learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LEARNING\n",
    "\n",
    "This notebook serves as supporting material for topics covered in **Chapter 18 - Learning from Examples** , **Chapter 19 - Knowledge in Learning**, **Chapter 20 - Learning Probabilistic Models** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py). Let's start by importing everything from the module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from learning import *\n",
    "from notebook import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONTENTS\n",
    "\n",
    "* Machine Learning Overview\n",
    "* Datasets\n",
    "* Iris Visualization\n",
    "* Distance Functions\n",
    "* Plurality Learner\n",
    "* k-Nearest Neighbours\n",
    "* Decision Tree Learner\n",
    "* Naive Bayes Learner\n",
    "* Perceptron\n",
    "* Neural Network\n",
    "* Learner Evaluation\n",
    "* MNIST Handwritten Digits\n",
    "    * Loading and Visualising\n",
    "    * Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MACHINE LEARNING OVERVIEW\n",
    "\n",
    "In this notebook, we learn about agents that can improve their behavior through diligent study of their own experiences.\n",
    "\n",
    "An agent is **learning** if it improves its performance on future tasks after making observations about the world.\n",
    "\n",
    "There are three types of feedback that determine the three main types of learning:\n",
    "\n",
    "* **Supervised Learning**:\n",
    "\n",
    "In Supervised Learning the agent observes some example input-output pairs and learns a function that maps from input to output.\n",
    "\n",
    "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the agent. The agent then learns a function that maps from an input image to one of those strings.\n",
    "\n",
    "* **Unsupervised Learning**:\n",
    "\n",
    "In Unsupervised Learning the agent learns patterns in the input even though no explicit feedback is supplied. The most common type is **clustering**: detecting potential useful clusters of input examples.\n",
    "\n",
    "**Example**: A taxi agent would develop a concept of *good traffic days* and *bad traffic days* without ever being given labeled examples.\n",
    "\n",
    "* **Reinforcement Learning**:\n",
    "\n",
    "In Reinforcement Learning the agent learns from a series of reinforcements—rewards or punishments.\n",
    "\n",
    "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATASETS\n",
    "\n",
    "For the following tutorials we will use a range of datasets, to better showcase the strengths and weaknesses of the algorithms. The datasests are the following:\n",
    "\n",
    "* [Fisher's Iris](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/iris.csv): Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica.\n",
    "\n",
    "* [Zoo](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv): The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make using the datasets easier, we have written a class, `DataSet`, in `learning.py`. The tutorials found here make use of this class.\n",
    "\n",
    "Let's have a look at how it works before we get started with the algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intro\n",
    "\n",
    "A lot of the datasets we will work with are .csv files (although other formats are supported too). We have a collection of sample datasets ready to use [on aima-data](https://github.com/aimacode/aima-data/tree/a21fc108f52ad551344e947b0eb97df82f8d2b2b). Two examples are the datasets mentioned above (*iris.csv* and *zoo.csv*). You can find plenty datasets online, and a good repository of such datasets is [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets.html).\n",
    "\n",
    "In such files, each line corresponds to one item/measurement. Each individual value in a line represents a *feature* and usually there is a value denoting the *class* of the item.\n",
    "\n",
    "You can find the code for the dataset here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource DataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Attributes\n",
    "\n",
    "* **examples**: Holds the items of the dataset. Each item is a list of values.\n",
    "\n",
    "* **attrs**: The indexes of the features (by default in the range of [0,f), where *f* is the number of features. For example, `item[i]` returns the feature at index *i* of *item*.\n",
    "\n",
    "* **attrnames**: An optional list with attribute names. For example, `item[s]`, where *s* is a feature name, returns the feature of name *s* in *item*.\n",
    "\n",
    "* **target**: The attribute a learning algorithm will try to predict. By default the last attribute.\n",
    "\n",
    "* **inputs**: This is the list of attributes without the target.\n",
    "\n",
    "* **values**: A list of lists which holds the set of possible values for the corresponding attribute/feature. If initially `None`, it gets computed (by the function `setproblem`) from the examples.\n",
    "\n",
    "* **distance**: The distance function used in the learner to calculate the distance between two items. By default `mean_boolean_error`.\n",
    "\n",
    "* **name**: Name of the dataset.\n",
    "\n",
    "* **source**: The source of the dataset (url or other). Not used in the code.\n",
    "\n",
    "* **exclude**: A list of indexes to exclude from `inputs`. The list can include either attribute indexes (attrs) or names (attrnames)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Helper Functions\n",
    "\n",
    "These functions help modify a `DataSet` object to your needs.\n",
    "\n",
    "* **sanitize**: Takes as input an example and returns it with non-input (target) attributes replaced by `None`. Useful for testing. Keep in mind that the example given is not itself sanitized, but instead a sanitized copy is returned.\n",
    "\n",
    "* **classes_to_numbers**: Maps the class names of a dataset to numbers. If the class names are not given, they are computed from the dataset values. Useful for classifiers that return a numerical value instead of a string.\n",
    "\n",
    "* **remove_examples**: Removes examples containing a given value. Useful for removing examples with missing values, or for removing classes (needed for binary classifiers)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing a Dataset\n",
    "\n",
    "#### Importing from aima-data\n",
    "\n",
    "Datasets uploaded on aima-data can be imported with the following line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = DataSet(name=\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check that we imported the correct dataset, we can do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1, 3.5, 1.4, 0.2, 'setosa']\n",
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(iris.examples[0])\n",
    "print(iris.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which correctly prints the first line in the csv file and the list of attribute indexes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When importing a dataset, we can specify to exclude an attribute (for example, at index 1) by setting the parameter `exclude` to the attribute index or name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\",exclude=[1])\n",
    "print(iris2.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attributes\n",
    "\n",
    "Here we showcase the attributes.\n",
    "\n",
    "First we will print the first three items/examples in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1, 3.5, 1.4, 0.2, 'setosa'], [4.9, 3.0, 1.4, 0.2, 'setosa'], [4.7, 3.2, 1.3, 0.2, 'setosa']]\n"
     ]
    }
   ],
   "source": [
    "print(iris.examples[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we will print `attrs`, `attrnames`, `target`, `input`. Notice how `attrs` holds values in [0,4], but since the fourth attribute is the target, `inputs` holds values in [0,3]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attrs: [0, 1, 2, 3, 4]\n",
      "attrnames (by default same as attrs): [0, 1, 2, 3, 4]\n",
      "target: 4\n",
      "inputs: [0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(\"attrs:\", iris.attrs)\n",
    "print(\"attrnames (by default same as attrs):\", iris.attrnames)\n",
    "print(\"target:\", iris.target)\n",
    "print(\"inputs:\", iris.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will print all the possible values for the first feature/attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.7, 5.5, 6.3, 5.0, 4.9, 5.1, 4.6, 5.4, 4.4, 4.8, 5.8, 7.0, 7.1, 4.5, 5.9, 5.6, 6.9, 6.6, 6.5, 6.4, 6.0, 6.1, 7.6, 7.4, 7.9, 4.3, 5.7, 5.3, 5.2, 6.7, 6.2, 6.8, 7.3, 7.2, 7.7]\n"
     ]
    }
   ],
   "source": [
    "print(iris.values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we will print the dataset's name and source. Keep in mind that we have not set a source for the dataset, so in this case it is empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: iris\n",
      "source: \n"
     ]
    }
   ],
   "source": [
    "print(\"name:\", iris.name)\n",
    "print(\"source:\", iris.source)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A useful combination of the above is `dataset.values[dataset.target]` which returns the possible values of the target. For classification problems, this will return all the possible classes. Let's try it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa', 'virginica', 'versicolor']\n"
     ]
    }
   ],
   "source": [
    "print(iris.values[iris.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now take a look at the auxiliary functions found in the class.\n",
    "\n",
    "First we will take a look at the `sanitize` function, which sets the non-input values of the given example to `None`.\n",
    "\n",
    "In this case we want to hide the class of the first example, so we will sanitize it.\n",
    "\n",
    "Note that the function doesn't actually change the given example; it returns a sanitized *copy* of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanitized: [5.1, 3.5, 1.4, 0.2, None]\n",
      "Original: [5.1, 3.5, 1.4, 0.2, 'setosa']\n"
     ]
    }
   ],
   "source": [
    "print(\"Sanitized:\",iris.sanitize(iris.examples[0]))\n",
    "print(\"Original:\",iris.examples[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently the `iris` dataset has three classes, setosa, virginica and versicolor. We want though to convert it to a binary class dataset (a dataset with two classes). The class we want to remove is \"virginica\". To accomplish that we will utilize the helper function `remove_examples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa', 'versicolor']\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\")\n",
    "\n",
    "iris2.remove_examples(\"virginica\")\n",
    "print(iris2.values[iris2.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also have `classes_to_numbers`. For a lot of the classifiers in the module (like the Neural Network), classes should have numerical values. With this function we map string class names to numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class of first example: setosa\n",
      "Class of first example: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Class of first example:\",iris2.examples[0][iris2.target])\n",
    "iris2.classes_to_numbers()\n",
    "print(\"Class of first example:\",iris2.examples[0][iris2.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see \"setosa\" was mapped to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we take a look at `find_means_and_deviations`. It finds the means and standard deviations of the features for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setosa feature means: [5.006, 3.418, 1.464, 0.244]\n",
      "Versicolor mean for first feature: 5.936\n",
      "Setosa feature deviations: [0.3524896872134513, 0.38102439795469095, 0.17351115943644546, 0.10720950308167838]\n",
      "Virginica deviation for second feature: 0.32249663817263746\n"
     ]
    }
   ],
   "source": [
    "means, deviations = iris.find_means_and_deviations()\n",
    "\n",
    "print(\"Setosa feature means:\", means[\"setosa\"])\n",
    "print(\"Versicolor mean for first feature:\", means[\"versicolor\"][0])\n",
    "\n",
    "print(\"Setosa feature deviations:\", deviations[\"setosa\"])\n",
    "print(\"Virginica deviation for second feature:\",deviations[\"virginica\"][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IRIS VISUALIZATION\n",
    "\n",
    "Since we will use the iris dataset extensively in this notebook, below we provide a visualization tool that helps in comprehending the dataset and thus how the algorithms work.\n",
    "\n",
    "We plot the dataset in a 3D space using `matplotlib` and the function `show_iris` from `notebook.py`. The function takes as input three parameters, *i*, *j* and *k*, which are indicises to the iris features, \"Sepal Length\", \"Sepal Width\", \"Petal Length\" and \"Petal Width\" (0 to 3). By default we show the first three features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ww/nz5wEABw4cQHNzs+wnafGVvZwnxuXlZQwPD4NlWdx6660IBAJlP34hARGN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\npSqjJ1eBBEFQLFrT09OIRqOgaRo1NTWIRqMIBoNpp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JKg1uAnQDRmcblcsFgsUjUhF6SK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ZQnr27EmysrLe6/g5c+aQzz77rEXG8vr1a+Lk5ERMTU1J3759iZmZGfnll1+ISCQit27dIvb2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\nnjD6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c8967390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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X4fV6FXOjVKt1cjtGFkqdDSFHZOG73/0uAOCBBx7IuP3555/H448/DgCYnp7O\n+D2srKzg85//PPx+P+rq6jA0NISLFy/i3nvvrfr5lAoVCwpDOhxIOoGkG0rZ/IqJhXA4DKfTiZWV\nFezatQs9PT1VfwGVmgQJVCYWotEonE4nlpeX0d/fj56enqKbnTSyUEuy6z2UCClvB4GipJ210WgU\nW9oOHz4MhmFkdaMsxHaqWVDLvREoLw0hR2ShlM/vG2+8kfH3b33rW/jWt75V9drVQMWCQuTqcCi3\n6C1fzQKxZ/b5fNi5cycOHz4sm+viRk1DpNNpjI+PY3p6Gp2dnTh37lzJc+bJe66EWMi2laZUj9Jt\njNI6IqA8N0qj0ZjRxulwOGAwGEp6/rRmofaQi7dia3Mch1gsJmuB42aDioUaIxUJ1c5wyD64pfbM\nzc3NOHv2rJhflQulXBXJWsWECXGbdLvdcDgcZRdsAh9Ec7ZiGmKzmzKVipKvsxRxks+NUjoiOZcb\nJfljMpnWraFGZEHNmgW1IhpAcX+HUCgEADUxZdosULFQI2oxw4GIBY7j4PV6MTY2BqvVihMnTmQ4\n3snJRoosELdJnudx6NAhNDc3V1WLoXRkQSk2egRjNbmKOmPlm67Sr6/SSEauEcnZbpQejwfRaBRa\nrTZnF8Z2SUOoKVIAFE1DhMNhMAxDp05S5IP075PiRUC+HnvyJX7rrbfAMMw6e+ZaoKRYyFcfEYlE\nMDo6itXVVfT398tid00jC+pwc/4mLkxcwBOHn0CrtbXix9lokYVSyXajBNYOaKmAmJ6eRiQSAQC8\n//77qKurE9MYVqu1pq99u4kF4ohb7DWHw2HYbDbVvCA2AlQsyEitBj0Ba9Wwo6OjAICuri709vYq\n8sFVsxsilUphbGwMs7OzstdiKBVZUHpENaD8lXepn3GWZ3Fx5iLuLt7F29638ak9n6poPaVrFmp9\nha/RaDAeH0dciOP0vtMAgGQyibfeegttbW2IxWKyuVEWQ61CQzXHU5dS3BgKhWC32ze8GK8lVCzI\ngCAISKfT6yIJcnywsu2ZV1ZW0NbWppjCVaMbgud5TE9PY2xsDA0NDTh9+rTs4T8lDvLs3/923mgA\n4P3A+3AuO9FmbcM13zWc7jxdUXRhs6QhSiWajuLfXP+GJJvE3qa9aDI3iet1dHSI33U53CiLoeaY\naCW8K7Ip1b2ReCxs5+8wFQtVIEeHQz6k9swdHR2iC+H09LRiV/qAsmkIhmGQTqdx+fJlaDSaio2k\nSkGJuQ3+ZTtJAAAgAElEQVQ0DfEBLM/i0swlaBktdtp34u7SB9EFlmcxuTKJXfW7oNWUdsBt1jRE\nLt6dexczoRkIEHDVexW/tfu31nVgkP+v1o2y2MHI87wic2Cy2ehmUHK1TW5mqFioAGLWkkgkoNfr\nxZyXHBsKx3GYmprCxMQEGhoa1lX7a7Xasi2fq0GpNEQoFMLo6CjS6TQGBgbQ1dVVc3dFpdMQy8vL\nSCaTqKurg9ForNkBpKRAKXUtElXocfSAYRi0WlrF6MJCbAGveV7Dw7sext6mvVWtyQs8nEtODDQO\nQKeRZ3urZQtjNB3Fr6d+DbvBDr1Wj0szl3Cq8xTMgrmkC49y3SitVuu6KIT0in471iyUk4bYzlCx\nUAbSDof5+Xm43W6cOXNGlo1EEAT4fD643W4YDAYMDQ1l9HETlLzSJ+ulUqmaPX4ymYTb7YbP50N7\nezui0Si6u7trth5BychCNBrF6OgogsEgjEYjYrEYdDodHA6HuGE7HI6SfSKKrbnRIFEFCICO0SHN\npVFnrMPo8iguzlxEJBXB5Ook3p17F7sbdheNLhS60r8TuIN/uPUPeHTfozi786wsz7+WkQUSVdjb\ntBcaRoPhxWFc9V7F/W33V3xoF3KjJAJiZWUFMzMz69woE4mEaDmsJJshsrCdPRYAKhZKIlcbpF6v\nFytpq2VxcRFOpxPpdBp79uxBe3t73sfV6XRbIg3BcRw8Hg8mJiawY8cOnD17VhRMSqBEgSOpcn/r\nrbfQ1dWFwcFBUUBEIhGEQqGM/nuDwSAKB7J5VyIgNlrr5NTqFBZji9AwGkyuToq3m7QmXJ65DKve\nin2N+zC+Mo6x4FhJ0YVc3w9e4PHLyV9idGkUv5z8JU60n4BRV70Aq5VYkEYVSBSkydyESzOXcLDu\noKxX+MSN0mg0ZqT2st0oQ6EQVlZWMDc3J6sbZTHULHCkaYjSoGKhCPk6HOQ4tKX2zKQlsNgHV+nI\ngtxpCEEQ4Pf7xQFXx44dE41s4vG4IqOjgdrWExDRMzY2BgBiKonjOKRSqZztcyzLiu1zoVAI8/Pz\nGQ6AUhFRaNPeiJGF3rpePHH4CXBC5ucozaXxi4lfIJ6Ow2F0IBAPlBRdyPd7uxO4g9sLt7G3aS/c\nQTfenXtXluhCrbohfjP3G0yuTEKv1cO17AKwJngiqQjenXsXbUyb7Gtmk+1GeePGDezYsQNWq1VW\nN8pibPQ0hFxDpDYzVCzkodigJ2K9XMnBlkgk4Ha7MTc3V3ZLoNI1C3J2Q6yurmJkZATxeBwDAwPo\n7OzMeO/IZqHEVUatIgurq6sYHh5GMplER0dHRq6zkDjR6XSor6/PMNciDoDkj1RAZKcw1ChKKxWt\nRou++vVjeN8PvI/V5Cp663oBAJ22zpKjC9nfORJVYDkWTeYmBBNB2aILtRKvdcY6/Ife/5D73/R1\nqjka1sKNshhqdmGUGllob29X4BltXKhYyEJqqEQKm3IVL+p0OjE9UerBxrIsJiYmMDU1VbE9sxo1\nC9Wul0gk4HK5MD8/j97eXvT19eVU89IBT7UWC3IXOCaTSbhcLszNzaGvrw+7du3CwsKCaBNbCbkc\nAMmmTVIYc3NziMfj4hAjk8kEnueRTqc3nIBYTa7iPf97uLfjXug1evxm7jdIcSmEkh+8R3E2XjS6\nkEt0kahCl6MLALDTvlO26EKtxMLR1qM42no0578tLy/DteKSfc1i5PvuVeNGabfbYTabC76HatYs\nlPI9CYfD2Lu3eHpsK0PFQhbEM6FYhwM57Erp0+V5HjMzMxgfH4fVasW9995bsce40jUL1VyBS2dX\ntLS04OzZswWLp5SaBknWkiMNIfWEaGpqyhCAtUh15Nq0pUOMgsEgBEHApUuXxMI1aRSiFr3spR6k\ndwN3cd1/HQ2mBnQ7usHxHNpsmaH2dls7UlwKMTYGu2F92Je8n9I1SVQhkoqANbNYSawAWEtzyBFd\nUGugkxoppXK6IUp1o4xGo2AYJqOF0+FwwGKxiK9RzTREKQWdtMCRioV1kHRDsS8quQ/LsnmL0ARB\nwPz8PFwuFxiGwcGDB6uaZwBsjsgCydm7XC6YTKaSZ1coNQ2SrFXtOouLixgZGQHDMDk9IZTyWZCG\njZubm3Ht2jWcOXNG3LBXV1fFyneLxbLuqk8JM5xgIoi7i3fBCzxuzd/CQOMAnjj8BASsf38YMEU7\nIqTfodXkKgLRAJotzYimo+LtTeYmRNNRzMfm0e2ovMNGacdIQN0WxmrW1Wg0cDgcGQcrz/OIRqNi\nGsPn88HpdAL4wI2S53mxE0PJ1027IUqHioUclHrVmW9kNAAEg0E4nU7EYjExPy/Hl2Cji4VgMIiR\nkRGkUins3bu3YGdHNiSas9EjC7FYDKOjo1heXsbu3bvzzqpQ05Qp1xhlUvlOKt6zBYQ0AiH3Vd7I\n4giCiSAGGgYwFhyDe9mdNwRfiFzvZ4OpAf/97H9Hilvf4qvT6OAwVrfJbyexUIt1NRqN+LkiSN0o\nV1dXAQB37tyR1Y2yFEp1jqTdEFQsVEUusRCNRuFyubC4uIje3l4cP35c1is3rVaLZDIp2+MVo9Ru\nCKkt9a5du9Db21vRF1xJsVDuOqTmxOPxoKOjA+fPny/amSA93JQ6cPIJlFwCIplMihGI5eVlTE1N\nIZVKie5/RECU4v6XDxJVaLY0Q6vRos5YJ0YXrHprRa8t+720GWo3DVCN6Y9qCBRAuRZGqRtlY2Mj\nvF4vzpw5k9HKWa0bZSmUkkYmrc7beTw1QMVCVUjrB6RDj6T2zLVcUwmKdUOwLIvx8XFMTU2hvb29\n6tetlFgo56pfEATMzc3B6XTCbDbj5MmTJW0cao2oLofs3nti3kMKKIn7H8uyGRu2w+GA1VraQU+i\nCnsb1wrEWqwtcC+7K44uAFvL7jkXWymyUAyyn2m1WlndKEtdm/oslAYVCzkodZPX6XTiDIfJycma\nDT2SopbPQvaGKQgCZmdn4Xa7YbVaSz5AS1lvI0UWQqEQRkZGEIvFKkqrqJWGqPSAI+Y9zc3NaG5u\nFh+LRCBCoRAWFxdFAWEymZBKpeD1esUrPulhE0qGMLI0gjSfxtjKmHh7kkvi9sJt7GvaB5OudHGp\nhvhSQyyoFc1QSyxotdqc73E1bpTkT6Fuh1J8FgRBQDgcppEFtZ/AZoW0WI6OjsJiseS1Z5YbNWoW\ngMwNc2lpCaOjo2BZFoODg2htbZVtM1VqFkWxAsdUKgW32w2v14uenh4cO3as7KuWStIQ48FxTK5O\n5u2/VwOGYWAymWAymTIERCKRgM/ng9frzQgZS0179GY9hlqHchYy6jQ6aJnKQsk0slCbNQGosm45\nKYVS3Si9Xi8SiYTYVpzLjbKUyEI8HgfLslQsqP0ENiOBQAAulwuxWAzNzc04cuSIYpuJWmKB4zjE\n43E4nU4sLy+jv78fPT09NSmGUrPAkbS5ut1uNDQ04MyZMyWH27PJFVko9DnhBR7/ePcfMRGcwEDD\nAHrqeipaUwnIFV99fT0CgQCGhobWTUD0+/0Ih8MQBCEjXGyz26AxaGAzlh6BI86GZo3ycwu2S+sk\n+d4p3cIoV9tkrpocaVtxthulzWYDz/MIhULQ6XR53SjD4TAA0DSE2k9gI5LvSxoKheB0OhEKhbBr\n1y5EIpGaTg/MRaEOjFpAxIDT6YTP50NnZyfOnTsny9CjXMjpGFmIXBGMpaUljIyMgOd5HDlyRLyK\nrpRy0xDX/ddxe+E2oukofjnxS3x+6PMVr63G1XC+CYjxeFysgfD7/XjjN29gMjaJ/7TrP2FH/Y6M\nqvd8z/ll98t43fM6/sfp/yGupRQ0slBbaumxUMiNcnV1FUtLS5iamsLIyMg6N0qr1Qqz2YxIJAKD\nwVCTGrTNhPIVNJuQeDyO27dv4+rVq7Db7Th//jz6+vrEYVJKomRkgVxlA2ve6Pfddx8OHDhQM6EA\nqFPgGI/HcePGDbz33nvo7OzE2bNnqxYK2WsUgxd4/Gz8Z+B4Dp22TlycuYip1amK1txIEAHR1taG\ngYEB9B/oR7A+iKg1ihXzChiGgc/nw29+8xu8+eabuH79OlwuF/x+P6LRKARBwGpyFT8d+ymGl4bx\nxvQb4uMqxXapWeA4rqSx2LVYV8nXSozN2trWDMFOnjyJ+++/H4cPH8aOHTuQSqXg8Xjwz//8z+jq\n6sITTzyBpqYm/PCHP4Tb7a54f3r22Wdx4sQJ2O12tLS04Ld/+7dFv4lCvPDCCxgcHITRaMTg4CBe\nfPHFitavFhpZKEA6nRbtmVtbW9fZM+t0OsTjcUWfk1JiIRAIYHR0FMDaAT44OKhIGE7JNATLsnC7\n3fB4PGhra8P58+dlFULliAUSVei0d8Kqt2J4cbiq6IKShYDlHC7v+N7BfHQeNpMNd6J38OD+B2HU\nGcVR3iRcPDs7i0gkAoZhcD1+Ha4FF6x6K152v4xHLY/W8NWsR42DW600xEaez1CrdRmGyelGeejQ\nIezfvx8/+9nP8KMf/Qjf+ta3cPv2bRgMBtx///34yU9+UtZ6b775Jp588kmcOHECLMvimWeewUc/\n+lEMDw/nTXVeuXIFv/d7v4evfe1r+NSnPoUXX3wRv/u7v4vLly/j5MmTVb3+cqFiIQeCIMDj8WB8\nfBx2uz1vpb/SKQHgg0FStbraIZMwV1dXsXv3buzcuRNvvvmmIgc4oIxYIH3TgUAANputZIfJcinV\nJVIaVSB+Aa3WVlycuYiHdj1UVu3CRossSFlNruLSzCU0mBrQbGnGWHAMNxdu4mTHSTAMA5vNBpvN\nJg7s4Xke/hU//tev/xcsWgsccMDpd+Jm80103OzIMJEqNnugGtRKQyh9gBZa07nkRJejq2xfjFJQ\n0+q50Lpmsxnnzp3DysoKLl26hHfeeQfpdBrDw8OYnZ0te70LFy5k/P35559HS0sLrl+/jvPnz+f8\nmW9/+9v4yEc+gq985SsAgK985St488038e1vfxv/8i//UvZzqAYqFnIwMzOD2dlZHDp0qKA9sxpi\ngVTky72ZSH0isidhKtWhQNaqpVgIh8MYGRnB6uoqrFYrTp06VbODoNTIwnv+93B74TYECGLqQYCA\nQCyAVyZfweeOfq4mz09p3vG9A3/Uj/079kPLaGHSmfDm9Js42nI05+wGjUaDa4vXsMQuYU/rHug0\nOiQMCVxbvYZHGx8Fm2AxPT2NSCSSMbzIZrchro2jt6lXlt+tWmJB6UFg+dIBvrAPPxv/Ge5puwcP\ndD9Qk3XVjCwUQ+qxoNfrceTIERw5cqTq9YlzpbSeIpsrV67gT//0TzNue+ihh/Dtb3+7qrX9fj/m\n5uag1+tFe26bzVaw44uKhRx0d3ejvb29aEhOrcgCIN8XjOd5TE1NYXx8PK9PhFJFh0DtxIJUDHV3\nd6OpqQmhUKimh0CpYoFhGAw2Da5rLxxoGIBeU9mBsdHMoEhUoc5YBwgAJ3Bot7VjYmVCjC7k+pmf\njf0MVp0VDJi1wVOWNtxcvonR1Cg+ue+TANYPL3rp1kt43f86Hm1/FLubd2c4UWr1Wui15b2n28XB\nMV8a4rr/OmZDsxAg4EjLETSYGnL8tPzr1ppSrZ4jkYjsKVhBEPClL30JZ8+excGDB/Pez+/3i8XC\nhNbWVvj9/orXvnnzJr761a/i+vXrCAaDoiMwOc9u3LiRUwxRsZADMkyqGCQloCTkeVV7pS8IAhYW\nFuB0OqHRaHIOQiIoWVQpdxRDEASxFbKurk4UQ9PT0zUXQKWKhWNtx3Cs7Zhsa25ERpdGEWNjiKVj\ncAfd4u0aRoMb8zdyioX3/O8hlAwhwSVEQyee56FltHhj+g18cs+aWJAOL0qwCfwf///BqmEVgboA\nTu04hXA4jMnJSTiXnPjV8q/w2MBj6NvRJ4qIfC1zBLVSAmrUSWSv6Qv7cGfxDnY17MJcZA63Fm7J\nHl3YqGkIQi2GSD311FO4ffs2Ll++XPS+2Z/NSoUk+f1+8YtfhCAIeO6559DX14dkMol4PI5EIoGl\npSX09/fn/HkqFqpAjcgCKcapZt1QKITR0VFEIhEMDAygq6ur4IdPqaJDudcKBoMYHh4Gx3HrUkpK\nvCay8apVTb+RONR8KO8VqcOQeyO+t+PedUOgEokEhu8O48PHPpzzZ675rmFiZQLdjm68t/Qefmvf\nb2F/134IgoDL71yGL+jDncQdtCfaEQgEEI1GYTAYMmys7XZ7RqHrdumGyCWKrvuvI5qKotvRjTSX\nxnX/ddmjCxzHKZ5yIeuWOkRKTrHw9NNP4+WXX8bFixfR1dVV8L5tbW3roggLCwvrog3F4DgOHMfB\nYDDgxo0beOONNzA0NFTWY1CxkINSNwal5zRUu24ymYTb7YbP50NPTw+GhoZK+pIqHVmo9hBPJBJw\nOp1YWFhAf38/ent71228SlgxV2u9XM2aSlHqe2jRW7CncU9Zj23VW9dFXKLRKNLWNPob1l/9JNgE\nLkxcgFFrRLutHXeX7uJ1z+t4/PDjcC47cX3+OurN9bgVvoVHhx7FoH0QHMdlmPYsLCwgFovBYDCI\nwiGRSCh+mKlluyxdk0QV2m1rBac7LDswujQqe3SB4zhVPAxKjSyEQiFZxIIgCHj66afx4osv4o03\n3kBfX1/Rn7nvvvvw6quvZtQtvPLKKzh9+nRZa2u1WvG1fvazn8Xw8DAVC0pCIgtKX3mUe3hzHAeP\nx4OJiQns2LFjXQuo3OtVA2lprATp62xpaSk41EqJyIJULCjNRosslAPHcxAgQKfJvT3l+66RqEJ/\nfT9WkitYii3hzZk38aGeD+HCxAXE0jHsb9qPu4t38ZrnNXzm0Geg1WpRX1+f0Q3DsiwikYhoJBUK\nhRAMBjE/P5/RgSG1DZabjdA6ed1/HYuxRRi1RsxH5wGspY3kji6okeYBSk9/RCIRdHR0VL3ek08+\nie9///t46aWXYLfbxYhBXV0dzOY1Z9LHHnsMnZ2dePbZZwEAX/jCF3D+/Hl84xvfwCc/+Um89NJL\n+NWvflVS+kLKM888g/r6ejQ0NKC9vR3PPPMMNBoNhoaGUFdXB5vNBqvVWlCgUrFQBSSEVWo4Sy5K\nPbwFQYDf74fT6YTBYMCxY8cKVt7mQ8luCK1Wi1QqVdbPkPqL0dFR6PV6HD9+HA0NhTcypSMLlNL5\n7o3vIpqO4r+c/C8587W5IFEFBgxYgcWdwB34Ij6wAot/Gf4XvB94Hx22DjAMg2ZLM349/Ws82Psg\nOuzrDwGdTpchIO7cuQOr1Yr6+npRPMzNzSEej2fMHSBCQo4ohNo1C4IgIJwKo7e+N+M+LdYW6Bk9\nIqmIbGJBzW6IUtMQchQ4fve73wUAPPDAAxm3P//883j88ccBANPT0xm/99OnT+MHP/gB/vzP/xxf\n/epX0d/fjx/+8IdleSykUilcuHABPM8jFoshmUxCr9fjD/7gD9bV59ntdni93pyPQ8VCDkpV9OQD\nXsrkMjkppWZhZWUFo6OjiMfj2LNnDzo6Oiq+UtnI3RCRSAQjIyMIhULYs2dP0fqLStepBDXEwkYt\ncCyV8eA4Xp96HRzP4W7/XRxszqwUzxfFm1iZQCgZgkFrgGvZhanQFFJcCsFEEK9MvgKb3oYex5pf\nRYulJSO6UAxBEETXP6kIzZ474PP5xMFFRDiQ/5a7P6hVs0DWZBgGv3/g9xVZV2kHRwLLsuIVfSHk\nqlkoZR9444031t326U9/Gp/+9KcrXlev1+Pf/u3fwLIsOI6D2WzG3NwcWJZFIpEQixsjkUjBPZGK\nhTyUcuWp0WhU6YgoFFmIx+NwuVxYWFhAb28v+vr6qhYyG7FmIZ1OY2xsDDMzM9i5cyeOHj1a1hXd\nVo8sbNZoxk/GfoLV5Co00OAl90s4sOPAOnGQSyzsa9qH/3rffwXHc/j7G3+P5fgyeup6MB4cX0tn\nMGsdGQRBEHDFdwUfH/g46k2FDbnypQRyzR1Ip9MZ6YvZ2VlxdHJ2CqPQ91KNNMRG9ztQa91IJLKp\nJ04yDIOdO3cCWKvnunTpEj7ykY+U/ThULFSJGmIhV4Ejy7KYnJyEx+NBS0sLzp49W5JqLoWNZMok\nCAK8Xi9cLhdsNhvuu+++ikKENLKw8dYcD47j4sxFtFpaoWW0eMf3Du4uZkYX8r2XGkaDbkc3hheH\nMbI8gl31u1BvqsdyfBlmnRmfO/o5GLSZ9QUmnUl0zCxEOTVJer1+3eRDMjo5FAphZWUFMzMzSCaT\nsFgsGdEHqSmO2mkIJVGzdbLYhZQgCLKlIdSE/G5v3LiBhx56KOfed+HCBfzhH/4hpqZyz6ShYqFK\n1OiIkF7pC4IAn88Hl8sFs9lcE+viSuoIKqXQIb6ysoLh4WGkUikMDg6itbW14oNqq4oFwkaKLLw6\n+So6bB040Hyg4P1IVKGjca2OwB/154wu5PudC4KAv7/59/CsenC28ywAoLuuG5Mrk2AYBh/q+VBF\nz7/aAuZco5OTyaSYvggGg5iamkIqlYLVaoXdbkcqlUI8Hlf0IN3ohYZqrStXN4SaxONxRKNRTE5O\noqenB/F4HKlUCnq9XhzPvbi4WLDwnYqFPJQaplZzPkQwGMTIyAhSqRT27duHtra2mlxZqp2GSCQS\ncLlcmJ+fR19fH/r6+qreXLZqGmKj1SzMhmfxw5Efot3Wjq82fnXd1T1BGlUgr6HN2rYuulDovbwT\nuINLM5cQSoZwY+EGbPq1qEEoFcLL7pfx4Z4P5+2wKEQt6geMRiOMRmOGEVoymRRTGDzPY2JiAm63\nGxaLJSOFYbPZanK4qmExTdbdyGIhEolsWrFAhO6NGzfwJ3/yJ2AYBktLS/jjP/5j6PV68bOVSCTw\n2muv4ezZs3kfi4qFKlFDLJAuh+npaezatQu9vb01/bIpnYYgaxEr6rGxMTQ3N8ueWlF6FLaSbJTI\nwmue1xCIBRBKhvDu3Ls403Um5/0uzVxCNBVFWAgjEA+s3fjvL+HizMUMsZBPEPkiPrRb29Fh60CD\nqQGnO09Dq1n7XpSSbsiHUq3RRqMRzc3NaG5uhtfrxeHDh2E0GsUUxuLiIiYnJ8GyrBiBkKYwqhU0\nal7hq1XgWCwNwXEcotHophUL5HNrt9vxwAMP4NatW7BYLGI7MCluZBgGDz744Lo5FFKoWKgSJcUC\ny7IYHx+H1+sVJ6IpYWaiRjdEIBDAyMgINBoN7rnnnowQrhwodYiXOnlS7jU3ArPhWVycuYgOWwdW\nk6u4MHEBJ9pP5IwuPNj74Lo2PUK3ozvj77leX4JNwLXswvH24+LMiVOdp3C09WjVr0MtB0etVguT\nyQSTyYTm5mbx9kQikWEiNT4+Do7jYLPZMto4i/XNZ6NWnQR5rUpTijgKh8MAsKkLHAHgyJEj+Ju/\n+Rt4PB7cuXMHH/vYx8p+DCoW8lCOi2OtxYIgCJidnYXb7YbNZkN3dzeSyaRirmdKpiHS6TRisRhu\n374tWlHXYgNTMrIArIWYnU4n/H4/rFarOMvA4XDAYrFsmANeTl7zvIZgPIgDOw7AbrDDueTMG13Y\n6diJnY6dRR8zn8C7E7iDmdAMdjfshl6rh1FrxBXvFQzuGMyb+iiVjWCQRGAYBmazGWazGS0tLQA+\nEBAkhZEtIKQpjEICQi3XSACKiwVBEEryWSBiYTMXOE5OTmJsbAwWiwUtLS2455574PF4YDKZYDAY\nYDQaYTAYiqagqFioklp3QywtLWFkZAQ8z+PAgQNoaWnBzMwMYrFYzdbMRomDlURNPB4PNBoNzp07\nVzN3PEDZK36fz4fp6Wk0Njbi6NGj4pWhz+eD0+kEwzDi1SDZ2E0mU1UHlFxRk1g6hvf87+F012lo\nmPUHSb51SFSh1bpWg2DSmaDT6ApGF0oh11V+gk3givcKLHqLOFGy096JiZUJDC8OVx1dUDqyIAhC\nWQJFKiDIzABBEBCPx8UUht/vh9vthiAIYgSCfNYsFov4HZdDLCTZJCZXJ7GncU/Oz4wU8h1UY1BX\nKRGNcDgsS4pHTV5++WU899xz6OjogE6nEwvWSTuvTqeD3W5HMpnEY489ts40ikDFQh7Ung8RjUYx\nOjqKYDCI/v5+9PT0iB9YpeskahlZkHZzWCwWHDp0CKOjozUVCoAyQ56CwSA4joPP58ORI0fQ1NSE\nVCqFuro6tLW1AVjbtKLRqLipezweRKNR6HS6DGMfMh2xFOR8PT8d+yl+NPojGHVGnGg/UfLPve55\nHd6wFw2mBqwmVwEASS6J0aVR/GbuNzjdVZ63vZTs1zceHMdyYhnRdBQjSyPi7ZzA4eb8zU0pFgBU\ndUAxDAOLxQKLxZIhIGKxWIaJVCQSgSAIsNvtiMViYuV/NdGuu4t38Zb3LRi0Buyq31XwvqReQQ1P\nCaC4SAmFQrDb7Zs68nfmzBlotVpoNBp4vV688MILSCQSGBwcRCqVwt27dzExMYG6urqC5k9ULFSJ\nTqcT54HLgdRsqLOzE+fPn193SCiZFqjlequrqxgZGUEikRC7OYq5iMkF2YhrUYmdSqXElINWq8Xh\nw4fR2NiY83VpNBoxREz85zmOE2cThEIhcbgRsRaWRiDyhVHliCwEE0H8ZOwnmFqdwovOF3Gs7VjR\nK0VCg6kBD/U9tO52hmFg0eduz/JH/IixsYIHTK7X1VPXg0/vzb3JVVPYKF1TyStLOcRCLhiGgdVq\nhdVqFcUqERChUAhutxvBYBBzc3NgGGZdCqMUARFLx3Ddfx3ekBc35m+gt6634GdGzeJGhmGKrh2J\nRDZ1CgIAjh8/juPHjwMAfvCDH8Dn8+ErX/kK9uz5YLDbX/3VX+Hu3bs4cCB/ezMVC1Ui11U+z/OY\nmZnB2NgYHA5HQbMhpcWC3N0Q0umXpBWSHHpK1xLIWeQoCAJmZmbgdrvR0NCAM2fO4Nq1a+Ja5diI\n19XVZRRVEWthIiCIMyBpfSJ/bDabbFdBr06+Cm/Yiz2Ne3Bj4Qau+6+XHF34+MDHy1qL4zm8MvkK\nwugsx1YAACAASURBVKkwHj/8OKx6a977Zr8+m8G2zsOBF3jE2XjBxykVJSILSTaJF10v4mO7PwYj\nszYeW4mrWamA8Hg82LNnD+rr68UIBPmsRSIRMV0mTWGYzeaM5zm6NAp/xI89TXswtjwGz6qnoPhT\n22Oh2HtMDJk2c2RBEASxxu0b3/gGPve5z2HPnj3geR48z0On0+HLX/4yTpw4gbt376Knpyfn41Cx\nkAelChwFQUAgEIDT6QQAHD58GDt27Ci4vhqRBTkOcJ7nMT09jbGxMTQ1NeWcfknEQq03aGlkQQ6I\nYRTLsjh8+LBYvZ4SUvjp+E/x0f0fRYulpeLXlMtamBj7kLa6iYkJcBwHQRAwOTmJxsZGsSq+3HVJ\nVMFusKPOWAd/1I8fO39cVnShHMaCY5hYmUCKT+Fu4C7u7bg35/1KFXcvu1/Gzfmb+Mp9X4FRZ6zq\nuSkhFl5yv4RnrzyLcCqMx/Y/BkD+yEIxSJRNo9HAZrPBZrOhvb1d/DeSLguHw5ienkYkEoFWqxUF\nhN6ix1XvVTgMDtgNdsxH5otGF9QWC8XYCoZMDMOIxYttbW24dOkSHnnkEbS2toqfsfHxcfh8Plit\n+cU1FQtVUo1YCIfDGB0dRSgUwu7du7Fz586SNgilaxZIZKGaTXNxcREjI2v55KNHj2aY0WSvBdR+\ngyaPXa1YSKVScLlcmJuby2kY5Yl7cCtyC3a7HZ/c88mq1som29iHVMVfvXoVGo0Gc3NzcLlc664I\nHQ5H0QJKElUYaBgAAHTYOnBz4WbO6EK1vyeO53DNtxaBqTfW4525d3Cg+cC6qECKSyHJJouutxRf\nwq88v4I/6sc13zWc7z5f1fOrdTdEgk3gH27/AwKxAP7vnf+LT/R+AoDyLbCFUgLSdBmBCAjShXF1\n9Crem3sPXeYupCwp6A163Jq5hcG6Qexr3Zfz9Wxkq2fggwLHzQ55j7/4xS/iqaeewhe/+EX8zu/8\nDlpaWuD3+/H1r38d+/btw969e/M+BhULVVJJN0QqlYLb7YbX661oCJIakQWgsgM8FothdHQUy8vL\n2L17N7q7uwsKIrJWrdu4qk1DkHZWl8uF+vp6nDlzZl2UJJ6O427oLtLmNG74b+BE+wnsMOYWSXJA\nquK1Wi26u7ths9nEsbRkQydXhKQCWlr/YDSuXYGTqIIgCAgmguLjh5KhmkQXSFShy9EFg8YA57Jz\nXXRBEAT8z/f+J6KRKD5iLTwE5+L0RcxF5mDQGvDLyV/iZMfJqqILtRauL7tfxuTKJLocXfBH/PhX\n17/igGb9AK1aU+53TiogYukYLiUuoUvfBZvGhkQigWQiCV/Yhx+99SPc33w/6hx1GSkMo9G44d0b\n5Zo4qSbS3+tDDz2Eb37zm3j22Wfx+c9/Xqy3e+SRR/CXf/mXYi1LLqhYyEMtuiGII+H4+DgaGxtx\n5syZgmGffGi1WrG9SolQJflSlVOMxLIsJiYm4PF40NHRgXPnzomHUSHI45c6a75SGIapuH1ydXVV\nnFFx6NAhsd89m9sLtzGfmsexjmPwJX141/cuHu57uNqnXhLSIjkSUiaQAkqSwiAFlEajEQ6HA4tY\nBJtm0WxpRppPYym+hFZrK7rsXVhJrCCWjslSOAhkRhXMujV3zjpj3browujSKK76riKdTGOvZi/u\nRe40xVJ8Ca9NvYYGUwN2mHfAHXRXHV2opVggUQUGa68/oo3g+6PfxzNdz9RkvXxUu5/E2ThMOhO6\n7F1rN/z7ttaDHtj0Nuxv349ULIVQKISJiQlEo1Gxt5/neSwuLmYI1lqzncRC9u/0E5/4BD7xiU+A\nZVmEQqGM1GYhqFioklJSAoIgYGFhAU6nE1qtFkNDQ1U5EpIPOcuyNW8xBDIP8GIREGJF7XQ6YTKZ\ncPLkybLcz+RKD5SCRqMpK7IgjQj19fVh165deTeceDqOt2ffhllrho7Roc3Whvfm38PR5qNot7XL\n9RJyUuxg02q1qBMENE5NAaEQhOZmpE6cQPjfNw+EgD9q+SPEE3FcjlzGVe4qHul6BA/2P4g6ex0M\n+vI+c0k2CYPWkPN5jQXHML6yNkbaF/EBWCtOnA5Ni9EFQRDw84mfI5aOIcWmcGXpCh4RHsn5eCSq\nsL9pP7QaLQya6qMLteyGIFGFJvPaftBgasB8ZB5vBt/Ef8R/rMmauSDfg0qv8pvMTfjMwc8UvpPk\nTCKCdWpqCuFwGOPj46KAkHZglNMyXA6lpiEikYjYerpZ+ad/+id8+tOfhslkwttvvw2DwSAWRptM\nJoRCIZjNZmrKVGtIZCGfKg+FQhgZGUE0GhUdCau9SpFe6SsB6YMuth55rbFYDHv37kV7e3vZr5W0\nMyklFkpZh4zFdjqdqKurKykidHvhNqZXp9FsbAaEtc3UH/bj3bl38YmBT8j1Ego+53wwbjf0//iP\nYLxegGHAaDTQ7d0L/eOPo0FSCT27Mov//av/jRAbwoXJC2iNtQI8MhwoWZYtuFaaS+O7N76Lwy2H\n8eGeD6/79ySXRJe9CwIyH6PR3IgEmwCwFlV4d+5ddNo6EUvEMLw6DNeyC3ubMvOrJKpQb6pfixoJ\nPDrtneuiC7F0DDOhmXU/n49aRRZIVCHJJhFLxxBLrxmtpfk0Xg28iv+W/G+oMypjM0y+20oVVZKO\nH9L+Ozg4CJZlxZbhcDiM+fl5MeIlTV/Y7faqBUQ5kYWBgYGq1lITjuPw3HPP4eMf/zj0ej2+8IUv\nQKfTQaPRQKPRQKfTQa/XQ6/Xw2Qy4YUXXsj7WFQs5KGcNASwPkSfSCTgdrsxNzeHnp4eHDt2TLaw\nOsMwG6ojQnrFLcdr3UhDnkjKIZlM4uDBg2hpKd7RkOJSeHv2bUTSEUTiEYSCIViSFiS5JG4t3MKp\njlNotdXuaqXg80uloP/Xf4Vmfh78/v2AVgshmYTm7l1of/ELsP/5P4t3/fXsr7HKrWKocwiz4Vlo\n+jS4t/lecTP3+/0IhULgeR7Xr1/PMJEiLXU35m/g/cD7CMQCON52HA5jZkj3cMthHG45nPfpkqhC\nPB1Hj6MHOlaHaW4aPx//OfY07sl4rbcWbiGcCiOejsOZdGY8zlXfVVEs/L+R/4dXPa/iLz/0l+i0\ndxZ8LwVBqJlYWEmsIM2lscOSWcfSaGoEwzIIxAKKiQXyfVPD7pkc2jqdDvX19aivrxf/nWVZsQMj\nFAphbm4O8Xhc9ByRiohy6r5KTXOGw+FNPxfi61//Ourq6sDzPD772c+CZVlxgBT5bzQaLfq7p2Kh\nAKUcJtKUgF6vB8dx8Hg8mJiYECclFpoRXikbwZhJ6g1BivwqqcHIZiNEFtLpNNxuN2ZnZ9Hb24v+\n/v6SQ7QaRoPj7cdxqOUQRkdG0dLaspYXFNY2KZNOmZkeOZ/b5CSY6Wnwvb0AeT1GI4S2Nmhv3wYb\nCgEOBxaiC3hl8hU0mhph0VugYTT4ydhPcLrzNFpbW8XQ7Pz8PCYnJ9HR0YFQKISZmRmxpc5is+CF\nuReQTqUxw87gmu8aPtJXuDgxGxJVaLN9UHjVZGzCO3PvrIsunGg/gQZTQ87HIWH++eg8fj7xc8yG\nZvHTsZ/iD4f+sOD65PtfC7HQZmvD67//+rrbl5aW4Ha7sbtht+xr5oN8D9Qoqiz0vdLpdGhoaEBD\nwwe/V+I5InWiTCQSMJlMGdGHQgKC7NfF2OzdEFqtFg8++CBu3LiBoaEh/NEf/VHFj0XFQpWQq/x0\nOo1gMAiXywWDwYDjx49nfMDlptYzKbLJNmaSzqyQ+grItZZSkYXsdUjKweVywW63VySAdBodznWf\nAwDYF+zY2b4THR0dEAQBqVRKtudfiLwiN50Gw3EQsjZKQa8Hk0iASachAPjl5C8RiAWwr2kfAKDL\n3oXRpVFc8V7JKBYkn//29vaMnvxIJIJLk5cwEZpAs64ZgVAA/3zln2EL2tDe2F7y1eC1uWtIc2nM\nR+YxH5lHMpVEMpVEA9eAa75rGWLBbrBjqHWo4OP9YvwXWIwtot3Wjlc9r+Jjuz9WMLpQS7FQaE21\nPBbUaNcsNwqZy3Mk27TM6/UikUjAbDavS2GQ1HEpg/i2QoHj2NgYHnnkETzwwAM4c+YMjhw5gr6+\nvrLr5qhYkAGNRoPbt28jnU5jz5496OjoqPmXTq00RDweh9PpRCAQwO7duzNmVsiFkpEF6aEaCoUw\nPDws+qa3trZW/XtUahR29pr54Lu6IDQ2gvH7IXR+cEhq/H5wg4MQGhrEqALLs5gJzYj3CSfDeMn9\nEu7rvE8c2JQLjUYDs9WMO7E72NG4A/0N/ehOd+P9hfcxyU7CHrGLV4NkmI3UgVJ6pfnwrodxqPmQ\n+PfFwCKWlpewd+9e7LQXn1IphUQV6o31aLG0wLnsLBpdUEMsqDHlUi3bZbl8FnIJiFQqJUYfVlZW\nMDMzI7qesiwLnuexsrICm82WU7AIgoBIJLLp0xANDQ341Kc+hffeew+vvvoqduzYgQMHDuDBBx/E\n/fffj5aWlpKM26hYKECxjT4ej8PlciGdTou/gFq2+0mp1QCrfJAhJIFAAK2trTh37lzNRmQrHVmQ\nzuPo7e3Frl27ZK0vkX6GlBAPvMCD5fJEnerrwX7kI9D9+MfQOJ0QrFZgdRVCYyO4j34U0GiQ4lPo\nq+tDh60j40d3N+xGvbEeLM8WFAsAcGP+BsaCY+it7wWwtpm32FtwN34XHzvyMTiMDnEzD4VCWF5e\nhsfjAcuysFqtGR4QQy1D4kHm432YZ+cx1FY4gpALElXY27gXGkaDJlNT0eiCWmJBjcjCZhYLuTAY\nDGhqasq4gk6l1to3XS4X4vE47ty5g1QqJXYHSDswTCaTaPe8mWlqasI3v/lNCIKAK1eu4LXXXsPr\nr7+OP/uzP4NOp8PJkyf/P3tnHh5Xed/7zzmzz0ijXZZkWZZkW96xMRgv2AbM4kLShISG0JYkJZTc\nQpvblCbpTZulT9vnaQhpLyRpk5KSSymBLBCDgUAxNosNGLzKtjTa910ajTQzmvUs94+jM57ROpJG\nkgn6Po8f0Ehz3jNnznnf7/tbvl9uuOEGPvaxj01ZzLlEFmYBSZJobm6mpaWFZcuWkZ6ezrJlyxaM\nKMDCRRZUVaW3txe/3080GmX79u0JBUjzgVR7UUwGQRBwu91cvHiR9PR0du/enXR+ssffw7OuZ7l7\n891kWie/Hgtpha3D5Xfh7fFyW9ZtE6vm7d+PmpOD4YMPEAYGULZtQ961C7Vc0/AvTi/mH/b9Q9Lj\nTTTGqZ5TyKpM41DjpRdVkBSJqoEqdi3fNW4y1zXs9VByb28vDQ0NMVtlp9MZ6zyaadGhHlUwG8z4\nI34ALEYLrcOtU0YXUm3qlAz5WCIL8wez2Uxubi7Nzc2UlpaSl5eXIJs+ODhIY2Mjd955J4WFhdhs\nNl566SUURWHLli3YbLYZj/n222/z8MMPc/r0abq7uzl48CC33377pH//5ptvcsMNN4x73eVysW7d\nuhmPrxfpiqLI7t272b17N9/61reor6/npZde4tChQzz44IMcO3ZsqRtithj7QOv57Pr6emw2W2zh\nPHny5ILWD8DC1Cz4fD5cLhd+vx+73U5JScm8EwVInRfFVPD5fAQCAUKhEBs3bpxxyuG3Db/l5YaX\nKUgr4A/WT27rOtUxvSEvjUONs9olTwZ30E1roJXAUIC+QB/LHJe6Ls72nsUiWtiQtwFl61aUrXOw\nbpYkhO5uTO3tWDwekOVLBZPAx1Z9jN3LJ7ahnsxYSBAErFYrVqs1JnQV74ro8/nweDyEQiGOHTs2\noQLlZNe71l2LiIjVaMUX9cVez7HnUNlXOenHnGxxj8gRfBFfrHAyWTx04iFkRebvrp1cdGkxaxYW\nGos1riRJsXHHyqYrisKJEyc4duwYf//3f8/x48f58Y9/jMfjYdOmTRw5cmRG+f6RkRG2bNnCPffc\nwx133JH0+2praxPqJWZbF6a3vV+4cIGOjg7cbjc9PT20t7dTVVVFbW0tBQUF7NmzZ8rjLJGFJDE4\nOEhNTQ2RSCRmp6xPIAvt1QDzG1mI7wQoKSnhyiuv5OLFiwu2Q57PNIQkSdTX19Pe3o7JZGL16tVT\nSpxOhE5fJ0dajhCRI7zS8Ao3lt04aRX+VJGFR049wvGO4zz2e4/FwvVzRY27hoASICyFqR6ojpEF\nT8jD6e7TmAwmVmauTPBdaPA0kG5OTyAWU8LrxfD224htbdiHhsj1ejEA8r59MBqyXZmxkpUZK4nK\nUQyiYdby0PGuiIWFhdjtdtxuN2VlZbHdYLwiYHwo2el0xgoo967Yy7qcdeP0HIApnSkn6xK497f3\ncqLrBOe/eB6bKbndZq27lpcbX0ZVVT619lNsyN0w6ZiXu9RzqnA5GkmJokh5eTkOh4Mvf/nLvPji\ni9jtdtra2jhz5kzSioc6br31Vm69debKrfn5+XPanOnRt8OHD/Of//mf5OTkMDw8THNzM1lZWVx1\n1VV89atfZdeuXUkV4y+RhWkQCASora1lYGCA8vJySktLx91kC92ZAPNTsxDvd+B0OhPC8guVGtDH\nSjVZUFWV7u5uamtrcTgc7N69G5fLNatJ+X8a/wd3wK21RrprONJ8ZNLowmQ1Cg2eBo60HKE30MvT\n1U/zt7v/dsbnMRbuoJsadw3Zpmzy7Hk0eBrYkLuBZY5l1AzU4Al7EBGpc9fFohm+iI+jrUfJseVw\n+5rbMYjTTNyqiuGDDxAbGlBKS4lmZRHu7ERsaACbDXn//rg/VXm16VWybdlcW3ztnD+ffkxBEGJk\nYPlokWa8oI/ejz+2Gl4nEjNZnCZKd1zsv8gL9S8A8MSFJ7h/W3LtaM9UP4Mv7ANB+/9/3PePk465\nGHoHi0UWFmvc6dLGfr8/JlYkCAIrV66c1L55PnDllVfGiq2/+c1vTpiamAr6vfvBBx/w61//mlWr\nVnHffffx0EMPUVxcPOPzWSILU6Cjo4MLFy5QVFTEvn37JtUt/12ILHg8HlwuF9FolM2bN5OXl5cw\nSS5EakBHqsmCnk4ZGRlJiArNpp5Ajyrk2fMwikYyLBlTRhcmIwtPVz3NYGhQK7JrPswfbfijOUcX\natw1+KN+0oxppJnS6In2UD1Qjdlgpmqgijyb5vVwvv88FTkVOEwOXAMu+kb6GA4N0zzcPH1v/9AQ\nQmsrSlERWCwQDKKazSjLliG0tMDwMIxWj7d6W6lx12A32Vmfs55s28x2ZJNhIoI3kaBPNBqNkYeh\noSHa2tqIRCIJCpS6hfdkC9ZEZOGf3/tnjIIRSZV4+P2H+ZPNfzJtdKHWXcuR1iNk27IREHij9Q2q\nB6onjC4s1SzML1RVTWpcr9dLenr6gl+XwsJCHnvsMa666irC4TD//d//zY033sibb77Jvn3Je5zo\n533XXXeRnZ3Nu+++y+HDhzl9+jRr1qxh3759bN68maysrKSK1ZfIwhTIyspi586d0/bZGo3GBeuf\n12EwGGKOYXNBKBSitraWvr6+SSMn+ngftsiCJEk0NDTQ1tZGSUkJ27ZtS9hNzNQbAi5FFTbmbQQ0\n62aX2zVpdGEistDoaeRIiyZLnGfPo2GwYc7RBT2qkG/Lp0foAaDQUUiDp4FAJMBQeIiKrApUVBo8\nDdS561idvZqzvWfJteXij/qp7KukLKNsyuiCEI1qWgxjibPZjDA0FNNpUFWVc73nkFSJwdAg1QPV\n7FkxdU40Gczk+zKZTJMWUPp8Pvr6+mhsbERRlFgBpR6FsNvtse8unixc7L/Iiw0vxn52B91JRRf0\nqIJer9E03DRpdGGx0hCXWzpgPscEpo0sLFYnxNq1axOsonft2kV7ezvf//73Z0QWdKxatYr777+f\n+++/n9raWo4dO8bhw4d55plnyMrK4pprruGGG27gwIEDU651S2RhCqSlpSUVMTAajQQCgQU4o0uY\na+ojXmkyPz9/2lZIURQXLHoyV7Kgm1nV1NRgt9vZtWvXhA/9TCMLvSO9HGk5QlAKUj1QHXt9JDLC\nKw2vcKD8AOmWxHEmIgs/r/o5g6FBKrIrMAgGnBbnnKML7d52wnKYkegIHcEOwkNh7A5NYrp1qJXV\n2au1aAoCTouT8/3n8Ua8uINuKrIrcMpOmjxN00YX1IwM1IwMBLcbtbDwUgHg4CBqZibq6GTT6m2l\nfrCeorQiglKQyr5KNuRumHN0YS7Sy1MVUOr1D7oHiCAIpKenx56JUCiExWJJiCoAqKjTRhcSogqj\n555jzZk0urAYu/zFSAfoTpeLRRaSiSykpaUtOHGbCDt37uSpp56a83F0IvKnf/qndHZ28sILL/Cj\nH/2In/zkJzz//PN84hOT+9YskYUpMB821anCbMdUVZX+/n5qamowGAxJK00aDIYFi57MhSz4/X6q\nq6sZGRmZ1sxqppEFi8HCgfIDRJXouN9ZjdYJd+Rjx2jwNHCk9QgWgwV/VGvhsxltdPo75xRdWJW1\nKlaZfy5wjpUrV5KVlUVlXyUfdH2AO+hmMDQIaDoMQSlI01AThY5CREHrEhAEYfrogsWCsnUrhrfe\ngtZWRFnG2t2NsHIl8pYtYDbHogqyKuMwOegf6U9pdCGVk3d8AaVe6KooCiMjI3i9XtxuN4qi8N57\n79EeaU+IKugYCA5MGV14qeElvGEvBtHAcHgY0EiGrMi83PjyOLKwWN0QizEmzN7pcraQJClmjjcV\nLif1xrNnz8YUUmcKn89HU1MTra2tdHd343K5aGxsjPn5GI1G1qxZQ2lp6ZTHWSILKcCHpWbB7/dT\nU1PD8PAwa9asYcWKFUlPvAudhpjpWJIk0djYSGtrKytWrODKK6+cVkp4pqQk05rJ56/4/IzOa2xk\n4VT3KQQETAYT3rA39nqGJYNzfedmdOx4pJvTSTdrUY0uSxdFjiJynbmoqOPElQCqBqo413uOsDVM\np68z9nqjpzEWXQhJIV5pfIVdy3cleDMo69ahms2ILhe0tREqLES65RbUsjLgUlShMK0QT8jD2b6z\nOM1OKhuPs7l5hGzRoSlJrlwJM1z4F0INUxTFmDRwWloaPp+PnTt38tXXvzrpe3565qfcVXZXTE44\nHreU30JR+vjvAGBj7sZxry3GbnuxohmwOOZVyZpIpSIN4ff7aWhoiP3c3NzMuXPnyM7OpqSkhG98\n4xt0dnby5JNPAvDII49QWlrKxo0biUQiPPXUUzz33HNTaiBMBP07vf/++6msrIyRYKfTyfr16/nS\nl77Ezp07ueaaa5K6HktkIQW43MlCNBqlsbGRtrY2iouL2bJly4wc2mDhuyGSHUsXjaqpqcFms02a\ncpgIC6GmOHaMO9fdmaBIGI/J2i9nM6aOEmcJJc6ScX8jKdI4Q6sObwcDgQH07sLzfec51nEMRVW4\nY11cf7ggoK5ahVxejr+rC3d3N2Xll7QTGj2NSKpEp6+TWnctrUOtOEIS+QM1tHsukB/JRXU4kHft\nQr7ttgR9hukwXw6Qk0GvHzAYDHxt99fYsWIHYTlMRI5gN9hjzn35Yj5VVVWxAsr4DoyNORsTJKuT\nGXOmz+dcsZjpgIUmC/EaC1PB7/enJLJw6tSphE6GBx98EIAvfOELPPHEE3R3d9PW1hb7fSQS4atf\n/SqdnZ3YbDY2btzIyy+/zG233TajcfXrWlFRwdq1a9m2bRubN2+mpGT8fJAMlsjCFJjJrvtyFGWK\nN0VKS0ub0UI60Xjz0Q1xvvc8y53LE8RtRFFMKuXh9/txuVz4fD7Wrl07Y0+OhZCVHksWRFFkTfaa\nRak8BxgIDPBm25vcuupWrim6JvZ6VI7ynePfoc3bhiqohKQQ73a+S0SOcLbvLDuW76A4fUy7lSDA\nBDuSrcu2UpZZRt9IH/0j/SzPSsd98X2Wq2msXnUNimgDjwfjW2+hrlgxN3GoeUY8OSlKL+KuDXfx\nS9cvGQwO8vltn8diTCz0jFeg7O/vp6mpCVmWYwWU+j+9gHIiLNYufyEVaPUxF8u8KhmykKo0xPXX\nXz/lpuSJJ55I+PnrX/86X//61+c8ro5vf/vbCT/Ha4fM5NovkYUUYDEiC9PVLAwNDeFyuQiHwykx\nRZqPNES3v5tjbcdYlb2KA+UHYuc3HTGRJImmpiZaWlooLi5m69ats9qJLYQU82IYScHk4fp3Ot7h\naOtR8u35Ce6RJ7tPUj1QTUgK8Wrjq1xTeA2tw62sz1lPvaee9zvfp3jdxL3ZY++rHFsOObYcLvRd\n0MhRyE5+wEF3voiHEOnYICsL+vsRq6pmRBYWOrIwdrx2bztnes7gi/io7KtMIFygqQHm5eXF1PZU\nVSUYDMY6MLq6uhIKKOP1H/R+/o9SzcJiqTcmQ4z01skPO3R5dL1OY7bf8xJZmAIzKXC8XNIQ4XCY\n2tpaent7KSsro6ysLCUP5Hzswi/0XcAT8lDnrmNz/uaYmc9kY6mqSl9fHy6XC6vVmlRb61RYiNTK\nYnhDTHbf9o70cqLrBGEpzNvtb3NV4VU4TA6icpSXGl9CQKA4vZhjHcfoHenFZrJhMpgocBRMHl2Y\nBF2+Ls71nqPAUQB9PWSpVrrUCO/LrZSIWrpFNZlgFl1Ei2kX/W7nu/giPqxGK8faj7Elf8u46EI8\nBEHAbrdjt9vHFVDqHRgtLS2MjIxgNBpxOp0EAoFYO7bZbJ73z6if02JEMy7ndk2/3z9jddfLEan6\nXpfIQgpgNBpjbUAL9cCNJQuKotDa2kpDQwN5eXns2bNnVqYnyY43V3T7u6l111KSUUKPv4cLfRco\nSiuKMd+xC+zIyAgulwuv10tFRQXLly+f86IhiiLR6PjOhpRhcBBrQwOSqkJJCcICtmFNFFk40XmC\nwdAgG/M2UjdYx+nu0+wr2ReLKqxIX4HVaKVusI7B4CC3r9HMbrJt2fSM9EwZXRiLk90n6RnpYZlj\nGQFLCINxBEGyUil0skNZSYmSjjAyglpRMefPNZ+IjyzoUYXCtEIcJgdNQ00TRhemQ3wBZVGRPs9I\n3AAAIABJREFUVvgoy3JMgdLn89Hf309XVxdWq3VcBGI+0gWLVbNwOZOF34XIQvw8Gj/3zGYeWiIL\n0yCZMLL+8EqStGA7AT1UrygKbrcbl8uFKIps27ZtRiYnMxkvlWThQt8FglKQFc4VCAgJ0YV4siDL\nMk1NTTQ3N8+6OHMyzFuKQFURTpxAPHGCzNGctdjcjHrTTURXrpyXMLOqqlwcuMj6nPUTTgR6VGGZ\nfRmiIGISTbzd/jZX5F8RiyrYTDYUVUFRFTp8HZzpPUOGRVNjDMthKvsq2V28m8K06Vu4VFS25G/R\nfrDmIvYHWdbWjsGiICmdiIOgrFmjtVvO8HMuVhpCjyqscK4AwGwwJxVdSAYGg4GMjAwyMjIYGBig\noKCA3NzcWPTB6/XS0dFBOByO2Snr5CEtLW3Oi+5i6CwsltRzsmmIVBU4LiZSeX2XyEIKoOeCFpIs\n6Df7mTNnGB4eZvXq1axYsWLeHr5Uhuz1qEKBQwvxpVvS6fZ3x6IL+lh6ysFsNrNjxw4yRmWEU4X5\nKnAUGhsRjx5FdTiIlJcTCYVQfT7cTz1F5ZYtRDIyZlTwlgxOdp/keye+x71b7yWPvHEkKBZVyNlI\nZV8lXf4uglKQX1T/guqBaqJylAaPZgdtFI3YjDYcJge3rbpUgS0KInaTPeG4k5Gt2yvGWPBuDGA4\nfRrx3DmQJKRdG5C3b4dZGOUsRjeEHlVwmp0xi+tMSyYNnoYpowu+iA+zaJ4RmdDHNJlMZGdnJxgX\nxdspDwwMjCug1KMQDodjRtdpKQ0xHj6fL+VzzkLC7/fz1FNPkZubi91ux+FwxFJidrsdm82GzWbD\narVOamUQjyWyMA2S2X0KgrCgdQu6pgBo/ux79+6dd5KSym6Ii30XGQgMoKgKnpAH0ISC6tx1XJF/\nBdFoFL/fz4ULF1i7dm1KUg4TYd4iC7W1IEmwbBn09RFVFGrDYdK7u7nquuuQt2+PhZz1grf07m6W\ntbWR4fViWr4c065dGLZuTUqHQFVVfl3za+o8dTxb8yz35t6b8PuBwAAnuk4QioY403uGc73nCMkh\nFFXBbrKzs2gnRnH8VFCWUcbNZTen5prY7ch79yLv3TvlnwlNTYi1tZCerpGJMZPYYqUhmoeaMYgG\nJEWKiVuBRnQbPA0TkgVZkbnvt/dRkFbAIzc9kvSYUy3cY+2UVVUlFAolGGjV1dUhCMI4QqoXUM50\nzPnCYpKF6RZHVVXx+XwxI70PI/r6+njkkUfIzc2NrU16B4TBYMBgMGA2m5EkiQ0bNvCjH/1oyuMt\nkYUUYSHaJ+OdE/Wd6KpVqxYkmqHv9lMRBk4zp028E1OhtaUVX7cPURTnnQTNW2TB60W1WIhKEsND\nQ4RCIQqLisgDJKORiM2Gw+Fg2bJRS+gLF+D114kMDREwm4mcPEn0xAkGr70W5dprp3VMPNl9krO9\nZynPLKfB08AZ4xnKSy7pHpgNZvat2IesyhxvP06mNROzwUymNZObS2/mQPkBzIaFiYhNikgE8/e+\nh/GVV8DvB6MRtbSU8He+g3LFFQl/uhhpiN3Fu1mfu37Cv0kzTbygHGk9wrm+c5gGTFT2VV5KyyQx\nZrILtyAIsR2ifj8pikIgEIjVP7S1teH3+zEajePqH/RF86NUsyBJEg7H5LbkOj7skYX8/Hx+8IMf\nxApq9X+BQIBgMEgwGCQcDjM4OBirnZkKS2QhRZjvyMLw8DAul4tgMBhzTjx69OiCRTP0hzoVZGFX\n8a5xr+kpB9WksnbtWlpbW+edBM1Xp4JSXIz/xAnavF5MFgvp6enkOZ0IAwOoY+tJJAnTu+8iAKar\nrkKfwpT2dnJ7e+kSxZhjYjQaTXBMzMjIwGaz8euaXxNRIuTYcvCGvRzpO8In5Esa706Lk1tX3Urf\nSB+/bfwtG/I2kG3NpsHTgMPsWHyiAJiefhrjc8+hZmRAWRlEIoiNjVi++U2CP/85jBaaLUTNgqzI\nuINu8h35sYXbKBrJs+fN6Bg/PfdTZFVGkiQer3ycH9z8g6TeO1cjKVEUSUtLS9gV6wWUegqjr6+P\nQCCAxWLB6XQSDodjOfqF0lu43J0uP+w1C2lpadxyyy0pO94SWZgGi+0PEYlEqKuro6uri9LSUsrL\ny2MP80JKMOsPV6qLkgKBADU1NXg8HioqKiguLmZoaGhB2g1n4zo5HbxeL7WBANkmE6vCYYIWC0GP\nByEYRF2/HmXVqoS/F4aGEHp6UPUog35uhYXYm5pYabFQsmFDgmPi8PBwLNxcO1LL8e7j5NhzCIfC\nFNgLqO928V7NS5Qs+1KCaNIbrW/gDrpZn7seURCxGW281vwaO4t2jqtFmC8IbjdCczMYDNq1cDpB\nUTAePAhms6a/AJoHRXExQns7huPHkW+9FVgY34QnLz7JC/Uv8Phtj8+anBxpPcKF/gtkW7OJKlHe\naH0j6ejCfCyi8QWUOiRJSqh/aGtro6GhAbvdnhCBSEUB5URYzMjCdIRIUZQPPVmASxoL+nVua2tj\ncHAQq9WKzWaL6XvY7dM//0tkIUVIdWRBUZTYw5udnc2ePXvGfaELaWClT16yLKekG0GWZZqbm2lu\nbqawsDAh5bAQyoqpHifeDru0rIzSv/5rjGfPEjp5EkUUUfbvR73qKi0HH/edqUYjmEwI4TBqfH40\nEgGTSVtAmdgxUZZlnn/9eSJEUGSF/v4OTO4BRNnDyx0/4JYjrZhu/Ti23btxh9y81f4W6ZZ0orLW\nLppnz6N5qJkTXSfYv3J/Sq7DpFBVDEeOYDx8GDweLaqTn490++0o69cjDA/DWNfT0ftMGBxMeHk+\nIwvuoJtfuX5Fu6+dg3UHOZB9YMbj6VEFRVWwGq1YVAtd4a6kowsLteM2Go1kZWWRlZVFS0sLW7du\nxWw2x+ofBgcHaWlpiYXtxxbkzvUcUzWXzGbc6UiKz+cD+FCnISBx3n722Wd5+umnaWxsZHh4OJaC\n8vv9/MVf/AXf/OY3pzzWEllIEVJJFgYGBqipqUFVVbZu3RorZhqLhTZ3EgQhJeP19/fjcrkwGo1s\n376dzDEV8QtFFlJV4Njb24vL5RrnTaEWFjJcUUHfwADLd+7U/nisrkNmJsq6dYjvvANpaRqZkCTE\n1lbktWtRp8glesIeekI95DvzUWUZg28ARQqQLljwWgVaG6so+XE71S0tnM73Mzg0iCIqBMNBjAYj\nCJpb5pmeM/NOFsSqKowvvAA2G+q6daiKgtDWhulXvyLyv/83SlmZ1ikRV/lPIKDVLowaVMH8Fzh+\n461vUD1QzfL05Txb8yzbt23HIMxs96tHFTItmbHzTTenJx1dWCwFR73gLTc3d8ICSp/PR09PD/X1\n9aiqGos+6P+12WwzIlayLMcswBcSMyELvws6C6IocvjwYf7pn/6J/fv3YzKZaG1t5c477+Spp54i\nMzMzwbtiMiyRhWmwkCqOgUCA2tpa3G43q1evpqSkZMpJY6E9KebaEREMBqmpqcHtdlNRUTGp6+WH\nJbIQDAZxuVx4PJ5JuzYEiwV1molJuuEGjENDiPX1WtRBEFBWrpzWZCnXnsu/3fJvhKUw4rlzmN7+\nOUppKT2DHjIcaazeUohQXU1WJELhFR9nbe9a/H4/fr8/Zs2clpbG8pzlhMPhpNqnJkIyz4h49ixE\no6h6GkYUUcvKEC9eRKyqIvpHf4SlpgahrQ01KwshHEYYHkbatQv5mkvFsPNZs+AacPFa02tElAg2\nk42+kT5ebX+Vjy/7+IyOc6j+UEKnjw6DaOC3jb+dlizMtWZhpoiXAx6LiQooVVVNUKBsb2/H7/dj\nMBgS0hdOp3PKe+pylnv2+Xw4HI5FOb9UQierL7/8MuvXr+fRRx/la1/7Gk6nk6997WvcfPPNPPTQ\nQ4yMjEx7rCWykCLMZeGOFx4qKipi7969yfW9LmAaAmYfyVAUhebmZpqamigoKGDfvn1TFi/qi/h8\nF7PNtsAxXi2zoKBgyq6NsdGLCT9PVhbS3XcjNjUheDyoaWkoq1dDEgqcugGXYeQ8RsmOas4FJUK6\nOloq6XRi6elhReEKVhSuiJ1/IBBgeHgYr9fLUNcQ79S/Eyt2mw+1QGFoaFwbJIIAoogQCCB98pOE\no1FM//VfiB0dYDYT/cxniDzwwIRmVfOBf/ngXxiJjmA1WOkP9JNhyeCV9lfYmz11u+dYfGX7V/j9\n1b8/4e825W2a9v0LHVnQn4GZdGDoBZSFhYWxY+jtwF6vl6amJkZGRjCbzePuKT31cDnrLOjqjQtt\ncpVq6HOP2+1mxQrt+R8cHIzNV1u3bsXtdnPx4sVpiyGXyMI0mElkIRwOz+jYqqrS09NDbW0tFotl\nxsJDC5mGgNkJMw0MDFBdXY3BYODqq68mK2t6G2Z90ppvsjCbAsehoSGqqqpQFIWrrroqQTBnTmOY\nzSjr1k36a6G+HuPRowgeD0pZGdJNN0F8Z4V+34TDWHt7sbS1IaaloQYCKGvXjjsnfbJfvlzz44gv\ndotXCxzbfTFTsR8dSlkZhnPnUBUF9EUpEkEVBNSCAhAE5NtuQ77lFoS+PlS7fULBpvm6J1wDLl5v\neR2TaMJitDAcGibbmk1/qJ+jPUfZze6kj7U6azWrs1bP6jwWWjYeZk4WJoIoirH7RId+T+n3VVdX\nF6FQCJvNFvPACIVCC0oa9E3IdCTY7/d/6FMQcGn9ysvLw+12A7B+/Xpee+01zp49S3p6Ou3t7ZOm\nuuOxRBZSBKPRmFQoR4fX68XlchEIBKioqJixvTIsPFmYSRoiPuWwZs0aSkpKkv58+qQ135PmTCIL\n0Wg01pVSXl5OWVlZUueWiroIw6uvYn74YW13rh0U43PPEX7ooVg+X964EUNBAYaXXybT7cYgioiy\nDEYjys6doKpTCjzFF7vpiO++6O3tpaGhASAh1Jyst4ayfTvKqVMI1dWaWJUsI/T3o2zciLx5c/yJ\nTFmnMV9k4bFzjxGSQ5hEE2E5TESO0DLcQqYxk3cH3k35eJNBv1cWOg0BqZUGhonvqUgkktCB0dHR\nQWtrKw6HI+G+cjgc8/Ls69HfZGoWfhciC/rn/MxnPsPZs2fp7+/nj//4j/nNb37DH//xH+PxeFi9\nejU79ZqqKbBEFlKEZGsWIpEIDQ0NdHR0sHLlSq666qpZh3oXo2ZhOnKiKAotLS00NjaybNmypFMq\n8YgnC/OJZHb9uhBWTU0NTqeTa6+9Nqk2Ix1JpSGmwtAQ5h/8ACEQ0PL9gqAVQDY0YHrsMSL//M/a\n3zmdKKtXY3zpJVRRRDWbUdPTISMDw5kzSI2NqKtnttsda7dc664ljTSEsBBzS9TrH86fPx+LPkyU\nvlCXLSN6770YjhzBUFsLBgPSgQNIN94I0wjkCL29CK2tmtbCPJDjbn837d52NudujqV1AtEAnpCH\nTxR9gm3Z21I+5mSYr4V7Kujt0AuxMJrNZnJycsjJyaGnp4eKigocDkcsoqWTUlVVxylQzrSAciLo\n89d01/d3wUQqHnv27GHPnj2xn5955hkOHjwIwN13370UWUgFUlXgqCgKHR0d1NfXk5mZybXXXpuU\nith0Y8409TEXTJeGcLvdVFdXI4pi0imHycYBUh81CQa1ne3goLaIFhdPSUhGRkaorq7G7/ezfv16\nCgoKZjxZzTWyYDh1CqG/H7Wk5FJkwGhEzc7G8MEH4PHEtAnElhaUigp8BgNWkwn7smVgMiFWV2O4\neBFphmQhHu6gm79582+4quAqvnXtt2KKb52dnXR2dpKRkYHX66Wzs3Nc+iK2U1yxAulP/gQpENBS\nEdNVwkejmJ56CsPhw7Gah5KcHHxf/CKUls76s4zF8Y7jjERHUFFxh9yx100GE+6Qm5K0kpSNNR30\ne2Wh0xCLJY5kNBrHtQSrqpqgQNnR0YHf74+5dY5VoJxpB4bRaJz2PXpk4cMOvZjzoYce4o477mD1\n6tXIsszKlSv5yle+AkB9fT1Op3NaEbwlspAiTEUWBgcHcblcyLLM5s2bYw/FXHG5pCFCoRA1NTUM\nDAwk1cUxHXT98pRGFvr6EJ98EnG07UsA7AUFWNaPl/BVFIWmpiaampooLi5m69ats+4Hn3MaQpK0\nFMLY62kwQDSKIEnEjh4Og8mE7HAgW60xjQZgfMtmPHTCOUUE6FD9IVqGW/CGvXx2/WepyNaspUVR\nxGg0snLlyrjDhWM7xb6+vthOUZ/oMzIytEr5aVIKxldewfjss6hZWSgVFRAM4nC5sDzxBOiaFSnA\ntcuvJcs6MbGVBqRFSQks9JiLQRYm64bQO3UcDse4Ako9hTG2gDKeREz1rEqSlLSJ1IddkAkuGQ5+\n4xvf4Prrr2f16tXjPv/GjRupqqpizZo1Ux9r3s7yI4aJyEIwGKS2tpb+/n5WrVpFaWlpSh/KxSAL\n8ePFdwUsW7aMPXv2pKxvOpXGVQDiiy8i1NSgrl0LZjOqJGGoqmKZ2w133hlrUXS73VRVVWE0GlPi\ndDmWLKiqOjF58PkQGxoQurvBbkcpL0ddsQJl61bUzEyt6K+gQD8IwsAA8s6dqHHhQ+XKKzFUV4PF\ncolAeL2oZrPWXTH23Hp6tLTAxYtageGWLcg33phwTNCiCs/VPkeGJQNvxMsvXb/kW9d+a9LPPDZ9\noe8U9e6LlpYWRkZGMJlMCdGHBKlhScLwP/+DarWi6uTa4SBYXExaczNCZSXKNeP9RYTubgyHDyPW\n1KDm5CDv24dy9dVT1msUpRdRlF7EcHgYu9GOyXBpsakJ1fxO1A9MN+ZiRRaSHTe+gDK+KDe+A6O7\nu5tQKITVah3XgRGvQJtM2vd3hSy88cYbZGZmkpaWRm9vLy0tLZhMJsxmM2azmeHhYWw22zitm4mw\nRBamQbITRfxCGq9OqOft50N8ZDG7IdxuNy6XCyCproDZjJUystDfj1BTA8uXX9ptG40oJSXYKyuh\nvZ1wYSG1tbX09vbGCjJTMYEmFVnweDC++ipCWxvYbAiRCOL588h796JceSXS3Xdj+ulPEZqatPMP\nhVDz84ned1/CIijv34/h9GnsJ08iZmQgmEwIkoR0ww0o8UWEAG43pp/+FLGpCXV0UTe++ipic7PW\nrhg3UR6qP0TPSA/lGeUMh4d5o/WNhOhCMtdA3ynq6QtZlhO6L/RKebvdjtPpJNNopGRgAMMY1z/V\nZEKQZQSPZ/w4jY1Y/uEfEFpatKhDNIrxyBGiX/wi0h/8wZTnGJJC/MfZ/6AiuyLBXnshvCji8VFx\nfxwrQzwbGI1GMjMzExa6aDQau6d0T5VIJBJLi8WPP9V19vv9MbL7Ycbf/d3fAdrn+e53v0t6enqM\nKFgsFlwuF5s2bVoiC6lCMhO+0WgkGo3GWiFNJtOc8vbJYCFtsUEjJ5FIhMrKSvr6+lK6qI5FSslC\nNKqF88fk5ASzGUGS6G5tpaqhgZycnJQTu2TuHcOFC5oY0Zo1YDCgAkJfH+LJkyhlZUS/8AWt9fDV\nVxF7e1E2bCD6yU9qfx8HNSeHyNe+Rt+TT5LT2oqSl4e8Y4dmCz1mN2U4fRqxqQllwwat2BCZ1hyB\n8tpaDOfOIe/bB1yKKqSZ0jCIBrKsWTQMNUwbXZgOBoNh3EQfn77oHRrCbDDgaGoiqiiYzWZMZjPC\nyAiq2Qyj4el4mJ5+GqGlBbWiIhYpErq6MD39NPLeveP8N+JxtvcsLreL/kA/1xZfGzONWmiysFjq\njYtBUGD6roSZwmQyxQoogZinik5M+/v7CQaDvP3227ECSj2FoTv5ghZZWDXGx+XDiC9/+ct4vV5a\nW1vZO2oPHwwG8fv9SJLEgQMHeOCBB5JKsy6RhRQhFAoBUFVVNamaX6qxkJEF3eZ0aGgoJkQ0n1Kt\nKSUL+fmoBQWI7e0JC6zc0UE4I4P2YJArtm1LWS1JPCYkC9EoQlMTgterRRJqazWZ47iJU83LQ6yv\n18hBZibyddchX3fdtOOpOTm4b7oJKSMDS0lcYZ7fj+H99xEbGlBtNi2iYLXGxnTRz+umRj5uN7Gq\nvT32tkP1h+jwdVCUVoQvokng2oy2WHQhndQVgY1NX4j33Yfh//5f5MFBgmlphP1+jAMDdG/aRK8k\n4WxujnVfmEIhDOfOQW5u4nUsKNCu4/nzyDffPOG4ISnEm61vYjPaGAgO8E7HO7HowmIIJC10u95i\nkAX92Z7viEa8p0peXh5msznWLqgT087OTmpraxEEgZdeeolQKMTw8DCSJM2JLL799ts8/PDDnD59\nmu7ubg4ePMjtt98+5XveeustHnzwQaqqqigqKuLrX/86f/Znfzar8QH+8A//ENB0Fj796U/P+jiw\nRBbmjGg0SkNDA+2jE+yOHTsSrGHnEwtFFgYHB6muriYcDpObm8uWLdM7580VKSULRiPqLbegPvUU\nQnU1cno6vo4OvOEwfXv2sGP//nmzwx5HFjwezAcPYmhqAv3z9fWhrl8P+fng92umUqPtmeosJvFx\nk9vQEOYf/hDD+fOa9LQsIwwMaMWQq1cTFRQ+oIMGBjllEilNu9Slc6rnFFmWLILRYOw1o2DEIBqo\n7KtkT8aeeVvclOuvR/B4MP/Xf2FtaQGbjc6rryZ4771k5eYm5KnTBIFtfj9GoxExGsWkV7yrqla/\nMdl1HBnhbM1rNPa5WLVsPZ6Qh3c63olFF5YiC/ODhWzXjIfeHWC327Hb7RSM1gHpm6GmpiaOHj3K\nxYsXOXr0KD/+8Y/Zvn0711xzDZ///OcpnUEXzsjICFu2bOGee+7hjjvumPbvm5ubue2227jvvvt4\n6qmneOedd3jggQfIy8tL6v0TQU8xffrTn+bFF1/k1KlTpKen88ADD2A2m2O1GcmQtiWykAQm2h2q\nqkpHRwd1dXU4nU52797Nu+++u6A3/3yThXA4HMvjr169GkmSYhGU+Uaq/SHUK69Esdvxv/Yag5WV\nyGVl5H784wz6fPMuKR1/7xiOHIGaGpQ1a7S8ejiMob0d4f33Ubu7Ebu7Y2kTZfXqS8V9M0T8mMYj\nRxArK5ErKi65WNbVYbh4EaGmhpq1mTQxyPohIzXOEHVlGejxl4euf4jh8HD8gTG8/TaG//kflv/i\nKQLFbzGyaxdceeWsznMqCJ2dGN97DwFiRZfWri4y2trI3nZJ+yASieD1eglt3UrakSN4DAbU0S6N\nNLcbMT2dQEVFYveFLGM8dIjo4Vc4ZjuN3RbFVhTFtGkzVb76WHRhMXwaPgo1C5eb1LPelnnPPfdw\nzz33sHv3bv7lX/6F0tJSTp48yQcffIDH45kRWbj11lu5ddRaPRn85Cc/oaSkhEceeQTQlBZPnTrF\n97///VmTBT11/Pjjj/Pwww8jSRJer5cvf/nLDA0N8ad/+qdcffXV0zpOwhJZmBU8Hg8ul4toNMqm\nTZvIz89HEIRFqSGYj/Hi7bFzc3NjKYfm5uYFdblM5VjBYBDXyAieDRtY+6lPsXL5co2MHD48r06G\nCWTB7UZsaEAuKrrU9mexIG/ZgumFF6CzEzU7W6svUBREtxuxpgZlx44ZjxkPw/vvozqdCTUb6po1\nqN3dSF4PJ7vrsZhCZFoL6F1byklDN+WKjEE0kGZOI818KVJm+tnPMP3Hf2g1IDYb9qZmVr//PobC\nQuT9STpXDg1hOHECsasLNTMTeccO1NEK93gYf/MbxLo6lPXrtWuiqgjnzpHxwguwf3+sCFN3ShT+\n/M+xDAxgb2xEBuRolIjVSvP+/bQ0NGBsaYlVyBe8+y6Zv/417xdEqMuQKA1YiLouooSDZG4ujUUX\nFqPA8aOQhphJJ0Sqx52uG0JVVfx+P/n5+ezevZvdu5OX+p4L3nvvvXH+DAcOHODxxx8nGo3OuH1b\nv3fr6+t59NFHefjhh9m6dSv79+/HaDSSm5vLzTffzPPPP79EFlKNUChEXV0dvb29lJeXU1pamsBS\nF1pR0Wg0ptxwyePxUF1djaIo4+yxU72AT4VURRbGtnfGmz4thFJkAlkIhxGiUdSMDOK/LWHUL0G5\n4gpIT0c1GlFzchA8Hgzvv49y1VVaGH0Gk2syBEjNzeXC7++k3lRDubWIaOFyCs0iDZ4GmoaaWJOd\nWEAp9PdjeuopMJlQi4sBiGZkILa2YvrP/9SKIqeZiIX2dswPP4zY0KDpR6gqxhdeIPLnf57YCjk8\njHj+vNYuqh9TEAgVFGDr7UWoqRnXOqmWlBD63vcwvvEGYmMjQmYmxj17KNu4kRJZjrXZ+fr6iDz/\nPP2BAG85giBBm03CYAJxoA5lOA1LRi6uARdO1fk7H1n4qEQzQEtDJKMouxitkz09PTFnTx3Lli1D\nkiQGBgZimhPJQl8XWlpaEASBO+64g0OHDiUIWRmNRgYHB5M63hJZSAK6SE9jYyP5+fmTFvcthgsk\nJN87PBXiUw6TaUKkWvtgKqRirHjTp23btsUqpHXoD8x8k4XY8XNyNBLQ1oahowNDTQ1IklZoKAgo\nmzZB3O5BlWXE9nYML76I4PejOp2o69drmgkzmNzl7dsx/eIXyNFo7PjCwADRNDvvFylExFy8ablA\nGCQISAFOdp+kPLMcg3hpQherqmBoSFOTvPQBkTIysLa2IrS3x7wqJoSqYnrmGS1asHZtLFogNjZi\n+tnPCG/aBLqU9iiRGPc59dcnI0M5ORO2SRoMBjIyMsjIyEAwGLCYzcirVvF5VaV/aIRINEo0HMbR\n1UX3mm1QvpWVhpX0S/3JXOKUYbFqFj7qaYixWCwFx7HENBVeIZFIJLY+6G3M+j2my/IngyWykAR0\nQ6Tp9AQWIw0ByfmzTwZFUWhvb6e+vp6cnBz27NmDbRJr5IXsvphLZGEmpk+zcZ6cCRIiCxYLyrZt\nmH72My0Er0c4QiFtkezsTJAxFjo6oLcXsaUFsrMRurqgrQ38fpRtk/sVjJ1YpP37EauqEC9e1FIR\no22kg7fdQDRLJj9qRFIu3bfL7MsISkECUoB0c9yEabFonQaSlNBxIMiydtzpumMGBxG4iBmTAAAg\nAElEQVQrK8dFC5SSEsSWFkSXS4uiAGRkoKxbh+HddzU569HvzzwwgJKTgzCN2txUUJ1OSEvDEAiw\nPLOI5RatvVn1elGdJvLX7cbtyKa/ux+v18vIyAgDAwOkp6fH1Cdnq+g5HRYjDbEYKYHFICiQ3FwZ\nDoeJRCJzFmSbKQoKCujp6Ul4ra+vD6PROG6jkwz07/TKK6+krKyMb3/725hMJkRRZGRkhBdeeIHX\nX3896W6LJbKQBCoqKmISxFNhocmCXk082wVcTznIsjwu5TDZeJczWYg3fUpPT0/K9CllstJ+P+I7\n7yCcOaNV4G/bhnLttQhjyIjQ04Pg96OM1rmoZjOq3Y7Y2IjxzTeRPvlJsNsR3G7EtjaUsjJNN0B/\n/8AAYmWlViA5xc4ngQDl5BD5q7/S6gRqa8FuR962jYytW7lHlVHU8Z9fFMQEJUMAeetW1BUrEFpb\nteiCKIIkYRweRj5wAHWaMKkgy6Ao4zs8DAatMyT+2REEpE99CrGtDbG6GtVm09I4ioLvtttwzkUE\nLC0N6YYbMD3zjJZSycrSWks7OpB37yZnxw5yRp/1U6dOkZOTg9FojBkdBYPBmM1yvEpgKhbcxUpD\nzBf5mQyXc2TB6/UCLHgaYteuXbz44osJr7322mtcffXVs/5+VFWltLSUL37xi3zve9+jv7+fSCTC\nzTffTFVVFV/60pf40pe+lNSxlshCEjCbzUmRgIUmC/qYM13AI5EItbW19PT0zMhueSHTEDMlC7M1\nfUpJZCEYRPyP/0A8eVKLEAgCwvnz2r977kk4vnjxoia8tGoV6mitAoAyPKzpL4yMIAwOolitKOXl\nKGPaVNXsbITGRgSPR3OVnAATfu6MDOQDB5APHEh42dzbj+H4ccTaWlSnE3n7dpTt2ydOc9hshP/m\nb7B85zuaSqIgYJIk/CUlWP7yL6e9TGpuLsqqVRjOnkXJzIypTwpdXdrvKhIVIdXVq4n87d9iOH4c\nobERcnJocTrJvu465jqNS7ffjjAyguHYMcTGRlSbDWnfPqJf/OI4aWiHw5GgwRGvEjg4OEhLSwuS\nJJGWlhaLPMzWJfGjVLNwuRY4+kdbcCeLsCYLv98fs3UHrTXy3LlzZGdnU1JSwje+8Q06Ozt58skn\nAfizP/szfvSjH/Hggw9y33338d577/H444/zzDPPzGr8+Fq222+/nZtuuoknn3yS+vp6bDYbjz76\nKNu3b0/6eEtkIYVYDLIwk9SAqqq0t7dTV1c3bcphrmPNFcmSBb2epLm5meXLl8/Y9CkVhZTCqVOI\nZ85ogk96KD4cRjh7FtPoYq8/uGp8cVXcZCkoCkp5OdE//3MYGUG12zG+/DKCLJNAZSIRre5gzGcU\nGhsxVFaiOp0IubmJ40x23h0dmH74Q8SWFlSHAzESwXDyJNLHP67l/SdY6JQdOwg98QSGI0cQ3G7c\naWm0rFnDFVPVKsR9Xumzn0Vsb0d0uVAdDoRgEGw2op/5TMw9Mx5qYSHSZz4T+3nk1ClyUrHIWK1E\n770X6WMfQ+jtRc3IQF25ctxnnigtMJFKYDAYjBGIeJfEsd4X0+l5LNUszC+SMZLS7ann+j2cOnWK\nG264Ifbzgw8+CMAXvvAFnnjiCbq7u2lra4v9vqysjN/+9rf81V/9Ff/2b/9GUVERP/jBD2bVNqnP\nN52dnbz11lt4vV42bNjAAw88MOvPs0QWkkCqbKrnA8l2YAwNDVFdXY0kSWzZsmVWuueXWxoi3vTp\nmmuumVWOcc6ukIBQX6/9T3zO3mIBoxFDfT2sXh17eJX9+xGffRahowO1qEgjDB4PyDLy/v2oOTkw\nuggpa9ZgeO89cDi0Y0sSne1V9BdmsEnf6UajWL71LYwHDyIEAqgGAxszMxk+cABTdjbk5CDv3Imy\nceO4hdD42muarfWGDSCKMZlp4+uvayZVK1ZM+HlVkwll2zbUjAz8qorc15f0tVI2byb87W9rHQv1\n9cjLlmkeGFdfndT7VVVlZERgAmsITCaYqR6aWlBwyaBrkvGme/4FQZhQ5Cfe5Kivr49AIIDVak2I\nPqSlpSUsXh+l1snLOQ2RCmG966+/fsq55Yknnhj32nXXXceZM2fmNG58F8RXvvIV3njjDSwWC4FA\ngP/zf/4PDz744LTp2YmwRBZSCIPBQFi3+13AMadawCORCHV1dXR3d1NWVkZZWdmsH9KFTkNM9rni\nOzfm6k+RkhZNi2Xi6nxZjhEIfdJQt28ncvfdWJ5+GqGuThMcMpuRr7+e6Kg0qw5l61YEr1drM5Qk\nFFRey+inIydKYWSIHFsOpscew/iLX6BaLKj5+QihENb2diz/7/+h7NkDgOHNN4l+7nOJKYhoVGtN\nzM1NiHCoeXkILhdiYyPyWLIQDmP81a8wHj2qdWfYbGRVVOBOQoY6HuqqVURnqbsfCIi89FIaMD56\nlJ4Of/AH0RkThqkw27bk+KiCjqnSF/rfhkKhpQLHeYKqqkmlIfROiIX+HlIF/Z79yU9+QltbG9/9\n7nfZunUrv/zlL/nhD3/Iddddx969e2d8by+RhRRioVsnpxozXmEyKysrqWK/6aATk4UQqhFFkWg0\nmvBa/GfKzs5OiT9FrMAxGkWorNSsoHNzUbduHWc8NRnUTZvg8GEYGNC8CQAGB7Xiuc2bweNJ2GFE\n778f9dprMbz9NkSjyFdcgXLDDeM1Cux25JtvRtm0CcHnwxXpxOXuIaj4Od19mlvKbsb09NPaYq/X\nLwSDqKKIMLpDVdatQ2hvx/jcc8jbt2seFKC9x2CAYDBxTP08J5jIjc8/j+m551Czs1FKShB8PhzH\njlE4NAR79kxpAz0hFAXx/HkEt1sr5Cwvn/YtkiQwMmIgOxtstkvXNBgU8Pk08ctUIpVpgYnSF6FQ\nKMF5Uy+u06vxk01fzAWLFVmYa7v3bMaE6f0ofpfsqT/3uc9x//33A1oB5aFDh+jq6gJmToSXyEIS\nuNzTEGPJwvDwMNXV1UQiETZv3pwyg6R4EaP53hWMjSz4fD6qqqoIhUIp/0xCXx+Ghx9GuHABQZI0\nUaT165H/+q81W+tpoG7ejHLrrQivvYbQ3Q2CgGqzoRw4oJGON96I7Wrq6+sZHBzE6XSS8dnP4nQ6\nsVqtk99jBgNqcTGyqnDi4gcgGiiwF3Cq5xRXZW3EMTgYa8FEURDCYRSjEUGSIBDQzq+oCLGuDkNd\nHfLOnbHjyjt3Ynz2Wc2iejQ6IrS3a8WG69cnnoffj/HoUS23P9qXrebkEA2HSa+p0TokJpDCFbq7\ntQLF/n7U/PyY+6PQ3o7lm99ErKrSvDAcDqSbbiLyt397SWthoms9SmZsNhWHI+E3hMOpJ7DzSYwF\nQcBms2Gz2WK97vX19YRCIbKyssalL8Z2X6TqGfyo1Cx81MhCT08PV1xxRcJrDocjRtJmShCXyEIK\nsdhkIRKJUF9fT2dnJ2VlZZSXl6f0gdSPtVBkQVEUJEmisbGR1tZWVq5cyapVq1K6IxEAx3//N8Kp\nU1Berhk4BYMIlZUYfvxj5H/8x+l3zKKIcuedCFu3ag6SgFpRgVpRgTC6uLndbmprazGbzRQWFuL3\n+2lra8Pv92MymTTyELeTHHt96wfrqR2sZXn6cqxGKy63i9OeixSXlyNWVaHGx95H0yqqXjA4aqak\njtVfuPlmrTDy/PmE90h33hnzYohdp+Fh8Ps1Oeo4KGlpiB0dCG73OLIgXriA+fvf1/QhRBEUBePL\nLxN58EHM3/ue1hWRn6+1RXq9mJ5/HrKziYwWgk2OhTV2WmgjKavVSvGoQiZo6QvdYnloaIjW1lYk\nScLhcCTcM/EWyzPBR6VmQZIkRFGc9rMuliBTqjEyMsLhw4djRZ0lJSX09fUxODhIf38/JpMJi8WS\ndJH7EllIIRardTIajdLR0UFtbS2ZmZns2bNnzimHiaA/ZLIsz3tftsFgIBgMcvz4caxWK7t27ZqX\nB9jqdmO+cAGKii7taG02KC5GuHgRGhoupSOKiycMz8ujPgrq2rWoa9cm/C46arx14cIFKioqKC4u\nJhqNJlxLfSEYHh6mvb2daDSasBCkO9N5r/M9UMFu0s4xz57HqZ7TXHPvH7L863+PMDCAmpaGKgiI\nkQhSTg6UlFyKFixbhrJuXeKJZ2YS/cu/RDl7VhOAstmQr7hC6woYAzUzU+u0GB5OICaiz4dkt48j\nF0gSpscfR+jp0cYdJQtiXZ0m91xVhVJQoF3r0XNRo1GMhw4Rue++STUk5lNAayIsdMGhqqrjFlGT\nyUR2dnZMEG6i9IVusTy2+yIZaePFqFm4nM2rPuxkQb9fKyoqePXVV3nrrbdQFCWWsv73f/93fv7z\nn2M2mwkGgxw6dIisCTqRxmKJLCSByzkNIUkS/f39CIKQYGo1H5irCFSyCAaDdHR04PP52LhxI8uX\nL5+3z2SKRLR2xLHs2mqFpiYMjz4KutNmaSnKHXdodtL6uUaDfOvNb3HbmtvYX3rJSEkXiHK5XABs\n376dzMzMS8WUgQCGs2cxNjZisVjI3rwZZdMmVLQCTp08dHV14ap0cXTgKCaLiYAvgNlixmK2MBgZ\n5IOtV3PbP/0T5h/9CKG3V9NCyM4mmpmJ/fx5LSWSn4/0h38IE3WL2GzIyRjlOBzIN96oeUMIgqb3\n4PNh6ulh8MorscRLQANiUxNiczPKihWXCihFEWX5cgx1dQgjI1o3SBxUmw0hEJhSQ0Lb6U9/uqnC\n5WgkNVH6QrdY1glEU1MTIyMjWCyWhOjDROmLxdJ2uJzJwoc5DaHfP//6r//K0NAQwWCQQCDAyMhI\nLEoVCAQIhUIMDw8nvbFcIgtJIpkWu4U0kopGo9TX19Pb20t6ejo7duxYkIdvPjsidLfL+vr62OQW\nH46dD0Ty85GzsqC/X9uJ62hrQ3C7ob9fK7xTVYSaGsTHHkP++tdhVK3wjdY3ONl9El/Ex+7i3ViN\nVgKBANXV1TGyc+7cuYQdnuzxYP7ZzzBUVmoP9qj7pfTxjyN98pNYrVasVmusLsMx4MDf5CcQDBAM\nBgmNhIgORcmx5NDV0UXr3pvJuOUW0gYHEZxOeg8eJOfFFxF8PlS7HXntWuQxucvZQPrkJzXFxtdf\n1+SqbTb8N95Iz9695I1d4EbVGseJO4mipgFhtYLfnxBBEHw+rbh0mrZeQRAIBgUgscBxPrAYZGE2\nC7dusZyens7y0Tob3Y5YT1+0tbXFolbx0YfLeZefSiQri+/z+VJWE7WY2KnXJ6UIS2QhhdDDPPM5\nwaiqSmdnJ3V1dTidTkpKSohGowv24M2XMNNY06doNEpzc3PKxxkHhwPfzTdjP3gQoaEBNTMTvF7o\n7YXsbK2bYfS7VNetQ7h4EfHkSZRPfIJgNMjBmoOIoki9p56jzUdZb1xPQ0MDhYWFbNmyBZPJxNCQ\nleZmsFoULOdPkv6Ln6LUXcB75W7EzHTS0jR9A8MrryBt3gxj2grX5a5jXW5iCkGPPugSxPVeL4Ig\nsOL4cQpefpmI1Up4wwaMkQiGc+fgZz8j+pd/OWEaJWmYTEh33YX0e7+H0N8PmZl4IhHk/vFmS0pZ\nGUpREWJnJ0p5uXYNVRWxu1szkdqwAeMbb6BGIlpEwetFiEaJ3nXX+ChPHAwGBYdDIRxmXEFjevo4\nrao5Y6FFklK5yzcajePSF/H3TU9PD3V1dSiKQk1NDVlZWTNKX8wFl3Pq48OehpgvLJGFFEJnrfPV\nFuT1eqmuriYUCrFx40by8/NpbW0lOLb9bR6RamGmyUyf+vr65h7BGB5GfPVVhKoqSE9H2b8fddu2\nhIJFQRDw3XQTuStXIr70kqbmt3IlrFyp7XzjSZ8gaPULvb2AFlVo8DRQnlVO82Azjx1/jC+XfznB\ncKynB374gy2US+18s+lLrPefQAFEwOyq5u2CP2DLHSXYc3MRXC5Ul4voihWxlI8gCBNOqhaLhby8\nvJi4lqIojPh8mF96iSjgz87GMzioydamp+P44ANCZ89i3bZt7pN0ZqZGqgA6OycmxlYr0t13awqR\nNTWxFIOanY30uc8hr1+P+uijGA8fRvB6UTMzid51F9HPfW7Koa1WidtvD2K3j28lnI0o03RYjALH\n+VpEBUEYF7WSZZm33nqL3NxcgsFgQvpibPdFKue0yzmy4Pf7P9RpiPnCEllIEsmkIfQbcS4ukBMh\nGo3S0NBAe3s7paWllJeXx46/GLbYqUhDjDV92r17N464XrgJxZIkSfMI8HjA6URdvXpyLYTubowP\nPqgRBW1AxN/8Bvl//S+UP/mThHFUQL3lFuSbbtJ0B2w2xIMHEX79a013QF8sVFWrb8jPj0UVTKJW\nR2AJWugVewkVhRJ2coF+P5/pfJy7PD+jJNKkjTk6tpEo1/X8mqHAX2BMtyKIIuIoOVBVNeHzjyUO\nYxcUURRJNxiwBIMM5eXhSEsjKzOTcDhMKBwm2tVF0/vvM+D3J7gnZmRkzNsuUt67FzUnB8PRo4jt\n7cglJcg33hgrtIx85ztE/+IvYHBQq19I7IWcFGlpE5dfpBqqql6WNQvzgaKiopiWgyRJsaJbr9dL\ne3s7kUgkQTzK6XTicDhmfa6Xc+rjw16zMF9YIgsphCAIKa1bUFU1VumsL6hjZUgX0q8hVeMlY/o0\nLoLh8SD+6lcILpeWDx81Y1I++1mYIL9o+K//QrhwAbWs7FJsuqcHw+OPo1x3HYx6GSSQElGMLVjK\n9u0Ib72FUFurOSzqXQUFBShXX80brW/g6neRIWcQMUVYXrgc2S9zqO4Q+0v3YzVakWWZ9Bef4Ybg\nUYoireMa/gTAgIRYdQ5FXIUxLQ3D+vWIFguKosQIg/5f/V/8NUogETYbamYmhv5+JKcTURS1QjhA\nzM1l8759+MvLGR4exuv10tzcPK4ILiMjY5wE8VygbNigyUlPgnh562SwkN0Q+lgfhpqFuYwHidoD\nRqORrKyshAr5cDgcu296enqoH5U4T09Pj5GHZImnfj9fzmRhKQ0xHktkIcVIVUeEz+ejurqaYDDI\nhg0bWLZs2YST1kKThbmkIXTTp6amJoqLi6c0fRrrBim+8grCuXOaWZNuV+z6/+y9eXRkd33m/bm3\n9iqV9larWy2pd6kX9Wr3apsJix2TmXhIAj4JwYwDwxCTmWEYTiAZHAIOYcs7OATMMnNmeJMMYAhr\n3pADTsjEbbxgG9vdrbW173vt66177/vH1e/qllSSqqRSSXb0nKNjS12le1VV9/6e3/f7fZ6nE/mH\nP0R717uy2wWqivzTn6KXl2c3sevqoL8f+dln0RbIwooVo6YmtHe/G/k734GREcCwKdbe+lbSu3bx\nV9/+K4KRIJpXwyW7mA/No+oqI+ERnh19ljv23YEeCFD2wlNE7VU4yP2aaUjYhvuYdWaYv3yZeDxO\n5fAwFRUV+P3+rNfHShjm5yGV0tHNeGkVSZKoPHsX7vZ27DMzUF5uJGIOD6O1taG3tuJzOPD5fOxd\nUCIsHYITGv6li8CqxlElRCl3+ltBFraikgFrG/S4XC7q6urM9oWR0REzVTuDg4NEo1GcTucy9cXS\nKmsuglIK5FPx1XWdSCSyrpyZ1zp2yEKeyPcC3mhlIZPJcOvWLUZGRmhubub8+fOrfsBLqcAQx1tP\nG2Jubo6Ojg5kWebChQtUip73KscxScncHFJHB/q+fYvDby4XenOzEeI0Pp7ttKjrRvVh6Xsmvl+y\nO1/p79FPnUI9dgwWkuH0pibGp6fpunaNe+rv4W1n34bTYZRudXSzbN1abZTZ7bEYeipB2FZH1FaO\nTw0vqy7Y0FHveCOVD70D/cAB5GiUubk5+vv7jcqE309lZSUVFRXmoj0/D3/+5w6CwYW8CV1fcGnW\nKfO8kd85105zzy+grw9cLjLnz6P89m8j5SBmS4fg+vp05uYUwuEoExNRotF54vFRysslWloWzaOK\n3cMuBK9lslDqyoKqqmZ1qhBIkkRZWRllZWVZxNPavhgdHSWVSi1TX4h2x1YMOOZT+dhpQ+TGDlko\nMtZbWRA9/O7ubnw+X86Ww0rH285tiPWGPpmZDWD4HKRSsJRguN3GDIHwQRCw29HuuAP5O98xzILE\nDmZuDsrK0C0Jh2vOojgccOgQ8Xic9pdfJhqNcuLECd5Q/wbzIcLK2bq4SJKEXlOD6q+kQp2no+IS\nF+Z/kvWrM8iMuQ8T+8ij7D9ipwaosezc4vE4oVCIUChEf38/0WgUl8uFqu5ibOwgfr+DqirHwt8A\ns7NJ+odCjP/KPTT/zttIzs0Z0sl9+4wWSzptnluu4cn+fnjnO71EoxJgfa11PB6VP/uzISRpltHR\nUbOHLchqLBZbt4NgIdiKNsSrVQ1R6uOt1L6wqnZ6e3vN17W/v9+sQrhcrk3/7OQzeC78KnbIwnLs\nkIU8UYgxU6GLt2g5xONxWltbc/bwV8J2bUNsNPRJVDB0XUeqrUWvrTXyBaxDcNPTRjDSgjGNFeo7\n34n00ktI/f3GEGQmAw4H2m/9FvrRo1l/z2qVEk3TGBoaore3l71792a1TkQlQbQGxAyBCZ+P9Bvv\noeypvySqeXnZf5Vj0Rdw6Sk04OmqX+HLp7/In/iXX4aSJOHz+fD5fDide6mslMyd29BQnFBIIZ0O\noihJnE4XmqYSj4Pfv4uTJ2so2yMZrRRNww4mmbHOQFiPJcsy4bCNaFTC5dKz0raTSUgk7Pj9DbS1\n7Vn4meEgODY2RiqV4vnnnzeTFq1l6M1w+nwtVxa2Qqq5me2ApaodXdeZmZmhvb0dVVUZHBwkFouZ\nlufWr2JXroTt8WqIRqPour5DFnJghywUGYVUFjKZDL29vQwPD9PU1LRmyyEXSpkEKY63VhuiGKFP\n4oap6zqSy4X+S7+E9PjjSLduoVdUIIXDoGlo99yTWy938CCZL30J+fvfR37pJfSqKrQ3vQn9jW9c\nJp1cifyEQiHzpnbb2bNU1dQsei5YSII431yvv/+BX6VRlnH9w4+xhXUS7l8j0HKS0K/cT+3uvfyJ\nW6e+fuXXYXYWPv5xB6GQhBHL7CGZhN5eCb+/mosXAyQSc9jtdmw2O/PzIZ59tpeDB71m62Lpor10\naFJURlTVID8ul4bPJ1leJomlyetCgpdOp5Flmba2NqLRqDkENzExQTKZxOv1Zg1PbmSCXrzupcJW\ntSFKebxS+x0I+abD4aB1QRVjtTy3EtCl7Qufz7ehc81nwDESiQDskIUc2CELRUY+ZEHXdSYnJ+nq\n6sLr9W4o90B8+EsV+bpaJWPN0KdMxohudjqXtxSWwJpwKcsy+oULaC4X0jPPGF4IBw+iX7yIfv78\nyr+koQHtfe9jNWqzdJBS/B2CxB1LpWjq6MD2139tRDP/0i+Red3r0BdI00okwYTNRvk774O33W3Y\nGJeX4ywrw7gVrb3wKYpEKCTh8ehmdEU8bnQVgsEUwWCE5uZ6fD4v0ajRmTl+3InLFTAHFhVFMeWS\nFRUVVFZW4na7s4LBjFO1WimLOQhRQTEqSpqWe+crqgrWm2w6nWZ8PEwgEGV6eo5odAgwJuirqsrY\ns8dfcPxyKQcAxevyWp5Z2A4hUjabjcrKyqw5Jmv7Ynp62mxfWAdv10xszXHcte6RkUgEr9e7ZfM4\n2xk7r0ieKFY+RDQapaOjg1gsRktLC3v27NnQzWizjaCWQpblnH/f9PQ0HR0dK4Y+STduID35pJFf\n4HCgHzuG9oY3wAoBJlayIKCfPo1++jQoCtjta6dB5vn3WI8xMzNDR0cHLpeLO91u/P/n/0A4jF5T\ngzQwgNzdjTQ+jvb2t69NFKzweIx0xXXC6xUFFJ1oNEoy6QAcOJ0NRKMS0ahheZxOQ0VFBfX1xjS3\nCB0KBoOEQiGGh4dpb2/H4XCY5EF82e02syUhy5hDkwKappLJLC6ga817pNNOnnyynnBYWni+Tjqd\nJplMYrdHuXhxAF2P4PF4stoXZWVlqy5gpWxDlFoB8mp2jMwX+ezwc7Uv4vG4SSCGhoYKbl/k04YI\nh8P4/f5tofzZbtghC0XGSuoE6667sbGRc+fOFWVxFzftUs0t2Gw20um0+X0ymaSzs5P5+XkzVXHp\nhSb19CB/+9ugKOh1dcag3VNPIQeDaO98Z06P3lxkwcRG+uC6jvT888j/+I8wNkZNRQXq2bOkW1vp\n7OxkZmaGo0eP0tjQgP1jH4NYzCA2ALt2wfQ09n/6J3jjG9EX8iHWfR6Dg0iDg8a3+/cbEc9L/SYC\nc9RF4+hVjaTTGnNzs4RCEqnUXlIpmWef1bNeDr/fqDwIWEOH9iycryj7CgIhTHcmJ3eTSp0iEpHQ\nNBlJMshQOi0tmFe6sNsVc1ZDURTm5+cBo4ogiIb4r6JAOCzhdoPbLUiFk2TSRTJZwZkzdZSVKab8\nbnZ2lv7+fjRNW9E4qtRtiFIvGltRWdgKv4NC/0brDM/Sz7E1fVO0vqzkQZDPfCsLOx4LubFDFooM\nu91O0jKdr+s6U1NTdHV14fF4ih61LIygSkkWjHL0YujT7t27ueOOO1aUJUnPPw/xOLolIlkvK0Pq\n6UHq68v6ufmcBRJU7NAq+cc/Rv7qV42pvbIyfDdvsvvnP+fm+DjSnXdyxx13GIOYc3MwPIy2a5fh\n8Kjrhuyxrg6powNpaGj9ZEHTkP/+77E9+STEYsbPfD7Uu+5Cu/deo8cwOYnzIx+h8cf/wKeiKkH3\nLr5/9HeItr2TffuqqK2VSKd17rpLNUc2EglIp6U1jRBzlX2TySTXr8coK9OIRCSiUYPwyrKMzSZT\nXi7h9WbM2QchhXW73bS0tJizLNbPoaJIqKqM02lURiRJLBA6yaSxCDscDmpqaqhZMGayqkDC4bCp\n3xfGUbqum99v9iJX6l0+vPZnFsQxi/He5focp9NpkzzMzMxkkU9FUQgEAtjt9hXbF9EFh9OdysJy\n7JCFPLEeNUQ0GqWzs5NIJEJra+uGWw4roZReC7Isk0wmeeaZZ8zQp5rVHPh0HeheLqwAACAASURB\nVMbGFrMEBFwuwwshEFj1WEUlQZGIUeGQJPRjx8goCvOShGtkhJM3buD83d81qxa6y4XucBikYmGH\nKYEh4bTb0XMpO3QdaXgYaWgIbDa0I0dyuktKXV3YfvpT9Opq00mSuTls//RPxizG4cO43v525Js3\nUW1O0rpMdXyM37nxSb5bv4+n970Vm82YT6irM7yXwIiymJtb30vjdru5cMHNt75l/B5dl4lGo8Ri\nUSKRCKoaYmgowOysD13XSSQSpvW4dbGxGkeJHxskAkBDkkBVJTTNtmAolb1QWXeQS/X7oVCI6elp\nurq6UFWVsrKyrOpDsY2jtiIXYqcNsTE4nU5qa2upra0FlkuQJyYm6Ovrw263L8u+cDqdZhtiB8ux\nQxaKDLvdboYjDQ4O0tjYuKpTYbGOWYrKgqIoTE1NEQwGOXz48LKFIickCWprkXp60ONxpMlJUFX0\nigrj31bxklhL1lgopIEBmJ5Gb24mvHDzcDqd6Lt3452fJzM6arQDdB3N7Ua/eBHH979vrMY+HygK\nUn+/saAfO5b9y1UV2w9/iPzP/0xiOoqqSmQqqwm/4T7i568Chp/U3r06cne3MexpJVk1NTA9jdzT\nAwMDyO3tKG43aV0mY3OSsHnxpQNc/fmf80Tlb5DJbCxAciliMeOUamuNLwNlQBl2ez0+H6YPiM1m\nw+/3MzQ0xMjISNbgpFV54XQaRNZu17DZNHQ9W26ayaik05k1Q7OEfr+yspL+/n5uv/12ALP6MDIy\nQmdnJ3a7PYs8bNQ4aivIAry2fR2gtLkQgnw6nU66urq4bcFjJRqNmu2viYkJ/u7v/o7vfe97NDU1\nEY/Hef755zl9+nRBw7dL8dhjj/HZz36WiYkJTpw4waOPPsqdd96Z87Ff+9rXePDBB5f9PJFIFCQ5\n30zskIUiQpRIg8Eguq5z6dKlkkhwNrsNYVVvOBwO/H4/hw8fzv/5t92G9M//jPzUU0Y1QVWRMhn0\nc+fQDx5c8XnFCq0y4XCQ0XVmx8ZQF+xrlUyGVCRiDF06HFlySO0tb0GbmkJ++WVjqBLQm5vJvPvd\nRmXEAvnll5F/8hMizhr+zy8OkUrq7MmMoP/dD/ha7WEmHE34/Tp//ddpGhUFct2gJQnSaZI3biCr\nKpos47I5SSVlNB3SkouaYD/xuSSZTBk+n14UwhCLwd/+rY0F1dgy+HwqR492Eg5PcPToURoaGswW\nkdjxi5tuIpHA5/NRUVGBJFWTTteRTDqQ5cVbTSajY7Pp2Gw2ZDm378NaoVkulwuPx0P9gu7U2r8O\nhUKm/E6EHwkSUYhx1FZZL5f6mKWeWdgKgiIqr4KYCoLb2NgIwOHDhzl9+jTf+c53GBgY4O677yaR\nSHD27Fl+7/d+j7e//e0FHe/xxx/n/e9/P4899hhXr17lK1/5Cvfeey8dHR00NTXlfE55eTnd3d1Z\nP9suRAF2yELeWOsCjsVidHZ2EgwGcTgcXLx4sWQX/WaSBRH6FIlEOHbsGJIk0dfXV9Dv0GtrkZJJ\nY+vqchmlfrsdKRYzwp4uXcr5vGJWFjKZDLcUhXK3m7rZWdynT4PdTiYUwjkzg3bPPai7d6Mt9HAl\nSYLKSjIf/CDSzZtGRcTvRzt9Omc1RHr5ZdB1Uv5aEgkJWYYZTxNHEtdpVW8y4WgkEJBIJDDCrZ56\nymhpCNKRTKJnMvQDqUSCNknCZreDTaKi0pAxyhEFtbqWD/43mb94TKW2Vs83qHGN1wYiERYGEbP/\nbXY2yiuvTNLQkOLy5ct4LIoOWZbNm66ACBwS5GF2NsLIiAOPx7PgzWD8t6pKxut14HJl+z6sFpq1\n2nDjSnMYgjyIQLZCjKNKPT+Qb05DMfFqnllYzzFXej/r6up429vexiuvvEJTUxNf+tKXuHXrFs89\n9xz79u0r+Hj//b//d971rnfx7ne/G4BHH32UH//4x3zpS1/ik5/8ZM7nSJJkkt/tiB2yUAByScVU\nVaW/v5+BgQH27dvHgQMHeOWVV0p6k9mMmQVN0xgYGKC/v5+GhgazlTI7O1vwAi61txs79ze/2djG\n2mxQXm4MOD7//KaTBeEY5/F42P8Hf4DnK19BefE6eiqNBMzs2sfw5bfgGjV2uy6XpRRvtxPYf4b0\n3oXv4wtfZNtFSJEIOJ3EYhAOA5KELEFElQmk0ozYJHRdYnYWDp06iXTqlFGxWFjtk3NzDNfWEti7\nl2NXryJ9//tIs7Pofj+yzQapJBIa6oPvYPdeGbvdUD1MTS3+ncaA4/pfJ7d7MSU6k8kwPj7G1FSU\nmpq9nD69H49n7c+0NXDoyBE4c0YjGIwtDJ1NEAqFSCaTlJd7GBoqM8nG0qRLK2EQrYvZ2VnAuOYU\nRVm1+mD8PYZxlDAF0zStIOOonTbE5kBV1U1ty650zHxaUpFIhNraWiRJ4ujRoxy1uL3mi3Q6zYsv\nvsiHP/zhrJ/ffffdPP300ys+LxqN0tzcjKqqnDlzhkceeYSzZ88WfPzNwg5ZWCd0XWd6eprOzk5c\nLhcXL16koqKCaDRa0mAnKP7MgjX06fbbb8/ara2niiElk0aJ3eXKKt/rbjdSKLTi8zZKFlKpFF1d\nXYtyyMZGpFSKyP4TTPxkCFciTszm58VwK9/+hJegPYLdbqemRua//bcYBw+Wk0i4eOwxO8Hg8kWj\nslLnoYcyVFaC1tKC/fp1Ml4VXbcjy+CREuiyzJRjHzJGJyOVksDjQf3N30Q/ehTtlVeYnplhoq2N\n2nvv5eyRI4Zc8X/8D5y/+7uGL4WmgcuF+mu/RuY//ScSE9DXJ5PrpauoMEjDRiDklB6Ph6NHjxCL\nOZGk3O/5zIyhwFgKp1Nn1y4oL5cpL/cDfsAI+0qn02b1YWpqip6enoVzz/Z9EP1iRVHo7u5mZmaG\n1tZWXAsR3mtGdi/BSsZRgjyI7ALArDioqko6nd5Q7zpfiErGa70Noapqycvr+XgsgLFgH1ylNZoP\nZmdnUVWV3Uts6Hfv3s3k5GTO57S2tvK1r32NtrY2wuEwf/7nf87Vq1d55ZVXOHLkyIbOp1jYIQvr\nQDweN1sOLS0tZg8XjIXbmhVQChSrDZFOp+nq6lo19Gk9CgV9716jmpBILKZGahpSOIz2S7+04vPW\nSxZ0XWdsbIzu7m6qq6sX5ZCA9N3v4njyp4w6D5KorKKMKOei1ylXv8cPWv4roXCGcDhDb+8Io6Nz\nJJPl9PW1Ul7uoLLShcvlRJIk4nEIBiVzJ6/dfjvaL36B/5kO9uq7cGoqVXKQl523c8vdBkljzZ+Z\ngbExCV33MVt+nOFmB413uDl27FjWDVS7coXkM89g+6d/gmAQ7dw5c6jS64XjxzUcDh2rz1MiYcgV\nhdNjoVDVDENDY4RCIRoaGqiuriYeX3nhmpmBhx92rkhaHnkkzYKnThacTie7du3Cbt+F3w8NDYuG\nO+PjYXp7+7HZwni9XtxuN+Gw8f+XLl3KaoNYraqtg5MCq4VmLT0Xq/lPLBYzlReKovDUU0/hdruz\n7LPXMo5aD0rd9hDHLAURWnrMrWpDrIViJk4ufS9Xq1RdunSJS5YK69WrVzl37hx/8Rd/wec///mi\nnM9GsUMWCoCmafT29jIwMEBDQwN33nnnsgvN6qj4aiELS0Of7rjjjqyb8tJjFbqA6ydPop05g/zC\nC+hVVca8wswMemMj2tWrKz5vPWRBzFhEo1FOnjyZxe71SAT5n/8ZtaKasKsWjxM0ZwVhRzP7w9c5\nIg8xWHsYSZI4f/48dXUKfX2RBQIYJRCYQtd1PB43quojmfQuzD06oLaWzLvfzYz2M+LX2olg56eO\nX+YZ5+uIKi7icQlNg8cec/CNb6gLskQ3u3Zd4KMftTE7m30TcTp16uq8qL/yKzn/TrfbyNCyjk9E\no4ab9noQiUSZmBimqspFa2trXgtIOi0RChmGS1aCEo9DKCQtVBxyzxkEAvDoow4L0XAiki4rKuC9\n740wMdHB/Pw8Ho+HWCzG008/nVV5qKysxOl0LrOtzic0ayXyYI1edjqdZDIZzpw5Y2r3lxpHidaF\n1ThqvdgKX4d/KTMLmUwm7zbERqWTtbW12Gy2ZVWE6enpZdWGlSCqurdu3drQuRQTO2ShALz00kuk\nUimz5ZAL4iLIZDIl68tthCwUGvq0ruAqlwvtXe+CAweQnn0WFAXtjW9Ee/3rYe/eFZ9WSBVD0zQG\nBwfp6+ujoaGBs2fPmjcHsevUg0Fs0SiarxpYXMiSTj+V0VE8qVDWFSEkexUVDmpqKvD5DLviRCLB\n/HyaQCDI0093sHev3Vy8xu98M4984TdBMiyTyRgVBbFeud0JFGUet9vDwEANo6MSf/iH2rLBwooK\n+NSn0lk2DRMTxoDk3BwEg8bPUiljXnS9myFFUejs7GViwklj4x7q6ytRFEmIP5alf+fCohX1ItZ6\nXjrNikRjejrNCy9cp75e4sqVK3i9XnPHL1wne3t7icVieDyeLALh9/vzCs0SWK36ID7jKxlHieFJ\nq3GUlTwsncNYC1tVWdghKIsoRmXB6XRy/vx5nnjiCd7ylreYP3/iiSe477778voduq7z8ssv09bW\ntqFzKSZ2yEIBaGtrw263r3pBS5JUUPJkMWC320ktjQVcA2uGPq0Aqw1zQbsDvx/tvvvgX/9rY+XM\ng0jlW1kIhULcvHkTXde57bbbqLLkTVjL1FRWQk0NtrEAsPgYbzJA0llOxLs6UZIkCZfLhcvlwm4H\nm03iypVKnE5jAZucnGR8fJi9DedwuWx4vcZCoyh2uroM5uBwBGloqERVvdy4YXyOlkZCix27dWc+\nMSHx7/6dk0jEmH2Ynpaw2TDNmd7+9gyybLQipqchF8dyOrOtHcTMjcNRyalTLSSTDpOEWOH3G1Ec\nmwEr0dB1nbm5eWZmkuzdu5dz5xbbe9Ydv5hOVxTDKjoYDDI7O0tfXx+apmUt2JWVlVluj4VUH1RV\nzXmt57IethpHiQCvTCZTkHHUVizcr3WfhUKOKaTvK20EC8EHPvAB3vGOd3Dbbbdx+fJlvvrVrzI8\nPMx73/teAB544AEaGhpMZcTHPvYxLl26xJEjRwiHw3z+85/n5Zdf5otf/OKGz6VY2CELBcDtdue1\n0y01WSi0srBW6NNax4IN9B3FCpcH1iILmUyGW7duMTIywsGDB7NMoqw9bLFDlLxe1De9CenL/y+7\n4kMk9Go88QhlyTmuN/4yo+zLylWwYunPxfcOhyOr593QoPOtb9kIBDSSyQyxWIJUCjIZHx6PRnm5\nD4fDTjyuL2Qw6PT0yMu407592eX7RMKQN7pcxthHKGRYNWiaITAJBg2zzOefl5mfdy6rVABUVOj8\nwR8o+P1puru7mZ2dpbW1lfr6ek6flshkcn+G7HaKItFcDclkkqmpSZJJJ7t372bfvrVzwlba8Yvq\nQ39/P9Fo1Jw3qKyszLv6kMlkCC30SITyIh/jKEFURYBXIcZRO2Rh81DKNgTA/fffz9zcHB//+MeZ\nmJjg5MmT/OhHP6K5uRmA4eHhrNc9GAzynve8h8nJSSoqKjh79ixPPvkkFy5c2PC5FAs7ZGETsF3J\nQj6hT6siHsc2NoYzGCyJ/Gm1+QirHPLKlSuUWergWdUEFkvNANo995AI6Sif/Ue80TniNi8v7Hor\nT9f8Oul54+KtrNRxOo3nGvJInWBQWqYyMB6X/bM9eyS+/GWNVEoiFkvT29vLxITOt751kooKBYgz\nORkgFHKjqnULlSgVl0tEjRvEYCWO5HYb5+TxGP4ImmY8JxiUcDiM771efVmY59ycUZ145ZV5gsEe\n/H4/R45cxel0IkkbIwMrEal8YEgi5wgGg9TUVFNdXUUgIANKwedh3fE3NBjKC7Hoh0Ih5ubm6O/v\nR1VVM6hKEAhrZLcYYI7FYqa3yHpmH0SAl9U4Skg3cxlHieOXUrK5XXf5W3XMYg44PvTQQzz00EM5\n/+3//t//m/X95z73OT73uc8V5bibhR2yUADyvYBLGeyUz/EKCX3KCV1H/pu/Qf7Wt5BmZ7ktGsXR\n3g7vf392XbvIyFVZSKVSdHZ2Mjs7S0tLSxbhWbo7zBkhbbPh+81f4eidryczNY9WVs4BfznGGKGx\nQDmduumzUFkJDz2UyelfYPVZsKKuzpifmJgYoKVlH2fOHOEf/9EwJPJ6/Xi9OqmUiq5LSJJOJpMg\nmdQWjIfsqKoDTVs5YdHhgP37dTTNWJjDYXjXuzJ4PDrhsJOqquwZgngcrl+XmJvLMDlpY9eu281y\neGWlzkc+oqzrbXQ6dSoqjGHGpTMKFRWYhGslpNMK/f0zuFwadXXNOByOgohGPjCksLmDqkKhEAMD\nA0QiETOoSpZlZmZmqKur49SpUyYhzmUctfSaW0u6abPZlplYCeMoMTyZSCS4du1a3sZRG8VWVTO2\norKw1j0vlUqRSqWK0oZ4LWKHLGwCtmJmYaXjBYNBOjo6yGQya4c+rQD5hz/E9vnPGwFK1dWQSOD8\n0Y8gGkX9sz8rbkiB9bgWsrCaHNLachBDYjmJggU1+zywr2Hhu9UXtcpK6OmBaHT57ysr07H6toTD\nYdrb2835iYqKCmZmWFhUWUhblIjFZEBGlnVkuWyBNKgoik48rjE7G+W5524wP2+U0MPhGnTdDG0w\n2xaZjPH/NTVGtSGXiCESiRMIyMiyncOHK/H7jcs+HtcX5J8rqxZWw65dhjxyNZ+FXNA0jfHxYWIx\nB7Jcjcfjz3ptDaJR8OnkhZWCqubn5+nr6yMWi5mT7LFYzKw8LK0+iL9jqXFUIbbVkG0c5ff7GR4e\npqWlxRyenJycJJFImLHL4lyEcdRGsTPguIjIgt95KSz6X43YIQubgO3QhlAUhVu3bjE6Orqsn18Q\nVBX5b/7GSGpc8FFXKivJOBw4f/ELtFdeQT93rhh/xjKIIbOhoTg3bvQQj8dpaTlDTU0Ns7MsOC1m\ntxzWIgnrQU8PvPWtrpyeA16vzre/neLQIcPJc3h4mP3793PgwAHz9d61C/70T7MX1eeeg//wH2xo\nGkxOGgQCZHQdNE3C43HQ2Hic8vJZgsEgnZ3TRKNnyGQk4nEZu92Ow2EjlVr5BqiqKrOzM8zPK7hc\nDdjtdvx+LavqsFEDJ4MQ5E80otEoN2/eRNM0HnmkDZfLA2RfK04ny9oom4lgMEhXVxd+v59z587h\ndDpJJBJm9UGoHRwORxZ5KC8vz+qDL60+WAmswGrVB7HjFtUEMcgpYpeF94MwjhKtFEEi1uOXUOpd\nvnhttmMbIhKJYLPZ8K7XqOQ1jh2yUAAKianeKrJgDX0qKyvj6tWr+DbSkA6HjaRGS2lOAjSfD2Zm\nkMbHN40sSJLE4GCML3whiqq2UFZWlvUeVFTo/MmfpKip0TaFJAhEoxLxuITDka1aSCYhHpcYH48w\nM3Mdu93OhQsXSCT8jI/n3m0LKeSBA0YLwOHQszKpMhlIJHT27tWx2SpwOMqprYUjR6Cmxk4sphKJ\nqKiqgqqmkGUZv19nfn6G8nI/ul69MNUdY3Z2Fo/HzZ49e2lvL6297lLous7Q0BB9fX00NTVx6NCh\nku8ul0JVVXp6epiYyA7IAvB6vXi9XlPtoKqquWCHQiGGhoZQFIWysrIsAuHxeNZdfVhJOpkrdlkY\nR4XDYfr6+ojH4+YgpyAP+RhHlXqXL+5T23HAMRKJbIrZ1msFO2RhE7AVZCGTyRCPx+no6CAcDtPa\n2sqePXs2voCWlRl1+Olpc7snSZKxJbXZ0DdpZiEYDDI6Oko47MRu30VNjX1Bj2+EKsVihrFPKrU5\n1YRccLvJ8gQw+t8q3d3d3HNPA83NzczMSHz4wyu7GgrvBKfT8BiQpOwASlk2CEh/v8THPmbPIid7\n98IHPqBTXW20MIRcT1HCyPIsN24MMjnZQiCg4XIp+P0VuN3lJBI2VHXz5I9rIRaL0d7ejqIonD9/\nPss+fKsg5LZOp5NLly6tuZu02Wwrqh1CoRDDw8NEIhEcDscy2+rVqg9WMhFfGNjIZDKrzj5YZaRi\nkFPISMPhMHNzcwwMDCwzjiovL19ms1zqNoQgStuxshAOh4uihHitYocsFIBCKguF+h5sBJIkoaoq\nP/vZz9i7dy+nT58u3kCUw4H2b/4Nti9+EX16GqqrsScS2KamjIjphXz4YsEqh6yurkZRPDidDrze\nxYRF4yark0zKC0RhsQw+Pb2Qv5ADLpfOGp5TBZ1nIpFG1x20tbWxf79RHlh0NTRaFAKBgMTkJAwO\nyqRSOvG4RGOjTlmZjt+/6IIdjcLTT9sQthA+n/E74nFDjVFfb5VV2jBcDyvR9UZcrinKyjIkk27S\naS+Tkwqjo9NkMk4SiUp8PolwOIOuO0zL6s2EruuMjIzQ29tLQ0MDhw8fLvkisRSaptHf38/Q0BAH\nDx5k//796yKaK6kdrF4LIyMjpNNp02tBfHm93qzXQVEUenp6mJqaorW1dV2zD+sxjvL7/SUnC6KS\nUWrzqXyCpIQSotTn9mrBDlnYBNjtdmKxWEmONT8/z82bNwE4f/481dXVRT+Gdv/9EAgg/93fIQ0M\n4EinSZ06BQ8/nJe5Ur4Q/g9CDjk3N8fMTBgwPASMuYRFOeTy58MHP+gkFFr+b9GosYC/731KVj/c\n79c4eTL/cxQ7ykwmg8Pu4pz+c1r+9w9w/NUs+smT2C79GtCM16ubswGJBLS3G62Mhx924HYbFZGu\nLmnBVEnn9ts1PJ5Ft0chZ1ycL9BJJHLfxIRCJBQK8cd/fJyKigpmZw3SlMlkmJiI87//d4JIRKO/\nP4XTqeFwOHE4HNTW2pFlnWLfChKJBO3t7SQSCc6cObMpn8tCIeYldF3nwoULRd9FWmOyhZ5eVB9E\npayzszNLFeFwOBgaGsLlcpkR4PlGdq9VfcjHOArgxo0bVFZW5mUctVFshWwS8guSKpbHwmsVO2Rh\nE1AK6aQ19OnQoUP09PRkeQ0UFQ4H2n/8j2i/8RtIAwP0j45SduECTQs3xI1iJTlkMBi09Ho1dH31\n6k4qJREKSQsWwou7+mAQXnrJjqrCL37hzCr7u93wgx8k8yIMsZhGPJ5Alm04nT7eGvpfvCv0Oap/\nEkd3yUhP/JSa2h9Q6/4KMc9Rc6FXVaPiIEmGN4PXa1QWbDbjfAMBiWvXJOx247Hz85LpxrjaW2qd\nT6mtreXy5cs4nU6mpuBP/9RJOCxhZC4YGRY2m6He+PCHg9jtQSKRCIlEkOvXw/h8PrP3XllZidfr\nXdeCIVQrPT091NfXc+bMmbzMcDYTosJx69YtGhsbOXz4cMl200LtIDIBNE0jEokQDAYZHx8nGo0C\nxj1jYGAg6/VfOvuw0dCspcZRiqJw7do19u3bRzQaNcnMasZRG8VWKCHE65YPWdhRQqyMHbJQALbD\ngKNVQlhVVWVKCHt6eshkMpubILdnD/qePaReeolizAuLv0UsdnfeeaephRbGNMlkknQ6jabZkKTs\nm0wyCaOjkMkY78v4+KJxksu1+F6l05Jpf+x2L/buFcX4HZGIDKzsFOl0prHZdGIxCYfDGGDblRzh\ngdAX0DToTu1HVkDSVernhni99Bf80cRj/Kt/pWbNONhsRrXA5zMqBzbbovmS3Z49UyDMlqxIpWB0\n1PhbUqkUvb29RKNRTpw4xYkTNZbHSYTDBmlamkqZTErs3eujqclreXzK7L2Pj4/T1dWVtfsVu861\nFoxkMmmGeJ06dcocyNtKJJNJ2tvbicfjnDt3LssKfCsgyzJOp5Pp6Wk0TePChQu43W5zty9ef1mW\nl73+DoejqKFZ4rH19fXmv1uNo8Lh8DLjKEEg1ksmt6KyIP7OfAccd5AbO2RhE7BZZCESidDR0UEi\nkVgW+lRKI6j1xFQvRSwW48knewiF0hw+fJaKihrGx41/c7t1du0yXPa8Xi/hcIREQqGszIbL5cTp\ndBCLublxw8bv/77TVBOkUtDTI5FIyHg8i3bBqmqoDCTJ6JpYZ7yUVYwCdV1nfHycmZkePvvZBurq\nDi50XTLU/uOTNH4hRE+yyciJkAFsxLUKLiSexpUKo6orq1BkWc/q4Ij2g7jXSxKkUjoLG0/m5iSu\nX5f5/d93IElp4nEVh+MoXq+Xigr44hfTLLTOTXg8+QU8uVwu6urqzM+T2P2KBWxsbIxkMrnM9dDj\n8ZjuhhMTE3R3d7Nr1y4uX75cshC1lWCtutTV1XH69Oktr3AATExM0NXVxe7duzl37py5cC59/aPR\nqGlbPTExQSKRwOfzZREIn8+3odAssYhaF/1cxlGCTIbDYSYmJujp6UGW5SzykK9x1FZZPcPaQ5U7\nlYXVsfVXz6sM4ua4GopNFlRVpbe31wx9On/+/LIbn91uLxlZWE9MtYCmaQwMDPDCC6N89asXUVWr\nHNJ4Xf1+nS9+MUN9vYezZ49z6JCD+XkNRVGIxxUUJUU8niSdrkDTkrjdMg6HA7fbvjDsmb0YG4RA\nWvAwyO88E4kEHR0dxGIxTpw4QV1dHRMTBiEBYxEWmzWJRV8qWTa+V1XDOVGSDOWGpmWrHjweOHVK\nIxKRkSQ4c0bD6zV+/4svyiQSkEhIzM2J8xHl1AguV4qGhjKcTheJBITD0sJQZ+HGSrlg3dU2NTUB\n2b136+S/3+8nmUySSqU4duyYOey3lRAtuvn5efO922ooikJXVxdzc3NrnpN1IRawVn8mJyfp7u42\nH2clcIVUH5LJZJZkc6X2QC4yGY1GzeHJqampZcZR5eXl+Hy+Fb0kSgnR+lir/bEzs7A6dsjCJqCY\nZGFmZoaOjg5zAGqlD3MpKwvrPVYwGDSHMVtbz6JpfjweHY/HWOR0XV9Y/CCVMiKeDUMjZcHQyLbw\nBYODGT70IZ3ycpDlBMlkiGTSjq5XAw40TcdYtrOhqovVhEzG+F4syOIcxAR/fX29afk7MQHvfa+Y\nA4DdqTv4f8LleFOzzNl3U1GhY5dUfEqIJ91vJkw5waBGKrWY9WC3Gx4KZrZHAwAAIABJREFUgmuq\nKqZ0UsgyvV44e1Zjfl7i4YczNDToC3G1s/zRH5VRXg67d1dltWTyiZHeKJb23g2zrCEGBgZwOAx1\nxc2bNxkaGjKH/MSwXCkxOztLe3s7FRUVXLlyZXPbcnlifn6e9vZ2fD4fly9fLsxqfQG5FmxrZHd3\ndzfxeHyh0rRIHsrKynJWHwyjr06qqqoKtq22kplCjaO2qrKQby7E/v37N/+EXqXYIQsFolSVBRH6\nNDc3tywDIRdK3YYo5O/LZDL87Gf9DA3N0NjYRGNjI2NjRp6Ax2PIAxfVDhLJpAh+Ml7nXC6BmYwd\nr9dBWZl9wXRKJxLJ4HAY74+iCOtnGVWVEcRhbk4yd/jGMeEzn3Fw/nwKvz9KR0cH6XR62QT/0jmA\nNI38MPa7/Gr/52nMDOKMy9jIMFfWxLXW/8hRReejHzUW+7k5eOQRB7GY4b4oJIvJ5CKJsC74mmaQ\nhz17dGprjQpHMqlSUXERv9++ZhrjZmPpzr2+vn6B6CXM6oPIXMiV+LgZA26ZTIaenh4mJydpaWlh\n7969Wy6B0zSNvr4+hoeHOXLkCI2NjUU7J8OMy4/f76dxwVk1nU6b1YepqSl6enoAsmSb5eXlTExM\n0NfXx8GDB2lqajKl12JwstDZB1jbOKq/v59YLIbdbsdmszEyMpK3cdRGsRUhUq9F7JCFTYDNZjMv\nuEIvhKWhT9ahv9VQSiMom82Wt4/E9PQ016718Wd/dgZNO2a+HqkUDA1JuFxw+bJRXdjYjVTC73fQ\n0gIvviixf7+Mz2fcKAKBDIODxg7XqDgsPkeWjYX61q1RFKWbffv2reoHYJAb4///4dB7+OnUSe5O\n/X8cq5xipPo0Tzf8BmM04Eoai/2+fTr79sFjj6WX+T/MzMDHP+5Y8FDITrUsL9eZnx+nr8/ouZ89\n27KwQ8y/1bDUynmj1s5gvJ+dnZ1UVFRk7ZIlSVrmepjJZAiHwwSDQebm5ujr60PTNMrLy7OUFxvd\n/YuKlVV+uNWIxWLcuHEDXde5ePFiSQbnnE5nVly64eS5mHLZ3d1NIpFAkiSqq6tNifdK1YeNhGat\nZBzV09NDLBZjfn4+b+OojSIfjwUwpLU7bYiVsUMWNgHig1moOiEUCtHe3r6u0KdStyHWmlmwyiFd\nrlOk0+W4XDou12LLAWTSaTH1vz6isNRYSMwGGDJLGbtdprx8caixpUXB6VTIZDJmcJOi2BgbG+PK\nlcPs3bs37zKpbJN4wXMn17iT440LVtALFYLy8sW/FVgwg8pe6Bsb4StfWU4ikskkQ0PdhMNB2tra\nqK2tZWgo/9fH5dIpL9cJh5enQS49r3yhKArd3d3MzMzQ0tKSlzuo3W6nurrarNAIoyBROu/t7SUW\ni+HxeLLIw1Jb75VgNVg6dOgQzc3NW15N0HWd0dFRbt26ZRLPrbIPliTJrD64XC4zTbO+vp5oNMrM\nzAy9vb1omrbMddLlchU9NMvhcOByubDb7bS0tGQZR4XD4ZzGUeXl5fj9/g21LgppQ+wkTq6MHbJQ\nIPK5GQnWnS9ZKEbo03ZRQ4ibZXd3N7W1tbS23sVDD3kZHjZ8BMQ1q6rSQgKjkcYoXtZ8d7/WBdFa\n5MhkjAwGRZEIG35O5oyC3Q5+vx23226JKk6jqg6qqqoYGRkx/SrEwlVZWbmwU13+vrvdcPy4RiAg\n8Sd/olicFY3zW2jvrwrjMYsEamxsjNHRHhoa6jlyZLmqIJ9qwe7d8Oijy0lIIedlxdzcHO3t7ZSV\nlXH58uV17/ysRkHW3abY+U5PT3Pr1i1gsXRuHdyzYrMNltaDdDpNe3s7kUhk2xhRqarKrVu3GB8f\np7W11UzaFLMn1nbBUgJnJQ9LvRbWG5plHXDM1zgqk8mY16SYlRBKnHxfg3zJwnb4HG1X7JCFTYAk\nSXm1BYoZ+rQdKgvRaNR07Tt16hR1dXUMD0MkIpmyRatqQJaNqkIoJGEdAykv13G7V9/91tfDF76Q\ne0GcnwfrNT86KvFf/6uT0VGJV16RTbto8ALGLrayspVLl46SSqXMne/o6CgdHR04HA7i8d2kUq2E\nwzZ0fdGuVtOM9Mu9e3WamtavRhDqi3g8ntOjoNBqgZWErBfWOYClQUvFguEimd3rtsoGu7q6lskG\n4/E4w8PDNDc3b4tAKlgcRK6qqtoW0lEwrscbN24gy/KK+Rer5UyEQiFmZ2ez2kdWArGeyG5FUXC7\n3Su2aJcaRwnHVHE+o6OjRCKRLOMo8bVSqyGfNoSu6zuVhTWwQxY2CWuRhWKHPpV6ZsFKTIQcsq+v\nj8bGxixpp7BoFlP/4pq1241FLpGAD30ow223Ld5U3G59mWdALhiPWb4gLjWWtNkMomJIKlVAw2aT\nkSQZRTGklr29EjU1EuAG6pGkehoa4Px5o+9+61YUlytFIADz80bErhjWqqqyrau0L14fUbaur69f\n0Q+gvt7wUlipWlBsxaKY4Pd4PCWdA7CWzq2De2Lu4datW2ZZORKJMDg4mDOwqVSwJlcWLbxtg7C6\naDY2NhZMqFbKmRC7/f7+fqLRqDm8mm9kdyAQIBAI0NTUZN6r8pl9EBkcViVOLuMoQSiXGkcV0obY\nqSysjB2yUCA26uIoFtb+/v6ihj5tVRsiEAjQ3t4OwIULF7ISBRd3FobKQdOMNkH27zIGAffvL45H\nQC643Toul9FuAOO9MUxpFsv4H/6wA2vHyG6Hujqdxx+HhoYqLlyo4utfN4YhE4kE4fA84XCYSCRC\nJhOlv9/G/Pxi393n8635WbHmJ5w+fXrNGZWVyFExITw9xsbGOHz4cFEn+NcLh8NBJpNhcnKS3bt3\nc/jw4SzlhTCNssZFi/bRZp57OBzm5s2b2O32vJIrSwFFUejo6CAYDOb1mcoH1naBaGOI4dVQKGQO\nK2YymazqQ2VlJW63G1mWGRwcpL+/n0OHDtHQ0LCq8mKt2YdCjaNEO1hRlBXvtaKitaOGWBk7ZGGT\nIGKjrRC7NVmWuf3224sa1Wuz2Uin00X7fWsdS1VVOjo6GBsb4+DBgxw4cMC8sLN7mDqyLOF0GkTB\n+pKI2OTNlOIrisLcXDdve5vCzMxtVFbaF3wddKamIBo1zjkQyL2oWAcoF9qqgGfhy9jpCMlaMBg0\nnQzFDU0sXhUVFebuxlpN2LNnz7bITwBDVdDe3o7D4eDixYvrbokVE+l0ms7OToLBICdPnjQn/Z1O\n54qmUaJ9ZLfbs8hDeXl5UTT+uq4zNDREX18fBw4cYP/+/duiFSJC5fx+v5kTslnINbyaSCTM9pEY\nVnQ4HOb9oLW1lfr6+pyZF0v/a7135iPdXM04amRkhHg8zrVr11Y0jopGo+i6vtOGWAVbf4d6lWE9\nlYV0Ok13dzeTk5McPnyY5ubmot9cSllZCIfDxONxotEoV65cyVpUlg46ybKMy2W4FS69dyWTRm5D\nXV1xd8sTE4YMcXZ2lv7+fsrKyjh8uBW73YEsL+YlrPVW5tvVWSpZs4YFCcdDRVHw+/34fD7C4TCZ\nTGbbDMFZ/QC2i6oAFucAKisr11z8cplGifcgFAplvQfW4dVChzVFNSiZTHLbbbdti8XFqgo5evTo\nmp4smwGrdFZUH6anp2lvbzffm97eXjo7O833wFp9WCs0azXb6rWMo4LBIH6/nz179iwzjlIUhU98\n4hMcO3aMuro6kkVwOHvsscf47Gc/y8TEBCdOnODRRx/lzjvvXPHx3/nOd3j44Yfp6+vj0KFDfOIT\nn+Atb3nLhs+j2NghC5sEQRaEMkCEPm1W7zdXJaPYEEZRs7Oz2Gw2br/9dvOmtHQiWuwE3G6oqNAJ\nhbJVC2As1nV165PyrYSJCYkHH7QzM5Mik/Hjdl/E4XCQSkmMjUnMzEicPKmx1LpCkhbJwzqdrE1Y\n7ZKbm5vNXVd/fz+Tk5PY7XYURaG9vd1ctAqRDBYTopQuy3LJ/ADWghisnJqaylumuRTWuGhYHJRb\nuvN1Op1Z1YfVTKMmJyfp7Oykrq5u21SDEokEN27cIJPJbBtViCAvw8PDWQZZS9+D4eFhs5K1NDTL\nZrMVLTRLDDjmMo6amZnhvvvu42c/+xmRSITm5mb279/PpUuXeP3rX8+73/3ugv72xx9/nPe///08\n9thjXL16la985Svce++9dHR0mFUwK5555hnuv/9+HnnkEd7ylrfwve99j7e97W089dRTXLx4saBj\nbzYkfS07wh1kQdOMjIK18NJLLxEKhQA4fvz4pvvTj4+PMzIysikfsKVyyKamJl588UXe9KY3mf+u\nqqrpMS++BKanyTmYB8ZwXrFeGl3XefbZGd773kq8XpmqKg+ybBw3kTCUEABHj2q43TA+DiMjxs9y\nkYXdu3V+/OMUR45s7BKJxWJ0dHSQSqU4fvw41dXVZDIZs2wubp5A1q53M4f2xOzM4OAg+/fvz2oj\nbSWEwZLb7ebEiRObOlipqqopGRTvgaqqy/IWZFmmu7ub2dnZklzL+UKEUtXX13P06NGS2yjnQiKR\n4ObNmyiKwqlTp9Ykn6qqmrt98T4oipI1f2INLRPIFZplXcqs96GXX36ZhoaGVXNLfv7zn/Nbv/Vb\ndHV18fzzz/Pss88Sj8f51Kc+VdDff/HiRc6dO8eXvvQl82fHjh3j3/7bf8snP/nJZY+///77CYfD\n/P3f/735s1/+5V+mqqqKb3zjGwUde7Ox9dT4VYa1djhiQGx6ehq/38+FCxdKsgPZrDaEVQ55+vRp\ndu3aRSKRMMmBlekLZr8UuQyJio1EIkFnZydDQyoezx5qamxYW+42mzEfoSgQjUqk08szFYpNm3Vd\nZ3h4mL6+Pvbu3ZuVMmi325dNnAvJoIgqtg7tWcvmG60+RCIR2tvb0XWd22+/fVsMdVlbIYcPHzZt\niDcTNpstp2mUWLT6+vqIRqNIkoTD4aCpqWlV2V+pkMlkTIOs7RKUBYtth927d9PS0pIXeTHURMul\nkoI8jIyM0N7ebkolBYEQyou1qg+pVIpEIrFgAa+sWH0QSojKykruvvtu7r777oL//nQ6zYsvvsiH\nP/zhrJ/ffffdPP300zmf88wzz/Bf/st/yfrZPffcw6OPPlrw8TcbO2ShiBA9VqfTyb59+9A0rWSl\nymKTBVFK7O/vXyaHFDdxRVGy+oZb0edeGvx07lzLwnlmr/xut05bm0YwKPGZz6RpbNT55jdlPvlJ\n58LvWf67RbjTehCLxWhvbyedTnP27FnzZrgSckkGlyY9tre3m4N9gjwUkrWgaRpDQ0P09/fT1NS0\nbTwKhB+AJElb2gqxTv3X19ebeQYNDQ04HA4CgQBDQ0Poup5lWV1RUVGywKpQKMSNGzdwu91cunSp\n5EFduaBpmikf3WjyqFUqKX6PmD8RBGJ0dHSZ+kVIJa1qBzHwWVFRYRLClWyrI5HIhtuAs7OzqKpq\nzs0I7N69m8nJyZzPEQqffB+/ldghC0VArtCnwcFBgsFgyc6hmDMLQg4pbt7WIS5dNzIcbDYbTz/9\ndNau1+/3l5QwWMv7Yliwv3/l4xt209DcrHPwoM7lyyp2e+4ZBVmGP/qjFA0NhZUbrJPya+VMrIVc\nQ3vihjk/P09/f3+WVa94H3LJwwR5URRl2wzmWV+r5ubmdTmXbgZisRg3b95E0zQuXryYNQdgzVsI\nBoNMTU2ZaY/W2Yd8pLOFwPpaHTx4kP3792+LIVSRgQFGCX4z5KNL50+ArOrD2NgYnZ2dpgKpvLyc\nZDLJxMQER48eNeW/S6sP1jmrJ598kjlr/OwGsPR9EffMYj1+q7BDFgqE9U0UF3Cu0KdSmiSJ4220\nsiAGy8bGxjh06FCWJMx6YUmSxF133WWGBM3OzpqRtFbyYJULFhPWHfJGFuQ3vAG++90EgcDy51ZV\nqbzhDYX9PuuCfP78+aJKYyF32dwaU9zT00M8Hsfn82XtuIQL30bJSzEhetupVGpTXqv1wGpm1NDQ\nkPO1slaArPHMgjxMTk7S3d2dNeS60fmTVCrFzZs3SSQS24bogTEz0dnZSUNDA0eOHCkp0VtKpIUC\naW5ujpGRERRFMd/PaDRqvhc+ny+LTCeTSR5++GG+8Y1v8N73vndD51RbW4vNZltWFZienl5WPRCo\nr68v6PFbiR2ysA5IkmRq0lcKfSrG4l0INtqGmJqaoqOjA5/Pl1MOKdg4LJbuli5couceCAQYHR0l\nnU6bfUDxlU+C5moIh8N0dHSgadqqi0wuBVSunxmEYGPvk3XXJxzzSrEgW616rQuXIA8jIyN0dHQA\nmK+96M1uFWHQdZ3x8XF6enqor6/n7Nmz20JVkE6nTUfVQs2McklnrZbV1vkTK3kQDoOrYWZmhvb2\ndmpqalZ09yw1VFWlq6uLmZkZ2trazL97KyHLskkOKioqOHHiBJqmmdWH8fFxurq6kGWZZ555hrm5\nOY4fP87XvvY1FEXhxRdfpKWlZUPn4HQ6OX/+PE888USW9PGJJ57gvvvuy/mcy5cv88QTT2TNLfzk\nJz/hypUrGzqXzcDWf/JeZdB1nY6ODkZGRkwzolw33lJXFtYTi93bKzE7m2JwcIBQKExz8wkqKuqY\nmJA4fDi7TCdKYyvd3HL13IVJi9UiViQMiq98y7WqqppyrNWm9z0eIxciElmeoQDGvxVzwF4MgGYy\nmW2xQxYLVyqVIh6P09DQwO7du03PgaGhIRRFMXvuYuHaKInLB2JBDoVCWQZLW43Z2VlTxnrp0qUN\nzx9YNf4CuTJHlg7tWStxKwVAbTWi0SjXr1/H4XBsm5kJMUjc29u7bDg2l1HT8PAw165d41vf+hbz\n8/O0tLTw6U9/msuXL/Orv/qrG9rVf+ADH+Ad73gHt912G5cvX+arX/0qw8PDZtXigQceoKGhwVRG\n/Of//J+56667+PSnP819993HD37wA/7hH/6Bp556aoOvSvGxQxYKhCRJuFyuNUOftoIsQP6x2Ldu\nQVubE3ACp5b9+40bKQ4cWKwmrEYUVoIYVBKJciJh0FqutfYjhcZ6KQkQVRy73b6mlnzPHp3/9b/S\nK6ZXejzGYzYKayuklNWEtZBMJuno6CAajWbtkK2qCyuJWxoTXSiJyxfT09N0dnbmZbBUKlgXZKsf\nwGbA5XKxe/furLK5kAxajbvKysrw+XwEAoFt5aRpbdE0NTVtm/kSYW8dCoXWJOuyLOP1ehkYGOAX\nv/gFn//853nzm9/Mc889x7PPPsvXv/51Tp48uSGycP/99zM3N8fHP/5xJiYmOHnyJD/60Y9oXgis\nGR4eznrdrly5wje/+U0+8pGP8PDDD3Po0CEef/zxbeexADs+C+uCoig5UxetiEQiPPfcc7zxjW8s\nyTnpus6Pf/xjXve6162pTY9Go3zve0P8+39/bsXHXLsW5/RpdVNVDqLPGAgEzMVL6NzFwOT8/DwT\nExMcOnSIpqambXGDEtUEVVU5ceLEtugh67puWk3X1dVx9OjRvDNHrCRO7H5Fz32j8ydC5jc9PW3a\n/W6H4a1IJMKNGzew2+2cPHlyy3MdBIkbGBhgYmICh8OBoihZ6hcxvFfqa0BRFDo7OwkEArS1tW0L\n11FYVIZ4vV5Onjy5JgGdnJzkwQcfZHJykm9/+9ucOrV8k7SDlbFTWdgkCHVCqSZbhUJhtbkFqxzS\n5zu65u/cbDmkdQgMFnXuYspcyNS8Xi/xeJypqamieQ2sB5qmMTg4yMDAgLm72g7VhFQqZfbb11Pe\nXxoTbe25i6yFdDqd0/NhNQQCAW7evInX6+Xy5cvbpmQt5ku2kxlVJpPh1q1bBINBzp49S01NzTL1\nizWsyaq82MwWknVB3i4VIWES19PTk5cyRNd1rl27xoMPPshdd93F3/7t324Lb5FXG3bIwiZBDCLl\nk6VeLKxGFsSNW9j69vev3ls32g6bcZarH9PpdJqLVEtLC3V1deauVxi0CIteq03yZt/whZGRpmnb\nZiJd13Wmpqbo6uqipqamaDdza89dWNRaUx4HBweJRCJmRPHS90HTNHp7exkZGeHIkSPbIrkSjBaN\nMBjbDvMlAisFQK1lGmWNiraSh2JcD9Y5gO0k1cxkMnR0dBAIBDh37tya/iWqqvK5z32OT3/603zq\nU5/ife9737Ygh69G7JCFdSCfi2aryMLSOQlFUejp6WF8fHyZHHK7QSx85eXlXLlyxdyJWoeUxG5L\nSDb7+vrMtLjNsEm2VhO2kxeASGMMBAIcO3Zs06VWS41yhF11KBTKeh98Ph+JRAK73b6tFmSh9qmr\nq9s2qoJCA6ByRUUripIlYe7r61vmvVGoaVQ6naa9vZ1oNLqt3sNIJML169dxu915EeO5uTne8573\n0NXVxU9/+tNtOQfwasLWXzGvUciyjCzLZDKZkkyaw3K5prhBlpWVcfXq1ay+7HYaVUmlUnR1dREI\nBGhpaVm1r51rt7XUJjmVShVFsrkdbZEhe1jwypUrW1IaXmpXLVz8RkdH8fl8qKrK888/nyUXrKys\nXObxv9nIZDKmzO/48ePbRr9erAAoh8OxzDY8l/eG1+vNIg8ruRUGAgFu3LhBRUUFly5dynvuZTMh\nhiu7u7s5cOAABw4cWPMz9Pzzz/PAAw/Q1tbGCy+8UJAUdge5sUMWNhFboYhQVdV0lJyfnzdlV0vT\nIb3e1clCKcLrrEN5NTU161r4VpNshkKhdUk2rSFL26maoCiKmQmwnYYF4/G4aW19++23my0aIRcU\ncw8dHR04HI6skvlmDuyJUCqPx7NtZiZgcwOgVvLeEFWglUyjysvLGRkZYWBgYMtirnNBkL25uTnO\nnDmz5qKvaRpf/vKX+ehHP8pHPvIRPvShD22La/e1gB01xDqgqmpeJODJJ5/kxIkTJWO1P//5z3G7\n3UxPT7Nr1y6OHTuWtfhaU9oA+vpkotHlNwS/Hw4fLk3wUyQSMbPkNwu5pv2FNWxVVVXWohUOh2lv\nbwfgxIkT26aaMDs7a1aJjh8/vi0WPqucbs+ePWsufCJh0Po+WNUvYuHaaKXEWt4vVShVPhAL31an\nV4oBVus1kUwmkSTJNJfK1zRqMyE8HZxOJ21tbWtWB8PhMO973/t45pln+PrXv87rXve6bfG+v1aw\nQxbWgXzJwtNPP82hQ4dKUvqMRqM899xzAJw6dSprIn5plOtWhT6Jc7EGPx05cqTkpU4h2RQ3ykAg\ngKqqOBwOUqmUmZpXqvbRahAW3JOTk5vuBVAIrAqMEydOmEqKQmBVvwjyEIvFzJwF8VXIohWPx7l5\n8yaZTIa2trZ1l/eLDWsA1MmTJ7cF2QODhN68eZOqqirq6urMwKZwOGwS6lymUZsN4biYr6fDjRs3\n+O3f/m0aGxv5+te/vqEwqx3kxg5ZWAc0TUNRlDUf99xzz7Fv3z4aGho29Vz6+voYGBgwB9COHDkC\nLM4liDhpa8b7VsAa/HTs2LFt00cMhUJmcJDf7ycWi2VlLFRWVlJVVVVyyeb8/Dzt7e14vV6OHz++\npn9GqTA9PU1HRwfV1dUcO3asqGTPmrMQDAaXLVqiCrR00RI20t3d3SvmOmwFtmsAlLhvjIyM5HSI\ntBJq8X5Y5bPi/Sj2NWG1kj558uSaJFTXdf7qr/6KD37wg7z//e/nj//4j7fF8OprETtkYR3Ilyy8\n+OKL7Nq1y5SfFRtCDmmz2Thx4gSjo6M4HA6OHj2aleew1dWEYgU/bcZ5iXL1gQMHspQiImPBumg5\nHA6zbbGZkk2rs+CRI0e2Vf/YarAknDk3E0urQMFgEEVRsgZYvV4v/f39BIPBdVc5NgPWAKi2trZt\nIbeFxeFKVVVpa2vLOxI8mUxmVYEikciyGZSN5I7EYjGuX7+O3W6nra1tzepLPB7nAx/4AD/60Y/4\ny7/8S+69995tcZ28VrFDFtaBfMnCK6+8gt/v5+DBg0U9vlUOefjwYZqbm5Flma6uLjRNo7W11SQK\nW11NsAY/HT9+fNvIsEKhEO3t7ciyzIkTJ9YsV1v77YFAgFAotCmSTTGU53K5OHHixJY7CwpYqxwn\nTpzYsjK6rutZi9bc3ByJRAJZlqmtraW6utokclu5cIgAqNraWlpbW7fNblcopIoxXGm9JkT1QZhG\nWdsX+XxWRIKlsE5fi4T39PTwjne8g7KyMr75zW+adso72Dxsj0/wqwz53oQ2Qw0xOTlJZ2dnTjmk\nzWYjmUySyWSQJGlLqwmqqjIwMMDQ0NC2UhRYA6kOHjxoEq21YLPZqKqqoqqqigMHDqwo2RRl2qqq\nqrxvlOK8RFn40KFDNDc3b4tdkqqq9Pb2MjY2xuHDh7fcYEmSJDweD06nk3A4TDqd5ujRo/h8PkKh\nENPT09y6dQtJkooWEV0IrFWhY8eOlaT6kg/EeU1MTBRNQmq9JiA7d8SqRLKad1VUVOD3+81rTlVV\nuru7mZqayivBUtd1vvvd7/J7v/d7PPjgg3zmM5/ZFq6S/xKwU1lYB3RdJ51Or/m47u5uVFXl+PHj\nGz6mCAgKBAIryiEnJiZob2/PCmeqqqrKujhLgWAwSEdHR9679lJBVBNE2ybf8mu+sO54g8EgkUgk\nL8nmZp/XehEOh80218mTJ7dFoBEY/hfCjTTXeWmaZnoNWKf9Retis/rt0WiUGzduIMsybW1t26Yq\nJMr7sixz6tSpks6+WM27BInQNI3y8nJ8Ph/z8/PY7XZOnz695nmlUin+8A//kG984xv8z//5P/n1\nX//1bUGo/6VghyysA/mShb6+PmKx2IYCS4R6oKenh7q6OlpbW1eVQ+q6bvZ4RUCTpmlZC1ZlZeWm\nzAxkMhlzF7qdgp+su/ZCqgkbxUoBTda2xezsLCMjI8tmJrYSuq4zODhIf3//tspPsFoQF1qtSiaT\nWe9FJBLJsg1fuuMt9LyEhDTfMnqpIFQFYlZoq89LmEaNjIwwNjZmus4KUm21rLYSgcHBQR544AEy\nmQzf/va3zSHuHZQOO2RhnUilUms+ZnBwkPn5ec6dWzndcTUIB8E1rzZIAAAgAElEQVRUKrVscEuQ\nhP+fvfMOa+rs3/gdQDaEIeIEXMwEUVBAxFFX9dfx2lpHiwJ11L3qW0fVum2rfR211ddqxdYiVq2r\nrdX6VoaK4GrZQ5aiAoIJCYQQkjy/P7zO6QlDAiThaM/nurhawwl5ss75Pt9x39R/myo5UF9OprMj\npXDIbNZrayqvoqICGRkZMDc3h7e3N2t2oUx76/betTOb9crLyyESiUAIgY2NDRwdHVslzatrqNHD\nuro6CAQC1jTlUb4OMpkMQqGwzb0vTNlw6r+UTDLzotXcpAdlkSwWi1nlyMjUdNBmqsBQUEqfzHII\nM6imshAAcOjQIXTq1AlOTk7Yu3cv3nnnHezZs4c1U0H/NLhgoZUoFIpmJZOLi4vx+PFjDBw4sEV/\nmzkO6eLigj59+mjUW6lJh9aOQ1J1RSqAqK6upscEqQBC2xQt1WxZWlrKqs59qtZeXFzMqiwHczLE\nxcUFXbp0gUQioZsmme+FISWSmbvjrl27om/fvqyYWAGeNeVlZmbqtVmwvkyyWCxuMD5bX6iIMoCy\ntbWFt7c3a2rnlIeCmZkZqzQdampqkJKSAkIIfH19myzTUGWk/fv348KFC0hPT0d1dTW8vLwwePBg\nBAcHY8qUKawp8/xT4IKFVqJNsFBSUoKCggIEBwdr/XeprnOqfs3c2elrHJI5JigSiegULRU42Nvb\nN1prp4yfbGxsWKMqCDwbKaWkhX18fFiT5aiurkZaWhpUKlWD95aiqZFNZtOkrntQKIElqVRqUMXR\n5mCOanp5eRlcaKex98LExAR8Ph8qlQpisRh9+/ZljUIk07rZzc0NvXr1YsW6gGfaHOnp6VpPYZSU\nlCAiIgLl5eU4fvw4OnXqhMTERCQmJuLGjRv47bffDJph2LZtG1avXo3Fixdj165djR4TFRWFyMjI\nBrfX1NSw5tzYFrhpCD3SkmkISvf/8ePHGuOQwN8NjFRvgq4nHUxNTRs4O1InyLKyMuTk5NC1dipw\nePjwIW0jzRaPgvrZBLZMFDBr7VRNu6mTZWPvBTWeVlFRoXOXTWrXTllcs8E4CPh7hJRyGGyPk239\n90KtVuPJkyfIyclBXV0djI2NkZubi9LSUo1MUHtkGKhySGVlJfr378+acoharUZubi4ePnwIb2/v\nZgM+Qgji4+MRERGBkSNH4pdffqEbpP/1r3/hX//6lyGWrcHNmzdx4MABrXrPbG1tkZ2drXHbyxAo\nAFyw0Gp4PF6zmQVtggVCCH3CbsodksomADDIOKSxsXEDR0GpVAqRSISSkhJIpVIAAJ/PR3V1NZ4+\nfWqw0bSmEIlESE9Ph6mpKYKCgliTTaBMlmprazFgwAB6zExbGhtPY/agPHr0SKPTn/pp7gTFNKVq\nj117UzBNvNgU8AF/Z9Ko3bGRkRFd0hOLxbh37x6qq6tbZFqmCyorK5GSkgJra2sEBQWxphwil8uR\nkpIClUqFwMDAZr+TKpUKO3bswI4dO/D5559j7ty57V46rKqqwnvvvYdvvvkGmzdvbvZ4Ho/Hmu+S\nruGCBT3SXLDAHIekZrLrj0NS2YT21EwwMjKCqakpnj59itraWvj6+sLKyoo+SVISztbW1hqlC0Oc\ntJhz7VRvAhsuLlRKODc3F127dsWAAQN00gPAdBWkXDaZI5uFhYWQSqUwNzfXaGBlXrCoUpeVlRWr\n3BiZvg5ssgRnNgv6+PhoGEBZWlrC0tKSlktmNuvVd3hkZoJ08VlgSkmzLbCiPCc6deoEDw+PZp9v\neXk5Zs2ahdzcXFy5cgWDBg0y0Eqfz/z58/F///d/GDVqlFbBQlVVFVxdXaFSqeDn54dNmzahf//+\nBlip/uGCBT1iYmKioaRIQaWlc3Jy4OzsjNDQ0OeOQ7a38RN10XN2doZQKKRT1UwbXLlcTu92KTEW\nyhCIumjpulHv6dOnyMjIgJmZmVY7F0NRU1ODjIwMyGQy9OvXT+89AObm5ujcuTO9o6Fm28ViMUpL\nSzUuWEqlElKplFVujJRGSFZWFuuaK5kGUEFBQc0GVh06dEDHjh3p6QMqK0e9H8XFxVAoFLCxsdEI\nIFoasCkUCqSlpaG6uhoBAQGsmVphek5oK0qVlJSE8PBw+Pn54datW6wpocTExODOnTu4efOmVsd7\nenoiKioKQqEQEokEu3fvRkhICP7666+XYtSTa3BsJUqlEiqVqtljLl++jFGjRtEp+ubGIdmSTQDa\nZvxUV1enMXEhkUg05trt7e1bLclL6TlQctftrSpIQZkZUZoYHh4erJD5VavVKCkpQW5uLt3zQsny\nsqXWzjZfB30aQDFVDinNB3Nz8wY6A02l4J8+fYrU1FTY29vr3MirLcjlcqSmpqKurg6+vr7Njimr\n1Wp8/fXX2LBhAz755BMsX7683csOFA8ePEBAQAAuXbqEfv36AQCGDx8OPz+/Jhsc66NWqzFgwAAM\nHToUe/bs0edyDQIXLLQSbYIFQgguXryI4cOHo0OHDsjPz0dBQQFcXV0bmCm1dRxSl+jD+Ik5106N\nCfJ4PI3gwdbWttmTBZVCt7CwgLe3N2vGp+RyOTIzMyGRSODt7d2sbK2hUKvVKCwsREFBAS38xOPx\nNGrt9cdnDTWyWVFRgfT0dNjY2MDHx4c1tXZDG0AxM0GUzgCziZWSrTYxMUF+fj4KCwvh4eGBbt26\nsSJIBp69l6mpqejYsSO8vLyaPV9UVlZi3rx5SE5OxrFjxzB06FADrVQ7zpw5gwkTJmg8D5VKRTeX\n19bWanVOnDVrFoqLi3HhwgV9LtcgcMFCK1GpVFpNOvz+++/w9vZGfn4+LZvLrMUyMwnt7Q4J/J35\n0Lfxk1qtRlVVFZ15EIlEUKlUsLW11ai1UztzpVJJa9uzSc+BEIKSkhJkZWXROgBs2elVV1cjPT0d\nSqWyweeuPtSYYGVlJUQikcbIJvWjq5FNtVpNT624u7uz6qLHBgMopu8IFURQZllGRkZwdXVF586d\nDaK/oc1aKedWDw8PDRn6pvjrr78QFhaGnj174ocfftCJT4WukUqlKCoq0rgtMjISnp6eWLFiBQQC\nQbN/gxCCQYMGQSgU4ttvv9XXUg0GFyy0Em2Chbq6Oly5cgUA0Ldv32bHIdszm9Dexk+EEMhkMg2l\nyZqaGtjY2MDc3BxisRiWlpYQCASsySYoFApkZmZCJBLB29tbo/GtPWH2mXTr1q1VmSHmyCb1U182\nvDUTMJR/Ao/Hg1AoZE2fCVsNoIBnAUxaWhpsbGxgbW0NiUSi12BOW6gMjFwuh6+vb7MeMIQQHDly\nBB999BGWLVuGdevWsaJMpy31yxDTp09Ht27dsG3bNgDAhg0bEBQUhL59+0IikWDPnj34/vvvce3a\nNdY0bLaFF+edeoGgxiEzMjLA4/Hg7e2Nbt26afze0OOQz4Np/DRo0KB2MX7i8XiwsrKClZUV3TRZ\nXV2NzMxMlJeXw9TUFJWVlbhz545G5oGpqGdIqHFXe3t7DB48mDUpdGrCpqqqqk3NlU2NbFKBw+PH\nj+lgTpuRTcrjJDc3Fy4uLqzyT2AaQAUFBbEmGGVmYOoHMMxg7unTpygoKKAzc8xgTl+fS6pvwsHB\nAf369Wv2ol9dXY2lS5fi0qVLOHXqFMaMGdPuWZG2cv/+fY3PsFgsxuzZs1FSUgI+n4/+/fsjPj7+\npQgUAC6z0GrUajXq6uoa3E51wovFYnh5eaGwsBC9evVC586dWdfAyFbjJ+BvrwlLS0t4e3vDwsKC\nbppkGjMx1Q2p3ZU+X9O6ujp6jM7T05M1glQANMohHh4eei+HNOaySTXqMQW8FAoFLdkrEAharDWh\nL5gZGLYZQMlkMqSmpoIQolUGhsrMMb8b1dXV9ESSroJrQggKCgpQUFCgdd9EVlYWpk2bBnt7exw7\ndowe+eV4seCChVZSP1ioPw5JuUPevHkTXbp0Qbdu3TSyCe1ZcgDYa/zE9Jporp7N3F1R5QsADZom\ndTWG9+TJE2RkZMDW1hZeXl6s0SegApiKigp4eXm1Ww2Y2ahHXbCAZ98VKysr9OnTBw4ODqwYi6RK\nSJWVlRAIBKwZ1wNAZyW7dOnSpjFShUKh8X5IJBIYGxtrjGy25PtBjWvKZDL4+vo2q4NBCMGJEyew\naNEizJo1C59++ilr+nk4Wg4XLLQSZrAglUqRlpYGhULRYPyLSpv36NGD1ltozyCBrcZPwDNhloyM\nDFhZWdHZhJbQmD13XV2dxslRGyfB+jA9Ctzd3bVq4jIU1ESBtbU1fHx8YGZm1t5LAvAskMvKykJp\naSmcnJygVqvp96O9RzbZagClUqmQnZ2N0tLSBuJPuoDpekr9UO8H8zvS2GdILBYjJSUFfD4f3t7e\nzX6H5HI5Vq5ciRMnTuDQoUOYMGECa74zHK2DCxZaCSEENTU1yMvLQ2FhYZPjkKmpqRCJRHBycqJr\nwO0VXZeVlSEzMxM2Njbw8vJijdUrFcCUlZWhb9++OuuOp94j5sRFTU2NhtJkc4I4jZVD2ACzIY9t\nEwWVlZVIS0uDqakpBAIB/ZpR70djI5t8Pl9v4l0UarWa7tx3d3dnVaBM9U0YGxtDKBQa5HNGCGlQ\nSqqqqqLlqqmRzYqKCuTn56Nv375aaZoUFBRg+vTpIITgxx9/RJ8+ffT+XDj0DxcstBKZTIZr167B\nxMTkueOQCoUCIpFIww6aulhRJ0d97wZra2uRnZ2Np0+fssr4CXiW2qd8MQzhXFlbW6uReZBKpRpa\n/vb29rC0tKQNcB49esS6DAx1Me7QoQOrpkOY9WxthYzqp8qZfSi67PKvqalBamoqlEqlVoJBhoIS\n8srOzmZF30RdXZ1G4yRV2uPz+XB0dHzuFAwhBL/88gs++OADTJ48Gbt27WJNqY6j7XDBQishhKCw\nsPC5fg6NjUPWDx6kUiksLS01PBV0taugZHRzcnLg4OBA91GwAaaRUXum9pVKpYY9t0QigZGREdRq\nNUxNTeHu7g4nJydWNL4xTZZ69eqlMYrb3tTU1NClOKFQ2Gpfh6ZGNuuXkloyckdJSbe1B0DXKJVK\nZGZmoqKiAgKBgDXqlcDf5lRWVlZwc3PTmIRhGpcxP4sbN27EoUOH8PXXX+O9995jTXDNoRu4YKEN\n1NbW0v9ffxxS294EZoc/dbEyMzPTCB5a08FcU1ODzMxMSKVSeHl5sUYDAGBvoyCV2i8uLoajoyMI\nIRpqetR7oisjoJZQXV2NtLQ0qFSqZgWWDAkVkGZnZ9NujLp8beqPbDL1N5ob2WQaQLFJBwMAJBIJ\n7TkhEAhY02vCHHFtypyKWbpYvnw54uLiYGNjA7VajXnz5mHixIno168f18z4ksEFC21AoVDQyou6\nGodUqVQawUNlZSVMTEzowKE5T4X6xk/u7u6s+dIyswkeHh4aWZn2prKyEunp6bTKJjUdwlTTo7JB\nCoWCbtKjAgh9vca6EFjSF3V1dcjMzMTTp0/h4+NjMIlruVxOK002NrJpZ2cHlUqFtLQ0WFhYwMfH\nhzUBKfNi3LNnT/Ts2ZM13wHKp6OyshK+vr7NqrcSQhAbG4tZs2bB398ffn5+uH37NhITE6FQKNrF\nPXLbtm1YvXo1Fi9e/FwPh1OnTmHt2rW0Y+eWLVswYcIEA670xYMLFtpAbW0tlEqlXsch1Wo1JBKJ\nRumC8lSgggeqpksZP8nlcnh7e+vd7bAlUM2VbMsmMJvetEnt12/SE4lEkMlksLKy0sgG6eL5yeVy\npKenQyaTwcfHh1XjfdREgY2NDby9vdt1Z1x/ZFMkEoEQAktLS3Tp0qXdskH1qaurQ3p6OiQSCYRC\nIWv0JoBnmY6UlBRaJbW5cqVKpcLnn3+OnTt3YseOHZg9ezb9vVGr1cjMzETPnj0N2k9z8+ZNTJo0\nCba2thgxYkSTwUJiYiJCQ0OxadMmTJgwAadPn8a6detw9epVBAYGGmy9LxpcsNBKEhMTsXXrVgwe\nPBhDhgzRSsVMFzA9FajgQaVSwczMDHK5HE5OTvDy8mJNb4JCoUB2djYrRYyokVcejwcfH59WK1dS\nfShMcSJmKcnOzg5WVlYtet5Und3JyckgAkvawlQVZFvjJyU/LJPJ0Lt3b7ofRSQStfvIplgsRmpq\nKj3iypbvJ5W5ysnJ0bop9cmTJ5g5cyYKCgoQExODgIAAA622aaqqqjBgwAB8/fXX2Lx583PdISdP\nngyJRKJh7vTqq6/SolEcjcMFC63k/v37OHr0KOLj45GYmAgACAoKwpAhQxASEoIBAwYY5IRA1T6V\nSiWsra1RVVVFaws0ZshkSEpLS5GVlQU+nw8vLy/W1GWZToz68MGgdrpUAFFZWQljY2ONiYumOvyZ\nqX221dmrqqqQlpYGABAIBKyZKACebwBFjQgyAzp9qBs2BtUInZ+fjz59+sDFxYU1wZVSqURGRgZE\nIhGEQqFWmavExESEh4dj4MCB+Pbbb1mTHQkPD4eDgwN27tzZrJW0i4sLli5diqVLl9K37dy5E7t2\n7WpgHsXxN5w3RCtxcXHB6tWrsXr1aiiVSty9exdxcXFISEjArl27IJfLMWjQIISEhGDIkCEYOHAg\nzM3NdXaiYKbPmRe8+toCWVlZGt3LVAChz0BGoVAgKyuLlaOaVVVVSE9Ph0qlQkBAgF7sh01MTODo\n6EiXgahSErXLLSgoaGDKZGdnB5FIhIyMDNjY2CA4OJg1wRWbZZG1MYDi8XiwsLCAhYUFunbtCkCz\nsfjRo0fIzMzU+chmbW0tXUbS12ettUilUqSkpMDc3BxBQUHNftbUajW+/PJLbN68GRs3bsTSpUtZ\n8xmIiYnBnTt3cPPmTa2OLykpaaBy6uzsjJKSEn0s76WBCxZ0gImJCQYOHIiBAwdi+fLlUKlUSE9P\nR2xsLBISEnDw4EGIRCIEBATQwUNQUFCLU9MUzzN+4vF4sLS0hKWlJW1exdxV3bt3j9Z6YAYPuuoh\nYBosse2CV1RUhLy8PLi4uKBXr14Gq2EbGRnRFyA3Nze6w596Tx4+fEhP1jg4OLBKIbK2tpY2pvLz\n82NV30RbDKA6dOgAJycnuilTpVLR6oZMYybmyCafz9e6HFRRUYG0tDTY29sjKCiINe6KhBA8fPgQ\nOTk59Cajuc+aWCzGnDlzcPfuXVy8eBFDhgwx0Gqb58GDB1i8eDEuXbrUonNY/edMqetyNA1XhjAA\narUaOTk5iIuLQ3x8PK5evYpHjx7Bz8+PDh4GDx4MPp//3A+sUqlEXl4eiouL22T8pFAo6F2uSCSi\nhYmohkmqQa8lXx6mXbOnpyecnZ1Z8+Wrrq5Geno6FAoFBAJBs13ehoQSWDIxMYGzszNtBkQpG9YP\n6Az5mlKpfQcHB3h5ebGmb8IQmY6mRjapILupRlYq43f//n3WKWuqVCoNXQdtGqD//PNPhIWFoW/f\nvvj+++9ZVRYDgDNnzmDChAkagb9KpQKPx4ORkRFqa2sbbAq4MkTr4IKFdoBSumMGD/n5+RAIBHTw\nEBISgo4dO9InmqSkJCgUCr0YP9XV1dE1dkrrwdTUVENlsqksCGXHnZWVBXt7e1Y1V1Jjavfu3UPX\nrl1ZJchTfwqjfmMZFdBRQZ1UKqXfE6ajoz4uREyPArY1pbanARSl/lm/kZXZ85CXl8c6lUjgWRYm\nJSUFpqamEAqFWpUdDh8+jFWrVuHf//431qxZw5rvDhOpVNrgAh8ZGQlPT0+sWLECAoGgwX0mT54M\nqVSKX3/9lb5t3LhxsLOz4xocnwMXLLAAKjVI9TzEx8cjKysLnp6e8Pf3R3FxMZKTk3Hx4kX0799f\n7ydulUql0aAnFothbGysETzY2NjQvQkikahd3Q4bo6amBunp6aipqWHd2CHVKEgIgUAg0GoKg6m/\nQf1Q5Q3qPbG1tW3zDptqmK3v68AG2GYAxRzZLCsrQ1VVFXg8nsb3hA0jm48ePUJWVhZdfmvuM1JV\nVYXFixfjjz/+wA8//ICRI0eyJljUhvoNjtOnT0e3bt2wbds2AMD169cxdOhQbNmyBW+++SbOnj2L\nNWvWcKOTzcAFCyyEEIKysjJ88cUX+Oqrr2BnZ4fa2lrw+Xy6ZBEaGtqoupo+YGo9MOfYCSG09bCj\noyMrGp6YNVlKUZBN9WJKkKdHjx7o06dPq18zykGQGdAxa+z29vZNavg3tTaqa1/bETpDwWYDKKaH\niIeHB6ytrTUCOkrAizmdZKggh3L+fPLkidZy0hkZGZg+fTocHR0RExND9z29SNQPFoYPHw43NzdE\nRUXRx5w8eRJr1qxBfn4+Lcr01ltvtdOKXwy4YIGlLFy4ENHR0di1axfee+89VFZWIiEhAXFxcbh6\n9Sru3LmDLl26ICQkhP7p27ev3i/YVMObWCxGp06doFQqIRKJoFKpNGq57bGjksvldDOet7c3q7T2\nmQJLAoFA5yNn9WvsIpEItbW1Gg6b9vb2jV6omL4OAoGAVV37bDWAAp6ZyaWkpAAAfH19GzRYMl0d\nKTXWqqoqg4xsVldXIyUlBcbGxvD19W22+Y8QguPHj2PJkiX44IMPsHXrVtb0qHCwAy5YYCmJiYno\n1atXo6l9SoL4+vXrdOni5s2bsLOzo0WihgwZAi8vL51dsAkhKCkpQVZWFjp27AgPDw/6wsO8UFF9\nD9SOikrJtqSTvC1rY5uIEXNtnTp1goeHh8EyHfW1BZgXKiqAEIvFyM7OhrOzMzw8PNo9Zc6ErQZQ\nwN9ro3phtA3SmSObYrEYEolEaw2OlqwtMzMT3bt31yp7JZfL8dFHH+Gnn37C4cOH8cYbb7Amc8PB\nHrhg4SWA0lZISkqig4cbN27A3NwcgwcPpssWvr6+rbpQyeVyZGZmQiKRaGVKxRTBoX4o8x9mPVcX\n6dja2lq64Y1thllMvQk2CCxRFypmIyvwzH64c+fOzfqOGAo2G0BRzZ9lZWU6WRtTg6OxclJLRjZV\nKhVycnJQUlICgUCglVdHXl4ewsPDYWxsjOPHj6NXr15tej4cLy9csPCSUltbi1u3btHBw/Xr1wE8\nU5mkyhb+/v7PvWAzHQXbumNnpmOpXW5b/RQoTQe22W8DQHl5OdLT08Hn81nRjMdEJBIhLS0NlpaW\n6N69O635UFlZSfuOUO+JLpomW0JlZSVSU1NZZwAF/D1R0KFDBwiFQr2sjRACmUymkRGqP7JpZ2fX\noPGUKonweDz4+vo225hKCMH58+cxd+5cTJ06FTt37mSNJgoHO+GChX8IlMpkfHw8Pa4pl8sxcOBA\numzBVJnMz89HTk4OLCws9LJjZ2o9UOlYSuuBulBZWFg0ustl7tip0T62QO3uHj9+DA8PD1YJLKnV\nauTl5eH+/fvo27cvevToobE2qmmS2fegUqnocpI+pcOZollsa7BkNs1qO1GgSxob2TQ1NaW/JyqV\nCvn5+ejWrZtWJRGFQoF169YhKioK+/fvx9SpU1nzWnOwFy5Y+IdCqUxSWg8JCQkQiUTw9/dH586d\ncfHiRbz//vvYtGmTQXbFlOkPsxmMeUKkdAXKy8uRkZFBj8+xaTckFouRlpYGMzMz1o0dVldXIzU1\nFYQQCIVCrRoFm9rl1pcOb+t7QBlA1dTUQCgUsqrBkumfoK2Qkb5hjjY/evQIcrkcRkZGGgFdUw3G\nDx8+RHh4OCQSCU6cOAEvL692eAYcLyJcsMAB4NmuMi4uDgsWLEBhYSE8PT2RkpICPz8/uuchODgY\ndnZ2BtmFUCdEZvYBeHYB69SpE1xdXdvcCKYrmKN9vXv3NthIqzYw1Q61bXh7HlQ5iXpfqqqqNDJC\nLe3uf54BVHvDLIkIBAJWBaY1NTVISUmhgz+1Wq0R1CkUCloLJT8/HyNGjEBubi7ef/99jB8/Hl99\n9RWrJks42A8XLHAAeCbrOmzYMEycOBFffPEF+Hw+rTKZkJCAq1evIi8vj1aZpJQmmSqT+oLS2Tc3\nN4ejoyOdKieEaGQeDF1fB1onsGQoFAoF0tPTIZVK9aZ2WL+7v7KykjZkYgp41f+MUAZQjx8/hqen\nZ6MGUO0FIQT379/HvXv3WFcSAYCysjKkp6fTOiL1MwjMkc0rV65gy5YtKCoqgqmpKfz9/REZGYnQ\n0FC4u7uz6nmxBc4nonG4YIEDwLN067Vr1zBs2LBGf0/Vbameh4SEBGRmZsLDw0MjeNBljV6pVNIX\nlPo6+9T4KNXZLxaLoVQqG1hz62vcjnlBcXFxYZUTI/Bsx56RkUFLcBtqlFSlUmk4bFIZIWbTpLGx\nMdLT02FkZAShUNgiAyh9QwVYVVVVEAqFrPIRUavVuHfvHoqLi+Ht7a1Vr05ZWRnef/99lJaWYvbs\n2SgtLcXVq1eRnJyMV155RUPyWJ/s27cP+/btQ2FhIQDAx8cH69atw7hx4xo9PioqCpGRkQ1ur6mp\n0WvTa11dHWvGrtkGFyxwtApKZZIpFJWSkgI3NzeN4KG1u7KnT58iIyMDZmZm8PHxafaCUr++TokS\n1W/O08WJgJKSlsvl8PHx0bnAUltgjs95eHigS5cu7bpLIoTQmSCRSISKigqoVCqYmZnR45q6el/a\nikgkQmpqKmxtbeHj48OKNVHI5XKkpKRApVLB19e3WW8YQgiuX7+OiIgIBAUF4dChQxqBT21tLcrK\nytCjRw99Lx0AcP78eRgbG6NPnz4AgCNHjmD79u24e/cufHx8GhwfFRWFxYsXIzs7W+N2XTczP3ny\nBFu2bMFrr72GUaNGAQAyMzMRHR0NZ2dnvPnmmwZ7jdgOFyxw6ARCCMRiMe1tkZCQQKtMUkJR2qhM\nqlQqevfUp08fuLi4tPpiV1NToxE8yGQyjea8phQNn/ccqVFSZ2dnVklJA898HSgHS6FQyKoGS4VC\ngYyMDFRWVqJv377054XS4GAqTerSMl0bKGO3goKCRqdE2pvy8nKkpaXRol7NZcvUajX27NmDLVu2\nYMuWLVi0aBGrsl4UDg4O2L59O2bMmNHgd1FRUViyZAmdmSu4gP8AACAASURBVNIXt27dwtSpUzF8\n+HBs2rQJWVlZGDt2LIYPH474+HiMHj0a8+fPx9ixY/W6jhcBLljg0AtUmSAxMRGxsbF06pNSmQwJ\nCUFoaKiGymRCQgLUajU9Y69LZ03g7xE0qnRBaT0wg4emLlKU26FYLIa3t7dWgjeGgjl22LNnT7i5\nubHq4tCcARTzfaFGAy0sLDRKF/qQRKYem5rE8PX1ha2trc4fo7Uw7a49PT3RtWvXZu8jEonwwQcf\nICUlBTExMRg8eLABVtoyVCoVTpw4gfDwcNy9exfe3t4NjomKisLMmTPRrVs3qFQq+Pn5YdOmTejf\nv7/O1qFWq2FkZIQjR45g9+7dePvtt1FWVoYBAwYgPDwcd+7cwYoVK2BjY4MNGzZAKBTq7LFfRLhg\ngcMgUCqTycnJiI2NRUJCApKSkmBmZobAwEAQQvDHH3/gwIEDePvttw1ysauvaEhZDjNVJi0tLelx\nTTs7O1ZZcAPP0tNpaWmQy+WsGztsrQFU/TFaiUQCExMTDVEiXUzCUMJZDg4O8PLyYlWWiHpfFQqF\n1p4Yt2/fxrRp0+Dl5YXvv/+eVd4oAJCamorg4GDI5XJYW1sjOjoa48ePb/TYGzdu4N69exAKhZBI\nJNi9ezd+/fVX/PXXX+jbt69O1qNQKOjv8urVq/Hzzz+jtrYWP//8M/0Y586dw2effQZfX198+umn\nrPp+GRouWOBoN2praxEdHY3Vq1dDoVDAzs4O5eXlCAwMpMsWzalM6hLKcri+HDIhBM7OznBzc2OF\nHDJFSUkJMjMzDe45oQ0ymQxpaWlQqVRa6zo0RX3XU2oShtnM2hLjMkqc6sGDB6wTzgL+nv5xdHTU\nyt9FrVbj4MGD+Pjjj7Fq1SqsWrWKVT4aFAqFAvfv34dYLMapU6dw8OBBxMXFNZpZqI9arcaAAQMw\ndOhQ7Nmzp03rWL9+PSIiIuDm5oZjx46hrq4OU6ZMwbRp0/D777/jyJEjeP311+njt2/fjjNnzuD1\n11/HypUr2/TYLzJcsMDRbhw/fhyRkZFYsWIFVq9eDR6P16TKJFW2YKpM6hOxWIzU1FSYmJjAwcEB\nVVVVtBwyM/PQHloPdXV1yM7OZqV3AqB/AyiqxMUsXVDGZcyRzcYaFCkXS10EMbqGEEJnYrQNYqRS\nKRYuXIj4+HhER0djxIgRrAp8nseoUaPQu3dv/Pe//9Xq+FmzZqG4uBgXLlxo8WNJpVLY2NigoqIC\n48ePR01NDQICAnD06FGcPHkSb7zxBjIyMjBjxgz06dMHa9euhbu7O4Bnm4jZs2fj5s2bOHz4MAIC\nAlr8+C8DXLDA0W48evQIJSUlGDBgQKO/V6lUyMjIoMsWCQkJePr0KQICAmihqMDAQJ3u9pmSyPUb\nLCk5ZOa4JlPrgdrh6jN4oHwdrKys4O3tzSrvhPYygKJKXMzShUwma+A9IpFIkJ6ezkqHTap3Qi6X\nw9fXVyu9joyMDISFhcHZ2RnHjh3TqqeBTYwcORI9evRAVFRUs8cSQjBo0CAIhUJ8++23LXqcGTNm\nID8/HxcvXoSpqSmuXLmCkSNHwsnJCX/++Se6dOkClUoFY2NjxMTE4PPPP8err76KVatW0e9Dfn4+\n8vLyMHr06NY81ZcCLljgeGFQq9XIzc2lJaqvXr2K4uJi+Pn50aOagwcPbrXKZFVVFVJTU8Hj8SAQ\nCJrddTK1HqiLFKX1wAwgdHFRYtb/2dixzzYDKIVCofG+SKVSAM/0Hrp06QI7OztYWVmx4jV8+vQp\nUlNTYW9vD29v72bLSYQQREdHY9myZZg/fz42b97MqhJUY6xevRrjxo1Djx49IJVKERMTg08//RS/\n/fYbRo8ejenTp6Nbt27Ytm0bAGDDhg0ICgpC3759IZFIsGfPHnz//fe4du0aBg0a1KLHTkhIwNix\nY7F27VqsWrUKMTEx2LNnD5KTk3H48GFMmzYNSqWSfg3Xrl2Ly5cvIyIiAh988EGDv/dPFW3iggWO\nFxZCCAoLCzWCh7y8PPj4+NA9DyEhIXBycnrul5s5TeDq6tpqo6DnaT00lx5/HtXV1UhLS4NarWad\nSiSbDaCAvz0xAMDFxQUymYxWmjQ2NtaYuDB0SYn6/Obn52vdAFpTU4Ply5fj7NmzOHLkCF577TVW\nvd5NMWPGDPzvf//D48ePwefz4evrixUrVtA79eHDh8PNzY3OMixduhQ//fQTSkpKwOfz0b9/f6xf\nvx7BwcEtelxKZGnfvn1YuHAhfv75Z7z66qsAgE8++QSffvopbt26BaFQCLlcDnNzcygUCkydOhV5\neXk4cuQI+vXrp9PX4kWFCxY4XhqepzJJaT3UV5nMzc3FkydP6AuxrhX7qPQ4VbqQyWS0pkBzRkxM\nt8Nu3bqhT58+rEqdy+VypKens9IACnjWO5GZmdmoJwbVNMnse1Cr1RoTF/pUAFUoFEhLS4NMJtN6\nZPPevXuYNm0azMzMcPz4cfTs2VMva3tZoEYjpVIpcnNzMWfOHCiVSpw8eRK9evVCRUUFwsPDkZub\ni8zMTPrzIRaLUV1djRs3buDtt99u52fBHrhggeOlhRCCJ0+eaAQPlMrk4MGDYWZmhmPHjmHDhg2Y\nPXu2QVK5jWk9WFpaagQPFhYWtIiRRCKBj48PK9wOmbDZAEqlUiErKwtPnjyBj4+PVpoYhBBUV1dr\nZIUoMyZmVkgXkzlisRgpKSng8/nw9vZuNtNECMHZs2cxb948hIWF4YsvvmCVqRVboPoOmMTFxWHi\nxIkYNWoU8vLycPv2bYwfPx4//vgjLCwskJ2djfHjx8Pd3R2fffYZNm7ciLq6Ovz444/0a/xPLTvU\nhwsWDMC2bduwevVqLF68GLt27Wr0mPbSQv8nQakG/vLLL9iwYQMePHgAV1dXyGQyDYnq5lQmdQlT\n60EsFkMikaBDhw5QKpWwsrKCp6cn+Hw+a05WbDaAAp51vaempqJDhw4QCoVt+u4ws0LUbpMp4kUp\nTWr73jBLNtr2nSgUCqxduxbfffcd/vvf/2Ly5Mms+SywiY0bN6J79+6IiIigv7tVVVUYPXo0+vfv\nj6+//hpPnjxBUlISJk2ahGXLlmHz5s0AgOTkZEycOBGWlpZwdHTExYsXWTUlwxbYsx14Sbl58yYO\nHDgAX1/fZo+1tbVtoIXOBQq6g8fjITs7Gx9++CFCQ0Nx/fp1mJubIzExEXFxcThx4gT+/e9/g8/n\na5QtvL299ZaO7tChA5ycnODk5ASVSoXs7Gw8fvwYDg4OUCqVuH37NkxMTDS6+ttL64FqADUyMkJg\nYCCrDKAoK+6cnBy4ubmhZ8+ebQ74LCwsYGFhQQdECoWCnri4f/8+0tPTYWpqqvHeNNU0WVdXRzuA\nBgQEaFWyuX//PsLDw2kxMw8PjzY9n5cN5o5fJpM1eM/Lyspw7949rF27FgDg5OSE1157DTt27MCi\nRYswaNAgvPHGGxg0aBCSk5NRUlICPz8/AI1nKf7pcJkFPVJVVYUBAwbg66+/xubNm+Hn5/fczIIh\ntND/6Tx58gS///47pk6d2uCkTln7JiUlIT4+HnFxcUhKSoKpqSktUT1kyBD4+vrq3GSI2hGbmJhA\nIBDQF2K1Wo3KykqNHS6Px9OQqNZ3Yx51Ic7NzUWPHj1Y57BZV1eHzMxMiEQiCIVCvVhxN4ZKpdKw\n5xaLxTAyMtLIPNja2kIqlSIlJQXW1tYQCARalR0uXbqEmTNn4s0338SXX36pc+nzlwmmU2R2djbM\nzc3h6uoKAOjVqxdmzpyJ1atX08FFaWkpgoKCYGNjg+joaAgEAo2/xwUKjcMFC3okPDwcDg4O2Llz\nJ4YPH95ssKBvLXSOllNbW4tbt27RPQ/Xr1+HWq1GUFAQHTwMGDCg1TVkZmpamx0xpfVQvzGPUjO0\nt7eHra2tzk52zN4JgUBgsAuxtlRWViIlJQVWVlYQCATtKsXN1OGgggelUglCCBwcHODq6go7O7vn\n9ncolUps2bIFX331FXbv3o3333+fKzs8h+XLlyMvLw+nT59GTU0NHB0d8eabb2Lv3r3g8/lYuXIl\nrl+/jh07dtA+GaWlpZg4cSKSkpKwcOFCfPHFF+38LF4MuDKEnoiJicGdO3dw8+ZNrY739PREVFSU\nhhZ6SEiITrXQOVqOmZkZ3c+watUqKJVK/Pnnn4iLi0NCQgK+/PJLyGQyBAYG0qWLgQMHwsLCotmT\nPHOawN/fX6tJDCMjI/D5fPD5fLi6umo05olEIjx48AB1dXUawQOfz29VAyLTACooKIhVnhjMIKt3\n795wdXVt94sq872hyg5isRhdu3aljchqa2s1HDb5fD5daiwtLUVkZCQePXqEa9eucSN7WuDq6orv\nvvsOd+7cwYABAxATE4OJEyciJCQECxYswKRJk5CVlYVly5bhm2++gbOzM86fP4/OnTujsLDwhROy\nak+4zIIeePDgAQICAnDp0iX6C99cZqE+utRC59AfarUa6enptNYDpTLp7+9PZx6CgoIa9BkUFBSg\nsLBQ574OlJohU2VSLpfDxsZGo7b+vFR4aw2gDIVCoUB6ejqqqqogFAp1Pu7aViQSCVJSUmBpadkg\n2yGXyzUyD19//TWSk5Ph7e2NW7duISgoCD/88APrnlN7Q41B1icpKQkLFizAv/71L/z73/+Gqakp\nPv74Y+zZswdnz57FK6+8gitXrmDHjh24ePEievXqhcePH+PIkSN46623AEBDkImjabhgQQ+cOXMG\nEyZM0EgFq1Qq8Hg8GBkZoba2Vqs0cVu00Dnah+ZUJgcMGIDjx4+jtLQUJ0+ehLOzs97XRF2gmF39\nzN2tvb09XUbRpQGUPqCyHdqOHRoSZpOltgJVjx8/xrZt23Djxg1UV1fj4cOHcHJyQmhoKMLCwvDa\na68ZZO379u3Dvn37UFhYCADw8fHBunXrMG7cuCbvc+rUKaxdu5bO7mzZsgUTJkzQ6zpXrVoFd3d3\njcmxyZMnIz8/H3FxcXSvz4gRI1BeXo6zZ8+iV69eAIA//vgDEokEgYGBrJvieRHgggU9IJVKUVRU\npHFbZGQkPD09sWLFigYNNY3RUi309evXY8OGDRq3OTs7o6SkpMn7xMXFYdmyZUhPT0fXrl3x0Ucf\nYc6cOc0+Fof2MFUmT548iUuXLqFz587o1KkTBg4cSEtUd+rUyWC798akkC0tLWFmZobKyko4Ozuz\nTjuBMlkqLCxkZbZDqVQiIyOjRU2WT58+xezZs5GRkYGYmBgEBQVBJpMhOTkZCQkJcHd3x+TJkw2w\neuD8+fMwNjZGnz59AABHjhzB9u3bcffuXfj4+DQ4PjExEaGhodi0aRMmTJiA06dPY926dbh69SoC\nAwP1ssZr164hNDQUAPDNN99g9OjRcHFxQVZWFoRCIY4ePUq/XtXV1XBzc8P48ePx6aefNggOuCbG\nlsMFCwaifhlC11ro69evx8mTJ3H58mX6NmNj4yYFaQoKCiAQCDBr1ix88MEHuHbtGubNm4djx45x\nqmU6RqVSYePGjdixYwc2btyISZMmISEhgc48ZGRkwN3dne6NCA0NNahtck1NDdLT01FZWQlzc3PU\n1NTAzMxMY+LC0tKy3S7OcrkcaWlpqK2t1dpkyZBQ0w7m5uYQCARaNbveunUL06ZNg0AgwHfffcc6\n0S0AcHBwwPbt2zFjxowGv5s8eTIkEolG1vPVV1+Fvb09jh071ubHpsoO1H8JIairq8OHH36ItLQ0\nGBsbo1+/fpgyZQoGDhyIKVOmoKSkBOfOnaPVMK9evYqhQ4fiiy++wOLFi1k1wfMiwp6twz+M+/fv\na3x4xWIxZs+eraGFHh8f3yLTFBMTE3Tu3FmrY/fv3w8XFxc6ePHy8sKtW7ewY8cOLljQMUZGRqiu\nrsb169fpHpZ3330X7777Lq0ymZCQgLi4OOzduxezZs2Cq6srnXUIDQ2Fq6urXk52TAOokJAQmJub\nQ6VSobKyEiKRCCUlJcjOzoaxsTEdOBhS66G8vBxpaWno2LEj/Pz8WJftePToEbKzs2lPkeZeE7Va\njQMHDmDt2rVYs2YNPvroI9btcFUqFU6cOIHq6uomvRgSExOxdOlSjdvGjh2rdU9Wc1Cf9cLCQvp1\nNTIyQpcuXWBvbw8/Pz9cvXoV06dPx6+//oqRI0di//79uHnzJkaOHAmVSoUhQ4bg4MGDGDFiBBco\n6AAus/CSsH79emzfvh18Ph9mZmYIDAzE1q1b6XpdfYYOHYr+/ftj9+7d9G2nT5/GpEmTIJPJWFUL\n/idBCEFlZSWdeUhISMDt27fRuXNnetoiJCQE7u7ubToBMk2MmquvUz4KzL4HptYDpSegyxOyWq3G\nvXv3UFxcDE9PT9Z1ratUKmRmZqK8vBxCoVCrzIBEIsGCBQtw7do1HDt2DMOGDWNVKSU1NRXBwcGQ\ny+WwtrZGdHQ0xo8f3+ixpqamiIqKwrvvvkvfFh0djcjISNTW1rZ5LWq1GuvXr8fmzZvx66+/IiQk\nBDY2NkhKSsKUKVNw5swZ9OvXD3PmzMHt27excOFCLFy4EMuXL8fatWuhUCg0GkubapDk0B4uWHhJ\nuHDhAmQyGdzd3VFaWorNmzcjKysL6enpjZ7I3N3dERERgdWrV9O3Xb9+HSEhIXj06BHXAMQSKBts\nSmUyISEBycnJbVKZbKsBlFqtbmDNrVKpNBwc+Xx+q3fMNTU1SElJgVqthq+vL+sEiaqqqpCSktIi\nSem0tDSEhYWhW7duOHbsmNYZQEOiUChw//59iMVinDp1CgcPHkRcXBy8vb0bHGtqaoojR45g6tSp\n9G0//PADZsyYAblcrpP13Lt3D1u2bMFvv/2GOXPmYNGiRbC3t8fMmTORn5+PP/74A8Az+2uRSIQj\nR45AqVSiqKiIO3/pAfbk9DjaBLNrWSgUIjg4GL1798aRI0ewbNmyRu/TmIJhY7dztB88Hg82NjYY\nM2YMxowZ00Bl8sKFC/jkk0+0VplkGkD169evVWl9IyMj2NrawtbWtoHWg1gsxsOHD6FQKMDn8zWy\nD9o8VmlpKTIyMtC5c2e4u7uzLkVPOVlqq2RJCMH333+P5cuXY9GiRdi4cSOrSilMTE1N6QbHgIAA\n3Lx5E7t378Z///vfBsd27ty5QfN0WVmZTqZ7KKXFPn364PDhw/jwww9x5swZXL9+HRcuXMCCBQuw\nbt06nDt3Dm+88QY2btyIP/74A0lJSSgqKgK3/9UP7PzUcrQZKysrCIVC5ObmNvr7pr7sJiYmrGy2\n4ngGj8eDhYUFhg8fjuHDhwN4pjJ5+/Zt2l3zs88+g1qtRmBgIF228PLywocffoju3btj7ty5Ot15\n8Xg8WFtbw9raGj169KC1HqisQ1ZWFmpqamith8YcHFUqFXJyclBSUgJvb2+DjJS2BMq3o6ysDL6+\nvujYsWOz95HJZPjwww/x888/4/jx4xg/fvwLFYgTQposKQQHB+P333/X6Fu4dOkSrZLYEi5fvoyA\ngABaW4J6jaiJhW3btuH8+fNYunQpRo8ejQ8++AD29vZ48OAB1Go1TExMMGbMGAQGBsLW1hY8Ho9z\nitQDXLDwklJbW4vMzEx61Kg+wcHBOH/+vMZtly5dQkBAgFb9Ci0d1YyNjcWIESMa3J6ZmQlPT89m\nH4+jaczMzDB48GAMHjwYK1eupFUmqeBh586dqKurQ6dOndC5c2fk5OSAz+drpTLZGng8HiwtLWFp\naUn3Gsjlcjp4uHfvHu3gSE1aFBcXo0OHDggKCoKFhYXO19QWqqurkZKSAmNjYwQFBWlVdsjJycH0\n6dNhZWWF27dvw83NTf8LbQOrV6/GuHHj0KNHD0ilUsTExCA2Nha//fYbgIbTW4sXL8bQoUPx2Wef\n4c0338TZs2dx+fJlXL16tUWPe+PGDYwZMwb79+9HRESERgBpbGwMQghMTU3x9ttvIyAgAP/3f/+H\no0ePIi8vD7m5uZg7dy6AZ4ENVU7jRJb0A/eKviQsX74cr7/+OlxcXFBWVobNmzdDIpEgPDwcwDMx\nk4cPH+K7774DAMyZMwd79+7FsmXLMGvWLCQmJuLQoUMtGnvy8fFpMKrZHNnZ2fRoE4AmRzs5Wo+J\niQkCAgLg7+8PS0tLXL58Ge+++y4EAgGuX7+OGTNmoKKiolmVSV1ibm6Ozp0707V6ysGxuLgYxcXF\nAJ65PObn59OZB30FMy2hpKQEGRkZ6N69O/r06aNV2eH06dOYP38+IiIisH37dlbJZDdFaWkppk2b\nhsePH4PP58PX1xe//fYbRo8eDaDh9NbgwYMRExODNWvWYO3atejduzeOHz/eIo0FQgiCgoKwZMkS\nrFmzBl5eXg02N9T7r1ar4erqilOnTmHfvn1ITU1FZmYmjhw5gsjISI3PCRco6AeuwfElYcqUKYiP\nj0d5eTmcnJwQFBSETZs20c1JERERKCwsRGxsLH2fuLg4LF26lBZlWrFihdaiTOvXr8eZM2fw559/\nanU8lVkQiUSclK2BuH//PkaNGoX9+/fjlVdeoW+nJg1iY2ORkJCAhIQEWmWSapocPHgw7O3t9Xax\nViqVyMrKQnl5OQQCAezs7DTMsSorK7W2f9YHarUa2dnZKCkpgY+PDzp16tTsfWpra/Hxxx8jOjoa\n33zzDSZOnNjuwQ6bYU4oBAcHQ6VSITo6mu6bqA9VWnjy5Al+/vlnnDlzBj/++GOrTdw4WgYXLHC0\nipaOalLBgpubG+RyOby9vbFmzZpGSxMcukMbpTpqjJIqWyQkJCAvLw8+Pj60UFRISIjOVCYpESMz\nMzMIBIJG0/pMrQfKR4HSeqCCBxsbG71cjGUyGVJSUsDj8eDr66tVWaSoqAjh4eFQKBT48ccf4e7u\nrvN1vYxQJQORSISePXti4sSJ+Pzzz1vkbsqpMRoGLljgaBUtHdXMzs5GfHw8/P39UVtbi++//x77\n9+9HbGwshg4d2g7PgKMpKLEhZvBAqUwyxzW7devWoos10zuhZ8+e6Nmzp9b3p7QemNkHAA2suds6\nS19WVob09HR06dJFKy0LQgh+++03zJ49G2+99Rb27NnDup4LNvG8C/vFixcxbtw4fPnll5g5c6ZW\nGQNOP8FwcMECh06orq5G79698dFHHzU5qlmf119/HTweD+fOndPz6jjaAiEE5eXlGsHDX3/9BVdX\nVzrrMGTIELi5uTV54q6rq0NGRgYqKyshFAphb2/f5jVJpVI6eKC0Hupbc2u746QMwB49eqT1NEZd\nXR02b96M/fv348svv0R4eDhXdngOzEDh8OHDKCoqgrGxMZYsWQIrKysYGRnRjpGnT5/GyJEjudeT\nRXDBAofOGD16NPr06YN9+/ZpdfyWLVtw9OhRZGZm6nllHLqkvsrk1atXcfv2bTg7O2toPVA78//9\n738oKipC//794ePjo5eGP0rrgRk8KBQK2Nra0qULOzu7Rid9KBEoQgh8fX1p58LnUVJSgoiICJSV\nleHEiRMQCoU6f04vK2+99RaSk5MREhKCW7duoWvXrti6dSvd3DhixAhUVFTgxIkT8PDwaOfVclBw\nwQKHTqitrUXv3r0xe/ZsrFu3Tqv7TJw4EU+fPqWV2DheTKgLdWJiImJjY3H16lUkJyfDxsYGvXr1\nwp9//omFCxdi7dq1ButUp8SrqMBBJBJpaD1QfQ+VlZVIS0vTWgSKEIKEhARERERg+PDhOHDggMZ0\nD0fTyOVyLF26FJmZmTh58iQ6duyIxMREhISEYMqUKfjoo4/g5+eHmpoa9OzZEwEBAYiKitJK04JD\n/3DFHo5WsXz5csTFxaGgoABJSUmYOHFig1HN6dOn08fv2rULZ86cQW5uLtLT07Fq1SqcOnUKCxYs\naNHjPnz4EGFhYXB0dISlpSX8/Pxw+/bt594nLi4O/v7+MDc3R69evbB///6WP2GOJqFEmUaPHo0t\nW7YgNjYWWVlZcHNzQ05ODkJDQ7Fv3z64urrinXfewe7du3Hr1i3U1dXpdU0WFhbo2rUrfHx8MGTI\nEISGhsLNzQ1qtRp5eXmIi4vDn3/+CRsbG9jZ2TW7HpVKhe3bt+Ptt9/GmjVrEB0dzQUKz6H+PlSp\nVGLAgAH4/PPP0bFjR3zxxRcYP348wsLC8Ouvv+K7777Dw4cPYWFhgR9++AGVlZVaZXk4DAM3kMrR\nKoqLizF16lSNUc0bN27A1dUVwDNZ3Pv379PHKxQKLF++nD4Z+Pj44JdffmnSqKYxRCIRQkJCMGLE\nCFy4cAGdOnVCXl7ec0cxCwoKMH78eMyaNQtHjx6lrbidnJw4d009IZFIEBwcjNDQUPz+++/g8/lQ\nKBS4devWc1Um/f399ToGR2k92NnZoaqqClZWVujRowdkMhnu37+P9PR0mJub05kHKysrummyoqIC\ns2bNQnZ2Nq5cudIiN9h/Io01MlpbW2PMmDFwdXXF/v37cfDgQRw4cADvvPMOFi9ejJiYGLi5uSEy\nMhIjR47EyJEj22n1HI3BlSE4XhhWrlyJa9euISEhQev7rFixAufOndPoi5gzZw7++usvJCYm6mOZ\nHACSkpIwaNCgJhvUlEol/vrrL9oc6+rVq6iursagQYNoW+6BAwfqXJiJsrx2cnKCp6enxgVNqVTS\nY5oikQjffPMNLly4AIFAgJycHHh4eNDpc0Ozbds2/PTTT8jKyoKFhQUGDx6Mzz777Lk1/aioKERG\nRja4vaamRisVyrZy79497N27F66urujbty9ee+01+neTJk1C9+7d8Z///AcAMHPmTJw8eRICgQAn\nT56kxbu4aQf2wGUWOF4Yzp07h7Fjx+Kdd95BXFwcunXrhnnz5mHWrFlN3icxMRFjxozRuG3s2LE4\ndOgQ6urqOCtuPdGckp+JiQn8/f3h7++PZcuWQa1WIyMjgxaKioqKQnl5Ofz9/enMQ1BQUKu1FdRq\nNfLz83H//v0mLa9NTEzQsWNHOhjw8PCAo6MjYmNjL2p2pwAAG6JJREFUYW1tjZs3b8LT0xOhoaF4\n++23ERYW1uJ1tJa4uDjMnz8fAwcOhFKpxMcff4wxY8YgIyPjua6ctra2yM7O1rhNX4EC88IeGxuL\nUaNGITQ0FFeuXMG9e/ewatUqLF++HHK5nB7FraysRE1NDSQSCS5dugQXFxcNR04uUGAPXLDA8cKQ\nn5+Pffv2YdmyZVi9ejWSk5OxaNEimJmZafRHMCkpKWkwBufs7AylUony8nLOypYlGBkZQSAQQCAQ\nYMGCBbTKZHx8PK00+uDBA/Tr14+ettBWZbK2thapqalQKBQYNGgQrK2tm11PZWUl5s2bh+TkZERH\nR2PYsGGoq6vDnTt3EB8fD4lEoqunrhWURwPF4cOH0alTJ9y+ffu5OiU8Hs9gdtjUhT06Ohr5+fnY\ns2cP5s2bB4lEgjNnziAyMhJdunTBjBkzEBYWhk2bNuHixYvIzc3FuHHj6NIOJ7LETrhggaNJCCH0\nboEN885qtRoBAQHYunUrAKB///5IT0/Hvn37mgwWAM6K+0XEyMgI7u7ucHd3x8yZM0EIQVFREV22\nWLNmDa0ySQlFNaYyWVRUhMLCQjg6OsLPz0+raYyUlBSEhYXB1dUVd+7coYPNDh06IDAwsEX+B/qi\nsrISAJpVOqyqqoKrqytUKhX8/PywadMm9O/fX2/rOnHiBJYvXw6ZTIbTp08DeJbdmD59OlJSUrBi\nxQpMnz4dK1euhJubGx4/foyuXbti8uTJAJ59N7lAgZ1wOR4ODagLqUqlAo/Hg7GxMWsuql26dKG9\nLii8vLw0Ginrw1lxvxzweDy4ubkhPDwcBw8eRHZ2Nh48eIBVq1aBx+Ph008/Re/eveHv748FCxYg\nOjoaixcvxvDhw9GjRw/4+Pg0GygQQnDkyBGMGjUKU6dOxcWLF1lnlQ08W+eyZcswZMgQCASCJo/z\n9PREVFQUzp07h2PHjsHc3BwhISFN2ta3FJVK1eC2wMBAhIWFQSqVQiqVAgBtc71ixQp06NABJ06c\nAPDMz2bp0qV0oECdczhYCuHgqEdSUhJZtGgRCQkJIZMmTSIxMTHk6dOn7b0sMnXqVDJkyBCN25Ys\nWUKCg4ObvM9HH31EvLy8NG6bM2cOCQoKatFjFxcXk/fee484ODgQCwsL0q9fP3Lr1q0mj79y5QoB\n0OAnMzOzRY/LoR1qtZqUlZWRU6dOkZkzZxIbGxtia2tL+vXrR8LCwsi+fftIamoqkUqlpLq6usFP\nWVkZCQsLIx07diS//vorUavV7f2UmmTevHnE1dWVPHjwoEX3U6lUpF+/fmThwoVtXoNSqaT//9Kl\nS+TGjRukpKSEEELIvXv3yPjx44lQKCSPHj2ij8vKyiLdu3cnV65cafPjcxgeLljg0CAlJYV07NiR\njB8/nhw8eJDMnTuX+Pn5kVdeeYXcvXu3XdeWnJxMTExMyJYtW0hubi754YcfiKWlJTl69Ch9zMqV\nK8m0adPof+fn5xNLS0uydOlSkpGRQQ4dOkQ6dOhATp48qfXjPn36lLi6upKIiAiSlJRECgoKyOXL\nl8m9e/eavA8VLGRnZ5PHjx/TP8yTLIfuiYuLI127diWTJk0iRUVF5Pz582T58uUkKCiIdOjQgXTr\n1o288847ZPfu3eTWrVtEKpWSO3fuEB8fHxIcHEyKiora+yk8lwULFpDu3buT/Pz8Vt1/5syZ5NVX\nX9XJWioqKkhwcDBxd3cnffv2JR4eHuTQoUNEqVSSy5cvk4CAADJs2DCSlZVFioqKyCeffEK6dOlC\nUlNTdfL4HIaFCxY4NFi3bh1xd3cnYrGYvi03N5f85z//IdevX9c4Vq1Wk7q6OqJSqQy2vvPnzxOB\nQEDMzMyIp6cnOXDggMbvw8PDybBhwzRui42NJf379yempqbEzc2N7Nu3r0WPuWLFigYZjeagggWR\nSNSi+3G0jX379pGvvvqqQWZArVYTqVRKLl26RD7++GMydOhQYm5uTvh8PjE1NSVLliwhtbW17bTq\n5lGr1WT+/Pmka9euJCcnp9V/IyAggERGRmp9n8a+2yqVipSXl5MRI0aQKVOmkIqKCkIIIUOHDiW9\nevUid+/eJSqVihw4cIDY29sTPp9PIiIiiKenJ0lISGjV2jnaHy5Y4NDgiy++IL179yYZGRkNfqdQ\nKNphRe2Pl5cXWbJkCZk4cSJxcnIifn5+DYKU+lDBgpubG+ncuTN55ZVXyB9//GGgFXM0h1qtJjKZ\njJw6dYp8/PHHrC47EELI3LlzCZ/PJ7GxsRqZKplMRh8zbdo0snLlSvrf69evJ7/99hvJy8sjd+/e\nJZGRkcTExIQkJSVp9ZhUoKBQKEhGRgaprq6mf1dQUED8/f3J48ePCSHPNhnW1tYa3wuRSERWrVpF\nvLy8yMGDBxv8XY4XCy5Y4NCgpKSEDB06lJiampKIiAgSGxtLp86pL/njx4/JgQMHyNixY8nUqVPJ\n2bNnmwwk1Gr1C596NzMzI2ZmZmTVqlXkzp07ZP/+/cTc3JwcOXKkyftkZWWRAwcOkNu3b5Pr16+T\nuXPnEh6PR+Li4gy4co6Xhcb6XwCQw4cP08cMGzaMhIeH0/9esmQJcXFxIaampsTJyYmMGTOmQXaw\nMZiB07Vr10hwcDCZNm0aiY2NpW8/d+4ccXd3JwqFggwfPpx4enqSGzduEEIIkclkJDk5mRBCSGpq\nKgkLCyMDBw4kDx8+JISQF/588E+FU3DkaJTo6GicOnUKFRUVmDNnDqZMmQLg2SjWsGHDYGtri7Fj\nx6KgoADx8fFYvXo1pk2bBuCZtoGZmVmbbYjZgqmpKQICAnD9+nX6tkWLFuHmzZstUoHkLLk5XiT+\n85//4OOPP8aHH36I0NBQDBkyhBaAevLkCQIDA1FUVISpU6di165dtJjViRMn8Pvvv2Pbtm1wdHTE\n5cuXsXXrVhBCcOXKlfZ8ShxtgNNZ4GiUSZMmITAwEFu3bsXs2bPRq1cv9O/fH3v37kVRURHKy8vp\nY8+dO4fp06fjtddeg729PQ4fPoxvvvkGW7duxZ07d+Dq6opJkybBycmpweNQ41dMLQdCCHg8HmvE\nWZoa2Tx16lSL/k5QUBCOHj2qy6VxcOiFs2fP4uDBgzhz5gzGjh3b4PdWVlaYNm0aDhw4gEmTJtGB\nQnJyMjZv3ozhw4fT4lejRo1CVlYW8vLyWPOd5mg5nM4CB83JkyeRk5MD4Jn0be/evbFt2zY4OTkh\nNjYW1dXVuHLlCkQiETp27Ah/f39s3rwZMpkM9vb2KCgoQG1tLUpLS1FSUoKoqCioVCp89dVXmDx5\nMmQyGf1YVJBgbGzcQMuB+t2ECRMwd+5cek67vQgJCWkgmZuTk0ObZmnL3bt3W6QY6ebmBh6P1+Bn\n/vz5Td7n1KlT8Pb2hpmZGby9vWlhHA6OlnD37l306NEDwcHB9G35+fn4888/8fvvv0Mmk2Hx4sUY\nN24c3nnnHYwZMwZTp07F6NGj8corr2D37t0wMzOjv8uzZs3Czp07uUDhBYbLLHDQHDt2DL/88gsi\nIyMRGBiIuro6/PDDD6iqqoKPjw8IIcjKysLevXsxfvx4nDx5EleuXMHevXthY2ODqqoqSKVS3Lhx\nAwMHDsT3338PJycnvPvuu5gwYQK++eYbLF68GCqVCv/73/+wc+dOAMArr7yCyZMnw8XFBQDoE0pS\nUhLmz5//XDEdKguhT5YuXYrBgwdj69atmDRpEpKTk3HgwAEcOHCAPmbVqlV4+PAhvvvuOwDPLLnd\n3Nzg4+MDhUKBo0eP4tSpUy3KRty8eVND+CYtLQ2jR4/GO++80+jxiYmJmDx5MjZt2oQJEybg9OnT\nmDRpEq5evcoK1UGOF4fCwkJUV1dDqVRCoVBgzZo1SE1NRVJSEgDA0dERcXFx+PbbbzFkyBB6k/HT\nTz/RbpHMLII+3UQ5DER7NkxwsAe1Wk3i4uLIlClTiIODA93B7+bmRmbPnk2qqqrI/7d37zFNnW8c\nwL9QoNykMoFIFaqiFgSmRhyCaH4mChOdqEwR48CxKUZFLtmGGnXq5JYsZjNZpnPjYgCnY2xeyKLg\nBYTq5izd5OYcFtwUdchKi1Sh7fP7g/UIUlBUkMv7SfzDl/ec89Jo+/ac50JEZG9vT4cOHepwbEtL\nC1VXV5NOp6OioiISi8Vc9LM+mGnJkiUUGhpKRG352Xl5ebR//37avXs3eXl5kb+/P929e5cLrrp7\n9y4ZGRlRfn5+l2t++PDhS38dutLTlM2UlBRycXEhc3NzsrW1JT8/P8rLy3uhNURHR5OLi0uXkfvL\nly/vlEMfEBBAK1aseKHrMkPPjRs3yNTUlMRiMZmYmNC0adNoz549JJFI6MKFC+Tt7d3lvyudTscy\nHgYhtllgDLp06RKlpqZ2youOi4sjT09PkslkRNSWIdHY2Mj9/MCBA2RnZ0fXrl0joscf6NOmTaPY\n2FiD19LpdOTp6Ulbt27lxjIzM8nOzq7LwkdKpZKCgoK6POdg8+jRIxoxYgQlJCR0OcfJyYn27t3b\nYWzv3r3k7Ozc28tjBqHy8nLKysqio0ePklKpJLVaTURtXwDeeustCg4OJqLHWVL9Pf2UeTHsMQTD\n0el0XCOXrhrm7Ny5E3fu3MG8efMgFovh4eEBS0tLREVFYdSoUaioqIBKpeKezfP5fKjVapSVlSEu\nLg4AUF5ejszMTJSWlsLe3h7vv/8+hg8fjqamJu7W5YkTJzBlyhQucEqP/nvsIJfL0djYCEtLS27t\ng7md7Y8//giFQoHVq1d3OaerDptP9sZgmGcxadKkToG9AKBSqfDw4UOu26X+/x3r6zC4Dd53V6bH\njI2NuWeM9F/HyfaICMOGDUNWVhbOnz+PJUuWcK2Fx4wZg1u3bqG2thbm5ubYs2cPAKCurg7btm2D\npaUlli1bhoaGBixevBjFxcUICAgAn8/Hhg0bUFxcjFGjRkGj0QAAioqK4Ofn16mdMP2X6VtWVga1\nWv3UZ/FEBI1G0+l3GWi++eYbzJ8/H0KhsNt5hjpssjdx5mV48OABSktLMX/+fKhUqm47vTKDD7uz\nwBikj7x/ckz/4WPoW4dcLkddXR2ioqJw8+ZNeHp6gs/no7m5GUlJSTA1NUVBQQEUCgWOHj3Ktcr9\n448/4OPjAycnJ/D5fPz777+4c+cO3njjjU7R0/pvMRUVFTAzM4Onpye3Nj39XQb9Wp+lLXF/Vltb\ni4KCAuTm5nY7r6sOm/2xcyIzsOzduxeXLl1CaWkpfH19kZGRAWDw39FjHhvY76JMn2tfC0Gn08HI\nyIh7s5DL5VAqlQgLC8OoUaOQnp6Ou3fvIiQkhNtYCAQC2NjYQCqVYurUqZDJZEhOTgafz4eLiwsA\nID8/HwKBgPv7k9RqNaqrqzFy5EiMGTOmw7qAtg1FZWUlsrKycPbsWYwdOxZhYWGYN2+ewTe29o9f\n+qO0tDQ4ODhgwYIF3c7z8fFBfn4+YmNjubHTp0/D19e3t5fIDHI+Pj64d+8eVq9ejcDAQACARqMZ\n8BtxpgdeUawEM8g8evSI1q5dS2KxuNt5Wq2WYmNjycLCgtzd3SkyMpLMzMxo+fLlJJfLiehxZsGT\nTZj0AVRlZWU0Z84c2rZtG3fO9mQyGTk5OVFISAh99dVXFBERQa+//jqdOXOGm1NdXc01wOnPtFot\nOTs7U3x8fKefPdkLoKSkhHg8HiUnJ1NlZSUlJyeTiYkJV4b3WYlEIoOlhdevX29wflpamsH5+oC4\noSAxMZG8vLzI2tqa7O3tKSgoiKqqqp56XE5ODrm5uZGZmRm5ublRbm5uH6z2+bQv6c6yHYYetllg\nXoqWlhbKycmh5ORkIiJqbW0ljUbT5ZtKQ0MDnTx5kuRyOQUFBdHWrVtJpVIREZGtrS1t2bKFWltb\nOxyjP9eRI0fI29ubazOt0Wi4jcSdO3fonXfeIS8vrw7HJiQk0MSJE4morXb9mjVrSCwWU15eHoWF\nhdGBAweooaHB4Fo1Gk239ex7Mwr81KlTXKvrJz3ZC4CI6LvvviOxWEympqbk6upK33//fY+vee/e\nvQ7NivLz8wkAnTt3zuD8tLQ0srGx6XCMvsHQUBEQEEBpaWlUVlZGMpmMFixYQM7OzlzKsSESiYR4\nPB4lJiZSZWUlJSYmPtfmjmH6AusNwfQ5MhB0p8+CaG1thbe3N3bu3IlFixYZPG7Xrl04c+YMUlNT\nMX78+A4/KyoqQnR0NCorK2FlZQVnZ2esXLkSCoUCeXl5OHXqFHQ6HSIjI1FUVITw8HBYWVkhJycH\nfn5+SE1NfWpQYPvntEPhVmxMTAxOnjyJ69evG3xd0tPTERMTA4VC8QpW1z/9888/cHBwQGFhIZc1\n8KSQkBAolUr89NNP3Nibb74JW1tbHD58uK+WyjDPhEWmMH2ufdyD/g+PxwMRwdTUFFKptNNGQX9c\nS0sLZDIZiAgCgaDTObVaLWpqalBSUgKJRIKwsDAUFhYiPT0dAoEALS0tqKurg1QqRVxcHD7//HMk\nJiYiLi4O586dg0Qi4a5TUFCAwMBA+Pn5ISMjAyqVCsDjIEsiwtixY5Gdnd0h4+LMmTPYtGkT1Gp1\nr76OfUFffTIiIqLbDVRTUxNEIhFGjx6NhQsXorS0tA9X2f80NjYCAF577bUu51y8eBH+/v4dxgIC\nAjo0LGOY/oJtFphXpn2/A/3fdTpdt2mODx48gKOjI0pKSjBx4kTMnDkT27dvx9mzZ/Hw4UOIRCI0\nNzfDyMgIYrEYsbGxOHnyJGpqapCVlQUnJyf8/vvvsLS0xNKlS7nzuri4YNiwYVAqlQCAffv2ISIi\nAtbW1vD398fp06exadMmzJ07F1euXIFKpcLBgwfB4/Ewfvx4mJiYwNjYGK2trbhw4QIOHjwICwsL\nDPQbd89S38HV1RXp6ek4fvw4Dh8+DHNzc8ycORPXr1/vu4X2I0SEuLg4+Pn5wcPDo8t5rC4GM6C8\nimcfDPMylJSU0NatW8nT05OEQiFXhnrZsmU0Z84cunnzJhERNTU1kUKhIKK22Ir4+PhOMQ2pqak0\nevRoun37NhG1xU188sknXHXKvLw8sre3J19fXyovL6eSkhISCARkZGREbm5utHbtWqqpqaH6+npa\nunQpvf3229y5tVrtgA0I8/f3p4ULF/boGK1WS5MnT6aoqKheWlX/tn79ehKJRPTXX391O8/U1JSy\ns7M7jGVmZhKfz+/N5THMcxncD1uZQYf+S9nk8Xjw9fWFr68vEhISALTddQCAhIQEbNy4EZMnT4aH\nhwdEIhEmTJiAmJgYNDc3o7q6Gm5ubtw51Wo1KioqYGdnB0dHRxQUFKCpqQnvvfcebGxsAACBgYGw\nsLCAs7MzhEIhJk2ahKlTp2LEiBHw9fVFTk4O5HI5XF1d8dtvvyEmJgYPHjyAsbExLCws+v6Fegme\ntb7Dk4yNjTF9+vQheWchKioKx48fR1FREUaPHt3tXFYXgxlI2GMIZkAxMjLi6iHodDpoNBquM6OV\nlRV0Oh0mTJiAU6dOobi4GMHBwRAKhfDx8YGNjQ2qqqoglUrh5eXFnbO+vh4VFRWYMmUKAODq1asQ\nCoVwdHTkKkr+/fffsLa2hpubG4YPHw61Wg25XI7Zs2cjLi4OEokE//vf/3DlyhU0Njbil19+wcqV\nK2Fra4uQkBDcv3+/j1+pF/es9R2eRESQyWQ9ascNtAWLbtu2DWPHjoWFhQXGjRuH3bt3P7X6ZmFh\nIaZNmwZzc3OMGzcO+/fv79F1XwYiwsaNG5Gbm8vV9ngafV2M9lhdDKa/YncWmAHL2Ni4U5Gl9pUb\nDVWZdHJywpIlS7g2ugBQXV2N8vJyhISEAGgLTrO1tUV9fT3Xm+Ly5ctobW3lsi9+/vlnEFGHwlFa\nrRZlZWVQKBQQi8WIjIzEjRs3sGzZMhw7dgwRERG98jr0Bp1Oh7S0NISHh3fK9tAX3UpKSgIA7Nq1\nCzNmzMCECROgVCqxb98+yGQyfPHFFz26ZkpKCvbv34+MjAy4u7vj119/xbvvvguBQIDo6GiDx8jl\ncgQGBmLNmjXIzMxESUkJ1q9fD3t7ewQHBz/fL/8cNmzYgOzsbBw7dgzDhg3j7hgIBALuztKTr1t0\ndDRmz56NlJQUBAUF4dixYygoKEBxcXGfrZthntkrfQjCML1Ip9N1W+tBTyKRkLe3N1cU6uLFiyQS\niejLL78kIqLS0lKaNWsWubm5kVQqJSKijz/+mLy9venq1avceRoaGig4OJjmzp3LjSmVSgoODqag\noCBuTQNBT+o7xMTEkLOzM5mZmZG9vT35+/uTRCLp8TUXLFhAERERHcaWLl1Kq1at6vKYjz76iFxd\nXTuMRUZG0owZM3p8/RcBA0WpAFBaWho3p7fqYjBMX2CbBWZIedZgwx07dpCVlRV5eHjQihUraOTI\nkRQaGspVfVy0aBGtWrWK6uvruWOqqqrI1dW1Q5tohUJBAQEB3IfEQA107AtJSUkkEom4DYpMJiMH\nB4dOQYDtzZo1izZt2tRhLDc3l0xMTDpUHGQY5sWwxxDMkNJVbwh9CmdLSwuampqwa9cuREVFoaqq\nCiYmJrh27Rrc3d25vHkHBwfcvn0bw4cP585TW1uLuro6zJ07lxurr6/HlStX8NlnnwFgbXy7Ex8f\nj8bGRri6uoLH40Gr1SIhIQGhoaFdHtNV+qFGo0F9fX2P4yYYhjGMBTgyQ56xsTH3Id7c3Iz09HSk\np6fDzs4OYrEYX3/9Ne7fv9+hgE54eDjKysogFAqxceNGAG2BkdbW1lwnTAC4ceMG7t+/j3nz5gFg\nm4XuHDlyBJmZmcjOzoZUKkVGRgY+/fRTrsNhVwy15TY0zjDM82N3FhimHQsLC7S0tCA+Ph4ffPAB\nbG1tYWlpid27d2P69OncPD8/P/z555/Iy8vjCjldvnwZI0eOBPA4xVMqlcLR0REODg5PLSM91H34\n4YfYvHkzVqxYAQDw9PREbW0tkpKSEB4ebvCYrtIPTUxMMGLEiF5fM8MMFWyzwDDt8Pl8bN68GZs3\nb8b169dRVVUFHx8fLitCj/4rTb148WJu7Ntvv0VdXR2Atm+1zc3NOHHiBJdBoa8PwRjW3Nzc6TER\nj8frNnXSx8cHJ06c6DB2+vRpeHl5wdTUtFfWyTBDEWskxTAvoH1TKUPKy8tBRPDw8GB3Fp5i9erV\nKCgowIEDB+Du7o7S0lKsXbsWERERSElJAQBs2bIFt27dwqFDhwC0pU56eHggMjISa9aswcWLF7Fu\n3TocPny4T1MnGWawY5sFhmH6BZVKhe3bt+OHH37AvXv3IBQKERoaih07dsDMzAxA24aipqYG58+f\n544rLCxEbGwsysvLIRQKER8fj3Xr1r2i34JhBie2WWCYXsTuJjAMMxiwbAiG6UVso8AwzGDANgsM\nwzAMw3SLbRYYhmEYhukW2ywwDMMwDNMttllgGIZhGKZbbLPAMAzDMEy3/g911SZz8hdt8wAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c8967400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XbwQ8EQsFTCYVDplA0zT8fj/OnDkDn8+H1tZW2dozF2rOglj62dPTA41Ggy1b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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c86b0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "\n",
