diff --git a/BE2_GAN_and_cGAN.ipynb b/BE2_GAN_and_cGAN.ipynb
deleted file mode 100644
index b68ee7d0836800a496feb6044a87ec831078b299..0000000000000000000000000000000000000000
--- a/BE2_GAN_and_cGAN.ipynb
+++ /dev/null
@@ -1,1733 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "UGwKsKS4GMTN"
-   },
-   "source": [
-    "<h1 ><big><center>MSO 3.4 - Deep Structured Learning</center></big></h1>\n",
-    "\n",
-    "<h2><big><center> BE 2 - GANs and cGAN </center></big></h2>\n",
-    "\n",
-    "<h5><big><center>Adapted from <i>Projet d'Option</i> of : Mhamed Jabri, Martin Chauvin, Ahmed Sahraoui, Zakariae Moustaïne and Taoufik Bouchikhi\n",
-    "\n",
-    "\n",
-    "<p align=\"center\">\n",
-    "<img height=300px src=\"https://cdn-images-1.medium.com/max/1080/0*tJRy5Chmk4XymxwN.png\"/></p>\n",
-    "<p align=\"center\"></p>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "16aVF81lJuiP"
-   },
-   "source": [
-    "The aim of this assignment is to discover GANs, understand how they are implemented and then explore one specific architecture of GANs that allows us to perform image to image translation (which corresponds to the picture that you can see above this text ! )\n",
-    "\n",
-    "Before starting the exploration of the world of GANs, here's  what students should do and send back for this assignement : \n",
-    "* In the \"tutorial\" parts of this assignement that focus on explaining new concepts, you'll find <font color='red'>**questions**</font>  that aim to test your understanding of those concepts. \n",
-    "* In some of the code cells, you'll have to complete the code and you'll find a \"TO DO\" explaining what you should implement."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "M-WNKvhOP1ED"
-   },
-   "source": [
-    "# Part1: DC-GAN"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "y_r8nMTGQI9a"
-   },
-   "source": [
-    "In this part, we aim to learn and understand the basic concepts of **Generative Adversarial Networks** through a DCGAN and generate new celebrities from the learned network after showing it real celebrities. For this purpose, please study the tutorial here: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "jiHCy4_UUBFb"
-   },
-   "source": [
-    "## Work to do\n",
-    "Now we want to generate handwritten digits using the MNIST dataset. It is available within torvision package (https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST)\n",
-    "\n",
-    "Please re-train the DCGAN and display some automatically generated handwritten digits.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Random Seed:  999\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<torch._C.Generator at 0x10aca9390>"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from __future__ import print_function\n",
-    "#%matplotlib inline\n",
-    "import argparse\n",
-    "import os\n",
-    "import random\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.parallel\n",
-    "import torch.backends.cudnn as cudnn\n",
-    "import torch.optim as optim\n",
-    "import torch.utils.data\n",
-    "import torchvision.datasets as dset\n",
-    "import torchvision.transforms as transforms\n",
-    "import torchvision.utils as vutils\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import matplotlib.animation as animation\n",
-    "from IPython.display import HTML\n",
-    "\n",
-    "# Set random seed for reproducibility\n",
-    "manualSeed = 999\n",
-    "#manualSeed = random.randint(1, 10000) # use if you want new results\n",
-    "print(\"Random Seed: \", manualSeed)\n",
-    "random.seed(manualSeed)\n",
-    "torch.manual_seed(manualSeed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Number of workers for dataloader\n",
-    "workers = 2\n",
-    "\n",
-    "# Batch size during training\n",
-    "batch_size = 128\n",
-    "\n",
-    "# Spatial size of training images. All images will be resized to this\n",
-    "#   size using a transformer.\n",
-    "image_size = 64\n",
-    "\n",
-    "# Number of channels in the training images. For color images this is 3\n",
-    "nc = 1\n",
-    "\n",
-    "# Size of z latent vector (i.e. size of generator input)\n",
-    "nz = 100\n",
-    "\n",
-    "# Size of feature maps in generator\n",
-    "ngf = 64\n",
-    "\n",
-    "# Size of feature maps in discriminator\n",
-    "ndf = 64\n",
-    "\n",
-    "# Number of training epochs\n",
-    "num_epochs = 5\n",
-    "\n",
-    "# Learning rate for optimizers\n",
-    "lr = 0.0002\n",
-    "\n",
-    "# Beta1 hyperparam for Adam optimizers\n",
-    "beta1 = 0.5\n",
-    "\n",
-    "# Number of GPUs available. Use 0 for CPU mode.\n",
-    "ngpu = 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MPS is available.  Training on MAC GPU ...\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load dataset\n",
-    "dataset = dset.MNIST(\n",
-    "    '', \n",
-    "    download=True, \n",
-    "    transform=transforms.Compose([\n",
-    "        transforms.Resize(image_size),\n",
-    "        transforms.CenterCrop(image_size),\n",
-    "        transforms.ToTensor(),\n",
-    "        transforms.Normalize((0.5,), (0.5,)),\n",
-    "    ])\n",
-    ")\n",
-    "\n",
-    "# Create the dataloader\n",
-    "dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
-    "                                         shuffle=True, num_workers=workers)\n",
-    "\n",
-    "# check if GPU is available\n",
-    "if torch.cuda.is_available():\n",
-    "    device = torch.device(\"cuda:0\")\n",
-    "    print(\"CUDA is available!  Training on GPU ...\")\n",
-    "elif torch.backends.mps.is_available():\n",
-    "    device = torch.device(\"mps\")\n",
-    "    print(\"MPS is available.  Training on MAC GPU ...\")\n",
-    "else:\n",
-    "    device = torch.device(\"cpu\")\n",
-    "    print(\"Nor CUDA or MPS are available. Training on CPU ...\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x141119d00>"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot some training images\n",
-    "real_batch = next(iter(dataloader))\n",
-    "plt.figure(figsize=(8,8))\n",
-    "plt.axis(\"off\")\n",
-    "plt.title(\"Training Images\")\n",
-    "plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# custom weights initialization called on netG and netD\n",
-    "def weights_init(m):\n",
-    "    classname = m.__class__.__name__\n",
-    "    if classname.find('Conv') != -1:\n",
-    "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
-    "    elif classname.find('BatchNorm') != -1:\n",
-    "        nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
-    "        nn.init.constant_(m.bias.data, 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Generator Code\n",
-    "\n",
-    "class Generator(nn.Module):\n",
-    "    def __init__(self, ngpu):\n",
-    "        super(Generator, self).__init__()\n",
-    "        self.ngpu = ngpu\n",
-    "        self.main = nn.Sequential(\n",
-    "            # input is Z, going into a convolution\n",
-    "            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),\n",
-    "            nn.BatchNorm2d(ngf * 8),\n",
-    "            nn.ReLU(True),\n",
-    "            # state size. (ngf*8) x 4 x 4\n",
-    "            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),\n",
-    "            nn.BatchNorm2d(ngf * 4),\n",
-    "            nn.ReLU(True),\n",
-    "            # state size. (ngf*4) x 8 x 8\n",
-    "            nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),\n",
-    "            nn.BatchNorm2d(ngf * 2),\n",
-    "            nn.ReLU(True),\n",
-    "            # state size. (ngf*2) x 16 x 16\n",
-    "            nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),\n",
-    "            nn.BatchNorm2d(ngf),\n",
-    "            nn.ReLU(True),\n",
-    "            # state size. (ngf) x 32 x 32\n",
-    "            nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),\n",
-    "            nn.Tanh()\n",
-    "            # state size. (nc) x 64 x 64\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, input):\n",
-    "        return self.main(input)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Generator(\n",
-      "  (main): Sequential(\n",
-      "    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
-      "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "    (2): ReLU(inplace=True)\n",
-      "    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "    (5): ReLU(inplace=True)\n",
-      "    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "    (8): ReLU(inplace=True)\n",
-      "    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "    (11): ReLU(inplace=True)\n",
-      "    (12): ConvTranspose2d(64, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "    (13): Tanh()\n",
-      "  )\n",
-      ")\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create the generator\n",
-    "netG = Generator(ngpu).to(device)\n",
-    "\n",
-    "# Handle multi-gpu if desired\n",
-    "if (device.type == 'cuda') and (ngpu > 1):\n",
-    "    netG = nn.DataParallel(netG, list(range(ngpu)))\n",
-    "\n",
-    "# Apply the weights_init function to randomly initialize all weights\n",
-    "#  to mean=0, stdev=0.02.\n",
-    "netG.apply(weights_init)\n",
-    "\n",
-    "# Print the model\n",
-    "print(netG)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Discriminator(nn.Module):\n",
-    "    def __init__(self, ngpu):\n",
-    "        super(Discriminator, self).__init__()\n",
-    "        self.ngpu = ngpu\n",
-    "        self.main = nn.Sequential(\n",
-    "            # input is (nc) x 64 x 64\n",
-    "            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),\n",
-    "            nn.LeakyReLU(0.2, inplace=True),\n",
-    "            # state size. (ndf) x 32 x 32\n",
-    "            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n",
-    "            nn.BatchNorm2d(ndf * 2),\n",
-    "            nn.LeakyReLU(0.2, inplace=True),\n",
-    "            # state size. (ndf*2) x 16 x 16\n",
-    "            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),\n",
-    "            nn.BatchNorm2d(ndf * 4),\n",
-    "            nn.LeakyReLU(0.2, inplace=True),\n",
-    "            # state size. (ndf*4) x 8 x 8\n",
-    "            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),\n",
-    "            nn.BatchNorm2d(ndf * 8),\n",
-    "            nn.LeakyReLU(0.2, inplace=True),\n",
-    "            # state size. (ndf*8) x 4 x 4\n",
-    "            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),\n",
-    "            nn.Sigmoid()\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, input):\n",
-    "        return self.main(input)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Discriminator(\n",
-      "  (main): Sequential(\n",
-      "    (0): Conv2d(1, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "    (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
-      "    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "    (4): LeakyReLU(negative_slope=0.2, inplace=True)\n",
-      "    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "    (7): LeakyReLU(negative_slope=0.2, inplace=True)\n",
-      "    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
-      "    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
-      "    (10): LeakyReLU(negative_slope=0.2, inplace=True)\n",
-      "    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
-      "    (12): Sigmoid()\n",
-      "  )\n",
-      ")\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Create the Discriminator\n",
-    "netD = Discriminator(ngpu).to(device)\n",
-    "\n",
-    "# Handle multi-gpu if desired\n",
-    "if (device.type == 'cuda') and (ngpu > 1):\n",
-    "    netD = nn.DataParallel(netD, list(range(ngpu)))\n",
-    "\n",
-    "# Apply the weights_init function to randomly initialize all weights\n",
-    "#  to mean=0, stdev=0.2.\n",
-    "netD.apply(weights_init)\n",
-    "\n",
-    "# Print the model\n",
-    "print(netD)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialize BCELoss function\n",
-    "criterion = nn.BCELoss()\n",
-    "\n",
-    "# Create batch of latent vectors that we will use to visualize\n",
-    "#  the progression of the generator\n",
-    "fixed_noise = torch.randn(64, nz, 1, 1, device=device)\n",
-    "\n",
-    "# Establish convention for real and fake labels during training\n",
-    "real_label = 1.\n",
-    "fake_label = 0.\n",
-    "\n",
-    "# Setup Adam optimizers for both G and D\n",
-    "optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))\n",
-    "optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting Training Loop...\n",
-      "[0/5][0/469]\tLoss_D: 1.3327\tLoss_G: 2.9109\tD(x): 0.4710\tD(G(z)): 0.3334 / 0.0759\n",
-      "[0/5][50/469]\tLoss_D: 0.3975\tLoss_G: 18.6086\tD(x): 0.9795\tD(G(z)): 0.2794 / 0.0000\n",
-      "[0/5][100/469]\tLoss_D: 0.1952\tLoss_G: 5.1273\tD(x): 0.9312\tD(G(z)): 0.1027 / 0.0077\n",
-      "[0/5][150/469]\tLoss_D: 0.0890\tLoss_G: 5.1653\tD(x): 0.9807\tD(G(z)): 0.0640 / 0.0084\n",
-      "[0/5][200/469]\tLoss_D: 0.2651\tLoss_G: 4.6875\tD(x): 0.8998\tD(G(z)): 0.1069 / 0.0145\n",
-      "[0/5][250/469]\tLoss_D: 0.2760\tLoss_G: 4.1801\tD(x): 0.9559\tD(G(z)): 0.1915 / 0.0223\n",
-      "[0/5][300/469]\tLoss_D: 0.1293\tLoss_G: 4.3041\tD(x): 0.9312\tD(G(z)): 0.0502 / 0.0233\n",
-      "[0/5][350/469]\tLoss_D: 0.2458\tLoss_G: 3.0678\tD(x): 0.8304\tD(G(z)): 0.0361 / 0.0767\n",
-      "[0/5][400/469]\tLoss_D: 1.5231\tLoss_G: 1.4075\tD(x): 0.3113\tD(G(z)): 0.0038 / 0.2935\n",
-      "[0/5][450/469]\tLoss_D: 0.8433\tLoss_G: 1.8634\tD(x): 0.5897\tD(G(z)): 0.1559 / 0.2040\n",
-      "[1/5][0/469]\tLoss_D: 0.3977\tLoss_G: 3.0405\tD(x): 0.8880\tD(G(z)): 0.2193 / 0.0712\n",
-      "[1/5][50/469]\tLoss_D: 0.2656\tLoss_G: 2.1538\tD(x): 0.8446\tD(G(z)): 0.0739 / 0.1483\n",
-      "[1/5][100/469]\tLoss_D: 0.2981\tLoss_G: 2.3471\tD(x): 0.8163\tD(G(z)): 0.0750 / 0.1179\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/l1/8_qbbp7d7hbgk9kkn4tkz2nh0000gn/T/ipykernel_35921/2435757670.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;31m# Calculate gradients for G\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0merrG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mD_G_z2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m         \u001b[0;31m# Update G\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0moptimizerG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "# Training Loop\n",
-    "\n",
-    "# Lists to keep track of progress\n",
-    "img_list = []\n",
-    "G_losses = []\n",
-    "D_losses = []\n",
-    "iters = 0\n",
-    "\n",
-    "print(\"Starting Training Loop...\")\n",
-    "# For each epoch\n",
-    "for epoch in range(num_epochs):\n",
-    "    # For each batch in the dataloader\n",
-    "    for i, data in enumerate(dataloader, 0):\n",
-    "\n",
-    "        ############################\n",
-    "        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))\n",
-    "        ###########################\n",
-    "        ## Train with all-real batch\n",
-    "        netD.zero_grad()\n",
-    "        # Format batch\n",
-    "        real_cpu = data[0].to(device)\n",
-    "        b_size = real_cpu.size(0)\n",
-    "        label = torch.full((b_size,), real_label, dtype=torch.float, device=device)\n",
-    "        # Forward pass real batch through D\n",
-    "        output = netD(real_cpu).view(-1)\n",
-    "        # Calculate loss on all-real batch\n",
-    "        errD_real = criterion(output, label)\n",
-    "        # Calculate gradients for D in backward pass\n",
-    "        errD_real.backward()\n",
-    "        D_x = output.mean().item()\n",
-    "\n",
-    "        ## Train with all-fake batch\n",
-    "        # Generate batch of latent vectors\n",
-    "        noise = torch.randn(b_size, nz, 1, 1, device=device)\n",
-    "        # Generate fake image batch with G\n",
-    "        fake = netG(noise)\n",
-    "        label.fill_(fake_label)\n",
-    "        # Classify all fake batch with D\n",
-    "        output = netD(fake.detach()).view(-1)\n",
-    "        # Calculate D's loss on the all-fake batch\n",
-    "        errD_fake = criterion(output, label)\n",
-    "        # Calculate the gradients for this batch, accumulated (summed) with previous gradients\n",
-    "        errD_fake.backward()\n",
-    "        D_G_z1 = output.mean().item()\n",
-    "        # Compute error of D as sum over the fake and the real batches\n",
-    "        errD = errD_real + errD_fake\n",
-    "        # Update D\n",
-    "        optimizerD.step()\n",
-    "\n",
-    "        ############################\n",
-    "        # (2) Update G network: maximize log(D(G(z)))\n",
-    "        ###########################\n",
-    "        netG.zero_grad()\n",
-    "        label.fill_(real_label)  # fake labels are real for generator cost\n",
-    "        # Since we just updated D, perform another forward pass of all-fake batch through D\n",
-    "        output = netD(fake).view(-1)\n",
-    "        # Calculate G's loss based on this output\n",
-    "        errG = criterion(output, label)\n",
-    "        # Calculate gradients for G\n",
-    "        errG.backward()\n",
-    "        D_G_z2 = output.mean().item()\n",
-    "        # Update G\n",
-    "        optimizerG.step()\n",
-    "\n",
-    "        # Output training stats\n",
-    "        if i % 50 == 0:\n",
-    "            print('[%d/%d][%d/%d]\\tLoss_D: %.4f\\tLoss_G: %.4f\\tD(x): %.4f\\tD(G(z)): %.4f / %.4f'\n",
-    "                  % (epoch, num_epochs, i, len(dataloader),\n",
-    "                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))\n",
-    "\n",
-    "        # Save Losses for plotting later\n",
-    "        G_losses.append(errG.item())\n",
-    "        D_losses.append(errD.item())\n",
-    "\n",
-    "        # Check how the generator is doing by saving G's output on fixed_noise\n",
-    "        if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):\n",
-    "            with torch.no_grad():\n",
-    "                fake = netG(fixed_noise).detach().cpu()\n",
-    "            img_list.append(vutils.make_grid(fake, padding=2, normalize=True))\n",
-    "\n",
-    "        iters += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "sIL7UvYAZx6L"
-   },
-   "outputs": [],
-   "source": [
-    "#TO DO: your code here to adapt the code from the tutorial to experiment on MNIST dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "5fbSgsrE1GqC"
-   },
-   "source": [
-    "# Part2: Conditional GAN (cGAN)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "7SjXNoT7BUey"
-   },
-   "source": [
-    "Let's take the example of the set described in the next picture.\n",
-    "![Map to satellite picture](https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/map_streetview.png)\n",
-    "\n",
-    "We have a picture of a map (from Google Maps) and we want to create an image of what the satellite view may look like.\n",
-    "\n",
-    "As we are not only trying to generate a random picture but a mapping between a picture to another one, we can't use the standard GAN architecture. We will then use a cGAN.\n",
-    "\n",
-    "A cGAN is a supervised GAN aiming at mapping a label picture to a real one or a real picture to a label one. As you can see in the diagram below, the discriminator will take as input a pair of images and try to predict if the pair was generated or not. The generator will not only generate an image from noise but will also use an image (label or real) to generate another one (real or label).\n",
-    "![Diagram of how a cGan works](https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/cgan_map.png)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "0JRaeHfzl6cO"
-   },
-   "source": [
-    "### Generator\n",
-    "\n",
-    "In the cGAN architecture, the generator chosen is a U-Net.\n",
-    "![U-Net](https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/unet.png)\n",
-    "\n",
-    "A U-Net takes as input an image, and outputs another image. \n",
-    "\n",
-    "It can be divided into 2 subparts : an encoder and a decoder. \n",
-    "* The encoder takes the input image and reduces its dimension to encode the main features into a vector. \n",
-    "* The decoder takes this vector and map the features stored into an image.\n",
-    "\n",
-    "A U-Net architecture is different from a classic encoder-decoder in that every layer of the decoder takes as input the previous decoded output as well as the output vector from the encoder layers of the same level. It allows the decoder to map low frequencies information encoded during the descent as well as high frequencies from the original picture. \n",
-    "\n",
-    "![U-Net](https://www.researchgate.net/profile/Baris_Turkbey/publication/315514772/figure/fig2/AS:485824962797569@1492841105670/U-net-architecture-Each-box-corresponds-to-a-multi-channel-features-maps-The-number-of.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "xFqMOsoYwzFe"
-   },
-   "source": [
-    "The architecture we will implement is the following (the number in the square is the number of filters used).\n",
-    "![UNet Architecture](https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/unet_architecture.png)\n",
-    "\n",
-    "The encoder will take as input a colored picture (3 channels: RGB), it will pass through a series of convolution layers to encode the features of the picture. It will then be decoded by the decoder using transposed convolutional layers. These layers will take as input the previous decoded vector AND the encoded features of the same level. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "yzy7y4hmbbX3"
-   },
-   "source": [
-    "Now, let's create or cGAN to generate facades from a template image. For this purpose, we will use the \"Facade\" dataset available at http://cmp.felk.cvut.cz/~tylecr1/facade/.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Q_jf9H_NDESm"
-   },
-   "source": [
-    "Let's first create a few classes describing the layers we will use in the U-Net."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "uOKvYDyu0w8N"
-   },
-   "outputs": [],
-   "source": [
-    "# Importing all the libraries needed\n",
-    "import matplotlib.pyplot as plt\n",
-    "import imageio\n",
-    "import glob\n",
-    "import random\n",
-    "import os\n",
-    "import numpy as np\n",
-    "import math\n",
-    "import itertools\n",
-    "import time\n",
-    "import datetime\n",
-    "import cv2\n",
-    "from pathlib import Path\n",
-    "from PIL import Image\n",
-    "\n",
-    "from torch.utils.data import Dataset, DataLoader\n",
-    "import torchvision.transforms as transforms\n",
-    "from torchvision.utils import save_image, make_grid\n",
-    "from torchvision import datasets\n",
-    "from torch.autograd import Variable\n",
-    "\n",
-    "import torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "import torch"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "Zk5a6B5hILN2"
-   },
-   "outputs": [],
-   "source": [
-    "# code adapted from https://github.com/milesial/Pytorch-UNet/blob/master/unet/unet_parts.py\n",
-    "\n",
-    "# Input layer\n",
-    "class inconv(nn.Module):\n",
-    "    def __init__(self, in_ch, out_ch):\n",
-    "        super(inconv, self).__init__()\n",
-    "        self.conv = nn.Sequential(\n",
-    "            nn.Conv2d(in_ch, out_ch, kernel_size=4, padding=1, stride=2),\n",
-    "            nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.conv(x)\n",
-    "        return x\n",
-    "\n",
-    "# Encoder layer\n",
-    "class down(nn.Module):\n",
-    "    def __init__(self, in_ch, out_ch):\n",
-    "        super(down, self).__init__()\n",
-    "        self.conv = nn.Sequential(\n",
-    "            nn.Conv2d(in_ch, out_ch, kernel_size=4, padding=1, stride=2),\n",
-    "            nn.BatchNorm2d(out_ch),\n",
-    "            nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.conv(x)\n",
-    "        return x\n",
-    "\n",
-    "# Decoder layer\n",
-    "class up(nn.Module):\n",
-    "    def __init__(self, in_ch, out_ch, dropout=False):\n",
-    "        super(up, self).__init__()\n",
-    "        if dropout :\n",
-    "            self.conv = nn.Sequential(\n",
-    "                nn.ConvTranspose2d(in_ch, out_ch, kernel_size=4, padding=1, stride=2),\n",
-    "                nn.BatchNorm2d(out_ch),\n",
-    "                nn.Dropout(0.5, inplace=True),\n",
-    "                nn.ReLU(inplace=True)\n",
-    "            )\n",
-    "        else:\n",
-    "            self.conv = nn.Sequential(\n",
-    "                nn.ConvTranspose2d(in_ch, out_ch, kernel_size=4, padding=1, stride=2),\n",
-    "                nn.BatchNorm2d(out_ch),\n",
-    "                nn.ReLU(inplace=True)\n",
-    "            )\n",
-    "\n",
-    "    def forward(self, x1, x2):\n",
-    "        x1 = self.conv(x1)\n",
-    "        x = torch.cat([x1, x2], dim=1)\n",
-    "        return x\n",
-    "\n",
-    "# Output layer\n",
-    "class outconv(nn.Module):\n",
-    "    def __init__(self, in_ch, out_ch):\n",
-    "        super(outconv, self).__init__()\n",
-    "        self.conv = nn.Sequential(\n",
-    "              nn.ConvTranspose2d(in_ch, out_ch, kernel_size=4, padding=1, stride=2),\n",
-    "              nn.Tanh()\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.conv(x)\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "1rZ5Qz1mBUe8"
-   },
-   "source": [
-    "Now let's create the U-Net using the helper classes defined previously."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "4Tbp_535EVPW"
-   },
-   "outputs": [],
-   "source": [
-    " class U_Net(nn.Module):\n",
-    "    ''' \n",
-    "    Ck denotes a Convolution-BatchNorm-ReLU layer with k filters.\n",
-    "    CDk denotes a Convolution-BatchNorm-Dropout-ReLU layer with a dropout rate of 50%\n",
-    "    Encoder:\n",
-    "      C64   - C128   - C256   - C512  - C512  - C512 - C512 - C512\n",
-    "    Decoder:\n",
-    "      CD512 - CD1024 - CD1024 - C1024 - C1024 - C512 - C256 - C128\n",
-    "    '''\n",
-    "    def __init__(self, n_channels, n_classes):\n",
-    "        super(U_Net, self).__init__()\n",
-    "        # Encoder\n",
-    "        self.inc = inconv(n_channels, 64) # 64 filters\n",
-    "        # TO DO :\n",
-    "        # Create the 7 encoder layers called \"down1\" to \"down7\" following this sequence\n",
-    "        # C64   - C128   - C256   - C512  - C512  - C512 - C512 - C512\n",
-    "        # The first one has already been implemented\n",
-    "        \n",
-    "        \n",
-    "        # Decoder\n",
-    "        # TO DO :\n",
-    "        # Create the 7 decoder layers called up1 to up7 following this sequence :\n",
-    "        # CD512 - CD1024 - CD1024 - C1024 - C1024 - C512 - C256 - C128\n",
-    "        # The last layer has already been defined\n",
-    "        \n",
-    "        \n",
-    "        self.outc = outconv(128, n_classes) # 128 filters\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x1 = self.inc(x)\n",
-    "        x2 = self.down1(x1)\n",
-    "        x3 = self.down2(x2)\n",
-    "        x4 = self.down3(x3)\n",
-    "        x5 = self.down4(x4)\n",
-    "        x6 = self.down5(x5)\n",
-    "        x7 = self.down6(x6)\n",
-    "        x8 = self.down7(x7)\n",
-    "        # At this stage x8 is our encoded vector, we will now decode it\n",
-    "        x = self.up7(x8, x7)\n",
-    "        x = self.up6(x, x6)\n",
-    "        x = self.up5(x, x5)\n",
-    "        x = self.up4(x, x4)\n",
-    "        x = self.up3(x, x3)\n",
-    "        x = self.up2(x, x2)\n",
-    "        x = self.up1(x, x1)\n",
-    "        x = self.outc(x)\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "1hmcejTWJSYY"
-   },
-   "outputs": [],
-   "source": [
-    "# We take images that have 3 channels (RGB) as input and output an image that also have 3 channels (RGB)\n",
-    "generator=U_Net(3,3)\n",
-    "# Check that the architecture is as expected\n",
-    "generator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "xIXFtHzcBUfO"
-   },
-   "source": [
-    "You should now have a working U-Net."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "RqD1katYBUfP"
-   },
-   "source": [
-    "<font color='red'>**Question 1**</font>                                                                  \n",
-    "Knowing the input and output images will be 256x256, what will be the dimension of the encoded vector x8  ?\n",
-    "\n",
-    "<font color='red'>**Question 2**</font>                                                                  \n",
-    "As you can see, U-net has an encoder-decoder architecture with skip connections. Explain why it works better than a traditional encoder-decoder."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "cchTp3thBUfR"
-   },
-   "source": [
-    "### Discriminator\n",
-    "\n",
-    "In the cGAN architecture, the chosen discriminator is a Patch GAN. It is a convolutional discriminator which enables to produce a map of the input pictures where each pixel represents a patch of size NxN of the input.\n",
-    "\n",
-    "![patch GAN](https://raw.githubusercontent.com/Neyri/Projet-cGAN/master/BE/img/patchGAN.png)\n",
-    "\n",
-    "The size N is given by the depth of the net. According to this table :\n",
-    "\n",
-    "| Number of layers | N |\n",
-    "| ---- | ---- |\n",
-    "| 1 | 16 |\n",
-    "| 2 | 34 |\n",
-    "| 3 | 70 |\n",
-    "| 4 | 142 |\n",
-    "| 5 | 286 |\n",
-    "| 6 | 574 |\n",
-    "\n",
-    "The number of layers actually means the number of layers with `kernel=(4,4)`, `padding=(1,1)` and `stride=(2,2)`. These layers are followed by 2 layers with `kernel=(4,4)`, `padding=(1,1)` and `stride=(1,1)`.\n",
-    "In our case we are going to create a 70x70 PatchGAN."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "ge6I7M0aBUfT"
-   },
-   "source": [
-    "Let's first create a few helping classes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "RYqomFO8BUfV"
-   },
-   "outputs": [],
-   "source": [
-    "class conv_block(nn.Module):\n",
-    "    def __init__(self, in_ch, out_ch, use_batchnorm=True, stride=2):\n",
-    "        super(conv_block, self).__init__()\n",
-    "        if use_batchnorm:\n",
-    "            self.conv = nn.Sequential(\n",
-    "                nn.Conv2d(in_ch, out_ch, kernel_size=4, padding=1, stride=stride),\n",
-    "                nn.BatchNorm2d(out_ch),\n",
-    "                nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
-    "            )\n",
-    "        else:\n",
-    "            self.conv = nn.Sequential(\n",
-    "                nn.Conv2d(in_ch, out_ch, kernel_size=4, padding=1, stride=stride),\n",
-    "                nn.LeakyReLU(negative_slope=0.2, inplace=True)\n",
-    "            )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.conv(x)\n",
-    "        return x\n",
-    "    \n",
-    "\n",
-    "class out_block(nn.Module):\n",
-    "    def __init__(self, in_ch, out_ch):\n",
-    "        super(out_block, self).__init__()\n",
-    "        self.conv = nn.Sequential(\n",
-    "            nn.Conv2d(in_ch, 1, kernel_size=4, padding=1, stride=1),\n",
-    "            nn.Sigmoid()\n",
-    "        )\n",
-    "\n",
-    "    def forward(self, x):\n",
-    "        x = self.conv(x)\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "5m4Dnup4BUfc"
-   },
-   "source": [
-    "Now let's create the Patch GAN discriminator.\n",
-    "As we want a 70x70 Patch GAN, the architecture will be as follows :\n",
-    "```\n",
-    "1. C64  - K4, P1, S2\n",
-    "2. C128 - K4, P1, S2\n",
-    "3. C256 - K4, P1, S2\n",
-    "4. C512 - K4, P1, S1\n",
-    "5. C1   - K4, P1, S1 (output)\n",
-    "```\n",
-    "Where Ck denotes a convolution block with k filters, Kk a kernel of size k, Pk is the padding size and Sk the stride applied.\n",
-    "*Note :* For the first layer, we do not use batchnorm."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "AH6u5a-PBUfg"
-   },
-   "source": [
-    "<font color='red'>**Question 3**</font>                                                                  \n",
-    "Knowing the input and output images will be 256x256, what will be the dimension of the encoded vector x8  ?Knowing input images will be 256x256 with 3 channels each, how many parameters are there to learn ?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "g_9LxNhGBUfi"
-   },
-   "outputs": [],
-   "source": [
-    "class PatchGAN(nn.Module):\n",
-    "    def __init__(self, n_channels, n_classes):\n",
-    "        super(PatchGAN, self).__init__()\n",
-    "        # TODO :\n",
-    "        # create the 4 first layers named conv1 to conv4\n",
-    "        self.conv1 =\n",
-    "        self.conv2 =\n",
-    "        self.conv3 =\n",
-    "        self.conv4 =\n",
-    "        # output layer\n",
-    "        self.out = out_block(512, n_classes)\n",
-    "        \n",
-    "    def forward(self, x1, x2):\n",
-    "        x = torch.cat([x2, x1], dim=1)\n",
-    "        x = self.conv1(x)\n",
-    "        x = self.conv2(x)\n",
-    "        x = self.conv3(x)\n",
-    "        x = self.conv4(x)\n",
-    "        x = self.out(x)\n",
-    "        return x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "W_sevZRnBUfn"
-   },
-   "outputs": [],
-   "source": [
-    "# We have 6 input channels as we concatenate 2 images (with 3 channels each)\n",
-    "discriminator = PatchGAN(6,1)\n",
-    "discriminator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "v_QubOycBUfv"
-   },
-   "source": [
-    "You should now have a working discriminator."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "DiI2CByRBUfz"
-   },
-   "source": [
-    "### Loss functions\n",
-    "\n",
-    "As we have seen in the choice of the various architectures for this GAN, the issue is to map both low and high frequencies.\n",
-    "To tackle this problem, this GAN rely on the architecture to map the high frequencies (U-Net + PatchGAN) and the loss function to learn low frequencies features. The global loss function will indeed be made of 2 parts :\n",
-    "* the first part to map hight frequencies, will try to optimize the mean squared error of the GAN.\n",
-    "* the second part to map low frequencies, will minimize the $\\mathcal{L}_1$ norm of the generated picture.\n",
-    "\n",
-    "So the loss can be defined as $$ G^* = arg\\ \\underset{G}{min}\\ \\underset{D}{max}\\ \\mathcal{L}_{cGAN}(G,D) + \\lambda \\mathcal{L}_1(G)$$"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "k4G_xewPBUf4"
-   },
-   "outputs": [],
-   "source": [
-    "# Loss functions\n",
-    "criterion_GAN = torch.nn.MSELoss()\n",
-    "criterion_pixelwise = torch.nn.L1Loss()\n",
-    "\n",
-    "# Loss weight of L1 pixel-wise loss between translated image and real image\n",
-    "lambda_pixel = 100"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "c12q2NwkBUf7"
-   },
-   "source": [
-    "### Training and evaluating models "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "vGKjO0UMBUf9"
-   },
-   "outputs": [],
-   "source": [
-    "# parameters\n",
-    "epoch = 0 #  epoch to start training from\n",
-    "n_epoch = 200  #  number of epochs of training\n",
-    "batch_size =10  #  size of the batches\n",
-    "lr = 0.0002 #  adam: learning rate\n",
-    "b1 =0.5  #  adam: decay of first order momentum of gradient\n",
-    "b2 = 0.999  # adam: decay of first order momentum of gradient\n",
-    "decay_epoch = 100  # epoch from which to start lr decay\n",
-    "img_height = 256  # size of image height\n",
-    "img_width = 256  # size of image width\n",
-    "channels = 3  # number of image channels\n",
-    "sample_interval = 500 # interval between sampling of images from generators\n",
-    "checkpoint_interval = -1 # interval between model checkpoints\n",
-    "cuda = True if torch.cuda.is_available() else False # do you have cuda ?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "PhPkU7BDYooV"
-   },
-   "source": [
-    "Download the dataset. \n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "8wyPjAxPYsNF"
-   },
-   "outputs": [],
-   "source": [
-    "import urllib.request\n",
-    "from tqdm import tqdm\n",
-    "import os\n",
-    "import zipfile\n",
-    "\n",
-    "def download_hook(t):\n",
-    "    \"\"\"Wraps tqdm instance.\n",
-    "    Don't forget to close() or __exit__()\n",
-    "    the tqdm instance once you're done with it (easiest using `with` syntax).\n",
-    "    Example\n",
-    "    -------\n",
-    "    >>> with tqdm(...) as t:\n",
-    "    ...     reporthook = my_hook(t)\n",
-    "    ...     urllib.request.urlretrieve(..., reporthook=reporthook)\n",
-    "    \"\"\"\n",
-    "    last_b = [0]\n",
-    "\n",
-    "    def update_to(b=1, bsize=1, tsize=None):\n",
-    "        \"\"\"\n",
-    "        b  : int, optional\n",
-    "            Number of blocks transferred so far [default: 1].\n",
-    "        bsize  : int, optional\n",
-    "            Size of each block (in tqdm units) [default: 1].\n",
-    "        tsize  : int, optional\n",
-    "            Total size (in tqdm units). If [default: None] remains unchanged.\n",
-    "        \"\"\"\n",
-    "        if tsize is not None:\n",
-    "            t.total = tsize\n",
-    "        t.update((b - last_b[0]) * bsize)\n",
-    "        last_b[0] = b\n",
-    "\n",
-    "    return update_to\n",
-    "\n",
-    "def download(url, save_dir):\n",
-    "    filename = url.split('/')[-1]\n",
-    "    with tqdm(unit = 'B', unit_scale = True, unit_divisor = 1024, miniters = 1, desc = filename) as t:\n",
-    "        urllib.request.urlretrieve(url, filename = os.path.join(save_dir, filename), reporthook = download_hook(t), data = None)\n",
-    "\n",
-    "if __name__ == '__main__':\n",
-    "    # Download ground truth\n",
-    "    if not os.path.exists(\"CMP_facade_DB_base.zip\"):\n",
-    "        download(\"http://cmp.felk.cvut.cz/~tylecr1/facade/CMP_facade_DB_base.zip\", \"./\")\n",
-    "    # Extract in the correct folder\n",
-    "    with zipfile.ZipFile(\"CMP_facade_DB_base.zip\", 'r') as zip_ref:\n",
-    "        zip_ref.extractall(\"./facades\")\n",
-    "        os.rename(\"./facades/base\", \"./facades/train\")\n",
-    "\n",
-    "    # Download ground truth\n",
-    "    if not os.path.exists(\"CMP_facade_DB_extended.zip\"):\n",
-    "        download(\"http://cmp.felk.cvut.cz/~tylecr1/facade/CMP_facade_DB_extended.zip\", \"./\")\n",
-    "    # Extract in the correct folder\n",
-    "    with zipfile.ZipFile(\"CMP_facade_DB_extended.zip\", 'r') as zip_ref:\n",
-    "        zip_ref.extractall(\"./facades\")\n",
-    "        os.rename(\"./facades/extended\", \"./facades/val\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6DHT9c0_BUgA"
-   },
-   "source": [
-    "Configure the dataloader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "rxi_QIpgBUgB"
-   },
-   "outputs": [],
-   "source": [
-    "class ImageDataset(Dataset):\n",
-    "    def __init__(self, root, transforms_=None, mode='train'):\n",
-    "        self.transform = transforms.Compose(transforms_)\n",
-    "\n",
-    "        self.files_img = sorted(glob.glob(os.path.join(root, mode) + '/*.jpg'))\n",
-    "        if mode == 'val':\n",
-    "            self.files_img.extend(\n",
-    "                sorted(glob.glob(os.path.join(root, 'val') + '/*.jpg')))\n",
-    "\n",
-    "        self.files_mask = sorted(glob.glob(os.path.join(root, mode) + '/*.png'))\n",
-    "        if mode == 'val':\n",
-    "            self.files_mask.extend(\n",
-    "                sorted(glob.glob(os.path.join(root, 'val') + '/*.png')))\n",
-    "            \n",
-    "        assert len(self.files_img) == len(self.files_mask)\n",
-    "\n",
-    "    def __getitem__(self, index):\n",
-    "\n",
-    "        img = Image.open(self.files_img[index % len(self.files_img)])\n",
-    "        mask = Image.open(self.files_mask[index % len(self.files_img)])\n",
-    "        mask = mask.convert('RGB')\n",
-    "\n",
-    "        img = self.transform(img)\n",
-    "        mask = self.transform(mask)\n",
-    "\n",
-    "        return img, mask\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.files_img)\n",
-    "    \n",
-    "# Configure dataloaders\n",
-    "transforms_ = [transforms.Resize((img_height, img_width), Image.BICUBIC),\n",
-    "               transforms.ToTensor()]  # transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))\n",
-    "\n",
-    "dataloader = DataLoader(ImageDataset(\"facades\", transforms_=transforms_),\n",
-    "                        batch_size=16, shuffle=True)\n",
-    "\n",
-    "val_dataloader = DataLoader(ImageDataset(\"facades\", transforms_=transforms_, mode='val'),\n",
-    "                            batch_size=8, shuffle=False)\n",
-    "\n",
-    "# Tensor type\n",
-    "Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Okb3LU76BUgG"
-   },
-   "source": [
-    "Check the loading works and a few helper functions"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "xuxq4TZRBUgJ"
-   },
-   "outputs": [],
-   "source": [
-    "def plot2x2Array(image, mask):\n",
-    "    f, axarr = plt.subplots(1, 2)\n",
-    "    axarr[0].imshow(image)\n",
-    "    axarr[1].imshow(mask)\n",
-    "\n",
-    "    axarr[0].set_title('Image')\n",
-    "    axarr[1].set_title('Mask')\n",
-    "\n",
-    "\n",
-    "def reverse_transform(image):\n",
-    "    image = image.numpy().transpose((1, 2, 0))\n",
-    "    image = np.clip(image, 0, 1)\n",
-    "    image = (image * 255).astype(np.uint8)\n",
-    "\n",
-    "    return image\n",
-    "\n",
-    "def plot2x3Array(image, mask,predict):\n",
-    "    f, axarr = plt.subplots(1,3,figsize=(15,15))\n",
-    "    axarr[0].imshow(image)\n",
-    "    axarr[1].imshow(mask)\n",
-    "    axarr[2].imshow(predict)\n",
-    "    axarr[0].set_title('input')\n",
-    "    axarr[1].set_title('real')\n",
-    "    axarr[2].set_title('fake')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "m2NxLrQEBUgM"
-   },
-   "outputs": [],
-   "source": [
-    "image, mask = next(iter(dataloader))\n",
-    "image = reverse_transform(image[0])\n",
-    "mask = reverse_transform(mask[0])\n",
-    "plot2x2Array(image, mask)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "zAvaxAbxBUgQ"
-   },
-   "source": [
-    "Initialize our GAN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "dVgF3qfDBUgR"
-   },
-   "outputs": [],
-   "source": [
-    "# Calculate output of image discriminator (PatchGAN)\n",
-    "patch = (1, img_height//2**3-2, img_width//2**3-2)\n",
-    "\n",
-    "if cuda:\n",
-    "    generator = generator.cuda()\n",
-    "    discriminator = discriminator.cuda()\n",
-    "    criterion_GAN.cuda()\n",
-    "    criterion_pixelwise.cuda()\n",
-    "    \n",
-    "# Optimizers\n",
-    "optimizer_G = torch.optim.Adam(generator.parameters(), lr=lr, betas=(b1, b2))\n",
-    "optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=lr, betas=(b1, b2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "rN3cbiWaBUgf"
-   },
-   "source": [
-    "Start training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "msmQQUX-BUgh"
-   },
-   "outputs": [],
-   "source": [
-    "def save_model(epoch):\n",
-    "    # save your work\n",
-    "    torch.save({\n",
-    "                'epoch': epoch,\n",
-    "                'model_state_dict': generator.state_dict(),\n",
-    "                'optimizer_state_dict': optimizer_G.state_dict(),\n",
-    "                'loss': loss_G,\n",
-    "                }, 'generator_'+str(epoch)+'.pth')\n",
-    "    torch.save({\n",
-    "                'epoch': epoch,\n",
-    "                'model_state_dict': discriminator.state_dict(),\n",
-    "                'optimizer_state_dict': optimizer_D.state_dict(),\n",
-    "                'loss': loss_D,\n",
-    "                }, 'discriminator_'+str(epoch)+'.pth')\n",
-    "    \n",
-    "def weights_init_normal(m):\n",
-    "  classname = m.__class__.__name__\n",
-    "  if classname.find('Conv') != -1:\n",
-    "      torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
-    "  elif classname.find('BatchNorm2d') != -1:\n",
-    "      torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
-    "      torch.nn.init.constant_(m.bias.data, 0.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "6UXrZLLNBUgq"
-   },
-   "source": [
-    "<font color='red'>Complete the loss function </font>  in the following training code and train your network:  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "7NUuGcQ0SiJw"
-   },
-   "outputs": [],
-   "source": [
-    "# ----------\n",
-    "#  Training\n",
-    "# ----------\n",
-    "\n",
-    "losses = []\n",
-    "num_epochs = 200\n",
-    "\n",
-    "# Initialize weights\n",
-    "generator.apply(weights_init_normal)\n",
-    "discriminator.apply(weights_init_normal)\n",
-    "epoch_D = 0\n",
-    "epoch_G = 0\n",
-    "\n",
-    "# train the network\n",
-    "discriminator.train()\n",
-    "generator.train()\n",
-    "print_every = 400\n",
-    "\n",
-    "for epoch in range(epoch_G, num_epochs):\n",
-    "    for i, batch in enumerate(dataloader):\n",
-    "\n",
-    "        # Model inputs\n",
-    "        real_A = Variable(batch[0].type(Tensor))\n",
-    "        real_B = Variable(batch[1].type(Tensor))\n",
-    "\n",
-    "        # Adversarial ground truths\n",
-    "        valid = Variable(Tensor(np.ones((real_B.size(0), *patch))), requires_grad=False)\n",
-    "        fake = Variable(Tensor(np.zeros((real_B.size(0), *patch))), requires_grad=False)\n",
-    "\n",
-    "        # ------------------\n",
-    "        #  Train Generators\n",
-    "        # ------------------\n",
-    "\n",
-    "        optimizer_G.zero_grad()\n",
-    "\n",
-    "        # GAN loss\n",
-    "        # TO DO: Put here your GAN loss\n",
-    "\n",
-    "        # Pixel-wise loss\n",
-    "        # TO DO: Put here your pixel loss\n",
-    "\n",
-    "        # Total loss\n",
-    "        # TO DO: Put here your total loss\n",
-    "\n",
-    "        loss_G.backward()\n",
-    "\n",
-    "        optimizer_G.step()\n",
-    "\n",
-    "        # ---------------------\n",
-    "        #  Train Discriminator\n",
-    "        # ---------------------\n",
-    "\n",
-    "        optimizer_D.zero_grad()\n",
-    "\n",
-    "        # Real loss\n",
-    "        pred_real = discriminator(real_A, real_B)\n",
-    "        loss_real = criterion_GAN(pred_real, valid)\n",
-    "\n",
-    "        # Fake loss\n",
-    "        pred_fake = discriminator(fake_A.detach(), real_B)\n",
-    "        loss_fake = criterion_GAN(pred_fake, fake)\n",
-    "\n",
-    "        # Total loss\n",
-    "        loss_D = 0.5 * (loss_real + loss_fake)\n",
-    "\n",
-    "        loss_D.backward()\n",
-    "        optimizer_D.step()\n",
-    "        \n",
-    "        # Print some loss stats\n",
-    "        if i % print_every == 0:\n",
-    "            # print discriminator and generator loss\n",
-    "            print('Epoch [{:5d}/{:5d}] | d_loss: {:6.4f} | g_loss: {:6.4f}'.format(\n",
-    "                    epoch+1, num_epochs, loss_D.item(), loss_G.item()))\n",
-    "    ## AFTER EACH EPOCH##\n",
-    "    # append discriminator loss and generator loss\n",
-    "    losses.append((loss_D.item(), loss_G.item()))\n",
-    "    if epoch % 100 == 0:\n",
-    "        print('Saving model...')\n",
-    "        save_model(epoch)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "Ed-ZbuVWBUgu"
-   },
-   "source": [
-    "Observation of the loss along the training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "nOLW054DTLpg"
-   },
-   "outputs": [],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "losses = np.array(losses)\n",
-    "plt.plot(losses.T[0], label='Discriminator')\n",
-    "plt.plot(losses.T[1], label='Generator')\n",
-    "plt.title(\"Training Losses\")\n",
-    "plt.legend()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "S58kJj9HBUgV"
-   },
-   "source": [
-    "If the training takes too much time, you can use a pretrained model in the meantime, to evaluate its performance.\n",
-    "\n",
-    "It is available at : https://partage.liris.cnrs.fr/index.php/s/xwEFmxn9ANeq4zY"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "i0TC5qK3BUg4"
-   },
-   "source": [
-    "### Evaluate your cGAN"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "fYBRR6NYBUg6"
-   },
-   "outputs": [],
-   "source": [
-    "def load_model(epoch=200):\n",
-    "    if 'generator_'+str(epoch)+'.pth' in os.listdir() and 'discriminator_'+str(epoch)+'.pth' in os.listdir():\n",
-    "        if cuda:\n",
-    "            checkpoint_generator = torch.load('generator_'+str(epoch)+'.pth')\n",
-    "        else:\n",
-    "            checkpoint_generator = torch.load('generator_'+str(epoch)+'.pth', map_location='cpu')\n",
-    "        generator.load_state_dict(checkpoint_generator['model_state_dict'])\n",
-    "        optimizer_G.load_state_dict(checkpoint_generator['optimizer_state_dict'])\n",
-    "        epoch_G = checkpoint_generator['epoch']\n",
-    "        loss_G = checkpoint_generator['loss']\n",
-    "\n",
-    "        if cuda:\n",
-    "            checkpoint_discriminator = torch.load('discriminator_'+str(epoch)+'.pth')\n",
-    "        else:\n",
-    "            checkpoint_discriminator = torch.load('discriminator_'+str(epoch)+'.pth', map_location='cpu')\n",
-    "        discriminator.load_state_dict(checkpoint_discriminator['model_state_dict'])\n",
-    "        optimizer_D.load_state_dict(checkpoint_discriminator['optimizer_state_dict'])\n",
-    "        epoch_D = checkpoint_discriminator['epoch']\n",
-    "        loss_D = checkpoint_discriminator['loss']\n",
-    "    else:\n",
-    "        print('There isn\\' a training available with this number of epochs')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "4V0DwQomBUg9"
-   },
-   "outputs": [],
-   "source": [
-    "load_model(epoch=200)\n",
-    "\n",
-    "# switching mode\n",
-    "generator.eval()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "gyvmvkIvBUhB"
-   },
-   "outputs": [],
-   "source": [
-    "# show a sample evaluation image on the training base\n",
-    "image, mask = next(iter(dataloader))\n",
-    "output = generator(mask.type(Tensor))\n",
-    "output = output.view(16, 3, 256, 256)\n",
-    "output = output.cpu().detach()\n",
-    "for i in range(8):\n",
-    "    image_plot = reverse_transform(image[i])\n",
-    "    output_plot = reverse_transform(output[i])\n",
-    "    mask_plot = reverse_transform(mask[i])\n",
-    "    plot2x3Array(mask_plot,image_plot,output_plot)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "nqvrxBoGBUhD"
-   },
-   "outputs": [],
-   "source": [
-    "# show a sample evaluation image on the validation dataset\n",
-    "image, mask = next(iter(val_dataloader))\n",
-    "output = generator(mask.type(Tensor))\n",
-    "output = output.view(8, 3, 256, 256)\n",
-    "output = output.cpu().detach()\n",
-    "for i in range(8):\n",
-    "    image_plot = reverse_transform(image[i])\n",
-    "    output_plot = reverse_transform(output[i])\n",
-    "    mask_plot = reverse_transform(mask[i])\n",
-    "    plot2x3Array(mask_plot,image_plot,output_plot)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "qkFVjRsOBUhG"
-   },
-   "source": [
-    "<font color='red'>**Question 4**</font>                                                                  \n",
-    "Compare results for 100 and 200 epochs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "k85Cl5_UDWyv"
-   },
-   "outputs": [],
-   "source": [
-    "# TO DO : Your code here to load and evaluate with a few samples\n",
-    "#         a model after 100 epochs\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "_GbMIfRXBUhH"
-   },
-   "outputs": [],
-   "source": [
-    "# And finally :\n",
-    "if cuda:\n",
-    "    torch.cuda.empty_cache()"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "colab_type": "text",
-    "id": "rVxSSPJgK60P"
-   },
-   "source": [
-    "# How to submit your Work ?\n",
-    "\n",
-    "This work must be done individually. The expected output is a repository named gan-cgan on https://gitlab.ec-lyon.fr. It must contain your notebook (or python files) and a README.md file that explains briefly the successive steps of the project. The last commit is due before 11:59 pm on Wednesday, March 29, 2023. Subsequent commits will not be considered."
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "collapsed_sections": [],
-   "name": "BE2 - GAN and cGAN.ipynb",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3.9.9 64-bit",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.9"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/BE2_GAN_and_cGAN_part_1.ipynb b/BE2_GAN_and_cGAN_part_1.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..86b6a227ba505325a677654a15ceac0cd33cb18b
--- /dev/null
+++ b/BE2_GAN_and_cGAN_part_1.ipynb
@@ -0,0 +1,24407 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UGwKsKS4GMTN"
+      },
+      "source": [
+        "<h1 ><big><center>MSO 3.4 - Deep Structured Learning</center></big></h1>\n",
+        "\n",
+        "<h2><big><center> BE 2 - GANs and cGAN </center></big></h2>\n",
+        "\n",
+        "<h5><big><center>Adapted from <i>Projet d'Option</i> of : Mhamed Jabri, Martin Chauvin, Ahmed Sahraoui, Zakariae Moustaïne and Taoufik Bouchikhi\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">\n",
+        "<img height=300px src=\"https://cdn-images-1.medium.com/max/1080/0*tJRy5Chmk4XymxwN.png\"/></p>\n",
+        "<p align=\"center\"></p>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "16aVF81lJuiP"
+      },
+      "source": [
+        "The aim of this assignment is to discover GANs, understand how they are implemented and then explore one specific architecture of GANs that allows us to perform image to image translation (which corresponds to the picture that you can see above this text ! )\n",
+        "\n",
+        "Before starting the exploration of the world of GANs, here's  what students should do and send back for this assignement : \n",
+        "* In the \"tutorial\" parts of this assignement that focus on explaining new concepts, you'll find <font color='red'>**questions**</font>  that aim to test your understanding of those concepts. \n",
+        "* In some of the code cells, you'll have to complete the code and you'll find a \"TO DO\" explaining what you should implement."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "M-WNKvhOP1ED"
+      },
+      "source": [
+        "# Part1: DC-GAN"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y_r8nMTGQI9a"
+      },
+      "source": [
+        "In this part, we aim to learn and understand the basic concepts of **Generative Adversarial Networks** through a DCGAN and generate new celebrities from the learned network after showing it real celebrities. For this purpose, please study the tutorial here: https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jiHCy4_UUBFb"
+      },
+      "source": [
