Fashion_MNIST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Classifying-MNIST-Fashion-Clothing-Articles-Using-Convolutional-Neural-Networks\" data-toc-modified-id=\"Classifying-MNIST-Fashion-Clothing-Articles-Using-Convolutional-Neural-Networks-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span><strong>Classifying MNIST-Fashion Clothing Articles Using Convolutional Neural Networks</strong></a></span></li><li><span><a href=\"#Part-1---Setup\" data-toc-modified-id=\"Part-1---Setup-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Part 1 - Setup</a></span><ul class=\"toc-item\"><li><span><a href=\"#Necessary-Installations\" data-toc-modified-id=\"Necessary-Installations-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Necessary Installations</a></span></li><li><span><a href=\"#Library-Imports\" data-toc-modified-id=\"Library-Imports-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Library Imports</a></span></li><li><span><a href=\"#Google-Drive-Connection\" data-toc-modified-id=\"Google-Drive-Connection-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Google Drive Connection</a></span></li><li><span><a href=\"#Create-working-directory-for-this-run\" data-toc-modified-id=\"Create-working-directory-for-this-run-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Create working directory for this run</a></span><ul class=\"toc-item\"><li><ul class=\"toc-item\"><li><span><a href=\"#Change-into-main-directory\" data-toc-modified-id=\"Change-into-main-directory-2.4.0.1\"><span class=\"toc-item-num\">2.4.0.1&nbsp;&nbsp;</span>Change into main directory</a></span></li><li><span><a href=\"#Generate-new-run-name-by-combining-random-word-with-the-current-date-and-time\" data-toc-modified-id=\"Generate-new-run-name-by-combining-random-word-with-the-current-date-and-time-2.4.0.2\"><span class=\"toc-item-num\">2.4.0.2&nbsp;&nbsp;</span>Generate new run name by combining random word with the current date and time</a></span></li><li><span><a href=\"#Create-a-directory-for-this-run-and-set-it-as-the-working-directory\" data-toc-modified-id=\"Create-a-directory-for-this-run-and-set-it-as-the-working-directory-2.4.0.3\"><span class=\"toc-item-num\">2.4.0.3&nbsp;&nbsp;</span>Create a directory for this run and set it as the working directory</a></span></li></ul></li></ul></li></ul></li><li><span><a href=\"#Part-2---Data-ingestion-and-preprocessing\" data-toc-modified-id=\"Part-2---Data-ingestion-and-preprocessing-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Part 2 - Data ingestion and preprocessing</a></span><ul class=\"toc-item\"><li><span><a href=\"#Read-in-data-and-split-into-subsets\" data-toc-modified-id=\"Read-in-data-and-split-into-subsets-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Read in data and split into subsets</a></span></li><li><span><a href=\"#Reshape-and-normalize-X\" data-toc-modified-id=\"Reshape-and-normalize-X-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Reshape and normalize X</a></span></li><li><span><a href=\"#One-hot-encoding-of-class-variables\" data-toc-modified-id=\"One-hot-encoding-of-class-variables-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>One hot encoding of class variables</a></span></li><li><span><a href=\"#Set-Global-Variables\" data-toc-modified-id=\"Set-Global-Variables-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>Set Global Variables</a></span></li><li><span><a href=\"#Other-functions\" data-toc-modified-id=\"Other-functions-3.5\"><span class=\"toc-item-num\">3.5&nbsp;&nbsp;</span>Other functions</a></span></li><li><span><a href=\"#Plot-some-images-to-see-what-the-data-looks-like\" data-toc-modified-id=\"Plot-some-images-to-see-what-the-data-looks-like-3.6\"><span class=\"toc-item-num\">3.6&nbsp;&nbsp;</span>Plot some images to see what the data looks like</a></span></li><li><span><a href=\"#Display-8-images-of-a-specific-class-to-see-variation-(Sneakers)\" data-toc-modified-id=\"Display-8-images-of-a-specific-class-to-see-variation-(Sneakers)-3.7\"><span class=\"toc-item-num\">3.7&nbsp;&nbsp;</span>Display 8 images of a specific class to see variation (Sneakers)</a></span></li></ul></li><li><span><a href=\"#Part-3---Baseline-Model\" data-toc-modified-id=\"Part-3---Baseline-Model-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Part 3 - Baseline Model</a></span><ul class=\"toc-item\"><li><span><a href=\"#Build-Model\" data-toc-modified-id=\"Build-Model-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Build Model</a></span></li><li><span><a href=\"#Compile-the-model\" data-toc-modified-id=\"Compile-the-model-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Compile the model</a></span></li><li><span><a href=\"#Fit-the-model\" data-toc-modified-id=\"Fit-the-model-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>Fit the model</a></span></li><li><span><a href=\"#Plot-training-and-validation-accuracy-and-loss\" data-toc-modified-id=\"Plot-training-and-validation-accuracy-and-loss-4.4\"><span class=\"toc-item-num\">4.4&nbsp;&nbsp;</span>Plot training and validation accuracy and loss</a></span></li><li><span><a href=\"#Evaluate-on-testing-data\" data-toc-modified-id=\"Evaluate-on-testing-data-4.5\"><span class=\"toc-item-num\">4.5&nbsp;&nbsp;</span>Evaluate on testing data</a></span></li></ul></li><li><span><a href=\"#Part-4---Hyperparamter-Tuning\" data-toc-modified-id=\"Part-4---Hyperparamter-Tuning-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Part 4 - Hyperparamter Tuning</a></span><ul class=\"toc-item\"><li><span><a href=\"#Create-model-and-set-hyperparameters-to-tune\" data-toc-modified-id=\"Create-model-and-set-hyperparameters-to-tune-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>Create model and set hyperparameters to tune</a></span></li><li><span><a href=\"#Set-parameters-of-search-space\" data-toc-modified-id=\"Set-parameters-of-search-space-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Set parameters of search space</a></span></li><li><span><a href=\"#Summary-of-tuner\" data-toc-modified-id=\"Summary-of-tuner-5.3\"><span class=\"toc-item-num\">5.3&nbsp;&nbsp;</span>Summary of tuner</a></span></li><li><span><a href=\"#Search-for-best-hyperparameters\" data-toc-modified-id=\"Search-for-best-hyperparameters-5.4\"><span class=\"toc-item-num\">5.4&nbsp;&nbsp;</span>Search for best hyperparameters</a></span></li><li><span><a href=\"#Choose-best-model\" data-toc-modified-id=\"Choose-best-model-5.5\"><span class=\"toc-item-num\">5.5&nbsp;&nbsp;</span>Choose best model</a></span></li><li><span><a href=\"#Find-optimal-number-of-epochs.\" data-toc-modified-id=\"Find-optimal-number-of-epochs.-5.6\"><span class=\"toc-item-num\">5.6&nbsp;&nbsp;</span>Find optimal number of epochs.</a></span></li><li><span><a href=\"#Refit-one-more-time-on-best-number-epochs\" data-toc-modified-id=\"Refit-one-more-time-on-best-number-epochs-5.7\"><span class=\"toc-item-num\">5.7&nbsp;&nbsp;</span>Refit one more time on best number epochs</a></span></li><li><span><a href=\"#Plot-training-and-validation-accuracy-and-loss\" data-toc-modified-id=\"Plot-training-and-validation-accuracy-and-loss-5.8\"><span class=\"toc-item-num\">5.8&nbsp;&nbsp;</span>Plot training and validation accuracy and loss</a></span></li><li><span><a href=\"#Evaluate-on-testing-data\" data-toc-modified-id=\"Evaluate-on-testing-data-5.9\"><span class=\"toc-item-num\">5.9&nbsp;&nbsp;</span>Evaluate on testing data</a></span></li></ul></li><li><span><a href=\"#Part-5---Analysis\" data-toc-modified-id=\"Part-5---Analysis-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>Part 5 - Analysis</a></span><ul class=\"toc-item\"><li><span><a href=\"#Make-some-predictions-to-visualize-whats-happening\" data-toc-modified-id=\"Make-some-predictions-to-visualize-whats-happening-6.1\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>Make some predictions to visualize whats happening</a></span></li><li><span><a href=\"#Confusion-Matrix\" data-toc-modified-id=\"Confusion-Matrix-6.2\"><span class=\"toc-item-num\">6.2&nbsp;&nbsp;</span>Confusion Matrix</a></span><ul class=\"toc-item\"><li><span><a href=\"#Compute-prediction-accuracy\" data-toc-modified-id=\"Compute-prediction-accuracy-6.2.1\"><span class=\"toc-item-num\">6.2.1&nbsp;&nbsp;</span>Compute prediction accuracy</a></span></li><li><span><a href=\"#Show-heatmapped-confusion-matrix\" data-toc-modified-id=\"Show-heatmapped-confusion-matrix-6.2.2\"><span class=\"toc-item-num\">6.2.2&nbsp;&nbsp;</span>Show heatmapped confusion matrix</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xSflq_1YGMA7"
   },
   "source": [
    "# **Classifying MNIST-Fashion Clothing Articles Using Convolutional Neural Networks**\n",
    "\n",
    "**Group Members:**\n",
    "* Dino Anastasopoulos: 1900661\n",
    "* Philani Mpofu: 1848751\n",
    "* Chloë Smith: 1877342"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qOIxg3DcHAjF"
   },
   "source": [
    "# Part 1 - Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qY_6sUR4kBla"
   },
   "source": [
    "## Necessary Installations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "idkUXdJdkAr8",
    "outputId": "cef0370f-af25-4b74-980e-3d2c62f7554a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already up-to-date: keras-tuner in /usr/local/lib/python3.7/dist-packages (1.0.3)\n",
      "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (2.23.0)\n",
      "Requirement already satisfied, skipping upgrade: ipython in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (5.5.0)\n",
      "Requirement already satisfied, skipping upgrade: packaging in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (20.9)\n",
      "Requirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (1.19.5)\n",
      "Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (1.4.1)\n",
      "Requirement already satisfied, skipping upgrade: kt-legacy in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (1.0.3)\n",
      "Requirement already satisfied, skipping upgrade: tensorboard in /usr/local/lib/python3.7/dist-packages (from keras-tuner) (2.5.0)\n",
      "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (2021.5.30)\n",
      "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (3.0.4)\n",
      "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (2.10)\n",
      "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->keras-tuner) (1.24.3)\n",
      "Requirement already satisfied, skipping upgrade: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (0.8.1)\n",
      "Requirement already satisfied, skipping upgrade: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (1.0.18)\n",
      "Requirement already satisfied, skipping upgrade: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (5.0.5)\n",
      "Requirement already satisfied, skipping upgrade: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (0.7.5)\n",
      "Requirement already satisfied, skipping upgrade: setuptools>=18.5 in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (57.0.0)\n",
      "Requirement already satisfied, skipping upgrade: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (4.8.0)\n",
      "Requirement already satisfied, skipping upgrade: decorator in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (4.4.2)\n",
      "Requirement already satisfied, skipping upgrade: pygments in /usr/local/lib/python3.7/dist-packages (from ipython->keras-tuner) (2.6.1)\n",
      "Requirement already satisfied, skipping upgrade: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->keras-tuner) (2.4.7)\n",
      "Requirement already satisfied, skipping upgrade: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (0.6.1)\n",
      "Requirement already satisfied, skipping upgrade: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (3.12.4)\n",
      "Requirement already satisfied, skipping upgrade: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (0.12.0)\n",
      "Requirement already satisfied, skipping upgrade: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (1.34.1)\n",
      "Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (1.0.1)\n",
      "Requirement already satisfied, skipping upgrade: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (1.31.0)\n",
      "Requirement already satisfied, skipping upgrade: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (1.8.0)\n",
      "Requirement already satisfied, skipping upgrade: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (0.4.4)\n",
      "Requirement already satisfied, skipping upgrade: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (0.36.2)\n",
      "Requirement already satisfied, skipping upgrade: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard->keras-tuner) (3.3.4)\n",
      "Requirement already satisfied, skipping upgrade: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->keras-tuner) (1.15.0)\n",
      "Requirement already satisfied, skipping upgrade: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->keras-tuner) (0.2.5)\n",
      "Requirement already satisfied, skipping upgrade: ipython-genutils in /usr/local/lib/python3.7/dist-packages (from traitlets>=4.2->ipython->keras-tuner) (0.2.0)\n",
      "Requirement already satisfied, skipping upgrade: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->keras-tuner) (0.7.0)\n",
      "Requirement already satisfied, skipping upgrade: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard->keras-tuner) (0.2.8)\n",
      "Requirement already satisfied, skipping upgrade: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard->keras-tuner) (4.7.2)\n",
      "Requirement already satisfied, skipping upgrade: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard->keras-tuner) (4.2.2)\n",
      "Requirement already satisfied, skipping upgrade: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras-tuner) (1.3.0)\n",
      "Requirement already satisfied, skipping upgrade: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard->keras-tuner) (4.5.0)\n",
      "Requirement already satisfied, skipping upgrade: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->keras-tuner) (0.4.8)\n",
      "Requirement already satisfied, skipping upgrade: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->keras-tuner) (3.1.1)\n",
      "Requirement already satisfied, skipping upgrade: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->keras-tuner) (3.4.1)\n",
      "Requirement already satisfied, skipping upgrade: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard->keras-tuner) (3.7.4.3)\n",
      "Requirement already satisfied: random-word in /usr/local/lib/python3.7/dist-packages (1.0.7)\n",
      "Requirement already satisfied: nose in /usr/local/lib/python3.7/dist-packages (from random-word) (1.3.7)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from random-word) (2.23.0)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->random-word) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->random-word) (2.10)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->random-word) (1.24.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->random-word) (2021.5.30)\n"
     ]
    }
   ],
   "source": [
    "!pip install keras-tuner --upgrade\n",
    "!pip install random-word"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RGHPFrIoIMpn"
   },
   "source": [
    "## Library Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8vLR1t5JjdAA",
    "outputId": "6e5d87d5-a6b3-442c-ce50-72b47c47e664"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All packages succesfully installed\n"
     ]
    }
   ],
   "source": [
    "#Standard/Basic Stuff\n",
    "import cv2\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import math\n",
    "import imutils\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "\n",
    "from random_word import RandomWords\n",
    "\n",
    "from skimage import io\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import *\n",
    "\n",
    "\n",
    "from google.colab import drive\n",
    "\n",
    "\n",
    "#TensorFlow and Keras stuff\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Dense, Flatten, Conv2D, Convolution2D, MaxPooling2D, BatchNormalization, Dropout, AveragePooling2D\n",
    "\n",
    "# from keras.models import Sequential, Model\n",
    "# from keras.layers import Dense, Flatten, Conv2D, Convolution2D, MaxPooling2D, BatchNormalization, Dropout, AveragePooling2D\n",
    "\n",
    "from tensorflow.keras.optimizers import Adam, RMSprop\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
    "\n",
    "#Keras Tuner\n",
    "import keras_tuner as kt\n",
    "from keras_tuner import HyperModel\n",
    "from keras_tuner.tuners import RandomSearch, Hyperband\n",
    "\n",
    "print(\"All packages succesfully installed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GMdB-HdIIbnC"
   },
   "source": [
    "## Google Drive Connection \n",
    "(This is necessary for working with Google Colab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8ow4b4q3KMdm",
    "outputId": "62d7308f-efc7-4403-887f-92dcdf5f2f1a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "drive.mount(\"/content/gdrive\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "emzZEClMJrpN"
   },
   "source": [
    "## Create working directory for this run\n",
    "The code below will create a new folder named by the current data and time. This will allow us to save all results, figures and models in a new directory for each run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G4RhLsx8Q2w_"
   },
   "source": [
    "#### Change into main directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DrH7eAMAQpqh",
    "outputId": "1c630e18-49f0-4c7d-f90a-8b8f5f0d9b61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/content/gdrive/MyDrive/MNIST_WORKING_DIRECTORY\n"
     ]
    }
   ],
   "source": [
    "cd /content/gdrive/MyDrive/MNIST_WORKING_DIRECTORY/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hd4xGcSFQ7hK"
   },
   "source": [
    "#### Generate new run name by combining random word with the current date and time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L5yMr0nfIyTO",
    "outputId": "a218ef65-abd5-42bd-e348-d622e8b379d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unbulleted\n"
     ]
    }
   ],
   "source": [
    "r = RandomWords()\n",
    "random_word = r.get_random_word()\n",
    "print(random_word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "r5HHrTTWNGbg",
    "outputId": "c4f67f78-7d7b-465a-f4a1-8653fc117447"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22-06@11:51\n"
     ]
    }
   ],
   "source": [
    "current_date_time = datetime.now() # current date and time\n",
    "date_time = current_date_time.strftime(\"%d-%m@%H:%M\")\n",
    "print(date_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rUIcXtotRSX4"
   },
   "source": [
    "#### Create a directory for this run and set it as the working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2a8tcH2BjZM0",
    "outputId": "d24e6ee4-dc69-4e32-9e3a-65597f97cfb1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unbulleted-22-06@11:51\n"
     ]
    }
   ],
   "source": [
    "run_name = random_word + \"-\" + date_time\n",
    "print(run_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "lX39kr-eLh-3"
   },
   "outputs": [],
   "source": [
    "os.mkdir(\"Runs/\" + run_name) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uI-ynXjEMouC"
   },
   "outputs": [],
   "source": [
    "cd \"Runs/$run_name\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oDWQYFlykN-L"
   },
   "source": [
    "# Part 2 - Data ingestion and preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nfWuViJmRzhz"
   },
   "source": [
    "## Read in data and split into subsets\n",
    "*   Roughly 68% training, 17% validation and 15% testing data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "id": "LZTez3XykQO0"
   },
   "outputs": [],
   "source": [
    "(trainX, trainY_original), (testX, testY_original) = keras.datasets.fashion_mnist.load_data()\n",
    "\n",
    "trainX, valX, trainY_original, valY_original = train_test_split(trainX, trainY_original, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HBgd8S1Nkc0-",
    "outputId": "01137a90-b344-44b6-aafe-ba3da8d2a3cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X Shape:  (48000, 28, 28)\n",
      "Val X Shape:  (12000, 28, 28)\n",
      "Test X Shape:  (10000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train X Shape: \", trainX.shape)\n",
    "print(\"Val X Shape: \", valX.shape)\n",
    "print(\"Test X Shape: \", testX.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9XeSe3wIlSQh",
    "outputId": "b6d90316-9b27-458d-de79-5788b17bbbaf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Train Y Shape:  (48000,)\n",
      "Original Val Y Shape:  (12000,)\n",
      "Original Test Y Shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Original Train Y Shape: \", trainY_original.shape)\n",
    "print(\"Original Val Y Shape: \", valY_original.shape)\n",
    "print(\"Original Test Y Shape: \", testY_original.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7ttVue2BTapF"
   },
   "source": [
    "## Reshape and normalize X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "id": "qEUfvn7AlRR3"
   },
   "outputs": [],
   "source": [
    "trainX = trainX.reshape(-1, 28, 28, 1)\n",
    "valX = valX.reshape(-1, 28, 28, 1)\n",
    "testX = testX.reshape(-1, 28, 28, 1)\n",
    "\n",
    "\n",
    "trainX = trainX /255.0\n",
    "valX = valX /255.0\n",
    "testX = testX /255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VEuWGU2Zo0tX",
    "outputId": "97055718-4dde-4afe-a036-f4b8318f8bce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train X Shape:  (48000, 28, 28, 1)\n",
      "Val X Shape:  (12000, 28, 28, 1)\n",
      "Test X Shape:  (10000, 28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Train X Shape: \", trainX.shape)\n",
    "print(\"Val X Shape: \", valX.shape)\n",
    "print(\"Test X Shape: \", testX.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k9EFVAvBpnod"
   },
   "source": [
    "## One hot encoding of class variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ItieUqNZn76U",
    "outputId": "a3864fb1-66cb-4546-974a-20fbabc962da"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded Train Y Shape:  (48000, 10)\n",
      "Encoded Val Y Shape:  (12000, 10)\n",
      "Encoded Test Y Shape:  (10000, 10)\n"
     ]
    }
   ],
   "source": [
    "trainY = keras.utils.to_categorical(trainY_original, 10)\n",
    "valY = keras.utils.to_categorical(valY_original, 10)\n",
    "testY = keras.utils.to_categorical(testY_original, 10)\n",
    "\n",
    "print(\"Encoded Train Y Shape: \", trainY.shape)\n",
    "print(\"Encoded Val Y Shape: \", valY.shape)\n",
    "print(\"Encoded Test Y Shape: \", testY.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Iyu_ETNpwb0D"
   },
   "source": [
    "## Set Global Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "id": "HBLuOKdcwba-"
   },
   "outputs": [],
   "source": [
    "class_labels = {0 : 'T-shirt/top',\n",
    "            1 : 'Trouser',\n",
    "            2 : 'Pullover',\n",
    "            3 : 'Dress',\n",
    "            4 : 'Coat',\n",
    "            5 : 'Sandal',\n",
    "            6 : 'Shirt',\n",
    "            7 : 'Sneaker',\n",
    "            8 : 'Bag',\n",
    "            9 : 'Ankle boot'}\n",
    "\n",
    "imgShape = [28,28,1]\n",
    "numClasses = 10\n",
    "classes = list(class_labels.values())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mhPpmQE3bsny"
   },
   "source": [
    "## Other functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "id": "kVkIN0y0bx-Q"
   },
   "outputs": [],
   "source": [
    "def Plot_Training_Progress(history):\n",
    "    acc = history['accuracy']\n",
    "    val_acc = history['val_accuracy']\n",
    "    loss = history['loss']\n",
    "    val_loss = history['val_loss']\n",
    "    epochs = range(len(acc))\n",
    "\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(25,10))\n",
    "    \n",
    "    #Plot Accuracy\n",
    "    axes[0].plot(epochs, acc, 'b-', label='Training Accuracy')\n",
    "    axes[0].plot(epochs, val_acc, 'g--', label='Validation Accuracy')\n",
    "    axes[0].set_title('Training and Validation Accuracy')\n",
    "    axes[0].set_xlabel('Epoch')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "    axes[0].legend(loc='best')\n",
    "\n",
    "    #Plot Loss\n",
    "    axes[1].plot(epochs, loss, 'b-', label='Training Loss')\n",
    "    axes[1].plot(epochs, val_loss, 'g--', label='Validation Loss')\n",
    "    axes[1].set_title('Training and Validation Loss')\n",
    "    axes[1].set_xlabel('Epoch')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    axes[1].legend(loc='best')\n",
    "\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Yjy14K-nm6EW"
   },
   "source": [
    "## Plot some images to see what the data looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 801
    },
    "id": "7KUJ2VLdlVBR",
    "outputId": "9c3a02fe-ec49-4c7b-e674-6ede8c4c59ae"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABpgAAANNCAYAAACDbH1jAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde7RdZ3UY+vmdp47etiVblvwQJrZFHGxeGTyGcSCPplBoEtqRFNIkhKTchOaONqSkgTZAkpJmJE7blEJJGQQSoLmEBEiBQBxfGxoIYIwBN36/ZEuy9bLeOjrvdf84h1zh2N9cS3tL50j6/cZgIJ85Nfe3ts6a+1tr7n1OaZomAAAAAAAAoK2BxV4AAAAAAAAApxcDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgOklKKW8vpXxosdcBnF30HqDfzva+Ukp5bSnlC5X4Z0opP3Uq1wS0cyb3r1LK50opP/sUsUtKKUdKKYOnel1wNjiTewuwdOk9LFUGTD0opbymlHLrwub9sYUbDNcu0lp+o5Tyf0opM6WUt/dQ58cXjudIKeVYKWXuuP8+0sclAydI7wH6bYn1lc2llJtLKeOllLtLKd9/gnXeclwfmSilzB7333f0a71N07ysaZo/rKzjKQdUpZR7SilXlFI+UEr5D/1aE5xNllL/WljPvyqlPFRKOVpKuauUckUPtd6yUOtIKWV7KeUjbf5e0zSPNE2zsmma2UrtpxxQAUurt7jmgrOH3sPpyIDpBJVS3hgR/yUifjMiLoiISyLi3RHxQ4u0pPsj4pcj4tO9FGma5sMLFyMrI+JlEfHot/574Wt/Zym8I66UMrTYa4BTSe/Re6DflmBf+eOI+HpEnBcR/y4i/rSUsr5rkaZpfvO4HvJzEfGl4/rKVf1d8pOr9YpSytMjYrBpmntPxVrgTLTU+tfCwOZnIuIfRcTKiHhFROw9wVo/FRE/ERHfv9DHnhcR/28f1lhKKe4DQMVS6y3hmgvOCnqP3nO6srE8AaWUNRHx6xHxL5um+VjTNEebpplumuaTTdO86Sn+zkdLKTtLKQdLKf+7lHLVcbGXl1LuLKUcLqXsKKX8m4WvryulfKqUcqCUsq+U8tdPdTHQNM0fNk3zmYg4fBIO+Vvr/EAp5b+XUv6ilHI0Il5aSnnGwrvfDpRS7iil/OPj8r/tXXHHv4N34cLmP5dSdpdSDi1MxL9rITZaSrm+lPJIKWVXKeU9pZSxhdhLFt69929LKTsj4v0n63hhqdF79B7ot6XWV8r8O/2fExFva5rmWNM0fxYR/yci/snJOP4nPPZrSykPLqz9oVLKjz8hfn0pZf9C7GXHff3ves5CjS8u9JnHI+IjEfGeiHhhmX+H3oHjSv6jiPiLUsrrI+LHI+KXF3I+uVCr1uc+sNCj/mphvZ8vpVx60p4cWIKWYP8aiIi3RcQvNk1zZzPvgaZp9p3gIX53RPxl0zQPREQ0TbOzaZr/8YScSxd6zuFSyg2llHULa9lcSmnKwg2ShV7yjlLKFyNiPCI+GBEvjoj/ttB3/tsJrhHOOEutt0S45oKzgd6j95zODJhOzAsjYllEfLzD3/lMRFweEedHxG0R8eHjYu+LiP+raZpVEfFdEXHTwtd/KSK2R8T6mJ9cvyUimp5W3rvXRMQ7ImJVRHwlIj4ZETfE/HH93xHx4VLKlS3q/IOIuC4iroiINRHxoxHx+ELstxa+/qyI+I6I2BQRbz3u726IiHMj4tKIeH1vhwOnFb1H74F+W2p95aqIeLBpmuMvYr658PWTppSyIiL+a0S8bGHtL4qIbxyX8vyIuCci1kXEb0fE+0op5SnKPT8iHoz54/zn8e2fnlp7XN7LI+LTCzeMPxwRv72Q88pSynDkfe7HI+I3Ftb0jfj2fwc4Gyy1/nXRwv++q5SyrcwPo3/tqW7atPDliPjJUsqbSinPK0/+jt7XRMRPLxzPSET8m0q9n4j5/cuqiHhtRPx1RPzCQt/5hRNcI5yJllpvOZVcc8Hi0Xv0ntOWAdOJOS8i9jZNM9P2LzRN8wdN0xxummYyIt4eEdcsTKcjIqYj4jtLKaubptnfNM1tx339woi4dGFq/ddN0yz2Sf/nTdN8sWmauZg/KVdGxG81TTPVNM1NEfGpiHh1izrTMd84tkREaZrmrqZpHlu4WfP6mH/n376FG0y/GRH/7Li/Oxfz72yebJrmWB+PDZY6vUfvgX5ban1lZUQcfMLXDsb8eXuyzcX8jeGxpmkea5rm+N/T9HDTNO9t5n+fyR/G/LFc8BR1Hm2a5p1N08w8Va8opSyP+U8nfO4parwg8j736aZp/vfCv8O/i/lPSV3c8ljhTLDU+tdFC///DyLimRHx0pg/Z3+m85HNr/VDMX9T5Qcj4vMRsbuU8m+fkPb+pmnuXeg1fxLze6Sn8oGmae5Y6E3TJ7ImOEsstd5yKrnmgsWj9+g9py0DphPzeESsKy1/JmMpZbCU8lullAdKKYciYutCaN3C//+TmH8X68Nl/kecvHDh678T8z/v8oYy/yNbfqUfiy/H/SK1UsolHf/6tuP+vDEiti00gG95OOanwFULDeK/RcS7Yv5i6X+UUlbH/AR9eUR8beGjkAci4rMLX/+WPU3TTHRcN5wJ9J55eg/0z1LrK0ciYvUTvrY6nuTHMpRSLikn+MthF34kwrf+7luapjkaET8W8582eqyU8ulSypbj/srOb/2haZrxhT9+288LP862p/j68b4vIv5m4WLwybTpc3/3OE3THImIfQt/D84WS61/fetmxG83TXOgaZqtEfH7CzWfbD3pvqiZ/50F3x8Ra2O+P/1GKeUHj0vZedyfx+Op+1JEu94ELL3e0olrLjht6T3z9J7TkAHTiflSRExGxA+3zH9NzP9Ctu+P+Y/obV74eomIaJrmq03T/FDMf/TvEzH/7rNYmEL/UtM0l0XEP46IN5ZSvq/XxTfH/SK1pmke6frXj/vzoxFxcfn2H/twSUTsWPjz0Zg/gb9lwxPW8V+bpnluRHxnzH9M8U0x/0twj0XEVU3TrF3435rm23/p22JP1mGx6D3z9B7on6XWV+6IiMtKKcd/Yumaha9/m6ZpHmme4pfDZpqm+bnj/u5vLnztL5um+YGYf0ff3RHx3i41jy+f/HfE/MXeX1Rysj4XEfF3n1YqpayM+R/p8Gjn1cLpa6n1r3siYiq+/Xx+yr1Dl33RwjuMPxoRt8f8j7k5EW16E7D0eksnrrngtKX3zNN7TkMGTCegaZqDMf9zGt9VSvnhUsryUspwKeVlpZTffpK/sirmm8TjMX8S/Oa3AqWUkVLKj5dS1jTzP6rgUMx/LC9KKa8opXxHKaXE/I+Hmf1W7IkWHn9ZzP+bDpVSlpUn/znd/fSVmH+n3C8vPP5LIuKVEfH/LMS/ERGvWnh+viOO+/EQpZTvLqU8v8z/joGjETEREXMLE+r3RsR/LqWcv5C76Qnv1IOzkt7zd/Qe6JOl1leaprk35s/hty30kx+JiKsj4s/6edxPVEq5oJTyQ2X+dzFNxvwnqZ60752AXRFxUSll5LivvSwiPv2EnMuO+++sz0VEvLyUcu1C3d+IiC83TeMTCpw1lmD/Go+Ij8T8ebuqlHJRzP84lk+dyPGV+V9a/Y8Wag2UUl4W87+P7isnUu9JPLHvALH0estCrmsuOMPpPX9H7zkNGTCdoKZpfjci3hgR/z4i9sT8x/l+Ieanwk/0RzH/cb4dEXFnzP/C1uP9RERsLfMfafy5mP+lzRHzv6jtxpi/yfGliHh30zQ3P8WS3hvz09hXx/zP4T+2UPekaZpmKuZP8pfF/DT43RHxk03T3L2Q8p9j/l18u2L+9xUc/8vmVi+seX/MPzePx/zHNCMi/m3Mf1zzywvPyY0R0eaXucEZT+/Re6DflmBf+WcR8byYP09/KyL+adM0e07o4NobiPnn4NGY/1Fz3xMRP9+n2jfF/CewdpZS9pZSvisijjzhnX3vi/mfkX6glPKJFn0uIuJ/RsTbFtb73Ij4531aL5w2lmD/+oWFvEcXcv9nRPzBiRxbzN8MektEPBIRByLityPi55um+cIJ1nui34uIf1pK2V9K+a99qglnhCXYW1xzwVlA79F7TlelWfTf4wUAAGeHUsovR8S6pml+uYcaH4iI7U3T/Pu+LQwAAAA6avWLwwAAgL7YGhGfXOxFAAAAQK8MmAAA4BRpmuZPFnsNAAAA0A9+RB4AAAAAAACdDCz2AgAAAAAAADi9GDABAAAAAADQSfV3MJVS/Py8k+Dzn/98Nb5mzZq0xo033liNX3PNNWmNW2+9Nc3JfoTiueeem9a45557qvG5ubm0xsc+9rFqfNu2bWmNs03TNGWx13Cizrbec8EFF6Q5W7ZsqcbPOeectMbY2Fg1fuDAgbTG/v3705zM0FDvv/5vdHQ0zVm9enU13qbXZo+za9eutMYnPvGJNOdMovfQ1Qte8II053nPe141/vWvfz2tMTw8nOZceeWV1fhDDz2U1rjhhhvSnF6Vkp9mZ9uPwdZ7zi4DA/n7JNtcY2R+/dd/vRqfnZ1Na6xcuTLN2bdvXzV+5MiRtMY73/nONCczODhYjbc53lMl64OnqgfqPTzRS17ykmr8oosuSmvs3LmzGl+1alVaY8WKFWlOdi3T5por6xtt+vXHP/7xNIdvp/fwRFlv+dmf/dm0xgMPPFCNt7n/0ab3nHfeedX42rVr0xrZvamnP/3paY23vOUtaQ7f7ql6j08wAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0MnQYi/gTDMwkM/srrvuumr8s5/9bFrjyJEj1fjFF1+c1jh8+HCac//991fjg4ODaY3zzz+/Gl+9enVaY+PGjdX4tm3b0hqwVH3xi19Mc3bs2FGNb9q0Ka2xbt26anxycjKt0SYnMzc3l+bs2bOn5xoXXXRRNT4xMZHWuOOOO6rxK664Iq1x4403pjlZT4fTWSmlGh8fH09r/O7v/m413uZ8fvzxx9OcZcuWVeO/+Iu/mNY4FZqmWewlwJNqcy3U5vs3y2mzD8i86U1vSnP+9b/+19V4m76ycuXKNCfrk+ecc05a45577qnGb7jhhrTG7OxsmpNpc32Y/fv143sEFstP/uRPVuPf8z3fk9Y4evRoT/GIiHPPPTfNueWWW6rxyy67LK1x7733VuMrVqxIa2T3wI4dO5bWgLPdf/yP/7Eab3Of+Id/+Ier8bGxsbRGm31Atu/J9jQReX9qc6/5D/7gD6rx7J44/z+fYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKCTocVewJnmp3/6p9Ocv/zLv6zGP//5z6c1jhw5Uo1/6UtfSmusWrWq58fZv39/WmN6eroaP3DgQFpj5cqVaQ4sRcuXL09zjh07luZs3769p3hExOTkZDV+7bXXpjXGxsZ6eoyIiB07dqQ5a9asqcYHBwfTGrfccks1Pjs7m9Y4ePBgNd40TVrj0ksvTXPuuOOONAf6bWRkJM254oorqvE2r88vfelLq/E/+qM/Sms8+9nPrsbf8573pDVe8IIXpDkf+9jHqvGPfOQjaY1f+ZVfqca/8pWvpDUOHTpUjd93330914CTYW5u7pQ8zsUXX5zmvPnNb67GX/7yl6c1Hn744Wp8165daY1f+qVfSnM+8YlPVONt9orZdeimTZvSGn/8x39cjU9MTKQ12uyv4EyW7Y0+/vGPpzWyc77N/usZz3hGmvPII49U448++mhaI9tvbNiwIa2R9af7778/rQFnu4GB+mdI2ty/HR4ersa3bt2a1ti2bVuak+3j9u7dm9bYuXNnNb5x48a0xjXXXFON6z3t+QQTAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0MnQYi/gTPMzP/Mzac6RI0eq8cHBwbTGFVdcUY03TZPWuP3229Oc3bt3V+PnnHNOWmNsbKwa379/f1rjoosuSnNgKXrOc56T5szMzKQ5R48ercaXLVuW1sj6wh133JHWePazn12Nr169Oq3x9Kc/Pc256667qvGHHnoorXHo0KFqfGRkJK0xMTFRjQ8M5O/TaPM90Oa5hy6e+9znpjkXX3xxmjM5OVmNZ/uEiHy/8fKXvzytkXnHO96R5mT7kYiIDRs2VOOvfOUr0xrbt2+vxg8fPpzWOP/886vxSy65JK2RvW5ERNx0003VeJvXJ+jqta99bZrz6le/uhp/1rOeldZ48MEHq/F77rknrVFKqcYvv/zytMYHP/jBNGfz5s3V+Be+8IW0Rtbj3va2t6U13v72t1fjX/3qV9Ma73rXu9Kcm2++Oc2BpWjLli1pzvr166vxhx9+OK2RXdsdO3YsrXHfffelOfv27avG21zbjY6OVuNt9hJZL73//vvTGnC2y65jsuuLiPye0fj4eFpj7dq1ac709HQ1fumll6Y1sn48NTWV1mhzfUg7PsEEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJ0OLvYAzzeDgYJrztKc9rRrfuXNnWuPw4cPV+OzsbFrjmc98Zprzta99rRofHx9Pa5xzzjnV+LnnnpvWOHjwYJoDS9HatWvTnKmpqTRn2bJl1Xibc76UUo0fOnQorXHzzTdX41lvioi47rrr0pymaarx2267La1x5ZVXVuOTk5NpjeHh4Z5rjI6OpjnQ1TXXXFONn3/++WmNRx55JM3J+sbQUL6V3L59ezXe5jU+219lPSMiYu/evWlOttY25/Ojjz5ajWfPaUTEnj17qvE2rxubNm1Kc6699tpq/HOf+1xag7NL9v370Y9+NK2xefPmNOfIkSPV+De/+c20xtzcXDWe7a0i8uu2Nufili1b0pzbb7+9Gl+3bl1aI9Nm7zQ2NtbzOq6//vo0J9u3Pv/5z09rtOn70G/PeMYz0pzp6elqvM09o+XLl1fj2T4hIuLAgQNpTtYns54QkZ+Lx44dS2u0uWaGs9nGjRvTnOz679Zbb01rZHujNvuANudz1icnJibSGjfddFM1fumll6Y11qxZk+bQjk8wAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0MLfYCzjQ7d+5Mc7Zt21aN79mzJ60xMFCfDe7bty+tMTc3l+ZkdS644IK0xsTERDXeNE1aY3JyMs2Bpeh7v/d705yjR4+mOcPDw9V4dp5FRKxevboaX7t2bVpj69at1fgVV1yR1vjyl7+c5rzyla+sxts8rwcOHKjGjx07ltboh5UrV56Sx+HMMTSUb882bNhQje/evTutUUpJcwYHB6vx2dnZtEbWv7JzNSLf9/RjPxKR9+Pp6em0Rna82XPa5nHGxsbSGrt27UpzNm7cWI2vW7curbF37940hzPHu9/97mr88ssvT2u06U9TU1PVeJvzqE0vzdx3333V+OjoaFqjzfVhdjz79+9Pa2Q9vc1+JLvmanNN1mZfm/We973vfWmN173udWkOLIaDBw9W423u92TnyGWXXZbWaNMDs97SZt+T7ePa9Os2vRTOZsuXL09zstffNvd7smvMNtcgbc75bD/Rj3va1113XVrji1/8YppDOz7BBAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCdDi72As9HWrVur8ZGRkbTGkSNHqvGVK1emNV7zmtekOVdffXU1/vu///tpjdWrV1fjg4ODaY2pqak0B5ai2dnZvuRk58DQUN7Os5xnPvOZaY3du3dX44cOHUprrFu3Ls259dZbq/Hh4eG0RtZ79u3bl9YYHR2txgcG8vdprF+/Ps2B423evDnNyb732pwjc3Nzac7MzEw13mbPkj3O8uXL0xpZnxwfH09rZMcSkZ/zbY43e5ymadIaWb8upfRcIyJf6wUXXJDW2Lt3b5rD6aHN90x2bTAxMZHWWLZsWc85R48eTWscO3as53Vk52ubdbTpcYcPH05zMv3YK2avHW16T5vXn4MHD1bjV155ZVpj06ZN1fiOHTvSGtBVdn0RkZ9rbXrP/v37q/E2vfYHfuAH0pysbzz44INpjez6sB97RTjbtbmHkl3HtLnvlL2Gr1mzJq0xOTmZ5mR9IdsntPH1r389zenH/ot5PsEEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdDK02As43axataoaf85znpPWuPvuu6vxUkpaY//+/T2vY2xsLM0555xzqvH/9J/+U1rj/e9/fzV+zz33pDXaPCewFN13331pznXXXZfmHDhwoBqfmppKa+zdu7ca/8QnPpHWyPrG3NxcWqPN+Xzs2LFqfPfu3WmNLVu2VONDQ/lL4LJly6rxT33qU2mNrOfDE23YsCHNGRiov0do9erVaY1Dhw6lOcPDw9X4zMxMWiNba5u+MTg4WI1PTk6mNdqc85k2x9s0TTXe5nj7IXveI/Iet379+n4th9PAz//8z6c5y5cvr8bb7EfafF9t3769Gs++dyPytWZ7jYj8fB4dHU1rtOlPExMTPT9OP3pt1ifbHMvKlSvTnKynr1ixIq3x6le/uhq//vrr0xrQVZvX1uxcW7t2bVoj6z33339/WuOyyy5Lc7LrsjbXstk5PzIyktYYHx9Pc+Bs9uxnPzvNufTSS6vxgwcPpjWyntCmxjXXXJPmbNu2rRpv0xOe9rSnVeNt9hLnn39+mkM7PsEEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdDK02As43fzDf/gPq/HDhw+nNfbt21eN79y5M61x4MCBanz79u1pjQ0bNqQ5f/qnf1qN79+/P61x2WWXVeNtjnfXrl1pDixFe/bsSXMGBwfTnKmpqZ7XsmLFip4fo5RSjTdNk9aYnZ1NczKrVq1KcyYmJnp+nKxGmz46MzOT5nzxi19svSbOfNm5GpHvA66++uq0xle/+tU0Z2iovlVsc85nfWNgIH+/U1ajTR/tx1rbWL16dTU+PDyc1sj2PW1qbN68Oc2Znp6uxkdGRtIanDle8YpXpDnZXqHNa/yaNWvSnKzHjY+PpzWyvtDmPMrOkTZ7jWXLlqU52bnWZq1ZL23Ta/uxR7v44ovTnOwacnJyMq3xgz/4g9X49ddfn9aArtqci9l59Pjjj6c1sv61cePGtMaP/diPpTn/63/9r2q8Tf/KesvY2Fhao819NDibfelLX0pzXvziF1fj73nPe9IaDz74YDX+ghe8IK3x3ve+N8256qqrqvHzzjsvrfHwww9X422uqbNrXdrzCSYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6GVrsBZxuXvziF1fjTdOkNXbu3FmNr1mzJq1x2WWXVeN/8id/ktbYu3dvmvOd3/md1fjo6Gha4ytf+Uo1vnHjxrTG1q1b0xxYimZmZtKcoaG8FQ8M1N8P0OZxspy5ubm0xvDwcM81xsbG0px+rHV8fLwaHxwcTGvs27evGr/uuuvSGp/73OfSHM4u559/fjV+9OjRtMb+/fur8SNHjqQ1tmzZkubccsst1fj69evTGrOzs9V4KSWt0Q/9eJw2/frYsWPVeNabIvL+tHLlyrTG8uXL05wvf/nL1fi6devSGmvXrq3GDxw4kNbg1FixYkU13uZ7ZnJyshrP9gkREVNTU2nOyMhINT4xMZHWyLTZB2TnfJv917Jly9KcbJ/XZt+T9do2a81qtNnDZTUi8mvm7Pssot3zCv3Wpk+22StksnMke+2NiPjMZz6T5jz++OPVeLZnjYi4//77q/Gsn0e063FwNvuRH/mRNGfz5s3V+LZt29Iaq1atqsYvuuiitMbrX//6NOfXfu3XqvE3vOENaY0HHnigGt+zZ09aI7umpj2fYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6GVrsBZxupqenq/Fdu3alNcbGxqrxubm5tMbU1FQ1fsMNN6Q12rjtttuq8YsvvjitMTIyUo0PDw+nNdo8J7AUPfTQQ2lOdo5ERAwODlbjWW+KiJiZmanGZ2dn0xpN01Tjbc7VNud8KaUab7PW7DmZnJxMa2SPMz4+ntbYtm1bmsPZ5aqrrqrGr7766rTGvffeW41n52pExOte97o055ZbbqnGs94UkZ/PbQwM1N8T1aYnnIp1ROR9cNmyZWmN+++/vxp/61vfmtb4whe+kOasW7euGr/mmmvSGv1YB6fGa1/72mp8dHQ0rXHw4MFqvE3vya5jIiI2bdpUjR84cCCtkZ1rbfpGdj636Qlt9hvZ47TpX9n+KtsHRuT7mtWrV6c12nwPZM9Jm3+bVatWVeOvfOUr0xqf/OQn0xw4XpvzOeul2f2giPw82r17d1qjja997WvV+OWXX57WyM7XNvue7LUFznZt7hlt3LixGn/44Yd7rnHXXXelNdr4sz/7s2r8jW98Y1rj2LFj1Xib56zNfpJ2fIIJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACAToYWewGnm8svv7wa3717d2WRdH0AACAASURBVFpjZGSkGl++fHla4xvf+Eaa0w9f/vKXq/F169alNQYG6nPMqamptMa+ffvSHFiKHnrooTRndnY2zRka6r1dN03Tc402a80MDg72nDM9PZ3WyHpLmxqZNv8ux44d6/lxOLPcfPPN1fhXv/rVtMaWLVuq8Q0bNqQ1XvrSl6Y5a9eurcbHx8fTGtm+ps0+INs7lVLSGv0wNzeX5gwPD/f8OCtXrqzGDx8+nNZos0fLeu1nP/vZtMYDDzyQ5rA0bN68uRpv87qY9ZY2r/Ft9vV79uypxs8555y0Rtaf2pzP2XXMzMxMWqMf2uzh+rF3WrNmTTW+devWtMZFF12U5mzcuLEab/PakvXBSy65JK0BXbX53uzHdduqVauq8Ztuuqnnx4iIuO+++6rxq6++Oq2R7cFGR0fTGjt27Ehz4Gy2f//+NOfxxx+vxttccz3/+c+vxt/whjekNdq48847q/EDBw6kNS644IJq/OjRo2mNNvfwaccnmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBOhhZ7AaebkZGRanxmZiatMTs729NjRETceuutaU4/HDx48KQ/xtGjR9Oc6enpk74OOBnafH8fPnw4zSmlVONZX4mImJubq8YnJyfTGk3TVOOrV69Oa5x77rlpTtZ7snVERIyPj1fjbfr1wED9fRiDg4NpjTb/vnC8I0eOpDnZPqDNPqHN+Zr1nuwcaWNo6NRsR9v0jex428j68dTUVFpj48aN1fiv/uqvpjW2bt2a5nB2edOb3lSNtzmfv/d7v7caf8tb3pLW+MAHPpDmbNmypRp/7Wtfm9a4++67q/Hh4eG0RqbNc9Ymp80+rtfHabPvedrTnlaN/87v/E5a41Of+lSa81M/9VPV+O/93u+lNf7qr/6qGs/2vXAiHnnkkTRnbGysGm+zD1ixYkU1vmPHjrRGG48//ng1nh1LG23u5ezZs6fnx4EzWZtrlOx+znnnnZfWyK5D3//+96c12sj2JG2uD9evX1+NX3bZZWmND33oQ2kO7fgEEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0MLfYCTjd33313Nb5ly5aeH2N2djbNeeyxx3p+nDb27dtXjQ8ODqY1Dh06VI3PzMykNY4cOZLmwOnq0UcfTXNGR0er8bGxsbTG5ORkNd40TVpjaKj+spE9Rlvnn39+Nb5r1660xtzcXE/xiLw/DQzk79N45JFH0hzoKvvea/P9/ZWvfCXNGR4ersanp6fTGtm+JnuMiHZ7hUwppecabfY9bfZxp2IdbfTj+4gzR5t/7xtvvLGneFs/+qM/Wo0vX748rdGPc74fj9HmfM16aZv9RqbNHi1b68UXX5zWeO9735vmfOQjH0lzYCm666670pzsPFq2bFlaY82aNdV4v/YaWV944IEH0hpZP26z1jb7STibrV27Ns3J7hmtXLkyrfGNb3yj9ZpOpp07d6Y5H/7wh6vxa6+9Nq1xyy23tF4TdT7BBAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHQytNgLON3ceeed1fiGDRvSGiMjI9X41NRUpzWdTAcPHqzGh4byb6HDhw9X44ODg2mN++67L82B09XevXvTnE2bNlXjbc7F5cuXV+PT09M9r+MP//AP0xpvfetb05ys1z722GNpjYGB3t9DMTw8XI3Pzc2lNbZt29bzOuCJ2nzvZR5++OE056qrrqrG2+xZZmdnq/FSSlqjaZqea/RDto6IvG8cOnQorZH16/Hx8bRGG22Oh7NHmz159j3T5rV3ZmYmzcn2AW3Wmq2lzVqz3tKv3pM9r232aKOjo9V41osjIiYnJ6vxNnvWNrI+2Wat2XPfpgZ01eYcmJiYqMaz1/iIiFWrVvUUbys7T/qx72nDfgTqnvGMZ6Q52eviypUr0xo33nhj6zWdTG3urX/f931fNX7ZZZelNdpcD9OOTzABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnQwt9gJON7t3767G16xZk9YYGqo/7U3TdFrTkymlpDltHmf9+vXV+NjYWFpjbm6uGp+enk5rHD16NM2B09X4+HjPNY4dO5bmDA4OVuPDw8NpjRUrVlTjN9xwQ1pj//79aU5mZGQkzRkdHe25xuzsbDXepjfpX5wM2et8m9f4Nt+b2TkwNTWV1li5cmWac7rI9jQRea/N4m0eJ+tvcCKy872NNtcgbWTXS/fee29aIzuP2uwDsuuUNr22Tc6yZcuq8Tb9emZmphpvc922Y8eOarxfvSdbaz+uh+FkaPMafu6551bjAwP5+7z70b/aOHz4cDXepqdn/avN3gmoa3POZ+fr6tWr0xpf/epXW6/pZLrjjjvSnLVr156CldCWTzABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQydBiL+B087d/+7fV+NBQ/pReeeWV1fj27ds7renJNE3Tc42IfC0XXHBBWmPdunXV+N69ezutCc40x44d67nG1NRUmjMwUH9PweDgYFrjwIED1fj+/fvTGm1kvSc7loj8eIaHh9Mao6Oj1fiRI0fSGnAy9ON1vk2Nubm5arzNuZjtjbLHiMh7XJv+VUpJc06FkZGRNCd7TmZnZ/u1HFiSsv7Upn9lfaFN78nOtTZ9pU1Odjxt+ka2n1y5cmVaY2Jioud1tJE9J/26loV+a3PNtWbNmmp8bGys53VMT0/3XCMiYs+ePdV4m+Ntc00F9ObRRx9NczZu3Njz42T3b0+VNvfIDh06VI1ffvnl/VoOLfgEEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANDJ0GIv4HSzd+/eanxkZCStcfjw4Wp8y5YtndZ0Mu3YsaMa37lzZ1pj06ZN1fgjjzzSaU1wptm/f3+aMzY2Vo0vW7YsrTEzM9N6TU9ldna25xptHDp0qBqfm5tLaxw8eLAan5ycTGusWrWqGp+YmEhrwOksO+fb7Huy87VNXxkaWhpb1sHBwZ5rDA8PpzkDA/X3gE1NTfW8DljKsr7QNE1ao5RSjbfZF2X9q00PzM7niIijR49W4236RrbWNjWy42lzvG20+feDpajN927WW9rsJbL+1eY6po1+XB8uX768Gh8fH+/5MeBsl/WEiIjdu3dX4xdffHFa48orr2y9ppNpxYoVac7atWur8a1bt/ZpNbThE0wAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0MrTYCzjdHDhwoBpfv359WuPgwYPV+MqVK9May5Ytq8YnJibSGm0MDNRnkG0e5+jRo9X4RRdd1GlNcKbZu3dvmjM4OFiNDw8PpzXGx8er8dHR0bTG/v3705x+2L59ezV+7rnnpjVmZmZ6Xsfk5GQ1nj2ncKYbGsq3knNzc9X47Oxsz4+T7VfarKONNmvNcrJ+HpHvBdusA05ny5cvr8ZLKWmNpmmq8TY9oU2Py7R5nOx42vSN7HizPU1E/ryvW7curdFGtlY4nU1PT1fjbfYs2et8mxptHDt2rOfHyfrT2rVrO60J+Pva3Hd41rOeVY232Y+cqvM1u3+V7UciIvbt21eNt9k70T8+wQQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0MrTYCzjT7NixI8258MILq/GJiYm0xqZNm6rxBx54IK3RRtM01fgll1yS1rj99tur8YcffrjTmuBMs2vXrjRncHCwGh8YyN8vMDo6Wo0vW7YsrXH48OE0px/m5uaq8VJKWmN4eLgan52dTWtkz+vevXvTGrBUZedZRMTQUH2rmO0TIiJmZmZ6eow2NUZGRtIabfpk9pxk62izlja9J3tO2tSAxdCmJ7SRXS+16V9ZTrZPiMj7xtTUVFqjzeNk2jyvY2Nj1XibPdz09HQ1fsUVV6Q14Gy3b9++anz58uVpjcnJyWp87dq1ndb0VLK1bty4Ma2R3c9ZtWpVpzUBf9+dd96Z5jz3uc+txtvsWbL9V79k957aXB9ma83uRdNfPsEEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJ0OLvYAzze7du9OcDRs2VOP33HNPWuOCCy6oxh944IG0RhtHjhypxtsc79zcXDV++PDhTmuCM83+/ft7rjEwkL9fYHBwsBqfnp5Oa8zMzLReUy+yvpD1wIiIpmmq8dnZ2bTGhRdeWI3/xV/8RVoDlqo250A/TE5OVuPLli1LawwN1bes2V6jrayXZutoI+tNEfnxjI2NpTX68doCXfXrXNyyZUvPj5Pte7J4RLv9VabNOZ+tpc3xjo6OVuMTExNpjWwvePnll6c1+qHNv82peg2DrrJ7Meedd15aI7vmWrNmTac1PZWpqalqPLsfFNGfvRFQ9/jjj6c5K1asqMbb7CUOHjzYek29OHbsWDU+Pj6e1sjurbsWOrV8ggkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoZGixF3Cm+Zu/+Zs051WvelU1vmnTprTGVVdd1fM62picnKzGZ2dn0xoDA/U55qOPPtppTXCmOXz4cJqTnWullLRGdi62qTE1NZXm9EPWF6644oqeHyN7Ptrk7N69u+d1wGJpcz5nfWFmZiat0TRN6zWdqKGhfEs7MTHRc502fWNubq4ab7N3yv5t2hwvnM6ya53sPOuXU/U4WZ/sxz6vjaw/rV27Nq3R5lp2x44d1XibY2nTS2ExPPjgg9X4hRdemNY4evRoNT4yMtJpTU8l20+Mjo6mNbI+2aYGULdr166ea7S5blu9enXPj9MPba4fs95z6NChfi2HFnyCCQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOhkaLEXcKb5zGc+k+a89KUvrcYPHjyY1rj66qtbr6kXExMTPcUjIkZGRqrx4eHhTmuCM81jjz2W5jRNU40PDOTvF5ibm6vGBwcH0xpHjx5Nc/ph37591Xib482Op03vyWrMzMykNWCpyvpKRH6ejI+P92s5Vdm52KZ/DQ3l2942vaXXGlkvjoiYmpqqxicnJzutCU43V1xxRTXe5hzox7mYmZ2dTXNWr16d5hw5cqQaz3pCRMTo6Gg1nl2TtVnH7t270xqrVq1KczJtXp9KKT3XgJPhrrvuqsaz+0ER+f5r/fr1ndb0VLK+0Y8+ac8CvWtz72LZsmXVeJvzedOmTa3X1IvsNbzNnmVsbKwav/feezutid74BBMAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdDC32As40Dz74YM81zj333DTnkksuqcZLKWmNpmnSnIGB+gyyTY2VK1dW41NTU2kNOJPt27cvzZmenq7Gh4eH0xqDg4PVeHa+t1lHv6xdu7YaHx0dTWtkz8ns7GxaY8+ePdV4m+cMlqo2r+HZOT85OZnWWLZsWes1naisv7XNyc7pNj2wH33hyJEj1fiKFSt6foyIdt8DcLzs+3tubq4vj3P48OFqfGgov4wdGRnpeR3ZNVWb873NOrJzuk2vnZmZqcbb9OJDhw5V422O97u/+7vTnLvvvrsa79f3ESyGxx57rBpvc68my1m/fn2nNZ2oNvemsv61d+/efi0HzlrPe97z0pysb7S5/9HmnO+HNWvWVOOrV69Oa2TXMevWreu0JnrjzhgAAAAAAACdGDABAAAAAADQiQETAAAAAAAAnRgwAQAAAAAA0IkBEwAAAAAAAJ0YMAEAAAAAANCJARMAAAAAAACdGDABAAAAAADQydBiL+BsdOzYsWp8dHQ0rfHAAw9U45dffnla4957701zJiYmqvFt27alNSYnJ6vxNWvWpDXgbDc7O1uNDw4OpjUGBurvKRgayl8SVq1aleb0w9q1a6vx7PmIyI+3zXM2PDzcUxyWslJKmjM3N1eNt+kb2b5mZmYmrZGd822O5VQd7/T0dDWe9aaI/Dl50YtelNZ48MEH0xzoKnvtzM6hiIjNmzenOdn1wcGDB9Map0KbvUR27dfG8uXL05ypqalqvB97ljb7r1e96lVpzgc/+MFqvE2vzY4XFku2D2jTN7K90/r16zut6alk92q2bt2a1sj69eHDh7ssCXgSN9xwQ5rzute9rhpvsw/Yv39/6zX1ommaarzNdVt2v/rGG2/stCZ64xNMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdDK02As43ZRSqvGmadIat99+ezX+Ez/xE2mNb3zjG9X4pk2b0hr33ntvmjMwUJ9Btjne2dnZanzz5s1pDTjbHTp0qBpfvXp1WmN6errnddx9990912jj61//ejX+zGc+s+fHmJubS3Oynt+P5xQWy7Jly9Kco0ePVuNt9gHZeTQzM9NzjSze9nGOHTtWjQ8PD6c1Mm2es2z/9ed//uc9ryOiP/tazi79+J5ocy4ODdUvU9vse7LzeXBwMK2RnfNtarTZb4yPj1fjy5cvT2tk11zZ8xERMTIyUo2PjY2lNbLr1DbafI/04zpVj+NkmJycrMazc7WN8847r+caEXlfyI4lImLfvn3V+NTUVKc1AX/fbbfdlubs3LmzGl+xYkVa49FHH229ppOpzX2W7Fq2zR6N/vEJJgAAAAAAADoxYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKCTocVewNnone98ZzX+ile8Iq1xzjnnVOMvetGL0ho333xzmnPkyJFq/JJLLklr7N+/vxrfs2dPWgPOdldffXU1PjExkdYYHBysxgcG8vcc7N27N83ph/POO68ab9N75ubmqvHly5d3WtOTue2223quAYtlaCjfBmbn4szMTFoj6y1t1pHVGBsbS2tkPTAi7wuzs7NpjWytTdOkNTLXXnttmvOZz3wmzcnWUkrpuQZnljbnfGb79u1pzotf/OJq/MYbb0xrZOfJQw89lNbYt29fNb5hw4a0Rpv+tHPnzmp8ZGQkrZH10pUrV6Y1Nm3aVI2/+c1vTmu8733vS3MybfqK3sNSdeDAgWp81apVaY1sz9LmWqiNbF/TZr+R3Vdqs/8CepftA8bHx9Mabc75fnjHO95RjZ977rlpjd27d1fjd955Z6c10RufYAIAAAAAAKATAyYAAAAAAAA6MWACAAAAAACgEwMmAAAAAAAAOjFgAgAAAAAAoBMDJgAAAAAAADoxYAIAAAAAAKCTocVewNloYmKiGt+zZ09aY/fu3dX4zTff3GlNT2VmZqYa/8IXvpDW2LdvXzV+//33d1oTnI1e85rXVOPPfe5z0xqTk5M9xSMiPvWpT6U5/fCud72rGh8cHExrrFq1qhqfmppKa+zdu7caP3jwYFoDlqrDhw+nOQ888EA1Pj09ndaYm5urxtucz1lOKSWtke1pIiKGhupb4+xY2uRkjxGRr3VgwHvEWJrafG+2OY+OHj1ajb/whS9Ma7zsZS+rxv/Fv/gXaY0LL7ywGr/pppvSGgcOHEhzNm3aVI2Pjo6mNbI++Ld/+7dpjXe+853VeHYN2i9N05ySx4GTYevWrdX49ddfn9b4ju/4jmr8ox/9aJclPaXsXszP/dzPpTWy8/Wb3/xmpzUBJ+bNb35zNd5m7/TpT3+6X8up+uxnP1uNP/TQQ2mNLKfNdSr94+oUAAAAAACATgyYAAAAAAAA6MSACQAAAAAAgE4MmAAAAAAAAOjEgAkAAAAAAIBODJgAAAAAAADoxIAJAAAAAACATgyYAAAAAAAA6KQ0TbPYawAAAAAAAOA04hNMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTAAAAAAAAHRiwAQAAAAAAEAnBkwAAAAAAAB0YsAEAAAAAABAJwZMAAAAAAAAdGLABAAAAAAAQCcGTIuslPL2UsqHFnsdwNlBzwFOFf0GOFFnc/8opby2lPKFrrEWdT9XSvnZ3lYHPJWzuW8BS4M+xGIxYDoFSimvKaXcWko5Ukp5rJTymVLKtYu0lmeVUv66lHKwlLK9lPKrPdTaXEppFo7rSCllVynl3aWU4X6uGehmKfWc49b0PQv94j/0WOfCUsr7Fo7rcCnl7lLKr5VSVvRY9yWllO291ICz0VLqN6WUm0spe0oph0op3yyl/FAPtV5SSpk7bo+zvZTyJ6WU7+7nmuFstsT6x4tKKbcs7C1u78c6SilPW+gj/70fa1yqSikf6HV/B6eLJda37HvgLLSU+tBxa3K/5yxnwHSSlVLeGBH/JSJ+MyIuiIhLIuLdEXHCL/49+p8R8b8j4tyI+J6IeEMp5R/3WHNt0zQrI+KZEfHCiPiXPdYDTtAS7DmxMHT+vYj4So91zo2IL0XEWES8sGmaVRHxAxGxNiKe3us6gW6WYL/5VxFxYdM0qyPi9RHxoVLKhT3Ue3Rhf7MqIl4QEXdHxF+XUr7vyZJLKUM9PBacVZZS/1jYX3wyIn4n5vcUvx0RnyylnNNj6Z+MiP3x/7F373FyVnW+73+ru/qWvibdSTpNSJokQIAkBEgUIThyiUOQgHgYQAVGRNStKPvoGd3jANvNazjHreMMitthj6MiggbRkS0gAdkaBlAUlKCRBNIJIddO0iF9S9+r1/mjirHNTq/veqhKd3Xyeb9evvbs/H58a9XlWbWeZ1VVm13pnCvLMQvAOCukeSuLdQ9wlCnAeYjrPTAzNpgOK+dcrZndZmYf997/m/f+gPd+0Hv/kPf+b0b5bx5wzrVmv2H07865U0bULnLOvZTdxd3hnPt/sv/e4Jx72DnX7px73WW+oTTac9tsZvd579Pe+01m9rSZnTJKbyLe+z1m9jMzO3nEmP+Lc25TdswvOecuG1Erds592TnX5px71Tl3Y3bHm4UK8CYU6JxjZvZpM3vcMicpufiUmXWZ2dXe+y1mZt77bd77m7z3v8+O7Szn3HPZ+/Occ+6sEffnOufc+uz92eyc+0j23yvN7FEzaxrxqb2mHMcKHNEKcb7x3v/eez/0xv/XzErM7Nhc76vP2O69v9XM/tXM/vuIcXvn3MedcxvNbGP23y52zq3NjvmXzrlFI/o/m71/Xc65l9+4aOOce4vLfBKx02W+Ef6PuY4bKFQFOH+cZWat3vsHsudI95rZXjN7Tw730Vlmg+lmMxs0s5UH1b1z7qPOuY3Z8f2P7H9zqKwvOeeezj5uB9fmO+d+lr1/LzvnrhBDm+sy39TqdM79L5e5mPNG1iXOuT9mx7PGOXfSiNpJ2X9rz/Zckv33D5vZ+83sM9n100ORDxEwoRTgvMW6BzjKFOI8lMX1HrDBdJi9zczKzezHCf6bR83seDObZma/M7P7RtS+aWYfye7iLjCzn2f//dNmtt3MplpmB/tzlllgHModZnatc67EOXdidoxPJBjfqLIH6F+a2bMj/nmTmZ1jZrVm9t/szz9Vc4OZrTCzxWZ2upm9Ox/jAI5iBTfnOOdmm9kHLbMQytUFZvZv3vvhUW5ripk9YmZfNbN6M/tHM3vEOVefbdljZhebWY2ZXWdm/+ScO917f8Ayc9FO731V9n878zBe4EhWcPONmVn2ZKjPMp+gW2NmzycYX4x/M7PT3Z//TMO7zeytZnayc+40M/uWmX3EMvPQ/zSznzjnyrLrrhvNbGn2fv6lmW3JZnzFzL6S/RTyXDP7QZ7HDRSSQpw/Dt7ccdmsN2uZmc00s1WWOZ7/+hA9F5vZUjNbZGZXWGZO+NMAnCtyzn0jW3+n977joHqlZT7c9z3LPC5XmdnXnXMn2+iutcy6bIaZDVlmzWTOuRPM7Ptm9p8t83j91DLf4ip1mU8mP2SZi0fTzOwTZnafc+5E7/2/WOa5+GJ2/bTy4BsEjhCFOG+x7gGOLgU3D3G9B29gg+nwqjezthGfKpG899/y3nd57/vN7PNmduqIT6sNWuZNvMZ7v997/7sR/z7DzGZnd6+f8t6Ptgh5gpAJzwAAIABJREFU2MwuN7Ney+wuf9N7/1zyu/Zn2pxz7Wa2w8wOmNkPR9yfB7z3O733w977+y3zKZe3ZMtXWGZRsd17v9/MvpDjOICjXSHOOV81s1u8991v6h79uXoz2xWov8vMNnrvv+u9H/Lef98y89xKMzPv/SPe+03ZT+U9aZkLJefkYVzA0agQ5xvz3l9smZ92ucjMHh/tBCUHOy1z4bluxL/9f9771733vZb5iZr/6b3/dfabEN8xs37L/NRM2szKsvezxHu/xWe+Tf7G/ZznnGvw3nd770d+WAc40hTa/PEry3yq9b3ZD+H9tWUueE56k/fPLLOh9Gj2HOd7Znahc27aQT1f8N63e++3mtkvLPOhuzeUWGbDZ4qZrfTe9xziNi42sy3e+29n1z0vmNmPzOyvAuP6rvd+XfZiyy1mdoVzrtjMrjSzR7z3P/PeD5rZP1jmJ2rOssz8VZUd74D3/ueWOad8b4LHA5joCm3eeuM2WPcAR49CnIe43gMzY4PpcNtnZg0u8iffXOYn477gMj8p12l/+nRHQ/b//b8ss3B4zTn3pHPubdl//5KZtZjZ49mvAf6XUfKnmNlqy+wsl1vm69N/6Zz72Cj9fxzx9cHQQdngva+zzEnYM2b22IiMa0d8XbrdMrvib9yfJjPbNiJn5P8NILlCm3NWmll1dnNZjWXWiPlmtMXJPsssdEbTZGavHfRvr5nZMdnbWOGcezb7Ne/27H1rMABvRkHNNyNlT4QeNbN3ulH+zuTI+cY5NyvmPmQdY5lP8LWP+LeR65fZZvbpN9Y92bnmWDNr8t63WObbAZ83sz3OuVUjfp7hejM7wcw2ZH/u4eIEYwImmoKaP7z3+yzztws+ZWa7zexCy/zCwyH/GLSaP5xzFZbZ5Lkvm/8rM9tqZu87qLV1xP/dY5lNnDfMy47pv3nvBw41DsvMN289aL55v5k1jtJv9ufz1WuW2chqsIPWUNmL1NssM+c1mdm2gy5c/8f6CjhKFNS8NRLrHuCoUVDzENd7MBIbTIfXryzz6Y3Yn357n2VOJC6wzE/KNWf/3ZmZee+f895fapmvNj5o2a8RZ3ejP+29n2Nml5jZp9yh/xDjHDNLe+/vye72brfMzzZcdKjBeO9P8X/6+uBTavDZT7DcbWZnusxvds42s29Y5mvR9dlNqHX2p5+g2GWZn454Q86/Fwwc5QptzjnfzJa4zG/+tlrm07H/2Tn3vw5u9N5vHTHfVP0fSRlPmNllbvTf/91pmZOckWaZ2Q6X+ePaP7LMJ3KnZ+ejn9qf5qNRPxkI4JAKbb45lJSN8gdhR8432W8PxLrMzH6X/fT/f8SN+L+3mdnt3vu6Ef+blP2EnXnvv+e9X2aZucpb9u8aeO83eu/fa5n7/9/N7Ifuz3+OBjiSFNz84b1/0nu/1Hs/xcyuMbP5ZvabUXrV/HGZZX6e5esj1kDH2KF/Jm806y3z8y6PuszPTB3KNjN78qD5psp7/58CuSPPt2ZZ5lPKbXbQGso557K9O7K1Yw9af83K1sxYQ+HoUHDz1iGw7gGObIU2D3G9B/+BDabDyGd+J/tWM/sfzrl3O+cmuczPLqxwzn3xEP9JtWUmi32W+TbQ//tGwWV+//r9zrlan/nZgk4zG87WLnbOzcueCHRY5qvIh/pq9CuZdvc+l/lN70bLTAC/z8f9zR7Q11jm03j7zKzSMgfx3mz9Ovvz3zL/gZnd5Jw7xjlXZ2afzcc4gKNVAc45t1jmk2mLs//7iWU2na97k3fxHy1zweY72Q1sy84f/+gyf0z2p2Z2QnaOSznnrjSzky3zMy6llvmJhr1mNuScW2Fm7xyRvdvM6t0h/oA2gP9Toc03LvOH7lc45yqy47jazN5uZk/mel9dxjHOuf9qZh+yzO+Qj+YbZvZR59xbs/9dpXPuXc65aufcic6587LrpT7L/FzxG/fzaufc1Ow3BN74lHC+f+YGKAiFNn9ke0/LjqHGMhcntnnvHztUb4S/tszfJFlof1oDnW2Zn6VZGBuSvUD7OTN7wjl3qIvGD1tm3XNNduwlzrmlzrmTArFXO+dOds5NssyvWvzQe5+2zHnZu5xz57vM31z6tGUe819a5m+79JjZZ7K38Q7L/BzNqmzmbst8kBE4YhXavMW6Bzj6FNo8ZFzvwQhsMB1m3vsvW+bnFm62zAt9m2W+0fPgIdrvsczX+3aY2UtmdvDv0F5jZltc5quNH7XMTyCYZf5g2xNm1m2ZHe2ve+9/cYixdJrZe8zs/zaz/Wa21jLfKPr7N38Pzcys3WW+4rjbMn907pLsb16+ZGZfzo5pt2VOsp4Z8d99wzK/ifl7M3vBMpPFkGUmLwBvQoHNOV3e+9Y3/meZk4oD3vvX3+R9e90yfwtg0Mx+7ZzrMrP/bZlFT4vP/MTNxZa5KLLPzD5jZhd779u8911m9knLXEDZb5lP8/xkRPYGy/ytg80u8/MOTQYgqJDmG8t8Ou3zlvnjrnvN7CYzu9L/6bfE34ym7Pqm28yes8w65h3e+8dH+w+898+b2Q1m9jXLzDUtZvaBbLnMMn9vss0yH8aZZmZ/m61daGZ/zN7eV8zsquw3w4EjUoHNH2aZNUNbdhwzLPOp/cScc8dY5hO9d4xcA3nvf2uZnypP8i0m85m/Z3Kbmf3cOdd8UK3LMhdPrrLMp3pbLfNNgLJA5Hct84sTrZb5yfRPZrNeNrOrzexOyzwOKy3zt58GfOYn+lZa5g9kt5nZ183s2uzaySzzR8JPzq6fDvX8AUeEApu3WPcAR6FCmoe43oORnB/97wUCYyq7w3yX9/7grzwCAAAAAAAAAIACwjeYMG6yX+e+KPvVxmPM7L+a2Y/He1wAAAAAAAAAACCMbzBh3GR/+/tJy/wR3V4ze8TMbsr+lB8AAAAAAAAAAChQbDABAAAAAAAAAAAgEX4iDwAAAAAAAAAAAImwwQQAAAAAAAAAAIBEUqGic47fzzsMiouLg/V0Oi0zTjnllGB9xYoVMmNoaEj2vPjii8H6L37xC5mhFBXpfc7h4eGcb+do47134z2GN4u5B5i4mHtwsPPPPz9Ynz9/vszYsGFDsN7S0pJoTKOpq6vLqW5mdvnllwfra9askRmrV68O1g8cOCAzjjbMPQDGA3PP2HAu/DCP1Z9++PCHPyx7vvWtbwXrMddhCuX+5sPf/d3fyZ7bb799DEZyZGHuKQzqWDUzS6WCl97NTF8HHqtrot/+9reD9Zi5J+Y8Zdq0acF6U1OTzDjnnHNkz1hQ1/jN4q7zTxSjzT18gwkAAAAAAAAAAACJsMEEAAAAAAAAAACARNhgAgAAAAAAAAAAQCJsMAEAAAAAAAAAACARNpgAAAAAAAAAAACQCBtMAAAAAAAAAAAASIQNJgAAAAAAAAAAACTivPejF50bvYhDKirSe3bDw8PB+ooVK2TGpZdeGqzfdNNNMqO/v1/23HzzzcF6aWmpzLj11ltlj+KcC9ZDr+Ojlfc+/KAVMOYeYOJi7pk4KisrZc8XvvCFYH3+/Pkyo6qqKljfsmWLzNi+fXuw3tLSIjMaGhpkz7x584L1VColM5Tm5uacM7q7u2VPzHrySMLcA2A8MPeMjbG6HrBmzZpg/S/+4i9kxsqVK4P1hx9+WGYUFxcH6+l0WmbEyMfjevLJJwfr69atkxmbNm0K1tX1LzOzl156SfYcSdeVmHtwMHVedu2118qMT3ziE8H62rVrZUbM+ZI6P5w1a5bM+NrXvhasP/roozLj6aeflj2FolDmr9HmHr7BBAAAAAAAAAAAgETYYAIAAAAAAAAAAEAibDABAAAAAAAAAAAgETaYAAAAAAAAAAAAkAgbTAAAAAAAAAAAAEiEDSYAAAAAAAAAAAAkwgYTAAAAAAAAAAAAEnHe+9GLzo1ePAIVFen9tlQqFawPDAzIjEsuuSRYP/7442XGl7/8ZdkzFq677jrZs3jx4mD9pptuynkcMc/d8PCw7HHO5TyW0DE1lrz3ud+ZcXK0zT2F4vzzz5c9S5cuDdb37t0rM7q6umTP1KlTg/WOjg6Z8eMf/zhYP3DggMxAcsw9E8edd94pe+rq6oL1tra2nMdRVVUle/r6+oL17u5umdHe3i57hoaGgvV58+bJjFxvw0w/JjGP2Q9/+EPZc//998ueiYK5B8B4YO4ZG+p8P+ZcX50bmJm9+93vDtZjzkE2bdoUrJ9xxhkyYyJ59NFHg/Wzzz5bZpSUlOQ8joqKipwzJhLmnonjhBNOkD2f+MQngvWFCxfmfDutra0y45577gnWr7zySpkxODgoe6ZNmxasf+c735EZb3/724P1JUuWyIxXX301WH/qqadkxr/8y7/Inpdffln2KOp69Vhdix5t7uEbTAAAAAAAAAAAAEiEDSYAAAAAAAAAAAAkwgYTAAAAAAAAAAAAEmGDCQAAAAAAAAAAAImwwQQAAAAAAAAAAIBE2GACAAAAAAAAAABAImwwAQAAAAAAAAAAIBE2mAAAAAAAAAAAAJCI896PXnRu9OIRKJVKyZ6hoaFgfd68eTLjfe97X7B+2223yQyluLhY9oSe+zc454L1dDotMz784Q8H68cee6zMuOWWW2SPUlSk91PVYxLzmBUK7334yStgR9vckw+TJ0+WPWpu6erqkhm7du0K1tva2mTG4OCg7BkYGAjWY+5vVVVVsH766afLjJ07dwbr+ZibjjTMPYXh7LPPlj2f//znZU9dXV2wvmbNGpkRs75S1PorRnd3t+wpLy8P1hsaGmRGe3t7sN7Y2Cgz1P2NeTxi1qQf+tCHgvVNmzbJjELB3ANgPDD3TBzqPMZMnz/ErCVUxoUXXigznnnmGdkzFmLWEhs3bgzWd+/eLTPUtRp1XcrMbNq0abJnIl3PUZh7cldSUiJ71LWLs846S2asWrVK9nR0dATr/f39MkMdJ6WlpTKjpaUlWFfXR8zM6uvrZY+aW37605/KjOXLlwfrai420+dUMde8a2pqZM/HPvaxYP2JJ56QGeo6f8xY82G0uYdvMAEAAAAAAAAAACARNpgAAAAAAAAAAACQCBtMAAAAAAAAAAAASIQNJgAAAAAAAAAAACTCBhMAAAAAAAAAAAASYYMJAAAAAAAAAAAAibDBBAAAAAAAAAAAgESc9370onOjFyeYVCole4aGhmTPGWecEax/+tOflhnve9/7ZI9SVBTeGxweHs75NszMnHM5jcPMLJ1OB+uf+MQnZMaMGTOC9c997nMyo7i4WPaosU4k3vvwk1fAioqK5NwTmruORl/84hdlz3vf+95g/Rvf+IbMeOaZZ4L1adOmyYyY+VjNLR0dHTKjvLw8WFfzuZlZXV1dsH7DDTfIjKPNRJ57jqR1T4x//ud/lj3veMc7gvVnn31WZrS3twfr3d3dMiMf1JwQ26Oo9eS8efNkRltbW7C+ePFimdHY2Ch7nnjiiWD9mmuukRmFgrkHE5k654oxkdbGai0Y83gMDg4G6x/5yEdkRm1tbbAes75m7pk4Yo4RtSbp7e2VGer129DQIDNWrFgRrK9evVpmxLj44ouD9Yceekhm7N+/P1iPuTalHrNJkybJjJhzu5deekn2TBTMPYXh29/+tuw57bTTZE9/f3/OY1HnXFOnTpUZkydPDtZbWlpkxr59+2RPfX19sB4z1oqKimB9y5YtMkO9L5SUlMiMKVOmyJ7f/OY3wXrMmqVQjDb38A0mAAAAAAAAAAAAJMIGEwAAAAAAAAAAABJhgwkAAAAAAAAAAACJsMEEAAAAAAAAAACARNhgAgAAAAAAAAAAQCJsMAEAAAAAAAAAACARNpgAAAAAAAAAAACQCBtMAAAAAAAAAAAASMR570cvOjd6scA454L10P1M4kc/+lGw/vGPf1xmtLa2BuupVEpmDA0NyZ5CUVxcHKyn02mZ8U//9E/B+je/+U2ZsW7dOtmjHvuJ9Lh778MHRQGbSHNPoVDHiJnZli1bgvXdu3fLjEWLFgXre/fulRmdnZ2yp6enJ1iPmScHBwdlj6LeW77//e/nfBtHGuaewqDee83M5s+fL3u6u7uD9Zj31rvuuitY3759u8xoaGiQPUrMvFFVVRWst7e3y4zy8vJgva6uTmY0NzcH6zGPWcztfPSjHw3WY94XCgVzD4CRTjnlFNmjziHPPPNMmcHcM3Go6zBmZtXV1cF6aWmpzNi3b1+wHnNtqrGxMVh//vnnZUZNTY3sOeGEE4L1trY2maGukUyfPl1mdHR0BOu1tbUy48orr5Q9DzzwgOyZKJh7CsMzzzwje9S5gZm+djFlyhSZ0dXVFazHXM/s7e0N1nfu3CkzjjvuONnT19cXrKtzUDM9H5eVlckMNddu27ZNZsTMter959JLL5UZhWK0uYdvMAEAAAAAAAAAACARNpgAAAAAAAAAAACQCBtMAAAAAAAAAAAASIQNJgAAAAAAAAAAACTCBhMAAAAAAAAAAAASYYMJAAAAAAAAAAAAibDBBAAAAAAAAAAAgERS4z2AfEmlwndlcHBQZnzpS1+SPS+//HKw3traKjNKSkqC9ZixTiTOuZwz7r///mD91ltvlRlXXHGF7BkaGgrWY+6L91724OiRr9dMZWVlsN7V1SUzfvaznwXrFRUVMuN3v/tdsF5aWiozYqhjUT0eZmb19fXB+vTp02VGTU1NsD558mSZsX//ftkD5Fs6nZY9f/zjH3O+nbvuukv21NXVBettbW0yo6+vL3pMo6mqqpI9au6JGYe6nZj7qzLuvvtumdHS0iJ7du/eLXsAjK64uFj2xMzHyu233y57Fi1aFKzHjFWtrzZt2iQzduzYkXNPd3e3zFi1alWwfu6558qM733ve7IHE8Oxxx4re9S63sxs3759wfrNN98sM9R79NatW2WGen8+4YQTZEbMeejOnTuD9YGBAZnR3NwcrN92220y46KLLgrWlyxZIjNOO+002fPAAw/IHmAkda25oaFBZsRcq9mzZ0+w3tjYKDPUMV9bWysziorC30OZMmWKzOjs7JQ9TU1NwXrMOVd5eXmwftxxx8mMjRs3BuvqPcFMv0bM9PlwTIY6Tx1vfIMJAAAAAAAAAAAAibDBBAAAAAAAAAAAgETYYAIAAAAAAAAAAEAibDABAAAAAAAAAAAgETaYAAAAAAAAAAAAkAgbTAAAAAAAAAAAAEiEDSYAAAAAAAAAAAAkwgYTAAAAAAAAAAAAEkmN9wDyZXBwMFhvaGiQGXV1dbLnhhtuiB7TaNLpdM4ZE8nQ0FDOGc8++2yw3t/fLzO++tWvyp5PfvKTwbr3XmYAIxUV6X38mDlhwYIFwfott9ySc0aMpUuXBuuPP/64zKiurpY9fX19wXrMnD5jxoxg/dVXX5UZ6vnr7u6WGd/5zndkj3MuWGfuQaFqa2uTPYsXLw7WW1tbZYY61qqqqmRGeXm57FE5am4y0+vJ9vb2nMfx5JNPygwAh1++3p/VecoHPvABmfHiiy8G66mUPvUvLS0N1s866yyZUVlZKXvU+kqti8zMbrvttmA95r0l5v5gYpg9e7bsqaiokD3qOHnmmWeixzSamGNEHYsx66+Y89D6+vpgvaurS2YoP/jBD2RPc3NzsL5kyRKZcfrpp8cOCYimXptlZWUyY2BgQPbs2bMnWO/p6ZEZ+RiHWteoa+9mev4y0+c66vEw0/N+zHW27du3B+sxz29xcbHsUfP+vHnzZMaGDRtkz3jiG0wAAAAAAAAAAABIhA0mAAAAAAAAAAAAJMIGEwAAAAAAAAAAABJhgwkAAAAAAAAAAACJsMEEAAAAAAAAAACARNhgAgAAAAAAAAAAQCJsMAEAAAAAAAAAACARNpgAAAAAAAAAAACQSGq8BxCjqEjvgw0PDwfrN910k8woLi6OHlMu1FhjOOeCde99zreRj3GYjc1Y1q5dK3sWL1582MeBI0vM61tJp9N5GInZr3/962B90qRJMuOqq64K1ru6umRGSUlJsD5//nyZ0dHRIXtKS0uD9YGBAZnxuc99LlhfvXq1zJg7d26w3tLSIjNijNWcDYwUs+5Rc9i6detkhnr/raurkxl9fX3BekNDg8xobW2VPcrQ0FDOPTH3d8uWLbFDAjCO8nFeZ2Y2ffr0YH3nzp0yQ809tbW1MkPNk/v27ZMZkydPlj3q/D5m/VxdXR2sb9q0SWbgyBFzDhJDnYPkY+0fs/6KOddRYs5l83WuGvLHP/5R9vzqV78K1q+99lqZUVlZGT0mINayZcuC9Zh1QEVFheyZPXt2sJ6Pa6/l5eUyo62tLViPub8x1/BTqfB2hKqbmfX09ATr6vzRzKyxsTFYV+8JZnHndqpn0aJFMmPDhg2yZzzxDSYAAAAAAAAAAAAkwgYTAAAAAAAAAAAAEmGDCQAAAAAAAAAAAImwwQQAAAAAAAAAAIBE2GACAAAAAAAAAABAImwwAQAAAAAAAAAAIBE2mAAAAAAAAAAAAJBIarwHEGN4eDjnjOnTp8uec845R/b8+te/DtYHBgZkxg033BCsb9iwQWZ472XPWMjHOFasWCF7PvaxjwXrNTU1MiPmNfDOd74zWH/88cdlBjAeent7ZU9RUfgzBbW1tTLj3e9+d7C+ePFimTFjxgzZ09raGqxv375dZkybNi1YX7lypcz4yU9+InuAiSqV0svAdDodrMesWdTtNDQ05Jyh5gwzs6GhIdmT6zhiVFVVyZ4HH3ww59spLi6WPer5BZC7T37yk7LnjDPOCNa3bt0qM5qamoL1119/XWaoc6ry8nKZUVpaKnucc8F6zDn14OBgsF5fXy8zcOQ47bTT8pITc5zkKuY4am9vD9YrKipkRsw6QK2N1LGaL2vXrs05Q537AW/G3Llzg/WY4yyGmhdirour6z0x50JTp04N1vft2yczYuY49R4eo6OjI1jv6uqSGccee2ywvmXLFplRUlIie9Tzd9xxx8mMQsc3mAAAAAAAAAAAAJAIG0wAAAAAAAAAAABIhA0mAAAAAAAAAAAAJMIGEwAAAAAAAAAAABJhgwkAAAAAAAAAAACJsMEEAAAAAAAAAACARNhgAgAAAAAAAAAAQCJsMAEAAAAAAAAAACCR1HgPYKy89a1vlT2tra2yp6qqKlifPn26zLjvvvuC9Zdeeklm3H777cH6hg0bZEY+XHnllbLn+uuvD9YbGhpkxvr164P1oiK9Vzp58mTZc8MNNwTrjz/+uMzA2HDOBeve+zEZRz5up6mpSfZcdtllwXp3d7fMGBgYCNZj7stnPvOZYP2FF16QGY899pjsOemkk4L1mLln1qxZwXrMPHndddcF6z/72c9kxvbt22XPWFDHTIyxOq4wNoaGhnLOKC8vz/l2Yo6RVCq8ZI25L2oNF5OTj4wFCxbknBFDPWZmZul0OufbARD2V3/1V7JncHAwWFdruJiemDmhtrY2WC8pKZEZMfOXWk/E3I6av5jfji5z587NS87GjRvzkhMSc+1CKS4uLpjbyYc//OEPwXrMHDhlyhTZo+4P8wYONmPGjGA95vw4H8eRWifEjCXm9X3gwIFgPeY4i1kHqNuprKyUGV1dXcF6f3+/zFC3k6/nt6+vL1jP13vYeOIbTAAAAAAAAAAAAEiEDSYAAAAAAAAAAAAkwgYTAAAAAAAAAAAAEmGDCQAAAAAAAAAAAImwwQQAAAAAAAAAAIBE2GACAAAAAAAAAABAImwwAQAAAAAAAAAAIJHUeA8gX97//vcH61OnTpUZu3btkj3bt28P1r33MqO7uztYnzt3rsx46KGHgvWuri6ZUVSk9xdPOumkYH3Pnj0yY/Xq1cH6z3/+c5mhHrOZM2fKjHQ6LXsWLVoke1AYYo61QriNsrIy2XPLLbfIHjU/dXZ2yoyqqqpgfceOHTJj/fr1wfrixYtlRktLi+xZunRpsK7mBDOzp556Kljv6+uTGWreuOaaa2TGAw88IHtiHpNcjcUxg4klldLLQHUMqHVRjDVr1siexsbGYL2trU1mDA0NxQ7pTY8j5nZi5p4tW7bEDulNjwMoZDHnKcrw8HAeRhIWcx5TXl4ue37/+98H6zHnOuqYr6urkxlqzo85n4p57tRYBwYGZEZxcXGwXlFRITNw5Ih5f47xwgsv5CUnJOY4cs7lfDsxa391O2N1/nDgwIFgvbS0VGbEzLWzZ88O1jdv3iwzcHRR12dj1vWVlZWyR52Xqfc8M328xlybUucgMXNCzDmm6unv75cZap0Xc82ouro6WI957mKosc6aNSsvtzOe+AYTAAAAAAAAAAAAEmGDCQAAAAAAAAAAAImwwQQAAAAAAAAAAIBE2GACAAAAAAAAAABAImwwAQAAAAAAAAAAIBE2mAAAAAAAAAAAAJAIG0wAAAAAAAAAAABIhA0mAAAAAAAAAAAAJJIa7wHky+WXXx6sd3d3y4zJkyfLHpWzbt06mVFdXR2s//KXv5QZP/3pT4P1mpoamTFlyhTZ89hjjwXr+/btkxklJSXBuvdeZpx66qnB+umnny4zXn31VdnT2dkZrJeWlsqMgYEB2YOjx8qVK2VPzGtz27ZtwfrWrVtlhpoXmpqaZMbQ0FCw/tprr8mMmOO1v78/WN+1a5fMqKysDNarqqpkhnrct2zZIjOWL18ue3bv3h2sd3V1yQxgPKRSeik5b968YF2t4czMZs6cGT2mXKg5LkZ7e3uw3tjYKDPq6upyHgcwkQ0PD4/3EMzM7Oyzzw7Wzz33XJnx29/+VvbMmDEjWFfzipk+ly0rK5MZ6XQ6WI95XmLO7dR7R3FxscxQYs7tceQYHBzMS86aNWvykhMSc22qqCj8WXDnXL6GE6TOyWK8613vkj2PPPJIzrcTsyZV18A2b96c8zhwZJk9e3awHnM8q2vAYyUf78979+6VGTHXlWJylN7e3mA9Zt1TXl4erPf19cmMmLWROsecNm2azCh0fIMJAAAAAACoro/1AAAgAElEQVQAAAAAibDBBAAAAAAAAAAAgETYYAIAAAAAAAAAAEAibDABAAAAAAAAAAAgETaYAAAAAAAAAAAAkAgbTAAAAAAAAAAAAEiEDSYAAAAAAAAAAAAkkhrvAeTLW97ylmC9tbVVZqTTadkzb968YL22tlZmtLS0BOs1NTUyY+HChcF6SUmJzBgeHpY9qVT4JeK9lxlFReF9zJjHrLm5OVjfs2ePzJg1a5bs2bdvX84Z6vlFfjjngvWY12Y+nH322cH69OnTZcYrr7wie+bOnRus79y5U2bs378/WB8aGpIZZWVlwfrUqVNlRl9fn+zZtm1bsF5dXS0zSktLg3U1N5np+bi3t1dmlJeXy563ve1twfrjjz8uM4CkYo55Rb1vmplt2LAhWI953+zu7o4e02iqqqpkj3pMYsah1k4xc0J7e7vsAZCbe+65R/ace+65wfrDDz8sM+rr62XP5MmTg/WYczvVE3OuG3N+mI8MNZaYNZqar+fMmSMz1Dqvs7NTZqAwTJs2LS856nxpyZIlOd9GT0+P7KmsrMz5dvJBnXPHuPnmm2XPI488kvPtqPWXmT6nfv7553MeB44sFRUVwXpHR4fMiHlfzMd5mboGFnONTB1HMWuamNtR657NmzfLDLXuaWpqkhmDg4PBesxcHDNPqtuJWfcUuol/DwAAAAAAAAAAADCm2GACAAAAAAAAAABAImwwAQAAAAAAAAAAIBE2mAAAAAAAAAAAAJAIG0wAAAAAAAAAAABIhA0mAAAAAAAAAAAAJMIGEwAAAAAAAAAAABJhgwkAAAAAAAAAAACJpMZ7ADHmz58ve+rq6oL1HTt2yIyBgQHZMzQ0FKw3NDTIDNXT1dUlM5xzwXp5ebnMGB4elj3e+2A9ldIvoYqKimC9rKxMZhw4cCBY7+/vlxmdnZ2yZ+7cucF6c3OzzGhpaZE9CFOvbzP92hwrb3/724P1jRs3yozS0tKcx6GOMzOzKVOmBOvFxcUyQ82Tra2tMqOkpET2qMcknU7LjL6+vmC9qqpKZsT0KLt27ZI9xx13XLA+ffp0mbF79+7oMQH5cv7558setSZRx6qZ2dNPPx09pjc7DrO4dY2i1oqqbmZ21VVXBeu33HKLzIiZJ4HxkI913uLFi2XGQw89FKzv3LlTZqhzyJj1V8z9LSoKf+6zsbFRZqhzu5hzP7UWjLkv+eiJmb/UmjRm/XXRRRcF66tWrZIZGBvqmK+pqcnL7ajrAQsWLMj5NmKOkbGi5tqYazUdHR3B+pIlS2RGzO0ovb29sufGG28M1u+///6cx4Eji1q3x7xfxVzvUe/RMedL6jrL4OCgzGhvbw/WY86nYnrUtYuY631qvo65BpwPMa8B1TNp0qR8DWfc8A0mAAAAAAAAAAAAJMIGEwAAAAAAAAAAABJhgwkAAAAAAAAAAACJsMEEAAAAAAAAAACARNhgAgAAAAAAAAAAQCJsMAEAAAAAAAAAACARNpgAAAAAAAAAAACQCBtMAAAAAAAAAAAASCQ13gOIsWTJEtmTTqeDde+9zHDO5dzT19cnMyZNmhSsNzQ0yAx1f4eHh2VGUZHeX1Q9MbejHrPe3t6cx3H88cfLjOLiYtnzwgsvBOuNjY0yA0eO9773vbJnz549wXpra6vMWLhwoezZvHlzsB5zHJWXlwfrQ0NDMkMdi9XV1TKjrKxM9igDAwOyR401Zr5Wc9zUqVNlxt69e2VPR0dHsL58+XKZce+998oeIN8uv/xy2aPmlqqqKpmxYMGCYH379u0yY6zew7u7u4P1mLln/vz5+RoOjhBqPR1zHhOzbh8LMedlymc+8xnZo47FNWvWyAx1HhqzHikpKZE9as0Scx6jDA4O5jyOmOcu5jFRY+nv75cZag3e1NQkM971rncF66tWrZIZGBvXXXddsF5XV5eX2zn33HOD9fr6+pxvI5WaEJfhzCzumpG6NhVzf6+44oroMY0m5jx06dKlOd8Ojhwx116VmOswY7UOUD0x7+HqOIoZh5oTYnJinht1bV2tA830eqSmpkZmxKzB83E76jpazNrpcOIbTAAAAAAAAAAAAEiEDSYAAAAAAAAAAAAkwgYTAAAAAAAAAAAAEmGDCQAAAAAAAAAAAImwwQQAAAAAAAAAAIBE2GACAAAAAAAAAABAImwwAQAAAAAAAAAAIJHUeA8gxjHHHCN7ent7g/Xh4WGZ4b2PHlMu1Fj6+/tzvo2Y+xLzmJSWlgbrJSUlOY+luLhYZij79u2TPTFjraqqCtbPOOMMmXHvvffKHoSN1bE4Z86cYP3YY4+VGdu2bQvW1WsqVnt7e7BeUVEhM9Tc0tPTIzPUnBAzf8XcjsqprKyUGWqsqm6m5w31/JuZ1dTUyJ6Ojo5g/eSTT5YZJ5xwQrD+yiuvyAwcXdLpdM4Zixcvlj0tLS3Besw82d3dHaz39fXJjFRKL3vV7eRrTs/VqaeeKntefPFF2aPWYPl4jSA/1NporNZOY2X69OnB+rJly2TGb37zm2A9Zp33xBNPBOtnnXWWzIg5l500aVKwrs51YzjnZI9af8WMo7OzU/aoHDUXm+m1YExGzLkdCsMDDzwQrL/++usyY8eOHbJn06ZNOY3DTB9HMeuRiSRmDabMmjUrWD/vvPNkxrx582TPlClToseEI9/MmTNlj7pmMDg4KDOKivR3O1RPPt7DY+YedX8GBgZkRsz5kroeHXO9R10Di5nz6+vrg/WYa0b5uBZXXV0tM9S6VZ1zH258gwkAAAAAAAAAAACJsMEEAAAAAAAAAACARNhgAgAAAAAAAAAAQCJsMAEAAAAAAAAAACARNpgAAAAAAAAAAACQCBtMAAAAAAAAAAAASIQNJgAAAAAAAAAAACTCBhMAAAAAAAAAAAASSY33AGKcccYZsmdgYCBY7+vrkxnee9lTUVERrBcXF8sM1eOckxn5EDPWVCr3l0jM45prxvDwsMzo7++XPVVVVcF6Y2OjzMDE8Z73vCdY37lzp8zo7e0N1mtqamRGe3u77Kmrq8v5dtRYY+aEdDodrJeUlMiM0tJS2aPGeuDAAZmh5oWiIv0Zi+nTpwfrQ0NDMmP37t2yRz1/mzdvlhnLly8P1jdt2iQz1POLiaOsrEz2xLwvXnrppcF6a2urzFDvrTFrtIaGhmC9u7tbZqh5NCZH3RezuMdEUe8LF198scx48cUXZQ/H/JHjQx/6kOyZPXt2sB7z2u3s7AzWp02bJjOOOeYY2fPWt741WO/p6ZEZu3btCtZjXv+XX355sK7mJrO48ym1nohZb6h1XMz9VY+rev7N9BouZizl5eUyo7a2NliPWddWVlYG6zNmzJAZGBtPP/10TvV8qa+vlz2vv/56sD5W13vyIeZaTsy1GEWdH/7iF7+QGTE9wEgzZ86UPer9KOb6R8wxoq5NdHR0yAx1nhLzvqjGEbMeielR86S6DmNmtm3btmA95nFX1/jVXkPs7ajz7pjXkboe3dLSIjMOJ77BBAAAAAAAAAAAgETYYAIAAAAAAAAAAEAibDABAAAAAAAAAAAgETaYAAAAAAAAAAAAkAgbTAAAAAAAAAAAAEiEDSYAAAAAAAAAAAAkwgYTAAAAAAAAAAAAEkmN9wBiNDc3y56+vr5gvb+/X2aUl5fLnlQq/JAVFek9O+99sO6cy3kcMRkxPcPDw7InH7ejqMc15jYGBwdlj3puJk2aJDOQu7KyMtmjjukVK1bIDPW6UvOKmX7t1dfXy4za2lrZ09HREazHjDUfx3M6nQ7WS0pKcs4wMystLQ3WY14jap6MeV9ob28P1o8//niZoZ47M7POzs5gvaamRmZUV1cH68uXL5cZq1evlj2YGGJe3zGuvvrqYL2lpUVmzJs3L+eM+fPn51SP1dbWFqyr+2Jm1tjYGKzHzNfqMVG3YWZWXFwse2LmYxSGj3zkI8H63/7t38qMbdu2BetDQ0MyQ732Yt6fY8659u7dG6x3d3fLjGXLlgXrvb29MmNgYCBY37Rpk8yIORarqqqC9ZjHTB3P6jzHTK+/Yta1MfdXrRdj5qaYuVRR75WVlZU53wbyQ523xZyDxKyN8nG+r8658nF9ZKzEjFU9NzHvLXPmzIke02hi5h41D+bjfBkTR0VFhexR1xRi3p8nT54se9QcFvPaVNc8Y9Zo6ppCV1eXzIgZq+pR6xGz/Kwl1GOSj2uCZnqejHnMpk6dKnvGE99gAgAAAAAAAAAAQCJsMAEAAAAAAAAAACARNpgAAAAAAAAAAACQCBtMAAAAAAAAAAAASIQNJgAAAAAAAAAAACTCBhMAAAAAAAAAAAASYYMJAAAAAAAAAAAAibDBBAAAAAAAAAAAgERS4z2AfOnr6wvWBwYGZEZZWZnscc7lVDczS6Vyf9i99zmPI4bKUeMwMxseHg7Wi4uLZUZRUe57oTGPuxpLvh5XhPX398ue2traYP2cc86RGS+//HKwHvOaGRoaCtZjXru7du2SPR0dHcF6eXm5zOjs7AzW0+m0zFD3p7KyUmYcOHBA9qjHNWZOV7czbdq0nDNinruY5+b1118P1ktLS2XG5s2bg/WTTjpJZrzwwgvB+u7du2UGJo6Ydc+8efOC9Zh5cv78+cH6BRdcIDO2bNkSrHd3d8uMmJ6qqqpgPeb+trW15ZzR3NwcrC9btkxmXHXVVbLnvvvukz0oDOedd16wHnM8q/f5mPXXz3/+82A95n0i5j1cHa8x6423vOUtwfqMGTNkhpoT6uvrZUYM9T4fs0br6enJ6TbMzEpKSoL1fJzHmunzQ1U30+dlMa8zlaHWoxg76jWRr+dq8uTJOWeoayQx54cx11nGQj6u98RkxMxxuY4jdiw4ejQ2NsoedZ6u6mZxr7tJkyYF6/v375cZNTU1OY9DrSdjrt+q+2Km36PXr18vM9T5Uswabe/evcG6WheZxc3p6vplzDUy9fyON77BBAAAAAAAAAAAgETYYAIAAAAAAAAAAEAibDABAAAAAAAAAAAgETaYAAAAAAAAAAAAkAgbTAAAAAAAAAAAAEiEDSYAAAAAAAAAAAAkwgYTAAAAAAAAAAAAEkmN9wBitLa2yp5FixYF6wMDAzLDey97UqnwQ+ackxnDw8PBelGR3veLuR0l5v7mQ8z9UdT9VY9pTIaZWV1dXbDe29srMzA2rr766mB9/fr1MmNoaCinupnZ1KlTg/WY1+aMGTNkz4EDB4L1jo4OmVFeXi57clVcXCx7ampqZE9paWmwXlJSIjMGBweD9ZjHTD2/fX19MqO2tlb2qPeomLGq12tlZaXMeOc73xmsf/e735UZmDgaGxtz7mloaJAZ3d3dwfqZZ54pM2bOnBmsx9yXmPWkmidj5lF1LC5YsEBmqPu7ZcsWmbF48WLZc99998keHH4rV66UPU1NTcG6WieYmR1//PHBemdnp8xQ50Kqbma2f/9+2TNp0qRgfeHChTLjlFNOCdbLyspkhlpLxIg5B1HzRkxGVVVVsB5z7pdOp4P1/v5+mRFzO2q9GHPurp6/mOdOjTVmrsWRRR0D+TBW12HGiro/+XpfUGLmySPtsUdujj32WNmj3vfUeY5Z3DUD9b4Xcxypa6/qGouZ2d69e3O6jdgete45+eSTZcaGDRuC9ZjrbGre6OnpkRkxayO17onJmDJliuwZT3yDCQAAAAAAAAAAAImwwQQAAAAAAAAAAIBE2GACAAAAAAAAAABAImwwAQAAAAAAAAAAIBE2mAAAAAAAAAAAAJAIG0wAAAAAAAAAAABIhA0mAAAAAAAAAAAAJMIGEwAAAAAAAAAAABJJjfcAYmzbtk32nH/++cF6aWmpzCgpKZE96XQ6WC8rK8s5wzknM2J6CoUaa1FR7vucxcXFsmdoaEj2TJ48OVjfuXNn9Jjw5p199tmyRz1X+/bty3kclZWVske9rp577jmZoeYvM32cxMw95eXlwXp/f7/M6O7uDtZjjsWYx3VgYCCnull+XiM9PT3BemNjo8zo7OyUPdXV1bJHUXNta2urzGhqagrWZ82alWhMKGx1dXWyZ926dcF6KqWXkn19fTmPo6qqKliPORYbGhpkz/PPPx+sX3jhhTKjubk5WH/wwQdlhnpM1HxuZvaFL3xB9qAwvOc975E9c+bMCdbb2tpkhnrvjDlfOvHEE4P1hQsXyoza2lrZo9Y1aj1iZtbe3i57lJjzQ8V7n3NPTIZaB8TMG+pxraiokBkxt6PWnDEZyvDwcM4ZmDhijpEYMXOpos6H1PUgs8K53hNzrUbNkzH3ZevWrdFjGk3MWJkXMNLUqVNlj1obqXMUs7j3tPr6+mA9Zm5S1y5i5h41l8YcZ/l4D8/H9du9e/fKDLW+jhlHPuaemPXVlClTZM944htMAAAAAAAAAAAASIQNJgAAAAAAAAAAACTCBhMAAAAAAAAAAAASYYMJAAAAAAAAAAAAibDBBAAAAAAAAAAAgETYYAIAAAAAAAAAAEAibDABAAAAAAAAAAAgETaYAAAAAAAAAAAAkEhqvAcQY/369bKnr68vWC8pKZEZRUV6v623tzdYLy4ulhnOuZwzvPeyJ9dxxPTkI2OsxDxmaqyrV6/O13AQMGfOHNkzMDAQrPf09MiM8vLyYP3EE0+UGV/72teC9eOPP15mqPnLTN/fGDG3o5SVlQXrMXNte3u77BkaGgrWS0tLZYaar9V9icmIed/YvHmz7Nm7d2+wfuGFF8qMF198MVivra2VGc3NzcH6mWeeKTMwcVx99dWyJx/zhhIzJ8ycOTNYX7t2rcxIpfSyt6GhIVhfs2aNzFDzV3d3d84ZapxmccfrI488Intw+F133XWy59Zbbw3WV6xYITOmT58erMes2dXrV712zcz27Nkje1ROzPGs1v4x5wZq/ZWvc6Hh4eGc6jE9MY+ZWsfFrEf7+/tlj1o/pdNpmaHWcTFzrVpPnn/++TIDhSFf1xxi5jAlH69vlZGP60H5oq5fxdzftra2nMdRKNedMHFUVFTIHvXeqs6fzcyeeeYZ2aPeO5cuXSoztm7dGqzHnNfV1NQE6zHv8THvv2otqNasZmbV1dXBesz1j4ULFwbrn/3sZ2XGeeedJ3vUNc7Ozk6ZMW3aNNkznvgGEwAAAAAAAAAAABJhgwkAAAAAAAAAAACJsMEEAAAAAAAAAACARNhgAgAAAAAAAAAAQCJsMAEAAAAAAAAAACARNpgAAAAAAAAAAACQCBtMAAAAAAAAAAAASCQ13gOI8fDDD8ueO++8M1ifPHmyzBgYGJA9fX19wXpxcbHMmDJlSrA+NDQkM4aHh3MeRwzvfV5yDrd0Oi17ysvLZc/+/fuD9cceeyx6THjzvvvd78qeq666KlhfvHixzNixY0ew/vrrr8uMnTt3BuvLly+XGTGvzdLSUtmTKzWvmJk554L1oiL9uYWY+6vmwZh5cnBwMFivqqqSGWrO7+npkRmLFi2SPV/5yleC9ebmZplx8sknB+uplH7L/9GPfhSsr127Vmbcf//9sgeFIWaebGlpCdZjXlcNDQ3BujrOzMzWrVsXrK9Zs0ZmNDY2yp758+cH6xs2bJAZ7e3twXrMY1ZXVxesb9myRWbceOONsueRRx6RPSgMt912W051M7Nly5YF6xdccIHMuOSSS4L1pqYmmVFZWSl7ysrKgnW1HjHT79ExaxYlZu2Uj9vJx7ldzPmSOveLub8xPSUlJcF6zFpR3Y56/zLTa6cTTjhBZqAwjNV1i46ODtmj3uf7+/tlRj7mjZh5Mh/U7cSct23cuDHncUyUa1coHBUVFbJHXSNU6xWzuPW2OgZixqrOl2KcdtppwfqkSZNkRsw8OXXq1GB9/fr1MqO7uztYj5kT1PN77733yoyFCxfKngULFgTrr7zyisyorq6WPeOJbzABAAAAAAAAAAAgETaYAAAAAAAAAAAAkAgbTAAAAAAAAAAAAEiEDSYAAAAAAAAAAAAkwgYTAAAAAAAAAAAAEmGDCQAAAAAAAAAAAImwwQQAAAAAAAAAAIBE2GACAAAAAAAAAABAIqnxHkCMLVu2yJ5/+Id/CNabmppkxmmnnSZ72tvbg/Xq6mqZ4ZyTPUpZWVnOGel0OueM4eFh2VNUlPs+phrrwMCAzKivr5c9119/ffSYML5WrVoVrJ966qky4+qrrw7W77///kRjOpTe3l7Zs3v3btnT09OT81jyQR1rMXNTzJyQSoXfnoaGhmRGPuY49fy1trbKjBNPPFH2qPeFJ598UmYcd9xxwfodd9whM/LxmKEwxByLfX19skcdazFrtO3btwfrM2fOlBmNjY3BupozzOLmjbVr18oeRa0VYx539Zi1tbXJjIaGBtkze/bsYP21116TGchdPs4NvPey5+mnn86pbmb2+c9/PnZIo5o0aZLsueKKK4L1mMdsz549wfqMGTNkhpqfdu3aJTNizlPUeqO/v19mKHPmzJE9XV1dwXrMOfWsWbNkj5oHH3zwQZmhzu1ijomWlpZgPeY94etf/7rsweEXc10iH2LW/s3NzcF6zGszH+8LMRlqLDHnbSojZk2qjsUYMY8rMNKUKVNkz+DgYLAecw4S8x6ujrWamhqZUVtbG6zHnBuouTQf84qZXgtWVlbKDLXOizn3mz59uuxRduzYIXuKi4uD9Zh5sq6uLnpM44FvMAEAAAAAAAAAACARNpgAAAAAAAAAAACQCBtMAAAAAAAAAAAASIQNJgAAAAAAAAAAACTCBhMAAAAAAAAAAAASYYMJAAAAAAAAAAAAibDBBAAAAAAAAAAAgERS4z2AfPmbv/mbYP25556TGVVVVbKnpKQkWC8tLZUZivde9jjncr6dmIzh4eGcb0dlFBXpfU71uDY0NMiMH/zgB7Lnxz/+sezBxPDiiy/KnpdeeilYHxwczHkc9fX1sqe6ulr2qOM15jhKp9OyRxkYGAjW1RxpZtbd3S171DwYM19XVlYG65MmTZIZfX19wXrMHBnzvnDccccF65s3b5YZd955p+xR1Oss5v0JheFf//VfZc+CBQtkT3l5ebAe8/7b3NwcrLe1tckMdczPmzcv5wwzs/b29mC9sbFRZlxwwQXB+plnnikz8iFmrr3jjjuC9csuuyxfw0HA0Ta39vT0yJ6777778A8EwFEl5vqHmo+3bt0qM44//vicxzE0NBSs5+t9Q51DxpwPq7VijJi1oHK0vZcidzt37pQ96jw95vxi3759sueBBx4I1p966imZodZXMceIut6j6rG3o8Rc31LXntQ8apafa2SbNm2SPcXFxcG6unZlZrZ27droMY0HvsEEAAAAAAAAAACARNhgAgAAAAAAAAAAQCJsMAEAAAAAAAAAACARNpgAAAAAAAAAAACQCBtMAAAAAAAAAAAASIQNJgAAAAAAAAAAACTCBhMAAAAAAAAAAAASYYMJAAAAAAAAAAAAiaTGewBjZcmSJbJn8+bNsqejoyNYLyrSe3b19fXB+qRJk2RGaWlpsF5XVyczUqnCePrXrVsne7z3wfqNN94oM1atWhU9JhwdBgcHD/tt/PCHP5Q9TU1NsufAgQPBemVlpcxIp9OyRxkYGAjWp0yZIjNi5qe9e/cG6zHPnXMuWFfzuZme09XjYRY3p7/66quyB0gi5jhbu3at7Hn++eeD9Xe84x0yQ603Ghoacs64/PLLZUZjY2POt3PHHXfIDDWWp59+WmZUVVUF66tXr5YZf//3fy97PvCBD8geAACOBOrcwExfd/jDH/4gM5YvXx6sl5WVyYyYddxYiDnnGh4eDtZjzrnyQT13wMHuuece2fPBD34wWH/hhRfycjtKa2trzhkTiZpXzMz6+/vHYCTa3XffLXsuu+yyYF1d4zcze+ihh2KHNC74BhMAAAAAAAAAAAASYYMJAAAAAAAAAAAAibDBBAAAAAAAAAAAgETYYAIAAAAAAAAAAEAibDABAAAAAAAAAAAgETaYAAAAAAAAAAAAkAgbTAAAAAAAAAAAAEgkNd4DGCtLly7NS08qFX7ITjnlFJnR2NgYrJeUlMgM51yw/qlPfUpmbNiwQfaUlpYG68PDwzln9PT0yAxgotq7d29eeoCx4L0f7yEgT1auXJmXnNmzZwfrd911l8zYt29fsH799dfLjCeeeCJYf+2112RGjDvvvDNYv/vuu2XGs88+G6zff//9SYZ0SMccc0zOGWZm3/zmN/OSAwBAoVPXUGL8+7//u+z54Ac/GKz/9re/lRlq7dTV1SUzYqjrW5MnT5YZy5YtC9ZfeumlRGN6sziPQVI7duyQPZWVlcH6/v378zWcoJj5S/XkYw6MMVbHYj7uj7qmna/70tbWFqxXVFTIjN27d+dlLIcL32ACAAAAAAAAAABAImwwAQAAAAAAAAAAIBE2mAAAAAAAAAAAAJAIG0wAAAAAAAAAAABIhA0mAAAAAAAAAAAAJMIGEwAAAAAAAAAAABJhgwkAAAAAAAAAAACJsMEE4P9n787DLTurAuGvt+69de+tujWmqjJVBjKHGIbEkDC0gPpAIAliYwuKoHwMbRRRxODUIN3tpzRO+GkDioIoKA3ERiODoJAgpoUEoTFhMiEJValUUmNqrjvt749zIpWY2uvdOTdVt6p+v+fhecJdq9Z5975nr/3u/Z59LgAAAAAAdFKapjncYwAAAAAAAOAI4gkmAAAAAAAAOrHABAAAAAAAQCcWmAAAAAAAAOjEAhMAAAAAAACdWGACAAAAAACgEwtMAAAAAAAAdGKBCQAAAAAAgE4sMAEAAAAAANCJBSYAAAAAAAA6scAEAAAAAABAJxaYAAAAAAAA6MQCEwAAAAAAAJ1YYAIAAAAAAKATC0wAAAAAAAB0YoEJAAAAAACATiwwAQAAAAAA0IkFJgAAAAAAADqxwAQAAAAAAEAnFpgAAAAAAADoxAITAAAAAAAAnVhgAgAAAAAAoBMLTAAAAAAAAHRigQkAAAAAAEEoMv4AACAASURBVIBOLDABAAAAAADQiQUmAAAAAAAAOrHABAAAAAAAQCcWmAAAAAAAAOjEAhMAAAAAAACdWGACAAAAAACgEwtMAAAAAAAAdGKBCQAAAAAAgE4sMAEAAAAAANCJBaY5VEp5UynlvYd7HEeSUsozSinrD/c44Eh3rPafUsr1pZRX9P/7x0opnz3cY4KjybHaWyLae8og/ebAvgUcGsdyLwMODX0GOBz0HuYDC0wdlVJ+uJRycyllVynlnlLKx0opTzsM4zi1P4YD/9eUUl43QM1fKqXc0a+1vpTyv+ZyzMBg5lH/WVNK+YtSyoZSyv2llH8spVw6QL1nlFJm+9u1s5Ty9VLKy+ZyzMDBzZfe0h/LU0opn+/3gi/PxThKKY/p95i3z8UY56tSyp+UUn71cI8DDpd51sueUEr5h/48aX0p5Q0D1Dq9f533wDXfvaWUt5VSRuZyzEBunvWZT5dSNpVSdpRS/m8p5fsGqHXg9dgD94M+UEq5ZC7HDDwyeg/znQWmDkopPxsRb42IX4uI4yPi1Ih4W0Q84oPpkWqa5ltN00w88L+IuDAiZiPi2kdSr5TyoxHxkoj43n6974yIv5+zAT/KSinDh3sM8GiaT/0nIiYi4qaIuDgiVkbEeyLiI6WUiQFqbuj3nqUR8fMR8c5SymMHHumjTO/hSDefekspZWVEXBcRvxERyyPiLRFxXSllxYClXxoR2yLihaWU0QFrAfPQfOplfX8eEZ+J3jzp6RHxE6WU5w1Yc/kB131PjoifHLAe0ME87DM/HREnNk2zNCJeFRHvLaWcOEC9B67HlkTEZRHxtYj4h1LK9zxcsusgODT0ngfTe+YnC0yVSinLIuK/RcRPNk3zl03T7G6aZqppmuuaprnmIP/mg6WUjf1Prn2mlHLBAbHnllK+0v+E7t2llJ/r/3xVKeVvSinbSylb+598q/k9vTQiPtM0zZ2PcBMviYi/bZrm9oiIpmk2Nk3zhweM9/pSyn/vP6mws5TyiVLKqgPil5VSbuyP+/+WUp5xQOxlpZSv9v/dN0sp//lggyilvKa/X9aWUkZLKb9ZSvlW/5N67yiljPfzntFf2f75UsrGiHj3I9xumPfmW/9pmuabTdP8dtM09zRNM9PvFQsj4txBt7Xp+XD0bgY/tjzkce/y7U/xppOK0nsS4qb+PriplPKU/s9fWEq5+SG5ry2l/HX/v/UejgnzrbdExFMiYmPTNB/s95b3RsSmiPiPA2xjid4c6b9ExFREXPWQeFNK+fFSyr/2x/c/+//m4Wr9Rinls/399tDYeaWUT/a37+ullB9MhnZm6T2ptaOU8lelt7j2QK3nlVJu7Y/n+lLK+QfEzu//bHs/53n9n78qIl4cEa8vvU//XVe5i+CINw97WUTE6RHxvn4vuz0iPhsRFxwkt5Omae6LiE9GxL99EKeU8gullNv7Y/5KKeX7D4gNlVJ+q5SyufS+reLVtXMpoGc+9pmmab7cNM30A/83IkYi4pRBt7V/Pba+aZo3RsQfRcT/OGDcTSnlJ0sp/xoR/9r/2ZWllC/1x3xjKeVxB+T/fH/7HviWiu/p//xJpfc0xo7+9dZvDzpuOBrpPf82br1nnrPAVO/JETEWEf+7w7/5WEScHRFrIuKfI+J9B8T+OCL+c9M0SyLiOyLiU/2fvy4i1kfE6uitTP9S9A7Yg+rfCHlp9J4ieKT+KSJeWkq5ppTynaWUoYfJ+eGIeFn0tmdhRDzQiE6OiI9ExK9G71N6PxcR15ZSVvf/3X0RcWX0nkx4WUT8TinloofZjjdGxI9FxNObplkfEW+OiHMi4gkRcVZEnBwRbzzgn5zQf73TordqDkeredt/InpfAxO9nnBbh/EdrNaC/k2R5RHxLwPUWRm9vvT/RcRxEfHb0XvK6rjoPSFxbinl7AP+yQ9H79PGEXoPx4752FseurhT+rUeqadFxNqIeH9EfCAifvRhcq6M3gdtHhcRPxgRz37QAHp96Z39+LOaprn/IfHF0bvZ++fR2y8vioi3lfanMF8aEf9PRJwYEdPR61VRSjknIv4iIn4mevvro9F7imth6X0d1nUR8Yn+6/xURLyvlHJuf6H/fRHxlv7T7Vc99AXhKDYfe9lbo3dtNVJKObc/xr/rML6DKqWcFL0+9U8H/Pj2iPgPEbEsIv5rPPjTxK+MiOdEb15zUUQ8fy7GAceY+dhnon9DeF9EfC4iro+Imw+W+wj9ZURc1J/rPOD5EXFp9D4M+MSIeFdE/OfoXXP9QUT8del9YO/ciHh1RFzS385nR8Sd/Rq/GxG/238C4szozdGAf0/v+Ta9Zx6zwFTvuIjYfMAqbappmnc1TbOzaZr9EfGmiHh8+fanXqeid1AsbZpmW9M0/3zAz0+MiNP6q9L/0DRNdoP3adFrAB/qskEPGet7o3ej4tkRcUNE3FdK+fmHpL27aZpvNE2zN3oH4RP6P/+RiPho0zQfbZpmtmmaT0avuTy3X/sjTdPc3l+NviF6N0b+wwF1S3/V+FkR8cymaTb1F81eFRGvbZpma9M0O6P3OOiLDvh3sxHxK03T7O+PCY5W87b/lFKWRsSfRcR/fehN145OKqVsj4jNEfErEfGSpmm+PkC9KyLiX5um+bOmaaabpvmL6D1qfVXTNHsi4q8i4of623B2RJwXvQmJ3sOxZL71lv8TvV7wQ/2bsj8avYn/oke4fRG9BaWPNU2zLXoLQJeXUtY8JOfNTdNsb5rmWxHx6fj2/Cai94m8v4jeovID/eOhroyIO5umeXe/33wxel9Z/J9axvVnTdPc0jTN7oh4Q0T8YP/DPS+MiI80TfPJpmmmIuI3I2I8ek93XRa9ryh9c9M0k03TfCoi/ib6vQyOYfOtl0X0js0fiIi90Zt//HHTNDd137QH2dyfK90dEbvjgGu/pvfk54b+tdj/it6ne5/UD/9g9G6mrO/3wjcPOA44Fs3HPhNN01wZva+Vem5EfKJpmtnum9ZqQ/Q+7LP8gJ/9ev86aW/0rpv+oGmazzW9JzbfExH7ozdnmYmI0f52jjRNc2fT/8ac/naeVUpZ1TTNrqZpDlwwB75N7/k2vWces8BUb0tErCqVXyVQel9F8ObS+6qCHfHt1dIHvlbuBdE7EO8qpdxQSnly/+e/Eb2nAD5Rel8n9wsVL/ejEXFt0zS7Wsaz64D/nfpwOU3TvK9pmu+N3gH84xHx30spB36Kd+MB/70nejc5Inqf4v9P/ccSt/cvfJ4WveYUpZTnlFL+qf+Y5fb+dq86oNby6DWHXz/gBvXq6N1Q+sIBNT/e//kDNjVNs+/guwWOGvOy/5Te18ZdFxH/1DTNr7fkpf0net+7u7xpmpVN0zyhaZr312xri5Mi4q6H/Oyu6D2NFNG70fzATdkfjogP928c6z0cS+ZVb2maZkv0vkv8ZyPi3oi4PHqf+F9/kPG09pZ+j/pP0f/UXtM0/ycivhW9Y/5AB5vfRPSeYvy+6C2iTz7cOKI3D7r0IfOgF0fvaceDWXfAf98VvYWsVfGQ3tW/WFsXvd51UkSse8gF3IF9DY5V86qXld5T1B+P3lfajEXva2OeXUr5iYPk33pAL/sPD5fzwPiaplkevXnKP0bE3x5Q46Xl218Tsz16n0p+YHtOigf3nAP/G6gzr/rMgfo3gz8WEc8qB/lbb5XXYw/n5Og9xbD9gJ8d2ENOi4jXPWQOdEpEnNQ0zW3ReyL7TdH7APP7S+8JzIiIl0fvGyO+VnpfZX5lhzHBsUTv+Ta9Zx6zwFTv/0RvNbT2KwV+OHo3JL43el9VcHr/5yUiommam5qm+b7oPbL44eg/ltdfZX5d0zRnRMTzIuJny0H+sFnEg26etH49XtP7upQH/vetJHeqaZoPRsSXo+5radZF75O4yw/43+Kmad5cen9M+9rofQL3+P5F0UfjwV+Bsy16n/59dynlqf2fbY7eJ/4uOKDmsqb3h9/+bagVY4OjwbzrP/1j+8PRu/F70L+r1q9b3X8exu548NMLbTdsD7QhepOOA50avU/9RvS+zmp16X293w/Ft78eT+/hWDLvekvTNDc0TXNJ0zQrI+Il0Xu68PMHyc16y/dH7+t531Z630O+MXoXKw/3NXkH89Xofb3vx0rv6xYezrqIuOEh86CJpmmubql74PeUnxq9T9Ntjof0rlJK6efe3Y+dUh78fegH9jW9iWPVfOtlZ0TETNM0f9r0nmpcH72v6Xzuww2maZoLDuhl/5ANvv/J3T+JiMtK728mnBYR74ze18Ec17/euiW+fb11T/S+KvQBA/+dBDgGzbc+83CGo/fk978zwPXY90fEPze9J67/rdwB/70uIv7fh8yBFjW9b4+Ipmn+vGmap0VvbtNE/2+qNE3zr03T/FD0tv9/RMSHyoO/Cgvo0XsOKHfAf+s984wFpkr9J2veGBH/s5Ty/FLKotL7+pbnlFLe8jD/ZEn0msCW6N0c/bUHAqX3PfovLqUsa3pff7Ijel+59MAfKTurf0Ph/ug92tf2qOH3R2+B5tODbF8p5cdKKVeUUpaU3t8aeE70/hDt5yr++Xsj4qpSyrP7q+VjpZRnlFLWRu/vsoxG7490T/frPuuhBZqmuT56n/b9y1LKk/qfzn1n9P5e05r+GE8uD36iCo4J863/lN7fAflQ9BZifrSZ+8ehD/SliPiuUsqppfdY9y9W/ruPRsQ5pZQfLqUMl1JeGL0/hv03Eb2F9Ij4YPQ+qbMyegtOofdwLJlvvaWf+8T+GJZG78Mp65qm+duHy63wo9H7bu4Lo/e1d0+IiKdG72siLqwt0r9Q+aWI+LtSysNdPP1N9PrNS/pjHymlXFJKOb+l7I+UUh5bSlkUvaccPtQ0zUz0LvKuKKV8T7/Xvi56+/zG6M3J9kTE6/uv8YyIuCp6N64jek99nVG7XXC0mIe97Bu99PLD/euqE6L39Zdfnovt7X/I5yXRe/pyS0Qsjt7Nk039+MviwR8S/EBE/HR/PrM8Ih76NehAYr71mVLKef3XHu+P40ci4rui9+cOBlJ6Ti6l/EpEvCJ6c6CDeWdE/Hgp5dL+v1t8wH2lc0sp393vWfuid+34wHb+SClldf/a64EnFB7Na0o4Iuk9B6X3zDMWmDpomua3ove1Lf8lehP4ddH7pNiHHyb9T6P3tSV3R8RX4sF/hDWid1FwZ+k9svjj0Vtciej9Iba/i4hd0VupflvTNG2LRz8avaeHBv3U6o7oHbzfit5B9paIuLppms9m/7BpmnXRWyH/pfj2frkmIhY0vb9f8proXdhsi95q+l8fpM4no/cHr68rpVwUvYuf2yLin/r76e8i4mCfHoaj2jzrP0+J3lOHz4qI7aXua10ekX5f+F/RuynzhegvEFX8uy39Mb4uepOr10fElU3TbD4g7c+j98meDzYP/k5jvYdjxjzrLRG9Y3VzfxwnRu+DNJ2VUk6OiO+JiLc2TbPxgP99IXpfXdXlKaZoet/r/d8i4lOllNMfEtsZvX74oug9ZbQxep+IG20p+WfRewJhY/S+Qus1/Vpfj97ftvy96O2Hq6L3t58mm95X9F0VEc/px94WES9tmuZr/Zp/HL3vGt9eSnm43x8cteZTL2uaZkdE/MeIeG30rn++FL0nin71kW9hRPTnXNFbTH5yRDyv6flKRPxWf0z3Rm9R/R8P+HfvjN7fwP1yRHwxeh/CmY7ezSOg0nzqM9F7GuFNEXFffyw/HREvbL7991QeiZP6PWZXRNwUvV7yjKZpPnGwf9A0zc0R8cqI+P3o9bvbIuLH+uHR6P3Nt83Rm++siW9/WPDyiLi1/3q/GxEvavxtW3hYes+/p/fMP2XwdQkAAABgviu9b5R4R9M0D/0qYQAA6MwTTAAAAHAU6n+NzXNL7yuDT46IX4mI/324xwUAwNHBE0wAAABwFOr/rbcbIuK86P0dgo9ExE/3v8oPAAAGYoEJAAAAAACATnxFHgAAAAAAAJ1YYAIAAAAAAKCT4bZgKcX358ERqmmacrjH8EgdSb2nlPbdPDQ0lNaYnp6eq+G0euxjH9sa/83f/M20xqWXXtoa/9znPpfW+OpXv5rm7NmzpzV+0UUXpTW++7u/uzX+i7/4i2mNt771rWnOoVDzPpqdnW2NH6qvxNV7gMNB7+GhsnNnNoeLmJs52sjISJozNTU18OtkhodbL/0j4tDNSY8mes+RY3x8fOAaNcfRzp07W+NnnXVWWuPee+9tje/atSutMTo6mubs27evNX766aenNbJrjG9961tpjbGxsTRnLuzdu/eQvM6hoPccW572tKelOatXr26Nr1mzJq2xfv361vjXv/71tMb27dvTnNNOO601fsYZZ6Q1sntGNX3y7rvvbo3fdtttaY1jzcF6jyeYAAAAAAAA6MQCEwAAAAAAAJ1YYAIAAAAAAKATC0wAAAAAAAB0YoEJAAAAAACATiwwAQAAAAAA0IkFJgAAAAAAADopTdMcPFjKwYPAvNY0TTncY3ik5kvvKSXfhW09dC5dddVVrfGf+ImfSGs8/elPb41v3LgxrfHMZz6zNX7++eenNV7ykpekOevXr2+N1/xuXve617XG9+zZk9bYtm1ba/yv/uqv0hp/8Ad/kObccsstaU4m2yeH6r2q9wCHg95zbFmwIP+c5NjYWGu8Zh7wuMc9rjX+1Kc+Na2xe/fuNGfx4sWt8ampqbTGH/3RH6U5maGhodb4zMzMwK9xtNF7jhzj4+MD1xgZGUlzduzY0Rp//vOfn9a4/PLLW+NvetOb0hqzs7NpzpIlS1rjb3nLW9Iar3rVq1rjW7ZsSWtkv5uanl+zvXv37k1zjhR6z9HlhS98YWt87dq1aY3snsLKlSvTGtn9nJNPPjmtcffdd6c5a9asaY1v2rQprfG1r32tNV4z/zr33HNb41/4whfSGn/3d3+X5hxNDtZ7PMEEAAAAAABAJxaYAAAAAAAA6MQCEwAAAAAAAJ1YYAIAAAAAAKATC0wAAAAAAAB0YoEJAAAAAACATiwwAQAAAAAA0ElpmubgwVIOHgTmtaZpyuEewyN1qHrPggXta+yzs7MDv8ZP/uRPpjlXXnllmrN27drW+K5du9Ia+/bta40//vGPT2vceuutrfFFixalNS666KI0Z8eOHa3xrVu3pjUmJiZa41/+8pfTGgsXLmyNL1++PK0xMzOT5mS/m+/5nu9Ja+zevTvNORT0HuBw0HuOLUNDQ2lOdv595jOfmda4+OKLW+PveMc70ho1c7TM1VdfneZk2/uHf/iHA48jmztHzM38+Uii9xw5xsfHB64xMjKS5uzcubM1/uQnPzmt8epXv3qg14iIOPHEE9Oce+65pzW+Z8+etMY111zTGm+75/iA7BpyamoqrVFKfiju3bs3zTlS6D1HjhUrVqQ5r3jFK1rjt912W1ojO05q7hdkOTXn+NHR0TRn//79aU5m8eLFrfGxsbG0xsqVK1vjJ510Ulrj937v99KcycnJNOdIcbDe4wkmAAAAAAAAOrHABAAAAAAAQCcWmAAAAAAAAOjEAhMAAAAAAACdWGACAAAAAACgEwtMAAAAAAAAdGKBCQAAAAAAgE4sMAEAAAAAANBJaZrm4MFSDh4E5rWmacrhHsMjdah6Tyntu6itPz7g5S9/eWv8Na95TVpjx44dac7k5GRrfGJiIq2xa9eu1vjs7Gxa49xzz22NL1myJK2xffv2NGdsbKw1vnnz5rTGxo0bW+MLFuSfsRgaGmqNL168OK2xb9++NOe4445rjY+MjKQ1zj777DTnUNB7gMNB7zm6ZOe9qamptMbTn/701vhTnvKUtMav//qvpzmZ4eHhNGd6enrg17n88stb4495zGPSGm9/+9sHHsexRu85coyPj6c5Ndd/mWzu/9M//dNpjbPOOqs1vmfPnrRGdq0bkfeemmu797znPa3xm2++Oa2RXVPVXKfW2Lt375zUmQ/0niPHBRdckOY885nPbI3X3EPJ7vdk8Yj8Hkl2f6SmRkTen2rmeVn/Wrp0aVpj0aJFrfHly5enNW644YY055ZbbklzjhQH6z2eYAIAAAAAAKATC0wAAAAAAAB0YoEJAAAAAACATiwwAQAAAAAA0IkFJgAAAAAAADqxwAQAAAAAAEAnFpgAAAAAAADoZPhwDwDgcGmaZuAaV199dWt8amoqrbFv3740Z+nSpa3xmZmZtMby5csHrvGVr3ylNV6zvcPD+akn+92MjY2lNZYsWTLQa0RELF68uDVesy01+3XLli2t8dHR0bTGxMREa3zXrl1pDXg0lFJa4zXH4lzUmAuveMUr0pzzzz+/Nf5zP/dzaY1DtT3Q1YIFg38+sebcOTk52Ro/9dRT0xpXXXVVa7zmWMzUbMv09HSak/W4mtf5+Mc/3hp/5StfmdZ4wQte0Bq/9tpr0xojIyNpTjZfzPZHxNz0yflybuHQqJmTZ+/f3bt3DzyO7/zO70xzNm3a1Bo/VMdIjcsvv7w1fvPNN6c1sp4wNDTUaUwwn6xatSrNyXpPzTGQ3TPYvn17WmN2drY1vnDhwrRGzb2a7Jjfu3dvWiObKx533HFpjex+T7Y/IiJOOOGENOeWW25Jc450nmACAAAAAACgEwtMAAAAAAAAdGKBCQAAAAAAgE4sMAEAAAAAANCJBSYAAAAAAAA6scAEAAAAAABAJxaYAAAAAAAA6MQCEwAAAAAAAJ0MH+4BAMxXCxbka/Dbt29vjTdNk9aYmJhIc/bu3dsaX7hwYVpj586drfGxsbG0xvLly1vj+/fvT2uMjIykOZma/To7O9san5mZSWvs2bOnNb579+60xooVK9KcufjdXHzxxa3xG264Ia0BXZVSjpjXqekbmUsuuSTNeexjH9sa/+QnP5nW+Jmf+ZnW+C233JLWqDEX+3UuamT9mvkj+13VnOMnJycHHscv//Ivpzm/8iu/MvDrDA0Ntcanp6cHfo2IvD/Nxeu8853vTHOuvvrq1vhTnvKUtMaNN96Y5mTvk5rtzXpPTc+fi/MCR46a/rRv376BXyc7h9dcY85Vb8lkx9HWrVvTGtm85znPeU5a42Mf+1hrvOZat2a/wuGwcuXKNCebX5166qkD19i8eXNaY/Xq1a3x4eF8GWEu5vVLly5Nc3bt2tUar9nv2X69++670xo1r3Ms0IEBAAAAAADoxAITAAAAAAAAnVhgAgAAAAAAoBMLTAAAAAAAAHRigQkAAAAAAIBOLDABAAAAAADQiQUmAAAAAAAAOhk+3AMAmK8uvPDCNGfFihWt8W3btqU1Nm3alOasXbu2NX7bbbelNX73d3+3Nf5rv/ZraY1SSmt8amoqrdE0TZozOzs70DgiIoaHBz/FZduzcOHCtEbNe2BsbKw1PjExkda45JJLWuM33HBDWgMeDdkxX3M8ZzVq+spcuOeee9Kc5z73ua3xmuP5X/7lX1rjNfusxlzst0O175kfhoaGWuM184Aab3vb21rj73znO9MaGzdubI3XzBOmp6fTnEOh5jhbsKD9s6PZ3Coi4u1vf3tr/C1veUta42tf+1qas3Xr1tZ4ti0R+fbMRQ2OLln/ioiYmZlpjT/+8Y9Pa1x11VWt8c9//vNpjUzNttS8v7PeUlNjw4YNrfEf+ZEfSWv8/d//fZqTmau5Ecy1pUuXpjnj4+Ot8bPPPjutcdddd7XG16xZk9bIcnbs2JHWqJkLjoyMtMYXLVqU1sjuxdVYtWpVa3zv3r1pjWxOE5H3p6PhesoTTAAAAAAAAHRigQkAAAAAAIBOLDABAAAAAADQiQUmAAAAAAAAOrHABAAAAAAAQCcWmAAAAAAAAOjEAhMAAAAAAACdWGACAAAAAACgk+HDPQCA+erCCy9Mc/bv398aHx8fT2ssXbo0zdm9e3drfGJiIq1x4403tsanpqbSGkuWLGmN79u3L60xPT2d5oyMjKQ5mZmZmdb47OxsWqOU0hpfsCD/nMbwcH6qHR0dbY3X7NfTTjstzYG51jTNvHidoaGhtEbWEyIiFi1a1Bo/++yz0xof/OAHBx7HxRdf3Br/6Ec/mtZ44xvfmObcfvvtrfHt27enNQ7Ve4BHX3bOi6h7/2be9ra3pTk33XRTa/zmm29Oa2Tn1mwOd6TJ5jU185FsjvaBD3wgrfHa1742zXnDG97QGq+Zo2X0Jh5qLvrXrbfemuasW7euNZ71poj82q9GzTGQzZ9q9lm2PZ/97GfTGpOTk63x7Bo0ou4aEw6HmnsG2TVGzf2Rj3zkI63xSy+9NK2xY8eO1njNcVYzn8z6U00PXL58eWv8r//6r9Mal19+eWu8Zj5yxx13pDnHwpzEE0wAAAAAAAB0YoEJAAAAAACATiwwAQAAAAAA0IkFJgAAAAAAADqxwAQAAAAAAEAnFpgAAAAAAADoxAITAAAAAAAAnVhgAgAAAAAAoJPhwz0AgPnq9NNPT3OGh9vb6O7du9Ma4+Pjac7U1FRrfOXKlWmNpmla4zt37kxrHH/88QPXGBoaSnNmZmbSnEEtWJB/xqKU8qiPIyJi3759rfGafXb22WfP1XDgkMp6U4256hlr165tja9bty6tceedd7bGs+M9IuLKK69MczLXXnttmjMxMdEar9neL3/5y63xFStWpDV++7d/uzX+6U9/Oq3B4GrOednx+oY3vCGtsWHDhjTn3e9+d5qT2b9//8A1jibT09NpTjY3uvnmm9MaP/ADP5DmvP3tb2+NX3311WmNhQsXtsYnJyfTGtl7fi7OT8wf2fVUxNzM/bdt29Yan52dHfg1ao7nmmNg0aJFrfGa/ZEdi9m8qMZc7DM4XLJjJCLiggsuaI3fdNNNaY0vfelLrfFnPetZaY0dO3a0xrP7X3OlpsctWbKkNX7HHXekNW688cbW+Pd+7/emNT73uc+lOdlYa+6jzXeeYAIAAAAAAKATC0wAAAAAAAB0U5bHawAAIABJREFUYoEJAAAAAACATiwwAQAAAAAA0IkFJgAAAAAAADqxwAQAAAAAAEAnFpgAAAAAAADoZPhwDwBgvjr99NPTnNnZ2dZ40zRpjeHhvBVPT0+3xhctWpTWuOiii1rjmzZtSmssW7asNT4yMpLWyPZZRMSCBY/+5x9KKfNiHBERQ0NDrfHs9x8RsWrVqrkaDhxxsmMoImJmZibNufjii1vjF154YVpj+/btA9cYHx9vje/bty+tUZOzd+/e1vjY2Fha44lPfOLANc4777zW+Kc//em0BrnsvFdzfs5cdtllac4VV1wx8OvMhZpzfNZb5mKfReTzxZr5ZE1OZi625xd+4RfSnGuuuWbg15mcnGyN18zz5mKfceSoOeZHR0db41NTU2mNDRs2tMbPPPPMtMbOnTtb49k4a2XHQM0+y67/aq4xMzVzODgcaq5Baq7TzzrrrNb461//+rRG1p9qxpqpud9T0yczNb0nu0eSXU9FRHzoQx9qjb/sZS9La6xZsybNyXp21vOPBJ5gAgAAAAAAoBMLTAAAAAAAAHRigQkAAAAAAIBOLDABAAAAAADQiQUmAAAAAAAAOrHABAAAAAAAQCcWmAAAAAAAAOjEAhMAAAAAAACdDB/uARxphoaGWuMzMzOP+mtERLzmNa9pjV900UVpjf3796c5n/jEJ1rjH/jAB9Iax5rv+I7vaI3/8R//cVrjiiuuaI1v3ry505h4ZE4//fQ0Z2pqqjW+e/futMYJJ5yQ5mS9Zdu2bWmNj3zkI63xD37wg2mNv/zLv2yN/+zP/mxa49Zbb01zlixZ0hpvmiatkeXU9Nq5UPM6o6OjrfHJycm0xsTERPWYYD4ppaQ52fFcM/+qOUZWrlzZGt+0aVNaI+v7p5xySlpjbGysNT49PZ3WqJnnZfttdnY2rZGdB2vGceONN6Y5DK7m3DmomnP87/3e76U5t99+e2v8j/7oj9Iau3btao3XvL9rcniwl770pWnOGWec0Rr/sR/7sbTGn/zJn7TGa97v2fnnUBwzHDqHar6RXcdk582IiFWrVrXG9+7dm9YYHs5v92U5O3fuTGtkfXLRokVpjUzN9dRc3IuDrhYvXpzmrF27Ns3JjsXPfOYzaY0TTzyxNV5z/ZCZq/PiXNyLyY75rBdHRHzzm99sjdf0r5rf76G693Q4eYIJAAAAAACATiwwAQAAAAAA0IkFJgAAAAAAADqxwAQAAAAAAEAnFpgAAAAAAADoxAITAAAAAAAAnVhgAgAAAAAAoJPhwz2AuVJKaY0vWJCvpc3MzMxJTuaVr3xla/y3fuu30hr33Xdfa3zTpk2dxnQwL3/5y1vjv/zLv5zW+Kmf+qnW+Gc+85lOY3qkzjnnnDTnVa96VWs82x8REcPD7YfV4sWL0xrXXntta/zpT396WoPBLVu2LM3Zu3fvwK+T9a+IiJGRkYHHsXHjxtb4ihUr0hrXX399a/yaa65Ja5x++ulpztatW1vj09PTaY3R0dHWeNM0aY0sZ3Z2Nq1RM9YlS5a0xvfs2ZPWyN4jMF/NxRztsssuS2u84AUvSHPuvvvu1vi6devSGtk84Atf+EJaIzv/nHzyyWmNiYmJNGf//v2t8Q0bNqQ1sj5Yc46ryeHIsHv37jTnwgsvTHPWrFnTGn/Ri16U1siO55tuuimtcf/997fGFy1alNZYuXJlmrNw4cLWeLY/IvJeWjPWbN5z/PHHpzW2bduW5mTzvJprri1btrTGr7vuurQGPFR2XqxxwQUXtMa/9KUvpTWy8/y9996b1ti3b1+as3z58tZ4zTVIdjw/8YlPTGtk94RqtiXro/BoqLmHMjQ0lObs3Llz4LGsXr26NV5zHGVq7m3UbO9cmJqaao3X3HvN1Oyz8fHxNGdsbGzgscx3nmACAAAAAACgEwtMAAAAAAAAdGKBCQAAAAAAgE4sMAEAAAAAANCJBSYAAAAAAAA6scAEAAAAAABAJxaYAAAAAAAA6MQCEwAAAAAAAJ0MtwVLKWmBLGdoaCitsXDhwtb41NRUWmNycrI1PjMzk9ao8bjHPa41/qd/+qdpjXvuuac1fv3116c1lixZ0hpfunRpWmN8fDzNuf3221vja9euTWtk27Njx460xtatW1vjo6OjaY2xsbE0J3uvZb+7iIjdu3e3xmvGeuKJJ6Y5PPoWLVqU5uzZs6c1nvW3mhoR+ft37969aY0s5/TTT09rnHvuua3xG2+8Ma1x2223pTlXXHFFazw7ziIiZmdnB4pH5Oe4mhojIyNzkjNojWXLlqU17r///oHHcayrmTtlmqaZg5EcGnOxvXMxR7v00kvTnPvuuy/NyXptzZzl8ssvb41//OMfT2u8+c1vbo2vXLkyrXH22WenOVlPP/XUU9MamZprgYmJiYFf51g3PNx6WRcREdPT04/6OLJrpYiI7du3pznZWNetW5fWWLCg/bOUT3nKU9IaWY+rub6o6XHZfGLx4sUD19i/f39aIxvrtm3b0hr79u1Lc7L5cc3rfN/3fV9r/LrrrktrHEnnWwaX9YSI/D1x2mmnpTWyc/SuXbvSGjXnzkxN78n2Sc0+y3pLzTXmXHA8czjU3FetOY42bdo08FjOPPPM1njNfafsfuVc3VvPjteaHpjNe2quhb75zW+2xmvuO9Xci6uZLx7pPMEEAAAAAABAJxaYAAAAAAAA6MQCEwAAAAAAAJ1YYAIAAAAAAKATC0wAAAAAAAB0YoEJAAAAAACATiwwAQAAAAAA0MlwW7BpmrRAljM7O5vWmJqaSnMGtXz58jTnhS98YZrz4he/uDW+Z8+etMZll13WGh8aGkpr3HXXXa3xmZmZtMbGjRvTnFJKa3zbtm1pjQUL2tcxFy1alNYYGRlpje/evTutcd9996U52ft1eLj1kImIiNWrV7fGx8fH0xqTk5Ot8VWrVqU1GFzNezN7z2TH0KGUva9qes9b3/rW1vj73//+tMab3vSmNOeKK65oje/duzetkR2vWW+KyH9/NefJmv06NjbWGt+/f//Ar1NzHrz//vvTHNrVvCeONdk+qTkvvvSlL22NL126NK1RcyyecsoprfG77747rXHzzTe3xmvOLaeeeurANb74xS+mOddff31rvGYen/XSE044Ia1x7rnntsY/+9nPpjWOdYdqvnH11Ve3xmvOrdl8JCI/h9fMhbNrzJrrpez8u2PHjrRGdh1TM5bp6em0RqbmPZL1lpqeUCP73dT065NOOmlOxsKxo+aYzzzpSU9Kc7J7QjXHc3Ys1hwjc3GdMhevUzNnOfPMM1vj3/zmN9MaNWOFuVbzvquZG915550DjyW7fqi5b5pdl9XMJWp6XFanZp9l27NmzZq0xujoaGt88+bNaY2aNY1joT95ggkAAAAAAIBOLDABAAAAAADQiQUmAAAAAAAAOrHABAAAAAAAQCcWmAAAAAAAAOjEAhMAAAAAAACdWGACAAAAAACgEwtMAAAAAAAAdDL8aL/A6OhomnPJJZe0xp/whCekNR772Me2xs8888y0xvj4eJqze/fu1vjIyEha47bbbmuNL1iQr/tlYz3ttNPSGscdd1yac99997XGZ2dn0xqLFi1qjdfs92y/llLSGjXbOzY21hqfmZlJa2Q5w8P5YZdtT837jNzSpUtb4zXv76ZpBh7H9PR0mpO9J6ampgausWfPnrTG1q1bW+Pr1q1La9QcR5s3b26NX3zxxWmNL3zhC63xmt9ddrzWbEtNj8vea/v3709rZH3hxBNPTGvcddddac7RrOZcMhfmom/MhZrtzXJq+uSyZcta46973evSGhs2bGiNr1+/Pq1x0kknpTkf/ehHW+Nr1qxJa2S/3xUrVqQ1tmzZ0hrfsWNHWqNme9euXdsarzkvZGPZuXNnWmP58uVpDu1qjsVMzfnq5S9/eWv81ltvTWvUzFkmJydb49u3b09rLFmypDVeM58eGhpqjdfM4Wqu7bJr5pr5RvY6Ne+RbL/X/O5qxppdc2XzwIj8HsKqVavSGjWvw9FjLvpkdt8pIr9nVDOOrD/NxT2FiLxv1NTI+kJNrz3nnHNa47fffntaYy5+v9BVNk+IqDteszlYTY3s3mvNPYW5mEvMRe+p2a/ZnKXm+mIu7t/WjLVm3x/pPMEEAAAAAABAJxaYAAAAAAAA6MQCEwAAAAAAAJ1YYAIAAAAAAKATC0wAAAAAAAB0YoEJAAAAAACATiwwAQAAAAAA0IkFJgAAAAAAADoZbgtedtllaYGf//mfb40vWrQorbF79+7W+PBw6zAjImLnzp2t8bvvvjutMTs7m+bUbE9m/fr1A7/G+Ph4a3zjxo1pjbPPPnvgnL1796Y1an5/menp6dZ4KSWtsXDhwjRncnJyoHHUqNln999/f2v8uOOOG3gcRCxfvrw1XvOe2bdvX2t8ZGQkrTE6OprmLF26tDW+Y8eOgV+n5ljds2dPa/xFL3pRWuPqq69Ocz71qU+1xt/znvekNV796le3xmt6fiY7f0Xk/bqmzlycBxcvXpzWONY1TTNwjQUL5sdnd2q2ZS5yVqxYkdb48R//8db45z//+bTGk570pNb417/+9bTG5s2b05zsvLBr1660RrbPxsbG0hqrV69ujd93331pjez8FJGfF2r6Rnaeq5n3fPOb30xzaDczMzNwjXe9610Dv07NfKTG8ccf3xqfmJhIa+zfv781PjQ0lNbI5go1Pb/mWJyammqN14w1m2/U1Mj2Wc15o+a6LJNdk0VEbNmypTX+gQ98IK3x3d/93dVj4shX877KnHXWWWlOdt6r6RvZ3L/m2qDmeM36Qk3fyOYBWX+LqNuvmbmYx0NXNfcUas6L3/jGN1rja9asSWtkx0DNvD7rkzXbUrNPspyaHpdtT839vBNPPLE1XrOWsHbt2jTnWOhP8+MuCAAAAAAAAEcMC0wAAAAAAAB0YoEJAAAAAACATiwwAQAAAAAA0IkFJgAAAAAAADqxwAQAAAAAAEAnFpgAAAAAAADoZLgt+KxnPSstsHz58tb4hg0b0hrj4+Ot8VJKWuO4445rjS9cuDCtMTs7m+ZkdUZGRtIaixYtSnMyixcvbo3PzMykNbZv357mbN68uTU+OTmZ1miapjU+NDSU1pgLe/bsSXNqfn+ZbHt2796d1hgebj0048UvfnGnMfHwli5d2hqv6RvZe2bBgnwdf8uWLWlOdixOTEykNbJjoGas+/fvb43X9J7Vq1enOevWrWuNf+pTn0prvP71r2+Nb9u2La1Rc/7JZD0wIt9vNb02e79m5w3qjoFsrlAzlzgUat672RwuImLJkiWt8TPOOCOtsWLFitZ4zXkx65M1/XrHjh1pTjZHq+m12Xugpvdk23P66aenNdavX5/mZP2ppn/NxXlhzZo1aQ6Dy+aXl1xySVrjs5/9bGu85lxTM9/Org/nYs5e8/7Ozgs1542aa51sLDXH0b59+1rjNXOJbHtGR0fTGlNTU2lO1idXrlyZ1rjjjjta4y95yUvSGpdeemlr/HOf+1xag6NLNu+puY656667WuM1PWEuelyNbL5YM46sf9X0npr5ZCabj8Cjoeba75xzzklz3vGOd7TGa651suM5m1tF5Ndl2VyyZhwR+X6rmaONjY21xmvGmvXjmuupE044Ic2pmccd6TzBBAAAAAAAQCcWmAAAAAAAAOjEAhMAAAAAAACdWGACAAAAAACgEwtMAAAAAAAAdGKBCQAAAAAAgE4sMAEAAAAAANCJBSYAAAAAAAA6GW4L7ty5My1w7rnntsZPOeWUtMauXbta49PT02mNZcuWtcYnJyfTGjU5mZmZmTRnx44drfGa7d2yZUtrfGRkJK0xPNz664+IiFLKQPGasSxZsiStkanZltWrVw/8OjX7dXx8vDV+1113pTXOPPPM1vi73/3utAa5k046qTVe8/4eGhpqja9duzatce2116Y5WY979rOfnda44447WuOLFi1Ka2Q9rqaP1vTJPXv2tMaz4ywi7y0125uNY+vWrWmNvXv3pjmZhQsXpjnZe3Hp0qUDj+No1zTNwDUmJibSnFWrVrXGa85pY2NjrfGa93fN+yrbnppz69/8zd+0xs8777y0xj333JPmZGqOgcWLF7fGa84LWU7NXGJqairNGXQcEXk/np2dTWtk76Nt27alNbJjYi7mcES8613vao3X9ISsT2bHUETevyLy9+bu3bvTGtlcoeb9vWBB++cxa67bamR9v+b8lI2l5tySzSVqamT7rKZOzX7N3iPf+MY30hqvfe1rW+MvetGL0hocOWrOi9n9rZr+lV0P1RxHo6OjrfHsWI2ou+bKjtea80LWS/fv35/WyM7zNXO47D4bHC419wA3bNjQGq+5tsuuMWpq3HvvvQO9RkRdn8zukdTMA7LXqZnnZWr6V02fPBZ4ggkAAAAAAIBOLDABAAAAAADQiQUmAAAAAAAAOrHABAAAAAAAQCcWmAAAAAAAAOjEAhMAAAAAAACdWGACAAAAAACgk+G24O/8zu+kBa677rrW+BOe8IS0xqWXXtoaP+GEE9IaExMTrfGlS5emNRYuXJjmDA0NtcZnZmYGrjE9PZ3WaJqmNb558+a0xuzs7MA52TgiIvbv398a3759e1pjLvZZjazO5ORkWmPXrl2t8R07dqQ1tmzZ0hp/3/vel9Z417veleYc65YvX94a37Nnz8CvMTo6muZ89atfTXNOOeWU1njWAyPy43lqaiqtkRkbG0tztm7dmub80A/9UGv8BS94QVrj/e9/f2t806ZNaY3Fixe3xp///OenNf7xH/8xzbnyyitb4xs3bkxrZP245r14rKs5p61YsaI1/rSnPS2tMRe9ZXi4dQpXpWYekNm7d2+ac+aZZ7bGlyxZktbI+lN2rEbUHQPZfKOmTy5Y0P75rZGRkbTGypUrW+M1PaFm7pvNW7M5TY3x8fE0J5sb3X///QOPg/ya6+67705rZMdaze+7pvdk12WLFi1Ka2Tz+uxYjciP17noxTVqzk9ZzlzM82r2WdZH5+p11q5d2xr//Oc/n9ZYvXp19Zg48tUcR9n7qkZ2rNUcI6WU1njNXGIu5nk1PS7rtTXjyOZop556alrj1ltvTXNq3gPQRc2xWJOTqblfnc3ra+ZOWf+qud9TIxtrzbGa9Y2aa+6sx910001pjfPOO2/g1zkaeIIJAAAAAACATiwwAQAAAAAA0IkFJgAAAAAAADqxwAQAAAAAAEAnFpgAAAAAAADoxAITAAAAAAAAnVhgAgAAAAAAoBMLTAAAAAAAAHQyPGiB2267baB4RMSHPvShQYcB0NnKlStb46WUtMb09HRrfHx8PK3xla98Jc1ZvHhxa3x4OG/nU1NTA9domibNyczMzAxcY9WqVWnO5z//+db4hz/84bTGD/7gD7bGly1bltb4h3/4h4FfZ2RkJK0xOTnZGl+yZElag9w555zTGq95b95xxx2t8dHR0bRG1p+GhobSGmNjY2nOwoULW+M7d+5Ma2QWLMg/77Ro0aLWeM0+q9ne2dnZ1njNfs1kx2pEvl9rzk9Zz4/Ie3rNfs1ep2Z7s9eZi/PG0e6pT31qmpP9vjdt2pTWyH5XW7duHbhGRH7OqukbWU7NcZSpqVFzDs9+NzXzr7noX1mNLB5Rt0+y7dm1a1daI+sL27dvT2uccsopaQ7HlvPOO681XtMnMzX9q2bOksnmcBF5f6rpG9n18Fz069NOOy2tccstt6Q5MNeWL1+e5jz/+c9Pc37/93+/NT4X1yATExNpTnZ+rrlnVCPrG1k8Ir8+3Lt3b1oju8921113pTWe9KQnpTk175MjnSeYAAAAAAAA6MQCEwAAAAAAAJ1YYAIAAAAAAKATC0wAAAAAAAB0YoEJAAAAAACATiwwAQAAAAAA0IkFJgAAAAAAADoZPtwDADhclixZ0hovpaQ1hofb2+j4+HhaY/PmzWnOiSee2BrftWtXWmN0dLQ1PjIyktZomqY1Pjs7m9YYGhpKc/bu3dsaz7YlIuL4449vja9atSqtcdlll7XGZ2Zm0hr33XdfmpPttwUL8s+DTE9Pt8Zr3ovHupNPPjnNufTSS1vju3fvTms85jGPaY1nfSUi/33XHIuTk5Npzr59+1rjNcdz1ltqtnfhwoWt8ampqbRGti0R+X6r6ZNZX6g5t2RqatT0p+z3t3jx4rRGtl+z311E3uPmYp8d7Wp+V/v37x/4dbIaNeeamuMom9fUbG/2/p6L91XWi2vGUaOmp2dztJq5U9ZLa8YxF/u1pm8sX768NV5zPq6Zg3NsyeaC2fsuImL9+vWt8Zr3d83xmqmZX2X9uGYc2XmhpudnfeOMM85Ia8DhcMMNN6Q52b2ciIiNGze2xp/3vOelNebiOiab19fcl6jJyeZGNefwbC54//33pzVOOOGE1vjf/u3fpjUe//jHpzk7d+5Mc450nmACAAAAAACgEwtMAAAAAAAAdGKBCQAAAAAAgE4sMAEAAAAAANCJBSYAAAAAAAA6scAEAAAAAABAJxaYAAAAAAAA6MQCEwAAAAAAAJ0MH+4BABwuS5cubY3v3r174NfYs2dPmnPPPfekOatWrWqNL1iQf15gZGRkoHhExMzMTGu8lJLWmJ6eTnMWL17cGt+2bVta49WvfnVr/Bd/8RfTGtl7YN++fWmNmvdAtk8WLlyY1sjGOjY2ltY41tUcA6tXr26NL1++PK0xOzvbGp+L3lOj5v2bqTmely1b1hqv2e+Tk5Ot8dHR0bTG0NBQmrNkyZLWeNaLa8Zy/PHHpzV27drVGt+5c2daY2pqKs3JenbN73f//v0DxSPy3813fMd3pDWOdffee2+ak/2+s3NvRMTmzZtb4ytXrkxr1JzTsvdN0zRpjez9W9N7hofbL5ezfl77OlmPq5nnZWqOxWy/1mxLze8mO/9kvTgi73Enn3xyWuPWW29Nczh61FynZPO8mrlTdrzWzEeyuUTWm+ZKNoeLyM8LNf0rm/uuWbMmrQHz1caNGweuUTN3yo61mmMxu98zV/OeTDYvmitzMb+quS47FniCCQAAAAAAgE4sMAEAAAAAANCJBSYAAAAAAAA6scAEAAAAAABAJxaYAAAAAAAA6MQCEwAAAAAAAJ1YYAIAAAAAAKATC0wAAAAAAAB0Mny4BwBwuCxbtmzgGgsWtK/TT09PpzX279+f5qxYsWKgccyV7HWGhobSGsPD+akne53Jycm0RrZft23bltZomqY1vmrVqrTG1q1b05xsexYuXJjWyMaavYeIuPPOO9OcN77xja3xJz/5yWmN7/qu72qNX3DBBWmNxYsXt8ZLKWmN7D0TkR+LMzMzaY3sWKx5f8/OzrbGa/p5TX/KXqemxq5du1rjt912W1rj+OOPb41n44yo26/bt29vjdf8fjM1Y12yZElrfHR0dOBxHO3WrVuX5mRzkprzc/a7qvl91xxH2e+85r05F++bqampgWvU9Npse2q2Jev7c9E3avZHTe+ZmJhojdfM87LXGR8fT2ts2LAhzeHoUXMsZvOJmvlVdrzW9NqRkZHWeE0frcnJ5nk125uNtaYnZDWWL1+e1oD5ai6uy/bt25fWyI6jmnlAzVgzNT2uJieT9a+anl+zT6jjCSYAAAAAAAA6scAEAAAAAABAJxaYAAAAAAAA6MQCEwAAAAAAAJ1YYAIAAAAAAKATC0wAAAAAAAB0YoEJAAAAAACAToYP9wAADpdFixa1xnft2jXwa4yOjg5cIyJi7dq1rfHdu3enNfbu3dsan5mZSWs0TZPmZEopA79OTY0sZ3p6Oq0xNDTUGl+yZElaY/HixWnO5OTkQOOIiNi/f39rfHjYKX8uZO/NG2+8Ma2R5dS8ZxYuXNgaf/zjH5/WOPfcc9Ocr3/9663xxzzmMWmN8847rzX+la98Ja2xdevW1vipp56a1rjzzjvTnC9+8Yut8Q0bNqQ15sJv/MZvtMaz339E3X49//zzW+M1ffLee+8duEY21ptvvjmtcazLjpGI/Hi95ZZb0hrZ+WhkZCStsWBB/hnH7JyVnTcj8rGOjY2lNbLXyeZWNTUi8rHOzs6mNcbHx1vjW7ZsSWvUzCczq1evTnN27tzZGl+/fn1aY+XKla3xiYmJtMY3vvGNNIejx4oVK9Kc4447rjW+adOmtEZ2DVJzDs96aU2Nmrl/VqfmHD4Xsh6XHe8wn83FPZR9+/alOdlxVDOXmAvZfYmanJp9VjO/ytTMSaljTwIAAAAAANCJBSYAAAAAAAA6scAEAAAAAABAJxaYAAAAAAAA6MQCEwAAAAAAAJ1YYAIAAAAAAKATC0wAAAAAAAB0YoEJAAAAAACAToYP9wAADpfR0dHW+IIF+Rr8yMhIa3zp0qWdxnQw9913X2t82bJlaY0lS5a0xmu2t2ma1ngpJa1R8zqDjmOuzM7OtsbHxsbSGjX7ZPfu3dVjOpjsvZi935k/at4PWc7111+f1qjJORQ1+Peuueaawz0EjkL//M//3BpftGhRWiM719Sc4/ft25fmTExMtMZrzq2Tk5Ot8YULF6Y1stepmY/MxXxyamoqrZHNWbJ9WvM6MzMzaY0dO3akOZs3b26N14x1xYoVrfFVq1alNWre8xw9svdMRMSWLVta4zXHYtYXsuM9Iu89NX1laGgozcn6Rs3rDA8PflsxG+tcvAYcyfbs2ZPmZL1l69ataY2sf9XMe7K+UqNmnjcX/Wsu7k3RY08CAAAAAADQiQUmAAAAAAAAOrHABAAAAAAAQCcWmAAAAAAAAOjEAhMAAAAAAACdWGACAAAAAACgEwtMAAAAAAAAdDJ8uAcAcLj8zu/8Tmv8ve99b1pjZGSkNX7PPfd0GtPBnH/++a3x8fHxtMbMzP/fvt0rNdVFYQDeh2CCIAIWyuAMNjhjZaMdw3AP3ASFrddjw13YWVhyAY4FzChqoyQwAflJjlfAXnsbZviNpPFBAAAEb0lEQVTi9zztu2flJDlnnZ2sZJTN27atOqa/NR6PwzVN00yUp5TSzMzkv6GIXrNutxvWODg4CNfs7+9n862trbDG6elpNn/37l1YA4B/18nJSTZ/9OhRWCO6h19cXIQ15ufnJ36caP+VUkrX19fZ/Pj4OKwR3edL9jTRXiKl+FhLXF1dZfO5ubmwRqfTyeYle8WoRkopra+vZ/OS82gwGGTzkue7s7OTzff29sIaTI/V1dVwzcLCQjaPrrOSNb1eL6zR7/ezeUlfKfksdH5+ns1LPmNGfbKkJ0TX63A4DGssLi6Ga6LPS1Cr5HuJ2/ie5devX+GaqH+V7DWi3lKy/yrpk5HZ2XhcEfXJkn1AyftHGf9gAgAAAAAAoIoBEwAAAAAAAFUMmAAAAAAAAKhiwAQAAAAAAEAVAyYAAAAAAACqGDABAAAAAABQxYAJAAAAAACAKgZMAAAAAAAAVJm96wMAuCvfv3/P5j9//gxrfPnyJZsfHBxUHdNN3rx5k813d3fDGk+fPs3mS0tLYY1ut5vN27YNazRNE66Zmcn//qGkxng8zuaj0SisET2fT58+hTUGg0G45uPHj9l8e3s7rDE/P5/Nj46OwhoATKeHDx+Ga9bX17N5p9MJaywuLmbzFy9ehDVK7r9nZ2fZPLrHp5TS1dVVNr9//35YY25uLpsPh8OJjyOllO7duzdRnlK8dyo5R6LX/fT0NKxR8nyjPUu0R08pPhcvLy/DGu/fvw/X8O9YWVkJ12xsbGTzkmsgul5LPse8evUqm5+fn4c1Svrky5cvs3nUA1OKr+eS1+zJkyfZvOS5lLyucNtKvv+I7s8pxed4yTW/vLyczfv9fljjwYMH2Ty696aU0vHxcbim1+tl89vYk5b0r+fPn4drKOMfTAAAAAAAAFQxYAIAAAAAAKCKARMAAAAAAABVDJgAAAAAAACoYsAEAAAAAABAFQMmAAAAAAAAqhgwAQAAAAAAUKVp2/bmsGluDoH/tLZtm7s+hr+l98D00nuAu6D3xDY2NrL5s2fPwhrD4TCbHx0dhTV+//4drsl9Rk0ppYWFhYlrRHmJ8XgcrpmZiX/T2TSTn77R41xcXIQ1ovcmev9TSuny8jJc0+12s/ny8nJYY21tLZv/+PEjrFGyJqL3TI9OpxOuGY1G2fzx48dhjeg6KumBm5ub2bzkGvn8+XO4ZnZ2NpufnJyENQaDQTbv9/thjZJeGinptdH7O030nulRco+P9iS9Xi+s8fbt22x+eHgY1oiu59evX4c1Sq6zr1+/ZvOS+3PUj0v2Ix8+fMjm3759C2v839zUe/yDCQAAAAAAgCoGTAAAAAAAAFQxYAIAAAAAAKCKARMAAAAAAABVDJgAAAAAAACoYsAEAAAAAABAFQMmAAAAAAAAqhgwAQAAAAAAUKVp2/aujwEAAAAAAIAp4h9MAAAAAAAAVDFgAgAAAAAAoIoBEwAAAAAAAFUMmAAAAAAAAKhiwAQAAAAAAEAVAyYAAAAAAACq/AFB07ivKSKG4gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 2160x1080 with 18 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,15))\n",
    "j = 1\n",
    "for i in np.random.randint(0,trainX.shape[0],18):\n",
    "  plt.subplot(3,6,j); j+=1\n",
    "  plt.imshow(trainX[i].reshape(28,28), cmap = 'gray')\n",
    "  plt.axis('off')\n",
    "  plt.title('Class {} - {}'.format(np.argmax(trainY[i]),class_labels[np.argmax(trainY[i])]))\n",
    "\n",
    "plt.savefig('Class_Item_Examples.png', dpi=400)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zm3BLJWKodFO"
   },
   "source": [
    "## Display 8 images of a specific class to see variation (Sneakers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 570
    },
    "id": "7Pu0dWvcmP-V",
    "outputId": "9abe7710-ecaa-4750-b35e-4dd871c59626"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABGoAAAIpCAYAAAD6q97+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzda4zdV3U//H1mxjNjz9jjy9ge27EdXxInzj0hCQmQhlsgCLUkhRAQhUSFiDulhYoIVa0KoqKUIhQVqkIl2tCWtkAKokQhIVUIUAiEXEjShOZqO76O7bE947Hndp4XT/k/Lx5mr/3vGew99ufzdn21zm/O+Z119lk+khvNZjMBAAAAcPy1He8LAAAAAOD/ZVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlejIFRuNhv+7G04AzWazcbyvoRVmEZwYzCKgBrN9FqVkHsGJYrp55Bc1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJTqO9wVwfDUajTDTbDZn5LHe9KY3ZetDQ0Nhj9tvv31GrgUAOLEcyzNN5MorrwwzF110Ubb+6U9/eoauJq+tLf5326mpqWNwJQD8kl/UAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUImO430BHF/NZnNG+lxxxRVh5i/+4i+y9V27doU9lixZEma+/OUvh5latLW1viudmpqagSthtmk0GmGmvb09zExMTLTco2SOHKv79PTTTw8zL3vZy7L1v/7rv56py2lZyYwwAzgRdHTkj6TRrEppZs40hw4dCjP/9E//FGZ++tOfhpl3vOMd2fqdd94Z9nj44YfDTKRkzs/EnCk5w+3du7flxwE4EfhFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACoRKPZbE5fbDSmL3LSeOMb3xhmvvKVr4SZH/7wh9l6d3d32GP9+vVh5p577snWX/e614U9SrS3t2frk5OTM/I4M6HZbDaO9zW0wiz69Wlry+/rp6amqriOlFJ629veNiOZ008/PVvfsGFD2GN0dDTM1PLcLly4MMwMDQ0dgysxi05WjUb8sufOo6W+9rWvhZlFixZl60899VTYo7+/P8x85jOfCTObN2/O1m+++eawxze+8Y0w8/73vz/MzIQ//uM/ztZf8IIXhD2++MUvhplt27Zl6/fff3/YY7bPopTMo/+t6Pye0rE7w/f09GTrl1xySdij5PvTqaeeGmY+//nPh5lIyayfCSWfF9G1lJw7j9V9MN088osaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFCJRrPZnL7YaExf5IRwyimnhJktW7aEmUcffbTla5k7d26YmZiYCDNLlizJ1r/97W+HPd72treFmZnQ398fZgYHB1t+nGaz2Wi5yXFkFv3/tbe3h5nJycmWH6e7uzvMLF26NMz09PRk61deeWXY45JLLgkz9957b5i54YYbsvW77ror7PGxj30szESvUe7z95empqbCTPS8/NVf/VXYY9u2bWHmmmuuCTMRs6gujUb8cpTcpzNh06ZN2frNN98c9jhw4ECY2bhxY7b+2c9+NuxR8n4588wzW+5T8vfceuutYSZ6Df/5n/857NHZ2RlmPvShD2Xrf/qnfxr2GBkZCTOvf/3rs/Vbbrkl7PGtb31rVs+ilE68eTSbnHfeeWGm5Fzz0pe+NFvv6uoKezz00ENhJpp7KaX01a9+NVv/yle+EvY4Ga1evTpb37p1a9hjurORX9QAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQiUaz2Zy+2GhMX+SE8O///u9h5txzzw0zR44cCTNtbfm94NjYWNhjzpw5YaajoyNbX7t2bdhj3759YeaNb3xjtj5v3rywxze+8Y0wc+2112brt912W9ij2Ww2wlDFZtMsiu7z0szExMRMXE6ot7c3W3/nO98Z9ij5e5YvX56tr1ixIuyxadOmMLNnz54w86UvfSlbf+aZZ8IeP/7xj8PMsRLNgNWrV4c9RkdHw8yb3/zmbH3r1q1hD7Mopfb29pm4lJQ7v6WUUqMRP9WTk5Mzci2R3/3d3w0zr3nNa7L1ZcuWhT0++tGPhpnBwcFs/Zxzzgl7lDxv4+PjYSY6r5R8DjzwwANh5iMf+Ui2fv3114c9Fi1aFGZ+9KMfZevvfe97wx4XXXRRmPne976XrY+MjIQ9tmzZMqtnUUpl8yiaAyWf31NTU2Emmkc1ec973hNmXvnKV2brfX19YY8dO3aEmeizt2TWnHnmmWGm5DtJdJb4+Mc/HvaYTS6//PIwUzKzos+Mks+U6c5GflEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACrRaDab0xcbjemLzAqf/vSns/X3vOc9YY/vf//7YWbFihVhZuHChdn62NhY2KOjo6PlzNTUVNhj8eLFYaa7uztbf+qpp8IeXV1dYWb//v3Z+rnnnhv2aDabjTBUsZJZ1Gjk/8TOzs7wcSYmJsLM5ORkmDkWVq1aFWZK7o1Xv/rV2fqOHTvCHpdddlmYiZ7/kvf/xo0bw0x0H5Rcy+233x72+MIXvhBmHn300Wx99erVYY8Pf/jDYebCCy/M1kvu65KZNzQ0lK2/6EUvCnucDLPoRHPGGWdk66ecckrY48/+7M/CzD/+4z9m63fccUfY4+KLLw4zw8PD2Xp7e3vYo+Szu6TP4cOHs/WS92XJzPv5z3+erR86dCjsUfI6Dw4OZuvr1q0Le6xduzbM3H///dn6XXfdFfaY7bMopZNzHm3evDlbf//73x/2GBgYCDNbt27N1ufPnx/26OnpCTOLFi3K1ku+Xz377LNh5vzzzw8z/f392fq//du/hT1+9KMfhZnoepcvXx72KJn10d/c19cX9ii5luj7XslzP9088osaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFCJRrPZnL7YaExfZFqNRiPM5J73UrfcckuYuf7667P13bt3hz0WLlwYZvbu3Rtm5s2bl63PmTMn7DE+Ph5moj4lz31JJnqdp6amWu6RUkr9/f3Z+vz588MezWYzfqCKnWiz6KUvfWmY6enpydZf85rXhD1K3lMHDhzI1pcsWRL26OzsDDOjo6PZem9vb9hjZGQkzLzoRS8KM/v27cvWBwYGwh7d3d1hJpqLJa9PyXMbzfGSOdPe3h5m+vr6svXTTjst7GEWpbRmzZows3jx4jATvR6LFi0Ke6xduzbMRPdpyftyx44dYebo0aMtXUdKZWeEZcuWZesl77m5c+eGmZLz1XnnnZetb9myZUYeZ8WKFdl6yft/eHg4zHR0dGTrBw8eDHtEr0+Jz372s2Fmts+ilMrmUfS+KXm+S2bJ6tWrs/Xzzz+/5R4ppdTWlv+NwapVq8Iejz76aJg588wzs/XoHJFSStu2bQszXV1d2XrJGSz6npBS2Zy4+OKLs/VTTjkl7FFyliuZ05GSM9jg4GC2PjQ0FPYo+Q4cvceiOZ9SStu2bfuV88gvagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKhEx/G+gJRSajR+5X8d/n+VaTabYY/o/zlPKf6/3UsepyQTueuuu8LMy172sjDzk5/8JFvv6uoKe0xMTISZkj5RZiaet5RSOnDgQLY+f/78sEfJPTk1NZWtz507N+wxMjISZnp7e7P1F7zgBWEPUjr33HPDzOLFi8PMVVddla0vW7Ys7LFhw4Yw097enq0/88wzYY+Se3Dv3r3Z+sqVK8MebW3xzn/nzp3Z+hlnnBH22LJlS5h54oknwsymTZuy9W3btoU9ovd/Sin19fW13COaZymVzatIybUsWLAgW1+yZEnL11G7kvf3G97whmx94cKFYY/Dhw8XX9N0jhw5Emb27dsXZoaHh7P1sbGxsMfAwECY2b59e7a+aNGisEfJLIpm3po1a8IeJe+56P2SUvz5Pjk5GfZYvXp1mInOcTt27Ah7RPMspfh6S16fgwcPhpmS1+hk8MIXvjDMvOc978nWBwcHwx7Rd6OU4s+Qktf++eefDzPRd4mS+6fkO8uzzz6brZd8pyw563V3d2frJddaci3r1q0LMw8//HBL9dLHmQnRmTKl+Bz99a9/PexR8h34E5/4RLZ+6qmnhj2m4xc1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASHa02aGvL73qmpqbCHs1mc0YykbGxsZZ7lLjxxhvDzKc+9alsfceOHWGP733ve2Fm5cqV2fr4+HjYI3qNU0ppwYIFYebo0aPZesnr09vbG2Y6OvK3dcm91N7eHmYmJiay9cHBwbDH6OhomFm/fn22vnbt2rDHbDd37tww88UvfrHlx4nu0ZTi+2vPnj1hj5/+9KdhZmBgIFufqffl5s2bs/WSWXTRRReFmYMHD2brQ0NDYY+zzz47zNxxxx1hJnLaaaeFmQMHDoSZw4cPt3wtJZ+hkTlz5rTcI6WUdu3ala2feuqpM/I4Nbv22mvDzMjISLa+c+fOsMdMvO4lM2ImzkWdnZ0t90gppb6+vmw9miEppbRs2bIws2TJkmx91apVYY9nn302zETnr5RSeu6557L1kjPa5ORkmGk0Gtl69JykVHb+ivT09ISZ6DO2JFNy758ISs4bX/7yl7P1knNCdB5JKaWurq5sveT9EM2AlOLzYMl92t/fH2aie6y7u7vlHinF1zs8PNxyj5RS2rt3b8t9Sr4blXymHDlyJFsvuSfXrFkTZi688MJs/Zprrgl73HfffWFm6dKl2XrJd4vpnByTDAAAAGAWsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEh2tNpiampqJ6zgmzj777DBz3XXXZeuXXXZZ2GNgYCDM/OxnP8vWx8fHwx4bN24MM9Hr09YW7+qOHDkSZiYnJ8NMe3t7tt7REd+Ohw4dCjPz5s3L1kue25GRkTDTbDZbqqcUPycppbR169Zs/Td/8zfDHrPdO9/5zjBz8cUXZ+tPP/102KPkPj5w4EC2vnjx4rBHV1dXmBkbG8vWS+7RkvfU8uXLW36c+++/P8w0Go1sveS9PTo6GmZK5u+yZcuy9b1794Y9St7fvb292XrJ/O3u7g4z0f20b9++sEfJ33PGGWdk60NDQ2GP2e7ZZ58NM+eff362vnTp0rBHyXO5f//+bL1knkWflynF92nJ51jJOSKaRc8991zYo+R5mzt3brb+i1/8IuzR09MTZkqe/6hPf39/2COarSmldPTo0ZYfZ3h4OMwMDg5m6yWvz+7du8PMY489lq3Ppu8mrSiZ7Q888EC2XnImLpkT0fsq+txNKaX169e3nJmYmAh7HDx4MMxEf/OiRYvCHiVzInpv9vX1hT1K7oM5c+aEmehviq41pZQOHz4cZqL3Z8mcKDnf3nPPPWEmEp3jUkrpwQcfzNZbORv5RQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqESj2WxOX2w0pi/+j7Vr12brf/mXfxleRO4afmnJkiXZen9/f9ijt7c3zBw9erSlekoptbXF+69du3Zl641GI+wxMDAQZqamprL1jo6OsMf4+HiYKRFdS4k5c+aEma6urpavo729PcyUPHeRkvspepz9+/eHPS688ML4hqrYunXrwiHxgQ98IFtfv359+DiLFy8OM9EcKXnvloiut+RxSt4vBw8ezNaXLl0a9jhw4ECY2bt3b7YezfiU4vd2SmWfJ9FMGx4eDnuMjo6GmZGRkZYfZ9++fWFm+/bt2frjjz8e9ijJRK/z/fffH/Y4cuTIrJ5FJeeiU089NVvfsGFD+DibN28OM8uWLcvWDx06FPYo+XyPzjQlPebNmxdmonk1f/78sEfJ53s0R8bGxsIeJZlotqaU0uHDh7P1PXv2tNwjpfic8Pzzz4c9SmZRyd98LKxYsSLMbN++fVbPopTK5tGqVauy9ZLP+JLP1WjelNwbJZ+J0fm85O/p7u4OM9HcKzlflXxPiM5yJeeemfqeduTIkWy9ZO6VXEuUKfkePTk5GWaie6Xk8yI6u6aU0saNG7P1uXPnhj3+8z//81feCH5RAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUazWZz+mKjMX3xf0T/N/iaNWvCi4j+//GUUrrkkkuy9bVr14Y9+vr6wkz095T8X+gLFiwIM4sXL87Wu7u7wx4l/4d8lOno6Ah7DAwMhJmZMDg4GGb6+/tbfpyS5y33vvilRuNX/pf3/8f4+HjY48CBA2Hm6NGj2fp3v/vdsMeNN96Yv9jKlcyimbBy5cows3r16my95N7p6ekJM9EMKJkRhw8fDjMjIyPZ+ujoaNhjzpw5YWbHjh0tP07J39PZ2Rlmoud/amoq7DE2NtZypqTHkSNHwkx0z82bNy/sEX0mpRTP35Ln7aGHHjrhZ1Fvb2+2PhOf3SnF86rkzLN8+fIws2TJkmy9ZJ6VzIinn346W29ri/9tcfv27WHm0KFD2fq+ffvCHkNDQ2HmZNTV1ZWtl5yhJyYmwkzUp+RzYNu2bbN6FqV07M5GJeeNaE6U9Ijun5TiObB3796wx/DwcMuPU3KflnwPiGZ9e3t72GMmvrOUXEvJDJ4JJe/fku+v0fWWfC6VnE2jeVTS4+DBg7/yBfKLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQiUaz2Zy2uGDBgumLhQ4dOtRqi2Oqo6MjW+/u7g57LF68OMxMTU1l6/39/WGPkmtZunRpyz1y98gvtbe3h5lGo5GtR899SimNj4+HmYmJiZZ7HD58OMwcOXIkWx8eHg577NixI8zs2rUrzESazWb+ya9co9FoeRaVWLhwYcs95syZMyOZ6L0ZzZCUUurr6wszR48ebflxxsbGwkxvb2+2vmDBgrBHybWUvHej92Y0q1Iqm4vRTCu51oGBgTATPbf79+8Pe0T3QUop7dmzJ1vfu3dv2ONkmEXR/VMyZ6LXNKWU9u3bl61Hn4Uplb3uJ5uS+Vzy+pScaaI5UjLzSq43OqOV3Cttba3/227JWbHkXDp37txs/dlnnw17HDp0aFbPopSO3dkI+PWa7mzkFzUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBKNZrM5bbGnp2f64v9YuHBhtt7R0RFeRHd3d5iZmJjI1oeGhsIew8PDYWZycjJb7+rqCnuMj4+3nGlvbw97RNfK8dXWFu9BS17n6P3R29sb9ti+fXsjDFWs0WiEswioX7PZnNWzqK2tLZxFuXPVTJo3b162XnJeKTl/RX1KznnRGa7kWkp6lJiammqpXnotjUZ8q0dngJJ7ac6cOWEm6lNynuzs7AwzJc9dpOSsvmfPnmy95O+Z7bMoJWcjOFFMN4/8ogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKNJrN5vTFRmP6YqHOzs4w09HREWYajV/534v/H4sWLQp7tLe3h5nI5ORkmCn5e3LPe+njlIietxJTU1NhJvp7SjIl11ryODPxN8/EtZQ8b21t8a40epwdO3aU9Gj9STmOZmIWAcefWQTUYLbPopTMIzhRTDeP/KIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFSi0Ww2py82GtMXgVmj2Ww2jvc1tMIsghODWQTUYLbPopTMIzhRTDeP/KIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqESj2Wwe72sAAAAAIPlFDQAAAEA1LGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFSiI1dsNBrNY3UhwK9Ps9lsHO9raIVZBCcGswiowWyfRSmZR3CimG4e+UUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACrRcbwvAAAAACDS1hb/1mRqaipbbzQaYY+STLPZbKme4xc1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVKLjeF8AAAAA8H/nuuuuCzOHDh0KM7t3787WV61aFfb45je/GWZmwtTUVMs9ms3mjGR+nfyiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUotFsNqcvNhrTF4FZo9lsNo73NbTCLIITg1kE1GC2z6KUzKOTwcDAQJj5yle+EmYmJyfDzP79+7P1rq6usMd///d/t5wZHh4OezzxxBNhZmxsLFvv6OgIeyxZsiTM3Hnnndn61NRU2GO6eeQXNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASljUAAAAAFTCogYAAACgEo1mszl9sdGYvgjMGs1ms3G8r6EVZhEnq7a2+N9TpqamWn6clStXhpmrr746W//bv/3bsIdZBNRgts+ilOqaR41G/unMfd881qJrTenYXe9b3vKWbH39+vVhj7PPPjvMlPzNy5cvb6meUtmZJcpMTk6GPSYmJsLM+Ph4tl7ynCxatCjM/Pmf/3m2fsstt4Q9pptHflEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJTqO9wWcrKL/u73ZbM7I43R2dmbrY2NjM/I4J6O2tvyec2pqquUeJUoeh5NTyf11ot0/8+fPDzOHDh06BlcyM47V67Ns2bIws3379mx9JuYZnEz6+vqy9csuu2xGHmffvn3Z+n333TcjjxNpb28PM5OTk8fgSphp0feamTIT349mosepp54aZj74wVsD+mIAABplSURBVA+Gme7u7mz96quvDnvcf//9Yeaxxx4LM4ODg9n6L37xi7DHwoULw0xHR379sHTp0rBHT09PmJk3b162fvTo0bBHyRksmtO33HJL2GM6TlUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIdx/sCZptGo3FM+jSbzRl5nLGxsWx90aJFYY8bbrghzFx++eVhZtOmTdn6xz72sbDHv/7rv4aZY2Vqaipbb2ubmT1o9Dgwndl073R0xB9HAwMDYeZd73pXmPn617+erd9///1hj5L392x6/pctWxZmbr/99mNwJfC/197eHmYmJydbfpze3t4w09nZGWYuueSSbH3dunVhj1tvvTXMXHbZZWHmWJiJ5546HavPu2P1/Sly9dVXh5l9+/aFmZ6enmz9nnvuCXuUnI3e/OY3h5nI6OhomBkaGmq5z+DgYNgj+n6bUkoTExNhJjI8PBxmfp3z1S9qAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlGs1mc/piozF98STV1hbvtqampo7BlZT50Ic+lK2/5S1vCXuceuqpYWbPnj1hpru7u+UeJdf72GOPhZlIo9FouUfuvXWsNZvN1v+g48gsqltHR0eYufTSS7P1m266Kezx+OOPh5lt27aFmSuuuCJbf8c73hH2mE1uu+22MPOjH/0ozHzyk59s+VrMopNTe3t7yz0mJydn4EpiAwMDYaavry/MRDMvpZR6enqy9SNHjoQ9duzYEWb+5E/+JFt/xSteEfYYHh4OMzPhxhtvDDPRefLzn/982GO2z6KUzKNfp3e9613Z+jXXXBP2ePDBB8NMNNdOO+20sMfq1avDzMjISJiJvuPOnz8/7DF37twws2TJkmx9YmIi7FEyj6LvcuPj42GPw4cPh5lzzz03W1+1alXYY8+ePb/yYv2iBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEp0HO8LmG2mpqaOyeN87nOfCzOvfe1rw8y8efOy9S1btoQ9fv7zn4eZT33qU2Hm4x//eLYeXWtKKX31q18NM1dddVW2vm3btrBHs9kMMzPhlltuCTPve9/7jsGVUJu2tvwe/VjNouuvvz7MnHPOOWHm4MGD2fpjjz0W9pg7d26YOfvss8PM4OBgtn7dddeFPUpm0Uy8RvPnzw8zf/AHf5Ct9/X1hT2eeeaZ4mvi16+9vf2YPM7k5OQJ9Tglz9uqVauy9Ze85CVhjwULFoSZFStWhJknn3wyW+/s7Ax7lMyIBx54IFv/rd/6rbDHP/zDP4SZyCWXXBJmli5dGmYGBgay9ZKZd7KIzhIljtV5o8TKlSuz9ZLvTyX32H333Zet9/T0hD1KZkBvb2+2XvJdb+fOnWHm/vvvDzONRiNbP3ToUNhjaGgozCxcuDBbHx4eDnvs2bMnzGzYsCFbL7mvS/6eTZs2tVTP8YsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFCJjlyx0Wi0/ADNZjPMtLW1vi8q6TExMdHy45Q8J+973/vCzCc/+cmWr+Wzn/1smGlvb8/W58yZE/b4vd/7vTBz4403hpn58+dn66Ojo2GP3t7eMPPwww9n65/61KfCHrfffnuY2bFjR7b+tre9Lezx3ve+N8zcfffd2fptt90W9qAuJfNqamqq5cfZvHlzmHnVq16VrXd2doY9vvnNb4aZrq6ubH3VqlVhj1NPPTXMbNiwIczs378/W7/00kvDHkuWLAkze/fuzdaXLVsW9njxi18cZvr7+7P1O+64I+wxd+7cMMOxMzk5ebwvYUaVfHZH7+8VK1aEPUrel9FMK5l5Tz75ZJg57bTTwkz0OkdnuJRSesMb3hBmovPK0qVLwx5vf/vbw8zu3buz9ZLZ+uCDD4aZxYsXZ+sbN24Me5wsZuIscaycccYZYeaP/uiPWn6ckZGRMDM+Pp6t33XXXWGPs846K8y8/OUvz9Yff/zxsEf0vksppSNHjoSZ7du3Z+vR97iUymZJdE+WnEcuuOCCMBMZGhoKMyXfk6PdQMlZfDp+UQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKtFoNpvTFxuN6YsnqJe+9KXZ+t/93d+FPVasWBFmoj6rV68OeyxatCjM/OxnP8vW3/nOd4Y9Pv/5z4eZkj733HNPtr548eKwx4IFC8LM/v37s/W1a9eGPebMmRNmIo8//niYOXLkSJg5//zzs/VXvepVYY8f/OAHjTBUsZNxFkXOPffcMLNhw4Yws2XLlmx9aGgo7DEwMBBmNm7cmK2X/D1PPPFEmBkdHQ0zv/M7v5OtX3TRRWGPqampMDM+Pp6tHzp0KOzx2GOPhZnvf//72frIyEjY47LLLgszN998c7a+ffv2sEez2TSLCsydO7flzOTkZNij5ByxcOHCbH1iYiLssXTp0jATKbmPDxw4EGb27NmTrb/1rW8Ne5TMmaeffjrMRK/hO97xjrBHyVlj/fr12fqaNWvCHjt37gwz0edFyT1ZMhfPPPPMbP0zn/lM2ONb3/rWrJ5FKZXNo82bN2frJef3ks+hRqP1p/O0004LMxdccEG2/vOf/zzs0dvbG2YeeuihbP3CCy8Me6xbty7MRM9bZ2dn2OPgwYMtP05KKfX09GTr8+bNC3uUnAc7Ojqy9dxu4pei81VKKbW3t7dUTymlwcHBMBPNz/e9731hjy984Qu/8gXyixoAAACASljUAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqkf+PzI+RtrZ4XxT9v+zLli0Le7ziFa8IM3/+53+erX/nO98Je9x9991hZvPmzdl6yf8h39XVFWauueaabH379u1hj3e9610tP05KKW3YsCFb37lzZ9hjfHw8zPT397f8OCMjI2Emeo0mJibCHj09PWFmz5492fonPvGJsAfHTkdHPFYXL14cZq677rps/eUvf3nY4+mnnw4zzz33XLZ+5MiRsMfzzz8fZvr6+sJM5Kabbgozp5xySpj5yU9+kq1/+9vfDnvs378/zDzyyCPZesnzNjg4GGZe/OIXZ+vr1q0Lezz11FNhpmSmkdKmTZuy9TVr1oQ9xsbGwszw8HC2fuDAgbBHZ2dnmOnu7s7Wzz777LBHdIZLKf6b9+3bF/aYM2dOmJmamsrWS96XW7duDTPR65NSSi95yUuy9ZLPiuuvvz7MjI6OZuuTk5Nhj+jcmlJ8Xim5J0vO8+3t7dl6yXN/svjABz6QrV944YVhj9NOOy3M7Nq1q6V6SmXnp4cffjhbL/mcKvkuEX1nXLp0adijZL5GM6vk+2DJ45R8Z9yxY0fL17Jt27Ywc/jw4Wy95PUpuVei6y2ZeyXfB6NzcsleYDp+UQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKtHRaoM//MM/zNY3bNgQ9jj//PNbvYy0cOHCMPPkk0+GmY9+9KPZ+lvf+tawR09PT5g5fPhwtj4+Ph72mJycbPlaPvzhD4c97r777jAzMDAQZo4ePZqt79q1K+yxbdu2MLNgwYJsveT16evrCzONRiPMREZHR8PM8PBwtv7000+HPX7jN36j+JpqNH/+/DBz1llnZeuDg4Nhj3nz5oWZ6F6fO3du2KMk8/DDD2frjzzySNij5D4+44wzsvWXv/zlYY+DBw+Gmehvjt63KaX07LPPhplvfetbYSYyMjISZkrup+heKZmbJff+nDlzsvVo9pZey2mnnZat7969O+wx25199tlh5rzzzsvWn3vuubDHxMREmOnt7Q0zkZLPoK1bt2brJfNsx44dYWbnzp3ZesmMKDlzrlq1Klt/7WtfG/Yoma0l55UtW7Zk6/fee2/YY82aNWEm+nwcGxsLe3R2doaZpUuXttzju9/9bpiJPpdK7uuTRXS2Lvm8iz5jUoq/y5V8Phw4cCDMTE1NZesl341KZlZ0L5ecF0u+J0TviWazGfYYGhoKM/v27Qsz3d3d2XrJ+7fk+Y9ew5L7raMjXmG0teV/jxJdR0rxfZBS/PyXfE+bjl/UAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKdOSKr3/968MGb3/727P1/fv3hz0OHz4cZkZHR7P1HTt2hD22bNkSZpYuXZqtP/nkk2GP6FpTSmnBggXZeltbvEM7cOBAmGlvb8/W58yZE/a49957w8zKlSvDzBVXXJGt//CHPwx7PPDAA2FmcnIyWx8bG2u5R0opDQ0NZesdHdm3V0oppYmJiTATXe/U1FTYY7a79tprw0x/f3/Lj7Nv374wE703R0ZGWr6OlFLasGFDtl7y3j169GiYiWb0Qw89FPbYvHlzmIlm9LZt21rukVLZ/D3rrLOy9YMHD4Y9BgYGwszq1auz9aeeeirssXv37jATfRaUPG+LFy8OMwsXLgwzJ7oXvvCFYSY6R3R2doY99u7dG2Z27tyZrUef/ymltGrVqjBzwQUXZOuPPvpo2GPXrl1h5vLLL8/We3t7wx6bNm0KM9HzUjKLSt6XV155ZZh505velK2XzPBnnnkmzERzseSeLJl50T35gx/8IOzx/PPPh5lo5g0PD4c9ThbLly/P1ufPnx/2aDabYSY6N69fv35GHufIkSPZeskZoKurK8yUnLEiJd8lou8BjUYj7LFixYowU/L3jI+Pt9yj5DWMlHwHLnmc6O/52c9+FvYo+X5b8r3yf8svagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJTpyxf/4j/8IGzz66KPZen9/f9hjYGAgzHR1dYWZyObNm8PMyMhItn7w4MGwx+LFi8PMxMRES/WUUpo3b16YmTNnTrZe8vfs3r07zDz00ENhZtmyZdn6Jz/5ybDHRz7ykTAT3bdHjx4Ne+zbty/MRH2WL18e9njmmWfCTHt7e7Z++PDhsMdsV/I8jY6OZutr164Ne6xbty7MTE1NZes9PT0t90gpnp0lPUpm68KFC7P173//+2GPH/zgB2GmoyP7cVP09xw6dCjMRPdBSil997vfzdZLZt6OHTvCzJEjR7L1aD6nlNLevXvDTPSZc/rpp4c92trif7cpubdPdM8991yYWbp0abZ+ySWXhD3mzp0bZqL3w/bt28MeO3fuDDNjY2PZ+mWXXRb2KDnD9fX1tVRPqex5W79+fbbe29sb9oiek5RSeuKJJ8LMgw8+mK2vWrUq7FGSic4RJXOz5LmNZtGFF14Y9iiZV9G9sGXLlrDHiaBkJl911VXZ+q5du8Iek5OTYabRaGTrQ0NDYY+Seyw6K5S8f6PzSErxZ2LJZ2aJ6HmL3rulmZLvPtFci840pZlo3pTM1xLd3d3Z+oIFC8IezWaz5cdphV/UAAAAAFTCogYAAACgEhY1AAAAAJWwqAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUInsfyS/d+/esME111yTrc+fPz/sccUVV4SZq666Klu/9NJLwx5dXV1hJvq/0OfOnRv2WL9+fZiZnJzM1gcHB8Mew8PDYaazs7Ol60gppSNHjoSZw4cPh5nHH388Wz/jjDPCHuecc06Yee1rX5utP/LII2GPktcwuhempqbCHosWLQoz0d/893//92GP2a6tLd4pHz16NFv/l3/5l7BHf39/mIles5JrnTNnTpjp6MiO5zRv3rywRzTPUkppdHQ0W9++fXvYo+Q+Hhsba+k6SjMlc76npydbbzQaYY+bbropzESvc3TPppTS6aefHmaGhoay9ZJ7/+GHHw4zy5Yty9Y3btwY9pjt7rzzzjBz3333ZevReSallPr6+sLMmjVrsvWSz8vLL788zPT29mbrBw4cCHscOnQozETv7+j+S6nseYvOPfv27Qt7LF68OMxs2rQpzJScnSIl57joepcsWRL2KJlXO3fubKmeUkr33ntvmHnuueey9ZL74ETw27/922Emeu9t3bo17FFyNormRMm5Z2JiIsxEZ6Poe09Jj5TiM3zJtZaIvmOVnHui81VKKTWbzTATvcfHx8dn5HGimVXSo+Q7VvQ6l9yTJZ9vJbuB/y2/qAEAAACohEUNAAAAQCUsagAAAAAqYVEDAAAAUAmLGgAAAIBKWNQAAAAAVMKiBgAAAKASFjUAAAAAlWg0m83pi43G9MXKdHR0hJn169eHmdWrV2frK1euDHssW7as5UzJ41x22WVhZvny5dn6yMhI2OPAgQNhZs6cOWFm3bp12frf/M3fhD2+9rWvhZnPfe5z2frzzz8f9pg3b16YOXToULZe8tzu3bs3zGzdujVbv/XWW8MeTzzxRCMMVaxkFnV3d2fr/f394eMsWLAgzHR2dmbr0QxJKaXFixeHmba2/B49qqdU9t7t6+vL1qO/N6WUJicnw8zRo0ez9ZK/Z2JiYkYy0d/8wx/+MOzxpS99KcxccMEF2fpDDz0U9jj77LPDzB133JGtf/nLXw57LF26NMycc8452fq3v/3tsMfXv/71E34WzYS5c+eGmd7e3pYfp+T+uvDCC7P1gYGBsEfJHInuweHh4bBHyfP25JNPZutjY2Nhj5L3yymnnBJmIkNDQ2GmZM5v2bIlW9+zZ0/YY9u2bWEmut6FCxeGPVasWBFmonu/5Fq/853vzOpZlFJK1157bTiPbrjhhmx9fHw8fJyS71iNRv7pzH3f/KWS7xLRaz9TZ4noeZmp80j0N5e8PlNTU2Gmvb09zESvc8lrWJKJHqfk7ymZ9dE9OTo6GvYouSejM/BVV10V9mg2m7/yYv2iBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUotFsNqcvNhrTF/+/TLae609r5s2bF2ampqay9SNHjszU5bTs/PPPDzPR35NSSv/1X/+VrUf3bEopjY2NhZnZdO83m834j65YySyqRU9PT5gZGRkJM/Pnz8/WTz/99LDHjh07wszcuXOz9aVLl4Y99u/fH2YmJiay9ejvLXXo0KEws3Dhwmy9ZEb89Kc/Lb6mX7err746W4/+3pRS6ujoCDN33nlntr5z586wh1lUl5L39549e7L1zs7OsMfKlSvDzPbt27P1jRs3hj0OHDgQZiYnJ7P13t7esMfo6GiYKXluI9Fzn1JKixYtOibXUnL+Wrx4ccuPMzQ0FGZ27dqVrT/22GNhj9k+i1JK6d3vfnc4jz74wQ9m69GZOaWy82x7e3u23tXVFfYoEd2HbW3xbxC6u7vDTDQnxsfHwx4l74fozFLy95Q8Tsn3mr1792brJee0kvk5ODiYrZd8ppTMo+ieKzm7lty30XfpV7/61WGP6eaRX9QAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQiUaz2Zy+2GhMXwRmjWaz2Tje19AKswhODGYRUIPZPotSSmnVqlXhPFq9enW2fs0114SP88pXvjLMdHR0ZOuHDx8OewwODoaZhQsXZuv9/f1hjyeeeCLMLFiwIFtfsWJF2OO2224LM5deemm2vn79+rDH7//+74eZs846K8y87nWvy9YfeeSRsMfHP/7xMHPrrbdm68uXLw97vPnNbw4z5513XrZ+ww03hD1K7tunnnoqW7/pppvCHtPNI7+oAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEpY1AAAAABUwqIGAAAAoBIWNQAAAACV6DjeFwAAAEC57du3t5z58Y9/PCOPs3v37mx9165dYY877rgjzLS15X9j8O53vzvs8fa3vz3MXHzxxdn63XffHfYo+XtuvvnmMBP55je/GWauvPLKMHPRRRdl6yX3wZNPPhlmoue2vb097NFsNsPMxMREtr5gwYKWe6SU0rp168LM/5Zf1AAAAABUwqIGAAAAoBIWNQAAAACVsKgBAAAAqIRFDQAAAEAlLGoAAAAAKmFRAwAAAFAJixoAAACASjSazeb0xUZj+iIwazSbzcbxvoZWmEVwYjCLgBrM9lmUUtk8ajTyf2bue+Cxdvrpp4eZsbGxbP3ZZ58Ne7zwhS8MM4sWLcrWb7/99rBHiRe84AXZ+tatW8Meu3btCjP9/f1hZmBgIFvfsWNH2GPv3r1hZvXq1dl6V1dX2OPJJ58MM5GlS5eGmZGRkTATvcdKekw3j/yiBgAAAKASFjUAAAAAlbCoAQAAAKiERQ0AAABAJSxqAAAAACphUQMAAABQCYsaAAAAgEo0ms3m9MVGY/oiMGs0m83G8b6GVphFcGIwi4AazPZZlJJ5BCeK6eaRX9QAAAAAVMKiBgAAAKASFjUAAAAAlbCoAQAAAPh/2rVjIgBAAAZi4F90kcEPiYLOf40QagAAAAAihBoAAACACKEGAAAAIEKoAQAAAIgQagAAAAAihBoAAACACKEGAAAAIEKoAQAAAIgQagAAAAAihBoAAACACKEGAAAAIEKoAQAAAIgQagAAAAAihBoAAACACKEGAAAAIEKoAQAAAIgQagAAAAAihBoAAACACKEGAAAAIEKoAQAAAIgQagAAAAAihBoAAACACKEGAAAAIEKoAQAAAIgQagAAAAAihBoAAACACKEGAAAAIEKoAQAAAIgQagAAAAAihBoAAACAiLvt9wYAAAAAjkcNAAAAQIZQAwAAABAh1AAAAABECDUAAAAAEUINAAAAQIRQAwAAABDxAF2EK38fJepkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "j = 1\n",
    "num_found=1\n",
    "\n",
    "while(num_found < 9):\n",
    "  i = np.random.randint(0,45000)\n",
    "  if(np.argmax(trainY[i]) == 7):\n",
    "    plt.subplot(2,4,num_found); j+=1\n",
    "    plt.imshow(trainX[i].reshape(28,28), cmap = 'gray')\n",
    "    plt.axis('off')\n",
    "    num_found+=1\n",
    "\n",
    "plt.savefig('Sneaker_Examples.png', dpi=400)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IHGt91Q6q_LA"
   },
   "source": [
    "# Part 3 - Baseline Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HzJwXBimXRGu"
   },
   "source": [
    "## Build Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "id": "MUhiVbNFrc1Q"
   },
   "outputs": [],
   "source": [
    "model = keras.Sequential()\n",
    "model.add(Conv2D(filters=16,kernel_size=3,activation='relu',input_shape=imgShape))\n",
    "model.add(Conv2D(filters=16,activation='relu',kernel_size=3))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Conv2D(filters=32,kernel_size=3,activation='relu'))\n",
    "model.add(Conv2D(32,activation='relu',kernel_size=3))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(numClasses, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Rl1t40PFXkZA"
   },
   "source": [
    "## Compile the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "id": "NBIQKQb1Xmqp"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
    "  loss='categorical_crossentropy',\n",
    "  metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "ysz7XgDDr5Ai",
    "outputId": "4ef44001-625f-42ec-b702-5d904430ed33"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiUAAAV5CAIAAAA75Om+AAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOzde1wTV9o48DMhIeEWLioXuUoQFUWx9UbUUmxXbXlFEVGsdl9vK7oqBbwgohQRUYsFPii0VVz2XbGCgIsuFe1HLfWlXmpXKAgtIqJ4R24mEhAI8/vj/DpvNkAyISQT4Pn+5ZyZPHMyAR5z5sx5CJIkEQAAAKBhLKY7AAAAYEiAfAMAAEAbIN8AAADQBsg3AAAAtIGtiaA3btxISEjQRGQAAACa5unpGRYW1u9hNfL95vHjxzk5OZqIDIAuyMnJefLkCdO90LibN2/evHmT6V4Abbt58+aNGzc0EVkj32+w7OxszQUHgEEEQYSGhi5dupTpjmhWQEAAgl/koQd/7poA928AAABoA+QbAAAA2gD5BgAAgDZAvgEAAKANkG8AAABoA+QbALTkwoULpqam//rXv5juSD/bsGED8YeVK1fK7rp8+XJERERubq6zszM+4NNPP5U9YO7cuSYmJnp6euPHj79z5452O/7/xcTEuLm58fl8Lpfr4uKyY8eON2/eyB7w7bffTp061cTExNHRcfXq1S9evGA8MtbV1ZWYmCgUCmUbz58/f+jQIalUSrXk5eVRH9Dw4cNVOkU/IzUgKytLQ5EB0AUIoaysLFVflZ+fz+fzz58/r4kuacKSJUuWLFmi9LCgoCALC4uCgoLKysq2tjaqPSoqasGCBSKRCG8KBIJhw4YhhPLz82VfXlBQsHDhwv7tuUq8vLxSUlIaGhpEIlFWVhaHw5k/fz61NzMzEyF06NCh5ubm4uJiZ2dnDw+Pjo4OZiOTJHnv3r2ZM2cihCZNmiS3KykpycvLq6mpCW92dXU9efLk2rVrH3/88bBhw5RGpvm59wHkGwBU1rd8ozUSicTT01P9OPTzja2trVzjgQMHXF1dW1tbqRaBQHDq1CkWi2Vra9vc3Ey1M55vfHx8Ojs7qU38WFVtbS3e9Pb2HjlyZFdXF948evQoQqioqIjZyCUlJYsXL87IyPDw8Oieb0iSDA4O9vT0lMten332GbP5BsbTABhsTpw4UVdXx2AH7t+/v2fPnr179/J4PNl2oVAYEhLy9OnTbdu2MdW37vLz8/X09KhNPOIkkUjw5uPHj21sbAiCwJv29vYIoUePHjEbedKkSbm5uStWrOByuT0eEB0dXVJSkpSURCea1kC+AUAbioqKHBwcCILA/41NTU01MjIyNDQ8d+7cRx99xOfz7ezsTp8+jQ9OTk7m8XiWlpYbNmywsbHh8XhCofDWrVt4b3BwsL6+vrW1Nd7ctGmTkZERQRD19fUIoZCQkK1bt1ZXVxME4eLighC6ePEin8/fv3+/1t5scnIySZK+vr7dd8XGxrq6uqalpV2+fLnH15IkmZCQMG7cOC6Xa25uvmjRot9//x3vUnzREEJSqTQqKsrBwcHAwGDixIl4oEVVT58+NTAwGDVqFN50dnaWTd74Fouzs7NORe7O3Nzcy8srKSmJ1KmKmpr40gTjaWBwQ30aT3v8+DFC6MiRI3gzMjISIXTlypXXr1/X1dXNnj3byMiovb0d7w0KCjIyMqqoqGhraysvL8d3lamhmBUrVlhZWVGR4+PjEUKvXr3Cm/7+/gKBgNqbn59vYmISExOjaof7PJ7m7Ozs5uYmd5hAIKipqSFJ8vr16ywWy8nJ6c2bN2S38bSoqCh9ff2TJ082NzeXlpa+8847w4cPf/HiBd6r+KJt27aNy+Xm5OQ0NTXt2rWLxWLdvn1bpbfc0tJiYmISHBxMtRQWFnI4nOTkZJFIdPfu3XHjxs2bN0+lmBqNPH369B7H00iSjIiIQAgVFxdTLTCeBsCQJhQK+Xz+iBEjAgMDW1paamtrqV1sNhv/N9/NzS01NVUsFqenp/fhFD4+PiKRaM+ePf3Xa0VaWlpqamoEAkFvB3h6eoaGhj58+HDnzp1yu1pbWxMSEhYvXrxy5UpTU1N3d/evv/66vr7+2LFjsof1eNHa2tpSU1P9/Pz8/f3NzMx2797N4XBUvWJxcXE2NjaxsbFUi5eXV3h4eHBwMJ/PnzBhglgsTktLUymmpiP3ZvTo0QihsrKyfoypJsg3AOgEfX19hFBHR0ePe6dMmWJoaEiNLOmyuro6kiQNDQ0VHBMbGztmzJiUlJSioiLZ9vLy8jdv3kyZMoVqmTp1qr6+PjWWKEf2olVWVkokkgkTJuBdBgYG1tbWKl2xs2fPnjlz5tKlSyYmJlRjZGTksWPHrly58ubNmwcPHgiFQk9PT/xVVRciK4A/gpcvX/ZXQPVBvgFgYOByua9evWK6F8q1tbUhhHq7j43xeLz09HSCINasWdPa2kq1Nzc3I4SMjY1lDzYzMxOLxUrP29LSghDavXs39azJo0ePqJvzSmVmZh48eLCwsNDJyYlqfP78+aFDh9avXz9nzhwjI6NRo0YdP3782bNneACT8ciKGRgYoD8+Dh0B+QaAAaCjo6O5udnOzo7pjiiH/8zJPm/YI1zRq6qqat++fVSjmZkZQkguu9B84yNGjEAIJSYmyt4woFnH5ciRIxkZGVevXh05cqRse1VVlVQqlW3k8/kWFhbl5eV0wmo0slLt7e3oj49DR2iw/g0AoL8UFhaSJDljxgy8yWazext5Y5ylpSVBEK9fv1Z65L59+/Lz84uLix0cHHDLhAkTjI2Nf/nlF+qYW7dutbe3v/vuu0qj2dvb83i8kpISlXpLkuTOnTubmpry8vLYbPm/hzjPPX/+nGoRi8WNjY147jJTkWnCH4GVlVV/BVQffL8BQEd1dXU1NTV1dnaWlpaGhIQ4ODisWrUK73JxcWlsbMzLy+vo6Hj16pXcQxsWFhbPnj17+PChWCzu6OgoKCjQ5nxoQ0NDZ2dnOvVP8aia7BMqPB5v69atZ8+ezcjIEIlEZWVlGzdutLGxCQoKohNt9erVp0+fTk1NFYlEUqn0yZMn+A96YGCglZVVj+vlVFRUfPHFF8ePH+dwOISMw4cPI4RGjRrl7e19/Pjxa9eutba2Pn78GPdk7dq1+OWMRKYJfwTu7u59jtDvIN8AoA1Hjx6dOnUqQig8PHzhwoWpqamJiYkIoYkTJz548OD48eNbt25FCM2fP7+qqgq/pK2tzd3d3cDAYPbs2a6urj/88AN1U+Svf/2rt7f38uXLx4wZs2/fPjxmQt1t3rhxo6WlpZub28cff9zY2Kj9N+vj41NeXk7dmPnnP//p4uJSXV09derULVu2yB45Y8aMsLAw2ZbPP/88Li4uJiZm+PDhXl5eTk5OhYWFRkZGCCGlFy0pKSk0NPTQoUPDhg2zsbEJCQlpampCCLW3t9fV1Z07d657V0mFj6cQBJGdnR0YGLh27Vpzc3M3N7fa2trc3NzZs2fjAxiJjBC6efPmrFmzRo4ceevWrV9//dXGxmbmzJnXrl2TPeb27du2trYTJ05U0A1t08Qka3j+BgxuSPPr2eBFyTR6CqX6/PxNVVUVm80+efKkxrqmGqlUOnv27BMnTgydyPX19Twe7/Dhw7KN8PwNAKBnSm+5647W1tZLly5VVVXhe9QuLi4xMTExMTFyyyEzQiqV5uXlicXiwMDAoRM5Ojraw8MjODgYIUSS5LNnz4qKiu7fv9+v3VQZ5BsAgLoaGxvnz5/v6uq6Zs0a3BIREREQEBAYGEhn4oBGFRYW5ubmFhQUKH4kaDBFTkhIKCkpuXDhAofDQQidO3fO1tZ29uzZ3333Xf/2U2Wa+NKks+Npe/fuHTdunImJib6+vkAg2L59u1gs7vHItWvX4ocAZFeDoKm1tXXMmDGRkZE0j//uu+90bZn6GzdujB07Fq8kaGlpuW/fPq2dOicnh1pgysrKasWKFVo7NX1Iw+NpERER+ElGJyen7OxszZ1IMfXHVS5duhQeHt5f/QF05OXlxcXFya5LrSrNjacNrfnQV69e3bx5c2BgIIfDKSgoWLlyZVlZWUFBQfcj09LSPvzww+XLl/fhLJGRkZWVlfSPJ3VqQT2EEEIzZsz47bff5s+ff+nSpcrKSvxUhHb4+/v7+/u7uLjU19erWn5q0IiLi4uLi2O6F/1g7ty5c+fOZboXQ8vChQsXLlzIdC96NrTG04yNjfFtWBMTk6VLl/r5+V28eLEfF5BACF2/fv3u3bsqvcTHx+f169cLFizox270qLW1Va4UoI7Q2Y4BAPrR0Mo3istRyKHqUtDX2tq6fft2Xas5QWG8LEpvdLZjAIB+xHC+OXny5JQpU3g8npGRkZOTE17ZguxrAYxx48YRBMFisd59912cRXbs2GFqasrj8f7+9793P7tcOQqSJOPj48eMGcPlck1NTbdv367q24mMjNy0aRNeV4OmgVIWRZsdo+N///d/3dzc8Ifr7u5+6dIlhNC6devwI3UCgaC4uBghtHr1akNDQ1NT0/Pnz6Ne6qN88cUXhoaGJiYmdXV1W7dutbW1VWk4FABAlyZuCtGcL4Af3Tpw4EBDQ0NjY+M333yDbw73uQBGZ2enk5OTg4OD7L2y0NBQuSWVsO7lKCIjIwmC+PLLL5uamiQSSUpKClJlvkBRUZGvry9JknhRRfrzBXS2LMq8efMQQlQVdK11jCRJgUBgamqq4KJlZ2dHR0c3NjY2NDTMmDGDeqrA399fT0/v6dOn1JGffPIJNRejt/oo+K199tlnR44cWbx48W+//abg1KTO15PuL5q7bwx02SB8/qajo2Pv3r3e3t47d+60sLAwNzdfu3bt1KlT1SmAoaen99lnn9XW1p49exYfJpFIcnNzqTmasuTKUbS2tiYmJn744YdhYWFmZmYGBgYWFhb0305ra2tISEhqamofL0c3OlsWRQsdo2PJkiWff/65ubm5hYWFr69vQ0MDTvMbN26USqXUeUUi0e3btz/++GNEoz7KwYMHN2/enJubO3bsWA11G4ChjLF8U1pa2tzcjP8HjeFsoU4BDITQunXrTE1NqTsoGRkZixYt4vP5cq/qXo7i/v37Eonkgw8+6Nvb2bVr1/r1621tbfv2cgV0tiyK7nQMP2SAn46cM2eOq6vr3/72N5IkEUKZmZmBgYH4pp369VFkLVu2jBjscnJycnJymO4F0LacnJz++c3shrH50CKRCP2x/LgsdQpg4BeuX78+Pj7+559/njZt2ldffdX92mVmZiYkJBQWFsouBo7XtlPp1gulqKiorKwsISGhD69Vn86WRdFox7777rv4+Pjy8nKRSCSb8wiC2LBhQ1hY2JUrVz788MN//OMfp06dwruo+ii7d++mjrexselbB0JCQjw9PdV4BwMAHvEODQ1luiNAq/DnrgmM5Rv8tx7fMZalTgEMLDg4OCkpKTExcePGjfb29nJ1bY8cOXLp0qWrV6/KpTQej4cQevv2rYrvAyGETpw4ceXKFRbrP74s7t+/f//+/bdv35b9rtbvdLYsiiY6du3atX//+9+hoaG1tbV+fn6LFy/+29/+NnLkyCNHjuzYsYM6bNWqVbt27UpLS7O3t+fz+Y6Ojridqo8SEhKifmc8PT2XLl2qfhxdlp2djRAa9G8TyMGfuyYwNp7m5ORkYWHx/fffy7WrUwADs7OzW7p0aU5Ozp49e2T/spAkGR4eXlZWlpeXJ5ds8HlZLNaPP/7Yh/eSnp4ue09Mdr6ARpMN0uGyKJro2L///W+8TnBZWVlHR8df//pXZ2dnHo9H/OfMdXNz82XLluXl5R0+fPgvf/kL1d63+igAgP7CWL7hcrm7du26du1acHDw06dPu7q6xGJxRUWFOgUwKFu3bu3s7GxqapozZw7VqLgcxYgRI/z9/XNyck6cOCESiUpLS+VmKOgOnS2L0l8d6x65o6Pj5cuX1Lr0uDzX5cuX29raqqqqut/b27hx49u3b/Pz82WfolVQHwUAoA2amPRGf/20o0ePuru783g8Ho83efLklJQUkiS7urri4+NHjx7N4XDMzc39/PwqKyvx8SkpKXgBu9GjR1dXVx87dgzPBXB0dLx3755sZG9v77S0NNmWsrKyHq9AfHw8PkAsFq9bt27YsGHGxsazZs2KiopCCNnZ2f36668qvX2V5kMfOXIEP5hiaGjo6+ur9A0GBQVxOBxbW1s2m83n8xctWlRdXU1Fa2ho8Pb25vF4o0aN2rJlC36EyMXFBc9LvnPnjqOjo4GBwaxZs168eHHhwgUTE5PY2Njuvbp58+b48ePxCKG1tfX+/fu11rGvvvpKbghU1tmzZ3HA8PBwCwsLMzOzgIAA/OiSQCCgpl+TJDl58uSIiAi59/X27dvw8HAHBwc2m43/h1FeXn7o0CFcP8be3p7mEvoI5kODwUtznztBamDxrjNnzixbtkwTkcGGDRuys7MbGhqY7og8XeuYj4/P0aNHqYd5+xdBEFlZWYP+xkZAQADS5Gg+0E2a+9yH1no2g4POlkVhvGPUWFxpaSn+LsVsfwAAsiDfKPf7778rmKuuuCCSOq8FqgoPD6+qqrp3797q1avx2khACzZs2ED9SK9cuVJ21+XLlyMiInJzc52dnfEBn376qewBc+fONTEx0dPTGz9+/J07d7Tb8f8vJibGzc2Nz+dzuVwXF5cdO3bIlYn79ttv8XoZjo6Oq1evpr9sueYiY11dXYmJiXJr3Z4/f/7QoUOy//nLy8ujPiC8aCRjNDFIp7P1bwY6HSmL0p2OdCwyMpLFYtnb22u6mBCC+zcy8JrrBQUFlZWVbW1tVHtUVNSCBQtEIhHeFAgEw4YNQwjl5+fLvrygoGDhwoX923OVeHl5paSkNDQ0iESirKwsDoczf/58am9mZiZC6NChQ83NzcXFxc7Ozh4eHh0dHcxGJkny3r17M2fORAhNmjRJbldSUpKXlxe1ElVXV9eTJ0+uXbv28ccfM1tPGvINACrTdL6RSCSenp6Mh6Kfb2xtbeUaDxw44Orq2traSrUIBIJTp06xWCxbW9vm5maqnfF84+PjI7viIr4tR8098fb2HjlyZFdXF97Ek1OKioqYjVxSUrJ48eKMjAwPD4/u+YYkyeDgYE9PT7ns9dlnnzGbb2A8DQCd048FGhip9XD//v09e/bs3bsXP0ZNEQqFISEhT58+3bZtm5a7pIDiMiWPHz+2sbGhnvGyt7dHCMlN6Nd+5EmTJuXm5q5YsYLL5fZ4QHR0dElJia7VRoF8A4BGkL2X1VCpQAODRSj6LDk5mSRJX1/f7rtiY2NdXV3T0tIuX77c42sVXDfFRTFQL/UmVCVXpsTZ2Vk2YeNbLM7OzjoVuTtzc3MvL6+kpCRSp+YJa+JLE4yngcEN0RhPU1xWQ6UCDdosQiGrz+Npzs7Obm5ucocJBIKamhqSJK9fv85isZycnN68eUN2G0/rczkSsvd6E/R1L1NSWFjI4XCSk5NFItHdu3fHjRs3b948lWJqNPL06dN7HE8jSTIiIgL9Z0UVGE8DYBCiWVaDPmaLUKikpaWlpqZGwUO7np6eoaGhDx8+3Llzp9wudcqRKK03QYdcmRKEkJeXV3h4eHBwMJ/PnzBhglgsTktLUymmpiP3ZvTo0Qih3p5zZwTkGwD6n6plNVTCYBEKOurq6kiSxKtR9CY2NnbMmDEpKSlFRUWy7eqUI1G/3kT3MiUIocjIyGPHjl25cuXNmzcPHjwQCoWenp64RqIuRFYAfwQvX77sr4Dqg3wDQP9Ts6yGUjpbhAIh1NbWhhDq7T42xuPx0tPTCYJYs2ZNa2sr1a7OdaPqTVDPmjx69Ii6Oa9UZmbmwYMHCwsLnZycqMbnz58fOnRo/fr1c+bMMTIyGjVq1PHjx589e4YHLRmPrBhepQl/HDoC8g0A/U/9shoK6GwRCgz/mVO62ISnp2dYWFhVVZXsk7nqXDeq3oTsDYMbN27Q6fORI0cyMjKuXr0qWxMLIVRVVSWVSmUb+Xy+hYVFeXk5nbAajaxUe3s7+uPj0BGM1b8BYBBTWlZDnQINOluEArO0tCQI4vXr10qP3LdvX35+fnFxMV7wG6lXjqRv9SZIkty5c2dTU1NeXh6bLf/3EOc52UXExWJxY2MjnrvMVGSa8EdgZWXVXwHVB99vAOh/SstqqFqgQWeLUHRnaGjo7OyMC+YqhkfVZJ9QUacciYJ6E4GBgVZWVj2ul6O4TMmoUaO8vb2PHz9+7dq11tbWx48f456sXbsWv5yRyDThj8Dd3b3PEfod5BsANOLzzz+Pi4uLiYkZPny4l5eXk5MTVb8HIfTXv/7V29t7+fLlY8aM2bdvHx70oG4Xb9y40dLS0s3N7eOPP25sbEQItbW1ubu7GxgYzJ4929XV9YcffqBukKgaSgt8fHzKy8upGzP//Oc/XVxcqqurp06dumXLFtkjZ8yYERYWJtui4LqlpqbiUscTJ0588ODB8ePHt27dihCaP39+VVUVQigpKSk0NPTQoUPDhg2zsbEJCQlpampCCLW3t9fV1Z07d657V0mFj6cQBJGdnR0YGLh27Vpzc3M3N7fa2trc3NzZs2fjAxiJjBC6efPmrFmzRo4ceevWrV9//dXGxmbmzJnXrl2TPeb27du2trYTJ05U0A1t08Qka3j+BgxuSLvrp+EFyrR2Okqfn7+pqqpis9k0iwlpgVQqnT179okTJ4ZO5Pr6eh6Pd/jwYdlGeP4GAKAc47UeFGttbb106VJVVRW+R+3i4hITExMTEyO3HDIjpFJpXl6eWCzu9+XYdTlydHS0h4dHcHAwQogkyWfPnhUVFd2/f79fu6kyyDcAAHU1NjbOnz/f1dV1zZo1uCUiIiIgICAwMJDOxAGNKiwszM3NLSgoUPxI0GCKnJCQUFJScuHCBQ6HgxA6d+6cra3t7Nmzv/vuu/7tp6qgvicAKtNmfc9du3Z9+eWX7e3tTk5O8fHxS5Ys0cJJMfXrPH7//fdXr149ePBg/3UKKHHu3LmKioodO3bITsRQiebqe8J8aAB0WlxcXFxcHNO96KO5c+fOnTuX6V4MLQsXLly4cCHTvegZjKcBAADQBsg3AAAAtAHyDQAAAG2AfAMAAEAbNDhf4MyZM5oLDgCzaC4EOaDhBVHgF3moefLkiaZWg9XEQ6R9K+MKAABAF2hofQGNPH8DwOCmzedvABg04P4NAAAAbYB8AwAAQBsg3wAAANAGyDcAAAC0AfINAAAAbYB8AwAAQBsg3wAAANAGyDcAAAC0AfINAAAAbYB8AwAAQBsg3wAAANAGyDcAAAC0AfINAAAAbYB8AwAAQBsg3wAAANAGyDcAAAC0AfINAAAAbYB8AwAAQBsg3wAAANAGyDcAAAC0AfINAAAAbYB8AwAAQBsg3wAAANAGyDcAAAC0AfINAAAAbYB8AwAAQBsg3wAAANAGyDcAAAC0AfINAAAAbYB8AwAAQBsg3wAAANAGyDcAAAC0AfINAAAAbSBIkmS6DwDouqCgoMrKSmrzzp07o0aNMjc3x5t6enr/8z//Y2dnx1DvABgY2Ex3AIABwMrK6tixY7ItpaWl1L+dnZ0h2QCgFIynAaDcJ5980tsufX39VatWabEvAAxUMJ4GAC0TJkyoqKjo8felsrLS1dVV+10CYGCB7zcA0PLnP/9ZT09PrpEgiEmTJkGyAYAOyDcA0LJ8+XKpVCrXqKen99///d+M9AeAAQfG0wCgSygU3rp1q6uri2ohCOLx48e2trYM9gqAgQK+3wBA16effkoQBLXJYrFmzZoFyQYAmiDfAEBXQECA7CZBEH/+85+Z6gwAAw7kGwDoGj58+AcffEDNGiAIws/Pj9kuATCAQL4BQAUrV67Etzz19PTmzZs3bNgwpnsEwIAB+QYAFSxevFhfXx8hRJLkypUrme4OAAMJ5BsAVGBkZPRf//VfCCF9ff0FCxYw3R0ABhLINwCoZsWKFQghPz8/IyMjpvsCwEAysJ+/CQgIyMnJYboXAACgJQP6L/aAXx96xowZoaGhTPcC6IobN24kJSVlZWVp9CwZGRmBgYFsNpO/PsuWLQsJCfH09GSwD0Cb8M82071Qy4D/foMQys7OZrojQFecOXNm2bJlmv6pbmtr4/F4Gj2FUgRBZGVlLV26lNluAK3Rzs+2RsH9GwBUxniyAWAggnwDAABAGyDfAAAA0AbINwAAALQB8g0AAABtgHwDALpw4YKpqem//vUvpjuiPZcvX46IiMjNzXV2diYIgiCITz/9VPaAuXPnmpiY6OnpjR8//s6dO4x0MiYmxs3Njc/nc7lcFxeXHTt2vHnzRvaAb7/9durUqSYmJo6OjqtXr37x4gXjkbGurq7ExEShUCjbeP78+UOHDnWv2jeEkAPZkiVLlixZwnQvgA7BT96o+qr8/Hw+n3/+/HlNdElDEEJZWVl9e21UVNSCBQtEIhHeFAgEeOHR/Px82cMKCgoWLlyobkfV4OXllZKS0tDQIBKJsrKyOBzO/Pnzqb2ZmZkIoUOHDjU3NxcXFzs7O3t4eHR0dDAbmSTJe/fuzZw5EyE0adIkuV1JSUleXl5NTU00Q8nq28+2ThnYvYd8A+To+O+kRCLx9PTsl1B9zjcHDhxwdXVtbW2lWgQCwalTp1gslq2tbXNzM9XOeL7x8fHp7OykNvHDRrW1tXjT29t75MiRXV1dePPo0aMIoaKiImYjl5SULF68OCMjw8PDo3u+IUkyODjY09OTfvai6PjPNh0wngaA9pw4caKuro7BDty/f3/Pnj179+6Ve4RIKBSGhIQ8ffp027ZtTPWtu/z8fKraEEJo+PDhCCGJRII3Hz9+bGNjQ1Vctbe3Rwg9evSI2ciTJk3Kzc1dsWIFl8vt8YDo6OiSkpKBvlJA30C+AUNdUVGRg4MDQRD4v7GpqalGRkaGhobnzp376KOP+Hy+nZ3d6dOn8cHJyck8Hs/S0nLDhg02NjY8Hk8oFN66dQvvDQ4O1tfXt7a2xpubNm0yMjIiCKK+vh4hFBISsnXr1urqaoIgXFxcEEIXL17k88Aq+OgAACAASURBVPn79+/X2ptNTk4mSdLX17f7rtjYWFdX17S0tMuXL/f4WpIkExISxo0bx+Vyzc3NFy1a9Pvvv+Ndii8aQkgqlUZFRTk4OBgYGEycOLFvCw49ffrUwMBg1KhReNPZ2Vk2eeNbLM7OzjoVuTtzc3MvL6+kpCRyIK8U0EcMf79SD4ynATl9G3N4/PgxQujIkSN4MzIyEiF05cqV169f19XVzZ4928jIqL29He8NCgoyMjKqqKhoa2srLy/Hd5WpoZgVK1ZYWVlRkePj4xFCr169wpv+/v4CgYDam5+fb2JiEhMT04d3ivo0nubs7Ozm5ibXKBAIampqSJK8fv06i8VycnJ68+YN2W08LSoqSl9f/+TJk83NzaWlpe+8887w4cNfvHiB9yq+aNu2beNyuTk5OU1NTbt27WKxWLdv31ap5y0tLSYmJsHBwVRLYWEhh8NJTk4WiUR3794dN27cvHnzVL0gmos8ffr0HsfTSJKMiIhACBUXF6sUEMbTABi0hEIhn88fMWJEYGBgS0tLbW0ttYvNZuP/5ru5uaWmporF4vT09D6cwsfHRyQS7dmzp/96rUhLS0tNTY1AIOjtAE9Pz9DQ0IcPH+7cuVNuV2tra0JCwuLFi1euXGlqauru7v7111/X19cfO3ZM9rAeL1pbW1tqaqqfn5+/v7+Zmdnu3bs5HI6qVywuLs7GxiY2NpZq8fLyCg8PDw4O5vP5EyZMEIvFaWlpKsXUdOTejB49GiFUVlbWjzEHBMg3ACiBC3p2dHT0uHfKlCmGhobUyJIuq6urI0nS0NBQwTGxsbFjxoxJSUkpKiqSbS8vL3/z5s2UKVOolqlTp+rr61NjiXJkL1plZaVEIpkwYQLeZWBgYG1trdIVO3v27JkzZy5dumRiYkI1RkZGHjt27MqVK2/evHnw4IFQKPT09MRfVXUhsgL4I3j58mV/BRwoIN8AoC4ul/vq1Sume6FcW1sbQqi3+9gYj8dLT08nCGLNmjWtra1Ue3NzM0LI2NhY9mAzMzOxWKz0vC0tLQih3bt3E3949OgRdXNeqczMzIMHDxYWFjo5OVGNz58/P3To0Pr16+fMmWNkZDRq1Kjjx48/e/YMD2AyHlkxAwMD9MfHMaRAvgFALR0dHc3NzXZ2dkx3RDn8Z07p84aenp5hYWFVVVX79u2jGs3MzBBCctmF5hsfMWIEQigxMVF2KP/GjRt0+nzkyJGMjIyrV6+OHDlStr2qqkoqlco28vl8CwuL8vJyOmE1Glmp9vZ29MfHMaQM+HprADCrsLCQJMkZM2bgTTab3dvIG+MsLS0Jgnj9+rXSI/ft25efn19cXOzg4IBbJkyYYGxs/Msvv1DH3Lp1q729/d1331Uazd7ensfjlZSUqNRbkiR37tzZ1NSUl5fXvbQdznPPnz+nWsRicWNjI567zFRkmvBHYGVl1V8BBwr4fgOAyrq6upqamjo7O0tLS0NCQhwcHFatWoV3ubi4NDY25uXldXR0vHr1Su6hDQsLi2fPnj18+FAsFnd0dBQUFGhzPrShoaGzs/OTJ0+UHolH1WSfUOHxeFu3bj179mxGRoZIJCorK9u4caONjU1QUBCdaKtXrz59+nRqaqpIJJJKpU+ePMF/0AMDA62srHpcL6eiouKLL744fvw4h8MhZBw+fBghNGrUKG9v7+PHj1+7dq21tfXx48e4J2vXrsUvZyQyTfgjcHd373OEgYqJSXH9BuZDAzl9mDN65MgR/MSMoaGhr69vSkoKvp07evTo6urqY8eO8fl8hJCjo+O9e/dIkgwKCuJwOLa2tmw2m8/nL1q0qLq6morW0NDg7e3N4/FGjRq1ZcuW7du3I4RcXFzwhOk7d+44OjoaGBjMmjXrxYsXFy5cMDExiY2N7cM7RX2aDx0cHMzhcCQSCd48e/Ysnq42fPjwzZs3yx28fft22fnQXV1d8fHxo0eP5nA45ubmfn5+lZWVeJfSi/b27dvw8HAHBwc2mz1ixAh/f//y8nKSJP38/BBCUVFR3bva2/St+Ph4fEB9fX1ISIiLiwuXyzU2Np45c+Y///lP6uWMRCZJ8saNGzNnzrSxscExra2thULhjz/+KHuMj4+Pra0ttX4BTYNgPvTA7j3kGyBHC7+TQUFBFhYWGj0FHX3LN1VVVWw2++TJk5roUh9IpdLZs2efOHFi6ESur6/n8XiHDx9W9YWDIN/AeBoAKhu4S/y6uLjExMTExMTILYfMCKlUmpeXJxaLAwMDh07k6OhoDw+P4ODg/u3YgAD5RiOUrnZOWbdunYmJCUEQqt5NRQi1tbWNHTt29+7dKr2qsrJyy5Yt48ePNzExYbPZpqamrq6uPj4+NOcLqUPBZZFdGB/T19e3tLR8//334+Pjm5qaNN23oSMiIiIgICAwMJDOxAGNKiwszM3NLSgoUPxI0GCKnJCQUFJScuHCBQ6H078dGxiY/oKlFp0dT1O82rkcvMyUqotbkCQZFhaGEIqMjKT/krS0NA6H89577128eLGpqamtra26ujozM1MoFH7zzTeqdkBVSi+LQCAwNTUlSRLfkP/hhx9WrVpFEISNjQ3N5U80PeYQERGBn2R0cnLKzs7W3ImUQmrUIyBJ8tKlS+Hh4f3YH6BUXl5eXFyc7LrUKhkE42kDu/c6m28Ur3Yup2/55qeffpo7d65K+ebGjRt6enpz5szpvhb6xYsXqdXDNEfpZaHyjazs7GwWi2VpaSm7VH5vBsHvJE1q5hsw4AyCn20YT9MIxaudy6GWPaevtbV1+/btqi5pHhsbK5VKDxw40P2Zg3nz5m3evFnVbqhKpctCWbJkyapVq+rq6r7++mvN9g8AoElDJd+cPHlyypQpPB7PyMjIyckJPzhN9nV99XHjxhEEwWKx3n33XfzncseOHaampjwe7+9//3v3s8utdk6SZHx8/JgxY7hcrqmpKZ4yq5LIyMhNmzbhx7ZlKVjfvr29/cqVK8OGDZs2bZri4ExdFgXw0y0FBQVKjwQA6C6Gv1+ph+Z4WmJiIkLowIEDDQ0NjY2N33zzzYoVK0g11lfv7Ox0cnJycHCQHR0KDQ2VW7ED677aeWRkJEEQX375ZVNTk0QiSUlJQaqMpxUVFfn6+pIkidfskh1PU7C+/b179xBCM2bMUBqfqctC9jKeRpKkSCRCCNnb2yvt/CAYc6AJwXjaEDMIfrYHdu/p5Jv29nYzMzNvb2+qpbOzMykpSSKRGBsbBwYGUu0///wzQoj6Y43/sFJld3FWuH//Pt7EOezMmTN4s6WlxcHB4fXr1907EBkZ6erqStWKl0gkhoaGf/rTn6gDVLp/I5FIpkyZ8uTJE7KnfKMAXonkww8/VBqfkcuC9ZZvSJIkCMLMzEzp2xwEv5M0Qb4ZagbBz/bgXz+ttLS0ubl53rx5VIuent5nn332yy+/9Hl9dYTQunXroqOjk5KSAgICEEIZGRmLFi3Cz1TLwqudf//999Rq5/fv35dIJB988EHf3s6uXbvWr19va2ur6gvxyr5Kb5aos+w8UuOyKNbS0kKSZPc4vTlz5gzNIwc0LUxhB7pjEHzcgz/f4KEYvLqtLHXWV8cvXL9+fXx8/M8//zxt2rSvvvoqJydH7pjMzMyEhITCwkLZtWbx0kndb73QUVRUVFZWlpCQ0IfXOjk58Xg8PKqmAFOXRTHc7bFjx9I8ftmyZTSPHNCSkpJUnTMCAIMG/3wB/EcNF5CXpc766hheiioxMfHatWv29vZyZRN7W+2cx+MhhN6+favi+0AIoRMnTly5coXFYuEnInHS2r9/P0EQsgv39ojL5c6bN6++vv6nn37qvrexsXHdunWIucui2MWLFxFCH330Ec3jmR420AYE42lDDB5PG9AGf75xcnKysLD4/vvv5drVWV8ds7OzW7p0aU5Ozp49e0JCQqh2kiTDw8PLysry8vLkvijg87JYrB9//LEP7yU9PV3250/2/o3sCFhvoqOjuVxuWFiYbB0t7O7du3iSNFOXRYEXL14kJiba2dmtWbOG/qsAALpm8OcbLpe7a9eua9euBQcHP336tKurSywWV1RUqLO+OmXr1q2dnZ1NTU1z5syhGhWvdo4Xx83JyTlx4oRIJCotLZWrAK8Oxevbe3h4nDp16u7du7Nnz75w4cLr1687OjpqamqOHz++du1avMAGU5eFQpLkmzdv8NK5r169ysrKmjlzpp6eXl5eHv37NwAAXaT9b4X9iP76AkePHnV3d+fxeDweb/LkySkpKaR666tTvL2909LSZFuUrnYuFovXrVs3bNgwY2PjWbNmRUVFIYTs7Ox+/fVXld5+9/lpdNa3r62t3bZtm7u7u7GxsZ6enpmZ2eTJk9euXfvTTz/hAxi5LOfPn584caKhoaG+vj6LxUII4Qlp06ZNi4mJaWhooHlNBsEcHpoQjKcNMYPgZ5sgSbL/k5i24ElQ2dnZTHcE6IozZ84sW7ZsQP9U00QQRFZWFl4TCAwFg+Bne/CPpwEAANAFkG90yO+//070rt8reQAAgDZBvtEhY8eOVTD0mZmZyXQHwUB1+fLliIgI2SJDn376qewBc+fONTEx0dPTGz9+/J07dxjppNKqUR0dHXFxcS4uLvr6+mZmZhMmTHj48CGzkbGurq7ExEShUNh9V2+Rz58/f+jQoYFbuK+PtHObSEN0th4BYMoguKdKE6I9XyAqKmrBggXU0kECgWDYsGEIofz8fNnDCgoKFi5c2P8dpU1peSQ/P78xY8bcvHmzo6Pj2bNnvr6+ZWVlzEYmSfLevXszZ85ECE2aNKn7XgWRk5KSvLy8mpqaaJ5oEPxsD+zeQ74BcrTwOymRSDw9PRkPRTPfHDhwwNXVlVrvjiRJgUBw6tQpFotla2srW1KI8XyjuDzS6dOnCYIoLS3VqcglJSWLFy/OyMjw8PDonm+URg4ODvb09Oxej6pHgyDfwHgaAKo5ceJEXV2droXq0f379/fs2bN37168qgVFKBSGhIQ8ffp027Ztmju7qhSXR/rqq6/eeecdd3d3nYo8adKk3NzcFStWcLnc7nuVRo6Oji4pKRk6ixJBvgFDEdl7jZ/g4GB9fX1ra2u8uWnTJiMjI4Ig8JJIISEhW7dura6uJgjCxcUlOTmZx+NZWlpu2LDBxsaGx+MJhUJqbVOVQiGF5Yv6Jjk5mSRJX1/f7rtiY2NdXV3T0tIuX76s6iVSXAYJISSVSqOiohwcHAwMDCZOnNi3hVhkyyO1t7ffvHnTw8OjD3G0GVkWncjm5uZeXl5JSUnkQJ7lrAJmv16pCcbTgByaYw6Ka/ysWLHCysqKOjg+Ph4h9OrVK7zp7+8vEAiovUFBQUZGRhUVFW1tbeXl5VOnTjUxMaHGalQKpaB8UXeIxnias7Ozm5ubXKNAIKipqSFJ8vr16ywWy8nJ6c2bN2S38bQ+l0EiSXLbtm1cLjcnJ6epqWnXrl0sFuv27dt03hRFrjxSTU0NQsjDw+P999+3trbmcrljx449evQoXodCFyJPnz5dbjyNZuSIiAhErxwJjKcBMPC0trYmJCQsXrx45cqVpqam7u7uX3/9dX19fZ8XFmKz2fh7gJubW2pqqlgsTk9P70McHx8fkUi0Z8+evnVDTktLS01Njdx6qbI8PT1DQ0MfPny4c+dOuV00L5FQKOTz+SNGjAgMDGxpaamtrUUItbW1paam+vn5+fv7m5mZ7d69m8PhqHpB4uLibGxsYmNj8SaeTjZixIj9+/eXl5e/fPly0aJFmzdv/vbbb1UKq9HIcmhGHj16NEKot9U3BhnIN2DIUbXGj0qmTJliaGhIDT0xqK6ujiRJvP5Qb2JjY8eMGZOSklJUVCTbrk4ZpMrKSolEMmHCBLzLwMDA2tpapQuCyyNdunSJKo+E746MHz9eKBRaWFiYmpru3bvX1NRU1f8iaC5ydzQj4w/o5cuXap5uQIB8A4YcNWv8KMXlcvHSdsxqa2tDf/zV6w2Px0tPTycIYs2aNbKrhqtziVpaWhBCu3fvph5VfvTokdJCf5TMzMyDBw8WFhY6OTlRjTY2Nug/q4ro6+s7OjpWV1fTDKvRyD2iGdnAwAD98WENepBvwJCjfo0fBTo6OvorlJrwHzKlTxR6enqGhYVVVVXt27ePalTnEuGyTImJibID9zRrU/ZWHsnY2Hj06NEVFRWyjZ2dnaampnTCajRyb2hGbm9vR398WIMe5Bsw5Cit8cNms6kK2aoqLCwkSXLGjBnqh1KTpaUlQRCvX79WeuS+ffvGjh1bXFxMtahTBsne3p7H45WUlKjUW1JZeaRly5YVFxc/ePAAb0okkkePHtGZxKy5yErRiYw/ICsrK/VPp/sg34AhR2mNHxcXl8bGxry8vI6OjlevXj169Ej25RYWFs+ePXv48KFYLMa5pKurq6mpqbOzs7S0NCQkxMHBYdWqVX0Ipbh8kaoMDQ2dnZ1x/XKlFyQ9PV32CRV1yiDxeLzVq1efPn06NTVVJBJJpdInT548f/4cIRQYGGhlZdXjejlKyyOFhYU5OjquWrWqtra2oaEhPDy8tbWVmunASGSlFEfG8AfUL+ltAGBiUly/gfnQQA7NOaMKavyQJNnQ0ODt7c3j8UaNGrVly5bt27cjhFxcXPAs5zt37jg6OhoYGMyaNevFixdBQUEcDsfW1pbNZvP5/EWLFlVXV/ctFJ3yRRREYz40ru0tkUjw5tmzZ/F0teHDh2/evFnu4O3bt8vOh1anDNLbt2/Dw8MdHBzYbDYuMFheXk6SpJ+fH0IoKiqqe1eVVo0iSfLx48fLly83NzfncrnTpk0rKCigdjESmSTJGzduzJw5E9+qQQhZW1sLhcIff/yRTmTMx8fH1taWzvTrQTAfemD3HvINkKP938mgoCALCwttnhGjk2+qqqrYbPbJkye10yWlpFLp7NmzT5w4AZGx+vp6Ho93+PBhOgcPgnwD42kAqEtnV/