gbcanuck71/6.3.2_SoftmaxClassifier2_v2.ipynb

## 6.3.2_SoftmaxClassifier2_v2.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"http://cocl.us/pytorch_link_top\">\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/Pytochtop.png\" width=\"750\" alt=\"IBM Product \" />\n",
    "</a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/cc-logo-square.png\" width=\"200\" alt=\"cognitiveclass.ai logo\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Softmax Classifier</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<p>In this lab, you will use a single layer Softmax to classify handwritten digits from the MNIST database.</p>\n",
    "\n",
    "<ul>\n",
    "    <li><a href=\"#Makeup_Data\">Make some Data</a></li>\n",
    "    <li><a href=\"#Classifier\">Softmax Classifier</a></li>\n",
    "    <li><a href=\"#Model\">Define Softmax, Criterion Function, Optimizer, and Train the Model</a></li>\n",
    "    <li><a href=\"#Result\">Analyze Results</a></li>\n",
    "</ul>\n",
    "<p>Estimated Time Needed: <strong>25 min</strong></p>\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Preparation</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll need the following libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Import the libraries we need for this lab\n",
    "\n",
    "# Using the following line code to install the torchvision library\n",
    "# !conda install -y torchvision\n",
    "\n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following function to plot out the parameters of the Softmax function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# The function to plot parameters\n",
    "\n",
    "def PlotParameters(model): \n",
    "    W = model.state_dict()['linear.weight'].data\n",
    "    w_min = W.min().item()\n",
    "    w_max = W.max().item()\n",
    "    fig, axes = plt.subplots(2, 5)\n",
    "    fig.subplots_adjust(hspace=0.01, wspace=0.1)\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        if i < 10:\n",
    "            \n",
    "            # Set the label for the sub-plot.\n",
    "            ax.set_xlabel(\"class: {0}\".format(i))\n",
    "\n",
    "            # Plot the image.\n",
    "            ax.imshow(W[i, :].view(28, 28), vmin=w_min, vmax=w_max, cmap='seismic')\n",
    "\n",
    "            ax.set_xticks([])\n",
    "            ax.set_yticks([])\n",
    "\n",
    "        # Ensure the plot is shown correctly with multiple plots\n",
    "        # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following function to visualize the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Plot the data\n",
    "\n",
    "def show_data(data_sample):\n",
    "    plt.imshow(data_sample[0].numpy().reshape(28, 28), cmap='gray')\n",
    "    plt.title('y = ' + str(data_sample[1].item()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Makeup_Data\">Make Some Data</h2> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the training dataset by setting the parameters <code>train</code> to <code>True</code> and convert it to a tensor by placing a transform object in the argument <code>transform</code>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the training dataset:\n",
      "  Dataset MNIST\n",
      "    Number of datapoints: 60000\n",
      "    Split: train\n",
      "    Root Location: ./data\n",
      "    Transforms (if any): ToTensor()\n",
      "    Target Transforms (if any): None\n"
     ]
    }
   ],
   "source": [
    "# Create and print the training dataset\n",
    "\n",
    "train_dataset = dsets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())\n",
    "print(\"Print the training dataset:\\n \", train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the testing dataset by setting the parameters <code>train</code> to <code>False</code> and convert it to a tensor by placing a transform object in the argument <code>transform</code>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the validating dataset:\n",
      "  Dataset MNIST\n",
      "    Number of datapoints: 10000\n",
      "    Split: test\n",
      "    Root Location: ./data\n",
      "    Transforms (if any): ToTensor()\n",
      "    Target Transforms (if any): None\n"
     ]
    }
   ],
   "source": [
    "# Create and print the validating dataset\n",
    "\n",
    "validation_dataset = dsets.MNIST(root='./data', train=False, download=True, transform=transforms.ToTensor())\n",
    "print(\"Print the validating dataset:\\n \", validation_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the data type is long:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data element:  torch.LongTensor\n"
     ]
    }
   ],
   "source": [
    "# Print the type of the element\n",
    "\n",
    "print(\"Type of data element: \", train_dataset[0][1].type())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each element in the rectangular tensor corresponds to a number that represents a pixel intensity as demonstrated by the following image:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/chapter3/3.32_image_values.png\" width=\"550\" alt=\"MNIST elements\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this image, the values are inverted i.e back represents wight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print out the label of the fourth element:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The label:  tensor(1)\n"
     ]
    }
   ],
   "source": [
    "# Print the label\n",
    "\n",
    "print(\"The label: \", train_dataset[3][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result shows the number in the image is 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot  the fourth sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image:  None\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the image\n",
    "\n",
    "print(\"The image: \", show_data(train_dataset[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You see that it is a 1. Now, plot the third sample:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAEICAYAAACZA4KlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAOT0lEQVR4nO3df4wc5X3H8c/HgFXkALX5YSzsGhIZ1KgypDK0QApuTZBjtTL5w0ksXFyFckgEqVGjqoiqCqpbCSqShlYi0vFDNsGFRDIGC4UEy6pwKoHlM3LBv00t7Jx9sotchCmI1PDtHztOL+Z29rw7u7N33/dLWu3uPDs7X4/8ueeZndl9HBECMPlNqbsAAL1B2IEkCDuQBGEHkiDsQBKEHUiCsANJEHZUxvbNtsP239ddCz6NsKMSts+R9IikLXXXgrER9knM9l/ZXnfasn+x/f0ubO7bkl6WtKcL740KEPbJ7WlJi23/piTZPlvS1yT9cKwX237R9rtNbi8224jtuZK+Ienvqv8noCpn110AuiciRmxvlrRM0mOSFkt6JyK2NXn9H7e5qX+W9LcR8b7tNt8C3UbPPvmtkbSieLxCTXr1dtn+E0nnRcSPqnxfVM98621ys/0bkkYk/YGk1yR9PiIONXntS8XrxvLziPjyGOt8X40h/AfFogskfSxpU0Qs7ax6VImwJ2D7MUm/p8YQ/o8qfu/zJE0btegRSUckrYqI41VuC53hmD2HNZL+XI0euFIRcULSiVPPbX8o6X8Iev+hZ0/A9m+pcUrs0oh4r+56UA8+oJvkbE+R9JeSniXouTGMn8RsT5N0VNJBNU67ITGG8UASDOOBJHo6jLfNMALosogY8zLGjnp224tt77X9lu37OnkvAN3V9jG77bMk7ZP0JUnDkrZKWh4Ru0rWoWcHuqwbPft1kt6KiAMR8UtJz0ri8kigT3US9ssk/WLU8+Fi2a+xPWB7yPZQB9sC0KFOPqAba6jwqWF6RAxKGpQYxgN16qRnH5Y0Z9Tz2Wp8AQJAH+ok7FslzbN9he2pkr4uaUM1ZQGoWtvD+Ig4afteST+TdJakJyNiZ2WVAahUTy+X5Zgd6L6uXFQDYOIg7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IIm2p2wG+t2iRYuatq1du7Z03Ztvvrm0fe/evW3VVKeOwm77bUknJH0s6WRELKiiKADVq6Jn/8OIeKeC9wHQRRyzA0l0GvaQ9LLtbbYHxnqB7QHbQ7aHOtwWgA50Ooy/MSKO2L5E0kbbeyJi8+gXRMSgpEFJsh0dbg9Amzrq2SPiSHF/TNJ6SddVURSA6rUddtvTbJ936rGkWyXtqKowANXqZBg/U9J626fe518j4qeVVNUFN910U2n7hRdeWNq+fv36KstBD1x77bVN27Zu3drDSvpD22GPiAOSrq6wFgBdxKk3IAnCDiRB2IEkCDuQBGEHkkjzFdeFCxeWts+bN6+0nVNv/WfKlPK+6oorrmjaNnfu3NJ1i1PKkwo9O5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kkeY8+x133FHa/uqrr/aoElRl1qxZpe133XVX07ann366dN09e/a0VVM/o2cHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSTSnGdv9d1nTDyPP/542+vu37+/wkomBhIAJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0lMmvPs8+fPL22fOXNmjypBr1xwwQVtr7tx48YKK5kYWvbstp+0fcz2jlHLZtjeaHt/cT+9u2UC6NR4hvGrJS0+bdl9kjZFxDxJm4rnAPpYy7BHxGZJx09bvFTSmuLxGkm3VVsWgKq1e8w+MyJGJCkiRmxf0uyFtgckDbS5HQAV6foHdBExKGlQkmxHt7cHYGztnno7anuWJBX3x6orCUA3tBv2DZJWFo9XSnqhmnIAdEvLYbztZyQtlHSR7WFJ35H0oKQf275T0iFJy7pZ5HgsWbKktP3cc8/tUSWoSqtrI8rmX2/l8OHDba87UbUMe0Qsb9K0qOJaAHQRl8sCSRB2IAnCDiRB2IEkCDuQxKT5iutVV13V0fo7d+6sqBJU5eGHHy5tb3Vqbt++fU3bTpw40VZNExk9O5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kMWnOs3dq69atdZcwIZ1//vml7YsXn/5bpf9vxYoVpeveeuutbdV0yqpVq5q2vfvuux2990REzw4kQdiBJAg7kARhB5Ig7EAShB1IgrADSXCevTBjxozatn311VeXttsubb/llluats2ePbt03alTp5a233777aXtU6aU9xcffvhh07YtW7aUrvvRRx+Vtp99dvl/323btpW2Z0PPDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJOCJ6tzG7axt79NFHS9vvvvvu0vZW328+dOjQmZY0bvPnzy9tb3We/eTJk03bPvjgg9J1d+3aVdre6lz40NBQafsrr7zStO3o0aOl6w4PD5e2T58+vbS91TUEk1VEjPkfpmXPbvtJ28ds7xi17AHbh21vL27lk6MDqN14hvGrJY31cyP/FBHXFLefVFsWgKq1DHtEbJZ0vAe1AOiiTj6gu9f2G8Uwv+nBk+0B20O2yw/uAHRVu2H/gaTPSbpG0oik7zZ7YUQMRsSCiFjQ5rYAVKCtsEfE0Yj4OCI+kfSYpOuqLQtA1doKu+1Zo55+RdKOZq8F0B9afp/d9jOSFkq6yPawpO9IWmj7Gkkh6W1J5Sexe+Cee+4pbT948GBp+w033FBlOWek1Tn8559/vrR99+7dTdtee+21dkrqiYGBgdL2iy++uLT9wIEDVZYz6bUMe0QsH2PxE12oBUAXcbkskARhB5Ig7EAShB1IgrADSaT5KemHHnqo7hJwmkWLFnW0/rp16yqqJAd6diAJwg4kQdiBJAg7kARhB5Ig7EAShB1IIs15dkw+69evr7uECYWeHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5IYz5TNcyQ9JelSSZ9IGoyIR2zPkPQjSZerMW3zVyPiv7tXKrKxXdp+5ZVXlrb383TVdRhPz35S0rcj4rcl/b6kb9r+vKT7JG2KiHmSNhXPAfSplmGPiJGIeL14fELSbkmXSVoqaU3xsjWSbutSjQAqcEbH7LYvl/QFSVskzYyIEanxB0HSJZVXB6Ay4/4NOtufkbRO0rci4r1Wx1Oj1huQNNBeeQCqMq6e3fY5agR9bUQ8Vyw+antW0T5L0rGx1o2IwYhYEBELqigYQHtaht2NLvwJSbsj4nujmjZIWlk8XinpherLA1CV8Qzjb5T0p5LetL29WHa/pAcl/dj2nZIOSVrWlQqRVkSUtk+ZwmUiZ6Jl2CPi3yU1O0DvbIJtAD3Dn0YgCcIOJEHYgSQIO5AEYQeSIOxAEkzZjAnr+uuvL21fvXp1bwqZIOjZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJzrOjb433p88wPvTsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AE59lRm5deeqm0fdkypiKoEj07kARhB5Ig7EAShB1IgrADSRB2IAnCDiThVnNg254j6SlJl0r6RNJgRDxi+wFJd0n6r+Kl90fET1q8V/nGAHQsIsb8IYDxhH2WpFkR8brt8yRtk3SbpK9Kej8iHh5vEYQd6L5mYW95BV1EjEgaKR6fsL1b0mXVlgeg287omN325ZK+IGlLsehe22/YftL29CbrDNgesj3UWakAOtFyGP+rF9qfkfSKpH+IiOdsz5T0jqSQtEqNof43WrwHw3igy9o+Zpck2+dIelHSzyLie2O0Xy7pxYj4nRbvQ9iBLmsW9pbDeDd+4vMJSbtHB7344O6Ur0ja0WmRALpnPJ/Gf1HSzyW9qcapN0m6X9JySdeoMYx/W9LdxYd5Ze9Fzw50WUfD+KoQdqD72h7GA5gcCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0n0esrmdyQdHPX8omJZP+rX2vq1Lona2lVlbXObNfT0++yf2rg9FBELaiugRL/W1q91SdTWrl7VxjAeSIKwA0nUHfbBmrdfpl9r69e6JGprV09qq/WYHUDv1N2zA+gRwg4kUUvYbS+2vdf2W7bvq6OGZmy/bftN29vrnp+umEPvmO0do5bNsL3R9v7ifsw59mqq7QHbh4t9t932kppqm2P732zvtr3T9l8Uy2vddyV19WS/9fyY3fZZkvZJ+pKkYUlbJS2PiF09LaQJ229LWhARtV+AYfsmSe9LeurU1Fq2/1HS8Yh4sPhDOT0i/rpPantAZziNd5dqazbN+J+pxn1X5fTn7aijZ79O0lsRcSAifinpWUlLa6ij70XEZknHT1u8VNKa4vEaNf6z9FyT2vpCRIxExOvF4xOSTk0zXuu+K6mrJ+oI+2WSfjHq+bD6a773kPSy7W22B+ouZgwzT02zVdxfUnM9p2s5jXcvnTbNeN/su3amP+9UHWEfa2qafjr/d2NE/K6kL0v6ZjFcxfj8QNLn1JgDcETSd+sspphmfJ2kb0XEe3XWMtoYdfVkv9UR9mFJc0Y9ny3pSA11jCkijhT3xyStV+Owo58cPTWDbnF/rOZ6fiUijkbExxHxiaTHVOO+K6YZXydpbUQ8Vyyufd+NVVev9lsdYd8qaZ7tK2xPlfR1SRtqqONTbE8rPjiR7WmSblX/TUW9QdLK4vFKSS/UWMuv6ZdpvJtNM66a913t059HRM9vkpao8Yn8f0r6mzpqaFLXZyX9R3HbWXdtkp5RY1j3v2qMiO6UdKGkTZL2F/cz+qi2H6oxtfcbagRrVk21fVGNQ8M3JG0vbkvq3ncldfVkv3G5LJAEV9ABSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBL/Bz7ZR5RP0G0AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the image\n",
    "\n",
    "show_data(train_dataset[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"#Classifier\">Build a Softmax Classifer</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a Softmax classifier class: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Define softmax classifier class\n",
    "\n",
    "class SoftMax(nn.Module):\n",
    "    \n",
    "    # Constructor\n",
    "    def __init__(self, input_size, output_size):\n",
    "        super(SoftMax, self).__init__()\n",
    "        self.linear = nn.Linear(input_size, output_size)\n",
    "        \n",
    "    # Prediction\n",
    "    def forward(self, x):\n",
    "        z = self.linear(x)\n",
    "        return z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Softmax function requires vector inputs. Note that the vector shape is 28x28."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of train dataset\n",
    "\n",
    "train_dataset[0][0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flatten the tensor as shown in this image: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/chapter3/3.3.2image_to_vector.gif\" width=\"550\" alt=\"Flattern Image\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of the tensor is now 784."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/chapter3/3.3.2Imagetovector2.png\" width=\"550\" alt=\"Flattern Image\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the input size and output size: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# Set input size and output size\n",
    "\n",
    "input_dim = 28 * 28\n",
    "output_dim = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Model\">Define the Softmax Classifier, Criterion Function, Optimizer, and Train the Model</h2> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the model:\n",
      "  SoftMax(\n",
      "  (linear): Linear(in_features=784, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Create the model\n",
    "\n",
    "model = SoftMax(input_dim, output_dim)\n",
    "print(\"Print the model:\\n \", model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the size of the model parameters: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W:  torch.Size([10, 784])\n",
      "b:  torch.Size([10])\n"
     ]
    }
   ],
   "source": [
    "# Print the parameters\n",
    "\n",
    "print('W: ',list(model.parameters())[0].size())\n",
    "print('b: ',list(model.parameters())[1].size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can cover the model parameters for each class to a rectangular grid:  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<a>     <img src = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/chapter3/3.3.2paramaters_to_image.gif\" width = 550, align = \"center\"></a> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the model parameters for each class as a square image: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EWFUlLS1qkUpXl0hurmzfLgLPCpQpOq+tlepqPd0tU7C4WBQBZGervCoqFGGGhMiIQSXnjXznwFUR8AyQ/WBM299IVpai+6IiMSjrmCxdqkAiOdkgtZYW/UVxsUhLi1RV6XgyMw3CWb1aLGhVWqq6JcXFUgEi/v5SWqpDlcJCHXtDg05oba3ec8ECRUUhISJtbdLWps+cmioiK1aoq6aoSLKyxkx/yxLKydH3pqeLSEaG7AeRujqBw5KQYFBwefn4CDgsTMe0bJlIWpp0dKhqZmer6q5fr8sgJERRnZeXPqo0NtqukJYWGyyLgD7LwoU6zsBA6TBWQ2ysIufBQaNXK1aorvT0SE2N7Rmzzf7kZPNey3WTmSkSGKjItKBAKip0fEuX6pK05m3zZh2juh5OX+EKKBfoFhgYVx7Tp8dLUZGoBdHTo3O2dKlIaak0NxtXirUfFBaqtWr5ZMrLVQjGndbSoq/qajUonE7RB1qwQGRoSBISjCxdLpGsLOPG6xJZtUoOXbluWlpUV/r6pLRUVW7TJn2tXWvWXm6u7Dcuh0HLPefxiPT1yUmQTcY11WZcrsXG0iosFGMN/o1cENNAnjN+W3E4VHMiI0V6egQGJDZWhdjTow+QkyMCdQK5Ulkptqvg7BV+msZGnZDBwSuEHxsrpWbRS0eHLqi6OpHmZlm2TE0TGRwUiY6WM2ZTrbU2sIULdffp6pLYWBHYpws0KUk6QPLMRqIm1vMSG6sLIinJWgj1on7K541b4dUv2ICjBE6IxMaqmyUjQxerxyNnzDOqOdNqTO+NxrXwtJpDJSUiycnyjjFbJDdXGhp00hMSxDZ/oU9lUFIiEhcnQyDNoK6Y7GyBOvWPO51ywDrccnJEhoYkL89adOsFdsv27Srm7GxdROnp+n09PWpeyqpV0tNjTL6SEl11ln0M8jJI+Dgb8A0glWDm/W2BAVmwQNeAw6EbQF2d2K4ll0v0H7Gx4vHosC3XRFmZ2RxKSuQ86EZZUKCHW1qaymvVKlH/ZZlITo6MmLm14wNZWWKc7yI5ObJ0qeVP3yiwUaS8XD4BaWsTgTdk/XpR+Tmd1tmjm0VlpUhysgyCOcwyBN6ToiLLJT/OBuznpw9UUSEyOGhM5QcFNojTqfOSnq57huUKkI4O/eIVK0S2bDH3r9O4hb+/NBpd3wtSAVIGugY9HpF162Q/yG6zRrusjaaoSGpqxlzQqam6x58HOYD66V8AWY/6NGstF15urqxfr+fY4KDlXugT9bs/K+pD/a05XDebuXh6fHncfLNIX58BImUiQ0N6gOTny4oVlltktzgc6pPfZJ7TMu8lJ0cPHuPn0zX8tixYoIeFdHSIrFsn1dUicEnS0hSE9PQosHI6RSQzUz4xLgIJCVEd6euTwkIxz6WgrKPDuOUGB0XS022Q9I4BGRIdLRIXJ5+AVKGuw5NmvK+be4/5lP9GLohrgdnANACXS1+RkQBkZk4iMhL8/DSAvG2bWsjQDcDjj8NhjweefZbWp58m+NQpzvV609oKx4+Djw/8vV89lJTAkiU88OSTMG8eW/ZOIzoabispgdFROjt/i0g1wz534X3sGKH+/iy+/nq4+WZ4+GG19V0uLvoEkJgI3d13cf488PzzTNu3j59v3Ai5ubAI4DpAvQ+vvAKzgz/nnoduZffuMmAJVVWzCQ+HG2/88VXl4ePzNW6+eQbU1/NYQQEXHv4p4Wvh7W3ePDA4yKftE9lzK4Af2dn3sWgRPPQQnDpVwsjIed6+9vuklH+fxQ/dD2fP8uc7/oUnH1bzKzJSg6pOZxLf+hbwyA/UPVBSwnGfW5jtusyspiYuhM8mPxSWLIHPr1c3C+HhEBfHe7t8efFFiIuDtWv/ieBguOkmGBiAo0dh/34PwcETCA1Vs3vyk09CVBS5uTonjz32fRJf/D719frVP+14jEeuv57CcfQj2N+fexYtIvEpiIpKs83plSth8WL93uPH1StUUwMjIz3UTriT23Nz8R4d5sUXfZk3D3w3/wHqWyG3HNxuAtPSCAwMhLo6bq/9AbS3s9zhgLlziY39IddfDzwcg09cHIEpKZxzT4T3N8GWLfAP/wA7dnCi3Zf5OyA+HhyO5QQGAhGHca1ZA3xGZeUy7mn9PR8BF9xuNmxQGV03eEJD9rm5TPTzw68XkpNf5ZFH4Hvn18O+rvEXjLe3TmZMDIePTyQ1Fd5997e4XFPJyIDrr4c77tD18vLLifT1Ae0fqctm0SIupjzAvrUAHpxOuNjfzzDwDZcLEhIgOFgX3JNP8u9rvVm+/BckHj1qs0nw81PBR0Vxe/dH/PGxXqXKAJRUcxF1y8xdtUoHU1yM+9gxnBER6iIZHOSfkj7QZxiNZGakH21tAZw5s4TERA/q1goHbgBmAneZB8+9qjg810yk61IAmzdDd/f9sO/PTEtMhMRErvlEv9LHZz4xMTDxqVq+V14+xmiJiNBFsW0bwy+9hG93NytWvEFrawJ/Kvkctm6Ftc3Q3k7ikzBnjpOBAfjF81OJjFSR+DYdhnkbuIy6DHzPnmX6m29CeDg/XrSI9pwlOJ3wT/M/hn1Hubjouxw8OJGotX9g+iuvMP3JJxl4+WVmAtTU8NnAZFztH+I6fhxefhl3TQ3TgDsBYmL484uHITAQrxvGV5EvhYBvRYMZkpionvmyMoVTXV029PV41ASHcwb1vWpOy+cF1svateq47gNpbFSYn55uzLycHEXXQ0MCZQL7xelUhDZkTnQ9JR+XtjbRAFNGhlRW6imdk6Pv3b5dLbjt2zUWmJ+vB51l+oaFKTKGfTZQkrQ0qUQj5PALWbpU5B2DDBjnBHO54o274LTAK+J06jgSEkTGIrT7BYZFUlPlXTBBvUaxAkpwWjIyLFPlI1GmxDnJyTEotKBAJDVVdpvTVRF1hsCjAk9IdLSIJCbKJfP3vaA36+kxyGqj5OWJ7DWntNTWSkOD5Wr5wDbVtm9X82vlSssKeE/gtEHhFaIMllbp7BSJnzPnqgg4ICBeXQcrVigKLSlRJFtUpAg2MFDq6hTE6LNulthYY1739dlm9fto0G+jQXPS16cIJzRUXjauh74+42LJyxMpV9SiEf+tAo/r90ZESF+foioYVjRbVSVDqLtKhoZU7/LypNJ8115jaorLpTdct06ktNQYAOeU4RMdLX0oc6TsC/QjfvJkkawsaWxUvaypUetIWlrUpC4q0nW0apXOc3GxvkpKpKNDxaHsj32SlibSYll6Ho9kZVmWzYCo6X9C/P3Veigs1Dk1MUypqhKRuDjZCrLFIMvdoJZBVpZBtRslO1vU0mpp0Q+tWqUo2t9fTZfaWmkzlpcyA04InBOHQ62pTZvErIery+OGG+LVGk1PV1ZNZqYdCF69WvXA+mrVu2MSFiY2wm1rUwS70ZJDWZlIVZW0gbxrXAACIqWlsnattQ7LJCxM5JhB97uNNbXTyOIAhjXi76/Cjo7Wfzud0ten+5K6K5+X1atFmlEXhFoz5WZ9H1O0HBcnkp+v/5+bK1V/RT++NAL2CQgg4FvfgiefhLlzYd8+DegsX87vd03H7VY0OTAAKSlBtLbeR1PTAeAIEIeX121ERsJtyckQFUVkJMyZA5s2wbFj8IfaJUS9+CIcPMiKFffT1AT794+wf78D31WrmHr8OFHAyZML2bULfpifDzEx3JN4kQ/rJ/L884qkb7oJpkyBXbsUxdx+OyxaBDt2wOuvKx8SEgkLm8CvfgX33gvc/gkTAe+aD4GVJCbCPVv1pMwaRx6TRnq5P/QjEhK+wYEDt+Lx6Hf09gIkAdcSETFDY1ADxnIYHcXL6xZE2o1cAoiMDMPlgqNHE6ipSSA4WO+TkoJaBC0tzAX8/P2JjoYdOx5EwzrtjI4Cc+bgHBxk9pEjXAZ9eLebxx4DWM7PH7nAh0/ARIDISNoPWtOXREyMPktkpMrh61+HDRtuxe0OJzNzCvfeC8uX30d//0nCwm7guto/Kpy4yuV0qgHCvIcJ9vOD2FhcTmDnJQ2OhIbicinA27AhgbNnb7LR90WfACaaSNHl3l580JBdOPDngwGUlMA/rVnD/T/5Cdx7LwHuz8Ht1mCvnx8PJyu6LS9fgkgYixaB+zunCNjzR1JS7mf+fLiu4U9QX49vQgLT3G5OtPuq/Do76UT5z5HmO1m2TOVYWwsHDzIa+QDQwL5987lt7lwCWlsZNjznca9Ll6CpidlRw8TF+RIWpvMPKN/1uuvUarO4wqOjajrW1zMtJISVK+8jJgZee+0uHn4YJr9jiPu9vcTETCYuDmASZ85MoqNjClFROofh4RDQelgXoitS7z1nDovq63E6HDpJU6ZAdLROxgsXgEts2AA+Pr6sXDmTaf2fQFQUU9PS1BSLjITOTtxokCwoCMBBSkoQv/qV3mpy++Gx57vK1denen1fXBzOnTth3jw+C76Nbdv0sefPh58+fIHAwABE3MB5Fi2CH/1IY+G5ufBqwfMseeklnHFxHI68n9FRuC00lIHOTi4Dp4Fp+/Yx/8EHGBqCAwfuY/58iHrLgXtkhJjQUEhI4PN336UecAHe69apxRAeDmfP4l1QAJ2dBOz4L/7w2mJ6e8N5993ZxMdDLzB64ACZmU62bfs24eEqTu+mj1Uoc+dSUgI/SE0l+Omn//oG+2UQ8KxZ8YZWoehWGhv1JGprMyj3WeP769Ljd/Vqw/GtV0SSni7S1SUulx78Fp1KA06/VEpIX59IcrK8DyJpaQJdAq2Smaknu+UgV/5ikfIao6MNBW6rwBtSVCTGr3dO4Hk92bOyjG9oo0CdnsQ9PSLR0TaFbRA0KFBXJ+LxSGnpF9NIZoE69BsaZO1aC1l/IEFBeou+PlFnW0WFotLUVJGODnnmGcvfVSRwWFFHQYHtiFa/31ZFMsYHNjSkz93RoShK5dooc+box6WtTewBV1So/JOT5aRBBh3GV1hZORYE6uxUlG0HfkJCRJKT7YCkREQooq6stGJYcgRk1jg+4FtvjbcZOzCqdDErGJaZqSZKSYmOr6/PoufK0qXm+7q6RFaskL0G4VwCkdRU40fbYNFh1S+XlSVSXS1r1xqeaESEzt+WLYpAEhKkFuWujpjXJSMDj0fB0zPPGOsnNVXWGUQrgYEiq1fL2rWKuoaMj0/ZZCsFRjVekZ0tZWjAZTz9uAX1SUturh0c7Ogw46+ulp4eRY6xsYoAN21SXa6w0Jy/v6KqVavUfEtMVDhYXq7zFRgoe633OhwioaGqQxERcsj4Ki9ZaL6n5wrUXCf+/qpLDQ2WBfe2qD83V9fmunUiPT2SmTkWBxWPR5pB3rSt1HMijY3yjpFdg9HV8eQBNwkc00B6SYls3y6GYnhKNDYyoFZBWZkoXXO/SHm5dGH53p8W2C8rV1pBwK0CFTofOTlyyoztkkHBUlurFofHow87OCgZGbr2pKJC10RZmVm3A6Lxqksa9Fu1auxZc3LUUqqokN8ZtC1pabqQ0tJU4dPSRNLTjTy7JSdH7PU8njxE5MulIl++rD+9vIKAMHC7lZoTHAx0oVlKQfoyp3p4OMB0fHyAqCjO+UyludmgxH37YNs2wsJuAu4iPh4sLstk853R0VMJC7vBdjd/85vq7wwLmwC4aW1FT/FwyxcVw7e/DdN7DxMbGwTEKQLdv9+4qy8BlxgYQJHBhAmA0tYmRkSov6u3FwYGGBn5wgMdQekm1Nczf74ZgvHXXX+9oVgFBiqCWL4cnnqKv3ROo7VVwYnKyo+u877qww4MBCy3ehx+fnpCzzTfFx4O01o/hOef56mnIDl5NrffrqyxT93Tx5BNXByXE+8EHx86UXpeIEBgIImJag3cHXWa6xr+xC1RF/VrR0f1+/38bP8zbrd+cWsr8+fD9PDL+KGko6tdPowSFaW3cTgmcPPN6OBGR9UXefvtivzOnoWDBwloPUxKiv4pMVG/h95evFHamDMhAebN07EQx8CAWjXn3BPtOQLzIyaGiSEh0N6O9z71W85CUX8rSmLzQefcu/cc112nn2ttBaKiCMboXEgIhIbS0qLsKN+QECbq7YH5REdPoLNTJ2kyY3p6dXlAoMOBPcjjx5k2+plaEJGRuN1w661qjcTFaUgFp5NgUIth3jwdz9Gj8Mkn+sxutyKt0FAGenv5FDgHOu8RETpxLhdTrxxbTw9dI5N56y04dmwEcBIYqOoSHGy9yWO/wsNRJ31TE06nLpNJk/QZJqJrRS8/aGqy6ZPw12obeAETqKkBli6lvt6yFo1fmgmqK/39REQ4gHDw92cyGjvQPaaZDRtg/343SjoN0HuEh+OHzrczJESF6nQqtN63jxPuaby3ZyJNTcaAmzePaS4XxOk6czonAeGEhJgHNoK5DDA6qvtAcDDTUMsMHx+di08+gUOHdI6OHCEmBvz9p+jjPPigBkC+6PoyCDh+xgxFM/n5ekLm5Ynk5xtE+rbAaamu1sM5O1sB7+rV6v9ShsE+gUuycqWCzwY0kiuRkbZf6Ig5vaKjDQk6P19k2TIZxES3XS49bbq6pKjIym4bUIL34KBISYmcNKhFIiN1ANHR6suqqzO+zXKBczJjhon+V1UpqigtlcFB/V6bFZCYOL6Pz8trLDpbUSGbNmmk1XD6TbbVqPGV7RcrG0mR7z4JClLUbJHNly0zCRQej6KmwUFJSjKZWC6XtBlkuNPIqQtECgvF318RgRQXi6xeLRkZ6j9rbBQ7WitpaSJxcXIMjeZ+YtChLF0q0tVlU/EaGhQs9PQYeRrkYGVGeTwi8fHjJGLMnKnJDSUlOpbQUI1g5+baORPS2KjwMzdX4afTKR3GAhlBkyWq0Sj14KDxg2/ZIrJmjR3J7+yUscwAj0fq6saQpHg8IkVF0tGh0yINDcqmCQlRGFdWZlMhla2xSxF0SIjq1sKFIkVFsnKlzoe0tal1smaNDQU7O0Vht0kBHVc/brnFpmhKV5fIwoVKI8vOlvXrDfPH4ZDzlh8yJEQRYF+f8ZOf1mdYvVokLk6OoDS0qipD4UtIUJ94aqqsXKlTmZysPwsLje+3sFAkLc1QARt1DlavFlm4UL9z6VKDQHMFTkhEhKLT9y39yskZcyqXloqEhMigmZuEBF2f+ZZPdt06kZKSceVx3XXxsnKltRcMSHS0qqWylS6pBWwc13V1qkJZWdYat1BykVm/p6WszOhpebkuWpN6Njio+r9mjegzJiTIypVKkioqUoBrxTuam80es3q1fn7BAtVNi9aYmWms4I90fEVFIpWVVg6IPnNcnDQzlqwkxcUyOKjoXtH13wgBMzSkR9Gbb0JBgfq4Fi0yp1Mw/v5h3Bn+GbP9PrMj3U6nooezZ0dQMvcxIiNh8ujn9KLI5C+trRzfuJEPgHr0NDt27Pe89Rbw8sv0vvkmB4HDwHBTk9Z62LqVH6acZsoUgC3s24c6ibKzOY56SFtbW+HFF7lw7JiS9gcGUC4HwOecPDnIvn1wed7dEBsL4eF0dioI9/MD97vv0llTM748nE648UY9DQcGCA1VX1h4uAZvFUA2ADVAJfA+8BczuhHi4vS9IyNncLubefPNEfLz4TLeUFwMAwPs3XuAggIYbWriYxTNdQJHgT0A+/bR339SGSe9vdDebubjCO++i5oLyckKdzwe6oF95vMnQOkN7e243fD++2qUjI7qI/25ZiJ/2DWNz7on4tv5GQMDmAj2OPLw8tIo+/bt8MILtHd2chrg2mt59103u3ah8HjCBBgchM5OWt1u6tEkjwEUcQQCfiEh1NQowGDbNsjNJSFBRX7ttWBncuzZQ2Qk7N/fxpEjNXx6XFV6xw74t3/z8FngLYp8AwMVYoaGcmJkhL/Yg25mzx4Uhiclqflx3XX4+6sMPnVPV3/wvn18/txzUF6ubIWoKKUwzJ8/rnoMXXbw2eg0hgOnqkIdP04TQFMTvb0wcfQCe0ZG2IXq9umzZyEqird3BfD88wAlbN1qbmYSiS6j8m9tBUJD8QOYN4+iIti61c3u3efZunWEDRs0fGCoNGqB4su3vgXdzz1H+/vv69wcO4Zi2gCCgmawfDlQXc0F1J/Kxo0q/+PH9YtdLia6XGBuDTAVg9jnzlV2xjiXn5/GfFR/juN2qxs8JgYWLHCqBRwYCE4nt0VdYPFiVc+tWz9H10wYan+1Ax7uj/uMydt+D11devPwcHC5FGFTx5kzqlfU19PcDKdOnSc4GGaFXqCzE8rKzBp9/XV47jk+3r2bz3buhLY2nfyUFHjsMd1bGFaZp6RwMfEeampg/37UdLn5ZgbQtfkxwMsvM7H9Uw4dusCZM83jygP4cgh4+vR4yc9XRPbMMyYporn5Ct7nubF048xMkWXLpKdH39vTI3qCejySna1Ir6VF1FHqckkXGoHeAobcvl9zr6Oj1RdmJSAMDioqyszUE7qqSuCSrF+vkc4W4y9du1YPs4gIJY9LQ4Ph+u0W6FO/aW3tGCpKTFSGR0iIHssOh2wH2foFPq05xk9ngaGUFD1Vh4YU2UhsrLS16aFpAajqahHp6ZHmZj3d169X35IUF0thoZ6aERFip5SWlWn0fGhI9D89PYqqystFcnOlsVEtCy8vlYmEhorU1kpzs8VoGFbfYmysjKD80EaUYz0CivgKC3UMXV0iQ0PS2anIwGKopKWpLIuLRaSyUuJvvPGqCNhONS0qUjnGxoosWCB1dZa/7pJOusV4LypSgTQ3S0WFfs/atWN+bqmq0nEZDrj1UamsFAkMlEHzLAKyebOOtbJSEfIzz6gsZWhIuszzir+/iMMhLQZpq3xeUaRbUWHXiFi/XofZ0aFDqKwURViBgQZdndBUWEO/GU8/4qdM0TlpaVG0u2qV8k/9/W1+8jsod1rS0xXpV1aK1NZKUZFaRR0dIp+YubIYArJunU7IihWqVFVV0tamc1ZTY2IPhgM9YlmOycmK9JxOedP8rq5O/c5tbYbzWlKi92tosNOcz1r+5YoKfWNfn+phR4daM4ODOl9btsgpY1mNJ4/g4HiNx3R1iRQX27546ehQcyMnR//W0TFmIbW1SXOzrh9lhJwW+I3qu4lt9PUZK3HZMkXicXE61qws2W18woq6d4vLZZJ82tp0T8nM1LldscIuY2AlctXVqYiHhsROfuox+mYV7rCSmSQxUV5HucCydKmOvaREpLT0CxHwl9qAJ0yIN/SlVoHDai43NhqKzmaBU6qsLS3SYilWQ4MGOnJzlfqycKFkZVlO9AE1ebKzpR+lhLwDIlu2CJxS8ys6WiVfXS0ej1oFCxaosIuM+ZWUpIvOIm4r7H/WBE2+J3BKioosnd+tTvilSzXgsmqVSG6unEUpI5XGRK8AyUcTT8YTYKwxOTQgtln8/Y3bZMsW2WgCE9YCkYYGO0Np0Jiha9eKXQimxZh11hjhAYET+tlGLQRilWmYM0fnv6FBzHfvUoVMT1cTKClJZNUqWbZMBH6oz+t0ynmjjB7LlZOcrAs/O3vM7Covl54eXciZmTpPSpw/pvOYkSHxAQFX3YDhBoEPdMwOh8q2osKY02riSnOzbjTl5SLV1bJ5s2VinhAl8r8nYWEyZnqnp0tfn268dXWGipSXJ/1mntZZQRGTRVdaKnbBl7Q0Xdxn0MSV/cZUPmnmTSlGr9p1P9SVZupQdHWJ1NVJWZmZ07Iykbg4Y8r/Qp+lr8+4ta6uH7dZm21VlbotCgpEXC4ZNOPYjQZ5rAy7xkazqefk2HFGKSuTYjSYp9SscyKhoZqAkZk5lhlaWKgnZHm5yJo18onR5WrzXXVowPgdkH/7b/cb1Y05NNR28+kh0ySVlbr5nweR2loZHFTZ5ucroJGMDHO4dklDg8jr5t7jyWPSpHgN8KWmKt10zRodf3b2WNB1cFBfCxcqmDAUvcFBPZw1IJsrISEiVcbsLyszSRZLl0qReV6P2ZzfMZuqPusbAl2ahZqXp+5Ph0NBQGGhnakrcXEiS5eamhjDImVlUmbA2CazrvvMoaaHwm6RvDzZinHbZGSIJCbKWTOGv9kG7OUVLxERqotDQ+bEtBxOfX0iHo+e9LW1cgiDZtPSFE5YVYKczisW3X6Bj+SZZ3RdWq/KSr2lx6Mni0RE2IjKipxLZaXuRnl5qtwtLTppdgUoLYxTViZ2FllLi07gypW6kYjLpY6shATlJTNWve0A6IRERo6PcMLCdOOqqhLp6JCiIo2wVleLfjY0VBdFc7OsXav7YkuL6O5pfldTo4dMvzVxW7YY/u4TAh9IVpZuhDNm6FnU0SG6yDIz9RfLlulDNjbaPlzLZy5r1owVu8nI0M+UlurnTQ6yx1Iah0N/V1Ii6ekq2spKETuX3BR3kcJCiY+IuOoGHB0dr5vIsmWKri1/fXOzrFxpCg81NCiaKivTSHSHDmntWh1iVdUVSMzpFHG5pLhYb7VypVn8+fmyH/XzV1qoo6rKzm7NzBzzh9oRfGtRJyTooZObaw67Mk1LNgyFzZt1LHfcYTbw8nK9mculm+ngoHh5qW/PKlQzrn74+KgOJCaq87qsTHXapLFKWZnqyYIF5rActhkpnZ0G7dfWqm5kZhr0d1rE31+j8/n5akGVl+smuXChNDcbH/maNTruxEQ7flBhDiLx9xcpLhanU6e9rc3MS36+Cq+xUXW4oGAsE23pUtX15mbb715aOhaDWblSVNfXrBlXHuAS6BYpLVUQEBmpi6KwcCwb1sqjNynw0thop4WnpYm9kQYFiUX0FenqsjMkj5nDVZKS9ARrbLTjGDI0JD09Rj6ZmbqJrlsn+fk65/n5Jq3dgrWm+JE4nbIdYzG2temrrEwFYEox9KMZhcfMPEhEhDSjsZq/2QYMptxgdrZ0MZZW/ImZ3LMG8Up5uVShaatdIBIXJwdA/tWcCB0dVuLBBoN63hZNZ3xW4GFNLigp0Qk3OaFl5jSTggJpbNTn7uwUhUWrVomUlEhl5Zi5n5cndknELut0zcuT1auNYq9eLZKUJB7GKGhDBo3kWYIcGjJ0tvETMXp6FMGWGmWBOg2IWfn8Js9cye6/kehoHXdNjU56VZVuWBISIicNUunoENEkjo0GDX8k0K3WgsllfwdNEnkO5FmzsBSp7hJZvVp2mvdsNDKXykqRwUEpLDRuj9xcEX9/OQTyO7OZSUiISEGBQaNlukmYpIMGa+HW1Ul8bOxVN+B4EDGyHjSvS6C7uYWwq6tVJqWldhlQGRpSQaxbJxIRIdsNyqgzc6K1JT4Qh8NYPwUFUgSy3UJnISF6r7Y2O43bemnw5jeKWIeGbLO5s9OiZH0ky5YZQNHSYgfwlJK1USQ6Wl43z99vFmxlpWV5fGT0dpwNeNIkEZfLTleVujqDmLQi3owZRn+HhowFmSEJCbqEWlqM266lReUyOChKkzohgkl3LS/XQyI3V14ysrYOn+3bTZXCtjaRigoZRClz4nBIX58i2aAgndL0dBlL/DFBTCv12rIKz1uHdHq6SF6eWb+N5lAYFS8vPS811Xu8DXiqwG9V1snJ0matm/Jyo+9dqh9ZWdJn9hOlZGr68hhd7CPx9zfPVlKi8snJ0b0iNtamiPX0iECdzJiha7TSeoYrSoNqok6fAYO/EHhDBZ+eLp8YxPuO0UfJzxcNACpo7OjQ+1YY2eZjaKlxcSKMpYx/0Qb8pRIxYFRZYoa4PhPwi4ggGHBNmKBBjKgoCA/n7vR07j5yRCtPNTYyDbgfmBYbywm3vs3L6x8ROYOSuazqTDdoAkF+vgaOvvUtCA0l/He/4zJwIf1RHl+isYHrtvxWAyu5ueB0Ms9HP/LQQ+ro//l8H1qBuYBz4UJYvJhdSzTI9IArSilLISH4nT2LG6VrTUVTCaMSEvhlri9NTeNL45prNCg0sbKSB44fZ8uR2cAgc+eiHKZrruFE+N0ceB/gJODk2LEecnKmkJIC/7zwsEYZ9u3j8tmzREZHEzllCgx8ytq1P2HTJjhy5C1gmOjoKUqne+QR4txu4lpbbcoeIyOQns68fVBf/02418O9GzdyurOTTzFUmuefh5QUfPx+qh+7NwkaG4l76y2cIyO4YmO5WHOYffs0UDIwMINhJ/j+7GcEFBYy+cgR5SK1t1uVtP/n9bWvwTe+gfPIEejpgX/9V1i1SqNB//qvOjlpaXofj0eDcTExXPbxxXvvXli3js7+fkAJRlMBX5fLBEE+ZmTEm507b+Cz1x7locceY2JcHKxfD3Fx/OeGibQWaFGtmBgVqwYj3UAADscMePE3mJtxXWgoDz/8Kv/yL1HExIBv+wl9hnnz8PODpUsz8fMD5l/m4Q0bNNgSGUnXgz+lYJXGO6OjNeB07Ng4CjI8DIODuBIScAUG0nX9bYSFYXTqDCdPeuwEh7y8B2ltfZDf5l5Q5V64kI87Z+IXNRPfe+9l2GciKSm3ER0NF18yVLCODo0BLlrGY2VlMG8ev/3VBY0s7dgB3fBB5A/YUzOdX2Zm4nrpJbjxRgJ8LhIePpHwcH2Ozk79yMGDcOmSN2H3/pDwh3/IxJU/5vLvfsdkILCwULmChqo4+iuAm7BK+Yv04HZ3Wdp21cvPL4K0tEx8D34I4eFMdzi4PDKiQWJeBY7wi23fZN78O7n/kUegt5fXXgQ4z8jIBAYGJjFjxiQiIxN0LbjdOh6PRxf1/PmqY7290N2N0wkpKbeRlAQTD6Zxz+7dsHChbj633w6xsfT2Qn5+AO+/n8iOHYNAAP/+ymQWP/UHZj/4IK7KSptiRkwMCQn3EBMD/2fJp1BzFEpLue/SJXjySY53dhLldGpw1uEg/MABDf5/0fXlEPBNakIkJSkadTrt/MGKCoXvNTUKbmJjDeHZSi+OjVUTpbFRGhvHLAOrfrrTqZ4Gl0vdCxUG8vf0iJijTDxgEMSz4vGImkb5+XbtGasurIWmZcUKec58TlpaTBBOSejS2DhGXYmMlD4MLcvhEFm61NQLfU++qBhP/OzZBpl0iZLEj0lamnErZGSIFBUZd8iwQbIbjB9qqzzzjKZSHzAWRBeI1NZqQkJmprpASktFU7g3yPr1Yqe0Ll2qJnJEhKICf38Vr+UnbWgw6Cc3V/oYKxIyCLJp0xVV5Sx/bE6OVFaKQYplioRaWqShwVgLpaVjSQE5ORI/bdrVEbDLpUh72TKRuDgTG/itVFeLbDEoYgjj2klIUCRSW6tz7HJJMeoT3W7kIqGhxqStE/WJf0/ghwKXpKdHp0/TpstEi9s/K0lJ6kZQgv8x89n3JD1dpM3ce70xF6W5WZKSDPIrKhJpaVEXwNCQ7ULKzxeb5jdWwPw9cTqNBebxjKsfVpDWyrLOyrIsv2Ej60Y1/01RoHdQv3AtiGRlyfbtimStOruWldCB+nZl1Sq74JKXl763BzV7K40Vp3L4hepVaKjqZV2d9PSMFb239Cg1Vf+scZ79Ulen6fh99rprleRkdQVZNYLViugWpZj+0qDIL/CJW66u3FyNPWRlGb96rqjb7VFzDyvNd595tQqMql+8rk5lYSl7XZ1Io1LsgoJU1jaN0gQ1bfeoCR4WFV1RuXDZMpHycqtWvihddIOoZf6eJCTYuVnqkjLF8odsqzNX6X3GrSZVVbovxsZ+oQvz/w8E7AAC4Sc/YXFNDTz/PBdXPMroKMwNNGiQi3wWNpGBAfSEf+ghorZt05O+vh5CQ3EHzyYwEKa5TzAtPJDQ0MkEBSnrY+5c4JNPCAV8QkK0RumoDyQn4+100lev4ygvh/uTk2HRIq5/3yQvHO8Ah4M5c37MoUN3wdG78QO8k5J4r2kmx49DUNBtLFqEkqePH4czZ6CnRxMxQAexfLlSTrgXq5jQ1S6P77Wc6JzInDkTOXToEZzOG+jsVNBvWQJOJ4SEOHA65+N0KhNuYECTIfz+RYnr0wFiYzns/AavvQZ/HxfMZ8DczZvBlL7p71fk5HarnEHvEx6uQw4PVxkEBupjnT8PsxcuJKCqioCzZ4lsbYU5c/jekmHmzvVVefUGw+LFDC/+Lsc3AGTicjm5JfAz2LufW5KduFzT4Mk9dPf3E6xCGWtV839fvb2aeHHXXbB4MS43BAVlcqfrHPswNZpDQnSQ/f2KgF0udr4PDy5fzrynn2YATXUNAH2g8PAxvSMImITL5WRy+2Giom5ByWvDaKL3JHx8lOGmtKsgNDmoF7db73sRTfIIMOP9+teNPK+7Dnx8CA6GYXzxXbUKgoPZtw/6+3vYv3+SGUcvMJNrr4XKSggMHJ/J6W0KBr3/vjK5QB/Z399Bf/8PueOOKUwPvgg9PVxGEzfCzZOweDE73tL1sGQJTOw9rSisu5tpERFM6+yEu+7i3ICvTfX08YHJLheTW1oYHRnBxyA8CNNn9PfHyqyY7DfMww/70toKGzaoYdPUpGhe81vCaWqC7zmdkJrKshior7+Be+9VkLltGxw4oO91OKYwMhKAWhvuceXh5eurdQISE9Wi6O6G3l6T7BRlZHserfv8TWbMcNLdrTr/zW8qwF21Cni8AFwuTiz5BQMDcEv7H+H4cQIDZ3P+/AihoQ4mH/0AgoMZnncP3d2w19D5HozthYMHcTpn09VljLneXujpITJS5bh//93ouq8BPqa7e6Gm2IPyTHt6mO50Qmwsfsfh/Pmp7NoF9957P3fGXND64u3tY4WPvuj6Mgg4KCheUlMtX8yoaApxhbIhYmPHIGxmpkELl+SOO0zasVWQJTnZZrTIqlUimZmSl2dI3W1tikhTU/VUskL9LS0ifX3S02N990fG/3RMkpIM2mtp0YBDaKjth/yEseIlUKG0pLo6hU7+/jKI0pHOgM1hMuwVdcaXl38hzWjixHglZ69bp10X+kTgtCQnj8VZ1q41yRWrVimFxelUJLlggQomL0+GhsQg0FMC60Wam8fI7atWiVRVSUqKujqtlk/5+VcgWZdLgw6mJnNent76mWcUDWZn61vy87UEYS2I5ObK4OBYW5/sbJEuTFprZKRdZPWSQVvrDbKSdeskPjLyqgj4RoNc9VlGRUpKZC+GApaWps+Sl6fmSmioSEiIQT9dNuVOqqqkx7Ja8vNNmvuwKI2tSeCSeDyiCQ15ecb6aJKICB3ysmWK0FJTLabNMbGKFlUbhG0XmKmqsoNWQ0MGMTU3G2pfl9GzEwZ9bRCL/ZCTY8nN6iYxjoV0000iHR0GJVmlSH+prIsVK8acr6YWrzgcIpWV0txs3X+3UqYWLNCHS0sTycqSzk6d18JCFVFDgyK7oSFFwYWF+vwJCbpeQkJEWR0REaoMls/d+EvXr7f85U0Cb0tIiN5DWSIGdRr2hiQkiGRkmKJQvxXoksxMCzlmizKPxkHAt8XbjJa8PLNuBweltFT1c8YMfaWn65qSxES10EyK+lmjg6Vm/tT/fFhk6VKlVvaodSGDg2rN5uSIliOoN3qgjI9+s1YdDmPhmXrUmzYZyqpJOtHP/lCTXvLzRXp6ZPXqMaqkleyjVm6TwKsaJLZ4pA6HiMPxt0PAkycrX33nTlDK8QjqX0NRzciIHldut0EgHvbv93D06AReXRTM5wCHDjF3Lkz2uaAnoEm/HR1FydSmSSHBwXpaHz8O4eGcYCb19VcmAbiBEYKDYbbzhPr2enqgp4fjKNJxon7Ez9q9gUEFUxs26D1vvJGJPT1cOHVKC6oMDMClS7S3a7prXBxQs50vKvN/8aKCekLbaQVmuT8336ofu+YadYsHBwPf2c1ngI/bTcDgoDbLTEyEqChaWzU7V5McZ0BwMLPNv2hqglOnzHvO09sbxMiIimbKFJgZepHPm5qY3NSET28vPs6pdvqo260c9ePHoalpkKNHJ3HZSI7AQNrbMaT1Glpbl/CZkVt4ayvebjeXDfk/EC1SMxMUbr/xxlXlISgW9bG0qqGBJvMck61fXrqksjbFU3T6HdTWarGW29+5myDA23qA7m6Skqazd+9dQBAOhzbsnAxGsBOAIJYvV8B88KAmKQwOWmnkDiCI0VHFWH7A1KQklb3Tyaxwq5yOj53h4BM5CzgANKFtNYIZQ3cOwsMtK6QHxdVXvzzX+nG4exojI5+jSDwACNbyok+9PlYBaXRUfbYAfn6cP2/5r4/R2jpff+92j5lPqNuzu/uKJbRjB77z5tHfP4vWVnj3XTfQjcMRrnrvduv6nDSJzzp9mR4aast33jzV1wMHooALhIbq8yl6Pk57+02MNjXRDswyDmMNAwwCk0hMVD06cMDB+InqOicHD6pKd3bq2hkdnUhrqybXXHutLv2oKGDXLi7W1DBxzx798KFDXEDTrj8HLvT3m3nxgJ8fflii8QbTaFazLHpRVD0MTIX2dj4HQn20HGp9/RQeWLwYwsO5ccQUGdpxFHp7cTofwO0OJjoa9QO73dTUqH4PDen6Hh01BajoBo5SUwM/nTt3LI0fNMNpvOvLIOBbLWTS3KzR+9JSPaVaWmzKj/RpkoP6jLrNyXNYOZUmGtxi+QLXrBGprjZUlmHz/lY9lSwekWmP8MwzVsri2wJrNJKfni4SEiLNmEi/v7/Y+Y0rVigcMl0BcnL0ZNttniElxaDuhASpQ6PcR8AUet6nyHbhwi/04WhB9v16ahrq0ObNikLq6lQWEhLy3yhm2nNuVFJSFAHYBPg1axT2mAL3VlKHhWC1f9d7AqMWCNHTtq5Omi2U2dxspxH39Rl/V2WlTdOpqVEjw0LJinDqRJsqmsaWBQV2LzgpK5OeHnWPpqZaHMwPBGZd3Qc8Zcp/l3tamkhqqgwZn2SVsUo+QdkmByyE39ZmENUGTb4JDdXvB4Vv2dkiZWWGnlcucMwgrktiFSSSuDgZAVm7VgxqbTTPdk7mzFEQuXq1RRsbFbikws/IGMs2sdq5NDYaRLNR1Ge/2aDhVgkLEzvtd8YMBTvj6YeXV7z5rlaxi9AsWCASGqqMoTVrBM5pTMKkGl8CkYgI8xzaNLKuzsyl+WwfykowOUzq24yIGLNcCgqMpVhv5PW29PRoQ0qpUEvwijrxFtxWWRQXG6ukW1JSjB6Z3oOSnS3i8Uh1tWVdnFIdLCw0ha6KRFlN43fEKCxUVc/KsvaIAbNHVBiLolvn0yLuZ2bafRyt+Xrf6I+4XCKrVklbmzJ7rFhSXZ3oA27ZImvX6nwlJalqSlyc1MIVXWI2iFWWVkpKtMdkjy4bi44pXV2KZhsbzVzuM7qx2aDwYWMJrTdr9IRAtzidf70p55dCwAPAB8D8V17h7sceg+/8iPd6e/n7PXvYvXuQ3btdzJ8fwOiontCKBrXnFc4pDKPpjZ+hhd1vM5XHFal4o73VPmDz5iIe6O6Gkydxt7bijIzU9NujmPOvh+XL4fCjG+k29wwHpo2MKBz45jcVXXR06LHY3s6jj06GnM20oiyHHTv+AnTBsijcBw7QraMkpqkJGDBI263H9rjXMNBGaWkiYWGzCUYR78CAQeq9vdSfPctp4O9vuIHPEr9LRgxADjt2LOPhh2O1dGB1NezezQdNTXwKPNLby44dO4FYWlpmm7ZjPUaWn3HgQDhTpjjUX26k7ATw88PthmnOc9DarhDj6FFobeX2xYs57Z7Mr36l6MoYKuapJwBt/H7PfJZnPIrvmjWM9vfjO38+rxTA00+DyJtoCmg9cPbq4ggN1fKK3/oWn7rdzEpKgscfx3fvXlpNiUmrb5+3+cmuXRAcjL//r4ED7N//jxARgXdnp1pMZ88y9YUXwOUiMuF+M4YT9PcPm1n/BnFx8Onr9bRi0pQ5YeZmBIjlwQe1nOFzz31sPv8XwMFn7U8w3YJjZ89q4RuXy0C/GWi/tPMoOyeMiIhwO/3Wz89YSYzfFk5kwHxXGHAdM/f8nl07dxKIWmYKnd6hv/8GcDq5AHwKTDx1ithYzPjbeOWVKB5+eBp3mm4HB1Fc90DvZ/T1TcfphOFTpxgGhltbmRwYSHDwo5w5gxn/BdxuI+/QUA4dusChQ9cyMOBg+XJYvncvNYDrd79j6oYNuPb8APV/fpuA1/5DrZa77oIZM3h7mzcHDyry8/cPVxbG0+UkPvJjIy8r1f/qlwUMfXxMOjUfmzH2mHcMK5J1udQhawVO4uL4z1e8+afvfIeA3/2OC8COpiaimpr4aN5/8vjjuvQjInQ6WbIEoqJY6NJ/h4SYcML7nYxiofszRlf+xJEjwdDYCN3dHDyoavn1r5siUTt30jsyQmB3N7pWzqB9MUGbOrjN76egSf5vAUG43Tfgdv8te8K5XDbhf/Nmg4BdLoUD+fkiQ0MSEqKHcV+f+nSV7N4kUlYmv7NQpnWqWVzQVatEMjIMwn1cCgvVx9fBFW25PR6prNRDOjlZ1I+1bJmIy6WEaqdTj7i8POnpMadgRYXCHutVXi4tLfpr434VCQ2VA8b32Wf8RjZ/MjVVJCnpCxBwtMBh9R+uWmW3QklONtHVhgY5ZNBfT8+VLY9+qwyRdetE0tLsdFGLT2jxTaWqSjo69PDPydHHG+NCjlp5CpKRIXa7nZAQRcWSlKTWSWKiSHW17Vq3ULDlu7oS+K1caXxiq1apnzorSxFxWZlIba0pyfi0QPBVEbDDEa+tcyoqRBISxGP51xMS1OfY3GxnsVrFipKTLRTZJLBBs5QMd9UidLe1KW9V33dY01lLS3VsWVkqgNRUkdWrTVT+DYE+WbBAn6m4+Eq0/4FAt3h5qTyLisQu0COFhZKVZQHCUwZFa6whJcW8t6NDBdbXZ/42vg948mSNEaSkKHjdtMkgzhUrZKexBGX1an2O0lIdR06OSEaGNDRYnNphgdPKDhocFDvdr6REo/ttbWO86sFBG+VfmXdTVycqhLg4w9pR3m50tO3SlZwcE5fp6jJr9lVZvVpjBpKQIKtX63MY2rJBwHVaHnTFCsOdXi9fxIKInzXL7p2YmSm2b9cuSr9ixX/zc1dWGl0260QiItRXbiwFO8V84UK9YVqaTl5fn9xxh96ustI8V12d7jVJSXqPykrZvt3ic/eJv7/ewoqbVFSIIm6TxGQnFuXkiDQ3m71q1N7vQkKu7PuoHaj9/f96S6IvtQH7+8fbpS+XLRM7B1yio5WonJxsFLxOaSYhIYb61SVSVCQvmM3FsEZUmdaskWqU8KypoE9LdbXIThMs6gMVmFXeybg4pKtLhWOqPVm0L6mutgsZSV2dnRYoTqdZNOUCJ7QKUm2tDGLI006nbTL/t51p6dIv2IBnC/SJFBZeQRrPFWhVpa+uljqUAqaUqbeN2bJRzSGzQX1ixvCukUMxSpM6CWMrqKdHBgfFmOp9ZsPabUyoDNHghzbelIoK6eIKyldLi1jJHNLXJ1JeLmVlqnzV1WIHnqyGkVJcrBMcEWFvog1gCPPrBaZedQOGCIG37fbmZ9AkD1m4UO64wzqAzpmxPy9jtKOVokT8feLvrwugrk43HJMLYjbEPoFRkdpaOy202DJHKyqkuVmMSbheoqN18VlJd+p60MxLqw28moenNInTdj+9J0prazVj1YW0fr0BFWlp2osvJ8fI4tFx9cPlipfqasUmViAUukW6umQjStx/BpNg5PHI2rWGPtXcLG1tOg1KI3tDoF6KinQJPPOMmaeKCt1US0qksdHKSntW4A1Zu9ZsPLm5uumYNdLXJ3YGnG4OA6L0sZXicFh9HDVpKD9fZLtx2aks9iv1rrTUHFKb1e1oun6Mpf+PswEHBIhkZEhVlQnMJyTYiUT/iqYxrzOApaxMxmpd5+VJH9gJMZKaqpuuyZarRhNGXgftzVhRIeoOaR3L2k1LGwOLV27W1dWSk2MdKNrdorDwir1t1Sp7/2hDg7gn0T6C+fm6hlJT9WdWls7LM88odrBeX7QBe+nC+X92ff3rc2XjxoO0t6tVMHcuzIochuXL+dPWrcwDWhsFPz+YdoMXxwHXunUMP/4LfGs+UIrSjTcq8drpVDuutRX+7u8419nJ5Koq/tB6Nz9YfEGTKyxbZe5c/qP1AatEKb29yuX391fGx/RFt6gJ2dMD8+bx6kN/ZscOeHv529qMztCZLrzyBl/72nEgiOxsTWz4+5PrlU/k46OvuDi1O8LDORc4k5ERCA31qhORuf+3PObOmSMHq6qwWoH8+FfT+N3vasjISOTV3u/CgQNcaG3FF3B2dPBq+TRGR9V8W7kSfJfcrzJ47DE1R3ftUv/ArbdqfeC4OP5Y7k1kJNxy8Pdw6RLfq36UN99sxuqIodcE1Ex2A1FIdiV/fvZZ7gkMHEtoiYlRW6ygAEZHqX30D+Tn61fFxqocX3/9Y1yu2XwScR8DO3fit3SpyqK+XjlHFRV43SBACiKf/A8umpfXLQL7yM4OYNEiuPP7M/h9ays/TErix64PCAzUsshOJza96MCBMU/RwAD86lcwO/d7MH8+/6f1x+zYAYcOVQPtpKY+yNq1MDtqWKM5BQX8+c03iQHeWi/k5sKZM28BR5gxY41N/QIVs2lfSHu76tDmzeqh+eOSP/DRP/4j31i1im8c/E+mTFGxRUaC7+hFcLu5HDiZgQEIOP4XDapkZnLz7QF0dEB//9X1Y+LEuXLvvQf5Y+5hlXtKitb4ff55Pn/uOZxAQGgoJCfzh5Q32LUL/vCrE/re3Fz+/cWJOrsTNDgUE6NTOXPgMHR20nXrfezZo+tgyhRoblZ6WGensiwjIuDTJ1+Fn/0MKirYcuZuGho03hcTA48+qkHa73znPNBOZ2cs1637OTz/PP/naW9WroTpz/8UHnkEr1sjgMvIlkr1Se3bx283T+Wn936sffcyMvhD8M85cwaeeurq8rjttrly8OBBvFtPqAIcOKBm/+ioKsPOnezo7CQAuDMnR7max47puj56VBe/6XdId7c+iNutv/Px4dOdO/kL8FBmJvc1/ZZFi+Cncz/UNWW9NyVFo3xmvZ9z3UlBgeqHy6VvsSoC/vZX5zBtQnQSwsL0TeHhfFAfwJkz2jBlYABefBEmlv+XKo3LBU4nw+r04Zprri4P4Eu6IK6/3k68sBoVl5aK3VOr2qClNrRwxevmdy1gOif3aTAsL09kyxapqTGpuJWVNmI+BSLFxZKRoXQPq3anItc1BnE8LBo4Stc8fqdTTprTqQEkP19sE/+UQeZSXn