canvas-recognition/number_recoginition_capstone.ipynb

{
 "cells": [
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   "source": [
    "# Capstone Project\n",
    "## Image classifier for the SVHN dataset\n",
    "### Instructions\n",
    "\n",
    "In this notebook, you will create a neural network that classifies real-world images digits. You will use concepts from throughout this course in building, training, testing, validating and saving your Tensorflow classifier model.\n",
    "\n",
    "This project is peer-assessed. Within this notebook you will find instructions in each section for how to complete the project. Pay close attention to the instructions as the peer review will be carried out according to a grading rubric that checks key parts of the project instructions. Feel free to add extra cells into the notebook as required.\n",
    "\n",
    "### How to submit\n",
    "\n",
    "When you have completed the Capstone project notebook, you will submit a pdf of the notebook for peer review. First ensure that the notebook has been fully executed from beginning to end, and all of the cell outputs are visible. This is important, as the grading rubric depends on the reviewer being able to view the outputs of your notebook. Save the notebook as a pdf (you could download the notebook with File -> Download .ipynb, open the notebook locally, and then File -> Download as -> PDF via LaTeX), and then submit this pdf for review.\n",
    "\n",
    "### Let's get started!\n",
    "\n",
    "We'll start by running some imports, and loading the dataset. For this project you are free to make further imports throughout the notebook as you wish. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O3vI8jSIz_Fs"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from scipy.io import loadmat\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from tensorflow.keras.layers import Dense,Flatten,Conv2D,MaxPooling2D,BatchNormalization,Dropout\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint,ReduceLROnPlateau\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8OrHY7TRz_Fx"
   },
   "source": [
    "For the capstone project, you will use the [SVHN dataset](http://ufldl.stanford.edu/housenumbers/). This is an image dataset of over 600,000 digit images in all, and is a harder dataset than MNIST as the numbers appear in the context of natural scene images. SVHN is obtained from house numbers in Google Street View images.\n",
    "\n",
    "* Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu and A. Y. Ng. \"Reading Digits in Natural Images with Unsupervised Feature Learning\". NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.\n",
    "\n",
    "The train and test datasets required for this project can be downloaded from [here](http://ufldl.stanford.edu/housenumbers/train.tar.gz) and [here](http://ufldl.stanford.edu/housenumbers/test.tar.gz). Once unzipped, you will have two files: `train_32x32.mat` and `test_32x32.mat`. You should store these files in Drive for use in this Colab notebook.\n",
    "\n",
    "Your goal is to develop an end-to-end workflow for building, training, validating, evaluating and saving a neural network that classifies a real-world image into one of ten classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YWdiz3n_z_Fy"
   },
   "outputs": [],
   "source": [
    "# Load the dataset from your Drive folder\n",
    "\n",
    "train = loadmat('C:/Users/bncha/Downloads/train_32x32.mat')\n",
    "test = loadmat('C:/Users/bncha/Downloads/test_32x32.mat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Sot1IcuZz_F2"
   },
   "source": [
    "Both `train` and `test` are dictionaries with keys `X` and `y` for the input images and labels respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_Q1n_Ai2z_F3"
   },
   "source": [
    "## 1. Inspect and preprocess the dataset\n",
    "* Extract the training and testing images and labels separately from the train and test dictionaries loaded for you.\n",
    "* Select a random sample of images and corresponding labels from the dataset (at least 10), and display them in a figure.\n",
    "* Convert the training and test images to grayscale by taking the average across all colour channels for each pixel. _Hint: retain the channel dimension, which will now have size 1._\n",
    "* Select a random sample of the grayscale images and corresponding labels from the dataset (at least 10), and display them in a figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "-WIH5hyXz_F4",
    "outputId": "e51e8a80-911b-41a7-df93-e1cfc2fb2733"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scaled image sizes : \n",
      "train images:(73257, 32, 32, 3)\n",
      "test images:(26032, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "train_images = train['X']\n",
    "test_images = test['X']\n",
    "train_labels = train['y']\n",
    "test_labels = test['y']\n",
    "\n",
    "train_images = train_images/255\n",
    "test_images = test_images/255\n",
    "train_labels = train_labels[:,0]\n",
    "test_labels = test_labels[:,0]\n",
    "scaled_train_images = train_images.transpose((3,0,1,2))\n",
    "scaled_test_images = test_images.transpose((3,0,1,2))\n",
    "\n",
    "\n",
    "print('scaled image sizes : ')\n",
    "print('train images:' + str(scaled_train_images.shape))\n",
    "print('test images:' + str(scaled_test_images.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6SR4gYffz_F_"
   },
   "outputs": [],
   "source": [
    "def plot_random_images(images,labels,rows,columns):\n",
    "\n",
    "  num_train_images = images.shape[0]\n",
    "\n",
    "  random_inx = np.random.choice(num_train_images, rows*columns)\n",
    "  random_train_images = images[random_inx,...]\n",
    "  random_train_labels = labels[random_inx, ...]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "  fig, axes = plt.subplots(rows, columns,figsize = (16,16))\n",
    "  fig.subplots_adjust(hspace=-0.7, wspace= 0.2)\n",
    "\n",
    "  for i,ax in enumerate(axes.flat):\n",
    "\n",
    "      ax.imshow(np.squeeze(random_train_images[i]))\n",
    "      ax.text(6., -1.0, f'Digit {random_train_labels[i]}',fontsize = 20)\n",
    "      ax.get_xaxis().set_visible(False)\n",
    "      ax.get_yaxis().set_visible(False)\n",
    "            \n",
    "      \n",
    "  plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "colab_type": "code",
    "id": "ghAbFwh57GBq",
    "outputId": "1ec22b47-ffd2-4e9e-f57d-755b7d024f58"
   },
   "outputs": [
    {
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dGT+rCtuwKIeQxtbnftPZdxhbTYPtleUYD2VZ2N3XX7DXXvm4HR/vWdvWlmW5bW1dt9tPvd+eevr9FkKwEP1amWyCyFxfBrLuhhSz4MbVnIx5ocS6aEn/7sl4P23TNpjWWD8VmQ8HAdupnWM55seQZJ1bgG1s4pz29DNbkPY3fvAjNMYvrKvq28n21o5tb+38ckJ485/kCnEe+dqv//X2tV//6xcv6IcBinlxcakGlVWDX54M+x4XnEJcZL7pt/92+6bf/tt/6fX0eLq+wgjxOeDGzXfZjZvv+qXXBflQXqwftYoQQgghhBBCiKVo4yiEEEIIIYQQYiln/qlq8Not9gu3PrgsTHT/eG3kIuPjs/SkEBk1c2C6PqbaJmnu0pzcqye6F/ZVe1URHY3/fToRijUd0ZdAilnTkp9pOeEB+104g1V/xxsdUrLcvSczoyC6mnURQrCqqiCN5XtcHm5kRLShpAG9lvAtHZlD4oiJsb3WcnWNI75lx8wXvPEE0QQxjTDTiTUN6ihqpxNoW9Ry5ENmhIP1MxyiPmJjAzWOrdOP9ETHM52et5+TBTM/hhM9rk9jutXGsI5ZjGd0fE5vyDTXPRlTmE66N6z3npQN4p7ELquKnMUl0aF1rn80RINoBdHeF6SPkmcyn0Y7Hz53QbRkTN+ZZfievn/Q+fL8DOFmtpjnyyLVA7E6CK4OAhkLclLHPZsUSb34NQ6TA7DxrSdpbU1MXeaY1tTp3N8SbXJPJp2uISZkc5JWOxMy4hvIPC0Ci1U2r5F+BZDYLYk2uRqjTm/coJZzOElFYeWIaP4KTFsnRZnbjVvbSVrX4bpvOj12r0kckXgLRA8YSEcvXbbhAOtpewvvNRhhJ5qMMZiGpC38qjQLeF2Vo4avLDBfwfxX/Li94jjek8ljTur2qE7XLw/20GNg/x7qc5uGmNiRbtWw1We2wlhHfGFO45wN+UIIIYQQQgghzhvaOAohhBBCCCGEWIo2jkIIIYQQQgghlqKNoxBCCCGEEEKIpZzZHAd8Auih5u5NmOPAKq43ZmaBGB84Y4VInBuYxJobnWC+SEwCssybOeA7lGQfXpCDQgcVVnuep2nUD4UchtuT98yY8YirkbCiUQuDmV30xBwgujRmrMDaZF2EgJ4trHjBvHHIas8QmKsCS3L3o4d6U0Me1hfZKeokKS5/fVoac03xRjtmaEzFTJHYAeQMb0JiZta6g4eZOU7R4v0jM3ghRg6B9HcYE8gBzCxt3fjQ4eOi67vE5Ksnh0nzPk7K4AI/kOBiR3j5McXMLGMmOuQ9CxdfFTONIaYQzHijI33ZH3Le0PLjdRkx+6Dzqh+gSV0U3oTKzCpi5DCoVjNyy1c5R+2cffwcLFiZpaYa/rWZWeENI0jM9KRp2HCcMQNAP08Q8zxmCNY22K71DPvafI7rAW/YxYd/ErvEHGc2nUPa1K1dStKHhkM0EsszzEdN9lwSMzQsiMlJTteYaIQTiOnUfJrOHZMRXldVaLSzTobDgX3gC9+bpB0d70K+3b37yev7d/Feew/wAPrZFNNij3U3LFMTmvEA6+7KFqbtXEfzmg1iojMeYD7f/fKMGOEQcxy/xl5cS9Yh7jkjMb1jYzYzxzmucbDYd/2qjtiPD4lhDln+W0fmPtav/DieEVOrnMwTp3HOhnwhhBBCCCGEEOcNbRyFEEIIIYQQQixFG0chhBBCCCGEEEs5o8YxWBb8QfWP11UxzZPXKZ7+llRYlbzKiTghz1f7Hbw/pH6R+HjNT06u297YgLRBhb+1rsghqbnTCvSkXuuaaBrIIcDzOR6Q7g9v78ihpi0RObJD35lIjunrQDNDdGJMK7ouYoyg26KyRKdVoZoR0n4d0d2xNNBKEW0B+909l1qyg75Zmj9Ul/z23/d94wfJssODM/ej+sD64qq6Rybc6R4f3zTmST02RExQ19invDyp74kGmbTdWgkRNJw9O5jcp5GDuVmfZ59EsvEfgnWFmDTjYz3TQZUkBkdOjzMiWqmSaLEKci/S1Na4WJqTtu/Z4ehsGibSWxBEk/7O5rjSn9JtZtWAHJhO9GrQLOzM9hW18U+KEDIr3YHt7AD33Nc7Gey7djV/hkjaNbrewM62Z2eLN0RvWJM0poWMffqebB1kROtFpFI2J2Pe0VF6/7Ik+rIK65rOOUTf6f0q2JxTsjmBNFNF1l6ZTSBttpHq+cYj1GiWrB7XSFHkdvXaVpI2xuWnjcauvdhz9PuQtHuXrDXrx88TORkrNyZE93gF65hpHEu2qHHrykAEe0zjyOYJ5vVhXfqeHRmLmcaRjQEFGchzN6Yw3X7PPCzIWqslfgxsne09GuiwgFV2KvrGUQghhBBCCCHEUrRxFEIIIYQQQgixFG0chRBCCCGEEEIsRRtHIYQQQgghhBBLOaM5TjRzYlJqopH5g2/JrYjmlZkhMOMRb7bAxKUdEaVyDT+mFqQco6E76HSMStKtCR4Sy4TBZYXK1ODE40xIPydGOD0xtGnnKDo/ODhMXu+TA0bbBg/8zQp8zs0NckDuAJ9pOp8mr+f1MeSZjFEkvTaCWfTCYhK8rXODaFtiZNSsdmDzrMY6b2pnjkPa+MHd+5DGjIzKEtuFmQb4NNaeA3KvghhqsEPON4vN9F6BHNBLjBDo4MGMWjr/mrhOkKScmHT1xDBnFXMcZqpz7pxDIh46zg4h974u3vDMjLdXRvJl1NDGmWAQw4ucmAEUJI1C3tMfel9VzHiDPBP5fLUJxJzKHfYcGtL2zBCkI4dJk1j1dwus/tnhz6RuC2ICVHkzM8N2ajuM8Y4ccr1OQgg2GKRz8YAc4J47NwhvemZmFjtmNEFikI0jsA56vJGgmVlP3pN6bEVs/yL3pkDEJIQMs02LbTg/PoK0qTsYfkCMcMYTnNMHxIwpY3UGBlDERI2ZidC4Jwe+k/JWLi0nYwBdkK2RPvZWu7XDcIhtPaiuJK9DxDrp5jivd8e4Vjtose978yVqQEPGzwExVWLlL0j7g3EM3YOQeCPdtmdrCT9nE7MZWCeaWUc6KR0vnQHjnKy7O7Ju6Mj4ZDmJ1ZwMFll6v3JADOVIHz0NfeMohBBCCCGEEGIp2jgKIYQQQgghhFiKNo5CCCGEEEIIIZaijaMQQgghhBBCiKWcyRwnWKBGAZ7ohMqRGNBQES0TwjIzAXf/nohGe/KeMWPmISgIHQ6wWjY3UnH99iaK7UdDvG5MRKgWiMlNcGUjovmiwPLnxLCkjnj/bpDWbY2XWSRi+5I809YWmuMMRig6L47T8o4afKatrQ0syLqIaDCTFcRYIqR1wmKtnaEoem8fzQbuP9zFfLupcdFsNoM8zDOGmTYw8wUeW2nbD4g5zniE7b69vQNp165dxfd0pgfMaqAsMdYm4wm+5w6+pzcKOTxEcX9NzGv2D/YhjZkRDYbExMkJ69uamOrMUfi+TqJFMJJgMRJcGhmGLRDTFWZA4A3VzMw6l5EZvRRE+F/kzMwM8wUy1g+d+H/EDDuIQUwkc16fYYUUs7TOyK2sI8YnLG5iy+ZZZyhUECMichUZ6q1k5nSknXzb9Q2WtZni+LRO8iy3zVE6r4xHOI5Ug3TOyojRFzMTMT9Xm1nfMecNn8ZMdUhsPb4ZzIwbSmWuz2Rk/ZGTOa0ihmDtHNu1aVKTsBnJ0xJjuOGQGG+Q8uNAQ8Ymsk5kc1og5juZYTly12t6YhRUk/l8ncRo1tRpPW8QUyLf1LMhPsdoiHNUVWEbxh4N4mpn2DgjBoAktCzP0AinzHAN2RMTsujmXeobxczxyDqtI8/Ut+kzES8mK5lnHzPpDMQcx63PmTkdK2tk8UzekyzjYS7KybBWVisaz5m+cRRCCCGEEEII8Ri0cRRCCCGEEEIIsRRtHIUQQgghhBBCLEUbRyGEEEIIIYQQSzmTOY7ZKcJkhzcYYeYLPTFwCUzVuQI9kcf2zBSE7JNzYsBAjUHGqXB3PEJznAkxkgmGz3k8RZOU43kq0m2IuDcEvP/GeAvLysoR0/I3NQqR2560CTHyyYjxREUcGLJJKoAOPdb1xgbW47qIFq3rUzFz7LDMPrQaov6e1yiKnhGB/WzK0tI6n86JIQg1cmBGC0R4TdwX2s73WbwuI0L1ARHbN8QcIbZppYUSy8CMVSpi/jQZoQlAB4YGWBfHxMSjI0Y4TY2C+Ya0px/nOtJnWdr5g5lUpGmky1uRU4ctTCNmItHdnwn/mXFIToxwmGFOSYxjRs70rCImaMywgz0SCRuLrjoCcVAgRaVzFTM+8d4qJelDgwrrpyIGcGT4sJKYprTO/CL2xPyiPV/GISEEGzjjm8EAzTi8IRhb2TCjGto2hMwbAJK1U0f6BuuPbN0ViPtS7gzG/GszbviW43LADg/ROKx2Zjis7TtSaX6sNDPqkOaNDxkZGSsiaT029/FulV7bk849JwZ16yT2EeakwnAtOHDmSCNi8jZk5jjlFNLyAuu4cWZZfU8MYkiTNsQocYbTrhUFzjGtiyXWgxpSDhaDrLw+REoy51jG1l/MMQfXQm3t1lpkicBM1CIpP+steY6plWu7ihjhFKR9T0PfOAohhBBCCCGEWIo2jkIIIYQQQgghlqKNoxBCCCGEEEKIpWjjKIQQQgghhBBiKWc2x2FGN5DHyVX7QASoXjhuZswfgQqoY3r/jOx/C+K0URBR+KDCKhgNUZDr8w2I6JyZEMxnKDI+PDyAtP2j1DBn3qDovKrQSGZIzEOGxDCnGqRlo74Whu8ZicA8RFQx594Zwsy871BeYv2X2ePj6ckRrXdmRiR0wRijJeY4DWm/ukWhNPNO6V08B8O4ykgDBqr8J4Y5KxhvRNKnWlJY9uyzOcbHxJsOsWciIvThEGN+c3MT0vIiNb8oS3R7yPJDSKuJ6Q1LY+3pjW+4Ec55im8zswADLTOWiBA3zEBnNYOASIzQzPd75qlDDEHyQIxemGEOcfNZxayEWy0wc7fHG4Awkw1m/pGTucrPoWZo7FGSuasg5jjFkBjmMHME0sStM78IzCUpO18GUCEEy51xjDfCMTPLfL2z2GUmG6QNA+kfmY8vMhbTNBIPbGwvctKGztjPvzbj5jhsGs5zNBQyZhTi6Mk4uKo5js8VyDMyG6NAx9nV2pNMfkBTkzFsncRo3TydkzpmUtWn82BJXLGGzAxygm2/sYFzalOnlTUaYZ6crFVmc2Ki8xBNeryJmplZ68Zetm+gRnXEqMaI+aMfQidk/GR0JLbqGaYd7adGS/Njsi4k5kHsmdg3fznp31WVplVsLU7Mtk5D3zgKIYQQQgghhFiKNo5CCCGEEEIIIZaijaMQQgghhBBCiKWcSeMYY4Tf2TKZiNdB9uwAaJq2qtYjLn29SCSHppOfKjON45CcZj90BwgzzURH9F7HR8eQtn+AGscjdzg5U9qUJf7uvCTlYAd5BqcNKslvnNkB7x0pSYz4W/qM/FbcF43pTjP6pOshBLPS61Qj1m/vDwwmeif2e3R2IHEkp796fU5FDq8ekIN8mXaF6azaFjWIvWsHfgA5O2QZ+15LtZz+oGAsl39uM7OSaHgnkwmkFa5vML0kO5g6RuyfvO3YwfD+mSDLyoeFPylCCJZlaZ02PWo/fV1lBcZgnhGhdEfafoVyMe18XZMToXuMwZaM6zkZj9o+zTg0cih8yeYgdpg0eU6X1pG6iD0pLBFS90QX6ufCPJDD3cnhz0zzQtNIOQo3HmZkjivJfGm2vgPTQ0A/A/8cZny88Xg/BTOmsDMLK2ljmZCXrV3IPEnai2p7K69xxBjJ2dqlIeMxyQdxQ8Y3piJk4z31y3DPzrSjtAWI0I00Hf2qxGupA/MFIPPouvGh07F5t3XPQtYgOVmLlwOsT2IbYJ0bp/oOx+z5DNeLR0c4PrR7WP77948wn2tE6ixAxt6+Rw0lmdZsY5QmXtnagDyTCa6/OlK3h+Q5799P1/97e7gGIbeiQwXTOQ9IWuX6rX9tZlZSPTHnfK1qhBBCCCGEEEKcO7RxFEIIIYQQQgixFG0chRBCCCGEEEIsRRtHIYQQQgghhBBLOZM5DoMZg/jTZNmh0BQi0mU2MZlTiVIBe7eaEUtODD+Y4NQnZcTUZHqMQt69vX1IO9wngl/3DHlJRO1EDM/MQ4ZDVPx2bXr/ihgDBGryspq5CkvLnCFPS+7VzIgBxtoIaGbCDoXu0jR28G5doxC7IWnMSMaHJDNiGpA25uY4RAxPDpdtiZgc8hDFdk3anT574w4r7jBuvaGFGTe5Kcghxv6JKmJ6xcwwAlGcM8MiZnSChg94L1b+tePKREddN+CxcZJeSeozsEO3fR0T8wxvQmVm1rK5hBjmdMQkpnBNyKauyE6TJuVgB1P7tJ6YbERWP9xO5LHX9iROW3Zgdov3qomhHKlGa1xi15Hr8LK1EkKw0vV/NoaCrwtrG/LRekbMa5iZBbJa2+fk/syopshxjCvceoCtIzIyDrK5qafmNem17F7ebMaMG+FEMi74lMCMD6lZFRtjWDlItuhfk3ai49/6CIZmJjkbR3q/VsH5uunQwKVuMa3pp5A2q9N8LblXzLFzHM2H+J415tt7gO/Z+PUX5DjFmIyYOg7HGL9XtlPzvZKYkBkZZ5mp3uEh1sf+bvpMs0NsE29macbHCmYoGsj44fc0rF91K9nYLdA3jkIIIYQQQgghlqKNoxBCCCGEEEKIpWjjKIQQQgghhBBiKdo4CiGEEEIIIYRYyls2xzEixPQic+o3QMwLqNCTGhM4Ixki0GZGGyUx1aiIeLwi1+b+mYiZQ9ug+HY2w7R5jcLd3L2nN5Y5yYVJzAyhYSY3qfCVGSYQ7xo0izHeJkwYPHVCbGbUUh+fJ3OcaH2ftk1PHCN8/c7naCwwnaKo+/joGNLYtb1r0ywjJgjELCEvSFmJ4Jk0g5kXnJNMLTEB8kZVZmYlKcfWfOTuheY+KxlYmFnXYTmaJm23ul7RsKghZgGkH7P68H2DGVUNBgNIWzcwHpNhJSvcGMvMJ8gYGIhhCzPdMnu8GVNk48xqXhlWkLTOmV6Q4tM0cKsys55UWu+m0570PTbHMdO2QI2H/M2IwQEZ19l807ePN4AzQwOohowd7F5rJQQwhcnIw3XO0YbPf+TZSEZmZuFNVqjZDKEsiTEGWUTlbG525WVGL8yQjc1DNRkHfV/LchzzQoFpzLwGzcXMgvsug10XSd9g43NFxuOmxnl5OnUGL6QPDYcjSFsnwcwGbkDwr83Mcjf2dqTOjxuMh1lH5sWI+RpnONMSg5uwfwBpBzNiyEPGqSJiG/r1lzeWNONrlWj4TBWJ1dyZdJa4VDGybbBIxoVI4jLO0+cMZFKLAcvaB1K3ZB5qDdccjetXLZl/a2IedBr6xlEIIYQQQgghxFK0cRRCCCGEEEIIsRRtHIUQQgghhBBCLEUbRyGEEEIIIYQQSzmzOY7X9q8i92YGK11LTDuImYA3pTEz894bzESBeBBYIKXNiElPTkw6/O26jpg7EKMaJvgNJJ/16Xu2xHAgEqOW42MUGWeBCYpT4WtNDHQsYLmoOQ4zAiAi3caZkezu7UOeh/d3sRxrIhozNMB83uSHmSLNifh7vqI5iwVn7EDagBnhFMT8yRtJnXa/ELypAsYti/maFH82w3LULiMzaOg6vK4jwvHpFOvx2Bkc7DNB/gGaEx0f473Ye5ZEIe+Nb0YjNFCoKuyLayWYWZbGb55jW2RF2l5ZT4yLmHEFNcehbkz+SsxB+h4zl2HjLhtjfXG53w8Zm5k5DjPW8cYe7HNZ5iOTsQfFN8idQUpGjE96Mq43zOBrRZMeb2ASO3JdZEZu6yMEs9yZ4RD/POLrxwxcSDyTtIwY6gXXrtRCiLRhJOsgZtLDzHF8LDETDGYkM5+haUxLxnvf//y8YcYNeZgRzkqwBSZbupC1C6txb75jZpZlafwys8VzZv9keRZsMkznpAGZ/2tn2DIlZo3HZD49PMZ4mBPDGR9vFVuDkIH2+BDXR+x7rJBjY5e5ay8SDy0x/BkM8P4b4yGkPXP7VvL66s4G5GlqLP/+w4eQdvfOHqT1zkBoVOC6ocuwTebEFDCQccfIc7ZuvdiSfdVxjeuj09A3jkIIIYQQQgghlqKNoxBCCCGEEEKIpWjjKIQQQgghhBBiKdo4CiGEEEIIIYRYypnNcTxUlOyEl3ElCx1OT8Xjq0iVielNzgxFmFga7+bTMmIGw8pKjRsMDTOyLG2KPCd5AjZXU6PQ/aBDYxAviJ8RQXTbECMAZgxBzBAiCaXOpU1rVDFPZ28+Nt5uggUrXL2zSGuy/rF5qNECMSXoiQmGj8myxLodVGjWUlTE1ImIoJnhQ9enYvK2e7yBjpmZxdXSujZ9zrbFclFDKNKn5jMUvh85sf3hAYr7mZGUN9UxMysLrNvhAEX0w0Eqah+NxpCnKs+ZOY5Fy4KL34Ci+yxL6zgLGDMlMezoiAkGM2jyH1nGjJlnkHgguViMsDknevMaEuORmE4ZNQNjsZq7PKuZ40TSRzNSfn9pIP5szFSnbUhdsHKwwrmxghmyVNlbXka87WRFdK+JUYqPX2LixMbKSNIqEoPQXqScgV1HgpwNveyZIOTInFPPibnYHI0xOlI2MCbJmZHgauM4DUK3nmSXsQqiJj3kUr5Gc4Y/ZG3H1nHrJJpZ58bxQ9KGR3U6tu8Sg7iDQ2La1xGjLFLv3kiIjQSRrPsiMVtjbcjiPndzUSSmg6HDtMFgAmnbWzhnjwbpU0RitHN4cAhpu/to/nhATIZim96/IvsSau7GTICI8VBDMpZuDgtkjTMYDiDtNM5XbxBCCCGEEEIIce7QxlEIIYQQQgghxFK0cRRCCCGEEEIIsRRtHIUQQgghhBBCLOVMqvYQgmVOHM+9MZwQ01YzBYlE6FkQQS7xZgGY0NaXfZGGN2P5vNSaib2Z+H1QoTlG1xGBdpE2RVYQQwZy2ZSY3LCytU2dvN4/RHHvdIYmGSX5bKEjwmlj9ZinAty+x3u13SpGR08OL+wPzGTDPT5rF2YaVRTY3SpSlXmZXssMCNoW24q6KpDyZ8TUxJteZMz0gDwTe/a2xf5eFKnwOidGT3MSf3WNaUeHKDg/OkpNbli/6EiV5QFF4sy8yiKWN3PXlgUa6JQl3n/dZC6AmRFL7pIyYhySB1JPGekvZDiNLl8kMcncLdh8c4rjDMnlCsLimRhjsDR6fz/v5RgzbN7rDdOYaUp0pges7zEDBWbSw+qxJ/n8NMqMTwIZ+9dKMPxInBhQ+DromaEZM3ZirLAeYKsKaphD0vjahRnCpOXoOzT2mM+IcdgRmqaw/ldU6TjO5jQKCbi4ghnKKnnMTqkL0iY9WWP6pEgXtefHxM9sEatHfbqm29vHtt5zc+X9XWKOs49pbYOTJVvHgwkVMY7qSX0WLRmTWB/NiGGj+ffEsg4qvNdkE81fRptojuN74JysJZj53sExlmNG6sMb34QV8piZxY6YY5Zkvmrw2tKvKYk5zsZoE9JOQ984CiGEEEIIIYRYijaOQgghhBBCCCGWoo2jEEIIIYQQQoilvImTe50GjGkAvC6C6UGYLozpBFY5Q5n9bp2JPQjZir+hj06HwvSYHTlsNyeHI49GRD/mDnnvyW/2mW5z7rSLZmaRaChns6Pk9eHhEeRhGschUVw0NTlUuMbf10/d4eqzOSkrOVx7nXgNBNNEgEaVxDLVERI9SM7koj7oSb9oSLv3RIfGdB70kHZ4CKYZIQe+s7KxfuC0fnmBeoOmxmeq5xhXx8eYbzqt3WuM5WOS5nVjZmaDAcYkG078Y7bkQHYql14n0cxL6oiMxNwZy0zlx2TNfMAmSZ1LY4dLc1iFMm0e0yqm8cviGXSQZpYRvQl7THOHYVPtX7+aFotpPv1Uy7Rw9Hxz1pfZtSserI7veb70X4vZ2o3jTA8K+k3SDkz/y/S4vBhvTx4zy0nAMd1jG9MxrmlxrJzXqM+az1DHVVU4RpduDiuYFp71UbqQI/Xok+i4sNoY0BPtGPNn6Jwmn63j2hXXk0+K3qLN3Xy/f4BrunsP07SDvRnkOSZ6PVbvTOMYCr8WWq2d6Vqf9gXi19H6Z8AY39jC2N25MoG04QTz1U4X3Bxine3vE5+FOa4v5iQuC6flZGtAvj/C9WNoif8FGRfmjfMUIH4jgww9Gk7jvC1rhBBCCCGEEEKcM7RxFEIIIYQQQgixFG0chRBCCCGEEEIsRRtHIYQQQgghhBBLeRPmOJ7HC1+ZlDwngtlInSQef7AuO6R3ZcH+CuJ/ej8mliaHXw9GeNDmgJgmZFXaFOzw6Ixc1xBB7myKwt167g+/Js9ND2DFbB0VLJODhl3ZeiLIrYhJyrqIMYJ4nonpoxPdMwMFelA2PaicJDknhy4wQT/WNytHT/pBSw6FbjtvEPD4g5LNTjOXwjg9PEwPGWaGI8yUwBssmZnN5mjkMHPmOPMZCuZ9HjN8bjNumpJleNi9z9e22E45MY9YJ8HMCtcPyxafrXSi+4y0aU7G60gMFNjnk2BUQ0xjArHkYcYbgTrVrHDc+irmHGZm2YruBX6IZT4RzPSA1C0zpfHmLcygjY0nzIiCTbU5SQNTOFI/Da209RGjWe3m546tEbxLFLcMgpSMVBSNQThYnjn0kPuzuYMajODtvNHLnBiOzYlJHVtDlWTsKt26JCfrlMDMg0gamztgDmNGbqSqezJWsNakxjduLmVrmbpl49r6CGZWuJhjvbB365d2js/RNyQGS2I2RE223FqF7CiiN9AxbkrHxuOeXNvV6fw/yPGZNre2II0Z5gzHuD7vjtL2PyRGOA93Me1winEzI2vqKk+fKSfuOAUzZCNpORkEaha/bj00J2aWbC10GvrGUQghhBBCCCHEUrRxFEIIIYQQQgixFG0chRBCCCGEEEIsRRtHIYQQQgghhBBLOZM5TrRo0YmomWjbq5IzIuAMAd+6jyjOZELr4O5H7SdIsYj/AjUUYdcG5ybQESOcqkKhbcjReCIrSIldPeYV1s9gOII0JmyeHaFwN3OfETCtdzw6wvtjNpvN0LCEmYDUtTNWIM4KJamzdRFjtNoZB3Qt1m9TOyMZYorSM+MK7oQDSa0TtBNvHMtr0i9a/ByoZfcn4mlvAsTK3/XEkIcYe7AOtLe7n7yeHWMMMXF/TcyfpsT4ZjpLBfPMAKJpsPw9EenPyP37Dks3m6Vl8wZAZit7bz0xQgyWeeObmojuazduUQMXYozBxnD2+WRM70d7C/WkWc3AqyfmTt7ZhXl4BNLZIjMmIeN/Btey0ZO9JzEPIg/VZ2ndsrKWxECNGShkpPyBlLd1hi5sDJgzo4s10sdo8ybtm8wQzBuH9Dkx8WBGWSzuyejlxxZmPsHMWjLiXMTWWexaP8Y1zASDzFcsbooCn7Mo0vmaGfaxsrL1ATML6r3xHO2jmNaTumDmQd6Uy8zMDxUtGTtYf1wnmQUbZmn7bI8mkK/ecDF4TIz2cIqF/mNm1lHDLmdyxhqH7DJ6upZgYxLpH84wZ3ML18VXb2xDWjXAGOwjluPwOF0H7x7ieno6J2NlT9ZCzGTIPWdjWIac7RtYZyB9KCeGjn1M27MmBoPezHIZ56s3CCGEEEIIIYQ4d2jjKIQQQgghhBBiKdo4CiGEEEIIIYRYijaOQgghhBBCCCGWciZznAVOjLmCLj5QYwXcsxJNOBV/BrBSYAY6q+2JqUCbWDV4XSrzvChKFLQyA4OGCIN75wRBtLFWMEOGihhbxCG+Z72RvJ5OURx7PEVTkKM5mpjsRzTRmc7wmWonzK+nKL4t8/PjHhL7aLWrg44I5f1zMbMZbyK1SGOGM8y8xpk2EMORnojGmZEUM2Rgab5szAyAmTHkpJtR04Y2bXtqTEI+x2JGDl1Dnql9fJswQT7zMGqIc1Tssb/496AmJysapDwxopm1ztCgJqL+Jh2AIjEEiazx2ZSSYR0EZ46DY7pZz8wASIx4469F4cilrhjMaCeQPsrmoIy0ax6ceQ177p4Z4RBjFVYM12nygoz9xIxhQDobm0uMjCmtM4Wa0r53vmI8WoTxho1J3ryGGpqxR2NxQxYv0d2PGTaxtqef57PxmIxn3hynnZMxj7RhVuCCoyCLkFXXVXB/Mlaweah3fY2ZDvH+QkxCWNUy8xaXxtqSGhqukSwEGzlznAF6xFh0MV7Psc7nJC5nB2Te7XBsz13fD2T8iWQctIrcn4w/sce0rY00LreJEc7GzgakdWQtPp2i8c3ewUHyen+GeZiNTFENIC0rWfw+3rirJWNAVeC9BmxvRdY+rRsrGjIu1MQ88DT0jaMQQgghhBBCiKVo4yiEEEIIIYQQYinaOAohhBBCCCGEWIo2jkIIIYQQQgghlnI2c5yIJhoZEUuj6BxFnXlOTG+Ibpl4h6A2neiWA7kZS+uI+LYl5hilE0znGXnTDgs7b1BGu7+3j+/Zp/kmmxPIUxCh+Gg4hrSKCNG3N1Kx8OyYmOMcoznO8RzTZnN8ppqohXsn6I8k3Orm/BgrxBitdsYBkbRp79KYnwYz7MiZ6J7EpO9jLB6Zp0IMWBBmEsOMInznyzJsK+YdUVCzAfbsaX/JWP9hfh3kmdhz+mJkpK4LanCw6mdnpBzeOYMZmhDB/3oJYEyT9UTU78T0PTHH6Tuszz4Qo6gV/K98mU67MJC4YeZrOTGE8f2PFosFOWtDMm9kLl9B4pQ5wLUtdfF4fFJBDEFKrIsBM8wh1c36/MzNXy3zUarPWYzHCPO6N6pZpC1/fdp1HenobGTp3LXMCI3NftQjkKQxM5+ude9JjE+M9LU8J/2KGf64mI6sZKTvMeM24lWCz0S6QUbWjqQ7WsHMo8h85U10mEljTo3A1kcIwaoy7a/ZAJ8tG6btOiMxuE/67yExTylmxGTLVXzZsXbG+3c5MQ9khmOkY125vpW83rqO5jiBGMkwg6mDA1yLP9w/TF4f1mQNRXp8oPHGzDcd1NQQ62JQoOllSeY+tpGau/sxgyw/Xi3jfPUGIYQQQgghhBDnDm0chRBCCCGEEEIsRRtHIYQQQgghhBBLOZPGMQSzDH6zyw61XeG3sky/Qn6bm7EfwrsfvtPryO+Gme6JadGo1tIfTEsPC8ay1jPUEh4dHkHabD5LXjc1agtLou+piPZoNER9ZDZI821M8LTY8SGm7R/PIK0mv38PhocFow6UHKTNfou+NgLEQ7dCHBWkF5XkgNtBiWlNhXXif34eItONMb0u036QNJLP61mYbqwgJyoXJerjyrKCtMlGGpOjAcZLS7QEkQiPSNeA/p6TsjKNKdPxFKRBmQbIww6mjkRXsX5cmdih3i6NxRvVn5OD5SM79D7zWim8GR2bSX1mRG/CtMO5y5exOYjEWyDzWUbzpWlscu3Jc0biAUAPIXf1UZJnHJT4rsMK7zUkB1MHMtf2Lq0i5Z+Rg+jXSYxmvdN2M/1i5tYR/rWZGXsy7+FgZtaTuO9cXXVUm03agejp2Jg9Jwd2z9zc3BLvgYxEZmAaR1IOH5eRjB1sHupojJA+76uDSo5JY7akTchYkZMblm6u8K9PS1snMZq1rp43J2QN5rSEkzmZmw9xDt87wHVrNyfa3iZNK5ifCYn7hsU90QhuTLBsm9vpWqKaoPaPCVWPDnDdvbt/AGn702laVjY+k+VAQfX3pM/78YOaAGAfjT3znSDzbcPi3sU4mTPPouM9j6saIYQQQgghhBDnCG0chRBCCCGEEEIsRRtHIYQQQgghhBBL0cZRCCGEEEIIIcRSzmSOE40dvs0OgHXGB0SZzg7x7lc59NjQoIeZ2TChOz1mmb0leSRvhtNHcuIsEbk25AD22QwNZ46mqRi5Ifrv0QhdQSYjLMdwiGmFM0gYDlFIPagwHIg3jLXk8NCsR7Ft6U6ZZkY4A3Jg9boIIVheunph4mMX80NyoDIT5rckrWGGDE6M3QfmBsMcnLBdenpQORG5F+kzVBUzO8LyVxWK7QfE+Ob69avJ640xCtpnTpRuZlburyY4zzI/JmAH8gcmm3FTiPF4TK7F54RSUFOIc3Y4uuFh3JGYEvg05ncWMzauk3x0DPdmTJiH9aFAjXCIwRH5TDR3jgZo3mVG/QHo/EKMPVz7gwmCmVnLDHmIARcxZChcrA6IQ8OATOklGSty0qDMIyn3plMkT8EmzHUSo7W1c4VpsR/mrt4LMr71pB0imfs7kta6GGE+Wb5NzcxyYs7VzNDl5oiMl4eHaVrdsLEex7KCjAFsDijdeB9IH2rZmNeuZvaX5+mzR2LuFtniiNR/w96zJOulQdrGw5LUxbky8VvMPZ0rEjMmC3malmOTcnO/AtuGrVXQyY+MuyTuKzJ2+fWimdnmJs7FY7d2YMPsse//ZrZ7iIY/B3OMkdoZOZEhwEj1wLxqBluhk7S0wMzwjZl5eTMkM7OWGfKR7pe7dilZ/TPHn1PQN45CCCGEEEIIIZaijaMQQgghhBBCiKVo4yiEEEIIIYQQYinaOAohhBBCCCGEWMqZzHEWpGLMwFwNVoDJ6dmdIhHx906MH+mV5P7M4IGIr70RjplZzN1zk/dsGhTfTmdHkDZrULg7d4JvZr5zeIz33yRGOxubaDySO4VyNSBGNUNiMkGU07mhoJiZZwSXSLwHrDxn5jhFNUjTWlQpd23afl1EA6SmxTauSRozEvAi66IgRjUkrSOGHYGI1dsG28/3Y29AtUjDmKlI+42JOc7AGdNURF0eyHVhY4LvSYwc2u1URF8TU6rITEhyjG9mhJNlmM+bJDGVfseU+2skBrPOVx8xTPBpkTwHM/tgpmfMzMiHZc/MbIjpFItnL/w340ZOPq0gZgA5M/chs1VBps7cG/ewvk3uxcxrWP8bunxD8twVifGc+biR8boj1/qpkI/zmLZOokXr3ZjMTKo6Z5iTE9OVnrhUdCRWY4/jTeeuZdXExmzriWFHh3NHQ94T1kKBLPFI3A+HOOZVFa4jMmJoQwqxUlpk6ywXYDTeyDzExgBm9lTmeMPKjwtk7GDrvXUSLYLx4hxDBObskjjVbIywnbfGxOzpEOOydQZpbD3dEXeZjKwrmVHUxniE+dyao55jPzgklXEwJQZTLYlBtzYpiMONN+MzM4sR78/2L2Xw8xDWDxuLjYwVdHVByjZw9T0gcTBghounoG8chRBCCCGEEEIsRRtHIYQQQgghhBBL0cZRCCGEEEIIIcRStHEUQgghhBBCCLGUM5njBDMDHWcghhzeI4CJoIlammqqqWPOqUX85SzMVKdFwW8kJhp9g9XixbxM0Dol5idzYoTDxMJlkYqAMyIU7ojJQdNgYkOE9LkztwgZPndBTEeYiQn7tMEbxpiZxdIZClXEfAc1+WsjZJkNh6nJynyO5kNHx9Pk9e7+PuS5/2AP0vYPjyGtJmLy3BmxFKSSRsQ0hvlUzYmJzmxKyuGMnZqatCcxFsiIecSQmNz0LnjrZgp5mHHIeDyAtMmEmNe4h49U6I3x1zPDHFKRzJABxhgy5nSrDFZPmD53hgYVGRddGh07yb2j4bjCjF58feasvXpsr4wYexTE4Kgg41bh+xUx+shZO5Mn7Um+MqRxWTIDBdZJmbkPidXSJZUd9r2c1Jl1LHYxW0sMkBo3PrXMQIbMQeskWrTWrTk6YoLhH5e1DTNFoWMBLYk3EiQ5SGJP2qHu0Bhv3hITHbee6cEJi68tiowYsJG+ljlHLLbOYjaHpCtQ8xqfmBG3qoyZOJE1Jms7Zu6T56V7TdZ/dCG6PkKMFtzcS5Z9Ft3zsucYkYYYFZivIC5bsyx90ykO/5aTGMw7nNcHBaaNxmNI8+P4EVnPHJOCHEyxvxyRdU7pDYUKZqBJ1kd0n0PWvObmCeJwU5B5lK0lOlI2ZvCWO3PCETPHwctORd84CiGEEEIIIYRYijaOQgghhBBCCCGWoo2jEEIIIYQQQoilaOMohBBCCCGEEGIpZzLHoTDF95skGlGJMvF18FlIHiIwjySNCdHZ/bzQOhAFakeMQhrmaEPEyGX1eHOckGFzMc12S4xq2jwV23akXBkxOmImQDkpf08MMMy1J/uUoqDXrYcQgmVOGJ+ROvdpNA8R2OcFMfEgMQ/3J7HgDXTMzIzUZc6uZWXz8UBjgbwlM7nqMba8Yc5kTFyRIl4XVnTHCu7Z2WWRmCWw6GPmOGycg2GCDUPnJ7zNzCyGaF3mjG8KMi66NDZ2dsScJQQ23hGjBTcYZESsbxkxFgikDxHDi5KkeRMd9p4+jhawPkTKVqbPPiD1w0xCaGyxanSJxFfLGmJew8xKAunMHalvPyz0xGgnI/WzTmLEebfpiNmEj2liBhPIWMmGB2as441dVjXaaYlhX0Pm9K7HfN7IKZA2ZeN/QUzUWL/K3DP0bNBb0VyMprl+FSIZF8j6LJLOwNaT7H4+fnMyxoSA9bNeAhjdRLL+hPmHTIzM/Msy5thC5gBXLT2J8YKMGYPRCNI2iOHfxnAIaW2Tmtwc7qE54cExDqDTOZrjRGaO52LQG/uZGRgTmZn1ZNAuiOFPdOvnjvR3uoAha7JVjdvMmWGytT5r8tPQN45CCCGEEEIIIZaijaMQQgghhBBCiKVo4yiEEEIIIYQQYilvXeNI8D/PZb+z9wfJLi4kv6EmacH9xr3v8bf4TFrYkt/Bs3x9T34H7ovhRTrGNTNMC8PqI/o0oi8wok1gekn282iv8WAaR/pbbvKbdaab68lvpr1eiEhIrGTCuTUS3Gcpgdavb6vV9BtUa8ROQQ6+DOQ69ht7cn/22ZB/RjNysDo7lJwdLEv0IEwDW7rYZYe2U70BvqVRMaGLP3qwNr0XuTurW6Lb9O/A9AZMA7pu/PAWifCud1rFSLR/VJNOoDJ1f9A3GRxKoglmerqCaYfJGBsyrwnCcvVUA4SwZy9c0QYDLFdJ5pa6JfMeq28nWGpInEaie+t61Gd5rZqZWYhM0+YOq2ZjXXm+xnCL0Vp3Gvq8QX2TnxN7EluBzcPkcdkB9P7w74zN++xQb6KNbRriW0C0UV7HnVN9MdMOk7ZfRV/O/CCoRHy1+dDXB+tnzIOCKnuZFpJp8h/z2uwUDfYaiRatdeu1kml0XRXXzIeDpNWR6GxZ3Lu+PyhxrKnIGmFjCzWON65fgbSSzE37+4fJ69nRFPIcH7BxEJKojtcHANsPsHUxrAuNa599PYYSyxA77O8sje0l2Nqtt7Q+moj3mnc4Rp7G+eoNQgghhBBCCCHOHdo4CiGEEEIIIYRYijaOQgghhBBCCCGWoo2jEEIIIYQQQoilnMkcJxoXDgNOKL6qdJ4e0E1NQHwpmNEOSSLi2I4IqJk43fsXFMTcJy/xUPOqwrQ8J2YIznyjI2L7PqLwmBpyMBF+9OY4RBBdE0E0EeX3THTOnCZcUjA0GMmL82MeEqOZPxOaHXhtvu1JLPCnYvVGTIVc25Oz1202QyEz62dtg23as5PDvZEDMefIiYibGpOQfuBLx56JGS2wA6bpAdyuHrkRxWqwWKaHJLt8zNyHdYt1Es2sc1YSOTEI8OJ/GqfMgIi0K2vDrE2vLYmpQpHj4clZxvIRYw9iqtD6MXaGfSMYMRwhRhE9GcsyZxwzqrCsXUfmKmJo05C4aVw5ZsywiTRASdKqnhhYkb7sTZGqCq8b5nhIt9ldkvZkiBatc888r+eQb16nc9ukw3jLveOR8YO+2XzKTNPgXmQsnpM5dzbD8h/PZliO2vUrUoSCzBT1DJ/dSKxClyfzBPFCsZzEFlu3+bF35eGTOLcFUn429fk1IF3LnDNCMMuG7pmH+LytS5q1GFsHNa4lDmuM8VlHDqp3RRhU2A4bFbb91ib2qwEJ1nmD/Wp3mpZt3uF6oyHXBWJyUxKTSzAYC2zdjQSylo3kPVtv5Ef6AVtr5S3eixlVMvMr/5x9TubHMxj56RtHIYQQQgghhBBL0cZRCCGEEEIIIcRStHEUQgghhBBCCLEUbRyFEEIIIYQQQizlTOY4ZmadE1DmREjsTRNWFRvzfET0HLw5CRGgElFqQ5TRTERbtygMrtr02g41qTYejCFtY2ML0mY1lu3YCX6ZA0hJjAmKAQqDiwLTwPSFmF2w526IIJqZk3CRblpnpW834+YqayNGa50xgTctMkORdRHYsxNxM0lr7PFGSdZghU+JgQL0CzOL1GTp8X2Wmt6QNGb+NCBpK40J1IDl8UY+DG5wsxo5EcyXpO18cbk5znkzWogW3WDQeucvM+vduNiRsaHI0TSAu5JhUt+n9dmQGI8dMdMi426br2YS401uQkauC8QAgpjjhID3914oRcbKQAxoiOmBEfOa1uVrmR0W6S8dqce2xnwVq2/3UCv4vZwPXL+bz9FcZuYMc7qI8zcb2ztiZsRMobzBFDOJqltiTHJ8DGn7+/sk7RDSGveYpaHpzbTEtK7F+ikLbOzMmdAwE4/BiBjtZFi37Fof0syAKyPzHBuzmYkRW/f4+TCyIGfOimsk5JmVW2k9x4L089YZahGzvL3DKaTtH2DafIrXjtyadECmhCubOOZd28E1QtsfQdq9Q4zLB8epKdT+HNdCZHlkhZF1GnNy8sZwhjeLkawHyBzJ1spdcCZtZNzdLLAPsbVWRfpQRSbJwSB3r7GhMtYfT0HfOAohhBBCCCGEWIo2jkIIIYQQQgghlqKNoxBCCCGEEEKIpWjjKIQQQgghhBBiKW/ZmSSsYLRBzTiIUJzp7pk3RvAmFJEJo1HM3BDx6myGwteCiEuzLH2PjJR/YwMF4OMxpk2ImLe3VPAbiap2MMDmKomgNZKa7FwdzYkp0LwmImNinMEE60WOZQvOHCIQgxFm6LIuoqGZDIvJvEifg4mWh0MUN9cNGiH0xFWhrdM0b+qxSGMdYwUjKTPLWfs5c4GqxPYclMQIZ4DPORgMyf3TOiPhQvsx7f8ZM5xJ789ilBmaUDuuFevR+2ZEYsjF2nedBAuWu6hmkng/rrNxPiMmEsxDoiPXdq5eGmLaQPxhLGuJUQ3JF5jRkvnxiOXBOcICpmUklgpfH9RQCe9Vz5mBFXaQrnOmRqSsPTHbynIyF5JGn5N6LPM0kfilWE478/qIMVrtjG+OjtB4Y3qcGoC07SbkGRDDq54YjkUWrC4wad9g5nw1zhOz2QzTiOGP97hpWd8jfa1r0AylzMk84dYb1RBNNkb9CNKyAd5rmOE8kbl5IpAxJiNByKbDtiFmJaR/xODHBbxXXp6fdYqZWbTeYkgbuybx4E0XD/aJ8dJDTDvcZcZLeP+tYdrWkyHW05UruEa4dh3XEneJ2dPd/buQtjdN+8K0Wc0ILydGVyMS45n/Po0Y4fRkYUKmf1qOzo0fbE7oibtPT8qaE5ObITHMHI/SZxiRvcSokjmOEEIIIYQQQoi3CW0chRBCCCGEEEIsRRtHIYQQQgghhBBL0cZRCCGEEEIIIcRSzqhqjxa92QMzAABFKBMzM9sRJBBhauYE+1lAgWhgBjFEnE4Nc4hJTOnMQ4h/hpUFloMZ7UxGKBYGIT0xABmOsbkyIphtyIM28/Q5p1N87ukxiuZrZlpB2qQk9YEmI8xUAK9bF8GwzDmJ78EgfY5xh+3ZNGgQ0LbMHAfbYR7T+GPmIiz+mFlJRs2HMM2b1wwqjLWKpPl+YWZWECMB3w0y9gDMlIbb15BLHz+eeIMrMy5ehzHOzFpiyoLXnX9zHDOz3DUGM7YCwydiXETrjqS2HdZd5u5Hr6N1zt4VCcS8wA+VzAgnZCSN3IuZ4zQwdjADOJwjYo/1z2Kp6J1xSCRGPmS8YjFIp1+S2LrxiRmcZcQsZp30fW/TWWqGc3CwD/kODg+S17PZNuQZDPF5W7Jm6End+TEukPGHjcXMxCMna4uKmJX5KwvDuaki9+rmaL5De19wqWR9ULZY/o7UWUfi168LMzLWMwMoFuMd6d8FGz98/yYeIZG5Qq2TGC26up8fo3nN0WG65jjaRROk+RExYukwLgcDXNNsbG4krzevYkxuXd+AtMkWxuVeiwZWRcHW4mlaj29J9xcVGdvLghifRW9eg/fvmW0i6bcsnx8vezIGRGJKGck8VGTEnIqYE25M0kqajDHPkBjtnIa+cRRCCCGEEEIIsRRtHIUQQgghhBBCLEUbRyGEEEIIIYQQS9HGUQghhBBCCCHEUs5kjhOjWfTCUaIczbxIlIhLqTaf5Is0pzeqIftfksY0/E1Dyp+hIDd3QveeGNAMShSXDgbEHGcygbRq5ES6xPSmLLG52LNPj9GEZT5Ln+n4AJ+xnjOxOqsfLEdOhP8+DJjRSX+ejBWCmfcJYeL86Op8OMD6qMeo2J42mDZvsB06H1vEJKQsiDECMWJipjEszbdfScwAWFpFXJEKMqpEZ1TQEfOPQAYKFjPMrMR6F3/EKGhVoxo/xp2kYoq7HbuM32t9BAuWu/gtiOFJ4RqReDhZTsYjZjbGjD081LiI1R0bL1g8sLjxr1f2uyBtT671aT0xSwjEeYMZzuTEjCh3MR0Nx4BITHv6HMvfZcR8gZlr+XGBmrmcr8+fu66zg8PUDGd37yHk29/bS14fHV2FPIMhxjgzdWHGGH4MyplRBqlPZl4zGozxWmJSF7v0PYqMGejge85I3Bg1j+rda7wqBmKEQwyyaD26skXS3yNx7WExWDe4DmL9Y945Mzo2D5HmXSexN2udweHsAJ/3aDc1PZqSPEaMcMYjXKOON4jpyk5qmDPZJgaRxAyyJeNnQcz3Nsj6uR2mFx/3GBBoE2QWIjGAYuOgC0tmSsPnJramYeVwBlArGhhmOTPIwrodjXGsmEzSthuPmDnO6tvB8zXiCyGEEEIIIYQ4d2jjKIQQQgghhBBiKdo4CiGEEEIIIYRYypk0jmYGcg92ULH/bf+qMh/2u+FVNFpMh8c0VA0R6mTsQNiaiTLdoZ3kXmEf77VJfhdekd8Se41ZyIlGkxwKOp+TQ1+P8Lfc82n6O/C9Azxs9WiG92o7cuhoTnQ07Hfgro76FvPM68cfrP6kyLLMRu73+F3HDgR3elei1diI2O4F0a1ubODv0RtXJy0tA9GUsjRIMao59gewZ0TvVBEN73BAdFakPnydFQU7ZXk13SPVr/kD5dn5vCuK2lbN5+uMHTp8HvEai4K0del0d+SMesuIppYdhN6zo8T94ehMd8s0XG9B4+hFqbSdqd6EaLFYW3udJhFGRa/FtVOeicyF/mBq/9rMjEgjqY6caYd5LaZt1xE9FNNyrpM+9jadpvPb4eEB5Ds8OExeHx0eQ57RGDVEecl0qiR+nd4wUH0odqwyx7ljMsJD1AdE5+41tHkg+mIypFZEvx571N9Hd4g6Kb4VZJ7wY6UZj8vc9VHmJdGRNUkg7zmvyWH3TA83T3V/bY86wEDWPOuk7Xq7cz+N1/19jN/dh+la8OEurg3rGhsxI/r1whtAmIGur2swjo4OSJ0TrWWN4WYZWUdF1z6ReBd0Hd6sIWN2a8Rjwu9xyLo10DGb6JWZVt3PMSS0ciNaUaZxrMj+omRp6ViRk3txrSVH3zgKIYQQQgghhFiKNo5CCCGEEEIIIZaijaMQQgghhBBCiKVo4yiEEEIIIYQQYilnMscJxoxpMJ8XPWfkgGN2ITvAnOIE1B0zCSCC2UjEt2BoYGZEj221E+TO5yi07SIK6esaxcjMZCSr3LMT74WWmOO0DaYdHZKDTt2185oYPmREJE1MTDpWOFLfeenNIjDPjJVjTcQYre2c8JoYL/n4I543VpF6iwNs95AR04AqzdcyITb5zCcQkxDmG8L6Ru8U2pEIpasC42M0RIOGQYVp3mwnsIGDPSY1qiFGDvQA6zcLKwgzzHpsAo35dRIsWOEcLUpiejBwYxQT5h+zA7aJMVJkB2r7+CIfYbIYYaZNzBSKGa1F84Y8zDSGtCExVeDP5O/PDA5wjmAmQ6HD98xcvw3MtCegEUVB5rg+X9EcxxvyMFMdZpC3RoJFMP/piVlGXadmcDUxmvNGZWZmGTMJYTHik0g7MPOaqsAYsSGWvy+YwUVatiJnBjpYjobMTdFI/3bxFckB6hmZJ3Ji5JPRdYQzHKOHr+N7NnMsa9ti2/VkzJrN0vXSvGEuLedrHK+bzl56dS9J29/bg3yH+6lB0GxG4jSScYrE8/ERGiqWhRuDyHp3lyy8Y0PahgxARzW21/EszXg0xXvVZK3MTM4CGbsyZ/aVkf7SkzVUxsYAtsxxMR3JhiMnY0zO9lGEjtyvdWkZMe0pyPr/NPSNoxBCCCGEEEKIpWjjKIQQQgghhBBiKdo4CiGEEEIIIYRYijaOQgghhBBCCCGWEpiJwKmZQ7hrZi9+7oojhJmZPRdjvPGk31TxLZ4Qa4lvM8W4eGIoxsVlRzEuLjs0xs+0cRRCCCGEEEII8fmHfqoqhBBCCCGEEGIp2jgKIYQQQgghhFiKNo5CCCGEEEIIIZaijaMQQgghhBBCiKVo4yiEEEIIIYQQYinaOAohhBBCCCGEWIo2jkIIIYQQQgghlqKNoxBCCCGEEEKIpWjjKIQQQgghhBBiKdo4CiGEEEIIIYRYijaOQgghhBBCCCGWoo2jEEIIIYQQQoilaOMohBBCCCGEEGIp2jgKIYQQQgghhFiKNo5CCCGEEEIIIZaijaMQQgghhBBCiKVo4yiEEEIIIYQQYinaOAohhBBCCCGEWIo2jkIIIYQQQgghlqKNoxBCCCGEEEKIpWjjKIQQQgghhBBiKdo4CiGEEEIIIYRYijaOQgghhBBCCCGWoo2jEEIIIYQQQoilaOMohBBCCCGEEGIp2jgKIYQQQgghhFiKNo5CCCGEEEIIIZaijaMQQgghhBBCiKV8Xm0cQwgfDSHEt+E+HwkhxBDCh996qYR4+1CMi8uM4ltcdhTj4rKjGL/YXLiN40mQPPpvHkK4G0L4dyGEHwghfGMIIV9j2T76Jq973L/f+zkosjiHXMYYf+T66yGE7w0hfCyEMA0h7IYQfjKE8F+/jcUU55jLGN8aw8WjXNIY/6IQwp8IIfz9EMJnHnm24jHXXQ0h/JkQwgsn9fBKCOGvhhCefdMPIdbOJY3xj5DnevTfN3wOinvhCDG+5U3/E+WRTyn+xMnf3Mx2zOxLzOxrzawys58ws98dY/xFd+07zWwcY/zYWyzDdTO7bmafiTEeu7L98xjjh894v4+c8r82zOyPmFlrZu+IMb72pgosLhSXMcZPrv2gmf0TM7tmZj9qZj9tZkMze4+Z/coY47vfSpnFxeAyxrfGcPEolzTGv93M/rSZdWb2CTN7ly3G7zLG2J5yzTUz+1/M7AvM7J+a2b8xsy80s28ys9fN7GtijJ8+SznE+eCSxvhHzOw7zexvmNkLJMvfjjF+8s2W97JwYTeOMcZA/t8tM/vzZvY7zewlM/tVMcbXn3DZ3tSi+pT7fZuZ/SUz+6EY4+94O+4pzj+XMcZDCFfM7GdtsZD+hhjjv3T/v4wxNm9bQcW55TLG95L7aQz/POQyxngI4QO22Bj8TIxxGkJ4wcyes+Ubx79sZn/QzP50jPE/fST9PzazP2tm/yTGqG9xLiCXNMY/YouN46+LMX707S/Z5eDC/VR1GTHGO2b2LWb2UTN7h5n90Uf/fzjld9UhhMHJV9SfPvm6/fkQwnedpMNX3sH9rjqE8Aceue/Xu6+2P/IWHukPnvz9y2/hHuIScYFj/A+b2TNm9sf8pvHkubRpFBc5vk9DY7hIuKgxHmP8eIzxX8UYp6s8ZwhhYma/18yObLEYf5S/YItvdH5TCOE9q9xPXBwuaoyL1Vj62/SLSIyxDyF8l5l92My+NYTwh+OSr1VDCMHM/p6Z/RZb/PziL5hZaWZ/wBZfua/CT9ni6/rvNLMXzeyvP/L/PnqW8j9Srq8ws6+wxeD6P7yZe4jLyQWN8d9li584/a0Qwheb2b9vZmMz+5SZ/eMY4+GK5RCXnAsa36xcGsMF5bLE+GP4GjMbmdmPxhgPHv0fJ8//o7b4YOXXmZl+rnrJuOAx/qEQwlfaYo/0gpn9WIzx3hmuv9Rcuo3jCf/CFpqSm7b4Hf7zS/L+HlsE6v9kZr8hxlibmYUQ/riZwTcjjBjjT5nZT4UQvtPMXogxfuTNFvwRvu3k7/fHGPu34X7icnFhYjwsfqb6XjP7RTP7iJl9u5k9+vOW+yGE3xdj/Eer3lNcei5MfC9BY7hYxmWI8WV84OTvL57y/z9x8vcLPsflEOvjosb4n3Kv5yGE7zWzP75s8/v5wqX6qeobxBjnZnb/5OWNx2T//Sd/v+ONQD25x65h8DwRQggbZvattuhwf3UdZRDnmwsW4zdP/r7XzP6Qmf3nZnbbzJ4ys//MzLbN7O+FEL7oCZRFXAAuWHwDGsPF47joMb4C2yd/9075/2+k73zuiyLWwQWM8Z82s/+9LQz7RrbQ8P4fzGzXzL7DzL77CZXjXHMpN44nvPGNxuM+HfigmfW2cP7y/Iu3tUSr861mtmlm/0AufGIJFyXG80f+/tkY4/fGGO/EGF+LMX6fmf05W7jzffsTKIu4OFyU+GZoDBercJFj/K2y6rOLi82FifEY4w/FGP9ajPH5GOMsxviZGOMPmNlvNrPGzP4vYeHk+nnNpdw4hhCGZnb15OXdx2TfNrMHp7iC3XlbC7Y6bxgq/Hdren9xzrlgMf7wkf/+IfL/30j76idQFnEBuGDxzdAYLpZyCWL8cbzxjeL2Kf9/y+UTl4zLEuMxxn9nZv/aFprLr1lnWc4Dl3LjaGYfsoV+806M8YXH5N03s6uBH2J76+0u2OMIIXy5mf0qW/wW/Eef9PuLC8OFifEY46snZTBb/OTD88bGcvS5Lou4MFyY+PZoDBcrcmFjfEU+fvL3NA3j+0/+nqaBFBefyxTjb2x8J2stxTng0m0cQwiZmf2xk5d/Z4VLftIW9fBryf/70Bnfvrdf/lnem+UNQ4UfkAhXMC5ojP/Tk7+/gvy/N9JeeBP3FZeMCxrfj6IxXCzlEsT4KvxLM5ua2deGEDYf/R8nz/8bT17+sydQFvGEuUwxHkIobeGQbSYH4Mu1cQwh3DSzv2sL+9/PmNl/tcJlf/Pk73eFEKpH7rVtZv/lGYtw3xZn1rwpTs49+l0mQwVxChc4xv/iyd/vCCHsPFKGnUfK8HffxH3FJeICx/cb76kxXCzlosf4qpwcsfS3bPENzUfc//4/28Jl85/EGD/vF+KXjYsY4yGEzZNfi/j0ysz+jJm908w+ZmY/ccayXDou7HEcjxzmmdnCletLbPGpRGWL3yL/7hXPXfmbtjio9BvM7OdCCP/AFr9j/mZbBMgHbPHpxSr8mJl9SwjhR8zs39pi8fDjMcYfX/H6b7HF7/5/UIYK4jLFeIzxfwwh/HlbuKr+3Mn1Zma/1cyeNbMftl+eOMTnAZcpvh9BY7j4JS5TjJ+YgnzfI0lvmIT8lUcOXf+eGOPHHsnzR22xefhPTxbl/9rMvsjMvsnMXjez/9OKZRbnlEsU49fM7CdDCD9lZj9jZq/awgn215nZu83snpl9q45WMrMY44X6Zwtnpkf/zW3RoP/WzL7fFkGXnXLtRxePDOlDM/uTttCkzG3xk7nvNrNnTt7jh13+j5ykf9il37TFV/J3bHHYeTSzj5zh2f7VyTW/ad31rH/r+3fJY/z32eInTIe2+BnTT9piM5mvu97178n8u+TxrTFc/y5ljNviG0L/XP7fh8l1V83sz9riQPbaFgvyv2pmz667nfTvzf+7bDFuiw/8/pwt1ievncTqoS2O6PgeM7u57jo/L//CSYUJQgjhP7CFucH3xBj/i3WXR4i3G8W4uMwovsVlRzEuLjuK8fPFmTaO29s78dbtp5O0LAuQL4SwQh68f54RySXJ578o7jv85rgjaW3bYb62t7ZtrSh++Ve7MZp1XWd3Xn/Z6mZuT916hw1H48cWjFUlq9/IjrOJj89DvyFn9ZijJriq0l8lP/q8v5SWY/1ntE1Y2ejDpy/ZM+FV9nM/8zP3YoyPOyz2bWdnZxifvp1o+C2yCl6BwOKDPG2gdenbGduz67Fd5nOMj/l84Wzddb3lJ+37Rj/o+97294+t63rb3BpbUT76PqzP4nvmpG+XFeYbDtK0gsjW2b04q9QZi6y3W9L95mLjYx+/u5b4NjObbGzGq9fcW5PH8Em0f7NBfMW4h0GcwgZ/li9a17WW54+MaWExht9//XVrmtpu3Lpt1WCQXsXutWIaHe5cRjr2k+fOSL9i47MvCJ+5WZuwcWfFdlrluh7n1Zdf+szaYnw8mcSdqztJ2krPtkpHOEM+1q4eFiN9jzESY7Su69K5PS7yPrh/39qmtavXr1lVVcl1rL1oGi0bLTFLfFP3p2NFn96frePqpsa0uoG0hqTx9W5aDlZ+xrw+XluMDwdVnExSE/JAfsU5qMrkNZ9i2fiwWjloX3jTRGu7HtahXd/b6/ePrGk7u3VtwwZuLbtK3172nm8GWj90niB9GRb7q1b2qnW92ry8Sp7PvrZLY/xMGsdbt5+2v/iX/3aSNhhVkG80SIN1WOEqsSRpWxvoxp/l+DDNLD3m5Xh/Bnl2d48h7e6dfcy3d2x/4S99t7300qftfe/9Ytva3LF79+/az/78v7G6mduHv+632O//3f+JVdUwvTDgc7cdRk7d4pE0bYcTbXSTRd3h4Nh0c0jLc3zP7Z0NSHv6mZvJ69s3r0KeKzubkDaa4HPmGb5n2+Ig3bbpM7QRn5stH9/z9O0XSfLnnKdvb9rf/P5vTtLYxhGengwYRVZCWhuxjrKC1Jtr+5hhuxweoiP0pz59CGmf/NR9MzP7hz/y/7O7d3ft6aevWwxmx8cze/mz96zrevuCDzxrH/w1H0jf07D8gwH2z40xDiHPPoP53vfetLw3ruKiamuMsWY9qdwe+0HXT9PXpI/lGbk/2UzyDwtW20jDvUiA/5qv+3+sJb7NzK5eu2Hf/kf/VJIWSH/2kzf70C2QGI9kcmNx3/UujTRzzibUhrRX39nf+Rt/yV59+SV77t3vtcnGpu3vP7SP/8LPWtPU9qs/9PX2zb/r91nr3qRpyZvisGtZi+/ZtdiwjYu52j+jmc1mU0ibjIaQduPaFSyIK3/HFiXEyb4no2xBPtzp2fjsqqgiH0o2M5xX/2//8f9xbTG+c3XH/qNv/7YkbZUPyNkH1xl5XraxYBt92MSRInSkX02nGCP1vLYf+n/9PXv9tTv27DuftfFkbPu7+/apT3zK2qa1L/9VX2Hf8L/5zZZnaXnLEvtoSfot/0CAHKfnPyQg9cre05fLzMw6rG+/AdzbO4I8n33pZUh7iaS9+goe/TebYQfPc7e5yrEt2Vj/yed/Ym0xPpmM7Bt/49cmaWV/APne+9xTyevRAJ8jkPm0JF8iBDJIZ65dQyTXrfgJXbRof+UH/5V99s6evefZa7Y5qezh/tR+4VN3rGk7+9BXvNu+9bd80Cz07jr2QQt5S5KPbbYxH/mwryDrAbJW8WvgRVoa413HPogi/YV+iUPqm10bfJ8kWz9y3R/57h+kMX5hzXHeTr7qKz5k+/u79pM//S/t+PjIyrKyZ556zv69r/1N9nUf+sZ1F0+It8z73/+sHR/P7FOfesXm89ryPLPtnQ173xc8Y+//gmetJt8aCHFR+BVf+hV2eLBv/+vP/7TNpsdWlKXdeupp+6qv+ZB99dd+3bqLJ8Rb5gNf8oV2dHhon/jYL9psNrOiKOz6jRv2pV/xZfZlX/nBdRdPiLfMl3/hM7Z/NLef/cSrNp3VVha5PXVjy77my5+zX/vBd627eOIEbRzN7Fd/9dfbr/7qr/+l1+TLMyEuNB/4wnfYB75w4U5998HemksjxNvLl37wq+1LP/jVv/Q6q6TdF5eLL/7SL7Ev/tIv+aXXPfnWW4iLzFd+8bP2lV/87C+9juxrerF2LtU5jkIIIYQQQggh3n7O9I3jdDqzn/25X0zSJhsDyHdlOzWSuUo0d1eubkHaeEhMXXKiS3K/x2+JVuXwAHWPr7+O37S88sp9SDsg1wZXVdM5fi15dIzXMY1jQTQA43GqC2uJviAG/L30iGjMbjfXMZ8TUk8mqKsZEo1ZXuHvr6sS03oqAnYwA4Fz9NFFtGD9mxF7M4MbI19bB/w5KNNT5M4M53iO/eLOa6j9eOnFXUi7/zrGZN9jO0820j4aiSalJv1s9wBjsngdY+HKdnq/nQ3UbXak/xMJLzXjyMw9U4b9Jye/4e+Jjo7q3lcwmaBmKG+nd8DbQN/3Np+mmpaWmEhURTpGRWJSMa8xthqi86iJNnveptf2ROtl5BuVfkZ0eDW2dT0nOrGY5pvVeK+WGExZg8/UNUSb5vQszNgjJ9qe596D51RvjnHOLMdu3iCDZ07Gk2wF47JFGnn2VQbx/pwFOYGbAaXQ7+9INVHDOKIJg/ZhYw2TNxHdEvvepSUmOr03pGMScbLqK8h4n9M6c2Me05etajhCv03yacyUa9W01YoBplMX5Esub6yXkfbKXVwycxwWz9xEh8SvryvSDoHejM27rDOwxnAaRz5hY5IRnT7VPbr3ZM/E0nIyh5Ex1ZeM9ndaZ0y7yBbQmK8F7SmZO4g+/jTO0bJdCCGEEEIIIcR5RBtHIYQQQgghhBBL0cZRCCGEEEIIIcRStHEUQgghhBBCCLGUM5nj1HVjn3kxPWj12jU0uclDenjxaIhvM56jQcx0Rg6mJVvb2KfiT3LWtx0doSHDa6+hEc4nP/ESpN2/hwca+8PDa6KkP5qiWQQTsFcjNBTa3nJmCMRIJS/wXjtX8CD4q9ceb9OdUTE/Mb0hIuCOqMc7IjKGNKL3zckBy+cedwAtE1izw7RjIAfJhhGk1X3aD/Z38bpXPouH/b7y2V1IOzjAcuxc24a0a+7A8SkxPnntHt6/nuL9H5AO+fpWer+nb6I5zvYGO5SW1GPEmMmy1OwpI+ZEPRHHs5j0BhCnZfTd4CJ4KtR1Y5/5dDqG33/tLuTrnXFMS8zA5jN8YmaO0xgaKEFfYEMWM8chRj7MMCcnDiDe26klYxsbjQpmEkJMD+qYlq1vmDkO3qu5eRvfkxwon5VpWsfOXmXjNTEe4mYxzPjGGYewKD9np0LEGK2DOHx8n+7JswXSzt44apER2ys6My5ugoG36pm5BTEgmpN27Z0ZX0a+GygLNEcbVJg2JCZ+3jiE9YNVzWtYDPokZh604vBMvxZh60l4TzqSn7PRPWD9sTWdH0cyugZhh8iT9mJ14H1kWFFXbnvSP+j8nPaPVb/9iiwnNa9Jy8vKbxmJe9JHswyv9QaMzNSIlpWtQahhDum3nb+WzC+BGJGegr5xFEIIIYQQQgixFG0chRBCCCGEEEIsRRtHIYQQQgghhBBLOZPG0cwsuL3maIwHyW9tp/qlycYY70N+uzyd4qHmjMxSjWDdou7l4Bjvdf/+A0h77bU7kPbg/iGkjUapLmy4gdrCqkK9WkZ+F54RXZ+X8+QFOcyVaA6GY3zPzW08PHpzJ02bbGH5h2P8jXNRsc8WyKG/9PDzVIPBftfuD6hdN1DEFQ4pZhpHpv3oid41RqLFqtN23ttFveGDe5g2PcKD0AcDbNMbN1DjePt2mvb6Q2yr+7vHkFYM8f5VTrSyTfpMXUs0QUwuk5O4IvpRPy5ZwD7WkzeghwcTeBi4w7BppvNFUzf28mdfTdJeffEVyDdwA1JB9IbsJPEZydZlpL1cuzItyGiAY1vsMe67FuPe5ji+hTadh/IMrwthCmlUwsfG58o9AzkQuutxvKs71OPnBdH2Og0NH08giR9oTs/axj4TvJaHaoLO1xjex2izWaqrZXXgtUZsUVKUGOOBxCo5Q91WmSdaooNl2sVZg7E6naF2eD5L+0dH9OaDAj0WJiPsa7aJaX5sJ0M9PcidHTLPBJ4hc7pQdjj9imnMP4GleY0fW6dQ8d66cfMN08p524qM+FgwHWxGvAV4FYBAFO+1gq70tGtZLIFQlWpqiR6wI/HGxjO4H8tDtIXkQXsyb/o+ubKnAnkmNjf1HaY2dfpMHclDdZuncL5GfCGEEEIIIYQQ5w5tHIUQQgghhBBCLEUbRyGEEEIIIYQQS9HGUQghhBBCCCHEUs5kjlOWpd166maSdvv2Dch361aatrON5jixR2F306LxQZyiWUHs0mt395gRzj1I2z/ag7SavScR25bD1NxjsokHmBcVis6Z3LQh5h7m0sBowcyqMQpyBxNymO8GMdFx+aohOSyWnGucM3MSdpA6OYzai86pQP7cfXThBcisBR9vgsLMgvIMKzh22H77e6nZ0+uvHkCe+/f2IY0d9H31KppX3biO77mxkZb3xc9i/8nJYbN5gX17QHTj/tDsktUFMfvoAo4TGTNbcXHE6p864ZAD61n7soPPIY0ak5wvw5y+6+14Px1Tm2M0Fxs68T8ZjuzoCONysIFj4GgDx8rOjf9H+2hcNidmaaHCNhxNMJYGG5ivLFLDnHJIzFAq7C8FMSUbEFO4MEgr6ZXPvg55Xn2J9Nse67+PGPfBmeNkxKCBHtJd4DTPwp7Ne978JJIL2aHq66TvetvfT2OnI+X2aXmB9VkNsJ1jj/kiMT3qXNgwC4ymwbafzpkRDqYdT9FU6fAgfe66xjgakIm+JeXIiAFUcGZoRcXMg8h1JI3ViA/pQNx3MpJWrGiEw81xfNqKzi1rJBj29VVMgzJigEKPmidzJa8BZwBFvWyYYw67/wpGOIbPxM1lGMwha4XnZGZYZKxky/pA1kzMhAyLxcykmOkU3ms6w758dOTmWzLnN93qMX7OhnwhhBBCCCGEEOcNbRyFEEIIIYQQQixFG0chhBBCCCGEEEvRxlEIIYQQQgghxFLOZI6T55ntXEmNDgZDJkBORZaDCsXYORHs1zXuY+saReEHR8fJ61dfvwN5Xn31FUibHqPZwnCIRiF5gYL48WSUvA5EMFt3KETviDkGM9qwkCpr+w6VtvMWxavTBut2OsfnnNVpnU1rfMaqYeXCNglEGNx1KLYtnakEE6a3LbbvRYPZn+Q56VoZ1jmL+Xv3UvOSV19FU6fDAzR1CkQcP5pgzGztYL6qTOPj4f2XIc/0aAvSrmxMIG1zhM+5tZH2s8GAiNIDxlCMxLSB6sZdHFHTG8ZqgnBmckP7sc9zvjwVzKJZ6NIKzJlXl3u0KRnbqh2M8fd/xfshbfvqNUhr5mn8vvzp5yHPS8/jGF7PsRxXntqBtC//ql8JaeUgzdcXaBSRDzDe8gzniLJgQZhW5LzGe73yPJrjZMwsgbQJGEqQIrBwixGfkxlKRGKE5sf6jJlEkflgnbRtZw/uP0zS6gYr1KeVFbbzeILjW9dg3Y3JOFtWab2zaurI/DedYowfHxMjnCOcAw4O0nF8PkNzwYKYAPU9lqMcYP+unGHOiORZ2RyH5eu8mQsxBKGGKcw4ZDVjnQw60vk3x4lm1rl4olOec6sJpO4KYrCSRbLuY3Ogd4Rh/ocl+36KGEyR7Qgbp1o/BhFHHm/GZ4ZzmplZTwwF/e2oeVAgYyqps9kU125376b7lfHWNuQZkXVVT4x8jg+wf7/yKhob3rmbzjuHUyz/dLr6WlzfOAohhBBCCCGEWIo2jkIIIYQQQgghlqKNoxBCCCGEEEKIpWjjKIQQQgghhBBiKWcyxynL0m7fupGkjSd4i6JMha8NEV5nRJAbMrzXMTG0uXvvfvL63n0Ug+4doglBMcB98pVrm5DWdES4G9KytR0+05wI8FsiyGUC7ZClYlWmJx5NxpC2tY1pFTEsattUSD+bH0Oeaorl71smRCcCayK4z10bM1G7EUHx+YKYSLi0QBxQmDlODCjYns+wzvd207bZ30cBNLtuMMa63NhAw4drV1F4XbepiDvP0IwhN+yL4wrLtrODsbC9mZZtUKIBRJ4xlxas256q3N211K9jNTeRSFwGVrH/YEY458s2ZNEHh0UaE6OSjBf76XiRFdg2X/DlaITzji9/N6SNJhuQFp1xzOgKGio1ZJx56cVXIW1y5Qqkbd7agbRsmJZj3mOMxxL7S0+myY6412SzNAD6hoydZG4J/WrmOJk35FnRwIIZk7Te1MLM+h4NE9AEDu/fE6OIddJ2rT14mI5ndU3McZzxHjPHmRNzvoyM45GMLQPnXpIREw9mjjOfo6lSTYyWWNq8TsfV2QxjvMixrCWZv6fEkKcZpyaB1PyLpq04Evpr2fKAGu28ecMcn8Kml/OI73U9nd8y9xLjza/TzMyIT9YpBnFuzCP3p/XJ2itn/YrMTW6ujyQIp8TAKnZs/Um2QLFd/trMIum39RzXQq+9vgtpr7yS7lfePcQ9yICM7QeHuGa/c+c+SXsIaXuHbqwIONYxI6LTOO+rdiGEEEIIIYQQa0YbRyGEEEIIIYQQS9HGUQghhBBCCCHEUrRxFEIIIYQQQgixlDOZ4+R5ZlvbqcHAxgYKWkfjNK2sVjOlqFsUsN9/uAtpL7/8SvL6zp3XIU/TonC8qIgJAROs16jmbbu0bD25rge5sllHDAd6JhR3gt/x1giybF9BEe2Nm9cg7erVbUgbDNM2YQJ5RktEwEwknRmaW7TO7IRUmXXd+TJWWMXOJPjgJaY/WGtmkcT3dIo5Dw9TUwLigWANcV0aZdgXJ8SY5MrWDr7ncWqqcPPqFuQZFtiAW1tocnPjBj7nzZtpeccTfKgMjDjMIjEOiaRPRe8mQoKNmtew5maBuoJjAjWKOGdGCyGYDcq0oGPUydteTM1xrt++CXne94H3Qlo1xHHAiHlKcM16/TqOY0/fwvd89TM41m+McFzMSuwL/mPSnJmcEFOrljRsCMQcYZaaRx3tziBPRsweYkfdIyCl9+VgccpgBm2sf5B8nbs40oBe3VThSdB1ve0dpG3RtRiDbZM+S5hjm7Y9PttggOZiRTmAtMyZ/WVkzu06YtrTkHKQuYMMg9A6vv3MzAK5kM3zdY3mON4UsF/RLImtjTJq5uLija2V3qzRjq1ockZznbOBPBpMOFlO1re5N8chhlrErJFXAXPMcUaBdOokRjhkzUSNi0i+o2m6dnj44BDyPHiARn4Pd9Fchj1SWcalr83MIjH8nM9xLbS/hyads2n6nO8KuNavyR7k9XtoenPv/i6kBWJ0de16OmYVBTECI6Zcp6FvHIUQQgghhBBCLEUbRyGEEEIIIYQQS9HGUQghhBBCCCHEUs6kcexjb/M6PeRyu0JNy8Zmqqsq/MHFZjYnh2UeHqIm5O59/F3vK6+9lry+c+cu5Mlzorkjv+M/Ihqz2Yz8UNvdL8vxd8kZOUib6bbaDn9L3PXp76MHQ9SYbW2hXm3n6g6kXb9xHdKu7KTl3d4YQ56KtFPX4e+22aHCM3IgfdOkbRyYLvScSQdAGLFK+dgB2OTH814na2Y2nWIs7O+nv8U/PMC+Mp1hX7mSo7Z1VGE7j4hGJ1ia79nbVyHPwQbGwpUd/K38U0+hdmxrK62jvGB1hsNRjESHFonG0aXFjNx/VU0Ya/RVdI80Vs5bgEeLlrZj12B/HmymbfHMe5+GPJtbGCP7xxjPcYBxX7mxcjBEjdj2zStYrg0c14sRpmVEw+FFYRUWy5qOadeJXn6M7Tpr03o8PjwgZcA3jeyAaaIJMzeXML0SFb6RpDwjUz/RE8XgD45mfeN8ff4cQrDcHSbOzoz3kkOu3sQLmd6QafVhCqAia/KmK5YjI+2fO41uUWA7Z2T8ZPNVT2K1j+7wdRJcTM/I0pgTQB/TeGN9Y9X70wPrmagNpnx2HV62TqKRMmW4/gwuHgJZj7LOEWissjZ0Mci0iySNTaeBtOF0imufF55P1//Pv4Dr/3v3cOzdO0CNox9TzcyqgdM44tRkbFBlOuq+wzbZGLt1fIFrtOM5ru9291C32ZPv/m4+hXP11Ss7yesBGRemM6xr+7FfwDTTN45CCCGEEEIIIR6DNo5CCCGEEEIIIZaijaMQQgghhBBCiKVo4yiEEEIIIYQQYilnMsep69o+89mXkrSceBAMhmmiN0IwMzs6QiHmg4d7kLb7AA/Q9GYhsxoF1FVJDtZlhwATd5bIDkd2CuqCCJFzcl3LBMXkUOG8eLxZxCYxx7lCzHGuXEVTic1Jer/RAJu+ICLppsHnbNm5veQx61lqwlF32E7s0Nd1ESxYFtJ6YeL/4B+WPHxJDns2cpj9fI6H1x4fO2F0YKYx5P5E9z4eo4lTIBmHLh6+6APPQR52QOywwgFgMiGGDKUzYKGGIMQIJ8N+wMqPzYRGPsw3hFUaq1t2EHUGhhWsXOfMVcGw3PMG23WyncbN6CqOPZ98/rOQ9uAA6/3Gu25A2rXrqfkXM/EoNjF2J1fRACqMMN+UGJwd30vnl26KZT3CsLS6wMRn3o3lOHb360jbFwWbW/D+Hekf/pxuNnLSuYv5YRBjIOZxs4q/CzM9Wyd5ltvWZhpfgX5GnqbVDRmTSN01ZL0xm6LB1GiUml4U5ID2bBXTLeOGLcwbqaqcGYqhcZRFbPyS9D82nnXOYLBpceyoyDxXkPmQmtCYN99ZzTiK3asjAz5Ls+Dek43Z52wYZwZQvWG7Zq5dczL+ZGT9krE5kNQLvie2PRupyPKZwowvP/2pdN558AD7XkfKMZhgX2DGZ22b7i/aOVlLEIPLQAzHNjevQdrt208lr8djNCt8SPY9R8fYFwLZgGUkrRqkz74xxrJe2SFjxSnoG0chhBBCCCGEEEvRxlEIIYQQQgghxFK0cRRCCCGEEEIIsRRtHIUQQgghhBBCLOVM5jjz+dx+8ROfTNKmczS52dtLhZ1bm2iswAT13lzAzKxjWmYnQmWi1HmDQuFIlP1FgYYcIce0LqZpkZi69C0z2oAkagQx2kgNHibbWGcbWyii3dzEtPEEzSLKIv2MIASiTiaC6Jw4JgxJ1FAPoDatI+ZNwgwZ1kewYKmwOAskjkIqUg7EOIXVG5qpmNVzrJW6Tu+X5aTCSfyFEtMmm2guUw1I7HZp2mQDhdItMQ6ZHmH558coHLcizVeQICoqTKsq7ItlRiMpecX6Hev/3vTqVJgriHsTFsrxnH02F0KAeOo6Mha4cbFpsR987Kd+AdIevI7zwRccfiGkXfnKzfTtNiGLjQsU+T917RakNccYD//2X2LZ7n/m1fQ9a4zT45oY2lzD8XRr+1dA2nSa1lHHTGlIOHQ91m3PzJiciUXPHDtI4AfWGcj4RN4STTLIQB+J2ds6KcvSnnYGFN5IxMwsZGna7h4aUjx4+BDTHmDafI4GHZUzDiu2tyBPUWDf60k81DXev65xvVSV6TNtjHEcZ/NVRmKpyDGtbtNyHB9jfx+QcXxQEsM07/ZkZsG9Z2Sue6T8zLysISZAs4YZnbhysb5xzgygFvg1HTHycwMOW/Yx46jAzPeoKeLjxwfmLMSqk62f93ePIW3qHMyefvYdkOf2O27j/YnR0uHhLqTt7t5LXh8f47jQE6PHQYVrrWvX0BjumdvpHLaziX30wesYu02NaVNi6NX2dyBt7ua6978b62c8wPKfxvla1QghhBBCCCGEOHdo4yiEEEIIIYQQYinaOAohhBBCCCGEWIo2jkIIIYQQQgghlnImc5ymae3V11Lh6NEURdv37+8mr2/fugl5rl27CmlMZJ8RUXt0ytqWiMkDMbPwYngzsyJHQWgeMV/dpqrijphqdBGFql2PBgxZifv1ycY4eX392hXIw9K2t9FVYkwE8Vnny0FEzKTOciKSjgHrh5k5DAepqYQ3CzAzi9ToZJ2k7RyJsDs6hTmTzXcYkta2mHOOWn2bzdK2aohovO5nWK4cbxZKZryBacdOPP3wPorSX3v1CNL27mF8914wb2iGM9zAGBpv4XU3rmP/3NmGJCuLtI5CTwT5rKVIWWnfYI3ce0Oe1UwA1o4zVmrJNOANx15+6bOQ5/jhA0gbTrFdX/85FOt/wXvenbzuNtC1oSDGSAUx8nn42i6k3bmL5Z0/TPNtZniv2QHOZzeuPAdpGzmOZff30+eczQ8hD/EboTHYkbgpQtrXejZe5/hMWItmkfQPZrjVu8+WM+Lu0/ZokLJO8jy3na2dJI0ajLlxvCZmSd7oz8ysIUY4M9JePl8k6xSL2F6RGG+wNGZEUjrjvfEIx09mjsPMPrqOmBU6h7SmwTrrmIESGweZG5M3eGHmOBm7DpPYeNz1eL/OuVgxAysW9+vGP14g41nwpojMHIfUXd4T0ya2pnYXR2qBSIyXyLrvmBgXzaeYbzhIDSGffe5pyPPc+5+CtDzHezUz3Jsc7qf3q2fY33tS1pKZE46x//m0cYHXbY3HkDaZoGHm4UNcp90nhkLDYbon6Mi4w0xGT+P89QYhhBBCCCGEEOcKbRyFEEIIIYQQQixFG0chhBBCCCGEEEvRxlEIIYQQQgghxFLOZI7T9dGODlOh6OEhmnQcHaV5emKwUg1Q/DkaouFAluO1wYn4eyKCLgsiOieCX6YWjsaEwT4PMU0hab2hID4jAuLRIG2Kq1fQAeTqVUzb2BhB2qDE8rcxFfNGIhJnhgmBiGihMk5Jq4q0PQMRAXekftZHZmYuBgOJPygzMQgg9dt2eK+GeB7MmrQuW1K3GRFilyNMawOW7eAYzSwe3Ev78ac/hcYnn3kRRdf7D/E5+w77cT5MLTrKEQrOx1tYrnc8ixX07ufQ7uOp2+mzl6QPEJ8qagjCCKRvwzh03nyeCCGYFW5M7SMxSzo4SF7P2z3I00Qc+0tieHF87x6k7T9I42v83G3I05Mx/OFDNCt5cAfv33cYv3lMzZ1CROOCjJR/PMR8BRkXu6M0VjvSuakXGBtPSVhm7mJqQ7HqR8HEwISEOCkI6wcrvucTIoRgVemXNmRsdA5mrI8XORlnydjC0rxPUST9rKeDBsZNzkxiSNkq17eJD5/1pO37Ht+zb0ka3HA1MxRmmtJT47m0jiIzwmFjNjGvCcQoKpD1ZObWj4G742DaOolojpMFXM5nrnNmxAkno7FF6pOss72hZSBr58DuT9Z9LZmg5zUxAXRxU1XsPdG8pp7ivfoZWbeGdLwfTXCvUpFwGAxJfyTmhH4h0s5w3bO9iaaX169eh7SD+X1Iq4nR1WiSmuMUzESNGL6dxjnrDUIIIYQQQgghzhvaOAohhBBCCCGEWIo2jkIIIYQQQgghlqKNoxBCCCGEEEKIpZzJHCf20ea1E+j3KP4cTFPx5/EURa9Hx2iOwYTibYdpVZWKOCeTCeTpiBFO2zCBPHtPFJfOmzRfXqCQlAmvAzFEMWLAUDq17WiMTTMYYlqRM0MbLH9wwvxABPLUJiSSVCLyZ8YKPjZoXXBHhjWRWW+pEDr0KLIG4TVpT8uwjlh8sM9ueic4z3LS7iT+IhHH7+6j8Jq19J2XU/OTT3xyF/K8+ir22a5Gc6aMjCr91MXCAdZrsYsxOasxX5bje0420jFgZ5MUIhCjKhb0JOapMYCL3UDiYDXrnSdICFY405mc9MH2ODVCqlvS9mTsDCXeq5njeHTszHaC3YA8fY3t9XDvIaTt76M5TjU4hLTc9duWtE4EUxWz8RYZ60mdzZ1RXGiZ8ddq42nGTGggvojBGRlPOpKPGdowj56VrMvO2cfPWQg2qNJ2bDt8uLpx6xIyJ7Ihu1rVHMddy0zxqG8IM6kjYVOSwvnnHlQYu22H67GerHnAfcXQQC+yPKQemTkOW1rAXMrGVLakomnMWJEZ37iykUnBG6GtnZBZEdK27TsyjsTUSC4na0O6niNzZU+C0NdLTtqrI/HQEQOoPsO4bCNZv7iyPbyLBjF3730G0g530eBteoBlK4t0LZGTfnbz1gakPfM0mlde30GTm8koXb80BZZhewcNeb7oi3Yg7cbTB5A2b9DEcGsjjYMrE2KE066+Fj9nQ74QQgghhBBCiPOGNo5CCCGEEEIIIZaijaMQQgghhBBCiKWcTeMYzeraHfiZ42HcXZ/etiaHbB4e4m+X244c2tni757LYfr73M0d/L3x4QFqctpmtYNI2e/2/e+0iwwPha4y1DnEDOunKLEcmzvp76pHY3LodME0VOTweaLniH1aH1hS/vt0pjHIyO/fQyCaUqelaNvzfUJ6H82OZ2nsbo6IdsX91j+S3+tHpg4iH9MUFXZB/5v6SLS5PdGLHh9i/d69h/Gx+wAPUX/ls2na63fxsNx5g+85IoejZ+zgZad9mxE9xiE5oPeVV9nv9TEmr191Y8IYxwSmCYrk0OFAtBzssGp/uLI/DHmReL60McHw3PCCCIQyN95lRH/ek7EnH2A8MG1M5w5Gzoj2rz8kMTjHeLAcy1Yw7bQ7TLwmGsRYYOyOt7GPdhHLMT/cdZlwDgpGtMl0DiJjrD1eX8Z0euzA9xBJX8C7gY6X5WHayHUDkjTixdA2afv0PY7jOdG2DcmYzdLK4vH6L6YjZG3IRhGucUzXG0MyPjdkHO9a4uvANMArxCCPS0xaxd6AfbPB9J7UX8IPdMa1il73yMZ/9p5rJUZrG+cbMMZC+sfNyTqNeWKwgGO6bq/FZuNWS8bBnGjhO1KOaY1zwN5+er/w2dchz9EU9YyzKereu5o9qBvbiRD53i7G24Nd1DO+57lnIO1dz74jeV3kZA1V4HhyZTCEtNEEr+0i1lnpdZQ11v/uA6yf09A3jkIIIYQQQgghlqKNoxBCCCGEEEKIpWjjKIQQQgghhBBiKdo4CiGEEEIIIYRYypnMccyCBWer0tZokDCdzt1rZnKAacxUo27RMKd1InZ2uCwTujOFMxVVk2op3GHXRYmmN0aMFSoiFB8Rw5Ur13aS15ubeADoaITGCmVBjC2ImBcPK2dCambugUn0oPMVzluPxBzHl2udNG201++l8Ta4RQ6bd+ZMscdY7iP2C/YpTUkMj4gumoCxMJtiTD64T+qX9I3dPX8IMoqut3ewYDvbWD95QYw9nCHU4RHGwuv3sB4f3DuCtJewaLazkdbu0ze3IM9wm5hCMHMcdpg08zpy3SUS4wVqmLNGokVrXPvTg7J7n0ZMFQIxhSImOhkxjxq4IGejwO4+mjixcT0j4xE90zqm79m15JkGmDaaEHOfFg9erme7yeuCGEBEcshyT9xlelKPYLREjJeoFxMxwqHvSa71pWD+Lj0zQ1kjMfZWu/XFfI4GHfPjdGzpajTsy4lx0WiA4+xwiONxAesZZlpHjJGIUU1g/aog7+nSqpIMlqS5ihzHdm/+tSiHCxJqCIZJ9NmpA4s3X3y8mY2ZWUbG3pxcm5H1no9fbo5zzsbxaNa5NWkeiBGjq6siJwMjjPV8XUZNibw5DolTtl7MSBtGMnhlxHDSP/dsjnN47DGeqxGuVTJMsulxWt7pHOvs+D6aox0e41rFMuyjm1duJK9vXd+GPMyUi/XHvMA2j2S8r9t0/Hv1/j3I84lPvgxpp6FvHIUQQgghhBBCLEUbRyGEEEIIIYQQS9HGUQghhBBCCCHEUrRxFEIIIYQQQgixlDOZ44RgVnhxMVPjO8F306DpRUtMb6pqAwtYDvH+Mb22mRNRe4YiXRSrm0Vi+tCRZ5o3qWC9Cyi2L4mhSDFAcexkCxW5165fT15fubIDecZE3MueifjlmLn66IkAvyUmFjkRJxfV4wXmZmbRGVn0xIikYyYQa6KuO3vxs7tJ2gYxyxhcT9OygHka8liBCJ5DjhlLZyRTEJF/7PE9ZzNM231IzERybKu5i4fxJt7rxvUJpN28hv1zNMBnart0DHi4T+pshs+5dw/79v37aJryymvpULZ/gOPL1W3si8ycIhAROjNfsFVMFYgJwDqJvVnjgrMnBhfRGSYwrzGrsO1jjfcqO6yDjSIdy2pinPXa3i6ktR2OIdZiWkPcjEJM36PrmdkYpo1HaEDQtGiEcDR3aQXWRUPMcWpiwNIR85oYvREFZKGxG8m9qC8JM0lyl0ZmRMRutkb6vrfZLDWvmM1wvvZ5uoYZmhFzOzKnD4dk7ncTMRtCuo6Y4zDDHGY6QubczLUFm5ty4mSXkaVgRr9XeLJtzeZMNhYzsxWaRsxxMtY/oCDnK8bN0MxwlTpgRmKsmbtV/a7c/XJWvxnOEx0xtMwyHGc3N3FP4JbKduXqVcgz2UBzyWpEzPE6nNheeXU3ef3aq3uQZ28fK2j/EPc5d+4fQtrru6mx2rXrVyAPW8OTZaANiZPitMGyvfxKaobzCx9/AfK88PIdfINTOF+rGiGEEEIIIYQQ5w5tHIUQQgghhBBCLEUbRyGEEEIIIYQQS9HGUQghhBBCCCHEUs5mjmNmpTPWKAsUtBaVMw8hRjXMvGbnyhakTcYoRD86SE06yowoeYnZwuEBpvWRmHR0qEI9nqVmG+0URbUTIlbfGqChyHCMwt2Nre3k9aZ7bWZWEiFs36KQnn0ekMe0neY1MaohhhLDIdZPxcwiyP36uauzHg1LelLX66Ket/biC3eTtKubGH8bG5vJ6+EQ67slz1WUpF2IUU1RuT5WEfOSiPciHlQ2n+H9ByNiHJKl5hHXbmCMvvtdm5B2YweF75vo4WSNM0155RWMhQd3iBEFMWJqiYnFfWekcnCE5iXRMG574jDiDSbMzHKiTO+dsUUgphPUmGSdBDNzhlqRGGDFmD4LqXILxJyl6vBeowmOgZNJGkv1McbD66/dhbR6hmZJwxLruI/E6MuVtyDtNRxiPJcDjJvZMXa241n6njHDsYN5DDUdmZdY3LhsGTFxyzM2peP9OzJXsXkjgDsOq+vzM4abmcXY23yW9v/5HOOrrtMxz5sPmZkVBTGHI/PwiJjjDEpvoob3b8n40xMjnFXTOmesw8Y3I2ZYGZlPcmas44xpmGcMNQmjZkxYHz6N3/+tpK1WtlWuWychoNEPL+Lj+y8zWYJ+b2Y5mRe9+VIgZlItMYAyYuI1HqERznvf9xykPf10Oopub6O5zOYOrp8zYlZ2dETmk/HryeueOgXhvR48xOd8uH8MaS+/ms5r733X05BnwNxxSH9hy//nP/0SpP38xz6dvH7lHhr+dPnq20F94yiEEEIIIYQQYinaOAohhBBCCCGEWIo2jkIIIYQQQgghlqKNoxBCCCGEEEKIpZzJHMeCmdNGW+YTzKxyRhLDAb7N5hYaJty4vgNpV6+gIUc7T8Wwm8RsZli8DGmvvoqC0OMZMR4hItGrV1IR8P39A8gzrdEwYdyhQUJBDIWaNnWfqBsU8zcNimO7jpjSEJF05swWMvKZQVViWStiDJGT+umJwLoa+Pvhe8b+/Hx20Xad3bu/n6Tdu4+GTbdupUYIBTFwIX4XVhKzAWaYUDgXj4qIupmgvZ6jUvrwAMXZGTETycs0jm49hX3qPe+5CmlbGDK2NcL6qOdpzOREcH7vLgrV77yG/ey4wees5+lz1s0c8kRi4sHMa5jHQJ5j2/XQyMxQAK9bKyFYcONPR+qlcCYrOZsqiNmVZTheDG6iUUFxJTWhOdrdhTxHL6E5zoD4FERiMEW812wQ0mDda9GqZmuTmONM0O3ptTsPIK1t07ZmkysznWAmJ8zEAvxLmH9Oj32jIEYUbLxm+MiI5E1Zv1onMUarnZsTM9Tq3dyZEZOoqsS0MVtvjNEcx8+dbKxpc2ybLGMmRZBkLAD8e3TEJBDHLTMjbZgRoyVfNLb+I8VnXiinGNWsYL5D3nPlNPqeaZ2t2DXWSjSzNrq+Tsy+vNEbW2+wcYQZxLHG8PdjBlOBxTO5/3CIc8ezz+I+oVvBcG6yiX00koXxoMT37NxavKtxnotkzN7b34W02QzXIfsHqXEX64/MdDQn68BXX70HaS+8dAfS7t5L3zPLcZ57+haaDJk9T9L0jaMQQgghhBBCiMegjaMQQgghhBBCiKVo4yiEEEIIIYQQYiln0zhasMwdiFtW+FvcjUn6+9mr2zuQ5/oV1Etdu3oN0m5cR31MdDqOUYm/160C6lL67hVIu/M6aqha8vvl0dD9rnoP8wSyDx+PsBxXtvCZOqfB2H3wEPIUHf5ue2uE9Z8RPZY/OzRjuhfy2/F6jjqgzJ+kfQpeF1YO2OcU50cD1jSt3bmT/mb8tVv4G/vnnkvb79pVjD+mA2VnMW+w+HC/z997gNrZKkdx4ZRIzo6PUONYYXHt+s1Uo/PUTdQWX93GWKt61A4NSqJ7dnqZWzd3IM/Tt/ABXvzMLqRNHxxCWuP0nfUMK5tphLMMK4PpYNghwCGmz8RlSOfrs7lgATRU/tBwM7PciQQLciAx0yc3bAy8fR3S2iq9/93P3sf738exZzTC8WJvivEwIHqQwmkcj46xb9zexn4VyeHuuw8xVvOY9o/cUN9SkLHZDGOVHbaNoYQx2cywXFmO5eeaOaKPdO/B5EUNj/y1EWMEz4C2w1jy9Z4TXRHq9M3GRPM6HOE44vtMTw5C9/O+mVlBdI8Nma+ZFq91J4I3ZP5mmiqmeWWaTx++Wf5WtIUkDWKJxdZq+juexvRxPu18xTMjxgjjNit1BnXADDAwidYAq06voVxRn9sTPaZFjLeWxOrBfro2PiRrnKs1vun2JvpVjMnYfuPqRvI6Iz4iB7v7kPbSZ/E9GzJHVlX6nhkZ/7OA/fZ4huV48ZXXsByv7UJaKNN17LWruAcZTbAuTuN8rWqEEEIIIYQQQpw7tHEUQgghhBBCCLEUbRyFEEIIIYQQQixFG0chhBBCCCGEEEs5kzlOlgUrq1QsXpADcgejVGQ5maCpy3hMBObkMM5hhYLN0glwi2v4GGWGpiZHxyhePTp+GdJ2D9CYxh8KWhAB+MbmBqQ989QtTHsG07zHzXyKgt+aHKyejYmgvyDNSkXhKX2Pgtz5MRqztCQfF9Ln7jWWKxAjgHXRd70ducNZD3aPIN98lj5/oM/ODuNFJmM0VdjeSPtGiGgc4g9oNzOLEY0xmoaZIGHM7DgDq6vEwKn0Dktm1hPTiZocOu08tawYkj5bYT0yo4isJ/mcUY3512bWdkSoTgwxAjEd6clh8XBIMjlg+NwR8ED4khhcFM4sgxp7RFJ31Q6kXb1xA9LaOh1PX/okivytw3LlgRn5kIPVmXGIM18YjYjZ2BjH8Dgn9z/GcvQu7kO5musEOwybGa2xfJiHmbaRg+LZtSRxFauSPJwfgzOzE3OcJjUmYgZQ0dVCTsaaakDWJCNck4yIOY43BenIGFKQ8acg8zebX9lh602dvscsoEETM2nrqGEO5jO39qJGfGzMXtE0xRvmMAOd1QxuTrn/BTC+WYkYLbq5lz1ZdKmRrFuZgRurO76C9OY4pB0yZv5FjBhrXL+89soDSPvkJz+TvN47xBh/5p14/y/+YtwTXCH9ezBI+9/WFs4Jkwkx5CQGck2Dzx5d/PZGDKxIm9x/uAdpr72Oe5VjYohVVumacjbH8fDwDt7rNM7Pql0IIYQQQgghxLlEG0chhBBCCCGEEEvRxlEIIYQQQgghxFK0cRRCCCGEEEIIsZQzmeOEEKwapOY4RPNshXPCqJyhjhk3l+kbImCvUeiZD1Nh6niIotewjeLVG9fQcOalz6L4tq7RqGE2myavhwOsupvXr0DaO565vVJaHtOy5RnWRVXie+ZEkBtI3WbuM4KMqJ+Jp4nNnSmQmdl0ioY5RKcP7T4YYpuwOFgfwcwZx8zxUS06045IjAUyDHnLybMOiLnUxDkljYkp0uEQKzwn5gs1MTLqIhpT5a4jD0om/kYhedMR84Wc9GN/KTXtwWfqiEFKMCzHaDB2r1HQ7s2aTgqyUjlomFL3CM95iu9FaZw3jjF/qtD7mCaGQRFja7KNbXPtKtb74b1Xk9cP7tyBPOOSmLqEKaSFDGOwifietXumaoSddGdzC9Jshv27OSLl8HVEzTlWo2cDany8cQgz/6B+GCR2mYkOmOOQ9wSTqDWzMMdJ+zXr09HVJzN1qSpiPkfSSjI3kzeEJDYmURM532nNrCMTdh18n0TDEeZyQuuHmNAMwAxlNSMcnraCG5M4lUgMyzxg3FewxsdxnDVDiGS8cc3Kxq3YkDV8QczFyPr/5RdfgbSf/9kX04QK1/+14Xzy9HO47h5MNrFsfdr/+h77Y1djXXQdPntR4bUbO2l5ByPMw3z2dolR4+t30dBmRsxx5k167e7BAeSpa7LYPQV94yiEEEIIIYQQYinaOAohhBBCCCGEWIo2jkIIIYQQQgghlqKNoxBCCCGEEEKIpZzNHCfLiMEJCloHZWo6MCyJOQ7Zs3Y1mhzUUzQhKDNv9IL3j1SIjqJ2lo+JRKMTEG9soCD3yg4Kba/soNnC1SuYNh6k1xbEHGdUEfF+zkS6KI6NTm1bVgPIU1RomsLcjzpiwjKfo8mIN2CIEcX8rP7XR7DM0viOPZoXFGBegM/FjCaYSQUzWdreHrnXGN+Hx3j//RkaIczmxDikxWubLm2/lqmzDesiML+ZDPtP06fxPK3xwmmN5Wf6/yxgnRWlN+TC8hfESIoZG9E4JUYR4BPCjAHIvdZJNLPOmVJ0JDCji19qnkEMDnZuYR0PRzhevPR8akAWOmz7bIjj9ZyNFwHrOA/EyMmZNJRDjKPJhMwlc3zOZopGaxa8IQtpe3CJMoskHxsXfUqgn/sSYyoaz8y5h5nt+CzEjI3da430MVrtTPVY2OSZa38ymAWfx8wyYmgTyDwJbcgGS1KfkViTMB+uloxdFtL2j/b4tcAiEeMmkH7Vu/mQ1s+KMUJD0D87XR5Q6xZIYc9JPaec6QtfkpyndYqZWYT4YsZY0bUhewpmpkiWNLTv+4yBLghwrdk3uC6ZT8n6n6xfiiKNudEWrsW3d8aQ1pPFRMOMz/r0OR/u4tz0YB/L1RPzoM1NLMeNa9vJ64JUWddiWedTXGPXJI31vytXnCHPmOzJiDnYT//C81g40zeOQgghhBBCCCEegzaOQgghhBBCCCGWoo2jEEIIIYQQQoilaOMohBBCCCGEEGIpZzLHybPMNiZOZEnMPW5eT8WfN25egzzMXCYjitz59BDS+joVhHYdlmF3D80L7t19CGkH+/uQ1hGTjrJM99g7Wyh6HQ1R5Rp7NArpOxTWbm1eSe81wD19QcTqgRjVtKiXNWuc2QVRgLNgGA28GZJZWWJOJuivXT12PZY1L84Ugp9TggWrstS8ogQjHLPROBV7ZzkzEcBG6IkIvSwxbWMjFS5vb6GQ+e4DjO8sw7gKxCyjqTHt8CAt7/ExiSti7BEKYuRAPo7y5kkPdrFf7B5gv4sR+9RkA9tk52raJqMJFoK2EzGY6EmcMsG5ecOc8+afQIiG9ikNcd7InbFE1+Hz5yNsm1tP34S0w0OMt1c+dZTef473nw6IwVS5jWnELCmQ/te5ti7HeF1FDHOY8dd8jvHrzUT6SAzDSDwzg6ZADUwe/zkvM+XyRkeLcrCLmUlSWPp61XI9UWK01plL5GQcL9w8xixXmBGcv/fi/sTFy8Hm3Eg/u2dGO6RspN823mAk4pjathhvzBhjsknm/iqdi6oK5yZmosKenUbNKj5LLHaZZxY1bWKGOd7Eb8X3XCMhBCsrNx+vYDZE64SYugRiBrdKHTODHmagFHtm4oX3J55TtrmVzvU3bu9Anve89x2QdmPnKt6swfI+2E33HJ/+zOuQ5879PUgrSjQBevrGdUi7vZPufYbEHaeeE6PDKc45JTEevX4T3/Mdzz2VvJ4Qw8XRCNd3//0P/zikmekbRyGEEEIIIYQQj0EbRyGEEEIIIYQQS9HGUQghhBBCCCHEUs4kMMvy3LaupAfVXyOH2T99I9U03ryKeTZGRF9SEF0Y0Rse1NPk9dER6hDuvI6/QX7lzgGkHR6ghtIfMGpmNhmnh7Jfc/VgZra9NYK0QUVO94xEQ+UPUmWaAJJGpAmW+9++m1nvDgZmB4zWNWofvKbBzGw0RH1nQX6nPZ2lv8me1ajBM6KRWxchBCtd21/ZQS3u0GlZswzbk2ocyaG6RYFtte20JTvk9+iTjSmkjQ8xGOZYNGtrrPN7r6f94JVXdyHPzRtYVt8vzMx6os+azdI62314BHmO9jGGimID0kaYZLdvp+20sYllNaJd5GeXs8Ok6YnI6e1XPWh9jQRDHUrXkXHF61mJJmVzC/WGk03Ukbz2Csbq0X1XLx3GeDbCurty6wqkzfZwrK9Jmh/zBhPs2/kI4/nhfdTBHx/ivAT6IRYy/ARySKLh5rRIWU5ii0wIOdFWt+xwdLwbprGynjMBWIyo/8vJPFOUaYwTyaDNiNZoSrRGVJ+V+/GMitBIGstHtOTkPaduvVQTfW5b4zg4GqI+a4P4OFROxzWoUAfJDoEnQ6OxhwfNHHluVmVUKUrinkr8vMaRlfW8jeMhWOnWDrz/+lTMRXXRJKCp7hGuxXmChINlxK9jMMCMG0RnOxq7547MxwHvv0d8IY72cB3yyp27yesXPvMy5Jl5LbGZXb+Oc9Nzz6Dm//p22q8GJT737Hg1P4adbVwMve+974S097zv6eT1aAPjeThYPcb1jaMQQgghhBBCiKVo4yiEEEIIIYQQYinaOAohhBBCCCGEWIo2jkIIIYQQQgghlnJGc5zMxpupsPPaNRSEXnVmOOMxGh8U5MDckgjYyRnKVjuR6JyI1e/dvwdpDx+gEJYd8LuxgaLw7Z3UCOLGtR3Ic+vmNUi7cRUNJIYDrPamScW2GRFod0R4zLxlvMGLmVnmTlLtiHMDO6SXmTSwzxvYocj+dkzoTk1H1kSWmY3dwfE3b6OBxmScPgfxBbKeHIKcEXE5E4lv7aQGHTdvYQy9voumDbuHeP/pHO/fdhgfew/S+PvFj70KeZh4/eYNNInKAhrTHB2k8fHayyguPzpEg4bhAM1Krl7Hcjx9O+2zkxGOOX2P4wT75CywU4dZf3HuFD11XiC3WjPey6noiKGKb0PiGHHlCo79A2Iu8+orn4U0f/j6tVsYR5vvwDi9/S40G3jxp7Fd27t4bejT5xxN8D3zEst/dPwQ0uZTHO/Grn8EwzhtVzQ+YYETXRvwA+VXuRMzzTDrme0IvAe77nwRLVrt53XSpwtnEkO8JyySOilKjDdmVFM6850sw3jwJj5mZoGYHgViesSNt8LSl6emESMZmrbCge90VGWHzLMD5eHAerwXM98JpG5zmobjQu7m4JjRk+4xbY3EiGtXZp6CJjfs2R4/1qxMRsxxyLrVyLpnMkEjnFs30Wzt1dd3k9evv/Ya5Hm4i0Y4wwLXBB0xbNw/SvcJxyTP1as4973vPWhK845nrkPaltsPZdSMD9tksoFz07MFrpmeJgZy153JYznAjVWer97m+sZRCCGEEEIIIcRStHEUQgghhBBCCLEUbRyFEEIIIYQQQixFG0chhBBCCCGEEEs5kzlOnme2tZWaUGxto5GMN8MpmNNLRFOajAh3sxL3toMqFZ3nOTEhIKY3TYuGIkWJ1443NiDtqjMBunEDRbvPPH0L0m4/jeLYyQRFutalAlzmGdN35Jl6TOuJCLgq0zRWZxn5HIGJzpsGxc7zOQqIg2vPjIjtW+ZIsCbKMrMbt1IB8q2n0OhlYzN9rhiwDSwjbUwE4d4UiTHZwDLcuIamPbuHWI55i+85nWLZjg/Scrz4AhqCWEaE2E9hm5YFlnd2nN7/4AGWNfZ4/50dHF9u3cLyb287wyIy5lAzBmLawIxDVnOZOD+xfBrBgg0sbZ9NYgjTt6m5QF3h898iwv/BBrbNl3zl+yBt+OXpWBMNDUfyKzg9tTM29nwa0gIxwTBnajIhZSUeJHZwuAdp2YAZUaTzS0XKwMxWuhaNCmIkZmNFGpe14XWBGKM1zOiCPCczL8ucOUnssG8UrGOtkRjNevfMHZlQG7dG6Jg5HLl/IHNnT8aMobufN8sxw3KamRkzdSFjalEQs7wyTWMGShkxl6kqYmBITfacARQzgSNGRNRvjBFdRmqOs6JhDjGnCiTwvTkJmRLOnTmOGfbXVfyNWB9ndcImS3qty5eTQrBy9aSSWf+4fesGpL332I17L96BPA+IeeDeMc4xbJ0wHqf1cetpLMM730GMcJ5+CtJ2ttgck5aNDQED0h9vEkPOrsd6vLKN66jcr1EbMuecYS2ubxyFEEIIIYQQQixFG0chhBBCCCGEEEvRxlEIIYQQQgghxFK0cRRCCCGEEEIIsZQzmeOURW5P3UxNYa5f34F815xhTmko4u7mR5AWa0yjgMkIijqHwyG58ABSmAB8a3sT0q7f3Ele3yTmOLdJ2vWdbUgrieFP6NNyRGJ60zXMWAFFzG2PwteiT4XiFREiZzmKapmIuW2xPZl5SOZMBIim3QqmnF4TZZXb08+mbX/rKayTapAavcxmaHDD6ogZMWVEwJ/laZvu7KARzo1bGB8PD1H87Q0gzMwOD/GZyjwVY+/tYV/87MuYNid9tiqwobv2MHkdehSvb4zRCOfaDayfmzeJOc5mOgbkgRiHrGp64w0aFomPTWKRHIkh0joJhgYGLTPdaqfJ6+1bW5DnBjED2yLj3XCDjM/OaKGLGA+xwjHqzvP3Ie3oCPtfJHNO4/pVPsAW62ssx+HBPqTlZOYEr5IGnzuPxLCDuSOQcMuCfwMWcdg3emIMkxHznUDiPs/T92DvmBEDlnUSDI1RmAGIL3ZPzCGaiOPI1I4hjTaYi3FqJEMMR8oSg2swxDE7knr3t8tJoDLDnDFZL43GaJo1GDjjQ2qgw8xrIAmMVcwW5l3Jvcj6gxmasWE2MpcbOgWEx+bx5Vo3IQQrneEhW1+ZM39h7UBnO2aUtcpajRQiRoyRghhMMeOt8Rjj8n3vfUeaZwvnpge7OCfM5zjmjQakrw3ScmxtYj+4so3z3NYGrl9YfXddWo6KGF9ZwHLdvEXqlgTrBjF9izAHrGZqdRr6xlEIIYQQQgghxFK0cRRCCCGEEEIIsRRtHIUQQgghhBBCLEUbRyGEEEIIIYQQSzmTOU6eZ7axmQpAxyMUYo5c2pC8Sz9AUWdfE/OXhpgctKmBQUfE/9NjFLDPaxTMWoHi23KAwt1BlT5EWZDyd2is0M6nkFYaimFzZ5gTApahINd1ZOvf91hn3iBhNsNy9cyggoh0CyK4L0tirOPes+vQaCDLz4/ovCiC3biVPsfWBubru7Tu6jk+17zBhtkaoMi6yDB223lqJFNW5F6bxNQJNeK2f4hlq2dohjJxIvS2w37NYnJ6jH2qzkgcOWOqKzt4r6vXMYZu3ML6uX4D+96W87PKSL32RHzP7T5WJS59+dbv//YTsmCDcdq28x7bsAlpG954Fo1wxltoEJAZxnggZl2+KcoSx+G+xxhvDh/gvVqs4xkxFysnadnG26TDkOaaE/Mri3h/b/RF/GeMBQn3tCDmFC6NTd6BGdUw8x1yf/bw3twhI7c/R0O4mZmFLLOq8iYuOHeWzJTCwcwnvLmFGTdDi316LasmZi4zHmEfyjMcL5nBXTVIx9C2xjhlITIa4Ni7uYGT38DlY3NCRk2A8D0zUrcBTK1Y71gtdqmf2UppK95/jYQQIHbKDOfswtL2KsBgi5tz9cTVhU9vaT5mvMQMAI0YsXTEyI+952icPtMzz1yHPDdvYfnrejXDQr+2HxKzv4r0W+L3Y32PT5Dnab/NSB9iI9Mkw3GBme/lBTF982MWMz8i5TgNfeMohBBCCCGEEGIp2jgKIYQQQgghhFiKNo5CCCGEEEIIIZaijaMQQgghhBBCiKWcyRwny4KNx6lss6pQZFmWaVpFBKiWo9DTKixOS4xHptNUcNoQ04vDKZq/dCC8NhsR556NTSzb1s4kec1MgSIxgZjN0KQnM7w2cyLmqmB1tppAOxKxc+vMIphonmj+LQQsqw2JeJ+IhZsmNZVoiTnOYPh4g4InRVXl9s53poYAkzF+tnL//sPk9Sc/fQfyTGu87t3veiekPXWDGIzEtM5jxFje3MB2eeezO5BWFphvMkCzj6PDNN7GA9KeHcZVUWDZhuhzYuNJGt+3bmGmmzcmkHb9KvaD0YSJ6NM0EIObWSCGIIFL/gEm+vf6cmakcc48FczMLHqDhJKYDW3tJK+fevfTkCcnfSPkGFsVGUKCe8uQ4736+QzSZtNdvFeG71mUGKvjsTN2I+NYTUzD5rMjSIsBx7LoDB9iiQ/eEzcz5tlEfXUczKjG1+uiICTuI/YrVo7obhgN67UjaeskyzIbubYuiaFb5cxxOtLHezKXsvk1J2YfhXPLKMkcOSSdIxCjmjEZVOcNjoPzuZtzSZ5A4sGbCZmZbWzgeFy5NVogJhuBDHoZTUOCM1KJ8a2Y46yWtoo5DnvOtRIjmDSVOTFLytN2zQOLB5JG6iCSNL+mZnkCMUtifc2I2RM1+3LzbEXMa9iWY0zWmoG4NmWuL7Nld0bMquIKZTVD06yGmFIymOkN7UXEHLPwkwWZKJozDOP6xlEIIYQQQgghxFK0cRRCCCGEEEIIsRRtHIUQQgghhBBCLOVMGscQgg3hN+5E8+D0dJFpLOhvoZnugugGmzTteIZamJocJloN8Hf82+SE92tX8WDoa9fStMmEHOZOfgzdNai/mZHflJsrb2RCMfJ76abB30d3REvYOf0lVXaR320H8lvonvyGuiXlaNv0PXt6Ivb50TgWhdn1a2kbZkRtdP9BGm8///HXIE/Xo05lOMKDaq/u7EDaltPnxAbbc1hguW7dvAJpo+Emvuc20Tjup/344QNsz6MZOfS8wPjY2MBn395J07a2cOiZbDC9MSRZSfRr0cVW7DEPO4h4NYUjp4exj2kQ3so7vP3E2NucjEmeySSNQX9osZnZ3gHqAbMM670i43rj9KZ9h3XXHGM5d52+2MwsI3PQgIxbudN/7b16H/I8JPE8nx9AmuXY/6Yu5rIM+0EfyZRLQqRmOnV/GdOXkQOnmba3J7FKsoGeD2PeLDtnGsc8z21nK9WOM41jCRpHbNOOtAM7RZ5pBMejdA4fEe3iaIhpeUlihLSN17iZmc2dJ0RL1kHsZr4uzLj+EvoaWQuQodeIPJ6dQW6+upk2MjDdI01jb4BJvj6obpPo9NZJNLOGmVL4fP552fOzhiCPy+od4mFFKSjTQjLojMraFTKxMYk9PGtXnw/zcD0juRPJ570QqH6WjLOMwMpGrvWloG95Bhnv+eoNQgghhBBCCCHOHdo4CiGEEEIIIYRYijaOQgghhBBCCCGWoo2jEEIIIYQQQoilnNEcx6z0h1AyEX+dGmvMmFEFE5cScfrBERowPNxLzQoOD4l5ARF6jsdoOLO9g+4bO1fwUPbtrTTfcECMZEhdsIPI2xrNTg5naZ01xBDFiAlBQ4wu2AHFRZl+RjCosC6KEoX6IWCIRGJk4Y1wFtemr/2By2Zm+TkSnWdZtNEobQd6UG2eGiO1hmZKMcNnnTdYbzU5dRVMhMhh40zoXZBDqFmcbm5gWulMTTYm2O5tT07VJSYkFTlQfjBKn2kyJodhj8nB1MQIJ2OGTS7m38pxzUysTsXwXqRPyrWihv6JEUKw0plgFcQ0qD5I+8Gnfv4FzENMb5jDSiDOGAduDGTD3YCYIMz39iCtmxLjE9I/5gfHyevn/9cX8V7E9KaZHkPaaIjmaE2bXpt1xIypJwfFM4OwSIzifN2SOM2YSQjpDD07CJ2ZKgTfr7B+ztEQbmZmWQg2HqTzW1GQGCkfb47Tk7ULGwv8vczMBlWaVhHTm5KlVXivjBjvsbKVRRo3TcPMcbDB8hzjrSJ11jvjPWbQ4w84X6RhKajHyQpmLoGZLa5imGLcUAo7yCp51k/I03bsybP5an+7pyNvcumNX06/bjVW8DJi0y73AGJGSyTNGzLxsq4217NyRN8qKxrVsLLCvcwssILApW/NyO+cDflCCCGEEEIIIc4b2jgKIYQQQgghhFiKNo5CCCGEEEIIIZaijaMQQgghhBBCiKUEavpwWuYQ7poZOgoI8fbyXIzxxpN+U8W3eEKsJb7NFOPiiaEYF5cdxbi47NAYP9PGUQghhBBCCCHE5x/6qaoQQgghhBBCiKVo4yiEEEIIIYQQYinaOAohhBBCCCGEWIo2jkIIIYQQQgghlqKNoxBCCCGEEEKIpWjjKIQQQgghhBBiKdo4CiGEEEIIIYRYijaOQgghhBBCCCGWoo2jEEIIIYQQQoil/P8BR9ZYwKy9PeoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1152 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_random_images(scaled_train_images,train_labels,2,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UXYwWhHpz_GD"
   },
   "outputs": [],
   "source": [
    "grayscale_train_images = np.mean(scaled_train_images, axis= 3)\n",
    "grayscale_train_images = grayscale_train_images[...,np.newaxis]\n",
    "\n",
    "grayscale_test_images = np.mean(scaled_test_images, axis= 3)\n",
    "grayscale_test_images = grayscale_test_images[...,np.newaxis]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "colab_type": "code",
    "id": "CGHZvq4zz_GK",
    "outputId": "2d7f65e0-f983-4b95-f2dd-1cb6594237be"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x1152 with 10 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_random_images(grayscale_train_images,train_labels,2,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7e7iSyWXz_GN"
   },
   "source": [
    "## 2. MLP neural network classifier\n",
    "* Build an MLP classifier model using the Sequential API. Your model should use only Flatten and Dense layers, with the final layer having a 10-way softmax output. \n",
    "* You should design and build the model yourself. Feel free to experiment with different MLP architectures. _Hint: to achieve a reasonable accuracy you won't need to use more than 4 or 5 layers._\n",
    "* Print out the model summary (using the summary() method)\n",
    "* Compile and train the model (we recommend a maximum of 30 epochs), making use of both training and validation sets during the training run. \n",
    "* Your model should track at least one appropriate metric, and use at least two callbacks during training, one of which should be a ModelCheckpoint callback.\n",
    "* As a guide, you should aim to achieve a final categorical cross entropy training loss of less than 1.0 (the validation loss might be higher).\n",
    "* Plot the learning curves for loss vs epoch and accuracy vs epoch for both training and validation sets.\n",
    "* Compute and display the loss and accuracy of the trained model on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l14VCBFVz_GO"
   },
   "outputs": [],
   "source": [
    "def get_mlp_model(input_shape):\n",
    "\n",
    "  model = Sequential([\n",
    "                      Dense(64,activation = 'relu',input_shape = input_shape),\n",
    "                      Dense(64,activation = 'relu'),\n",
    "                      Flatten(),\n",
    "                      Dense(128,activation = 'relu'),\n",
    "                      Dense(128,activation = 'relu'),\n",
    "                      Dense(11,activation = 'softmax')\n",
    "\n",
    "\n",
    "\n",
    "  ])\n",
    "\n",
    "  model.compile(\n",
    "          optimizer = \"adam\",\n",
    "          loss = \"sparse_categorical_crossentropy\",\n",
    "          metrics = ['accuracy']\n",
    "      )\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "colab_type": "code",
    "id": "7eUgirn1pTWS",
    "outputId": "913e6df2-c806-497c-d10e-816c446f5b56"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 32, 32, 64)        128       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 32, 32, 64)        4160      \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 65536)             0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               8388736   \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 128)               16512     \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 11)                1419      \n",
      "=================================================================\n",
      "Total params: 8,410,955\n",
      "Trainable params: 8,410,955\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = get_mlp_model(grayscale_train_images[0].shape)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 717
    },
    "colab_type": "code",
    "id": "beEZO1kvz_GR",
    "outputId": "0f9d1b8c-9422-4270-d9e8-9861d415d7c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 1.6253 - accuracy: 0.4408\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.65784, saving model to /content/checkpoint_mlp_best\\checkpoint\n",
      "1946/1946 [==============================] - 233s 120ms/step - loss: 1.6253 - accuracy: 0.4408 - val_loss: 1.1043 - val_accuracy: 0.6578\n",
      "Epoch 2/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 1.0069 - accuracy: 0.6870\n",
      "Epoch 00002: val_accuracy improved from 0.65784 to 0.70907, saving model to /content/checkpoint_mlp_best\\checkpoint\n",
      "1946/1946 [==============================] - 244s 126ms/step - loss: 1.0069 - accuracy: 0.6870 - val_loss: 0.9389 - val_accuracy: 0.7091\n",
      "Epoch 3/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.8827 - accuracy: 0.7282\n",
      "Epoch 00003: val_accuracy improved from 0.70907 to 0.73164, saving model to /content/checkpoint_mlp_best\\checkpoint\n",
      "1946/1946 [==============================] - 254s 131ms/step - loss: 0.8827 - accuracy: 0.7282 - val_loss: 0.8736 - val_accuracy: 0.7316\n",
      "Epoch 4/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.8145 - accuracy: 0.7509\n",
      "Epoch 00004: val_accuracy improved from 0.73164 to 0.73655, saving model to /content/checkpoint_mlp_best\\checkpoint\n",
      "1946/1946 [==============================] - 250s 128ms/step - loss: 0.8145 - accuracy: 0.7509 - val_loss: 0.8578 - val_accuracy: 0.7366\n",
      "Epoch 5/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.7623 - accuracy: 0.7660\n",
      "Epoch 00005: val_accuracy improved from 0.73655 to 0.75312, saving model to /content/checkpoint_mlp_best\\checkpoint\n",
      "1946/1946 [==============================] - 259s 133ms/step - loss: 0.7623 - accuracy: 0.7660 - val_loss: 0.8117 - val_accuracy: 0.7531\n",
      "Epoch 6/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.7200 - accuracy: 0.7816\n",
      "Epoch 00006: val_accuracy improved from 0.75312 to 0.76349, saving model to /content/checkpoint_mlp_best\\checkpoint\n",
      "1946/1946 [==============================] - 253s 130ms/step - loss: 0.7200 - accuracy: 0.7816 - val_loss: 0.7769 - val_accuracy: 0.7635\n",
      "Epoch 7/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.6870 - accuracy: 0.7912\n",
      "Epoch 00007: val_accuracy did not improve from 0.76349\n",
      "1946/1946 [==============================] - 254s 131ms/step - loss: 0.6870 - accuracy: 0.7912 - val_loss: 0.7934 - val_accuracy: 0.7590\n",
      "Epoch 8/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.6575 - accuracy: 0.7998\n",
      "Epoch 00008: val_accuracy improved from 0.76349 to 0.77923, saving model to /content/checkpoint_mlp_best\\checkpoint\n",
      "1946/1946 [==============================] - 253s 130ms/step - loss: 0.6575 - accuracy: 0.7998 - val_loss: 0.7486 - val_accuracy: 0.7792\n",
      "Epoch 9/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.6316 - accuracy: 0.8075\n",
      "Epoch 00009: val_accuracy improved from 0.77923 to 0.78606, saving model to /content/checkpoint_mlp_best\\checkpoint\n",
      "1946/1946 [==============================] - 270s 139ms/step - loss: 0.6316 - accuracy: 0.8075 - val_loss: 0.7351 - val_accuracy: 0.7861\n",
      "Epoch 10/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.6104 - accuracy: 0.8145\n",
      "Epoch 00010: val_accuracy did not improve from 0.78606\n",
      "1946/1946 [==============================] - 248s 127ms/step - loss: 0.6104 - accuracy: 0.8145 - val_loss: 0.7439 - val_accuracy: 0.7826\n"
     ]
    }
   ],
   "source": [
    "early_stopping = EarlyStopping(monitor = \"val_accuracy\",mode = \"max\",patience = 3)\n",
    "\n",
    "\n",
    "\n",
    "checkpoint = ModelCheckpoint(filepath = '/content/checkpoint_mlp_best/checkpoint',save_freq = \"epoch\",save_weights_only = True,verbose = 1, save_best_only = True,monitor = \"val_accuracy\")\n",
    "\n",
    "history = model.fit(grayscale_train_images,train_labels,\n",
    "                    epochs = 10,\n",
    "                    validation_split= 0.15,\n",
    "                    callbacks=[early_stopping,checkpoint],\n",
    "                    verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "pPPbzGhVz_GW",
    "outputId": "514028b1-670c-4e13-e206-51abc942834a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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     "metadata": {
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   ],
   "source": [
    "\n",
    "try:\n",
    "    plt.plot(history.history['accuracy'])\n",
    "    plt.plot(history.history['val_accuracy'])\n",
    "except KeyError:\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "plt.title('Accuracy vs. epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='lower right')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "7b5_8VsCz_GZ",
    "outputId": "a31960e5-06c1-40c6-db22-2ad53e44ab01"
   },
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Loss vs. epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='upper right')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "b0kH6VYqz_Gc",
    "outputId": "bed011f6-70b7-43ad-bb91-9c265421f55a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "814/814 [==============================] - 27s 33ms/step - loss: 0.9151 - accuracy: 0.7503\n",
      "Test loss: 0.9151020050048828\n",
      "Test accuracy: 0.7503457069396973\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_accuracy = model.evaluate(grayscale_test_images, test_labels)\n",
    "print(f\"Test loss: {test_loss}\")\n",
    "print(f\"Test accuracy: {test_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ncPtDtCLz_Gg"
   },
   "source": [
    "## 3. CNN neural network classifier\n",
    "* Build a CNN classifier model using the Sequential API. Your model should use the Conv2D, MaxPool2D, BatchNormalization, Flatten, Dense and Dropout layers. The final layer should again have a 10-way softmax output. \n",
    "* You should design and build the model yourself. Feel free to experiment with different CNN architectures. _Hint: to achieve a reasonable accuracy you won't need to use more than 2 or 3 convolutional layers and 2 fully connected layers.)_\n",
    "* The CNN model should use fewer trainable parameters than your MLP model.\n",
    "* Compile and train the model (we recommend a maximum of 30 epochs), making use of both training and validation sets during the training run.\n",
    "* Your model should track at least one appropriate metric, and use at least two callbacks during training, one of which should be a ModelCheckpoint callback.\n",
    "* You should aim to beat the MLP model performance with fewer parameters!\n",
    "* Plot the learning curves for loss vs epoch and accuracy vs epoch for both training and validation sets.\n",
    "* Compute and display the loss and accuracy of the trained model on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yk2mH3Npz_Gh"
   },
   "outputs": [],
   "source": [
    "def get_cnn_model(input_shape):\n",
    "\n",
    "  model = Sequential([\n",
    "                          \n",
    "\n",
    "          Conv2D(64,input_shape = input_shape,kernel_size = (3,3),activation = \"relu\",padding = 'SAME'),\n",
    "          Conv2D(64,kernel_size = (3,3),activation = \"relu\",padding = 'SAME'),\n",
    "          MaxPooling2D(pool_size = (2,2)),\n",
    "          BatchNormalization(),\n",
    "          Conv2D(128,kernel_size = (3,3),activation = \"relu\",padding = 'SAME'),\n",
    "          MaxPooling2D(pool_size = (2,2)),\n",
    "          Dropout(0.5),\n",
    "          Flatten(),\n",
    "          Dense(64,activation = \"relu\"),\n",
    "          Dense(64,activation = \"relu\"),\n",
    "          Dense(11,activation = \"softmax\")\n",
    "  ])\n",
    "\n",
    "\n",
    "  model.compile(optimizer='adam',\n",
    "                    loss='sparse_categorical_crossentropy',\n",
    "                    metrics=['accuracy'])\n",
    "\n",
    "  return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 527
    },
    "colab_type": "code",
    "id": "lbgRgZ5cz_Gn",
    "outputId": "af258135-eee7-4573-831e-5f1b1195bae1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 32, 32, 64)        640       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 16, 16, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                524352    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 11)                715       \n",
      "=================================================================\n",
      "Total params: 640,907\n",
      "Trainable params: 640,779\n",
      "Non-trainable params: 128\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn_model = get_cnn_model(grayscale_train_images[0].shape)\n",
    "cnn_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 649
    },