l1cXGJiYmJiYmRWw6ZEVKpNC8vTywW93tljYEYGYuOjvbw8AgODtZEcB0E+QaAwSwiIiIgICAwMJDOxAGNKiwszM3NLSgoUPxI0BCJjBBKSEgoKSm5cOECh8Pp9+C6CfINAH23a9eu9PT0169fjxo1Kicnh+nu9Gz//v3BwcEHDhxgthsffPDBqVOnqNXkhnjkc+fOvX37trCw0NzcvN+D6yw20x0AYACLi4uLi4tjuhfKzZ07d+7cuUz3AvyfhQsXLly4kOleaBt8vwEAAKANkG8AAABoA+QbAAAA2gD5BgAAgDYM+PkCN2/eDAgIYLoXQFfg1UGGyI9EYmJidnY2070AWkJnaSIdR5ADuY5pQkICzXVnAehHBQUFkydP1sQ0WQAUG9D/wxjY+QYARhAEkZWVtXTpUqY7AsBAAvdvAAAAaAPkGwAAANoA+QYAAIA2QL4BAACgDZBvAAAAaAPkGwAAANoA+QYAAIA2QL4BAACgDZBvAAAAaAPkGwAAANoA+QYAAIA2QL4BAACgDZBvAAAAaAPkGwAAANoA+QYAAIA2QL4BAACgDZBvAAAAaAPkGwAAANoA+QYAAIA2QL4BAACgDZBvAAAAaAPkGwAAANoA+QYAAIA2QL4BAACgDZBvAAAAaAPkGwAAANoA+QYAAIA2QL4BAACgDZBvAAAAaAPkGwAAANoA+QYAAIA2QL4BAACgDWymOwDAANDc3EySpGxLS0tLU1MTtWlsbMzhcLTeLwAGEkLutwgA0N2cOXN++OGH3vbq6ek9ffrUyspKm10CYMCB8TQAlFu+fDlBED3uYrFY7733HiQbAJSCfAOAckuWLGGzex58Jgjiz3/+s5b7A8BABPkGAOXMzc3nzp2rp6fXfReLxfLz89N+lwAYcCDfAEDLypUru7q65BrZbLaPj4+pqSkjXQJgYIF8AwAtvr6+XC5XrlEqla5cuZKR/gAw4EC+AYAWQ0NDPz8/uUnPBgYGH3/8MVNdAmBggXwDAF2ffPJJR0cHtcnhcJYsWWJgYMBglwAYQCDfAEDXvHnzZG/VdHR0fPLJJwz2B4CBBfINAHRxOJzAwEB9fX28aWZm9sEHHzDbJQAGEMg3AKhg+fLl7e3tCCEOh7Ny5creHsoBAHQH69kAoIKurq6RI0e+fPkSIVRUVDRz5kymewTAgAHfbwBQAYvF+vTTTxFCNjY2QqGQ6e4AMJAM7NGAGzduPH78mOlegKFl+PDhCKHp06dnZ2cz3Rcw5CxdupTpLvTdwB5PCwgIyMnJYboXAACgJQP6L/aAH09bsmQJCcAfsrKyEEKaPkt2dramT6EUQigrK4vpXgDtwT/bA9qAzzcAaN+SJUuY7gIAAw/kGwAAANoA+QYAAIA2QL4BAACgDZBvAAAAaAPkGwAAANoA+QYAdOHCBVNT03/9619Md0RTLl++HBERkZub6+zsTBAEQRB4lQTK3LlzTUxM9PT0xo8ff+fOHUY6GRMT4+bmxufzuVyui4vLjh073rx5I3tAR0dHXFyci4uLvr6+mZnZhAkTHj58yGxkrKurKzExscf1JnqLfP78+UOHDkmlUvpnGQQg3wAwsJ+hU+rzzz9PTk7etWuXv7//gwcPBALBsGHDMjIyvvvuO+qY77//Pjs7e8GCBeXl5e+88w4j/bx69ermzZsfPnxYX18fFxeXlJQUEBAge8CyZcv+8Y9/nDp1SiKR/PbbbwKBQC5taD8yQqiqquq9994LCwuTSCTd9/YW2dfXl8fjffDBB83NzTRPNBgw/QyTWpYsWQLPewJZ2nnes88kEomnp2e/hEL0nvc8cOCAq6tra2sr1SIQCE6dOsVisWxtbZubm6n2goKChQsX9kvf+sbHx6ezs5PaxAu31NbW4s3Tp08TBFFaWqpTkUtKShYvXpyRkeHh4TFp0iS5vUojBwcHe3p6dnR00DmXjv9s0wHfbwDQnhMnTtTV1WntdPfv39+zZ8/evXt5PJ5su1AoDAkJefr06bZt27TWGaXy8/P19PSoTbxOHfWl4auvvnrnnXfc3d11KvKkSZNyc3NXrFjB5XK771UaOTo6uqSkJCkpqQ+nHogg34ChrqioyMHBgSCIo0ePIoRSU1ONjIwMDQ3PnTv30Ucf8fl8Ozu706dP44OTk5N5PJ6lpeWGDRtsbGx4PJ5QKLx16xbeGxwcrK+vb21tjTc3bdpkZGREEER9fT1CKCQkZOvWrdXV1QRBuLi4IIQuXrzI5/P379+vobeWnJxMkqSvr2/3XbGxsa6urmlpaZcvX+7xtSRJJiQkjBs3jsvlmpubL1q06Pfff8e7FF8ihJBUKo2KinJwcDAwMJg4cWLfFmJ5+vSpgYHBqFGjEELt7e03b9708PDoQxxtRpZFJ7K5ubmXl1dSUhI5qEd0/w/D36/UA+NpQE7fxhzwKuNHjhzBm5GRkQihK1euvH79uq6ubvbs2UZGRu3t7XhvUFCQkZFRRUVFW1tbeXn51KlTTUxMqMGZFStWWFlZUZHj4+MRQq9evcKb/v7+AoGA2pufn29iYhITE9OHd4pojKc5Ozu7ubnJNQoEgpqaGpIkr1+/zmKxnJyc3rx5Q3YbT4uKitLX1z958mRzc3Npaek777wzfPjwFy9e4L2KL9G2bdu4XG5OTk5TU9OuXbtYLNbt27dVenctLS0mJibBwcF4s6amBiHk4eHx/vvvW1tbc7ncsWPHHj16tKurS6Wwmos8ffp0ufE0mpEjIiIQQsXFxUpPAeNpAAxaQqGQz+ePGDEiMDCwpaWltraW2sVms/F//N3c3FJTU8VicXp6eh9O4ePjIxKJ9uzZ03+9/j8tLS01NTUCgaC3Azw9PUNDQx8+fLhz5065Xa2trQkJCYsXL165cqWpqam7u/vXX39dX19/7Ngx2cN6vERtbW2pqal+fn7+/v5mZma7d+/mcDiqXp+4uDgbG5vY2Fi8ie+xjxgxYv/+/eXl5S9fvly0aNHmzZu//fZblcJqNLIcmpFHjx6NECorK1PzdAMC5BsAlNDX10cIdXR09Lh3ypQphoaG1FiT7qirqyNJ0tDQUMExsbGxY8aMSUlJKSoqkm0vLy9/8+bNlClTqJapU6fq6+tTI4dyZC9RZWWlRCKZMGEC3mVgYGBtba3S9Tl79uyZM2cuXbpkYmKCW/DdkfHjxwuFQgsLC1NT071795qamsrlPwYjd0czMv6AcMXYQQ/yDQDq4nK5r169YroX8tra2tAff/V6w+Px0tPTCYJYs2ZNa2sr1Y4n6RobG8sebGZmJhaLlZ63paUFIbR7927iD48ePepxrnCPMjMzDx48WFhY6OTkRDXa2NgghPBtMExfX9/R0bG6uppmWI1G7hHNyAYGBuiPD2vQg3wDgFo6Ojqam5vt7OyY7og8/IdM6ROFnp6eYWFhVVVV+/btoxrNzMwQQnLZhebbHDFiBEIoMTFRduD+xo0bdPp85MiRjIyMq1evjhw5Urbd2Nh49OjRFRUVso2dnZ2mpqZ0wmo0cm9oRm5vb0d/fFiDHuQbANRSWFhIkuSMGTPwJpvN7m3kTcssLS0Jgnj9+rXSI/ft2zd27Nji4mKqZcKECcbGxr/88gvVcuvWrfb29nfffVdpNHt7ex6PV1JSolJvSZIMDw8vKyvLy8uT+16FLVu2rLi4+MGDB3hTIpE8evSIziRmzUVWik5k/AFZWVmpfzrdB/kGAJV1dXU1NTV1dnaWlpaGhIQ4ODisWrUK73JxcWlsbMzLy+vo6Hj16tWjR49kX2hhYfHs2bOHDx+KxeKOjo6CggLNzYc2NDR0dnZ+8uSJ0iPxqJrsEyo8Hm/r1q1nz57NyMgQiURlZWUbN260sbEJCgqiE2316tWnT59OTU0ViURSqfTJkyfPnz9HCAUGBlpZWfW4Xk5FRcUXX3xx/PhxDodDyDh8+DA+ICwszNHRcdWqVbW1tQ0NDeHh4a2tFbhLpgAAIABJREFUrdRMB0YiK6U4MoY/oH5JbwMAE5Pi+g3MhwZy+jBn9MiRI/iJGUNDQ19f35SUFHwLd/To0dXV1ceOHePz+QghR0fHe/fukSQZFBTE4XBsbW3ZbDafz1+0aFF1dTUVraGhwdvbm8fjjRo1asuWLdu3b0cIubi44AnTd+7ccXR0NDAwmDVr1osXLy5cuGBiYhIbG9uHd4pozIcODg7mcDgSiQRvnj17Fk9XGz58+ObNm+UO3r59u+x86K6urvj4+NGjR3M4HHNzcz8/v8rKSrxL6SV6+/ZteHi4g4MDm80eMWKEv79/eXk5SZJ+fn4IoaioqO5d7W2CVnx8PHXM48ePly9fbm5uzuVyp02bVlBQQO1iJDJJkjdu3Jg5cya+VYMQsra2FgqFP/74I53ImI+Pj62tLZ3p14NgPvTA7j3kGyBHC7+TQUFBFhYWGj0FHXTyTVVVFZvNPnnypHa6pJRUKp09e/aJEycgMlZfX8/j8Q4fPkzn4EGQb2A8DQCVDZRlfV1cXGJiYmJiYuivPqk5Uqk0Ly9PLBYHBgZCZCw6OtrDwyM4OFgTwXUQ5BuNULr+OWXdunUmJiYEQdC8vxobG0v8J+pBB5oqKyu3bNkyfvx4ExMTNpttamrq6urq4+NDcwaROhRcFtml8jF9fX1LS8v3338/Pj6+qalJ030brCIiIgICAgIDA+lMHNCowsLC3NzcgoICxY8EDZHICKGEhISSkpILFy5wOJx+D66jmP6CpRadHU/z8vJKSUlpaGgQiURZWVkcDmf+/Pm9HYwXnqKzoAVJkrKTVrHx48fT71haWhqHw3nvvfcuXrzY1NTU1tZWXV2dmZkpFAq/+eYb+nH6RullEQgEpqamJEniG/I//PDDqlWrCIKwsbGhuSCKpsccIiIi8LONTk5O2dnZmjuRUoje+tDYpUuXwsPDNdofoJK8vLy4uDjZVauVGgTjaQO79zqbbxSvfy5H1XzT5+H4Gzdu6OnpzZkzp/v65xcvXqRWD9McpZeFyjeysrOzWSyWpaWl7OL5vRkEv5M0qZRvwCAwCH62YTxNIxSvfy6HIAjt9Co2NlYqlR44cIDNZsvtmjdv3ubNmzXdAZUuC2XJkiWrVq2qq6v7+uuvNds/AIAmDZV8c/LkySlTpvB4PCMjIycnJzwqRfZ1xfVx48YRBMFisd59913853LHjh2mpqY8Hu/vf/9797PLrn+OzxsfHz9mzBgul2tqaoqnzPYLBevbt7e3X7lyZdiwYdOmTVMchKnLogB+uqWgoEDpkQAA3cXw9yv10BxPS0xMRAgdOHCgoaGhsbHxm2++WbFiBanGiuudnZ1OTk4ODg6yo0OhoaFya3hgcuuf48gEQXz55ZdNTU0SiSQlJQWpMp5mZ2dnZmbG4XCcnJwWLlz4888/U3sVrG9/7949hNCMGTOUnoKpy0L2Mp5GkqRIJEII2dvbK+38IBhzoAnBeNoQMwh+tgd27+nkm/b2djMzM29vb6qls7MzKSlJIpEYGxsHBgZS7T///DNCiPpjjf+wUoV4cVa4f/8+3sQ57MyZM3izpaXFwcHh9evX3TsQGRnp6uoqEonwpkQiMTQ0/NOf/kQdoNL9m9ra2jt37ojF4rdv3964cWPy5MkGBgZ3795V+kK8NsmHH36o+DCmLgvWW74hSZIgCDMzM6VvcxD8TtIE+WaoGQQ/2/Lj+INPaWlpc3PzvHnzqBY9Pb3PPvvsl19+6fOK6wihdevWRUdHJyUlBQQEIIQyMjIWLVqEn7KWhdc///7776n1z+/fvy+RSD744IO+vR17e3t7e3v87xkzZqSnp3t4eKSkpKSmpip+IV45SunNEnUWokdqXBbFWlpaSJLsHqc3+OyDXmJiYnZ2NtO9AFpCZ2kiHTf479/goRi83q0sdVZcxy9cv3799evX8X//v/rqq+4PbfW4/jn+ocFr6KrP3d1dT08Pj5Up5uTkxOPxlB7J1GVRDHd77NixNI8HAOigwf/9Bi8/LluFAlNnxXUsODg4KSkpMTFx48aN9vb2coUUjxw5cunSpatXr8r97ebxeAiht2/fqvg+etbV1dXV1aW4xgnG5XLnzZt37ty5n376aebMmXJ7Gxsbd+zYkZaWxtRlUezixYsIoY8++ojm8UPhf/0EQYSGhuI55WAoOHPmzLJly5juhVoG//cbJycnCwuL77//Xq5dnRXXMTs7u6VLl+bk5OzZsyckJIRqJxWufz5hwgQWi/Xjjz/26d0g2YFBhBB+CtLT05POa6Ojo7lcblhYmGxlLezu3bt4kjRTl0WBFy9eJCYm2tnZrVmzhv6rAAA6h9nbR2qiOT8NLzy+ZcuWJ0+eSKVSkUiEV6v9/PPPORzOyZMnX79+XVpaOnnyZBsbmzdv3uBXyd0YP378OELot99+k42MVyl3d3eXbbx7926Pl5pajzYgIEBPTy8tLe3169e//vqrt7c3oj1fYPz48adPn25qampvb79+/bqbm5uDg0N9fT3eq3S94ZycHENDw3ffffe7775rbm5ub29/8ODBsWPHXFxcqAWDmbosJEkKBAI+ny8Wi6VSaVdXV11dXWZmprOzs7W19S+//ELn+gyCe6o0IZgvMMQMgp/tgd17+usLHD161N3dncfj8Xi8yZMnp6SkkOqtuE7x9vZOS0uTbVG6/rlYLF63bt2wYcOMjY1nzZoVFRWFELKzs/v111+VvpGtW7cKBAIjIyM2m21nZ/eXv/zl2bNn1F4669vX1tZu27bN3d3d2NhYT0/PzMxs8uTJa9eu/emnn/ABjFyW8+fPT5w40dDQUF9fn8ViIYTwhLRp06bFxMQ0NDQovTLYIPidpAnyzVAzCH62CZIklX8J0lV4GtJQGKwHNOEx7gH9U00TQRBZWVlw/2boGAQ/24P//g0AAABdAPlGh/z+++9E7zRUgQMMBZcvX46IiJAt+vDpp5/KHjB37lwTExM9Pb3x48f3rXay+uhX8Whraxs7duzu3bsZj4x1dXUlJiYKhcLuuzo6OuLi4lxcXPT19c3MzCZMmPDw4UOE0Pnz5w8dOjRQCin1G4bH89Sjs+tDA6YMgjFumhDt+zdRUVELFiyglnIQCATDhg1DCOXn58seVlBQIFtPWvvoV/EICwtDCEVGRjIemSTJe/fu4QcMJk2a1H2vn5/fmDFjbt682dHR8ezZM19f37KyMrwrKSnJy8urqamJ5okGwc82fL8BQDWtra09/k+W2VC9OXjwYGZm5pkzZ2SXckhOTmaxWEFBQYwXYZNlbGyMa3WbmJgsXbrUz8/v4sWLjx8/ljvs+vXrvc111H7kX3/9defOnRs3bvTw8Oi+NzMzMy8vLzs7e/r06Ww228bG5ty5c1SBxM8++2zSpEkff/xxZ2enSicduCDfAKCaEydO1NXV6VqoHt2/f3/Pnj179+7FTxlThEJhSEjI06dPt23bprmzq4pOuYrW1tbt27cnJSXpSORJkybl5uauWLGix2euv/rqq3feecfd3b23l0dHR5eUlKh60oEL8g0Yisjeay4EBwfr6+tbW1vjzU2bNhkZGREEgZeoCAkJ2bp1a3V1NUEQLi4uycnJPB7P0tJyw4YNNjY2PB5PKBRSa82pFAopLCfRN8nJySRJ+vr6dt8VGxvr6uqalpZ2+fJlVS+R4rIUCCGpVBoVFeXg4GBgYDBx4kQ8EKSqHstVREZGbtq0Sc3loDQXWVZ7e/vNmzd7/N5DMTc39/LySkpKIgfyrDMVMDucpya4fwPk0BzjVlxzYcWKFVZWVtTB8fHxCKFXr17hTX9/f4FAQO0NCgoyMjKqqKhoa2srLy+fOnWqiYkJVbRUpVAKykl0h2jcv3F2dnZzc5NrFAgENTU1JElev36dxWI5OTnhh3nl7t/0uSwFSZLbtm3jcrk5OTlNTU27du1isVg0y4FTeixXUVRU5OvrS5Lkq1evkIp3WTQdefr06XL3b2pqahBCHh4e77//vrW1NZfLHTt27NGjR7u6umQPi4iIQPQe94b7NwAMPK2trQkJCYsXL165cqWpqam7u/vXX39dX19/7NixvgVks9n4e4Cbm1tqaqpYLE5PT+9DHB8fH5FItGfPnr51Q05LS0tNTY3c+nWyPD09Q0NDHz58uHPnTrldNC+RUCjk8/kjRowIDAxsaWmpra1FCLW1taWmpvr5+fn7+5uZme3evZvD4ah6QeLi4mxsbGJjY2W7FBISonQddAYjy8FT4EaMGLF///7y8vKXL18uWrRo8+bN3377rexho0ePRgj19jT0IAP5Bgw5qtZcUMmUKVMMDQ2poScG1dXVkSSJ14PoTWxs7JgxY1JSUoqKimTb1SlLUVlZKZFIqLviBgYG1tbWKl0QXK7i0qVLsnMcdu3atX79eltbW/pxtBm5O3xHZ/z48UKh0MLCwtTUdO/evaampnI5G39AL1++7N+z6ybIN2DIUbPmglJcLhcPyzCrra0N/fFXrzc8Hi89PZ0giDVr1siu4qrOJWppaUEI7d69m3p07NGjR0oLL1F6LFdRVFRUVla2bt06mkG0HLlHNjY26D9XptfX13d0dKyurpY9zMDAAP3xYQ16kG/AkKN+zQUFOjo6+iuUmvAfMqVPFHp6eoaFhVVVVe3bt49qVOcS4VvuckXEb9y4QafPR44cycjIuHr1Ki4jQjlx4sSVK1dYLBZOYPgU+/fvJwhCdi1zRiL3xtjYePTo0RUVFbKNnZ2dpqamsi3t7e3ojw9r0IN8A4YcpTUX2Gw2VbFUVYWFhSRJzpgxQ/1QarK0tCQIgs4TNvv27Rs7dmxxcTHVok5ZCnt7ex6PV1JSolJvSYXlKtLT02Wzl+xdfdlBPy1HVmrZsmXFxcUPHjzAmxKJ5NGjR3LTo/EHZGVlpea5BgTIN2DI4fF4W7duPXv2bEZGhkgkKisr27hxo42NTVBQED7AxcWlsbExLy+vo6Pj1atXjx49kn25hYXFs2fPHj58KBaLcS7p6upqamrq7OwsLS0NCQlxcHBYtWpVH0IVFBT043xoQ0NDZ2dnOkWI8aia7BMqSi+R4mirV68+ffp0amqqSCSSSqVPnjx5/vw5QigwMNDKyqrH9XIqKiq++OKL48ePczgc2WWccDERpRiJrFRYWJijo+OqVatqa2sbGhrCw8P/H3t3HtfUlTYO/FzICoSwQwQBISwiKLVqBWXUOqWtjAsqiltLfbVUqwi4IC4IiLhggVFBx+WlM2Jl0wFHRftapa1VrK2iFFtFFBA3BNnDEsL9/XE+zeQXIAuQmwSe71/eJeee3BvzkHPPfZ7W1lap2Rn4Asl4RmcwgXgDhqIdO3bEx8fHxsaamZlNmTLF3t6+oKBAX18fb129evW0adMWLVrk4uKyc+dOPNbh5eWFn0hftWqVhYWFm5vbjBkz3r59ixBqa2vz8PBgs9k+Pj7Ozs7Xrl0T3zVRtqmB5efnV1JSIr4x8+9//5vP55eVlY0fP37t2rWSe06cOBGnclHkFKWmpiYlJSGERo8e/eTJk2PHjq1fvx4h9NFHH5WWliKEkpOTw8LC9u7da2pqyuPxQkND6+rqEEIdHR3V1dV5eXndu0r27wEUtbSMECosLJw8efKwYcNu3bp17949Ho83adKkH374AW81Njb+8ccfbWxsPD09ra2tf/755wsXLkg9kXP79m1ra+vRo0f3p5NaQ3VTrSkAz98AKdQ/o4ATpVB5RAwp8PxNaWkpjUY7efIkNV2SSyQS+fj4nDhxAlrGampqWCzW/v37FdkZnr8BAMi/J68ufD4/NjY2Nja2t3TIVBKJRLm5uU1NTQOe6VwbW8aio6M9PT1DQkJU0bgGgngDwGAWGRkZEBAQGBio9tScBQUFZ86cyc/Pl/1I0BBpGSGUmJhYVFR08eJFOp0+4I1rJog3APTdli1b0tLSGhoaRowYkZOTo+7u9GzXrl0hISG7d+9WbzemT59+6tQpcTa5Id5yXl5ee3t7QUGBsbHxgDeusWjq7gAAWiw+Pj4+Pl7dvZDP19fX19dX3b0A/zV79uzZs2eruxdUg983AAAAqADxBgAAABUg3gAAAKACxBsAAABUgHgDAACACgSpzXVMAwICNHYSKgAADDit/sbW7nhz8+ZNnIcKACotXLgwNDTUy8tL3R0BQ86CBQvU3YW+0+54A4BaEASRmZmp1f/zAaAe3L8BAABABYg3AAAAqADxBgAAABUg3gAAAKACxBsAAABUgHgDAACAChBvAAAAUAHiDQAAACpAvAEAAEAFiDcAAACoAPEGAAAAFSDeAAAAoALEGwAAAFSAeAMAAIAKEG8AAABQAeINAAAAKkC8AQAAQAWINwAAAKgA8QYAAAAVIN4AAACgAsQbAAAAVIB4AwAAgAoQbwAAAFAB4g0AAAAqQLwBAABABYg3AAAAqADxBgAAABUg3gAAAKACxBsAAABUgHgDAACAChBvAAAAUAHiDQAAACrQ1N0BALTA6dOnm5qaJNdcuXKlvr5evOjv729ubk55vwDQJgRJkuruAwCaLigo6J///CedTseL+H8NQRAIIZFIZGBgUF1dzWQy1dlFADQejKcBIN+iRYsQQsI/dXZ2dnZ24n/r6uoGBARAsAFALvh9A4B8nZ2dlpaWb9++7XHrd9999/7771PcJQC0Dvy+AUA+Go22aNEi8XiaJDMzsylTplDfJQC0DsQbABSyaNEioVAotZJOpy9btkxXV1ctXQJAu8B4GgAKIUnS1ta2qqpKav3PP/88fvx4tXQJAO0Cv28AUAhBEEuXLpUaUhs+fPi4cePU1SUAtAvEGwAUJTWkRqfTg4KC8KxoAIBcMJ4GgBJcXV0fPnwoXvztt99GjRqlxv4AoEXg9w0ASli2bJl4SM3NzQ2CDQCKg3gDgBKWLl3a2dmJEKLT6Z9++qm6uwOANoHxNACUM27cuF9//ZUgiPLycltbW3V3BwCtAb9vAFDOJ598ghB67733INgAoJT/Lz/0zZs3ExMT1dUVALRCW1sbQRDt7e0BAQHq7gsAGs3Lyys8PFy8+P/9vnn27FlOTg7lXQJAm7BYLEtLSxsbmwFprbCwsLCwcECa0mRVVVXw3TLUFBYW3rx5U3JND/VvsrOzqeoPAFrp8ePHfD5/QJrCP5IG/X+6rKyshQsXDvq3CSR1HwCA+zcAKG2ggg0AQwrEGwAAAFSAeAMAAIAKEG8AAABQAeINAAAAKkC8AUD7XLx4kcvl/uc//1F3R1TlypUrkZGRZ86ccXBwIAiCIIhly5ZJ7uDr68vhcHR1dUeNGnXnzh21dDI2NtbNzc3Q0JDJZPL5/E2bNjU3N/e4Z1tbm6ur67Zt29TeMtbV1ZWUlOTt7d19k1AojI+P5/P5DAbDyMjI3d29vLwcIXTu3Lm9e/eKRCKlDiQF4g0A2mdwp6HasWPHgQMHtmzZMm/evCdPnjg6Opqamqanp1+4cEG8z7fffpudnT1z5sySkpKxY8eqpZ9Xr15ds2ZNeXl5TU1NfHx8cnJyb48Ab926VTKtuBpbRgiVlpb+5S9/CQ8PFwgE3bcuXLjwX//616lTpwQCwe+//+7o6IhD3axZs1gs1vTp0+vr65U6nCSINwBoHz8/v4aGhpkzZ6r6QK2trT3+Faw6e/bsycjIyMrK4nA44pUHDhzQ0dEJDg5uaGigsjOyGRgYBAcHm5iYcDicBQsW+Pv7X7p06dmzZ1K73bhx47ffftOQlu/du7d58+ZVq1Z5enp235qRkZGbm5udnf3ee+/RaDQej5eXl+fu7o63rlu3bsyYMTNmzMApa/sA4g0AoFcnTpyorq6m7HCPHz/evn17TEwMi8WSXO/t7R0aGvr8+fMNGzZQ1hm5zp8/r6urK140MzNDCEn9aGhtbd24cWNycrKGtDxmzJgzZ84sWbKEyWR233r48OGxY8d6eHj09vLo6OiioiJlDyoG8QYALXP9+nVbW1uCIA4dOoQQSk1N1dfX19PTy8vL+/jjjw0NDW1sbE6fPo13PnDgAIvFsrCw+OKLL3g8HovF8vb2vnXrFt4aEhLCYDCsrKzw4pdffqmvr08QRE1NDUIoNDR0/fr1ZWVlBEHgR1wvXbpkaGi4a9cuFb21AwcOkCQ5a9as7pvi4uKcnZ2PHz9+5cqVHl9LkmRiYuLIkSOZTKaxsfGcOXP++OMPvEn2KUIIiUSiqKgoW1tbNps9evTozMzMPnT++fPnbDZ7xIgRkiu3bt365Zdfmpub96FBClqW1NHRUVhY2OPvHjFjY+MpU6YkJyf3bUQX4g0AWmby5Mk3btwQL65evTosLKy1tZXD4WRmZpaVlTk4OKxcuRKXvg4JCQkKChIIBOvWrSsvL79z505nZ+cHH3yAB2cOHDiwYMECcVMpKSkxMTHixeTk5JkzZzo6OpIk+fjxY4QQvl3c1dWlord24cIFFxcXPT297pvYbPbXX3+to6OzcuXKlpaW7jtER0dHRkZu3bq1urr6hx9+ePbsmY+Pz+vXr5G8U4QQ2rx58759+5KSkl6+fDlz5szFixf/8ssvSvVcIBBcvXp15cqVDAZDvPKnn34qKytbvHixUk1R1rKUFy9edHR0/Prrr9OmTcN/mowcOTIlJUUqtLzzzjvPnz+/d+9eHw4B8QaAQcLb29vQ0NDc3DwwMLClpaWyslK8iUaj4T/83dzcUlNTm5qa0tLS+nAIPz+/xsbG7du3D1yv/6ulpeXp06eOjo697eDl5RUWFlZeXr5582apTa2trYmJiXPnzl26dCmXy/Xw8Dhy5EhNTc3Ro0cld+vxFLW1taWmpvr7+8+bN8/IyGjbtm10Ol3Z8xMfH8/j8eLi4iS7FBoampqaqlQ7VLYsBc8LMDc337VrV0lJyevXr+fMmbNmzZpvvvlGcjcnJyeEUHFxcR8OAfEGgMEG/yEs/uNdyrhx4/T09MRjTZqjurqaJMkef9yIxcXFubi4pKSkXL9+XXJ9SUlJc3PzuHHjxGvGjx/PYDDEI4dSJE/Rw4cPBQKB+K44m822srJS6vycPXs2Kyvr8uXLknMctmzZ8vnnn1tbWyveDpUtd4fv6IwaNcrb29vExITL5cbExHC5XKmYjS8Q/uGoLIg3AAw5TCbzzZs36u6FtLa2NvTnt15vWCxWWloaQRDLly9vbW0Vr8eTdA0MDCR3NjIyampqkntcPDq3bds24k8VFRU9zhXuUUZGxp49ewoKCuzt7cUrr1+/XlxcvGLFCgUbobjlHvF4PIQQvnWHMRgMOzu7srIyyd3YbDb682IpC+INAEOLUCisr68fqPo9Awh/kcl9ohCX8CotLd25c6d4pZGREUJIKroo+DbxLfekpCRSglThlt4cPHgwPT396tWrw4YNk1x/4sSJ7777TkdHBwcwfIhdu3YRBKHgnSHVtdwbAwMDJyenBw8eSK7s7OzkcrmSazo6OtCfF0tZEG8AGFoKCgpIkpw4cSJepNFovY28UczCwoIgCEWesNm5c6erq+vdu3fFa9zd3Q0MDCS/cG/dutXR0fHuu+/KbW348OEsFquoqEip3pIkGRERUVxcnJubK/W7CiGUlpYmGb3wr8mtW7eSJCk56Edxy3ItXLjw7t27T548wYsCgaCiokJqejS+QJaWln1oH+INAINfV1dXXV1dZ2fn/fv3Q0NDbW1tg4KC8CY+n//27dvc3FyhUPjmzZuKigrJF5qYmLx48aK8vLypqUkoFObn56tuPrSenp6Dg0NVVZXcPfGomuQTKiwWa/369WfPnk1PT29sbCwuLl61ahWPxwsODlaktc8+++z06dOpqamNjY0ikaiqqurly5cIocDAQEtLyx7z5Tx48GDfvn3Hjh2j0+mEhP379yvyZtXSslzh4eF2dnZBQUGVlZW1tbURERGtra1SszPwBZLxjI4MEG8A0DKHDh0aP348QigiImL27NmpqalJSUkIodGjRz958uTYsWPr169HCH300UelpaX4JW1tbR4eHmw228fHx9nZ+dq1a+LbJKtXr542bdqiRYtcXFx27tyJx0m8vLzwhOlVq1ZZWFi4ubnNmDHj7du3qn5rfn5+JSUl4hsz//73v/l8fllZ2fjx49euXSu558SJE8PDwyXX7NixIz4+PjY21szMbMqUKfb29gUFBfr6+gghuacoOTk5LCxs7969pqamPB4vNDS0rq4OIdTR0VFdXZ2Xl9e9q/1MKaSWlhFChYWFkydPHjZs2K1bt+7du8fj8SZNmvTDDz/grcbGxj/++KONjY2np6e1tfXPP/984cIFqSdybt++bW1tPXr06L50TvKnGX7KiQQAUGX+/Pnz589X6SFwZhSVHkIuBb9bSktLaTTayZMnKeiSIkQikY+Pz4kTJ6BlrKamhsVi7d+/X5Gdu3+24fcNAINfP9P6UobP58fGxsbGxvaWDplKIpEoNze3qakpMDAQWsaio6M9PT1DQkL69nKINwAADRIZGRkQEBAYGKj21JwFBQVnzpzJz8+X/UjQEGkZIZSYmFhUVHTx4kU6nd63FiDe/Nf+/fvxDJkjR47gNQNYZUTxghYrVqzgcDgEQSg1YUZGQYveSBYX6e2J8cTERIIgdHR0XF1dxYO8ypI8EEEQdDrd2tp6yZIlv//+e98alKSuqyb1pgiCYDAYFhYWU6dOTUhIwKP/mmDLli1paWkNDQ0jRozIyclRd3cUsmvXrpCQkN27d6u3G9OnTz916pQ4udwQbzkvL6+9vb2goMDY2LjvrUgOrsH9G3zz8PDhw3jx/PnzhoaG586d63/LU6ZMSUlJqa2tbWxszMzMpNPpH330UW8740yCd+/eVbDxR48eTZo0CSE0ZswYZTuG04dYWVl1dHRIbers7LSzs0MITZ8+XdlmezwQl8slSbK5ufncuXO2trYGBgZ//PFH/1tW41UTvyk8AezatWtBQUHC4p2XAAAgAElEQVQEQfB4vNu3bytyCAru32gC+G4ZguD+jXIGsMqIggUt+kB2QQtFvPvuu69evcrNzZVaf+bMmQHPmYEQ0tfXnzlz5t///vfm5uaDBw8OePtquWoEQRgZGU2dOjUtLS0rK+v169e4G/3vAwCDBsQbVSFJMjs7W5x6SJGCFmIEQSh+INkFLRSxevVqhNDhw4el1icmJuJpo6owYcIEhJCy1aJUrT9XTWz+/PlBQUHV1dXiIT4AAOpDvElOTtbX19fR0Xn33XctLS3pdLq+vv7YsWN9fHzwY7pGRkabNm0S7//jjz+6ublxuVwWi+Xh4XH58mWE0Ndff21gYEAQhLGxcW5u7i+//GJnZ6erq6tIem3Z9TyQzDIYcrdKUqrKCEJIJBLFx8e7uLiw2WwzM7MRI0bEx8dLJnuXJFXQgiTJhIQEFxcXJpPJ5XI3btwo9zwoSJGCJe+///7IkSOvXbsmWZj2p59+EggEvr6+UjsP1AXFJQLFMVIbr5oM+GnK/Px8uXsCMIRIDq4pOMa6Y8cOhNCtW7daWlpqamo++ugjhNCFCxfevHnT0tKCp8oVFRXhnbOzs6Ojo9++fVtbWztx4kRTU1O8/sGDB3p6ep9++ilejIyMPH78uILDgsHBwfr6+g8ePGhrayspKRk/fjyHw6msrMRbo6KiGAzGyZMn6+vr79+/P3bsWDMzs1evXimyVepOAB45OXjwIF7cunUrQui7775raGiorq728fHR19cX3/bYtWuXrq5uXl6eQCD49ddfLS0tp06d2mP/W1paOBxOSEiIeM3WrVsJgvjqq6/q6uoEAkFKSgpS5v4N9t5773W/f3P+/HkOhxMbG9vbqxwdHZ8+ffr3v/8dIRQaGipe7+/vn5aWhhNSSd6/6fMFFd/qwE6ePIkQ2rhxI17UxqvW/U2JNTY2IoSGDx/eY1OS4P4NGKy6f7b7Hm+amprw4j//+U+EUHFxMV78+eefEUIZGRndXxgfH4/+zDpOkuQ//vEPhFB6evo333wTHh6u+NsIDg6W/E9++/ZthFBMTAxJkgKBwMDAIDAwULwV9wd/4creSir2zdXa2ooXcVR4/PgxXhw/fvyECRPELX/++ec6Ojrt7e3d+79161ZnZ+fGxka8KBAI9PT0PvjgA/EOys4XwHqMN3LheFNfX6+vr29sbCwQCEiSLCsrs7GxaW9v7x5vJCl1QSXnC+Tk5FhaWlpYWFRVVZHaedWk3lR3+I5Oj5skQbwBg1X3zzat/7+QcCUJPDyCEMJTs3vMAIg3iR89+/zzz//v//7viy+++Otf/9qfmZqS9Txkl8FQtkiGbFJVRtra2iSLrotEIjqdLjn6j+GCFt9++624oMXjx48FAsH06dP70IeBwuVyFy9efOzYsYyMjM8++ywpKWn16tUMBgPngu2Nshe0oaGBIAhdXV0rK6sZM2bs2LEDz0fQxqsmW0tLC0mShoaGiuyck5Oj1B077TVE3iYQmz9/vuTiAMQb2S5cuJCQkFBSUtLY2Ng9CO3atSsnJ6e6urqfRxHX85BdBqM/RTLkmjFjRkJCQl5enq+vb0lJSW5u7t/+9jepb66MjIzExMSCggLJHOM4/90A1iHvm9WrVx87duzIkSP+/v7Z2dm9PRzTnwvK5XLxJZCijVdNtkePHiGEXF1dFdl54sSJYWFhyvZcu9y8eTM5ORn/ygFDBM5ZJ0m18aaystLf33/u3Ln/+7//O2zYsIMHD0pOJRAKhevWrcOToOLi4vAwXR9I1vOQXQajP0Uy5IqOjv7111+DgoKam5t5PN6CBQuk7tIfPHjw8uXLV69elfrqxH9ft7e3978P/eHp6Tlx4sTCwsLg4OCAgIAen+pS0QXVxqsm26VLlxBCH3/8sSI729jY9DZDYTBJTk4eCm8TiGVnZ0utUW28KS4uFgqFq1evdnBwQN1+Ta9du3blypVz5859/vz5zp07fX19vby8+nAUyXoesstg9KdIhlwlJSVlZWVv3ryh0aTPKkmSmzdvrqury83N7b7V3d1dR0fn+++/X7VqVf+70R+rV68uLCzMyckR5xWWoqILqo1XTYZXr14lJSXZ2NgsX768/z0EYNBQ7fM3tra2CKErV660tbWVlpZKDrinpKRYW1vPnTsXIRQfH+/m5rZkyRI8q0cRvdXzkF0Goz9FMuRas2aNra1tj1lqZBe0MDc3nzdvXk5OzokTJxobG+/fvy9VMLw/lCpYsmDBAjMzM39/fxxOulPRBdXGqyZGkmRzc3NXVxdJkm/evMnMzJw0aZKurm5ubq6C928AGCokJw8oMockOTkZZ4Kzt7f/8ccf9+zZg6uNWlpanjp1KiMjA9d9MzY2Pn36NEmSERERJiYmRkZGAQEB+JEIR0dHT09PgiBMTExu3LhBkmRYWJiOjg5CiMvl/vLLL3KnPQQHB+McXDQazdDQcM6cOWVlZeKtXV1dCQkJTk5OdDrd2NjY39//4cOHimz96quvcOf19fXnzp178OBBnIZIT09v1qxZKSkp+I07OTmVlZUdPXoUf5vY2dk9evSIJMmrV6+ampqKTyydTh85cuSZM2dIkiwuLu7x5CckJOBDNzU1rVixwtTU1MDAYPLkyVFRUQghGxube/fuyT0bN2/enDRpEq49jhCysrLy9vb+/vvv8daLFy9yOJy4uLjuLzx79ixOZmNmZrZmzRq8ctOmTfiikCS5bds2fAZ0dHTc3Nx+/PHHvl3Qn376ydnZGXePx+MFBAR074zWXbVz586NHj1aT0+PwWDgN4snpE2YMCE2Nra2tlbuhcNgfhoYrAZmPrTaaUI9j+5SUlIkn19pb28PCwtjMpl4hjHQTGq/ahBvwGClkvnQaqFp9TxevXoVEhIimdGZwWDY2toKhUKhUIhrJgJNA1cNACppXP60P/74g+idiooI9R+bzabT6SdOnHj9+rVQKHzx4sXx48ejoqICAwP7M4ivpWdDW6joqoH+u3LlSmRkpGTRh2XLlknu4Ovry+FwdHV1R40adefOHXX1E/VeCkTxEiSa0zJCSCgUxsfH8/l8BoNhZGTk7u5eXl6OEDp37tzevXv7+4e+5I8drfjNGxkZiR/Zs7e3z87OVnd3/uuHH37461//amhoqKury+Vyvb29U1JShEKhuvsFZFH7VYPxtO6ioqJmzpwpTuXg6OiI77GdP39ecrf8/PzZs2cPcEeVJKMUiFIlSDSkZZIk/f39XVxcCgsL8V9gs2bNEueOSU5OnjJlSl1dnYIHGiT3bwAYNCiINwKBwMvLS71NKf7dsnv3bmdnZ3H+IZIkHR0dT506paOjY21tXV9fL16v9nhTVFQ0d+7c9PR0T0/P7t/dfn5+nZ2d4kX87JE4zaNmtnz69GmCIO7fv9/by0NCQry8vBT8gwzq3wAw5Jw4caL/KTwGvKkePX78ePv27TExMZJJhhBC3t7eoaGhz58/37Bhg+qOrizZpUD6VsxCvS0fPnx47NixHh4evb08Ojq6qKgoOTlZkWN1B/EGAC1A9l6RISQkhMFgiEsIf/nll/r6+gRB1NTUIIRCQ0PXr19fVlZGEASfz5ddzkOpppBi1S6UcuDAAZIkZ82a1X1TXFycs7Pz8ePHr1y5ouwpUqQsRVRUlK2tLZvNHj16tCry7ihezEJdLXd0dBQWFsou22hsbDxlypTk5GSSJPtyDMkfOzCeBgDFFBxPk12RYcmSJZaWluKdExISEEJv3rzBi/PmzXN0dBRvlV3OQ6mm5Fa7EFPwu8XBwcHNzU1qJU5hTpLkjRs3dHR07O3tm5ubyW7jabJPkeyyFBs2bGAymTk5OXV1dVu2bNHR0VGwHDgmNzV7j8UsNK3lp0+fIoQ8PT2nTp1qZWXFZDJdXV0PHTqEn2UWi4yMRIplr4fxNAC0T2tra2Ji4ty5c5cuXcrlcj08PI4cOVJTU9PnPBQ0Gg3/DnBzc0tNTW1qakpLS+tDO35+fo2Njdu3b+9bN6S0tLQ8ffoUP4PcIy8vr7CwsPLy8s2bN0ttUvAUeXt7GxoampubBwYGtrS0VFZWIoTa2tpSU1P9/f3nzZtnZGS0bds2Op3etxPSm/j4eB6PFxcXN4BtDnjLeJKbubn5rl27SkpKXr9+PWfOnDVr1nzzzTeSuzk5OSGEensaWjaINwBouoGtyCBFspyHeuFCSjgfRG/i4uJcXFxSUlKuX78uuV7ZUyRZluLhw4cCgcDd3R1vYrPZVlZWA3hCcDGLy5cvK1jMQl0t4zs6o0aN8vb2NjEx4XK5MTExXC5XKmbjC/T69es+HALiDQCaTqUVGZBEOQ/1amtrQxIlxnvEYrHS0tIIgli+fHlra6t4fX9OUUtLC0Jo27Zt4ifbKioqFLz9LldGRsaePXsKCgrs7e0HpEHVtYxzYuF7dRiDwbCzsysrK5PcDT8HjS+WsiDeAKDpVFqRQbKch3rhLzK5TxR6eXmFh4eXlpbu3LlTvLI/pwiXnkpKSpK803Dz5s0+vAUpBw8eTE9Pv3r1quKVk9TYsoGBgZOT04MHDyRXdnZ24gyZYrgGY9+yb0C8AUDTya3IQKPReqyoqwjJch79bKqfLCwsCIJoaGiQu+fOnTtdXV3v3r0rXtOfohXDhw9nsViSaY36jyTJiIiI4uLi3NxcpSonqbFlhNDChQvv3r375MkTvCgQCCoqKqSmR+MLhJPkKgviDQCaTm5FBj6f//bt29zcXKFQ+ObNm4qKCsmXm5iYvHjxory8vKmpCceS3sp5KNuUUtUu5NLT03NwcMDlbuWekLS0NMlnUPpTtILFYn322WenT59OTU1tbGwUiURVVVUvX75ECAUGBlpaWvYhX47cYhYa2DJCKDw83M7OLigoqLKysra2NiIiorW1VWp2Br5AMp7RkUXyJyTMhwaAYgrOh5Zdr6G2tnbatGksFmvEiBFr167duHEjQojP5+NZznfu3LGzs2Oz2ZMnT3716pXsch5KNSWj2oUUBb9bQkJC6HS6ODl3jyUzxDZu3Cg5H1rGKZJblqK9vT0iIsLW1pZGo+F6VCUlJSRJ+vv7I4SioqJ67K2MUiByS5BoYMvYs2fPFi1aZGxszGQyJ0yYkJ+fL9WCn5+ftbW11CTpHkE+GwA0C/X509RSzkPB75bS0lIajXby5EkKuqQIkUjk4+Nz4sQJaBmrqalhsVj79+9XZGd4/gYAoHHlPMT4fH5sbGxsbKyyCY9VQSQS5ebmNjU1DXgidm1sGYuOjvb09AwJCenbyyHeAAA0SGRkZEBAQGBgoCITB1SqoKDgzJkz+fn5sh8JGiItI4QSExOLioouXrxIp9P71gLEGwCGkC1btqSlpTU0NIwYMSInJ0fd3enZrl27QkJCdu/erd5uTJ8+/dSpU+JsckO85by8vPb29oKCAmNj4z43oq31PQEAfRAfHx8fH6/uXsjn6+vr6+ur7l6A/5o9e/bs2bP72Qj8vgEAAEAFiDcAAACoAPEGAAAAFSDeAAAAoEIP8wWysrKo7wcAQxPODjLo/9Ph9JeD/m0CSVVVVdL5UiUf/lRFFVUAAABDk1R+AYLsWxlqAIYwgiAyMzMXLFig7o4AoE3g/g0AAAAqQLwBAABABYg3AAAAqADxBgAAABUg3gAAAKACxBsAAABUgHgDAACAChBvAAAAUAHiDQAAACpAvAEAAEAFiDcAAACoAPEGAAAAFSDeAAAAoALEGwAAAFSAeAMAAIAKEG8AAABQAeINAAAAKkC8AQAAQAWINwAAAKgA8QYAAAAVIN4AAACgAsQbAAAAVIB4AwAAgAoQbwAAAFAB4g0AAAAqQLwBAABABYg3AAAAqADxBgAAABUg3gAAAKACxBsAAABUgHgDAACAChBvAAAAUAHiDQAAACoQJEmquw8AaLrg4OCHDx+KF+/cuTNixAhjY2O8qKur+89//tPGxkZNvQNAO9DU3QEAtIClpeXRo0cl19y/f1/8bwcHBwg2AMgF42kAyLd48eLeNjEYjKCgIAr7AoC2gvE0ABTi7u7+4MGDHv+/PHz40NnZmfouAaBd4PcNAAr55JNPdHV1pVYSBDFmzBgINgAoAuINAApZtGiRSCSSWqmrq/vpp5+qpT8AaB0YTwNAUd7e3rdu3erq6hKvIQji2bNn1tbWauwVANoCft8AoKhly5YRBCFe1NHRmTx5MgQbABQE8QYARQUEBEguEgTxySefqKszAGgdiDcAKMrMzGz69OniWQMEQfj7+6u3SwBoEYg3AChh6dKl+Janrq7uhx9+aGpqqu4eAaA1IN4AoIS5c+cyGAyEEEmSS5cuVXd3ANAmEG8AUIK+vv7f/vY3hBCDwZg5c6a6uwOANoF4A4BylixZghDy9/fX19dXd18A0Cqk1srMzFT3yQMAAErNnz9f3V+9faf1+aEh6gApSUlJCKGwsDDVHSI9PT0wMJBGU+d/n5s3byYnJ8Pnf0jBn23tpfXxZsGCBeruAtAs2dnZSMUfjFmzZrFYLNW1r6Dk5GT4/A8p+LOtveD+DQBK04RgA4DWgXgDAACAChBvAAAAUAHiDQAAACpAvAEAAEAFiDcAIITQxYsXuVzuf/7zH3V3RFWuXLkSGRl55swZBwcHgiAIgli2bJnkDr6+vhwOR1dXd9SoUXfu3FFXPxFCXV1dSUlJ3t7eUutjY2Pd3NwMDQ2ZTCafz9+0aVNzc7OGt4wQEgqF8fHxfD6fwWAYGRm5u7uXl5cjhM6dO7d3797uRfwGMYg3ACCEEDmoCw/u2LHjwIEDW7ZsmTdv3pMnTxwdHU1NTdPT0y9cuCDe59tvv83Ozp45c2ZJScnYsWPV1dXS0tK//OUv4eHhAoFAatPVq1fXrFlTXl5eU1MTHx+fnJwsVSFCA1tGCC1cuPBf//rXqVOnBALB77//7ujoiIMZnlU/ffr0+vp6xY+l3dT9wGnf4Sfd1N0LoHHmz5+vyc9gCwQCLy+v/rej+Od/9+7dzs7Ora2t4jWOjo6nTp3S0dGxtraur68Xr8/Pz589e3b/+9ZnRUVFc+fOTU9P9/T0HDNmjNRWPz+/zs5O8SJ+9qiyslKTWz59+jRBEPfv3+/t5SEhIV5eXkKhUJFjafhnWy74fQMApU6cOFFdXU3Z4R4/frx9+/aYmBipZ4a8vb1DQ0OfP3++YcMGyjoj15gxY86cObNkyRImk9l96/nz58XFhxBCZmZmCKEef1JoTsuHDx8eO3ash4dHby+Pjo4uKipKTk5W5FjaDuINAOj69eu2trYEQRw6dAghlJqaqq+vr6enl5eX9/HHHxsaGtrY2Jw+fRrvfODAARaLZWFh8cUXX/B4PBaL5e3tfevWLbw1JCSEwWBYWVnhxS+//FJfX58giJqaGoRQaGjo+vXry8rKCILg8/kIoUuXLhkaGu7atUtFb+3AgQMkSc6aNav7pri4OGdn5+PHj1+5cqXH15IkmZiYOHLkSCaTaWxsPGfOnD/++ANvkn2KEEIikSgqKsrW1pbNZo8ePVoVeXeeP3/OZrNHjBihsS13dHQUFhZ6enrK2MfY2HjKlCnJycnkoB7RxSDeAIAmT55848YN8eLq1avDwsJaW1s5HE5mZmZZWZmDg8PKlSuFQiFCKCQkJCgoSCAQrFu3rry8/M6dO52dnR988MGzZ88QQgcOHJDMMZOSkhITEyNeTE5OnjlzpqOjI0mSjx8/Rgjh28VdXV0qemsXLlxwcXHR09PrvonNZn/99dc6OjorV65saWnpvkN0dHRkZOTWrVurq6t/+OGHZ8+e+fj4vH79Gsk7RQihzZs379u3Lykp6eXLlzNnzly8ePEvv/wygO9LIBBcvXp15cqVuByRZrb84sWLjo6OX3/9ddq0afhPk5EjR6akpEiFlnfeeef58+f37t3r5+E0H8QbAHrl7e1taGhobm4eGBjY0tJSWVkp3kSj0fAf/m5ubqmpqU1NTWlpaX04hJ+fX2Nj4/bt2weu1//V0tLy9OlTR0fH3nbw8vIKCwsrLy/fvHmz1KbW1tbExMS5c+cuXbqUy+V6eHgcOXKkpqbm6NGjkrv1eIra2tpSU1P9/f3nzZtnZGS0bds2Op3et/PTm/j4eB6PFxcXN4BtDnjLeF6Aubn5rl27SkpKXr9+PWfOnDVr1nzzzTeSuzk5OSGEiouL+39EDQfxBgD58J+64j/epYwbN05PT0881qQ5qqurSZLs8ceNWFxcnIuLS0pKyvXr1yXXl5SUNDc3jxs3Trxm/PjxDAZDPHIoRfIUPXz4UCAQuLu7401sNtvKymoAz8/Zs2ezsrIuX77M4XAGqk1VtIzv6IwaNcrb29vExITL5cbExHC5XKmYjS8Q/uE4uEG8AWAAMJnMN2/eqLsX0tra2tCf33q9YbFYaWlpBEEsX768tbVVvB5P0jUwMJDc2cjIqKmpSe5x8ejctm3biD9VVFQoePtdroyMjD179hQUFNjb2w9Ig6prmcfjIYTwrTuMwWDY2dmVlZVJ7sZms9GfF2twg3gDQH8JhcL6+nobGxt1d0Qa/iKT+0Shl5dXeHh4aWnpzp07xSuNjIwQQlLRRcG3aW5ujhBKSkqSnAt78+bNPrwFKQcPHkxPT7969eqwYcP635qqWzYwMHBycnrw4IHkys7OTi6XK7mmo6MD/XmxBjeINwD0V0FBAUmSEydOxIs0Gq23kTeKWVhYEATR0NAgd8+dO3e6urrevXtXvMbd3d3AwEDyJv+tW7c6Ojreffddua0NHz6cxWIVFRX1rds9IkkyIiKiuLg4NzdX6leXxraMEFq4cOHdu3efPHmCFwUCQUVFhdT0aHyBLC0tB/bQGgjiDQB90dXVVVdX19nZef/+/dDQUFtb26CgILyJz+e/ffs2NzdXKBS+efOmoqJC8oUmJiYvXrwoLy9vamoSCoX5+fmqmw+tp6fn4OBQVVUld088qib5DAqLxVq/fv3Zs2fT09MbGxuLi4tXrVrF4/GCg4MVae2zzz47ffp0ampqY2OjSCSqqqp6+fIlQigwMNDS0rIP+XIePHiwb9++Y8eO0el0QsL+/fvxDhrYMkIoPDzczs4uKCiosrKytrY2IiKitbVVanYGvkAyntEZNCDeAIAOHTo0fvx4hFBERMTs2bNTU1Nx4d7Ro0c/efLk2LFj69evRwh99NFHpaWl+CVtbW0eHh5sNtvHx8fZ2fnatWvi2ySrV6+eNm3aokWLXFxcdu7cicdJvLy88ITpVatWWVhYuLm5zZgx4+3bt6p+a35+fiUlJeIbM//+97/5fH5ZWdn48ePXrl0ruefEiRPDw8Ml1+zYsSM+Pj42NtbMzGzKlCn29vYFBQX6+voIIbmnKDk5OSwsbO/evaampjweLzQ0tK6uDiHU0dFRXV2dl5fXY28LCwsnT548bNiwW7du3bt3j8fjTZo06YcffkAKJBzSwJYRQsbGxj/++KONjY2np6e1tfXPP/984cIFqSdybt++bW1tPXr0aNndGAwozWYwoCCfDegRBTk/goODTUxMVHoIuRT8/JeWltJotJMnT1LQJUWIRCIfH58TJ05Ay1hNTQ2Lxdq/f78iO0M+GwCGIm1J68vn82NjY2NjY5VNeKwKIpEoNze3qakpMDAQWsaio6M9PT1DQkJU0bimGVrxZsWKFRwOhyCIgb2TSb29e/e6urqy2Wx9fX1XV9ft27c3NjYq8kLJdPQYg8GwsLCYOnVqQkICHvEAg0xkZGRAQEBgYKAiEwdUqqCg4MyZM/n5+bIfCRoiLSOEEhMTi4qKLl68SKfTB7xxTaTuH1h917fxNJzi6e7du6roEmX8/Pz2799fXV3d1NSUlZVFp9M/+OADxV/u6OjI5XJJksQ3va9duxYUFEQQBI/Hu337tsp6TRFVjzlERkbiZxvt7e2zs7NVdyDZlP38X758OSIiQnX9AcrKzc2Nj4+XzEstF4yngYHR2traY7GmHjEYjC+//NLc3NzAwCAgIGDOnDn/93//h+f/KIUgCCMjo6lTp6alpWVlZb1+/drPz0/tfwV3p9TJUbX4+Pj29naSJJ8+fTp//nx1d0dRvr6+e/bsUXcvwH/Nnj07MjJSck7goDfk4g1BEOruQs+USlN/9uxZyfTy1tbW6M9kTX02f/78oKCg6urqI0eO9KcdVaA4hz8AQBUGf7whSTIhIcHFxYXJZHK53I0bN4o37du3T09Pj8PhVFdXr1+/3tra+uHDh2TvOdhlJ6JHMvO3K5umXimlpaVGRkZ2dnZ4sc8p7vETJPn5+YPp5AAANIU6B/P6R8Hx661btxIE8dVXX9XV1QkEgpSUFCRx/2br1q0IoXXr1h08eHDu3Lm///57VFQUg8E4efJkfX39/fv3x44da2Zm9urVK7x/cHCwvr7+gwcP2traSkpKxo8fz+FwxHUAZb92yZIllpaW4o4lJCQghN68eYMX582bh9PUK66jo6OqqurgwYNMJlNywuv58+c5HE5sbGxvLxTfv5GCJx0MHz5cq0+Oto9xKwieBxiCtP2zrcWfV0X+vwkEAj09Pcl76VLzBfBXqrjUrkAgMDAwCAwMFO//888/I4TE393BwcGS39S3b99GCMXExCjy2gGPNzgBhqmp6d///veOjg7FX9hbvCFJEt/Rwf/W0pOj7f8nFQTxZgjS9s82jcrfUtR7/PixQCCYPn26gvsrm4NdMhG9sq/tv2fPntXX19+9ezcyMvLo0aNXr161sLDoT4MtLS0kSRoaGva4VYtOTlVVVVZWlipa1hw4/eWgf5tAUlVVlQamhVXcII83ODERzlariD7kYBcnou9P/va+odPp5ubmvr6+I0aMcHZ2jo+P72cV9EePHiGEXF1de9yqRSensLBw4cKFqmhZ0wyRtwnEtGhKZHeDPN7gSVzt7e0K7q9sDnbJRPT9yd/eT3w+X1dXt6SkpJ/tXLp0CSH08SlL/mcAACAASURBVMcf97hVi07O/Pnzs7OzVdGy5sjKylq4cCE5BIreA7GAgAB1d6FfBvn8NHd3dx0dne+//17x/ZXKwS6ZiF7uawcqTX1tbe3ixYsl15SWlopEouHDh/en2VevXiUlJdnY2CxfvrzHHbTi5AAANNYgjzfm5ubz5s3Lyck5ceJEY2Pj/fv3pSq5SlEkB3tviejlvlapNPUyOqmvr//tt99evXq1sbFRKBTevXv3008/1dfXFyf3VSTFPUmSzc3NXV1dJEm+efMmMzNz0qRJurq6ubm5vd2/0YqTAwDQXOqcrNA/Cs7PaWpqWrFihampqYGBweTJk6OiohBCNjY29+7d27t3L84VP3z4cPF84q6uroSEBCcnJzqdbmxs7O/vj587wYKDg+l0urW1NY1GMzQ0nDNnTllZmXir7NfW1tZOmzaNxWKNGDFi7dq1+EkgPp+PZwzfuXPHzs6OzWZPnjxZPEu4N7NmzRoxYoSBgQGTyXR0dAwMDCwuLhZvvXjxIofDiYuL6/7Cc+fOjR49Wk9Pj8Fg6OjooD9TDEyYMCE2Nra2tla8p/aeHG2fw6MgmJ82BGn7Z5sgtXb8Vy3j11988UV2dnZtbS2VB9UWGnJy8Bg33L8Bg4+2f7YH+XiaKmhLInq1gJMDAOgNxBuN88cffxC9U1ERDgAAUDWIN0rYsmVLWlpaQ0PDiBEjcnJyVHQUV1dXGQOgGRkZKjpuP1FzckCfXblyJTIyUrIA0rJlyyR38PX15XA4urq6o0aNunPnjrr6iRDq6upKSkrqnhE8NjbWzc3N0NCQyWTy+fxNmzYpm6OW+pYRQkKhMD4+ns/nMxgMIyMjd3f38vJyhNC5c+f27t07tIYEqLlNpApwvxT0SNvvqSpIqc9/VFTUzJkzGxsb8aKjo6OpqSlC6Pz585K75efnz549e4A7qqRHjx5NmjQJITRmzBipTVOmTElJSamtrW1sbMzMzKTT6R999JGGt0ySpL+/v4uLS2FhoVAofPHixaxZs8Sze5KTk6dMmVJXV6fggbT9sw2/bwBQ2gDW46GgtM+ePXsyMjKysrI4HI545YEDB3R0dIKDgzWq3NG9e/c2b968atUqT0/P7lsNDAyCg4NNTEw4HM6CBQv8/f0vXbr07NkzTW45IyMjNzc3Ozv7vffeo9FoPB4vLy/P3d0db123bt2YMWNmzJjR2dmpyLG0HcQbAJQ2gPV4VF3a5/Hjx9u3b4+JiZEsmIQQ8vb2Dg0Nff78+YYNG1R3dGWNGTPmzJkzS5YsYTKZ3beeP39esjqZmZkZQkggEGhyy4cPHx47dqyHh0dvL4+Oji4qKupnJiptAfEGDFHkANXjkV34R9nSPn2uXdSbAwcOkCQ5a9as7pvi4uKcnZ2PHz9+5coVZU9Ramqqvr6+np5eXl7exx9/bGhoaGNjg5OvYyKRKCoqytbWls1mjx49Go/+Daznz5+z2ewRI0ZobMsdHR2FhYU9/u4RMzY2njJlSnJyMjkUprardzivP+D+DeiRgmPcA1iPR3bhH6Waklu7SEzBz7+Dg4Obm5vUSkdHx6dPn5IkeePGDR0dHXt7++bmZrLb/RvZpwiXq/juu+8aGhqqq6t9fHz09fXFdTE2bNjAZDJzcnLq6uq2bNmio6Nz+/Ztub0Ve++993q8FyLW0tLC4XBCQkIUb5P6lp8+fYoQ8vT0nDp1qpWVFZPJdHV1PXToEM7rIRYZGYkkiqTIAPdvANA+ra2tiYmJc+fOXbp0KZfL9fDwOHLkSE1Njex0RzLQaDT8O8DNzS01NbWpqSktLa0P7fj5+TU2Nm7fvr1v3ZDS0tLy9OlTR0fH3nbw8vIKCwsrLy/fvHmz1CYFT5G3t7ehoaG5uXlgYGBLS0tlZSVCqK2tLTU11d/ff968eUZGRtu2baPT6X07Ib2Jj4/n8XhxcXED2OaAt4wnuZmbm+/ataukpOT169dz5sxZs2bNN998I7mbk5MTQqi4uLj/R9RwEG/AUKTSejyShX/Uq7q6miRJPT09GfvExcW5uLikpKRcv35dcr2yp4jBYCCEcHa7hw8fCgQC8V1xNpttZWU1gCfk7NmzWVlZly9flpwBoYEt4zs6o0aN8vb2NjEx4XK5MTExXC5XKmbjC/T69ev+H1HDQbwBQ5Gq6/GIC/+oV1tbG/rzW683LBYrLS2NIIjly5e3traK1/fnFLW0tCCEtm3bJn5OuaKiQsHb73JlZGTs2bOnoKDA3t5+QBpUXcs8Hg8hhO/VYQwGw87OrqysTHI3nKgQX6zBDeINGIpUWo9HsvCPeuEvMrlPFHp5eYWHh5eWlu7cuVO8sj+nCFc4TEpKkhy7xwVJ++ngwYPp6elXr14dNmxY/1tTdcsGBgZOTk4PHjyQXNnZ2cnlciXXdHR0oD8v1uAG8QYMRSqtxyNZ+KefTfWThYUFQRCKPGGzc+dOV1fXu3fvitcoW+5I0vDhw1ksVlFRUd+63SOSJCMiIoqLi3Nzc6V+dWlsywihhQsX3r1798mTJ3hRIBBUVFRITY/GF8jS0nJgD62BIN6AoWjA6/H0VvhH2aYUqV2kOD09PQcHB1xVXe4JSUtLk3wGRZFyRzJa++yzz06fPp2amtrY2CgSiaqqql6+fIkQCgwMtLS07EO+nAcPHuzbt+/YsWN0Ol0yo+D+/fvxDhrYMkIoPDzczs4uKCiosrKytrY2IiKitbVVanYGvkAyntEZPNQxKW5gwHxo0CMF54wOYD0e2YV/lGpKRu0iKQp+/kNCQuh0ukAgwItnz57F09XMzMzWrFkjtfPGjRsl50PLOEUpKSn4LreTk1NZWdnRo0dxmT47O7tHjx6RJNne3h4REWFra0uj0XDZw5KSEpIk/f39EUJRUVE99vbmzZuTJk3Ctz0QQlZWVt7e3t9//z1Jkr1N30pISMCv1cCWsWfPni1atMjY2JjJZE6YMCE/P1+qBT8/P2tra6lJ0j3S9vnQWvx9DfEG9Ij6/5M4FQqVRyQV/vyXlpbSaDRxxTy1E4lEPj4+J06cgJaxmpoaFou1f/9+RXbW9ngD42kADACNzfLL5/NjY2NjY2OVTXisCiKRKDc3t6mpacDLamhjy1h0dLSnp2dISIgqGtc0EG8AGOQiIyMDAgICAwPVnpqzoKDgzJkz+fn5sh8JGiItI4QSExOLioouXrxIp9MHvHENBPEGgH7RisI/u3btCgkJ2b17t3q7MX369FOnTomzyQ3xlvPy8trb2wsKCoyNjQe8cc1EU3cHANBu8fHx8fHx6u6FfL6+vr6+vuruBfiv2bNnz549W929oBT8vgEAAEAFiDcAAACoAPEGAAAAFSDeAAAAoILWzxcICAhQdxeAZiksLERD4IOBk6AM+rcJJBUWForz8mkjgtTaIqY3b95MTExUdy/AUJSfn//OO++oYo4sALLhZN7q7kUfaXG8AUBdCILIzMxcsGCBujsCgDaB+zcAAACoAPEGAAAAFSDeAAAAoALEGwAAAFSAeAMAAIAKEG8AAABQAeINAAAAKkC8AQAAQAWINwAAAKgA8QYAAAAVIN4AAACgAsQbAAAAVIB4AwAAgAoQbwAAAFAB4g0AAAAqQLwBAABABYg3AAAAqADxBgAAABUg3gAAAKACxBsAAABUgHgDAACAChBvAAAAUAHiDQAAACpAvAEAAEAFiDcAAACoAPEGAAAAFSDeAAAAoALEGwAAAFSAeAMAAIAKEG8AAABQAeINAAAAKkC8AQAAQAWaujsAgBaor68nSVJyTUtLS11dnXjRwMCATqdT3i8AtAkh9b8IANDd+++/f+3atd626urqPn/+3NLSksouAaB1YDwNAPkWLVpEEESPm3R0dP7yl79AsAFALog3AMg3f/58Gq3nwWeCID755BOK+wOANoJ4A4B8xsbGvr6+urq63Tfp6Oj4+/tT3yUAtA7EGwAUsnTp0q6uLqmVNBrNz8+Py+WqpUsAaBeINwAoZNasWUwmU2qlSCRaunSpWvoDgNaBeAOAQvT09Pz9/aUmPbPZ7BkzZqirSwBoF4g3AChq8eLFQqFQvEin0+fPn89ms9XYJQC0CMQbABT14YcfSt6qEQqFixcvVmN/ANAuEG8AUBSdTg8MDGQwGHjRyMho+vTp6u0SAFoE4g0ASli0aFFHRwdCiE6nL126tLeHcgAA3UE+GwCU0NXVNWzYsNevXyOErl+/PmnSJHX3CACtAb9vAFCCjo7OsmXLEEI8Hs/b21vd3QFAm2j3aMDNmzefPXum7l6AocXMzAwh9N5772VnZ6u7L2DIWbBggbq70HfaPZ4WEBCQk5Oj7l4AAABFtPobW+vH0+bPn08C8KfMzEyEkKqPkp2drepDyIUQyszMVHcvAHXwZ1uraX28AYB68+fPV3cXANA+EG8AAABQAeINAAAAKkC8AQAAQAWINwAAAKgA8QYAAAAVIN4AgC5evMjlcv/zn/+ouyOqcuXKlcjIyDNnzjg4OBAEQRAEzpIg5uvry+FwdHV1R40adefOHXX1EyHU1dWVlJTUPXdDbGysm5uboaEhk8nk8/mbNm1qbm7W8JYRQkKhMD4+ns/nMxgMIyMjd3f38vJyhNC5c+f27t0rEomUOpDWU/ec8n6ZP38+PH8DJPXt+Zvz588bGhqeO3dOFV1SEaTw8zdRUVEzZ85sbGzEi46Ojqampgih8+fPS+6Wn58/e/bsge+oMh49eoRT0o0ZM0Zq05QpU1JSUmpraxsbGzMzM+l0+kcffaThLZMk6e/v7+LiUlhYKBQKX7x4MWvWrOLiYrwpOTl5ypQpdXV1Ch6ImmfLVEq7ew/xBkjR8P+TAoHAy8trQJpSMN7s3r3b2dm5tbVVvMbR0fHUqVM6OjrW1tb19fXi9WqPN0VFRXPnzk1PT/f09Oz+3e3n59fZ2SlexGldKisrNbnl06dPEwRx//793l4eEhLi5eUlFAoVOZaGf7YVAeNpAFDnxIkT1dXVlB3u8ePH27dvj4mJYbFYkuu9vb1DQ0OfP3++YcMGyjoj15gxY86cObNkyRImk9l96/nz53V1dcWLOIudQCDQ5JYPHz48duxYDw+P3l4eHR1dVFSUnJysyLEGAYg3YKi7fv26ra0tQRCHDh1CCKWmpurr6+vp6eXl5X388ceGhoY2NjanT5/GOx84cIDFYllYWHzxxRc8Ho/FYnl7e9+6dQtvDQkJYTAYVlZWePHLL7/U19cnCKKmpgYhFBoaun79+rKyMoIg+Hw+QujSpUuGhoa7du1S0Vs7cOAASZKzZs3qvikuLs7Z2fn48eNXrlzp8bUkSSYmJo4cOZLJZBobG8+ZM+ePP/7Am2SfIoSQSCSKioqytbVls9mjR49WRSKW58+fs9nsESNGaGzLHR0dhYWFnp6eMvYxNjaeMmVKcnIyqc1Z0RQH8QYMdZMnT75x44Z4cfXq1WFhYa2trRwOJzMzs6yszMHBYeXKlUKhECEUEhISFBQkEAjWrVtXXl5+586dzs7ODz74AOcpP3DggGT63pSUlJiYGPFicnLyzJkzHR0dSZJ8/PgxQgjfLu7q6lLRW7tw4YKLi4uenl73TWw2++uvv9bR0Vm5cmVLS0v3HaKjoyMjI7du3VpdXf3DDz88e/bMx8cHF/6RfYoQQps3b963b19SUtLLly9nzpy5ePHiX375ZQDfl0AguHr16sqVK8W1VjWw5RcvXnR0dPz666/Tpk3Df5qMHDkyJSVFKrS88847z58/v3fvXj8PpxUg3gDQM29vb0NDQ3Nz88DAwJaWlsrKSvEmGo2G//B3c3NLTU1tampKS0vrwyH8/PwaGxu3b98+cL3+r5aWlqdPnzo6Ova2g5eXV1hYWHl5+ebNm6U2tba2JiYmzp07d+nSpVwu18PD48iRIzU1NUePHpXcrcdT1NbWlpqa6u/vP2/ePCMjo23bttHp9L6dn97Ex8fzeLy4uLgBbHPAW8aT3MzNzXft2lVSUvL69es5c+asWbPmm2++kdzNyckJIVRcXNz/I2o+iDcAyIH/1BX/8S5l3Lhxenp64rEmzVFdXU2SZI8/bsTi4uJcXFxSUlKuX78uub6kpKS5uXncuHHiNePHj2cwGOKRQymSp+jhw4cCgcDd3R1vYrPZVlZWA3h+zp49m5WVdfnyZQ6HM1BtqqJlfEdn1KhR3t7eJiYmXC43JiaGy+VKxWx8gfAPx0EP4g0A/cVkMt+8eaPuXkhra2tDf37r9YbFYqWlpREEsXz58tbWVvH6+vp6hJCBgYHkzkZGRk1NTXKPi0fntm3bRvypoqJCwdvvcmVkZOzZs6egoMDe3n5AGlRdyzweDyGEb91hDAbDzs6urKxMcjc2m43+vFiDHsQbAPpFKBTW19fb2NiouyPS8BeZ3CcKvby8wsPDS0tLd+7cKV5pZGSEEJKKLgq+TXNzc4RQUlKS5ETYmzdv9uEtSDl48GB6evrVq1eHDRvW/9ZU3bKBgYGTk9ODBw8kV3Z2dnK5XMk1HR0d6M+LNehBvAGgXwoKCkiSnDhxIl6k0Wi9jbxRzMLCgiCIhoYGuXvu3LnT1dX17t274jXu7u4GBgaSN/lv3brV0dHx7rvvym1t+PDhLBarqKiob93uEUmSERERxcXFubm5Ur+6NLZlhNDChQvv3r375MkTvCgQCCoqKqSmR+MLZGlpObCH1kwQbwBQWldXV11dXWdn5/3790NDQ21tbYOCgvAmPp//9u3b3NxcoVD45s2biooKyReamJi8ePGivLy8qalJKBTm5+erbj60np6eg4NDVVWV3D3xqJrkMygsFmv9+vVnz55NT09vbGwsLi5etWoVj8cLDg5WpLXPPvvs9OnTqampjY2NIpGoqqrq5cuXCKHAwEBLS8s+5Mt58ODBvn37jh07RqfTCQn79+/HO2hgywih8PBwOzu7oKCgysrK2traiIiI1tZWqdkZ+ALJeEZnUKH4+dKBBfkFgJQ+PIN98OBB/MSMnp7erFmzUlJS8C1cJyensrKyo0ePGhoaIoTs7OwePXpEkmRwcDCdTre2tqbRaIaGhnPmzCkrKxO3VltbO23aNBaLNWLEiLVr127cuBEhxOfz8fPqd+7csbOzY7PZkydPfvXq1cWLFzkcTlxcXB/eKVIgv0BISAidThcIBHjx7NmzeLqamZnZmjVrpHbeuHGjZH6Brq6uhIQEJycnOp1ubGzs7+//8OFDvEnuKWpvb4+IiLC1taXRaObm5vPmzSspKSFJ0t/fHyEUFRXVY29v3rw5adIkfNsDIWRlZeXt7f3999+TJNnb9K2EhAT8Wg1sGXv27NmiRYuMjY2ZTOaECRPy8/OlWvDz87O2tu7q6uqxfUmDIL+Advce4g2QQsH/yeDgYBMTE5UeQhGKxJvS0lIajXby5ElquiSXSCTy8fE5ceIEtIzV1NSwWKz9+/crsvMgiDcwngaA0rQlrS+fz4+NjY2NjVU24bEqiESi3NzcpqamwMBAaBmLjo729PQMCQlRReMaCOKNSiie4XzFihUcDocgCMXvr/aW4VxBDx8+XLt27ahRozgcDo1G43K5zs7Ofn5+AzKDSDYZp0UyVT7GYDAsLCymTp2akJBQV1en6r4NVpGRkQEBAYGBgYpMHFCpgoKCM2fO5Ofny34kaIi0jBBKTEwsKiq6ePEinU4f8MY1lLp/YPWLxo6nKZXhHCeeunv3roKNy8hwLtfx48fpdPpf/vKXS5cu1dXVtbW1lZWVZWRkeHt7/+Mf/1CwkT6Te1ocHR25XC5JkviG/LVr14KCggiC4PF4t2/fVuQQqh5ziIyMxM822tvbZ2dnq+5AciGF6xGQJHn58uWIiAiV9gcoJTc3Nz4+XjIvtVyDYDxNu3uvsfFGqQznSsUbuRnOZbh586auru7777/fPf/5pUuXDh482Ic2lSL3tIjjjaTs7GwdHR0LCwvJ5Pm9GQT/JxWkVLwBg8Ag+GzDeJpKKJXhnCAIxVuWm+Fchri4OJFItHv3bhqNJrXpww8/XLNmTR/aVErfEr/Pnz8/KCiourr6yJEjqu0fAECVhkq8OXny5Lhx41gslr6+vr29PX6UmuxrxvWRI0cSBKGjo/Puu+/ir8tNmzZxuVwWi/X11193P7pUhnOSJBMSElxcXJhMJpfLxVNmFSE3w7mM/PYdHR3fffedqanphAkTZB9FXadFBvx0S35+vtw9AQCaS82/r/pHwfG0pKQkhNDu3btra2vfvn37j3/8Y8mSJSRJRkVFMRiMkydP1tfX379/f+zYsWZmZq9evcKv2rp1K0Lou+++a2hoqK6u9vHx0dfX7+joIEmys7PT3t7e1tZWcnQoLCxMKocH1tLSwuFwQkJCxGu2bt1KEMRXX31VV1cnEAhSUlKQYuNpT58+RQh5enpOnTrVysqKyWS6uroeOnRIPHn//PnzHA4nNja2+2sfPXqEEJo4caLco6jrtJC9jKeRJNnY2IgQGj58uNzOD4IxBwUhGE8bYgbBZ1u7e69IvOno6DAyMpo2bZp4TWdnZ3JyskAgMDAwCAwMFK//+eefEULiL2v8xSouxIujwuPHj/EijmFZWVl4saWlxdbWtqGhoXsHtm7d6uzsLK4eLxAI9PT0PvjgA/EOit+/wc+mffDBBz/99FNtbW19fT1+Vjk9PV3ua3Fukr/+9a+yd1PXacF6izckSRIEYWRkJPdtDoL/kwqCeDPUDILPtvQ4/uBz//79+vr6Dz/8ULxGV1d33bp1v/zyS58zriOEVqxYER0dnZycHBAQgBBKT0+fM2cOfspaEs5w/u2334oznD9+/FggEEyfPr0P70UywzleExMTc/jw4aNHjy5ZskT2a3FuKLk3S/qTiB7147TI1tLSQpJk93Z6g48+6CUlJWVnZ6u7F4AiiqQm0nCD//4NHorB+W4l9SfjOn7h559/fuPGDfzn/+HDh7s/tNVjhnP8ocE5dJWlYIbzHtnb27NYLDyqJoO6TotsuNuurq4K7g8A0ECD//cNTjAu+R2N9SfjOhYSEpKcnJyUlLRq1arhw4dLFVI8ePDg5cuXr169KvXdzWKxEELt7e1Kvg+EFM5w3iMmk/nhhx/m5eX99NNPkyZNktr69u3bTZs2HT9+XF2nRbZLly4hhD7++GMF9x8Kf/UTBBEWFiZZvhoMbllZWQsXLlR3L/pl8P++sbe3NzEx+fbbb6XW9yfjOmZjY7NgwYKcnJzt27eHhoaK15MyM5y7u7vr6Oh8//33fXo3CmU47010dDSTyQwPD5esrIX99ttveJK0uk6LDK9evUpKSrKxsVm+fLnirwIAaBw13z/qHwXnp+HU4mvXrq2qqhKJRI2NjThb7Y4dO+h0+smTJxsaGu7fv//OO+/weLzm5mb8Kqkb48eOHUMI/f7775It4yzlHh4ekit/++23Hk+1OONsQECArq7u8ePHGxoa7t27N23aNKTw855v3761t7f38fGpqKioqalZs2aNjo6O+LVy8w3n5OTo6em9++67Fy5cqK+v7+joePLkydGjR/l8vjhhsLpOC0mSjo6OhoaGTU1NIpGoq6ururo6IyPDwcHBysrql19+UeT8DIJ7qgpCMF9giBkEn23t7r3i+QUOHTrk4eHBYrFYLNY777yTkpJC9i/juti0adOOHz8uuUZuhvOmpqYVK1aYmpoaGBhMnjw5KioKIWRjY3Pv3j1F3ouMDOeK5LevrKzcsGGDh4eHgYGBrq6ukZHRO++88z//8z8//fQT3kEtp+XcuXOjR4/W09NjMBg6OjoIITwhbcKECbGxsbW1tYqcGXJQ/J9UEMSboWYQfLYJkiTl/wjSVHga0lAYrAcKwmPcWv2pVhBBEJmZmXD/ZugYBJ/twX//BgAAgCaAeKNB/vjjD6J3KqrAAYaCK1euREZGShZ9WLZsmeQOvr6+HA5HV1d31KhRfaudPFC6urqSkpLET5hJun79+qRJk/T09Hg8XkREhLKTPFXR8t69e11dXdlstr6+vqur6/bt2/EDGJiMAhznzp3bu3evthRSGjBqHs/rH43NDw3UZRCMcSsIKXz/JioqaubMmeJUDo6Ojqampgih8+fPS+6Wn58vWU9aLR49eoQn648ZM0Zq02+//cZms7dv397c3Hzjxg0zM7PPPvtM7S37+fnt37+/urq6qakpKyuLTqdLpg75f+zde0ATV9ow8DOQkIRwV27lIpcISMVqq66gvtS6pVVWFBXFW0VXS9WKeKsiggiIIi6wKNT18lI/UQGRoquiLlJqqda2q1TErUUsCnjhIhAgICHM98f5OpsvYDIhZELg+f3lzJk5c3IS85C5PI/8AhzJycne3t6NjY00jzUIPtvaPXqIN0AGA/8nRSKRp6enxruiGW/27t3r4uJC3U9IkqSzs/OpU6d0dHRsbGykSzxoPN6UlJTMnTs3IyNj7NixPaPCwoULHR0dqVSBCQkJBEHI3BjJfM/+/v7Sc4uvKD979gwvKizAERIS4unp2bM+SK8GQbyB82kAKOf48eO1tbUDratePXr0KCIiYvfu3fgpY4qXl1doaGhNTc2WLVvUd3RlvfPOO+fOnVuyZAnO2yStq6vr0qVL3t7eVPGOGTNmkCR5/vx5zfacm5srPbc2NjYIIeqkmcICHFFRUSUlJcnJyXSONQhAvAFDEfnmmgshISF6enpWVlZ4cd26dXw+nyAInKIiNDR08+bNFRUVBEEIBIKUlBQul2thYfHZZ59ZW1tzuVwvLy8q15xSXSG55ST6JiUlhSRJPz+/nk2xsbEuLi7Hjh0rKChQdorkl6VACEkkksjISHt7ex6PN2bMGPyHuSoeP37c2tpqb29PrcF5K+7duzegei4vLzcxMRkxYkSvrT0LcJiamnp7eycnJ5PafNcZfRBvwFAUFRUVFhYWHh5eW1t748aNqqqqqVOnvnz5EiGUIv3fuAAAIABJREFUkpIifZNxamrq7t27qcXk5ORZs2Y5OzuTJPno0aOQkJCgoCCRSLRhw4bKyso7d+50dXV9+OGHVVVVynaFEMJXj7u7u/vrZV66dMnV1RU/MiWDx+N99dVXOjo6q1evbmtr67mBnClau3btxo0b29vbDQ0Ns7KyKioqnJycVq9eTaVt3b59+/79+5OSkp4/fz5r1qzFixdLZ6zogxcvXiCEpLO7crlcHo+Hx6PxnsVicU1NzaFDhwoKCg4ePIiT2MoQiUSFhYWrV6+WaR03blxNTc0vv/zS11egTSDegCGnvb09MTFx7ty5S5cuNTY29vDwOHz4cH19/ZEjR/rWIYvFwr8D3N3d09LSWlpa0tPT+9CPr6+vUCiMiIjo2zBktLW1/f777zL566R5enpu3LixsrISV7WQRnOKvLy8jIyMzM3NAwMD29ranj59ihDq6OhIS0vz9/efN2+eiYnJzp072Wx23yaEgm8Ykz43hRBis9k9MzNppGc7OztbW9uoqKj9+/e/KcVZXFyctbV1bGyszPqRI0cihN70NPQgA/EGDDnK1lxQyvjx4/X19alTTxpUW1tLkmSvP24osbGxrq6uqampxcXF0utVKUvx8OFDkUg0evRo3MTj8aysrFScEHyNpKurS3plZ2cnj8dTpdv+6rmqqqq2tvb06dMnTpwYN25cz2tyuADH1atXexbgwG+Q6j/UtALEGzDkqFhzQSEOh1NXV9cvXamio6MD/VEz6U24XG56ejpBECtXrpT+i16VKcJn53bu3Ek9OvbkyROFhZfkw9fApB9tEYlEHR0duEKHxntms9nm5uY+Pj6ZmZllZWVxcXHSrfILcODAht+sQQ/iDRhyVK+5IIdYLO6vrlSEv8gUPlHo6em5adOm8vLymJgYaqUqU4RrO8kUEb9161YfXgLF0dHR0NDwyZMn1Bp8xWvMmDGqdNvvPQsEAl1d3bKyMmrNwYMHMzIyCgsLcWGUnjo7O9Efb9agB/EGDDkKay6wWCzq0reyioqKSJKcNGmS6l2pyMLCgiCI5uZmhVvGxMS4ubndvXuXWqNKWQo7Ozsul1tSUtK3YfeKxWLNnDnzxo0b1M0U+fn5BEH0eusdYz03NDQsXrxYek15eblEIrGzs0O0C3DgN8jS0lKll6ElIN6AIYfL5W7evDk3NzcjI0MoFJaWlq5Zs8ba2jo4OBhvIBAIXr16lZeXJxaL6+rqpP/4RQiZmZk9e/assrKypaUFx5Lu7u7Gxsaurq579+6Fhoba29sHBQX1oav8/Px+vB9aX1/fycmJThFifFZN+pq5wimS39uKFSvOnDmTlpYmFAolEkl1dfXz588RQoGBgZaWln3LlxMREfHy5ctdu3a1tbXdunUrISEhKCjI1dUVt2qkZz6ff+3atcLCQqFQKBaL7969u3z5cj6fv2nTJoTQgwcP9u/ff/ToUTabLZ2YCpdHoeA3iGYJK63H6NOl/Q3yCwAZNJ/BllNzgSTJhoaGadOmcblcR0fH9evXb926FSEkEAjwk+F37twZMWIEj8ebMmXKixcvgoOD2Wy2jY0Ni8UyMjKaM2dORUVF37qiU06CgmjkFwgJCWGz2SKRCC/m5ubi29WGDx9OlTuibN26VTq/gCplKV6/fr1t2zZ7e3sWi2Vubj5v3jxcccrf3x8hFBkZ2etob926NXnyZOrCiZWVlZeX17fffktt8O23306cOJHD4VhbW2/durWjo4Nq0lTPfn5+jo6OBgYGHA7H2dk5MDCwtLQUNymsS4L5+vra2NhQ2Q3kGAT5BbR79BBvgAzm/08GBwebmZkxeUSMTrwpLy9nsVgnT55kZkgKSSSSqVOnHj9+HHrG6uvruVzugQMH6Gw8COINnE8DQFUDNsuvQCCIjo6Ojo6mMqxokEQiycvLa2lp6fdM59rYMxYVFTV27NiQkBB1dD4AQbwBYDALCwsLCAgIDAykc+OAWhUVFZ07dy4/P1/+I0FDpGeEUGJiYklJyeXLl9lsdr93PjBBvAGg73bs2JGent7c3Ozo6JiTk6Pp4fRuz549ISEhe/fu1ewwpk+ffurUKSqb3BDv+fz5869fvy4qKjI1Ne33zgcslqYHAIAWi4uLk3m4b2Dy8fHx8fHR9CjAf82ePXv27NmaHgXT4PcNAAAAJkC8AQAAwASINwAAAJgA8QYAAAATIN4AAABgAkFqcx3TgICAAXsTKgAA9Dut/sbW7nhz69YtXLgXACYtXLgwNDTU09NT0wMBQ450hXKto93xBgCNIAgiKytLq//nA8A8uH4DAACACRBvAAAAMAHiDQAAACZAvAEAAMAEiDcAAACYAPEGAAAAEyDeAAAAYALEGwAAAEyAeAMAAIAJEG8AAAAwAeINAAAAJkC8AQAAwASINwAAAJgA8QYAAAATIN4AAABgAsQbAAAATIB4AwAAgAkQbwAAADAB4g0AAAAmQLwBAADABIg3AAAAmADxBgAAABMg3gAAAGACxBsAAABMgHgDAACACRBvAAAAMAHiDQAAACZAvAEAAMAEiDcAAACYAPEGAAAAEyDeAAAAYALEGwAAAExgaXoAAGiBM2fOtLS0SK8pKChoamqiFv39/c3NzRkfFwDahCBJUtNjAGCgCwoKOnHiBJvNxov4fw1BEAghiURiYGBQW1vL4XA0OUQABjw4nwaAYosWLUIIif/Q1dXV1dWF/62rqxsQEADBBgCF4PcNAIp1dXVZWlq+evWq19br169/8MEHDA8JAK0Dv28AUIzFYi1atIg6nyZt+PDh3t7ezA8JAK0D8QYAWhYtWiQWi2VWstnsZcuW6erqamRIAGgXOJ8GAC0kSdrb21dXV8us//HHHydMmKCRIQGgXeD3DQC0EASxdOlSmVNqdnZ248eP19SQANAuEG8AoEvmlBqbzQ4KCsJ3RQMAFILzaQAowc3N7eHDh9Ti/fv33377bQ2OBwAtAr9vAFDCsmXLqFNq7u7uEGwAoA/iDQBKWLp0aVdXF0KIzWYvX75c08MBQJvA+TQAlDN+/Ph///vfBEFUVlba29trejgAaA34fQOAcj755BOE0J/+9CcINgAoRbvzQycmJt66dUvTowBDS0dHB0EQr1+/DggI0PRYwJBz9uxZTQ+h77T7982tW7d++OEHTY8CDCDV1dU5OTlqPQSXy7W0tLS1tVXrURTKycnp+fApGMQY+Gyrm3Zfv8F/YGp1wAf9Kzs7e+HCher+VD969EggEKj1EAoRBJGVlbVgwQLNDgMwhpnPtlpp9+8bADRC48EGAG0E8QYAAAATIN4AAABgAsQbAAAATIB4AwAAgAkQbwBAly9fNjY2/uc//6npgahLQUFBWFjYuXPnnJycCIIgCGLZsmXSG/j4+BgaGurq6r799tt37tzR1DgRQt3d3UlJSV5eXj2biouLJ0+erK+vb21tvW3bttevX2u85/j4eDc3Nx6Px+fz3dzcIiIihEIh1RodHe3u7m5kZMThcAQCwRdffNHa2oqbLly4EB8fL5FIlHoJWo/UZvPnz58/f76mRwEGkKysrD58qi9evGhkZHThwgV1DElNEEJZWVl0toyMjJw1a5ZQKMSLzs7Ow4YNQwhdvHhRerP8/PzZs2f3/0CV8dtvv02ePBkh9M4778g03b9/n8fjRUREtLa23rx5c/jw4StWrNB4z76+vgcOHKitrW1pacnOzmaz2R9++CHV6u3tnZqa2tDQIBQKs7Ky2Gz2xx9/TLUmJyd7e3s3NjbSPFbfPtsDinaPHuINkDHA/0+KRCJPT89+6YpmvNm7d6+Li0t7ezu1xtnZ+dSpUzo6OjY2Nk1NTdR6jcebkpKSuXPnZmRkjB07tmdUWLhwoaOjY3d3N15MSEggCOI///mPZnv29/eXnlv8ROCzZ8/woq+vb1dXF9WKH5Z6+vQptSYkJMTT01MsFtM51gD/bNMB59MAYM7x48dra2sZO9yjR48iIiJ2797N5XKl13t5eYWGhtbU1GzZsoWxwSj0zjvvnDt3bsmSJRwOR6apq6vr0qVL3t7eVHW7GTNmkCR5/vx5zfacm5srPbc2NjYIIeqk2cWLF3V1danW4cOHI4REIhG1JioqqqSkJDk5mc6xBgGIN2CoKy4utre3Jwji0KFDCKG0tDQ+n6+vr3/+/PkZM2YYGRnZ2tqeOXMGb5ySksLlci0sLD777DNra2sul+vl5XX79m3cGhISoqenZ2VlhRfXrVvH5/MJgqivr0cIhYaGbt68uaKigiAI/MTolStXjIyM9uzZo6aXlpKSQpKkn59fz6bY2FgXF5djx44VFBT0ui9JkomJiaNGjeJwOKampnPmzPn1119xk/wpQghJJJLIyEh7e3sejzdmzBj8h7kqHj9+3NraKp0g1dnZGSF07969AdVzeXm5iYnJiBEjem2tqanh8XiOjo7UGlNTU29v7+TkZFKbswbQB/EGDHVTpky5efMmtbh27dqNGze2t7cbGhpmZWVVVFQ4OTmtXr0aV5IOCQkJCgoSiUQbNmyorKy8c+dOV1fXhx9+WFVVhRBKSUmRTjCTmpq6e/duajE5OXnWrFnOzs4kST569AghhC8Xd3d3q+mlXbp0ydXVVV9fv2cTj8f76quvdHR0Vq9e3dbW1nODqKiosLCw8PDw2traGzduVFVVTZ069eXLl0jRFCGEtm/fvn///qSkpOfPn8+aNWvx4sU///yzKi/kxYsXCCFDQ0NqDZfL5fF4eDwa71ksFtfU1Bw6dKigoODgwYN6eno9txGJRIWFhatXr5ZpHTduXE1NzS+//NLXV6BNIN4A0DsvLy8jIyNzc/PAwMC2tranT59STSwWC//h7+7unpaW1tLSkp6e3odD+Pr6CoXCiIiI/hv1f7W1tf3+++/4r/VeeXp6bty4sbKycvv27TJN7e3tiYmJc+fOXbp0qbGxsYeHx+HDh+vr648cOSK9Wa9T1NHRkZaW5u/vP2/ePBMTk507d7LZ7L7NDwXfMCZ9bgohxGaz29vbVem2v3q2s7OztbWNiorav3//woULe90mLi7O2to6NjZWZv3IkSMRQqWlpcqNWztBvAFAAfwHKfXHu4zx48fr6+tT55oGjtraWpIke/1xQ4mNjXV1dU1NTS0uLpZeX1ZW1traOn78eGrNhAkT9PT0qDOHMqSn6OHDhyKRaPTo0biJx+NZWVmpOD/4GgmurErp7Ozk8XiqdNtfPVdVVdXW1p4+ffrEiRPjxo3reYkuNzc3Ozv76tWr0j+kMPwGqf5DTStAvAFAVRwOp66uTtOjkNXR0YEQ6nmFXBqXy01PTycIYuXKldJ/0Tc1NSGEDAwMpDc2MTFpaWlReFx8dm7nzp3EH548eSJ9kbwP8CUx6UdbRCJRR0eHtbW1Kt32V89sNtvc3NzHxyczM7OsrCwuLk66NTMzc9++fUVFRQ4ODj33xYENv1mDHsQbAFQiFoubmpo0Xg6nJ/xFpvCJQk9Pz02bNpWXl8fExFArTUxMEEIy0YXmyzQ3N0cIJSUlSd8Iq2JdREdHR0NDwydPnlBr8AWwMWPGqNJtv/csEAh0dXXLysqoNQcPHszIyCgsLHzrrbd63aWzsxP98WYNehBvAFBJUVERSZKTJk3CiywW601n3hhmYWFBEERzc7PCLWNiYtzc3O7evUutGT16tIGBgfRF/tu3b3d2dr733nsKe7Ozs+NyuSUlJX0bdq9YLNbMmTNv3LhB3VuRn59PEESvt94x1nNDQ8PixYul15SXl0skEjs7O4QQSZLbtm0rLS3Ny8uT+aUoDb9BlpaWKr0MLQHxBgCldXd3NzY2dnV13bt3LzQ01N7ePigoCDcJBIJXr17l5eWJxeK6ujrpP5wRQmZmZs+ePausrGxpaRGLxfn5+eq7H1pfX9/JyYlODVB8Vk36mjmXy928eXNubm5GRoZQKCwtLV2zZo21tXVwcDCd3lasWHHmzJm0tDShUCiRSKqrq58/f44QCgwMtLS07Fu+nIiIiJcvX+7atautre3WrVsJCQlBQUGurq64VSM98/n8a9euFRYWCoVCsVh89+7d5cuX8/n8TZs2IYQePHiwf//+o0ePstlsQsqBAwekO8FvkIeHRx9Grn0Yfr60f0F+ASCjD89gHzx4EJ/E19fX9/PzS01NxZdwR44cWVFRceTIESMjI4TQiBEjfvvtN5Ikg4OD2Wy2jY0Ni8UyMjKaM2dORUUF1VtDQ8O0adO4XK6jo+P69eu3bt2KEBIIBPip8jt37owYMYLH402ZMuXFixeXL182NDSMjY3twytFNPILhISEsNlskUiEF3Nzc/HtasOHD//8889lNt66dat0foHu7u6EhISRI0ey2WxTU1N/f/+HDx/iJoVT9Pr1623bttnb27NYLHNz83nz5pWVlZEk6e/vjxCKjIzsdbS3bt2aPHkydeHEysrKy8vr22+/pTb49ttvJ06cyOFwrK2tt27d2tHRQTVpqmc/Pz9HR0cDAwMOh+Ps7BwYGFhaWoqb3nTLWUJCgnQPvr6+NjY2VHYDOQZBfgHtHj3EGyCDgf+TwcHBZmZmaj0EHXTiTXl5OYvFOnnyJDNDUkgikUydOvX48ePQM1ZfX8/lcg8cOEBn40EQb+B8GgBK05a0vgKBIDo6Ojo6msqwokESiSQvL6+lpSUwMBB6xqKiosaOHRsSEqKOzgcgiDcADGZhYWEBAQGBgYF0bhxQq6KionPnzuXn58t/JGiI9IwQSkxMLCkpuXz5MpvN7vfOByaIN2ohp+6FjFWrVhkaGhIEQfN+nvfff5/oQc7dLz09fPhw/fr1b7/9tqGhIYvFMjY2dnFx8fX1VfGOVTrkTIt0aRZMT0/PwsLi/fffT0hIaGxsVPfYaNqxY0d6enpzc7Ojo2NOTo6mh0PLnj17QkJC9u7dq9lhTJ8+/dSpU1RyuSHe8/nz51+/fl1UVGRqatrvnQ9cmj6hp5IBe/1Gft0LGTjR4d27d2n23PNN/Oijj2gO7NixY2w2+3/+53+uXLnS2NjY0dFRUVGRmZnp5eX1j3/8g2YnfaZwWpydnY2NjUmSxDeAffPNN0FBQQRBWFtb//TTT3QOMQjOcdOEaNe/AYPDIPhss5iNbkOFgYFBcHAwvsF0wYIF586dy87Orqqqwjfmq4LL5QqFQumsGJ999pl0jkg5fvjhh+DgYG9v76tXr7JY/++td3JycnJyMjExKS8vV3FsCtGfFoIgTExM3n///ffff9/X13fhwoW+vr6//fabsbGxugcJAFATOJ+mFgrrXkijCm/QceXKFelgU1VVdf/+/Q8++IDOvrGxsRKJZO/evVSwoXz00Ueff/45/WH0jVLTQpk/f35QUFBtbe3hw4fVOz4AgDoNlXhz8uTJ8ePHc7lcPp/v4OCAU3eQfa3wMWrUKIIgdHR03nvvPfx1+cUXXxgbG3O53K+++qrn0WXqXpAkmZCQ4OrqyuFwjI2N8SMafbNv374NGzZQi3LqqXR2dl6/fn3YsGETJ06U36empkUO/DRlfn6+wi0BAAOXhs/nqYbm9ZukpCSE0N69exsaGl69evWPf/xjyZIlJElGRkbq6emdPHmyqanp3r1777777vDhw1+8eIH3Cg8PRwhdv369ubm5trZ26tSpfD6/s7OTJMmuri4HBwd7e3vpYrEbN26UyRmFtbW1GRoahoSEUGvCw8MJgvjb3/7W2NgoEolSU1MR7es30qqrq93d3SUSCbXm4sWLhoaG0dHRPTf+7bffEEKTJk1S2K2mpoWUun4jA6dTtLOzUzj4QXCOmyYE12+GmEHw2dbu0dOJN52dnSYmJtOmTaPWdHV1JScni0QiAwODwMBAav2PP/6IEKK+rPEXK1WcHEeFR48e4UUcw7Kzs/FiW1ubvb19c3NzzwGEh4e7uLgIhUK8KBKJ9PX1P/zwQ2oDpe4XkPb5559/+eWXNDfGubD+/Oc/y99MU9OCvSnekCSJr+gofJmD4P8kTRBvhppB8Nke/OfT7t2719TU9NFHH1FrdHV1N2zYoEqFD4TQqlWrjI2NqcLjGRkZc+bMwVk9pPWse/Ho0SORSDR9+nQVX9ezZ88uXLhApe1SCN8zrfBiiaamRb62tjaSJHv28yY9bxkffBBCCxcu1PQoAHPeVMlNiwz++9PwqRicX12aKhU+8I6ffvppQkLCjz/+OHHixC+//LLn0xiZmZmJiYlFRUXSqchxej6cs10V8fHxq1evxtWi6HBwcOByufismhyamhb58LDd3Nxobo//EhzcFi5cGBoa6unpqemBAIbcunWL+ktOSw3+eIO/1Orr62XWq1LhAwsJCUlOTk5KSlqzZo2dnZ1M4d6DBw9evXq1sLBQ5rsbRwhcxbbPXrx4cfr06YcPH9LfhcPhfPTRR+fPn//+++8nT54s0/rq1asvvvji2LFjmpoW+a5cuYIQmjFjBs3tad4grtUWLlzo6ek5FF4poGh7vBn859McHBzMzMyuXbsms16VCh+Yra3tggULcnJyIiIiQkNDqfWk3LoXo0eP1tHR+fbbb/v0av6f+Pj4pUuXmpmZKbVXVFQUh8PZtGlTz9rs9+/fxzdJa2pa5Hjx4kVSUpKtre3KlSvp7wUAGHA0fP1INTTvT8MFJ9avX19dXS2RSIRCIc6OvmvXLjabffLkyebm5nv37o0bN87a2rq1tRXvJXNh/OjRowih//znP9I946oYHh4e0ivv37/f61RTecgDAgJ0dXWPHTvW3Nz8yy+/TJs2DSlzv8CLFy+MjIyePHnSs0lhfvucnBx9ff333nvv0qVLTU1NnZ2djx8/PnLkiEAgoBLUa2paSJJ0dnY2MjJqaWmRSCTd3d21tbWZmZlOTk5WVlY///wznckZBNdUaUJwv8AQMwg+29o9evr5bA4dOuTh4cHlcrlc7rhx41JTU0nVKnxQpk2bduzYMek1CutetLS0rFq1atiwYQYGBlOmTImMjEQI2dra/vLLL3Rey6ZNm5YuXdprE516Kk+fPt2yZYuHh4eBgYGurq6Jicm4ceP++te/fv/993gDjUzLhQsXxowZo6+vr6enp6Ojg/5IMTBx4sTo6OiGhgY6M0MOiv+TNEG8GWoGwWebIElS8Y+ggSogIAAhdPbsWU0PBAwU2dnZCxcu1OpPNU0EQWRlZcH1m6FjEHy2B//1GwAAAAMBxJsB5Ndff5Vz972aKj4BUFBQEBYWJl0SYtmyZdIb+Pj4GBoa6urqvv322/jinEacPn16woQJhoaGI0aMWLFixYsXLxjYNz4+3s3Njcfj8fl8Nze3iIgI/IgFJqfExoULF+Lj47WlNB9DNHw+TzUDth4B0JRBcI6bJtRP128iIyNnzZpFJXpwdnYeNmwYQujixYvSm+Xn58+ePVv1w/VZZmYmQig+Pr6pqenu3btOTk5jx44Vi8Xq3tfX1/fAgQO1tbUtLS3Z2dlsNls6OYj8EhvJycne3t6NjY3KvtheDYLPtnaPHuINkMHA/0mRSOTp6anxrvol3uzdu9fFxYW625AkSWdn51OnTuno6NjY2DQ1NVHrNR5vpk2b9tZbb3V3d+PFQ4cOIYSKi4vVva+/v7/0/OBrxs+ePcOLvr6+0tkC8eW0p0+fUmtCQkI8PT1pxjb5BkG8gfNpACjn+PHjtbW1A62rPnj06FFERMTu3btlslR4eXmFhobW1NRs2bJFU2PrqaqqytramvijeAeumfTkyRN175ubmys9PzY2Nggh6qSZwhIbUVFRJSUl2v6cZn+BeAOGIvLNNRdCQkL09PSoEsLr1q3j8/kEQeAUFaGhoZs3b66oqCAIQiAQpKSkcLlcCwuLzz77zNramsvlenl5UbnmlOoKyS0noQ4pKSkkSfr5+fVsio2NdXFxOXbsWEFBQa/7yplA+UUrEEISiSQyMtLe3p7H440ZM4Zm8iEnJyfp2IwvwDg5Oal7Xxnl5eUmJiYjRozotbVniQ1TU1Nvb+/k5GRSm+8r6zea/XmlIjifBmTQPOcgv+bCkiVLLC0tqY0TEhIQQnV1dXhx3rx5zs7OVGtwcDCfz3/w4EFHR0dZWRm+KE2dUVGqKznlJHpCKp9Pc3Jycnd3l1np7Oz8+++/kyR58+ZNHR0dBwcH/KivzPm0PhetIElyy5YtHA4nJyensbFxx44dOjo6dIqFFxUVsdnslJQUoVB4//79UaNG0S+jrsq+WGdnZ3V19cGDBzkczsmTJ3vdptcSGyRJhoWFoT4lgJcxCM6naffoId4AGXT+TyqsuaBsvJGuofDTTz8hhHbv3t2HrpSiYrxpbW0lCGLWrFky66l4Q5Lk5s2bEUI48YR0vFGlaEV7e7u+vj61r0gk4nA4a9eupTPmnTt3Un8o29raVlVV0X+9quxLkqSlpSVCaNiwYX//+9+pwCmj1xIbJEn+7//+L0Lo//yf/6PUEXsaBPEGzqeBIUfZmgtKGT9+vL6+PnVyacCqra0lSRJni3iT2NhYV1fX1NTU4uJi6fWqFK14+PChSCQaPXo0buLxeFZWVnSmKzw8/MiRI9evX29tbX38+LGXl5enp2dVVZXCHVXcF6uqqqqtrT19+vSJEyfGjRvX86qbnBIbeJJfvnxJ/3CDFcQbMOSoWHNBIQ6HU1dX1y9dqU9HRwdCiMPhyNmGy+Wmp6cTBLFy5UrpHK+qTGBbWxtCaOfOndSDZU+ePFFYlun58+fx8fGffvrpBx98wOfzHR0djx49+uzZM/x7UX37Uthstrm5uY+PT2ZmZllZWVxcnHRrZmbmvn37ioqKHBwceu7L4/HQHxM+xEG8AUOO6jUX5BCLxf3VlVrhL0GFTyN6enpu2rSpvLw8JiaGWqnKBOLKTzIlxm/duiV/r/LycolEIl0wycjIyMzMrKysTOERVdm3J4FAoKurK73vwYMHMzIyCgsL31TPqbOzE/3rn9RSAAAgAElEQVQx4UMcxBsw5CisucBisaiKpcoqKioiSXLSpEmqd6VWFhYWBEE0Nzcr3DImJsbNze3u3bvUGlWKVtjZ2XG53JKSEqVGiyPZ8+fPqTUtLS2vXr3Cdzarb9+GhobFixdLr8HRC+9L0iuxgScZXwEa4iDegCGHy+Vu3rw5Nzc3IyNDKBSWlpauWbPG2to6ODgYbyAQCF69epWXlycWi+vq6mQe1DAzM3v27FllZWVLSwuOJd3d3Y2NjV1dXffu3QsNDbW3t6fqfCvVVX5+PmP3Q+vr6zs5OeFqs/Lhs2rST5konED5va1YseLMmTNpaWlCoVAikVRXV+NgEBgYaGlp2Wu+HEdHx2nTph09evTGjRvt7e1VVVX4WH/961/xBmral8/nX7t2rbCwUCgUisXiu3fvLl++nM/nb9q0CSH04MGD/fv3Hz16lM1mS6eewgVQKHiSPTw8FE7O4KeRuxT6C9yfBmTQvIdHTs0FkiQbGhqmTZvG5XIdHR3Xr1+/detWhJBAIMB3Od+5c2fEiBE8Hm/KlCkvXrwIDg5ms9k2NjYsFsvIyGjOnDkVFRV964pOOQkKUvl+6JCQEDabLRKJ8GJubi4uxjp8+HCqGBJl69at0vdDq1K04vXr19u2bbO3t2exWObm5vPmzcP1qPz9/RFCkZGRvY62vr4+NDRUIBBwOBwDA4PJkyd//fXXVKv69vXz83N0dDQwMOBwOM7OzoGBgaWlpbhJYeURzNfX18bGhspu0GeD4P407R49xBsgg/n/k8HBwWZmZkweEVM93pSXl7NYrDc9TcI8iUQyderU48ePa9G+CtXX13O53AMHDqje1SCIN3A+DQBVaWkOYIFAEB0dHR0dTWVn0SCJRJKXl9fS0tKHPOia2peOqKiosWPHhoSEqKNzrQPxBoChKywsLCAgIDAwkM6NA2pVVFR07ty5/Px8+Y8EDah9FUpMTCwpKbl8+TKbze73zrURxBsA+m7Hjh3p6enNzc2Ojo45OTmaHk5f7NmzJyQkZO/evZodxvTp00+dOkXlmtOKfeU7f/7869evi4qKTE1N+71zLcXS9AAA0GJxcXEyj/5pIx8fHx8fH02PYrCZPXv27NmzNT2KgQV+3wAAAGACxBsAAABMgHgDAACACRBvAAAAMEHr7xeorq7Ozs7W9CjAQIEzPw6Rj4TCNJdgMBkEbzdBanOV04CAAC29CRUAAPpAq7+xtTveAKARBEFkZWUtWLBA0wMBQJvA9RsAAABMgHgDAACACRBvAAAAMAHiDQAAACZAvAEAAMAEiDcAAACYAPEGAAAAEyDeAAAAYALEGwAAAEyAeAMAAIAJEG8AAAAwAeINAAAAJkC8AQAAwASINwAAAJgA8QYAAAATIN4AAABgAsQbAAAATIB4AwAAgAkQbwAAADAB4g0AAAAmQLwBAADABIg3AAAAmADxBgAAABMg3gAAAGACxBsAAABMgHgDAACACRBvAAAAMAHiDQAAACZAvAEAAMAEiDcAAACYAPEGAAAAEyDeAAAAYALEGwAAAEwgSJLU9BgAGOiCg4MfPnxILd65c8fR0dHU1BQv6urqnjhxwtbWVkOjA0A7sDQ9AAC0gKWl5ZEjR6TX3Lt3j/q3k5MTBBsAFILzaQAotnjx4jc16enpBQUFMTgWALQVnE8DgJbRo0c/ePCg1/8vDx8+dHFxYX5IAGgX+H0DAC2ffPKJrq6uzEqCIN555x0INgDQAfEGAFoWLVokkUhkVurq6i5fvlwj4wFA68D5NADo8vLyun37dnd3N7WGIIiqqiobGxsNjgoAbQG/bwCga9myZQRBUIs6OjpTpkyBYAMATRBvAKArICBAepEgiE8++URTgwFA60C8AYCu4cOHT58+nbprgCAIf39/zQ4JAC0C8QYAJSxduhRf8tTV1f3oo4+GDRum6REBoDUg3gCghLlz5+rp6SGESJJcunSppocDgDaBeAOAEvh8/l/+8heEkJ6e3qxZszQ9HAC0CcQbAJSzZMkShJC/vz+fz9f0WADQKiRtWVlZmh4sAACAAWT+/Pn0g4jS+aEh6gCQkZERGBjIYqk9vXpSUhJCaOPGjeo+kGbdunUrOTkZvlu0Dv580qf0f5gFCxYouwsAg4yfnx+Xy2XgQGfPnkVD4z9dcnLyUHiZgwz+fNIH128AUBozwQaAQQbiDQAAACZAvAEAAMAEiDcAAACYAPEGAAAAEyDeADDYXL582djY+J///KemB6IuBQUFYWFh586dc3JyIgiCIIhly5ZJb+Dj42NoaKirq/v222/fuXNHU+M8ffr0hAkTDA0NR4wYsWLFihcvXjCwb3x8vJubG4/H4/P5bm5uERERQqGQao2OjnZ3dzcyMuJwOAKB4IsvvmhtbcVNFy5ciI+P71lUsB9BvAFgsBncRRR37dqVkpKyY8eOefPmPX782NnZediwYRkZGZcuXaK2uXbt2tmzZ2fNmlVWVvbuu+9qZJxZWVlLliwJCAiorq4+f/78jRs3ZsyY0dXVpe59v/vuu9WrVz99+vTly5cxMTHx8fHz58+nWgsLCz///PPKysr6+vq4uLjk5GSqyga+y3/69OlNTU19eL20KJtfgP72AAAVzZ8/X6nntxkmEok8PT1V74f+d8vevXtdXFza29upNc7OzqdOndLR0bGxsWlqaqLW5+fnz549W/Wx9dm0adPeeuut7u5uvHjo0CGEUHFxsbr39ff3l54fHE6ePXuGF319fbu6uqhW/MzT06dPqTUhISGenp5isZjOsZT9fMLvGwBAHx0/fry2tpaxwz169CgiImL37t0yzz95eXmFhobW1NRs2bKFscEoVFVVZW1tTRWEtbOzQwg9efJE3fvm5uZKzw+uP0udNLt48SJVwAkhNHz4cISQSCSi1kRFRZWUlCQnJ9M5lrIg3gAwqBQXF9vb2xMEgf8oTktL4/P5+vr658+fnzFjhpGRka2t7ZkzZ/DGKSkpXC7XwsLis88+s7a25nK5Xl5et2/fxq0hISF6enpWVlZ4cd26dXw+nyCI+vp6hFBoaOjmzZsrKioIghAIBAihK1euGBkZ7dmzR00vLSUlhSRJPz+/nk2xsbEuLi7Hjh0rKCjodV+SJBMTE0eNGsXhcExNTefMmfPrr7/iJvlThBCSSCSRkZH29vY8Hm/MmDE08+44OTlJB2N8AcbJyUnd+8ooLy83MTEZMWJEr601NTU8Hs/R0ZFaY2pq6u3tnZycTKrjrCz9n0JwPg0AhvXtfFpVVRVC6ODBg3gxPDwcIXT9+vXm5uba2tqpU6fy+fzOzk7cGhwczOfzHzx40NHRUVZWhq9RUydYlixZYmlpSfWckJCAEKqrq8OL8+bNc3Z2plovXrxoaGgYHR2t7IBpfrc4OTm5u7vLrHR2dv79999Jkrx586aOjo6Dg0NrayvZ43xaZGSknp7eyZMnm5qa7t279+677w4fPvzFixe4Vf4UbdmyhcPh5OTkNDY27tixQ0dH56efflI42qKiIjabnZKSIhQK79+/P2rUqI8++ojWdKi2L9bZ2VldXX3w4EEOh3Py5Mlet2lrazM0NAwJCZFZHxYWhhC6e/euwqPA+TQAQC+8vLyMjIzMzc0DAwPb2tqePn1KNbFYLPyHv7u7e1paWktLS3p6eh8O4evrKxQKIyIi+m/U/9XW1vb77787Ozu/aQNPT8+NGzdWVlZu375dpqm9vT0xMXHu3LlLly41Njb28PA4fPhwfX39kSNHpDfrdYo6OjrS0tL8/f3nzZtnYmKyc+dONptNZ368vb23bdsWEhJiZGQ0evTolpaWY8eO0XyxquyL2dnZ2draRkVF7d+/f+HChb1uExcXZ21tHRsbK7N+5MiRCKHS0lKljkgHxBsAhhZcn1QsFvfaOn78eH19fepc08BRW1tLkqS+vr6cbWJjY11dXVNTU4uLi6XXl5WVtba2jh8/nlozYcIEPT096syhDOkpevjwoUgkGj16NG7i8XhWVlZ05ic8PPzIkSPXr19vbW19/Pixl5eXp6cn/ump1n2xqqqq2tra06dPnzhxYty4cT0vs+Xm5mZnZ1+9etXQ0FCmCU/yy5cv6R+OJog3AID/D4fDqaur0/QoZHV0dCCEOByOnG24XG56ejpBECtXrmxvb6fW4xt8DQwMpDc2MTFpaWlReNy2tjaE0M6dO4k/PHnyRPoCe6+eP38eHx//6aeffvDBB3w+39HR8ejRo8+ePcMnJNW3L4XNZpubm/v4+GRmZpaVlcXFxUm3ZmZm7tu3r6ioyMHBoee+PB4P/THh/QviDQDgv8RicVNTk62traYHIgt/CSp8GtHT03PTpk3l5eUxMTHUShMTE4SQTHSh+TLNzc0RQklJSdLXIW7duiV/r/LycolE8tZbb1FrjIyMzMzMysrKFB5RlX17EggEurq60vsePHgwIyOjsLBQ+hDSOjs70R8T3r8g3gAA/quoqIgkyUmTJuFFFov1pjNvDLOwsCAIorm5WeGWMTExbm5ud+/epdaMHj3awMDg559/ptbcvn27s7PzvffeU9ibnZ0dl8stKSlRarQ4kj1//pxa09LS8urVK3xns/r2bWhoWLx4sfQaHL3wviRJbtu2rbS0NC8vT+bXnjQ8yZaWlgoPpyyINwAMdd3d3Y2NjV1dXffu3QsNDbW3tw8KCsJNAoHg1atXeXl5YrG4rq5O5hEQMzOzZ8+eVVZWtrS0iMXi/Px89d0Pra+v7+TkVF1drXBLfFZN+ikTLpe7efPm3NzcjIwMoVBYWlq6Zs0aa2vr4OBgOr2tWLHizJkzaWlpQqFQIpFUV1fjYBAYGGhpadlrvhxHR8dp06YdPXr0xo0b7e3tVVVV+Fh//etf8QZq2pfP51+7dq2wsFAoFIrF4rt37y5fvpzP52/atAkh9ODBg/379x89epTNZhNSDhw4IN0JnmQPDw+Fk6M0+reywf3QADCsD/dDHzx4ED8xo6+v7+fnl5qaii//jhw5sqKi4siRI0ZGRgihESNG/PbbbyRJBgcHs9lsGxsbFotlZGQ0Z86ciooKqreGhoZp06ZxuVxHR8f169dv3boVISQQCPAN03fu3BkxYgSPx5syZcqLFy8uX75saGgYGxur7Muk+d0SEhLCZrNFIhFezM3NxberDR8+/PPPP5fZeOvWrdL3Q3d3dyckJIwcOZLNZpuamvr7+z98+BA3KZyi169fb9u2zd7ensVimZubz5s3r6ysjCRJf39/hFBkZGSvo62vrw8NDRUIBBwOx8DAYPLkyV9//TXVqr59/fz8HB0dDQwMOByOs7NzYGBgaWkpbnrTLWcJCQnSPfj6+trY2FDZDeRQ9vMJ8QaAgYuBfDbBwcFmZmZqPYRCNL9bysvLWSzWm54mYZ5EIpk6derx48e1aF+F6uvruVzugQMH6GwMz98AAJSj1pTA/UggEERHR0dHR1PZWTRIIpHk5eW1tLQEBgZqy750REVFjR07NiQkRB2dQ7yh68CBA/iK5eHDh/Gafsz6LidJuIxVq1YZGhoSBEHzAib9nmVIJ3t/0xN8iYmJBEHo6Oi4ubnduHGDTrfyD0QQBD63s2TJkv/85z9961Capt41mRdFEISenp6FhcX777+fkJDQ2Nio+tGHprCwsICAgMDAQDo3DqhVUVHRuXPn8vPz5T8SNKD2VSgxMbGkpOTy5ctsNrvfO0cIrt8oo7y8HCH05Zdf4sWLFy8aGRlduHBB9Z69vb1TU1MbGhqEQmFWVhabzf7444/ftDHO7EQn24SyPfeEz49bWVlRuT0oXV1dOCnT9OnT6Xco50DGxsYkSba2tl64cMHe3t7AwODXX39VvWcNvmvUi8IX5L/55pugoCCCIKytrekkRCHVfz4tLCwMP9vo4OBw9uxZ9R1IPmW/W65evbpt2zb1jWdoysvLi4uLk84erRBcv1EjmW+ufqQwSbg0peKNUj335OzsjO8Zzc7OlmnKysry8vLq93iDff311wihdevWqd6zBt81mReFnT17VkdHx8LCQjp5/psM8HoE/QW+W7QUXL/RDiRJnj17lkrfpDBJuDQqSzkdSvXcq7Vr1yKEvvzyS5n1iYmJmzdvpt+PUiZOnIgQun//vpr67xtV3jXK/Pnzg4KCamtrqVN8AAwR/RxvkpOT+Xy+jo7Oe++9Z2lpyWaz+Xz+u+++O3XqVPzYlImJyRdffEFt/91337m7uxsbG3O5XA8Pj6tXryKEvvrqKwMDA4IgTE1N8/Lyfv755xEjRujq6so8x9Qr+fnVkdy05ApbpSmV9R0hJJFI4uLiXF1deTze8OHDHR0d4+Li8F/EPckkCSdJMiEhwdXVlcPhGBsb43tS+0amZzoJ5D/44INRo0Z98803Dx8+pFZ+//33IpHIx8dHZuP+ekNxKUMqeYk2vmty4Kdb8vPzFW4JwKBC/6cQzd+8u3btQgjdvn27ra2tvr7+448/RghdunSprq6ura0N3/ZQUlKCNz579mxUVNSrV68aGhomTZo0bNgwvP7Bgwf6+vrLly/Hi2FhYceOHaM5Tvn51eWnJZffKnNmRqms73v27NHV1T1//rxIJPr3v/9taWn5/vvv9zr+nknCw8PDCYL429/+1tjYKBKJUlNTEe3zafJ7VphAHid7//vf/44QCg0Npdb7+/unp6fjBCHS59P6/IbKnHo6efIkQmjr1q14URvftZ4vioLrydvZ2fXalTQ4nwYGMs1fv8HxpqWlBS+eOHECIUQ9cPTjjz8ihDIzM3vuiDPK4SywJEn+4x//QAhlZGScPn1606ZN9McZHBws/Z/8p59+Qgjt3r2bJEmRSGRgYBAYGEi14vHgL1z5rSS9by6qkiuOCo8ePcKLEyZMmDhxItXzp59+qqOj8/r1657jDw8Pd3FxEQqFeFEkEunr63/44YfUBkpdv5HTMx043jQ1NfH5fFNTU/yoXUVFha2t7evXr3vGG2lKvaHS9wvk5ORYWlpaWFhUV1eT2vmuybyongiCMDEx6bVJGsQbMJAp+/lkqe+XE4bvfsGnRxBC+Da7XjMy4SbqUYBPP/30X//612efffbnP/85JyenzwOQzq8uPy25sknL5ZPJ+t7R0SFd5FUikbDZbOmz/xhOEn7t2jUqSfijR49EItH06dP7MAb5PdNnbGy8ePHio0ePZmZmrlixIikpae3atXp6ejiv35so+4Y2NzcTBKGrq2tlZTVz5sxdu3bhUrja+K7J19bWRpIkfohdoerq6uzsbFqj11o4/eWgf5mDT3V1tVKpXdUeb+S7dOlSQkJCWVkZzvYj07pnz56cnBzVC6RT+dXlpyVXJWm5QjNnzkxISDh//ryPj09ZWVleXt5f/vIXmW+uzMzMxMTEoqIi6bytOJcRTlLbZ732rJS1a9cePXr08OHD/v7+Z8+efdPDMaq8ocbGxvgtkKGN75p8v/32G0LIzc2NzsY//PDDm+plDTJD5GUOMvPnz6e/sSbvT3v69Km/v7+VldXt27ebm5vj4+OlW8Vi8YYNGxITE2/dutWzAh190vnV5aclVyVpuUJRUVEffPBBUFCQkZHR3LlzFyxYcPToUekN3pQkHP99/fr16z4fWmH6cTrGjh07adKkH3/8MTg4OCAgwNTUtOc2anpDtfFdk+/KlSsIoRkzZtDZGM6ngQFLqWCDNPv7prS0VCwWr1271snJCfW4zXf9+vWrV6+eO3duTU1NTEyMj4+Pp6dnH44inV9dflpyVZKWK1RWVlZRUVFXV8diyc45SZLbt29vbGzMy8vr2Tp69GgdHZ1vv/12zZo1yh5Ufs/KWrt27Q8//JCTk4OviPSkpjdUG981OV68eJGUlGRra7ty5UrVRwiAFtHk7xt7e3uEUEFBQUdHR3l5ufQJ99TUVBsbm7lz5yKE4uLi3N3dlyxZgu/qoeNN+dXlpyVXJWm5Qp9//rm9vX2vuWTkJwnH+WhzcnKOHz8uFArv3bsnU3RdDoXpx5VKIL9gwYLhw4f7+/vjcNKTmt5QbXzXKCRJtra24lS7dXV1WVlZkydP1tXVzcvLo3n9BoDBg/5PJzq/eZOTk3FWHwcHh++++27fvn3GxsYIIUtLy1OnTmVmZuIaPqampmfOnCFJctu2bWZmZiYmJgEBAfiRCGdn57FjxxIEYWZmdvPmTZIkN27cqKOjgxAyNjb++eefFY5Tfn51OWnJ5bf+7W9/w4Pn8/lz585VNut7YWHhsGHDqGlns9mjRo06d+4cSSNJeEtLy6pVq4YNG2ZgYDBlypTIyEiEkK2t7S+//CJ/KhT2LCeBfK/J3r/44gv8ppAkuXPnTjwDOjo67u7u3333Xd/e0O+//97FxQUPzNraOiAgoOdgtO5du3DhwpgxY/T19fX09PCLxTekTZw4MTo6uqGhQf4bR4H708BApvn7oTVuIORX7yk1NVX6+ZXXr19v3LiRw+FQxTzAAKTxdw3iDRjIBtz90Box0PKrv3jxIiQkRDqjs56enr29vVgsFovF6qgTDlQH7xoA/UvL8qf9+uuvxJupqSCE6ng8HpvNPn78+MuXL8Vi8bNnz44dOxYZGRkYGKjKSXwtnQ1toaZ3DYAhS8vijZubm5wfa5mZmTt27EhPT29ubnZ0dFTlKdH+ZWxsfO3atfv377u4uPB4PHd39/T09H379uHkC32mcDb6a/xDk5reNaC6goKCsLAw6SJDy5Ytk97Ax8fH0NBQV1f37bffvnPnjqbGSeno6HBzc9u5cycD+8bHx7u5ufF4PD6f7+bmFhERIX1jjpyiTRcuXIiPj1fvySH6p97gHCsADIPrNz1FRkbOmjWLSh3k7OyM7+m4ePGi9Gb5+fmzZ8/u54H21aZNmxBC4eHhDOzr6+t74MCB2tralpaW7OxsNpstnQ1LftGm5ORkb2/vxsZGmseCegQAACW0t7fjOkYDqqs32bdvX2ZmZnZ2tnTqoJSUFB0dneDgYI0X/ezVzZs3+1xZow/76unprVu3ztzc3MDAICAgYM6cOf/617+eP3+OWw0MDPAdVYaGhgsWLPD3979y5QpOKogQ2rBhwzvvvDNz5kwqA1n/gngDwJB2/Phx1VNG9XtXvXr06FFERMTu3bulk9ohhLy8vEJDQ2tqarZs2aK+o/dNe3v71q1bk5OTGds3NzdXen5wEkLqpJnCok1RUVElJSV9G7BCEG8A0HrkmysAhYSE6Onp4aeOEELr1q3j8/kEQdTX1yOEQkNDN2/eXFFRQRCEQCCQXz5Kqa4QvepKSklJSSFJ0s/Pr2dTbGysi4vLsWPHCgoKlJ0iOmWQIiMj7e3teTzemDFj8Nk/msLDw/GvDWVeaD/sSykvLzcxMcGl33vqWbTJ1NTU29s7OTmZJElVjts7+qfe4PoNAAyjeX5cfgWgJUuWWFpaUhsnJCQghOrq6vDivHnznJ2dqVb55aOU6kphdSUKze8WJycnd3d3mZW4ZAZJkjdv3tTR0XFwcGhtbSV7XL+RP0XyyyBt2bKFw+Hk5OQ0Njbu2LFDR0fnp59+UjhakiSLi4v9/PxIksT5gpW6fqPKviRJdnZ2VldXHzx4kMPhnDx5stdtei3aRJJkWFgYolfxBK7fADC0tLe3JyYmzp07d+nSpcbGxh4eHocPH66vr6ef90gGi8XCvwPc3d3T0tJaWlrS09P70I+vr69QKIyIiOjbMGS0tbX9/vvvOOdFrzw9PTdu3FhZWbl9+3aZJppT5OXlZWRkZG5uHhgY2NbW9vTpU4RQR0dHWlqav7//vHnzTExMdu7cyWaz6UxIe3t7aGhoWlpaH16sKvtidnZ2tra2UVFR+/fvf1Pi7bi4OGtr657Jc0eOHIkQelMGDVVAvAFAu/VvBSAZ0uWjNAsX7sP5h94kNjbW1dU1NTW1uLhYer2yUyRdBunhw4cikWj06NG4icfjWVlZ0ZmQHTt2fPrpp/jyibJU2Rerqqqqra09ffr0iRMnxo0b1/O6Gi7adPXq1Z5Fm/Akv3z5ss9HfxOINwBoN7VWAEJS5aM0q6OjAw9GzjZcLjc9PZ0giJUrV7a3t1PrVZmitrY2hNDOnTupJ6mfPHkifYG9V8XFxaWlpatWrVLYf//uS2Gz2ebm5j4+PpmZmWVlZbjYLiUzM3Pfvn1FRUUODg4998W5M/CE9y+INwBoN7VWAJIuH6VZ+EtQ4dOInp6emzZtKi8vj4mJoVaqMkX4cn1SUpL0dQhckFSO48ePX79+XUdHB4co3MmePXsIgpCundHv+/YkEAh0dXXLysqoNQqLNuG6verI2ATxBgDtprACEIvF6rWCOx3S5aNU7EpFFhYWBEHQecImJibGzc3t7t271BpViiTZ2dlxuVzpNHp0pKenS8cn6Wv+0qf1+n3fhoaGxYsXS68pLy+XSCR2dnYIIZIkt23bVlpampeXJ/NrTxqeZJxYvX9BvAFAuymsACQQCF69epWXlycWi+vq6p48eSK9u5mZ2bNnzyorK1taWnAseVP5KGW7Uqq6kkL6+vpOTk64vLrCCUlPT5d+ykSVIklcLnfFihVnzpxJS0sTCoUSiaS6uho/PhkYGGhpadm3fDlq2pfP51+7dq2wsBAXdL979+7y5cv5fD5OUkCzaBOeZA8Pjz6MTT6INwBovV27dsXFxUVHRw8fPtzb29vBwaGoqIjP5+PWtWvXTps2bdGiRa6urjExMfg8iaenJ36qfM2aNRYWFu7u7jNnznz16hVCqKOjw8PDg8fjTZ061cXF5ZtvvqGumijbVf/y9fUtKyujLsx8/fXXAoGgoqJiwoQJ69evl95y0qRJ+BuWzhSlpaUlJSUhhMaMGfP48eOjR49u3rwZIfTxxx/jUrbJyckbN26Mj48fNmyYtbV1aGhoY2MjQqizs7O2tvb8+fN9eC1q2pfL5U6ePHnVqlU2NjaGhoYBAQEODgQfvHMAACAASURBVA4//PADvt+BpPdIzU8//WRjYzNmzJg+jE0B+rdOw/M3ADCM+fxpGikfRfO7pby8nMVivelpEuZJJJKpU6ceP35ci/ZVqL6+nsvlHjhwgM7G8PwNAEAlA618FEUgEERHR0dHR/da4ZthEokkLy+vpaWlD4U/NLUvHVFRUWPHjg0JCVFH5xBvAABaIywsLCAgIDAwUOOpOYuKis6dO5efny//kaABta9CiYmJJSUlly9fZrPZ/d45gngDAKAMzPJRMvbs2RMSErJ3717NDmP69OmnTp2isslpxb7ynT9//vXr10VFRaampv3eOTY460kDAPogLi5O5sHAgcnHx8fHx0fToxhsZs+ePXv2bLUeAn7fAAAAYALEGwAAAEyAeAMAAIAJEG8AAAAwQen7BQICAtQxDgBATz/88AMaAv/pcAKVQf8yB58ffviByq1HB0HSLhp669atxMTEPo0KgEElPz9/3Lhx6rgnFQDtghNy09xYiXgDAMAIgsjKylqwYIGmBwKANoHrNwAAAJgA8QYAAAATIN4AAABgAsQbAAAATIB4AwAAgAkQbwAAADAB4g0AAAAmQLwBAADABIg3AAAAmADxBgAAABMg3gAAAGACxBsAAABMgHgDAACACRBvAAAAMAHiDQAAACZAvAEAAMAEiDcAAACYAPEGAAAAEyDeAAAAYALEGwAAAEyAeAMAAIAJEG8AAAAwAeINAAAAJkC8AQAAwASINwAAAJgA8QYAAAATIN4AAABgAsQbAAAATIB4AwAAgAkQbwAAADAB4g0AAAAmQLwBAADABJamBwCAFmhqaiJJUnpNW1tbY2MjtWhgYMBmsxkfFwDahJD5XwQA6OmDDz745ptv3tSqq6tbU1NjaWnJ5JAA0DpwPg0AxRYtWkQQRK9NOjo6//M//wPBBgCFIN4AoNj8+fNZrN5PPhME8cknnzA8HgC0EcQbABQzNTX18fHR1dXt2aSjo+Pv78/8kADQOhBvAKBl6dKl3d3dMitZLJavr6+xsbFGhgSAdoF4AwAtfn5+HA5HZqVEIlm6dKlGxgOA1oF4AwAt+vr6/v7+Mjc983i8mTNnampIAGgXiDcA0LV48WKxWEwtstns+fPn83g8DQ4JAC0C8QYAuj766CPpSzVisXjx4sUaHA8A2gXiDQB0sdnswMBAPT09vGhiYjJ9+nTNDgkALQLxBgAlLFq0qLOzEyHEZrOXLl36podyAAA9QT4bAJTQ3d391ltvvXz5EiFUXFw8efJkTY8IAK0Bv28AUIKOjs6yZcsQQtbW1l5eXpoeDgDaZNCeDaiurr5586amRwEGoeHDhyOE/vSnP509e1bTYwGDkJ2dnaenp6ZHoR7kIJWVlaXpqQUAAKXNnz9f01+f6jJof99gJFydAv+/gIAAhJCKP01ycnLmz5/fTyNSi+zs7IULF8LnX+vgz+dgBddvAFDaAA82AAxMEG8AAAAwAeINAAAAJkC8AQAAwASINwAAAJgA8QYAAAATIN4AoNjly5eNjY3/+c9/anog6lJQUBAWFnbu3DknJyeCIAiCwGkUKD4+PoaGhrq6um+//fadO3c0NU5KR0eHm5vbzp07Gdg3Pj7ezc2Nx+Px+Xw3N7eIiAihUEi1RkdHu7u7GxkZcTgcgUDwxRdftLa24qYLFy7Ex8dLJJI+DHJQgngDgGKD+0GWXbt2paSk7NixY968eY8fP3Z2dh42bFhGRsalS5eoba5du3b27NlZs2aVlZW9++67GhwtFh4e/vDhQ2b2/e6771avXv306dOXL1/GxMTEx8dL3xBfWFj4+eefV1ZW1tfXx8XFJScnU8/Q+Pn5cbnc6dOnNzU19W2ogwzEGwAU8/X1bW5unjVrlroP1N7eznBatn379mVmZmZnZxsaGlIrU1JSdHR0goODm5ubmRwMTTdv3rx//z5j++rp6a1bt87c3NzAwCAgIGDOnDn/+te/nj9/jlsNDAyCg4PNzMwMDQ0XLFjg7+9/5cqVqqoq3Lphw4Z33nln5syZXV1dfRvwYALxBoAB5Pjx47W1tYwd7tGjRxEREbt37+ZyudLrvby8QkNDa2pqtmzZwthgaGpvb9+6dWtycjJj++bm5krPj42NDUKIOml28eJFXV1dqhWn1xOJRNSaqKiokpKSvg14kIF4A4ACxcXF9vb2BEEcOnQIIZSWlsbn8/X19c+fPz9jxgwjIyNbW9szZ87gjVNSUrhcroWFxWeffWZtbc3lcr28vG7fvo1bQ0JC9PT0rKys8OK6dev4fD5BEPX19Qih0NDQzZs3V1RUEAQhEAgQQleuXDEyMtqzZ4+aXlpKSgpJkn5+fj2bYmNjXVxcjh07VlBQ0Ou+JEkmJiaOGjWKw+GYmprOmTPn119/xU3ypwghJJFIIiMj7e3teTzemDFjlMp2GB4ejn9tKPNC+2FfSnl5uYmJyYgRI3ptramp4fF4jo6O1BpTU1Nvb+/k5OTBfVaWDog3ACgwZcoU6Vzja9eu3bhxY3t7u6GhYVZWVkVFhZOT0+rVq8ViMUIoJCQkKChIJBJt2LChsrLyzp07XV1dH374IT7BkpKSsmDBAqqr1NTU3bt3U4vJycmzZs1ydnYmSfLRo0cIIXypubu7W00v7dKlS66urvr6+j2beDzeV199paOjs3r16ra2tp4bREVFhYWFhYeH19bW3rhxo6qqaurUqbgykPwpQght3759//79SUlJz58/nzVr1uLFi3/++Wc6A/7+++8rKir6VsZblX0RQmKxuKam5tChQwUFBQcPHqTKvEoTiUSFhYWrV6+WaR03blxNTc0vv/zSt0MPGhBvAOgjLy8vIyMjc3PzwMDAtra2p0+fUk0sFgv/4e/u7p6WltbS0pKent6HQ/j6+gqFwoiIiP4b9X+1tbX9/vvvzs7Ob9rA09Nz48aNlZWV27dvl2lqb29PTEycO3fu0qVLjY2NPTw8Dh8+XF9ff+TIEenNep2ijo6OtLQ0f3//efPmmZiY7Ny5k81m05mf9vb20NDQtLS0PrxYVfbF7OzsbG1to6Ki9u/fv3Dhwl63iYuLs7a2jo2NlVk/cuRIhFBpaWmfjz44QLwBQFX4j1nqj3cZ48eP19fXp841DRy1tbUkSfb644YSGxvr6uqamppaXFwsvb6srKy1tXX8+PHUmgkTJujp6VFnDmVIT9HDhw9FItHo0aNxE4/Hs7KyojM/O3bs+PTTT/HlE2Wpsi9WVVVVW1t7+vTpEydOjBs3rudlttzc3Ozs7KtXr0rfeYHhScY//oYyiDcAqB2Hw6mrq9P0KGR1dHQghDgcjpxtuFxueno6QRArV65sb2+n1uMbfA0MDKQ3NjExaWlpUXhcfHZu586dxB+ePHkifYG9V8XFxaWlpatWrVLYf//uS2Gz2ebm5j4+PpmZmWVlZXFxcdKtmZmZ+/btKyoqcnBw6Lkvj8dDf0z4UAbxBgD1EovFTU1Ntra2mh6ILPwlqPBpRE9Pz02bNpWXl8fExFArTUxMEEIy0YXmy8SX65OSkqQrcd26dUv+XsePH79+/bqOjg4OUbiTPXv2EASh8NqPKvv2JBAIdHV1y8rKqDUHDx7MyMgoLCx86623et2ls7MT/THhQxnEGwDUq6ioiCTJSZMm4UUWi/WmM28Ms7CwIAiCzhM2MTExbm5ud+/epdaMHj3awMBA+sv69u3bnZ2d7733nsLe7OzsuFxuSUmJUqNNT0+Xjk/492J4eDhJktKn9fp934aGBplbDMrLyyUSiZ2dHUKIJMlt27aVlpbm5eXJ/NqThifZ0tKS3msdtCDeAND/uru7Gxsbu7q67t27Fxoaam9vHxQUhJsEAsGrV6/y8vLEYnFdXd2TJ0+kdzQzM3v27FllZWVLS4tYLM7Pz1ff/dD6+vpOTk7V1dUKt8Rn1aSfMuFyuZs3b87Nzc3IyBAKhaWlpWvWrLG2tg4ODqbT24oVK86cOZOWliYUCiUSSXV1NX58MjAw0NLSsm/5ctS0L5/Pv3btWmFhoVAoFIvFd+/eXb58OZ/P37RpE0LowYMH+/fvP3r0KJvNJqQcOHBAuhM8yR4eHn0Y22AC8QYABQ4dOjRhwgSE0LZt22bPnp2WlpaUlIQQGjNmzOPHj48ePbp582aE0Mcff1xeXo536ejo8PDw4PF4U6dOdXFx+eabb6jLJGvXrp02bdqiRYtc/y97dxoWxZX2DfwUdNNNN92AsogssrRLUNwiE0EdYnhCEhlFRBSjJsYxQRNF3KK4IAISEYNcGLgcl4cnlyYCIkFjRDNqSMZxiXmViDgaJKKAC6Ls+1Lvh5rU9DRLL9DVNPx/n9J1arnrpO2bqjp1n5Ejo6KimHssHh4ezIDpFStWWFlZubq6zpgx4+XLl9o+NV9f3/z8fPbBzDfffCOTyQoLC93d3VetWiW/5uTJk5lfWNb27dtjYmIiIyMtLCy8vLwcHR1zcnLEYjEhRGkXJSQkrFmzJjY2dvDgwTY2NqGhoRUVFYSQ5ubmsrKykydPanAuWtpWKBROmTJl2bJltra2EokkMDDQ0dHx6tWrzHgHFV+puX79uq2t7dixYzWIrV+h+ynmDTJdRwF9zty5c+fOnavVQzDVTbR6CKVU/P4XFBTweLwjR45wEJIq2trapk2bdvjwYT3aVqny8nKhULhnzx5VVubg+6lDuL4B6H36UhJYJpNFRkZGRkay1Vl0qK2tLSsrq6amJigoSF+2VUVERMT48eNDQkK0sXP9gnzzH8uWLZNIJBRFqfsksy9Tq/S6fDl6hpGRkZWV1euvvx4XF8fc8YB+JiwsLDAwMCgoSOelOXNyck6cOJGdnd39K0F9alul4uPjc3Nzz5w5w+fze33negf55j8OHTp08OBBXUfRy9Qqvc6Wozc1NaVpur29vaysLD093cnJaePGjaNHj9Zg8OhAs3nz5pSUlKqqKicnp4yMDF2Ho5KdO3eGhIR89tlnug3D29v7q6++YovL6cW23Tt58mRTU1NOTo65uXmv71wfId/oB83K1PekbDshhKIoMzOz119/PSUlJT09/dmzZ0xZfo13qCXc1/DvRkxMTFNTE03TDx48kJ8lpY/z8fHZtWuXrqPob/z8/MLCwuTH9Q1wyDf/haIoXYfQOQ3K1PekbHtHc+fOXbJkSVlZ2f79+3tlh72I4xr+AKCZgZ5vaJqOi4sbOXKkQCAwNTXdsGED27R7926RSCSRSMrKytatW2dra3vv3j266xrs3ReiJ93Wb1e3TL0quiq9rnGJe+YNkuzs7H7QOQCgAzocG6dVKo4H3bJlC0VRn3/+eUVFRX19fVJSEiHk5s2bbCshZPXq1fv27ZszZ86//vWv8PBwIyOjI0eOVFZW3rp1a+LEiRYWFk+fPmXWDw4OFovFd+7caWxszM/Pd3d3l0gkjx49Ylq733bhwoXW1tZsYHFxcYSQ58+fMx8DAgKYMvUqunTp0qxZs+j/fpWacfr0aYlEEhkZ2dW27PMbBcyc7fb29nrdOf17vCkL7wPoqf79/ey330hV/r3V19eLRKI333yTXcJMCaWQbxoaGtj1TUxMgoKC2PV//vlnQgj72x0cHCz/S339+nVCyI4dO1TZthd/Uuvr6ydNmlRSUkJ3lm+U6irf0DTNPNFh/ltPO6d//3tmId/oqf79/eRxeCnV59y/f7++vt7b21vF9dWtwS5fiF7dbXui56XXO1VXV0fTtFQq7bRVXzqHEHL16tXAwEBt7LnvYAqo9PvT7H+uXr3Kltrrfwb08xvm36Tqk8tqUIOdLUTfk/rtaumV0uud+u233wgho0aN6rRVLzoHAHRoQF/fCIVCQkhTU5OK66tbg12+EH1P6rerhS29Lr9w586dO3fuvH79utJquN04e/YsIeSdd97ptFUvOocxefLk48ePa2PPfUd6evr8+fP7/Wn2P/37knRAX9+MGTPGwMDgxx9/VH19tWqwyxeiV7ptb5Wp70np9W48ffp07969dnZ2S5cu7XQFvegcANChAZ1vLC0tAwICMjIyDh8+XF1dfevWLYXZ1xWoUoO9q0L0SrdVq0x9T85alRL3NE3X1ta