{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**<font size=48>CS228 Python Tutorial</font>**<br>\n",
    "<br>\n",
    "Jupyter notebook by Volodymyr Kuleshov, Isaac Caswell, Shachi Deshpande, Alexandre St-Pierre-See based on the [CS231n Python v3.0 tutorial](http://cs231n.github.io/python-numpy-tutorial/) by Justin Johnson."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "Python is a great general-purpose programming language on its own, but with the help of a few popular libraries (numpy, scipy, matplotlib) it becomes a powerful environment for scientific computing.\n",
    "\n",
    "We expect that many of you will have some experience with Python and numpy; for the rest of you, this section will serve as a quick crash course both on the Python programming language and on the use of Python for scientific computing.\n",
    "\n",
    "Some of you may have previous knowledge in Matlab, in which case we also recommend the [numpy for Matlab users page](https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we will cover:\n",
    "\n",
    "* [Python](#python)\n",
    "    * [Basic data types](#python-basic)\n",
    "    * [Containers](#python-containers)\n",
    "        * [Lists](#python-lists)\n",
    "        * [Dictionaries](#python-dicts)\n",
    "        * [Sets](#python-sets)\n",
    "        * [Tuples](#python-tuples)\n",
    "    * [Functions](#python-functions)\n",
    "    * [Classes](#python-classes)\n",
    "* [Numpy](#numpy)\n",
    "    * [Arrays](#numpy-arrays)\n",
    "    * [Array indexing](#numpy-array-indexing)\n",
    "    * [Datatypes](#numpy-datatypes)\n",
    "    * [Array math](#numpy-array-math)\n",
    "    * [Broadcasting](#numpy-broadcasting)\n",
    "* [SciPy](#scipy)\n",
    "    * [Image operations](#scipy-image-operations)\n",
    "    * [MATLAB files](#scipy-matlab)\n",
    "    * [Distance between points](#scipy-distance)\n",
    "* [Matplotlib](#mpl)\n",
    "    * [Plotting](#mpl-plot)\n",
    "    * [Subplots](#mpl-subplot)\n",
    "    * [Images](#mpl-images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id=\"python\">Python</a>\n",
    "Python is a high-level, dynamically typed multiparadigm programming language. Python code is often said to be almost like pseudocode, since it allows you to express very powerful ideas in very few lines of code while being very readable. As an example, here is an implementation of the classic quicksort algorithm in Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 2, 3, 6, 8, 10]\n"
     ]
    }
   ],
   "source": [
    "def quicksort(arr):\n",
    "    if len(arr) <= 1:\n",
    "        return arr\n",
    "    pivot = arr[len(arr) // 2]\n",
    "    left = [x for x in arr if x < pivot]\n",
    "    middle = [x for x in arr if x == pivot]\n",
    "    right = [x for x in arr if x > pivot]\n",
    "    return quicksort(left) + middle + quicksort(right)\n",
    "\n",
    "print(quicksort([3,6,8,10,1,2,1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python versions\n",
    "There are currently two different supported versions of Python, 2.7 and 3.7. Somewhat confusingly, Python 3.0 introduced many backwards-incompatible changes to the language, so code written for 2.7 may not work under 3.7 and vice versa. For this class all code will use Python 3.7. The latest Python version is 3.8.5 currently.\n",
    "\n",
    "You can check your Python version at the command line by running `python --version`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"python-basic\">Basic data types</a>\n",
    "Like most languages, Python has a number of basic types including integers, floats, booleans, and strings. These data types behave in ways that are familiar from other programming languages.\n",
    "\n",
    "**Numbers**: Integers and floats work as you would expect from other languages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "x = 3\n",
    "print(type(x)) # Prints \"<class 'int'>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "4\n",
      "2\n",
      "6\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(x + 1)   # Addition;\n",
    "print(x - 1)   # Subtraction;\n",
    "print(x * 2)   # Multiplication;\n",
    "print(x ** 2)  # Exponentiation;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "x += 1\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "x *= 2\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'float'>\n",
      "2.5 3.5 5.0 6.25\n"
     ]
    }
   ],
   "source": [
    "y = 2.5\n",
    "print(type(y)) # Prints \"<class 'float'>\"\n",
    "print(y, y + 1, y * 2, y ** 2) # Prints \"2.5 3.5 5.0 6.25\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that unlike many languages, Python does not have unary increment (`x++`) or decrement (`x--`) operators.\n",
    "\n",
    "Python also has built-in types for complex numbers; you can find all of the details [in the documentation](https://docs.python.org/3.7/library/stdtypes.html#numeric-types-int-float-complex)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Booleans**: Python implements all of the usual operators for Boolean logic, but uses English words rather than symbols (`&&`, `||`, etc.):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bool'>\n",
      "False\n",
      "True\n",
      "False\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "t = True\n",
    "f = False\n",
    "print(type(t)) # Prints \"<class 'bool'>\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(t and f) # Logical AND; prints \"False\"\n",
    "print(t or f)  # Logical OR; prints \"True\"\n",
    "print(not t)   # Logical NOT; prints \"False\"\n",
    "print(t != f)  # Logical XOR; prints \"True\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Strings**: Python has great support for strings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "hello = 'hello'    # String literals can use single quotes\n",
    "world = \"world\"    # or double quotes; it does not matter.\n",
    "print(hello)       # Prints \"hello\"\n",
    "print(len(hello))  # String length; prints \"5\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n"
     ]
    }
   ],
   "source": [
    "hw = hello + ' ' + world  # String concatenation\n",
    "print(hw)  # prints \"hello world\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world 12\n"
     ]
    }
   ],
   "source": [
    "hw12 = '%s %s %d' % (hello, world, 12)  # sprintf style string formatting\n",
    "print(hw12)  # prints \"hello world 12\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "String objects have a bunch of useful methods; for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n",
      "HELLO\n",
      "  hello\n",
      " hello \n"
     ]
    }
   ],
   "source": [
    "s = \"hello\"\n",
    "print(s.capitalize())  # Capitalize a string; prints \"Hello\"\n",
    "print(s.upper())       # Convert a string to uppercase; prints \"HELLO\"\n",
    "print(s.rjust(7))      # Right-justify a string, padding with spaces; prints \"  hello\"\n",
    "print(s.center(7))     # Center a string, padding with spaces; prints \" hello \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "he(ell)(ell)o\n"
     ]
    }
   ],
   "source": [
    "print(s.replace('l', '(ell)'))  # Replace all instances of one substring with another;\n",
    "                                # prints \"he(ell)(ell)o\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "world\n"
     ]
    }
   ],
   "source": [
    "print('  world '.strip())  # Strip leading and trailing whitespace; prints \"world\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find a list of all string methods [in the documentation](https://docs.python.org/3.7/library/stdtypes.html#string-methods)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"python-containers\">Containers</a>\n",
    "Python includes several built-in container types: lists, dictionaries, sets, and tuples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"python-lists\">Lists</a>\n",
    "A list is the Python equivalent of an array, but is resizeable and can contain elements of different types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 1, 2] 2\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "xs = [3, 1, 2]    # Create a list\n",
    "print(xs, xs[2])  # Prints \"[3, 1, 2] 2\"\n",
    "print(xs[-1])     # Negative indices count from the end of the list; prints \"2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 1, 'foo']\n"
     ]
    }
   ],
   "source": [
    "xs[2] = 'foo'     # Lists can contain elements of different types\n",
    "print(xs)         # Prints \"[3, 1, 'foo']\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 1, 'foo', 'bar']\n"
     ]
    }
   ],
   "source": [
    "xs.append('bar')  # Add a new element to the end of the list\n",
    "print(xs)         # Prints \"[3, 1, 'foo', 'bar']\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bar [3, 1, 'foo']\n"
     ]
    }
   ],
   "source": [
    "x = xs.pop()      # Remove and return the last element of the list\n",
    "print(x, xs)      # Prints \"bar [3, 1, 'foo']\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xs.pop(1) # remove element from a specific position\n",
    "print(xs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, you can find all the gory details about lists [in the documentation](https://docs.python.org/3.7/tutorial/datastructures.html#more-on-lists)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Slicing**: In addition to accessing list elements one at a time, Python provides concise syntax to access sublists; this is known as slicing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4]\n",
      "[2, 3]\n",
      "[2, 3, 4]\n",
      "[0, 1]\n",
      "[0, 1, 2, 3, 4]\n",
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "nums = list(range(5))     # range is a built-in function that creates a list of integers\n",
    "print(nums)               # Prints \"[0, 1, 2, 3, 4]\"\n",
    "print(nums[2:4])          # Get a slice from index 2 to 4 (exclusive); prints \"[2, 3]\"\n",
    "print(nums[2:])           # Get a slice from index 2 to the end; prints \"[2, 3, 4]\"\n",
    "print(nums[:2])           # Get a slice from the start to index 2 (exclusive); prints \"[0, 1]\"\n",
    "print(nums[:])            # Get a slice of the whole list; prints \"[0, 1, 2, 3, 4]\"\n",
    "print(nums[:-1])          # Slice indices can be negative; prints \"[0, 1, 2, 3]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 8, 9, 4]\n"
     ]
    }
   ],
   "source": [
    "nums[2:4] = [8, 9]        # Assign a new sublist to a slice\n",
    "print(nums)               # Prints \"[0, 1, 8, 9, 4]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will see slicing again in the context of numpy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loops**: You can loop over the elements of a list like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cat\n",
      "dog\n",
      "monkey\n"
     ]
    }
   ],
   "source": [
    "animals = ['cat', 'dog', 'monkey']\n",
    "for animal in animals:\n",
    "    print(animal)\n",
    "# Prints \"cat\", \"dog\", \"monkey\", each on its own line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want access to the index of each element within the body of a loop, use the built-in `enumerate` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#1: cat\n",
      "#2: dog\n",
      "#3: monkey\n"
     ]
    }
   ],
   "source": [
    "animals = ['cat', 'dog', 'monkey']\n",
    "for idx, animal in enumerate(animals):\n",
    "    print('#%d: %s' % (idx + 1, animal))\n",
    "# Prints \"#1: cat\", \"#2: dog\", \"#3: monkey\", each on its own line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**List comprehensions**: When programming, frequently we want to transform one type of data into another. As a simple example, consider the following code that computes square numbers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16]\n"
     ]
    }
   ],
   "source": [
    "nums = [0, 1, 2, 3, 4]\n",
    "squares = []\n",
    "for x in nums:\n",
    "    squares.append(x ** 2)\n",
    "print(squares)   # Prints [0, 1, 4, 9, 16]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can make this code simpler using a **list comprehension**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 4, 9, 16]\n"
     ]
    }
   ],
   "source": [
    "nums = [0, 1, 2, 3, 4]\n",
    "squares = [x ** 2 for x in nums]\n",
    "print(squares)   # Prints [0, 1, 4, 9, 16]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List comprehensions can also contain conditions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 4, 16]\n"
     ]
    }
   ],
   "source": [
    "nums = [0, 1, 2, 3, 4]\n",
    "even_squares = [x ** 2 for x in nums if x % 2 == 0]\n",
    "print(even_squares)  # Prints \"[0, 4, 16]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"python-dicts\">Dictionaries</a>\n",
    "A dictionary stores (key, value) pairs, similar to a `Map` in Java or an object in Javascript. You can use it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cute\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "d = {'cat': 'cute', 'dog': 'furry'}  # Create a new dictionary with some data\n",
    "print(d['cat'])       # Get an entry from a dictionary; prints \"cute\"\n",
    "print('cat' in d)     # Check if a dictionary has a given key; prints \"True\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wet\n"
     ]
    }
   ],
   "source": [
    "d['fish'] = 'wet'     # Set an entry in a dictionary\n",
    "print(d['fish'])      # Prints \"wet\"\n",
    "#print(d['monkey'])  # KeyError: 'monkey'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N/A\n",
      "wet\n",
      "N/A\n"
     ]
    }
   ],
   "source": [
    "print(d.get('monkey', 'N/A'))  # Get an element with a default; prints \"N/A\"\n",
    "print(d.get('fish', 'N/A'))    # Get an element with a default; prints \"wet\"\n",
    "del d['fish']         # Remove an element from a dictionary\n",
    "print(d.get('fish', 'N/A')) # \"fish\" is no longer a key; prints \"N/A\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find all you need to know about dictionaries [in the documentation](https://docs.python.org/3.7/library/stdtypes.html#dict)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loops**: It is easy to iterate over the keys in a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A person has 2 legs\n",
      "A cat has 4 legs\n",
      "A spider has 8 legs\n"
     ]
    }
   ],
   "source": [
    "d = {'person': 2, 'cat': 4, 'spider': 8}\n",
    "for animal in d:\n",
    "    legs = d[animal]\n",
    "    print('A %s has %d legs' % (animal, legs))\n",
    "# Prints \"A person has 2 legs\", \"A cat has 4 legs\", \"A spider has 8 legs\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Dictionary comprehensions**: These are similar to list comprehensions, but allow you to easily construct dictionaries. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0, 2: 4, 4: 16}\n"
     ]
    }
   ],
   "source": [
    "nums = [0, 1, 2, 3, 4]\n",
    "even_num_to_square = {x: x ** 2 for x in nums if x % 2 == 0}\n",
    "print(even_num_to_square)  # Prints \"{0: 0, 2: 4, 4: 16}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0, 2: 8, 4: 64}\n"
     ]
    }
   ],
   "source": [
    "another_dict = {x:x**3 for x in even_num_to_square}\n",
    "print(another_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"python-sets\">Sets</a>\n",
    "A set is an unordered collection of distinct elements. As a simple example, consider the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "animals = {'cat', 'dog'}\n",
    "print('cat' in animals)   # Check if an element is in a set; prints \"True\"\n",
    "print('fish' in animals)  # prints \"False\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "animals.add('fish')       # Add an element to a set\n",
    "print('fish' in animals)  # Prints \"True\"\n",