+        "## Work to do\n",
+        "Now we want to generate handwritten digits using the MNIST dataset. It is available within torvision package (https://pytorch.org/vision/stable/generated/torchvision.datasets.MNIST.html#torchvision.datasets.MNIST)\n",
+        "\n",
+        "Please re-train the DCGAN and display some automatically generated handwritten digits.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SkmiIdRlZiew",
+        "outputId": "b4ff14c7-c423-4402-b895-b84877b9c610"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Seed:  999\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<torch._C.Generator at 0x7fc0bc186230>"
+            ]
+          },
+          "execution_count": 1,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from __future__ import print_function\n",
+        "#%matplotlib inline\n",
+        "import argparse\n",
+        "import os\n",
+        "import random\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.nn.parallel\n",
+        "import torch.backends.cudnn as cudnn\n",
+        "import torch.optim as optim\n",
+        "import torch.utils.data\n",
+        "import torchvision.datasets as dset\n",
+        "import torchvision.transforms as transforms\n",
+        "import torchvision.utils as vutils\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.animation as animation\n",
+        "from IPython.display import HTML\n",
+        "\n",
+        "# Set random seed for reproducibility\n",
+        "manualSeed = 999\n",
+        "#manualSeed = random.randint(1, 10000) # use if you want new results\n",
+        "print(\"Random Seed: \", manualSeed)\n",
+        "random.seed(manualSeed)\n",
+        "torch.manual_seed(manualSeed)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "urUeAODHZiex"
+      },
+      "outputs": [],
+      "source": [
+        "# Number of workers for dataloader\n",
+        "workers = 2\n",
+        "\n",
+        "# Batch size during training\n",
+        "batch_size = 128\n",
+        "\n",
+        "# Spatial size of training images. All images will be resized to this\n",
+        "#   size using a transformer.\n",
+        "image_size = 64\n",
+        "\n",
+        "# Number of channels in the training images. For color images this is 3\n",
+        "nc = 1\n",
+        "\n",
+        "# Size of z latent vector (i.e. size of generator input)\n",
+        "nz = 100\n",
+        "\n",
+        "# Size of feature maps in generator\n",
+        "ngf = 64\n",
+        "\n",
+        "# Size of feature maps in discriminator\n",
+        "ndf = 64\n",
+        "\n",
+        "# Number of training epochs\n",
+        "num_epochs = 5\n",
+        "\n",
+        "# Learning rate for optimizers\n",
+        "lr = 0.0002\n",
+        "\n",
+        "# Beta1 hyperparam for Adam optimizers\n",
+        "beta1 = 0.5\n",
+        "\n",
+        "# Number of GPUs available. Use 0 for CPU mode.\n",
+        "ngpu = 1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 449,
+          "referenced_widgets": [
+            "5b1284d051074026b654029985de0475",
+            "1b0dd88ce7f040929eaf2051798e09cc",
+            "c2c54b034a8740d1bc0f7f0d14c982e0",
+            "545173732009421fbf046b6a7a4815bc",
+            "95ace5c2fee448ef853fb7b07d686b10",
+            "869c96d78fca4491a92e2263f769cc46",
+            "08fa2de34044430e9be9255d9a418797",
+            "451bbec4a51142efa60ee78d1e92d472",
+            "cd2538c04bac43c38c69a8d16d169246",
+            "a989ff56146d4a44bc0c899867f5caa8",
+            "340cd5c542224a46a5d9044c2363abd6",
+            "94814e5bd82f4f8695e8108d25ee3aa2",
+            "81e4ec4c5ff14000bbeb49f813077100",
+            "989524472070470cb31e9730f062df8c",
+            "7cbf03adfce548d1b7a9e090d729c062",
+            "53803a4fefa548e1bf09f3eeecd75818",
+            "33ddb270ba0948f7b9774da98d078c82",
+            "ba94fb941b064461afee0d3a7ca2efe4",
+            "d9c7e8208fb54c7cb6ebe83e544d1923",
+            "c7db9cf34d5543d094d7272eeb7c29b9",
+            "1f7c6037ac0040c6a867696029b8a8ed",
+            "88f53d171b1f4c70a392a4fa30b7760a",
+            "ea73a0d46a1641a6958bf5009ed09f7c",
+            "cbe55fd5da3843a5bc35be8551bb828e",
+            "f35c291e36ca407cbede47c52b1023c3",
+            "6b927538004a47d8a1198afaa1f550a4",
+            "1b7b3ec7d686496794b2bcf38621b402",
+            "e00e3c8a02524dab92f987eaa6751038",
+            "15c398aafa8e45369ec37be532b7e72b",
+            "0268aa9b96db4ee9a71248d34ab4fa4e",
+            "36fdb48445f34cef9115c1210f1bf2d8",
+            "519f25858adf431ea6d4bda6375d17b1",
+            "91d2339790334e879e299e1ef56f6f82",
+            "a9e313ff6c864d4d8972e1ac9aa8ca62",
+            "20b6927f70f646a2a6b5ef9db424f435",
+            "4bcfdc0994fe49129b9672aea700e6ff",
+            "23596e042e3247989541d2f7492e60d2",
+            "80ed97a5707a4a93bdf3a08091e0e5c1",
+            "7bd59329ddf64d92a8e2ef75b5fa240c",
+            "bdb9d506f68d46a2a5aa5c21cd3188c5",
+            "a1ceeaf66926402793dd2089c94cf08c",
+            "277c1f5e1c9e487aa159c98d02a76745",
+            "18ddcb15a10b465f8caf79b57245c0ce",
+            "89c16f16b3a0470d8a158c5933851c29"
+          ]
+        },
+        "id": "rAM2-_EOZiex",
+        "outputId": "e7b5ee70-1652-4205-ccf3-8f3c7ca35219"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to MNIST/raw/train-images-idx3-ubyte.gz\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "5b1284d051074026b654029985de0475",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/9912422 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Extracting MNIST/raw/train-images-idx3-ubyte.gz to MNIST/raw\n",
+            "\n",
+            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to MNIST/raw/train-labels-idx1-ubyte.gz\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "94814e5bd82f4f8695e8108d25ee3aa2",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/28881 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Extracting MNIST/raw/train-labels-idx1-ubyte.gz to MNIST/raw\n",
+            "\n",
+            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
+            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to MNIST/raw/t10k-images-idx3-ubyte.gz\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ea73a0d46a1641a6958bf5009ed09f7c",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/1648877 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Extracting MNIST/raw/t10k-images-idx3-ubyte.gz to MNIST/raw\n",
+            "\n",
+            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a9e313ff6c864d4d8972e1ac9aa8ca62",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "  0%|          | 0/4542 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Extracting MNIST/raw/t10k-labels-idx1-ubyte.gz to MNIST/raw\n",
+            "\n",
+            "CUDA is available!  Training on GPU ...\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load dataset\n",
+        "dataset = dset.MNIST(\n",
+        "    '', \n",
+        "    download=True, \n",
+        "    transform=transforms.Compose([\n",
+        "        transforms.Resize(image_size),\n",
+        "        transforms.CenterCrop(image_size),\n",
+        "        transforms.ToTensor(),\n",
+        "        transforms.Normalize((0.5,), (0.5,)),\n",
+        "    ])\n",
+        ")\n",
+        "\n",
+        "# Create the dataloader\n",
+        "dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size,\n",
+        "                                         shuffle=True, num_workers=workers)\n",
+        "\n",
+        "# check if GPU is available\n",
+        "if torch.cuda.is_available():\n",
+        "    device = torch.device(\"cuda:0\")\n",
+        "    print(\"CUDA is available!  Training on GPU ...\")\n",
+        "elif torch.backends.mps.is_available():\n",
+        "    device = torch.device(\"mps\")\n",
+        "    print(\"MPS is available.  Training on MAC GPU ...\")\n",
+        "else:\n",
+        "    device = torch.device(\"cpu\")\n",
+        "    print(\"Nor CUDA or MPS are available. Training on CPU ...\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 500
+        },
+        "id": "lFnqmFq1Ziey",
+        "outputId": "7d6f4dac-e96c-4b4a-a6a3-adbcdd37a3b6"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.image.AxesImage at 0x7fc0337459d0>"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 576x576 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot some training images\n",
+        "real_batch = next(iter(dataloader))\n",
+        "plt.figure(figsize=(8,8))\n",
+        "plt.axis(\"off\")\n",
+        "plt.title(\"Training Images\")\n",
+        "plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=2, normalize=True).cpu(),(1,2,0)))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IJc0V1GyZiey"
+      },
+      "outputs": [],
+      "source": [
+        "# custom weights initialization called on netG and netD\n",
+        "def weights_init(m):\n",
+        "    classname = m.__class__.__name__\n",
+        "    if classname.find('Conv') != -1:\n",
+        "        nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
+        "    elif classname.find('BatchNorm') != -1:\n",
+        "        nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
+        "        nn.init.constant_(m.bias.data, 0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0qBH4NSEZiey"
+      },
+      "outputs": [],
+      "source": [
+        "# Generator Code\n",
+        "\n",
+        "class Generator(nn.Module):\n",
+        "    def __init__(self, ngpu):\n",
+        "        super(Generator, self).__init__()\n",
+        "        self.ngpu = ngpu\n",
+        "        self.main = nn.Sequential(\n",
+        "            # input is Z, going into a convolution\n",
+        "            nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),\n",
+        "            nn.BatchNorm2d(ngf * 8),\n",
+        "            nn.ReLU(True),\n",
+        "            # state size. (ngf*8) x 4 x 4\n",
+        "            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),\n",
+        "            nn.BatchNorm2d(ngf * 4),\n",
+        "            nn.ReLU(True),\n",
+        "            # state size. (ngf*4) x 8 x 8\n",
+        "            nn.ConvTranspose2d( ngf * 4, ngf * 2, 4, 2, 1, bias=False),\n",
+        "            nn.BatchNorm2d(ngf * 2),\n",
+        "            nn.ReLU(True),\n",
+        "            # state size. (ngf*2) x 16 x 16\n",
+        "            nn.ConvTranspose2d( ngf * 2, ngf, 4, 2, 1, bias=False),\n",
+        "            nn.BatchNorm2d(ngf),\n",
+        "            nn.ReLU(True),\n",
+        "            # state size. (ngf) x 32 x 32\n",
+        "            nn.ConvTranspose2d( ngf, nc, 4, 2, 1, bias=False),\n",
+        "            nn.Tanh()\n",
+        "            # state size. (nc) x 64 x 64\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, input):\n",
+        "        return self.main(input)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TPJmdQACZiey",
+        "outputId": "533f8454-d018-433e-831b-7139a946a1c0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Generator(\n",
+            "  (main): Sequential(\n",
+            "    (0): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
+            "    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+            "    (2): ReLU(inplace=True)\n",
+            "    (3): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+            "    (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+            "    (5): ReLU(inplace=True)\n",
+            "    (6): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+            "    (7): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+            "    (8): ReLU(inplace=True)\n",
+            "    (9): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+            "    (10): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+            "    (11): ReLU(inplace=True)\n",
+            "    (12): ConvTranspose2d(64, 1, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+            "    (13): Tanh()\n",
+            "  )\n",
+            ")\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Create the generator\n",
+        "netG = Generator(ngpu).to(device)\n",
+        "\n",
+        "# Handle multi-gpu if desired\n",
+        "if (device.type == 'cuda') and (ngpu > 1):\n",
+        "    netG = nn.DataParallel(netG, list(range(ngpu)))\n",
+        "\n",
+        "# Apply the weights_init function to randomly initialize all weights\n",
+        "#  to mean=0, stdev=0.02.\n",
+        "netG.apply(weights_init)\n",
+        "\n",
+        "# Print the model\n",
+        "print(netG)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "jLVbsW7HZiez"
+      },
+      "outputs": [],
+      "source": [
+        "class Discriminator(nn.Module):\n",
+        "    def __init__(self, ngpu):\n",
+        "        super(Discriminator, self).__init__()\n",
+        "        self.ngpu = ngpu\n",
+        "        self.main = nn.Sequential(\n",
+        "            # input is (nc) x 64 x 64\n",
+        "            nn.Conv2d(nc, ndf, 4, 2, 1, bias=False),\n",
+        "            nn.LeakyReLU(0.2, inplace=True),\n",
+        "            # state size. (ndf) x 32 x 32\n",
+        "            nn.Conv2d(ndf, ndf * 2, 4, 2, 1, bias=False),\n",
+        "            nn.BatchNorm2d(ndf * 2),\n",
+        "            nn.LeakyReLU(0.2, inplace=True),\n",
+        "            # state size. (ndf*2) x 16 x 16\n",
+        "            nn.Conv2d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),\n",
+        "            nn.BatchNorm2d(ndf * 4),\n",
+        "            nn.LeakyReLU(0.2, inplace=True),\n",
+        "            # state size. (ndf*4) x 8 x 8\n",
+        "            nn.Conv2d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),\n",
+        "            nn.BatchNorm2d(ndf * 8),\n",
+        "            nn.LeakyReLU(0.2, inplace=True),\n",
+        "            # state size. (ndf*8) x 4 x 4\n",
+        "            nn.Conv2d(ndf * 8, 1, 4, 1, 0, bias=False),\n",
+        "            nn.Sigmoid()\n",
+        "        )\n",
+        "\n",
+        "    def forward(self, input):\n",
+        "        return self.main(input)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pYBdOIyaZiez",
+        "outputId": "7ed5db6c-eaf1-4b6b-9565-1638fa7002d6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Discriminator(\n",
+            "  (main): Sequential(\n",
+            "    (0): Conv2d(1, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+            "    (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+            "    (2): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+            "    (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+            "    (4): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+            "    (5): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+            "    (6): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+            "    (7): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+            "    (8): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n",
+            "    (9): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+            "    (10): LeakyReLU(negative_slope=0.2, inplace=True)\n",
+            "    (11): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n",
+            "    (12): Sigmoid()\n",
+            "  )\n",
+            ")\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Create the Discriminator\n",
+        "netD = Discriminator(ngpu).to(device)\n",
+        "\n",
+        "# Handle multi-gpu if desired\n",
+        "if (device.type == 'cuda') and (ngpu > 1):\n",
+        "    netD = nn.DataParallel(netD, list(range(ngpu)))\n",
+        "\n",
+        "# Apply the weights_init function to randomly initialize all weights\n",
+        "#  to mean=0, stdev=0.2.\n",
+        "netD.apply(weights_init)\n",
+        "\n",
+        "# Print the model\n",
+        "print(netD)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qTIyB0ZyZiez"
+      },
+      "outputs": [],
+      "source": [
+        "# Initialize BCELoss function\n",
+        "criterion = nn.BCELoss()\n",
+        "\n",
+        "# Create batch of latent vectors that we will use to visualize\n",
+        "#  the progression of the generator\n",
+        "fixed_noise = torch.randn(64, nz, 1, 1, device=device)\n",
+        "\n",
+        "# Establish convention for real and fake labels during training\n",
+        "real_label = 1.\n",
+        "fake_label = 0.\n",
+        "\n",
+        "# Setup Adam optimizers for both G and D\n",
+        "optimizerD = optim.Adam(netD.parameters(), lr=lr, betas=(beta1, 0.999))\n",
+        "optimizerG = optim.Adam(netG.parameters(), lr=lr, betas=(beta1, 0.999))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4wgBdKIAZiez",
+        "outputId": "4de0d92b-8d38-4d08-b5bf-19d629a35341"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Starting Training Loop...\n",
+            "[0/5][0/469]\tLoss_D: 1.6647\tLoss_G: 3.5638\tD(x): 0.5276\tD(G(z)): 0.5480 / 0.0425\n",
+            "[0/5][50/469]\tLoss_D: 1.0761\tLoss_G: 36.3118\tD(x): 0.5811\tD(G(z)): 0.0000 / 0.0000\n",
+            "[0/5][100/469]\tLoss_D: 0.0011\tLoss_G: 37.8798\tD(x): 0.9990\tD(G(z)): 0.0000 / 0.0000\n",
+            "[0/5][150/469]\tLoss_D: 0.0003\tLoss_G: 37.7210\tD(x): 0.9997\tD(G(z)): 0.0000 / 0.0000\n",
+            "[0/5][200/469]\tLoss_D: 0.0001\tLoss_G: 37.2140\tD(x): 0.9999\tD(G(z)): 0.0000 / 0.0000\n",
+            "[0/5][250/469]\tLoss_D: 0.0000\tLoss_G: 35.0420\tD(x): 1.0000\tD(G(z)): 0.0000 / 0.0000\n",
+            "[0/5][300/469]\tLoss_D: 0.3094\tLoss_G: 5.0151\tD(x): 0.7723\tD(G(z)): 0.0106 / 0.0118\n",
+            "[0/5][350/469]\tLoss_D: 0.3624\tLoss_G: 5.4329\tD(x): 0.9018\tD(G(z)): 0.1934 / 0.0069\n",
+            "[0/5][400/469]\tLoss_D: 0.3947\tLoss_G: 3.5341\tD(x): 0.8058\tD(G(z)): 0.1155 / 0.0408\n",
+            "[0/5][450/469]\tLoss_D: 0.2421\tLoss_G: 3.2379\tD(x): 0.8830\tD(G(z)): 0.0971 / 0.0579\n",
+            "[1/5][0/469]\tLoss_D: 0.2054\tLoss_G: 3.0700\tD(x): 0.9322\tD(G(z)): 0.1097 / 0.0654\n",
+            "[1/5][50/469]\tLoss_D: 0.4544\tLoss_G: 3.9403\tD(x): 0.8996\tD(G(z)): 0.2575 / 0.0312\n",
+            "[1/5][100/469]\tLoss_D: 0.3328\tLoss_G: 2.9637\tD(x): 0.8577\tD(G(z)): 0.1295 / 0.0812\n",
+            "[1/5][150/469]\tLoss_D: 0.8733\tLoss_G: 1.1586\tD(x): 0.5426\tD(G(z)): 0.1110 / 0.3794\n",
+            "[1/5][200/469]\tLoss_D: 0.4456\tLoss_G: 2.6109\tD(x): 0.8740\tD(G(z)): 0.2449 / 0.0988\n",
+            "[1/5][250/469]\tLoss_D: 0.9665\tLoss_G: 1.0699\tD(x): 0.4570\tD(G(z)): 0.0252 / 0.4171\n",
+            "[1/5][300/469]\tLoss_D: 0.4754\tLoss_G: 1.6058\tD(x): 0.7410\tD(G(z)): 0.1413 / 0.2322\n",
+            "[1/5][350/469]\tLoss_D: 5.2394\tLoss_G: 0.4646\tD(x): 0.0134\tD(G(z)): 0.0015 / 0.6867\n",
+            "[1/5][400/469]\tLoss_D: 0.4361\tLoss_G: 1.7491\tD(x): 0.7918\tD(G(z)): 0.1565 / 0.2079\n",
+            "[1/5][450/469]\tLoss_D: 0.3503\tLoss_G: 2.2397\tD(x): 0.8102\tD(G(z)): 0.1130 / 0.1271\n",
+            "[2/5][0/469]\tLoss_D: 0.5412\tLoss_G: 2.2170\tD(x): 0.8335\tD(G(z)): 0.2641 / 0.1409\n",
+            "[2/5][50/469]\tLoss_D: 0.4603\tLoss_G: 2.9827\tD(x): 0.8956\tD(G(z)): 0.2702 / 0.0663\n",
+            "[2/5][100/469]\tLoss_D: 0.4714\tLoss_G: 2.1066\tD(x): 0.7862\tD(G(z)): 0.1760 / 0.1602\n",
+            "[2/5][150/469]\tLoss_D: 1.1968\tLoss_G: 0.3174\tD(x): 0.3675\tD(G(z)): 0.0331 / 0.7600\n",
+            "[2/5][200/469]\tLoss_D: 0.5540\tLoss_G: 1.9371\tD(x): 0.7174\tD(G(z)): 0.1566 / 0.1802\n",
+            "[2/5][250/469]\tLoss_D: 0.5890\tLoss_G: 4.7816\tD(x): 0.9730\tD(G(z)): 0.3806 / 0.0138\n",
+            "[2/5][300/469]\tLoss_D: 0.4398\tLoss_G: 1.6311\tD(x): 0.7036\tD(G(z)): 0.0544 / 0.2466\n",
+            "[2/5][350/469]\tLoss_D: 0.6743\tLoss_G: 3.2679\tD(x): 0.8714\tD(G(z)): 0.3736 / 0.0508\n",
+            "[2/5][400/469]\tLoss_D: 1.0301\tLoss_G: 1.7936\tD(x): 0.4652\tD(G(z)): 0.0655 / 0.2398\n",
+            "[2/5][450/469]\tLoss_D: 0.4778\tLoss_G: 1.8480\tD(x): 0.7730\tD(G(z)): 0.1704 / 0.1949\n",
+            "[3/5][0/469]\tLoss_D: 0.4503\tLoss_G: 1.4237\tD(x): 0.7138\tD(G(z)): 0.0664 / 0.2883\n",
+            "[3/5][50/469]\tLoss_D: 0.4492\tLoss_G: 1.8684\tD(x): 0.7674\tD(G(z)): 0.1449 / 0.1897\n",
+            "[3/5][100/469]\tLoss_D: 0.2579\tLoss_G: 3.1759\tD(x): 0.9062\tD(G(z)): 0.1385 / 0.0528\n",
+            "[3/5][150/469]\tLoss_D: 0.2720\tLoss_G: 2.8981\tD(x): 0.7885\tD(G(z)): 0.0133 / 0.0740\n",
+            "[3/5][200/469]\tLoss_D: 0.3770\tLoss_G: 2.4892\tD(x): 0.8960\tD(G(z)): 0.2123 / 0.1158\n",
+            "[3/5][250/469]\tLoss_D: 0.5292\tLoss_G: 2.1836\tD(x): 0.6829\tD(G(z)): 0.0881 / 0.1470\n",
+            "[3/5][300/469]\tLoss_D: 0.3888\tLoss_G: 1.9216\tD(x): 0.7715\tD(G(z)): 0.0892 / 0.1902\n",
+            "[3/5][350/469]\tLoss_D: 1.0527\tLoss_G: 1.6183\tD(x): 0.4803\tD(G(z)): 0.1593 / 0.2506\n",
+            "[3/5][400/469]\tLoss_D: 0.5676\tLoss_G: 4.4470\tD(x): 0.9490\tD(G(z)): 0.3568 / 0.0182\n",
+            "[3/5][450/469]\tLoss_D: 0.3094\tLoss_G: 4.9702\tD(x): 0.9597\tD(G(z)): 0.2205 / 0.0109\n",
+            "[4/5][0/469]\tLoss_D: 0.1506\tLoss_G: 3.5363\tD(x): 0.9301\tD(G(z)): 0.0714 / 0.0397\n",
+            "[4/5][50/469]\tLoss_D: 0.5106\tLoss_G: 2.3718\tD(x): 0.7621\tD(G(z)): 0.1785 / 0.1204\n",
+            "[4/5][100/469]\tLoss_D: 0.5713\tLoss_G: 2.9989\tD(x): 0.8433\tD(G(z)): 0.2796 / 0.0702\n",
+            "[4/5][150/469]\tLoss_D: 0.3277\tLoss_G: 2.5302\tD(x): 0.8208\tD(G(z)): 0.1038 / 0.1076\n",
+            "[4/5][200/469]\tLoss_D: 0.4611\tLoss_G: 2.6754\tD(x): 0.6867\tD(G(z)): 0.0335 / 0.1001\n",
+            "[4/5][250/469]\tLoss_D: 0.1429\tLoss_G: 3.6879\tD(x): 0.9212\tD(G(z)): 0.0548 / 0.0351\n",
+            "[4/5][300/469]\tLoss_D: 0.5275\tLoss_G: 3.5465\tD(x): 0.8795\tD(G(z)): 0.2897 / 0.0406\n",
+            "[4/5][350/469]\tLoss_D: 0.1198\tLoss_G: 4.0385\tD(x): 0.9521\tD(G(z)): 0.0648 / 0.0264\n",
+            "[4/5][400/469]\tLoss_D: 0.5846\tLoss_G: 1.7904\tD(x): 0.6716\tD(G(z)): 0.1197 / 0.2124\n",
+            "[4/5][450/469]\tLoss_D: 0.2202\tLoss_G: 3.2276\tD(x): 0.9008\tD(G(z)): 0.1000 / 0.0552\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Training Loop\n",
+        "\n",
+        "# Lists to keep track of progress\n",
+        "img_list = []\n",
+        "G_losses = []\n",
+        "D_losses = []\n",
+        "iters = 0\n",
+        "\n",
+        "print(\"Starting Training Loop...\")\n",
+        "# For each epoch\n",
+        "for epoch in range(num_epochs):\n",
+        "    # For each batch in the dataloader\n",
+        "    for i, data in enumerate(dataloader, 0):\n",
+        "\n",
+        "        ############################\n",
+        "        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))\n",
+        "        ###########################\n",
+        "        ## Train with all-real batch\n",
+        "        netD.zero_grad()\n",
+        "        # Format batch\n",
+        "        real_cpu = data[0].to(device)\n",
+        "        b_size = real_cpu.size(0)\n",
+        "        label = torch.full((b_size,), real_label, dtype=torch.float, device=device)\n",
+        "        # Forward pass real batch through D\n",
+        "        output = netD(real_cpu).view(-1)\n",
+        "        # Calculate loss on all-real batch\n",
+        "        errD_real = criterion(output, label)\n",
+        "        # Calculate gradients for D in backward pass\n",
+        "        errD_real.backward()\n",
+        "        D_x = output.mean().item()\n",
+        "\n",
+        "        ## Train with all-fake batch\n",
+        "        # Generate batch of latent vectors\n",
+        "        noise = torch.randn(b_size, nz, 1, 1, device=device)\n",
+        "        # Generate fake image batch with G\n",
+        "        fake = netG(noise)\n",
+        "        label.fill_(fake_label)\n",
+        "        # Classify all fake batch with D\n",
+        "        output = netD(fake.detach()).view(-1)\n",
+        "        # Calculate D's loss on the all-fake batch\n",
+        "        errD_fake = criterion(output, label)\n",
+        "        # Calculate the gradients for this batch, accumulated (summed) with previous gradients\n",
+        "        errD_fake.backward()\n",
+        "        D_G_z1 = output.mean().item()\n",
+        "        # Compute error of D as sum over the fake and the real batches\n",
+        "        errD = errD_real + errD_fake\n",
+        "        # Update D\n",
+        "        optimizerD.step()\n",
+        "\n",
+        "        ############################\n",
+        "        # (2) Update G network: maximize log(D(G(z)))\n",
+        "        ###########################\n",
+        "        netG.zero_grad()\n",
+        "        label.fill_(real_label)  # fake labels are real for generator cost\n",
+        "        # Since we just updated D, perform another forward pass of all-fake batch through D\n",
+        "        output = netD(fake).view(-1)\n",
+        "        # Calculate G's loss based on this output\n",
+        "        errG = criterion(output, label)\n",
+        "        # Calculate gradients for G\n",
+        "        errG.backward()\n",
+        "        D_G_z2 = output.mean().item()\n",
+        "        # Update G\n",
+        "        optimizerG.step()\n",
+        "\n",
+        "        # Output training stats\n",
+        "        if i % 50 == 0:\n",
+        "            print('[%d/%d][%d/%d]\\tLoss_D: %.4f\\tLoss_G: %.4f\\tD(x): %.4f\\tD(G(z)): %.4f / %.4f'\n",
+        "                  % (epoch, num_epochs, i, len(dataloader),\n",
+        "                     errD.item(), errG.item(), D_x, D_G_z1, D_G_z2))\n",
+        "\n",
+        "        # Save Losses for plotting later\n",
+        "        G_losses.append(errG.item())\n",
+        "        D_losses.append(errD.item())\n",
+        "\n",
+        "        # Check how the generator is doing by saving G's output on fixed_noise\n",
+        "        if (iters % 500 == 0) or ((epoch == num_epochs-1) and (i == len(dataloader)-1)):\n",
+        "            with torch.no_grad():\n",
+        "                fake = netG(fixed_noise).detach().cpu()\n",
+        "            img_list.append(vutils.make_grid(fake, padding=2, normalize=True))\n",
+        "\n",
+        "        iters += 1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 350
+        },
+        "id": "Nh9-QIo5baTv",
+        "outputId": "3b3e8b87-e96e-4e9c-8c0f-adbeed904e4b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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PhlXT63adO+0NJw6p23XWQCJqvE4EJjnnVvvPV5tZRwD/b9Tw0Tk31DnX3znXv127dgkopoiIiEhFCxcuJD09nfbt22/3uhJxk+zzKGtmBPgYuAQY4v8dnoAyiIiISKpJYs1U0Nq1axk0aBDXXnstVgetR3ENvMysCXAs8OewyUOAd83scmAxcHY8yyAiIiJSE/n5+fTt25fi4mIyMjK46KKLuOmmm+pk3XENvJxzW4E25aatx+vlKMmgAVRFREQqVVpaGrd1a+T6BkPJ9SIiIsmmwEtEREQkQRR4iYiIiCSIAq8GRzleIiJSv9XFCPGJUJtyKvBqKDSAqoiIpICcnBzWr19f74Mv5xzr168nJyenRsslYhwvqU/q+Q9ZREQatk6dOrFs2TLWrl2b7KJUKScnh06dOtVoGQVeIiIiUm9kZmbSrVu3ZBcjbtTU2NCoyVFERCRpFHg1NGpqFBERSRoFXg2GarpERESSTYGXiIiISIIo8BIRERFJEAVeDY5yvERERJJFgVdDod6MIiIiSafAq6FRr0YREZGkUeAlIiIikiAKvBoaNTmKiIgkjQKvhkZNjSIiIkmjwKvBUE2XiIhIsinwEhEREUkQBV4iIiIiCaLAq8FRjpeIiEiyKPBqKNSbUUREJOkUeDU06tUoIiKSNHENvMyspZm9Z2ZzzGy2mQ00s9ZmNtLM5vl/W8WzDCIiIiL1RbxrvJ4AvnDO7QHsC8wGBgOjnHM9gVH+cxEREZEdXtwCLzNrARwGvAjgnCtyzm0CTgeG+bMNA86IVxkknHK8REREki2eNV7dgLXAy2Y22cxeMLMmQAfn3Ep/nlVAhziWQURERKTeiGfglQH0A551zu0HbKVcs6JzzhFjfAMzu9LMJpjZhLVr18axmCIiIiKJEc/AaxmwzDn3i//8PbxAbLWZdQTw/66JtrBzbqhzrr9zrn+7du3iWEwRERGRxIhb4OWcWwUsNbNe/qSjgVnAx8Al/rRLgOHxKoNEo+EkREREkiUjzuu/DnjDzLKAhcBleMHeu2Z2ObAYODvOZRDQAKoiIiL1QFwDL+fcFKB/lJeOjuf7SiU0gKqIiEjSaOR6ERERkQRR4CUiIiKSIAq8GgzleImIiCSbAq8GRzleIiIiyaLAS0RERCRBFHg1OGpyFBERSRYFXg2OmhpFRESSRYFXQ6GKLhERkaRT4NXQaABVERGRpFHgJSIiIpIgCrxEREREEkSBV4OhJC8REZFkU+DV4CjHS0REJFkUeImIiIgkiAKvBkdNjiIiIsmiwKvBUVOjiIhIsijwaihMNV0iIiLJpsCrodEAqiIiIkmjwEtEREQkQRR4iYiIiCSIAq8GQzleIiIiyabAq8FRjpeIiEiyKPASERERSRAFXiIiIiIJkhHPlZvZImALUAqUOOf6m1lr4B2gK7AIONs5tzGe5RA0jpeIiEg9kIgaryOdc32dc/3954OBUc65nsAo/7mIiIjIDi8ZTY2nA8P8x8OAM5JQhoZLufUiIiJJE+/AywFfmdlEM7vSn9bBObfSf7wK6BDnMoiIiIjUC3HN8QIOdc4tN7P2wEgzmxP+onPOmVnUOhg/ULsSoEuXLnEuZkOiKi8REZFkiWuNl3Nuuf93DfAhMABYbWYdAfy/a2IsO9Q51985179du3bxLGYDoeR6ERGRZItb4GVmTcysWfAxcBwwA/gYuMSf7RJgeLzKIFHoJtkiIiJJE8+mxg7Ah+YNY5ABvOmc+8LMxgPvmtnlwGLg7DiWQURERKTeiFvg5ZxbCOwbZfp64Oh4va9URTVeIiIiyaKR6xsKDaAqIiKSdAq8RERERBJEgVdDo+R6ERGRpFHgJSIiIpIgCrwaHNV4iYiIJIsCrwZDyfUiIiLJpsCroVGOl4iISNIo8BIRERFJEAVeDY5qvERERJJFgVdDoQFURUREkk6BV0OjHC8REZGkUeDVADz+9a9MXLIp2cUQERFp8BR4NQCPfz0PU26XiIhI0inwanAUgImIiCSLAq8GwmkAVRERkaRT4NXQKLleREQkaRR4NQAaSUJERKR+UODVAKRFRF6q8RIREUkWBV4NQJopx0tERKQ+UODVAFh4jZdyvERERJJGgVcDYKiBUUREpD5Q4NUAKMdLRESkflDg1QCkKb1LRESkXlDg1QCkaTwJERGReiHugZeZpZvZZDP71H/ezcx+MbP5ZvaOmWXFuwwNXVqakutFRETqg0TUeF0PzA57/jDwmHOuB7ARuDwBZWjQVOElIiJSP8Q18DKzTsDJwAv+cwOOAt7zZxkGnBHPMoiS60VEROqLeNd4PQ7cAgT8522ATc65Ev/5MmCXOJehwdMAqiIiIvVDtQIvM2tiZmn+493N7DQzy6ximVOANc65ibUpmJldaWYTzGzC2rVra7MK8WkAVRERkfqhujVe3wM5ZrYL8BVwEfBKFcscApxmZouAt/GaGJ8AWppZhj9PJ2B5tIWdc0Odc/2dc/3btWtXzWJKNKrxEhERqR+qG3iZc24bcBbwjHPu/4A+lS3gnLvNOdfJOdcVOBf4xjl3AfAt8Ad/tkuA4bUquVSbcrxERETqh2oHXmY2ELgAGOFPS6/le94K3GRm8/Fyvl6s5XqSqrg0UPVM9YTG8RIREakfMqqeBYAbgNuAD51zM82sO17NVbU450YDo/3HC4EBNSplPVJYUsoF//2FCYs3AvDlDYfRa6dmSS5V5RR3iYiI1A/VCrycc98B3wH4SfbrnHN/iWfB6qsf560LBV0Axz/+fejxA2fsxaE92vLr6i0c0as9uQXFpJnRJDud7IzaVhBuv4gaL7U0ioiIJE21Ai8zexMYBJQC44HmZvaEc+5f8SxcffTbuq0ATLjzGD6bvpKnv53P6txCAO78aEbM5fbs2Jzf1uVxWM92zF+Tx7kDOnPavrvQJDudZjmZ5BeVkp5mmEFGmkX2RNxOulejiIhI/VDdpsbezrlcM7sA+BwYDEwEGlzgtWZLIVkZabRpksXFA7ty8cCuFJaU8uGk5Qz+YHrM5WavzAXgq1mrAXjwszk8+NmcSt/rjSsO5ODd2lBc6nC4WteaKbleRESkfqhu4JXpj9t1BvCUc67YzBrkGXxbUQlNstIjaqSyM9I5d0AXzh3QBYD8olLmrt5C747NCTjHxm1FFBYHeGLUPJZvzGfcog0ANMvJYOcWjZi7ekvU97rghV8qTDunf2dO2GsnjujVrvq1YqrxEhERqReqG3g9DywCpgLfm9muQG68ClWf5RcFaJxV+dfWKCudvp1bhp53bNEIgMfO6VvpciOmrSQj3SgoLiW3oIQnvv6VdXlFEfO8M2Ep70xYGnp+zJ7teer8fmSmp5Eeo00xTQOoioiI1AvVTa5/EngybNJiMzsyPkWq3/KLS2iUFZ9E+ZP36Rjx/KKDdgW8npSzV25h8fqtXP/2lIh5vp69hj3u+gKAtk2zue+0PhXWowFURURE6ofqJte3AO4BDvMnfQfcD2yOU7nqrW1FpTSOU+AVS3aGV4PWt3NLDurehpKAo3lOBks35PP17NU8OvJXANblFXLNm5OYuWI3bjlhj9DyyvESERGpH6rb1PgSMAM4239+EfAy3kj2DUpBcSnZGfG+t3hsHZrnhB733jmT3js355ojewDe0Bbz1+TxzOgFLN+Uz7//b18y09PqtIekiIiI1F51I4jdnHP3OOcW+v/uA7rHs2D1VWnAkZGWvMArmvQ0Iz3N+Pqmw3norL0BGD5lBVe+OgEoN5yEcrxERESSproRRL6ZHRp8YmaHAPnxKVL9VhpwMZPY64PzBnRh8l3HAvDt3LXMWpFLmhnO1d8yi4iINBTVDbwGAU+b2SIzWwQ8Bfw5bqWqx0odpNXjwAugVZMsnji3LwD3fjyTNIMGOvqHiIhIvVLdXo1TgX3NrLn/PNfMbgCmxbFs9VIg4Eiv33EXAKf33YVZK3MZ+v1CurRuHPaKAjAREZFkqVGyknMu1zkXHL/rpjiUp96r702N4U7osxPOweL129TUKCIiUg9sT5Z4gzyTB5wrNzxD/bVPp5a0bJwZOVHJ9SIiIkmzPYFXgzyDp1KNV3qa0btjc6CBbiwREZF6ptIcLzPbQvRztgGN4lKiei6VAi8ou11RGYVgIiIiyVJp4OWca5aogqSKUpdagVebplnJLoKIiIj46tdIoCmgNOBIT5EcL4AWjZTjJSIiUl9U95ZBDV5RSYANW4sIBFy9H8crXDDw0k2yRUREkk+BVzUN+XwOL435jUaZ6SlV49Uk27uhtym3S0REJOnU1FhN385dA0B+cWlK1XhlZ6SXm6IATEREJFkUeFVTaaAsYElPoW8tO8MrrJoaRUREki+FQojkCgYwABlpqfO1VajxUnK9iIhI0qROBJFkjbPKAphUGbkeIDszWOMlIiIiyRa3wMvMcsxsnJlNNbOZZnafP72bmf1iZvPN7B0zS4mBpjLD2hdTsamxjEIwERGRZIlnCFEIHOWc2xfoC5xgZgcBDwOPOed6ABuBy+NYhrhIqRqvCsn1IiIikixxC7ycJ89/mun/c8BRwHv+9GHAGfEqQ10qCaRmTVGFUfaV4yUiIpI0cW00M7N0M5sCrAFGAguATc65En+WZcAu8SxDXSkJBMqepE6FVyjwUq9GERGR5Itr4OWcK3XO9QU6AQOAPaq7rJldaWYTzGzC2rVr41XEaispLaspshQKYioOOaYaLxERkWRJSJq4c24T8C0wEGhpZsER8zsBy2MsM9Q51985179du3aJKGalUrWpMZXy0URERHZ08ezV2M7MWvqPGwHHArPxArA/+LNdAgyPVxnqypaCYgpLSkPPUymWSaVR9kVERHZ08bxXY0dgmJml4wV47zrnPjWzWcDbZvYAMBl4MY5lqBN73/tVxPNUCmUq3FdSyfUiIiJJE7fAyzk3DdgvyvSFePleKaE0RZsYg4IVXkquFxERSb4UGgo0OYpLAxWmpXZTY2oHkiIiIqlMgVcVoiXVp1avxtQpq4iIyI5OgVclCopLOejBUckuxnapWOGlGi8REZFkUeBVieWb8skrLKkwPZUqkdSrUUREpP5Q4FWJQIzE+lQKZdTUKCIiUn8o8KpEqg6aGq7CcBIiIiKSNAq8KnHiEz9EfyGFgpkKRVWOl4iISNIo8IohVjMjpFZTo26SLSIiUn8o8Ipha1HFpPpUpBwvERGR+kOBVwy5BbEDr1SKZSp2alRTo4iISLIo8Iohv6i06plSgKVSlCgiIrKDU+AVU2U5XikczCi5XkREJGkUeMVQWXySipVISq4XERFJPgVeMey49UI77icTERGp7xR4xRCopMor1eqOOrKeI9KnJrsYIiIiDZ4Cryicc/zri7kxX0+1psZrMj4qe6IcLxERkaRR4BVFcalj1Jw1yS6GiIiI7GAUeNWChmgQERGR2lDgFUVl+V2pb0f+bCIiIvWbAi8RERGRBFHgFUVVFV4p3dK4Q9fmiYiI1G8KvKJwVTTHpfTI9SIiIpI0Cryi2LErhXboDyciIlKvKfCKoqrQJKWbGkVERCRpFHhF4aqo8kq1uCuivDt2dZ6IiEi9FrfAy8w6m9m3ZjbLzGaa2fX+9NZmNtLM5vl/W8WrDLWl0ERERETiIZ41XiXAX51zvYGDgGvMrDcwGBjlnOsJjPKf1ys7dK9GhZUiIiJJE7fAyzm30jk3yX+8BZgN7AKcDgzzZxsGnBGvMtRaVYFXijU2hn+cLYUlSSuHiIhIQ5eQHC8z6wrsB/wCdHDOrfRfWgV0iLHMlWY2wcwmrF27NhHFDKlqOIlUNvS7BckugoiISIMV98DLzJoC7wM3OOdyw19zXhZ71CjHOTfUOdffOde/Xbt28S5mufdO6NvFXXj93JothUkrh4iISEMX18DLzDLxgq43nHMf+JNXm1lH//WOwJp4lqE2qoq7duQaMREREYmfePZqNOBFYLZz7tGwlz4GLvEfXwIMj1cZaquq4SQCKRx3mYJGERGRpIlnjdchwEXAUWY2xf93EjAEONbM5gHH+M/rlSprvFI8dlm4Ni/ZRRAREWmQMuK1Yufcj8Qea/ToeL1vXagqsEq1psbw0hqO+Wvy6N6uadLKIyIi0lBp5PooqgqsUq3Gq3z0m2LFFxER2WEo8IpmB45MjKpz2ERERCQ+FHhFUXWOV2oHLilefBERkZSlwCuKqgKTVO7VCKlffhERkVSlwCuKVEuer0r55Pod7fOJiIikCgVeUVTZqzHF4pbyyfWq8RIREUkOBV5RVBWXBFIt8gpjuJTPURMREUlVCryiqCowUdgiIiIitaHAK4oqbySd4jVGqVxjJyIiksoUeEVx1jM/Vfp6qoctirtERESSQ4FXLaRy4GI4JdeLiIgkiQKvWkj14RgmLt6Q7CKIiIg0SAq8aiGVa4wMx1vjllY6z4pN+Tz0+WwCqfxBRURE6iEFXrWQyk2N1XH925N5/ruFTFu+OdlFERER2aEo8KqFVG9qrEpRqff5NN6XiIhI3VLgVRspHI+UH8U+KgVcIiIicaHAqxYUloiIiEhtKPCqhVRugjOFjSIiIkmjwKsWGkpnP7NqNUyKiIhINSnwqoUUrvCqVo1XCn88ERGRek2BVy3s6L0aRUREJD4UeNVCatd4eWau0BhdIiIiiabAq4G6+MVxVc6jDC8REZG6pcCrFnaEXo0FxaVxe494rPv9icvoOngE6/IK63zdIiIiiRK3wMvMXjKzNWY2I2xaazMbaWbz/L+t4vX+tbW1sKTKeVKtV2O0hPrCkkBc3uuXhevZ464vGDN/XZ2u981xSwBYtG5rna5XREQkkeJZ4/UKcEK5aYOBUc65nsAo/3m9Mm9NXrKLkBAllUSP21OhN+63DQD8tKBuA6+gFIt5RUREIsQt8HLOfQ9sKDf5dGCY/3gYcEa83r825q/J44a3J1c5X2r3aiwre1VNgrUZxistzVsohVtjE+7JUfMYPmV5soshIlIrWwtLyC0oTnYxUkaic7w6OOdW+o9XAR1izWhmV5rZBDObsHbt2oQU7sZ3prBo/bYq50u1pkYXI01+zqotcXvPVPuOkunRkb9y/dtTkl0MEZFa2f+Bkexz71fJLkbKSFpyvfMy1GOenp1zQ51z/Z1z/du1a5eQMgVra6qSyrU54Z+wNFD3eV5pFqzxqtsvST0sRUTqp4Li+OQM76gSHXitNrOOAP7fNQl+/0qlV/vsnlqRV6zR6ktKo0+vTlNqSWmALVGqloOxayCVo1MRkThxzjFvdfxaG6T+S3Tg9TFwif/4EmB4gt+/Uks25FdrvjhUFCVMeBC2fFP1Pm80N747lb2jVC0H88IUd4mIVPT2+KUc+9j3dd7zW1JHPIeTeAsYC/Qys2VmdjkwBDjWzOYBx/jP642GNkbUTe9OrfWyn0xdEXV6sKlROV4iIhVNX+7dNWShhsZpsOLZq/E851xH51ymc66Tc+5F59x659zRzrmezrljnHPlez2mhFTu1Vidm2QHla+12lZUwmH//DY0ZARAIEaElcrfUSpasDaPDyYtS3YxpJYuevEXdrv9s2QXo07c98lMnhw1L9nFqPeUt9pwaeT6Wki12pxYvRoBiktjt5uW/5hfzVzNkg3b+Mdns0PTyo8HVpZcX/NyVoeaMKM7/rHvt6sGU2ru46krmL0yt07W9cO8dZSm2oElitfGLuLlMYt4dOSvcXuPQMDx8BdzWJ1bELf3kOQrLCnltbGL2LStKNlFqXMKvHyxam6iSbWTf3gt1+2Zb9GUsiEz/jHCC6LGL9rAgrWRg8eWT5C/4Z0pABSFjXpf/mRx/6ezoi67eL2q1eOpsgFxpXrmrd7C17NWV3v+v7w1mROf+CGOJUo9dw2fGff3mLhkI8+OXsDf/qcLjR3ZeUN/5q7hM7nq9UnJLkqdU+DlC080v/zQbpXOm2rNaCfv0zHi+cC0WaHHExZ7zYb/99xYjn7kO6AssIwVYIbXkpXE6GkQvuyIaSs5/F+j+XZOverEGmHq0k3MjeO4ZlL/HfvY91zx6oSEv++aONXcvPbzYn7aARO4gxd78brtWWUKS0q548Pp25UPvHRD1WNF1qWC4tKU/B1MWrIJ2DEv2hV4+f782sTQ49ZNsiqfObXiLlo0yox4nhb2ASppaSTWBw0PvMLjrvCxu8JrvILJpLO2o1mmNqPo18TpT4/h+Me/j++bxHBPxjC+zLolKe9d3yxZv40vZ65KdjESauO2+Iz4fddHMzj/hV/isu56oYbH4Ye/mEPXwSNq1LpR3hczVvHGL0tCLQW18cO8xAZBf/90Fue/8EudNYsn2o5Yma/Ay7dTixwAxt1+NJcc3JWz+u3CT4OPijpvqv8O0iiLliob6DTWS+FNjeE1XtOWeQFWc7biAmW3I9qeoGlLQTH/+nJOUq5uE+WyjC/plVY3ifF1PXBtoh33+HcRF0FSO7NW1OwkO3zK8nrRq3vj1qLtCowqM/T7hQAs3Vj7Gqfg7pXocQqHfD6Ht8YtqdWywfsPb87fvgB/c34x5w4dy7Lt+P5qo7SS7/qLGavoOngEeYUlCSzR9lPghZff9Y3fDNa+eQ5NszN49Oy+7NyyEd3bNqkwf+qd3CIjn/Aar8oOILGOfys3lzWNhOd4lQQcWRQzLedPnLr8sbL3C43tVfPv7YFPZ/P0twtCQd32fPcbtxYxcXHsjrT/yHgRZn9S6/XXByn30yynOiNgz1yxuUElVp/21I8c/q9va7TMS2N+q/a8a7cUcv3bU/hTEppZw23YWsR+fx/JIyPnVmv+mtagB49Vl7w0rsZlC0pGmsnkJRt57rsF3PbB9Ern2xrn4OPTaSv4eeEGnv52flzfp7zwY/7q3AJWbvbSgrYWlvDvr7zfypJq3OqvPlHgBSzbGHsg0S5tGleYlnpVn+V6HobVeJX/LAXFpWE5XlV/0KfK7YQ5eD1Q9ts0MjTNiD2214zlm3njl8Wh5+9PXEbXwSNCJ9aa3nj1hR8W8s2c6AnSF774C79/dmzMz3VBxih458IavV990xDuGHDykz9yyJBvkl2MhJm2bDOLa3hiqcnPIJg6sHJTcoPZDVu9GrcvZlTe1By8jMwrLGHEtJWVzhtNVTU/m7YVMcNPj6iqDIlQnW3/+fSV9LnnSyYu3sjVb0xkUSVjhG0pKObOj6azrahmgVrwOJ7oQ8y6vCImLPIumA98cBQDH/L2/T73fMl8vzZv/to8ug4ewZxVqdGcqsAL2FIYe0d84tz9uPTgrhHTMqp5T8f6Kjzwmr8mj7OfHxt6/p9vysbfmb0yl2vfnMTHMQZLBXh17OJyU5z/v/cdLd+UHwrOou2wp/znR+74cEbo+UOfe7kTJz/5Y/U+TDkPjJjNH1+peOVeXBpgpt/8kojAecWmfA5+aFRCrsQic+vi/nb1Qir04iwoLq16JupPZ526zqNMRBJ58MRbE1UN23HO8z9zyn8qP/7Ujy1W5rtf1wLw4o8L+Wz6Ku4aPiPmvM+OXsDrPy/htQrH7spV564k8eoo8ofnxlb6ejAv9KPJ0c9VJaUBXvrxN7YWlrBhaxEllSc3x50CLyC/yDtADj5xjwqvtWiUGerlePPxvbjskK7ceUrvhJavrqWVO2yED4i6Pq8oVIV/7yez+HTaSv7y1uRqrdesbN3BdwivmSh/gnnxx4rNIcET6rq8Qob9tCjitW62stqXW8FtGvTw53NCj6MfeOv2UPrh5OWs2FzAW+Nrl5dRE+EfZ3tqvHILihk9t/Y9T1duzmdzDRPFl23cxvPfLaj1e9aV8PuOVjdgqswHk5axx11fVBiipT6r66aq2n72qn7B5wz9OWze6v3eg4EJVH1xMrca91HMzS9mwqINvBTlGJYIn05bwcatZeNbBXd786OjaMe44DzBnKlo38MXM1bxvwlLa12u4VNiX6SHm7RkY4XWjDmrcrli2PiIHOLqClaGxAqo3hq3hPs/ncUfXxlPv7+PDHX4ShYFXsBW/yR9QNfWUV/v3Loxk+86lquP2I17Tu1Tda/Heq584BWuNleQQR9NXk5GqDat4uVzeEywdMM2/v7prArzlIbduPuej2cyftFGAPa2hXyb/Vc6zH6ZktJAhcCqvFXlrrxmrCjb0aIFJ+kk5wooIpG4lkFTacDRjG3ckPEegdLYJ8+lG7bxXCVBzjVvTOLSl8ezdkvtkqwHPvQNh9UwF+mKYRN46PM5Fe4bWtMEa+ccExdvqHUOYPiJao+7vqjVOsJ9NdNr7k7WECU1qUUb7zfj5BZE/+10HTyCroNHVBpUr8kt2K6BLpdvyueYR6vuVVx++1Z3c78clvNW3YuT8M4Ga3ILuO2D6RSXeMt+O3ctf3hubGjcwq6DR/CfBI3Wv2pzAde+OZlBr1fshBIMWoK/56lLN7Fqc9W1UI9+NZef5q9j0OsTufm9abw30evs45zj7OfGMnLW6tARPdZv6+L0L2m97Gvu/XhmheCppDTA/z33E2Pmr6OwpJSznvmJK4ZFtkzc+v50vp69pla93zPSvFAmeOG+dkshXQeP4PPpXlP0Fv+iIrhNM9OTG/oo8AK2+RulSXZ6zHlaNckKXU2kujSLHWRMWLwx6vTKroaXbtjGtGWbeHXsYtLxAiIX5bt6wj8wTVqysUJTUfCAWr4HS3BH6WpeVXLTtZP582sT2fPuyk+OlZ2Ao10NZlK7q/2ikgCFJbWvIXl17KKyJ4HarSfgHLdlvMkNGR9gcz6NOd8lL49jyOexR/wOdnOP9XnmrMqt9Ma+fWwRD5Q8UqPPscU/2ZcPtGpac/fexGX8/tmxjJhe85wfKDuBN2crdVH7+YXf9FGdj1Hdj7pha/UDm/B8rapq8K5/e0q11nnxy7GT0gc8OIoB/xgVMa0m3+LRj4yu1oLl993abKnq3iHg+re9mv41uQUMeHAUb41bEuqEFVEGfwM+MvJXLnlpHF0Hj+D7X9cyctZqug4ewb+/LOssEJ47FnCO2z6YXuNegsH985ewlorg4XakPwBw8Dd1+tNjWOI3+VZ2+nrym/kRQ48EB6ctLAkwbtEG/vTqBAaXS+6//cPpEYPY3p85jN//ejOv/LSowpAww8YuZvyijdz07pTQ9z/FH6erNv715ZyI559M82ragvmK8/xay2Hhx1eg2L+wz85Q4JV0wRqvxpkZSS5JYlRW4xXL4f8aHfO13/3zW057agwAGcHAK8a8A/7xNWc98xNH/jtyfcFAbFuMmqzwY8aoagzEWtmxNVpVdibVDxamLdvEf/2u6Qc9NIq97/mqwjz/+jJ6z6x7P55J18EjQjV2i8JzwAKxaxRmr8zllTG/RT2JlgYcjc0/0ZbEvrrN84Ocqk700S4wthQUc8LjP3BBJeNCPZ35BKem/wwbF1X+BjGEB8uVdSGPJnjD4WvfnFyrpsJ/fjmXnraMaTl/4v/Sv6vx8rEEe2BVV/ngKrxmt7pB5YpN+YxduD7mOmtrQZTa8EDAhYLmomrkzRSVBHhs5K8VErvDe7NWlr9X29y+8J9TtJ/W97+urVDTu2Grtz9e/UbZyOnRmqi+DWueDzZpvjNhaShVIpjjOm3ZplCLwoE2m8nzl/PWuCXc/L9pAGzeVhz1t7tmS+Q+Hf4VFBSX8tu6rRU+U7QLl3OH/szCKM2/lSXiR/u+352wjPV5hbz5yxLem+g9Li/8/acs3RTRuhEMvMrv46EatRj7frBmFuDpbyNr7oPH9BI/sMr0A6vZK7f467aI+VTjVQ8EDwKNK6nx2pFUp1ntAJsTkYRf3TF+0v3atMISF7U5cE2MZqwVm6o6QUUm7UezISLnIXLnDb/Kvefjirc1qazG67PpK5m6dFPo+WlPjQndr3LD1qJqnXCCXvEPxg9/Mafii6WxT5AnPvED934yK2ozWPhBrrJ4JfhSVSfinxesrzCtOuNChX4vVv3DSngTY/g2ivU5Yh2Uw38VsZr3Ppm6gq6DR4RyY0oDLlST+9a4JexpXj7eYWnTqiz3GU+PqXIe8Dp7VGbl5nzW55Vtj/I3l/7u17KTerTAJ5rwExTUXfZitJqi/R8YGbt5OcobvzthKU+MmsdT38QekqA04Fi6YRsHPzSqwnHhifLNeZX84Ccu3sivfs1HoJKgPhBwXPzSOM4dGpnAXRoI8O8v50a0ApRvEgeiduYZMW1lxHFh8fqtnPbUGO78aAY7sZ53sv/O0b/e530E/4va9/6vOPOZn0LLvD1uCec8P5YHP4s8VoTvA3/731SO/PfoCqkVsWqMX/+5Ys7pre/H/r0Xx8i32v+Br6M+Lm/Zxm0R+8rq3MJQLXf47+m/3y9kStgx9sooQ5v8XxUJ9gBbi0o4+ckfmOCnqGzOL44Y8DtYW5ilGq/k21robYwmWQ2lxityZ0ozmHbvcVzhdyI40Gbzv+z7uTp9eI3XXVbjZUxdtqnay1V31PhoQc66vEKGfr+Afn8vG8LC4V0N/rJwPflFpaFcMfBubry1sIRiP1espDRQaeB19RuTOL2KE21Nb3AcNfgprd0Ah5F3D4g9X/AAdPFLlY9m/tco98C78MXKlykuDZBmfs1HoHpN8uFNLM5BftjVfqzv8+Uxi6JOnxh2coz1FVzndxKZtyaPa96YxNGPjKbPPV+Gmi2CzeTFZFR68/jSgIs4SVSlfO1ObkFxqJZo4EPfcOGLv9CcPI5LG19pDtMrYZ1N5q3ewtvjvBqHroNHhPJ4iksD/POLueXWUTehV36U2piN24pjDscTLRcoWLsSq2YbYG1eIW+OW8KKzQUc8e/REUnY746PTPyu7JP9/tmfOO6x75mwaEPEaPHlg5Jg/ueCtVsjOlmUBFyF4XJqIrzT0iY/Py6vsIQmfu10MNAPL87slbnMXLGZ/4yax+APpkc0J4I3kOqCtWU1VJ/6w2mEdx4A73Y7M1dUrJ17acxvoS8tWLsW67f+2fSVtb4fZvAzBXMdwx0c1uHq6W/n8+m0FaELWfDy576qwT1Twy1Yu5WZK3IjLmwLSwKhZtZgHmOya7waRqRRhfyiEswgJ7NhxKHtLTKPa9Jdx9I8J5M7T+nNX4/rxa33ekHG7mnLqEELHFBWc+SAP74yvtrLFRQHKu3NErzR94TFm0LT1ucV0qZpNv2jXHHNXpnLAyNm8/2vazn/wC4VXr/slfGhA+ODZ+5NplWd41XxpFj2fPiU5QAs2bCNG47ZPery4SfgguJSthaWRI64XIPAa8PWolAnj7ELw04qlZyKgh0X1uXVvOmpuNTRmlyaWPST7KDXJnK//95nPfMjn97dlYLiUoZPWc7Z/TtHNF++NW4Jt30wnTP326Ws3M6x971lTbaL12+j987NQ89HzlrN9OWb+XBy9BH+y5+gKvPn1yZE3KYn2GyRYd6PvdSl8eGk5Zx9QOeoy/9YLs+tNOBIr2SImcLiAI2zvAF8txWXcsiQb/jjId2YuKRsP3wq8z8clj6d02f1oeCkPcnJ9GrfY8XzJzzxA6UBx8DubQDv93fOAZ0Z8I9RoYuTdmzk6PTJwJExyzayihNcZUHbD/PWxnwtlhf8XoDB4Gd9XiHHPx55o/Hw40BRSYB7P57J6X134eMpK1hf7oLlP9/M56/H9QK8przTnhrD0+f3o23Tsg5Q5YcicM6rCQneSi2YJgHebZaCanoxVZnwC7dgrb3F2FcrG0qnss4xNVkPeLWHJYFA6J6I5YU3sVat4mcpLCnl3Sp6SEZLyQjvVXlq2k+scq0Z7yqOOBBNtNsi9fv7yAqdDpJd46XACy/Hq0lWxg6TPF+Vv2R8xIslJ7GZprx0aX9aNi47SDXKSqemwwM+f9H+odu83JbxFgA5FFd6VRvN7nd+XuU84bv3/g98ze96to06X3jC8Ju/VKxeD78avf3D6XSPEngVlQQiduTlm/LZqXlO6HlxWA/MLQUloSbMY/bsEJr+7OgF3HqCd9DIC+s19tWs1fS550sA/h1cZYymxvInv7/9byrvTVzGB1cfTL8urRj0+iRGZC335426Cq+MMTpI3P7hdD6ZugJwzMu+mPtLLgJOrjDf2OxrybYSfpx3Gq+OXcR1R/Wkc+tGLFq/jVFz1vBAtvfmefneFf1jX//K898t5Nb3vaTck/fpyL6dWoSaTraEfR/lm39OevIHFg3xyjBzxeaoo6oXFJeGApRwwe/r61mr6dG+KSUBFzGSe6x7IwZra0tIj9ktPRBwoZHPn8p8kkmBnuQXH0/T7NiH0mK/SnK/sBrZ8iPL72peALQ5dzMPjJjFeQO60LtjcxyO5myli61mhusemj94Agnmcj30+Rwe+jyySerVrCHsmbaU5dtugFaRA0Fvzi/myVHzKgzpcs/wGfzx0G7s2sa7Y8e8cs2bN70zhQ8mL+fNPx3IRS9WPgJ8Zb/F0oDjz69N4MsoNSLg7TdBH0xazgeTlsdc14zlmyPG3brmzdgBQzZFlJDOvvd9xd/P2IuLDto14vXwmvGS0roLvMKVD7zi8y6xPe/np0LFXKnayiZyn7rp3Sl8MLkdc2rRqzf8zij/yXoKgK4Fb9a6bNEu6LNU45V8J+61Ez3bN012MeLHVfzhDc16lNJLRnDwbtEDF4h9RRbuggO7cHyfnULPj0j3qqabxagZqa1YoWBd3XA2vKlx7ZZC2jXLrhAIBgKRwVZ4U9kHk8tODOUHX5yxfDOfTltZ5dXqxi1badWq4vTyV97Brt4zV+TSr4u3QJ80bzDExeu3snfYvMs35XPIkG/45x/2ifm+wcA0m2IyrZQ7M14HnqgwX7YfnP7xlfEUlQb4atZqWjbO5OHfe+sONmEHcwhXlBsJfcS0lREjjYc3G8+sJIcs1kjjuQXFpKcZPe+I3E7Bb+uKVyeQZtUfVDZY7lLSotb0rNiUHxHcnJL+M6ek/8y3v93N4bu3I82v9SrftDjgH6MYctbeVKbUz/rIpJR3xi/l9Z+XcMMxPWmancErWQ/TL20+3Qpe9z5fNZsO90zzag4CRd7vdNO2IpZvyqfPzi14/OtfozbbDhu7mGFjF3NCn5245sgenPpU5G85+Ds//7/Rm56bk0e/tHmMDuwX+t6vGDaeX1fn8drlA0LzjV2wPtQhYntVNdhpuLk5lzKqdD8uL76Zuz6aUeGWcOE5XNHyuepCsEk72MmpoLi0yhzaPraIgWkzeaG04gVRfZBN5EVjwHkdFuqrzPTkVrIo8AL6d21N/xhjeO0QogReu9kK2sYIuoKH9fCfZusmWVHzku4+1RtMduKdx1SaZAnw4dUH89a4Jbw7ofY3hK4suX57hAdew6cs54rfda8wj8Px3sSyavCjHinr/Ta1kpyf6p4YVm7cQquKraKhnjnlLVq3lcvKdfF/8cdFPH5S2fMhfqBwy3uRCbRLN2wjI90Y9HpZ7UBjvEDJYUxftpllG7exU4scmuVEHibCA6ZN24pDtZ3BE0kGpTjn/Fq02MIPzC/+4F2F72bL+U/mUwwuvoL8olLMvPt1RnPXRzNYuqHs5Njf5jAk8wW2FH3Ng5953dlr0loUXuMVvuCWgmKa5WRy5L9HR71Z+2WvjOes/XahZ4dmZGekRR3PqnxX/PK6p3nlbUxBKLh//GsvkXxRjpdnlEMRb49bUuW6ynvpmxm8PG8+u7VrwoK1W3n50gNi5soFfTFzVWhIjJp4MvNpjkifygEFz+Cc48PJy/h6ttdBILxndKygqyPr2Uo2udT9hXBvWwTgNb/6sXxlvXTjoXvbJmSu935nwQvbacs2R02XCDci+3YAXig9icTesCi2ptkZ5BWW0LN9Uzav2Vj1AjUw9e7j2Pf+ir3F60qGarwkGdparleFk1bxB/h//TvDNAivBJ9017F0HTwC8EbwD7bNZ2d4TT1tmmZ7V4+VdLzqs3ML7jttr5iB19wHTqDXndHH56pO7dv2yAoLvGLldsxfk8ddwyv2iIzO8UDGS7xXejhTXI9qLTFt8Rp671v2vKC4lD889xMzlkevDYo28j94o0//68vIJNzyfvfPij3RGvlXraWkVajpqA4LBV4But32WbWWOTntZx7OHMr+y54DsjgybQq90xZzavpYfph3Nks35sccULF8M9XtmW/SI20F09ZMZ+j3NR9SIlgTkUUJYxZt5KcF62nTNJu3xkW7+0DkbyS8xnN7NLbCCm1Ppc5IN0djCmscdAFMmr8M6BH6PVxWg9zLmupmXo3mzRnvcOVrLahpkDA25zrWuJYMKHymTsrTsUUOY287mq6DR7CTVT8PsK5dfcRuHLVHe375bQNffOVtw2BnlJrIpJTiBJ22WzbODHUKAOjSunFoTDCA0TcfwbOjF3D+gV345onntuu9ztxvFz7096FT9ulIi8aZ27W+ykS7Q02iNYxscolu5eSok5s18nK+DMfpfXfm5uN7RbzetU2TaIuxZ8fmTA5EDzI+uuYQsjLSaJSVzs3H9+LZC/rRKmznGvbHAaEgLpqyfIi6v9pLTzPvBtlAiUtj5eaCUJAZ7vJhFfOMYmnFFi7MGMWrWUOqvczbYxeycWsR+9z7JV0Hj2CPu76IGXTFYjgGvT4xatDVlG08nDGUnYnePNvIvOaO0loeFtJDTY3VD3puz3yDplYQ6vARbK50GFe+NjHq3Q1iKXDe7/bhT6dUe5lwwbsunJ/xDZ9NX85Xs1bHCLrKascAsojeFJpNUSgQqa5grWO44PboXoN1WVjP5WAvusockzaRa9I/qvb6y/vjId147sL9KcLbp8/O+I7eVrN7AQa1t01VztOUbZX2RL731N7ceMzuEc2b5fOQEumWE/agf9fWXHX4brUerBmgEdW/q8T+u0bJW4iifJ5b0JWHdeeR//OuBN/988CIDiTPXdiPtk2zueuU3uzWril/yii70Do0bXqFpsfKdG/bhMfO6Rt6ft9pfQAq3CO5tkb/7Qj6+9/F21cexJ+itGYkmgIvgPyN8NN/IvvlhyvOh+d+B6OHwL0t4IvbE1u+7RYjWCmJvhMHK3w6tsjhiXP345ojq1djg8GUwG5hExzvXHkQ7191MH07twxNvebIHpy4d0cm331caNphMZLkw1btrxEOS5vKfRkvYwS4//Q+Uef/4OqDq1dmoGubxvw+3etZFcAiuu3XVhvzAqaaXNNmUMJ+fx8Z89YtGZTEPMmH5rHYQc++aQs4J2M0QzL/G/X14EE9UMVhISPGiSMYjGTUIPAKBtLN8JoMg82VgUoC7L1tIYemRdb8ZFHMwelekNa4BiencOHlbk3lScHhNaStqRgcNyGfuTmX8m32X8mpQXmilT3L36b/y76/2uvpYWXNvL2s8p5lXW0lL2Q9ws2Z70ZMv+qI3bjLvy9tD1tWIWAP1hz8/Yy9uPvU3pyw107khJ1wd7PKm5oBbjp2d567cH+g4jA30dzrpzbMyLmClzL/GZr++36dQo+fPr8flx7SjeuP6UmP9s1C06sbeGVTxP+lj6Yp1RtRvleHZlGn3+OXNbwDUFqasX/ar95jArRlM7GOEkf0ascLF/evULZYutlK+tmvoefH7NmBjDRjJ9azKOf8CvsMwKIhJ/P3M/aKur5LD+7K7/fvxKIhJzOgW+vQkAxf33Q4J+zVMWY5Xs96iPsyXon5elCwo1Lw0/9wy5G8f9VA2jTN9iZUI5exJVt4P+seOltZ7fen1x3KYbu3Cz3v2rYJL1zSn5cu7c9B3dtU2gM5URR4AXw4CL66E1ZE6Q2zYgr8YydYNQ1GP+RN+/nphBZvu8X6AccKvPy/5Ue4b0Eej2Q+Q6Ot0Q/kf9i/U8TJa3/7lX07t6zWlVesHqXBoSAsrFr+jow3uCRjJFfv7bh4YFcWDTmZRUNO5tPrDuXUtJ+4MeN/oaRzgCN7tauw3gO6lr3++hUHhh5nWWlEbQFAOzbRvRonkYhlzBtDpya1R5mVBE0AH2bdza85l4Se79Q8h8l3HRsxT1YlV9PBpsRYTS5N/ZqREiJrHssn5ufEOPgHg8Ka3Pcy4Lzt3tK8Nuq0qBmGkT7JvpPXsx6KmNYyrI07uK6ayrayz7VTuSFXBh2+W+S8Yd9BMMgOd2n6l2WvRwnMAG44pmeFacFax3CLAh0qTKuJqmrK9rayJuvwoPrWE/agh9/p6OvsW/gp5y+h164+YjcGHb4bi4acHFFjMjWs5+VuaSt49oJ+Ud/zkoHeMgd1b8MJe3mdc5rFCHS+v/lITvTnMbNQgPa79BmA4/2rBvL3M/owoGtrbj6+FyftvVOFdbRtmkWWlQVeTfxAf9q9x0XM9+fDu3Nc2gT+lTmUazOij2N4YfpI9gyrzRt8UsWmq7ZNs+jeLnqe2h2ZXg+9nW0DE3Ku4sr0yNt8BW/4/Jeje3JM7w7su3NZj9QcK+KssGFY3vrTQTx3ofcdf5v9Vz7Ivjf0Wq+dmjL+jmPol+blCr6e9RCntijr4NMkK3oLQydbCzgalxvXMs0/Rlenc0fwPSszoFtkXnXn1o3Zf9eyaWlh55JXMh8mnVL26dQiYpkz0sewf9o8/ux/h7/cfjR77dKC8rnzLRtncdQe27cf1SUFXgCN/SuSgk2R0wvz4M2zE16cOhfrVjRboifPNvX3t0bldsw7Ok/j9+k/0mXRe4y88TC+vOGwiNeP7NWeJhllJ927Ml/z7olVXAAj/garZhBNrFE8Dt+9Hdf6tW0WdkLuYV4uwGm9Ips8M9KN/2Q9xfUZH0L+Rr6+6XBeu3wAz1zgXVHvb3NZlHM+jxzVhP8NOpgfbz2SCXceQ8cWjSLW0z/sqhHgqawn+Sb7b6GD9TFpE3kk8xnA0cnW8HLmwzQvl9zmXclCqR/EhDerhjc1tAhbrnzQ9OR5+wFwVr9dAMfeaYsiXn/hkv60apLFH/Yvu9r/S8YHxLqCDjZjtbI83rziQBYNOZmhF+0fGgqhj38C3upyGNCtNT3aN+Wig3bl7P6R41m1ssjaoD128q74g70e38z6R9T3L++w3duFate6N/YCjsP9XrGDMj4hgxJ+uKVsDKr0NKvQjBkse3jA0qqK2qpYWoVtiw7lgtPrjurBnL+fwKNne00vz2aV9foMBl4H79aGSw/uytjbjoro1dvatnD3Kb1D45Y9/Pu9efvKg7jhmN2ZcvexEcFOk3JNjZ9edyjznLd9i1w6aQQixj8Ll5OZxqS7jmXO30+IqBk9KG02Pawsr/K3h05iwYMnMfv+E3jwzL3JDgtI7j+mA2/+6UBu82uzurdtwu5RaswOjVFDvc2VDbdyQ8YHnNCnPecN6MKDZ+7NpQd3pZE//McVv+vOlLuPjTj5fpp1R+hxMAj+6JpD6NKmMU+f348FD55EmkEbygYG3dcWAEbjrAzeHTSQa47sEfUi7qsbD+eItLLBQGfmXM7nfzmU5jll++WiISdz24l7hpq997TFvHnFgZw3oDO/PXQSL196AKfv04EHMl/m8+zb2KPRJl794wCO7NWet/50UGjssGcu6Mdn1/+OXVt7AdMRvdpH/a6CLkz/mnf/PJDbTtyDf/5+H+Y/eBLT7z0udPHYypV93qGZjzLAyvJMB+7WhhP26sis+48PTcukhDtP3pOj9uhAqyZlQwUB3Fn0ZCh15CB/DLhwHVnPj9nX80jbivd8vdgPltuHDakTSz5erVW03rwD/I5snVs34uKBu/Lfi/ePuo70sPPWEelTeeuMVnw8aEDo2LoT69nDH4S2meXzdObjZBd6++3OLb1j+mPn7Et9pOR6gN/dBFNeh61hVelf3ek1P+4IYg3M+dEg6Htehcl7tPd2rJ6tIxMc05x30sssymXX7E3w1R1w+tOQXVbVHl7j1TdtIeQuhx8fh/H/9QLAUyOHKZh+XXcyf/smlOj/461H8tx3C7j7lD6hQe6m3nMcQx/4CCDUJAjQq2VkudPCD7hr5tBj14GhK/bWTbK4rMirhdh141jgCDqVG9soaFjP77l+dRO+39yBpuRzYJrXM/Drs9L4rc2BHPjqBaSb45mS07kr43WOSJ/Kz2em0dtvqbnqiN0o+MEb4qCEdD697lB6d2zOrJW5PPX0ozyX9TjnFN7F03dcx/n/eDn0vldnDOe7on1Y+ODJoaEJTtt3Z5Zu2EbrqUND82VTRCFZ7LWLd/W3/66t2DKjEc0snw62iVsOacFFxx7E4vXbKAk4znh6DGlWluvTkjwO7uGdOI/rsxOj/tqSOz6cwV2/vQFAIZm8++eBEd/Jm1cMAG80AzrZOpa6DnRp3Zju7Zrw/EX7M2/lJnjRez3TSll454F0fyCyx9gPtxxJk+wMnHMUlQbYuLWYrkO9JoL92jlezyP0XQNMvH4vmrdqxC4tG7F8Uz7f33Iks2bPAL8y6dQ9W/HkxQOZtmwztz/zemi52zPfYmjpqQD02bk5D565tz+ApaMNuXTrsisTogwaeVFGWc+ync0bHys4lljQWf06cfBubdnpsfND0x7PfJrVF33Pnj3KasXCg6lP/rgH9PTuCvHo2ftGBAYtG2dx3n4dwO+4OSjna24adAuP/5LHFb/rRscWjdjg165lWSmfXNiJ3ffcJ5SI/OwF/bjKH+jyl9uOCSUlhzdZ9kxbztfZt3BC47fIadwcMyPdvAur8w/swryPy2qazu/TCDq2DQ0z07l1Y97NKmviHHfrYWRlZ0eM/Rcux4rY5rK9TgKArZjMQ2eVNZeNnLWagk2rQp89KJMSOqeV9XL9Met6jip8JBTUB/eHg7q3YUxYbcrw7LtZmnNh1LKEa90ki5PSI3sA7xmYD+zP/wYNpE1YgNLBzzFrZVvYp0fb0L5y5B7tGdi+GPzrsv81+RfNdr8A8AKgt688iE+mruTEvXbytnEzmHDnMRHrjqZNi2Z06dY6IghtFhYQHt81HfwK2D3TltJsyXOcmnYYPdKWExxvL7x2al7OxUzmTcCrfdx/15bgV3rmUMA1R/bgsJ7t2K192YXrrm0as2FrETsXeufAY7ZV7Bxz8cCuXDywa9TP8NtOJ9BtVVnHqH3TFnLVgNacO6ALh+3ejke++pVDpt/OWek/8mTbdxm3CDq2aMTNx0fPLwPIDkTmqQ7ImAcf/JcJOcNZds5XdHqnbB88Pd271dK2Xz+F1n/krlN6M6Bba07bd+eY60+mpNR4mdkJZjbXzOab2eBklCFCE//qLc+/L1qgdMcJuqDSmy9HC8os4I8zM38kTH0b1nvV072ae/Pu+ttb8Na5MGs4LB4bkRvXqHxy8LOHeEEXQNFWL5/uqzth7a9QWkyzz/9Czte3w3Ivcb1TxhYe2HI3WYu+hdUzoaSQFt/fy5npUXrZfXojlJbA7E+gaBs54b/m3MheZl9f259T0n8GYL/mldeINF4ymv8W3srcnEuZmHNVaHrHzy7j4JWvke43e348cIF/8IOcFT/zt6wP+SjrLm4dkEWXDO9I2dE2sNfK90lzJey1SwtOSveCkUc6/0DbJlmc0r2sVvHAtDksbHkNaaumwvKyZu/OrRtzdvro0PMZV3dh0h1HlS3XpWlEDcvVS/5GszUT2StnHX3bZ/DFqaVMu353upp3wsu0Uq8W0teheQ5PnVOW59Ehu6RC8/TBG8uaXV7bZzqLTpzF91f24JWzdiE7I529ciLzf9KmvM7nF3ZkUqvb+bHR3/hfq2fo3DyD1lkB2rCZji0a0SOz7J6QJ7dewcS/RObrtVj4KWbGmFuPZNFl6ezSqJRem8oC739suhnLXc6+nVtyXudNEcv+3PMN5h/2AyOO3cS+7dJYdGEBt7b5kYk5V/H2oSu5/yB4+VhjwV93545jd2XktQdELP9A5stM7v6s99vPjWyq26lF5BV/a8uj6/c3QX5ZGSISxN/4A4x+GIjepH7TEWUnhw6lK2k29CDu2msjHdO8mrTuaSvZ4LwLiD7vHUHm+Od5aJ9V/K/LBxzTowmvdRrOhEtbRvQEe7SfF8Tku7KT/hfbzuOjrDthVWSuT0RZnz8M1s6NOC60tLITYPu5b9Byc5T7jPrapeUxx3XmrMJ7vQkvHA1Dj4A5n0HuSl44vICJOVex87LIsdcmX9Yycj22mek5V5C9NHJU+54dmvFc1uMR0zovqNngmrcVX+6X7Sj4cBAHpM+n+9IPYMkvsGoGnc07D+yRtc47Zk15y9tfvn2InFUTQ+tplvcbTHvX21fmj6JH/nRuzPoImzcSCr1jTNum2RW2+ejAfqxwrfm21KuNabJlIbz3R1jyM0x+vcK+d84ekYFbmispq9kPzrs58ni336jzYeFoAC7vV9Y818Jtgbw17P35WTSe/kZo+rc3Hc7UmwcwrOP73nyBzdFTVFZMhoKKTec7N6n4u751yZ8hbw07Nyrhkb0WcZZ/DL+SjxjT52PO71j5cCUHtix3y6NPb/TOOUCnd46LsgQ0HnkzfPsAOZnpnN4pH6uj22XVNaur+3hV+w3N0vGuGY4FlgHjgfOcczG7L/Xv399NmFD9HmU15hw80AF6nQhnPOPtbP+umH8R4d6K98Gqt946D+ZW0r1/535w4CBo3R06HwDf/wu+eaD662/dHQaN8cYLe8hrBnmt5JiIGoRqOfdNWDoOxjxeNq19b1hT/Z5tIZ0P8mriVk2DfhfDL0OhMGybHXkn9D4dirdBhz4U/7MnmYUb6VPwIjN2fQxzgYj3/bG0D4emV3coiWgMclpUaM52TTtgeav5uHQgpzaajhXVID+p74XQoQ+MeQLyVvFKyXFcmhF77JtSy8QCJWXd2LsMhHa9YN18WFwusG3TExq1gq1ry77HaljjWlarVxrNOkJaJmyO3mswgqWDi2xiXBjoSPc0PyDaae+IYKKkcXsytq2hNq4tuo5D06Zzbsboii/ueih0ORA2LYHp/wNgYqAn+5fPZ2ncFrZF6Tna6yRvP2zcBnbpDwdcDssneifvjb8xrORYLs76DguE5dB12AtWz+DJkjM4I20MXcJqhQDve9wS9j103Bea7Qzfe4nn5xTexfNdv6XlynLbN6sZ9DoBMhvDpGHMDnRhz7Ry26L/H6HpTjD6Qd4v/R1nZIwl3fk1eZlN4JC/eJ+l2U6w0z6Qu5zcdwYxZkt7riq+kUV9XoQFo2J/2X0vhL3/AC06w4+PwpQ3Ys/7u796v9cWneGZAyu+3r43ZGTDgCu977Z1d0hLL8tjKNoKD+5MrmvEPoUv8v2JG+jy7bWx368unDkUZrzv7WPH3Bcauif/8QH8uL4Jfyr+G/f0y+eyWZdHWdhgvwu9ALj5zvDjo5xTeBcPNH6LnqXl7h+Z2Riymnj7anltesL6SvKtznwe9jjFuxie+HLkawP+DIff4gVbHfp4weTTA6BpB7hxFqRneOdOM4qGHsevy1bzXWAfzms9j9a5ld8cPuS0p7zP1+Nob11rZkPT9l5lyOQ3YPjVrHCt2bmmQ4F0OgCW+cOmnPe295so2gI5LSGrKWRUXgtZF8xsonOuf9TXkhB4DQTudc4d7z+/DcA591CsZeIeeAG8fQHMqdiuHdNx/4CWnb0dI1Aa0dzm8b/X0Pdb/nl15om2DFXM4yrOM/YpWB2WX/WHl+G9y6J/ruzmUJgL2S3gmLthxF+jz1eJ50tOZtPBd3Dr/ItgwwLvoBkohXFDYbejIr/ntrvDul9jryzcMffCnqfx/A+LcS27MKjxt/DZzUR85o59vdy1vIpXU6WH3w7F20j/6fGoq3+15Fje73gjw685pGziht8oLCyguNkuNN04B6b/j5+LdmNDq705advHrC9w/JB5KGdseQs69Yed9oXPb2Fb7npu2XYRV196Cb2/vtg78G9dh8MROO0Z0r+51ytnSQFvlRzJbSV/8pq15o30akjA2xYtOnkBYPNOHLTmVq5v/CXnBaL/TvsXPMvHV/Vn59XfwWd/807ETTtAehasnkluSTpnr7+St1sPpeXWhVHXcXLhg9yw20qOXfUClESO3D2idABDS05heN/xXnCxeEzEthtacjIPlpzPtJOX03zULd7EnBbQ56zQlSpZTWDzUu/EX7SFFa41FxXdxqi9vobibXzqDubaufvwxP7rOH3lE7ChYjmnBHZj2hEvc/FvN3uvZzeDTUt4sehY/llyDnOHnAlr5sDmZTDhJa/TTPNdvPftdEDM/fytppdy27rjuOyQrtxzZDuY/zV8cgOUlkt4T8+iIKc9D206mgXdLuD1s7t4QcOvX8Iyrzkr15pzR+HFbKYJrx643KvNWF/5DZf7FzzLmAfOIXvSS16Ne9HWUAB3WOFj7N+3H4+d1BHevcg7oW5aDIt+iLm+84tu56fAXt7vKm+Nt75fnodfnq0w723FlzM20JvRrYdEPYHPPOxZ+nTfFV45qcJr5f26x9W8mHEeD5/RC76+F9rvCXO/gCU/ecH85uUVv1OAPU+Fc16HjYu8i8V1v0IgRmeRC97zTqDDr/GOMdGkZVRY/veF99Bqj9/xwiUHwG/fw7BTve9y81IoKasFnt3jCvbI/Qnbsgryy530e5/hXbAtHB3zNl9RdewL6ZmwbDxPlJxJm1Pu48KDdoXVs7zPsXFRxffyBRq1ZfwffubAZuth5N0w78sK86zuehpZ6+fQKr3AuzgIt8cpXlD+bdX5l5tPfYnmE/+DrYg+3FAFrbvDhoW8XXIEjzf+Cz/fcqj3e5/2TsVgutfJMLfiUD002xm2hHVg2nk/7ze7bT15Ny2icNzLtBn/GGxdA0feUfY5+v8Ruh+Be/8KrLrbwtLh2vHQZreq590O9S3w+gNwgnPuCv/5RcCBzrlry813JXAlQJcuXfZfvLh2Y8JUWyAAP/y7Wj/MlHb1L9De74Uz6VUYdT/seZrXXNd5gHegKsiFQ2+A3Y/3AsuxT0PHfWDlVO8KuCgPdukHc0Z4zTCNW3vB2po55O33J35udDjH9PZ7kJQUVby6KCn0mhybdvCuSAs2e/l1v33n/d37/7wD2+qZ3lVWp/6w+CevVq58U826eV5w4gJegJGW4Q3/sW0dNGkH29Z7J7ymHbwTgHPee8/80KspWDMbAqVsbLEnh/7Qh8Gn9+eiGHkMNREIeHlMEfcSDJSCpZV9hs3LYd1czv+mMT8tWF8hnyjEOQiUsmRTES0aZXpNSs7B0l+8JtUWXVjeuBekZ7JLy0bR14HXG2nkrNUcs2cHL2dm/QLve0vLgCbt+G3Fao58fg7DrzmEfTu3hKJtXnkX/QBN2tLtP8s5qlcHXrw0rFmutMTb9sX5nPbab0xbtjn25whXUgRzP+ORxV2ZsbqIly8bEPre5q/NY/doXfRLS6Aoj4K0RmRnZUU24TjHuxOWMXHxRh6OdXsk/+qcQKlXG1KYV3ZXh+J8Xp2Rz93DZzL2tqMqdLhg2wYvcGnkJTyvLszgwAdH8dg5+3Lmfp0i5y3IJd8aM2NlLs1zMunl5ylRuMXbn4LvvW6u12t6r9/jGreBrCYVmyKLC6CkgGnrYfcOzSremzJ/oxfELvre23fyVnv7ZK8TeGJBR+auzg11Lon4HpzzagCKtuHMmLQ+k707tSq7eXBJoVeLuHWtt+/svF/Z7zZ/o7f8r194wYoLeL+lrWu9QPuou6BRy+jbIGjdfO9EO+tj73jSfk+vZrpRuR7QwfPTquneyXzDAq/mq88ZZfMESr39OW+Ntz/kLodW3SB3BaydHTpGbdj/Oi78ZVeG/XEA7ZplRy9XSZEXkHU/3AuSggq3ePtJZtjvIvh72rbBe++SfK8GMn+jlyowa7h3cd5uD1g2wds2pcXQvCOc/Ij3XcUya7hX+9+qq7fcrgd7F63htm3wgv3W3bxj6O5lCfZsWQXj/gudD/QuTDod4NVQlRZ7tbU9j/N+L2Oe8MrdcldvPTvv5wVouSu8Y+78Ud6FX8suXqC/30XeBeH0d2HBN96Fc9P2lBbkceaiMznsiOP5W7lxH8ldASunedu2y4GQt9b7va6eCU3ae7+b9fO9761RS1gwGhq3goxGXg7yIdd769m4yJuvxzH+d77a+92Ad/7OXQYZOd5xffFY6HaYt6+Nf8H7ffS7xAuw0zK8ygAFXhUDr3AJqfEqr6TQO4GnpXsnn8I8L1cqPcvb4bauwevy7h/EotyWpyxIsBo+r+0ylbye2cQLgCrb2Ru4zduKad4o8TdLd87hXFkCcaoqKQ1QEnBRb1ydCpxzFJYEUrb8IsmUX1RKTmZawo+f9VVlgVcyejUuB8L7p3fyp9UvGdnev6CscqO1N6s/Y4JI3YjnbSoqY2Yxh9RIJRnpaVRy84F6z8wUdInUUvnhhyS2ZPRqHA/0NLNuZpYFnAt8nIRyiIiIiCRUwmu8nHMlZnYt3mg86cBLzrnt6S4mIiIikhKSMoCqc+4zoJLxDURERER2PLplkIiIiEiCKPASERERSRAFXiIiIiIJosBLREREJEEUeImIiIgkiAIvERERkQRR4CUiIiKSIAm/V2NtmNlaIM53yaYtsC7O7yF1R9sr9WibpRZtr9Si7VW/7OqcaxfthZQIvBLBzCbEuqGl1D/aXqlH2yy1aHulFm2v1KGmRhEREZEEUeAlIiIikiAKvMoMTXYBpEa0vVKPtllq0fZKLdpeKUI5XiIiIiIJohovERERkQRR4AWY2QlmNtfM5pvZ4GSXRzxmtsjMppvZFDOb4E9rbWYjzWye/7eVP93M7El/G04zs37JLf2Oz8xeMrM1ZjYjbFqNt4+ZXeLPP8/MLknGZ2kIYmyve81sub+PTTGzk8Jeu83fXnPN7Piw6TpeJoCZdTazb81slpnNNLPr/enax1Kdc65B/wPSgQVAdyALmAr0Tna59M8BLALalpv2T2Cw/3gw8LD/+CTgc8CAg4Bfkl3+Hf0fcBjQD5hR2+0DtAYW+n9b+Y9bJfuz7Yj/Ymyve4G/RZm3t38szAa6+cfIdB0vE7q9OgL9/MfNgF/97aJ9LMX/qcYLBgDznXMLnXNFwNvA6Ukuk8R2OjDMfzwMOCNs+qvO8zPQ0sw6JqF8DYZz7ntgQ7nJNd0+xwMjnXMbnHMbgZHACXEvfAMUY3vFcjrwtnOu0Dn3GzAf71ip42WCOOdWOucm+Y+3ALOBXdA+lvIUeHk/5KVhz5f50yT5HPCVmU00syv9aR2ccyv9x6uADv5jbcf6oabbR9st+a71m6ZeCjZboe1Vr5hZV2A/4Be0j6U8BV5Snx3qnOsHnAhcY2aHhb/onHN4wZnUQ9o+KeFZYDegL7ASeCSppZEKzKwp8D5wg3MuN/w17WOpSYEXLAc6hz3v5E+TJHPOLff/rgE+xGvmWB1sQvT/rvFn13asH2q6fbTdksg5t9o5V+qcCwD/xdvHQNurXjCzTLyg6w3n3Af+ZO1jKU6BF4wHeppZNzPLAs4FPk5ymRo8M2tiZs2Cj4HjgBl42ybYK+cSYLj/+GPgYr9nz0HA5rDqeEmcmm6fL4HjzKyV38x1nD9NEqBcHuSZePsYeNvrXDPLNrNuQE9gHDpeJoyZGfAiMNs592jYS9rHUlxGsguQbM65EjO7Fu+HmA685JybmeRiiZe38KF37CEDeNM594WZjQfeNbPLgcXA2f78n+H16pkPbAMuS3yRGxYzews4AmhrZsuAe4Ah1GD7OOc2mNnf8U7oAPc756qbAC41EGN7HWFmffGaqxYBfwZwzs00s3eBWUAJcI1zrtRfj46XiXEIcBEw3cym+NNuR/tYytPI9SIiIiIJoqZGERERkQRR4CUiIiKSIAq8RERERBJEgZeIiIhIgijwEhEREUkQBV4iUq+Z2U/+365mdn4dr/v2aO8lIhIvGk5CRFKCmR0B/M05d0oNlslwzpVU8nqec65pHRRPRKRaVOMlIvWameX5D4cAvzOzKWZ2o5mlm9m/zGy8f5PnP/vzH2FmP5jZx3gDgGJmH/k3W58ZvOG6mQ0BGvnreyP8vfzRv/9lZjPMbLqZnRO27tFm9p6ZzTGzN/wRxjGzIWY2yy/LvxP5HYlI6mjwI9eLSMoYTFiNlx9AbXbOHWBm2cAYM/vKn7cfsJdz7jf/+R/9EbwbAePN7H3n3GAzu9Y51zfKe52Fd+PofYG2/jLf+6/tB/QBVgBjgEPMbDbeLXf2cM45M2tZtx9dRHYUqvESkVR1HN696aYAvwBt8O4pCDAuLOgC+IuZTQV+xrthcE8qdyjwln8D6dXAd8ABYete5t9YegrQFdgMFAAvmtlZeLdsERGpQIGXiKQqA65zzvX1/3VzzgVrvLaGZvJyw44BBjrn9gUmAznb8b6FYY9LgWAe2QDgPeAU4IvtWL+I7MAUeIlIqtgCNAt7/iVwlZllApjZ7mbWJMpyLYCNzrltZrYHcFDYa8XB5cv5ATjHzyNrBxwGjItVMDNrCrRwzn0G3IjXRCkiUoFyvEQkVUwDSv0mw1eAJ/Ca+Sb5Ce5rgTOiLPcFMMjPw5qL19wYNBSYZmaTnHMXhE3/EBgITAUccItzbpUfuEXTDBhuZjl4NXE31eoTisgOT8NJiIiIiCSImhpFREREEkSBl4iIiEiCKPASERERSRAFXiIiIiIJosBLREREJEEUeImIiIgkiAIvERERkQRR4CUiIiKSIP8POhCXT3gOKFQAAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 720x360 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10,5))\n",