2t7czGSstLW3KlCmGhoZZWVldPb/pH50DAFrE+QgFjqg4PqempmbZsmWDBw82MTGZOnVqeHg4IcTOzu7XX3+NjY1lasXb29uzBXTb29vj4uKGDx/O5/PNzc39/f2Z904YwcHBfD7f1taWx+NJpdLZs2cXFhayrd1v++LFi+nTpwuFQicnp1WrVjFvAslkMmbE8I0bN4YNG2ZsbDx16lR2lLAqOo5PO3PmjEQiiY6O7rjyqVOnxo4dKxKJjIyMmDtyzIC0P/3pT5GRkS9evGDX1N/O6d/jf1gYn6an+vf3k6JVm79B7zD3rzk+u+XLlx8/fvzFixdcHlRf9JHOYe6P9/sHGzr5/kPP9e/v54C+n6YN+lKIXifQOQADGfKNnrl79y7VNS1N4AH93vnz58PCwuQnpFi8eLH8Cj4+PhKJxNDQcPTo0ZrN2dy71Jpoo4fbxsbGjho1ytjYWCwWjxo1atu2bUytDUZkZKSrq6tUKhUIBDKZ7NNPP2UnEzp16lRsbCz+zPoPHd/P0xru71+HhYUZGRkRQhwdHY8fP87lofu+vtM5/fv+OEut7394ePjMmTOrq6uZjy4uLoMHDyaEnD59Wn617OxsPz+/Xg5UU8zk1moVztB4W19f3z179pSVldXU1KSnp/P5fPmiJF5eXklJSS9evKiurk5LS+Pz+W+//TbbmpCQ4OXlVVFRoeKx+vf3E9c3vUZPC9FzY0B1Ti/Oj8DBVAu7du1KTU1NT0+XSCTswsTERAMDg+Dg4D44/QTp2UQbGmxrZGTElL41MTEJDAycPXv23//+9ydPnjCtJiYmzAziEolk3rx5/v7+Z8+eLS4uZlpXr149bty4GTNmtLa2ahZwf4J8A9DLenF+BG1PtXD//v1t27bt2LGDefeZ5enpGRoaWlpaun79eu0dXTM9mWhDs20zMzPl+4epFMXeNDt9+rT8DDcWFhaEkPr6enZJREREbm5ub80MoteQbwA6QffS/AjdT8Sg7lQLGs8l0ZXExESapmfNmtWxKTo6esSIEYcOHTp//ry6XZScnCwWi0Ui0cmTJ9955x2pVGpnZ8cUw2W0tbWFh4c7ODgYGxuPHTuWufunoq4m2tD2tqyCggIzM7Nhw4Z12lpaWmpsbOzk5MQuMTc39/LySkhIoDFcUJc387QJ7x9Ap1S8P96L8yN0PxGDWrtSOpcES8Xvv7Ozs6urq8JCFxeXBw8e0DR9+fJlAwMDR0fH2tpausPzm+67iCkffuHChaqqqrKysmnTponF4ubmZqZ1/fr1AoEgIyOjoqJi8+bNBgYG169fVxot3e1EG1rdlqbp5ubmkpKSffv2CQQC9p0zBXV1dRKJJCQkRGF5WFgYkSs83w08vwEYWBoaGuLj4+fMmbNo0SJTU1M3N7f9+/eXl5d3X36iGzwej7kOcHV1TU5OrqmpSUlJ0WA/vr6+1dXV27Zt0ywMBXV1dQ8ePHBxcelqBQ8PjzVr1hQVFW3atEmhScUu8vT0lEqllpaWQUFBdXV1jx49IoQ0NjYmJyf7+/sHBASYmZlt3bqVz+er0iENDQ2hoaHJyckanGxPtmXY29vb2dlFRETs3r17/vz5na4TExNjY2MTHR2tsHz48OGEkLy8PI2P3j8g3wAo0ur8CPITMehWWVkZTdMikaibdaKjo0eOHJmUlHTp0iX55ep2ETM6kak2dO/evfr6+jFjxjBNxsbGQ4YMUaVDejLRRs8n6SguLi4rK/v666+//PLLCRMmdHyulpmZmZ6efu7cOfmRFwymk589e6bx0fsH5BsARdqeH4GdiEG3GhsbmWC6WUcoFKakpFAUtXTp0oaGBnZ5T7qorq6OELJ161b2vbGHDx/KP2DvVE8m2uiVSTr4fL6lpaWPj09qamp+fn5MTIx8a2pq6q5du3JychwdHTtuyxR/Yjp8IEO+AVCk1fkR5Cdi0C3mR1Dp24geHh5r164tKCiIiopiF/aki5jH9Xv37pW/s3/lypXut2In2mBSFLOTnTt3UhQlX1m817ftSCaTGRoa5ufns0v27dt39OjRixcvDh06tNNNmpubyR8dPpAh3wAo0ur8CPITMfRwVz1kZWVFUZQqb9hERUWNGjXq5s2b7BJ1p5+QZ29vLxQKc3Nz1Yq2JxNt9GTbFy9evPvuu/JLCgoK2tra7O3tCSE0TW/cuDEvLy8rK0vhak8e08nW1taqnWu/hXwDoKjX50foaiIGdXelylwSqhOJRM7Ozswst0o7JCUlRf4tE1Wmn+hmbx988MGxY8eSk5Orq6vb2tpKSkqY1yeDgoKsra01q5ejpW3FYvH3339/8eLF6urqlpaWmzdvvv/++2KxmClScOfOnd27dx88eJDP58tXltqzZ4/8TphOdnNz0yC2/gT5BqAT27dvj4mJiYyMtLCw8PLycnR0zMnJEYvFTOvHH388ffr0BQsWjBw5MioqirlP4uHhwbxVvmLFCisrK1dX1xkzZrx8+ZIQ0tjY6ObmZmxsPG3atBEjRvzwww/sUxN1d9W7fH198/Pz2Qcz33zzjUwmKywsdHd3X7VqlfyakydPZn5hVemi5OTkvXv3EkLGjh37+++/Hzx4cN26dYSQt99+u6CggBCSkJCwZs2a2NjYwYMH29jYhIaGVlRUEEKam5vLyspOnjypwbloaVuhUDhlypRly5bZ2tpKJJLAwEBHR8erV68y4x1o1V6puX79uq2t7dixYzWIrV/R5mBrXcL7N9Ap7t9vYIqdcHlEWuXvf0FBAY/H6+ptEu61tbVNmzbt8OHDerStUuXl5UKhcM+ePaqsjPdvAKBH+myFYJlMFhkZGRkZyVZn0aG2trasrKyamhoNypzraltVREREjB8/PiQkRBs71y/INwADWlhYWGBgYFBQkM5Lc+bk5Jw4cSI7O7v7V4L61LZKxcfH5+bmnjlzhs/n9/rO9Q7yDYAWbd68OSUlpaqqysnJKSMjQ9fhdG7nzp0hISGfffaZbsPw9vb+6quv2GpyerFt906ePNnU1JSTk2Nubt7rO9dHPF0HANCfxcTEKLwY2Df5+Pj4+PjoOor+xs/Pz8/PT9dR9CG4vgEAAC4g3wAAABeQbwAAgAvINwAAwAXkGwAA4EI/H59GUZSuQ4C+aIB8MQbIafYzc+fO1XUI2kLR/XRK7ZKSksuXL+s6Cuif5s+fHxoa6uHhoetAoB+yt7fvr1+tfptvALSHoqi0tLR58+bpOhAAfYLnNwAAwAXkGwAA4ALyDQAAcAH5BgAAuIB8AwAAXEC+AQAALiDfAAAAF5BvAACAC8g3AADABeQbAADgAvINAABwAfkGAAC4gHwDAABcQL4BAAAuIN8AAAAXkG8AAIALyDcAAMAF5BsAAOAC8g0AAHAB+QYAALiAfAMAAFxAvgEAAC4g3wAAABeQbwAAgAvINwAAwAXkGwAA4ALyDQAAcAH5BgAAuIB8AwAAXEC+AQAALiDfAAAAF5BvAACACzxdBwCgB44dO1ZTUyO/5Pz585WVlexHf39/S0tLzuMC0CcUTdO6jgGgr1uyZMmXX37J5/OZj8y/GoqiCCFtbW0mJiZlZWUCgUCXIQL0ebifBqDcggULCCEtf2htbW1tbWX+29DQMDAwEMkGQClc3wAo19raam1t/fLly05bL1y48MYbb3AcEoDewfUNgHI8Hm/BggXs/TR5FhYWXl5e3IcEoHeQbwBUsmDBgpaWFoWFfD5/8eLFhoaGOgkJQL/gfhqASmiadnBwKCkpUVj+888/u7u76yQkAP2C6xsAlVAUtWjRIoVbavb29pMmTdJVSAD6BfkGQFUKt9T4fP6SJUuYUdEAoBTupwGoYdSoUffu3WM/3r59e/To0TqMB0CP4PoGQA2LFy9mb6m5uroi2QCoDvkGQA2LFi1qbW0lhPD5/Pfff1/X4QDoE9xPA1DPpEmT/t//+38URRUVFTk4OOg6HAC9gesbAPW89957hJDXXnsNyQZALagPTa5cuRIfH6/rKEBvNDY2UhTV1NQUGBio61hAb3h4eKxdu1bXUegYrm9IcXFxRkaGrqOAHrl69erVq1e5OZZQKLS2trazs+PmcPJKSkrwXdVHV69evXLliq6j0D1c3/zb8ePHdR0CaI651ODsf+L9+/dlMhk3x5KXnp4+f/58fFf1Di6FGbi+AVCbTpINgL5DvgEAAC4g3wAAABeQbwAAgAvINwAAwAXkGxi4zpw5Y2pq+u233+o6EG05f/58WFjYiRMnnJ2dKYqiKGrx4sXyK/j4+EgkEkNDw9GjR9+4cUNXcbIaGxtHjRq1detWDraNjY0dNWqUsbGxWCweNWrUtm3bqqur2dbIyEhXV1epVCoQCGQy2aefflpbW8s0nTp1KjY2tq2tTYMgBzjkGxi4+ncxp+3btycmJm7evDkgIOD33393cXEZPHjw0aNHv/vuO3ad77///vjx4zNnzszPz584caIOo2Vs2bJFvvy2Vrf9xz/+8eGHHz569OjZs2dRUVGxsbFz585lWy9evLhy5cqioqLy8vKYmJiEhAR2TPOsWbOEQqG3t3dlZaVmoQ5YyDcwcPn6+lZVVc2cOVPbB2poaPD09NT2UeTt2rUrNTU1PT1dIpGwCxMTEw0MDIKDg6uqqrgMRkWXL1++ffs2Z9saGRl98sknlpaWJiYmgYGBs2fP/vvf//7kyROm1cTEJDg4eNCgQRKJZN68ef7+/mfPni0uLmZaV69ePW7cuBkzZjDFW0FFyDcAWnf48OGysjLODnf//v1t27bt2LFDKBTKL/f09AwNDS0tLV2/fj1nwaiooaFhw4YNCQkJnG2bmZkp3z+2traEEPam2enTpw0NDdlWqEJJggAAIABJREFUCwsLQkh9fT27JCIiIjc3V7OAByzkGxigLl265ODgQFHUF198QQhJTk4Wi8UikejkyZPvvPOOVCq1s7M7duwYs3JiYqJQKLSyslq+fLmNjY1QKPT09Lx27RrTGhISYmRkNGTIEObjJ598IhaLKYoqLy8nhISGhq5bt66wsJCiKOZF0bNnz0ql0p07d2rp1BITE2manjVrVsem6OjoESNGHDp06Pz5851uS9N0fHz8K6+8IhAIzM3NZ8+efffuXaap+y4ihLS1tYWHhzs4OBgbG48dOzYtLU31mLds2cJcbahzor2wLaugoMDMzGzYsGGdtpaWlhobGzs5ObFLzM3Nvby8EhIS+vdd2d6FfAMD1NSpUy9fvsx+/Pjjj9esWdPQ0CCRSNLS0goLC52dnT/88ENmAumQkJAlS5bU19evXr26qKjoxo0bra2tb775JnODJTExcd68eeyukpKSduzYwX5MSEiYOXOmi4sLTdP3798nhDCPmtvb27V0at99993IkSNFIlHHJmNj4//7v/8zMDD48MMP6+rqOq4QERERFha2ZcuWsrKyn376qbi4eNq0ac+ePSPKuogQsmnTpt27d+/du/fJkyczZ8589913f/nlF1UC/uc//1lYWPjuu+9qcLI92ZYQ0tLSUlpa+sUXX5w/f37fvn1GRkYd16mvr7948eKHH36o0DphwoTS0tJff/1Vs0MPQMg3AP/F09NTKpVaWloGBQXV1dU9evSIbeLxeMwf/q6ursnJyTU1NSkpKRocwtfXt7q6etu2bb0X9X/U1dU9ePDAxcWlqxU8PDzWrFlTVFS0adMmhaaGhob4+Pg5c+YsWrTI1NTUzc1t//795eXlBw4ckF+t0y5qbGxMTk729/cPCAgwMzPbunUrn89XpX8aGhpCQ0OTk5M1ONmebMuwt7e3s7OLiIjYvXv3/PnzO10nJibGxsYmOjpaYfnw4cMJIXl5eRoffaBBvgHoHPPHLPvHu4JJkyaJRCL2XlPfUVZWRtN0pxc3rOjo6JEjRyYlJV26dEl+eX5+fm1t7aRJk9gl7u7uRkZG7J1DBfJddO/evfr6+jFjxjBNxsbGQ4YMUaV/Nm/e/NFHHzGPT9TVk20ZxcXFZWVlX3/99ZdffjlhwoSOj9kyMzPT09PPnTsnP/KCwXQyc/EHqkC+AdCQQCB4/vy5rqNQ1NjYSAgRCATdrCMUClNSUiiKWrp0aUNDA7ucGeBrYmIiv7KZmVlNTY3S4zJ357Zu3Ur94eHDh/IP2Dt16dKlvLy8ZcuWKd1/727L4vP5lpaWPj4+qamp+fn5MTEx8q2pqam7du3KyclxdHTsuK2xsTH5o8NBFcg3AJpoaWmprKzUySw43WN+BJW+jchM/1VQUBAVFcUuNDMzI4QoZBcVT5N5XL93715ajtJJXw4fPnzhwgUDAwMmRTE72blzJ0VRSp/99GTbjmQymaGhYX5+Prtk3759R48evXjx4tChQzvdpLm5mfzR4aAK5BsATeTk5NA0PXnyZOYjj8fr6s4bx6ysrCiKUuUNm6ioqFGjRt28eZNdMmbMGBMTE/kf62vXrjU3N7/66qtK92Zvby8UCnNzc9WKNiUlRT4/MdeLW7ZsoWla/rZer2/74sULhSEGBQUFbW1t9vb2hBCapjdu3JiXl5eVlaVwtSeP6WRra2vVzhWQbwBU1t7eXlFR0draeuvWrdDQUAcHhyVLljBNMpns5cuXWVlZLS0tz58/f/jwofyGgwYNevz4cVFRUU1NTUtLS3Z2tvbGQ4tEImdn55KSEqVrMnfV5N8yEQqF69aty8zMPHr0aHV1dV5e3ooVK2xsbIKDg1XZ2wcffHDs2LHk5OTq6uq2traSkhLm9cmgoCBra2vN6uVoaVuxWPz9999fvHixurq6paXl5s2b77//vlgsZqZ8vnPnzu7duw8ePMjn8yk5e/bskd8J08lubm4axDYwId/AAPXFF1+4u7sTQjZu3Ojn55ecnLx3715CyNixY3///feDBw+uW7eOEPL2228XFBQwmzQ2Nrq5uRkbG0+bNm3EiBE//PAD+5jk448/nj59+oIFC0aOHBkVFcXcY/Hw8GAGTK9YscLKysrV1XXGjBkvX77U9qn5+vrm5+ezD2a++eYbmUxWWFjo7u6+atUq+TUnT57M/MKytm/fHhMTExkZaWFh4eXl5ejomJOTIxaLCSFKuyghIWHNmjWxsbGDBw+2sbEJDQ2tqKgghDQ3N5eVlZ08eVKDc9HStkKhcMqUKcuWLbO1tZVIJIGBgY6OjlevXmXGO6j4Ss3169dtbW3Hjh2rQWwDFD3gMW+l6ToK6JG5c+fOnTtXq4dgqpto9RBKqfhdLSgo4PF4R44c4SAkVbS1tU2bNu3w4cN6tK1S5eXlQqFwz549qqzMwfdTL+D6BkBV+lISWCaTRUZGRkZGstVZdKitrS0rK6umpiYoKEhftlVFRETE+PHjQ0JCtLHz/gr5BqAfCgsLCwwMDAoK0nlpzpycnBMnTmRnZ3f/SlCf2lap+Pj43NzcM2fO8Pn8Xt95P4Z8o4ampqbVq1cPGTJEJBL9z//8DzMQaP/+/bqOq3Pt7e179+5Vqyyx/EQpCpj3D/bs2dPHz1pLNm/enJKSUlVV5eTklJGRoetwVLJz586QkJDPPvtMt2F4e3t/9dVXbHE5vdi2eydPnmxqasrJyTE3N+/1nfdvyDdq+Pzzz8+ePXv37t2EhITly5fLV9/qawoKCv785z+vXbtW6Qt38tiJUkxNTZn7ra2trfX19c+ePWP+SFy/fn1fPmvtiYmJaWpqomn6wYMH8rOk9HE+Pj67du3SdRT9jZ+fX1hYmPy4PlAR8o0asrKyJk2aZGZm9tFHH6n+o6Mw9wkHU6H8+uuvmzZtWrFixfjx43u4K0NDQ2NjYysrqxEjRqi1IfdnDQB9HPKNGkpKSjS4Xasw9wkHU6GMGzfuxIkTCxcu7L6oiVqysrLUWp/7swaAPg75RiV///vfZTLZkydPvvzyS4qiOn3l+B//+Ierq6upqalQKHRzczt37hzpMPdJx6lQOp0yROlEIz3Ru5Ov6MtZA4DOId+o5M0337x//761tfX7779P03Snw0yfPXs2f/78oqKix48fm5iYLFy4kHSY+6TjVCidThmidKKRnlB38pWLFy8qvFatj2cNADqHfNNr5s6du337dnNz80GDBs2aNevFixdKiwcrnTKkm7lYNKbK5CtVVVXsyDRvb+9u1tSXswYAnePpOoD+iXnMo/T1QNWnDOl+LpZeZ2pqypSmJ4Tk5OSoWG1Xt2edkZFBUZQqa+q7AXKa/YweDWvUHuSbXvPdd9/FxcXl5+czFQBV2YSdMmTr1q3sQhsbG22FqJHXX3/99ddf76q175z15MmT16xZ08Od9HFXrlxJSEhgHneBHmHqzgHyTe949OiRv7//nDlz/vd//3fo0KH79u379NNPlW7FThkSGhqq/Rh7X586azs7u3nz5vXiDvumhISEgXCa/czx48d1HUKfgHzTO/Ly8lpaWj7++GNnZ2ei8h0PzaYM6TsG5lkDgGYwXqB3ODg4EELOnz/f2NhYUFAgP9+7wtwn8h8NDQ27mjJEe3px8hU9OmsA0D0d1aXuQ1Sp8V5UVDRhwgRCCI/HmzhxYkZGxueff87M6ycWi+fMmUPT9MaNGwcNGmRmZhYYGPjFF18QQlxcXB49enTjxo1hw4YZGxtPnTr16dOnCh+bmpo2btzo4ODA4/EsLS0DAgLy8/OTkpKY+jHDhw8vLCw8cOCAVColhAwbNuy3335TekZXrlyZMmUK+0RkyJAhnp6eP/74I9N65swZiUQSHR3dccN//vOfbB2BIUOGeHt7K6zQZ896gNR7x9wZemqAfD+VomjVZhbqx9LT0+fPn49+0GuBgYFkANwlx3dVTw2Q76dSuJ8GAABcQL7RM3fv3u10vgCGlqaWgoHg/PnzYWFh8nNSLF68WH4FHx8fiURiaGg4evToGzdu6CTI6Ohohe88+yIXq6uZOCIjI11dXaVSqUAgkMlkn376KVso5NSpU7Gxsfoyn57+Qr7RM6NGjerm9mhqaqquAwS9tH379sTExM2bN7NzUgwePPjo0aPfffcdu873339//PjxmTNn5ufnT5w4UYfRdqObmTguXry4cuXKoqKi8vLymJiYhIQE5jYXIWTWrFlCodDb25t9zRm0AfkGQCW9OKVCX5udYdeuXampqenp6RKJhF2YmJhoYGAQHBys8xlCFRw5ckT+b6zbt2+zTd3PxGFiYhIcHDxo0CCJRDJv3jx/f/+zZ88WFxczratXrx43btyMGTNaW1s5OpOBB/kGQCW9OKVCn5qd4f79+9u2bduxY4dQKJRf7unpGRoaWlpaun79el3Fpq7uZ+I4ffq0/CRpFhYWhBD5y6CIiIjc3NyEhAQOQh2YkG9gAKFpOj4+/pVXXhEIBObm5rNnz2brtoWEhBgZGbHTD3/yySdisZiiqPLyctJhhoXExEShUGhlZbV8+XIbGxuhUOjp6cm+fqTWrkhvzxChrsTERJqmZ82a1bEpOjp6xIgRhw4dOn/+fKfbdtOfSmeX6HROCi6VlpYaGxs7OTmxS8zNzb28vBISEjACUFu0NdBaf+Cdhn5AxfcbwsPDjYyMjhw5UllZeevWrYkTJ1pYWDx9+pRpXbhwobW1NbtyXFwcIeT58+fMx4CAAGZKBUZwcLBYLL5z505jY2N+fr67u7tEInn06JEGuzp9+rREIomMjFQavza+q87Ozq6urgoLXVxcHjx4QNP05cuXDQwMHB0da2traZrOzs728/NjV+u+P7ds2UIIuXDhQlVVVVlZ2bRp08RicXNzM9O6fv16gUCQkZFRUVGxefNmAwOD69evK402KirKzs7OzMyMz+c7Ojr6+fn9/PPPHVd77bXXxo0b181+6urqJBJJSEiIwvKwsDBCyM2bN5VGoha8f8PA9Q0MFA0NDfHx8XPmzFm0aJGpqambm9v+/fvLy8sPHDig2Q55PB7zp72rq2tycnJNTY38rAqqU2WGCC2pq6t78OCBi4tLVyt4eHisWbOmqKho06ZNCk0q9mens0sonZOiK++///6pU6eKi4tra2uPHTv26NEjLy+v/Px8dU88JibGxsYmOjpaYfnw4cMJIXl5eeruEFSBfAMDRX5+fm1t7aRJk9gl7u7uRkZG8mV4NDZp0iSRSNTprAp9WVlZGU3TTFmHrkRHR48cOTIpKenSpUvyy9XtT/nZJVSfk0KBvb39hAkTTExMjIyMJk+enJKS0tDQkJSUpHRDeZmZmenp6efOnZMfH8FguuLZs2dq7RBUhHwDAwUz1FVhLnAzM7Oamppe2b9AIFA611xf09jYSAjp9Ok6SygUpqSkUBS1dOnShoYGdnlP+pOdk4J9jebhw4cdRzAr5ebmZmho+Ntvv6m+SWpq6q5du3JychwdHTu2Ghsbkz+6BXod8g0MFGZmZoQQhV/DyspKOzu7nu+8paWlt3bFJebnVel7jh4eHmvXri0oKIiKimIX9qQ/2Tkp5G/uX7lyRd3429vb29vbu8+X8vbt23f06NGLFy8OHTq00xWam5vJH90CvQ75BgaKMWPGmJiYyM9Veu3atebm5ldffZX5yOPxNJ5BNScnh6bpyZMn93xXXLKysqIoSpU3bKKiokaNGnXz5k12idL+7IbGc1K89dZb8h+ZIQYeHh5KN6RpeuPGjXl5eVlZWQrXZPKYrmCK0kKvQ76BgUIoFK5bty4zM/Po0aPV1dV5eXkrVqywsbEJDg5mVpDJZC9fvszKymppaXn+/PnDhw/lN1eYYYEQ0t7eXlFR0draeuvWrdDQUAcHhyVLlmiwq16cIUJdIpHI2dm5pKRE6ZrMXTX591eU9mf3e+tqToqgoCBra+uu6uWUlpampqZWVla2tLRcuXJl2bJlDg4OK1asUHrEO3fu7N69++DBg3w+X74czp49e+RXY7rCzc1N6Q5BE7oYFNe3YDx0P6DieNP29va4uLjhw4fz+Xxzc3N/f/979+6xrS9evJg+fbpQKHRyclq1atWGDRsIITKZjBnlrDClQnBwMJ/Pt7W15fF4Uql09uzZhYWFmu2qmxkiFGjjuxoSEsLn8+vr65mPmZmZzHA1CwuLlStXKqy8YcMG+fHQ3fSn0tklOp2TgqZpf39/Qkh4eHin0a5bt87FxUUsFvN4PDs7uw8//PDx48dsazczcXQ15CwuLk5+/76+vra2tu3t7T3sVQUYD83A7yzyTX/A/b9npjIKl0ektfNdLSgo4PF4CkVidKitrW3atGmHDx/m/tDl5eVCoXDPnj29vmfkGwbupwFoqH+UE5bJZJGRkZGRkWyxZB1qa2vLysqqqanRSaXziIiI8ePHh4SEcH/oAQL5BmCgCwsLCwwMDAoK0nlpzpycnBMnTmRnZ3f/SpA2xMfH5+bmnjlzhs/nc3zogQP5BkBtmzdvTklJqaqqcnJyysjI0HU4vWDnzp0hISGfffaZbsPw9vb+6quv2NJznDl58mRTU1NOTo65uTnHhx5QeLoOAED/xMTExMTE6DqKXubj4+Pj46PrKHTDz8/Pz89P11H0f7i+AQAALiDfAAAAF5BvAACAC8g3AADABYwX+Lf09HRdhwCaY8qQ9Pv/iUxFy35/mv1PSUmJ3tVy1Qpdv3Cqe9xPZAsAAw3qC9A0TdGYqRtATRRFpaWlzZs3T9eBAOgTPL8BAAAuIN8AAAAXkG8AAIALyDcAAMAF5BsAAOAC8g0AAHAB+QYAALiAfAMAAFxAvgEAAC4g3wAAABeQbwAAgAvINwAAwAXkGwAA4ALyDQAAcAH5BgAAuIB8AwAAXEC+AQAALiDfAAAAF5BvAACAC8g3AADABeQbAADgAvINAABwAfkGAAC4gHwDAABcQL4BAAAuIN8AAAAXkG8AAIALyDcAAMAF5BsAAOAC8g0AAHAB+QYAALiAfAMAAFxAvgEAAC5QNE3rOgaAvi44OPjevXvsxxs3bjg5OZmbmzMfDQ0Nv/zySzs7Ox1FB6AfeLoOAEAPWFtbHzhwQH7JrVu32P92dnZGsgFQCvfTAJR79913u2oyMjJasmQJh7EA6CvcTwNQyZgxY+7cudPpv5d79+6NGDGC+5AA9AuubwBU8t577xkaGiospChq3LhxSDYAqkC+AVDJggUL2traFBYaGhq+//77OokHQO/gfhqAqjw9Pa9du9be3s4uoSiquLjY1tZWh1EB6Atc3wCoavHixRRFsR8NDAymTp2KZAOgIuQbAFUFBgbKf6Qo6r333tNVMAB6B/kGQFUWFhbe3t7sqAGKovz9/XUbEoAeQb4BUMOiRYuYR56GhoZvvfXW4MGDdR0RgN5AvgFQw5w5c4yMjAghNE0vWrRI1+EA6BPkGwA1iMXiv/zlL4QQIyOjmTNn6jocAH2CfAOgnoULFxJC/P39xWKxrmMB0Cd4/6ZH5EfHAkC/l5aWNm/ePF1Hoa9QH7qnQkNDPTw8dB0FKHHlypWEhIS0tLRe2dvRo0eDgoJ4vL74z2f+/Pn4TmrJ/PnzdR2CfsP1TY9QFIW/d/RCenr6/Pnze+vb3tjYKBQKe2VXvQ7fSe1B3/YQnt8AqK3PJhuAvgz5BgAAuIB8AwAAXEC+AQAALiDfAAAAF5BvALp05swZU1PTb7/9VteBaMv58+fDwsJOnDjh7OxMURRFUYsXL5ZfwcfHRyKRGBoajh49+saNGzoJMjo6mvpvY8aMUVinvb197969np6eCssjIyNdXV2lUqlAIJDJZJ9++mltbS3TdOrUqdjY2I5z6IH2IN8AdKl/vy2wffv2xMTEzZs3BwQE/P777y4uLoMHDz569Oh3333HrvP9998fP3585syZ+fn5EydO1GG03SgoKPjzn/+8du3a+vp6haaLFy+uXLmyqKiovLw8JiYmISGBnVRi1qxZQqHQ29u7srKS85AHKOQbgC75+vpWVVVxUCetoaGh49/mWrVr167U1NT09HSJRMIuTExMNDAwCA4Orqqq4jIYpY4cOULLuX37Ntv066+/btq0acWKFePHj++4oYmJSXBw8KBBgyQSybx58/z9/c+ePVtcXMy0rl69ety4cTNmzGhtbeXoTAY25BsA3Tt8+HBZWRlnh7t///62bdt27Nih8CKRp6dnaGhoaWnp+vXrOQumh8aNG3fixImFCxcKBIKOradPn2bnKyKEWFhYEELkL4MiIiJyc3MTEhI4CBWQbwA6d+nSJQcHB4qivvjiC0JIcnKyWCwWiUQnT5585513pFKpnZ3dsWPHmJUTExOFQqGVldXy5cttbGyEQqGnp+e1a9eY1pCQECMjoyFDhjAfP/nkE7FYTFFUeXk5ISQ0NHTdunWFhYUURclkMkLI2bNnpVLpzp07tXRqiYmJNE3PmjWrY1N0dPSIESMOHTp0/vz5TrelaTo+Pv6VV14RCATm5uazZ8++e/cu09R9FxFC2trawsPDHRwcjI2Nx44d21vlhVRXWlpqbGzs5OTELjE3N/fy8kpISOjf9077Chp6gBCSlpam6yhAOeanTd2tmBsv+/btYz5u2bKFEHLhwoWqqqqysrJp06aJxeLm5mamNTg4WCwW37lzp7GxMT8/393dXSKRPHr0iGlduHChtbU1u+e4uDhCyPPnz5mPAQEBLi4ubOvp06clEklkZKQGZ6rKd9LZ2dnV1VVhoYuLy4MHD2iavnz5soGBgaOjY21tLU3T2dnZfn5+7Grh4eFGRkZHjhyprKy8devWxIkTLSwsnj59yrR230Xr168XCAQZGRkVFRWbN282MDC4fv260jOKioqys7MzMzPj8/mOjo5+fn4///xzx9Vee+21cePGdbOfuro6iUQSEhKisDwsLIwQcvPmTaWR4N97D+H6BkA9np6eUqnU0tIyKCiorq7u0aNHbBOPx2P+8Hd1dU1OTq6pqUlJSdHgEL6+vtXV1du2beu9qP+jrq7uwYMHLi4uXa3g4eGxZs2aoqKiTZs2KTQ1NDTEx8fPmTNn0aJFpqambm5u+/fvLy8vP3DggPxqnXZRY2NjcnKyv79/QECAmZnZ1q1b+Xy+Kv3z/vvvnzp1qri4uLa29tixY48ePfLy8srPz1f3xGNiYmxsbKKjoxWWDx8+nBCSl5en7g5BXcg3ABpiJvpsaWnptHXSpEkikYi919R3lJWV0TQtEom6WSc6OnrkyJFJSUmXLl2SX56fn19bWztp0iR2ibu7u5GREXvnUIF8F927d6++vp4dymxsbDxkyBBV+sfe3n7ChAkmJiZGRkaTJ09OSUlpaGhISkpSuqG8zMzM9PT0c+fOyY+PYDBd8ezZM7V2CBpAvgHQFoFA8Pz5c11HoaixsZEQ0unTdZZQKExJSaEoaunSpQ0NDexyZuiwiYmJ/MpmZmY1NTVKj1tXV0cI2bp1K/sazcOHDzuOYFbKzc3N0NDwt99+U32T1NTUXbt25eTkODo6dmw1NjYmf3QLaBXyDYBWtLS0VFZW2tnZ6ToQRczPq9L3HD08PNauXVtQUBAVFcUuNDMzI4QoZBcVT9PS0pIQsnfvXvkb+leuXFE3/vb29vb29u7zpbx9+/YdPXr04sWLQ4cO7XSF5uZm8ke3gFYh3wBoRU5ODk3TkydPZj7yeLyu7rxxzMrKiqIoVd6wiYqKGjVq1M2bN9klY8aMMTEx+eWXX9gl165da25ufvXVV5Xuzd7eXigU5ubmqhvwW2+9Jf+RGWKgyoRyNE1v3LgxLy8vKytL4ZpMHtMV1tbW6gYG6kK+Aeg17e3tFRUVra2tt27dCg0NdXBwWLJkCdMkk8levnyZlZXV0tLy/Pnzhw8fym84aNCgx48fFxUV1dTUtLS0ZGdna288tEgkcnZ2LikpUbomc1dN/v0VoVC4bt26zMzMo0ePVldX5+XlrVixwsbGJjg4WJW9ffDBB8eOHUtOTq6urm5rayspKXny5AkhJCgoyNrauqt6OaWlpampqZWVlS0tLVeuXFm2bJmDg8OKFSuUHvHOnTu7d+8+ePAgn8+XL4ezZ88e+dWYrnBzc1O6Q+gpXQyK6z8IxkfqCQ3GQ+/bt495Y0YkEs2aNSspKYl5sDx8+PDCwsIDBw5IpVJCyLBhw3777TeapoODg/l8vq2tLY/Hk0qls2fPLiwsZPf24sWL6dOnC4VCJyenVatWbdiwgRAik8mYAdM3btwYNmyYsbHx1KlTnz59eubMGYlEEh0drcGZqvKdDAkJ4fP59fX1zMfMzExmuJqFhcXKlSsVVt6wYYP8eOj29va4uLjhw4fz+Xxzc3N/f/979+4xTUq7qKmpaePGjQ4ODjwez9LSMiAgID8/n6Zpf39/Qkh4eHin0a5bt87FxUUsFvN4PDs7uw8//PDx48ds65UrV6ZMmWJjY8P8oA0ZMsTT0/PHH3+kabqrIWdxcXHy+/f19bW1tW1vb++VvoVuIN/0CL5/+kKz92/UwtRN0eohVKHKd7KgoIDH4ykUidGhtra2adOmHT58mPtDl5eXC4XCPXv2qLIy/r33EO6nAfQafSk2LJPJIiMjIyMj2WLJOtTW1paVlVVTUxMUFMT90SMiIsaPHx8SEsL9oQcg5BtOLVu2TCKRUBSlwVNTLemmYHv35IvYM4yMjKysrF5//fW4uLiKigptRw49ERYWFhgYGBQUpPPSnDk5OSdOnMjOzu7+lSBtiI+Pz83NPXPmDJ/P5/jQA5SuL7D0G1H/+popJ6VK8QxueHl5JSUlvXjxorq6Oi0tjc/nv/3226pv7uLiYmpqStM086j8hx9+WLJkCUVRNjY2qpQq4Yy276eFhYUx7zY6OjoeP35cewdSSq3v5Llz5zZu3KjVePqsrKysmJiY1tZW1TfR4N87yEO+6ZF+kG98fX3FGtRoAAAgAElEQVTl/8nNmzePEMJW/VKKzTfyjh8/bmBgYGVlVVlZ2WuB9gwHz2/6CPwmag/6todwP41rFEXpOoT/orRguwbmzp27ZMmSsrKy/fv39zQ+AOgvkG+0jqbpuLi4kSNHCgQCU1NTZiAsq9MK7Urruv/4449/+tOfRCKRVCp1c3Orrq7ualfqUijYrnFhfOa9k+zs7L55mgCgA7q+wNJvRIXr6y1btlAU9fnnn1dUVNTX1zN1Btn7aV1VaO+mrnttba1UKo2NjW1oaHj69OmcOXOYsvaaFXuX17Fgu9LC+J3eT6NpmskN9vb2feQ0cT8Neg5920MD4l+g9ij9/tXX14tEojfffJNdIv/8pqGhQSQSBQUFsSsLBIKPP/6Y/uOHuKGhgWlistT9+/fpPybTPX36tPyButmV6rZs2TJixIjq6mrVN+kq39A0TVGUmZlZHzlN5BvoOfRtD/E4vpwaaO7fv19fX+/t7d1pq+oV2uXrujs7O1tZWS1atGj16tVLlixhSt5qXOydxRRs//777zsWbNdAXV0dTdPM6+V95zTT09N7emL6QIMimABc0HXC029E2d87Z86cIYTIvzgtf33zz3/+s+P/kcmTJ9Md/vA/ePAgIeRf//oX8/H27dt/+ctfeDweRVHz58+vr6/vZleqOHbsmLu7e2lpqbo90NX1DVMLy8fHp4+cJh7zQK/A9U1PYLyAdgmFQkJIU1NTp60aV2gfPXr0t99++/jx440bN6alpe3Zs6cnxd6VFmzXwNmzZwkh77zzDukzp0kGxl9XBL+JWqPxPwdgIN9o15gxYwwMDH788cdOWzWr0P748eM7d+4QQiwtLT/77LOJEyfeuXNHs13RqhVsV9fTp0/37t1rZ2e3dOlS0gdOEwD6AuQb7WKK4GZkZBw+fLi6uvrWrVvyM713U6G9G48fP16+fPndu3ebm5tv3rz58OHDyZMna7YrpQXbVSmMT9N0bW0tU173+fPnaWlpU6ZMMTQ0zMrKYp7f6Pw0AaBP0PEFqp4jKty7qKmpWbZs2eDBg01MTKZOnRoeHk4IsbOz+/XXX+kuKrR3X9e9qKjI09PT3Nzc0NBw6NChW7ZsYQoEdFXsvRtKC7Z3Uxj/1KlTY8eOFYlERkZGBgYGhBBmQNqf/vSnyMjIFy9eyK+s29OkMT4NegP6tocoGjcle4CiqLS0NKYGDPRl6enp8+fPHwjfdnwntQd920O4nwYAAFxAvunP7t69S3VNJ9ONAMCAhXzTn40aNaqbe6mpqam6DhB07Pz582FhYfJTGS1evFh+BR8fH4lEYmhoOHr0aOadKl1pb2/fu3evp6enwnKlEzh9/fXX7u7uEolk2LBhH3zwwdOnT5nlp06dio2N1Zcp8voJjp4T9VMEzw/1BMYLdBQeHj5z5ky2fJGLi8vgwYNJhxpC2dnZfn5+vR+oOn777bcpU6YQQsaNG6fQ1P0ETswfVbGxsZWVlTdv3nR2dh4/fnxLSwvTmpCQ4OXlVVFRoWIY+PfeQ7i+AegdDQ0NHf/61vmuurJr167U1NT09HT58kWJiYkGBgbBwcE6n/RT3q+//rpp06YVK1aMHz++Y6uJiUlwcPCgQYMkEsm8efP8/f3Pnj1bXFzMtP7tb38bOnTohg0bTE1Nx48fv3bt2tzc3GvXrjGtq1evHjdu3IwZM1pbW7k7nwEM+Qagdxw+fLisrKyv7apT9+/f37Zt244dO5j6FyxPT8/Q0NDS0tL169dr7+jqGjdu3IkTJxYuXCgQCDq2dj+BU3FxsY2NDTvplL29PSHk4cOH7PoRERG5ubkJCQnaix9YyDcA/0HTdHx8/CuvvCIQCMzNzWfPns0WAw0JCTEyMhoyZAjz8ZNPPhGLxRRFlZeXE0JCQ0PXrVtXWFhIUZRMJktMTBQKhVZWVsuXL7exsREKhZ6enuyf1WrtivRgFqKuJCYm0jQ9a9asjk3R0dEjRow4dOjQ+fPn1e0ipRMacTB3kcIETs7OzvKZm3l44+zszC4xNzf38vJKSEigB8BYed3T7e08fUdwP1dPqPj8Jjw83MjI6MiRI5WVlbdu3Zo4caKFhcXTp0+Z1oULF1pbW7Mrx8XFEUKYWXlomg4ICHBxcWFbg4ODxWLxnTt3Ghsb8/PzmUfW7ETdau1K6SxE8lT5Tjo7O7u6uiosdHFxefDgAU3Tly9fNjAwcHR0rK2tpTs8v+m+i7qZ0Iju8RRNr732WsfnN/I6TuCUk5PD5/MTExOrq6tv3779yiuvvPXWWwpbhYWFEdWmeMe/9x7C9Q3AvzU0NMTHx8+ZM2fRokWmpqZubm779+8vLy+XL0GkFh6Px1wHuLq6Jicn19TUpKSkaLAfX1/f6urqbdu2aRaGgrq6ugcPHri4uHS1goeHx5o1a4qKijZt2qTQpGIXeXp6SqVSS0vLoKCgurq6R48eEUIaGxuTk5P9/f0DAgLMzMy2bt3K5/M165CuxMTE2NjYREdHs0u8vLw2btwYEhIilUrHjBlTU1Nz6NAhha2GDx9OCOmq1gb0IuQbgH/Lz8+vra2dNGkSu8Td3d3IyIi9D9YTkyZNEolEas1IpCVlZWU0TTOVhLoSHR09cuTIpKSkS5cuyS9Xt4vkJzTq+RRN3WMmcDp37pz8CIgtW7YcOHDgwoULtbW1v//+u6enp4eHBzuagMF0xbNnz3orEugK8g3Av1VWVhJCFOpkm5mZ1dTU9Mr+BQLB8+fPe2VXPdHY2MgE0806QqEwJSWFoqilS5c2NDSwy3vSRXV1dYSQrVu3sm8cP3z4kH2w30Opqam7du3Kyclh5uVjPHnyJDY29qOPPnrjjTfEYrGTk9PBgwcfP37M3L1kGRsbkz+6BbQK+Qbg38zMzAghCj+dlZWVdnZ2Pd95S0tLb+2qh5ifV6XvOXp4eKxdu7agoCAqKopd2JMu6uHcRd3oagKngoKCtrY2+YVSqXTQoEH5+fnyqzU3N5M/ugW0CvkG4N/GjBljYmLyyy+/sEuuXbvW3Nz86quvMh95PB5za0gDOTk5NE1Pnjy557vqISsrK4qiVHnDJioqatSoUTdv3mSXKO2ibmhj7iK62wmcmCwoP11FTU3Ny5cvmVHRLKYrrK2tezEw6BTyDcC/CYXCdevWZWZmHj16tLq6Oi8vb8WKFTY2NsHBwcwKMpns5cuXWVlZLS0tz58/l3+NgxAyaNCgx48fFxUV1dTUMLmkvb29oqKitbX11q1boaGhDg4OS5Ys0WBXqsxCpDqRSOTs7FxSUqJKh6SkpMi/3aK0i7rfW1dzFwUFBVlbW2tQL6f7CZycnJymT59+8ODBn376qaGhobi4mInzr3/9q/xOmK5wc3NT9+igNp2Mius3CMZH6gkVx0O3t7fHxcUNHz6cz+ebm5v7+/vfu3ePbX3x4sX06dOFQqGTk9OqVas2bNhACJHJZMwo5xs3bgwbNszY2Hjq1KlPnz4NDg7m8/m2trY8Hk8qlc6ePbuwsFCzXXUzC1FHqnwnQ0JC+Hx+fX098zEzM5MZrmZhYbFy5UqFlTds2CA/HrqbLup+QiO667mL/P39CSHh4eGdRnvlypUpU6bY2NgwP1lDhgzx9PT88ccfaRUmcCovLw8NDZXJZAKBwMTEZMqUKd98843C/n19fW1tbZkJA3vet9AN5JsewfdPX3BfP40pssLlERmqfCcLCgp4PN6RI0e4CUmptra2adOmHT58mPtDl5eXC4XCPXv2qLIy/r33EO6nAWhLn609LJPJIiMjIyMjFUop60RbW1tWVlZNTY1OJsiIiIgYP358SEgI94cegJBvAAaisLCwwMDAoKAgnZfmzMnJOXHiRHZ2dvevBGlDfHx8bm7umTNn+Hw+x4cemJBvAHrf5s2bU1JSqqqqnJycMjIydB1O53bu3BkSEvLZZ5/pNgxvb++vvvqKrSbHmZMnTzY1NeXk5Jibm3N86AGLp+sAAPqhmJiYmJgYXUehnI+Pj4+Pj66j0A0/Pz8/Pz9dRzGw4PoGAAC4gHwDAABcQL4BAAAuIN8AAAAXMF6gp/bu3Xv8+HFdRwFKMDVLAgMDdR0IF/CdhL6JojGLag8MkN8vUJCdnT1hwgTuh/CCzq1du9bDw0PXUegr5BsAtVEUlZaWNm/ePF0HAqBP8PwGAAC4gHwDAABcQL4BAAAuIN8AAAAXkG8AAIALyDcAAMAF5BsAAOAC8g0AAHAB+QYAALiAfAMAAFxAvgEAAC4g3wAAABeQbwAAgAvINwAAwAXkGwAA4ALyDQAAcAH5BgAAuIB8AwAAXEC+AQAALiDfAAAAF5BvAACAC8g3AADABeQbAADgAvINAABwAfkGAAC4gHwDAABcQL4BAAAuIN8AAAAXkG8AAIALyDcAAMAF5BsAAOAC8g0AAHCBp+sAAPRAZWUlTdPyS+rq6ioqKtiPJiYmfD6f87gA9Aml8K8IADp64403fvjhh65aDQ0NS0tLra2tuQwJQO/gfhqAcgsWLKAoqtMmAwODP//5z0g2AEoh3wAoN3fuXB6v85vPFEW99957HMcDoI+QbwCUMzc39/HxMTQ07NhkYGDg7+/PfUgAegf5BkAlixYtam9vV1jI4/F8fX1NTU11EhKAfkG+AVDJrFmzBAKBwsK2trZFixbpJB4AvYN8A6ASkUjk7++vMOjZ2Nh4xowZugoJQL8g3wCo6t13321paWE/8vn8uXPnGhsb6zAkAD2CfAOgqrfeekv+UU1LS8u7776rw3gA9AvyDYCq+Hx+UFCQkZER89HMzMzb21u3IQHoEeQbADUsWLCgubmZEMLn8xctWtTVSzkA0BHq2QCoob29fejQoc+ePSOEXLp0acqUKbqOCEBv4PoGQA0GBgaLFy8mhNjY2Hh6euo6HAB9grsB/5aenq7rEEA/WFhYEEJee+2148eP6zoW0A+enp52dna6jkL3cD/t37qqxggA0ENpaWnz5s3TdRS6h+ub/8B3AhSkp6fPnz+/499kGRkZc+fO1UlIWkJRFL7/WoK/ZVl4fgOgtn6WbAC4gXwDAABcQL4BAAAuIN8AAAAXkG8AAIALyDcAAMAF5BuAXnbmzBlTU9Nvv/1W14Foy/nz58PCwk6cOOHs7ExRFEVRTM0Flo+Pj0QiMTQ0HD169I0bN3QVJyGkvb197969HStBREZGurq6SqVSgUAgk8k+/fTT2tpa+RW+/vprd3d3iUQybNiwDz744OnTp8zyU6dOxcbGtrW1cXQC/QvyDUAv69/vUG/fvj0xMXHz5s0BAQG///67i4vL4MGDjx49+t1337HrfP/998ePH585c2Z+fv7EiRN1FWpBQcGf//zntWvX1tfXKzRdvHhx5cqVRUVF5eXlMTExCQkJgYGBbGtaWtrChQsDAwNLSkpOnjz5008/vfPOO62trYSQWbNmCYVCb2/vyspKTk+mX0C+Aehlvr6+VVVVM2fO1PaBGhoaOK7htmvXrtTU1PT0dIlEwi5MTEw0MDAIDg6uqqriMpju/frrr5s2bVqxYsX48eM7tpqYmAQHBw8aNEgikcybN8/f3//s2bPFxcVM69/+9rehQ4du2LDB1NR0/Pjxa9euzc3NvXbtGtO6evXqcePGzZgxg8lAoDrkGwB9dfjw4bKyMs4Od//+/W3btu3YsUMoFMov9/T0DA0NLS0tXb9+PWfBKDVu3LgTJ04sXLhQIBB0bD19+rShoSH7kamJx14GFRcX29jYsHUB7O3tCSEPHz5k14+IiMjNzU1ISNBe/P0S8g1Ab7p06ZKDgwNFUV988QUhJDk5WSwWi0SikydPvvPOO1Kp1M7O7tixY8zKiYmJQqHQyspq+fLlNjY2QqHQ09OT/Ts6JCTEyMhoyJAhzMdPPvlELBZTFFVeXk4ICQ0NXbduXWFhIUVRMpmMEHL27FmpVLpz504tnVpiYiJN07NmzerYFB0dPWLEiEOHDp0/f77TbWmajo+Pf+WVVwQCgbm5+ezZs+/evcs0dd9FhJC2trbw8HAHBwdjY+OxY8empaX1+qmVlpYaGxs7OTkxH52dneUTOfPwxtnZmV1ibm7u5eWVkJDQv++d9j4aaJqmaUJIWlqarqOAvoX5aVN3K+a2zL59+5iPW7ZsIYRcuHChqqqqrKxs2rRpYrG4ubmZaQ0ODhaLxXfu3GlsbMzPz2eeUT969IhpXbhwobW1NbvnuLg4Qsjz58+ZjwEBAS4uLmzr6dOnJRJJZGSkBmeqyvff2dnZ1dVVYaGLi8uDBw9omr58+bKBgYGjo2NtbS1N09nZ2X5+fuxq4eHhRkZGR44cqaysvHXr1sSJEy0sLJ4+fcq0dt9F69evFwgEGRkZFRUVmzdvNjAwuH79uuqn9tprr40bN66bFerq6iQSSUhICLskJyeHz+cnJiZWV1ffvn37lVdeeeuttxS2CgsLI4TcvHlTaQD4bWHh+gaAC56enlKp1NLSMigoqK6u7tGjR2wTj8dj/vB3dXVNTk6uqalJSUnR4BC+vr7V1dXbtm3rvaj/o66u7sGDBy4uLl2t4OHhsWbNmqKiok2bNik0NTQ0xMfHz5kzZ9GiRaampm5ubvv37y8vLz9w4ID8ap12UWNjY3Jysr+/f0BAgJmZ2datW/l8vmb905WYmBgbG5vo6Gh2iZeX18aNG0NCQqRS6ZgxY2pqag4dOqSw1fDhwwkheXl5vRhJv4d8A8ApIyMjQkhLS0unrZMmTRKJROy9pr6jrKyMpmmRSNTNOtHR0SNHjkxKSrp06ZL88vz8/Nra2kmTJrFL3N3djYyM2DuHCuS76N69e/X19WPGjGGajI2NhwwZ0ov9k5mZmZ6efu7cOfkREFu2bDlw4MCFCxdqa2t///13T09PDw8PdjQBg+kKZqZXUBHyDUDfIhAInj9/rusoFDU2NhJCOn32zhIKhSkpKRRFLV26tKGhgV3ODB02MTGRX9nMzKympkbpcevq6gghW7dupf7w8OHDjuObNZOamrpr166cnBxHR0d24ZMnT2JjYz/66KM33nhDLBY7OTkdPHjw8ePHzM1MlrGxMfmjW0BFyDcAfUhLS0tlZWUfnAuS+XlV+p6jh4fH2rVrCwoKoqKi2IVmZmaEEIXsouJpWlpaEkL27t0r/xjgypUrGpyCgn379h09evTixYtDhw6VX15QUNDW1ia/UCqVDho0KD8/X3615uZm8ke3gIqQbwD6kJycHJqmJ0+ezHzk8Xhd3XnjmJWVFUVRqrxhExUVNWrUqJs3b7JLxowZY2Ji8ssvv7BLrl271tzc/Oqrryrdm729vVAozM3N1SzsTtE0vXHjxry8vKysLIWrLkIIkwWfPHnCLqmpqXn58iUzKprFdIW1tXUvBtbvId8A6Fh7e3tFRUVra+utW7dCQ0MdHByWLFnCNMlkspcvX2ZlZbW0tDx//lz+FRBCyKBBgx4/flxUVFRTU9PS0pKdna298dAikcjZ2bmkpETpmsxdNfm3W4RC4bp16zIzM48ePVpdXZ2Xl7dixQobG5vg4GBV9vbBBx8cO3YsOTm5urq6ra2tpKSESQZBQUHW1tYa1Mu5c+fO7t27Dx48yOfzKTl79uwhhDg5OU2fPv3gwYM//fRTQ0NDcXExE+df//pX+Z0wXeHm5qbu0Qc0nYyK64MIxixCBxqMh963bx/zxoxIJJo1a1ZSUhLzYHn48OGFhYUHDhyQSqWEkGHDhv322280TQcHB/P5fFtbWx6PJ5VKZ8+eXVhYyO7txYsX06dPFwqFTk5Oq1at2rBhAyFEJpMxA6Zv3LgxbNgwY2PjqVOnPn369MyZMxKJJDo6WoMzVeX7HxISwufz6+vrmY+ZmZnMcDULC4uVK1cqrLxhwwb58dDt7e1xcXHDhw/n8/nm5ub+/v737t1jmpR2UVNT08aNGx0cHHg8nqWlZUBAQH5+Pk3T/v7+hJDw8PBOo71y5cqUKVNsbGyYH7ohQ4Z4enr++OOPNE13NagsLi6O2ba8vDw0NFQmkwkEAhMTkylTpnzzzTcK+/f19bW1tW1vb++Vvh0gkG/+Dd8J6Eiz92/UwlRV0eohVKHK97+goIDH4x05coSbkJRqa2ubNm3a4cOHuT90eXm5UCjcs2ePKivjt4WF+2kAOqYvxYZlMllkZGRkZKRCKWWdaGtry8rKqqmpCQoK4v7oERER48ePDwkJ4f7Qeg35RkPLli2TSCQURfXuk0zuRUdHU/+Nfdehe/Ll6BlGRkZWVlavv/56XFxcRUWFtiMH7oWFhQUGBgYFBem8NGdOTs6JEyeys7O7fyVIG+Lj43Nzc8+cOcPn8zk+tL5DvtHQoUOHDh48qOsodIktR29qakrTdHt7e1lZWXp6upOT08aNG0ePHi0/Hgk6tXnz5pSUlKqqKicnp4yMDF2Ho5KdO3eGhIR89tlnug3D29v7q6++YovLcebkyZNNTU05OTnm5uYcH7ofQL7ph9QtU69wR/727dsaHJSiKDMzs9dffz0lJSU9Pf3Zs2dMWX4NdqVV3Nfw70ZMTExTUxNN0w8ePJg7d66uw1GVj4/Prl27dB2Fbvj5+YWFhcmPvgPVId9oji1X3tdwXKa+o7lz5y5ZsqSsrGz//v06DKNTOu8cgAEL+UYNNE3HxcWNHDlSIBCYmpoyg1MZu3fvFolEEomkrKxs3bp1tra2zHDPrmqwd1+InnRbv13dMvU9oXGJe+YNkuzsbNJ/OwcA1MP5iLg+iqgwZnHLli0URX3++ecVFRX19fVJSUlEriA5U1N99erV+/btmzNnzr/+9a/ua7B3X4i++23VKlPfvaioKDs7OzMzMz6f7+jo6Ofn9/PPP7OtSkvcs89vFFRXVxNC7O3t9bpzOBgP3Ueo8v0HzaBvWQPi35IqlH4n6uvrRSLRm2++yS5hpoRSyDcNDQ3s+iYmJkFBQez6P//8MyGE/e0ODg6W/6W+fv06IWTHjh2qbNuLP6mPHj26ceNGTU1NU1PTlStXJkyYYGxsfPv2bRU37yrf0DTNPNFh/ltPOwf5BnoOfcvicXYhpe/u379fX1/v7e2t4vrq1mCXL0Sv7rY9YW9vzxaGmjx5ckpKyvjx45OSkpKTk3uy27q6OpqmmRfFO9KXzmEEBgZqac99yt69e48fP67rKKA/w/MbVTHlkphqtarQoAY7W4i+J/Xbe8jNzc3Q0PD/t3fvUU1c+QPA70BCQkLCQ56CIAQUEYS1PpqgSy1n6VqOKLI8uuoWrVt0a1NEKYJCERCluMBi4bgqy/aIFRAoWCu2iy54POW42xUE8aigoigiD4GE92t+f0w7v5wAIeQx4fH9/GXundy5cw35Zmbu3O+jR4+UbIdowdHRccLaWTo4AABlwPmNvJhMJkJocHBQzu2nuwa75EL0yqzfrqSxsbGxsTHZaU7kce3aNYTQxo0bJ6ydXYMzH371Yxi2f//+gIAATXdkDpqxE1mpB+c38nJ2dtbS0qqoqJB/+2mtwS65EP2U71XhMvXvvfee5EsiMzyfz1emzZaWltTUVCsrq127dk24wWwZHACACkG8kRexMG1BQUFWVpZIJKqpqZHKvi5FnjXYJ1uIfsr3TmuZetnH9fLly9zc3K6uruHh4crKyt27d1tbW+/du5eolWeJexzHe3p6iIVy29ra8vLy3N3dtbW1i4uLJ7t/M1sGBwCgSpqcrDCTIDnmkIjF4t27dy9YsEBPT2/dunUxMTEIISsrq7t37yYlJRGZ/hYtWkQ+ri9jDXZ8qoXoZb93WsvUyz6oAwcO8Hg8NptNo9GsrKz+/Oc/Nzc3k7Uylri/fPnyihUrWCyWjo6OlpYW+nWJgTVr1sTFxXV0dJBbzt7BgflpQHkwtiQMx3FNhboZBcOwvLw8Kq9f79mz59KlSx0dHZTtcRaZIYOTn58fGBg4H/5GqP/8zx8wtiS4nqZJs2Uheo2AwQFgjoF4M5c9ePAAm5xGEoeAOaCsrCwyMlIyIcWOHTskN/Dy8uJwONra2suXL1cg37MKjY2Npaamjl+hNS4uzsnJicvlMhgMe3v7zz//XCqpzzfffEOsamFjY7Nz586Wlhai/PLly0lJSfBjSEGavqA3UyBqr7FGRkbq6OgghBYvXnzp0iXK9jsrzJzBgfs348XExGzatEkkEhEveTzeggULEEJXrlyR3Ky0tFQyn7RGPHr0yN3dHSHk6uoqVeXh4ZGRkdHR0SESifLy8uh0+u9//3uyNjc3FyGUlJTU1dVVVVVlZ2fn5uY2PDxM1KalpXl4eHR2dsrZDYq/W2ayefG3JA/4TIDxKIg3fX19fD5f403J+fk/fvz4kiVLyHWJcBzn8XgXLlzQ0tKytLTs6uoiyzUeb6qrq7du3ZqTk+Pm5jY+3nh7e4+MjJAviZsr5AJ9GzZsWLhwITHlEsfxr776CiF069YtcnuhUMjn88kIJBt8t5DgehoAmqTC/AjqTrXQ0NAQHR199OhR4tlnkkAgCA0Nffny5cGDB9W39+lydXUtLCzctm3bhA8vX7lyRTKHjbGxMUKor6+PeNnU1GRhYUE+p0ks+CQ5sT42Nra6ujotLU19/Z+TIN4AoCxcRfkRZCdimG6qBYVzSUwmPT0dx3EfH5/xVQkJCUuWLDl37lxZWdl0hygzM5PNZrNYrJKSko0bN3K5XCsrK2IxXMLo6GhMTIy1tbWuru6KFSuIk07Vevnypa6urq2tLfHSzs5OMnITN2/s7OzIEkNDQw8Pj7S0NHwezF1UJc2eXs0cCM55wThyXk9TYX4E2YkYptXUlLkkJMnz+bezs3NycpIq5PF4T58+xXH8p59+0tLSWrx4cU9PDz7ueprsISKWD79+/Xp3d3dra+v69evZbPbQ0BBRe/DgQQaDUVBQ0NnZGRUVpaWlRayCIae1a9eOv54mqbe3l8PhCIVCsqS8vJxOp6enp4tEonv37i1btuy995uRdAMAAB5NSURBVN6TeldkZCSSWB5eBvhuIcH5DQBK6e/vT0lJ2bp16/bt2/X19V1cXE6fPt3e3i57+QkZaDQacR7g5OSUmZkpFouzs7MVaMfb21skEkVHRyvWDSm9vb1Pnz7l8XiTbcDn8/fv39/Y2Hjo0CGpKjmHSCAQcLlcExOToKCg3t7e58+fI4QGBgYyMzN9fX39/PwMDAyOHDlCp9MVG5DJJCYmWlhYJCQkkCUeHh4RERFCoZDL5To7O4vF4nPnzkm9y8HBASFUW1urwp7MeRBvAFCKWvMjSCZi0KzW1lYcx1ksloxtEhISli5dmpGRcevWLcny6Q4RMTuRWG3o4cOHfX19zs7ORJWurq65ubkKB6SoqCg/P/+HH37gcDhk4eHDh8+cOXP9+vWenp4nT54IBAI+n9/U1CT5RmIoXr9+raqezAcQbwBQirrzI5CJGDRrYGCA6IyMbZhMZnZ2NoZhu3bt6u/vJ8uVGaLe3l6E0JEjR8jnxp49e0be2FdSbm7uiRMnysvLFy9eTBa+evUqKSnp448/fvfdd9lstq2t7dmzZ5ubm4mrlyRiiSZiWICcIN4AoBS15keQTMSgWcTX65TPOfL5/LCwsPr6+vj4eLJQmSEiMk6lpqZK3gaorKxU4BCknDp1Kicn58aNGwsXLpQsr6+vHx0dlSzkcrlGRkZ1dXWSmw0NDaFfhwXICeINAEpRa34EyUQMSjalJFNTUwzDuru7p9wyPj7e0dGxqqqKLJlu+glJixYtYjKZ1dXVinV7QjiOR0RE1NbWFhcXS511IYSIKPjq1SuyRCwWv3nzhkyDSyCGwszMTIUdm/Mg3gCgFJXnR5gsEcN0m5Inl4T8WCyWnZ0dkeV2ygHJzs6WfLpFnvQTMlrbuXPnxYsXMzMzRSLR6OjoixcviGAQFBRkZmamwHo59+/f//LLL8+ePUun0yVXeDp58iRCyNbWdsOGDWfPnr1582Z/f39TUxPRz48++kiyEWIoXFxcprv3eU0js+JmIARzFsE4cs6HVmF+BNmJGKbVlIxcEuPJ8/kXCoV0Or2vr494WVRURExXMzY23rdvn9TG4eHhkvOhZQxRRkYGce/dwcHh8ePHZ86cIdIm2djYPHr0CMfxwcHBiIgIa2trGo1GpKGqq6vDcdzX1xchFBMTM2FvKysr3d3dLSwsiC86c3NzgUBQUVGB4/hkk8qSk5OJ97a3t4eGhtrb2zMYDD09PXd392+//VaqfW9vb0tLS3INAiXHdp6AePML+EyA8ahfPy0kJMTIyIjKPRLk+fzX19fTaDQyg5HGjY6Orl+/Pisri/pdt7e3M5nMkydPyrMxfLeQ4HoaADPLjF172N7ePi4uLi4uTmopZY0YHR0tLi4Wi8UaWeY8NjbWzc1NKBRSv+tZDeINAEBekZGR/v7+QUFB8kwcUKvy8vLCwsLS0lLZjwSpQ0pKSnV19dWrV+l0OsW7nu0g3gAwU0RFRWVnZ3d3d9va2hYUFGi6OxM7duyYUCg8fvy4Zrvh6el54cIFcjU5ypSUlAwODpaXlxsaGlK86zmApukOAAB+kZiYmJiYqOleTM3Ly8vLy0vTvdCMzZs3b968WdO9mK3g/AYAAAAVIN4AAACgAsQbAAAAVIB4AwAAgAoQbwAAAFABwyEfKkIIITJXOQAAqFZeXl5AQICme6F5MB/6F+pIig7mqsDAwNDQUD6fr+mOgNlBIBBougszApzfADBtGIbBL1YApgvu3wAAAKACxBsAAABUgHgDAACAChBvAAAAUAHiDQAAACpAvAEAAEAFiDcAAACoAPEGAAAAFSDeAAAAoALEGwAAAFSAeAMAAIAKEG8AAABQAeINAAAAKkC8AQAAQAWINwAAAKgA8QYAAAAVIN4AAACgAsQbAAAAVIB4AwAAgAoQbwAAAFAB4g0AAAAqQLwBAABABYg3AAAAqADxBgAAABUg3gAAAKACxBsAAABUgHgDAACAChBvAAAAUAHiDQAAACpAvAEAAEAFiDcAAACoAPEGAAAAFWia7gAAs8DFixfFYrFkSVlZWVdXF/nS19fXxMSE8n4BMJtgOI5rug8AzHTBwcFff/01nU4nXhJ/NRiGIYRGR0f19PRaW1sZDIYmuwjAjAfX0wCY2gcffIAQGv7VyMjIyMgI8W9tbW1/f38INgBMCc5vAJjayMiImZnZmzdvJqy9fv36u+++S3GXAJh14PwGgKnRaLQPPviAvJ4mydjY2MPDg/ouATDrQLwBQC4ffPDB8PCwVCGdTt+xY4e2trZGugTA7ALX0wCQC47j1tbWL168kCr/z3/+s3r1ao10CYDZBc5vAJALhmHbt2+XuqS2aNGiVatWaapLAMwuEG8AkJfUJTU6nR4cHEzMigYATAmupwEwDY6Ojg8fPiRf3rt3b/ny5RrsDwCzCJzfADANO3bsIC+pOTk5QbABQH4QbwCYhu3bt4+MjCCE6HT6hx9+qOnuADCbwPU0AKZn1apV//vf/zAMa2xstLa21nR3AJg14PwGgOn505/+hBBau3YtBBsApgXWh1aKv7+/prsAqDYwMIBh2ODgIPzvz0NhYWF8Pl/TvZit4PxGKQUFBeMfAAQz0IsXLwoKClTSFJPJNDMzs7KyUklrKgefSfUpKChoamrSdC9mMTi/Udb+/fsDAgI03Qswhfz8/MDAwEuXLqmktYaGBnt7e5U0pXIYhsFnUk3gWSslwfkNANM2Y4MNADMZxBsAAABUgHgDAACAChBvAAAAUAHiDQAAACpAvAFgUlevXtXX1//uu+803RF1KSsri4yMLCwstLOzwzAMw7AdO3ZIbuDl5cXhcLS1tZcvX37nzh1N9RMhNDY2lpqaKhAIpMrj4uKcnJy4XC6DwbC3t//88897enokN/jmm29Wr17N4XBsbGx27tzZ0tJClF++fDkpKWl0dJSiAwAQbwCQYW6v9vTFF1+kp6dHRUX5+fk9efKEx+MtWLAgJyfn+++/J7f58ccfL126tGnTprq6upUrV2qqq/X19b/97W/DwsL6+vqkqm7cuLFv377Gxsb29vbExMS0tDTJ53Dz8vK2bdvm7+//4sWLkpKSmzdvbty4kVgBz8fHh8lkenp6dnV1UXow8xjEGwAm5e3t3d3dvWnTJnXvqL+/f/wvd7U6ceJEbm5ufn4+h8MhC9PT07W0tEJCQrq7u6nsjGx37949dOjQ3r173dzcxtfq6emFhIQYGRlxOJyAgABfX99r166RT2X+/e9/X7hwYXh4uL6+vpubW1hYWHV19e3bt4nazz77zNXV9f333yciEFA3iDcAaF5WVlZraytlu2toaIiOjj569CiTyZQsFwgEoaGhL1++PHjwIGWdmZKrq2thYeG2bdsYDMb42itXrmhra5MvjY2NEULkaVBTU5OFhQX5nOaiRYsQQs+ePSO3j42Nra6uTktLU1//AQniDQATu3XrlrW1NYZhX331FUIoMzOTzWazWKySkpKNGzdyuVwrK6uLFy8SG6enpzOZTFNT0z179lhYWDCZTIFAQP6OFgqFOjo65ubmxMtPPvmEzWZjGNbe3o4QCg0NPXDgwOPHjzEMI54kvXbtGpfLPXbsmJoOLT09HcdxHx+f8VUJCQlLliw5d+5cWVnZhO/FcTwlJWXZsmUMBsPQ0HDLli0PHjwgqmQPEUJodHQ0JibG2tpaV1d3xYoVeXl5Kj+0ly9f6urq2traEi/t7OwkAzlx88bOzo4sMTQ09PDwSEtLm9vXTmcKHCgBIZSXl6fpXoCpEV9t030XcVnm1KlTxMvDhw8jhK5fv97d3d3a2rp+/Xo2mz00NETUhoSEsNns+/fvDwwM1NXVEfeonz9/TtRu27bNzMyMbDk5ORkh1NbWRrz08/Pj8Xhk7ZUrVzgcTlxcnAJHKs9n0s7OzsnJSaqQx+M9ffoUx/GffvpJS0tr8eLFPT09OI6XlpZu3ryZ3CwmJkZHR+f8+fNdXV01NTUrV640NjZuaWkhamUP0cGDBxkMRkFBQWdnZ1RUlJaW1n//+1/5D23t2rWurq4yNujt7eVwOEKhkCwpLy+n0+np6ekikejevXvLli177733pN4VGRmJEKqqqpqyA/D3riQ4vwFgegQCAZfLNTExCQoK6u3tff78OVlFo9GIH/5OTk6ZmZlisTg7O1uBXXh7e4tEoujoaNX1+v/19vY+ffqUx+NNtgGfz9+/f39jY+OhQ4ekqvr7+1NSUrZu3bp9+3Z9fX0XF5fTp0+3t7efOXNGcrMJh2hgYCAzM9PX19fPz8/AwODIkSN0Ol2x8ZlMYmKihYVFQkICWeLh4RERESEUCrlcrrOzs1gsPnfunNS7HBwcEEK1tbUq7AmYEMQbABSko6ODEBoeHp6wdtWqVSwWi7zWNHO0trbiOM5isWRsk5CQsHTp0oyMjFu3bkmW19XV9fT0rFq1iixZvXq1jo4OeeVQiuQQPXz4sK+vz9nZmajS1dU1NzdX4fgUFRXl5+f/8MMPkjMgDh8+fObMmevXr/f09Dx58kQgEPD5fKk1nomheP36tap6AiYD8QYAdWEwGG1tbZruhbSBgQGE0IT33klMJjM7OxvDsF27dvX395PlxNRhPT09yY0NDAzEYvGU++3t7UUIHTlyBPvVs2fPxs9vVkxubu6JEyfKy8sXL15MFr569SopKenjjz9+99132Wy2ra3t2bNnm5ubiYuZJF1dXfTrsAC1gngDgFoMDw93dXXNwDQ5xNfrlM858vn8sLCw+vr6+Ph4stDAwAAhJBVd5DxMExMThFBqaqrkBf3KykoFDkHKqVOncnJybty4sXDhQsny+vr60dFRyUIul2tkZFRXVye52dDQEPp1WIBaQbwBQC3Ky8txHH/77beJlzQabbIrbxQzNTXFMEyeJ2zi4+MdHR2rqqrIEmdnZz09vZ9//pksuX379tDQ0FtvvTVla4sWLWIymdXV1Yp1e0I4jkdERNTW1hYXF0uddSGEiCj46tUrskQsFr9584aYFU0ihsLMzEyFHQMTgngDgMqMjY11dnaOjIzU1NSEhoZaW1sHBwcTVfb29m/evCkuLh4eHm5ra5N8BAQhZGRk1Nzc3NjYKBaLh4eHS0tL1TcfmsVi2dnZyZMDlLiqJvl0C5PJPHDgQFFRUU5Ojkgkqq2t3bt3r4WFRUhIiDyt7dy58+LFi5mZmSKRaHR09MWLF0QwCAoKMjMzU2C9nPv373/55Zdnz56l0+mYhJMnTyKEbG1tN2zYcPbs2Zs3b/b39zc1NRH9/OijjyQbIYbCxcVlunsH06aRWXFzBoL5kbOEAvOhT506RTwxw2KxfHx8MjIyiBvLDg4Ojx8/PnPmDJfLRQjZ2Ng8evQIx/GQkBA6nW5paUmj0bhc7pYtWx4/fky21tHRsWHDBiaTaWtr++mnn4aHhyOE7O3tiQnTd+7csbGx0dXVXbduXUtLy9WrVzkcTkJCggJHKs9nUigU0un0vr4+4mVRURExXc3Y2Hjfvn1SG4eHh0vOhx4bG0tOTnZwcKDT6YaGhr6+vg8fPiSqphyiwcHBiIgIa2trGo1mYmLi5+dXV1eH47ivry9CKCYmZsLeVlZWuru7W1hYEF9Z5ubmAoGgoqICx/HJJpUlJycT721vbw8NDbW3t2cwGHp6eu7u7t9++61U+97e3paWlmNjYyoZWyADxBulwOdvtlDs+ZtpIVZVUesu5CHPZ7K+vp5Go50/f56aLk1pdHR0/fr1WVlZ1O+6vb2dyWSePHlSno3h711JcD0NAJWZLYsN29vbx8XFxcXFSS2lrBGjo6PFxcVisTgoKIj6vcfGxrq5uQmFQup3PQ9BvAFgPoqMjPT39w8KCtL40pzl5eWFhYWlpaWyHwlSh5SUlOrq6qtXr9LpdIp3PT9BvKHU7t27ORwOhmGqnaWjjKSkJEdHR11dXTab7ejoGB0dLRKJ5HmjZNIUgo6Ojqmp6TvvvJOcnNzZ2anuns8oUVFR2dnZ3d3dtra2BQUFmu6OXI4dOyYUCo8fP67Zbnh6el64cIFcXI4yJSUlg4OD5eXlhoaGFO96/tL0Bb3ZDU3/ei6xfKE8izVRw9vb++TJk62trWKxOD8/n06n/+53v5P/7TweT19fH8dxYmrWv//97+DgYAzDLCwsprU0lrpRcP9mhlDgMwnkBGOrJDi/me90dHQ++eQTExMTPT09f3//LVu2/Otf/5J8ZEFOGIYZGBi888472dnZ+fn5r1+/JpLHqKPPAIDZCOIN1chUHDNEUVGRZBIUS0tLhJCSt5H/8Ic/BAcHt7a2nj59Wtn+AQDmCog3aofjeHJy8tKlSxkMhr6+PvHgBWnCjCBT5hGpqKhYs2YNi8XicrkuLi7EHReVJBepr683MDCwsbEhXiqciIV4zrG0tHRmHiYAQAM0fUFvdkNyXM89fPgwhmF//etfOzs7+/r6MjIykMT9m8kygsjII9LT08PlcpOSkvr7+1taWrZu3UqkUVEmucjQ0NCLFy9OnTrFYDAkH8uYMhELef9GChEbFi1aNEMOE+7fAOXB2CppXvwFqs+Un7++vj4WiyV5B15yvkB/fz+LxQoKCiI3ZjAYf/nLX/Bfv4j7+/uJKiJKNTQ04Dh+7949hNCVK1ckdySjKXkQi0ctWLDgb3/7G5kdSx6TxRscx4k7OjPkMCHeAOXB2CoJrqepV0NDQ19fn6en54S18mcEkcwjYmdnZ2pqun379tjY2MbGxuk2NaGmpqbW1tZvvvnm66+//s1vfiOZglcxvb29OI4Ty5nMnMPE5gGEUGBgoKZ7MTcp8xcBEEI0TXdgjiOWAiRWYh+PzAhy5MgRspBcJ2oyurq6N27cOHTo0LFjx+Li4gICArKzsxVrikSn001MTLy8vGxtbZcsWZKYmJiWlibneyf06NEjhJCjoyOaSYc5H272BAYGhoaG8vl8TXdkDgoMDNR0F2Y3iDfqRUz9GhwcnLCWzAgSGho6rWaXL1/+3XfftbW1paSknDhxYvny5cRaIAo0JcXe3l5bW1sqQYgCrl27hhDauHEjmkmHGRAQoMC7ZpfAwEA+nz8fjpR6EG+UBNfT1MvZ2VlLS6uiomLCWsUygjQ3N9+/fx8hZGJicvz48ZUrV96/f1+xpjo6Ov74xz9KlhApqqQShExXS0tLamqqlZXVrl270Aw4TADATADxRr2IRdcLCgqysrJEIlFNTc2ZM2fIWhkZQWRobm7es2fPgwcPhoaGqqqqnj179vbbbyvWFJvN/vHHH2/cuCESiYaHh6uqqj788EM2mx0WFkZsIE8iFhzHe3p6iOXc29ra8vLy3N3dtbW1i4uLifs3Gj9MAMCMoOH5CrMckmO+ilgs3r1794IFC/T09NatWxcTE4MQsrKyunv3Lj5JRhDZeUQaGxsFAoGhoaG2tvbChQsPHz48MjIyWVNTHoKPj4+tra2enh6DweDxeEFBQbW1tWStjEQsly9fXrFiBYvF0tHR0dLSQr8uMbBmzZq4uLiOjg7JjTV+mDA/DSgPxlZJGI7jmgp1cwCGYXl5eXCtfObLz88PDAycD592+EyqD4ytkuB6GgAAACpAvJnLHjx4IONhAo2ktwIzWVlZWWRkpGSmiR07dkhu4OXlxeFwtLW1ly9ffufOHU31EyE0NjaWmpoqEAjGV926dcvd3Z3FYllYWERERJCzQy9fvpyUlDRbcuLNSRBv5jJHR0cZ11Jzc3M13UEwg3zxxRfp6elRUVF+fn5Pnjzh8XgLFizIycn5/vvvyW1+/PHHS5cubdq0qa6ubuXKlZrqan19/W9/+9uwsLC+vj6pqrq6Oi8vL09Pz7a2tqKion/84x979+4lqnx8fJhMpqenZ1dXF+VdBghBvAFAVfr7+yf8ua3ZpuR04sSJ3Nzc/Px8DodDFqanp2tpaYWEhMyovBJ37949dOjQ3r173dzcxtfGx8ebm5sfPXqUzWbz+fyIiIh//vOf5AoUn332maur6/vvvz8yMkJtrwFCEG8AUJWsrCzl1wFSeVPyaGhoiI6OPnr0qGRmCoSQQCAIDQ19+fLlwYMHKevMlFxdXQsLC7dt28ZgMKSqRkZGvv/+ew8PD3LtmY0bN+I4XlJSQm4TGxtbXV2t5PIZQDEQbwD4fziOp6SkLFu2jMFgGBoabtmyhfxpLBQKdXR0yLTHn3zyCZvNxjCsvb0dIRQaGnrgwIHHjx9jGGZvb5+ens5kMk1NTffs2WNhYcFkMgUCwe3btxVoCimRFUJO6enpOI77+PiMr0pISFiyZMm5c+fKysomfK+MEZsy34TKU0s8efKkp6fH2tqaLOHxeAihmpoassTQ0NDDwyMtLW0+TFaccdQ/5XouQzAff5aQ8/mbmJgYHR2d8+fPd3V11dTUrFy50tjYuKWlhajdtm2bmZkZuXFycjJCiMiSgOO4n58fj8cja0NCQths9v379wcGBurq6lavXs3hcJ4/f65AU1NmhZCkwGfSzs7OyclJqpDH4z19+hTH8Z9++klLS2vx4sU9PT04jpeWlm7evJncTPaIycg3gSuXQQPH8bVr17q6ukqWEAt5JCcnSxbq6up6enpKlkRGRiKFcrrD37uS4PwGgF/09/enpKRs3bp1+/bt+vr6Li4up0+fbm9vl1wSYlpoNBrxw9/JySkzM1MsFmdnZyvQjre3t0gkio6OVqwbsvX29j59+pQ4D5gQn8/fv39/Y2PjoUOHpKrkHDGBQMDlck1MTIKCgnp7e58/f44QGhgYyMzM9PX19fPzMzAwOHLkCJ1OV2x8SMRUNG1tbclCOp3e398vWeLg4IAQqq2tVWZfQAEQbwD4RV1dXU9Pz6pVq8iS1atX6+jokNfBlLFq1SoWiyV/6gTKtLa24jhOLPQwmYSEhKVLl2ZkZNy6dUuyfLojJplvQsnUEhMi7j9JzQUYGhrS1dWVLCEO9vXr18rsCygA4g0AvyCmyerp6UkWGhgYiMVilbTPYDDa2tpU0pQKDQwMIITG33uXxGQys7OzMQzbtWuX5LmCMiNGppYgHwh79uzZ+PnN00LcEiNyyxL6+voGBgakMlYQ4Yc4cEAliDcA/MLAwAAhJPVd2dXVZWVlpXzjw8PDqmpKtYgv3ymfguTz+WFhYfX19fHx8WShMiNGZqmQvL5fWVmpwCGQbG1tORzOs2fPyJKGhgaE0IoVKyQ3GxoaQr8eOKASxBsAfuHs7Kynp/fzzz+TJbdv3x4aGnrrrbeIlzQajbgWpIDy8nIcx99++23lm1ItU1NTDMPkecImPj7e0dGxqqqKLJlyxGRQR2oJGo32/vvv37x5c2xsjCgpLS3FMExq6h1xsEQOdUAliDcA/ILJZB44cKCoqCgnJ0ckEtXW1u7du9fCwiIkJITYwN7e/s2bN8XFxcPDw21tbZK/oxFCRkZGzc3NjY2NYrGYiCVjY2OdnZ0jIyM1NTWhoaHW1tbBwcEKNCVPVgiFsVgsOzs7IhGtbMRVNcm78VOOmOzWJkstERQUZGZmpth6OdHR0a9fv/7iiy96e3srKyuTk5ODg4OXLl0quQ1xsC4uLgq0D5SimWlxcwWC+ZGzhJzzocfGxpKTkx0cHOh0uqGhoa+v78OHD8najo6ODRs2MJlMW1vbTz/9NDw8HCFkb29PzHK+c+eOjY2Nrq7uunXrWlpaQkJC6HS6paUljUbjcrlbtmx5/PixYk3JyAoxngKfSaFQSKfT+/r6iJdFRUXEdDVjY+N9+/ZJbRweHi45H1rGiMnON4FPnlrC19cXIRQTEzNhbysrK93d3clbMubm5gKBoKKigtygoqJizZo1DAbDwsIiPDx8YGBAqgVvb29LS0siY9O0wN+7kiDeKAU+f7MF9flvQkJCjIyMqNwjQYHPZH19PY1GO3/+vJq6NF2jo6Pr16/PyspSR+Pt7e1MJvPkyZMKvBf+3pUE19MAUJfZshSxvb19XFxcXFxcT0+PpvuCRkdHi4uLxWKxmtYvj42NdXNzEwqF6mgcyAbxBgCAIiMj/f39g4KCNL40Z3l5eWFhYWlpqexHghSTkpJSXV199epVOp2u8sbBlCDeAKB6UVFR2dnZ3d3dtra2BQUFmu6OXI4dOyYUCo8fP67Zbnh6el64cIFcXE6FSkpKBgcHy8vLDQ0NVd44kAdN0x0AYA5KTExMTEzUdC+mzcvLy8vLS9O9UJfNmzdv3rxZ072Y1+D8BgAAABUg3gAAAKACxBsAAABUgHgDAACACjBfQFlKrjAIqEH8N+Xn52u6I1SAzySYmTAckqoqgUyTDgCYD/Ly8gICAjTdi9kK4g0AAAAqwP0bAAAAVIB4AwAAgAoQbwAAAFAB4g0AAAAq/B+sxGSUZMZAOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 