXkgPJLwwIOhSgUCXuzDI9PVxbfg6Ht3acE0BBalFEG724PlQuCBNYtn04bseClzT2SWKoDr+KyqgJ9IZVASFAKVeaAvzwXTiWQuR0F+JktSOfE6BNJLelvnwVdnvTZ9qhxh1Ax923ikGI18sNP0lTdGHdyckl7rfUtO9O+g14IPHi94cnROE8DIYbuJp4pDpc8mKMmyAuckvTYICUU5DqTECHx7uRDhJJoOKR2T6ZS2aajXLSHFiXHpUDK0q8Bn9MCmfwaxSCFoW82m0SEoKlOQuGkNZ4smPDuFUMorGYQeuBzlWNOitaUoop9GmhF4E1sl0MJS1z7QmikPxCPwfoBCpOfhf8bePLD7ZfCcMgbdGzIpyXOFbV2SQYn3EaNk1nq5o77vwQnK8YhXXn+Dn/zpv0xeTHDW0reGVmv6rqNta5qqoq48QL5Zb2jbFX3ToHsdgjA9PnPv8jVGjGmoj+7xtjgBiWNU+Cx1tlGbXIXtvh9QMkh3spWlQwhcWDc467yhKg6pcpTKKIoxk+mM+XzOfHefnfkus/mM8XiCynx97dqGulqxXl3w5MNvs9msqZuGi+WaVdUQ55NOhHaNRfddYp1nUpIp6I0JGTPb+YCL7TT0IzEwoqSk7y1978f9TEpGowyVC4oiY8KMutEYJ8gwaOsDGqazZAoyJcM5PDCf5bknuEmHXFuWWYsUkizz474qAonYOZ99IzxjPRryVo1FnzUh6Pj9yws5l7/g5dPoicbB/YctQoBwLX/8m/8NH/7hr7E6/hCRFRhgVioyoTh/9B0+fOfP6VfPKKXgzChWT7/NL40mzF75CkIonPSSNT+qIhCMpJ/8GSd42o1YOYMWFiVzlDU8/N538Padl8vQjC9O9MdlyUuv3Gd5PkLYCV/7G/9TpMyCbphnBbqu36anSYmTCiNzhMiQIkMKhRUSpzKEzLBIjIO1keAsIkRDPaHgh39XL8qL8qL8aEvTNDx58pT7L7+UmOdRZiUCoDZIlXRdx+3bt5NUVJzcCSHY3d3l5OSEruuSmVoEneP3Y78fgQQhPEM6yqvUdZ3kUm7evMnx0RHz6Yy7d+6QBUmpqqoA2N/f9+CYc+zt7HJxfk7XttRVxc58jtaae3fv+r6w9yxzIQTL1ZK+16yWSzLpzS5v3LrJ+dkZs9mMqq559uwZt2/dZn9/P+nkjicT8sKbZe7u7Ia0QA+Ad33PNLDLIyMxMvh3dnY4Ojri7OwsMbTzvGBnZ4fz0xMEXnKmaRqy3E8g26ahczV1XSGloKo2TCbTFIgYdT1NXTObTsmznK7vPAgUzKtu3rjJ2dkZSmVsNhWT6ZS69mzI+E5nsxkXFxcpEyC+s8QOD/cihEis7q7rmIVnGyePVVUlbXUXggTvv/9+CrTkec7t27c5Pj5OQZf4TNNzCs8qatLv7O2CEPRGo2vLnbt3mUwmCSzvuo7NZsPNmzeTzND+/j5nZ2fJBDVmNETZndlsloIBsT7G4JBzLgVtVqslT5489nVluWK1Wv3TaJb/QpersHKEhxKrWmz38TrfYrsi9yjgpQNEeCixoyIO4CKZIYJrH3ElUQ+drRJtWv3HRTDbBfG1WQtXtonh/gPI6hJkHm7nEmyR8Bl36bm4cIrE/uLKhuExIjx3+TENv/j89RMWzenndpHsjxUWu257DG+zJsI3CODlFrCXWHTT88HDp/zRb/8WP/WlL2IWuxxkLcvFkj9/9pD1kwtev3/A7XLG/iuvcOPePaQq+ODhE5wa8d13H/BsdY5pKkyrkULy5OkDEI7JvGCcjRBlxnh3ztOzM27dvYfrTALboo9C/DkEzNu2TYzzq3I82/5RJpnDmF0TQfQYPIyG2UMJoJh5FTN6YKvv2veG3//9P+Ds7JzJdEpW5D4wYixZmSe9WidIAPvwHcZ3KwZMP5fe3rb+OERoMkNpDxfqnUj1z6/LbQIdI7A+EOdObW7Idk5AdgCGt0CjuLQtZVpcu//2WPbqZ257fVG/Pa2GQh8Rmbfba3LpcqOB5+W2taV8DeVujNtuHzacKGFj0+c+yGEjZh+uIV5s9Cpww6Ok77r0/NL5w05W+Pc57Pv8fbigw+4YxC4SWJ9+hmvztxRbYGyZAZSKQNLlW9zeyKcs06mXR/GGhRYlQUtoGo221ps5hs+0tr4rCdcoFUFaVeC0l2KQxqGyKP0QZD7Etn931huFSuUDeBG89mC3Y9k72jZmjeABzTzzQUa5rZtKKYwz6WE46+tXBEojidAD+1HyYfDwtvEcbzoYNN6NNthgKqkU3D7MuFgYVKY4O6+2+EhYuzvrgo68DDKqkeimIMjBSqGCLnrQQ5YkprMgAMrSkQmFEopMCqSyKOVQmSfeyQj04+V0pBTYHhoj/f1KgcD4+w4a7Nb4/stnqvtrqVvL4+M1T49OaZr22joxBNDB94fleEqeFV5ZIPS35XhClucYrVFK0jYVxngZjvgSsjxDqSnr1RKjfV8lhWelC+nIpNeTF/FvlXnt/wAAZ8qxOy88WTPocUshuFjWNK1DZH6OKoTAWI2zFpVJyiJHm451VWNW6yBn5uVQsI4iD6a3jUljyzYojZ+vp8BFMDRNgRoVPBP8uzHGKz70zlEUOW3rZYNif4IgBRr8uswHR5TKmM+njCYZjx6/y2ZVUa3WXqe8rmjrCm001ngyqE0DSugng4eekDI809h/u62HROyEL01noqJCCimEoFDImnEghMeerPWhxzYQaPxj9IbjWVEyGo0Yj6ZMZwfMd+fs7MwZj8YU5Rjpqf70fUNTr6g2a06PP+TBB0uq2muya+0NPo3xKgqTkZf4E0Jycl7RdhohR2Fw0cgs82C29Y22yBRKSrSNLHX/PrQO2bRRIw/n258M8wPl36HWmrLwkk9lMaLIcybTGafVObp3GHwmgGt9v1PkCpX79XfTOhwGIxzW9Hh1wh7d9/RNFzIPRDBp9qoc1gjaVqA7ESSBBHkhcBgP6ndbj4qPKi9A9L9A5ZMC5h+b5uwc1x3p06RHXzx7l3e+/huYk3cppMFgvbZWDzKXfPit36euVt512Tgm0wnr1Snf+v1/xNfuvokWI6/FlDQgfwTFAdrRZzU5CptNWSuF0w5nNW21ol+dw9C5OH7VuUvPXQrFdDLH0VOUCrOBXt0iHweN3dAR59jwnF2YSBgM2nfoQiGQKKe9VZUD6QxYR69850EAloyFTI5+dM/mRXlRXpTPpDjnePLkCffu3WWxWHBwcOAB3QAeDEGBaCwaQYOyLNnb2+PRo0ecnJwQWdRR+iUy8SLzLoKZWuvEIK+qKsnHjMdjLi4umM1m1HXNbDajzAvyLMNYixMwn88BaJuGUVEynU45Pz/n1fuveHPMtsOUGt33TCYTqqpiPp+n9HwpvATJ8bMjD0KXJXlRILOMO3fv8vjxY7TWXqqlLLkIetxae4NqlWVsag/kl2W5BdGc86z0wJKP5qiz2QxrLev12uuwBza3MYb9/X2auubs7Iwol1JVFaPRiOVqRds2KCW96fMAFJ6Mx4zLEQ+OHnBwcADWsTi/YLVaIYRgOpmyXq9pmoaiKFhcLKiqiizLGI/H1HWdjDMjqN22Lbu7u4k1H81J4/OOZnld16FD8GB/fkBb1WkMzrKMh48ecXx8nIIpEWCPLPAIYEe9YKUUN2/cTDIqTdNQPWt48uwpQkpms1kCu621rFYrjDFJgsdam/T8q6pKIHk8z1DDPmYUKKW4efNmkliIdc+DYJbRaEy1qXn06Ekyg7raZn7Q8kLG5ZOXS2DwABePSxUx2DH+nrCLuKIMaG4ECmHLpo4TySFwfPWNJuPGuAPxeNe/+8sA83bbEOCMbPWoXbpVMh9cyPcpl0DSwUN67luBwRvZXBGVuwQYsg0IDBRPgS0TTwz6tqEuemRhXrqu8KX0rAOYSDA2jUnrijjftAgsTb3mz//kj+nbiv/Rv/ovYy4ec/rBN6iPPmA0niJ2Jb/wc2+g8ynTgztMdg7RFu7M56hyysXTJxx3FaKrmRYZPZblZkk+GlP1G7TUCKkoduc8efyEXYCwSCbomEc5rNh3x778KvA9/JmyahyXQHO4bGastU5975Cxnp5deLZxuwzz7IcPHvLd736Pr3z1q+TCf884i3IijYURyIjgtAvouQighoAfgETsrvk9QsYRKAnbLv28+vuwNoqP+PmDfvYR+6c1obhmj6utYtsQ010992xcAuuv7nvpFgf7p2c/eBYxKGCHWjsEWGp4/OdPfe1jje84BhgugehhW8SxYBvbuPQzBkTCF2M3GeuIYBuktHH71Wf3KUvf2dCmBLkKLGcpcU4GaY4giSLBKInug9yIdl46xDmfpRcAcQ9uba9NCUmWSZTyNyKl8+Cw8lrpUjqEDT2tczRdwXg6p64XiUkLJjBjCWbBBlWWONfi7FZvfgtwb+/P2shK979fM5hsn6TY/vTtHtpeYjEQzCpTXQp9gzYmZPBsSXvD8dH3vVsd7uRbJiRSKZCxT5IIkSOk9vtL6TnZwoWQxVZdwOGC1IxIPbkajmXpfrY3ZwGrPXO27Rrqurt8nYMSgxdxs1RZ8JizaOu1tKVSjNUcEVQNpFBIlXtAVHtPOSsMVpkQAoq61l5MyQamtpLbPtvL68TMF4ezkOU5UgrqpiPPPPNbZRkni8ZnfQqFkMpjLS5mYiryTLFcdDRth8BrUQvn5VRwXrJI25629RI+EbiPxEYBGN2DEEHyxUuSKGSY2/g1l1SCvrNYB12vAyGpIvY7PgvAm8H6DA6HNtoHC6RktapY/el3sPbbaY6ylT126feoEBHnFzG0Z6xOwDlhjxhEiT2FkCKYhm97NxH03W2SmIkMc4XIcopyyng2YjqZM53NAcmNw5uMxiPyvCBTEtCYvqWuNlR1TV1f8OD9hzR1S9M0tF3t5Yy0zxDrtaXtNU3d0fQ6GPgajLYolfHKSwdMxjNfL5TgfFlhrSPPRAhWudAfaR9kDZksQkp024PzhqQ+iE4KusUMXZUpL40cOlsvD2XJCkWZeyZ6OcoxaL7xrW/T9gZjBUgfyCtGitlOEaRefB2qGs2m9kH/yTQjywt6A13b0zT++fp5g593aW0x2mKdD8ChJCoTCKm8/8QnKD8AiP6DL4b+6ZUf97UOz/d8CmdKtbhSPm6xeJ00zMed/bq/P+n3rhbhLE8/+CbN6pnX5c4n9AZK02GdohhnNOtzxkqgtWEymVNmGTmGkw+/Q7U8Y+fWy/RtiyoKnHk+qnNpgXHdglvAdSLew10FXvexFzWFrnGMqBWoYMJmdEeWSxqjQeXxxGnSJ4IhhO/8fHS0bTZI3UDdIcwGwSgNZgDG6rCgDJ2lFJShY7TO+oiXlDincM545fJMUFiNc17zzanSdx72B2fpf1Jw4tq9PqsK8mm/ds2E/FOtX36gzz/+DJ+21/j0R76mXn/MXv8s9cL/PJa6rjk5OeXevbu0bZt0W4cM8qghHaU+4qTh6OiILMsYjUZJpzyCrTdv3vRGaAHQjkCwUirJt0Qd6whglGVJURQsl0tm01kyaCvyHItLExiBSOeKYO3+/j5931PXNeOgox0NMnd2dmgaL2sym80wxnBxseDOvbsJ0J1MJklWpOs7RkXBaDRKoHY8V9d1SY99d3eXuq7Z2dm5ZLIEXDKti2OCtZaHDx96wATHerViNBoloL2u63TO8XhMVVVMJlPA65Z3XcfR0RHHx8ccHh6yXC5ZLpesVqsEBj1+/DixvlerFUopbty4gWdXL1mvvZmsUiqZaUaQpyzLxMqWUibTzciYNM6yv7uTTF4xXk9YSknV1ClwsNlsElg/n899wAFSlkF8ZmVZ0us+yQI1TcN8bzddTzT8K4qCi4uLJKew2WwAOD09BUgM9SgpBCQAfzabMZvNODk5SXrEnqmvUnBAax2kXozPVFivefz40Y+zGb4ooURcAIbAktuyziPEE3aSEUAM6NV2ERhZq0M4aCBcIR08N+fwx3aD40f4yROkrgf/4uKUAF6kax98J4EPAZyQbivz4kS8uiGEueUHi+grlC7Jpesbwv3pp/RGY9Ztwfv0bLhCshheK5AYyi6C/Vxi0IIYvJ/LTHTC83YIjJAYFBKHwnqwxjm8nItFYGibDd/9zjf5wpe/yM/87E8z1m/xjd+1PC06fuWnv8Dm2ft86c3XWZxX9FpQnx2xOl8y2dnh9q2bHJYwMh02F9y4t8ey08jcUhlH1W1YdxWyKNkdH6KLgvO6YZyXOKdTRk709ojBz5hBM/RuiH3KsB/v+97rFEMaw4b7AEkTPcpQmbQAd+k4ccxwdivt0jU1v/u7v8dP/ORPeaNK6znWbduBlJdA0fR7BOjcFhz9JG5aH1c+TdDwx1We9+6K+v0ffc3PAaLf55gf9ftHHSu+C+uel5uPX//o815zznArUYHqeSb6YB/cpWNdAt1dBNpDnxEas2euxvoSsluC/oxLZ/nhSttr33/bEKCzDq29tMsAL/agY2/pOkuvvSxK33uNciFEqsy+f/HrUy+n5JDKS2xEbXIpHXkmKXJQmQh9j7/PGzdf4+d/ccof/s4fc3R84VnwnUVlgnxUMt8dc3FRc3DrNlYb+k6jTdR/9n2CNcaPCOHhmghOXVrDb9to7KmTGrfw1y9VhqGk6Tom4xxoEQNp+AiIp3rNFnsZgu3giPilko6+83IfCKJ/Y7oqIWNbiVcVNdzDuYI+dGSWi6DBvpXZ2XY4Ity/r1sGIRTSdMGL56NLYmPj76WpW7T2RvFZniNFhnCO5fkpzvngQt9lyCynHHlze2Gh6xxt02KsZ5gXZRGuoyPPfMMwzsu4SOGz7GNb8eO1I8szGEwFZGCjn5xXIagQAPAABHsA1svuaK09IxzCc4tyccKzfrXPZMiyjCiiFI1HnbM4qwOTOICyQvrj4IIhpwjH9wTHvtOMSo/3xCCHB5sDxg+BnYz3AQQwDJ71NoATM+ykHcyPxHacl0IOtm+rmnMWa0Sal0VGv8Dfv1A5eV5SlBPv3TTZYTKbMRlPGY09acmJ0Nbb2huRVgtWywXV+tj/Xbd0bUOnO3Rv6IzX+u96Tdtr2pBNZqzfrrVBG4swQVpLSPIs9wSkTFGUBTcO9rhzYxb6PO+5d3qx8S1AKHA2vZ++aX2tlt53QAbd/K7XEINT6R3644mBdGAMYoHPnhmPCqpMMi4U8/mMhx8+RPdh7DcW3RiYhKwQcqYTyWTsJZ2qjeb4dMP5Rc1iJcgyX99Ma9G9Z6Eb67PwZOfrUF5kiF7QG9Da0NQWKS3aGOrq47G5fw5B9B/3dT4PYT+PET6v/fiJj/7cxOfHV4yznJ88QegKTYbWoKxGixIpBN2mRrgMY3xnqhBBj0zQrJ9Rby6Y2Jd8R2j1p3sz21H18rZLfzoMHcKM0AKgJ9cG4xpsNsPlJc4pjO/tQhqON2Mxrme0d4fu4pgSi3YFVV2j8hG27ynyEqxMs+7YaQqRpb8JEzOjw4WFQR9ntwwHCOmSKi3SCClIz4vMPH/Ln6b8s9JiP6p8drX9L8KT+Dh4/KP2eVH+opUHDx5w+9YtqqpK0idlWSYzy6R7zTbqHsHToU5sVVWeLT2ZJBZ3UXj5kouLC4QQTKfT9L1MKYq8QIdgZFmWrFcrJpNJcH/3+tdaa6rWS2+UZUmRF7QBdJ3NZkCcuPhrjKz3zWaTZFVigCCysderFVrfvMQ83N3d9eD1ZsPyYkFZll7mpakvGYw65xiPx0wmE5RSCZSOwG0E13d3d2nbNjGgIwtfKYXuWu7cucN6vUYISZZtZXDG4zFd17G350HkCIpHIEYIwfn5eWJORpNTKSUXFxdsNpsE9Eyn0xRciNcaDfKi8eZQP9ynborE4HfOsbOz4/XDpxNG4zHz+ZzvvfMOTx4/TrIHWQh49H3PeOSlU8ajEXfv3mW9WrO7u8vh4SF93yeGvtc7VTStD2Q44Pj4GIBXX301XWdVVYk9HwMeUdt8uVwihA+qxEBNfKfj8RghBE+ePCFKFHVdlzTk+74nz3P6vg9BnRGbTc2TJ4/puj5Bri/Kj69cYkDH559+DJnepAWLcmAGr8q5SzsMsOfL41GSeOH6kWowU70CVv9g97Kd57r0dwT0LwPp8c4Ii9QtsP5R0i1++/Mq5xF4cREsG1z98Pfvd+1XSS9JmGU7LQwAwuCChF9QG6FweLajl5VwXrcah0MDFq1bTs+f8Xf/zr/HdDrm7MED3vjc2/zMV19n8/Q93nnwgEfvv8vF01PQlr39A17/3Od49f4N/vjPf5/CLHnlluKi7imyjr/5b/9b/NYf/Rm//lu/R99anMxx2vG4espoNGO53DC/OUcVij6YKEcAPY5JUd4FSGaisa+KYHoE1520l+trGIOGGTFt0FpOYPkVYNYf04Xj+vOV4wn/6Dd+g3//P/gPUNYbG2d5xsVymaSzhuC5E1uGs8QDoRI+lon+yddfH7Of2DIZf3wlBoOGz38LFF03F32eOT74+zkZKLf9Odx+DbDuD+tDfVEXfSiRc/15r267ntGe1lbDn5cPka71yubLP90g6yWxSq98Ldzf4Klec8IfrKw2PUKK0GYIkgRenshZh3Ekhq6XHpAI6RDS+5d1ncVYAvDumdkQQGhpPYM888C5102XZBKEp2+ThTW8CrIL9fqYW3v7/If/4b/LO999jz/4wz/xshe7E87OL9AafuYX/hr3X3mTZlNRVxvqekPdbGibNXXT0nWaru3QpqE3DcZ2WGOx2uGMxWqLcyaZSsYsqG0fgTeixLJa9TinsE4iMv9wEnzplWyCHA1pXU4EQlPl2ALaQiqE0N5HIDKGo4fAgOnuvM6QP4QFF86htWfjxrLt34gnTkBsBA+zovDGnkazCWaWw65lWO9jwCSNY0HqxwiNs4bxZIwqJpi+pqk2CfA3xgTQu0AKQVYUoATriyWjsvSSh3i9byG8xI91nkGvpA8GADgbfA6UZ/9GFnMakS1s2obluoFBIMEav48UjiLPMcbSa4sQXg+bUI+lkjgHutdoYwMhRKJ1fE9+furHFa+xnQDouIZJ72iraW+do9eGrMhS/QeSJ571Yvs4BNpZchE1y4djeJTi2gbZwIU5kogn8n1YkqcUOCFQKkOpnKwYUY4mTMYTRuMdxpMJo9GYoijIvEA/xnbotqdrK5p6w8X5EU8frqmaNW1T07QtXQdN39Prnk736N74mUEIsHVhrq+NxUTplFAfpZRkypvtSikYZSXFJGdUFJSjkvG4YFwWlGVOWSjKYoQwDc61/nkLSdcZlqsu1vLQHvyMSxuNUnmQ8Qk688bvl4e1njYGrW1qg/ERK6X8vC68R+d8v2SF5dbtA8ZlxtOjhlVTYzT0nc+qGI1yZlNvbnx+XnF+4fE8nKTrNZlQlEoymXkpo1p6Y9Fu44N4PhPBZ+II4TBaejkXpbDGB2B63dPWH9+nf3IQ/S/o+uifaurvj/nUW42oTzdY/6Dfc87R1pU3rwpu4EqBEwZjLFL5zswaixoeXwhMMJCLk+Ew3n26a/oE+1yaoOGQMg+McEdeFpR7u8izwuv1hX9KSYTL+cmf+WX+6J/897jqDGyD7TYcP37IJM9ATSlH48SUGC4UL7//KyPhYOcrS8KPHDB/ZGV4We7jq+2nrdbpVj7BAa5c0kfs8+mu5Fpo+poTPrfpmi9+7HVed4kfcUOf5J4/4YN5Uf4pFedgvd5wdn7B66+/Rte1QR96FSYKXvIjggJxfNrZ2UngsHN+0bBer1AhjXAymaS+8vz8PJmPRpmR0WhE17Y449NFIwt6NB7RN20COiMwOs9nrAJbO2qjn52dcagPuXP7Nqtqw+HBAV3bsVmvmc/nieE9Ho+ZTqeMx2NGo1FilK8WCw4PDjlZb1gtV/zUT/8UT5488UwaKTA4srLgjTffTBPVCMzOZrO0Lcv+/+z9aaxmyZnnh/0i4izvete8mVkrq4o72Wwu3WTv3dMaT2t67BlYtiRrNJIGsj+MvxgDa2AD/iDDC2xrDBhexgMJsGAYhmADtqAxLFiWNCPP1sNmLySbW5Es1sJacs+7vftZIsIfnog4572ZVZksJjfxBpl13/e8Z4kTe/yf//N/MsbjMSqAz+PxmCzLOD8/T6ycKIljjGE+n+Otlc1EkBAZDIRtLdIzImVyfi5SLHHhGD0DZrMZ0+k0BcJbrVaJTX1+fo4xhmeffZZ79+4lYCjKqUSWe5TtieBR9AYYDod4BZOdKVmWMRqNEvjc1jXH8xmvv/qqsD0CY9N7z3w2Q3sYFiJzMzo4ZDyZUG8qdnd2uH7tGmVRkBcFeZ6zXC6Ffb9ZUw4GVE2TPBEGgwHHx8cMBoPksaCUSsFA1+s1WQh+2rZtYrqLq6zIEPUlZGLZ92WKZC4H51o2mzXOec5nLev1hhs3bhGEzS6B9J+ipHoTXWRQRrZ1HyhPTMsAOkTAMUUNZBuQj8n/iOs6gtCR8RUemsh9ck78EDZh8TpFYHJ3QEov4+GS3jVbwLZPc/97vWNinv8Qbxiz06JxWjaUWUebRGMD1GipNnNe/87LHBzusljNyIcFJ7du8J3vf4e733+Zxe1bXJ+W+KzlV3/lc3zykx/nfLnk69/6El/++teZ1w2nJy27V67xz/3eb/LnfudX+fbLLzO0ntzklIMpXhXcvXtObj3lYEi7qjG5Dq7uc/b29jg7O0txLKLMVV8fPXoX9Znjzjm8dckDqb/J7ydrLYvFIhk/43wWx2QB7DVlmdM0nrpuGRUDXn31db72tW/wwodeYndvD6Vhva4oigEekd3og+h951btSZ4O75W0fjRXPYJ+75k8+CdBe/8BUn+f0WFEKvX9/vFtEK874d32Kh0Pe/sE591Dz4ufhT6kApje3f/h+8SHf+7n8YF+/m7Jbwd8fLfUD2Wset89nUeKSuPRkxkNT85r2VO3obSUwrWi8WyUIssCSJzA+xDEWSsaJRrqADoXQ2FjFW0TzAtKApWaAKK3RmEyAa3aNjDVtQTfzEwc8x2+PWe2eJUrV4/4vd/7de7cucdXv/pNytEOv/pbv8dk55C6qsjKAZM8YxSM/mB7bNwWaxvqdkNdV9RVTV0FBm3VUNdipG8CSGudxXlLVrQ4Z8XDGy+yJzisXWHbvhwGKN1JFgmW3JvYUj2qnpa7x2vEaBkkNgQsDXrpKs4/3dwYxy5tNN6BVjYZoZTqcAgxCPTnJ/msTUY+HqKVZb1ZIu22Z2iNBi0vQSFtK/nIS9GJdtahPYGEklFvGoyTtZnJCmzbEKXJPAK6eyXyNN55WTvWNX61RBuFbVrKTJGXOc7KdSbIuNAzNjvrcFbKu21aTCba3845ZosNdeNQOscrFfpy6BFaJGAkFpHFo5JUiZZKw3tp37Gc6qYVdrhSmNxgVEbTbLp+jkNjuv25ViinQu1qFBLYs23bxDz3QZ87Bue0NtalwnvBtsR7TtjizhPasETSS3XkQZmcLB+EtfeE0WjEcDBhMBpTDofoTIfnemnz9YZqtWK1WnHvzjGr1YqqWlHVa6q6oapqmtrStC1V3VI3jqZtaNqWthVGdJTckUaoMVr6ageSG8q8ZDI2lJkRQLwcMChyyiJjWJTkeUZRagwi2yQBpC04K/XrGrytsOt1MkYpJTLmq01DVVu6EO2x74mxQuscbXKKTLG/u8e1azlvvX0DQvBTF2R7Ui9UJsQT7LyRfQDRBcS27O7us1jXnM3XrNqG4aggy0Vr3mgF3mIbS121NEEf3vtA6NWavMzIc4R97kWXvV6L9Mx4VFKW0hbq1tPU0gbbFry3WOtpGgT4f0S61ET/KU4/adfAPlskDlEKAdJB43QmCmBOWG4pgBDdRuZR939YeqTEy2Oc4xxkpqRpKvLhmCsvfIQ7b3wN11bJLV0pTbl/laOXPsvVm6fc+NY/psw81XrO8Z273LXwkd/5dZzJHnyGSv+5TJfpMv2cpVdffZWnrl+nKLLEKK+DlEbTNIm1d35+Lr8FJp+1En0cfGD1tpydnVFVFTs7O4xGI9brdQIRElg+GIhenNZkWRaCbgZpqhz29vZYLBai1723xzLom0fJk/Pzc5RSLJdLbt+5k/JT5gVXrlxJMiB5nqfnR2PjcDjEGMN6tWb83JgrV67w2huv89Zbb5HnOYPBIOmaV1Ul7L/e3LGzs5NA677rf9R+j+UXmfdAkiE5PT3l7t27HOztk2ei1S2gikuM+PV6nRjbkd0dF2ZRxibKpUSmY9REH41GiWUd81EUhYDj3nN4eJhY+m3bpmCc3nuuXr3KdGeHrJB8OedYzRcpP23bsNmsqSuRTqmqKpVrNCrE9y7LUvLmPXmWMQgAuXOO6VQA+vl8jgrGgSgZMx6Pmc1mnJ6eJt39CDydnJykep5kWdJKT5qEAfja29sDRKpovV6TZVkCxsqgdR/Brwi0ew87O7t897uvUNXCULkIiP2k1y8/TymxxBUBIezJm4R1m0vHlJAeVC+mS0IJIO0W5VSigvmD6JbvrYN6df8ApddvHY7NRId/8dJtpXR5B9Oh+NGjOoDefUCunzfZZAYIYXvjRtj6qfCU8L7plWNeEoD+qOQkSJwnwNxd0iGjPoD0kcfYeQuo8BwfQDmXykBhU5nY4NauvCEbTHjpE7+AUzl/+vWX+c/+3v+d1f13eGZ/yPNXRpTTPb75+lt88PoYMxkwqzf88de+zle+9i3ONzXZeEhtPftXnuJzv/RZ/uCf/RP++Et/TJkVuArqdYvJcnZGO8wXC4oi5+zslNFkTFEWzM7nXL9+ndPTc4oiI88K6lq8UjJTIBx/LaxSHzf8PiHW1regvci6qG68cKHAI+C+WMySgc8EdlsnO5WjlcFkOXXVMJ8vWS3O8Vrzp1/5MvtXj5ju7VO1LUpnVM5LQFzfgejg8b32Y5IK/Xsn5aLAxLsnHTyTHkgXCRn+McB2Ht4Gf7hdhxMQORqmQp+SPuBRW2GJ5XxRenbJqCWHO0hc7tMh4F05PzgHROZ2Oi/0gBRX4T2AdOiMLw8C6Y9vzHqceUnY6t1IRHqfOEL1DG5PcB+4WnhMoSgzQ5ZJn9CZScErY1BOjYyB1jpaC1UdADHv8NYLcO6VyBSpCAqGim6BKOXiFN4qbBh5vFKBfSz5qedzPveLn+PWrWO+9fK3OT4+ReN49tkrPPPi59k7vE5TrYO+cwAbiZ48ApD2+3Kel4yHPhBJgBAzDA/ONxIQsrEiS9FsaOqN/K0a6lpYuLVt8I2jbQUcc77B2Qa0RqkW59pktMN39a2CxUOFl4tguvymhBRIqOwwB7p0pGsAcV3rg+aN7t2nny72b2UM491dtLas5wusfRCgEyAw6NsH/XiTqbDeM8kzdbOpqTYbydlmE9qGISvzJC/T1DVNvWI0mUDQSs+LIVlesFltsFUj7WWQp/KI5qB+cNk+Q1spYaaXmbDSjVGczYVUEUFd8Cl4sHg0itetdWG+C8YcAVGFie6cF41sVAj4CHmeoU0mnrSbqsOXFFt/Y0HLR53WCNY6CRob5hYVgtNC9BAWQ4UYAKQf66zEmJy8HDEcDBmMJgxHY4bDQSC0ZJhAHLVtQ1Vt2GxWbFbnnJ3eZrlZsalXVFUjxgLvQGuaFtarDfP5mvlqQ92Ilr21MUCpJK1C4FpDiF+QMRwMKArDYFDI5zzHWcdkUlIUhjKQXbLgVSFrCSsBigODH1a42lE1sT1vL3Li/KqN6bH8SfI4i/VG6k+LUYw4AioximgdCVIjPviRT7Jc3OPW7dusqhgA1aU2ppUhzzOyLASP7U2OPngQNI3l9u27FGXBndNTqkoY6MWgAOXIlAenMbl42GTIejYGK7atFQOTztC5Zjgo2Nlx+BbqjSfXmkGWoRRkWhpM0wjY77zGOC3GxOzRY/sliP4zlh5XJ/1JJKUUu/v73DQZrg2BhJzHNh50wd7BVRbrhnp+gvd2a7AtyoLpRNiNkc324wSdnbegLGBp/ZDnP/4r3PzmF7l/47UeiJBx7aVPM3rqF3nhM4q7b7/CZvkOrm1xa8fBtee4+qFP06i8xw66TJfpMv28p/V6zSvf+x4ffOmFBHhH4HV3dzdpb4MYF9u2TXImp6enjEaDoKXeae5676mqSnTFZ7MEeGZZxnotG5XJaJwYfhGsjvqyEewu8py6yBMwEaVXiqLg6OiI4+NjZrMZb731Fs8+/QxXrlyhqWvm87mwGrKM+/fvJ4mamIf5fM5ms2FnZ4ciL1gsFjz11FOpTJbLJcPhMOUnAuKR0RyB3YODg3ROZIJnWUZZlty9exetNXfu3AEk/8899xzj4Qgb5F1EekWCYO7s7DCfL5nP59y9ezex57XWHB8fp7KNgTRjeWWZLH3quqYsyyQdEzV+1+t1Ym7HYHeR5V6WZSobbeSdBoPBFohf1zWLxRznLFlWEF1SgaS/HuVyRqNRkoJZLBZcvXqV4XDIzZs3mc1mVK3kN8rsRKmbzWbD4eEhg8Eg3e/o6Eja52aT6kMpFWRwRKJlf3+fpm05OT5OcjlVVVFVVTLYxMCqy+WSKPcStf6VUhRFwcnJaaqn2H5/op6BP4fJ2T6lNYLQEJGxBHAlxnmEriLw5AOgprEKlO82NH1mqobA2opyAg8G/Uv4ffise8eFlNZn+BG0Vzsxh7hxi3rpIFrlSV5Fpa1byFgHZkW2oOi6ey5CdDFjPrwH8bwI5gXmXFynRuKI66ugC/qXoPDEzg8awhHki/J8EUz0SpjOPdNED1gXrpuiwQU2tMYR9nV4JVx0fEY53ueTn/l13rpxyj/9p1/hO6/dYif3XD0ccrLx3H7rDs/sj/jQ5z6L3tnj+/fOuD1rWdoR5xXcutXw9PPX+PgvfpByusPt42Pun62xbsC61nhlyUsLWjHaG7JpF3hlsG6AUprxeMKtm7d56voz3L59E+cgz0s2mzXr9SbUrRGmXGAyOh/YqEgMIeesMOCU1I51Iush8rZSjtY1UvZWC/DuZfOd5QVN6wRp2qzJM8Nzzx7xwksf4p/8wRf5R//4H/LRX/gFdq9cBWOoAkDogwVmy04SNOd9gIjxqmvwSuR0+n0gJh0B/969+kxU5aRdb1/Xsdyj8ceGOEj903Qy8wjyExmRgVYbzve9e/c9fzrgWwWDUz8PHidApxLRIK8kvGDocAKeq0Dl2wLRAxirHCrS5+N40pM88Vz4kOxa/XOkb7mQ72TmezyL1SNTvM0DIbQupMd5lAyToccGA2Uoqm6EVR3YFJ//w6aqdhgHNpPgoUorikxR5DF2hE9AeqypCESZTHIRwSTdyj1EAiTs3R045XEaXKvwmcJqFYKWOnQWdMB9kCnx8P/7h/9fdkcjDq9c59rVp5mfzckHE65cexHrJMC41iqwnsEohdOAC2zj7VE7jthh3ulKLdMFRiP63EPwaqdrJV7aMF5iHjjX0jYttW2E5VzXNLUA7U3b0jQVbbuhaVyny+4c1rWhL/jAZFeYTGPbtmPs91LqQ1oluRcf+qT3PjHYt2wqcR4I63TvRWt+NJG11mYxSwB6H1bwThjSEAKV5gLsQ9C614a8HKFNQVWfhICIiNEVaSvj6YiiHKGUxjYt1rds1gFsV8IANrmwyLU3YgS+CParCJZ2HTNJpyhP68Q7IGpln5ytiWNWxIgIZEodNNJFAoakJy9yJwJiOgda50zHJYv1GmfFMKGNwWjZF6yMEUmj3mDRlxSJxIFkqfdiYDJKdNK1gcyI3ExWTri29zyD0RRnAT3g6OhKmL8c1jrquqKqNlSrBcvZHU7uyhy3rtY0VU1VN9SNY1M3WNdS1Q1tC40NgLHS5LkEbm0bK2zzVoxc0eAxKMogn5JTZEPKQc6g1JSDjEEWvLBUgc4kmK0Ockdt22CdJdMt+BZfr3BasQlU7FDyaY0X94HRCKqVCnEjttu6NmIANiGCcdvWrGvx/FisFjJnhoHdY/FeI5JHPpFnTZ7T2BrnZC+7WdcBONeh7mWvl2XyLOuDDE7qD566tuxMxuztHfDdV99gUS+SYcQ7j8mhLHMyIaUzKAviKKONxxhQ3lGUGWWZozONMopMixzm7KyWdYHKUNqTaRgqmXulPxnQlqaB6jH2M08WRH+32em/RPuqh1mxH7VxfLdrHtCc/CHy8DjpB93gKqU5PHoene/g6xPEYV+Ta41VGU99+AssTu/x1stfQmuFbWuczXDaMNi9wnRnRwINhNn4iTIFHnGOUh7rGjKTs64bDq99iE9+4c/zrT9RnJ/cJ8tLnn7hQ3z6t/8yarDHsx/9HAPzb/K9l79Es7Hs7z/NJz79S6ij50Bn0Dbbz5dMXMzUg+dcOOIv/OAfSqG4UFY/7ALzx43/X3ze++j/D7vksV7jMRfkF+//7pc95Ogj7/+4d3o/975MPw3Je88777zD9WtXUcowDQE4mxAYNLKk+wB6HRi74gbfMhqN8X7JYDAgz/NkcJxMJikYZ18+pKlr0doeDmURXFWgoBwMmM0luOjh4SG3bt0iLwrRImxaxqMRTd2ghyM26zXXr12nbVvu37/PbDHnnXfeoWmaoNMnASqLokBrzWw2Y7lcJqmSt99+mw9/+MNMpxNMltE2DYNykPIV2fGRzRzfP2ppRzZ0ZCYURUFRFCyXS46Pj9Fah6CVbQp6OhgMKMqctWu5ffcORV6wqdZUawGR4wJ/Op0mVnxkfUdWenR7jLrmMTDnYDDg2rVrVFWV3jcaRWJ+tdHs7O5SDMq0iJzP59y5c4fxeExeyCLu7u3bzOdzirzg6rVrgJzXtq1oqmfi0aSCa2Oe5xhjEkO+ruvE9Jkt5lRNTTkccP/mSZLqKcuS8XjMer2mKAqm0ynvvPOOlFFRcHp6Kl4N0ynnQVc/guRaa8qyRIJTrZM3QMzDZrPBGJMMCePxOJVjZPTL/WQD9I1vfJ31ep36xCWA/uNPzoUgjnSoWQT15FAH6Ko+wzwcjdCu4ALbzMu+Jm0EkZz3DzDKt24ZjgV52u5fAh5VYp9rfOIA67AZTlIsIW+R2bb9mAiK+61nCvgZ36a/yCJtrvvSD2kNGVnT/dfol0VCBPv6x9uv7AHtt9d3rvd7vGhLFrAHhmrXptpIhoRUCsItK0d7/OJnvsDr3/4G33r5VVqXkZUlq8ZTn85pVcYvfP5zXH/xJRabDW/dOuGN2zPeuOfwyjDbOH71gy/xwkc+yOlyw2tvvkPlYTLdw9gSR4ZViuV6gabB0oITtnmRW4qiZD6fhQDXwhpUaIzOqKoarVXYHMcAfyoA1Z2OtgBY0go8EqdIgrMGtXql8MHYqVV08xdUR+tMJL2cQyOR4coi47Of+UW+/corvPP227z6+uscPvUMOwdXwGRUVgsTPbUJ37XJ0Ix8aBe+138EhO5qOvaTntxyB6pGgMkLdKF7Fd6B7PG+sf31PUViKwj5Uz4BodarFCBzux/FeyYOd++YSNR0DwjMXyf6xz4YAnwCVsKzI1DZ6z+9WxBZ6b2ilGt7XiBbYLq6+GbbpZm6ce+N+rEF3mvf3NfWTy+dCvyBy95H6qm0x3GQfn2GcUp17eRJPNY6j2scVR3lLmJQRMhzRI5FB1EF52lbkTFwLmj9WgK7FZraU9eepg3HPIGlLHk2ztO2ce4Ixh8N2nSGIAHUFfPVkuzkTfAZmdqjsQ23vveP8CoHXaIyE8Zujdal/FN56F9hLIuGqVhwznb1GMeIVI69SSeM1YEjjTGKLJOAjMPQHHFIIEw8+Li+FFmGtm1wjQDrdVsn75kIuLeB+dpaYbFb14pWu/N43+IDeCltoRVDspXxzzonXjf0jMZxuiC2T0UxmJDlhvXinKZpw2/x/ABwG0UxyMiyoC+tMwmU3DZ4a7Gu4fzsFKUMRW4YjYdihPQ2gPmatm5p2zlKGbQK8oNtQzSiK6VwraJtxZBgjMJ7E941rBcC2P4wrxsJwKpiLdK2jtmy6hmUgtk6eCBoI5rUtm1lLHeQZQatpWxyI9r2B888zYc++AJf/8bXse2CLBNvozwvKMsBeZ5hbYPypPYeAVwV+ggqSPT224z3iVU/Go64+tRzTCY7ZFlJtV6xXMw5vn/CvXcMtpW9wnLTMl9tWG1qqrqmbj11Y2kbl4Kaemel/r1Im4hOewChs4wiU+TKkRWG0c6YwpQUZUaRa/JM2rDRGVpZOm8Mj3OtANTO46oGxyq9S0xN25LlGa0N4zh+20vKd3ibR4yd8TyQcT+WSZq7pBOG8xRNXbFarshyQ1FIPXT91Id3DjEJQrvSSpMpTbVaYhuPtyDeMJoUeFdJGWWZxAeIe70sz6RNKYVtG1547irLTc2mWbEzUZSDkqrywWNBAPEY9cZoj9IieqeUI889ZWGCQ6Kl2jQ0wqdlMigYHBZsKlBe6sI5hdEebSx1qGOtNEVuuHrl0bprT5yJ3p9InjQe9LO8SXsk+P5TaIBQSnP12U+y9+wnOP7eHzLQDRUGZxuG4x2OPvUXuHr+JidvfZPN8oxMOerNCsoJH/rF3yUf7lArTes9ueqcibee8aPKvAetSpyDEk/rDc/9yr/A/gd/Cb86pVU5wyvPU5Z7KFuDyTn66O9w+OHfwlorbplaywBkHxIU9eJu6qFg+PY53j+kHfgHy+DdVJh+0LL6cTedhz3vccaAXpiO9/eQiydcOEddLON3ucfFwxfNPj