    "print(len(animals))       # Number of elements in a set; prints \"3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "animals.add('cat')        # Adding an element that is already in the set does nothing\n",
    "print(len(animals))       # Prints \"3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "animals.remove('cat')     # Remove an element from a set\n",
    "print(len(animals))       # Prints \"2\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, everything you want to know about sets can be found [in the documentation](https://docs.python.org/3.7/library/stdtypes.html#set)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Loops**: Iterating over a set has the same syntax as iterating over a list; however since sets are unordered, you cannot make assumptions about the order in which you visit the elements of the set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#1: cat\n",
      "#2: fish\n",
      "#3: dog\n"
     ]
    }
   ],
   "source": [
    "animals = {'cat', 'dog', 'fish'}\n",
    "for idx, animal in enumerate(animals):\n",
    "    print('#%d: %s' % (idx + 1, animal))\n",
    "# Prints \"#1: fish\", \"#2: dog\", \"#3: cat\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Set comprehensions**: Like lists and dictionaries, we can easily construct sets using set comprehensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0, 1, 2, 3, 4, 5}\n"
     ]
    }
   ],
   "source": [
    "from math import sqrt\n",
    "nums = {int(sqrt(x)) for x in range(30)}\n",
    "print(nums)  # Prints \"{0, 1, 2, 3, 4, 5}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a id=\"python-tuples\">Tuples</a>\n",
    "A tuple is an (immutable) ordered list of values. A tuple is in many ways similar to a list; one of the most important differences is that tuples can be used as keys in dictionaries and as elements of sets, while lists cannot. Here is a trivial example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2)\n",
      "<class 'tuple'>\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "t=(1,2)\n",
    "print(t)\n",
    "print(type(t))\n",
    "print(t[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n",
      "5\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "d = {(x, x + 1): x for x in range(10)}  # Create a dictionary with tuple keys\n",
    "t = (5, 6)        # Create a tuple\n",
    "print(type(t))    # Prints \"<class 'tuple'>\"\n",
    "print(d[t])       # Prints \"5\"\n",
    "print(d[(1, 2)])  # Prints \"1\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[The documentation](https://docs.python.org/3.7/tutorial/datastructures.html#tuples-and-sequences) has more information about tuples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"python-functions\">Functions</a>\n",
    "Python functions are defined using the def keyword. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative\n",
      "zero\n",
      "positive\n"
     ]
    }
   ],
   "source": [
    "def sign(x):\n",
    "    if x > 0:\n",
    "        return 'positive'\n",
    "    elif x < 0:\n",
    "        return 'negative'\n",
    "    else:\n",
    "        return 'zero'\n",
    "\n",
    "for x in [-1, 0, 1]:\n",
    "    print(sign(x))\n",
    "# Prints \"negative\", \"zero\", \"positive\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will often define functions to take optional keyword arguments, like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Bob\n",
      "HELLO, FRED!\n"
     ]
    }
   ],
   "source": [
    "def hello(name, loud=False):\n",
    "    if loud:\n",
    "        print('HELLO, %s!' % name.upper())\n",
    "    else:\n",
    "        print('Hello, %s' % name)\n",
    "\n",
    "hello('Bob') # Prints \"Hello, Bob\"\n",
    "hello('Fred', loud=True)  # Prints \"HELLO, FRED!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a lot more information about Python functions [in the documentation](https://docs.python.org/3.7/tutorial/controlflow.html#defining-functions)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"python-classes\">Classes</a>\n",
    "The syntax for defining classes in Python is straightforward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Fred\n",
      "HELLO, FRED!\n"
     ]
    }
   ],
   "source": [
    "class Greeter(object):\n",
    "\n",
    "    # Constructor\n",
    "    def __init__(self, name):\n",
    "        self.name = name  # Create an instance variable\n",
    "\n",
    "    # Instance method\n",
    "    def greet(self, loud=False):\n",
    "        if loud:\n",
    "            print('HELLO, %s!' % self.name.upper())\n",
    "        else:\n",
    "            print('Hello, %s' % self.name)\n",
    "\n",
    "g = Greeter('Fred')  # Construct an instance of the Greeter class\n",
    "g.greet()            # Call an instance method; prints \"Hello, Fred\"\n",
    "g.greet(loud=True)   # Call an instance method; prints \"HELLO, FRED!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read a lot more about Python classes [in the documentation](https://docs.python.org/3.7/tutorial/classes.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id=\"numpy\">Numpy</a>\n",
    "[Numpy](http://www.numpy.org/) is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays. If you are already familiar with MATLAB, you might find [this tutorial useful](https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html) to get started with Numpy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"numpy-arrays\">Arrays</a>\n",
    "A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the rank of the array; the shape of an array is a tuple of integers giving the size of the array along each dimension.\n",
    "\n",
    "We can initialize numpy arrays from nested Python lists, and access elements using square brackets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(3,)\n",
      "1 2 3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([1, 2, 3])   # Create a rank 1 array\n",
    "print(type(a))            # Prints \"<class 'numpy.ndarray'>\"\n",
    "print(a.shape)            # Prints \"(3,)\"\n",
    "print(a[0], a[1], a[2])   # Prints \"1 2 3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 2 3]\n"
     ]
    }
   ],
   "source": [
    "a[0] = 5                  # Change an element of the array\n",
    "print(a)                  # Prints \"[5, 2, 3]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n",
      "1 2 4\n"
     ]
    }
   ],
   "source": [
    "b = np.array([[1,2,3],[4,5,6]])    # Create a rank 2 array\n",
    "print(b.shape)                     # Prints \"(2, 3)\"\n",
    "print(b[0, 0], b[0, 1], b[1, 0])   # Prints \"1 2 4\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy also provides many functions to create arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0. 0.]\n",
      " [0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.zeros((2,2))   # Create an array of all zeros\n",
    "print(a)              # Prints \"[[ 0.  0.]\n",
    "                      #          [ 0.  0.]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "b = np.ones((1,2))    # Create an array of all ones\n",
    "print(b)              # Prints \"[[ 1.  1.]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[7 7]\n",
      " [7 7]]\n"
     ]
    }
   ],
   "source": [
    "c = np.full((2,2), 7)  # Create a constant array\n",
    "print(c)               # Prints \"[[ 7.  7.]\n",
    "                       #          [ 7.  7.]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0.]\n",
      " [0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "d = np.eye(2)         # Create a 2x2 identity matrix\n",
    "print(d)              # Prints \"[[ 1.  0.]\n",
    "                      #          [ 0.  1.]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.60760649 0.1445073 ]\n",
      " [0.25996118 0.02291291]]\n"
     ]
    }
   ],
   "source": [
    "e = np.random.random((2,2))  # Create an array filled with random values\n",
    "print(e)                     # Might print \"[[ 0.91940167  0.08143941]\n",
    "                             #               [ 0.68744134  0.87236687]]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read about other methods of array creation [in the documentation](http://docs.scipy.org/doc/numpy/user/basics.creation.html#arrays-creation)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"numpy-array-indexing\">Array indexing</a>\n",
    "Numpy offers several ways to index into arrays.\n",
    "\n",
    "**Slicing**: Similar to Python lists, numpy arrays can be sliced. Since arrays may be multidimensional, you must specify a slice for each dimension of the array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create the following rank 2 array with shape (3, 4)\n",
    "# [[ 1  2  3  4]\n",
    "#  [ 5  6  7  8]\n",
    "#  [ 9 10 11 12]]\n",
    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use slicing to pull out the subarray consisting of the first 2 rows\n",
    "# and columns 1 and 2; b is the following array of shape (2, 2):\n",
    "# [[2 3]\n",
    "#  [6 7]]\n",
    "b = a[:2, 1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "77\n"
     ]
    }
   ],
   "source": [
    "# A slice of an array is a view into the same data, so modifying it\n",
    "# will modify the original array.\n",
    "print(a[0, 1])   # Prints \"2\"\n",
    "b[0, 0] = 77     # b[0, 0] is the same piece of data as a[0, 1]\n",
    "print(a[0, 1])   # Prints \"77\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also mix integer indexing with slice indexing. However, doing so will yield an array of lower rank than the original array. Note that this is quite different from the way that MATLAB handles array slicing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create the following rank 2 array with shape (3, 4)\n",
    "# [[ 1  2  3  4]\n",
    "#  [ 5  6  7  8]\n",
    "#  [ 9 10 11 12]]\n",
    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 6 7 8] (4,)\n",
      "[[5 6 7 8]] (1, 4)\n"
     ]
    }
   ],
   "source": [
    "# Two ways of accessing the data in the middle row of the array.\n",
    "# Mixing integer indexing with slices yields an array of lower rank,\n",
    "# while using only slices yields an array of the same rank as the\n",
    "# original array:\n",
    "row_r1 = a[1, :]    # Rank 1 view of the second row of a\n",
    "row_r2 = a[1:2, :]  # Rank 2 view of the second row of a\n",
    "print(row_r1, row_r1.shape)  # Prints \"[5 6 7 8] (4,)\"\n",
    "print(row_r2, row_r2.shape)  # Prints \"[[5 6 7 8]] (1, 4)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  6 10] (3,)\n",
      "[[ 2]\n",
      " [ 6]\n",
      " [10]] (3, 1)\n"
     ]
    }
   ],
   "source": [
    "# We can make the same distinction when accessing columns of an array:\n",
    "col_r1 = a[:, 1]\n",
    "col_r2 = a[:, 1:2]\n",
    "print(col_r1, col_r1.shape)  # Prints \"[ 2  6 10] (3,)\"\n",
    "print(col_r2, col_r2.shape)  # Prints \"[[ 2]\n",
    "                             #          [ 6]\n",
    "                             #          [10]] (3, 1)\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Integer array indexing**: When you index into numpy arrays using slicing, the resulting array view will always be a subarray of the original array. In contrast, integer array indexing allows you to construct arbitrary arrays using the data from another array. Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([[1,2], [3, 4], [5, 6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 4 5]\n"
     ]
    }
   ],
   "source": [
    "# An example of integer array indexing.\n",
    "# The returned array will have shape (3,) and\n",
    "print(a[[0, 1, 2], [0, 1, 0]])  # Prints \"[1 4 5]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 4 5]\n"
     ]
    }
   ],
   "source": [
    "# The above example of integer array indexing is equivalent to this:\n",
    "print(np.array([a[0, 0], a[1, 1], a[2, 0]]))  # Prints \"[1 4 5]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2]\n"
     ]
    }
   ],
   "source": [
    "# When using integer array indexing, you can reuse the same\n",
    "# element from the source array:\n",
    "print(a[[0, 0], [1, 1]])  # Prints \"[2 2]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2]\n"
     ]
    }
   ],
   "source": [
    "# Equivalent to the previous integer array indexing example\n",
    "print(np.array([a[0, 1], a[0, 1]]))  # Prints \"[2 2]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One useful trick with integer array indexing is selecting or mutating one element from each row of a matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3]\n",
      " [ 4  5  6]\n",
      " [ 7  8  9]\n",
      " [10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create a new array from which we will select elements\n",
    "a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
    "\n",
    "print(a)  # prints \"array([[ 1,  2,  3],\n",
    "          #                [ 4,  5,  6],\n",
    "          #                [ 7,  8,  9],\n",
    "          #                [10, 11, 12]])\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  6  7 11]\n"
     ]
    }
   ],
   "source": [
    "# Create an array of indices\n",
    "b = np.array([0, 2, 0, 1])\n",
    "\n",
    "# Select one element from each row of a using the indices in b\n",
    "print(a[np.arange(4), b])  # Prints \"[ 1  6  7 11]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11  2  3]\n",
      " [ 4  5 16]\n",
      " [17  8  9]\n",
      " [10 21 12]]\n"
     ]
    }
   ],
   "source": [
    "# Mutate one element from each row of a using the indices in b\n",
    "a[np.arange(4), b] += 10\n",
    "\n",
    "print(a)  # prints \"array([[11,  2,  3],\n",
    "          #                [ 4,  5, 16],\n",
    "          #                [17,  8,  9],\n",
    "          #                [10, 21, 12]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Boolean array indexing**: Boolean array indexing lets you pick out arbitrary elements of an array. Frequently this type of indexing is used to select the elements of an array that satisfy some condition. Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False False]\n",
      " [ True  True]\n",
      " [ True  True]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([[1,2], [3, 4], [5, 6]])\n",
    "\n",
    "bool_idx = (a > 2)   # Find the elements of a that are bigger than 2;\n",
    "                     # this returns a numpy array of Booleans of the same\n",
    "                     # shape as a, where each slot of bool_idx tells\n",
    "                     # whether that element of a is > 2.\n",
    "\n",
    "print(bool_idx)      # Prints \"[[False False]\n",
    "                     #          [ True  True]\n",
    "                     #          [ True  True]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "# We use boolean array indexing to construct a rank 1 array\n",
    "# consisting of the elements of a corresponding to the True values\n",
    "# of bool_idx\n",
    "print(a[bool_idx])  # Prints \"[3 4 5 6]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "# We can do all of the above in a single concise statement:\n",
    "print(a[a > 2])     # Prints \"[3 4 5 6]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For brevity we have left out a lot of details about numpy array indexing; if you want to know more you should [read the documentation](http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"numpy-datatypes\">Datatypes</a>\n",
    "Every numpy array is a grid of elements of the same type. Numpy provides a large set of numeric datatypes that you can use to construct arrays. Numpy tries to guess a datatype when you create an array, but functions that construct arrays usually also include an optional argument to explicitly specify the datatype. Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([1, 2])   # Let numpy choose the datatype\n",
    "print(x.dtype)         # Prints \"int64\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1.0, 2.0])   # Let numpy choose the datatype\n",
    "print(x.dtype)             # Prints \"float64\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 2], dtype=np.int64)   # Force a particular datatype\n",
    "print(x.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read all about numpy datatypes [in the documentation](http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"numpy-array-math\">Array math</a>\n",
    "Basic mathematical functions operate elementwise on arrays, and are available both as operator overloads and as functions in the numpy module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1,2],[3,4]], dtype=np.float64)\n",
    "y = np.array([[5,6],[7,8]], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6.  8.]\n",
      " [10. 12.]]\n",
      "[[ 6.  8.]\n",
      " [10. 12.]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise sum; both produce the array\n",
    "# [[ 6.0  8.0]\n",
    "#  [10.0 12.0]]\n",
    "print(x + y)\n",
    "print(np.add(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4. -4.]\n",
      " [-4. -4.]]\n",
      "[[-4. -4.]\n",
      " [-4. -4.]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise difference; both produce the array\n",
    "# [[-4.0 -4.0]\n",
    "#  [-4.0 -4.0]]\n",
    "print(x - y)\n",
    "print(np.subtract(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5. 12.]\n",
      " [21. 32.]]\n",
      "[[ 5. 12.]\n",
      " [21. 32.]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise product; both produce the array\n",
    "# [[ 5.0 12.0]\n",
    "#  [21.0 32.0]]\n",
    "print(x * y)\n",
    "print(np.multiply(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n",
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise division; both produce the array\n",
    "# [[ 0.2         0.33333333]\n",
    "#  [ 0.42857143  0.5       ]]\n",
    "print(x / y)\n",
    "print(np.divide(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.         1.41421356]\n",
      " [1.73205081 2.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Elementwise square root; produces the array\n",
    "# [[ 1.          1.41421356]\n",
    "#  [ 1.73205081  2.        ]]\n",
    "print(np.sqrt(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that unlike MATLAB, `*` is elementwise multiplication, not matrix multiplication. We instead use the `dot` function to compute inner products of vectors, to multiply a vector by a matrix, and to multiply matrices. `dot` is available both as a function in the numpy module and as an instance method of array objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1,2],[3,4]])\n",
    "y = np.array([[5,6],[7,8]])\n",
    "\n",
    "v = np.array([9,10])\n",
    "w = np.array([11, 12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219\n",
      "219\n"
     ]
    }
   ],
   "source": [
    "# Inner product of vectors; both produce 219\n",
    "print(v.dot(w))\n",
    "print(np.dot(v, w))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[29 67]\n",
      "[29 67]\n"
     ]
    }
   ],
   "source": [
    "# Matrix / vector product; both produce the rank 1 array [29 67]\n",
    "print(x.dot(v))\n",
    "print(np.dot(x, v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[19 22]\n",
      " [43 50]]\n",
      "[[19 22]\n",
      " [43 50]]\n"
     ]
    }
   ],
   "source": [
    "# Matrix / matrix product; both produce the rank 2 array\n",
    "# [[19 22]\n",
    "#  [43 50]]\n",
    "print(x.dot(y))\n",
    "print(np.dot(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy provides many useful functions for performing computations on arrays; one of the most useful is `sum`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "[4 6]\n",
      "[3 7]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1,2],[3,4]])\n",
    "\n",
    "print(np.sum(x))  # Compute sum of all elements; prints \"10\"\n",
    "print(np.sum(x, axis=0))  # Compute sum of each column; prints \"[4 6]\"\n",
    "print(np.sum(x, axis=1))  # Compute sum of each row; prints \"[3 7]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find the full list of mathematical functions provided by numpy [in the documentation](http://docs.scipy.org/doc/numpy/reference/routines.math.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apart from computing mathematical functions using arrays, we frequently need to reshape or otherwise manipulate data in arrays. The simplest example of this type of operation is transposing a matrix; to transpose a matrix, simply use the `T` attribute of an array object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n",
      "[[1 3]\n",
      " [2 4]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "x = np.array([[1,2], [3,4]])\n",
    "print(x)    # Prints \"[[1 2]\n",
    "            #          [3 4]]\"\n",
    "print(x.T)  # Prints \"[[1 3]\n",
    "            #          [2 4]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "# Note that taking the transpose of a rank 1 array does nothing:\n",
    "v = np.array([1,2,3])\n",
    "print(v)    # Prints \"[1 2 3]\"\n",
    "print(v.T)  # Prints \"[1 2 3]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy provides many more functions for manipulating arrays; you can see the full list [in the documentation](http://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"numpy-broadcasting\">Broadcasting</a>\n",
    "Broadcasting is a powerful mechanism that allows numpy to work with arrays of different shapes when performing arithmetic operations. Frequently we have a smaller array and a larger array, and we want to use the smaller array multiple times to perform some operation on the larger array.\n",
    "\n",
    "For example, suppose that we want to add a constant vector to each row of a matrix. We could do it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# We will add the vector v to each row of the matrix x,\n",
    "# storing the result in the matrix y\n",
    "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
    "v = np.array([1, 0, 1])\n",
    "y = np.empty_like(x)   # Create an empty matrix with the same shape as x\n",
    "\n",
    "# Add the vector v to each row of the matrix x with an explicit loop\n",
    "for i in range(4):\n",
    "    y[i, :] = x[i, :] + v\n",
    "    \n",
    "# Now y is the following\n",
    "# [[ 2  2  4]\n",
    "#  [ 5  5  7]\n",
    "#  [ 8  8 10]\n",
    "#  [11 11 13]]\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This works; however when the matrix `x` is very large, computing an explicit loop in Python could be slow. Note that adding the vector `v` to each row of the matrix `x` is equivalent to forming a matrix `vv` by stacking multiple copies of `v` vertically, then performing elementwise summation of `x` and `vv`. We could implement this approach like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1]\n",
      " [1 0 1]\n",
      " [1 0 1]\n",
      " [1 0 1]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# We will add the vector v to each row of the matrix x,\n",
    "# storing the result in the matrix y\n",
    "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
    "v = np.array([1, 0, 1])\n",
    "vv = np.tile(v, (4, 1))   # Stack 4 copies of v on top of each other\n",
    "print(vv)                 # Prints \"[[1 0 1]\n",
    "                          #          [1 0 1]\n",
    "                          #          [1 0 1]\n",
    "                          #          [1 0 1]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "y = x + vv  # Add x and vv elementwise\n",
    "print(y)  # Prints \"[[ 2  2  4\n",
    "          #          [ 5  5  7]\n",
    "          #          [ 8  8 10]\n",
    "          #          [11 11 13]]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy broadcasting allows us to perform this computation without actually creating multiple copies of `v`. Consider this version, using broadcasting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# We will add the vector v to each row of the matrix x,\n",
    "# storing the result in the matrix y\n",
    "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
    "v = np.array([1, 0, 1])\n",
    "y = x + v  # Add v to each row of x using broadcasting\n",
    "print(y)  # Prints \"[[ 2  2  4]\n",
    "          #          [ 5  5  7]\n",
    "          #          [ 8  8 10]\n",
    "          #          [11 11 13]]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The line `y = x + v` works even though `x` has shape `(4, 3)` and `v` has shape `(3,)` due to broadcasting; this line works as if `v` actually had shape `(4, 3)`, where each row was a copy of `v`, and the sum was performed elementwise."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting two arrays together follows these rules:\n",
    "\n",
    "1. If the arrays do not have the same rank, prepend the shape of the lower rank array with 1s until both shapes have the same length.\n",
    "1. The two arrays are said to be compatible in a dimension if they have the same size in the dimension, or if one of the arrays has size 1 in that dimension.\n",
    "1. The arrays can be broadcast together if they are compatible in all dimensions.\n",
    "1. After broadcasting, each array behaves as if it had shape equal to the elementwise maximum of shapes of the two input arrays.\n",
    "1. In any dimension where one array had size 1 and the other array had size greater than 1, the first array behaves as if it were copied along that dimension\n",
    "If this explanation does not make sense, try reading the explanation [from the documentation](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) or [this explanation](http://wiki.scipy.org/EricsBroadcastingDoc).\n",
    "\n",
    "Functions that support broadcasting are known as universal functions. You can find the list of all universal functions [in the documentation](http://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs).\n",
    "\n",
    "Here are some applications of broadcasting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  5]\n",
      " [ 8 10]\n",
      " [12 15]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Compute outer product of vectors\n",
    "v = np.array([1,2,3])  # v has shape (3,)\n",
    "w = np.array([4,5])    # w has shape (2,)\n",
    "# To compute an outer product, we first reshape v to be a column\n",
    "# vector of shape (3, 1); we can then broadcast it against w to yield\n",
    "# an output of shape (3, 2), which is the outer product of v and w:\n",
    "# [[ 4  5]\n",
    "#  [ 8 10]\n",
    "#  [12 15]]\n",
    "print(np.reshape(v, (3, 1)) * w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 4 6]\n",
      " [5 7 9]]\n"
     ]
    }
   ],
   "source": [
    "# Add a vector to each row of a matrix\n",
    "x = np.array([[1,2,3], [4,5,6]])\n",
    "# x has shape (2, 3) and v has shape (3,) so they broadcast to (2, 3),\n",
    "# giving the following matrix:\n",
    "# [[2 4 6]\n",
    "#  [5 7 9]]\n",
    "print(x + v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5  6  7]\n",
      " [ 9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "# Add a vector to each column of a matrix\n",
    "# x has shape (2, 3) and w has shape (2,).\n",
    "# If we transpose x then it has shape (3, 2) and can be broadcast\n",
    "# against w to yield a result of shape (3, 2); transposing this result\n",
    "# yields the final result of shape (2, 3) which is the matrix x with\n",
    "# the vector w added to each column. Gives the following matrix:\n",
    "# [[ 5  6  7]\n",
    "#  [ 9 10 11]]\n",
    "print((x.T + w).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5  6  7]\n",
      " [ 9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "# Another solution is to reshape w to be a column vector of shape (2, 1);\n",
    "# we can then broadcast it directly against x to produce the same\n",
    "# output.\n",
    "print(x + np.reshape(w, (2, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  4  6]\n",
      " [ 8 10 12]]\n"
     ]
    }
   ],
   "source": [
    "# Multiply a matrix by a constant:\n",
    "# x has shape (2, 3). Numpy treats scalars as arrays of shape ();\n",
    "# these can be broadcast together to shape (2, 3), producing the\n",
    "# following array:\n",
    "# [[ 2  4  6]\n",
    "#  [ 8 10 12]]\n",
    "print(x * 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting typically makes your code more concise and faster, so you should strive to use it where possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy Documentation\n",
    "This brief overview has touched on many of the important things that you need to know about numpy, but is far from complete. Check out the [numpy reference](http://docs.scipy.org/doc/numpy/reference/) to find out much more about numpy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id=\"scipy\">SciPy</a>\n",
    "Numpy provides a high-performance multidimensional array and basic tools to compute with and manipulate these arrays. [SciPy](http://docs.scipy.org/doc/scipy/reference/) builds on this, and provides a large number of functions that operate on numpy arrays and are useful for different types of scientific and engineering applications.\n",
    "\n",
    "The best way to get familiar with SciPy is to [browse the documentation](http://docs.scipy.org/doc/scipy/reference/index.html). We will highlight some parts of SciPy that you might find useful for this class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"scipy-image-operations\">Image operations</a>\n",
    "SciPy provides some basic functions to work with images. For example, it has functions to read images from disk into numpy arrays, to write numpy arrays to disk as images, and to resize images. Here is a simple example that showcases these functions (**some SciPy methods were deprecated after the original production of this tutorial, imports were adapted in order to keep the code working**):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8 (400, 248, 3)\n",
      "float64 (400, 248, 3)\n",
      "uint8 (300, 300, 3)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from imageio import imread, imwrite\n",
    "from skimage.transform import resize\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Read an JPEG image into a numpy array\n",
    "img = imread('../assets/cat.jpg')\n",
    "print(img.dtype, img.shape)  # Prints \"uint8 (400, 248, 3)\"\n",
    "\n",
    "# We can tint the image by scaling each of the color channels\n",
    "# by a different scalar constant. The image has shape (400, 248, 3);\n",
    "# we multiply it by the array [1, 0.95, 0.9] of shape (3,);\n",
    "# numpy broadcasting means that this leaves the red channel unchanged,\n",
    "# and multiplies the green and blue channels by 0.95 and 0.9\n",
    "# respectively.\n",
    "img_tinted = (img * [1, 0.95, 0.9])\n",
    "print(img_tinted.dtype, img_tinted.shape)  # Prints \"float64 (400, 248, 3)\"\n",
    "\n",
    "# Resize the tinted image to be 300 by 300 pixels.\n",
    "img_tinted = resize(img_tinted, (300, 300)).astype(dtype=np.uint8)\n",
    "print(img_tinted.dtype, img_tinted.shape)  # Prints \"uint8 (300, 248, 3)\"\n",
    "\n",
    "# Write the tinted image back to disk\n",