    "colab_type": "code",
    "id": "nkmS2vV2z_Gs",
    "outputId": "fe2c9d1c-3e6e-4ce2-d475-54e20c33464a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.7535 - accuracy: 0.7612\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.86113, saving model to /content/checkpoint_cnn_best\\checkpoint\n",
      "1946/1946 [==============================] - 521s 268ms/step - loss: 0.7535 - accuracy: 0.7612 - val_loss: 0.4573 - val_accuracy: 0.8611\n",
      "Epoch 2/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.4506 - accuracy: 0.8624\n",
      "Epoch 00002: val_accuracy did not improve from 0.86113\n",
      "1946/1946 [==============================] - 514s 264ms/step - loss: 0.4506 - accuracy: 0.8624 - val_loss: 0.4984 - val_accuracy: 0.8509\n",
      "Epoch 3/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.3974 - accuracy: 0.8786\n",
      "Epoch 00003: val_accuracy improved from 0.86113 to 0.87606, saving model to /content/checkpoint_cnn_best\\checkpoint\n",
      "1946/1946 [==============================] - 554s 285ms/step - loss: 0.3974 - accuracy: 0.8786 - val_loss: 0.3995 - val_accuracy: 0.8761\n",
      "Epoch 4/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.3600 - accuracy: 0.8904\n",
      "Epoch 00004: val_accuracy improved from 0.87606 to 0.89180, saving model to /content/checkpoint_cnn_best\\checkpoint\n",
      "1946/1946 [==============================] - 609s 313ms/step - loss: 0.3600 - accuracy: 0.8904 - val_loss: 0.3685 - val_accuracy: 0.8918\n",
      "Epoch 5/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.3314 - accuracy: 0.8991\n",
      "Epoch 00005: val_accuracy improved from 0.89180 to 0.90891, saving model to /content/checkpoint_cnn_best\\checkpoint\n",
      "1946/1946 [==============================] - 645s 332ms/step - loss: 0.3314 - accuracy: 0.8991 - val_loss: 0.3094 - val_accuracy: 0.9089\n",
      "Epoch 6/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.3104 - accuracy: 0.9049\n",
      "Epoch 00006: val_accuracy did not improve from 0.90891\n",
      "1946/1946 [==============================] - 632s 325ms/step - loss: 0.3104 - accuracy: 0.9049 - val_loss: 0.3592 - val_accuracy: 0.8950\n",
      "Epoch 7/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.2948 - accuracy: 0.9112\n",
      "Epoch 00007: val_accuracy improved from 0.90891 to 0.91300, saving model to /content/checkpoint_cnn_best\\checkpoint\n",
      "1946/1946 [==============================] - 675s 347ms/step - loss: 0.2948 - accuracy: 0.9112 - val_loss: 0.3000 - val_accuracy: 0.9130\n",
      "Epoch 8/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.2776 - accuracy: 0.9159\n",
      "Epoch 00008: val_accuracy did not improve from 0.91300\n",
      "1946/1946 [==============================] - 641s 330ms/step - loss: 0.2776 - accuracy: 0.9159 - val_loss: 0.3266 - val_accuracy: 0.9032\n",
      "Epoch 9/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.2622 - accuracy: 0.9201\n",
      "Epoch 00009: val_accuracy improved from 0.91300 to 0.91373, saving model to /content/checkpoint_cnn_best\\checkpoint\n",
      "1946/1946 [==============================] - 625s 321ms/step - loss: 0.2622 - accuracy: 0.9201 - val_loss: 0.2985 - val_accuracy: 0.9137\n",
      "Epoch 10/10\n",
      "1946/1946 [==============================] - ETA: 0s - loss: 0.2498 - accuracy: 0.9240\n",
      "Epoch 00010: val_accuracy did not improve from 0.91373\n",
      "1946/1946 [==============================] - 713s 366ms/step - loss: 0.2498 - accuracy: 0.9240 - val_loss: 0.2917 - val_accuracy: 0.9126\n"
     ]
    }
   ],
   "source": [
    "early_stopping = EarlyStopping(monitor = \"val_accuracy\",mode = \"max\",patience = 3)\n",
    "\n",
    "checkpoint = ModelCheckpoint(filepath = '/content/checkpoint_cnn_best/checkpoint',save_freq = \"epoch\",save_weights_only = True,verbose = 1, save_best_only = True,monitor = \"val_accuracy\")\n",
    "\n",
    "\n",
    "history = cnn_model.fit(grayscale_train_images,train_labels,epochs = 10,validation_split= 0.15,callbacks = [checkpoint,early_stopping])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "VytQECDVz_Gv",
    "outputId": "2dcb2ab2-3619-4617-feb5-dd12495c861e"
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    plt.plot(history.history['accuracy'])\n",
    "    plt.plot(history.history['val_accuracy'])\n",
    "except KeyError:\n",
    "    plt.plot(history.history['acc'])\n",
    "    plt.plot(history.history['val_acc'])\n",
    "plt.title('Accuracy vs. epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='lower right')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "60mJypwQz_Gx",
    "outputId": "7e1a0317-1578-47fe-c561-a46023e39ff9"
   },
   "outputs": [],
   "source": [
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Loss vs. epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Training', 'Validation'], loc='upper right')\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "w2v80qosz_G0",
    "outputId": "fa802da4-ddf1-4373-9a50-e1e48b2a9d2c"
   },
   "outputs": [],
   "source": [
    "test_loss, test_accuracy = cnn_model.evaluate(grayscale_test_images, test_labels)\n",
    "print(f\"Test loss: {test_loss}\")\n",
    "print(f\"Test accuracy: {test_accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3iBYFPWUz_G4"
   },
   "source": [
    "## 4. Get model predictions\n",
    "* Load the best weights for the MLP and CNN models that you saved during the training run.\n",
    "* Randomly select 5 images and corresponding labels from the test set and display the images with their labels.\n",
    "* Alongside the image and label, show each model’s predictive distribution as a bar chart, and the final model prediction given by the label with maximum probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "XMYYWs0oz_G5",
    "outputId": "ed72eb4a-ed5f-4322-865b-9cadd3fd0059"
   },
   "outputs": [],
   "source": [
    "model_best_epoch_file = tf.train.latest_checkpoint(\"checkpoint_mlp_best\")\n",
    "model = get_mlp_model(grayscale_train_images[0].shape)\n",
    "model.load_weights(model_best_epoch_file)\n",
    "\n",
    "test_loss, test_accuracy = model.evaluate(grayscale_test_images, test_labels)\n",
    "print(\"MLP best weights model \")\n",
    "print(f\"Test loss: {test_loss}\")\n",
    "print(f\"Test accuracy: {test_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "colab_type": "code",
    "id": "8yUPWbFCz_G8",
    "outputId": "eba978e4-d225-4488-f3fd-4600fa4b51b6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "814/814 [==============================] - 44s 54ms/step - loss: 0.3344 - accuracy: 0.9015\n",
      "CNN best weights model \n",
      "Test loss: 0.3344075083732605\n",
      "Test accuracy: 0.9015442728996277\n"
     ]
    }
   ],
   "source": [
    "model_best_epoch_file = tf.train.latest_checkpoint(\"checkpoint_cnn_best\")\n",
    "model = get_cnn_model(grayscale_train_images[0].shape)\n",
    "model.load_weights(model_best_epoch_file)\n",
    "\n",
    "test_loss, test_accuracy = model.evaluate(grayscale_test_images, test_labels)\n",
    "print(\"CNN best weights model \")\n",
    "print(f\"Test loss: {test_loss}\")\n",
    "print(f\"Test accuracy: {test_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 716
    },
    "colab_type": "code",
    "id": "W48syko0z_G-",
    "outputId": "c841bce4-44eb-405b-8071-25c16b30810f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iWkmsmnMAfNkivwXQRfLQA1OdiIiITBezuYuFSL2WIlpSeNym+Lp9+tGSvAjARQCwYsWKA1KcHBxWXvbDRpeADVe9stEliIg0VF0T5D25dty4OfrEeW7TcHL9kXN2JeOjW7Yl45VNO5NxVypsDwBtLgKx2CUHUu4Lf3OL4ctqoyVf+At36HHjnOtckbNMxXGx7KR5ysVAulLheF3p8OW4xZneZLwh1Z2Mm1PhMQxkwxf2+sdCJqNnTviyYK69qeTY+dZQS6El1MjmsB1z4RjVuoXIlKn0FFf8FqeZXQvgWgBYvXr17GwmLiIicpBSxEIk2ARgubu8DMCWBtUiIiIiDaIJskhwC4A3xN0sTgHQq+V+RUREDj51RSxGB5uwYc0yAEC+I3R/WLcwfI/pmEMWJeOT521Ixs9o9dFO4Mjs7mRc8At/uA+rfR/lza6v8aNjhyTjPw4vTsabRrqS8YDrUVxtoQ8AmJMNvYXnNYWIxeLmvmTcnQldOgruU/i2VFj5I+2iGq2Z0JWDTeF6K2tC4Uth0S28UnCdNdxCIbN1WfADheRNAE4H0E1yE4DLAWQBwMyuAXArgFcAWA9gCMCbG1OpiIiINJK+pCcHDTM7b5LbDcAlB6gcERERmaYUsRARERERceo6g5wZAeY9EI1zHeGug0tC/OG+kXB93i0v3dIdYgdA+eIiQ+6WECPYXmhNxg+MLE3Gv+5ZlYwf3hPiFnt7w7LOhRGXZ8i7LENZxALpcLx0W+gYMbczdLE4vCu0zj2sLYwXZMM2KVd3JhUiEqyylDUAuM2QGnP3GXXPVd4tllJUxEJERERkf9MZZBERERERRxNkERERERGnroiFpYDcnCii4BpDIDMYYgv5vWGRiw3t85PxPc3LSvY1Nx1iFS18wo1D1GB3MUQmnhhdEPbbF/a7e3eId9AdOzMWakq5xEJ6uCxi4VILxaZsMt47L+yr/5Cw8MfYoeGBpzq2h/u66MZIIezHiuF699CiWkZDh4qSWMVYGFuxCBERERE5cHQGWURERETE0QRZRERERMTRBFlERERExKkrg1xoAgZWRKHdlOs+lnJ53/RImHMP9YXs7ta5c0v2tb09XO5pCu3culLDyXikGHLAoy70XChZgi4MzbVsc3ctae2WLuuUlhlxF8KhQbfs3UhTeBx7utrC5m3hICm3kl6+GJ6DkgxyeZzYXWbeXfCt3bwUK18vIiIiIlNGZ5BFRERERBxNkEVEREREnLoiFunWPDqP3Q0A6O0PUYP8jhBB8LEBG6s+/866jMaIZd04tDhrT40m46XNPcn4yLm7k/GcprFkPHRI2E/arWA3MBqiEHt3dpTUkd4T7pMZChGGQqvLYjSH/mytWVdfJtSXdg+8LRNqSmXD9S61Ed8YhpYJF9gc6qVWzxMRERE5oHQGWURERETE0QRZRERERMSpK2LRlsnhxEM2AwAezC5Krt/cFyIB6UE3585X77rQlgoxhDQqxwjaXMRiWVOIVeQ6QlZhVbuPVYQ4w9x0aEmxPdeZjH/dcmTJMTa1zEvGY24lPnPRiNaOUMfCloGKx/DHbsuEGEYqFR6blb0dKWbC82NZl79I+yyGW36P6mIhIiIisr/pDLKIiIiIiKMJsoiIiIiIU1fEIsMiurJDAIDmTOXFLOhiFX6cKotRdKWHknGHWxyky8UqvKZMiBr47hYtdF0lGGIbc902Wwqhc0Vf3nXcAJBzi3rsYIhiZJvC41s+ryeM2/Ym4+5sfzIuusVIOrNh9ZFMNtRtZc+2pV1kIuUWF3ELgtAlLFAoX2lERERERKaaziCLiIiIiDiaIIuIiIiIOHVFLFIsoi0dxRia0y5i4Rbl8EkKFkJUYDgfuk0AQM6tmtHhOlrMT4VMQc5tn+VIxe1bXAahI+UjHcGeot9TKd8XwschOtvC8Za09yZjH6tocZGOEYQOGLlieGzFoqupxjU/ShYH8WPToiEiIiIi+5vOIIuIiIiIOJogi4iIiIg4dUUsCpbCQL4ZADBaqHJXn1lwiYCxQrpks/5CazLOInRn6EiF/Y5YwW0TdpZzsYo2t3iG7/Gw08U7Hhk7JBlvGFxQUkfPUKgjnws10sVG2tM+0hGiJWm3jY9VjLpxoRDeg6R9Rwqg5PkpiU/4cbFYeSwiIiIi+4XOIIuIiIiIOJogi4iIiIg4dUUshgtZrOs9FACwo39Ocj1Hwzy7pKGFS1V0NJcuADI/M5CMR92GOwuh40TO5TV8D4wWH+NwCi6asLPYnowfGw0Ri029c0vuM9QXFg4xF8sYaG1OxnvG2sIx2lxNLm7hu3KMFcPTaq6LBSZoQuEXB7GMi3qURCz0fkZERERkf9OMS0RERETE0QRZRERERMSpK2IxmsvgT5ujuEJxMIQesgOV59nWFOIBc5uGS27rSg8m4xFzZVRp1NCWCnGGFpdV8Jv3uAjCxlzoVvHwwKKwzZ458FI7wwIfPh4ykArdLXZ2hvv05kPcYn46xESKFo6dd3VYPozdQ4gvhwP6YyOVqjyu9uSIiIiIyJTRGWQREREREUcTZBERERERp66IRWo4hZYHougB/af9rlHDWJeLDbS7WES6NF9QcJGE+emRMK5hyl50B+wvhuNtzHcl4weHl4TxrhCxaH4sdKcAgPZN4f4pt5BH71EherF7QYhVbOkIXTCybsGSNCvHH8x1+MiUpkyQGQz352jo3lGyIIhN0PpCRERERKacziCLiIiIiDiaIIuIiIiIOHVFLNJjwNzHoo//3VoYyLnFM3LtbvtMiApkfX4BQM51rsi6rhRtDNGGNMN+/SIgQxbiCGMuqpFDWGDDRx5SqTBmaRnIDof9ZgfDdsN9ob6BoRDL2DkSOlp0ZUNmosPFRPJFt9CH72IxVhqXSI+6iEW+Sqyi4K7Pl7XBEBEREZEppzPIIiIiIiKOJsgiIiIiIk5dEQsWDE19USzAUiH+kBkJ47G5IV4wOBbGBXOtLlAagej3cYviWDJucRGLLFMVxwvSYT9F7A3btP8xGe89NHSh+MFRHaUPykJ8om17OJ4vN9cXtulf2IJKchYe60jBPa0u0lHe6KLkKWHp85NwHS0sX6i8jdSM5JkA/gNAGsB1ZnZV2e2nA/gegMfiq242sw8dyBpFRESkseqaIIvMZCTTAD4N4KUANgG4i+QtZvZA2aa/MrOzD3iBIiIiMi0oYiEHk5MBrDezR81sDMDXAJzT4JpERERkmtEEWQ4mSwFsdJc3xdeVO5XkvSR/RPK4SjsieRHJNSTX7Ny5c3/UKiIiIg1SV8SimCZG5sdZW9+xzMVnffs3MGw0Viw91GDR5XqLobVbi+vD1lQlltvCsK+0C/kuSofx/FTII4/MXZeMH13ZXbKvdYUwP7JMqKOYnXwFO7+S3kgxm4xzvs1bITwIlu+yWu5Y9pdKT3j5T2UtgMPMbIDkKwB8F8Cqfe5kdi2AawFg9erVWu5QRERkFtEZZDmYbAKw3F1eBmCL38DM+sxsIB7fCiBLsvRdlYiIiMxqmiDLweQuAKtIHk6yCcC5AG7xG5BcTEan9kmejOh3ZPcBr1REREQapq6IRaEF2Pu06FPqVC58Wp0KndkwOt+tTNcSVn7LF0vn4ltyXcl4YbovGc9PhRXpCub6ovm2a1a53VkLQ7ShOx3asZ2EHcl44yH3l9xn1LVkW49F7iCh3nR7eBytmbCKn+cfX67gIxZuo7I2b9WwWOUT+5QiGU+FmeVJXgrgJ4javF1vZutIXhzffg2A1wH4O5J5AMMAzjUzRShEREQOImrzJgeVODZxa9l117jx1QCuPtB1iYiIyPShiIWIiIiIiFPfGeSWIrBqEAAwOhruasMhUpDpCBGEJfN7k/EhLQOlu2KILQy51ex2FsOqd3vcKnIFN5f3q9Z57QxZjyOyg8k467pFPK255DtZOK4rxCp2dbcn48Hh0NEimw05iYzLTPg6Rku6WIRaWZygi4WIiIiITDs6gywiIiIi4miCLCIiIiLi1BWxaMnmcNyhWwEAg7kQi+gdDR0j5jaHLhRHdu5Kxoe3lq42tjATOlekXHuHPYU5ybivEPa7PT83GW8bDWMfvZibGU7Gz257LBmvyIRFQ9Jl60J0Z0P0o6ttGJXQZSMyqVDrqFv8ZLgQIhajuXC972LBsi4WJd0qrMpYRERERA4onUEWEREREXE0QRYRERERceqKWBBAKo4b+NhB2sUO2rOjyXhRU4hRHNuyuWRfyzM9ybhoodPD5kKIT/hYxQMDS5Lxhv75yXgkHx5CSyZ0xhiYHyIgq1q3J+NUWSuJ3nxrMi647hO+JrjxmFtYZMxFLPrz4XgjYyFukXLriqRyZdEJH7HIuyyG696BQuVFUURERERk/9AZZBERERERRxNkERERERGnrohF3lLYOxot5DHqog0+muC1uHxBV2qo5LYl6RAd8M0dRiws8OE7Tgzmw8IdPUMhFjEwGDpdmItCDI6F7f8055Bk3JYJi4kAwN4RtzDJYBjn8+ExNTWF6EaqvBXFeN2ui0U+5xZOGQs1pQqlEQtW61bhohfmtymqu4WIiIjI/qYzyCIiIiIijibIIiIiIiJOfRGLQgq7Btr3ud53sfCdHYaKLhZRbCu5z5CFyMWidOgAcUQ2LDSyu2VLMt7e3pmMdwx1JOO+/hC3sJ5wvF17QqeLXakwtvQEMQWXnuCcEKtobw2dOea4Lh2tqRDXSLk4SGkHDDcseztiaXcFiUrori+/v4iIiIhMPU25REREREQcTZBFRERERJy6IhbFIjEYd41IuVhFU3OIIwzmQsxh51iIQmzILizZVztDPCGLsKBI2kUKFqRCR4vDm3cm460dLm7RNycZjxRCVKOpN+wnPVw9pmCh+QTybSEPUWgPj6mzJcQq5jeFaEhnJsRBmtK+04XLVbjjWVmKwjKurmz4UTCTdhuFfVXteiEiIiIiU0ZnkEVEREREHE2QRUREREScuiIWKBCF/iiTUGiqvGBGXzbEHDYMhO4RhbJ8wUAhLPDR17I5GR+S7k/GflGOI5p2hH3NDfP6HcMhxvFwr1s0pC9kJ9x6JShf58Ov3WFzwoWm1nCnpe29yXhF855k3Ox23J5ZEI6Rcgt9uLREMVP6HBSy4XGkm8OGNubiFv4OiliIiIiI7Hc6gywiIiIi4miCLCIiIiLi1BexKBKZvigKUGwKc+tcLgQBeovh+nwhxAZGC6WHGi6ECETBtZY4qmVbMl6a2evGodNFVzp0kti1IEQshlwHjY1ucZBcZzgWy1IKxZaQuch2ha4URy8KXTNO7HwiGS/Jhpp6CmHxE79QSCpVJQpR3sUi7a5IufcqvotFvuDuoIiFiIiIyP6mM8giIiIiIo4myCIiIiIijibIIiIiIiJOXRlkFoFUvKhcatStAjcUdlMYDPnZvo5w/cioW7IOQM7lk9MuGNziWqctSA8k47mpkDs+LOWyu533hm0yYZu1nSuS8a6RsNpeJlXa521OJqyS190cjnd8+8Zk/LTmLaFuC4+pr9iajIsuYFwshnFJHHmiCLHPFxdcjQWXQfZ5ZBERERHZL3QGWURERETE0QRZRERERMSpK2JhaSDXGUUBMsMhRtC8J4xZCOOCawU3cohrXQZgm4shNGfyyfiQ5rCS3khLiGX4e89JhRXzjsgMJuNi6yPJ2LdjGyqG1f3aUiFSAQAtDJGOzlRo87YkE+qY795G9BdDFGJHIbSY6x0LNeVGwtPa4lMR5W3efFTELenHoVCHDYTHZ2NjEBEREZH9S2eQRUREREQcTZBFRERERJz6VtLLGDAv+pi/6Fata+oNm7TsDVEBcwmC9GjpXHyQoQPE5uzcZLywJXSS2N0auk/0Z3cn424LuYU2hhjGEdkQTViU3pyMfd+KtrKYQ5bhihaGIEerqy/NUHsaQ277EM/oz4WIhQ2Hp5UTNZ5wTwldtwobHg6194eoh+VDFEVERERE9g+dQRYRERERcTRBFhERERFx6otYFAEbjWII6ZEQTWjqC7GK1h2h00JqLMQGMiOhkwQApHJuwY1MezJe39qdjNc0H56MO9MhPtHCbcm4zUUk/Gy/PVWWpYhly1tJOAW3kkceofbRYog2bHORiY25Bcl4YCxETphzNbntU/nSlULoLjPnNhzLQUREREQaQ2eQRUREREQcTZBFRERERJz6IhYGIB/FB1IuRpAZDX0iMoMhHpAaCnGLltJ0AfKtYW4+vDiMewdC94gtnaG7xea2ecl4ZSZ0tMimw/GyVeIWXg5W5RYA5m+r3DGi30LXjN58W9i64JYy8bvxLTTKDk23OAjcAiTm66BvdeHiIRM8DKmO5JkA/gPR2jPXmdlVZbczvv0VAIYAvMnM1h7wQkVERKRhdAZZDhok0wA+DeAsAMcCOI/ksWWbnQVgVfznIgCfPaBFioiISMNpgiwHk5MBrDezR81sDMDXAJxTts05AL5skd8C6CJ56IEuVERERBqHJR/nT7YxuRPA4/uvHKnDYWa2sNFFzCQkXwfgTDN7a3z5rwE8x8wuddv8AMBVZvbr+PJPAbzPzNaU7esiRGeYAeAYAA8fgIfwZHQD2NXoImqgOqfOTKgRUJ1TbSbUORNqBFTnVJvOdVadS9WVQdaETGa4Sj3+yt8h1rINzOxaANdORVH7E8k1Zra60XVMRnVOnZlQI6A6p9pMqHMm1Aiozqk2U+osp4iFHEw2AVjuLi8DsOVJbCMiIiKzmCbIcjC5C8AqkoeTbAJwLoBbyra5BcAbGDkFQK+ZbT3QhYqIiEjj1NfmTWQGM7M8yUsB/ARRm7frzWwdyYvj268BcCuiFm/rEbV5e3Oj6p0i0z4GElOdU2cm1Aiozqk2E+qcCTUCqnOqzZQ6S9T1JT0RERERkdlOEQsREREREUcTZBERERERRxNkkVmK5JkkHya5nuRlja6nEpLXk9xB8v5G11INyeUk/x/JB0muI/mORtdUCckWkr8jeW9c55WNrqkakmmSv4/7jk9bJDeQvI/kPSTXTH6PA49kF8lvkXwofo2e2uiaypE8Jn4Ox//0kXxno+uqhOS74t+f+0neRLKl0TWVI/mOuL510+l5rPTvOcn5JG8n+af473mNrLEemiCLzEI1Lqs9HdwA4MxGFzGJPIB/MLOnAzgFwCXT9LkcBfBiMzsewAkAzow7sUxH7wDwYKOLqNGLzOyEadzH9T8A/NjMngbgeEzD59XMHo6fwxMAPBvRF6C/09iq9kVyKYC3A1htZs9A9GXucxtbVSmSzwBwIaKVYY8HcDbJVY2tKnED9v33/DIAPzWzVQB+Gl+eETRBFpmdallWu+HM7JcA9jS6jomY2VYzWxuP+xFNQJY2tqp9xcujD8QXs/GfafctbJLLALwSwHWNrmWmI9kJ4DQAXwAAMxszs56GFjW5MwA8YmbTdVXeDIBWkhkAbZh+ffCfDuC3ZjZkZnkAvwDw2gbXBKDqv+fnAPhSPP4SgNccyJqeCk2QRWanpQA2usubMA0ndTMNyZUAngXgzgaXUlEcXbgHwA4At5vZdKzzUwDeC6DY4DpqYQBuI3l3vLz8dHMEgJ0AvhhHVq4j2d7ooiZxLoCbGl1EJWa2GcAnADwBYCuiPvi3NbaqfdwP4DSSC0i2IWpLunyS+zTSovG1BOK/D2lwPTXTBFlkdqppyWypHck5AL4N4J1m1tfoeioxs0L8MfYyACfHH8dOGyTPBrDDzO5udC01ep6ZnYgoqnQJydMaXVCZDIATAXzWzJ4FYBDT+CPseIGmVwP4ZqNrqSTOx54D4HAASwC0k/yrxlZVysweBPBxALcD+DGAexHFwGSKaYIsMjtpyewpRDKLaHJ8o5nd3Oh6JhN/zP5zTL989/MAvJrkBkSxnxeT/EpjS6rOzLbEf+9AlJk9ubEV7WMTgE3uk4JvIZowT1dnAVhrZtsbXUgVLwHwmJntNLMcgJsBPLfBNe3DzL5gZiea2WmIIg1/anRNE9hO8lAAiP/e0eB6aqYJssjsVMuy2lIDkkSU8XzQzD7Z6HqqIbmQZFc8bkX0n/1DDS2qjJm938yWmdlKRK/Jn5nZtDpDN45kO8mO8TGAlyH6eHvaMLNtADaSPCa+6gwADzSwpMmch2kar4g9AeAUkm3x7/0ZmIZfeiR5SPz3CgB/jun9nN4C4I3x+I0AvtfAWuqipaZFZqFqy2o3uKx9kLwJwOkAukluAnC5mX2hsVXt43kA/hrAfXG+FwA+YGa3Nq6kig4F8KW4g0kKwDfMbFq3UZvmFgH4TjRPQgbAV83sx40tqaK3AbgxfiP8KIA3N7ieiuK87EsB/G2ja6nGzO4k+S0AaxHFFn6P6blM8rdJLgCQA3CJme1tdEFA5X/PAVwF4Bsk34LoDchfNK7C+mipaRERERERRxELERERERFHE2QREREREUcTZBERERERRxNkERERERFHE2QREREREUcTZBERERERRxNkERERERFHE2QREREREUcTZBGRWYbkAMkjnuI+biD54Rq3XUnSSGbiyz8i+cbJ7lfjvl9A8mF3eQPJl0zFvuP9rSN5+lTt70AieXq8Ylkt215B8iv7u6Yqxy6p88k+5+WvBZH9SRNkEZEKSJ5Pck082dwaT/qeX+N9jeRR+7vGasxsjpk92sDjn2VmX5psu1qeJzP7lZkdMxV1VZr0m9lxZvbzqdj/JMc2ktvH30TE12VI7iB5UC1pW+tzXv76mMrXQo3HHox//wdIXncgjivThybIIiJlSL4bwKcAfBTAIgArAHwGwDkNLGtSfvI1G8y2xwOgB8BZ7vIrAOxtTClPHsl0o2s4QI6P32zOMbO3NroYObA0QRYRcUjOBfAhAJeY2c1mNmhmOTP7vpn9Y7zNySR/Q7InPrt8Ncmm+LZfxru6Nz7z9Jfx9WeTvCe+zx0kn+mOeSLJ35PsJ/lNkl/3ZzpJXkhyPck9JG8hucTdZiQvIfknAH9y1x0Vj1tJ/hvJx0n2kvw1ydb4tm+S3BZf/0uSx9X4HKVJfoLkLpKPAnhl2e0/J/nWeHwUyV/Ex9hF8uvVnqfxj+JJvo/kNgBfrBIjOInkAyT3kvwiyZZ4n28i+euyWiyu4SIAFwB4b3y878e3J5ENks0kP0VyS/znUySb49vGa/uH+KzvVpJvruX5cv4bwBvc5TcA+HJZvUvin/Ge+Gd+obutNT4LvpfkAwBOqnDfb5PcSfIxkm+vpSj32D4Q/4w2kLzA3X4Dyc+SvJXkIIAXTXSsGur0z3k6Pu4j8ev/bpLLJ3p9uP08PX6t9TCKbby6rOZPk/xhvN87SR5Zy/MhAszACTLJAqP/ZNaRvJfku0mm4ttWk/zPGvZxR/z3SpLn13Cse0jeMnWPQkSmsVMBtAD4zgTbFAC8C0B3vP0ZAP4eAMzstHib8bNPXyd5IoDrAfwtgAUAPgfglnhC1hQf6wYA8wHcBOC14wci+WIAHwPwegCHAngcwNfK6nkNgOcAOLZCrZ8A8GwAz433/14Axfi2HwFYBeAQAGsB3DjBY/YuBHA2gGcBWA3gdRNs+y8AbgMwD8AyAP8FVH6e4suL4zoPA3BRlX1eAODlAI4EcDSAD05WsJldi+jx/Wt8vFdV2Oz/ADgFwAkAjgdwctm+FwOYC2ApgLcA+DTJeZMd2/kugNNIdpHsAvACAN8r2+YmAJsALEH0vH6U5BnxbZcjesxHInr8Sc47/n/w+wDujes7A8A7Sb68xtoWI3o9L433ey1JH2c4H8BHAHQAuGOSY1Wts4J3AzgP0dn0TgB/A2BogtfH+OPNxjXchuj1+zYAN5bVfB6AKxG99tbH9Y/f/wckL5vkOfklozeQN5NcOcm2MtuY2Yz6A2DAjQ8B8D8ArnyS+zodwA9qOZb+6I/+HBx/EE2+ttV5n3cC+I67bACOcpc/C+Bfyu7zMIAXAjgNwGYAdLf9GsCH4/EXEE3qxm+bAyAHYKU71ovL9m0AjkJ0EmQY0SRjssfQFd9vbnz5hvEaKmz7MwAXu8svi++biS//HMBb4/GXAVwLYFmF/ZQ/T6cDGAPQUnbdJnd5Q9mxXwHgkXj8JgC/rnaMSo8p3t9L4vEjAF7hbns5gA2ujuHxxxhftwPAKTW+RsZ/JtcheqN0MYDPx9dZvM1yRG++Otz9Pgbghnj8KIAz3W0XjT83iN4gPVF2zPcD+GI8vgLAV6rUdjqAPIB2d903APyTe96+7G6b7FhV66zwnD8M4JyJnrNKrwVEby62AUi5228CcIWr+bqy18lDtfys4u1PA9CE6PfiagD3+5+9/sz+PzPuDLJnZjsQ/eJdysjpJH8AACQXkryd5FqSn2P08WJ3fNtAvIurALwgPkP8rsY8ChGZZnYD6OYE+VeSR8dnoLaR7EOUVe6eYJ+HAfiH+KPgHpI9iCZDS+I/my3+Xzm20Y2XIDprDAAws4G4xqVVtve6EZ0Nf6TCY0iTvCr+aLsP0aRl/D6TWVJ2zMerbYjojDUB/C7+5O9vJtn3TjMbmWSb8mMvqbZhnUqe6wr73m1meXd5CNEblnp8GVG0Yp94RXysPWbWX1bDUnd7tef9MABLyl5jH0CUoa/FXjMbLNu3f+z+uJMdq57Xx3JUeH3WYAmAjWZWdNf55wqIJtDj6vpZmdkvzWzMzHoAvAPA4QCe/iTqlBlqRk+QAcCib2qnEJ1N9i4H8DMzOxHRx5crKtz9MgC/MrMTzOzfK9zewuhb7L8l+ZqprFtEpq3fABhBFFuo5rMAHgKwysw6EU0OOMH2GwF8xMy63J82M7sJwFYAS0n6+y934y2IJiQAAJLtiGIam9021bog7IofS6Xs5fmIvnT4EkSxgZXjh5jgcYzbWlZjpX9fo8LMtpnZhWa2BNGZ089w4s4VtXR0KD/2lng8CKBt/AaSi+vcd8lzXbbvqfIrRFGZRYg+KSg//nySHWU1jP+sJ3reNwJ4rOw11mFmr6ixrnnxa8vv2z/28jdwEx2r5tdHvK8nkw3eAmD5eMTSHWdzle2fKkNtvxsyS8z4CXKs0ov2+Yhzemb2Yzy5bwqvMLPViP4j+ZQC/iKzn5n1AvhnRPnS15BsI5kleRbJf4036wDQB2CA5NMA/F3ZbrYD8H2IPw/gYpLPiT/taif5yngi9BtEH6tfyqjt1zmIsq/jvgrgzSRPYPSFsY8CuNPMNtTwWIqIss+fZPSlqjTJU+P9dAAYRXQ2ui3eb62+AeDtJJfFGdyqWU6Sf0FyWXxxL6KJRiG+XP481eqS+NjzEb05Gc+n3gvguPi5akEUK/AmO95NAD4YfwLZjeh1MKW9g+NPCl4F4NVlnxrAzDYiyvd+jGQLoy9yvgUhG/4NAO8nOS9+Tt/m7v47AH2MvuDYGv+sn0Gy5Atyk7iSZBPJFyDKmH+zynaTHWuiOstdB+BfSK6KfzeeSXJBfNtEP687Eb0hem/8+3k6oue1PJ9fN5Ljr6E0yTkA/g3RxPvBp7pvmTlm/ASZUTP8AqIsWMlNT3XfZrYl/vtRRJm6Zz3VfYrI9Gdmn0T05aEPAtiJ6CzXpYi+ZAUA70H0xrkf0eT362W7uALAl+KPn19vZmsQfbHtakSTxPWI8rIwszEAf45oItQD4K8A/ADR5BVm9lMA/wTg24jOzB0J4Nw6Hs57ANwH4C4AewB8HNG//V9G9JH0ZgAPAPhtHfv8PICfIJqQrgVw8wTbngTgzjjadguAd5jZY/FtV8A9T3Uc/6uIvpz1aPznwwBgZn9E1IHkfxB19Cg/Q/sFAMfGx/tuhf1+GMAaAH9A9JytHd/3ZBh1YvhRLdua2TozW1fl5vMQnc3fgujTz8vN7Pb4tisR/cweQ/T4/9vts4BognhCfPsuRJPPubXUhCiOsDc+7o2Ict4PVal/smNVrbOCTyKaUN+G6E3nFwC0xrddgSqvj/j35tWI2ubtQtSG8Q3Vai7HqK/5B6rcvAjR73QfotfXSgBnm1muln3L7MCyN7DTHskBM5sTjxci+kX+jZldHr+DfI+ZnU3y04i+RPBxki9D9I/5QjPbNb4Pks8G8Ekze2GF48xD9E3a0fhMwm8QfZHggQPzSEXkYEXyTgDXmNkXG12LzH7x/51fMbNlk2wqctCYiWeQWxm3eUN0luA2RO9Wy10J4GUk1yJ6h7kV0dke7w8A8ozaxZV/Se/pANaQvBfA/wNwlSbHIrI/kHwhycVxxOKNAJ4J4MeNrktE5GA141YpMrOqK/hYtHTlz+OLvQBebmZ5kqcCeJGZjX9kOSf+O4eof2Olfd0B4M+mrnIRkaqOQfQx8xxE3+h/nZltbWxJIiIHrxkXsagVyVWI/sNJIeqr+fdmdldjqxIRERGR6W7WTpBFRERERJ6MuiIWXfPTduiy6C5+Wp2zsJtRNx4pZJNx/2hz6c4sNJlIZwrJuDUTviTamg7jLMM2WeTd9aFHeMZVVXBNLJpce9FUWXOLAsL9i+7NwpiLZ+dcqiNXJeGRdnVkEWpNsf43INXu4St/6L6xXWa2sO6dy5Tr7u62lStXNroMERERqcPdd99ddS5V1wT50GUZfPH7hwIonShuy3cl40fHwnEeGjg0Gf/ikVUl+yrmw3Sva15YvOcZC0Ps7s86Qr/vJdmeZLw448bpgWQ8PxUmpv1uAr4sHSbqbammkjoGimHBpv5imHhvKYTtNubnJ+Md+c7wGNwxutJDrr7eZNyRmmxBqH1Vm4T7Nwknr3xiopWJpAKS1yPq7bnDzJ5R4XYC+A9ES5IOAXiTma2dbL8rV67EmjVrprpcERER2Y9IVp1LzcQuFiJP1g0Azpzg9rMArIr/XIRotTQRERE5yNR1BrloxFAxikoMmjvDmgtnWP1Z40f6upMxN7aU7CvlTpL2Z0M8oWduskooBgrhPoPpENHwkQ4fpfAdvEfcWdg/jKXd9aXvCQatKxnvdGeHt+dCb/WtY2E8VgzH7siEs8OHN+9Mxv5scgfCNi3uDHB5jV62bDuZGmb2S5IrJ9jkHABfjle3+i3JLpKHqpuAiIjIwUVnkEWCpYhWTBu3Kb5uHyQvIrmG5JqdO3dW2kRERERmqBnXB1lkP6q0PHnF70ya2bUArgWA1atXqxWMyDS08rIfNroEAMCGq17Z6BJEpE51TZDzSGN3YQ4AJH8DwB+HFifjh3sOScbbdodowpzNpXOPXLg7hg8JJ7LzxTAeKYYv1/kvrvUUQgzD28nwJbv+Ymsy3p0PB+stu++efHsy3jzSlYy3D3ck477REPXIpEIcpLs1fEHQxyI60iFW0ZEaTsblEYt0la4b0jCbACx3l5cB2NKgWkRERKRBFLEQCW4B8AZGTgHQq/yxiIjIwUcRCzlokLwJwOkAukluAnA5gCwAmNk1AG5F1OJtPaI2b29uTKUiIiLSSHVNkHOWxrZ8FJvYOtaVXO+7VWzdFWIV3BqiCc29pTHNQouLFLhhi1scZNR1jNjrohC9ed/pornieDDvu1642IZbvAQA9o6Gfe3oD1GMwcFQe2Es3D/lOm70dYZjNKUqRywWuD7NXanRkmP7RUSybsESX6+iF1PHzM6b5HYDcMkBKkdERESmKUUsREREREQcTZBFRERERJz6ulhYCrtyUXeHh/oXJddv2BUWCvGxivYtIR6QHgsRAgAoNIXbmltDrGJuU4gn+EU5Hh9ekIx3joQoxPahMN7dG2IY+dFwXyu49wG50sgCR8NtmYEwzo6E7ZpcOiTfHi7s7Q6dNXa2h8VBettCB40x130jVdYxrFp8IsvwXPlASLWFRURERERk6ugMsoiIiIiIowmyiIiIiIhTZ8QijZ1jUcRi62Bncv3Y3hCrmLMzxAbat4aoAAvlXSzC5TktobtDq+ti0ZcL+906FI73+I4Q6cCWsE3LrjDfbw/NI9Dc4yILg6V1pPLhcmosHBsMj2OsM0QbBpa4RU06wvVZ18ViRfPuZHxIuj8ZF59iR4ryhUZEREREZOrpDLKIiIiIiKMJsoiIiIiIowmyiIiIiIhT30p6xRR2jEZt1fpHwipyadceraknZHqbe0JmdqyjdC5u7sgt2Xy4fyqMh/KhydnOgdDCrbgrHLtjU9hv1yPhvs27Qq7ZMik3Ls0Bp0ZDjamxQsX7AO6xznf3d3FmvwLgEU07kvGi9HB4PBO0afMt3Hybt3RZazgRERER2b90BllERERExNEEWURERETEqStikaahIxNFF9qaQqRgwC01Zym6cbhvrr10Lp7vDDGCRW2hFdrcTIgk7B1dmoz7t3Uk4zmbQhzBt5LLDoSIhTewPEQkhheU1uHbvKVDKgM+DTHWGR7T0KHheB3L+pLx6q4nkvGC9GAy9usHlq+cl7PK708UqxARERFpHJ1BFhERERFxNEEWEREREXHqili0pcdwYufj+1y/a0GIP4zOa03Gw4Nh90OLS+MFTQuHkvGfzd2SjA/JhtjC4FhTMm7eHvbV8UQILjT3hFjFyPzQ9aJ/WVhhr39V6E7RvrSnpI6s66DRPxSiGE1N4fr5bSH28ezOsEren83ZnIzPmnN/2I+FOjYX5oRjcwwiIiIiMr3pDLKIiIiIiKMJsoiIiIiIU1fEgjCk474Mw4UQIyjmQ3yCrgEDLVzIt5ZGLNpaQtxgrltMoysdohfzWsL1u+aFmMTg4tBiYrQz1DGyIBxj5JAQwzjy6SHC8bJFD5TU0cIQpdiVD3GILMPx2lKh1jnpkWS8ID2QjH2sYmNuQTJOu0U/2jN7ICIiIiLTm84gi4iIiIg4miCLiIiIiDh1RSxSNHTEEYPWdFgoxIoh2pAKV5csGlJoKV38orMlrMrR7O5UMBfX8HkNt3BHPjTKQDEbts91ugVL5oV9rmjfm4wPa9pVUodflCPr4hYtriYf++hKhXFbKjyGDobt/X5yVtdTLCIiIiINpjPIIiIiIiKOJsgiIiIiIs6T/vzfd7HAWJhnu4YPKLpYRLGpNGKRTYcuEaPFsK+chTsNjIWFO5ir3CnDUlXGbpsnBucl41+mn1ZSR87dae9YWzKe1xSiFEe0hljGUc3bk3GLi1UsyIQDFhEWE+kvhsVOCijt5OH5qIeIiIiINI7OIIuIiIiIOJogi4iIiIg4dUUszIiROA6xe6Q9uT41FGIRqbzrJOH2bpnSCEHRdavoLYS2FDmXy+gbCRGL1GjlThk+mZAOTSVQGAwHf2xbdzLeuLurpI58LmxXGAjj9JzQieLB7p5kfMKCcP/ndqxPxkdlNyXjo7MhVrG9EBY72eTbbwBIuUVERERERGR60BlkERERERFHE2QREREREaeuiEXO0tiemwsA2DsS4gKZwRB/8DGHfEu43lpK4wStmZCT8LGKnnzoJDEyErpb+FiF75SRHgsZC7rYRjEb5v5FF9UoFFpK6sgOhfu094Trx7rCsTcOh/p8NKQzM5KMT2rZmIy702H7FrrFUsq6WFR7dzJRtwsRERER2b90BllERERExNEEWURERETEqTtisWW0CwCwp2dOcn1rr49YhChFrt3FFxaEhTcA4JiOsODGHJfLWD+4MBnnd4cYR+f2sK+OzWGRkfRwOF6hJcz3s32pitf7SAYANPeEfbXsCZ0rhhaFiEVvPnSl2Jyan4zXNi9Pxs9oDV0sUghxCy/LQsXrRURERGT60BlkERERERFHE2QREREREaeuiMVYMY0nhuYBAAp9IYKQHQixBR9h8AuFLOrsL9nX8pY9yXjAdZbYNtiZjJt3hm4Q7dtCPKF1a+gekcqF6/MdIQpRaArjfEhOoGmwtJtG+6awkEdmQ4h9NO0KUQoW5iZjc4uArG8LC5A8PP/QZLw8uzsZd/iWGzXKWeX3LVktLCIiIiKy3+kMsoiIiIiIowmyHFRInknyYZLrSV5W4fbTSfaSvCf+88+NqFNEREQap66IRb6Ywu7haCGP1HCYW2dcg4p0LkQsiiGFgblNIcoAAC0MK3/sKIRYxd6h0LmiaW/YvnlvyEmkxsK42Bwewsj8cMChQ0J9FpIaSOVLF+Ggj2hsCxGLVP9AMu5oPiIZj3WG7h17FoRaN6xckIx72sNiJy0MtaZR2kFDDiySaQCfBvBSAJsA3EXyFjN7oGzTX5nZ2Qe8QBEREZkWdAZZDiYnA1hvZo+a2RiArwE4p8E1iYiIyDSjCbIcTJYCJU2qN8XXlTuV5L0kf0TyuANTmoiIiEwXdUUsRGY4VriuPPeyFsBhZjZA8hUAvgtg1T47Ii8CcBEArFixYorLFBERkUaqa4JcKKbQNxy1ZMsMhblGZjTMMVzkFnBdycaKpYfakw9Z3p58yOzmciEw3OzOb+fnhOuL2bD98MKw3/7DQk3DS0MhLITr860ukAygdbfLC7eEdnPFwcFknN4bxs297ck4MxgK7B0L9x0phlZwLZmQcR6x0mNXo3Zu+80mAMvd5WUAtvgNzKzPjW8l+RmS3Wa2q2y7awFcCwCrV69WuFxERGQWUcRCDiZ3AVhF8nCSTQDOBXCL34DkYpKMxycj+h3Zvc+eREREZNZSxEIOGmaWJ3kpgJ8ASAO43szWkbw4vv0aAK8D8Hck8wCGAZxrZjpDLCIichCpa4JczKUwsD2KRnTsCbGFpn7Xymw4xAOae0OkYPtAR8m+9na6mEQhtGdLp8P9B5eGcb41lOoXmhs5NEQYDjt6WzKe2xRW2+sZDe3YHm8Oq98BQE9viEMs2n5kMs7s7EnGxbbmUEdzeNyF5srzpo5UaGnXxkLFbQCgUDESK/uTmd0K4Nay665x46sBXH2g6xIREZHpQxELERERERFHE2QREREREaf+DHKFBgsF127CGGIDufYw7m4ZKbnPoU29yTjrYggLO0LHiI2LQvxhqN0ty9cUiliydE8yftWh9yXj5lRYqW/dYGh1u62nNOoxNjccIzcvdKJIjYUuG/nOcP1YR3hMxc5wjHlupcD21GgyzvoUhZKsIiIiItOeziCLiIiIiDiaIIuIiIiIOPVHLOLIQCE0dsBoR5hnpwohR5BzaYZFrf0lu5mTDpGLkWKITyxpD9GL0e7QBWOsK4wXtocYxmnd65Pxy+esS8YtLraxOBP2uWXR3JI67jskLPwxeGiow1IhYpHrDE+TW98EzIaox6hbCGVjboGrI3T40AIgIiIiItOfziCLiIiIiDiaIIuIiIiIOPVFLAggjhXkOkOUYmQstGpgMYxH54dIQSZVumDGUDF0jyi6BTM6siF6cXhn6FDRnhlLxke3hwVBntv2p2R8WMa3jAgPbWV2VzJe1tZTUse9HSECMdrV5G4JcQvfjWPMPe5MUx6V7C6EHMbCYl8yXpAaqri9iIiIiEwfOoMsIiIiIuJogiwiIiIi4tQXsUgZ0q1RVCI3zy0Okkq7cdici0JcIs3SVTL25kP3iIK7U1c2LLixIBu6VfiFRY5o3p6MfZeIx/PhGLuLrcn4zqEjk/H9ew8tfUgD7ilwJeZbQ6xiZIGLWHSH4y2fFzpzrGgNcZCFmRCraGFYTEREREREpj+dQRYRERERcTRBFhERERFx6opYpFKG9vYoNpFvCV0lRjua3Dahc8XyhXuT8aHNISIBAG2pcP/mVIghzE8PJOMmt9iHv749NZqMe1yUYsPYwmT8yMghyfjO3SuT8eObwyIeANDSE94jpMdCxsJHLMbmhuub5oXYyOL2EKVY2hwe6wJfa40RiwI4+UYiIiIist/pDLKIiIiIiKMJsoiIiIiIU1fEojmTx2HzoihBUyp0cxgpZCtuf9zcrcn42e0bSm5rcRGLLEKUYkE6dK7wsYOi63Thr9+cm5eMf9FzdLh+sCsZP75jfjJO7S2t1TXBwFhn2G+uM1yf7w4xicPmhVjF4pbJu1WMufcgTShW3EZEREREpg+dQRYRERERcTRBFhERERFx6opYtKTzeHrnNgBAdzYsklESf3DjZ7RuTMarsrtK9jVqYXERH5locZ0rNudDzmFnIYy35+Ym4/sGlibj329bloyHh0NnDdvRkoyz/dXfE4zOD90qxhaG7MXipaFDxQkLNiXjP2sL4yObdiTjhS4mknKrj/jHLCIiIiLTk84gi4iIiIg4miCLiIiIiDj1RSw4hqe3bgEALM/uTq5PuxjBmIsRHJkN0YS2snUwthTCoXcWOpLxiIUuEw8Mh/jEQ4OLkvET/aErxZbdIW5R2BoWDUnlwwEzw2HsmmcAAIrNrvYFId6xaHmo/fmLHk3Gp8x5JBmvdLGRRemw465UeGz9xbBP/5iB0ufNj0VERESkcXQGWURERETE0QRZRERERMTRBFlERERExKkrg9zEfJI9Pibb664PGd/+YsjSzk+F+XdPsXQVuQ257mT8+FgY9xZCjviOXUck4yd2hRXzxnqak3F2b3gI7Ttc0NkNR7tcq7Xu0qxvsS1khDsWDSTj1QtDi7rndfwpGa/K7kQlvcWQvR61fMVtsix9DorGituJiIiISOPoDLKIiIiIiKMJsoiIiIiIU1fEIk1DV2oYQGmkwK9+5+1xsYpHcvNKbls/Gtq23d27Ihmv2744GQ9vCu3fmnrCXL41F/bjFu7DWOj4hmJTiFJkVoVV/56+sDQiMSc7moyPbA+3Hd4cxm0M2/QXwwp9Wfe4dxfak3EO4bnpSI0k4y43BoAU1dpNREREZLrRGWQREREREUcTZBERERERp66IBWHJim8F1yZixK2eV82C9GDJ5cOawip0W1tDNmJDS1glb2hO6AaRK4ZSfTMIH6Uwt326KWx0tItVPKsrdKcAgI50iD34mhakQ0cLH5PwK97556Caouk9iIiIiMhMotmbiIiIiIijCbKIiIiIiFNXxMIrWeTCDX0EwVueyZVcbuGWZFxw8/SxQ0JJf2oJ0Ybdg23JOFcIkY6mTIhVdLWG7ZvT4fpjOrcn42VNe0rq6EgPJ+PFmZ6wr1ToXNGRCt0q/DsK38mjKz2UjPuLLWH7ssVBRERERGR60xlkERERERFHE2Q5qJA8k+TDJNeTvKzC7ST5n/HtfyB5YiPqFBERkcZ50hGLeo1ZafQi5aIYXa7DxeGtpQt5jFvUGmILedcZojUdohvt6TF3fRgf17opGR/ZtKNkvy0MUYz5qXCf9lTIjaRchsQHJnz0olAM2+RYGieR6YFkGsCnAbwUwCYAd5G8xcwecJudBWBV/Oc5AD4b/y0iIiIHCZ1BloPJyQDWm9mjZjYG4GsAzinb5hwAX7bIbwF0kTz0QBcqIiIijXPAziCLTANLAfhG2Juw79nhStssBbB1/5YmIgerlZf9sNElYMNVr2x0CSLTSl0T5Afvy+169sqNj099GVvc+P6p3/3sdFijC5iBKq3sUt52pZZtQPIiABfFFwdIPvwUa9tfugHsmnSrxlOdU2cm1AgcRHXy41NUycSeUp0zocYDSHVOrelcZ9W5VF0TZDNb+NRrEWmYTQCWu8vLUPrurNZtYGbXArh2qgucaiTXmNnqRtcxGdU5dWZCjYDqnGozoc6ZUCOgOqfaTKmznDLIcjC5C8AqkoeTbAJwLoBbyra5BcAb4m4WpwDoNTPFK0RERA4iyiDLQcPM8iQvBfATAGkA15vZOpIXx7dfA+BWAK8AsB7AEIA3N6peERERaQxNkOWgYma3IpoE++uucWMDcMmBrms/mvYxkJjqnDozoUZAdU61mVDnTKgRUJ1TbabUWYJmlZeGFhERERE5GCmDLCIiIiLiaIIsMktNtqz2dEDyepI7SE7b/o4kl5P8fyQfJLmO5DsaXVMlJFtI/o7kvXGdVza6pmpIpkn+nuQPGl3LREhuIHkfyXtIrml0PZWQ7CL5LZIPxa/RUxtdUzmSx8TP4fifPpLvbHRdlZB8V/z7cz/Jm0i2TH6vA4vkO+L61k2n57HSv+ck55O8neSf4r/nNbLGemiCLDILuWW1zwJwLIDzSB7b2KoqugHAmY0uYhJ5AP9gZk8HcAqAS6bpczkK4MVmdjyAEwCcGXdimY7eAeDBRhdRoxeZ2QnTuE3VfwD4sZk9DcDxmIbPq5k9HD+HJwB4NqIvQH+nsVXti+RSAG8HsNrMnoHoy9znNraqUiSfAeBCRCvDHg/gbJKrGltV4gbs++/5ZQB+amarAPw0vjwjaIIsMjvVsqx2w5nZLwHsaXQdEzGzrWa2Nh73I5qALG1sVfuKl0cfiC9m4z/T7ksmJJcBeCWA6xpdy0xHshPAaQC+AABmNmZmPQ0tanJnAHjEzPbDomNTIgOglWQGQBsq9MFvsKcD+K2ZDZlZHsAvALy2wTUBqPrv+TkAvhSPvwTgNQeypqdCE2SR2anaktnyFJBcCeBZAO5scCkVxdGFewDsAHC7mU3HOj8F4L0Aig2uoxYG4DaSd8erZ043RwDYCeCLcWTlOpLtjS5qEucCuKnRRVRiZpsBfALAEwC2IuqDf1tjq9rH/QBOI7mAZBuitqTLJ7lPIy0aX0sg/vuQBtdTM02QRWanmpbMltqRnAPg2wDeaWZ9ja6nEjMrxB9jLwNwcvxx7LRB8mwAO8zs7kbXUqPnmdmJiKJKl5A8rdEFlckAOBHAZ83sWQAGMY0/wo4XaHo1gG82upZK4nzsOQAOB7AEQDvJv2psVaXM7EEAHwdwO4AfA7gXUQxMppgmyCKzU01LZkttSGYRTY5vNLObG13PZOKP2X+O6Zfvfh6AV5PcgCj282KSX2lsSdWZ2Zb47x2IMrMnN7aifWwCsMl9UvAtRBPm6eosAGvNbHujC6niJQAeM7OdZpYDcDOA5za4pn2Y2RfM7EQzOw1RpOFPja5pAttJHgoA8d87GlxPzTRBFpmdallWW2pAkogyng+a2ScbXU81JBeS7IrHrYj+s3+ooUWVMbP3m9kyM1uJ6DX5MzObVmfoxpFsJ9kxPgbwMkQfb08bZrYNwEaSx8RXnQHggQaWNJnzME3jFbEnAJxCsi3+vT8D0/BLjyQPif9eAeDPMb2f01sAvDEevxHA9xpYS120kp7ILFRtWe0Gl7UPkjcBOB1AN8lNAC43sy80tqp9PA/AXwO4L873AsAH4lUZp5NDAXwp7mCSAvANM5vWbdSmuUUAvhPNk5AB8FUz+3FjS6robQBujN8IPwrgzQ2up6I4L/tSAH/b6FqqMbM7SX4LwFpEsYXfY3quAvdtkgsA5ABcYmZ7G10QUPnfcwBXAfgGybcgegPyF42rsD5aSU9ERERExFHEQkRERETE0QRZRERERMTRBFlERERExNEEWURERETE0QRZRERERMTRBFlERERExNEEWURERETE0QRZRERERMTRBFlEZJYhOUDyiKe4jxtIfrjGbVeSNJKZ+PKPSL5xsvvVuO8XkHzYXd5A8iVTse94f+tInj5V+zuQSJ4er1hWy7ZXkPzK/q6pyrFL6nyyz3n5a0Fkf9IEWUSkApLnk1wTTza3xpO+59d4XyN51P6usRozm2Nmjzbw+GeZ2Zcm266W58nMfmVmx0xFXZUm/WZ2nJn9fCr2P8mxjeT28TcR8XUZkjtIHlRL2tb6nJe/PqbytVDDsV9F8v749/8OksceiOPK9KEJsohIGZLvBvApAB8FsAjACgCfAXBOA8ualJ98zQaz7fEA6AFwlrv8CgB7G1PKk0cy3ega9ieSqwDcCOBiAF0Avg/glln4epQJaIIsIuKQnAvgQwAuMbObzWzQzHJm9n0z+8d4m5NJ/oZkT3x2+WqSTfFtv4x3dW989ukv4+vPJnlPfJ87SD7THfNEkr8n2U/ymyS/7s90kryQ5HqSe0jeQnKJu81IXkLyTwD+5K47Kh63kvw3ko+T7CX5a5Kt8W3fJLktvv6XJI+r8TlKk/wEyV0kHwXwyrLbf07yrfH4KJK/iI+xi+TXqz1P4x/Fk3wfyW0AvlglRnASyQdI7iX5RZIt8T7fRPLXZbVYXMNFAC4A8N74eN+Pb08iGySbSX6K5Jb4z6dINse3jdf2D/FZ360k31zL8+X8N4A3uMtvAPDlsnqXxD/jPfHP/EJ3W2t8FnwvyQcAnFThvt8muZPkYyTfXktR7rF9IP4ZbSB5gbv9BpKfJXkryUEAL5roWDXU6Z/zdHzcR+LX/90kl0/0+nD7eXr8WuthFNt4dVnNnyb5w3i/d5I8spbnA8DLAfzKzH5tZnkAHwewFMALa7y/zAKaIIuIlDoVQAuA70ywTQHAuwB0x9ufAeDvAcDMTou3OT6OOnyd5IkArgfwtwAWAPgcojNSzYwm1t8BcAOA+QBuAvDa8QORfDGAjwF4PYBDATwO4Gtl9bwGwHMAVPoY+BMAng3gufH+3wugGN/2IwCrABwCYC2is2a1uBDA2QCeBWA1gNdNsO2/ALgNwDwAywD8F1D5eYovL47rPAzARVX2eQGiScyRAI4G8MHJCjazaxE9vn+Nj/eqCpv9HwCnADgBwPEATi7b92IAcxFNlt4C4NMk5012bOe7AE4j2UWyC8ALAHyvbJubAGwCsATR8/pRkmfEt12O6DEfiejxJzlvkilEZzrvjes7A8A7Sb68xtoWI3o9L433ey1JH2c4H8BHAHQAuGOSY1Wts4J3AzgP0dn0TgB/A2BogtfH+OPNxjXchuj1+zYAN5bVfB6AKxG99tbH9Y/f/wckL6tSE+M/5ZefMcHjkFlmxk2QSRYYnYVZR/Jeku+O/2EAydUk/7OGfdwR/72S5PkTbPdxRhmk+xmfBRKRWW8BgF3xmaOKzOxuM/utmeXNbAOiCe9EZ5cuBPA5M7vTzApxPncU0WTsFAAZAP8Zn6m+GcDv3H0vAHC9ma01s1EA7wdwKsmVbpuPmdkeMxv2B43/bfwbAO8ws83xse+I9wMzu97M+uPLVwA4ntEZ9Mm8HsCnzGyjme1BNIGvJodosrvEzEbM7NcTbAtEk/fLzWy0/PE4V7tjfwTRRGgqXADgQ2a2w8x2Ippc/bW7PRffnjOzWwEMAKgnEzuCaFL3lwDOBXBLfB0AgORyAM8H8L74uboHwHWuhtcD+Ej8s94IwP9/dxKAhWb2ITMbizPon4+PU6t/ip/3XwD4YXy8cd8zs/81syKAP5vkWBPVWe6tAD5oZg9b5F4z211DracAmAPgqriGnwH4AUpfCzeb2e/i3+UbEb3xAQCY2dlmdlWVfd8O4IXxGesmAB8A0ASgrYa6ZJaYcRNkAMNmdoKZHQfgpYjedV4OAGa2xswm/UjJzJ4bD1ciele8D5KvBHAiol+o5wD4R5KdT7l6EZnudgPo5gR5Q5JHx2egtpHsQ5RV7p5gn4cB+If4o+Aekj0AliM6S7gEwGYz81/U2ujGSxCdNQYAmNlAXOPSKtt73YjOhj9S4TGkSV4Vf7TdB2CDu89klpQd8/FqGyI6Y00Av4tPbPzNJPveaWYjk2xTfuwl1TasU8lzXWHfu8veOA0hmqTV48uIohX7xCviY+0xs/6yGpa626s974cBWFL2GvsAogx9Lfaa2WDZvv1j98ed7Fj1vD6Wo8LrswZLAGyMJ+z+OP73Ypsb1/yzMrOHEJ31vhrAVkS/Ew8gOrMvB4mZOEFOmNkORB/BXcrI6SR/AAAkF5K8neRakp9jlL/rjm8biHdxFYAXxGek31W2+2MB/CI+QzSI6KOkMw/MIxORBvoNorN6r5lgm88CeAjAKjPrRDQ54ATbb0R0Rq3L/Wkzs5sQ/Qe8lKS//3I33oJoQgIAINmO6Cz3ZrdNtS4Iu+LHUil7eT6iLx2+BFFsYOX4ISZ4HOO2ltW4otqGZrbNzC40syWIIiaf4cSdK2rp6FB+7C3xeBDuLB/JxXXuu+S5Ltv3VPkVoqjMIgDlZ9O3AJhPsqOshvGf9UTP+0YAj5W9xjrM7BU11jUvfm35ffvHXv4GbqJj1fz6iPdVazbY2wJg+fgnyO44m6tsXxcz+5aZPcPMFiA6CXcYgLumYt8yM8zoCTIAxB/tpBBlkLzLAfzMzE5ElO+r9At6GaIg/glm9u9lt90L4CySbfHE+kUo/YUXkVnIzHoB/DOifOlr4n8DsiTPIvmv8WYdAPoADJB8GoC/K9vNdgC+D/HnAVxM8jnxm/l2kq+MJ0K/QZRpvpRR269zEGVfx30VwJtJnsDoC2MfBXBnHO2Y7LEUEWWfP8noS1VpkqfG++lAFPPYjWhS+dHanyV8A8DbSS6LM7jVspwg+Rckl8UX9yKaaBXiy+XPU60uiY89H9Gbk/F86r0AjoufqxZEsRFvsuPdBOCD8QmWbkSvgyntHRx/UvAqAK8u+9QAcRzhDgAfI9nC6Iucb0HIhn8DwPtJzouf07e5u/8OQB+jLzi2xj/rZ5As+YLcJK4k2UTyBYgy5t+sst1kx5qoznLXAfgXkqvi341nklwQ3zbRz+tORG+I3hv/fp6O6Hktz+c/KSSfHT+uhYgiVN+PzyzLQWLGT5Bjlc54PB/xL4qZ/Rh1ttIxs9sA3IroH6ubEP0nVjWTKCKzh5l9EtGXhz4IYCeis1yXIvqSFQC8B9EZ2H5Ek9+vl+3iCgBfij9+fr2ZrUGUQ74a0b9F6wG8KT7WGIA/RzQR6gHwV4iylOM54Z8C+CcA30Z0Zu5I1JcrfQ+A+xCd/dqD6Bv5KUQf7z+O6IzbAwB+W8c+Pw/gJ4gmpGsB3DzBticBuDP+5O4WRHnox+LbroB7nuo4/lcRfTnr0fjPhwHAzP6IqAPJ/yDq6FF+hvYLAI6Nj/fdCvv9MIA1AP6A6DlbO77vyTDqxPCjWrY1s3Vmtq7KzechOpu/BdHJncvN7Pb4tisR/cweQ/T4/9vts4BognhCfPsuRJPPWjLlQBRH2Bsf90YAF1ebENZwrKp1VvBJRBPq2xC96fwCgNb4titQ5fUR/968GlHbvF2I2jC+odZJLKO+5h+YYJP/QPT7+HD894UTbCuzEMvewE57JAfMbI67fASif/i7EX1J5j1mdjbJewG8ZvwfYpJ7ABxtZrvG9xG/43yPmZ1dw3G/CuAr8RczRET2G5J3ArjGzL7Y6Fpk9ov/L/yKmS2bZFORg8aMPoMcf/RxDaJvNJfP9H+N+Bu4JF+GqM1LuX5EHzNW2nd6/GOe+GOuZyJ6hysiMqVIvpDk4jhi8UZE/978uNF1iYgcrGbiqjCtJO8BkEUUefhvRB/RlLsSwE2M2rP9AtFHk/1l2/wBQD4+23xDWQ45C+BX8fdm+gD8lU3Q9klE5Ck4BtHHzHMQfaP/dWa2tbEliYgcvGZcxKJW8ZdQCmaWJ3kqgM+a2QkNLktEREREprmZeAa5VisAfCNuATMGBexFREREpAZ1nUGeMy9rC5a27HN9mqFPd8q1SvRjsuw47mLRNaEouFh0wVKVt3HXm7s+7683v30YF600du2r8vfxfO0pN864x92UCumLJhaScZZ5Nw7XA0C65LkK8iXPRxj7+v54/+guM1tYsWA5oLq7u23lypWNLkNERETqcPfdd1edS9V1BnnB0ha871vP3uf6rvRQMm5J5ZJxG0crXg+UTlQHrSkZ9xdak3FPIazqOGLZZNybD9ePFsND6PHXF8L1/fnmZDyQC2MAyBXTYVxIo5JsOkxs2zNjyXhBc1h0aFlr6CK3oimskrk425OMl6Z7S/Y71z0nLW5u3lMMz01vMdTrn4MzjvjjRCsTyQG0cuVKrFmzptFliIiISB1IVp1LzeguFiIiIiIiU21aZ5B9JMGfPa121rgvF+IfY+7MsD9rPJQLZ6sBIOfO1g6Nlt42LpMOUYpcU9hvJuXPLIc65qaHk7E/u47KJ6hFREREZBrRGWQREREREWdan0EWERERmW1WXvbDRpcAANhw1SsbXcK0VdcE2cCSLhPj/HUphDhCe8p9SY9la2y4L6Vl3fobvkNFXzF8YW9XLix4t3MsjHePhmjDXjceHAtxiZFceJj5fGnOITcWbiuMhdss5x5nOnSb2NscYhV72kN9/R0h3uG/gLgkG76811zWxaIjFZ6ELMN9Rixst91FS3YXkhW2RURERGQ/UcRCRERERMTRBFlERERExJnyDLJfNMQvhNFWFrHwC3/kLO2uD3P2gUKILfTlw3jvWIg2bB3sTMb9w2GbkZEQTSiOVW8fYaMuHjIYtkuPhfr88iHFdNhvf2d4+nIuxtGcCY91cE5p32Wvjdkqt4SIhY9V+L7QIiIiIrJ/6AyyiIiIiIijCbKIiIiIiNOwNm8pF7/wHS6y5d0uYv0uYrF5YG4y3rk3dLQo9IfIAkdCXMI3j7CytIVb6wPpkRCmyAyFcapySRhzsYoRuPpaQuzjia4Fyfi45s0l9x+10OWjmWFfvcXwOLblw2PdkQv7FREREZH9Q2eQZdYheSbJh0muJ3lZhdv/keQ98Z/7SRZIzo9v20Dyvvi2NQe+ehEREWk0LRQiswrJNIBPA3gpgE0A7iJ5i5k9ML6Nmf1fAP833v5VAN5lZnvcbl5kZrsOYNkiIiIyjdQ1QU7B0MLchNuMFMMCHYXUUNXt0gwRC5e2KNuX6xiRC90gegZCN4fizhBtaOqrHJHwsYp8W+nBzK8HMupiFe5hZgf8NuH+/hjDhfBU9rS1J+Nti0IsYmROadeKERtOxgWEA24uhFjGHwaWJePdo2G/UtXJANab2aMAQPJrAM4B8ECV7c8DcNMBqk1ERERmAEUsZLZZCmCju7wpvm4fJNsAnAng2+5qA3AbybtJXrTfqhQREZFpSxELmW1Y4boqn1HgVQD+tyxe8Twz20LyEAC3k3zIzH65z0GiyfNFALBixYqnWrOIiIhMIzqDLLPNJgDL3eVlALZU2fZclMUrzGxL/PcOAN9BFNnYh5lda2arzWz1woULn3LRIiIiMn3UdQaZNLSkJs4g+1XxBoshN9xell3OIqy4l3J55Jy5dmeFsGLejqHQzm2kL+y3ZU+Y4zf1hf2nxsLYZ5BZLD3B6GLOJe3gPJ87bu4NdWeGXc455VYA7Aj1bRycl4w3dHaX7Hd+unJG+0+ji5Pxo/3hPtv6OiptLqXuArCK5OEANiOaBJ9fvhHJuQBeCOCv3HXtAFJm1h+PXwbgQwekahEREZk2FLGQWcXM8iQvBfATAGkA15vZOpIXx7dfE2/6WgC3mdmgu/siAN8hCUS/G181sx8fuOpFRERkOtAEWWYdM7sVwK1l111TdvkGADeUXfcogOP3c3kiIiIyzU3JBNm3Y/N8HGPERkpuK6BynsHHMnpyIWLROxzauaX3hOM17w33be4JUQgWwrjQzIpjACjW8AyUtnxzEYu0byUXxmOdIW6xsacrGf+xK0QnAKAtNYpK1g2EpgsbtoeWb/m+pkqbi4iIiMgU0pf0REREREQcTZBFRERERJy6IhYZFNCVjr7TVHBL0BUsxAt8F4qJpF1r2oJrXev31Z8LsYrhkRCryA6EbXysork3xDZS+XD92JzQxiLXXj1i4RYBdD02SmUH8smYeb9ViIaMzAs7HRgM1z8xPL9kX3Mzw6hkfV/oXFHcEZ6D5r5KLX5FREREZCrpDLKIiIiIiKMJsoiIiIiIU1fEIgVDO6MVOAoMH/cPuc4TPmIx5lboGLHKnS6A0ohFfzF0rugZC+PcYMg/NLvOtU2uq0RTb4g/eMVM2H8qVx5T8B0ufBeMyrWmh0NLi5Qb+wWOmxeEx93fG+rePly60EdXNkQuRl3WY0ffnGTc5BdC6a1ck4iIiIhMHZ1BFhERERFxNEEWEREREXHqiliQhhZGsQIfi/ALghTcnLupWk6hjI9l5Fwso1B08/d8OF7GrTniYxWZXrfwRsZ12WgO41Q+7L+ca8xRdQy3AAmHw/H8E9nSGyInmb5wvG19pRGL9sxYMh4rhu2GekO0pNPFKlp3V+utISIiIiJTRWeQRUREREQcTZBFRERERJy6IhZmrNiNoo0hapBNV+4kMWhNJZd9h4tq0ikXKciEaINfxSM9FI6X7hmouJ9spitcKKvfLw5SaDVUUmgJ8Q5rdnUXQyEcDIt+NPW0h3GvWzRkb1vJfjdnKz9XHAjH8LGKlt21RVZERERE5MnTGWQREREREUcTZBERERERp66IRRHcJyoBIOlsAQBNCDEA3+miPJqRQ+WIRdG1jGhKhX0x7bpHFMM4NRJiCtbvIhaFEE1Id4SuEKl8acyhRLpKxKIp1FTMurpT4XobHErGmf4QOWnZHTpajOwpfbr3NIUoRioVjp3tC/tt7gnPQfNu175DRERERPYLnUEWEREREXE0QRYRERERcerrYgGiYPvOqf3iIOkaFwfxqi0UUnQRjZI60u56tyAI6GsrVhyybK0NXy7dYiTmDsGS+7sYxliIlmA4xB/SeweTcXYwLA7ioxMAMNoa4ip5F+9oHwgHT4+EAlOjlbteiIiIiMjU0RlkmXVInknyYZLrSV5W4fbTSfaSvCf+88+13ldERERmv7rOIItMdyTTAD4N4KUANgG4i+QtZvZA2aa/MrOzn+R9RUREZBarO2Lh4xDjfBcLL43KXSGA0ljFYDF0ehhyK3eMFap0unCNNHKd4UJzV4gzcGQsbN9cfVGSlCs9NVI50pEZcR00ci6TkQ+Rh6KPWIyGY2eHQj4jO1h6wr7gIhc+uZJ1zTgybiEUjlR+nqXEyQDWm9mjAEDyawDOAVDLJPep3FdERERmCUUsZLZZCmCju7wpvq7cqSTvJfkjksfVeV+QvIjkGpJrdu7cORV1i4iIyDShCbLMNpU+Bij/KGMtgMPM7HgA/wXgu3XcN7rS7FozW21mqxcuXPhkaxUREZFpqGEZZN8Nw3euyBXDOEUXbciEqEIhJDKQ6wjbZ7vCwhscDdGLYktYpIRWOt9J5cKcKDNcOWKRHnV1uAVIzEUsLB/iDzYYulg09eTduDTqYalwvKL7SWQGXY2+3GJZCw6pZBOA5e7yMgBb/AZm1ufGt5L8DMnuWu4rIiIis5/OIMtscxeAVSQPJ9kE4FwAt/gNSC4myXh8MqLfg9213FdERERmP3WxkFnFzPIkLwXwEwBpANeb2TqSF8e3XwPgdQD+jmQewDCAc83MAFS8b0MeiIiIiDRMnV0sgEKFmGbaraSRZeXFLLJWGi/Ila/YUam4VNgmnQndI3LtIXcw2hn2mxkIsYrMUDg5Xqixi0V6NIz9AiIlEYu8q9vFLeCiG+YWEMn0hp227Sp9uumeEx8baXIRi9SYez5TOuFfCzO7FcCtZddd48ZXA7i61vuKiIjIwUUzLhERERERRxNkERERERFHE2QREREREeeAfUkvXZY5TlnlDHJzKmRuW9Ihy9vcHK4fbg0Z3ZF5YY6fyoV2bk39Id/rF/8rZkoz1D5rnAnd2ZAeC8fIDoZa6TPB6XBsZtxB0q5Vnds+2+8OBqDQFGopjPp2c+65oas3VbkNnYiIiIhMHZ1BFhERERFxNEEWEREREXGmJGLhV8XzXeCyLOy78Th3l6Fi6HHW4vquNaXC/VuyIaow0BmuH5vn5/hhnG8NhfgyilmUSBVClCI9FK7PDPuIReXWdT5WkWprCze0tiRDy1R/D+KPkc658XDl583S1dvViYiIiMjU0BlkERERERFHE2QREREREWe/drHIWW2RgJTrcNHsIhad2ZFk3NU6nIx75oQ4w+j8MMcvujhD0XWISI2FY5Uv4Oe7VfjIQ5PrOJEaCWMfc2BLiIawOaziZ20hYlGYE7axsg4aPo6ScnX4GksiGi1aGVxERERkf9MZZBERERERRxNkERERERGnrs/sCSAN2+f68kVAxrWwcveHfbcLsQrf+aLVZSPaM2E8b25Y0WOv289YU4g5FJtCFCIzVDluAQDsC7elx8LjaOoJG6aHQ31wkYdCd2cYu/hDvt2PQx1+YRAAoO+g4eryNRazLtKhhUJERERE9judQRYRERERcTRBFhERERFx6oxYGLIVYhODbqGPalPu8riFj2r4iIaPW7Smw3hOdjQZL5nTl4zbsmGbnc3tyXioqTUZF3vDw/Rxi/LLKb9Yx6DLOVi4vtgWYhxjc8N4ZH44Rq497NMvWIKyhER2wHXNGKjcxcJLFRSxEBEREdnfdAZZRERERMTRBFlERERExGnYyhN+cZB2hjhDRzosCDI/E7pVjLouEYPZEOloc90t0qmwz60W4gjDCAt35MsecjE7eWzBmrOhjvnh2MMLwr6GFrlYxRy//xCdSOVKj+WPXRqrcO9bXNMQy1XJXkgJkmcC+A8AaQDXmdlVZbdfAOB98cUBAH9nZvfGt20A0A+gACBvZqsPVN0iIiIyPWhpNplVSKYBfBrASwFsAnAXyVvM7AG32WMAXmhme0meBeBaAM9xt7/IzHYdsKJFRERkWlHEQmabkwGsN7NHzWwMwNcAnOM3MLM7zGy8hfZvASw7wDWKiIjINFbXGeQUimhPje5zve9iUa2jRXkXiyaEuEDRda7oSg0l46FM2FfBtYAYKoTr/WIieQsHHJnjukqMhXFhICy8AQDmLhbdQh7F1hCryLeFcUmsYrGLcSwKj6fQGRY7YVO43kZKj+0XAUmNufiFX4vFRUXSab2fqcFSABvd5U0oPTtc7i0AfuQuG4DbSBqAz5nZtVNfooiIiExniljIbFMpVL7v8o8ASL4I0QT5+e7q55nZFpKHALid5ENm9ssK970IwEUAsGLFiqdetYiIiEwbOiUps80mAMvd5WUAtpRvRPKZAK4DcI6Z7R6/3sy2xH/vAPAdRJGNfZjZtWa22sxWL1y4cArLFxERkUabFmeQffxiLBXiFm0uzjE3HUpNVz4hiO7m0PViIBdiGH2toYvFQFNTyX0K4SaMtYf3C2nXrSI3J0QhRuaHE5Qj3a6OQ0Ot8zpCTKS1KTye/hEXPwHQbx3heEPh8bHou1uEmixV+XFLibsArCJ5OIDNAM4FcL7fgOQKADcD+Gsz+6O7vh1Aysz64/HLAHzogFUuIiIi08K0mCCLTBUzy5O8FMBPELV5u97M1pG8OL79GgD/DGABgM+QBEI7t0UAvhNflwHwVTP7cQMehoiIiDSQJsgy65jZrQBuLbvuGjd+K4C3VrjfowCO3+8FioiIyLRWXxcLGlpcx4lxgwjRgRELHR9co4p97udjFT4y0YRCxevTbmc+euG7W7Snw/Ut6XC8jFtABGUxhWImXB7rcN/vYnhqRue6WMXCsH1hUeigceTi0DZ35Zw9ybgrG+IW20dDpAIA1uTDl7vGhsLqIulciFWkXPMPS0++qImIiIiIPDX6kp6IiIiIiKMJsoiIiIiIU1fEggjRiBGrfNeRootY1Dj9TjHEFlIuSpFm5XHOShfcqKQp7RbrcPsv75LrH0auM4wLrWHDnEtG5OaFOjq6QnzimM4dyfiZc8I6FQvSA8l4W8vckmNvHwoH/OPe1nCMwXDskgVEqnTvEBEREZGpozPIIiIiIiKOJsgiIiIiIo4myCIiIiIiTl0Z5CyKWJSOWpv1FkPGd3chtCgbMbdSneuuVtL+DaUt3Npcy7dieUh4quWq7z/fFmryMedch8tCzwut5A6fF9q5re54LBmf1PJ4Mp6bCs/T4/mekuOtbT8sGa9vDcsVF7Phx+LrKKrNm4iIiMh+pzPIIiIiIiKOJsgiIiIiIk7Dlpr2K+Dl3Dx9zI19y7iSsYtrDBXCKn5FY8WxuXH5WwJ/U64jRCyKrSFWkekMK+Ytmt+XjFd1hNZuT2vamoyXuWd1birET4oILd8AYFnr3mTc1h6iG4PtIaaSH5i8pZ2IiIiITB2dQRYRERERcTRBFhERERFx6opY5JDC9kL08X+1lfRaGOIILalc1X0V3Ny8p9gSxoW2ZOy7Y+xwy9ztzYVthgshjtCfD3GLgVwYZ9KuC0VHaU355tBlork91N7ZPpKM57eGFfMObQsRi0Obet3jCVmNXYWwz6FiiFXsKZR28piTDsc4bF6IWzxWDPsaag8r7GFM72dERERE9jfNuEREREREnIZ9SU9E5EBaedkPG10CNlz1ykaXICIiNahrglwwoqfYus/17anRiuMWVo9YDBZDBGKPi1LszIcoxaaxecl412jYpjcXIhk+SpErho4PuUIYN2XCQiTz5g6W1NHsblvW0ZOMD2sLi4DMTQ8n4w4Xi2hzj3VbvisZ+2hIsUpXDgDIMkQxnjl3czI+tDXEOHoXhceaL4Z9PQERERER2R90BllERERE9nEwf/KmDLKIiIiIiFPXGeQiUiXRiHE+VlGrHEIEwu+ztxAiHIOuK0W1DhVDudDFwi8OkmJY9CPtxq3Z0thHZ3OITCxt6UnGK5p3J2P/+FIIHTG8nKUrjv1j8wucAEAaoa7ubOh20ZwKsY+u7L7Pt0yM5JkA/gNAGsB1ZnZV2e2Mb38FgCEAbzKztbXcV0RERGY/nUGWWYVkGsCnAZwF4FgA55E8tmyzswCsiv9cBOCzddxXREREZjllkGW2ORnAejN7FABIfg3AOQAecNucA+DLZmYAfkuyi+ShAFbWcF+Rg950yCUC6gpyoE2Hn/tkP/PpUCOg1+ZsUNcE+ZH7h3e99qg/PL6/imm0HzW6gPoc1ugCpqmlADa6y5sAPKeGbZbWeF8AAMmLEJ19BoABkg8/hZr3p24AuxpdRA0Oijr58SmspLqD4rkE9HyWmQl1HjQ/c9VZYjr/u1l1LlXXBNnMFj71WkT2K1a4zmrcppb7RleaXQvg2vpKO/BIrjGz1Y2uYzKqc+rMhBoB1TnVZkKdM6FGQHVOtZlSZzlFLGS22QRgubu8DMCWGrdpquG+IiIiMsvpS3oy29wFYBXJw0k2ATgXwC1l29wC4A2MnAKg18y21nhfERERmeV0BllmFTPLk7wUwE8QtWq73szWkbw4vv0aALciavG2HlGbtzdPdN8GPIypNO1jIDHVOXVmQo2A6pxqM6HOmVAjoDqn2kypswSjL/KLiIiIiAigiIWIiIiISAlNkEVEREREHE2QRWYpkmeSfJjkepKXNbqeSkheT3IHyfsbXUs1JJeT/H8kHyS5juQ7Gl1TJSRbSP6O5L1xnVc2uqZqSKZJ/p7kDxpdy0RIbiB5H8l7SK5pdD2VxAsdfYvkQ/Fr9NRG11SO5DHxczj+p4/kOxtdVyUk3xX//txP8iaSLY2uqRzJd8T1rZtOz2Olf89Jzid5O8k/xX/Pa2SN9dAEWWQWmkHLZt8A4MxGFzGJPIB/MLOnAzgFwCXT9LkcBfBiMzsewAkAzoy7tExH7wDwYKOLqNGLzOyEadzH9T8A/NjMngbgeEzD59XMHo6fwxMAPBvRl6O/09iq9kVyKYC3A1htZs9A9GXtcxtbVSmSzwBwIaJVY48HcDbJVY2tKnED9v33/DIAPzWzVQB+Gl+eETRBFpmdkiW3zWwMwPiy2dOKmf0SwJ5G1zERM9tqZmvjcT+iCcjSxla1L4sMxBez8Z9p9y1skssAvBLAdY2uZaYj2QngNABfAAAzGzOznoYWNbkzADxiZtN1Vd4MgFaSGQBtmH698J8O4LdmNmRmeQC/APDaBtcEoOq/5+cA+FI8/hKA1xzImp4KTZBFZqdqy2nLU0ByJYBnAbizwaVUFEcX7gGwA8DtZjYd6/wUgPcCKDa4jloYgNtI3h0vLz/dHAFgJ4AvxpGV60i2N7qoSZwL4KZGF1GJmW0G8AkATwDYiqhH/m2NrWof9wM4jeQCkm2IWpYun+Q+jbQoXmcA8d+HNLiemmmCLDI71bxsttSG5BwA3wbwTjPra3Q9lZhZIf4YexmAk+OPY6cNkmcD2GFmdze6lho9z8xORBRVuoTkaY0uqEwGwIkAPmtmzwIwiGn8EXa8ANOrAXyz0bVUEudjzwFwOIAlANpJ/lVjqyplZg8C+DiA2wH8GMC9iGJgMsU0QRaZnWpZcltqRDKLaHJ8o5nd3Oh6JhN/zP5zTL989/MAvJrkBkSxnxeT/EpjS6rOzLbEf+9AlJk9ubEV7WMTgE3uk4JvIZowT1dnAVhrZtsbXUgVLwHwmJntNLMcgJsBPLfBNe3DzL5gZiea2WmIIg1/anRNE9hO8lAAiP/e0eB6aqYJssjspGWzpwhJIsp4Pmhmn2x0PdWQXEiyKx63IvrP/qGGFlXGzN5vZsvMbCWi1+TPzGxanaEbR7KdZMf4GMDLEH28PW2Y2TYAG0keE191BoAHGljSZM7DNI1XxJ4AcArJtvj3/gxMwy89kjwk/nsFgD/H9H5ObwHwxnj8RgDfa2AtddFS0yKz0ExZNpvkTQBOB9BNchOAy83sC42tah/PA/DXAO6L870A8AEzu7VxJVV0KIAvxR1MUgC+YWbTuo3aNLcIwHeieRIyAL5qZj9ubEkVvQ3AjfEb4UcBvLnB9VQU52VfCuBvG11LNWZ2J8lvAViLKLbwe0zPZZK/TXIBgByAS8xsb6MLAir/ew7gKgDfIPkWRG9A/qJxFdZHS02LiIiIiDiKWIiIiIiIOJogi4iIiIg4miCLiIiIiDiaIIuIiIiIOJogi4iIiIg4miCLiIiIiDiaIIuIiIiIOJogi4jMMiQHSB7xFPdxA8kP17jtSpJGMhNf/hHJN052vxr3/QKSD7vLG0i+ZCr2He9vHcnTp2p/BxLJ0+MFGWrZ9opGLetdXueTfc7LXwsi+5MmyCIiFZA8n+SaeLK5NZ70Pb/G+xrJo/Z3jdWY2Rwze7SBxz/LzL402Xa1PE9m9iszO2aibWpVadJvZseZ2c+nYv+THNtIbh9/ExFflyG5g+RBtWJXrc95+etjKl8LtSL5xriOtx7I40rjaYIsIlKG5LsBfArARxEtObwCwGcAnNPAsiblJ1+zwWx7PAB6AJzlLr8CwLRYJrge8XLmsx7JeQDeD2Bdo2uRA08TZBERh+RcAB8CcImZ3Wxmg2aWM7Pvm9k/xtucTPI3JHvis8tXk2yKb/tlvKt747PPfxlffzbJe+L73EHyme6YJ5L8Pcl+kt8k+XV/ppPkhSTXk9xD8haSS9xtRvISkn8C8Cd33VHxuJXkv5F8nGQvyV+TbI1v+ybJbfH1vyR5XI3PUZrkJ0juIvkogFeW3f7z8TNuJI8i+Yv4GLtIfr3a8zT+UTzJ95HcBuCLVWIEJ5F8gORekl8k2RLv800kf11Wi8U1XATgAgDvjY/3/fj2JLJBspnkp0huif98imRzfNt4bf8Qn/XdSvLNtTxfzn8DeIO7/AYAXy6rd0n8M94T/8wvdLe1xmfB95J8AMBJFe77bZI7ST5G8u21FOUe2wfin9EGkhe4228g+VmSt5IcBPCiiY5VQ53+OU/Hx30kfv3fTXL5RK8Pt5+nx6+1HkaxjVeX1fxpkj+M93snySNreT6cjwH4TwC76ryfzAKaIIuIlDoVQAuA70ywTQHAuwB0x9ufAeDvAcDMTou3OT6OOnyd5IkArgfwtwAWAPgcgFviCVlTfKwbAMwHcBOA144fiOSLEf1H/XoAhwJ4HMDXyup5DYDnADi2Qq2fAPBsAM+N9/9eAMX4th8BWAXgEABrAdw4wWP2LgRwNoBnAVgN4HUTbPsvAG4DMA/AMgD/BVR+nuLLi+M6DwNwUZV9XgDg5QCOBHA0gA9OVrCZXYvo8f1rfLxXVdjs/wA4BcAJAI4HcHLZvhcDmAtgKYC3APg0o7OMtfougNNIdpHsAvACAN8r2+YmAJsALEH0vH6U5BnxbZcjesxHInr8Sc6bZArA9wHcG9d3BoB3knx5jbUtRvR6Xhrv91qSPs5wPoCPAOgAcMckx6paZwXvBnAeorPpnQD+BsDQBK+P8cebjWu4DdHr920Abiyr+TwAVyJ67a2P6x+//w9IXlatKJInI3ptXzNB7TKLzbgJMskCo7Mw60jeS/Ld8T8MILma5H/WsI874r9Xkjx/km07SW4mefXUPAIRmeYWANhlZvlqG5jZ3Wb2WzPLm9kGRBPeF06wzwsBfM7M7jSzQpzPHUU0GTsFQAbAf8Znqm8G8Dt33wsAXG9ma81sFNFHvqeSXOm2+ZiZ7TGzYX/Q+N/GvwHwDjPbHB/7jng/MLPrzaw/vnwFgOMZnUGfzOsBfMrMNprZHkQT+GpyiCa7S8xsxMx+PcG2QDR5v9zMRssfj3O1O/ZHEE2EpsIFAD5kZjvMbCeiydVfu9tz8e05M7sVwACAejKxI4gmdX8J4FwAt8TXAQBILgfwfADvi5+rewBc52p4PYCPxD/rjYjObo47CcBCM/uQmY3FGfTPx8ep1T/Fz/svAPwwPt6475nZ/5pZEcCfTXKsieos91YAHzSzhy1yr5ntrqHWUwDMAXBVXMPPAPwApa+Fm83sd/Hv8o2I3vgAAMzsbDO7qtKOGUVIPgPgbfHjlYPQTMx3DZvZCQBA8hAAX0X0jv5yM1sDYM1kOzCz58bDlYjeFX91gs3/BcAvnkK9IjKz7AbQTTJTbZJM8mgAn0R0hqkN0b+ld0+wz8MAvJHk29x1TYjOEhqAzWbmv6i10Y2XIDq7CwAwswGSuxGdudtQYXuvG9HZ8EcqPIY0osnlXwBYiHBWuRtA7wSPZbwmf8zHJ9j2vYj+Hf0dyb0A/s3Mrp9g+51mNjLB7ahw7CXVNqzTEpQ+lvJ97y57TQwhmqTV48uI3lAQwPsqHH+PmfWX1bDa3V7teT8MwBKSPe66NIBf1VjXXjMbLNu3f+z+uJMdq57Xx3JUeH3WYAmAjWUT2McR/V6M2+bG9fys/h7AH8zsN0+iLpklZtwZZM/MdiD6CO5SRk4n+QMAILmQ5O0k15L8HKP8XXd820C8i6sAvCA+I/2u8v2TfDaiL+jcdmAekYhMA79BdFbvNRNs81kADwFYZWadAD6AaMJTzUZEZ9S63J82M7sJwFYAS0n6+y934y2IJiQAAJLtiM5yb3bbVOuCsCt+LJWyl+cj+tLhSxCdZFg5fogJHse4rWU1rqi2oZltM7MLzWwJoojJZzhx54paOjqUH3tLPB5E9IYFAEBycZ37Lnmuy/Y9VX6FKCqzCED52fQtAOaT7CirYfxnPdHzvhHAY2WvsQ4ze0WNdc2LX1t+3/6xl7+Bm+hYNb8+4n3Vmw1GXNvy8U+Q3XE2V9m+HmcAeC2jfP42RPGkf9MnyQeXGT1BBoD4o50UogySdzmAn5nZiYjyfZV+QS8D8CszO8HM/t3fEP/S/RuAf5z6qkVkujKzXgD/jChf+hqSbSSzJM8i+a/xZh0A+gAMkHwagL8r2812AL4P8ecBXEzyOfGb+XaSr4wnQr9BlGm+lFHbr3MQZV/HfRXAm0mewOgLYx8FcGcc7ZjssRQRZZ8/yehLVWmSp8b76UAU89iNaFL50dqfJXwDwNtJLoszuBNlOf+C5LL44l5EE61CfLn8earVJfGx5yN6czKeT70XwHHxc9WCKDbiTXa8mwB8MD7B0o3odTClvYPjTwpeBeDVZZ8aII4j3AHgYyRbGH2R8y0I2fBvAHg/yXnxc+o/kfgdgD5GX3BsjX/WzyBZ8gW5SVxJsonkCxBlzL9ZZbvJjjVRneWuA/AvJFfFvxvPJLkgvm2in9ediN4QvTf+/Twd0fNans9/Mt4E4OmIIhknIPpk+kpEGXU5SMz4CXKs0hmP5yP+RTGzH6P+Vjp/D+DW+B8sETmImNknEX156IMAdiI6y3Upoi9ZAcB7EJ2B7Uc0+f162S6uAPAlRt+uf30c/7oQwNWI/i1aj+g/YZjZGIA/RzQR6gHwV4iylOM54Z8C+CcA30Z0Zu5I1JcrfQ+A+wDcBWAPgI8j+rf/y4g+kt4M4AEAv61jn58H8BNEE9K1AG6eYNuTANwZf3J3C6I89GPxbVfAPU91HP+riD7ZezT+82EAMLM/IupA8j+IOnqUn6H9AoBj4+N9t8J+P4xoMvQHRM/Z2vF9T4ZRJ4Yf1bKtma0zs2qtw85DdDZ/C6KTO5eb2e3xbVci+pk9hujx/7fbZwHRBPGE+PZdiCaftWTKgSiOsDc+7o0ALjazh6rUP9mxqtZZwScRTahvQ/Sm8wsAWuPbrkCV10f8e/NqRG3zdiHKDL+hWs3lGPU1/0CVx9cTf/Kxzcy2ARgD0Be/eZaDBMvewE57JAfMbI67fASif/i7EX1J5j1mdjbJewG8ZvwfYpJ7ABxtZrvG9xG/43yPmZ1d4Tg3IvqGcRFRbqkJwGfMrOqZEhGRqUDyTgDXmNkXG12LzH7x/4VfMbNlk2wqctCY0WeQSS5E1ILl6vKPqhCdOXh9vN3LELV5KdeP6GPGfZjZBWa2wsxWIjoD82VNjkVkfyD5QpKL44jFGwE8E8CPG12XiMjBaiZOkFvjL9WtQ/Qx2m2IPs4pdyWAl5Fci+gjmK2IJsTeHwDkGbWL2+dLeiIiB8gxiOIKvQD+AcDrzGxrY0sSETl4zbiIRa3iL6EUzCxP8lQAnx1vDyciIiIiUs1M7INcqxUAvhF3oxhD9AUZEREREZEJzdozyCIiIiIiT0ZdZ5CbUi3Wmoq/01bDxHriybdVHFbDlItLp1xXt1SVGLXfZ0kTuLKOcH4RHquhJr9NybHd9b7ffzpd+XoAlnaXi+H+zLmFmnwd7th9hV27zGxhlSrlAOru7raVK1c2ugwRERGpw9133111LlXXBLk11YFT574WAGCjo5Nub4VCuFAsm3G6ian524ruPqkwuUy1NCdjtrWGcWsYl+7f7TPtJrJlk1SMjoW7jIUxfO1+t4VQN1taKtbNpqawfVdokmFZN1kGUOgI908N5UK528Iy9FZ0E/ixsM1P9nx+oqU75QBauXIl1qyZdIVzERERmUZIVp1L1ZdBJpKzt2xtqbyNP/vpz56WTzj9xM9NOkvu48/u+oktK59NtuYwMfX7Lzkj68flUunK17vamXFPma/P7bd4aFjUb8+JobvcwNLSyXkxzPnRui1M6A+505W0aUf1ekVERERkys3ENm8iIiIiIvvNbO5iISIi+8HKy37Y6BIAABuuemWjSxCRWarOiAXBbBZAaRa3JGrQlA2b50PswKpkegGALiNs+coRDZaM3Ynv8mxzcmx3PJfdLckZA6WxCv/lP6vyRTv3OGwo5LB99GJ4ecgd7zwpPDfH/tkTJYduSYe67n7g8GTcubEtGbdtcnfI6P2MiIiIyP6miIWIiIiIiKMJsoiIiIiIU99n9mZJBILNoQVDSSxieCSMfdyCZXNxH8vwxqrM2V3XjJJWa80hCjGyMnSMyM0JsYj2jUPJOLVhW+X9A4CPhIy4+IRr2wb/uAcGwzauvuHu8LSuOnZjMv7oyu+UHO6+0aXJeO1jK8K+CpVjH3St7kRERERk/9AZZBERERERRxNkERERERGnvohFKpWsXGetbmU7t0hGtS4UmChi4W/z8QnXrYJuoRCz0Emi2NGejLevDlGIkYVhP10PzknGi3pChwkAQE+/O3ah4tg/ppRbxS81tzNsvyDEO4YWh1rPmB86VxyVKX0O7hhyK+ntCLU39YRISMnjzqqLhYiIiMj+pjPIIiIiIiKOJsgiIiIiIk59n9mn0yjOm7Pv9UUXl/ALb1RZxANAaayiGOIMJYuG+MVI/NhFN3ILw6IanS/Ynoyfv2BLMr4988xk3P2H0vrTO3dXLM8s1J7yC3Rk3OPrnp8Mxw4NcYvB5eHxPLMtdLEYsrAwCADc0XtkMm7fGB53une4Yk1I6f2MiIiIyP6mGZeIiIiIiKMJshxUSJ5J8mGS60leVmWb00neQ3IdyV8c6BpFRESksdQWQQ4aJNMAPg3gpQA2AbiL5C1m9oDbpgvAZwCcaWZPkDykIcWKiIhIw9Q1QS42pTC8LGqTlsqHjG7TzpDLTedde7T+gXBnn03ep4pMxe1YHK2wcemKciPzw2p9f3XY75LxqqawYt7/6z46Gefbw/YAkHZ5ZhsLGWGmK69m51lzqDvXEcbp7lD3wkxfMn4071bkA/DbDYcn46UPu2MPh/tbZ8hMW2vp/aVuJwNYb2aPAgDJrwE4B8ADbpvzAdxsZk8AgJntOOBVioiISEMpYiEHk6UANrrLm+LrvKMBzCP5c5J3k3zDAatOREREpgVFLORgUumjgPJWKxkAzwZwBoBWAL8h+Vsz+2PJjsiLAFwEACtWrNgPpYqIiEij1DVBzrUT254TRRToFp3LDIWP/tu3hZXqFvxqczK2Phe32KcKV4ZfYa/KynHmIhajc8NJ8CObwqfhLakQWciPhv2wrPUcsy5y4VcE9K3r/H3cmKNh+2ImzL1a20JEIu3mX2uHV5Ycu7glrMrXvD2s6OdXzCt0hTZ26YHKkROp2SYAy93lZQC2VNhml5kNAhgk+UsAxwMomSCb2bUArgWA1atXT9DPUERERGYaRSzkYHIXgFUkDyfZBOBcALeUbfM9AC8gmSHZBuA5AB48wHWKiIhIAyliIQcNM8uTvBTATwCkAVxvZutIXhzffo2ZPUjyxwD+AKAI4Dozu79xVYuIiMiBVtcEOduew+JTo0+ks+mQsXjl4vuS8defeHYy7h1Zkow7fnBvyb78Knkp16nBxy18/MFaQ6yi2BbGw4eEaMMjY6Ejl+9ikd0cIiBNW3aW1jE3REIwJ8QZbEP4LhdLumyE49FFMsY6wsn4p3WHqMeidIiWXO9WzgNKV89jzmVW3Ip5qZHK3S3kyTGzWwHcWnbdNWWX/y+A/3sg6xIREZHpQxELERERERFHE2QREREREaeuiIUByBejOXVbdiy5fmXTrmT8rO7QueI3ixYl47nzukr2VewNC2iULA7iu0qw2gIdYft8W2ggsDjTm4w35hYk4+Y9LhYxOFyyr8LieWHsFhFpcvGJkmO7DhPFjhDJGFkQjnH0nBCx6EiFKEnRSt+PpNwhUv2DrijXQSPjFgpp0UIhIiIiIvubziCLiIiIiDiaIIuIiIiIOHVFLPKFNHb3twMAdvW1J9fvXhxiAGfMfSAZ3778Wcm4uGh+yb6Yc90Zmlyswi8O4mIOzIcuD4WWsM3YvBBH6EqHmMIfhsN6ENn+EMOwtpaSOvY+PXSxGFgWYhIL5h2ajNs3hE4UHA11Wza8vxibG/Z5eHPolLGnGGrd0Ff6HKSHXV3+cbeECEmhI3TsYEHrUYiIiIjsbzqDLCIiIiLiaIIsIiIiIuLUt5LeSApYF0USsq7pwjcWrE7Gbz/sf5JxcelIMs7PDVEBAGja7mIVLmJhabd4xohbPCPjYgcugmDtYZuuVOhQMVIM+yxmQnRiZGXoWgEAu04MsYU5h/ck490I2zX1hFhG044Q+0iNhrGlwn7SDLGPjfmusM/+EEsBgFb3FAwfGbpu5NvSqKR1+0jF60VERERk6ugMsoiIiIiIowmyiIiIiIhTV8QiPQp0PBZFCdp2hnjB+kOXJeP/nX90Mj50YVi4Y2TBwpJ9NWXCoW1gKBmz2S2GkXZRipIYRtikec5oMu5KhcVLhgth+/RoiD+Mzi2NL3QdtScZv+mIO5Pxv/e8JBnn7gu1Zve4CEg+RCl8TSmE60eK4fEs6HC5FACbn9majPuODPUWusPjwGjY8fy1YWES/C9EREREZD/QGWQREREREUcTZBERERERp66IBYtAZiSKK7RsDbGIti1hlYxNI13JeFlHTzJ+ZO6ikn11zAnxAhZDJAGuiwWKbmEM18XCd6VoagpRjwLC9aNugY50WNsDxWzYBgAWtofYw6rmbeE+Ta6DBnx+ItTnu2kUWkKtnenQbWJxOsRMXrX0vpJj71kUulpkGY53QVeIevRbiF6c135RuPPnICIiIiL7gc4gi4iIiIg4miCLiIiIiDj1LRRihsxIFIdIb9udXN2+tSMZ7xkNsYGV7aFDxMNtpdGGYntYOKRklp4PUQPm/EIclefyZmG/OwshttGfC4t7mLurlZaBzqYQh2hhyGLkXfeIzHABleQ7QocKl+go2c/CdFi85PDmHSX3X5QtXTxlXJohrnGy695xybN+kYz/seI9RUREROSp0hlkOaiQPJPkwyTXk7xsgu1OIlkg+boDWZ+IiIg0nibIctAgmQbwaQBnATgWwHkkj62y3ccB/OTAVigiIiLTQX0RCzJ0gXALejQNhi4Uu4fDYhY+YjGhguti4aMUfpwJY9/FIpMK992cn5eM+1zEgm73KItYzGsK3Tg6UiFugVw4XmrUtcFwi4PANdlIj4Qdb8uHrh5HZMNzMFQsjVT8oueYZPyHnYcm4+uyz0/GF638ZTJ+fvvDkKfkZADrzexRACD5NQDnAHigbLu3Afg2gJMObHkiIiIyHegMshxMlgLY6C5viq9LkFwK4LUArploRyQvIrmG5JqdO3dOeaEiIiLSOJogy8GEFa6zssufAvA+M6v8zczxO5lda2arzWz1woULJ9pUREREZpi6IhZGIN8SzTGKc0O3ioJbfGNoNEQv+vIhUpAeKZ2HpAZHkzHHQoTBslVKsnB/35WCruODjzAM5kIdqXzYxsczAKDVrSKS9nOlfNguPRK6abAQ5k3p4XB9aiwcb2c+dPXoL4YuFF/bUvqJ/WP/uyIZz3s4HHvYdfz42EvPSsb//qxvQJ6STQCWu8vLAGwp22Y1gK+RBIBuAK8gmTez7x6QCkVERKTh6ssgi8xsdwFYRfJwAJsBnAvgfL+BmR0+PiZ5A4AfaHIsIiJycNEEWQ4aZpYneSmi7hRpANeb2TqSF8e3T5g7FhERkYODJshyUDGzWwHcWnZdxYmxmb3pQNQkIiIi00t9GeQUkG+O87EuE5xvCaHgjpaQLc66/mou6htxrd3MrRYHuozwcNgXLLRt89KpUMfCTF8y3jUQMtILd4SD960MWWEASCPUsa3QGcpwbd6Yc9/Xcqv7ZfpCWzhLh/Z2CzP9YXMLK/L9acshJcde+fOxZNx8z2PJuHj4kmS8eW6oadufhfZxIiIiIrJ/qIuFiIiIiIijCbKIiIiIiFPnSnpAIW7z5pIJ+P/t3XecXGd5L/Dfb2Znq1ZaSau6ai7CBYILcgvBGJyADQ4m3YTQAjgkJkBCLnG43ACpJhe4kEtxHGNsAphQTKKAbexrEkrAxgU3ucq2ZK3qSruStu+U5/5xzp73mdHM7o690tny+34++uidmTPnPHO0kt4585vnLbr0w9KmEDt4djCsbOdbrUXPL6EqH7HIVp+/069gl6m+H7Own1Jj2I9LPAAARl0btgPFBWG/bmU8joVYBQ4NhPvbQ4yDNbrmjiEcsDRSfvDMWHgSc6EOurZyTb3hxR4shhiHiIiIiBwduoIsIiIiIuJogiwiIiIi4tQVsSg1ACNLo/HgiaG7wtDKEEdYkAudJx7aHboxdA6XRyxYcrddZwg0VGQgkieEY1jGrXLnIhZtDF0h8vls1e0rFxbOuE4bIy5uURaZ8LGP/Jh7IEQsGobCNk8Mr0zGG3L7w25y5XEQH/3wx/Cr9TUOhIJ3jCyBiIiIiBxduoIsIiIiIuJogiwiIiIi4tTXxaKlBPxCtAjGjlUtyd2da0OM4MS2nmR8d/9xyTiTr8g2mI9YuFVEfByCNcbuuSXXrcLHJYqF6lENVpTRXwgtOEYsRCzyS0LMoefcpcm4tacjGbc9fSgZN/WFHT9yeFUy/o2Oe5Jxx+LBsmPnF4SFP8qWQcmE9y2F5vD69o4uhIiIiIgcXbqCLCIiIiLi1HcFWURERETmhQ1XfjftErDtqtemcty6JsiLmobx6uMfBQAUNoQIQ0duKBkPFJuScfOzjeFAw6G7BYDyyERLCBhYjcVBfKwiOxbGQ2MhFtGRCYuUtLSG4x1eF+Igo2HtEgBAprKtReykF+xMxk8uWJ6MD3WHWk/YEc5By4EQ73imz3WbWBeGL1ga4icA8OTKsF17UzhX1hDOQakRIiIiInIMKWIhIiIiIuJogiwiIiIi4tQVsShYBr1jrQCA41oPJPc/MxS6PNy5fUMyXrI1xA4ae8o7OGDMda5oDDEJ5FxJxfB85kNXiYyLWAyPhudmXVzijJXdyfhHZ4WIRbbJrwACnLxgdzI+oXFfMn5T153JuHtZiEJ8f9VJyXj0+2FBkIaRcOzD+8MCIgdL4dhnLNxRduwHl52cjM1FLFhw3ThyIYrS2TQAERERETm6dAVZRERERMTRBFnmFZIXkXyc5FaSV1Z5/I0kH4x//YTkaWnUKSIiIumpK2IxMNiC/77zVADAXatD54rC7tZkvOSBEAnoeKw/GXN3iGQAKO8d0RI6X6AQIhB0EQu/faYYbuXHwkvody0fXr1kSzI+4cywkEkJrnsGgJe3PZaMj28YS8bbC6H29sxwuH9hiJP8vHNNMm7bHZ7bvCN0unhqbEUyPqf1qbJjX73h5cl4bFVYBMRHLIbD07GhObwOqR/JLIDPAvgVAN0A7ia52cwecZs9A+DlZtZH8mIA1wA459hXKyIiImnRFWSZT84GsNXMnjazMQBfA3Cp38DMfmJmffHNOwGsgYiIiMwrmiDLfNIFwH9Tsju+r5a3A7il2gMkLyd5D8l7enp6qm0iIiIis1RdEYvGQ4b1txQAAEPLQ6yiuTfEIlq39oYnuMU9kA8RBABgW+j0YJkwT2e+EDZynS5YDMdoGAzbWCEs1rGv2J6Ml2ZDx4eTF4ZOFf2lEH8AgLyF52/Nh8ceG1uVjJ8YDt0qHjywOhk3HnZdOvaESMbCZ0Jk5N7+9cn4Zcu3lh17/boQmdj/4rDfhsFw3kbXhPN2vOuyIc8Jq9xXdaUYkq9ANEH+pWqPm9k1iOIX2LRpU/XVZkRERGRW0lLTMp90A1jrbq8BsKtyI5IvBnAtgIvN7EDl4yIiIjK3KWIh88ndADaSPI5kI4DLAGz2G5BcB+AmAG8ysydSqFFERERSVtcV5MxoAc1PRxfUmrvd4h6u20RZrKIhxBfYEhbMAAC4hTFQcs93UQrLu4gFwvH8oiHsC7GIHfnQYSLHsM1gKUQeKiMWKxsOJePD7rEbdpyXjJ99KMQt2naG9xRd21ycpCeMW3o6kvHPdoeIRdtK9zoBXHl8iLd+5KJfTcaHBsO5+tXjQpcNHxuR+plZgeS7AXwPQBbAdWa2heS74sevBvCXAJYC+BxJACiY2aa0ahYREZFjTxELmVfM7GYAN1fcd7UbvwPAO451XSIiIjJzKGIhIiIiIuLUdwWZBHJVnpJxzQEyLlYxNJKMrb3NPwOl9hBnKC4IcYvMcOhQ0bD3YHiC62iR7Q1Rg/W3hPjEx5suDttnQ9Qj1xdqLjWUNxz4jQvvTMYvXRAip88+HGIVJ10b4hPsDwuk2HBYQMRHSJr3hW0O/PeSZPyejt8oO/YfdX0/GV9z6peT8epsiIcMucjKfwyc4p79NERERERk+ukKsoiIiIiIowmyiIiIiIijCbKIiIiIiFNfBtlQ3tIt2Uv2yPsAlFzuOL+steyxgyeG7PBgV8gwN7tVe1fcGY6V6Q4PcCBkfBt7wn7X3RyOlx0Jz20YDNsfOrG8ji2bQtb4jNbtVV9HmULISLPBnb589Yz0kkdDTQ8vO75sV/94Vnh/csnyB5PxxqY9yfhgMTz/pl1nuGf/5+S1ioiIiEjddAVZRERERMTRBFlERERExKlzoRADC1ELMsu6ubWPXbiWb5YL0YuRpW7lPQAHNoWowjkveioZP7QnRB4OHmpPxkuGxsKTXau1zGiINix4OEQTMDIa6nDxh5bFJ5TVkS+GGlsz4Tml1tBqrbggxEEaBsPrMNfGzqM7dttj+5Px6kxn2XZPjm5Ixp84fkUy7lzcn4wHR0MLvNEtHVWPJyIiIiLTR1eQRUREREQcTZBFRERERJw6u1gYkI+iEXRPtYybZ+dDdCLj7i/m3Gp7ALrWH0jGf79mczL+5qLTkvG/LH91Ml5SCivK2UDoElG2V7pbvsOEi4BkR8u7cAzkQ4RhqNSEapgPzymLVbg4CXMuetEc9km3AuCCJ/rK9muZsMre2BNhJb7RtjDOuXIX7Qs3tlatVERERESeL11BFhERERFxNEEWEREREXHq7GLhWIg8lC0UUgzdH1AI44YRtz2AwyMhztDmogrLGkIHB3PVcTR0sfB7Mnc/XTTBH5uu40ahpXxRkwFXx4i5ThvurQP9ax0NHSrY5hYdcfEOumPDRUM4HJ4LAG3bQlSkpbW8y0dVZpNvIyIiIiLPi64gi4iIiIg4miCLiIiIiDj1RSxIWFMUBSiLC7iIBV0XCx8vaO51C30A2PXMomT8sbUvS8Z371+fjNt2ue4Rg8PhyTnXJSJbfY7vFwfxdTQMF8u2GxxsTsY9hbAwCRrCsUeXhyhF86Bb7MN1qCjroOG5+qylvEuG5cJjpUb3OlySIuM6aEAJi+eN5EUAPg0gC+BaM7uq4nHGj78GwBCAt5rZfce8UBEREUmNriDLvEEyC+CzAC4GcCqAN5A8tWKziwFsjH9dDuDzx7RIERERSd1