    "show_iris()\n",
    "show_iris(0, 1, 3)\n",
    "show_iris(1, 2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can play around with the values to get a good look at the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DISTANCE FUNCTIONS\n",
    "\n",
    "In a lot of algorithms (like the *k-Nearest Neighbors* algorithm), there is a need to compare items, finding how *similar* or *close* they are. For that we have many different functions at our disposal. Below are the functions implemented in the module:\n",
    "\n",
    "### Manhattan Distance (`manhattan_distance`)\n",
    "\n",
    "One of the simplest distance functions. It calculates the difference between the coordinates/features of two items. To understand how it works, imagine a 2D grid with coordinates *x* and *y*. In that grid we have two items, at the squares positioned at `(1,2)` and `(3,4)`. The difference between their two coordinates is `3-1=2` and `4-2=2`. If we sum these up we get `4`. That means to get from `(1,2)` to `(3,4)` we need four moves; two to the right and two more up. The function works similarly for n-dimensional grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manhattan Distance between (1,2) and (3,4) is 4\n"
     ]
    }
   ],
   "source": [
    "def manhattan_distance(X, Y):\n",
    "    return sum([abs(x - y) for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = manhattan_distance([1,2], [3,4])\n",
    "print(\"Manhattan Distance between (1,2) and (3,4) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Euclidean Distance (`euclidean_distance`)\n",
    "\n",
    "Probably the most popular distance function. It returns the square root of the sum of the squared differences between individual elements of two items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean Distance between (1,2) and (3,4) is 2.8284271247461903\n"
     ]
    }
   ],
   "source": [
    "def euclidean_distance(X, Y):\n",
    "    return math.sqrt(sum([(x - y)**2 for x, y in zip(X,Y)]))\n",
    "\n",
    "\n",
    "distance = euclidean_distance([1,2], [3,4])\n",
    "print(\"Euclidean Distance between (1,2) and (3,4) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hamming Distance (`hamming_distance`)\n",
    "\n",
    "This function counts the number of differences between single elements in two items. For example, if we have two binary strings \"111\" and \"011\" the function will return 1, since the two strings only differ at the first element. The function works the same way for non-binary strings too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamming Distance between 'abc' and 'abb' is 1\n"
     ]
    }
   ],
   "source": [
    "def hamming_distance(X, Y):\n",
    "    return sum(x != y for x, y in zip(X, Y))\n",
    "\n",
    "\n",
    "distance = hamming_distance(['a','b','c'], ['a','b','b'])\n",
    "print(\"Hamming Distance between 'abc' and 'abb' is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Boolean Error (`mean_boolean_error`)\n",
    "\n",
    "To calculate this distance, we find the ratio of different elements over all elements of two items. For example, if the two items are `(1,2,3)` and `(1,4,5)`, the ration of different/all elements is 2/3, since they differ in two out of three elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Boolean Error Distance between (1,2,3) and (1,4,5) is 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "def mean_boolean_error(X, Y):\n",
    "    return mean(int(x != y) for x, y in zip(X, Y))\n",
    "\n",
    "\n",
    "distance = mean_boolean_error([1,2,3], [1,4,5])\n",
    "print(\"Mean Boolean Error Distance between (1,2,3) and (1,4,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Error (`mean_error`)\n",
    "\n",
    "This function finds the mean difference of single elements between two items. For example, if the two items are `(1,0,5)` and `(3,10,5)`, their error distance is `(3-1) + (10-0) + (5-5) = 2 + 10 + 0 = 12`. The mean error distance therefore is `12/3=4`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Error Distance between (1,0,5) and (3,10,5) is 4\n"
     ]
    }
   ],
   "source": [
    "def mean_error(X, Y):\n",
    "    return mean([abs(x - y) for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = mean_error([1,0,5], [3,10,5])\n",
    "print(\"Mean Error Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Square Error (`ms_error`)\n",
    "\n",
    "This is very similar to the `Mean Error`, but instead of calculating the difference between elements, we are calculating the *square* of the differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Distance between (1,0,5) and (3,10,5) is 34.666666666666664\n"
     ]
    }
   ],
   "source": [
    "def ms_error(X, Y):\n",
    "    return mean([(x - y)**2 for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = ms_error([1,0,5], [3,10,5])\n",
    "print(\"Mean Square Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Root of Mean Square Error (`rms_error`)\n",
    "\n",
    "This is the square root of `Mean Square Error`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root of Mean Error Distance between (1,0,5) and (3,10,5) is 5.887840577551898\n"
     ]
    }
   ],
   "source": [
    "def rms_error(X, Y):\n",
    "    return math.sqrt(ms_error(X, Y))\n",
    "\n",
    "\n",
    "distance = rms_error([1,0,5], [3,10,5])\n",
    "print(\"Root of Mean Error Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLURALITY LEARNER CLASSIFIER\n",
    "\n",
    "### Overview\n",
    "\n",
    "The Plurality Learner is a simple algorithm, used mainly as a baseline comparison for other algorithms. It finds the most popular class in the dataset and classifies any subsequent item to that class. Essentially, it classifies every new item to the same class. For that reason, it is not used very often, instead opting for more complicated algorithms when we want accurate classification.\n",
    "\n",
    "![pL plot](images/pluralityLearner_plot.png)\n",
    "\n",
    "Let's see how the classifier works with the plot above. There are three classes named **Class A** (orange-colored dots) and **Class B** (blue-colored dots) and **Class C** (green-colored dots). Every point in this plot has two **features** (i.e. X<sub>1</sub>, X<sub>2</sub>). Now, let's say we have a new point, a red star and we want to know which class this red star belongs to. Solving this problem by predicting the class of this new red star is our current classification problem.\n",
    "\n",
    "The Plurality Learner will find the class most represented in the plot. ***Class A*** has four items, ***Class B*** has three and ***Class C*** has seven. The most popular class is ***Class C***. Therefore, the item will get classified in ***Class C***, despite the fact that it is closer to the other two classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "Below follows the implementation of the PluralityLearner algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource PluralityLearner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It takes as input a dataset and returns a function. We can later call this function with the item we want to classify as the argument and it returns the class it should be classified in.\n",
    "\n",
    "The function first finds the most popular class in the dataset and then each time we call its \"predict\" function, it returns it. Note that the input (\"example\") does not matter. The function always returns the same class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "For this example, we will not use the Iris dataset, since each class is represented the same. This will throw an error. Instead we will use the zoo dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mammal\n"
     ]
    }
   ],
   "source": [
    "zoo = DataSet(name=\"zoo\")\n",
    "\n",
    "pL = PluralityLearner(zoo)\n",
    "print(pL([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output for the above code is \"mammal\", since that is the most popular and common class in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-NEAREST NEIGHBOURS CLASSIFIER\n",
    "\n",
    "### Overview\n",
    "The k-Nearest Neighbors algorithm is a non-parametric method used for classification and regression. We are going to use this to classify Iris flowers. More about kNN on [Scholarpedia](http://www.scholarpedia.org/article/K-nearest_neighbor).\n",
    "\n",
    "![kNN plot](images/knn_plot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how kNN works with a simple plot shown in the above picture.\n",
    "\n",
    "We have co-ordinates (we call them **features** in Machine Learning) of this red star and we need to predict its class using the kNN algorithm. In this algorithm, the value of **k** is arbitrary. **k** is one of the **hyper parameters** for kNN algorithm. We choose this number based on our dataset and choosing a particular number is known as **hyper parameter tuning/optimising**. We learn more about this in coming topics.\n",
    "\n",
    "Let's put **k = 3**. It means you need to find 3-Nearest Neighbors of this red star and classify this new point into the majority class. Observe that smaller circle which contains three points other than **test point** (red star). As there are two violet points, which form the majority, we predict the class of red star as **violet- Class B**.\n",
    "\n",
    "Similarly if we put **k = 5**, you can observe that there are three yellow points, which form the majority. So, we classify our test point as **yellow- Class A**.\n",
    "\n",
    "In practical tasks, we iterate through a bunch of values for k (like [1, 3, 5, 10, 20, 50, 100]), see how it performs and select the best one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "Below follows the implementation of the kNN algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource NearestNeighborLearner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It takes as input a dataset and k (default value is 1) and it returns a function, which we can later use to classify a new item.\n",
    "\n",
    "To accomplish that, the function uses a heap-queue, where the items of the dataset are sorted according to their distance from *example* (the item to classify). We then take the k smallest elements from the heap-queue and we find the majority class. We classify the item to this class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "We measured a new flower with the following values: 5.1, 3.0, 1.1, 0.1. We want to classify that item/flower in a class. To do that, we write the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "\n",
    "kNN = NearestNeighborLearner(iris,k=3)\n",
    "print(kNN([5.1,3.0,1.1,0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the above code is \"setosa\", which means the flower with the above measurements is of the \"setosa\" species."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DECISION TREE LEARNER\n",
    "### Overview\n",
    "#### Decision Trees\n",
    "A decision tree is a flowchart that uses a tree of decisions and their possible consequences for classification. At each non-leaf node of the tree an attribute of the input is tested, based on which the corresponding branch leading to a child-node is selected. At the leaf node the input is classified based on the class label of this leaf node. The paths from root to leaf represent classification rules based on which leaf nodes are assigned class labels.\n",
    "![perceptron](images/decisiontree_fruit.jpg)\n",
    "#### Decision Tree Learning\n",
    "Decision tree learning is the construction of a decision tree from class-labeled training data. The data is expected to be a tuple in which each record of the tuple is an attribute used for classification. The decision tree is built top-down, by choosing a variable at each step that best splits the set of items. There are different metrics for measuring the \"best split\". These generally measure the homogeneity of the target variable within the subsets.\n",
    "#### Gini Impurity\n",
    "Gini impurity of a set is the probability of a randomly chosen element to be incorrectly labeled if it was randomly labeled according to the distribution of labels in the set.\n",
    "$$I_G(p) = \\sum{p_i(1 - p_i)} = 1 - \\sum{p_i^2}$$\n",
    "We select split which minimizes the Gini impurity in childre nodes.\n",
    "#### Information Gain\n",
    "Information gain is based on the concept of entropy from information theory. Entropy is defined as:\n",
    "$$H(p) = -\\sum{p_i \\log_2{p_i}}$$\n",
    "Information Gain is difference between entropy of the parent and weighted sum of entropy of children. The feature used for splitting is the one which provides the most information gain.\n",
    "### Implementation\n",
    "The nodes of the tree constructed by our learning algorithm are stored using either `DecisionFork` or `DecisionLeaf` based on whether they are a parent node or a leaf node respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource DecisionFork"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DecisionFork` holds the attribute, which is tested at that node, and a dict of branches. The branches store the child nodes, one for each of the attribute's values. Calling an object of this class as a function with input tuple as an argument returns the next node in the classification path based on the result of the attribute test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource DecisionLeaf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The leaf node stores the class label in `result`. All input tuples' classification paths end on a `DecisionLeaf` whose `result` attribute decide their class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource DecisionTreeLearner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The implementation of `DecisionTreeLearner` provided in [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py) uses information gain as the metric for selecting which attribute to test for splitting. The function builds the tree top-down in a recursive manner. Based on the input it makes one of the four choices:\n",
    "<ol>\n",
    "<li>If the input at the current step has no training data we return the mode of classes of input data recieved in the parent step (previous level of recursion).</li>\n",
    "<li>If all values in training data belongs to the same class it returns a `DecisionLeaf` whose class label is the class which all the data belongs to.</li>\n",
    "<li>If the data has no attributes that can be tested we return prurality value of class of training data.</li>\n",
    "<li>We choose the attribute which gives highest amount of entropy gain and return a `DecisionFork` which splits based of this attribute. Each branch recursively calls `decision_tree_learning` to constructs the sub-tree.</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NAIVE BAYES LEARNER\n",
    "\n",
    "### Overview\n",
    "\n",
    "#### Theory of Probabilities\n",
    "\n",
    "The Naive Bayes algorithm is a probabilistic classifier, making use of [Bayes' Theorem](https://en.wikipedia.org/wiki/Bayes%27_theorem). The theorem states that the conditional probability of **A** given **B** equals the conditional probability of **B** given **A** multiplied by the probability of **A**, divided by the probability of **B**.\n",
    "\n",
    "$$P(A|B) = \\dfrac{P(B|A)*P(A)}{P(B)}$$\n",
    "\n",
    "From the theory of Probabilities we have the Multiplication Rule, if the events *X* are independent the following is true:\n",
    "\n",
    "$$P(X_{1} \\cap X_{2} \\cap ... \\cap X_{n}) = P(X_{1})*P(X_{2})*...*P(X_{n})$$\n",
    "\n",
    "For conditional probabilities this becomes:\n",
    "\n",
    "$$P(X_{1}, X_{2}, ..., X_{n}|Y) = P(X_{1}|Y)*P(X_{2}|Y)*...*P(X_{n}|Y)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classifying an Item\n",
    "\n",
    "How can we use the above to classify an item though?\n",
    "\n",
    "We have a dataset with a set of classes (**C**) and we want to classify an item with a set of features (**F**). Essentially what we want to do is predict the class of an item given the features.\n",
    "\n",
    "For a specific class, **Class**, we will find the conditional probability given the item features:\n",
    "\n",
    "$$P(Class|F) = \\dfrac{P(F|Class)*P(Class)}{P(F)}$$\n",
    "\n",
    "We will do this for every class and we will pick the maximum. This will be the class the item is classified in.\n",
    "\n",
    "The features though are a vector with many elements. We need to break the probabilities up using the multiplication rule. Thus the above equation becomes:\n",
    "\n",
    "$$P(Class|F) = \\dfrac{P(Class)*P(F_{1}|Class)*P(F_{2}|Class)*...*P(F_{n}|Class)}{P(F_{1})*P(F_{2})*...*P(F_{n})}$$\n",
    "\n",
    "The calculation of the conditional probability then depends on the calculation of the following:\n",
    "\n",
    "*a)* The probability of **Class** in the dataset.\n",
    "\n",
    "*b)* The conditional probability of each feature occuring in an item classified in **Class**.\n",
    "\n",
    "*c)* The probabilities of each individual feature.\n",
    "\n",
    "For *a)*, we will count how many times **Class** occurs in the dataset (aka how many items are classified in a particular class).\n",
    "\n",
    "For *b)*, if the feature values are discrete ('Blue', '3', 'Tall', etc.), we will count how many times a feature value occurs in items of each class. If the feature values are not discrete, we will go a different route. We will use a distribution function to calculate the probability of values for a given class and feature. If we know the distribution function of the dataset, then great, we will use it to compute the probabilities. If we don't know the function, we can assume the dataset follows the normal (Gaussian) distribution without much loss of accuracy. In fact, it can be proven that any distribution tends to the Gaussian the larger the population gets (see [Central Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem)).\n",
    "\n",
    "*NOTE:* If the values are continuous but use the discrete approach, there might be issues if we are not lucky. For one, if we have two values, '5.0 and 5.1', with the discrete approach they will be two completely different values, despite being so close. Second, if we are trying to classify an item with a feature value of '5.15', if the value does not appear for the feature, its probability will be 0. This might lead to misclassification. Generally, the continuous approach is more accurate and more useful, despite the overhead of calculating the distribution function.\n",
    "\n",
    "The last one, *c)*, is tricky. If feature values are discrete, we can count how many times they occur in the dataset. But what if the feature values are continuous? Imagine a dataset with a height feature. Is it worth it to count how many times each value occurs? Most of the time it is not, since there can be miscellaneous differences in the values (for example, 1.7 meters and 1.700001 meters are practically equal, but they count as different values).\n",
    "\n",
    "So as we cannot calculate the feature value probabilities, what are we going to do?\n",
    "\n",
    "Let's take a step back and rethink exactly what we are doing. We are essentially comparing conditional probabilities of all the classes. For two classes, **A** and **B**, we want to know which one is greater:\n",
    "\n",
    "$$\\dfrac{P(F|A)*P(A)}{P(F)} vs. \\dfrac{P(F|B)*P(B)}{P(F)}$$\n",
    "\n",
    "Wait, **P(F)** is the same for both the classes! In fact, it is the same for every combination of classes. That is because **P(F)** does not depend on a class, thus being independent of the classes.\n",
    "\n",
    "So, for *c)*, we actually don't need to calculate it at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wrapping It Up\n",
    "\n",
    "Classifying an item to a class then becomes a matter of calculating the conditional probabilities of feature values and the probabilities of classes. This is something very desirable and computationally delicious.\n",
    "\n",
    "Remember though that all the above are true because we made the assumption that the features are independent. In most real-world cases that is not true though. Is that an issue here? Fret not, for the the algorithm is very efficient even with that assumption. That is why the algorithm is called **Naive** Bayes Classifier. We (naively) assume that the features are independent to make computations easier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "The implementation of the Naive Bayes Classifier is split in two; Discrete and Continuous. The user can choose between them with the argument `continuous`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discrete\n",
    "\n",
    "The implementation for discrete values counts how many times each feature value occurs for each class, and how many times each class occurs. The results are stored in a `CountinProbDist` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the below code you can see the probabilities of the class \"Setosa\" appearing in the dataset and the probability of the first feature (at index 0) of the same class having a value of 5. Notice that the second probability is relatively small, even though if we observe the dataset we will find that a lot of values are around 5. The issue arises because the features in the Iris dataset are continuous, and we are assuming they are discrete. If the features were discrete (for example, \"Tall\", \"3\", etc.) this probably wouldn't have been the case and we would see a much nicer probability distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3333333333333333\n",
      "0.10588235294117647\n"
     ]
    }
   ],
   "source": [
    "dataset = iris\n",
    "\n",
    "target_vals = dataset.values[dataset.target]\n",
    "target_dist = CountingProbDist(target_vals)\n",
    "attr_dists = {(gv, attr): CountingProbDist(dataset.values[attr])\n",
    "              for gv in target_vals\n",
    "              for attr in dataset.inputs}\n",
    "for example in dataset.examples:\n",
    "        targetval = example[dataset.target]\n",
    "        target_dist.add(targetval)\n",
    "        for attr in dataset.inputs:\n",
    "            attr_dists[targetval, attr].add(example[attr])\n",
    "\n",
    "\n",
    "print(target_dist['setosa'])\n",
    "print(attr_dists['setosa', 0][5.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we found the different values for the classes (called targets here) and calculated their distribution. Next we initialized a dictionary of `CountingProbDist` objects, one for each class and feature. Finally, we iterated through the examples in the dataset and calculated the needed probabilites.\n",
    "\n",
    "Having calculated the different probabilities, we will move on to the predicting function. It will receive as input an item and output the most likely class. Using the above formula, it will multiply the probability of the class appearing, with the probability of each feature value appearing in the class. It will return the max result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "def predict(example):\n",
    "    def class_probability(targetval):\n",
    "        return (target_dist[targetval] *\n",
    "                product(attr_dists[targetval, attr][example[attr]]\n",
    "                        for attr in dataset.inputs))\n",
    "    return argmax(target_vals, key=class_probability)\n",
    "\n",
    "\n",
    "print(predict([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can view the complete code by executing the next line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource NaiveBayesDiscrete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Continuous\n",
    "\n",
    "In the implementation we use the Gaussian/Normal distribution function. To make it work, we need to find the means and standard deviations of features for each class. We make use of the `find_means_and_deviations` Dataset function. On top of that, we will also calculate the class probabilities as we did with the Discrete approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.006, 3.418, 1.464, 0.244]\n",
      "[0.5161711470638634, 0.3137983233784114, 0.46991097723995795, 0.19775268000454405]\n"
     ]
    }
   ],
   "source": [
    "means, deviations = dataset.find_means_and_deviations()\n",
    "\n",
    "target_vals = dataset.values[dataset.target]\n",
    "target_dist = CountingProbDist(target_vals)\n",
    "\n",
    "\n",
    "print(means[\"setosa\"])\n",
    "print(deviations[\"versicolor\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the means of the features for the \"Setosa\" class and the deviations for \"Versicolor\".\n",
    "\n",
    "The prediction function will work similarly to the Discrete algorithm. It will multiply the probability of the class occuring with the conditional probabilities of the feature values for the class.\n",
    "\n",
    "Since we are using the Gaussian distribution, we will input the value for each feature into the Gaussian function, together with the mean and deviation of the feature. This will return the probability of the particular feature value for the given class. We will repeat for each class and pick the max value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "def predict(example):\n",
    "    def class_probability(targetval):\n",
    "        prob = target_dist[targetval]\n",
    "        for attr in dataset.inputs:\n",
    "            prob *= gaussian(means[targetval][attr], deviations[targetval][attr], example[attr])\n",
    "        return prob\n",
    "\n",
    "    return argmax(target_vals, key=class_probability)\n",
    "\n",
    "\n",
    "print(predict([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The complete code of the continuous algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource NaiveBayesContinuous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples\n",