    "imwrite('../assets/cat_tinted.jpg', img_tinted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IEBpKmLjtkLyT4Ud33UXGgOpcBRXHaDLGVpKCaTCUz7OsVGJJvkjgxegqoQVCi5mtLZDv78eLFMPFDFkFxH5CW9AI0Dg4NkMrWtSMJSESJ4hpKY84DnBiiTIEegihiNDDkGrQItQlcWwzO+uy7rTTaAjF6OwEh2drrFy7hrM2XEXeHqbZqqNSUJcat7zvTzHNtah4gaLtMX1klNiLQJgYToZAhHiy0aUTyrbwohgHuO/uH/Lhj36Ai6+4jEhThCEs7x1kprJAId+LJXQcs81xy0eQbpFWOWb38//EwV3f5aLLLyFW0KxBSBuXFl/8279ivhrwk/seZGF+jA+/93p60jZ+E5b2D1GeneX//fa3WLKsB2nGOJaiVW+jm7DxTSegRxloZFF+C0MLabuKmBjDNrEyNqvXrT0mbl4X4I3jCE3TOlWVmCAIkFJimmY347cNHcexsW07icCahWGnmSlXef+Hf5NQCaIYrFQWiUEQJrqr45i86byzedO5Z+FHkM3nMHQdpUTCE5VK+CtJpcxKO0k1CZgvlykUSghgdmqaTRdcRMoEhJa8XiW55Pe//310XSOMYxaqNXQ0DF3HNkwswyCTMfnGt7+BYUKIotZqksk4aAIO7N9P0Pa48PwLsISgp5ChkElx8omrueC8U7j2zVfy3HPPoRkpenqHGVzSy5IVx6M7JVqtNpOTk2zbtg3fV5haopJY5itc0Y0VhqVTaQb09PQwemQc0zTJ5UwsE+bmymhCcGT0EIcPjdJbzOOkTC699FKOHppmx7bDtFsmW7c9hQ7kMqCh8bV/+Do9hV6uvOp8vMhlybKlNH2F0OCJJzfjOA7l+Qqzs7PEEhbm5lm6bDmRlIQSVhx3HI7jMDc3RxB4+L6PbpoJVTRNLNth2bLjjomb1wd4OyAyTRMhRDc5i6IIXdcxTRNNCFAxSiXl4Fy2h7HxMgPDywkUIAx02yKIQSJAs7jnRz/mM5/5Txia4NqrL+X2793FwdHDiU7ped3KFCRJly4UaAmXbbbb6LqJhsAU8ORjT1DIWsgQ0HSkhChO5LKNZ55NrdrC0HWKxRJS+qRtC1M3OokonHLqhiSZQqd/oIQQMUHksWvXDlAxxWwGAxjuKZJLWWQtMHRIpZK5ZjK9SGnxh//9S9TbAaHM0myG1GoNdF0nbQtabohhWGgYIHSUgFgXVFoe//P2O7j+nW9n966Xuf3221EKduwcpTy/QHl6llPWncT555zN/PwUTz/1CJ/42MdYtuQEjow22LFtGlMPWagsEEUxOhZnnH4ut956G5sf38KnPvUJpIxwbMHokXHWr19PNp/i4MGDREHIk09swbHT1OtNJqdnOrzcpFKrUKsv4Ps+ftBGKUW+lKHYU2DZ8hWoX1EAfl2Al46uCwnvXfxZVCCiKELFEXEUdBO3WGpIDKJYUK66xApcN8LQoR3GPPjQw+zfv58/+PznsYzkg87Oz9Nsuijo6Mha15OQLLMJz40UtNttstksiUEOUqlUkgAZyXylUhgGeEHMhg2nIaXEdX10wBBaR88XaJ1K+bp165ASKo0FEsFDYBgG1WoV27bJpTPEUYgOpAwBEXh+hOs2krnke1DC4PIrruC57dvQrDSaphMGEUopypUaURShiWTVkJCAV8K+0UMk6wosW76Eubk54hief/559uzZQ6mYx/M8oiDk0KGD2LbBwGCWTRdfRrMeMj/XQiNiz64XQAW88MKLrFm9hv3795PLZUhZ0NfXB3RutFwW24J8scB73vMeHn/0MQ7uP4Bp6hiajqGD57epLMzjRz5RHHSSTYWmJfPu6xvAdjLHhM3rArxSJp4B13XxPK+rOCxG4QSwCQhSqRROOkvKKWCnCnz6M5/l/gcf4PFHnmB2Zo6vfuMfCYOY0dFRGq0mu3btQNeSKPbe227jhReep+2FaJ2yqkAQySQmakLD813m58vkcjlMwwbAa8dsuugidJFU3ty2j2YkVbfR0VEsy6JUytFstoAEtJZlgIqT7FmAYzloGvh+QCwhUiGgqFQqnHfOuQgFlmEmOp0CXQfLkNRrs2w84ywOHhhHEw4rVq7g83/4X0lne8lmelFCw/d9fnzfPeiGIAig7UlCKZEIqvU2D9z/EO95z22kdDjj1JPo6+vjO9+5k7e97W3c9r7fxNAEsR/gu20G+nu54MJzkcDHP/1RoijFQiXmpz/8EZsf+xHPP/s4991zL9uef4m7vncnG05djxdA4LeJgXQ6RU/RolILWbNmDSefsoaFhRovv/wylq4z0FfEawe8uP1ZZqbG8f02fhSgGxqaoRMEPvl8ESud44KLrzwmbl4X4NUNnVar1QVtcge+AmDDMNB0M0mINIFl2fSUhtl4+nmMjU8xNnEUxzQZ6uvjpBNWk3EsRkdHeemll/jLv/xz/uZv/4qnt2xmoCdLTyHPwX37sC2zoxlr6JpJrASRgp/e9xP6e3tJmSaB72MI2LZtGz09JYJYEQQRTjqFH4QEkcI0ddIpQRzD4EAP0PFa6Rq6aaCbGmEEoQxptUDFElNLCiBPPfUUMozYuPGsJMIv1jyEBCGp1xYQQuI4GbZt3cXFF13Glmc2g/A55+zzuXjTVZy0dj29vSUuuvg8fnj3PVSrDYSugaZRbdW5665/4oQT1tCo1pmdazI/V+WySy/mmmuuJpu3sR2LFcuWIuPE6zE8tAzLtBB6os9+6j9+BjcI2L/zACODRcYP70HGIffeczerVh3P/v17mCtXKGSyEML05CS7949x730/5vhVK9F0eP8HP0C93uQjv/VuCimdmYmDVMvT1Gtz+IGL0DUMyySVSZPL5dAMk3agSKV/oZmsO14rV9n/r6Gk7BYcfN9HiI7FUCRJlabpYFhYKQPTdjCsNEIz+cl9D3HN9W/ndz79SUwpUAouu/hc5uYqKKUIfY+q2+Dhhx9ix/YXGBub5dJNF/Lcz5/l1JPXJsuujFEdZ5brhbzt2msxteSutgwbKeG551/grHPOwjAEsdKJZYBl2cxWa6w9cSUK2H9gH+vWrEZ0qnWLBRDDMNA0WChXccwhhgaGiUMwTI2Jo+Poukmp+L9KQkopxo5OoAlFo9FCSIOnnnyGWz5wE3d//zs8cu9jNFtV2n6LSIYMDi/hD77wF9z9g3uI45jLr7wQ2zawdYOhvn4qs7Ocespqghi0oSIPPfQYl166iVbbRdMTeUroFggDP4oxDQ0jBW+9/goMW+OrX/oik0ePsG/0IAMDG0BKCvkMGzacTCghq0lSBgz2D7DjpR00Gg2EAc+/uI+RwSE+9NEPcu/3bidrKp54+KeMHdpP6DeBCKHrYBhopkUUhOi6yQUXXsrB8elj4uZ1EXkXgep5HrZt4/t+V2OFDgB0G2FapNIOumFRKvbzjhtu4sILT8fSBJYOlp7cjUN9PVx26SZsO1EnqtUqhw6P8qN7f8CWzU9wwXnnoms6izYaSVJt+9mjT7B0eAQZgYyTzNnzQk488UQMI/FICCE6hhHJHXd8l3YQMV+pUijk0XWBJkAhEbrAizz8KEQp6O3t5ac/fgBNga0n5w/DmIGBIYIgmYl69Y+A6elpXNdlqH+Am2+6lVKhwI3vuoGvf+1vGD3wMvPlGmEYMTs7y8zsFN/85jf5yEdu46KLNnH3Pfew/+ABzjvrHDZdsJFiIUccJbRHA44cOYRtwGx5Fs3QcDIZekoDmHYWJSCUAYEMaAceV7/lMkq5IQ4fPMDVV11GvpDh85//r7zj+utQSLY8vZORgT50AXEYoJTi8ssvx7Sg1NODYVs02wFnnLKWE45bSr08Q+jV8dotojhRluI4JlKSbDZLLltg5eq1PPvctmPi5nUBXpRCRyFUYnNMZzMoHUzHIjYkbenimB6OAivUMCRYKcXpb1pDLAGp8GSYmM4VRAKuuurNFPJ9xL4ia6WQQcTc5AR33XE7P/zB95kYH6fdaqNrAhlEqAhytkmGKEnwdEEs4LEnN3PW2acDsPnJnycl2AjCSBD4AtsyWKg0WDoyBHFiIrJkCkOEWPShkUEoRUqaLFQOITSLtgZtV5Gy85x7znmYppYYXlh0nQmkhFAoVqy/mKUnn8dd997FV7761zSnq4Q1DT8K8VoVglab3mwflekm2196CRcYnTjECavW8ewT2/n59q20QxhZNohmgN4x5vcWVxBE0GrP0nBb6KZJoVRMEk0FBgJDCRwjhYjh1o99iEq1zZ6XtnP+BSfTNzxAuRkhY41VQzpSQAjsGz3ARVdcRq63xN5Ds+RKfVQbPppmkZJNAneGA0f20IpjpEijRxny6QyOKegpGfT1rWTFso089fAB9r5YOSZsXhfgVSRqwyJNSKiC9iraoBFEMbFSnWgJmqGDEHS+867rTIgkcukGBEGAZVlYlkUul8O2bZrNJs899xxHjhxhfHyc8YlpDCORtJYvX45mJDKYlK90DuRyOcIo4eF+SIeTC1avXomUdJWSxXkoldCefD6PaZq4rRa6DiMjSeeDRlIlNAyD3t7eRH7SF+2MSRUxiiJSqRT9/f089thjVKvVblkc6GriURTheR5BEBBFEWNjEwwODrJkyRLOPHMjlmWxf/8kngehjFAobAPS6TRCQHl2Dsdxuqtfcg0FoYzRdZNFD2Xba1FdqFNdaLB8+Qp0oJA12L17L70Dg8lqoRJL6djYWKKAzM3SarUY6i8wNTXL1NQE9XodAKkioijAMDsQ1DVM06TRdukbHGLfwQMUe0rHxM3rArzAP9N3fd8n9APCMERIhSE0mqEPhoYwdaSh0Q4DYg0iofBklEhrMkIAbS+kXK5z6qmnkk6nabXahGGE67pEUcTExARf//rXOXr0KPMz02zbto1GvcaKFSsSDy/JF9FqtVm6dCkAliE4/YxTMU2wTAvXbfOWN18JCnK5HLFMFAJN1xG6QS5bYvWqNfT09BCGIZ4XkMk6SAXtIOLQ0SOYKZPB4UGEoXcM3klEDeMYSXIjfPUrX+HJJ59kenqahXKFdrsNgOM4WKbdNSiBpFGrEfptzjh9Hc16jYsuuJB333gdjz/yIJZBYjklRgFnnbmRhUqdttvA1BWB10Z0Wpp03SAKk4phGCliCXv37ub0087jyOEF7v3h/UCEBuze/RK5XA9RBE9ufgZdNzlh1Sr6i2lyToqZ8SNYAvryKbY+/ywzs9NoKNrtNrZtEquIIPLRTQPDcrjyuut506ZNfOjjN3HFmy87JmZeFwmbQOC67ivPVQJgy7Jw0klU0IRCs0wiFOgammlQbzXxwgjHtpKopCWZlm2YHJo+yFvf+la2bf05QtPwvKAbxaWUNJtNvvzlL7N+/Xquv/569u7dC0B/T6njGzBxfY98Lsv0xCSZfI50Ot29213XRQpB4EeYukaz3iTQNMLAJYqTFePKK69GCjpWTp0NJ59Eo1mnUqnQ399Lb2+JWCp0TeAHPpCsFkII7r//fm6/4zuMj48TtD1SqVTSA6frhJ1jUk6KlJnCNCOiehOhYgb6+jh6dIzzz38T+bROCHzkw+/jz/70S7z7trfjZ9Kk7SylnMkTT27GbVTIO4n9VEMSBjHCMrBSDu0oQtcMxienGD24F11lCf0827bu5kc//h5mOsWNN15HEEGj7jI4PMLVV13Low8/wpIlS3DrNS67+FwiN+SFpx9loVYmCD0ajQamrmOZAhm3sfN5LCdNqX+ETVf8BqgUEZB24mPi5nUBXoXCMAzCyMfrWP50oSEUhH6AbdsINEQEsRehSxjoKdGTzSXRIQ7RSADj+wGGaZFOp1mxYjmWZSGEjpPOEoV+Es2FIFhYoFAocPDAPr71rW9x0003Ua/XmQkDJiYm6O3tpVKr43kezWaTJUuX0dOTSGGtZo2+vgHq9aSA4LouutCQUUgYeOTyJaSUzMxMUygU0C0TkEhiqrurycoSJt0g9WYD09RRStFoNJifn2XPnj089thjTI2NI4SgWCwSeD66ZdFsNjE6K5Tnh1i6hVIKv91i9fo1vLzrRQ4fGeO3bnl3R9WQGKbGLe96F7ff/i0++tEPI2ILU7N4adtWBkY00k6aKIo7ETxZjqUQNNwWzWqThx76GbZj8YXf/2se+ukj/P03/pQnH/8Z7/7N24CIct95y4cAACAASURBVA0O7N3LGWeczqHDR/BaLt/6xv/gUx//MIQh1ZkjHN2/gzBq02w1GB8fJ59PITSJmTJQmo6dznH9u97LPfc8yg3XvQWh4JEHf3BM3LwuwAtgWRZuu5nY4QwDKZOLqXVsiSndQkUxOdshbaSoTM2yak1HjtLMJENWIZad6MGWlRjGb7r13Xzly3+H22oRRVG3C0NoBuVyGU3TqFarfPnv/oZbbrmF+UYD27aT0mYUUW00E67r+1TKRWrVOvlChlqthlIC0zSZn59POKppIOOYRjMpdVarC7iui2ZqxHHI3NwcuXyGMAzxvRi32WJyfIy+vj4Mw+CZnz/Nww8/3KU3GoJMOoOMXukkSafT6JqGrutYtoOugR7FmKbOcH8vC5VZzjvnzMTxpoGMPXQ9zQknjLB06XKef/4FLM3hkovOp7+viNeepGkp1px4CiNLl6OZBu1QYpsac3MzPPqzR9myZQuf+MRH0R3BxVdcxhf/8vNMHh3nxz+4m9PWvYknn3qM005eh2UlK9KRw4fpKRbwmk1mJ46wZ8dW5qcO0WzWmZ8v4zgZlIrwgzaGaVIoFOgdWsay49ZxyskjbH/hIIbwmJs8fEzMvG7A22q1uiYcnSR5UEqhJXYDLCuF42TIZDJYlsXExARn0emNjEOUYXS/YIBcPkMkJZdccgn/7Y++kNgsAxfHcZBSouuJlhlFEbaVpljI8eTmxzlu1WpMw6bVanUbMMMwJvIDcukMuoBqtUqj0cD3fYq5PPNzc8m8u4lbUvJtt9u0Wo2ErujQbrVASmzbRBeKyfGjFItFZBSxY8eLbNu2jdrCAlJKLMsinXZo1OvdJDCKIhzHIQYM06RcqUIcYWiga4JlS0cgCli7ajWopM1fIQhi8Dx4140387nPfY6Np52D68LSpUup1JoIISj19mJYVqd0ruH6HrNTk+zauY2NZ5zM6rXr8QXsGR1l06WXsHfHZsb3jfHAT+/jhre9gyefepZMxuGB+3/CRRddxEknrmH39q3k7JiF8gxeu8bOnTtpeT7DI8uRcYSpC3LFHKtOOImR5Scyt+ChKdh4+ir+5At//s9spr9o/JsSNiHEYSHEDiHEdiHE1s7feoQQDwkh9ncej50yQrexUinVdXr9y518ZKf5Txg6kYyTdncAkfyfUJ0OguRPWJaF0fnSHcdB0/Sukd227S7Qc7kcYRiSTqcplUoEQdAFp+u61Ot1giDo9lYt3lStVgshk6Xe87wufWi32zQaNdrtFkrFuK5LGIa4rkutVmNmZibJ4qVCxZI4jGjWG8xOz9CsN0AqdKFhdagEJH6PMAwTT0cc4/s+zWYz4d0y8fHGcczAQB+u20x05k6LUhAlKoRuJc68QqHACy9uRzd4JViYNkLXUTLplNY1SNspdu/ZSRwGZNMOuUyWxzZv4YR1K9m06WL8dsDCfA0NnTCOOO/8s8nn8xw+fJhly5bhOA71RpVVq4/nyJFDLNQX0PXESC/Q8TyfOI4pFApYtkMUgx8o8jmYLbeZq5TJF3qOiZv/E2rDJUqp05RSZ3ae/y7wsFLqBODhzvNfORaX9H8JYA2BLjRCoTAcmwBJOw5Zd/qGxHgCyYXvHCtVIqalUhY7d+9GCMH5F15EqVQin89jWRZSStLpNIODg/T29rJixQoAKpUKUzMzTEyOMTZ+hIWFBcrlclJ80DTq9TqNRoNWq00URQRBwNzsNF67RavZRElJu+0TxAFB6OH5LrohaHstpqamqJSrzM9VePbnWzl6+AgagvnZOV7es4t6rUYYBBi6wDJ1GrXkvUzT/GdNp67r0mw2KZfLhGFiH202myAjdKHwWi6TU2MIFFJGWIZFo9HAMpNrddPN7+ad77yR73znbs44c2Mi6+kGCDPxSQRw+PAkW7Y8RbVSwTTgwgvO5Qf3Pcj6009GtxVXXX0ZvhsTe/DIAw9h6Ymt9OFHHuL441eQy2XYtXsHl1xyCVu3Pku1WcPzXNLpNPl8Hl03SNvJ7/V6nWyuwNDIcvr6lzI2N0mx16F/eARlZI+JmddCKns78O3O798GrvvVL9GI0dFNG93SMR2DmBjNMAhk0vmbNgws3SD0QnTNIJ0pIEkaKRUdN1LHLrlocjx0ZBzL0vnYJz7Jmy68CNsyaLtJxGq9igObpkmj0WBqaorJsUkM3cJrB5im2QV8LpchigLiOESGAaGXJJZ+EFGrN9ENA7dDE4q5IrblIGMIg5gwiJGdxDkIAqampti7bw+Hj4xycHQ/MzMz9PX14TgOhmF1lJHEodVoJEmh0aFFUkqiMOniEDIkCBW6lSOT7+fIgd0MZlpUjr4EGEQYlJsOC82AKHaxiMimM6w58UQC2eLB+3+I8CW2mSKdyYNuIHSoVuY55aR1zM/VKA2cyE8f2c5vvPlKhgtpUgqOzjT42B/8GTMiw76JGSYO7caOQtYsW0E+W+AnP7yPnJUibC5waN9OmrUKXhyS0m0sIuJgBkkTLzIp1xy2PLGXxx54jn7b5rjsEKMv7WGwaPGms078Faj5tw0FPCiEeL6zAw7AoFJqCqDzOPArT6IUtm13l8nAj7DMFKZpdgG0mDSVSiUKhUJXUI+iqJslJ/bGpNQbhonFTgI9pQy33HILYRh2AGJQLBaTbY9qNRYWFpifn6fdbtNutxkbG0NKiW3b6LpOtVplbm6OarVKtVql3W4TxzFhGGLbNtlsNpH1HIdisUi9Xqder3ftm4tUJQgCgiApn5ZKpe45dV1ndnaW/v5+8vk82WwWTdO6/mbDSHYNWnzvMAwJwzDxFEeJc+3EdSeSyWSYnp5menqaKGrjB5IggC996Us0Gy5eHNE/2Eu93uCDH3g3btsHNJQKmS/PoaRi1659rFu3jgce+ClNt83y41Zw5VXXMDo1hxI6XiCplBe44vILef/7biN069xzx3fBgL37djE9Pclll1/Chg3reeCn9zEzOdX1TocyTjYT0cyExtkpVq1axVe//hXWrl3Dzl37UEbM7n0vUW83+If/8c1j4ubfCt7zlVJnANcAHxNCXPSvfaEQ4kNCiK1CiK1BFHXNOIvRcLHi4zhOZ2nrdBVrBoZh/bNu4sWEZhH8OnS//JYbUa21WVhYYHBwkGw2y8jICI7jdPlqpZKUIeM47t4wqVSqs3NPnTAMu6CoVhOpa5EHK6XI5xM/bLVa7VKLRWBHUcT8/Hy3srRYjCmXy3ieR6vVYmJigmq12jXkFwoFstks6XQ60Ztdt9u/t1gxNIwk185kEs/ru268kbYXYFhpojhg/74dpMyAfAFWrFiBF0pMPYUfJqtSrRlSKvUShhGWJnhmy2b+/C/+hNWrVxLLgBjF2vXreO97bmb/6EEWmpJyI2LLs1up1Wq0qi6/eduNhI0ae7Zt46GHfszmpx/jdz/72zTqZaKwja4pmo06XrtN5CXFCCWSllXdtli/fj3rN6xHaYrb3ncrjzz+MA13joVWmXKjxg233nZMDP2bwKuUmuw8zgJ3k+wOOSOEGO58UcPA7C95bXe7J1PXkiVZhigl0HUD205hGGY3QTIMi0KhQDqdxnJSxKojk+kGanGTuw6FADA1wYknnohSipm5OWzH4bfe/0Fy+SKabtL2AoRmIDQDy3aQShBLumVWgImJiW4CtxgBpZR4vovbblLtdALMzk0jVYQipu21WFhYYHx8nEqlkvBREonLMIwusJUUpJ0spmEThRLTsPHaAYEfIdAZ6B8im81iGAaFQoEwDMlkMt2VI5lPovFetOlirrn2LWCYNFserutSmR9jfu4wsYRPf/ojFPI9Xadb20/KyccddxxOKseBg/soFfJc99a38E933s7Xv/5Vtm/fxic/9nHaEZxz7ptwTJOnHnuCE1au5JxzTyGfT+P7kuvfcTOHjkzyzLOP86EP3oaTNlh30vFsefJRIq9Fu1El8j3iKMJMWYmiYWgMDAxxYPQQdiqFqWtIGfDe993CH3z+9/DbLkfHjtBqtV4b8AohMkKI3OLvwJXATpJtnd7bOey9wA//NedbTNYWfxY7HSCRoBaTuMUet1+2r7CKZbfOrus6mUxS7l3Myi3LSjTUV3mHbdtGiESzXdyvYbGDWSlFKpXqzsNxnG6P3eKxrut2E6vFrudXz19K2Un0WgRBwqUXnXOLEXSRhvi+311m0+l0UlmL406nhtvdt0EphWPZ5HJZzj77LMIoJIyTHRkDz0fKgGplhpSZrESmaXSvS6FQoFTKJDqyk+bQoUPYtk2pVMJtN9m/fy+1Wo2YmEajzeOPP87uF7dx2abzWbmiP7EbC7BsjdM2vgkpUkxNTTIw0IeSAciYQ6P7mJ2ZSjYR6VwvNIFuakkA6lCyOI5RKPygzb0/uodWs8lAbx+bLtrEWWdsPDZm/jXA+iVjELi7wz0N4LtKqfuFEM8Bdwoh3g8cBd75q04kBIRRUv1aBEpCH7QulQiCgJ279zA8spJcBxhhpNANQRwrDKWSi6PrHesiDA0NEcawevVypITnn36s6w1QSjE0NIRSSTdDPp9ncHCQsfFJlFJdfrpYQFiUqFqtFpqeAHnxZshmszSbTWZmZshms2QyWeI47iaF9XodwzBIpVJkMhl83yefz9NqtbBtm3w+TxzHNBqNrkkojmOkikinE4P23NwcmqZRqVQwDINcLoeBxciSJZT6eolQ3HzLe7jne99mZrpMKn2UanmGTP8JmJpJsdCP0BLwphyTp59+knBhhpdefIH+gRF27NrN6NgMN916C3/9lxMEvsu257bSM3A8l116KT2WwNQBJELXCICZSptc7xIKw6sY3beXb3zt7/nP//kPePAn9+E16zTrC9TqZYI4QHVooVJg6BYrVhzP5HSZWrXKX//1nzE1M0cUC/xGyN13/ZDzL9rEQPHYUtn/NniVUqPAqb/g72Xg2I6KfzGkjLu6ZBQmAr1SIUGQgDabzeCHyRdZb7Zwmu0uJ1a8souOQHQ39ZBAvdkgn8sRJ3YIxiemSGdyVKtV0pkcfhDR19dHOpNLSrBBwJIlS5idnaXdTuSwxW5lz/O6UVAREkUBINE0CEOfcrmMbZuUSgVMw+kUKFpUq9VuBS6dTnf3XrA6+6TNzc3R09PT0blFN+LHHT+xruukUil6e3uZnJxMvA2dFaFQLFBZmMdxbDTDYtnxJ1BZ8OkrmsggQIqY3Tt/zomr1iGzJSQGugaTk0eZmTrM2O7tLB8ZgnSKDWduYs1JG6g3PWZnZ7n4gnPoKZY4btkAmgamAqSfVIwwiGKDvh6HmcmYd7zzZr72t/+J3dt2EDRbzI6P06hXqC7M0Wo1iYnxZIStO0CyepbnKsgw4n9+61ucuvEclixdzuGjY6w/+VSOjk9zZHQSEem/CC7d8bpwlQlNJFbGDl2QUnbVhsUtnxbtg6Zp4nptEDqhlHh+lOywIbTOfkuvjJ5CjiCIiWN4ccfLrFixoptgaZpGT09Pp9qm02w2sW2bWq1GJpMhnU53l9JFi+MiLQjDsFvEWFhIul8XCyBxHFOv17vKxWLUXbRWLlu2jMHBQQzDYHh4mOOOO667ecrihtnpdJqenh4Mw8AwDBYWFroehyiKuhRoYLCPK666nFTaZqFWxfMU733fh1GhTnm2TOC3MYSHk9JJ2wa6gIP79zB+9CCbn/gZG05Zy3B/D36s8dMHH2Z6tkIYx8RRRKlQZM3qlcgwoR0RgG4ilUQR4SQtepjSozY1gS0M6pU6d37nDtxmi8jzaTbrhHFALGRi9jFMbNPipLVr2bZ1G309/axauZKMk2J87BCDfSVGjlvOhz/xMW6+7b385V/9wzFx87ooDyv1CnATPmogZYhuJDy01WqRTme6USiXy3X73KATcVHdzcGFSLY6OnD4CAP9Q9x///1Uq1VqM1PJFk5K0d/fj2VZ1Gq1rgwHSTa/KIUtgmQxijpOElGlSgoDSin6+vqYm5sjlUpRLBap1WqknXyyHVWHZmSz2a6OWygU2LdvH6V8gbGxMXp7e7FtG8/zKJVK3cia+C8Sfu84DrVaDdd1KZVK1Go1SqUS/b19Sc+XphEEAf39RVIrVqKUhm2a1CoLtLQDpMwsQwMrsXSNH9/3I9zWApduuhDfazE1PoY5vBrXbYGm8cyWZ7nhhhuQgUsUhKRsgygEZQJohFGMbRkEsY+tm5gEGLLNaaecxnPbXqJWrTI3M0mrWcV1XWIZgmlhpWyq1QrFYg/VapWVK1eycuVKwgj8zl7LCMWug3u57sZbsQyLp5568Zi4eV1EXhSoKGkZ14jw/DpSBUgi0KDRatJs+di2Q+S1SOkKQwRYWsKNFYJI04g0DV9AEzgwOYfh5Dlw4ABz02PUyzPMztfI5Ipk03kyqQxB08NUAhlKDMNKdohUDeoLdeqVFplUiTgKCKIajdZ814zTatZxWw10odFqNInDiHTKYXZ6DhkpAt+nVqsxOTmNbtlEClzfI4gDas0auqFoNGrYtkmzXsGxdYaG+yiWsuSLObLFLEbKYKB/iFKxl1y2QG9PPyPDS3FSGfr7Bukp9RGbGqViLyndwYp1hAQnn+LCqy9lvhXSlimi2VmCZp2te3bwjdvv5JMf+QAb165mav9eyuU5SsuWcfMtH6ZaKbP5kfvZ9uyjXHz+Jeh6EUWyKaBlQktBrGIcM42mLGzdJpIaoeVQXLOKz3z2S2w4/VxsJ6ThHmG2cohQKKSRTqqkcYuRpUvo6R1i+4v7OTo/w8PPP0t6ZB1f+OOv8earb2Mg14OMXOLYx4sUb7vpzceEzesDvPBKH1NnWXx1F7GUkvnZOXREsn+CUoCe7FHQoRWu5xKrpFgxOTVLEAQMD5YY+/+oe/NYS8/7vu/z7u979u2uc2ffh8Mhh6tM0aZ2yxIJWXYsO67iFU7ruimSBnZbo0D+cA0YdVK0buI4cCDYaVy7jSXBSrTLEsVN3GeGwyFnOOvd77lnP+fd1/7xnPMOHYsjo4GD6QMMiHt5527zO8/7PL/f9/v5bqxCKixGlWqRarmEYaoEgYdhKkiqRJwGOM4EN3DFyDmLGU2GpFmM40yAlCSJUDUZScpyTUHGbSfDbKceDof4vksYeGRpiJRFGJpEtVwkjUPiUOywUZSgqiphnCJrKo4jWmxhGKJIMoZh5GPs2dNmRpoUvDKTZrOOYQq5ZZJlSNPfx/33PYAXhBhmAVnRGAwGPPPd7/ChJx7j4vnXuHjhHPv376fRXMCPUoZjF1VVWV1dZXt7G6tUmB6DhD3JD3xC37tN7ZwuSYa1zU2OHj3OwuI85XI514UwcwjKoKgSum6CpNFqLuK7AZah43sOH/vIR3EcYRDYbnfJ0oSyaaGQsdiq3LFm7opjA5C3yGbTsjRNSeOEYrFIvV4n9QT3djwcEUQwvZshKYKfoGsKvudgFSqM+z1W1zaInAn2YAhpjJSlRIGA1UkAioTteYRRSJSEjN0xcZoQeRNq1QXGkwlh7JJkFq5t49hjdNUkibO8k+B5HstLK0RRRK/Xyydj41FXnF91mY21m5w4cQJdgX5/iGeLS0ixUsMwTaxiQRhPzQJWoZRfDAFaC0t4npcPamYv6OFwSLPZpFgvI6sqcSbaUJkEsqSiFypIapG1zT6NQsyl55/niY88ydNf+TwL9Ro/9emn2N7aYOzF3PfwD1GpFdh3YD/f+tY3WFgUdnPdNNnZ7dKaq6MbJlaaosnT89hUUxJm8ML3XuI3/8ffgCTmnnuOcOny93IXeJhGqLpBSoSmaZw89QiH9x/h2197mophcPLEMQ7uXea3f+t3UeWUueUD9PvrfOvrXyZJNX7s45+4Y83cVcWrKApRGpGlWX6RmozGDAYD5koNPMclSTIKhQIZMlEGuqGz0W7TtHQqlQoZcPaek+iqytb6Br7nsL0pRN1BJJhYmqqiJQbDwZhMSoXIZWYlihOcnW3iNKXTbbPbiSgWLKqVJt1ulyTOiBMxyCiXy4zGA2BqXZqOn9u9juiGZNBoNYiSiN6gjyQp2I5Ho9FiYo+I4gDdNJFUjShOsUyVYrGIIgu4iu2KVlulVs1f2OVSmXqzIZzRJXHBQ5ZIJQgS4aBGNvjZz/4Kn//8v2P1xmvsWd7LXMXAyAoUTQU5ibD9iEQtMvQzbDvkpVdfoVgps7x3RViQVKEWU1SFsTNBtwpIWZpT5BMJtneGHD0uYHgvPvcNRuMuw16fdrtDsWgRxBGSnJDGKbXGHE899Rme+86zSJlM1TSpFSy+/uWv8FM//dMsLy/x7/7831IMx7zyyuv86q/+w//4/v3X1t1RvBJTtwGETogsy9i2TaNRJwgCWq0W+1pLVColFhYWKNeb4mihguMF1KsVKqYYn6ZZiiTJSEnM1uY6o4EYwxq6Tugn9HsjfD/EdhyGo/4UkZTg+w5BENBoLk6VbTGTJENGYdh3qFYjXNejaBXwfXGh832fVquFbdu53K/f7xNqEqHvCze0bojCRSYIIqIkozdxqJd0HHfI/NwSsqrT7w9ZnG+hKULQrkoymiU6Hmtra5RKJer1OrZtC4d1ocBkMqHVmieKgulgJZsaOSFMFFCKSHqBNE0ZdTfZ3dpi3/5DRFYRSbX48Z/8WT73R3/C2q02n/3sZ/mjP/ocYRgSxBGHDx9mZ2eHZqtGsSgGGnImWMQJQiz14isv8yOPvx9DkXjn2huMhx3iOEbTDBzHQ1KEYEozLZ74kQ8Tegm3bqySxRnVahmrIIZDY9vlxosv8/RzL/Do++7hxWdfp9asc2cT0F1y5s1Soed1XVfALyQJU9NJoghNUaiUSqi6mGBlCP+aompMXDF2LJkWGQpRInLYRqMh3d0dRoMegedjTMe/w+GY4WjC9s4u3W6fXqfLrevXsId91CzCkmN2t7fYWLuK5/QpFRQ0VUaTDbJUxjKLUzSUAMVlWcr29nbeZ97cWmc8HuN6AXGaEAQ+URQyGQ3ZbW8xGHbp9dsMh112Nq6ytXqVmzeuMB52KRRMJpOJODN74dS+JNpzi4uLWJaVi3hmA5I9i3sggTROGHS7QoXmxDz3/EscPXaYX/sH/5DDR09SrdeYDAa0mnUUwwTN4NiJe8lSib/zEz9Jr9PlgQceYGlpiUP7D2CpGq1Wg3PnzlE0TaGV5nY+WZrBhQsXefnF52jVKvyr3//fGPQ2ae9uMhk7SMjijAsgSxw+dJQ0U3nm6W9hj0fUW3Vai0us73SIJZlH33eaVnOeZnOOH/87n+WDP/YkW10HL/7rtfLudVfsvGmW4bgupWKROBbnI5KUMAwxTZPxeEy1UAK5RJylKKqObbtoWoFKwSSNI2RVQ5bFCLTd7rCzvUuagOeHJEmG5/lESUi1VkbTFdI4gtTBnaQc3j/P8nyN0bDL5WttWrUCfjBi2PdoNfeTodHp7JJmIXEcoulyLo4xDEXQYabjXFVVMcpFkiAgVVPsUYdBR5BfypUamqZSqxvEwwFaucTlt89z69YtqrU5KpUazXqD2LTQVZW5PUt5i20ymWCa5m34YJKQxQmyLuGMHSaTCfNLCTdvrvHQQw+xurZDmqacuucMb59/ET+M0QwdJ0rRZZ2jR04Sej5f/H/+jHtO3UutXKFaKlOpVJCAKIgpF0vTLArBdGMGJcxSvv2X38TSVcqWjCUlXO9sMuz3iMOEKErJpJhUTqlW6hw4eITvPv0C9x4+ytV33mBursl2r8fS0eN88tM/SX8EJ06dJE0CUuDjH/84f/pnf8JHPvzEHevmrth5genk6q9KHGf6hSwRToLZpUSSBNHcMlTiVLiFAZDA94XLQGCjpFyZJfxmErIMkpShG6roBOgKipxgj3aRUo/RqEOxqFCvWUiZB4SoGuiGiqykqNo0sC/LprLEdGr8FH1iVVWR05iiqeEOu4x6uxR0maVWjf3LLVrVIg+dOcnjjz/CA2fuoVywqFVF0aiqiu/7+fc+s0bN1HW6rudf0zTF5VHXdSzTJE0SBoMBxZKFponx+dLSEntWVoTvTVFRNB0UgyiV0E0h8Hnz/DnOnDnD+dfPMRmPaTWahFGMqasYmkYcpyiywruyY6ZQQJXDRw4S+h6ePc59d5pmoCo6qjo1vloWum6iGRaD/i6OPULTVbY7Xepz82y2d5EkqFVFq3Tf4iLLiw1MXbCN77Tuip0XRKF6nifGNoBVLBFGGUkUU6lUCOIoH9WapkmtWsNLBSo/Cn1CWcdUZUZjm+FwmE/Coihi4no4vkecBCIrTYowLZOVvQsokovvDShrBrLs06gYVEsacZZhWlWCKKCgyew/