+        "plt.title(\"Generator and Discriminator Loss During Training\")\n",
+        "plt.plot(G_losses,label=\"G\")\n",
+        "plt.plot(D_losses,label=\"D\")\n",
+        "plt.xlabel(\"iterations\")\n",
+        "plt.ylabel(\"Loss\")\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "ibToMTlEcveb",
+        "outputId": "d500adcf-06c9-42af-b16d-6c363cc1ad75"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "<link rel=\"stylesheet\"\n",
+              "href=\"https://maxcdn.bootstrapcdn.com/font-awesome/4.4.0/css/font-awesome.min.css\">\n",
+              "<script language=\"javascript\">\n",
+              "  function isInternetExplorer() {\n",
+              "    ua = navigator.userAgent;\n",
+              "    /* MSIE used to detect old browsers and Trident used to newer ones*/\n",
+              "    return ua.indexOf(\"MSIE \") > -1 || ua.indexOf(\"Trident/\") > -1;\n",
+              "  }\n",
+              "\n",
+              "  /* Define the Animation class */\n",
+              "  function Animation(frames, img_id, slider_id, interval, loop_select_id){\n",
+              "    this.img_id = img_id;\n",
+              "    this.slider_id = slider_id;\n",
+              "    this.loop_select_id = loop_select_id;\n",
+              "    this.interval = interval;\n",
+              "    this.current_frame = 0;\n",
+              "    this.direction = 0;\n",
+              "    this.timer = null;\n",
+              "    this.frames = new Array(frames.length);\n",
+              "\n",
+              "    for (var i=0; i<frames.length; i++)\n",
+              "    {\n",
+              "     this.frames[i] = new Image();\n",
+              "     this.frames[i].src = frames[i];\n",
+              "    }\n",
+              "    var slider = document.getElementById(this.slider_id);\n",
+              "    slider.max = this.frames.length - 1;\n",
+              "    if (isInternetExplorer()) {\n",
+              "        // switch from oninput to onchange because IE <= 11 does not conform\n",
+              "        // with W3C specification. It ignores oninput and onchange behaves\n",
+              "        // like oninput. In contrast, Microsoft Edge behaves correctly.\n",
+              "        slider.setAttribute('onchange', slider.getAttribute('oninput'));\n",
+              "        slider.setAttribute('oninput', null);\n",
+              "    }\n",
+              "    this.set_frame(this.current_frame);\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.get_loop_state = function(){\n",
+              "    var button_group = document[this.loop_select_id].state;\n",
+              "    for (var i = 0; i < button_group.length; i++) {\n",
+              "        var button = button_group[i];\n",
+              "        if (button.checked) {\n",
+              "            return button.value;\n",
+              "        }\n",
+              "    }\n",
+              "    return undefined;\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.set_frame = function(frame){\n",
+              "    this.current_frame = frame;\n",
+              "    document.getElementById(this.img_id).src =\n",
+              "            this.frames[this.current_frame].src;\n",
+              "    document.getElementById(this.slider_id).value = this.current_frame;\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.next_frame = function()\n",
+              "  {\n",
+              "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.previous_frame = function()\n",
+              "  {\n",
+              "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.first_frame = function()\n",
+              "  {\n",
+              "    this.set_frame(0);\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.last_frame = function()\n",
+              "  {\n",
+              "    this.set_frame(this.frames.length - 1);\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.slower = function()\n",
+              "  {\n",
+              "    this.interval /= 0.7;\n",
+              "    if(this.direction > 0){this.play_animation();}\n",
+              "    else if(this.direction < 0){this.reverse_animation();}\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.faster = function()\n",
+              "  {\n",
+              "    this.interval *= 0.7;\n",
+              "    if(this.direction > 0){this.play_animation();}\n",
+              "    else if(this.direction < 0){this.reverse_animation();}\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.anim_step_forward = function()\n",
+              "  {\n",
+              "    this.current_frame += 1;\n",
+              "    if(this.current_frame < this.frames.length){\n",
+              "      this.set_frame(this.current_frame);\n",
+              "    }else{\n",
+              "      var loop_state = this.get_loop_state();\n",
+              "      if(loop_state == \"loop\"){\n",
+              "        this.first_frame();\n",
+              "      }else if(loop_state == \"reflect\"){\n",
+              "        this.last_frame();\n",
+              "        this.reverse_animation();\n",
+              "      }else{\n",
+              "        this.pause_animation();\n",
+              "        this.last_frame();\n",
+              "      }\n",
+              "    }\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.anim_step_reverse = function()\n",
+              "  {\n",
+              "    this.current_frame -= 1;\n",
+              "    if(this.current_frame >= 0){\n",
+              "      this.set_frame(this.current_frame);\n",
+              "    }else{\n",
+              "      var loop_state = this.get_loop_state();\n",
+              "      if(loop_state == \"loop\"){\n",
+              "        this.last_frame();\n",
+              "      }else if(loop_state == \"reflect\"){\n",
+              "        this.first_frame();\n",
+              "        this.play_animation();\n",
+              "      }else{\n",
+              "        this.pause_animation();\n",
+              "        this.first_frame();\n",
+              "      }\n",
+              "    }\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.pause_animation = function()\n",
+              "  {\n",
+              "    this.direction = 0;\n",
+              "    if (this.timer){\n",
+              "      clearInterval(this.timer);\n",
+              "      this.timer = null;\n",
+              "    }\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.play_animation = function()\n",
+              "  {\n",
+              "    this.pause_animation();\n",
+              "    this.direction = 1;\n",
+              "    var t = this;\n",
+              "    if (!this.timer) this.timer = setInterval(function() {\n",
+              "        t.anim_step_forward();\n",
+              "    }, this.interval);\n",
+              "  }\n",
+              "\n",
+              "  Animation.prototype.reverse_animation = function()\n",
+              "  {\n",
+              "    this.pause_animation();\n",
+              "    this.direction = -1;\n",
+              "    var t = this;\n",
+              "    if (!this.timer) this.timer = setInterval(function() {\n",
+              "        t.anim_step_reverse();\n",
+              "    }, this.interval);\n",
+              "  }\n",
+              "</script>\n",
+              "\n",
+              "<style>\n",
+              ".animation {\n",
+              "    display: inline-block;\n",
+              "    text-align: center;\n",
+              "}\n",
+              "input[type=range].anim-slider {\n",
+              "    width: 374px;\n",
+              "    margin-left: auto;\n",
+              "    margin-right: auto;\n",
+              "}\n",
+              ".anim-buttons {\n",
+              "    margin: 8px 0px;\n",
+              "}\n",
+              ".anim-buttons button {\n",
+              "    padding: 0;\n",
+              "    width: 36px;\n",
+              "}\n",
+              ".anim-state label {\n",
+              "    margin-right: 8px;\n",
+              "}\n",
+              ".anim-state input {\n",
+              "    margin: 0;\n",
+              "    vertical-align: middle;\n",
+              "}\n",
+              "</style>\n",
+              "\n",
+              "<div class=\"animation\">\n",
+              "  <img id=\"_anim_img72f33255262a4b48ab598f45be08e0e8\">\n",
+              "  <div class=\"anim-controls\">\n",
+              "    <input id=\"_anim_slider72f33255262a4b48ab598f45be08e0e8\" type=\"range\" class=\"anim-slider\"\n",
+              "           name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\"\n",
+              "           oninput=\"anim72f33255262a4b48ab598f45be08e0e8.set_frame(parseInt(this.value));\">\n",
+              "    <div class=\"anim-buttons\">\n",
+              "      <button title=\"Decrease speed\" aria-label=\"Decrease speed\" onclick=\"anim72f33255262a4b48ab598f45be08e0e8.slower()\">\n",
+              "          <i class=\"fa fa-minus\"></i></button>\n",
+              "      <button title=\"First frame\" aria-label=\"First frame\" onclick=\"anim72f33255262a4b48ab598f45be08e0e8.first_frame()\">\n",
+              "        <i class=\"fa fa-fast-backward\"></i></button>\n",
+              "      <button title=\"Previous frame\" aria-label=\"Previous frame\" onclick=\"anim72f33255262a4b48ab598f45be08e0e8.previous_frame()\">\n",
+              "          <i class=\"fa fa-step-backward\"></i></button>\n",
+              "      <button title=\"Play backwards\" aria-label=\"Play backwards\" onclick=\"anim72f33255262a4b48ab598f45be08e0e8.reverse_animation()\">\n",
+              "          <i class=\"fa fa-play fa-flip-horizontal\"></i></button>\n",
+              "      <button title=\"Pause\" aria-label=\"Pause\" onclick=\"anim72f33255262a4b48ab598f45be08e0e8.pause_animation()\">\n",
+              "          <i class=\"fa fa-pause\"></i></button>\n",
+              "      <button title=\"Play\" aria-label=\"Play\" onclick=\"anim72f33255262a4b48ab598f45be08e0e8.play_animation()\">\n",
+              "          <i class=\"fa fa-play\"></i></button>\n",
+              "      <button title=\"Next frame\" aria-label=\"Next frame\" onclick=\"anim72f33255262a4b48ab598f45be08e0e8.next_frame()\">\n",
+              "          <i class=\"fa fa-step-forward\"></i></button>\n",
+              "      <button title=\"Last frame\" aria-label=\"Last frame\" onclick=\"anim72f33255262a4b48ab598f45be08e0e8.last_frame()\">\n",
+              "          <i class=\"fa fa-fast-forward\"></i></button>\n",
+              "      <button title=\"Increase speed\" aria-label=\"Increase speed\" onclick=\"anim72f33255262a4b48ab598f45be08e0e8.faster()\">\n",
+              "          <i class=\"fa fa-plus\"></i></button>\n",
+              "    </div>\n",
+              "    <form title=\"Repetition mode\" aria-label=\"Repetition mode\" action=\"#n\" name=\"_anim_loop_select72f33255262a4b48ab598f45be08e0e8\"\n",
+              "          class=\"anim-state\">\n",
+              "      <input type=\"radio\" name=\"state\" value=\"once\" id=\"_anim_radio1_72f33255262a4b48ab598f45be08e0e8\"\n",
+              "             >\n",
+              "      <label for=\"_anim_radio1_72f33255262a4b48ab598f45be08e0e8\">Once</label>\n",
+              "      <input type=\"radio\" name=\"state\" value=\"loop\" id=\"_anim_radio2_72f33255262a4b48ab598f45be08e0e8\"\n",
+              "             checked>\n",
+              "      <label for=\"_anim_radio2_72f33255262a4b48ab598f45be08e0e8\">Loop</label>\n",
+              "      <input type=\"radio\" name=\"state\" value=\"reflect\" id=\"_anim_radio3_72f33255262a4b48ab598f45be08e0e8\"\n",
+              "             >\n",
+              "      <label for=\"_anim_radio3_72f33255262a4b48ab598f45be08e0e8\">Reflect</label>\n",
+              "    </form>\n",
+              "  </div>\n",
+              "</div>\n",
+              "\n",
+              "\n",
+              "<script language=\"javascript\">\n",
+              "  /* Instantiate the Animation class. */\n",
+              "  /* The IDs given should match those used in the template above. */\n",
+              "  (function() {\n",
+              "    var img_id = \"_anim_img72f33255262a4b48ab598f45be08e0e8\";\n",
+              "    var slider_id = \"_anim_slider72f33255262a4b48ab598f45be08e0e8\";\n",
+              "    var loop_select_id = \"_anim_loop_select72f33255262a4b48ab598f45be08e0e8\";\n",
+              "    var frames = new Array(6);\n",
+              "    \n",
+              "  frames[0] = \"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAkAAAAJACAYAAABlmtk2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\\\n",
+              "bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAAsT\\\n",
+              "AAALEwEAmpwYAAEAAElEQVR4nOz9R4ykaXYejD7hvfc+0kV6X7aruqvN9HB6DIdUU6JEQhIkCFxq\\\n",
+              "IUDQWmsJ2mkjcCNwIUojQSQ1o+F0T/sub9L7iMyIDO+9d3dRPGey/rusi0v8yO8BBpjursqM+Mz7\\\n",
+              "nvecx4jG4zEECBAgQIAAAQKuE8R/3x9AgAABAgQIECDg/98QCiABAgQIECBAwLWDUAAJECBAgAAB\\\n",
+              "Aq4dhAJIgAABAgQIEHDtIBRAAgQIECBAgIBrB6EAEiBAgAABAgRcOwgFkAABAgQIECDg2kEogAQI\\\n",
+              "ECBAgAAB1w5CASRAgAABAgQIuHYQCiABAgQIECBAwLWDUAAJECBAgAABAq4dhAJIgAABAgQIEHDt\\\n",
+              "IBRAAgQIECBAgIBrB6EAEiBAgAABAgRcOwgFkAABAgQIECDg2kEogAQIECBAgAAB1w5CASRAgAAB\\\n",
+              "AgQIuHYQCiABAgQIECBAwLWDUAAJECBAgAABAq4dhAJIgAABAgQIEHDtIBRAAgQIECBAgIBrB6EA\\\n",
+              "EiBAgAABAgRcOwgFkAABAgQIECDg2kEogAQIECBAgAAB1w5CASRAgAABAgQIuHYQCiABAgQIECBA\\\n",
+              "wLWDUAAJECBAgAABAq4dhAJIgAABAgQIEHDtIBRAAgQIECBAgIBrB6EAEiBAgAABAgRcOwgFkAAB\\\n",
+              "AgQIECDg2kEogAQIECBAgAAB1w5CASRAgAABAgQIuHYQCiABAgQIECBAwLWDUAAJECBAgAABAq4d\\\n",
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+              "AAAASUVORK5CYII=\\\n",
+              "\"\n",
+              "\n",
+              "\n",
+              "    /* set a timeout to make sure all the above elements are created before\n",
+              "       the object is initialized. */\n",
+              "    setTimeout(function() {\n",
+              "        anim72f33255262a4b48ab598f45be08e0e8 = new Animation(frames, img_id, slider_id, 1000.0,\n",
+              "                                 loop_select_id);\n",
+              "    }, 0);\n",
+              "  })()\n",
+              "</script>\n"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 576x576 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig = plt.figure(figsize=(8,8))\n",
+        "plt.axis(\"off\")\n",
+        "ims = [[plt.imshow(np.transpose(i,(1,2,0)), animated=True)] for i in img_list]\n",
+        "ani = animation.ArtistAnimation(fig, ims, interval=1000, repeat_delay=1000, blit=True)\n",
+        "\n",
+        "HTML(ani.to_jshtml())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 427
+        },
+        "id": "a-vMraLldu6Z",
+        "outputId": "c754902d-6cc4-40bc-e692-0126e9f220c8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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YbDbY7XZYrVah+AYCAWxtbe2x3BNkcsUVFdnKS/JxEqal+MhhftxqL59P16VziYjk5OSIcdCSXbaMc+IjyywTBmpTtrhrkTsae5nA8vniY8mJkexZki3fpHwROeHzTYqmTI5I+eV94X3g88KvyUk5XSebF5P3la63ubkpQj75tWgctRRYWaHn4ar7XZfAjQNE3OS5421wjydvU54HLgcnIfSZ3C79L68Ped2TEYK+2y8Ul98HfC2YTCZUVVXh448/Rn19PaLRKPr6+vDnf/7ngkxxLwmdy/ske/Xk+1SeB3ns+TNH6z6h7y0WC2w2m/jJzc3F7OwsIpHIHmMDERn+bOPeVS2iJN/bPGyQjz1fC/ye5/er1r0uzzF5a+W1wseLz68c0id7lbXWEYdOpnTo0KFDh47vCDkkRn7xykRK9hbQS52swm63G9XV1Th79iyqqqqQn58Pp9OJtbU1fP3113jx4gUWFxf3WK+5Z4kge5tIoQWwh8gRtELEZHkpzEbuI51vMBjgcrlEGJDRaEQ4HMbm5qYYM1nZk8dGJgtkteZjK1+bt6P1OZ8vmcBqKdw8v0IOldRqW1bMOUmhPBFOWLSUSa3+aYU7al1/P6WPfyavHb4+tcCV/reRg86RZeJrTssrI8v7Nkqs1tzxNvgcy/JrkUYuu1Z/shEzWSaTyYTm5mb8/b//93Ht2jV4vV7E43GYzWb88pe/RCAQ0DxPvoaqqoKMcy+RHEaXzcDCyTZ9J/fbYrGgoaEB7e3taGlpgc1mg9VqxVdffYX79+8jHA7vaY//zz2BXH5au5w80eeywUQ+V8v4ozX+WuRLaxzkMc72vywHPXNkI4MMnUzp0KHjO8NkMsHpdMJqtcJgMCAajSKRSAiLpw4dvwuQvQL8BQxkvpgpNIXyVOhzo9EIr9eLpqYmnDp1Cj/4wQ9QUlICu90Os9mMjY0NkfNy+/ZtLC8vZ+QccIUbyCRpXEHgXiZ+HP0QqePhSnScHOZEPzk5ObDZbPB6vcKyXVhYiLy8PKE4LSwsIBQKQVVVBINBhMPhPbk/WsSKQGE6byKscggW/1y+htlshsPhALBLLhOJREZuCLUrewrkcdGSnYPCh+TQQ1kBlXOj5D5xYilfRx4DeX75Mdm8mRzy2GfrG1cwecEN3idaT1rEPdsa0/r+bSHLLt8LsoeQrkWEheZAlofPHX0uV53MycmB2WxGeXk5rl27htOnT6O0tBRWqxUmkwmFhYVwu917DBK8bS35ObSUegoVlY0Q2eaW+pafnw+fz4cLFy7g5MmTaG1thdVqRSKRwMTEBF68eIGNjQ3NAiv7kc/9iKv8mZYBRW5Da02/iUjz87TGRZYxmxzy9bWgkykdv7XgVWj4S3E/68JvGvTAogc1kPlC+y4viqMCk8mEoqIinDx5EvX19TCbzXjw4AFGRkawtramWSZVh47fNsiWdh4CJytvWso+sKuAFRUV4b333sP169fR3NwMn88njBRmsxlWqxWXL19Gfn4+TCYTvv76a6yurmJzc3MPSeKKNfCt8kFhXtn6wf/mxIH+1kqMNxgMcLvdaG5uxokTJ3Dy5Em43W643W6RpE65T4FAACMjI3j27Bm6u7uF8UVL4ZdJBCcBMoHJ5tXQUvxoXoxGI/Ly8tDa2oqioiKsra1heHgYa2trgqTK+V1EHEnpzgaSi+f2yMRaS5Hj5/MxoHeIbM3nay2bciqTBfpci1DJyiPJlE1ZzubZyaZIy/IpiiI8pXw85WIkWjJl62e2dbOf4i33U16LvJiMqmaWAufXMRgMcDgcKC0txZ/+6Z+is7MTRUVFGXmTsVhMU55sHkq5X1xmnt/Fj5HDB/m9zA07Ho8HnZ2d+Oijj9De3o7y8nLk5ubCYDAgGAzCaDTC6XQiHA4jmUxqji2XVR5/eSwJ3EulZeiQnzlcf5OfaXw8aF6yeRazrSf+XOEGDRpfLTIoQydTOn4rYTKZUF1djdraWpSUlGBnZwfLy8sYHByE3+/fE1v9LmA0GlFcXIza2lpUVVXBbreLWOxAIICpqSksLCwgGo2+UzneBRRFQVVVFTo7O/HBBx+goaEB8Xgco6OjGB8fP2zxdOg4MHDFhecXyQoyP5YUA/o/JycHhYWFaG9vR2dnJyoqKmCxWAB8q2ibTCZ4vV60tbVhc3MTS0tLAID5+fk93gBgr/VWS1Gj39wDoOW5ouNIuaBjyaDS2dmJ48ePo729HceOHYPT6RT5MbwEdiKRQHV1tQj/6+3tFTlg+1m5ubIqK6Ba5DBbX/m4qOpuefC5uTnE43F4vV40NzcjkUjAZDJhfX0d4+PjiMViewiz1tjIf8vjJo+5lszZCCD3Er5NWKn8ez+iI48J/c+VSi2ylY2o8vboM5l4EoHSyj+i/mYjrJyocgVZbkf2au23/uV7VV5j/HM+n1x+o9EIt9uNrq4unDlzBhcuXEBdXR1cLhfMZjMUZTeny+l0orS0FOPj44Iccw+zFrTmWC4os9/4A9hTaMVqtaK6uhrt7e04efIkGhoa4HK5hBcxGo1ic3NTeGtlMqRFTmQZuSyyoYcfLx8jk1s5RJAfR3PtdrtRXFwMt9sNANjc3MTm5ibS6TQ2NzeF4Ub2kMrzr0Xu3gaHTqZMJhOsViscDge2t7extbWFeDwuXO0yeJWV74NVPycnB1arFTabDQaDAbFYDFtbW1n7d1Agy5zNZoPZbBYPt1QqhXg8Lkp4fh9B8frnzp3Dxx9/jK6uLmxtbaG7uxt//ud/jo2NjT0Ph980FEWBzWZDW1sbfvzjH+PKlSsoKCjA9vY2wuEwhoaGcOvWLdy5cweTk5PCuvx9AJXY7erqwkcffYQrV67A4XBgcHBQPMCOal9IqaNkW7PZnBFLnk6nkUwmsbm5KUo6HxbII+F0OmE2m7Gzs4ONjQ3xgtBxdEDWagKvzia/nLkHixRVq9WK8vJyNDY2Ijc3VyiSMjkzmUwoKCjA+fPnYTAY8MUXX+CnP/2pqMpG1+DKL/2Ww/dkpUfed0VWcnhFN4PBAKfTCbvdjpMnT+KTTz7BqVOnkJeXh+3tbaytrWFrawuRSAQejwculwtOpxNOp1N4sZ1OJ4LBIDY2NvaE15EsvC8Wi0UUUeCKlZYSnU0J58+l7e1tbGxsYGNjAwsLCygqKkJLSwt++MMf4uzZs5ibm8Nf/dVfobe3F+FwGPF4PKOinRxtIOcV8fEji7kc/iyTQV6UQe4T/eZzabFYxDVIaZRzwrh3lMulRfh5SBsPqeTlvvma52OrZeXnv0kWugYv3829PpzYySSC2jObzRnheLIuKK9hGbx9+p6H3cnkk36MRuOecE1FUWC32+FwONDe3o4//uM/xvXr11FYWChKmPN1WlRUhBs3biAcDmN6ehrRaBTRaHTP/m/UNm0xoFV0gT9D6JnB5zZbnpTZbEZ+fj7a29tx8eJFVFdXZ3jPaDxMJtOeEFd6zsmeLr426Zqc1GrldPGx5xUT5XPpHuKfG41G8R53Op1oaWnBhQsX4PV6YTabsbi4iKWlJUSjUSwtLWF6ehrhcBiRSGQPoeL3Mo0vlbPf3Nx8K+P7oZIpk8mE8vJynD9/Hu+99x5CoRB6enpw69YtLC8va94gTqcTHo8H4XAYGxsbR1ZpI+Tl5eHs2bM4d+4cPB4PvvrqK/T09GBhYeFQyaDb7UZFRQVOnz6NtrY2lJWVAQCmp6dx69Yt9Pf3Y2Fh4dDk+65QFAUejwetra04f/48Tp06hZKSEvj9flgsFjidTlgsFmxsbLxTOcxmM4qLi3Hu3Dl8+OGHKC0thcVigaqqyMvLQ15eHkpKSuB2u3Hr1i28evUKiUTincr0m0JBQQFOnjyJDz74AOfOnYPX68XY2Bhu3bqFqakpEcZw1GA2m2GxWGA2m9He3o4rV66gpqYGeXl52NnZwfr6OlZXVzE6OoqnT59icHDw0EIVaR23tLTgRz/6ERobGxGJRPC3f/u3ePToEVZWVnRCdUTAPU30giYiTkYqGfxFTuE2J06cQFNTE1wuF3Z2dpBIJBCLxYSiRXkYJpMJDocDnZ2dSKfTePnyJcbGxrCxsaFpRScZuayylZgfS2uekt7pOzJAkUJcUlKCU6dO4cMPP0RTUxPMZjPm5ubw4sULjI+PIxQKIRKJIDc3F06nE7W1tbh06RKqqqpQWFiIM2fOYHp6GrFYDGNjY0IeYK93ioyS2Z6RMknS+lz+m/+k02msrKxge3tbKJkVFRX44z/+Y/h8PnR3d2NwcFAQHXlc6W9ZceSWdV698W1kpza5EkubvzocDuTn56OmpgZOpxPpdBpra2sYGxtDKBQS3j5ODuQ1wcdaJoEkC/eAkBzZIK83Pr5aXi05v0eeF632SS4qZCKPn7yG+Rjul/MDIIPMy3NAkMMqSamvra1FW1sbfvCDH+DcuXMoKCgQJIiO3dnZEaGlV69eRUFBAfr6+tDT04Oenh4Eg8GMCoJcdj4/BNnbSce/qViJ0WiEz+fDe++9h9///d8X9y6VQbdareLY/Px85OfnIxgMZhgMePlymeTT9YgI8vnQkgeAeF7K2zBw0sWNGFarVdynx44dQ2lpKWpra1FaWiqcA/F4XBCh9fV1zM3NYWBgAHfu3MHCwoLwUvHx5OuD8ljflmMcGpkiZfP06dP45JNP8P777yMYDMLr9YqOhsPhQydLZI3Iz89HSUkJgsEg/H7/W1nfFUVBXl4eTp48iQsXLsBgMKC7u3sPCz5I0AKvqanBlStXcOXKFRErCwBTU1Mwm82w2WxQFAWBQCDjwXWUQUpJY2Mjrl69iuPHj6OoqAjb29uYmppCf38/1tbWDiTEz2azoby8HPX19aioqMh4kSqKIip0kRc2Ho9jZmYGkUjkncr17wsKRzp58iTa29uRn5+PlZUVvHjxAg8fPsTy8vKhe3RkUIU0n8+HiooKFBQUoKOjA5cvX0Ztba2wpq+vr8Pv92NoaAg5OTmIxWJYWFjI2PDwoGAwGFBUVITTp0/jgw8+QHt7O8LhMILBIAKBAEKhkE6mjhBkpYdXa9NSjmQyZTKZYDKZYDabAQDRaBThcBhTU1MYGRnB1tYWSkpKYDQaUVJSAovFgry8PDQ1NeHGjRvw+/2Ix+NZlVGuZGpZ+7ls/Byu2JAiYzabYbfb4fP5cP36dbS1tSEnJwfDw8Po7e3FgwcPsLCwgPX1dWxubooiNSUlJQiFQrh+/TrKy8tFjtjq6irm5+eF50Yu9UwyJRIJTa/Bft4HLWgp06q6G/K3urqK7u5ulJWV4cKFC6isrMSpU6fg9/uxurqKeDyeYdmW29PKe6HP+dzzMac25HkhyzgZ3ywWCywWC3JycuD1euHz+VBXVwePx4PNzU2Ew2GUlZVhZmZGvFc2NzexsbEhPJfkAXwbIxHvH/ceyQou/c3/l4kUkElEZLIDIKNIhezN4oquFpHVGj+tseZGBf637BGUvSgkA9/YWFEUOBwOeDwedHR04MaNG7h8+TKKi4uFZ5o8hRRZoKoqrFYrSktLYbfbUVVVhYqKCiSTSfT09Ijy4rxf8p5Mcp/lnB4tQs6Pz8nJQWlpKVpaWlBaWgqj0Sg8xE6nU3h2rFYrampq0NDQgLm5ORHuqrX25ecOXYv22ePhuVr3AV9ncrt8nqhke0VFBZqamnDmzBkcP34ceXl5ohAWhRXz92M6nUZLS4sIu/ziiy8wNzeHZDIpnivy+G5vb/9ahuFDI1MejwfHjh3D7/3e7+HDDz+Ex+OB2+1GOp3G8PAwwuEwotHoHrfy1tZWhkv0XYOUx/Pnz+PatWt4/Pgx7t69i5WVlX2VRkVR4HQ6UVZWhpaWFhiNRoyMjGBkZASrq6uHZvGm2NLTp0/jj/7oj1BZWQmLxSIIU35+Pj766CPk5+fDZrPhwYMHIib/KINC+9ra2nD9+nV8+umnqKysRE5ODpaXl/HgwQP8zd/8DZaXlw/Ec+J0OlFdXQ2v1yvIUjQaxdbWlgiTczqdOHnyJCwWC7a3t3Hz5k309fUdugEhG2iMq6qq0NHRgaKiIqyvr+P+/fv4+uuv8erVqyNDBklBNZvN8Hg8qKurw7lz53D+/Hk0NDTA6/XCbrfDZDKJWHKn0yksvoqiIJFI4Ouvv8bKysqBz4nBYEBZWRk6Ozvh9XqFNe7YsWOYn58XCrZe5OPwQd4CCoOSSS5X+HlpbyILRKRIydjY2MD4+DiePHmCu3fvYnl5GSaTCQ0NDWKNkmW4sLAQly5dwsDAgAgXyrZPkBbR4mFE2TwC3CthNBrh8Xhw/vx5XLlyBSdPnkQ6ncbAwAA+++wzjI2NYXR0NCNsz2AwCOPo5uYmJiYmcP36deHRunTpEvr6+rC8vIxQKJRhvOOKFymVnGDRuGnt75WNNGYDte33+/HgwQPs7Ozgxz/+Mc6cOQOv1wuXy4Xe3l4MDQ3tGS8+r9mURK4U0m+r1SoqGvJ3EkXh1NXV4cqVKyKyxW63AwDsdjtsNhscDoeYv1QqheXlZaytrWFtbQ2BQABLS0uYn5/HzMyMeI6Rgi+Tnf3IiVY+nVwkQoskUl/4GpMJME85oBA6Iv10bjKZzNh8lsvIyYRWX7Ty6ihEkq4lzydvh8gA91wZDAZYLBbU19fvidLIyclBOp3GxsYGIpEI4vE48vPzxblmsxkmk0kYJChE0Ol04sGDBwgGgxleRS1dhUISaRxkaHmNVFUVXs26ujr4fD5sbm5icHAQN2/eRDKZRFtbmzAiOJ1OnDhxAn6/H729vQiFQhnXJEdDNiIl/03ht/RupnHX8iTLBhLyzNbU1ODy5cu4ceMGKisrUVhYKML1aa6pTRo7i8UCk8kEi8UCl8uF8vJyWCwWPHnyBMPDw/D7/ZoeYz7+b/MMOTQy5XK5UFtbi8rKSlE9hErDNjQ0YGBgAIODgxnn5OTkoKSkBC0tLRgdHcX09PQ7V4rtdrsoU9vR0YFAIICBgQEEg8F9yZTZbMaJEydw/fp1NDQ0IBAIYHR0FH6//1As3QRyM1Oi3srKChYWFrC6ugoA8Hq9qKqqQmNjI7a2trCysoL19XVEo9EjTahIYf7ggw9w5coVUfAhGAxiZGQEw8PDmJ+fz4gpf5cgC66iKPD7/eju7sbAwAD8fr9I/KSkz5qaGpw9exaTk5MYHh4+MEPBrwvKk7px4waampqgqirGxsbwzTff4OXLl1hfXz8yyn1eXh7q6upQX18vrGt1dXWorKxEfn4+zGYz0uk0IpGIUPLy8/Ph8XjgcDhQXFyM8vJy5OXlCQv7Qc8J5S+SEkke8urqajQ0NCCVSmFtbe1IrpXfJZAHIR6Payr0WqF1/Huz2Yzc3FwUFBTAbDaLvOGxsTFMTk6KAjVGoxHz8/MIBoNwOp2iBHlZWRnKy8vhcrkyQldkLw6QGXZGx5Biw99LXHb+vKR39LVr19DZ2QmLxYKRkRG8fPkSvb29mJmZESHUdN10Oo1UKgWDwSDC+goKCnD27FmUlJSgsbERra2t2Nrayggn4rk+XG5SEImIUti21savb4KsZFMe1ezsrKhQePbsWTQ3NyMUCmF2dlYYYDhplZVxPvZkzZe9UwaDATabDVtbWyKXiubFbrejtbUVH3/8Mc6fP4/q6mq43W6RI0Wy87FRVRW5ublIp9OIRqNYXV1FLBZDMBjEzMwMFhcXMTQ0hOHhYbFPGZ9vOcxJy1ovjx0RWVn5lcdCJiz0Hb0nc3Nz4fP5oCiKqPposVhQWFgIl8uFUCiEsbExRCKRrKRNy9shy08yU7EIMs7LcsnrQvbmORwOlJWV4ezZs/jkk0/Q0dEhCFMoFEI4HMbMzAwmJiYQiUTEd9Qnt9sNm80m2rbb7SgpKYHT6UQkEhG6ZTaSSPdBtvctH3uZpNPzCgBWVlbw6tUrfPnllzCZTIhEImhqakJ5eTkMBgO8Xi8qKytFVVF5jHhOpzwnfO1zTyAZBre2toThJJsRgv9N4ZQXL17E8ePHkZ+fL3KtkskkotEoZmdnEQ6Hxbrf2dlBVVUVcnNzUVNTA5fLhcrKSnz88cdwu93CcyuHSMvXp37t92w5NDLl9XrR0tKCwsLCjImwWCwoKioSBItAVr3Kykp89NFHwjVJuzO/C5jNZpSUlODSpUu4evWqcCNms+ARTCYT8vLycOnSJXzwwQcoLS3FyMgIBgcHsb6+fqikxGg0ivGNxWLo7u7GkydPMD8/DwDw+Xw4ffo0Ojs7ceLECbx8+VKEDRxFMkWWlvb2dly+fBk/+MEP0NzcLLxtU1NTuHv3LoaHhxGPxw9Utp2dHUSjUYyMjOAXv/iFCIOzWq1obm5GIBCA2WxGTU0N2traUFNTA4/HcyRDuGw2GyorK3H16lXcuHEDPp8Pg4ODosTx9PT0kVDqKaSvvb0d169fF2XbS0tLYTKZRH4GJb0HAgEEAgGkUimcPn0azc3NsNlsKCgoQGtrK5aXl+FyubC4uIhgMHhgZHxnZ7f65MDAAM6cOQMA4uVWU1OD1tZW+P1+hEKhI0Ngf1ehqqogC9mUCvqtVRyA8o/KyspEoQKj0SjCrdfX15FOp7G1tYVYLIZEIiEUGFJES0tLUVpaikgkIrxjskIoEzqSi4fVcSWXg2RyOp2iUIbP50MwGMTz589x//59LCwsCKWEPBGkcNH1qJLp7OwsFhYWUFxcLLzd8/PzmJycFHJo3Weygr+zs5NRslkmMftBLinN2wyHwxgeHsYXX3yBmpoa1NTUoKWlBeXl5ejr6xNFpLLJul8oE80B5cVxEkh5GjU1NTh16hQ++OADVFVVweVyifyTVCqFRCIhiuTwyndU7Co3N1d4SSi8LxgMYnBwEH/913+NO3fuiOfZm5RGec1w+cljoFUwgEOLSJE3yufzoampCSdPnoSiKMLzXlBQgHPnzol1/a/+1b9Cf3//nvLwWuF/8tqX+0RrkX8nn8eNAXx+HA4HKioqcPnyZfzwhz9ER0cH8vLykE6nEQgEMDY2hqWlJfT29qK3t1fkQimKgtzcXJSVlaGhoQHFxcUAdqvOLS4uIhwOw26379mYWksmLUONnM+ltfa2t7eRSqUQDocRCATg9/sxMDCAtbU1GAwGrK6uilxHyi8uLCxEQUHBnj3EaP61vIH8mjzXkj7jz0stow+fN95fl8uF3NxcpFIp4YWnQjdTU1O4f/8+xsbGEAwGRd2F/Px8lJeX40//9E9FsaympiYYDAZMTU1hZmZGPFO17uM3GRUIh0am8vPz0dbWhqKiooxFnG0hALuKUl5eHtra2rC4uIjp6WlMTEy8k8R9WvhNTU1obGxEQUGBSGKbnZ3d17vkcDhQWVmJ9vZ21NfXI5lMCnkPu8gAlbJ1Op0IhUJ48OABvvrqK2EJGRwcxNDQEJLJpCCC+fn5WFhYOJIbsVosFlRWVuLDDz/ET37yE2FF2d7exvLyMp49e4YvvvjiwMt1kxKxsrKC2dlZUXQkmUxia2sLw8PD2N7eRk1NDfLz8+F2u1FaWorCwsI94a1HAZQn1dHRgdLSUmxvb2NoaAjPnz/H+vr6kSBSAARRvXHjBj755BNUVFTAZrPBaDSKTQh/+tOfYmRkBNFoVFR29Hg8yMvLEx7NiooK2O12lJWVoaenB93d3Xj+/DlmZ2cPJCdse3sbc3Nz6O7uxo9+9CPs7OyIkGOKs3e73ZovMB0HC7K680R94NuXL0/O5p4EUoDy8vLQ1dUlCP/29jYcDgcaGhoQDAZF5bLKykp4PB5YrdYMhctqtaKxsRELCwsIBAIZpca5oiKXUubKIj+O5JJJOoWelZWVwWg0wu/3Y3R0FJOTk5ienhbGKgr/oj6SJ4dySLa3tzEzM4N79+6huroaqqqKhH1Obgha+UacCMpKkBaJ5KC25Ap53NJOytrS0hLW19eRSqXgdrtRVlYGj8eDeDwu3oe8DX49LY8IH/ednR1NPcLhcKC5uRlnzpwRESQUThWPxzE3N4fR0VF0d3eL6mREYih94vLlyygpKRFluYmY0/pSFAXffPMNVldX38o4RO3zfsleCTJ4y+tOJgP0P1V2/NGPfoSuri4AQH9/P/Lz83HmzBl0dHSgvb0dRqMRy8vLKC0txfDw8B5PC1fStdaAlm6ZTqcRi8X2GOxlGfmaI+NFS0sLzp49iw8//BCdnZ2w2+3Y2NjA6Ogo7t27h6GhIaysrGB+fh5ra2sZUT0mkwkTExN4+fIlHA5HxnpzOByIRqMZpBT4tuqhPPb8WSOfo0Ue6bx4PI6+vj4Rks+raU5NTeHnP/85DAYDzp8/L8gXkXxeoZTGn4wJWuSNe7Pkda9lsNlPj0in03jw4AFWV1dRVVUl+kLepWAwiFAoJLzU9KzZ2NhANBpFd3c3mpubRfn3goIClJWViVxOLZ0rW2VQLRw4mVKU3aQ9r9cLr9ebUTkE+Ja1ZitdbTabxV4bS0tLiMViWFpa+o0WSTAYdjdeO3bsGD799FO0trZiY2MDPT09mJqaEow4W/+qq6vx8ccfo62tDdvb2+jp6REx4YdNSMxms3ghhMNhrKysIBAIiO8pr4fiqym+9ygqbZSIe+rUKZw8eTJjL6dYLIa+vj48e/YM8/PziMViBypbbm4uGhsboSgKJicnRalgAKLYwfj4OH71q1/BZDLh3LlzcDqd4kY/KiBPT0dHB37v934PbW1tSCQS6Ovrw/3799Hf3//OKyO+LchQcO3aNXz44Yeoq6uDw+FAOBzGq1evROWk7u5usdkpvRxcLhfu3r0Lp9OJa9euiVBYu92OwsJCFBYWYn19HYFA4I17gvymQEnl3PJOpdxpz4yjQmJ/l8EVCTk5myvQ2azFqqpmFJ+g/L3KykrMzMyIF30sFsPs7CyWl5dFiAvllJSWlqKurg4vXrzA8vJyxrW4MsgVGE6q5PBErpQRyMOxtLSEV69eIR6Pi/sqEAgIIwNX8Hi4Hh8fv9+P2dlZxOPxDHKjJYusMMreIK19Y7gc8v9a3jnZK0HKGHmASHnd2tpCMpnUbFP2ymiROPrNFUt5PeTk5CA/P18YBomgxGIxDA4O4vnz5xgdHcXdu3cRCoWEsk6eqYGBAZhMJly6dEl4OnJycmA2m4XRju9jxuXj61X22PAx0upntvwXue/0U1ZWhhMnTqCurg4FBQV4+PAhent7EYvF4Ha7cfz4cbhcLhiNRuGB521yMkzX18r940YOTphJeebrSJaXt0N5Vi0tLTh37hxaW1vh8XiwtbWFoaEh3Lt3Dw8fPsTQ0BDW1taE15B7HimENxgMZow3zQ8dwwtx8I2y5XwgLj9fS1pEivqoqiqmp6extLQkwkbJELKysoJHjx6hsrISzc3NwpBDeUnkYeP3s5YBiY8hL4XPZefzJZ8rgzzQCwsLWFtbw6tXr8T7W/Z60rjR2BOfII9VdXW1IIc7Ozui4p88fpyokwz7yXjgZIpydohIyaVjKY+BKhNxqKqKRCIBv9+PnZ0dOBwOYW35TZMpp9MpqsIVFxdjeHgYT58+xdzc3L5EymKxoKamBlevXkVlZSUCgQBevnyJ0dHRfUnYQYGqKNrtdqysrGBra0uUnSXSRPsNyAvsKMFoNCI3Nxf19fU4e/YsampqMuQOh8Po7+/H4ODgoXgDnU4nKioqRBKqbPXY3t5GJBLBs2fPUFlZic7OTrFfwlEhU4qyWz2nuroaXV1dOHv2LDweDwYHB/HNN9/g+fPnwtt2FGC320X1rWPHjgmL+MrKCm7fvo2vv/4a/f39e0qiGgwGbG5uihj3s2fPIjc3FyaTSZR1VhQFd+7cgc1mO/BNlmUrazKZxNramuYzUsfBQ2vPI0A7zE/LYqyqqqgqRaWR0+k0gsEggsEg1tbWEAqFoCgKQqEQUqkUYrEYOjo6RPhNcXGxyKuh57gWqXgbxUVLOSKZAoEAnj9/juXlZUSjUQQCAcTj8YySzjJp0WorHo9jZWUFiUQCRqNReIi1CgHIHp1sY6oVbsTJACmGMrHN5kkiWShfhvpPpcfl/moVWODHZFNwZYXdarWisLAQ+fn5sFqtUJTdYjhjY2O4ffs27t27h7GxMQQCAeGZI+NzMpnExMQEXrx4gfb2dpSVlQnlNZlMIh6PY3FxUVQy0yI5pIjK48/JCAd9x0O+eP4VJ8CkfOfk5KC2thbnzp1DUVERQqEQPv/8c/T29mJnZwcFBQWoqqrCqVOnRJVFqqIoy6UVnsXXgxa0vCn7eTRp/RQVFaGpqUnsqba5uYmFhQXcvHkTX331FaamphAOhzNCA+X2tYg0L3Ygk0YiW/Ka5f/zMZbvDS2P6ObmpiBG/Fiq+jg2NoZwOAyv1ysKVgwPD4v8R57XzUmT1l5YdAy/vlb+Vbbx4u3RteVtkUj3NplMYv+pWCwmSCJdi+5buq+pMNh+VVe11oMWDoVMlZSUwOfziV3SORKJBKanp/fsM0U34tTUFP7Nv/k3mJubw8TEBAKBwG9cWabEUNpocGdnBwsLC3j58qWw+mXrW2FhISorK1FaWgqbzZYRD3vYxIQe1C6XC4qiiHyzvLw8VFdXo6ioSJSE9ng8iMViR7IYAnlLTpw4IfZ18Pl8wtKwsbGB6elpDAwMiA1xDxrJZBLr6+sixlqLIPGHA7C795fX6826N81Bw2KxoLi4GJcuXcKpU6dgs9mwsrKC58+fiz2ljgqRUpTdbQjq6+tRUFAg1kIoFBJrgcq7yiFBdrtdlIqtr6/P2JyUQhKoCiMVFTlIyIqk1gtXx+GBK5DyZ1yp48omKZWkoFVUVAhln55fvb29mJiYwPLyslAe6DeRqlOnTolQsPLyclRWVoq93ggUIiZ7cEhOejZxK7OWIkNtUK4yWX21iA5vR4scORwOlJeXw2w2IxaLYXp6GmtraxlGJ+51kJVHPqb82vIeO/w4Ukhl7yEPVePXIT2ANu5cWFgQBaTk/pAiKRM7Lfll4wiXlbdrNptFZTgq2U5eKaomLM+VrATSHCWTSVFYY3h4GKOjoxn5UrLiLYdA0hjJ3huZxHKyReeTx5XIsslkElXVysvLRTVIyrnLycnBxsYGlpeXEQ6HkUqlMDMzI3QombwZjUakUinNa1PfgMx9mWSDgdbxvH9GoxEVFRW4ePFiRrGJhYUFPHr0CKOjo2JrE+qnVhgbb5dfi+TTUui18s20ZJdJMG9Dvla2e4mKU5BH02KxwOfz4fLly9jZ2cGjR4+wuLiItbW1jKIiJD8fR05y5dBMmajI4YBa/dQygNAxVE+hrKxMRG5MTU0J3Y+H8gLf6mfLy8t7Qm25LG/yRnEcuNZmMBhEKUgeI03YzzO1vb2NxcVF3Lt3D4FA4J3lalitVjQ1NaG2thYARIwrVWbJBrPZjIqKCpEwSlYgIn2HTabIchUIBFBVVYXKykqxz1R9fb0gU+FwGFtbW7h79y4mJyc1K50cJnJzc9HV1YX33nsP165dQ1VVlYg/jkajGBsbw82bNzE8PPxOC5Tsh1AohL6+Pni93n0JKbmS6eVJVXMOGwaDAQUFBWhvb8eFCxfQ2toKABgZGREFJw46dPJNsFgs8Hg84uUNQDxjotHonmdKTk4OnE4nampq0NHRgYsXL6K1tRU2mw2JRALxeByxWAxzc3Po6enB5OSk2GvjICErtgdN5nTsD1kR5UqorMhwJYdygCkXymAwIB6PY2RkBF988QVu3bolPD/0Ut/c3BSh7USs3nvvPZEo3tzcjFevXmFhYWEPqZErygGZoVEytDwRZNF9k5EtmxJEYYnFxcVobGwUW1cMDg5iZmZGlMbm52oRU05KqR/ydWWPCnmY9lOQiEgYDAbk5uaivLwcbrcbq6uruHPnDmZnZzNyzDiRkp8tAESeydu+g2i8KdSLCo/Mzs7i3r17uHfvnqiaSkq+xWKB0WgUVnkyyHGFkEp1Dw8Pi/0W0+n0nlwomawYDAYRoaKVz8bHWv6et8HL5FPJ9/r6etjtdrx48QI3b94Uii3J9PLlS3z99dcoKyvD0NAQAoHAHoWaDJLyhrzy+tMqlMCPp60GZJJNMpvNZjQ0NODDDz9EbW0tLBYLQqEQBgcH8fDhQ7x69UpUj5PHiI+LvG75sXKeI5dRvo+zGTBk8it/n23d5+TkwG63Izc3Fw0NDWhubobb7YbD4YDRaERnZyecTqcIxxwfH8f6+jo2NjYyijKRDPK6l8kqN7LQ53wtymMoy83vPZvNhvz8fHR1daG9vR2pVAp9fX0YHx/PIHNGo1HcF7FYDIFAAAsLCwiHwxlhlSQHf05oeapkHIoJXE7UfVtQrgklmL0rpcbpdOL06dM4fvw40uk0nj17hgcPHuy7txRZ2xobG9HQ0ACz2YyFhQUMDAyIWM2DVsJkUMLe4uKiyDOqr69HKpUSyc9bW1vw+/347LPP8Dd/8zfY2dkRVoijANpI7qOPPsKVK1fQ0tKSUeVoaWkJz549w2effYbp6elDk9vv9+Px48fw+XyidKcMg2F3zy+32y2STH8dS8i7RE5ODsrKynDu3DkcP34cxcXFCAQCGBoawsjIiPBy0ov2KMhM7v9YLIZkMin2mKLqlRQyo6qq2AyzoqICp0+fxqVLl3D69GmUlpYKS/DMzAzm5ubQ29uLp0+fivv4MMi5lgVax9EAKZoymZKfPVwBMhqNcLlcuHDhAn7wgx8IBS0cDuPzzz8X+5vJJZmpetXa2hoePHgAm82GpqYmVFRUwOv14sqVK/D7/SK3l7yYsidHDjEC9uY4yAqZFrni0DpW/t5utyMvLw8nTpzAmTNnsLm5idnZWZH/LD9HZOs1XZ/nuWhZ37NZwrU8RFrPLiJTtbW1UFVVhDYvLy9n9E+2qMueMvk7rRAu3g55Jyl3GdgtELC4uCjyPKlv5Dmg0DPa+8/tdostN6j8ttVqhdfrxYULF5Cfn4/i4mLcu3cPs7Oze0rZy1EU8ljJ3oFsHiAac54DT3rS5cuX0dDQgFAohCdPnuDp06fCaEtG36mpKXz22WeoqKiA3+/PGmEih4wRst2PWnLSPSzfC0RYHQ4HXC4XSktLRfn5YDCI6elpkSMlt59trWndF5woZeufVi4hV/DlcEct7xS/Dwj0Lm9ubsbFixdx5swZNDU1iVQcKtZgs9lQVVWFs2fP4tWrV3j8+DGeP3+OxcVFkUcoX4c/S/j4yM8WrbHnc8P7QfllVqsVJSUl6OzsRGVlpUirWFxcFOkHJBNVTSaHwebmZgaXoPb52ufE6m30m0MpQEGbFH4XkGXsXcJqtaK+vh5VVVWC5VJiZDaYTCaxd0ZXVxdycnLw+PFj3Lx589AUMBmKoojdt30+H/Lz81FQUCAW9szMDPr6+vDkyRN0d3djfHwciqLs2SzvsGA0GtHc3Ixr167h6tWrqK+vz9h3Y2trCyMjI+jt7RUhXYeFWCyG8fFxLCwsAIBmkQar1Yquri6cOHECFosFiUTiQDYUfhsYDLubxp48eRJFRUVIJBKYmprCysoK7HY7zpw5g4WFBWHZOezCKqqqYmVlBU+fPkVDQwN8Ph+qqqpgsViQn5+PCxcuYHV1VZRBb2howAcffIATJ06gubkZJSUl8Hq92N7extLSEoaGhvDkyRM8fvwYMzMzCAaDh7Y9gJYV8iiQVx27kJWDbMq6qn6baF5WVoaamhr88Ic/RGdnJ3JzcxGPx7GxsYGZmRmsra3tyVklBYXyXzY2NrCwsIBgMIiysjJYrVY0NDTg1KlTuHnzJqampoT3ipRFChvLlvidzaot/72fJ4sresC3pIyqUZ45cwZdXV1wOp0YHBzE48eP4ff7RaiW3L7sTeNjoUVMZCWS9ylbkj4/nkrAt7W1oby8HIuLi+jt7cXY2JhQ0MgwQ889vokqkOkxky3eWtem/2nOef5vMBgURIPyQmgPrMrKSrFXF21GThvAkldeVXcLnFCOORnwDAaDKCVNhIfGgefwABBVALNBJuhaY09jYLfbRX7f4uIiNjY2NEMnU6mU2FeNzpXXFZAZwsW9CiQX6ZtvSgeRSSD9bbVa0drais7OTvh8PlitVmxsbGBsbEyEJ3Jdgwot8Lb4PcWNFrRPmsfjERtbc/3WYNjdHFgu4671PuCkjJMZIpI0NnzbBCriRZtnE8nNy8uDzWbLWLNWq1VsfOt2u2G325FMJrG5uSny9wBkeH9pHe/33lRVVTPXSst4SJuGl5eXi71FGxoaEIlEMDo6itXVVYyPj4ty52SgIE94fn4+DAYDQqEQlpeXM97p8vXoefm2qQwHTqZMJhMqKirg8/m+M6F6l3A6nSguLkZxcTE8Ho/YFFPLosDhdrtRU1OD2tpaFBUViUlyuVxoa2vTzAM7SOTk5KC0tBRnzpzB8ePH4fP5YLFYRAz80NAQXr16hZcvX4owkaNSpQ34tnBJV1cXLly4gOrqarhcLgC7N18gEMDIyAgePXqEvr6+QyclVH1LVVW4XC5hOXa5XMLyZrVacf78eTQ1NcFkMmFtbQ1LS0uHnodEyh5ZNx0OB9bW1hCLxVBRUYHc3FwUFRXB7/djaWkJ4XAYGxsbCIVCojyznCB6EIjH45iZmcHjx4/h9XqRTqdFCfHOzk4sLi5ie3sbTqcTXV1duHbtGhobG8Ved4FAANPT03j58iWGh4fR19eHvr4+rK+vH2g/dHz/IK91rkTz9wd5o5uamvD++++LBPxUKoXl5WUMDQ3B7/fv2QhVy/pO+aGLi4toaWkRil9tbS3a2tqwurqaESLIZQCwx5IO7FX+tKzrWpCJpJy7YTDsbtRZVVWFGzduoL29HRaLBS9evMCjR48yNk7VGtf9vElc8ZUVyzeRQ602SPlqb29HQ0MDbt68iefPnwvljEgOESgut6xIEzgRyHZ9Ut48Hg+cTic2NzcRjUaxubkp3t+pVAplZWU4fvw4rl+/DofDIQpVEGmgtUKhmDs7OyKMzWQyobCwEA6HA1arFdFoFH6/XyjxsrGalHFSqrXWoZanh/6X1x15edxuN1R1N2xVUXbzXSkyhl9TURQhL+V/EeGkeeXkgM8j7wNt0q4V5sk9rvyepXNNJhOamprQ1dWFvLw8AMDi4iJ++ctf4uHDhyJkkt8rWmuNt6koCpxOJ7xeLzo7O5Gfn4/BwUH09/eLAke0Jng5e5ko8nXPQzG1yK0cRkchcvX19fiTP/kTnDhxAtXV1bDZbCKKiYqW0DzRGvP5fGIv2Gg0ip6eHpF2Ixs1eIhlNvDz+Bjyv3l7DocDPp8P4XAYN2/exMrKCtbX18U+fLRGFEURlZXb29vhcrmws7OD2dlZ9Pb2ZqTfyNeisFnyqr1JnzxwMmWxWNDa2orGxsaM3IajAIPBgPz8fNTU1MDr9cJisYj8qRMnTmBiYgLRaDSjmgkt0pqaGmHFp4loa2sTeyTcvHlTVCE8aJhMJni9Xpw/fx5//Md/jK6uLhQWFgKASGz9y7/8S/T392N1dVXsOXBUQCEXLS0tuHTpkrBqAt9aNQYGBvD555/jyy+/xMTExKF6SugB6Ha7UVFRIdzLbW1tqK6uFnmBqVQK586dQ1VVFcLhMJaWljA1NbXvHmYHAZfLJTxmhYWFoiCDyWQSLxSbzZaRgxGNRjE+Po5bt27hyZMnGB0dPfB+qKqKaDSKhw8fIplMIhaL4b333kNtbS1aWlqQTCbR1NSEhoYGQW6NRiO2trYQDofx5MkTPHz4ELdv3xZhR4c9F0BmGAj/X8fRQDaPDJCpJFgsFlRUVODs2bM4c+YM3nvvPRQXFwtL6cDAAG7evImlpaUMLwH9zqYAUjEIUkDz8vJw8uRJDAwMiEIVpFxxpY8gVwojyNZ0rrDK/eXHaYUaWa1WVFRUoKWlBY2NjbBYLBgfH8fg4KDYW4crhVrQIn1a1mtZuZe/12qPQLminZ2dokAG5VTw3A5FUfaE7AHYY2HXuk+57GS5p7ao+ujTp09F/jVtrltbW4vW1lYcO3YMDQ0NyM/PF54NnmebSqWwubkpcs8DgQAMht0Nvz0eD1wuF6xWK9ra2nDt2jWxTcf4+HiGXsMVWr6GZIKfbSxlIk5tJhIJzM7OIi8vD3l5ebhx4wZaW1tFYRObzSbGIhqNor29HXV1dRgdHcWdO3cwPDycsRdjtlwuPidaBIpvNizLTMfYbDaUlpaiubkZTU1NsNvtGRXlaG3IoWj75SEajUbY7XZ0dnaira0NV69eFdsFkf7FSRPfcoCPPe9zNqMDfUbrlcthNptRUFCAxsZGdHV1oba2VrzXI5EIxsfHMTU1hYWFBYRCIaiqKnI8GxsbUVlZia6uLlGi/NmzZyLnka5PeXl8LOg5pWWo0SLD/P+dnW/z4mdmZoRHlQwdvBQ9kfeqqirU1NSIMvvxeByTk5OYnJwU73dZRpL910knOpQCFOQuPIoKgclkEuXWFWU3vvfSpUvIzc3F1NQUVldXxe7KZDGy2+1oa2vDuXPnBEmh0p+bm5vo7+9HKBQ6FCJFL4fr16/j/fffR1dXl6h2Fo/HMTQ0JBIoFxYWDqXy3X6ghNWmpiZ8+umn6OrqQn5+fkZlpng8juHhYTx48CDDvXsYUJTdvICqqiqcO3cOx44dQ319PRwOh8jdoQTjdDqNkpIScS+k0+mMPUwOCw6HA62traLcPD1UaLPNgYEBsZYdDgeqq6tRXFyMrq4uEfNPCuFBh4dSLHt/f794kFZUVMDpdKK1tRWVlZXIz8+Hw+GAqu6GBo6MjKC/vx9PnjzB4OCg2P/mKIRbAm8fZqXjcED7+HBruaxsGwwGuFwu1NXV4Uc/+hE6OjpQXl4ORVFEZADtU0PvCq50ANkLMtBxJEt+fj46OjpQWFiIkZGRDAVRSwGle5YXleC/td7TXOmR5STQOFDuYmtrK86cOYP8/HwEg0E8f/4c09PTGZ74/da1lpU6m7cnG9Hb7zrUF8oVpUpzcl4oH3/ed56/xeeGW7b5dY1GI9xut9jPisI3qbqdw+FAZWWlmBePx4POzk4UFRWJ7WC2traE14BCrkKhEEKhkNg0lioeU1jUiRMnUFlZCZfLhfPnz4sw7aWlJVEmmuSWvXt8DOkdLPcrG7GicQgEAuju7hY5aadOnYLBYEAwGBSl8oHdIk7Dw8Ooq6tDY2Mj6uvr4Xa7sbW1henpaYRCIeHN414ZvrEsGRDk7TCoah1/52rJ7fV6UVtbi9raWni9XphMJkQiEUSjUayvrwt9SV5r2dqjaxcUFODMmTO4fv06WltbkU6nEQ6Hce/evYx8H75uuIeHCilw7x9fnzLp0iKTNpsNzc3NOH36tAgTTqVSWFhYEMS1p6cHKysrIszU7Xajvr4ely9fRm5uLgoLC/H+++8jEokgEomIIk18DLj8JDt5Cune1yKfWs8d2v9N3u+KvK/8XMoprK6uRm1tLVwul4jEWlpaEvu+8lxMPpZvegbumdc3HvE7Bnoh0kDabDa0t7ejuroafr8fa2tr8Pv9mJ+fFzc+xQC3tLQgNzcXsVgMa2trWF5exujoKF68eIH5+fkDV84UZXcz0oaGBnz88ce4cOGCKIhA1pXFxUVMTk7um+B5WKASqjU1Nbh48SI++ugjVFRUwGq1QlVVUdpyfn4e3d3dGBoa2rfa4kHAYrGgubkZFy5cEBs3l5SU7HmhcyWLPjeZTLBarWKvh8OSv6CgAE1NTSgrK0MqlcL09DTGxsYwODiI0dFRzM/PCwuU2+1Ga2sr3nvvPVy6dAnnz5/H0tISHj16JFzuBwlV3d1LIhAIYHJyEsFgUGxmSeG7wG5I4MLCArq7u3Hv3j28evUK4+PjCIfDR5KoHEWZdOwiJycHJpNJVBXj4MqPz+dDc3MzampqMgr+TE9P44svvsDz588z9jzhITsykSILNxVYsdlsQpngCob8PtPy5HAyJYMUM1kW/p3sVZKt0A6HA21tbfjkk09w8uRJEa3R39+PSCSi6QnZD9m8tFrFAzip0ioKwkHKayqVEuFMVKqeyA7JJhdp0Oq3rFjzZz7JRvPMQ42IcDx8+BAvXrwQFvbc3FxMTEzAZrOhuLgYRUVFIj/K6XRiYWEBS0tLWFpawvj4uAg/I5Jmt9tRVFSE+fl5fPzxx2hvb0dRURFOnTqFxcVFDA4Oik1l5bGU+0bkRVGUrFEgWgSXQlN7enqQSqVQV1eHc+fOibB9qlzY398vqjx2dHQgnU7j8uXLuHLlCtbX13H37l08ffp0D+HIZnjiY8+3JdA6j/qtKArcbjdKSkrgcrkEaV5YWMDw8PCevdGIWJP3Vwt031qtVhQVFYlKxNFoVBRN0hpDmazRRr70HpaPk0PXZCLFC5JcuXIFbrcbqVQKU1NT+Iu/+As8evQIgUAA4XAYm5ubogz/+vq6qIxbUFCAc+fOIS8vD2fPnsXy8nLG/ofZxoAcKrTlSLbjtOaS+qZlQJHzVukYypt2OBxIpVIIBAJYWVnB2tqauL4ckipfn66937PpUApQyBYOIHPhcxfnQYM2RCTXJcUgO51OWCwWlJSUCEbNE2t5WeuFhQU8fPgQN2/eRE9PD2ZnZw+lpLLBYIDP58OxY8dw7NgxUaksHA6LGGqbzQaHw3EkvYRkmbt06RIuXryI4uJi4cWhvSfu3buH7u5uPHv2TGzQdljIycmBy+XC9evX8fHHH6O1tRVutxvAt3ts0HH8hQp8W9nP6/WKEMDDgMvlEqTD7XYjGo3im2++wa1bt9DX14dIJJJhycvJycHY2JiogJmbm4uSkhIUFRWJkIWDBiU5FxYWwuVyiXuYK11+vx/d3d346U9/iidPnuzZePQoQeuZeRTv199V0L1NCru8hqhaVktLC9ra2uDxeER4VyQSEXv/UL4kJwDZiJTVakVZWRlaW1vR0NCQUamSCMHbbkRJ8lM5Y/oc+LbqoNvthtFoxPr6uijNrZW4za9FxrC2tja8//776OjoQHFxMTY3N2G321FcXAyn0ykIFUHOmeHjm41w8bHnlmb6TGufv2zKkaru7ueVSqXgcDhQUFAAt9uNpaWlDHLKx1tWwuR7VUsxpAgFGmeeF0MVG7nHMRgMYm5uDoqiwOPxiKI5Ho8HNptNGHDpJx6PZ5BHk8mEcDgsno2UF15WVoYTJ05gZGREhGrJBTS0cnG09iXLVpWNzqVqlKurq0in0wiFQtjc3BRe2vn5eSwsLGB2dhbhcBiBQABmsxlerxdnz55FXl4eysrK4PF4xFiRHORRk68rE32Sg0hjNpJtMBjg8XiER4rGIxaLYX19PaPoCK+4p7XO+N9a/xNpp7/5GuPrjMB1T07w+T3P1x2/HwwGA8rLy0XIqNfrRSKRwMuXL9Hb24sHDx5gZGREGHVlQ0w4HBY5R62traI6JIUJcsKqRUxos1xeHCMbgck2jtnIr2xsMplMcDqdKCsrQ05ODuLxOPx+P+LxeMYzUo4s0BrDN+kGB0amyEtSXl6ewfQ5qKPz8/OHEha3s7O7yefIyAhu3boFVVVx7NgxsUDMZrNmnhdfCGTJ//LLL/Ho0SMsLS0dmpJmNpvR2tqKCxcuoKioCJFIBENDQ3j8+DEKCwtx+vRpkcB6VDaK5SgpKcGpU6dw9epVnDhxImPz2+3tbeGRev78ORYWFg41z4vWN4VRNDc3w+PxIJ1OY2FhAf39/ZiamsL29jaOHTuGxsZGcYMDux5QCrn4/PPPMTExcShFHCjpmRKfKX56dXVVrGUZ6XRaFGzo7OxEaWkpjh07huXlZQSDwQOV32QyoaioCF1dXbhy5Qrq6+tFyC4HvbRCoRACgcCByvjvg7d9sOs4OJASxRUb2TJrMpnQ0NCA1tZWeL1eUSVqbGwMY2NjGRXB5IpkBFIWrFYrysvL8emnn+LSpUuoqqoSZIrKak9MTOzZKJ5CokhJk8MQtazJtFkpedSGhoaE14A2feUkQPYCeL1evP/++/jhD38oPGhGoxFlZWU4f/48RkdHEYlExL5KAMRG506nE6qqijA1kpnGAtibF0WfyeSTj5+W4sbnLRwOY2RkRCiaHR0dGB0dxcjISIaXi4fCcQJB1+feAf78IQVSVdWMcC5OnEmX4HNP4XzAbq7z1NSUMBSRLMlkcs9c0t9bW1sIBoN49eqVKJjV2toKj8eD9vZ2BINBsfEsL7ZBiin1jRNKec752JMcsoduZ2cHm5ubwpPR09OzJ4eG2qG9RcfGxrC8vIzc3Fxsbm5mbDhMpIKuI2/8bDQaM8ggKcgUDigXdOBrgu4Zfm8Hg0EsLi5mRDFoESl5/fG+r62tCfIEAOvr6xgZGRFFkjg4QeT3iNY6kyEbUAyG3eqAZ86cwUcffYSWlhak02kMDg7iX//rf43nz59jZmZGRCnxIiu0bmmrF/LI0/hubm4KgkoykV5J1yeiSJ6ubJ5BrTGU15HcRzkkmgxBZWVlKC8vh8Vigd/vR29vr9jmSOt5zdc9z6uTCa2MAyVTDocDJSUlwuXGyZSqqvD7/ZicnMTc3NyhhdvEYjHMzMzgzp07wnNTVVUFj8cjjqHFQA872i2arAszMzPo6enB4uLioSWwk3W+vr4ezc3NSKfTmJycxJdffolvvvkGLS0tqKysFBueaVnuDgu0mWpDQwNOnz6N9vZ2lJaWivWSTCaxtraG6elpjIyMCCvcYSqYFBJQU1MjLGepVApzc3N49uwZbt++jb6+Puzs7CAcDouwM1XdrWi0sbGBqqoqfPDBB1hfX0cqlcLk5GTGS+MgYDKZYDabxYNje3sbsVhs3/Hd2dndi2xychKNjY1wuVzw+XyiSMhBgFf4aWtrw5UrVzLCWsmink6nUVRUJCp2lZaWIi8vD+vr60cmR0qG/PLQidTRAlcAgeyFDqxWK1wul/CuJ5NJDA0NYXh4GOvr68LoxhUnbp22WCxiL8Ouri6cP38ejY2NwshE92p/fz/u3r2LcDi8R4knjxhXDLgir+WtURQFtbW1+OSTT9DR0YEHDx7gyZMnGBsbE1VTtZSinZ0dpFIpsQE2D4uyWq2orq7GyZMnYbVaRRENyiepqqoSOZrd3d2CSMgKKh9f+pwr5fQZz6nIZpCg49bW1rCwsIB4PA6j0YjKykqUl5dnkCBeIESLtO2n2BLx4aSBH6PVL/kzkoFyRzi51PJqEtLptFDcb968CZfLhby8PBE2qBX6RoSC8mcpz0UeY9mKz+8H+Ttaf/t5Nzm2t7extrYGRVGwtrYmwmGz3Wv8PqJ1z3PbtIig3I6qqlhcXBSGg3Q6LdI6cnNzRXSSFnnnfZZlpFwe8vZSWOf09LQoxkLgZJCeGbSFgEzg5HeEPDekE+bn56O+vh51dXUwGo2Ym5vDX//1X6O/vx9LS0tiLzOZ7JBny+Vyob6+Xmxwn06nMTc3J/Kk+XXlfCS+NrQIqNY8UBs8T56PDx0n7xfl9XrR1taGuro6YZiJRCKYmZkRW6TIa1LrmWAymYTx60iQKWDXU2K328WO3Rw7OzsYHR3F06dPsbq6emhhTqq6uztyb28vEokENjY28PHHH+P48eNiUjc3N0XMpaIowgtB8e8TExMIhUKHWuLaZDIJl7jb7cbIyAi++uor/PznP8fy8jJKSkoQjUZRVFQkqpodFdjtdrS3t+Pq1au4dOkSSkpKMtzofr9fbPQ3PT196ESK4HQ6UV5eDpfLJRJ6v/jiC/z85z/H5OQk1tfX4Xa7RZl8cndPTExgdHQUdXV1qKurw5/8yZ+IcALaEO8gYDAY0NDQgIsXL4oHUDAYxNjYGKanp/eNjefWqHQ6LbxaBwEqAtPe3o6PPvoIZ86cEfekoihYWlpCb28vuru7EYvF8Id/+IdobGxEU1MTrl69imAwKL47itAKYXiT8qHj8MDniRQQs9ks9mYhpSCZTAorN609UkBIUeQFm4qKilBZWYkzZ87gwoULaGtrExXdSFHo6enB7du38fTpU7G/IV8vvNqVTDboM9lzQ0SOyiG7XC6UlJTg3r17GBgYEDlF9HzgVuhYLIbR0VE8efIEdrtdbA+Rk5ODvLw8XL16FceOHQMAURK6srISZrMZg4ODWFpaEuMqkz4+3lqWam5R5r/lkDX5Xkomk0gkEtjc3BTkrqCgQOR5ZFOe5TwVvhb45/L3WpZ2LZmzWeY5IZHJhDw2Ozs7SCQSWFxcxIMHD9DS0oKmpiZYLBZUVlaisrISMzMzgqTx9UHKpCw7hxa54QVS+JjL3gB5POW2qfAADx/nXkKTyZTxruRrXlGUjPUpr3EZdP319XUsLS2JcES73Y7KykrU19fDbreLXEmtfD0tzyUVBGtvb0dBQQFMJhPW19cxOzuLQCCQUf2OrwG6b3mIpUx4+RzzYg98DRUXF6O5uRnV1dXwer2IxWIYHh7GkydPMDU1JcaVCDQRN7PZLEraU3hgfX09zGYzlpaW8OLFC/T19e2ZF5pvXr6exj2bEUEL2e5VrfnLyckRTpCOjg7hNEin01hbWxP77xGor2To4UU7shk9tHCgnimn0ylc/bJCoKq77nXa5fswlWMa9KGhISSTSZEUSS+ucDiMoaEhrK6uIjc3V3iANjc3MTU1hYmJiUNX8MmiQRVaent78fz5c0xNTQnr1PLyMvLy8mA2m4+McmY0GpGfn4/Tp0+jq6tLVJkBdhc3VWr7+uuv0dPTg0gkciTKuNN4UxiL3+/H3bt3cefOHfT29mJjY0NYhFpaWuDz+QAAgUAAfX19uH//PtbW1uByuVBbW4vz58/D7/fjzp078Pv9B9aHoqIi1NTUiH0/KMxvv5BDg2G3dH1ZWZnYo4LniL1L0L4szc3NuHz5Mm7cuIHa2lrY7XZEo1GRLHz37l28evUKNpsN7733Hurr6+H1etHS0oJXr169cVPuo4ajYDzQsQv52Wm326Gqux5nnvvDlTl6QZOyRlZSUnyMRiOMRiMsFgvy8vJE6Or169dF2IrX6xVh58lkEhMTE/jyyy/x8OFDEcYCIEORzba3FH2Wk5MDo9GYsZcPVbBNJpPweDzo6upCY2MjWlpa8NOf/hSvXr3C/Pz8nsR7InhfffUVHj9+LN5HZWVl6OjowNWrV1FdXS1C6XkOw9zcnAit42WwtcaeX1MrD4P3kStLcmgZnxcKdQOAgoICsb9etpBg2crOvSL8O34dWf/JRia4MUVWouW53O8Zza8XjUYxNDSE7u5unDx5EpWVlfB6vairq0N/f7+IDCLZt7e3M0Ld5L5ryc3ByW22PmrJDAD5+fmiGtvGxobIpyL5iLTwYhBayrw8P5zwZAtz3djYwOTkJF6+fImGhgZUV1ejsLAQ7e3tqK+vx8zMjCgbrjVfQGZoHoWhX7p0CU1NTQCAsbEx9PX1YXV1VYT+8fOJYGl5d2hM+SbPBLoezZ3FYkFpaSlu3LiBpqYmGAwGjI2N4dGjRyLMWMsgkJOTg4KCAjQ0NODs2bOCoDgcDiwsLODBgwf46quv9pApVVX3kCg+11oeLLqeVlERuqe1DBGyF9Lj8aCmpgaXLl1CdXU1jEYjNjY2EAwGEQgE9njU+djKZEorD1YLB0amSOGqrq7OWvBge3t730ooBwValIFAANFoFEtLS3j69ClqampgtVoRDAbR09ODeDyOlpYWnDlzBmVlZUgkEhgbG8Pk5OShb7xKfaA9jYaHhzE9PS3GNxgMYmJiQij1RwFkBWxtbcX58+czEquB3fUxMTEh9gKanZ091P2k9sPy8jJu3bqFnp4ebGxsiH3H3n//fXz00UeoqqpCLBbD0NAQnjx5gvv37yMYDMLtduPatWs4duyYeJBTTsFBwGazifADUury8vLg8XhEaAWBEutpH47m5mbYbDaRd/Uu7wGyluXn56OhoUFUEzx27Bh2dnYwPz+PkZERQZSePHmCSCSCpqYmbGxsYHNzU4S4Ug7LUQSFt4TDYbjdbhHzThtZHhUjyO8yZIVQzn+hYyj8hBQHKtvb1NSEeDwuNoi3WCyw2+3w+Xzw+XzCWFBfX4/Ozk54PB7Y7XYRTUBe8JGREfT09GB6eloYJLXCY2TyQNbnbJ6MaDSKubk5zM3Noba2VninqPKoxWLBzs6OCI3jyg3tFUThSwaDAUtLSwgGg/B6vSgqKkJJSUlGGM7c3Bx6e3vx6NEj8YzPpnS/yVote3NoHOTP+f+kfG9sbIhQf6rEODg4iMXFxYy5lUPruCIs7+GVjWjw62vNRTbr+Jt0JVLCaa2QkkphkyMjI3j69Clyc3PFpsq8mAm/jnx92Vi2H4l6G9l5HhD/npRbs9mMra0tJBKJPSW1+fjR8XTtbARQ9tpqEcF0Oo3l5WXcvXsXpaWlcDgcyM3NRXFxsYhqePHiRcY7mq8zvm5JMaeNcgsLC5FMJtHf3y8Mw3Qvao0x/1smKTLJputT7pOqqnA6nfB4PKivr0dRURG2trYwOTmJ8fFxxONxzRA9r9eL0tJSNDY24tixYzh9+jRqa2uRk5ODcDiMgYEBUc6dVyHW8rjK9wuBr3eqMmixWDLSN/izS8tDS/JSxcpz586hra0NVVVVcDqdIqcrFothY2NjTxVEvta0nifZjAQcB+qZys3NRUVFBex2u+Yxh02iZKjqbtLm0tIS1tfXMTY2BoPBIDZsKy8vR21tLUpKSmCz2eD3+zE9PY35+flDV/LpgUnhCpTgSwtwbW0No6OjIsTisEFhHxcuXMAf/uEf4uTJk0LJ5S/mgYEBPHv2DH6//9AJqwz+UOBhEVarFZWVlbh27Rp+8IMfoKqqCvF4HH19ffjZz36GBw8eiNAFq9WKwsJCtLS0oKOjAx0dHVhfX8fU1NSBeHr4A4oMIFevXkU8HsfDhw8RjUbFS8HtdqOxsRGffvopLl++LF4OMzMzePXq1Tst7GAymVBSUoLr16/jww8/RHt7O0pKSmAymbCysoJHjx7hpz/9KaamprC+vi7yR1KpFEKhEKLRKJxOJ0KhkNh48ShifX0dX331FXJzc0UJ24qKCnz66aeYnZ3FyMjIkZX9dwWyJZznlADf7l1IYUH0jLDZbLhw4YL4n0J6KZ+vs7MTXV1d8Pl8KCsrg9PpFBVjueU3FArh0aNHuHPnToZXgQgbHUsVyShUjZBOpzP2ypKt6vF4XITgUxU5s9mMwsJCXLt2DXa7XazBSCQiiBzJx8N8LBaLyMuhPbEofzqRSGB1dRV37tzBz3/+c4yPj2ckict76/AxJsjhVbyaHydSXOHmYVjcY0HHWCwWlJWVob29HcvLy1haWtrjYeGV1LiCR+SFy6KlYHIPCW+X91fLcyKvQ74eZZIgezMURRF7A62srKCsrAx1dXWoqqrK2MCXl/7Xkj3b/ZBNKeWkSSbJsreQ5sHpdMLlciESicBgMAhySO3R+qA8PeBbgsE9NHyMeDvk/ZEJLM9DpGfviRMnkJubiw8//FBU4L1//z4WFxf3hFzyflksFhQXF6O9vR1NTU0wmUxYXl7G0NAQJicnRTViCjnj/eMhftQ32QDA50T22iiKgpKSErS0tKC2thZOpxPr6+ui4Ag9H8iAajabYbPZcPbsWVy6dAlnz54VZdyB3QIoVKitp6dHECluvOF5ityzLMtF8hqNRuGBP378OP76r/8as7OziEajmsSJzqfCcJSH3tjYiJ/85Cdob2+Hz+eD2WwWRT+okp8clikTcPqe1urbvGMPPGfK6XQeqfycN4FejslkEuFwWHxOewLl5eXB6XQiJycHW1tbCIVConTsYYJio0OhEBKJBEpLS1FYWIjZ2Vmoqiq+S6VSMBqNgrQcFqG12WyorKxEZ2cnzp49i4KCAhHCkkqlEI1GRWGP0dHRQw+jlKGqu+V0Kf/B6/Xi9OnTohT9mTNncPHiRVRVVSGdTmNoaAiff/45uru7MTc3J14C/f39ePjwIYqKiuDz+XDp0iWsr69jZWXlnW8kq6oq5ufn0dfXh6qqKtjtdjgcDpw/f14oeEtLS4jFYjCbzaiqqkJ7ezuuXbuGqqoqKIqC8fFx9Pf3i2TadwWn04m2tjZcvHgR58+fR1FRkSiX/+zZM9y8eRPPnz8X1QRJkaP9Y+gZ9Dax0IeJra0tkdxLSoLT6URNTQ28Xq/umToi4NZT/hwlJdlkMsHtdsNms4lzSMG5du0aKisrEYvFRHlyp9MpLOFOpxMOhyOjxD95lEKhEPr6+vDZZ59hYGAg491Dx/CKZIqiiEpastLIy2GTfKRcLC8v48svv4Tb7UZZWRm8Xi+A3a0U2tvbYTQaceLECUSjUQSDQeGNIot9LBYT+03V1dWhpqYGx48fR35+Pra3txEOh9HX14df/OIXuHfvHqampjKIFPVB673K799sZaW58i6H7cjETFXVjIIDlLfmcrk0Q+JlRY+TAS6/XL1YPp7Gnrf/JgKVDVyZVRRFeB14W4qiiH0cydPpdDozCiPwPc+yEUKClpeQy8OvK4+B7I3h3gCz2YyioiJYrVZRSU4O+6Tz5ec5J/Ba3gi+nuhv2YtH+cuDg4P47LPPYLVa0dHRAYfDgfb2dvHeCQQCe65BMhiNRpSWluLKlSu4ceMGvF4vQqEQhoaGMDo6KoojyUU5aNsdvh2J1nqVQyg5aP0eO3YMra2tIqJhe3sbeXl5IlWFDL+lpaVoaGhAeXk5Tp06haamJhQUFAgnSCAQwMuXL/G3f/u36O/vz/D0cPAqiNnCdPm6dzgcOHv2LC5cuIDOzk6YTCY8ePAAvb29opQ+b9tisQhDB6VPVFVVwefziTB+0svD4TBWVlZEcS+te1a+F2Xv8JtwKKwm28OIGOBRVm44qI49L9e9tbWV8RI4LJCLNxgMYmtrCy0tLUJRTiaTsFgscLlcIjGPXtSHQQIpT6qjowNtbW0oLi7OKMUai8UwOTmJ27dv4+XLlwdalOFtoaqq2LE9EAiguroaP/zhD1FSUoJAIIBr166hpqYGADA5OYl79+7hF7/4BZaXlzMKN6ysrOD+/ftobm5GaWkpzp07h7W1NfT29mJxcfGd7tukqiqGh4eRk5MDn88Hm82G8vJyNDU1wefz4dSpUyJG3Gq1oqmpScROUwn4Bw8e4MWLF1hbW3tnc0R5FxcuXEBHRwfy8vIQj8cxOzuLp0+f4le/+hUeP36MSCQiFACr1Yr8/HyUl5fD5/PB7XbvUbSOIuhFLJeY1XF0oPU+k7+nKlwUyk7rkp7BBQUFopodKa5ySWdStLa3t5FIJBAMBjE5OYlvvvkGIyMjmJ2dzdinit6rcgl0XtWMy6ylCFMbGxsbmJiYwOPHj1FdXY329naxh1tubq6oDptOp8Wm9VTCOpFIIBaLobS0FK2trWK/QLPZjHQ6jfn5efT39+P27dv45ptvxPNdVmpIfn6/cmWHW+r5mMkeG04O5GOoHX6/qaoKh8OBqqoq5ObmZlT1o+Nlz4C8Fn5dosF1Ivn7t30GaM0xgTwlXq8XBQUFKCoqEv/zfFlSYHnRCFmWbOtfS05SrLPpgNxrQddwOp1CtyK5Sefi9weREa3rydtjcEOBHNolkwKa67W1NTx48ADV1dXweDwoLCyEx+NBU1MTLly4gM3NTRExQ3lK5PGqrKzE8ePHRcEVq9UqCrNQOJuco8PfT+SVpfmg+4D6ojX+9B2FFlL1WtKtqEhDJBKB0+nE1taWCGk9efIkqqurUV1dLd6V5CwYHBwUuhhV/5ONE/K60LpP+TzYbDbk5uaiq6sLJ06cQE1NjQh5tFqtmJmZEXtT0VzS/moNDQ1oa2tDW1sbfD4fHA6HuH+3trbg9/sxOzsr9iPVkpdDNmbIz5VsOHAyJVs16H+qACZvNHdUoSi7pd6pEl62h99hgojIzs4OGhoaMDMzg/z8fESjUTQ3N+ODDz6Az+fD5OTkoZFYRVGQn5+P1tZW3LhxA8eOHRM3u6ruhlkuLi7i+fPn+PzzzzE+Pn5o5eb3g6ruevuWl5cxPz+PlpYWlJSU4OLFi0gkEigrK4PJZMLi4iLu3r0rQvtkwhGLxTAxMYGenh6Ul5ejrq4OTU1NOHHiBDY3N8XGke+qDxsbG5iamsLdu3ehqipOnTqFmpoa5ObmwmazobCwUFS5ysvLE0RqZmYGd+/exVdffYWBgQERq/2bhtFoRENDA65evYoLFy6guroaW1tbuH//Ph4/foxnz55hfHxchDpRFTXa/+vcuXNoaWmB2+1GJBLB4OAgRkdHj+SaAr7NI8zLyxP3BfDm6kc6Dg5EengCPAflrU5MTGBhYQFlZWWinDnlUFGIkqzYkcKXSqWwtbWFeDyOeDyO1dVVDA4OYmBgAPfv3xcFJ8iaTfIA31quybMpy2gymTQTsblllvavGhgYwM9//nOsr6+jvb0dxcXFwnNAnoxUKoXq6uoM+YFdIwhFG1Bhm0AggJs3b+LWrVsYGxuD3+/fU/qYCKCWsZUr01qeF7o+b4e8NTych4Mq4ebm5gojo8PhQFlZGfLy8kSOGJeTb+Cq1WY2YkTlxrVyXuQwKN6X/ZRB+boc1I7H44HP50NdXR1qa2vFevR4PKioqBCVC/m4ankesnlLaG7of05+eDtEbLMp2sBuEYj19XXxPLfZbLBYLGI+yWNFbXBjBV/7/LpEyuge4RtDZ3u2JpNJzM/P46uvvsLa2ho++OADNDY2orq6Gn/0R3+E48ePY2lpCVNTUyKSwGAwoLS0VOw9SVsZRKNR9PX1oaenR+QT0hjxOSfdmIdnAruhp0R25cIafB4URYHdbhdVhiORCEKhEPLy8pCfn4/3339fFLtKpVKwWCxifG02G8xmM1KplAiTm5ubE8bKxcXFjNxMfl16jvHnCoGMg5z8qqoqDCwOhwMOhwMtLS0oLi5GR0cH4vF4hqdOVVXY7XYRPu1yueBwOEQYdCqVQiwWw+rqKl6+fIknT57g7t27YmN0ea3xdUHy8Hv2bXTjAyVTtOeETJZ2dnYEg+RViI4yTCYTCgsLxYOH9nw4SkRwZ2cHKysrWFpaQmtrK06cOIFr164hGo3izJkzuHr1qhj3w9pYmB7s1dXV6OjoQEVFBYDdxUz7jr18+RJ37tzBixcvMkpaHjXQ/lfd3d1wuVzo6uqCx+OBw+HA+vo6FhcXRSXCvr4+zTFPJpMIBAJ48uQJ3G43fD4fGhsb8f777yMajQrr0LtaZ+l0GsFgUBRsWFxcRHt7u6geRvtU0N4wCwsLWF5exsuXL0Vo3bvMlTIYDCgqKkJDQ4OI/Z6dncWLFy/w6NEjTExMiLwOirUvLi7G8ePH0dnZiZaWFng8HkQiEfT29uLx48cYGRk5smQqJycHLpdLJNHqOHqgeH3KJ+Sg+zQej6O7uxsmkwktLS0itIrnUpHFlVvZk8kkotEoZmdnMTo6Kp7VgUAACwsLWFhYwPT0dEbZatl7I8sCZCoS5CGS37v8fAobX1hYgNFoFLmR9fX1qK6uRmlpKdxutxgLrpSqqio8c/F4HH6/H4uLi5ifn8f09DR6enowNDSEcDgs9rghGTmJ4EoNjZNssdfqH8nAyQhXTGWCY7fbUVdXh5KSEmHAoOceKaHBYDCj+pmc0yJDJqn0m0jYfmFQnBjI4IT3bd7fJpMJLpcL1dXVOHXqFC5evIiKigqRT5dOp8UPD1njXihedpuIihYZ4h4KmXCRAYKHxvI+cRBJp+d5YWEhpqenRbu8CqSWJ44TDmqbttNIJBIZ72EtrwrwrY6aTCYxMjKCWCwGVd0NBz1x4gTKyspQVFQkCqiMjIyIe7Gurg4VFRUoKCiAzWZDJBLB8+fPRTg85ezK65Kuy/Oj6LfZbM7wHL5p7mOxGKamprC1tYXNzU00NDSgtLQUXq8XhYWFKC8vF/Km02kkEgkEAgGMjIxgfX0da2trCAQCYpuR2dnZjDx8LgNfk1peZKPRCIfDIfad4/c3cYR0Og2HwyEKStD33NhkNpvF/Uk/ZMxZXV1Ff38/Xr58iZGREQwODmJ2dhaJRELTqMHHW8tL+TbeqQMjU2T19vv9e9xsFJK2uLgoOnyUQTu0l5WVobq6GjabTeQgHSWljKrf9fb24sKFCzh27BicTieSySRKSkpQXFyMx48fY3x8/NDK0ZO1gReaINmHhobw2Wefobu7GxMTE0d+XZC19auvvkIwGMTm5iZ8Pp8IEXj27BmeP3+O0dFRsQeMDHqg9Pb2wmg04saNGzhx4gSuXr2K+fl5sXP6uyTtlKezurqK3t5eVFZWorm5GR0dHaIsc0FBAcbGxtDT04MXL15geHgYMzMz77y8OL3AYrGYUM7C4TBSqZTY0PTkyZNoaGgQG/fR/jwulwsGgwHRaBQvXrzA3/7t3+Lly5cIBoNHyggig6yS/OV0UKXndbwZpFCStVV+jpIiRuGvvb29OH78OHw+n6gm6XK54Ha7RaJ/MplEPB5HNBrF4uIinj59iu7ubiwsLIhNcEnRkosdcY8LDxsCtMPEuEVePk4mVFQZdmZmBn19faivr0dbWxtOnz6N8vJyoeDIhR8ikQji8bgwYtBmxbOzsxkRKbJSzi3cXC5Z2aUcH0I28pFN4aa2c3JyUFJSgqamJuTm5op+U7GMkydPIhQK4fnz5yKXWstrw/ugJS+XhTyJNN7caELkhY8Bfc4Jq0wOtTxSVJre5/Ph3Llz+MlPfoLOzk64XC4Au8/9ra0tsf6yRavIyjP3UGUjdrJHTVZoZQ8VybyzsyM8Uzs7OzCbzSgvL8fo6Khoh++JJcvIry97UCgETwvcMyiPq9/vx8bGBsLhMMLhsNgax+12CwPoqVOnMuaXCCptznvnzp2Md4+WV1Iea3n86N6WPX0yoaV9xW7fvo3S0lIRVphMJoUzQFEUYcAhL/T4+DgePHiA8fFxoZfHYjFEo9EMfTGb4SLbM4dCNvlxOzu7m2W/fPlS7EdHeXJOpzNjTcj3CRkjqFgO5bfdvXsXv/zlL0V/5bzBN60ZrWP2w4GRqZ2d3XLFT58+xbFjx1BYWJhRaYheSEch3+hN8Hg8OH36tLBImM1mzM3NYXh4WIQXHQXs7OxgaWkJPT09uHPnDs6dO4f29nbxYKUqNT09Pe8sLOtNUFUV0WgU09PTePz4MVRVRXV1tVDkHz9+LBI0vw/KI+U9PX36FGtra+JBkEgksLS0JPaSeJPyHo/HMT8/j4cPH4rqW1RV8l0r/mRJJoWOPFT9/f1wuVxwuVyw2+0IBoNYWVnBysoKwuHwgRUFIRIVjUaRl5eH0tJSvPfeezh27BgURUF1dTWKioqgKLux2JSEOj09jZmZGfT396O3txevXr0Su7YfVaRSKfj9fgSDQbHJ8/r6OsbHx7G2tva9uCd+2yFvF6ClTAC74Upzc3OigpfL5YKi7OYEkKWcQn14Bdb19XWsr68jGo2K/d60PDFc0dDyvpDBSt5rilt9qS1eUYxfg4fyzc7OYmVlBYODg/jVr36V4WGjdUkkh97ttMFvIpFAJBIRVcBkD4yWcqlFBGVrOMnOvXNauSXZwud4SOHW1hbW19dF0ZBUKiU8OJTHIhNZmSBwUsg/o+Pk/XSyeWhkz4TcZ0I2UmcwGITxt7W1FR988AEaGhpEUYGdnd3qs5FIRLxj+DW50s49PtnmgcYzm+dJy+Ivt8/HkoiP0+kUIWv8GJlU8O94TjsfJ74Pm5anko8vl4U8cX6/H48fP8ba2hrq6+vR1dWFpqYmFBUViSpw5EWenZ0V2+bMzc1hZmYGi4uLGWs/G3HKZtiQPc1a59BYk7eHvMu9vb0oKCgQIceqqiIWi4lnzsbGBjY2NrC8vCwKldFa1QqJ1CKCfOz5vJPHnRNsMkQ/ePAAy8vLCAQCOHv2LOrr60UOH39+cU8W3/x8ZGQEU1NTGBgYwOLioghFlMu+y89mPuby80Bel9lwoGTK7/cL12ZFRYVYdLFYTCS2fx+UA9oUjML7aJ+PwcFBhEKhwxZPgG6Q6elp3Lt3Dx6PB3V1dbDb7YjFYoIAUpjIYckYjUYxOTmJW7duYWNjA01NTVhcXMSzZ88wNTWFcDh8pBVeGalUCsvLy/D7/XteFr9OTt36+joePnyI1dVVWCwWDA0NIRAIHKgXhR5ytNeM/ILhfToIIkXWcVq7TqcTJSUl6OrqQjKZFEopKTq0f8/U1BTGx8cxODiIFy9eYHZ29p2GS/6mQOR8fHwcNTU1cLvdWF1dxcOHDzE7O/u9eF7+toMrNlypkC2vm5ubQolYWFgQ5xOB4BXf5LAx3g79zcsD09+knHEFgCuVdF/Q5yQ792bxsBuuUHOSs729LTYrXV5ezngucGWZkwj52ccT7bVyn+g6dCz3PsmKJVeAuKLHryl7twh8jMgT8urVK+zs7IhqimazGYlEAs+ePcPQ0BBCoVBGhTVZGZZBJE9eH6TkaY23DE70+DN3v7wxaktRdvOzaBNot9stSC5Z9KmAUH9//x7PN429PD+yEi0TFi4LrxisRVjkECv6nnKRZ2Zm4HA4hFdFJqDZCDhtaMsVZVLEtdYlXxcy6abjqUrvxsaG2NNwenoap0+fhs/nE9eMRqOYn5/HwsICxsfHMTY2JvZg5ERcnjc+Pjwfj98T/J7l5/C/ueElmUwiFotBURQsLy+LLRu4wYNvZQBArE9ONPh8apFg7jXjxg3enlxRj4w08/PzCIfDCAaDWF1dRWtrK/Lz8+FyuUROVzKZFF5U0r9XV1cRDoeFxzsQCAiDVLb7Umu8SH4+51rPEi0ceJjf7Owsnj9/jry8PBw7dkwoif/23/5bDAwMfC+UZkoeLCwshN1uR1VVFYaGhvDixYt3mi/yXaCqKsLhMJ4+fYq8vDwR3764uIh79+5haGjoUEu507qYmpqC3+/Hw4cPYbfbRTnL7xuRIsgPuu+CSCSC+/fv4/nz5zAYDFhfX3/n5dG1wBWvw8b29jZmZmbExsDpdBrXrl2DxWIR8dNkYV1bW8PExATGxsYwMDCA0dFRzM7Oir1wjjqRAnZfgAsLC7h9+zZWV1fR3NyMYDCIn/3sZ5ienv5e9OG3HXIIk+wlos+4IgTsva+yhWrJFlL6jEM2bGTb04h/J4OTNMpn0brntUKL+G8tsiJ7zeTjqf9vMshobUAsX48XHtDqH7Uj53MAu8+X6elprKys4OuvvxYExGw2i+cv5X3L58oKp6zcZ1Pe5D7Lc0dFM7TWE/0tK4BaZIU8DlQF0mAwIB6Pw+FwYGlpCX19fbh37x4GBgb25PPydZNNgefQInQ0vvI2LHxdanmXYrEYZmdncffuXRQXF2N0dBQLCwsZRFwed35/kJdNa11q5RHKBFkOu+RyU2VN8tI+ePAANpsNJpNJGPc2NjZEEQee3yVDyztFIGJCupBWBcr95oO3yYk43yRYixhlIyKcWHLiytcoPY/e5L3i8lGxnqGhIczNzcHj8QjSRwaVZDIpiBJ5ikkvisVi4t2uNT7Zxkj2nGoR/jfhQAtQbG/v7ifx4MEDrK2t4eHDhzCZTJifn0d3d3fGpmdHGZRT8uDBA/j9fhQWFmJiYgJzc3NHMq8nlUqJ0DMAyM3NRSgUwujoKObn5w9dIaMHUiKRgN/vP1RZjhJoQ86j5O08CqCQyefPn0NVVYyNjYkqYYStrS1Eo1EsLy+LjTZXVlbEBoXfF5AHYGRkBH6/H+Pj40gkEpicnHzn+Wk63g5c2SJFgazJ8rNVa48j2aNCkJUpHjpHigcdI284ypUbXgqdK0BcVl5ZS7bmc6We5MhGqIBMUsjbzEa2ZOuvrLxyIior8xRSKCvFWmRC9vrwsePGL1J46f6iseWeDN4e9wrK15bnmSurspJGxEDeT4t75fiG0LJyzOXh40DXCQaDQo6ZmRm4XC5R/Gl8fBwTExNYXFzMyOelNrUKHVAIV7bwOhl0HDeO7rfmCalUSoTIRiIRLC8vI5FIZHhm9zNoaK1Xvibk+eJjmZOTI6oFcuLBDQA0L+T14eMgr3W5n1qGDXldaJEvrT3Xst2TWsRVy9jAj5XHQuu5pUVQZfnpvt0vJFEeEwqPTKVSGdubyF5ZmlfZuMLb0pJT7iM/nq8nPl5vY+hR9jtAUZR3onXwkrDAtwvysJX6Xwd8gsmKd1jlxd8WvNADLcBfJ+xMh46jBFI8eFw2B38IHmQo4rsAPeR5uMS7gKqqes31XwOKoqi81DaRFyIj9F7g7wv5BS0XGNBSYLjCKCv/RIiATMWLlHCykmfL77HZbCLEB9h9T8glu/n+LrxUs0zi6DcZN7gSrqrfVt/j73rKhyASw8ZWXFNOWKfxsNlsUBRFFEygZ4Ic3seVMT7GstJN/ec5V7IixgkAl1Eu2iArZrLsNG/0Hqb/+Ya5Ozs7YoNSUtqzeQn4WqCS7jSvnFTztWgymeB0OjOKCnACrBVqSr9JJr5G+BqQx1n+jtrk8yK3QXNHJbNp/yvav4xvCsuf70ajUawD7vml3xTyyvN/eNgatUOeyVQqlVFenb9z+NqmdS9vssu9ejQetM7kkDe+hmVPNl/3FI7H+8MNDnQNQPu5IBMGKjdPhJ2uT2tF3jSXy8jJPd+agfrOx14um07rQAZ/38nrnHRurVBhHgbJ15/8vxZ55s9LrXy2121oviMPZdPe7xtx0oIW4z7q+L7Jq0PHfqAXpawk/jZiP2uijsOFbF1X1W9zAvhnwN5QomxzyhUifjxdTzaCyQq7LJ9MBrhMstKzs7OT4QEh8NLtWvkz/No814IrMnLlMmCv5V22jGv1jX7zEC7eV3kMZBm5QqVljedtyYoeV+joOE7ACLLSRtfRmjeubHNFnINXvNVaW/I15TUj94kIIIVF7fds0VJ2eT4bX2PyWpfHQB5DPjekjBNRoDVD+YZcaZbnR54X3n/5vuD3Fx97LQVfXtty23w85PBIeVxkebhHja7HiYqWpwSAeOfxdS+TMrqntbw6dCzvF98zis8pv295v2RyrQW58IrW+sh2rnzPysYNkovLJFdn1Hp+ZoNWP+S1vR/0jUt06NChQ4eOfw9ks7ByJVH+mxecIGstgIyoDf55NsWTrM884oM+I4s6WXHJg8Vl5Z4RklmLxJAVnCvRXPnifZIVQdn7Ru2SPLLix0kbXY+TOQKXR0s5y6bMceWexoxyM6g/lINJ4yorZNxLzNvhpeEJ5HGS1wYnAFpkkcZMVuhkcsLXDTcycQVTiyxx7waXg4N7OORz5XUjnyP3KZtSywkEH1uZVPP1wL0iWnLS+qNy/XJ7dB6dI8+tVt4hgdYrv2dJJg6+vun6JpMpIyxd9lRtb29nhN3K64aPh5bRgM7hzw0up0zo6D6UDRx0jHwdnrfIyTsfAxoHs9msuXa4DFrrjf9NXjez2bzHU0X3Lf+c5k4eN601KRN+Gnu6byjC4G1wKJ4pHTp06NCh47cF9DImZUhWqkgpoT1xVFXNUOK0vFvcIsyVDlLkZOWHl7OWK21xpUFLSeTyZ1PSNjc3MxQw3ldZNlJ05b7y6xGyEQXqPycaWuFVXGnUKijAySA/F/jWg8bnQcs7pOVVomtRNTw6hhRT+uHEWB57mShnG3taN1qkl3sn6BjqazZllZN6vlZl7wsnNbLnjUMOi6TzORnkbWqRFDpWyyvK2+MeCa7U83ni64CvIS255ZA63hblc8vGEHl9y6GEvB0uOz9G3pOO/ubjxccsm/cv2z1L8vOx4KGPsmFCXk+yXHLbXE5OZvjc0j2r5THizxCtcQAyDT38OaAlN93jdCw3pPB1TOPCx4AbkCg3jj6Tw4az4VBypnTo0KFDx9GDqudM/Vrg70iuoGqRAw7+gpcVVDlvhCtxXOHhbeynCMlESg6FkxVRLaVMy5ote5q0xkCWUysM7m2uyY/nirnsock29lrta7Uht6/1OUHu09uMfbYqY/uNg9ymPD7fpa9ye9nakMdXiyy8Sfb95jZbO1r948dqtZutr2+aVy1SyT/n13ubMebQIs9cnv3Wqky0te7ZbGPB1yY/Vot48bUpQ2se9jPGyM8hIjVa4b373f+yDPx4rftQqw0tgq+1jvnnWutGw+Cg+Y7UyZQOHTp06ACgk6lfF/I7MpsS+Gu0l/X8bErov8/1ftvx7zsfOt49vu/r+E0Ei3/3ferrd7133kR4jyJ+nXnJ9o7Uw/x06NChQ4eO7wgKY9Gy8MphJTyxHMhMmqc4f15Zj2AymUQlM7LO8kR9CmWiz0kRkvfYkS3cwLf5FVqWY61QPpKXW4llBYq8UfJ51CaQWSxCyxrMx0VOhqfr8BAiGmdqg8ZBHp9s3jy5T1rhRHKoUTaPHR0vexBkrxrPqdGyovOxpM95bgofKy4T9UO+PsnHc6u0vpe9oVqFPXheDoA94XbyGPP5kj01VP0tnU5nfC9XaOPzKocP8rHn55IM8nrj8mt5pugzeW3ysTcYDLBYLBkFIGhszWYzTCaTyF2j73JycpBMJjMqXcrzwsPUqJ+8r7wYSbY2eAgez1uU1zQfIwoPlqsdyufI9z21TfMjV6TknikaAz638r2oFU4qP0f4PGXzIMrH82Opr3ys+Rrm91O28FYOnUzp0KFDhw4d3xE874IrxKRYcMWL74vEj+VllTkh4ISEKzCy0qiqqih3zBVNaltW1rkSQso5J3DUJ1mh50UCZEVfVtTkxH4e4sT/JznltukzTnLkfauocIFc+pkUOq4QUbl3mQhx+TmRIOKWzePAP+f5RLy/NCdangn6PluOHf+biDZX7nNyckRSPsmqRUiytcvniSuM8jjy/cwIXKGXx0grt4X6ydcxL/LB16ZWOW9+P5nN5oz+EhHjc0fHyXurkbzUN/4dvw6fIyp8IoeqcVLAiQhtLKsoSkY5dWpTHistpZ0XPZHXDd1zQCbpoPmQw4TNZrOo/kd95XLzHyL3nHhmW0f8PuH3sNFohMViwdbWlpCFl92n47QMEnK7fB1zksTnT96TTWvd8DGhMePbScgGGpnc7VeMhKCTKR06dOjQoeM7gpQtvvktvbDl5Hd5Xx56QVPFPdofh7+0uSLJq/PR50TQuFJDCoSsKMiEh47j8nCyJyuXXNnjVdWAb71IJCe/Hrc4c+WIEzZOAEnBpmRwUi7lzWOp2hYnLVy5slqte8aB5JcVRiK7NE9aSefc4i+TUnm+tBR1rTnlYysrl1xOvm54BULqv9VqxcbGBqLR6J7CJVxZ54qivB5oDrlHgq4p59QQWZWJBl8PWqSUe06o0Af3LmqNJV9riqIIb1AikcggFHwfs62tLc02af75XnBaez3x+5N7lmh9qOpuwQOai2z5SHQcjZU8PjT/9AwhzzRfZ/IaovXOr8kNDTxXicaYzzcvWGMwGDIIl9FozCjxrkWytQwKdC/zPe3o2cRl56RPfobIzy4tIsWfFXIBkWz3G2+fGx/4mqK25f203nY7oSNBprI9lHT85qF1E+jQoUOHju8GUvhJCeUKAPcUcSWAwI8lBYeTI67I8XAZWdHi38sEiCvEJCO/PpeZKyOc2PB2ZEsvJw68DTm0R/aw8f4Dez0RfPxUdXdTUfm9RUobKT10PCcTslWeK758nlRVRTKZzPAwkixyX7Xmj3/PdRlOGjlkxU1WGLnMfHw5yLrucrng9XoxMTGxRy4uc7b3vhbxonEkUid7BanqHl/fJDP/nI8PXwv8c943LU8RXwexWEx4fGSFmI8798LIfSeSIW/sqzU+8j3Lx4wIHSfh/F7UKviiRQwIdC/ye5kTEnmM5PuJGzvk63Jyy8fKYDAIwkUEl8ZeyzMr90XrWOofGZr42udGAZl8EkGS7zF65mitG258ktcNH2u5bdkwws+n8aLQajpmP7350MiUouzW/y8oKIDP50Nubi5mZmYwNzcnXII6fnMwGAxwOp3weDwwGAzY2NhAOBx+a9atQ4cOHTq0QS9mLcWLKz4cWgqyqmZu+MsVn2xeJi1FUFYq+WdcQdU6J1s7BB5iw6/LSYksj5bXRT6G90smXLJiJI+fTN7o+EQiIZRDGfK4ZVOatQgS7wP/X/bAye1xT0u2+Zf/11IQiUxsbGzAYrEgkUhgeXkZGxsbWa3z2drVIrSyosm9KdnakMeHE1oO7rnNNv+yEixfi5R/edy1xnQ/IsTXjdb31L68RuT1K3u25DGQ25aRra/UBoAMIiDnncn3slbVSK3wSWBviXY6PpunR+se4PLT/yQXD+fkZFFGtudONuOQPD5cDn4sz4OSPbMcfE3Sd/Ssk6+jhUMhUwaDAVarFSUlJTh79iwuXbqE6upq/OxnP8Pnn3+umYB7FKAoCmw2G6xWq6Y1jqwdWovzsECk1ePxoKamBk1NTVAUBaOjo+jt7UU8Hj9sEXXo0KHjewueC6KleMhKgWxB1zoW2FvaV7ai8r+1EqhlyORDVl72s9LKCg2FIcnHvum9Rwn7PKdpc3NTeI+0FFI+ZrI1W0sp52E83BvFx11WwLTGnrcrW9dl+eTcDj6OJCfPzeKf8/7QMW/KQ6I+kL6xsbEBg8EgxvFtvFH0nXyslvIvezxJdrl9OQ+Hy877SteVlXE+1vsRV942/U/X1ponLY+i7JGSC1bwtuT7W77O2xAO+TytvCct0NqR+88JoVZOEL+GfB2Z/Gj1Z7/9n6gNOoeMK3IbyWQSFotFXIsfw9cU16f5WGrlKmmNsXyP8+85seN91TKGyCQ+lUpleMT2w4GTKYPBALfbjfr6ely4cAFnzpxBXl4eRkZGMDs7i83NzbcS/DBgNptx8eJFnDhxQliDNjY2oKoq4vE4/H4/RkdHsbi4mGE5OSwoigK73Y7KykpcvnwZ7e3tKC4uxvLyMiKRCAYGBg5VPh06dOj4vkPrpSwr41z547lJWkolKU9yYQC5MIWW0qAV0iTnbMmyyl4mWR76jCtm+ym/2ZQYeh91dXWhoaEBFRUViEajePnyJZ4+fZoRkcKvyduRLfBa4y6PL7XHv5OVOA5ZftkLoNVnPmacZHLFj4c3aYFbwWWFnhegkCuO0fiSEZfnH2mREi2SpUXWtEgW76esZGqtS621SePD1yV9TiSbrxsKo5XHU55HLSOF3KdsoLUhkyb6jqC1Dvg4cPko70eLRMvepmwEg8ZFNpJoFbwhOeT1KpMEPn98XLSMANkMCLxtnrcke6ZUVc3Iv9Jql9YG97rz77TGn757W5nl9czb1loz9MNlepM+f6Bkymg0wu12o6mpCefPn8e1a9fgcDgwOjqKr7/+GsPDw4jH40eSTBmNRni9Xly8eBE/+tGPYLPZEI/HEY1GsbOzg1gshoWFBbx48QIvXrzA1NQUIpGIsBQdBgwGA0pKStDV1YUf/ehHaGlpgcFgwMuXL2Gz2bI+1I8q6GWcl5eH4uJiEbK4urqKxcVFhEIhPWxRx+8EDAYDcnNz4fV6YbPZkE6nEYlEsLa2hs3NzcMW73cK2azhwN4qb6Qw8vOyKTf8Ra5FDPixXEGVlQOuJGrJriW/rKDRZwSt5HDZKi23qygKPB4Prl27hk8++QQ1NTWIxWL4sz/7M/T19YmCAfL48QRz3haXSVbgZeWJxoeKBfA8CJPJJIpb8MR/eR54X7l8cnigrLjKMsljxP/X8j7S5/waVJCDZN/a2soIoZLPl8HHT1F2CzpYrVYYjUbEYjGhAGsRr2xjz5V2LUWUjwUdI4dQ0Xl0rFwIRb4mJyuyMq/VNpdHK8xVHnv6XM6z4wRJPo97nOhacjEOrXAzWXb5nuXHkl4pVyuUwT972/5yMk4knrzJyWQSm5ube3JD+f9axFYef9kby8eV3wd87jn2M4Jo/a11P8iedVl+2ZO2H7EEDpBM5eTkwO12o7a2FteuXcPp06dRVFSEx48f4/PPP0dPTw9CodCRDO8DAJvNhuLiYlRUVKCiokJUgqEJ2dnZTURsa2tDeXk5fvnLX2JoaAjhcPjQZDYajaitrcXp06fR1NSE0tJSRKNRJJNJbG1tHUnSuh8MBgOKiorQ2dmJGzduoLm5GSaTCXfu3MEvf/lLvHjx4kiRKVIEqEIXPQB2dnb3hqE4a6PRmFGm8zAJeDbQA55+tKyK9EMVzY4auMWJ5oYrm/zBSlXTjto9QmNvtVrR1taGjo4OlJWVIRqNYnh4GI8ePcLS0tJhi/k7Ba0XsZywT795IQZS9vieLFr3lZbSrXV/ZbOuykqarJhzpSGb5V1V1T3WY648y+RNJiR07dLSUlRXV8PtdsNsNiOdTsPlcsHpdCIej4tnHymvXKnkxEUmcqQAkvKXrZognUN9MZvNsNvtcLlcsFqtWFtbQzgc3lM1kINkoWtlywOhe5XKdvPnv0yMuTIpzw+fRxqT3NxcFBcXo7W1FfF4HCMjIwgGg1hbW9NUPOW+0N85OTmwWCyoqKhAe3s7fD4fent70dfXh0QiIUiVnLhPv/n6IGLKQw359fhcKYoiKshpeTPkMuT7eQbk+0/+jtaQ1ntVJvsymZX7R39T2XVeNp6/Q2SvM++TLJ98ruwBJo8kXzf0rKD1QjoGlWSX+8fvgf3GlL/bTSYTCgoKUFlZifLycuTk5CAYDKK/vx/Ly8t75kjruUN9kcmXLFu2eeTPlmz5elrEikNrPOQxofb5Gqe1o/UM1sKBkCmj0YiCggKcPHkSN27cQEdHB7a3t3Hv3j3cuXMHvb29CAaDR5ZIAbuLNx6PY2JiAs+fP4fD4YDZbIbNZkNeXh7cbjdcLhfsdjuSySRisRjC4TDW19ffOAnvAkajEbm5uWhpaUFnZycKCgpgsVgQDAYxPj6O6enprIm5RxlmsxklJSU4deoUmpqaxIvEbrcfKU+bxWJBfn4+Ghoa0NLSgrKyMlgsFqRSKayurqKvrw+PHj1COp1GQ0MDzp07h3Q6jYmJCbx69QrRaPSwu5ABl8uFqqoqNDQ0oLa2Fvn5+RkbTkYiEaysrGB5eRkTExMYHx8/UutLUXbzHZ1OJxwOB0pKSlBZWQmv1wuv1wuXyyXCneLxOJ4/f46nT59ibW3tyJBbk8mEsrIylJaWwufz4eLFi+js7EReXh7W19dRWFiI0dFRnUwdMLRe6tlC+GSLraw80HlyWA8pOFohL9lyObQIh9FoxObm5h4FRG6DrsnJX7awLg5OEuXcDZvNhrq6OlRWViI/Px9msxnr6+vY3t4WCi9XKOlvvi9QNiMNVzJlAqWl1FN7ZWVlaG5uRm1tLUpKSnD//n309vZiZWVFszoZHyMefsXHiYOTJFk+3g79zQk4XyucjFgsFnR2duLjjz9GS0sLAoEAHjx4gHv37iEUCmX0NVsb1P+SkhI0Njais7MTx48fh9frBQCsrq5iZWVFeLmzKZXy39mKGXB5ZE+M7OGQ5ddaY7LXhj7n8tEeVFRpT36OczmzGRLoOLlP/No0Z7IHk9qQwzJlGeR7lh/P7ye5DbmwAu+L/Dcfey0vFjcCAbvvGovFAq/Xi+PHj+PUqVPIzc3F5uYmfvGLX+Dhw4fw+/3CKC9fh5Nr7lXOtna0cr/k+4qMTgQ61mw2Cy8t7dVHhNdqtcLpdIoKkLFYDJFIZM86lZ+HNB5WqxWJROKtdPh3TqZycnKQl5eH48eP46OPPsIHH3yAdDqNR48e4Ze//CUGBgawurp6KITj10EqlcLa2hqePHmCUCgEj8cDq9UKl8uF0tJSlJSUoLS0FAUFBaitrcW5c+fw9OlTjIyMHCqZqqmpQW1tLWw2G5LJJEKhkMhPOypK4q8Li8WCwsJCuN1upFIp5Ofnw+12HxkyZTabhQft4sWLuHLlChobG+FwOLC1tYWpqSn8/Oc/x/DwMGKxGBobG/EHf/AHUFUVT548QSKRwNTUlHgAHOa9QUpQbW0trly5gvPnz6OzsxOlpaXiwZVOp+H3+zE1NYWZmRncv38fGxsbWF1dPfSQM7K8ulwulJWVoaKiAvn5+aitrUVrayvKysrg8/kEOdzZ2UE4HMbnn3+OZDKJZ8+eIRQKHZiHikIqrFar8AI6nU5YrVbY7Xa0tLSgvr4eNTU16OzsRG1tLQwGA9bW1hAIBGCz2Q5ETh3fQlbigMwQPf6dHKoFfGu1lS28pCBYLJY91nGuEFEBBwofkkODCBQSxje55ZZkLUWHW8Rlqz1XhkkRymYlNhgMyMvLQ1VVFfLz82Gz2bCzs4PNzU3EYjFsbGxkvI+494PO1yIrdH3eDzkEUVba+HjU19fjhz/8IY4fPw6n0wm3241gMAi/379HsZfHRx5DLqc89lxJlBVOfq78vbx+yHNQXl6O9957DwUFBcIjNTQ0JJTlbGuA/jcYdguBNDc349NPP8XZs2dRUlKCZDKJ2dlZPH36FKurq3vk3y/igJNBecy0yKj8buNrnJ6DWkQK2J3HZDIpStlrGRHouGyhYrxNPt7yGpYJLm9Xa2y1PDRa3iuta3G5aUyyXYfAN9ql3/sZa7TWJx1DMm1vbyORSCAWi8FgMKCurg7l5eVQFAVbW1vY2NjA/fv3EY/Hs+on9KzhHj0tyMSYr3k5PJfPIXnjPB6PeGd6PB54vV4RCWcymeByuRCNRhGJRLC8vIzBwUEkEokM77XWfPD9w+Tx1MI7JVOKshsj3draij/4gz/AuXPnoCgKvvzyS9y8eRP9/f0IhUJHnkgBEDkJL168wPDwsJhIo9EIu92OgoICtLS04JNPPkFzczPKy8vhcrkOTV6TySReDk6nEwaDAYFAAOPj41heXkYsFjtyIUxvAt1URqNRPGTdbrcgrrdv3z5cAbF7gxcXF+PcuXP4e3/v76G1tRWlpaVwOBziweLz+VBZWYni4mL4/X54PB5UVFTA6XTCbDbDaDTi7t27GBwchN/vP1QPj8ViQUNDA95//338/u//PsrKymC325FKpUSxmFQqhWQyidzcXOTn58NgMCAcDuPp06eYm5s7FLnJIpmbm4vS0lK0tbXh3Llz6OjogMfjER4qs9kMi8Ui7md6OF+6dAlbW1ui3LBWPsdvGjk5OfB6vSgrK0NNTQ0cDgecTifa2tpQWVkJl8uFvLw8OJ1OEZpEIU2BQAAzMzN6dc5DgBYZkZVh/jlXkEhh4C/1nJwcWK1W+Hw+lJeXo6ioCIWFheLZoCiKIE/RaBTj4+OYmZlBOBwWoXKyAklKEPcwECjcipde55AVMq3v6G9Z6aDntN1uR2trK9rb21FUVASDwYCtrS0EAgFsbGzs2dSVkzdAO9yRjpU9UVpjTuNKYXkmkwlerxdVVVVobm5GYWGhkNPpdMJkMgmSm430yv3UynnL5pEguahtOlfLgs/76nA4UF5ejpqaGuTn58NqtWJ7ezsjz4mPu9Y7nshKXl4e2trahHHM5XJhe3sbeXl5MJlMwqNDfZHlIk++lneFj7vWPGkRBAp3d7lcIryssLAQNptNvPMtFgvS6TRmZ2cxOjqK6elp4TnQmnNg1xDOvTucvPKx52tXvnc4yQD2hq3xY+UwPVob1A5fK/RbztXL5qniY09t82Nlg0O24g3ZdG75mRQMBrGwsIDNzU2YTCbYbDa0tbVhfHwcfX19iEQiGUYFPveqqmbsX8VlykauZPmpj3z9kYe9sLAQ1dXVOHXqFEpKSoQROz8/H6qqCpJJHq319XWMjY1hY2MD8/PziMViQh4eskg/29vbv1ZBvHdGpsjFd/z4cfzgBz/A2bNnYTKZ0NPTg9u3b+Ply5dYW1s7krkVWiBLSigUynClA98+5ILBIJqbm1FZWanpnj4o0IPy2LFjKC8vh9lsxs7ODubn59Hd3X1kqg3+usjJyUFtbS0aGxuFBd5kMgkv1WF7pkwmE/Ly8nDlyhV88MEHOH36tFCCCOl0Guvr6wiFQojFYmKTTrvdjsLCQkGCw+EwVlZWEAqFDpVMOZ1OXLp0Ce+99x5qa2sRCoXQ09OD6elphMNhYRmk2HGylGpV8DkoGI1GlJeXo729HY2NjaiqqkJ1dTUaGhpQWVkJi8UiXugbGxuIRCJQFEXkTVA4XVNTE1wu174W99+kzF6vFxcuXMDZs2dRW1sLu90Oq9WKqqoqFBYWwmKxwGQyZazzeDyO2dlZ3L9/H9988w0CgcA7k1GHNkixkffPyabgqaoqNrPkyrrZbIbD4UBBQQEKCgpQX1+PpqYm1NTUoLq6Gna7XTxLKO91ZWUF/f39ePHihVAu6b6Uw5XkHAf6jrxEcp6QVv4IQU6gl6239L/RaBTboJw8eRLHjh2D0+mEqqoIh8N49OgRxsfHMyzxRDa1FEUuN42dljJE53AvHy/f7HQ6ceLECXR2dsLn88Fms2FtbQ19fX1YWlrKqLxHYcChUAiRSGTP81jOndJSKinkTB577lXI5k3gXg6n04ljx46hpqYGTqcTiqJgc3MT8XgckUhkz5xlAz1XWlpaUF5eLqz429vbcDgce8aZt8st96R0ahFpPgf0N2+LxoOU9IKCAhQWFsLr9aKiogJtbW2or69HXl4erFYrDAaDIP7Dw8O4f/8+bt++jcnJSSQSiaxeFy0STmMvy7IfMaDjaM3t7OwIwweFidNznOeCmUwmsQ0AGTTIMALsepYTiQQSiQTW1taEcUE2JvA55aG0Wv3mBgTurX0TieJzRO9IMpYajUZBssmoR8YZeWz5upfXAfeOa+UH8rHWuv+pkN2JEyfw4x//GF1dXSguLhay0dzQM5A88olEAj6fD+Pj49ja2sLW1lbWvdDomm8aN453RqbMZjPy8/Nx9uxZXL9+HXa7HQMDA7h58yb6+vqwurr6vSFSb8LOzm7xifX1dSQSCcTjcYTD4QOxZsugsKzy8nKcPXsWlZWVMBgMSCQSmJycFO77o5yflg1GoxGtra04ceIEHA7HG18YBw1SGq5du4YrV66gsLBQeA42NzexubmJ9fV1DAwMYGBgACsrKxlWasotdLlcqKioEN6swwJZb8+cOYP29nYkk0m8ePECv/rVr9DT04PV1dWMqk+UsGqxWJBMJrGxsXHgMlNYcUdHBz799FN0dXWhtLRUVCMiJW5rawvRaBR+vx+bm5twOp2ora2FxWIR+7eQVeogSKHL5UJrays+/PBDvPfee+LlQC99erHF43FsbW0hHo+L8Mre3l589dVXuHfvnrC26ThY8PCu/bwRnKjQ3BqNRthsNpSWlqKpqQnXr19HVVUVioqKYLfbYbfbxfrl4WIGw25BnsbGRnR1deHly5f42c9+hlevXgkjh3xtrmxpKWcE/l02JVmGfA6RmOLiYrS0tODMmTMoLy8HAESjUSwsLOD58+cYGBgQFcJobOR2SXbqv+xtAiDOlz0KJA+V2FaU3eIHDQ0NqKmpQU5ODsLhMMbGxvDq1SssLCwIYmSxWNDa2oqGhgYsLCzg/v37e8LTSGnM5tWTFVStcZc3fZXfbUQyvV4v2traUFFRIQoO+P1+4ZWW5yrbPFHoMxmQeOW7lZUVEaItkz9ONPha1CqYwWXIRnIogqCyshIffPABTpw4AZ/PB6/XKzzwpFRToQVVVeFwOFBRUQGXy4V/9+/+HWZnZ/dsWKwFvn64V4gbv0mJJsjzSjl+RqMRTqcTRUVFaG5uFvmAZWVlgmRwz9T29ja2trZgs9lgNpvFfG9uboooiPv37+P+/fsIhUIZHmZuuOBjL2+sy8eWvufn8znSmg++/nJyclBYWIhjx46JnG8AiEQiWFpaEvmO2dacVgEe+k1ezzdtuMv7TO06nU5UVFTg2LFjuHjxIkpLSzMKwqXTaYTDYaytrcFsNqOwsBAOhwMOhwNVVVX4u3/372JlZQV+v1+QcLpmNuL/NrrmOyNTBQUFuHjxIs6cOQOv14uXL1/iq6++wq1bt0Ry528LyLpXUFAAr9eLdDqNmZkZrK+vH7gsOTk58Pl86OjowNmzZ1FWVoZkMom5uTkMDw9jaGgIkUjkwOX6TYA8DtXV1bBarYctTgYUZbdsu8/nQ1lZmfAyJZNJBAIBTExMYHR0FDMzM5icnMTw8DASiQSsViuSySQSiQTS6TTMZjPMZvMeD8Rh9MfpdIr+GI1GDA8P4/bt2/jmm2+E90l+iW5sbIgX3mF41KxWK1pbW3H16lWcO3dOvARUVRUx08PDwxgbG8P4+DhWV1eRl5eHrq4u5ObmIjc3F5FIBP39/bh79y6Wl5ffed6aoigoKyvDH/7hH+LKlSuoqKgQLy667ubmJiKRCKampjA2Nob+/n6srq5ibW0Nq6urWFhYENs06DhYcGVG66VLyqXD4RBbOjgcDni9XjgcDhHzT96otrY25ObmwmDY3YQ1EokgHo8jHo8jGAwKT3ZxcTHy8/OF5X57exuvXr3C0tKS8HpzBUH2sPK1lU6nMzw3WqFz/DfvK1dM6RzyLJnNZpSXl+PatWuor6+Hw+FAMpnE4uIihoaGMDMzg0AgkGHck70CBDk5HUCGB4knpCeTyYw9e3h1LioO1NTUhKqqKhgMBszNzeHVq1dYXFzMCIF3Op1ob28XYXWjo6PiPpPHIps3g/eDK8cAhPWce9q4J4HOzcnJEXtGNjY2iupq1EYwGBTP2/3IPIEIPM8LoXGiKn5ckeTJ/7KyzD0Icm6blkeICFRxcbEIZe3q6sLJkydRXFwMi8WCra0tYegKBALC60ZG+oqKCng8Hly8eBHRaBRffPEFZmdnNXOZsnlUtXL05Pmj9UIeKCo0Rh60kpISlJSUiNDLvLw8eL1eETbOyRqNC/eeqKqa4ZVaX1/H7OwsnE4nAoGAWBtUNIHAjQs0D0Tg9stPkovI8PExGAyCwObm5qK6uhpnzpzB5cuXUVJSgnQ6jUAggCdPnqCnpwfxeHyP9zTb84GTIT4mtK5IZ5AJmCwnGZw7OzuFR8pisYgc7UAggLW1NYyPjyMQCMDhcODatWtobm4WfaupqRF5VTT3/PnBw65lz+x++I2TKVIqq6urceXKFdTV1SGRSKCnpwfPnz8X8Ze/DaAXZHl5Oerq6tDZ2YmKigqEQiH09fXB7/cfiFWbQDfDiRMncPnyZVRUVMBut8Pv96O/vx9DQ0MZD93vE0jZyM/PF+7lowRFUZCbm4vGxkYUFBSImPvp6Wk8f/4cPT09GBsbw9LSEoLBIMLhsPCAhMNhLC4uiv4dBSjKbr5jeXk5PB4PotEoBgYGMDIyIsqiyqAwhsPwyBIo5NbtdsNisWB9fR3r6+vw+/2YnZ3F5OSkqGY5NzeHWCyG9vZ2nDp1SuzXsri4iGfPnuHhw4cHForscrnQ1NQEn88nyN/CwgJGR0cRj8dFOOLU1BQmJiYwNjaGQCAgciWOYhn33xXIyqOWAmk2m0URl/r6ehHb73a74fF4hOJFzw3aLywUCmFychITExOYnp7GysqKyCmhKmxdXV3CAuvz+VBQUICZmRlxfa4gcgMNKezkVZGVZU6OgL25Jbx//DNah1TC++TJk7h69Sp8Ph9ycnIQiUTw7Nkz3L59G4uLixnVsmQDEpeBxohb5UnRIUWMwt7kPlAbBoNBhLc1NTXB4XBgaWlJbK+xuLiYoWSn02nEYjGRXymHwBGRINm4sr6fJ5CHQfHx42SL/yavf1lZGcrLy5GXlwcA2NraEko45UvyOcsGTrT5+k2n0yJZn6IOuCeB+khrRSYhb3pWKspuhbSKigqcP38eXV1daG1tFcUNwuEw5ufnMT8/j9nZWQwPD2NiYgJra2vY2dmBxWJBdXU1/vAP/xDnz58Xxb6Gh4exsrKiWXmNkwtOarU8KTRPRDI9Hg9yc3NhsVjgdruFMbe6ulqQWlLMabwoxIxvJUI5X9ygQd7jRCKBSCQiQvqNRiMcDgfi8TgKCgpgs9nQ39+/x1Mih6Bx+bX6zudA/jsnJ0d4x8vLy3Hx4kV88sknqKiogNvtxtbWFmZmZtDf34+vvvoK3d3dSCQSezxLskeH+kz95uHH/H6RvcpEcGQ5ySB1/fp1nDx5EhaLBdFoFIODg7h16xZGRkYwPj6OpaUlpNNpOBwOWK1W5OXlCSM1/eY5lFxu+XnJQx73w2+cTJlMJvh8Ppw8eRKXLl2C0+nEq1ev8PLlS0xN/f/Ze8/mNtMrTfhCzokECII556xA5ZbUuT1OUzWbamv3w/6C/SH7K2anZl7bZc/abtud1JJaakVSzBnMCSAIgkQGSeD9wDq3Dm4+YLdnWqR6zFPFIgk84U7P/ZzrhOssnGkuxQ8hxLBEk1RWVoY7d+7g2rVraG9vRzqdxtdff41nz55hc3PzVPtKYVk3b97E+++/D7vdjlwuh52dHbx48QITExOK409WF3oYON3r/v5+XnLkWYhKpYLdbhfx09/H5Xraolar4Xa70dnZiaKiIvFSHx4exq9//Wu8evUK4XBYKC300GazWYRCISwuLqKyshJFRUXiZXWWY079aWpqgtVqRTAYxKtXr7C+vv5We5UPDw8RDoexsLCA8fFxxONxzM/PY2xsDBMTE1haWhJgT6fTweFwoKGhAZ2dnbDZbAiFQhgaGsKjR48wMDBwaoQOZK0lpSwej+P58+f4x3/8xzwSjL29PcTjcfFMnlYY4rmcLEqeG+C1NdXj8eDjjz/G7du3UVZWJvIoyEodj8cRCoUQDocxOjqKzc1NkQezsbEh8nWTySSAI6KEyclJBINBlJSUoKqqCsCRx0Gv15/oIeNKgpLSz/shJ44rAQWloqjA69DV+vp6+Hw+mM1mpFIpUT5hYmJCGCuUCBOUrNw8p0o+/rvmhZ6x5uZmXL16FT6fDwAQCASwvLyMlZUVxGKxPDCSzWaxs7OD2dlZ4UVQGlO5hpC8HmQLvsz2yK8nK/rZbFbkcba0tKCoqAhGoxHZ7BHzaCgUQjQaFfuaHFZF1+FzbzQahWeUg9NoNIpQKCQISfj55EXgY6/kQZTBNRetVouSkhJ89NFHuHnzJmprawEA6+vrGBkZweTkJAKBgACJlDIRDoeRyx2F91HeGuW+chIEJSkEZPn8AK/zoQg0l5eXo6OjA1VVVWL9kjfYarXCarUK7148HhfPaSAQQDQaFYDM5XLB6/XmEUxRaOnm5iaGhobg9/uxvLyMjY0NrKys4ODgQBRi9ng8eTl/fOyVnhH5+ZTHQYnkwWw2o7u7G++++y66urpEnibVDfP7/fjss88wODiI+fn5Y0Wd+X3le/I6mjx0UWl+eNtlcKjValFWVoba2lqUl5fDZrNhf38f09PT+POf/4wHDx5gZ2dHhO+RDjYyMiJAr8vlEu0hg7zSePFn5aT+cflBwRRZHVpaWnDhwgU4nU6MjIzgL3/5C2ZmZkRM649VSKmvrq7GpUuXUFNTg7KyMjQ3N6OiogIGgwGDg4MYHR3F2trasY33NNpHVJBEyEDWzbW1NUGcIYcjuFwutLa2wmQyIZVKIRwOw2g0wmQyYXV1FVtbW0gkEmc2dyrVETlATU0NHA7HmRNNFBIKa6FQDQCi5lgsFlNcD/SCp002m80inU4fC7U4beH5GDabDX6/H/Pz88fIV942yWQyYrMfHBxEJpPB7u6uUFRjsRhUKhVKSkrQ3t6O69ev4+bNm6ivr0ckEsHQ0BB+97vf4dWrV9+7vsQPJbSBJ5NJLC8vY2JiAqOjo4JQgAo6/5j30P+IohTSxBVau92Ozs5OQSxCSjDlSiSTSQSDQWxubmJ5eRmvXr3C6uqqoO/lYUJ0H8rXqK+vFwWmU6mUKCRayCNCShi3WHP2PH6evPZlb4+S8Gv6fD50d3ejoaEBdrsdKpVKGDo2NjawurqaV8eIgwyeXyQb+XihVH5f+XhZYaYyA+QVcTgcwhMSDAYRjUbzQqboWltbW4jH4wgGg9ja2sqzuvNae/QZn6dCTHEn7StK17BYLGhqakJjYyNcLpdIqF9dXcX6+roIu5ZFVlrpHUWlO3guTDKZxNraGsLh8LF9hvLN5LBu3neufHJvIH1H+XMdHR24e/cuent7cXBwgNHRUQwODuLx48cYGxsTIaoEIg8ODgQDo9VqFeUh9Ho9AoEAVlZWhKFS7jsHD7K3T54HIlZobGwUdbcaGhpQVlYmjLgajUaQSK2vryORSCCZTGJlZUV4z8grQkYSr9cLj8cDq9UKm82GoqIiHB4eIhgMYn5+XkRsUZ5aOp0WnpxIJIJ4PC72fyXCDD72SgBangs6nsaTai3evXsX77//PlpaWmA2m6HRaJBKpbC+vo6HDx8Kzw9FN3FWPp4jJt+X1o+8Nvj3nDFTCWjRs9vZ2YlLly6JsMNgMIivvvoKX3/9NWZnZ0W6BG8DeVmJcCKTyeQdQ/2gOeNtlEmFTpIfHExZLBa0traipaUF4XAYT58+xcOHD7G2tvajDC8jIcrOvr4+XLt2TVhViNVEp9MhGo1Cq9WKYq3kdTgt5jxaGDxeNx6PIxwOIxqNwuFwiGQ92qAoNranpwdmsxmJRALb29swmUwwGo2YnZ3FwMAApqam8l42py0U5/625UqRkKua14aiMAHKn5KFQhY6OjpQU1MDm82W94KkJNSzEGp7RUUFzGYzkskkQqHQW0+9TRvs1taWopJLuQft7e345S9/iVu3bqGurg6pVArffvst/vSnP+Hx48fY3t4+lWeWWy7pBRYOhzE4OIjh4WFsbm7+6L35/9GFW/aB/PwBIkTp6OgAACwuLmJiYgLPnz/H1taWSEqnMK1MJiMs79z6LFOaazQaQZ1PhTFjsRimp6exvr6eFw5E53KLsOwBkr+nc+n7QiCFhy3JyqnRaERFRQWqqqoEWcLCwgL++Mc/4unTp8K4RPkSMmDkIUJKtORKQqQtFE7EFX1SHisrK1FSUgKNRoO1tTXcu3cPs7OzeaFG9C5NpVKYmZmBXq9HLBYTnisaQzkHSgm8yF4E2RvC55jmUp4jlUol8ouIfTEUCuHhw4e4f/++qIXHwQtXoOlvvV4Pr9crmE2plMXh4SF2dnbw8uVLLC8vH/NKkfdQycMjJ+nL96Q5VqvVqKqqEmQNOp0OExMT+NWvfoVvv/0WkUhEkBbJ1zCZTHA4HOjp6cHf//3fo62tDblcDnNzc5iZmUEwGMzLGZPBklIb5Tw/vV6P+vp6/Nf/+l9x+fJllJaWij2ZgNHOzg42NjawsLCAsbExoRdtbGyI3EMil+A5ZQQSKGcyFothe3sb6XRa6AwEYHlbs9ms0AFkLxA/Vv6e91Vmu6TPTCYTGhsb0dXVhY6ODly6dAmVlZUiSml/f1+E9lFkEzENys86v7Y8tjyCQukcWleFvLXA61I/fX19uHLlCpxOJ5LJJGZmZvD48WPMzs4KAhbajyl0kXKhTSYTDg8Psbe3dwxg8XvzvfavMVz+oGCKKAsrKipgtVrx/PlzDA0N5Vmg/hr5LgvOaQolf3744Yf46KOPUFZWJh40WvRGo1FQv9bV1eGPf/wjBgYG8h70Nyn04uQWGWKRIzfu5cuXhdIej8fR3NyMuro6katD4UP0gltfX0dRURGSyST8fj/i8fiZzAlZFN7mELNEIoFgMCg8GlqtVoRmDA0NIRqNivFVq9Ww2Wy4efMm3nvvPeEB2trawujoqMiJOcvCyhTyAHy3i/ttE6X2kjXf5/Ohv78fH3/8MdxutwjNePDgAe7fv4/d3d1T66tOp0NtbS3a2tpECMLe3h4GBgYwMzMjLMI8N4Mre3J4BC/eei6nI9yLorRuDg4OMD8/j62tLezs7MDv94tcIZpP8kDRC5wrwzJxhEr1OnTQ5/PB4XDg8PBQhA1R7UauVMrCLcNKjFwkfH3J1OnydaiNarUaZrMZRUVFokYWAMRiMezs7GBmZgabm5vCyEh5C3L+BV/nXPFS8vTwNnLQx9uq1+vhcDjyQiG3t7exsrKCvb29PEWT0zZHo1EYDIZj3holZUtOXlcSWcnn7VcK3SUDqclkQklJCfR6PQ4PD5FIJLCxsSEUeXle6Fy+bnK5HGw2m2Cdo9IPnGZ/ZWWlYNgcnyPeVz4WSnPE20D18eLxOEZHRzE2NibCxzmoNBgMgvyBAOCVK1dQV1cHo9GItbU1TExMYH5+Hru7u+Jdyb0ZNI9y7hBfsyQUGULvCIqCSSaTiEQiGBsbw9OnT+H3+7G1tYW9vT1BWELMrzKg5denfEGDwZBXD04JZHyXN0Suc8Xnl68v/hzQ86PVakV4+/Xr19HV1YW2tjZUV1eLsL50Oo1UKiUIpWw2m6DOV2II5GuB2iKHEXMjAR8b+k7pmaXvVaojBsqioiL4fD5otVrBCBoOh4U3U8mYwPfPZDKJQCAgnBwy0Qs/56Sab0ryg4Ips9mMiooKeDweRKNRUXj0pCrJhYTCngg5nrUUFxeju7tbxJMS4wzF61LFbkokJy+K2+3Gn//851NhMLRYLCgtLYXVagVwtKk5nU50dHTgf/7P/wmDwSDq7NDLm5Kgc7kc5ufnkUgkUF9fD4fDAeCoKC4l8v3ud7/D+Pj4qYf85XI5BINBPHnyBDdu3EBXV9ep3fv7Si53VOBucnISd+7cEUoCJXNmMhm8ePECc3Nz2NraglarRU1NjUjCtdvtSCaTmJ6exqeffirG+SwBDG0usVgMu7u7IozoxyhqtRperxddXV14//33cePGDbjdboRCIYyMjODZs2d48uQJAoHAqQJYg8GAnp4eXL9+XRARZDIZBINB7OzsiEK+NpsNJpMJXq9X5DoQrTFw9HLd2dkRysWPqYbfj124YiYr0LlcDolEApOTk6KGDD1H/MUN5CuBXJmSvQS0JlpaWtDa2gqHw4GVlRXMz88LJYGuSzUG+b1kdkpu4ZYVopNAouz9oM90Oh3q6+vR1NSE+vp6GI1G7O/vY3l5WbDc7u/vixwcbk3mippsTSeLPxkYqL10X95+pc+pgLfdbhc5R9FoFMlkUuTLkNJNY0VjbTabRXgT9xzIoEUGFxxgUoiY0ljyNcCT3kmJdLlcgrVRpVIhHo9jfX1dGOiofIJc8oT6RH2kOmYej0cQK2SzWUFuMzMzg0AgkNdHtVotdAYl4fMvh4/KoIA8YzqdToCRg4MDobNQ0fSSkhLRV6fTicrKSty4cQMtLS1wu92IxWKYmJjAq1evsLi4iHQ6LYwMuVxOUGXT86JWq/MihPj8KPXHaDQKkL+3t4ehoSH83//7f/H06VMRzkd9JKBLuipFBhmNRgHaaa+mmmD0bpeBppJRQ6nQthJRAg+1o2vReNDntJaIdOnjjz9GXV1dXooHsSgSQYPT6cS1a9eQTqcxPT2NtbU1bG9v560H7v0GkAeMyTPHhQAu91xyMMz7Snui2+1GaWmp8DBFIhFMTEwIojf+DOVyOUFW0djYiOrqami1WkHoQ+yI/Fnnzy55YrVarVg336WL/eBgiippx2KxYw/md4nNZhPsKTabDQaDIY+tKh6PCxfdaVteCRUTDSNZhLa2tpDJZITVq7i4GF6vFz6fDzdv3gQAjI6OIhqNIhqNvtE2EmUueRP4Q+x2u4U72el0ihpGtHj29vYwOjqK5eVlpNNpwUpH1LBWq1UoaLOzsyIJ/jQkl8sJy9Ds7KygUFUKnTsryeVy2N3dxczMDMbGxgSzlt1uR3t7OywWCzweD169eoW5uTnodDr09vais7MTPp8Ph4eH2NzcxMjIiCis/DYow6S8aDQawZD3pqnCf2jR6XQoLi4WQOrnP/85vF4vQqEQBgYG8MUXX+Dx48dYWlo6dU+gTqdDXV0dWltbYTabAbxWOOrr61FeXo6qqipRf6yqqgperxd2u108xyqVSgCwp0+f4tGjRxgbGxO5D+dyOqLkMeHKPA/lkS3RsjdFfr44IKIw1Q8//BA9PT0AgKmpKQwODmJ7ezuvXhP9zUNfZA+TkiUYyA/XkftH3ytZda1WK3p7e3Hx4kXBOheLxfDNN9/g/v37ApTIRBl0P9kqTIoX0b8rjZ88Tjx0iL4/PDyEzWaD0+kUyl8mk0E0GhWeBfkawBF1PO8fb7MMFmRPMQl5WwgsylZvOo8DRRKbzYby8nI4HA4xZltbWxgZGRG5dUrFRck7QN46ooW+evUqbty4IfKAKJTxq6++Eu92DpJpTmSRw8e490P2TlFf9/f3hbJuNBpx8+ZNGAwGLC4uippqRDdus9kEGURxcbEgflCpVJifn8eDBw8wNzcn6h3RfHAgSIo4bxMXPv46nU6w9fHyHqurq/j2228xPz8vwtyonwRSKJqHfvR6PcrKyuDxeFBdXY22tjbodDpMTU3h5cuXiEQix551pef+uzzL9Df3wPHQUq6jqdVq2O129Pb24pe//CV6enrg9Xqh0Wiwvb2NFy9eYHR0FFNTUzg8PITL5UJ1dXVese26ujr8+c9/FikfVFJBNm7w9tO9aX7oOP43J09ReraJHIVyG1WqI+IRyoGm+/BwVLfbjY8//hg3b95EVVUVtFotIpEIRkdHRTkGJXBN46jX68We8328VD8omCLAYTabj8VzniTE5d/R0YG+vj709vbC6/VCr9cLDv7d3V34/X4MDAxgcHDw1AHV3t6eKOpHjF9jY2NYXl5GKpWCXq9HUVERamtr8c477+DOnTsoKipCY2MjWlpaBI3xm2xzMplEOBw+lteSSqVEjO/GxgZu3bolkgxp8cTjcQwNDeH+/fvw+/34+OOP8e6778JkMsFsNqO6uhr/8A//gNLSUvzqV7+C3+/Hzs7OqSr8mUwGIyMjqK+vx+3bt8WL+m0QskAvLi7iX/7lX7C5uYlf/OIXqK6uhsvlEjWorl69irm5Oej1erS0tIiiymS9fv78OcLh8FtTVJnCEevq6nDt2jWoVCrMzc0JxfAkFqW3RRwOB/r7+/HJJ5/g9u3bwiP1/Plz/Pa3v8WjR48QDofPpGQDKTkUbgMAJSUl+OUvf4kbN24gl8vB5/PB6XSKGmTE7ET/03k1NTWCOc1oNIq8nHM5HZEVWRJ6Rsxmcx4zquwBka8BvM4nIs+DXq8XRXpbW1tRVFSEvb09DA8PY2pqSli8Ze+OHDYne7vkNtPnhZQI2fMCvLbSU2mU9vZ2uFwu5HI5Eb5MRFT8OrISKYcM0XHkgeHtl8GYDGb4dZPJpCBTIg+CzWaD2WwW1m7ZI1RUVASv1wuTyQTgqNgw5TEeHBzkhZ7zUCV5LJXyq5Taz9tOn/l8Ply6dAlerzePFY1CxWjsZd2CxozWl9vtRnNzM7q6ulBfXw+LxYJsNisUzFevXglgInsklYwy3IuqlK8lh0TmcjkEAgGsrq4iGo2ipKQE1dXVUKlU6O7uRmlpKYxGI4xGo2C7JOIJqs0GQLxnx8bGhPFABh3y/BNzJtdLObgiTxIRP+3v70On0wlGTT7W1G+z2Qy73Q6fzycY98jLZjKZUFVVhYqKCjQ2NsLj8Yh1sri4CL1enwfgqc1Kwr2VSs+qvNZkEhc6hmp7Xbx4ES0tLfB4PNjd3cXq6iomJyeFTkthnhQJQbWdamtrYbFYEAqFAADz8/OIRCLiXkrrMJvN5hnteZv5j9Kc8edof39fGAspT3RkZAQLCwvI5V57IvnclJeX4/r166itrYVer8fOzg4WFhYwNDSU513k9+brl4NF2YCjJD8omOJsZt8HNJA7lSgor1y5gtbWVpSUlIhNg8Ln1Go1WltbBcgaGhpCIBA4NUVub28Pk5OTyGQysFqtWFpaEvGatGFZLBaRvGm1WtHf3y/IKGZmZkRRuTclVGTVZrPlfU6ofmZmRiT+hkIh4a2iGNpgMIi5uTlEo1E4nU5UVVWhrq4OdrsdFosFjY2NIvzyD3/4A549e/bGASKXbDaL3d1dRCKRt9LiTiEB4+Pj4iG8ffs2+vr6RAXu4uJilJaWipcbbQLxeBwrKytYWFg4Fv9+lkKbcF1dHX7605+itbVV5HyQMeE018C/RUwmE+rq6tDU1ISysjKoVCrMzs7i888/x8DAADY2Ns7MC0ibP7fuWa1WUWaBK96UEBwKhZBOp+HxeFBRUSFCe4mQ4MKFC9je3sbCwsI5mDoFIdIDAkr8xUyegcPDQ5SXl6OsrAzLy8t5zJ46nU6EndG+RuDKZDIhl8vBYDCIWlK3bt3C7du3UVlZiUwmg9nZWRF+k8u9Dq3heyS1SUkpUKJcBvIVHjl8iAM0UpgoJ7G6uhqNjY2i+HQsFkMgEMDu7i62t7dF+6hdPMTmJDIDXg6APuc5yye1nfq2u7uLlZUVRCIRuFwu9Pb24n/8j/+Bhw8fYnJyUkRcUI70tWvX0NPTI/KM5+fn8eLFCywuLmJnZ0cksgMQyhfP/+LtV1KGKXRRiUyAxqisrAwNDQ15hUYpCsZms8Fms4l7Um42XZeuYzKZ0NTUhDt37qC/vx8Oh0N4pUKhEFZWVhAKhYRlX87l4tEI9DmFCCrlktG+JoN50kPm5uZQVFQEj8cDh8MBo9GYV6srlUohmUyK/D/KSaf5DAQCWFpaEhTYBBjJS8+BK7WR59RQeZt0Oi2OpzDpFy9eoLa2VqSZWK1WQXpAZGplZWWorKxEfX09Ojs7xXhSqgeVzqEIoFwuh2g0Kt7/pCfTujwpR0opNJRqEcrsnXzdkNBcVFZW4tq1a+jv74fRaMTS0hIePnwoqNnJY0iMeBSS+Omnn2JrawuXL19Ga2sr+vr6RM49rXdOmCN7qcizQ+0i7102m83z+nAvlpxXRaD/j3/8I549eyb2N6q/SOdTGOOlS5dw+fJldHd3w+FwIJVKwe/3w+/3Y3NzU3iAZY+z7OXiIdnfJT8omOKWsO8SSlKtqqrCpUuXcOvWLVRUVECtVmN5eVnEVVO16ZKSEni9Xly5cgUajQa7u7unWoA2Ho8LAEVuep7oTYAlkUgIEo76+nq4XC4RLqfkrv0hxWg0Cgs2X7zA65yesbExJBIJrK6uwuv1YmdnBwaDAc3NzQgEAojH41hYWMDLly9RVVUFk8kkrKImkwktLS1wOp1YWFjAyMjIW8/udtpCrEgTExNigzGZTPB4PHC5XDCZTEKhp82UNnp6Ib4tQAp4rZTYbDY0NjaipqYGV65cwcrKCu7du4dnz55hZmbmTJkev0u4ZZOsh5ubmxgbGxMKxFkJ7ZlyIqxWq0UqlRJ5BZFIRAD1xcVFJBIJVFdXCyamqqoqeDweGAwGVFRUoLu7G3/605/OrF9/S0KKnNKzS4rh3t4e7HY7ysrKUF5eDpPJJOqkWK1WAaaA16FeFotF5BKQh8pms+HChQuoq6uDwWDAysoKnj9/jmAwmJcHICvu3OorW/FJ2StkoJItzXRN+bder0dlZSUuXLiA6upqERK8vr6O58+fY2FhQewTXPmj85U8HHRMLpcTdMxcYSRDBOkBXJmTreFkRJybm8PAwAB6e3thtVpx6dIlmM1mdHZ2IplMQq/Xw2azwefzoaGhQTCaHhwciD1wfX0dwWBQEA6Fw2EEAgFsbm4iEonkMXBS+znLK60byifjYU587jQaDYqLi1FZWZlXY5FCKff29lBRUYH9/X3s7e1he3sbKtVReYVEIgGVSiXW3a1bt3D58mVBCEK5Uqurq1haWhIeO67YKwFwrvQSeYW87mVgTAZ2Yr775ptvkM1mUVFRgfLyckH1TrWWFhcXcXBwgEAgAI/Hg3feeQfFxcWCiTgWi4n2yoYA3hbefgJdwGt25lwuJ4xW6XRahE+2tLSgoqJCMPdtbm4imUyKgvAEpPr6+tDc3CyMoiaTCXq9HgaDQXjXyJN6eHiIuro6xONxxONxTE1NCfKQVCqlGD6vNK65XE6Ee3IvL++v/GxpNBrU19ejpqYGwWBQFLcdGhoSQJruT+NF0ScbGxsYHh6G3W5HW1sburu7RchcKpVCMBjMM2bwda+0J5LThcZd9kRxkEPAin5CoZDIJab1Rx4+Yu9zOBy4efMment7UVxcjEQiAb/fj/v372NwcFAwX/LxLORVVmJoLSQ/KJgiJCdXM1YSo9GIqqoq/PSnP8WtW7dgMpnw+PFjPH/+HIFAQDwoBoNBECtcv34dFy5cwK1bt0RYw2kVlOVKz3cdk0wmRRE9qnNzGjVrtra2MDQ0hBs3bqCnpwcmk+kYoIrH45icnMTy8rJ42anValitVpH/pVKpMD4+DqPRiJKSErFQKXnXZDIJa+y5HBeyQs3OzuIPf/gDVldX0d7ejqtXr6K1tTUvX43G1mazoampCR0dHSIO/m0BVWq1GouLi3j48CFMJhNqamqEl7i5uRn/+I//KGhT35Y2c6FnkhQutVotQnI3NjZE/PpZCD2Te3t7MJvNUKvVoqL7+Pi4CGUgWnoy2GSzWUFZ29jYiBs3buD27duorq4W+XlvaxmB/2jCKZC5pZOHSO3u7gpA39vbi+rqakEGRFZ52guoGCkBBeC1h4uMMwaDAclkEktLS3jx4oVgx+MWedlLIysKJFzR5HkXslJaSNEjMFlcXIyLFy/ik08+EeFbsVgMw8PD+Pzzz7G6upoHevg4ye9wOfznpHYAyKMU59Zs3mfKjxofHxe6RVNTE+rq6tDQ0CD6wtsAQJAR8HCnTCaD7e1thEIhzM/PY3JyEouLi+J8HrEiK4z8b9kYzOeMwiapDiF5m1QqlchlrqioQDKZRDabRTAYxNraGnZ3dwUbrMvlgs/nE0o/Rf3s7+8jk8lgamoKz58/x8TEhCCz4O2QvVG8/TJ7Hh9reezp/2QyifX1dXzxxRfY2dlBY2Mj2tvb4fF4sLm5iW+++Qbj4+NYWloS9+jr68OFCxfE2BKBgxKjXSFdUF43h4eHiMfjeTlGZMy8d+8egsEg+vv7RRFdAm+RSAQajQarq6uCwIv0oVzuiPTAYDCIEECLxSIM0haLBdXV1aJm5sOHD0V+9NLSkiIYpH1ABrUU2iiT1fB5ojVkNBpht9tRV1eH/f19fPnll5iensb8/LyoZXd4eJjnoebzlk6nsbi4iPLycmQyGdTW1sJsNmN9fR3b29vY29s7MWRRnqNc7nX4Kfd6y8cogZrDw0PFdUfnmUwmAXTb29sBHOWTfvnll/j6668xNzeXFwpN11HaG5XG8yT5QcFUPB7H6uoqIpGISBY0GAyKYUvEMtfX1we3241vv/1WuBx3d3fFg8Ljfe12OxoaGlBbWwu73S48Jm+zpFIpBAKBPOrVNyWxWAyrq6uYn5/H+vq6eFlTMToKMwkEAsKaoCS5XE5Q2A4NDcHhcKC2tla82M/luyWbPar7Mjc3J+LzW1tb8zZ98mIBr2tOXblyBbu7u5icnBSV7c8if4pe9OTKDwQCePToEfb391FRUYGGhgZ0dHSgs7MT7777LlQqFV6+fPnW5HpxIeanmpoaEWZRXV2Nmzdvito+W1tbZ9L2dDqN4eFhlJeX4/Lly0in05ibmxN1PWZmZsQ6kD0HOzs72N7exvb2NpxOJ9rb2+Hz+WCxWKDT6RSTxs/lhxfZ+srfSdyrsrKyAuBI8Xe73TCZTLBYLHm5qxT+k8vlM2FR7oZKpRLejEAgIOrsRCKRvP2c8k24csKBGf9cydske4d4iJSsJFNtxWvXruHy5ctob2+Hw+FAIpHA2NiYyJWid6DsGZPHjETJwi4rZnKeEweFpD9wr1w8Hoff70cqlUImk8HFixdx+fJlQYVNJBGUo7GwsACv14uysjI4nU6hc+RyR6RNT548wcTEBAKBANbX1/NyjggYk+JIln7efjJmyrkmsvJGngg+FmTsJCOux+MRCvOtW7eEgcZkMqGoqEhEx1D43PLyMj777DN8++23CAaDiuFkPIRONiTLfeHKPV8rtJ7puEwmI94nr169gs1mg9VqRTweF96Og4MD2O12UY6ms7MzL+9lY2Mjj1mxENCnNUB/83XDjWt8zVGdPyJi4P2kv6nEwcjICFwul1hnTqcTDocDRUVFaG9vR2dnJ5qbm+F0OqHT6WC32wWxQTKZRHFxMb788kvhoeLjTm3X6XTCyM3bwdnwZDBC35nNZpHzNzQ0hKdPnyIQCCCZTCIWi+U9J3wfozVJqQt6vR77+/vY2dlBRUWFKCI9MjICv99/bM/j/eC/+TjLnii+3mQPtRKY5+NEdfcuX76MX/ziF2hvb4darcbq6iq+/vprPHjwAPPz84hGo+LePE9VNj7RmHAP63fp7z8omNrb28Pc3Bw2Nzfh9XpRVVUFv9+PWCyWp6ioVKq8Ctzr6+v4zW9+g9HRUTHBJDwZkh4AmR/+bRIqLkY0x7u7u1heXkY4HH7j7T04OBAekampKZSUlIhY+7KyMnR3d2N+fh6Dg4PCSsip52XLKhFXEOClYoFKjEqnISqVSljpfgyKIll1ksmkqJtBHr10Oo1wOIzR0VEcHh6ipqYGZrMZFy9ehMViwfPnzzE8PIzJycljitJpSC53RIlOYSx7e3uYmprC4uIiAKCoqAj/7b/9N9y9excffvihUJzksIO3QXZ3d/H8+XNBWXvz5k14vV7cvXsX+/v7iEajIkTqtCWZTOLhw4eCmWhrawuvXr3C6OioiGE/aa/LZDIid4OUof39fcHwdS5vXkiZpb9loWc3lUphY2MDAwMD0Gg0iEQigrmWQr2IoZRyRChkmwr8NjY2ilAiCjMjUgUZ5PBcIRJOH8wL4fIfHuql1Ff+jtBqtXC5XGhra8OVK1fQ3d0Np9MJAAgEAnjy5IlI+JaL1yuBUN5+JSEFiPQBOlbpukpeJooUoQLre3t7iEajsFqtKCkpgcvlgsFgwOHhISYnJzE7O4vW1lZcuHBBeHoXFxcxOzsLv9+PP/3pT9je3hZFl8l7SHsJ5RPxPDFqDwFZpZBM6gdw5OVaWlqCwWAQexgBCfJc0Q/Pl+bheKQUxuNxbGxsYHp6Go8fP8azZ8+OKZmFAJU8J/x4uc0yC6QMBDKZjAhDpPbRD4HEzs5OXLp0Cb29vbDb7UilUhgfH8c333yDxcVFRQZDpfbzKBD6TQYO+dklrwnlZvNxlGVvb08Uh6cfvV4Ps9kMl8uFjY0NJBIJuFwuQZpAxEEUxh2JRLC7u5sHDKktfOyV2PAoLE8JDABHuiiB6O3tbQQCAcRiMdFv2Qih5B3i47S3tyeMBWQQcrlcx8CyvHa4kYPmjOuRcn9PMkzx//m1LRYLKioq0NPTg97eXhQVFSEcDuOrr77CixcvMDY2JlgAqa1yOKuSyHTrJ71Tf/AwP6rsrFKpcPnyZUEfTiCJahZQ8nQ0GsXU1JQofqa0iRLKv379Ourq6rC5uSl47t+0kqkUalBIiIKZqkqrVCpBKhAOh09FIaYq85OTk7h48aIAdUSJSRvu2NiYYHKhTYMs2plMBg6HA42NjWhoaBCFAlWqo+TOvb29M1HW1Go1PB6PICF524XGnYrj1dXVCctnMBjEwMAAPv30U+zu7qKpqQmdnZ1obGzExYsXUVlZibq6OmQyGUxMTCAej59q27PZLLa2tjAzM4POzk7xObFIpdNpPHr0CHq9Hnfu3BF03TzM920R8gC+ePFCxPdfuXIFJSUlaG5uRk1NDV69enXqY0xtC4fDGB4eRiQSQSKREHHhnICikFDSf0lJCcrKyqDX67G1tYXZ2VmRmHsupyeyJVaev2QyiYWFBQQCAdy/f1/QP+dyOQGc9/f3hcJFHsZgMAifz4df/OIX8Hq9cDgc2NzcxNramlB2ZMVLVkxOAnxcIZNDw3nfuGJEBqKGhgb09/ejv78fdXV10Gq1SCQSWFpawsjIiCBukpVrJZBXqF2FFFrebt5/JZBCQtb2ZDKJ7e1tjI2NiTA6t9stPIWLi4vCE9XS0oJEIoHt7W3cv38fv/3tb7GysoLNzc08Y6/cJlkxVRpXeT64N41A3bfffov9/X1EIhGYTCZBX05hZUTSRXkjnPmTxprIJl68eIEHDx7gm2++EeUTCqVm0BriwPuktUF/y2Oh5K3ln9O8abVaeL1e3LhxA5cuXcKdO3dgt9tF6PMf/vAHPHr0CCsrK4pgQAb78jPA20AKvZLyXsi7ojSvfM6A116vTCYDo9GIpqYmlJSUwGazCR2KGBT/8pe/YHZ2VryXZLBAoEnJU1xofAGIsMKioiKkUins7u4KBkol8CCPA/cM8RwqGjOVSiXKFn1X+ovsiebPvdJ3fP6UDBB8zVAotMPhQE9PD9ra2lBaWor9/X0sLCzgyy+/xNLSkoiO4+vhJBBFwsf3VD1TZPnw+/1ob29HR0cHgsEgwuEwpqamEIlEhOvR5XLB4XCIJEgl5Vyv16O4uBg9PT24e/cuWlpasLe3hwcPHmB2dvaNUhlTsT6fzycSTCORiOKiobhUj8eDrq4uUVxue3sb09PTInnxNIQYv4aGhtDX1wcAwtpWUVEhKJhpLInFJZfLCVadZDIJt9uNxsZGNDU1oaqqSlhMQ6EQnj17hsXFxWNxsm9aNBqNqLtjMBhO7b7/VtFqtaiurhbWNa/XK5IoX716hd///vd4+PAhIpEIZmZmxIv53XffRVlZGTQaDe7fvy+KKZ+mJ5CDqWg0KmrIbWxsIBaLIZlMYnx8HC6XC++88w7Ky8tRX18v4srfJiHr2urqKgCIYqKVlZUoKytDaWmpYA89CyFP5ebmJuLxuMi3/K75VqlUIteuubkZPp8PBoMB29vb8Pv9b7yu3bkciRxGpBTuRJLNHtU2IoWeQsHk2jB0Dcpl2N/fFyQ2RNk8MzOD9fV1sQcrARK6Dt1btsjSMfx/kkLrjyIEPB6PMBRdu3YN1dXVMJlMyGQy2NjYwOzsLGZnZ0+MyuDgjLeHxk4OweIWdX4N3mYlBVO2vFPUwP7+PlKplIgYCAQCIoIgl8uhrq4OXV1dcLlc2N/fx8TEBD777DP4/X5FfaCQMl4oH0bOqZL7AxzlQj969AhbW1uCEZZyeAiEUH6O1WpFS0sLysvLUVxcDLPZLEgbIpEIRkZG8OjRI1GCg/Jcld7jnGGUe4FkcMH7K88j/ebPg5xPxcelqKgI3d3duHjxInp6euByuRAKhTA2NoZvvvkGz58/x9ramvBqKd0zl8sJDxARO/E1xYGW0tzJopQ/KAM3Gj9aD5wQJBKJIJlMCvbDjY0NfP7550KPIvY8Pg409vIY8e85OYJM3+1wOFBaWory8nLMzMwgk8kIMKTUdz52SuDeaDTC6/XC7XZDq9UKZkvSBwqNY6HnUel4eW0pecu40Gd2ux2NjY3o7+9HdXU1AGB8fBxPnz7F6upqHuu3vPZkgKa0X/J5PUl+cDa/ZDKJqakpNDQ0oLm5Gf39/UgkEkilUiJ/hBJuaSCKi4vzlBmyehGQeu+993Djxg0kEgk8evQIv/vd7zA3N3csbOCHEkK67e3tuHLlClZXVzEyMiJqENCAc0tQcXExmpub8d577+Hq1atwOp0YHh7GzMzMqdavyWaz2N7exsTEBL7++muoVCp0dHQIJhybzYZr167hwoUL4qW3u7sL4LVnKp1OiyrkFCaoUh3FWvv9fvzxj3/E+Pj4qVJ4E/sjFfR7mwr2KolKdcR+2NDQgL6+PhFXTZ7YBw8e4IsvvhCFkJeWlqBSqeDxeHD16lXxjPAX2mlKLnfk1l9bW0MsFoPD4UBbW1ve5rS3t4dwOAyVSoWKigp0dHRgcXHxrQNTJNxqT/+bTCaRIHwWQon71dXVqK6uxtraGvx+P0Kh0IlMpVQyory8HJcuXUJLS4sI8YlGoyJE8FxOR7hSTEoAvSPIS0Kfc68J0QqTcIWGW+tpb6isrIRWq8X29rYgPSBFTLac82sU8tIoKb5K39Pf9L1er0d9fT1+/vOf4+bNmyKPAoCgvx4fHxe5IErKlGz1VVJuCXBwim35Gvw3B7ZKx8nXBo7yFrnxgt41Pp8PXV1d6O7uhsViwfr6Or788ks8f/5cRNHIYywr60osj4W8P0qelFzuqBj84OAgRkdHBRMaUVLTeeSJotIId+7cQVtbG+rr62Gz2XB4eIhEIoEXL17g3r17WFtbE2FWSp5IrszKY8/byMGRPLZy2BqfK3mNUV53bW2tIGKqr6/HwcEBHj9+jE8//RRDQ0NYX1/Py+3ieUN8zet0OpjN5jxSJCUFXe4LT3WQn1nefyXQSPenME9iHaRxJtrxhYUF/P73v8fw8HBewWV5TdA9yKBC/eVpFoVAbElJCRobG+Hz+TA/P583t3K7ZYDBhXRGn8+H1tZWwby4vb2Nubk5rK6uHtMDZSPBSUYbClWVwS4fSyUdk65lNBpRXV2N2tpatLa2wufzIRqN4smTJ/jiiy+wvLwscjVpbdB48b7TZ7TfFgKBJ+m7P7gGQQnUz58/R3t7O+rr6/HJJ5/A5XLh5cuXmJqawubmJjY2NrC+vn5MOSZms7q6OuHmraurw+HhIQYGBvDw4UMsLi4KN/cPLSqVCkVFRfjoo49w69YtNDQ04Msvv8TU1JSgXXS5XCgqKoLT6YTH40FtbS0qKipQUVGBqqoq6PV6LCws4OnTpxgaGjpV+vBc7ihcZGlpCX/5y1+Eq9dms4mYXapNkc1mYbfbhRuZL2oK4QAgKEPHx8fx4MEDDA0NIRgMnlqYn0qlQmlpKa5du4aamhoYjca3PmeK6qe1t7ejvb0dVqtV5BTeu3cPT58+FaGqwFHoCb0M1Wo1IpEI1tbWEAwGz4QlL5c7YiRcX1/H1tYWurq6cPPmTfj9fkxPTwvrOln9yNv8toJctVoNl8uFhoYG9PT0iGLJFHdOjEynXb/MYrHgk08+wQcffACXy4WZmRk8fvwYX375JYLBoOI5RqMRpaWl6OzsxNWrV3Hjxg00NjZCpTpiT1tbWxN5EOfy5oW/dLnCpRTOwnNPZIWVHwvkKzlmsxkOh0PkXhCdMj9PVla5YkhCyj1XImQCCt4nWQhoXL58GT/72c/Eu51CFSmHb3p6Gn6/X7z7yDBEpQnkd4eSMkmfn6T0874Wup6SB6SQFZ5fk7ztREmeSqXycoz5db7Lu6RSqYSHSKbz5uyJnLmNJJt9XScJgFBeuYeRKN339vYQiUSwtbWFW7duCW8VJejX1NTA4XBgdXU1bwyV1i/vjxKJQyEgLoffyUVbZa+OWq1GSUmJ2Mt6enpQWloqmCDv3buHoaEhhEKhPAApz7nsXeD3I0IW/kzJfebATF4P8twWGgN+nNFohNvtFobsXO6oPM3s7CzW19fzorFkIwbfAwhI8XUnPz/ca6vValFaWor6+npUVVVhbW0NgUDgWK0teezkedRoNPB4POju7sbt27dx8+ZNOJ1OkUs/MjKSV6OR73FKa0IpNJG+k3Ps+Hwo5ZLRs2K329HT04NLly6htLRUGKanp6cxPT2dR7/O5+gkUgmea8rrYX2XDvaDg6ls9qj42eTkJB4+fAgAqK6uxsWLFwVN58zMDOx2u1AgqdaU2+0WeTHt7e3o7+9HT08PNBoNpqamMD09jdnZ2TcKpKxWK2pqanDnzh3cunULVqsVa2tr2NraQmVlJaxWK9xut3hIfD4f6uvr4fV6YbPZkMlkMDk5iSdPnmBwcBDLy8unVguLhBTh2dlZUbNLKY9FpVIJa6LSZpDLHVlOKQfj3r17ePLkyTHmmTctKtVRrYympiYUFRUpbgZvm9jtdjQ3N4vq57lcDnNzc3j06BGePHmC+fn5Y6QsVOSPPFiU2HxWHgaK7V5fX0dbWxuamprQ1NSEgYEBbG5uCu8IWeDlF8JZi0p15B202+0oKSlBbW0t+vr60N3djdLSUhHKSPmMZ9F2g8GAzs5OvPfee9Dr9XA4HNje3saTJ0/EMaSM0MujrKxM5JD29/ejqakJFotF1Iibnp7GysrKqYUW/62L/HImAECKoqxY0N/8fP6ZDLgoz9hqtcJsNkOj0QiafNmrRecDx+vucC+P3H75fyVgoNfrYbfb0draiv/+3/+7ICYiRXV/fx/xeBw7OzuIxWLCQ80ZZcmoRJ5XJUWe/pcBihxuVaj9vN3yM83nhN9bHietVouysjI0NzfD4XCIsDrZk6A0tzT2wGulV6PRiHxZGdzJ71zZkyKvqUJ9JRKDdDotdKzu7m7U1tYKQ53P5xNzJnsr+f3kayux+SnNgWwoUOofX4MWiwVlZWW4cOECrl27hra2NhQXF2N1dRVTU1P45ptvMDY2dsyTxvMCZa8wleghRboQSJLHm4eWyvN6EsiUx4vW+qVLl9DX14eKigqYTCYcHh6KqAHOD8DvyZ9ZHr4r5zvKwJTrEmq1GnV1dejp6UFJSYkgkPL7/QiHwwDy2Tl5vhzRqFMR+NbWVly8eBFXr15FeXk5tFotxsfH8erVK6yvr+fVl+QAUN6LZJHBjGx44mMvh/qSsb+0tFSQw3R2dsJms2F5eRmPHz/G/Pw8tre3j61xKgvAry97yuRczu+byvJGYltyuRxWV1fx61//GjMzM7h48SL6+voEVWQikUAsFhPFCOvr6/G///f/xtbWFrLZLLxeL4qKikT+TjQaxe7urmCtelN5Omq1GpWVlejv70d3dzfKy8uhVqvx4Ycf4tq1a4LilGpOUFE2qgtC1PAPHz7E7373O0xNTYmaMGchtLHGYjERyseT9uhvJUsoMYLt7u5iaGgIjx49wldffXUm+TvAa4XltO/7bxGNRoOysjJcvXoVTU1NUKvV2NnZwf379/HrX/8aCwsLIs6Y5oIYCuU+nnV/iUGptrYWnZ2d6O3txdzcHL7++mvxctbr9Xm1P05DePgTt17Ty5qeS2r3rVu30N7ejqqqKuFBCwaDgrqZErHPQujlSbHpZE22WCzC4EEsUR0dHbhw4QKuXr0qqN4NBgMymQw2Nzdx7949PH78GKurq6IQ6Lm8eZGVHPLY0mdcIebHnXQtEiowarPZ4HQ6cXBwICjxld4vhTwktN8rgSz5HFJi6HMqfH3x4kX89Kc/xY0bN4RHisKaKNE9FAohHo/D4/Fgf39f5B9fuHABFRUVGBsbw/DwMJaXlxGPx/PChGSAo9QHeaxJqG+Fwst4v5SUfv4bgFAsqYZVNBrFzs5OHvGVDMqofUqeKY1GI/YsrtDJypwSwKO+K4VPyWOUzWYRiUQwOTkJv9+P/v7+PMKmQoBUCXjSfbl3ie9XShZ/7k2Q50OeL6PRiIqKCly6dAnXr1+Hw+HAzs4OHj58iHv37mF2dlbQtvP5omsTeKJ2ykCE2qXUPz52hc6h55j39yTwSx7AiooK3Lp1C7du3UJ9fT30ej0ikYiI7JBD4/iapvHjoIKv2UL6AbWRuAYaGhpQXl4On88Hm82G58+fY3p6GpFIRJRkUavVwttnMBhErlV7ezuamprQ29uLqqoqGI1GZDIZBINBvHz5Eg8fPsTa2tox4gz5OZJB9UlrVv5MBmn8umq1Gg6HA83NzaitrUVtbS3S6TQmJibw+eefY3Z2Ns/QRGuY16WVwRpfB7zt39fI+sYSBSihOpvNiqrjVVVVKCkpgdVqFTG+a2tr2Nvbw+HhISorK+FwOOB0OgUbEHlXyHVXiPHvhxCVSiWK8tGLAjhKiiwuLhbH8QVBicQbGxuYmZnB4OAgvv32W5G4f1ZAito2NjYGlUqFp0+fwul0wuVyobS0FF6vFy6XC16vF1arVcT3ZjIZ6PV6zM/PY3h4WORfjY2NiQr2py253FEYw8bGhvBKyi/ct0WIJKO9vV14QIj8YG5uDktLSyKGV6fTifomTqcT/f396O3thcVieWsoxhOJBF6+fIny8nK0tLSgubkZfX19GBwcFPlgVqtVhO2ehheNEq5ra2thtVqRTqexvr6OSCSC/f19OJ1O+Hw+8ZLu7u4WBA1WqxW5XA6bm5t4+fIlnj59itnZ2TMrkkye3/X1ddTU1AgSmzt37qC+vh4ajQZVVVVwu92wWq2orKxEZWUlampqBDsUeaSePXuGBw8eYGZm5kyNOH9rUkiRAPKttACER0A+nq4jK30q1VE+TElJCUpKSkSoGBWh5komxfrza3NFRg5Xoe84CFHySlCI7M2bN/F3f/d36O/vR3FxcZ6CrlKpBOBqaGiAVqtFR0cHYrGYUHy8Xi+Ki4vR0dGBrq4uDA4O4uXLl1hbWxMKHYXRye2g9su1DrlHXAkMyGPJFVN57LlnQKvVCh2FLPY2m03Ut1RSoumeSkCViBBkZVImZuBt57+5979QjhK/L9UEmp2dRSAQEIXiPR6PmDtey5N+y/cHXtdE5EqnPPacplteO3wcuPenqKgI1dXVuHbtGq5evQq3241AIIAXL17g1atXmJubE3u6DKgL5SzJ4yGvB3ntKO2Rcu4bp+On6yjlINL6J2B45coVkZZA8zE0NCTC5LlHigMoeV9Qei7lsEv+vVarhdlshsViESHh7733Hjo7OxEIBBAIBBAKhZBOp8V19vf3Rd070hHLysoEc/L+/j4WFxcxMzODly9fYmFhQfAH8PbI+x0P4ePH8nzSQiQufP3w8zUaDcxmM6qqqtDf34/a2lpotVosLy9jcXERm5ubiEQiYm8jKZR3KbeZj/1fU4LpjWZdZ7NZMXGjo6NiQ3W73YJphDYrtVqNmpoalJWVwWAwYGNjA+Pj46K2DSlLb1pZo8Udi8UQi8VgMplEnDNNPNUOoqKmy8vLmJ6exqtXr/Dy5UssLy8rFsA7bTk4OMD4+Djm5uag1Wpht9vh8/lQV1eHmpoa+Hw+ARzpYU+lUjAajRgaGsL9+/exs7MjGBnP0nJPYYsLCwtoaGiAx+MRiglRdJ71eANHD7rP5xPFpV0uF6LRKBKJhGCdSaVSgvygubkZpaWlKCoqwsWLF9HV1SWKE25tbZ16iKgs3DP1wQcfoLq6WoTK9fX14cqVK7BYLNjZ2TmV0DJSbCorK/Hee+/B5/MhHo9jampKJMNWVFSgpaUFTU1NuHDhAmpqagRbVzKZRDAYxODgID7//HNRgf6s6Nz39/fh9/sxPj6OkpISFBcXo6urC2q1GqFQCFqtViQSm0wm4ckEjmi2Q6EQFhcX8ezZM9y/fx/Dw8PCw38upyM8p0G2Hhcy+ChZnWWFmq5tt9vR1dWF+vp66HQ6EfrL80dI6VOqHcWVSyWFnjybvAYM9YGUnrq6OvziF78QuQnkraFrUaQG5XaVlJSInEpKvs9kMnA6nWKdl5WVwWg0wu/3i7W8traGZDJ5zEND7aHnlJQsrVZ7LAxKCRDSObJCT+97GWRSm+mdr9VqYTQa86zWsoIn1/3hQqFn8trgeVSF2i9fR0mRl+9LhZ5XV1cxMzODsrIymEwmeL1etLS0YHh4GCsrK8es/zIQBI57y+hzYqeTgZQMarhHhxRpnU6HhoYGvP/++8JwlEgkMDIygvv374ui9TLtPwd9/NkpNO8c+PC2E1jmjMTys8LXDQc6ckhaLpeDXq+Hx+NBX18fbt++jbt376K6ulqsmUgkgtnZWYyPj2NtbU3oUkqgiPeDr3n+OWcqVFojlCJQWloq8rfLysqEF/ng4EBELqhUKpFyQ88TGVdisRjW19exsbEhyhxMTk5id3f3WJii7E2jvsiAhOafvuN95aCGFy3n69BoNKKsrAydnZ3o6+tDSUkJwuEwBgYGMD4+jnA4LJ41rVZ7rLC3vLb5/3zdynle3yVvnMKKFgP9RKNRUYDOaDTmdcJkMonP0uk04vE44vH4MSvcm2wrhf4UFRVhf38f1dXViMVi2NvbEwU+g8GgYNwKhUKYnp5GMBgUxT/PCnQoCVFiEkDc3t7G/Pw8jEYjDAaDSLynh4yYY6LRKMLhsIiFP+s+EUPUw4cP4XA48M4778DpdCKZTGJ5eVkUtTxr4ZsHEX5YrVY0NTXhH/7hH3D79m0Rr6zX64ULnlhz9Ho9otGoqFZO7viz7A+59+fm5uB2u9Ha2or/9b/+lyBcoSKH38VA90OISqWC0+lEU1MTPvzwQzQ2NiKdTiMQCGBrawvJZFJQnpvNZthsNkGkkkqlsLKygr/85S94+PAhBgYGRFLzWUkmk8H4+Dh8Ph/a29thMplgNpvR3d0tworNZrOgz6ZNPZ1Ow+/349mzZ/jmm28wPj6O5eXlY0XPz+XNSyHLMn0nK7mAMt0y9yBxgGSz2dDQ0IDq6mqo1Uc1pxYWFo4VLeUhLEpWea4IKinP/Fj+N4UZUrgpAQ3ZK0YhbBTORvWLqFgx5bBQ+FxPTw+qq6uRSqWwtbWFgYEB/OEPfxBlN5SUSFkZovvRvsvnQj6X56Dwa8hCoVJ6vR7hcBg+ny+P5YuPHd2T007L15ZBBu+LErmAPBfcuyWfz4+T+5pOpzE/P4+BgQH09fUJ71pXVxfGx8exvb0tjJD8PjJw4kCI35fANs9948KLKvM2m0wmlJWV4d1338Xdu3dRW1uL/f19TE9P48GDBxgbG8sL7aM5kcdBBkoyCJSF1gbvGwfiskIthxHKHgy6BhlQu7q68NOf/hSXL18WeVJUl/PZs2f413/9V8zPzyuGtdL1Cz23sgeLwJTs2aH39cjICBoaGlBaWirqjRGQJbIYq9V6DFRQn6im2ejoKF69eoVXr15hbW0NoVBIFO7lbeWARW47B9V0jJI3XD6X6lfxsdJoNHC5XLh8+bKgzs9ms/D7/Xj69Cn8fn9evUiluaVr8bmWgTSJDMJOAlWnygdMgOptFfKkvXz5Etvb23j58qVI4KN8EKpavbGxIcBTIBB4q2mI+Yt0f3//rQkh+2uE6mQ8e/YMbrcb3d3dUKlUmJubw1/+8hcMDAy8FXOQzWYF8Jienhbxy8XFxbBarWhoaMjbhKkOSDabRSKRwOzsLB4/fowvvvgC4+Pjb8Vc5XI5LC8v4+uvv4bH48Hly5dFeJ/RaBT1SiKRyKk836TAuN1ulJeXI5vNoqSkRDyjDodDEKvQy4zCVV+8eIEnT55genoaGxsbZw48Dg4OsLq6ikePHkGtVqOqqkoQ21RUVIhCvGSh29jYQDAYFPvUs2fPMDU1JZKaz+X0hV6w3MrMrfFKISTfpQCS4mCz2VBZWYm2tjZ4vV7s7+9jY2PjmFLGr8/bxL9XyrnJ5V4X41QKe6L2bmxs4NGjR9jZ2YHVasXu7q7Ym6i8gMViEcYA4HUIfDgcxsLCAra3t+FwOOBwOESSO7XLYDCgtLQUDQ0NODg4QCgUEvnRSt4TEjIAkrLJ88K4MsyBCwey1G85NIkIF4hEg5RQyo9WWgNKXkgOrkgRlseePFScFEBWTgtZ0mWAwJXTXO6IVn1hYQHBYBAulwtarVbk0wwODiqWsZD7x6/PATl5pJSO42uH/qYyMpWVlbh165bI+zw8PMTc3By++eYbLCwsiAgHDn4JDMj3lJ8v3g4SyvdSejfJ7IlKOW+yQUTuk81mEwQaly9fFh6pXO4oomZ8fBzPnj3D6OgoIpGI4jMm5+AVGlMS8jCTh0cGNouLi/jss8+QSqVEODyFhXPSMRob0mtpT4nFYtja2sLo6Cj8fr+oq8Y94nQN3lZZ+J7I20chgtz7x+eBzpE9Wi6XSxDatba2QqfTIRQKYXJyElNTU1heXj6Wx8UZTAsRYvB70m8O4OU+KMnZVap8CyWXO6Ku3N3dxfT0tHiA5QeJI2oli9G5vBk5ODjA9PQ03G43rl+/DqvViufPn+NXv/oVpqamztx7BhxtFBsbGxgaGkJFRQUymQza29uFx5U8UuRSTyQS2NvbE3k/jx49wj//8z9jdnYWiUTirVlba2truHfvHmpqalBdXS1qkJHCQQxebxpMZbNZwYgUCoWws7MjnlOHwyHC+TKZjAjp29jYwNzcHL766it88803CIfDgvnqrIVCQIaGhjA5OYni4mKhPHd3d6O7u1vkmKZSKYyNjWF6ehqLi4sYHh6G3+9/a0Jc/1ZFSdHkll4l74iSYkZCL3Iq+n3jxg1UV1fDbDYLptyXL1/mKWbcgi1buDnYU/KI8fA+WZE8PDxEMpkUxgeyZkejUQEEyGtqMpngdDrhdruRy+UQj8cRiUSwvb0tcp052Q5FRrhcLjidTkE+sLe3l+eVon5xa7w8npyEQKaQ5qBWVp5lwEv/p1IpBAIB+P1+4TFOp9OCjl5JISalWFYIyXPH54L+5u2X28F/K1FacyAlK9w0rzs7O5iamsLo6Cg8Ho8AJOSdkL0HMsDnIZEykONjqxQuJQN5YmDr7e3FT37yE7S3t0Oj0WB6ehpff/01Pv30UywtLeXlexIwJi+9DBaVRAa2HEQTuObgu5B3S/aqcDBMotPp4PV6cenSJbz33nuicHUul0MkEsHw8DB+85vf4Pnz51hYWFDMzf2uUEUlIwetQXm9AUd60vLyMjY3N/H8+XPU1tbC4/EIYiOv1wun0wmz2SyAUygUEjVHOYlMNBoVRmqlZ7LQeuXPmzxXfJ+hv+VnptD8UppKS0sLfD4fstkshoaG8OTJE1HMWQamcimCQmBZnlsC8fIaLyTnYEqSXC731nvQ/paF8sD+z//5P9BoNAiFQlhYWHgrgBTJwcEBVlZW8Kc//QkzMzNobW1FTU0NTCaTsMD6fD7Y7XZB+xqJRDA+Po6BgQHhqn4blH0SIkr485//jIODA7z77rtobGzE4eEhRkdHsbi4eGrAb39/H4FAAI8fP0Y4HIbNZkNRURE8Hg9KSkoAAIFAADMzM3j69CnGx8exurqK1dXVUwlF/GuFLNT0E41Gsba2hoGBARQVFeXlhUQiEeEVoBzHcyB1tlIotA4onDMlf6akRJlMJjQ2NqK7uxtFRUUinIVyFrhyo2TRVromBxT8HceVSNkam8sdhQ7RuuMKSi73Osmb6hxtbm6KfA4qiCvnrVA7NBoNgsGgYN9Mp9NCIZJz0LgCpzTmsuIrAyngNdjiIUfc60N/R6NRjIyMQKVSobW1FTabDZOTk6Jv32dOuVAIJN1HzgNRUkYLXU9mNCykHAJHSvfW1hY+//xzpFIpOBwOLC4uYmJi4lh+6/dZkwRu6HslEKV0rk6ng9vtxs2bN3H37l20tLTAZDIhEAhgeHgY8/PzCIfDx/LlZA8jN24rtU8eCz5mnGzh8PDwmM4gA0F5TuSwNDIIuN1ueL1elJSUCJBKhA0vX77ExMSEKCcjh8IVAkTy80geJB5yqAQMuOJPz93s7CxWV1dFaQODwSDeKXQ8MT5nMhnxzNIY8XHgnjt5DpTWKx93iozi4ylLoTVPY+Dz+dDZ2SmKB+/s7GBmZgbLy8uKhG/fB3Cf5IUEXudufRc77jmYOpcfnYRCIVHD7G2UXC6Hvb09TE5OYm1tDdPT0yKGmhJVS0pKYLPZhMU3FothYWEBq6urby2QTyaTGB0dFTkONTU1yGazGBsbw9jY2Km1++DgAMFgEF999RXGxsZgtVrhcDhQVFQEt9sNAAJkDw8PCxbKt3VcudCLjEKjzuXHIXKoDlf6ZeYxJQ+ErACSp4e8Nmq1GltbW/j6668FGxg/jzxZ/P5KNY2A114c2Xos5y/wc7LZbJ4Rgisb9FxRGQ5ZlBQuleo1PTgV9pW9ZrK1ulAOFVf2qA9yQjx9T8qcHIYpH5fJZBAIBDA6OorPP/8cZWVlGBwcFKHBHHRSjhjPh5Ot45lMRhCE8LbzzwopdoXWE/+uEKg6ODjAzs4OHjx4gK2tLdjtdmxtbYk0BTqOzisUckbXl4k65L7ycC0OOhwOB9ra2vDRRx+hv78fJpMJ4XAYIyMjGB8fx+zsrMjF4Qo799rwMZfDtfj3Sl4cWk98fOR8KQ5YZK8cP58/c2azGW63Gy6XC2q1WtR0Wl1dxYMHD/D06VOsrKwII4S8xgsZW2TAJQM7OWJK9ixyI8j+/r4ojaMENPkYFjKqyPsVP0fpePqOexR53+TryeGAfE3RHOj1erjdblRWVkKlUol0ipWVFWxsbOQ9f/z6fF0o9Yl7WvmcEGDla/IkOQdT53Iub0gozysWi2FpaUk81FqtVlg7iFiFrDZvs8JPeV0TExNYXFwU8drcknUakssd5QK8ePFCWNjIyk2KIRGnpNPptyak71z+Y4qsoKhUqmO5DLJXRcmyLr/kCRzFYjFEo1FMTEzg0aNHCAQCeWFhpAjq9XpBuUzXoWeD7zHyfSmnQFaM5GdGto7ze3AFjrPkyfuZktIle5NoLGSFi3tE6Du5thSBDdl7xL1CwOs6YFz5l8MHqXbbH/7wBxgMBsHwy/tB+zmFIMpAg/rEk+k5ICBGWk7mobQWOHiQgQq1n7eJK9akTM/NzYmagLQe5GNJqG3UbqX9Uy7kS2tNDknU6/WorKzElStX0NraCovFgr29PczMzODevXsCpNI+zUGkDKT42NB+LwNtJU+N7IXlBgclwMr7xJnzlJ5bh8MBlUolcoanpqbw7NkzzMzMYHh4WBB58bbLYywDPe4BlMPPgPwSCyRKgEfJUMDD12SPMZ9T2dggAxz5maLv+LpX2uv4OuGkMXyvlNtMY0LtDoVCWF9fx4MHDzAwMICNjY0T6fnlNU76FwdQSmuMA/FzMHUu53KGQi/lty207N8qFD5w1gVhKZfjXM7lrIUUeP7ClhVKGeAoAQP5/L29PQwMDODg4ABVVVWYmJjA7OwsotHoMSsyASUSWQGVrdZcTlJoZIClpIQqXZNEVlD58RwocLCg5DE6SZGRrfQnhUxxC7hSW/g5h4eHSCQSYp8pBAS5144DJT5msrLMryOvHdm6z8+TPTVyyJ18Tw4eg8GgGB9+P3nseV9PGn9Z0VbKPaL7bW9vY3p6GsPDw5idncX8/DxGR0cxODiIlZUVxcgB2Rght1ten0qhpIXGUsmjQgBbDi0lhmMiAeLzF4vFMDo6CoPBgCdPniAej2N9fR1+vx9bW1uKBYfldnBQT3Ms1y2ThXsoZfpueZ2dlPfD20HrkNeAUgJM8rqV54fnZirtc3I7OaiThc/hwcEBhoaGBENyOBzG6OgolpaWFFkp6Tftb/KaVLqHPE/fBaDy2nrSwSqV6jwY/1zO5VzO5W9Ecrlc4TiQczkmKpUqxxVneknLdN0yOFBSapSsqkRrTIUzSbGXQ44ACPIVuZaUbIGV8414UVI5NJALeYH59ZUUQw4o+Odc2aH2073J6syVolwul2eh56xcdD3edp5fwxU+agsP1+IAgmjc5TDHk+aJK8AcpFFIED+elHTeb3nsgdfeMnncaZw4YCCRmQt5n8jyryQyQJMBLxFGkLdIDlGksZdBnRJApXWs1+uPEZiQN4GHZ9L4yf0t5IWjMZC9mnQ+nxu+NmlMCUjJoWZ0DYPBAI1GI3L5lELQeCFn3h/edqV1pNPpBCkI3ZvGloetycYLuh/VN5P3Hz63nH5fCcjwOeUAie6l1Bfeb77O+DVo3SjNDb9PITAoA2L6nEeg8OeWH8vXn7xu6Fo8vFlpf+JrkI9XoXfkuWfqXM7lXM7lXM7l3yFcSeDKhWzdl5VMJUs6/5zC9sgTLCt6XFGQlUWuZHPFU/aiyEqSrMzL16JzSeGTFVdSDmWFRlZESRkjenO5LXL4E4U2cSBAwi3wdD6QD8Bk4Cf/yAqpPHf8Nx8/+XiuYJKVn5Ramk9+30Jjz+/NQS4Po+LtlsMt5Wvw6/LPOeCQr03XJ6WYj4PSWMjrkz7jpA/0v9I1Co2xrAzLXhkSpdA9HtKmdD0ZOHNwTF5H7hEkob/p2StETELH8GcQQN66B16vVbkUgJxzxK/Hv/+uceP7k7wP8aLTfB+S51ppz+FrkJ41AqocyMlgRxZ5buX9kvfnu9Ih+PqT+0/jx9eDHJYst+X7yDmYOpdzOZdzOZdz+QFFVvplxZE+44oCV+xk5ZQfK1+DjinkhZBDdJSULxIlMEWfyx4tfs1C1ymk2NP1ONCRlTx+bVLUlBLv+W9Z+ZHBqVJblPIm5D4pWcj5MVwxkymk+bHc08bbx69baByV7sWV1EJt+D594vk5HKjwc2XvFL+OPB6ykPLKvW/yXMvXUXqGlP7mY1AI7HBPiNI1uAdCaSzl+ZfvIxsD6DilY5XmidrJ/+ZrnXuXla5F/xdqG7WHezj5GCg9J7IHV96blAwsdC364TT0SlJojArNE/9M6b4nrUV5j1Ba60rXVgKzin056QFQnYf5ncu5nMu5/M1I7jzM76+S83fkuZzLuZzL344Uekeee6bO5VzO5VzO5Vz+jcI9SoBySBW3ahcKVePnci8J/X9S6As/hieAK1l55c+JNZBEicChkCcIyM+hIA+SnEPB81b42Ci1kY8ZDysiazI/Xs7xkBPL+WdK3oVCVm9ukZcL/fL2FWo/XUfJGyKPvRySxPtGY6bkzSlknedehkJeKdljJlvila4pt4/neymNfaF1Ix+rdLx8Hj+Oh0Ry7wJnm1TyosrPpewxUfJUyXN40nMl90X2kiqtGTnvrtDzLd/vpLHn5/Ex5LlMSutGzueTRWlO+Trj46C0b9H38n7JP5f7I69L3k657XJbT3rGedv4mi8UUXCS04nkHEydy7mcy7mcy7n8G4W/jAuF+tCLm/Jp6NhCyjAlWRuNRgBHCnU6nRaJ8koKDNGxF/qc6LBlhi0CU4XCdpRABO+fRqMRSfrUTp58Tkqu0rjJeVSFrk3U1Lw9NE5caeX954Aom82KnCsZUPGx4soVXYcTLfBrU9tJoVdS6ORcKKWx52Ql/Dyep8SVPX6+kmJKY87D9Pg5SgqiEmhRAif8WpzAQR57+T7UF56bJYO9kwAUjQGNGQcFNK+cbIS3R6nt1D8ePie3ifrOmfXoOjL4KdR+Pv78eeKgSQavQD7Zi0zQQG2VwVchICT3i3IVedson03ex2SwJAM3ed3zZ4ZLIQIKDljl54v2S/k5pjmne3PKenkN8GuR8GNlKfTMFgpx5nIOps7lXM7lXM7lXP6NwpVKJQWVK2b0spatwPwFTgDCbDbD6XQilztK2A+FQqL4p6xE8fsB+YqkkqWcn2M0GkVdNn6OkiLFa1LJSiNXTLkyTj+yUkv95v2QSQ74by7UNmIwVGIMKyRK4yZ7Ifg80XccDCgRH3CruVL7ZeCjVh/VMAJwrIYRV3zpR55f2ULP78sBh5yndVJf6Xy+XuTxp7E3GAx5gJLO5eCAr3u+BgoBD37eSYorBzOFxl5ecxwccGIKpXHnY0L9kdkMlYgS5M85OKGxL9RXeU7kdvB7GwwG0WeZvVNprGShdcHHkAMJuS6WvK/Jz5B8v0KfA/n1ouTx43MHQLAd0mfk9ebzpzT/SmQscju4J50+lw0i8vydg6lzOZdzOZdzOZc3ILLnhisSchI3L5orW6QPDw+h0+ng8/nQ1dWFS5cuwePxIJvNYmRkBIODgxgaGhJ0wPx+uVzumFJFykYmkznmpSAhoKYU4qME/rj3iit+yWQSKtVRsWL6jBMOkHLPr00KEbWDfvNrZ7NZpFIpATJlpYbaIiuc3GNEChVnKyNFku4lMxwCrwGObK3n48L7Q/eXqag5AYKsQPIiy/zzdDp9zGtF7dLr9VCpVIJOm9qj0+mEYsg9GrydJ4EjWaElTyZfS3zsaXxkQET35Z5SDri4cszHRwnQ8bmlYwks8nGnNskeFz4fMiCRgRb9yN5U2fMhG0NkEKTUdj5uHGTyPnGAyMdLr9cjlUrlzZlSCCLvo7xm+brnHmrZG1XIa05eMvqfj6EMjPl65aCJt1HeK/la4CCPr0Gj0SgMAwcHB8hkMuIefL9Q2l+5h1c+ln8ur0m5iPFJcg6mzuVczuVczuVc/o3CX7JKyir3bnDrLHlyuBKj1WpRVlaGGzdu4D/9p/8Ep9OJdDqNkpISrK2tiZpI3Lqq5FEgRQmAolItK82kQMj0zryN/J78ulwpJUWH34eDPdnyK48LD2eTw4eUPB5cWVS6Pu8Pvw/1VQZFXMHiCr+s4NL15DZyZUxeC1zJo+85U5zs7To4ODgGkAlgUcgnvzbRj8vjelIOkQwquOLJ+yX3Uaab54CDzuX059zLouQxkEEFP0dek4XmWq1W5xkr5PbL4VsckMt9lo/hyj+/LxkP6N50Pb4OlKjIZQ8s7yv/TKvVQqfTidIISvMmPwd8/vm1ZOp1PjbyelUC+LydcrgpX9Py2MvzxM9T8kRzLzONI5UV4IXJ+bVkUKfUft5X/h19Lpdz0Gg00Gq1xwweheQcTJ3LuZzLuZzLufwbpZDyRt/JCgoJt9DTC95sNqO5uRktLS2wWq3QarUIh8MYHx9HMBg8Boi4gkaipNTI1nKyNHMrcqF28vsVAghK48A/Vwrx48oZD0VTug63FnNRUsa+qz30nZJCJ/eX/600LkqevkJggQMKrrzKgExuH5AftiQr50rX4WOiRKn9XX1VOk6m1T6prpISgFUCmXIfThIOhPj/MkCSARkp40rXL+RZUQKT/LdOp4PVaoXBYBAgKB6PI5vNCk+w0rNF91S6rhLQJUAtGzBOKsjMryV7iwodW2i/oL+VQGghECUfq/TsUN++7zqQAZnSviPvLfLfSnOqdA1qM+WR0dr5rtpW52DqXM7lXM7lXM7l3yjcI0EvZTm0iCsVpITwczQaDfR6PaqqqnD16lXU19fDYDAgHo9jYGAAL168wOLioghHI1FSaJWUTiWrLP9eDo/h4S081IuH+xQKKZJDd0iJlnNa+G+u3PAwKiUljh/Prdm8/YXGvtB8ycraSe2UAUIhj4fcfrqH7AmSz5HBFv8cwDGvkNwvAj08DI+PvaxIFgLPPMdO6fyTlGtZwabngdoiz688/3wt8R+l9irlAvK/5XAufp6SR6NQWB59plarYbPZ0NHRgdraWpSWliKRSCAUCiGbzWJnZwe7u7tIpVKIRqPY2NhAKpVSBO087IyDTd5OeublcaLP6HOldcOJKuScRpmYAYDwhNH9U6lU3h4jj68cSsfHSwaThdrOryV75uS5l+dOaSypr0rPIX8e5T2YA2+lsf8uOVMwpVarYbfb4XQ6YbFYEIlEsLOzIxbeufxti1arhdFohMPhyLMC8YV9eHiIaDSaF5uv1+thMBiQSqWwu7uLnZ2d77QqnMt/bFGpVGKv0ev1iMfj2N7eFvki53Iu/16RFW7+N1cESWHhlmKNRgOn04mmpiZ4vV5YLBYkk0kMDg7id7/7HQYHBxEOh8XLnisvXJTuzRU2OexNbj9XXMhbxBUjpb7Sb57vQZ/J7ZE/58q6EsMXcBwcycqt0j3oPBmA8RwO+V2iNJdK46QE5OS2y8crtY9/J499obAp2TMnK6kEyvV6vQBd5MXg40+AppA3kIN+eU6VxqrQXBfyTMjXlxVfpbmRjQInGQTk9tE6LnQMF77e5LVGz5PD4UBbWxt+9rOfoaqqSniQiM0ykUhga2sLIyMj+OyzzzA/P49kMpkXcimPn5w7xedJzq/kfeafy4BQvg8dI4M4ejYsFgt8Ph/UajV2d3cRCoWOATAytsieUCVDykkhfrwf9LkSwOOAkAs3+PDrynsLB32FQvz4OMpj/33lTMGU1WpFf38/7t69i5aWFjx48ABfffUV5ubmEI/Hz7Jp53LGolKpUFxcjIaGBly9ehUdHR3w+Xx5Fi6VSoW9vT0MDQ0hHA5Do9HA5XLB5/PB5/PB7/fj0aNH+PLLL7Gzs3NqbSe2GuB1/O/3sWycy5sTvV6Pvr4+3LlzB+Xl5RgeHsYf/vAHbG5uIplMnnXzThQlZYS/LM7lbEUGLyfNCykYlN9C1MtarRY1NTVobW1FWVkZLBYLdnZ2MDQ0hFevXiGRSIjYfX4fJeWVRFZKlazDwPGwI64EfR/vjXwOv24hMCWPifzD7yEzjvFjuAKl5PniQkoZkJ/Pw8dKvudJQIK3gd9bSZSUPK6cnwTe5M/4OHGwbrFYUF5eju7ubhQVFYlzMpkM9vb2BAPc4eEhYrEYIpEIFhcXhcIs56kojWWhsZfXjDzXND4cFHAvFj+vENjggJC3qZCCLJ9fqB98jpT+l40P2WwWW1tbeP78Oex2O27evImWlhbYbDZBgLG/v49IJILi4mLYbDYMDQ1hamoKq6uriMVieXmKfMzl+eZASqnPfOxlQEXnFAr/5Gtfr9ejtLQUra2t6O/vh1qtxtTUFO7du4dQKHQsD5LOl8kZCj3j8nzwOSm0P8l94PcoFMos7wEyWJfvofSs8X1LBmEnyZmBKaPRCJ/Ph97eXty+fRuNjY04PDzE9vY2AoHAOZj6GxatVguHw4Hu7m5cu3YNnZ2dKC4uFgqIVquFxWKBw+FAeXm5sKJoNBrYbDbY7XYYDAYsLi4qJqS+SdHpdGhqakJlZSUsFguWlpYwMzODeDx+YozzubxZ0Wg0aGhowN27d1FWVgaVSoWBgQHs7u6+1WBKo9HAbrfDZDLl0damUikkEom8xORzOVuRPQuyN0BmkqJjtFotnE4nGhsb0d/fD5/Ph1QqhbGxMQwNDWFzc1O8D7nyyi2oShZW2ZKvUqkEgYWSgiUrY7JyzRmvqLYP9ZGur9PpoNFooNPpoNfrhaeELN9kZCIChVgshlQqlccUpgSO1Go1DAYDDg4OkE6nFYEMtU+n0wmSDq64kWJENWqIUVCtVkOn08FisYjoB4PBgEQigVQqJZ4xunYmk8nzQigp5UpWdEqiV1LkSenjNb/4tXiNLA5E1OqjmkE2mw0tLS24ffs2bt++jaqqKuHBpPE1GAzY399HKpVCPB7H3NwcfvOb32BgYADRaPQYIQe/B42XklLJc/80Go0Yez53fD45vT6fH3nNcik0XjS2JwG+Qmx+PPdMBoAc6MkkL4eHh8KIu7GxgaWlJfzyl79Eb28v3G43jEYjTCYTrFYr3G43qqur0dLSgj/96U9Ip9M4PDxEMpnMIx+hcVFaSzRvSmCLt43GVWls+DzSs0b/O51OlJaWor+/H5988gmamppwcHCAiooKTE5OYnt7O28t0BxzryUfb6VwOyXgI+9lSqGf8vgoEY/I+xbdX96/lNYq2gFWyQABAABJREFUHzv5mVOr1SJC4K0GU06nE+3t7ejt7UV9fT1MJhNaW1uxubmJp0+fYnNz86yadi5nKGRha2trw3vvvYcbN24gkUhgZGQET58+RTKZhNFoRElJCerq6lBRUYFsNguDwQAASCaTCAaDWF5exvPnzzE2NnZqwFylUsFqteL999/H+++/D6/Xi6+//hq/+tWvMDMzg2g0+h8qpIy/LHU6HQwGA3Q6HXQ6HbRaLXK5nIgbJyXitIXaVlxcjLq6OrS1tcFkMsHr9cLr9WJpaQnhcPjU26UktMHr9XpBA2symVBXVwePxyNq6iSTSYRCIayvryMYDJ4D9bdACilzhSyb3GLsdDpRVlaGqqoq8S6cn5/H2NgYAoGAULxky6ucWwIoe1hISZbrPskWd24NL6TQcmttIc+NyWRCRUUFqqqqUFZWhrq6OlgsFphMJphMJuzv7yMQCCAQCODx48eYnZ3F7u7usbAarlCR946DJKXxz+VyeXll/HNZydfpdDCbzTAYDKipqUF/fz+qqqqEIub3+xEOh5HJZJBKpWAwGKBWqxEIBLC9vS3CoDiLntx2Dn64V0bJQ0Bt53NFopRzRsfZ7Xb09PTg5z//OS5duoTS0lJYrVZxLAeNVE9sf38fWq0WDQ0NmJycRCwWE/eR14GshJPIwIfPn6xo87WTy+Xy2P9k4Uoxv24hTxIpz3KoJY0RgRECxdybwq9L4EYGe7xP9CzRegoEAnj48CGMRiNyuRwuX74Mj8cjPFQ6nQ4ejwcNDQ24cOECVlZWkEwmRVtkD5G8T5Bhgt+XzqOx4deRvS5yDpLsBTSbzWhqasIHH3yAmzdvorGxEWq1GsFgEFtbW8dAr0yowQG+0n4g71GyJ4t+8z2K3082msjtl/vL73VSyK0MsPjnfL5pHpVytGQ5MzDl8XjQ19eHmpoamM1mEYfqdrtFIbtz+dsTg8GA8vJy/OQnP0FjYyPm5ubw+PFjYaE9ODgQSqbVaoXVaj12jUwmg1gshlAohEgkcmqKpl6vR1FREdrb29HV1QWLxSIe0l//+teYmpoSRTf/IwgBJ6PRiPLycjQ3N6OmpgYVFRUoKSlBOp3GyMgIPv30U6ysrCCRSJxq++hlUVFRgVu3bqG7uxtGozEv7vttEbX6qICny+VCTU0Nent74XK58sAU7YupVAqBQADT09MYGhrCixcvsL29fQ6ozkhkCyu3assWbe7VIdFoNCgpKYHP54PdbgcALC4uYnZ2FoFAIM+TxBVIui5wPFSPhLwJpMjICo5SX7goeQS4YkF91+v1MJvN8Hq9qKurQ1VVFdra2tDc3IyqqiqYzeY8Aovd3V2sr6/D5XLh3r17mJiYQDKZRCwWU8yNymazIudECazy9smKFwntVWVlZXA6nXC73XC73bBYLKiqqkJzczNcLhfC4TA2NzfhdDrh9Xpht9vhcrmg1+uRy+Wwvb2NjY0NbG1tYXh4GH6/X7xnuGLMf5NFXQkI0hqQ85NkhZ4rrLR/kUfq2rVraG5uRklJCcxms6BQ5+MjAxy9Xi8MkslkEslkUhGcE/gptFZICaZ+8j7Q3NFxBBa5MkxeweLiYpjNZlitVgG+Dw4OkEgk8kAthcYaDAYYDAYUFRWJ9UVASKfTYWdnR0Q5kXfupFBVElmR5+uMzwFwFC66ubmJr776CuFwGNlsFhcuXIDT6RTRBCaTCT6fD52dnZiZmcHu7i4ikYig+ebPpFIbqIYbXzuyN417XAp5ivjn5OktKytDS0sLPvjgA9TW1sJisYjxo+eNrkO/T8o7U/qce534Z0qASAbfdCw3CPA9h6jp9/f382rFKQF/GdDz8VMyIB0eHiKRSBTcK2U5MzBltVpRXl4Ol8slBkSv18NkMr1VSg4JPbylpaVwOp3C4m21WuF0OhEMBjE9PY2dnZ3/kKE3tHiNRiOMRqNIsvwhw+g0Gg08Hg86OzvR29uLRCKBJ0+e4P79+/D7/ceURaWNkES2fJyG6HQ62O12eL1euN1uEfIXj8fx8uVLrK2tIZFI/CiVXrLSGAwGERbg9XrhcDhEuGVjYyMqKytRXl6O4uJipNNp2O12jIyMIBQKiZf1aYlKpYLJZEJVVRXu3r2L1tZW4TE7SyGF2mg0wmw2o6ioCE6nEzabDTU1NWhpaUF3d7cgy/B4PLDZbMJavb+/j93dXRFOarVaRTz+3t6eYi2Oc3lzwpU4bnUn4d4g2UOhVqvh9XrR1dWFhoYG6PV67O7uYmFhASsrK9jd3c1ToOSQJa6UcJG9IiRKioFWq1WkcJaFKzNcqVKr1TCbzejo6MDNmzdx8+ZNFBcXo7i4GHa7XbCDcUXGbDajuLgYlZWVYu1PT09jYWEhb/3ycByZGSyXy4mQPVKUZQs+b7vL5UJjYyNu3bqFa9euCaWdvOZarRYjIyOYnZ1FMBjE7u4uqqqqYLFY4HQ6xbOm1WpFBERDQwP+8Ic/YHR0NO99KIMpeey5QkhzqbRu+Drh11GpVDAajaioqMDFixfR0dGBoqKivKLJPKyU35fPQWNjI65cuYK9vT2Ew+G89nNFV7b607oh4QyDspLMCQR46Cu11eFwoLa2Frdu3UJVVRXKy8tht9tFWFckEsHq6iqWlpaEgdRsNqOkpASVlZWoqakRJGY8/HRxcRFffvklHj16hEQiccxjQsfKwFvOP6JjldYW9Wl9fR3JZBI6nQ67u7vo6upCfX09bDYb1Go1rFYrampq0NjYiI2NDczMzAB4TTdP+wf39PDry+3S6XSinzK4kUELP5avn6KiIty4cQPvv/8+qqurYTab8/pnsVhEiK48HjyXqxBAlT1LfD3Qe5h7XGXwxNcOvwYZRex2u/AIJpNJEYFE4bcy8UohcMzXgbyXKgHwQnKmBBRKyvBJCvJZCA02KcmXLl1CQ0ODsAr5fD7U1dVhaGgI//RP/4SxsTEEg8EzV9j+rUIPNv3W6/WCfcput6OoqAhFRUUIhUJYW1vD1tbWD6K80cuhuroa3d3dAIDp6Wm8ePECGxsbipaxt2mM6cVos9mg1+vFi8xgMMBiscBms4l4/JOuQb+/rzXkNESj0cBsNsPlcqGoqAhNTU24ePEimpqaUFpaiqKiIhGWBhyF+ZACRTls9OI8bdHpdHC5XKKtfKM+K6Fx8Xq9KCsrQ1tbG6qrq2Gz2VBXV4fy8vK8506n0wmLLL3MSBGiOXn8+DG++eYb+P1+7O7unmn//taEKweFchtI5Je2SqUSIJpy+VZXV7G6uoq1tTVEIpFjoS2yYg0cT0SXPST83rKiz/MqThKu/HDFx2azobm5Ge+++y4uXryI7u5uATrIIk6hilqtVjCukjHh+vXrokbP6uqq8MRxJUjOF6LrklGCzldSdumc0tJSXL9+HZcvX0Z3d7fwNIVCISwuLmJxcVF40ckgurGxgcnJSbS0tODSpUtob2+Hz+eDw+FAUVGR8CTqdDqMjo4K5lhZKZNBBo01KelyHhifU9mrQOe53W5cunQJfX19qK6uhsPhOOaNojVJ73PeBq1Wi8bGRkSjUUxMTGB6evrYuuKeMWoTtYuMCOl0Og9Iyf0FcEzpp1wdr9eLvr4+9Pb2oqOjAw6HAyaTKc+jWlJSgsbGRuzs7CCRSCAWi8FgMMDlcokfk8kElUqFdDotdJOFhQUsLCwgGAyKvBeuROv1+mMhmvK8KXmN6HPez/39fezs7ODly5ciJ4oAHnkR9Xo9SkpKUFxcnOcwyGazx8I6+bXl3EvgNQgrVEOMt1f2VFHfXS4Xenp6cOHCBdhsNjFPqVQKOzs7iEajYl3KRgwOqgoZc076jufgKbVV9oTRuiaSlZaWFjGWsVgMu7u7WFxcRDgcRjQaRTweF3NL9yejiWz0Iv1Vaa+m82UPmpKcOZjiD+fbBqSIKrK8vBx9fX24evUqWltb4Xa7ARx50sgzpVKpsL6+LiZWKTH1bRYae6PRCLfbDavViqKiIrS2tqK6ulpsDBaLBUajEaurq/j222/x2WefIRQK/bvvbzQaUVlZic7OTlRUVODTTz/Fs2fP4Pf7fxRkJBqNBmVlZbh69Sq8Xq9Y0+l0Gnt7e9jZ2TnRM0PAiwBXOp0WL6mzFJ1Oh6KiIrS0tODKlSuoq6tDbW0tysrK4HK5hPUqFothdXUVY2Nj8Pl8ghGI3O9n1Q9SGgjgvQ1iMBjQ2NiIrq4uNDY2CiVibW0NoVAICwsLmJycBABUVFSgoqIC8Xgca2trMBqNKC4uFrkoRUVF6OvrQywWw/z8PNbW1s7B1CkLvXR5CBw3iHAlhLwzwGuFzmQyoaioCFarFZlMBgsLC1haWsLW1pYIM6HjeZI2z2mRX/Zy2IqSksvbpwSoCnlJ+Gdmsxn19fXo7+/HRx99JKzxpKRQyPXm5iai0SjMZrMwyjmdThgMBvh8PrS1tWFhYQF6vT5v3+M6At2bK2CkuHLhBhM6j/Kiuru70dTUhOLiYqjVaqTTaWi1WiwuLuL/+//+P0xMTGBvb09cZ2NjA7lcTgDcRCKBvr4+1NXVCePHhx9+CL1ej0wmg6mpKWxvb+fpNLKngc8BJfIXAsDyPJCYTCbU1NQIr5Tb7YbBYBCehFwuJ8gzdnd3RfFRTuBEP42Njejr68OLFy8QDocVDZc8NIqPPVdWAQijghJ4pGvQmq+trcWFCxfw4YcfihDsSCSCra0tkZOm0Wjg8/lQWlqKiooKwUhIAIXAfTweRzwex+7uLqanp/H06VM8e/YMS0tLx4AqB4gEZpUMbPLzJANEOoaufXBwgI2NDVgsFng8Hly6dAmVlZViDKjfJpNJePVkpt/vcjDIYIlAGT3DsuGFP9P8mbLZbCLktbi4WADLTCaDcDiM2dlZTExMiNBFPg50Db4XKRkPCrWZAA3vHx9H/hxoNBpB6GG321FXVycAYE1NDex2O/b39xGNRjEwMIC5uTlEIhHEYjGk02nx7CeTSezt7QniJirD9F2EFrwP36XPvzVFe98mIEXxpEVFRWIDvn37Nq5fvw6bzSYsSfSztrYGnU6Hy5cvY35+Htvb24hEIgL9xmKxM7eGA0cbsMPhENa0VColcjUoEZdCtpxOJ0pKStDX14empiYUFRUJdiZ6uezv7+Pbb7/9QcCUzWZDW1sbamtrkU6n8fDhQwwPD/8oACltlFVVVbh+/XqeB2R7exuLi4tYX18vSECh0+ngdDpFiINGo8H29jY2Nzexvr6OeDx+6uNAVqDS0lK0tbXhypUruH37Nurq6uBwOJBOpxEOh7G1tYVkMpmXw3Pp0iV0d3dDrT5iWQyHw0ilUqfa/rdZaL0YjUZotVpkMhlsbW1hcnISGxsbWFxcxMuXLwEAtbW1qK+vRzQaxcLCAkwmE0pLS1FfX4/3338fly5dQklJCUpLS+FwOPJCb87ldETJeiznFQHHawdpNBpYrVYUFxfD5/PBYDAgGo1iZmYGW1tbeVZhDia41Vf+XlZivktRo+P49zIQKHQ8KWPvv/8+3n33XVRWVsJsNiObzSIWi2Frawt+vx9+vx+vXr3Czs6OCOO+cOECOjs7YTAYhLGD55Bw8EHvTq1Wm8eABhwpsDycTTbQ0mcajUa829xudx5BzubmJubm5rC4uCg8wLR/0xiurKyIvKhYLAa73Y6ysjIYDAZUVVXh2rVrWF9fF/kwND88HJOPHf1N/ZEVOHnceV9UKhVcLheam5vR3NwMr9cr9hLgKH8kFoshEAggFApheHgY6+vrIhzS4XCgsbERvb29AoDZ7XZ4PB5BBMKVYwIupHhyMMXXi7zGlNYk5cZTns7Vq1dRUVGBXC6HsbExfPnll5iensbW1hZSqRT0er3I6yGvIHlk6N1Cnqj19XUREri7u4tEIiGANjcekHAwID8bciiuknFCiWSDcrwor4uvzf39fQSDQWxubuZ5sPk4K+XWKQEN7iHmniK+txTyCqlURzUXq6qq4HQ687zq8Xgci4uLePHiBUZGRvI84/LYKIUY8/Urr2Xe9u8jFB7Z29uLnp4etLa2oqqqCi6XC8XFxbBarcJDl8lk4PF4EIvFxPjTnkzsngSo1tfX8ejRIwwODop9tpAR6fsQT5Cc6ZtXCbG+DaLT6VBRUYGenh68++676OnpQW1tLcxmM0KhEDY3NxEMBhEKhbCzs4N4PC5IB65evYpsNovNzU2hTI6OjiIajZ5p/8jCc/HiRXR1dQE4CmHQ6/Vwu92orKxEaWmpWKQUZmQ0GgWIosJ02ewRex7lcfx7Ra1Wo6SkBP39/TCbzRgYGBBg9McgBDwqKirQ2dmJoqIi8YD7/X4MDAxgbW2toGfK6XSit7cXf/d3f4dbt27BYDBgdnYWT58+xW9/+1v4/f5Ty4MhT47ZbEZzczOuXbuG999/Hy0tLSImf3d3FysrK3j69CnGxsawubmJQCCA3d1dkTtxeHiIeDyOQCCAtbW1MwGEb6skk0mMjIxgcXFReLYBCArsdDotvLETExMiX5CSt/1+P6ampkSIoNFoPMPenAuAY9ZfTrsMHPdIkBeLF61Xq9UCNK+srCiG8GSz2WM1kug7JTDFhStctJ/LyqDSefwzroDa7XaUl5ejqakJLS0tcDgcAIC9vT2MjIxgenoaw8PDCAaDwuPjcDiwuroKlUqF+vp6QZRgNpsFcFJKcKf/eYgfHcdzQQr1QaVSCeBFIWGHh4fY2NjA6OgoFhYW8oq/8/tSInomk8HQ0BByuRxqa2thMplQUlICvV4Ph8OB0tJS4ZXjHgL6TaCEPFLcQ6LUZiVrOI19cXExOjs7UVJSIoDUwcEBAoEAVlZWMDs7i6mpKezv72NmZkYQ1BweHsJgMKC6uhrz8/PweDzweDyCJZfTt1O7CBTwYtE8j4e3nYNDPo+09mw2G2pra3H37l3cvn0bFRUVCAaDWFpawqNHj/Do0SOsrq4KJV6tVmN6ehqTk5Pw+/1wuVzCmxSJRJBIJBAKhUS+aCqVQiqVyht3mcWSt1kGJdRWJSMGfadkyODzZDAYUFJSIp5pWmvxeBzLy8tYXV0VhpJC3g9+PQLJdB2e58WfS9IRuNFG9nry/YeePe7B2t3dxf379/HZZ59hfX29YIQVXx98zGRRAlpyX3l/ZSBDkRhNTU3CqE+swbQmKYTSZrPllU7IZDJ5IdgUKbO9vS0cIS9fvswzWsntpP/l9awkZ27GfBsVLJvNhmvXruGdd94RdT+0Wi22trbw7NkzDA4OivjMZDIpXPXXr19Hf38/ysrKEI1GodFosLCwgEQigbm5uTNjclOpjvKRWlpa8LOf/QxNTU3QarXY29sTLn+XywWbzSZYcQ4PDxGNRrG5uYmtrS2Ew2ERe0wP6NTUFKLR6L+rbWq1GtXV1ejt7RWhHkNDQ4hEIj9M509B6AVtt9tF4T5KnJ2amsLg4KCw+ClJeXk53nvvPVy7dg0tLS3QarVwuVwAgAcPHoh6WW9aSEEqKytDd3e3iMcnlqtsNouVlRUMDAzg6dOnGB0dxdraGvb29hCPx2G32/HOO++gt7dXhP5xBqZzOZJsNivGjKhvAYhx4nvEwcHBsTBXstj+2EKJ/6MKV4rksBc594eOIUWRIgBMJhNyuRxisZjIDSGRGevoMzlURulvruwVUty5YkjXVlIuuJVdrVajuLgY7e3tqK6uhsViAQBEo1FMTk7iyy+/xJMnT7C8vCxC/dLpNFKpFGKxGHw+H27evAmz2QwAQjmSx4kTbhAYkpVO3hclpZmPGRkG6ZobGxt49uwZJicnjz1PXPE8PDzEwcEBgsEgRkdH8ac//UmQxxAxj8FggMlkEu1Q8gwUAon8XqQ0c+HjbjKZUF1djaamJpHrkkwmsbGxgVevXuGbb77B4OAgNjY2ABzV9KJQa2rXwsICBgYGRK1Pq9UKr9cr5ovX/SKPi9wXpfAoMhLI7ad9rrq6Gu+88w7effddlJWVIRKJ4MWLF3j27BlevnyJlZUV7O/v53loYrEY/H4/lpeX89bgwcEBdDodcrmcqFumRCRQSGlXAlGyd5afx88BkOeN5mPg8/lQUVEBo9Eovqf5WVlZweLioghB496kQmBEDhvm7ZGNDPSdvP7kc+l5IsY+lUolvLQvXrzAyspKXjkTPi6yR0zeY5RE6RnlbVU6jsaYvEmUckBEGVqtNs/wTyGgBKIoEojy1Z1Op6jZePnyZWQyGczOzmJ7e1uxzUA+0c93yZmAKWIYIRf/2yQGgwFerxdXr17F1atX4fP5kMlksLi4KCpCP3/+XNQL0Gq1KCoqQjKZhMFgQH19PaqqqoRFZHR0FM+fP8fW1taZWOcplKS2thYXL17EtWvXRJgDKbq0EVHNjEwmg3g8LvI3VlZWEAgEsLy8jGg0KhZuNBr9d4MpKqba09MDj8eDmZkZhMNhwcyTyWQEYH1bRafTwev1wuPxCEsP0f8uLS1hfn7+xMKwxcXF6OrqQmVlpVCsi4uLUV5eLujV37QQ3XxzczMuXLiAW7duoaurCxUVFVCpVIhEItjY2MDg4CDu3buHb7/9FsFgMI8Jy+1248aNG+ju7oZWq80LeTmXfCEF5eDgQFC6ft+9gSyflHNyGuvjXApLIeWgkJJPL2aKgCgvL4fBYBDhcZlM5ljIm5JiV0hk664MQID8vCM5pIhChGSwRtciT0VDQwMuX76MqqoqkXPh9/vx//7f/8PTp0/zvD0ERkhJ39rawubmJsrKykSuB1mROXDg7GwneWroO7K28zpGHKRQrTYixdje3s6joOfKIYEa/nwdHBxgb28Po6Oj6O7uxuXLl8W4EDDkSjIPBZOVfDqOK2xEisGBL7VFo9GI4s63bt1CWVkZ1Go1dnZ2sLCwgJcvX+LRo0dYWFjAxsaGIOXgxXY50KR3v06nw7vvvotLly4hHo9jcHBQGHB4+2WAyteVSqUS+VpKOVdEGHHz5k28++67KC0txfb2Nj7//HM8fPgQ8/PzCIfDx6ios9ms2B8JgMieIyWwpLQ2lICsvEaUjAhKXgn5uaRr6HQ6lJSUoKKiAg6HQ8zv+vo6JiYmBKkMncfLJCg9g4UMILwPSmBE3m/kc6PRqAiHI6C9u7uL1dVVBAKBE5kl+fMhgyn+7AJQfG7l/ZKOV+pLMpmE3+9HLBZDcXGx0AsJRFO+KTEWWiwWQR6TyWSQyWQE+/DFixfFfkOOAyWjhRIw/D7G4FNHMvTQ2Ww2FBcXC0sO8Nr6dFbWVmL86erqEkQIqVQKw8PD+Prrr/H48WNRyI8SV4uKinD16lV0dHRAr9eLGGTqK7EA2my2U1d6VCqViE/+yU9+gps3b4qCcqlUCuFwGAsLC1hdXUUwGBTxx4FAALFYDPF4XLjSyarIY9RpQ/73CDEKNTc3iw27pqYGDocDh4eHCIVCIg+Nu7e/y+V6WkIx4JcuXUJPT4+Ye0qM3t3dFV4pJRYuAMJNTRZT2kxJMTiNUgFWqxV1dXX4+c9/jg8//BBlZWWwWq3I5XLY3NzEyMgI7t+/j6GhIczMzOTVyaDChI2NjYLWNplMYnx8HCMjI+f5Ut8hf+06drlc6OvrQ0NDg3hhf5eCfS5vTkhplnOkuDItK31UaqO8vBwejwfZbFawUJGSe5KVlyszRA9OgEVJ0VTy1PDrydelvUrOJaCkfUqmb21tRXFxMVSqI9Kc2dlZ/P73v8fGxkZeH2hMCMSsrq5ieXkZLS0tos4kKTc830i2esveD1mRU7LOkzfHZDLlRV7s7OxgY2NDEEfJ4V/8fc3HhHJEA4EAEolEnneE8i/k5Hwl70chTwS/HylxpAA2NTXh7/7u79Dd3Q2Hw4FwOIyxsTE8ePAAL1++xNjYmAjnk3OCuKKbyx3lyITDYUxOTsLr9aKnpwc+nw9ms1mAKQ5quPeDPuNjT6CY/qfv1Wo1qqqqcOHCBXzwwQfo6elBJpPB/Pw8vvjiC4yMjIgSAPJ7XR4jOT+OPlOaL07IICvp/F5KnhUOtDhJgWyAkJ9ps9kslHeqgUnEJF999RXGxsby1hpnfuTXpkK9MjOk7GlSAn68b0phswRSQqGQKFlisVgEI6GS0Z+vf95WpWeVGyLk9tH9uS6kNKfU9kQigcXFxTyvJA87pdqeKtXr4sy7u7swGo0CiNvtdtTU1MBms8HhcMBms4n9QAbK8j4ij8FJcmZgyuFwiAJz1KGNjQ0RFncWQl6SGzduoKKiAgCwvr6OJ0+e4N69e5iamsLe3h5yuZzwQl28eBF37txBZ2dnXo0HEnKd8nohpyEq1VGNnd7eXrz77rt45513UF5ejkgkgmAwiIWFBSwuLmJubg6bm5uIRCLY29sT3gQCTrxg5A8ter0excXFqK6uRk1NDfR6Pdra2gSLokqlEnk3wWAQOzs72NraQjAYRCAQEC7qH7LW1V8rRqMRJSUl6OzsRFNTUx5jHD3gDofj2BjSRrm/vy+AlPywUlE9t9udVyfjhxaNRoPS0lK88847uHbtGhoaGpDNHtEUz83NYXZ2FmNjYyIMg0IwDQaDSGRtb2/H1atXUV5eDgDY2dmB3+/H4uKisCyey79fVCoVvF4vbt26hcbGRuj1eiSTSUSjUcHadS6nK1yRk63VJFz5IoIDThGey+UEyxRZ9pVY3mTAQ1ECKpVK7NWFrKjcCMaFW4f5Z0pKGJ1P+S+Uw5LL5QSTbTwePxauqjRm29vbYu+maBWe38KZynj7CKzQ39z7JudPEdj0+Xzwer0wGAyiDeR14sYqee5onKnfNNfRaBTJZFJY9am/VINSvh4PLZRzXfiY8HHj46fX61FaWoru7m5cu3YNXq9XGHp///vfCyY+8t4ovStkBZjmmMBhPB5HZWUl3G43wuFwXluo/XxtcbAGQKxbDmAIJPf29uInP/kJ6urqYDAYEA6HMTMzg42NDWQyGbGGOHAp1AcZqHKgw8eMjzP3Vsrrg/6XDR7UByVFWwngEAlVWVmZoKnPZrNIJBLY3t4WbJBKz4UMjDgI4iBVnkMZKMufFyI+SafT2N7eFgRSFC7ncDhEWQOuV9G9eZ1GugcH1fxY3g4lowwfS+49k4EX9Yt7zGkuqeQCXZMcMvScp9Np8ayOjo6ioaFBhP253W44HI5jIJz3heeffZeOeWZgym63i+rjwNFgLS4uYnR0FLFY7LSbBeBo4JqamkTRQaIjHh4exvT0NA4PD+FwOERS3J07d/DJJ5+gs7MTDodDhGdQyJRarcbe3p5g6TpNhd9oNKKmpgYff/wx/v7v/x5erxfxeBzz8/MYHh7Gq1evMD09jZWVFRGjTy/j77NwfgjR6/VwOp0iltVqtcJgMIixN5lMwopA1syZmRlMTU1hcnJSWFHC4TD29vbyYnxPS5xOJ+rq6tDW1iZoUIHX4ZV1dXXo7+8/9mIg5pxYLCbC+eSQV6olNDs7i9XV1TcCpogxp7GxER9//DHa29uh0WgQCoUwMDCAL774AhMTE9je3kYmk4HZbBYGEJfLhaqqKly5cgWXL19GT08PHA5HXnjj5uamYtjHufx1QjkHFosFTU1NuHPnDioqKpBMJrG+vi48zOdewNMXrsTLCpaSRZwUfKvVivr6epGTS8QuFAak5BkhA0ZxcTFKSkpE4Wyr1Yrd3V08fPgQm5ubefm5MjCQP5fBGkUfcG8b9z64XC7U1dWhurpaKF7EYkZF62WFmiugVOPG6/XmKekUEsjbQj9kif6ueoMysCWjYnd3N+rq6vIY74g0Rw7lk0EwD3WjtlJ4Llf8lZRs8oJx6nnZss+FhxpyIOByuXDjxg3cvn0bVVVVSCaTmJ2dFaQNOzs7efVyZODN+yMru9yomclk4PP5MDs7K9pHoL3Q2FM7KXecj73RaERzczN6e3vR1NQEj8cj2AY9Hg8uXLgAj8cDg8EgcnjIoBuJRMQYy8+DfG+6J/+c2q5kqJSPpb7SfZRACAcovIAvnVNUVIRLly6htrZWFOsldjwqd0DGeG4MobGj+8khuES7r/T+lwElfUbt52kH/FkmOnEyflCUWGVlJTo6OrCzsyPC/fg15eLC9GzLewsBcfIiyUBLBldKQEr+nxszONDn+XwEusi4xGtK6XQ64cWXx523Rd67vq8+fGYJS0quNI44z7JNNBlarVYUVOzq6hJW4fLyctTU1KC9vR11dXU4ODgQoQJbW1twOp1oa2sTMdTfZaV7E/0oKyvDz372M1y5cgVutxt6vR6pVApGo1EUL7x06RJmZmYwOjqKkZERhEKhU1XGyGs3MTGB8vJyFBUVYXJyEg8ePEAoFILD4UB1dTWqq6tRWloKu90u/r5z5w5SqZRIuh0YGMDCwoKIlz0t8fl8gsGPWyR1Oh18Ph8++ugj9PX1HZt/sphEIhF4vV5UVlYKVjY6logo3mSIqE6nQ2VlJdrb21FZWQmr1SriqOPxOBKJBCwWi6hrRBYstVoNj8eD0tJSVFVVwev1iqr1gUAAg4OD8Pv92NvbO/OcKaWxOy2DwQ8lVqsV5eXl6OzsxO3bt2Gz2URNor/85S+4d+8elpaWTszNO5fTEdnCzRUkWotGoxEWiwVutxtutxu5XA7hcBijo6PCK8DDWnQ6nagBWFNTg+bmZjQ1NaG6uloUeY5EIigtLcWDBw8wOzsr9nKy1nIFUFa+ZGWGt1U+1mKxoL29XTDakbIWjUYRi8WEEkPKLDfQkSGV9nUK7aM6QVxhJ5DK87R4+/hxvO0yONLr9aipqUFjY6PYY4nkgL4nJVFp3uTPqR1kvKPjYrEY9vb28vJEOGkGb6PSmpFBFP0mI0p9fT26u7vhdDqxvr6Op0+f