    "\n",
    "We will now use the Naive Bayes Classifier (Discrete and Continuous) to classify items:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete Classifier\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "\n",
      "Continuous Classifier\n",
      "setosa\n",
      "versicolor\n",
      "virginica\n"
     ]
    }
   ],
   "source": [
    "nBD = NaiveBayesLearner(iris, continuous=False)\n",
    "print(\"Discrete Classifier\")\n",
    "print(nBD([5, 3, 1, 0.1]))\n",
    "print(nBD([6, 5, 3, 1.5]))\n",
    "print(nBD([7, 3, 6.5, 2]))\n",
    "\n",
    "\n",
    "nBC = NaiveBayesLearner(iris, continuous=True)\n",
    "print(\"\\nContinuous Classifier\")\n",
    "print(nBC([5, 3, 1, 0.1]))\n",
    "print(nBC([6, 5, 3, 1.5]))\n",
    "print(nBC([7, 3, 6.5, 2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the Discrete Classifier misclassified the second item, while the Continuous one had no problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PERCEPTRON CLASSIFIER\n",
    "\n",
    "### Overview\n",
    "\n",
    "The Perceptron is a linear classifier. It works the same way as a neural network with no hidden layers (just input and output). First it trains its weights given a dataset and then it can classify a new item by running it through the network.\n",
    "\n",
    "Its input layer consists of the the item features, while the output layer consists of nodes (also called neurons). Each node in the output layer has *n* synapses (for every item feature), each with its own weight. Then, the nodes find the dot product of the item features and the synapse weights. These values then pass through an activation function (usually a sigmoid). Finally, we pick the largest of the values and we return its index.\n",
    "\n",
    "Note that in classification problems each node represents a class. The final classification is the class/node with the max output value.\n",
    "\n",
    "Below you can see a single node/neuron in the outer layer. With *f* we denote the item features, with *w* the synapse weights, then inside the node we have the dot product and the activation function, *g*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![perceptron](images/perceptron.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "First, we train (calculate) the weights given a dataset, using the `BackPropagationLearner` function of `learning.py`. We then return a function, `predict`, which we will use in the future to classify a new item. The function computes the (algebraic) dot product of the item with the calculated weights for each node in the outer layer. Then it picks the greatest value and classifies the item in the corresponding class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource PerceptronLearner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the Perceptron is a one-layer neural network, without any hidden layers. So, in `BackPropagationLearner`, we will pass no hidden layers. From that function we get our network, which is just one layer, with the weights calculated.\n",
    "\n",
    "That function `predict` passes the input/example through the network, calculating the dot product of the input and the weights for each node and returns the class with the max dot product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "We will train the Perceptron on the iris dataset. Because though the `BackPropagationLearner` works with integer indexes and not strings, we need to convert class names to integers. Then, we will try and classify the item/flower with measurements of 5, 3, 1, 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "iris.classes_to_numbers()\n",
    "\n",
    "perceptron = PerceptronLearner(iris)\n",
    "print(perceptron([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correct output is 0, which means the item belongs in the first class, \"setosa\". Note that the Perceptron algorithm is not perfect and may produce false classifications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NEURAL NETWORK\n",
    "\n",
    "### Overview\n",
    "\n",
    "Although the Perceptron may seem like a good way to make classifications, it is a linear classifier (which, roughly, means it can only draw straight lines to divide spaces) and therefore it can be stumped by more complex problems. We can extend Perceptron to solve this issue, by employing multiple layers of its functionality. The construct we are left with is called a Neural Network, or a Multi-Layer Perceptron, and it is a non-linear classifier. It achieves that by combining the results of linear functions on each layer of the network.\n",
    "\n",
    "Similar to the Perceptron, this network also has an input and output layer. However it can also have a number of hidden layers. These hidden layers are responsible for the non-linearity of the network. The layers are comprised of nodes. Each node in a layer (excluding the input one), holds some values, called *weights*, and takes as input the output values of the previous layer. The node then calculates the dot product of its inputs and its weights and then activates it with an *activation function* (sometimes a sigmoid). Its output is fed to the nodes of the next layer. Note that sometimes the output layer does not use an activation function, or uses a different one from the rest of the network. The process of passing the outputs down the layer is called *feed-forward*.\n",
    "\n",
    "After the input values are fed-forward into the network, the resulting output can be used for classification. The problem at hand now is how to train the network (ie. adjust the weights in the nodes). To accomplish that we utilize the *Backpropagation* algorithm. In short, it does the opposite of what we were doing up to now. Instead of feeding the input forward, it will feed the error backwards. So, after we make a classification, we check whether it is correct or not, and how far off we were. We then take this error and propagate it backwards in the network, adjusting the weights of the nodes accordingly. We will run the algorithm on the given input/dataset for a fixed amount of time, or until we are satisfied with the results. The number of times we will iterate over the dataset is called *epochs*. In a later section we take a detailed look at how this algorithm works.\n",
    "\n",
    "NOTE: Sometimes we add to the input of each layer another node, called *bias*. This is a constant value that will be fed to the next layer, usually set to 1. The bias generally helps us \"shift\" the computed function to the left or right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![neural_net](images/neural_net.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "The `NeuralNetLearner` function takes as input a dataset to train upon, the learning rate (in (0, 1]), the number of epochs and finally the size of the hidden layers. This last argument is a list, with each element corresponding to one hidden layer.\n",
    "\n",
    "After that we will create our neural network in the `network` function. This function will make the necessary connections between the input layer, hidden layer and output layer. With the network ready, we will use the `BackPropagationLearner` to train the weights of our network for the examples provided in the dataset.\n",
    "\n",
    "The NeuralNetLearner returns the `predict` function, which can receive an example and feed-forward it into our network to generate a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource NeuralNetLearner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backpropagation\n",
    "\n",
    "In both the Perceptron and the Neural Network, we are using the Backpropagation algorithm to train our weights. Basically it achieves that by propagating the errors from our last layer into our first layer, this is why it is called Backpropagation. In order to use Backpropagation, we need a cost function. This function is responsible for indicating how good our neural network is for a given example. One common cost function is the *Mean Squared Error* (MSE). This cost function has the following format:\n",
    "\n",
    "$$MSE=\\frac{1}{2} \\sum_{i=1}^{n}(y - \\hat{y})^{2}$$\n",
    "\n",
    "Where `n` is the number of training examples, $\\hat{y}$ is our prediction and $y$ is the correct prediction for the example.\n",
    "\n",
    "The algorithm combines the concept of partial derivatives and the chain rule to generate the gradient for each weight in the network based on the cost function.\n",
    "\n",
    "For example, if we are using a Neural Network with three layers, the sigmoid function as our activation function and the MSE cost function, we want to find the gradient for the a given weight $w_{j}$, we can compute it like this:\n",
    "\n",
    "$$\\frac{\\partial MSE(\\hat{y}, y)}{\\partial w_{j}} = \\frac{\\partial MSE(\\hat{y}, y)}{\\partial \\hat{y}}\\times\\frac{\\partial\\hat{y}(in_{j})}{\\partial in_{j}}\\times\\frac{\\partial in_{j}}{\\partial w_{j}}$$\n",
    "\n",
    "Solving this equation, we have:\n",
    "\n",
    "$$\\frac{\\partial MSE(\\hat{y}, y)}{\\partial w_{j}} = (\\hat{y} - y)\\times{\\hat{y}}'(in_{j})\\times a_{j}$$\n",
    "\n",
    "Remember that $\\hat{y}$ is the activation function applied to a neuron in our hidden layer, therefore $$\\hat{y} = sigmoid(\\sum_{i=1}^{num\\_neurons}w_{i}\\times a_{i})$$\n",
    "\n",
    "Also $a$ is the input generated by feeding the input layer variables into the hidden layer.\n",
    "\n",
    "We can use the same technique for the weights in the input layer as well. After we have the gradients for both weights, we use gradient descent to update the weights of the network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "First, we feed-forward the examples in our neural network. After that, we calculate the gradient for each layer weights. Once that is complete, we update all the weights using gradient descent. After running these for a given number of epochs, the function returns the trained Neural Network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource BackPropagationLearner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "iris.classes_to_numbers()\n",
    "\n",
    "nNL = NeuralNetLearner(iris)\n",
    "print(nNL([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output should be 0, which means the item should get classified in the first class, \"setosa\". Note that since the algorithm is non-deterministic (because of the random initial weights) the classification might be wrong. Usually though it should be correct.\n",
    "\n",
    "To increase accuracy, you can (most of the time) add more layers and nodes. Unfortunately the more layers and nodes you have, the greater the computation cost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LEARNER EVALUATION\n",
    "\n",
    "In this section we will evaluate and compare algorithm performance. The dataset we will use will again be the iris one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = DataSet(name=\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes\n",
    "\n",
    "First up we have the Naive Bayes algorithm. First we will test how well the Discrete Naive Bayes works, and then how the Continuous fares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error ratio for Discrete: 0.040000000000000036\n",
      "Error ratio for Continuous: 0.040000000000000036\n"
     ]
    }
   ],
   "source": [
    "nBD = NaiveBayesLearner(iris, continuous=False)\n",
    "print(\"Error ratio for Discrete:\", err_ratio(nBD, iris))\n",
    "\n",
    "nBC = NaiveBayesLearner(iris, continuous=True)\n",
    "print(\"Error ratio for Continuous:\", err_ratio(nBC, iris))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error for the Naive Bayes algorithm is very, very low; close to 0. There is also very little difference between the discrete and continuous version of the algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k-Nearest Neighbors\n",
    "\n",
    "Now we will take a look at kNN, for different values of *k*. Note that *k* should have odd values, to break any ties between two classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error ratio for k=1: 0.0\n",
      "Error ratio for k=3: 0.06000000000000005\n",
      "Error ratio for k=5: 0.1266666666666667\n",
      "Error ratio for k=7: 0.19999999999999996\n"
     ]
    }
   ],
   "source": [
    "kNN_1 = NearestNeighborLearner(iris, k=1)\n",
    "kNN_3 = NearestNeighborLearner(iris, k=3)\n",
    "kNN_5 = NearestNeighborLearner(iris, k=5)\n",
    "kNN_7 = NearestNeighborLearner(iris, k=7)\n",
    "\n",
    "print(\"Error ratio for k=1:\", err_ratio(kNN_1, iris))\n",
    "print(\"Error ratio for k=3:\", err_ratio(kNN_3, iris))\n",
    "print(\"Error ratio for k=5:\", err_ratio(kNN_5, iris))\n",
    "print(\"Error ratio for k=7:\", err_ratio(kNN_7, iris))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the error became larger and larger as *k* increased. This is generally the case with datasets where classes are spaced out, as is the case with the iris dataset. If items from different classes were closer together, classification would be more difficult. Usually a value of 1, 3 or 5 for *k* suffices.\n",
    "\n",
    "Also note that since the training set is also the testing set, for *k* equal to 1 we get a perfect score, since the item we want to classify each time is already in the dataset and its closest neighbor is itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perceptron\n",
    "\n",
    "For the Perceptron, we first need to convert class names to integers. Let's see how it performs in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error ratio for Perceptron: 0.31333333333333335\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\")\n",
    "iris2.classes_to_numbers()\n",
    "\n",
    "perceptron = PerceptronLearner(iris2)\n",
    "print(\"Error ratio for Perceptron:\", err_ratio(perceptron, iris2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Perceptron didn't fare very well mainly because the dataset is not linearly separated. On simpler datasets the algorithm performs much better, but unfortunately such datasets are rare in real life scenarios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MNIST HANDWRITTEN DIGITS CLASSIFICATION\n",
    "\n",
    "The MNIST database, available from [this page](http://yann.lecun.com/exdb/mnist/), is a large database of handwritten digits that is commonly used for training and testing/validating in Machine learning.\n",
    "\n",
    "The dataset has **60,000 training images** each of size 28x28 pixels with labels and **10,000 testing images** of size 28x28 pixels with labels.\n",
    "\n",
    "In this section, we will use this database to compare performances of different learning algorithms.\n",
    "\n",
    "It is estimated that humans have an error rate of about **0.2%** on this problem. Let's see how our algorithms perform!\n",
    "\n",
    "NOTE: We will be using external libraries to load and visualize the dataset smoothly ([numpy](http://www.numpy.org/) for loading and [matplotlib](http://matplotlib.org/) for visualization). You do not need previous experience of the libraries to follow along."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading MNIST digits data\n",
    "\n",
    "Let's start by loading MNIST data into numpy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `load_MNIST()` loads MNIST data from files saved in `aima-data/MNIST`. It returns four numpy arrays that we are going to use to train and classify hand-written digits in various learning approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_img, train_lbl, test_img, test_lbl = load_MNIST()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the shape of these NumPy arrays to make sure we have loaded the database correctly.\n",
    "\n",
    "Each 28x28 pixel image is flattened to a 784x1 array and we should have 60,000 of them in training data. Similarly, we should have 10,000 of those 784x1 arrays in testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training images size: (60000, 784)\n",
      "Training labels size: (60000,)\n",
      "Testing images size: (10000, 784)\n",
      "Training labels size: (10000,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training images size:\", train_img.shape)\n",
    "print(\"Training labels size:\", train_lbl.shape)\n",
    "print(\"Testing images size:\", test_img.shape)\n",
    "print(\"Training labels size:\", test_lbl.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing MNIST digits data\n",
    "\n",
    "To get a better understanding of the dataset, let's visualize some random images for each class from training and testing datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5KWO5bfA3kh03bpx3zkpNW+n9zp07e22Wc22zeRUrVvTakmlNQk5Y2W377HUj\nOfaZnhcsQlGvXr08e85U4K4zcmdzARYsWOAdW1lfdw1jRlbeXcLZ2sKJEycC4RufW+Q+qzUVQWRR\nCQhfK5KTn0uXCI556aWXgPDNse3z8Pjjj494vG1i/P777wOwZMkSr822r3jkkUcAOOaYY7w2Kyut\nSI6IiIiIiEge0U2OiIiIiIgESiDS1SzMNmLEiBz93GWXXZbnffnxxx+B8LKD1i83RW39+vV5/tqJ\nYAvl9+ett94Ccl7kIdX98ssv3nHPnj0B2LNnT8TjbKfvDh06ZPpcljrUpUsX79wnn3wCKCVtf6w4\nhJtG88QTTySqOwnjfi7ZsaVKumVmrViKlf611D4IT5FJRR988AEQnq5mpdUtrTY3LOXU0tX++usv\nr23mzJm5fv5kZeVlo3GLUzz++OOA/znmpj9aYRBLv5VwVjTJLS5irJSv/Q4SRLZdA8DQoUMBv5Q9\nRC5dsBQ+8NOSbSsPW0QPsHjx4jzvaypwiy8sWrQIgLJlywJw8MEHe22rVq0Copd7N8ma9q1IjoiI\niIiIBEogIjnFihUD/MV4tjgPcr7Bny1OnTRpUkx9ufvuuwH47bffYvr5VJPVAlG3bKjNBARZtAXZ\n7mZYVnLcNiiz6A1Ay5YtgehFNCyCc9pppwGpW+7WjaJYIRAr5xtt07Bly5Z5xxaJyc77qmbNmt6x\nLXi2krYW+QJ/8zOJFOT367fffguER1ktwuIumh0/fjwAu3fvzvS5bNbznnvu8c7Z+zTj8wA8++yz\nsXY76WU1k+t+F7iz8QBff/21d2xFMLZs2ZLHvUtd0aIXxsrGA7z88stx61OiuJkjVkQl2vfubbfd\nBvjfLy7LKunYsaN3bvTo0UD6ZZq4rCiU/fnDDz/k6Ofd7QySiSI5IiIiIiISKIGI5GSc3ShdurR3\nnNPZWss5XLFiRe47FmA2xlnl53fq1Mk7dmfrgsZKkEeLwlSpUsU7/u677zJ9DvtZ+9PKTYO/duDj\njz/OfWcTyN0gcOrUqZk+zjYXcyMyNus2ffr0TH/O1jy5s3e2Gdmff/4J+JtiAmzYsCHbfU93brlz\ne89bKWB3JjW7G9Umks1Qurn8tmZk+PDh3jm75ixK465nsqisbSQbbW2ire+566678qzvycyyGMDf\nKNbK0brRCMuWsLU5gwcP9tqCuMVAbtlGleBvYGwsAgHBjn5Zto6tO4LonzX2+e6uL8zo+++/B8I3\nL7f1J0FjB66TAAAgAElEQVT+PSW/2bpN9/vgrLPOAmDhwoUJ6RMokiMiIiIiIgGjmxwREREREQmU\nQKSrZeQugLKSlJK3LrzwQgBOPvnkTB/j7pwb5DCw7Sbvpj/dfPPNMT2Xpan17t3bO+emCgVV3759\nvWNLFXV3SbYyvCVKlAD8Bcrg77R++umnA1C0aFGvbc6cOYD/72ElkSVn3PF+4403AP8zwE2tTKWF\n9W552VNPPRUIL3VsRQgeffTRHD2vpRBZmmm67Dzvlsru3r074O+C7r4nrTy0m6YmmXMLthj7rrEd\n6IPOiknZZ73LLf9saWrR3nPVqlUDoEGDBvnQQ8mYcp/xOFEUyRERERERkUAJZCRHkoMtBAd/g7wg\nskiLW3bcFtpalMdl0YQXXnjBO2c/a5t6RtswNNW5G0reeuutAAwbNgyABx98MOLxbolfi8i6EYWM\nnnrqKQDmzp3rnXvuuedy0WMxtlg3mkMOOSRu/cgvFmm2jQLBX8Rss8fuDPCMGTMAfxG9/T/4ZcrT\nJYITjY2nRcgk52yrASu377Jztr1A0LVr1y7TNjcL4KCDDgL8wkiTJ0/22izrxDatdQuJ2PtY8pZb\nBCxRFMkREREREZFA0U2OiIiIiIgESoFQMqwMyiDaDrbpKJZ/mniNnaUh2E7hAFWrVg17jLtjfYcO\nHeLSL5PMY5fs4jl29957L+DvqwH+AtFobGG4W8jCFsLbAtSsdqjPbzkdO11z/9H7NXYau9gl89hZ\nqtVVV10V0WYFVdz9RxYsWBCXfpl4jp2lvrtpocbSQwGOOuooAIoUKbLfPtg+VuAXUYmXZL7uYtW+\nfXsgvBCEFQErV65cnr1OTsdOkRwREREREQkURXKSWCrc7b/44ove8RlnnBHW1rJlS+843qW8U2Hs\nkpXGLnaK5MRG11zsNHaxS+axe+uttwBo1qxZpo8ZO3asdzxu3Lh875Mrmccu2QVx7KpUqQL41y3A\n33//DUD9+vXz7HUUyRERERERkbSmSE4SC+Ldfrxo7GKnsYudIjmx0TUXO41d7JJ57GrUqAH4G1yC\nv+bk6aefBvwS/BD/bQeSeeySncYudorkiIiIiIhIWtNNjoiIiIiIBIrS1ZKYQpqx09jFTmMXO6Wr\nxUbXXOw0drHT2MVOYxc7jV3slK4mIiIiIiJpLSkjOSIiIiIiIrFSJEdERERERAJFNzkiIiIiIhIo\nuskREREREZFA0U2OiIiIiIgEim5yREREREQkUHSTIyIiIiIigaKbHBERERERCRTd5IiIiIiISKDo\nJkdERERERAJFNzkiIiIiIhIouskREREREZFA0U2OiIiIiIgESuFEdyCaAgUKJLoLSSEUCuX4ZzR2\n/9HYxU5jF7ucjp3G7T+65mKnsYudxi52GrvYaexil9OxUyRHREREREQCRTc5IiIiIiISKLrJERER\nERGRQEnKNTkiIiKSmgoX/u9Xi5EjRwIwatQor+2ee+4BYMiQIfHvmIikFUVyREREREQkUBTJERER\n2Y+yZct6x7Vq1QKgd+/eEY+rWrUqAPXq1QPgpptu8tqefvrp/Oxi0rj88ssBP5KzYsUKr23WrFkJ\n6ZOIpB9FckREREREJFAUycmgZMmSADz77LMAtGrVymuzGbm77roLgH/++Se+nUsCpUqVAmD69OkA\nnH322Zk+duPGjd7xwQcfnL8dSwFHH300AF27dgWgbt26XtuFF14Y9tgff/zRO542bRoA8+bNA+DL\nL7/M136KSKS+fft6xwMHDgSy/lz76aefAPjjjz/yt2NJqEOHDgBs3rwZgBtvvNFr+/jjjxPSJxFJ\nP4rkiIiIiIhIoOgmR0REREREAqVAKBQKJboTGRUoUCCur3fggQd6x9999x0AFStWjOiLDVWvXr0A\neOyxx/K1X7H80+T32N1+++0AXHvttft9rJuuVq1atXzrUzSJHruGDRsCMHz4cO+cpfYVKlQopuf8\n999/Abj//vu9c9n5d8ipRI/dscceC0CLFi0yfcx9993nHe/bty/Tx7311lsAnHXWWQBs27YtL7qY\nqZyOXbw/65JVoq+5rBQs+N9coF1DAM888wzg93vv3r1e24wZMwB/0f2mTZvytX/JMnbHH3+8d7xq\n1SrALzhwyimn5Pnr5YVkGbtUpLGLncYudjkdO0VyREREREQkUNK68EDjxo0Bf3My8CM4WbGyoUce\neaR3bvz48QDs2rUrL7soKcCuo+bNm3vnrEhFmTJlIh4/c+ZMALZs2ZLpc1oZWoBu3boBfgSoXbt2\nXlt+RHISwZ2lWrZsGQAVKlTI9PFu9CarmR2bQbbnyu9IjgRH9erVAbjtttsA6NGjR8Rj7Lvg0Ucf\njV/HkpQV6wH//TlhwoREdUfSmBVIcqOLVgzDuN8577zzDgALFy6MQ+9ST+nSpQG45pprADjhhBO8\nttNOOw3wy+O7m/zmdxQ7OxTJERERERGRQEnLSM5BBx0E+DnTJ598csRjNmzYAITf7VeqVCns8e7P\n1axZE4Arr7wSgJ07d+Z1t1NO8eLFveNOnToBsHjx4kR1J98MGzYMiF5O+/PPP/eOO3fuDMD3338P\n+GtsoilSpIh3/O233wJwww03AFC7dm2v7dxzzwVg/vz5sXQ9KVmEK6tIl621AViwYAEA48aNA8Jn\n77Zu3QqkZ7l3ybnChf2vRIu4nnrqqRGPs2vTvQ7TVfny5YHwz6xffvkFgBdeeCEhfQoCi+b369fP\nO2e/Z1xyySUAvPzyy17b6aefDsDDDz8MhJc8TxeW9WARxEMOOSTTx7q/29lYWQaGm92Trux3C4BH\nHnkE8CM60dg1edRRR3nnmjRpkk+9yz5FckREREREJFB0kyMiIiIiIoGSlulqkydPBuDMM8/M9DFj\nx44F4LXXXvPOWUize/fuAFSuXNlrs3PG0tYgfVPXLC0QYMyYMUCw0tWsTLRbWtY88cQTAIwYMcI7\nZzugWxjYvbYy7orupldZOpalq7klqEeNGgWkfrqaWzzADXfnxNVXXx1xbtasWQD8+OOPsXUsBblp\nQyeddBIAd911F+Cn9EH+vBe7dOkChKd7rF69GghPf0hWAwcO9I4zpqlZCjNAgwYNgPAy+enKFh67\nRXsmTpyYqO4kPbeozGWXXQbAOeec4507+OCDAShWrBgQvsWFpVPa52Xr1q29NjvXp08fIH3S1cqW\nLesdP/DAA4BfaCa75YZtjKtUqZLHvUsdNo72vdC0aVOvLSflq9evX5+3HcslRXJERERERCRQ0iaS\nYxsMQvSZd3PHHXcA/kIrd3H4ddddB8C8efOA8JKZNgNgEZ3ffvvNaxs8eHCu+i7JyQoJRNvc00pS\ntmzZ0jtn16CVYZwzZ47Xdumll2b6Op9++ikAr776KhA+e1eyZMlYuh4otrjUFuW60nFheP/+/b1j\n+zyzGWA3wvLGG28AsGPHjly9XteuXb1jiyi6M39Dhw7N1fPHw+GHHw74EWeXbWzpvpf//PPPuPQr\nVbnff7GwaIRFqiFyVv6qq67yjlOhwIFFty666CLvXLQtK7Zv3w7Anj17AL80L/hR/V9//RWAOnXq\neG32e0m6sTLuELntgL13AQYMGABAvXr1AHj88ce9Nvu8ys516xYzsO0cmjVr5p2z6/Swww4L+xPi\nvyn6/rhRMHsPuREcY1uj2PYOS5cu9dqs8IgVe4hWyCuRFMkREREREZFA0U2OiIiIiIgESuDT1Swc\nd/vtt3vn3AXx4O9bAn7ILas9TN577z0AevXq5Z1bsmRJ2GNsQSHAvffeC/gLz1OZhcSt/rkbphXf\nlClTMm37+eefAbjvvvuy9Vx//fUXALt3745oK1GiBOCH0N1rOcjclAFLG41WsMDSO9KJfd6Anypr\naRLuNWfXVU7Z/ldWwOX888/32iztY/r06d65jJ+Nyeibb74BwovE2PfEnXfeCShFLTPXXnstkLPF\nyS73vWzXZ8eOHQE/dQvgiy++APxF4s8995zXZsUPLAUzGVkq1fXXX++ds367RWhs7xtLSctKrGMe\nJHPnzvWObe9De+9ayh/4xXxatGgR8Rxr164F/M80l6WZX3755YC/Hwz46eLuv4MtvLff9yxNLhm5\nafIZ09Q+/PBD79iWeEQrKlCrVi3A/9052SiSIyIiIiIigRL4SI7dZbZp0ybTx5x33nnese2Qnh3L\nly/3ju+//37Av2svU6ZMRNsFF1zgnXNnGFKJ3d3b4uYZM2Z4bY0aNcr052zx3S233ALAjTfemF9d\njBubWfzss88AqF+/fsRj3NLFttDRZrnzoqxxpUqVAL/c7aOPPprr50wFbqTUyvn+/fffQHqVr3Vn\nEDt16gT4BTHAL0dr5Y+ff/55r83GKzvcstTDhw8H/AW/7oJwW7j70EMPeeeiRSCTVbTo1rRp0wA4\n/vjjvXMWMf38888B+OSTT7y2vXv3Arkv6JAq7N8/u+V6M3LL9nbo0AHwy4736NHDa/vyyy8BqF69\nOhA+03zzzTcD4UUwNm/eHFN/8kvPnj0jzs2cOTNXz+lmUthngRW9SRdudMEyKIYNGwaEFwuxaJm7\n2N5YUR+7ttxiKf369QP869sW4YO/VcQzzzzjnbPxT4Xy8tHK+n/88cdAeMTLrq3mzZsDUKpUKa/N\njeQnI0VyREREREQkUAIfybFyvdHYrOaaNWtiem531m/SpEmAv6mXzaCCP7NaunRp71yqRnKMRS8y\nbmKZGctdtQ00g+Cpp54C4M033wSil4d0ZxPTZb1MfrBSqxYBdDdttBk2i+C4ZWeDyj5LjjzySO/c\nokWLIh63b98+wI865/QatPftkCFDvHOW927j7l7j9llnM/Gpxt0g2v7ONnvp5uIfcMABgF8+1WUz\nuLaWxy3tm06b0maXm+Fgzj77bMBfv+iyczbjDP7aFncD11TfIDk7Dj30UO/Y3o/p8PfOjG2+bVFq\nNyITLYJjbCPpaBtKGyvlfffdd3vn3BLVqcS+P6KtY7US21999ZV3ziL5GUt0pwJFckREREREJFB0\nkyMiIiIiIoESyHS1iy++2Dt2Fy5mZIsVbaFoblgJ0m+//RYIT1cTny1ms/Q+yDqlMBXYYuvc7vIt\nmbPwuqWpFSzoz8989913gL8INB1Y+fr9FVmwcvcXXnhhTK9jn5HR3qO24NfKi0L4YvBUZKVkwS+v\nailpVjob/HQ1W5TsLtK1YiB33HEHEJ4CY99Nr7/+eh73PL7cIgxZFZzJStWqVYHwHetzwlLawC8Y\n5PYlyGlbdv256ZWWmhpr+n2QjBkzBggvtGIFoGz7C1fGUtxuMQMrcvPKK6/kdTcT5rjjjgPCC2QZ\nS00Lyu+wiuSIiIiIiEigBCqSY7O7brnowoUj/4o2W5efm3OmyyZd7oy6ewxQqFChiMfbbGjGDVlF\nMnLLf9oGera41mYtwV8MGW3hvb0PLSLx4osvem2pUOIzI4suZLXx2qxZs7zjm266KabXadeuHQCn\nn356RNumTZsAf0zd8slBlFUpYitOYDPrADVq1AD88XEX1ltp7bZt2wLBKEZi77GcfufZ94Nt7gn+\n4u5oBQey04dWrVrl6OdSlX02HnHEEd45KxOfzBuixpu7oaqVdj7xxBOB8Os1Y/nzcuXKeceWnRMk\nK1euBMIj7xbdya1k+z5QJEdERERERAIlUJGcypUrA3DRRRd556JtUGZ51DbzkR+ivW6JEiXy7fUS\nxZ1Rd4+zehzEvnFcOrLrJlr+rJUxt9n1ILC/p7uxac2aNTN9vM0EH3744RFtNltnz7V06VKvzdZH\n5GQD4ES79tprgfDNOTNyc6lt3cKKFSv2+9x169b1jh9++OFMX2f27Nlhj5Hw7QS+/vprALp37w6E\nr1mqU6cO4G9W2Ldv33h1MU+5JcJfeOEFALp06ZKj57AxcyNltv7JImPRNmc1zz77rHds3yeLFy/O\nUR9SnRuNSLdNQLNiG8y6m45nfK/ZRr4ATz75JACjR48GoFixYl7bkiVLAH8dmrsZaKqyLUzctYS2\nwbv7d89o3bp1Eecs+mrcEtvJQJEcEREREREJFN3kiIiIiIhIoAQqXS0r7u6t7nE8uTtmW1hUJBo3\ntfG+++4D4JRTTol43F133QWEl8pMdZZGllWKmmv8+PFA9JQ9t1Q5QPv27b1jKy9vpUVTge3gbX3v\n2rWr12bXjFssIFrhgFi415e7i3gqc3dAt0W3r776aqK6k7LmzZsHhKeruQviM/P7778DfjEGgOHD\nhwNQsWJFIHoBAivscMwxx3jndu/eDcDy5ctz1PdUZdeum/ad6uXbY1W/fn3v2AqB9OzZEwgfH7tG\nrOy+e93t3LkT8H9HO/TQQ702SzEtWbIkEIx0NWNjAnDnnXfu9/FFixYFom/XYAUakq3whSI5IiIi\nIiISKIGK5GS18NEtMpCfBQeyMnXq1IS8rqQOm4236A34m5EZd6OyRx55JD4diyMrQelGWGxxspXl\nzS57ji+//BKIXpwglbz00kthf7ql2Fu3bg3AyJEjvXM2C2mFP/7880+vzQo2RCuIYjOgVmRg1KhR\nXtu///6by79FcjjyyCO9Y1t0mxeRHFs0b5/37iaixmaOg8DeW+7fycpm33vvvWGPicYKF4AfybHI\nmhvJsc1D7fFumd+xY8cCfmncoDvnnHMizrnjmA5sU08rDADh0VkIz9rJznvcruF02QIkp84//3wg\n/Prbu3cv4Bf3caNDyUCRHBERERERCZRARXKOOuqohL126dKlAW1yKbGxWfXJkycD/qaPrr///huA\nBx54wDv3448/5n/n4sxyevMit/eQQw4B/Nn0oJUu3759u3dsJXXd0rpWFtoiOO7mk1Zy9qSTTop4\nXlvzM3fu3LztcBJxI6S2BsTd1DOr8sUZHXbYYd6xla3NGIEFfzPacePG5ayzSezjjz8G4JdffvHO\n2Xex5e6fccYZXpvNltsMsBt9qVevHgCXX345EP59ahFKW+/jvp47m58Ozj33XAC++eYb79x3332X\nqO7ETalSpbxjK13sbq1gn++25vmee+7x2rJaS2Preqz0tPs9oagOHH300UD4OiZj78Nbbrklrn3K\nLkVyREREREQkUHSTIyIiIiIigRKodLWs5PduwFbKtUGDBhFttngyJ+kPktps1/mCBTOfR3B3mL/+\n+uuB8LQO888//wAwZswYACZMmJBX3Qw8K31s/x7pxt3VG+Daa6/1jk888cSwNrdM9KJFi/K3Y0lg\n7dq13vEVV1wBQKtWrbxzy5Yt2+9z2Oe+WyikevXqYY9xF+Tbwno3zTAo3BQ8S1Nr1KgRAO+++67X\nZovBb7311ojnsGIQ/fr1A/w0XvBTiKxUvO1AD3456qCz8vrGvbZ27NgR7+7EnRUPAKhWrVpEu103\nDz/8cKbPYdeNpZUCNGvWDPDTVl1WvMaK36QLK2QD8NhjjwFQrFixiMfZe7R8+fJA8o2TIjkiIiIi\nIhIoaRPJOfnkk/PsuWzxmztb0KFDh0wfb7N8W7ZsybM+SPJo06YNEL4xoxUOcBcyx+qzzz4DYNq0\naRHPmTE66EaO7HF79uzJdR9SiS3KBX8hs80Cb9y40Wt7880349uxBLJZz+uuu847V6hQIQB++ukn\nILzEfbKVAc0P0TaOdBewv/zyywCsW7cOCH/fWeSncOHIr1B7T1qpWvd6DPJ78cknn/SOa9WqBfgz\n6+7moHbcsWNHIOtiIFbUAOD1118HYMqUKUD6RG+yErRCKvvjRv+i/d0zFl/o1q2bd2xRRStqUaFC\nhUyfyza2BLj66qtz0ePU5RZqsO8P2z7ALUhjUdtki+AYRXJERERERCRQAhXJsU0Eo2ncuHHE8Qcf\nfLDf53R/zkqtDh48GICaNWtGPN7W37g52pMmTdrv60jqatiwIQBXXnllvj6/zVwuXbrUa3M3O4Pw\njQctv33NmjXeuYULFwL+rGiQnHXWWUB43rZFtmyGz424ZlyvEkRFihQB/HVcbh67lSS39WBW3jhd\n/PDDD97xoEGDALj99tu9c23btg37Mytff/21dzx+/HgAHn/88TzpZyqycbQNKnv27Om12ZqIli1b\nRvycbRpqm/4uXrzYa0uHNSf7k3GbjHTbAHR/5Zyzs44uGlvbZNszjBgxIqbnCYJKlSoBMH369Ig2\n+5y75JJL4tqn3FAkR0REREREAkU3OSIiIiIiEiiBSld76qmngPAyqRbedUvfWUjT0jWyUrJkSe+4\nRIkSYW3uIuY777wT8EN8KjIQnZUSvemmmxLck8Rz0ytt8V6fPn2A6KmQtsi5ffv23jn3OCO7vv/4\n4w/vXKqmqdl7r0WLFkB4yp4tbrYUGbfsrKWpnXnmmUB6pKi5rPy4/elavXo1EHuKR6pz058spdi9\nriy1M2OKkMu+cyzdDeDXX3/N036mMivTPXTo0AT3JBiOO+64sP/funVrgnqSGBMnTvSOBw4cCEQv\n/mFpbW5BAUuFtEIi8+fP99peeuklIHkXz8eTbWPhfu7t27cPgBkzZiSkT7mhSI6IiIiIiARKgVAS\n1iDc3+Ky/XE3/rMFjL169Yrpufbu3esdW7TGNs1zy1vmR2nQWP5pcjt2OVWmTBnv2Ery2iaXbjlj\nmwmwsqxWPjS/xHPsbCOx1157zTtXtGjRTB9vs8a33Xabdy475VDPO+88ILwca0Zu6d977rlnv88Z\nTTJed7Vr1wb8SIzNvAE0b94c8Gf03AXlVtY7XhGcnI5dvN6vVapUAaB79+7euUceeQQI31AwUZLx\nmksVGrvYpdrY2aaMVsjB3tcAGzZsiGtfEj12tj1AtEhONFakJxnKuCd67KKxggMW6SpXrpzXZlkn\nVgQpkXI6dorkiIiIiIhIoOgmR0REREREAiWQ6WpBkYwhzVShsYtdMo6dpUX26NEDCN97yvpr6X+j\nRo3K175kJVnT1ZJdMl5zqUJjF7tUGLtSpUp5x++99x7gp4S7qfnplq6WypJx7Jo2bQr4xaHcgkVt\n2rQB/GI1iaR0NRERERERSWuBKiEtIsFkpVLvv//+sD9FRIKsfPny3vGRRx4JwF9//QX4BX1E8opF\ncEaPHu2dS4YITqwUyRERERERkUBRJEdEREQkCZ177rkR52yW3d2QXCQ3Vq5cCYRHDoNAkRwRERER\nEQkU3eSIiIiIiEigqIR0EkvGMoOpQmMXO41d7FRCOja65mKnsYudxi52GrvYaexipxLSIiIiIiKS\n1pIykiMiIiIiIhIrRXJERERERCRQdJMjIiIiIiKBopscEREREREJFN3kiIiIiIhIoOgmR0RERERE\nAkU3OSIiIiIiEii6yRERERERkUDRTY6IiIiIiASKbnJERERERCRQdJMjIiIiIiKBopscEREREREJ\nFN3kiIiIiIhIoOgmR0REREREAqVwojsQTYECBRLdhaQQCoVy/DMau/9o7GKnsYtdTsdO4/YfXXOx\n09jFTmMXO41d7DR2scvp2CmSIyIiIiIigaKbHBERERERCRTd5IiIiIiISKAk5ZocERERCb569ep5\nx2+99RYA8+bNA6B///5eWyzrGEQkvSmSIyIiIiIigVIglITTI6oi8R9V4Iidxi52GrvYqbpabHTN\nxS5Vx6548eIAPPDAA965Sy+9NOwxBxxwgHf8zz//5HkfUnXskoHGLnYau9ipupqIiIiIiKQ1rcnJ\noFChQgB07twZgMGDB3ttJ598MuDfUffq1ctre+eddwD48ssv49JPSV09e/YE4OCDD/bO3XbbbUD2\nZikmT57sHV9zzTV53LvEO+aYYwAoVapURFvNmjUBaN26tXeuTp06AFSuXBmAI444IuLntm7dCsB1\n113nnXvsscfypsMBdsEFFwAwd+5c79zvv/8O+OMtEovjjz8eiIzeAGzYsAHQOhzJP0WLFvWOV65c\nCUDFihUB6N69u9f29ttvx7djkqcUyRERERERkUDRTY6IiIiIiASK0tUyGDJkCAC33nprRJuFzu3P\nadOmeW0ffvghAF26dAFg/fr1+drPVGDpCACrVq0CoEqVKgBs3LgxIX2KN0txBJg4cSIAxx13HOCn\nRgLs27cv28/Zo0cP7zjV09VsYfEbb7zhnatfvz4AJUqU8M5lJ21l8+bNQPi1ZSkJpUuXBuC0007z\n2tIpXa1x48YAfPDBBzn6uRo1agDh45/qn212za1bt847V6FCBQDatm2bkD4BdOzYEQjvl5smGBQl\nS5YE4Oqrr870MXPmzAFg7969cemTpI9ixYoBMHv2bO9co0aNwh7jvu+mTJkC+CnlkloUyRERERER\nkUBJ60iOzeg1adLEO3f22WfH9Fw2O9+sWTMAnn766Vz2LjV07drVO3722WfD2o466ijv2GaCjzzy\nSCB9IjmVKlXyjnfs2AGER3BiYaVXwY8cLly4MFfPmSjt2rUD4IQTTsjycV999RXgz3K7M21//fUX\nAM8991zEz1nk8PPPPwfgoosu8tpmzpwJwCuvvBJT31PBwIEDAbj77rsBOOyww7y2n376ab8/b+Pn\nStWCA6effjoAo0ePBsLHwrz77rtx7VM0dj27ghTRsff8ueeeG9H23XffATB16tS49kmCz743H3/8\ncQDOOeeciMds2bIFgOrVq3vnxo4dG/bzN910U772MxnZ7yyWEWGfoeBHZO13PLfUu73Xly9fHpd+\nRqNIjoiIiIiIBEpabwY6atQoAMaMGZNnz2mlai+77DLvXLQZ5uxI9Q2j3Nxyi+DkNoqRXck4dnfe\neScQXsY4t/79918A6tWrB8DXX3+d6+eM59jZmpm+fft65yzCOmPGDO/cnj17ANi9e3dMr/PWW28B\nfqQV4LzzzgNgwYIFMT1nNMmwGWiLFi28Y5tBGzFiBOBfg5D1OrADDzwQgI8++ggIL8ttJaSjRXli\nFY9r7ptvvgGiR3CSla377NOnT6aPScbPuozc9XWvvvoqACeeeGLE45o2bQr4azjzWyqMXbJKtbGz\nsv2r74QAACAASURBVNC23su1Zs0awF8Xd/HFF3ttw4cPB/xIjvtd9eijj8bUl1QbO1tb/Oabb+bo\n53bt2gX4UfS8eF9rM1AREREREUlruskREREREZFASZvCA1Y2EPzw49ChQ3P0HJ999hkQuYgeoEiR\nIgCUKVMGCC9P26ZNGyDn5VtTnTs+SZgVGXdWbOGLL74A/JLH4F+T0VxyySUA9O7dO6LN0v8KFkzN\n+Yq///4bgPvuuy9fnr9WrVoAHHPMMQB8+eWXXltepqklEzcl1FIc2rdvD8Dtt9+erec4+OCDgfA0\nNZOxwEiqeOihhwB/e4D9pc5aaqT9aYUqwL9u69atC/iFLfKCW/wgGQoh5IaN8QsvvOCdy5im5n43\nbN++PT4dy2fuQutWrVpl+rjXX38dCC+hb/IyjV787wDjXndWTvqXX34Bwj8nP/nkEwCWLFkCwPjx\n4722WNPVUoG75CJjsQV3KYJ9d2/atAmAefPmeW0Zfy9OhNT8zUhERERERCQTaRPJqV27tnc8cuTI\nbP+cO2t5wQUXAP4GZW4J1qpVq4b9nJXag/AoUjq44oorAC3QzOiOO+4I+zO7spoJlEg2ewQwa9Ys\nAEqVKgXAM888k5A+xZOVs3e5Y5Jbv/76a549VzzZ++79998Hwj+jH3zwQSC8PPaECRMAuPnmm+PV\nxcCxMXaLYRgrNTto0CDvXF5GxBIpu5/Z9rhoj3fL9BorZ2wsErS/cxJp9erV3rFt1G3cKK9tFJwu\n6tSpA4RHEsuWLQvA9OnTgfAsqG3btoX9vBt9tkyKl156KV/6mh2K5IiIiIiISKDoJkdERERERAIl\nkOlqts8G+KHGe+65J1s/a7tNW3j922+/9dosTc24YXYL41lajKt8+fLZeu2gcRf2pUOaUF6qVKmS\nd2x7nERji/5sD5B0Zu/1cePGeeesvv/69esBePjhh+PfsTiLtpN8btlCe4BFixbl+fPHU7TdtzOm\nq0jeGDBgQKZttmfG1KlT49WdlJcxhS1aSptx09YyFjZQUQNYuXJlxLnq1asDfnESCN8zJ8gsTe3l\nl18G/LEAv0iPLUWIxsbJvnPBLyRi+/i5BQviRZEcEREREREJlEBGcpo0aeIdR5u1y4qVusxOuWe3\nBG3Dhg2B6LPutmt1qs+AZlfFihWB8MIDVl5QssfGEMIjk+DvIgzwwAMPAPDvv//Gp2MJZqWNzzjj\nDO9c165dATjrrLOA6OXKrYDIDz/8kN9dTBiLPp9wwgkRbX/88UeOnitjuVUrfw7w8ccfx9A7SSdW\niKdbt24RbXv27AH878UgcgsEZIy2uG0WbcmqUEFW0ZqsuM+Z8fnd57T+pHN0p0aNGgC8+eabABxy\nyCERj9m5cycAl156aby6le8segN+cQCL4Cxbtsxrc8tJZ8a+k93f+yySk4gIjlEkR0REREREAiWQ\nkZzsshlft0x03759Y3oum0nft28fEL45o93ZFi1a1Dvn5rgHTZcuXYDwGfVU3UAw3mxzwaw2qnz6\n6ae946+//jrf+xRvVnZyzpw53jnblNLeQ9HWvmXF3teNGjXyzq1YsQKATz/9FICFCxd6bTZrl0qs\nVH20jWHvvvvuHD1X06ZNs/3YKlWqeMf2b/fee+/l6PWS0TXXXANA9+7dAahWrVq2fs7WP9g6RHc9\nYlA2u9yfs88+G4D69etHtNlmio8//nhc+xRP7nqY7KybyarssxthyVhyumXLll5btA1FjT0uq1LV\nbh/SoQy1O3a2WXK0CI5F/y+66CIA3nnnnfzvXJwcf/zx3rFFs6y0u7u21c0eyejYY48F/N/7XNld\nC5+fFMkREREREZFA0U2OiIiIiIgESlqnq82bNw/ww5C5YSHlc845B/BL5oG/W+yUKVO8c5dffnmu\nXzPZWKpKzZo1Afjwww+9tkTueJtKbKGupWdFs3Tp0nh1JyFmz54NwEknneSdi1ZMICMr454Vd6Gl\n7dpsz/3II494bbGmrSbS+eefH3HO0gzWrl273593d/a+5JJLsv26nTp18o4tFclNYUtVVvo/p1sA\ndO7cOezPHj16eG1WHCOr9I8giJamZn755Zc49iQxskr3ctPXcrrYPzvpbVmxdLVoBZncVLYgpqtZ\ncSjToEGDiMdYOulTTz3lnbvlllsA+P777/OvcwlSsmTJiHP3338/EL3EtqWLlytXzjt3/fXXA1Ci\nRAkAtmzZ4rUlw3WkSI6IiIiIiARKICM5gwcPzrLdFsEPHDgwz17zggsuALK/ODWIbCbYZj5//PHH\nRHYn6RUqVMg77tOnDwDDhw/P9PG2sDRICx+jsUIArj///BPwixHMnTs3oi2nrEzyu+++C4RvdGab\nrCay9GVORSvXa9FUtwR0ZtyIReXKlcPa3HKixq7fxo0be+dyWhAikU499VTv+LDDDsv0cVu3bgXC\nN4bODtvQt3Xr1t65JUuWAHDmmWcCsG3bthw9ZzKzoingfx8a9/pzZ8kzY+Wl27Rp452zMVuzZk2u\n+pnOkmFmPR6KFy8OhEfN3PdhRjt27ACgY8eOALz99tv52LvkYVk3Liu44kb2jX2mtW3bNtPnHDly\npHecDNsNKJIjIiIiIiKBEqhIztFHHw1kvbEW+LOzOd0gLyP3dSz/unTp0hGPe+211wDo169frl4v\n2Vk0wkpmp/MGoBYlsNnHaNxr5brrrsv0cTarZGVZbWY5qK666qq4vM4nn3wC+OWV3Y18bUYv2SM5\nzZs3945t9tL18ssvZ/u5ouVnmyOPPNI7tmijzYyedtppXpttphcEtm6hV69eQM5z8q0kq33+g79h\nq5WVtusMYo9IJgu3PHvG70E3gmCbgZrChf1fQx588EHAH3OXjVlWUbdkZJttxrqpZ7y4JZVTlUWS\nLeLvrhfMaPXq1d6xRR6DuCVDVtzP6yFDhgD+upuLL744pufMztrYeFIkR0REREREAkU3OSIiIiIi\nEiiBSlezlJ9oaRdu2crFixfnyevNmjXLO65atWqmj7Nwte0kG3RWktcKPARV9erVAb9cuO2MDn4a\nSk7Lz0ZjqR5BT1NLFFt0mopOOOEE79gtZGEyXjMHHHCAd2wLxW1X6qzKlp988snesaVibtiwAQhP\nL3QLQiS7zz//3Du2Yhf2ngY/fTHW0rE//fQTAAMGDPDOvfjii4Cf4ud+V6V6ulpWstpCwC1S0Lt3\nbyB6yfho13cqSKY0sKxS+a2wTaqwtHi3OMWtt94KwHHHHQeEX0f79u0D/OvICs5A+qWpGTed+ZRT\nTgGgWLFiQPjyCiu6Yu/PjIVpwN+S5d9//82fzsZIkRwREREREQmUQEVysuIWGVi1alVMz2GLKSdO\nnAhEv5u12TjbFC83r5eqbIYl6KwQgM2E5/frPP744wD8/PPPXtvff/+dr68dZAceeCAAPXv2jGh7\n//33492dmEQrG+2yUp+2yZ0tLgW/UEtW7OeGDRvmnbNoRKpvjvfrr796x1ZO2i3e4L7PcsMtR2sR\nDZt9dv/9pk6dmievlyhu1NCyFmzWvGBBfz719NNPB/ziAvb/kPWmvwcddBDgR86TvSiI2V8hpHjK\nqi853Zg0EdzIp21aGe3z+/fffwfgpptu8s7Z1gvRtigQ+OCDD8L+3/3cKlOmDOBnqET73deiQhYx\nSxaK5IiIiIiISKCkTSQnL9gGgT169Mj0MXfccQcAt912W1z6lIyymo0LkkmTJgH5P3NhM5dfffUV\nALNnz/babFbKyjZu2bIlX/sSJOeffz4QvomhSZXZvqOOOirLdpt5c0sVm82bNwN+DnaJEiUiHvPb\nb78BfmnfoLKxyA+7du3yjm022SI5FqWF1I/kLF261Du27AVby/XQQw/l+vntPZkqEZzM2BrdREim\n9UE50aRJEyB8PXW0zSqt3SLWX3zxhdd2880352cXA83WnB977LERbba578yZM+Pap+xSJEdERERE\nRAJFNzkiIiIiIhIoaZOuVrt2be/YFqNZaplbutMWSNquyoMHD/baLrrookyf39IdnnjiiTzqceqy\nwgMbN25McE/y15dffgmEX1vZsWLFCgBmzJjhnbvkkksAv+R0/fr1M/35aOmSVu7RTXm56667ctSv\nILMiA5aiBv74W3rlsmXLvDY3xSiZuelOzz33XES7lUm14hjubvPvvfce4BcVGDVqVMTPP/3003nX\n2TTllu1u3LhxWFu0BbxBYOVk3dLjsXCv13PPPTdXz5Uolp42evRowN8SIBGiFR5IZPpcdllpejdF\nbe/evUB4qWPb1uOvv/6KeI6gvtfyi1ssxC08k5EVU0m20tFGkRwREREREQmUQEVybCbW3VzMZtFs\ncS34MypWFm/9+vVeW5EiRQC45ZZb9vt67mLKBQsWAOm7qRRA165dgfTZDHTChAkATJkyBYCiRYtG\nPMYt8Wyzm1dccUVE26OPPgr4mxF27tzZa7OIjF3L0Up0H3LIIYC/GRr4mzZ+99133jlbfGmzYEHk\nzkCdd955gB/BOfPMM702u05tfNxyvqmyMePy5cu9Y1uca2WfIfYyyPYcVtBCYueWuHXf1wBz5syJ\nd3fiwmbUL7zwQgCaNm2ao59fu3YtALfffrt3zsoCpxorzZwKJZqTjX2m1axZEwjfUL1Tp05A9I1m\nbaH8DTfc4J277LLLwh6TKsVlEsXNXMqYxWQFVAAWLlwYtz7FQpEcEREREREJlAKhJKz3m9vNJH/4\n4Qfv2GbG84KV57UhczfT27BhQ569jonlnyaRG3Faf23GLZE5sPEcu2rVqgFw/fXXR7RZ2XGAb775\nJqbnNzYT2L17d+9cTtcD2czxxRdfnOlj4jF21m9be3TEEUd4bVYqO6dsps6isQDt2rXL9PE2A2gR\nnJ07d8b0uq6cjl0ybJxrpT/dtV52rbr/LvkpHtecRfNtltfdDNQim+5Mcazsc8/WCgwfPtxryxjt\ndb+f3IyCnEjm7wnbXNXWGoJfatre+272w7hx4wB45plnANi9e3e+9i+Zxy4/RPv72pqcnEaa4jF2\nVvLa1jG5pd5tM1nLyAH/vWcRHPe6M1Zm2s34ifcazGS+7iwTwjYfBz8ia1q0aOEdu1GdeMjp2CmS\nIyIiIiIigaKbHBERERERCZRAFR4wbiqOuzA3Fm65R0t9yYuUhiDat28fEFsoNpVZmsk111yTr69j\n6QRWpACgVKlSAIwfPx6Ak046yWuzcptuGlZO09vyi5U+vu222zJ9jBuez841ZY+P9lgr8jB58mTv\nnJWST5UiA3mtbNmyALRu3TrBPYmPG2+8EYARI0YA4QuP7RpwSxYbS6+Klk5mJZKPOuoo75wdR0vX\ntdexkt6pupg+u2w83QIYDRo0SFR30la00tEmkSWt98dSSi1t0U0/W7Ro0X5/3i0lPXLkSMDfZiFV\ntgmIN9s+JWOKGsCSJUsAWLduXVz7lBuK5IiIiIiISKAEsvCAe7dvM20PPvigdy6rBfE242slZ1ev\nXu21xbowNFbJvDgtmjfeeAOA5s2bA1CoUKGE9SXVxi6vuDN2hx9+OBC+KWu0DSMzisfYWbGGU045\nBQiPJtSpUyesLbt9sj7Mnz/fO2cLmD///HMA1qxZk6N+5lQqFR6wcsbRZkSDWHjA3gfuhoLx4EZS\nrbTyVVddlWfPn66fdXkhXcbOvheiZbaceuqpQM4jOvEcu6FDhwLh3wkZy7G7Vq5cCYSXb7fNu5NB\nMl93Nk72+wP42yzYZsZbt26NS1+iUeEBERERERFJa7rJERERERH5f/buPG7K6f/j+KufvVSWIkt2\nQir7vi/RhpAiS0QRoSwpX0uyfL+kLCEpIb6W7Nl3lZ0kW2RJyJJ9r77x+8Pjc65z3TP3NHM198w1\nZ97Pf7q6ztwzp9M1M/d1Pp/zORKUINPVQpHmkGY2lhJ4zDHHALD44uWra1FpY5cmGrvkKildLU1K\ncc3ZgtrjjjsOiPZpAWjfvj0A77//vjvXokWLvJ/b7/+YMWOA7MUuLG2ymPR+Ta7axi7bvzfpv6fa\nxq6Y0jx2v/zyCwD169d3526//XYAunfvXpI+5KJ0NRERERERqWqK5KRYmu/2005jl5zGLjlFcpLR\nNZecxi65ahs7KzzgF6ixggODBw/OOJdLtY1dMaV57CyS4/fRrpcpU6aUpA+5KJIjIiIiIiJVLcjN\nQEVEREQkYtEaP5Jjx7YFBKR7g1CpW7bBeCgUyRERERERkaDoJkdERERERIKiwgMplubFaWmnsUtO\nY5ecCg8ko2suOY1dctU6dn66mik0Ra1ax64YNHbJqfCAiIiIiIhUtVRGckRERERERJJSJEdERERE\nRIKimxwREREREQmKbnJERERERCQouskREREREZGg6CZHRERERESCopscEREREREJim5yREREREQk\nKLrJERERERGRoOgmR0REREREgqKbHBERERERCYpuckREREREJCi6yRERERERkaAsXu4OZFOvXr1y\ndyEV/v7774J/RmP3D41dchq75AodO43bP3TNJaexS05jl5zGLjmNXXKFjp0iOSIiIiIiEhTd5IiI\niIiISFB0kyMiIiIiIkHRTY6IiIiIiARFNzkiIiIiIhIU3eSIiIiIiEhQUllCWqRaderUCYA999zT\nnevbty8QlZCcOHGia9tll11K2DsRERGRyqBIjoiIiIiIBEWRnFqstdZaABx11FHu3L/+9S8AJk2a\nBMCwYcNc2wMPPFC6zqXUYostBsDZZ58NwLnnnuva+vTpA8C1115b+o5VgM6dOwNw9913A/ENr+zY\n/lxnnXVcW+vWrQGYNm1aSfpZbg0bNgTgmGOOcefsffjXX38BcOWVV7q2n376CYiuyf/7v2he5447\n7gCgW7duddhjEclm//33B+D8889351q2bFnr499++20g+l6577776rB3Us2+/fZbd9ygQQMATjvt\nNACuvvrqsvRJklEkR0REREREgqKbHBERERERCUq9v/28mJSwBdbl0K5dOwAuuugiAFq1apXxGOvf\nb7/95s517NgRiC8KX1RJ/mvKOXYbb7wxAG+99VZG24wZMwDYe++9Afj000/rtC9pHrtGjRoB0KFD\nB3fuxhtvBGCJJZYA4JtvvnFtt956KwA77rgjAFtuuaVre/jhh4GoYEExpHns7H3mp6rYa+fTb7+f\nv//+OxAVb5gyZcoi96/QsavrcbPnP/LIIwG44YYbMtosDffCCy+s077kkuZrrkePHkA8rap58+ZA\n7n4/+OCDAGyyySbu3KhRowD497//XbT+pXnszNJLL+2Or7rqKgC6du0KwIIFC1zbY489BsDYsWMB\n2HnnnV2bpTx//vnnQPbv5kJVwtilVZrHbvXVVwdg8cWjFRn23WHuuusud7zVVlsBsPXWWwNwyimn\nuLb69esD8O677wKw+eabu7b58+cn6l+axy7tCh07RXJERERERCQoVV14YMkllwSgf//+7pzNZuZz\nt2h3+ACnn346ALNnzwbgww8/LFo/Q7D++usDcPDBBwNw6aWXlrM7ZXXggQcCMHr06Iy22267DYgX\nbbBryRbU+5Gcxo0bA9G1aNGJUB1++OFFe65lllkGiK7NYkRy0sYifGPGjAGyF7SwWcxsmjVrBsSj\nGObmm28GYPLkycXpbMocccQRAIwcORKIz9q+8cYbAFxyySUAvPTSS67NZoMtsv3LL7+4tlDHqjb2\nWXXPPfe4c6uuuioAr776KgADBgxwbTUzISyyA7DKKqsAcNhhhwGw/fbbu7YXXnihmN2WCuJHCddY\nYw0gik7bd63/uJoRbICmTZsu9HXs/exHhO69996k3a4oNmYXXHABACeccIJrs3EcMWJE6TuWB0Vy\nREREREQkKFUZybE78jPOOAOIZoYWha3lsT9tbYUvhcufSs5mfwVefvlld/zss88C0Yz7Rx99lPF4\nW0/hz6LssMMOAGywwQYATJ06tU76GrITTzwRiEpKh8TWwOXy5Zdf1tpm6yB69uyZ0WalzP2Nayvd\nxRdf7I67d+8OROsJ7fsC4JFHHqn1OWbOnFk3nasg22yzDQD3338/AE2aNHFtTzzxBAADBw4E8v/M\nsm0aunTpAsTXxEp1sLU2EK23GT58uDu37777xh7/5ptvumPb4sLWcvnRG1sXZtH8XNHt9957L1Hf\nK42/3YKtHx40aFDG4/r16wdE6+jS9r5UJEdERERERIKimxwREREREQlKVaarWXpaMdLUavO///3P\nHVsqUbYUpGrxySefAPDnn3+WuSflZ2WfrTQ0wLx58xb6c1aAwE9zs7QQiVxxxRXueNdddwWgTZs2\nZepN6Vn6AECvXr1qfdyPP/4IRCV9C3X77bcn+rk0Gjp0KBAvHWvjYjud+6WOJZNfKvuhhx4CYLnl\nlgPiBQWsdPTPP/9c0PNb2fhNN90UqNzvUz+VfYsttijoZ63AkS0E/+6771ybpcPvscceQJROClHJ\ncksRrFS21ACiIh7rrrtuxuMsZdQvatGiRQsAvv32WwAef/zxjJ/79ddfAZg0aZI7Z8Uz7PPSfpcJ\nnZ/qbKmic+bMAeLfsVaMwD47/aJS+fxeU9cUyRERERERkaAEH8mxGY/p06e7c1ZmMJfnnnsOiC9q\na9myJRBFgDbaaKO8+mAl9o466qi8Hl+p/E2yarKZvJ9++qlU3Umtr7/+OtHP2SyTzShVo969ewPw\n2WefuXM262aLnA855BDXtvvuuwPR54C/mPKvv/6q286WiR+N8P+9NVmEwja5K9SsWbMS/Vxa2Aap\nEBWf8Mfrgw8+ABTBWZgGDRoA8XK6K6ywAgBPPvkkAPvtt59r++OPPxbp9So1gmMsagjRdVcX/M+3\n448/HogibJVaytyPvtg1ZptrZ/Paa6+5Yyt4kc2yyy4LRJvAW7QQom0ZLEI+d+7cQrtdUez3Y78Q\nj/3O0bZtWyAq4gBRJMf+bN26tWuzqG05KZIjIiIiIiJB0U2OiIiIiIgEJch0tSWXXNId9+/fH8i+\nOC0bW9xtqWV+SsaDDz4IRAtuDzjgANd23nnnAVHo3ufvERCykPbLSKMVV1wRgNVWW82d+/zzzwH4\n/vvvy9KnUrOwuaVa+Wwn6nPOOcedW3/99YFoUa6fwmHnhgwZUjedLQE/9dYWJdvO8D5LuTj44IPd\nOdubKZfNNttsEXuYXv5nu+1746dV5cOKyvjjZHtz+CnSIbM0lbXXXtuds4Xfdr0taopaSOrXr1/y\n12zYsCEAO+64I1C56Wo++8475phj3Dnbq2WttdYC4vvy2ef8K6+8kvFcluLsF20x9j5++umni9Dr\n9LJr5K677gLi16mdsz2tbK+qbJo1a1ZXXUxEkRwREREREQlKkJEci95ANMuUi92lAnTr1m2hj//0\n00+BeFECi2Lks8O4SBJWmtEv1WrlqCt9EfiisFm4MWPGAPFIbj4qsRiGLZD3ZzH79OlT6+MfffRR\nIIpY5MsWmobomWeecccW1bJx8llkbIcddnDnRo8eDcAyyywDxMsCWyTRfs7KrwI89thjxeh62fkZ\nC9ki+FdffTVQ3UVSanPPPfe446OPPrrWx9nO8dmiYBaFts88n13X7du3d+dOPvnkZJ2tAFbcAuDC\nCy8EovLv/hiYTp06AfGozSWXXBJ7zBtvvOGO7Tm++eabIvU4PfwCAoMGDQJgq622AuLfFfZ7sX3O\n+cV90k6RHBERERERCUqQkZydd97ZHVvp2Gxslumaa65Z5Ne018lWqtZmAvbZZx93LtuMoUgu2dZ7\nDRs2rAw9SRfLAS40gmMzVbYOrxIsvvg/H9lWlv6ss87K+XjbcNFmOPNlm6jmKkEdEls3ud1227lz\nv/zyCwB9+/YFYMMNN3Rttgnjf//7XyCKXEA062mf+4cffrhrs1lk26KgUvmfRdm2UrCNOyWTv7bD\nX9db0zvvvAMk/3zyr+VqMXLkSCDabsEvYWzvx/fffx+I1u1A9Dln72tb3whhRnDMyiuv7I7PPPNM\nIPrOyLZxrG3G2rlz51qf00rvp0V1fIOJiIiIiEjV0E2OiIiIiIgEJah0tV122QWIyiRCtEDPN2fO\nHAB69OgBwMSJExf5te11spWqtUWtxXgdqT4WSrdFf375T3/xtBTmuOOOA6IUhbTyF4faIu+zzz47\nr58999xzgaj0Z76sJHK2dDVL37AywX4arqVqXXnlle7clClTCnrtcrCCMbbzOUSpRJZ+ccIJJ7g2\nKzwwf/78jOey/5tbbrkFgPfee8+1WQpRpaerLUyudDUrvmAFGarte3Hu3Lnu+P777y/686+33noA\nHH/88UV/7kph7z3bYgGidLWa2woATJgwAYBevXoBYaeo+XbfffeMc1asy0rh++za/d///ufOWQq1\nGT9+fDG7uMgUyRERERERkaAEFcmxzYsWttnWa6+9Bix6OU9/8WWu17SN+OzPamQLdKVwrVu3BqBD\nhw5AtOhc4nIVGTGVuJC+ZcuW7vihhx5a6ONHjRrljvMpqmKbzPoLyHOV0l9ppZUAuPHGG4H4gvzG\njRsD8cX2fiQqrQYMGJBxzsrI2vvuq6++Kug5LQLklwzeaaedgKjErZUJrjR+wYts7zsrc28ZDbbY\nG6Bnz55AVMLXLzNts8g2Pv6MseTHSsn7i8pnz54NVF9BCH+T3prFoXx77LEHUD0RHONv6vn1118D\nub8zPvnkEwC++OILd27NNdcEoqjZiy++WPR+LorK+8YXERERERHJIahITqn5papthk6y83NjZeGW\nW245d3zHHXcA8PbbbwPRpqAhsAiozZYDzJgxA8h/HYnNmN95551APM/YohTGXytnaydsbU5a2eaS\n+dpiiy3ccT5rtlZYYQUgHpHJxzbbbFNrWyWV5YZovZu9xyDaQLHQCI6xnH+7LiGKaFukLNtmjmlm\nm8/a2gWI/p3++hJ/TRbACy+84I5tTcRuu+0GxDeqtM82Ww9l0UKovLEqtYMPPhiIrx0zTzzxeXYR\nVwAAIABJREFUBADTp08vaZ9KwY8UW0TGSkD7v6PVXJ/t/9029bXvoXwi5iHwf8+w8bDIjEVtAFq0\naAHA4MGDY4/x2Wenld5PC0VyREREREQkKLrJERERERGRoASZrpbPAuRi8EOhNV/TX+Dsl/yVcPk7\nKFtZxaRpO/5zWUlQKwccgoYNGwIwfPhwICrnDlF6j39u3rx5tT6XpRbYn/4i8gsvvLDWn7NF8mn3\nww8/FPR4P12tXM4///xyd6Egdu1ccskl7pwtxF1Ufrra5ZdfDsBWW20FVF4K1hprrAFklo0FWG21\n1dxxrhLZ7777buzPn376ybVZutoGG2wAwIgRI1ybvZeHDBkCwNVXX134PyBg5513HhD93/iFjq64\n4opydKkkrCw7wCOPPFLr46yIxdFHHw1EW45AVAxj6NChAEyaNMm1/fzzz8XrbMrY9y/AvffeC8A7\n77wDxNPVVl11VQCWWmqpWp/L/5xLE0VyREREREQkKEFGcrJtAFpMF198MQCnnHJKra/p3wXffPPN\nddqfcrIZeYBNN920jD0pDX+RY9u2bQHo27cvEP/3L7nkkgC88sortT7Xq6++6o5tYaiVsLRZFYjK\nf9oGhCGwssh+tMZ07doViEdhbHYpG7sGbRHlQQcdlFcf/NK1aZa2zUoffvhhICr4YH9CVKLWNlyu\nFKeeempJXue2224D4tsPVBIrguEXaLAowZZbbunO2eaq+WzTYBs3+sc2Y+wXBbEIjv1f2SbbkPvz\nIUSWOWK/i0C0ONz4Ww1k29ix0h155JEADBs2rNbH+P9uK5ZhW4j4235YJMfO+QVqQvbggw+6Yysn\nPXDgQACaNWvm2uz3WyvC4rdZUam77767bjubkCI5IiIiIiISlHp/13XYI4Gka2qaNm0KxO9Os+Wn\n2yxj7969AZg4caJrqzm7azPyAP379weiGeZcQ2eboUHyso1J/mtKtR7J+Jv++eU+Ad566y13bOVC\nC11fkFRdjd3xxx/vjv18cYjn7to6LNs4EWD55ZcHYN111631+W2TLT+/3SI5W2+9NQBffvllrT+/\n+uqru+NWrVoBUZQI8ttcrxTXnc1AWqnPbM/lbyBo0YMJEyYA0KlTJ9dm7/F8yrj7/bT1Bf7GZouq\n0LHLZ9z88tq33347EJU8tj8hysH3o402I55toze7nnJFG20dmL/GsHv37rG+FEMlfNYVw0UXXQRE\nZbuLUb683GNnJe4PPPBAd85KRvvrVheVRSasjK3/ebvOOusAhX+/lHvskrL3dbYNxm3srZwy5F7T\nmFS5x87Wfe24444ZbbbJrpXVBnj00Udjj7HvY4h+B9x4442B+O8yFuUppnKPXT788tJNmjQBonVw\nlsUC0bi2a9euJP0qdOwUyRERERERkaDoJkdERERERIISVOEBS0P79ttvcz7O0truueceIJ6uZilW\nFhLzQ3aHHXZY3n0JcWfhbDbbbLNa22bOnOmOS5WmVtf8dLCa/IWeFtZt1KiRO2eLjffaay8gvtO3\nFS2w5/dDsla+8fnnnwdyL5i3NBiA5s2bZ/Qhn3S1UrDyndlC8JYa5S+kteN+/frV+vh8FotaCV8o\nbppaXfJ337brY6ONNgLi77/x48cD8UWhlsoxcuTIRK999tlnA/F0NUnO/v+ypRlVKkvjtlQfiFJH\nrST3oEGDXFvSz6ALLrgAiFK1/Oe0a3/PPfdM9NyVYtlllwWiAhY+u6YshbwuUtTKzS/xvu2222a0\n2+8cluKb7fcwS/G97LLL3Dn/2oX474TVyv89w34H8dPUjF+GOo30zSUiIiIiIkEJKpJj/IW6Vt7y\nxBNPrPXx/qZQtkA+16xwtpnjBx54AEj/XW0prbjiiu7Yohi2ILBSTZkyxR3bZnY2C7TyyitnPN5f\nHGvHL730EhBt3ubLVlLZFkhaIQF/NsUvUAAwY8YMdzx27FggPdEb39SpU4Fo8bvP3lf5LjAs5PGV\nXkrVNk60a8j+9PmRbL/UbyFsEalfxKAa2Oc/RAubF7WcrB+R7Ny5MwDXXnvtIj1nmlhE9LTTTnPn\nrrrqKiCK8vibh1rJXys9WygreGGlbgF22GEHIP5dnmtD0kplBY3at2+f0XbWWWcB8QyKUFj07pBD\nDnHnsm1Ia5toWzEa+86EqODPvvvuC8A222yT8fNW5Ofll18uQq/DYdlPxi8q9fTTT5e6OwVRJEdE\nRERERIISZCTHZxuInXDCCXk9Pp9ZYXuMvxmZ5Qu//vrrSboZpO23394d2/qQSl+r5G94ZdEpi5gM\nGDDAtdlmeLfeeqs7ZzO6hx56KBBfK2Mbm/kb49XGL31pM1zmjz/+cMc2659GNlbrr78+EJ9BL6a5\nc+cCUdnZkDfmLSa7HtNQLrcULOpsazsAXnzxRSDaMiBb1Cwf++yzjzu2jV3teykk/saftjbG/p22\nYTJEaxItkv3MM8+4tu+//36hr/Phhx8C0aaOEG1EWnPGOTQ1t2nwv0/teyhE9ruErQ1ZmFyf8/aZ\n5mdl2Bony86o9N9Tiq3mWlh/24E0Zor4FMkREREREZGg6CZHRERERESCEny6mqUH+MUIrNSvhT7X\nXHPNgp5zzJgxAJxyyinuXEglQetCr169gGghaggstcxSos455xzXZukT9idEIfARI0YA0eJciMqf\n5yOEcty//PILEJV0Lka6mr0H/TSE//znPwA88sgji/z81cQ+2w444AAgXoDAX0gfCvue6NKliztn\nn/OWTjV06FDXdv311wMwa9asWp/Txmz//fd35yyly67/UNm4WNqjXwa9d+/eQJRetWDBAtf2+OOP\nA1Habc30LIhSXf3P1vnz5wOFfY5WiiOOOMIdW3qv8a/JkK+padOmATB58mR3zlLYCi1vb4UZ/DSr\nO++8E4DPPvtsUboZlPXWW88dd+rUKdZWSQW2FMkREREREZGg1Ps73zqtJVSqxa620ae/yaeVnLZh\n8WeGbAGqzcTXtST/NaVeKFy/fn13fOyxxwJRiVBbIArRYtNcM5/FVAljl1alHLuGDRsC0LNnz4w2\n24gSoHHjxrU+h5Wuff/994HyRm0KHbu0X3NWStWfNbcy3JtvvnnRXieN71cr8LHzzjsD8Q1VreCH\nRRqyfSecfvrpAGy11VbunJX+/eabb4rWzzSOXT6spO8qq6ziztkGn5ZlYSV9IRpH67u/Iebo0aOB\nwkvEp3nsbCPf++67z52za9K+W8sZVS332Nli+K5du7pzthm2FQnxo4Qff/wxABMmTACibQzKodxj\nl4/u3bu7Y8tasS0J2rRp49oWtcR+oQodO0VyREREREQkKLrJERERERGRoFR1ulraVUJIM600dslp\n7JILLV2tY8eOANx///3u3HXXXQdAnz59ivY6lXDNNWnSxB1fc801ABx00EG1Pv69994D4KijjnLn\n/P0liqUSxi6t0jx2l156KRAv1jNv3jwAjj76aCCesldqaR67tKuEsRs4cKA7vuiiiwB49NFHAWjX\nrl1J++JTupqIiIiIiFS14EtIi4hIMg8++CAQLyFdrb799lt3fPDBB5exJ1IN/BK+xhZ+lzOCI9XB\nL/rx+eefA3DIIYeUqzuJKZIjIiIiIiJB0ZqcFKuEvM200tglp7FLLrQ1OaWiay45jV1yaR472/S5\nUaNG7pxtd5GGSE6axy7tNHbJaU2OiIiIiIhUNd3kiIiIiIhIUJSulmIKaSansUtOY5ec0tWS0TWX\nnMYuuTSPnaWrff311+5cy5YtAViwYEFJ+pBLmscu7TR2ySldTUREREREqloqIzkiIiIiIiJJKZIj\nIiIiIiJB0U2OiIiIiIgERTc5IiIiIiISFN3kiIiIiIhIUHSTIyIiIiIiQdFNjoiIiIiIBEU3OSIi\nIiIiEhTd5IiIiIiISFB0kyMiIiIiIkHRTY6IiIiIiARFNzkiIiIiIhIU3eSIiIiIiEhQFi93B7Kp\nV69eubuQCn///XfBP6Ox+4fGLjmNXXKFjp3G7R+65pLT2CWnsUtOY5ecxi65QsdOkRwREREREQmK\nbnJERERERCQouskREREREZGg6CZHRERERESCopscEREREREJSiqrq4mIiEhlmjZtGgAtW7bMaBsx\nYgQAJ598ckn7JCLVR5EcEREREREJiiI5C7Hnnnu648cffxyADz74AIDBgwe7tvHjxwPwv//9r4S9\nExGRYllqqaXc8UMPPQTAHnvsAcT3Z5gxYwYAY8aMKej5BwwYAMAKK6wAQMeOHTNeLwQWwcm2p8Vu\nu+0GQKNGjQD4+eefS9cxkUW06667uuNnn30WgE6dOrlzm2yyCQDPPPMMAC+99FLJ+iaZFMkRERER\nEZGg6CZHRERERESCUu/vbPHkMqtXr15JXmexxRYDYN1113Xn9tlnHwBGjx4NwO+//+7aDj/8cCBK\nU1trrbVc2zvvvANE6W1ff/31IvcvyX9NqcYu7TR2yWnskit07DRu/0jLNWcpVAA//PBD0Z/f2PdD\nu3bt3Lk333wz0XOlZeyOOuood2zfn9n6Nn/+fADatGkDROnf5ZCWsatElTp2Sy65JADHHnusO2f9\nsn/TZptt5toOOuigrD8PMG/ePCCe5rrEEksAMHfuXAAaNGiQ0YdKHbs0KHTsFMkREREREZGgVGXh\nAYvgnHHGGQBceOGFGY85/fTTgfhM27hx4wB48cUXAXjsscdcmy20fPLJJ4F4UYK77rqraH0Pxf/9\nX3R/3bp1awCeeuopIFqUC3DYYYcB8N///tedS2HwsehWW201d2zXYJ8+fQBYfPHa37Z2/QEMHToU\ngNmzZ9dFF0tq4403BuDAAw8EosXgEF035ssvv3THNqMs/zjppJPcsc1k2uL3Tz/9tCx9qhRffPGF\nO27YsCEQj/zUZMUJ3n///Yy2Sy+9FEgevUmT5ZZbDoBWrVrl9Xgr0lPOCE7IdthhByBaAA/R5+Ze\ne+0FwF9//ZXx+NAXyNv36KhRowBYZZVVXFvNSE6+LKrjZ/wcffTRQDTWIVlnnXUAOO6449w5y2iy\na+y1115zbY888ggAV111FQDfffddKboZo0iOiIiIiIgEpWoiOSuuuKI7vvLKKwE45JBDan28zaQP\nGzbMnWvbti0AH374IQB77723a7OojkV0OnTo4NruueceID57Uq0sCmH/BwC9e/eOPcYfp549ewLR\nGAL88ccfddnFkvHzePv16wdE180WW2zh2mx26dFHHwXiMyU2G2ozJOedd55rs+t08803d+emT59e\ntP6XkkVDN9xww4y2XXbZBYjGyZ+NW2+99QA488wz67qLFcEfv4022ih2Llskx8b23XffdefmzJlT\nl10sq+OPP77Wtueee84dDx8+HID69evX+vjPPvsMCD9CtummmwLxKGFN/me2/50qhWnatCkQ/X5y\nwAEHuLbu3bsDsMYaawBRxorPPhv99R22JjnESI5FXAFOO+00IIrg2DpqiL4r1157bQA++eQT1zZo\n0CAg+n3Pz+Ax/tYhH330EQC33Xbbov8DymiDDTZwx/b7Sa9evYDsES875//usuWWWwJw4oknAtCl\nSxfXZiW265oiOSIiIiIiEhTd5IiIiIiISFCCT1ez9Ci/XGDNNDUrAwjw/fffA9HC+LvvvrvW57YQ\nJ0ShTAv5Hnnkka7NdsW96aabCu5/KGyB3mWXXQZkpqj5/LC57SQcSooaRCku1113nTt36KGHAtG1\n+MADD7i2m2++GYDmzZsD8UIW3377bey5999/f3dsYfZ7773XnbMUpUpjKQDZUgXMqquuCsRD4pai\ncPDBBwPQvn1711apqXtJWBqW/7779ddfAfjtt99q/TlbpPvjjz+6c1aoxb9GK52lGfvfEzXZdwPA\nlClT6rxPlWL55ZcH4ilQNcvd2gJkgKlTp5amYxXAUsyylQfeZpttgHiKqZXdbtKkyUKf2x9zK9yy\n5pprJu9sBfIL+FjqrX3e2XYhEBWrWXnllQFYZpllXNvMmTOB7AVErGjSzz//XMRel9dFF10ERIWO\nAJZddtlFek4rTmLFvkDpaiIiIiIiIokEH8mxmTm7O81m4sSJ7thmViwC5JejzcWiOjY76pdBHjBg\nAAB33HGHO/fnn3/m9byVzBZ9QxTBsXK1ubz88svuONcsc6Xp0aMHEC3iy1bi02ZFbYEpwBVXXAFE\nZRhzlbncbrvt3PG+++4LwO23376oXS87KyFuJXf9hfDGNmGzqA9EBQeszKW/KLIaIjl2HWWLUNjn\n3uTJkxf6PP642cLmSucvzLbolC089j399NNAFBmVuG7dugG5FyNn26ahWm277bbu2LI7/C0VkrJI\noxWveeWVV1ybLYK3SI5ll/ht1cIiXNl+t8u2ibt9J1vkx9/01goi3XfffUXvZyn4EasxY8YAUbZD\nvtEb28LBPidPPfVU1+YX/IJ42W4b17rcdBkUyRERERERkcDoJkdERERERIJS7+8Ubh+fbRFeIfza\n6LZA1GrB+ywNyFKpAD7//PNFeu1zzjkHgH/961/unKW++btjW1pbLkn+axZ17IrB9n+xXb0BTjjh\nhNhj/B2Cbd+Nxo0bA9FiPojvMl6INI6d1d63dB+/Fr+9drNmzTL6cuONNwJwyimnAPFCGcYW9vnj\nZft0+Cls+YSG0zh2Fla3/Qjmz59f62OtyAXATjvtBMATTzwBwFtvveXabBFvMRU6dnU9brbPlBW5\n8F/P9kHw0/tqssW22T4/7XOtGMpxzZ177rnu2D63s7EUW38hd5qUY+z8he+zZs0C4u87e/77778f\ngIMOOsi1pWm/uHKMnf97gBWOyfZe+umnn4D4Z3bN1LIJEya4Y/u8t+8A/3UsNdW+W/09jUaMGJHg\nX5HO74mahgwZ4o4HDhwIwLXXXgtA3759a/05K1QD0ffuL7/8AsA111zj2iwlMN9lDabcY2f71zz0\n0EPuXM3UsmzsOjr//PPduRdeeAGIfi+ZPXu2a7NCDtn+vba/1ttvv11Q3wsdO0VyREREREQkKEEW\nHjjssMPccbYZyLlz5wLRzNyiRm98dofrl/K1O1abpYd4+enQnHfeeUBm9AaiQgJ+OW2bUbFZpqTR\nm7Tzo1eQfZGzFaewBfMQzZTmYiWCl156aXfOCg7U9cK+UiikhLgf6TriiCNibbmiFqHwi1bYDtXG\nypFD7uvKxi1XkYGzzz4biM+WVgJbmG39Xxgr4evvAG5j55dnryZ++V0/glOTRRrSFL0pN7/csG2R\nkG2W3oqr5PP5n43/HWvfrZMmTQLglltuSfSclcZKZ/uy/X5h2T8WcTzxxBNdm43VuHHjgMxtGyrF\nzjvv7I4tguMXF6j5HvWLY/3nP/8B4hEcY7/f2veARW8gKqiR7f1fjGIb+VAkR0REREREghJUJMfy\n9k8//fScj7OZkccff7zO+mLl+CAq/XvMMce4c/5MfShs8zI/klbTySefDMDYsWMz2qZNm1Y3HUuJ\n/fbbD4ChQ4dmtNm6m9dffx2I8qsXxmZnLIfYz9H2S3FXE3+z3wMOOACI8qmHDRtWlj6V0vXXX++O\n/dLPAB9//LE7tnVNtmGeX07UIuBWljub8ePHL3pny8DWS+ab457t/WrZAN999x0Q3yzVZoOzbR4Y\nimwZEtn415uxjbPPOussIP493LVr14U+p20HEULp41ybGydlW1ZYlMhn0Qj/eq02tv7ONo+G6HPS\nIg62cSjkt346zWyrCit3DdFnvR9hsbUu9p71f4+2tXU2Zn6GhGXsWHlof82MPX+2dTSliu4qkiMi\nIiIiIkHRTY6IiIiIiAQlqHQ1K8Nou5v7LL0AqiNlpVQsRQ3grrvuAuJhYGPpQq+++mppOpZCVmzC\nL0qRhL/jspWztHFd1OeuZJamZuWSARYsWABEYzZ58uTSd6xELMXCymZnYyVAAS6//HIgSmux3dAh\nSuXKVa5z+vTpyTtbRn5p3aSsTL591vmfeVau3BYo+/8fVnil0vmpftnS/mqe80tIW3EV449PPiks\n//3vfwHYbLPN3LlBgwYBUYn5atSyZUsgSsf0F3aPHDkSiNKiq5kVyujTp487Z9fr4MGDy9KnumDF\nFKwQlG0zUZtnnnkGgG7dugHRZxxA27Ztgeg6ylWQJhdL74XSpQEqkiMiIiIiIkEJKpKTy1dffeWO\n/ZleSWbrrbcGougNZI/gGJv1/fTTT+u2YylmM+W28DZbeUvjLxq1zbasBPWxxx7r2myzW7+kazXw\nyyRb0YWLL74YiM8G33rrrUC4pX79BbJPP/30Qh//7LPPuuNcs+a5Sn9WOtuszkpDQ7Qxr23AmM0b\nb7zhjq1MqkUh/A2orZCD/fnAAw+4tjPOOAOICoxUKj/Cl8/mfBZlyPb4bIufc7HH9+/f352zRdUv\nvfTSQn8+JA0aNHDHVsDBzn3wwQeu7YILLgCqL9JlxS0gd5aDfd7Z92mlFxsAWGeddYDc/24/smLf\nowceeCAQ39B+9dVXB/KL8Gdj21j4hUVmzpxZ0HMkpUiOiIiIiIgEJahIztFHH11r24wZM0rYk3DZ\nZpO2KVSu6I1vvfXWA6B58+ZAtNFZqBo3bgzAoYce6s7ZWrBsm+dZyWhbt+Nv0uU/B8RnUWxG+dRT\nTwXipcttxjokRx55JBDNiANstNFGscf07t3bHfvllEPUuXNnd1zILLj/eNsU+ZRTTnFtNguc7Tlt\nVrhS9ejRA4hvjmdrtfIt3W522203AFq0aOHO2cZ59h7eddddXdvdd98NRLOllR7RWRi/LHk+bO2s\nbZjpf1baZ2o2tmagWiI59evXB+CGG25w56yEr22cbGsrIMzvglzs9wzbEBtyfz7mKnUcsuWXX94d\nv/POO0C0difXJr+Fsoian0lQKorkiIiIiIhIUHSTIyIiIiIiQQkqXS1X6lTNspXlMHr06HJ3YZFt\nt912AOy11161PubMM88E4rtiT5gwAQgrTc0WFrdr1w6IxgaiBeFrr722O2flO4cPH57xXLYA8JJL\nLgFgyy23zHiMlYv2C2fccsstQLRI8Msvv3RtIVxvNVlaXs0UNd+ee+7pjkNNV7NrL9uu5rn46YyW\nOmUpU1byeGHyfVxaffHFFwDcdttti/xcVnbV/gR47733AOjXrx8Q/z+yNBorhGGfHRCli4TESs76\nBWpqsoIqEO2kbuPjp/o9+eSTddDDyrLEEksA0QLuLl26ZDzGvkOmTp1auo6lhKXFn3766UD8d5Bc\nnnvuOQDmzZtXNx0rA/tdwLaX2HzzzV2bbbey2GKLuXOW+m7+/PNPd2wFK6z8fq6CNP772dLKR40a\nVfg/oEgUyRERERERkaAEFclJk2zlgf0y1pWkSZMm7rjm7Kdt8glw0kknAVHJ1B9//LEEvSstf+bD\nZinbt28PwJw5c1zbwIEDgXgE0RaEWrnZ7t27uzaLxFg0ctq0aa7t7LPPBqJomL840hbi24Zf1ieI\nyixbaeUQHH/88UC8MMOmm24KRP9OP3oWKlvQ7W/gmYvNpPfq1avO+iT/sFlh+9MvS23XrUXi/I2r\nKymSM3bsWHdshVGyzZpb6Vn/+/D+++8HYL/99gPipWRDLfVeLLaBZbZsACujHdLnfaEuvPBCIMqE\nyLfwhWVe3HnnnUC0oW8l++abb4Aow6RDhw6uzbJt/O9R+73CIvx+VMvKS1s0KFeBBj9yXY5CAzUp\nkiMiIiIiIkFRJKfILCe0ZtnfSmZ5wBDfhBFg1qxZ7vjmm28uWZ/KxZ8JtwiOzcD6/+dvv/02EI+C\n9ezZE4giXrZZl8/Kz1511VXunK0hyObNN9+MvbZtagjRBqH+rJ+fZ1uJnn/++YxzNfOobQPQkFl0\n4K233nLnWrduDcQ33LUZ9yFDhhT0/DU3A7XS5gAjRoxI0OPq5Zc19teLVTL/c99Ktj/11FPunG0a\naGwDZIjKGdt6m2wRf1sz4Ee67DmzbVRb8/VC4o+dfT8Y///BvgNCWldSk61pA9hggw2AeOl7Kwu/\nYMECIJ5dUfN3l48++sgd2+8uoa7hBHjooYeyHte04oorAtG6JoDNNtus1sfbemL7f7DNy9NCkRwR\nEREREQmKbnJERERERCQoQaWrWYqPH2YzliIE8TKqxWJpao8//jgQ353ZUpfmz59f9NctBX8H+Zr8\ncsbVIFfJXr+EtIW9LfQL0cJcSzXyd2O28tJJFzxaUYOOHTu6c7awN/RdnP2dvQGmT59epp6UjqVh\n+KXc7TPHL+HplxQvRM0dwCtpUXza+Ivpa6arDRgwwB3nSiFJMytV7Ker7bHHHkD2z55VVlkFiBYl\nT5o0ybVZ+qWlx/ifqfZc2Xan99MpQ2FFaKwkNES70Nv7f//993dtIaep2ViceOKJ7pxtJ+CbMmUK\nEBVhOPnkkzMeY6lsr7zyijtnBQsELr/8cgAOOeSQvB5v21gUoyR/XVAkR0REREREglLv7xRO8yZd\nRLj00ksD8YWethjXX6Rom7V17twZKHwWyMrpWdlBiBb92Wyq/2847LDDgMIXRCf5rynmAkwrw+iX\npNx9992BqJzx4Ycf7tr8ctLlVldj5886brPNNkC8rLSx680iLBBFd2xmd/LkyQX3sRTKfd3lw19I\nb5FGK83tL9SdOHFiSftV6NildcG0zXbav8cvP/rYY48V/fVKec3Z51qDBg3yerwV/vC/J7bYYgsg\nXoK1Nn5Zd1ssbfzNkVu1apVXf2pKy/vVL9dr3w877LADEC9ek6sv+fxb7PGvvfaaO2ffS7/99lsB\nPU7P2GVjUa2WLVtmtFm2ymWXXVaSvmRTyrGzf2e2yIzPLzQA8WIDtqGlRcbOOeecRH0phrRcd/5n\noG39YdFTixpm64NlnkC0rUOpIomFjp0iOSIiIiIiEpSgIjnGL9v75JNPAlFEx2ezPoXegTZq1AjI\nPoNv6338TZAsV9GPJuWj3Hf7VnrYX89kevToAcC4ceOK9nrFVIqxs/UQa6yxRkabzSjjDb38AAAg\nAElEQVTZ7EglKfd1l43NEm+//fZAfD2TjbXNbpZzbUNokRxbi+OvRfNLVBdLKa45i+A88sgjAKyw\nwgp5/ZxtVulHXXbccUcg+i5I6owzznDHSWfl0/h+NcOGDQOisYfoPZytL/n8W1599VUA9t13X3eu\n5gx+vtI4doMHDwaiDaL917PPNltvWejvFMVUyrGrGVnOl5U3Bhg1ahQQba5dTmm57vxtLGbMmLHQ\nx9sGo7aurhwUyRERERERkaqmmxwREREREQlKUCWkzbfffuuObXG4pVcBXHvttUC06CrfBag1+aVq\nLS3JdiT+4YcfEj1n2llxgffff7/MPSm/pOWepXBdu3YF4IYbbgDi5djPO+88oHJL8KaR7SpvqWl1\nkaJWarZQvVevXkC8zL8Vh9lkk03cOfteWGuttWJ/Loq5c+cCUTGXO+64Y5GfM8369+8PwOKLR79q\n7LrrrkCUctWnT59af3727Nnu+P777wfg3HPPBeD7778val/LyU+/bdu2LZA9PclS6+39Wc50tbTw\ni0188MEHQPQ9YampEKWdSlTMwr/u8kkDq8StBBTJERERERGRoARZeGBhz7nyyisDuWeQdtppJyBe\nMthYFMOfhbPyhMVU7sVp2QoP2Iyuv2Atjco9dpUsLWNn5WchKkm73HLLAfFSorYJcBqEVnjg4Ycf\nBnJvglsMabnmfFZcYNtttwWgTZs2rs0W4FofcpW29bc0sPK1FpUohjSOXaVIy9j5WSE1y4xnM3r0\naCCKSpZDKcfuzDPPrPU1bSwgXmggzcpx3VkGE8CRRx4JwFJLLVVrn/yNpMeOHQuUt+y2UeEBERER\nERGparrJERERERGRoFRNulolKnco3dLVevbs6c7ts88+ALzwwgtFe526UO6xq2RpGbtPPvnEHTdv\n3hyIdgFv166da/vqq6+K/tpJhZauZmlZHTt2dG2vv/560V8vLddcJdLYJZeWscuVrvbjjz+640sv\nvRSIChxVyz45oSnH2L3xxhvuuFWrVhnPaX2yNLUhQ4a4NttjKA2UriYiIiIiIlUtyBLSUhyTJ08G\noFmzZu5c2iM4Eo4nn3zSHdsu52maUQqZLaS3XdebNGlSzu6IBM3fbd4iOS+++CIAffv2dW1Tpkwp\nbcckGHvuuac7tlLQTZs2defmzJkDREVmpk6dWsLe1R1FckREREREJChak5NiynlNTmOXnMYuuVDW\n5JSarrnkNHbJaeyS09glp7FLTmtyRERERESkqukmR0REREREgqKbHBERERERCYpuckREREREJCip\nLDwgIiIiIiKSlCI5IiIiIiISFN3kiIiIiIhIUHSTIyIiIiIiQdFNjoiIiIiIBEU3OSIiIiIiEhTd\n5IiIiIiISFB0kyMiIiIiIkHRTY6IiIiIiARFNzkiIiIiIhIU3eSIiIiIiEhQdJMjIiIiIiJB0U2O\niIiIiIgEZfFydyCbevXqlbsLqfD3338X/DMau39o7JLT2CVX6Nhp3P6hay45jV1yGrvkNHbJaeyS\nK3TsFMkREREREZGg6CZHRERERESCopscEREREREJim5yREREREQkKKksPCAiIiKVrXXr1gA8/fTT\n7tyuu+4KwNtvv12OLolIFVEkR0REREREgqJIjkjKNWjQAIDmzZsDcNRRR2U8ZurUqQDccccd7txf\nf/1Vgt6JiMSdcMIJAOy4444ArLDCCuXsjohUKUVyREREREQkKIrkLMS//vUvdzxkyBAAbrnlFgAu\nvPBC1zZ9+vTSdizFmjZtCsDXX3/tzrVr1w6Axx57rCx9qjQjR450xzvttBMAG2200UJ/7uWXX3bH\nH3/8cfE7JlVhhx12AKJ1Ez/99FM5u5M6G2+8MQAdO3YEYJVVVnFtp5xyCpB707qePXsCMHbs2Lrq\nYskdd9xx7ti+Gxs1agTAGWec4dree++90nZMRKqWIjkiIiIiIhIU3eSIiIiIiEhQlK5WwzrrrAPA\no48+Gvs7RAu5Dz30UAB2331317bXXnsB8O6775akn2lkaWoPP/wwEE/X2H///QGlq/mWX355d/zv\nf/8bgBVXXBGAzp07u7Z69erl/ZznnHOOO+7Ro8ci9rA81ltvPQDef/99d+7//u+f+ZhsxRTuvvtu\nAFZaaaXY37Pxx/Kmm24ClIplxowZ446POOIIACZNmgTAfvvt59p++eWX0nasTJZcckkAunfvDkQp\nfABdunQBYNlll834uVwFP+yaXnrppYvWz3Jr06YNAJdeeqk7Z8VSxo8fD8Dw4cNd24IFC0rYO6lG\nDRs2BKBbt27u3NChQ2NtufjfEx988AEAHTp0AODDDz8sWj+l7imSIyIiIiIiQan3d67VkWVSyMx1\nMay77rru+JFHHsk4l4+vvvoKgLZt2wLwzjvvLHK/kvzXlHrsfFtssQUAr7zySkZfttxySwCmTJlS\nkr6keexsnKyQBcA+++xT9Nex6Eehyj12FmGxGXT/+XP1rZDHANx8881A9pLcSRU6duV8v1oUwqJ/\ntnEjRJ9jpmvXru7YZueLqdzXnLEIBEQzt7fddlui57KIjkW2AU466SQAPv3006RdzFDusbNCC8OG\nDXPnbPbbPtdmzpxZtNcrpnKPXSUr99hZNLRFixbu3BVXXAFA48aNgfhnWj4+//xzAFZfffWMNisu\n1bJly8I7W0O5x66SFTp2iuSIiIiIiEhQqnpNjuX++zNtNSM4FpWAqFzouHHjANh7771dW7NmzYBo\nE7Q+ffrUQY8rg91p+2W1VWI7uraefvppIL/cYIDffvsNgNGjRwNw4403ujYrZ26zSyGUZ23SpElJ\nXsdmmS3K+Nprr5XkdcvJXwfWr18/AE477bSF/ly2mc2QWFTLZoIh95q2N954A4B58+ZltFnkxx4z\nefLkYnUzlWxNju+AAw4A0hvBKafzzjvPHa+99toZ7bYO2DZSzTcybY+75JJLgPgaqe+++y55h1Nk\n6623dsd9+/YFojXS+bruuuuA7Nk2zzzzDBDPsrD1xOuvvz4QrceDuolql5tFyDbddFN37sADDwRg\nww03BGCrrbZybbYW9osvvgCgV69ers0yo8pJkRwREREREQmKbnJERERERCQoVV14wFLRbCG478EH\nHwTiYUtLZ7GQ3UMPPeTa1lprLSBabGo7WkO0wLlQlbY4zcbl1VdfBeC+++5zbYcffnhJ+5LGsbvq\nqquAKKUxFz/Fxcpgzp49G4A111wz43GrrbYaEIWVAe69995E/Sz32FkhgOuvvz7j+YtZeMAed8MN\nNwDxMHtSaS08sNxyywHRNQjxwg4QLz9uj+/fvz8QT8taZZVVAPjhhx+K1r9yX3M2LrnSjP33pBUl\n+PXXX4vWh6TKMXbHHnusOx45cmTGc9rn0ZdffrlIr1PXyjF29v0IsPnmmy/0dQr9PDN+kR8/vahY\nSjl2lqZm6WSQXxn2wYMHu+P//Oc/AMyfPx/IXep9scUWc8e2LYgtbxgxYoRrO/nkkxfah2zK/XmX\njX3mX3nllUDm90Ntfan5b3nhhRfc8U477VTMLmZ9vYVRJEdERERERIJSNYUH7C4cohnbbAsmbbb8\n1FNPBbJv/GSL6P073eeffx6IyvaeddZZri1pJKfS2LjYnxbZkX/kKjQwbdo0AM4++2wgvmlqtsXN\ntbFF9JA8klNuY8eOjf0JUYQq10af2Rx33HFANPPsL6Y0dTHblBbLLLMMEH2e+Z9Ztinj7bffDsAF\nF1zg2vwINkQbY0KYpUxbtWq10MfYQnCIFtumIZJTDtttt507tuvhzjvvdOe+/fbbkvepUpx44onu\n2Ao0/Pnnn+5cIZ9xu+22mzv2S3hD9LtMJbPf22xM8t1E14ov+EV65s6dm/fr+hvWfvzxx7Gft0hH\nCCziCvDss88CUeGL33//3bVZVo4Va7jrrrtc2worrABE3x9+8S77/vnjjz+K3fW8KZIjIiIiIiJB\n0U2OiIiIiIgEpWrS1XbZZRd3bKkb2RxzzDFA9jS1mn788cda21ZeeWV3bCHXfJ6zklkBB1tMefzx\nx5ezO6lj+93YdTNmzBjX9uSTTwLxNLXa+HudLL74P29hCwf7eyOEpNA0NfPoo48CcOGFFxazOxXj\n4osvBuCkk07KaLMx8fftqCZ+Oq0VjsmXpW/YXmlpX2BfbNkWJb/88svu2BZ3SyZ/nPzjJHL9LmP7\nNFWyn376CYBPPvkEgFVXXTWvn7M05aeeesqdmzVrVq2Pb9GiBRD9/vfcc8+5tnbt2hXQ48oydOhQ\nd2xpZpaS5n9n+AUfatO+fXsA9thjD3fOxtWuU/+75qOPPkrY68IokiMiIiIiIkEJPpJjM3RWCtVn\nd/n+TucTJ07M+7ltdgGiBYA33XQTAGussYZrs1LTdlcbuhRWJU8FK0bx73//G4DPPvusoJ+3CNmE\