sESn1yZJIpIopVyuCiuQJGFZhVzPa5omhhwzGfUZ99os1MrsWWoRBB6+02fQHzDszGEqGXqhjGVqzC/M0Wgto2kWru1BKtpxasFka2trWhRaPsCY2WdC38eZ2DTn5pFlmclwyMLyMmmWsndlHj8UEs/F5RXG7Vvi6aQa6IUSsgSrt27x6//4vyPwfF599VU2NzeZn59HV1TSVGBeZUkijiI0RX1XhmmKYWr8yPsfwzA0OjsbeK4tRPOKKoiemcz8/DyHDh0ijlNOnz7DxqWXSWMfs1hAqcs4fsStjQ0OLFWwjAL9fo+MhBeef4lmtcygu/We9QJ3yc4rSzJpLKHLCpokoWcpie+iA7osIaUJpixjKap4O4mRskwkzaQQSzqd7oCNdp8wFH4x09JRlYxCUUdTEkwddNVEQYPMIMss9u0/ydFj9zHo2SQJaLLJPUdU6gWfuilR0i1CJ6RSFj6rxvwijfkl5moLzDUXOLDv0DR7oSIYu5UKhVKBoDjP1iQkkmQ0XWbvvMreusPBps/RpkYNH8+9Qr+9ipqUOXnwMUhMMkJQQlQjQ9YNyiUTTYUodLHtMa5ro6oyiiIJkEmc4IdiVG3qMmnkMVcvU1QFNkCTZYqFBr6XoGsl1EwitIeYkoCzfOfFVygsH0Ku1Th+z33Ua00MSUz2WUh16QAAIABJREFUJBWsisXa9ipoGVHskWQyiaRw4+YGh/ftY2WhxRf+/N/iRi5uUCDTNHypT6QOwQgxC1WOn3gAxxlRsBIuXdskkmVqFZNsknDh2fNsXHmbhWaBjfVNyAy+9p1nuPfsWT7x1JNceO09o/tE3fxnqc4fsDKyqcUmud3rlSTC2Zh09nGylLsbojjG9RLGtocsy8zPN1lcaNBut0nTlMFgwPb2NqPRSDT+FQV9qg+2LAvH9tje6aDoJc4+/H52ug69SYJsrKDqe4izGmsbNkGgoJs1TKPCfHOJerXJ3OICrZYYzR49cpzl5WUWFhYwTRNFVlEym8gdMhl69Hu7aJpCa65Cqa5QX1LJtB56cZmtXZv3Pf4jtPs7+JFNEHgUCialUgnP8bl582YunZz1ej3Py13NM4XcTCYqyzI7O7vEiWAC66rEcNAFpn9flmg0GtRqNb76zW/wy7/4X9DZ3abb7/Pwow8zNzdHt9/L3SgPPPAAG6sbZHGGourIsqDBnz/3Ko89+iCuM+bymxeJA19ktUkZqqxgGIZ4Auk6L77wAmkYce71V+n1uhiGwcrKClGU0O32GI9tdnZ2OHRoD8ePH+f4wYM0ChaWLLOy0Lpj3dwVxQsShqqJRrwqoG1xIsTp79bUJtNznRv4OS+hVrFQFRgOJzz73Eu5tX2miZ31i4WlRjCyACRVIYhi/CAmygo8+vgnKDcPcvHtMa+c3+StK31SqUKxvICs6JimRblYpF6pUq01iGJxcYyiiEq5Rr3Rynlovc0O9eIclWKdyUjjuRdu0J8UCZIaYVZkbWdMd2Ry/N7340QJmZQxv9SiVDZRZdFyKxQKU9eznGuIZ4U7uxfopkacRkgKGLrF/n0HeePceVRFJoxCAAaDbbLUwwtcNN0U7TerwMbmGnGc0qiVMAyd7e1teoOueLGQEUcpi/OL7Gxtc+OdG2SyyEz2PYdeZxtThWe+/Q1MFXxnhD3uY9sT4jikaBWoVupcevNt3rl8Bc+1OXn0MBmCyba926bZalFvtPjkk09y8dKbfO5z/ydzc02WF5cIPB8FidDx7lg1d8eZdybKyWJkNFRdIYpiNEUhigIkSfQ7a7VarvbKsoxSRcgdJeCLX/win/rUp3jz/Dk8z8ttMzM70EwQres6iqohqzqm2SSOYzzXY3cUsHzwNCuHHkBRM9rtNhvrWySpIj6fqZJlEYaposmCH5EkCUkmdsEkiZhMHCFGiTVUo8Cho4+hSaArMVdWOyi6yv5D91BbLNKzE0aegqSqJFFAELpUy2UCN2U0GmEZFYrT48hkMqFaazAajQBh/ZEkaZoA6uWKtrm5Ju12m36vS6NRQyYmDR1kKaVQsugNR+iNmN/7/X/Jb/zmb+E6Y5rVGv2tXRaadXRd59nnn+XxJz6MqsqkMezudnnFfY0DJ46jSALPutCo8I2vfonO9gZbm6t4nousZBDEpKmCoprouoksq/Q6feqlCrVSGUlKaC3MceDgQbojl43RDrdWN7hy5QrXr17jU5/8BI4bUbYsJFVhMM1Wfq91dxTvdMmyjG6opGmMJusokkxBN1BlMe2RJIlCqZRreSVJQpFgMnZ46KGHpjh9PfeTzdwMYRhO9bJCNSYrGpomE0xv8jMtbxCmKHJC7PvIioJuGhh6EVmGufk6qpYRRQGlQhXXdSmVLdIszrUOcSy6ImbRJPQiZM2kWCpz5MA+UjwyOUFSZK5cuYqk6aTTbopmKdOxtUKpVEDXEkI/YX19mwMHDgBiXL68vMzVq1exbZtjx07Q7/dz5zNLgmxeq9VwHIdKRXRZSBMMQ0MiQ1Y0Xn3tvGAnSBL1ag3IqJbKXL/2NmQJjUaDdmeX+flFkiylUq4RTzscmirT7+2iaxLj3ojJSFyMHWdCmMYkSYQhiwjehYVFbt7YYtAb4jgO515bR1ElDh48KBwbkUQYp7x2/hyVSoX77rtPqAfLJTzHwx46QoR9p3r5W67Hv9HKstncBqI4Fu0wOQMpzY2Os0zimQAmA6oVixj42je/waFDh6bSyds8gJnNRlGUKU4qQFMNSoUiqiojk5LG4vEqSQq7nR4jx8EJfOI04eDhA9QaVXRTJwxDisUie/bswSqVKdfqQntrlTANITIvmBZZAkHaxaxmmDUZOxuzNtpikoT0PJfrm21KzSXSTMYwLErlAlKa4dsB/d6E4XCEoii05pu0Wq0c0zqZTGi321iWxcLCAo7j0Jxv4ochw/EYNwjJMolPfvKTvPLKS0LeTEq/16ZcKqAqOq25Rd6+fI2f+6W/D7JCFEdkaUQWhXzv2WewR2NOnTrFhQsXcIIIRZP51Kc/TYbEpTfPo5Dw/DPfplUt0tleZzLui+y8GWZWU0kziRMnz3Dg8ElOn34ARdZ55aVX2VhdwzBUer1dvvPdb9MZDPmff/t3+MAHP8wnnnySD3/4g3zqU0+haFCsWiwutUiJvn/BTNddsfOK1MYEWRK7XRyHxEmCrqpkcfJXzq5xLMTnURyx2Z3w+c9/nl/5pV+gYBkoSoskEH3SQqGAPR6h6lqu+jp46BjDUZ9+v49hasSRS5KkRLFIfg8Cj+b8HLV6A8PQ6HeEA6Jeb5EmMrpWJApTdEPPd/lqqcjm5ibyUKVQKGGaNsXGElGYYpklgiDACwdIni2SiFIVOUtpNMu4rk0YxlTKZSRq9HoDhsGE/mDE0aOHAfLCLVdqOUkzDENUVaff72OaZn5EUlWJNBVW/UGviyRnGLpMHISkksK5c2/yEz/1s7z19jWe+OEFZBRUEpaaVbyxja4Ji/69D95PkMRYkoak6Zx54EE21t5GI6S/u4WZeUSBQ+i54pKtKMSRj1UsUK61WFzZz6U3L/PW5eukmUK3N2Aoxew/eZCjxw6j6yVqi4dozS0wDoS+utaok2Upjp3SKGlMRl1M/c7Fe1fsvBJiDKoowrM2m3nPLlpItyWSszGypmq89dZbLC8v50zdmag8DMN81KzrOnNzc1Oego09EY94z7OJQpc08fHcEf1eG12TIImJg5DOTpvt7TaFQgkJg1KxSRKpxJFCkmYEU8xoEAtpY6FQyM2Xm1dtCtIialJBl6oYssXazTWc4ZjEjYjdkM7uJpoqUSwYSJlMvdpiYX6ZxaU9LC8v0x90CEPBKK5Wq7n93TAMEcBtGCiazMQWInVVVXHdAMPQuO/MaV5+5UVefvlFzCkx03V9SuUqDz7wKCeOn+L//vMvEQbi8jrujxkN+9jjCb1eB6tY4Bvf/EucECRNRi9YXH77TW5du4ouZwz7u4SBx2g8YDgeoemm8BFqBqdOn6ZQqvKBD32M8cTBKpSRZRWjYLFnz5LQZZRLeF6AF4rLrqwqNBtiSDPu7SCnIVffuYA3+U/MpJAk6XOSJO1KkvTmu973fXlkkli/J0nSNUmS3pAk6YG/SfFmZKRSimXqQhU13XU1RQVZwTBLjCZ9XG9IoaSgGxkRLs+/+DxPffopwkzm4oU3cUYDatUKRcvAMG67hnu93pSbYKPrKb3+LpOJS6czwXES0kTDNCosLOxhz8oSqqpjGEWRfikneEGX7fY1NrdvEMUu2xs3GfV3cSYD2tvrjCdDgtAjSkJUXWHl4Bx6McGP+0iKj+NMaDWXSFKFIAkYuD0U2URTCwwHHsPRhMGoi6wkKDIEXogqFXJiebfTZzwc0e/2UGVFjGCDECVUMRWDLEnIpJjdQYfecMTC3Bw7q1ewJJck9emPely++g4PPnSWRtmkZil87AM/zDvXr+ElMlajwi/8yn/NwUNH2VzfZmd9GznNePnlV7n01jVeff0cv/rf/hMS2aDULPPWlcvYdkKcGMSpih+5zJUbNOpzzC3vZ35xhc21Td535giPnFnCDnpQLFKvVXHcLpghkSrT69g0SgZ7WkUMQJcklheXkPyIq+ffRPsB5fk3OTb8EfDPgX/zrvfNeGS/I0nS/zB9+79HwEeOTv88CvzL6X/vuGaXryAIkGVDhFJnGRGRYPImEbVGnbE9AcTk5/f/j39Oq1HnxrU1rr59mR/98BMUCwau6+E6PtVqnSSJRDutViPLMjq72zncYzAYYBoFCgWhp11Z2ceePXtIspggCHJBvBCX+/i+T6fTmY5alXw8PDc3x9GjR7FtMdm7PY7O2Lt3bw7qG41G+QVS13U6nTbDYZ9SqYSmKXieg++Lp0Waxui6SalcIMsyxuMx1lRhZts2cSw8b6qs0ZxrYFjikhrHMW+88QYlQ2IymdDpdFhaqqIjJKZ79u4nyVJK5QqqkeD4HhcuXOC+s/dz+PBh7n/gLI4taD+NRoNb12/wwKc/xYnjR0j8iHZ7G1OaIMvQHw+wbWeK2MqQNRnDKJKlKl/56pd4+hvf5DNPPsXlNwcUrRb22MEZ21gVg3a7jWZVaDVEB6VZMYnIMGQNw1DRU3ExnvWx32v9wJ03y7JngP842eK9eGSfAv5NJtaLQG0GIPmBK0nzbIUkikjj21TGKAgF2CNN8ENRWG+9dZksSXn1xe/x5Mc/SqlgTM2WCY25Vn5WtG2b7a0NdrY3UVVVjDxlocwyDAPXdacDgOSvuBlmR4/5+Xl2d3c5f/48vV6Pzc1N+n1xbm6321y8eJGvf/3rvPjii7RaLZrNJqqqUiqViKIIXddzCs/MdTGZTAgjnzgJMS0dw9QwLZ00i0nSSIBLhr1p16Aizu+2nWuICwUzxwFsb2/TbrcZ9LsMh0MajQZvvnmJ5twCml7AdV06vR77Dx5ip91BUjSCKEXTFaEiO3SIl186hyJLHDhwgKtXryJJEh964v00mjWKloGlw7C3RRK69HodgsBj4o4IIxfdkMULTopYmNvHscOneenFZ7n/4ZNoBZM0a5BEFeIYNtc3CP2A0I8oGEKbEYUhuqpPa03spuPRAEXKKBTN/7TifY/1XjyyPcD6uz5uY/q+v7bejXuK4tsBKu8Gj8gZKIqUAzlmUJA0hfFwxJFDh/n5z/40pjqzwAuXr2maBFP7eBiGQqY4BXa8mxkmQkWk6XnRJYoCtra2cjBIkiR0u12WlpZQVZVuV/jYZi+Kra0txuMxaZoyPz+fi2tM08ydFjNn8UxklCTJdEfJSJIYx7Gx7QlB4JOmCVmWkmUptj3JXcuWZVEoCvxUsVgUkzxF/JyapmFZFpVKhTROqJbK096wg+25BEFErzugUqsThjGZJKNrwn2SJAmNWoVyuczlK+/Q6/VIs4wTJ06QAUXLFPafBMbjDpalo8oK48mIMPRJ03BadBmyqlIs17n6zk2QYubmqxRLJTSjSancQtcMojAhDmIKhugB65pwW6ekqLIsRFNAu72dY7/+Nor3vdb3075/X7TNu3FPhq4gySBnsvgFpxJJnOWtriD0qFRKDAYDxqMJw8GY3/zN/4mnfuxHBZcMYc8WLASZM2fOCOZDLI4AruvS7/e4dWsVRRHFNRt0GIaBZQnL+3g8FnGigUe3u8toNECSRMrlgw+epVwuUqtViKKALEuYm2ty5sxpHnzwLEtLCwwGPTqddm5VX19fz6k6M7u9qqosLi7mVMs0jbHtMZ1Om83NdTY21nINQxzfFhcpioKqyXnErXiBColmlmVosoQ9Fv6x+88+iOMG3Li5hu+F1JstmnML7D98VIyVpzrZm6urxGTs3buH3d0d0SJzHMaOTX845t4z93Dh/Gu88fprPP/014k9m0GvI9qQxEhqBrLwqi3t3cORwyd59unv8cEPPIGiQrs3IoiLPPLIh3Acj0qxQrM5R5KkgIxtOzknTpKFPSqJPS69eR4/cEQE7h3W/9fifS8e2Qaw910ftwLcWRrEFD43pWAnYUQQCBF6kiTEUYSuawSeT71WI/ATWnOLPP3d50hguoOEOcZeURSCMEbTTVxf6AKSKMJ3vXwnrVarPPjAwxw5coQjR45MtQIZ4/EQWZbzHbPZbObesWazyfHjx/PRrOM4LC4usnfv3nyXndn0dV1YkiRJ4q233srxVOVyGRDcsrm5ufx8PFOLmaaZUyAty2I8HuM4NpZlIssySZIwHPVZW1tja2uLTqcjWoiy+B0KXvAuIPPwo+9nbn6F0WjM4UNHQVJpthZp922cICIBGtOfr16xOH36NI899hidfg9N02jWKqRxzOsvf49zr79MycwYdncZ9IaUSiVQEpBidEOlUmuwuO8wzbkWvmvjOh7N5hKXr63z27/723z04x9FklM2NrbY2WljGBZHDh+jvduhVK4SZ6Jv7wdw/eplAs8WR6Pkb6dV9l48si8BPzftOrwPGM2OF3f8JmQZXZ/xvVSkTHxbWQJRFEIaUy6VUGSdUrWBbpb4pb//q/z7r/4lXpSiqDJpGkOWUTB0Go0Gew8coGCV8jOnoSkoskYcpaiKkC+ura1x8eJFbty4wfb2dg4MCcMwp4/PzpqFQoGDBw9y7733IssyxWKR5eVlarVaToQcDockScL6+jrnzp2jXC5z6tQpQf+etrn27t07LdQCi4vLTCYOnids/UEQEUUJg8GIW7fW2NnZYX19faoVznKG2uycHMfx7fy6JMa1J1P2sMf8whJ79x/ADwPGjkuaSVy68g6FSolSUSMCWo1aDrMzDIN9+/bhuj7tdpswTmjWq/zID78fzx6hJD7OaAjJ7IINKBKGabKysp/3/dCHGTtDZDnk1LH7eeD+J7j/kcfRivC1b38B04LR0GZne5ckk6i3mowmDvsPrCBLGilw/tJFzr/6IpNhjyQN8OPgjnXzA7sNkiT9KfABoCVJ0gbwT4Df4fvzyL4CfAK4BrjAL/6gzw/TnLqpYVFVVaQMslRCIsvn9roixM1kCmGUouo6teY8tudjaRqqrJBlKaAgywr79u3j6uXLuJOesNRkCX5E7vSd2eyLxSKHjxzMyYvlaiUXwwdBkFMdL126RLPZzBVqjUYD13W5cuUKxWIxH4SYpkmlUqHb7dLpdHAcJ4eSzPQQYRgKIc2U4KPrOoPBAMdxKBaL+XkvjsXZvN/v52R2XRequnq9jqoUcsIOaXY7Jcj3MQtF9u0/TFlzUU2T4cTm8q3zrLWHLC4uc2DfHiRTEuy0KKFQMDH1Js1mk+dfeIHPfGYfmiKzf+8yXx31SMc9PMfFnrgM7T6KIQT9pVKJNIFe3+P5p7/NoYML7GwPWF1/mV/4r36dm1s3OHisxaU3SnTX++z2+pxQNJI0o1Aq4sbgOz0uvXGJB888wva1F5lkCWQxxg+4sP3A4s2y7O++x//6azyyTNxKfu0Hfc7v8/fyi1OcpKiqQhKFSMxEL+KSU4oiMgk6nR43b21z/4P38od/8Af8N//lz1NWFdJEhGCHYUi1XkMzDYrFMhO9TxIFpKnYQZeWlti/fz+DaXjJtevv0JmGomSII4IAjGT5AMQwDHZ2dgiCID82lEolyuUyu7u7xLHgno1GI0zTZN++fXS7XSqVCouLi3m/eTQa0e/3GQ0VTNOiVCozHo/Z3t5B03Qcx512QTysaQtsNnAR3QsB5guCgHKzSblcplar5vzfyWRCqVghCCJKZUHombgJmmrwd3/mU8QIosJ/+PK3eOLxR6iVCsiyko/nz549y/r6Oru7u+xfnuOP//iP0RWVUa9P4PsEvi+IRlKcg7/f//4fplCd49VXX+Z//6e/zc6OzzMvXEBRTb78tS+SuKscOLiXUdvFcd3cBxilCZcuXcI0Eh597Ie4/MY7tHe20KQMPwgYReM71s1dMWHLJIlEU4lliImAGMNU0E2VKImRVVWMZSUF3x5SbxR44fyz/Pk3v8z19iZb3T6DUGY3UegrEr6lYEgylZJJL3QZeT5ZquKHHmEcIKsKg9GQd65d5crVd9jabtPrjwnCFEhFKEmWkCQRQeBRLIpWmuva2PYYGYnQDwg8n8B3kSWB/FHkDNcZEvgOYeBSrRQZjwaQJRQsg5XlPezds0KjJujuom0nyI+lUinf1WVZplwuUy7X0DSdIAgxTYtKpQqZjKYaKLJGGIpCItOY2AFhlCDL4Hpj4jAhcBM0s8lgLLCpaZoQhhFZAqE9ol4u0+v0cROJUahiY2FU96CpMot1DS3zcEc90ihgEmh07FWUgoeglRoomopVKXDq3iforV5icX6e/Sffx81RSFyts97fpdVqUdHLtKwaflzA9yyGOyOkaEBZj7j25hUePnGGShYS9d6gqae4tkcqtdh/8CN3rJu7QtsAt/1qMyeu7/uoikzBskiShIWFeUxLx7IMrILB5uVV9p+8n4999MN87g//NQ/e/yipKhMYUFI0lKHN/r37uL61Sua4eH0xRlUUjbW1NRqNBktLS3laT71en4p4pLzLMaP4DAYDTNPMb/qTyQTLsqaxrP704qVOgXliHLu6upoz1b7znbcplUrMzc2xtLiHs2fPUq4UGQwG+UVvdhSYsXfjOCaOoxwwPWvJ1WvNXKhkGAblchnP8xiNRvi+nw9kZiPjoiGg3F4sC6G4rtDpDpibm0OTJZYX59nu2zz77As0m01OHj3C1s2L+I7NrbffpFYqTonvQ9JYESwGRabRaKIXVMqlFntXlvn9Vy+wb+8hrrx1hWe/8zSHjp1g/dZNfu5n/h7/17/+XfadeYCvf+s8WRayvXGTixdeJpGv8bM/88u8dfF1hp1NdtY2WN+6jl5popXhyZ/42B1r5q4o3tmETdM05DQRbSHZgCwlDiOK1TITd0K5ViNOQlRVZu/yEu3NDXQkDuxd5o0L59izf4XqyjwkAdtba5w6fZKD+/fidPpgWVSrdWq1GrIs0+v1BON2mhcxE73HScju9MZumibaVHAehiHOVBxdLpfFzX84RFZquXBoFr0aBB6tVivvVBw5cpher8/6+jrb29s57fLMmTP0+/38RTIajfL8tzAMqdWqKIrC3Ny8aI0FAbIiXtilUolSqSA8dUmCbY+nEQA2uq4iy+B5CnONFkUvwh+7Mxwhi606X1nfJJx6AEuWxk9+6mN02hOuX3mTW1ev8B/+4gus33gHU9HxJkP8YEySaiAlVGoGsqLQrK/wM5/5ZW6uvsP3Xn2Dj3/sRxl2+6SuzdaNq3z2Mz/F7/3eP+P6pVc5fmAPFVPFCyYETpfBznWsashLz3+D1PHRZEi8iPriHEMv4diRw6Tft/N6e90VxRvHIousVJgKvCUoWyYZqSCdZxkTZ8xoNKQ2v8Jw0EdVSqRJSGt+ETP0eefSVV57ZYsHi4/SKhQ5sHeR9dUb7F3ZQ3djG1czicnY2tqiWq2SRDEX3zg/pYTX2d7eFi0tQooF4drwXQc3dZAk0V+tlkUyUEaSj4cnk8nUji5E4oqisLa2hiwLArjgLiwjSRJLS4s5sT1NU9bX1/PddzAY5Dv+7Fzuus60febmQGpgyicjH1nPXCMzzoPv+zlBs9PTMK0SjHxSQEMAQ+r1JsOhLaaLcYyGwZ6FMmX9FF8pWdy4comypTPqtnEdmzQJIdNR1RiUhEKxgm7UWZg7wD/7vd+i2pzjMz/z07zy/PO8/6H7sF0HA58PvP9hqkZAwcg4cfoYr73yAoHrQBZx/NgKSTLhxLETbK9t4NoJfTdErzRp1g/gunfm894VxStJcn5cEF4zdfr2lBWQZZiW8FBJMjieTXF+jj1Wha3VdeqKxJnTp9gZdNjeWKd55DCapBAEHnJc5sjR42wp6/hxlCfvDEeChu66LpIE5XJJqNaCMbbt5qo2xxlTLlepVMq0WnM4joOmSnm+hKqq9HsisyL0A1GYUkTJKuWW+Js3rxOGIfPzi7RarSk0JGBnZyc/isx282q1im3bRFGE57jUKlVqFeFUJk0pF0voqkaWiknDDIIN5E+P2SU3iiIcN2B+aS+2l6AgLmtpLC5m5y5c5PHHf4iKVSSOxAXIm4y559gxNtbeRlcFul9TUzIiVNVCt1RUI+PM/Y/wUz/5i2SpynC0y4Gjhxg7Y4JgQr1mcuvm2/zFF/4E1ITz555HIWDYianMzRPGY8Iow/Zc7rvvLPuXD/H8cy9i+zFrN25y5pFFdFmlNJV6vte6Ky5ssiwKVwwUBBnc8zx810PObkP4gigkRehoUzK2Om1002CnJ9hYBw4cIAwD7MmI0WhAGonsiWqrxdETJ/M2lCRJOefW81x6PTEZc5wJSZLlFnZFlag3qui6imnqpGmEriskich8KBR0siyh2apSKpsgxWREuc9M1WQKRZN6o8rCwgJZlrC7u8NkIpIwNU3LUzezTDDYHMdhPB4LImStDFJKsVjM04FmwGrP8+j1O3S6bcLIJ4oDJDlD0xV0QyVJI4LQI8lgd3cXq1jg8jvX8IMUS4fJZESxXMDQIMxEvKymianfwuI88/MtPM9la2cL2xX5y+WqiqJJzM0f4KmnfppipcWzLzyDYWj8+I8/ia6LLDpVN4kyOHn6Hib2gIWFKrWawfzSIoZZYDyJaO+OsEcOYejzF1/6IlEUoKgq95xeRFPG+N4671x94c5185+jOP9GK83y3W62C78bIJ0mwu0QhiF+GLO2sYWu6xSrFayC4HEVi0VWVla4ePFC7paVNU04KEyLYqGApqpUymVIM0xdxzJMdE04lH3PI4lSkkictRVJplIqY+gqmiqTpTGylBHFIWmWYFqGcGTIoOsqqipPheEpcRzmITBA3oqaTCZMpuHcm5ubjMdjjh8/TrfbZWdnh4WFBY4ePcrVq1dRFCXHhqqqOm3TxbnAZ8anSJIkz7KYAf9mwSxhGCJPAwjb7TY3bl6j0x+SpOIcP/FiQd+Z/jPMLoiuZ9Mb9uh2d+kPB6iKTpwIpEC51KI5t4ckibl+44rIC7l4UdwVZI2J7bF3/yEefexxMmRhKCBB1aBUKiBJCpOhA6nE+totep1tVE3CshSMgkK1WWBz5yZf/dqX7lgyd8WxYWaSZJqxq0hCtBElcb6LzW7kkixQ93P1eexMwzQLlA8d4tJLr1HthVQqAAAgAElEQVSu1zhwzxHeCgJW126hWEVkRaNUqVDWizz4wMMMh8Np/q8ixpyAohpMJuK2LktC+5Ck4kVjGJpIvcxiMmYCogTL0kjTkE53h0ajgWVZxIlw+8apNg1gEenvjuPkQxDdUKek9QHNZhPHEaimcrnMwYMH8wtcrVbLmWSLi4sMB+P8iSAujw4p4ow8HA7zCyKQq/EMw2A0mRAlCSv79hKEHpubG6xtrCEpKsfvOU2YxBiG0Ja4YUqhXGJja5NUgp3dNnbg0Ww2CYKEWkXCsBQ++tEfh8wgSTxGkx0iP2J7dQe7Y3P08DHqjUUaUcgrr10hjlUMzUBCI5EjThw9xI1L75A6MYN2B5WMkgHuaJeyVaCxcpyhE/Dcc6+wsP/IHevmrineQqFAMjVEhmFIwdBvA6aRkCQFVRHhfkEYYRYKoBSIPI/Aczl9zxmu3rqG7/t85CMf4forF6haRcIkRkoCdEkkVZ46dWoqloHhcCp/jGI6nQ7VahVNE9lmSZxhmkJ1hpTh2BM0TZlygwN2tgQZsloukiURcaBgqOKCMcummNmWfN+lVKrkXYkg9Fle3sPW1haLi4uAsPskSZK37FzXxVA1yoUiw14fw7CwDINOu50HfevTC+GsjTeLyJrpYKMoojlfxCxYU21FkfWNLTRD563LVzl38U2OHj/GvuV5Th05iakb+G5EEEVcvnKFW+trtFotzGKB1I2RZJtGs8aDZx8j8BK+891vsbW1jqkb/Pzf+0UmE4fxyOPI8Xv5znef4dKVq1QrLfysR6+7wal7T3D88Am+8YWv0N3ZRYlSUl8Ejcd+yLi7Q2Ac4eqtbRrzxzh06M5ehrvi2KAoEpIaI6kJkiKCsMMkJiEjVTJSVcbKZAxJQdc0CrqBO7Cx+2NkSSNWNKSFIo39C2hBiL25S6e7K/QJmzvE4zEqMVapjGYUWdyzQrVeQ9U1CiWLKAqoVEo47piJM6I36OIFLsPxgP5whOcHJKlErz9hYgc4PviRTJzpDMYeEzdi5HgYxTKqWcA0SiiyQRRmpImMZYqk88CPCIMYQ7dwnTGLCy1uXH8Hxx5RLBg49ghFzsjSiFq1hOtHbLe7KJqJWSgymtj4YYRZKGIWiqRxhu8GuLaH5/hYRgFFUimYReaa81RKotVGnJD6IWoYUSEl6bfZX5XZV454+7m/4O2bb/LKa98hS4a8+uK3WFt9B1XVqRVqyEFMNByjlWJct8rS0lm2B6v82Zf+kOde/y611gH+0W/8Lzz9yvdYOnqcTSekuVThwMH93Hd0D6mzy2DQ49jZhzlz8mHSRKO+sECUBWxs3iSTfCICYi1Fb5Rw/AnL+5ZIVQjkO0NO74rifXdc6+zRN1NRzXaTNMtIyShXKkiqQrFSxrQsMgmKlTJSllEqlfKxbq/Xy02bswSeWX6vJEnUqg3q9Xr+KFZVlUq5ymQyEedTKZ1KKbt55m8cRwSBjyxLxHFElqU5xjQIfLrdDsPhgP6gSxQHZIgsNaQUx50QJ6GIAkiEDnY0GnH48OFcjTa7vM0ge0EQUCwW6XQ6XLhwIX86zHbmGep/5qieDTxmsbGTyYTQ9/O+cbFQJpVkdrsDXnjpNc5fvMSNjS2c9XXGa2v85Rf/nN3V67jDHp4/Ri/J6EWVgBDHC9i3bx9PPfljfPlLf8HqjeuMB2P+wT/6dexAolRs0OkO+cQnPsj1Wzt4/gh70mU46HBw5QCPPPgI4zAhVS0+9LEfI0VlMBZHtSxNCf0AiZS5bEKye4PlApjx/w+4DbPzmRtNBcjp7Wnb7DIiqQqpLOGHAUqpgOP7KIUakqogJwG2PaJZrcHYxt5to031ujMheDDN+QVxaZIQj2pFUUS0qKaJUXBJ5KTNQgzjOMuLQYjAxdHAsizxaJ8m+cwsOoIaefv7nk3LgDzXbfbiqlQqjEYj5ufnKRQK9Pv9XORj2zaWWWJ9fZ1yuZwLz2cJ8DMYySyfolQq5e8DciGQqsrEYYQrCQNnuVanOb/Eg9USqZRxX8Ggf/MadhJh1esMO9s4zoD+YBvN0HBDD12VaTQWWJhv4U3GpGHAaDjksff9EJNJyO/+r/+Cx973MGtrG6zs28M3vv5FirpEAZulhRYPnr2PUWfCja0xpmaycvAoYZbRbneIIsGWiyNx33F6G+hykUK1SWqP7lg3d8XOO+sqvDu9/d39SlVVBZlGkYlS8fZsh5FlmTCKMHUjz0nTdZ16vY6sCq+Zpqh5TsKMaC56qXH+9YV00pj605KpbkDOoSXiDKvmYvZ3p7LPvtfZaBduX5pm3+u7M+WyLEPXTAb9EfVaEwmF0XDCeGQzGTvomomhW4AwkM40wjPqz0wTkWVZ/jO9G8Qy6zgkSYKMmPiJXLiAJBauB0nRiOKE3U6fPSsreeay6ztTsX2W58MpsoYkq3mPvL29xcH9B/j4xz/O9154AVXRWVnew8ryEjKwvDRHFLuocsbxo0fQZI3V1XXcICZTxNfOkAjjiGQKfdA0jSiKiGPQdIMwjJG+v48hX3dH8UKeAjSzfkTRbSGyoijEaUoqCRC1omuoukYQR9i2ncsph/0+V69eZW5ujkqtSqvVysHPM36sVRDYKMuy8rxgCQVJUvD9EEWRpn1XSbg6MonJxGEycej3hwwGI1zXm07KRMB3qVSe0ngMZmaSmVFz1r6auR9m07A4jmk2m0wmE8bjcf6Pp+s6N2/ezAnx9brAMO3u7gqU1PTFN2NTzH4/URRx/fr13Cm9ubnJ9vY2a+u3poMYYSfSTIPW/AJppqCaRZb27sOWMpRKidhQmfgBSQaGYSFlCkW9SMWqsGfPHpb37OXpp59hdXWV+8+cJg4Cvv6lL/Cb//jXePyh+9ATn6KcoksBu5s3yTIfTZF4/fXzXH77Cq2FeZBlMkk8uXzfZzweU67UqTfmWFo5RGgt4EgVCo0VUtm6Y93cFccGdRoOGPoBoRcShSGlgimE6IjdR5t2H/LHjFUizkIKhokixSgkpEg89NBD7GxuUKpWaLZaDCcbU5uMkhN4ut0umjILtb6dMyyhkCRZvnOFgTA5xlE6/diEKEzw3Nu+tNkxYrZmkGnfEx65GfTPMAxkSRWdFKtEkgR5mHa9XidJEpaXl7l+/Tr1ep1Lly6xd+8+lpaWpjqHWs5xmGkjBoMB9Xo912bMTKdRFLG4uEixWGR+YYFSqYSiiK9tajpBEDA/12RrdwtNqSJXSkycMddXb9Ib9xmOx8gKWLqBqVgUrSLHjh1ne6fD2uYOpVqDf/UH/4KzZ8/SsGKcrcvMqcvE402++KdPIyspjz5whnpBZdDbZXVjnVRRaNaqNBsN3nj1dR568AGef+brrK6vU59fptpaxCxU0E8v4AYJrqSim8U71s1dsfOmaZYzCmagkVn+Wp4CL4nHZJokRH6A57hC95CkqJLMeDDkypUrpHEytYoLNdryyh7KhSJJFOePVfE1UyZjZ4qF4nbSeygKdDYUkWUVRdGJopQoSgnDBJCJ45Q0FXww3w/xvIDJxCEMY6IoxfNChsMJ47FDHGeEYYLj+IBCFKUYhslgMMSyCmQZmKbFzk6bU6fuwfcDCoUitm3nMbAzAvvs8qXrOktLSxiGkaNha7Ua+/bt48SJEzSbTarVKgCu6zIajUQPXZUwNIXQc9FVBXvUZ7C1wbizy7C9zaS/iyyFxIlHKsXIusLyyhLd3Q7Xb64jGyUee+JDhHHAt7/57/l/qXvTGMvS877vd/bl7mut3VW9zs7hcEgOhzMURYkSF8mmiAi2EcBJrA8OYNlwgHxJviUwDBiwk0CfjNjIYjuGZVmSLSlSlFAyxZ1DzT6craeXquqqW8u9dddzz37Omw/vPad7JLFJUXEwPkChu6pv3Vr6ue95nv/zX/puytnbX+N7f/jrDG+9jJV5XN/ZZr3R5Oa7N4mSjFxVqTbqLCYnqFnARrfJI9d2ESLj4PAE1XRJVYs7gyHzJGcaJrx54yZ/8NWvP7BuPhDF6/vy9CmKtYiytyyr7GsVRSGN7nn4KlD2nN58wWAw4OLFi2iaxnw+x7JtJvNZKRFfLpclqd0wjJJiqCr6KqgbKpUqqqqTJNmqCOWboUtPsmajS6PeQVUMshSSOCcKU7IUshQUdBR0ECppsnpuw8a2XFRFL0/ts9MRQRDQbrdlGs5ceiUUwYG9Xg/DMErNG8jbbLVaLb/v4+NjFosF0Sptczqdcn5+Xio6wtWAOp5OyHNWiMaCOIwQWUqeJTSqDhXXJB+OmO3fJhweE4xPsdQUt6Jj2Ao7D12k1qsxODzEDyKe+/RneOfmbQxD49qlCzz71HWa2oLMGyCCIZc2utx++21e/O4LTEYT5rMllutgug4iXnLrnTdYTM4kexCFMAz57h+/wswLqdQ7jEYDXn7lBba21/nUp3/igXXzgWgbkiRDwSLPVZIokMC/kDBXlEvb0ySHLAxRM4FruqSaSZKmqMmMrhGRVwVtM6Ruw8eefpiXXn2XvaMJP/eX/hPmS58oCcmSlCgI0VXpLK6pUvtWDGaj0zOECq5bKXvwPM/xA2+Viayutn06uqGj63LrV8BbRVtj2xKJEGRkuSAJI6Iounc3WGnfjo6Oyu2haVsomsrdo0PiOC5ziI+OjqhWq2xubpb8hkZD5hb7vrcyapFnUKPRkAbQK/mQaZpUbRsli1FMG8OuoZkVKo6LPRsRBVPC5YQ8nuBnS3xilpZOmkJFtalXW6y3+oRhyEmQ8rNf+Bz7+zdoODm6ahBlNkdhnTsTn63+Dqcnx9x69RUmsylRFPHkk09wfDBgu9nB0HX29vbI0xTTyMjTjM2dHW7uH5HlCcfHt9g72uenPvMLfGrrGqpVIY3/ggLMH2D39N8pinKkKMqrq7cv3vdv/+3K7uldRVE+9yNVr0J5uiqKUk7KxdAmPcoCmWG7mrKlCV+CaztEQcjFnV3yHO7sH/Diiy/z9jvvYts2Ozu75ck7m83KkxsovcwKlKNSqaxeTPeCUlRVLZ2+DcMo/yyEnYULZdHmFJ9fFPT9iImu6+UG7X7ubvFcBQZdqVSwLIsgCPA8iXUWGjbbljkVBcpQ+FHoul7+vgr3nPt5wuPxmDAMy6+lKfKFK53kJTvN9/1VwQjSNMOxXY5PT7l7dIRb63Br/5DHn3iS42O5XpdhKTqbG2u4rst8Pmc6lZams8WCweCE+dKj1Wrdw+C5B0W6ro2KwF94hGHIT//kZ1ZOR1n5Mz/o+nHtngD+JyHEP3pfDSrKo8BfAx4DNoE/UBTluhDigauSLJNatZpbYbYyyjMMiZFaFWm6lwURZrVO3XFoOTbDKEDEKZpqkicpt+6e8N67b3Nw8wb1aoPPf+HnaLS3JF6rqNir9kNqwaISZlIUUfaNuq6X+WdF61KE9RVFKDHfezL1IlSlKPTCCqp4jgIGtCyrlMOfnZ2V6T66rpep7q7rli7wrusyPD2jXq+vSOYSMTg5OUFRFKrVKsvlkkajUUJyRZsRhmE5Q/i+T7fXp9KQgtEg9FFFzng8gizk0u4O771ygu95xGmErevkCly+dJUr1x7lxZdeo9FoYbWvcDSZcv617xAHIVv9Dq5lUrd18jTi1skZJ6NzHn/ycW7dukkYxxwOBqx1utzZ30NkORc3N0nTkCiQhKH19U1eevVNsjRmPj5HyVNm0zlxplBvmJwMj/5ixSuE+LqiKLs/7HGr60vArwohIuCOoig3gY8D33nQJ9mWRaNRK19xBQivapAnKZptYmoCkefoqoKh5JzcvcXJeMKdPOHlF77JFIdHHrrOL/2tv8sL3/w2qCZRmuE4DsPDu4S+9O4tvMcURcEwzRWmG5coRhCH5SlWLBeKE7M47XRdK7kLxVBU4tFZxmw2Lx3ck0RKeXzfL1GNgpBTr9dLKE3e8tUyb2I8HtPpdFYB2Z3SpadWq72v8IvibrVapRwoiiJ6PZkhrCJYhgFC8zAth+V8Rn+ty6Xdi9x891VOj84ZTSQZSBMCshjVMACVW3fP+Oinv4BpORwtdXa2LjC48SokIZ/7/OcxdJ3ZbMIL33uRzuZV1i/s0mj3iG+8i6pLO60cQa3ZII0SFJGCyNB0hXa7Q6UKruOwmE6oVx3u3rnJ7pUuZi4w8oSm9Rc/eX/Q9bcVRfnPgBeB/1oIMUFaO333vsc80O4J+JsAtqUTBgGQkyURmmmgqWDZNrPZhIZaI0xCWpZOlMeMvXO+8pXfIVNNNMPkE5/+DFeeeh5TN/B8j/XtHZxagyBM5K05ywiWS0ajEb7v43nevSEwzUprUsuyyBDvs0ktNmKT83E5UCWJhMEKNx5DEmHxPckeMzQdTdWwTYs8zfDmcuuVRDG2ZRH6AY5lk6UZFcctYweiNGV7e5vT01MURaHblpDYW2+9VVo9FcVtmmaZCp/nOYvFolRDr63JwBeA5WJetjUyQTRjOhny5muvUKsovHe0L/tjXSNDRUdFNS36/XXc3g66U2c0mdIyKthJwscfvUr/mUeZjIYcHQ9YBDGPPPVR3rxxwCOPPMTx6SnXr19HkHPjnbfxfY/Lu5cYno1IgiVxHKEaOrZTpWqvkknPx7i2QcN9groBwWLGbOCjpv9hzKX/MfD3kPuFvwf8D8Av8ee0ewL+CUC9ZolCOWsYBlkqOavpwiuD9eqdJsskwU9jqrbJF37uZ8lVk+EsoNHbIkoEaRqhJjmtdo/ZxCtzhAtn9OIkLbZjYoVuFF5mqqA8CbNM9mbF51uWRRRFq8WDUq6aa7XaqqATmdGrqiSJNAMp7P6LFHfTNMtTtvgcoAwK7HQ6aJrG9mrjpes6h4eHpdYOKAc0IQStVovhUK5Ya7Ua3W6XdrtdvhhmsxmqWujrQtIsw3U0qk6Vr331Kzz3zIdIQ5/J5ByVlDgKqFYcOu0el3Z28XOLOI24sLlGLZjQrFrMxkPe2jtlcHpGpdnk5tEZAy8jzRT2jwZYlmA4mjKfjrENFUs38bw5igpRGDBfLKh3WiiGzWS2ZGvrAnt33mV6PqLuOnjDQ8aDIXa1RtN9sJLixypeIcRp8XdFUf4p8H+u3v2x7J4MQ2bd7t+UyTeLFWFDXaWf53lKvipoVJ0ceOP1V9m5+ijt/kXc/mWy0MMPlszPTrmwvlYa5UVhQuBH77PHL6b+k+NjsiwpjUIMVWM4Pi+JQgWmWmy/ipO3VquV6EMxWBV9ZyEkrVQqJQ+heJ44jnEcp7R0ur+vLpYNBaGo4FMUOjiQxd/pdErFc5qmtNvtkjNcWFXlec5gMEBRFNbW+mQiR8QxeR6SNRzIUz71/CeZDPeJ/QWqlpOEMQIVRbNodrvomoItInY21gjjhI6ucDoa8Na7Nzg7n9PobtC/eB2ru8vx8JwPPfE4r7/6Ipc668xHAyxTJYtDckUhCBd4C7+kvKa5wmwZkGHQ6W+wv3cT07S4c/MWH37kSSpVG7de4d/+xm88sG5+LJz3T9iWfhkokIjfBv6aoiiWoiiXkD693/uhTygEWZzcW0jct7Uqbnd5lKDkAt/ziIIQJRcspjMqbh3dcKlVHBzLZjobE62iropBLE1TslSUU3ghnnRdl0qlUq5yC1/e+79uscot3CeLHDh9pU5IkuR9jy1y0wo1Q8GLKJ0vVwLJAlq7fzFT9N6r3zFA6XtWq9XK30vBIrsf8ShIQIX38GQyKWMAipao+Pw8S7AtEyXP8JceSRIjVAXdMjEdh3qzQcW2MHUFU0kwcp+7h/sMRyP2BmfsPPQYwnI5Hk2Z+9LbOEpiTMdmuVwAOaqqoKgC29RJwghzNQ8IVXIagjAmR4pTC2Tk/Pwc261iWja/9399hfb65gPL5se1e/pJRVE+jGwJ9oD/UtageFNRlF8D3gJS4Jd/GNIAoKjSDFnVoF6vE0chiiKAnCxPMNExcwVTM7A0k5pdZXE+wzCbvPLymwTWiJY5lUEpikKepMy8c7a2LzE9n+F5HsulVy4G8vyee2SWJeVpWRRS4bcQhmFJOUySpGSNOY5d9pHp6o5QnHiymLWyXSiI9mXhrIquaAOKgMCiKIthrDi9C4jv6tWrNBoNxuMxURSVkN0777xDGIZsbGxINccKN97d3ZXLGG/BeDolTaDX69Fr1zg+nrKYTjg7Pcb3PQxDQdEsbNfFrTfYuXSFt97+Ptd2LvLWi18jCgIUp8PxaE598zKH84Sd3Yfk4RBHGDqcn59z9epVjvbewTJ1wiDEsUwuX95lcj5lOlugqCoVxyHTbVSnxmuv3OC5Zz6O41ZZLmXrNhiN+c3f+X2uPPah8iD5sYv3B9g9/S8PePzfB/7+D3ve+y/bqZBpBppj4c3nZLnA0OR/YBr55EbGwrWws4zjd9/izttvoVervHDjNovlARutXY6TAWtra1QbdV64O2B9c5unNEEWzshFvLIXVfE8T564VYcwcIjjwt8rIPR9VDLiSAoiW426NNqLY+pVt/TpnS+9kmyjxmrpwVutyoioxWKxMuCLSy+xPM9Li9ICQruXVqSiGwq6YZMkWknVRMlotmqEocFkMub8fISiKIQrlfJweEaaJjiOjWkaVCoyILs4qcMwxD9N6bbqKHlG1TZ47XvfYHOjw2J6jB9M0K2cIE3Y6PdQcoXtdpPBO29Sq9Y4W8QMsiaH45grLcHd4YRnPvsMqVojxiJFwXFd9vbe5vGLHQ5vvYsqYtIsx7Vc+r0Ovhfg+zIUMLMNDKdOsjTIAw0thl6zxvb6Gjdvzjg+mbM3Pmfj8kM4rV3QmsC/+oF184HYsPn+kp0LF7lz6y1pbRlIVheAqmugKiiKytnZGXduS23aJ37uy7x05wUMs8psPqfrgGFbDIdDmq1OKWjMcxnK580XJKl0xlksFqVJdJEwCaxwUrnZK07S4pacplIqJIRgPJuWywFNlXTHZrO58p2Vt2nP8zBNg7OzM1zXlYZ0QVr2uUCJGxdW/8VpX/S4xeBYr9cZn08xDIONjQ00TT5/GAb0+32yLGM8HpMkCbu7l0rtW4FlR2aEoSpkScTG5hpp4jOZTFZ9c0aawNKL6HS6dHvr+PM5hu4ymS3p97bZ2LzE8TuvcfHiRZIoIlI0dFPDsauoWUStUgWh0m63mU3PSJFYs6mpjEcjhFBQdQVbdbF0kxt391jMQx599BGGwzOe/MhTnJ6eMpt6KMmSxx+6wk/+7C/wv/2Lf/vAuvlAcBvSJOXme+9Sr7iIPCXPpelymidgaMR5xvFgyODomPXNLT73+S8ymy9JUoWj0zMOTg9ZhgHdbp/9g0Nmsxn1SpVGo1GKH7vdLs1m8315E/f3gUWBjkYjFotFiVDkeU6r1WJjY6N8v9gCFv1okTtcqBy63W45FBY9aYH3FiTxdrtdWvabpnTluX37NkmScHp6WqqMgyAoaZNFsS+Xy1JRbFnWyhQ7kuYtnsfBwUEpMN3or6EI0BRIkog0DjgfneEHHqqukeYZrVYHw3TQjQp/8IffJMciFzpbm5cwNYs4SNk/OKRer1N1HZq1CpamEPoLNDXHNLSVJZaLqRtUXZedi9skSURORppHnI1Omc2WvPjii/T6bf7GL/2nVOsag9N9Xvje93jsiScJI8H58T7vvf0yWuqxu9F6YN18IE5e0zTKSX0xnRDFCapuYagaimHihUuSZULDdXniiSfY3rnI6GDMs5/6DCkWeq7iJGMUQ+f6Iw+viCgWcRiVCwYpmQ+o1+slt9bQ9NWWTUFVFeIwpNFoUK1WV+rfiFqthu/7ZShhMVA1Gg0ajQYiV0ofhaJtmEwmUrVQq5ZkdM/zODo6Kou12ZS5apPJpBzyCtrj1tZW2VIUa+OC//rGG29QrdZK1ANgbW2tHEYV5V6iZ8Hz9QMPzdZxbGlzFcc+URQQJwlC0UizjLVuD103+egnnmXp+diu/FmHZxOCMJIMujhCpCEqBkqiYasqZ4NTOk0HTUnodruMTu9irBiA3mIBilR/m47J0ek5a711nn3maV743jfZP7jJ+vo6zz73PN4sAM1meHrEZ7/wi3jeGP0/Bg1bkkiJjbniDmiaVsrc8xW/1jRsNjc36a/Uto5bwTIdGdznWqxvbuGHERsbW2xvb2NZVqlgALm7LzDdwjAa7k31QLmqLdaq9+cVF0y08/PzEuoqsNQwDAE4PT0tbUyLoiw4DsVjOp1OafFf4LVipb9TVZVGo7HCZ9XSq6HgQRR+ZbVajVarRaUiTfBs26bX65Wu7UB5RykI60GwBHKZ5Swk5yEVeZk6pGigmwa5AkKVnIzFfEqWBNiWPOEHgwEqYOkqs/GQOAwYnZwQLH1OT09J05TDA2mGraGQrb6fDOnF+8wzz/DzP//z8gWU+Lx7420MU6Pf7+P5S+I0I0rk0Hp+fs72hftR1z99fSBO3jzPUVSdaAUVGZpOJlTJk01DFFVn6+IOF3b7jCcjdLdKs9lFNOp4QUzkz5hM56iagUAlSxNIMrQVVlsEodi2WTorhpFPrVIt/cYKamKtVin5CkVhe55X3pqLRKEidT5N8tLxJo4l9lq0D0Wx7e3t0e120Q21fGGMx+NVCpFgbW0NTdOo1+ulgqIo3Ol0Sq/Xo+LWypPf930uXLjA6emplKbbNp7nUa/XS7JO8XfTNDE0DVORHATfm8iizCRCoqgSj/7QziVqjTbDsxn7R7eIooCjsxMwbOZzj4999BOcT4ecn56AMub2e/s4TgWRZrz5xjFp4HHt6iWiKGKjv0Ec+ZIXjcB0HGaLJU8/vcv4/JyXXnqJw+MD/spf/asEQYBTq1NvtqjU6iyjGTfuHLCVVvn4c089sG4+EMVbazQYTaekQt7ukygiiRLQdBSka7h2xUQzpBoiyWK6vQ7H+x6KCo2KBVab8XhEGqeoAhr1ZgldmbZFz+6jIjg7O0MIsRIn6mWBpmlKs9kE8jLNcjgcloYe/X6f+XxOmqasNzfLYrdMp2wpNE1feT9I1hTbhvoAACAASURBVNnduwdMp9OyL213mpyenuL7Ptvb2yXOWzjbjEYjHMcpORJF0n2/3yeOJCRX4MoAzWazTCkqVNCF3q30LdMNsjyhUjM5HdzEX86IYh+UHN0wsNwKne4GDz/yFJmiI5QR771zkzQXfPSpj/C7/8/vc+nyZabzBXEcc3p0QKPR4erOBQ4ODmm1WnzjGy/w9FNP0mw2efyRRwn9gCSJabf7eMsl1Vab5z/9JP/u3/wWd/YPiGJ49IkPE/gp61s76LpJo9tm68IWp8cjzs7O+fgnt+n3uw+smw9E27BYeLz8+hs4bgXTtPE9H9eu4JoVvFlAp9njm9/+Fq+//iq2o9Nq1BmdjtBQsBWdeDkniCMU3UAoWuk2k2UZlmFimvJtuVyW2yi417cWw1yaygT3QitW8AQ8zyuHuF6vR61WK6mLRdJP0QoIITg+Pua9995jOp1imiabm5tsbGyUVMwi+LpQHMv+uMbW1ha1Wq2MaC1+juVyWZLM4Z7qoyj6wqSvYLPdnyDkOA7b29sYhkYaRyy9BYE/XzHrFAzT4uHrT5MkFlFo0uvtsnvpMbq9C7hOnYevP0IcxLz80qsMBgP6vQ7Rco6aRvz0p58nXHqogF1xVr15hdlsQRoLJqMZ7dYaWarxD//hr5DnMU889igVt0az0aPZ3uDuwTGm7XDx4kU+9omPES6WnJ+eYGkKp4P9B9bNB+LkRQHdkJTB2ekJruvS769xejZGFSrn5xNqtRrLwJNS8OYardYmp3cDbNdGFxaaU8dxHPZv3qZRb7G11i97VoA8T0tZuWEYjMdjXNsp16yFMtY0dZrNJpZloes6y+WS3d3dMlwwiiKSPCuLR9fMksMQx8kq0GRZBgmapnnPglS9x9UtUIM0TUvUoUgqKoSaRXEbhkGvt1aGthStzvr6GvP5/B4nZIVuFPzlAvEQQuDNFxwfHxGGPlEcIRS5em82m6z3d0BY1KpNkhSeeuoZzo/3ePWV72AYFo89ehmMfYQI+P5rr7O5vsU7b73FdDrlU89/koU34dq1a9Trdd7bv4OCtlr590HTee/Gu3z+8z/P5PQOaaby0EMPsfQitnfb5KvBdP/kAMPSsS2D8+GQLPa5e+fmA8vmA3HyKorCMknI1QqpsKk3uxyPTpkuJ4QixI8jAsVl//YxFXTmJ2d0Ow3a2z3625vkqaDVvYphdgiShIQA27XwFylxkBN4M5aLMZPpOfPFlCCUJ9nZ2SnT6YQsk4XY7XYxDYcwSDg5HjKZzEoTaU1TME0d09TLUBRd11E1qNUr0rXdlW+tVpNut1Pyc4ufsVZtoCBTiYr2YHNzc2W5n5cr0v39/ZLVVqlUaDQaDEenvP7Gq0xnYyxbJ8tlm1GoKxazGdmqqF23CorBwgtJF2NsEpaLMYapkZIiVAXDtanUK9iuTWraJCJnvjgnjOb4aYzT7a+WBV3uHA6oOylqnqCpDnGms3lxl/PRhH/1L/45m90ebR1uvPYihi5QTKh3mxg1m3fv3MKpV7m1v0dAHT+3wKgSZRlHR0ccHR0xnSxZ39hBURzCtIVqZLz+xv/N9Oz1B9bNB6N4VQVW3gkFg8vUVLI8hVzguBaGDmtrPVqtDo4rDfSCICCNYqbjCWQZIkvRFAiWHoamM52Oy1MxW6EGQJkCpCgKQRCUmcK6rsswQ03Bce3S7MO2bdI0L+OtgiAoN2eFsUlB7imKrcB0Cz8KIUTpDlkgDwUa0u/33yf9L9bNQNmK2LZNq9VifX2d2WxWYrwFvlsMc+fn5yUzLs9zNAUsUwYxLhaz1V2m4E3YuLa0TZXfIysYr43rVjEM+fuq1Rroloti2BiWi6Lp1OpN4iyn1eujrLgew+GwHBILJETOFYJut03FNUHk+P6CLInZ2bmAbVkIkeHYJrWqW5KgCkLTg64PRPFqqkoaBGRRiKYJBscHjCcjRBbT67akk6CRcPHCJrrrolouUQaXLu6QpxGTkwF5OKVqCBquxXq3Rxz60pJJEaBoxHFa/jKlFFwrqYhnZ2ccHh6yubmJZUlr/MVixngsFxbT6XQFfWVEkeQ5HB8fr+z/ua81yUu1RuFw3u12S++Fom8uOAsFojCfzxkMBtTrdYQQXL58uYwfKFhohblJHMclZTJOwhK2S/Mcc/V1R6MzFFIQCZPJMW++8SKT4TEiT3BsE01V0RULx67zE5/6LKPhlOFwxPh8ymg05vR0SJblPPmhp7h29RFq1RZRbtDsbGJVW7S66xiOy2NPfhjHrRLG6Wp4tUmSjCtXrnHnzj6np0POzk5QFPCWc5LUZ3Ory6OPXCNNAk4Ghzxy/Tqht5D/90LQbjVQVZ3bt/ak2uMB1weieE3D4MOPPYptmeRZgq5rGLqCoSkkgceHHn6IC2tNLm6vE+cCL0nxo5CDvTtcXOvhGhqXtzqYSoSlCDZ7ErRfeDNQcrliVtRySl8sFuUt+f4lwsnJCW7FpNms4zgWliUx4tFojKrKdiGJ5RaraB3G4zHT6bS0Ji1w5YLlVa1Wy5ag2WyWL6BiJdxqtUpoqxi+ikBDVVVLmG8wGDAYDEp0AUCkmUyc9H2azebq5NZwLJOzkyNODg9QlYgwnLJYnJOlYelHYRoOGxs7bG5eZWNriyTOEEIh9COWC488FfhhimlVqdbatFo9Gs02a2trciiMQ2oVi26nwXq/y3AoMz6effZZvvWt7+D7vvR9E4J6o8r29ga7ly+hGwatXp/dqw9hORXevXmTOEt57Y03ODg65PnnPsFy4TGfL/6UJ8afvD4QA5umqJBk7O/dljzY5Zw0i6laFeI4YTo+o9+tkOUJuYAsTRiPztByg3e//yqPPHydP/rK7/Lqq6/y8Y8/y3Lhs1j6pHnCZDYqxYUil6B5t9stJfFF8HThQjMcjahUKtTqFbyFT63mSCnR8FxSIV2diuGWLQTIBUjBBivUDAUrbDobEwQBlYr0YWg2m6XbeRzHJXTXbrfLQa0wPTEMyY1oNBolib1YmLiuS7j0S17GZDJh++JFCb35Hv12QzLWDIXQn5NmPkHoYVo2lVod262xtX2F0blPr7tG4CelN7JhGEzGMzrdFvVmh1anT+PIASVneH5GsPSYDUfEC5MsCqk2bIbTJT/x6U/xrW99i2azKWG7IOTpRx9i4klp/zIG16lza++QP37pdb70pb/Ewg9od/u0u5vs7e2zWa0h0ozIT8p54QddH4jiTdOUPMs4OTmi322SKUgDPRUQOdoqs2K2WGK6Mbpq0XRUrl6+yNe/+nW++41/z5Vr13jk4esymtVtIjSb/PgQ17ZKvZnI5WBWZAvHYVj2V41GA0UILl26JLdmQVwuETRNo9/vlqdpRrZCECTs1ut1V4sFFZn6AMvlsqRNrq2tldTJotgKwlC1Wi1ZafcPYEVs6+bmZhn9WkTFFls/Jc05Pj3BrdbJgeFwyIWtLcjq+PMpnXaTyfQQTVOJogDdMNB0E8N2WN/apb+xi1Adao0qm5vy+4mCkGjVA89NOX8ANGsNzscjlrMphi7Q8ghL1bl4ZYfQW/Dc8x/nxo13SsK9t1jiLwMmkxmvv/Umjz3xKJ3eBq1WF02p8u1vvrQydDFoNKUu7+7BHoE/xzJMvFmArv8HUFL8f33lacbgYECtVSFFSChqZR5XhGdrugtCR+QZShbjKBF/+Hu/ycuvvMlf/xu/RLNZ4/btfXYuPoZTaRIkQxrNJnkWs1wuUDGwbG01fEm3xnTlMu77PqPRCE1RMIwmpmGzmC/Z29ujXq/TbrdL4B+QdlGrE/t+AWahZ9N1mX1WfKzgGRQy+UIpXISnqKparoALnm8BmRUvIFVVGQ6HdDoyhy3LMuaLqVRaAJbjyNM48jkbHHBt9wI112F0tmQwGGDbLnNvimOZOG6V9e0LZJjEqUrkL6hUpE/xfBWc7fseo2FKb60v4bdMB82k0epBHrD0pmiGzunpKQ9du8p7N99hOByRZyq///u/T+CHtLs9rEqVZ575BP31NbLcxEglQb1ZqdCtVdiPFkTzc6y8StNRUKMU17ZJkpTxePrAuvlAFG+ywlm3t7eJUo/p7JwMgQFlfyhQQVXI0wyhp6h5ysHtm/T6bWr1OgcH+ySpNJBO0hxFM1Yu5IE8AbmnVihWvePRaJVh5hHHMb1Oh5OTs5XdaUKz2ZSIgKmRpglh5JeDXlG8BeegWIRId8WMPIcsS8vTOMtyyZRLY1zXLuMKCpvTYrou3CSLib2gSRaJQQV27bouS8NgOltQq0n23PaFC6hkqxengbdcMB5P0TSd2XyM7dhYjotpO1SrdZIsxVJUonCJbjmrF05eohupyEskQ3McNMvFcFwiP2J9YwtERjiboxkmh0cHMqjcMPnYxz5GGES8d+s2r776KvNgyfUo5NL2ZQwFNJHSqjgMDvYwRMZwcMDCMVGJSdNYtli+z3z2H4E/r1TG1giCkLPxEMt2iYIQFBllJXvAnChK8L05ipCn71NPPsLx+ZLT0ZBqvQ5KyMsvvcLO5cewbJMolfTCqlXFX0YYplKegIqisL6+jqIoHB0dcX5+zsnJyUpuI1lnW1s70qTZk6lB8nvVSkHkeDx+n5FzIRb1PI+NjY2SCyGEKEO17969i+u6eJ5Pt9vl7t27bG1tlcScZrPJYrGgXq+X0iTTNEvOQxiGdDqSr9zvdElX6UV5FHF0dESzXqHVaECecXT3AF038QIZK5CIiPX1dexKk063RyRU6fqeJfh5tiLUVzkfjTBMeVcq7gy5YuK4DTx/wTJOiZcz6lWXD33kI3z/1deo1RyEgMuXd1EVG2+xRNUsNi5ug6EwXyz47V//dRA6jz/2NJcvbPHeG69y6co6zab0wBhOZ2RpxuXLl/njl17i9PT0wXXz/0dx/rCr1mhwMBrx5LNPkeQBw+O7EEXkaYaSrvx4mZJkNSaJQS40hosln/z4J/mf/9df5YtfvIbX3KKnqLz0jW9QcVUsU8PSHfr9XZbTOYahsJh5NBo1bNPC8xbylq/kCNaxHRmRmsQCkUOUJkRpxnLpE4QxSSpNoudeQL1SJ0syHNPBX/olVyLPc1Sh4jgVQEXXzXJNLWVEcyoVB02T5iSLxYIrV66QpimTidwiDgYD2TN6XunLUMiDigGzyKCI84wkzwiiiEatCVnKcjzm2qU1FtMjKk7C3bMRpqNSaRg4TptKZZ1Wa4vZeYZdE8TJGMeqSnfOMEczHNAsPG8l6U8zDEtHxSeLTJrVDkoiaG1dpuYa7O+9i++fE2QTslzjzXdVnnv+Z9GrCbM3bhPsD7iw00dTBc99/rMoisZv/NpvYJFzdbPP+eESx9jh+GRIpmlkiU+31yDJfJZh8MC6+VE0bBeQbjnryGnknwghfkVRlDbwr4FdpI7trwghJopkuvwK8EXAB/4LIcTLD/4asLm5UcJDhu0QRxECheqKUL6Mc2wTTEVBEQlV20IzLTSnxu/+0bd58ic+w3w64+rlXZLAJ15GqHmCkgb4sxGqAkLRSsunYrLXdKVcOCiKgqqYKKooMdX7hZDFLbToQZfLZcnLNQxj5aYuDfUKw+cC+5WIhl9yi7vddZbLZRkC2Gg0Sl5DscgojKQtyyqTMgs+RbVaxXEsXG+JyBUURRDGIYaaU6k4jIcBWZrQbteZjD18P6TVXKdeb6EoOvOFR7XVw3YtvPkCyzRXRt0h1ZqLrq6SSb05UMWsGKiGir7id7hWznRyJvPkLJ35VMVfRvjBOf/y//jfCcOY45MzPvr0U3z+p3+Gf/Nrv4o3nxCngrWtbSqmg+XqbF+9xOnolHkY4scJpqrR6WyQpjr+8i/O502RpiKPAJ8Afnll6/TfAH8ohLgG/OHqfYAvIFXD15CmIv/4h32BLMt5/rlnqTq2dLFBwa7UMSo1gjTHcOsI3UHTbZr1OpaqEYdSCdC7eIVnf+YX+OrXvsHZ6BxVN+mtrUlKomWgkKGpAp2sLMY0TUvSDNwTQbbbbTY213AcpzSfhrx0symKM4oC0jRmfb2P73tomrISdQbYtlRXSNVFSp6njMdjzs7O0DSj5CYkSbIKMbwXnF3I8gvaZsGvSJKEo6OjMiq24C6Mx9MSLUnjCJHHfPjJx5jNz0kzuT5eeiOEyDANB1V3MUxXZqMJQRKlePOg3Bre7xBUbDqLtij0o1L4Wa263L59m9u3b6OvEInN9StUKy2UXPD4Yw9z/douVy9vo2Qxt995B0fVefV732Q0OOALX/gcRqXCYDTjeDplNJvhRQGaodPpX+CrX38B06kzmz345P2hxSuEOC5OTiHEAngb6YLzJeCfrR72z4BfWP39S8A/F/L6LtD8E1L5P3UFvs/J4BBvKWl3qAo3b9/h6PgEP844ny2wq00UTccyNExDIUsTbrz3HndPhqiVJj/3C7/Ih57+ODf37+KFCZrlEiQpQtGp1OoohsxtqNfrWJa1yhaLyuCSghw+m83KIENZjDaz2Yxms0616iJERrPZpNFolIVWnNzF6Q058/m0zBMuelZJ0cxk/5kk7OzslKfyZDIpieOnp6csl8uSDJRlGVtbW3Q6nVJLV2TGpWmKSk4S+7QaLqahEgU+cRyCClHsSWK+1aDZ3GCxiPDDBMOQEFgYxCVsV5ivFCLRgn5ZsO0C31+tgAXrW5tsbm8TpzK7bT6LCIOMTqfHbHLO7Zs3UERK3bV54VvfQkXh6naPtZbDYnzK5Z0tPvHM05iGSrddZ2tzjcCfYdfrXLi0y+7Va8Q/xDHnz7VhW3mWPQW8AKwJIY5XRX0M9FcP2wLu3vdpf6blk6Iof1NRlBcVRXnRW/rcuXOLmzdvlsB0cWsubtVBEJQMrWIaPjg4QAjB/t0DRtMZcy/gwsXLZCjESYqm26iGiWZZaCs70uI2XnASisn+fp2YvOWLUp7TaNTKFW3h1VA4kxduOIXpR8GnnU6nJfehUqnQ7/dLtKAYwopVcnHyz2az0pe3SMMsYDrHcSSvN47LwGwpPaqupPsaaRKRJyFplpSsMsuSCw3TcGg2uoRxThglZBQeGXopNi1+JwW/olC0lIWsyNU6qoqq6Kv/R5UsB5ErmKZNrVrHcSrlituwLUajEd1ulzwJ0Mk4Pz2m32mQxT5x6BGFSyajocSl5xMqtSobm2slBfQHXT/ywKYoShX4DeC/EkLM75fP/MmH/hkf+1Orkvvtni5f2hZf/vKX+dV//S949JFr3L51g3q9urJ+iqUiIfDR1fb7nM0Hx4c8/dRnWe818NOMPMtQdA2nWsFfTElR8OOEIEjI0owoXNJs1leElpDBYECWJ6v+V6obCgv9YkMmdWGivGW6rosq1BJJKIk/99mkTqdjer0OrlstfcQKdpqq6iwWS4Lgnh9acdJpmsbe3h61Wo3Nzc3SPLqAxqbTKa1WSyYFOQ5CUfCXIYYm0JWU3sY6x6cHeN6U9967wbXrV8jSmChK2NndwXF6ZPkJihAkSUQQLHHdOm7FZrnwyLKkNGkxNb3UxKkqKEIhjHxmU2maUnMNavUe3d6SOJqTpBG241BvdkknIy5ubJMmEaenp5g1F6ErxFHK4PAIjBoX1tZQQw81CVnOZ9Qbddxmk9bGLse2ya0b72FXXOAHY70/UvEqimKsCvdfCiF+c/XhU0VRNoQQx6u24Gz18T+35VOWZQwGh9QqVabTCRe2NzkdHK8KQhD7C+qbbRAZaCqW4zKbz8mSmNHxXa4/maEqAsM2qQiBPz0n8meoeYKugCJk/Crc8/qC1SLAMVcsrHupkUU/bNu2hJFW9kqaLluLmltlNp/IEyqTEqAszwgjyR4r2ojlUvrOjsfTVR8rFxrj8bjkCheEm4IoX8iPtFVOR6fTodWSKtoCcSgWI47j4DomZCkqPhtrHfbuDHn15T+m022VLwjbqnJyPKHeiNAMmziOCMMA142QurakJCkVd5iUuIQUkyRBqNKfTcFYcQ4cKq5N1uxz92ABaka93QLFYGPrMotlwHTuMQ9iGr0Oi8RnY/dR9g4OuHzpClkUouQRWRxhV6pgOCh2k4P9QxzbZjyek6U/8IAEfjS0QUGajLwthPgf7/un3wb+c+AfrP78rfs+/rcVRflV4BlgVrQXP+iKoohOp8P169eZz0dopJxrAkM3SKMU27EQaUS49KSAsdmmVqtj3LzLt7/2Fcx6h2uPPk6SCwQCsfQg9MjCObpIqNkmYZ4RoJXyHs+T9MQkjcpCWFtbI45j7t69u0oZEvcWEY5V4rUnJyclnCVWkU+FYFIIUfaIhQypWD/nOczn87LPjeO4tEIt7KSKoalwUi/k7KPRqFQeFxb/UZhgWQp5nrDe7zKdnTMdj8iFRAmCOGBjq06n0+PKlefIlAZCmzGZjUmyGMPQSJLofYT8wiBQyUUp53cchyiN8X352DCMmeQptapLs9mXBa+FBP6SSi1lcHDCnTt7fORjTxOLFMsWqI6BqjZx2ynN7jqD2+/hqBlJkmG222BVsFvrOFbM7dt3+NJf/kUOD4bAD/bo/VF63ueAvw781J9wQv8HwM8oivIe8DOr9wF+D7gN3AT+KfC3ftgXqFVs/OkZ3/jGN8iEShylGBropChGBhaMwhA9S9CmE+LhgCAKcC5e4u/89/+IS48+y7e/8wL//o++hrcMyTWbIFXINYcghShXiYWGZihMZhNUdSVwjAOm52PIclQh02+UVXxUu91msViW3reqotGoNxFC4NoOvrckiWI6rTYXty+wub6Boem0Gk0URWM8npaBK41GY2VSEpAkEevrfaq1BseDUwZHJ4RhSLtR58LGOuvdFp1GlX67wfbWRdrtNmkakuUhUewRhHNsW9pF6aqBrStk8Zzdi03C+ZjJcEHD7WLrNo6hYtiXqHevcjycACmGodGo1WXuRpaiGBoIlUzkpHl2T2yaxvjBkuHojJPT49XwJtD0DEFElEayJcsMNGeNwfGMm3unzJcRDz32GA8//hjHx0dkUYgIfOLpDMv3+Pj1S6SzM5oNG+GaLFVVimfzHCeN8eJTDgb7RCl87JnnH1g3P4rd0zf5s/tYgJ/+Mx4vgF/+Yc97/5WmGf/ut36HT3zq03jenDCJEUKS1DVFQ9VNdN1EKHJ54IchShCy9CL2Dw+5uPMUf/nLv8hyPqOiqIyODqVTTBSQpSlxsERkOaouN1hJFEv7UmSykLyFL94nyfF9H9u2yxaicMGRg6TMV+t2u2UmRRAEpRPkbL4oFb3F2tdxnPLEXSwWxJF05un2pPlIo1oBRUHXzfeZ9lm2fPGYlkxIUhWtJKsbii3jrOpVoiBkdD4EJNk8I5eZGabFbDqn4vSZzRaMJiPyPMO0rdUC5V4EQRrLQc/zPFQE+coT2LIsoiRZwXpmKQ4tSPWVSoWdnUtsXVAIU8G3vvUtNE3jkYevMTo9wNIzfH/Kdmed4WhEGPl4/gLdMqWMyZYq7oOjQ1587QWee/azvPPOO2xurj+wbj4QG7al71OpbCNyQFNXbKOVI2KSkqc5JmDoFlmWk6QpfhijKTp7dw7JRI9WWyOLIjLLkbS/SoVFMAOkAiIKQjxf6sGa9YYE/20TZZXDVuQUp6vFABSLCTnd1+v1MisjV9XSXbLoV4viBUoUo5jSXdctGWWFuPPk5JR+v0+z2cTUdMIwxrZZDUg6mnYvDVTTTFRFBWRPHMcRlqWgWyoi08rB0velKrhSdYiSHN3U6PfWWXqS0Zav+lkAITKyFQ4tkAEnxQApRF6iPgXao97nD1z8jAUiVK1WuXLtOi+9/DqPfehJur0+OYLDwSGPXL3ES9/7BrZVmBkKklRaHIRphmlZVOp1TNtisQxx7Arra5vUnB69Tu+BdfOBKN48z/mZz32RN27coN6okGYZqCtZegy5UGUMaZqgI6joHSpug81qi9rGE1Q7lzk8fgdb15j7IT3HYRRJfy5F5ATLJfPpGMWQ/rZ5nlOv15nOxiShNPUwTb0MOinWuQVQX+RP5HlOo1mnarmlf0LBlajVaiU2K1ZxsYUlVEFjLDZ7w+GQas2l2ZKi0cBbrhx69LJHdlcc426vzfn5kEpFYsEFTi2EwDZU2o02H3riYb7/yteJY584DjBtC6fiUm/V0VSbLF2iOMbqe17KlbhqEIcBru0gsrykbKqqirm6y0iplEBRROmeWcTXAvfFGMwwDY2NrYucnp1jWhauI3/X3/3ed2hUHa5d3mEZ+CyXC8IkIldUTMfFVVSqtRoZgqPjQ9bWNmm12rRbFl/76h89sG4+EMWrqhq6YXHhwg6z+TmapmMYsv/UYgWhGPgLj6TqIiybMEsQ0xlXn/ooRm+Xcayxsb1D3XWIxyOUNJJMsFlKlsg0zZqzSbbCec0VRhtGAXmSlkEo4/GYYGXtVAxZjivNo7vt9mohYZIlWUkKL4g4BVJgWRb6ymCvwGUrjlWalUjCt0zkrDougSfFoFLiHqwCFHWSNGXmLZl+f8ylSzsrroFUD7fbbWq1GrPpKU9/5Hlmk6H0WJtPWQYebaeHU+tQa/U5GpxRrbRwXXlrPjs/xXENGrUqcM/MD1aZz1laMuSyLGG58GThtmT2sW07JXwXx2m5HUzSFBQDlJT9w7u0GjUsU2F39yJqJvFyP1yWkbuWY3P38Ij+5hZCVUjShCRL2Vi/IKXziUeSpQ+smw9E8TqOy539A2ahz3e//XV2t3qQxORJjqZYTBc+a01pia826sRpREVVULKU9959myk1dDNDEbBVsTFUST6PIxmkp2sGtmky9WRuw3Q8kacXGaYmlQN3797l8PCwdHQsTpckSSQktbr1J0mCN/fKML9qtbry/M3Z29uT5G1VKeUyhQtkEARl3GqtVkNVdITIUFVwXZtidm42m/grc2w7TRBCIU1zlku5elYUeaKPRiOeuLzO+OyI8eiMNE5QVaSps26wuX2Fjc0LvPXuGc1mW56ojk2315brYtNEX9OmwwAAIABJREFU5DnT8ZQ4jggCn3QVng3SmK8QdRYrddkm3fONUFVRJhW1200q1TqZSBlPzkiSmMcffZjD/XdpuHWOjw7wPemnFqcJF3d32T86xnEqjM4nHA6OuHT5Cjkat27uU683ePTRRx9YNx+I4hVC0O+usW7rDA7vIJIleZyQJSl+EDCfBWw2+kSBj6qrVCoOddfC1lUeurbLOVUMy0SkCU7kk89GZIlkYFmOSxzGZIIyY2Ixk55jWZ6QJ+kqa0Kh3++XbULBBiv4uoWPWZompX5NCMFwOATkhq0wkTYs831+DQUpvfAX0zRN8goWC+LVi0MIhVwBMwqZzWZYpkN/rY/v+xwfHxPHMdOpIAx9qlUXx7XZ2X5CCkPDJfHqNKzWazSabeJU4+TMx7AsvMCn1uiS5/kqpFuj0+lgGjLvOQoClqvM4iiKyNOkdODJRUoUZ9hZbQWjBe/jHRf+EiIVCE1gWSaOZYGSMjjcgyxkcu6jibQ0LfR9n1dfeZ2Hrj2MapkcHBysBkiVweCEz3z6ZxmPp9QbD84e/kAUbxiGJEnEQ9evUKu6aAnMQo80z7EMkwtbbbJgjlupo6gCQ1fI0ojDOzcIxzG1K08QhSk6QkqHVh67qqqSCUgEWKqByKVMfHNzk7W1NWbzCYaqrXi40m83WrnQxCszDNu0SOME31uSxglxHFGr1Uvi+WKxKIu01+sRBAG1ak2a5SkKIhfMpzO5fBAQh1GpyNA0jYrrIoA4TliGAdOJbFnctSrz+ZRarUGn02M0HMvlipKjqjmOa3NycsBoeM7ZyRBvOSeMfLrVDrVmi/OxL+8WIibLFNI0Ln0iPG+OqZn4i4DIT6RYdbXN8zwpw7FtG91Q0e8TrhazwP0ZGUWy/WQyod/vcjwYyDW1iDENF1UzQU24fTSkXt+gXqny/de+zyef/wle+f7r6JbNk48/wf7hXRCCn/z0T3FycoJlVdi782DHnA+EelgIwde//nUm03PW1tYwLR1d1VAUQZ4l5Hm2mroV6fma54gsxfeXiDzl9GzwvgTHwiuhCAR0nSqmLeEv13VLA7xCAVFM1gWbrCDcFASd+xM5CxsokD1icdq6rlv2rkV7UVAii4y2wmRvNpuVTpWFUqRoMQrehqKppX5sPB6TZRm1Wo1+v0+9XufKlSuEoc98NiPNYiz9XjqnZdoEQUQQRGS5VHMUfmrF77uAx+I4fV9WR/GiL4bM+2kA9//8hSfFcrmUPGNNBjC6ruRqiDQt71Jh5NPrdnFdp/w59vb26HfkneW1114DII7j0ojk+Pi4zNT4QdcH4uQ1TJ2/83d/idncQ6SCmacwCgxEppGuFMMtxyALIjIvIjSXmHaCXjHoderklssUgzRPmGYJRgZeluN5S7Qsp1LRgRhV1Sgga9u2OR8PUXJBp9MiigJUDSo1Bz1Sy5VwlmcomkK1Xi05vWkSlYF/aZrS7/eYz+fEsSTsBIGEm5IsZb70qTSl7syPY0xDJ0fw/1L3ZjGXZtd53rP3N5/5nP/8U809d5PdalIiKUoiRUUSHDkKYsiOESsB4gBOkItcBPBNhivnwhcBHDgIkosgV8lVEgRB7MjOYIeSKEuUKJEtks0eq7rm+uf/jN887Fzsb+9ziiaLbVMSmgcoVNU/nHF9a6/1rne9b+AFXM4WnJ1fMt0/AKHr3W5vYxtbJg11uWR6uEuRCaaTXfI4wZM1N8c1R3GPOEkoqoxO18FRPfrDGyAmxNl94uKYfm9MGEJe5pyfa2X3KOyTpSVh6FCUS4RqqIrcygGAdorv9/vUjsB1XKhKqqZGUtNUJVWRI10XKSHPU55kObWrOJwOOKkrHCmYP3nMcOBTqYZVntLt7fP+3UfEREyGB8wX59x86SXm8zlpmjPs7tKJxty82WVn0mMxv3hm3HwiMi8o7t39iH6vQ7/fpVE1J8dPePjoPovFDFVpGaM0byWWxMbH9+zsFN9zGHQiBlGXThQiHSxn1yiwGAjMZLtOp0O/37eeaGaYYFbPjSS+WZ40AtJCCGtlFa9TS7gxrDfzGEbNJo51PZq0wnpCCKbTKTs7OxafNZoOWqHHt1xejRF3ydKiVdUpieM1N69f45133mG9XrXCf+Z+YP/wKhezBVEUcXBwxWqYNfWmjjXH/snJCWdnZ1xcXDyVmUHDlyb7GWzXnFJmJG4x4Bat0XYIOb1Bn+n+AY7nc7lcsVguUQLOzi956ZWX+dKXv8Kv//qva0y5rgg8j+l4wtXDfb791jfwnYYo9PjozvvPjJpPROYt8oLzk1OtWONLotBldzokT3LKvMClQSlBFEV2eOG5AVmW8Pb33qc/vsJZHtM0FZELQ1/QDSO7MmMCLy9Ly6s1yuOukBRFZuvXspVPMvBR6AeWhtnv9nClw8XFBShpcVH9IWKDXamGKOpY6uNsNmM47JOs1ggafN9jvdIY8Wg0oheF9sLxWoOV5XJOx9XKlrv9PY6ePKLKK67s7Vi04PL8hKJMmc3PCCKf0XiM73Vp6pxud0hdKXZ2djh68oTd3V2SJNEE/LYcMOT2PM+t/JVSisFgAGwMxPXgQgdsp9vBGG07nofTss+CIGA5XzC6to/nh6yShLSsaBQsljGf/9mfxo0OmM9WvPjCp7h7965+T/t9JILhcEQnjPibv/kbvP/he3z9976K5/352bf+md0cx2FnZ8zt99+jrnJkUzDud6k7IfF8RVkk9AcBXhDihx28dsNgMhrx4i0YdAMG0wOqqqApc5wqJRZoh3UpefDgQZu5tSevRAPsvu9T5YWtUU09Z4IbsOo4Zqy7XC6h/f08z+l0Onieb6drWrBvh/U6pqlpA1g/1tnsiOdv3aIT+hSVompq6kq1cJqgbJ+Hkex3EHT6HdIko8xzPMdlsThjbyxav4oFZV3gupJup8909wrvf3AXNxjhej5FVXJyfEwQBCRJoleHWuhuuVyyXC3IkpSqKu0kMIoia3WwvdFsCDtIgeNsPO5EK+EvhWA4HFDXiiDqE3QiZqsz4jRH+gG9wYTzZcpsvuR777zLerWiaRre+ua3+Hd+898iCCI+eP8OF6cPWS6XPLp/m5dfe/WZcfOJKBv02olHUWY0VQai0n+aAkcqet0OfhBRVg394RDHCzT/oa544dYN3v32t0jnZzTpmkgoZN0QtOVC3Mr5D4dDu9JiSortoDVbD4aMDljpfjMtMztlhhCu4SLNItMlg8J19bG/itdWLKTf77NeztkZj3GEIs8SjZuGmlZo3OA9qfkMRis4KzOKomI1X1FkGUIVvPbqc+RFgkBR1zmuq2vv/miH/cMbFKVgNl9zeTmjqeqW0qk3gLM40Rd4U7FaLKmK0hLsTXMI2Ka21+s9JYkFBhkqLepgdvWUqlkul8wWK6Tv47ghQTDA87tMd/YpS0UQRFy5dp0kyRiNRvzyL/8S63jJvXv3+IM/+GdUZcbs8pTl6pL9/X12Jj8B42EzscnzHC8MgAZqPXd3XEFTFQRBXwtuKIHv6fFroyrSxZL55TmXpydIBw7391sCdmp1GoyQh0ESJLp+kw4IX9E0GoBHNIh2UgbYDGywWqMvZtABKR1LzDGBrrkOiaVIOltTIs/zkAhqpfAD3y5tVnVhs2PTVPYC8TynrbGHFMWS+eISSYWq86e4tgCD/ogsL4m6A7xKUja1RTmqqmK9mNMNI7vQGWxlYCmxXAxDuDEXnkFPTKAWVUlV6R5BFQW9Xt8iDkbRpyxdPM/B83yisMve3pQ0TYmLmrxVIppMJqg64caNG5ycHCGEg+t7rOMKzw+hrKl+XBPBv4ibdByOTk4RbTAoGpzAoaz0h1RkBeeXc9IiZ76OefPNN/XGwuUM3D5X93a4vqfpilW6Jk0SPMclCrXfQm8woKpK8pavoFq/3+VqTlNqjLKua9Is1m9IWxMGnkft6elcVVX6yI0iZouFRS50SdFtHeJ15nTai0tKqWmWCKIgpN+NyPJEM7ZEYeE6pRQKaVV0sizTwacElxdzppMJoqm5cfUQKQvOL06IVwnJesVgMiHq9Ig6Q2bzmKgzIE4LRpGP4yqS1Zq6UYwnE5bLOXvT/RavzlrNYZ8kWev1HnQiMebb5oI0gRuGIUpo64X1eo3jeYCGCw3HodvtkiYl62aNwKOpBKqAdbwio0NRVPzCz/8cH93+gPfe/y67+1NUXbG/f0BRlHRHO+R5ReMq7j44fnbc/HkH5se7CbwgJE4TirKkQdtb6QmPZjSZrVnHcUiytNW69XGl4PVPv0q6mDEIfao8I11v9tEMnyDLMuv0A7okMF7Cplve1HHCYrQGezUq40mS4LqeNdE2ptgGKzWK5yYLO45DnKzodHSzGfoevaiDRJEksZXmT9rlU1PGDIdDGrRm7uxCcy4mOwMeP3lAmq5atUkPVUMU9ZlM90mTGoF+r+pG60QYXbbFYqH5BS13w2wLV0VmTwBTEhgivcGgNd9C2oA23hxml08pxWym1TJPzk5Zrtc0tSTwIsqkoExyvFqynC0ZdPt89OFtHj16xOPHjzk/P2e+XHDn7m2SLObOg2PO5jH9yR4/84UvPzNqPhGZt2ka7t57wPUbByilj8EkS5FCH7210kHkuF6rtpiwf9jh8vKSR4/P6XYGOLJPlibEywXz+SUKTVs0ckp5vnGE9FolmnW8pCkrG6gmsM0HYgLcwEMbA2xs3dfp9Dg/P7fDBf0YOlut12trRuh5HlVR0utGnJ+eUImAXrdL3TaG2qGosF7Fvu9Tu4o6k4Sux5WDfVyhLamWixnD4Q7UMcPhmO5gCEjCTpc41tJQQQhlVSCkstTNstTrUFWlS6AoCMg8j6KFD81r73a7rFYrq9Jj+oSNNoVvS4nlcmlNEdfrmDjViEbgebiBy0svvMQglISBy/h6D9XAep3Z96ShpK5Lgk7Eh3fu8NwrP8Nrr32KxTzmvdsfPTNuPhHBq1TNC9euML9Y0+lFeAS4SuJKh3VVELgRXqQbHz+MtAhzVTMZjeh1d2jqGkLJPFmTVgXzhdaF1e4+4Hmal3B+NqPbi2gaXccO+iNUXbJaSSCmKBRBt0sax5p95kjKXL/RDYK6asiNsEhZUquGvCjwgwDhasZaVtVI5RIGPdJkiWzAqcF3FHXdsFinZK20f5Jr/7m6gXWct/JOJY7jsVotiIZ94vSEqOfzxkuvcfTkIbJ0EASacB4EyKhHZ3jIn3zzfYbjPTwfel2BKisip4tyWxXOqsIPPNtoLmaXLb+2RtUlVZHZ9X1Vl0galvNLqxjfCFitMsY7E7rdLkmSUVRVO/0E6YS4bsmw7+JLSNZzdsa7XFyccjZLefHF57h35yO+/vU/5IUXnsfvSKTUum2uP8J1fT7/hU9xPK/4xje/i5CObRJ/2O0TEbxVqfenxjsTdnd3uH37bd3AlRrAF65oM0duV8erquK977zNG5/5Iqv1mlF3VxtFtxsQhoRjIJ7ZbEZV6SYmXq0ZDgdtFpV2otU0DYot8L2Va3JdlzQvEEKvqhsCC8KhEjoDCSkRwqGqchwpqOtWLC8KOdjb1a4/nkeD4uT8jLJMtZdEK8AshGM1y4xWw+X5BZHrsF4tOTp+RJwsWiaXw3I94+D6dSbTPeZzXQYEUUajFFVd4bvanDsv9Pa1HrcLa/htyhM9kOGp8sqoU27Dh8LVv395eUkcx3hegGzFSVzXJUvjFpWpCIIOv/d7f8ju7q8gpeTg8DrvvfcBu1df4Vf+8r/OennJxeUxq3jNcNxnd28P3+8ghcunnj9kMBhoHFk+u6r9kTWvEOK6EOK3hRDvCiG+J4T4j9uv/x0hxOPv22szv/OfCSFuCyHeF0L8qz86fLUz5WAwsBMhd+uNMUeXObabpiHJMw4PD3nvvffwfc1VSJLEWj4ZjNI4X5ru2dRzps4F7LFqSgNTQpipkbkItmthczN17zY2rEnapfWc2K6djWK4uT8zLJFS2kmWWSfK0hgh4IUXnqMsNSxWVDlVrcuLMOgwGk0Q0qXfHz7FpzDKkmaC9/2aFdsCgIZXYSBBU8Ob52OQE4NtG4+37f7A8CoMojOdTpFSMp3ucXp6ysHhFYpSUVYNRdUwm83o9XpPeXEMh2PqIuej2+9x+923uf3e954ZNR8n8xq5p28JIfrAN4UQ/6T93t9XSv297R9upaD+BvBp4ArwT4UQLyulfqiCRNMojo+Pee75l3VWqHU2K5W+8pMqoxtGrbmJphuGQYfOYIoMMmQrQqzFNzxUU9mGykA9WZaxXqWkWcxoMLS2T5qLkFqvM8XGz2ybVCOlQ14UqMas6XiUbQmRZQWyUfZDNvXgeDwm9D0btHqKF3P92k2ko4VOjPZYFOkJ18nJSRsoPlcme6wX53z+Z97k29/6fcoiZbGcEccrDq9eoVYucVyymMfUhHS6Paq6JQfVNWVZ4PqeRQvqZjO2NmhIp9Mhr7TFquu6CGCxXjFobbeW8dqqV67XCUmatBKwOVXT2D293d0D5vM5StVcXOS8+uqnuH/vIdKpODjcJc1rZC+iKjJmyxVRT4/yO52I3d19FsuUd955j+moz6DX5/orB3zzG3/84wVvu7ZulHFWQggj9/TDbn8F+J+VUjlwVwhxG/gC8PUf9gtCCo5O9CToycmFhsFmZ7qJCBzqEmsltc1yOj465aVXfgrhdnlyfG5ZZQKBIyWr5RIahSsdoiBkuYhtA2UyD7DhpCpFVRfk7Q6b27Kg0jSlahSdqNsqzWh8taprpNzcD4AjPYQoqVt93qZxSJLYDj60/H9AXlQ2eAALm7mu29bqEilq9vcmPHxwl+XiUu+d1SVVrShrRRSN+e7bHzAcTMkTjZMXZapH1mVNt9MnL3XZkMaaoWVQAgdl1+jLOLHZVjPDOiwWCwBrIl4ULRadpRsuSLuXJ4SwCpjGNy5JEnb397l69YDZ/JS3vvUnOGNtHdtxG64c9PG9iqgTcDlfUBUOntdlts5IipplktMZ/Bm6vn+f3NMvoPUZ/l3gT9DZeYYO7D/c+rUfKPf01JNoV3A+un+Pa9eu2OOnyguaRht9OK7Aa48ux3FoEIx3pswWMbi1hdL0BxBa/QRzzJk3XEhl1cY9z2O9XFvySZIkVkEnCIKn6JJeWzNKIVivkxaPlvaoBqgrRbye47swGPZaymFhSxilBE3dLmxmBb7vWyNs0GWMmbbleYEi48s/9/M8uvu2noypCpAMxhOkE3H9xos8eHBJkpXUSg9LhBSIdjReFzWDwUBLUSHsBnSSJDgom5HNBM0MY7Issz2DKXl836cs9AVotiq6LWHfbBMbZR8hoNvvM50MyPMSzwt5+ZVPkbkhxaSPKmOK7JKqyNg/eBHkkMFgjzv3Tvjw7nvaWcj17ADmh90+Ns77/XJPaPXHF4DPoDPzf2V+9Af8+j8n97StVdYonYGOj495+eWXWRv7VNG6sreK6EWRWYEMIwvqhyFNzVP2pgaXNKIfpkY1fg/maD89PSVNU7sZbMT2jFqNEFolxiw9Gkl+KSUCZ6tGhjzTu1l5nuO4wtazVVXY+7QXXqPXxQ2UZ2pQcwKYaaNqCqJA85oFUBUli/kKlMf1Gy/z1rff4fxywaPHJ7iOB9IlN9sQ7br9dr1tMGnDCDMNWZbnJGlKmmWUVUWjFAiB47pIxwEh7J5ev9/H8zy7MW2C2fQSuvGrtCxtluP6AVlRId0QVyqi0MV3GxxKOqHuDY5Pzvg//s/f4ut/9C0+/8Wf49/4jb/KF770Jb7yK7/6zJj8l5Z7UkqdbH3/fwB+q/3vx5J72tYqu3VzX2VFwaNHj/jqV7+qLevXc9y2yfC8sD2CdRAqqRlmrheQZ4o0L2yHrrcadCNmPM6KIrcNUaN049fpdPT9NZX1iNDYb27Za2WuVcizLCMrShypt4SrEpq6sAR1pSoa+D57LB2odVPZrAaSJNVQlXQ2UJCWU9VBG8cxZVkyHA559YVdnjx6yGq50Co2QjGZTAm7Ywb9HR4fP2Fv9wpxmrFYxwyHPX2BCOxjmgs4yzL8wLXB7LaJwfd93BaFMaeM8YAzmblpGlCKulKEnWhDZt9q2JbzGb3B0G4Um8Y7z0vyXFM5iyYjCgPixYy9aY+j4wecXMxJ8g63nnuJqon48IN3CCN9Ud/9cTcpfpjck3hatvQ3gLfbf/9D4G8IIQIhxHNond5vPOsx0iR5SrnRvAH6GQq7bpKmqZ0MmVrL8HOroqAuS8osR1U1nnQIPZ9e1CHwPJz2aDdEFONsaQLI7JwZ7oIRmTNySyZrmaxtspfZSDB8B5NhgyBASPUUCrGNZpimMMsy+9p9X7PTzLG8P93dZGGlWCxWuK5Pp6OJ8WmaIV2HTtS1EKIpAcy00GRcYxFgnrt5PoB9ztvrPrAZWBgbg+3a3gT3NtpSl4UtlUyZBvrYjZOsJUYV9rkul0suzy/47Gd/htdee40XXniJF198keFAD362UZ0fdPs4mdfIPX1XCPGn7df+c+A3hRCfaZ/bPeA/bN+Q7wkh/lfgHTRS8R89C2kAEH5I/+oeKo1pkpKeM6Lfi6jqjH63Tzz3GI0Fg7BP1+3SdzqUSYnqNSgJhSrIc21CXVYZii6L9YKszMjKglI1ICBsWWUaMtKO6LkDju8QL1OiXpcqLzTv1/eRgTYX7A367TFbU+YZYeQTJw1KCnAdagFVVVpxPr2NIEA5dKI+QrqkcUZRV+A5qKpC4lPmFVFHK2EqIcmzlDhLGQx6LJMzHO8Ki9mMMlNkcUI3cqnqFchD3nv3Ma4T6r24PCFdzRB1Th779Ho9El9n+CLRnN26LROyrE0UXmvYQgONgys9XKkhtvUyptMNicIQlKJp8WDP8xA0dKIuqs3crtTK9jUN63RNA+2E0NPwnucShjAYuHQGE6RTs1wf8ejiFLe/y+tv/BR+f5cMQTgMqOtd+sNDnr8VEK/W/D3+63/54H2G3NM/fsbv/F3g7/6o+zY3IRz6wynL1Zq3336bvX1t8hdGIbJ2aVRB0SiyqqZSTevFoAcJSZyyWmX2CE/TFNddMW2zYlmWtubLqsIy/+u6spkTJJPJhLOzM529vMhmTt/38R0XpQRK6behaI9Z01AYOqFQm3/7vo+qG0RLmTT1c2+gt3B9JyDPE5TS2GnQuv8gSlxXcPXKPqdPHlIWKXGyQEmNMEjp8sqnP80ffP19+j2tBOl5G8X1IAisdrDneXR6WtBv3pYj5pQw5UTUCVnHMWW72mTwXOOaNBgMbFY2qEMU6lUhQ6WUUpJXpR2JSylby4QBnh9uOL9UZMmKuioIgohudwfPD5nNVyjHpWqW0NSouqEXdXDFT4ADpjaLC8jLgkwlFsrStVYFssGJOmSqIi1TFqsFo+mhLjMcB4mwDds2QmBqOj/Us3in61jKYRRpt5ter8fl5aU9xjdEFAfQRPEsTijLGtFWWVVVbhYm2Ry7aUuFNPBXURTIVufA+EmkaapXwFcx0JDmmjAURD5RGOB5fRQFn/7USzx574+pyjWIEj/0CDsRB1dfIMsUD4+OuXHV35j8Tcb2Mc19ep7Hcrm0jZsefmRtTa8vJi3bCp4XWjRCSEXkR5arsZ0AjLxV2MKIRl3SiFV3O32UgigKcRwPpXQZsVrF2qosvWyfm8vewRWE30M5A2ohGHV6CBo86aDKijL/CRAdEdJlNNlnfvQBeZZrMWPhEMcrHKnAcTibX+I0cHBwQKUapCtQaKGRum5snayzbW3FQtI0Zd0C7XWpqxe9RRzZSZZuOlw0XOUhcNo6syHLEzpBByEK8myjRZakuqOnrVvjOKZpCS/r9dqaczdlZQcUJksul0ua0mEyHdKoyoqeTCc7OLJgf29MupyxmJ1x9OQuXhDQ7Y1phMQLh3z7e+/SKI0AmJ00TYxZWyMW01QZdMaMtJXSGTf0fLsnZy78MNT7f6bG9jwH48u27U5k2GWAnYA6nkRKz9phmc3twA9RqkRKh6bKOD89wfMhCEJq5bI/PSSvXYJOl7yoiDyJUJJw6NONOs+Mm09E8GZZjut38UMPT+js5roerqOdEF0/AsehzjP7gcXxisNrIfXlQlteWWhKqySa5UszlNBZoiRNE7uXZUebQYf5QnuqdYJQC00rYbOzMcw2918jbJOl70t/+F5L2On3Ohb/bNrToCxLut0u+XwGQBj6FpILw5BdtFypAD73mTd4+PBDfUT3OggHbaoYdrly9RYf3PvTlszd2CAyMJtZRzduR64rbUDrC1XZ5q1pGhpVU5YVjapYLFO7HqVUbQNW++Rpvm9ZlkShwN9CM7Iso4gLer0BrqO923zfp9frtrTJAClc3n3nbRQVjXCRTkCWVsxnS4TbJcsKGgGl1O9BON6lLn8CMq/vt94RjoPjaF6BwEE4LrJRNEVDVqRI1VBVBUoF1GVFFIQ0zYyqamwHq5XPtdKNEdUzgwraevf7uQpCCBzpEfgOqtbBsE2T3Ky6tJuzLS67fTMlhwlwbXQi7Idv2FzmsZXQ5Ybjyjaj+aiyYHc6Yjzq8dHtmLpuhatdiesHeG7IxWyFFA6uK1mnK8auY6dajtJNpJKCsqn13y1XwfzteXqY0Q21cLWi0cJ7jbKlk3mt29wFoyivlCLwc3yBfW2u6+JUzlN8CfMe1LVCyM37jnTw3IBub8Dtu0fMVgolA5QAKUE1Db7jchYeocqfAONsIR2GownZwQ6Lh4/bmtPHkT61qEiynCgMCDxJ1w/p+j6eo/emLJ/hKRhKZ41JK47n5ZocM19sHNmNnKdxnhyPxzx+/LhdOQpwHV22SClax3gtFN00DY7v0VT6frIt8oqzNXEKw/CpzGEe08BgdaWQDpZc70qPJkv42c99ifPTI+YXZ1zO1/SGXfImZ+/gGoPBHrc/eownA5wQ8lbkz5DCzeswFlndbpd42e7ctZZZeZ7ackorw2s9NNMfaMnSjm266rpCqQYbUX8XAAAgAElEQVTX9S1JHniqLJFS4rYY8jYMaE64wbDLzs4u75zcZjAeooRksUr4zJufQ7h9sKetbtBEo3CFxP9JMM4uioLDa1epFmNOb9/Bc1wc6QGSptZ1qKv0GvpkPLZLjUZmqcgrS7LRnbJe7ZlOp7bxK4oC6YAf+KyWsd2jMhnBlBEabw2pKx1o/X4PobQrZprqWrCoK5pG/XMflAncOI4BcKWD034gSqnWUEW2e2ma+qgcwXK5Igo63DiYcnpyxPnJA5qqwA86OK5g0Omwf3BInjs8uH9EFA3xA8e6C2nvjNr6Whi7ANNszedz6lZAz/Mc+7yzLKNRNUWlbLPb7UbM5/N2kuZiqCSm6UvTlF7Xs6/DZGdN9vFwHWVLGa1HnBGEuhQpqoayqhn4ERrC1dK1nquFWKRscITL6dkx3bDHPHu26MgnInhd32G+zHn4UUmdOpTpHFdVBHhkTUVVxijHxXUClNDc0rDTo2qUtltVgkZIcNw2ENc0lSJZr9g7ONBLl66D47Q16dBhurfL2cmp7qipWcdLHKHAlTSNHpF2+z2UglpBo2rqRkNtpQLRTv9U0+BKh3pLPdGV2ppgMB21Hb2gllCrBseRRFGI67sUBaxnCYNuj1Gvw69+5ef402/+M+bzU8qmJuwo4jzhcPdFFnPB7TuPmE5vMJud4YeSshStmnuPosgoskSPl8sCjx6ryznz1Zq8qvH9AFWV+IFuVGfrpdWk0HZYEiFUu/OnCT0GeWmaRpPjowiQlFVOtS5bxp6WfvIdH0860FR69N3vA9AJe0ipGXPXp4csVnMWFzMOb77An37nO9SVh+v4KGqWywXv3nmfz372s62M6/DZcfMXEJs/+iYE/eEOQRghHYemzQpxqhsNx3OecqusqooHD+/xyqcPiJOVZXnBZiyqVKM1cg8O9JHesr9MmRHHMcPhUNMQT4/06ktZoVqbqrquaVSlhwBt02bkPxslrLaBxktri4PmeY7EodfraQSi0X7EpqzRvsS0zZTOdOPBENXkLJdz0izecGaBXm/AzmTK7Tt3qSrHUhnjOMbxhKUnpmnCZDQiyzK63a4tUQyUZZpLgzkHQYAU0m4/GwlXw9k1zZ+p7R2hy4hOp8d6vW4tCFwGA72m3+v1LMaslLKQYFmW+K3uxZWr11l8sMZxPBzpcXFxyWRyheneLtPphF63w5e/8iVAT++eXfF+QoK3LCuOzy8pGoHXiUirAulJnNrBc6EGyxCL45iyqugPp3aztaryp5oqkOR5slEqb8e8pjb0fT0SNkwywzKr69p6nJnvhYFnPxTa55Es9QVT2aPYs8e1lJJSNhsFHM+jKpst8Q4Xx3EJohBV1URhSJqt+cVf+AInx08oy5yzsxMcV7G7d4VSNYzGu6T5Cd3OgPU6YzgaIGRIkmnRuigKaBpdKrmuS1ODIzbkcXPhSaVLG8dxCISHamEwM3o3yEfWckHMeFwpZRGW9XrdGo4LW44NBgPrHKSbXAfREv2NF0eWZVyczxDSB+GxWqd88Ys/jx8OqcqGqBvhBi77012KVmbAeF78sNsnZHsYyqKmQSBdnyRNwZE4jjawcxzxVD1pasqqKjS/td2yMH9vWzLVdWPZZGa9ZXuKZLposzlQs9FtEEKAcJ4S1TNTKpuR2scyXTno08FkZv08K0uIMZl/g2YohGoIPJcg0IrqVaODMi1yut0BWVbqOrxpqFvNMdf1LARnsqnhE5hBgiGdm6xralQzWTSsMsPJMNoUZsiyMRGUFkc3NE7z/hj+hCEUGajNPA/DV+l2u1y7/hyHV2+g0OXGap0QrzVXJc1zLW3QGi4CuM5PQMPm+QHXbr7A6uHbSHdNGV/g+h4Uet+qqSvqUuH7WhpfAavViludDlV1rqX3s6A9Kjcr291u31IiDVfWdV0uL3UjcHamVROTdG11xqJOl0pp5po59qqmfgr8N0FnmsR4vVk10uVDw3y+5MqVA5uBy7Lk4HDPZjglvLajduiPeiTxjMdP7tGoktFogOO6KMfn1nOv8q233sPxB6xWK7ygy3y5pBttTg/fd20GlVIrXPa7A2sHawLLDwNbw+qTwLWnla5n9f15gW8JPmaQE3haCUgIx5YNWmdiTJ7nDIdD5vM53W6X8XiHaouwYy6UMAiIOkOSJ0cEXUjWKYtFjpKCvMxoVAmlFuqu65rJdPrMuPlEBK9SUJUNk+kuWX3O6fFdev0OldKjwlI2uC0U5Lra0GM0GnJycgQonjx5RJ5nFsvUBGv9oS0WC3b3DlBIkraMMBoKh4eHejlzfvEUs0sbCDoUtW7IirxBSJeiSlF102ZOZ9OgtVlpewUI0Dzflq4ZBAEoac1Z8mzNdGfM2elDvvDTr3P0+B5NpaeL0pNkVcb+7nWCsM9kesjp6ZKo198sRhY1QeBajjE09Ltda2xtRK+LenNSNI3eHQMYjvoW3tvepzOjbCGELacMg0xn0o2iZpqmGOemi4sLRm3NrTXN9HvgOA6iPR0gIgr7TCa73LrxHNKJKJWDciDqBdR1Tl3pTWXXlbYs+2G3T0TZ0NQNl5dz6rpmuV5TVQ1R2CX0QmgEqmgs7GPwzPV6zXg85sHDe4Rh8NSRjRL2mF8u1/Y4dV1t/Gxm9UZEz3zvpZdeQkhpj78kScir0ma0ptbN2oZUvnH48X3fZmbzfdNESulaOdMw6JAmOb3egLOzM6LAZ7ozxHFq6ianM9BS+p1uF88Peft7HxCGI6Jun26nT1lquC6IOu3p0rUcBEPp3H5u2/9WSlkSvLngzNFu6sttso0pC/SOnc7MpvwxPwNYuVizgWIez3h7GHMaXcIN6EYd7t+5x8P7DxCqQdUF88sL1vFSTwBRxMsVYRj8gGjZ3D4RmddxHfb29ijFKed3FZ4X4Lsupe/jOz6NrGmqppUtdaxnb5rpvw2bDMB1PNIibT+UjeZY2B6ZutbNSdOU8XhMkiRIRzdd6/Wa1SombLnCBpHYFtozGaoocquQsz2alu0KvGynW+YDNh20EEJLOqWaGzzd7ZPGS9I0pipTlssFo8kY6blcuXaV9QfnFJVisrNH3RRUKodG4HkBVZ3b12RwZH1xpgjVZlDftc/bPD9zsUqE5TeYi78oCpQwGG3SrihtduwMxzZoTcR9P7SnoQnoulbUzcYey20FRnqdPuvlil6nT5kX1FXBR7c/4HRxxsMn9wk7Poej63iBlnadTEbPjJtPRObN05QkXuFKrV0wGAxwpIfn+DjCRShptyRM47ZYLLTWw3hM3WjegFJwfn5u192n0+lTwbXdPZtmxJBQzBaDlmtKuZzNrPkJzoYov+0PvA0lme95W+WNaY6MTq/ZYr64uODs7IzF5YzDw0