z/5KeyOxzNoCtZvY0AJD8GoBLATzitrkUwJfMzADcSbKD5Coz233syxWR+WDDld9NuwRsu+q1aZcgMqPQ6uiMQLIHwPajV47UYb2ZLUu7iNmE5G8CuMjM3hHffhOAc8zs3W6b7wC4ysx+HN++A8Cfm9k9Ffu6HNEVZgA4CcDjx+AlPBedAPanXcQUqM7pMxtqBFTndJsNdc6GGgHVOd1mcp0151J1XUHWhExmuWpB8cp3iFPZBmZ2DYBrpqOoo4nkPWa2Ke06JqM6p89sqBFQndNtNtQ5G2oEVOd0my11VlIGWeaTbgBr3e01AHY9h21ERERkDtMEWeaTuwFsJHkcyUYAlwHYXLHNZgBvZuRcAIeUPxYREZlf9CU9mTfMrEDy3QC+h6jN23VmtoXku+LHrwZwM6IWb1sRtXl7W1r1TpMZHwOJqc7pMxtqBFTndJsNdc6GGgHVOd1mS51l6vqSnoiIiIjIXKeIhYiIiIiIowmyiIiIiIijCbLIHEXyIpKPk9xK8sq066mG5HUk95F8OO1aaiG5luR/knyU5BaS7027pmpINpP8GckH4jo/mnZNtZDMkvx53Hd8xiK5jeRDJO8nec/kzzj24sWMvknysfhn9Ly0a6pE8qT4HI7/OkzyfWnXVQ3JP4n//jxM8kaSzWnXVInke+P6tsyk81jt33OSS0jeTvLJ+PfFadZYD02QReagKS6rPRNcD+CitIuYRAHA+83sFADnArhihp7LUQCvNLPTAJwO4KK4E8tM9F4Aj6ZdxBS9wsxOn8F9XD8N4FYzOxnAaZiB59XMHo/P4ekAXoLoC9DfTreqI5HsAvAeAJvM7EWIvsx9WbpVlSP5IgDvRLQy7GkALiG5Md2qEtfjyH/PrwRwh5ltBHBHfHtW0ARZZG5KltU2szEA48tqzyhm9kMAvWnXMREz221m98XjfkQTkK50qzqSRQbim7n414z7FjbJNQBeC+DatGuZ7UguBHA+gC8AgJmNmdnBVIua3IUAnjKzmboqbwOAFpINAFox8/rgnwLgTjMbMrMCgB8A+LWUawJQ89/zSwHcEI9vAPD6Y1nT86EJssjc1AVgh7vdjRk4qZttSG4AcAaAu1Iupao4unA/gH0AbjezmVjnpwB8AEAp5TqmwgDcRvLeeHn5meZ4AD0AvhhHVq4l2ZZ2UZO4DMCNaRdRjZntBPBxAM8C2I2oD/5t6VZ1hIcBnE9yKclWRG1J107ynDStGF9LIP59ecr1TJkmyCJz05SWzJapI7kAwLcAvM/MDqddTzVmVow/xl4D4Oz449gZg+QlAPaZ2b1p1zJFLzWzMxFFla4geX7aBVVoAHAmgM+b2RkABjGDP8KOF2h6HYBvpF1LNXE+9lIAxwFYDaCN5O+lW1U5M3sUwMcA3A7gVgAPIIqByTTTBFlkbtKS2dOIZA7R5PgrZnZT2vVMJv6Y/b8w8/LdLwXwOpLbEMV+Xknyy+mWVJuZ7Yp/34coM3t2uhUdoRtAt/uk4JuIJswz1cUA7jOzvWkXUsMvA3jGzHrMLA/gJgC/mHJNRzCzL5jZmWZ2PqJIw5Np1zSBvSRXAUD8+76U65kyTZBF5qapLKstU0CSiDKej5rZJ9OupxaSy0h2xOMWRP/ZP5ZqURXM7C/MbI2ZbUD0M/l9M5tRV+jGkWwj2T4+BvAqRB9vzxhmtgfADpInxXddCOCRFEuazBswQ+MVsWcBnEuyNf57fyFm4JceSS6Pf18H4Ncxs8/pZgBvicdvAfDvKdZSFy01LTIH1VpWO+WyjkDyRgAXAOgk2Q3gw2b2hXSrOsJLAbwJwENxvhcAPmhmN6dXUlWrANwQdzDJAPi6mc3oNmoz3AoA347mSWgA8FUzuzXdkqr6YwBfid8IPw3gbSnXU1Wcl/0VAH+Qdi21mNldJL8J4D5EsYWfY2Yuk/wtkksB5AFcYWZ9aRcEVP/3HMBVAL5O8u2I3oD8VnoV1kdLTYuIiIiIOIpYiIiIiIg4miCLiIiIiDiaIIuIiIiIOJogi4iIiIg4miCLiIiIiDiaIIuIiIiIOJogi4iIiIg4miCLiIiIiDiaIIuIzDEkB0ge/zz3cT3Jv5nithtIGsmG+PYtJN8y2fOmuO+XkXzc3d5G8penY9/x/raQvGC69ncskbwgXrFsKtt+hOSXj3ZNNY5dVudzPeeVPwsiR5MmyCIiVZD8XZL3xJPN3fGk75em+FwjeeLRrrEWM1tgZk+nePyLzeyGybabynkysx+Z2UnTUVe1Sb+ZvdDM/ms69j/JsY3k3vE3EfF9DST3kZxXS9pO9ZxX/nxM58/CFI59DcnHSZZIvvVYHFNmFk2QRUQqkPxTAJ8C8HcAVgBYB+BzAC5NsaxJ+cnXXDDXXg+AgwAudrdfA6AvnVKeO5LZtGs4Bh4A8EcA7ku7EEmHJsgiIg7JRQD+CsAVZnaTmQ2aWd7M/sPM/ke8zdkkf0ryYHx1+TMkG+PHfhjv6oH46vPvxPdfQvL++Dk/Iflid8wzSf6cZD/Jb5D8V3+lk+Q7SW4l2UtyM8nV7jEjeQXJJwE86e47MR63kPwEye0kD5H8McmW+LFvkNwT3/9Dki+c4jnKkvw4yf0knwbw2orH/4vkO+LxiSR/EB9jP8l/rXWexj+KJ/nnJPcA+GKNGMFZJB8h2UfyiySb432+leSPK2qxuIbLAbwRwAfi4/1H/HgS2SDZRPJTJHfFvz5Fsil+bLy298dXfXeTfNtUzpfzLwDe7G6/GcCXKupdHf8Z98Z/5u90j7XEV8H7SD4C4Kwqz/0WyR6Sz5B8z1SKcq/tg/Gf0TaSb3SPX0/y8yRvJjkI4BUTHWsKdfpzno2P+1T8838vybUT/Xy4/ZwS/6wdZBTbeF1FzZ8l+d14v3eRPGEq5wMAzOyzZnYHgJGpPkfmFk2QRUTKnQegGcC3J9imCOBPAHTG21+I6GoTzOz8eJvT4qjDv5I8E8B1AP4AwFIA/wRgczwha4yPdT2AJQBuBPBr4wci+UoAfw/gtwGsArAdwNcq6nk9gHMAnFql1o8DeAmAX4z3/wEApfixWwBsBLAc0ZWyr0zwmr13ArgEwBkANgH4zQm2/WsAtwFYDGANgP8LVD9P8e2VcZ3rAVxeY59vBPBqACcAeAGAD01WsJldg+j1/UN8vF+tstn/BHAugNMBnAbg7Ip9rwSwCEAXgLcD+CzJxZMd2/k3AOeT7CDZAeBlAP69YpsbAXQDWI3ovP4dyQvjxz6M6DWfgOj1JzlvkhkA/4HoymcXop/J95F89RRrW4no57kr3u81JH2c4XcB/C2AdgA/meRYNeus4k8BvAHR1fSFAH4fwNAEPx/jrzcX13Abop/fPwbwlYqa3wDgo4h+9rbG9Y8//zskr5zknMg8NusmyCSLjK7CbCH5AMk/jf9hAMlNJP9xCvv4Sfz7BpK/O8F2t8bvTL8zfa9ARGa4pQD2m1mh1gZmdq+Z3WlmBTPbhmjC+/IJ9vlOAP9kZneZWTHO544imoydC6ABwD/GV6pvAvAz99w3ArjOzO4zs1EAfwHgPJIb3DZ/b2a9ZjbsDxr/2/j7AN5rZjvjY/8k3g/M7Doz649vfwTAaYyuoE/mtwF8ysx2mFkvogl8LXlEk93VZjZiZj+eYFsgmrx/2MxGK1+P8xl37L9FNBGaDm8E8Fdmts/MehBNrt7kHs/Hj+fN7GYAAwDqycSOIJrU/Q6AywBshrtCSXItgF8C8OfxubofwLWuht8G8Lfxn/UOAP7/u7MALDOzvzKzsTiD/s/xcabqf8Xn/QcAvhsfb9y/m9l/m1kJwC9McqyJ6qz0DgAfMrPHLfKAmR2YQq3nAlgA4Kq4hu8D+A7KfxZuMrOfxX+Xv4LojQ8AwMwuMbOrpnAcmadmY75r2MxOBwCSywF8FdE7+g+b2T0A7plsB2b2i/FwA6J3xV+tsen/BtCK6KqPiMwPBwB0kmyoNUkm+QIAn0R09bQV0b+l906wz/UA3kLyj919jYiuEhqAnWbmv6i1w41Xw+UgzWyA5AFEV+62Vdne60R0NfypKq8hi2hy+VsAliFcVe4EcGiC1zJekz/m9gm2/QCiq8g/I9kH4BNmdt0E2/eY2WQfa1cee3WtDeu0GuWvpXLfByp+JoYQTdLq8SVEbygI4M+rHL/XzPoratjkHq913tcDWE3yoLsvC+BHU6yrz8wGK/btX7s/7mTHqufnYy2q/HxOwWoAO+IJuz9Ol7u9x42fy5+VzGOz7gqyZ2b7EH0E925GLhi/2ktyGcnbSd5H8p8Y5e8648cG4l1cBeBl8RXpP6my/zsA9FfeLyJz2k8RXdV7/QTbfB7AYwA2mtlCAB9ENOGpZQeiK2od7lermd0IYDeALpL++WvdeBeiCQkAgGQboqvcO902tbog7I9fS7Xs5e8i+tLhLyO6yLBh/BATvI5xuytqXFdrQzPbY2bvNLPViC42fI4Td66YSkeHymPviseDiN6wAABIrqxz32XnumLf0+VHiKIyKwBUXk3fBWAJyfaKGsb/rCc67zsAPFPxM9ZuZq+ZYl2L458tv2//2ivfwE10rCn/fMT7mnI22NkFYO34J8juODtrbC9Sl1k9QQaA+KOdDKIMkvdhAN83szMR5fuq/QW9EsCPzOx0M/s/R7dSEZkNzOwQgL9ElC99PclWkjmSF5P8h3izdgCHAQyQPBnAH1bsZi8A34f4nwG8i+Q58Zv5NpKvjSdCP0WUaX43o7ZflyLKvo77KoC3kTyd0RfG/g7AXXG0Y7LXUkKUff5k/KWqLMnz4v20I4p5HEA0qfy7qZ8lfB3Ae0iuiTO4NbOcJH+L5Jr4Zh+iiVYxvl15nqbqivjYSxC9ORnPpz4A4IXxuWpGFBvxJjvejQA+FF9g6UT0czCtvYPjTwp+FcDrKj41QBxH+AmAvyfZzOiLnG9HyIZ/HcBfkFwcn1P/icTPABxm9AXHlvjP+kUky74gN4mPkmwk+TJEGfNv1NhusmNNVGelawH8NcmN8d+NF5NcGj820Z/XXYjeEH0g/vt5AaLzWpnPf07i89CM6A1jLv7zmPVzJpm6ufKHXe2Kxy8h/otiZrdiFrbSEZF0mNknEX156EMAehBd5Xo3oi9ZAcCfIboC249o8vuvFbv4CIAb4u8w/HYc/3ongM8g+rdoK4C3xscaA/DriCZCBwH8HqIs5XhO+A4A/wvAtxBdmTsB9eVK/wzAQwDuBtAL4GOI/u3/EqKPpHcCeATAnXXs858BfA/RhPQ+ADdNsO1ZAO6KP7nbjCgP/Uz82EfgzlMdx/8qoi9nPR3/+hsAMLMnEHUg+X+IOnpUXqH9AoBT4+P9W5X9/g2imN6DiM7ZfeP7ngyjTgy3TGVbM9tiZltqPPwGRFfzdyG6uPNhM7s9fuyjiP7MnkH0+v/F7bOIaIJ4evz4fkSTz6lkyoEojtAXH/crAN5lZo/VqH+yY9Wss4pPIppQ34boTecXALTEj30ENX4+4r83r0PUNm8/ojaMb65VcyVGfc0/OMEmtwEYRvTl1mvi8fkTbC9zDCvewM54JAfMbIG7fTyif/g7EX1J5s/M7BKSDwB4/fg/xCR7AbzAzPaP7yN+x/lnZnbJBMebdBsRkelE8i4AV5vZF9OuRea++P+5L5vZmkk2FZk3ZvUVZJLLAFyN6BvNlTP9HyP+Bi7JVyFq81KpH9HHjCIiqSH5cpIr44jFWwC8GMCtadclIjJfzcYJckv8pbotiD5Guw3RxzmVPgrgVSTvQ/QRzG4c+YW7BwEUGLWLO+JLeiR/hCiDdSGjJupT7ScpIlKPkxDFFQ4BeD+A3zSz3emWJCIyf826iMVUxV9CKZpZgeR5AD4/3h5ORERERKSW2dgHearWAfh6/K3TMURfkBERERERmVBdV5AXL8lY15poTl2o0SqzwbVKzLhxsWL7krvtt8u6sd/GdwI3d39D2SOBP17RQpKkVFGH31eOxWScqdEus9b9eZdWKbnjkf61Va8VAAqWdbWH52cRavJdUp94aHS/mS2ruUM5Zjo7O23Dhg1plyEiIiJ1uPfee2vOpeq6gty1pgHf/G4nAKC32Fx1myXZsABSq5sc9pfK4879lkvG7cwn47ZMmESOWJgRDpVCqX7y25EZS8b+CIdKYf8HSy3JeNAay+rIW9jv2oZeV3tYLMlPqlvdJNrbWwzHOFwK56Y5E15bR6b24lB7CuG7gv2u3o5sWNjIT+BfcdzWiVYmkipIXoeot+c+M3tRlccJ4NMAXoNo1aW3mtl9ldtV2rBhA+65Z9IFHEVERGQGIVlzLjUbv6Qn8lxdD+CiCR6/GMDG+NfliFZLExERkXmmrivIB4ptuL73PABAxl0dXtIQrnKe07o1Ga9vGErGjSyPF2RdtGPIXcVFKVy59c9Z5K7Eenl3dXdnMayS+dhoWEJ+20hnMh4oNpU931+VPWvBM8n45MawhHtrpuC2D8/dXwxXqe8eDov9/PfBsGrmUCFcsV7a5Je5B/rzoZZDYy2oxkc6xkpZ98gnqm4vtZnZD0lumGCTSwF8KW4ZeCfJDpKr1E1ARERkftEVZJGgC9GKaeO64/tERERkHpnLXSxE6lXtm6dVv5VJ8nJEMQysW7fuaNY042248rtplwAA2HbVa9MuQURE5oi6Jsi9Q2248f6zAADZxhB/WLH0UDIeWh0iBa9qfygZL8uMlu3LfwnOa3aximY3Xcm5Fg4jLp6xPR++3HbL4Rcn4x/vDZGHvb0Lk3FxuPwlL1waYg+HV7mYQ0cYrs71JeM2hi8FPjm2Ihlv3hOO/fSD4aJjY1+4SP9kxfX63OEwdt9thE+c+GRKZmxu9qyeQboBrHW31wDYVW1DM7sGwDUAsGnTJv3BiIiIzCGKWIgEmwG8mZFzARxS/lhERGT+UcRC5g2SNwK4AEAnyW4AHwaQAwAzuxrAzYhavG1F1ObtbelUKiIiImmqa4KcHSIW/TzqvNB4KHyq3HNK6Pu7ffHeZJxfELou9JbK+w/nXHYg5xbQ8L0u2jOhvAWZcIxHx0J3jB8MnJyMb9l+Sjj2/YuT8bInwl4bRso/DT9wStju8fNDzmFFU8g/dGTD8bKuT/P2sdAd46nu0Gd61U/D/hf9PHTDYLFioZBSuG3D4dhsDOfK2lvDOKML/s+Hmb1hkscNwBXHqBwRERGZoTTjEhERERFxNEEWEREREXHqiliwADT3RrGA5r6wwMbAQFgwY9gtnuHlKhYKKfllpF0ZJYT9jlgYDxVDt4m7R9Yn4+/sCCsGF+4JcYnVPw1dM5ofC9+zsv6BsjpGF74wGfccXpCMDy8NkY5W14HDLyzSWwgLk+BgiEW07XQtKfbuD8detbzs2MVFoWuG5ZaE19ESoiljHe7cZF1bj9AgRERERESmka4gi4iIiIg4miCLiIiIiDj1tXkjYJnoY/5iU5hb+4UtfHQi6xYha6tYGGTQHXqwFGIZ/aUQbegJaQbsK4YFQX5wMHSu2N/dkYxXPhViHE27QhcKG3GRh0z5Ymm5oVDj2GioqSkT6u3IjKCa/kKotWEw7DczlA8bLV+aDPteErpeAMDA6nAOfVqjlAs1FVrD2Nz9+FrVkkRERETkedIVZBERERERRxNkERERERFHE2QREREREaeuDLIRGI/dlrJhbl1scjnekmtRhjDOlUd/0ezauQ25rPJBC+3SduZD67NHhlaHcd+KsN++cIymw+U553FctDC8hsGhqtsAAF2NGVrVbfrdioB7hkMu2meQrSnUNLQq5I57XlK+r+bjDiXjNYtCZno4HzLZTQ3hNS1uCrVvr1qdiIiIiDxfuoIsIiIiIuJogiwiIiIi4tS3kh6A8e5nxaZwv4VEAQouYpF3D1Sur+cjDEOuBVwzQ4u0Udf+rXuoIxn39beGmlwruHybO/bS0Det4XBo08bevrI6io2oyrer21VYlIz3uPH+4bDyXnbM7bMlnNaBVWGc6QqrAQLA+WufSsanL3g2Ge8e60jGQy7Ssa7pQDL+dvWyRUREROR50hVkERERERFHE2QREREREae+iEURaDocrVY3vCTMrUsN1Ts+eM0sn4sXXeeKJpeT8BGLDMPKeD7ykMuF7QeWhXhG38YQyRhtb0nGHU+FYzfudHmQCfjjHSyGSEdvIcQq8iX3mtwpKDb6FfKq1w0AjW61voWZ4WTclwnxEB+x8JEVERERETk6dAVZRERERMTRBFlERERExHkOC4VEkYGRpW5hjM7RZLy0KXRqyLE8UlBL1nW0aHYdLVY2hIU0Nrb3JOOShXl9T0voULGnLXSYsFyIWLT7WEWxvKZaJTZlqi86siAbjreoKYwPuzYdlnHnxh26VCp/P7JrONT7aENYCGXXSEcy7hkJkY7+1ubqxYqIiIjItNEVZBERERERRxNkERERERGnrohFsQXoOyWKDxTWh64Lp6/rTsYnL9idjH1HikELHSkAIO+7PrjIRM51rlibCwtjIDR2wJKGEOPYN9aejB/MdSXjbd1rknHDQKijeDDENgAg6wrxJfqFTHw3Dd9torM51PGsi1K4pyI3EG4MdLsXAeDu/uNC7QtCxGJsJOQ1bCycm+3LFkNEREREji5dQRYRERERcTRBFhERERFx6opYZFsKWHhaFHt4ceeu5P7T23ck4/WNodtEI0I0YcgtvAEARXd71LV6GKsxZ+9q6Kt6v+8qcbgQOlc81RLiFqXGsP+G5vJOEC49ASu6xU9cfXlrcGNXaymMfbeKYnN4bsNQiFgs3Fr+2kq5sAgIGMatw+E52dAgBEMrl0BEREREji5dQRYRERERcTRBFhERERFx6opYLG0axJuPuwsA0Jpxi4M0DCRjv7hHk1uFo9m3dgDKWj0MFauX0e66YIwiZBiaM2PJOOtiHIOFEFPIjoaYA0vhWFxQ3kkiO1LeXSOp3S0U4mMVg6WmZHx4LMQ18u1hP30nhtdTaA37zC8sP5avsbknjFt6XSzjkYPuCWGbx6pWLSIiIiLPl64gi4iIiIg4miCLiIiIiDh1RSyamMfGpj0AgHa3YEa7izy0seC2D89tZnkXixGriFzEfOeLtkwYl0rh+TmE6IaPetTCQtiPjY6VPZYddY+5RTmGSiGu4WMVXldriJM83dWZjAfaw3Obl4QuGyvbw8IiADA0FhYEOXgwRD9GF4foRm4gLITS+sT+qnWIiIiIyPTRFWQREREREUcTZBERERERp66IRQaG5rizRK6sQ0Wx6vaNFbEKz62rUfb8IsJzii6F4e+vpSlbmHQbG6uIWAy55xRDlCJbFvUIMY72TIhMnLPo6XDsE8N+Rl1XjlXNIYaRZXkXi5FSiFjs7OxIxj/B8cl4YEeoqe2R6udZRERERKaPriCLiIiIiDiaIIuIiIiIOHVFLEhDLu5S4btN5C3Ms/P+CaUQCWiqSEjka8QnRtyiHH6cL7s/RBO8lmw4einnDuCiHqyIfdB303DDRQ2hS8eyhsPJuCMzlIxXNhxMxgtdVw9fn4+i9BdDdwoAyFv109/U4hZIWRKeU1y2KGz0TNWnioiIiMjzpCvIIiIiIiKOJsgyr5C8iOTjJLeSvLLK4xeQPETy/vjXX6ZRp4iIiKSnzoVCDBtzUZTAz6zzLqYwVH39D5Qqbuen0JWiZJPP39vcIiVLcmEhDnMRC2twx8qU77PY4k6Be8hHOrIue9HsFkLJua4US7IDyfhgqdXVFzpgLMuGqAYA9BYXJOO9+YWoJh/WD8Hwipaq28jUkMwC+CyAXwHQDeBukpvN7JGKTX9kZpcc8wJFRERkRtAVZJlPzgaw1cyeNrMxAF8DcGnKNYmIiMgMowmyzCddAHa4293xfZXOI/kAyVtIvvDYlCYiIiIzRV0RC5FZrlqupzIUdB+A9WY2QPI1AP4NwMYjdkReDuByAFi3bt00lykiIiJpmpYJcrFsXD1bnKuYh7Qz3G51ud5RC2O/r6FSKDXvVrMbc1nh9my43xpcBjlTO+9cyroWcPkw3p8P+WDftm2krDVbqNVnjf2Keb7NW7YiiZ136wk2Z0Jrt8Zc2O9wWZa65suQqekGsNbdXgNgl9/AzA678c0kP0ey08z2V2x3DYBrAGDTpk01kvciIiIyGyliIfPJ3QA2kjyOZCOAywBs9huQXMm4WTbJsxH9HTlwzCsVERGR1OiapMwbZlYg+W4A3wOQBXCdmW0h+a748asB/CaAPyRZADAM4DIz0xViERGReaSuCXLBgAPFI+MKo+6zfx9ByLioQYdrxxY9Vn3O4Vfc8+3jfEu1KXH7L/k2b6Xy/WRHw+3MaNhusNAU7i9bNTC8vgOuTdueQkcyHio1JuNW97r9antAefzCb9e5ILSr2zW6OBk395SfQ6mfmd0M4OaK+652488A+MyxrktERERmDkUsREREREQcTZBFRERERJy6IhZZGNozUdzABxVKJRdNsDAec90feovlc/ER17XBy7k9N7pYxaCLNgxaI6op1lh5r0aaAwBgvotFIYybMtU7VOwpLErGDw6FhghPDCxPxvuHQ/RiZVtYPe+CxY+XHbsjO5SMF7nxipb+ZLzPpTJy23tqvxARERERmRa6giwiIiIi4miCLCIiIiLi1BWxMACjcVzBd5soiz+UQveH5dmBZNxEv5wIcMht57td5N2cfdR1sWh2z8+5pUmGLOyn5BcpseqdK9jWWv6a3CIixQW+W0WoY2c+dJLIurzG3b3rk/G2u9ck48ZDYZ/b14boxdALy6Mhv7Py7mR8QuO+ZHwHTnEF+mLVbUxERETkaNMVZBERERERRxNkERERERGnrohFCcSIZQEcGZmoxm+TrWglUXRxiGK8TwCAXzyDoZNER8bFH7JhIY2S646RQfUIQjYfnmuDQ2WPNe0JHSMyw0uTsV8o5FCxLRzb1drd2xHqcw0qlmwJ+zzwC6GjxY6usD0AFFeEc9DqOmUUXLyj5P6ErBDOh4iIiIgcHbqCLCIiIiLiaIIsIiIiIuLUFbEYKjXi3pFocYyF2ZHk/jEXO2hmWABk1N3fhPJIRtbFIXzcwner8LGK9kwotbkUthlxi5H0FVyHCreSSamh9vsAHg5xjYahzmQ8Vgq1+/pGSuF4nl+jJDsQ4hKt+1uS8a6BFv8UPDsWjjfkunrsGgiLkWTDrmADoVYREREROTp0BVlERERExNEEWURERETEqStisW+sHZ9/5uUAgNZciFIsbgqdIU5u35uMGxc8kYyXZUNnB6B8cZCci1u0uc4VObpog1WPVRwohC4R24eWJOPskOsEkQv7zywN2wCADQ8n46becP/j+8MCH8ubwoInCxvC9u2tIWYyuLw9GQ9tCBGJUi68Bu5oLjv2dxa/KBk3ZsPr2/XUsnDsvW6Rk1xdf1wiIiIi8hzoCrKIiIiIiKMJsoiIiIiIU9dn9sXBBvTeE0UPDrhnPt0ZYhF9x4VOEksaQteFU5t3lu3LL4zhO1+MuM4XxVKIFwy5FTMOuIU79hdCtOGpvrDQR+vuMPfPHQqxCFTGFIZC/KJ9p4s5bA8xiUcXrEjGpywKEZK2xrFk3Lsk7Kd/bTiGS2SgbWeIWwBAr4UoRbEpPL91r6t9yEVLNp0Ynnw7REREROQo0BVkERERERFHE2QREREREaeuiEVmDGjfduT9gyOhq8S+ztBVYv/SMB5pKl9go9lCrCKPEKvwS2Hk3WIdOwuLk/GufBhvGViVjHt3h1jEsv0hsuAVli8qu22rwr7MJSAaD4b3Dr2DITYyvCC8jqZsiJYUO0Pcor/YmIxzh8JOcxXrfDT3hMfyC8PYr0Uy0OXOTZd7P6OIhYiIiMhRoSvIIiIiIiKOJsgiIiIiIk5dEQsakImbKmRH3eIbY24xDIb7F7kWDkuyYbENAOjIhEU2Slbe3WHckDUl457CwmT86GCIVTzWGzpMZAZDHGFkadjnvrNC14tCSEtEx866G+7twlhH6KDR6l7TcDHkHzqawus7cd2+ZNy/ItR9aLAljHvCGChfzKTYEeIa2ZbQuWJ4NBR44vrQQeMpiIiIiMjRoCvIIiIiIiKOJsgiIiIiIo4myCIiIiIiTl0ZZGPI7DaEiC5cRBe5bMjPLnYr6S3NuCXlACzKhO2GXAbZr5iXY8jlLnUZ5qWuX1pzQ9iGK0KueWh52OeihWH7BbmwfaXDIyE7vLwxtKFbt7AvGS9pHErGC13G+oyFO5Jx0cL7jj4Xet7dVd5ibu9wWAVwUWPY18rm/mScYTjRx7f0JOPv13wVIiIiIvJ86AqyiIiIiIijCbKIiIiIiFNfm7cS0BC3d8vmQ64iOxq2KRRDW7IsQjwg56ICExmx6iUd3xjaqDVn8lW32b0oRBg6G0Mko6spRCQOVfR5W+JiIP3F5mTcng1xjWUNh6serzUTXriPgAyWQlSjv+Rau4WFBQGUrwjooxRduVBvK8MxRqx8NUIRERERmX66giwiIiIi4miCLCIiIiLi1L2S3vgKesXG0CXCNZ6AXxMv62IDxYrV8obcOO+6PuQYulsUXdwiixDp8Kvyvbg1dI84uWWXO3bYvt110GhrdnkQlHec8PX6Okpum0yNqEje1drmohcd2aFqmwMANuT2V73fv9aMex05TC2mIiIiIiLPna4gi4iIiIg4miCLiIiIiDh1RSxKWWB00ZFz6kJbGC9oCNGEjIsE+MgDAORcjCDrYxVlIY2wqEerWzTEb9OeDfGJg0VXiK97gvcB/jEfpfAxh1p8l45G9xqmyr8Of+yii3E0WhiXWB5TEREREZHppyvIIiIiIiKOJsgiIiIiIk59EYtGoH/9kfePLQ8Ld2xsD4tqtGXGkvFEkQU/S292UYVsWQwjjNtc9KKUGay6vZdz8YxcRRSi1nN8t4ryBU/qi1JMJaoBlMdMcmXHtqr3y3ND8iIAnwaQBXCtmV1V8Tjjx1+DqNnKW83svmNeqIiIiKRGV5Bl3iCZBfBZABcDOBXAG0ieWrHZxQA2xr8uB/D5Y1qkiIiIpE4TZJlPzgaw1cyeNrMxAF8DcGnFNpcC+JJF7gTQQXLVsS5URERE0lNXxGJ0V/f+rR96//aJtnnWjb/7nEqSKaoSdpFJdAHY4W53AzhnCtt0AdjtNyJ5OaIrzAAwQPLx6S112nQCqL4izczyvOvkx6apkonNhvM5G2oEVOd0mw11zoYaAdU53WZynTXnUnVNkM1s2fOvRSQ11frkVYbEp7INzOwaANdMR1FHE8l7zGxT2nVMRnVOn9lQI6A6p9tsqHM21Aiozuk2W+qspIiFzCfdANa622sA7HoO24iIiMgcpgmyzCd3A9hI8jiSjQAuA7C5YpvNAN7MyLkADpnZ7sodiYiIyNxVV8RCZDYzswLJdwP4HqI2b9eZ2RaS74ofvxrAzYhavG1F1ObtbWnVO01mfAwkpjqnz2yoEVCd02021DkbagRU53SbLXWWodnU+vSKiIiIiMwHiliIiIiIiDiaIIuIiIiIOJogi8xRJC8i+TjJrSSvTLueakheR3IfyYfTrqUWkmtJ/ifJR0luIfnetGuqhmQzyZ+RfCCu86Np11QLySzJn5P8Ttq1TITkNpIPkbyf5D1p11MNyQ6S3yT5WPwzel7aNVUieVJ8Dsd/HSb5vrTrqobkn8R/fx4meSPJ5rRrqkTyvXF9W2bSeaz27znJJSRvJ/lk/PviNGushybIInPQFJfVngmuB3BR2kVMogDg/WZ2CoBzAVwxQ8/lKIBXmtlpAE4HcFHciWUmei+AR9MuYopeYWanz+A+rp8GcKuZnQzgNMzA82pmj8fn8HQAL0H0Behvp1vVkUh2AXgPgE1m9iJEX+a+LN2qypF8EYB3IloZ9jQAl5DcmG5Vietx5L/nVwK4w8w2Argjvj0raIIsMjdNZVnt1JnZDwH0pl3HRMxst5ndF4/7EU1AutKt6kjx8ugD8c1c/GvGfQub5BoArwVwbdq1zHYkFwI4H8AXAMDMxszsYKpFTe5CAE+Z2YSr8qaoAUALyQYArZh5ffBPAXCnmQ2ZWQHADwD8Wso1Aaj57/mlAG6IxzcAeP2xrOn50ARZZG6qtWS2PA8kNwA4A8BdKZdSVRxduB/APgC3m9lMrPNTAD4AoJRyHVNhAG4jeW+8vPxMczyAHgBfjCMr15JsS7uoSVwG4Ma0i6jGzHYC+DiAZwHsRtQH/7Z0qzrCwwDOJ7mUZCuitqRrJ3lOmlaMryUQ/7485XqmTBNkkblpSktmy9SRXADgWwDeZ2aH066nGjMrxh9jrwFwdvxx7IxB8hIA+8zs3rRrmaKXmtmZiKJKV5A8P+2CKjQAOBPA583sDACDmMEfYccLNL0OwDfSrqWaOB97KYDjAKwG0Eby99KtqpyZPQrgYwBuB3ArgAcQxcBkmmmCLDI3acnsaUQyh2hy/BUzuynteiYTf8z+X5h5+e6XAngdyW2IYj+vJPnldEuqzcx2xb/vQ5SZPTvdio7QDaDbfVLwTUQT5pnqYgD3mdnetAup4ZcBPGNmPWaWB3ATgF9MuaYjmNkXzOxMMzsfUaThybRrmsBekqsAIP59X8r1TJkmyCJz01SW1ZYpIElEGc9HzeyTaddTC8llJDvicQui/+wfS7WoCmb2F2a2xsw2IPqZ/L6ZzagrdONItpFsHx8DeBWij7dnDDPbA2AHyZPiuy4E8EiKJU3mDZih8YrYswDOJdka/72/EDPwS48kl8e/rwPw65jZ53QzgLfE47cA+PcUa6mLlpoWmYNqLaudcllHIHkjgAsAdJLsBvBhM/tCulUd4aUA3gTgoTjfCwAfNLOb0yupqlUAbog7mGQAfN3MZnQbtRluBYBvR/MkNAD4qpndmm5JVf0xgK/Eb4SfBvC2lOupKs7L/gqAP0i7llrM7C6S3wRwH6LYws8xM5dJ/hbJpQDyAK4ws760CwKq/3sO4CoAXyf5dkRvQH4rvQrro6WmRUREREQcRSxERERERBxNkEVEREREHE2QRUREREQcTZBFRERERBxNkEVEREREHE2QRUREREQcTZBFRERERJz/D5LZkbvOjkKrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x864 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_test_images = grayscale_test_images.shape[0]\n",
    "\n",
    "random_inx = np.random.choice(num_test_images, 5)\n",
    "random_test_images = grayscale_test_images[random_inx, ...]\n",
    "random_test_labels = test_labels[random_inx, ...]\n",
    "\n",
    "predictions = model.predict(random_test_images)\n",
    "\n",
    "fig, axes = plt.subplots(5, 2, figsize=(16, 12))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=-0.2)\n",
    "\n",
    "for i, (prediction, image, label) in enumerate(zip(predictions, random_test_images, random_test_labels)):\n",
    "    axes[i, 0].imshow(np.squeeze(image))\n",
    "    axes[i, 0].get_xaxis().set_visible(False)\n",
    "    axes[i, 0].get_yaxis().set_visible(False)\n",
    "    axes[i, 0].text(10., -1.5, f'Digit {label}')\n",
    "    axes[i, 1].bar(np.arange(len(prediction)), prediction)\n",
    "    axes[i, 1].set_xticks(np.arange(len(prediction)))\n",
    "    axes[i, 1].set_title(f\"Categorical distribution. Model prediction: {np.argmax(prediction)}\")\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import PIL\n",
    "from PIL import Image, ImageEnhance, ImageDraw, ImageFont\n",
    "from IPython.display import display\n",
    "\n",
    "img = Image.open('no_3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "img = cv2.imread('no_0.png', cv2.IMREAD_GRAYSCALE)\n",
    "img = cv2.resize(img, (32, 32))\n",
    "my_img = img[np.newaxis,...,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 1,figsize = (2,2))\n",
    "fig.subplots_adjust(hspace=-0.7, wspace= 0.2)\n",
    "#for i,ax in enumerate(axes.flat):\n",
    " #   ax.imshow(np.squeeze(my_img))\n",
    "  #  ax.text(6., -1.0, f'Digit {0}',fontsize = 20)\n",
    "  #  ax.get_xaxis().set_visible(False)\n",
    "   # ax.get_yaxis().set_visible(False)\n",
    "axes.imshow(np.squeeze(my_img))\n",
    "axes.text(6., -1.0, f'Digit {0}',fontsize = 20)\n",
    "axes.get_xaxis().set_visible(False)\n",
    "axes.get_yaxis().set_visible(False)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "predict = model.predict(my_img)\n",
    "\n",
    "fig, axes = plt.subplots(1,2, figsize=(25, 10))\n",
    "fig.subplots_adjust(hspace=0.4, wspace=-0.2)\n",
    "\n",
    "axes[0].imshow(np.squeeze(my_img))\n",
    "axes[0].get_xaxis().set_visible(False)\n",
    "axes[0].get_yaxis().set_visible(False)\n",
    "axes[0].text(10., -1.5, f'Digit {0}')\n",
    "for pred in predict:\n",
    "  axes[1].bar(np.arange(len(pred)), pred)\n",
    "  axes[1].set_xticks(np.arange(len(pred)))\n",
    "  axes[1].set_title(f\"Categorical distribution. Model prediction: {np.argmax(pred)}\")"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Copy of Capstone Project.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}