5FL7RRcTj0PVY1KCkttUnq27crEggJ+eLUyiiDXBX5av+0GTNEpK9PtphTWRIT9cRdsEDRivUydRc0DfWSwEdWATVj2j5vkNglRctJSSL+/qaCl9Vr72nRYEC3QYhvC3wkEhsr60AkNlZNqfx82YimJR9DE1w6O8eK+SvKahUr6NPDWNp3dbXYlcmsz/j7X70ecHBwvCxbZiGrUZGcHE2Zdblk/XqTuBAdbRejqbIsnIgInf+ODpHkZHnZ/F7H9KyZ7x/abca152C5ZGWZvoCRkUZmTxhrIENmzNDv6+kxZn9BgezE9EIzRYI8HmOJRUTIc8YN1NkpIm1t4kFr77ZYlsi6ddLXp6g0JcVU3zLV6cbTD5gh0GjffwtjCTGSkyNSVSWlpYqSYmNNwKe4WC2X6mpZudIgsQULRFatUiTn8Yj4+8sprHrGzwoMS0WFQXqmk4UVEJPycunAogaWCZyT9HSz7tLT7USI1atFxN9fCo2OSFKSSFXVFd0nzmmJAJdLqriiPbvTaZcksF7jycPhiNdU8qwspRp6VFesV2mpaJU2rgjSJiTY7s2iIjNfdXW6dgxdtbPTUBiTkjTlOzRUTTtT/EhiYxWi5uSIdHTYVe6Ki9WysS0P01Vd0f1plZERbE2N1b1aLbXiYv0Ku3X9ihVyyLhGrO4Ysnq16Vr+t0rEcDoVlR2dya4X9TQODAQWL+ae557TI3f5cujtZeq//iufo6GQUTDe98ns2wcf/OrnXHONUjnCw4HXdin6c7uVehUayugoNDRo8KSzE7THWhRaU/gE6ux34nLBxXfd+KFpE8EREXxynSnTWlOjAbr2dvB4uCXyAq+8EkBr6wTKy/WRRkf1MJ3e26tEd7eb9nY9xN56S9/zd393dXF8zeEgLjGRP/tAQkKYTQu6QACTMTVSi4s54TOLmfVv69FqccTuvZc/u+80XW/bgGFGRno4enQKGsyIJyhoPnv2GFrO8QJwOmn9GWgKqFKaII4FC6YwMIBJFvCDbdv4xcqVEBPDfe2/h95elqem6lF95gw4HFy3/fe8/fx8/mPbTJ54wqOfI52kpCice02Q6NprdX5ramDDBq77h34efvjbSr27yuXrq2NNSYHRUe1uMBNg4ULefVdlOuXYMT433xYIBOTkqJVy9Ci88w50dzMbCIiNhSMX0PDiPwFR3OPzAeeamrS3Gu3s2gX/kZioFtWzNwDxwDFgkOBgDbx4Hz1sWxb3hobagcLLc79Bfb3q1uz584nZuJHgyEg+aIYOv+ncVlenelNTo5N6/fXaZ41b2LFjhNhYBzz3nMm6ufrlcEzmm9+cDSHzeWDjRmZGRyuPMzTUrl179BUr09UNeGDRInwXLoTRUdLTYVr7R7B3L/T34+u+oLozbx7hhw5pgIifkJDg4NAh/dPd994LTidpaT/UYFbvJqZFRzMt/FPWr7+fuDi4M+6iymTvXpgzh329+s/fzJvH1Pff17mPioLjx3l5/Ty6znrT2BikrL/Nady9a5eOPzgYvvMdgqOjleI2OKg/tRnh/7imT4e1a4GHd9ELTB4dZmDAl5QU+P734YGYTzEJ4UQaDbeKZn983Nfu8RgVdRsxMbdx91Mz4Px5rvOc5v55Ttgzl7uPHYOnn+bT4DuZleSnKfghIVBQwGfdE5nud4FpvZ/RHTjdTmSKioLJxz/iwqlTBJw9y5NbIDQ0TIPcOwZg2zZuB1577bvce+9PmDcPfhD5AXR20xv7gK4Hp1O7cViNEePiYPlyLjsnwrPPjqsjXwoB33ZbvPH1NBk02ieZmXoKlJVZaZanBAb0pGpoEImI0BO1ocEEUU6IpmHus0tX9hlkYBf7ML2oFH2uEdgqmzdb/au6DPrbJdAkjY3aLVacTrs2aWOjnmhn0QSCPlAYbTXFysqymkdIUZHpR2YKgMiWLZKZqWjivEFAjHOChYTEy+rVpqTkoOVnPGWf5NpNo0usjrNWa6q6OjHVryyf8D5RH229wGHx9zc1RhsbpcygCnXubxaHY4xkP2OGKVk4NCQez5WUtf0C2lFjI4hkZBgLQlNQpbhYPrH8sw0NAu9ZLBu7cl0/iJSUKKpKTZVaC/Xk5Un8DTdcFQHfeGO81e5NJyAzU+fU4xH4QFas0K6yuy2kkJBg0NXzimAsn3ViokkprxDYbFf3OmWsHEV4PxX4SJqbxfZ/ap89pQOtWKHys8YuoaF2kauMjLF02owMRbwSGytSXn5FoHODQTvDGlg0wTftjvCKpKdrf7z3v0A/4qdPV6TW1ycdHZYPuNyuU9zYqEBT9fxtgQ2KRK3MHAOND1k6XF1tWsk06P+bEqdWb7XUVEV0VoGoFlDLoqVFhP+e5n0IU+UsMlLUb/6CSEOD0jkXLrR5WC3mfXVo0Ku52WbqXVGK1tJfK2llHHn4+oqEhMgnxjK2klSam0XE4ZAuYxUdMjorW7boYunoMGi/y3zXCwLvSUqKKSyVna3ruqFBKip0bq1YgoSGmkpyXWL3j1u1SsTjkWeeMVZZXp6Nnm1q5JYtujkUFIg4nbIbQ3VNS9PeisaKk74+peLl5ur6WLBAZMsW6eiwAnLn/nYI+Px5tMsCAOE4HAH4+ekBY1W2d7nC8fe3+i7FQGAg3qdOgdtNYCCcOhWA0jVu4J64c1BSTgCKbbn3Xu2UOjCZY8eUUuR23w3cyoMLL8DRo6Sk3MmOHQvN+XgtUVFK3cHh4OPWiRw/PubiCYiL4976egKsvlpWF91589i/U91P7e16YP19TIymIjud+PmBb+/nY/22xrn6+xXUPvSQpoq6XNNwOMK5/nqD+js6SE2dyrvv3k9np7qTrMvK6oUgQkLuIioK05tKGT933AEcPcpMFNWPnFIa2rx5yvSqqdFXXBywdSveM2bQ0XGnKfQSDMQy7DORONBn/t0kYJIaIlOm4HI4YGSEYdctQB9+fth9r3j4Yfx6eyEtjX374J7AQC5inik4GKw+Z//XFThphPvmnuOz3skc757MbYsXw8AAl/EmOjqJ+fNh1q4IZp06pTml8+ez/0EP8DlOpxIM/Xp7mdjaajiHS4BBrus6DDU1+AGzgHNMRq0ENzt26JCM254XXpgJXKC3Fy6EzyZg8WK+sWMHLFtGZ6fqZVOTMqusGi6HH5vOLUuWwMKFHMyGM2cGwS6jcomzZx12AzVtFXYboaGMJcuMdw0PQ1cXh1sD2LYNmpqUxnfoUCJvl0+ht9duWMHOnYsAD4sXoxO7Z4/6LU+dIhwI8PfXBdjdPaYo+/ZBUxN///jjtK+cRne3uutdLi1A5QZISuLDzpncGRuL95EjhAJxKB2w1ShiWloY77yTDsFDTEtI0HXockFgoJ1mEg5MDQlh9yGV37594DN/tqGGXkZJblP4okQMAgPhW9/Cd+NGHVtvL4GB0wgOhgsjIwyA3Q+Qe+9VH7DpgxcXB3fcMZWmpqmcP38dEEZIiIqC9nZdUEeOMHfhLXR26j8jIlCfbGQkSuH0MHHgc3WQ79rFkiX3cZ3ntI5tZMTuOEJgoCqVKXg1bLhzo6Dy93gIRi24E90BioBXLmBiRQUsWcLlJd+ltUaNnf7+IDvH5qrXl0HAmniwW+bMUfRYWGjQY0vLGCyzHJmDg3qyLVyoVKTKSrt+ana2IsZG6xQpLZW2NvXLbN5s0YAOq7+opEQkPV06rBPdpObW1Rm/1ZYtWlgZ7Vy7bNlYE4X0dOOTbWlROk1+vtXsQjSCvUbU11qkaDwjQ6S2Vn0/W7bITr64IwbECJzTiH1lpQwNGV9UaalUo9HcIyjzQ2lQGwROSUKCHthOp9gFqgeN763OOmmNn7C42Ops0ScwoJSj6GiRtjb9bF+f+ihjY0V9wZuVhWBqAa9cOVb2sqVF7Eo9isDPCQyL0zlWWjgtTdGUoklN85XmZvWdRkRIVZXIjBlX9wHPMnOklsBmCQoy/rHaWtWBpUuvSOi4ZL5/n0CZSE+PVKBMkC2YurUNDQqnLUZ8SYnI4KCRpVWspU8TakyvOZXVqN1nT+d6VObMUR+jFtluFGUWPG904JIUFlrpuGUCH0hIiP7bajrR0CB2Nwgr/mEx4MbTj/jwcJGyMnNfC7lliDJltgoMK9qtqFBnfEiIdDGWqGIXL6qr03nPzx/LPW5pEQkMtBHjSYN24ZL6anNzRZYuNbLSAliSmioej8qluFjkjNG3U5Z109Mja9boui4rE5Hycr1vaKiUlakuLVig+qGofUBCQvR3a9YYX/vQ0BeslzlKp4uIkK0gUlys5TQHB2UIpZRZBbEKC9X33NenFptkZ9u0UImIUEXt6dFXXJz0GYTeYVC9rFmjsiorExkclIgI1TvJypIW895BC+3X1opkZcleo3ednSbJyfiqBzG1gE2Si93Zu6vLxJT2yYIFql/qJz6heh8YaGIZf7NiPJeBYa67Tg9IU29GT4rOTjhwgA+B4CNHmHXwIJGJd0NgoKIEk3J8441aEW5izZ85Z50qUVHs3z+GSBUdXqtdYrdsgR07+BTFJDPfegtGR7nt+TjqXQF2SutlxspD+vioK2rbNujv38tpZ5Ki2xtv5J2fXUDJ311oEkIP0MOePXBPSgoEBhIXCmzoZDL8FQGNAj20tgZxW1ykXR2P472MmrufRpMUNdnkY+A2/PzC7RZlKSnA93dyAmzEHWXuPN3tZts2k+6MBy+vAGhtZfTYMXxaWwkMnA7t7XQCkU1NqL/Uj1sGPsT9r/9KwFNPUVjoy8MPwz+v+HyMehAezptvDgKvAfcREXErwcF2azsOHTpv5NIFuCE0QeUwZYqNHq92eVBkpSmhvZw/f4YdO8L45RI/TgChp07xbo0HTU7wmE8FANdpaVD9NjpRfsesd9/V4idWskRCAn94a6JB+WFomvEF3O4ARY1HjxIZeT/+/hMYGICRETcW2f7QobsIDXWaFOJJRlYWe2SQ9nanyTrvBgKYP18tushIRVm9vfCX4wHcFncZ3z1/JjDyHl5+GdzuL0jUueYaiIrSfn00G43wNd9xAUiEPR/D5s20797N54wVc+w2chgFzl9/G9deCwFnz2rgxOQKj/b2ctromDcQWVaGw/EveoPoaADqtwCc5K23ZvPLxx/nuefA7X6H115L4wdoQkcran24nJN58021vubNA+bfTCTAggVs26ag3Lr0uT8nPHwGDz2kjKiJRz+yU6Wvfg3R1DQCC1wEnDoF/f1qwbnd+EZHM7m3l8lnz8L8+Rw9quSFgQEImDABDh5kYOdO3GhCWOSVzRsdDnyNRC8A01pb9cM33QSRkVwYnUhoqFmDTid+6F7SCcx0uy3TnQAUfXd1mbjTlCn4Ab4OB3YbaSujIzGRv9R7098/CIzQ2amEDd27urjmmhmcMPPzhdeXQcDxkZEiVVXmRD+syQbp6SK1tdovrLlZqtAI+iFzslrtRHJyxqpXSn6+HrNW/6fsbJEVK4wPebdW1y8tHStVWF0t1QYZib+/ouCGBkV0ubnSaPnIcnP1c11dMjg4VrDGIsDr/d8TqJf0dIuE3SRWeisM6D1NqyKrpjrjnugzxWIe6H1aBbqlomIskURWrRIpKzPk9jo7dVMKC/U0rayUsjJD8rcyTbKz1S9VWGh3ala0t0EyMxUVVFUZ/1dzs/RgCmab6GuzmQNZsECtEI9H5XJF5F/9nHUCo5KUZCyZ7GyR4mLJydGTfOlSg9DLy+1WK+vXi4SGXh0BT5umPQOtMoBVVaoey5bp68ruTDY7YWhIH8hYOpdQbu8h0IF2ddntqqSry2YLWD341q41/si4OJHISFMEStsgSXOzXcRpcNAg2I6OMXJwT4/qV22tSG6ucZWekpAQkyJeWamoKy7OcNRPS3q62AVlQkIshD1+IoYion2ihWt2i8ZI3hPIFn9/5R9/YpBfF4hs2aKsnIgIecEgL4uWrRbOaUlKMtZfZqYcMX7TLRZ6zM9XxLhunUhRkaxdayWwWNzxYwInZMYMsRsbPPPMWIMYh0Nd9w0Nql9z5uh3W6JTf/Fmq9GJSF6eHe/weNSnOp484oODx7JDLIK0SUXPztYxpKSM9QYsKhpLHLKaQMjChVYnKCksNH5ec6++Pv2dlTSSlyf64aEhycoaS4hJSTH898ZGqahQa2/tWhlrpWLyCfLyTF9Bq7N2ZKTdtkt6ekQ8HmlpMXLYskU/m5en+rRli52WP548vjQCHm5thXvvZTRmGNjDtdfGKpnyscfIzYWoDbPwRk/VP5vT5MeRkTz9tFIn3e4efHymwLZy+Lu/4xfPTwXgN+2tjL75JgMZvwc+4Oab5zP6D/+AT2Ymb8V8g+98507A+LSsXMbubsJdgMdDgDnRyM+3O99OjPPj9tsn2q6cxx+3WA29gItf/UrdPDt23MSZMx409fIEra2/5FxvL3M7O1m0CE6edP8VqQwCf0TRYjQwg127lpCIieK++CLv7fJldBS8vG7TAHHgkwrRHQ5YuZKVJfdw5sxJKLqHH76YaPOK2bMHnnuOiaOjqMfpY155RZGqXakRLUzzOfDBO+9wAT3ZB4DhnTuZ1f0htA4ohLvmGtrbx/zeZ85EMWPGBIKDFfEMf/tZfF0uUop+gI+PAoPrrwcau5jqcEBUFG63+keudp0+fY6f/ewv/DTwKOf+8R+5u66ObcG38eKLIPIi8CC/eWxEo+9vNeuYensV5syfD08+ibOzE7+dO9UPGBnJZedEejEdegMDOXLEjcvlZP58pUovXgzT3Z9aZgK9MwDalQXz2GMQHMzk8HB9YKvqzPe/T7fbTXB0NJNdLs0l7uhg5sKF+PvfRmIieJf/EcrL+fzdd+nFSl0tZ8eODFpbx1yxo6Pqmr3ade7cZXbuVAtLke9NJCSEceCAWgGRkfDxEeX03GLm8dO47/LQYvjL4sWMvvQS3uiQtY78bwE3e/c+wcDAVHjidqa99BKXUcupE5j6s5/hFxgImZkQFcWccPj2t+GVVz4G/hOYDzxIdzc88YSivUOHKlGb4y5gBk7nBK67TpkRhw49x6FD3+aNx3oJGBhAvcFHiYt7kFkHNjH8xBO8BSyuqeFiq1VE6OrXQHc3H7/0ErMfe4xX98zixxzkwssvE/D44zz7bBtqrZwEprN48V1861tqFe/fD9NDQ9U6SExk5cqxIkTh4ZCaeicTw8NZuRLefLMNCwu3tt7Fz5cAAwOEh0+mvR22bj0A/IWGhp/w/POzee01OHnyAIWFCfzz5uVj/veDB3ll150cO5ZH7aUniABlebjdGjwwXblnHtynE/TSS3ze2krg1q34RkSoSfCjHylJ+803x5WJl4y3mq5yzXW55OAbb3Au8jba2+EW1zC2rWBxk0wFs/qNGxkAojuFiAgb5fPKKxBwU5hKb9Uq2yfx6XElfu/efYbs7DD+/aHDet99+2D3brbV19ONKmooMD0nh3OP/5rJLz9D69NPMxGYGhkJCQmce+W/2HlFkM3icD/+OPx60UcQHs4be6bZ694i6Y+MFNLZ+RP2hnqRCITX1kJ4OF7XX18nV0vEmDVLDpaX4xszi5GR3zFjRgbbtsEtTf9F64MPEhkayveSz/DOO3Cp5yI89RRs28ZpU4HtAnCbw8G//2qYbdv0LJvZ/ZEOtqkJ7rqLEzd/mwMHdD1Z3PP7Yk4rZausTHeGU6dUKfr7uex204SasHeXlnLLrx7Ax0enJTISHnlED549e1Q2jz8O0/b9FyQm8qem6YyOjllbM8t/q1/42mv8+rVp7NkDu3e3AwsROXKVRIyZAm8hRfV0/+hHBKelwcMP03X7/axerYGyf0r8i85rTQ20t3N561Y6gWktLUybN5NXXoH7NzygCh4Xp6dNby84nfx89De88MIBcnIS+HXk7/VvVlUva+UvWcJw6HR8f/V/4IUXtDjHkiVQX8/Ayy9zETXznUDg6tXw0EOcCLyNrVuhpUWHde+98B/f3A49PXya+AM7Aai9XXMQZu15FRYv5udrp9LbC6+/fnWifUzMXPnjHw8SGgoTfYZV6DU1sHw5b9TMZMkS8J17i05OeTmnR6cy7dB2qK7G/eyz/Bn4BhCcn687TYwGtT9zT6W7W+dzst+wDu7IEY1am/oqfzo+k9FRk2i0fTv/Pu89/uVf3jFPfi0QRljYTfj5qfh8fPQwi4nRf/f3KwV061YPaWkTePsfNkFPD2+H/5R9++A/Ev8LfvITXcNPPcUf9wTwne94jFZPvqo8Znh5yUvAopISaqO+z+27nmH46afxbWjA69avExExgSef1DV5S8GPdU8pKOBE8DeY6Tytsrt0STfCefP4+Wuz6exUnY6K0nPc7Vb9Dg0F3x1/hAcfhNhYut79iD17dI57ezW+N2+eJlA88cQgGRmTeNX5U+wofnCwLZDaA97U1en7b3F/NJY11NkJzz/P5WPH8C4u5sLiH1jNOOjtHSuk+I//+DdKxAgJiVeonpWlqZt1dQrz8/LkTeNEN5auiNMpp0DWr1cTLCHB5HZXV2unC0ztWTBdc7vtQmZWbrp0dMgRtEZCEUopK8T0w1qwQEnqubl23zerOpLVojwpyaL+XBI4LI2NWuH/jPnO1FRRU83lMmPYKtLTIy+gNUf7TBCEcUyISBNoU/NUOwhIRMRYenNsrFhVtWThQvu+tSA7zXMcQduNr11rTGTLdImIEKmqsituZWerK0XKy0XS0+U8mujRaL5HFi4cq3nh7/9/pUb/UjRpo0yKi9VMtHoLyurVGiCrqDAm8mE7PXnImLdqmlv3eFog7KouCHXJ1IkUFdlBDo8Jvkld3VgnAyvpJTnZ7gShvfd+KtCq81pdra6gwECRvj5TRW5Y4A3tgWoFRkxZOI/HuA1cLjlp3D9dVuDEuBJeRtN/j1guq4oKqamxUqR/K1AnKSnGTVZRYapfHRZ4WmltLS3q5oqIEMnJke3bxdDSvqAtfVqanDffWW0Ca6dAlTM93e7EoW6CAZFly+QdlJZpuSY6zP+/aebcavC5fr26kqxSwVaFvOxssYOI4nAo3bCuzrghPjLP+rbAgISFqQvCTtltaZHSUh3esmWql9XVusYlM1O/oKBAPEaPx9K3i4xujJ94MM2sX8nO1uBadrb6POq0b1t2tlmPSUlywKwRWbVKpK/PrjogVVVKa42IkGeeEXu+rFrSsmKFSGysXDKy24IGGm13ppU9VlYmUl4udXXqgigqEnUxOJ26+RQUyJBZx5r4ky3Qp2u0ulr1wLj79mJ1kh4wMu4WGJawsC+uliciX24D9vGJF5fLRNPr6qShQYONPT1iD1qam0XKyqTDevDycrvTbGam4d1ZudzWTtvVJR0dune4XGMNSVtazH0XLLAbG5ahzITzliLGxo51WM3OFikqksFBlZEurI/sNud9faKbQWmpuFwqHCsxXX1sb4gMDcnvzPd0mINi3AXm4yMSFyednepfbmhQf5GsXq1+sbQ0sYrMSEODDJqF32Mm9nfmcLH8xFJXp/JbulQ3xdhY9fH2jNWb2b7dPEdmpr4nLU2qqkxmk1W5JytLZOFC6ewUe2GrH7JvzOdZWjpWdTsy0hRpeVvghB4CCQlyylpkDQ1m8+sTZUbcOs4GPFugS8eXl6eLKTdXpK1NNm3SA6SiQuwWRH2gCzA01PgOtYD/5s2iu2lEhEhExBW86UaBF8a6Wicl6SYcF2dX3pSiIt2NiopUloZvK9XVdt2Nk5b+9PVJVpaVCTggSUljDJrOTvlvPF2rcI99OLS1SUuLGF78OPoxZ45OVkvLGG0iOlp1PydHX4aMbmej5eYqR3rZMp1Uw1EdATubUaKj7WYFQ0OqcsXFZr2UlIg0N0tCgsmsS0zUZpQLF4r09Rkf+RqB92yGTH6+2C2vBget2MgJueMOfV6rGFJxsT5GW5uIXfHK+NGbm1U3y8r+ig949Wo7s0zq6kSys01r+2N6yIWEKPMjM1OkpES2b1dRFBZe0WQ0I0OkoECqqnROiotNRu66dSIGYNWadWYV4bLOj7Y2GevwXVZmt4uyMjUtX3dHhwFEhme9YIE5ABob9bN1dfqw/v560K9eLdLWZheasnROgc3frCtylEFJWsPUqrGqCnxKF4ahhFSYjdKu/evxiJeXUbLaWruPXGOjiDid0ghmEQ7YlaPy8sa63svSpSIOh93v6k208EYj2FXQ7KBecbHIli1ms3hQqS6rV4t0dUlqqrUp1Qm06mZQXm4Cixs0wGKQyicoNWw8Ad50U7xuuhUVdhW12FiDGKKjDS3omECRHTyTkhKR5GTpMeN/CaVeHQCRoiJdRKbn3jvmb/1givCcMKfrJbvvnFZge1rgFcnONlXYPB7VNEPYb2sb69JtBbvE3183wKQkkfx8Ux/1FYFzct7It9rIQFaskMFBsamCs2ZdPQjn7R0vISG6/2VmqlJbC0iDN10Cw7JihXbfKAWRnBxpaTFBssFBqa7WqS4rEzs/V62Tc6Jp148L9OmBU1gopQZdWuDHqn6lVMY6gQG54w4TkCkvF8nOln40uUUDrMM2dXFwUOzuyitXih1wk5YWQzc7Z/dOy88X1beWlnH1w88v3qSWHxYok5QU+0xQxbeqcyUn6w7S0KACrqgwRVwMFbO5WWTFCnndIPhLIJKYKCNckRxgEMxec8ivXKkqKGvWSA9Ka9tiI+FHBT6wC1pZOUrFxWL04JLAE1qn2KQBa8JQrkCfKY50WGCjyNCQvIxap6+jAdTx5KFt6RvtAFllpQ5b50r7+VUYvdNDQAPP8Lwth8FBPaCrqsRe75YlIsnJapGiRX20a8aw0YPdAnWSlWUKPXV1qcwbG6WnR60AKzHKqjKooOUVnWur4pqZJ5MDo5t+crI0GEB1zMzHJSOTdV8gD5EvTUO7BphBamoQiYnq0nrtNTh/XulcfX3h2uMJJakDhIeGwpw5nOv1JirKcKLb2yE4mPJydX/9JiuL2SUl0PEXKipuIyYGpo1+BsHBfLpooqbiNoVDSgoxfn7g48Poxo00AbMDA9VplJjIlt1TiYiAOw1BffPm7/Lww5t58klghxMCA+nu1kI+sbG3ERio3T1uvvnbPPQQxMT8IwHtH5MCdo+u8Tt+aVbvr34FjzxyH3Er78PdqTWE7oy7qE54f3+ys6PYty8KHx+V1c8fXwZdXUwOD+exrVsZcLs5iqHj7dnDTJcLUF/3RDQl2HvBAnJyYM6cGWzeDCdPDrJpE0T+26tsywO4D4jiZz+D6/o+5cOaWRw9Op0f3zsKa9cy/aFuVq68XylykXM0MBAaSkBrq07A0aOkPAbwXZYuDWJgqz63C5gcHQ3h4Uw8fpjpgYFMn6eJNle7Ll/WoJRVz+i++er3dC+5jzff1L5tmZkOFi2CqceTuH/vXli82E5KmRnpJDRU/XP33guXv3OEYeA39ZcJDQ3iiScSgGZcrgDu9vsL7Nhhz4+VZbxrl7qtNWh1HnDj5zdJg4nV1dDait/SpfgFBuLjA2lpDrtGTm8vHHd9l9Z67c5hZY3jdpt0+JN0dgbZPdh4/nkrT/6ql5WafeTITGACLS0aA1m0CO4J1fvS26v+xkWLqD3ky+AoDLgtyte1+j1bt8InnzDb6KMzJweWLMGnpIRpe/fC44/z+x3T+OFjjzH7uecICAxk2TItssSOgwwYPZoMJkPqlyxYMBW2vQq9vSx+5BZcLg3HBAXBHXc42b8/TjuqPPhTtpeAroippKcHsGQJrF0by/794XD0KIHm3nH8NdrmRByO2fj762OHhuo6X7x4Onv3LiE0FO5buBCCg/m3M6C1dj8Gemlqgguhs9i3R5mp8+bB3Z17wO3myJkfsGcP/OCb3yR892673vTl/n5KSx0cPXobvb3YgeXubvhgYCo+oQ8QiMaUtFDTfSit8YKhOk4A1M/PvffqvlVQAIsWscfnFi5dgn+OVn+x1WlmAOD4cZz+/izq72cU+OcvEsmXQcCTJsVrv6biYpGEBINsHhUlmO/TvzkcSnA2JmBnp6Iyyw0xOCg2dU0R3W7LKyASGytl5rRuM6+TGMJ0QYFIR4chPmvKqHg8dmampiruEy8vTcPcaFBsteWeMLWA58wxtUhNB1Orc4AUFqrPKTV1LJ22oMCg2PFO9DCDPn8pkCvbt5uuA0lJdsGQQYNgtcNBvSxYMOYlgFN6svv7S5151rOgiKikRMTjkcxMQxEzpO6qqrGutVqI5ZQiblNAZgiMmfmKiMcjFRZKysoSKSuTzZtVVmvWGL9zaelYCmVHh8iWLdKAKVpTXDxmKpr+fZKTI/Fh4/mAbxEvL1GIt3q1dBhEpAVJTqslYtrpVlaqx2jNGkUfFgCUoSFFhsuWyW5jBTQYuShKeUVqajQNeL+xtE6C3dFY3SRvX/EaGEsDN7pUVqYotq1NbFNUCgultFQMyt4lCxaonDwela26s14QOKeFeKqrpcygnvH046ab4qWyUtGeovhLAtrZw/YjOp1XFIA5LJqg8YpAk8yYoevlE4zfOD1dpLTUIFDVpeJiy9X2ijgcMlaEJitLJC1NTlpzmZCgz1pcrPOYnCx91tpKTdXfmT51HR2WVWt1enhPNC28WyQ7W903q1fbyPysGZtl5o8nj5gYLWVg0d6kpcW27fv6rC7XuwV2SUKCVV6zXLTE5THJzLSs1/cU3Zv6wFbqeF2d2F1shozMthsrsgqT3NPYaErkXhIoEn9/m60o0tdnXEqNBnmflrCwMVe9FBTIRmNhaCLKCZvfegyNU9ViYhOrV8v69VZpgPER8BcBvP9xeXsbRHD0KMMHDujpzFQ0qtqrSCYujokhIUpCR0HHhg3YEciDB1HEOncuoIz+KVNMe6/gYELNiRQKNr3MG2DBAj5snUZ/fzPwMTt2wHs7vGltHasLogeKm4nR0cxEiVs+oOTpsDA7UcLHByw4rlSuKMAghMhI2tsNsAkMtBNIrn55oaT+cCCKKVPM97ndSr1oaKAVJbrffDOApp9aTBeoZufOC/DQQ7jQ+PRFzPfecQcf1nizY4cGuC/09mrqKNDffwGl7HQBk7jT7zBs24Z3dDS+CQmGMuWEgQEuWvIzsHTPHkWHJqMS/Py0m4ZFfYiL45bAQO4Ghh/6AVu2QNf1t+l8WTne43RFdjgcWiDGdOFwYjr8mjqQTic27czHR9Fye7sGng8eVJlcHPVVCHv0KFGoJWUlxCxaBLCcm29WiU83958GXAydaVDLCGPpsMOAdoD2bj3BORShHDyoX3H8uHnumBhISTFptQ7AaWewend/Du3tmhRENA5HkN7axwcnppP2OJefc5R7Ei+yZIkG1KOjncAkJkxQuRMdDStWwJIlJlEnAE0MuQCE8fDDMLH9U/ww6bmmhKIyMS/h56e6r5/tYmTkJKcj79RMJ6cTRkf/e3ptZKRO/MaNcPQoAbGxTExK0ptYfRhDQ21moF6mwJOFKwcGlA66Z49dqjE4NRUeecRuwDLeZfJSSElRYgrHj9ssl4Dez0y3kSAgmPnzFXSOyeQ8TU3WuK4zXb17udzba96XwLFjKK0nLQ3fyEicKOm010h0ACAy0vRBBLhBS+o6PyOg/gPYsIHZvR8SETEbSMTpDCM0VMc8dy7Q06MFpBwO3O4LwHk78yvK4eBu1Gr0CQmBG27gyJEvlgfw5RCwl1e8RlatAJCJxCoZ/RcC+9Qv0tCgqNPf36DkV6947TIo5ZLAJSU6Z2TIINo1IjpaUxBTU8dKXUptrTlx6g1KaJKwMPUtejzG37punaxYIXZvOqtodVaWom/Lf5mXJwYhqg9Y6upEli6Vqiq9TWGhOZmtbhqZmeOeYF/7Wrye5Fbh9+pqff6CAqlFo6MnzYkppaV2oFGf5bRYSRwrVpjA05o1GvUvLpaGBkWo/v76GSuqq77gbOOTOyabN5sOwYGBJu10WNLSTApwQYH6yPPzjbXxgf3cNTWiz1hSIlJQYCK9+wzSaRX119abz5wTl8tEqQcHJf6WW66KgONvvHGsiHVamojHI6tXWym+p+WOOxRNSlmZDX8VGZ4Wq8/W+vUidQbJr1//33jx2nkBRCoqJCHBzJXRDyvQaKVU6zOs1w4bJhNop5kPleF+CQqyUke7jDz7ZP16GbOAurrGSok2NEhdnY4lI8P4INetE1m9elz9uJUrym0mJop0dcnatWrRDA5aDIoTAl1SWam/0w67A+onBhWAsUx6zP0sn6Xk58slMAkSZQIfib+/rh/rO8ThUCvQ6kqSmqo6sXSpaL+6LnnmGX3mzZt1TKqfReLlpbE2ZaDsE9inwanCQjtuI9XVAgMSHT0WDBtPHvHx8fos/v7aGWfBArtTing8itAXLhyjYOTmmjFmmLVySdLSjOViBVz8/W35qt98l/r7rUrpcXEq+xUrRAoLDYLerNZfSYndI64WjSe1gd43I0NKS00gs6JCC8H7+19R+P+YwDHb9755s1oiNlOpsfGvBmm/tA9YxORB+PnpB/ft45+ens+77wawY4cH6OGtt+BHP7qFoNZWOrE65J5hLFHwc6weW2FhsZry+O5OWoEjO+HYsbc4diwMuAEfn3AeWBkDc+fidtegnWq/CdyAn58S4L17z9F97BjBO3Ywb/kvAOXTdnbCyMh5YJDU1HBCQuza7uZUeh8Ig81NtG7dSv08OHbsd+zYkcGPJ+3XMoPd3Ra8uOrl76/t5ygo4MLrrxNw773sOQqzwsLoRJHHLExCxtGjTAsNJTDwTtzuNtRjdAMQQHm5+pMfXD1PnZgxMRw9qnWog4MNakcRsnaj9QA3EB0dxdKl8MFDMLW3l40bLwDvEBz8jzzyCJDbqOg2JgbYZeYhGogiKOgGqKrXL4iIoLYWYDvqNw0y33aD+f/jNDVNZc+eGzj35MRxi/Fw4YIiI7cbQkN59TVvnnvuOIpIHXR3w2dMZ/pcH52kgQH6rwdNRWgA6jlyZDkx6PO+9BI0NbVx/PgN3H473Hn0KH8Glo+McODAHnp75/Pj1+YaePIRcJyYmLt46CF4881pwLAWazEkXm9z364ugB7Onz/Jzp1hRq8OAHexeHEA0wIv6mfOnBlL396zh9sS3RD3DcrLVYfufughRfTPPXdVcbhRD2bkqVNMPnUK3507mT//+7YPVKnzbwDhzJv3j/iW/J4lS35IZ+ckfPf8iY+B2b29/LlpGvekpDCMorjpxz/ioYe+ASvLaQVcPp+beYL+/jPU1ITx7rvK5X3Q5cLvyBFVItNp+gIYk/H3wOeUl2v6ssVfVf1sx88PJu/7I2lp94NJ9j16FKbNnas97NxuI/v/5Nix7xAVNcNODhr3crtp7+/nMBDT2amo3OqAXV/PifffZxhwBQaC243LNYGmpnCjjxcID3cyvf6PKryRERgZ4cSpU1w+dYreOwDKKS//Jj9Pcenf580bI8HPncupn5wHzmiiTmoWx8+e5ajRi0DUorl45AhTjxzhrn97VfVnxw4+AKL6+6n3u49tr4FaBkHQewLcbjZsgLNnC1m79id8ryYZgJnOy1z+a06GL4OAp06Nl+xsQ3fp61NfU2qqSHOzpKeP1ePJy9NSjhWgJ5rpSQa56iO0or2mbmRhoQINq7iP3S04P1+j8C6XOXFekBkz1DdXXGxQSEmJ3XfMsD70P0VFkp+vh6N1oNqoYvVq8fISHYu/v7wLhk3wtpSVmfY0gYEKpxcuHPcEg1sEuiQkxKQsmi7IdvHsvj4d7KpVCuGGhkz5xSItSrJunf7dpNxaxV7Uj3ZK/dwlJYoWExP1OLY6Piclaam9yEg95XNyJCvL9Mszvrkzxgc7ghbk1kI19QK7JSvLIIlVqzRya7dxeVWcTuVXDg6KSFmZdHaOFfSRxkaJd7muioCnTYu32rhJfr5VXP60mbs6GwVb6aaKRI8Z9LlGtMj+ftm0yehYZaUiakMuN6wlWbZM9ayxURRhRkba6dNSXCzichk09IrKw+pRZeC0WmAbjW9zn6IWk96ek2N80aGhIoGBUl09loo71tVaOwZLW5tBgFfXj9mW3zE6WiQ93XKNq0+/p0fq6pTHm5IiIqmpWgLUsHmsRgTFxYqqysrGUriloEAkM1Pa2nTdWGnl/v4qMm1g0CVwSgeekWG7W6WjQ1FeVpYoRbJOdd6j68MqG+Dvr0jf4xHjWz8t0G0jPhkcNJSzUVH+9oDdaHA8ecTfeutYy+6eHl2neXlqpi5YoHq9ZYtIba3U1Nht7mwaY2ysYU2ZztD7MSwdU4bSSkE2TbRtSqFNHayokJ4ewxTKyZF+4wO/hFLXqjDU2eRke116PCISEiLrzV4G3RIUZPaSykodd3S0VFbqfmJ1PXrmGVWPnp4vRsBfagOeOTNeNm3SsVmUpkErODA0JNu3q/mydKnWw91kHqoRDGz/rWRkiAxZzv/ERJG8PFFq1QtK9ygq0i9IT5chkBeMkFUBnrc5/T09olLIybE5s83N5veBgXIWbdttVW+aMUOVb5NxzluNLLtMIEXNuF3S0SFyyApcJSSYPndf1PGgSLQTww+lo0MpbLJggQQFGfOlpUU30Z4e6eiwKoH9QvuxGTeFJCSIrF4tfX36+F5eKqulS1VW/WA3L6xCe2itA8nDJHzk54vU1UlFxVht4xGUrrcGE4hcs8b0xOsS2CV33GGoVCZBQXPWywTes88EiYvTDSQjQ+Vq+snF33jjVTdguFUsmtwYb7jbbLKW++O0cQ+UmU1QN311q3xPNKC5S2vwhobqPPj7iyQlmUDVEwIn1ASvrZXtJkhnyk+I+PvLXjDc0g0a+LNOhYYGM89N5juLBN4QqaqSZ82hazHCmlFKkR4SZeoOKygwAZzddpU9qawcVz9iLD0yXMqaGt0gNm8WG2Xk5ene0GfWinWA6yb6gdUr1N4U58wR24+lB/ULRpaNethUVcnQkJjDZb0GjxoaDK3rsFRWiupTdrbZPId1k6uoUNO5vNxu2lpYOFZzAYaVlmZKfmlA9CPRgGG6BgBDQr6YNx8TIzI4KCtX6nrs6xM7caLPbHxWDYroaMPTLyjQIF9VlQb5e3qkzWy+RUbHtcLdKcnO1s22sFDs3pUWsJOGBl2HFiAKCdG5cTr1hI2OlgPWgVlYqCDGlM87j0kgsfsAntLTyeGQIUyykccjS5eOJX4lJelXaoLR36gnnJdXnMABioocLFkCAYvv4S+7d3NbVhY/HvgPBgYU7UdFwf9ZdBh27KD9n/+ZvwDzeoQpU3KA75Kfr63Cr0u/T+2kqio+POjLnbt+rTyukhJe7f0ut94Ktw99QNdNdxMaugVox+N5Au/5d6tpERenveC6umDuXD4MVqrV7A2/0EhLTAyEh/MfPr/giSeqqay8C77pRRQQ2Ce0t8PsX30X9u7llxln+Ld/e4v09CX8YdF/wbx5/H7HNHp74Yknrp5KeOut2iPvwQfVVF6z5gZ+ufgwduO33l41yZ1OXg39pd3mbnRUU1r/aeA3Gog4cEDN+tde41PnLTz0EBw69BYREUv4bNeneh/T94qjR9U0fvpp6ltbmQ345ufDokUsfHQmLS0aG7kz8jQ8/TS9r79OICgvLDpa8zZDQ9XE7u7W