9YefoHP10YHB/lGnyZfrpT0zS8c+MmH/7Qh/FetP9Wq2UKbNmXY4mLhTzPEwu6L+kRddTruubk5ITxeJyCkMbglh6o6hqlNYPhkLqVQI86MwyD/vW9e/dkoWwtdVUxGg5lIa1a0eszhqapmUwmiV0eU9XUHO7tM5vNyLKMo6MjdnZ2Eju8LMvETh6Pxmw2G+pNxc5kynQ6TWUyHA6THjmQ3m0wGPD0008znU7x3qfyiNrk3ntOTk7S8f39fTabjUid1BVKK46uXkUpxb17LeWwY4jFMjw/P2cegq1GGZkI5FdVlTbE8X3ipjw+P8qyjEYjrl69ymAwYDAcYr3Ip9y6dYurh1c4OztLxoB5CJ4a9dwbGu7fu8d6vUYpTVEIWG7xwnw3omGo6QDuTrLHcnZ+RhX0f+/du5fuq7Vmf3+fsiy5evUqWZbx+uuvs1qt8N6zXEpgqaIoUjwSIMnUZFlGlMy5e/duMtb0jTgxKSXBWKPRI+oTe+9Yrda8/fYNTk/P0vnvpnF8mX60KRLHEwBIB/+oh4Ys34ayujmuY4N3gDsBgOs0VuN5WwBzQJfiMeV7gFOCUHqMTdUDMi++TDyuiErJAU/xCYyOfT5h290LdX97LtJyPCF23aH0Ih0M2YGK3c8PrmL6ZRjx9QvHHng/f1EQQ0CXhCaGPtQD76NZId6pKIa8+MJL/Mk//SecHJ9wZWfIaJzjXMWm2vD5X/4Ev/qbv8Hm/IQb92/z6jt3efWte7xyY81zT0/ZPTA8/9KLjPeu8vrbN/nmd16j9XD92Q/g1JC6hcV6zf23zlifnzKdjvG2ZbVeozBMp4bBQAJNj8cTmqYNXj25BBZW+oI9Qt6kc//u0NfYijq2cn+RFtjh8ZfYnkKlKQ9lUeBcw3w+5+Bgn8989jP843/6JV7+1svsX32KX/jsLvmoxDklHhahgUYmt7TTKD2iccGMA2B8V3tdkxJhh7iLkevp7hXfWJE8HSC0dU+6l07gmkrgZbo2gJx4h/ca51VgGst90rW9Uoqr1E4mKDWn7WabnisZ32qJwaOjb7DaTg9Zyzr10FPSaNIHElO+Os+RPqj+xGaMH9HU87A5TcUxJQ1IT+ZZTW0F4FLSR5zvwHTbIgCyDmCo8wK2+aCf7aSveC9sYB8aVxxRPD4qr4Ly2Fb+dkYjkWoVxrWAY9rIjw0KoxoOdsDQkpscu7oHSuKk4C1eOazzzBc1davR2YhyMESbEVkxIC8EXDdZjjJZMEzEwI+6Z1iK44GMHR7pF8rpUNRRl1lkrrzywpzGQRhrhBxuyDJPUQT9bD9JRps0xjpZ/1nX4JwVQL1taJuGppZ4D3VTC7u9FfBdgmv6wIhvRK/dWjwuaT/rLGhqK48xBYNyTL06o6kbUAZjdNCRLsiLHJMNBHAWPySRpfAebE1TndG0DbZpEsu7VoqszBmNJ0L4qDesV2tcX8NZCZt7OMxBG5T3rJYbXABflZIg41GeLw57Rutk3L5otGqtAIxxXl5XDeuNAMqdMVHGda1U0K6Gpu1JfHWhYNHaUJYDfvkLv8bzz12hbVd85avfQpscTyba35FyHMZuiQkQ5ukA6Hpv0xjvw1TvgdaJUcgDvq05vv02x3hciKxeNaKR7WzGatNy4945904WNG008MgcLLECDEWRUeSGMi8oSyEqlbmhLEVOxWSazKgQj0W8NAR0J3hl1WChjUatXluE7fVMJ6nTr1IlcbEyqV8xJsT625534vjkfPc9SqZJuIqgz4JGa4tWOiyHNAoXYkAJqWa9abrhTgXZKC/GEm2MGGOIsb7OOT05Zzg5xJQNjQ0erLQioaTEG9cE4Bs02uRA8FZwjo995DneeecmWbHE5BV5pihzjbUiaWXyaKx3DAca66Gqpc6KzFHkmjYEWM2Npm0VG+/ZeE2ZGcbjDKOHFFkGStNUjqpuWKxrNusK6yuMsbim5VHpUs7lMr1rct6jd3b55T//+3xpc8zZ3TdxypCN9vnkr/weV55+CX3tKp/7vXO+8s/+Ie3iFIohH/nU5/jQ534Xq8YUKkPbCoVlK4z1TyBVfoTZfYnRoaNxGmuG4Ddb50TwvAsQEhbzvYXUJfR5mS7TZQK4ffs2zzzzDOPRiHIgkcibpknBOJWSAI+RhR4DUkZpjgicRjkYpRQnJyeJ+T2fz5M2dbwnEICMjilsW4sPoLtoT0rch9lsBpAkSPYP9jk+PWUStLV3dnZSoEnbWnJjUqDPs7Mz9vb22Nvbw1rLdDpNoO/u7m7Kz2w2Y7q3S1VVCaiN42Zkf0cgOAva3IPBIBkbiqJgPB5zfn7OZDLZAtdXqxXWWnZ2poBom49GI/b39zm+d5/FYsHBwQHWWk5PT5PhYjQaJQmX/f19bt68mcbwqI8e9dwXiwV1XaO1ZmdnJ8nNLEJg1h1nmS0WSXrmjdk8BYmNwHwssxgIdrVaJZmY+NxxCB6rtWZYDlgtl+keUff87t27TEI5R2mY4XCY/uV5zmw2YzKZcOfOneQlEFnlMWBrZIi3rSwCY50cHBxwcHCQAozWdZ104qNxJwLuffA+Cyz62WzGer3he6+8kiR5LrXPf4KpB24nQDjUR2Swsn04pT7gRQQEexu7eP94dgTFAw5H4JRvM67jh/doEh2g3gPNH3ZeYpuSGG4PP6cHfKd3iTq1UTs1AKShMCLzsQ/mXbyv9z4E9ez4uWmzy8UPP0TyCuWNSL4oxDO+h/lHxr63LfPZiqeP9pkMDa5e8sy1F/n0R5/Bbo75b/7lP8f8/D7fe/X7vPq9N3j7rTusGstzT084euopZqsltct4+/YpX/7aK9w7W3MyBzMcYRsBXEqjqVqLUzmb2qOdp6nWNJWMI1F6am9vrwsY6v2W0S4yAC9+7qwgCu2jr0IqcaIGtTiim2SAib/H/3mgriuca8ibkrfefod//i/+Pn/yze/wh3/wB+wdHPHJz36ek7NzJtM9rOoZLBSAReHQETXSwkKP7Tjiw1tM9MisjCB8/BeCEEaAQifQMt7Dh2e7BLjHckklEK4JWZPnKof1CheZ6N4HkZUOZoz9MRZtMhSo7Z7SdeEAqvqL5/f7y3unaDy4CO70U9I9h2Tc6zt9QBebYCt/P2zqd9AfMkWPmPR9676+Yx0ngOrJpLoSSQKpk44drLRUn7NgI+sXYZy3LvY1OoNnxCvDX6O9FLpRCff3AeyMbSAy3j1yrlOCAch47jm3ijx3FNk5ebsmz0ZkWRHALB26kmMy1JyfLbh3/1iYpV7hvA4SHQplDJnOMFmGyUqyfEBejCiKAUVZkuUlWV5gTIHSJkjCKFA6tW3xXAl5D7NSf6yQ/yuEDivv5HuGnDQ3oEF7Mm0AQ5mr2MCJY44P7w9gvWhMOxtkYpylqW2ICVSHwKhNihHU2pb1agW6QLVjRjt7KJ2jEGKJ8y1t66nrDYKPi7a9/I068y4w7UWWwoe1dZYJWz0zGbZtpR6MMKJR8f2ChEtWBk8U8V7Ny5y8yCnzgqad98YllYwohLHHIdJPCmhbizE6GY7P5msBqsNg5kPDi/1F9OKdAPeRDU8E5jWZ1gyHExbzc/70T95kuViQ5xkeLbrwPupyR9A51G9vkfGg90oHflvbYkJw0bwQ8F/mLDnWtpYsy9lUDW/fPsehuX5lj8Egp8xL8iyjLBRZHjTclZg5FC6B0WLIEk9N34gHSKfV3jMVqm6ISmx5+udup4uky24ejdhUlKOL7+/TQk/KPY69Mvd45cEFKS0VpO68h+D15L0EDtXKJY8V71par7l7f5UWS53Xkk/zhrUhT2Qslktar5keXGE2u0ezkBhYKjDsxbii8V6Y7ALWFyg2oBx7uyWjYcPJ+Vs4vxHmfJ6Joc1JvTrvgqEJqrVFG4NzSoKSGk2dCaAfi1ahwUoQZqMVw4ElL4IMkQdrHLmxGCyT0pPnhrzU9Bxt3zVdguiX6d2TUji1z871z/J7f/V5zu+9w3ozZ+foCpP951ljaLMdnvqlv8Jf+Pifw9UrlCkoJ3ugh7TWY5RIGugY0eEHTO+2QX8/jLehsqhMBZdLj/PVA3ugi8+LgPoD+VDpP5fpMl2mn9PUti3fe+UVjn7tSgBZdQrSGceOCCSDZ7VaJ2Z5BM4loCSJNb1YLBgOh4kpHbW4Y9DHwWCQFoxx8bjZbBgNBglAj8zhCPxqrVmtVhLgsm2Imo3j0RjnHZPxBJRAF0VRBL12R1VVCZi2gaG9XC6TsXEwGAQNyia9q1IqBeuM+QABaWPg0Qg2xzKMMjhnZ2epvIwx7O7usl6vWa2WKBTDoUrAtPMCqp+dpoLwAQABAABJREFUnYncTZSkCQBwVVWpfGPetDH48B5ZnlEWZQKAiqJAaZV03bMso2ka7t27z/HpCVevXhWjwfmMuq6ZToWBf3Z2xmw2S4aFfmDWwWDAcDjk/v37Sfc9zzJM0AZcLpcAQeddPAtOz07xIHU6GnF0dMTJyQnWWiaTCYv5nJOTEzabTTIgRONNnufcu3eP2WyWdMxBgoju7e1xdHTEtWvXEvgejTDRwyC2q9hWYrlNp1OOj++jjeH09IzVI1aXl8D6z1hKgGD07uhYUEp1MikRxozBGiEC3BduF8Hs90DUL66e+i7hHSz+Pl5FQbcv7clCPAa47+mASnqb87Rt77Hl338OH/LsACprT5DFFs1uhZdgY95TGMX0aJ8XnrvO+vyElz78NC88c439nSEr58mwLGbnnJyuuHVvyZu3Vty9V/PcB8bcPZ/TeM9Xvvk9yjdu8Mbbb3O2cFx95ipf/dY3+cznPs8z159mMJrwL/3r/wp/5+/+Hd753iuMhwPQom96enqavKMODg6YTqc0TZPG7H5KevMJTFcBxL0I76oLn3v3CTrlyYPBB21+pchMTmOhalvuH5/y4Y9+nJde+iDf3nyPO8cnvPLa67z44Y+zaW2Cm6P7d4TLtQ+wuVZ4HYEXTwupAaseKG5UrPsLGv4hgyqAiEbHhhbuEa5BdQFn5Vjf86cPriSzWAJIoiJ2d144w8dXCPJHqJ4euk/lFsE4KWGVgLF4ghhwos55p1UdP3d9X27br8YtP4PI7u8B2glQ52KK9f2zsY/q13k3+D3ZeW4wjPEEZIy1VgAwoxWZkUB3xoiVLQJ4thcoEx9kXYzIvzSZpywVsakrsfLgrLBxRS0pyLRlAbiLgUU9NM6LbrQDZzy37tfkmWKQ1ZT5kmGpKfOMLCtxtsA5z2K55ny+oa7BKtg0wvD2TsA9rX3oIx14H9uLCtrjRhu0ycjzgjwvyPIBZTmgHJQU5ZA8H2CyIVmeBaayhgDkC0grwJmPElKADrJYnXFVAF6NDnNGjNUQTlKI+TR4wsT+q00u1lgf5J3GD7ZtH6wT3jtOT+7y2uu3UWZH+oaT5zSbDU2zAh893eN/fTJsxD7unE2BRrXWZCG4/GY5Q+sM8OTFIJBXesYQ72ibJjGiizLHthZrW5wtyCcTqnaejNTOg8kyrHUCVHpP6+I8ZKgbReYBLK2G+2er8K4i56NVzzijfEdIVGorSLTRWmSJjAdtyQcZv/7Z3+GrX/kjvvPKm2JIUIbc5ti6az+xhJJhJc3vQQLLW+JZSkHTWNHIxlMUWRhUgxEAjXWeQium0wkfHpTSnX2QyAnv6XDQkAzpNtZvbz2Rhrs478WjqWGkxRIAzrsgp9VvL9vtp2+UjmssYXCLcSA20wsXAt24ERu0C2WkYptGB2c9yUOMc9PFCekIpWfLNefzCo9OhpLIQhf5tfC+Mjmzt3/EnVdeZzTep8j3qExNXW3wtsbjyTJDqxVkAt6bjGQAKHLPL/7iEMzbPPWMw/sBq5VlXYHSPgVYhhAk1olsTGvBW9BOs6kti9ZSt5ambmialtbKc02QQlpmiqIMxRUMbc4b6gaaxtG0DQ5Pay+Z6Jfph0neY+0cVZSo4inGo0MOSk3tPYtKM6bFm4xVayiG++SjXZxXbJwi8xUKJxF984zK/eQbm/OiO2nD5k77zg1p67zgvg6XYMBlukyX6d2T957TszO+99qrPHX9KmVZpsCMEUQV6RLR29NaFsSRCd00DWWWg5IAOOvNJgRekcCOi8VCmOYBdI8g+97eXgLMJSq6SbIlSikmkwlHR0cCmlcVi8WC69evM5vNyPNc5FGUYr1cCiN5s2G1XrO3u5vOz/M8Af6j8Qi05nR2zmg0orUCpLz99tvs7e+jtU7vo5ToiUfWeF3X6Z2iDngEmsuyRGvNfD7fMipsNhu890wmE8bjMbdu3GS1WnL16CpFXuAV7OxK4Kn1es1+yEPdNFRNTdu07O7uMj8/B2Bvby9swAacn51JINXlkqwoGIxHPPXM01SB+T5bLpIXgbWWuml45vpT3D8+lgCddAvb6XTCbHZGlhlWqxXQBSuNMikxYOt0MqE+PmG1WG5J9YzHY1arVQKy27oRVvx4wuGVKwxGYlBZrVbYRvQ6lYfDvX0G41EKZHpwcJDkcGIw101oT5G9PxqNuHv3rmyaA/De156PddNnme7s7HB8fIzJcvym4bXXXw8bTR6Jf/Q3A5fpyaf++kSpC9+JrL3HvJlK+6AOLA6YnlGRFesTVddHcXPCRaonRec6EED11lhbIFQPIH1YXra/9MC8tIGW5/qQ0R7+uFUW8vzuh85IEPIRv28xpi/IxxBZ6aIG+mRXhSIbqLyRN9UhQp+EFEV7j8Fh65b7Z8d88mMf4Vc+/zlKP+dP/vDLfDuHv/U3/wVsveDv/2d/ny9/7Q7nS4vKcg6f2eN0U3F6+5R1AzdP1jJe5yX3jmv87ISPfPIT/LV/89/g87/yq5zP5nzxj/6Y+ydz8ukhyovmqDA1xYvHGMO9e/cYj8eUZUnTNA94b/aZ6DFFcEj+BFB9S2LHpL+KDgQQHfHuPAfUbZB5cJ5vfOtlTk7P+MKv/iZ3j5fcOT7jlVff4EOf+DSrdR2aa4KkSfrmPoizaI9X0mATpK+iOaXTQddEg4oLbaMz+ph4jlICVOJRyhB13onXhGTojkWAXW6pupgDIVhfHGaFHdqHncN7aN1ruh04Hsm6KjBaI+gTqiAAJ6qT5FUEYxnE9p+OBYAlVGryWIn1Ef9um0giu30r8kLKOT6+u7pw5Q+e+nd4JK79OI/aGkcDqNQz7m3NaWr7/B8mNbWnCRIrzkkbN0bkH6xTNFbkG6LkhpyHAJb4QD4Akwm4XpTmwXHReZzTWOtpGh8Y0CQDqlOAleuFodqx3gWY9FSNxS+hzCHXDdVmTbUJYL3ybEJQU+sUi6WAbtaS+rVSXfsU1ixpPSG/N12ZJx13+a5DoMLMGJHYKHOyLCfLC/J8QDEYBB3tgQDwWYbJRNIEMrQyoZGIlvKW1Se19zQSPNgkIoCqtptSBOe7ZiEs8MPDI7QxvPnWLZYb8N7gvSIrhnga2roVkBtHZNQrwb/TmBkZ3hFIbZtWGONaoWi6tt8zfsd5Tamgge47bXed5YxGe7TVeRAI0LTWkhnNlaN9Wo8QPXTOIBtSDEYUg4LxcExTNyyX51TVhtPZjdT3YjH6XmVGkFUpYR9Hr0bZCxi0UWzWK26+9QZvfOfbVK0E6fQWskKIO5uqAq/QOgvtQNwyREokVKsCrUwYc8OQosS71mjxlBiWRYrP11Wa5KmtK5GwCeOfjg2BMA5vXdOx3pPBINWcyCtJvcV1Qs9gmYyMHSt/S+Xgwr37xx0ii6OLTGSDQiNUvbaagngqhQSKDqf5KP2i0Doaq8S/QGsjc1kI5hp/09qglOK1m6c0ttOWj+/swxwQ9fhN8H5uas9gMGGzmZFlBVkme77WxrID71wwjCiJsehE139QKPJ8Q9W0FIUhy6Dc1ey04f2Up6od68qynLcsNi11A651YnjTYmC0raeqGzablvVazpF4DaKnnpkgjWWgCFIxRZGHduqx2lNXopH+qPTEcc0HppKf1f3Tu82JD3mfi0Dr42waH8puftij3PaBx5mqkzbVD5pP77ceoABjPM5WeKVRmWbTCoehyDXeW5xtKbUGZyXqt4dChyAjQdNPNgj+oQuN97v0eD/gtk1rONF+st6nxW8/XSyXd33ShUHuoXm6eOiB7w9zUv7ZSI/dRZ7kC/4MFlZa4F/oWxfTT6Q8L9MPnZxzvP322wwHJfv7e4nBHAHkGBCyqjYB3MwC8J0xHo+Zn51zdHSEtZaqrhmPxwkQjyzyyBqOjL88zxNDfTweUzUVykugzr6URwRLrbUcHx8DUFVVer4xhqOjI+7evcswBKe8d+8e1lrG43ECeZeLJcVwkCRn4r+dnR0JUulsYtJb5xgOh7Rty3w+ZzAYMBqNEjv7+PgY7z1XrlxJwVcjKzuCxTH/zjnG4zHj8Zj9vT0B+JcLdnZ3WSwWLJcrtBIJnKqqaNoGk+eMxiO00ezt75MFL6hyUOJDeZDnjMcjrHPs7e0RmTJXr16Veis3iWHprOX8/JymrmmDvEpk6kdQSQLryaIzsuHbtqWua3Z2diiKgtu3btO2bTKIxHo5Pz9HKZWCe+Z5jrVWwO/hMC0BjDEYJSz/yXhMlmfoUIdNI/rAUSv95OSEyWSSApCWZcnBwQH3799PzPMI3FdVlRjrkXkeGepRiqiua3aGQ775rW9RVSIp1Ad+LtNPOnXyCokAQIfvPE4VCUtJ9eYbYai6gKLpCMaorb0Zfd2YB5/TYxL3foxyBJDwiK1xRT1wrwsM2YQadGCej9c8UA79dW3nghzPMchaMOYlvkfULIV+AfbPe5Jrtx6AuwUJdhsArWFQFPyj/+K/4J2334bNMUc7BZ/8xAe5dnSNr/zRP+CtN+8wGO6wspbGe6b7e1wZlfz+f/1z3Lx1wuvfv8nde2esz+YcHD3LveO7/Ppv/DqzxTn/4B/+59y+c5e68fwf/u7f5X/6b/9PuPf2WxjXUgbgIxrXTk5O0EESLHoWOefSGN//1wE7Hbji414jgugdqiqs/KSbn2Dm3jdh4ikjgetu3bzFN775Mh/7xCf5gy/+MXdOZyzXGza1lUCLPXJ7H45XwaXfey9tHGF0pyepDnQXKZcA5iGBFwPmhlIeG95LG4IHAaCE4aaU6+4Z7mcDiK6jcSn9xwWAHXl+6LwqtGGVWoRPQJGJLMP4e3f5djygPtijBSjqcEG/JSMkYHroc6GqVKqr+B6SaRfYupGNroKxTScmcHxGPyOhBz3hueNHMxOl0e7CmHRhP/0EnrRYWtwWbuPRRoAvYaFL5WQasizqlwM+mJ681LD3YC20bQhQa4Wh7n1koRMA8mBMQRjiUe46AusJGO4B2QIygtfQWJGT0bnCt57zM0vbdiEpZEy4IIHUC266XYBxbLgIxl4o8jjdBEBQx7/BqJuCrwbAVSvx+suLjLwoyfOCoiwp8oKiLMiyAVlWSMD0LBfNdmXolMEFoPXeJRkMl+aUjvUtxgqV8pdiAqmM/d0r7H5yn7vHd7l95w6bOhO5GjVGaUW9WeNcG7H9jumsuu/JEyT0yyC5jmhi9wRttuY6UoEJeKnIioLRdB/sDG/X6ExT1y2jwZCPffhZDnZKXLPGtw5LTbtZUC0FLD0Llg+lYN1Y1pv6QvVEEFn6dmRQa0UY84ISQADUR6Mh909mZEWB1nB2+wRtCqpmDW1D09SCM3mFMbmAtUbjbYtSkOUG70ArjckLVJYBm1DvWrw0tbSPPM8k8Kj2KCf5ynJDXmQJGFZhgRODW4uxVzqKd9GnoZP5lXoI2uKBnf2glAthfujWDQSgvBvf5cckR9Nb38TPrbW44FJi4xwTxu1wMeI1Ecs3thZZu+ksrrFCn0ndz+GcGKtta3v2JM981fDmO6dby5+4DoxrtDwT/XrvFJtNjXWOnZ19ZvMFbW3FIJLlOGdwLWgdytWJLIvS4jEbg8PePl4zGoi2uTGaLBgGbOvYbDxN46hbz3LZcr5sOF+01LXFZIJXmkyjM8lw21g2laepXQL9XZCUyXLNoDAUmWY40AwdlKUE5B0UhslIPIYflZ4siP4us8hFS8ujzvlpSRdz9OPGrmJD7X8HnthK4YG6uPg8ACeW7OgSmBb4vpVBUYUzte507nw8UzSkcJ73J+byZJNoEz44MT/KuPDEtkpx4/bgoe3nP5mn/XjSI17mSb7Lj7tcHtcY9qhru4A5733/B9rZj7g8L9OTS6vVijt371CWRWKIr9drptNpAEQ10RXT2hats6TF3Vqb9MHzAKzHRWYEwvvMPqVU0kofDAacnZ3hrWM8GiUANgLuzjmMMRTB/dMYw3Q6ZbVasVwumU6n3LlzJ+mrr1YrikLeYTgcBhZ9y87uLpu6Cowiy2q1ZrlYJuDEWptYzZOdLnBolLVZLBbJUBBlAE5PTymKIsnPlKVIq0T973i/1WqFbVvqwKSJi+Msyzg8PKQK2uFlWZIXBXlZJGmX0+MTXDBqbKqKYlAyHo+oq5piUKK03Ovw8JDRaMR6vWaxWNBsBFg+PT3FOZd01rXWCUSPoLdoi7cJgI4yNdE4EPXVI6gkbq0qldlgMGC1WqVrh8MhZ2dnTKdTirLgbDZL4FUdQHxjDFmWUwzKpGme5znL5ZLz83OKotgKZBvB+miEiZrus3Bvay2j0WgrBsj+/n7S9iyKghvv3ODk5OQn0Lsu07ulfujQB+eGEOBLqw6v9P1ft/9GsDPKaDxw3/76RfV+jeBHOqzS7wn+3CJ2JAT73cH9hxxX4b6RNQkBtOjlP+5ftxn5289J83FvMxjin21NuZH5u11uHS/Nx3w+ieVhyEtaT19YLHokCNtoPObV117n+OSMdnnKpz76Wb7whc9zfP+cV77zBqtVA3mJ9TVV09B62B2PeO6F52m84ebdcxp7zmJRY/Kcj3zsE8wXc/7ZF/8prW8pyiEf+/in+bVf/XV++Zc+zx/O58yOxaga42zYMF/FcXAymSTZr1i+fTZ6/yU7ZmVEiBRJLoGI2gmMrPw2aNkF6QybcuWFmZflfOXP/oyPfvqX+cgnPkn+zi10VoAxWASA6deVRtNnhluHAMHoBCxEJjp0GIVRIQ/43nkkgB2Czrnpg8ykPHcseAJ5p2846Zq8612nVBS4Ub3/+q3zu9IN/yII4jvGeOqdIajoRTkCHY6p3l+pyyD/sgXiEWsrvVXMlY+58yJH0gfQ+63B++0yemLpcfriY/bX/tjV7X07IErO6d/vhx8INhsfMbsEfEYJgzbzAgAiskECHAf5ExPkDXAJjLNND5RTJG1oq4J+cisNLENARhMYvT6wVm3HOpN2r/rmDxIzXbb/nslU2MZn5466RsB01/XhLJN2lGUKayMDvTOy8cA43Rk7L44lkX0vy5UOoPSQGL5SfhYJaFgFYF3YrEZLsMg8U+S5ocxzyrIQyQcjhaFUiTayfsryEmVKclNi8hyTGbQywuJFd4WS5tDwlY5VnGU5T117mquHhxwf3+P23TNWymP0AKOhacFbD84FiQyXxq0oMxIf4ftAK6rXJrtK2pIiC//JBwWDQQnuHO8aGQWd4qnrR3zwuauUmaVdLQTQj4zmWB/OC+tYKcCxXrc0jUttIraPNHuFPJt0nwCuE7W4NYPBiOHAcff2TYbDA/YOP8CyvoPSZ1i7wXlP3Ygkl+yfAvkkqQhA61zwqjEpz5Ifhfc2GZmsddR1I+OUE4KnaHWD0gZngzxJkLER0FxAZdE/F4kbH9uj772Lu0Ci7BmQ+/u3rcVUGntlbIkMdu+6OcPHZwBV3aC1YVXVwUtB7usCeC+GL8m3zMG9vhPHhDZImsW2EiYNHWYZozuJMussJ4ua5aZNDP5tBr4HNMbkgbEtAUetbbDWcnRlj9OTFa2tUNagTYlSLU2zIc+LUB4CWltboZWidXDjZsVoqER7PsRbcFazXjls03l6SR/JGGY5uSLsiUQKNDMZeZ5JENx9RaZzOZaJ10qRZwzLnOFwSFGUlLmmLAqKIg8a7TqMBTmPSj9phY3L9BNKDwP/+su0n5d06W5+mS7TZfphk/eeO7fvMhqOGQxHeA9FIWxx0RFfMxwO0NpgrUua4DFwpBJKDa1zjCfCRI/yKCIDo9O/qIEd5U9OTk4kWEulWW/W7O3tkRd5WFRkCTyN4O66WrOzt4N1ltVmzdF0mha+RVkmFnxd1xRFkUDaYlAmCZa2rtmsRUt8EfS18yJnMBwyXywSoz4ysr333L9/fwtAjnrpffAWfLimSdrrANPdXcmPUtRNgw8Aftu2OGuFTU6UEoDVYsl5eyYBoAKjfblaMfVT8hB8sywK9vf3OTs94/atW7RNQ5bl6Z6np6dJc7yqqgQiAUnbPW4SI8s75iH+M8aIREvIQ2wrkd0dA7EWRUFkp6/Xa0ye0bQt8/mCtpZFaQwE5bM8AdxVXWObFtu07O7scHxywt7eXvKAiAC69548z9FaJ9ZoDJwan9u2bQh2WwZ32yJppy+XK05OTlNbeFRfiOlybv3RJt/7JABCFHHpg3J+S3f24rXBPLeFi/eBoeTMHPftqX51d6rqAXs+AoHvUvfqPb92x1PbiSDdQ9asW+Dz9rWJRdl7n63TomHywvG41Ze9d8fEjc/r7X+fWPJESZEA4EZKogpl6cEpjTIZB1eucXTtWfx6xMc+8Rk+8OLH+JN/9p9z5+6CplEs65pN7Wm9YrmpGFUbbty8wdn5Oa11aJ3jnGY5W/DP/5Xf55sv/wmLzTmT3Skf/NCHGQ0LVoslv/s7v8utN17jlcUsMCVdkqGq65rFYpGMfg/zft0eB2LtCWQQ1bll+97Vr+ztY+vZFs6JVzgEkGq9pbGOfFzyrW9/m8VqzS985rNMrz5Dg8EphUUCrCWPiQRJdHVuvRcvVbq9QPpLBKHBRwZtPB4BCNWRi7QK2Q8gdmQZ9gFuFes0gGNb4HcANjQqybY8YIoIjdAEsBqvohx0D5SX32JAQBKQrtOnzoNErvTprCQ+kErKhftENnqUVopP2x5LIuTcgaDxt+1t55OdG570TNMxNbeB3ovgf2y90ZDyw6QyV1gHbQDSBSQLn4M2uQptLhO8VYKGohPI2SHJQUbD6GAklPyLd7mDHj6kYsNGgpfGZK2T/hPItqrXB0zWgd6RCT440Ozua05PYDZztD32e5YrmiYYIehkIeIaqiOKsEUgjO1Sa7hylDEda9YbxZ17NW0TxmpF59QSgEflY9QBBy00ars/pVdWDYoKrUTiQSnIjCbLNLlRQUs5aESbICejjUhfqIwsz1E6Jy9EuiLLRuRFgckl6GeUn3FO6kmbkmvXnubq0VPMF3POzk5YrBbUzRBvHc75BNpGQNd5D0HXPM3BKgDbgEInvfu+fUxrjQkuJirIv4gBoZS193jM0cEOw7zBbRZUdSvXelKdREazVjqwhRVKZ6zXK3mnAF5vW8ziHKZTjCNftThEJsgI7ozzoE1GUQ6pGsVoOGJn5xpa5azXx2hEHhEl75flBZnxEqgUhTHioetdaMMeGQ+DYVN5G9YkMU9ajItBt05pYStbV3eBOr0YMQjMcx/KGR8BaRU0xklgsmJ7vvO9kdNbaZxehXsF7X28SN0QgPMYAjf2dxBAX7xIbBhlW7kurLGcc5LH0Jd8kIBqW4u1kv/ocVI3LU0bjAO+F6hVxf6gt4B/yU1/9vJ4J8zxWJwOUEbhaid7LQfH947Z2Z0yLAx+OsI7UaxAgbNrWqeD0cChVY5SGd56TGF49ugprh6NKfKMstAMArCd5wNyk4c9jUEb0dXPMo02Gq0ydPDaTmNS/NBNU1uGfhmH4kwrUkqxfSZvsn6MlndJlyD6z3F6gIEdLVM/Z+n9yPFcpst0mS5TPznvefudd9jd22Nvby+Bx2VZshekSMpykIDLCKIulgtW65VoqefCUI/gpzEmga9N09C2LWUAuouiYLVaMZ0K87soRNqjbmsyk1Ej9+8HMnXOUQwK7t67G5jSuUigZIbWWVbLJXt7exRFwWw2Y7FYoLXm6OoRxWAASnF+doYNgGvbtgxHI46uXWVvby8F+jw5OWG9XrPZbNjZ2UmM/KidW5YlQNLtFtZ0RlVtqKo2MHeyxKxXShZdy9WaPMtY9AJfRk3wp59+msFwyOz8nHFPSqevyx6lSpqmYW9vj81qzWa9ZnZ2nhjnxhiGIYDq2dkZZVnyzLPP0tR12hDMZjOyLKOu6wBK+wQw7+7ustlsUkDOzWaTQOr1ep2CjfZTZM63bYv1jrwoGE8nIhWz2WC0ZjISeZ3FYsFgMODGjRvSTtqWvd1dJpMJy1AueZ6nsouSQC7I7MT3r+saYwyj0YiqqoL3gqKuK4zRnJ6eBA8GT9O0vP3W2wlH6DSzf/i58kne6+ctPaCJHiEH1ds7+O683n6iuw66mI+963oPATowMG5OEhBI/xq1vTN7CAzYMcMjsq06veV3ebfEnI3v2Tu9L18DnofcaosNFq/vyqCntb71zN71/mKenmBbVbKBiwXstCNGGO26hKK2jpPNkn/5X/3rfPiFDzDNGp47mnDr3gl//NXvov2I04XlZHlGMTlkPJ3QtEtOT0/54hf/GdOdI/IsZzrdYT6v2LQt165e40t/co71a+ZnFd/51obclHzgqY/z2U9/hi9/5KPcv3WDO7dupuDNcQyPsR5iAOz3Si4gM0ob8qKkzIYsZmthmSkVpEei7EMUGunqS4rbCfPTOolZkWeUwyHz2Zz5uuH+ySkvfPhjHFx/nuP5ivPzOSofYJWma/1dHevwDOt1ANE7JK6HKYY27/Feo2NfCJlSPoDocePu5Z8PQA4+9pcgC+NVYLNDr8qDgaCDDZ0Xw5fGxzCoJH11v92rROFD96Bw37uTlGVw8kcBLQK6SWC77qpOn357dOieH/qu9x3gTwBSiOCl3NGh0D6ARr17Xey78OD+62Hp/ch3PqmUWOcqsjS741IOj9bN/UGeFUvfRcQ0gqUqMqnjWK8CsCflYwNumQw9iqA7Ltr/0WsnAo46uVOE64VIStfvhN3uHAEA7gKRaiXSRSaA5zqWi/JkWrG3p1hvFMoqnPXY8ChjBADURp6ltfwusYKiJ1GnuQwdKB6DEx/sZ7QuY9MYzk42Se9b6/i+XevvAOFUWWmeS28a7u+8kFjkR0uENVPdxDIN9SCBMTUmyO0I0B60vw0ordFEHXCTZPVEpz0XI4TO2NspONg7ROkARivT9cpgHZCcu5T/CNF64twWA7T6boxQXT1Km4jAb5Qnsdi2wdUnLFc1SvkuqKsOjO6gIx29aONxFGwCq7sDqUkM7XhI5EckP845FJq29Wjjseua9XqDtS2zmWa9OkNlU/JiwGA0pm1W1FWFtwEY1QISN67BexskeEq8FzKPViYw6BX9kS2xxQNw7KyMr230nlUdAB37kwDcLqxx+p7AsS3GVhHv79KxaPTw3gejiML6nqxVqgf53SNGLYvsz2zoizYYU9rWpXciFG96RgjqGceHvNChv0odWyfjgncBqHe9sQWZa2O70T3jQJyf4hjRJ5t25STvXZYFm40E8NTac3Z+ztUrY/Ks5fDKBIvGuoZNVbOcV1IroZ3keRHaqafIFL/9a7/Mi89fl3uz3aZdgvSjrnwaqohzS6oVH+a4MB/KveKYsk2clffovKy0gdj1Hscm+tMJor9bxn/Me6yHZuNhBy/k60Fw+iEnXTz0hNcI72fR8b6z8BgX/iQXQf30fiU63peky4O70TQIvudpPLqpv98u8sS71k9Htf7UpZ9dpfvL9H6T9wKkvvXWW+zs7ID3Cbz03vPcc8/x9ttvpzFIa53Y3s65xFovyzLpmkfQIgLhzgmLfTAYJEagsNZlKh+Px5ydnVHrmt3dXYAEnud5znQ65fjsODEo6rpOYEhkuY9GI9pWAnPGIJPr1Zplj7W8DMB327YcHhywM52mvBweHqKU4uzsTPTHIbGho852lKqxVjYqcq1NsgFVJVri8/mc0WiU3qEsS7xzlIEl3TQNBwcHKe+2bZNUTZSjiUaLGzdusF6vaZqGnZ0dlssldV0HA0TBZDJJsivH9+/jvU9M9Fs3b1KWwtCWjZCwvOfzeZJ2iYFY79+/n8rw7OwsgdkxyGfUUgcSsD+bzRiNRgCsNxuKccF8PpeAqAHYj+1rNptJOYR8A0wmE/KiSEYFpVQCvXZ2djg/P2c4HEqg1LpOzPp4LJafc6QgtfEZbdvwnW9/R7TqL3Hun6oUNz0Ru44AtWyuwzkQQGqfgrmhhB1H77ym7sAgnzYq3dYvblRUACWN78NHHbjsidIBCrwLeXpwsdtt7iUTit76rA909DawHZDeB/wuBvnqb/r6ULnCa5eAJtmMym/aB1mKsJEUKYMewyxtqCIAq0LYzyeTrA6BwhQ45XoAiDDQtdcUJmcy2eO73/8+H/nFz3MwNHz7q1/iD//RP+XeOczuznjnnme8M+bo6CrDacG9kyXWNnzghY9y//5c2Gm2YbNeoVzLINcc7e+ishGNE8bf1d0dXv7aV/l6/RUKY7h+7RpnJ8ccHx9zeHiYYjkAbDYb7ty5k2IsRO+iWD/RkwoDy8WC5196kd//r/1l/uq//G/wG5//NXYOr6Mx1JsaZy3T8YCD/UMWixOaes10OmY4GLBerbh16w4vPv8i33/rHTGgOot2nsFowma+4P79+1x5ZkU+3mV3b49sNGG5bhDH8ADoBPqcc1HxG7xWAdXrXPKFiRhc30PfyfM8BXOUuumETGSXDsbrBCamOkRtDZud8JHrdNZ7yF4HqkfwhASAR2BU9e4rwRoD61zF+0V2uhOdd+9Q3vVkaFQPKOsxDy8YieSwSuNLRCWdjWFEt/utAA/SWRroApDGPCUQuAM6fQx8eiH9oDKwj7vHfFxjbadX77fLlfjWAbhFWLgCev5w636pF9G57yseEYC9TvpICWiONNO2CSd5ifOl9MX7dm07gUVa9MMjYh1BKWn63Q7GBQAuos8OJKi0leN5piALMimByT0cwGCgWK0EwjdG9NlNpmlrJ6x0K8FHI/M9Df2+83PoV6n3cHzSMp7krGtPXUtgSRMmu9Q+icCyimhaAI07CZrOENGdH8G1stAC8raiJR8ww1Q2eNG4bqxHNZ1nC6l0fdKcVlqRBfa60ZAZRZ5pikJTlj2mekDtVMhYBPD6UmSdAefCvHZhHg+4YQoEifdpxOlOUKkty/AnHgsohfb0gkvq5A2KlzhFkbG7qepegW89IOQ9PD7I7jSNQ0WviY1Fa5jNFhidUQx2aJqKtqlAa7TOKPIx9aYhgtjWOmGWqzrIORYSi6lp8MhY5lqLUia8u7D4NSrE6YOqbpOXaNu0eAdN06a1jneKaEz0XtTvuzFKmpP1su6yzuO8wznxXG2t3MM5T2s9TSvxoWTa8WG9ET0NOkA+FmA0mrmw0NmWhwmFnCwo3frgcG+XnfGQ6bTk4MoAY3IKk5NlGuc0m9oyn62YzRbcO5lx+/4szUhxjdQ3DPSHnH5K40dgXHjvca2lKIQgFPNvG8vtOyd85voBk+mYdeUo8gPeeP0ObdNgTJbupc2Axop00GhQcnT1Cnmeb3ntxnxkOs6EHRgeZcG67y55FDhvAzNfxu++qTMOpzr0uVS88e11lBzikemxQfTH3TtdQkc/+fS+Aesf8Qb5Yr5iw33vi55ctt4v081vR3p5+DnwWO/yGIcu0894ej9Gkcc95zL99KfT01O+//03eO7ZZwGYzxdp4SagZJvGwhgwNLKzN5sNVVUxHo8Tg3i1WgmLIwDYESiNoDpECZGO6V1tKk5PT7l27RpRysMYw9n5GdWmShIxoBgMhB0/KAeMxyMWiwWbzSYtIibjCShYr9eJ4TydTlkqzXq14sqVKzTOJi145xyj0Yiz07OU77quibIlEYCJwG1knFfVhsFAAOO9vV2UEvaO9z7puiulKPKcpq5Zr9doozk9PeXwyhVWqxWnp6dBo1yME3Vdc35+niRZfDBsRAb5aDRKz3DOJXZ7nudsNhtOTk4SQBeD6C0Wi8TmjpI66/WaOuQpgs9R6zzLssRc78urlGXJeDxOuvcR7N8Ncjzz+ZzZbMb+zm7Keyy3TZCTiXr0o/GY45Nj6rpmMBhQVVUKHLtarZJGfww02tU/KYBtWZYBUBcX5tlsDsAbb3yf+8f3u7X7ewxUPy3G8p/X1Gf1ex831g/WycPY/0oHsKTbSiQu3sM2VfF4uuf2Ex4jszzQliL4/cSN0HGD6LePPfBePgJ/fbNA/4tKoMQTzV6UO1Eer10IyiYM7ohLblrH8uSMD378Fzh++y3auuFLX/02/9H/5x8yLjxf+OzH8YM9WqtRBlpbMZ2O+cALz/Dss8+Cv0vb5Dx1fchLL77IH3zxi+ztjPnX/uq/wpvvvMbtu7eoqppqtaReLfiP/9//Kc8/+xR5ZiQA9nzOarVKzPNo2Izj1sU5KdblaDRiOB0x2h3zqV/6HL/+279DPhzyf/t7/y/+5n/vb3Fy+x6DcsBwNGa2mHF8codrVw+4/tTTrFdztNF8+rOfYTR6hbOzGaPJkMwWbJqWTdPi25ajFz7IM88+m+plNB6zbFq8CkxqrwCDVx5woA0SsA46iZNerSsnFNsAMKKEaWhjGwnAnOm1X+9ko651t0GX9tPdPRqydGj8lh7QTA+M9vSY4p1wkgrvIix1uZ9OeQpMdwIzOH4OEHzU3dXo0IwjMK/QXoKAxgChnQEqBAj1ju6JKrHPt2VdJN8Jv03uLRF46Y71gdKfzXQRMA/I5Q+ZOoCtb6xRqVAjIzsaDJUSdjcJ6Fe986IBoOfXEZuzEqarGAvZQqulHfd6Q5pHUgsWaRkCX9t6TBsCnbY+yJ14RkNFtfZ4rQMw6AJwHdp4YI2r0IC3tudqG0CPb980nndu1AyGJdVGxptOpzsAw7rrN1v61q1L9wa1pdfegXrw0ks5h0eGthHWclPLc9tGUdfyvao9dePkeOtpg6FBgERh7DsA52naWPoq/VVK+mhmNGWRMR4aBgNNlmtMlE0JYHw/jsGWWkC8ne+MU73iS991KhvSX48HH0KnaoVydJJNRge5GgGmZb0u0iJKaw6Prso68c3TXtX41O5iefoECPfGwvDXeYe3MJ9VXDkoaZuK4XBEkSlW6wWtFcmTLM+wtg5gqsPaGoJOOegw57RoI4bb1rYYI8FHrZPr0IqiDOvuxgqwjQ8EpRzfRgOrvIf3UWrGYUOgUGFqC4M+yzRFJvrbymRonYFXNI3HOrCtwvlAIgj1rJWRMdwTjAJQty2L5Zr1pmI8GvHhD36A9XrNycmM47NzmrYNQXENRZZRFjlFKeSdIsso8pzpdMSv/PKn2NsdY7SROUAZPB3z2kMKlPrWO7f4O//+f8h802yB5rF59skJ0XDwQC+MhAU8TdtSFCbVv7Oyz713POcP/vCbXL16hdF4wsn9eyyXcxn3Q/vTSvZ9q7XIjR4e7DAeDWitlWeHuSjmy7lASvNx3onteLsZxnHQeyRWYywEujaaxklF8hqIuvY/KE74A4Doj75xaB/vfZ8fIIPvvkD/MaSHZdO//zw9znU/FSDa+8zET0XefwrTY7WXi4X3uA3rx33dI27z85geNp49sDWIi+FHlNjF33+Wtxg/z6ltW27fvsNkMuWp69clQvimkkCRIdBmZPMdHBxw9+5dAMqyRCvNIBeQEytBNA8PDzk5EWmNKM8S212U5wABfmNg0Kh/HQNWnp+fJ33rCP4qpzCBGV2WJdZ14K4xhqZtUUZjihyvFJPJJLHCz87OsGGReevuHXYnIikznkxwXtjyVQg+FwFtpRRt2wKkMojg8WAwCOxVAbNPTk4ZjcYJBI5pNpuxt7fH2ekpe/v7NI1op6/Wa9q6pmkajDFcuXKF09PTVA7eeyaTiUSB7+nDK6U4Pz+nLMv0nKZpAJgGdn00HsxmM05OTsiyjIODA4qiYLPZcHp6mqRRqqpKEjynp6cJaIrBOa9cucL9+/eTpEv0LIiA9nA4ZDAasqkqrLUsFwuMUgwHEsRvOB6hc6mzzGSAR2UG6x2z+ZzhcJhY+FGSp65rRqNRWiBGAN05R1VVSeoGYDAog3vtjPF4yle/+me8/c47vZHoESj6ZfqxJhsZSXFtGpmSXsC67XojaT32JVDi+RD35S7tz/vCGinQZvq9N2OlBzm80vwg7eTdzuwY6Bffovvk07fHmyvlHbonJsgp4H3dpg0McqqLmeuV0ZMTcJDnp51sqEivPK4HDjk0pjBMhhO+f/M2Q5Nxulxxvm4Y7h7wS5/7RV797tf46IdexLY143HJYJThWTOZjrh16zZ37t5nvbA4l2GdZjzM+D//+/8ef+53f41nn3+apz/1FJuq5tXvvQmuYTLKuX3rJm0rQZsPDg7YbDap7UQDXjQkRq+j6KEUx/qyLAVYQmOKknw4ZFk3XH/mOf63//u/w9/+d/7XfOcb38KuK8Y7eyzmjpPzOa1rqTdLRqOS8XTC2fk5HnC+pao2tM6TZzmurvhLf+kv8sLzz6PKCTWaddNQbWp8Ngh9IuowI8zzAKp3ATcDvTtFY+sgXh/Apza8awo4iQDdXUOS36zgaJhe3UofC49ykOkuPK3qLpfvSmRiXGK6d3IZCdoOQA86KP8kEDUIvwSpDKUC6957dH9M8Mj3CNZB0P8X/XiVmMfyHK+FodoHmRJY3mHlPZC0A89jnpXrv0PqVE9sOnmSs1KqkwvgrgplFhn9SQf9CT3c2sD89sGDKI7TzhM9gGzby48C24ay1gE0Dy+gTKiXPhgWQaYEmpFY6xFHlM992Zje5QmQUrRNeL4SaYmmEba1UsFQpBQmA+sVvvUCbMa/1gVvRGGvWyvgZGTyRmWhYLPcmiTqqqFtLCZTSR9ZKSPyKVo8+1BiIHLe0tQV1boOAJ7k33n/ELY+HBxkHB4Z8gwGpQRaxIaCEHEWorSGNh1Lv26Fad9YqDaO9cZSVY712rGpnIDvjcNahXVdH6obR9XUzBbyiCxTFJmmzBV5oSgDWGuyIKsSTVmqexeljBD9VG8+TsNSAMkjqLgFpkvAThcM5z4EzaQJgKUR/XDvg2eZ9xRhjZ5nGUqbfrOS9aXzCdiMYCsossKQZeId4BxisPeOpm3IigzbNGRmwMHeLm1zRrXZ0Dor76RNALk7FrkO4GgrIvtoU6KVzElFbtBK0TRVkNtSjAelMM590AN3ErBUaQG+69riAqHnYG+HZ5++ztWjPcpBQVkWFHlOWRjy3JAVikzlookfDTfKCFjrghaIDhJaulspKXQyaHhknLXW0lqLMTllJPN4K8FSrQDJWsv7JG36sJ+JbTbUZqqH2E9VOD9K2SmleOr6VXanQxZVm8YVdGwTPrSDTge/6xwdQN1997StFaDfmMDs72RtVquWN9+6TZZ5bFMH/f4Ym8ejTI7yHtc2ZHnOB569FtjzHuVUMiI7L2x+vKe14gkh79PF94jzsvc+GMplrHbeB4OgSt+T74hWApy7OEerVFY/SPrplHO5TJfpMl2my3SZfkbTer0WWY/hkNFwSNM03Lt3L4GbcTF4584dnHMMBgPqumZ3Z4fRQACJe/fugSIxpqMEhzEmMbtj8EpjZGEVg3RG9ncEqiVg5ABjhFW42WzI85zxZMzp+Xm4h6FuGo6Pj2nblsFoyKgYp2CiEZyPzOXhcIjWmuPjYzKlBeCva2aLOWdnZwLYt22SMYksxRjAMrLQi0KkSxaLOcYIu2SzqTg4ENZ+BLd3d3eZTqecnp4mPfK44LHOMpvPaYI8TdRrj7rk8XM0IiyXS0ajUdJqj3lcLBaMxxLYtQlA0HA4ZDweM5vN0sbg+Pg4BYWNsjij0Yg8l2hd3nvG4/EW0OScSxItg8GA5XKZ8joej9FaJwb8WQD2XVhMp3oNC73Iaq+qiuFwmPK5Wq0SAz7K68T8z+dzyrJMgWb73hGRiR/lew4OrvDNb3yTd27c6BaVHdLzvr26HpYutdB/+BSX/Wrrewcxq4cc5eK3CKzEo31sSG2D3ZHl2A+x17/uB8t8l6+tTcyFZvGkTMrdO0V2X1Sc7n7TPTBw6w19B4o+ycwoH0tP/vqwqfW9vmG9p7ItusyxTUODwmrDZO+QT/3SL7NanXO6XJBrxzgrGY4H5PkQ51uOj8+pNjWrTUVdO8CwuzPm1t2bfPEP/oSr1w946qnrjMYTvv2t7/Ddl9/k7GzG4eERg8EuTdOQZRk3btxIRsaYotGwLw8SpbucE/3yxWqJGZfU1nJyds6zz36IuoIPvPRB/sX/1l/lP538J/zZn36ZalMzmkzZrGbiCeNbrLO89vobLOcLjo6u4jebYPAVRre3LUWeCcDrbDBwGHKT0UbAIiCGsuV2ATaOzNRoUHlIGFPVAZAugehSNyohfHEMC+i5EoAkmpp0r73IEOqCNnrXADrwswtS2h8Wo+56BAsiI1XbqJkeA8KF5ykRsYk665oQSM56YZ/6npa7F+kYr73IGiVwTtBWFYGHJHMQjBHh4giOh+wnw0QEYB8UPepA9i49md79pPplhLpS/+wDlFyszyf39NaKHjIBRE8jbLCc9EHteDwF0NvKFPiq36a6OtKxDaWAkbA1ukdPm/SgHkSn+6ziDuAWnXWPDwFOJXaiZzBQ2FaMMNaDa0OA0dqT5/JbfBEf2pxSXVt+GJ7lvQQ8NZmhKHJMbjC6EEYwhGCKIrPR1DXVqg7jE8mxZLuq5EtZwsc/VnK4H3S7lQBxZGFech5jXPCglNopC5HE0VmQR1Me6wzaZJgijDZO4a2irjybjaOqPHUNbeOxDVSVZblsWVcOG8B423jWG8syaN0Yo8gzyAzCVNcqeAxqjLJEbWvdA06jxcQ6F+o/jIHBmCBYZscMtr4Fq2gaaYBiLAo66EHWpbGOzc3b5EZzfh7JLT4Y13xnZAkgegQth8MRTWXZVAKMa61F8sQ55ssV168dslg69vamVFXLerOmCbGRktQO0LRC8fca6rrCthaFwgRpF2s9ujRkRlNXNZlRjAYZo2FGnmcYrcizAq/knfIsw5gcrTR7e1NeeuFZnrp6hTw3W21E00m/hRk6tbV0RjBS+CDTFTuXZF8nc2k0hon0Wexv0RCmMcqQA4oIlvfQa9+XLiGByfHJfcA8eQH1JpLhaMjVo31uHc8lbLfaZl7HPu17ncSH56rUR0NWnMzxeJETXVdN0r3vPEMM3rcppkJHdvAU+RDraqk/pXjphWdx1vbeMZSz7xm+iMF1Pd7bLaJFAtdDpnWYI6NXn1ISVBett41OnS0orQlT9ekHZ6+L6RJEv0yX6TJdpst0mZ5wunv3LuPRiOzqtcTq2Ww2FEWRJFTqWvT9VqsVw+GQpmlZ2iVFUYhetRddvQhCRymQeN3BwUFiH2ut2Ww2AJjSYLRIv0T97ghGt23LZDKhrmtms3kCltumJVMisTIej6nrmqZtOTo6SvraUfO2qoRZn2UZq+WKu3fvorVmb28P64XhPAiBlJwisRIjWN80DVVVJSa4MSZJuQyHQ7Js/UAA1bOzs8Sankwm5HnOvXv3EpjehL+R8R/PiwB5X//78PAwycnkeZ4WnoPBIGm2X9RLPzk5IWrJHh4eJm3xqqpSUNC4iO3/iwB+DOI5nU7Z3d3l9PSU8XjMeDxORgWA5XrF9evXOT4+FqmWckCWZek9nnrqKU5OTthsNlhr2dvb4/j4OIH/q9WKnZ2dEDg2Y3d3l5OTkyTXkmVZ0mSv6zr9FTkeMWjcu3eP1157/QFphsv005W2GUkP6mh2e68eoB7Zjb4D4/pw+BbGEABA1/stHKbbFm1D9yr98sjcb4Ek6sL7PMBE7zDL9KxtCY5HPjDgNQFo2LIKROC895LhXN0HtHqA6xPB/cI9BLwMpekB73Gq0+r0eLxzrOsNB9Mp9XnLaGfMYDxA5ZrBzpjnXnqBb3z1D8l1y2CsGU1zdD6ktbBYVLQOrHNY16KUaMfmRc5bb9/knRs3OTy8yf7BIbdu3uLsbE5ZDhmPxkmiy1or2qfBSJukd7xPIHsc5+KY2ratBDFeeY6u7GGB4/MzytGIdb2iblt+/Td/E2uhaSwvf+0r4CErS+r1Bm0MjbXcuXeP3GRETwGtRMLMByPyq698l89+/jcwI4XLB2RZTp5JkDWRdFG9+o5mk23T07YRJwLMaqvdJVBj68pwPCCcHgF5uiqOIGbnu2HpPkctbd1ryxHYiqC6jdeHjX80VujUX7pyUfGvjwC+AOc6aLzr8EI6sllV0GSOoJfq9V4lJggCo68DzAhsywDEEdl+8ZoIRIv0i5S26sClcF5C3J5EZ1Lqh5UkD8lHa4DcNv5THTCTDCC9On0S72BCkM4YTNIDneJKb8wJxjzvxbiW4je6bcBNxczTeSbY+D5BzkSlmorvEAA4Hx/nOmZqK223IyGrAGrLZUZ7ylKRZfKssgRvYbESjfHIRpe1F5hMAotmmbDRRVM9AOmaJBvTw/flq4K6EtC0KDRK1wJU+gigewloap2w4vW2BE6c12K9Ga346IcGfOKjA4pCpfknGgyMDkzwEBi1bhy2cZgstGitMJkSVnol7GLrPHiFVQ5yzWSo0DpHKTCZwbYhgKQNsQ+UAMt4qCvPetNSVw7biIdIYx2bytK2EpPAWVivHLNZI2VqVAIwtVaBva5FY1+HAMMqGlGC1JMOgG4c0wmGgzAUJa3s4B3Q2hbvNU3jmC0iiUV3FdQHIX2UJ5KePxgUjMeyJ1msNjiv0cZw//icF549YnI0YjIyNFf2OF8sqJsGqzRaSf6b2uJiZ3BeQHbEWzfPDG3boIIXgtGOqzsTfuNXf4Er+zvs7UzFm9OIJKY2GSidDAVdn1K9cbzz2uu3G3q9JYUUoFsjC9gcpbTiWkgu9qrbF6heP+1GjgiAx1G/m1tie413lgC/undOsBIFqHprbqJrzy9+4Gm+8d03t+a0/jtuLSG9l36lfNCzj0GGg+GldbStZTDMmc3XYmkwMcJvBNFtiEXQGdi1yijKktXyHK0Vu5Mhzz59Nax/fJJQ7q9bUcGQEcas/vojGrn6Y3Esg269K4OkVgqVidE9zoVxaL24z3kcVvpjg+gPu9WPekv1pObDh6b3mfn3myd/4dvFxbrq/feRz3qcTPRv9diZfhen2K1NxUMe5R/9iHdtjD+yhvXgjR+jPzz2vSRdKOSHnvYYL/PAdY/ZaJ9MY7xMTzj1tvWX6ec4WWu5c/cu0+kOJtMUZZG0ufsa1MaYBGoqwIzGrDYbWfgjwHOUA7EhojxAVVWsg2TKaDhksZgzHo/xTtwcG1+TZfKcpmkoAqi9XC5YLOZp0eydZWe6w2azSQz39XqNMqJ5vlwuOTw85LnnnksA7HQ6TTIko/GIxXwOSnF6foZSiiITxvxoPGZTC9N6tVolnfeiKGiahslkAhACdJokiTIej1NQOh0YI8PhkEybxL5v21Z0v7UmMxkHu3ssFoutYKZ37txhsVgkBv/+/n4ySDRNw/379zsN97OzxBYH2NvbY7lcslwukzEiXnf37t0UJDWC2dE4ErWBtdG0zjIYDbGtgPXL5RKAxWIRGPfiZeC8Jy8K5os53ntWiyWDssS2FusdddswmkwYTyacz85ZLBaiRd80CTRfLBZkWcbOzg55nidt+9lsRmTCR+A/stlF432O1lKei8WSg8Mr/IO//w+oe4zTxwHQ348e+iU4/2RSZCdtMZESSB1Zrd1mCrhwTtg/dqcl8BYE5HURefAPWSlu4UiP1w7SRi9u8uKtIt3y4nLnIcfiiY/d8uJm1PseUKTSu7u0+VLhe7cy7z/6iYHoAF40qYksYO2S7I6LD8dL4Moc6naNKTxHB1c4OtrHtyv+7Bt/Smkco70h1fKEZTXnbJ6xqcVVvLGGTVVTty1OWbQ23Lt3h9Z6rhwdociwrefu7XNGoz3KcsSNmzf4yle+TJYX5HknIbazs4P3PsXviMa/OMZGRno0ZDZNw7ppGI8nFMMh67ah8ZbWO8rBhPPTc77wa7/G9aef5t/7u3+H73z1S2RDw2A6xbuaptpgsozRaMjZ7AytFFkuLv6tbRhOR3zpi1/kL/2Vf4lJOcQUCp0ZFtUaZQY9Bm7HJoy12G8XEcyIQHVkt8Uze9v5BDgmaRelQvDSMKfa7vahlyUgHSCEsuuCgRL7lKfTjo75ic+IDN0AWiuFClIdSnf51hEoUxEoR4KKBhDdhFzp8JwIonsvEi9eR6Am9H0cToXrQlv03oGly0sC0uUyFYxP3bvRSUYplfR65UdHZ6Z7eIrv/TjpyWwflQRk7d1Toy6wtoPhQBrLEwkzbO0FT5xUDd14E2UiItaJ8mjBhIL+f3iF9C4i7yMAZMh3wAolVl/PmOnAR3XsZHVSqQ/FtpjiIPfHfRXaqFYUpUiTGAPjieGtt9rQ93wCzyXAqOjtR2Oo63s1KCWyHwFIj+2lX79t47CtC7rw3Xlp3gu2sggGJ+OH7yRjtIKrV3J+57cO2JlKTAOTBfDWSK1GMLiqLHXjUUFrvVo32BAkUmlhjHvXVYAmGLJ0MI6GF2yqBu8UTetwXuFan/KWZ5oy8+RDhRrn8QWQt8h7ZSP53Kw81dqhjWKz9izmDcuVY7FsWSwa2saFWA3dO2mtMVqCiRql0CZIjUQAV8V2LY1CayUBYMM8tVjVrCubzoss4G4uD96NVjTjMy1xGES+xNE0UufGyFr86996g1/95U8zGWZ4VXKwt8tmvQLvcDpnuViLXIiPs3M0PoAKDPP1uknzz1OHU/7yn/8MB3vj3hheJ/kd1WpiLAx0ZwRIIHivoUUGthgcurrtzw4qGBM646eUoe0DskSdcWnzYuSSfPjQv6XfudQHXJIa6acoIiWrFa16RozwnJi2u6fc6KUXnqHIMjZNDJoa143RFy8ek+frIIHXrRNVyJujsZZ1bRmNBxgjbSSx0LUEqo2BVSMByeMpyzF4aG1DkRk+9OLTTEYD6MnBQAywroMhZzs0aFyZ6fCcuO/zTuanPtAe5yR5p+4+1rkQf4RubUiYax5zInl8JvrFG/4oUaInvKd6v1nXF65079PMLQuh3veH3Oah+wMuHPQPHnq35z3qVg+97uIG6CHPe9hGJk3mP2D60TepJwWkPxrpVw8979Gl8vAzHvd5P3h6wl3rMj0kxQ34ZbpMi8WC1994nY999CPs7Ey3QAdrLdPplNlsxnQ6Fbafc7S2TQyRyACMOnjL5TItApRSKdDbdDJhWA5xrcilDAZFkPsQ9nLb2sTS2WzWiU04GAzJ84LMGIYDYTyfn58D0NaWIsi4xECnN27cSMz2KBVjjCEPGuObzYYyz5Ornw9s6CgfU1VVkhjZ3d1NQPLOzg5nZ2cMBoayHOC9sJIGA/lsmxbbtIxHIwbDITozqfw0cPfO3WSg2N3dTQYH0ffTqcxu3LgBwGAwIM9z6roWN9XAso/l3AYZmvj7/fv3KYoiaalHoLyua5Hh2d1N0iiR1d00LVW4vuxpwsfyHQ6HolneNFRNjfWO8WTClYND5rMZTdMwKEo2TY02BpMZZnM5HoGs0WiU5GmiHEyU3Ins0L5W8WKxwHvPzs4OVVVxfHzMcDigqjYsFksOD4/4sz/7GqdnZ6JDSbdo3gZft9P7AdAv0w+fHgD5wmYh6pJ2uLdPf8OFIckJqld/HUOq/5xOEuJda7q3Dnzc1hDdby8uKzvdy17bi8DbFqTZBZ967NVN75mpfELhhW11YoGnTRXb79b//iSSxqC8ID6yAbQBkIy6sqJROyhLFmdnHO6MuX9yi8/+5i8xmjr+j/+L/zGf/Y3PM9rJme4dcrCzy2gwYb10LFYN1hvWVUNtG5xvUE5x9NQV6sqj/IjVqqVtxE9+sVwwGhumOxOqTYtS4qES4ybEMRtInjvr9Zq2bdNcFaWzEou9rSmGAxxwdj5jWVXoLCcvh+yZnOVsybPPfoD/+f/yf8Xf+d/9bf7hf/If4UYFravAWUaTMeA5O5sxGY9RQG4ytFa0bYNzhpe//S1+43efZjgccTxfUq0byulQNFGV7jHtAgO7B4XH1A1t2xu2zu29A3HiPrvvndARirqABF71hGJCXQqg6fEX7iXJCbBJCCzbc8s3xO8dK10uERBXBZkHpSwGFXTVfdBEd2JIwAYZB5fmPIs8TAD3fnmoFFx4a/+rpOfpsCHUvXIRgLwrc52KM5ZCkJZJ7/Fk5g559pPqlQ/LU1cPHdDbAbNP4sk2gPFpjJEiCgYdkfPQpttzdyNhyG8X4683dkJV+XQPOaZA+RQotz99R5mihKmCEF/D58hK92yXkvK9kAJIPotcMxxpTFbw2qsV60rA9bZ1AqQHNrdtvPxt5XuUconDuu+B6/0Uj6UAqReT787rGwI6TEYxGmo+/8sT6s2G+5UX6RLVgabOQmsd1cax2Tg2taNuHVXlaHsTYjK/KsGOjCIYt7rsCFs8AqaglQTBjIB4ZhQuhzxTFJnB6CAXE/SfjRYGtQ43zozBDRTeGQnUaEqMzoKsoKGucu6dzPny197g9bfupMDHmdEYI6C/8iKer5UYjiQwa499rzSZMSIhE4guN4+XIWhoN/Z1evmhzvBY53DOojJDnila63CNk3aiVfA8gE3d8Edf+RaLdcMzz73AtauHrNczzs7WzOdL6nqVbhxB2rgWMFmJB2zrKIcluYEPPjtloBcszmehvYfB34EKDHQfJn6vtIDZsU/1QGml429a5I+03urp0Xgp+TJELf7Y9GQM7AwSvtdx5Twdnh/Lsddoo2EivbMOQG+aeEDFoKg9Ym54l47EHXqqkjH56pU9ruyPefvuPO2Nunfu2OLdfBfXlh1JIyZnLU3b4N2A4VACxPpoDPA6AOm9bABaZwzKMevVQvTUteZTH/+QGNNCf0pGGZkFiYu/KFXWZaHzhEz5iiz2aFjujZ/eh1gjPgRP9l3b8EoY8PE3z6Wcy2W6TJfpMl2my/QTS957ZrMZ77zzjjDGR6MkSdIPRBllQyLTO+ppe+8Tc7ofkDRKnXjvBZhvW64cHDKbyaIxgsiRhW5MlgD5qMc9Go3QwS83SpfM5/Ok6x3zs7+/z9WrVzk+Pk6LrqjdHYNjRlb0cDiEoOMd36lqm8QajwB80zRJY9xam7TCvfcpGGpcwEwmEzJtMFpTliVVXbNerVmulhhjmJ/PGJRlkmtZLpcMBgOUUhweHqbgmiC66pFV7pzj6aefTsYJa21ircfrYxlHeZcIhEeN+ghkR+B9tVolVnrTShCcPM9BKYlkn+ddYFelGI1GnJ+f0wat+KZp8M7R1nKOtZZiOEhyPGXvPWO7iQFjYx7Pzs44PDxkZ2cnScmcnp6m+onleufOndQGV6s1xhhefvllXnvttUtQ/GckPVBLvQ1a3OI+tCYVFzZzsifT/kFwJP72IAAfb8RjeSO+1zu8GwCVdFa3TuwDoOHze92kl/SFEtk2MGyntK1ViSuWNm/2CULo2muM1ZgAZjgkWJ/XjkyJFigKvG04X825fvUQZSvW8zl50fKRT73If/vf+hv8h/+P/4BPfeKD7OxN2Nvfo8gmWF+TFTl7h0/zzjtvoExNUSrAcf/eMdX9Fc5mTKf7eBekvCYlg7Hlq3/6JbxXaJ3hnE+eK3HcixvtGCOjb/CIcw2IsdjVFVlmGE7HjHd3GIxG1Gu4eesOV69cZbqzR5WvaaoVf+t/8D/kzt13+N43vkxbVWSFwSs4n88px8IsH5YlWhuq1nJ6ukCNNP/B//X/Qjaa8PFPfw7yEbt7e2xsBFZlEy7BGgOrLoLQXm01H7/VPgLQm1A3ldpCZOrG99ZovFYoF1zZ6QPgumu6uLDRDzqu4WwFXZBHLzzD6Ikg84VLwSYFHAj65iEvEhBU4bwA6T4ACAKiO7SPzPcglxGZeb773OUx3LYHFqe8KynTCEqEIuzKz4WAxDqyVBU9mx7xEUmt/sl1pSec5KUfNBp3BWTefYR9X8m24AJIbXRXJ0opTOYZFNGPoAOYO0Rdhb4XzDgpYKgA800Lbc9DAgJoijTHOA72Nc9VkJfR8W9gQFuraL2wyiOeByRGuFLhsxYAemfH8NRTGTfesVReAhdGlq0PsjLeC4ArQUeljUY9475Oj39I++xY5j0jAnHe6lIHNMr3Mld8+pMjrh4qqqoVzfIUBDSUixdGbl17NpWnqkHUCVUwRohEhADTmsxoMq3RmXzP80wCUhpDkWcUJqMocooiIzeZ6GBnEngzN1p0zo3oepueHjleoTDBuySCngGoDOObaG9HoNHBSHHtcI8PPX+dP/rKa/yjP/ouJ4sNeNEmN1qRGZskX0QNJRgZo9yPEpB/sRSJGqUUVe3ZlsTqPvZ7hG1FAmw4MNKGvLDvQ7GhULSNBQ/rTc3Xvvldbtw9Zn9vh+PTexzfPw5BYemMOuHdnRMt+CIvsDYEGNWazHhcM+f731+hiOUXZFt0bMsx+GxvgAugfAeKq97oTDBQBo34YMTQcSxWoJQhmuG7OghXB816pYOnlhajhPzU1xAXsF5HUD/8JkC8FU8RpdgGksM4m8om6H3H52uZm+JQsTMe8okPP8eNuy8LWBzA6v6c7qwL0kVhhgrF5HxvvPaBjV63NE3NznTAem1D35B7OmfxtpOlcQ52dw8kXkFTk+UZ16/s8NyzVyRIbOzAsXnh6aIMx74Y5k4Xfbc6SZu+t1Msex2B/8Cmd2FMjbGzpVw6Fn6cjUVD/RJEv0yX6TJdpst0mX5iyXvP7du3KYqCZ599JrHOo25sHyivqgoQtl9ZlmgtAUL39vY4OztlOp1yfn4uYGsAoKM0R1VL8FCA+XzBdDpBKY21LXW9FkC7qphMxul50f2xLEtGoxHL5VI0bOuaIis5ODhIoPtsNkNrnVjpEdRXSiWJmbIsiXuexWJBG0D7pumA9JjnyIaOIHee5+zv7ydAfxSCdkbQIOZLKXE/rTYbVI+hP5/POTo6Is/zxISMGuKnp6eJLRmDm/Z14lPelZJ8t21gwxum0ymr1Sqx9GMw1RhoFTp5lliWVVUJgF6WtG1LWZZkWgLCRv30WA6DwYD5apmMJpvNJnnBjcZjxtNJCj4bA5BGdn8MNjsYDLhx40YKjLrZbBKAvlgsKMsyeQLs7Ozw5ptvJkmdunEsFgLEv/HGGykfF8GDPkj2g+oGvlu6lHJ5MukihvwA3twLXCls056fpX+3euhcZsPWRDYdifXUASbvCdg/MvMd26ljQfVPUL0AXo+8WbcVSpvDeHcCJt6BppEJH8vI988J18esKAIb119gncYdbI9B9qikUr7CdSoEuxSffxQe4z34zrXbA8O8oG0a1otZ2NgbvNfkgyF/7V/713n1u9/g+rXrDIsBy3lFtVny1LUXGI1GfP3rf0zdLjg4nHJ0dMiVq0fs7Rn+6Isvc+VoxFNPP8Pu3j5OtZye3eS/8lf+G+Dh3p17vPPODY7v3+dsXVHmGYPhCK0UTV2xWC7IjBhIQ2y9HoDs8d6CbxgNc4pcY9sK7y1379/n6Oqz1G2LbeQ9y/GIatXwN//7/xb/s3/7f8Rbr38X5RTG5CIO7Q2bVgLG0TTUjeWFF57j+zfv0KzmvPz1r7J3eIUPffIzVLZBqaKrkUQHjABu4JspHRGI4AVAz5LUq9cAluheuw+NRdqLCq7rERDxoEKdboPRhsa5rTYu4CUR/5DcqQDop+akY8tJ93YIm1ywBp/0jr3zuAh6ItIqERARjXSdQMbkOek7ze+Yq9T2E0oemH0RiQjglksawLH1ktYBSXqG7eC9Kj753Qxgvneu6gN2Dx9pPGLcem/Y49375sUsqFAoSXJHdUBtJyAVpU/e86GPnVorhkwBaIWRnOXC1h8MFJORJziWSXkbRaa1SKdkJE1xAB00670D6zxNC1Xtaa0LzG1FZiAPTGetwFnwzgVwOLbLHjCu5YN1ntbBpvbUTRy7hX0uYDAScNYpXNBRv349B6e5casGD7Ujgek6gedR19wHI4AA6/geOK62gfRk3+rXW2DaX0y9Lssg03z2Y0d8/ANjdCXAal4YpoVB6wwdGNhGZxht0NqQ5Tk6AKGmx1jXIdin0hpNlEaJIHRsHx0g6F1g26f2H+SDwnk+eIqK3Ej3js5Ju/C+Bxxal17OO4m2sBVHBAGOf/VzH+SFD1zlD7/8Gt994yazZSWBbEN9e9Hy6co1fW5RSou2uZJ7OUdohyqdG1m9fQ1q6yxt63BWGP6+bWnaOGZ5NlUIDhqMM23bcvfOPe7duU3TVLi2M6L7sE6RwJzynLwYoE3GarUhC5Jjw0KxWKyoluLnozMVQHTJbwTRO3mRbixPut3RoBTqNh6XgK4mjJ2qU4JRMkZpZYgyRAqVAPGtNhP2TJHlL0SmMMZokXfR2uCJv8d2IGUWgXUZd4JBJbalMPdoZVLdOBROdasepeBTn3iRP/jT77CoujhOkTTUZ18nMLov+RQNWwgLvmklgO+wHLC/N+T4dENaCPjQJgMRaTSaUuY55+cnYihSjs/94gdlXWDb/tSDD95apLkrNkwX3kPagcel+TAZmONkxEUgXG29h9zObe1httbCj6HRdQmiX6bLdJku02W6TD/CZJ3nnRs3GY7G5MVAYq8E8DvKbcTFS2TxGWOC9IqlqtZBSkTY1YvFgtVqlZjbTdNgAmu8bdsg6WExRrHZCNAagfn1et1jeKot8N57z2AwYDgcMp5Oktb2+fl5Ap5Ho1ECqkG0cCNrWikJPFpXFYPRkNZZfGDeD4fDpItujElg+fXr16WMAuB+cHBAXVXokLfVfAHAcrlkPB5TFAXr9Zq9nV1msxlNuH/btqzX68T2r+uayWSSNmYxmGkE/K9du5aMEWVZMhgMOD8/T++zWq1SHY1Go8Tsj4aDaBDol0EMjOpcB/uNRiMGgwFVYHtHJme8j1KKsizZbDZSL86Tl0Uqs52dHQCOjo4wRjThb968mQD59XrNfC5a6tPplOFwyHwumvfHx8dJ3iUaIG7fvp3kagaDAadn55gs57uvvMIm5O0y/YykxNLpQHK8bNBVkK1QEeBVbAcBpLtU5B6kP4etBQoVNHZVd1xFcCy49hKAPoIxJQZiREA9Yq58HwUJzF56mtJ0m5eYr06XNILL/bR9bshAvGorxa2WnKIunHEBJI0ZDmroskXrzgohKrEJ0HxI0LDujg/0pXh2Auu9Au2wygaWl0dkNiwGAdSNE4A2+PzTVC1aFZSDgoFxFMrw4gsfZ1QolqfnTIoB09GQoapYn6yYnd7g9VfuUS2OyQvN4e4OH3rxJb76Z9/g9r1zPvoLH+E3f/v3+MAHP0pWDGm9Zb46Z3dvB41mNV8yOztjPp9hbUum4Oz0mK/80Zd4/XuvMJhO2KzXKFraTUWZ5xRFDkh8icPDfZbNjGtX97l6MCVTLXWzRGWOVlucUaIrjIBntYMPfuhj/Kt/7a/zH/+9/yffefkbNLXDmBKtCrSB1lpwLd47zmen7O2OWNaOV77+Za5du8ZzL76Iy0fkowHeapTOWG0q6qZmUBgyiUwq3kxZickGxNaz7Zkgu3Gf2l+npZxqNjJqfY+5GttmAJH74JQQ2HVib0fine8ajQDyPVmVBGDHdkPXA5IWrBeWXWjKxCsUDoFmnLDoA8ijA4Cug5s/StzzU88KvxOAQGHnSd81CGqmYq6UC/eSa3XIj455jSAUEeyQ8UA03XuGqhQAUfXeMWoDdzUCHb6RxiEVZZguguyqOz+47PdvJDWsemUWS84SQfJUEz6Ol70vfqvH88OkYdmxgYsCRiPNoFRkGRSFYjhQDIqMooCy1PIvN+SFSvrdAtwpnLVY62gaL0CmE+Z42zrqxlHVTpjpniDlEfApb2hqCQQKBLJGlDeILFsBUxUiMeMB2wrQlyktc4mF1krJWePJc8XVg5xhXvDOzTXny5ZaCMQpuKZto/52MHYFw1QEXaMHSb+cL473UgsP9y6KWOSoNPzW517it3/lI+SZTvcU5nKsb59AydTp4xyb5rZO/oLYJiKYTG9uTHr08TSbjGWRZe3wgWkc51KpszgHiY626u4ZQG+pMx/GER+Cy4r5xznRbveOZIj+wmde4OMfuc7d+wtOZ0vWVUPTWk5na96+ccLWVB2Ae6nvDmztSzD1yRUXJdicc7TW0jqPUYZNtaJtJdCkkDV6M7D3QaMfYRm7vqF5u7a98yhtKIdjnG1x1lMWGUbDuJC1fuODQamO82pgkislOvDB6IECE+f+EGi5H3/CQzAy9cF0yU30ctIm9LvQfgQgj95I4bnGYHoAbTQIxBgicqsIgIexW2m0CXtDBUqbUBcCtBtjQKlg1OwZ0IK0DMSmG9jqIv7NtSsTXnzmkG+9fkfGzSDpkvaFPUDdBX3xUKkBw1Zh7hIi16ay5HlLWeTs7Q5YrmXMjvIuxhgmkwmTyYjF4hyPx2jY3x3wsQ8/T1s3gZmvt9apQNJW37ah+uQIEYO4+q11amirSPuXeVju6Jw0cM/2+iwau3z0XhPrMI9Kjw+iP+ReD73948whF0e9H2beedgI+j4ueVgWHr5sf/R1D82Sf8Tv73Kv9102j1tfT+p5j3GLx64qf/HLw2pCPeKcx3ni49z7B7n2cc95nLw/6l4Py+cPU1YPy+P7Kb/3c5/LdJn+y5+apuHVV18lzzOuHB4kULZtWwbDIZkxW8Er1+s1Wndg7XA4oqpElqQfxC2CwLPZTPTJ85wyz9nb2+Pk5IQsy9JzRAd8s6VvOxgM2Ww2KfBoGXTQR5NxAoajbExk0Uc2d3Tnn0wmSSPcGLM1MkSwN7Lvo9xKVVUMh0PW63WSnzk/P2cymXBycoJvbQrA6r1PTPIoaRKB4JgH5xzHx8eJER6B9Xv37iWmeATQnXOsVqsE5McAnZPJJJV3WZaALFj7wThjANF4LLIs1uu1BGRVir29PayXDUTUUTdZRhtA+2hEiGVdtQ17e3tSTq3FBomEeP6LL77IYDDg9u3bHB8fc3p6ynA4TEaLyCofDAbM53M2m03SLa7rOv3WNA2LxYKqqkKQ2RXOed58801OTk6AblP0bulJsdAv05NIfUQvQFoK8JFx3gPLO3iMniPzhS1q5wa7LWvRcS+7u0QAiYAEBpYc6oGVgYfkatsxYgPYEfZHWik5JwIX8RERuHjX1HtSdOe98MzEdvcPu8/2sYjRxDyqAFAo73tl0pVGBPs7gKd73vZTHn7MK3A6AnYerRwGK1IdkbkYsqm0oWkcRheYTGOMp9CGZ57+IIVquXr4LH59Bq2nUIpRoVit5mwW97l6OGE4GvL8U9d58fnneeP1t/jy117mv/pXfoNf++1f4ekPfJDKKkw5YL6WcVV5hUGjAds2eNdivOXmjbc4PTnl5s0bNG0NWqUAyEKndhit8VhQDmVgZzJkfzrEamiqJXmhqV2NUgU2sBtba3EIw/C3f+efY3k+w9Ytr732XUbjPeq6xegc6yoUHmM86/Wcyd4+hYOTuzd47bvf4KVPfIKPfPqXqZQEKtMmaNrihQ2IQ2uPSTInnfHjYhuR1pfg3K4+kkdG99knkKvT/77YFCKIDvQA+QiC9ZuX6pqm7/2JAFfsiX2m3UOGYxVAbRO7lIoBDwN7PGIXHtoU4LAHJgXQO2AMKTZCB3b7AE6GkJRK3P6lOwStdhU+qxDQNAKRnqgQE0B2lZ6zNc840YmPu4nut/A3fHcQWJe9MrrQ5X1vNFO9a9HRwBiekQCU3rlbJQ8JBXxgXHh/6WBfALgsV+SFJstJ2tVZJsBO4xyuhsY6NrVCqZbYzgTUklcymYypLoCRRoVy8yL5k2cFeaZTMMBMKzJtyPMM76GpDWKQFRDQBIBPaRWkZqKMCGhjcC7IkhhDFv4abVBB99uYcB9tuH9/wT/5k+/w2tvHrOtgsA1M5/Q3zENxnojt8UFPpW3Qt/93q5qU6JTvTQr+wq9/lE9/4nkxAPjYwwMr2avgmBKMzcr0RoeA3IXxXrwyXJrTooHGxwDDEZT0LtzBg/NBTzzWmbyrdzaB3ULC8AKcO43FYW0A1cM9nfdYB87GWEry1zae1vngLWBpvcdbhXVi2ovXe6RdjYcloCiKnJu3zmjaGHgxGNdi+08gbPzc6eN35dxnjsvnthE2uilKlFrRNhvayEYPg88WQuGcGB3i3Bfn9AA6+xCrpyhH5FlOtZ6TZ5lo8BvLzkCIJoQYI/IKgb3sZAxq+2OMiveOY09Xdx0o3QPGA76q4lorjIFKq3RMhXsK8B6+R4+F2A+VCmBtdy7p+g4Ql2KK9wq66BHIT/mOa6YA3AfPibhHjEB9XPkppfnCp5/n1bfusW62gzp3rPS+1AupY0WjcDzUNi2VqckqjVKG4cBQ5DBf1GA9+WDKdDrCaM35fE7VOPJMgsF+4bMfYncyAqJMiw1gevfuLmiaRTmdrf3JBWgrrhT7+xIxPoU+GQw7/Xb1wDqV0M6DQfdR6bFB9ItLT/8uBx8Fl71blt4PzPZY05XamvLe9T7qcTJxwY3zodc9Rhk87Iz3W56Pc3f/bj88JBPv53mPlZHHqKwn+uz3fcdHbdh+WtLD8vnjBjQe53k/K+X505ceBmZdglY/26mua1757ivoj32Eg4MDom64d4466J5HsFlkQdYS+MvaFAw0grRaa87OzhLoOxgMmM1mHB0d4Z1IyESQPeqbN01DUZTCKMwyikKA4vh7ZCxfu3aN4WDIbDFntVqxWq2SHm6WZSngZ57nrNfrpHde13WSGInvG/Xbh0GeJRoAIrBf1zUuBCB1zlFVFXt7e7RVnVjwk8mE8XiM957FYsFisRCWemC27+3tJcPDer2mLEuee+45nHMJ2F8ulyn/kbHtvU9gdGSIR0b4U089xe3bt2maJt0HSMC9gNDLxCIX7XlZwK/Xa9G6zDuZnshEj5vSWG+R4R/11/M8l02B1mLMCMz609NT5vM5y+WS3d3dJFcznUrA2itXrnD//v1kMOjr6Eejw3w+D4FnB8FwknP33j3u3Lmztfm5lFn52UgPA7YjYLCN3m0HykzrYh9/0Vv3iwww77evj5vLeE4CzX0Ex+OxCH4/mOPunt3vkt2ggRt/CQ986Nr4XVO4OmTOxxv1bvLIGTRtEDtDgnzyD7lDhNx6RoHwQhfZiBdzKecFgQ0dEBl82PD7Toc9lX3nYo732NbRAkbnDIcTSu0p8iHrxSnzs3NcW5Fpz97OmCv7H6OqRVt2f2eH3Bg++9nP8gd//BWeee5pBuOSxjfUXjEud2g2Dd4rmk1NkeUM8hwHFGVBoT3PPPcBPv6Zz/Lm22/ytT/+QwbTEd5aVDAOtnVNOczZP9jh3v17aO3BNhRaYQYlbbUGRnjvcN5ik