nCRPcOYsY2nPmii5WC7/4gkW2GjduXOvjbQfsUPhFT/xFzhAVtoAoylOtPvjgA3dss7zNmzfP62db\ntmwJwMyZMwH4/PPPXZvNaL7//vvF6GbFqJYCO2niRzasAIQV/gnhc23OnDlAFDFdYoklXNvpp58O\nwPrrr+/O7b///gA0atQIgAceeMC12ZYhhx56KBAvzGCR2Q022ACIf25aUZIQM3H8AiLff/89APvs\nsw8AX3zxRUHP9b///Q+IFxmwiJgVXfr6669d22mnnZagx4VTJEdERERERIISfCTn/vvvB+L513YH\nf/TRRwPxWbhC+CUJLQJk0SHb0BCiknz+hpjjxo1L9JqVwGaU/NK8o0aNKld3UsOiLEmjLTbzYZsx\n+vz1PdXK1t2dcsop7lyu8tBW8rJSS23XZBvT+XnPVtLeSn760Ztcm+FVA//fb6V8/bLlFpHJxdbE\n+Wt6bDsBm2n2nzNEtl7u559/LtpzWqTMX39oEQqVp4Y999wTiK9/sAwKi/SHtE7MogN+lMCPShvL\nqLHNPPfbbz/X1rt3byAaH/868jNvIF7S2yIUobPf2wrdIsDKetvWLJdffnmtz+2vVS8VRXJERERE\nRCQouskREREREZGgBJmudvDBB7tjP03NWGpP0jS1QlnKSLNmzUryeuVmYfNsYy/5sTQYgOHDhwNR\nioJv9OjRQOGLBCudn4ZgqQa2E3WuNKy33nrLHY8YMQKo7PSXfv36ueMBAwYAUaoGwPjx4wE44YQT\nAKWo1caugb59+7pzU6ZMAaJyyWuvvXZez2UpVueccw4AkyZNcm0hLl6279FCy0ZbMRBb6AxRmrdt\nR7Diiiu6tjfffBOIFi/76XEXXXQRAFOnTi2oD5Uq16JtP9Wq2tQsdWxbM0D0nWppVdnY2B100EHu\nnBUXCZ19blmBBv+9VPN9tcwyy7jjjh07AtGyjGyFpyxF/9xzzy1ij/OjSI6IiIiIiAQlqEjO6quv\nDsQXpNnd+8MPP+zOWbndupCt8MCCBQuA0kWOyq3QhWsS6dq1KxCVzATo0aNH7DFfffWVO7YZzGqZ\nvWvatCkAgwYNcuds1teiFLlKmI8cOdIdV3IEZ9NNNwVg4MCB7lyTJk2AaCYOog15rTxoMVj0KFe5\n5datW7vjzTbbDKiMzwV/1tZmHb/55hsANtlkE9eWazbYWITRNqSFKGoRUkTHrjs/+pxrsbZFcG64\n4QYgKvoAud+7bdq0if3dv54ssmuFhiAqA2z/f9Ui6cbJoXvkkUeA/CI5/iaiIbNiNRB9p9oWDP7v\nIP4xxN97ud6z9jlgvw9//PHHi9jjwimSIyIiIiIiQQkqkmObPG288cYZbbYpINTtrLdf0tH8/vvv\nANx222119rppos1A87Puuuu6Y5tFOfLII4FoHZfPNpX181qrJV/YZmptnCx6U6hKn+W0CI5Fpm0W\nHaIITocOHdw5ey/aJnfbbruta/M30YN4xNA2b8vG32y0Nv76DD+KXomuvvpqAHr27OnO2RivtNJK\nQHyTwpr8tTz2HrafDyGiY2uWrCQ75N4I9dprrwWgc+fOAHz66aeu7ZprrgHiG23XxjZ+BGjXrh0Q\nX0vx6quvAvHv/kpn63rbtm2b0fbYY48BMG3atJL2Kc38bTtsXaJE/MinfX9YJMeyACC+6SzE1xn2\n6dOn1ue3rQv81yk1RXJERERERCQouskREREREZGgBJWuls13330HwHvvvVenr2OLIq3MrxUbALji\niivq9LXTxhal+SFNiVLQ6tevD8DNN9/s2rbbbrtaf84WyJ955pkAvPPOO3XVxdSyRdxJ09TM448/\n7o732msvoLIKEFiqT7Zy9FZit0uXLu7cscceC0SL/5PyP88mT54MRJ+pfrrRc889B8C8efPcOSvF\nXOls6wH/2FKiNt9887yeY7311gOigiH+dgeV5IcffnDHlrL3wAMPuHOWFuk/zlgBB0ursvchwJw5\nc/Luw7PPPuuOreiIpawD/PTTT3k/V6Ww97GloVZCMY9yWG655YB4AaitttqqXN2pCF9++SUAEyZM\niP3pW3nllYHcKaCWogbQu3fvYnYxEUVyREREREQkKEFFcqz8rs8WMhYyQ5Qvv6TlfffdB0RlVadP\nn+7ayrEBUjnZLNNGG21U5p6Uj82wtWjRwp2zzQEPOeQQIHeBBj+6sPvuuwPVGcEx+Wz0aZGyXI+x\nhfsQlbKtpEjONttsU2ubRW1y8WfWn3/++VjbuHHj3LF9btpmjFZ+FWDffffNr7NVwN7LfpnofDcN\nrWRDhgxxx/be9AtZWHQnWyTHzJ49Gyj8u9lKVdvrQvTd72+KPGrUqIKetxKcccYZtbZdddVVJexJ\nOlmE27Ikdtlll4zHWLTZL7Ry/fXXA9F37XXXXefa+vfvD8Bvv/1W/A5XCNuexTIh/N/t7PeYO++8\nE4Bu3bqVuHe5KZIjIiIiIiJBCSqS07Jly4xzxZzdWGqppQA4/fTTgXhJUVszYLPCfjnNamNRDL+8\nbYgaNWoEwDrrrJPRduqppwJReVVfPiW27echvvlntXr77beB7NHBd999F4hK0u68886uzWbojB/l\nufzyywE4+uijgbqJ9hablfXMlev82muvueNLLrkEiDYD9Tdp/Pnnn2t9jssuu2yR+lktrAS0/z5/\n4YUXFvpzVhLdNqwEGDFiRJF7V3esrDbA9ttvD0RRLYB7770XiKIt/vrDxRZbDIi+r/1S+h999FHs\ndfwNRpdcckkg+gzwZ4x//fVXIColHSpba2L8yFU+113obrnlFgB22223jDYra2+fibNmzXJtRxxx\nBAD33HMPAMccc4xrs6i2rXmsFhaNhWhduWWm+L/D3HrrrUB8zNJEkRwREREREQmKbnJERERERCQo\nQaWrFZOlpvmlV62Eb6dOnYD4okoLy1sYP4SdrJPKJx2r0ljZZ0uJAujXrx+Qf/nYQtx0003u2Er1\nfvbZZ7U+3hZKbrjhhu7cxRdfXPR+lUvHjh0BOOCAA4D4NWapMVbK2E/DsgXz2dJY7Jz9/9mO4Wlm\n/8/+wti6YCkd/uJuqd3cuXMLerylYR144IHuXCWlq/msnKztmA4wePBgIEr1sXQgyEw5tXLaEJWx\nNVb4AqK0NitP7aee3n777UCU1hqShg0bumNLkbaU8NVWW821Lb300kCYpbNz2XXXXd1xrq0Y7Pva\nLxJivv76awA+//zzjDZLa86WAhci+2yyNDSICjIYvxBN3759gcI/A0tFkRwREREREQlK8JEcm4G3\nTUGzsagNRLMmVva5T58+GY+fMWMGAB06dHDnai6YrGY2y7Tmmmu6c7aJ4+uvv16WPiW19dZbA9EC\n9latWpW8Dzbzmaskd9u2bTPOhRTJsSjN8OHDF/pYv4CAzd6FviC52PxNU6uJX7zGSnLnKtttGjRo\nUNDrWIGaEN6jU6dOjf0J0WawVpTA36DW2pZYYomMtlwsemuz7ueff75rs01yQ+QXtllrrbWAaCzs\ndxGIii9UG//3MItmZXP33XfX2rb88ssD0LRp0+J1rEJZcZua0RuINvq0xwD8+OOPpelYQorkiIiI\niIhIUIKP5EyYMAGAYcOGuXM2M2KbAfozJZb7bxYsWOCObdbE1uR8/PHHddDjyjVo0CAgmmXy86mt\nvGClRXJsRttyodPGctFtY8ds+cYSRRdtw1CIcvqtTSK27qFaWGlUP4JlGwsWk63jPOywwwB44okn\niv4aaWCRFfvz8MMPz3iMbcGwyiqrZLTtscceADz11FPunGVjhBy1KZRfyruaN6usjUUeIHfJfLum\nsm0eWi323ntvIFpj57O1dZYZ4W9FkHaK5IiIiIiISFB0kyMiIiIiIkEJKl3NSsD6KWe2UHzs2LF5\nPYelsHzwwQcAXHDBBa7ttttuK0o/Q5ctNahSjRw5EihuWWxbAOmXu1x11VVjjxk6dKg7tmvSUohe\neukl12a7OFfCotMtt9zSHffu3RuIdph+7rnnXNvvv/++SK/jpxycccYZQPT/55edtXMhljxfVNOm\nTQOisuV+eeAQLbvsskC0ALkY7D35xx9/uHPdunUD4Nlnny3a61SqMWPG1Nrmf+9K7dK+6LsULFUb\n4MQTTwRgySWXBGCZZZZxbVZIJJs999yz1rZnnnlmUbuYWv6WE7ZthY2dFd8CGDJkSGk7VkSV/1uo\niIiIiIiIp97fKZzGTLoQ2MpVDhw40J1r3779Qn9u+vTp7thmkNIQtUnyX1PORdRWMvrll18G4uPa\nv39/AKZMmVKSvlTa2KVJscfOIjgPPvigO9ekSZPYYyZOnOiObfM1/5zNWFqp3myLlW3jyh133NGd\nsxLy2fppM+1WgnTy5Mm1/hvyVejY6Zr7R1rer/vtt587Xn/99WNtO+ywgzved999geharbmJJcDT\nTz8N1P1nXlrGrhJVwti1adPGHb/xxhuxtiOPPNIdjxs3rmR9gnSOnY2HbTVgxaXyZRFsv1CGFZwq\n5maXaRm7t956yx1b+fw777wTgO7du7s2vwBXuRU6dorkiIiIiIhIUHSTIyIiIiIiQQkqXS00aQlp\nViKNXXLFHrvrr78egKOOOqqg5/TT1SysvsYaawDRXlXZ+pCr/34/be+mfIuS5EPpasno/Zqcxi65\nShg7P12tZupjjx493LHS1SLt2rUD4gV9LrvsMiBaYN+8eXPX9uKLLwIwfvx4AGbOnFmn/Sv32Nl1\nY9/NAHPmzAFg4403BtJb1ELpaiIiIiIiUtUUyUmxct/tVzKNXXIau+QUyUlG11xyGrvkKmHs/GiE\nRXKaNm0KKJJTqco9dpYlsfbaa7tzXbp0AeJbVKSRIjkiIiIiIlLVgtoMVERERCQUs2fPdse2hsIi\nOB9//HE5uiQVaIkllnDHiy/+z6/+fln8WbNmlbxPpaBIjoiIiIiIBEU3OSIiIiIiEhQVHkixci9O\nq2Qau+Q0dsmp8EAyuuaS09glp7FLTmOXnMYuORUeEBERERGRqpbKSI6IiIiIiEhSiuSIiIiIiEhQ\ndJMjIiIiIiJB0U2OiIiIiIgERTc5IiIiIiISFN3kiIiIiIhIUHSTIyIiIiIiQdFNjoiIiIiIBEU3\nOSIiIiIiEhTd5IiIiIiISFB0kyMiIiIiIkHRTY6IiIiIiARFNzkiIiIiIhKUxcvdgWzq1atX7i6k\nwt9//13wz2js/qGxS05jl1yhY6dx+4euueQ0dslp7JLT2CWnsUuu0LFTJEdERERERIKimxwRERER\nEQmKbnJERERERCQouskREREREZGg6CZHRERERESCopscEREREREJSipLSIuIiEgYllhiCXe89dZb\nA9CnTx8AJk6c6NpefPFFAKZNm1bC3olIqBTJERERERGRoNT7O8muRHVMmx79o9I2jGrdujUAb7zx\nBgD/93/RPfT5558PwLhx4wD48MMP67QvaRy78847D4Bzzz03o23w4MEA7LLLLgt9nueeey7jOYsp\njWNXKbQZaDK65pKrhLE7+eST3fGwYcNqfdwLL7wAwE477VTnfYLKGLu0qoSxO+2009xxp06dALj6\n6qsBuPPOO0vaF18ljF1aaTNQERERERGparrJERERERGRoFR1ulq2VJ9sqUTGUoqeffbZ2J91pdJC\nmp07dwZg/PjxGX2xf8thhx0GwO23316nfUnj2NXFW2233XYDinstlmPsGjZs6I7tfdmhQwd3bv31\n14893k+F/Ouvv2p93kmTJgHw+uuvAzBjxgzX9tBDDwHw2WefJex1pmpKV1trrbUAGDt2rDtn12Oh\n0vh+rRRpHruTTjoJgCFDhrhzyy67bK2PV7pa5Ujj2G2//fZAdN0dfPDBGY+ZP38+AG3btnXn/BTw\nUkjj2FUKpauJiIiIiEhVq5oS0s8884w73nXXXRM9h0V57E+L7EDdLACvNM2aNVvoY5ZZZpkS9KT8\n7BrLFRksBruuK3WWZ9tttwXgmmuucefatGkDxGdsas7e+NGbXDM7NiO84447ZrSNGDECgLvvvhuA\n4cOHu7aXXnopv39AFbNrPOnnqcStuuqqAMyePbvMPUlmvfXWc8dWCrpBgwYALLXUUhmP/+abbwDo\n2rWrOzd16tS67GLq2PhYFgREn1UbbbQREI9q2Wedfd77n3127r333gPg4osvdm12ziLaIbExBDj7\n7LMB2HvvvWt9vJUz98uaS6bmzZu744MOOgiALl26ALDddttlPN4Kipx66qkl6F3+FMkREREREZGg\nBBnJ8aMqizqT7kdraj6X/3cr/Zs0Jz0E3bp1W+hjhg4dCsTz+EPkRw4LYddbXUeA0mKPPfYAovLj\n5WCzVE2bNnXnbMbqu+++K0ufKsF+++1X7i6UxKWXXgrAgw8+6M7lyuG3aPUGG2wAwO677+7aLFqz\n+eabA/GoTceOHQH4888/3Tmbqf/1118B6N+/v2vz+5MG+++/vzteYYUVan3cp59+CsDxxx8PxDcD\nDZl9vgwcONCds4hDixYt3LmaUZpcEe1sUWx7rptuuinjcfa5du+99yb8V6SPnxNt/JsAACAASURB\nVEGSK4Ij+bHsCr/Eth/VgShSC9Ea7GzRnZo/X8z1r/lSJEdERERERIKimxwREREREQlKUOlqdbHY\n2099szK92VKR7LWtrZrT1qpVoSlquQpX+H/PtbC+rsuY1xVL2+nZs2dGmy3CnTJlStFez8pRt2/f\nPuOc2Xnnnd2xlaxWulqm5ZZbDojSk2bOnFnG3tSdAQMGANFCWj8d19IsLf3iggsucG12XdUsew7Z\nF4zX1LhxY3dsj1txxRWBdKer2Xhl88svv7jjY489FoCnnnqqzvuUJvXr1wege/fu7txKK60EwLvv\nvuvOXXHFFUD0OThq1Khan9NPO7My3YMGDQKyF6OxIgYhpKstvfTSABx33HG1Pubmm292x506dQJg\n+eWXr9uOVajLLrsMiFIa/eI7a6yxRq0/Z6lofgpbzbZZs2ZlPE+pUtcUyRERERERkaAEFcnJdybd\nZtBttjzfzYVs1tyiNLkiOv5MfMjlpf3yjdnKhFaDQkvp5rOBZ77XjB8NqiS24HratGkAvPnmm67t\ngQceKPrrPf/88wCcc8457pzNdNqfftRm3rx5Re9DGvjXVdJiKTUj5TbzHAKbxYTMUqgWfYRo08F+\n/foB0KpVK9eWdNNfm7G/5557Ev18uTRq1AjIXcbeykVD9UVwzJw5cwBo165dRtv06dPd8e+//w7k\njuBkM3r0aCCKlPmFVOya9F+n0m266aZA9pLFc+fOBaBXr17u3EcffQQokuPziwvYZ59Fi/0tFXKx\niEy2yIwV9TGrrbZaxs/VNUVyREREROT/27v3eKvm/I/jL1Nuo35UaKjcxiUal5BSiVESya3GLaM8\nXMatiJhucqk0ZCiXcp1C7mlyTTJIGXId10lIojEhuiCM+P3h8fmu795nnd3e6+xz9trf/X7+Y1lr\nn72/fc/ae5+1Pp/v5yMSFF3kiIiIiIhIUIJIV8snTShXKN1P+bEUjlxpQPks9o4rfhBi2tqWW27p\ntv3weCUpZpqasfNwTcq18IDxe2vUhq5duwJw8803A9C0aVN3LDut6IwzznDbxSx6kAbnnHMOkPm5\nVMi54/+e7LmWLVsGwKRJk2o+wBJr27YtADfccIPbl53Wcs8997jtPn36AJlpaibXd439nPGLB3z1\n1VcFjLi0tt12W7c9efJkIHcakPUAKgb7zrF0JYjSS6dPn1601yk2S0Orrc8W6zhv38P+eTh79myg\n8BS4NMvVE2fUqFFAuGnHNWVpatYTB+Doo4/OOJaU31PH0nktNc0vZlBXFMkREREREZGgBB/JyWdh\ndtIIi3+nxIoQ5HtXPxStWrVy2/6iskoSd/7E3SXP5855oUUMpCq/uIBFLnItBreiBIWWAC8HFoGJ\nW0Saz2fjVlttVe3P2106i+iUG4veQBRRsfLYEJ0zFsHp37+/OzZlypSMx3z99dfumC1wfuCBB4Bo\nQTjAkiVLivcPKKEjjzzSbbdp06ZOXtM+Z/faay8g806+RUnOOusst++2226rk3GVUsuWLd22vdft\nnPziiy/cMYtkh8TKuPsskjd69Oi6Hk7qWYloiIoM3H///W5fTSM4ca9jUR2LMpaCIjkiIiIiIhKU\nICI5udYv1NU6mFmzZgHxd+DtbnKIa3Lq168fu12JivH7zaeRbbmWja4tlu8/bNgwAFq3bp3Xz9ka\nnLvvvhvIbFhYzmzNDFSNwFj0BfKLLFrTQIvoQLQGp9zX4lx33XVuu3HjxlWOn3766UB0t9NfM2N3\nPS3X3H/vL1iwoOhjFTjzzDOB+N+VNdo87LDD3L5KiOT07NnTbWevBfPX/tx55511Nqba5GeLxK0B\ntvYDq1evXuNz+aXzrXGvNVKNc8UVVwCZZbhnzpy5xtcpNYum7L333m6fNe6MK7+d9PnvvffeKq9j\nxo4dW+PXSUqRHBERERERCYouckREREREJChB5BeVyyJtP6UhlNS1Y445ptRDCEI+BQcsvSiUcwei\nMrB+WpWln/rFAmzxt3Wd91NUcxUVsBSOpUuXApnpCP6C8BBYmlpckYATTzwRyD/FzNLU7Pfzr3/9\nyx0LJV1yjz32cNt2DllKCkSFA+JKO0+YMCHjv5XGT43KVTI7qebNmwNRKilAkyZNqh2D/f46derk\n9h177LFVniMUVnBg0KBBbp/Ngf3X3sMh2X333d32DjvsUOW4zYu1DujQoYM75rcPABg8eHBBrz1u\n3DgA3nzzTbevS5cuAHz++ecFPVddsvQxe09BNC+Wbluoo446ym1feeWVQJS25hczMElfpxgUyRER\nERERkaAEEclJA7vLns/C8ZD4d9Sz7+j96lfRNfRPP/0U+xj5RaUWHLAFn/vss4/bl31HEqJGZdmP\nyd6ujhUGefbZZ5MPNuXiIjgWgbHCAX5Tz2nTpmUcO/vss90xe5yVh/bPvYULFxZtzGlhdxr9SE45\nNeesa4W+//Jld5stQta+fftqXyfudf33d4gRHGNRWyu4AFW/W0P8rDv44INzHrfPMP+zrDpW6h2i\nojM//vgjkBnxt0jI0KFDgcwGwEcccQRQfk1W84ms+I1CreS0zYVfXMAiNwMHDgSgV69e7pgVOCgl\nRXJERERERCQoZRvJSdu6hHzKsYbIIjRQ9c5a3LERI0bUzcDKgH8O57MWJ8RzzO4orVq1yu3z704W\ni91x8yOPts+agZY7W3fjR3RsTY3912cRGWuA6TfCNLaGx6I+ofryyy8BRW9KzaIvfgRHqrLPrrho\n1qhRo4DMUsehsHVyEH3erbPOOgU9h0W8rEQ8wJNPPlnt4+1Y3759Adh6663dMYsYlUMkx9bMACxa\ntCjn8WzZkR8/s8LK6Vvkx48AFaNEdU0pkiMiIiIiIkHRRY6IiIiIiASlbNPV0iZt6XNp9emnn5Z6\nCCVn50q+RSr8zsyheeGFFwDYfvvt3b569eqt8ecaNGjgtk866aSMY6eccorbbtiwYcYxv1v6o48+\nCsDDDz8MZKZSzp8/f41jSBtLLfPTGq2AgHWCt0ICAK+//joQfx5aetqAAQNqY6ip4KdIWgGMtm3b\nun1z586t8zFVkk033RSIOqVDVNq20GIGr732GgB9+vQp0ujS6dRTTwVgk002ATLnydKwQk4t9dPK\njj/+eCBqK7AmVjxlww03LP7AUszSyaxYBWQWDqiOXwr6qquuAqLv6zhWnMB/TClLRxtFckRERERE\nJCjBR3JsQXcaFm0r2lPZConghBy9iVOTCN/5559f7f9bJMIW6vrN4SzK07t3bwC+/fZbd6x///4A\nfP/994nHVSp+ieexY8dm/Nfn39mDzIaftqg3ZHHltJ9++mm3z96vfllp+YXfENEWMW+xxRbVPr57\n9+5u295TVibab+BZiNmzZ7ttWwhtpYBDYlEbiKLUcWX2rflniAUH4kyZMqWgx9v3QqVFckx2GwaA\nZs2aue1cUZpcrNDAueeem/HftFAkR0REREREgrLWz8Xs5FUkhTaMzPVPsDzM2oii+GV//TuAxRpD\nkl9NXTXbbNmyJQAvvvii25dd+tcfy9KlS4GoUVRtNyorxdz5v18rVZyrNHScYp6vdk76Y7AIUa7I\nZprPu6SaNGkCwODBg90+i2TY2P1/d8eOHYHC724VOnd1PW/W+BOi88NKR1u0C+o+8l3qc87WOowZ\nM8bts0ifRRn9Y7feeiuQjshBqefOomA9evSo9jHfffed27aS7Z07d67yOGsg7bcfyGYRnJNPPtnt\ne//99wsYcaTUc5ePG264wW1bJCfuMyuftYzFVA5z57P1IRa96Nq1qzuWq4S0seahfgnpnj17AlEU\nLV/lNne5WLaErdvx1/skjQ7lUujcKZIjIiIiIiJB0UWOiIiIiIgEJYjCA5aCE5cyZou8ayNdLd8S\nwGkoelBsJ5xwApB/d/p33nkHqP00tVIotCR0LpbmVgxxqXL2Hklr6Lu2WLpkpXa0tzQ1P63C9lmK\nZIifU/myjuX+++Lyyy8HYLPNNgPgr3/9qzvWrVs3ICqbmoa0tVI588wzAdhnn33cPkuBNOutt57b\njktTM7lSUaxMtC2gXrJkSeGDLUN+Gmn2/IwaNaquh1NxrDBNixYtgMwU/RkzZpRkTGli6WrPP/88\nUDspajWhSI6IiIiIiAQliEiO3YG0/+a6gw01L89rz59rUbn/GpV8h7QSFCOCY+yc8u/Y2fkza9as\nNf58vpGgtJRWtyagfoTBooSvvPJK0V7HFuwOHTq02sf4ZaxDa1o7ceJEAHbbbTe3z5qHqrR95MYb\nb3TbM2fOBKJmlbvvvrs7dsABBwBRA9nsctyVZPHixUDm55M1n03qvffeA6IMAIgafVZK1MwWcvsl\npO17wSLTt9xyS90PLGBW+ML/TLSmo/Xr//Lnsn9O+m0HKslRRx3lti3CNXDgwFINJydFckRERERE\nJChBRHJM3NqcuKiL3Q2xu9h+1CXXXc18ygJnR5VEaiqfyGGhSn1+2noQe6/+5je/cccaNGiQ6Dlt\nfdgFF1zg9g0fPhzIneu/YsUKAE477TS376OPPko0hrS5+uqrgejcsXK/UBkNP2tiwYIFAHz44YdA\nZiTHWAnZSo7kGL+k8w8//ABEa5byZe87K+kd4hrONbH2DLYWx//ssm1bB/HFF1/U8ejCtPbaawPR\nd8ewYcOqPMayDdLW7LIU/M87W4tz3333lWo4OSmSIyIiIiIiQdFFjoiIiIiIBGWtn5O0Xq1lxew8\nX8xF4fkoZmneNHfF3XLLLQG455573L6ddtoJgA022KDKWKxLdTFTrnKpy7mzlKu4f5ufFpZd8KKu\nzlN/DPmUC66LuWvXrh0Ac+bMqXLszTffBGD+/PkFPWfz5s0BaNu2bZVxxf2brLiAlbQt9PXiFDp3\ntfF+Pfzww922pVgsXLgQgNatW7tjy5YtK/prJ1Xqz7qGDRsCme/RCRMmALD55psD8WO0Rfe2+LYU\nSj13cZo2bQpE3eH974nsufLTRC315a233qrV8Zk0zp0VcOjYsSMQLYaHaNF7q1atanUM+Ujj3OVi\nhRws1eqJJ55wx5YvXw7Ep1c+8sgjQFRKuhiFL8pt7oylSdpcQpS+Z6nRta3QuVMkR0REREREghJk\nJCdObdw1t7vi2c9fLOV2tW93kM8++2wAOnXq5I6FHMkx/r8t6cJ+iwrFlYu289Z/7uzHxb1uoWOp\ni7mz0tEWyWncuHGV58o1Dv/18nmcLdD1SwTfeuutQHGLDJQykmMNGK1por/PIjgW0UmbUrxfL730\nUrd9+umnA5nnYfbr+GN8/fXXgagk+fTp02s0lpoot++JNEnL3Pml7fv37w9AkyZNgMxo92WXXQZk\nRiFKJS1zly/LNLHS8Nbk17d69WoAhgwZ4vaNGzcOiIppFEO5zZ1ZtGgRkNnw0y8nXRcUyRERERER\nkYqmixwREREREQlKxaSrxYnrP2K9cOLSheq6B065hjTTQHOXXF3OXYcOHYDMxfK2kLEY6WojR44E\nYPz48QB89tlnicaZr1Kmq02cOBGAvn37un3Wa8Pvj5NGpXi/WrEBiNIvGjVqVOVxzz33HABjxoxx\n+6w4xqpVq2o0hmLQZ11ypZ67PffcE4C5c+e6fVZo4KeffgIyiwzMmzevaK9dU6Weu3JWbnOXXXDA\nLzxw3nnn1elYlK4mIiIiIiIVraIjOWlXblf7aaK5S05zl1waSkiXI51zyWnukiv13O2xxx5AZiTH\nnn/q1KlAfFnjNCj13JWzcpu7++67D4jaNLRv375kY1EkR0REREREKpoiOSlWblf7aaK5S05zl5wi\nOcnonEtOc5ec5i45zV1ymrvkFMkREREREZGKposcEREREREJii5yREREREQkKLrIERERERGRoKSy\n8ICIiIiIiEhSiuSIiIiIiEhQdJEjIiIiIiJB0UWOiIiIiIgERRc5IiIiIiISFF3kiIiIiIhIUHSR\nIyIiIiIiQdFFjoiIiIiIBEUXOSIiIiIiEhRd5IiIiIiISFB0kSMiIiIiIkHRRY6IiIiIiARFFzki\nIiIiIhKU+qUeQJy11lqr1ENIhZ9//rngn9Hc/UJzl5zmLrlC507z9gudc8lp7pLT3CWnuUtOc5dc\noXOnSI6IiIiIiARFFzkiIiIiIhIUXeSIiIiIiEhQUrkmR0RERMIwbtw4t92/f38AhgwZAsDo0aNL\nMiYRCZ8iOSIiIiIiEpS1fk5S5qGWqYrEL1SBI7m0zF3btm3d9tChQwE45JBDqn38vHnzAOjcubPb\n9+mnnxZ9XLmkZe7KkaqrJaNzLrk0z12jRo0AWLx4sdu33nrrAdHn2nbbbeeOffvtt3UyLpPmuUs7\nzV1ymrvkVF1NREREREQqmiI5KRbi1f7YsWMB6NevX5VjPXv2BGDatGk1fp1Sz93f//53ALp37+72\n1atXL++f//HHH932hRdeCMAVV1xRpNHlVuq5i2Pnxvrrr1/l2BlnnAHA+PHjM/7f37d8+XIAHn74\n4VodpyI5yaTxnOvYsSMAd911FwDrrruuO/buu+8C8Nvf/haAmTNnumP2b6lf/5clr717967y3B98\n8AEACxcudPvss+KHH34oaJxpnDvTpEkTAD7//PMqx2zc+++/v9s3a9asOhlX9hgKoffsL9I4dwMG\nDADguOOOA2CPPfao8to27hEjRrhjF110Ua2OK1sa565cKJIjIiIiIiIVTRc5IiIiIiISFKWrZena\ntSsQhTkvu+wyd+ynn37KeOw777zjtu1xd999d9HGEmJI8+233wZghx12qHLMUpIefPDBGr9OKebO\nUtQADj300IJ+9vbbbweihbo9evRwx1avXg1Aly5dAHj22WdrNM41KfV5Z2k+p512mtt35ZVXArDO\nOuskes5vvvkGgNmzZ7t9Z511FgALFixI9Jxx0paudtBBBwHwyCOPAPDee++5Yy1btqzV1y5Eqc85\ns9NOO7ntV199FYC11167oLEk/Upt0KABAKtWrSro59Iyd3Fypat9+eWXAOy8885un4qslI9Sz902\n22wDwOTJk92+Nm3aAPD+++8DcM0117hjU6dOBaL3+J133umObb755kUbVz5KPXflTOlqIiIiIiJS\n0Sq6GajdNbcrfIB27doB0d07P3qTfQW54447uu1JkyYBUYOzww47zB0r5p3icvWHP/wBiBbqhsj/\nncfdbZg4cSIAI0eOBGDlypXu2LJlywD43e9+B0TnIcAmm2wCRCWoazuSU2oDBw4EMqOoNbXBBhsA\n0K1bN7fvjjvuAKBDhw5Fe520ss+xFAbuU8UvbJEdwfG/C1566SUAXnvtNSDzLqsVubCIdPPmzd2x\n3//+90BUqMCilgDff/99zf8BKeMXAclm0dW6jt5I+bLPcYBHH30UgK222srtO+WUUwC49957gfio\n6IoVKwBYunSp22fvy6effrra17a/YT7++GO3zz4HLNsiBPYZ2KtXL7fPsmwsA+ekk05yx/75z3/W\n4egKp0iOiIiIiIgEpSIjOSeeeCIAo0aNAmDTTTd1x/73v/8B0RoJ/w7dCy+8AMAnn3yS8RiADTfc\nEIiiOyeccII7dvHFFxd1/OXCL536l7/8Bci8c2m++uoroPzv6DVr1sxtW0nK0aNHu312Byh7bZfv\n9ddfB+Dwww93+2ytT6dOnQDYb7/93LFnnnmmZoNOIVtHkos/B9kldy3iBbD33nsD0Lhx4+IMrgz4\n5+FNN91UwpGUnxtvvLHaYw888IDbPuaYYxI9/5QpUxL9XLmxO76DBg2q9jF1VRK/3Oy6665AFPm3\nvzeg8LWeofHXb22//fZA5mecZdTkYtGdG264we3bd999gfhIjkVwrr/+egA23nhjd6x9+/ZA9Ldh\nOdt2220BuPzyywE48sgjq32sv9bJvq/j1t2lgSI5IiIiIiISFF3kiIiIiIhIUIJPV7PSgNYBF2D4\n8OFAtFhs2rRp7pgtFrVFybn43XQt9e3oo48GoG/fvu7Y3/72NwAWLVpU8PjLmT8/m222WbWPe/zx\nxwF48cUXa31MtclPt/PLHyfhh7+tHKaFxvfaay93LMR0tWOPPRbITA96/vnnAfj666+BzMWOP/74\nY8bP++W3rWv9/fffD0DTpk1rYcTp4qdh2OefpUg+9NBDJRlTufDLyrZu3TrjWK5FyZLp4IMPBjIL\nOWSbMWNGXQ0n9WzhO8D48eOBKOXv6quvdsc6d+4MROXG89WiRQsgWpCf1tSiNfELF9lSgqQL3y39\nzNenTx8gKn4D0KpVKwAWL14MREVDoGqqdDk777zzgPg0Nfs7Y8899wQy/7YbN24ckPk3dpookiMi\nIiIiIkEJvhnoEUccAUR3cn3WIM9f5J2U3WGJuztlhQ7yiQ75yrVhlN09njdvntv361//OuMx1mgP\n4MADDwSiAgTFUK5zF8caWFok5+WXX3bH2rZtW/TXS8vc+QVBrHFgdtQmjpXchqjctpUZXXfddd0x\niw4Vs4R0KZuBWglzP5Jjc2iRHL8B6AcffLDG57QiBocccojbl2txflJpOeemT5/utq0xtLHFyQBz\n5swp+msnlZa5s/L3EL23/JK/Jvt7N1chltpW6rk74IADABgzZozbt8suu2Q8xv/Mq1evXo3GYN+1\nfjQiqVLMnX8+2b9ho402cvvsb6033ngDyN1Y1/9+sSIPtojeojYQRTEsW8f/uyapUp93ZsCAAW7b\n/n1W6MgyniBqg2LfB37hAWtSbt+7tV0KX81ARURERESkoukiR0REREREghJ84QELX/r+/e9/A1F3\n3GKwztf2X3/Rqt/xuhLYArbsFDWfn5pWzDQ1Ccdnn32W6Of8oh+2eLcSbLnllkBmGsavflWz+1i2\n2N5f8PvRRx8BUcGQkNjCWp+lplkKlsSzBdoQn6ZmrGdaKdPUSsG6xPupaX6qlZk/fz4QFQl59913\nC3odSx+yIkgQpXaVe/GMb775xm13794dgHPOOcfts3+npeJeeuml7pilYVl/Hb+wzU477ZSx79xz\nz3XHrL9dSKwAj9/P0f52te9P6xnps55NI0eOdPssZd56OMUtDSklRXJERERERCQoQUZyNtxwQ7e9\n4447Vjm+cuVKoLhlFG1hdFxUwsoJjx49umivl2Z2ZyXXArEVK1bU1XCC4y8sl2gx7kUXXQTAn//8\n51IOp2Ts/RZ3h7zQu+a9evUCoHHjxlV+PoW1amrM7mg2atSoyjGL/PvfJfXr//LV+eGHHwKwfPny\n2h5iatlc+He/sy1cuNBt+4VTCrHffvsB0LBhQyD6vUBUZj+Ndt11VwCuvfZaANZbb70qj7FjEH2O\nLVu2LNHr+VFXY1HIfAq3lAv7W8vmC+Cxxx4DovmcMmWKO/bkk08C0KlTJyBzLizj5/bbb6/FEafH\n6aefDsBuu+3m9lkEJi6Ck80veGRRISsrrUiOiIiIiIhILQoykmOlVAG23nrrKsf9XEypGb+sYc+e\nPdf4eGv4eeaZZ9bamEJld4vffPPNEo+k9Py1E2eddRaQmV+cD7vjaY34yj1fPR+TJk1y29nrFdu0\naeO27U6oHxUPmUUJ4sq02trN3r17u30WvbB1Y5MnT3bHrrvuOiCzOXDI9t9/fyDz/Mn28MMPu+18\nGij269cPyGwwaHeP7XfkRxRtnZi/Bi8tjRrtc3vu3LlAZvuEq666Csg8V5KuVbJ/u72vrXEywC23\n3JLoOcuNzbE1zJ4wYYI79qc//SnjsW+99ZbbfvTRR+tgdOnhR3CMXz5/Tdq1a+e2LZMiu3lyWiiS\nIyIiIiIiQdFFjoiIiIiIBCXIdDW/q7nxF5m99NJLdTkcVwoyRH6K2j333LPGx1soPWl54Eph4XaI\nOolbmlrShbsh8VPLcpWrzcVKLT/xxBMAbLzxxu5YqAvJ/TQDf+E2ZJabrrTyvlOnTgWgT58+1T4m\nriS+tQcYPHiw29e/f38gWsTslwz2F+CHIi71xdji8EGDBlX7mBYtWrjtO+64A4AOHToAUSqML67w\nhZVP94v7WCuDUrPfuaXF1hb7brWF4Lfeeqs7ZqV/K4UVpzjwwAOrHLvkkksAOPnkk90+K9N9zDHH\nAFGRgkqyePHiNT7GUkXLKY1ZkRwREREREQlKkJGcCy+8sMq+WbNmue1nn3226K+57bbbAvHlG22x\nfYj23Xdft21X+XZHuNLuBhfTzjvv7Lb/7//+r4QjSae111672mN+6VVrwGd3i8eOHVvl8XF3i6V6\ndid0xowZJR5J8XzxxRdAZsQ/+xybPXu2237mmWeAqNS2NROEKLJoZVp79Ojhjtki/TSXPM6HH/U7\n8sgjq32cLf5ftWpVlWOXXXYZEM0T1PwOsV90aMSIEUDyUszlwC9Hbc0YTdpK+dYli8hahA+i4gKj\nRo0CouagAHfffTcQFaUaMmSIO3bTTTcB+ZVWLhfvvfdeop+zz0T/fWYs+8QiiQBLlixJ9DrFpEiO\niIiIiIgEJchIjl8G1LbjSoMW01ZbbZXx39A1aNAAiNaLQNVmhH7u9Nlnnw0kv4NQKaxMqp/Hb+66\n6666Hk5qTZw40W1369YNgO+//x6ARx55xB2zu3X+nfZQWdnnTTbZxO2zUturV69e48/7Ea2WLVsC\nmXdCTW1/lpbCCy+8AMDuu+/u9tmd8aeeegqAt99+2x2z8rwWcfUbhWavGWnWrJnbfvzxxwHo2LEj\nAP/973+L8w+oY5a5AJnrB7NdccUVVfbZXXZrI2DrJ9bE7grb+glr6ujbZptt3LatsQs5knPqqae6\nbXvP2nqS2shYSTtr5jtw4MAqx/yIKsAbb7zhtrt06QJE0dprrrnGHbP1TA8++GBxB1tC9h7yI9fX\nX389EJ0/9hiAefPmAZlrD7NttNFGABx11FFun9/ktlQUyRERERERkaDoIkdERERERIISZLqany5g\n23FlJ2vKD41bWM5e57bbbnPH8imtXG6sHOY+++xT7WP8ErV33nknUDnFze537AAACjdJREFUCKz7\ndPv27d0+S6vKZfPNNwfiF+B+++23QGZKZDmVpPUXK//xj38E4kv29u3bF4BFixZV+1z+YmVLmbTQ\nu4XWK9Xw4cNr/BxWoMFSior9/Gnlp6T529WxLur++9BSc+NsvfXWQLQ4t1zT1dbECitYiq0t6AY4\n/PDDgfi0RyvSc/PNNwOZi8Nt4bcV93n11Ver/Pw333zjtq3oQYisTYZf+Me89dZbAHz33Xd1OqY0\n2GKLLTL++9xzz+X1c59++ikQffdYsQGAYcOGATBnzhwAli5dWpSxltItt9wCwHHHHef22d90fipq\nEn5Km9LVREREREREiizISE5d8ReCb7fddkBUitQWcUFYpQcteuVHqqrjz0HIiz+NNbKDqKmYH72o\nKVts/+WXX7p9tpDZzrtx48blfA77PdgiQf/3Utu/I/+ukV84AKKF31D4HUi7c5lL9+7dC3pOkXx8\n/vnnAKxYsSKvx1vE56OPPqqtIaWC3Q22u99xrRWMH5E5+OCDgegzzi+RbNHxXN89fpQxVyS43Fkk\n+4gjjnD7bIG83wTU2F36xo0bA5kN0UOaJ2tObu/HU045paCft/PVL15jTWU7d+4MwH333VfjcaaF\nlXGH4jWr9du1pIEiOSIiIiIiEpSKieTENQFMynIOd9llF7fP7ujZ3eq4fOEQWCneXA0q7W5lPtGe\nkBx//PFuO27tkeWLr7/++kDyKI/djYPM6AhA//79c/6snZdWKtc/T9u0aZNoPPnKHitEUU5/3dpn\nn31Wo9fxmzhahPXEE0+s9vG2LqJS1otJza2zzjpAFDm1XP41Wb58OVD+ke3//Oc/btvWwFkJY1+u\nCI7x75pbBMjW7fjPafuMlYwHOOecc4BorUHo4r5/raFl3Fqyp59+utbHlAbWnNeyAT788MNEz2ON\nQyGK5FiUMaRIjh91sX+fNUL11xm2a9cOiMpo+2sJrWXBBRdcAKSvTYgiOSIiIiIiEhRd5IiIiIiI\nSFAqJl3N0skK5ZfrtXSkoUOHAplpMVbqMsSwsJ8yYKU9c+natSsAq1atqrUxlSPrBHzssccCmelt\nxsqe+p3CrbxlHCtFG1fqt0GDBkBUbhQyO7rH/X9ds7LY77zzTtGe0zqdQ35FCUaMGAHAypUrizYG\nCZst2LWO83455FztCurVqwdA/fq/fPX6HcfLyddff+22x48fD2R2iS+EXyzAvlttnuJY2m/v3r3d\nvoceeijRa5cb+2yzwjb++fPYY4+VZExp8sorrwBRarSfRnrjjTfm/Txx34vl1K4hX35RrOnTp2f8\nN1+77bYbEKWrpY0iOSIiIiIiEpSKieQUqkWLFgDMnDnT7bNGbsYW+gEMGjSobgZWAn7jRVtkFsei\nZR988EGtjymN1tRw9rDDDgMyy34auzs5YMAAIL4MaC6XXHJJlX1dunQBomIREEV19txzTyD/Zmm1\nxZqe3n///W6flf30Fzdn22uvvdx2dvPFuLvAdqfd/x1Z0QVrVCsRm6+44hhWoGLGjBl1OqZis2gK\nQL9+/YDMMqr2ORa3eD5pw7zmzZsD0KxZMyCMUtKTJ08GMiOo9jnWsGHDgp7LfidxTbytzLxFXq18\nfiWx6L9lV/h33dNWurcUbEG8fX5ZSWnIL5LTpEkTIDNKaNkVlRItLFTaI1yK5IiIiIiISFAqJpKT\nKwLhr62xO1BWctaaX0LUoOzcc88FYNq0ae5YiPn81ojNci7jLFmyxG0feOCBtT6mNFu9erXbjrsD\nbvn7cSyyMn/+/KKN58knn8z4b6lNmjTJbXfr1i3jmEV0oHZKdNq6JrsLDNFdv3wbOVYSu4MeV1b7\noosuAmDp0qUATJgwoe4GVkR+lKBHjx4A7LvvvkV/na+++sptW9QxhAiOsXLYF198sdtnUZe4dSIW\n+Tn00EOB+HLIFtn1I7xTpkwpzoDLjP/3ydFHH51x7MUXX6zr4aSanSP2N5o18AS49tprM475rFn0\nsGHDAGjdurU7dt111wHhtgUJnSI5IiIiIiISFF3kiIiIiIhIUComXe2mm25y27aobO+99wYyywX6\nC9UAFi9e7LatLOGcOXNqbZxpYotwO3ToUO1jRo4c6bbzKdcbMn/RspVCtXLaPgt7++mOlvoTMj/1\nxFKFbDHoRhttVOPn//jjjwF44403qhwbPHgwoHO0mKxDdrmmq/ml1S1NzT8/LFWvUaNGQFQ0ADJL\nRkN07gEsX74cgDFjxgDwj3/8wx3LVUwjJFYUIC5tVwpjBWsA2rZtm3HM/w6R6LPf/i45//zz3TFr\ns7DffvtV+TkrzmPva78VSCGlpyX6uxpggw02AKLCSqWgTyAREREREQlKkJGcqVOnuu0zzjgDgM02\n28zte+qppzIe7y+utTLIl156KVC+dymTatWqldv2o1+yZn6E76CDDirhSNLJX+htUR27622lOwGu\nvPJKIPM9+/bbbwNRE8Y4CxYsAGDu3LlFGnHlmj17NhCVrPULQ4TCb6R48sknA9GieMhslAeZd3Rt\nQb0VrQixCbSkQ69evarss89Sv9iNRA3IrTjKgw8+6I69/PLLQGZLhWzWhNv/uy+kIiG1wUpsWzTb\nWldAFM22v8NLQZEcEREREREJii5yREREREQkKGv9vKY27SWQvaizJqx7ui0Eh2gBqaWm+Yvnr7/+\n+qK9dk0l+dUUc+7KmeYuOc1dcoXOXdrn7d133wUy+4UNHz4cgCeeeAKAV155pcavo3MuOc1dcmme\nu6ZNmwKZhVQ23XRTAIYMGQLA6NGj62QscdI8d2kX4tzZd4T/fWDnp6UBFkOhc6dIjoiIiIiIBCX4\nSE45C/Fqv65o7pLT3CUXWiSnruicS05zl1ya587ufg8cONDts4IghxxyCAArV66sk7HESfPcpV3I\nc3fttde6bSsGdPXVVxft+RXJERERERGRihZkCWkRERGRcvXJJ58AsGzZMrfvmmuuAUobwRHJpV+/\nfqUeQgZFckREREREJCi6yBERERERkaCo8ECKhbw4rbZp7pLT3CWnwgPJ6JxLTnOXnOYuOc1dcpq7\n5FR4QEREREREKloqIzkiIiIiIiJJKZIjIiIiIiJB0UWOiIiIiIgERRc5IiIiIiISFF3kiIiIiIhI\nUHSRIyIiIiIiQdFFjoiIiIiIBEUXOSIiIiIiEhRd5IiIiIiISFB0kSMiIiIiIkHRRY6IiIiIiARF\nFzkiIiIiIhIUXeSIiIiIiEhQdJEjIiIiIiJB0UWOiIiIiIgERRc5IiIiIiISFF3kiIiIiIhIUHSR\nIyIiIiIiQdFFjoiIiIiIBEUXOSIiIiIiEhRd5IiIiIiISFB0kSMiIiIiIkHRRY6IiIiIiARFFzki\nIiIiIhIUXeSIiIiIiEhQdJEjIiIiIiJB0UWOiIiIiIgERRc5IiIiIiISFF3kiIiIiIhIUHSRIyIi\nIiIiQdFFjoiIiIiIBEUXOSIiIiIiEhRd5IiIiIiISFB0kSMiIiIiIkH5f1A6nO45ed8sAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c868b828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# takes 5-10 seconds to execute this\n",
    "show_MNIST(train_lbl, train_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zaCyK4V6926Ks69evB5LHIN1icGWhrMZu7Nix3nZQGcV0x8ykT1a61xY6g/S5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XAAAg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\nVIPXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c6b00048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# takes 5-10 seconds to execute this\n",
    "show_MNIST(test_lbl, test_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the average of all the images of training and testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of all images in training dataset.\n",
      "Digit 0 : 5923 images.\n",
      "Digit 1 : 6742 images.\n",
      "Digit 2 : 5958 images.\n",
      "Digit 3 : 6131 images.\n",
      "Digit 4 : 5842 images.\n",
      "Digit 5 : 5421 images.\n",
      "Digit 6 : 5918 images.\n",
      "Digit 7 : 6265 images.\n",
      "Digit 8 : 5851 images.\n",
      "Digit 9 : 5949 images.\n"
     ]
    },
    {
     "data": {
      "image/png": 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pju/szzoWUmf5fKhecPZ19lnNxWbJ6VdeeQVAtv0HkHk/8t5+IM1dAlr2Vc2j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\nDccg5ugAmdeMctIxi3M451P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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72bf4aca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average of all images in testing dataset.\n",
      "Digit 0 : 980 images.\n",
      "Digit 1 : 1135 images.\n",
      "Digit 2 : 1032 images.\n",
      "Digit 3 : 1010 images.\n",
      "Digit 4 : 982 images.\n",
      "Digit 5 : 892 images.\n",
      "Digit 6 : 958 images.\n",
      "Digit 7 : 1028 images.\n",
      "Digit 8 : 974 images.\n",
      "Digit 9 : 1009 images.\n"
     ]
    },
    {
     "data": {
      "image/png": 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r856q7MKNaZvTsh4+G8R0hfqgngD1yhPKjB4vfc7gtYSleYFUnjynusXv4HgQ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\nJA/kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c44b81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Average of all images in training dataset.\")\n",
    "show_ave_MNIST(train_lbl, train_img)\n",
    "\n",
    "print(\"Average of all images in testing dataset.\")\n",
    "show_ave_MNIST(test_lbl, test_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing\n",
    "\n",
    "Now, let us convert this raw data into `DataSet.examples` to run our algorithms defined in `learning.py`. Every image is represented by 784 numbers (28x28 pixels) and we append them with its label or class to make them work with our implementations in learning module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 784) (60000,)\n",
      "(60000, 785)\n"
     ]
    }
   ],
   "source": [
    "print(train_img.shape, train_lbl.shape)\n",
    "temp_train_lbl = train_lbl.reshape((60000,1))\n",
    "training_examples = np.hstack((train_img, temp_train_lbl))\n",
    "print(training_examples.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will initialize a DataSet with our training examples, so we can use it in our algorithms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# takes ~10 seconds to execute this\n",
    "MNIST_DataSet = DataSet(examples=training_examples, distance=manhattan_distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moving forward we can use `MNIST_DataSet` to test our algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plurality Learner\n",
    "\n",
    "The Plurality Learner always returns the class with the most training samples. In this case, `1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "pL = PluralityLearner(MNIST_DataSet)\n",
    "print(pL(177))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual class of test image: 8\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f72c4e819b0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nivADSRF+ICnCDyT1/zuzOYWa4hAXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c5683ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "print(\"Actual class of test image:\", test_lbl[177])\n",
    "plt.imshow(test_img[177].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is obvious that this Learner is not very efficient. In fact, it will guess correctly in only 1135/10000 of the samples, roughly 10%. It is very fast though, so it might have its use as a quick first guess."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive-Bayes\n",
    "\n",
    "The Naive-Bayes classifier is an improvement over the Plurality Learner. It is much more accurate, but a lot slower."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7\n"
     ]
    }
   ],
   "source": [
    "# takes ~45 Secs. to execute this\n",
    "\n",
    "nBD = NaiveBayesLearner(MNIST_DataSet, continuous=False)\n",
    "print(nBD(test_img[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual class of test image: 7\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f72c8487860>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c5683dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "print(\"Actual class of test image:\", test_lbl[0])\n",
    "plt.imshow(test_img[0].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### k-Nearest Neighbors\n",
    "\n",
    "We will now try to classify a random image from the dataset using the kNN classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "# takes ~20 Secs. to execute this\n",
    "kNN = NearestNeighborLearner(MNIST_DataSet, k=3)\n",
    "print(kNN(test_img[211]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make sure that the output we got is correct, let's plot that image along with its label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Actual class of test image: 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f72c8665cf8>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCD8QFOEHgiL8QFCEHwiK8ANBEX4gqP8B1flLsMvfVy4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c55f74e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "print(\"Actual class of test image:\", test_lbl[211])\n",
    "plt.imshow(test_img[211].reshape((28,28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hurray! We've got it correct. Don't worry if our algorithm predicted a wrong class. With this techinique we have only ~97% accuracy on this dataset."
   ]
  }
 ],
 "metadata": {
  "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": "3.6.1"
  }
 },
 "nbformat": 4,
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}