4unTpwgEAscs/UrXVBo7quNTXl6O2tpaHBwcoLS0VISZyWMs/83XCp8v+p+8Ul1dXSgqKhLHFxcX4+rVq6itrUU4HIZGo0EikYDf78f8/LwAH5FIRISL8nUkt0HuO2+bDCrluZABJj9P6XoyIObAsb6+Hs3NzbBarVCpjnKNnz17hqmpKVHXiK8BJe+PDMYpn7JQTSo5d4338SRDQSqVwtraGoLBIMrKymA0GlFdXY3bt2+LeoU83DObzeaFsfK5VwK6JDzcUj6P94OPgSzcuyWPO12f71f0bBJRTnV1Nerr6+FyuaDRaERh4kKGXgJS3JDyXfrCW0GNDhwNQCgUwvr6+plZV3O5nLCM7O/vi5j227dvo7GxESqVCm63W1gErVYrotEonj59KlhoHA6HoIY+C9FoNPB6vejt7cXdu3fR1NQkvGQGgwEejwdGo1Eku7a2tqKlpQV1dXV4/vw5pqenT60uFjHePXnyBDs7O7DZbCKRdnd3VzAMlZWVobS0FCUlJXmFlN1uN9rb21FaWgqfz4fh4WHMz88jFAoJC9CbFp7AyYU8U42NjWhoaDh2HtU9iMfjwkPHPVNkOdra2nqjIaI6nQ61tbXo6OgQiZwq1VGuX2NjI/b397G/v4/i4mLU1NQIS7RarRbshZSHkM0eFU/0+/2CpOUsQ/won40Yt8hatbOzg7W1NaysrPwoCkJrtVpUV1fj+vXruH79Ojo7O2E0GhEKhTA2Noavv/4aw8PDP6pyAv+RRM4lkZVhLtwzU1FRAbfbDZPJJJhTFxcXBSDW6XRibygvLxdgura2FuXl5aLeCtUN9Hg8sFgs6OnpyWMQjUQiWFlZEYncsgLBPVBcuIKWy+WEcZEIgyismZ4rYnyl61PeJ88PoZp6lZWVoggreSEODg6OWbKB4/TbvE1yKKCSks1zWYhhTa/XCyt8TU2N8OwoKeTyZ/Q/KZacmllW9HgivlL7yGMlX5tfh97n9fX1cLvd0Gg0iEajWFlZOVaQlt+XK738e34vCiWvq6tDeXk59vf3RV41Xxc8F4+EQr5OynNKpVKYnZ3F8+fPodPpBH08edtMJhMqKiqg1+txeHgowFUoFML09DQmJiawvr6el8ct5+IQ2Oa07hxUFCJBoHeCHBqr5B1RWhf0Hd3b5XKhpqZG9Ic8QETZTveTPaeycQPIJ/6Qcw+5R0gmOOFzQM8szykjrw5dd2BgAEVFRSguLobb7YbVakVfXx9WV1dFPiE3wnBPFW8r/4w/n8DrEMtC46k0rnwty55C+XiZpp/+prkpKytDbW0tSkpKRB7b5uamqJWqNNf82t/XGPzWUOnlcjlsbGzA7/efWc4UAbr5+XmUlZWJGgxXr15FOp3OW1REjjA5OYnf/va3mJqagsViwUcffSSsdalUCslk8lQt8zqdDvX19bhw4YJIUM9msyIHiqxctFh9Ph+cTidqa2thMBiQSCQwPz9/anNwcHCA0dFRzMzMiM2Q8oOokvXy8jK0Wq3YjMxms3gR9vf3i8KFtbW1grJ7dnb2VMY+EAhgcnISa2trohaZEk2pnAhLLmfKDeOhCLlcTjA0jY6OYnl5+Y3mSzkcDhQXF4sXOzHz2Ww2dHR0iM84fXE2mxVzQp+l02msrKxgfHxcVE8/S1Gr1fB6vaiqqsqzSAcCAfj9fszNzWF3d/dM23iSkAHEZrOhvb0d7777Lnp7e+HxeHB4eCjCoqmOzDmQOhuhsBFK/C+kdPHfRUVFgvFLo9EgHo9jdXVVGBNVKhWsVitKS0sFDfaFCxfQ09MjlFEKK9/Z2REkCDabDY2NjaipqREGGSoc/k//9E95OVnUHkrc5p8rKeFarVYYVVpbW0WBccoLoSLjFC7OFTeucJtMJng8HtjtdpGbQUXNlfKNCnnTgNeU6uQN4+3nlmt5/yJCh+rqarS2tmJ+fl54lWSRPRZksTYYDMKQRACNcqboeG6Rl4EWeTN4DTl+P1IiGxoa8M4776ChoUHQP29ubgovZiGPhtJ4yUCttLRUFEQtLi5GIpE4ZtgjfUe+DhVoptxlWYkGIHJV1Go1MpkM6uvrYTKZRDh/NpuFw+EQ67e4uBilpaXQaDS4evUqlpaWMDQ0hMnJSUxNTWFlZQXb29t5xAz8fSuTNcneLO7NoTFSAiJ83JQILvha0Gg0ApBYrVZRTJaM8/Pz8wgGg4q1pZRycmSAKhfvBiBys2WgqLTGlNYCAb35+Xk8fPgQTU1N6O7uFlFXd+7cwdLSkjB0U5uInILrNeSBltcHtY90Z34MH3t5zE8S3j/6n889jVs2m4VOp4PJZEJHRwf6+vrg8/nEvrm6uor5+Xnx3MmeRlo7f03b3iowFY1G82J/T1sODw/x6tUr8QCRa1qtViOVSmF7exsbGxtYW1vD4uIi1tfXsbKygqmpKUQiEbjdbqTTaREy4Pf7MTIycqqhN1qtVryAiaGF6F+3t7cFxXU0GoVKpRK03u3t7ejp6RFVot9Ujo6SJJNJxTEi5UQO3dNqtaIf8/PzuHTpErq6utDW1iZe9P/v//0/zM3NYWdn5432IxKJYGJiAp9++ilyuRxu3bqVx1AJvPYyUZI1KSYGg0HRqn14eIjnz5/j008/xatXr7C1tfXG+pDJZDAzM4OBgQHU1NTAaDTCYDAIa67cF3pB7OzsCM8Uyc7ODn7/+9/js88+w9ra2pnn72i1WtTW1qK1tVXU8aEQACrueNYhiIVErVbD5XKhtrYWXV1duHv3Lnp7e+H1enFwcID5+Xncv38f9+7dw9bW1nkdrzMUeq4BZQpdWTkjT45erxfnRaPRvOLWWq0WTqcTnZ2dePfdd9HU1ITS0lJR4HlnZwczMzOYnZ1FMBhENntEC2y329HQ0ICWlhaYTCak02m4XC7EYrE8ow4XXoz1JCsxRQXQDxkoSMkKh8PHcrW4sid7UHjf5+fnsba2JhQvJQVRtlbTeJ5ktZat8eSFIAXQbDajoaEBFRUVIr9TVki50k4GVZ1OB5fLJTxdmUxGMM5y4Cd7mvhnNKaF1gkp6na7HZWVlbDb7WKsl5aWsLq6Ksabg1Dqr9I4cMCs0WhQUlKCvr4+VFVVQa1WY2trCxsbG4qkTrJBgELQKISUzxFXpqmIMxUpz+VysFgsqKqqEuGLBMwPDg7gdDpRVVUliJ3q6+sRDocxNDSETz/9FA8fPswrlyKTdsjAUun545/Jz0QhD4t8DPf+tLS04MqVK6itrc1j9CWv8Pr6el6UBvcmKa0Npf7wdU9rA3idM8Svw8FOIZpzepdvbW1hZGQkL/S2sbERP/vZz5BKpfD8+XNBSqYE/Og+Mtik75XySQuR4cjnyvPJx4jGgUT2sBN3QXFxMRobG+FyuQTOoFBSpfqf8jjKY1ZITh1MkUWIqkNz2d/fP9NClNlsViRxu1wurK2tCbd6LBZDMBgUlK7z8/PC9cytUOTyPTg4wOzsLMbHx0/d06bRaIRFaH9/X1R5J0a8lZUV7O3tCWtaa2sr3G43qqqqkEql8PTpUwSDwTNXhgsJ1XDa3d1FIBDA9vY2otEoLl++jI6ODlitViSTSZSUlGByclKEQ7wJSaVSWF9fxzfffCPCPYhUhSSdTiMcDgtrpkajgcfjQW1tLSwWi1BmcrkcUqkUgsEgXrx4gUePHol6Zm9KiKL24cOHcDgcaGhogNPpFEA8FosdK7pLhTcpqRg4Uu6IPn1kZETUODlL0Wg0KC4uhtfrzWNZfNuF9siamhpcvnwZN2/exIULF+Dz+aDT6bCxsYGHDx/i4cOHmJqaOs+TOmM5PDwU3iR6jnmdGPlFTPsEGSoODw9FREF7ezui0Sg8Hg8uX76MlpYWdHZ2wuPxAADm5uawvLyM2dlZLC4uYnV1FZubm0gmkygrK0NLSwvKy8sFM146nUYoFMLc3JxYJ7JyQuFt5F0jzwsPmSIvSklJiQhNpGeKFEOiWeb9lj0hVBeGPDj7+/tYW1vDwsICgsFgXn0tWZQ8PjwXjFvguQ4Rj8cxMDAAp9OJrq4uGI1G7O/vY2FhAXNzc3j16hUCgYBiqBefU94Pi8UiPCo0hwcHB+KdqaTEk/A+0HuJDLD8GMpnstlssNvtMBqNos5VNBrNs6pzIg8lMCF7Xih3raGhQXil0uk05ubmsLa2diw8m4cj0u9MJiPWLnkjZYU2lzsKDdvZ2cHOzg4ACBbL5eVl2O12+P1+wQqZTqdhs9lEWKvdbhd/X716VRgiFhcX4ff78wA7rQO57zKAormUjfY814sDZ5mAhB9PBZSvX7+OO3fuwOPx5L3Ld3Z2sLe3J54LWU4C2rQG9Hr9MUBEnmTuQeRjz0GX7KHi/SA2ZA6GVKqjMP/Lly8jkUjA4/EgGAxiaWkJ29vb2NnZEfTuJ+VeynsffzZJVyZiEyUAy+dS7p8cCkx9oXtTrhSlsTQ0NECv14tSALOzs9jY2BB7ogxUOVh7a8P8dDodqqqqRNIsidLmeVZCFvbPP/9cKLrkJaHQCqoSToOu1WrFxmcymZDNZrG8vAy/33+qpAjZbBZbW1t4+vQpfv/73yMUCiEcDgvaVHp4uIWNig47HA5BjWoymd5aMMUlkUhgYmICwWAQY2Nj+Id/+AdcuHAB/+W//BdcuXIFX331FX7zm99gfX39jbDKkXVnfn4eqVQKMzMzxxT3TCaD3d1dAV71ej0uX76M//yf/7NgxKLNnYDZwMAA1tfX3/jaoXorT548wcTEBMrKylBVVYWKigrEYjFhNKCXAb1AfD6fKAadyWQwODiI3/72t5iZmXkrgJROp4PVahVK39u0vygJvQAoF62pqQkXL17ErVu30NPTIyy3VMvnn//5nzEzM4NMJiPyTEi4NfBcTkdIGZETrYHjlk5SQInSnMhcLly4IFhVGxsbceHCBREKtbu7i/X1dXz99dd4+fIl/H5/HmkDkcJUVlaKd9bOzg5mZ2fx7NkzfP3118LzwkOXuDJHjF9KgIQUHHoH0mfAawv8xMSEiGhQspIDRzXS6uvrBRnB3t4e5ubmRP6irPDy+5OSw8NwuPLIFULep93dXTx69Egw6zqdTmSzWUxMTGB6ehoLCwvHyC+A19Z/An10LyrMyguqx2IxLC4uipAoWamUw6BIdyCCikJFiXmoLxXFJR2E3ue8vzKQ5ePBr01U69XV1aJY/NLSEr766iuMjo7mkWvIzI68bTQuhbw7fA1wEJZOpxGLxbC5uYnZ2dk8wEPj7nQ64fV6cffuXdy6dQttbW24desWHA4H/uVf/gVLS0vCy6hU/Ja3k3t2ZKWe2sZDQ7kyTSCE56GpVEfhsRUVFbh8+TL6+/vR1tYmcnIoGoXqc3InAa1jHjLP28A9MlqtFna7XeQ6kh7KWeoKvWv53BOJTSF9wmAwCNIMMv47HA689957uHDhAvx+P168eIHZ2VmRxxaPx4UhRsnLRm2QPZq8z4W84XzdyHNFc1HIW0RzVl5ejnfeeQe9vb1wuVxIpVKYm5vDP//zP2N4eLggAQVf92Sk+C4vGnAGYEqv16O2thZVVVXH4nLfFiEF868Rq9WKqqoq1NTUwOv1AgDC4TC2trZONWyRwrbW19cFG0s6nVYkSaDcmPr6ejQ0NMDr9SIWi+UpoG/TvCgJxWxvbm4K6/Dq6ipu374Nn8+H69evIxwO49mzZ5ibm3sjbJGUM7C6uopIJHLsRUIWSFIIiAgkGo2Kl2Emk8Hi4iIeP36MP/3pT5iYmDg1UHJ4eIhYLIZYLIZIJIL19XVB9U9eP1LCjEYjWlpacOnSJVRWViKdTsPv9+PZs2d4+vQpQqHQW6HIl5WV4dq1a+jp6UFJSclb75kiqz8xnd29excdHR0i6VylUmF3dxcTExMYGhqCRqOBz+c7dp1cLofd3V1BUf22P7//EURJCebfyb/JUk+eHODo/dHU1ITi4mIcHBwIYgmVSoXp6WlMTU1hZGRERBjs7e0Jkh0qg3H79m1cv34dlZWVSCQSePHiBR4/fozBwUHMz88LxUcJeCt5L3ifaA8Lh8NIJBJCiSOv3NbWFra3t/MsvbKQR8dutwtDaiaTwd7eHsLhcJ43ROndQ8Y/Hq4mAxelMT84OEAoFMKrV68wOzsLh8Mh5oCK0svhfXQ+Kc3UV+BIyayrqxP7CnlLiP2UX4Mr7jwCgXu75Bw22XOVTqfzlHrujeNemULeQFkRVKmO8o7b2trQ3t4Oi8WC3d1djI6OYnp6WjHXldNi83cWv68MVPgY0n359/J48/YdHh4K2vpnz54hHo9Dp9OhqalJ1HKyWCwCBJ/k9eHtk6OhlISfxw1T3CtiMplQU1ODK1eu4IMPPkBra6tg3aVz6F1PYFkOAVUaC/k3haXz+ZfbSGMmGzDofx5KKofLabVaFBUVoaOjA3a7HalUCoFAAKlUCqWlpaIek8lkQnl5OdbX1/Hw4UM8e/ZM6ChKeaKyZ5ePvRLw/2vnhPeZjymBLPJo9vb2orS0FIeHh1hbW8PMzAwGBwdFbiq/VqG1S3nh32XYPhMw1dDQgNraWpF0mMlkxCb9Y335u1wuNDY2orq6Gk6nU7h2T9tKf3BwgLW1tbyFpoTcLRYLSkpKcOnSJdy6dQv19fXQaDTi5fI2KMR/jezv7yMUCuHx48cIhUIAgA8++ACNjY147733kEgkEAgEsLe390Y8VASoksmkolWOhBelJGsycBQKODU1hXv37okwy7OozxSPxwU4BV5bkCi/oLq6Grdu3cInn3yCiooKhMNhQU3PmcjOUlSqo8Tqq1evorW1NY+2njZHnU4nEtLPyotGyqHFYkFFRYXY+Gtra/HOO++goqIiLyeDmKF0Oh1u3rxZ0Kq2vr6O5eVlUa9FDmE6lx9eZHpiJSWW7wNE1kLeGIoQcLlcwhNAIXBjY2N48uQJBgcHsbKyAgBCCXK73SgqKkJfX58gKYjFYnj+/Dm+/PJLvHr1CktLS3l7ulKeBv+Me4FkRY8UV1JaiIiJQsdlQMTHBzh6P5lMJhHiSIY+ClkjpVGlUgkDFN2b5zvJSiEfXyVjViKREMqQ7JU7SbHnihqFSpWVlaG1tRUVFRUCYFAf5NA7Dpb4GBcCgLLIgEmr1cJkMsHhcMDlcomcD1kBVLoe3Uuv16O8vBzXrl1DTU0N0uk0hoaG8ODBA6yuroo9nHtQaAzk+j7y2PHwVmoLZ1yk8VJSoHnbDw8PRV7UyMgIwuEwamtrUVFRgZKSEtTV1QmvLdX2pGtwzwIHMOTRUFLgucIvA1F6PogB2Ww2o6ysDFevXsWdO3dw9epVAdD5XBCY0mq1Iv2D952Pr1KOG60ZIvhQ8uLIz7EMqKhPSkCSvF719fVobGwEAIRCITx58gSbm5tobm4Wjg+HwwGHw4Gqqip4PB5RmoOIW2Rvngzq5Xby4/m4y+GGfD7k6/F55ve2WCzo7OxEf3+/KIewvb2Nly9f4sWLF4LshtY09z7T9egecj7eSXKqYIosIs3NzairqxPWYrK4Ur2EH5uoVEcF2yjf5KSE2B9aiEmKbxh88+cPE21sJpNJMP59/PHH6OnpgdVqxdzcHIaGhrC0tPRG6bjflORyR3S1S0tL+Prrr1FTU4N33nkHFy9exPz8PF69eiUqZr/JNpw0blqtVtT0KC0tFUncmUwGs7OzGBoaOrH+wWmI0jOo1+tRVlaGjz76CD//+c/R3NyMRCKBmZkZfPvtt5ienj5TGnRZiH5aZlckq6LdbofD4RBEFKct9Bw6nU40NDTg8uXL+OSTT1BaWgqLxQKXyyXCwACIhPf+/n50dnYWDAsCgI2NDSwuLmJzcxPDw8P4y1/+gkgk8qPcW38MIiv2sgVWPi6Xy4ki5Jubm0gkEoIBDHidx0AKwL/+679icnISsVgsz0rt8/nQ09OD69evo7W1FVarFXt7exgcHMS//uu/ijpvFJXAFUlSEKm9/J2hZL2n44mhluoQZTIZjI+PC6+0vG/x5w6AUH5JmaL8y1Qqlefp4IqgbH2Wry+DWKV54fNRiCq70LUpLJ5IeSorK9HZ2YmysjIAEDW2Njc38+rqcS8UV6Jlqz33IHDwRNeOx+PY3NwUxZptNpso+UB5Wtxbxfsr95EYGTs6OtDe3g6n0ykU6OfPnwtvEM2ZDESUPFAyOOH/K3kRlMZc6Vmh7zOZDGKxGEZGRgTrnM/nQ1FREUKhkHjvy2NLa65QvSxZgVdaL1yRpvlpbm7GpUuX8OGHH6KlpUUAKVLQqYYcFbin8Ey6Dvei8PvIoJT+lkEa76fSupf7wIEwN0aQYaCxsRFOpxOZTAZ+vx9//vOfMTs7C5vNhmvXruGDDz5AQ0ODIJ6pqqrCO++8g4ODA/z+979HNBrNWwfUTiVPD58b+p681ErPd6EwQH4sHUPr2+1246OPPsL169fhdrtxeHiIlZUVPHv2DMPDwwKc8jGU6fYL9eEkOTUwpVKpUFxcjKamJvh8PhFvTBuqTObwYxMiHqAH5/u4Lv+9olarUVVVhdbWVrHhBoPBvJh2q9UKu90Oq9UKm82GoqIilJaWorGxES0tLejo6IDNZsPKygpevnyJwcFBQT36Y5RcLifi1+fm5tDW1iaYkMjlfpZisVjQ0dGBtra2PGrgvb09hEIhbG9vnxmb5UlC+VFdXV1oamqC0WjE4OAg7t+/j+Hh4bfOEJJIJARrJX+5EzsW1YqjGiCnKXq9HhUVFWhsbERjYyNaW1vR1tYmnkUO/miPJKtocXExiouLT7y+2+1GWVkZtre3hfV6eHgYa2trP9r99W0WWRmj/5W8BaTc7O7uQq/Xw+/3o76+HrW1tXnseIlEQhRRJ8WD8qw8Hg8aGhrE2uns7ITFYsHq6ioeP36MiYkJTExMYHd3V3hKuBVXVoY50JI9MSSkcOzs7GBrawuxWAzpdBrJZBLLy8uCiU8+j/9PCune3p4Iz6I6dhzgKIERrnDy51kpT0oWeS7kY+X7yH2mtun1emGhd7vdcDgcAmxFo1HBSkvXKqRMcsVZ9lwqjfnGxgaGhoYE6YXZbMbdu3ehUqkwMjKCsbExwezHc7t4v8gLUV1djQsXLuCdd95BdXW1SMifnp5GIBAQocFKwElurwxSZMDOFX2+/rLZ7DEGSXm/4/c7PDwq6jw3N4fp6Wm0tbXBYrHA6/Vidnb2GBDhctJ+JwMRee7l/lKNtMbGRty5cwetra1wuVw4ODjA0tISFhcXkcvlBHsssR06HA5YLBbxfPDxUcpD4+ygch0x3j55vcjCvXwEynifKUqso6NDvCe2trYwOzuLpaUl5HI5QQ7T3d2Nuro6wXBdWlqKO3fuYHV1FYFAQDDKfp/3i9x2XnKIf19ovqhvfExojRcVFaGzsxNtbW2CWTEYDCIUCmF5eRmLi4t5+YDy/bgDQtaBv6tvpwam1Oqjmi+kRHKhWOy3IUToxyQajQZVVVX46KOPkMlksLCwgNnZWRG3TYnNXq8XbrcbHo8HlZWVqKurEwQChNq/+eYbPHz4ECMjI4jFYmfcs3+fkDVva2sL4XBYMbfkLISsghcuXBCFV1UqFfb29uD3+7GxsZEXx/s2iEqlEoUVu7q6UF9fD4PBIGKnv/jiC8zNzb1VxW/JUkl0rlwo9IKshnJdkjclRCtNhaj7+vpw9epVdHd3izAKUqIp3ICIbjQazf/P3p82tZWmaeL4JYH2HbHv+24MeE1vaaedmbV2dVR198R0TMzb+TLzKSZ6oqd7auJXXVXdWbm5vBuMMfu+rwIkhIQkJCGQ/i/4349vPTzCziqDqWrdEQRwdHTOs55zXfcqAs5JWaNK667RHMV3kGY0JycH9fX16O7uxsbGBjweT9bd7xSEW1HIssBdd7gGmkBDPB7H9vY2Hj58KOoZ6vX6tOLSkUgEZrMZ7e3t2NvbEy4sTU1N6OrqEpaFRCIhnuG///3vsb6+Dr/fnwZaeZISXrcGwDHwTW2WLQxkTT04OBAKO6oLSFp5+Rp0XbPZnEbAdnd3kUgkRHA+T8Uta+y5qzEH4Fy4yxQvEiqDerouiQzeVe2nvttsNpSWlgpXfsqy5vF4sLS0BJ/Pl5aNUKfTCQDL51UGxdRmOg94mwwEgMi2WlRUhPLycrjdbhFLeeXKFbx+/RqvXr3CwsIC1tfXBaGjupJ6vV4UkL9y5QquXr2K+vp65OTkYHx8HP/2b/+GkZEREWZBwJTGkVzUVG3nYJ6IgAyWZULF54SLyqpGNdAODw+Fqz4pIsrLy2G1WkV8O7Wbxk2uRSW7Q8rtVLkAEkHUarUoLCxEY2Mj7ty5g2vXrsFisWB3dxebm5vo6+vDy5cv4XK5BNmgpGRWq1Vk8aXx5PeTx4Wst3ScMt5xkdd/JksgzR2P+aM1p9PpUF9fj8bGRlgsFuEKTFbOVCqF7e1tPHv2DENDQ2hubkYwGMRnn30Gu92O8vJyfPnll/B4PHjx4oUwhvC9xq3tvO1ysg/ZvVKFgTJZDqkvDocD165dw09/+lO0tbXBZrOJ/Tk9PY2dnZ20rIp8vul5zdtE71O5Bl8mOVPLlE6ng9FoFA8WEtJunXZNoLMQWYN22vfa29tDJBJBZ2cn7ty5I6qykxgMBhgMBpFFjkAkZXZZXV3Fq1ev8Jvf/EYU/zyPlpEfIlQMt6SkBMXFxSI27yzm5CRxuVyora1Fc3MzSktLxYNmcXER//7v/47BwcFzkQmPi9FoRENDA27cuCHieJaXl/G73/0Ojx8/xtLS0rnL+qjRvI2ZKi8vT3vW7O/vpxXt3d3dPZM2Wa1WXL58GZcvX8aVK1dQVlYmEk6YTCbs7+8jEAhgaWkJm5uborjw+vo67HY7vF4vlpaWxLqZm5s7Nu5GoxFlZWWor69HWVkZtFot1tbWRAHl80TS/9qEa4B5xi/+cqbzSA4ODrC8vIyXL1+isbERVqtVpDR3OBxob29HQ0MDOjo6cOPGDWFlcrlcKCoqQm5uLhYWFjA4OIj+/n4sLS1hY2MjLW0xB4o8vTUHX/KzkUAnPTcJCJGbm8ViEcVjKZPf9vZ2WuFZujaNBylbUqkUTCaTyHoWDodFHKtskSAhMki4gc7j4JDfi7svymMu7wHVO4Ffi/pjMBhQXFyMO3fu4N69eygqKkJOTo4ojUG1JnkyD3JnpLZTm2UyyC0UnABQ23Z2dvDmzRuxlm7dugWXywWHw4HGxkYUFBTg8uXLmJ6extjYGJaXl7G1tQWNRoOioiJUVFSguroatbW1KC8vh81mw+HhIWZmZvDo0SMMDAyIGll8bXJ3sEyun5x8yHF31H6V6yefM74W5XcfjSHF6zY0NMBut2N3d1dYD+R1o1o7vKCrilDR3ND5/DqkgKMU6JS1Lx6PY3l5GU+fPsXjx48xPDyMhoYGUaKF2k9p6Le2tkSbeL+Bt6SD2sjTsvN9mOkZrrIm8+Mq/EPWSofDIdpjs9ngcrmwsbEhrOEUV0XWHqfTKeK/jEYjCgoKYDabsbe3lzb2hMMo/o3GWH4W8r2W6Vkpi0x6DQYDKioq0NXVhQsXLog1TpmKv/76aywvLwurFF8f8r2oD1QLVA6fySRnHjOl2pSRSATLy8sIBAJ/FS/8RCKBvb29NB/w05BkMgmPx4Oenh7k5ubixo0baGtrg9lsTjuPtKDRaBThcBh+vx/b29vw+/1YWlrC6OgoBgYGxMvgY4per4fRaBRpoDPVZ1CJVquFzWZDQUEB2tra0NbWBrvdjs3NTQEyPkb/6GHc1tYmkn1QAUZys+jt7cXS0tK5ca/UaI4y95WWluLOnTu4e/cuWltbkUqlMD4+ju+//x4TExPnNrbOarWKOiVcCNgFAgHs7OycWZyXy+XC/fv3cfv2bVRXV+Pg4ACBQADz8/PY3t4WySKWlpZEBtDt7W34fD5YLBbs7OxgY2MDBQUF0Gq1WF1dPdZ2cgGjIG1yqVpeXj4GlrLy4UTWZqo07PL5wBGw8Hq9mJ2dRW9vL+LxOKqqqmC329OKe5NrEcUqabVaEa84OzuLJ0+eiPioaDR6zHWMgABvCwctMiiWwTE/j1xNuZsWASW9Xn9M0y5bekwmE0pLS9PAjqwt5q5f8njq9fq0jHL8M9kCoop3oDbLx/n/hFP0er2o81NVVYVr167h6tWrqKurE7UEQ6EQpqensbKycgxw8T5QCnTV2slEIkko82B/fz9isRhWVlZw6dIlVFVViWQUNpsNhYWFaGhowPLyMsLhMHQ6HQoKCkSyKYfDIdbc4uIinj17hv7+/rT6XtyaKK8flXCynslqKK8zsvTQZ7KFiF+XlPAWiwXNzc1oa2uD1WoVz3C5Lpl8LSIhKg8Eeb3LhJw+MxqNQoFB4061R2dmZvAf//EfmJiYwP7+PiwWC0wmU9pc0z7ma1mjeVtIOpMlhtrE6zDxa8rrlStD5LFV9f3g4CDt2pQu/+rVq3A4HACA8vJyVFVViUQUpMShDJxTU1MIBoMA0pNrAcdTwKuEyKtMcPm88LGQhZ4pJSUluHXrlihAbTAYEIvF4Pf7MTMzg+npaZH9VL62PEY0J8lk8p0Z/LicKZkis6ecLWNvbw9bW1tpdSb+kiUej4vCZqpK4h9KkskkNjc38ezZM+HHnkql4HK50s4LhUKYm5tDIBBAOBzGwsKC0IBvbW0hGAyKoLyPJbQp3G63SMkZDAaxvb393u2ih0FjY6NwZYjH4xgeHsb09PRHI4tarRZGoxFNTU24evWqAMT7+/vY2NgQ7hnnxVWOQFNxcTEuXrwoajWYTCbMzMyIFMPnWfkhgwBqJ7lP/RCS/iHEZDKhpqYGbrdbWJymp6cxPT2NhYUFeL1exGIxkU2T3DK4yxhl6qR+yBKPx7GxsQGfzyde3vS980LS/xqFEwsgPY5H5X7DAdve3h4mJycRiUTw5MkTtLe34/LlyygoKIDdbhdxRQDE+3FnZwcjIyPo6enB9vY21tbW0rTCPNkDX+NyTSJqj8FgEEkqZA03Bx25ubkoKSkRxcaNRqMAMkVFRYhEIgI4plJHKds5IKEkKmVlZaLWk8/nE9nAyLWMAKQcg8MtBhwAUp8InJLIBJGTxpP2PgFoctdqbGxER0cHLl26JBRhFOsajUbFO5S3l88Dt1bx9hFho3bLlh05MUgsFsP4+DiWl5fx8OFDdHV1obOzE42NjXA6nSJdd0lJiSBvZEGg9PCrq6sYHBzE1NQU3rx5I1x/uQWNE1VqEyfWNKY0z7JFUx5vPhZ8TVGxXxXZov/JSltcXIy6ujpUVFRAo9HA6/Vibm5O9FPlYkjPP5VVhxJ3qcoCyOSESg9UVVWhsrISFoslbTx9Ph80Go1I6kWZqmk8dnd3RZ1J3n9V1jt5jOTP6Hy6PrdkZVrLcmITbvUihT8AkRzuv/23/4ZoNAqDwQCdTgez2Qyj0Sis0ZTMa3V1FRMTE/B6veJzTkQoOQq9x3i/+fjy9XbSOKj6ZjQaUVtbi+vXr+NHP/qRiKcDjt6Ha2trIlM4T+MPvF2/Mvnkz2jubvkuOdOYqcLCQrS0tAhtMb1MqB7SadQAOmtJpY4SIKysrIg6IqepESYN99jYGPx+P549e5aWPQY4cm2iiumJRELUFIpGo2mF/z6mWK1WtLa24tq1a7h+/TpMJhMCgYCoW6R6uMgPJ51Oh5KSEpSUlKCgoACJRAJ9fX34l3/5F4yOjoqaCGctyWRSxEJRzQJ+/DzNA3A0jsXFxXjw4AH+4R/+Ac3NzcK17Pe//z3+/d//HT6f71xbOviaNxgM4tjm5qYoUHyWBGNnZwdff/013rx5A7/fj5WVFXg8HgSDQYRCIREDRf7qmeRdLrhZ4nT2QgUxeRzbSe8xDlABIBAIIBqNYmVlBfPz85iamhKxF1RqITc3V2TzI+vE8vIydnd3lZnMZGBAbVJZAGTSQ+fyvhAYI6UbgXSDwYDW1lb4fD5YrVb4/f60RFLxeFy4mZeXl+Py5cuoqKhAbm4uQqEQfD6fUCIRGZHJBbeU8XgT4OhZlZubm6YcUVlGCKRnOofGQqM5qlFDRZO7u7tRX18Pl8uF8vJyEfuSSr2NaeSZ3PjYq+JcZOsEJRrgdbNUAJLihigW2Ov1wufzYXZ2VlgMyPVPo9GI+pJarVYoWqke2Pr6OjweD7xeb1o5Ghp72eqXydrIS9uoACf/jkyq5HVJQgSDYkzz8vLQ2dmJtrY21NfXi31CBaLpeUjrVeUJRHPBiRq5rFLMHj+XX4+Av9lsRmFhoaglRXujtLQUt27dgsFgQG1tLS5evCisN4lEQpSmCAQCaXNM1wbUpQq4hZbirGTFRjKZPOa2xoXGnbsf8/5RLa+trS243W4YDAY4nU6YzWYxjpSePh6PY3d3Fx6PB9999x16e3sRCAQQDAbFO0cmrnLWTE6mKA7sT8E8ND7kCn3v3j1cvnxZuPdpNBoRwkKlITKNE1cS8PVN4yVn4TzpuX4mZIqybFRVVaG2tlb4SyeTR1mNfD6fKJz3lypcA0CFCCmY/LQlmUyKIo5TU1Onfr/TEAogdLvdKCgoEH9TzBP56QcCAfHyNRgMIqiffNLJfL2zs4OpqSk8evQI/f392N7e/mh9o4ceFVPl7lmyRvJji81mQ0lJCbq6unDv3j1cuXIFALCwsID+/n709vZienr63MfVUZFOilPT6XTCre7169dYWFg4U/IaDAbx/Plz6PV6hMPhNBCZlb9syaRpp89kbT0XAlaUCW5nZwdzc3PHgKYc/yMTHbnODgcJXAMuX5fAMBE2nrhBbidp48fGxtDe3o5kMgmHw4GOjg4UFBSgtrYWCwsLWFxcRG5urtBKUxKEzs5OXL9+XWTRXF9fx+zsbFomMA44VSJbn3hsDwe5vN3y35muTfEptbW1+Md//Edcu3YNtbW1IlENr6FESY78fj/W1tYEgaQxlIGrvBb4b7KQqOovEXDklgWydoyPj2NqakokmLDb7SgpKRHvGrJIEeGmQHoOHmWCJ6+rTJYmvtZ4wg/VuPM+6HQ6mEwmEcMNvC3+yzPZOZ1O5Ofno6GhAffv30ddXR0AYG5uDi9evBBxXnIqfb4/TnIvI6sD3VNljeN9jMViCIVC2N3dhdVqhV6vFwlAGhoa4Ha74XQ6RYw6cOQRREp1bizg1ht5PPle5SITXB7HyMeZz6s8T/Ixqm358uVLkSiDXFtJubK9vY2NjQ2sra0hEAigt7cXr1+/FutLZWWjtlE/uYWV7s2tuHz/ymtHXkfUZ5vNhosXL+Lv/u7vcPv2bRQXF8NsNgsDzcrKCnp6ekRct+q5JhtvVF4EfPzeJWdCpgwGA8rKylBSUgK9Xi9So0ajUczOzmJ2djbN//UvTchVwmaziQdEVn6YUNHd169fY21tTdRgysnJQVFREUpLS6HX60VBUgBwu92oqKgQ6WLJZWZ6ehqTk5MYHx/HwsKC8On9mJJMJrGxsYGZmRk0NTXBZrOJos6qzGwfQ3Jzc1FbW4ubN2/ixz/+MTo6OpBKpbC8vIyenh58++23wrXiPEsqlcLs7Cz+z//5PwCOxt5ut2NhYQE9PT0YGRnBxsbGmZKpWCyGxcXFNI1XVv56RNZqyqAYULvOyZJJscIBmEyM+O9MzxGulZcBM88cx11gZLetw8NDbG1t4dWrV3A6nbh48SIuX74Mo9GI8vJyYUmjbGs8QYBGoxFab579bGxsTNRA465ZKuEZ5bhLpWpMeP8yWaHk4/Qer6qqwsWLF0W2OHIHovtRLO/W1hbm5+dFgXPyCJEJL4BjY0/t4/2WteKZRAbNpKyLRCLY2dkRpInPs6pWEReZYKnGXp4XAsmypVMWihkmpXpTU5PItkbeGaRMdzgcInGU3W6H0+kUyvfp6Wl8//336O/vx+bmprA2EGCnv2XrsCoRCZ8Lvl+5qyB9Fo/HRe0+j8cjMjna7XZBYHNzc4VClyw6s7OzGBsbS7P+kdIi01xwBQi1j8aen68iB5yc0LVUa4quk0gkMDg4iPX1dTx//hw2m01YeskStb29jd3dXVH7bmdnR1ij5P3Gr82VPKp9KbtCq0gXfSYTHp1Oh9LSUrS1taG1tVXUZEyljrIPrqys4OHDhyI7NVmNZcWBSokgrw3Vus8kZ0Km9vf34fF48M0332Bubk48dA4ODuD3+88N4P1zpKCgAK2trXA6nQgEAh+7OX9xEovFsLy8DJ/Ph+npafESy8nJETWyTCYTfD6fGF+z2SzqfpBWmNwf6CccDp8L8J9KpbC6uoo3b96grKwMgUAAoVBIZFP62GUByDLY1dWFzz77TKxlv9+PR48e4Y9//CNGRkb+YrLCUXDw//t//w+vX7+G0WjEzs4OlpeXsbGxceZrIpVK/cUqi7JysvBYGG4RAo4HOsvAUwXoVaICuipNrvwdahPwFuzLbjgqoK0K7Ccrx/7+Ph49eoSVlRWsra2hqKgIDocDDQ0NKCwsRFlZWZo73OHhoXBnXl1dxePHj/Hy5UsMDg4Kd3MCkKp+khabg2b+GScVmcZNZZFTnXtwcACv14uFhQUUFBSI1PPxeBzxeBw+nw8LCwvweDzY3d3Fq1evMD09LQAbt5Rx4RYQwj7AW0ubKp6Nt0tV90nuNxW3Va0plaWIg0sZaNN16d5ykgAS2ZVLtirQtclVzWw2o6SkRNR/5LE/+/v7sNvtyMvLE0m0QqEQFhYWMDs7i9HRUfT29mJlZQXxePxY+nuVpYfIC1dEUF9prjhRkckuWXC2trYwPDwMm82GhYUFUSLEYrEIJTqtH7K4Tk5O4s2bNyK8gN/jJDJF56RSqbRMkDJRkr9PySRU8yGvf1IKUNjH2toajEaj+Hx/f1+EhchpzVX3VhEO2arKz1OtN/lvlSWUvkvrYmRkBMlkUtReHB4exuvXrzE4OIiRkRFRRFt1HRXJ439TFkt5TjLJmZCpRCIBj8eDzc1N9Pb2pn1GC+QvWVOr0WhE2nHygc7KD5P9/X0RyAmoszQBx2OmZC2w7AN7noD/5uYmBgYGRHKHcDiMV69eYW5u7gdljTkNsVqtqK+vx+XLl9Hd3S2qy/f39+O7775DX18ffD7fuSCm7yPkr97T04NXr16lvXD/kp81WTl/wkGjrP3kzyCexUx2bQHUoFf+TOW+lOl5l0lrrGo/f6aqXHaozalUCpFIBCMjI5idncWbN29QWVmJ0tJS3L17FxUVFbDZbHA4HMLlTaPRwO/3w+v14vXr1/j//r//D8vLyyJpRibrhgpgyX1RgfhM7jo0B5kIFYHhlZUVvHz5Uijw9Ho9fD4fFhcX8fr1awwMDIj2U0p4XnhU9d6Rx1eeN/ourRFVzBjv30nzLLteAcfdxOSxPAnY0+c8vThdUyaxKtJK4wocKbkoRh6AIFNERmjNUJbHkZER/PGPf8TY2JhI9CEnrchkfeT9UO0FTsBkCw6fE5pfyoo6PDyMyspKFBcXi1g1sp7FYjGMjY0JD5vNzc00d26y1nCCoVqnvJ2csGYiR/Izh4Q/c2jt80QMqVRKWDL39vbS1ovqnie1VTXmtG5kQibXlaJxobGXLeV8PVHirlAohK2tLVy7dg2lpaUIh8MYGBjAxMREWiInea/QNblrNFd0yGvkffoLnGECCprEv0Ygk0ql0iwqWfnT5LyRnw8te3t7WFpagt/vh06nw+HhoQgM/tj7wuFw4MKFC6iqqoLBYEAoFEJfXx9+/etfC//0vxQiRfLX/MzJyvkR2VoiH+OggopAyi5g3GWKvsezpRH44q4zsiWGwAgJB+i0FzhY5CQPQBqI5ESIX4/OI4BJQDgUCmF7e1vUTSNvgVgsBq1WK5Iera6uwuPxiOxafAx4ezmYkjXDvI88jonaLs8HXfNdoJvuEQqFMDo6Cp1Oh52dHVRWVmJhYQEDAwOYn5/HzMyMAPXc1Uy2GsikiRQ5vG+yaxZl+IvH42mKRV7Ml4+TDHBlsq1ypZKzmvFx5r/5PHDgT/ejdsrucjIApd/kiTQ2NoadnR00NjbCZrOJJCVkLTEajYhEIgiFQtjY2MDKygo2NzcRi8XSxpvGi5I98L7wtvD3QKb+yYRKPpZKpUSiILJQFhUVidqp/DrBYFAkhyGLpbzGeDvltc73AK0NmRTQ/uTnc9JC1yLvHh6nzeflJCLM/1btHbn98mfUdtm6JWc/pXnkY0/zQ32SLVkHBwcIh8OYm5tDIpEQ8VIbGxuitEim+E+6n8FgSEsPz0kdj2+T751JzjQ1+l+rpFIpkWUnGo0qaxpkJSvkH37eitwCwO7uLsbGxrC/v4/Hjx/j8PAQs7OzGBwcFA+nrGQlK8eFB1NzkAuku+YAEKBOdifiiopMQJy7KcnaW54ljQMvApuyOxOdy8ExB9Oq+BgO/OkeoVAIk5OTaaCL7mk0GoUVgfebt4ODfA4gqS08HXomVyKZRKo06rxwa6aswTQPlNxhdXUVL1++RFVVFWZnZ7G2tiZc/rg1jbvwyW2UrR0yUJWBMh97GShzYqOytsj3pevyVNjyfGeq8cNTh1Mb+RoD0pM8cOLB+yeTk3A4jNnZWZHQCEgniBzM8vg43l++3vnYcPBM8y0rLmRirpoPee5o3Ig8R6NRbG9vY2Zm5hjQ5mMjW7nkVPl83fNrUNtVzwoucowR9ZvfX9Vnvpf5uFPZIjnmjl+Tk/5MFlZ6/tGe5fegdur1+jTCQ+tUfj5R9mOVu+HBwQGCwSDGx8fTxoPv70zrkuaB+sP7RUos+vxde40kS6Y+kASDQczMzMDlckGv12NxcRHhcPhjNysrWXkv8fv96OnpQW9vb9oLQX7hZCUrWUkX2VWIXvTyC13+WyUysOWZxk4CgpQNjZ+nuq58D7oegDRgLQMJDrIJqMlxPHQuJ0ycHMiWbRnk0DF5vPhYyH3hAFEmJfJ1qP38PvIYkQWQMvEGg0FRx4tcsTmoU4E0+Zry2uBzJ48JafJVZJBA5buE91Um9ap1I4tMgPh4yd9NJpPH3LNkoswtEZxkENGRx4C7X6nGlY+57JrFSYvcFplg8D0jg2p+n0yWUk6+eBszWQZlosD7K/dV/lyeJ9lVL9O6yUTE5PFJpVLCGsrXLO+j/DxQWUE5+eTHZELLszfyZ438fMy0BngfVNZ4vudk0kffkzOHqvan3I6TRHPSSRqNJoui3lNsNpsIWNVqtdjd3cXGxgZ2d3c/dtOykpWsZOW9JJVKvVsFlxUhGo0mRaCAW3U4aCNwQW5+h4eHAphzbbxMDAhAyxpUDja4hplAqKzpJ8sUAQt+b7oGAGGJ4EHj9B0CTtxNjBMrTsjocw6i5cx+1C6dTidSs+t0ujSNOKWupnYajcY0qxCNEW+jDDp53ynjVyZSJFsV+I/KOkKWNz52NGay9YjPjWrsVWCft4uSh8Tj8TSwx+Nv5DXHrym7f/ExI5FJH59fcunjhFmv14s5UJUEIA0/X4M8EQEnGHLbueWJJ/Wgz/laoXirRCKRto+onVQPK5FIpI0JVwhwl1Z57XLLDHcrpXHl1+BzTcfJxY7az/c1ueFRgo5U6igmiGfBM5vNCIVCaURAXjf8+tQGOo+PFW8DzQP1lQog83ngxIf6TdeT9zt/TtB9aF/wdc+fl3xPkHBySXPH3e74eqFxPsmCxPc2J0xk9eJ75+DgIC17Z05OzrE6ZJnekVnL1AeSUCiEUCj0sZuRlaxkJStZOWPhIIFrfOkzAl5UhJcADQEEToBIMllyZIsL1/rLIrvaAMeTMMiafZmQUBtlDXCme6pc3njb+XW5KxMRE9miwt0LOZilz+X7ym2ntlLGVJk0yACM35sTk0zadjmmQ+4rB+6yK9NJ40OSTB65h8vX5MRddZyPi4q48D7KIl+bADVf23zueZwJt1zQ9Xl2SJkwqu7L3a5kawG3fCaTyTSLCreQcUIpF73l91MRSz72meaE35OuT8CfKxHkvvJ+7O/vK2P8uMWPCKFMWEmo7bKihfYy7xfdi4iT6l6qfcqvwTPynTQ2dK4853wuVXuFPy/lxFyqc1Vjy68rt5Hmhitr+DzRvFE7MsWoypK1TGUlK1nJSlYAZC1TP1ToHanSfLJzAGR291O97DMdf99zVee8q30ngY9MBEB1LicamdqpAliZrs3bB6hdsejvd933XfI+Y6war5OAOu9vJgCaaYz/nHa+z/H3vafsDseve9K6ed/7/tC2c5KlAtj873cB63ft2T+1naprZ1o7vJ3ccpZKpStdPsSc03HVfVVj87576qSxz3SNH9L2Hyo/9HmpUq5kIGzKd2SWTGUlK1nJSlYAZMnUDxWZTAHHARU/9qeA5pMk07UyWSJO+s67rqNql4pInHS+LO/T/vdp40nyvu1RJWj4ofKn9FN13ocCm38KmfxTzqfvnEQqVZ9zcnGS5v8k5UOm9tD/f8p8ngTEVcfeRdT4uXRMZUlStfNde0Qe2x8yhzIZzdS3d133h677k+Sk9sjjycmOiiCqlB1/bvszvSOzbn5ZyUpWspKVrPyJIsdMcZDEXY1SqbcxBBzgyS96DgY0Gk3GuBhZW81dU7jrEHdBkpNDAEgrCsp/03VU8QhyzAVvoyrGJZOrmcrtjfotCwdN9D/dV9bq032pTarj/H45OTkiBkoOxCfh4yDPA3cXkuNOAKSlseZjQDEzfD75uiLXK95mOf5KNT485iiT1UU1r3y9yteWf1MxXH493kbeJ3Jx5GuUvk/nU5pqHqslA2vuQiorKPjY0/WprYeHh8LVkO+RTKSJ71k+lnxseLwetZ0Dep1Ol5aFkdqn1Wqxv7+f5mrH206xXXzPymuHjx3vEwBR65TWB40Z37Oy8HUmrxP5ecTHnseDymPG28yfUzRm8triY8/XMY/XlP+nOEqeMp/WJk/Ywp+JsusjfcbXHR8Xuf2ZJEumspKVrGQlK1n5E4WnMpaBHy+TkUwmYTKZsL+/r8y8BaQDCwL4Op0OOp0OkUhExDfEYrFjIISC/GVNLRVGVbnaEBBSxUTxQHoCMHQuJ3c5OTnQ6/XQarUitofak0ljL/eVRCaZMkiTASQHQRwQ8rHn4FcmkhxU8/vTMQ7A6XwV2eOgmQfJ80QAvA98boC3ZIvfg4+XTqc7VluIrk/X5pn+CGynUqk0kK8iS/IxPgZyf/jYn1QviNYcjbfBYMD+/v4xUCrHwNExXjtKtUfkfUZrkn7rdDrYbDZBcii1Nh87mWjSuMsxTER45QyXOTk5op+c2PL4MSIutCb1en1aYheDwSDGmO9BImJEEqjfnCzQuuGf8TZR3ywWi6hlyYkdXYMTDp5JkY8NV1rw+eaxXPK15RgvmQiplEHyvuKEl9+D5oQrreg43ZenytfpdGnJJihZiUz4NJqjxDK0VuSsrCdZT7NuflnJSlaykhUAWTe/HyoajSZlMBjStLay1pyDQ3qB86xvdB4HFQaDAS6XCyUlJTCZTHC5XAgEAvB4PAgEAvD7/ccsVXKmMQI+3BoApBdgJfBAQEFFqoikqQLZuQWBADyBaA6oCezI95bBGQeFKvLHwSyRU05W5EQHvGaMbFXgx+mYrJ3mgIyTDU70SOsvZ/DjVg0S3n/K7kjXJoueDC7pOvxv3jZZuIVBtkLJY6+y7shFcOU+0fVMJlPaXPD2UBa5VColAv1lqwUHyXwsuUVRtnqpAC0np1x5QfcnEkrJDEghoaqfxNvP55DfQx4z2YJE40H7nl+fCBXtC66coDbt7e2lXV+ev2QyCavVKo7xdUNrnggBZQbkGTbldlK75OeJauypndRfvl90Op14/nHixEkltxplsjJyEi8/F6kvdFyv16f1CUgnh3zN0XM1JydHJKMhgknrlayC/J6chP//r5d188tKVrKSlaxk5UOKnEKcg1b6m7+QM4ExDlby8/Pxy1/+Evfu3UN+fj70ej1mZ2fx+9//HuPj49jd3VVmrgPSiQ7/rXJvA95aHnj7OKBQWXPk/vF70PXkjH6ye6N8DfqfgDRvBwdx/Hy56CeJDBI5eKTfXHtP11C5xamOyWPDtf4cpFKbyArAhcgfB5z8HhxM8vu/K/mCDOz5nPC2y/PALVkygJbJDQBhAeBkQh5b1Rrk58rjqiLzqvby46p9Rtfi95bHXt578ueq8eFpu4G3ZQhksk33ld07+Z6Qx1omkbIVhwu3mMhEi8gErUmuEOB9o79VbVcpOugaZHlTXYvvByJ2/P6cDKvmRt6PchvldRyLxY49F/g+lK8h95tnJuSp/2mtym06SbJkKitZyUpWspKVP0Nk0JEJIHCQr3Ivy8nJgcvlwvXr1/Hll1+ira0NFosFoVAIKysrCAaD2NvbS3MfkoFCJuDPQZJM/OT28/+59YxfXwWE6bdGo0mL2VCRAnmcZFERONnSw7XPKtCTKT5NJlK8D/yecuyH6r78mNx+Os7HXu43Jx+Z+i+3/33Gku4nrw35mPy3amxU31MB35OIET8ut1/Vf5WVQmUh5H3lbaB1m8mCJ7c30/F39Yn3XV6fmcZSNW6qczMRS5mUyNdX1f2SCYncBnlueXv42MqWME62Mz1HVK6kmUiKas2c9FmmcgOq6/NnEu8Xv7aKpMukXiVZMvVXKlqtFiaTCUVFRXC5XNDpdGkvJJfLBbfbjdzcXEQiEXg8Hni9Xuzs7CAUCiEej79XtfWsZCUrWfnPLLKWE0iPN6GXsmwZUgFUs9mM5uZmdHZ2orm5GS6XC4eHh/D7/Zifn8fU1BS2trbStNsy2OEiH5eBEwdNHPhQ27lwYqECv6rvyKCP7n2StjcTQVH1UwXMVYTjpIB3+Rg/TxWsz6/NRQZcNLfv6qtM4FSEQR6HTNfn5/O5kvursvjIFlK5nfw8+Xqq9shkR7UW5OuTkLshuWUBR3sjHo8jFAqlWRszAV0+tyrrjnzfTGMvE+pMREueJ/65fJx/56S2y79JZMtqpjnnhFS1Pk8iCHLbZUVEpnXJ/5etmfw83nZulaPPeFIO1f7O1M5Mz0N+DXLD5PfiSg86l+//vygypdEcVdq22WyiqnUkEkE0Gj0XwF6jOfIL1ev14ofM97TJKcjyY4ter0dBQQGuXLmCtrY2mM1msWByc3NRU1ODpqYmGI1GbG1tob+/HxMTE5ifn8fKyoogV5lY/4eU3Nxc6PX6tGBr4PiLlEsqdWTi3dvbSwsuzcqfL/I6V33Og9Fp3WcCHlnJyl+78Bcx18LKL/5MyREoM1VtbS0+//xzdHZ2wmw2Y39/H4uLi/j1r3+NV69eYXd3V/j183vLf3PgIFuGgHRtLgdVHCDJgIRroHl/6R7yuRys8N9ym99nbDkYl++rIiPyPeSA+5PIiyzvS/pU88DXgtx21TiorJXyceAtgSegz5NMyGOnAsV8/viPPLb0t+r9yuPg5GtR+7lFiY696z1NcUV5eXmorKxEc3MzCgsLkZeXh9XVVXz33XdYW1tDOBxOI8l8z9FPJsWAPDZ8fFRFWmVClYnkyMkVuFWXt5WTDNXaof2j1WqFJUhuO28/T8Qit1PehyeNwQ8V2eIkPw+A41ZHue38mUnn03E5AQ3/Pn8e8bbQOdwFkwsnVKp1Ke+z95VzQ6Y0Gg2sViuqq6vxxRdfoLa2FolEAo8ePcLQ0BBWV1c/KqGiDV5VVYWamhrU1taiqqoKhYWF0Gq1GB0dxcOHDzE3N4dAIPDR2kliNBpRUVGBK1eu4ObNm7BarWKxaTQaOJ1O5OXlITc3F06nE3a7HR0dHdje3sba2hqePn2K7777Dn6//1gV6g8pWq0WRUVFqK2tRUtLC2pqalBSUvJOrcf+/j5GR0fR29uL1dVV+P1+xGKxc0Fk/9LFaDSKdV5RUZEW1AscBfaazWbk5eVhd3cX3333Haanp7Gzs5MltFn5TyeyxvMkckNxEAAESKIMXPn5+WhqakJbWxsaGhqg1WoxPT2Np0+f4sWLFxgcHEQoFFICK1Vb+OcyWODtkuO5CEzK1+UkSgVCVSSMkywOcHU6HYxGowjQB4BoNKrM+EYiA2V+DzmWgpMkGnc+9qoYokyaeP4Zl0wWikzWAvk+HLDT+SoCDrxNQS+DUFXMiap/fOwzkdpMFixV/Jrq+7KlQZXK/32IlNFohNvtRmNjIy5cuIBbt26hvr4eJpMJMzMzWFlZwc7ODsLh8LFxzUTqThLeN5XwdcPvJc+vTI5kFzeemly1ZziJkBUI/J7UJrk9qlghuX+yxYqnd+ftVilH5fGUnwkyocr0nFApxlV7hsgoP8atVfLY832hcu+Vx4T2mnx/eT3xtp8k54JMURrLxsZGXL9+Hb/61a/Q0tKCaDQKo9GI/f19bG5unjmZog1AVp6qqip0dnaira0N9fX1qK6uRlFREfR6PR49eoTFxUVsbm6eCzJFC520cHa7HRaLRaTmJC1dMpmE0WhETU0NampqcHBwAL/fj2QyiZmZGUSj0VMnU/X19bh9+zZu3ryJ5uZmlJeXiw3BNxOdr9UepeAdHBxEfn4+pqamMDMzg7W1Nfh8PsRisVNr71+7GI1GlJaW4s6dO7h06RJqamqOBU/rdDpYrVbk5+eLtXJ4eIiBgYET/dOzkpW/ZuFAlTTKJDJokkFITk4OysrK0N7ejoqKCjgcDgSDQQwNDeHRo0fY2tpKSzvOtc6yKw3/nGfNov/l2AwZBPF2ypp/2Voik0jZnY4Hn9PnJpMJbrcbTU1NaGpqEu0bGhrC5OSkcDOXwZDBYEAqlUpLF61yI+J9zKTFPmm8+BzS/eUYCxVp4SIDSk4wuPDPgHSrCG8jZSWkdvH4FJXFSx4PnglNbqM8r/x9S9fmWRFV5JPGiKdvP4mgZBqznJwcOBwONDY2oq2tDW1tbaisrERJSQm0Wi3Kyspw4cIFLCwswOfzKUkDiUwc5Lbz9QEgrYQAXwt8TchjLCsoKCMfWUU4kVIRjkyEgMYagFA4qMZezgYoJ5uQLVZ8rnQ6HUwmEywWC+x2uzg/HA6L500msqIi8LSGucKCMnwmEgmluy/96PX6YxY4+VlHY0lZA7nlKdO8Z1qr/HrUR5V1OpN7qEo+OpnSaDSw2+1oamrCz3/+c9y9exf19fUwm83IycnB5cuXMTs7ixcvXoiUkWclRPLy8/Nx/fp1/OQnP0FbWxvcbjdSqaMgv+3tbbEQyU2Nu3D8kIfJh5RQKISRkRFoNBpsbm7i6tWrqKqqgtPpRDQahdlsFjFTtLGo7TabDRUVFaivr8fq6uqpksOcnBw0Nzfj1q1buHDhAlwuF4CjBR0KhRAMBmEymcQGMhqNMBgM0Ol06OjoQEVFBZaXl9HT04OXL1/i5cuX8Hg8p9beP1XkgHOuFQLSNdf04DxrK5tGc2SxbGtrwy9/+Ut0dXXBYDCItsgvJ3ID/Pzzz+Hz+TAyMpIlU6cg/CUtu/uQ0MuX10LJytmIrOXkL2iZjKhiAAwGA4qLi3Hp0iV89tlnKCsrw8HBARYWFjA0NISpqSkEg8Fjtal46mWuVZaJFCdecu0auR+A2mqjIixymmUZgNMxg8EAp9MJt9uN0tJSFBcXo7S0FB0dHSgvL4fFYkEikUBnZyd6enrw5MkTLC0tpSnxtFotzGYzDg4O0jJw8fHgGmZqs0rDT+NCP/Tepjhio9EIp9MJi8WSlqo7NzdXxBjv7e1he3sb8/PzCIVCwtUcQBoopN809ipLEv+fk175HJnM8XklIkMgU0Uc+PdV7pH0Wx5HPgcktOb4NZLJZFqWOT7+KqsFF+qz3W7HpUuXcPXqVXR1daG0tBSFhYUiFTh50PT29mJ8fDztWqq9RWOfichykYn/SSSYn8fHIJFIpLmRnjQHdFwm+/J3MxF5fk1uXebzRPPC9yQ9b2pra1FXV4fS0lI4HA4xfysrKxgdHRWKDZpTlbsetYOTRd5PrgSQhfeDP9v4+aoYTLn2k+o8/jyiOSTDiE6ng91uh9lsRjQaxe7uLmKxmKjFxa8hKyBOeq9+VDKl1WphtVrR2tqKv/mbv8Ht27dRX18vXNIikQgGBwcxOTl55lYpnU6HxsZGfPbZZ2hsbERraytqa2uRSqWwsLCAgYEBLCwsYH9/Hw0NDVhaWsLY2BiSySSqqqpgtVqxubmJ7e3tjwIwk8kkwuEwJiYm4Pf7MTIyAofDAaPRiIODA6GV0Gq1cDqdaG9vx+XLl1FXVyfGfmtr61StUsDRgjUajbBarTCbzWmb79WrV3j8+DGamprgcDiwvLyM8vJyNDQ0oKamBmazGQaDARaLRTwIFhYWsL29jf39/VNt9w8Ro9GIwsJClJaWwul0QqfTobS0FK2trcLqQ0Hm6+vr2NzcxMjICJaXl8987Wi1R7UY3G433G43IpEIBgYGMD09Da/Xm5YNp6amBoWFhQiHw+dqvP+ahNy/Ghoa0NTUhJqaGqG8IUkmk9jY2MD09DQmJiawsrKCcDicdXk9I+HgiGs7gZOBLInFYkFBQQGKiopQWVkJs9mM5eVljI+PY2lpCV6vN61IL78Or5Mi30cGX1xzLANpFaiT+8G12gCU4FQmM+TVUVZWhqtXr+LGjRuoq6uDzWaD3W5Pi8ksKCiA1WrF4uIiPB7PMdJHtau4a+BJc5Kbm5um3CQtOXdtIuVcXl4eSktLUVdXh/b2dpSVlcFmswmlEd0vNzcX4XAYGxsb6O/vh1arxdzcXFrSJj62Mrk+qb18vuTvyIH5/Huk9KW6T7Re+NzTNWQCIM+XPJf8fwKkMlni56hcp+Tr8/5x0Wq1yMvLw40bN3D9+nWUl5fDZrPBYrEIixfNmd1uP2bVkUU+LltXTvoOn0NuaZP7K39X7ptM5OX+8+vKBJq+z8eU90XVdt4O1fk6nQ4OhwNtbW148OABbt68idLSUuj1ekEEfT4fhoaG8PDhQ4yPj8Pv9yMcDivdcFVzqXoOqdrL2yZfM9NzRx5LuX+0Ro1GoyBCZCgwGo0CaxYUFMDlciGRSGBychIejwfxePzY3NE1gXfHT300MsWJ1L1793D37l3U1dXBYrGIxsdiMYyNjWF+fv7MwJpGo4HJZEJlZSVu3LiBn/3sZ6iqqoLRaEQoFML09DSGhobw/PlzzM7OIpFIoLm5Gbu7u5ifn4fdbhfAZ2pqChMTE9jY2PgoYDOVSmFnZwe7u7uYm5tLe9HTAtfpdKivrxcb7PDwENFoFFtbW1hdXT11a2AqlUI8Hkc4HMbu7m5a3ZDR0VF8/fXXCAQCcLlc6O/vR3l5OS5cuICLFy8KN0uLxYLq6mpEIhFMTk4iEolgZWUF8Xj8o1h3jEYjzGYzLBYLnE4niouLUV5ejqqqKhQUFECv16O6uhqXLl0SyTYODg6wubmJpaUlrK6uori4GMPDw1hYWEAgEDgT18VU6m1SD3qA7+3toa+vD48fP8by8rJYx1qtFs3NzaiuroZGo8Hi4mI2AcUHkpycHFgsFuTn56O8vBxlZWXo6OhAV1cXmpub4Xa7084/PDzEysoKBgYGUFxcjN7eXszMzIisV1k5XZFjWWQwQEKAiazsJDabDc3NzairqxMa+Lm5OYyOjsLr9SIejx9z38kE0E4CfGRlkdvEyQK/Fv+fa+lVdV14u8jiYzKZUFpaiq6uLly9ehUdHR2oqamBw+EQ7yKu9ddqtSgoKEBlZSXGx8cRiUTS7hGNRsXY0f3kttDvnJwcWK1WuN1uFBQUoLi4GIWFhdDr9TCZTOI57Xa7odfrYbFY4HA4hAWNK/fke8TjcUF+q6urMTQ0hI2NDaHI2N3dPfbeUbkQ8fbLcyjPgZy8gOYuNzcXbrdbZH88ODjAN998g6WlJUSj0YyB+xyc8rWQqZ3UVr5uDQaDsIKdRKx5jAt9pnovJ5NJ8f6h9UNeKLRW9Ho9XC4XHA5HGqmm9yj3oqD7HRwcHLOQkCWPx9RRG2WCIFt6VMRBTn4gX1P+rgzYVeSSX18mI7wgtnyOrMCh/Zibmysswl988QWuXr2KiooKWCyWNKup3W5Hfn4+Ojo6sLCwAK/Xi5mZGQwPD2NycjItjp572MgEjmKvZOWO/AxRkScSXghc9Xyl65KShFwWKysrUVBQAK1Wi9LSUhHmYrfb4XQ6xd72er0wGo2IRCJpCkiaP74GTiKEwEciUxqNBmazGXV1dfj7v/97/OIXv0BBQQFycnJEpiI+2GcJiA0GAyoqKvDLX/4Sn376KZqamhAKhTA6OoqBgQEBVHZ2drC3t4dUKiVAy+HhIZxOJxoaGvB3f/d3mJmZwffff48//OEP8Pv9Z9YHLqlUKqN1g1z6ampqcOfOHVRXV+Pg4ABerxerq6tYX18/dcsUadJmZ2dxcHAAk8kkji8tLSEYDMLj8cDv92N4eBhjY2Po6+tDRUUFHjx4gJ/+9KcoKiqC2+1GR0cHEokEHA4H/u3f/g2bm5tnFj9FIMlsNqOiogKVlZWor6/HpUuXcOHCBZhMJqEtSSaTQmNKD2K9Xi/S2FPQ7fT0NP7lX/4Fvb29WFhYSDNvn5bs7u7C6/WKh2AikcDS0hKmp6exsbGRBuq8Xi/6+vqg0WgQiUTOpYsfBxD0EJc/B9LdLM9inGWhtuXk5MBut6OxsRH37t3DF198AZvNBofDIV4IOp3u2AuzpqYGeXl5aGlpQX5+Pn7zm99gZmYGkUjkTPvxn1HI3Y5cvVQB44DaNY6SGn3yySe4cOECdDodVldXMTY2JsB5ppTrJPx9KcdG8HNUbjqpVApGoxHJ5FHRSjlrWyYgTp9xV8JUKiW0wvn5+SgpKUFXVxfu3buHq1evwuVyCQtDIpFIiytJJpPY3d1FNBoVbt28jclkUnyHjgNHWWs1Go1oOz2HS0tL0d3djcbGRtTV1aGwsFB4ZjidTqE0NZvNwtrFnw0yOOREx2AwCPf/iooKNDY2YmRkBIlEQrgAcuWpDBo5uKT1kCnVPR97GdwZDAYUFRWhubkZX375JZqamhAOhzE1NQWPx4NoNHoMvPNrc7dHGazyMaBzCQvQ9ymene6jemZyoMufsfIapeOBQABv3rxBU1MTamtrRRgCrRNyGTWZTDCZTOL5xsdPbrsMjIGjPWsymRCPx8VcyeTvfdYC7X2r1SpcxwwGg8CxtGaTySQikQiCwaCYb+6uyudbVmxwUsbHXmUFzSRUv66yshJffPEFbty4gerqapEEhn9fp9PBYrGgqKgIjY2NSCQS4l3/9ddf49GjR9jY2DhGnFT35wocajtlb5ZdIvl487GV54Tam5ubC7PZLPa1w+FARUWFSL5WUFCA0tJSlJWVCQJF785AIAC/34/l5WUxF6qMle8ztqJd7zzjAws9jOrq6vD555/j+vXrqKqqQjJ5lPDA4/HAbDajpKQEBoMBFy9exMLCAlZWVk7d1Y9Y7OXLl3Hz5k1UVVVha2sLfX19ePnyJaampjA/P4/t7e20wd3d3QVwNLnRaFQESppMJng8Hjx+/PhU2/2nitFoRGdnJ27fvo3a2lrYbDaEQiFsbW1he3sb0Wj01AHy4eEhpqamsL+/L+phAUeLd3x8HIFAADMzM9BoNNjZ2cHBwQG2t7extbUl1tLt27dFPNiFCxews7OD4eFh7O3tpQUyn5aQdrC5uRmXL19GWVkZ8vPzUVpaivr6elRWVooHSSQSwdzcHNbX10X2O7PZDKfTidzcXDgcDtTV1QliReAiHo9ja2sL0Wj0VPtycHCQBqpII0xjyYHdaRPtP0foYUwvN51Oh4KCAuTn56cFe1ssFqRSb0sw+P1+LCwsCEXJWYher4fVakVRURFKSkpQVVWF9vZ2XLlyBV1dXdDpdGmaMXqxh8NhHB4ewmq1wmg0Cg17c3MzqqqqsLa2liVTZyBkIZAVV7Imk4g6iVarhcPhQFlZGaqrq5GXl4dEIoGZmRnMzMxgdXUVwWDwGLmRSY1MdDggl7W5spaYvBO02rcpmFWaYL72eFtkdzF6Jre0tODu3bt48OABSktLYbVaxRglEgkEg0GEQiHo9XqYzWYkk0ksLCygp6cHL168wObm5okxGrwNFBNDSqq8vDx8+umn+Pu//3uUlJSgsLBQaM8NBoPQdnOgrho/Ar0Etol00bOFvltWVobt7W1UVlZieXkZGxsbx+LbMq0bupYcqyH3VY6h1WqPakV2d3fj/v37uHXrFmw2GzY2NlBcXAy9Xn8s1kcFDFUWBX5/lVWFjvF4MpWcBLZVFplU6shLZXZ2FgMDA2hoaIBerxfZLmn+HA5HmpuWnNxAJj7UVnns+XqU+3bSHuLXMJvNKCgoQH19PVpaWlBQUCDWM90zFovB4/EIN2wiVJwoye2Xx1Ee41QqJRQ4KqsW/59IaH5+Purr69HY2AiHwyHWsNxvbrnkJYCuX78Op9OJ3d1dPH78WODD9zV2cMsUkRrC9PJnXNnMSZdGoxGx85QUrru7G7W1taiurobb7RYhLES2iDDSd6PRKJaXl/HixQthxd3e3s6YXOV95czJlF6vR2VlJW7duoW/+7u/Q11dHfb397G9vY3Hjx9jcHAQxcXFuHnzJq5evYpbt25haWkJT58+PXVgkJOTg4aGBty7dw/t7e2IxWLo6enBb37zG7x69Sqt8rxK+MslJycHJSUlKC8vh8FgONV2/ylCvuz37t3DgwcPhLYuGo1ic3MTOzs7Z+IidHh4iLGxMYyPjyvBB2kRgLcLPB6Pw+fz4eXLl9jb24PdbofNZkNxcTGKi4tRV1eH6upqLC8vHyO+H1rsdjtKSkrQ3NyMH/3oR/gv/+W/wGazAVD7Cu/u7uLRo0d4+fKl0Dq7XC5UVFTAYDCgrKwMt27dQlNTE1wuF37yk5/AbDZja2sLPT09abETpyWHh4fY29s7k3u9rxAYksk9f/DqdDoBlujvgoICocmsr69HXV2d0GzpdDqRTMbn8wni/vXXX4u4o9MWCrquq6vD1atXcfHiRTQ3N6O2tlbE15FWeH9/H+FwWMRmbG1tAYBYK7m5uTCZTILIm83mU29/Vo72C3d7ISDC3Y04WKe/LRYLuru7cfXqVTQ0NMBgMMDv9+PNmzfweDwIhULCss5BD4+V4pYbLioiRMBX1uJHo1EB6g8ODoSFhkCqDHxVoJwsUlVVVWhra8O1a9dw8+ZN1NXVQaM5chcOBALY3t4WpSxycnLQ0tIiNPhTU1N48uSJKIMiA0JKsAAgDYRSdtpUKgWTyQSHw4FAIICxsTEB+HNycpCXlycsaRxIUl8SiQTi8TjW19exvLyMzc1NRCIRHB4eory8HK2trSguLobBYBBjsb+/Lzw5ZmdnRYY5Pte0Hoik8nty6xydR8Q2k1aclL7kwXD9+nUUFRUhJycHwWAQlZWVKC0tFW1/n/gyui/Vd7Lb7TCZTNje3hbxYJFIRKxpWjcy6OT9ltfjSQCVkx6v14vHjx8jFAqhs7MT7e3taGlpgdVqFQqykpISOJ1O+P3+YzGIqv7JhIPeccBbV0ueOEzeq/Ic5OTkoKioCNevX8fly5dRW1sLl8sFi8UikjZRv+LxOGKxGNbX1zE3N4eNjQ2Ew2EsLi5ia2tLuPHL6chVBI+E9gdXBlDb5fmgdUfWsbW1NVG3i68xOufg4EC8ZynpitFoRFFREVKpFKqrq0XmXk5AZKWA3Ha+Z0nJTXNH+9Fut4ui5ZQAZnt7G7u7u+I+5OZZWFgo4kxJ+UzxdXxcuAJnbW0NIyMjeP78OaampjA9PY1wOHzseUPf5a6w78JCZ0qmNJqjzH2fffYZHjx4gKqqKlgsFqytreHhw4f4+uuvMTs7i4aGBpHsgTb5WYhWq0VxcTGam5ths9lEHMLKygqi0eg7yYXJZEJzc7N4MarStp4XcTqdaGxsRFNTE8rLy6HT6XBwcIC9vT34fD6EQqEzA9Lv0uCpPqMEG6urqxgfHxeuHLSRzmLNaDQaXLp0CT/72c9w8eJF1NfXw2azpaUSl7VawWAQvb29ePLkiXgZ6XQ6jI2NCUDy1Vdf4X/8j/+Bn//85zAYDMINc35+HltbW6c+L3t7e5ienhZuFh9byPVNq9WmxdVptVq43W4UFxfDZrOhsLAQVVVVyMvLg9VqFTEbbrdbWKEsFssxLRhwVLcskUigvr4esVgMT548wdTU1KkrFHQ6nYjP/Nu//VuRvIZcEg4PDxEIBOD1euHxeNDX14eFhQXEYjEEg0GUlpbiV7/6FS5cuID8/Pw04J6VsxFukZLBPj+Hzwm5BLe3t6OtrQ0OhwMazVHm1Tdv3qTFDMkuQDKh4UAlkxadNNncfY6AELeicLAsgwhqi+yyRRaD8vJytLe345e//CXa29tRXFyMw8ND7O7uYmtrC1NTU5idncXExAQikQjKy8tRWFgIi8UCv9+Pvr4+TE5Opu1xErIkyGSEjzkB1+3tbQwMDGBnZwcjIyMoLCwUihRyxeLkhd7VgUAAkUgE4+PjePHiBcbHxxGPx2EymXD79m3U1NSkvVvi8TjW1tbw4sULPHr0SNyTB+lz6xORJHkeudVGdvlSPevJGnbr1i3cvHkTlZWV0Ol0Ika4sLAQlZWVIomHbHlRgUZSxJSUlKCpqQmNjY2Ix+MYHBzE6uoqotHoMa8IOT07b69MQjjhIXDPEwpwok6ZiBcXF/H48WN8+umn+O///b+jpaVFuIA6HA4YDIY0AvIu0MuVCeQ2Sv3g+0VO8MH/pjnV6XRoaGjAT37yE9y8eVMkxKDrcS8qGouGhgZ88sknCIVCWFlZQW9vLyYnJzExMQGPx6NUisj35esik2saF7pmIpFAIBDA4uIi/vjHP4p9X15eLt6tRI6IvOTm5qKgoEAkYqFxz8vLE3GWvF2ZXEW5FZjay2PbaOxzcnJQW1uLH//4x/jJT34i4ke3trYQDAYRjUZhMBhETJfL5RKxTwDS3HTpHoRpt7e3MTc3h6GhIfz2t7/F0tKSILB0f3kcad/KazyTnBlSIqLS1dWFu3fvorOzE0ajEfPz83jx4gW+/vprDA0NIRgMwuVynXkadBLy56QNxRftSeJyudDc3IzPP/8cV69ehV6vx9TUFObm5s5d3aOcnBxUVFTg2rVrqK2thcViwf7+PlZWVvD69Ws8f/4c8/PzZ5684YcKpaZ/8+aN0F7JMTGnJU6nE11dXfibv/kbfP7556isrITValWeS1qo6elpDA4O4vXr11hfX1eeq9FoMDs7iwsXLqC2thbNzc0i9uC3v/3taXZJyMHBAQKBAEKhEJxO55ncUxaj0YimpiYUFhbCZrOhoKAARqMxTalBZIqygOXl5aGoqEhoVcmXPZk8yna3u7uL7e1t4btO2i4qE0A1fshV47REozmKlSktLUVzczOuXr2Kq1evor29HQ6HA0C6sqC/vx9TU1NYX18XL979/X3E43FRg8Vmsx1LTJGVs5FM8QLyMR6jQpap0tJSVFRUiJTbfr8f29vb8Pv9yppIHKSclDQik3AFJddEy9dWuX/RPVSlEpxOJ27fvo2uri50dXUhPz8fiUQC6+vrmJmZwejoKPr7+0X8KwGgnZ0dAMDk5KRIxayqhcTbzvutIn57e3si5fH09LSIoSgsLITZbIbNZktzL6KstpFIBDs7O1hbW8PKyopQKJaXl6OiogJFRUVp9Rm9Xi96enrw/fffY3R0NI1IyW3npEomvzK5yTRvwNv4vJKSEly8eFEoEA8ODhAKhRAIBBCPx4XVPZlMioRChGtkd22yZpNrZk1NDWw2G4aHhzEwMCBcvFWJR06ynryL6KvWN12XrCiJRALz8/MYGhpCcXFxWs0lck+V7ysLVzBxQC/HHcrrns6Rs8jl5OTA6XSipaUFFRUVwrJ6cHCAaDSKYDAoCgpT2vzc3FyhcCCrEL13qK4TtYP2Gd/T8hiRe52KZMl9p7EOBoOYn5+H1+uF3+9Hf38/fvzjH6OrqwtmsxnhcBjBYBADAwPw+/0oKChAd3e3UECSsiIajR6z7Ga6P3C8LIzKNVg+32w2w+VyCXdWSsJDljLyPKHnAT2TCKvT/t/a2sLk5CSmp6cxPT0Nj8eD5eXlNKutPPd87Ljy5F1hRmdCprRarfDlv3//Pi5evIiioiKEw2G8efMGX331FQYGBuD1ejNq9c9CUqmj2AlKre1wOFBbW4uZmRkRr6LSmBmNRlRXV+P27du4d+8empubEY/HMTIyIh5G50V4ey9fvozCwkIcHh6KNOpPnjzB8+fPsb6+fmbj/udIPB7H6uoqfD5fGig47bbn5+fj5z//OR48eICamhoYjca0hwL52kejUSwtLeHx48f45ptvMDQ0BJ/Pl/G69OCbn5/H8PAwysvLUVxcLGLwzsriwB90BoNBuLaclRiNRjQ2NqKlpQVutxslJSXCTYdACQWQG43GtO9yDeTW1hY8Hg9GRkaws7ODaDQKr9eLtbU1eDweoclta2uD0+mE0+k8VSUCuank5+fj2rVr+Pzzz9HZ2YmKigpB/CiQfXl5GX19ffjqq68wPDwstHPkApRKpbC1tYWhoSG0tLSgu7s7a5H6CKICMByUyS43PD1xU1MT8vPzAUAQj0gkIsArvdDJBY/fiwMUTo5k4aBH1gbz46o+cJAsWxpoH1Lyn1u3buHKlSsoLi7G3t4eZmdn0d/fj7m5OfT09GByclIoFi0Wi4hRXFxcxJMnT7CysnKsjxwEE4Dk1ptMAJ0AVTAYFGNLsR+U5Ijc2qxWKzQaDfb39xEIBIRlTKvVoqioCLdu3RKAk9oRDAYxMTGB7777DuPj4/B4PMeK1XI3IWq3CsDLa4iPs3wtSu1cVVWF8vJymM1mhEIhrK+vi+fc5OQk1tfXhSdQQUEB3G432traYLFYsLOzg0AggEAggP39fZSXl6OoqAhdXV24dOkSUqkUVldXsbKyIpRQcnIVahuJbNU5iSDKViDeX/7Z/v4+Dg4OsLa2htnZWdy6dQtOp1O4OweDwWOWGZnA8nuqxp6PM69HxcecrkOfU6xaR0cHDg8PhaWVstzt7u6KOLtgMCj+djgcuH//PsrLy6HX61FWVobV1VVhIZHJKR/Ld1mfuFubrNyhfUFKhp2dHQSDQSwtLQmCRPF+wWAQCwsLyMnJEdkvuetpLBYTiWLoOHfBlRUJ1HaK7eLjryLZXq9XKASAIxzAE1jQnJESiEr40Hon4rq5uYnl5WUsLCxgcnJSZEVWJbHhbs2ZxjKTwozLmZAps9mM1tZW3LlzB3fu3EFxcTG0Wi1CoRCGhobQ29uL7e1txOPxNDcc4OyIFHD0QiHtb319PQoKCvDpp59Cqz1K0zk2Nga/359W/4KyEt66dQuff/65iAGbnZ1FT08PBgYGzlUQONeIUyAi+fOOjIxgdHT03NVpOkmoPy6XKy3N7GmL2WxGY2OjcJGkBxY9PEKhEJaXlzEyMoKenh68efMGs7Oz8Pv97wXWyX+ffP4pgPKshMZRr9ejpqYGFRUV8Pl8Svel0xjzvb09vHnzBgsLC7BYLCgsLERxcbGII7Lb7WhpaUEqdeQ66fP54PP5RCApgamVlRWsra3B5/OJ8YxGo4hEImmZoKqqqnB4eChS75rN5g8+3lrtUR2VpqYm3LhxAzdu3EBbWxsKCgoEUQ6FQvB4PBgdHUVfXx+ePXuGxcVF+P3+NLAGQLw8qA5IVj6ecGDG0zSrNJ06nQ41NTVoampCXl6ecFdaW1vD6OgoAoFAmsY0lXpbqJKDF7oXEbRYLCYstxwM6vX6tOeTTJRI+No6yWJCbSIlaXt7O27cuIH29naUlJTg8PAQa2tr6OnpwX/8x39gcXFRKDKIpJDV2OPxYGlpCVNTUyJGkwNtbgni5FRuJx2nJBE8QxcHRLJrT25uLgKBgLgnuV5TwP79+/fxD//wD6ivr4fBYEAyeZR1cHJyEk+fPsWLFy+wu7ubNrbyHpWtM3wseSY92YLCv0ffoZqFlHDp4OAAy8vL6O/vx+joKF6+fJlmITMYDGhqasIvfvELXL58WbjzU5x6JBIRiWso69nCwgL+8Ic/4KuvvsLi4qLQyMvJJuhv3nf+Ge8zKQNkJYNKEUG/6e9oNJpW5ygcDmNtbQ3r6+tpmfhksi+PIV8vMlimTKp8zuT20HXKysrQ3NwMv9+PmZkZkS48GAwKUkb7NxaLIRAICKVDXV0dOjs7odPp4PP5MD8/f6ymmorYyXuP+sKfE7SWuPWWEzI+X8FgUMTBLS0tobGxEXNzcwgGg8jLy0NraysKCgqE9SeVOooPpBTiVFuSExt+T76uM80x3wPUzkgkgomJCczPz8PhcKQlfaE5oH0cDofh9Xrx7Nkz9Pb2IhaLIZFIQKvVYmdnBz6fD5FIRJxLsWAy0eRKeNX+e18ccOpkirQjXV1duHz5MmpqamAwGLC1tYWBgQGMjY1hbW1NdJAymlmtVuHHHAqFzsTlLJlMYnV1FX19faiursbFixfR2NiI3Nxc2O12VFVVYW5uDh6PR8SuFBcX49atW/j000/R1tYGo9GI2dlZPH36FENDQ/B4POeq1ovRaERraytaW1uF5iEejwuNSjAYPJcprjMJPXhIw3JWyT4ikQhGRkZENh9ynwiFQsKdbHFxEcPDwxgcHMT6+vp7u3tqNBoUFBSgoqICJpMJsVgMOzs7Z1a4mh5W5C5SU1ODlpYWoaEkLS49YAkofch1vr+/j7m5OQBvs5653e60rIdLS0twOBzY39/H1tYWvF4vfD6fAFGxWAxra2snliWghyf3vT4N0pqbmwuXy4WmpiZ8+umnuHv3Li5cuACn04mcnBzs7+8LkDY0NIT+/n4MDAxgfHw8DVBR4UGqYVZYWIjq6mq4XK4P3uasvJ/IYIFiZTno4WDs4OAABoMBVVVVcLvdIgvs9vY25ufnRSZJ2Q2FkqxQzIDb7UZhYSHsdjsODg6wtbWF/v5+YXHgbaM1rYo5koEwP37SuZTFtKmpCZcuXRJKUipqS+8SCsCnvuh0OrhcLlRVVQE4AsuxWOyYxlhF+CjIndrDx4fGVrZyEGDKVPKAk0PaY8XFxSI5E7caUzzPs2fPMDg4CJ/PlxZzxgPWZVBGf8tkIhOxVbWVwiXy8/ORm5sLn8+H/v5+PH36FPPz81haWsL+/r4Ap0ajUdQ4pNhRijMifEUZIw8PD0Wtxjdv3mBtbU2sI06CSFRriY8pFzlT2knPWNkC4fP5MDIygtevXyMcDmNlZQVv3rwRlin+HfoegXCe6Y+3WSZZ5CIm90V2OSXCNT09LcrHcDLNszySRYb2u9lsFmT98PAQCwsLmJqaEm6YNL+0bjgpky1O9Izh60ulAFHtZe7eSzU+l5aWsLW1hcPDQ1RWVqK5uRl2u10oa6gv5PJJ8XlyvBv/W35+8D3LCRf1m1xS5+bm8Nvf/hbBYFAkj6JnHmGPxcVFLCwsYHl5GUNDQ5iZmUEqlRIxkUajUSQlAwCPxyNyAciZNlVWed5OVX9Ucupkih5Mly5dQktLi4h7GB4exr/+679ibGwsrZEmk0kUYz04OMD8/Lww/5+2JJNJLC8vIx6Pw2g0IhaL4e7du2hpaUFDQwN++tOfYnBwEL29vejt7cXh4aHQ+rS3t8NisWBrawu9vb34t3/7N8zOzp4rIkUvv2vXruHChQvCPYq0l1Qk76wSfnwIiUajWFxcxNLSEvx+/zGXr9OS5eVl/M//+T9x9epVXLp0CXq9Hn6/H/Pz85ibmxMvWXrwvO86ILDR2tqKa9euweFwwOv1Yn5+/sySglAsWjAYRG5uLqqqqnDlyhWRuj4nJ0e4wgQCAQwNDYk0zqeh9Egmk8IPfWVlRbxEenp6BJHmmmjg7cP7Xc8NjeYoZsLtdotnEwHBDznWlE3w1q1b+NGPfoS6ujq4XC5oNEeZzkjL+dvf/haPHj3C2tqaANXcwkDp0ysqKtDW1iauU1dXlwbksnL2wq0lchA7ucnQ/s7Ly0NjYyPsdjsACBezjY0NoXThWlKdTicKgFO20traWpExNhaLYWJiAslkEiMjIyK1OPAWyFIQuGwB4oBeBb7kPtIP9eHy5ctoamqCxWIBAFFrKT8/XygYV1dXsbGxIWob1tbWorGxMc2NjN+TwCSBLK7xl9smExKNRiOIkxyUzudDBoJ0fkVFBT799FP87Gc/w9WrV0VyAXIpHxgYwMOHD7G0tJSWDIeTKWonB9lUR1O2LMiuZHJbqZ25ublwOp24du0aampqcHh4mGbF3traEmuHiLfT6RSZPff29sQzOxgMikKsqVQKLS0tAoSOjo5idnY2zTuFF7jlkik+jNY/tV0mmXwtqeaTg/N4PI7NzU3MzMzg8PBQeNDQfpKtCZR4gBIqqO4jW3Woj9ySSecR8DcajSgpKYHZbMb6+rrynUzn03uJFHR5eXki7k6n0yEUCmF+fj6tDIdsBaO1xZMgqNa9PJbyXqD28WyF1PdEIiFKFBgMBlH3rqSkBC6XS+BBUk4Gg0FRU5IrIDgB4XuKW9jk+eWxVGQxJaXQ48ePMTY2BrvdjkQigerqanR1dYnrjI+P4/Xr1yLrKbn3JZNJOBwOXLlyBaWlpWhra8PBwQFmZmbw1VdfYWJiAmtra0rFtKz0ojUhE/JMcqpkSqPRoLa2Fjdu3BBmQ+rY69evMTg4CK/Xm/YdCpx0OByIx+OYmZnB8vLymVlLDg4O4PP58PTpU2xtbWF+fh43b95EW1sbiouLcfXqVZSXl+PatWtIpVJwu91obGyEwWDA8vIyvvnmG3z11VfnrmAmpX2nytelpaXCPW11dRW//vWv8fTpU6ysrJzr+kGykMk6EAi8M3X9hxQiHP39/VhdXUVOTo4w6VOq0z9FTCYTysrKRJYrjUaDQCCA+fn5M1tP0WgU8/PzWF1dFS9ul8uFzs5O8XCk1Ka7u7vo6urCo0eP8OTJE1H76ENLpniQP0c0Go0oEVBVVQWNRoO1tTWMjY1hY2Pjg96P6lxVVVWhrq4OTqcTqdSRi+LIyAj6+/vR29uL0dFR4UdPsTUajQY2mw0VFRW4f/8+2tvb4XQ6UVBQILJa8SDhrJy90LhzQAe8taTwpClWqxUVFRUoLS0VBISsOfTs4GQnJycHVqsVdXV1ePDggbACURpmu92O/f192O12HB4eIicnBw8fPhTWY669p+K0HOjLJIoDUN433secnBzk5+fj5s2baGxsFBZj0sI3NjaitbUVnZ2d2NrawurqKl68eAGLxYKqqio0NDSgqKhIFJflcYAq9yy6LwfvKhDOLVCq+B1+feqfrISoqKgQ8cQmkwk5OTmCSP3+97/HV199hbm5OZFtUWVxkckFdz+UwboqHTYnI9RmrpApKytDNBrF0NAQhoaGjhFSyoBaV1cHi8WCyclJkcJ9Y2MDOzs72NzcxPb2NiwWC2KxGNxuN+rr61FUVASbzZZm0c9kQVNZrDixoc8zJRWRryETLvqfssdRHLIqBTe33mR6B2VSGKRSqTTco5pXq9WKmpoa5ObmCndyrjTJtIcMBgM6Oztx7949dHR0IJFIYHZ2FqOjo/B4PGnFkLmFTNV2Vbv5D7eKyePJU5fza9G7iRJi3Lp1C62trWmZiVOpo9CFzc1N7O7uZnTh4+uXnhOZniXcvTUej4vzAYi4UXoXLi0tYWRkBKlUSpSQ2NjYSKt1lUweFX2mGMjKykpUVlbCZDKJpCrfffcdfvvb3x6zJsvrWEXSVf3lcmpkioBKXV2dKGSq0+mwvb2NoaEhDAwMwOPxHEvOQIHler0e+/v7WFhYwNra2pm6nsXjcRGnsLm5KVITl5aWory8XKQUp8knc/vCwgJevnyJwcFBBAKBc2OVog1dV1eHO3fuiBTeqdRRDafJyUl8//33mJiYODOXyg8lPBGBwWA4s2x+JF6vF9vb22mb8c8ZP4fDgYsXL6KsrAwARAaewcFBUeT3tCUej2NpaQnDw8OoqamB1WqFXq+HzWYTtRzI3B6NRlFcXCy0bUNDQyJD13kX0sK1trairq4OiUQCS0tLWFxcTIuj+BBCYMhut8PhcECv1wut8tOnT9HT04PR0VEcHh7C7XbDbDaLODFyc6yqqsKDBw/Q1NQkLIQq8JaVsxV5DvjLV3Yts9lsqKmpETEvFM9ESUdisVga4DEYDHC73aitrUVTUxMqKiqg1+tFzILD4RBu8QaDAVevXsXCwgKePXuWVidKo9GkATw52F0lqvVPhMzhcKCpqQn3799HbW0tDAaDCE6nAHJKWkNAjrJzXrhwAS6XSwS0cwwga9Rl9zBV3A4HPUB6qmtVrBW3StB80fepf1VVVSgrKxMB8FR/imJkqOaSbBGQQSIHYdxtiLeft03+DicTlCinpKQEVqsVm5ub2NjYEF4QnGjm5ubCZrMhHA7j+fPnSCQSWFtbw8bGRloBdmoXWbEo2yHPlAcckTr53aoiVySZ6mTJJDcTQKU1Q9b49vZ21NfXp80vnUfzpnLjlC03XGRSzdvK1wyt966uLnz55ZcIBoOoqqrCwsICQqEQrFZrmvWF9j0nUw8ePMCdO3fgcrmwtraGb7/9FlNTU0pXRZnE8TbK/eNEgKcd59+n76qSu+h0OhQVFYni2oWFhSgrK0N5ebnIiAu89Q5ZX19Ps6RlInjcKq7X69P6JZNt6gPtD1IkkmKWrGcbGxvC7ZDHlPP7RiIRrK+v48WLF0gkEsjPz0dDQwMKCwvR1dUFv98v9gPFFWYiTZwUy9Y1lZwamaI4o7q6OrS0tMBut2Nvbw8rKyvo6enByMjIsboFtHAp604ikcDq6io2NzfPPI6HzOETExPY2trCs2fPUFVVhb/927/F7du34Xa7odfrARxNYjQahc/nE/6vcpaajyW0YRwOB8rLy9HQ0CCqX0ejUUxMTOD169eiSOl5IlK0iEmbqnI/dDgcIplGcXGxyKp3VuDyJC3YDxVKpnH37l3U19fj8PAQi4uL6O3txaNHj069ADHJ/v4+1tfX8fDhQ6yursJkMqGgoAAtLS24ePEiqqqqxP5IpVIoKirCZ599Br1ej93dXVEE7zyLRqOBw+FISxwzMzOD+fn5tJiTD31PDgbn5ubwb//2bxgeHsbW1hYsFgsqKytRX1+PyspKtLS0oK2tTShsDAYDHA6HSFZBGjkZYGblbIUHuwPH3ZSAt3NfUFCAW7duidpSZPWgGASyXGi1WthsNpSUlKC7uxuXLl1CIpGAx+PB+Pg4AoEAdDodOjo6cPfuXaHQKC4uRm1tLfLy8kSyFZJUKqVM3U0uNpliB/gzR6s9Slvc3NyM7u5uQTgo5mt9fV24iMXjcXg8HmxubmJvb0+4+DmdTthsNgQCAVG/SLYU8fvy9ygnKsDbYt5kteI4QTX+XHMua6FzcnJgMBhEnTeehGZ3d1ckPOCZ16it3A2OEyL6nICs3H4qNsxJn0xyNZqjOK66ujpcu3ZNlEAgJS8lE+BtIkvO4uIi9vb2hKafWwBJy19QUJBWp1GFtTj55vFYcr0g/hwiCygd48keqJ1EOuT+UqFYl8uFhoYG4U6/u7uLgYEBQR5VSUo4QJaJLl9jstuoitgRfiorK8O9e/dw9+5dkRkvEAjg4OAARqNRWM2o3hetp9zcXFgsFlFkeG9vTyg7NjY20izWctp8vgfkdpFXEfU7097lSgYVEbNarejo6MCPf/xjlJSUwG63w2g0wmAwCELNiUU0GhWujdzFTya4nOBx0kJrnrv1ye75dD1uxaVzyYqnIuL0fzQaxdjYGHZ3d6HX6/Ff/+t/RVVVFQoKClBfXw+3251GYmUrMH/u8b35Ljk1MmWxWFBXV4eGhgaUlZVBr9djfn4evb29gqDIINRms4mYAIfDgd3dXZF952NIMpkU2sKdnR1sb2+ju7sbkUjkWMC3zWZDU1MTvvjiC5hMJgwNDQkW/zGF3EBKS0tRXFwstOLJ5FEdG3IxoiDK8yAUI6DX62E2m1FbW4vS0lKhAeJis9nQ3NyM9vb2tM8/Non9oUJZ865du4bu7m4UFRWJ2l9LS0tnmmExlTpyeaBaKxaLRbh/eL1e7OzsYHh4GNvb2ygoKMCXX34pQN/ly5cRDoexsLBwrgkVuVN2dnaiqakJOTk5mJ6exsjIiKgRctpCLobXrl2DyWQSz7+ioiKRDr60tFS5pillcCQSEW7RZxUvmJV0IQ06LyCqshiSdbK0tBR5eXkCPMViMXg8HhGnRwCuurpaFKTe3NwUikXy+Tcajdjd3YXdbsetW7dEZsC8vDw4nU7hpsUtJjzgm1s9yNWGiwyOSTQaDfLz85Gfn49U6ig9//j4OIaGhrC1tYWZmRkAR0oZn8+HYDAorKvk0UGa3nA4nAaQSOR2EjDjRdnJWkIE4F0xPTKglq0AFFPM35MARLKAp0+fYmFhQWkB4clCOMhTATLeDnoXq9zCOSEnQkWJckizTiSKrkulFyhGKhaLiWRCMlHT6XQoLy/HgwcP0N3dDavVisXFRQwMDGBzc1M8v2VrBifhZEFQKWFVcTL0t6xoJlJE5KOyshIVFRUiPvDq1asoKCjAxMQENjc3xZpJJpPCRYwAvlZ7VIiZYlIJR/JxIozBU2WrhNadz+fD+Pi4sK4WFBSgqKhIxBoBEJkqaS1QnJzRaBTPB6/Xi4WFBWxubqYpEYhAZIqN4mNPhBSAIJX8e3wNyuOs+ttqtcJmsyE/P1+4tdI4kpAliFyQyZKkInsqgsOFvImi0ahwt5MttDKZkq+r+p/aRIWqc3Jy0N/fjzt37qC0tFQ8e7u7uwWplbN9ytdXkf1Mcmpkym63o62tDfX19cjLy0MymcTc3Bz++Mc/Yn5+/lgqX61Wi8LCQjQ0NAjNWigUOq3m/SA5PDwUxcpocGmD0YuCGH5VVZXInPPixQssLy9/FIsPPVQrKipEsHJTU5OolREIBDA7O4ve3l4MDw8fsxJ+DNFojpIBuFwuuN1uuFwuFBQU4OrVq2hpaREAhJ9PmjW73S60g+TiQCmKz7tQYOvNmzfx5ZdfoqmpCRrNUfzO8PAwFhcXz3z9kMmcAjrtdjsMBgNWV1cxNTWF3/3udwgGg2hsbITL5cKdO3dQVlaGGzdupAG+8ygEBtva2tDa2ioyA7558+bU9gK99Dn4IStkXl4eSktLxRoG3lq7eUzp4eEhQqGQAEeTk5OIx+MiAUqWTH0cobnlwEGVuU2jOSrUm5+fL5IapFJHRWYpjS+tDXLrslgsmJiYEAl2OHikJANVVVXo6OhAXl6eUEKRdpm0yFyTLGt1ebpyVRwVcDy1cjweRzAYxOjoKHw+H3p6evDkyROsr68jHA4fsyYRMYlEIsJ1LhKJCKDLx0lFSrgrDn8WkjWP4kHoPNkFj/rA+8OvodEcZR3u6OhAc3OzSMtMZHdychI9PT3HvGRUbQTeWmW48D6SUNyNTqcT7ZfBMYE6qm1VXV0tspaRkpSKPNOzjbKsUuFbTjTI6llfXy8yFhYWFsLn8+HRo0d4/vy5sLrIVjIaLwDKwHyVhVYeF1q7pEAiEmi1WqHT6ZCfn4+LFy+iq6tLlOWgjJUOhwM1NTVYXl5O6w/dk9ZWQUEBysrKkJOTg52dHSwsLGB1dfWYBZBbBlVrg/q5tbWFJ0+eQKfTobu7G5WVlaLml8lkEgrIVColSC/9kCvv3t4e1tfXMTc3JwrGZyJSMvGXhSf2yJRkg86Tk5rQ38nkUfa8tbU1eL1elJeXp6WI5+fSvejZIpMplWTab/T84NZB3nZKICKnhT9JZEskWcSWl5dFUh+quXb79m2Mjo6mtYeuIY+5akwzyamRKZPJJEybwNGC9Hq9mJubU5IkjUaD0tJSQb7OKsX1+4rdbkdtbS1qa2tRWFiIg4MDPHv2DE+ePIFer0dnZydu3boFm82Gq1evori4GG63G3/84x8xOjp65nVgLBYLGhsb8aMf/Qg3b95ESUkJ8vPzRYrXiYkJ/O53vxPVrs+DVYq0sZ988gkuXLiA+vr6tGrh5C9LQg9huRaBy+VCc3Mz+vv7hYXkPFuqSktL8dlnn+HHP/4xrl27BrPZjKGhITx69Ah/+MMfMDk5+VHmhyq937t3D+3t7dBoNHj58iWeP3+Ora0tJBIJLC4u4t///d9hsVjS/K7POnbth4hGo0FVVRVu3bqF0tJSeL1e9PX1YWpqCtvb26fiUry/v4/NzU1R/Ju04LSuDQZDGpGKx+OYm5vDyMiIeMnu7e2hv78f8/PzwkrZ0tKC5ubmY1mVzvN6/2sTAsR8/FXghltTOCDx+/1YWlqC1+sVADYSiaCvrw9arRaJREL8cCvN4eEhdnd3sbOzIwgJgVUCS3w9yOuDAw9Zmy2vI/5/LBZDf38/ZmZmRPILr9crrEzkfkU/5ApFhWYpUYbf78fOzo74DidMQHoaa3L9onGUCYmsAZfBlXycrk/XzsnJQV5eHm7cuIErV67A7XaLtgeDQQSDQfj9/mPWBE6e5XGSx09FUInIclEB6EQigfX1dSwsLCASicBut6O4uBj3799HTk4OlpaWEAgERBZHsiQQiaI1mpOTI5IJ/cM//ININODz+fD48WM8e/YMq6urac9APmZc5GQJfMz5cb43LBYLjEYjTCYT6urqRAyY0+mExWKB2WwWylSHwyHOJ7JsNBpx8eJFmEwmzM3NwWaz4eDgANvb27Db7WJcGhoa4HK5EIlEEAqF8M///M9YX18/RpreZW2g8w4PD7GxsYFvv/0W/f390Ov10Ol0oi/k+qfT6URs2y9+8QtUVlaKayQSCbFfVZlnMwlXFPCxz2Sx4UqHk/qXSh25/Y6NjeF//a//BbfbDYvFcswqxZ8lGo3mGJ7KpLCgz+g3f8bwmDIuPNZKvod8fb7eaJxkCxeRMnou6vV6uN1u2O126PV6pTWQ71k5Fu4k+eBkSqM5qtlSVlaGtrY24a40OzuL6elpbG5uKt2VtFotysrK0NjYCKvVinA4LDL9fEzRaI5M8dXV1fj000/R2NgogsdHRkbw7bffQqPRiPpTXV1daGxsRHNzs3DFCIVCIgXmWbSX0st/+eWXePDgAdrb22Gz2USc1MzMDJ48eYLHjx9jeXn5oxfoJQLU2tqKTz75BHfv3kVVVRWKi4uFuwK5LPCFrtPpROIJvrHy8vJw+fJlxGIxVFRUYGpqCsvLy9je3v6o/ZSFXmwXL17Ej3/8Y1y+fBl2ux1erxfPnz8XRCoQCJx527RarShpcPPmTeTm5uLx48cYGBjA0tKSsDoFAgGMjY1hamoKN27cEICJgM95ldLSUnR1dQni+vLlS1EW4TSICCX16OnpQX5+PqqqqkRabC4E3qanpzE9PY35+XnxMKfn6ObmJlKpo1i1+vp6sQfohR0Oh08t7isralG9kGXgfHh4VMcnGAwKQEKB3ZQ9M5FICODE9z2BBO7ySRYGSmQBHK0zn8+HaDQqAJDsBkZgQyZnHKBl0tSTGw0VyKZrcYsW3Yv+ptpYzc3NaGhoQG5uLjY2NrCwsIBgMJhmmSLgLSeeoGvyeEFe6JQTKv49FZniAIneIw6HAw0NDejo6EB1dbXwatjf3xdtJLe6TMCRgzn53nRf+uHuWfJYq54/pIweHx/HxMQEdDodSktLUVdXB4PBgNnZWfT394uEWbu7u2k1oqifdrsdV65cwZdffonu7m6kUiksLy/j2bNn+Oabb7CwsKDEXATOZdcv2TohA8/c3FxheTebzSKmj1L8l5aWCqUSt7ZSvA4H27m5uSgqKsLly5dRXl6O27dvw2g0iucdZTalumBa7VHxVr/fL/APt0JRW1UWFnkNkSvm6uoq1tbWhDWRk8ecnBxYLBZcvHgRdrtd3JPuQSURKIsnuR7S/sv03lEpBqgf8nqn81VupfwckmQyCa/Xi0ePHuHSpUtwOp0oKSkRyh7+3CKvFJ/Pp4wPPKntcrto7FWEku9lmTDJe5lb3bhiiI6RW7C8B+h5Kc897xOtR7oeJ8Aq+eCIhx7wtNHdbjdCoRDGx8dFunC5QZQtpaKiApWVldDr9aI69MdOL07kkCwmFRUVwuy7trYmUjeSTzstNHIhSqVSmJ6eRiQSwfLy8qm7a+Xk5KCgoADt7e24d+8eWlpaxKY+PDzEzs4OXr16hRcvXmB2dvbMLWYq0el0KCkpwWeffYZPP/0UnZ2dMBgM4gWyuroKr9crAALwtpBraWkpqqqqRIphjeYohWlzc7PI5PL48WP09vZibGxMJEf42Jp78ulubm7G9evXcenSJdjtdqyurmJ8fBwvXrwQKW8/hlC9levXr6OmpgaTk5Po7e1NI1LA20QtpJmurKw815YpShlcWlqK0tJShMNhTE9PY2xs7FSzJdIzo6+vD3t7e2J9crBAZMjj8aC3tzctcxL//ODgADqdTvi5O51OGAwGAXR3dnZE2tisnJ3IVggZaND8BAIB4dJHcxoMBtPIr0rDy9cKxYWUlpais7MTDocDqVRKWFDC4bAAA3Rvuq4MxPj/dC9+Lm8Db58KhFKfyeJC9QtLS0vR0dGBuro6Ee+3tLQk0ixT/3htKU4CgbdghsgUtYO3SwZucrtoLMh6R+5mlZWVaGtrQ3l5OaxWq7jf3t5e2tzIIJvfTwasKpKkArX8b57ZjAM7it9eWVnBt99+C61WK5IF5OXlwW63C7dgivMmMKnRHGVeJTLT3d2NTz75BG63G5OTk3jx4gUePXokCsjKpFjutwx6+RhziwDFEhYWFgpvk7a2NpSVlcHhcKRZIIm8Epni1lW6JykfbTabqKtH65vWBZEaInqJRAI+n08Q70w/qv3K+8RJpCrhBoFvs9mMhoYGQUzIjY0wh8vlQn19vcjWS3Ozt7cnYn3kdS/vXXnNnDRXqqRdfL3S88fn8+H58+fCVb+4uFiQz3g8Dq/Xi8nJSQwPDwvMqNp3Jz0v+N/yflD1lcZd9Tyl5x+5xkejUezv74vnAq0psnbycaA9IZM1+fnNSa6slFHJqZApk8kk4iz0ej2i0Simp6ePATFqeEFBAS5fvozu7m5UVFQIzdX4+PiZFSrNJOTLW1JSInyVA4EAFhcXhZWNAMz09LRYnPF4HJ2dnaivr8cvfvELkbLxtLXFRqMR7e3tuH37Nurr60XGqGQyiZ2dHYyPj+PRo0cYHBw8F2nQNRqNCDi9efOmSCQBAGNjY3j69CmGh4eFLz6PKairq8OtW7eEiwAPJNRqtXC73ejs7BT1M54/f47nz5+Lyu4fel2pzNb0P4+hIHfGmzdv4v79++ju7obD4cD09DQePnyIR48eCddQPj+ZYh5OQ3JyctKK3q2trYk54EIAkTS3sjbnvInD4cD169dx4cIFaDQajI+P482bN1hbWzt18kGEam9vD9PT00pX5mQyKTKD8qBpWWh9kxaRtN17e3vweDxYWlr66Iqo/ywir3X+4paJSTKZxO7urgCt9KySEyWohAM/i8WCmpoaNDc3o6mpCTabDbFYTGSk3N3dPbaeM2my5b7wNsuASRUTo7oHfY+y0HV1daG5uRlOpxObm5sYHBzE+Pi4qNUkExPeFk4oyHInj7UK3Kr6RqCcADG93zs6OnDjxg3k5+eLvRSNRoUVMS8vD8XFxWkFS+k+nADJc68iBdwqRkpOeZ9nGvednR309vaKBCAOhwOLi4t4+PAhBgYGhEKICCpP2kEZ6a5evSrm4dWrV3j+/DmWlpbSlJU0ticRRJl88Od+bm4uXC4Xamtrce3aNdy5cwctLS3Iz88Xz714PC6U61TTiAA8WR4Jo9C7hbLMJZNJUVCWrESyqyjFBJESUJWxkgNo+o4MnjOte75uqYTBZ599hhs3bqC0tFR4AoVCIQQCAVFLLpFIoLy8XCRaI2y2ubkp0u6rFB2yMkVWOMjPEX6uas/QmqXfXq8Xi4uLaGlpQVFRkVir4XAYQ0ND6O/vh9frFRhefk6olEmqdUJtVcXk0ffkfsiYR6fToba2Fg8ePIBer8fy8jKWl5eFJXJvbw82mw1tbW2ipipwVENza2sLwWAwYwZTTtDp83fFhwGnFDPF41ho0AKBwDFipNEcZaiprKzErVu3xEuBNDBU9+hjCpkGTSaTqLcTCoUwNjaG1dXVNHNlKBTCzMyM6HthYSGam5tx4cIFlJeXZ9QSfCixWCyoqKhAV1cXOjs74XK5RIAluRm9fPkSY2NjymyKH0O0Wi1qa2vR3d2Nuro6kdKXxvjbb7/FxMSEcF0hMkIBpqSZiMfjCIfD2N7extbWFra2tkStnrKyMthsNhgMBmFBoaJ7H0o0mqNU20VFRcKd02q1ikQgpaWlIm14SUkJWltbcfXqVVy5cgUlJSXCFfTly5d49eoVDAYDysvLRVphcuuw2WxIJBLCneM0hABGcXExSkpK4PV6RX0Secy0Wq0IxLVarefavc9oNKK0tBQ3b95Ea2urKAo+OzuLUCh0JvuBklD8OS6nWq1WKCBqampE4ol4PC5eKn6//9wmAPlrE9lawY9zEAYcxb2trq5iZ2dHgAnuUnOSZpeun5OTA4fDgQsXLqC5uRmlpaXQ6/Xwer3o7e0VLuUU5K3SusskRe4HnZOJlJwELnJzc2EymeB0OlFUVITr16/jxo0bqK6uRk5OTlq9Jq7Qo7ZwkMjd4ghky0H1RI7kMVdZ0qjtdL7JZBJ7qaOjAw6HQwDIubk5rKysCJJRUlIigtn5OGUCXKr5k4/LIJjHufDz6NqhUAiRSAQPHz7E3t4eysrKsLS0hIcPH2JjYwP7+/tp1kgOPsvLy9HR0YGysjIcHh5ibGwMr1+/xsjIiFiPJKQUozHn48kz1lHfuGXRYDDAYDCgu7sbX3zxhcA/9P4lsBuJRODxeGAymdDW1obCwkI4HA5RHHZ5eRkejyft3e92u0VKdpfLBavVCovFApfLBbPZLPABZXkMBoOYnZ3Fzs5ORkvhSYpJTmb4nNO8UbIrylz92Wef4dKlSzCZTAIPDg4OCosx1aWMRqNoampCZ2cnCgoKoNfr0d/fL95JPKRB9XxRKQ7kfSuTePqMv6NlJY/P50u7NlnNv//+e7x69QrBYFCp4FORJdmiJhMjWVRJW7jw+9J6/uSTT1BcXIzd3V1MTU1hbm4OS0tLCIVCoqg4EdtU6iip08LCgiC1qpplquRBfFwzyakW7eU/PKiN/idw09nZidu3b6OqqgrJZBIbGxuYnJwUlqnzJsFgEH19fZibm0t7AFFA8MTEBBKJBLq7u9HY2Ai73S58vU9LcnJyUFZWhk8++QTXr19HY2OjAFnUrsnJSTx79gyBQEBkoJGFNhZP9UoaI7oWr1Xy50pubi46Oztx48YNkco0kUhgY2MDExMTGB4ehtfrFYueTP3t7e348ssv8ZOf/EQAidnZWfT19aGnpwd9fX3Iz8/HnTt38Ktf/Qrl5eUihfra2toHLaqs0Rz58jc2NuL27dvwer1IJpOoqKiAyWRCcXExbt++LTIKXb16FdXV1dDr9SItcTJ5lKo+Go3C6XSio6MDRUVF6Onpgcfjgd1uFyk+A4EAnjx5cmpkiogcFRadmprKSL4NBgOKiopQWVmJkpKSNOBznkSr1cLpdKK+vh43b95EZWUllpaWMDc3h/X19b8o4mEwGETx0/b2dmGVpefSxMRENl7qDIVeuFxZxrXcJMnkUSa+6elpjI6Ooru7G0ajEQ6HA62trVhZWREuZXKKdf7CJ2US1dczm804ODjAxsYGRkdHRdIdenbz/cgJlAzC+PtZBXhkdxiVUHKN8vJyXL16FZ988glaW1tRWVkJs9mMUCgEn893LDsuv7ZsnaP7kXKWCnnSZ0SuZLLCryVbiehvh8OBgoKCtIywVENocHAQ3333HfR6PVwuFxwOB1wul7BW8fZx7bxs3ePHOLil8wmUc9dFeczpXMp0tri4iGAwCJvNhlAoJCwGmUArj7HS6/VYX1/H48ePMTIyIqxDPLua/H0eq5fJ7YzGVafTob6+Hl988QUePHgArVaL5eVlDA8PY3BwEHNzcwiHwyItfHt7O9xuN4qKihCNRrG2toanT5/i66+/xsbGRtqeoPTpvB6p1WpFZWWl2AuULKy4uBgWiwX3799PK+TK2ytjMnlPZDpGc2IymdDc3Ix//Md/REdHh6h16ff7MT4+jm+//RavX78WhJDctMktu729HWVlZXC5XCgtLUUoFEI4HMba2pqIz+N7jhN4lTVKthxmIu68L5TBr7KyEnV1dSLTKHCE98gQwi1SqmcAf37IuJ97AvGxp/uQVZNb4eT2k2GGFERWqxWFhYWoqamBXq/HhQsX4Pf7sby8jEQiAZPJhKamJrjdbuH6ub29jaGhIQQCgWOKC5lI0T3fRbhJTo1M8QZQnILFYhF+tHl5eaiursaNGzdw79491NTUwGw2w+/3Y2RkBJOTk8IUd96EfKlVrmL0woxEIuJzOZDyQ0tubq5wlfzJT36CtrY2MdbA23ofANDa2ory8vKMYCsej8Pv9wtTKKXtJrPv1taWqNz9IQBbMnlUWHBqagoXLlwQG5mCUHmGM61Wi6KiInR1deHnP/85rly5ApPJhIODA6yuruLVq1f4/vvvMTY2hs3NTeHyEIlEUFhYKDRmLpfrg1oJLRYLOjs78eDBAzx48EAQP3JdIMuU0+lEQ0MDioqKYLPZ0q5hNptx48YNFBUVCa2KxWLBF198ISxTZWVlCAQC6O3tPVULkNVqxcWLF9HU1CQKEqoCZCnWkXzADw4OMDs7i7m5uXNHTsgKTu7HCwsL+M1vfoM3b96cm2yW7yt2ux3t7e1obGxEQUEBcnNzEYvFsLGxgYGBgbRsf1k5O+HAR+WmAhw9X9fX1wUYJmXE/fv3BahdW1sTsZ1y3JPVakVtbS3u3r2Lzs5OVFZWIpVKYXV1FW/evMH09DS2trYEKCGyQoCJlFWyNYr/LdcHon6owDXw1pJdUFCA8vJy1NTUoKmpCa2trSgrKxMxfX6/H69fv0ZPT0+axUceJ/pbBlZ6vV5Z40kG9zKBofbKc0EKFkoaRa5iKysrePLkCX73u9/h9evXMJlMaG1tRWFhIUpKSuDz+RAOh4WVTAX8TmqLat3I86FqL78eKd6IhPHnMxEjWjs2mw2tra1ob28X9ccGBgYwPj4Oj8eTVjuIg20O2MkdUW43l8PDQ+j1etTX1+OXv/wlPvnkE7hcLqyvr2N4eBjffPMN5ubmsLW1hdzcXFG2paSkBLm5uVhfX8fk5CQmJycxMTGByclJbG9vH8v2yNtESagCgQB2dnYQCoWQm5uLxsZGlJSUoKKiQhRiptT/8lqm36q1Jd9Tozly6XM6naitrUVHRwc6Oztx+fJl5OXlIRwO49WrVxgbG8PMzAxGR0exuroqiBSJVqvFxMQEnj9/DpvNhq6uLlgsFnR3d4uM11QKQSZJ8tqS962KZHEcKscBUaxXfX29iLGntbe7u4uNjQ34/f5jdejk8eHtoXsTTpEJmGrsZYUUXUtFAHU6nbBMUtkWq9UKh8MBt9st2m+320WB4Gg0it3dXeGVpHJZVVmq5NTpmeSDI7JU6iif/s7OjqjMbTab0d7ejkgkgsrKSlFLoL6+HleuXEFbWxusViui0SiWl5fR09ODycnJNFPnxxLy1w2Hw9jd3RX9aWxshMfjScvsQpqz/Px8tLa2Cv9r0qqcVl+o4Gt3dze6urpQXFyctojpRVRVVQWbzXaiVSkWi2F7e1u4kVFx0+LiYqRSKSwsLMBoNGJsbAwbGxt/dp8ODw8xPz+P/v5+tLW1icBam82GxsZGfPLJJ0KTpdPp0NbWhps3b+LWrVsoLCyE1+uFz+fDq1ev8PTpUwwNDYlsZ4lEAjMzM9jZ2REpV0n786Esa0ajEWVlZfj000/x4MEDdHZ2ikwxstbDaDSioKAAwNvq9TJwqaqqEg+Qw8NDFBYWIhQKYW9vD1tbW8KaEgwGP0j7VaLX61FcXIz8/Hzo9XrhwmcwGESsR05ODsrLy9HS0oJbt26htrYWwWAQPT09GBsbO3dgnjJB1dXVwWKxYHJyEn/84x8xNzf30Qtrc6E6QRQky4VeemQBbWhoEPGFVDh1enr62LrKyukLJwHyvudzQQB4Z2cHPp8PbrcbJpMJtbW1aG9vx9LSEmKxGLRaLaLRqCBAZGUitzlyi3c6ndjb28ObN2/Q19cHj8eT5rJKAIDIFGUg48VKZe21Sgsra8RJKUqF1SsqKtDe3o5PPvkEHR0dyM/Ph9lsBnAEyKgsyuvXr/H06VN4PJ40ZaTKUsbbJJMilZWAvitfh4MjTtQoiRG5StK4zM/P41/+5V8wOjqK3d1dRCIReL1ekSQomUxidnYWgUBAYBTZyqF6L3LrmAwsM1n9+LmyhYIwhQw0OQglokvFyQ8PDzE1NYXBwUGsr68L9zkSThB5P/i4Z3p3UlbEjo4O3Lp1CxUVFdDpdALXUVIck8kkXLSuX7+OwsJCaDQaLC8v4w9/+EOaJYe7ZJLQsYODA1FI1u/3Y3FxUbR5cHBQeHiUl5cjFAodU2pnskrxz7kigta73W5HVVWVMAQUFRXBarVid3cXw8PD+N3vfoc3b96IZCDcMsx/b29v4/vvvxcugG1tbcLDhVxg5aK2ctszrXuZFNBxmZTxeSsuLkZZWZlI5pVMJhEKhbCxsSHKNmSa+5PWu7y25f2c6VqykkG2wCWTSVELjpQHpCAh/EuWVsLw4XAYoVBIrHtOROV2qcbvJPngZIrqRywuLmJhYQHFxcVwOp349NNP0dHRgXA4LDLoEGgmlzPSrr169UpUJ/7YQguKiq3l5eWhoKAAP/3pT3F4eIiXL1+K4nMmk0kExl27dg2NjY1IpVLwer2iSNtpiE6nQ0VFhfBLlrVger0eRUVFyMvLEy9W/jlZHmjhU0KBeDyepgVKpVIoLi7G3t6eiE36c+colUrB5/NhbGwMDx8+RCwWEymrr127BrfbjYmJCXi9Xuj1ely+fFkQ1VAohLm5Obx58wYPHz5Ef38//H5/2jhToTzqw8TEBGKx2AeznLjdbrS2tuLWrVui/kWmBwS5SFJCllevXp1IOvb29rC0tITl5WWsra0Jq2c4HD7V5ALJ5FEa2FgsBo1Gg8LCQlRVVQnf44ODA5hMJly/fh2ffvopbt68CYfDgYWFBaEIOU9kSqM5ysjZ1NSEK1euwGq1CneA8+RGTC8CCiyXrY9Uw6SzsxOfffYZKioqoNFoEI/HMT8/j56eHqyvr59aevesqIW7qHAtskoDTs+Ara0tvH79GoWFhTCZTMjLyxMKosHBQZEQxWg0Clc9i8UCh8OB9vZ2tLS0wOVy4fDwECsrK3j58iWmpqaE9pie11yry1NBU/sACJcjWVsrg7fc3FxB9s1mM0pKSoT7dG1tLSorK1FRUSFiQJLJJLa3tzExMYEXL15gYmICHo8Hm5ubaYknZCIqAy8aw/39/WOgkt5Z9Js03Lz9HBSTqxC5irlcLlH7CjhSJvp8PpjNZrjdbqRSR66ATU1NuHHjhrDaP3z4UCQ2oHGmsc2092QSpdVqRa0buX8qYimPTSZiQ66W9CxpbW0VtSYpjmdoaAjBYFC0nRM6Fakjt1G5/dRnsvxXV1ejvb0dBQUFwv3Y6XTi0qVLsNlsODw8hMFgQFVVlfBG8ng86O/vx8TEBEZHR0U9L7k4sjweMgnk6+nw8BBerxcvXryA1WoVbqV8TeTm5gpQLveXFOQ2mw3FxcVwOByorKwUSX8KCgpQV1eHsrIyJJNJgUOeP38urMN7e3vHrF18fik2b3R0FNFoFEtLS9Dr9cIjiKyOqvVE1mqyGGaqhcStPRwXcvJAfSssLITT6RTJGvi+y2S1k8sB0HH6PsXwyXudxzxyDClbi7hVlBRTNDe0l3nfZEs+Zfnb2tqCx+OBx+NBPB4XzwI6T6Ws4Wvufd6np2KZ2t/fh8fjQU9Pj6hV43a74XQ6xSRSgCA9ACORCEZGRvDixQusrKykpQT+mJJKHQWtzc/P4+nTp3A6nWhpacHFixeRk5ODCxcuiCBWKi7ndrvFA3plZQV/+MMfMDY2dmrkkOrP9PX1CZezkpISsQjC4TAWFxdxeHgIp9OJwsJCWCwWsWh2d3exubkpipnRIvT5fNjb28Pm5iZ8Ph8SiYTICLi9vf1BrDs0vsvLy/j++++xubmJjY0NtLe3o6SkRATMRiIRkZGIkpQsLi6ir68Pjx8/xtDQEPx+/zG3UNJKkHxoEkIZi8g/W+4b/3tpaQnj4+MYHh7G1NSUmJNMkkgk4Pf74ff7xYvvLCQcDmNwcBDl5eUoKipCcXExPv30UzgcDni9XpFmt6OjQ9SFGx0dxR/+8AehkTsPihASg8GAkpISNDY2oqamRqSnjkaj56qdOTk5qK+vx7Vr19DU1CSsTvxBT5lFKysrYTKZsL+/j42NDYyNjWFoaOhYoHVWTl/oBc41t/TsVblqUXbMb775RigoCNQXFxfj4sWL8Pl8iEQiIoW60WgU7vF2u12sjfX1dTx58kTESvGistxqQT9yMgHyWjg4OEirO6UClzabDQUFBcjPz0dlZSVcLhfu3r2LxsZGlJWVifhPcitfXV3FxMQE5ufn8c0332B1dVUATFVshNxmEhkA0rFUKiW8ALir20nPSQ4KKbU8pUOnNuTl5Qn3IavVirq6OnR0dKC5uRl2ux01NTXC+yMYDIoMf+8iUnItLtrP0WhUZF5UkQZ+jUz9o2tSchKKJbly5Qq6u7vR1NSEWCyGlZUVzM3NiZp69N1M1gsOLDkYTqVSIrkVtYcKM5P1nwhLYWEhrl27htbWVlEUFoDIatrX14eXL19icnIS4XA4jdTLVl4CvtQOTqb59+jvg4MDoTDjpJcAeSZri1Z7lDnw6tWruH//PiwWC/Lz81FSUiLqYhFhm5mZEX2gcjMy+SBCIQP0eDwuvICmp6eFhZSUwrJljvpO8fCqlO983Ph3uMKHE4+CggLcvXtXeIhx6+3m5iZmZmYQCAQyrkk6l68T7lIojy9Z+chjS/6c2kvXkK9Nxhhy3eTxfEQsqVwRKUzX1tawsbGBjY2NtHqS8n6TMdsPeZeeSuBFMnmUZvHZs2eoq6tDTU0N8vLyRBwMPy8SiWB7exvr6+t4/vw5ent7T7Xeyw8VIofLy8v44x//KDRYlBnnwoULIpMMaZIpgHVlZQWvX7/Gb3/7W0xOTp4acIvFYhgZGUE8HhcVtuvq6kT7t7e3MTw8jEQigaKiIlRXV8PpdIrv+3w+zMzMoKysDPn5+fD7/VhZWREB0WQdoaKFVOn+Q83RwcGBIAyrq6tYWVnB8vIyrly5InyfASAUCgk3xKWlJYyNjaGnpwevX7+G1+v9IG35oWIwGETcViwWE5ZB4K1VMxgMYm9vDy9evBCpz9fX1z9Ke99HiExZrVbk5eXh2rVraG9vR1tbm4jloBS1sVgMExMT+I//+A/85je/EWTrPAnViautrYXT6cTq6qrwRz9PkpOTg6qqKty+fRu3bt0Svt+yNpokHo/D4/Ggr68Pr169wvT09LmoG/efTTh4eBeYT6VSIsFOPB7H7373OwSDQVy5cgVlZWUiA2leXt4xa5FGoxFuLTx77OPHj7GwsCCsPcBbcMO1rvw6HDxzIpAJqBuNRuGW39LSgpqaGrjdbpFYQq/XC3C4srKCnp4ePHz4UFjLyOVJBq8yyMt0/0wkg4N6VXw1Hw+6Fv1PyQuIBFI/m5qa8LOf/Qyzs7PIzc1FdXW1sEqQZa6rqwsHBwfw+XwYGBgQMclyu1Vaew50qf0qi9D7vF85OaOisY2Njbhx4wauXr2K+vp6uN1u5ObmihTqU1NTIj7vJEuYahxlkE6/U6kUYrGYcOcLBALQaDSw2WyClNLcUymZ8fFx/NM//RMmJiYEKZULtspjxu9P1gWeHEtus8pawv/nVht5PLVaLZqamnD79m243W7o9XoYDAZhdV1bW0NPTw8ePXqE2dlZbGxsiFh5+R48+QlXIiSTb9P9x2IxQSYTicSJiZwoaQklJMk0X4A6ppDaptfrUVBQgO7ublRVVaXFqEejUczOzorsgqp7ZCL48nFOZGVynqntmcgMWZQpcQe1mXIZhMNhTE1N4fvvv8ezZ89EmAQpjHjRdPme3ENLlkztFe3K+MmfKaFQCFNTU3j9+jWqq6sF6eASjUYxOTmJV69eYXh4GK9fvxY1gM6bhEIhTE9P45//+Z8xPDyMu3fvilSe7e3tMBgM4sW3sLCAvr4+DA0NYXx8HHNzc2kvug8t9IKen5/H9vY2nj17BpPJlPYZaTgNBoMwIZNQfRoCyFQnKx6PC5LAfcNV2oQPIeQi+ubNG8zNzWFgYACXLl1CZ2cnNBpNGmFcWFjA8vIy1tfXP1phWwDY2dnB7OwsJiYmYLPZUF5eLsY2FothdHQUPT09WFpawvDwMGZnZ5Uv3vMkpOTo7+8XRSuvX7+OhoYGuN1uMfdTU1N48+aNqAW2vb197hJPABDZxfLy8pBIJLCysoLNzc1zR6YANQCgv/nz4+DgAFtbW+jv78evf/1rvHnzBru7u+cyYc9fu9C80L6njFOZnvfJZFLE6fX19SEQCGB1dRWNjY2ora0VAFR+HlPdI0oQtLq6ipmZGYyMjKQpB04C5twNioT2LLnTcIBNGvySkhLcv38fXV1dqK2tRUlJiSAilPFrdXVVvPvGxsYwPT2NnZ0d0X45jkMGsPL/8m+V2xO9i7jbDtdmy1YMGn+yoFH67by8POFZUlhYiDt37uDixYsihtdoNApFGRWkdblcAmTzgH9uLSGLJa0HckPkhJj6RgVnZW28LJlAHQHsoqIitLS0oKmpCS6XC6lUCmtraxgeHsbw8DA8Hk/a/J+0Xrg1QI77pr91Op2wCHg8Hvzv//2/MTQ0JJRwRqMROp0OkUgEfr8fo6OjmJqawvT0NCYnJ+H1ekWSCX59DpJVQngkE0GX+yeTCloH5O5Ha4MkHo/j1atXKCwsRFtbG4qLi6HRaLC1tYXl5WWMj49jcnISCwsL8Hq9aaVDaMz4PpLvx4k+3ZfHSPEEIPJ8kws+nzeeiTGTdYePA5ESyuJHVinCjNvb25iZmcHi4mLae4XvKXndyKRVTihB/SW3Ub5vZFE9E7Tao7IgLS0tqKqqgsFgEAYDypK6ubmJ2dlZTE5OYn5+Xjx76Jp8vHlf+H34/d4XJ5wamaLaUi9fvkQoFEJ5efkxNyhyB5yfn8fq6iq8Xu+xgMjzIvTgo5oFXq8XDocDFotFaBSBo4WytbWFhYUFrKyswOfzIRgMnro7USp1lPiDinb+pQovghwOh+HxeDA8PAyNRoOVlRVB7vx+v7BUfUwhzWQkEkFPTw/y8vLEAySRSGB1dVUQKIqdO48gXpaDgwPxkjs4OMDw8DBKS0vTNFcEnmZnZ7G9vf3R5yKTkFsALyC+srJyrlz8gKNnh8/nE2lxMwll7nvy5Am++eYb9PX1YXNz81wS2f8sQmPPYxdk7ToJgWrSmG5sbGBmZgZ1dXVoampCXV0dioqKBFDnGVY3Njbg8XgwPj6OpaUlET/JYwcygXAiHDx2ip+rcrch8FdSUiKsUnl5eQLEUFru4eFhDAwMYHBwEH19fQLsyZpeWfMva5/ltnOCwoPZuRDIV8WNZAJNFFqwuLiI/v5+JJNJNDU1weFwwGAwoLi4WGSwJfAXiUSE2/ve3h7GxsYwOzt77JmeTCbTwDNZn3j/qZ8EKmWiotKMn2Q1omtFo1ERc0Su8VtbW3j8+DEePXokXOnkdnDCIc8BEQPVPHESQjXU1tbW8ObNG/T09ODOnTtobW2FzWaD1+uFx+PB73//e8zOzmJvby8N5MtkwGKxCEUvt7DK45Ep7omuoyIjtF5UYw9AkMO+vj4sLCzgwoULuHnzJlKpFIaHhzE+Po7NzU2Ew2FB5vn6Ii8sijeieab2yuMoz68M6OU54RY8vp9V+4xfg/dVr9ejpKQE1dXVIvMwraOtrS0MDQ2JhEaq95GsoMlE8DnhUhFa+XuyRVImg0ajERcuXEBlZSWAo+RL09PT+L//9//i1atXwp2Yx5KdJJnGVn7mvAu3nWqFzVQqhZmZGSwtLSmzUwFHDzX6Oc2Mdx9KEokEvF4vAoFAWvpR2W+T9+kvATyfR/H7/aIYG4C0OhznZa2Ew2HMz89jZWVF+O/yBxo9SE/Tondacnh4KGoXDQ4OHktcQn3jqZbPo/CHNgWjnsfYomQyCY/HIzKFUSA//5yyK42MjOC7777D06dPz6Vr5X8mUYFP+XM6zoEFPceoeGk4HMbCwoLIRkUJjRKJhEiaQ0qLYDAosp2pAvXl+/C2EfCTtfEcvMmWC3KhoTINlOVuaWkJ/f39ePHiBdbX10WmLA72VKSJgyxZm51pfGViRG3nwDhTXRj5GH1vfn4e//zP/4yXL1/izp07uHHjBiorK2Gz2USAOyWloGLDExMTWF1dxfr6OqampsQ8ZLofzREBbHov8D5x60Km9qvwkzxO+/v7mJiYwL/+679idnYWxcXFCIVCePz4MaanpwUxyfR9lchEh9ab3HbgrTIhFAphYmIC6+vrqK2thdFoFK58PDMcb4ucgZJjQhlYk8gEjLdZZbHhfeXkQ16D9DuRSIhswYuLizAajWnx5PJYkPD3fab9L9+T/y/vWU6GaKxUzxx538skn46TkqSiogINDQ3C0EHPo+XlZTx+/BhLS0sZs35mspDyewBIs47JonpW8j3KLcokZLggZXtPTw9evXqFvr4+bGxsCCL1PvhQdY68Bk5qP5dTJVMABNj6a5L3ZbxZ+fOE/KvPM1CkF8Jf2xonSSbfZvb7SxVaR+RuYzabM2Zd/JhyeHiI9fV1jI+PY2pqSgT8E8CiFNi9vb149uyZyHL517r2/lKEuyNxraYMFEj5BqQD0mTyqOByMBhUWrKA4y94GYDxv1XuMfS3qr4KuerRORw8kALF5/Ph9evXCIVCMJvN2N/fx9LSEubn54UrO7kuchCr0WjS3MBoHPi9uJBVh7ddJkAcBNPe4GnCubulbNmSSSR5QGxubiIYDGJnZ0dkLz08PMTOzo5w7VpaWsLCwgIWFxeFF00mC7I8T0T2uEJQ5UbEa4HxueUZymRNPb8PeQSNj49jdXUVbrcbiURCWDE5YeFjSN/lBJcXQqZ1k4nIyMTv4OAAu7u7CIVC8Hg8aQkiZHJB88NJEbmaywRDJiOcPHOAL+8BGjsSlbWNu4Hye1KcDe1PlRVG3nt8ParWA91PReJUe1oeO56lUx4L/rm8bmSLF5E+SspEdV6HhoZE2A1/98vPFdnqJ7efxopn2aQEFDxWk9Y9fw7R9Wnt0PcTiQQCgQAmJiYQj8fx6NEj4S59UpzZSXKS1VNed5nk1MlUVrKSlax8TInFYlhbW4Pf70dDQwOampqwuLgIj8cj6pmcB0mljjJvzs3N4euvv8b8/LxwG02ljgK8x8bGMDo6ivHxcezu7maJ1DkQGRSfpLVVgTECDBzYURpyDszkejWy9penCibhrn0qiwfdi1tL5HMoa9l3332H3t5ekYSG6iyR9UYmeNQeXk9KBY450eH3poxq5OWhcgniyQc4iFbVjaEU8/LckAV+eHgYCwsLMJlMcDgcwsWMEu1QX6n+D59vAua8D9ySQgo3GTSrwKlsZaC2EzhXkRF5fZGrFsVRUsy0SuvOgTm/NvWJLGuZXOlkQA8cdyHl59F9qe38XNmtSmV94dcwGAwi3kr+jOY7U1pv3m9u+XofawUfa5ngyf3ibedrhvY33+M0BvLYy/fmFkzqC49rk/vFXSHpJxKJYGNjA9vb2/D7/YjFYpibm8Pvfvc7TE5OYnV1VcSjqxSPRIp4MjK+hqktcgZRrVabVoCbrxt5Tohk0blarRZ7e3vCpZiSlgUCgTS32pNINd2H7iWTVNXz8n0IWpZMZSUrWfmrllAohNHRUczPz+OTTz7BlStXEA6HRWzSeSFTwJF2eHl5Gf/0T/+U5j4MHHch/lM0cFn58CJr+2Uwo9I2yy97VRwFWVJlMHAS4JOBHZENfm8Z0HM3HtJUc80sFUedm5sT53MQomoP9YsTNRlQyv1V9Y0D7Ex95kCSrNDyudQvrk0n8EV7an9/XySE4O54fE4zAXA6n5MsrrVXgXE+B1zrTu1VEed3gUT6n4LueeIo1dipvCpUFiPV2HPvnEygk5OiTMRK/p8DbFlpwIUTVDk1uuq+/DrymNCzlSs75DGX+yXHMsnEmPdLJsI0ftx1PhMpVY0XtzTRXpQth3QPCj+Qx2J/fx+Tk5P413/9V0xMTMDn84mkE9vb29jf309ztZP7R9ei5Cl0rpxERRa+9/jY09/yuuc1qcia/Pr1a7Fn6XnEE7+oxozPHVdYyJ/JbtDvK1kylZWsZOWvWkjzPDU1hdHRUbS1tcHhcADI/MD9mEKulVn5yxDZKiIDBflvVVY0Tqh4IU6eyIDuxQGA7GbDQbBMBoh0cLdpIgAckMpgkhMrmYzJ2t5MIDJTViyugZe17VwrD0DEj/F7Uv+4ayInHKrgfeorB4rcvZC7B6qALieKvH30fVXmO66t5+3gc66KG+cae25Z4/MtE1FOiGW3KVlkIiBbu6h9lEyBi2yJpb/5fpCJ/Umafpo/bp3ic8r3CB9PVYpwbk2jMZTHnltGZNe+TERVJrMqIsUVYHydqRQomRQlNId0DXldcPc4FeGkdc/HkdY6T1ixs7ODnp4e9Pb2KuOM6Ht8TWeywsvFefm5/Lq8np1MeuU2U3Ftvj5isdgxa5iK1MrPTmqP3F56zvI2yPvyfXBClkxlJStZ+auWZPKoCPXMzAxevHiBg4MDLC4uCh/rrGTlzxV6Uctpj4H0OAr+Ys6k9eaWEw5gVMKBonxPDjDIfUVOL86BgwoAc+CnIm2qMaD7cpc6Fbnk4E5uA32PwLIM6OgaBJY58eTglZNQVaIOnm2Q94kDO9mSRePJSe/+/v4xVyW6Nv9RWRp4W2UiRd+hWkS875xYy+3MZImSNfKc7NI9+VqVic1J2n5O3uT7yv3KlECBr0fZ5Y+fR+fycZGBvIoo8u/Kn8mEhF9LdVx246M2EsHna0p1DbnINwmfO5moyuMpk0E6h69NlfJDbo9sCeX3la3V/LhMakghwK1EfGypDbIbrqrPsmssH3uVyOdxIsfbo9Vqsb+/n6aY4HPKrehcQSTfQxbNiR9qNOdPbZuVrGQlK1k5FUmlUienDMtKmmg0mhQHohQnpHqxy0BTBookPP4GOJ6sgAMhHrjNAQt3E5I1vXImNVW8gtxO1TFqm9x2ui+JTLL4NbhlSiZy9DdpkElDzV3h6Bi3xPH2cYIht523PxOw45/J2nhO3jLdkx/jxJH3jZNtmQDLgF62bHBRjfFJohpv+dp0Ta7Fp3HLBPJPItDUZz4+qvWtGnt+7UzrTHVPVRwMv7ZKWaAaH7k98tjL7ck0J5meB/QZ37P8Gpx4Uf/5Z3Kf5WO8DSoFjYr0Ztr7qnWmcsvkz0QaH75/ABxTEsjEXV4X8pyojqmel9Rn3h7eJ9V1+POJ3V/5jsySqaxkJStZyQqALJn6oZJ9R2YlK1nJyn8eyfSOzLr5ZSUrWclKVrLyJ4rsiqPS2ssaUq4V5xpTnU4n6rep3G5Osjzwa8maXS6yppWsOyQqtxrZaiFn0ZPPl/+W2ynfS3aBynSubGXilh2y7vAxky0R3Aoi90t2OaTvUCIYOT6L2sItLOT+x604vO2ZLHn8PFWf+TEeYE/9p2OUfENlVZHvo/pcFYPD287bL9cdlK0awNs4JTkuTWX9k6159L33sSbJYy9brFTWFTnGTx4PbnGk8eBWUpPJJNx64/F4WlbBk9afPFa8Tfxv2VrKr8XHXmVZU/VHjiukMaAxJouv/HyS3Rnl/c7bK99TtXZoHKlNcrwa/1u2NHGLkUZzVMAXgMi0SWOlstjzfSM/J1TPHLktJxmegCyZykpWspKVrGTlTxb+opaPcVcTAnwqcEwFXfV6PSKRiLgeAQIV0FK95FVEigo/E4igdvAgdhVooj4QSJHjB/g9ZeCjSjohE0jgLWBVgWw+hqqYDQ6gSQgoyvenfspgnYCdiqzK4JDH8HA3LYpFk7PB8bgqGdzL1+Rkm68nImscCMrrRq/XiwQRNMc8Nk4G5XQ/PgaqOaUfIvYyceJAXBWzw9tNqbBp3PhaoO/wbIu8r/I64nuDfsv9kgExJ0QcXKu+Q33iv+X2UFpwh8OB3NxcbG9vI5k8yiSpuqaqTfxe8jm5ublpdb7k+CqKI+T7SL6W6h6cFNIPfUbjL7uz8r3B+6DRaMS65+2n49zllu8NmcSpngl8rOV55HNgNBpFQWwSHrPGSTyfb3m98HbwkhRy/OFJhCpLprKSlaxkJStZ+ROFwPhJWueThAK8OXAhEEFWI/naMtCXQUkmYMWFzjcajUIzLZMWWcMugxSVxl/VHpmM8OuprC90XLaiyZ/r9XpxXSITqnlQaeJlbT+RInmcVXFXfHy5pp33J5PVjv+mlOqZtN+cOMgkiILlqd1UHFmrPaq/RAXv5bWSKR6Pkz46RxWrQkIkXT6Xg2Qec0UAliseOPniY8kBLxe6jwyU+f0yfVdej5yEy3E7fM5lKxvtlXg8LopYy6Cfz7N8nK8F+W9OHFUkhv43Go2in7xdqnmSx4B+8/3I/5f3rup6KqJF95bXDb8vEUFK105JMk6aL6644Pvw8PAQu7u7aXtRp9OlKSb4Hqe9otpT8r7lfeLtyZKprGQlK1nJSlZOQeQ6UyQEdGSgTS9vchECIFz79vf3RW0mTnA42AHUrkt0TyDdPYpAN7WBa2OBtwkdZEsOP07Xlt2zSOheHChzQGU0GmGz2VBdXQ2DwYDx8XGEw2GRNU51XZlgyBp2GjdOSuRx4DVqeOFP7trIC51yMsV/OFjmIJvGc39//9g8ETDj58rtl9Pq0xzJwI+PJ3dlo5pzsVgMVqtVXOfw8FBYlHgWRJkUUltU6fo5KKZrcOHjyb9LfeZEkepekaWU9gDNvU6nS7OmcUDM55DPBx8bmfjw87nllX/Gs7Zx8iPXZlKRbvpONBpNA+GqhBgqIqXah/xvch0k66M8PzROlLqbrxF+D/m+NCekfOBWG06K5ecNH0/ef1UZAd4+epbxz7kSQO6z3HZOiPgzjaf+53uf1plMfFTrX/UM5fPIyfn7SJZMZSUrWclKVrLyZ4oKqHJ3EVnzDeAYQFW5lsga90wgjRMpAqq8TRy8yCCJrquqM6XS2vPjvJ2qdhF5MZlMKCgoOAZw+b1IZJdDug4/JoNX2RWKrq9y/ZIJHI2BXPCXA19Z287vJc8LB2qytl+l4VYRHX5Pmjs+t1ybn0qlsLe3l0bsOVHnxzMREd4O4K3blwpU8j7KY8OJLtf6E3DnIJ6+R8RPtR5U5StUJIfarFo7RMpycnJEOm/VD7+OfIzPm06ng81mg9VqhcPhwN7eHvb29tIUF/J48vGXj/E5k7/D1y8fY1rHqnUvjyH1n68Ffi25zfQ9VQZEVZ/kvsprXVY08fuepEgh4TFQfN3TnlV9X6X8kZ9twPFnsRzrKI99JsmSqaxkJStZyUpW/kSRyQ2gTrIgE4iTfPHl62UCL+8Sfh05doBrd+W4B/m+sqZbJlh0XAZB/Njh4SFmZ2exu7uLQCCQUastA2F+XLbwyCTwXe2XwbOK/PFxk+cpk/uPPEZyuzmx5GPPAV+m9shkWuWmR2NAYyLfn65DsTh6vT4tBX88HlcmPeHf58kp6D4q64rqviQykOeET/V9mcCqrnvSPuJCZIqfm+l8VdvJSuR0OlFSUoLOzk7U1taioqICAwMD+Oabb7C+vo5IJJKxAK587Uzrj48PJ8J0PJFIHBt71XOBt18ulpup75kIk/y/bNVT9eEkq3qmOMhMzxzVfPH+nLQeMvWXkz1Z+J6V4zZVkiVTWclKVrJyjoSDHu5fTkIggwoPvushn5XTFTmJAKCOcQGQlqiAEwmVCxsHIDLo4ppf+j7FC9D5/Fz6W0WCSDgwIfDO78vdrfhvmUzQcYrjcTgcqKurQyQSgdfrxc7OjtAmy1piua/8mjKpydQWVUY3HislAzYec3MScZDJEt1DZW3iY0K/Zesk75+8Fui6srVRNQbvAuk0D8XFxaioqIDT6URRUZFw3QuHw5ifn8fKygp2dnYQjUaPxbBkqi9FhIyPswr0ZrJMqNa3bLnh653PNy+KzNcq3xM0ZrwIsQywVeBdXoMUh1ZYWIibN2+is7MTbW1tKCoqgslkQnV1NaqqqtDb24uRkRGsrKwIS6E8T5niouS+yuRAZcHh1yG3Ydm6x908VfPB28P3uLz++HxwNzi6Hl/fKhKkWjfyGPN1xt1xVdZGeS29zzvwJHLF149M9t/n2lkylZWsZCUr50iMRiPKyspQWloqfttsNvF5IpHAzs4O5ubmMDs7i7W1NcRisY/Y4qxwYKYChvzlTP9zIM5d8FQkAHirvZeDqCnexGKxADiKG9jb2xPX45IJ7MjnyqCfn8PP44RPdgXLzc1FWVkZmpubUVdXh9HRUaytrQlXOg52VQCJ+pdJU87bpgJJcnv4tQiIc9Ata8qJ+HKrCR1PpY5c02RSy8dKBvSZxlkGmycBavn6/IePAy9mbDKZ4Ha7cePGDXz22Weora1FXl4ecnNzEY/HEQwGMTw8jG+//RZ9fX3CDY6+L2v/uZZeJh2ZCBFfJ3KyFdWc8yQUcl8zucHKBMJgMAilE1dgyHtAdX9ZcnJyhFLgV7/6Fdrb22Gz2aDT6aDVapGXl4fi4mJUVVUhNzcXkUhExO/wNvLry4BeRXZkoi2vD/49OQ09faZy1aM+qcivbAWldspKCj7e/Fy6hnyuas/y42Q5V5FbOlf1OVcAcXLPx44IZaakMPIYv+uZo5KPSqboBeByueB0OtOC1Q4ODrC3t4etrS0RqHoehR5WNpsNJpMJOzs7CIVC57a9suTm5kKv14sAYbPZLDQc0WgUwWAQ4XD4mC95VrLylywEQmnvWiyWNO0b8PZFFAwG4ff7la4oH7pNdrsdVVVVuHbtGtrb29HW1obm5ma43W5xTiwWw/r6Ol69eoXHjx/jxYsXWF9fT4uZyMrZigwW6Jj8P09XLX+mAgncJZBrifV6PaqqqlBTU4Oamhrk5+ejoKAABwcHmJ+fx1dffYXFxcVjbZBBDm+7rCHnbQFOTkBBn/O+mc1mtLS04Mc//jGcTifm5+eV5E1F2jiokTX7Mjl9F0jj7aMxoOtSXS+ZwPK/eUY6mTSr2shTM/P28P69zxjw76niOGQyw+91cHCA3NxcWK1WXLhwAZ2dnbh9+zaam5tRVFQkLN+Hh4eIRCIwm82w2+3QarX4/vvvEQ6H0/rJ143sqqf6W7a0qlwPuUWQxppIIHeDlEG5ak7luaZry6RPHmcet8XXOT+P9pvL5UqzRnG3QZPJJNJz+/1+BAIBHB4eYm1t7dha5eN6kgKDKxpkN7NM7rGqNcGvR0STlAfyuueWLXnMZesfb7Pcbv49mZhwJZJq3fPv0PNSvhcRJ0qPbjKZUFRUhNraWhgMBvFej0aj2N/fRygUwsLCAjY3N7G7u4toNJrRoyMTaXoX/v1oZEqr1cJiseDChQu4d+8ebt26BbvdLgY9FAphamoK/+f//B+Mjo6KzX3exGg0orq6Gt3d3aitrcUf//hHDAwMIBKJnPjiOQ+Sk5MDu92OoqIiVFdXo7OzE01NTXA6nYhGo1hYWMDz588xNDSEjY2NUweTWcnKWQm9HOvr69HV1YXW1laYTKa0cw4ODhCJRPDs2TM8fPgQwWDwVPdAbm4u2tvbcf/+fdy7dw9VVVWw2+2wWCxpdTTMZjMqKipgtVpRWFgIi8WCly9fYnJyErFYLEuozlhkQMxBGh3nAJ5n1pOFg3CuNaY4F6PRiPz8fLjdbgGQL168CKfTCZPJhP39fSwvLyMWi+EPf/gDtra2RHwFgVVenJMLd5eiNnBwK2v/ORiWrRIGgwEtLS34/PPP0d3dDQBwOp3Q6XRCe0yacbq2ql4Nv0empB0qQM4ztRGA1Gq1x8isyn2OxooDOT6v3NpAvzlYlMeJvpfpmvxa1EcO6GXQz2PvVCBWo9HAZrPB6XSipaUF9+/fx40bN9Dc3AyTySTWIIFpwjButxupVArr6+sYHh4WiSKAI5yTSCSOJYNQrXU+fzSWvL2ZzqF+y65mMpkG0ssRcALLyST1j0C3qu1ym1VC82mxWFBWVoa9vT2srq4KRbTdbofVaoXBYEBRUREuX76MWCyG3d1dbG9vpymiaS1y4VYdFblQZfPj45OJ0Mjn83vz+mcyieTrWDVW8j35XpYthnQ/OfZN/l81B7TnecZOl8sFq9UKq9UKvV4Pg8EAl8sFh8OB6upqdHR0wOVywW63i+fM3t4eNjc3MTo6io2NDczMzCAUCiEej2N1dVUQLrlN3GL9PnLmZCo3NxdFRUWor69Hd3c3WlpacPHiRbS0tMBsNovNEI1GUVZWhv39fTgcDvT19Z06mPkhotFoYLVaUVtbiy+//BLXr19HcXGxAGT9/f2IRCIfuZXpQla0/Px8oclsampCdXU1ioqKUFNTg7KyMlgsFsTjcTQ0NMDhcECn02FoaAhmsxmHh4dCC34Wc5Gbm4uCggLU1NQIwBuPx7G1tYWVlRWsrKxgd3c3zTXhLKW9vR0XL15EXl6eKJy4svL/Y++9mhvLsuzgBe9IwhL03nub3pfLMt3VUj1IMaGYeZD0oP+jP6BRxExIPdP9TXV1l+2sSp9JMpMu6b0nCBKOcARo8D1Q+9TG4QUzq7tIZk1zR2SACVxz3D13r23WXobP50M4HIbP5xNeDTnR+1xOVwwGA7KysuBwOFBfX4+uri6UlJSgoqICxcXFUKlU2N3dxd7eHgwGA3Q6nXgpDg4OnuiaN5vNKC8vx40bN/Duu++ioaEB+/v7mJubw9zcHAKBALRaLRwOB4qKilBZWSkUarVaLZSdhYUFbG9vn0gbz+XNRU4az+QtkV/eQHooIH2vUqlgsVhQXFyMO3fuoL29HTU1NSguLhaKBSk3LpcLH3zwAXZ2dvDs2TOsrq4ikUgIxSeTV0Tpvtw6fVyeB31SH+12OxoaGtDS0gKn04mNjQ3E43EkEgnF+3CRc5hI2aaaUqScchYvbvXmYJXuw8OWeEglL0jL+8EVb94OHkqlNJ782vIYc8AkK270dyZiD3ns+bzJY6/T6VBQUICrV6/ixo0baG1tRXFxMcxmM1SqQ0rsWCyGeDyOaDQqIoQ0Gg2amprw3nvvYXt7G8vLy4hEIgDSCQ9kkeefJ+zTnMhtzrR25JBK/kljzovN0tjwcVFa40rzIP+uFE4H/Ei/7/F48OWXX2JiYgJmsxlms1mEsVZXV8PpdEKv16OkpARXr17F9PQ0FhcXsbm5mcYgKOsASgCLt1cpf1E+n8+/DO5J+PPMyUb4PTONk5JnUD5eBksARLQZzatSuwAcMVDw78mAoNFoUFdXh+vXr6O6uhpWq1Wsd5PJBKvVipycHBwcHIjCu7RmCHNEo1F4vV7EYjF4PB58/fXXGB4eRjAYPLK+KV+Z9iy5/7KcCpiijdBsNsPhcKC5uRk3b97E3bt34Xa7YTabodfrRXwyWcRKS0vx0UcfQaPRIBgMYmpqCn6//zSa/FpRqVSw2Wyoq6vDe++9h7a2Nmg0GqFMj46OvhVgih5Us9kMm82GwsJCVFVVobKyUnjUysrKRLI7Lb6srCyYzWYBEEwmE9xuNyKRCJ48eYLV1VWEw+ETa7dGoxEhoDU1Nejo6MDVq1eRk5ODaDSKhYUFzMzMYGJiAktLS1hfX0c4HD51b2BzczP+4R/+AdXV1bBYLIhGoxgcHMTc3By2trYwPz+P+fl58eLa2dnJ+ECSghCJRI5YME9CtFqtmGNyjfO4bm7Y2NnZSatbotfrYTKZhBIHHG6gOzs7iEQib02oK++b0+lEUVERKioqcPPmTbzzzjvCir23tydCWvf391FYWIiCggKkUikUFRXB4XBgc3MT8Xj8Z2+jWq2G0+nE5cuXce3aNdTX10OtVmN6ehr37t0Tz5tOp0NZWRlaW1tx+/ZtlJeXw2azobu7G/v7+yLEmNikzuV0RMkqDCiHa9GxsqLIFXZ+HZ1Oh5ycHJSUlAjL6+XLl9HY2CgMXST0bObk5KCrq0usl88//xxzc3PY2dlRVApkZVWp7UqW6UxjYTKZ0N3dja6uLrjdbmRlZWF9fT2tZhYfA1kRpO/kUMSDgwOhJygpX/y6stLKr8XbINcakoFdJvCZ6fnK5C2i7wkE8N+40kf95GMujz39TsoxtZne8wSkPv74Y3R3dyM3N1dY6QOBABYWFjA5OQmv1wuv1wu32y0iawoKCnDr1i0AwDfffIOhoSFRA02e90xkDjT2suJN4yavMeoXkagYDAYRdkigRafTiXcNeer39vZgMpmwu7sLv9+PWCyGhYUFQXByHACQlXalNU3HUd4VpZzMzc1Bo9EIBb65uRnd3d3o7u5GRUUFzGYzKioq8MEHHyAQCOD+/fvw+/0id0vJA6QUBkmfRJzBx423nxtheB/52NL9eCgfF4PBAJVKBbPZLECJy+UCAESjUayvr8Pn8yEej6fpJrK3WO4ThZvKHld531OaJ/qdPo1GI7q6uvD++++jrKwszflCBoKtrS14vV6hhxDuMBqNcDgcIrctlUpha2tLGBf6+/vFGich48nr9jySUwFTRqMRFRUVaGpqQltbG5qbm1FVVYXS0lLxgKRSKQSDQQSDQSQSCdhsNrhcLoHyI5GISLw+aSXzTYQWnsvlQn5+Pux2O+Lx+JHN+CxFpVJBr9fD6XSKB/7SpUtwOp2wWq3CSm8ymZBKpUTicip1WGHbaDSisLAQ7777Ltrb22GxWDA/P49AIIBYLHZioZdEP9rW1obW1lZkZ2cjFovhX//1X8XDqNPpUFtbi+7ubszOzuKHH37AixcvROL1aYlOp4PFYhEbUE5ODgwGAxobG7Gzs4NwOAy/3w+fz4eNjQ0EAgFFwEebJuXCbGxsIJFInGjbnU4nWltbkZ+fj/LycjQ0NIgkdpJ4PI6JiQkRb7y7u4vs7GwUFhaisbERRUVFYqOPx+OYnZ3F8+fP8fTpU0F/fBZCL+b8/HwUFxejpqYGDQ0NqK2thdvtRn5+PsxmM5aXlzE9PY2FhQXMz89je3sbNpsNd+/eRVFREbRaLWw2G9xuNxYWFk6srUVFRfj000/R2dkJjUaD2dlZ3Lt3D//6r/8q6HbVajXW19exsLCAubk5XLx4Ed3d3aivr0dHRwd2d3exvLyMra2ttzYs+t+j8HA12ePARaVKL9QLHM0r4KLVapGTk4Py8nJcvnwZXV1d6OzsRG5uLrKystJyH7giq9frodfr0d7eDpfLJdbD0tLSkURxlUoFg8FwxHOu5B3hoVVKfSNlLTs7Gx0dHWhraxPWejKYyhZ3+pvupQRSeP4LeY75GAFHvWo814SHmsn94gyIZCQiY24q9SPJBIXPyix/mf7mwtskg1heoJnnq8jKvkwWQGPNgZjZbEZhYSFaW1tx/fp1XLhwQQAp0p1GR0dx//59PHnyBCsrK1CpVCgpKYFarUZFRQWysrLQ3NwMg8GAubk5TExMHMkpo7ZR20npVPKEcIAqjw+tJzJ2Ud621WqF2+1GWVkZ8vPzUVpaKvK5dTodsrKyhIKu0+kQDAaxuLiIyclJPH36VBgy4/G4CE2UwTeFgyqREvDxp79pLYTDYYTDYTHuer0ey8vLePXqFdbW1vBf/st/QVFREcxmMy5fviyiBfb29hAOh9P0E/Lg8fvKIEJp3dDY8/UirzUloE7/5/2j3DnSB4nxsaCgANXV1dBqtdjZ2cGjR4/w4sULrK6uivtQ+7nBQG4HL6BLfaV5Uxp7ue+0N5BnymazwWq1isLPVLA6HA5jbm4OY2NjmJmZQTKZxObmJra3t0X4ZVNTEy5evCjWdyqVQkFBAYqKijA2NiZ0X5LX7eeynDiYMpvNKCsrw507d3D16lU0NTWhqKhIVOsmBT4ej2NkZAQzMzPY2dlBdXU1uru74Xa7UVlZiatXr2JiYgJzc3MIhUJnno+k0WhQWFiI+vp6QZ7Bk+LkzfC0hUL6Kisr0dLSgqtXr6K7uxutra3Q6/UADt3XgUAAKysrAtEHg0GoVCpUV1ejubkZJpMJNTU1qK6uRjKZFGN/UpZvWvgXLlxAc3Mz7HY7VldXMTIygoGBAezs7ECn04mEfLfbjcbGRni9XiwtLWFtbU2xiNtJCVn6srKy4HQ6hfKdl5cnNhEat62trWM9Nnt7e1hcXITRaMSzZ8+wuLh4omCELGikpNXV1R0BUzs7O6irq8Pi4iK8Xi/29vbEpltfX4+CgoI0MLWwsCDA78jICLxe76k/q/RsNjU1iTDWyspKVFZWoqioCDqdDl6vF8+ePcPg4CBGRkawvLyMlZUV7O/vo6amBt3d3Tg4OIBWq0VBQQGampowNTWFjY2Nn729KtUh8URdXR0cDgeWl5fR29uLJ0+eYHR0NG29RKNR+P1+eL1e+Hw+7O/vo6SkBIWFhWhubkZ9fT1mZ2eFJ/FcTl64cqBk+ebfccstV55kxi0KvWpra8Pt27fR0dGB6upqYbwgL/DOzg7i8TgMBoNgMCMFymQywWAwpBksZaWYDFMAjpAMyW0mhYh/L3sk6DuHwwG32w2DwQDgR681Kebc6s+BlJI3hs7nXhj6pPcuWf25cO+U0h5EfTEYDMjOzkZxcTEqKipgt9uh0+kQjUYxPT2N5eVlsY9xq76SJ0MW3h8l0gDyGCmFXWU6X+4PGT9ra2vR2dmJixcv4s6dO+IdFI/HMTY2htHRUTx//hyjo6MYHx9HOByGwWCAWq1GJBIR0SnkGZJDIklkRV0mMpCVa/mTjjGZTHA4HCgrK0NNTY2IHHA6nYLkgXIEDQYDDAaDUP65xyWZTKKoqAjV1dVoaGjAxsYGJicnMT4+jomJCayvryu2UabnzjSXHAzLxycSCcTjccRiMZhMJjQ3N8NiscDhcMBisaCwsBB5eXnw+XxHDFxKXiIZyHEvJD+HjAgUPqjkUeYiGxvoWmQwv3LlCpqbm5Gfny/yvyh1QavVorCwEDabDX/4wx/g9/vT8i7l/U72OMu1pmi/JANypjHn19nf30csFsPz58+hUh2G7e3s7AjQFAwGsbCwgK2tLYRCIezu7iIajYq9cXNzU7zLc3NzkUwmMTc3h56eHgF25fnnbXgTJ8mJgimVSiW8Ir/5zW/Q0dEhWFCIRWZzcxPr6+vY3NzEN998g76+PiSTSVy+fBkGgwEXLlyA1WpFWVkZGhoaMDY2JhKtz1K0Wi2qq6tx+fJl2Gw2AD8CGB4udVai1+uRn5+PO3fu4O7duyKvR6vVIplMYmdnB6FQCJOTkxgbG8Pw8DCmp6extbUFi8WCX//61yLUieJGaZMaHR2Fz+f72S3fZHloaWnBf/pP/wmpVApPnz7F/fv3MT09nfayDwaD2NjYwOzsLP7bf/tvqKysRG1tLSKRCLa2tn7Wdh0ni4uLePz4McLhMAoKCoQXliyyZFF0OByw2WwZrZckZB0MBAJYXV09UTBFL8vS0lLU19fDaDQeOcZgMIiYcG7d02g0Ii4ZgFBKKisrsbu7i0QigUQiIZhzTstLolarYTab0dnZif/6X/8rWltbhbGDrHlbW1t4/vw5Pv/8cwwODopxTqVScDqdsNlsgt1PrVajrKwMFy5cwKNHjzA5OfmztpeUWVJot7e3MTMzgwcPHmBsbExRCaSQk97eXmRlZQnFyWg0orq6GsXFxVhbWzsHU6ckpBRwOudM+z/NJ/cA8X/AjxEFJSUluHjxIt59911UVlaKvBZSLILBIKLRKCKRCJxOJywWi1AaE4mE8HIPDg4iFAoJRUZWWigfhtMKy8fRsRz08dwfbkEmo6LRaIRerxfPluwdkK8jh9sphWLRtbiCzq9Bx3GwRax2Op1OKJ/kLbNarbDZbMjNzUVTUxNaW1vhdruhVqvh9/vR39+PV69eCa81zV0sFhP3jkajSCaTadT1MvBRst7T/8nbxVkAZQU70/5JYZ1OpxNNTU1499130dzcLEK0gsEg5ubmcO/ePTx9+lSExFO4sslkQm5uLux2uwCmBwcHCIVCCIfDYg+R8+doHGTQq+S54/MH/Ji7Wl5ejqamJhGxlJ+fL/ZeYsejZ4kMuPRuobGndxClJVRWViKZTGJychKPHj0S7dze3hb5XzTu5GlUAvHyujtuHshjtby8jOHhYZSUlIg+FBQU4MqVK4hEIsKrw9sgj63SmlcKhSOPKff+0fOhlF9H59I5xKDtcDjQ2tqK3/zmNygvLxdh7xQSbLFYxDsxHA6jr69PePz4c8jBHgfbfNz4Px66qOSNk8MT9/f3EY/H0dvbi7W1NdjtdhwcHAjG72g0iu3t7bScRuqv1WoVnjetVotQKIQXL16gv78fY2Nj2NraUsQTfPzeRH85cc+UzWZDUVERcnNzBVtfIpHAyMgIvvvuO6ysrGBzcxN+v18k6wHAq1evkJeXh+LiYsFM09HRgfn5eSwuLp45mFKpVKISNimhlHfBJ/WsxOVy4fbt27hz5w7a2trgcrmQSh3Gic7OzmJkZASTk5OYnp7G2tqaKNbncrnQ2dmJlpYWuFwu6PV67OzsYGVlBf/2b/+Ge/fuYWNj40S8PzqdTrAPaTQaDA4O4smTJ1hYWDiSq0Ib/ubmJkKhkHBJEy3paYWXLS0tIR6P4+XLl7BYLDAYDCguLkZ5eTmqqqpQXFyM4uJiuN1uRbAii9FoFFbmk/ZuJhIJ4Qrf3d0VVmQupNiRN/M4oWPLyspw69YtkcNzmsYPo9EowlwaGxuRn58vQncon/GLL77Aw4cPMTg4KMJB1Go1rFYr6uvr8emnn6KlpUUkBZO19iQMJDqdDlVVVWhsbIRGo8HAwAC+/PJLDA0NYXNzM+Mmvre3h+3tbayurmJychIOh0MAXK1We+ae8b8l4eEgstLDk/FlMMMtz1zo3fLBBx/gnXfeQXl5OcxmMwAIZTIYDIriqrQ2d3Z24PP5EI1GMTMzg3v37mFoaCgt7DOTUiyHcmVScPinDMq4skYRGtRfXnOHgydSljQazZH8JbktSrklSm0moSiB/Px8QV5Ehja73Y66ujpUVVXBZrMhOzsbTqcT2dnZot3FxcUoLS3F7du34fF4BBkNKV/b29uYnZ0VxuBgMJiWW8RBJlfu5LmWvXryOuGKJc89oUT78vJy3Lx5E52dncL4zI1G33zzDcbGxuD3+xEOh8W7VKfTob29HR9//DEuXLggatl5vV7Mz8+L/FF+Xz7WSmQZfE6V1jYZTLu6uvDuu++iu7tbgCgiGCFQR+cmk0lBXkKRNJRq4HQ6RXh9bm4ucnNzYTAYUFtbi4ODA/j9fqRSKUxMTIgwLu5pyrRPyko+75eSkWFvbw9erxdPnjxBeXk5ioqKYDAYYLFYcPnyZSwtLWFwcDAjyzMBaCVPj+zFBtKJSmRPbyYPD13LbDajo6MD9fX1aGxsRENDA+x2O5aWlhAKhTA7O4tkMolAIIAbN27gwoULwotYVFSEjY0NhMPhtOeSRB7TTEaZTPoZ7R1kZCCvMwk9c0ajMc2wQqCbE3mo1Wq43W589tlnuHr1KgoKCpBMJvHo0SP84Q9/wPr6utB9MunrMhg9Tk4MTJHFtbCwEDU1NcjJyQFwqMAtLCygp6cHX375pSAOiEajYkNXq9VYWVnB8PAwbt++jZKSEhiNRqF0PH36VCTFn4WoVIfJcGR9oMW0u7uLtbU1kWx7lmKxWFBTUyO8JBqNBisrKxgYGEB/fz/6+/sxPT0Nj8cjXKFlZWXo7u4W1i2j0SgWb39/P/785z8fuyH8NUIWttraWjQ3N2N8fBwDAwOYnZ1NsyhxIeuSWq1GSUkJnE4nnj9/jsXFRfh8vlMJL9ve3k6LodZqtbDb7SLsoLS0FGVlZSgsLBR5aAUFBeIlwF+O0WgUy8vLgsLzpAF5NBrF4uIihoeH4XK5kJeXJxSugoICZGVlKVqIXyfZ2dkoKSlBWVkZcnNzMT09fUI9OCoU/01MPxRCvLe3h9XVVfT29uKbb77ByMiISEC12WxwOp2ora3F1atXcfHiRbhcLmxubgrGyJGRkRMhv9FoNHC5XCgoKIBGo8Hy8jLGx8exubn52v1tb28PkUgEm5ubCIfDsFqtP3v7zuX1oqTQcyVG9lIoeaNIVKrDHBK3242SkhKh3CeTSSSTSWF9JgNHPB7H8vIyFhYWsL6+jsnJSQQCASwvL2NpaQlbW1siBI6UE7ktBAaV+sGVM1kxo71CVlCBQ6MGT07nQIH+Lyeyc+VVHhvZ88Wt8EpeBco3u3jxIm7evImqqiqoVCr4fD4AEIZau90ucop0Op3w0hHhgcvlQnZ2NkpLS6HVahGLxRAOh7Gzs4ONjQ3Y7XZMTEwIyznlgXAvHAeO9J7gyiQPH+M5bfK4EyinsdFoNMjOzkZ5eTm6u7vR0dEBh8OBvb09TE9P49mzZxgbG8Pz58+Fp5rycHNyclBYWIhLly7h0qVLqKyshEajQTQaRV9fHx49egSv1yv6wZVU2SvIhQNi/jv3iLS3t+PXv/41bt++LcL5+FrZ29tDMBhEIBDA2toaZmdnBTsu7XWhUEiss+LiYuj1enz44YfCA2ePWb0AAQAASURBVGGxWOB2u5GXlwe3242pqakjazdTuKZsNJDBgRI1PnlJxsfHMTo6isbGRuTk5ECn0wnjanl5uQDhfE0oCX8uKKRPflbl9aEEWqhtZNSx2+0iB7OjowNWqxWJRAL37t3D3NyceOdRjpfJZEJxcTGqqqqQm5uL6upqTE1NpfWfhyvKbZBzFXn9sOPGXg5v5B45CuHj+xEBKbX6sCxDeXk5ampqxPvcbrcjEAhgfHwcX3/9NWZmZgTRFx8vec/hHrfXGehPDExptVpYrVY0Njaiq6sLNpsNyWQSGxsbePjwIR48eCD43uV41oODAxEDubq6ilAoBLPZjLy8PNTW1qKurg7BYPBM8jGAQzd1bm4urFZrGo9+NBrF7OwsFhYWTjVvR0l4mAVtANPT0/j973+P/v5+rKysCFeuXq9HQUEB7ty5gw8//BBtbW3IyclBMBjE+Pg4vv32W9y7dw+zs7Mn5nUzGAwoLCwUlM+jo6MYGxt7LXAj8JKfn49EIoHa2lrMzMwoUl2ehPAHkMIQKNl3ZmYGZrMZ2dnZgpmooKAA77//Pi5evCjyBvf39wVL0A8//IDf//73mJycPHFAHo1GMTU1hVQqhfX1dVRVVSEWi0Gv1+O9995DTU2NYE76KaCKNiE5QfU0ZG9vD5ubm5iZmUF+fr4AGIlEAk+fPsV3332HiYkJRCIRGAwGWK1WVFZWoq2tDbdu3UJDQwNsNht8Ph+Gh4fx+eefY3R0FB6P58SYRGl8dnd3EYvF3piCnRRSCrs4l7dDuOIvK8bycfLLm+jvqX6Yz+dLo4KmUBXyRtE79fHjxxgeHhZkJZRPIIcwyX9nCl2S28n7JYck8U8CRZwZixQcMj4mk0mxXmkMlLxOsoWdAGEm8MqVK6pJc/XqVfzmN78RhB3kISPWOJVKhUgkgo2NDezt7QnvBYFYAq1kPCWDWCwWE6QUVP6CnluuKMpeEK5I8v5zwKS0Xvh3FLVgMplQVFSE8vJy1NfXo7CwEKlUCpubm3j27Bl+97vfiVIJ5MUEIN73165dEyx+ZrNZGE5fvnyJ3t5eeDweRWCnND98fXDAQUJroLKyUrCWlpSUCC/6wcEhY9729jYCgQAWFxexuLiIV69e4fHjx/D5fCJ0nHsv9vf3YbVaRZmXrq4uMd77+/vw+/0YGBgQHiolwCG3n4OTTF5FJa8VhRN6PB54PB7Btkr1vrq7u0VorpIRQQ6bJeFjqfQs8/bTb/IzS0AjPz8fly9fxoULF1BaWorFxUWMjo7i888/x9LSEnZ3d4URL5VKYWRkBI2NjSgrKxMGx6dPnx4BgjLo4aAoUxivPIZK3k95XuRnh/fNaDQiKysLVqsVra2tuHPnDjo7O2GxWBAOhzEwMIAffvhBvP8z0bHL96R+nJlnKisrC1VVVaitrUVJSQn0ej3W19fR29uL+/fvY2hoKC0mVxZSEmgjAA6tEFarFU6n841Cpk5KcnJy0N7ejvLycpHrQIXBVldX4ff7zzzM7+DgQDA0yd9zRprs7GxUVlbizp07uHXrFlpaWmCz2bC9vY3R0VF8/fXXePDggUDyJwVQTCYTCgsLkZOTg+3tbSwtLWFzc/O1SiV5QGkeKGTjLHPWiGo7Ho8jEolgZ2cHWq0WZWVlaGxsFEnOtAF7vV68evUKz549w8OHD8VYv+7h/Wvl4OBAgLhwOIzx8XEkk0lYLBYkk0m0t7ejqqoK5eXlIgQkk5wmYDpOiNHuwYMHmJ+fTwvBnZubw8zMDLa3t+FwOFBbW4va2lq0traipqYGRUVF2N3dxaNHjzA9PY3h4WG8fPkS6+vrJ8YSub+/j42NDczMzGB+fl6QwbwpOKJwDMqbOZezERlkcAps+o57JZQsuORNyc/Px/7+Pu7duydCVWpqaoR1mwrvRiIRLC4u4k9/+hPGx8exvr4uIjz4PeTwMKV2AUcVIdlTpBS+Q4xodDyFmnKlkAqbOp1OmEwmUVJDts4reWOo7bzdXDGTSQS4cksEHEajURQL5kooeTiGh4fx4sULBINBhEIh7O/vCzplu92OyspKEYZrsViws7OD8fFxPHjwAH/6059E+BzlkfB7ZApzIoVSztmRj+FjwI/R6/XIy8vDtWvXcPPmTRQWFkKr1cLj8eDx48d4/Pgx5ubmRBgTKch0XmdnJ27duiVYeqPRKAYGBvDNN99gYGBAeDRpnGWCFN43DjpkUEznm0wmlJaW4tatW7hx4wby8vJE6Dh5dTweD/r6+vDq1SssLS1haWkJgUBA1CjjtOJ8fVksFqGbEFAPhUJYXV0VHlpeFoA/E0rPA/+kv5UAMp8rEvLoRCIRJJNJASJLS0vR1taGqakpLC8vH2HglL0sfL6VvDhy22lty15QHqput9tRXFyMS5cuoaSkBKurq/jiiy8wPDws1grdj5fK2d3dFfnqpG9lAv78ueTt58+sklFGDn+W5yKT3smNJ06nE52dnYJ4qqioCF6vF2trayJHanZ2FuFwOK3IszzX3AAsh28fJycGpqj+SX19Pex2OwBgZWUFT58+xYsXL7C4uPjaBiaTSaytrWFzcxPl5eWi4rHJZDrTvICsrCw0NjYKywoA+P1+8aDQhnyWQoVtKbaVXsqtra1YXV0VnrPa2lpcu3YN//E//kdUVVVBr9fD6/ViZGQE9+/fxx//+EfMzs6euGJPVjaNRoOFhQVsbGykWXDeROSX/1kKWUqcTqcoOHzt2jV0dHSguLgYJpNJhIoMDQ3hu+++w+PHjzExMXGq3taDg8PaI8FgUITjmc1mBINBzM7OoqWlBR0dHXC5XEc2HZfLBbvdLkhl3gbZ29uDx+PBxsYG+vr60n4jS7Pb7UZbWxveeecdtLW1oaysDDqdDvPz8xgaGsKjR48wOjqKpaWltIJ9J9Xe1dVVvHz5EllZWRgeHobX630jMMWV5LcFzP6tC1eOePhLpvwMmkMqPJmTk4O9vT1sbGxgaWkJOp0OFRUVqKqqEu8+4JBFdHh4GL29vfjzn/8sIgbktcoVKllpJyXldblS3PPOLcRKyg71hd6LwI/1j/Lz81FUVIRQKCTCzY7zfHClULbYc3CoNAfJZBI+nw+jo6MoLCwU9Szp3FgshrW1Nbx69QqPHj1KC/tJpVLCG5+VlYWSkhJ88MEHwtsVDofx5MkTEa3BFXx57GVgyn+Tx1HpOFnZJkU7OzsbRUVFqK2tRX19PSwWi4joGR8fx6tXrxAIBIQnju6j1WqRl5eHrq4utLe3Iy8vD/F4HK9evcL9+/fx6NEjzM3NIRqNpimUMuhVys2hY+U+aDQaFBQU4NKlS7h58yZaWlqQk5MjrkM18vr6+vDll1+ip6cHfr9fvP9lowABUFrPsVgMGo0GoVAI8XgcFosF29vbWFxcxMTERJoewedDCchQe2Wv4nHHk4GE1iRFCnBQZDabRUgpH7Pj9EQytsrfUZsyncOFg/rCwkIR/haPx9HX14enT59ifn4+jRWQ2nxwcCCYCjnFPF2TF8fm7yHZaKTUJhmkyJ4rfhz/ju5BzKVEkGG1WlFcXIyOjg7cvn0bbrcb6+vrePDgAXp7e/Hy5cs0vZzvLfz6Sl6w455PLicGpux2Oy5cuCAKwu7t7SEajYrkzTdRdmOxGAYHB1FTU4O2tjZhyThrxUFGr6lUCl6vF6Ojo1hZWTmRnKKfKpT4T3HPWq1WFJLb39+H3W7Hzs4OPv30U9y+fVsQaaytreGHH34QxUKPS4L/OYVCJ5PJJObn59+4IDB/2ZJFant7+8zBFLEpXrlyBe+99x5aW1tRUFCA7Oxs6HQ6xONxrK6u4s9//jMePnyIFy9enFnYKpC+SVC9KPKY/fDDD0co03U6Hd5//31cv34dpaWlQlGRr3VW86BkRSXA3tXVhTt37uD27duwWq3w+/0YGhrCH//4R/T19WFtbQ3hcFi8YE5SyDs4MTEhyEDk/SPTfqfT6eBwONDQ0IDCwsIz9cb+LYvshSAvk5zTIwu9R/R6Pex2uwghI++SRqNBXl4ebt26hdu3b8Nms8FgMCAUCmFmZga/+93vMD09fSRigCsEfH/kZBhcKeJKg1JblTxH3LIve6EsFgssFktasU6LxYLKykrU1dVhaWkpLcxGCcAprWW5rha1XUkp2t3dhcfjwR/+8Ae8ePEC+fn5yM3Nhclkgs1mQywWE8ZPyhvmuUAEHKn4ZyQSwfz8PC5evIjc3Fz4/f4075+S0igbnygni+9NpJDSepEVeS5ckXc6nXC73aLmmFqtFgQNVB6EWHh5eJ/dbkdzczOuXLmC3Nxc7O7uYn5+Hg8fPsT333+P5eVlJBKJtNppfD1zI4+sfNM8cFZDleqw/lV1dTW6urpQWVkpQi75mgqHw+jt7cXTp0+xsbFxBLzRvPBoG75udDodksmkYNAlbxylOSh5GDIpy0qeFvn/x31PhhGKliFwT7k+Sp5p2QPJ648p1cPi482vIReZpflTq9WoqqrCjRs3kJOTg5GRETx9+hQej0exJALNYTAYFPsL1Xij9cbnn5wbvDYm/11+VmRjjFKYr+zBksdMr9ejubkZ77//PpqamuBwOGC325GdnY3FxUX89re/xR/+8Ad4PB6RC0lt5ePE2yvfh+pZvYlx88TAlF6vh81mE0pWKnUYR03u2jeR/f19QdF51uAEOJxcSmwsKiqC1WoVC2VxcRFPnjw5cTrrNxVS0AYHBwWzHL3QPvjgA9TX12Nvbw9tbW0oLy8HACwvL+P58+f48ssvRV7VaSnDOp1OsD1SiMFx91ar1bDZbCKWV6VSwe/3Y25uTtQLOiuhkL67d+/i2rVr6OrqQn5+vvACzc/PY2ZmBqOjo+jv78fMzIwoiPs2COX/xeNxQdHOLc3A4fNdVVWFtra2I2OdTCYFdf3bEPKanZ2NhoYGNDY2oqmpCfX19aitrYXL5cLi4qIoMky1vU4DRHEhZr5oNAqNRgOTyQSz2QyTyQSLxSLIQFKpQ1a0UCgEj8cDtVotvBYOhyMjUcu5nKzIL2GlsC35WHqRE8lBfX298J4EAgGo1Wq4XC60trais7MTdrsd+/v7WF5extDQEL755hsMDw8L5lLuDePAghRQrnxxxZ/+lhPaqY1yKA+BHCozIHvDKFeXagLRdXQ6HYqKilBUVCTyMLniSvfhijP3WqlUKsHgpVRPin/SPQ8ODrC9vY1kMomDgwM4nU4UFBSgrKwMgUAAqdRhblR2djbC4TDUarVILSDgQHOZTCaRl5eHqqoqZGVlobKyEuPj44pzKgM+GhcyKssKPfd4UPgR9w5wpVOr1cJkMqGurg7Xr19HTU2NACekY9H86PV67O7uiryvCxcuoK6uDjdu3BBRIL29vfj+++/x/PnzI/Xp5FwhGXTL487JJwCI8M7Lly/j5s2baGpqgsvlSsszpzyjpaUlTE9PY3NzMy28UGme+bpVqVSoqqrChQsX0NLSAqPRiIODA1EWgOjIlZRmJUVdac2TcG8oBz+kmJMHNjc3F4WFhTAYDDg4OCxTsLKygidPnmB5eTmtphhfM/yZJB2SPDDxeDytTfJY8PGQn20iI7t9+zaam5uRSqVEBBVvB38WaV1ub2+LMgDAYcQTZ/IjAwetNb4uVCqVyEtUop+XPVN0PXlM+ZjTHpednY2bN2/iH/7hH1BXVydqkanVaoRCISwtLWFjYwOxWOwIAQ5/5vg9ON07B4LH5b1yOXFqdBqIRCIhLDxvA9j4S0StVosic6WlpaK+FOW9jI+PIxAInG0j/59QnZGBgQEUFxfD4XCIJNrGxkbU1dUBgKgEHQgEMDQ0hPv376Ovrw8ej+dUFUqqBaJSqYT1JpOoVCpRkLijowN2u13Q0lNY6FmBb1ojjY2N+OCDD9DS0gK32y3CF0dGRkTtF6Kr/SkGhtMUem45yKON02AwZKThjsViWF9fx9LSkij0e9pC4UaU83Dnzh1cvnwZDQ0NyMrKQiKRwOjoKPr6+vDw4UMMDAxgfX391IEUCd3T6XSK+m5Wq1XkdjkcDqRShzVtNjY2BHEIFVo0m82IRqOn3u5zORRZOVJSdvhxFK5ExZovXbqE0tJSUYiX2FXr6+uRm5sL4NDY9fjxY3z33Xfo6+sTJDtK1nT5nvx3UpL5npPJ+8kVfTqXvidLrwwaCKBw75JWq0VxcTEaGhpQUlKCra2tNM8dV27ksSM5ODgQVmWl0EQupAjRPkAlK0ihr6urQ319vajXtbW1JcL/JiYm4PP5sLKygkgkApPJhIsXL+L27duoq6uDRqOBz+fDq1ev0NvbK9qWSdHln5ms4aTUydZ7+TwizbHb7WhtbUVhYWFaxI7NZkN9fT1CoRDm5+fFNaxWK2pra/HOO++gqakJer0egUAA/f39ePHiBaamptLC6rhCzRVKmThAXj9caTUajSgpKcG1a9dw4cIFlJeXizBVAquRSAQ+n08wTyop5HysOLjW6XQwm80oLCxES0sLqqurRV0zv9+PlZUVkW+vpAzL15PHXV6TpODz8zndv8FggMvlEmFnZDCgPF7Sq7iyLt9bSVKplDBeKIEffpwSEUR2djbq6+tRUVEBl8uFnZ0dUQOSjDn8+P39/TTARHT61H+5llMqlTqS00ZCZDkyGY68buQxpesqhUdTqGp7ezsaGxvhdruF15eXL0gkEsJTToy+mcBzpjVNY5optzGtXRl/+StERtvkcp+ZmRGxyb9EUasPa080NjaiuLhYuDuBH13QZ6GIKQkt8P7+fphMJtTW1iInJwdZWVlHyDt8Ph+Ghobw9ddf4/vvv38j4oefW4xGI/Ly8hAKhUS9DiUhS0tJSQlu3ryJ999/X8SKU+L1WXoxdTqdqHNUX18Pl8slXPZ9fX0iNMfr9Yp+vg1e1+OEt4+qiF++fBmdnZ2ingaXQCCAyclJzMzMnMlaojVSWFiIu3fv4vr166iurhZFq7e2tvDgwQN8/vnnAnxTHsdZPb8E/G7evInbt28Li7NOp0NWVpaolbW/vy9Cj1Sqw4KEVGQUgEgcJpD7tuxH/55FtmTTC1hJKSSChuzsbDgcDly5cgW3b99GdXW1KBRNIMVqtQriF5/Ph76+Pvz2t7/FysoKQqFQmkeKgzhuOZeBlJJyT6Es9A7jIlusOYsakE5hTEYup9OZVtAbgGAprKysRGlpKVZXV5FKpYRix8dHVhqpvTw3iYeg8XbI82KxWFBbW4s7d+7gxo0bKCgoEB4oyrnIzc1FZWUl9vb2BMirqKhAaWkpIpEIgsEgOjo6UFpaCpPJJECLzWYTHieuYHKwx8eb1yPj/aX2HwcQKQe3sLAQXV1duHjxImpqamC328Va0+v1KCoqwvvvv4+Kigqsr6/D6/UimUwiNzcXzc3NaGxshNVqRSAQwPPnz9Hb2ytIeYh4QB5P3jbeLh7GpuSJKywsRHt7O9ra2kQ9LwIFRHM+MzODly9f4tGjR1hYWDjyLjwOKBuNRpSWlqK1tVXkf5Exdnl5GeFwOGOEC4FX3i8lQCgDSzpX/peTk4O8vDy0tLTg4sWLcLvd4r1P4X1+vz/NK0XjxBV6ua0HBwdpzNDUJk44cRwIo3lobW2Fy+WCyWSCXq/H1atXBemH2WxGKnXofaXix69evcKrV6+Qn58vQvz39vZExASfAw6oqI0k5GXk40vnynP8OpBOz5bBYEBJSYmoKQZAOGuAQ06DW7duobGxEa9evcLIyAiGhoYwMzMjPFXAj0Qf5FVTygOUQfVx8rODKdpQs7OzBVEEEUksLy/D5/P95HAm2X15VkIJqXa7HRaLRSg4FHJWWloqrC1vg+zv78Pj8WBsbAzDw8OC2lJeyIFAAC9evMDQ0JCoMXDaotfrYbVaEY1GM3ovVSoVHA6HcOu3trbi4OAAT548QUFBgbB6naXQRkEWEV7sNisrC3l5eaJ/ZC35pYhOpxNMUHfv3kVzc7N4QQI/boYejwcDAwOioPFpilqtRk5ODmpqanDx4kV8+OGHaGpqQjKZxMLCAubn57G8vIynT5/iwYMHaTHeZyUajQa5ubm4dOkS3n//fVy7dg35+fnY3d0VRViDwaA4npiZHA4HcnJyhAJkNBpRUVGBtrY2hEIhTExMpJ13LicjPBeJK2dKHiGau7q6OnR2duLmzZvo6uoSlOfcO0FKCBXh7evrw/j4OKLRqHj5KykechtIlMAdGR6SyaSgBeciK/jHeU5MJhMqKirQ3d0Nh8OR9t6mnMWysjJ89NFHyM3NxezsLCYnJ7G8vCws25n2fcpbkFnleG6J3FeqOVdSUoLCwkKEQiHhaaKSK8TOd3BwWCJkdXUVq6urohAsUaRT3SnKwcnJyRGhjEQ+oKTY8jmRFWm1+rCA6v7+ftrYKwEpnU6H3NxctLS04KOPPhKkQLJSTaH8eXl5iMViiEQiiMfjyM3NhdPphMFgQDgcFmVSBgcHFUPrZDCu5EmhWmK8IDMH3hSdQXVGiZp+fX0dIyMjePXqFSYnJzE8PIyFhYW0vDWlNcDHwul0orq6GhcuXMD169fR0NAgKLBpTXm93rRCvbQOCYTzsZPHXSZUoO9UKpXwtpBnjHt+WltbRWkZqg9HuXBKBDF0Dw5c+TPFDRd8HAgIymBQaQ2p1YcFvf1+Pw4ODoQB4eOPP0YkEoFer0csFhO5cjS+oVAIer1eRDXt7e2JOedrgzPj0b3pH4XY8vbw8LlMxh7gx2eb9gTStSm1hurpra6uwuv1YnV1VYQTl5WVobKyErW1tcIQ8vXXXwvmYirdsL+/L1gXZSEg/KZGyZ8dTGk0GtjtdhQUFMBms0Gn0wmX8/r6+k8KZ6LNxmg0pm2oVN/htK2uZIGRPVBUeLOmpgY+n++tAVPAj3lnExMTqK6uRnV19RFGmWg0ivn5eQSDwTMDrPyFQC58cpMTra3RaERDQwMuX76MGzduYHd3F/fu3cOXX36J27dv4/r16yLm9qy8PUTBTYQCFotFxPJeu3YNVVVVGBsbQ09PDx48eCCqjr8uR+xtEIPBgPr6erzzzjt45513RPgikJ4vsry8LOqUnKbodDpYrVZUVVXh3XffxSeffCIKJb58+RI//PADnj17htXVVWG1PGshha+xsRF/93d/h46ODthsNgSDQczPz2NsbAybm5tp1OxUr6yjo0PUAiPa5o6ODjEOKpUK4+Pjwhp3nBXzXP5y4ZZWpSRq4EdFwGaz4fr167h69Sq6u7tRUlIi6hWSlVpWrmKxmCiyTsrZ6/a3nzLX5Onh91YKRaO/lay1FLJYX1+PO3fuIC8v7wjDJ+XFXr9+Ha2trdja2sLz58/xhz/8Af39/YjH40eULvm+SsIVXtn6vbu7i6mpKayuroqitXq9HpFIBIlEQtDMUx/IU2Y0GoViubOzA51Oh7q6OthsNphMJsG8SDWnaC9RaqcMQuU2ajQaYXTK5EUxmUyCQIAiAnjeGhF8UX0x6idRkFOeWiwWw/z8PAYHBzE3Nwev1yvAkEqlOlJImcCH3B5aN8BR2nTqF4Xf8cLICwsLuHfvHr799lssLCyIsg4ydXmmeaac0paWFrz//vu4cuUKamtrkZWVhYODA6ysrIicb264l43x1F7eP/77cSF1nDSmrq4OdXV16O7uRnV1NfLy8mCz2YQRlfZdk8kkctu4nkN6LX/eldaN3D4+zjKQ5X/v7+9jbm4OX3zxBWZnZwUgJ9Ko6upqGAwGsYYDgQA8Hg+8Xi82NzfR2NgIl8sl8tkpxJ+uTWBVzq9T6gNf99R2zggoj7XSvKhUKiSTSayvr2NmZgZqtRpra2uYmZnB2toaDg4OkJeXh5s3b4rCxPX19bBarfB4PIhGo4LgSTYMyZ7JN/VIkZwImHI4HKJmEHlHhoaGMDc395Ms8RaLBRcvXkRzc7OgvlxYWMDw8PCpscxxOTg4gN/vP0IWQFS2o6OjJ1bY8y8RleowXrasrAxXr14V8d7yMQUFBfj4449FwbxAIHDqOTx+vx+9vb1wOp24cuUKVCoVlpeXkUqlUFhYiKKiIlG1XqfT4dGjR5idnRX1VdRqNZxOJ3Jzc5GdnX1mjH603nt6epCbm4tr166htbVVhOxQzHtxcTHq6+vx5MkToSidthfnpwqF+JWVlQm2LhKyIPn9fmxtbZ1qbiSB7/Lycly9ehXXr19HW1sbHA6HKPz43XffYWRkBKurq2mu/rMUKmze1dWFd999F01NTdjb20NPTw+eP3+O8fFxLCwsYGdnJ20sNRoNnE4ndnZ2xIubLJ/RaBR5eXm4e/cuHA4HXrx4gRcvXmBjYwOhUOiNmVTP5c2FFCAeBie/mCmqobq6Gm1tbWhoaIDb7RY5HhyUyJZbyi+huVNSDGWRAQZXEGSFjYA6rSE5R4Cfz5U/brG3WCxoamoSpR/Igk3jwz32xLiVk5MjajaNjo6mMc/J+UPymNBxfOz5cWT4DIfDaYXm6Tkiq34mbxCBW7KOLy4uYmxsTITfEjih+ZKVbU6gQdcD0oEHAWU56Z73mc51OBy4cOGCYBMk9rpAIIDR0VGMj4+LpHutVov29nZcvXoVDodDeD1pfxgfH8fw8DC2trbSQJycb8OVYCWvBxXQlSOHyPtKRWv39/extraGsbExkZM9Nzcnwt6UPBf83uQdqaioQH5+PsrKytDR0YHLly+LZ4jy1vv6+vDs2TNheOBjL4MNpXVMc0Prj6i39/b2YLFYkJeXh5KSEtGOiooKoZtw7zJ/1uLxOHQ6XVo0Exkw5HVBzx2dL88JiUySwAGN/Pf29jbm5uYQDAaRm5uLV69eCXBHDJc7OzvCk7a2tob5+Xl4PB50dXWJnE2v14vBwUFsbGwcAdBKJA0Efqn98vjztpMuQfuP7HXnBhziA3jw4AF6enqQTCYRCoXSKPLJgaPX61FXV4fCwkJcvnxZ1IOVvamZ9lCeT/W69+bPDqZ0Oh1KS0tRX1+P7Oxs7OzsiHwpj8fzk5QYvV4vFm4qlRJJoaQQnYVSkEwm06wo8Xgci4uLwvp1UsU9f6qoVIcJoMT609bWhoKCAuGJok2KrKVdXV1YXFzExsYGBgcHRQG30xKfz4enT5+ivr4e+fn56OjoQHl5OQ4ODgT9KzH/bG5uYmhoCAsLC9jc3BThM1lZWaL2EafePU1JpVKiEO53332HYDAIr9crmKxcLhdcLhesVivy8vJgt9vhdrtht9sxOzv7VrH6caFE1dLS0rSkZy6JRALz8/OC+vi0wJTJZEJ9fT0uXryId999V3hniGCiv78f/f392NzcPNO8KFmoUPXly5dx8eJFAMDw8DAePnyIZ8+eiaKVcnsNBoNQrmmzX11dxdTUFObm5oS3a39/H7m5uWhsbIROp8P+/r6gtz2Xn09Icc+UW0KhQS6XC7W1taLoPIF6Yr/T6/WCUple8Ht7e0gkEojFYqLwKhfZ2su9ZCSy9VpJMZOT7jN5CZRCcwCIqIHS0lIBMpTuRwpWKpUSxX3j8bgAUrJVWu6HPLZKzzK1nda6XFuG/j7OE0JjQkrj5uYmJicn4fP54HQ6hdeKrs+Vcq6oyyCLhM6hPF+lorhcUT04OEBOTo7w8sTjcfj9fgwMDOCrr74SNNeJRALZ2dnY3d1FU1OTiPBQqVSIx+NYWVnBixcv8Pz5c2E0lb16/P4yeOcAmQNGDrqBQ0M45Qvv7e1hfHwcv/3tb/Hs2TOsra0dITXi88HboVarYbfbUV9fj2vXrqGpqQlNTU0oKCgQuTyxWAybm5sYGBjA119/jenpaZFTCKQDc74++HqnOeHHqNVq5OXloaamBkajUUQ9tLe3o6KiQgARyk+VlX+aU61WC7PZjJaWFqhUKuEh4euUQlhlRkRqv5KXRvb6yECKjtnd3cX29jbC4TCWlpaOXIsTmPDwwf39feh0OhiNRuzs7GBiYgIvX75MY3kmMC0bJ+g30tvIECQbmmgMlN5JsleIzxMVRuZeUpqDRCIBn8+Hzc1NOJ1OZGVliVqfy8vLohg6zTEZGuSIM/pNNuBkkp8dTOn1etTW1orK2ltbW/D5fCL2/00VGWJHoYS5vb09rK+vY2Vl5Y1cwichavUhJaPdbhdIOhwOo6+vD2NjY29F2BCJwWBAYWEh7ty5g08++QRFRUVQq9UiORMAcnNzBetMbm4uLl++DJ/Ph8XFxVMHU8FgUFSh7+zsRFVVFRobG5FKHcbdUmHTpaUlsZZ43gBtWhQbvrq6eqbeh2AwiKGhIaytrWFwcBClpaW4dOkS2tvbUVJSguzsbOTm5uL27duoqqpCVVWVKNwbCATeqrA/Sh6ndubl5R3xSlF+z8DAAEZHRxEKhU4UFNLGT9Xlf/WrX+HWrVtobm5Gdna2KDr9ww8/YHp6OmN9j7MUm80mQkTKy8vx6tUrfP311/jhhx/Swm9kMZvNqKqqEuxMADA0NIR/+Zd/wcjIiKgjl0qlBPtoKBSC1+tFKBQ67W7+u5fjLJcEpMxmM0pLS0XO5NDQkGBfLCgoQHFxMQoKCpCXlwfgRyVlb29PsGnt7u7CYDCIHAx+Dx7ayZVIsjpTKDEPq+HKpKw4y4omP4f3m1vgeY0/GZjxHBQiuohEIujr68PU1FRa2H6msBquwMljrgQQ5XZyoMn7qCSc7IKusbOzIwDw/v6+8EAohVTy8eTty6RwH7cvpVKHyf10/2g0irW1Ndy7dw8PHz7E7OwsVldXhaGIiAKoLhm1cWNjAxMTE1hcXITf708D5zSmx3mJyJPCc1iUxp/aSwaGra0t9PT0CIOWTA1On3I4F+3xRUVFuHLlCm7evIn6+vo0IgtS8snj9fLly7S6a0oAkbdd7iv9plIdMideuHABn3zyCfLz8wVLJfEBUGFnmkdaX3zMtFotcnJy0NbWhtLSUiwsLGBpaQlra2vY3t5GKBQS5UcCgQDC4bDQk8lLw4GsUj4kB6PyM8rXmdIzAaQX46b7EAW/0+kUeYXkyKBSRccZD/j80bhw0hqldnJQxtuo9GzIIFKe01Tq0Kh9//59tLW1iflzu93Izs6Gz+cTx70uZYjm4HX6/c8Opsjln5OTI4r2kXWEP4ivuwbVECosLITZbBZFaCcmJhQtdKchWq0Wzc3NuHz5MrKzs5FKpUQNgbOk45ZFrVbD7XbjnXfewbVr11BdXQ2j0Yjt7W1MTk7iu+++QyKREH0pKyuDTqdDWVkZGhoa4HA4sL6+fqrgcG9vD8FgEDMzM4jH45iZmUljmaFkQ6ISl4XG3u12p7GbnZUcHBwWYyWr0NLSEpaXlzEwMIDGxkY0NDSIWkfl5eUivn9nZwcDAwOC2vOsRavVwu12o6qqCq2trSgvLxdJ2cCPG/XGxgZevHiBBw8eYGxs7MSfUTIANDY24vLly3jvvfdQW1sr2K0onn1gYCDN8vtT5HWhVH+t5ObmorW1FaWlpdjf38fk5CSmp6exsbGRMeSTXvIUx06UsF6vF5OTkyIskHI+srOzsb6+Dp/Pl1YB/lx+PlHKl+IKkMvlEvku1dXV2NjYEKFtsVgM1dXVIgTFZDKlXY+MSFNTU/D5fNjf3xdEO1qtVih0JSUlqK+vF+/c3d1dBINB6PV6UeR3fX1dGDi4MsJDgrhCJSs9pByR9ZwrPZSb1NjYiJ2dHdEObo2mvynUZnx8HM+ePUsL/5eJAWTLN4Aj3hSuTMnPLD+Oh6TxOZIBjyx0LleeKbfq4OAgLTRLXgvcmyaH/sntz9QWut/09DR6enrgcrlEDujMzIwwLJJxqampCc3NzSgsLBRtC4VCePnyJZ49e4aVlRVRO4jAPm8zDzEjJZj2eZkMIZMBgfpLRsVXr15heXlZRBTxfsprj4c3Umhsa2srGhoa4HK5oFYfsjGura1hbm4OPT09Yix8Pl/a2PO28HUigx5+X+AwaqCkpAQXLlzAlStXhD4L/EjrHg6HEYvFxPNAYdvcW0WhrVqtVijzzc3NiEQi8Hq9wgg5NzeHyclJDA4OilqlctuP27u5V4n6KD/bSusKOJqbRB6lsrIykdMZCATg9/uFkZTuR8YaeZ+gdU/AUGYk5OtKCUjz9lNb5bBM3k9qN19L8Xgc8/PzGBgYQHNzszAsUooCryvFjUj8OrxG3+vkZwdTpERub28LVOt0OsXfr6OCJmWhrKwM3d3dKCsrg8FgEDG3k5OTZ6ZkarVaEfNOaJ1qJLwtYIoUycbGRnz00Ufo7OxETk4OIpEIxsbG8O233+Lzzz9HLBbD7OwsHA4H3G43jEYjnE4nSktLUVBQIDxApylETbu1tfWTz43FYojFYnA4HMjPzz+SG3ZWkkgkhNt5cXER/f39gsnr2rVrIsegoqJCKCXhcFgUTzzrNUW0+hcuXMDVq1fTaoWQ7O7uYm5uDg8ePEBvby+WlpZOBHxwycrKQltbG+7evYubN2+KotS08eXn5+PatWtYXV1FNBrF3NwcAoHAa/cOeoFnZWUJC2Q0GoXP5/vZSStsNhsqKyths9lEjDcxaymJSqUSRXzr6urgdDpF6M7s7CzW19dFOBiFG29ubmJubu5na/O5HBVZsefKgUajQX5+Pj744AO0trbCYDBgbm4OU1NTmJiYQCKREF4CtVqNhoYGwfTp9/vx9OlTfPnll5icnMTW1pZQ2jjoyMnJQVFRkWBNSyaTom5POBwW+XLk6eQKA6Cc45NJceEKN79GLBbD6uoqFhcXEQ6HYTAY0qzSwKHSFo/HMTs7i2fPnqGnpwcjIyMIh8NpihVvC+UTvc5Tr6QkygrQ64CA7E3in1lZWSgpKRHsezs7O8JbKEdI8Ovw+8h7uaxEHmcATCQSePXqlSjWTXsFATq1+pCmmnKrurq6YLFYoFKpEAqF0NPTg8ePH+Pp06eiqL28bpU8bLK3Rgmg8nVEa4tCET0eD/r6+jAzMyOY3eQ5kp8Z+ttgMMBsNqO4uBjV1dWidplKpRLg8ssvv0Rvby8WFhbScmF5HhpfT0rkVBw4khiNRpSXl6O4uFjkmqnVh6x45Cne3NwUUTLkDWxqaoLb7YbVaoXRaBQGD5VKJcLmzGazeMccHBzA5XIJkEL5sUQywz0+fD3JXjUlhZ/PixIjoNL6p2fO6XSipqYGxcXF0Gq1WFtbw9raWlqYPIGN48iN5PINdJ4MnmXjDfds0X2U+sqvw9f0wcGBMDAsLy9jdnYWFosFJpNJlKDg+5k8DvI130Sf+dnBVDKZxNzcHEZGRpCXlwer1YqioiIUFxfDbreLegZKolL9mGzY3NyMGzduwO12I5lMwuv1YmVlBV6v90ytq3xB+3w+TE9PY2pqSiTlnbXo9XrU1NTgypUraGlpEUXaZmZm8O233+L//J//g/X1dbE4r169ivr6ehQUFAhFsqioCA6HA36//8SV4p9DUqlDevf19XVBsX7Wnikl2d/fF6DW5/OJ2HGNRoPi4mJUVlYiEolgenoa6+vrCIVCZwqmeIjCzZs30dnZCbvdLn6nzYjYIAcGBhAMBk98zahUKjidTtz+fwyO5eXlafUuDg4OoNPpkJ+fjw8//BAOhwN/+tOfMDIykvE5pU04OzsbVVVVqK2tRX19PUpKSjAzM4NHjx5hdHT0ZzUwkNJBL1pi4soker0excXFgrCirKwMXq8X3377LZ49e4aNjY23KtT4b0W4B0VJqbfZbGhubkZlZaWg2KbinuFwGLW1tWhoaEBdXR2ysrKQSqUwPT2NR48e4cmTJxgeHk4rZE5WayIAiMViCIfDmJmZEWGAyWQSm5ubQuEnhUfJwnqcFZsrPLJix2Vvb0/sacvLy7BYLIKFl7wj8Xgck5OT+OKLL/DkyRMRbsbDxpTGkCvs8tjK3pTjPCXyubI1XP6dezCo3pvRaBQsZpz2Wb6v3GbeN368HC6Vqa9EsBCJRJBMJuHz+cS46fV6OJ1O3Lx5E21tbbh48SLq6+thNBpFSYgvv/wSIyMjWF5eRiQSSZtzpVBD7o3jngu57XI7SfHlXhsKZ+VeBhlEUmgZ5Q5SYVbyshHRBO2Vfr8fy8vLgtiMgFQmUMHD8TKtHW5AiEQi6O/vF15fiq5Sq9UIh8OiHiYxEZKRo6GhAfX19aivrxf5kUShT7ojKfp+vx/r6+uYnp7G/fv3MT4+fiSvVwadJPI7TF5X3DAhX+t1RgkCQE6nEw6HQ3i55QiPg4Mfa2DJzxInVJEL3iqBGCUjBNW8ohwppXFREv48UU4meQGpDInRaBTrMpPxRWlsjpOfHUwRs93i4iJisRjsdjvsdjva29vTrGVKVpqysjLU1NSgtbUVly5dQktLC3JycjA3N4dXr15hbW3trVEWUqmUsB76/f6flA92UqJSHZJONDc349KlS8Il7vF4REHepaUl4d7c2trCyMgIqqurBcOSxWJBaWkpnE4nZmdnz7xPbyKpVAperxezs7OC7e9N3LJnIQSodnd3kUwmRQHlDz/8EE6nEyUlJbh06RKmpqawtLR0ZmQBGo0GpaWloqhoa2srnE5nWq4UveAfPnyI77//HrOzsydOwEKhthTCWlFRIajBKaRyenoasVhMWAivXr2KvLw8rK6uYm1tLY0ohrw9TqcTNpsNVqsVLpcLubm5cLvdorzDyMiIIunGzyVWqxXt7e2Ynp5WzPcjy/gnn3yCd955B5WVlUgmkxgfH8f9+/cxNjb2VoSF/i0KvbRlQJVKpUTZjNraWkFTbbfbUVhYiIaGBuzv76O4uBgul0soIR6PB+Pj43j48CFGR0extbWVESgAwM7ODra3t7G2tqYYFkMie0roODmfQilkR7aMk8WY1inVa/H5fBgbG0NRURHsdnuapdjv92Nubg7Pnj0TIY50PrWbxpDaxNtmMBgEiQpvP+8btVfJkp0JkMkeEa7UUXhWUVERqqqqkJOTI0KzOOutfH+eHwL8yNIo7+fcKyJ7t2SAl0wmRZglFX4mAqOKigrcvXsXly5dgtvthsFgQDwex9zcHB4+fIiXL1+KkEB+z+PGKJVKCQWWgyqltstMhNR3yr0xm83iGvv7+8K7ajQahbeA6ObJc0N5xlTPkMJXY7EY5ubm0Nvbi/n5ebGOZE8frX8ae56rlelZorGnMM5vv/1WgCHg8P1Dv3EQSqBhZmYGk5OT8Hq9AkA5nU6xrgk0plIpeDwePHv2DA8fPsT09DS2t7eP1I/jYOR1Ius88l4kf6d0LH3qdLo0Y3wwGEQsFjuyzqmOrCw09hTyKD+zcnv4XkVkH+Xl5SgtLcX29jZWVlawsrIi8uHktcbngdYkeTbtdjtMJpMgZrp48aLwZFK4q7wGSGQD03Hys4Op/f19bG1tYW1tDeFwOM39uba2hkAgoFi4V6PRoLOzE5cuXUJ3dzcqKioEJeXy8jJGRkb+ovCvkxBugaGaDm8DQxa5xktLS1FVVQWDwSDYFF+8eIHp6WmxodBi3N7eFsXc6ME1Go0nqjiehESjUWxtbQkaz7dF+AbPH8pEIiHyjAoKCnDt2jXY7XZkZWWhpqZGAOGzEKK2bWlpwbvvvovW1lYROsn74vf7MTY2hu+++w4vXrwQVLcn3a4LFy7g/fffF6Ef3Fo5Pj4uSDwqKytRWloKt9uNpqYmtLa2IhqNYmFhQdSCU6lUQrm12+2C7Ab4scYP0a7+3M94PB5HIBDAzs4OXC4XOjs7MTExgfX1dQSDQRHqSbTv3d3deO+999DZ2QmVSoXJyUm8ePFC1KI6l7MRblWVvRrZ2dmoqakRRD9ERkFrGYBgVSUw8sMPP+CPf/wjRkdHEQ6Hj7zIlcJ0SCGRk8yVzlMCVXI/6DjZU0LnUq4eKch0f4/Hg97eXnR0dAjSo1QqJSz9T548wdTUlCBCUVKKZIWYlCOz2XxEiVXqK2cuzDQWXDhBhtwOvV6P3NxclJeXo76+HhaLReSOEOW6bP2nsaB+8HwreY743HFPggykCEQajUZYLBa43W7U19ejoaEBlZWVKCoqEtThVE9qcHAQf/7zn/H9999jbm5OeKR4vzMp2xwoH+f1499z4GixWJCVlSUM6US0QGy1VACZcoarq6tRU1OD0tJSZGVlCYBFBWMJRIZCISwtLeGHH35AX1+fYO3lYF8eYw4A+NzKa0OJ3IHIUvhcyt5Iut7BwYHI7Q8EApifnxc1McvLy0V4GV0rFothbW0NGxsbwsCqlPNE4CtTKB3NFX++eX/eRGQAYbPZBGlDIBDA+Pg4lpeX02qy0T1obPlzR7okkeJwUpBM96XnrbKyEl1dXbh+/ToqKyvh8/nw/PlzPH/+HDMzM2nRbbyvtOcSAyGxD3/00Udob29Hfn4+9vf3ceXKFYyNjWFrawvJZPJIMW3eHpn04zg5Ec/U+vo6Jicnsba2hoKCAmRlZaG+vl4MlN/vP4JmNRoNGhsbUVNTA4fDAZPJhN3dXVFgjuoivI3ytnhvyJJG8bi0iClcTKfTiXwXSuCvrq5GRUUFdDodgEMrJ4Het6VfbyJEVfu2hfdptVro9XrodDokEom0Oj+7u7vweDzweDyi+juB2bP0rmVlZaGrqwvvv/8+bty4kQakqO3JZBIvXrzAN998I2i8T5rS3WazoaWlBRcuXEBTUxMsFot42W1vb6Onpwe/+93v8OrVKwQCAUGVX1pais7OTlFvorGxMW2dUCHCaDSKlZUVERtONVxGRkYwPT2NcDj8s/ZnbW0NPT09qKmpQUFBAWpra/Hxxx/D6XRicnISKysr8Pv9sFqtuHnzJj766CM0NDRArVZjbm4Of/zjH/GnP/0JHo/nzHPr/paFgxM53IXqAfEwF7LIAz++O2jf7e/vxx//+EcMDg4eYcT8a/cDrnAqWbG5gsSVSd5uHi5F+xVXYsmKzEOUU6kUwuEwRkZGMDU1lVbWhFu56TslZr9U6rAelry/ZwKWf8nYcNFoNELZb2lpQVNTE3Jzc6HVauH3+4X3QR5TDob4GO/t7Ym2832Uf6fkSeBran9/HwaDAbW1tcK4kp+fj8rKSjidTuTk5IhcpVevXuGbb77Bq1evMDU1lVYqhHvC+BhmagfNt9IY8fPl88iYUFlZiWAwKIgj4vG4qLtYUFCAkpISuN1uFBcXi7A4MuaSdzIajcLv9+PVq1d48OABHjx4gPn5+TR2Sj6WdC4p+Uo51NwbmGkdEIghyaRY0zhRDls8HkcwGBR1R41GI4qLi2EwGESfgsEg1tfXEQgEjij0vA9yYVulfYa3KRMwyPQb/c5BEJVrIBAbDAbTPLG8DRxI0bjS9YgER6lvSm0hY/Ldu3dRV1cHh8OB0tJSOBwO1NbWYnx8XBgz9vb2REgoB0Xk2SoqKkJdXR1aW1tRUFAgSPBKSkrQ1dUlWJO3trYUPcavG1NZfnYwRZvexsYGpqenhaXBarUKizuP/yZRq9WiejRdY3l5Gffv38fTp0+xurr61hc1fRvk4OCQLpSUdr1ej4KCArzzzjuoqqoS1imdTge73S5IBcgiv729jcXFxTMpivzXCMVbv01gihI5y8rKUFpaivn5eUxOTooaE/SyoWJ/KpVK5DpksuSchhgMBhQVFaG8vBz5+fmCXpeE2BXHx8cxODiI9fX1U3k2XS4Xurq6UFpailTqsBhhIpEQNUaePn2KJ0+eYGNjQ4QkmEwmLCwswOPxYGxsDAUFBSJ0j/dXp9Nhc3MTGxsbggQiHA5jbm5O5Cr83GDR5/NheHgYL168QG5uLioqKtDS0gKbzYaGhgZsbGxge3sbWVlZaGlpEcXLV1ZW0Nvbi5cvX2Jubu7M2E3P5VAoZIlCmPgLOB6PCzZFMpDQ8cDhs5RMJkXNoO+//x7j4+NpzIuyVV1OnuZKEHC8RVpWoKk9PFFc9kLQsVTIlhLkuUWcH88pxLmEQiGRr0tt5hZ4nr8jSyr1YxI6V+R4sU/ZW/CXCB9Ll8uFtrY2fPjhh2hraxPAmHKllYwYmRReAlnckq9SqQSoJsKB13kTqaYekUyYzWbk5OQIxZeK8n755Zfo6+sTDHoEfDkRgwxo+Zwo9Yf+0XfcmCUDYHofZ2VlifDI1tZWwW5HHjYK8yOiBiKOoLVA+VdUduObb77BwMAAPB6PMMiT8s5ZIOV+yeuKPBiZmF75eMjARQnMyJ49AiBjY2NIpVJwu93CU5dKHXpq19fXj1DFK829EoCTi1rL4FuJGERuo/wM0icRmdjtduFNB5BGAkPzpNPp0koVyWOmtK4pV1jec/j5drtdhO8bDAY0NzejtrYWN2/eFGHNdD7tNxRWajabBatiTk6OiAqgHK+DgwNUVFSgs7NT6C7RaFQxlFLeZ4+Tnx1Mkfj9fvT19aGwsBBWq1WEMFGhNaUHlpRJYsh7+fIl/vVf/xWjo6PHElechdADmulBOAtJpQ7jqj0eD5aXl2G1WpGTk4OWlhZUVVWJsCHgx80kOztbJO8T49zs7Oxb6wVUEnIPU1X6s/LoyEJJtFevXsUHH3yAZ8+eAYAgCjAajWhsbBS5gWq1GqFQCOPj49jY2DiT9U50p1SDjFv0aNMLBoMYHR3F6OiooOI+DaG8oZ2dHQwPD4u2TE9P43e/+x1mZmbE5k3tjcViwttEL2k5HIKMPYFAQIBY2Up+Es94OBzG1NSUKCL40UcfIT8/Hy0tLejo6BB9IIs3MaH19/fjm2++wfT09FuTQ/q3LBqNRtS9kSWRSGBiYgK///3vhSGAv9yJbe/ly5d4/PixCGdPJpOKShpXVHguE1/btF5kyaSokxGKDHBKVneuJPLniwM7OobeLXT93d1dBAIBbG5uIhgMpq1ZuheFZh1n+efhVhzgcYWZ9+2nPLNcQabwpMbGRty9exdXrlxBbm6uyPtaXFwUHoVMwI/GhECzHHZGn5QHJhcTl6339P9YLIZgMAiz2SxC+igMjEIp//SnPwkgRbTpNC90Tb7HKXmilPokC601uX6fSqWC2WwWhBGk0NKaJlDOgRM/n+Y6Eolgfn5eUKvPzc1hfHxc1OCTwR9nv5PngoTnLlFxbG4QkNeXDEzk/vPx4+AnlUqJotHDw8P4+uuvkZWVhdbWVsHiB+CI11pp3PlzT940yqM6Li+St4mPFS/MKwNprVYrPDt6vV4YToPBoGKuolxwWp4PJdZIAkg0Rrztu7u7WFpawuPHj+H1ekWdWYfDAYPBkJbTzI07u7u7IiSSjFXJZFJ4NHd3dxGJRBCJRODxeDAzMyPCMSm8Un7W+FzKHlslOTEw5fP58OjRIxFbffnyZeTn56clsJPwTXd9fR3ffvutoE2dn59XJKw4a0mlUgiFQoJ95W0QUrjGxsbw7Nkz5OTkiFhdIpfgVhvOJhaPx/H48WN88cUXWF9ffyuYCX+K2O12FBcXw2QyvXXg9uDgALm5ubh79y4qKirg9Xqxs7MDg8GAmpoa1NTUwGazAYDwhvj9/jOZg+zsbJSVlYkq7+QxI9nf38fs7Cz++Z//GU+ePEljlTppmZ2dxf/6X/8rjZqdPEjLy8sZgYVSngIXuga9/E5LyKo2PDyMcDiMxcVFXL58Ga2traisrITZbBbHLi0tiRyI/v5+LC4uIhgMvnX74t+icDpmOQ+FmFQ///xzzM7OoqKiQtROVKvV2N7exvLyMh4/fozp6WlFjxRXomRLOYA0ICUrjrLVmdrFP+n9xeslKXkoZIu8EvsfHceVJCKnSCQSiiQABP6Uwn+U+sPvSYBF9kooKZKyKH1PAKekpAQ3btzA7du3UVBQAADwer0YHR3F1NQUAoHAEW+8rIzztip5eGjsCWDweVfK+QF+DBe0WCwCBJP3ZnZ2Fj/88AMePXokPNZ0HRojmg9ZcaT2ymFvJLyGER1LoenyHkREEaSoUsgY7dt8LEjhTiaTwkNAOegUcfDs2TO8ePECPp9P9Im3n4MYpflXGnsOovhzIo85Z1ule/Br0nW5kY73jZjw+vv7kZeXB7PZjLKyMmxvb2NzczNtjuTryv3j91dS/GmPINDMcxn59eS6UvwZIwM7GabD4TC2trbSPEE0/9zgIwNyuT+yTkZeJHnOYrEYJiYmEIvFUFZWJiJ3CgoKRFkQt9stPLKUhwYcrlHStxKJBILBoGh7JBIRXA4rKytCx4rFYkeAuRz++aZ62ImBqXg8juXlZTx79kyEOlCYHy1QcsnRS2VpaQnj4+Oi8Cen8H4bhBJsl5aWUFZWhpWVFYyOjopk9rdBkskkpqenodVqkUgk0N3dLdj6zGazCCsgqlqPx4P19XXMz8/j+++/R09PD0Kh0FsDSF4nRN9dVFSEgoICLCwsvDVgcH9/H4FAQFQ7r6qqQmFhoQgZ02q1oviqSqWCx+PB6OioSI48C0XZaDTCbrejqKhIsBBxOTg4gM/nQ39/P5aWlk7VMxIIBETiOpe/1nNEYUtnIfv7+9jc3BS1rNbW1jA+Po6SkhKhfKRSKaytrWFiYgJ9fX1YXFxMq9lyLmcrvHYTkK5AUL7HzMwM1tbWROgKeW4o1j8ajaaxknElnBs0ZIVQ6TsONOh34EerPF831EZSujIpdrJHjPrNf6dr8e+5gk4KuRL7m6yIKilpFI4oy3EKJhcl5Zrfk/qWk5ODhoYGtLW1wel0Ajis1zY+Po579+7h+++/x/r6eppHnvdPaW6UQDDwI829fBz94wBao9HAbDajpKREsD/u7e1hbW0Nr169wg8//IDvv/9ehDnT9WSQz0UGe/I48jVIXkmuZMp7L7U5Ho9jY2MDPp9P5ABzwEHhgYlEAqFQCF6vF0tLS1hYWBDh1sT+TB5N7gHjc8k9LfK8HrceuCdLaezpGP5cy6BSfibk8aBjV1ZW8MUXXyAQCKC9vR3BYBAvX74Uhbhl4f3TarWK3isZvLyu/7LXitpNzy0d43a7BfEHRYlxkjJ+L/IuysXA6ZMAvBKYUlqTpJfOzc1hdXUVer1eeLLIm2gymWAymWC32wXrLjH27ezswO/3Y21tDZubm6KUUip1mM9GOXs7Oztp+57Ss8GNWG+iX5wYmAJ+tGJvbGxgcHBQWOHJ/Ws0GuF2u0VhrZ6eHoyPjwurz9sEpIBDz9n4+DiePn2K3d1djIyMYGBgANvb22fdNCG0uQaDQczOzmJ+fh5XrlxBRUWFoHpWqVSiWOTLly/R19eH3t5eeDweUcfilyAq1SGtNTEB5eTkYHZ2FpOTkydOhvAmQsyWk5OTGB4ehtVqRXFxsQifo/CgVOowXG14eBgPHz4Uz8BpK8u0UZKyJyft0qa7s7ODaDR6Juvk3yOAIIvc0tISNjY28OTJkzQylVQqJRSPWCz2VoUWn8uhyEo0CXlpyPIeCAQAKIeSZFK6uWKdad6VgBX/Hvgxt4sUGZ73pHSeDHa4pT7T/amQ/crKCjweD0wmE3w+H0ZGRrC2tpYWJsjPkz1h8lhyAMo9GlyJfpPchuPaTn/v7+8jFApheXlZUEMPDAzgxYsXmJycxPLysvDK8PYreWn471wp58on9UUJ1PD5oBwiSppfWVnBzMwMnj9/jomJCczNzWFubk4AWlLE6R5yOB6fV3m8+X0pfIryTKmotOyFJSFP2bNnz6BSqXDp0iXk5uZCp9PBYDAIb4fX64XP54PH40EikcDW1hZ8Pp+g+SegwUEcN1rQPXm4m1L/qN18ffD1kwn4KgE3pWecvD3y/PE2RqNRLC4uCp2YyNU4IZXSeTT23IAh91fJ+MC/VzJ+cJENKFarFQ6HAzqdTnh25BpP8hgpiWwIApDGlJvpXU59JI+5klGH1iOVByCG1FQqJcoWEIW9DDaVxlm+P92Lh6G+Tvc4UTAF/JisTrG+s7OzwkIhe6ZWV1fPLLzpTYQA1Pb2Nh49eoT5+XlFJpCzFqq3s7Ozg4cPH2JmZiajZ2p9fR1ra2tYX18/Erf9totOp0NhYSF+9atfobW1FbFYDNPT01hYWHhr1tDe3h5WVlbw+eefIxgMoru7G+Xl5cjNzUVOTg4AYGtrC0NDQ7h37x6ePn2a0Vp10kJKezweF1XezWaz2DT39vYyljY4l79OUqmUICP4uVkDz+VkRVYsuMInC3+xyx4S2SpOn5x4gd9TCUDJio6seHIwpaQ80zGyt4SEzpPLJPA9IhqNYm5uDoODg9BoNAiFQujt7RW5olzJI0WUe82UclQ4GJAVYFmpU1KEqV/8mEx7LBk2BgcHRYRNb2+vYMVTqgXEvS5KCrfSOAM4Qi3Pc8Bk5Y08UYuLixgYGMDCwgJevHiB58+fw+PxiJzoTOtIXg/cq5IpvJOO3d/fF3l+8tgq/T8Wi2F4eBgbGxuYnZ1FdXW1KHocDAaxsLAgSDxov0smkwI4US4RBwlKfZLHW6n9MkmKDDr4+GYyZGQylMjrSOm5obnc3d3F8vIyvF5vWkiafG85lJC3+02NiXKomtI15esDEJTi5OSIRqPCWSCPDwEaCj1V2lOU8tFoHSoZGl5nEKGxpZB8Cufj60LeKzgQVwK88vVJuPf8TeTEwRTwY+z4+vo61tfXT+OWJyIHBweieNjbLqnUoVtzdnYWs7OzZ92cExGylGq1WiwsLIgQKZ/P91Z5MAKBAJ4+fYpwOIyVlRXU1NQgPz9fFCJcX19Hf38/enp6RC2wsxLyWC4vL6OyshLFxcVQq9UIBoOCnOTVq1fnzJrnci7/TzKF+LzOcpvJEk7fkWeYkquV7kfCC5NyTxZdh1u0ZYu8TCKRiTSB5ywpJWaTkhMMBtHT0wO/349AIIC9vT3MzMxgc3MzLcyPX1fOv6J28Wsr5YDIIWxyOzmo5OFS8jl87JPJJDY2NtDf34/Z2VmEw2EsLCwIunelcZFDEGlcOAU5bzsBUkryl5kKlZR8jUaDSCSCV69eYWRkBDqdDsFgEF6vN00x59eQFVzOokYGbZ5LJSu+JLu7u0fWDc/zUgJU4XAYiUQCiURCeMzoN6rTFYvFRIQDD5dV8nopsfVlytvj86qUR8XbqrTe+DF0X6XnW34OeI6VDAzI2MBBjvxc8zwuOociEfj9ab29jnSFPzd8TwDSDTp8PG02G5xOJ/R6vTDMy8YDmg9qs9J+Io+9fB9+PfIqye1X2lv5vBIJHG+X0jMkr08lQ4DSfpbJ66kkpwKmzuVcTkKSySRmZmbwP//n/xREJ29jUj69oEdHRzE3NyeouOkltre3J6pxn3UIVzQaxfr6OkZHR1FaWgqbzQaDwYDJyUl8/vnnom7J2xTaei7ncpZCShT3uiiBJABpFllZCZMVJq40cmUlU44QvzcpWTzMRvbEyB4cWQlVsubKjHByfyj/cGBgACMjIyKMSQZAcq6G0p5HIIVbomVlX0nRkb0W9B2/p1KoIV2XPPDBYFAo91xhkz1nfJz4nJGhLxOxDVc+lb6nv6nN5K2Zmpo6QsEvjxtfB3wOuWJO64raK68H7sXkYyZ7GDJRSpOnPRKJYG5u7gioUfI0ETshjRnvm7xWZOIVPqfc66kEDpVAFgcxBHp4LS3u0VVS1vn5/Hnm40Vzwo+l33hb6Xx6npT2kUygk9+L90teJ5mAIwCRx61Wq+H3+9OMpwSs5Jw5GVBy0M5FKVeNjx/NM+VIyQYMCnvk1OoUBkx7Bd2HG4p4O2lf5GCJt58bnjKNlyznYOpcfrGSSv3o8XzbJZVKieTHt1kODg6wtbWFP//5z5ibmxMFez0eDyYmJrC5uYlQKPSLyas7l3M5aTnOy8E/M31H/1eyZMukFpmEQuWoPfx7rkxkap9KpRIGKbqGEuDgIoMvLhQKJyfOKym+cvuBH9nnNBpNWjkPGTgpKfJK3iP6JM8M77fcP67wkuLIgYQMYvm8yUBBaRz5eZR7RL9lCnOiNmUq1aC0PjKBZRmwy8BZbqd8LfrkoYFyn3m7+RpWWv/83hx4Kq05WQE/ODhIywVUugcHVpmuxcdYaX1ypstM46u0ZpXGkt9b9g7Jx8j3IaH2kWcz074igymaHwJN/D1O7cjJyRHpNxTmrxTCR+F98jhT+/izwMeTQh7pnhSWx8dFHif+PQdRSmOi9J28bvb399NCapXuJ4/9cUAKOAdT53Iu5yJJLBbD2NgYJicn00JAyFr3tnn+zuVczlJIIZJDRGQPD5dMngCysnKlmStCskLHSWJIOZAVCLqHzLxFIivcXOkkCzhZaqm0CVeSuMLGvRm8r6+7H/d8UTspdI3GgAqEKl1LDn2k63GmN1lJouOVwICsrMmACoDIF1GyzBMwkD0VMtOhUoiSPG5K38nf0xjIbZHBK58nApbUZjkEjId9arVaoXjzNU7rVQlk0rqh+eNgn68bGh8Aad6MTPOhUh3WlaRrkZeBzqP3FN2TCM92dnYyzrvSM8vHLtOx9H+9Xp8GMGi85Tnh51E7aC5IuIeK6ibJRBVKYbbcaCCPGz+eP1MyWCCK/YGBAVitVjx8+FCwx8oiewL1er0Ye9onjEajIK2SwbzSWpaBDc0jHz8ZgNLa5QYZGjsZLFK7eAg0n3cehknrnhNhHAeozsHUuZzLuaQJveTPSSbO5VzeTGQlVkkZJ2VaSemRlWxZGZIVMPlvzuAmv/BlQCeH5lFb6DelcEA6n+8JXDkmpYT3VQlQycoSiZLlmyvdAI5YwmXhyju/Ln2SQnecN0CpbXIfSLj3ihcU5kBDKb9KaQw4EOW/yZZ+JVCu1HYZECuBQa5AcgWezx8Pc6O6QPI8k2KeaeyV1r08xlwRlpVt7k2Uz6XSA/SdHDLJryGvUfpUWq+8fwSKldrInwHqJynwx7WHX4OvAfp9f38/bby5gYVfi4AlDzGW14S8BuX78nN2dnbw+PFjjI2NAYCo1ZSJZE3pGeZjSR6gTIQ8MkCS9zYe4shBu9LzyddZpv2SwNnBwUFanSw55JK3S34uM4nqOKSlUqnOLnnjXM7lXM7lXE5VUqnUm9MXncuJvCNfZwH9a45XOlZWvpR+zwQufko7f4oc186TuudPlZ9znk56LIHXe72Oa9dpjP1PWWc/pT0EYmXA/rr7/hT5ucaHt+W451IGpj9XtAh5a2RP35uOcyYvXCYA9Je0703Xws+xb2UwTim+I8/B1Lmcy7mcy7kAOAdTP1VUKlWKKz2ZXtSve6mT0sETpjOdR8fL1z5OeTiuLce1P5NHScnqy8+XvWHH9eF14E4+7jilMtP9Xtce2ZL9unHPpLgptZ1/Jyv0mTxLr/OcKZ1zXHuUFHSldshMiPKxPwWQKbUhU39fJ6+b5zcZs59iRDgO1GRq208FfG+67unY162bTNfn3heldco9VT+l3/x8pbbLe8ZxfVM6lwMxJVD2ur1FyfMme+Tka8vH0zmSx1hxYM7D/M7lXM7lXM7lXP5C4axiQDopAn9JK4WX0f+JxcxoNGJ3dxc7OztHmNj4OTx0i4ejKCkYvG3UPq5UyIU1ZeYsHm4DHFXS6HuesyUTOJDw4zNZrLkyo1L9GF6mRGtOIUR0PG+nTNGcScGj+1E+RSYCBKV2y4on/6ScC66sESMfjQPdk487/UYhdnxulRRdJeCdCcjIYFduM92Tt48Xd+ZjL+epyffn96a8FpkkRUlBlseZr3dOJsFDB+V1xg0TJPLzRGPP14c8ftR2pe+pj7yffJ3xsZTnTQ5h5denseV04ZTLI697GQjxNsphnTy3jP6mdiiFCfPnSgmY8085N4yzGdLYU7/5fvk64wIVXKbjed52pnVD16F/lJNGY8LZApVAG+Wp0XV5/bPj5BxMncu5nMu5nMu5/IVCFNhyTgOPw6cXMVeQuPKRnZ0Nu90OlUqFUCgkFHoijJBBAFcUSAmS8yreRGkEDhnB5LwtDlK4AsQp4Hk/KSeF95Xq1MjgRFZA6Tuem8CBHid64IqQzOQmJ8TL48HnSFbgCAjKijsv0HuctVyJpppTO9M48rarVCrxu8wwyMcTAAwGA9RqNeLxeJryzD2ZfF5p7JWAlgwSZZIRurZer09L7ufH8zw5JYVYtu4bDIY0pkj5ODkXiwNxjUYDo9EIk8kk8pZ2dnbEWPD552BEJnKh7+katB5kECu3XafTCUIUGaSQss8NDpz5j9qlxMpJzyaBQz4GZFzhxAp8HRoMBnFP+RmiMeaGGiUac2784AYRPpay4URpfgiI8fXB15k8z1RfTQYovP10vtFoFDlzHEjxZzCTgYbuSc+SXJhabieJyWQSv/O6fa/LnToHU+dyLudyLudyLn+hkFLCFU+u+HPljCtMBGLI+hkMBoXSyCmzuYIvkzBwQMUVKx7So2TFpX+kdFL76Ro88V2v1yORSAhFhpRDrqwSiYHRaBT35mxxcvgiV5RlzwtXvMgqLFu/ef9JQePXIOVU9hhysELHUF8JuMmKL1fA+H0NBoOgwVaqw7S/v49YLCbWCJ9/rsyRUs0VamorZ7mTvQa8bWq1WgAuAplc8ePKtBIQ44CMrkEsZnS8DMy4Uqq0zrgXidjoZGVZBlYySLdYLCgtLUVRURGKiorEfK2trWFiYgLBYBCJROKIQiyDE77u+f04Wy0/jq8Rub4Zbycp29zTo8RIKI893w+UgHYikRAECTSGXOia5DmkvnIjDB2jtO4JGPK1IK/3TAYODvLlcafveDtk4wWtAV5Li84j4EjtjMViaUCPj+NxQI/2LTqXCLX43sJJTfg+GolEjuy5ch+U5BxMncu5nMu5nMu5/IXCgVOmOkD0f+6hIms3AKE8xeNxaDQaxOPxNCWCFCq6F1caZEVetvZza66ssMrKDFcEuRJFXgUOZjiNMAmFsdFY8AKlvGBsJiWcX59bvqlv8rjytnPvFAcSSkq+Ui0gAm9K1mfusSGwkpOTA41Gg1gshng8fsQryP9WsoJzhY6DOn4/GZjL48C9glarVXhLDAYDQqEQwuFwGruc3Cf6nuZQXg9c2T5uTJTAuuwlkM/lyruS0m42m1FfX4/PPvsMN2/eRH5+vlhHMzMz+O1vf4v79+9jbW0tbUzk+8v3pmN4WJvSnMteK5lNTj5HJmpQMgDIv/N54M84HxslpkzO3pepgHOmPsnMfGq1WnynxOoot1nJUy6Prezp4kLrnu9R8u9876I2ys9BpjWm0WjgdrthtVqRSCSQSCQQDAbT9h/ebroGB6U030pjn0nOwdS5ADhcKEajEdnZ2bDZbNDpdEgmk/D7/Wkb8rmcy7mcy7n8KJlesJle9hRWxQEEKRdkmaWXOSlkvGArFyWPCBcOivh38jW4gsGVBu5pk8+XzwEgFBZS0GSvkKyEyWE+9JlJ4eFt50BMKZyN2i8rp/S93HbuIZOP5+0wm82oqanBhQsXsLu7i56eHszPz4vfeVtlQCV7GjIpwvL/+f0JfJhMJtjtdtjtdpSUlKCxsVEUXPV4PBgaGsLAwAB8Ph9kUZrP191byduhFHLFx16+fqY+yf3TaDTIzc1FR0cHLly4gJqaGphMJmGASCaTuHnzJvx+v6DuzgRWeBs4+JBD2bjIIZu8P0rzpRSyxj9525TuKc+B3A/uSaS2yM81v4cMdPkaz/SMyO2VwUcmyTS//Hfujcv03CuNq/zMKwE8+l6r1SIrKwulpaW4e/curFYrenp6MDw8LDzrSmOv9KzSuPBwZ6UC4VxODUzJbkT+InndZL1NwsMJlBb0L1V0Oh2qqqpw5coV3Lp1C06nEx6PB59//jl6enrg9Xpfu5jO5Zcn9OLirvvXibxh/xKf41+qyCEYXFk9fz7PRrgHhZ4DOdEeSLcyy2F5ShZTHmbEny05MZyuTd9xUgOSTM8nfc/fZTzMhitamXIMuCeMX5f6T8qK7MWSgQYdJyui8vH8NyUlPpMCy8OE5BDMTICG94nCzsrKyvD++++jqakJwWAQ4+PjGdtJ91CaX6V9U2kseZ+oH9nZ2SgrK0NTUxMqKipQU1ODkpISWK1W7O3tYX5+HpubmxgeHv5JyrDcDqVxUVJ+ucIvK+y8T5n6xcdCq9XCZDKhsLAQ7e3tqK6uhsViAXDo+dze3sbm5iY2NzcRDAbTPAqZQHAm4WBWaRyA9NzH454jpWvwNa0EZOTz+bjwZ0jpukpjLP/G5zETaFJqrzyWx/VLCfzw95J8HwIpshdIPl6+byavP+UmOp1OuN1uXLt2DRcuXMDBwQH6+vrSPGl8rSqN5XGGptfJiYMptVoNo9EIl8uF7OxsmEwmWCwW7O7uYmtrS7iid3Z23npgotfr0dzcjOLiYkQiESwsLGB5efkXX9zUYDCgoKAA165dwyeffILLly9Dq9ViZGQE2dnZR9igzuXfh2g0GthsNrS2tqK2tlYxlENJdnd3EY1Gsbm5iUAggO3tbQQCAQQCgWNfGOfy14larUZ2djasViscDgfKy8vhcrmws7ODyclJjI2NCRa4czk9OU6Z4En6XPngAIXABP+NnkWeoE5J80ajETqdTiTh0/uHA20ZqLyJMsfbLpNn0HV4f3jek3wdJYXlOAszvzfvL++XHAJF40dtpGP5OfI9MimgSoohBwZqtRp6vR51dXX41a9+hQ8//BBqtRpjY2Np8ysLv+7rnkvyWFJb5XEhXcput+POnTu4e/cuSkpK4HQ6kZWVJYq3er1e+Hw+rK+vHwnJUmofn49MbVcCUySZQDC/pvwdn2vKF6NQM41Gg5KSEnR0dKCrqwtmsxnxeBybm5uYnp7G0NAQRkdHMTo6ivX19bScqdcBI1mUlHOlseA5b5n0VP7s8jVL1+QhY3y9Kq1h+l5+npSeIT4Hct/pOtyDzX/LNEfyHkDzxIEZiUxckgnYyfeV2w2kFxTnews3MMjzo9VqUVpaik8//RRVVVVoa2uD0+nEzMwMgEP9VmlvVBK+99KcyG3MJCcGpmjzycrKgtvtRlNTE4qLi2G325Gbm4toNIqpqSksLi5iaWkJXq8XkUgkjYHmbRKVSgWTyYQ7d+7gnXfegcfjwddff41gMIhgMPhWtvlNRK1Ww2azobm5GTdv3sTFixdhMBiwuLiIoaEhLC8vIxKJvPX9U6kOY8UtFotIxN3d3UU8HkcsFnvr238WYjAYUFxcjM8++wyfffbZEXYmJUsjMSltbW1hfHwcc3NzWF1dxfz8PGZmZrC1tXWu0P+MQuualOiqqipUVlaiqqoKt27dQl1dHQKBAL744gvEYjEsLy8jHA6fA9pTFB4eJit1cmgO/aakVNLzRpb2ZDIJnU4HvV4Ps9mM/Px8FBUVIS8vDwcHB3j+/DlWV1fT8pnon6x4KClNdI7sUZIpkuV+ylZtbk2WFSFiL+THc3AhUybT33xseN4FH08uXNni3/G28zbIoIm3i/eFvtNoNDCbzSgvL8fFixdRXl6OUCiEUCiEQCBwRG/hbZfHWL4Xv5+S8knH63Q62O121NXV4dq1a7h69SpycnJgMBiENzIejyMQCGBjYwOTk5MIBoPHWtflNrzuGD6uHEAqXUdp3Stdi3soSGmvqanBnTt3UFJSgmQyic3NTTx//hwPHjzA+Pg4tra2sLm5mTHHja7NwaCsUCutBSV53dqT78W9MnzNc2CTaWy5HOcxUTLgyMfS+a/zrtD6p1w7h8OBnJwckRNIOYJ6vR5arVbkIWm1WsRiMYRCIayvryMcDh8btiq3i/9fBpJyP5XCLul4vV6PvLw8XLlyBe3t7XC5XNjf38fMzAzW19cVn4FMRgIOYn+qYfhEwJRKpYLVakVVVRXa29vR2tqKpqYmuFwu6PV6GI1G7O3tIRAIYG1tDZOTkxgfH8fLly8xPz//1iq/Go0GxcXF6OrqQjgcRiAQwOLiIsbGxhCJRM66eT9ZyNJdV1eHTz/9FB0dHTAajfD5fHj8+DH+5V/+BVNTU4hGo2/lfHAxmUwoLi7GRx99hNbWVmRlZWF+fh7Pnz/H/fv3EQwGz7qJb52YzWYUFRXB7XYLD2QikUAsFsPu7q5gFotGo9jd3RWWUYvFAovFApfLhe7ubsTjcYTDYUxPT+N//+//jaGhoV/EmvkliNFoRGVlJfLz8+F2u/Hee++hqakJ2dnZyMvLg9Vqhcvlwq9//WtkZ2fjt7/9LYaHhxGLxc4B1SkKt2hyazs3StBvXDkjkZU+epnv7+9Dr9ejuroan376Kd577z1kZ2djc3NTWPOJIprawZV4ruRROK9cs0Y+R6/XQ6fTIZFIpCk3RJohMwVyRY7XrDKZTILCmfrLCRXo+tx6T5/0N1dgiYaaU7PT/flYcjID+Zry8UrWea6E0rEmkwlutxttbW0oKioCAITDYXi9XsU201gAEAyMBJRlRZ57o2jdyGOi0WhgMBhQWFiIDz74AN3d3bDb7dDpdGlek2g0iqWlJczOzsLv9x+5jqw80rVlQgglEMtzjbjwfvP55MLvy9cmkE56oNVqxbulrKwMJpMJS0tL6O/vx7fffou+vj5BJsA9srLwNUoeVT72HPhw77CScs29Izqd7sgYqFSH+eY0BzzHkRsmZGAlP59KcwRA3FMJECiBcj4ema7Jf9PpdNDpdOKd0tXVhcuXL6OiogImkymNAIfWxO7uLnQ6HUKhEJaWlvAv//IveP78OcLhcNr1ORslX/P8OZT3EPn55OtDqR/Z2dkoLS1FYWEhrFYrtFqtiJ6Rqc1lQgzZqMLZS+X7v05OBExpNBrk5+fjzp07uHLlCurr61FUVCTiXmlQSktLUVZWhpKSElRWVqKoqAivXr3CyMgINjY2EI1GT6J5f7HwpM/s7GzU1NSgqqoKCwsLv0gwRXlSFy9eRFdXFxwOBzY2NtDX14eHDx9ieHgYkUjkCPvL2yhWqxXV1dW4efMmrl69CqPRiL6+PiwtLUGv159p23Q6HbKysmCz2WC324VRIZlMYn19HV6vF36//9TDRff39xEKhdDf3y82nUQigXg8jmQyCaPRCLPZjFgshmQyKXIGyG3ucrngcDhgs9nEczwxMQG/34+ZmZlz0pK/QlQqFRwOB2pqanDp0iVUVFTA5XLhypUrKCoqQjQaFQQxJpMJ5eXluHPnDnw+HxKJBEZHR8UL4VxOVmQLZyqVSiNiULK0cuVbflnTeVqtFg6HAw0NDfj7v/97NDY2oqqqSiThOxwOZGdnK3pllO6nxGIntz+VSqUx8vF2yiFOHIRpNBpYLBYUFBSgqKgIJSUlyM/PF3ViotGoCPMPhULw+XzweDwYHx/H+vo6QqFQGosXtYfXlSIlidole5FkTwgdJx/P/y972+S8Ep1OB7PZjKamJly7dg3Xr19HUVERdDodwuEwlpeX08AsD6nk703KtZZzSGSrPPcy0tjr9Xrk5uaisbERv/71r3H79m3k5eWJfZiOTyQSWF9fx/j4OEZGRtLo9WXJBFi590YO71Sy1PPzlACZDJp4fi6vxUTtqKurw8WLF/Huu+8iPz8f0WgUT548wbfffouRkRF4PJ60ENBMbHn8urT2Zc9nJg8agQc+d0peNQAC/HV2dqKgoAAAMDc3h/n5eUQiEezs7Ci2Tb43tVFmzSQDJhEo8DbI3lYlEMVF6b4GgwGVlZVoa2tDQ0MD6uvr4XA4kJeXh6ysLAEmotEotre3EYvFoNVqYbVaYbfbkZ+fD4fDgYGBAYyMjCAajabtHUTRr+RZ4/84Q2kmQwdvN9978vLyUFJSAofDIUozUOFzanOmMEQaY27UoDl4HXujLD87mKINqLy8HP/hP/wHNDY2CqtAJBIRtRlo0Gw2G7KyslBVVYVr165henoa//RP/4QnT55gbm5O1Cd4W4RbKwoKClBXV4fnz5+fdbN+smi1WrhcLty8eRN3795FcXExotEo+vv78U//9E8YHBwUbtu3XVQqFfLy8tDQ0IDy8nLY7XYkEglEIpEz7YNafVj7w+VyoaqqCvX19aitrUVbWxtsNhuCwSCeP3+Onp4eDA4Owu/3C4rd05BQKISBgQFMTk6mWda49Y6H+fHNTq1Wo6GhAQ0NDWhsbERHRweKi4tx48YN+Hw+eL1eBAKBE+0LbdZ6vV4oMZSMyjdkJdnb2xPWTSos+rbsMyrVIWNYS0sLPvvsM1y/fh0lJSVCCVlfX8f8/Dz8fj80Go0I/SouLsZ//s//GXt7e/B4PNjY2PhFPL//nuQ4pYEfI1ur+bF0jMlkQlVVFW7fvo2uri4UFRWJ4qfEaJZJZAs8t7zLobv82aY1Rt/JAIcrIjqdDiqVCllZWcjJyREGre7ubrS0tKCkpATZ2dnCmEXXTyQSwuBy//59vHjxAvPz82LPViIwSKXSaduVFCyl8ZfBUSZPlDxmtHeTYnnt2jXcvXsXlZWVMJlMSCQS2NzcxNzcHCKRSJpCLl+TAww58Z7/pkRakkqlYDab0djYiE8//RR37txBYWGh0KNofkjh3djYwPDwMFZWVo4YQTN5J+T7yV4SAoI854ePHbWdAyq+7vl7gwwNsleWrl9WVobr16+js7MTWq0Ws7OzGBwcRG9vb5qnTQaAcv9kcK1UZ0u+Px97DlSUQlDpPKPRiPz8fHR0dKClpQVmsxl9fX3IysrC1NQUtra2xDm8hpu8RmRvndw2XsCXj73cf6V9hR/DhfqYn5+P9957D93d3XC73VCpVAgEAggGg0gmkwgEApifn8fc3Bw8Hg9cLhdaWlpw8eJFmM1mEYYuE0TIYDqTyL/LZQAyEd8AEKyPpaWlsFgsYv/a3d1FLBZDIpE4QkBBn/I4cZCeSqWEZ1l+ZjPJiYX56fV62Gw26PV6eDwe9Pb2wmAwCPpOci0aDAYYjUYYjUZkZWVBo9Hgo48+EpXBFxcXReG7t0nUajVycnKQm5sLg8Fw1s35yVJYWIju7m5cvXoVDQ0N0Ov1GBoawrNnzzA5OQmfz/eLUsTMZjMcDgfMZjO0Wi12dnYQjUYRiUTOpB/kxSFLW1tbG8xmMw4ODrC1tQWfz4e9vT2Ul5cLOs/h4WGMj48jEAicijfw4OAAsVjsL3q+VCoVdnZ2sLm5ia2tLVgsFpSUlKC5uRlra2uYnZ3F+Pi4Ii3vzyWUeFpeXi6s3llZWSgqKoLdbhftlCWVSmFrawvz8/NYXV3F0tISNjY23prwYrPZjKqqKly+fBm3bt1CWVkZcnJyEIvF0NPTg76+PoyMjCAUCgkP1oULF3D9+nXxomtra0Nvb++Jjv+5HApXJGXhCg43RHClR8kCazQaUV9fj1/96lf44IMPUFBQAIvFIl72ZAxQUsrJmkz3p+8zKTt6vV4oeZm8OlwhJoUlJycHBQUF6OrqQnV1NYqLi1FWVob8/HyYTCZkZ2cLSzFvy8HBASwWiyBSyc/Px/LyMubm5jA4OIjt7W3RNz4uSmxeMjBVUii5FZvPi6yMckINi8WCqqoqtLa2or6+Hu3t7aivr0d2djZSqRRCoRCWl5extLQkvA9c8ZPJMpTGl9YOHxfeNrpeUVERPvnkE9y4cUMAaj6m8Xgc8XgcS0tLePr0KcbGxhCPx9P6lkkJzLT+uDeBFEk5GZ8Xnea/y6CGDOzUV15bizxVLpcLdrsdV65cwaVLl+B0OuH3+/Hs2TNMTEwgEomk1VbioEYJiMjkDRyIk8hFYOU20/gphccSwKmoqMAHH3yAjz/+GNXV1dDpdKirq8P777+PsbEx9PT0YGRkBJubm6IPMrkKAXfKk5TXLV/31C6ZJIHPNe+HkmecvED0f6PRiJqaGuTn50OlUmF9fR39/f2Ynp6Gx+NBIBDAwsICvF4v4vE4HA4HvF4vamtrYTabj6wZfj9es4y3nYfiKq07vv5p7ClMlvpKYLaoqAgul0sYd2i+ZU+p0tqW9w4+NjRPb5o7dSJgKpVKIRaLYWVlBbu7u5iamsL/9//9f1CpVCKczGw2IycnB3l5eXC5XDAYDMjJyYHNZsO1a9dEnaNIJPJWgimVSgWLxQK73S5CGX4p4EOj0YiwoPb2dthsNqyurqK3txdPnz7FxsbGLypES6U6zNErLCwUD/f+/j68Xi82NjZOPXxOrVYjNzcXLS0tuHDhAlpaWpCVlYXV1VUsLCzA4/EgFovBaDSioaEBVVVVqKmpgdvtxv7+PoaHh9Nij99GSaVS8Hq9SCQSyMrKgt/vF+CmsbERdXV1WF9fPzFlnuiB29vbcefOHbhcLrGnFBQUICcn54hiqNVqYTAYhIFnfHwc8/PzGBsbw+TkJCYmJuD1el/LgHWSYjQaUVJSgjt37uDOnTtoaGgQ4U1erxf37t3Dn/70J0xPT2NnZ0cYrubn57G3t4ePP/4Y9fX1uHHjBhYXF8/B1CmIDI6UPCXc88PXJVeKSCjq4caNG7h48SLKysqQlZV1JKdDyVPA/5aVG/l5IIWFW71lkKHkfQAODRn5+fn47LPP0NjYiNbWVhF6RpEn/N7y80RkQS6XC5WVlVhcXMTDhw+RTCYxPT0tnkPeViWvCE8WJ1HyPnDh1+Hgh67rcrlQU1OD27dvo7OzExUVFcjPz4fVahVAam1tDevr69je3j4SosTHj5QxUgZlRV2n0x0ppCr3s6ysDO3t7SJskgMpivgJh8N48eIFvvvuOywtLaWF+ClZ1JW8GrL1nn7PpNcQmCI2veMMUTLA54qyxWJBTU0NOjo6cPXqVRQUFGB/fx/Ly8vo7++Hx+NJq1nG2yg/e9QujUYjgIvsTaLzaN3LxZv5JwEpOY9Nr9fD5XLhwoULIk87JycHKpVKhL3X1taisbERDx48wNDQEObn58W7H/gxbHZ/f1/s5fL9Dw4OjvxGbeAGHCVvEI1vJkBG58XjcVHYdmdnB/fu3cPz58+xvLyM2dlZEeZPx+t0OsRiMUFEFQgEsLW1dYRwg8CJ0lqgd1omL7TcT6VrURRKTk4O7HZ7GjiOxWIiZ4obAeh5pPzLTCyH/De5TZnkRMDU/v4+NjY28PDhQ1itViwtLWFoaAibm5t49uyZKK5VUFCApqYmNDY2Ii8vTzD+Wa1WdHR0IJlMYmBgACsrK2em3Bwner0e2dnZwgL3S0j6po2gqKgITU1NcDgcCIVCePnyJXp7ezE9PS2sWr8EIQWlsLAQdXV1Qone2dkRrunTzB1RqQ7Dc1pbW/Hf//t/h9PpxPz8PH73u99hcnISgUBAxD9rtVq8ePECd+/exd///d/j4sWL8Hq9mJmZeevBFG1MJpNJhJkZjUZhLbJarSfqsdVqtXA6nWhvb8fdu3fT4rsDgQBmZmaO5DKQJ9npdMLpdKK7uxutra24evVqWngx1S45baGQhfb2dnz22WdobW0VL/JAIIC5uTkMDw+LZ5T6trOzg/7+fqjVanR0dKCurg7d3d346quvTr0Pf6tC+xBX+pRewjKTlwx0yEqdm5uLzs5OUWNHDosyGo2w2WwwmUziPlxpkD+V7imLkrdGVsBImXI4HOjo6MAnn3yC/Px8EYUiW8Ll68rjRWveZDKJkN3f//738Pl8ikVD5WtzwCaTLdC9SZQSz+WQO51Oh9LSUnz00Ue4du0aKisrRZ4ozW8wGMTc3JwAfHxsZOWdX1+pL9ybJQspnW63G06nE3q9/ogXKZVKIRqNYm1tDRMTE1hYWEjLEXlTUQKsSvMmjy95GTIxQJKQZ4h7jKjvhYWFIk+qtrYWKpUKCwsLePnyJaanp4UHXgmcUygWNwwQMUcgEDhiiJfHn3II6Xx57RBg5aLVamGz2dDe3o4rV66guroaZrNZzKNGo4HRaERubi6ysrJQWFiI5uZmfPXVV+jt7cXW1pYI01XyzvAxJrAlP8uZAABvu2zgUXr2Dw4O4PV6MTk5ib29PczOzuL//t//i9nZWVF6ga5PzKJtbW348MMPYbfbsbKygpGREYyOjqbpv8cBJD6OfN+ShZ4npWeH9zknJ0fkjqpUKiSTSQEEKU1Ifh7lT1qbdF95zDLtmWn9ee0RP1Fo8a2uruLrr7+GXq9HOBzG2toawuEw/H4/gEPL1OrqKlZWVjA7O4v6+nrY7XYUFhaKBLfCwkI4HA6YTKY05eFtELVaLWjf3W43srKy3ro2yqJSHca319fXo62tDaWlpdDpdFhYWMCTJ08wNTX1i6BBB37si9vtRnl5Obq6ulBYWAiNRoPl5WU8ffoU4+PjCIVCp+oxNBgMuHr1Kt577z0UFxdjYmICX331Ffr6+o54/FQqFUKhEBYXFxEOh6HX6wUd6dsq5BHKzc1FcXExampqcP36dTQ2Noqw3KGhIQwPD2Nra+vE2kFWqezsbGg0Gjx79gxLS0uIRCKC0INAKx2flZUFu90Ou90uPFidnZ2oqqqC2WzG/fv3YTabEQqFTqzdmYRe0O+++y5+85vfoLGxESaTCR6PR9RUmZiYwMTEhKKCEA6Hsbq6itXVVdTU1CAvL08Q/pzLyQoHMWQdpTB1OdxOZk7jyhQZYhobG3Ht2jV0dnbC4XAoeqRsNhsqKiowMTGhGIoin8ONCnKbSFnU6XTCg8LDmjhYo3V669Yt3Lp1S4TY8kLBdCz94/kLGo0GVqtV0CyTkkZKZ3V1NQoKCoTnlYtS7gS1T/YIKeWWyOMjh90ZjUaUl5fj1q1bePfdd9M8gnRNYk/c2tpKKxjLFVclhVwGK/RJYExpvqgcQm5urkiNkIXWHIWQ7ezspO17cr//UpHD5oBDVkVqO89Z5ePKvaP83/7+PnQ6HfLy8tDd3Y2uri7U1dXBbDZjY2MDz58/R29vL9bW1oSHQWkeqQ3A4XORk5OD1tZWqNVqDA0NIR6PK3pHU6kfc7eozXLhWDl8lwzRxcXFwoh39epV4RWhXEaeY0bh7xaLRdR0JEV/aWlJEJgohbXSPemZzbTueXiqTBzDryf3h3T15eVl/OM//iNKS0uxt7eHpaUl7O7uwmAwCAbJpqYm5OTkwOFwoKurC3l5eVhZWcHDhw/x4sULkRvG751KpY544vn4k3eQhwLS+UqgXn7GSAeksH56PnZ2drCysoLFxcUjYYGZjAZvcv/XyYl4pg4ODuD3+4VFgRAml93dXXFMMBhEIpFAd3e32HA1Gg1MJhMKCgrgcrmwtrb21rHKkQXebrfDYrGcqPL4cwhZbS5cuIDOzk5YrVYEg0FMTU1hYGAAHo/nFwGkgB9p3aurq3H79m10dHQgJydHhIl89dVXmJ2dTYtBPmkxGo0oLCzErVu30N7eLjyxDx8+RCgUUmTRolpYlMTMCxCepXDLMW3aBoMBNpsNJSUlqK2tRVNTE2pra1FTU4OcnBx4vV709vbi0aNHGBwcRCAQOLH2cavh5uYmvv76a/T19SEUConQYE4qQS9Ck8kEs9kMm82GxsZGOJ1OUf/OYrGkUTufppjNZrGWb926BaPRiJWVFfT39+PBgwcYHBzE6uqqoGJWEgoFjEajwpJ+LicvSi9nrlhwyye3oPPQITrWbDajvb0dly9fhtvtFscRMCOQRooEeX95LgddSykEiAMSDni4oiyHvsjKhs1mw40bN9DR0ZGW9E3toKT17e1tJBIJUYspEokIwpTy8nLRP7ovhWXxtisJtxTz8ZXbnMkjx5VrOp88162trbh48SIqKipgNpvFHkNU0NS36elpLC0tvREpBh97mcxACYhxK/r+/r4gyNnb20sbLxKirA+FQtjZ2flJXik+bpmUxkxt5wo+zZsM1Pl1OcU3cGh4dLvdaG1tRXd3N2w2G3w+H6ampvDkyRM8ePBAgER5XPmnDKbq6+vh9/sF0JX7wSneeR/Iqywr7vyZNZlMaGlpEazBJSUlIuzN6/Via2tLEDJQOovZbBZlRGpra+Hz+dDf34+vvvoKOzs7wsMpG1aAH+m6lYCkUmgr32d4CCQ/jq//g4MDhMNhjI6OwufzifxJh8MhjNXXrl3DpUuX4Ha7YbPZkEgksLy8jL6+Pnz//feYmJhAIpFQpDBXypekT1oL8lzKRgn5WeZjQwCKxohwxczMjND/ZOHjxK8n/873GRrT4wzzJ1a0lzahNzkmEonA4/FgfX0dW1tbKCgogE6nE6E49P3bAKZkqwHx0lPy29sqFJJVXFyM69evo6WlBQDQ19eHe/fuCSrPt0GRfxMhMFVeXo4rV66gtrYWWq1WhDs8f/5c0KieVnuKi4tx584dXLhwATqdDl999RVevnx5bNgYFbbOzs7GxsYGNjc3Tz3HC/hxLdMLh2KRqf6UyWRCXl4eWltb0dHRgaqqKthsNmg0Guzs7GBmZgaTk5P4/PPPMTAwcOIeQVK+fD6fsPBRgWmlWGzg0IoZi8UQDAYFKPH7/Uc2ztMWtVqNvLw8vP/++2hsbIROp4PX68XXX3+Nf/zHf8TGxoZQlI5bGxSCFIlERB4nT/Q9l9MRHurG1yEpLyqV6sh+QGCfyERaWlqg1+uxt7eH7e1thMNh7O3tifBZWuNccVFawzKI4IBKVmyUQIrcRtoXXC4XcnNz09j/yBi0vLyMkZER9Pf3C1bPYDCIaDSKrKws5Obm4p133sGnn34q8kwSiQR8Pp8IzVaK8uAKEP8u0zvruO/5OJGhKC8vDzdv3kRtbS30ej12d3cRiUREXSPyEsZiMWxubiIQCKQprTJ4VVICeQ6OUvu5Qknh6rOzs5icnBR5tRTuR8fSNd+EQCfTHnfce58DTu7Z5B4opbni55OHknuq7HY7SkpKUFZWBpfLBa1Wi9XVVYyNjWFubg7hcPjI8yN7imj+gB/rJqpUKoTD4bSwMzqGe3Ez5Rvx7zghgdlsRmVlJa5du4Zr164hPz9fsFMuLi6it7cXg4ODCIVC0Ol0oiRQS0sLcnNzYTQaYbfbYbPZ4HK5xPqhOo1KzxvfMzhY5f1XMhzwdcXXitLcUNpNR0cHamtrEYvFYLPZRJFwInhQq9WIRCIYHh7Gd999h6GhISwuLh4pnMzvlckrJYfXyuvmdc8unZNMJtPquCWTSWxsbGBhYSHNW8bvy8eI1gT/v9J+ytd9JjkxMPVThaqlk2VYrVYLF7fNZssYV3yawq0Z9BIxGo2orq7GxMQE5ufnz7iFyqJSHYaPUFXxxsZGWK1WbGxs4OXLl3j27Jmw5PwShCxQ1dXVuHDhAioqKpCTk4Pt7W2Mjo5icHAQHo/nSJjISYpOp0NJSQmuXbsGl8uF6elp9Pf3p7malfphtVrhdDphsViQTCZPPSwROFwfNpsNpaWloi1UpJJeGAaDAXa7HZWVlaiqqhI1yaanpzE8PIylpSUsLi7ixYsXp0LJvb+/j2AwiN7eXqyurmJ2dhbhcPhYTyS9/Pf29oTF/Kw9gWQRr6qqwpUrV1BWVoZgMIh79+7hm2++wcDAwBs/l8lkEisrK/B6vSgtLUVeXh7y8vLg9Xp/MeQ4v0SRlZRUKpVmTebKDFe8uRJBIU+tra2oqKiAzWbD3t6eyGdYWVkRdPm5ubmiHhyFfMrX4koYVxbksDQgXcGk4yk8h1uOtVot8vLyUFFRgby8PNhstrTIk0gkgvn5eTx58gQDAwMYHx/H5uYmYrGYqF1nNptRUlKC0tJShEIhAQzJ2zMwMJBWUuG4kB/gR4s0f0aUACUPReN95qFbVGvH6XSK5PrNzU1sbGwIevecnBykUod5SpzFjxLquQJM96E2y0q83D+uvPFjyUCYm5sLh8MhDLcc+JJCmwlcUj+Vrs/HiR/PdS76Tc4Ro085jIpfRw5PJO9qW1sbrly5gpqaGmi1WoTDYfT29qKnpwfr6+tH7sX7RvoihYsCQF1dHcrKyuDxeDA3N6dILCEbDvjYy0o1fU/hes3Nzbh16xa6u7tRUVEBvV4Pv9+PpaUlPHz4EL29vRgfHxfstouLiygtLcXq6qrgAygsLEROTo7QG6enpxXHjBdiVvL2ybln9E8GmkreFd5nqgnb3d2NDz/8EKWlpUilUgL0UWkDykN6/vw5+vr60NfXJ8gnlPK36J7c06wE/uhYMjDIzwav9STfAzh851Eul1qtxs7ODlZXV+Hz+YRxQTZoyM+jfE0lUP0m7+BTBVMGgwFms1kwrUSjURwcHECv14tiplTB+G2UVCqFSCSCUCgkYjQtFguampowNjaG58+fv5UseJQw3N3djV//+tcoKipCOBwWCe1TU1O/GCAFQMTxdnZ24urVq7DZbNjZ2YHH48Hz588xMDBwqt4dAndlZWVobm5GKBTCyMgI5ubmjg11IwBGNRLIEnraiq9Go4Hb7cbNmzfR1NQEt9sNi8UiLFPcY0UGj4ODA6ysrKCvrw/ffPMNZmdnBbnGabR/b28PgUAAjx8/FvvJX7uGTxtUqVSHRAKVlZVob29HQ0MDLBaLYD/t6en5SWO5s7ODpaUleDwelJeXo7S0FKWlpSde7+tc0hPGubeGi6zocKVGp9OhrKwMt27dQkFBgSh0OzQ0hH/7t39DIBBAYWGhIGvY2tqCx+OB3+8/ohTI4a2cklpWHriSqqTU80+dTofCwkJUVFTA4XCIfeHg4EB4lnp6evDP//zPIhdEDqlKJpMIh8NIJBLi/vF4HOvr66I0Bxlj5DYoKT5AOqNbJqWe94v3m7zu9fX1eOedd1BcXAytVotIJCLe6ZFIRFBHazQa+Hw++Hw+8a7noJPfkxfq5cBFyTuUSqXE3srDpQ4ODrC8vIyenh4RTSL3Sa1Wi7IbmX43mUzIz88XoYuJREJ4PJUKwvJzeTgiXxtyX/i6z+Q9SqUOqfitVisuXLiAGzduCPa+zc1NzMzMYGRkBD6fLw0A0bXIC0dGKKfTCbVaDb1eL/J2ybgnPwd0vtI8cAOC/DxotVoUFBTgypUreOedd1BVVSUK6c7NzaGnpwdfffUVRkdHxZqnsixzc3MYGRnB0tISmpubcfnyZUFWQUW3M4Vlyus007pRmhf+O33K86LRaGA2m1FYWIjr16+ju7tbMGtrtVphqIhGoxgcHMTjx49x//59LCwsCO4Dua3UDjnPK1P7KA8z05qSx4XvOcAhWKMxp5DFubk5rKysiOdTfubla8p7JTcQye05Tk4NtWg0GlRWVqKzsxN2u13URAgGg+JhoH9vgxdKFtqAyBJPMb5k8TmrXIs3EaPRiLKyMtTV1Ql2or6+Pvzbv/2bYHH5JQnFRTc2Noq6G5OTk3j8+DF6enqwsLBwqn2ipPG2tjbk5OTgq6++wnfffSdCyDKJxWLB7du3cePGDVgsFsTj8bQK4qchtNFkZ2ejpqYGnZ2dKCoqgl6vF/XflJ5HlUqF4uJidHR0IBAIiJC70wwpIyXurxUlhfI0RK/Xw+1244MPPsBvfvMb2Gw2zM7O4vnz55iZmRFhIG8qpDDT+qEX4rmcrHCPDv2f5yKRZFISKBejpKQE7e3tcLvdUKvVSCQSwvvhcrlQXFwsGML0ej06OzuxvLyMly9fpoVccapurhDze/P7KymXSpZbCq0mZZKDtHA4jKGhIQwNDWFyclJ4zLiHjqJNiDLabDZDpVLB7/ejp6cHjx8/FqUs5BAcah8pUryv5HXix8kWbg52+adWq4XFYkFZWRlaWlpgt9vh9/sxMjKC77//Hs+ePRNghEhtCIRwLzhPdKd28zbRGpHHlbdZzhej37a3tzE1NYXV1VW0tLSIAuvUL51OJ8KyOD00zVdLSwu6u7tx5coVGAwGUZdqYmICf/zjHzE7OwuVSjn0VKvVpoFG2UvIx1b2/nAvrAzkiWqePG3b29uCbp7eJ/I6VavVcLlcgrE0Ly8PhYWFACDCNImVmHLH+DjJBgXuIcn0DlCpDvMYy8rKRC01m82GVColcp8ePXqURpNP1yVvSzgcxu7uLsbGxpBIJJCdnY28vDzY7XZUVVWhqKgI6+vrGWnE5bUr/81BEl9P8vnyWFJ4eUNDgyhtYDabhRd2f38fS0tL+Pbbb3Hv3j1ROiQWi6V5YZVEBkeZwp05cOFAVolshl+XG2d2d3fFuCeTSVGyRX7GuCiBPALUMoh603fwiYMpyn1qb28XldFtNhvm5+dhMpkwPT2N3d3dI7Sq+/v7CIVCmJqawvLy8luh8BNF8dbWlvB8UH6JTqfL6JI8a6HaNcXFxTCbzYjFYqJAYigUgsPhEMWHE4kEvF6vyD05TSGlPisrC5FIBPF4/MgmDABWqxXNzc0oLy+H0WhEMpnEzMwMnj59irm5OWxvb5/qHBgMBpSWlqKgoADxeByTk5OKbFQARMhAbm4uamtrBYslVa/f3Nw81bVOm1csFoPH4xG14TJ5euglYTKZ4HQ6UVNTI6yqe3t7mJ6ePvXx/0vEYrEIxrtYLCbyMslbfhpSUlKCmzdv4tatW6iurkYkEkFfXx8ePXoEr9f7k9eBwWBAcXEx8vLyAACbm5vY3Nw890qdsJBywBOaZWtxKpUSFnVSHug3jUYjSoUUFBTAaDSKuc/Ly0NdXR0sFgvKy8vhcrlgsVhEUdmqqipkZWVhe3s7bb1wZYSH9hFLH1caZaVYzushhd1ut6O+vl6UoOBeqa2tLfT19aG/v1/UOKL+0jvS4XCgpaUFd+/exaVLlwTbpNfrxaNHjzA/P3/sO4e8NvLYy14sJUs9r7fDj8/JyUFxcTFKSkrgdruxu7uL6elpfPvtt+jt7cXy8jLsdjsSiYQw+lKEjdFoFGxx9N6XQyppjDhDIrVbaey5gk+fBoMBJpPpiAJIQFmr1YrwSbvdjmQyKRiROzs78emnnwplmQgGYrEYqqurEQqFRCgh37flvDqDwZAxhFr2SvA+yMfpdDoUFxcL2n9K4QiHw5iensba2prIl9NoNNDpdKL/drsd1dXVuHv3Lrq7u1FYWCh0L7VajWg0iqWlpSPEAmTolvdTuc1Kirter0dubi66urrQ3Nws8gRDoRBmZ2cFG/XGxoa4Ps0JPccEhl0uF1ZWVrC9vQ2Xy4Xs7GzBD6DkTaX1pNPpMj4X8jmy95WivMjoQHuVRqOB0+lEV1dXWgkAIjihyI/79+/j0aNHGB8fF7qBvGY5cOT5dMCP3lmlcFIlbxX3ICr9xssl0DV5nde9vT3BLsoJf7jIIJu3Q9Y3KcTxTQytJw6mzGYz6uvr8T/+x//ApUuXYDQaodVq0dDQgMrKSlHbiIppkiSTSWxubuLp06cYHh4+1VpBxwlNGA0svQgpDpZYxN4mMZvNgj1pd3cXi4uLmJubQygUgtVqRVFREbq7u2G32xEMBkV9h0zWkpMSesCLi4vTlFtevJHqGrW0tKCoqAjJZBIejwevXr1Cf39/WnjAaYlOpxNFYzc3N+Hz+Y5QzBMAsVqtaQX9TCYTvF4vVCoVlpaWsLa2duqhont7e/B4PLh//z4WFxeRnZ0Nv9+vCAZVKpXIZbx9+zZaW1vR0tIiarF88cUXmJ6ePlVQ8lOFGMU6OjqQm5sLv9+PoaEhEZZ5Gs+vSqVCQ0MD/u7v/g6tra3Y29vDq1evcO/ePTx+/BjBYPAnX5OSiKurq7G/v4/5+XksLCycCaHJ35rI1mHurcpkseeKK4XVZmVlCcUgKysL1dXVAIBYLIbs7Gzs7u4ikUiIHMaWlhaUlpaKUO1M1nUAQjlVIg7gCs7u7u6RNWM2m1FRUSGoy4lFkJTFpaUlvHr1Shg+uSXaYDAgOzsbV69exWeffSaUUp1Oh3g8jpmZGUxNTQnCAS5c8TkuRIcfz63edIxcFJfmx+12o7GxERUVFdBqtdjc3MT9+/fx3XffiVxDMrhw4GI2m2EymYT3WB5HGSzxMDLeBnnslfJjdDodcnNzBXOi7K1IpVIwGo1obGzElStXhPevsrISdXV1KCoqgtFoFCxzlAOr1+vxq1/9Cru7u/j+++9FVISsrBKoICpxPn4kShTY8lqkcPiKigpcv34dDQ0N0Ov1iMfj8Hg8GB4ePvL+1mg0sNvt6OrqEkXMS0tLkZWVJUAIAQaDwYCSkhJUVlbC6/XC5/Mpjmem9aPkSTabzbDb7aipqUFpaakASB6PBw8fPsTz58+xsbFxpDYnhfrRmJhMJhQWFqK8vFyUP6HnUalt3PhBtOuvazuQTn5Dhga+zmjeKLrnV7/6FS5evAi73Q4AAqzs7OxgcXERS0tLCIfDYhx5LiUHTErgmYAgD/mTwaL8yb3PNAc0BtQ+DtjJQEHrMpFIiHc4FcSW78n3Eu4Fk3NJ6b60H585mCLXvtvtRm5ubpq1gLxWra2t2N7eFnWC6EHe3d1FOBx+a+o3kUWH3LnAIXIli6LVahWsS2+L6HQ6ocDn5uYiHA6jr68PMzMzgi2xpaUF7e3tyM7ORiwWQ3NzM7777jt8/fXX2N7ePjVl7ODgAIFAQDyEZrNZWP5SqUNqVSqyWlJSApPJhM3NTXz77bd4+vQpPB7PmYBubkmhMCv5ZZiTk4O2tjbBhBcOhzE5OSkAY25urqjFchZrPRKJYGpqCh6PRyg5mdYx5Qr6fD4sLi7i4sWLcLlcuH37NpLJJIxGI4aGht7KItYUslNXV4fbt2+jsLAQXq8XY2Njf5E36K+R7OxsUVdvbGwMv/3tb9HT0yOsxT9VSJmwWq1YXV1NM0Scy8mJHCoCHM1VkK2xXBklpkyHwyEMigcHByKvOBAI4MmTJ9jd3UVJSQmuXLki9r+CggJ88skngvVN3n+49ZjCrZTazxUZrjDT9y6XC5cuXUJJSQlsNpsgFTg4OPj/2Xvvp8bSLE34kZBDFgGS8MJ7l5lkkt5VVlVXV3dNj5/d2B829h+bmIiN3Zntjt12UzVdJh1pgEy890YIISSBBBISQqDvB77z1tHLFVnVk4bu5kRkQIqre19/z3PMc4TlfXl5WSSkkxciLy8PtbW16O7uxscff4zW1lbY7XbhyabzOj8/H7FYTLw/iQ6cj2G2nKBs3gUlsMLniYxg165dQ0NDA46OjjAxMSGYbakURGtrKy5fvoyysjLk5uYiEong6tWr0Gg0ghWUK2Iy0OBKm2ylV/LkyMollaMwGo1COeXfPzw8hEajQVlZGf7u7/4OTqcTLpdLMLHG43EEAgHBWki5OmazGS0tLYhGowgEAohEIojFYifeoel0WoAFJZBOawZAVqWT6hM2NTWhq6sLTU1NcDqdODw8xMrKCgYGBjAyMoJQKCQM1A6HA+Xl5bhy5QouXbqEqqoqkWMUiUSQTqeRm5sLi8UiPFQmk0nUEgyHwxmeSLntnEJcNobQ3ysqKtDR0YGysjKRJ8jB39TUlBgbmlvyipBHkozZVNuT9s/+/j7C4fCpodh0DsjC170SiKWfnHCDiNPUajWqqqpw8eJFVFdXC7IbemcQYYxKpUJVVRVMJhMsFgvGx8cRiUSwu7ubQcHPw/NonKlNFHXGhZPB0BrKBuKpHwSY+PzwMHbqs9VqxYULFxAOh7G+vi4KDxOTL61vOQxQyRBGYYT8/qfJe8mZkl1pZOkggFVXV4dQKASr1SoGLJFIIBwOv9c6QW+Sw8ND+P1+eDweYbWn2k35+fnCTXpWhB/EVVVVKCgoQDQaRSgUgsViQUNDAzo7O1FTU4PCwkIxP6TsDw8PY39//72CqXA4jHg8DofDkRF/TLH6n376qVCCtVotgsEgHj9+jOHhYezs7HwQb8jR0TGFbSqVQl5eHhwOB1wuF/b29qDX6+FwOFBZWYlbt26hpaUFh4eHePnyJfr6+lBdXS0oyGU2m/cpyWQSwWDwB9dKU6vVCAQC8Hg82N/fx507d9Dc3CwO693dXSwvL2N3d/cdt/zHCe3X+vp6XLx4EVarFSMjIxgbG3tvXk2NRoPCwkIR0rW2tobnz5/j22+/xdra2o++n0qlEiGXlMxNxS7P5d0LD+/OpvQDmUYX+j8lore2tqKiokIwrhFImZiYwO9//3s8f/4cyWQSNpsNyWQSd+/eRU1NDWw2G7q6utDb2wuPx5NVcaR7cq8UCa9dRNZsEgId5eXloo16vR5qtVqAHgrx8/l8wjtG+TqXLl3Cz372M9y8eRMOhyODYIoUIZvNhqamJhHuHIlE4PV6M3JOudJGfQKQAeq4V4r3m481tY3Ct1pbW9HW1iaiNoaHhzE+Po6joyNYLBa0t7fjzp07oig8GYEptHFwcFC8p+j8lttJv/O8EL4elLxtHGA5nU7U1NSgoKAgI6ecriMmP6PRiLKyMrjdbphMJqRSKcRiMczNzWFqakqArq6uLtTV1SEvL0+kYExNTWFycjKj8Cxvg5JXkEAD9U0JSFH/Sd+7efMmbt26JYiNIpEIXr9+jRcvXmBlZQUHBwfIy8sT53NDQwMqKipQU1MDrVaLcDiMlZUVbG5uQqPRoKGhQTDrabVa5ObmQqfTZQAU2TBAa488F0r5jbRGWlpacO3aNVRUVIi1trOzg7W1NYTDYSQSiQyQAkDosaQjchBJBeJVKhW2t7extraGaDR6Yp1ycK2U6sD3LJ8jPu6yd5b+r9frUV1djcbGRhiNRkGqNjIygmAwKEJEnU4nOjs7odPp0N3djefPn6O3txcLCwui7isHOdyowdsjrx1iYgQgnCZK710CTGSkpjOAxohC9e12uxgzipiprKzE4uIiNjc3EQ6HEQgE4PP5RCqF0WjE4eEh9vf3M/KMldb5D9UJ3rvmTy5C/mIhCxsNCB3Qy8vLZ8q6fXh4CK/Xi/n5+RNuXSU354eWnJwclJSUiHoHZrMZqVRKkDfQC+zg4AA+nw+7u7uiFlVBQQGKiorg9/sRjUbfa7upVgC9oNTq4xpOP/3pT/HJJ5+gubkZBoNBFLxdX1//oIxlqVQKW1tb2Nvbg91uR2trK3Z3dxEKhVBQUIDm5mZcvHgRRUVFCIfD+P3vf48XL17A5/OhsrLyTJOXZBPyIs7OzsJisaCwsBA1NTVobW1FPB7H5uamqJ9xlkSv16O4uBilpaWwWq1IJBLweDyYm5v7o0Lr/hgxGAxob29Hc3MzVCoVBgcH8ezZsz96rChs+qOPPoLD4cDm5iampqYQiUTecsvPRUko/IsnQpPIHgeer0OKPTGBOhwOEWIzNTWF58+f4+XLlxgYGMD29jYMBgMKCgpgsVhOGO64RVjpfUmfK9Wp0ev1wvsge0U0Gg0KCgrQ2tqK1tZWoQTT9R6PB8+fP8fw8DBisVhGiIzb7cbPf/5z3LlzB2VlZRmFpEkpoiLFNpsNqVQKJpMJo6Oj4ozkipY8rhStQDWW5LbzfnMlj3SOixcvCvZEnU4nCsSaTCYYDAZ0dnbi5z//OZqbm0UYt0qlQn5+Pjo7O0Ve79TUlDCwKhF9ZLO409rh6yLbe4BC8+T1lEqlEI/HBVkJKYbkbad/oVBIKKR7e3swGAwiNJs8D1arVbDPck+aEjvk0dGRIAwjQMFF9tJpNBpYrVZUVlaiuroaFosFqVQK6+vrmJycFPUYSWe5efMmbt++DbvdLgqzT09PY2xsTJBmVVZWwm63w+12i7kOh8NYXV0VNYaUAAa1SafTCWVaCazYbDbU1tbi0qVLcDgc4hlUO81gMMBoNGasUb7OiKW6srJS1I4jQH54eIhQKCSMXnyNngZiKWSOmB9pzcneWaV1z/fG9vY2FhYWsLm5iZ2dHfj9fqysrGB7e1v03W63o7OzE1euXBGGnsLCQnz55ZeinmG2MyWbYYa8dWazWZR3kIXvY56DRaCNDDxFRUW4e/cu3G63IMQhIhKbzYbKykrEYjEkk0nhnAmHw/B4PMLY++rVK5FXrESwonTmZJP3CqbI3fzq1Svo9XpcvHgRBQUFGYdsPB7HysoKnj59iq+++gp+v/99NvGNQgvgrOaDcFGr1XA4HKioqBD1AqxWKzo7O5FOp6HX67G4uIipqSkMDQ0hkUigsbERn3/+uaCw/RAhi+l0WnjDKGa6rKwMlZWVghr44OAAoVAIa2trgi3nQ0ksFsPo6ChcLhfKyspw7do1tLW1YW9vT1jvotEoHj9+jOnpaQwMDGBtbQ0ajUaEZMi1OP4UhJJUx8fHYbVaYTabcfPmTRQXF6OhoQGjo6NYWFg4E8YQUqCqqqrw05/+FFeuXEE6ncbIyIjItXtfa91sNuP27du4dOkSEokERkZGMDQ0JBjQfqyQhfb+/fsoKCjA5OQkXr58KUKQzuXdCr30CWTIgIlfx8PBeKgKxf6HQiH4/X709vZmUIxT+YIrV64I5Z6MYK9fv4bP5xNRHNzKzZ/NlSuueJEnghQ1+i55ly5evIjW1lZYrVaRDJ9MJrGwsICnT5/i0aNHWFtby4gkIGWnuroadrtdvOP58ykFgCjfyZBqNpuFonNa/Sjge28P5XJQmJWSBw743iqel5eHmzdvoqOjA0ajUbzv0um0yLv5+OOPhfeaewvIu33t2jUYjUb83//7f4WSTcpbNlArf8Yt7UrKG/0tGzMnrbfd3V34/X5sbGxgb28Pm5ubePr0KQYHB+H1esUcm0wmlJeXo729HdXV1TAYDCL0z2aznfDo0Jzy9vEQLgIitG6U+pFOp2E0GlFYWAir1Sq8k4lEAltbW6IAMuXDELNlcXExVCoVpqenMTc3hydPnmBhYQGhUAh2u10QDZC3LhqNwuv1wu/3ZxCyZFs3FOolt5mAVkFBAex2O3Jzc8X7OZ0+Di10u91oaWnJmCsaY1ojRJrR0tKCW7duoaKiQhCJJJNJLC0tYWFhIcMDKxtaOHjlbeRRLNwzlG3d87lIJpNYXFxEKpXC3t6eABeU70/9NxgMgl3z7//+70Vo8eLiIubn55FIJLKmVcgeKr5uyBDDjQfZ2Pvk+aH1YTQa0dLSgs8++wzl5eUiF5TGhsJKTSaTGBM6Z4lkbWRkBKlUCgsLC1hZWUEsFntjvbrT5L2BqXT6ODnM5/Ph22+/FW7UtrY2FBcXi+uogvrIyIiwVpwVoUODUC5ZeM6qqFQqmEwmcXipVMfkAS6XC36/H5OTk3j9+jUGBgYwNjYmwhWTyaTwKnxIkEL5XkQ7XlpaKtgIyUM4NDSEnZ2dD9ZG4HjNLi0t4fXr1ygpKUFLSwscDoeoV7KysoLBwUG8evVKeECSySTy8/NRWlqK4uJiJBKJMwE6fqwcHBxgY2MDg4ODovhsYWEh3G438vLyMhisPqSo1Wo4nU60tbXh1q1bqK2tFaGs4+Pj780DTla5xsZGOBwOzM7OYnFxET6f70cbaCi3oLy8HC0tLaiurkYikcDy8jKmp6c/+L74SxEOkrKxVpFwCzIpS9vb25ienhb5Hk+ePMHQ0BBWV1eRTCah1+tRV1eHn/70p/jiiy/E+3J5eRk9PT343//7f2NjY+MEm59s5ZaJL6iNnDCCt1mlUsFut+P27dsCVJAC5vP50Nvbi1/96lciUkP2IPEcH3kMeDtJoSclS4kV900hcbICLz+HCzEjNjc3w263Q6VSIRqNYnt7G01NTbh58ybq6+sFIQgps6RoklJms9nQ0tKCnZ0daDQa9Pb2inGQlVo5BEpeN3JoIr+OahbJ9aCoz7FYDNPT0+jr68OLFy+EYhkIBETOyMHBgfBuUeg+J3Y6PDwUYW8yCKd5ktd4tvNSNiRQaBl9TqGsRDNPdNsUKkhheoFAAAsLC/jVr36V8d7U6XSi0Gx9fb0ghVhYWMDLly+xsbGR0TeZ3Y/GkIeoZesDtYWvL3q3l5WV4eOPP4Zerxf5fwTuVCpVhgE7Pz9fhMcmEglsb2/D6/Vm7FulkDwiLpFBC9/rfA0phcJywwmBMCK7Ao4jgTg4I1394OAAQ0NDiEajcLlc6O7uhs1mQ319PaqqqoQnF0CGniive/rJ1xVFHHEPmrzu5HmjfpnNZjQ1NeHChQsi54vyoeLxOLa2tsS+pTpUwHFuKrGhUu6n1WrF8+fP8bvf/U54duUzkMuZ8UwdHR1hb28Pq6ur2NzcRG5urmAxokZTzYrt7e0zR+YAHOdybW5uYmJiQlSDJznNTf+hhFtuAIhq8y9evMDvf/97jI+PCwt2V1cXLly4IELolNzf71PIqvO3f/u3uHHjBoqLi2E0GrG6uop///d/x4sXLzA8PPzBLfCkBIyOjsLv98PlcsHpdMJutyMYDGJlZQWhUEgAcFrTGo0GFRUVKCkpEcnjf4pC4RpUbLGyslLE+P9QJpx3LXq9Hs3Nzfjoo49QU1ODnJwcrK2tYXR0FPPz8++NuMRgMAhr5+bmJvr6+rC2tvajgZRarUZeXh7a2tpEKFUqlcLU1BSmpqZErY1zeT8iAxHZE8RBDYAMJS4SieD58+dYWVmBWq3G0tISNjY2EI/HRb7S3//936O7uxtVVVU4OjrC3Nwcfv/732NkZEQUqOReL66k8N/Jcq7VajOSyDnZAwd7FotFFHvd3d3F+vo6ZmdnsbCwIBLwibmU73FijV1dXUVtbW3W/U+KWzKZxPb2NpaXl/Hs2TOMjY0phkzyd6xcm4ZEZnDjf6MxcjgccDqdgpWQ2NaIsrqwsBA6nQ5HR0eIRqPw+/3wer3Q6XRwuVywWq2in4eHhwiHw4hEIicUMrnt5AEk5VNeN/x6+pvX68WjR4+E8S0/Pz9D0czNzQVwTDG/tLR0Yi4JLBNIpdA+rhMQmJTBGj2De0l4jomsfMrjTf/na417RHd3dzPqM6XTx2GIKysrWFlZwejoqCCmACCY9e7du4cbN27AbrcjGo1iaWkJv/nNb/Ds2bMThjEOhvj6Oc2Lk0gksL6+jpGREVRUVKC5uRk2m00ATyLwqKysFGx76XQ6I4+MQn/1er0Ak+RFnJ2dxcrKCoLBYAbBBLWbxorAFOVI0bgrpTTIIDebd5zm4zSh62hsv/zyS6TTady4cQM1NTW4du0aVldXBckHX8cyGKF1R2QodD7R2lEyMvD+8L5QpFJnZyeam5tFaOro6Cjm5ubg9/vh9/tRWVmJw8NDQaAWjUah1WoF2Vp9fb14f4ZCIXzzzTcnzhF+XmYbcy7vFUyRp6G8vFww/FEcMgktGL5hz5LInila8JT8eJYIKNRqNYqKilBZWYnc3Fzs7+9jfX0djx8/xh/+8Ae8evUKm5ub0Gq1IuHT7XbD4/EI6vQPqeC7XC7BgFdXVycU4KGhIfT29mJ4eBirq6sfrH1ciGGIklPNZjMsFgt2d3extbWlCCiI6UelUiEYDL733LS3JaTYl5eXC2pn4Icnbr5rIWrh5uZmtLe3w2azwefzYWRkRJBkvI+wXZVKBbPZjKKiIuTm5iIcDmN+fv5ENfkfIjqdDhcuXMBnn32Ge/fuCUthf38/xsbGMvJXzuXdCg9F4V4gHtpCIr/rkskkwuGwiOEHvk/mp1zi7u5u3L59GxUVFVCr1djY2MDIyAh6e3sxOzubEd7HLfFKII7WhMz4p6T8Ezsasbx6PB709vbi0aNH2NjYwObmZsbe4crb0dERYrGYSNJX8sgcHh4KL9f6+jqWlpYwNzcn+iVb7ClvQgZHXEHnyptcT4uE+k5/p34ajUbxLifvTiwWw8zMDAYHBzE6OgqbzYa6ujpUVVXBbDZjfX0dz549E6HC2XQWJWVRHvtsFvpwOIyZmRmMj4+joaFBhDkB34eTGQwGpNPpDI8ThQeSIq7T6QRBUmFhYQa9Pc0HeabkxH/yyvE1reR94+tNBrixWCyDfv3o6Ei0m8vW1ha+/PJL4VXY29sTtOputxvXrl3D3bt30dTUBACYn5/H48eP0d/fj8XFxQygIIfAyaBKZvDjHhyq+0e1u5xOp8gRo6gfGmMaQ051bjAYkEgkBHEIga5AIICRkRFBqU4eMjI8yp4mHjZ62jria4l7hORrZeHzJt+DjAl9fX0oLCxER0cHCgoKBMU7AUb5fryN8jnEwZd8bmUrmJtOH9cdI09fVVWVcGTMzc2hp6cHjx49gt/vRzKZFEyWkUgkw+M5MzOD3d1dYRCx2+2w2WyK5wStgx8yhsB7BFNkQSkrK8Pt27dxcHCAtrY2uN3uM+fNeZMoDTwVlNve3hau9Q+pzGg0GuTn56Ourk6wtgSDQUxMTOBf//VfRXIvFdG7c+cOLl++DJPJhMePH2NgYABbW1sfBEzRYe52u3Hp0iWUlpZCq9WKw+3bb7/F+Pj4D2aee59ClrV4PI5gMKi4VkjoEIlGo1hbW/uTJAygkIvLly/j448/Rnl5ubC8hUKhrLkD71MoLKCtrQ3l5eXCg/P06VN4vd73ZrSRwRTlx/zQXClScKnW2k9+8hP89V//NVwuF+LxOCYnJ/HkyRNMTk6ee6Xeo/DwOQJWXFHjig3l9nBlghQxXhBTq9WiuLgYX3zxBR48eIDq6mpotVpsbW3h+fPn+P3vf4+lpSWEw+EMT5KsFHJwxZ/L96US8KMzWKvVYnt7WxBNTE1NYXFxMYMFS0kJI2s6he8BmRbnZDKJra0tDA4OoqenBzMzM9ja2sLm5qYg86E+0XeVwCm/d7ax58oiAVgiLUqlUqKdXPEmq/bg4CB++ctfigL3JpMJs7OzKCsrw9HREUZGRrC0tCTelUpWee5RkMOwOFW6kmdKpfqekOv58+dwOp1wOp0oLS0VXhCj0YiamhqRiM9BDoVTUc5dS0sLLl68KEg3CNASEQEHoHJYnKyncTIRGZjL16ZSKQSDQezt7SEajSInJwcGgwEWiwX5+fkCkKTTaWxtbWFnZ0cQW+zv78NqtaKlpQW3b9/G3bt3RbH4hYUF/O53vxNsgJzMgTMN8rXCx17pc/rs6OgIKysr+PLLLzE2Ngaj0Yj8/HyRa0V1rjihGi+ZYzAYYDAY8Fd/9VcwmUw4PDwUOtjDhw8xOzuLeDyesTbkPCj+WbZ20nlB15LRgc8f/77s4T0NoNHcRSIR+P1+RCIRWK1WOBwO4SFVIvrg+1KJnIR/j9YxBy50/nBwRZE81dXVGeyfvb29ePnyJcLhsDhDiZCMgDX1l9giKXctEonA5/Ph4OBAgFhqtwz23iTv1Y1CiZ/37t1DOp2GxWIRnfpTA1RAZjxxW1sb/vt//++wWCz49ttvsby8/EEVmvz8fFy5ckXQhh4dHYnitn6/X8T3lpSUoKurCz/5yU9QWVmJpaUlUUj5Q3kGc3JykJ+fj9raWlGXJJFIYG1tDU+fPsXjx4/PfBjTaSAKgHjBqdVqJJNJESLypyZFRUX4p3/6J9y6dQvNzc1wOBx49uwZfvnLX2JycvJMhC4WFhbio48+QkdHB9RqNebn59Hf349Xr169d0DOQ2ZkpfZNkpOTA5fLhZaWFnzyySe4c+cOioqKAACPHz/GP//zP2N4ePi9edrO5VgohArIfAnLQoqFkheIrwNKnm5ubhbU3RaLBaFQCOPj43j58iVevXolFAZaU5wtizwJXMlTAiPULrk9tD739/cxPT2N0dFR9Pf3i7AZpf5xC3NeXh7sdjvy8vJgsVgylBQKNZ+bm8PXX3+N7777TtQIpDOd7k+eFVJg+TuXruO6A1cclSzNwHFYG9XLofOJxg+ACMWam5vDN998g6dPn8Lj8Yh5DofD8Pl80Ol0ggFNHhPuoTyNjEIGmkpeAgJ2ExMTcDgcaGpqgkajEekRRNhw69YtrKysYHp6WgAXapfRaER9fT2uXLmC+vp6QSZCYXbRaBTBYFBcLwMQrmhyJTubB0Tu797envDCrq+vi6LBBQUFcLlcyMvLQzgcRjp9HFpGc6HX61FYWIjKykp0dXXh2rVrotiv1+vF69evMTY2hqmpKQEGac2Tgq7Udr7XuJ4jg0haJ3NzcwIcnVb/jFOk5+XloaGhAR9//LEwIGxubopcMM4iR8/jz6f2c6NCNi8J7x+BKwJ6Six1sgGIrz35dzL4bGxsYG1tDS6XCzqdTnh/ss15tv8rGUTIe8SvIyMTjYVGoxFlflwuFzQaDYLBoPBsJ5NJAYroJ/WFiG5qa2tRVVWF3Nxc7O3tCXZIHqosA9UfCqjeOZii+jUTExNwOp0oKSnJyJECMi0yVHiyu7tbMF0FAoEPVsz0h4hKdVznpba2Flar9UxY4/Py8tDV1YWKigrxos/NzYXT6UR1dbU4YC9fvoybN2+ipqYGm5ub6O/vx8zMjKjs/r6FPB1Xr17F5cuXUVhYiHg8jqWlJTx9+hQDAwPwer1nQkn/Y4VegES3S8yEfyyb2/sSlUol3OyUvNnQ0IB79+4JeuP9/X0EAgFsbGx88FAzGufi4mIR8+7xePDo0SP09fVhY2NDsSDiu5J0Oi3CQGOxGPLy8lBTU4O5uTkEAoFTv5ufnw+32y32RVdXF1wuFwKBACYmJvDVV1/h5cuXZzLP9M9dSBngCgr3OAA4oQzRZ9zymk6nRThTS0sL/u7v/g7t7e3Cqr2+vo6vvvoKExMTooYYKW+yEii3jT7jwEpWbshrRvehc2l+fh4ejwfRaPSN+Y86nQ65ublob2/Hz3/+c5G0Tu8gshavrKzgxYsX6O3txdra2gkLMv1OoWgcLCrV45PHWwnY8L9RyJVcXyadPo4soLIPfKw5iDg4OIDVaoXL5coKMDngIzY+CsnkCij3ppEni4d60Zjv7+9jdHQU//qv/wqVSgWr1SqY5qxWK65fvw673Y4//OEPeP78OZaWlrC7uyvC/S9fvow7d+7A7XaLQqpEFb29vZ0RLsrBCJ8TrtsQcOf9zBaqRcBkdnYWXV1dcLvdMBqNImzP7/djcHBQtFev18NgMCA/Px/37t3DrVu3RB098tAODAzg4cOHmJyczPBI0bN53hH9X143NC8HBwcnvCO87bTueN1TvhcotJTmkGjXaZzoXyQSEdFLskdPHi8S8iDKoI+PsQwebDYb1Gq1qL2ZzbjGPXTy51RPjYAMlTwhwy99LgNVDsTkcaTfaV44sQn/DgeS9B2VSgWn04nm5maYzWaRYxkKhZBIJGA2m3FwcJBRooCzKra2tuLBgwdoaWkRBbeJgIqfP/x5SmdqNnnnYIo8Ct999x00Gg1u3LghEC3w/WSS2zc3NxeNjY0CnPzrv/4rhoaGBO3oh2SXyya0OJLJJAKBwKmx0+9DVKpjJpnGxkbYbDbE43Go1WoUFhbi+vXr2N/fF9ap27dvo76+Hnt7e+jt7cU333wjUP77bjMV9rt06RL+5m/+Bi0tLdjd3YXX60Vvby9+/etfY2Fh4Uzm0v0YUamOw71KS0thMplERfX3UZNJp9PBaDQiNzdXkL1QTHa2tup0OhEKU1hYiKqqKtTW1qK0tBTV1dXicNvZ2cHKygp8Pl9WKt/3KRQW0NLSgqKiIuzu7mJgYABfffUVxsbG3vtZkk6nEQqFMDs7i3A4jLa2Nly7dg1LS0siEZm/EDUajWA6a2hoQHd3Nz7//HM0NDTg6OgIwWAQQ0ND+PWvf43Xr1+L+PBzeb/ClUuulHOFgV7O9Hs272ROTg4KCwvR0NCAxsZGOJ1OHB0dwev1YmBgAM+ePYPP58ugdeaKN7ekynkWlA/EPQ/Ufrqe+kH/393dFQQ6SoQQvF9GoxF1dXXo6urCnTt30NXVJUK46JnhcBiTk5N4/Pgxnj17Bq/XmxESL3uZ6D1Ez1AKGZLngivz3PpO1xLDKOXw6PV6kZ8TCAQwOTmJpaUlTExMYGJiIsPzRAqqXq9HaWkpysrKRFkFbvAlaz6NJXkKZKVRScmkueKhXsD3HrPJyUl89913yMnJQW1tLZxOp8j7amxsFDnQFB2TSqXEvJSUlMBisQA4LkMzMjIiQoNJIecAnOtnPIxR9srIXg0ObmnsDw4O4PF4MD8/L94ZZrMZ165dE3MWi8VQWlqKpqYmYbCrrKxEZWWlYPKdnp5Gf38/BgYGMD09jc3NzYyznK8BmUmSC/dgyH3hbJh0LQEqJS8P/4zmnXvXVCoV4vE4vF4vlpeXMxR+mmuVSiUMYdxLQuFu2UAKrQ1aXzqdTqRs9Pf3Cy+lvOflfsj/ByDo9GtqakQpAZvNJsCULLKXKhtbogwYSThjJ19P5KEmfQMA/H4/+vr6BAkOeTT1er1gUKyqqhIlddra2lBfXw+1Wo2RkREMDw+jr68Pq6urJ2rEyd5+uX9K8s7B1OHhIba3t9HX14dUKoXNzc2MKujAscekoKAADQ0NKC4uhslkglarFei+uroaExMTGB0dhcfjEZ2SF8CHFCKliEajWcMg3qfQAbixsQGPxwOj0YiCggKUlJTg/v372N3dFeAlHo+LKuTcyvM+RavVwuVy4dq1a/jiiy/Q2dmJQCAgiCampqawsLDwwb0db0vIesXjkd81mFKr1SgtLcWFCxfQ1taG7e1tfPfdd6IKu5Lo9Xq43W4UFhaioKAAjY2NaG1tRXl5OWw2G8xmM6xWK/x+P0ZHR9HT04OhoSHMz88Li+6HECo8+PHHH+OLL76A3W7HyMhIhsX2Q6yjo6MjJBIJ7OzsIDc3F52dndjZ2UFBQQFWVlaEhUytPqZyr6mpQVVVFdxuN4qLi+F2u5FOpzE9PY2HDx+KfJNgMHgOpD6gkFJFwIG/gGVrvqxUc5BAIXIU+sTDUV69egWfzyfYz/h9SNGThVuLSTmQ83hkRZjfk/YJ987w63NycoRnxOl0oqOjA3/1V3+FtrY25OfnCxCRSqUQjUYxPz+PJ0+e4PHjxxgdHcXe3l6GN4zGko8d/z2bYkzeFKVQHdm6fXR0JBjjPB4PDg4OsLe3h/X1dYyNjaGnpwcejweBQADhcDgjjyidTgvQpVIdF/Cl4rVcMVeag2wiW8H55zIQjMfjWF5expMnTxCLxXDlyhVcvHgRJSUl0Ov1In/K5XKhqakJqVQKOTk5KC0thd1uz6AlJ+DY19cnckeVFFm1Wi0IFDg9u+xl5R41eR5J/H6/OK+sVitycnLgcDjQ3d0tGCPJSEfeMaPRCI1Gg3A4jLGxMbx+/Rr9/f0YHR0Vdb34OCs9n4MqpfUlh7zJRhEgkxWQ30e+ju9/6sPR0TGZCTFcym1+kz5LtdOyjSvdh0jeqqurYTabsbq6KoAUMTTLIcnZ7kPU7hUVFbh37x5u376Nmpoa6HQ6rK2tYXt7W7EkgmwYkUGg7H1WGku+xsgYQSyU5CULhUIIBoMwGAzIy8sTa5UKDjudTly4cAHd3d0oKyuDxWJBOn3Mjvny5Uv09PRgbGwsg5FQKWLutPWcMUen/vUtCdU+SSQS8Hq9MBqNGVbrvLw8uN1u4SKlCuTFxcW4desWKisr0dDQgKqqKiwvL+Po6Ajr6+vw+XwIBAIf3FuVTqcRDAYxPT0t2nMWFP7Dw0MsLCxgcXERKpUKNTU1uHjxIhwOBxwOB2KxGLxeL6anp/Hdd99hYGAAfr//gyhlVATx008/xe3bt6HT6dDb24s//OEPGB4ePpNkE3+sUIhGWVkZdDodYrEYIpHIO88BU6lUKCwsxIULF/CTn/xEJPaurKxkBRcGgwHV1dVwuVzCW1xdXQ2bzYadnR1RI2d8fBw9PT14+fKlCNv5kHvAaDSirKwM3d3daGtrw+zsLF68eIH+/v4P7jmOx+MYGhqC2+1GY2Mjbt++jfLyciwtLWWAKZfLhfr6elFLg4xR09PT+Oabb/D1119jbGzsPKzvAwsBFHqnUfFRWUjhJ6WflCwOGAwGA9xuN0pLS0UEB4GQlZWVDI8UkTxw9jYg+0ufAJfM5EekBXK9qVQqhWQyKfJrnU4nVCqVCGsDkBFGe/XqVTQ3N6OlpSVDcSeSg6mpKTx+/BhffvkllpaWRNggtYOPk9yHbKHzPLzqtNwkui9dG4lE8N133yEYDMJkMiEUCmFjYwPb29vCUs3JAfh9yYvl9XoRDAZF6RHO6sb7lE6nxZoghY1+cuKA0zwFdL+joyNRi3Nvbw+Li4sYHR0VVNH0TiksLERhYaHQqfR6vQD6+/v7CAaD6Ovrw+DgIDwej2CoI6Wdt5PaL4MoKmRL61jOJZLnjzPZDQ0NwWKxwOFwwGg0oqqqCg6HA9FoVLATplIpUTfI5/MJjxTpfhTCJoPRbKBE9vxxgMKVaVmxV/JunbbHqC1Go/FErTXKTeOsiTLY52sVgNAnZXCo1E/Kg29qakJlZSX0ej1evnyJlZUVBAIBQf5B3+N7Hvi+qHVBQQGqq6tRWVmJlpYWXLp0CW63G3q9XoTBkmFbzpvKNlb8GXQGymPP9wbvK80XFXomsESRbrFYDMXFxTAYDLDZbLBYLLBYLCgvL0dRURG0Wi12dnawtLSEgYEBvHz5EktLS1m94rJR6bQ5F2N/6l/fohwdHVcI397ePrE48/Ly4PP5YDabcXR0JGpAFBQUCOt3TU0Nbt26hXg8jv39fXz99dd4+PAhXr169UEK+9JLiWgtKRRtbW3tTOR3kfVtcXERPT098Pv9qKurQyQSQVNTE1QqFTweD8bGxjA6OoqhoSGR0Py+hSxnP//5z3H16lWYTCZRz2R+fv5PkuXuNCFF+eLFi8jPzweQXVl420LWm9zcXNTW1qKiokLURsnWVv5CIHc7hVuMj49jZmYGo6OjGBsbOzVB/X2JSnVcbLShoQFOpxPb29v4+uuv8fTpU2HU+ZCys7ODf//3f4darYbJZILb7YbL5cLly5czXiT0YqPY9d3dXQwODuK7777D7373O6yvr58DqTMgRKVNll95L8uWTQIicu4PKdf19fVobm4WkRmbm5uYnZ0VNYRkbw33BsiKMH8+KeJy2ygnizzj3EpOBh6LxYKysjJhAaZ3X2lpKUpLS9He3o6rV6+isLAQBoNBADvKuxofH8c333yDiYkJzM3NYXd3N+NdI7P2KY2fPFbpdFqAKTnRXgYt/DtE003MhGq1Gnt7exl5LARc5HOMK6GUQ8PJhGgdnAaOSFEjj48cHqgEukhovx8cHIj8y8XFRczOzuL69eu4fv066uvrRXQPD7NKp9OiTubU1BS+/PJLTE1NiWK4fOw5IOQAnLeFwtd4vg59X0mhptDK9fV1/Md//Ad0Oh1aW1tRWloKnU4HvV6PeDyOSCQiivSGw2Gsrq4K0oaJiQkR/cPJV+T54WBEad3Q2NOelT1T/Holr6H8fzn/Sa1Ww263w263w2QyAYAAAjJBEFfa+Trg3j55L1PoH/8bvdftdjsKCgpQXl4OjUYDi8WCmZkZBAIBbG9vIy8vT4C4SCSCg4MD7O/vC89mbm4ucnNzUV1djZqaGlHcOp1Ow+/3Y3JyEoODg9jc3DxRK4rax8Ge/DdKGSC9IxtY4ecmedbC4TCWl5eh1WqRn58Pm80mKNILCgpgsVgy2AGJ9MTn82Fubg5DQ0PCw7+7uyuMHErz+6b8UFneK5tfKpVSfPmT9Wt3dxd9fX1wuVwoKytDZWWlYO8oLS2F1WpFIpFAMBgUYXUfwitFIGVkZER4fLa2tjA/Py8YaT6kpNNprK6u4p//+Z8RDofh9XoRjUaxtbWF9fV1FBQUQKVSYXd3F4FAAMFg8IPRoGs0GtTX16O7uxsNDQ2w2Wzw+/349a9/jUePHiEQCPzZKYwqlUooJyaTSXEzvwuhw3N5eRmrq6ui5ogc8y23VaVSifW9vr6O5eVlEa5AOYKhUEgUffyQolarBRX6gwcPUFJSglAohOnpaWGB/dD78+DgAF6vFw8fPsTe3h6uXLmClpYWuN1u2O126PV6kRM1Pz+PaDSK5eVlTE1NYXJyEvPz86JQ67mcDSErs9JZxZWKbKF49HdibHQ6ncjJyRFlE1ZXV4Wxi9YvT2yXQ2dk5UsGKfy8OS18jrwSlJ/T1NQEp9MJs9kMk8kkGPucTqeoO0PnCdGfDw8P41e/+hWGhoawurqa4ZGitvAkdBmE8J+y10qpP28SysU6ODhALBbLAE5KQEDp/nw8uTJLVn/Zy3eakGKcDbjJXhfqAwFaIvvZ39+H0WiEy+WCzWYTniMSAjOLi4sYGhrC0NAQvF5vBpjj5CRKHkJS8nl/lZgilRRpWquBQAB9fX3Q6/WIRqO4cOECnE4nkskkxsbGMDg4iIGBAeH9IAPd/v6+CAvlngz5mXzOZC+TPI+84K4s8hgo7SE5r43fNycnB2azWYBOyl/b39/PoEOX55u3PdvaUSJuIGMMGQd0Op0wmN6+fRvJZBLxeFw8N5FIiPWfSqUEiLJYLMjJyRHAymAwCBKwqakpfPPNN5iZmYHf788A0rzt8jgq/V/pvJT3Nt2LDCBjY2OCSI3WeXFxsRhvWhO0VrxeL5aWltDb24vp6Wn4fD7R7mzvT+7B/jFyJirMEuMfMejY7Xa4XC4BpBoaGlBTUwOtVivilScmJj4IUQJwDKZmZmYAAIFAQHh4PB7PB80T4RIMBtHT0yNQfTp9HOvt9/szrICnhUe8a9Hr9XA6nbhy5QquX78Ol8sFtVqNYDCI/v5+TE1NffB6Xe9C6NCzWCyCYIBYmd6lcSCdTouwvOfPn2Nra0tQjL5J/H4/JiYmsLKyIjyGpBR9yDUkC4Uo1NXVob29HWq1Gqurq/B6vR8sT0oWshDPz89jZ2cHm5ubWF1dRX19vbDsHx0dlzKga2ZmZjA5OSkKPZ7nR50dIeWc50cQ0QMncwBwwnDBFSYin6isrEReXh4ODw+xtbWF2dlZ+Hw+kfsgh8AokQDQ//nnFC4jJ6Lv7e0JSz332nIlcn9/HyqVCtXV1aiqqkJ+fj4MBgPMZjMMBoMI6aPxiEajmJmZwfPnz0UZgkAggHg8fiIMjvohg02uhNP4yAoOL+zLPbrZiBF4n5RAJCfGojHjbVDydvC2GgyGE2FZHIDIyjNZ9gmUcEDGAYCSl4run0wmhe6UTCbh9XrR2tqK+vp62O124Snc3NzE4OAgXr16hfHxcbGmTgPTfFxkAEBrgkBFOp0WoXOyN4WDg3g8jmQyidHRUWxubuLhw4dCsd7c3MTGxoZgjCPmRKUaXnw+sp3r2caM9iz1jcIhZfBHRpJsJQ24B0beew6HA2VlZaLWmlJbsrWftyMb8Qq9t8m7olarEYvFMD8/jy+//BIejwc2mw0VFRUi7JPaolarhZFEr9cLTx/9fXd3V+Soeb1ebG9vY2VlBV6vF7Ozs4hEIhnMhrwf1BYKZZaBNnnI+VwqAWM+DtT/2dlZ7OzswOPxCI4FvV6PVCqF7e1t7O/vC5BIXtBoNCq8csSkyMeRnsvLJGTbs6fJmQBTwPcHC7nmtra2sLi4iL6+PoGQaYFT6MCHogGORqMYGxvD/Pw8Hj58CKPRiFgsJijcz4KQRZGLHJ//IUWlUiEvLw/t7e24efMmOjs7kZOTg0gkglAoJOJ7z0p737bQAUGgym63Izc3972AKSo0aTQaM5Sg0ySZTIpQGPp5FhV6Isyoq6uD3W7HwsIC+vv7BR35WQBTJIlEAuvr69jZ2cHAwIAA1HSIkyWRcgeIzfQs9eFcMmmYSemjfcyVSK4g8e9wxVCr1Yo8qGQyKQoxy7nBSiFA2Szo/Ds8JIoMIfQOVSIgoPuTd3RnZwdmsxk2m00YgbgSSqQV09PTePbsGX77299iYWHhRFFb6i896zQPg+yFkD01vK3UD/5d/ixZZC8Dp4cnTyG/Tr4XHy8KRVIKW6L7c0s7N3RyLxZfK1w5JQVPHh96r/v9fmxtbWF1dRVTU1O4evUqHA6HqI+0srKCly9fYnh4GFtbW0gmkyfAuZKnhMZF7hMPM6Pv8XxBrpDyuSKP2tTUFKanpzPuK3tZZYps+oyPuwxI6Cf1jdO88zHnXjgOrpXuJ4Nj/rk8LvRsnU4nIk8o92trawvRaFTsBSKEkL2Y9Dt5XJTOfG7IpPMkFoshHo/jyy+/xIsXL1BcXIzbt2+joqJCFHVWqVTCcEKGEAKuWq0WiUQCoVAIHo8H/f39WFhYECGK8Xg8o6SAPLc0h/S7VqvNuJYDcvKu8u9zYwYJXZtKpeD1euH3+7G+vo6pqSnk5+dDpVIhkUjA7/cjGo2KSDfyvnHnAQdutCa4XsyBG3nZZRbLbHJmwBQJvYiIYecsCsWXvw8q6z9XSaePrfM+nw/Pnz/HysoK1Gq1qE7v8Xj+7ML7SCgBemVlBQ6HQyQ7v4+QVdpbOzs77/xZ71so8frChQtob2+HwWCAx+PB7OzsmSxkS9ZResmey5+mcIWUK81KSpj8QqfPAQhliGL8d3Z28OTJE0xMTJyow8SVWyXlXhYZNHAvkFI4muzFOTg4wMzMDB4+fIjCwkLBGkb1iuh+e3t7mJ+fx4sXL9DT04OlpaUMyn4lr4JS27OFOfGxlYWPqzzuPCQrW6il3Gd+P5mgQKkNBKay0XErtUHuY7bcMfmZ/P58HZGFfmRkBH6/X+S4plIp7OzsiJwdGdgqjTt/BimWwPc5fzKYV2KJI++E0ngoefd4X2VCFSVvFAfxSmPD+0ieHKWwxGwAWd6nHNjKfeX/p/sTiQvVaAoGgycYEeW5lttNucrA996TbJEg9DnpE2SEIza83NzcjHwinU4HrVaLZDIp+krlUqi4NnkvCURlMyzIc0kAj+9ZOUyZ7iGPA59reT3RGidGTpVKJcZZNgzJlPK0pojqP1teJN/7BHbfpD+cOTB1Ln85EolEMDIygrGxsQwLFreW/jnK0dERfD4fnj17hlQqhbm5ORG+dS5/vGg0GtjtdjQ3N6Ourk6EPcjJ7udyLm9TlMLnKNyJLM+yBwD4/oVOQmVEent7sbi4iEgkgqGhIeFFACCsyqRAvAmcKHl8ZIY6Uhay1WciSSQSmJmZwbfffotkMomSkhI4nU6RU7G9vQ2fzyfCyKanpxGJRDL6yEtCcHp3UtYpsoMrxrLyxcPSeA0r6i+FWHLjFCW98/Auug8px0pexMPDQwESZDAkAxpSOGktyN4zAjvy2pFJBGTwRuPDn5sN7BGgJeU9m9eGC59v3ifuBSOPOFcoeYgpCR97Dj7IQyEz/snzzA0FVKuQ1iVfp9n6peTt5B5Leg7tWd4GpZBOztJIIntR5LGg8aTwsq2tLezt7WFpaSkjp56vJz4Gcj+4B1Des0oKPl1H4Z/b29sZYyLXf5RD8ZQYLLlkA7Vy++me3HDD+837xOdDzomT9xHlT/2YlBrZUJItR5M/T05hyDYeJOdg6lw+qBBT0F+SHB0dwePx4He/+x16enoQi8XOPRNvQcjjRvH2eXl5qKmpQVNTk/BOncu5vG0hBYUsnbyOk6wccKu2rFgfHR1he3sbv/rVr4TySSUTeBgKt3xz5YpCUmSljhREUuZl5eFNBDT0t4ODA6yuriIcDsPv96O6uhqtra0oLCyE1+vFxsaGqIcVjUaRSCQEuYHcbh4eRmGCXLEEMgkOeDiT0jW8pACBNVK8ubIsW6O50kfjKyt79Hy5fUoeFN43+pvMuiaPvRzuKY89nyMlDwz/O9X9Ar5/t8oeGFmZ57k3fKzIIMBBkFL75bBKWcFX8h7RepXXBgcvBLhlxZcDXjnHjX/O1322nCcgE1TLijv9TiLnzil9nk6nBX396OgoysvLkUgk0Nvbm1F6hs4NJW+PDKbepMjTnMlt12g0wvDC783Hhn5SSCEHwhy48v1HbafcKyVQyudOHnduSJINQvQ7Dy+l8aX9DWRSxvNabzLw5+ebDJSVctHo/Jb3LN87SqI69Y8q1Xlw/rmcy7n8SYhKdZyHd/36dXR2dsJsNsPr9WJqagqvX7/+s6PYfxeSTqffPa3kn5GoVKq0wWAQoTKnxdfTC5x7N+QcFSUFUVaGZas8D0dRuq9sbeftIkWEgwbZ40JCypNarYbFYoFer0deXp7IGU6lUgiHw9ja2sp4Brd0y8CAnkFlAGTqdwIbvF10n6OjIxG2REQFsoWbK4pU04kri0peOKUcIe5Fky3nMniW5yqb90DFvGgy9bjshSClWKneE18TvN4ZASH5ep4bRIovV7j5GHBPkAwwOFtgNk8cjQ+NPV9jROzA7yF7TbgXgX9G/VYCEAAyAKV8H/ophyUqkUnQOBA4kOeXxpN/TvehYrJ5eXnQ6XRYWlpCLBbLAFM8fE/ez3yd8HHne5bvDaUxUhof3m4uSvuejAh8PmncaVw4rT6AEwCI9+Po6EiUkiCwLBsS6Hu0btLpTE8nnRN8TGgc+Vrh4yzPG+Um8zbQM2Vvsbwmsr0jz8HUuZzLufzZiFqtFkm1XMni1v1zyS7nYOrHiUqlStOLmQMSIFOh4YolrUsge8K/rFj//8864c3gVley2FL+AylbdD19l9fVAb5Xfkhh5MIt/LxALrVdSblU8g7Q9bz99B0CO0S8wRUv3lfOqkZ/J5DBlV1Z8afncE8LiUxOQN/ltWq4MsWBmkxWwRV2rnjyuZRzOLKNPf8OhclxpZK+wxVdDm7Is0khjUoskDS29Dv3FNCc8GR8/mz6u+xd4WNG40NKMS+NQ9fKfeX7RM4rUxo33g9iQ+Tr7/DwUMw7kWPInigelsnnkI8D99LQPXQ6nfD+8fnj+43uISvofJ64l0ZpzyrVIqN+Kj0rG9AmkT19cg4l3xtKYJbCZWmeOJEJX5vyfuF7loNmMoIogTsa42wlJ2i85f1O35eNMLReuNGFF9SmfvOx5+VU/v/nKL4jz8P8zuVczuXPRo6OjotVn1XymnP58xMlT4qSQpPNU8AVb7peSSniSqP8ORkN6H6yMkr3V1Ku5OcoGVgJKMrfla/hio3cTrpG9kiRN4oXAJUVW95XrvTJivFpAI9/T/aOyf3ggFNuOw8HozZwkeeP/5Ovla9RuhcBAf4Zfa7ksZNrXMlzLoNy7sHjwIo8RRwYZcun4d4CrugDmbWraOxlL6gS4JDXgnxv2XjBx4uDMvqXLZ0g29hTG+Q8NWoLlUNQmnc+RvLak9ckB8N8Pvg8ZQNjNJ5Kf+Nt4XtE/pvcJrkv/Doe6srDMOXnymcfv4d8P3nsuXGEe71OA03AyeLMcnv4ZwSC5XOWnznkfTsnoDiXczmXczmXc3nHwpVsJWVHvlZWtpSukX+XFRv6yb1gXGHjlm75XkqAKZuyw+/PlQ6la09TmrJ9R/Zy8TYqXSt/rqRccaU3271+iKIrK/X0O881Oa1/Slb9bGP0JkWNexN4O/nnBABlxS+bQiuDHLofX2v0vWykDxwgKY2ZDKz5ffnaUmofV6KVPs+2J+Q+ERiUlXY+9krfkZ8JIMP7dVoblX5XEg6CuEeZCzeSyPf9IWcJn0PuDeTXKAEiOtPk+8jrP9u8Kd2DfvK1qrSHSeR1JwNWGkOlfhNQy3auKK0XvmfT6bTi2GeT8zC/czmXczmXcwFwHub3Y+X8HXku53Iu5/KXI9nekeeeqXM5l3M5l3M5lz9S5LAvbqCUQ3QoxEkOuVH6KXs05NAf2ULM8xS4hZvaQPfjJAbAcd7GaWFr3JtA96XwJrIwAxDWfwoz45/LifZyqJrSePIcFN4Ofj3lPFH/T/My8THkQm2hnCzZe6GUgC97ReSQQGo39Zt7aPjY8zAx+X5yeBvdk3KjeGFo6puc+yWHN2UbFz6e2bxusgWf53sBJ8OslDwOcr/kfSN7qeT2ZyNUUPJayt/lc0YhgEpeSxIeLiiPJd+Dcnvo/6f1g+9NulYpRFjJAwd8XzNL6dmyJ4fGR6kOWLaxp+/JYyuHx/KwS6VQSdl7ReuGr3sltkKlfcX7Rc+jPcvPFfq7Uh6WfH8+1ryPfI2+yXtGcg6mzuVczuUvXlQqFUwmk6gQH4vFzvOuzuUHiaxY8c9J2SdFn+h7gUxlhxL0+cubKzOyssSVUg5wlEJvdDpdBsOWfD9SzpUYxWQliIgN6F68jzIhA9WA4mCHh0PJfaHncMWF5yxwAgDeJjnEjIM4Pp6cwIJfSwnx8n2BTEYw3geuUCqBAmqnwWAQbZdpvunZSoBSBkBcAZW/z0Ey/c6Z8k4DU0Bm3oqSoiwrtrxNfM7k+eTrjK8BpWuV1rc8n3z9yGMv30MeJ6XPOLEEfw4P1yUQorQvOfMbD1fNBtCU2sFZ6OQ1CZwsn0D35ftZaez4/6lUAG+TbAACTpKjKLVdHlN+HvDnc1IPue1kEODjxu/P94hSWCKNvUwMAnxPHkH9UxoPMmrI4Zo0Lnxe6Tqlc1WWczB1LudyLn/xotfrce3aNTQ1NUGlUqG/vx+vXr36sy4efS5vRzijnKwgcMWNK9P8hS8rq1yZ02g0GUnY3FoqKyKytZWew5nWlKztBoNBACn6qQSCZIs1kKnM8b6q1WpB3wwc7690Oo14PJ6RYC8DHFn55ePF+0jtJwZDUpC4lTvb+MjAjTPWkeLMgQ6J0tjLhBi8bUdHR6IIsVIbSKnkY67kHeDPpv9TP5XAHr9/Ns8JFxmI83UsjxnvH60bmZiCrx3ZyMDXDX8+by9fu/wect7Ym4SPmRIQ5GtHyZBAa0F+Hl8DvJ1yH/lc8n+nEUrw8gAyQOBzSWCEr4dsYyB7Vng7VCoV9Ho9tFotdnd3FcdMBhzy/eknBz7y2pEBmF6vFwQTSmPI140M5LKBMD53fD3JQIyMRzS/3AihZCSRz/Nscg6mzuVczuUvXrRaLVpaWnD9+nXs7u5ieXn5hFXtXM5FSWQFBMhkFeNMZ7xIOVdWKPSOhBQloqPmzGj0d66s8c/kdvFiwrLyTwBHSWEiiy8PfyOaYLIKk9JC3jai8AaOPSUyFTYpQnRv7hXhSp/MGgZAAFK+L3momzx+R0dHGYCI+iArszT+xF4nX68EVmXAlg0w0LwS1bq8bqhvHEjQ37hCy8eMPH7ck8Q/VwIHSuCFK+u0NvR6Pfb39wUI5EBHVtiTyaQiwKF1TsqqPGZKxAB83ORr+Wecvp0LB0BKoY58/vjcKP3t4ODgRHimfA1wkqVQ7qvsFSHh/ZeLaPN9TWOYm5ubsUepjfxe9I/miANBMsbw9UTt0mg0cLvdsNvtmJiYQDQaVaz9RP2i/UP/56UM5Ou4V4/PGY29PI8yyONnHs0t328UYsz7yo0p3IvJ+099ILp/biyi5/PPs4UKynIOpn6A0KBqNBpotVrxkxYF30T7+/sZNKbnci7ncrZFo9HAbDajpqYGtbW1mJyc/NBNOpc/MeEvc/o//eQ1zkj55CBCyapMlnODwSD+HRwcIBgMirou/NkkpJxw5ZeUEa4UyMCG2qbUFgIEMlBU8nJxUMivJ7AiK9AyWxa3Bsv94koZbwN9plTjSwYV/Kd8bbZwO2qzklIlt4n+KbGVcZDIFUFqu0zVzJ8rt12JklopjJCHmMpggIC+0WiE3W6H2WwWxWV5XSN57GTQI3sLeHvkZyqBF/qcryf6TAa9slB7dDqd6Dd/rhKAkIEOv55+KoWDymNHnkUKuaN9Lhs35HE8jeKbf4faSJ5vpXAz2rPy2J+2lvl6UqvVMBqNioV25efw8ZSNB3K4oywUPsfr6/E9LIN9GTTTmMm5UXJf+TzLBXn5tWq1WhiZOM0+AGFI4BECb/JKAedg6o1CG9Vms8HlcqGsrAzl5eUoLy/H9vY2vF6vmIRYLIapqSl4vd5zQHUu5/InIoWFhejo6EBlZSVsNluGNe1czuVNIoc4AZkvX1mZIuWDx/fTdaQ85ebmoqSkBM3NzSgqKoLdbkc0GsWzZ8/g8Xiwvb0tCrLye5PICgdvg5ycraQ4ZrPG02dKYC6b4sGVbiVlmvr8ppBaHgLGP8s2zkrjLiuT/Nps1/PPOMX2aQBLyZvF87JozPkcAJl5SkqAQAYL/Jk/1IJOotVqYbfb0dDQgPLycqTTafj9fgFM6L5KHgneBz4+cp+pT1xxVvLWKIGdbCCNhIBAXl4ejEYjtre3EYvFMtoi35sDaaWab7zPSuPPxy4vLw9WqxVmsxnxeBzhcBjJZBKpVErszWxKfrZ9JY8jGezJi0Iir/tsYy/3W96XyWQSXq8XZrP5BBik7yntZbnNSl5x+pznVJKhQTZQyOuNPuP5ifxvSs/ie0RpXcrjI+89LvyckA0I2eQcTGWRnJwc6PV6FBQUoKqqCtXV1XA6nTCZTCgqKkJHRwfC4TA8Hg8MBgO0Wi3C4TCMRiNSqRT8fv85oDqXHyz0YigqKoLb7cbR0RFCoRAWFxcRjUY/dPOyisFgQHFxMYqKimA2mxEKheDz+bC5uZk1jvssiVarRUVFBW7dugW32410Oo1QKIRoNHoOqM7lBwtXOgisyAouV8aBzBc6hViVlpaivr4eRUVFqKqqQlFREQoLC4XCVlJSgpmZGQwMDGBxcRHxeFwx9ElWSPjzlCzip1nPlfoFIKPtXNLptMg/UrKaZ1NusjG10U/u3ePPVPK6KfWRE2TQZ9m8H/z7pAvYbDbU1NQgPz8fy8vL8Hq9CIVCJ56h1P50OjMkivpLYYi84C1P3udjrHQeKXle+DpTAogajQYmkwklJSWoqqrCtWvXkJOTA5/Ph0QikUF0IXvxlJ7P15QSUOJrUKmtMvDnHjWtViu8ZPzeZrMZhYWFaG9vR3FxMXZ3dzE9PY25uTlFACEr2Lx/2bwuSt44ALBarairq8PFixdRXFwMp9OJYDCIcDiMeDyOSCSC7e1trK2tYWNjA+Fw+ER46mnjReuA/kYhfm/yjCqtY3nsgUyjhMFggEajwc7OjmLoHe8/tYsbRrIRRMjgX/bQ8rOI7xsOPsnzJ+dUAifPHPqMh8tmA8kAFL3Y/D5KIPFN+sA5mFIQtVotlMSLFy/is88+Q2trKxKJBIaHh7G5uQm/34+cnBw4nU5UVlYiPz8fsVgM+/v72NnZQTQaRSQSObMKGaerpU3BNzBZbrh16EMox3T4a7VasVFSqVRGeOWfg+h0OpSVleH+/fv4u7/7OxwcHOD169f4l3/5F8zNzZ3ZvtpsNty6dQv3799HTU0N+vv78fXXX6Onp+fMs+Hl5OTAYrGgtrYWV65cQX5+PrxeLyYnJ7GxsXFmx/xczpbIL22ufMmJ/WShlfN3TCYTCgoK0N3djfv376OpqQlWqxXAMXkDMXI1NDTA6/WirKwM//Zv/waPx6No6VZSbuRcCCVrNleClBQWGWhxy62s8PE2yF4l2YrMwx95KI8SMM0GxoDvlSyZ9U3+nbf9tDEisVgsaGlpwd/+7d+isbERY2Nj+OUvf4kXL15kKIoysOBt55T4HDzwPLrTwgz5/3mblRjugEzadXq2VquFzWZDc3Mzbt26hZKSEpSVlSEej2NrawtLS0uIRCKKoJtEBr1vOifl6+R1w68hoTwu3gdSujUaDaqqqnDjxg3cvHkT6XQavb292N3dFQQn8hjL8831Gj7fMqin5/Lxrq2txX/9r/8VN2/ehMPhQG5uLvb394VXKhKJYGVlBU+fPsW3336LeDwu8pbks0BpDOU1Ihtk5DHlnymBM35Pfi+NRgOLxQKDwYC9vb2sc8nbysdFaY6ztZN/Tx57zsjJwbkcwij3L9u6Py2MMlv/5LFU6me275OcgykF0ev1qK6uxtWrV/HZZ5+hsbEROzs76O3txcuXL+H3+zEwMCByLS5evIiuri7U19fjzp07SCaTSCQSGB8fx/b29ofuzgmhpMP6+nrk5ubCYDDA4XAgPz8fZrMZ6fSxdX5tbQ2hUAh+vx/BYBCBQOC9KshqtRpmsxkdHR1oa2tDWVkZNjY2MDk5icHBQYTD4R8d2nAWRa/Xw+l0oqurC/fv30dbWxsODw+xv7+P4uJirK2tnVlgYjQaUVVVhcbGRlRVVWFnZwejo6MZL8GzKCqVCnl5ebhx4wY++ugj0faJiQkMDw/D5/P9Waytc3n3wl/E/CWutH5kBU6r1aKwsBB3797FhQsX0NTUhMrKSpjNZqRSKezs7CAQCECn00Gj0aCgoAButxufffYZQqEQenp6MDExoQgCZMVRVhRlJURWxGRFjazM2UCIDFaUFKxsygj3OtG13LhHbZc9IRx0cRAht5P3k/dLDiOSr6XrcnNzUVtbi4aGBlRUVECtVmNmZgZDQ0NIJpMZYV2cYEDJY8bD43iOWbZ5kEEB/zybx4oTEVBeFJ3TnZ2daGtrQ0lJiTCo7uzswGw2Z3jJ+FjJa4n6Kv9NbjeJnAsk34vaSvegsE/KayExmUyoqKjAL37xC9y9excmkwnj4+NYWFjA7u5uBnOlDELlOVYKG5X7LK9hjUaDkpISFBYWilyjw8NDkTulVqthtVpht9sFUPn666+F54rWidK9CVgQaCQwIRse+FgrtV0G20rjr9frYbfbcfnyZeTl5WFkZAT7+/snIjKyASklj5TcHrlvfJ3L60BuO4kSyyKfm2zGHHm8TgNSsvA+Kq2bbHIOphTEYDCgrq4Oly5dQnNzM6LRKF68eIFf//rXmJycRCQSEdfabDasrKxgb28PVqsV5eXluHXrFkKhELa3txGNRrPGyL5PoRohJpMJ+fn5uHLlCq5duwadTifCS2w2m6iLsbq6isnJSXg8HpjNZuTl5SGRSAiX8/sQs9mMqqoqfPzxx/j444/R0NCA5eVlPHnyBPF4HNPT09ja2vrgY/ufldzcXFRUVODixYvo7OyE0WjE/v6+qHtERTXPWj+1Wi2sVitKS0uRn58PIHs4yFkTo9EIt9uNu3fv4vr167BYLHj9+jUGBgYwPz9/Jo0g53I2hV7i2azsHHiQsmQymZCbm4uqqirU1tbi6tWraG9vR1lZGVKpFHw+HzY2NrC5uYlAICAIKRobG1FRUYH8/Hx8/vnn0Ol0WF5eRiwWy2AJlD1A9BnlXpxmoeXf5WEz3FPFKcR5n2XLMbU7Ly8Pubm5SKePaZ0TiQQAiHpuPF8jm9KUm5uLVColEsSV2q8E9Pjc0O+kBL/pPrJ3yWg0wmw2o6SkBOXl5eJ9yUP6eagfV8zI68ZFDoGS1woHlxwQy3NFn5PyTfTThYWFIq+no6MDly9fRnNzM8rKyqDRaLC3tyfAYFFRkaCwl8eDmALp/jxPkLeZf0cOueL9Utoj/PsU2sc9jSaTCXV1dbh//z5u3ryJmpoabGxsIBAIwOv1inwp3hY+jkpgX+4niZJ3g/qxsbGBmZkZ8f6j9qZSKRgMBjidTuTn56OxsREqlQpra2sYGxsT1ONK64qeKY8DX6syOJH3BxE8cKAjMz/Sd6h9169fR35+Pra2trC6uqpovOCfySBT6Xdaq3xM5bFU8lTLa4d+53uWX8ufQe2lazQajaB8J4NBMpnMyD3jzJWJRCIjl4vmQ6fTZTBbnibnYEpBtFotnE4nDg8P8ezZM4yOjqK/vx+zs7PY2dnJ2JS7u7sYHh4Wn3366acoKSnB3bt3sbi4iM3NTWxtbX3QejU5OTkwmUxwuVyor69He3s7Ojo6UF5ejlAohJ2dHfh8PvT29mJjYwOpVArBYBA+nw+7u7soLy9HTU0NNjc3sbOzIyhR36Vyn5OTg+rqaty7dw+3b99GfX099Ho93G43bt++jYODA+j1ejx9+lQxIfdPSaxWq2CSc7lcyMnJQTgcxvr6OoLBIGKx2Jnrn0p17NmprKxEU1MTnE4ntra2MDMzg5WVlTOdL5iTk4OSkhJcvnwZHR0dKCwsRCQSQX9/P/r7+89MjpqS1fQ0UVIQzuX9Cbew8jwmWVE2GAyoqKhAXV0dPv30UzQ1NaGgoAA5OTmIRqOYnZ3F06dPMTIygq2tLaGE6XQ6VFdX45NPPsGlS5dQXV2NGzdu4KuvvsLCwoJizoOsVPIQQyWrLVeEyIgjK0QydbFS0jpZ6c1mMyorK3HhwgUUFRXBarWKfNDl5WXMzs7C6/WK8VJi/CNwQLTrsjdG7gc/e96kNCuFrxPw4Qxn8Xgc6+vrCIVCqKioEJ5CAhhcAeOsevQsJUWZt0ceYwIASooqX1PZyE8IfLrdbjQ1NaGrqwuNjY0oLi6G0WiEWq1GPB5HMBjEzs4Odnd3sba2ht3d3VPzo5Sorvnvp3l1+BwptZd7bfn8AMfGr+rqaty8eROff/45ampqkEwmsby8jPHxcQQCAcV5pxSBw8PDDJp+JSX/TSQgtN7X19fx1Vdf4eXLl9DpdEgmk8jJyUEymYROp0NtbS1u3LiB9vZ2OJ1OXLt2DZFIBKurq6I/nMiE/s/bxj3NnJExWw4PjR0HsXxt8nWk0WhQXV2N7u5uXL9+HVqtFhMTE3j9+nXGfWUgrBTeKa9PpbUorxEudB2nb5f/zkE1B2tKXmxyGuTm5oq8upKSEqjVauzs7ECv1wvQRMaZubk5eDweRVZHvpbfJOdgSkESiQRmZmYEO8zi4iLW1tYQjUZPHDSpVArb29uYmJiA0WhEU1MTysvLUV9fj+bmZkEg8CHBlMFgQFVVlfBG5eTkYGtrCwsLC/B6vcL97PV6EQwGxctjb28Ph4eHMJvNqK2txYMHD9DY2CjCoN6V0mkwGOB2u3Hjxg3cv38ftbW1SCQSGBkZEVaf69evY2lpCb29vaL2yZ+qEJgqLi6GWq3G1tYWBgYG8Pz5c/j9/jNZOFatVqO9vR137txBWVkZDg4OsLS0hIGBASwsLJzJNgPHCqLD4UBXVxc+/fRT1NTUIBgM4tmzZ3j58iUWFhayWr7fp6hUKlitVhQUFCjmrnBJp49rBVHOwFkd+z9XyTY/slKh1+tRXFyMhoYGdHR04Pr166irq4PZbMb29jZWV1cxOzuL4eFhDA0Nwe/3C88BcKxwbWxsIBaLIRKJ4JNPPhGkCJubmxmWVQ6i6DPZ8srbL+dlcEVdVpSB7ynNuXKXk5MDg8EAu92OkpISOJ1O1NbWoq2tDRUVFbDZbEKB2dnZwfr6OhYXF7G8vIypqSn4/X6sr6+L85y3/eDgALu7uyc8TTJA4YqwUjigrMzLfydFjDwOsVgMOzs7SCQS2N7eRiQSyShUK48bV4RlhTLbOpG9UErKMn0/m3GFt99oNKK8vBxNTU3o6OjAhQsXUFdXh8LCQuTk5CASiWBtbQ0TExOYmJhAMBhEPB7H8vIygsHgiefSWpBDt3meC183SoZWnpOtVOT46Ei5JhFwnE/scDhw7do1QRREHtne3l4sLCyIc4+vVfrJjQwyOCJFXSk8jF/LgTUBUJ7HTQVoNRoNFhYWsLKyAo1Gg4aGBrS1tYl3497engACvFYUX7N8/OhvVKeMwkL52Mu5Q3x8uVfn6OhIrO+amhpcuXIFZWVlAIDOzk709PRga2srA3jKBoHT6O6VhAMhpT3L92S2e/OcKr6+6B56vR5WqxXFxcVwu91wOByw2WzIy8uDy+VCaWkp1Go1wuFwBqBMp9NIJBIYGxvDb37zG3g8nox0CpqjH6pbvncwRR2hDfRjvApkLVI6lORY3/+MxGIxDA4OirjdRCIhkHM22drawtzcHEKhEFQqFSwWC/Lz82Gz2YSb8UOJxWJBe3s7bt++jc7OTrx48UJYPv1+v3Cp80RY/lIOhUKIRCL44osvkEgkEIvFEI1G34nHhJTd7u5u3LlzB62trUin0xgfH8fXX3+N6upqNDU1wWw2w2QyZbBj5eTkwGq1ilCFvb29DPftWRR6URBLVDKZhM/nQ39/P168eIGtra0P3cQTolarkZubi87OTty4cQNWqxXBYBBzc3OYmpqCz+c7kwq9Wq2GzWZDS0sLbty4gStXrkCj0eDVq1f48ssvMT4+jkgk8t6IVuhFSCE5Op1OMHyZTCaUlpaisrLyjflnR0dHiEQi8Pv92NjYEN7jeDx+ohjsubx94aQSSmCFrrFYLKirq8MvfvELdHZ2oqqqCgAQCoXw5MkT9Pb2YmxsDFtbW9je3j5Re+jo6Ai7u7sYHx9HTk4Ompqa4HK50N3dLUL9ZLp0IDMsRg534oqi0ruYe6FIceTKEf2jM8HpdKK1tRWXL18WbGcmk0l8X6fTIZ1Oo6CgABUVFeju7obX60V/fz+ePn2Kg4MDbG5uZigxp1mH6d1K/eLnDv8eAQ25oCvwPRDJycmB0WiE0+lEWVkZEokEFhYWMizfdF9SnnlBUfob9wzIugrvA+lCXIGW/04is/MpjU1OTg7MZjPKy8tx5coVYfwsLi6GwWAQ3qjV1VU8efIE33zzDRYWFgT5APB9jR15vOUwMeD7As18XWXzStA8yYo1fZ+DAw4ScnJyUFRUhM7OTnR3d6OzsxNWqxXr6+sYHR3F2NiYiIQg3ZCHugHK3k46d2WCA3lc6XceFkrFqLlHjcZIrVbD7/cjmUxicnISbW1tKC4uRnFxMcxmc0aeNy98rZSnxu9J48SBDfcgKRlR+Lqla3Nzc1FQUIC6ujo0NDQgLy8PBwcHKC0tRWlpKRYXFxGLxTKMDjIJSDZdSsmrRO3jIp+XSuuG70nZYCKPnd1uR3l5Oa5evYru7m643W4UFBQgNzcXubm5UKvVSCaTiMViUKuPWZNp3pLJJAoLCzE2Nga/3y/SWKgNPybC5r1p+dRxrVaLgoICaLVaxONxxGIxxOPxN36fDgqTySSUDgAZh0AwGBSx2P8ZOTw8FFYwmuw3CR3ktED29/exuLiImZmZH9S/dyVqtRoFBQW4du0aysvLMT8/j4cPH+L58+cIhUIZBSWzid/vx/z8fEbs+7sAiDk5OSgsLMSFCxfws5/9DN3d3dDpdHj+/Dn+8Ic/4MmTJ7BYLKiurkZlZSXGx8czXrpWqxV37txBZWUlUqkUxsfHMTs7K6y2Z000Gg2Ki4vR3NyMjo4OuFwuxGIxhEIheDweeDyet7Ke37YYDAa4XC643W6UlpaKsgAejwc7OztncqxVKpVIwv7Zz36GGzduQKPRYHx8XBgWQqHQe2Ws1Gq1sFgsKCoqErXrcnNzRe2XkpIS5OXl/SDPVCKRQCAQwOTkJJaWluDz+TAxMQGfzyesoefyboQr0HJeBv+7y+VCXV0dOjo6UFNTA71eD6/Xi6GhIXz33XcYGxvDxsaGKHDLFXSudOzt7SEejwvyiu7ubvT392N5eVkYxmSlXLa2y3kVAE7sW/5cHlpD9yGlyGg0oqamBjdv3kRJSQkuXboEh8MhvKoU3UFe9lQqBZvNBrvdjsrKSlRXV6OgoADV1dUZSj4vFiwr6NyAplKpsho7eZ+5wiozBhKzWVVVFex2O1QqlfD0Ul/z8/NhsVgAHOsbfF8p5TYptUNWdinnam9v74Siytsue+PkuST24dbWVly4cEGEMOfl5QmvRDQaxczMDH73u9+hp6cHKysr2N7ePjHvsnKrBLDpJymqSiCQ95nOVZ1OlwEQlCiqqX90Fn700Ue4c+cOLl68CIvFgt3dXbx8+RJffvklFhcXhTeFzwOJUi0sGZCcZmyVvYbyvMjjw/VAnU4Ho9GYES6r5DkjT6e8NnioHvfsEXDkbVdab0rjoVarUVpaiqqqKlgsFuGhyc/PR1lZGaxWqzCS0783AQoOfOmZ/D1K/SEARUCW1s6bhLyW9DutJ61WC7PZjFu3bqGzsxNXr15FXV2dCCWm8dvf3xfM2gaDQeiv5IFLJpOiGLoSoKU+yHMtyzsHU4QEXS4XHA4HHA4H3G43DAYDdnd3EQqFsLu7+8b7aDQa5OfnIz8/X6BNAOIgWl1dxYsXL96a8vljlQ8in7Db7WKitra2EAwGP6hySXHrFRUVODw8RF9fH8bHx0Vu1JtEpVIJ0gqKD6awh7fdTqKf/eSTT9DR0QGz2QyPx4NXr16hv78fKysrUKlU2NjYEIcoHSYWiwVutxs3b95Ee3s7kskkrFarCCk5K3kwXDQaDSoqKlBbWytCMAKBgLC2xWKxM6cEq1QqOBwOcXCZzWYAwPb2tgjROWtCycstLS24e/curly5ApfLhUAggJcvX6Kvr094aN+XEKNmR0eHYAgrKiqCwWCA1WpFVVUVbDbbibwVJaEXbjQaRWlpKdbX17GxsYGKigqMjIxgZGREMF2dy9sXUh6UFCZ6Ket0OtTX16OzsxMFBQXQ6/U4OjrCxsYG5ufnMTMzA5/Pl8H2RZZ/mjd6Bilp5NkoKCiA0WiETqc7FThzLwZXYGXQQgxwSnkJwPHa1Wg0sNvt4p3e2tqKjz76SBgE/H6/KC4cDAYRDAaxuLgoco7pXd7R0YGqqipUVVWhpaUFyWQSKysr2N3dhc/ny7D8y94MGnsgk/SB2p5N6eXzQonqRUVFqK+vh81mw97eHpaXlxEKhYSXIy8vDyUlJSLShACtDGxkkZ8vg8P9/X2Ry0ZKI++z0v04mNXpdCgsLERBQQFaWlpw+/ZttLa2orKyEna7XYTsh0IhzM3N4eXLl3jy5Ikw8r4JSHBvFD2X2kZjwz0X5BniAJwLGT+VWAzpJ9X1Kisrw4ULF3Dnzh3cvn0bVqsV4XBY1FkbHh7OqI8kj5OsDPPnUQhXtrOV72XZGEH/V8rdy8nJEUYCYvyj5/A+KoFu+j5/Fv1LJpMZbaWzgJN8yHl2cn+AY0NoQ0MDSktLYTAYRF9oHblcLuzs7GR4xU8bGwJ4er0eOTk5GWcJAScgE6DTmcZrrHFvLn8GB4V83avVatjtdpGTdvfuXZSUlMBgMODw8BCBQEAQwIXDYezv78PlcsHlciE/P1+Mfzgcxvz8/AmDO++fvE6zyTsDU/QysNlsKCsrQ3d3Nzo6OlBXV4fq6moYDAbs7OyI+FMlOTo6wv7+vrDSFRQUCKWeJjuVSmFjY0OESGxubr6rLmUVlUol+lhRUQGDwSBiqz90PSTaZFqtFsFg8EdZ4AkINzc348aNG1Cr1VhcXMTq6qpIin5bQknZt27dwi9+8QvY7Xasra1haGhIFKgkRYMYfMiqlZOTI0Ibrl+/jvb2dhF3HwgEMD09febAFHlKmpqaUFdXB41Gg62tLUxOTuK77747s3lHGo0GlZWV+OKLL9Da2gqtVito9JeXl88khTvlDH766af4xS9+gfLycoTDYYyOjuLx48cYGRn5QRaytyUUjtrV1YX/9t/+G1pbW2G32zNCFyhG/sfes6mpCfX19djf30dXVxeePXuG/f19zM3NYXt7+8yB8z8HIeVMjpYgoTM4Ly9PePZVKhVisRhevXqF0dHRDI8uKafc6qykCEajUWxvb2NnZ+dEbSAlS7WccyN7afizlNYJD2kjptUHDx4Imm2j0YidnR08efIEPT09WFhYQCQSEWfx3t4eUqmUIAXQ6/V49OgRLl26hH/8x39EZWUlWltb8dd//dew2+341a9+lRGCJjN8yf2QrfHcwk99lxVYOhvu3buHhoYGfPvttyLUkgxDWq0Wbrcbra2tKCoqgkqlymDq5ec0V+J5u5T2MoEKAh+y94qvH3ne6DqbzYbu7m48ePAgg1Zfq9Xi4OAAW1tb8Pv9ePjwIXp6ejA1NSVypLIp3vy59JNSHng7yIhAuTTy+PL7cm+WPDayN0Cr1aK4uBh37tzBz372M7S0tMDpdCIej6Ovrw9/+MMfMDk5KXQAAiHy2uBAW54b/nelfstzwOdQBi/UZ41GI871jz76SEQ/UK4dMW6Sks7XJD0jmydWniO+xuXxznYGEOgpKSlBfn6+MPrv7+8jHo+jtLQUV65cgVqtxurqaoaxWh4/vicJUJO+K4+9EhghECXvR6X+0v3Jy0RArLa2Fnfu3MGVK1dQXl4OtVqNSCQCj8eDR48eYXR0FKurq8Lw8w//8A8oKSkR79l4PI7BwUGMjY1lsG5zsKwEmLPJOwNTubm5KCoqEjWYLl68iPLycthsNpjNZjGxFovlBOCgDZZIJLC4uIhQKISDgwPEYrEMqxUBlnA4jJWVFQSDwXfVnVOFwNTVq1fFYUv9+NBCcfaLi4swm82oqanB/Pw8/H7/qd9TqVQwm81oaWnBrVu30N7ejtnZWTx79ky4RN+m5OXlobOzEy0tLcjLywMALCws4Pnz51haWsp4qfLcLo1GA5vNho6ODty7d0+AWQAZtOJnTYhCtb29HbW1tTg8PMT09DRevnyJubm5M1nwmYrcFhcXo7a2Fna7Hbu7u+jr68OLFy8wNzd3JsGUw+HAJ598glu3bqG4uBj7+/sYGhrCb37zG8zOzr53wgliZWttbUVdXR2cTif0ev0ffT+u9Oh0OvGMyspKYZn+8ssv8erVqzPp7fxzENmLIysOBwcH8Pv92N7eFi9sCi+TczpkBUaWRCKBlZUV/Pa3v0V5eTl2dnYwPz8vQtKUgBQJD7mh966Sh4Csxfzv9F2r1Yq2tjZcuXIFn3zyCTQaDTweD2ZmZkQNRqJ2TyQSJxRsbvUlhdjpdOLevXuor69HY2OjKMlBeUn0fPLWkdLGQSD9n3sJlcAoHwubzYbq6mro9XqMjo5iZGRE5Hym08dhQTabDaWlpWhraxN5L1wv4eCGv/v5e4crnSqVSjDingaAlfrH2240GtHR0YFr167hwoULqKyshNFoBAAEAgEsLCxgYmIC09PT6Ovrg8fjwe7u7ol3t7xelYQAFQlfs9wwK3tU+OfA93W1snmlKK/14sWLuHz5MlpbW+FwOHB4eIj19XXMzs5ifHwcy8vLwlvDx1tmZOPrnYeKye2SlWaeI6Y0H3wM6PsFBQX46KOP8NFHH+HixYtwOp3Y29vD0NAQ5ufnxdkre645cFKaZ75f6LlK4XvyWpLvpdFokJeXh8bGRthsNuzv7yMQCGBtbQ3Pnj1DIpGAwWDA7du3sbOzg7m5OWHk2d/fF0yPe3t7wgNOuhgPSZT7xj1Y/G/Z8tXksSbASc+gMMHCwkLcunULVVVV0Gg0iEQigrhrcnJS6LkOhwMmkwkmk0lEjqVSKaytreHp06fo6+sT/eHvUvnnm/bIWwdTFFJTU1ODa9euibCroqIi7O/vY21tTdC97u/vQ6fTCSsKDRb9Ho/Hsbi4iGAwKGLB4/G4CCmiZD7iiX/fYSw0qXa7HTU1NWhubkZeXh5isZhgu/vQCvHR0RG2trYwODiIS5cu4fLlyxgZGcHKykpWQKRSHdchoPonJSUl2NzcRE9PD/r6+k7Qw/9nRKU6JuuoqanBrVu30NzcjFQqhZmZGfG8jY0NRQYgUuxbWlrw4MEDXL58Wbhwz7JQ/kRXVxdaWlpgtVrh8Xjw4sUL9PT0YH19/UwwynFRqY7j+wkEuFwu6HQ6hMNh9Pf3Y3Bw8AeHjr4PIWBBSe6ffvopWlpaoFarMTk5iWfPnuH58+cIBALvtV1EsHLlyhVcvnwZTqdTAKC3KRTeW1tbC5VKJYpvLywsnMlQzD9l4R4QrhjRZ6Tk+Xw+rK2tifAqrVaL6upqeDweLC4uCsXk8PBQeHPIesvP21QqBb/fj0ePHsFkMiEejyMSiWTNGzpNIaS/c4ss8D3xBFcoqUg9hdZcunQJBQUFWF1dxfDwMJ48eQKPxwOfz3eCPEMeK+D43ZRMJrG2toaXL1+itrYWjY2NcLlcKCoqgsViQTQaFWdhNu+HbMmXPQhKVn66zmw2Izc3F8vLy1hYWBCGQgIPJpMJ1dXVaG9vR3l5ObRaLQKBgCCbomfIBAr8OfQZV9CzAV553LO1ndIempqacOHCBVRUVMBkMuHw8BDhcBiTk5N4+vQphoaGMDY2Jhh7ZeVdvreScICqpOwr9ZVfz+ebK6xKa1On08HpdKKjowOdnZ3Iz89HTk4Otre3RakZKudCijUZJOSx5M/le1Gp7el0OsPTpNRHfi3/v1qtFrU6P/nkE1y+fBkOhwPpdBpra2v49ttvMT09LcL9+FrJNt5K/5fzkOTreB6a3H7KqysqKoLT6YRGo0E0GkV/f7+I0FCpVGhqakJjYyMqKyvR2dmJSCQCvV6P3d1drK6uYm1tDXNzc0LflnPV+J5TqVTCmymDSOqPvG85uJLPCvo/MVYWFhairKwMJpNJsFG/fv0az58/F57jdDoNl8slCL4oPHpnZwcrKytYXV2Fx+PJIF7hrJJyeOJp8tbBlFarRWlpKW7cuIF/+Id/QH19PYxGI+LxOKampvD06VNMTU3B6/UiEonAbDZDr9dnIF0KByCgJIc7cCYdGvgPYW2l0A0KrbHZbFCr1fD5fBgaGvogIYeypNNpwQDV2NiIrq4ulJWVYXx8/ER1ceB4MVExScqLWV9fx8DAAIaGhkSC9NsSCtHr7OzE5cuXUVFRgUAggP/4j//Ad999h+Xl5RMEHuTVLCsrw40bN0SOlcPhEAmV8vVnRTgoefDgAdxuN6LRKIaHh9Hb24upqakPSliSTdTq48ruHR0duHTpEoxGI5LJJLa2trC4uIiNjY0z4/Gg9WG323H37l189tlnaG5uhslkgtfrRW9vL4aHh7G1tfVe62GRNbmsrAzXrl1DW1ubONxlUbLqytZ3rhTSP/4s4NgDWlZWho6ODszPz8Pn852Dqbcs3HrM54QULQAiP2pmZgY7OzsoLCyETqdDQ0MDTCYTiouL4fV6Rej7ysoKwuEwIpEI9vb2Muid6b14cHAgWP+yhctw5YBfw/cqV1h4+BH1gxQinU6HpqYmXLlyBXfu3IHNZsPi4iKeP3+Or776CsFgMCP0TQkIyHJ0dIREIiFAWDweh91uh8ViyUjep7ZzFjU+9hyM8LAz/k+eM61Wi5ycHASDQWxubmJ5eVmAWFI+6+rq8POf/xyXLl0SyfmvXr1CX18fVlZWhF6STRGksXwTsOTAADipOFMfycPS3NyMmzdv4qOPPkJDQwPMZjNSqRS8Xi+ePn2K58+f4/Xr1yKNQqa5J5E9MvIzuXdSBgEyyxwZhbhiSvJDvD4q1XE0jMvlQmVlJcrKyqDT6RCPx4Wi/PLlSxGuTGNF12TL8aNnyvPDheet8bbzvZFt7sj4XFZWhpKSEuH9WF5exvPnzzE1NYXV1dUTzJw0frJnSg4JzeZV5X3hJDJKc0xnTVdXF4qLixGLxTA6OopvvvlGeGPJc0cRTB0dHVCrj1k6t7e3Rb1Vj8eT0W6l9cMBSbbrZM9VtnUoGydMJhOqqqpQU1MDq9UKtVotHAaDg4NYX18XGMJoNKK+vl6E6KrVauzt7eHly5fo6ekRJF88LFGn0wmHDn/PvkneKpiiYrd3797FL37xCzQ1NSEvLw/xeBxLS0uYnp6G1+sV4QiUbEoUvrTIPnSe0Q8RlUolchT+8R//EdevX4fRaITP58OzZ8/wm9/8BktLS2dCwUylUiJet7CwEBcvXsTy8jJGRkZOhGXZbDY0NzejubkZVqsVAwMDmJmZEWENbzO8j0L06OVcXFwsrOhTU1MiB0ceQ6PRiMrKSty8eROfffYZ2tvbodFosLm5icLCQkGKQHKW1pLBYEB9fT26u7vR3d0Nh8OBqakpjI6OYmlpKaOK+1kSYovq6OhAa2sr9Ho9Zmdn0dvbi7m5uQzK1w8pdPgTucODBw/Q1dUFi8UiwhkePnyIqakp4e1+X0Jgqri4WLx0T6M9p7hyMiiR7OzsYHV1FfF4HHq9XjCn5eXlCZYkEirY3dbWhsXFRfT19Z1Juv0/ZVF62coKEoVbb2xsYGVlRdTLczgcsNvtKC0thd/vx87ODvx+v8hxW1lZwdLSEvx+vwDBstU0G5Ci3+XcDiXhoIMredxqbzAYUFlZiStXrqC0tBRra2vo7+/Hq1evMDU1JTw6pBAqgQelMTo8PEQkEhH07na7Hen0MalQbm4uwuFw1vGWf5f7KHsLZdna2hKeDl6vMCcnR3hIurq6UFdXB5VKBa/Xi8XFRaysrGBzc/OEgs6fRXmPSqFn9Ptp88G/w8GiXq9HbW0t7t27h6amJlgsFqhUKkGo89vf/hZDQ0MIBoPi2QTq+HlDIYfEHimPHQemSmuHACuXbKFmSt4S+f85OTkCSLlcLpjNZqjValGTbGxsTHhFCExRBJNsHKB1zPvPha8VPnenGa743MqeIZpvo9EoGBoXFhZE7iAHtPLz6P8ykKPxlD9TWjey90Rum16vR2NjI5qammA0GuH1evHVV1/h4cOHIt8SADweD/Lz83H79m2RkkPs2Wq1GtFoFIlEIiOSTB4Xmks5jE/ep/I4y+NKY8+BFhn7y8vL4XK5oNfrxdhFIhFsbGwIg47BYEBJSQnKyspQU1MDm82GVCqFzc1NDA4OYnh4WETT8Odzpj95HE/TF94qmKKcirq6OhG+RBabg4MD6PV6VFRUoKSkBPF4HFtbW/D5fKI4IYXrUZ0UJZfrWRFKkuzo6MDt27dRW1uLcDiMqakpPH/+HK9evTozSgsdOJubmwgEAqivr0dbW5ug0uUHHZGG0Iujt7dXWOze9lxYLBY0NjbiypUraG9vFxaQpaUlrK+vK1Zj12q1cLlcuHz5Mm7duiWsJzMzM9jc3MTly5dhMpkEWH/fCvNpQiEM3d3duHr1KsrKypBMJrG6uorp6ekPlvP3QyQ/Px9tbW0iDOfo6Aizs7N49eqVyI04C0IJthcuXMDdu3fR0dGBoqIiUb+GkuJJgU2n04Lxh86ddyUqlUqEBOfl5Yl6e7Ls7++L2m6RSAThcDhDkd7a2sLs7Cz29vaE56m5uVkQWcj5VxqNRtCv/2dys85FWeiM4h4dCl3nigG922ZmZlBUVCQKwxKTlsViES/1jz/+GMlkEn6/H0+ePMGTJ0+wuLgoLK7AsYJ6cHAgQm7kc457KwkQcbY1EiXLOHmBqD1arRb5+fmCdVKlUmFzcxO9vb1YWVnJYOeSgRjJmxRCqo1GkSdarTbjuxygcYZDrgjRPzn/QfYC0Xfj8XjGe4IUQZvNhs7OTty5c0fQSMdiMWxsbIi8I17YlHtt+HMoFIo+lxVzHr6kBELo/3SNTqeDy+XCxYsX0dnZKUon7O3tYWRkBP/rf/0vwXKntCY0Gg10Oh0sFgvy8vJweHgoSEzk/DY+blqtVljq5XHkBA9v0tmUlGe6v9lsRmtrK65evYqKigoRZRIKhbCwsCCMuTRHSuPEf9LvlKsm70c5x5EDHHlO5fby+SMvJu0ZlUoliNHIE809g3wd0//5epX3psysKY+fDAy5kAGvpKQEbW1tqK+vBwB4vV7MzMwINkkAgowiGAwiFAoJpks6R6jEAeVzy0Ybeb/RegOgOPbUd76+5POC72e6lmqpFRQUiHcovffLy8sRCoWQSCTgdDpx5coVdHV1ob6+HjqdDtFoFIuLi1hYWMD8/LwoSUBrjYAUzddpDImyvFUwRS77UCiEzc1NaLVaUSyurq4O5eXlGR6o/f19RKNRbG5uYnV1FX6/H16vFyMjI6JmVDQafWPB3PcplCeVn5+P9vZ2XL9+HXa7HbFYDMvLy3j69Clev359JvKlSOil/OLFC1itVsHLPzo6ir29PcGmmE4fhwSOjo6KGN/t7e13AqQovO+LL75Ad3e3UNDn5+fR09MDr9ebAYRo3B0OBy5duoRf/OIXaG1tBQD09/fjq6++Ep6p8vJyHB0d016Gw+EzQwntcDjQ0dGBn/zkJ7h8+TIODw8xMjKCFy9eYGpqCtvb2x+6iYqSk5OD2tpa/OxnPxOHcTKZFN7ms+JN0+v1cDqd6Orqwqeffopr164JbydwfLA7HA7cu3dP1PxJpVKYnZ1FX18fBgYGsL6+/s7yvtRqNcrLy3Hz5s0MohoSWuuhUAjffvstRkdHsbCwAJ/PJ9gzSSEj0EdJxffv34fNZkNubq4iYFKy2p/L2xN5fDnAohdxKpXCysoKfv3rX2N7e1t4XHQ6HVKpFPR6vfAwGo1G2Gw2WK1WFBQU4Pbt2/D5fIL0gIqnr6+vi7IRMsOq7CUgynNS+JSUTwAZif0kBoMBdrtdeNOIfph7pXmCv1L4uJLiC0DUniwtLYXVakU6fcz8t7a2hmg0qmidp3twYCWDAB6GJoM7nstGYVBHR0cwGo0oLS3FvXv3BAECldgIh8OC4Yy8TlwhlAHTm7wxSmNBeTWyEkeflZWVCQZF8kTTWL18+RLj4+OCNICPBymjGo0GdXV1uH//Pmpra3FwcIBAIIChoSGR90pnCw/H0+l0SCQSioCYg9PTzpdsIFGv16OwsBB1dXVobGxEXV2dyP9KpVKYmJjA119/LYrzyl4mahMnYaD9BkDQdsueBvl3OURQ9q7KYdQyGKJIAr62tFqtKCXA60VxUE1gTx5DurfBYBD1QLMBRg5y+RzRmrl//z6uXLmCvLw8eDwePHv2DMvLy6K95EWifE2KZKK0m8nJSYyOjgoSE3pOtn1N7SHCGJoLJYMBGb5pjOU1xPcsnWFUWJsMLhQ5U1VVBa/XC5vNhqqqKnz++edobGyExWLB3t4eJiYm8Nvf/lboW3LxZdm7LgPf09b3WwVTqVQK29vbePbsGQ4ODkQBLZ1OB5PJBIPBAJ1OB41GA4PBAJPJBKPRCKvVitLSUmG56+7uxtbWFjY3N/HixQuBhj+0l4osy5WVleju7sbNmzfR1tYmktr/8Ic/4MWLF1hbW3uvuRhvkqOjI8RiMczNzQmmnJKSEtTX1wsFnqwSyWRSJOXzmNG3KXSAulwutLa2CoWXcs2IGIAOJa1WC5PJJEJI7927h/b2dgDAxMQE/vCHP+DZs2ei/geJwWAQtRQ+pJDlo7OzEz/72c/Q3t4Oi8UCv9+PZ8+e4eXLl0JJOmtCnsDGxkY0NzejoKBAGA4oV+p90opnE5XqmFHz3r17uHXrlogNJ4thTk4OSktLcfXqVRwcHKCgoABFRUU4PDwU8flqtVrUnHoXc6FWH9ercbvdMJlMitccHh4iGAyit7cXAwMD2NzcxN7eHmKxGBKJhKB5dbvdQrGtqKhAR0eHyMOR1zvVvAsEAmdyjf25iJK1lj6nn5FIBNPT04KBz2azIScnB7FYDA6HA/X19SI0xel0Ijc3F06nE06nE/X19UL5SSaT2N3dhcfjgd1ux69//WscHByIXGIZVCkpglxpU/Kq0E9SLCiEyWg0IhaLIRqNIh6Pi+gGpdwOeWxkZZzCdqqqqlBZWQmr1YrDw0PEYrGMXDESAoNKFnh57E8zIFC4XF5enrC+q1QqwbB6//59NDU1icLDRIRBRmC73Q4ACIfDoh/JZPLU/SX3Xenv3Dshe4l0Oh3sdjva29tRWVkJg8GAVCol1tTy8jIikciJ+ZfnksgSLly4AIvFgv39fdTX1+Pg4EB452nsaFy40imvGxpP3g8lz6eSEM1+dXU17t69i7a2NhQXF8NsNiOdPiYcW15exszMjCD8oPWYDVgojTcHvErtl71CSt4I/jw5HyuRSIhoKgI3BMw5iQOnD5fbLwMMEvL6KuUh0u9Kij7t2ZqaGty4cUMQqASDQXi93hOeSLqXyWRCQUEBdDqdOLMGBgYwODiI7e3tjDUuj49sTKKyRjJFOgfAfCw5QFc6M6iNeXl5sFgs4rtGoxF1dXW4dOkStra2UFRUhJqaGrS1tQnG8NXVVYyNjaGnpwerq6vCWK/EhEjP4TmPbzLKv3XPVDgcRm9vL+bn51FbWyuspTabLSOp1GKxiJwBg8EgqosXFBSgo6MDOTk58Pl8MJvN6OnpES+gD6m4abVa1NfX4969e/jpT3+Kmpoa5OTkYGlpCQ8fPsRvf/tbLC8vv/UaTG9DDg8PEQqFMD8/j0gkgsrKSly6dAm7u7tQq9UipGhvb++djzG9QPPz88XBGYvFMDs7i6GhIYyPjyOZTAo2paKiIpSVlaGurg5/+7d/i4sXL+Lg4ABjY2N4/Pgxnjx5gqWlJVRWVmYc7haLBVar9dS8lHct3M1+69YtPHjwAIWFhdja2sLY2BieP3+OiYmJMxMmJwsV+WtubhYEH6urq2JPUnmCDynknblw4QK++OILtLW1oaioSIQYkFW3uLgY+fn5wtsdjUYFYU5hYSFWV1exvr5+4oXxNoUUuGwMfty6mZubi4qKCuj1euGl1+l0QhEqKytDZWUlGhsbRdig0lqnvb+5uXkOpt6BEJEBpwkGThJT0GdksAqHwxmJ4zqdDnl5ebDZbKitrcXPf/5zEVar0+mEYYCUVavVivz8fFitVkTXHDHjAACq60lEQVQiEYyPj4s9yT1jlBsDICPXgdql1WoV80K40mM0GlFQUACr1Qq9Xi8iL+Q6Sz/EcCUrKw6HA21tbairq4PBYMDu7q5gPOQKDM8Ro+fwvvF+ce+UTL7AARzlf9G+LCoqQmlpKcrLy0Xuy9bWlqjNVFtbi9zcXHR3d2NlZQVDQ0PCIBkIBEQR2WzWbNl7QB6xbACIxGAwwO124+rVq8IASYrk9vY2pqensbi4+MZ884ODA6yvr2NhYQFXr14VpFkAcP36dUxMTAi2Xq7I8rwpeex5WCjvJx8DTsRC/SOjalVVFT755BPcvn0bTqcThYWFoqg1RS/t7u5mFNmVQ8p4u/gaIMODHNpK7wT6/TRFmYfK8r7RZ4eHh9jd3RX1pI6OjmCxWPD555+jqakJfX19GBoaQn9/vyhsz8cGwIk9mG0t0HqmsLRsBgPyaFVWVuL69etoa2uD1WoVzJ+UdkP7jMB6YWEhbt++jc7OTuh0OsRiMXg8HvT39wsmWHoehQ3L7eVt5pFo1HYZXMmlDGThZ4pWq4XVaoXNZhPribxV+fn5uHHjBpqbm2E2m2GxWDKY/qj2FKUVcQ8k3YufLdQPpXlXkrfO5kcuu83NTUSjUfGCJ3cnIT0q3EfKpsPhgNVqhdvtxoMHD9DQ0AC3241/+Id/QHFxMb788kvBOvIhRK0+pnz/6KOP8E//9E8oKSmBSqXCwsIC/s//+T94+vQpFhYWMuohnTUhRS2dTsPtdouEvMPDQ4yNjb23dpC3iUJZ9Ho9Njc3MTMzA4/HIzZXfn4+ampqcPfuXVy+fBk1NTUoLS3F4eEhZmdn8fTpUzx8+BB+v/9Ua+WH9EyRm/1v/uZvcOvWLRQUFCCZTGJiYgL/7//9P8zMzJzZ2j9EENLe3o6GhgZotVrEYjFMTk7il7/8JcbGxjKStj+UWCwW3Lx5E5988gkuXrwoChLylyvlbW5tbWFqakqEK9jtdtTV1aG4uBg2m018910InXs6nS4rwNdoNKiqqsL/+B//Q4TfErvQ/v4+NBoNLBYLnE4njEYjjEYjTCbTG9vMqarP5e0K5RaQYkKidB6R0klKl/yC9nq9gglMpVJhY2MDHR0dsNlsSKfTMBqNgpiBaqcYjUb8/Oc/h8FgEO9d+TyRlS4eykLMnGTQyWbl1uv1yM3NFddQNAPPu32TwiGPG+XNXr9+HaWlpTg6OsL6+rqwHCvVMKLnAMjIA5OvU7JoE3jMy8vD7du3cffuXeHBtlqtgl04JycHW1tbAngsLS2hoKAA7e3t6OzshEp1nFNcX18Pr9crcn1DoZAgh6Hx5F4m2Sqfm5uLVColIiqUlFIKz6uoqEB9fX2Gx31vbw/Ly8uYm5tDMBg8VfHjij+1tbS0VAD1wsJCAa54O2UQIivF3GqvpFgrzQH1X61Wi1y8kpIS2Gw2kRpC6ysSiYi1wEEMASJa23Jf6ScPa+UgjJRoOUyU34P0B76+lby3e3t78Pl8ePXqFex2O1pbW2Gz2VBfXy+MHkajUXgQt7e3hQeQSIay3ZsMy/R8ACKPiuftyZ4tvV6P8vJylJaWwmazQavVIplMwmQyoaysDGtrawCAeDwuHBoVFRXCcJpOp+Hz+TA6OorZ2dmMou9Kxgp53Kg9BIJJiDGP5x3KRhIufG5JN5S9dBRKWVxcDJfLleFRWllZQX9/P7799ltRO4t7F+k6ef5pfGUDSDZ5Z0V7U6lUhjKQn5+fgYp5wyn5MDc3Fy6XC5FIBM3NzaKA382bNzMKhykxvL1LIcW+qakJ7e3tqKqqQiqVwvj4OB4+fIinT59iZmbmB+VJyZvlfQotYmL4ohpfiUQig1HxXUs6fVzJemdnB6FQCIWFhVCrj2t+uN1useHdbjcaGxtx6dIl4eX0+XyYmppCf38/ent7RVL2m573IYSo82tqanDx4kVUVFTg4OAAw8PDePz4Mfr7+0XRyrMmZDFub2/HpUuXUF1djXQ6jcXFRQwODopciQ/dRgoRuXnzJi5evIiCgoKMxHVKsJ6YmMDKygo8Hg+WlpYQCAQQj8eRl5eHGzdu4N69eygqKoLb7UZ/f/87aS+9dLe2thQBEL2grFYrWltbTyTBUsw9Twz+oc+Nx+NnFrT/qQu3vJKcZmV9E0kD5bE8fPgQs7OzmJ6eFp4SvV6Pjo4OlJSUCA8nJe6HQiGR9yIXtaXfs7VNDrWR+0HRColEAolEAvv7+4jFYsLiLz8j2zjRGieSB6pFWV1dLZj7ent7MTY2JvI2fsj4yx4o3h5ZiTYajaioqMD169dx8eJFmEwmaLVa4WGksPiJiQl88803gmSHcnHv37+P4uJiOBwONDY2Yn9/H1tbWxgfH8fY2JiojUi1nYgoBDhZ+4os/FzZVxpDAoAOh0PkRB4cHCAYDGJpaQnz8/MZIYfZDIvUt9XVVczPz6O6ulqEHNP4UdiTvB6UzhsOSrIBKaU5oM8PDg6EV4fSP8jQlEwmEQ6Hsbu7m9VzpwTifgygp3VzmmdE6cxU8k5tbW2hr68PdrtdeD7VarUgnSotLUV/fz9evnyJoaEhURuO90kJFCrR6gOZ5Aiy0GcOhwP5+fkCXBgMBrS0tIixn56exu7uLkpKSlBTUyP0LYvFgng8jpGREfT09CAQCJw4F/hz+GdyO+R5p7GmPDI5J/JNc7e3tycYHOV7016i/8fjcfT29uKbb77B/Pw8Njc3BRDjNbJ+yHvxvYIpIgmgRUGLgPIuiK1H9iIcHByImhpkDRoYGMDVq1dRXFyMuro6aDQarKysYH5+XsQuvw+hcLHGxkY8ePAAtbW1IoZ6ZGQEjx49QiAQEEms8suFJCcnB3q9HhaLBalUSrit34eiTwuXKrmTdZFoItfW1sTB/z7k6OgI0WgUGxsbWFxchMvlgtFoRGtrK8xmMy5dugSXy4Xq6mqRZEjhCU+fPkVPTw8GBgZErbIfCmDfp1AoSXV1NS5evIiqqirodDqsr6/jm2++wbfffoulpaUzkW+kJFqtFlVVVbh79y4uXbqEoqIiBINBvH79Gn19fR+cdILCEiorK3Ht2jVcv34dtbW1wttNL5+9vT3Mz8/j3/7t3/Dy5Uvh+SRLO1njkskkCgsLUVxc/E6K6ALf58ysrq4iPz8/a94UWayzKQc/dj0ToHzfdbX+UoS/z8iqT4qC0rzJihhXIuhzIg2KxWLY2toSHgO1Wo3h4WFcvnwZ9+/fh9PpRE5ODqxWKwoLC2EwGIRXVgZspPjyUCvaI/R3HjbDzyaj0Si8NhRt8iZvqBwuQ5/pdDqUl5fjxo0buH//PioqKgAAk5OTePnyJR4+fIiZmZmMtcqVL34/smzznBiZlpmPu0ajEXXeGhsbxfiRzhKPx+HxeDAxMYF///d/x9jYmKhFs729DZVKhUgkgo8//hglJSUwmUyCJMRut6OtrQ337t1DIBBAMBjExsYGpqensb6+jo2NDYRCoRNeB+5hkb0l3ANAxk+1Wo2DgwNEIhEMDQ3h9evX2NzcFPPFx1peWxT2RnToRHNN4FgmH5E9QPz+5O2XiTh4OKCsp/H+Uft2dnawvb0tgCXNayAQwODgYEaNph+y1mSRw/Tod84EyY0hfB44E+CbcgL39vZEgeSenh7B2trS0oKKigqUlpbiwYMHaGpqwuvXrzE6Oor5+Xmsr69neGHls0E+L2h8+BxT//m6SaVSWF9fF2FtRqMRer1evOMcDofQ+3Q6HYxGI4qKilBUVCR0WTLcGY3GN7JTZzPSyCCJjAuceIPPezaPMl1Humxubm7G+NAc0jPJKDU3N4exsbGMgty01k7DEdS+H2rsfqtgivJb0uljVrhwOIyDgwMxqZyFKtvipxeD1WpFbm6uCAekcJb3TShgMBjQ2NiIjz76CB9//DHcbrewDhcXF4saFGtra5idnUU4HBY5Gfwwp0Jj165dw8bGhqj3QgXu3qVoNBqRi/bxxx/D4XAIBYAzubxPIVraUCiEvb095OXloba2FiUlJTg4OIDBYIDFYkFOTg48Hg/m5+cxOTkpmO/IsyBvXiULzofwTBFl9e3bt/HTn/4U5eXl8Pv96OvrEy+Is8RSyYVKHFRWVqK1tRX5+fmIxWKYmZnBq1evMDk5+cELC5NXqru7G3/zN3+DmpoaGAwGAJmHtsfjwcjICEZGRkTokNFohNPpRHV1Ndra2nDz5k04HA4MDg6KcgHvQkh54oYUpbNMKazgjznz+Is3GAy+M2KNv3SRWfzI6isDJfr7acKvpxA6UuJVquPwaCIlaWtrE1ZnCqWXvQRcueVhLVwB4RZaIDM/hoTOBMojkvOZlPqhtGYNBgOcTieuX7+Ozz//HLW1tYjH41hcXERvby++++47rK2tnaBf5iCJs/RxwgA+H3zM5Xkwm82oqqqC1WoVSheRd6ytraGvrw9Pnz7FixcvBB13On0cjjU8PCwKJlO0gcPhgNlsRmFhIfLz8+F2u5FIJIThxOFwYHR0VAAy3j4aQ/4+lpV+7sEhY208HkcwGMTIyAhGR0cRDAZP7G0CONmUf2LLI/3M4/GIqB957ihHh9ZTKpUSwFwmH8nmHZPPO963WCwm9gwRkKyuruLFixeCep8LBztc5LBQILMgLz2Pj4Hs5eJrSAbp3Hskz1MqlcLW1pbIX7TZbCgrK4PP50N3dzeqq6vhcDjQ2toKp9MJt9uNhYUFPH/+HCsrKwgEAoqMgxTKSuFoSp5tpXOG6ruOj4/DYrGgoaEBBQUFIq0mLy9PGE/ouXq9XhBPUC4yeUNlAw2tMSVjkDxXZMjgXiGediL3Q1439CyKrjIajaL+FX2Xe1aBY6/UysqKAJSkL8prR16v3DDG0wX4WakkbxVMlZSU4PPPP4fBYMD29jb8fj+SyaRw155Gc05MV8QyV1JSgoqKCuTl5SEUCmFychJra2vvNcTPaDSiqqoKt2/fxv3799HS0gKz2SySwxsbGwVt6sbGBmZnZ0UtJ7IEk8ctPz8fjY2N+PTTTzE2NobJyUlEIpF33gcienC73XA6ndjf38fs7KxINvxQkk4f14949eoVcnNz0dHRgbq6OjgcDiSTSWxtbcHj8cDv92N4eBhjY2OYnZ3F4uIitra2FNcA1RIKBoMiQRF4/54plUolFIbbt2+jra0NGo0GPT09ePToEWZmZkRIxlkUi8WCzs5OXLlyBXV1dcjNzcXi4iJevnwpEjg/dNvJwu12u9He3i5qo3GhQ9JqteLChQtwu93QaDQwm83C89nY2Ai32410Oo3Z2VmMjY29U6B4cHCQUSvoXUssFsPKygrm5uawtrZ2Zj2hf8oie59ksMKvUfJOyd+V78uV26OjI/j9fiwuLmJzcxM1NTXCk8rpjfmz5Odxema6pww8lLwQAITitbm5mZFjxdvP+8WVXbVajcLCQnR1daG7uxslJSXY29vDwMAAnj17hsnJScWahrzvb1K2sp31NBcqlQrb29vY3NzE1tYW9Ho9tre3EQwGsbOzg+fPn6Ovrw/Ly8sZQIqeT+F/xLhZXV2Na9euob6+XuTF6PV6odzt7u5ic3MTPp9PeF94m5SIP3if6Hdee44ICHZ3d+Hz+bC1tZVVryIAwJVAqiVZUVEBq9UqQOT09LQwJJHizn/nXgYSvnZ4Ir+sIMuKMve48WuorysrK+jr68P4+PiJcePrQQYS8rql6wiQACdrHsnGK/43+rucGySDCi7kddvd3cXy8jK2trYwPT2NpqYmXLt2DaWlpVCpVGhpaUFzczPcbjcePXqE58+fi7mU+yq34U2gBjgG6H6/H9988w0mJibQ1dWFxsZG1NfXw+FwCJBJ5BcElGmfaLVaVFZW4tatW5iamsLa2lpWoiy+9+X9TyCIgxe+RuSxz7ZeKJqECjrztU2eQwrXValUiMVigriOG7c4eD5tHqk9HMieJm8VTLndbvzTP/0THA4HdnZ2RFhJNBrF3NwcfD5f1rwitVqN2tpaNDY2oqGhIYOdanBwEP/xH/8h3NnvUhHhOQklJSXo6urCrVu30NramuFW1Ov1gr6WFse9e/cQDAaxvr4On8+H+fl5zM3NYW5uDvn5+airq0NdXR18Pt8Pznf4Y/tAYYUFBQWimNn29jZ++9vfChKHa9eufRCvFHC8SP1+Px4+fCis5hQOSiQHExMTmJmZwejoKFZXVzPoaZVkf38fCwsLWFxcRFtbW8az3qeQkv/gwQM0NzcLtsTJyUm8evXqna/h/4zk5OTA4XDg7t27uH79OpxOJ46OjkQS5/r6+gcHUiTcuqpk1SI6dKvVis7OTmFN1Wq10Ov1IiTq8PAQGxsbmJycxOzs7DsDU6QoRCIRUZz0NAXwh9yP/yQhhZpYjPr7+zExMXHumXpHIisS9OIHlEko6Fo5NyXbfbnyCHwfCkMh86Tkh0IhRKNR8V1u1SbFQQlocWrgbG3X6/VwOBwir2l8fByLi4sZe4+3lSvLtOcKCgpw4cIF/OQnP0FZWZkgHSK2MxmgyZ4VJUY5ee/Q83hfefsAIBKJoL+/H3q9Hm63G36/X9QwGhgYwNLSkjB4yHuM6u54vV7s7OzA4/FgY2NDhHQVFhbCbDaLOmIrKyuYnp4WOdVyQrs89kp9os+SySRisZhYOwRsldIF5Lmg+1N4FxmsDQaDKIq7uroqCIVIAeY5Xul0OiMMUGbmI6VTSTlVmk8AouwJn7fd3V0MDAygp6dHGNBl0MDXKXksZSWcP4uTCcjrQV73ci6OksLN26P0Pkyn08LjSWUtiJTE4XAgGo2itbUVzc3NuHDhgvC0PH78WBgr6R8BHN4fHn6m1H76bjweF57e/f19QQ9OjIkErCknj4q+m0wmUdi5uroaHR0dGBoayqB/5946pbpovA/JZFK0n68TeX0ofUZ7hBwuDocDNptNkHAkk0ns7e0Jzzx9Hg6HRX60PEcUokzvSvk8JJE9gafJWwVT5O0oLS2Fw+EQCZvJZBKNjY0iJldJVCqVmEybzQadToednR28ePECX3/9NR49egSPx/NOlQGVSoWCggJUVlaio6MDzc3NaGtrQ0NDA6xWqxhkqqdFmwSAYGzJy8tDaWkpYrEYurq64Pf7sby8DKPRiOrqarhcLoGc34UQ6UF5eTlaWlqg1+sRiUSwsLCAjY0NRKNRBAIB1NTUoK6uTngMPwSg2t/fx8bGBvb39+H1ejE4OAiz2SzYILmH74ckzycSCczNzWF+fh5NTU3vqReZkpOTA5fLhfr6ejQ3N6OwsBA+nw/ffPMNenp6RH/PqhDoJ9IPleqYsXJkZATT09NvjJt+X3J0dFzbw+v1Yn5+Hi0tLbBYLBnXqFTH4bW5ubnCC0v7bnd3FxsbG1hbW8Pk5CTGxsbQ29uL3d3dd7YXjo6O4PP50Nvbi+LiYlitVjidzgzCjB8rxHjF2xwKheD1erGwsIDJyUn09fVhdnb2vRP3/CWJkudHJhsATob5yX9XssDzn1RKhBQeUqrC4bDwrsheCmoLV4KUvATpdFqwYhFIo++azWYUFBQIquHZ2VlMTEycUOSVvFRkMW5raxPFYin/8tmzZ5ibm8Pu7u6JdvN7yTkzSiQJKpVKMAYrMbhRW7a3t/Hq1Susrq6KunnxeBwqlUqE3nOwKxtqSKfZ3t7Gzs6OCNvX6XSwWCzQ6/WIxWKC4j0ej/8gjzBXHPm6II8BGWAov4TYFSncSQbK6XRmsVTyyjc2NuL27dsoKioSDKc+n0+AWXoG7zP3TCkp7dnmn/oie6BI6SWd0el0ijw9v9+PsbExjI+PC08cvz9X0rk3ha8Dur9MNpBNIeZ7Qg6V5Xnw8l6V9TgOKrnXjkLuvF4vVCoVEokE+vr6cO/ePdy5cwculwt37tzBwsICgsFgxnqnMZQ9s/wzJYBOY0VrcHt7G6OjoyKUj1JnqGZTZWUlHA4HDAYDXC4XWlpa4HQ6UVpaiu7ubjx69EiQZtBYUDt5X6kNSn+T1wmtBflvfH75eOv1egGkKNR4c3MTu7u7cDgcos4igfLp6Wn4fL4Txmv5TOTt1mq14m9KHvJs8lbB1O7uLmZnZ0XtIjrwTSYT8vPzBXqlBEjKHyBry/7+Pvx+P/x+PwAgGAziu+++w/Pnz7G0tPROa/HodDo4nU40NTXh0qVLuH79uqBMzsnJEeF60WgUkUgEGxsb2N7eFlYTh8OByspKuFwu2O124REibxsd9Ds7OwgGgydCGd6GkHWhsbERjY2NKC0txfb2NtbX1wUVKrl2qe4MWTM/lHeKgFMwGMTU1JQ4dGmj/Jh2HRwcwO/3i4K/JO8KuMqiVh/T51OInMvlgkqlwtraGh49eoTJyckzTZ2v1WpRXV2Nrq4uVFVVwWg0IhQKibj8QCBwZoBgOp0W4PnZs2dQq9WoqKiAyWQSHiij0QgAggWUzpydnR2sr69jfn4eS0tLGB0dxdTUFMLh8DtlVjw6OhLjWVhYiMPDQ1RVVQl2rj9G4vF4BrFEOp1GIBAQFvH5+XksLi4K9qNzefvCrcX0cpZfxPK13OrKFS+eZysrEsDxGWOz2ZCXlyeMcqlUCouLi1heXs6YZyVPAN2Tr3PuIT06Osqok8S/p9PpRO0Zem/zv8sWZ1LoKGf36tWrqK2txcTEBAYHBzE8PCzC34heW7ZQ031lCmhZOaZ+EYhSotHmsr+/j0AggEQigVQqJcKD7HY7HA6HAFaUoK+Up0JtJu8DARaioOYKGQFhOaxPBhuyJ4x+T6VS8Hq9onity+VCUVERPv/8c8RiMZHfRUCQAw1aNxaLBeXl5bh16xauXr2KgoICURCX6mhxGmslZV6mQedt58CNrwt5HfN/FPpZU1OTURNoeXlZFBmX50/Ja5TNs8CfoxTOKgNteX/yz7N5oHi7eCgk33v7+/sn3v3RaBSxWAwWiwWffvopysvLsbCwgJWVFVGgmPeXt4H2Cs8fVPKSpdPf528S6QN5cajOXW5uLgwGAwYHBwXBg91ux4ULF/Bf/st/QVNTE+rq6nDnzh14PB6EQiHFM4KLPF98z3IAyNvM1xtvP58X0l/X1tbg9/vh9XoRjUZRUlKC4uJi8VwiaAkEAop1X2m9ygCflyzh+Znv3TO1tLSEf/mXf4HZbBZJ3lVVVaiqqkJZWZlQdILBIMLhMEKhkODdV3JVx2IxjI+Pv5dYf5vNhps3b+Ljjz/GlStXRC2Ho6MjBINBTE9PY2RkBHNzc1hfXxcF+sgtbrfbUVtbi66uLlHFm2rB6PV64X0ZHh7G69ev33phUAqRa2xsxN27d1FYWIj+/n4MDw9jaWlJ0NRTcTNS8jc3Nz84ZTJtNHkT/TH3IaAuv4zeh+h0OrhcLvzVX/0VPvvsM9hsNoTDYayvr4t8urNIgw58H0d/8+ZNfPHFF3C5XNjd3cXCwgKePHmCwcHBM1VYmIwvw8PDCIfD2NzcRGdnJ9xuN/b29pCbm4uysjKk08e5eXNzc9ja2kIoFMLU1JRI+I3FYoJq9X3MDVko/9//+38YHBxERUWFIM74MUKHO4Uy8Do1VEaCKKzfJ/vpX6qQF4dEBgTAScVLo9HA4XCgsLAQ8Xgc0WhUhO4R2xp5uYDvyZmamppw4cKFDEPfxMQE5ufnRRg9z1XgidkcmCgp7tQPTvagUh0zk5rNZhgMBiQSCcRiMbGu6Dk8QZ/yVHQ6HWpqanD58mW43W7R1oGBAczPz4vzmoNLzsbHwQiRIMg1pWSvkRKAVKlUAjAVFhaKIsSUv0JebPL6qdVq+Hw+bGxsiIL2FFlDAIwDT2oneYploZQFOYeOt0+mdpcV/b29PczOzmJpaQl2ux15eXm4fPkyEokENBoNhoeH4fV6heeAE45QjndzczM6OjpEesLU1BSGh4exsrKS4c3jniTujZLfq3I7ZVDD70HrRKVSiRwXyoEpLCyERqPBxsZGBpDi55acf8OBHwceskGDvJXZzncZhMjgVp4PXqxWBmhc6ZY9eFzUarUoLKtWHxfYpfpUlZWViMViAqArgSR5Lng/aB/S77zEBjdU0/eVGJENBgMCgQDu3LmDpqYm2Gw2NDU1oaSkBPF4XOiTfH3wMecgVimcOdv7iAOsbABod3cXHo8HXq8Xr169QllZGaxWqyhtABxHKZHBh/Rs8joRiFICopxYhc4MeR1mk7cKpkKhEHp7e8WhRWEsxcXFgkb08PBQgKnt7W34fD6EQiHFhU4u6PcRnkKUkVVVVSgvL4fJZBLK5ODgIF69eoWZmRn4fD5EIhHs7e1hf39fHOyBQAChUAgbGxsYHR1FYWEhCgsLBQ35/v4+gsEg5ubm3nrIDVFEd3d3o7OzEzs7O+jp6cH4+DhWV1czyA7MZjOuXbuGyspKeL3erGQOH0L+s6CHAKXD4Tih2LxrUalUIketuLgYBQUFGQc9UdCeVbHb7ejq6sKlS5fgdruh0+mwuLiIZ8+eYWxsTBRGPktydHSESCSC+fl5HB0dYWZmBvn5+YLqNS8vD8CxUYZopmOxGHw+H8Lh8AcJe6NwHcofXVtb+6OLBKfTacGIeU4s8WGFv5S5VyqbZVOlUsFsNuP+/fu4cuUK9vb2MD09jfHxcWxsbIhwPbqWPFJ1dXX44osvcPPmTZH7GggERI6pHGXA8y14CJC87mUlmX8HgAAfZrMZ8Xg8Q1HnfaLvkyJHpS7u3bsHvV6P4eFhfPPNN/D5fBnGGdkrw38ngEVEG7KnQH5vKOkSKtVxjZ2SkhJ0dHSgoaEBNTU1KCsrE7WNqBwB9T+RSIhaiERaEQwGsba2Jj7zer2KhhiufHMwoiQcdMvrhuTo6Aj7+/uYm5tDb2+vUCCtViu6urqg0WhgMpnw7NkzEUFATGRarRbl5eX4/PPPcePGDUHEQ7nsr1+/hsfjyTjfOW04p8Hn4yMr+XxO5LXA1xLdl8L8HA6HKLTq8/my5tbzMSVAoDTGSsCIqL7l+/DraexJIad+yvcym80ZoWBk/JCVbvm73FhA5QHu3buHW7duIT8/H1qtFsXFxbh48WIG+5wMwuWzRJ4T/nx5LOSw1Wy/c+BB/6dQX5/PJ8BUNiDJ5532rTyeXGRPG//H9SgynFIIaDgcRldXFyoqKpCbmyvAI4Wu7uzsCAMV77+8vvne42NG6zYboOXyVsEUMUcB3y8eciPm5eVBr9fj6OhIFN6l+FwlFEpy2t/eptBEk8s+HA5jcXERL168wNOnTzE2NibCaZTalEgk4PP54Pf7MTIygtzcXOTm5op8CLIW7+zsvHVLMRViu3nzJqqqqvA//+f/xO9///uMJFpy8zc0NODevXs4OjrC0NAQQqHQW2vHhxatVouioiK4XC5RANDpdKK2thYmkwnRaFTE5r8LJZpKA1B4GXAMdCmu/cfULHifkpOTg+LiYjx48ABtbW0wGo2IxWKYmprCo0ePsLy8fGbDE+k8GR8fx+TkpGK4B7cg8hfPh5TDw0NEIhHxUvpj5X2dj+eSXeRQJjlnQVZo6BqTyYS6ujq0t7fDZDLh8uXLmJmZwfLysgD/lBtgNBpRWFgownBLS0uh0WgQCAQwNTUFj8eDcDicAYL4upcVBa4cyIo7Xcet4txTRmU+qBYU1aOh31Wq72tJtbe349q1a6ioqEBvby9mZ2dFWQulUGzZ2s/bfHBwcCLfWEkpVpofi8WC7u5u3L9/Hx0dHSgrKxNJ/+RVI/DBdRKywhNgSiaTCAQCmJ6eRl9fX0YY4GlGO+714e0ibxtwktZbCeDG43H09PTA4XDAarXCbrejsLAQFy5cQG5uLtxuN1ZXV7G3tweNRgOj0YicnBxUVFTg8uXLqK2tRW5ursg3XV1dzQC2fM1ykEEMgkrrhnsk5e/RGqJ/FGqWTqeh0+lgMBhgNpthtVqRTqexvr4uQre4R4iDd64Iy+OjtJ54aCIXOY9LXkfyXOn1elitVsHIl04f15ai0MTV1VVBfc+fSXuGwDzNndvtFh5bYoQOhUIiVFTO2VMC20pnjLyOOACV+6Z0PnFKdIvFIoA07fuVlRX4/f4MQgl5PkjkMFZq+2meTiUwTN+zWCwiZ8pkMqG9vR0XL15EeXm58EwR0QbVlOTjJz+L2sjXBG/XjzEgv1UwBZx036VSKcFCQwPKXfhnRRKJBJaWljA9PY3Dw0PhkXr58iU2Nzexs7PzxoHlrn6qMyFbCt6FdZ88U0ajEXNzc4L5jodgGAwGtLa24mc/+xnq6+sxMDCAycnJPyswBXy/UXNycuB0OkXh56WlJUxMTAjWqLdtyVepVLDZbKipqRFECPSiyc3NFYnJSrWxPqTQS6KkpARXrlwRB/vKygrGx8cFtehZarOSZHthnmU5B0J/HsItm7JCqWTV5IplLBaDVqtFWVkZNBoNGhoahMWVQsgp/9hsNotkcZ1Oh729PSwuLgpiGx7eRYVG+fPo3STnYhA1shKIoXdIKpUSFl4iUyooKMDm5mYGeCNPiN1uR0tLiyA6INKKyclJ0S/eNtmTIQvpDfQ7CYERJRBG96d3wWeffSYKHVPBbB7qQyGQxBBKIXukwxDBRE5OjmA15bnPsrLG+6Vk9dZoNLBYLEgmkyLXKRsoo3vF43FMTEzAZrPBYrHgxo0bIn/KarWipaUFBwcHIhSRxGg0Ii8vDzqdDslkEhsbG5iYmBDJ+wT23kS6wc9YDgRlYxW/F6/zxMemqKgI1dXVKC0thV6vRyKRgN/vh8/nEyFuJFzJ5XuKEx1wcCGvKbl+E3mH5JA3Gajzvuh0OhQUFODatWu4evUq9Hq98IKMjIxgYmICy8vLgvExmUwilUoJ0oSioiK0t7ejtbUVFotFFHxOp9OIxWJYXFwUBFoUeqd0lgAn96y87rknVClkka9TPq7AsXG+qqoKLS0tsNvt4nzLz89HTU0Nent7T+SGZdPn0+m0cB5Qmwgs0jtb3tdKxid6DkW92Ww2OJ1OlJeXo6mpSXj2gGOjy/r6umDo5HPIGSpp3crt5c98k7OHy1sHU0pCG/Esy97eHqamppBMJtHf34+NjQ2srKxgdXVVMZ/rNDnNGvIuhOpZJJNJGI1GuFwulJeXQ61Ww+l0wul0wmq1ChrO3d1dLC0tiYrQf46Sk5Mj6D+dTicSiQSGhoZErPvbFlIijEajYJihBMlXr16JUNazpjzTIUP1yIxGIzY3NzEyMoLx8XFsbW2dufC+czmXsyTEHMZZrkiUQBRwfGZHo1H09vYK9lqq+2O1WlFSUpJxTlHeB9H+RqNRzMzM4PXr13j8+DE2NzdFmBHlk3CrMLVFVkaB41qEBwcHoiQAt3ZTGzweD169egWz2SxqFpaXl2N2dlZEmBDoKy8vx40bN9DV1YXq6moEAgG8fv0aQ0NDIqcnW5hYNqH28HBFntOVLYSaLO12ux12u10YtUhB5Lk0VCCZagMFAgFsbW2J34nxb39/H+vr69jc3BQeqzf1QwYG1H/yHHEjG1ee5ftSDuTk5CRSqRRisRg++ugjEfZnt9sz5o0MuDR+VPdqfn4ePT09mJ+fx/7+fgY5gOwNkT1B3EukVqszCDq48PWr5BWhdU6EH3t7ewiHw/B6vSdCQOWQvv+vvT9ravTK1sTxRwLNEghJIOZZkMxJjuTstF3lclXZFedUd3R0n77p/i590x+iL7rjRHTXxanzqyrb5bKd8wCZSTIkM4hZDAKEJIQGQNL/gv/aubTZItN2DmSVVkQGSukd9vi+61nDszjQUAEp3hZV24CX9OIqAwJ95m1OJpOiBIFGoxGGjZqaGlRWVqKnpwd+vx8ARFSQXq8X9O8GgwGlpaUoLS0Ve3R7e1vU0Xz8+DHW1tawsrIiWGXldUNjT56rbIRQ3CumAgMq4ELXdblcaGxsRHt7u8hlSyaTcDqdaGlpgcvlEqCErzMZ+PE1wAEKhf/GYrGMXN9s7eNzajAYkJ+fj4KCAtTX16O0tFSEXabTaQGkent78ezZswxQrvJ88eur7i2XLznuGfVOwNSHIFSFfWFhQTzsPhRr997eHrxeL6qrq3Hq1Cl4PB7s7+8jPz8fjY2Notq71WrFwcEBhoeH33px0vchqVRKME0dHByITba7u4tAIID5+fnX8jD+FKEX3cbGBiYnJ7GwsIDx8XFMTk5idHQUy8vLJ4YJjwt/yc7OziKVSmF5eRkPHz4URRxPGgDMSU5OkpBCRrkWlMTMY/RloZDyqakpGI1GGI1GtLS0oKysTFisKTwYyFROotEo5ubm8PjxYzx9+hSrq6vC+isrDLLlWRXGRcZCAmoEWrgCHA6HMT4+jurqarhcLrjdbnR1dYnwwmQyCbPZDIfDgebmZnz00UcoLy/H/v4+ZmZm8O2332J5eTkjqV4OVVKFLPE+cKYt+TdO586VafJM7e/vY35+XuTU8iR98rjt7OyIgsjLy8sIBoOiKC7lSXNFm3QEuo/KA8lDoXiYFq2bcDgsAF+2XC/+l95d6+vriMfjMBqN0Gg06OjoEN4Oug95FIkoa29vD5ubm1hdXcXCwoIAt3KdPt4PFUikdqoAkhxOJf/G16HL5UJTUxPsdrs4JhqNKmny6TMHchz4yDnS8lyo+kL7lNepUp3Hxz4UCmF8fBw1NTWoqqoShoWamhrU1NSIUFTy9Ml7jtZMNBrF2toaFhcXcf/+fTx79gxer1eQnGTzztDY056ldQwgw+uimg8ZaMr9JWOIw+GA0+lEfX09CgoKxDo2Go0oKCiA3W7PMEKo8rDktsvAOhwOH/Fu8eNU85CXlyfaVl1dLYxPFotF5LCFw2FMT0+L+lLy2paBMvfa83BCmQzmdSQHpph8KOBJlkQigampKeTn5yMQCMBut4ukVJ1OJ+K+V1dXMTU1heXl5WOrWX+oEovFxAv/zJkzKCsrw9bWFp4+fYqhoSH4fL63BmhSqRRWVlbw3XffYXBwEFqtVuTEUJHWkyjp9GGoyvDwMP7n//yfsFgsiEajgu3uQ9wPOcnJuxSel6R6WXPhL+f9/X1sbW3h2bNnWFhYgMvlQlVVFTo6OnD69Gl4PB44HI4M63IqlcLc3Bz++te/4ptvvhHPcc7YxdvF20fKi7ynOSOdrFRTXyKRCCYmJlBXV4fa2lqUlpbi888/R319PSYmJrCysoK6ujpUV1fD7XajsLAQ29vb6O3txb179zAxMSHypHiuDSmXqtA2rqhTiI8MtrjSky3MKB6PY3Z2Fv/2b/8m8o0sFgtisRgSiQSCwaBg/qLiqqSEkSdI1g1kBVVlvebKfjYgSO8jbuDj4y+HUvE+HRwc4MGDBxgfHxehi1VVVSgoKBDgcnFxURRtpdIQFH5GeesqS70sKkY27rGS/3KRPbJ0PbPZLEL8aB729/ezGvCOAwW8/bwfqvaQ8P0h30cFRmjMnjx5gvn5eXR1deHSpUtoaWkRRCbkQebGFQobpeKy6+vrGBgYQH9/P5aXl+Hz+bC1tYVoNHrEU0ZAWw4TlZ81/K88FvL4EDhShbgRYDEajSJclNYapY7wZxHNpSqMFcCR8E4aD2rHq6J1+P4yGAyor69Ha2srTp06BafTKdiygcNUnampKdy6dSujHAgZVHh75DWUrX3HsUDKkgNTfwdycHAAv9+Pg4MDbG1tobq6WtT1SiQSwp26srKC2dlZ7O7u/l2Gbu3t7WFhYQEPHjwQxQCDwSCGh4cxPDyMSCTyVu+/s7MjGCA/JEkmkxn13XKSk5y8vnDFgn8HqCnS6fd0Oi2Kv+7s7MDn82FpaQlLS0vo6+tDZWUliouLYTabM0L1vF4vXrx4IajQZaWWXx9AhtJDtM4ARChgtsK7/FqJRALLy8t48OAB0uk0Pv/8c9TU1ODSpUvweDzY3d2F0+kU1Onr6+uYmppCb2+vsLhz6z+FLMm1XuTx42NHBAikXFKIOgeCqvMJDHq9XiwtLcFms8FsNiORSAgFlwApvw5XyPk1+RjxsVKNPfVNr9dntJP+UniifF/ZSydb/mlO9vf3Rd08g8GAiYkJQX5FZBm8f3I/5PUof0d9lNn4+LWynSe3Xb6Gz+dDb28vtre3UVJSgqdPn2JgYCCDKp9fk5OrZPP40lhykQlWZDCebd6o7/y3VCol6osCh+GvVN4gnU7DarXCYDDg4OBA5OURkKIcSSoYPzc3h93dXUQiESWoBQ6Vf/J08X7J/Zf3Pj9WdVy28SPyBo1Gg/n5ebS0tKCiogLRaFSwWXq93qxkFvyeBGLkcEp5/ajaKUteXp4g4SH9lpNjRKNRkVLx5MkThEKhI4QklOOnygnk9+d7mzx/r1pzQA5M/d3I/v6+oJyfmprKoPekBUQUnieJ+ONNCrl5nz9/jsnJSbGJybqYk5zkJCdvQ2QwIOcqyN4LEno2E1sehQpTfpTJZEJhYSHS6UP2s1QqJcqK8Bwt2YJN96S2yPeURW6nXDOKQqi9Xi/S6bSocVVSUgK9Xi+eseQNmZmZweTkJCYnJ+H3+zPytyhRnNMhq4gPZOVwb28vqwdK7rOsBCeTScRiMVF/Rmage9U1VYBYFRaX7XgCCColkitpfK7k9cTvx0OsiEQhFouJAqU0j6T0HqcIZvPccFEp5zR3qjUur0PZq5RMJrG4uAitVouNjQ0YDAYMDAxgeXk5oy4QeQbkdUmhmypvlbx+ZOILapMcFkeGBpmsgo6n69B4T01NiTaSR4pfm65HQILnr9F+52tPtW7S6Zd1oVRzyP/P1zQn/+BjyPeZ7FFKpw89nhSVotPpMDU1BavVCuCwyPDm5iY2NzczPMJ0b55vR9ekvEaV949/Jz8DsgFi6hvNP3lbfT4fxsbGMDU1JZhQedsMBoNYdzxnkV9fZRBThU5nkxyY+jsSCkX4RwYOqVRKuKZzkpOc5ORtC8+VIlGFPJGCRcoRP5aOJ4MXKWik4NC1yBjGC+aSkkSMaNwKTNfhDLpy2A2/P/1fzhug33d3dzEzM4M//vGPePr0qUjCTyQSKCgoQDwex+TkJNbW1hAIBOD3+5VhW9zSTsonKf+qceHjK4MMVaI+gAxFmYTuKSuDJNyLIitQqlA+DgY5CQafF1Ki6TwOgLOFxMlghAAo98bJgJHWjzxucntlUCGPoWoMVGCTM7Lxc2RwwMNL+ZgGg0HMzMxgY2MD+fn5IrdLJjaga3GwQPPM957cLxlEymuE9iLfK6q5lw3TdB95rdI1VcCaGyZ4W/jxfG54GziBCB1Le5qvY3kNUns4qM5m3KHxpX1P7Nvz8/OixE8wGBTeXHlc+F/uPZSfOfx3PpY8X4kDHb7GE4kEJicnMTMzg/z8fFErbWpqCv39/Zibm8Ps7CwCgUCG/sf3IN2Tzz+/D42JnA8mrx+VaI47QKPRvF7mVU5ykpOc5OSDl3Q6/fYrXP8diUajSVOtIl6/TqYjJoVYp9OJekz8WFkxJgWdX1dWurjwcCBZueXkDFz4vXhbSQEDkOEV4H3hrFo2m01Qpu/v72cUslXlgHArM/9ObpfqWH6dZDIpFDru1ZJDwXi/6Pp0DQoT4lZ18jJwb4BsySYhTxsprJx6noc4qcIQ6f5c2eSKJh8fChPk15JJF3g/uVJK58gMbLQmqe38N65Yq4BUOp0W9RSJMj0biOMgg4+zrDDzMeXrgI+X7InKz88XSrG8Bmi8VN4cYtiLx+MZZA7cqEFtIaMG31c8ZIzPh7xe+ZxwzwqNMe1ZHmpLfSLvFW83iclkEgCBe33lueJ1Tvm801/aJ9Qnvl7kZ5HcFj5eADK8RmT4kYEI7S96Jsm5nipDAo2hXq9HTU0NPv30U5SUlECj0Yh87+HhYQQCgaz5jbTP+RxyEhr5GZXNk/v/P1b5jsx5pnKSk5zkJCc5+RlCCtqrQnK4MiULV3ZSqZQAX7x8hayckGKUSqWOKBD8nvK15XYBR0PMVIoZXYvyQGKxGDY2NjJCi+S6P1x4e+l3fqysQMvtU4ESft1sbZeFW/35/wFkAAgVAFKNvVzQll8zGwimdvLrqryGfOz49TjA4144Gaiowq9IeDFhDliytZ1fm87lSf3UFrntfDyyec24yB401TXkc2XAK4M1/ns6/TJfjQwc3HsmCx8P1bqRFXB5LFXhYqnUy3xFfh73IvOx4ELtpGLZdD1+jmqMuEdKtU84o6fsVZPXJAEq/lzjY51t7OkZQWuHPy84GOYAkcZqdnYW//qv/5rRBiJVoWvKa4zaKFP8y21V7VNq3+tIDkzlJCc5yUlOcvITReWJIOEWUB4WRBZfUkR4zgUpd6Sg8AKdcliSzE7FLc2yssQ9XSpRKdVcoZG9DzKAo2tQ//j/5SR0Wanjx/KwOVLYUqmUILeQlWI6R3VfGTxSXyhMiuaBhLeTK4eqmkQEoEjZo3bLXjE6n6z1qnEnRV71PYAjiiL1lc85Z4/jiiK3wPNx4GtVVmC5x4O8AnL6gMqbxq9F3ge+DlXeKH5vObSU9ggBN/qNgD15J2XATaGysgeCr13ufVJ5RGiMsyng3JsiM1PSPzmUkL6j4sl8jHnb+P3oWBJ5X/O28/VKfeS5XHwN8b3AgSRfd9RXvjdVfeJ5TLw0BH92ye1XAT6NJtNbTPufhIATCQ/p5X2kNvE9S95W+VnMQZ28941Go7IGnEpyYConOclJTnKSk58psjVaVhRSqZckClyJJys5V6DoO1lpUVnq+fEqKzZviwzASPi1VWBFpWjyz7yN3NIrAxkVYMhm+ab70jglk0kBNFXHyiQWsvdDDv951X1lZVEWHkbHr8vnlY8FDwOT54ivG3leSCnm60pWvOXPKi8lF953WXmn36kv1CYOjuR2y4CFrkHtz5YjqBLZi8PHU+VNIJAnzyf/y8ee34eUZg7kONhIJpMZ+4p/LwME+bPqGUD3lUNC5XmSz1WBeTpHBZbpNxnUc3DOQyHp+lTCRV5nvF187AnY8HvxPqnCP/laU/1G65EbKfg8yh47edzka8vPM/pMgFsGtECmlz5bmLQsuZypnOQkJznJCYBcztSPlQ/tHXmcEvshyIfe/pycPMmtqZxkkyzGqVzOVE5ykpOc5CQnb1K4RTObYsatn9ks1lxUFn7VMXI7VMe+6lzV78e1h5+j8upks5Ifd21+jCrsKluYjTymqvHk1882lq/qa7b+qO6Xbd5U46Ii3pD7xa/J2yOHZMnXzzZWqnvx9hzXV34e9+pwz85x/ZXv92NF5YXI9pvKq8JFReAhy6vWguxRVO1x1XGqfCjVmso2t3wuVe3MNj6y95a3Qb4+D6E7ro3AyzBWVU7jcevwVf39MXuW90UeV7ndcv/l9nHJRkYhSw5M5SQnOclJTnLyE4VC53iyNA8f4S9i+p4DBlnRAtTJ0PR/Eq7cyoCOHy/nIMgJ1zL1s3wvlVKiSjyn/vC8l2wKjarPdBzPbeDt4nlD9D1nHpRDjHiIGh2rArKqMVUBR94nrmjyeZYVdJ4XJ489nxv6ja8H3n6ev0X3orwd3hYKHZPBKF2Pz5Mqj0nFQsnvyYkXdDpdxv15uJ08H7JSLCvUfPxlVjp+Ll83PKdGDgfjc0HtUYXxyXTrXBHnoFFuM78GD/ujOeWhZcTaR3MtEybINP3yfNPa533h657mnhsj+PzS+HCGRi48t4jvEblcAbWLt4fuaTAYxPd8DmU6fHncaOxVz65sNap4/+UwRn4Nvl5kYMjXorxOiE2R+iOzG2aTHJjKSU5ykpOc5OQnSn5+vkiIz2bdpRc/JWRzhY0rKXQs/eW1UAC1BfpVFmYiW1ApLFzxOc7SD7ykieaKp6zsy4ALUNeHUt2LlD7+OydVUCm8nPpZBq1y+4leXAY2nF6dckeoH3Jei0wHzb9XjSFXBlW/6fV6JJPJjDmm8eDAhxQ8ugflUOl0OlGQlAqY7u/vC5a3bN49DiS4x4L6zOdUBo803kajURCFyBTdr/IU8HGTFWq50KzspeB9ofWtYmXkRXl5m0hJ5/Tm/HoyvblMsMGvT8QxBDo4IAEOnw1msxmVlZUoLCzEwcEBtra2EAgEsLOzI+aN70/gJaU5n2tqXyp1SMZCv8mlEPh1tNrD+nOqWlXy/NB6kkGwbHzgBgN6ntHayOZx4/NH4ybfg77nbeW1zOR5V5FPcHIM+Vg+Tul0WlkWQQZf8trMgamc5CQnOclJTt6CkILDlTD6R4ob/UYEFPILW1aI6Huu1BLLmmzl5RZauV10T65AZ6P6VSXoAxBKJylvXLmm4zjVsQzuZI8HtZ2O4X95n+XCmce1nVvRueeCxp7IP+heXEmjucrLy4PBYBAsayrvjWzxlz0l9D1wqLTT2PM+c+HeDD63fOyBQ/DCARQp2wAQiUSQn58Po9EogJkMEGSgabVaYbfbxfGxWAzRaFSMF7Wb2kcskHIfiLyAU3yr1g0/jxMm8GK71E4OYGVwwxV67i2j9ccVbBW4UQF6rmir1o0KoHESCe7JoP2WSqWg1+tRWVmJ06dP4+zZs7BarUgmk1hdXcXAwAD6+/sRiUQy9hSBYBpPPi7ckMIBAR9jTldPx9E65+Qz3Auan58PvV4vjicQzutB8XnR6/UZ+4fGgRslePuPW/f07OJjytvP9wRvOzdAyF48Pq80n9ywwAlt+F7jbT84OBDsrHzPHyc5MPUPLvQAIgsLvXSOQ+A5eXNCL8VsxRFzkpOcnGzhIT6qkBBSROgzKQsqRVO2CvPfVPWPVMoKV/y5kHIgW1m5ksYVVvpe9prw0CbVe4IDgmzXlo/lfePhQTwUh7x/Kms596jIgFC2xJOCxGnK6V5U9FMOB1KFq8nzmu17Ok8+l5RHVWFf+suZC2kcSGnm3iVinIvH40ollrcvLy8PhYWFOHXqFFwuF2w2GwYGBjAzM4NEIoFYLJYRNqfT6WA2mzPAKJCp6MoFWuW9ILefrx15jGWPkKoOEZ9n+Xd5X6nyhDhoUu1D2QsKvPSYyWyS1B/aJwTsjEYj6urq8Pvf/x5XrlyByWRCMpnE9vY2dDodxsbGEA6HwYWUfmqPbJwgoT5z0MiFGzh4/1ReHvm5wj9zFjvae3JpBfpeFQqn1+sz9g4HJjw8Ut6j8v6nZ5fsdeJzJX/mfeFC4yLPsbz+uBc6GyjkkgNT/8Ci1Wpht9vR2NiIy5cvo6ioCMvLy/jqq6+wurqaA1RvWcxmM27evAmdTofh4WGsr69jd3f3fTcrJznJyY+QbEoc/x04PnledTxwlFpYVg74//n1Vdfj1m9ArQCrwlm4ZZgrZSpwpAIE9PlV75Ns40afyVouK+Kq8ZXvS33jx8gKElcGZTCcrU3y52znAercGxVwU11L9mLSvBP4A5ABDnloEr+fRqOByWSCx+PBf/7P/xlutxvxeBwHBwfw+XyIRqNHADt5HihHit9PPpb/lb16Ku+Baqzk9a5aO/I4cVGBND7/fA3IoWaqsEjebq7M8zbKnsC8vDw4HA5cunQJra2tsNlsAohsb29jdXX1lW3n88jHhL5XGQ/ksZe/Vx2rWm/HXSfbvKrGnp45BEw4kMtmPP6xeqe8dl51vuoZp/qevG3c03zctXNg6h9UdDodCgoK0Nraihs3buA//sf/iNLSUjx58gR9fX1YW1s7cWBKo9HA4XCgoKAAeXl5CIVCCAQCWUM/TrJotVoUFRXhk08+gdVqxcHBARKJRA5M5SRD5NCVk7Ync5Kp+ND/5aRmEtkyftx8kjfGbDbD4XAgEolgc3NT6fmSgZDquhR1oEpAlxV6VR6VHKpH/XnVfbmFma5D4Wo8dDGbckjXoBAgDhT4mMvjIANNOX+NPqvafpzCJSucKq+T3GfVtfi64Yq4ChTysDa+dnh7eMgc7wPPP9JqtbBYLCgrK4PH44HT6cTGxgYMBgPi8biyHhQAUbiU/67yGMk1j/h6ygac5P9zchZ5LOWx4fOU7RgVcOPgib7PRtDAr6MC7vK9ac+2trbi/PnzcDqdSCaT2N3dhdfrxfDwMEZHRxEKhY4AeL6O6HoyYKWx5yBOFZbIr5PtOcQ/832vmhu5r8ddjxsLOAEGP0ZlLKC9LX+vyilV7UP+f7l9NDd8HfPxprHj7XwVgOJyYsCUbD0BMhPdAByxAJxkoQeLHFYhJ4W+r7Y5nU60t7fjP/yH/4Dr16+jqqpKWC1fB92/DzEYDLh27RouXryIgoICPHjwAF999RWi0eiRUJSTLGQddLvdqK+vR3FxMebm5uDz+eDz+d53836WkBWKM4RRaMFJXFMnVej5YTQaYbFYkE6nEYvFhDL8Ia33v3c5TlHhiiFnoJKP5YoCV0BdLheuXLmCTz75BKFQCH/84x8xOTmJYDB4JORNpZjyNgCZCresgMhWedkaLivvdA+VIicbAagdWq0WJpMJdrsddrsdBwcH2NjYwO7urhgb+f3I2/4qRZmDWJXizfPX5GsAL3OUjivKqlLg5bFReUGOA9Zye+XzsimOGo0mI2+GfqdkfhlkmkwmnDp1ChcuXIDVakUgEMCLFy8wPT2NWCyWEbLK/3Gvl0roecWVcT7vXK/I5l1VrSt+rMwAx8dCVsaBlwQR5FlTzQGND297NsCm6rO8/jQaDSwWC1pbW/Hf/tt/g8fjQSwWw8LCAvr6+nDv3j3Mzs5ieXkZ0WhUCQJ4f3gbs4n8vOHnEIBQec/4niFAxkGFHBqpWsuqsefPAx5uKOvu8rOC2iDPkwqoqcZdPo+LVquF0+lESUkJkskklpaWEI/HjxhC5PupQiizyYkAUzqdDlarFSaTCXq9XlhP7Ha7yCnZ29vD2tqaCIV634DkOMnPz4fNZkNrayuqq6thsViQl5eHra0tPHv2DKurq4JG9F2LRqOBwWBAQ0MDPvroI/T09KChoQHpdBpTU1MYGhrCzs7OiVR8tVot3G432tvbUVFRgWg0ipGREczNzSESibzv5v0ooYRhk8mEgoICFBUVwWg0vu9m/SzR6/UoKSlBfX09GhoaoNPpsL29jdnZWczNzWF7e/tErquTJJQMTNbj+vp6lJeXI5lMYnNzE7u7u1hfX8f4+Dji8fj7bm5OoFbcVYozV1BUx/KXOSmnZrMZHR0dOHPmDA4ODjA/P49AIIBQKHSkHbLBQgZZKgDB2ySDJn4NlTU3G5jgfSWF1mg0Ij8/H2VlZaisrERtbS1KSkoQj8exuroKv9+PYDCIcDiMjY0NhEKhIyyAqmcHt2RzZUzVb5lkQB4jo9GI+vp6aLVahMNh0Z5s4C7bd6+rm7xKSVb1k58ngzf+He8np72mZ3RDQwOKioqwu7uL7e1tbG5uIhaLZYw53Y97P3gulQw8VOtbXh/UHvqOn8v7pgrvO84TIY8LiczgJrdV9VmWbG2QhZjz6uvr8fHHH6O1tRWFhYVYW1vDw4cP8Yc//AHj4+NHQCuJPMdyn+T9JreJg1d5f/PPsteZ5kMGr/weBJC4x0luowoYqsYxWz8BdZ6obICSQSABdQI+qv2n0+lw6tQpXLt2DWazGf/6r/+K2dnZrOHK/Dqy8SWbvHUwRRz7BoMBRqMRBoMh43dC8m63G8XFxbBYLMjPz0dpaSlqa2thsVgAAMFgEH19fejt7cXs7Ox7AyPHCVndnE4nampq8C//8i+4ceMGSkpKoNfr8eLFC/yP//E/EA6H3xvJg06nQ1lZGXp6evDrX/8aDQ0N0Gg02Nrawp07d/D1119jY2Pjnbfrx4jBYEBJSQlaW1tx+vRpbG1tfXBgil5sKqaiD1WsVitOnz6N3/3ud/jtb38Ls9mM8fFx/H//3/+HP//5zwiFQh9kSOa7EI1GA71eD4fDgeLiYlRVVaG7uxs3btyAx+PB/v4+lpeXEQqFMDw8jEQigfX1dUGry5mVPvR19CFKNvAke0JkUMKPBTKjMfLy8mC1WgEARqMRqVQKjY2NmJ6exvz8fMY15CRw7lXgSpKKOEJWTri1lj+f5ER+fjxvMz8mLy8PFosFFRUVcDqdOH/+PK5cuYLKykoUFBRAp9MhFoshHA4jEAhgaGgIP/zwAwYGBrC9vX0kDIv6KgM/btnW6XQZSfIqLxGxptH5+fn5qKqqwr/8y79Ar9djZ2cHd+7cwePHjzPe1XxMOS02sd3J+4/GkfKNiOlNbj8fb9V64muIH6OaW74muJdJr9ejsLAQTqcTpaWlghyAIjtkEE2ftVotzGYzEomEaL/cNjqHwja5x4KHc9I8yuD9dUVm7ZPBGl+THNATQyEXWtO0rnQ6XQYzIB8LTkqgirKg8ghlZWX45S9/iS+++AIulwuJRAITExN4+PAhJiYmhJ4iz7s87vye5Fni9+VzzueYGxVkQwOtcz6WKgMEn0u+1olFMp0+ZCwkUCifS6LVasUak/V17qkn75mKlZGPg/wsojVdXV2NdDotjB+JROIIEDQYDDh16hSuX78Op9OJ6elphEIhBINBxOPxDAI2jUYj2qHX6187muytginKC6msrERTUxNaW1uFxZoLxZiSZ0qjOXRHW61WgTrD4TDy8vKwvb0Nn893IsGU2WzG2bNncfHiRbS3t+PMmTMoLy+HyWSCVquF0Wg8UmztXYpGo4HdbsfNmzdx48YNVFZWwmAwIBAI4OnTp+jr6xPu/pMqNHY6nQ6VlZVoa2tDX1/fe27VjxONRgOr1YqysjKYzWYcHBwgGo2+MpTiJItOp0NRURHOnj2L9vZ2FBYWQqfTwWg0ipdpTrKLyWRCVVUVLly4gDNnzsDj8aCyshJutxsFBQVIpVKw2WzY29tDaWkpbDYbpqamsLKygu3tbQSDQQQCAWxtbZ3o/fv3LMdZ2oGjOTyyIgi8VIhJgSksLITBYBCGl/LycgFYSJExGAxH8j14qJUKYPF2kcIiW+dlRUwGCZyNiys69H+TyYSKigp0dXWhpaUFHo8HjY2NKC4uRkFBgWh3On0Y/r6zs4OCggI4HA6Ulpbi0aNHwhvLgR21R2bSo/6SbiADKPos14TKy8uD0+nEqVOn0NXVBbvdDq/Xi8ePHx85n5Rvh8OBlpYWVFVVQa/XIxAIYHp6WuxHOfeDaKaJdU9Wovl4y59l4fOiYiYj4UqowWDAmTNncO7cOXz00UcoKyuDwWAQz2VqFxcO3ngeiXyMfE+uDAPIYLnj+XJUayidzmSNy+Yd4mMjjwe1U7Xv5HXA78ENG6SEq64v708ZdBQXF6O5uRm/+tWvcOnSJZSXlyMej8Pr9eJvf/sbnjx5gp2dnWO9MvyzynOqylmUvT3y/qY9yX+T1xkX8iTbbDZUVlaisrISFRUVKCwshNVqhdVqxf7+PtbW1nD79m3MzMyI0HPOMEj7lYNwefxV+Xfc+6UCdnyN6HQ6FBYW4je/+Q2cTifu37+P/v7+I/fSaA69zuXl5SgqKoLVakVBQQEAZBCKyB5Tbgg7bj+SvHEwpdVqYbPZUFFRgcbGRlRXV4vPzc3NqKmpOQKmaALi8TgikQhCoRCMRiPsdrsAVAUFBWIwsm3s9yn0gL18+TJ+85vfoLa2Fna7/Ygn7n1JXl4eampqcOXKFXz++ec4ffo0bDYbotGo2PCDg4PY3Nw8kfkYZFUxmUwwmUwiBMZut2dYWz4E0Wg0sNlsIgR0d3cXa2trH5x3jYRejkVFRWhtbUVtbe0HNyfvS7Taw5ovtbW1uHz5Mq5fv47u7m5UVFTAZDJlvHipUGNhYSGKiorQ2dmJ1dVVBINBBINBbG5uYm1tDdPT05ibm0M4HD6Re/nvVWQliT7L3lhZ4eGKtZyvAGTmGjkcDlF8ln4nTwBXSDiA4tZqfn/eVq6wyO3i/6djqV9yX9PptKjXVFFRgdOnT+PChQu4du0aKisrMwyk1DfgpWVfr9fD6XTC6XSisLAQw8PDmJqaQiQSEUBNlXNG3iFOMw4cJajgnjn6f35+PkpKStDU1ISSkhJYrVaEQiE4HI6Ma9A9LBaLUJy7u7thMBiwsLCAx48f4/79+wiHw0eUeVUBXa7cEmDRaI6GK/HPqrnl1yPdyGg0iqK6BGqvX7+Oq1evorm5GRaLRYxFtoKuJKSbHQd25Fwd1bhTW8xmM4qKigRVeDqdRjweFx4yalMikThChS3fWwbXKkO1HJ5Kn2VlnR+rCivjyjzfs1qtFlVVVbh58yZ+8YtfoLq6GjqdDktLSxgYGBB61XEhviqAxMdf5fXhbeHtkceLxp6M+TynmfQq+o4MoC0tLWhqakJzczMaGxtRUFAgQnVjsRh8Ph/S6cMQO5/Ph1AohGg0mjEnAARAlUMIeT/IoELvKjm3i1+TP4fy8vJgMplQX1+PyspKLC8vY3l5GTs7O+K+dG+K+rDZbCJCjsaD700ZNP2Y9+cb13jy8/NRWVmJjz/+GF9++SVqamqE14km4+DgAHt7e8J9nkwmEYvF4Pf7sbq6Cp/Ph9LSUnR1dYlz9vf3EYlExEP1JAkpRDU1NThz5gw6OjqE1e19eaHk9pnNZpw5cwb/9b/+V7S2tqK4uBipVAo+nw8DAwN48OABFhYWTqzyRZvebDaLHLRkMnmkDseHIFqtFgUFBaiqqoLFYhFKsFxz4n0JWTLJo0RF7LK5uynH0e12o6KiAna7XTw8Dw4OBPXuzxXZqsblp64B/kIlZUSn04mXs4r97E1Kfn4+amtrcenSJXz++edob2+H2+0WRRRp3Cl8ibz2VVVVcLvdIswvkUggEokgEAjgzp07+Otf/4qZmRkEg8Fc3bi3LJxsRd4fMljK9j6QrbHAoceppqYGtbW1MBgMYk+SF4quSyEzBoNBFHflygcPq1J5mOg8FekCbw9X+Dngky3KNpsNHo8HFy5cwKefform5maUl5cLT4hKaSeFx263w2azweFwoLm5GXNzc/j+++9x69YtQd3NhQOzVOpl7ShVnhON3f7+vnh/0DuluroaPT09KC4uxs7ODlZXV4/kS5GFu7W1FV988QUuX76Muro66HQ61NbWora2FkVFRfjf//t/Y2trK8NDxoEgB1Bms/lI2+V8FFpjZKDiXkkau1QqBZ1OJ3JwGxoa0NLSgqKiIrjdbpSVlaGqqgpVVVUivDKVSmF7exvb29sZHjNqM1fMqS98PLhCrvIEcfCRn58Pg8EAj8eD5uZmXLhwAXV1daJ2UDKZhM/nQyAQQDwex/T0NEZGRsT/OXudKk8um3dOBnZ8zGgM5H3LDRt0bLZ8Ilo/586dw82bN1FVVQWj0YhEIoHFxUU8evQI8/PzR0LPVCI/K+g7FdCl8Ll0On2k/hf3UNM7jTsnSkpKhFOCjBbFxcWwWq0oLCxEYWGhYE0uKiqC2WxGXl6e6IPNZkNRUREqKipw5swZPHv2DHfv3sXk5OSRMNBEIiHGkrdPLiosr3f+vCRPMn92UQi02+2G0+mEwWBAIpEQKUW0R1KpFCwWC8rLy2G328V7NZVKCVzC8wRlQ8V7ZfMzGAxoa2vDxx9/jPb2djgcDrHY9/f3EQwG4fV6MTExgYmJCVEnYW9vD8FgEPv7+6ioqIDD4RDIMRqNwufzYWxsDF6v90SF+NEDsbu7G//0T/+EtrY2mM3mjJeQ6iX2LsVkMqGurg6dnZ3o6OgQc7K9vY3bt2/jL3/5CxYWFgS7zEkUslxEo1FRC8NoNMJms31wXhCN5jDcsr6+XoRwvW2l/ceIzWZDd3c3GhsbYbFYsLi4iPn5eSwsLGBnZycjHJGAVEdHBz777DOUl5cLzzOFAwwMDGB9ff1nGUEoXpu/AIGXln+V5ZdEpUDSA53+kkXO6XSivr4eZrNZMF29TW+txWLBp59+in/+538+4s2ORqPY2NjAysoKCgoKUFdXJ7xVer1evBgACOBaXl6OwsJCNDQ04M6dO3j27BnGxsZyZBVvUciqy5Ua1XOU1p2soMs5JCSpVAp6vT4jHC4ajYrCrCSkGPKwO9njxBUFuU0mkwmJREI8/2WrMD9WBcpI6dHpdLDb7WhoaMDZs2dx+fJlXLhwAQ6H4wjdMA8NopAvAmoUZkRRCKFQCP39/VhYWBCGAW6dJoMPB3dy+6mNpBwR4DQajSgqKkJDQwPa29tRUFCAra0tQRIlg4pUKiXaZ7PZYLFYxLNDr9dje3sbjx8/Fsyb8lzI4NRisWBvbw+7u7vHUsPTeqD5pOcRXTM/Px8ul0uA766uLpw+fVqECZMxW6/XZwDi4uJioVjL3kr+l485ncvXFf9e9qRQJEZtbS0+/fRTnD9/Hq2trXC73RkKazAYRDQaRSwWw+zsLDweDwKBANbW1sT35AGhfcC9eNxrJ3vG5DVBBkNu1Jf7yfcPiTyXlA/Y3NyM6upqGI1GRKNR+P1+DA4OijwplZGFt0d+P/H9lc2AqdFoMnRhFfAi/bSkpARnzpxBcXExKisr4XA4YLFYYLFYxO8lJSWwWCzC851IJBAKheDz+bCzs4NgMAiDwQC73Y7S0lIUFxfj6tWrqK6uFs6Qra0t7O3tifaT15yPIz3zZAOOPPZ0rAxwyJtcWlqKlpYW2O12aDQaQUzHDU2UakQ5mwaDQdD88xxH1RqR1/KrdPg3roXm5eXBbrfD4XAglUphY2MDOzs7CIfDiEQi2NrawvDwMAYGBjA0NCSqbROCrK6uRnt7u8gLODg4ELSSz58/x+Li4onKLTGZTGhqasK1a9fw6aefoqKi4siCSKfTCIfD8Pv9iEQi77T9FH549uxZAaTy8vLg9/vx4sUL3L59OyOe96SKDKbS6TQMBoPwUn0oQsqL0+lEdXU1TCZThhXlJIjNZsOFCxdw5coVOJ1OeL1e9Pb2CgVBBlM2mw0tLS24evUqnE4ngMMXQCQSwerqKqanpxEMBn8yUNfpdCgvLxcvAVoLpETF43FB6kJCYSJ6vV4AMPJi6vV6GI1GmM1mETpqtVqFd+3UqVMCRFKf3wYTocFgQHFxMc6dO4fLly+L6+/v7yMcDsPr9WJoaAhzc3Nwu904ffo07Ha7UN4LCgpQWFgowl7JO9HW1oaqqiph1PH5fMJjkZO3I6TQHqcMA+qitjQvcl4hgQz6fHBwgLW1NQGM+XVovXPFQwZRspJFQkqHnMwuexxIUeHn0bXIUtzQ0ICbN2/iwoUL6OzshNPpFCCPwrfi8Th2d3cRj8exv78Po9Eowv/ISMDBTyqVEjml2cZXBfRkDwUpdlxpIrrk2tpaOJ1O6PV6RKNRrK6uYm1tLcPARQom6SOlpaUoKSkRXiPKgz19+jRWVlYEK2629UBAgit/vC90DPeS8HkhPYPCI1taWvDRRx+hu7sbHo8HbrdbrB8+bzwE0mKxoKGhAW63G4uLi+JdpFJsVe3n15TbTe3Nz89HeXk5PvnkE/zqV78SBme+LgjY0TXq6+tx8eJF7O3tYWtrC4FAAEtLS5iYmIDP5xPfRSIRAcz39vaO1MqS28r7RMCYR7eo+qwChwQ+XS4X2tra0NjYCLvdDgBYWVlBf38/7t69i8XFRWW+EAd1fBx5OR1VUWQ677hnOd8HtC9PnTolDHYul0u8+4h4gVPXU6TD9vY2BgYGsLCwgN7eXgQCARHievnyZVy8eFEwbldXV6OwsFAYW1WedjkPjBeAPm7s5XEjY63VasX58+dRVlaGYDAIrVYrwCWNF+XWk1FBp9MhFApha2srgwhOpXvJe/BV7/83Dqb29/exsLCA/v5+7O7uwu/3Cy/UxsYGotEoNjc3EQ6HhYeBXPwdHR34+OOP8emnn6K2thZarRYrKyu4e/cu/vCHP4j6GifFgq/RaFBcXIzf/OY3+MUvfiHyHLjQi2BiYgKPHj3CwsICIpHIO/EAaTSHRAf19fX45S9/ie7ubmg0Guzs7ODp06f44x//iOfPn39wLGv8Zf+qB/5JE51OB6fTibKyMrhcLmHRflOhcD9X8vLyUFhYiFOnTqG9vR1OpxNNTU0wGAwYHx/H5uZmRm4Xz2ckcAgcuvdXV1extLSEaDT6s9aXyWRCT08PfvOb36CtrQ0ajUYw8MTjcVFRnntWw+Ewtra2UFRUhIKCApHgvr+/D6fTCYfDAbvdLuL3nU6nUF4p12B+fh5+v18Yg97k/NCLuLW1VQBE+rezs4MXL17gq6++wr//+78jGo2itLQUDx8+FC8vg8GAzs5OdHV1idw7su4DhzkT7e3twni1v7+fo6Z/S0JeQUCdDM+9UapcBzlsiYMhyj+2WCzw+/2YmprKCD/jSgt5r2SRra4ysCKDJin1pADLnixei4e302QyoaGhAefPn0dLSwuuX7+OyspKFBYWAgAikQg2NjYwOzuLmZkZjI+PIxAIYHd3V6zlmpoaOBwOtLa2oqWlBUajEQcHBxgfH8ff/va3I55tDkTld0F+fv4RSmyeTE5KNACUlpbixo0baG9vh16vRyKRELkXVECdW6qTyST8fj/6+vpEeOSlS5eEkaOoqAhdXV14+PBhhheJ1xjj40/AmMAO/ZXXiOp8jeYw7JDIvc6fP4/Lly/D5XLB5XIJDze/H4EXupfD4UBXVxfm5uYQDAaxuLiInZ2dDFZCeT1k85iolE7ymH3yySf49a9/jbq6OhgMBuzv7yMWiyEYDMLv9yMajcJgMKCoqEi0vbCwUBiDE4kEmpubcf78eUQiEWFUJeM0lYyYmJjAs2fPsL29LcB7NuIGzi5I61/es7JhgXsFiQb92rVrqKurAwCsrq7iz3/+M+7cuYOpqamMMj4qgEFCc+xyuVBaWopkMomFhQXhRZHfPSpGUDm8j76j0NZ0Oi3C+MgAR2uKSg9Rzu309DQCgQAWFxcFCQyF2EajUZhMJrS2tsJqtQqPp81mO+Jpo7GVjUj8eUgGBfnZxb1Z3KhABtHS0lI0NTWhsLAQm5ubIsqNAymdToeqqiq0tbWhoKAAiUQCc3NzosQSEZ/wZ6ccOfAqAxnJGwdTiURCcOk7nU5EIhGsr69jfX1dWJd4XCU9gDweD3p6enDmzBlUVlYimUxiamoKd+/exZ07dzA2NoZgMHgiFE4gM1Sru7sbDQ0NgrWPSygUwuDgIB48eIB79+6JRMR3IZxbv62tDcXFxYhEIrh37x7+8pe/4PHjx0esbx+CvO7iPomi0+lQUlICt9st4ny3t7dPBAubRqMRFkGPx4Pi4mLhtaF4Y3l96/V6uFwuOJ1OAUKSySQCgYDYuz+XWMNgMKC1tVWwJAEQL8mDgwPEYjHs7Oxk5AYlEgns7u7CbDaLGGqyWprNZvGP8gzIUsrXVTweR0tLC8bGxjA+Pv6z+sBFo9HAbDajqakJN27cQHV1NQAI5Wp5eRl37twR8fapVAo7OzvY2NgQY6zT6TA3N4exsTGUl5fj1KlTIheSwqOLi4vR1taG69evIxqN/izvYE6yCympQKbVn0T1vJdf0hxU0TVJASooKIBWq8X6+jpmZ2eP5PLwED8O0Ph96DMHS6Rs8nA02QvFvyMliF8/Pz8fbrcbnZ2duHHjhmDsJYWZQNTo6CimpqYwMzODkZGRjDpSBoMBbrcb5eXlWFxcRCgUEsaNvr4+3L9/H1tbW0cABmcP4+PMPefcCyBb93U6HSoqKtDT04P6+noAwNbWFrxeL7xeLwKBwBGPSyqVQiwWw/z8PEwmE3Q6HZqammC1WoWHyGq1ZoQfcgVdnnfqAx0nK8myp4d/1ul0ItTpo48+QkdHh8hRJ/BDz0cikCAjFBEKGI1GFBcX48yZM/D5fAgGgxnPaw7s6S/3XsnPExVoIU9CNBrFysoK/H4/tre3sbOzg7W1NSwsLGB3dxeFhYVwu93i/Wg2m+F0OmG1WgUZCH/+07qlnPvFxUXYbDb4fD7h+eRGBlLa+XpR9UUGhCqPp0ajETW7WlpaYLPZsL29jZGRETx79gz9/f2ibhe/lsrjS9/r9XpUVFSgrq4O6+vrmJ+fF+Mue0hlg4jsXeHrKxwOY21tDUtLS3A4HEgmk7BarcKbvbu7i6WlJSwvL2NtbU287wigyEYep9OJaDSaES5Kubny/fna4W2k68l7l697DhRlAKrT6QTrp1arRSAQEDTnnF6d8tOrq6ths9mQSCQwOzuLQCBwJN9eHlPentcBVW8cTB0cHMDn82FtbU08jHkcNx9ko9EorFHXrl3D+fPnUV9fD5PJhO3tbbGgYrGYiHkki7PsOn2XQgu/srIS7e3tItdBDoHY29vDysoK7t+/j3v37mFkZERYu962GAwGlJWV4cKFC7h69SrKy8uh1Wrh9/tx//59PHjwAIuLiycq/+zHyocIqojlqrS0FBqNBpubm1hfX0cwGHyvc2EwGOB0OnHhwoWMRFp6kFN8Mx9vs9mM0tJStLW1obKyMiOOe319HU+fPsXAwMDPztchha28vFzkCREZQzqdFi9d1YscULOn8Qc0T57nD3O9Xi9onGUQ+XP7U1RUhKamJpw7dw6lpaUADl8oW1tbmJycRF9fH7xer+gD5QzwEInNzU1MTk7CbrfjypUrMBgMMJvNYt5MJhOqq6tx+fJljIyMYHR09MSEkv49iSoUJ5vCRMKVIK5E82O12sPaOBQO4/P5MDk5iVAolHEvylHIpgTSZ56zwI9VFfvNZs2n44HDPWi1WuHxeNDd3Y2WlhbU1dXBaDQimUxiZWUFt27dwvj4OAYHB7G0tCRIpLgiE4/HkUgksLGxgaWlJayursLpdCKZTIp8y729PRGOJANV3kfVXudAiP5PEQINDQ2C+pkiSGZnZ4XlOlt4TyKREMrujRs3MmpkkndeVhZ5XR36jsZBVib5eMtjTwCBmMxOnz6Nzs5O1NXVwWKxIJ1OY3d3F3t7e9jZ2cHU1BQGBwdFeHN1dTXa2trgcDjEHDY1NaGpqQnPnz+H3+8/kooge25Uomo7gc/h4WH4/X5YLJaMEL1gMCio7ym8k8KtPR4PTp06hba2NjQ3NwuQSONIhCZEJnBwcIDa2lo4HI6MkFvV2MpKvfw77wdfT7SHiNWRyBq0Wi1mZ2fx7bffYnx8XITccs+K6h1C68FoNKKyshIej0dEdfBrZNvb8jOHt5k+R6NRLCws4Ouvv8bMzAzOnz+PoqIiLC4uYnV1VXj0iIiEcqV4YWFav2Sg7+rqgs1mQzp9GEkRi8UQCASOtFU2FtF32XIJZUDI9yytPwoHpkiS7e1tLC8vw+/3iwgSeobl5+fDYrGItbK2tobJyUlRvgA4ykSZbY5epWu+lcx97kqWG0WDbTabUVZWhjNnzuDjjz/G5cuXUVpaKjYMKZ2//vWvcf36dZG3MDg4iL/97W/Y2Nh4b14qCr9oa2vDlStX4Ha7M+je0+lDt6zf78f4+Dj6+vrES/BdtJmsB9euXcPNmzdx+vRpWK1WbG9vY2ZmBtPT0/D7/TmWr/cgRqMRHo8HtbW1SKfTmJ+fF2w/70vy8vJQVlaGa9eu4be//S0uXryI4uJi4fkJhULi5UcvWa32kA720qVL+N3vfoeuri5R0G9nZwc+nw8bGxsZYQ5vSvjDV/UCfFPCDUFvSviLs7GxUTA6knV6enoavb29mJmZyQjLkx/05P0jqyPlm9bU1MDpdIpxKSoqwqlTp+Byud7qWOUkU+R8AODouiXFQGVspMRxm80m9uHe3p4IcZLnUg5Hkr+XLdgqMgruheDhOXQdUmao4HhZWRlaW1tx+fJl9PT0oKamJoPJ7OHDh7h7967IdSEFTfXOIWMB0WMXFhYiHo9jbW0tA3DIHjyVJ45/R+22Wq3CU6PVauFyuXD16lWcPXtW5D2trKxgcHAQs7OzGSFDKkUqmUwiFAphY2MDy8vLCIfDMBgM4njyhshgjiu6fNz5vNN31B/Ko+HKn81mQ01NDc6fP48LFy6gvr4eVqsVyWQSm5ubmJ2dxcrKClZWVrC4uIje3l4cHBygoKAAPT09qKioQFFRkSBisFqtcDqdov6XnHtCbefPQnmNUNtkhXhnZwczMzNYWlqCwWBAMBgU9NWyd4BSPra2thAKhbC6uop4PI69vT2UlZUJsEqFmakNu7u7mJ+fx9DQEFZWVoSxnQNAOeeP94/GWv5ODg+l+SgoKEBjYyM8Hg8sFguCwSAmJiYwNDSUsWb5tfk6pWsTOCwuLkZHRwf0ej0mJyexsbGRwWCoMsyors/7xUFgPB7H2NiYMGbodDpsbm4iFArB7/djZWVFhPvytcifCTqdDsXFxejs7ER7ezuMRiNisRgWFhYwPz+PSCQiQlppvLnHnrePhyTK9+Ltl5lIOXZwuVyCU8Hr9SIUCgnDLT2jKPKkqKgI+/v72NzcFA4a3k++v/gY/hj9+J3RoJElhUKGKMa6tbUVra2tgqkKeJkYWVFRIRhfKNTFZrMJhrCNjY33AgbMZjPq6+vR1dUlYjH5Jt3b20MgEMDjx49x69YtTE1NIRAIvDPwR/Gk58+fx6lTp1BUVIR0Oo3Z2Vncvn0bs7OzwhqUk3crNDfFxcU4ODjA7Ows5ubm3punlRI0e3p6RF5daWmpKGHACzqvr69nxPjX1taiu7sbTU1NGcQTa2trGB4e/tkMfiT7+/tCKSCP2buQZDIpkuTfxF7RaA5Dgz0eD65evSoKhNJYRyIRTE5OYnh4GFtbW698XhCpxN7eHmZmZtDf348rV65k3E+n08Fmswmrb07evMhWWDm3SD5G9qTwY2mOdDqdIBghYEahmlwRICFFjVt/SflQ5TnROcdZYuX+AS+VXb1ej+rqavz6179GT08PamtrYTQakUqlsL6+jocPH+Ivf/kLnjx5gr29PRHCrFJm+T8Kc11fXxcsnXKSOvWJ5/5w8EH/J8MtByNEE+1wONDT04PW1lZYLBbE43GsrKzg0aNHGBoaOsKYqAo1onzohYUF+Hw+kZMRDoeFV0H2lHGiD/47bzvNpUqBJs98ZWUlurq6cOHCBZG7kkqlEAgEMDo6iufPn6O3txcLCwvY2NgQoaF6vR77+/vo6upCeXm5SE0wmUziWcSVTK4M85w61XOd/87XeCKRwNraWsY1VZFKdJ/9/X2EQiEkEgksLS2JqIhwOCw8QcBhSGY0GkUoFMLY2BiGh4cxMTGBQCAg3qeqOeTzwedF5VGW20jHWiwW1NTUiPfR6uoqFhYWMsLH5L3P9zitQwKylZWVsNlsIpQ3EokojTF0vWzeEhkQUjv29/eFQXRqakq0h87hBYs5sOHHEMFMeXk5ysrKkJeXh7W1NTx//hxra2uCcEWm8Kdr0tzzcVeFE5Nw8M6PJapzt9sNjUaDyclJDA4OIhQKCfILPk/co723t4fNzU1BuEZzwr1w8vp9XcPtOwFTxFpVXl4uah1cuHABv/jFL1BQUCAaz+lBOdUpJYUXFxcjnU5jfX0dfr//nQIU4PBBYDQaUVFRgStXruDChQuiBgiX7e1tDA8P409/+hPu3r2Lzc3Nd8bgRwmbDQ0NOHPmDMrKypBOp4VX76uvvsLy8vJ7DZN8E/IhKoYUmuBwOFBYWIhkMon5+Xkl48+7EJ1OB7fbjZ6eHnz55Ze4fv26IMUAICw5d+7cwePHjxEKhcQDymg0orm5GR0dHYJal5QLr9eLR48eYXV19Y20Mx6PY2hoCNXV1YJqWaM5LG6p1+thMpkyatgAL0kBKCSE4uW55ZSUXlUYBfWfh6H8XMnLy0N5eTmuX7+Ozz//HF1dXcKAFI/HRfmHycnJHxUOnEqlsLS0hOfPn4uQKMqjycnbF5XFWLbYk8jfycfR+RaLJYNhjt4f/LknK3/ydUiJoTbKll9Z2ePXltvFFUHK+/R4PDh37hyqqqpgMpmQTh/mTgwMDOCbb77ByMiIYMPkyi3dRwYOdB/+7ucKJc9DkhVN7gmUxyKVSgkwQYCkuLgYpaWl4lmyvb0tvOl0LFda5bbzsYvFYgJA7ezsYGVlJSPXhPoKvIza4f2TARYJncsZ7yhUr6qqCp9++ik6OjoEkNra2sLg4CD+3//7f5iamhKkPLu7uxkGoVAohLm5OZw9exaFhYVCryktLYXL5YLJZDpC583XEQ9d5Mo3X0NcQSZ9Tj5GfqbyfCz6azQaEY/HEY/HodfrkZ+fLxTi/v5+DAwMwOfziZx8XmJE9l7ycad9I++nbOCLg1/yjpWXl8NisSCZTGJ2dlY8t+WcPfpLCr7BYEBJSQk6Ojrg8XhQXl4Os9mMUCiEoaEhQdjEwQ2tAxJ5/ct7mX/m8yjnZcvzxz1IshGF3l2NjY3CCzo9PY2//e1vIveeU+zztcCj1fga4s9Lvj54v/n4ARCkEk6nE3t7e1heXhagm/pA1yNiLwr9IwZD1bNPtRY4yH+VvHUwlZeXh9LSUnzxxRfo6elBVVUVbDYbnE4nbDabKAYWDofh8/lETZfNzU2srKwgGo0KRrH29nYUFRWhpaUlI5TlXQjFXno8Hly8eBG//e1v0dbWdsTim06nMTMzg3/7t3/Ds2fP3jng0+v1gvWmoqICZrMZ4XBYVOJeW1sT9TNU8qF4qz6UdnIxGAyiPgmFUiQSiSOF7t6VuN1u3LhxA5999pmgOeVJpWSFttvtqKiogF6vF3Vh7HY7Ojs7RWgPcAg+1tfXRShpMBh8I+2Mx+N4+vQpdnZ2MDo6ioqKCuh0OgQCAZSVlaGlpUUw2tG6iEQi2N7eRmFhoQgboYLP0WgU+fn5KCwsFJYr+QWSTCYRiUSwsLDwRkhjSAktLy/H6dOnRXgfvchCoRAGBgYwMzPzk9g10+nD2Pj5+Xmsrq5m1Pv6EPfKhyzcEksirx/ZC8EVOY1Gg4KCArS0tKCsrAwAREI97U+i49ZqtYjFYlnpoCmsja5LFOuy0qWydMsWY43mMESVikxfvHhRhJfR+puZmUFvby8mJydF7k22/BSVQkNhOVqtVij0PLyKjuNtl8O3uBLMjyFjVmtrK86ePSvIGuLxOGZmZjA5OYmdnZ0MazqNA/AyX4krxqQXkIK2tbWF0dFR8ewjgEDWepXXQgZQ1FeufAIvS0SUlpbi9OnT6OrqQnFxMVKpFEKhEEZHR/HgwQPMzs6Ksg58rqk+VnV1NSorK4UBiu5H+ZaUc8nbwwtCy54d3nZZOZWVc9kTx8GmrNBSaJndbofT6YTL5YLNZsPe3h4WFhbw8OFDEbomjysHCDT2MokDbydvD98LNMfcmEEFcMvKygT1fzAYxPLysmBq5muQADO9SysrK9Ha2oozZ86gpaUFLpcLkUgEjx49wubm5pFQWF60lrdd9p7IOp0KWJGo9D859JfWvdFoRFlZGbq7u9HR0SEIk2itLy4uIhAIZLSXxo7aLj+fsr1P5fng3jG6NjEJVlVVIS8vD5FIBOFwWOxP6i/3clKhbpkkjgM3GeDRGLzuu/+dgKmioiKcP39eJGpSHsbExASCwSDW19cFutze3sbBwQG2t7fh9/sFoxZV9dbr9UfQ/9sWYlnp6OjAhQsXcO7cORGiw4VixXt7e9Hb24vl5eV3mgtDNQU6OzvR09Mj3OF+vx93797F6OioqHFERRFpsVCy6vb2tlh8J1lUD4+TLgUFBSgtLRWhO5SAqyrq9zYlPz8fxcXFOHv2LH7zm9/g3LlzqKioOFKvi4gSLly4gIqKCkE9TjVV6GVOSvve3p6w7gJvznt4cHCAlZUVRCIR+Hw+wfQVDAZRXFyM+vp6EbJCD8FoNIpwOCxYtoLBIIxGIywWCw4ODgR7qMFgELledO7+/j7m5ubw8OFDDAwMYGVl5WfPD415Y2MjOjo6UFxcLF7ye3t7WF1dRW9vL7xerzKM63UkEomgv78fdXV1Yoxy8m6F1jwpsao9ICuasvIPQNRuKy0tFYyfRBhEigIZ6WTFkFODy4oDt8qqwhBJ+ZO9XvQbL7XR1dUlDBgU2j48PIyvv/4ai4uLIlROVrrpflzhpVCzkpIS1NTUYGdnB9PT0yJaRW6LLHysZVZDOo8UYaoLScpYIBDAkydPBAW77IGSPR0k5GVwOBwoKSlBKpXC8vIypqenBY008DJ/g5Q7GTjz8efAkOaCrmEymVBZWYmPP/4YH330EWpra6HRaBAOh7G4uIgffvgBf/jDH0SYGx8v0sM6Ozvxu9/9DhcuXMgo1ss9BDL45WOfTWGnY+RaXvLaVPVdNbcazSGBjsvlQmdnJz755BOUlpYinU4LVmd6J5Axkl+b55fx8Tzuucqp0qntnHiJxsdkMgmHgNvthl6vF4yyMlU/eVPNZjOsVivq6upw8+ZN3Lx5U+izxKi3vLws3rEc2KnGXh5Hvk75uMuf5XNlrxB9R8cQ+dMvf/lLXLt2Da2trbDb7YjFYvB6vZifn8fGxoZgi+T7nQw+vO6UPMf0nbxOsu07o9EIl8uF6upqlJSUZHi+5fDRdPrQM7W6uorNzU2UlJTAarXC5XKJd6/8fOLjTkCa6NNf9U5+62CKo3Oq87Kzs4PFxUWMjY1hcXERMzMzIoGMGk4vi3Q6jfLychiNRlFwa3JyUtCWvm3hD6EvvvgCFy5cEFZlWXZ3d/H06VP09vaKcJt3KRROeerUKTQ3N0Ov14uxHhoaws7ODtrb21FWVoby8nLBekP5Gj6fD3Nzc/B6vYKq+314TF4l8kPlQxGHw4HKykrY7Xak02lB7CBTHb9t0ev1OHXqFK5cuYKenh6UlpZmuOdJqAD3pUuXxL7kv1F9Ce4KJzpyj8cj8hDelEQiEczNzWFpaUncj3KF5LA2soaRxZM+63Q6WCwWfPHFF+ju7j4y7vScGhgYwF//+leRv/Rz54dq8ZAXzWw2i/sFAgHMzs5ieHgYa2trP/lesVgMk5OTWFxcxMWLF39We3Py+sK9GLJFWvUCVimk9B3F9lssFlitVhEGT0YX8kapzqX/cyu6bL1W3Z+TJXDhCgTVnyNDHRkwyIOys7MDr9eLiYkJrK6uZijzKiDFwWR+fj4cDgdOnTqFnp4eNDU1iTxjHuKmqkfDQSjf67JyCUAkrldWVqK5uVnkOG1sbGB+fh5er1cw+JESTjoIV2jpN4vFgoKCAthsNgEqA4GAKJLN26Ui5SJlW/ZOAcggvtFoNCLss729HVeuXBFRMZRP+ujRI/GsSqfTooQCgTOz2YzOzk784he/EMYcKnVB7SBFtbCwUOQ4cQOTLPLYy4q5vN5e5Tnh3iBiWzxz5gxaW1tRVlYGg8GAQCCAgYEBfPfdd1hZWVFSj/M1R3qkqi2yEYOHL6rACK3VmpoaQUNvtVoBAB0dHejs7ITRaEQgEEA6fRiSSSHxTU1NqK6uhsfjQVtbm6i3tbe3h2g0KpjoeL4lXwsyEOWGT5WnigsPY88GZmQQQUDI6XSitbUV7e3t6OjoEBFhPC+ayFpUa0Dl7eHPS3k9yG2k9UDn2O12uN1uQZAVDAbFmqe+ckCVTCaxvr6OhYUF4UU/c+aMKH9AIcOqceFj8zry1sEUFfH9X//rf+FPf/qTiHkmL8jOzg5CoRDC4fCRDUuLlyiYzWYz/H6/KCL2U6y3P1YsFovIKbl8+bIozKt6MEQiETx58gRPnz595woyADidTrS3t6O+vh52ux2pVEoUsaPigl9++SXsdjusVmsGtSgVvltdXcXIyAgePnyIvr4+wdBykoRvuHexBt6EaDQalJaWorm5GTabDanUYc0Psqy8y36YTCZcvHgRly9fFi9VVXuBl95O1W9c0ulDivKOjg64XC7U19cjFou9UTBFHlTZSPFj8ouI2cdut8Nut2cUtgQOAdv4+DiePn2K4eFh4Sn/ueJyufDll1/i5s2bMJvN4mUYj8fR29uLv/71rxlFGn+KUN0TYunKybsRUj6ATE+GrFTyz1zRpRc2/UbkEwUFBcJot7OzI0Lg+frg58sWVn5PUnJ4PhIdo9frhRWeP1f5fchr1N3dLbwiRqNRgLelpSWRw0LepNcRbqz85JNPcPnyZZSUlECv1+PWrVsZ4UM8F4KPFc+1OQ7AEvkL1dDLz88X5Vc2NjZE6QGdTieMt8FgUNyTA+a8vDw4nU5BQkDHUpHvVColgKbs8aFrUOhXMpnMeAfw+aQxMpvN6OrqwvXr1wUQBA6fH1NTU+jr68P8/DySyST0ej0cDgesVqsopFpVVYWbN28K9kKqRcfBhcViQVlZWQa1OweR8jrla54r0SovCoV7ZVNaeb81mkMPaFVVFS5fvozm5mbBEjk9PY2vvvoKY2NjGfk/Mtjgwj24vL+cKY63AcgkSpFzxxwOB+rr6+F0OpGfnw+NRoO6ujrcuHEDRUVFIldNo9HA4XDg7NmzuHz5MsrLy1FQUCD0RyIP8vv9gu2ZcuKIwESVSwQgI+wym0eK5oj+ke5N45DNS02RJ1arFe3t7bhx4wY6OzuF7hsIBDA/P4/x8XEsLS1leFO5h1AVvkpjSEYj2ZMkg10+v1rtIYNwU1MT7Ha7MMjw0EqV4WZvbw+hUEisq7a2NvT29sLn8ylDEPmzVDZqHPdufutgKpU6ZJh58ODBjz6Xajk1NDSI2GCiIqUBfNtiMBjQ0tKC7u5uVFZWigcNl3T6MAl1bW0Nc3NzWFlZeS9KvtVqRVlZmbCSpdNpkUDf2NiIy5cv4+bNm8K1HIvFMmLaHQ4H3G63qF6fl5eHsbExkWd10oDLSWvPq8ThcIgHUjweRyAQeC/ev/z8fFRXVwslIBs4IgWLhwzxOh9cNJrDxO6qqioRXkp/T4potVqRi9LQ0ACbzSbc/cChBXBrawvPnz/HyMjIG/UuW61WdHR0oLGxMeNFvbe3h5GREfT19SEUCv3ktaDRaGCz2YQxRQWQc/J2hBQTrfZlXSgeKiULVx64l4b+r9frRViQTqdDOBzGysqKoFzm4UiccU1+2csKJgEf/j0p0UTSwNvIhYiXGhoaMizyBwcH2NjYwODgIHp7ezE7O5sBaI57RtN+7O7uxn/6T/8J3d3dKC8vx97eHhKJRAYrm3wtrpgRTbZKWedjbLFY4HA4UFBQIPa93+/H8PCwKFRLiuHBwUEG0QVXPomAoK2tDZ9//jk8Hg+i0SieP3+OmZkZAaboeA7yuNJJ40pgSl4fvI9Exd3e3i7qGtH4azSHdNGVlZXQaDQoKysTtN0EjtxuN8rKylBUVCQoo7ln4uDgANFoFJubmwgGg8cCU1pztOZprFSghAMtnvejms90Oi2Md8RWSKyD9Jz8+uuv0dvbKwqQyx6nbICUt51+o33Kw9C4l0q13lKpFFZXVzE/P4/R0VEkEgmRe/vxxx/j6tWrCAQCoqityWRCXV2dAO801ru7u4KJkAjB9vf3UVpaKtJcZEZCDjD0ej0MBoMILVSNJzEHE0+BTqcTJYUODg7EHjYYDIK59uDgAKWlpSgvL4fL5RK6LxnowuEw7t27h/v37+Pp06dYXl4WOiQnzVB5wvgc6PV6ASZVBiCVdyovLw/V1dVob28XbaYCxFarVZkuQfuWyKisVitqampQXFwMg8EgcES255S8Zo+Td0aN/mNFo9GgsLAQN27cwNWrV+FyucSDb3R0NKMGy9sSco87nU44HI4M1ypfLORqJzf7+xJ6cBODj9FohNPpREdHBwoKClBbWwsAog7Q0tKSCL0kKSoqQnNzM7788ku0tLTg//7f/4v79+9n1CHIyY8XsopQocTNzU1MTU0hHA6/c1BI1ppoNCpCh1TCj6FYcVJGAHW8fzqdRjgcht/vz8gbeN+i0WiEQnj16lW0tbVlFHYkwonZ2VncuXMH4+PjR+iRf47QC0TOY6LY/4WFhZ8M3Og5VVVVhStXrqC1tVV43EhZyKYY5eTnC40xhU1xjxN9lp+bXOmTPRac0ICuvb29jXg8fsQKLddx4S9+rjhmuzdw6NmlsB5SzKjNdE0qMk1sb3RtqiE0NjYmyFOyeYbor0ZzSGhQVFSEtrY2/OpXv8L169dF4e2NjQ2srKwIRVFW0HjbaW1zEhnuEeL9t9lsKC4uhtVqFe0n2vZ4PJ4xT+RZUlncyfPT1taGS5cuwW63Y2pqCs+fP8fCwoJQzqntPH+Hg2jgMDSXACEHJHwMiTiirKxM6CEEwPPz81FVVYVz586hvLwckUgEHo8HjY2NqK6uRlFREQwGg3jGk5FFJt0hSvq5uTkBBvncydZ64GWxZ51OdyT/ThVCp+qbHGpGz2jy5tTW1iI/Px9ra2vo7+/Hs2fP4Pf7M3LROdOgfF1ZuNGC5zfJ11Fdi8ZgdXUVT548QTKZhNfrhcPhgNPpRHV1NdxuN+rr6wXQ5ONM0SjE+ghAlK5wOp0oLy8X61DOdaR20bzQu0I2bPJxLCsrE3UjKbVgbW1NsB7Ss4kYE5eWlhCPx1FWVgaPx4Ompia43W4UFhYCOGSpnp2dxcOHD/Ho0SNBEHfce0XWEejz/v5+BrkHHyM+1vSZwlCrqqpQW1sLi8UiDCAFBQVHxoHPKeVKE5Of1Wo9kh/O55+eH+QNO84wxuXEgimDwYDS0lJcvHgRbW1t0Gq1mJycxNDQkKjU/LaluLgYXV1daGhoEDUYgEwLAT2EBgYG8PTp0/dW+wo4BEnLy8tio+bl5YmK4OTWn5+fR29vr0isl5mgSktLceXKFRGr/Pvf/x4OhwO9vb0CxObkx4tWqxU11vLz87G5ufnOjAKyRKNR3L59G1tbW3C73UowlU6nRSV0mnOiUe/q6jryQKIk2t3dXQwODuKbb77B/Pz8u+rSK0Wr1aKkpAQtLS3o6OhAZWWlaH8kEsHGxgZmZ2cFE9nW1taPZtR7lahe8Dw5mXuJX1cod6O7uxu//e1v0dDQINgVyTM9NjaGzc3NHJh6iyKHasmKv2xFl2P7SdLptPCgEKHK1taWKORJSj0pIlqtVihgJOQ5kMkE6DzeFp6brCpQzUPaqBQIsb1Fo1GsrKygt7cX9+/fzyCdkEVWjIhm+dq1azh//jyKi4uRl5eHYDAolGZi6CKh0CA+ZnLdGlkJ430nRi8ORigEiMLZaQzoXjw8bW9vT3iAu7u7RTHscDiMkZERDAwMwOv1ZhBmUBgY/ePX4wo9z82Shaz+Op0uo4YPgdL6+nqRjK/X62G1WmGxWKDT6Y4omXJuH/VraWkJo6OjWFpaUgJiWlMyQKJ/r2OsUYEW/lerPaTfvnr1Kj7++GP09PTAbrdjc3MTXq8Xjx8/xujoKKLRqHKdZvOGyOuAPnOvAz/vOMNHOn2YF9fX14cXL14IJb+iogKfffYZvvjiC5hMJuj1+ox1Rp4fCuvX6XQwGAwi1NxqtWJxcRF+v18QbMjAgs8jZy+UgZ9Wq4Xb7cbVq1fxX/7Lf0FNTY0A65ysg0edrK2twWw2Y39/H0ajEXV1daivrxd6IzFC//DDD3j69ClmZmaEIUP26vG/Op1O+Vyh5w1f97IxiEt+fj70ej3cbjccDofIFyQvG3nVaI9xBsWGhga0tbUJOvdIJILd3d0jnj9qI409J9B4nXfniQRTeXl5goqxs7MThYWFoo7K6OjoEa78Ny2E7CkOtrGxEWazOcO1HolERLLp6OgoBgYG0N/f/149U+FwGHNzc/D5fAiHwzCbzSguLobL5RI1jR48eIA//elPeP78udi0XJxOp4jN//zzz3Hu3DlBmbq1tXXkBZeTV0teXh4sFovI1cnPz0coFBJVw9+1xGIx9Pb2Ynp6WpCQyEJgKhKJIBQKiXAcj8eT8eJMpVKCxXJiYgKbm5t4+vQpbt++/UbzpX6uUKx7bW0tKioqRKFtspIRo9eDBw+wsrIiihO/Kcn2MNbr9Whra8Ply5cxOjoq4u3l43nhUT7+JpMJ1dXVuHr1Kn75y1+ipKQEwKGSubu7i5mZGdy5cwcLCws5r/JbFG6xlxU02WLPLc9c8aZzbTYbSkpKUFhYiPz8fGGF5y9/AkH0fzqX/slEDUBmbhflTwAvlXUuHISl02lBbkTh3+n0IYHO1NQUxsfHRb7f66wxg8GAyspKXLhwAe3t7SgvL0d+fj7i8TgWFxfx4MEDTExMZLBocSs/fce9cnJ+hgys0um0KJdAoFej0Qjlit5rXNFUkYro9XrU1NTg+vXraGtrQzp9WAqFShJQ4VI6jwwk1H4CwbSHVSBK5X0jQH3q1CnU1NSIwsmpVEoQAaVSKfE9L3HB88xkpZVqCRKb4dra2hEPOe+/7AVRXV/uB12D/5WvQSFxp0+fFoWICwsLkUqlMDo6itu3b2NqakpQ18vyOt4R3n6VlwzIHt7H70PRP/F4XCjvoVAIwKH+lJeXJ0jKuDeJgAnftwQoZmdnMTo6KsLwOJBSeVz43pDHnsJCqaBwLBZDKBTCzs4OdnZ2kEgkBEBaXl5GLBbD+Pg4lpeXYbFYcPr0abS3t4u8wXg8jrm5OfT29uLJkyeYn5/PiDqRx1I2ZnDvnJxDmC2viu8JGmOz2Qy73Q6z2YxU6rAcgNfrFfqqbDQg5j4i/8jLy4Pf7xfFrIk4QxbV85qeCcfJiQRTRqMRHR0d+OKLL1BTUyOovZ88eYKlpaW37pWiooQdHR24du0aKioqMh5MwWAQ4+Pj6O3txbNnz+D1eoVF4U0rYT9Gdnd34fP5MDIyAo/Hg4KCApGot7+/L5I3X7x4kbWQMCnDBQUFKCsrw4ULF9DS0oJkMolnz5797CT5f0QhL2tpaamo5USu/te1erxJodygUCiEpaUlpTWIHmb0sC8uLkZbW5vIlyCljGrC3bt3D3/4wx+wtraGra0tBIPBE0WvTzTGFotFhGMBLyl4LRYLpqen8fz58x9FaPG6QqCNFxIGDhnGPvvsM5SVleGrr77Cw4cPMTc3l8FqSt4rk8kkch3pwV5SUiLKNdTW1sJsNgtr88LCAh4/foxvvvkGc3NzOTD1FkUGRPQS5i9rGeiokvLz8/MFha/JZEJeXp6g7lcR1cg5U/wfV8iATBrkbF4QWRECDoF8QUEBiouLBaEB5ULPzs7C5/MJ5UoV1iO312w2i3Co4uJikTe0sbGB4eFhjI+PY319/Uj7ac1za7jcbjpexWCWTqexu7srQqvpOO7xkcEC/U5zU1xcjIaGBrS3t8PlcmFnZ0ewEhPxBoFWOWyQxo2H+9F95LXD+5RMJkVBV7fbLWrmFRQUwGw2w2w2w2AwwGAwiMK2dD49vynHiYcZky4zNzeH8fFxjI+PZ7Aky2uJK+5yu/n80vdyeCsHcry/FMZYXl6OU6dOoaOjAyUlJTg4OMD6+jqePXsmjFwq5VfluZHnks+BfJ7cfjnEkfdJdS/yOq2vr2N9fV35/uDGAMqtDAaDCIfDIrR8eHj4yHszG9BWebXpPuQMSCQS8Hq90Gg0guAtEomIIsgrKyuYnZ0VNV1TqRTcbje6u7thsVjEOz4cDmNyclJQoVM0Dd9j8hzLOX1yDSj+l7dbBcjoNzIU0HiTI2N9fV2ECnKq/5KSEjQ1NcHj8YgCv7Ozs7h16xZWVlYyjCqqtZDN2JFNThyYIgphQsc2mw2Tk5MYHR3F8vIydnd337pSYDKZ0NzcjI6Ojgwa9GQyiZWVFbx48QLff/89BgYGMDc3J+LZ37eyQswmY2NjqKioQFlZmSgqSBuMPCPZFsfBwQGCwSBWV1eFhb6yshKVlZVwOp0wGAxvNJfkp0g2S9lJFfIeUBIqWYqCweA7p88n4e7xVwkpU3V1dXC5XOKFnEqlhDLR398v2O9OmueSCvTW1dWhublZ0JIDLxWm/Px8kRD8NvZxJBLBwMAAnE4nmpubhcJDRc05KxRRS/v9fqyvr4uczfz8fKyurgoqWIfDAY/Hg0uXLqG1tVV4z6nmT19fH+7fv4+FhYW3AhBzciiyAk7PJRUxBCmmFP4ig4C8vDy4XC5RroAsrLRXubLBc5q414YDNLomB1CyIkagjntQOGECFdI1mUwC2KVSKWxtbaG3txdLS0sZhcePU1gpLLWqqkqUidBoNIjH4xgfHxdKM4Ez7mEjoMKF/y4r+vK7IRaLwefziZAgihigkDjZc0P9TCQSgib6008/xWeffYbq6mrs7e3B6/ViZGQEL168ELlGNPcqpZsDvFe9w/jY7e7uCoIPt9uNoqIiFBYWorKyEqdPn4bH40FpaSkMBkPGeBCYorAl7pHb39/H8PAwvv/+ezx79iyD8IOeiYDac8mFe0ReBWgoBIvWINXgqqmpwZdffomLFy+ipKQE6XQac3NzuHv3LoaHh0UNJvnafK5kQEpgQNV2Wufc88mJY3hxbPqes13SnqM9SmyXdXV1Ii+axoYMacBhOHwwGMTMzAweP36M6elp4RWi0kAckMhhutnGlXuB8vPzsbu7i6GhIVE2hIp+p1IpxGIx5OXliRQPyos2GAwiKsztdkOr1YpC3Hfv3sXg4KAgbKCxJU8b907SuNG6kZ8BqjWRra/0l4xKBoMB0WgUS0tLePDgAW7duiWiwWhO9Xq9qNP3+9//Hh6PBwcHB3jx4gVu374t1rpMmEFCIb5EcPG6OuZPBlMajUZ0zmAwZGxQ+vdjlSp6kfT09ODcuXMoKSlBJBLB/Pw8ZmZm3lltKaPRKJI4yQqfTh+GPY2OjuKbb77Bn//8Z6yurr42an1XQh4o2tzJZBI1NTUwmUyoqqrCjRs3EI1GodVqsb29fcTVSS+YgoICsbny8vKE1Ssb+1tOsovFYoHH44Hb7QYAUaw3HA6fONp5WcgaW1NTg+rqathsNvEbWdUePHiA4eFhhEKhEwekgMP9XF1djdOnT6Ozs1P0gSxPoVBIJOa+Ldne3sZ3330nQjBcLpfIPdFoNCguLsaNGzdw/vx5xONx7O/vY2ZmBlNTU6ipqRGhujMzM/D7/YLZiBLNiZlpb28P6+vrGBkZwffff48nT568szIS/6jCyQmAzJA/EnpmEmDJlh+n0WhQUFCAkpISQfut0+kylH0ZNKhyULjlmMLK6Fmjstir2sr/b7Vaj4SP0TtRtqRns/QCL2m+bTYbzGazAGexWAyLi4sYGRnJKEcge0ayjT1vt8rjAxwCEirHsru7C6vVisLCQpSWlmawX3LASUqtzWaDx+NBV1cX2traYDKZ4PV6MTo6Cq/Xi8XFRSWjnQzu5N9V36vmgrxTVDeS5sLlcmFhYQGff/65oNXmc0TjR943Hl4aDocxNjaGp0+fYmpqSoRpUp+5dyFb+7nwtZitfzx/hhRfejafP38eLS0t0Ov1WFlZwfPnz/HgwQP09/eLUC4+lnxs+JzTuifgxteRPMfy/zmYInZkt9uNeDwuAAmFyVGuK5Xu6ejoQFtbmzAQJJNJwfJMKRJ+vx9ra2uYn5/Hs2fPsLy8LDgAZDDIAYa8J1XrjK+1ra0tUUxXo9GIqAg+9kAm4YPD4YDL5UJJSQlMJhNSqRT8fj+mp6eFM4OD7Wz7ktoul3EgUY07Xy+qc7hH7uDgQJT8WVhYEEYmynU0GAxoamrCqVOn0NLSApPJhOXlZTx+/BiDg4PY3NzMKCiuMnDwtfO68pPBlE6nQ1lZmchBIDS8vr6Ozc1NbG5uCtfi67zItVotCgsL0djYiJs3b6KjowN7e3t49OgR7t27JyhH35VwJh/g8IESDocxPj6OoaGh91JH6nUkmUwiGAxiZGQEe3t7WFlZwaVLl9DS0oLCwkJcuXIFpaWl6O/vx+DgYIYVEIAAXdevX8fVq1fhcDhESBq5iN+3YpZtA7/vdmUTi8WC5uZmlJaWiocMWVlVrvKTJFarFVevXsVHH32UQYmcTCYxOTmJO3fu4NatW5icnDyxwNBms6GzsxOnTp2C0+kUVkNSKAYHB/Htt9/C6/W+tXmIRCIYHBwEcPhsuXnzJhoaGoTCrNFoBOuW1WpFKpWC1WpFY2OjSGhOp9OoqqpCIpEQ1lCLxSLY1fb397GxsYF79+7hm2++ETmcJ/E59fcoXOHh4Xe0z2WvkqxwUPRARUWFCKej8BYKT+VKE8+/UVnn6V5ciSLGPr7OqS3cQ0XXIzYsIPOdqNEcsu3W1tZiZWUFgUAgIwxPBfI0Go0IT6N8DPKkUqiR3+8XRA9cYSNAwK9P1ybASO8FGhOZYIDC2kgBI6KPnp4ejI2Nie+5Ak5jXFFRgQsXLuDSpUtwOp2C+fOHH37A7OyseC/SOMo1u7iCJr+/OMU995aoFGg6n54B+/v7mJqaQltbG1pbW8Vx/N1CXm/qE+VTrqyswOv1YnZ2VoQ+8nNorAEI0Cv3i681uien36c1xwEq9ZGedR0dHfjd736Hrq4uWCwWbG1tYXR0FN9//z0GBwdFSQB5vPj95TXHPYs0J9yAIQMXDqboWdzZ2Ylf//rXaG5uRiKRQDgcxtbWlvBqFBcXw+12w+12o7y8HE6nU0QBRSIRRCIRrK6u4v79+xgYGMDy8jLW19cRiUSwv7+PaDSaUSdK3tf8/zxHUjX28n6nCBHqP/fw0D8CRlRXzePx4MyZM6ivrxflBkZGRvD06VMEAoGMeZUNF9RO/pc8WLQmZCMH7QkZ0PA+y+G6u7u78Pv9GBoawvz8vOgDPSfJUHnt2jVcu3YNbrcbgUAAT58+xf379zExMZFxDvdgcrBObSJP7+vUAv3JYMpms6GnpweXLl0SbCHxeBxbW1vY3t7GwsICRkdHRYwluc3k2g30oigsLERLSwt6enrQ0dEBq9WK+fl5PHz4EAMDA9ja2nonIVHEzkKFbWkyI5EI5ubmMDY2Bq/X+9ZJMH6O7O3tYWNjQ9QyWFxcRGNjI8rLy+F2u2G1WnHx4kV4PJ4jeV4GgwElJSXweDyoqamBRqPB4uKiYP97n2AqnU6LIs+cwpesdCdVmTcajSgvL4fdbhcFOTs7O/HP//zPuHv3Lubm5l5JMfo+xGQyoaysDD09PaLWBH/JLiwsYGhoCLOzswgGg++3sceIXq+Hy+VCUVGR8AYBECxWz549w/3797G2tvbW5mB/fx9+vx8DAwPipR2Px1FdXY3CwkLheeBkICaTCS6XK6NNRUVFADKVhb29PWxtbWF2dhb9/f24e/cunj9/jo2NjfcWRvqPJrLizC3AMhmEDDJISHmnFze9M+PxuKCDlvM5ONjg3/Prc1DHw/RUFmHZYs/bxaMStFot7HY7rl69ilgshkQiIcgd9vb2RBid0WgU55aUlODSpUvo7u5Ga2urYCskMp6ZmRlEIhHRf055TtERBLTkMefPfs7qxYELKUVkGHQ4HCgsLER7ezt6enowMTGRYX2nXB6z2YzW1lZcv34dNpsNa2trGBoaQl9fH0ZGRpR5RnxcSZmXQQUdwxkJVR5NGViRkkl1OD/99FPcuHEDZWVlQgmVPWL8XrFYDMvLy/j2228xODgowvs4GOGKMyn3BGiyeajkArIq7wc/3mKxoLq6GleuXMG5c+dQWFiIra0tjIyM4O7du3j8+LF4hsn94GPL1yj/ja97DjpkMMivScdXVlbin/7pn3Du3DlUVlYK0jFOoZ+fnw+j0SjCXynaZ2pqCsPDw5iYmBBeqUAggEAgINJU+LzzNcNz7KgtpOyrjK68r3wMeOgdX0u07zltv0ZzmF90/fp1fPTRRygrKxPlfu7cuYN79+5hbW3tCDCj9nEwxNcBgRGNRnPEgCN7oXjIstwvMjrFYjFEo1FxbTIyJpNJUQ+2p6dH5DQWFBRgfHwcfX19ePDgAaanp0UYPx93eW0R+QwxCJJ3762BKbPZjLa2Nly5cgW1tbUCwVEhroWFBTx8+BBPnjzBwsICDg4OBDvY3t4ekskk8vLyUFhYiJKSEmH56e7uht1ux8bGBl68eIEXL15geXn5nSnKOp0OVqsVpaWlgpkFOAxp8nq9WFhYyAhDOKlC1qexsTHMz8+LPjU1NaGlpQWtra1oaWmBwWAQMfH8gUPgmCz39+7dEwXa3pfSn0wmsb6+LuLeyc1eXl6O+fn5DOvoSRF6wJHXgXIgWlpasL+/j8XFRayurh6pIfO+hTzF9fX1aG5uFt5nACJ5dmlpCcvLy++VdOV1RaVs7u3twefzYWZmBktLS2+dWTGVSmFzcxPPnz8XJCTnz59HbW2tYEmj9UIkGfwly/MfAAjrZjAYxPT0NHp7e/Hdd99hZmZG5IXk5O2LvLZkhY17UrgXRb4GKTuhUEhEdRDLHVcMucLFKZJ5W1TPaK48yW2WARk/h4q7E1Aiq3lRURE6OztFHbrZ2VnEYjHs7u5mFMm0WCwwm83o7OzEzZs30dTUJMLIU6kUNjY2MDExgaWlJRF2LlurSSGTx5d/lkGqnBtGXitisCsvL4der4fdbhfvxGQyKYiByLBYV1eHnp4eNDc34+DgAENDQ7h79y5evHiBjY2NI54FHhbHhTwC1BYZgMv9Va0zOodyoTs6OnD16lXU19dnkAbwNcLzpcjwQlTjMzMzSCQSGWMm34s+czDI+5tt3ckKs6yQOhwOnDp1Cs3NzSI0bn19HV6vV7C08bZxQ4UcWit7NlT3VpFLqD5zwhWHwyG8TZS7SPuOKNrX19eRSCQQjUaxvr6OoaGhjALO5EDgOUS8P/z+1A9aUyrjhmp+sgkHCzRmPJSTaqa1tLSgsbERjY2NsFgs2NjYQF9fHyYnJ0UIPL+fvLeytSGdTh8pEM3bpTIsqeaRnptEzFNXV4elpSUBtIqLi9HZ2YkzZ86gpqYGALCwsIDe3l5R1kfFRK0C1nzsVN7AbPKTwVQsFsPk5CRaWlpQVVUl8mvoplTg89KlSwKNh8NhbG5uCtYbnU6H0tJSuN1uWCwWkUewubmJ/v5+fPvtt5icnHxnuRjkKSMKZbL0pNNpRKNRrK2tfRB5LiSUbBiPxxEMBrG1tSUo5mtqalBbWwu3243a2lqcPn1aMMDs7OwIhXl4eBhPnjzB6Ogo/H7/ewNSAETOyPDwsHiBtLS04L//9/8Oi8WCb7/99p3l1b2upNOHyac7OzuIxWKCYv7Jkyf493//d4yOjiord79vIXrXixcvora2FjabTbyM1tbW0NfXh0ePHmF6evpEFef9MUJzQ3kf72JtJ5NJbG9v49mzZ1hbW8OTJ0/Q2dkpGIfIU19TUwObzSYUmHg8jp2dHaytrYlipisrK5ifn8fS0hIWFhYwNzd3JGw3J29fuOUYeEmUwJUCEh62IpMpkBJF+RmklNtsNtTU1MDpdArvFFeISGQlTfZQ0PXlMDKqL8OJBriCf3BwgIWFBVE3jnK4rFYramtrUVBQgPb2dpFTsrOzI3IXiB7aYDAIIhXKQ9ZoNNjd3cX09DQGBwdFjUbeJxpHUmCpbfLY0zFcyZYVPLoGAYny8nKUl5fDZDLh/PnzKCoqwuPHjxGLxUQ5EJfLhbKyMhQVFSEWi2FkZAS3bt3C48ePsba2JpRjAiuyN4wbQ3hdH/qNPIW0blRC88yLEZMhuru7W9SW44QLPIGeAMDBwQE2NjYwMjKCwcFBoVtxgM3JSmRrPfe4AsioocWNPLK3hc8nATutVov6+npcuXIFVVVVyMvLQzQaxcjIiCgRIXukZKHfuLdVXh/y2qDfyNPJa4vxNobDYfh8PlRUVMBoNIrQymQyKQDp8vIytra24PP5MD8/j1AoJGqWhUIhRKNRJBIJpVeGj7c8rnzeZVBBYrVahVNC1kflNUjzJOePabVaFBUV4ezZs/jlL3+J7u5uOBwOxGIxjI2N4e7du5ifnxfszdzDSkV3edtUXlHuAeft4+ssmzeWrkltpe+dTic+++wzdHZ2igLEer0e5eXlsFqt2NnZwf3793H79m2Mj4+LulIyeM22V3leoSosN5v8ZDAVjUbx4sUL2Gw2xGIxlJWVCZc4Jysg64/NZsP+/r4IKyOqTofDgYKCApFguby8jCdPnuDx48ciVOVdUizT5BLbGQlZsChM6H2Cih8jtChTqZSoM7CysoKlpSWMj4/D5XKhtrYWS0tLohI2eQ9DoRBGRkbg9XpFnPD7lGQyCb/fj6mpKQwNDYnaJxSvzOOMT5KEQiH09vYiEonAarUiEAjg2bNnonDuSQvF0moPGbdqa2vR2dkJl8slrOmRSARerxe3b9/GyMjIWylu+/cuRANN1PQrKyuYmJgQTIk2mw0NDQ2izhAAwTTo8/mEcWllZQWLi4silOR91CzLyUuAxJWybMYRUkxl5Y5+o++5J4rWQ0VFBdbX18X5sjX+uPvye8ttt9ls4rkvH0/tWFlZwa1bt9DY2IjOzk5UVFQIFjyn04mioiLRHroHKV5cgSHFkUDj5OSkYAKlGjvUD65kyu9buiZ5+2UPBv3l7+r9/X2Ew2EsLS2hv78flZWVOHv2LOx2OywWC9rb21FZWSkUxb29PRGquLW1hb6+PmHppnpwXAHj71pZ5JwX6oNOpxOhuse1HcjMFSNGxKamJvGcIEU/EolgZWVF0GxTiFYqlRLvz+npaWxubmbkgnDgKreBK8n0l3LpVGMvf+ZzqdfrUVRUhNbWVnR3d8PtdiOZTApihrGxMayvr2e9Fl9L3JOTTSdTrSHyyqiU5VTqkKny+++/RzgcRmVlpchzImPzzs4ONjY2sLm5iY2NDfj9fkQiEdEO+Z6yx5CDE7md8pipjDJE3kI8AjQnRIxhNBqxu7ubYSSk+SUgabVa0drais7OTpw7dw7l5eUiT+r58+eieL3saeUhiHK4MG87AGGkkceeANlx80ZCzxSq7ZpKpeBwOAQ78sHBAXZ3dxEMBjE7O4vx8XGRp0a5YzJgktcPNzDRsa8Lokh+tmdqdXUV9+7dQ0VFBUpLS1FeXg6XyyUYs4iW2OPxwG63izAnerhQwbxIJILJyUk8f/4c33//Pebm5kRV8ncl6fQhOxGFGLpcLrS2tsJoNApXaHFx8Ttrz9uSdDotGI18Ph/Gxsbwww8/ZCwsAOLh/FOYGd+WxONxzM7O4i9/+QvC4TBaWlowPT2NmZkZZWX0kyA+nw//5//8HxG6lUwmkUgkhIf2pAkZOWpqatDU1CQqh+/u7orifbdu3cLCwsKJbL8spLTIYPt9A+/9/X1sb29jYGAAIyMjwkqp0+lQVFQEg8EgXoC0ZqjoIr1g6N9JXPf/KCK/qFX/z+Yt4tcgQEa5GQQU9Ho96uvr0d7ejrGxsSMU6dmEKwwqxYCDDO5J4ELn+Xw+/OlPf4LH48HS0hLOnTuH6upqlJaWwmg0Ij8/XwB/Ts/NFXT6R7XSiFr9yZMnInSbK47cOs3HSPZW0DGqZ5GsJMdiMayurgrdY2ZmBufPn0djYyOcTieKi4vFPiTAt7W1hWfPnuEPf/gDpqenRWgfb5tKGeNtyAay5P6p2i73OZ1Ow2q1oqGhQYx/Op0W0SRTU1P46quvRA4a1eyisU8kEojH4wJI8bXJ7ye3PxugIYVWPlflVdFqD+uMdXV1obu7G9XV1ULxn52dxdTUVEYph1cZrlVg47g5UHnbVP3d3t7G48ePMT4+Dr1eL/IAKY2Fh11zA4k8b+RV4UQxNA60v/nekNvC555/T7qOfCzlZ3s8Huzs7MDr9WbUrqL5MhqNqK+vx/Xr13Ht2jXU1dVBp9NheXkZ33//PZ4+fYr5+fmMItTUN1r7XDfP5g3mc8TXOfeGZnte0mfyfO7s7GB8fBzNzc0iNJSIRTY3N/H111/j1q1bwuBI+pW8j/g7lQsHWnKbeP+zyU8GUwQ8AoEAIpEINjY2YLVahXeKqEaJDcjpdKKwsFDUdaCHFLnSyHVK1on3FfpETCj9/f0iebaurk6w37xLRsG3KbR5eS7bhyD0oHv+/Dn8fj9cLpcgPDmpxYSpdteHIsRyR6FGqdRhgc6RkRH09fXhzp07olDehyDEUOl0OjNCTt73WqEHOiltJFqtFuFw+EhbKUwpB5xOlnBlk1uiOb2uSqHgCgi9tA8ODjA1NQWn04nLly/DYDBgf38fNpsNDodD5LhywoBs61hWxLgHjbedvCLEGEnXI+VPozmkVl5fXxfkBS9evEBlZaUoillZWQmXyyXC/UmJ4cxxKysrWF9fh9/vx8bGBra3tzE3Nydy/HgYkkqZ4XWA6HdKFielVaWQylb1WCyGjY0NhMNhBAIBzMzMoLa2VuQRV1ZWwmQyiZzWb775Bt9//z1GRkZE1IYceiRbvGVAwQEa/54XG+VrQQVcSAhgU5QM5bQtLS2hr68PfX19mJ2dFd4SnrRPY8BJL1T34P+nvsptp/Gm9Sh7E7m3joOK0tJSfPLJJ7hw4QIKCwtF6OXXX38Nr9ebkUrB15LctmwGAFlkrxB9Jq8NtY33LZlMijA97v3K5imiNvGx4c9vWrsqb5rKO8bXlcpQwOu68XN0Oh0qKirwi1/8AjU1NVhbWxPU/aSDlJeXo7KyEs3NzWhqahIswzMzM+jr68Pg4CC8Xu8Roi9qC2fnk+dABtA8P5R+47lj3FtGa0cVqkve23v37mF1dRWNjY2w2WwCN/h8PkxPTwsnAQe38jp+FdCm7+gZ/ropAD+7aC+xw8TjcWxubioXOHXCaDQKsEXHywleqhjLdykUDjc8PIx4PI5AIICGhgYkEgk8f/4cS0tL710J+0eXeDyOpaUl+Hy+I27anPx8ITA1MTGBH374AZWVlQgGgxgaGsLAwAAmJiY+qLwcSoQ3GAwAXiqQ29vbWF9fRzAYPFEetlQq9UGQeuTkpchKFg/5UwEU+p7/BSAUeMrfqaioEOHvZMBQ0VNzkYEdfSZFSFZuCKCr6sJw0BIOh0XentfrhdvtxszMDLq6ulBVVYWSkhKYzWahJBqNRmFImpubw9zcHBYWFrC5uYm1tTWh9GQLE5PbzvOjuLeNhwYeNy70PSnK0WgUsVgM8/PzsNvtaGxsRHd3Ny5evCgU0cHBQXz33XcYHh4W9OGyh4N/l+3+2cgneFjocaBY9mju7u5iY2MDs7Ozom7h6Ogobt26henpaYTDYZFTI+fIyXObbZzoL2cilPvH1708FrxPBCyo/iWVqSCysoGBAQwMDGB6ejoj9DAbcJHXtBwSpwI2qj1KuoMs3Nis8iTTvPC+8WPy8vJgMpkEkE0kEuK6nAFPNd78L3mv5PFVATv6fyqVQlVVlaiz1NrairW1NWxsbCCRSIhcQUrNycvLQygUQl9fH549ewav1wufz6ekkqf9xseMAxTeHm5wkEME5XUvk7jI1yMAtri4iI2NDYyNjQGAiPAhwiWVIYVfV55Hle7Ivc6cBOpV+uXPBlNyA4+74cHBAaLRqEDpJ9WyTZTJm5ubGBoaEm50CknMKe3vX/gGzMmbl/39ffT392Nubk4k6u7u7iISiYjK6R+KUBhAKBSCy+UCAMEgNjw8nFGvIic5+bGSzWouW0G5AsXD7/h56fQhVfjjx48xMTEhPCR0XiQSybimnATOryW3LxsQIO+orPjKijJJMpkUoC4QCGB8fBw6nU4wlup0uoywIiJ4IQWfmAHlxP9s7eb/l+m25b6rLOUq7wUdT0VYd3Z24Pf7MT4+jgcPHsDj8WB5eRlLS0tKNjPZK5VtLvn9uGeCe3nk9st9V/0WCATw8OFDzMzMQKvVCmBICjMBY9V1ZOHjky3UShWWxcNNZe+OCtADgNPpREdHB5xOJ5LJJAKBAO7evYvvvvsOs7OzwkhH1+IgiLfxVd47+Xs+X3xsVOtGBoWq8C95bOTr8Dw4uR0ccFEImzz2JHxPcp1HBlJ0XzI0e71e4S2uqalBdXU10ulDIyJRudP5FBr67NkzUQKHe55IVCCQ2nIcUFcR7cje52yhrrKQ84a8wwSyZOIcvm74/MtyHNjKNvbZ5I2BqdcRjvZPsqTTaRFb/CGFZ+UkJ29K0uk0wuEwwuHw+27KzxZiDKupqYFWq8Xm5iZGRkbw6NEjjIyMYG1t7cQ/k3JycoUrUPwFLCt8KiU/G7DZ29sTtQxVyhU/X37pc+GKi2wVpt84qxV9R3kwKtIEOp8Kk25ubir7yPvF266KXOHjICtwdAydz5UanU6XQejBr8+vpQKdfKwoFyYajSIcDmNtbU3kT+/v72eAQ1VfVaFYfF3wkC5+nhzGpho/Ct+i8CrgEMisr6+LotzcOyf38zjhiiz1gyugdA1VeBvlAdH489wgeSy02sNaY7W1tfB4PIjH44LF8datW3j69KmIEODeDD7/2dpOYyjPu7zuucLNw3CpX3IYLF1D9igSeOBjpfISyfMhU7PTnKryzXhfaG3z63ODBT+Hcov6+/sBAM3NzTAajTCbzSK/joDS9vY2wuEwVldX8fTpU8EGS140bizguY8qhr1sgIPmRc4X47li/FrHMfvx56NKOGCSQyx5HVKVt0kGf9Q2Cqd8nb30TsFUTnKSk5y8a4lEIhgZGYHFYoHP58Pc3Bz6+/vR399/YklAcvLhiOyZ4EogV7JliykAQYzCC56m02nBiMvD4LjCx5UA+p7uxUERV1K4Ff04Syz1hyuk/Fq8rxSGxL1MBMZIuVOFe3GFl/KreUI/HavT6QAgg8GQ7sGP4QognxfeduBoAVl5LEkZJdKlbEXqZUWQjyUHqVxpk3OO+FjzzyqPixyqBkCQIsjHZ8vPUnk+aPxkhZ0r0DS3fDxpDPjvXMnm/aWxoNC3vb09PH/+HPPz87h9+7ao8ccZLLnhXR4vGRCrjBREv85rPMnH8mtl8wrJoEy+Nx8X2ofyWqA+yf3gvAIcMBAopfUoe134/MjgnLx9f/vb3zA0NIT29naYzWY0NDTg4sWLMJlMSCQSyMvLw/j4OHw+H9bX10UuUjQaVa4pDg7lfcjHhtbf6wAfFXDi39Mc0rW4oYcfR2uPwvhlZkzebroPkeXIAJCHE5MR5HVFcxzi0mg0uXi2nOQkJx+05Ofno6CgQBRgDIfDCAaDyiJ+/+iSTqdPXm2BEywajSat0+mEd4crkHK+gax400s9Ly8vI0eOK+IcRMjXlBVDUmK4pZv+cWVEBk96vT5DmeYKJG+7Xq8X1nyybssghZ/P20bXzkZKwC3PXAGlBHa+T/m9qNh8Op0+kmsjjw0HlrJyLYfoySFYxwES2UpPQmPD+8Q9GeRx4h4POoa3jQCPXq8XOTi8PTII5jlONHZyzhVvIymWcs7ZceFN6XRagAE+P9nWQ35+PvR6PcrKylBcXCzCxf1+v+gTH3s+DjKA4fPA54qDG/rHab3lsaf9Iq9Nfp+8vDwxRzwclq9v3k/ZkMK9IXxcNBqNYGs9ODjIyE+i+dZoNBlFuXnb5HXDnw80p1R7ia5XXFwsACqBLgq3DQQCGSQw2cZbtS752pGBJu83ASAC1vyZI9eLk40RMhiSn2MEguRnibymqd28XXxOaa3Ke5bPc7Z3ZM4zlZOc5OTvWii3IxQKZSiWOcnJmxAVCCEhRYEryKQwEDDhBVGpBgxn65MBGldsuBWVs91xZYUUB1I25DAerkxyz5assMtKP1feyJvAlRmucJL3TaWM8XZyICYrRJyCm4Qs8+Tho/wy2UpO9+OfZSWK94mUPhIVCOXfcY8b9zTSMdQ3OaSLjsnPz8/IseGAQDXu8vNLHjdSRjkY5GPPj0+lUmIe+BhwxVYeLwAZ5Awk8rrh7UulUlhdXcXq6mqGt1VeK6RE8/FQ9Vf+TgbrJKrv+Lo/ztulArb0nQwU5L7T2HEgQNeSc3r4eHEwABwaO+Q9m42wgT5T2KrX6z0yZhwcHwcKufEm29jz+3PwSL/pdLojoEY2XMhtp71CzzQOhPkxNE7c4MLHW24/B110bfnZwOeQQCk30hwnOTCVk5zk5B9Ccl6onLxtIYVCtpDn5eUJKzCQmX8hKxP0N5uVWFZkuGcmm3JLCgoPneH3JQVPbhNX6Pn1uYItAzwyWMht5G3n7aNryN4NDjb4+HKQwhU0Ag8q0EBeBTqPW8JVwsELH2PV8aS8ysqwDBB5bo/qOtlCn1KplyURsgFD3udU6rCeFH3PvaF8LjhwVHkOOUjga0PuOwcHfC1w4eCSgzW5/9RXWYGW204iAxIOUrLlgsmgSWUE4f2T28ONECrAqAIr8h4hDxEdI4MFKhxNbSfAoHoGyOGf/PfjgJOqjfIzgY8TfcePl0nk+JqktnEjDz+OP3P4Pud7ls8Zfy6oQB61R7VH5HvLAI/uK89rtvFVSQ5M5SQnOclJTnLyE0W2XstKtypkhCsEslWc6uVl+x3ItDKrwrG4R4IrBzyfQLYUy5ZZfr9sn+W/2cLJ5NwS+k7OqaLxUnmCZJIB+i7b9el71fip8pfk/qgUNpXI1+ahWao5lK305LGSrej0u3zOcfeXP8tt4MLHhsZVHmN+/2z9fZ3vVcBI3jNye44Dr6rry21QzV+2NquOU61B4CiRjNw3+Vz5XvJ1sx1H7ac9q1oDqva9Suk/ro38N3nO5eeb/MyR9xpwuBe4Z15un2p/8nvJ+1q1LlTedD5+2b7Ltj74M0zF/JhNcjlTOclJTnKSEwBAOpcz9aMk947MSU5ykpN/HMn2jjwWTOUkJznJSU5ykpOc5CQnOclJTtSiffUhOclJTnKSk5zkJCc5yUlOcpITWXJgKic5yUlOcpKTnOQkJznJSU5+guTAVE5ykpOc5CQnOclJTnKSk5z8BMmBqZzkJCc5yUlOcpKTnOQkJzn5CZIDUznJSU5ykpOc5CQnOclJTnLyEyQHpnKSk5zkJCc5yUlOcpKTnOTkJ8j/DyUnVblIjoiXAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 1080x1080 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Grab a batch of real images from the dataloader\n",
+        "real_batch = next(iter(dataloader))\n",
+        "\n",
+        "# Plot the real images\n",
+        "plt.figure(figsize=(15,15))\n",
+        "plt.subplot(1,2,1)\n",
+        "plt.axis(\"off\")\n",
+        "plt.title(\"Real Images\")\n",
+        "plt.imshow(np.transpose(vutils.make_grid(real_batch[0].to(device)[:64], padding=5, normalize=True).cpu(),(1,2,0)))\n",
+        "\n",
+        "# Plot the fake images from the last epoch\n",
+        "plt.subplot(1,2,2)\n",
+        "plt.axis(\"off\")\n",
+        "plt.title(\"Fake Images\")\n",
+        "plt.imshow(np.transpose(img_list[-1],(1,2,0)\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3.9.9 64-bit",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.9.9"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}