5avRpuMqmkZjCb8hb/4+4xHQ/5P/+7fJR8UrO+fyEbeC3tQ5xmjLGM5P6cYjDGZ4q03X+crf/RFfvW3fodbZxVt48mznDLPyPSAwmiazYoi10zHE1onnONoPHq3lispiZaTkLR+43yM/Wb8OYH1EZyK7NZen3v4nfqtUGHdez8v/aoUWjkJYKsCmOxEokh7QDmRZQmb2ohxJ2DI69QjfDA+JAZlaNHxv8pHbxMBtrX3Md6dfI/58kqA2thvfIQzu3av+jfq0GvJf09mwKvQYxKA35VLkk9SEQCVl1Oq32td7/x+8myPnw+DXS4ef39pdzfIMChNbgzaiJyDyTQmyEWYwF7NjciMGG2ChrIGDLqM+tuG3MhnbTSZEa9HHe4jgHZGDJqYZwGkg2SITdrMBHY7NoGCsf1vAVZdJFTCLZIXkksol+fZpw/4F//SF3jj7RO+8q3v8/rb9yUAsus02vuA+nZZ9wGyxytXpUS25sVnd/m93/goz17fw3sBd73vwGwfbuqC3FKUW0mAYpSoCM+O7+QQY513Qf85eNa0NrimWHkhG8pLbiMa0Q6P9Q5vwVspp9ZK8M2mDd4EztK0YiBx3mO9xXmwrTC2nXfdHOyjaFPIW5j7XOoo8V391vjl8JSlpo1Bj6Okj1Y9wDwYlIO8S1pvaNLz0j/AO0/TtLRtw6AU2cGiWEu5QyR+d5VEkKJybqtu45pZIcYAkxUMB1MIciLD4QitPMPSoY2iah14YbpHqTWVniGF0B+3NL7nZdEHsFUyPMnY7oOniN86R8cZPa27AvNdBTNQfKwOZIEwT5jIMFdBkz580ToC9B0rOmrh9/MnbPpu5NUpGGw0eOk0fnZSM3LeU4cjPvyBPb756knwdAjehVsAtE/1mN6tW7yE9mCxTUu1achMhvcG71oO9kp0toO1jrrecH5eYZ2nCIGDn7k65Zc//VGM8aG/q+TBK48V8Ds0uyA1pVMZdCnmV2YNWY86Ih/DB2NYjE4cSWHyL0i59ObtGMQXSDFK3itdyrlcpst0mS7TZbpMP6a03my4dfsOh1eOcF7Y4BF01lqzv7/PfD4P4LIs7vK8QKHITUY5KNmsN4yHoxSA0jlHkeXYtmW9XFLkRQJly7Lk/Pyc0Ugs/lGLHVQPVLfMZjN29vYoMsNoMuatt9/CtqJnV28CsB2Y4IvFguFwmGRiIsjeNA3LxYJ50zAajwWAGQwS4Dufz3HOsbu7mwDnmC8JcClBRff29mTdGiJaRdA3At0RmFZKMR6POTs7Ey3xADxnWcbJyQlKKZbLJXVds9lsePrpp7l9+3YKQLrZbBIbPTLpo2Z5BNSjUSOWc2Nb3Npzen5GUzeUQWs+AuHR4JEFGRWlFZnJtqRgrBPWpncek8lidzqdAlBXFTs7u2w2a65ev85yueTs7GxLrzBK4njvGY/H1HXNyckJt2/fTmUt7pPC6j8/PydK/+R5EYIBZty+fYdXX30tsTPg4ca7h6VLg95PPjnfq68tUM5HlEqAV2STEKsshjVUSiWN55jSPjhsvuPmONa2dx7XP9cTWD70giH6pJMcr/S9a/r56jCR4LZOgip7m7X3bpPbbTECLREMuAh7vff9OiZx9zf+r59d2TzHsvHpzn2WfQe8v3vqQ/CKAA6rDpAkbMQdMkbmRU6eFRgsmXLgG9brJSezU15++Vt84Rc/Cs2Sk3t3qNWGk5MT9vd2g1fOfV559TVefuUV/vp/57/L3/gbf4NPfe7zTKdT8izDZxnHx/cZTPbJsoKz+iywHkOg0Dyj9Y7BaMQnP/UL3Hr7Tb7xJ38sG/TM0NQbTJ6TZxJQzyvFZt1wcLSDaxxGG3Z3dlmvVhTDEd6Jbq6z4iZuFORlgVYSfG7/yiG//tu/hfWW/83f/nc4uHad2fkx9dqilaXUGVFKLFOK8XDE+uyMV7/6ZV779stce+FjrOs1vtmQmYKiKNB4GkDrjHyUs6miJnsnV9BvVw/zvLl47oNj4aPHxtRtvU9GpP7xCMS8V5J5q3nPc3TsC6pjhwtoIUCh1mJEAIdSphdQMEgAEPH9KAETmLAg52q5pw5Aa4S/I6CqILEvlRcZEBPup/Ao54g4k4DzwgTWujdyxLLxvSC/RKZwAHOdg6hxLIWTxrhUJdEyEMCZFCRWkFCiBIPcI4B68uT06eHp0WPU46Tf+fzvYkyGySTopDGF6CkrjcYk0EwnMWYSsOWs27qXd4GS3htfH5CX6rXfZHxVsbw7I2SHi0r56mRsCZIPQZu/U/noYkq4EHtAPBil7bWuxVrHlcN9futXJnz4w2e88votvvf6bZYr0ZGJoK8PYK2K85nq5pzt1BlUYpUrpciN4miv5JMfOuAjL+5R6g23b938/7P3p7G6Zed5IPastfbe33jOuWNNLBZZFGdxEm2RsmRbbsttwx3LdjcMAwkQJJ3hh5F0nOSP0UDyK50/CYIgCZAEHSAIECNoNNL5Ebttp9uWZZkyJVoSKVEcRFIcari37nSmb957r/Xmxzustc+9t+pU1WWpqvS95K1zzvftvfaa91rP+6znRSxA4JKVqixvBp65LOos0HoSBRV2Aio7XPJKgJRTAMHEGHp2LnPZkrLFZe0ZY0JMxIxzfU7UexKz1QugmhS4J2fSJ1QA2BYE0hXvIyIQeQOWM5JNQgxxWK11fuNLSn+8dice58UqQL5Q6Q/nHJBcsa6OaJsE+IDJZIztdmUyGyYRI+Cvgpva5gzgSzBR4n42m87RjIDVcoO6qeEDvyFH3mG92YmjIp/OyBI04hAoWN9wJH9rT9d5wA36kSvGC6BgeAbS80+yeATKTte4AjavOD65Y1IuJiHEV3nH8nDGUHcwgD0osxzEMjL6bJkfhv98/hkY6s96/gG/8Lnn8eqdBR6cdegLh5XuD5Mw9wlZFtDWKsV7KsYObcvlwWQM7wOWqxYpba0v84kUltZpKoe/+OVPI7iI3W6b5x4U3UrqP7kiPgC4bxjzHuIkIgDoJO85X8bcR2b4w+X2BAKfQive69lxAn4fvoHtQfS97W1ve9vb3t5Be/DgGD/4wR/jgx/8IG7euM4guASqVC3s1WplAPd6vYGbOhzM5qhChebgANvtFqObN03mpG1bjOoGsY+opwxuK3u71OBu2w7OeWZveG9M6LquAQc89fTTuHX7NtrdDqlnNvN8NjPgPOeNZUqapsF2u8X9+/dRVRWDbDHh/PQMV69eNbCbAfvMiFdGusrDKDCswT6JCCenJxiNRmiFDb5arTCZTIzF3nUd7t27B5U1Ub33Bw8e4N69e2iaxvT4NpsN7t69CwCYTCYikTPB0ZUrODk+Rt/3mEk5+77HycmJMfyPj4+xWq1Q1zUmsynqpsH84ABd2yJ27FhQGRnVfj+Yz7Hb7VgzcMMsdN4sRTjvkcCLznHTWNDXEAKuXrvG0jmHh7h1+5YFXVV5GQ0uquD597//fXRdh/v376PURVd5nPv372O326Lv2fHQ9xskYnmgb3/nO8awfzO2B9DfPaYbHt2iAQVpSAEHuZaBqsHdSLYJK4DuElTQ62QTmyAsNWRQoQTeDYiAQks0wJcU3zcw3XEwPQXOACGdKijj3hw89Xp9Mz32mzdvKnfyKND1zZzm4I0iN5YHWJ8eTvSrBfwUNtpoVCEB6PsWMUWQS2jAjrqrV67gxQ9/GJvNGug4NsSN69dwcHCAw4MZ5gcHOD4+wZ279/Dg9Az/8B/+Q3z8M18AiE/XUDVGmIzMCbfZbDAej+CIkFJEcuz4i+0G69jj6Ogqfu7nv4wf/vCH+Mr/77/E5MoRqIsYTccIwWO73eD8/BzXrh1hsTzH/Xv3EHcdDicH2K12aCYAUkKKrJErYh4Yj0c4uX8X09phsVzC1w3+2n/jbyCMJ/jf/Cf/CVLlgHoE52u4ELDZbjGbzNC1O4wmNepAuHvnVfw//9P/C/5X/7v/E7o24Hy7Rep7oG4AX6HyAc4FdF1ktisuA3u/QTsW/30zaZX95a3Mq290TxLXlCOWC8pgZJLnAxoE1hfMRelyxfdKBs/PUyxNNYUtSoAHkgYMVOYlcWDKREAPYi12ELPUgRz8VJiG6lxwTpjJkvcEICiISyQBGZ0BmTZp5Bpi4I8rSySelPEoriqnPUDB+su1gzOq4pN5Hz7/7EcKlrC2LZ8mBCkQGFWdgp9ssjMZhHRgYA7OXZg7L6DMEOdD8beB0aQSJQwqM/DrDCBmEDiiTwldz3IjXezQdQltG9H1Pdq+R9slbNsOXduLpF6Pto9o296u6WNE3yVM54eox7xGSjGCYpYxUVIIO46p0CvO7yothXNAU3s8dbXGM9cmePraCHXV49VbD6wutc+AnMQgIAH8WGO6BMuzxEqWhGBGuALYDIgTeWNRJ5VwKUF2eRemlP924E6twDGJQ0clYeyEkjkJgjWjAuw8FDL4p/Iy6kgHMsCu85SC7s6Jw8s5BEcWOJQPPBEHBEXBQJcXuHOav+yooUQgn0+ocD8Bdm2Hpu0xmdSYjcfYTlqs1t2FZcvQTaVjUsFPZb5PJnNMpnP03QYxJkxmYxnbPc4WW6xXUfTJCZUEFWXwWMBscgg+wQUdUw7eRRguSzlL+lxRwtHYngZGwxXSKvKMzExHDjDqdCyTnARxAt5Hq0cFhk06S/55Hwz0ZUmXKNIwTqtN5JY4LV8NAX6PABdc1rSXucGLg+nLP/sMfv13X8FiQ3Lyw2XmOcQZAxRBrHWxltc4LO8Zsdls4QCMxiM+QeMDrx8iQCJRR6nDlz77Mbz4wRtIfQRRZ2tGBb21/TmfQRws/G5SHfrsRCvnrrLfuMFnTk7VEKU87lx2LsHywIOFnSDvVhD9Dd43j/v6zSyk38bj38RFb+HadyKdS6X/Nh72CA/wGz/vSdtlEn+3bvTf6Q7zVp938b63OgLf6vPfyvN+2rPHu8Herf16b5e1ruvw0ksvcaDLdoef+ZmPDKRB5vO5saCVTb7b7VD5YBIfCqqqjIrKmChwrCwHBZxVr1vlXVQ6RgHg6XSKyXyG+/fvM9N7u0Mt2una4/Qe1RfXgKTeewOR67rGTljjp2dnuHr9mgHayijvRfP75s2b6Pse5+fnqOsah4eHBuhvt1s888wzcHBo6gq77Raz2Qz37t0zSZUYI6qqMja56pfP53PTAldwXdnnh4eHxjSvqoqDqTmHzWaD5XIJ59yA9b5cLs0B0fc9dm2LUFWcBwIfnZb67Psey+XSNOM1kKhqoKuzoGt3HNW+67BYLDCZTIxF773H0dERFosFdrsdttutORtUe15Z63fv3rV2eOaZZ6wOFguW4akkn9vtGs4F0dR3mB/M8JWv/Ju3BKDv7d1jUQam8hJNx1U2ek5kIggAKCEU7CrZRtjGeggjQTYc+ofqhDo5qs6W9LMC+EmeRN1bNjDQRPPfWVeTnxgEQk0JdjxZ4UhlQl3OMjiR2eMKPjC764IX4S2bgnp+8KRiIwaSU+Cvv/YgqPeA9aNVEtQbuOcFWGQ94TZFVM6jajwan1DBwcWIK9MRPvThF7C8fwvwCePGYy6nfO7fu4MoJ2pe+NCH8NFPTjE9vIb752t89KM/g230uHOywPLBGT74kY9hselY9qkWEJ0IoWmwXS9R+4AEdsJ99GMfx1//1b+B3/zKb2C73aEaT9ADaLct4q7DaDzGs88+i8X3Vnhw5xjL0wUq59HHhECe5Qx6liZw3qEHYbleITQ1CAnNZIqUeuxA+Pf/7t/FN779LfzGr/9L7LY1Kg94lzCpGtQeaHcLxHaLUajhQfjxH34dt3/0fcxuPIOJd2gpIvUdoiN4X4HIoe16AYVEtuIRDpASpC6ZuyXona9XEOWNbQB+a1pvFkgvwePHP2nQP1EABdkYaA8hWd+Fsff0XwFWyHjWAHzJQAkBQAionM5HZFrnqQDY9CQHy8JQljlwJCxxBVIEwCvqWoOyOgeej2TCcylmoEdp0UXbaMBgOP2aAM19CQbi9U6QOFPGV733Nx7ll7NIAr85KaCUWcE/EjGcLLUD9j6oiQOOBCFNlAZge3Zy8mfKiE5ESFECQaaIPgpAHjkoadf16PqEro/o+og+RnQxsd51Sny9SNMp6B0jn16JibW6SYDnlFJmbEePRBUAj6p28CkhVHKCQMgfZOB5HGiCs3TKkCHuHOADEILHqALq2uHBssOD5Q6KqbmierkVYX+orITKsbBnJ1+g0mU2Txg4PpwvzLFdtKGOPXsW5YfbyTHKKZH1RbnGOqe296NGPT9X61e7Uh6ylLNkC4E8rhKAqnLoOs0zIccykeCiotfuvASflBOR6rBR2UktV0oJvewXmqaGCw7Xrhygj+foumg517MeJsWhnzqV9SE0zRTzgysAemw2O4zHYwTvkfodYrfDtm2H9eQ02DEQBDjW+AKVJ1TeowoeOZAot4scjoH3QBXkGjhEp+98gKIGcwBCYvA3SVBXZba7JKB40WzeS48xR4RKzYgTU9bwhMRrH0oCICc4lyQ4KgCXJFoJseyTOuJbsvcTy5jAgPY8RXMQVO8drl1p8KWffQr/9lv3cLbui76YBu8l7dsGTdv0mk8cEBFWa54jRqMGVa0gNLFTLO3wc5/8AH7hz74IUM9AuDgdyOeTe9b7kkNyYCkxBe29SkyprAvPdQQFy6VRBgOJ53ldH5bvKu/KwOHWGWXMvPGs/t5hol/iDfV+gsneGXtS4OY7Der9tMHj94pdBuR+UgD649K/zDXvJIj+qOftZ4a9vfuMiHDnzh2E4PHRj/4Mrl69iuPjYwNK5/M51uu1AaskutzOe2yF3a2rTQXONfDlbDYzffGTkxOTJCmDgp6dnWEymaBpGhwcHGA2m+Hk9AzL5RIHBwcgkOmdz+ZzNOMRptMpFosFAHYEbNYbyZ/D4eEhgvdody3m87ltuheLhbHrt9stdtst1usNlqslRg2nOZvNkGLElaMjzGczrDcb0yav6grr9QbniwUcgBs3byD4gOVyifV6LQFYvemqqwNBAfnFYoEYo/1dBiXtus6cDHpv27ZclhCwWCwwGo0wm81MEqceNRIktIf3DtPxxDTIFShXKRiVgGm71iR25vM5TkR+Rp9JROi7HkeHhyAi3L9/39qk1HWfzWYYjUao6xp37941TXjv/cAB0Pc9drtdEUQHSIkXuc8++yz+yT/9J8L4f/Nz456F/u4xZYQzcxvwylD0GWRxpHq+hOSGmAsnItfLDbzn4HRVXxUOmY1IeZOTSEEDheogOq9g9jQUMNcdFwzgh+UPiALcyd5QNowlg+tN1EkBrGjxDFhPl2OOXoZFbse9iX9PLglMl7ETA/pg+7IBeOMAJAcEkOnBewgjUK91DBImx8CWp4SqaTBuKtSIQBfRth26APRthyoEpOjQti3OJfgzEWG1WmE6nWF2cIh6PMbXvvY1xDDC3bv3cO3pD+Da1Wuodz1Igi73fUTlAyppB+88n3aaTeFdDYJDNRrjhQ//DH75L/8V/Kt/+o9BTc19BAB8wHjMwbC9B04ePMDxvQfoty2q5gBBQBaKxHCk6Ftvux2O5lP0mxUAh3o0RlUH3Lp3D/+t/+5/CPIVvvF7v4sHt18F9T2uXj1Ev1lgVNfwlNDvNtjtIhBqfP23v4Jf+Et/FePxITw57CTYHnxAjLLZln/a1y4ywi/2hcedNrC/L9F3npQZiP861+SD8BnovTikcnkzuMO/K/sZxXh8WCqCWZFycoJHA8iFAWDpPTOZNX0nIHRwDJBr4EtmxsKAcp1wytaC4jouz0WuKEvpxNB2SRJrQIOoDirO5qfXqclCLisj8QKWIdff27OQUyidKoJaUTL1+AFzWTBzUPKmb2xs6UToe7BUCCX0PbODNRilssiTgOG9SYvIvyggdoyIPX+WKIludwLHNSA5URIFLJfrIn9PSYGunC8n9e1YKBoEOdEg6Snan4NXasBIlRiT9nQalHDIKG17QtujAAU5FXXWWFMa3qb9XPt3BmMVegOVgYVhc5bTIJjaORV4LPqZAZAlal+8F6lEJodfFZ+6wVjVV7WS1dUJpf0h31W+e0qQefglEVAFh66XcongkfMCZIPZwjGRxVvKaL/A1wmgkCVaiFgXfbfjE5XjUQNKG9y8fogHx0vsusSyjc7DyYkDyHqCn8F5G42nODy6huASFqs1qrpG1QRQjEipRd91SOWLViaHXhJQrXerE8cxIgYnV2RcazPCMfheVx6Tmk83+PL94Dyq4NDUhOAFYHcOEdHSNCkRTdKBQXFw/zGGuHO27kHkxuT0CERR8qwnfZKMnSjpR7meij6dJJh2kj6fTFaGc9MjeO48N643+HNfvInv/OAMt+5vsOtymzpAmOlWcVyShByAV76z0yJxg7bdIVRezpcRmkD4wqc+gH/nFz+JyhFS6uDAgYfVU6tzgjLpmeyv2u4FQC71amvcEKT/m8vWxmIe7zpX8ukaEq0kG44kazfS96EOrNe3dxxEf1PLi4sXX3jBPZQWPebzt5iRN7rkSS6VfprLrnc6n+/cEvK9ZY9fnz28SH8bTylTehvpvB17N+Th/Wnv4P5sbz9lU6Dn9u3X8KMfv4SPfexjGI0m2Gw2A2ayBvFs2xZt7OFjj8aN0HatsZ2D8xaMUqVdFNi9fv06bt26ZUyBqqps8991rKneNA3atsXi7AwxRhw/eICxgLWHh4fMMJfAo957TMcTrBZL7NrWmCq77Rbz2Rzb3Q4+eIxnUwN+1+s165KvN9is1/DOYVTV+MmPfoTnnnsOBwcHaNsWd1+7Y3mZT1hjPbYd4B1Czazqszt38NT1G1CG/ng8NufAarUybfKu60z6Zb1ewznWVnfOWfDTuq4t2Kay04k42Kgy4pOASqr/Pp5M4BIhOIe6qk2GRuVa+p41PceTMTa7HapRg27Dn1+7do1Z8Sla3njT73Dj5g1uu90OM3Gk1HVtQLlqvxORydLo6YXZbIY7d+5gt9vh+vXrOD09tcCrXddhvdmhrhscHl3Bf/lP/onI3uznk/e62f7VETw5JNmIGFA9aF+HZEG2FFjDcGNvgE0J0DsoK0cBNNJLZV+aKLNdEzQQpqRTZoMMe4BmT1mWSUH0C/+g11+mryr2os+nYT7Kn4833VAryzdvsIfgKktJDNhKUjgnus4EYa3ZZ1QALgRnATUzuzYQMiMd+ai9cwIyJa781Pe8We8jQLxBDs5hFyNi3zMzTySrJpMJTs/O0AuTtE2Eb33723jquRfw6quvoJ4eYnx4DbOqkbnDIYQKRIn382Q7ZjneH9ALu3QyneMv/cpfwW/+2r9A7Fq44AAEwLM0wvHxCbx3OD05xfG9+9ittjiYXuMj77qhNd1ewPmABCA6Z3NU8AGnyxU++OEX8cv/7l/Dto34g02L87u3kBAQk0NT1XCJJSJS16GuAn7/d34bn//ilzCvJwjeIxBYgoGAGFkD3IJTPgZAv7gef5z8ymXley52s4c6ZPH5G3HMbfy84aNEVkXHNnJfLsvA4KXAE27IRIcA54k0FgNnwDkJHKrgk9KkvaRB3JdTYoeR93l+CgDgncVG8JKmxBtF6YArVMAzeAIbclCSIlCAnqUJAKpIVh655fjNNZ4xcwFkVEd6cA8DLTSAdN6G+SaDiDqvmlxIQoxAlGCYDIgTukgMZINlE2IidKqrnVhnOyUF1pHZ4CJrkIQ5rvN4ipx2VA1zeSfExFWYEpCSF8ZuAaITkBLLLOhPJnowq1bfGc55C2NdnN+R9iQE7+EEiOfqDvw9ieayjAuy9lDmLhkAxjIk0ipkkTpgwPOFl4P12wtGinoO0DZ9t1DuJIP3THY2ueFtUCzSFQ/Wd6imYOkUL6wM9A2vUdN3PWlQkeJdmsuQ81d+D+nb9rsjhAD0PfftlAgheMuT90Dfc0EcYHGbynpJiQSgZeZ6SoTdjokkVeWB0MD1Ha5ePcTp2Qp9L30+wfq7k/nfB4/pdI6DgyuAi1iv1nDwGE8mSKlD1+2Q+m7g1OR3r7xfCOYAHNaXtEIR1FnPECWZz7Tfxy5h1zFYrfWkLHLvgFHtMZ0EVN6hlpgFLB9jTShzNWxxYg549jgb0Ms/c1l86RFUgJnYQcFzq7dCmWNT1lSIXCYG073FrFDPRCIeZ3zKBHjqxiF8CHj13ha7XQ910HGz6ixX9D6RdRnOzIRYzAe197h6OMIvffEj+PzPfghN8LKu4jZ3UBkxibfAH/LcRZ4rqFcnQZ53s4AYP5cstoYXp4wbDD4dk+ScxO8A16H3Io8TUPsKvqrgQ0AIFeq6wRvZe4eJvre97W1ve9vb+8xijPja176G+XyOa1ev4vR0CQA4ODgYMMcnkwkAXqCpTnbf9zg8PMRuu0Xf9YgxYjQaCetwiuVyibZtMR6P0fe9gct1nQN+qh73ZsOsctXtjjFiNpthPp/zxi7wc6uqQpgGk1FRSZjj42McHR6h61pUYHBZ5UpUZsQ5h6OjIwsieuXKFcznc6uLpmlwfHxs8jWaftXUODo6wrVr17BaLkGRjzVOp1Ocn5+bbAqzF5iVvtlsjGneNLw5PT8/x2QyEXYMy6BoINP1em1BQRX0d46juWu9a95VP12Dk3oJ9qNa5ACwa1v7valrUEzGyr9ydAW7jr+fTae4cnhkDpGrV67hXORYmqaBstlffvllVFVlgUa7rsN4PDYJGwVDXnvtNXMUqFMBADabDb761a+a9M2bBn329q6zEsDlg/6yiVdQiQo4STeNsgFxBv0U4NBFoACugB+cHYhlYmGWklBggiAguoIDBViPQaqQDVTe4PBG3hWAn5PU/KWwqQwGZhC/3AQOnv5GfV8BUtl0GvAiG3sgBz81sPwChqZ3eoXXhns63gIK6zb/rQEPhSUHyKZYWimIXnSMaPsekXrU6NFUFcajBk0dcLLdgvoOnvjdolJdd+7cQdt2WCxXOFksUQVmeK1WK5ydnQH1BGh4Pp1duYkQKiAlYfqxbMNoMobzAV3fMzgVOVjcz33x53H9Ax/EvR//UPSaAwgRu7ZDt1uhqQIWZwuc3D/FdrXBjecaAdGl3ZwAag5oRiPsRGaFiBD7Hi0ljOeHuHtyik99/gs4PVths9rhD5YrrNYtw2yJEAA0IYBqgqOE73/7D7FZnGI8P0IKAKFG8AHkPPrYI9QVFH16XXb5hT528doBk/v1e9agf8B6F/928fM3SktBnDd6khM5IMqICN9N1rsLMA+ZnUsKohdABLIUhbID8xwjY5rI+rfX78mBvDN9cwZLCK5A5Eu2fJ4fhsC1wSiELP3kAEdkEi0l1HjR2UHS7xRUUcCKiIrTMwAZ1JvHZobu5Tn6BQ1y/pbtteMNYsr63DGyrnaMyWS0ogCNsMCSegJI/4bdT8mI/AYqsiSKA5FnJ1mCaYCroEIiZiCTwFqK5BKpeJXIyuicpm0ZAKQE75LEUhAwm5IEPhXAn9gRELQP6suDMjPYIfdXAqebtcS4vvmgncwdBDiRX/GmIw5QknySXsclM5TbRlp+a6jD0Jipcr3N40nfD/JcaK/jPpDshWFdxUYzST+1d4FhmuU8Y6W2+wvoffCV9fcS7LR5Sb9Qp4OUVhqOYPjxYBYy9r860kUrO8aEqgrsTIsJPjg+beACbKQ4iONC5waWl+n7Hutth6oKGE/G2FFEcAk3bx7h9HSF7ba3gK7OeVRVhdFojPn8AKPxFF27wXq1AqWA6XSGRBF9u0MUAJ2s/oqf7sIcSqpXTjbBkct1a66wArAt10+wd7jOE/xz1wPtUlnh/HkIniVkvEPwDqHyCI6dnsGzrIyTv73MzU6d7AL0MhbuoasNzoeX+RwwB54wynV+ElxenGZA7LgNdx2hj4SuB9pOZJk6BtH7BMRIIBphMmswGkNAdmZts9yTEo0i9ASi9jmbrZ06DByO5g0+/pEb+OwnPoCnrvIp254IPjl2YMMB3iOByQEEBuC9AOpAhEaysXeQeRVknQgGwmFsdT694ODhQ4APNQdrDgF106AejdA0E4yaEUajMUJdo6pqhJqvc54DtF6WYbQH0fe2t73tbW97+xO0ruvwa7/2a/jCFz6P+YzB7xgjbt68ie12awEu67q2e6qqwpUrV9B1PW7evInVYjnQwVYtb4AXOXVdDzeTRLh69SpGo5FJogAMZCuLves6lgeJPeA9QggYjUamgX5+fo7r16/b9efn56BEdt9sNjM29Xa7RRU4jaeffhrOORwfH+Ppp5+2ZzVNg8PDQ8u7Bk0dpQl2bYtr167hytWr2K03BVPDc/DRtjUG/mg0sgCfCk7PZjNjnLdti6ZpDExX5r/Wq6ap+SIiY5s3TYO+703LXHXFQwjGXI8xAg7GZj88PEQAg+H37t0DiJ0jR0dHuHr1KlLXm1Ng27KG+nQ6NXb9crk054Dq3KsTQ+V6nHNYLpdomsY04J999lm8/PLLuHXrFl566WWTd9nb+8OUWaOgue6ZGTQQPUm7lgzwMjBI911kW8u8GYcCSygA8qzDTsI2AlTixRk4wHIusrEqGJwZfsq/R0A2jsKqdfp0BxDzT6lE+AYpXXqvw/e4ks96AYsoUh6ANxe/dxe+ID3mLgCEyg2gAPgMACwzy78HNwwsCkAO0cvG0fHGNBKh3fIJmqZp4MijgkdNDj4C3W6L1+7cwXq9wtF0hOlkhu16Jc7NOXwIeOaZZzGdzXG+3uDTn/sifv8738dHP/YxjGZHPGfFiGeeeQYpjHHv3j3MxmM48BwZ+x43bvBpmW67QS0O2NhXOJxO8e/8lb+Gf/z//s+xWp4DsQP6CNeMMJsfYnF6BqSExfkam/UWo9AwU5WV8A2sSiDsug5XDmc4Pl6KTIjHbt1ienCA45MznC23+Pyf/TLqaoKTByf4wdd/C/OZx2K1xuEo4Gg+hXMet167h822x261wna1QqoJsZoiTCbQ+BbOV+YNucwJ0Ecx08vPB47JC/2y7Pu572Qwt7z1ElmR58LeW2XqDz+DgRgFExmMyaBh1rql4vkCglE5Xrhv9/IAnXdYGkFmFfkuwjGQKn2ZHR35+pIHTpSQLA2eoxIp17CU35DTMGDnAT9fGZkyDomDJXurW55tmIHIc5UxQl2u7IEzALk/KKtziHOWYHoGJ9+una/IGOIZ9JX5V9nk5STtIGxxAcWh14ruOHE9R5FZAASElwk9EQxA5zJL3QtIzKxyAZURWC+MCB4MwDOJt4In4lgYJPknZtB7ddakBAoqt0JSjghlCmshSYIlm0RYkR6fbJETT6LL7aUhlX1OAIP4AAAGAIM0U9JnEJCdZqoZr22XZDDys729i2DgsHPsDOJyUdlhipaUE0SU5wZvTyHFmgWQHTqwHrb8DDf4W4FX/mbg4JM7vctOcNhYGAL4KOc0ymB7VQFdK2Mn5bqOstZUZwwcM5qzzIm2CWt5e89Ae4wJ7XaLTXDwPmA8mmG9WqDvtrhx7YAD33eEvo/wzqEZjdGMRqDYY7NdYLPZIoQakwnH+ejaHbp2mwFvn51eWu+6xtH209a0UzNeZHvKAMPFQHeyNoJKlqjDbDDPl+f6+EFJ6qcnB0cePgIuwt7pzgdoloN3qH1A03iMagZwlZnN82qWI+E2DfI3WZ75ZAohRkIXgbZnB1wfZexLzqL65eFBHgjBwdcOI+dtrik8Y7mT6FhElHdVNGeQSRqBnS+1d/BVwKhpcONqgw8+c8hOkxjgYo3gKqicDb8LLHdQCRcgBzv2kjclKgVfoWpGqOoR6maEUcP9pBk1/FktMcFCBe95L+cDx5NhaZhibpG9Wt+32Jwv0XU7tO0OXdexPFDs8dmf+yt4PXt7IPrbf2f8VJ538bJLr7HfpD0uO2/teZdJ7UlW+NtJ6/XvfbL18uTscYvkhxl573THfpL208z7ZdN+Unn4aZXlSfbQn2Zvfy/3w729Weu6Dt/4xu/j537uC7h5c46UgOPjY8xmMwN2FSwt5UO6rsX52TlAhMViMZCAUQBZdbUVSHfOWfDN9Xpt4GrTNAyEE+HmU0/BeYfT83NcvXoFfYy4du0aM+G71hjOq/UK84ND1KMGp+dnSMT6u0dHR8aIV+mV1Pd4+eWXjYF+5do11KMG41ANgp+qVvhoNIJzDtvNBvP5DGcnpxxIdbfDbrezIKsajLVpGgvOGkLAarUy5r2ytTXQp8raELF++dHRkQTGirYwV93HzWZjDgwF4Q8PDzEajXD9+nVzWnhxNFRVhcVqidl8jrpmVv7d268ZWx3eYSqLwtPTUwTvsdlscfvOHRAldH2P+XxugUVjjDg9PcXNmzdNl/74+Bgqz6Pvtr7vxFEQMRqP8eqrt3F4eAW3b//OAEB/Kyz0tycztrefhkXYnt+0uAcgr4JcBYgt+x5hcWIglWL4DGWJFtXYBRiwYUDGyfMVwBCoQYCMKKCVcykDyAqySdacy/yqEs9yysKS3HZ9nwFl+Wmg2qAfX1wrl//YvA8CBCKDHqVjESJpMUiVd7tl6hWANnLO3fDi4Y59mPwj8s0pJB8Az+C5BysjewQB0Rn4r5xHXVfY9R0cRYyqAPQdunYH9Ftcmc5xdnIC9DtsQ8J0XOP555/HfD7HrVdfxr1793Dv3n0kAhabLSiM8Ct//W/gxo0biK7BYttznIQqwo8Cv0PA6F1Vy5y2XIII8KGWgIIRAQ6nixX+1n/wd/Cv/vE/wvL8TMrv5Z3Fc1TtCMvzc/zkxy/hz/y5v4QHqxXctEYzGmG1a7FpdxiNxvAAjk9PAV9x/0oAqgarTYtqNEEYebQR+JlP/Cz+e3/vf4z/6/9hhZ985+u4eXAFAT1Oz9fo2g7Xb9zArftn+K2vfhW/+nc/isObN7HsHDYR2Gy32LU95kcT9LFDbHd4lD0ONB+2YXltlo54mJn8SBh9+FXRTx4l0zJ0gutThp8NdIrtM32sG6STL+PB6ejCMy8MEQLHVcgqFiIRQ4RoY0Ee1idmXnp+lwbH/YElcFkOLcaINrbMXHYMIFWemZkKJnmXy6nOKueBQEO5GZYpiQZ8eqf6187mHoJDr9rOUs/MDpUqcFr3g0rn3iygnMDJ0k42oB9uxLdgbZcECE92EsMAUifa30mfIyz1BHNUcHYFDJMjMsaQlgt0Hcl4mWeHhATFiFZeB5BHSHySQOdE5wI8JQP0kcIA7E4uA1RIJMC19kEeGyT0eDsBoTrYJVhH/OZJRqUnJES+r3g/SYmRqDPQn1QrHcVJC5Igq9B3HCED6rD6zQNlCKZrPyhp2+xgLoDTlL9XJw2VaWi7lLIx8sL1Xt99MGBcT3oV0Hl2O2l+we2cAVAFiVHck9veFWnJgJdWcPa9lzS8B3xgySAQt5HzngH14DgWswDl6mzntDkvMSVhW/O7PBGhjx02a4dQBcA5TOYHaHcb3D8+RVPXGI9HGI8aOYWxw/J8g7brAThMJ3M4H0AJaLsNdtslvGMQmlAN6iobg9hwoiNezJacL72lmK2dnoKQ8S3XleuPwROKsa+nbSUZ+0zBbu4bznBqlYTp4ZA6j13PoLn3AaHKjPXKewQPOO/QRY8+FuuzyE60VATCIXJwgfXtK2LQnopMmSY7eDJVB2gGrjXvKKcOWU86+8yJprtzklfv0FQB41GF+azGeOTRVB51qADv0CcPSkHAcFnrhQAfPKqK1xhVXaGuatTNGE0zQj1uUDcNqtCgbmpUdYOqbuBdhRBqWdPpeCY7Rd33Lbp2hb5r0bUtYuzR9R1iv0PfdoixQ4w9UooioVVQH1Se6BJ7nrcMoj8JiOiN0n6zx6PK9cDDH/4U7Yk97+LNjyr/u2Eje8k8vNPt8MTsTzazT/aY/TsxUt/cdZcHY95qO/g3vuSRab/VurrMuH2r9qh8XqZ8e3svmgLpn/rUp/CZz3wGr92+ZZIeKr2SUjKpFADG2F4tl6x/LTIpGoxSJVhUGubKlSum3a3SJNevX8dqtcJ6vcZ0OmWguGtR1TXGkzHWAiIreNzD4f7iHsuJOI9ECaGqsBFpk/F4jGvXrlnwTgC4e/cudpstHICzszOsNxt84IPPYyGs8+1uh06Y5Ar+O+dQ17UxzfU7nUOU7a6A98HBgTHptSwKyC8WC3M+eO9NBmYymRhjX50PVVWh6zocHh7iwYMHCCEwc9GxTIpqmS+XS9OJXywWGI/HdoJgJZIwm80Gse9BMZlDI/jKHCJd14GqCscnx1auqqpME32z2WC5XFrA2VMJSkqysdntdpjP5zg/PzMGSwg17t9/gLZt8bV/+2+x2z0aJBqw7V7H9gD6u9NS3hHavjqD1LI1FoDbC8CR9Ds92mwX8d0KPCQDIfKz9J/AHwprIcum6CY1s8J0I8/5Ex6YgAzJFbIoUMCYMYLodKNriMQj+6ltdt9gvaCa7lpBeb/weKBUr8t1Wkri9MVVrriyBD4ysMhsYLJNuV5HHoW2OkscOMdAHWtTc9MkAfea4JFih8Vmi7EnHE4mOJweYbO4jytXruBgcg2jioH4xWKB5XKJL3/5y/iLv/zL+PGPf4Lj0zPU4wlWu4jnn/8gtpstUgDqeoSDyRiLTWfzbOWdMTkJunFX5qpyKwmjusZ4Cnz+L/wltL/+a7h/+1W4egxKDl3PQd/ggNOzM9x+7TX0HZ8IGnmPbexB4PdYlGhlBpkKEJXAIEHvHEABoapQBcIHXvwI/sF//L/E/+zv/Xew7pcYIaGqaoyCx/l6henBIX7vG7+PX/x3fxXV4Q2sd4TO1Tg6vIqDwwrbdoeu74q+9HA/eDPa5687TRZAlqXnBj8eeu4w7aF8zJAV/Xbm6BJAuuw9edRmGPEC0AdCJDDo5oiDF0eCJy/DPJnWfnIeFTkkAnriExk+OQs8ajCfTFfeJPqd6at76zP5VIjmx8uYU1AXTiSTZL4KgDgQ+JmaBoP2uVL486LsyTGT0j2ZXWPfRyjUa2CvwuAELbQB0zaXUBo8nyjrFyd9SaiTx9Bavsd7P5CQUNZ3MqA5wAlgp3Wjz8ggtH6eTx4wuJfzlCgJyzwZoMxr0shlJHYQpCL9lFgWRkFoZaAT5TpyFEHE67EkMhOSAL/vTAs/WT5ASdLK9ZWdTwK2Uj4JwPnjYJFcdVlChWRO9M7Ocdhb0DlOK7ncnr64xspBGfzOp0G0fmkIZELzisELv0yLPyhORJXzjfUN/cgQVGiQSCcdOowDtpseMUqfkptSJDjP8i5eIpVTyoA6AFAEz9oKLEtA2bZrsVhJeZ1DXTe4du0m2l2LzXaLGLcAiIFkX2M6OUQIlenv73ZrbLcreA80owZFIwpQrDOak9LJzGQOL/lUGewDK5xuwgTXdtPrh2B6+ffwVJATMB46TzmepSwPrgDmUfzuM7O+J0JMQEcOPg7TtvFfBQQA1SDvtpQCyNtn5fyu4D5In5nTVoehF6B7+GIjA90Z8CeMmyCAeYWmCahrJ0A5z/Ue2YGam8bDVxVCHVDVHk1ds7O+rlGHBqGqWT8fCX23Q9+22KzzmOY9bAT10WQ4Y98jph4pMmOe+2TR9aGnMSn3e3JQfXZyYGmYqF3qjWf1vZzL3va2t73tbW/vEmvbFt/85jdxdHSEZ56+icVigaOjI6zXayyXS0ynU5MiUU3vuqownU4RY2TG4GKB0Whk8iUleLzdbg2MVymT7XaLg4MD00U/ODjAYrVkEH08NsBZF4mnpycG7Pd9b2xr7z2WyyU+/OEPg4gZ6arT7pzD1atXsFmzdEwnrPTRaIT5fI71col215osigLb2+3WHAdquiidTjn4qOqdxxgNOD84ODAZmZs3b5rWuWrKXwSsFaRXaRf9fDaboeu6gYa6AucxRqtPDUZ6dHSE01NmzLvAwWG32y3IJ9OpV7rb8fExFosFAFi6o9EIV69exWQywfn5OVJKmIgEwdnZWWaS6SbTOSs74LDdrnFyfIYf/eTHFoh0b+9PS8QbX2WGM3DO2wWFIgHFyfNmzwHDYJ0ZUYZuxDVGmYLjCQxCWeBQucZAFHlGgsiTIEtBKACmDHMFvpyAObyx8ZbHJA4ATwJruVSUpEQOuDCaR8Y85NlQ6E1Zj/KbinbCFZ+XNgyeVdZiueXWvMOxbMUgeCiQc0EOkGCK+h3jYCTBEsvNP1/HeqhJWF9U5CohpR51XWE2PsAoEGr0OD8/xU9+8AMcHcxxOK1w5XAGih1e+smP8corr+CFD34AH3j+eTTNGH/8wx/ij37wx0iB5+QQPCIltF2HGJlRWY/YYYoCSFLnif408BZAQoCvGvyFX/5LuPXDH+L+3buAI0Qw0ELk0FPCyekZXnnlFlabDYAagEPfdyBy8IHnU3gv4KrIPkCD1TokCiDyEtzWoQkNmtkMf+8/+p/i//5//N/ifHmK2hMq70CuQjOZ48Gdu9i1HeADyEe0bYfQdQhNwGazAsDMyCcxT5Y4xUN9pfj8MqJaD+HZBZh/MadZjgX2PcnncDomCoDNXfjukkW3gH3I/TJnaAijMziKXCnmgGEddHWg6Xygsk46lyg4mVgxl/WuwTItBIB8lrFyICSXYMrMAlCpRMxAaocyE1QdUxzwNKft4CD8Rqg6LzsfZcxLMEKVXXoiCDqQT7wU4ClPagXwLGBp1t3Okg4KpHL9F3D8AGiFYIo6sykwTxlrJJZZItHyogsF1FndOQHNFdiHg1cZnooQtDsQ6zPDqaZ6CRQrCDsEpiFsfJ3TjaFu4L7OTTEz46WuTMqGFPwGg/RIIBJd50Smza55geWn+EwqjhRc1/FCKl+T8+jsHmfPA5w4KeSUCrE8irO25f6WyEZW8byivXLL5o/tWfzu4Ws4CCPliRqm/S4OFHOyyLvQ5ix19ALGwh5PamzWPWJMBp6nRCalxNroXhwp+i7nTEfVD/HgINriNOl2LVZpAUoRk+kEIchppeaQ2e5apxIUN6WEru+w2WzRtmtUgTCfHzzk4Bz0TjlqZ29tl9/fTjSvfTF2HgbINa08dzjPwLiT9YN3QdL1A+BagXC9LtetSslI9BqVvNMp1Wt+izGJDGbzd97yxVFJZVUlxxmc42CsTgZzCYI7X5AtrLwpA/plv5DnBi2P5KEKDnXjMKo9Rk2DOgB1yOXlOpJVmDo5fXYa2YlYItl38dwbgpe8s+yKSSlpGycttay1tJ6I+55KASaRgqmkvUnLqXO+1oXJ0JD1dR0jlyUXAXsQfW9729ve9ra3d5WllPDVr34VL374w/jzf/6XcHZ+hhvXr+P+/fvous400yeTCQO/4wnayAFIExGmk4kBvXVd8zG5qsJkMsFms7FgmMqwBoC6YS05lSbp+x6tby145dHR0WCzpsxvlTrZbrd4/vnncXx8DJWcaduWGQgADuZztLsdnnrqKbz22muoqoCjq1fR7losl0t0250B6ApyK3Cui5npdIqTkxNjtyu4rlItCpArY3wymaCua5ydnWE0Gplki95f17U5HACYnvxms8HBwQF2ux3G4zEmUp+qj66BWVUPPaWEGzduQKVfxuMxOmFYbrdbBO8xmUyxWq0AB6zXa5wtzk2GptRu32w2uH37Ng4ODqydy7rkgEsjc6pongBgsVzi3t17+MlPXkbbtU+kL+5B+HevlQw22TEAcFnaBbbdgl5S/lbKAAAZrFBWVwmUJzBon5TxBWUKQ4APBVT1ngK8gQAFeevDGyZyEjrU2XHwDMgxQ4ikXA8RouSyEto2AN0ApYxHGSdcNFAflsbQ3y88xCowA4RaYIORFYhw+TPLlc+btovMMwdmdZF3grU7CSzK4B4HHs3sfeeAMGpAKYlkADM4HQi7zRq73RZ9PeaAx+sl7t+/j1u3bmGz2eC1117D6ekZzs7PWcqqGUlsiBG8qxE7QhcjnGNAOcYom19tf9mwQn/PbbXrI2oHfOzjn8SHP/ox/OiPf4DFyQPpAg4h1KDUYb3e4v79+zg7P8fsxrOIKSGmhCTHy6MAaF46VgbunQDpHolY7mPbdlhulphVI3zpl/4CvveHX8dv/+tfx+nJfVCo4UIFqhq07Qqr7Q6brkOfmBW6a3eoBKhDGXDzQh94c3OfAGMSAyADU8ONuBOnyxuZ9pHLsOOHucj3Dz43dLacEco0yu/coz9zeMR9Fz8r646yo82YzoyGZNkIBYfAzqiUT6g4SgxUOwXtCvCdmAXJp2kEwFRddAF0vNNxpOPP8ERLU5/vklyvwIof6sSrJeT2TfRkz4UyQDsE/RTgS9oEToKzyoRIZXOBmfIlAMvAGQ8kDvKqcyk7AcynSAFO4nY6J2PQ83XR0ssAmM6xQeuHIHIurug2Cjh7pKAgtjYA7P2h5SSQxA5laZ5KxpPKhei9A0Bd/9Ygo6YHL72PorzX5NlpxL+naPVSgvKJpIXJAYhyX362vnHtPaj51rS0JYs8Bq0g6U2UdIxodWqQ1ZyGOr6sHS9cr8/2cPn5AJw+zZxcelZMypD4nUKySHBleoAxdzVOhXMe40mN7aZDHzlArAHP8p62zxKK9x0/NQrIzuNR6i0l7NoWKQJd12MyHaOpGl4JZG0RiS9EaHcttt0WXbtDUwccXbmKSh2fMt+SU4gaOlBgbPGiXPq5dwpke5mP7ALRf1fwNZ9YURCdtdS5bhycSCyxDIjFddE5Bw4ykKCnWB4pCePInquziubJ+eEcpCxqfdTFlR2fLkG+QOpTJWwGzgKXjNQAkd9zVkcOdcWyMHXt0NQBdfCoKgcPYZaTzq9yT/AITupC5h4SWSgNMm2LGUjw0igOS+/FsZVk3s5xMgCWgLG+KgPC3pPc1dW/qW8h/q8w0tUJbCe6ir6vpxlJy4Ocx9ezPYi+t73tbW9729u7zLquw/d/8AMcn5zgF3/xF/HSS6/g2rUr2O12SImPSa5WKwaduxaREqYHc/R9j23XYjabIcaIZjRCFQLWIi+iTAADuB0HWTs/O2O9boC11tsWDsCDu/csmKYyoNebNVIfsV6vMZ/PsVqtjJmd+ojzk1OWnwnM5iaOZoXVag3nPaoRM+FT1yM4hy7yhkbBcz2ep+mORiP7u2kaA7uVBa4yKlVVYblcYrvdMhPcOWw2GwAMXKuOvNafOhlUMkeZ3SklnJ2dmQwOABweHqKuawuqqkz1pmns/vPzcyhDf1yPcSZM8qZpcLo4N9101WlfrVZYrVbo+x5N0wBgzfWDgwMcHx9bfevJA5Wt2W63OD8/R9tuEQI7R26/dgdnZ+f40Y9/YpI3T1YabG/vNjPGli76BbwgRxLgMu+jIghBN4MATHilQK9141FgC6aJzkxg/l1VnxUEyfqtZEC6oSSSN5NIoJwnp1kmZCaebPQ8ZflfD4IVxiB8N8i11Yn9D4M86W8KtgzqsPj5OHtI1uN1gUS9BoONWAmi2+eOK8A5Z8xc1kTP7gFyDEJWVUCoaywXC0Q4IPVIqcO4rnD16AiL4zs4PTvF8f0tVstzPHjwALvdDnfv3sWt27cBOEymM3z4xQ9j3RJu376NFz++wuTgGurKYZcSvPPmZGSJBAWqmHfLwWW54ZxIBsSuQ/IOV4+u4iMf/yS+/93v4NvHD0CAyXLFnuf1xXKJu/fv41Mf/AjOuw4i0CABEskALZdUj5/7GYs+CJgYHHZ9h/Vmjdm1A0xmU/zVv/nvY3F+hj/8/a9jtVoDvgHGU1z/wAE6eKy2LZJnTdWYenSbHiF4ULF5vtgnHtUHHm8DNPMRrMb8+WXm5ctep/O8stG1X9n8r4CCIiV6fL2QZAEUeRggEBc+076bGcP5/gvVYJ9Rfr7MIeRJgsgCKAKewjnR0db5gfMfoKA4GYjsBZzypKc2GChMGudBAPOsi57TcJI/dVbZ8+Rv1msn+OQQ7ATMxTlG5j0/1OZ+u6ZsU41X6QDABwZLZeIkgujZXABaxSlnzkrP/yBz9kCexCGPa+QmpwJIhNO5PiEI0EiSP0rOPAgMUHECKpeT1ItrleVMLiezPBmBIyqCpRKBPGs7k+cRr97TnFtk1rq8bZjZzQxpXTPxY/TEngYxLd4LpFI35b/8XWa2w4B0rUvNN5ymr2NO2P/U66ADIQIDIB52v36Wxwen4RyyDI/ekRTc1zrJedHv4ZQpP3QiQ9IkApLj9nKmy8bzrsrm5McW7HsCmlEN30d0bc9MdOfsmdom0HekAJwakJdSZCDdcz/Qeu+oRVz12O46jJoKdVXBV176MNB3TAyKfWes9WtXryKEobSJzV8AA/Eyd7rAb1SVczKg12RXvLDJid/BEJBc5hn1MA3nYWF9g0FxXZLoFKbvc29MeO3rRQoCanP3tpEuzZrncZvPrU6HawiN+WD1QFoOWJreB25fXUspkA8evxxumUHu4Dyq2qGpWce8rhzqEMBcJjLHgz7DeT+sV6nbLIPFjgEvsRcsoKjPa9UkrPwkQT45zz7PYVI/QYsnVZk0AK54RZOuS1M+vcb9LRXrSpG0Qna3AOKgJTLWutb7ZZzofwIg+qMy9aiFQvFysGsuuSF8w3LzJPNwHt4o/Yt5etTz3kY6b/m6d6m9h7O+t/eyXex4l+2IFw/ZXhzLjxuPF8f7Zcbto+aJ9/h439sTNyLC/fv38U//6T/FF7/4c5hMRphOp2jbFm3bGoNawXDV0d7tdhYgczqdYikBRy9qiqeUDChWRrMC6QAvPEajEXoWRcRsNgMATMYTHMznCCGw3EliWZO2bRnkF5kTZZIvFguWoRmNsBRgfDwem1RL0zSoq9pAaAWNFfRer9eYzWbGeJ9Op5hMJgZAaz2MRiOTXqnrGsvl0kDxEmzQuui6zu7Z7XZwzmEymVg9dF1nTHfVJFcm+Ha7xdGVK7ax6brOyrTdbrHZbgHPjPwHDx5gs2EZm81mgxijnQSo69q0zZ1z2G63AGDsc+ec5VOZ6nqtBhFdLTdYr9b4/ve/bwvRywI1r9f39vbutt7YZxl0UnYO4SLzMm/QjK35qOUsKTSBC5t82YTnvbf8nsEAIDPWCRnzzt1MmKLEm7mSkeuSskJzebRwCnRhsP3RsuQ+XEIrGUo3qEh0cFV3nQY/36wx6E1vuNr3fnjFRXA15ZIWQB4QtM2cY3DLeVRVDQLQ7VrMjmagLnKciR748IsvINAWX/+d3waoh3cMQLz44ov4jd/4DUwmE/ytv/0f4PNf+AIWmy2++4Mf47/+x/8MzfQIH/3U5zA9ug44lljZbrd8+qZbg/oIBewSJUR4JJN6kJaIQOo6bPwWH//Yx/HKZz6L737rm4h9i+ACB6JzDeB6tG2Hl15+FZ//cxNsVsdANWbAJPZcTpUK0P4GBmhKZ85qt4ZzwJXr19H1WxyfL3H9mQ/gr/7tv4NmMsW/+hf/HNEFTI5u4M//5b+Kq8+9gN4FJDjMpzPElHB2eorxZMrvB5L+caEvlH+XereP7xTcHy8GBb3Y5u4yacn1ZY4e9buCgpmNR/lec2KB2adw0HUumWCTNqON+mJpykAhTxTKQnaP7PNOAEfVfKYyiQJ40ZiNBDCj0ztEAnxSdmce+845RGKHN0yvmXXSnXNwSZxNPDwQhD7rwUHxkuJr4LgDzgnT3AvI4sBpeHmucxZktPKq460AEV8HiPSMA6I6/gAMW+otGikTO9ddGbxS51lloua6d/oL/9BxeSFPdl9Rx1naI8+dCugCDkGCLKqzC2BHiPcivxV0PkioCsmKAoMegMNZggTSGwWIVrBLokEk6rUCLszqkj9S2Q8CBQHSCgkTKjoauVwTDuADKCWoDmZnq7zLgMkuhTCHtb74kJnmevIqS+iwljuD57G4V8sRpX703QnRiydoYFRtG3MImNOjmKco56FoZCtD7kRanvxOpZjsGQCJvE3JoOc2LVnzIXi4UYXYJ5HpynOks7Sc5b+0lCKDqMLWpsR1o4EgY1cA3AbMc78JwePqlUMcHh3xuwQOTgMVC6rKjh4GuJ3LACxBYyLkz3KQYAc591WAz5QZ55A524ZXlgbR4ac68LaWdirTwnWmYHKeiCWlh9ZGkD4FLgdKmRmf8yDzVHB5PBMkOLC2NzKwDXhUjk/WWPPLPBcqZndXlUcTAmofEII6IL2sGZPlwYFBeZV20XYvGe52AimyA0ZcZdLrE8+1SfPu7JSCEQlE8in3Te4DvdSFlzp1nqQdxaEqwLwFBYWzcgIwZjyKetVnlCcxBsSB9HA/vmiXBtEv6xG/XFqP/LRM6VGpX7jm9VN4/eve6svu4gvpcddcNifvT3u3ln7Pynt32JNqh8ezhS5e90Qe9zr2KDD8jexR88RPPaN7e49a13X42te+hk998pP42Mc+htlsaprfUVjcKq0CMABbso8UUJ5Opxaskoh1CNXquoaywQ8OWG9Qg5P2PWsi9n2Po6MjDpSZGGiez+e4ffcOYow4OTlB6iN6AX0XiwWqqjJt8qeeeRo7cQBUIcD5zJBXRrfqjuuzr1y5guPjY+x2OwP6FXjXIKgaPFTZDAqyHxwcYLFY2D0lwxJg+ZbdbmeBROu6NjB8uVyiaRqsViuklEz2RaVxDg4OMJvNjD2/Xq/x4MEDY+x3fYdEWZJG20tZ8HVdM+NfgHp1GGhetE1UUmaz2aDrOnMK8O8Bi/MlXnvtDl69dSsH0XqbtgfQ3xsWBXjigG+AMi5186bs1AHMTLoRexiMAWQTgby557sYaEyiU6sgmAJ4uplmBpAofTvY8/WZBnAIE45ESkFOZBebcAE+iMEOZTo+au3wRl21ZP+lgYxLyT7OvxtLrKizvPt19lkQ9uvrmxukp79rMQii2Swyp7x19wX7VsN3OiQH9H2HqmkwGjWYTiZwTUBLPVzc4HA+B1HC0dEh2i0HXwOAo6MjPPfcc/js5z6HzWaNr371q+jhcL7mU0mj0Qh1XQPg00m72AGuwmw2w+Jsi1bO/VNKiHACelUC5kDaJSCix+n5As88+wF85rOfx7/9rX+DH//Rt9E7wIOBEPiAzW6HP/r+H+Gvp4jdrkVVjdAnQttHVOJQBJhsyx3Msy6ugAQJzHT1gZmGhIDj43PMGo8XP/lp+PEE4fAa/qt/9I9x6+Vb+NKf/2XMrt7Aetdh1/bMlnVA37eIfQ0fKmSd2Uf1m6I1X3ft6qSvXlyDukG7D4GVy/adAQRov2dW76PHQf48XwfLA9l32WFVAG9m5XeqT/uY/bp6PpABZ7uEyrzzz0RgNiucAZFevi7HViUfat1Gj8EYYTCcPzcJBJlzvCME74zt7CBAurHTHYM6jk/qOMcgce2SgWr635TInCkGfEJx6bf/7vUhAF6Dm2p63uZFA7GlfnjezW5Fff+rG9WCS1IO9AxXgK4655ICXSqLU8x0lBn8JAAgXH6/ECSoK/lBN3WAPZcBMgUlYfM5QAzSw9m41IwFqiD6CwzmatJey5qBxtzFTUyM3z2kdeCt7KrnbfXlmHGt7zI9D8TzUMyAdJITM0Tc2QBQhADu+h1PWjbmFOCmhJTI6hoayNAAcAIRE1WSxoPIVcH5KeJEKCSZsfRMELHb9F1KRTtDyiovdy4zoLrxep9KruT3u+af5VyICClGAdMjUoqgqOlrf9KuQNCTas5FG3PelyfZCDHy+I5JArgCqILHfDbBtWuHmEzH8KHigKPOw/uKsW8HAXg94DILHQr8GptcmdDMPBcyOfdfx44bZ4wDybtOmAa4c31yEFmCOSzsNF/RZsXclE/xyOmQYg3wSDY5cv1pmxhEru8OXhxZdg3YdlVe3zkyWZQQHKoAVCGg8h5VFVCLfrnzXk4V6GnArPvuUEFP9sDJc8BjXZ9pU704KNj5z8C+Oh50TPjgEYQsocpPXMbE73nqrV5V/oUZ7uwY9SL5Q3JP1H6s86K9azkvScqPRIgWN0DHulalnOIgdvYY1eQS+6C9nMve9ra3ve1tb+9yIwK+890/wmK5wuc+91nEFNE0NZaLJdqWgedPfvKTeOmllyz4qHMOi+USDsDB0aFJmtSjBkRA37UWZLRtWwPVl8slqqrC4eEhRqMRZrMZDq8cGcDrvMdmuwEtF4B3DLgsFkgpYblc4vDgwEBwlTzZbDa4e+cuQggcrPTsDE3dmCZ7SgnXr1/HZrPBcrm0PJWAeqkProC/AkEM9EcD/Xe7HdbbDZz3iG1rUiwaALVpGsvbdruFMt8BDAKQ6j3KfG+axgKl3rt3Dymxc6FtW8wO5jg/PzcHxXK1wpUrV7DdbnF8fDwIWKqBTEejkUm8dF1nJwgUjG/bFtvtFlVVgSjh5OQU3gcsFktsNlu8eusWa63v7U+lEQkAAQVheSPBm33dAgq7F1niQXaWD0E/uhkZHH2HbDwSA5gSb65g7cCAdTi9z9keHiCVSjXQKRUgdCAH+JJJ6xDhEJxerds0N/hJlDej+nkG4m27BMsFlZs2V3w+/L1MU/8uP3fFv9czl7djdnX5uwLkWhMeWWPYwEEoCy2h3fW4fv0GUt/j9u3bGAeH64czHE5m+NH3vo3/z3/xX+B73/0WPv3Jj+PLX/55/MKXvoTNZoOv/97vYDQeo64b/OjHr+C7P/gx/sP/4X8fv/qrv4pPfepTqKdzRF+hcQ7r5QYIFU5PT5H6DgzwMNcNYEdJFDkQa1vZ1DvPnz39zNP40pe+jB9/749AFJEost65d9hRwvd+8Mc4Pj1Blwjo+TsDAKU/8Z7eCwDDm30mmjo0zQiROqw2GzQ+4eYzz2JzfozlrsMLH/04/vbTz2ERPb76z/8lTtcbrN25HRvv1hGTUY3Dozm8C4iRmdBPxAwEKdrb+rC2qysA0jebfAHQ2eOo+Lvs6wot5CPqQ2YyXfh5yTwYMzinY6Oz+MKYoQV4pzeWAOxFsMjiKVDBaicYkMMnaEQWBs4CJLPDiecMJ891IguQokjDuNwSJiul/4RY7YkYkJcopV7KlxRIIxi7MZ8AeOMTKZexlBIMkjIUO4Nrqv8OB5PU0IuJCMF5nlfLetY6ljFGyACmcxAZDmVxD0tBgICNorv9yL4s7Gh7Ln/vvQdpLHgF83niNweLgtLkHDxlZmnumj4DzU57sJe5kgadh8dFgnOJgVIFbr2X+bV0HpBIwBASRWOWWznJAcEVgLOA7SmX3xjicEiIUseZya51y05nD5Z14XkyUbJiGqAt4LiVV9JXZ4kCp6rtruVVwNHB5fS08bSWNTCtgpkFSM/Xp2L+TayNTwUzn/Q9hUJLPop8joLrUQg9CTH2iD0D7iklYZ1zYFWVcrGx7MrOzoBpqDymkwoHsykO5jM0TQNf1wh+DO8D6hDYAe9YjsVpXAMZyE7XPTqoHQBzeF0EvMv5UwBuX0jCSB1laSTwvKb3urwuKJn43ulckZ9HAp5rsGOU+XHlygD2LslpSzkgTH6Kll+L6+AA55PtWZrao64cg+YBcC4xY93iLqCYb1wBohN0YmX2dqHRruW3uTMD/2bOAXI6hWOsWOnZYUJyGtFnhr33Ev9KnHZaH5p0IuJ5HMlO2uQmLOsJ1p6x+F5PLegsUq7fZMUBgs6rGHzzerYH0fe2t73tbW97ew8YEeGVV17ByckJvvzln8f169cAAJPJFOfn5/jGN76B+XyOo6Mj0xEnImy3W5ydn2MymVhQUV4kA2dnZwYwA7yRG4/HBvDWdY1eQF+Vf2nblgNXOuD07Ayz2QzOOYzHY+w2G5N10U2eMsVj3zMDHeBNnbAOlX2uwTWfe+45nJycYLVa2QIohGDBPhVods4ZmzzGiBs3bqCqKpydnWG1WmHXtfAh4MrBoYHvKSXM53MLFNq2LQ4PDy0vCog3EmhVy7zZbLDZbAw01/tVoqXtOnR3mX1e17Vd8/LLLyOlZMFJQwgGmKtjQBnwurBWzXV1JmQQfwfnPFJMWC5WePnVVyy46ZPsY3t7b1g0YNbZxlH3EAoqOds68iYmALZB0Y2NL5g3tgcXgETEgXiD4UR+RMCDCGQWORT7YpCDnILTedtqx5z5AbZhyoFQXd7ZABJ0NAOOpECSpcf5MHxGypqPpJM9SyHF3L2FeSQ5UiDdgGvb0qo+uTL7OXCfBTh8bOuQgXsZQMfgJxc3b+358wA7Qq4yBALuBSTcvfUSJk2Na/MxAhLOHtzFr//ub+E7f/ANTKZT/MxHX0TX7/D9738PwXvcvvUqDg+vYL1eY715gC7u8IEPPIWvfOU38dwHfwZf/e1v4OpTz+Gjn/4MPvrJn0XtHUJdIzKyLfrM2VkCiF69AnXk0MceLgKz8QSb7QZ1M8HHP/lphGoExJadL3HHfabvcPzgDpZnx3DVDH3skVwF5yquafHQEAQAhUd0XpxFCQkcaC5RRBU8QqiwWO8A1PAOeHC+QdsS/tbf+W/ihY99BjefewEnZwsQgKrioKyhaiQmxZr1jF0YAMwPMzof0bZF/yjb1QkYUTb0w9DkG5tTeQF9WQKFQwe20S//zhgOCbgG6JByjtvtwiVmqsNtOOjr5e3iB6TzTO7pJn/DiBwD3wVgaAVIzrS4mc2cwfaeGMB2XjjYpEAR32dAF0HkCvIpHOeFcU4AeVH+1e+gw0tlCpg1qRC2A/Hc6hhAomKeVKelB4M0CuZyvb39d6cFBSUBh7RdLgCf5Lw4VZKAWgIukxwUsL6iciDynrBGlPYSkNyH8FD2Vd6D2bPqONPy5rncnAgykZfvFgWcbU4VT4bmK7NwM/Bpfl6S+id1GGTA0kJbGKCZAAQ+HZC8tYkjCXjKTzEHVvLS/iLlYWPZiaOYuKNlUI7gAtm4sfeLg/SGzHItQVWKDMyrgyAhSewHAWUNqAeDzU6cHYn7ofWHqE7mDHZTcarAmq6IT6IdSa8ox3yWyshjUh3smVWPnDeQgJfq/BL5GgKozFPBmE9Jmfwia5NYAsaAdCjLWKUNK9R1hdGI5R2bUYXgKzgE+AB4XyGD5fJ+Fbdzufax9irwXT2doW9bm1sL0JUoy3ZZeZ3cJZrvDKZzXXpXSK0QmBlfAPqyqoI5L7SX6DLH5fq1d4fm0OU2UYkfdgroOybwc7xnlrn3aCqHppbYKV6DuWrAUqmlYp2jqy7uGzx+nL5vLI/cY7xjBjl3MVkzOq1LXevIGPU8l7PEl9SC1HMiwEeW6UJUqZjsRFcHj0r75JMayPN9kprUuQMAOUnLXK+aHMsIpZTXn7bOK5wBREDwXqS9xPtXnNB4nL0NEP1xL4u3/xJ5d9jbKcf7pQ72trc/7XaZsfxWx/s7fd/e3g9GRFgul/jX//or+NKXvoQPPv88bt26jaeffgpVVWGxWGC32+GZZ57BM888Y4E4R6MRdrudMdS7rkNT14AA7crQBmDs7u12i8VigRACbj7ztOmwKxiveuTKFCci+BBQVzXW67WB5wrKa/rL5dKuL3XKN5sNplN2CCio7L3H0dGRydBoAFP9/OTkxIKDqryNStLExTn/3XXo1mtUokG+XC4xm81wdHRkgPZ0OrV8agBQItY6TymhrmuTVVGpG5V4Ua35tu/hvLOAryrFEkUjXtny2o7eexwcHBh7fTQaYblcgoiwWCyszlJKuHPnDna7Hc7PFthud3j11m1mUT3hvrW39461Kbe/MpzKo8EeNNjMOefAh1VD3uABCOBNhG04TVPXIQoSR4CA6vm7rGcrAckEEGNmYs4Xb3IUyOeHMUsy619akD+7J4MPmdGdgREg44v202VwV/OYYYh8fDyD6qIC7XSTndMxMFBAEicbW647ZilGZdPbZpIz74pMRvs8Bws1oEXZXhktZRDFVQJMyT9iSHnsIzbrBQ5mV7HarnDv7mv48Q9/gG9/8xt48OAefv6Ln8PibASPBO8DvvXt7+Kbf/BNhABcuTLHnfvHaLuI+fwA3/j6t3By2uMX/vxfxM1nnsWV+Qwh9Rg5j3azBcijCh597NHHCBdYddUTmJ5KLJHhnYevHNbrLbbOwSegmczx1LPP4+Of/iy++81vwAVpzdQDXYfQbbE6vYvpUx9Gh8RMdOdBkRD6JHrFfBohOocegQOTgXWSSWQWEjnsIkAd4FDBAegSIfmA2fwQH/3Zz6OLgPOVMKK5F7V95P5GDJzK6CgARutAGRBDMhCqBJEN7JacGUnO5WvJrhUAo0ihTKvsuSDtnYrzCEqjVxTgmUEDMvYK5BWaoRxOjq/Oril141CJJebnXsigMsFzTgupA2ROdjIsXEYSlU66EkgvmJEFHGbAHwByvviWB3lS8FRu9QkIIQMlKtHinIOLmT0uOI8FHfWkwe84Ka3hXZ8QXEKASBR4loBgTXWGalKUAKWalnt0a17WLgKcCjKqtIF2RgepNgAIFRRwLmNi6HtA+J2FY1FrMk+UxEihvQBUZiGQ5kWDZ2bgLMuK5DyzzELxORWwpbSXBjkkwuB+K7sGrRQAjjSwLQBoYEN5+yg4SDahSt5EpoL/z1EnjCUsgHqgTOKAsNUVQGQAk3uD5cUpmCcOFe9ElYXHvMmpWBcmgBJQ6QvQy/ik7Ew2wNnldh+MBX57meNCruXSRXvZ6WMZ1LeXuAR9zR2/XG+rnIW+Z3OdZ6DZ2sYN88YgaX4O5H4bY5Z/ZMYyBHCW7520USn7RFSMIafgrIDMroIGOC7r0HqOZUf7isqQSX8r+mHOd75HHWXaR6RTyToq5HWJK8BbVwDuLs9buZxySkdA9JIkwOsxy4C1TRmEND9HvwOCZ3C5roBR5YqYIxpwWYeC9g3uh0mBZ9Oxye8TzYf3nlntkgXjbZfzjozjINr1PBYgThMZ/dIv9ZSCjhNATgcRv6u8z3XJTPTcp/Q0gPXZlCWd9NVRxrqyeZ+9G1Zf+Qa9rwx+6qXPEhCUElGM80vsh94mE/2NH/Boe3svmnfO3mr53up9e9vb3v702H5+2dtbt67r8Zu/+W9w5egKnn76KZyfn+PFF1/EdMrB0k5PT+AcLEgnANO+Xa1WzDRvGgn0k8FzlTdRYLyu6xxNHUC7a+EccO3aNSwWC/QCQs9mM0ynU2xWazjnLECpBi5V+ZTZbGbpf+hDH8K9e/dwenqK7XYLZZbrc0ejEdbrtYHcmnfW+GM99VIvXOVcVF+9qRvs0g737t7FeDweSMKolIuy8GOMODs7Q1VVGI/Hll91MCwWC7QiC6MM9rquMZlMsFgu4ODQdS3gnAUYJSLTfK+qCs45HIjUjbbBZrMxuRaVZeFj3cKScx5nZ2eIkaVy7t69h816e+lZwDbkr7PB34Pn703bpWSraQXRdZNjIDpgGwpPgPdkGzjFcTVoIyRYZMnaYsafL7b0bCITW4DoAvsK8lZsSaEAfv7bCeuToXOFdBIyGK3/FFi3LQ4N34CDXp1JnCgB9DLPmQHIjE0CTM6j3JbnfyIHoCwo3jYW8CZZ+bxu0nWv7LheSshRAyEiY2JWWwwNAcRuDTgw+9wRq5HXLoJch7g6xsmdW/jjP/ouvvdH38Xp8T0EkR+4ev0qbl6/gs16h1//F1/Ft779Pbz4kQ/gqWeuYzQeY705w73793F8/xyj8X384pd/CUc3roK8A1IHSh67bUJdTwDPTHAPEmkV3linBNbAJQdfMRuc4LBrezQBCFWD2eE1fO7P/Dy+861vAalD8DUzLNsW2G2wenAXh0+/gI4IyQEdObiOMOoTc/3JIzqH1nm0noMX1ojwiIBswCkBbUpwFBiuIGIk1QPLbY/J9BCbzU6ClUEC1Tm0beS2cxxUzkaRNZRCSGQ/DUEfzKfKVsyQr/YhhftyUFz5SUX/oMfthnUjnwag9qCD6w/DhPK4zQ8kw4SUvWeAaZFX7Zc2MMp0L2YLKlGgwB8MoChN5wYbSSVoqkAdKduQE1dAz8qs0lAqfmy5LvJJOf8EMCO9LILLIB903hPAzwsYHhBYBsYL6EwMxgQk0V/mfu8Fa3GJ51p2IvFngZFBvB2rA0MyJpFQvLNNhksxWRQOCeRA0Dqrmg6x0/pmAFadiAqMMpSW69baQNIn0RGuirzpnO+goLhDitGA2vI6bu7cpvkMVNH/ir5jbFZtOy89w4BBLoMn7ROF4wqQ95e8SRwA8tL98sg0Bw4BRAUwL+M4EXFgRc6Q5ZUo2czAQ5nbyWaLlOsOgDkVnMwzZZ9lsJUMQH1ErYgmNDORI6Jks2D9D2abXC59k+j8wGxwuTIXF6YUZmOTrK/oMCr7HQmI7Zx6yOwcVwZ8wX0QRYny2JZApY7LxLcUzhU9haV1A8uEgdckbaPlsmZPw7rTFQOpowS40B91DiOpa29rFANhUcxrxZhUJ3me3by8z53JlAwhWZiTDpZcrgOCOGVs+ubFQRWAug5ogkdde1QKcjuZb8wpoislqfGH1vMZsPa+slyVcjB6L9SBJJ2HifdZNohfmw4pJv4JcZiKtI/2KX4WEw5Q6RpUx4KMC+g8RtL1KC9KIfugAkjXZ8PS9+YM8sjznTq1+DqhLmj7ODmhSWRxe8qGyf3Yw/mAN7InKufyOnu0i1c+ycfubW97+1NorwcKvZXrLvnUN33NHqDa20/DtF+dnJ7g5PQEhwcHWG+2ODw8wLWrRyytstthNpvhypWrqOsaZ2dn6LoOV65cQdd1LLniPWphU6NgTOsCpa5rHB0doZXPSZjUo9EItedF73KzznrpLi9w6rrGfD43WRYAJokyn89x+/ZtA6mVXa6sbmWwa1DSMpBomQ7Az1P9cgDYbrcsu9K2cMQBUL0wG1TPXIOWqmzM4eEhay96j9lshvPzcwPKF4uFMcjLRV3X99i0XIdLKaOC4XVdY7vdoq5rTKdTk5JR0FwZ/V3XoWmawglQoW07bNZchuV6hbZtce/ePcuHbWyAt7Wc2s9N722LxAxJBZ4ZU+HNSiCH6OTIMVCwanSTzYxS75jxmxG5DISbPEqGEm3TE0mlDWxLBoLoUJKylESOwMAGIMAZW67UcmdAatgflTGnIIhtuyhvWC/AfHACymo+9RlkGEEGBZS1roC77P5sfAdSEFTrheshaZBFWFIZXBFgTdmDmkPBH4ZB+7TmKOTqz64PBgy0jighgDDywPe+9Qd45dWX8KMf/Qj37tzhUzq7Dl/56r/Br/zlL+PmM0/h/GQF1oet8KEPfwif/7nPA4j4w299B//y138L16/dQBVqPPvMM/B1wGKzQNdv0UeP2h1i3DRYbpZwlcdkNEXbJ7R9Dx9qtG2HPhHgKviqBiXCbH4Iarfoui1LpjRjfOqzn0eYzhBPj+EqD+88+hixOD3B6f07eDZFkOdTDBEOqYsYS49gGIhB/N6xhE4FAgfB44CiWkemnv2Y+ezRn2ufdlDIW0GYy06pOQUq2m+YwJudpofQDqDQ5jC1YYqqfQ0gA6d6DeXfc9oKOpclzqDQ498K0oEfyrHmbfjdAEgtvx+AxO6R95af0YVyPKo2kyPV4TDAXB9VphlCUPwUBI/MAgY8WUuy9I1zSDIP+ISsJQ6OXaA9j/Rky9t8nTYS3H3AFnbBQE5n81o+FWGySo8AggACIrOTSediKWNKee4lqBSCAGZaIZJEBj2dgJX8PMG4hfE87DepAC8BGDtVW8YbmpvfLbntnPVIKt4J5lwwiQjK8zaK5EgBXwzfjdJ11PnAgGWwkVDKmbDjjaybZua9OFJdfn9xvr2pPxiYrWV0gAV3VfAT6shIlhcdKTo+FGTOR0TsUnZ86EutqFnV+i9lgbIUF1n5Mrgtba2f6/0SaBI2vwpDH4CIwvH7pXhXUTH2ynrT7BF5kC/d4lIqGWO2MnHKLM6OovK97LxOH0OnjfU1Ku6Tvw2ERxnM01naCvzawJLc8ZjXMkg9eJfbBnkq0480fe132no25xKfYCEFmh2hCg4heHjn0DT83Mp7DsSJIn2U2dM6GXxqQDN/mp0PSsqx8yJERR+EtLH2K5knKNet1oU6R9VBoXOfOSHkPwqC88kVZY3LCQHonMDj2aSJHJ/QtQDR2h+Cs3rUYL6peH5CJjLoPlD3omW+8ptSTjqoZIx2S+01RfqvZ3tN9L3tbW9729ve3uN2vljg+9//Pp5//nlcvXIF4/EEx8cniJE3TOPx2IJanpycYDqdmoSLArjT6cwCWgLMIvfeY7VamSSM6p3z0T8+7r/ZbIzV3fc9UkwCqDucnp4aG14DgLZti/Pzc9NfJyLEGDEajYyprcx03vQySF4GIVWZE2WJN00DAMYUV8kY1Rwv5WpmsxnatsXx8TFijJhMJsY6V+C+73scHBxgu90CgOVhsViIY+IKFssFzpdL2xQqq10DkyqbfrfbWcBTAMZUX61WmM/n2G63xqxfLBZody3G4wnW6w0IhFdffRWbzcbaOm9WXt8ue93e3ptGBBHABXj3phvXzDhNTqUMePuQorDRFFC4kGC5L8/8uJQxFVKGYgGy6Oc5ocEmFgA0KOKQ5VjCdxrUiQbMKE2nBPxKKYtB9q0YeZM4hAZdLiP0z7xpHmzItTzEm8zk+MRO1oBXAOdCPRS1pp8MxqBmAxegQLKaySU10CzXXdu2+O4ffRc/+tEfmxPy05/9LLZtj69//XfwO1/7XcS2RXANzs7O8elPfxpN0+CVV17BJz/1MXzyk5/AH//wVbx2e4XP/uyn8b3v/RGOrl2BrwnbbotXbt3DfP48Xrn1+2hTxHMf/CCe++AH0bYRbR8xHTXodx26SPBVlnwYNSO0fYsomuZNU+OTn/pZPPX0s7izWSPGLeRMOVIknJycwDneaEdRO+fAh8r6JJDP5QdYp9cVbVt2gAHL8DE2YC6WneD9YMrUK4Al/fxxUt1lP3/oPfEIh0CeNC4A+AXw+6hnPOr7N/NeeuP3GAMehF7yqxrC+qxcEO8dKMp3BbhocicCyagCv/esn+29Z1EhyrIVyQsYpk6ft4mi67pLWd4kc48C3YknI5try/lOJ6skxXfmfEQGXkVTGUQIAgYCGQQcSLRIMyugRAK2kYBeAI9jQWE5yGQBgqtUiZ7M0TQdPDLGlx05wYdB/81Bl7UNJa+FfJLmvXTUqCyJAtllsFLnnAXcBikYmiXF7P5BOxbvC8D6jdYLs6Il8GLlDUyURoAWXv/rfcFIl74jBcugIthRAlcAmeX7VkHb4rXmTPakePtIWb1T5m8JNDM4X/ibLsyPhbyGfTcchwq+ZqdPGswXDvlEykVH9MXnGfBbLkAUTBcnVX6OXJqSAeAwsJabw8a+tZuSAx41Rl1RtgykM8vZiyNH/3kD3Elu9UXNlmdRnNMgoGRTZxAQu3Kw4J+h9iLTwhcFn+VJdKxp+XVe0DxoPyD0cA+dhMntQtYOSWYrWXNRnicBx5J8VKzB9L1SzA3Oe+6reorDTnCgWBvxHGwkDpmDyxNB3vPpHvLSO+2EgcoccRBz5wDigy451kaxgnLeD04BsnGZo+iue+eH/VvHvK75eBBBg496z06NN7I9iL63ve1tb3vb2/vA+r7Hq6++iqefegqnJ2f4xCc+AULCvXv38MEPfhCnp6cgIpNU0cCZyvKOXW8g+nq9xnw+t4CfCgyrlnlVVcxMl03DcrkUvXWPyWQC55wFF40xIsZoGt8qbZIZA84A8zt37hhIfnJygvF4jLqucXh4iOPjY5OkUXa6aqPvdjsLMqrBQJXR3jSNfV8L2+vw8BCTyQSvvPKK6ainlCzP3nucn58bWK/SLKqhvlgs0KeIRoKvqsb6gwcPDMjXvM/nc9M6H41GOD09hXPO2sE5h5OTE9N0D1XA+fk5lssVjk9PTBpG6+lxdpGVc5lr9/beNtKdHDBg0yTkDTI5J+ADf5CEVQkUG8uMJ8EwGQNWgLwhI3vmAKwm3gjZPQQM0DviXCXwhoxhKmaVOejGTfKeVN89b97zf4tnO9j2tQSUyv/pw2mgyewKQB/2u0Epuuknhnl0Y5WQ4FW+o8gbrAmc5cG5nCtlWCn+ZLj9BfDe8lR8rvWbUsRmt8GPfvhD/M1f/Zv47d/+Kv7wW3+Ivu/x2c9+Fv+v/+w/x8HhASbTCb797e/ApQqf/OQn8Hu/+w3sdjucnkbcuXMHL774Iv6j/9Hfwz/6//4Gfu6LX8B/+n/7P8NXDrPDCcazCerRFHXzFH7449s4Wa7xxS99Gb80GqMZTRBRYV7X8HXNbeMDegIoEeJ2h75NaJoxKhD61KOpa3z6M1/A8vwEq7uvAimhDhUIEZvNBlVdA73WT+4H0gqglJB8knpIRf+TTlrUd/nzjWy4mX6v2yXA6BJwe78aAVGIg6UURMnKdp4MVFHCJTtrCMlFduA5YukMT4gOCJQQk4P3CUHAmMoTekesw+7Z+Zc8s9zfXhFk7hFAXoFPgrCJtd9eYMx6lwH8DBoWIB6QGdDG4s2nL3SuYpktyUsBLjnHQFYqgwQ6N0AMvYKs0tmUrSr/L9Lj95Gjwqkq4FXunxn0VpA0UTJXaPm6Mrx6ANLpCSw9LaTzKCRyAnG8W5vxleWaQT7NhraLPlxBeue86be7EtjTunRV4dwoMo4CaKbyHZdEjg0wORqA4wEYaKrvFj90JmrSXqvccXDV/EZjMNjAfwG/vbWIvH/ziR5mHRcSIShAXJSSg7A+RRYUOkv+aFsQinUBMhNf28wBRT0i95Xyt8J5AedYukvLLu9suuDU1vJkmSIFq/P7AwBcGvYVbS+u4xxk1VtZFVDO6QIZlM7zDoOxwcPY5qPGoQ4qE0XGatD24S7krK/B6t5ZYFNHua/qXZQkIKyucXTwJbLxyOkQQBxlxPl88lCzTKSEBv7pbR6KjLPHqDVp72budd7mGG0255zopxegt/yXtcll7A7WSxqo11l7mdvQ5jUZi3RxzUA2nrRPeVeWjcTRogGLc1tpuyZK6CMGUkCPszcBol98+76dt/Fbvff9vALY2972tre97e2tGxEzFX/rt38bTdPgBz/8Y3zp5/8snnvuOdy5cwfT6RQPHjyA9x7Xr1/HbrfDer02YPro6hWcn51htVkjOeB8ucTBwRyz2QyrzRoIHtPJBG3s8cqtW5jPZ6x9ngjb7QbtdgsQsHUOfd+jqniJoWxwZaSrZIwyEa9cuYKU0oBhrprlKqNycnKC3W5n+uIAL+S7rrNnpJQs2CkAk1Qpj+XFyACOBj7dbrfous4Y533f49q1a2jbFm3LGufNqMFysUQXeyQAdVOjF6a99x7Xrl3Der3GvXv30HWdlXUymWA0GuHs7Ay73Q673c5AoJQSVquVBQ3VheBsNsfLr7yK09NTrJarwSL6SfWRvb0/LEV1ligAoA4pAShc1u4VTJh3vM4XWs0CKGmQUtXNhQK+BOUFlptHA6AoMeBT7F1TIgbtSYRJZOOW80XCYGYA1o7Hp8yA4n1xEiJWwbxU/OghxqDeQ3Y0eACjkxChC2BEvzPJlzIt2agyGzAhqX4zGOTIm/IM4D4Ewlgd4+Ke3e7hDCQAXpifVASESwaUpERIXYdnn30WBMJTTz+FZ4+fxR//8If4Z//sn+HevXvo4w6f+PgLOJjNcHq8wE9efgU3n34K2+0aXb/C9773Xdy5cwdPP/U8fumXfgGrTYfdZoV7J3fRxS0mBxN85vN/Brdvfx+vvXaGB8fneOHDH8F626KeHCImQk8ECo31q67n0KndrmXN6KpBcIR+22O92+FnP/c5fO1f/5o4Rjyci/Ce8ODBA3RtCxemDJHI3J1cModF0vI7+Y2Yrc56PQJ5PAJAL52JA0mR9+Hcl+Gg7MAqQQH30JUFaAAq2I3DPkuDO/IXCm0MHUZu8Fy1sh2G+Xrz77Q3ajvB0eRazZ+CJ2RsxuQ4LoTNK+QNMAIivCMEEZEPLiF5z1rpCYiOmefRM15ZBSAlh+j5REaV3magbwOKuW9ruzhw0EBtX5u9bB4UVX/KOsOCOSJCgV+uE8bSCkDSWtgNGtuAzQKMDhpQ0gujWMFFV4JxnKYCZJyWwqZ5jlawXVnGuQ5gaRa5QVDldpV9ICfBhiXaK9T56XOnN62bApJjZBouKCvdFekW48k5jiNALA/mykEBL1nX+UUzLmkQ9x8GFPk+0iDUEhgUlBnAJO9hb3kK0kSSf8UgQeK4kDeWnNy6OFKN+cyZsXvFJwEVrh6MPutLkn5RGXSBcQxSwD/Pq3k9UM4jKgPj7F2av8x9vCR9DPuCgv/gclqAWVsE8ICW92ae1bIDSpuNwBJ3mYhS9HuAB7BzJjs0rE9v9aHOGe/ZkeGc9EPn4R0zr4N3CMGhEqC8qfiNpll3UocpyZmZ1NsjSddMXuevfHohORInfqYODOq8iLjuivQ0Ha1j/sVfmPc5eHBZN8ZsF2Z/cFWxluOAogp0c75Jkx7m3ee+4aRf5r7GYLzzxBrkVLRLsaaEAOOUeNzZqRfrEnnccxsH61vO59MmydZWZE4HBt5h15gT8BLrhbcBor8de6tpPZk8vBGbCwAuWX9729ve9va6dtmNwvtxg7e3Pzlr2xb379/Hv/gXv4Y/+2f/DG7evInNeoNm1GC1WmG9XuPGjZsGvCsgXVUVrly9is1mg7ZtQQBiSmgEmCYAu90O4/EYfYzwPqCpa4xHIzRNg8ViMZAvGY/HcM4ZGzyEYMFGSw10lY5Zr9eYTCZo29a+u7hBb5oG4/EYy+XS3ufK6FbgvKoq3Lx50yRjQgg4PT1FVVX2fA0Q2ve9sce999jtdjg4OEDTNFiuVzg5PWUZGvm+73tjzRCRlVkZ8GWAUGW6d12HEAKWy6U5Au7fv2+SNF3XYbVa4cc/eQmr1dqcB2/G3gikuAgm7aVe3tvGmx5CqbdqQDrAQEmBZCQ4Y3sra93AdKaIG/gCZC3VzHjL/avcNA+Z1CJL4PNmjEBwxYbfgQS4Un1O5Gtlw59k48x3kKWdrzGYyepDARrbXCJvjGxNX2zMCu55kQMUwGB5LZgZDYccdS3nt7xf67E0LXvJ7C/ZURbQSwGyXPMAsXNhPJsBDfDNP/g62m5rc9lPXnoJ165dw4MH9/DgwQlWiyWWiw222x2uXbuGvusQKj4R1Pc7OJ9A1OH+/Xt45unrmB81OF2eYNutsVmfYzw+wG63xvzoCPOjK6hGE9SjCbrdDps2IiZm/UYCYiIE55Hg4bxj0I4cyAVEIrzw4Q/DJ2U5MmA5rnnuO18s4A7HgON3TB97UND2SiAJzKpOmSELnX8+DiAvwd2L3z+01novT4MGqCiu9LBOsOJsAzkHAZke9S4gFEB6WTeGMRQAyGMA8jLNi23xZt87jwLoH74IKCa7/MOmLZ7kzJnGGYFqfSvYlwCQSyCfEF1CkFM7SRiN3ifD7wgO3hPHeSAgXWDSvll7bF91btB2nPX8twK1F+so17mzOmAsULWQFWDLc3fGTkswKwPg6niFywxs0uodeAxLSRqVWCnzTgL+sxiGF9CMlB1dvG+ck5NVln8FtUtpF84TFRl1nvMdNSCoK5y9KbtO9UmMC2fQWd8jsLeUE115dT5JfyZlp+eswIm0jvZ5+56dy4xjl8/2uTz8srE6UGe1NUciRSqLai9AUqdtnwbtrIx6a1+tp1zVF0yY3Q/1y1y3WkRCEucKoAxle8e53HacRw0oPgTadFwNJ53cB+wT8wY4URQRsF3S0z2BQ9bez1nPc8MQSpd7woW5UDtccZ1OKi4lhMAOlariQKBVYOa09y6XTfKXFOh3xdoJ+R0GKvTUncNQnkVOiqQo7QIhIGhbcgh1dUQ8Ck/wxfxERT2VZSvXkMEHmVsUEJe1iNzHsVCz/nie3/1gTiUHkdvSo5Klw61YfxV7nvJkio4gOG+nNTR4rQ4TEjA8pZxwSqwBM3Sc6NrM2RrWnukdfCi0gB6uwodsL+eyt73tbW9729v70IgIu90Ov/s7v4cv/pkv4oUPvoDVaonReISD+RwnJ8cD6ZaRBBh1zuHw8FBkR1jCpW1bAMzunkwmqOsabdtiuVxgt2G5kYODA0wmE2N4r9drtG2L69evo+s6pJQMsAdg0i4xRhwcHGCz2WA6nWIymWC1Whmorc/TgKeazmg0MrC5aRqTbzk7O0NVVQghGHtdgfwYo8m+7HY7dF2HF154AWdnZ3DOmW75arXCZDLBZrMxiZfJZGJs97ZtMZvNsFwuTa98vV4DAM7Pz9E0jcnWnJ2dWZu0bWtBUVl/fYPF+QrL5RInJyeIsvB7PYbf3vYGAKW+qAEPjkHezMtSIGsIfsmXfJ/XjQVdAJozzHAR2DEQ/eJnQJFGsQvyOWGFiSmR5DVfmxJv0jKVSgE/zms+Ev7wHkc3j5ar4vcB8H4BYLeyKphEF+8Z/jNQAQRlRtrR6wKEGm7G5ekF0GSbTs23gjzFhlSf0/U9FqcPcO/Wj/HNP/wmgndIxKd3Rk2D7XaL8XiCdtdit1lju+4QwgibzRbTSY37D87w1NOHuHLlCN4BP/nxj/DavVPUtcO16SFc1eHO/RXu3nkVzz7zCcznEzzz4Y/jAx/6EMbTGULTgPoe25ZP40TyiImdHXAOJGyvLjI45X0FSoTDK9cwnR9gvTpB3O7gvcPBwQEWiwXOz84wm14FBcpBygrARkGREmzIwcjyd2W/HPSHR8ydD4MLl9gpv0vNSibj+pHvC+lPztBFvVdBtYeZ6GX6Fz81HvolAfLHvcfeLGnkctdnHYH8qPK5HpScYe2ZyEiAU0CXkFwCBZZ2SXJqJhFLOQSCnfAhwAB08ipp8vbsUSS/N+7DsM6gdV2CZFTMmza+FDYu5m3DgAuAkYE1lvDzKPXaLawmjGGKLIlxAZoTdrZqlCOjv5T/vAiqOvtvZugLZgrSzyjyLYkEPOYG0rnaziCqp0D/9BkEL4HaDH4W8KrM7YVSRga1+UUm9ZDrsUxZAXbjD8s8NhymwztzIMYLJ66s0sX5U7Sp9hNdXyuwSUQmm3YxWKKD4vFkLGzntR04fWcqReGhvpfngRKgdVnqRkB0rSBOVV14fK3im5klXaxl7HEcDDk7JyT31lGd5VmBbu6r3M9dGJ6UUPOSht6V6y7PcwSC1/HvVJaFYywx25zzBRkHeiqDiIwsZDmTLPu6GoxJb8Cz5MuLjFNMxRqGA6W7lINzamhNUfIG4Gz9lNdKcn9KohevAU1LhjqzwXW+HJQ/KTNb+njk/tq7BKIe3qRlpL94ApPKk4xDbhueIorTieKIkzutXRmEh8kEIUkfJ49EUdZ5MecpSX+yMcZzgfdBxm2uh4unHUpHBREh9jE7Gy6x7dqD6Hvb2972tre9vY9tu9vhq1/9Kr773e/iQx96AZ/57Gdw69Zt3Lx5E0dHBzg+PsZiscByucSzzz6LxWKB8XiM+XzOCzzvMRLtbwW4R6MRiAjb3Q7tbgcAODs/w3QyNWCciHVClZmtjOzpdIoYI5z3eOqpp1BVFZbLhUmzEBF8CHAxIhEDSJvNBso0B1h2RUH2GKMF8kwpYTQawTlnIPtut7MgnwTCdruDAwcYnc1mOD4+BhHh+vXrJnejmuqHBwdoRiNjyQOwfGw2G4xGI2O5932P5XKJ7XYr4P8UV69eBRE7DFbLJbq+l2CiO5ycnGCxXGG1XGGz2QxZEcUi9s3aG4EVe1D+fWKkYAibbWiTk+PwLsPPwo5mPXS7gbcccmzdwGEDjxVwUQYRFZdlhDkD6AXAXVyjDLwMupPkb/iZFcsN09Vv89Zb+rUAN/Z9kX+BXfO1yJu5h+HBC/ddrAcBzgxaScRHlHWLaKB33rxqmS+zE6PiPzQAJHOdrpdL/NZXvoI/+L3fwrRx2GzWAAjNaIRnnnkG/9U//zV87nOfwbXr19HttnC0Ruz4VMxsOsGt22f4mZ95Ac88/RTu3jvBN//wR+h6h123RTOtsNwucHp6jM32NTThCl588SP47Je/jA+8+FE04zHnCx67tgOFwAxxYrZcSqyX7B2h7Xok5zCuRDLIezz9kY9gsbiP1fYcdV3j2WeewTImLBYL1NdbxGYMgOdv6rdWzwacy08GRcj0lEtQ7FEs3ovs6EeB7JfZKL8nTACnh0A3ZZri4fp4OImiMlzGpgYfAninA4texggl+KwyEvpNcV0BpA3mMfkMLsFLwEVyLKFAcAyiyTyQvMqrSCUJ+JTeZpEGc9sgz492BpV1exH807IaICbZhS9+f8Tcya+JKABgBMu2ZAeh4pbKeNZn8C9c1yp5YRVPALxKYhRgtgHAZD8l9WL2dgAlRAFEowJnyrq1utLPh/VkgB0j7PYMfULOu6SSKJdV60VPwaQhsFqmr0/LTNncCF7mcedSBo1dMVYptwWnn3PvhZlPco1+lywIaW5jfjenIThvAHoCnEchsmP62pA5VkHnskqVtJLzVtZvBruV+c4BJ+3pouWfrA2cFNBZ+fQ3/jw7WbJzz+qamNGtOUhFRhPF4TjXdhLHAPXZ6aOv/0S5D2mfNQY1lMEMNIGljELQoJ8AkERyJYLjm0r5QXBJAeU8bu0nYLrqZXvqWsfrvJO07mzyZmCYyng2Gr7TiROaJWJCYAkqBuZV5iQxQ16irmq/tznQaX92fPJL21L6mjrR9BSAA5BF/HVsqQOBQCp/RyJZow4Uyt2Gn5Hy3CP/JUTIY/jZKfdzNR8gklEO5F0R86cYA84BiRCq4t2XO4f+BZt6IPIuJOvdR78mB7YH0fe2t73tbW97e5+agVYEPDg+xoPjY/zht76NT3/605jOZtjutgy0zGaYjEY4Pj42ne6maTAajTCZTLBYLAywVtkW55iRMZnPcHZ2hjb20K1gCAHXrl0zQFnlTBSgnkyniJQQKWGzWmK73hhb3XuPqqmxXK+M8e6dM53xruugDJu6rlHXNY6Pj9E0DbquM+318/NzALLY8h5t7LFarZi9HpOx1yeTCXa7HZbLJcbjMQDg7OyMme/BYy1SMbpZVemZ0aiRYKh30e46rNZLjEYj3Lx5E+v1Bl3b4/69B2h3HdbrNUIIiH3C+fkSt197Defn58x8N2DvCQMLjwFL9vY+Mm3iAhQBFBdwBRDm7HoDPuyzh/vJZXsOXbiy3IA/qjsbS1E2cAoUZ2BH8/cwyP1WxscjWXwP5enRH2q+qNyAlcB8sQEeMNEH5bhUJgXIcheKmMGl5XKJf/2Vr+Bg7LE4XyPGDqv1imWsJGDy9evX8aEXXkDftbj1yh3cu3OOX/mVX8Hv/e6/wj/4B/8DHB6M8eDBA7z6yh385Cc/RtcDXWqx7bbY9REJDvOjGr/zm7+N/8n/4t/D5z73WfjJNSx3CbHdgeDQUwKEhQ54VFVA30fE2PO+MyYgOCRyLJW12+HzX/gC7v3xd7G6ewuTwxk+9KEP4aV799G2LXa7HRAifDXCZDQBFtthGxQsxodb7f2CgP+UzQ0wA+urTtCMN/uWKHHQ4eePBtLfCeMpLgf2zHrMw+uIaZT8O8rJSn6SAOeeEImZpwmA0LBNnzoSIThAlCngkOM5vFXzIQdaL0Hyx7F/dT3CzNJHS8lcBFYvOhTJ5rkC2JYxF7zjOpWvvADhWQIHgHMMIoLBQWWqD56PDDbn95F8KOXLWsWSiXKeBQOo+ncGAGkIjulvAvKVwVapBCThRC4994/sYBrO2SE4g90RIAB5KWHB6ZXt5BwV9SwSE/aZFy1myT8UlNSAt/w0HyBPHfapZKCjOgHkjUpelpFKlyfBAQmmO08R2ho8H2RJQu5D0p/IcxmFoa7tPhj01qEURAW8C1mOC+WSxBug7ATEZc+9REKVyLCDU0gW2DIHYvUEJESQ1L8C9PpAlTgpT2LYPFeA2Hqtd7kdPYAAQqgIdeVRVwFBgHVmmic4z/00RUk/5TnGe5/ryXwLBVityL2TAMjWV0onAqwdnHN8ygU6DyRApVwU9ZUyMOnIi/64gP/k7fm8xkoGTOvSxcRgCIUWPGwc5GdlKRhCHschSN5tuDrpW3kscRwT2BgHgKAOOOg5ARnxdh/J+E4AOcQkAL6cpnDOSTwgbWddR+b1pfMBznloQHLzUOjIiVmWy+ZZnd+1n17CHF3yjfff/t9/7VIJvtP2Vr3Zb3TfO70QuOzzLlPeP4lFzKWOm+1tb3t7rL3ZMfMP/+df/inl5N1texbtkzPvPZ599hk8++yzmM1mqILH0dERbty4YTIpIQTUdW0btmvXrmE+n2O1Wpm2+M2bN3F+fs6SL0SYjiemNa5A+3w+R1VVIgGzRF3X8KKPvmt3aLc7k2OZHxxg2+5wfs7MxQDeJIbAx0lDCIOAodvtFnVdG1NcpVQ02OhoNEIfI7rIcjCr1QpNVSPIwlcDfmpQUmXMd10nK2xni2SVY2Ft9A7bLcvCbDct6iaYnMzifImmGVmQVJWx6fseJ6enWG/WP5U2fT+9ey9blvdTmfe2t73tbW9/+uzv//2/P2AhD52HCjAOQXRjTV6wzOzm+0vJD/2d2czKhi3AegPcU8HuLFNPglpl9IoAY1yXXhnD5sv7XanlnPNbypEYaDdwIlDxPAVPM5hf/GD3sX3l4RAYrBawV39nGQsvrNbsxEhEBsARRaTYwxGz85UFrsAgkeZT6zFLUznPwLg+y0BGIvub2ekS9VpRXmHCDnzAlDWfiSIIGSyGBL/kpDl/zsqYQWQtHwON3pjf6nBPVLSf5SX/Wa61DHwXz0gqdNvF2wDl/LuiXMa+F/D94gmVITOZ42Uo5AsiRBIZsdJhQ9l5okE5s3NI61tAajCz3EsA0BD4BIGyzAdjJamDO8IH+T6JxIvPwTn1ZIblxbKU2fcAM+1DEHBe2+ICqCyJcL+S/LgUuT8mHf/FPU7T0nGRv88a7vnfxeVyKfPzuLbQv8t2V4a3I+7DlGigle/stJ6w2yEnyZSQpM4Oyn3HTh243KYck0H7du7PKSWoM02b2SRzHGTsMfveez55oex+jomgJyEERI9kmvgp8dz3H/+v/x94PXtPM9FL3Z6fRtrvtD0pIP2d3lBebIf9hnZve9vb3t79llLCq6/ewu3br2E6neDjH/sYxuMxCBDN7zmccwYyp5RwfHyMrusQY0QIwaRMxuMxA81wmE4mmEwm9oz1eo3VagXvPTabDa5cuWLs9I3oprdtawFAd7stnPc4PDzkgJ+7Fk7ypAFEFTgfjUaYzWYWTFTBf2WpX7lyhVngMaJ2HFR1Op2i8gG9BC7dbDbGLtd32cHBAYPtowab3Q7z+dwCnWre23aHlKKA5Dtstr3V1Wg0AsAa62dnZ0iUsFqtsN1sURwsfSK2f+fubW9729ve9vbeNQ2CCGEYK7iTGc9ACZgzWKsgc8HizFeATLJi+H1mug+ZzYLaQYH7VCC5mobKwxANJVU0LOAbydGoXnZpJRO9ZKabTI13ApI6cIDBSoAxXoP60CD4ClVdIVQ1QuCgy6Gu4ENA8DWzU61m8nMSEWKfQKlnCZuUEPsefb9DjLrWZVAtxQ6JeoASkpA0zClhMhAX2iArs5dPh0b3Lh0KgxNiAjxbXZEyjxOQlC2rgLOAtD7fz46NofTMQ+CqdK4kbe+dK07/FBA6kTGF9b6LUiXeFUE8FUB34HgMwlRmUJlM9kMSsD7C4CVEVgMoOz9BHD9OScUMjLrsL4HewsXLToSqCqi8AOUgBrJ9Bso1YKeB81I+XwUpn+wNHDH5XfqitxNzespCAm5exCepqHtOphhXKgxH+XPvrVBKRCLycMFZPjhFlmzRMVzE5gQlDqprDggQLlZWHptDJ5sC+yqZovlU03vUiQRioNxLEGJf3K9gdxLRc+dz/Trif8GZwBJXUErZ6eQLh4Q5YwjBO45ToGkNnD7Sb4j49EJ0QHDiiCgdQS73S3OycT37sv0eY+9pEH1ve9vb3va2t729PUspYblc4fe+/g343/d46qmbeO655/DpT/8sAGC1XGI8bjAejy2oZggBJycnmM/n2AnI7JzDZDxGnyJmsxkAXvjUoxFiZIC5GY9Q1RzgczydoI09XAqYN6y/rtIxypzZbVjmRRdy4/EYMUYcHh6aBrkuvieTCYiYIdI0DXa7HdbrNbbbrWmoExH6lJBEAmEymaDtOkxnU2w2G/QpIlLCeruFryrbLCyXS2acC1jP8jQVvKtw7/wBFoslXODNQqgCdl2L8/MFFosFBxKNSfelbE/IT78H0Pe2t73tbW97e29bCF50yAEGIwXYUwyWMhxWMo6VrfnwWoC/N5ZnUnBcQTFl3hYsaAU/jfwtIH0BtJXs8BzcOgP5+enJmKFEydZ05XO4LJ7XUj4YK7yqGQj3oUFV1aiaClXVIFT8tw8VQqjgXDC5Pq0Y1V1OlASMZeDTZCKSaB+nCPgERAbsIxRIpQKkDAAknRiFlR75s9gbG9xkRErk2UBQ/ZlBZ2csZgBOWOmGXRfse2XaIqflnIOTU5lOgr4qw9fcMIIku6LPDMH0zNDOC9MkEjhZupBA8MK6VujfmO3WLwQwFSCeinLCgWNYDDjWCjlHvt76SJYw8gqKMqXY2iUE1UQXMNQFAaXZ8eQdA/TBO5Zbkn7NzgVxTg3AbbI60BMF2jDcBiwpojFQDDPX66RGS9a/gq96cjal0hFGML+U9hcZA8as13pTxrbq9Dtnzh/RI0KkHur7cgAHD9U8OHYa2DThRYuKyMZjCaDHlE935G6RrIzD0y2wceM5iqi1LQgcAJU4P+rZ8Mr0R2a9mwNJ6kBdPj4Eu4YdFiXg71SZBWQKXpmZzuMru5fKsQTnuA85lRbi4LJcdJHLkfEXL7G12oPoe9vb3va2t73tDQBED/0ujo9PEEU3fDwe42A+Q103mM1GaNsOs1mNpmlw//59TKczEBEODw+x3myw3e14ISTM7hACur6XjVHAbrfDarXC4eEhmqYBADRVhaZm4FtlU7quw40bN7BarYw1pBIybduCiDCdTjGZTHBycoLtdoumaeze8XiMuuZ8bjYbHBwcYLvd4uzszNj0bdsys323QyLCTkDy0ajGcrlkNnrTYDabmayN3rvd7HBycordrsVkMkE9Yqb7ZrPFrVu3DLQHkBfce/B8b3vb2972tre9FdZ3rcLUCuUMMCsSMJII0OCfgICVqokt9+vPhwNwQgBl5Hs1kGoigPJpPH0mUJBXFSWTD03XGwDLljDA6p1HqJj9XYWG2eF1hSpUAoSz5F5VjeB9BV8FZoyHwLrOYMeAwq1Z41r1vZMBdgoq8nfJpFhSZEZ5ij1SH5H6iD626GOH1HdIqUXse8TYgVJCHzsgRQHLCwBeA0Yq3EcqRxEHdeU0cGNRdwqKPkpOJ+tVK5Ap4Kv3RT0r+OuMjTs8naCq35xmQg4CCQX4FUhX4FEbsgCsNe3MJifWrvZB2Necy+A9s8glDSVOO/tdgFZSsFTK4Mr+ye3nDJbnwNTDxbEDkJA9SFn+xED2iqVyKi8yKd6zLEvwBkKrY0NBepWScfIIz9RjHQyP8gUBxAFes/SKM5C/PD1RnvIAUJyK5bSVUW0OKfV/UVK5c8PmqZdyG2NfgXpxmEgmK9VBl77pKOQe4dgZ5BIztlNKiCla8Fxxs2SfwoB5zWmw5MrQyZG9Pdxvkn0u/jGvTgBNM4D1+BUUz+UBhrEkiLhNytgKJfdIpZKS1CPJyQSn3ducaRhINAG5/dSZpqcQoozv7CSgYb28ju1B9L3tbW9729ve9mZGRGjbFr//+78P7z0+8IEP4Auf+zzu3r2Hl196BR/5yIu4f/8erl69ivl8jrqukRLh9PQUAAxoHo/HmEwmzDofj7HZbHB6empSMPfu3UNKCVevXmW2kTC8Y+TNyXQ6hUqjaEDTzWbD2uvgRWEIAffv38d8PkcIwXTN5/M5+r7H6ekpvPeo6xrb7RZt2+Kpp54y/fTNZoP1eoUoC9+maXB8fIzpdIqUEmYzdhCsVivsdqzTvlgsZCEfEGNE3/e4d/8e1psN1uu1HUHWutQ6eVJts7e97W1ve9vb3t5fZpzglAyoBGCAOsGZJncG+zQIHz0iJTIdYGWeGkMYxbpEAUHK0FWWOPAsm+I9fKgRAgeHDwqOVzWqpkYINeqqggsBIdTGEHcuMDirrGUI6OiEQktuWNZEHNw1JWa6E4zpnYiDGKfUI/YdYuzRxx0o9ohdj77vDBiPXY9EEZSiMPwF1BXJGgY1mQVuzgvKzFjjTzth/ioj1sm1YKmLMgbhAFTX36kA8ITVrZ/nXwqAr/yME4L9MIyarM4KTHbwrJyHos4lXZJ7ckBagoLn2rUSEQezLHFTIqSYTMaG2zGx7rQbBiqFAZHDYJ9W6qK/mhyHFUYDRIpkCwgezDD33jPTXIOvmn9AEXAOzOqMLS/pF2Vw3vHpUM2HoOoa1JPb1Q3yOWgbIguoS0lOWfhhQOBcTulThYyIo9wejJEneOeM9Q+RaSnBaDvVgWSnLVTSJ6kjQ+aBhKwXzmzsDLqTp5wxp3WYnTXqYEgiW8NjT29JuZal4i9KCJm8UYxgrXTtQ8lOBOhcZIB54RzR+jWmvFynGveDvZSXzBbeD74n5d+LadHOdBTjw06xFO1bluP17F0Boj9qc/mozP9J6JS/k/akyvduqKd3Qx4u2mX71MXr3mpZnpTG/WXTumw+/6TLtweB9ra3946llPDyyy/jlZdfAcBHFL/17W/h5lM38NGf+ShG4xHqusYzTz+D+XyOtmP9cg3+qYFJz87OAMDY4KpXXtc17t+/D6oTzs/OdCmPZtSgazu89PJLaJoGfd+jqmo4x0C3c45PgwJoJHBoVddw3qGPEa/duYO6qnD12jUsl0tsREu9bhqcLxZIKWK9XuP09JT5WgTM53Os1yt0XYuzsw6j0QhNMwJRxHa3w8nJCZYLZqev12ucn59js9kKKx5QZhaQ570n+S4cLsyfzDy+t73tbW9729ve/mSt6zsYSoocElAZv5DfGYyVK1R2RLSOM2tVgzmy3kFwLLPBQfYCS6eECsEH0Q5nQLypG2aPVw1CXSP4WjTHA4PpLhi4yJCfgp/8CetFs4xLxqSSBRFMlJB6AiHy75FAkRmyKUbEPiKmHWJklnjfCRgeGRjvY48UWwbmKCFRBJBBvqLSMrOY4UckSihkxJUcC+eSXZ4ZzEJQ1ioO/IcCfd6bjoSB4/ZcpRRr8Z2wwY3VPoDIc5alLWOStApt8hyuUYHPDHjrx9w9mEUbibXF2U/hssa45tk76VdZKqMEDrn9kjkv4ABPzsgmAAPwDIoqGJ3B/ovrX+4eWfOfs6wMZwgYy9Vs0ijwOfAnaKCz7eXUg/g2MgiLoUNGxwT3vQzAWhBMAdd9UCcPGOwu6nPQzjq+vAMSweUMMOBMhFIn3xISEJyzxI6c4A0hF312D+dUjiUIlszOJkpZI94VzwH6Ydspy/zCfsAcYhi2i51UkDRi5DaPMQqOnQHyEJzMHY7rC0l8HiyHkk/dKoucWfLw2UHiAKSYLL9R24SyZJB33hwk2p9yT5VadfoEL82W5WY0zwQNIIrCYaQOkdwvBv3FnIfI4+917F0BogPDDeDjQLb38ybxSZbtT7qe/qSf/zi7TL96kn3vSYHobwZ0fqtpvZPl24Poj7Z9Pe3t3Wy6jetjj7v37uHuvXv47ne/h6eeegqf+MQncPfufUwmE8xmU4xHIzz77DOIMeL+/QfY7ba4cuUqzs/PkFLCfD5HSsnkUWKMWC6XAIDVamVagjFGTKdTTKdTqEZjXdcMZi+XJukCAKPRCIeHh6iqCkSEZjxC13W4f/wAu93OWOtxs5YAoR0fJ25qpJQwrmus1yzd4n3AarXCYrHAK6/ewm7XYrFgfXMNbrrdbh8GslEE5XmSdf8Gc8G79Z27t73tbW9729ve3tgMTBWaeMZ1ss5yZoA6kdYI/L33cF6CaYYKVc1yKU1Ts3SKb+BDYCZ5VcH5CnDCdrVnqzAIAfAZhFbaqxgJkzsRMfidEhIxmBf7yEzw1COlDklO6lGMiH3HUioxIsYty6zEhJQiUhIwMLGMSkp9zlUqni8AHUmQQqsfnzWtVdzECbCtMjAGcDtltHq9XQJdCqOYcb8LDFUGor0CdJRZzMruhj5PbyEyYn++Th+hYK+zZyYqQexMFC7XlKpvb+Ct9AdrHQFYy789JFiiV71n/qqsQqRStRzI0jwYBGJ0erpA03QMhpeSIyX1t5Qz1HyWJyGCZ3CzErC88gKKCiCqwLmWM8Ys7wJwYM8MhhJ6cSx40kbMQSw1L5l9b1VkXZy0Pl1+DAESeLVsN8mbl3amBCJvdZ8Ba86XD+y8YpJ0ln5R6SFu1nyaxEH7v3aGXAfWzikBXmVZJLCrMvAN1Fe6vIDHSfNLFwB/Z+3rK6CqKngFsmVM5UrJsk0DaSKvreLyNfbo4nSCl3IX+xb9VV0s3OdSSSKX9i7brXQEpFxXEIeEtm15HSRGxIUYEs5p8FZYnyllZh5n7xoQfW9729ve9ra3vb13LMaI27dv4/bt2/De4/DwEIeHBzg6PISDw3Q2xc2nnsKVK1cQE2G33WA2m2Oz2WKxOEff97h27ZpokI9Qy8YvpYT1em2SKq+99ho2mw2apgERYbvdYjQaYTabMXuBCE3DwUrX6zWOj4+x2+1Q17Uw2CtcvXoVi8UCy+USKhEzGo1wdnaO0WiM5XKNqqpw+9ZLWCyXHEhUAPPdruXgoAOGyetbubh8Pdb4W3WW7YHzve1tb3vb297eHzaeTBjodgFVYAZ4VdXwVSX64Syhwn/XqAJLp8CzrAgxGmyMTWUqqwZwgckbWE+JNYkV0E0EAcWTSKd0Aox3iKlD27X8d9ejjx36vhcG+Q4gBsM58CZllniha02kayiR9xAs2ATwMiEUKk1hEJ9zhYzFBdKRBfZk5reyyYOwW0vwloSVqux4Jj8o4Ml5SERATPkz5LWcAaTytwJ4BSRo1xW5H7C8GZTNz1OwHy5/7+Q7dQkom9k5AEHZ8s5AVOe4DwzWlEW7p5QGUoNwhfb0gKbOIKRXhq+vhkCk3AewtIsT3X0UZVVHgfcSRNWJFIsDvCdUQfW9nTH+QQRHccCIZ+A0g/OhAMCt7QoUXDFj5wDysGcrMJ7bu2hXBbRjKvqVpinAu9aF1lUBDGeJJAfyAc4Fc35wOVhzGzFy3aXCASOeEj1dopIv+l+TlrE60Com+MCF1BMeypzOEkUEeOLBJb4IPgzgjJmthKXcJTh/zqmMS0JKwhKnvP/hutRTIHxiAA6ZTY48zpwTeaokrHpQjnsg84M6dDgIK7e5Og7VsaGsea4CyZ/jwoUQpIC5DpSZD6h+PThugQ/aSwfzCOedJI6tuwwRfQ+i721ve9vb3va2t7dnKSWcnp7i7PQUr+jixEGOAfMi56mbN3Ht2lV86EMv4IUXXkDbtrh16xZvxCRoqC5qNCjpaDTCdDoVVlWDrustGOnJyQmICHVdY7PZYLvdwvuA6XSCo6MjY1pst1u8+uqrWK1W8N5jPp/j1VduwfuAk5MTLBYLhCrg7OwMq+UKvbCr3oqMVrnZeBSArt/tbW9729ve9ra3vQHAJ37252Xd4wFyAhAChoAJ+1s54yb3kBTqJZCLcBIclLE+AaKV8U0cWDDFhD726LsWXb9jlnjXcoyXTgJupo7Z5CmBYg/okwUvLfnpMSWjTpdSFpnBmlmspcZ0KclRApuUqAAOFewu1lumhSzM1wv3Z3a2gruUQTgiJHUkiB67l7+HgCJAlIF/79xD13hxWjgHYUXnuhkAjpr3TMyG8prVQcD5yOxdZtNGK7tpU5fMYukjJahvD6GL69NcD/oM5Z8ryJgblZBSBvmJRP5E6wYAiIH2Ekj23sF7QoAC5nxKAo7lQFxRAQ4X60NATpf3D45gsivWLuqkQGaYq152qWcPAVHJa9m5gEn6gLUJYIztBIKnrL+t8jsAEHwQx47WWUmGgUmHKHk+M9+5nFqOlCvQwF47PVH57BCiHM8gA/8KCqs8SzTQXMFiOGG3E4BY9H/rjHShb2j98Tg1VjfUCUQWkLR0GARxrhjbu3BecfUl9CILAyILaGryLjJQVKjFUXaSWBf3Q0BdNdO135YSMsqw589zPpklL/kDywaRNELpGNMhkmIazCWvZ+8ZEH3PuvrTasULwax8IV/eHgdo/LTsMs97W/36ITfZOztG3mr53ul22Nve9vbOmS3XZKEWC+bLT156Ca+88gq+9e3v4OBgjmvXrqGualR1hel0gueffx6TyRQzkXupQoXTs1P87u/+rrCwAsbjMaq6xnw+R9d12G13iLHn4KFNg6ZpsFqvcffePaxWawtUevXqNbRdxJ07r6LdtVgul9judmh3O+hxxsstm7K93vxdznP79cve9ra3ve1tb3t7nIUwMrwcyOTgUnYh6b+UQDGBUkQfOdhmjJHBrr5H7Dfo+xZ936HvWF6FYoeYIqIEhgQlZolTAjlXPJfgQEgCuJnet8uwtOkOEz2CVZ3XUcq01fWVF4asYmia3kXAV2VTvFfg2MMr4OZUloTscQxy60NLcA2SN2FPC5ipAKoypqkoJwc8LZEHMlCvkFRXGI/LnwqgUoB5u07kTwDHLPmCZKJXZqcIRMqCAJcM0FTNaS9pmS69ORqUQVzmGkOAEDDZG77PWwkcCvkNFIx3KKAPAEnKXwLvGujTo6oYoKy8N6cEtysV6Znb4EL+Mfg5YF4js+itXQXQJXWeEOvwK5Nbdx0evAdx8AyseoFsxSkA75EuBE9NwtTW8ilsaw4pynni+qXsFHBOrkOR90yoyWUgwLFkpYLf3nsI4p/rB5T7qbYZScHAYHGWJpFgpzpmCHAuSJvSoC84FIxzA54jlzKqfr4DEQeMdSFZHyBy8AhQLI6QJC1hcycvfSbAe1idO3FgpLIeHJ8W8UV/cyITpGx6Lq7mU8bDADAnxJ5PjZhkjHOgsg8nsjrk5/MAV2mf8oSGphGqYVs8yi4Nol/UEPpp2sXBs7c/zUYPg8X01vrGZfrVk+x7TyqtR6eTHsbML4H/vBvKd5n7qJgg3+7zBsfXfgr2RnPjo8pyGdvPgXt7P1pMCbFt8eDBMR48OB4snH/v976Bo6MjTKdT3LhxA5PJBCcnJ3ju2WdQ1xWOj49x9+5dnC+WWK/XiDHK4r0qNk2cXtM0WCyWqOsaq9XK5oHyKOXQnhyAXl5jTJP9eN7b3va2t73tbW+PsO1uiz7tEPuE1LXoupYDaXY5qGbfdyKxwgxxR5HZsollF5Sp7pyAd8gsYmCIcSuEShZkErYMUgDW0Dggy14Qg+F6XVLGrXMiz1LIZThVk2CgOTmWtvAAgneDdVtmX+f9nncsASFJCSmDr00ggwc0aKDgtHy/gcWqPS3UZjDI6ABhx8p6ThwUTsqigSi9y0xg/Z4M3oPdT8ValiVKmNWrjgCu04JcoQ4IaPxFIQgGQXQd4FAZ0zifbrQW4udfCBqq5kQyIwOx3kBhyTUAh0SukPQRSJ8rBwDBi255cAFVVUizgLW+tY/BCetaAFsPkXOBH3Q6Z/otkheXAV2W/biohV2ApgWIrkR2lR5xDqCU4JznoJ2eyxyc3pIG5da+7QswXs2AeXF2EDhtk3whGLM5qZ8IXpjqGDKZL5BpSszA9j8ig5JBepZZyoCy5E/3Lyk7kvh5BXtcWdaq8y817WSMcNwBMlBfy5v3KNJuTqV4BIwnZW5n2Ru+DwjBW3BUUUaCZomDlHJ/kEKLnnwe6yXDfmBSF9w+BdBNue9niSdfOBNkfHAH4bnCyWgQmSjvHKI4gWy+kf4sFY43svcME31ve9vb3va2t729v6xkE7Rth3v37gMAfvKTn9g13/n2t+0oZUx586JWEJDeEXszgHi52Hwr97/de/e2t73tbW9729u72773zX/DcisC4CVi1qcTinIJThMgMgeZRMsgLwABfD0cooLqqQDs5FpyGRRTqZKhbEYBNoPgKBloRikKFI7BAkwBKkjeCE7AMwdfFUCiEyDMyiTM0Qt1olIm/ByHIHrF3nkBtDPwZox5A9GELSwphyKdpCxV7+AUQPSefzf5Fk7LHBHOozIAVJ0VAk5aRTmrf2Yoe8A0wx3IM7tX2zMV7ZmIBuBgac4JGOqY5et9Zjgz6JmZ0wYSQ9t0yKC2vAGAI+YUK4gvzOAQGCRXAFelXOQSCSLpcr9R9jaA5MBa/SRyNyVr2DEgikKyRBBqAXmdtWFMGchWR42CrkGAT1KWdq1BYqUPaTDU4K1ferCsTIpkTPmEIrDmBUcSM/XJAquSSBZxX5EeZ/dCxqsGpeQGYYfRcA+goC3LrvhizBEo6XjJzPuSyX5xL+DEWVBEsDVnAyWysohXBFqJzjkgcTBXSysUeZEf2r7ZCyROEPVnqIMiRW54CBseOsZd7jvF6Nb5yjntQ9zRnCNzNDmrXzJd9rLOORseiqIPWfX8GdlcpA4lwCHkgLXmVBInWczj7jLkxz2Ivre97W1ve9vb3t61loiQRJPzUfZOgef2vLdwsuRJ3b+Xwtrb3va2t73t7f1lu+06szgp6x8DAoc6X2BiDpGSAcfKZGXMKAPPBh45w8D4egWcDBRnhncSMNPAKBTgtMhWeAS4KsuCqMQCPzMzVhVxzQxn/kXlNCTKoQCfmrvMhh0yozNrmZMheAxlIRjn4wp0yoyW8vP/vZZGsibgrOZV68t7BvaoAEmLemcgzwE+FG2TJTWU/Z5Ba34qB2uVmioAWSdBTpVl7r2H8yHXFZThXYLkhBAAlCAwYHlQ4RkFKRX45Ker7jjfFYLIiTgy/WmvjHF7Hj+Lq0kgUUkzOE1ph7IAAAxeSURBVAekghAjbGcFlqHsfU/MTk6O9dblFIAvmPkKripYq4BzcCrxon3bF+A0ADnVoKxnl1gmhaIzoNQhA8KRElIkZHXtYh/hGJT2xRi4yCbnFD1CJY4i70XiJJ88NbmcC/fq04iSxvjMnUWu5z894FLBoH74RKtK/6hEkQ8ipSJziPpTFEw2XXNpP2ffS6+SeUDbMWm+S2eWzgq5wqy8DoCvoCR/mLQn+BSJd976sqYUZR7SMcjSPPlzza8GPHY2uFiex4sevJbXIbP3nXc5n/IYdszIXOH0dAsD9SmmoeTOG9geRN/b3va2t73tbW/vAtsDxHvb2972tre97e1PnzlIoEgFigzIYyCxBNYZ/UqAc0a8VcLpgJUJAlIcgF5QhrD8TcgB/Jhl7EEC6hqD1knQQ0HejMz5EMMUxhKWpxlYSQqsFqXQHCiSp0ESAWWrAio9UZbLWKRSXwSABJj3xVpSQcpSR12gdgbWinxq4EvOo7Jble2egTUD4ZCBX6sr12cguETThUnvnRepkSEwyr8LMO+ouK0A9K3NIJIbWn8sY+I8t5FKhrAUi7CCRYKFnSqE4EXqBVznpietIKlzg7wNygggdWmQNwsJWbQHIERhYSirJekLGRznmg3OwwdJKRQgtLQ/A/MEIHHMTOYrI/WZre0ApBjtOYlkmDBib6Axt6C0E0opDwAexnaHgPQOLsv/QJ1HjFhTIkSKkodovbrs5cO2FoeEDqLiDriybwSp82h9QdMyWRgHqTOuCz5NUfQZfY7+Dv0uFQ+VNksqHcQNp2NPL/Se5wfxpnEdJ+0XAk4X7XyR9EMoAtZqOUW+h0xaJvvKgkm+eMlLDjzKKlQyXwIDxrv21dSnQR60TlLSfJA4egoHl9SFw/A0yKNsD6LvrTDVi1JzuDgJ8PcXO5Z/xDWX8+IM7VHPe1w+f1oms+fr2qPKd5n7HpfWT6s+L2uXafcned/l7Elqt+9tb3vb2972tre97W1ve9vbu84UFCvANmPJCtCTGaAC4AqIzighDAgtFIgFYGKWLANoysJ0cPZpBpCNQSpoI4PIGdDmrAlAf4Gtq7h/FGCsjGWV9Z9LNq9YIsu/BhA0QNOqx+fvjREOKPtXGdClHrXpiFs9ZFkHgNHlIExeoTQLuOisrow1bO0iIHJKAjlrftSxIHXrh3tP53JNZyA3G+dbg1JmPW69UB0cCj56YWZ7aUfvVdaFNcvhWHde28uLgyElym1PF5+vjOOhtMVQ5oe/D3L6IbgspcKnBryUj3EL1mTnEwrKINYIrYzVFtIuToBWAlwXwQFuc0ZVWgVgprIB+tru2keF6R61rFInzLIv9OEFwC+hjHxKQ/u1Dj0JxKt1IeOE7x8yuJ20twHSBT7CY8hzW4OGwLUmDK0/eZZ3XM+WWenDpWNMPzXdfQGTS1Bb8yplo0TGSKcLdQCtm6JkiVgaisF6aW9kx4QRuO0+B4nTa04fY+obYO3tVENSB07J3hdHRhLdeyDHQADpiYZiTHIGcxwI7V82N4j2vuRLAw6D5MSH03nrjSHyPYi+t8LIOj7/WYyEx10jH71hWpd6/KOe9wj7aWKil8apL9bV23jgT6s+32oeLtsOb/m+N7YnCXzvQfS97W1ve9vb3va2t73tbW/vRosxZgBPzNnvBZGKBKgEDNAzYJo1DBjGM+ZyAS46wPtgOstDhnXeKymj1wnLncGxYTyaEqDO5swZ4IrrABjTFCVLXgE1ZbEqYGxMbS470ePSyrIlDFASVB+6BOK8goJFntRRwMBgBtmsHg34TAXg6uR5XvLMTPXyeVY/eFjlfRAAsgA6ncuyKgaHOg/ntZ2YeazK8d7DtObNWVIw07VO2PkiQShdRPG1tdtAHsM5CXLqjX3NwPagEAwQq3Z1EdDxgjsGAMsOaaBHogSXwKo/YD16xkK1PzjrY8pg1/+yb0E7owSWFccKOw8Ca3AbKK4YRXYkkLVZAiUZEG7Y761flwxmIDtI1JniyeoNcBa4U51JIFecnkjIpwwgALVXfwoc+ay9rsz+IgCpDyE7Jv7/7d3LkhtJCIVhyOr3f2GrklnAgSz1yJ6VxxH+v41vatVVCseBIo8nI/pz2XPbdT7tX7l2QPeZ54KgXfyw+vjGUfQyjW7K+/e4LHVveb0+z7dftX+R+7MUklvM+5ddA9R7wVbTucmN7PoO609iLZa8/di/+lVPuOwalRQ1rmffu2fU93lQwceyCDRXUQH6zOH/GUJ0AAAAAACA30wzqXuObweq1kHtdKmaTRCbOvitTu78u3zd2RU987efow7yfc+Zzur6Pp+OnrBUQZe2kUGp5yKD3aE+iZ46qzWaoX4s/+2ta/vYoyO8/h5Ua/HTbRquYhViPn9OJ63D0Np+WM6Vj60iw4R9Z8e92mkzSNS5q8Cu98UmPLTjAnrY0izvs7vcVaw4OqLPa1Zhtq8aveK12Gklmjm2xfu1mk9/dszn/uQs6iygaAZHaIcf58jdzbSQpVVncBz3hWV4v+3o1t/3FCV21GKp85RAB666xhEWPyYc1/XK3dI1mkBaBZAuIriZ+conCDoDv47rf1yRLsKEUvl63yOE7ttj/qDwu++7Oo4d28J2z+He3TUdPUNdx2g+hQ3N/T87sDUQPd/zPgoRZubXlLXceuRJB8a1RpTbdKcr8s4g2x6hfO9TTEg8QXoW1iaU9/xcaPHX6m4/kvMpTK0pFKhooSMMUyGgvpd87vkuFXkn9vXNUqF5nbNLY2r2roVmFay/+umD+3V3YWEt73uvC3xudr/23G/H561Hv9TpsQgLzz0hRAcAAAAAAPgDZT66J9ir/DoDTIVOM5u6w0GzIzh9hoE9s/r1XJSwA87jNVbbn4BL28ikUnGiXvfcnjqyNYbk+TNrrQ58lXa+j3p5D8hnX3e9//M4z2NVQJ1ZnveIkMdrfLq+Z5/fg/lzlMRcmTymCudMye3OY4p1dD/vOcTYpvGsceyPFlZcPcN5ChHL9e9Rv69TuSrwM4XZbv5lz053r78/Wn17JIx+3xnt+nYeH9fCJjjPOda7Q9gco7J7EO1bv3Y/JaAu477W8XxqQdvc+xwNG73o6VpXh7Wrz6kOwDvcV8XpiOUrFJ5jsKNAEhG2rjCryfke3++nvJKh3HdC1+pg7ute7239GhU1KoytgkcGvjvvj2N7Z6C9Lk2otypkZBCe6xBUB7aFpjrVCXnleyy3e4dpor3XdjPnnkV6zwuwfMYj7XtmhecpuHvfcg56dqy7ijhm/Z2UY5asZ9mr2GLH+KW+TCqW7BzXlON1rgzP3WpW+V3z87ft/cP2rULXtq3Z8Arb9RnODdvrNusLMrF813LymOvJD30fWRYj3OvJhjWLBP8KIToAAAAAAMBv9hw9Wd2QHSpNIHwu9Pf83dH1Wp3mVr3DZuqa1niJ3IbGNHzftvI/rwA3TIFwzvbWaIozgKwRFepSd3XDegex30Jrn1Er6kCdRQ+9j1vbCsvRDKs7hL07mestjvc+/hzW4ex9n7PNq4u3O5rNrut6a2mOfo2ZV6fqhPLLo0NA13nuzvJZRNWX27WWXZcW/JyFEK9rRqc8rkR3nc8s7tWdtK4qS/+M1xgczaEOFTIqYNc5mQKDwv6j4BLxWOQxaha4YsUppkRvK++Tq8L9r+6A33vPvHKvwPoovrjrfGeAea3aaC+BahXy7rn+ngH2juhA30Md6lW42DqfkeF0BfaxPfvHuztb52SezAjVelTY6ZfW+T/u+SnM1PvYBOfrymuzo+aO21txo46zu9HvOMbe6GmRCaXXtXq/15WjYPY998nSvqigVQG/Ci+xjwKUm937fhRhZmRRmPtX3pPLu5iR77Trvc3cr3yCZG+bz1/U8egei+7et3jeQRZmrzDLRVPD7nvbjv0oKqmAlce77PKvPpb5jNTaBHUOvYLy3EQuyKzvjOcTNvmz17Edc415mYD9Zzw+lf4AAPjDPP+zD+Bvx39jAQAAAPwO69cvAQAAAAAAAADg70SIDgAAAAAAAADAB4ToAAAAAAAAAAB8QIgOAAAAAAAAAMAHX//3DgAA8F+xiCAAAAAAAPjd6EQHAAAAAAAAAOADQnQAAAAAAAAAAD4gRAcAAAAAAAAA4ANCdAAAAAAAAAAAPiBEBwAAAAAAAADgA0J0AAAAAAAAAAA+IEQHAAAAAAAAAOADQnQAAAAAAAAAAD4gRAcAAAAAAAAA4IN/ANWLMMImGw+tAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import json\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "from torchvision import transforms, models\n",
    "\n",
    "# List of test images:\n",
    "image_list = [\"Plane.jpg\", \"Moon.jpg\", \"eagle.jpeg\", \"Audi_Q8.jpg\"]\n",
    "\n",
    "# Prepare the labels\n",
    "with open(\"imagenet-simple-labels.json\") as f:\n",
    "    labels = json.load(f)\n",
    "\n",
    "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n",
    "data_transform = transforms.Compose([\n",
    "    transforms.Resize((224, 224)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "])\n",
    "\n",
    "# Load model\n",
    "model_3 = models.resnet50(pretrained=True)\n",
    "model_3.eval()\n",
    "\n",
    "# Set up subplots\n",
    "fig, axes = plt.subplots(1, len(image_list), figsize=(15, 3))\n",
    "\n",
    "# Loop through the image list\n",
    "for i, image_path in enumerate(image_list):\n",
    "    # Load image\n",
    "    image = Image.open(image_path)\n",
    "    \n",
    "    # Apply transformations\n",
    "    image_tensor = data_transform(image).unsqueeze(0)\n",
    "    \n",
    "    # Perform inference with the model\n",
    "    with torch.no_grad():\n",
    "        out = model_3(image_tensor)\n",
    "    \n",
    "    # Display the image with its predicted class\n",
    "    axes[i].imshow(image)\n",
    "    axes[i].set_title(f\"Predicted class: {labels[out.argmax()]}\")\n",
    "    axes[i].axis(\"off\")\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Experiments:Study the code and the results obtained. Possibly add other images downloaded from the internet.What is the size of the model? Quantize it and then check if the model is still able to correctly classify the other images.Experiment with other pre-trained CNN models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Exercise 4: Transfer Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n",
    "Download and unzip in your working directory the dataset available at the address :https://download.pytorch.org/tutorial/hymenoptera_data.zipExecute the following code in order to display some images of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Data augmentation and normalization for training\n",
    "# Just normalization for validation\n",
    "data_transforms = {\n",
    "    \"train\": transforms.Compose(\n",
    "        [\n",
    "            transforms.RandomResizedCrop(\n",
    "                224\n",
    "            ),  # ImageNet models were trained on 224x224 images\n",
    "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
    "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
    "            transforms.Normalize(\n",
    "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
    "            ),  # ImageNet models expect this norm\n",
    "        ]\n",
    "    ),\n",
    "    \"val\": transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(256),\n",
    "            transforms.CenterCrop(224),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "        ]\n",
    "    \n",
    "    ),\n",
    "    \"test\": transforms.Compose(\n",
    "        [\n",
    "            transforms.Resize(256),\n",
    "            transforms.CenterCrop(224),\n",
    "            transforms.ToTensor(),\n",
    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
    "        ]\n",
    "    \n",
    "    ),\n",
    "}\n",
    "\n",
    "data_dir = \"hymenoptera_data\"\n",
    "# Create train and validation datasets and loaders\n",
    "image_datasets = {\n",
    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
    "    for x in [\"train\", \"val\", \"test\"]\n",
    "}\n",
    "dataloaders = {\n",
    "    x: torch.utils.data.DataLoader(\n",
    "        image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n",
    "    )\n",
    "    for x in [\"train\", \"val\", \"test\"]\n",
    "}\n",
    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\", \"test\"]}\n",
    "class_names = image_datasets[\"train\"].classes\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Helper function for displaying images\n",
    "def imshow(inp, title=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "\n",
    "    # Un-normalize the images\n",
    "    inp = std * inp + mean\n",
    "    # Clip just in case\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "# Get a batch of training data\n",
    "inputs, classes = next(iter(dataloaders[\"train\"]))\n",
    "\n",
    "# Make a grid from batch\n",
    "out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "imshow(out, title=[class_names[x] for x in classes])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "----------\n",
      "train Loss: 0.5579 Acc: 0.7090\n",
      "val Loss: 0.2821 Acc: 0.9020\n",
      "\n",
      "Epoch 2/10\n",
      "----------\n",
      "train Loss: 0.4667 Acc: 0.8074\n",
      "val Loss: 0.2217 Acc: 0.9542\n",
      "\n",
      "Epoch 3/10\n",
      "----------\n",
      "train Loss: 0.4692 Acc: 0.7828\n",
      "val Loss: 0.1792 Acc: 0.9477\n",
      "\n",
      "Epoch 4/10\n",
      "----------\n",
      "train Loss: 0.4409 Acc: 0.7746\n",
      "val Loss: 0.1982 Acc: 0.9281\n",
      "\n",
      "Epoch 5/10\n",
      "----------\n",
      "train Loss: 0.4173 Acc: 0.8320\n",
      "val Loss: 0.2318 Acc: 0.9020\n",
      "\n",
      "Epoch 6/10\n",
      "----------\n",
      "train Loss: 0.3639 Acc: 0.8279\n",
      "val Loss: 0.1922 Acc: 0.9542\n",
      "\n",
      "Epoch 7/10\n",
      "----------\n",
      "train Loss: 0.3660 Acc: 0.8279\n",
      "val Loss: 0.1792 Acc: 0.9542\n",
      "\n",
      "Epoch 8/10\n",
      "----------\n",
      "train Loss: 0.3840 Acc: 0.8443\n",
      "val Loss: 0.1892 Acc: 0.9477\n",
      "\n",
      "Epoch 9/10\n",
      "----------\n",
      "train Loss: 0.3703 Acc: 0.8607\n",
      "val Loss: 0.1863 Acc: 0.9608\n",
      "\n",
      "Epoch 10/10\n",
      "----------\n",
      "train Loss: 0.3013 Acc: 0.8730\n",
      "val Loss: 0.1911 Acc: 0.9608\n",
      "\n",
      "Training complete in 2m 49s\n",
      "Best val Acc: 0.960784\n",
      "test loss is 0.122250 and test accuracy  is 0.950000\n"
     ]
    }
   ],
   "source": [
    "# Get a batch of training data\n",
    "# inputs, classes = next(iter(dataloaders['train']))\n",
    "\n",
    "# Make a grid from batch\n",
    "# out = torchvision.utils.make_grid(inputs)\n",
    "\n",
    "# imshow(out, title=[class_names[x] for x in classes])\n",
    "# training\n",
    "train_loss_list_7 = []\n",
    "valid_loss_list_7 = []\n",
    "train_accuracy_list_7 = []\n",
    "valid_accuracy_list_7 = []\n",
    "test_loss_list_7 = []\n",
    "test_accuracy_list_7 = []\n",
    "\n",
    "num_epochs = 10\n",
    "\n",
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    epoch_time = []  # we'll keep track of the time needed for each epoch\n",
    "    \n",
    "    \n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        epoch_start = time.time()\n",
    "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
    "        print(\"-\" * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in [\"train\", \"val\"]:\n",
    "            if phase == \"train\":\n",
    "                scheduler.step()\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()  # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # Forward\n",
    "                # Track history if only in training phase\n",
    "                with torch.set_grad_enabled(phase == \"train\"):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == \"train\":\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # Statistics\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "         \n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "            \n",
    "\n",
    "            if phase == \"train\" :\n",
    "                train_loss_list_7.append(epoch_loss)\n",
    "                train_accuracy_list_7.append(epoch_acc)\n",
    "            elif phase == \"val\":\n",
    "                valid_loss_list_7.append(epoch_loss)\n",
    "                valid_accuracy_list_7.append(epoch_acc\n",
    "                )\n",
    "\n",
    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # Deep copy the model\n",
    "            if phase == \"val\" and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        # Add the epoch time\n",
    "        t_epoch = time.time() - epoch_start\n",
    "        epoch_time.append(t_epoch)\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print(\n",
    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
    "            time_elapsed // 60, time_elapsed % 60\n",
    "        )\n",
    "    )\n",
    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
    "\n",
    "    # Load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model, epoch_time\n",
    "\n",
    "\n",
    "def test_model (model, criterion):\n",
    "    since = time.time()\n",
    "\n",
    "    running_loss = 0.0\n",
    "    running_corrects = 0\n",
    "    model.eval()\n",
    "    for inputs, labels in dataloaders[\"test\"] :\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        loss = criterion(outputs, labels)\n",
    "    \n",
    "        running_loss += loss.item() * inputs.size(0)\n",
    "        running_corrects += torch.sum(preds == labels.data)\n",
    "        \n",
    "    test_loss = running_loss/dataset_sizes[\"test\"]\n",
    "    test_accuracy = running_corrects.double()/dataset_sizes[\"test\"]\n",
    "    test_loss_list_7.append(test_loss)\n",
    "    test_accuracy_list_7.append(test_accuracy)\n",
    "\n",
    "    print(\"test loss is %2f and test accuracy  is %2f\" %(test_loss, test_accuracy ))\n",
    "\n",
    "    return test_loss, test_accuracy\n",
    "    \n",
    "\n",
    "\n",
    "# Download a pre-trained ResNet18 model and freeze its weights\n",
    "model_N1 = torchvision.models.resnet18(pretrained=True)\n",
    "for param in model_N1.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# Replace the final fully connected layer\n",
    "# Parameters of newly constructed modules have requires_grad=True by default\n",
    "\n",
    "num_ftrs = model_N1.fc.in_features\n",
    "model_N1.fc = nn.Linear(num_ftrs, 2)\n",
    "\n",
    "# Send the model to the GPU\n",
    "model_N1 = model_N1.to(device)\n",
    "# Set the loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that only the parameters of the final layer are being optimized\n",
    "optimizer_conv = optim.SGD(model_N1.fc.parameters(), lr=0.001, momentum=0.9)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
    "model_N1, epoch_time = train_model(\n",
    "    model_N1, criterion, optimizer_conv, exp_lr_scheduler, num_epochs )\n",
    "\n",
    "test_loss, test_accuracy = test_model(model_N1, criterion)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performance of resnet18 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "fig_6 = plt.figure() \n",
    "fig_6.set_size_inches(10, 4)\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.35)\n",
    "ax_6_1 = plt.subplot(1,2,1) \n",
    "ax_6_2 = plt.subplot(1,2,2)\n",
    "ax_6_1.plot(range(num_epochs), train_loss_list_7, label = 'train_loss')\n",
    "ax_6_1.legend()\n",
    "ax_6_1.plot(range(num_epochs), valid_loss_list_7, label = 'valid_loss')\n",
    "ax_6_1.legend()\n",
    "ax_6_1.set_xlabel(\"Epoch\")\n",
    "ax_6_1.set_ylabel(\"Loss\")\n",
    "ax_6_1.set_title(\"Performance of resnet18 model\")\n",
    "ax_6_2.plot(range(num_epochs), train_accuracy_list_7, label = 'train_acc')\n",
    "ax_6_2.legend()\n",
    "ax_6_2.plot(range(num_epochs), valid_accuracy_list_7, label = 'valid_acc')\n",
    "ax_6_2.legend()\n",
    "ax_6_2.set_xlabel(\"Epoch\")\n",
    "ax_6_2.set_ylabel(\"accuracy\")\n",
    "ax_6_2.set_title(\"Performance of resnet18 model\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now modify the code to replace the current classification layer with a set of two layers using a \"relu\" activation function for the middle layer, and the \"dropout\" mechanism for both layers. Renew the experiments and study the results obtained."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hidden dim = 256**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "----------\n",
      "train Loss: 0.6311 Acc: 0.6270\n",
      "val Loss: 0.4179 Acc: 0.9020\n",
      "\n",
      "Epoch 2/10\n",
      "----------\n",
      "train Loss: 0.5302 Acc: 0.7213\n",
      "val Loss: 0.3562 Acc: 0.8366\n",
      "\n",
      "Epoch 3/10\n",
      "----------\n",
      "train Loss: 0.4946 Acc: 0.7500\n",
      "val Loss: 0.2454 Acc: 0.9150\n",
      "\n",
      "Epoch 4/10\n",
      "----------\n",
      "train Loss: 0.3952 Acc: 0.8156\n",
      "val Loss: 0.1762 Acc: 0.9477\n",
      "\n",
      "Epoch 5/10\n",
      "----------\n",
      "train Loss: 0.5007 Acc: 0.7582\n",
      "val Loss: 0.1992 Acc: 0.9346\n",
      "\n",
      "Epoch 6/10\n",
      "----------\n",
      "train Loss: 0.3985 Acc: 0.8279\n",
      "val Loss: 0.1783 Acc: 0.9477\n",
      "\n",
      "Epoch 7/10\n",
      "----------\n",
      "train Loss: 0.4048 Acc: 0.8238\n",
      "val Loss: 0.1834 Acc: 0.9412\n",
      "\n",
      "Epoch 8/10\n",
      "----------\n",
      "train Loss: 0.3712 Acc: 0.8320\n",
      "val Loss: 0.1913 Acc: 0.9346\n",
      "\n",
      "Epoch 9/10\n",
      "----------\n",
      "train Loss: 0.4335 Acc: 0.7828\n",
      "val Loss: 0.1894 Acc: 0.9477\n",
      "\n",
      "Epoch 10/10\n",
      "----------\n",
      "train Loss: 0.3929 Acc: 0.8115\n",
      "val Loss: 0.1828 Acc: 0.9346\n",
      "\n",
      "Training complete in 2m 54s\n",
      "Best val Acc: 0.947712\n",
      "test loss is 0.101250 and test accuracy  is 1.000000\n"
     ]
    }
   ],
   "source": [
    "train_loss_list_8 = []\n",
    "valid_loss_list_8 = []\n",
    "train_accuracy_list_8 = []\n",
    "valid_accuracy_list_8 = []\n",
    "test_loss_list_8 = []\n",
    "test_accuracy_list_8 = []\n",
    "hidden_dim = 256\n",
    "num_epochs = 10\n",
    "\n",
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    epoch_time = []  # we'll keep track of the time needed for each epoch\n",
    "    \n",
    "    \n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        epoch_start = time.time()\n",
    "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
    "        print(\"-\" * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in [\"train\", \"val\"]:\n",
    "            if phase == \"train\":\n",
    "                scheduler.step()\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()  # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # Forward\n",
    "                # Track history if only in training phase\n",
    "                with torch.set_grad_enabled(phase == \"train\"):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == \"train\":\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # Statistics\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "         \n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "            \n",
    "\n",
    "            if phase == \"train\" :\n",
    "                train_loss_list_8.append(epoch_loss)\n",
    "                train_accuracy_list_8.append(epoch_acc)\n",
    "            elif phase == \"val\":\n",
    "                valid_loss_list_8.append(epoch_loss)\n",
    "                valid_accuracy_list_8.append(epoch_acc\n",
    "                )\n",
    "\n",
    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # Deep copy the model\n",
    "            if phase == \"val\" and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        # Add the epoch time\n",
    "        t_epoch = time.time() - epoch_start\n",
    "        epoch_time.append(t_epoch)\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print(\n",