1M6hIVi5kgeWT7S7BERGwi8j/6BUq8cf5+JDP6S/X7NAv/OduRw5cvB/ROJuvXWuvP76QRYtgjNnmlmx4iaeekoDJxbbKiZG//+VVzSDOjJSW21Z6aPd3Vq748ePXKZ1wgR6gbjsbHj4YW7+ziyamt4E7iQh4QZtdPE1L3yAW9LSID5eS3lZsvLxofbMdPLyVM36+nQs2n2iG6gEjpOf/2/8tGAWlJfjddMMFixw8KfvrNeWLCkpOrjnn+fyu+9yvFm46aY3SUpaxgcjd0B9PV5u91X1Y+7XviYH77prrEQf6M/HHuON1jtxueC27j+pQAID1WTOzubCkSMElJXx70fvB7QdWUiIqvys8Itw/fW09/YSPjjI3SkTWbxYa0Q7nTCRi5xzT6SgQK36f3/qgkYc583jNKrPnZ2qRq+9puUB/mv5H7VQSlOTrqfkZJg/n08Tf2CVVuHJJ3Xu7os5De3tvFr/DV55RWN3gYFaMyQhQR9vvNTbuaGhcvDXv+anR3/MSy+NkJ6u+8iiReD98nq90Zw5ukAOHlQ9b2hQRUlI0PXe2qruB8utYEUiDR31tHsyoaHg3Xna7jV3OWoWjz+u8nh1+QeqfO+/T3dvL8Hr1vFr9y8IDdXA4MiIZvgPDMDP536gQjpyRGWzfj0/P/pDfHx0zFFRMK3kNzpWq0azy2V38TgXOBO3G66//m+UigzBAs8q0jAnw5tgJxx4eSkC275dLeQ77lBE8pw5IdSZ/kPRHl+NImlp0o9mJVm9oN43JoXST8pEaSlbRakoz4ukp8sm8xlJTxcpK5PmZjUrysrGzGTLNfEOVoLHLyQjQ+lLJy1EaIjYMjhozLgisehSViBC0zbHRzhnQaRLAxlaQ/ew3tZCpyEhIitWmL89L9HRitKtVvNVVSLicil9LT9fpKvLBNP2CXTJggX6mOvX658VQZuuzlZWVV6e6RJx2shtdCz1u6JCJCtLLjFGyyk1qPEMiLS0SFiYoeaZymGW1TA4KHZaz9CQynj1apGpU6+eiBHv6yuSlmbcTe8pLSgxURF+XJxIRIT0W4hh3TqxWzMnJEiPQYDnjYtIEhPlXUw6alub5OVZpnaTaEWxc7J5s9a0PWR05wxW6vtpkXXrNGW9qkqUKplh0HWRRERYwbcNAg8I9EldnVxB/u+S/HxFaEeMFbYRkwbc0WFbYgf44nrR8bNn60SnpooEBkol2v1XEhIkOtpQmxwOkYgIycoyVtqCBVrJzuGwqYjZ2WYuTYp6o2XVpKer62r1atWfpUvtCme222XNGqW7JSRozd0eMShul7V8LL6XNJv7HjG6Il1dosH1CkX7xcV2DLW01CD5wUGRNWtMEPdZ+aIu0fETJogkJRkK5jEzl00SG6vPNzRkrJWkJCkDtcxCQtTlEhqq7oHAQJGGBuns1GGvWSNj3beXLVN3Q1GRvi8kRCxB67wW6XcsXGjTOjWF+LeitaHfkLHKiMO6N1hZh42NYy6Tnh7RxKBTKustW7TsAprsstvS8cHBv+qC+JKJGKHA49rJYvNRApOTeai9nXOr/gWe3gtE8fcpysYvKfHVKlGTJuHX3w8lJcBv8fJSsn1uLrh/9A6ngVn+/uByEWwKsL5xVLs0QCKbNjlobYV/+ZebgGshsA0XWkjlxMrfMNPnM2Zt+DXMm8cbA/eo07ygAN5/n8lo7afA6rd55pl1/J9bt/On3ym16eKTv2TXLrh0BDz12tXi+PEfsngx/PDhy/xplzePPfZDenvh7NmfXlUaI2gFquANG3jqqV9w/DicPDlBSeYYShnAwIBdI9cqdnNn3EXuDO/W07ynB6epjPbrtb5s3DiC1o8dYdeuqfj5YXf47e8fAcKYFTnMxWPHmDh3LicW/5zyEtDeclNZtmwCDz0E94z+SSuCRUXhzMsjvL2d0Bde4CIQkJQEc+fyp+MzOXPmPCUlQbjdAXR2ws6dGig9eDCMu10ucLk4dEARZEyM1ny56jVxIoSG8tZrsG3bQp2LykpFHE88AQ4HfpGRmiHw5JMcPurNLd/sB7ebyaGh3G3Bb5ONsWjGDL3vxo38PCyMR5p+SHn5Tbz2GuzefRKPJ4huFMtaQRStyXOED+f9gocX7uEvfnej1chuIDX1Nq6/XhH3wADs3BkLtJOUFMBtzo95+unZuN2J+Plph5eBAYhxOrnsdjMdCPT35736aUAvBw/CXH9/Ivv7x18uVhuU+fNh8WLm/uhHdAPU1/Op+7LyEadMgZgYuruVkte1/08s3vJbRVIuFydaNYhz9iyqpHFxzD56VFHz/PlY5e3OAdMGBmhqUiA2PTJS4ejy5XzW6cv0wEB8e3uZfPwjli79J/z8TPwMdG0+9RTT4+NpBe6MjYVt2/i4eyrLlq3Dzw+2HACH30yObhizUjSGNpHbnU7tPsG30UDv+quKY9jj4eLevTy9DQYGouyKfjfdBLcnXFZ+ZHk5lw8cYBQNu7FoEcMZj+IL3FZQAM88wy/fuoVJk7S/3MzQi3A8nUeOHOHCK2/w5Ep44/EYtRBcLqit5dPOALKyYHQ0Ed/Oz1Ru+/fTCcx6ZxP+/plWq0BCQxVYBweb+XvoIUW/Dz8MK1fy57if07lDvzssLIjpezdBRQXhISEsP3uWAbBfAQ89xGQ/v/H1A74cAp4zJ15PvPR0DVJ0dUlWlkVDe1X9QGvWiGRnS16eHtjicmkKpMMhdgvYuDgZMui51PJvNjQYCs5HolQzpVgdMsimutpQb/LyRFwu4/d5WxoaxO4cYKW+NmIK9sTFqV8L9Uf/DuQ5tEiLjrnCoKlRjTakptpdCSQiQsf7BT6+GWb8/QbVKAo4phSwuLixYjILFhi/7sNKi+rqElmzRjpQ2sslEHG5DBDZKpry+T1Rv2iZaJDqUfM6pf7RLVu0TkN2tqiftd4ginOKXI1v/CUj+8xMkwbe1SVSW2snQijSrxD1xz5tEM/jAs/KypVjDRtycjQY1NkpEhc3DgKeOVPrP8MVqeXajaGZK2o2NzTIHXeor7utTRFLT4+Op7p6zI8ZFKRIvN/44A6hsQNFLdqZ9l3ztzfN3zXI+bh5pgHx9x+rxzNk9ERaWqSmxrJYGhWpx8Wp0zA3V6S8XBobTTCurEx/19UlDQ2WvIwFZ+qejKcfN1vW1tCQUviKiuQ86lvei6nfm5wsUlho/NuWb/wVCQoSO8XWSprZvFnnwIpf9PSoHkh0tOwHkcREWblyzJqRjg67w29NjYx1lQgJEVm2TAYHLdpYn8ApkbQ0edOW7y8lI8N0nU5ONtbVVvNzVNSnX6S9C02RDKum7xetl3dBzePiYpHERF07LpfKeOFCaUb9u/be0NYmyclq/bW1WTS9ZwW61bry97fpYroGXpWeHtGOGwsXisYdhkUWLNDvKi62g+VlIGIsblm3Tk4aHWowcyQFBSrj0FDJB9NbUemKsmqViPP/x977x0V9X/n+z1Ego4IdFXQ0YDCBhCZiUCTFVrLSSirbkA1taEM3dkM2tLoN3bgtbbgN3bDVraZhUxpzI0nYSoOJbkISGkykSpUoFRNJQCWBCApBFAIoCOgg4Pn+cd6fz9i9Ym/2ptd9fO+cx2OCgZnPfD7nfd7n/Tq/ndJr9IkUFqosFxeLBAVJm9E3T13BQvrUCHjCBY8efffcA1/9Km83zuTDD/W0OHXqTj1R77kHPB6uPWrmNZX3aiJGVBQX7/46E0JDoaeHAKeTRTU1+jeTcqLt25qBL7JkyXWEhUFMZCTMn8/vm01P1vtXwPnzhIaC05nKgtDT9KBJMgMDent/HR/PnJoahQLbt9Nn/j4TM9sqJob7FsKOHXfY+efs+QPs2cO5gQHtLex2c3rZ169Uaco04C7Az/hXw8IgKiqCr34V2NiBp6+PQSB4506eyIcf/vBn3Hcftn/4E3OdYAB/f5PxNoYmrl0HLMLpvAuP1QMZD2FhoWYarj4Lfn4kJMygsXEG3d1jBAVN1OuEhuKOiuLOxka49157IsBHfTP53HUz6axUgDQ2ptfVCRO3oqmClYA/y5ZBwOv/wfJ7vklPj5aqzvI/zUQZJzVvwgSYNIlRFP3PMPdcUwMPREcrfF69mo96ptsuPLcb6Otjes9HPPjgjYpoyjsJ6DnJ8uVzmDEDAiWG2+vqiAoJgeXL+WMdQKCKItrJ9jSKgO+7D/bvX4Wmvp1iYGAadXUzqKuDL0dHq5D4+REUpMjr+HFtyUhYmMKxyEiYNo2ICAjwuwgl+6Cxka4Hf8qvfmWlAaYSFHQzBJ+2i0wuRwHodN+PWgMUmd8TgyssjMD2dpxAqNOpPWNTUjix3vrUImAiK1Z4iywGB7WIITLSW73c2Khu2xUrvsncf+zmtocegnnzyM01l9ExHHz/+3OIiJhNVxece/RfmVzyrA527Oxksuc0UVHTSUiYSl/fVED3iJaG97F3L4THxMBXvkLkidkcPfolIyvNaBFVgK5fczO0tuJ2z7Fd3ZcjwRSFWDXeJiUucGzMnjThAhv9+gGMjuJyKerev98U85BKSsoMeLeVCwMDLF5sGQO3cubMVE03BQgPJy5ukRZ43PJXBFo+2sBAuOEGQru7df1Mg+3wqCivPz4kBBIT6emB6Q89xP2PPqoo+DkXUVFTdVGCgpiemAjh4Zy887v86gn4t1XxOv8SiMc74WR8pnwKBBwbFiZSUWHKFPeJlYBdUCB24nFQkPrqOjoUyZwy6FP9uu8IXJA1a4z/a2xMfUv5+SJ5eeYUy9M2kfn50t8vBqE2ClyQadO8LfekslJRS1CQ+g3T0yU7WwwCOi9Op4gkJsoWg8bE6RQpKrIqchVCxMd7O4JERUmD8X2dB5Pc/6JBo5c/wfz8YiUy8k/TZaSyUiQtTerRCO1zBqEdt+5hzRq749kbqM97wKB19Y3Vmuc9qYg1N1ekoECqqtSt19EhCn0qK5URGzYoRCovt6cBWA1pCgoUYK1fL7Y/Kj1dAUhRkUa4NWq7Vf31eXnmuQ8JlIvk5WkZa3S0ogTznbGhoZdHwDfdpMx1uaTDRsA9Mnu2Gjnqdz0v/v76VRUVoswzPsgPDcK1U+1yckQ2bpTaWhWRxERloVoTF2TePO/zWtF0SU4WycgwWQSNotkAjwsckqoqb8crq8AgL8/w1HTjsaZvS2mpSFaWvGt8v1o0kSdBQXqNoSEjv2Nj4/s8588X6e+XzEw1qKqrDaI+cECko0MaGhSdq1+/XaBLTQ6rHWBlpXR2emMAUlIiUlwsa9ZY5ei1Ai9IUJDJfqmokFrjDxe3WyQmRgYMQtNMhse1cCg8XIWgtFQtxPx85XVcnEhIiHnWx8Walp2eLnrPDQ2ihSN5hq9H1XowYzRMRfcVEXCFsYKGh0Vk5UpFj6ZoR2prdf3cbjmAyQopLZUtW3TbLFkidjaKZGR4s6mSk/UN27bp4g0NKRqtqtJ7S062mxOVlhrLoaREv3fbNmlqUstiyRKr1LlHoEuyslQstPT7TXE6TQrb8LB+X36+0Td1xjp4WgfymHsy2bBXRMCfqhSZyZNh/nzjZ/sECLCqOGHjRq73+5iBgV5OnfIwJ/As1NTgQn2uXbMWoCfnYbZuVZ/mH2smcM59vV67p4cvfQngdp0TZftNj6OJ+mc5c2aMujp1nXHiBCfr6ugZGLBhfHg4fO5zALvxeF6Dr3yF6SgKISMD7ryTI0fMTLPXX+d0TY36YE+cgHnziABC/f1xhoUZZHMeM4fjsjQ66uHo0VMcOWKaLzud9hRXi6zG64eAGo9HR4OcOAGDgwSgid+B/v4wezZRUeDvv4hp025i9uzZWqRiaofj49VnFxiI/mf+fK3VdLm04/6WLXZiSFQUzHVfwM9Pv8rfH9i1i+mt7zF/PqbkU29VR4clcv/9uoY8/zxBQdHArXDihDYJOnpUmb5/P14GXoYshBEVxUy0UGX27Bk4ncpmBWVHGRn5xNusp7VV+eXvjx8w2enUjI0bblBk1dRkTzLu61P/c3g4LFzoj9uNt9zTKhnfvx9ee015x3XmNRtQxFhaavrRmPsLDjbLFR7Oxz2Tqa421xschJ4evScwZc5NDAyMMWvSWSaPnvVmu4wnHxOv4aPOqTQ2Qnu7WgIsX66L5HbT2qo8OXPmDDCbsLCZmrHwyiu67s3NzJp01naL09sLHg+DgzAwMIY1uXlgYEibDwUG0ojuMksGe4CPjchAj96u1cS/pka7rht5ZNkyWLWKU6cAbgKmMjJyQmX7xAk4fhy10DzAHKZNi2BRzEUGR0bgxAnvAOJxaDLasIclSxRlL17MTLcbvvIV3t43gZPuRYoy09K8Jd4nTpiWB9YaoHGF998nwO1mcljYn7YLGB0Fp1PX2M+PcyMj9oJ7PHqNgweBefP0cxER7Nun8mmNmYNG4LDdvOfo0TGgBI/nOF+IPqd8czph1iz27x9Bp+k0A6dobISZTicsXmxPLr8ifRoEDDECo1JQYFqxVVfb3Z/3G19OSoo5MQ2qGRsTO+9PT/n9ojmYrd7GKytXKryxouVdXXZJsdXMx5pXNW2aabNXVGSf9u9b/sUNG0yEcr/AWnu+VWrqfzo9U1LkMCbnr7JS6ustdNaj+c1dikT+XClhSEisZGebnFmTB52RYfyaCQkyhkbxt6HR8teMz1ciIxWh+Pvrg5WWehMu164VaWmR6mpTjFJUJFJeLvX1ekonJir6SUw0udgtLfK+5U/MzxcpLZVhtLSy20JC/v7SbZBQt/F7lZUpmpSxMRF/fxngktahVVXewXMlJSJJSdJvPnscJBoui4CnTImVlBRz31Z3nd5eGR5WQ0czFE7r+qWlab/WmBhFM8O6TopAGiUkROy8Y7ubTViYrnNKiiIy61VQIB2WpRMVpWaSmVJt9XTV1pNaxm56oMv69SqrW7eqKGozmHYtkqmu9jahHRsTzZ++X6BCoqONVVFdfUUfcEyMtivV5i55AkclLs7snaQke2LykiXibca/erUubkKCvrKydB1Mr+jhYSvmsl9LbltapLfXWHXFxXYTKikuVkE0jcOlv1/zYQsLZbdBom8Zedy+3SoXHxQ4JgkJyhOrtallLWjDqzqBF/W7KytFUlM1myYkRL/zCvxwOGKtpCC7IY93SnadQfR1EhWlpfdvWOtZWGgKa3ZrrnNlpUhVld2S2KTxGh/6LrWsjJm1Zo3JA05PF0lKsjN0ZGxM90Jbm8A+XfO8PHsSuWV9DtjWQ6b6fisrvfu0tNRcz7LSD0lYmMpRVZXoQtfWfnY+YG1rsYfly7/CjTt+DeXlsHcvH5kxOPMbG4kwfUB4RUcI/ccm2LSpBkUhs9Ao6Qm9Vuuwoiv0tPr43Xf5AFixbx+7dy8hOHg2syp+y6zwcCIjb6ery/IB4W0KYqgHmPqb32ivSb4KwMjIazz1lNUsyJ+jR6Nh6/NcqKvjHOqjIzycIzWwc+e7wK948skn+da3ZvKFuItcv+ffrzjl1eVS9B8RAfT10do600q55IHFi5kwMMCcujoCUB/lOcPBOUeP4mf1XYyKUlQUGAiPPornrbdwdnXxxcceg06Pwl63m75GvZWDB2Fg4AN2747k1Cl//m2tm5Moyr6+rg76+qgD3kbLYIM7O5mMepU96OTl6cDsWeDxnIDy9ygZGWEq6lP2A+auX6/f+6Mfcch1OwsiqvnYTKQ9BwyPw4+hIQ1k79sHty9dqsf/jh0EXHst10dHs3TpAmASwcFw8eWXeQ9Y3tICCxeyeTO89tpbKF47THd3Modav8aCefN01t+ePbw9MsIR4I433iDC5fI2kDlyxG60Q0iIwpYPPyTAGpW1axe3v/IK0AJcwOO5CafTO9preNibtg2naG0N1eteey1/6LyZXXaGiQs4y+HDZ2hpmQbzTlyxHeXEiZeW4n8AHObdd2fT0zOFG+vqoK6O8HDTJvT557XtZFiYClRzs76cTuXjtddCcjKtrTAycgr4hHvvBb72Dabn5DDrum9Cp4fJ5i6JieGk31zmpKRoJ6i6Or4b74K/e8aeGdiHttnZuhXefbca2Au00dz8DN9KOaemwBN7mL50KfX1X7xkKvIM7r4beOgJ2LdPraTuboWXU6aMyw+RE3R3r2PHjp9y773aE0hbDHxi+HsWOEZj47dwut3K25YW2LcP1+LvAl2KgmNiIDCQ10v0Fl966QyaNbQHOExn51d4Z2SE206c4MnnTrFw4Wy++f5LOqE8GuD3nB1M1gwmpxPYS0fHl2DtWi6MjPCd4Uc58H4ArNzKEcyEaGYRFDRVIbLlJ3a5iIiAgwdDGRwEEQ/t7SP86Ef+PPII3B5c7p1LND5T/vcR8E03xaq/LDJSo4SZmepQWbNGfZyrV4s1NaGpSexo89NPW8hmtzbzsapMsrJE1q6VggJFAdbEE+nq0qOxuFhKS/Vgraz0TmGVxEQZNpHKYbDLAU9Z/9/UpPdZVqZHWW2ttLToib5liznZDxwQqa+XrCxF54oqziuyOXDA7m5/pUqW2MBAhW1dXerfs5ryWJMaoqNleFh9jMMGAUt4uH1/OTnGt9fSovywusUnJdmjWMLCxM6T7O01fi0Tcc3IEJG2NtmPdv8vLtbbkf5+kQ0bZNj4LyUkREpL9T76jcUgYWF2gxJpaBDJyJADVvQ3N1ektlZWrVK+NDSov07CwuRDkJvHQcCxQUHqV7daksbHK/8TEtSvXlRkUM4xXdTwcEU40dEivb329OSVK5U3dkl6Xp53ykV9vdXLU2TjRklIUPRmZcDY1ZCWj8/p1MYvMTGybt0l02+tCbctLd4ZPpb5MjSkVVqWPzYyUoaH7WQdXau1a8X68nHlw+0WKSkxsv+qpKTovRUXq7yvWSPqTDZlyVbprDVxRnJyRIaH7eeT8HAz5mpQIE8traAgkcJCu7Lf9qXGxen7zZRwWb1axozP9JSxZoZAJDHRZBYcE82BLRJolYULjY8+M1N5kJkpUlRk0GqF+pzz8620BJGcHCkosNpujjOSaE6sotHSUpGVK01Zd5eEhSkvFIW/qfJx4IBa0UlJahEMDdl5vzU14u1cFB+vv9y2TaqqjPU9NKSQODPTbpoveXkiGzbYU6KlpUVNwLY2gfMSHa1Lb5Vk9/errrAzcywLJSZGJCtLqqtVHK3pRtnZKm4JCfrVVVXa0qD7z2RBfCoFHDttmkhCguxEO/NLTo5C9d5eNXUrK80iviDR0crU3l65JMXsWSkoEDlgBQpMFzM1HR42rx9Kf7/IL0wwzDbRhoZEioqkHk3QrjYmtcTH2w1296PpPcMm4LXbBMA6QCQjwwT1TgoMSmam1RthUKBREhJUnuxk8FodIa9m1zj9Xi2zt6lJD47MTHnNuB0OGwVsFU7IypWyHUSys2X9ekvY3hYY1F2dk+NdwagosWajW/O7KipUaIbR4J7k5qoyqa/X73K7RUuAX7T7ZciaNaqAc3NF02daRdLSpMMcBs8Yt41VFnrABMKkuFjGxkS0nNiU8zY1ieTkyKkruCCuRUukn0HTAPvNIT1sXDCSkCCaZvWyKriCAl0/p1OksFCamkxwq6nJ7ofQ1maU6dq1Ulys5qr09uqBX1pq+PND8/NlSUgwJmdZmUh4uLQbF9BxsCeEHDdrJMad0WvM8VpLKSUl2dOhjmKS/K1AZGqqPaftPFeeGRhrAkB6Xmgam6xebXpDf1ugzh5LtHKlHnbWqDhr0oq6RZ6QadO08GMviKaBPSAtLaIHfUGBVFZayq9fVq7U3sc7zSHV2Ski0dHylsWHqCixGx5XVRkz+rRxlewTLX6q0IBdaqqIyyW1RploaXaF8thqwBAZaXpi9BtTfBx+hIfbY+z3g0hHhzgcRlabmoy75rTAoAZWh4b0+tHRIiUlVusN/e7+fjtd7S3DF0lO9o7b2rBBT8yQEGVqb6/9ebtA6ZI+NgbDSVKS0VlDQ+ZQWiUVFSItaIC+H0TS0yUtTVMk29oUBNXUqP5YtUq/bmhI1+utz1QBh4bqpjEt5OrrL1FaWVnmpt82m/2YwAV7tptWGZ2Wjg6RnRj/a1WVyPCwHZXu7PxPUX6rGUZDg0h4uPSjVSbbzCku8fF6IkVFKfM3bhTJy5NhI2hvgd1AWfLyvMOaOjrsETuFhWIrMjtUHxlpI1BVRJdn4AJM5VZJiSoOq8n2tm0i/f0yPKyPWF0t+vvISBt16qDL8wKjkp2t92F15mtrUz7oAbFV4O0/aeIilZVmE7frWCdbAR8TeFqVteGFrFljUPx+zUEOCvI2jY6JESkr00j2hg3yMiZKnZgokp8vtbXe5jFDQ6JrEhcnsU7nZRXwzWgOpeWbHzZKV6KiVMlWVYnmG5crU6yZVgUFIi0tUl9vZMliWlOTnaMp2dl2HxirabxWYO63eQRHpaDA3Gtiol1ZWWh+itOpqNDtFgkJkfPW+sXF6foUFdklWStXKgCz7yc9XTezFZyIj5ej5gAeV+HccovI0JDJGjqmGz8vz045txoi9fbqz5YWXffOTlFZjYkxa/eEBAV5N7T6JLsUFVsz5w4c0PssL9fPrlypr44OtRwbGlTIysq8VV1mDpEq1fMyb54FDEYFDsm8eZdYngapS1fXnzSOksxMtZoyMmTJEsufe3l+REfHKj/NeDFZvVq/YMMGlQW7reioMiQ/X6SjQzo61Ae9Zo2xDszwyAqwR1J1gUhLi3f0V3a23XRe/P11TzY06MOY/G3ZuFFaWhQRG10s/f3eobVPP617UFpaVIYt2UlNFRkako4OvWRtrR4iK1ZYsa7zttKu/kwVcFSUUbJ1oqXBo97OVfX1pqPYIYNynhbYJGFhdm2Gbp7ycik1CnT9ev3bcbzNZQrALESelma63dKClvn9wiCpSgs9b9smEhWlJ31YmCQmmr5AqAP/NfOyyzqjouRl6/RdvVqVVHGxyMaNchQtOX2DS4JRGRlX7Acc6+enq2dy46xgoQpRucAFO3hkj51NTlb0v3KlpKRcWgix1SiTRsPDWtG0vX0CDRIZeWmg5KhoE5R7ZMmSSxXwboGfiWzcqOljhYWXNAjpksxMLYh4GUS2bdMuYC0tdj/mXxr02mXxLDFRn62hwTtMs7RUYsPDL6uAY10uPQjDwvTBnE41kTMyRBoaTHrTrwV2iWRnK9qsqJD6ehXimhrjcigpUWVhCY6ZomFPtk1P1xSl1FTDvy6B8xISYjZfUZG0gPzcvNYZ+XoS45Ix6XZnrP8vLJS4OD0rUlO9/YtVzjcLvCBSUGAX92zfrnJcyZVLkT/3uViDLnsEDmnprlUinJwsUlkpkZH6vfYBnZ4uVnXTATBW5RM6PNhau7IyqaoSc+1+Ncnj46XbPONOkOFh/aqKCj3LMjJUOcbFXdLPeOVKkdxcSUvT1L71621vlEGzx4xMvWkOzlYRyyLIytJrpKbKc8ZysBTWePyYNi1WFVpXl0hBgXQbgPQGZvhraqrYKaTZ2SIhIabg6phZY+2TXIpaWC/hnZnoTePcqojauMys1FJJT/c24snIsF0zamEclbQ0fRZJTrbLrSU/X5VuZqbqmg0bvG4RA/pqavSZdY+NmnvdJWvWqLvvw89SAYeGartBjTiO/ukk4LQ0kQMHTH6i5U96U4WuqkrKykz2Qn+/vGXMGStv1SrJqjRKWJKTZeVKPXTbDMrotpCsv78XiWRk2LmOVvPm2bOV+b1gvyQ/X1GoyZzoBrHSDOzx7MnJXsVrtX4MCjKD/a5gYsbEiNTXS1OTd9qLFdlduVLsRauvN2NYtm3TjIWYGBkaujTP8LxRtlvNz2Mye7aewlu2KAotLbWUw3mxOo3l5IhtSqrLoEEVvZnzNDwsJoK8XxYu1ObtpYZfLS0i9ljdrCxpsSyLlBRdmOJivUGrD2JVlciaNRIbEnJ5BXz99YoizVgYSUxUwTfjv72Ifp9q2o0b7QYe/f2X9BawhNwa7WvV9Xd0eH30Rgls367PFx1tOoV1dXkrviIjZQgdeHrUQsAxMd7nio/X9e7osD9vtS612hlajf1tjWcNf9240c6xHVc+Fi6UsTGxOwgOD4udKSDV1dLZKbbi7+8XL5wy1kE7mHXdra1TTRvKigox/ULOi9W+05ZZA+eGhvRA6+jwgtfOTpUje8xVYaFIZaVRHur3zcxU/W8NbM7KUn7Mnm1anVqj5AsKxGocsttYOkuWWPnJ41QGfj5Wamv1Ma3hs3uN8pbsbNOtT5G4NdhSLb19BpA0qHJNS1PGud36zNu2iXR1mdFMv9Z9l5enTLKsYzMIWPLzveO11qwxoHG/OJ3m8DcVjzU1qi8kKUmkosLrX7f8/1Yr2Opqu7l+ZqaFgFvVF22c4p+ZAtYZaJtVkhISvGPo4+PlZXMSFRV5u/rDqEh8vBQZIb904sEQOt9MN+WoQL9Iero8jfawfc2ccM+g5qx1ilsTC6yZY5KQINLQYBz6Lwsoorp0pfVE11NJ4uJEcnIkK8s6tU4KNNobpK3NML+4WN5Ci0jG3WBOp0hqqsm42+0NAHR1eTdTRYWZnNsq8AM1G/39pR91wwwYFKhFC/sFviVQLiEhumFqjQKRsDCR9HS7aYyMjYmsXGlcJOrXtixoNXn7RZqa1D9cWSmaBnVIhtGGPBIXJ1JUJA0Nul7r13sLFLZvV6VRUHCJeyYmRsTtlvMgC+GyCviGG2LtacWSmqrCOjwsW7ZYQdhRg+4PCZxWf64pz5WqKj2gOjrkKBoofN88+3bUNaJum1o7b19KStSVEh8vTz9tDjhT4qrI5lXtgVtRIUNDqsOjokRl1igQKx0tLk7/Nnu26sd3DcqyEJp+935VXGFh+nwHDog0NY0rHxHmvm2LIjNTpKxM6uv10F240Du4QeWjVhISDM+bmsTqaeg9dDcbC+JF0QkRTwrkKKpMTDRydkxgUBW61SDbzP17yax7QYHqls5OMel5Rw3C3SywWb9/9Wpv8NRqiFNb6+273NSk/zByoft7s2jDo3H2y5QpIpmZZmJJo1ozJsDplYt+LcRyuaQJTAym6JLXVtGpOkclJcWabtNqfvekwA8V6FVXi7S1SUODrmdamr6sXptDxlLwusWKxGrEU1Ym3gkdpaUGhbfKvHlm+rJJ3exCXVjnDRiQqiqpqdGlsPCbWmifVSEGFzRl5MQJbaHY0aF5PL299swl7xg1J07nRGht1ZLbwUFgSKeTonPKvKWwHwMfgNvNBKxkFDiJpj1dRDNc6ur0Z3Mz4HR609DcbjPlYAgwE2xDQ+2XllZ+ohkhwcEQHExPjzUZ4RPgE5qb9dqtrfpYBAZyzjzTuORwgJ+fyUTq0tx3q9/f8ePaTs/pNAzpBU5qTrjTySiaOncaoLWVWdMumIt6gDH8/GBWyEVOGD5cbG+HEycIDjZJ9aYt34RBi1v6t9BQ7Am++Plp85dTp8x1Pd4FHxiAwUFGR/X9ra3K16NHdXnb2/X3yiO0CKC3Fw/jl1eOjJhULqv9YmAgZz0B9PSYZjJ40OS/icAnWoAwOqoTGgYGlH+jo5wz6+4xL/vfHoAzVv8ZmDRJ+dfSYncDxOMBj8dkN9Zp0n1EhOm0eIrGxhG9N4+Hi67pNDdrkyGLTB4/nahUtpqXZiMeVt52dur3WDI2Hj+wpAt6rIKAidqsyWpmY7pmYqVinTihrMZKs+PSOoMm4DA6C/AMulPOm/JoTE39B5hSDL1pjwcGB+lD5cgeQY2mDWrq3Xl0b+ucM39/877GRk1Z6+6GpiY4oUUZdXVmjbu7dQO7XOa7rak349CFC9DZqU26OGXPXdRb0kIG8K7jKFady8glr17zjE2XzKD7GG8a25h+xuy71lbsGY/W3gOVp7NgGGA9+wnghH5+dFQX5pprTB1Hrz0g5eyg7qJRVD+cBvtLQkJ02UZGYGCgF4/n1Pj8gE/bD9jRbZ70/zW6TkRC/vMvffz4U/Lx40/Jx48/JR8//lf6VArYRz7ykY989NnRp3RB+MhHPvKRjz4r8ilgH/nIRz66SnRVFLDD4XjM4XD86P/C90x3OBw7HQ7HUfNz2l/6O/8r9H+RH2kOh6PB4XBcdDgci//8J64O/V/kxy8dDkejw+E45HA4XnM4HK6/9Hf+V+j/Ij9+bnhR53A4fu9wOOb8pb/zv0r/t3hyyff9yOFwiMPhCP4sr/v/dwT8CFApIpFol/FHrvL9XG06Anwd7dXjI9gJzBeRBWifopyrfD9Xm34pIgtEJAYoB352le/nvwU5HI4wIAlNt/hM6S+ugB0Ox3fMqVrvcDheuMzfMx0Ox7vm76UOh2Oy+X2aw+E4Yn7/tvndLQ6H4x1zQh9yOByRf+br/wYoNv8uBu7+DB/tv0RXkx8i8qGINP1lnuy/RleZH78Xscd71GCGjFxNusr8uDSHbAqaA3/V6SrrEIAngR/zl+DHpynE+LQv4BY0eTHY/P908/Mx4Efm3zMuef9aIMv8+zBwrfm3y/x8Cvhb8+8AYJL595vAnMt8f99/+v8zf8nn/e/Oj0uuuwdYfDV58d+JH+Y9bwD3/b/OD2Ad0I5aSyH/r8sIOnWswPy71bqPz+r1l0bAXwZeEZEeABE5fZn3zHc4HHsdDsdh4G9RhgNUA5sdDkcmmrkPsB/4Hw6H4ydobt15c92/FpGTf8kH+YzIx48/pf8W/HA4HD9F8+q3fBYP9X9AV50fIvJTEQlDefHQZ/Vg/wd01XhikPRP+Qu6Yv7SCtjBn4ftm4GHRCQayMNMoxaRVcCjQBhQ53A4ZojIi+iJdB6ocDgcX/4z1+5yOByzAczPT/7M+//SdLX58d+Nrjo/HA7H3wF3oqjoapvcV50fl9CLwDc+3e3/Rehq8uQGYB5Q73A4WlEX1XsOh8P9X3+cP6W/tAKuBL7pcDhmgGYlXOY9QcAph8Phj55emPfeICIHRORnaNVumMPhuB44JiK/Bn4HLPgz3/874O/Mv/8OKPs/epr/c7ra/PjvRleVHw6HYwXwE+AuETn3mTzR/xldbX5c6g+9Cx2OdrXpqvFERA6LyEwRCReRcLRWeZGIjD8G5VPSX1QBi0gD6lOqcjgc9cC/XeZtucABNCJ96YL/0uFwHHY4HEfQqH098C3giMPhqAOigN8COByONx2XT5lZDyQ5HI6jaBRz/WfyYP9Futr8cDgcqQ6H4wSwBNjucDgqPrOH+y/Q1eYHsBHdvDtNUGbTZ/Nk/zX6b8CP9SZodQi4A/jHz+bJ/uv034Anf1HylSL7yEc+8tFVov+/5wH7yEc+8tF/W/IpYB/5yEc+ukrkU8A+8pGPfHSVyKeAfeQjH/noKpFPAfvIRz7y0VUinwL2kY985KOrRD4F7CMf+chHV4l8CthHPvKRj64S+RSwj3zkIx9dJfIpYB/5yEc+ukrkU8A+8pGPfHSVyKeAfeQjH/noKpFPAfvIRz7y0VUinwL2kY985KOrRD4F7CMf+chHV4l8CthHPvKRj64S+RSwj3zkIx9dJfIpYB/5yEc+ukrkU8A+8pGPfHSVyKeAfeQjH/noKpFPAfvIRz7y0VUinwL2kY985KOrRD4F7CMf+chHV4l8CthHPvKRj64S+RSwj3zkIx9dJfIpYB/5yEc+ukrkU8A+8pGPfHSVyKeAfeQjH/noKpFPAfvIRz7y0VUinwL2kY985KOrRD4F7CMf+chHV4l8CthHPvKRj64S+RSwj3zkIx9dJfIpYB/5yEc+ukrk92ne7HA4BT4HhDF9Opw+PQB0Ay4mTpzOlCkwezYE+g9z9sgRzgOz5s3j1PB0ZnOKwZMnGQNGrOsBAowBF83N+JnfO4ApgF9kJIyMMNbaigB+sbG0t8O0aRA4RTjT5+DkSZg3D+TDWiYCZ+bEcvLkGDD4J9927bUzmNZRa3+X/zXX8P7IfC5e7CF2fhDDR45wzbRpjF13PR9/DKdPfwyMAmd6RCTkP/NjhsMh8yZO5ERwDF1dQ4SFTWEmn8DgIBfPnOGieT5/4Ni0WM6c6cXPbwbXXQdOJzjxgMcDJ0/CyAjMnAlTpujvLlyAoCA813yOgQH4+ONh4AIwmQkTJjJhAjgcMDIyDPQCM4gN+IjhCxe4JjaWlhaYOhU+9zkI8LsIZ8/C4CCjXV2MGK5cBFyTJ8PnPw8iMDoK/v50d+stfPLJMDAETGPWLAehIcN4jhzhBNAn4vjP/JjucMgc4Jy5/qxrr+XYOTd9fSBy1tz/IDARmI7DMYVFE+s5NTrK54DJkyaBnx/4+8N111H7fjcwSnj4tcyY4uFigJPhYZjUeZz+06dt2ZkKtEyJZWjoHIopJhATE8DExgbOht3C0aPtQAAwy9zpACDceONUgoZ7IDCQUT8nx47BwEAr4GTSJDdBQSpnTif49fVAfz/dU2/g449PMXnybG46V8sFoAEuKx+TJgWLyxXOrFngN+qBjg7G+vqY6HDoOk+eDCEheHDS0OABPMTeAMMtLZy/IZaWljYmTLiOhcHtMHMmtUcGgQnEOo7TJcI1RramzJrFqYmhzP7cOc58+CETgM9Nn67fcf48jIxwvr+fs8CsSZNom3IzU6ZAsF+fypmfH0yYgHzOxfnzMPniIHR3c3bGPMO7QD7/+WlMmAANDX3AALE3TaOvqYkxs0NcwDULFzIyNoFDh2ovy4/pDofMBfzmz+fMuWuYNAmuuQbOnVPxnD3Nw+mGBvyASQti+eQTuPaaHt0fEyfChAngdtM9Oo3Jk2FK/0nw8+O9EzMR6cWLJyeal5Np0ybi7697YdIkCDjexOjgINwaS329ELtglDOHDhEIXIiKJSAALhyq5RwQ4u8PoaFw6hQejwfn/PnUHvkECCI2Jgg++QTPyZMMo1qRGTMY6+2lHwgC/G++GQICqK2ruyw/ABCR/+0XzBB4WGBQkpJE4JDALwT2y8KFIhkZItXVItLUJG+BPAkixcWybp2IFBTIhyD7QV4zrzLz8zcgz4GUglSY11sgvSBSUSGybZtISIiIyyUdHSKZmSK1tSLS0iJlZSKzZ4uUlYmcAukHKS4WCQsTgTqBFwU2CzwrWVkiw+a6wyCSmCjz5olAuUhHh37fypUi/f2ybp2Yzz0uwMHL8eOmm2Klvl5ESktFsrKko0NkyRJzb1FRch6kCqQaROrrlQ8VFSLm+8+AiL+/yNq1ImVlMjQkUlNj3pOQIFJbK1FR+nypqfqCfoE6SU8XGRoS6egQqaoSkd5e6QBpAnPv5QLHxOkUiYrStdm+XUTi4kRApLZWqqv11p9+WmTrVr3voSER2bBBZO1aWbdObw9aBXZLYaFIE8hNIJfjx60gHSDbQApApKBAkpJEHA4ReFvgVSMvvxZolGnT9H6KzH1LVJRIfLw+aFubWbtfq0yVl0tvr66zbNggh0HeNbwdA0lLE4Eec6/t0t+va1BfLwKbBN6UuDjr8RsEavVaVVUiTU0i/f2Sni5GvjfJypUihYUiLS0iY2OiX5yTI3l5IvC4LFkich6kRc/Yy8pHcHCsrFmjl5f+fpHcXBXM6Ghd36wskaYmaWqy9tKrIpWV0gGyfbsI5Mi8eaICX18vsEvgHRkze2s3yHEQKSqSzEwRaWuTUrN3JDNTpLhYvzMzU9pAXgCRmBhZv15FTIqLRfLz9UG3bZP+fvP76mqR5GSprRWBnwmUS22tmPvcJ/ALkYYGedfs4adA2kCGh0UaGmRcfsRY8t7fL8XFKrvS0SEdHSJFRSLS0CBvGR3R0aG3LqWlumgpKfbGt++/oECkpMTI1wsCFQL7BY4JDMq0aSJpaSLZ2XpfMjyssuV0Sn+/KG/r66UUpAukpUXl/0OQl0EkOVk3RXy8HAaRAweM/O4T6eoSyc2VD0G2g4jbLZKfLyMgW8w+kJYWkeHhcfkhuhM/jQK+1iikzQJlUljoZYIlyL29IlJbKxXWJszPly1bVP6kuloFeeNG3eTZ2SI5Ofr/xcV6rbVrRcLD5UNrscrKRIaGZOVKldtp00SCglThiNst/f1iGF8n0dFm0TIzRcLDparKUsRvm41VLkuW6J+rqkTq6/VnVZUKw9iYqED6+4u4XKoQoqPHZaDDEStLlqgSeQNEuroEKiQ9XURSUmTMHDDbLMXe0SHidstLIC+ZA+cwiJSVyfCwKsG8POWfhIeLtLQINAq0S0GByiIMCpTr+zZuVH6tWSOSmCjvG0Wmh+PDRtGVG6HskqQkw5uYGMnJUUUUEqLKOSdHvOsUEiLnQTo7LaV/SOCHkpoqcvQKCjj2lltEysqkAaQIRDo6zGe7JChIWSBtbSJdXZKVJZKUZDZGQ4PIypUyADIEch5EUlKkuFjFQsLDpQXM2bVfQkJE1q/Xx46L00eS2lqR8nLJyBCBC/ocSUn6ptJSZazLJRITIx0deuikpIhERuo9nQeR6mqBVklIEFWQoEqyqEjKyvRwVf5XSFqaOcycznHlY9KkWFm4UMUoMlIP0qAgozzS0vTeDhyQjg4ReEfgWWlpEZHUVOns1M/U1OhBIqmpKp+9vfIhSDGI5OTI9u26L+BVyc0V2Q9Sbw7Yjg5lbX292Xtr1ujzrF4tkp4u9omUlCSSkSHbt4ssXKi87AKRbdsELkhIiN5zfb3q6nXrdFvW1+u9bgERp1OyskQSE8dXwOHhsVJUJLJqlX69lJTo98TFiZSXK9DKzRUpLJT6et0PHR0iMjQk+fmKwYKCRJxOL9CprhaBLoFyWbVKdawUFyvPtm2TtDRda3G5pBtEGhr04O3o0P0TEyOHDXCTtWv1ATduFNm2zchSu0htrVSCSEKC5OXpPpFt20QyM+U4yAEQGRtTvVReLi8YkNfWZuGtz0wBhwsUCeQI/FCGh0XetZBqQoJIbq4yoL5edpqTUdaule3b9UYqK5VpbW2qP2VoSGR42EZiTU2GgdnZcso6VcrLpa3NUrKPG0E9LXLggLSASG+vQTh5Rum8IyNGuUlVlWRlWYjnfoEfm/fukshIoxCGhkQqK6WmRpWQFBdLrTnVfgnyiz+DcPLyzCEUEqKCXVkpkp0t5y1FEh6uwhAUpOxOTlYlHxYm20A+NIolKUkFC/olK0tsBKAKrFagR/z9VT46OxXN5uXpISs5Ofrdq1eLrF0rTz+tmx1OClSIv78XJUtYmIjLJSkpIlAkWVlGYPv7JTtbb1+CgnRjlJVJU5M+GjyuZwjI58dRwBERsdLSItJt0NbwsAiUCbRLQoK5B6dTxO2WykpV/KtWqV6QsjLpAjlloQeXSwWivl7eN9fbulUEHjUA4B2BlwW+L/C2auGgICku1q/o7TVKzu2WNpBao7R2GmQ+NqYKDvaJdHTIbhDJyJCoKN1gtebwFLdbZM0aWbVKBHabw6xBlX5a2hUP6ICA2EvWoc7c94/10F69Wtetutoo4H0Cv1YrJT9fZGxMFUdXl8pOSIhqraQkOQB64A8NCYwKvCmwSuLiRBosVDw0JGVlelBlZxvLdGxMZMMG+6CzgUZkpEhSkhQVqbUi9fXSYA6f1FR9zO3bzWHZ3y/S1iY5OQYIVVUpn6KizEEwOi4/YJHABZk3T9WFZGXJy9Z6m4NOhoelt1cPnspK/d6tWy1L7Ne2PDmdKv+6R9oF9ulhtWaNWsFGhlJTVbwPGCDU2albQOrrZdgo3lPWXk1I0HUZG5PKSjHg51Hp7BSpsPhlmZ1r14qkp0ubAVGFhSKwSXp7RbYZ/lZX63d/ZgrY7Y6VlStFYKvA9xUBJyTICMj7IBIfL0lJuqkGLFMoIUG1a06OKp/ERD3xEhN1B27YIBUV+uf+frEVmISHi2zYIFu2iDENdwu8KVu3GrSTkiIDIFJSYszPC0bBPiFZWbqAw8OqdNRsHNRN09Iiw8P23pGQEH1t2SL6gY0bdYXS01UBdHWNy8DIyFgV7N5ekcpKGTKofwtqHveDfaq0YFwyOTkSF6cn8BaQo2AUQZk5HJ4wiP282bi1ZoPlCDwpUlUl7cZEg12yYoWoJBYUSH29srqtTfeaote39aDJyBCJjJRTqPtDXS+rpKpKFIVnZMiKFbr3xemUowaFSnGxUT6bJStLpBfklnEU8E03xUpnp8iQpbxCQkRKSqS+Xv9ZWqoKvBeksvISRJydrZCqq0tkbMxGDikpRjnHxOj9bNsm27frPWZkWIi0XxVaQYHI6tWSna1rvWWLQX3Z2bpxQkJU/lavVujW3y/2yV9aqohm2za15kpKVBkUFqqllp8vMjwslZX6VusQlOxskYyMceUj9tZbRYaHpaBAn7WpSRWWjI3J9u16rS1b9BnVPfO0SEuLynV5uaxbZ6yy+HgRt1uVhMsl0t8vpaUqw6rgWwVelpQU0X3jdOofc3NlaMi4nqqqVCBcLjkO+h3p6SJr18rQkFGuZWWqCHt75emnvaDIUtySlaWMX71atm/X75aODlX4aWmyapUY6+vy/LjuuljZutW4uVpaRJKT9aDfsEFycvT7rDUYGhJbngsLdXnXr1dxkfx8kaoqWbdOl2B4WPQBhob02mlpeqAmJ0tYmN6TbS719yvIWLNG2rnEHWkQs5SW6nuN26e/3+ib/HwZHtZL5OeLbeHWguwFkYoKyc7WezVLrDzLyfnsFHDsDTcY39whgQd081dVibhckm9OCAshSFKSnqIgEhMjXQZ97ER9vPstIYiOlrIy3WwyNqYnS3a2MqCjw7gQ+o0CPiqydq20WJ8NCtIN1dtr/J6PCnzboN0cRVyZmSJtbWrKlpRIhflsbq6FOPMEViqSXbtWXxs2GNS93yjGyzNwzpxYWb9e93Fnp37XUdQf1m2erbJShUcyM+UMyNCQCBxTtJ2aKpKTI+vXWxbvoMBRc6J3ib+/Ck9iovqrFi40yj43VwoKVAGvWSOK0uLjpanJHCRdXSKlpZZlr2ZcVJS+zxx8tbW6lzo7VXn19io/UlNFN6bLJVJdLU1Nlom7VdcoMVFi/f0vq4A///lY6e8XGTNrvMWyglauFDgvRUVqMR02Cjg9XUSSk9VHXloqCxdaa9J4ycFzTDeF2+195efbKCk/32zcigqRsjKD7Ntl1apLFF58vEhOjrS0XOLn7uiQM6grSHJz9TDo6FCZDQqS/Hzj2ujqUhiTnW0rLNtczs8X2bBhfAV8zTX63U1NqjhA90lamgEMF2wQoJblJpG1a/Xwys6WnByzPunpImFhUmsOdThtZPzQJTxrl4wMI8NJSV5LoqzMvsZxY3HVWvunqEikq0vi4owyDQuTJ0HGxkTgAYFBfdasLBkwSHHA7Om2NpXJ4WHRfZiTI0VFYsDOOPy45RaRAwf0ecLDVbDi443f+FW9h8xMVQYdHdLWpuh3/fpLdIPTKUUG5a9bp2eCuN3yLohUVuq+Ki1VF2Z2ttlTo3rA9PaqVq+sFAkPl3rzTOJyiaSn67N0dckISMMlL9m4UdavtyymrRISorz60Oiz7ZYCLy01YPD7Eham8aYrxQg+tQK+/vpY2bJFFUp+vmFKcbFIaqoGlPLzRc39Y2Lv8MJC3Rylpfre8nI9rYqK5Lx1+nR0SGWlbijbJ5iVJVJSIh0dYuD9OwLviBQV6amZmKh/GBqS9estNLRVoFzS0ozvsLJSF7SlRTdpS4utrKS2Vjo7RTRwU6YowQivmE0yNCRGEY9vYkZGqvJMSTHme329zRMpKTGeh0bJylJdoALxgDGnTioarq4Wyc+XtjblrSq8RlWGVVX6y+RkkdRUqay8VFm/raw1iNvileXisK2MqChpNwfk2JhRTJaF0tRk1my/qL/4tGrssjKDfPvNgVuuplt/v8TecstlFfCiRbFeKyIzUyQqSkbAuIkOqZIDEbfbKMouDVhu2ybS0CBjY6oT1NxUfzrUqZVhyVNIiEhTkyQkqKVVW2vQRm6uyoxxabW0GIvKPIu0tMj27QYNDg/rOkVH6wasqlJeNzXJTqOkpLhYFzQxUXnZ1qYugbY2r2/ZRKbGk49o6yDOz1emR0aqvzAyUm+ut1fXt6tLUlN13aWyUiQyUnp7lQ8pKcYP3Nur95KcLFVVyoo1a/SxLcBWVSWqAbOzdQ3Ky2XrVj2Ea2vNvior0/eUlUlNjfI7MlL1h4SE6AFRXW0OvwsaHBselvOXPktLi7GE2xUANDVJR4fek1pd4/AjOlYPv5UrRYKCZMyY6lJWJgkJRnZXrtT9mZwssnKlNDWJ17ebkyMSH6/7PzVVoqL08LGVekGB6pqYGNkPIpmZZn+fltJSc/CuXi3idssIJgjuduuNV1bKqlVGr8XF6bPGxChjKyuls1NZl5amh4IkJ4uYw6zKyIvqqX6BBzTA7O8vHZ+lAoabBS6IJCXJW2j0WbKydAMNDZkg0UmBk5Kd7Y2r5eWJMXkPifrtHhbokRGDhixHjfpzehTF5eSI2aUia9YYZFgmUlWlm7qyUrZsEVmxwkJMRwW6ZMUKNYG3g65OZqZIQ4NUVKj8jY3pCdlhBKqqSuV6bEyFdwgNID1nfhZdgYEQI3BeoqP1VmtrRQ+YoiLdFWVllv6TNWs0GKAR2m8JFElkpAm65eXpglZVyfCw2GZ0erpBYNu2ifj763OPjUl2tgpCXJwqhGI0EFBdbRDBtm0qPGFhik4sH2JKivT2iu2nfc4gTxXSLoEuCQsz/lMTKJs2zTqk6mTePOXhggWxl1XAsVFRIk1Nkp+v61JUpN+lZ1qR+hcLCkTKy0V9nj8w8nBI+vtFXTb5+ZKebpnWb4tG/kdVOVVXiwQFmQyBRwW6VJHm54u1G4etTZ2TI9LVJR0dKodPP63KylaiVjB47VppalIZkOpqedqg9t3GtLSCpWphfVsKC0WDXGFh0tR05SDLdZhoeliYfnl6ukhCgvQb+SxFfds7jYxLdbX096vCVb4/IfCsfTA2NKiyE5dLD46NG9W6sQ6U8nL9nooKSU21DrI6o0wrDC97JCHBOuTPCwxKUJDxyTqdssHs6YQE6/P7dH8Y9L1+vRgEvkvUZbZf/P11q1miNh4/3G4Nwsnq1TKEBoyrLEv2wAGx/SphYfIGJnjd0SFLlhhLYPVq3SvbthlgtEvgRRuLSVqavGvWrgxEUlJErcljkp2tLDoF8oxB0BIXp3JiAnPKo6PqOnC7Rfr7VV6Gh+3MIMnL09fatSKrV0uH0WH6+XLRONW3VNbdbjnzZxTwp8oDBg9wDPz8uABMsPLkRkbgyBFWrLiNlStn09cHAwPQ2Aj33gsL5l8kPn4CO3ZEs29fNO++G0tc3Az8Hixk/sGDsGIFREUR6oG4uBmEhwNnrgW3G5YvhzvvJKTkerq750FPGX4uF4SH07wPWlsBzgA3k58/lb/9Wxh1w3TQBM7RUQgPZ8fzmu7odMLdd3+dRcnJ4HTy+uuwe/cY69dP5L77f8bcmBgeePRRLhw+zEE0c3V86gFewOPJJD4eFp3azoU77yQgIQHWroWeHv79wyUQNMbZx95h/XrMFScREvIAH3Ejnn8+yidoLmXAW28RADxeWsqM9V+nqQmuj5/Jj370Tb488i3eA+6eOJHHExPh/fc53ddH3XV6xWBg1WOwc+cJ7r//myzK9YNvfIP/AL7p78/vXzmLxwN3BV5g+pEaPsYkgT/2GJ2dhwgKgsmDn3DBBc8/Dz09M/nbv4XHHoOSkhls3ToDpxMefRTa28dhx5QpXIy4kQcfhPvvh6NHoa7Oev9ERNo48IUf0N8P8B4wH7idyMiZTO05Rh9ATQ0bN8GOHfD3f5+AxzPGmjUTWboU2L2biwMDuFygWeL+EBioC9vRwQWPh4CkJAKioiA4GPbsYY7LxXfmB0NEBG/um4qfH1xwzyVgsR/s2gU7d3JjcjJ33rkImpuZani5LC5OZfutt8Dj4dlggFUsWwaTAaKiaGyEEyfGl44ZwD1OJ/ziF5CUBM89Bx9+iBOYYyTBA8wFePBB3vPcTN0res2774YTJ37Izp0e4AXgCE89lUBq6o3cEReHa+dOWLKEHTsgKiqAxYsXEACwfTu0trJ0KQwOws6dM4ycBqB5slOJiNDvOHx4DJjI4sUQEwPEZPLtp54Cp5PNm6G8HNav/xJf/SpMeN+Fs6+Pn2RfJChoGiUlX2H//mXAECMjJwgMDOUnP9G86e997/L8OHcO9u2DB9Y+yuTwcK7/yU84CVwYGCCgpkbfFBEBHg+h7e2cAz4anMP+/UNUV0/h6zExuuFLSpjb10d29ndpblZe3XQT4FnK4nffZbS1lU8AnE6efjqUvj59vlmzYJJZ3/CoKNi0CaKi+KB1MqOjkJJyB/Pno4wLD4fWVpYtW8BZTwBTV63yKpDGRnjiCejsZE5qKnNcLuYshTVrvsbBg1+jpkb/zI6FuI4f1/ePR58OAQcLbBLJyVEnd3y8HisFBfrvpCQbEWdmGoSWkyNHzWn3ISiKy8oyvuRBgUY757KtzZhaHR0iLS0GXZwWf3+1zCyUKikp0tFhmeLnBdT3d8by2fj7q4/J+Ab0Og3mxHxa4G1ZtUpMhkSZaIDrcfO3TQJFmiOYlyeSnn4FBDxH4EmJjDR+3m3b1EeWnKz2TnW1dKGBNunoMKlfpwWekIwMDUjVGz9Rm3m1g0hurtTXq+/L6dS0n3qQQsPHYfO5lw1K2IIGE9Rfvt+b34rmWEtYmKSlmaCX8WkeN+hOwsL0d7W1ugi1tXaKWE2NuhwqKtQMTEtTV8/kyZdHwDNnxqrrBxOlNwFSzdMsE83VfNzw+LSEhRmXkxWRMyjrKIi4XNLba9wlaWki/v4yYKwWza54U5xOIxPbtkmX4V1Li0Fz9fUiQUHShgYEj4MiRJODWlSkqO45SybLy0WSk9WXGB1t5Gq3zJunX9/SYhB4bq7Cx5ISyci4ctApMFDTFIOCTHaBRk7FjgwlJ6vptHGj8QPvM4jtvEhlpSLj8nIjnz8QzYtutOMAikbValm50vAyPl7hfEWFyNCQyXs+ZqyY85KWpjwtLvbGJisq9Na2bBE7vbHJMs+rqtSiio8XCQmRFiN7Eh8vUlQk+fmK/FasEGW8y3WF/bLAWMinBUZFSkqkGuOrjorSh6ivVz4nJIjExcnChSKQJ5GRYrsvthjLYbe131NTRYqKpKFBrcfaWuOWyMiQWrOvZNs2O72zG4zfuUHgtF1HMITmA1sBTMnOFsnOlqoqS4a7BN7RZzWWlgZQT4vk5MhrxmpqslB9ZaXJ3/7MEHAQEAejLbgAEhI4FriA66M6oa8Penv1p9OJ0/lN/UhzMx+j528AWiU1dXAQ+vqYNm0uZ87MZMoUPVzmBp+D8l16+oSHG3Rbz8hIBF9eOhNeeV1LtEJDaW62DpaJwK3Ex8PZN/T6F0ZGCBgaUnQUGkpnJ2hFlxO4GbiJz3/eeqbZwEwURfeAwUDHj4/AQw/pifvSS5flhr//bEJDH2b+fPjwQ/i30W8Stf2b7NgBT005S0bGF/kXoBmIeP99oqLmIEPXwPP+EPjvcF8lcwYH9UGcTj5a8QPKy+Gflr7DgntuZEF4OD959K+gswv8/blxZARnVBSEhRG8cycfAdcDi4OC4LHHaP9hL+BhxQrg3l9BcjL3Dw7Crl28fM27wM389lvvwiuvMBO4DWDzZu64byb33TeT7wS3QlQUTz11HKhj5867WLJkKn/zN7BmjaKmujpoa7u8dEyeDPPnKxc/ARgepq/P+us0tG7tAjCBxMRp3HknzJgBOMMhMJALwFngBDC5r485B3/PdJcLamr4ZGSEC6ilEHzkPWA5S5boPc0NDWXU/G3fPti79xNOBi9gTny8IkXzN3p7obOT0NCpjI56qy8vrce/CCjEPgwcpq9vGU6n+aOfn5ZuuVzQ18c990BnJ5iv+F9obAzOnIGBgTHAo+jJ3x+uvRbi41WWDYSePx92744iKGgG114L7NrFB8DyoiLgUcOVMX35+YGfH9OmAcxgcBDefVfv5eaWFq0aO3ECmpu5994FBAbOw+XS7XD33XBj4Emcy+awYoX+bvlymND4AaOjN+ueemghc6yHeOUVRYTGrP0EOA0s6OvTD/cBTFGrtUjXaXwS8wyfAL2Qns4XMzKUn2Nj+hbLogkM1P8f1B99ffp81wcHg1kzp1k/Dh4EPz88MQ+wb5+C6EX3zYeYGGaiVXpaeuqEefMI7ujggx5Q3THEqVMBnD8/hQ9Q6Zw5f74yZetWqKsjfi0sXgwnTszk1Ckns2cDSUlMHh1l3z6A49DczBEUXbuByQMDhLa2aiXilejTIODY665TqJeQIBIWZnLcKvTUTE62/XCSlCSFhQZxRUfLy5ZfLjfX9hHW15vPREcr+mhrU0hq+ct6e83pulVPGMv5bnxl6h5+VVOQmpq8/q+UFNlv+YAzM0WGhmzkuW6d8S0nJMhxgxqHhsT8vUfgBb1eV5fx6XSJ+pcvf4KFhmoWhIUONUp6wfjdHhcYlDYLaRYWapAuPV2RjdstVVXeoNDYmFU0sk+kv18OGKTRhgkWWBViWVkiOTkyYBDw8Uv8neoH36VwzeXS9x44YPj4pECtN+pifMoaiDymhQUVFSZL40WBVaKpUYfUr9zRIcPDipYiIsbxAV9zjUhYmHRb99XVJVFR6v8PCdHnW7jQ5FsnJsoYmHurExkakgY0mFiJVri1GUQybFBqvUHW51FfsbS1SV6eAptu835FTD8WOCYpKQYVrlmjX2qlmVl503FxelNlZVocsXatbDHX17Xfp4KakiKlpYqCGhpEJC7OTkE6cAUfX+yNN4rU18uWLSojtrPZ5CFLRYU3EBUWpn77rCyR/HzpR+MPVRZCz8yUvDyTlpefr+/dtk1keFgyM1VuCgo0y6QedJ0PHBCJi5MhNHuhHxO3sQLRVsAuJEThsMm5t2I2vb0ix42P80PzqressGHvmloFIwNmDcbjR0BArMybp59RWdesn/x80efOy1MGaxKuDIDRMUclMdFUQdbX22l2xcUaEBs2aFiammTJEmVPf7/+LSTETlBQK8ZKia2vl44OMYHmQ1JQIPKGuY6V1i8bNshxwzdxOnVPhYWJrF0rK1bor/z9TV5xdLT8HM0/3mvk+EPDq/H48ekRcEeH+rNCQiAigl27APawY8cd/GzpfO97RkcJDNRSdPr6tIOBywX33sshz42Ul+spNfett/gEuHH7dj3i9u7lbHs7U0+c0BPNA3Yvh5YWeoCZo6MQGGiQlUdP3s2b9XT7whfgmms498Yb9JhbPsdkAgNh9uxp3HsvnL7hF3wMHET9xF/f9TtWrLiLkpIZtLfP4557gPXrWfrovwG1KPK4PM0KHuMnWee46JyMx6Ng4fXX/Vm8+Fb+/u9vZdo0CPD0M7evD7Zu5cvPfxvi41n+8MOcDL2NjQ/rY2/aFEBgIJSUQHz8l+DB7zAdiEhM5MAv/kBoHHgmOjgNBPYL990Hv2u4j6/fcgsTwsOhpwfKyykre4DBwQh46K9p7usjIjCQ9/xuw+WC1NSH8fODHzcuIuJHD/Ddzn/B78knaWkBOEZg4DxYv57J3zgKLDfPfQo4i8cTDQcPEuB0ckdwMJ/zP3dZflwYHubj9nacKBK44JpJb6+68hcvVlDjdsPf/A2c/cVujql4AO/wcc+tgCKbAHO90yjKmenvD243ge3tTEbx09xf/lKREj9gcBACUTT04Yf6PFDDG284mT9/Nv+6bJmiTT8//cJ9+9QxvXChItFly2jcDHdERjLZ3ENQ0EwGBvzhjf8Jb7zB4D0g8i6HD8dx88SJnAbeRi2ucam3F0pKWPrQ4/qcfn4QFQXz51NXB4tCQxlELYbR9nYmtLcT0dICt9zCoJHPHuB14IvPPce9Tc/S3AzsaNVrG79pePg3gV7On1cLIgBs1Nr57rsY7yoTUH/zzZ2dBPT26l4dGOBEXx8XgblNTbiTvkZdHRw/vpedOxO4FQWhAYa/k1Ebsbwcurv3Al9k5cqJfP7zxnq4Ak2apL7a/n61DOBd4C02b97AP90X6l2fU6cYRdHoTTeBv38E4eG6xXG51KG7YgWPPqo65htGJjh6lNDQG3G7VdaamqC7+xW6u2dx5EgCbjdcbwlieztzmptJSfk6mzYFExysazkZBdTd3cfB5bKiXlzweBj1eBjt6+P2bdvYcfgBoBqn8x71nx8dYTIq93PRdWjFWF5Xok+FgBcs0BMqJUUkJMRkPewTOCb+/uaULysTqaiQnByDgP39tQAhNVVyc/V02rpVX1u2GH9NfLx0m1NjO4gkJkp1tZicuhcF3lHE3NCgkeTISJGqKqms1F/19xvfZlGRSHa2jBlf4ZjxJUpKikYt4+KkwZxyTxrfoD7DecnL81boDQ/raVlcbKXAjYNwZs1SBGGVd3Z0aAZHebkmZ4NYaW6Sna3VNNXVoqkqp82rXTRKrei0xfjEesGUIj8p0CUNxu+lPLlfo/ZW4mVamvJl9WpN3cFEgfPyJDPTIIfKSuntFYNqnxSrFEwRhlbEdYBIeLiJZL8s2gfg+5qNYaV5paVJrMt1WQQcbRBQE+rHVd/7Zv18VpZIaak8/bTKQGeniAwNyfCwWb+WFr3R3FyFLDk5UlCgMlVWpm7B9es1Y6i/X9S60bpXlYfaWhMZPyqa231IczGHFR1LSIhepLpa/a9RUYqGu7qkt1d/vX27srS2Vuzs/7Y2BU1WmqR0dekDxMXJfoPWx5WPgABFmv39Ulam8l5UJLbV1Nam/STGQGRsTMrK9Duamoysb9jg5XturrdYZPVqXfPCQpHqaqmo0MepqBCRkBBFzC6XSFCQdKMxhuOWLzQ9Xa/f1CQSFCT9Rt7OGL9oWJipmisoEGlqkrIyk0eekaGWb1KSyMqVJt3sWc1OGRtTXvr7G+thHH58/vO2VQR1EhenWSktLSqfVp6vBAXZ+dZW+pdVfCVBQYrwTTbM00+L8mTlSt0DVom1y6W/M8UZnZ22MaP5xvX1akmFhIgkJckIGg8oA5GqKm/ecG2tMrakRCQ1VXmYkCCJiZqpUlUlKpBr1ui12tpk4UL92IDh+3j8EJXe/30FPG9erH5hRoaMoQEPqzZeS0L3SW+vrkdmpvJiBOTnIJKdLfPmqeBZDn8NDlwQCQ+3m/I8DSIxMVJUZAXZXhaokLg4EyCwmBAToxvBKL2WFmOaWRGkmBhpM6ZsvWHEh8ZkfAtkg2G2CtJWFd7sbJGWFtm61Zia1dUiFRXjC9SMGfqgOTkqMP39mmZTWirbjRKyirGkokIkNdU0OOmXxETdA5pSNSowqN9nSl/lwAHDo90aJ0OLG6S3Vwsi+vtV0DIy9NoHDuiOrq2VXnO4SHi4/q6lRaShwSiokwKNItu2qWKqrfX2ZAgPF8nIMJWH5437J08P1txc1R5NTRI7f/5lFfACNBD2vjHPtVjkAVmzRrRsPDLSNscTEzVVzcrblIoKddEMDysvx8bMYfG4wPlLqgW/7618CwmRp43JV1ZmpVbtEy1qOK0VUJWV3iCc1VEmJUXfXF8v9fXKns5OKw74goSFqdI+ijftylbK27apslizRrr+zAaLtcz9lhY7d3faNG8/nooKDQRKeLhJpzwmkZGqWwsKvLfb2yu6jlaRglXGXFoqUl8vLS22d0/lPz5e2oy8W5VeI5air6qS6GiTy2qUbwfqdtE00F0iQ0OaR1tQINCgubb19Zq2V1goUlpq0krXKo+LivSeQkKu2BsjdupUWw/Ai+oStPjZ1CSSmyu1hu/S0CD19WIXR0hamrSY/bvNHCoyNCS1teZAHhoSiYyUd9Eir73Wddxu5Vlbmwm8tQtsEmlrkyqjA4rQAPfTmBYGiYkiGzdKS4uuw5Yt5nCrqlJ3UXKy5OYagNnQ4O0hUVJi1vHrup+jo2X4s3RBBAQY3/ijjzLhG9/g+n2/RTYOcCz5+yxbdgegvuvAQLjlFrXu/HqS+OudO2HvXo69fghCQ/l4cDpf+QrI8LVQs58P/qqVs0C4gfD4+/NA/Ac8sDwQXHpd+j5WL3z5CdwAOTkcuO6bfGHwI7694zuwtQ+6u+Hzn+fYpt+zbx924OGLSy9oXtPhw1BRAYcP4+7rwwU88wwEB3+LTZvgicHHWd8H3+r4N6hqtlOQxqULF/SeoqIgIoLTo1MZHASuOc8nQCdw25ccPAa8d69wT90dnPoSQACbN0PjdQ6igLIyoaRkCqejvsj00lK+u/mLPPeF7cAHzJu3jIcegplPhXNjayvvzZjBj4E/fE7NmzsGBvh1zL9z5Ag0N0NExFyeDQtjtL2d37e24rruOm5zu7nYcYqeOlizZjbBwbPhwTh2AbfHxvL75GR4PgoKCyE0lPWBcP/9Tr761WRgorp5hoB77uGLK6Zy5Og1l2WH/8KFhJaXE7ppEzQ386uDAJGMjsL1QUFw771s2qSm5B8eextef13XLToa6ur4cgywqRGqq2HjRgoLf8DBg3DkiHpZ4GtAPI8+CmzeB+Hh/MPAACQm8qYfLFsGwcFfoq/vSzz/PCw68TvYsY+7w8N1HZctU5t14UKNej38MAvOnNHvj4jgt6uW86Mf3UdEBEzelE9waCgFDwN8wOuvf4lFj9zHsc7J1NXBske/zMzwcDWZ//mfL8uPrtBY/nX1QQ7+SO8/NBTuu08f+/Dhah588Et8XFUFPT1s/hK0ts7D318/+9xz8JOfQFiYWtxRUXNZ9dizuFwas/V44IvvPw0//SnXr1nDgw9+l7mjx1Qep01jbkqKN+oWHq7+sR07YGCABx9UVhAdzZzDh+lDZelfQp/lX8YehIcfJgrMm07h8QTw4pEFxCcv4HrPB9DXZ1IBr+PIETi0+AEWhIbS88wznB1/t2jPya1baWx+nE2b0rnvPqAxVG3+J56AI0dwYYJmjY0sWB7KRb+pXHDNJMDj0eAfcFteHixfzrMlmj626NR2KCtj8OhRLgLxLhcUFGgAfdcu+MIX+PrDc3nttRE0uDoFmps5AXwT8OvqUobW1RkdUw5lZbgzvs/goLq1OjrgYPzt3NcmANzTp+vJg4/qdzz0ENx5J3FxsGPHw2RmToGX26/sooJPh4Cjo2PtctfCQhGJjJQCjJO6v98EVIoEXrT7MUh+vkh0tIwZdCQpKYpWGxqkw5xURWjy+3HLnIyL0wCdv7/sNO8ZA6/3PipKOjq87eReM6bgAIikpZmUkSLRgo9vC9RJRoY59dva9NSNitJr5eWJVFYat8DfSG6umuIN5po7r4RwLBdETo5CkLY2DaoVFsprxmXwlkGEMjZmeW4ETkpFhaLC82gxiJU+MzRktUzcLFqIMKqBl5AQ+57eQN01baAl3w1qpq1bZ9YlMVEOg933QdLTpb9fQUxZmTGbUlI0mT8kRI/y3FyxfDqdnbp2Vq+BsjJFqk1N+v8Ox+WDcBAjDofyub9fjKn5siLo9HSRhgaBkxIWpjwuNKakrF2r9xAXp+tvNYhZuVJk40bJydG1zsgwlUr19Yps0tNF2tpkbMzbtKW21pjMqala0RYdregqK0u2YQojkpLsVoK/Mbw8b+SnF0Ti46Wy0grQ7BN4VhYuVD6sWqU8SEnR79Rsv/HSrmYYy/BlgVpJTVW+agBKi1Bycsw+ycnR+8rPF6msNN6VR40cHxLokbg4tQqdTlMBlpenwaeQEL2RwkJ9Q1qaXSJYUKB8k64u5afpsyBNTXY1j1UVdtTIqtUiQDsNvihQJmFhGgO20Lhap7vFak07PKwNjCqusF9usWTWqjArLRWrv+xRg2p7QQ3zDRsUdg4Pa3pqUpJagMnJkp6u6ZBW5pqsXKmpZQb9Sn6+pKQY1Gqq3NSau1+0uOVlkcJC+aV5rxa7vCDQquX+6enqxjEBfLWsrFantRqga2oSycuTDoPKrZr/7dvFtm6HzR4cjx+fGgGfO6dopK9P6xtYtYpv//CHTA0JgUce4Z/uuQfPugdob1d/+p498IVpgdDXx4S1awnNzAQ/P64f/QT6PMzJzWVOTw9LPR694LvvQkuLZvE//DD09LC8sVELPfz9vafU4KAJFAxxOnQBN6Mn+GkgcM8enngCNm58gPBwzce/9174+p0XuEAAJ3vmMmd5oAYwRkfhkUf448EAliyB/fs38i+PXuDgzzUA8uWQEIVr41QenJ8RyulHHmd6/I0cOXqU+Tt28Nfr18ORI9yOIuB30HBWzJQp/C47m39Z/C/88z9X89WvfoXiYiE4WANIK1Zo1ktjoz4iRJtP/gtPPvkDfkw3NUAUailcn5PDgb/5V+Y63uHmWxRJ35WYqFGu++9n/vr1vNlzG489BvHB8GCrBj4XNL8KnaM8e+fv2NyjAbGUFLjZfVorMK69lpZ5NzM4CP8R/A9cPPUME9wHWP2bO2hqglOnRlCZ+l/J4ZiI26393af2fUx09FwaG0M1w2j5cggNZd68qcTHQ/BL+hyMjGi0ZOlSeOQRRSAffqid/Zcv5+TgVN7/e5W5a67Rx8Pp1C9xOqGmhgkxHhobb6SzU389x30R7r+fqIgIuOce/nDiRuLX/5pvWqlIjz7KyZ4AoubPJ2rPHoWnfX0qLOHhsGIFe3ZpnCsx8Uv4+X2JF16AWVX/wV0bVpCSMpXwcLg5+JMrWkgORzjXXLORhQv10gD5+dDefgYIBW7ivvvg5tCz0LFYF2jxYggP55574MSJn9PeDh7PKcCfEyf09oeHlRdERXE9cKG7m4DvfQ86O+nr62MyEDA6ChER/GDpUlgaCo+a6pYvfIF9+yAw8EbmP/8fTPV8gt8jj+A6eJDJhw9zFgiMi4P77mPPHutJrmPTJl3CgJe3QFcXX/vaP7F37yIyM6fwq19BwME/sigxETsn7nL8QIOqJCfD3Xfzsfs2du2AB+67j5nPPadBvpAQzU1sbVXZcLmYGxEBLhcRAG43i+crq4KDtbiChPUE33cfwa2t0NnJ24v/iTd+eIYTJ6bh3vzvGrjkDBBNUtLNfOUrQPOPWQEQGMjs2ZmcOuUBTvD++x4OPPMiX3j+eQ4cnkxUlMpcT88MXn99BocPH9dg4Nq1WuiTnc2ciAhVeLt28ddLlxKx6XpCQyGgvh63x6PJAePRp0HAM2bE2i7Pdev00O3vF5HVq6XKINUGEHE6JSfHOLvLykT8/Q06XiX+/mJ3M0pNtUou9wlUiDQ0KEo2teHqh9wsUCEZGabRjOkjYbUHXLdO7GDMXkwKTl6eSHm5nEcDbpYvTHJyJD/f9Ino6BDp77d7vFZWGn+ky2X387QavjDOCTZ3bqyesi6X9lUwJ6qkpcl5c9q/hKamnDHISv26Wn67cKGCOCvAY5UtK0/OGwT8gECDtBkf1VHQ3Jfqak3namuzC13E6VR00dQkNTVWF7nTEh2ty9DWJnaXLy0g2C9xcSYVbmhIUYnpTrd1q4jExSl6LymxUYeWp14eAU+aFKt+3d5ekYICE8AclIwMsftqPP20QeAJCd6WiG63jI2pvCQmit1kJSTESoA/L3BBsrMNWuzoUIS0Zo2ixoICSUlRZNnUpAh561axS27V6hgUDXa+aZBMj2j/izftfsK9vSrbVil0XJz6/SQtTc4YdHXG8hEmJdmv8eQj1u22+9tKWZlZjzLRktUK7b9g+mL394udllhWZhBWZqbI8LCRhwvmGQYFzisC7u+3UyqfMRbFb4zM7Tb74S1jMe21LLHcXKvaVzRd8rTu4bIyFcD4eJGKCqmutsr8X9A9m5zsbbFq0GFGhtgxB0lPl+JiMSXbl+fHAkzKnenfovfQoPsxJka/e80ab+ey6GixexqsXq3v2bBBOjuNb9j0F1CEelS8RR5W98C1YrUuNeEiOzYkCQl63bIykQMHzD4/aj77uMATkp5ugsUHDojk5hpr52VZuFDbW+4E20rq7xe9X+vV0mL4W/7ZIeCLF/WACw31nuiDgzA1MNBOPbo+Lg6WLuWee/SUIjQUHnnEJLPfp36fEyfA5eLuu2/H5YLf/GYWcAqcTi3zdLtN8cRZtFhziOZm/c6oqJuJuOdmhvYARKtP+itfgZ4ezh0+rKkwe/ZAXx8ec+IGA9PDwiA+nrpX1A14002aaq6H1xQWLwZeOApxcdy1cydT09L4Y+MVym5R4LR1K9wREsKFvj5agfBf/Yqz7e12mtu9aWk2qsHtJtwPUlLuweNR0NXTo4Bh2jT7LfzjP8KsXaW8Oulv+cY32oBQ5oaFcUd7OxFjY3z7vgmsGoV/vf8jeH0HERbsfP11Dnw4lbTl0N7exNatN/Fi+C85/aN/ZcaMU8AU8vMfwO3GlLieZdUq+OvXvwsP76H56FEiXC6a1/2Y11+HoMfe0RLg13/Lix8uhAcf5M1Hv8/3v/9n5MQ1nQnh4bTWAHzCNdfMY/LBt5kbEcGMGXM0e2zxYiZ7THGCnx8T+k5z553Tbb/9yAh0d58FzuJwhBIcrAZLRwcQ6fRm5jc2gsvFI4/8wMrH5+L8BUT7KaC2eNzcPIWtW5Pp6QEdX3MBLcoJ4IYbvDLX06MpUibbUWXp9dcJBBYBASEhmlxvLaBVQHA5sipTzE2EhoIW/kwDZpOcjN5/Tw9T697m9ogI/dzoKLy+Q536Hg9nzowAF5g9expOp3610wlnmcrUZctw791LIJoGdhG1qJYBhIRwsLubE2ga2VSAvj5WrLCqrG8lMlKtFXp6IDZWX0uX0v6GyqO//338/d8Dw26c0dHqFw4NhRMn+NWvboTN+9Rn63IRajLJxiOHuQ+am1myZAH9/VBTczPLlwPPB9myQF8fNDZycWCACQ89hK1MYmIgIoJZ0y4oEwIDwelk/nxobY0gPh5uuAEqKpI5fjwSmEpIyEy7W0Jzs5aAe0DX5b771H+7aRNfeNhJSMgCurtnALfjcExh2TJNnWNwEAYHVZ8Rw/z5EPy+8rO+HuA4r7zyJR546CH7vRdCr0cDJ1PHZwh8OgQcGKgIx8oSGBoyaGfDBvXRFhWZU+S8SFycpr606Uln+eUkOdkuM5TISJGUFLupjhw4oKfH8LBpxnLenCKNdtK3Xn+fJCUpcqyuNmlDxcXyEppKctw6qSMjFa2Ul9uRYm/jmQsSGWlO0rIyDXfm5kpHhxYLpKToaa7IffyJGAsXKlJ8zaDvfjRx/TeWb6ijQ6qr9fmHh8Vu4LJ+vdg+PSsH/emnDTovLlakUF0tVrMTyyeuaWgveItKrMj4hg1SUGAZA1q+2tCgfjmdbLBJYJOEhVnZJXraNzVpCeZRNHPiFEhenrpm7RaYRUXyFqYgoL9fFi68PAK+5ppYiYtT+WhqstokPinr14vdnrO+3nbtqey0tXlrY02qWm+v1bhon1ijhNasUYCWlmbKjxMSRIKCbPTfZta8rc34BXNzZb+xyLos5AoiqamSkaHPNzYmYnWhHx4Wu5lRWppaIpmZmsWzHUQ2btQMl44OhcjR0dL9Z3x8wcGxtpUihYVSU6Nybve1PXBAJChIhsx1rMbxVZbFFhNjUgdfENgvRUWKktets3s9KeJfu9Yua38NTcFMSTF+4pUrpdZCnlFRKkdW+7SkJOVjTIxIQoJdmJCVpWyx089MoY9mEZwXqythpWXBlJR4e+mWlo5vETgc3rTQ1avt8maJjlbdkJ5up7sdMCheq7dHJTJS37J1q+Fbfb0MDV0y2KGlRSQqytuKwBTatBk9YMVfrIb0MjysFkhtrZQZGentNTMiLIvS9ACWAwfsLnB2xlVGhkhurrFIHheoE4fD24QwJ8c2sj87BDw8bBCIHnhMHj2rKMTpZEJMjIma7gUiTDmnBjjb25sJDIzg3GuvMdnfX7MGJk60T7zFi60oNxATw8edAQYBT0RRygSv35ku4BMGB7FLRPv69IYC0ETq6YDT7dYjPDQUwsJobbWqPnuAAJzOmbjdEFD3jp6CLhdccw01NfD++/UcOXIrd97pRfqXo8BAfRQe3sjdTzxhl4jOd7mYHx4Oy5dz+71zbL9uQPmrhK74Ovv2gVUp7XZroDo01Ho+eM/zHTzV38HtBuk/C3t+R8/fvEUf8B8P/p63H7qP2xufZfSnv8DPFClw9Cg/iD7IDxaH894Tj7N589fUR5mUBMHBZGZ+k85ORQJjY5CYGIGfXwQ3hl+A+HgioqOJuOcezi29g3+eMgKcoKRkHndFfQSexayoraXr2kXk/mj8UmSLJk3SYaALF8L+/XEsW6b+8OD2ds6fV5HZsQMCA6fz5cV+3uhzZ6daLh7LtToRCGDGDPX1tbYqijlxAuaaSp+AgQE8ZlVB37NnD3DPIM2op9WFxggGgdD9+yFZy4MnDA7pxXp6CBjVNHzrNrq69Kefvz9+IyMQGcm7r8E998xhamgoeDwEq4N2XD9wQIDxWQNMmUJQkF1lz1z3BWgehBtuYHJLC58MDHAWrewdxBQ1zJ5tKnLjgFBcLpVHq64E9B5vDAmxiySCgWCnkz17wOP5AJYv5/oXXmCytVmcTvWxjo7qxhkZ0Ym2pmR/aMi7r4KDgd27YeFCzgXO5OAOgGrgPYj5ivpzb7gBlizR6w0OXlkwRPQLGtW0PFdXx0kgoq9PH8p6QD8/Rvfu5Zy5h5CQiQwOqi75wheAMP2uyZ7TTPbzgxNqupxtbKQOmDAyQtTu3dDYqFkZAwMcPQrd3Z/AsmUERkfzdk0AjY1nuBiziOkoMnd5TkLzCd5Biy9uHB3l4EH4YqhZxJ4e5s+/XrOCnE5jIjlRlHsGkSba26fQ3j6N0NApzGl+G6PIrsST/30EDNfbftehIYMcV6/WE3B4WLZv9/rskpKsQorTAu/oSX1pOV9BgYSF6WFlN4JtaZGhIavByQsC5+1u/XrSHJKoKG8tBozqDLj6epG1azUbw+WS7dtNM+2yMj3dDVzQwoBGzZ4oKVGoZGVDbNgg0tBg7vlJRQ+FhaYV5uVPsNmzdSTR1q2KxoeGxM5f9Lb7+7XA04p40tNFurpsH+eKFaa0dGhIYXxXl8jwsEGxinLfNwjsDePPO4VmhLQYhHDYoJsOg7qfMX+X1asVlXR1KX9Mz901a7wR/N5ec7qvXWtO6vOiDUo2C5SLVFVJP1bzm5OieduHBG6+LAKeNClW++qWldnl6uLvL73mPiUuTrKyrNaktQJ1Ihs2qKWQnm434E5JUX/ztGnqi123Tv3/VrzA8j1Kebkik7w8XfD8fCN/T8jQkJbRSl6eZGebpu3GEa7o/1mZNk3XYWxMrQnNkz4mGt1/WqBCoWpamuaADolC97Y2aWn53+h/a9CoFBSI9PZKVZVaYdZ8v61bvRMUJC/P26AnOdkuhJANGxS5p6fLunVm5mFqqsp6UZGi1PR0qTSWn6SmihQW2j3kK8wjSEqKrsG2bQL94nB46xasqTCdnWp9dXba1bp6g+Xl0tRkFSXtM4ivRxITVX63bhUzP+2osVgvz48oNOPEauzebiwUiY8XGR6Wqipd95wctTyqLOQeHS0yNiZLlpjm7L29IsXFcsZcp7PT/D4mRltYOp0iLS12dwMtTy8TeFEWLrSaxp8XzfDokawsg6wbGkQKC2XAIGL1Z1dosceaNSLl5Xa/8nbzHJZchoVZct0jsEuysvQZ2q9gIX1qBKx+sy4AJjsvqnZvbYXly/njwQBaWzX3t7nZyc6d75n3TgMm6eFoRswzNgYxMbS3/ztwBgYzFRLdfTc7dsDevQBHgTEm7/odN8+fb64zRFwcfO1rVhXmu3R2xmt+79AQU6OjYflyHnoIjh9v4tn1S/W2Ozuhro7we+8CTP7ehg30HT5s+690TnewQeJn1fezZw+8//643Ojt1ZTBhx6C6ft+BxsbNX/xsccIDJzLwoVOvvCFLCIjYW7j77WNp2smdXUKQlJSNPjPj36k6SV33w3z5+N23wEM0dgID6Nn7MeG+ydRz/ggWlp6FvjIvOebmJLvsjI+Dr+dxkZofgUiImZyxz3z+fjEBFpbdcmefx7eeAOeyfSDvDymjo1RWPgzzpy5maCgm7VJjusQUwsK+KgVkpJmU1cH3d2HGa/o1N9f0R179nB2717eAT5Asx3mA4SHc+QIHD9+Bvg9MBFaW3kHuG1wkNdfV5SzZ48CDKurpFVaqmXLn1BXB2cjFjE16pwmyg4O2vnY8sMPgCb27dOCau6/n19e9wmRkTNZfvAuExH/PfAxZ868x1NP3cqDD05kQWur8V+eBNqAJuACf2i+gy+vWgXBwUzuOwmBgVwMncuu5+E3v2kzK3B5Oo2WET/Y3MxF13RAn8Xj0eVubVWxj4iAt0vu1z+6XPrw8+dzNvh6ph45wr8DX37pJTy5Lyqgeu01/gis2LOHOfffD52dDGIa09x5J8TEsNytZbzbt2sK8LPz5zP4xhvq++QlRJy89NIys5Y1gD9BQffA888za/lyRkfnKL9NusHgoGWhTTRP9yK7dzvZvftb5OZONRkTZZjuPJelMcOTyWCXGvsBzJ7NB80B7NkD+/c309wcwb+Gh+PX2spBgMOHuX3zZoKDHyAoCLU0e3qoQ5Gre8j4YiMjmVpXB0uWcGjwejZvhqeeehctVD4BTOT999uoq7sOzQc+D1Tz1FOh7Ny5iG8VD0JfH4FhYQSi8R2o5PDhO7SP5diYykjfIJ+g+2/ZMn1Z7uvjx6cAn+Dnp7J/YlxuGPo0CDg2JsbbINx0+bejhBkZIuXlNrJbseKSNn6mIXpKip7gVqtFRRp6wlgjx70Nn38g8KbdR8UKiubkiN3oJyrKjLOuqhJpa7Nny1ltJ4OCvKWsQ0NiKrP6ZfZsb8mnNWlGqqr0gi0tds+b7dutviDj5XmGC+wT2bhRDhv/3X4QSUuzhwG2GfRqjU+qqVErITNT7OF+FWgutNXKUk/ex7XJhynN3YvmsBbjLZncb37+0qAFq1LbGsapWRT7xZqHl55+aYZFhcBmkfp6edJ83po9tt3yew4N2cFia7aZIpybLouAY4ODdWHUWW9Xk0lKinR2WhHy/aI5mWsFfi3S26uIfds2ux+3NkA6LZmZKg/WMNeaGm9JcmKiKZm10hxMV3yrmUxtrbEsSksVFeblydiYXsMa8ZSTo8hnzRpFXtb48i1brGyPQVmyRO9p4UJF4y0t4nXcmzK68eQjnEsasufk2JBZK0hrbfnXdeoXp9M7REaGh+1yuNpa8VaFWcPSrGkvUVHeCRKWH9XypebkSH29t7NiVpbJjTWzDltavGPzsrKMVZGZaWS+S6eFG6uyttbKi37R7M2TFtiWzk5jRTQ1mVzvy/NjkYmRVGEqzlyuP52S098v1dXGkvT392b8xMSIFBerxVZW5oXsCQm6mNHRdjVlZ6fGD5YsUXZYWU/V1br2eXmXjOIqKdHqPqspUXq6zbPCQt3/1dV6ax0dl8S8hoaks1N/b4VhpKpKJCbGZEq8KuvWib2vx+PHp0bAI3V19MyaRXBYGEREcPHhfyL7fvjtY8Gc/M1vmNPcTMQ9X8PPT5FdVBRc/8g36Xv5ZQZLhTfeyOWNN+LZvPlrBs3MBCbyyisYZGLRkDmdPuDJJ2NwOmdz//2aGhoYqAgiKkqRbEQEEBzMB4Nz+epXR9Bz523gEwYGfsmmTTPZvPnvKChQV6PDMZXOTkWfGhH/n0APL66/nyONjcxftYpXd+zg1dcncP/9l/imL0uBwG2w0MF8t5vOzk6OAfj50XlCn+k7Ticej0fR7d1309Wl9x0ejr5heJhFwI1AYGoqfOMbeJoAFik6bm0Ff3+W+vszOjKi7TbxouC5aGR3sb8/P95sndrNaCOdaOBmoqKcdoT6c5+D3l4nL78cD7TB4ABuTAP7kBAAbuzsZAJwwW8yLhecOgV/PKgtcpYsuZW6usDLcmOgp4c/vvACC4BAl4tgp5NgpxOefx73rDGzNpV4W4M6ebNmOn+dnw/LluE6Yvk2j6EVeNP4/OcVBbtcMPfgqwoZ73+WRx5p5siRCNymkiu0u5uphw8z1/UdfvaFL/BR4Pf57sOTeTaqlcdfe41lr73GbY8+CkzgRz/S+12+HAI6P2bDhrm8/34uZ878nMd7HuDm+fPp+9U/sWfPFF5++RPlE8eAXlpb/4GLe/fit3cvwU899SetLP8zTTQSMtjeTuBLL9HZ2korEP/8aaAYlfHZKAr9dzwe+M1vHmbXrlu5994Apr70El3f+gGx7mLgDqR0P3z4Ib8N+ym7dsFvA1/hYGMji+Pi1KTZt0+r8jo6ODswwGRgwSOPsMs1lR/+sB74BX5+Wwl98ibNWMrK4vqbbuKLD6cqg1/7ENxug2Z/D9zH68Civj4CUL+4etvPEB09m2eegVmTzqoc5z3PoWeesbpHXpYcn/scU+fNY7CuTvfJkiX8IfQ7fHnpBe1Edf48XxwehldOcGFkRK3Te+5Rk3dsjOnlv4WNG/lDdzcRwNySEgD++NWvcghYtXw57omWIplIYuI8/unuY2pWrdf3Rm/9Ha+/DrPSv8yru3dzEUXjocDSoCB48EFWrIBTpw4jvdcqvH39dX78q+tZsQK+HNhM13W34XafBU6xcOFNuk+/9z2ebWzku32/AyYSGKh79PQV+AF8SgTs56cnrml8MjRk/LfZ2dJkRZozMkQ2bpTqaj1FrGY5isysEev7xOEwPsjeXnvSrVZOaeNof3894LZuVRBjZSTYCNyadTQ0JJmZYs9z0tZ8Vi5fnsDPxOnU089CUNbw18xMPfjWrRP9kshIHUtfq0ihsNCKxo8T1Y2MFKmvN/6vUZGODq0uKykRazSTVWSWmKiXz8xUhDU2Jvo/RUWycuWlyPS0pKR4B002GPSsfvFjxs92TIaHtQG95ObKqlWW/8marLxJ4FlF9tXVaqE0NNj9KOzefr29akKkpooUFNiTbbKzlZ/WZGn9brVuxOWShXBZBByBmRCblycyPCw1Nco/9cHtFhiUigoFWkVFul5WK8/t23Vt1T/3ssCb4nDoZysrDfqzhMpklthVbx0dioLCw6WpSWVNMzCOSkOD6UMREmKPYZPsbEWMGzaIlJXZ/b/T0vTWLT+1XqPfXpOCAvFW4VlzcIqLx5eP+fPtEdXDw2K3TVUr7XGBWqmstMYaqf99zRrTlyMrSyv5zAy4mhpR+F1frxvPmlQcHy/S2yuJiXrftbXmGWtrFZX19totV0NCxG6JavUbkaoqjRekp9tzEnUfbtIWpSYdpKBAxbWwUPekNXCht9dYBCEhcurP+Dxj581TxBkerllK9fWSmyv2KydHXxUVIhIerntp5UqR0lIZG/P6accsP68x6941VqYi980SGakIt7PTyEdXl8qH2y1DQ8Zf3N+vi71hgz0/b2zM27TH5uPYmBQX63IXFRm+Dg9LRoaybGjI/M56tbTo9+XkyAGu3KxJ1Lv/KRTwjBkia9cahdOlMN6MpbfKEAvMz7Y2K0Bhdfs6b/6/x5gwg9oxC0Sqqkz/3VaBRzVdxMpMX71aZONGb/lzSoq0GTPmJVvZvSh2M5uGBtGk9TLRvrDf8k5OqKzUXbZ2rTQ0XDJtwelU6S0rs10V9iDIqqrxBcrtFikuNhb3Zqmu1u5WUlZmNm67zJ5tBQwHBdq9wzuHh1XaqqvNRn/HDhTYM+rWrrXnj+nnN5mN+7hs367pMlpmedIouE2i3dNeEHhVgwemuEIOHFCTy+qWlZqqUm/Nt6qqsueJWv17rYZEWqySIxkZ2m/2hnEU8DyzLtpwaNTcx0rR3sI/lqgoDSq+ZgWnoqLMwfMtTUNcu9YooyLzHBoIKy01m8hKWcvL0xvs7zeujArdRGlphu/3m9dKdePk5YmUlKj7oKPD7oY3Zg4wKSgQKSkxKYc/NHJTbtZk1E5Xshu7mIbW1iEynnzAXNEy1wZzX4NG0TYKvCoZGSL7jSupt9cAAVP40WRcSy9jynfDw22z1+ppLdu2XVJM9DPDMx1kGhkp3pmCBQXepjcdHdLRoWy03DS9eMvl9VoNAr8QaLe7uFnuwzGzxtpF70mpqdGiBLsQ6AoTMWJiYr2Nc3p7LzmcTxo+/drI9yGRtDR9blNKrHxu9/ZDXrlSxN9f+tH0u8NYRRErZd06nf0mqane7nb+/nIGDSivWuVtiOcFPv0CJ7W7W1qajKCNtKZNU++hv79pqlVf73W9jo2JREVdMhnjcRkaEnnayHivCSJeSQF/KheE9PbC88+zfN1PKS+fyZ498K2SEvyam1lUVwcvv0xATQ0z0TSb5csDCAwM5cSJUB5+GO6K+ohnV9zI9773d8TFTSGiO1y7xrvdzLoG1q27jiNHfs6yZXBycCpzwsPVNOrq4tsL/6hRgH9+1+6GHwga+cEFjHHM/UVGRzWr5vjxBPz978Llgm98QwM7X54/X+3wri5uHnxHPecrVmihhPFpTPa7wIoVASaY1HPFUtPjF67lrle+Q3v7e8AFdu2CL2ZlQWsrUt+qn933HxAYyOr3v8umTRdobdXA3S9/GcCOHT8n6eBEzhf+FpqbmfP8v3Dq1K8IDIQJB9+B1FQWrF4Nfn7I6DA4V3KsU1Olvtj5qvox5s9HTeQpJCZ+j4gIbyznH5Z9AC8chvvv57aHbsPtvo1XTvwtAQf/CN//Pn2vvcYENJgQmJjIguefZ0H8KN95ZI8GtdY2Ql8f8+e/yf79p/B4NJ1svJkHY2gI5vbBtykouJ1du+6jpuY+urs9wDHS0sD1c3WZBAcFwbx5vF4IjzyylaQkOHb3T+ncBwkJD9jte4OD1e30sUeDl62t8IMVK7R5S10dcA8QAZNOgNvNihVT2bHjQTSI1qZBk5gYCAzUNT14QmeCARNSU7Xs3STPa3w5EpjB7NlfIzgYDh8eorHxAhdjFjHh859XGaypgaVLef31P5dlNBW4k7i42XZAUbMzb8LP7yb+x6rTvPcbDUq5XCZlLSICVqzgxj17WNrdjRuYGxICqamUlurX39zaysW9e5mwcSPPbp1q4sTBaEjqLFbQe3QUTfnat0/9XqGh0NrKnFmzNBDe1we7OpkeE8P0oSHODk5g8WKIiLiZvr6b+du/hbsC/wCB8O1NUXrB0EJuHxzkwbUAvVRWwv+wOgiZ1NPxqLNTly0iYjLBwZNZulT3ZkXFbDZtygQuTe1cz9x77+VYzNfZscPMWOMD9uwJha13qo/z0UeZ2tfHgs2bobOTlSthz57favlwZSW4XDwQY6rHfvMbXMPD0PoRzxSE8+NHA9i1S9dk4UIn58876e3VEnOWL8fPz4+9e+HMmV7D07Ns2nQr4eELAI37f/7zE/jBwoVMbmnRz3Erzc1wp3leK/XxivRpEHCYFVSorpb16y2z96hBCq9q0r51tFRUKBzPzxdJT5d2vPO3amsVYfb3KxC0evCKy6XBn5UrZf16b4GFlUxeYYI6Vo/Terhkqm+P3f0+K8ug5cxMRVr19ZKQYEwbKw/L5RKJiZHqau9I77Exg3Q2bvS23issvALCuUU0KPWywLPqjLc67kdGigQFySlzCmrf4UGZN88a9mxZAg0yhLbNVPfJL9T0Ky2V4WGx5+VJeroisLQ0fQYzYXdsTESDjoekrOyS1nxdXYpsw8PNtIz9AockO9ug+8hI2Y8Wixyw0KCZFNxv+DtgUI26Bb4l6ekiL4FcOw4CnmMsIKuIQCykm5mp1kZLiz5DSIgdHTllBWQ6OgQq1M0RF6fvM7PTTKdCGykVFoptglrtJGXbNl2z/n5pabEaGr0tcXGXBE8qK/U9pm/t8LDYJe5WKhOcVjluajIpa60Cv1b317ZtIsnJ+nzbtom6014eVz5uuilWZbipSeUiN9eKMtpTEzos5Njba7vIqqtF0X5+vlottbV269egIBEJC5PdIE1NIpe69dTS3CTwqkybZgpS1qxRV5XLZRc+nEFT1naiZfrS1mb31u7oEJGsLHsSRhWa/lhsrFzd8zmiJfLfEhj1ugXj4kSioq6wX24wCL1Ln2PbNqnFzGELDxdxOhW5RkWZ8vEXDSLOMZbJDwRq7SC8pkv+Wvz9la2SkyMNYKa6PCGRkUaWsrLM9XpEcnJ0BmObIv3oaN37FRVqKBQWKsi1Bm8oT580VlyOeF18vxbYrZZlQoI0NalRkp9vXBemt/f7nyUCngrEAzQ3s3r1F9m2DTRdrA84yfvvn4GqRxWmLF3KBb/JBHR2wv79nMWbvDQ4qIGdwUFFOYODE3GLHwAAWqtJREFUenje7vFwEqCnh95ezZFfNH8+NDbSh5mEgJ7zcwG/oCC45hoyMmZw6pR+bUgIfOlLkJAArN1BMxB88CDh4QsUDUVFaQ7Y/PngduNyaepcRARM6DypqGB4WI9GCzGPQzfe6OSZZ25lcPBWRkfh6+e3cPEb9+m0aJcLBgboMdz5evh7FBUt4oFlx3TUcPUqfrajAD8/6LxPiHHDRj/YtOkR+vrgWN/Xefc1vZ09e8C54kViHoEF67/N4JNPErhxI3/4/Pfpex0KCr6CYRvl5XDnnZOZyiCsXg1ZWRAYSG/vTI4cUcD3/PPQ2fkR8b96HDZupLG9nQlW+hNaqnkRCNywAR56iEf84Hvf28qsDf/Eb2HcwNM04C5gekICLF6Mp66ORmDp669z48MPw5FGTRsLDLRLWt3R0Sw+fJiTzAHe1wt5PMq/hAS49Vb27LHSDmcCidx6KxAUBD09LAg8pvdtpRL29HC92w+XazIwZmUgEho6nbmLF+t7r70WZ2srnDjG8uXXExWlxSNLlkB29jRCQ+Fc6I3WeEM8nmValGAKeyaDQXtD5nV5CpwiLIo6B80ee44cjY1cRK2AAEzwMyyMi67p9khFffy53Pzgg3ahh2cQnM4p+rV33smiZ57BLxQWLozm/fdHgDo07OcPBLNqlWakUROKKyHBC7/7+rjY3W0HnwBO+s1l1y41BmfMACIiCA8K4uTAAM3mfU4UqXsNwmQgkpUrTVpaRARY9/uP/zgOR0aBXsDfLnByopMoenQApMpWUBCeDvDOELTKxqOZNm0RgYEmiE0ycJ577jE1YI0eXPY9TlMLID4evvAFup/qBU5CcDB+wNzQizgcN7NiBdwe9QlnnTOpqVFddOaMGggezwiq9Saae5iBasAxNMFsBoTPgsWLcTq9hvR05zkYHWUmxkq/En0aBDxxYqxOnE1PlxYb1WmhgfpwXjWIuEucTlPGWlgoQ2iArhtEiotl9mwFQWlplp9Y/S+Sl6cpW9nZkpZmV6aKdHXZbStbQMSg8MJCETlwQP2uSUm2H1wKC+17LAN1wPf325MuNJ3qkECror7wcL2RNWv0CCsrExkbk8xMC7BcocG0NdcuLEyajF96v0E11qyqnaCwt7xczoCss3zf8fHGx7xP4AWR7Gy939xcycqyQa4py94tUCeycqW+p6FB4JCinLw8keJiWb9ekUFTk/o6S0s1eNDba0qBk5LM+jxhd+KU4mJ7EoYV5LKmWjQ1mXlXBQXyAlroUQRy3TgIGGLMWo6qHz87W16wLBWrLHzNGpGSEsnLs1Bqj7GgToq/vyk0qKw0hSHtBjGVC+zSwoCVK5WX8fH6ionR31ltC9vaROrrzbU3CRySuDhNS1q40BS+ZGZa8EZfq1er4KxerfKVmmqQ79v6nZWVIr29al2YemW9v31XRMCxM2aIZGdblcgqzG63SGGhtxw7PV0kK0u2bvU+WlKSssvpNPdrIt5btnhnDqxZo+veDiJDQ6KpbMqrkBC1EFrQIoWkJDM7bdig+OhoEadT193tNpbXBZ2+Xa/XdzpFZHjYbvUq8fEi+fmSm2v2dX6+ome3WyQ/X4aGrNS901dAwGECL0pamomDWBZyeLi30VFFhcjYmKxadal/eL++PyFBrToznHDIIPlT1mdLSqShQQO6BQXekm21OBsFjqk5ZIYYjI3pc1jzKtUf3CrqAz9kWxy6934s06aJWlBlZfZaNDTo/rIDfoWFyoi4OLtJ+3j8+NQIeGzsHHv3eiCuWRPswzEnQyg6n3cITQELwOOJp64uAubPZ3JIiBYIuFywfLlp/TZRZ24BVsMdYmO5EWDxYk7sg4EBRSAxMTNxY0pJQZFRfDzvvwC4O9kHxOzcyaLNmijPr/bBwYOMoqe2PX024mbefVezUtRHqPWtg62tBIaF6fFl1cqOjtLcHHDlNLQ5c7SIorwcGhu5cflybrQy7QcHCfDzY5kpraSnB0pLOYdOTb0etBlIRAQpKXPo7PwS3P1H7jp4EFas4M5BfZTgYD2V9+xZpmA89G7uGhzkf+65GXiJ7u5o6KmBujp+UnIPXQOTmbX932HfPr4eH8/XfxUPuxph5UpYvpzUwAj27fsh996rifqEhDInKgpWr+Z3PV9k8Z1fZG74L6Cjgz/2GFAcE8NiFLVNTU7mf5pZZP8rnQa2ATF0d193SR0uWoAzOqq8CAqisdFahzK0EGAVy5bN1rRCl8tMxP7AvAKAaeobfPll9elaI5H37IGQEM56ArREOcIJo6PGHTkDuMjhw4poRkY8OJ1OiA+EsTEGjfROt6CuSSO8/sgRNI3vpCLsH62HH/0Iv6V36DMtW2Zaho6gaGgcGh6GEye8w4+jDAqNiGBHuX7l7SaD39/fWwLs8WDaULbx4YfX2b7VqCiViZ07tQiIxZ00AqEHD2JGGQBTWb4cel7y0AN4erXoo6VFxXpuVJT6T4ODmb57N4SHmxTQk7S2Xkdnp77f42nijwdvItS68n33QVISU15T9rN5M28DgZ2dhHZ24ucHIyMnMNPZxiF/YDbz55truMLVJDt4kNHWVkW/4eF0dU8gPFzXrLl5NgMDE1m6FC7s3UuA06k5pHv2sAstm7HSKG+fNIkdO1QvLV+uls+uXVaK6xgwQa3y0FDo7tY4S2MjHnQK96xZcObMJOA4miJ4nTFkrwMWqUVRWgoREcQ8fBeNjdgtDqzJ0uzaxccjI8zt7lYTxO1W3o1HnwYBxwYF2XPVdoJpFLJb0zoSEkTq60X9kS+Kv7+eYFZEPTra2/sjOtrbM9oet9LUdMkYovPmOu8I9EhYmDedLS1Nrzc2JiJJSdKBd5TIANjtDW3ElZYmcuCA3cQkMtJEM/PzvVnvFRVSU2N+X1ysCNbtlupqMdHey59g06bFarPx3l6RwkIThX1WYFSGhhQs6Wm6T6S0VE4ZZFlrfKRVVSaqnZ8vkpsrW7Yoz6zxJ5KbayNpy5dcWWkhjVcFvi1RUTp7qgOUMW63NBiE/ZLhzXljrZSVmeteMgtNentFDhww1swhcTjUqpCCAiku1oO8rU3RrK53u4xXijx9eqz6cIeHFe2ZWX+SmyvFxcYX63aLxMVJUZGYiPt5gWOa4mNKvyU+XmFLcbFIS4uNhNasUXS/fr36+p9+Wuw0oqYmRVVWUUBHh8qX+grrRP2kDVqGHh+vvkaTwmg1n9m6VT9rVYYPDWk5qe0jT0wU6e83aXn9opkHPxwfAU+frmjbNEeXri5vOqDplD6EaZqfnW2X9FuFSmb8mqxfb49/0+j7mjW6USor7ecsKxO9YWNlFBaa6dMlJfrezEy75HzJEpNSlZ4ukptrsnRqxeGwe2HpZ0pL7WxPazSdlZpXXy92AyvJyND7Lyw0Dbkuz4/IyFj1b/f3izQ0GOTdqG1tLRRr8k8bGvS5rSZMVml6WZnYMymtKc0dmKEQOTkibW3S2aniV1ur8q2+8kGBLr2A02lnxViNsTo6VC4kM9PIpSJmaNX9aBrIWwMpjNEriYmm4X1urrSY/X3YxD3UX37+s0PAZwYGePXdd+0MBD2YZ/OTNRf47SN7+c5Xvwr8DughKkpPotde2wP8nu7uUA4fDgVuJSnpOqKiUP/YwYOwfj2cOkX5vPfZu/dfUMQzEUUwY7S3n+HuuyOYOBEaG19BUdHPOLRzJ61g+5f3AdNHRljU2Ymf06mOoaVLORl6Gwe3qh/x6NEzBAZO49APf8gnwPJly/g46g4KHoGXXhrj8dJA3gFmdnayePGVA7ujozrh1WpNqMFgD/AxJSXzDIob0+e5+27cKSnMfOMNPgJwOrGMAqsDzFe+r8gnNBQIDFf46/FwDi1FdqFTl0ZGms11dVrshEZzP93dgBWzVQpE/WpWp0MG/dTh2dnJ9OBRLgTPodV1Gz11EBkZrcjEzw9mzCBwkt5LTw/M/au/MqjvLOOhvpkztQUk99/PxZdeYoLLRXB4OKxaxYPh6t/8h85O/Hp7iYhQ+RgdddLTM48H7rsAEWthYICLZpL24BtvEOxycV/uGY4cmc2+fYo4XnvNA5Tz2GP38A+vxMPoqN3IqL9fkdOcnkPMOVjDihXf5YUXTpo7DFb/oGlVuuGpyTz33O+AOCIjZ7Nrlw7YTkqCfwp/FTaWcxAtwQgEwnfvZrLTyc6dTXgLTcfPkmHiRIWsH36oD79sGe+dmMkiv0548kkutrbSadYn/LnnrP6PLFi+nMGE6fT3K+/37FHX+fz5MCvoHD1PPskRYFlUFMuW6eyCf7j7JDz8GG/u3s3tu3fzPX4KvMO/Zlbx+717+eLevQQmJ3MiFfbv/z2trXfwwMMxEBxsiqLOI9LG4cPTCCjfxda9e7k3JoZ/fOoGYIzZsxfh5wceTzNwhrVr43jooR9wu/sj3b979tD3y19ecQrwVMcAXxz8PezVAQu7dt0MbGLTpid5MSSEs93dHBoZIXTvXsLD1c0/bZp+9je/6QX+hfXrC7jr+Lt4uruZY13XigEMDsLevcxKT6evb4IaYM88w41JSTgctyHigcZGOj0e3MPDtLbqer/0UhtBQdcxv6aGqTU1LM99lv37g9GiEw93hA7x5nPP8deNjWyM0WZahw+fAi7g8VynN9G8hz+gaDwU4LrrOH78PTRGdgX6VAh42jSR5GTpNtFTjcy+4y1ZzMkxvpNGsaYQ1tSoe1VRW51AqzgcesA2NBj0GxSkvqzaWsnLsxrZHPMObezoMLm2XWI1EpGyMqm3Iu6aOS615tVlUJ8kJ+vxPTYm69bpfTgcxs/ocul7hoY0wbujQ09wkyupwzIHjQ9q/HaDubmicCI6Wnp7xfiMThqE+o5kZOgtKHI4JmNj4r3nykpvZD4nR4as07OwULKzDfJMS7Mbvldi5V/uFqvRek2NiISFyZhByL3G77wNMwLKah6ekiKSl2f7yKSiQmTNGpN/3a5FBuHheqzn54tUVNgTga3XunWKRCZNunw7ypCQWM0WWLlS0UxamkhZmeFljwQFKdrYulWRXG+vhYJbJSTE64YvLvY2C2prswGPrFjhbZdoNYGRri67YbasWWOn14yN6efVh79VoFHWrTPP3tsr0tVlZFIn+3Z0XFJO298vlZX6XmtSdlWV3ld+vn6N+gt/JhqZHwcB33STftCss+ayjkpmprdxk2RmKszdsEGkuFhaWhTZW1PF09P1GZ5+WuwKoibLL1tYKFu26PuCgvR9li+yqMigxcREe29lZHjDHJWVYhcVKJqvEBjVgafl5SqHph3m2JjYe0ObHb0smlVzVJYsMRZbf7/ut/j4cfkxaZLGkKzhALm5Yg8LsEuCMzJECgpk/XrLGu4XOCTp6TZwluJifb7iYm/r07Y2BeHR0eJtG5qfr1ZxSorZy7UiublSb6Ftl0v539AgMjxs1/moXPSo335sTH8ZHy9SXS0pKdba7xKoU2shL0/E318+xLRsTUrS6xYXm2yZzwgBe+Zcz9mtbxK8/DZ4910+OAFwln374PaEBPV5/CIAf/+b6PurKN4Evt3ayqtPPMGGLyzikUdmAxcQaaamJoLGRrj57lBODwzwARC6YgU/e+ghHtzxM+68cx5lZfDmNV9lETA6W4DDJCZ+hXvuQZ07aMT9bPnbTN27nel33kkfitEuAKFHjtj+V49nJn5+3mYopKfj7Onh7YOTqa+HbxXdz46dO1lx4ACOaz8CtqOtNS+My48JEwyC9fMDp5PprovExExg714nipsCmD9ffXcezyfAbo4cmacnd2en5me63dqkKCSE00AjML+vz855nRsYCC4X0/v6cAKdfgAzCAubxp136jxJ3G4meDwKj1G/91TUZ8uKFeoE27MHAgMZGDDl1VEuCAw0eawncDpD+dj44UJbWyE4mLlLzzEaM9luVB4YqEi6qeny/Ojr00rhfwp3Mgi4Fi/mg4i7eP1XAGcYGKjjkUdqgBhkKBFKSvDz+y5whO7uYl57rR7Nb4lBS3TjgKnMnj0Rl0vRUEiIVqcuGH0PXC7OBV7PZGcn537+c04DoWbsUKNzETt2mNaUDAGh/I8V78HoKD947DbKy2Fk5PdAL8uXw5yN/0P5VFcHDz7I+l0/Nk3rq1F/8AVgjKioTBoalBc///mscWUDYGxSIBfibyeg8RD09bF5M8B6nnvuKxw8GM/y5Tfz+BNPKHONObT5MR3aGRjofblcJjd27a8Y3b3b2/5n3z6+vfFe1q+fysDAQ7z88jRefnk+cAdS/aGW9x4/TmhQED/euojf/CaXefN+zrH1/wGjLi7Mv8PwpwPwMG+e+lqZNEljMXV1TL9lNhc6O3kHzZ2OWSi8//4ptKT8BPv3p/LVr/4d2dlTefzee9WEGydGcP78SfbufYq+viwefFCr8x97TP92Ee3yP4GLHGudQOm98O67Q0Ah8A4PPvgyH33OweKSEm75Oxfq/R0BpgB3ERQ0jYGB14C32br1SR5o3QTPPccfgC+/8QZqqZwCj4c+NGeks6+PiOeeI+a557i+sJAvfa8fRaz3ALF8N/z3XJj4VQKysmDbNv54Yi6dnZgG+c1AAAF33sEfdu5kARAVEqLzzx58EB59lN/+3d9deUgpfDoF7LxGtHu+x0MAVrrHBQ0eGO99ZOR3VWh+VcW3X39dO+wPDmp3MZykps5k61YIaP1IAyo72pju789tIyM622rtWuZMnMjrr/+UgNaP+OvkZFi8mFV+UFf3FTZvhqkH/wCNsCA6Gtxupm56HPr6CE9OVmW4fLlKbXu72qStrTz44EzbId/ZCWc3/k+mDp7k9ua3uX1xD+zzsAy4uPg21LzsQk3t8coOdHNcey286v8d/B79Dvse0d8vWTKN0dEf4nbb7U35/9p7/7Aqryvt/3MUzIkCAQUDFlI0Ek+MGBKhPbbSkSSm2kgrGWnCTGi1jRlsQyY0sRPehiZOSKNtnNYE38SpdOKENDhKlIqNVKmSQMUoUxBJoYiCgB4iKChEfrq+f6z9PMd2xL5p05rvNezrOhe/Dud59n72j3utda977do1GR+fbzD76S/zFuDX1cXkZ54hJjSU++eeZvv2YerrhaVNv+LXPvfSkW/0WPPy4Lbb8KkU9pfBA3eDtN8IQ8asPnxMD77WVsbv3Mn43l4+M3++mrOxsRrtGBrSQEBfH59t/i8+Gx7Md/LvIifnMzz4IBQWurnnHhg/mAOVlXS8/DI9QOSLLzItNpZpe/dytqWFL6en81LsS/zqV1cej8HBQfbsGeZs578TPm8evwr9Gk8v081j/frpHDs2nddeuxu3G3jqMdi6lZziR3j44fuoq7uPCxfOERQUxD336OFz6FA90MXTT0+14xnj+s5zsiuAtw7ficsFM5veh6YmxiclMd7Hh5NpP+C11/RsM/FMHnroG7qxmErKXV16WMydey/BwfcSGwuXHvwBY4YGsDIYfvXQKT4MnML8+Xdz6FAnWt3vBNHRMOaVDfzrk6lUVHwLjwdqav71iuMxtqebcWW/hnnzOOkZZxiNn8fX160gAvjOswFcvBhAe7s+pthYdSnExKjLwRJ6mzQJyMnBx+Mh9uhRjVBnZvLWjjFGM/ghYAbLlwdpdZfAQNVQWLOGs0MBNKUBfItXXwUeehTmz6fPfS/h4VBfPxWnc6pWVf7d78DHn3EHD8K2bXz4ox8xgB6JfqGhbNsGP/lJOi+/3IrqRQQDh9i9280Pl8X+YeD1j1pg4BQWLUonNlbntlPjpXR0aB/vjDwLy5YxbWiI9575NkRH84O8VezYAXedfoMyMBVIBlAX5TTg0+TnB5GYCGvWJLFjRxKPPw70xcDGjXzZBD037JjE0NB90HGQLzidfCEuzq5mjtvNltNfAP7VfO4J/ep2My4qCubP50jXTRQVwaFD59AD/V5CQqYCSo4M9vfX4OaDD/KLptl8+emnce/ceRVtONM+kgvi5psVVkdFST9Ifr4GgzZsEBEQSUmRggI1EdVU6VFHeFGRMZ0HND6DErsLMdoBCQlajcKYzr+zaFEbN0pW1mUaoEVFIiEh6u5IT1ebJDNTAypRUdLfr6ZVQoJa+G1torazlTPa3y/JyWr2+vuraSuBgWrORUeLbNliTPIeY+Y/ILB4RBNixow5tsqbFWhMSFDrIz9fTav8fGP21taK5OTIGZB1aCrsy8Zdos7+79vWf2qqflZzs6GPud12YFK6u5V6ZUw12bhR+7h+vVcyzpJ/6+8X6e+3k12ks1P/b+VKY2Y9obTC0lKR+no1i4uL5YAJ4J0wJtW7aFpstwnmfepTI1VFvlmUuD5gAjsHBLJ1nJOT1UZsbxcpKZEajEpcaqo3zbixUZ9xZqZJM/6lQJE+1NBQnRMul9EnvqhUrLVrlZpkUmw1BfotsUj6u3aJlFhUuPh4kZwcE0Qb0vFISrJjV3rPA1r8wAqWmsq4+oxe9M51l8sOAI40P+aMHy+SnCyVld5AUliYCY4ND0turhjTf4NYNczWrDHPw6LKxcWpiVtdLSkp3vRZp1M/y+VSizctzQSdVq5UP0N7uwk+nRV4XVwuY+pnZMhmNL25uVmnyO/MenwbTQiS8nLx99f1U2u5sjIydJyNpkJtrSV6VylavblOXWbDw1cfj8REke5uKS3Vx93bq260NWt