185,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.utils.plot_model(model, to_file='Baseline_Model_Architecture.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E6pHQXuTtSg8",
    "outputId": "bd5d82d8-60ff-4a65-a1c6-ac1b256bdc70"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_4 (Conv2D)            (None, 26, 26, 16)        160       \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 24, 24, 16)        2320      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 12, 12, 16)        0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 12, 12, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 10, 10, 32)        4640      \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 8, 8, 32)          9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 4, 4, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 128)               65664     \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                1290      \n",
      "=================================================================\n",
      "Total params: 83,322\n",
      "Trainable params: 83,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8g0L0khnz73M"
   },
   "source": [
    "## Fit the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9nvQUFIXsZkJ",
    "outputId": "6be81ba7-73eb-49c8-8f1e-bd9f76c07edc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1500/1500 [==============================] - 8s 5ms/step - loss: 0.6684 - accuracy: 0.7509 - val_loss: 0.4155 - val_accuracy: 0.8513\n",
      "Epoch 2/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.4510 - accuracy: 0.8355 - val_loss: 0.3377 - val_accuracy: 0.8764\n",
      "Epoch 3/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3957 - accuracy: 0.8553 - val_loss: 0.3254 - val_accuracy: 0.8826\n",
      "Epoch 4/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3708 - accuracy: 0.8643 - val_loss: 0.2987 - val_accuracy: 0.8916\n",
      "Epoch 5/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3529 - accuracy: 0.8718 - val_loss: 0.2781 - val_accuracy: 0.8976\n",
      "Epoch 6/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3416 - accuracy: 0.8745 - val_loss: 0.2792 - val_accuracy: 0.8974\n",
      "Epoch 7/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3294 - accuracy: 0.8778 - val_loss: 0.2620 - val_accuracy: 0.9049\n",
      "Epoch 8/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3234 - accuracy: 0.8820 - val_loss: 0.2630 - val_accuracy: 0.9046\n",
      "Epoch 9/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3188 - accuracy: 0.8826 - val_loss: 0.2612 - val_accuracy: 0.9033\n",
      "Epoch 10/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3118 - accuracy: 0.8868 - val_loss: 0.2558 - val_accuracy: 0.9089\n",
      "Epoch 11/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3072 - accuracy: 0.8868 - val_loss: 0.2530 - val_accuracy: 0.9054\n",
      "Epoch 12/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3051 - accuracy: 0.8887 - val_loss: 0.2539 - val_accuracy: 0.9088\n",
      "Epoch 13/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.3012 - accuracy: 0.8890 - val_loss: 0.2472 - val_accuracy: 0.9090\n",
      "Epoch 14/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.2996 - accuracy: 0.8906 - val_loss: 0.2424 - val_accuracy: 0.9097\n",
      "Epoch 15/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.2950 - accuracy: 0.8914 - val_loss: 0.2416 - val_accuracy: 0.9110\n",
      "Epoch 16/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.2951 - accuracy: 0.8931 - val_loss: 0.2391 - val_accuracy: 0.9120\n",
      "Epoch 17/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.2887 - accuracy: 0.8956 - val_loss: 0.2416 - val_accuracy: 0.9112\n",
      "Epoch 18/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.2897 - accuracy: 0.8933 - val_loss: 0.2326 - val_accuracy: 0.9146\n",
      "Epoch 19/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.2908 - accuracy: 0.8937 - val_loss: 0.2463 - val_accuracy: 0.9098\n",
      "Epoch 20/20\n",
      "1500/1500 [==============================] - 6s 4ms/step - loss: 0.2860 - accuracy: 0.8952 - val_loss: 0.2597 - val_accuracy: 0.9057\n"
     ]
    }
   ],
   "source": [
    "Fashion_MNIST_Baseline_History = model.fit(trainX, trainY, epochs=20, validation_data=(valX, valY))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KbfbEPeO0Bjf"
   },
   "source": [
    "## Plot training and validation accuracy and loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 621
    },
    "id": "9WwishXwcHVJ",
    "outputId": "8257b457-65b3-4b0d-b615-d2059f8538d0"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt = Plot_Training_Progress(Fashion_MNIST_Baseline_History.history)   \n",
    "plt.savefig('Baseline_Implementation_Plots.png', dpi=400)    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fw6ghaojz-mf"
   },
   "source": [
    "## Evaluate on testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CnAB2RasvnZH",
    "outputId": "f393ef7b-edb1-492c-d8ea-bb0fac31bfd8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.2862 - accuracy: 0.8976\n",
      "Testing Loss 0.28618142008781433\n",
      "Testing Accuracy 89.76%\n"
     ]
    }
   ],
   "source": [
    "testLoss, testAccuracy = model.evaluate(testX,testY)\n",
    "\n",
    "print(\"Testing Loss\", testLoss)\n",
    "print(\"Testing Accuracy\", \"{:.2%}\" .format(testAccuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PSzAfgv4wCHm"
   },
   "source": [
    "# Part 4 - Hyperparamter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WIdXNVWrfr5f"
   },
   "source": [
    "## Create model and set hyperparameters to tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "id": "T3gk1XS0qjmQ"
   },
   "outputs": [],
   "source": [
    "class MNIST_Fashion_Hyper(HyperModel):\n",
    "  def __init__(self, input_shape, num_classes):\n",
    "    self.input_shape = input_shape\n",
    "    self.num_classes = num_classes\n",
    "\n",
    "  def build(self, hp):\n",
    "    model = keras.Sequential()\n",
    "\n",
    "    model.add(Conv2D(filters=hp.Choice('Conv_Filters_1',values=[16, 32, 64],default=16),activation='relu',kernel_size=3, input_shape=self.input_shape))\n",
    "    model.add(Conv2D(filters=hp.Choice('Conv_Filters_2',values=[16, 32, 64],default=16), activation=hp.Choice('Conv_Activation_2',values=['relu', 'sigmoid'],default='relu'),kernel_size=3))\n",
    "    model.add(MaxPooling2D(pool_size=2))\n",
    "    model.add(Dropout(rate=hp.Float('Dropout_Rate_1',min_value=0.0,max_value=0.5,default=0.25,step=0.05)))\n",
    "  \n",
    "    model.add(Conv2D(filters=hp.Choice('Conv_Filters_3',values=[32, 64, 128],default=32), activation=hp.Choice('Conv_Activation_3',values=['relu', 'sigmoid'],default='relu'),kernel_size=3))\n",
    "    model.add(Conv2D(filters=hp.Choice('Conv_Filters_4',values=[32, 64, 128],default=64), activation=hp.Choice('Conv_Activation_4',values=['relu', 'sigmoid'],default='relu'),kernel_size=3))\n",
    "    model.add(AveragePooling2D(pool_size=2))\n",
    "    model.add(Dropout(rate=hp.Float('Dropout_Rate_2',min_value=0.0,max_value=0.5,default=0.25,step=0.05)))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(units=hp.Int('Last_Dense_NumUnits',min_value=16,max_value=128,step=16,default=64), activation='relu'))\n",
    "    model.add(Dropout(rate=hp.Float('Dropout_Rate_3',min_value=0.0,max_value=0.5,default=0.25,step=0.05)))\n",
    "    model.add(Dense(self.num_classes, activation='softmax'))\n",
    "\n",
    "    model.compile(optimizer=keras.optimizers.Adam(hp.Float('Adam_Optim_LR',min_value=1e-4,max_value=1e-2,sampling='LOG',default=1e-3)),\n",
    "        loss='categorical_crossentropy',\n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "\n",
    "    print(\"MODEL SUCCESSFULLY CREATED\")\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "id": "0WudoSG9rCP9"
   },
   "outputs": [],
   "source": [
    "hyper_model = MNIST_Fashion_Hyper(imgShape, numClasses)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MFqTKIgpf080"
   },
   "source": [
    "## Set parameters of search space\n",
    "i.e. Number of trials, epochs per trial, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xERRkZ0krG-T",
    "outputId": "84b75c42-0cbe-4aa3-d395-de161731a1d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL SUCCESSFULLY CREATED\n"
     ]
    }
   ],
   "source": [
    "maxTrials = 20\n",
    "execPerTrial = 2\n",
    "numEpochSearch = 10\n",
    "seedValue = 2\n",
    "\n",
    "model_tuner = RandomSearch(\n",
    "    hyper_model,\n",
    "    objective='val_accuracy',\n",
    "    seed=seedValue,\n",
    "    max_trials=maxTrials,\n",
    "    executions_per_trial=execPerTrial,\n",
    "    project_name='MNIST_HyperTuned'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "348eENUXvZA4"
   },
   "source": [
    "## Summary of tuner\n",
    "i.e. The hyperparameters that will be tuned\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ou30OztXwBdo",
    "outputId": "b8db9ee2-934d-44a8-dc71-acf796510c51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Search space summary\n",
      "Default search space size: 12\n",
      "Conv_Filters_1 (Choice)\n",
      "{'default': 16, 'conditions': [], 'values': [16, 32, 64], 'ordered': True}\n",
      "Conv_Filters_2 (Choice)\n",
      "{'default': 16, 'conditions': [], 'values': [16, 32, 64], 'ordered': True}\n",
      "Conv_Activation_2 (Choice)\n",
      "{'default': 'relu', 'conditions': [], 'values': ['relu', 'sigmoid'], 'ordered': False}\n",
      "Dropout_Rate_1 (Float)\n",
      "{'default': 0.25, 'conditions': [], 'min_value': 0.0, 'max_value': 0.5, 'step': 0.05, 'sampling': None}\n",
      "Conv_Filters_3 (Choice)\n",
      "{'default': 32, 'conditions': [], 'values': [32, 64, 128], 'ordered': True}\n",
      "Conv_Activation_3 (Choice)\n",
      "{'default': 'relu', 'conditions': [], 'values': ['relu', 'sigmoid'], 'ordered': False}\n",
      "Conv_Filters_4 (Choice)\n",
      "{'default': 64, 'conditions': [], 'values': [32, 64, 128], 'ordered': True}\n",
      "Conv_Activation_4 (Choice)\n",
      "{'default': 'relu', 'conditions': [], 'values': ['relu', 'sigmoid'], 'ordered': False}\n",
      "Dropout_Rate_2 (Float)\n",
      "{'default': 0.25, 'conditions': [], 'min_value': 0.0, 'max_value': 0.5, 'step': 0.05, 'sampling': None}\n",
      "Last_Dense_NumUnits (Int)\n",
      "{'default': 64, 'conditions': [], 'min_value': 16, 'max_value': 128, 'step': 16, 'sampling': None}\n",
      "Dropout_Rate_3 (Float)\n",
      "{'default': 0.25, 'conditions': [], 'min_value': 0.0, 'max_value': 0.5, 'step': 0.05, 'sampling': None}\n",
      "Adam_Optim_LR (Float)\n",
      "{'default': 0.001, 'conditions': [], 'min_value': 0.0001, 'max_value': 0.01, 'step': None, 'sampling': 'log'}\n"
     ]
    }
   ],
   "source": [
    "model_tuner.search_space_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qhXMkLJ9ve-_"
   },
   "source": [
    "## Search for best hyperparameters\n",
    "Note: This code block takes a while to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MdAjjgKcwBgB",
    "outputId": "50df81cd-c23c-44fa-f9ae-8632c00baaa0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 20 Complete [00h 02m 24s]\n",
      "val_accuracy: 0.1014999970793724\n",
      "\n",
      "Best val_accuracy So Far: 0.9162916839122772\n",
      "Total elapsed time: 00h 50m 10s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    }
   ],
   "source": [
    "model_tuner.search(x=trainX, y=trainY, epochs=numEpochSearch, validation_data=(valX, valY))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qqUMEQCOwBm6",
    "outputId": "c449cb26-7ca3-4ba3-9d27-8a4b1647f5e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results summary\n",
      "Results in ./MNIST_HyperTuned\n",
      "Showing 10 best trials\n",
      "Objective(name='val_accuracy', direction='max')\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "Conv_Filters_1: 64\n",
      "Conv_Filters_2: 64\n",
      "Conv_Activation_2: relu\n",
      "Dropout_Rate_1: 0.25\n",
      "Conv_Filters_3: 128\n",
      "Conv_Activation_3: relu\n",
      "Conv_Filters_4: 32\n",
      "Conv_Activation_4: relu\n",
      "Dropout_Rate_2: 0.25\n",
      "Last_Dense_NumUnits: 80\n",
      "Dropout_Rate_3: 0.15000000000000002\n",
      "Adam_Optim_LR: 0.002312113349326375\n",
      "Score: 0.9162916839122772\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "Conv_Filters_1: 64\n",
      "Conv_Filters_2: 64\n",
      "Conv_Activation_2: relu\n",
      "Dropout_Rate_1: 0.25\n",
      "Conv_Filters_3: 32\n",
      "Conv_Activation_3: sigmoid\n",
      "Conv_Filters_4: 32\n",
      "Conv_Activation_4: relu\n",
      "Dropout_Rate_2: 0.4\n",
      "Last_Dense_NumUnits: 32\n",
      "Dropout_Rate_3: 0.2\n",
      "Adam_Optim_LR: 0.0007393219895594406\n",
      "Score: 0.9050416648387909\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "Conv_Filters_1: 32\n",
      "Conv_Filters_2: 16\n",
      "Conv_Activation_2: sigmoid\n",
      "Dropout_Rate_1: 0.30000000000000004\n",
      "Conv_Filters_3: 32\n",
      "Conv_Activation_3: relu\n",
      "Conv_Filters_4: 128\n",
      "Conv_Activation_4: relu\n",
      "Dropout_Rate_2: 0.30000000000000004\n",
      "Last_Dense_NumUnits: 48\n",
      "Dropout_Rate_3: 0.15000000000000002\n",
      "Adam_Optim_LR: 0.0009749332269823054\n",
      "Score: 0.9045416414737701\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "Conv_Filters_1: 64\n",
      "Conv_Filters_2: 32\n",
      "Conv_Activation_2: sigmoid\n",
      "Dropout_Rate_1: 0.2\n",
      "Conv_Filters_3: 32\n",
      "Conv_Activation_3: relu\n",
      "Conv_Filters_4: 128\n",
      "Conv_Activation_4: relu\n",
      "Dropout_Rate_2: 0.45\n",
      "Last_Dense_NumUnits: 48\n",
      "Dropout_Rate_3: 0.05\n",
      "Adam_Optim_LR: 0.00036276965095376624\n",
      "Score: 0.9035833477973938\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "Conv_Filters_1: 64\n",
      "Conv_Filters_2: 16\n",
      "Conv_Activation_2: sigmoid\n",
      "Dropout_Rate_1: 0.45\n",
      "Conv_Filters_3: 128\n",
      "Conv_Activation_3: relu\n",
      "Conv_Filters_4: 32\n",
      "Conv_Activation_4: sigmoid\n",
      "Dropout_Rate_2: 0.25\n",
      "Last_Dense_NumUnits: 48\n",
      "Dropout_Rate_3: 0.2\n",
      "Adam_Optim_LR: 0.0009719978423652108\n",
      "Score: 0.901666671037674\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "Conv_Filters_1: 16\n",
      "Conv_Filters_2: 64\n",
      "Conv_Activation_2: sigmoid\n",
      "Dropout_Rate_1: 0.05\n",
      "Conv_Filters_3: 64\n",
      "Conv_Activation_3: sigmoid\n",
      "Conv_Filters_4: 64\n",
      "Conv_Activation_4: sigmoid\n",
      "Dropout_Rate_2: 0.1\n",
      "Last_Dense_NumUnits: 80\n",
      "Dropout_Rate_3: 0.25\n",
      "Adam_Optim_LR: 0.0016721944783785415\n",
      "Score: 0.9007916450500488\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "Conv_Filters_1: 32\n",
      "Conv_Filters_2: 64\n",
      "Conv_Activation_2: relu\n",
      "Dropout_Rate_1: 0.1\n",
      "Conv_Filters_3: 32\n",
      "Conv_Activation_3: sigmoid\n",
      "Conv_Filters_4: 32\n",
      "Conv_Activation_4: relu\n",
      "Dropout_Rate_2: 0.25\n",
      "Last_Dense_NumUnits: 64\n",
      "Dropout_Rate_3: 0.45\n",
      "Adam_Optim_LR: 0.0011418581028573094\n",
      "Score: 0.8997499942779541\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "Conv_Filters_1: 16\n",
      "Conv_Filters_2: 16\n",
      "Conv_Activation_2: sigmoid\n",
      "Dropout_Rate_1: 0.25\n",
      "Conv_Filters_3: 32\n",
      "Conv_Activation_3: relu\n",
      "Conv_Filters_4: 128\n",
      "Conv_Activation_4: relu\n",
      "Dropout_Rate_2: 0.5\n",
      "Last_Dense_NumUnits: 112\n",
      "Dropout_Rate_3: 0.35000000000000003\n",
      "Adam_Optim_LR: 0.0005674460859990781\n",
      "Score: 0.8996250033378601\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "Conv_Filters_1: 64\n",
      "Conv_Filters_2: 32\n",
      "Conv_Activation_2: relu\n",
      "Dropout_Rate_1: 0.25\n",
      "Conv_Filters_3: 32\n",
      "Conv_Activation_3: sigmoid\n",
      "Conv_Filters_4: 32\n",
      "Conv_Activation_4: relu\n",
      "Dropout_Rate_2: 0.1\n",
      "Last_Dense_NumUnits: 48\n",
      "Dropout_Rate_3: 0.35000000000000003\n",
      "Adam_Optim_LR: 0.0003243806953412315\n",
      "Score: 0.8948749899864197\n",
      "Trial summary\n",
      "Hyperparameters:\n",
      "Conv_Filters_1: 16\n",
      "Conv_Filters_2: 16\n",
      "Conv_Activation_2: relu\n",
      "Dropout_Rate_1: 0.30000000000000004\n",
      "Conv_Filters_3: 128\n",
      "Conv_Activation_3: sigmoid\n",
      "Conv_Filters_4: 32\n",
      "Conv_Activation_4: sigmoid\n",
      "Dropout_Rate_2: 0.30000000000000004\n",
      "Last_Dense_NumUnits: 64\n",
      "Dropout_Rate_3: 0.25\n",
      "Adam_Optim_LR: 0.00032962260207930136\n",
      "Score: 0.8853749930858612\n"
     ]
    }
   ],
   "source": [
    "model_tuner.results_summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lI82dxd4p8v2"
   },
   "source": [
    "## Choose best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "QQLgLbvKm8Eh",
    "outputId": "4fb5fa48-8dfe-4b31-905c-f59870d0d74f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL SUCCESSFULLY CREATED\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 257,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = model_tuner.get_best_models()[0]\n",
    "keras.utils.plot_model(best_model, to_file='HyperTuned_Model_Architecture.png', show_shapes=True, show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "o_vakhqo8Zc2"
   },
   "outputs": [],
   "source": [
    "testLoss, testAccuracy = best_model.evaluate(testX, testY)\n",
    "\n",
    "print(\"Testing Loss before refitting\", testLoss)\n",
    "print(\"Testing Accuracy before refitting\", \"{:.2%}\" .format(testAccuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4KaTA0-fp_kD"
   },
   "source": [
    "## Find optimal number of epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bqHuJsOixJkX",
    "outputId": "dd4eef7c-d05e-46b2-81d8-75a8224af549"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "188/188 [==============================] - 4s 23ms/step - loss: 0.1178 - accuracy: 0.9564 - val_loss: 0.2318 - val_accuracy: 0.9314\n",
      "Epoch 2/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.1079 - accuracy: 0.9581 - val_loss: 0.2372 - val_accuracy: 0.9312\n",
      "Epoch 3/20\n",
      "188/188 [==============================] - 4s 23ms/step - loss: 0.1023 - accuracy: 0.9611 - val_loss: 0.2340 - val_accuracy: 0.9302\n",
      "Epoch 4/20\n",
      "188/188 [==============================] - 4s 23ms/step - loss: 0.1007 - accuracy: 0.9622 - val_loss: 0.2397 - val_accuracy: 0.9311\n",
      "Epoch 5/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.0997 - accuracy: 0.9615 - val_loss: 0.2374 - val_accuracy: 0.9310\n",
      "Epoch 6/20\n",
      "188/188 [==============================] - 4s 21ms/step - loss: 0.1000 - accuracy: 0.9620 - val_loss: 0.2395 - val_accuracy: 0.9326\n",
      "Epoch 7/20\n",
      "188/188 [==============================] - 4s 23ms/step - loss: 0.0967 - accuracy: 0.9632 - val_loss: 0.2396 - val_accuracy: 0.9316\n",
      "Epoch 8/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.0956 - accuracy: 0.9633 - val_loss: 0.2481 - val_accuracy: 0.9323\n",
      "Epoch 9/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.0978 - accuracy: 0.9630 - val_loss: 0.2452 - val_accuracy: 0.9325\n",
      "Epoch 10/20\n",
      "188/188 [==============================] - 4s 21ms/step - loss: 0.0956 - accuracy: 0.9636 - val_loss: 0.2359 - val_accuracy: 0.9307\n",
      "Epoch 11/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.0949 - accuracy: 0.9642 - val_loss: 0.2433 - val_accuracy: 0.9272\n",
      "Epoch 12/20\n",
      "188/188 [==============================] - 4s 21ms/step - loss: 0.0973 - accuracy: 0.9641 - val_loss: 0.2517 - val_accuracy: 0.9310\n",
      "Epoch 13/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.1005 - accuracy: 0.9616 - val_loss: 0.2438 - val_accuracy: 0.9300\n",
      "Epoch 14/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.0960 - accuracy: 0.9633 - val_loss: 0.2530 - val_accuracy: 0.9313\n",
      "Epoch 15/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.0966 - accuracy: 0.9630 - val_loss: 0.2481 - val_accuracy: 0.9306\n",
      "Epoch 16/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.0975 - accuracy: 0.9625 - val_loss: 0.2459 - val_accuracy: 0.9300\n",
      "Epoch 17/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.0958 - accuracy: 0.9650 - val_loss: 0.2661 - val_accuracy: 0.9287\n",
      "Epoch 18/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.0945 - accuracy: 0.9641 - val_loss: 0.2618 - val_accuracy: 0.9269\n",
      "Epoch 19/20\n",
      "188/188 [==============================] - 4s 22ms/step - loss: 0.0983 - accuracy: 0.9645 - val_loss: 0.2531 - val_accuracy: 0.9308\n",
      "Epoch 20/20\n",
      "188/188 [==============================] - 4s 21ms/step - loss: 0.0955 - accuracy: 0.9639 - val_loss: 0.2550 - val_accuracy: 0.9323\n",
      "Optimal Number Epochs: 6\n"
     ]
    }
   ],
   "source": [
    "numEpochsToTrain = 20\n",
    "\n",
    "best_model_history = best_model.fit(trainX, trainY, epochs=numEpochsToTrain, validation_data=(valX, valY),  batch_size=256)\n",
    "\n",
    "val_acc_per_epoch = best_model_history.history['val_accuracy']\n",
    "best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1\n",
    "print('Optimal Number Epochs: %d' % (best_epoch))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NjJD1kker9ye"
   },
   "source": [
    "## Refit one more time on best number epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DYUwYCWHr-SD",
    "outputId": "7b1c8c0d-47e2-4c0b-9954-798dec84d61d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MODEL SUCCESSFULLY CREATED\n",
      "Epoch 1/6\n",
      "188/188 [==============================] - 5s 22ms/step - loss: 0.2092 - accuracy: 0.9230 - val_loss: 0.2077 - val_accuracy: 0.9254\n",
      "Epoch 2/6\n",
      "188/188 [==============================] - 4s 21ms/step - loss: 0.1944 - accuracy: 0.9268 - val_loss: 0.2065 - val_accuracy: 0.9258\n",
      "Epoch 3/6\n",
      "188/188 [==============================] - 4s 21ms/step - loss: 0.1898 - accuracy: 0.9303 - val_loss: 0.2051 - val_accuracy: 0.9256\n",
      "Epoch 4/6\n",
      "188/188 [==============================] - 4s 21ms/step - loss: 0.1855 - accuracy: 0.9301 - val_loss: 0.2000 - val_accuracy: 0.9274\n",
      "Epoch 5/6\n",
      "188/188 [==============================] - 4s 21ms/step - loss: 0.1810 - accuracy: 0.9323 - val_loss: 0.1989 - val_accuracy: 0.9277\n",
      "Epoch 6/6\n",
      "188/188 [==============================] - 4s 21ms/step - loss: 0.1762 - accuracy: 0.9352 - val_loss: 0.1997 - val_accuracy: 0.9291\n"
     ]
    }
   ],
   "source": [
    "Fashion_MNIST_Best_HyperParameters_History = best_hypermodel.fit(trainX, trainY, epochs=best_epoch, validation_data=(valX, valY), batch_size=256)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IaAjDyB3MMgE"
   },
   "source": [
    "## Plot training and validation accuracy and loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 621
    },
    "id": "_1kmE7uxC_gU",
    "outputId": "68c4396b-e8fb-4be9-b9f8-8c9100b2fccb"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt = Plot_Training_Progress(Fashion_MNIST_Best_HyperParameters_History.history)   \n",
    "plt.savefig('Best_HyperParameters_Plots.png', dpi=400)    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a5jval0ysNQ5"
   },
   "source": [
    "## Evaluate on testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wsN4l3PXsNx-",
    "outputId": "b9d3fb32-fbe7-4fff-be74-30cb09f6fc86"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 0.2233 - accuracy: 0.9231\n",
      "Testing Loss 0.22327379882335663\n",
      "Testing Accuracy 92.31%\n"
     ]
    }
   ],
   "source": [
    "testLoss, testAccuracy = best_hypermodel.evaluate(testX, testY)\n",
    "\n",
    "print(\"Testing Loss\", testLoss)\n",
    "print(\"Testing Accuracy\", \"{:.2%}\" .format(testAccuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hux4DrFHEIH-"
   },
   "source": [
    "# Part 5 - Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cb2kdmt4MQDP"
   },
   "source": [
    "## Make some predictions to visualize whats happening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 316
    },
    "id": "sT2we-BpDDOa",
    "outputId": "21ea6896-3cda-4c1e-c75e-9bae65c9b2cb"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1800x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 5\n",
    "fig, axes = plt.subplots(1, n, figsize=(25,8))\n",
    "\n",
    "for i in range(n):\n",
    "    #Get random image\n",
    "    randNum = random.randint(0,9999)\n",
    "    random_image = testX[randNum]\n",
    "    random_image_class = np.argmax(testY[randNum])\n",
    "    \n",
    "    #Make prediction\n",
    "    prediction = best_hypermodel.predict(random_image.reshape(-1,28,28,1))\n",
    "    prediction_probability = np.amax(prediction)\n",
    "    prediction_idx = np.argmax(prediction)\n",
    "\n",
    "    title = \"Actual class = {}\\nPredicted class = {}\\nPrediction probability = {:.2%}\" .format(class_labels[random_image_class], class_labels[prediction_idx], prediction_probability)\n",
    "\n",
    "    # Print image\n",
    "    axes[i].imshow(random_image.reshape(28,28), cmap='gray')\n",
    "    if(prediction_idx == random_image_class):\n",
    "      axes[i].set_title(title, color='g')\n",
    "    else:\n",
    "      axes[i].set_title(title, color='r')\n",
    "    axes[i].axis('off')\n",
    "\n",
    "plt.savefig('Prediction_Visualizations.png', dpi=400)    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EQq45kaaMDOL"
   },
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6jwbtDWcuheT"
   },
   "source": [
    "### Compute prediction accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "id": "-XtJmHBPsubZ"
   },
   "outputs": [],
   "source": [
    "y_pred, y_true = [], []\n",
    "for i in range(9000):\n",
    "  image = testX[i]\n",
    "  image_class = np.argmax(testY[i])\n",
    "\n",
    "  prediction = best_hypermodel.predict(image.reshape(-1,28,28,1))\n",
    "  prediction_idx = np.argmax(prediction)\n",
    "\n",
    "  y_pred.append(prediction_idx)\n",
    "  y_true.append(image_class)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R8XJ_m4-unXf"
   },
   "source": [
    "### Show heatmapped confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 893
    },
    "id": "t9g6JpD5Mnxm",
    "outputId": "9e55a3e5-0a7c-46b1-bb17-7248b1420dd8"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "conf_matrix = confusion_matrix(y_true, y_pred, labels=np.arange(10))\n",
    "conf_matrix = conf_matrix/np.sum(conf_matrix, axis=1)\n",
    "sns.heatmap(conf_matrix, annot=True, fmt=\".2%\", square=True, cbar=False, \n",
    "            cmap=plt.cm.cividis, xticklabels=classes, yticklabels=classes,\n",
    "            ax=ax)\n",
    "ax.set_ylabel('Actual')\n",
    "ax.set_xlabel('Predicted')\n",
    "ax.set_title('Confusion Matrix')\n",
    "\n",
    "plt.savefig('Confusion_Matrix.png', dpi=400)    \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "EWgpvG537SQt"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Fashion-MNIST.ipynb",
   "provenance": []
  },
  "hide_input": false,
  "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": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}