    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
    "            time_elapsed // 60, time_elapsed % 60\n",
    "        )\n",
    "    )\n",
    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
    "\n",
    "    # Load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model, epoch_time\n",
    "\n",
    "\n",
    "def test_model (model, criterion):\n",
    "    since = time.time()\n",
    "\n",
    "    running_loss = 0.0\n",
    "    running_corrects = 0\n",
    "    model.eval()\n",
    "    for inputs, labels in dataloaders[\"test\"] :\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        loss = criterion(outputs, labels)\n",
    "    \n",
    "        running_loss += loss.item() * inputs.size(0)\n",
    "        running_corrects += torch.sum(preds == labels.data)\n",
    "        \n",
    "    test_loss = running_loss/dataset_sizes[\"test\"]\n",
    "    test_accuracy = running_corrects.double()/dataset_sizes[\"test\"]\n",
    "    test_loss_list_8.append(test_loss)\n",
    "    test_accuracy_list_8.append(test_accuracy)\n",
    "\n",
    "    print(\"test loss is %2f and test accuracy  is %2f\" %(test_loss, test_accuracy ))\n",
    "\n",
    "    return test_loss, test_accuracy\n",
    "    \n",
    "\n",
    "\n",
    "# Download a pre-trained ResNet18 model and freeze its weights\n",
    "model_N2 = torchvision.models.resnet18(pretrained=True)\n",
    "for param in model_N2.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# Replace the final fully connected layer\n",
    "# Parameters of newly constructed modules have requires_grad=True by default\n",
    "\n",
    "num_ftrs = model_N2.fc.in_features\n",
    "model_N2.fc = nn.Sequential(\n",
    "nn.Dropout(0.2),     \n",
    "nn.Linear(num_ftrs, hidden_dim),\n",
    "nn.ReLU(),\n",
    "nn.Linear(hidden_dim, 2))\n",
    "\n",
    "# Send the model to the GPU\n",
    "model_N2 = model_N2.to(device)\n",
    "# Set the loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that only the parameters of the final layer are being optimized\n",
    "optimizer_conv = optim.SGD(model_N2.fc.parameters(), lr=0.001, momentum=0.9)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
    "model_N2, epoch_time = train_model(\n",
    "    model_N2, criterion, optimizer_conv, exp_lr_scheduler, num_epochs )\n",
    "\n",
    "test_loss, test_accuracy = test_model(model_N2, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_6 = plt.figure() \n",
    "fig_6.set_size_inches(10, 4)\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.35)\n",
    "ax_6_1 = plt.subplot(1,2,1) \n",
    "ax_6_2 = plt.subplot(1,2,2)\n",
    "ax_6_1.plot(range(num_epochs), train_loss_list_8, label = 'train_loss')\n",
    "ax_6_1.legend()\n",
    "ax_6_1.plot(range(num_epochs), valid_loss_list_8, label = 'valid_loss')\n",
    "ax_6_1.legend()\n",
    "ax_6_1.set_xlabel(\"Epoch\")\n",
    "ax_6_1.set_ylabel(\"Loss\")\n",
    "# ax_6_1.set_title(\"Performance of resnet18 model\")\n",
    "ax_6_2.plot(range(num_epochs), train_accuracy_list_8, label = 'train_acc')\n",
    "ax_6_2.legend()\n",
    "ax_6_2.plot(range(num_epochs), valid_accuracy_list_8, label = 'valid_acc')\n",
    "ax_6_2.legend()\n",
    "ax_6_2.set_xlabel(\"Epoch\")\n",
    "ax_6_2.set_ylabel(\"accuracy\")\n",
    "# ax_6_2.set_title(\"Performance of resnet18 model\")\n",
    "plt.title(\"Performance of resnet18 model with a classification layer composed of two layers and hidden dim = 256\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hidden dim = 1024**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "----------\n",
      "train Loss: 0.6665 Acc: 0.5779\n",
      "val Loss: 0.3460 Acc: 0.9085\n",
      "\n",
      "Epoch 2/10\n",
      "----------\n",
      "train Loss: 0.4609 Acc: 0.7951\n",
      "val Loss: 0.2454 Acc: 0.9216\n",
      "\n",
      "Epoch 3/10\n",
      "----------\n",
      "train Loss: 0.5151 Acc: 0.7336\n",
      "val Loss: 0.2336 Acc: 0.9150\n",
      "\n",
      "Epoch 4/10\n",
      "----------\n",
      "train Loss: 0.4218 Acc: 0.8074\n",
      "val Loss: 0.1946 Acc: 0.9477\n",
      "\n",
      "Epoch 5/10\n",
      "----------\n",
      "train Loss: 0.4359 Acc: 0.7705\n",
      "val Loss: 0.2747 Acc: 0.8954\n",
      "\n",
      "Epoch 6/10\n",
      "----------\n",
      "train Loss: 0.4504 Acc: 0.7746\n",
      "val Loss: 0.2014 Acc: 0.9281\n",
      "\n",
      "Epoch 7/10\n",
      "----------\n",
      "train Loss: 0.3751 Acc: 0.8156\n",
      "val Loss: 0.2223 Acc: 0.9150\n",
      "\n",
      "Epoch 8/10\n",
      "----------\n",
      "train Loss: 0.3668 Acc: 0.8197\n",
      "val Loss: 0.1859 Acc: 0.9412\n",
      "\n",
      "Epoch 9/10\n",
      "----------\n",
      "train Loss: 0.3874 Acc: 0.8361\n",
      "val Loss: 0.2115 Acc: 0.9281\n",
      "\n",
      "Epoch 10/10\n",
      "----------\n",
      "train Loss: 0.3605 Acc: 0.8648\n",
      "val Loss: 0.2050 Acc: 0.9281\n",
      "\n",
      "Training complete in 4m 6s\n",
      "Best val Acc: 0.947712\n",
      "test loss is 0.056507 and test accuracy  is 1.000000\n"
     ]
    }
   ],
   "source": [
    "train_loss_list_9 = []\n",
    "valid_loss_list_9 = []\n",
    "train_accuracy_list_9 = []\n",
    "valid_accuracy_list_9 = []\n",
    "test_loss_list_9 = []\n",
    "test_accuracy_list_9 = []\n",
    "hidden_dim = 1024\n",
    "num_epochs = 10\n",
    "\n",
    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
    "    since = time.time()\n",
    "\n",
    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
    "    best_acc = 0.0\n",
    "\n",
    "    epoch_time = []  # we'll keep track of the time needed for each epoch\n",
    "    \n",
    "    \n",
    "\n",
    "    for epoch in range(num_epochs):\n",
    "        epoch_start = time.time()\n",
    "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
    "        print(\"-\" * 10)\n",
    "\n",
    "        # Each epoch has a training and validation phase\n",
    "        for phase in [\"train\", \"val\"]:\n",
    "            if phase == \"train\":\n",
    "                scheduler.step()\n",
    "                model.train()  # Set model to training mode\n",
    "            else:\n",
    "                model.eval()  # Set model to evaluate mode\n",
    "\n",
    "            running_loss = 0.0\n",
    "            running_corrects = 0\n",
    "\n",
    "            # Iterate over data.\n",
    "            for inputs, labels in dataloaders[phase]:\n",
    "                inputs = inputs.to(device)\n",
    "                labels = labels.to(device)\n",
    "\n",
    "                # zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # Forward\n",
    "                # Track history if only in training phase\n",
    "                with torch.set_grad_enabled(phase == \"train\"):\n",
    "                    outputs = model(inputs)\n",
    "                    _, preds = torch.max(outputs, 1)\n",
    "                    loss = criterion(outputs, labels)\n",
    "\n",
    "                    # backward + optimize only if in training phase\n",
    "                    if phase == \"train\":\n",
    "                        loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                # Statistics\n",
    "                running_loss += loss.item() * inputs.size(0)\n",
    "                running_corrects += torch.sum(preds == labels.data)\n",
    "\n",
    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
    "         \n",
    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
    "            \n",
    "\n",
    "            if phase == \"train\" :\n",
    "                train_loss_list_9.append(epoch_loss)\n",
    "                train_accuracy_list_9.append(epoch_acc)\n",
    "            elif phase == \"val\":\n",
    "                valid_loss_list_9.append(epoch_loss)\n",
    "                valid_accuracy_list_9.append(epoch_acc\n",
    "                )\n",
    "\n",
    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
    "\n",
    "            # Deep copy the model\n",
    "            if phase == \"val\" and epoch_acc > best_acc:\n",
    "                best_acc = epoch_acc\n",
    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "        # Add the epoch time\n",
    "        t_epoch = time.time() - epoch_start\n",
    "        epoch_time.append(t_epoch)\n",
    "        print()\n",
    "\n",
    "    time_elapsed = time.time() - since\n",
    "    print(\n",
    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
    "            time_elapsed // 60, time_elapsed % 60\n",
    "        )\n",
    "    )\n",
    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
    "\n",
    "    # Load best model weights\n",
    "    model.load_state_dict(best_model_wts)\n",
    "    return model, epoch_time\n",
    "\n",
    "\n",
    "def test_model (model, criterion):\n",
    "    since = time.time()\n",
    "\n",
    "    running_loss = 0.0\n",
    "    running_corrects = 0\n",
    "    model.eval()\n",
    "    for inputs, labels in dataloaders[\"test\"] :\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        loss = criterion(outputs, labels)\n",
    "    \n",
    "        running_loss += loss.item() * inputs.size(0)\n",
    "        running_corrects += torch.sum(preds == labels.data)\n",
    "        \n",
    "    test_loss = running_loss/dataset_sizes[\"test\"]\n",
    "    test_accuracy = running_corrects.double()/dataset_sizes[\"test\"]\n",
    "    test_loss_list_9.append(test_loss)\n",
    "    test_accuracy_list_9.append(test_accuracy)\n",
    "\n",
    "    print(\"test loss is %2f and test accuracy  is %2f\" %(test_loss, test_accuracy ))\n",
    "\n",
    "    return test_loss, test_accuracy\n",
    "    \n",
    "\n",
    "\n",
    "# Download a pre-trained ResNet18 model and freeze its weights\n",
    "model_N3 = torchvision.models.resnet18(pretrained=True)\n",
    "for param in model_N3.parameters():\n",
    "    param.requires_grad = False\n",
    "\n",
    "# Replace the final fully connected layer\n",
    "# Parameters of newly constructed modules have requires_grad=True by default\n",
    "\n",
    "num_ftrs = model_N3.fc.in_features\n",
    "model_N3.fc = nn.Sequential(\n",
    "nn.Dropout(0.2),     \n",
    "nn.Linear(num_ftrs, hidden_dim),\n",
    "nn.ReLU(),\n",
    "nn.Linear(hidden_dim, 2))\n",
    "\n",
    "# Send the model to the GPU\n",
    "model_N3 = model_N3.to(device)\n",
    "# Set the loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "\n",
    "# Observe that only the parameters of the final layer are being optimized\n",
    "optimizer_conv = optim.SGD(model_N3.fc.parameters(), lr=0.001, momentum=0.9)\n",
    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
    "model_N3, epoch_time = train_model(\n",
    "    model_N3, criterion, optimizer_conv, exp_lr_scheduler, num_epochs )\n",
    "\n",
    "test_loss, test_accuracy = test_model(model_N3, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_6 = plt.figure() \n",
    "fig_6.set_size_inches(10, 4)\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.35)\n",
    "ax_6_1 = plt.subplot(1,2,1) \n",
    "ax_6_2 = plt.subplot(1,2,2)\n",
    "ax_6_1.plot(range(num_epochs), train_loss_list_9, label = 'train_loss')\n",
    "ax_6_1.legend()\n",
    "ax_6_1.plot(range(num_epochs), valid_loss_list_9, label = 'valid_loss')\n",
    "ax_6_1.legend()\n",
    "ax_6_1.set_xlabel(\"Epoch\")\n",
    "ax_6_1.set_ylabel(\"Loss\")\n",
    "# ax_6_1.set_title(\"Performance of resnet18 model\") \n",
    "ax_6_2.plot(range(num_epochs), train_accuracy_list_9, label = 'train_acc')\n",
    "ax_6_2.legend()\n",
    "ax_6_2.plot(range(num_epochs), valid_accuracy_list_9, label = 'valid_acc')\n",
    "ax_6_2.legend()\n",
    "ax_6_2.set_xlabel(\"Epoch\")\n",
    "ax_6_2.set_ylabel(\"accuracy\")\n",
    "# ax_6_2.set_title(\"Performance of resnet18 model\")\n",
    "plt.title(\"Performance of resnet18 model with a classification layer composed of two layers and hidden dim = 1024\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantization of resnet18 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_size_of_model(model, label=\"\"):\n",
    "    torch.save(model.state_dict(), \"temp.p\")\n",
    "    size = os.path.getsize(\"temp.p\")\n",
    "    print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n",
    "    os.remove(\"temp.p\")\n",
    "    return size\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:  fp32  \t Size (KB): 44780.42\n",
      "model:  int8  \t Size (KB): 44778.17\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "44778170"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print_size_of_model(model_N1, \"fp32\")\n",
    "quantized_model_N1 = torch.quantization.quantize_dynamic(model_N1, dtype=torch.qint8)\n",
    "print_size_of_model(quantized_model_N1, \"int8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dynamic quantization only helps in reducing the model size for models that use Linear and LSTM modules. For the case of resnet18, the model consists of conv layers which do not have dynamic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test loss is 0.121837 and test accuracy  is 0.950000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "test_loss_list_10 = []\n",
    "test_accuracy_list_10 = []\n",
    "\n",
    "def test_model (model, criterion):\n",
    "    since = time.time()\n",
    "\n",
    "    running_loss = 0.0\n",
    "    running_corrects = 0\n",
    "    model.eval()\n",
    "    for inputs, labels in dataloaders[\"test\"] :\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        loss = criterion(outputs, labels)\n",
    "    \n",
    "        running_loss += loss.item() * inputs.size(0)\n",
    "        running_corrects += torch.sum(preds == labels.data)\n",
    "        \n",
    "    test_loss = running_loss/dataset_sizes[\"test\"]\n",
    "    test_accuracy = running_corrects.double()/dataset_sizes[\"test\"]\n",
    "    test_loss_list_10.append(test_loss)\n",
    "    test_accuracy_list_10.append(test_accuracy)\n",
    "\n",
    "    print(\"test loss is %2f and test accuracy  is %2f\" %(test_loss, test_accuracy ))\n",
    "\n",
    "    return test_loss, test_accuracy\n",
    "    \n",
    "# Send the model to the GPU\n",
    "quantized_model_N1 = quantized_model_N1.to(device)\n",
    "# Set the loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "test_loss, test_accuracy = test_model(quantized_model_N1, criterion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantization of resnet18 model with a set of two layers for classification and hidden dim = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:  fp32  \t Size (KB): 45304.25\n",
      "model:  int8  \t Size (KB): 44911.014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "44911014"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print_size_of_model(model_N2, \"fp32\")\n",
    "quantized_model_N2 = torch.quantization.quantize_dynamic(model_N2, dtype=torch.qint8)\n",
    "print_size_of_model(quantized_model_N2, \"int8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test loss is 0.101985 and test accuracy  is 1.000000\n"
     ]
    }
   ],
   "source": [
    "test_loss_list_11 = []\n",
    "test_accuracy_list_11 = []\n",
    "\n",
    "def test_model (model, criterion):\n",
    "    since = time.time()\n",
    "\n",
    "    running_loss = 0.0\n",
    "    running_corrects = 0\n",
    "    model.eval()\n",
    "    for inputs, labels in dataloaders[\"test\"] :\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        loss = criterion(outputs, labels)\n",
    "    \n",
    "        running_loss += loss.item() * inputs.size(0)\n",
    "        running_corrects += torch.sum(preds == labels.data)\n",
    "        \n",
    "    test_loss = running_loss/dataset_sizes[\"test\"]\n",
    "    test_accuracy = running_corrects.double()/dataset_sizes[\"test\"]\n",
    "    test_loss_list_11.append(test_loss)\n",
    "    test_accuracy_list_11.append(test_accuracy)\n",
    "\n",
    "    print(\"test loss is %2f and test accuracy  is %2f\" %(test_loss, test_accuracy ))\n",
    "\n",
    "    return test_loss, test_accuracy\n",
    "    \n",
    "# Send the model to the GPU\n",
    "quantized_model_N2 = quantized_model_N2.to(device)\n",
    "# Set the loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "test_loss, test_accuracy = test_model(quantized_model_N2, criterion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quantization of resnet18 model with a set of two layers for classification and hidden dim = 1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model:  fp32  \t Size (KB): 46886.33\n",
      "model:  int8  \t Size (KB): 45308.838\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "45308838"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print_size_of_model(model_N3, \"fp32\")\n",
    "quantized_model_N3 = torch.quantization.quantize_dynamic(model_N3, dtype=torch.qint8)\n",
    "print_size_of_model(quantized_model_N3, \"int8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test loss is 0.055903 and test accuracy  is 1.000000\n"
     ]
    }
   ],
   "source": [
    "test_loss_list_12 = []\n",
    "test_accuracy_list_12 = []\n",
    "\n",
    "def test_model (model, criterion):\n",
    "    since = time.time()\n",
    "\n",
    "    running_loss = 0.0\n",
    "    running_corrects = 0\n",
    "    model.eval()\n",
    "    for inputs, labels in dataloaders[\"test\"] :\n",
    "        inputs = inputs.to(device)\n",
    "        labels = labels.to(device)\n",
    "\n",
    "        outputs = model(inputs)\n",
    "        _, preds = torch.max(outputs, 1)\n",
    "        loss = criterion(outputs, labels)\n",
    "    \n",
    "        running_loss += loss.item() * inputs.size(0)\n",
    "        running_corrects += torch.sum(preds == labels.data)\n",
    "        \n",
    "    test_loss = running_loss/dataset_sizes[\"test\"]\n",
    "    test_accuracy = running_corrects.double()/dataset_sizes[\"test\"]\n",
    "    test_loss_list_12.append(test_loss)\n",
    "    test_accuracy_list_12.append(test_accuracy)\n",
    "\n",
    "    print(\"test loss is %2f and test accuracy  is %2f\" %(test_loss, test_accuracy ))\n",
    "\n",
    "    return test_loss, test_accuracy\n",
    "    \n",
    "# Send the model to the GPU\n",
    "quantized_model_N3 = quantized_model_N3.to(device)\n",
    "# Set the loss function\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "test_loss, test_accuracy = test_model(quantized_model_N3, criterion)"
   ]
  }
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