0jbxp5trLaOJWuxnrNnN/mhj1c7HkBxYsEJFFi1QL2aohZ+b/qlUimlL9nv7e6dQ+lJebgPkR0SB5g3EpfFc04WKFwNPqpklPtxN21L34c4EDlkSFyIIFGgBMThbJyzOp9EPqiktMFImO/vhcEHR1KeWqpYUh4IHkS2RlJakZvmULuN1U5Gj2o0gr0MvRozO4c340kU2QlubLuNbjdhVjm6hsuFZWirFNdPd4SEtTpD3gN5FxCQmwaRNTWlo4n/ptXnsNHnvySUIrKuDuuykr02tbqbhDQ9jpl5SVwYwZLFlyH01Nl2Xx9sUxc88eRRTbthH7k6/i6zuBwcE4YDqKgouuOBxWMQrLVFy8WIFma6taYceOweuv9wJjkc5QKC0lOCqKRxsa+D2wH2gCCgun8tprq3lrk6lM/NkJ8MVAaJ1q1xyTvj7oulVzMsPCYNkyfuPzBYKDvQG2rocew+mE8a/+G2zaRPtPf8H8+ZCcDP+apoj5+NM/o6kJ7j4B5eUvqidn+kPw0EN4Hv4BPq57cdfX466rgwceoK6vj3AUXwX4+nL/7kf4wWDLCBPED/gM0EFDA/j7u3G53GzbBgPXbWWcy6V2JzDL5WJWUxM88ADtMpnGVn12Dz00jXHFxSbH99PAWCjbQofHo9V+UQUzf/+b1PL61KfA35//9mjlkfT0sbS2JrF9+yDwHlVV8H+SktQMiY2FqCjuvhtCQ8fyoXMi49PS8Hj0T/PnQ3CwL99Ku8TJJxTbvd80nsOHwdd3KoOD/8CyZdD0bYjs7dWHHRqqgtBXamFh8OijVFToFPztb7XC8e7dYcyapVVeFiy4F4/nXmpq+oAB2tvh10Nf4K5HzQSOiYGlSznFFG64QT+yru4c0MXixVNJS1O9302bNHh457ZtmpDy8MPgMxmL4hkRYZIVNh0DlNI50/MeNPXhhzp+gjEVzFpbefhhmOJ3ngDAzzKtnU7Iy2PA48Hlgttug5aWmWgwcjo9PXC26yr6cAMD4PHwm6MBvPiiIv1pzlN8NgI+mwps24sbTXc/ijcBhKgoxtfVKU1t927i498CFOBHRwNDaUTGxsKSJVyKudMOyOpXJxAEe/Zo8C0pibfqZhIUBElJ0aa6+gem959G0fX1QDh3xZ6Hb+6kL/sl3nwT+vp6gTiCgqbz1FMwM/AUvAoBgYG6mdx6K6E+kJo6Vq2Qpkgds5qakcfkoyDgW02QqAXsGl+Sni7V1WJoG7WixPpT5kQplsREc/KaSrk2YisvV+d+f7964xct8gahnE49sRITlf4TH3+ZVmylKMH+dRNcGZANGzRAZGkQFxerY97jMcGO9HQ9SaOilJZjqqna6lKlpTKMSZhYv15cLhPgSUgQiYwc8QS74445dh0tl8sExDZuFOnttQMjeqo+oX0NCVHkXl4uEhMj6zGp3RkZIlu2yDCaunjABNIkPV2sygMXrPvz9xdZvlwaG5Xil5TkBcBW3TxZuVJqsURxsjVoWVQk0thoqDHd8vzzJoU3MVEtgNBQqaiwUn7PClwUSUqSXVZgITtbxOWSEpDpcEUEPGPGHGPpVAm8KNLeLqWoMMkrmBp5GzeKNDfL8897ZWuVjtQg8F2tpG0kseLiFN2Jr68UcFnV63XrbGUu6e6WtjZ9tFOnisjy5SoMU1IiGRlW8PN1geNSWGisovJyOyjmcnk/R1avljaU8ie+viKJiSZI+J4K5XR2ikRGyk/NeGmCTPuI88PpnCMulz4Xb1prpqjYUJVSGK21FB8vkplpEkhek6Agr1ZxSIgC4c2bLXW+AwK5IuXlchCLDvqYLFwodrBW8vLE47Hu71UNsmZnizid9liK6Ye43V7Om4WgV63S55CVJVJeLqtWmcQdt1sRcXGxbN6svzMyurZq20jjcacJ7OlYfNdLB42MlHITRJbOTpHqajkE8oYV0KqsFImKkn1oBY1B6/kEBookJxuaYrFocPB1mTpV57bqTx+R+HhF+W+DpKeLQK7S6EJCRA4evCzZqUXglFfIKCZGtoMZx0yBKlm/3kunHAQNPKani9TX63pqb9c9xqqT93EG4XxQKtQlNH2QCxcuO2n6gAn4+0+irw8GBz8ABoiMNAGEujp9TZ+Oxe/S6q7jGPPZz0JgIOP37dNgktXOnOE4MP3QIVpb+SMa1KeB6fj6+pKfr0AkPl4B773zB6C1lbO+0zh2DGb7+WkorakJKiv1htxuBpwBdnHVMZi6Ynv3snLlYzyw6DznH9x3VR+OVd5q1Sp4/nmY2PF78PGh/cJ4du+GCxfOAXcDwYx57Wc0nTlDpJ8fR/w+x+z0dNzf/CahoOjC5WKMry89g4NMB0IjImDpUsK2TQLAr7qae3bvhu3bYft2ps2axdGj37HH/+JFdX07ncC2SGbGx/ObPoB/wO2GgQX3AQqgu7oCuO02k2W9Zg13zZ8PS5ey/0393RtvBKkrL/hZvrRwIbjdfDh9NuOHhxn3zDM4RhiP667T5A5f39sZHLwIXV0Eo2iry3zlzBkYGqKiQgNkpuCCGX2t9UdXF/T04ONj5lZMDJGHDmlfAfz8aGrS5/Z+awA1NUovhOvB5eEkcEt+Po8+dRdlZXDo0HFgEmVlU/HxgSl+Q5oLb55heDhahqWqirOYKguD6vtXv+1YfU9VFTidRALExeEpgqulIjsc/FGChcv8JYqQkOkkJsKpV3Vcgk2BQHWhTrdpWFoJ3Ksh3dcHvr5uBgfD4HAhH2LFYtqpr8emZtHVxY3+HxIUNJlz56brGtzWzFBfHz6mjxhdb2JjVSi6uVktD6uW4oULKgLc0WFbeQQFqdU6fTpH96pv2pp3SmcbuTkmToTUVPqeGAbMHPN4wNeXycD46Gi27JlIZOREPo3R0rVS5OfN4zMNDWodR0ZCZycfdHUxef9+U3WiHg3chtlxFx13rSRtjYvuIa1aVfrMGQLKyliy5DPU1cH+/eE4nZob8vnPAwsa8QFu7D+Jpj3PpKtLh2V8Z6cGmkEv1NHBNPd0ePsQlJTo+FmlMq7WPpIPeM4ckcZGuWAhtPXrbcV5S3vHqm1mFV2VwkI9ZRct0lM1O1shR3a2vmHlSvuUl9Wrpdz4GmXRIhk0vshmkIULReCUXVHDRrWdnQKFmh4ZGipnrP83J1dSkogcPCjd1glr0JxKVh6RhQuNzmtUlLxu0EOD8UP/lquLaQQFmaq3SUnqt46JESkoMH7kIoF9dj7BBTQpQtaulYgIU2Vi+XKRtWtlwwaDLkJCVJgmMVHKyxUBZWVZerz5Am+JZGfLPlQkpdTqq+FGtZuxktJSKS4W+5Wbe1lad3KywsXSUmlrs2hgbwlUSViY8cUZ35uOebbAAQkKUvRYDXLLCAh49uw5SssxSQaWP0wOHpQfWz7fpCSRwkJDMTorvr5ihFI07XjVKn0WsnatWNKNGzYYkaGEBKnHElvZIFBkfNndoinxr4sUFckZlJx/AkQKC817BsThUMTb3CwibW3S2GjqsW3Z4hX/MaIwjWZsS0oUnFZWeud2b6/+X2rq1WvCzbntNpH2drvgs7S36zOPiJBGy78ZFSWSlWUsE+NHtbIeLNHZpCSxeYlW2WeXy040UqtlmcABHafKSn3/qlVSX290tEtLReLi5Bxa7aQXpKJC50ZJiRkTX199Rnl54nIp0qsHW3xIyssV6mZkmDm5Qdd4UZFIf79kZlpVM0ZKRb5VoE4WLjT3VF2tfTO6wDoX3xE4q28IDVXzKCdHKis1SckS1cnK0pp/9TaqrVWfba5aBtLba5KLhv6gAo7Gd7ZqQojbrQ93wQKxhZhLSmxRoGEQCQmRYQxtrbpaYJ+maaen61yJj9e9LSZGf7bGKz5epL/fWCcjI+CrCfr/j9bVBUd6puGHEdaJjSUvz5uKN63vfeXQFBUxb57qYLB7N6c8Ho2ugs1KoLaWS3v2wIEDlJXBmTPlMG8eARi/j7nGAIp6ensBBrjnHpjd9Q5dL7/MNlAHmKlT1+TxcAQlqJxFEcTQEODjg4/5vB7QRATeAUqorNTbsTIuTqK+2aPm/VcboO5uw4abPl1RQVQUxMTg5wchIfcREjLfyqXAz99fT+GYGFpaetm9G8jOZuDx77Jjh/mc+fO5CSA0FD8/RU8WQocEYD4kJDA/MJBw05ezoA7nAwc4ae6fd9/F7Ybbb1f0FBPjFRxi1iz9oauLKUMn9RkxA7jJVKD1jnljI1jpwOfOneDoUR0TGWE8fH2EcZ6T8MornP2P/6CuDmANvxn6DE4M5WZoCC5cIDQUfH1VMOXMmWHUBxeniSCDgwZ6au2P/HzIyTH3joW0TgPNDA6+j1KClCbGHXcQHBFBF8pbID8ftxsiInwRGaS319DwgoOZMMGg37o6ujwehTYLFsDcufZ8mT5dxy8yUi98sm8yTz+t0y442ErHH6H19EBZGTNmGCvwtdf44D/+g9+0tLAbOAL6AfPnc+JEObCdmwLPM/Dcc8oQ+t3vlMqxfTumzDH09DD0+uu8X1enHbn9dkMl9AXOsWkT/N7vTruI3i3Okzz4IMoICgxkPOqpHx8RwRtvKMnm0CGNV/QMDur8CQmhrq6GqiqdXx+AQse6OvUvR0YaJFmj66u0FKqqrqZbZdoHwGb8/U0ooK5Ox2jWLH70IzhwoBX1J5/WORoXp/OlsZHDh+G3vy3nzJlfANuUDYUieQWZAeqzfe45XUxDQwQHQ3T0WEJDobjYbBU4gZm63ENCwOOhb88eHQjz+n1fH8eAMaGhsHQpZzFRoN27gSr6+mqU8RAXp6a30wl1dXQ0NXEKUxXvwgVOdYyj4U/kYXw0BDxjhkKqrCyRlSuNfF+ZwIAdlO/vV79ud7fX/yohIXo6REfbEo0nUDm+M1jVR7slKUl9XNXVilBkyxY74l9crD4d+7Reu9auKbVhg0EyRVrJ1/KJysaNIpGR0m/81uLvb1Slh43PKFdcLoM+Y2LkTYPs6/HWZ1OR75FO9BsFfqwsh5wci5QhJkwvsmCBLF+uIMcq1eBNpCgWFWDfatDcVu13Vpae/hs3Sm+vnvqZmYr+JCTEROSPiHR2SrXxiZ0wvvM3UMEcO0Fl+XIdrKIiu1LJmjWKUmTjRn0uK1bo37u75Y03jJBPQoKI222YLA+IRoW/awuITxsBAd9uWRlr14o0NhpkdlEaGzXVVEJCvLB80SKRBQuM/3efIiKjhG8h0zVr/uiZO53qF4yLk5ISq/7fkERFGZZHb69kZBixmMxMW9gpLMwg0JwcHV9TKUJReJ1Ifb3WxFuwwHZ7VlYqIMrNNT7ilSsVURcU2On0lqjUSPPjppvm2PKGHo9BwMb/bLNzsrJEioqMH7Nb37d2rV1Z26rUkZjoBWsmT0YkJUXqwbA/Vgt06MVqa20hGmluFlm/3q4QXF9vxtnUPkxONtbFwYMigYEag2lrE+jQebdunUhBgbcqx4IFMmiuGRVlkjScTrEX0tq1f2K9fF80jjOgz3T5crWGrXlRVKTrPitLZMUKGbQsmYICSUsTb9LU8LBs3GhVY94r8JL6bYeH7aoWUlur+46lqO7v77Um4uN1/4iPt2VCe0EkMFCGh/XxLF+uvnerSodaO/sEKu005DVrTIzLJFKJr69aUnl5kpRkCQp9XCyIc+f0JF68GGbNYmMMwH8Dl3jzzXiOHYMbb4SoqHHcGXzSQEv0+Pd4oKEBT18fR1CEZUU5Y2IgKCiA7dvr2b79GFOn3sdrr00hZuFXCZgwAa6/nnvmKwA6ehQOHICbli3T09jp5FuR74DLxa+P3kdFGXzve+8DvTyWsp+fNTVxExrlZdIkWLKEX+8fg/L9JuDvbyFMRWgTUe7DGLdbnUGBgYpGrtiGgA+oq4MvxUbT0WGA29GjlAGz9uwhNFMP8UvTb2HM5z9vxH2cKIKrQDH2FGCQ/fth9rJl2smqKpxOtS76+qBv3z48WBz3PH5RtpYv4PWv9qHIZgDFGT3A50xCBcPD+pmRkew/oBVkX1kQrIivokKfTW8vfn5f9Ur4DQ0R3IiK/Rg+tI+PIo6RrIJzKDN0idvNS0XT6OmBlBQnEyaoiA+zZilyOHyYd95+m1bAnQs//WkDS5fO59in/5npq1bxeN3nCA5Wkn54OMrP7erC09dHK1B36BB3bfsWdz37LPn5k7n7bph49x3Q1ESe7znOnPk/sPdJxr3wAtxwAx6PzrGmbz5KH+Dy8YHISN59tx94mfOhOSqDGh7OCy80A72sWTOT8HB1z8fGAnl5/Ap4pKyM1tb7OX0a+vrOMVKFaNBh3bRJu3xj/0lOchOHPZPxeLxW4138DIaHWbwYgoMD6OyEG5cuZdlSqKl5Bh3xAPOaCTjZs2cQCEIyQqkAvtZxBAgiKGgS5K+D2Fi+971pQDP/9vj1sHs3e/0e4+WX/5uysjvJz3+MYwZ8hodDQNMRVfgJCeGWri7D5RXq6uDkT75Dayu8uEaX8WcbGngfmL33P3nxxa8xZtO/c6qvD5+6OvyeeeZPmNTWuisHati//+t8LTBQfaarV9MzOEirmcfhKSkQGckpNGkictMm0l+8n5kndvHhpxYzPiuLf3puCfA2ipqHefPNPmbNchIcrNN+dkcZ/7epiS50XY8bHGTi9u2MR5NKwjFVxJcsgW3bqAOmd3Wx5B7Yt+9drBrkhw4F8MILAebOwoDztLT8lJaWsezb9xWioibx5Zz5+LzwgrJy4uIgIYEdqSByNXEiPiICDg+3o/xef8rjxj/ZIy6XClWUlhr0292tp9uiRXrad3d7q7mmpoqtOdmt4h7KGtiqaGXtWuVogkhCgmFAqD8nOtogSqsy6qpVim7q62V4WGwpR1m9Ws4YP+5Oy//b36/HtslftKTr6usVQUliopRjBFLKy69a9RbmaOS9vV2jx1FRWvbEQnsLFtg1wWTdOj1Cy8ulouLyFNn3pKBA36Y+1wFZtUqHSi2MBoFTVpFbw73skYULdaylvV2hiNttV+m9iGFMVFYKnJWFC7Xb1jUs0fr160Ud94mJIs3NEhLyhyBBKitt1kZzs/nZ7ZY5TucVEXAkyniQ5ct1QDdv9ua+FhUpjCgpUYSTnCySkmLGoUji48VOfU5ONr5oU5yvrc2gLxPFH7R83263/lN5ue3DtfyBsmWLynbm5QnslZQUkQZM9N/4FtXnmKuoyuUSOXhQoqJM3KCtTaSxUVJTDbuiqEhT103UPDFRn4eK1V95fgQEGLEmowva2alTYuNG8VZsdjpFEhKMT7VJXC7tvxWVN6QXc69lJrZQKdCisD8iQqS9XVwunfMVFTov1H9+Sp5/3qT8t7Xp9TZv1rWSl2fPF9u3bD2b2lqds1a1gqIiSU7WuSOVlSJZWdLf760PV1Ji1qO/v/RfJWYCtwi8J3FxOr8ssRxJT5dGy2pzu0UyMuzKxPX1eiu7dhlEPzysyHXtWlGGxy8FOsTpNDEOUy8wNVW/LzSWYSXYtQElPl5OYyRho6N1fIqKpMRYv2qVtdgWua7LIfH1VQ62y6X3YsWunE61XPeY+JGASGamrF8vhov8cdWEu/lmkeJiaWy0VMKOCNwvqhiv9c9yc3WwKiuN2bhxo0hysng8unFaBOZdu7xsMEsFSyH+z6W4WPUSDqEVFlpAVBktVZRS9J7IwYNyEC9R29aaNZL0tvZpRoY0o8kP51D9Cikpka2YmnKVlUYf+KzAayK9vbIeb6UA9SZceQCnTFET05INqwRZbdwYsny5mjbZ2bpKLd1gw59Sus4BgSFdEMuXm0SAH4uvr05s1TF9x7gqXhPItzdIK3CVmSl23by0NLN5xMVJMRZ9JlugStat0w03MdEyi3oEmjQYkZho3DJHjDm3T6BdJC5OfmpN3IQEfXjr18uciIgrbsBTrQ3YmPMNKMFf/P1tkr5kZ4usX2/PIT10K41L5ucCTZKSYhaTKRdeXu51C8jwsEhCghSCTV0atK4ZFSXl5WajWLFCtphDFDK1xh2a/PM783/63FWvQ/r7pbJSp1Bjo9jB3IQEEfilBk23bJHmZt3o1q83m2hb24jzY/JkMz+io6UaTPWSdtulIaGhssfMYdUX2GDGoFAszYjSUhHJyzP6w3vNeyoF2jUpxOheeDy69nQ8h8wGbNU6GxBZuVJ+hKGo+fraVUEkIsIuB2/Vf5MtW0TMeqkHkZgY+yBas0afjRU41mtkq5stOloGr7oB36nB9uFhkZwc6e01h9HGjbIHQ/8rKTHzdsisj9fN/lIpQUHG9RIdLbJ+vaGznZKNG42LZ/NmkaQkW5OlulprGL5r9TkuTuTgQRke1qD4M8blkJmp42jR89as0bdLW5uujf5+W5cjJcVoTeTkiOTmmvnRLRIfL8+YPaXGfK6VaXK1DfijuSA+/BDKyphWVcW0wECeCnuE06d9URO1l8hI+Mas99TcPdajalDLl3PE/QhHSxTpP/ggzCz7dzvlk6M+agctXsziJmhqSsHphIk5OUysrSX2zTdhxQocL8Yh8i1SUtwaVHAe4TPp6fo5HR1qHz38sH5mdjbjurogO5sjrq8yOzKSx/75n/FZsYL+CUBkJEsDA9U09/Nj3jxISAhi3z5NZb4Lo4H66KOMu4ocWljwII/Mex8ez2Po0CFmA7MBn8xMttz+A+LiYNqcIFq7umyVKJ+aGsb99Kd8dn0fYWGPaRCsthZ++1t27IBnn32cp5+G2S27iPvJfRw+HG9rEvv5Kfc/JsbKZwgnOBiOhP9fnE5Ym2oCEh07uLeri19WQkLC93j0Ubi/6d/APY81az7D6dOt3HFHOLGxE/ic5y1ad+4k/IUXiIr6nqFz/Uqf6ZNPcs8DDxAZHw9PPMHZuffxj/8Iv+v8zyuOx0SHgyU33ghr14LbzZQZM5SuODhIRwfU1Y2hL/p7dHVBVY4+No0H3mkrw7W2arDE44Ele7/PmJ7zVGzSINGaNTA0NIacvF/z5exvweHDfHDokLqOXC644w6GhjRZgdBQpYupiDBdXSb4hLppulAP044dcfzkJ0BaGne++irzN6mu8H9t2QJdXcxrha6uRaoFm+ehqUlV83p6xpKWNp5xfkOM1EJC1M3A/G3MbmrifA/k50/mgUXn1a5OTuae8HBYsoRzM943dxZPREQ4aWmQlgYTD+zS2me9vcDfo86mVqCdHTsWMX16ALNmwRROMW/eFL1PxjJv3liGhuCf/3kYqIRly5j3yiuMB04ODjIGdbkFtLQw/tln+dy8efys6342bQJXzle5c2MXgT4+SrOKiWGhYZC2taln0d9f51pEhJOWlvn09MDA0SOM6/pA/ZAjtAsX4KWcMcyf/21yHtdnvczzCNP/6Z+YDDA0REgIzJ07lgMHYlBXXSvQzLlzTp58cib5+Ufo6oIVKyAwMIxHpv8ant9hyR0y8Sffp6vrXxnzwvOUmX4yaZLuMzEx7N0L92Zm8q0XXgCXix8sfAdefpe7gEB/fz68Q+cGgYGweDEnPePs+x8aMoH9668HHx+uu07Hm6Iinv27v4OqKg1kmmywAZ/xI44F8BER8PXXi4SGyrtoyrDSrVJF6zaVSXq6SItBgPtMgEirhb4kcXGKMiQ1VbYbs+B1Cy2Wl+tJ39wskpcnlZV6yM2dq6ArLU1P24ICNRVrQKSx0ZteuXatSHW1ZGToz9Xms5UG9UsJC9MT2ypvbumFFhbqySfV1VJdre8ZHjafFxkpW01Qi5FoRoGBIgsW2Mr8kpws0t5uyPLvSXy8VgF+Aa0ksQVko/n5Isjq1QZxaDTI6+1fsULNZBM0y8y043JSWWlO+9pakY0bpbNTbCUxS+NYzfAGDS5WVoqsWKGI3uUyiChfVq0ybiJDvxN/f1m3zlKie0ngRb3OokUixcVSW2uZwWkCQVdEwFFRc6SgwM7AFElMlLUGfRQUWN180Ubl0KPI0oJC8fEmoPSawFsydapXP1pdL1vN9V8XaJfCQiXY14PNsystNUFVg+LU0lgmcFYuGquq3JiksnKl2rihoUr/y8kxfX9VoMdWSZPKSh1bU55d+9AijY1iUO0I82PKlMtocLnq5jJmr6xYYZTI9hlU+32BH6r7rbtbrIfRbubgGTDumlOiAbdlAkfkjjsMSs7JESkttU12SUkRWbFClG75XUXsqanSiyb/FJqx+K352ggmaPpzgW477RmaFN02N4vU1hpXSY/Ex6u1kJqqbhVFo2+ZZzMyAlbE3CKauJUr8JhS2eLjvVlFtbWyebNFaTtgxsZ6fVfg2wJl+nBWrZJq06dSNAD9Lkq1s16HQNFvVpY9P9etM7X3MjOlGKOnbZQQrTT+7m6vslxysreCz/r1opuHcc1Y7h6oU2E1EElMlO5uMYqRHxcCvuEGSEoi5pVXCAU+jAT4up4A6Il4Hk3SsOhkVlqgy4XSOIaGiEXpLccwxPq9e/nCw5HQ1aNULvN+H58/LMnW1wcsWsSstjbw8yM+3nz+ww/TPjjRRlHBaNBtfwXACW64Qauv3hg0AFVV3Ojnx/1LXPrPRXvh9Glmzx1i9jIXvL1PYVhvr1Jcrn56ATDe6WRyXx/ExnK8Z7Kh6HRw6BDEREUxrqGBKSgC+w2qW/wBmorp4wPUzYdZszj75A/Ytg0eydGfL638NgXKGCM0VF/33AM3dr5vU2YmOp38/d9/TXVTH38cPB6mb4PExOk8/TTw4BrYs4ceoKeujurqAPbufYDvzP9veDafsw0NGjgJDaWsTMc4LCydG25QtHNjTg54PMyse4sf//h+3O7voRS+/9k6OyE/X4dvcBB49FG+sXMnPPmkVdoMrQlmKcyN5fRpONkxnps0CmXGbiYQxsMPa5/371cE3NIyDkWAk7njjslMn66BlDGg5oGPDx0dpvrEU0tg1Sra2wHuIyIiCOf8VGJ37uSYZZFYgxoZSbDHo4kIvICirtPU1EymqSmAKeHBCskrKpj1IOgMm8DQ0FWLZitKioxk7FgYHAzglsgBTgJTamrwsWrEGXSnYzKOhQtRqF9URFdNDUNocndwfLwJwHYAXwA+T1RUNIsXGypcSyB4PNzS9Qso67Dr6iUn+7J160O6/joiGR8aSrjHoyE9X18uDQ5yBA3aPvQQPPfcVwkLG0tdnUX9nKKfPzQEXV08+STMnz+Bigp4+21sPeLgYHjzzXFcLTFl/HgHM2Y48XjC6emBCxd0nG++Gciv0cSQjg5Tidsa2xlYQTYNePqiqcIxXAqdwJjbb8cPXaeh5tWK7kPBZib5WZoB2NLddoEWAgMJwMgfXN6Ghgjo6SCgrw+3WwPKN94Id9wBt0QOwJoqcDpZtuw+Jk1yUlISRkNDMIcPw8zcXAgOJqDvAwKCP8ZEDItWU1Ji/HGm7IOVHpmRIVJpTp1zxienfqIjGosyKG/XLkXD54y/5HeYBILyclm3zlBIGhttv1xhobpUExP1FFq+3NDdsrOltlZPoDvuMD7n9naFjJmZ5vT/hhQUKDLvtnxeRruzxtxnv/HDnTAn4TmDOFrMffGnasLFx4sVRcrKsgIorwq8p/So0lK7Hp2EhMjzli9u0SJ1bra3m5PylLEmBmT9egtxWjXRDgi0aL9XrpR24189ByL9/RbPXqKijF/9svTNEjTgsAfsSsglaGrmdoPMtQ5at6YCG995bq6h0NXWqn8uLk6krU3m3HnnFREwTBUokogIRUZWja2oKLUutD/fFxVv6bADqvr7HoF3xOG4rLKsFeEwlCNLtCg5WRTxZmaq388S6s3LszVXrLDA6tViayDb6cuLFikS7e7WYJ9V0tYIzWj6qPofExKMzy8xUVOpi4sFemTuXP1c9VeOXAGioMBb209WrZKdBoE32oj73w2aXSrwhMj69fKmQb2voL7qxkYLDb4oNu2xtFQ7Y2hs0t0tsmqV7LEQ3+rV3iyc9ev165Ytat2lpops3iyFhdr1i5ZFEBoqsny5SWEfkoULFSXatfaKiqTbWLYaIH5BrY2ICJEiKylm5NTs2bPn6HM10FKt426R/n4pNp8rK1fq/VZXi3R22nGLhQsVaW/eLHa5EssytCtVr1ypdLqUFI0NJSfbSWKSnS2yapVd5aWgQHSSWb9ITNTrmmrknZ3ijbKtXKnjnZQkBw3K7gedaBZXsKhIhoetgJ2mQ1t52iONh8hHTMS4cEHRyO7dylqhtRVefVVPbcIAcAG3YER27DQ8TeX0AHg8XLgAZ3vGERgayhQuS5AID2f/fnMylZVBWRkxMep76unRyx0+7EVEVjozKAC6KXRAHVRGIT8oyBfQ6hvnMWjb+HRP8Yf1DHpQbHHWvADCQ0K4KSJi5AHx8cEuq/zFL3J86CYOHbK6nURISJwiwchIL1KaNYtZ6MnMfffBkiX8omIymzaBt8pCA2VlRo+GMGAqEENERDjjWo9bf7BpZxQV4XbrmDQ0qC+eHTugpQU/lOQWi0oKhqLWSZ/pbzDwOfQely8P4Mkn9fPYt4/bbjPo6uhRPMClQ4fUz9Y7EsoZB4TZMoPnzvUC9bS06PNREBIFzGTu3El85Sv6XA8cGERTSY8jco7gYJgYaOhdw8NQVsZXvgJTp04F3CoteeyYl0cVGanwJCSECxe0FFtbm1XbDGa6LjFpks6dw4cBp1PT6X0CqK1ViiAuF0yYwIBVRpmZwAyuu04vQ2SkxgV6eoiPn4DbrY9eZVav3K67Tueu9cLPj1A0fdUHNM2XBOBe4PNAnF2j7HJ6oTfFdxowU/3KlthxRQX09/N+awAEB9OKSZwwyJ7Tp7XTXV02bROnE8LD2bZNDSnnggWq/+vvDx6Pzlk+wNcXPhfzITf5mFqL/v50oDM0OhogDLcbPC0tsGuX0aeePOJ4+I69xESf87pGm5pYvBhSUgKgogInxpKxJAtMsTVr7BYsgG9+E7629EPNpy8q4tgxQ8ucNYuJvr4aHImNhdtuU9+1282mTWqVERICQ0O2vnJPD5z3mahjNH269q+uDo4ds/caJk3yvrmmBo4d40N0L+kAzvb1MVBTozdRVcWYpuOEhAAMq2Xb02Mmz1XaR0HAMEeCgpQcX16uO3wviKxbJxs26IExd66pOBEVJZ1gTu5c6e7WiGS15S8FhbaVlXo6ZWWZE7FSfbMul9iOppISI1IyJOnpYiMzqFWaT2Ghl16zapV0Wz6d0lIbbUtenkhnp56gw8N2fTbJzPRS41wu6TYoUVwuyc21yP4jIODwcJHcXLvSsqaTHlAKXGamclCqq9WRVFqqx+6WLSJ5eVYhEIN6Xzfo5nGBbxgf6HEJCbmM6G2R+FNTtfNKg5CLBq3rSdsiUCiyZYs0YlIp3W6RzZvljTcMid3Qi2pRSUxZsEARXmOjdF+G+s+AoqXmZpHAQNlsLAZJTZU5kydfEQHPmTxZZN06WbVK5UjtygSdnSL9/XbRkYoKRbfnbL9mtsApWbFCEUREhHZx4UJDR4uKUnSUlKTj2N2tKN/iQhUUSFubPsb0dG9mqSXSZLJyJStLr91pnnFzs95nRYVBOwcPSm6u+dnU6ZO2Nuns1M/OzTV+87w8fZYlJaaq9ZXnx0wzD63KHFZShrS16Zxfvlyj5RER+vPmzYq2Fi2SRoOAd5rPGLQQ3tq1OgbV1XrPMTEmNXtAqqvF6/Ps7pZdu7QvtbUG7ZWUiCxYoIJGiYnGb9ti12tMTLSYNy0CGyQhQfTGMzKksdF8zurValqsXavQMjpaSo0FaUHSkcbj9tvniMejt19cLCJZWYpcLUi6YoVUW+vSrPtdu0zikOGpWghc3G6z3ob0xlauFGls1HssLta9o7NTlLrXoVbCypUiw8Oya5e3EsvGjaaPJj28H00/twSG1qwxiD0jw8uLW7tWDhqLUpxOHTwTR5KDB7302s2b/2RV5I+EgGGYc+eG8fMzKZytrar4XlXF4sUKCA8ceJPnnquHwEC6sNQUOxkaUt/wMbRq3HvmhPq9352QkgJLl5oquR16ejQ16alz4AAcPWr7gj/1KQUrDQ2dwEbN9XjmGXjlFfXhtbZyErTu2rPP8qWhX0BTE5dS/pG39k/kxRfhZ6+NIRSVw2TxYk657lLnalwc4zGoOCKCNWvghReu4uQbOxaCgzl61AsyIEDRzttvq8CLxfaoq1NUb6q67t9v+cCawcYt16N41Qn0EBgISUnw5Xs+VKiSn+91CLtckJCA099fEfuxY2h1gAnQ1MQHwBh/f02aWbyY3buV7/5O2RguuT/HeEwKwfTp7NgBOJ28hwbnhzAI7dgxePddLnV14YcRcOnrg0tXTj64+MEHdDzxBLNmgUivIrWnn4Y33wSPhynOszzy8CU+2/0rdp85w1tYUpqn8fUN4/HHtdhxS0svDQ3N7N69n6ee6uNSQwO/AY5v3w4PPABVVeTlwTsV43Q8r7uOqioFroOD2BKdc+bony2LKTwcbr1V0UsX+mhEPtAKw04nxMby7LNQXg6XnngCz3PPQVERE33OU1QE3/62yS3KydHwfVfXVZ3AF1Cf/8SekzidmlGclgb/uXcKLFoEPT38rKuLV1taOP700wx8/et6808/zTSnk0AUbf0aTYWteuUVPP/yL9oZS3Gmt9fkO71HWZmX6XF2KIBjx7Qve/eqnCs9PdDURBMo0u5TC6Wm5hfs2fMLdu78b3buPIYmSjRrJ/bvh6oq61+1HMmyZfr7pUtpqqmhB2PdbtoEL7884ngMDSnob2oy03X3bt4COHSI9s/fr8L1aC0ZenpUQsDHWBm7d/ObCxeoQq1XmpoMO+g0A9NnKvoND9ffGd/+eZ+JWJYVQUH6jJuaiInBpMn/grIy7DLUTeazz5zR9ZyXB+vXw5GmAP1ft5ufld0CDz/Mh2YOWdaEXV16xw72b91K18svK3o2Ma0R20dBwDffrALkMjzsJXC3tdkoR2t/HZewMLGTAUpKRKBKUYuJVFrEaMnN1dPLEg/fvFlRwbp1ekwaX6RkZuoJWV0ta9bo6ZWSoodMdbXxWR48qD7Wzk5pML7N+sv9vqYM86pVJoppcqVTUoyfs7RUxNdXGvGKlUh3t+FfjoCAp00TKSqyCAk6JikpIuXlf3h6FhVJr+m75WOUzZv1RgoLpbJSu2flKaxereivslK8/OHQUJHoaBkevixxpLnZ6yDPyhLp79cIeFyc+mzz8mTNmsu41pZ4ueFG7wQlPJowry1AYuqqyZYt+v6SEttvJ5WVMufWW6+IgKcZX7MkJ9vMluXLvckfFqslP18RcKNB7lJYqF+NNbVunYXEtCrthg2G+ZGervdXX28bAZbAjpVToH5n5fZ2dlpE+AZxOPR5VFSIIqGEBMnPVyukuFi8OccbNyrCNSizt1ds4XLp7fUiqfZ2c62ekedHTIyOWV6ejn1npzIWhof1+ScmygFjFUpOjkh3t+HCn1XLbfVquyagOJ02f9c2EVwusagaVs7PGWvuGktByst1ckVHS7P1fBcsECkosI0pRbwNsmKFPmpFh8qPt9WSTFnmykrD3Bke1msYtC6LFsmGDVdPPAgKmqPJFMXFXpSYna3r2yQy7TP+8ZISw+wxnGGJiVGr1ihZ6bpsF6iVjAz18efnG6urvFzH7OBBUc50nbzxhmG0tLeLNDYaX+0vpbRUpMZC3U6nSEqKrFih88iKM1VWqnWbkmKKKzQ3SzPKIJGYGJGMDOnsNOwbK9vGIotfRbBfdMZ/BBrarbcqxC4qEikokIoKXWDFxQq9m5ttGVU5YTba/n5dJNLYaN/0TmvSrVghkpsr/f1mDwd5DkT8/e01Lzk5IitX2kpm6vw/pZPAyuTo75faWjVVCgpEmlEa2ha0XP1vrY04I0PeeEMXYnq6tcg1+UCqq6XZmG/bMYkYSUmGyjPCAjOJKW1tZgwSEzWglZoqqo2syllWqfvnjHkvq1eLxMfLCcvUX7FCF2hWllhpcBs2iEh9vR0cPGMmiFZR6JbUVG/pdCkp0UW+fLlu2CYjSfr7xdfXTBp/fzVjo6JEoqKkGQ3GnTbjbS/wxETJzzf6E9XVtqJWdLSehZ2dInfeeeWKGOHGbK5GtTRUjeotUfpYvijlqkdN2+xskZAQKUcDTrkoRe8CqnZm9dObHPKOSFubanS0t4u3kOQ/CGywFc6gSeBpDXr19hoX2FaBOsnKMu6c5maRykq7vH1xseicTk7WTKbQUNm1Sx9Hfv5limmLFtnPSsHGEyZ4NhLtKlqzpDIzdfNYsUKvXVxsZ4i2GLO3vt7SRf65wFcETklBgXm+VrBtwQIRl8teR4PWBlBU5HVTBQbqYb12rQ5iUpKIyyWH0CoTneYA6+3Vvpl6kmJpdkhuruzapVOiokID5/Z10tPtMbPdkJs3a0JHcbEx969GQ5ujOh2LFqmbJCdHN8yDB6XfzMUDZjyUAndA5OBB6UVdbZYutRWY07nxnoSFqVckLU3vS0pKdL3n5orqkzfJggUmu7O3V6Sw0Lg7j4j098tPMXRYk/kSF2cSMTIy5BAWlfExgTqdC/X1ctFyuyxYIJKVZT+/8nLtjxQU2MpxH9sGPGPGHPWhderBVFFhopL9/baoeHGxmbCbN4tkZ5sF0CTS3i415nTrBJHoaOntNQjWpAa3oeVIbF5nUZHN3b1oNiJZt04nmvUUtmyxJfegWwXJ168XiYiQNuuUSkzUgbdQXHW1Tk6TYVRbKyJutxw099drnYZG/WTEDXjOHLsM0ubNet1m8yAzMrwCKikpuuFctDb1ggKRykrp7dXbGh42i8cSoK+vl6Qkg3JNxed2MwFTU0UgX6O/GRn61FNS7PTQ2lrtosdjgcoG5ZZmZCjKKCoSKSqSQXPYyNq13sVkkO/ChbpPV1ToZrVunR6iYWE63wICrrwB34SWkdmFKW2zapVUV1sbS5Mo97dIoMfefCQrS6Hr2rXqC6yutkXMrTJPu3Zpf+rr9dH19up9KTNiq0CD/iE7W6SzUzfjoiK98PCwuFzGj9jerjuwyyUSEWELdmdk6OYtK1eqZRYYKOnpigQrKsz87O8X6e0Vj0eHqbhYkaBmhF55fsyaNUeGh3UTK8UwVqzYRkqKLQ8pISFSXGxF0AtFuc4t8vzz6se2rCNTxFgkOlo3mMJCXWu1tV4x9XXr7PI7/aBjmpxsi1JJSorN6ZW8PJHaWjPFehTwZGZKd7ee5TI8LP3WejCp9Va2YFaWbnYWotf1t888jxEE2e+cI9LWJt2WpbR5s87N8nIbhVprxCp35PGI9is1VReUEYc3wyfR0d6XZs+1i/T2SqXZyKWoSLq7rXhLtz7P6mrxeMxhXFCgIMQgZuWhnxV4xyqoLhs26LXKy0UXQEiILYXZjlc+1043NXKs/QZQfGwbsL//HKu6uR1M6ew0m63TqcpDq1aJFBVdVkPpiECxSG+vlFubrzkq5s7V+Si9vSLr18sJg6DexDi4k5IELkpCgm7AJea1D3XEnzaLPD/fIvn/UCBX1qwx6Dk5WS5ipeQ2KKpbvlwkJET2mY3id2jgqRQlc9v6uunpenNXqYhx881zbOW15583iNEgBWlrM4kgWwVetGteSW2tSH29WbjvCdRJWppBqRERmjZZUiJBQcZVYpDPabMQNPDwDSkuFpu+dxFEkpNNQGtANLB3XJRKVaRjXFkpUl1txY1EXC6pxHIRfUOg2K7srhOwSdLSdJJaOgVeNBp1xQ14KkqN24CmkBdbh2lOjgminjKbS5GI0ynFWIk6mQIX5Y03rISL7wrkyoIFxl3hdMpvwU6CqKwURSwLFthyIJKVZT/XXDNHGszha9Oo4uJkELWMtoChSK6WsDCxdXhLUItAqYQdOr8LCy05BzP+F6WkRLVy373KArvTzKdd5mDabK5ba23EkZF2oHTjRuusLRYNSjbZ0s2Wi0APsAHdOGNijLlfKNLfL5tRK6m5WUQaG+Vts5ZsNFxcrIdMe7su2o0bFcEFBtpuD+ntVYBw8KDqO5SWSi9eqqaEhmqVk1KdJ/HxVgLGcXNvvzQH7JXHI8ZsWLtQC1UKCjRBq7RUtmAqpqSmipSUmBhzra4BC1kUFdkBzbQ0BT7WwaXv/7nAMikvF3kDtajWGeCm4/eC7gG9vbrGrcPJUPmslHe12jJFE15+qLoSmzeLBAbKy+Zz3wW7duJP8VrcbWYtitNpJ5xdbQP+SIkYIhZtyEu/8fUF59KvMd7t1qCAnx9nfSbTtE5/TEuLZu7caPjx80zEUMG2bgWnk4ce+hx1dfDO4fHELHuMyG3bmP/uuwSi6kUsXEhYhZPISHD6+hI4OMhENMjQg9LFQuvquPUhzUKeMGEVERHwL0m/h/z9EB6OMzWVd6oBfDWI8NTjsHgx8y2OUmQk+PgQXlGhnvkvflFL6e7ezVBdnabSXqWZLgNwhNnMLi2l/WIA+/erXnB6+lL6+pQa9dLemcTEeAsV+/rGMWsWvLJOU7x54QXmDQ/za+7C6TSBip88C2VlhBp6XagfnDgRw7FjcG98vEYM2tqgp4fsn8Dixb5ERobh5wd5eVPJz9cYJ3190NHBXUPH4PRpPHV1fABMCwcNr33Aa68pUykiIojAwCCWLYPPTv2AvLzJKEUuhtTUaHbvDrjiWEz09+drsbHK/zpzRqk/gYGQmsqhR3vRYONkYAKsWMH8l19mvxMgnvh4J243ZGdDTs5apk/X+N3M0LPgcjG7qor3gwES6OqCu0JDISaGbU+bQN59O+k7c4ZwlOYVExioHU9LY/dueOThcAYOHeIU3lqEVX0AQZw+PUxR0Vi+9fjj3LV1KyQkkHMP1NVNoqEBwmK+jPOoptGHhkJ4uJM7T++iCVNDbYTmCA4mYNEivlRTAx4P73s8vI9ZA52dMGsWY1avhvnzyXsI6uoGCQq6Fz+/ey2JWWJj4amnwm32WGAgMD0eoqKoeUbv/1THOGKB4JAQCL8EXYEsTE3VpIbsbD503cn4w+9oVCkwEEsubHJBAcTGcnieuWErs6SvD6cT2m/+AjfW1zO+p0cDjn5+HO+ZzNCQxnaXLNGAWl2dVhqZMOF2AJ566srjMdbHh8nBwXwJwOnk97Pup6YG3ur4Al/NyPBStjo6mDQJzpwJ1nJ7yx7WvnR0MPGecv7rxTRO+dzEs88qIzMsTBOtGhtTaGlJ4XMdv6ADDSQ7MXsJ9kfTfmE8N7rdWkYmMFDnaUgIN39a69xp8scAsJSEhLHMmwftX/waN66HZV//On1oALsPDZ33mWuNMfMKtxv6+ojdudOmuY7YPgoCnjBhjoH6HQJ1EhenB4milp8LvCNhYXpqBwWZdNS4OHkD9Qta2p6WIIrk5Yn09xt01KOnblGRrcZUXa1gsrBQRKKi1P+Xmiqybp1cxFRJ9fVV5Ll+vZw26OIA3iCfycQVS0lt4UIrUPOqQRr5BnU0CHRLQoIJMpSXyyGD4rgK0b642Fv9WMemUiz/b0qKmp8nbKSnYjobNuhr40ZF6u0Grel9vipwXO64w7aU9QQ2PCh9T7skJxvzuLFRoVlcnJ2JUYMJsMXFeWXQCgtVZcr87RWDDlQs5LvmtP93gQOSmWnMLeMWUCreYxIUJCJJSSOqoWnFgyMSFKToyJR/M4IlL4mm3L4ldvWC/n7JzdXn09Ym+g+LFkmbQUp2RsXGjSIHD0p1tc63DRuMy6etTS4aC8bSRha3WyQ7W0pL9V/DwtR9UlysqHmrMQslLs6MZbHAcXE4vNrLtbVq5ktMjKxerZZcZaWaqxIRIXvM/LKEgEaaH9OmzZHcXC9yluxs2YW6xXpBJDvbILfjxlIqVPeKMfVB0WabuV6h6Wd+vtdvC906FqZCsxQUGFpapWiqb52i5vJyr7XkconU1hrE+qqxxDoUHZukgupq67lliyU1YFlFBQVio3BxuURCQ6XXrLurWQRzbrpJpLBQnn/eQvZnzX0WilYvHrCD7pbglERFyXpjVW1E4wVvGjSuQlUvKUru7FRlMzO+B82Y1YNIQoIZh5es+KOUlOj6aW42CLu9XX233d0yd6659rp1img7OwUu2r7vykrR+4yOtmNGu8xeIS6X+i2am9XN+SeCcB+Rhma1AaCXCxf0wNS8gONAM6dPD9PRoYeLptnWcRJFrBbc/tC8aG+Hri5D3+pQ8nNEBERGcrJ1DB0d3orDjB2r/x8YCDfeyBgUSVwaHLRvohXFWB0ofYfrrqO1VQ9Wh2MsfX0KFs+dOw/UmNcx82oAjnHsmKGT+fgwgDdp9v+laT8sStk5+vr0O4/9jk4uXBi0Sulxww0Q4BzgvLlfHcc60wNFzR6P+X1gIPj5MXYsgNPuC8HBCo0GB7XqgaGg6UfV6T93ddno5jxqOXxo9c2mUQ2au+i1yep0dcGZM4YCeFETTI4duwr1qg+o59y50xw6pJdvbbXKr/UCF9G09bF4PHCqYxw+PjYg0+sZGmEr0GNNrgkTIDQUHx9FoBarDz8/O3mmD4Msg4IgIoJjx6C+Hk6fHkTkvAI7rIRf7IQc/WkYkV5aW/VzOzqAhgZobKSvDy5c6NUxOH2aoZYWu+qBD1zVhBzQ0oQcPQonTpw3KkHaLgFcfz1nzliWwXlggPFD56GqytzeAH5++iytxCEPmgdj1VyEcfo4THVoLl40V2gAqlE6mc6nHsy66+oyi/OEed+Vn6fSvKy1cQJoNdc2n9HWpmPk8fDBZfc4YjMPu65OE4a8aemn0bV4XN/T1+et6ebxcBYdb2vufgBc8njMb84ZAaoOqKmhybx3DKaCNvxBXbahIb3/nh41Qnp64JLPOPu69PR4539dnfanpwfooKnJWxjE0j2wkK+9kTqdfNg3xt6nLn/mV2oORS7/b83hcJzBJgj+r2qfFpGQP/7l6Hj8YRsdjz9so+Pxh210PP5n+0gb8GgbbaNttI22j6/9mS6I0TbaRttoG21/aRvdgEfbaBtto+0atWuyATscjmcdDseTf6PrtDkcjirz+tJf+5p/TvtbjYe5VrrD4ah3OBy1Dofjh3+La37U9jecH1sumxtNDoej6q99zT+n/Q3HI8bhcFSY8TjscDg+89e+5p/b/oZjcrvD4TjgcDhqHA7HTofDcTXm4UduH4kH/P/T9mMRefFa38QnoTkcjgTgK8BsEel3OBwjawf+L2gi8oD1vcPhWAd0X8Pb+SS0HwKrReRtA1Z+CMy/trd0zdsm4EkRKXU4HN8AVgFZH9eH/9URsMPh+JrD4TjicDiqHQ7H61f4+wqHw3HI/L3A4XCMN79PdjgcR83v3zG/u83hcLxnTugjDofjT0gNffLaNR6PlcAaEekHkD9ZM/uv3z4J88PhcDiArwJvfpx9+3PaNR4PwZtbcgNGeOxat2s8JjPwloDZgxbm+/jaR0nE+Kgv4DZUDy7Y/DzRfH0WPVUAJl32/mwg3XxfA3zKfB9ovr4M/KP5fhxwvfn+l8CUK1z/WaAJOAL8DC1m9lft8yd8PKqA1cBBoBSI+988Hpd97he4Cln+f8t4ALcCJ4EWoA2lT/1vH5PfAF8x338HuPBx9u+vjYDvAraJSAeAiJy9wntmORyOdx0ORw3wj+iAg4qSvuZwOFZgFZ2DA8D/cTgc/4JOjovmc78kIlc6rV8BbkaLQZwG1n083fqz27UeDx8gCHCjptR/GfR3rdq1Hg+rpfAJQL9c+/FYCWSISASQAeR+XB37C9q1HpNvAN92OByVgD8fLTfrT7a/9gbsQM2aq7XXgEdFJBpFZ04AEUkDngYigCqHwzFJRH4OfBlNqSp2OBx3Xe2DRaRdRIZF5BLwU7S+4bVs13Q80ESlt0Tbe2hCVvCf25mPoV3r8cDhcPgA9wNb/txOfIztWo/H10E10oGtXPv1Atd+D6kTkXtFZA56SDf+JZ354/bX3oBLgK86HI5JAA6HY+IV3uMPnHY4HL7o6YV5780iclBEvo/mUkY4HI5pwHEReQktrDH7ahd3OByX5wEmAUf/ot785e2ajgewA0UUOByOW1ATrOMv69Jf1K71eADcA9SJyFWzaP9G7VqPxyng78z3d6E5yNe6Xes9ZLL5OgbdzF/9GPpkt7/qBiwitcDzQKnD4agG/u0Kb8tCfZJ7UCEEq/3IodSPo6gTvBp4ADjqULqQC/hPAIfD8UuHwzHlCp/9Q/MZR9DqhxkfT8/+vPYJGI+fAdPMZ+QDXxfj3LoW7RMwHgAP8slwP3wSxmMFsM5c+wfAIx9Lx/6C9gkYkxSHw/F787mngP/4WDpm2mgq8mgbbaNttF2jNpoJN9pG22gbbdeojW7Ao220jbbRdo3a6AY82kbbaBtt16iNbsCjbbSNttF2jdroBjzaRttoG23XqI1uwKNttI220XaN2ugGPNpG22gbbdeojW7Ao220jbbRdo3a/wdINOboJUie+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the model parameters for each class\n",
    "\n",
    "PlotParameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the learning rate, optimizer, criterion, data loader:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the learning rate, optimizer, criterion and data loader\n",
    "\n",
    "learning_rate = 0.1\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100)\n",
    "validation_loader = torch.utils.data.DataLoader(dataset=validation_dataset, batch_size=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model and determine validation accuracy **(should take a few minutes)**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = torch.tensor([[2,5,0],[10,8,2],[6,5,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "_, yhat = z.max(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 5, 10,  6]) tensor([1, 0, 0])\n"
     ]
    }
   ],
   "source": [
    "print(_, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 5, 10,  6])\n"
     ]
    }
   ],
   "source": [
    "print(_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model\n",
    "\n",
    "n_epochs = 10\n",
    "loss_list = []\n",
    "accuracy_list = []\n",
    "N_test = len(validation_dataset)\n",
    "\n",
    "def train_model(n_epochs):\n",
    "    for epoch in range(n_epochs):\n",
    "        for x, y in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            z = model(x.view(-1, 28 * 28))\n",
    "            loss = criterion(z, y)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "        correct = 0\n",
    "        # perform a prediction on the validationdata  \n",
    "        for x_test, y_test in validation_loader:\n",
    "            z = model(x_test.view(-1, 28 * 28))\n",
    "            _, yhat = torch.max(z.data, 1)\n",
    "            correct += (yhat == y_test).sum().item()\n",
    "        accuracy = correct / N_test\n",
    "        loss_list.append(loss.data)\n",
    "        accuracy_list.append(accuracy)\n",
    "\n",
    "train_model(n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Result\">Analyze Results</h2> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the loss and accuracy on the validation data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss and accuracy\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "color = 'tab:red'\n",
    "ax1.plot(loss_list,color=color)\n",
    "ax1.set_xlabel('epoch',color=color)\n",
    "ax1.set_ylabel('total loss',color=color)\n",
    "ax1.tick_params(axis='y', color=color)\n",
    "    \n",
    "ax2 = ax1.twinx()  \n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('accuracy', color=color)  \n",
    "ax2.plot( accuracy_list, color=color)\n",
    "ax2.tick_params(axis='y', color=color)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the results of the parameters for each class after the training. You can see that they look like the corresponding numbers. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the parameters\n",
    "\n",
    "PlotParameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We Plot the first five misclassified  samples and the probability of that class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ie39T07TT+N9X3P0PJd0qaVm2u4qBeVTSFNXmAOyR9N0qm8mmGX9W0gp3/7jKXvrqp6+WfG5VhL1b0qQ+jydKOlpBH/1y96PZ315JP1LtsKOdHDs/g272t7fifn7D3Y+5+1l3PydprSr87LJpxp+V9JS7P5c9Xfln119frfrcqgj7G5KuMrMvmtlwSQskba6gj88ws5HZFycys5GSvqb2m4p6s6TF2f3FkjZV2MtvaZdpvPOmGVfFn13l05+7e8tvkm5T7Rv5/5L011X0kNPXlyTtyW77qu5N0tOq7db9n2p7RH8u6QpJWyW9m/29vI1626Da1N5vqxas8RX1dpNqh4ZvS9qd3W6r+rNL9NWSz43TZYEgOIMOCIKwA0EQdiAIwg4EQdiBIAg7EARhB4L4f/JT8N9JlgJOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([6])\n",
      "probability of class  0.9896867871284485\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([6])\n",
      "probability of class  0.4178355634212494\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAEICAYAAACZA4KlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAAAO8klEQVR4nO3df6hc9ZnH8c9HY0VtRV2JDVZXG126pbhWohYqS7XWTcVfRa0KQqRdUqJiwsZ1xQ1WkUKVrbu6inBF07RYYyE2hrBSf9A1rUIxitUYW3XFtsm9JPFH0CixRp/9Y07KNc585zpzZs7kPu8XXGbmPHPmPA75eM6ZM/P9OiIEYPrbo+kGAAwHYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdjRN9u/sr3F9lu2f2f77KZ7wseZL9WgX7aPkbQ+InbYPlHSI5L+LiImGm4Nk7Bnn8Zs/6vtFbss+2/b/1XndiLi2YjYsfOhpL0kHVbnNtA/9uzTmO1Zkl6WdGhEbLU9Q9K4pG9GxFNtnr9a0kkdXu43EXFGYVurJZ0qaW9Jv5R0ekR82O9/A+ozo+kGMDgRMWF7jaTzJd0paa6k19oFvXp+xzBPYVtn2N5LrcB/gaCPHg7jp79lki6u7l8s6aeD2lBEvB8RD0r6J9tnDWo76A1hn/5WSjrG9pcknSHpnk5PtP2g7W0d/h78BNucIWl2f22jbpyzJ2D7TkknqnUIf0rNr/0FSUdK+l9JOyRdIOluSV+JiKfr3Bb6wzl7Dssk/bOk7wzgtS3pOklflPSBpJckXUDQRw979gRsHy7p95I+GxFvNd0PmsE5+zRnew9J/yJpOUHPjcP4acz2fpI2SfqjWpfdkBiH8UASHMYDSQz1MN42hxHAgEWE2y3va89ue67tP9h+2fbV/bwWgMHq+Zzd9p6SXpT0DUkbJD0p6aKIWF9Yhz07MGCD2LOfIOnliHglIv4iabkkBi0ARlQ/YT9U0p8nPd5QLfsI2/Ntr7W9to9tAehTPx/QtTtU+NhhekSMSRqTOIwHmtTPnn2DPjoayefUGhgBwAjqJ+xPSjra9pG2PyXpQkmr6mkLQN16PoyvBhe8XK0hiPaUdHdEPF9bZwBqNdSvy3LODgzeQL5UA2D3QdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBLMCDMNnHjiiR1rN954Y3HdO+64o1hfvXp1sf7OO+8U6xgd7NmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAlGl50G7rvvvo61c889t7iu3XYg0r9asWJFsX799dcX688/z+jiw8boskByhB1IgrADSRB2IAnCDiRB2IEkCDuQBNfZp4GFCxd2rN10003FdWfMKA9p0O3fx+uvv16sL1mypGNt6dKlxXV37NhRrKO9TtfZ+xq8wvarkt6W9IGkHRExp5/XAzA4dYxUc3JEvFbD6wAYIM7ZgST6DXtIesj2U7bnt3uC7fm219pe2+e2APSh38P4r0bEuO2Zkh62/fuIWDP5CRExJmlM4gM6oEl97dkjYry63SzpF5JOqKMpAPXrOey297P9mZ33JZ0maV1djQGoV8/X2W1/Xq29udQ6HfhZRPygyzocxg/Z4sWLi/VFixYV67Nmzaqxm4+6+eabi/VbbrmlWN+4cWOd7UwbtV9nj4hXJP1Dzx0BGCouvQFJEHYgCcIOJEHYgSQIO5AEP3FN7oILLijWu116mz17drG+YMGCT9zTTmNjY8X6pZde2vNrT2cMJQ0kR9iBJAg7kARhB5Ig7EAShB1IgrADSdQx4CR2Y6Xpnqdin332Kda3bt3asXbllVcW1+12jX5iYqJYv+GGG4r1bNizA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAS/J4djVm5cmWxfuaZZxbrTzzxRLF+6qmndqy99957xXV3Z/yeHUiOsANJEHYgCcIOJEHYgSQIO5AEYQeS4Do7GtPtt/Dbtm0r1rv92y2Neb9ly5biuruznq+z277b9mbb6yYtO8j2w7Zfqm4PrLNZAPWbymH8jyXN3WXZ1ZIejYijJT1aPQYwwrqGPSLWSHpjl8VnS1pW3V8m6Zx62wJQt17HoDskIiYkKSImbM/s9ETb8yXN73E7AGoy8AEnI2JM0pjEB3RAk3q99LbJ9ixJqm4319cSgEHoNeyrJM2r7s+T9EA97QAYlK6H8bbvlfQ1SQfb3iDp+5J+KOnntr8r6U+Szh9kk5ieLrzwwqZbSKVr2CPiog6lr9fcC4AB4uuyQBKEHUiCsANJEHYgCcIOJMGUzehLt5+pXnPNNR1rixcv7mvbDz30ULH+5ptv9vX60w17diAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgqGkR8Ctt95arF922WXF+vj4eMdat2mRt27dWqxv3769WD/rrLOK9Tlz5hTrJevXry/W587ddRzUj9q4cWPP296dMWUzkBxhB5Ig7EAShB1IgrADSRB2IAnCDiTB79lHQLffhPczNfGCBQuK69ptL8lOedvdbN7cef6Q22+/vbju0qVLi/Ws19F7xZ4dSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Lg9+wjYO+99y7WTznllGL9+OOP71jrNi3yzJkzi/UDDjigWO+mdC194cKFfb022uv59+y277a92fa6Scuus73R9jPV3+l1NgugflM5jP+xpHZDgvxnRBxb/f1PvW0BqFvXsEfEGklvDKEXAAPUzwd0l9t+tjrMP7DTk2zPt73W9to+tgWgT72G/Q5JsyUdK2lC0o86PTEixiJiTkT0PvIggL71FPaI2BQRH0TEh5LulHRCvW0BqFtPYbc9+TeV35K0rtNzAYyGrtfZbd8r6WuSDpa0SdL3q8fHSgpJr0r6XkRMdN0Y19lHzv7771+sX3vttcX6okWLivUtW7Z0rJW+HyBJGzZsKNbRXqfr7F0Hr4iIi9osvqvvjgAMFV+XBZIg7EAShB1IgrADSRB2IAl+4oqi4447rlh/7LHHivXSMNnHHHNMcd1uUzajPaZsBpIj7EAShB1IgrADSRB2IAnCDiRB2IEkmLK5BkuWLCnW16xZ01d9lO2xR3l/0W1KaAwPe3YgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSILr7FN03nnndaxdddVVxXWXLl1adztTdtRRRxXrV1xxRbF+8sknF+vdppse5ngJKGPPDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJdL3ObvswST+R9FlJH0oai4hbbB8k6T5JR6g1bfO3I+LNwbU6WN1+l33xxRd3rO27777Fdd9///1ifebMmcV6aex1Sbrkkkt6qknS4YcfXqx3u07+7rvvFuu33XZbx9qLL75YXBf1msqefYekxRHx95K+Iuky21+UdLWkRyPiaEmPVo8BjKiuYY+IiYh4urr/tqQXJB0q6WxJy6qnLZN0zoB6BFCDT3TObvsISV+W9FtJh0TEhNT6H4Kk8rEogEZN+bvxtj8taYWkRRHx1lTHFrM9X9L83toDUJcp7dlt76VW0O+JiPurxZtsz6rqsyRtbrduRIxFxJyImFNHwwB60zXsbu3C75L0QkTcPKm0StK86v48SQ/U3x6AunSdstn2SZJ+Lek5tS69SdI1ap23/1zS4ZL+JOn8iHijy2uN7O8du13+Gh8f7/m1u53yNPkz0G7DWK9cubJYf+SRR4p1pl0evk5TNnc9Z4+I30jq9K/16/00BWB4+AYdkARhB5Ig7EAShB1IgrADSRB2IImu19lr3dgIX2efMaN8FXLVqlUda6eddlpx3X6vs3cbinr79u0da8uXLy+u+/jjjxfr2P10us7Onh1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkuA6OzDNcJ0dSI6wA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkugadtuH2f6V7RdsP297YbX8OtsbbT9T/Z0++HYB9Krr4BW2Z0maFRFP2/6MpKcknSPp25K2RcR/THljDF4BDFynwSvK06C0VpyQNFHdf9v2C5IOrbc9AIP2ic7ZbR8h6cuSflstutz2s7bvtn1gh3Xm215re21/rQLox5THoLP9aUmPSfpBRNxv+xBJr0kKSTeodaj/nS6vwWE8MGCdDuOnFHbbe0laLemXEXFzm/oRklZHxJe6vA5hBwas5wEn3ZqC9C5JL0wOevXB3U7fkrSu3yYBDM5UPo0/SdKvJT0n6cNq8TWSLpJ0rFqH8a9K+l71YV7ptdizAwPW12F8XQg7MHiMGw8kR9iBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUii64CTNXtN0h8nPT64WjaKRrW3Ue1Lorde1dnb33YqDPX37B/buL02IuY01kDBqPY2qn1J9NarYfXGYTyQBGEHkmg67GMNb79kVHsb1b4keuvVUHpr9JwdwPA0vWcHMCSEHUiikbDbnmv7D7Zftn11Ez10YvtV289V01A3Oj9dNYfeZtvrJi07yPbDtl+qbtvOsddQbyMxjXdhmvFG37umpz8f+jm77T0lvSjpG5I2SHpS0kURsX6ojXRg+1VJcyKi8S9g2P5HSdsk/WTn1Fq2b5L0RkT8sPof5YER8W8j0tt1+oTTeA+ot07TjF+iBt+7Oqc/70UTe/YTJL0cEa9ExF8kLZd0dgN9jLyIWCPpjV0Wny1pWXV/mVr/WIauQ28jISImIuLp6v7bknZOM97oe1foayiaCPuhkv486fEGjdZ87yHpIdtP2Z7fdDNtHLJzmq3qdmbD/eyq6zTew7TLNOMj8971Mv15v5oIe7upaUbp+t9XI+I4Sd+UdFl1uIqpuUPSbLXmAJyQ9KMmm6mmGV8haVFEvNVkL5O16Wso71sTYd8g6bBJjz8nabyBPtqKiPHqdrOkX6h12jFKNu2cQbe63dxwP38VEZsi4oOI+FDSnWrwvaumGV8h6Z6IuL9a3Ph7166vYb1vTYT9SUlH2z7S9qckXShpVQN9fIzt/aoPTmR7P0mnafSmol4laV51f56kBxrs5SNGZRrvTtOMq+H3rvHpzyNi6H+STlfrE/n/k/TvTfTQoa/PS/pd9fd8071Juletw7r31Toi+q6kv5H0qKSXqtuDRqi3n6o1tfezagVrVkO9naTWqeGzkp6p/k5v+r0r9DWU942vywJJ8A06IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUji/wHOd/jgOPKOmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([2])\n",
      "probability of class  0.6869593858718872\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([7])\n",
      "probability of class  0.35746705532073975\n"
     ]
    },
    {
     "data": {
      "image/png": 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p6RENH3b0kx0HZ9Atbnc23M//i4gdEbE/Ig5IulcNvnfFNONrJP0kItYWixt/71r1NVbvWxNhf17SKba/ZPtzkr4l6fEG+vgU25OLD05ke7Kk+eq/qagfl7S4uL9Y0mMN9vI7+mUa77JpxtXwe9f49OcRMeZ/ki7Q8Cfy/yvppiZ6KOnrDyW9VPy93HRvkh7Q8LBur4ZHRFdKOkHSM5LeLG6n9VFvP9bw1N6bNBysmQ31dpaGDw03SXqx+Lug6feuoq8xed/4uiyQBN+gA5Ig7EAShB1IgrADSRB2IAnCDiRB2IEk/g+5ltO9+t98eAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([7])\n",
      "probability of class  0.5495302677154541\n"
     ]
    }
   ],
   "source": [
    "# Plot the misclassified samples\n",
    "Softmax_fn=nn.Softmax(dim=-1)\n",
    "count = 0\n",
    "for x, y in validation_dataset:\n",
    "    z = model(x.reshape(-1, 28 * 28))\n",
    "    _, yhat = torch.max(z, 1)\n",
    "    if yhat != y:\n",
    "        show_data((x, y))\n",
    "        plt.show()\n",
    "        print(\"yhat:\", yhat)\n",
    "        print(\"probability of class \", torch.max(Softmax_fn(z)).item())\n",
    "        count += 1\n",
    "    if count >= 5:\n",
    "        break       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We Plot the first five correctly classified samples and the probability of that class, we see the probability is much larger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cFkiCK+iAJAg7kARhB5Ig7EAShB1IgrADSRB2IIn/A+X7ormomSq9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([7])\n",
      "probability of class  0.9966046810150146\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([2])\n",
      "probability of class  0.9454600811004639\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([1])\n",
      "probability of class  0.9739599823951721\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([0])\n",
      "probability of class  0.9995682835578918\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([4])\n",
      "probability of class  0.9469406008720398\n"
     ]
    }
   ],
   "source": [
    "# Plot the classified samples\n",
    "Softmax_fn=nn.Softmax(dim=-1)\n",
    "count = 0\n",
    "for x, y in validation_dataset:\n",
    "    z = model(x.reshape(-1, 28 * 28))\n",
    "    _, yhat = torch.max(z, 1)\n",
    "    if yhat == y:\n",
    "        show_data((x, y))\n",
    "        plt.show()\n",
    "        print(\"yhat:\", yhat)\n",
    "        print(\"probability of class \", torch.max(Softmax_fn(z)).item())\n",
    "        count += 1\n",
    "    if count >= 5:\n",
    "        break  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"http://cocl.us/pytorch_link_bottom\">\n",
    "    <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/notebook_bottom%20.png\" width=\"750\" alt=\"PyTorch Bottom\" />\n",
    "</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other contributors: <a href=\"https://www.linkedin.com/in/michelleccarey/\">Michelle Carey</a>, <a href=\"www.linkedin.com/in/jiahui-mavis-zhou-a4537814a\">Mavis Zhou</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright &copy; 2018 <a href=\"cognitiveclass.ai?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu\">cognitiveclass.ai</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>."
   ]
  }
 ],
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