OePHmClDCbXVrdhigK2d8/xHV8PC+wj+G6OtvG6/QpeQCz3mQ0eI0rpzk1DGZrWGgmK5vX7Lqu9ZozI3WzHmVeg6ndzftpGjf93itLdjfSWeZ9NhJSUkoGvR6qLknjmNsffMhkNOKj2x8SuB4/9ebr+MLBrcEHPvP6G8+Mm09E8Cpqvvvdtzg5nfHay68jyxRPpQROzbooUOEQJ/BoBJbNpQXzwEHg4LBaaUaVcCV+x8Pv+qRFQl2XFHluqYnz+cweZVmW4XrSWlUFQYBQelfMkbLNuq0NQDfE8R2KukDKjW6t67ra5MRBm734Dl4QUjYlipK60XoLSZJR1oJK1GTNmrq4wHNzjh/fJk1nrFe6Fi3SDFUrfCfg6Kxgkay5d/9djo/vIagospr1KqHXD3BwOD85xXd8Il8rCTW1Lp2SLNX7Y2lhS5m61jBiGIZI4doV/iwrWqxaM+t8NyBPC5J1iqpB1dAoB9eLENJFOh6u6xOGHfs+mu0JE/hGM2IbxcjzlNliyWC8S5wXLNaX3Ln9PQ53dgjxeOt3v8Vb33ubWoJw4Pd/96vPjJtPRPCGYcQrr75OklX80Te+xeVijXQdq1GW5Xpt2wDvpqZatQLFcRzbWs3Uf9twmVGD0UwnvedlMoipV+u6thoKYeRbfNJo+ppMZWAlY7pioC+T4cyCZVPrbWId5AGe65OmKZfnFwgh2BmP+KUvf4mjR49xpWPNWJpG4To6MO7evU8cx9Zh/ujoyD5Xs4dnBi0GEjN1qsmw27CdydLbf7YhtO3Vne2g8zxPK7j3elbnwhjCpGnK+fm5pVyaxs7ItAqxMZ1xHAfH0xk+6nTY3d3lxq1b9AZ9/vKv/2v8xl/7q/zSv/IVDg/36bRyrM+6fSJqXqUE0o0YTw+5/+Q9krxCOa4WwvAAKrQVk4Oipijqp4QzPM9DCWkbOoR+41WbCcytqirCMMJxdJAZ56BG6WNxNddKip2Otmc1QnlpK2Wkaz4oy/opZprdt0Nn97qF6xxXUNclWVHRVdogPOoERIFHD8X87JgiTciSEKkkvh8ReD5+p4uSWpRkPNIGhVevXrVHeKM0TrteLgkCr11vL20Dt1wubeYrqgrPbzHhTB/rvu9aSM00tibojNyT4eluyPnSHv/mfTdTNMN70Cebtt8ypYSxU9jZ2UE4EkcIAi9gur/H7sEu6yTm1q3nee/DDzg6OmI0nHBRZCxml9y8eu2ZcfNxFjBDIcQ3hBDfFlru6b9ov/6cEOKPhBAfCiH+FyGE3349aP9/u/3+rR/1GHme8+57t/nwzgMq4RF0hqRZTllXZFlC6G+WGqMootMNGU+GlOVmRGtwSdO8mHXsuq55+PAhR0dHtjYErYtglL8FG/KMH2xq1aIoOD09ZbWKieNUNyK1slnMfKA2q7TZTzWCquUYOzKgLCvKQmf2frerZUVVzYOPbhMvLjk9PtHk+6SgO9ghDEeUjUeeFS0PuN+y0Xp2Bd/3fUajEUVRcH5+TrfbtVskZrHTKDtuL2VusOjavk+w2QYxn8c24caQ3A1n2Jxu5v4Myd7oNpjTykzemlZZJ040Vk47CJGux8HVK0x2p9y8eZMXX3qJosygUfzs5z9PvF79eMEL5MAvK6XeRGs0/JoQ4ovAf4mWe3oJmAF/q/35vwXMlFIvAn+//bln3lTTMO4P8FyXPKvpDAbUCMqi0gYkng4UE1iAJXdraGZjgGKgIYNP5nnOo0ePODo6stnBTOiaRuOhx8fHbQPkW9TBrBJtQ0LmIjEbv1VV2VLDdOt1rVUXPc8jLyqUoNXnKulFHaqiwJeSl1++hWxKaGqOHp9ydHTCoD9GyAgv6rNY5gyHYxaLlQ1go7EbRVGrv7u2AWo4vUaX2JQ5G4SloNvtWuz2KS5zi6aY0sEgC0Zq1bxGA8V1u92WzKPpomZSaU4r2DRxpt42F0ac5ZbXqwcYrTSXUlpvLcv51Ksv0++EHD9+9My4+TgLmApYt//12j8K+GXg326//j8CfwctRPJX2n8D/G/AfyuEEGp7b/r7boHr0JUlo90pDxf3WF2mfPvOh7z84i0A1os5UrrUtUvT1FYntigKewTm5QYGMh+KzhY5g8GoPc4VdVXa4D45ObE1mRGOdnyH3d3dTTmgJK7TYqe1HjwYgT5DRQRslhJCIBsHzwkpa904xWlCvyxJ0piD3T5f/OJniMolX/u9E5SstZh22KHbHzMY7nG5XJMXkjBw6PeH3Lv3AM/zLGylp4SXiEZYxMB1tTum2cnTJ4FnyS3motYr8TpQHSntKHcb9zV0SlNO5HmOkLqO1lrH2jfD2NyaksP0CEmSINv7k1JuGHhKoeVaJHVZaZHqy0seH51weXnJ888/z2J2ybvvvsPuqE/k/xm4AQmt4vFN4EXgvwPuAHOllBmBbEs6XQUeAiilKiHEAtgBzn/Y/Z+eHPE//ff/DXWZcDjx+cLPPE++6lNXFaJROFLiuBvWlglMswGsg7atY6vNFoSU0nJeLy8vrZhH0+jdtKDd46prTce7fv06Z5dndnDR7w3J3KyFgPS6DGBV0s10zXTbpjb0HZ9KVRSV3uwQrtZZ290dI0XKfHbGRXzK9HAXVZUkeUF/OGC6e8AyVxwe3OTkfIHvBvqjlpJHjx4hWwRktVrpwUEYkaba81hKKEpNBDIDB6W0xOm4O7RNri6lSnuRm2Db9towXASzKlTXNbKVo5pMJriua4UJjdG4dkXy2j7AIYiizUZJC+lpHrD+zKfjSaseX3Pr1vOsVisuLi740pe+RJEkJKsLsnT9w0IG+Jhog1KqVkp9Bq1+8wXgtR/0Y+3f/+JyT1XJcn5Gma6pygKhtLHJarXCdz1UsyF7m+xaFJmtyyxkBU8JbRi803S9pj7VO2m1ndrp+3ValUNlGyPHcdoJVmR/z5QO5oO26y1slhGVqtvjciOjqoRGRTrdkKLIaai5ev0Kg/GIyWSCH4T0hiPiOKWoakCrjvu+T7fTZzQa2bramAGa7GjeG2Ontd1AbmfGbejKNLdmkGKOfaOqabKoue/tC9aUAobbsb2MaRCM7aVTS9Rpd+7qqqKqGoa9Pu+//yFKKfb29jQu73kEoUeeZqRp/My4/BdCG5RScyHE7wBfBEZCCLfNvtuSTkbu6ZHQK7lD4PIH3JeVe3JcR/mqxAWyZInvety6dQtXCh7du08YBNTUwKZ+Mh1xvx/heXOEI6xurBnV5tnmTTVqO1JKq3zj+671Vzs/P6cTBjRlY4cJaZK3ASHsMWw+XNhMoQzyYQcW0iFezqhVQyNoxecE63jJm2/+LB99+DaerDg6eULg+XR6HVy3w/vvfcj5vGSxzjk4vIHXkuuHw6HmRGQxp6enHB4e6mCrSptppdSNFsrU9BLHgcFoZBEX4+mxPZHb2dmxJ8g2smBINmb4IoSwG8pKKTv6Bez9LxYLaxm2alltOzs7ts5OkoRGVYzGA2YXl/zRH/8hL730Eh+89z6O43Dl8Br37t7l4uwJDnqR9Vm3j4M27AohRu2/I+BXgXeB3wb+zfbH/ibwD9p//8P2/7Tf/+qz6l2AXr9DdyzoDRyeu3Wdhw+e0OuOEcLD8RWNWOM5vuY5lKBKSeB0EI0+GqW36Z7Nld40DVVdtGssDa4rmc0W1LWW+59MJvT6Q5CCTq/LjVs3GUwmdDsDDvavMh5NuXbtOq7r2eNPCO0q3+30tUFgVrJcrPG9EM8NSJMc1/HJswZHBqimwhXQCUKqrOH5qzcokyVNccF6niNqB6UaClWyd/06adEw2TtACIfLywuaRttimRWlXnfA7u6uJY9LD/qjfjs8aVDCY51mFBU4rXJ5kq5tA7eZKGojwG6nT5YWFj83CIUpzdbrtaWSGvrlNm96vV6zXC5ZrVY265+envL48WNLxK+qyv7bDV06/R55UeAFPj/9uS9QqJrR7pidw10yldH1XcajkCyfM95/tg/bxykbDoHfFkJ8B/hj4J8opX4L+E+Avy20/u4OWs+M9u+d9ut/G/hPf9QDlFXOS69d4eU3brDILnjzc28ifY9CQSk8KulbC1UhRCtlmtpj3OCVplEzR6n5vwHQj46OSNOUyWRiqXpxHHNxccH5+bkllfd6PSvxube3x8HBATs7O/ZINGozWp3HtaSVwWBgM7EuPVpxakdy7coeV67uE7euPxcXF9rxsq5xZEBTS9Jclyuj0ajVIkvtgMJMsHZ2dmxmNFsQUdS14oAGEjQlgillTACbi9zAasvl0gajWZc3zZfrulpjotGOnKb8MNYGZmQ+mUzsMMj3fYbDIaPRyIrtmVJHVhWiLJFKUaY5sq7wlCK+vCQE9gYD9vemHD85oSxrPvXa68+Mm4+DNnwHrcn7/V//CF3/fv/XM+Cv/6j73b4JoSjKNbs7N1BlySuvv8ri4pTl20uiTo/5LCdwHFvnmszqOA55ltI0FSjHBuTTBJrC1n2aZbak3+9uto7RkyjtMrQmCAKOjo5slprNZk99qFrMznkKHzX8CFMrK6EHIhqpcHCEJAwle7tjTh4f4TgeURQyX8Z0hiOmB1d58PiE3ekhjdTIBkLgCF1TdjodK5/qtzRIE0wnJye2PlVKkcSZnRxuk22iKKJIMxu0q9WKoNUrS9PUTsEMXlyWJePxGN/XwxIjJWBOIYP3mvdgOp1auG77YjH1bp7nBELiey6e41DmKd9759vcvHmDw50pPor15QVPHtzHk4LlMuWtt777zLj5RIyHPcfnpeuv8vu/83Uuz+b81j/4R3ztt78GjaJIYlRZgGiIosB2q9sBbAgp24Gt1338pwjVAHfu3LGjWAO+LxZaQvT4+FjribVqN2VZMplMAGwWNyPh7UGFEWw2apJIqJoaz/GJgg6hL+n4kiydIaiZXVyyipfkZUFeNIzH+yRpTVVrn4nFfMVo2LdkHpP5bt68aRulNE2ZteQhE8hmK0E3rEoPR9pmykzGDPb6lLab4+M6PmmS40iPs9MLpHDx3IDLizlFXllVR+P7URSFJUeZ8bup7Q0Dz6BDoPFyLwpRjiSvC8J+xC985RcpqXj/9rt88OF7jMY9Hj+4T1PXHO4dcnjwY07Y/iJuSsFHHz7EFQGHe1cRCkLPJ12tKeKUsHUWN0chYBcyt488A6EZTNMc7/ox9Kjy4cOHFt4y407Xdbm4uLB8AMPIMuD7di2tG6Qc39eTM8dx8TwfKR2Uaon1VdE2kwN8PyAKQ/xAEq8XlgdguBQHB1dYxxn9wYRVnNr1nePW8NogKYZfYS4eM9kyZZTr+FvE+M3Gx0YBR9nTwbQgZufOlBagXYGMJrHBc82S5+XlpWWHpWlqoTcjA2uGP2bMbC4QcyL6nRDpu9QSHjx5zNvvv8Pu3h5XrlzhxVde5P333+fk9BhXOgS+z2T4E0CJFApeeeU1wlA3B8/deA6poNfpMohCnGbTcECrrELN+cWZPdaOj4+fqt/MdMjASttBb3iv3W6Xq1evMplMLFTj+77FhCeTiXVCN6QeEzzmKDYfssFE9dZv3V4sOapSDLodPv/ZN6jLGEFFlVfUqqLT75FVDY+enFM1Hp7boSy1KMm9j25z//59q7k7HA7ta9mgJT6qEQS+JgnpDQhpl0XNxWwwaIPfGiplp9Ox92Vupo42J5PZAtneqDCnmGnGDCGnrmvm87l1AN3e11NKUTUKXJdSgd+NGO5MyKuSm8/d4sMPP0RKyc994fMsV5dMJ+MfiTZ8Iog5ji956c3rDA7+Et/50++ScUhaFYS1T9gZEK8SmiYHPBs8SZIQ9XYpipzVeslwOG0zbq3li9rMYxoU2KzNn56esr+/b0fFZiZvCNo3btywEp8GMzVZzRzhZtZvHsfSNJXSjViu8EVII2pUU7NaXSBFxXq91JxdKrzAZ3/vkIdHMX7QQQYdPK/Bc2D/C5/n3v3Ttpk8YzAYEEURB4d7dhVotYxtgMxmMy4uLiz2bDJ1pxvY17AtoB1FEXVZWTspkxiMR4bJ0np65+L5G6kqE/DmYjABulmX93FaLkWWZVoosNulLmocx6MuKn7va79PFDj85l//a3z9d79GUxXk8xVFOiddr+iELt/+5jefGTefiOD1I4f7l2/z/MuvMdz/WX7n//ld3nj+VbJVTFYoOv0pTXquJzvJGtXAdP8aSil2d3e1DD1+S5RR1A02G+hRsv7wzGDh9PT0KTTCZJKTkxPqcsOTEEJwcXFhZ/tmkdEc+WZR0DRv1u+hyVHKQwptSfXSC89zcvSQ4+N7hL7LcpFAAL3ugHWSUtaK8+Nzqhx2p0MEBft7Q1588cUWfRjaLH96emq3mff3922We/fdd5GypVRahEFZ7oO5iA2e6zgOgbfZzjXIjRBiSzHHs+/bNuXTQGnbxHMb5O16v2hHz+PxGNH+zqi/Q13VXByfcmVnn89/7jN89f/+p9x7/13KLOe569eYXhkwGg/5v/7xP+L6teefGTefiOBtqpr90ZTHD++SZyVXb+6wTM7od3usL2s9dXMkRa4XIk1XH8fa3GTU26EojFumlviv8pbmV9eaXNvUOKqhrGtml5dkaUoUhixXpV0s9DyJRJNczs7OCMNQb3W0gwlDi1zHukGRjkecZFT1RvqprmuccITnVSTpMdPRLjeu9Hjy6ATR+KzyGNEJ6HR9JtND7txbIZwRk4lREofA7VPmHpWK8TyP6XTKxcUF4/GYo6MjW8okyzVhEHD30RMuTs+1hlrTgNTZWLqCugEXSd3U2nAlz3B8XUJcXl5yeHhIVeaEkW8nmKDLsk63p3uDSm+C5GkGcsOBbgRcLuZ0yoIybcjSkp3piEZU7IyHVE1NXmY4+ERhB5waP3TInSW7L4T8v3/4v+M2grRc85Vf/CXquKKs5pyfnON7EU1ZPDNuPhE1b9MoLmZznjx5QtnUjMdjPrzzAWWVE4Y+TVPZrCalZDAYWABdH5ObrGqyh6lzoyii2+1qKqTYNC2PHz+2mGy/37fbsIvFgvV6zbVr19jf37fTre1O3cz2TR1oyhMzaUqzmKrMmU7G7O1Nuby8YD6fUdYFriu1UmOp2kHGhpll6tKqKqhqPTi4f/8+FxcX7Ozs4DgOn/70p1kul/ZnHzx8aDFf2DSw5o8hrwtFa7u6MRV0HMfyJEzztv06taqPzm+mbDJ1tMn4Zhs6igKEhDjTq/1mS7ksS3wvaJvkFJqaYbfDk3v3+PQrL9OPAv79/+Df47kXr3P/6C6FqpnHK1bZmvPFs7XKPhHBixBkWcFkPOXOnTvUVFy5coWHD+/rWtLdrGULIbS0aWVof8LWneZN365BzYdqAmtbUdLATOZDHA7HTCYTBoMBnU7HdswGrjJO747j2I2M7UAxf2iNucPQ58rVQ1arBVVVoND1OEAU9pHtqHt3d7dFSxTn52fcvvMh5+fnFlUw9axpJKdTbXug0NoTxvDPvD9CbSRXt9+L7dWlDWU0tTWrES40PYJ5fBPApuQwQwpz8UspKavCfh2wnF7NadZiif1eB+nA/Y8+YtjtcnnyhJvXr4GoufPR+6ySBdJ3wBUsVgsq8eMbqvy531zXY2/3gCsvkzMAACAASURBVNPZGbeev8n+4R6v3HiOxemMe9/7ACU0ycYVLr6rG4Fev4+ipiwKyipHKecpMglgPzTzofq+j3Rd4nhjGui4LrP5Bbu7e8znMzsxW61WtiY2xGvTbc/mS8uX2IblQGcoLwgp85y9vSkvvHCTR3ffpm5ymqZC+C5hJ2A4PuTBgzPOzhc4ywqk4NbNKziiYTjq8+7b77KIV7zxxhuW5LK94pNlGffu3OXs7MxizvUWNGbq1bquCALNA0nSNaLp4DsujdACffpnNsMEc2pt83c7nQ4onZ1X8dq6w1dKv2YhBP1RV9+P43JyckIQBIzGO0SRRjcCzydPV9y9exuVJVy7MmZnepPFcsbX/uD/QwqHxs8pVYXfCVisZ1y5eQi89cPj5s8xJj/2raoqnhwfcXDzKjg1Dx8/4qx2OH30hJ7w8TwHRUiy1ivdQRDghx67nS4XF2cM+wMWS73nVpYVStV4nkNVmc0HzUloGmVHrrPZjDfeeIOqrgmDDo2q8P2ANE4srmoA+dlsZhsTLXgyYrFYEIYhZ2dnFjs1E7gsT9gd9/mVX/ky2XpGmqyoVUVDQ+B5DMYTJpNrnJ7d59q1W5SN4HJ2rkkrI11j/9qv/SXu3L3PN77xDV5//XUGgwFSSt566y1GIy1ud3J+RtlsSPgGJTCS+qY0GI40D7gqspYxRrtnV1t83JwuBhYsisLSR6uqIok15OaHelW+1+vhOtI2t/P5JdPp1GZtx3FAKW2n24EsT8jjU1SV8sqLt9gZhdy+8w7HFyd0uhFZXtKbhOT5ildeeY6XX74F/ARkXkdKdnZ2WKzmTK9MWSQLrt14mTxOKM/NAmWNAPvGatD8gvFuz+6kGR6uGW+a+s0ci1VVEUY+UujuN4oi0qxos5RnTQDNMWyOWFM3GmZauYotXGX+mOAVQhAGDr7//7f3ZrGWpdmd1+/b83jGO98YM6acM5xdrirkwmW7kLHdNDamHxp1Cwuh5gGQ4AXJiFd4aB5AQuKlEUMjtegXg2ikNvTgcpWrbGdRmc7KIXKIOeKO594z7nn6Ng/77BM3raoou4rqjLRiSUf3at/h7H3OOt/+1lr/AU5PDplPRxRFRpalKLqObroM1naYTFMct0NVK1BLPM9F11UWixlSQmIX7O7ucvHiRYqi4O7du5w/f57r16+v8BhB0NBkWvhnm3xC1phGg9N1uw3WtkizJV5jyf9b7m8bCa3sM/VCe5dq4ZLt4MdxHFRdW70Hluus9slCbcxhXNen73oYWkMRMi0d12t606PFKb2+x8Z6h7ff/i6759bxBz66bXP33gMymdPv+aytrTVj76ebAT0byauoKn7PZ9gdcnCyz3BjwCyc4/e6nByHmIZLXccrAp+CRF822jc2NoizCWGUr+gw8ESWtO1fNiFJ0xzfMxuvsqipnkGhKDJ8r0u29Clrp1unp6crpFVbJOmGWAG+zxZI7RuvkPNv/eZfZzI5Qalz8iKlLCtU3cIwO0xnKUejGGqN7e2NhsmclBRZA7LRVAvX7TCdnq62PNevX1+19KSUfPzxx2TFE2nSlnZjG+bq9zRNw9R0lJrltkAhzzKyNGUwGFBVxaqojeNG2/csFqRlWCdJgmN7n8EtdLtdNNNY1RW1KLEsB9tq+r7GktNmmjpJElBkKVvba5Rlzp++/Ra2Z3Nvf391x7AsB2W2IKkiHh4GZFlOJZ++8j4TBZthaFi2RpRGXLh8gbWtdZI849z585iWQ142lXxVNrf9ZkAQYxkaUbhYusHXgFwalQhaGCQ0yorN1kHBMJ6ApVfJqOt0u93PCIa0b4rv+yukVPuzdl7fFitnH0IIdA0MQyVPY4JgQRDMMUwbgcHm5nnGpyG9/hCv02E8mVAUGY7TqtEogKBeKuwMBgN6vR5CiFUftdVFaD9g7eAGnhRqmqZhL8mPTWuxmZS1w46WEZ3Fyao91k7zWqsAw2jE9baWGsftowXktB9Y0zRRNQvTcnAcD103UVR9uX2psQyV/sDn/fff5Tt//G3CMuGDe3cZpzFWp0dR1MhSoFU60qiZpXMSpUDzvwATNlnX6LoGmUQsjTriOGaxWKAojcR/SbIqTHRdW60MRVGgYqw4WO3K0L6Jf15voeWgVVVDFtSMJ+PLdrVeTcqWwJV2otR2Ls5iWs8mTfucvV4H2zGZ0kzupqdPmBamaeO4PpqhY9smuVFCXWGaKlKqVFWDZsvyOa5jNaJ1NH7AraLNaDRa7W1Xt+0zo9gVy0Ndmm4LgZT1UtEyXLXG6rpu5P6Xo+5WJ6HlCK7abMsPTnut7f6/rJ9MMlsK0NnXRNdVNEUglJrx+GRZ2HWZRBN2L5ynN+hyeniMJTXm0zm2qnM8GTFc38C2XOL0/yfX959lVJXk3qcP8VSHOmk+hbphc3A8akA6ioaiCaTIyKvGQNr3+oRRRVkJhCGoLRNsi1pdSiCpKgIwdQuJRpJVTOYLaqkCotHY0hWGgx6ObUJdYegqQpGkWUScBKRpghCgKGIl01kv0TdxnDKeL1AME6kKJDWGrmAoBX/37/w76EVOkcTkcYCiwyKdYXkuSapgWdvIvCaNEzRVNCxow0NgYVteA5R3dUyzcY8cDtdQ1aZL8tZb3+PBg4domk7XtXFNHdfUMXUFQxPkRQKioqwyJCWqArIq0FSVetknb7w2uoCCbtlkWYJlGWxsrNFYXQk0TVluKwR5nqIZKkJt7pJSSKSQq98zLJ2eZqBLSSUzalmiC4We0aEKFoz2PuFk9CFKv6DyEsxdhZH6kGNxm8g6Ym98G69rcHw8xuvopNEcU6npmtZT8+bZWHmlRFviVPMiRcrGS63KKpAFZd2sAlJRG/+HJZppfWejeTGVhqpS5QVpGa1WUCklWVEglGbl830fbUnlaQceLZcrSRr50zAMGmVxRccwGq1dTdMBsSoUTdMkWHqUhWGIa9lQV+RxiK0rmKbK4f4JvV6X0ckBiqLgWj472xcYjWfUuCv6Ttt3XiwWSyXFajWwiKJo1Vlo8RctAL4pJNVVi+4s8EhV1dWePMuyFUipLcDacXGWZatedwvgabEOLdT07OvUqgTZnrtajVsdibSUyKJEljlrwyGWbSBFxqPjx6xdcIjTBNc0EYqk43l4wqIm58tffZO72n30ykKuawT5KcPuGvNJjKW5T82bZyJ5FUXB67icTk/JRc5kOl3KLalk0wTUijxtbq8oS9n+IgckguXgoQypqmI1BKikpMybcWeSJlSymfGnSUZdN95vwGdYEa1m2Ww2W0IhWf4ONCqP2vJNb8DbcdrseYNgjmNb+J7Fv/lv/DpBOKOSGePJEXVdUVRgqy5BWCArgzhNECJeya3CZ3UO2n1lv99nNBrR7Xa5e/cuvV4P13VXCaVQrRKyhXOe3bMrioLteJ/RYrAsayWI1/5Nm/hte7BVP2+7N51OhzgpUNVm26OqT7DSuq43HiJVTVrkdAcOftelyjNuffoxW5d7HCeHJG6GEZbEwRw1N6j0EqhQKwVZQpnXCDRkqqJ3XIoq45WXbwL/x4/Mm2ckeQVJnmJ7BqPTEbpj0nU62BjEhUo0btws86ICRVAUJaenI7Z2L1EUGT2vh2VqqEpNGgkUXcOUNopSsFgsVlSWum50wFqtspa20q6mbTtNWU6EVFUnywpm03AFtkZRsG0Iwgb8XhUFjm2hayDqkp//a6/z1p/8PkfH+9i2Tq1INNNnc/MycVI3vDylRtWWzjtLq9MWYtmqsbfn3el02Nvb49q1a4zH488we1XxZEjQTs3albyV5leUJ7rFZzV72/0psGqxASsu29lhT9vNaKeNju6tVn9VVRF1jenoqKWCbWnIMmIyPmZz2+fDj99G2wDplJw3tig0napUONo7QsqSaPMadaaAVDENl901i+nxlJduvML9j+89PW9+XGKJHy339L8IIe4LId5dPm4ujwshxH8nGrmn94QQb/6458iLgvXNDVRL49yl8+xc3MX2HdyBT14XCOMJ4zcrciQVizDg8GgfXVWoihxTVfAsk3pZJBWyIskKCll9Rgy5LXCe0NqfCEMnSbJkZDyRQ20LIcdxPjOCFqJVDq+wTJ00DnnpxSsc7t/FUFUcx6KocoSmYjs+nc42muIjK4Hr2Sup1LMKiy0CrEXL2Xaj/DgcDldWqgCtEk5LSW/ZuZ7XTMzarkQrFtgk8ROYZJuwLdvkLGW9bQkCnzGSURQYDHpYlrEccjTDoLpeQlCLHN/RGPg2s/E+RTrlD7/5++zurqNrYFsG4SLAN108zeba9hU23XXUTCULM4pC4vf6DPwuu+ubuLrJ5Ojop0tefrTcE8B/Vtf1zeXj3eWxXweuLR//AY2KzlND1pLZbMbOzg6GZWLaBoZtIlQF23EoZLF6c9tbZr/fX40xK1mg6QqqJvAdd/XmNMVVe4lypbLTUoZatNjZ/9tsI7KVRUALMm85ZGdbUZZhYmg6cRzi+x5Xrl6krJp9JICyNILRdBuJhmk1q1ey1OBqP0jtc/951cZOp8NkMlnJNLWvwVmO2Nn21Vl2yVmthrPja2C1z2+3Be3/giecvLN0nnYPfVYxvQXntH/nGBqmoRKEU+aLCXkRY5rNh942bHS0Faiq43ZQa5VBb513334PQzPRl2yZjutx4fx5xifHDPs/JZOibuKHyT39qPhN4H9d/t2f0ug7bD/tOcqyotvvsYiTpv0iVEokaZHi9nwMt3HwqaVANXS8bgdUGoXzKEQTCtQlopZsbW0QRRFJmlPKiqIql6tEvdJ6kFKu6C0tRrdN1uZ8SooiI4oChKgpygzT0inKjMn0lDLPYPn/HMfBcyw0tWZ9rcf4eJ/bt+805tGWRY1Cf7jFeJxweDynVpqOQCtw0gp+tKtfy4NrPyxBEPDNb36T4+NGEqlN2PbD2VJ82oKsbZct37vVoKHdbrQFYUswPStMCI0WRa/X+4wKZ5qm+B2XKA6wHZPj40P29h6R5yllmaMo4NlwMnpAmI1Zv9DH2/C5cP0yjx/tMR0tKAJYxAn7B0fomoVndghOQ0b7Jzx48IiqqpgHMxazgMV0TpGWmD/GDegv1CoTQqhCiHeBEQ31/a3lj/6r5dbgvxVCtAYCK7mnZZyVgvqR0RY/puXQ7ffQDAM0nbQqkOKJ1227ophmMyWTUuI5NkkYkEQhSqut4LrImtWqc/bRFi7tqnQWiRaGAWVZUFUlVVUutwcmeZ4CEtdt9obtitPxXdI45m/+9m9xOjrk6HiPk5MGypcVBY7n4no9Hj0+JktLxuMxqlavEFztMKCl5wMrpoPv+1y4cIFf+IVfYDwes7+/v2L5tgOVdpU929NuCadtMdieb7s9adnQqqquOHxtrxiaAnh9fX21L27qAUlRpNy58wl+x+HlV27Q7XmoGpiWxsHJfUolRloFom8QahmJWnHrzj36vR302mV9axun2+funYeUaU2ZSr765X+Fay9c4d69e4ynp3S8AXkGSq0zOZk9NWd+IrknIcSrwH8OvAj8PDCg0XGAn0TuqQDH86kklLXk+GREVhSkacrmxhayrlFVHUXXVlBEy2n2eu3wYXtrC9e1V9Kapaw+U3X/+SlS+3eWZREEwap4c1yLza11dna3uHTpEhsb66ytDdnZ2WFjY4N+v7/UBlNWFfvv/M7vMBwOybIE09DY3NhG1bUlvkLnzu17y5daWco0Sbrd7gpHbNv2Sjapqhojw/Z8WprS0dHRavTd4irOiluf/RCeZU+cxWechYe28qRnJZzaBG8HGGf/JooD7t2/w6uvvcxg0KMostWqK0RNt+8yCSacxlPuHDxgf3qK0e3wy7/66+iGA7XONAgJ4oSXXn2Nk5Mxb958k3PbO6iqYGNro2FHqyZ5VnF4cMK9e49++uQ9k8Qz4A+BX6vr+nC5NciA/5knGg6t3FMbZ6Wgzv6vv1/X9Zfquv6S1/M5Gk0aCc8qgzqjkilVXRDlCcI0AQmVQh7BYpyymAXMF2OSdMFsPmXvZE4qDSrhY+gbmHoPx3LRFBVLd7D1hs6+tb3B+saAJAkpiow8T+n4PTx3gGP3ULCRpU4SSYocojBHU23W17bZ2jzH2nALt9tlfXNIv2/i2pLLF9eJghGyKpC1SloekMYZl3e/zLn1L9F1d3A8gdDmVHWBUFxUVSEIFti2xXw+I4pCFKWxnVUUQb/fYzabUZYl9+/fX3YUVKBGygrHsTEdEykal3uQWKZOsJih1pIsClFrCXWjjtmurEoNrmVTpAkUBaaiLOnoEkXVMS0HKUEIcCyVOBrz0a13ALj5xs+zWEASG6C4mI6PNDIOpx9R+yGblzpsrQ3p2V36To+TvSPKMOG7/9e/4LXN6yQRLBYZdx8/ZOvKOe4c7/HuJ5/w3q2PKMscz9UIx1PGx4dUdcjVV59Off+xrTIhxDpQ1I1OWSv39PeEENt1XR+K5l7zW8AHyz/5x8B/LIT4R8BXgHld14dPe46yyhFawXQ+o45rdi7sIlRI8hhVUajV8jPU7aIosGqNJAyQa82Ks7Gxhuv61LVNFOeI4wpUid/1CBYLagnDwTp1XTOfBVy5coUgCJoCJS8az9+0IAjnKx2Cs8o0rZdwkiQ4rttMrRTBm2+8zq1b7zV7bgFxHFJUAr/Tx16ae5+7cJFOOFya7fUoS8lodLgq0oAVAbS9G7TunZZlrURQ2qIJWG59nMaTY6mdUFYSVdGp6nrZnG7261VV0e93mbR8vDxD0VSEopDmGQgTTSmpRI1S1wghqdEJ4gxVMfn6L36DEtEwlU2TOE2wDAVFVUnLlLyIOT1pukHTeMpsuuDy5ctsvPgiihS88eWb3N6/w+37txsS6fltDqanOJZDOE7Y2NhAU1WuXHyBH3z/HaqqUVJ/IkL6w+OnkXv6h0KI94H3gTXgv1z+/j8B7gF3gP8B+A9/3BMoCmRF3NB6VJ1KKqR5hUBBisa+td3rnkVvaZpGGsVEi2A1YBCiXs7jl7hSrYH3NZ0JFU3T6XS6OGe6EuNx44SZZvGKYdGKJ59VYGxv61m27ETkCbvntsmzBAXJIpghZUlVgmnYJEnKfLYgWITEcfLE1HB9nStXrmKaFlEUo+sGQjSAHClryrKiLKtVMdfqHrTj6Tbpn1hnPRFBaa8pKwqSLEMKSPKMoqiopCRJU6JlO7BWBGUtqfK2fVY17vHL/X0SZ2xs7ZIVshnYqA3mxLIaqzFFgTwNqeuKMExRVZOjx8dcvXSFjuPj2w7z+ZRuv8Od+3f4uZ97g/6wx9H4BMOxOT49Qegaw+GQ3a1tjh7vMw+DZR8+WxWRPyp+GrmnX/kRv18D/9GP+79nQ9VUNnc2OD2d4nh9ZtMUWzfJ0hytzkF90hCv6xpZNx2EvEixHRNBY1riuj5R1NDVPa+DDAqSJMf1Oo1g3tKDYmtrh263TxAEtN7FQiRYViPg3PKz2j2jEILpdLqq3HVDgKzY2dlCUCCUkko2HQopKkyrg+32yHIQaNy9e79JlDJHESZ7j4/I8oALFy6s/m8cx/T7/VWft7WRev/995nP58viq14VWtCoUbajctu2my6CqiCokVlTgAqlgZxOF3NU3aCsK9IofAI2p0YWBa5nUpaNiPTm5ibHRyM8d0gtVTy/S5SEzGcBnueTJhGGZZDEMXVdsbWxxqe3D/HNkivnrrDd3+D27U+YzieolorT9zhJTrjZfZUNfZPj8QSz6xEc7OMJhc3dDQ4ePmYUHaJoEqdjkScpk/EXgMNWlCWHxyfomoNr9Dg9CvG9NWyzh6rZ2G4HoamrlVBRFNKksVJdzKZomkI4j6gySRzGqOIJvduyHIpcUuT1anY/HA7J0mLZH3XodDpkeYLjWiuKuO/79Pv9FSQSnhQ6vuug6yrXrp5nf+8+RRYTRlM0Q0XVNHqDTYRikaU5QZTQ6TeQxn5/SBRF3L17d6Xe4/v+qhhsjVBa1JcQgnv37hFF0YpG3hZirXpjq0rTCoC0mIWsyCmqkjxvcCGKqlJWFabZdDFQFbKyIMkKuh0PTQXXsRgOBtz66CPiNGsSG8FkNiUrqiUUNaHTcXBsk7LMQQree/dDunafF6++SsfqMToYoSsNGzqIFhTkvP6lV5nMZ5y7cB7b9djb28dzupw/f77pNIxOmE/GCKXm6OiQLEuZzZ7ebXgmxsOqoqJrJrqioyoK/V4HFcFsMkFNCxRFo64Fju01EMGlNy21JI4iDvYf4292STs9Tg4Pmc0WGLrA0AVZmlLkKVEYIpQS37+EbdsczUarzkUYHuC5HaqqarC3hkFVFeR5imnqVFWBYWioqqDT8QjCOWW2IM9CkniKptaURbMvF6qJ5w2opMoiigmCDM206HQ8FAUmkwm93hqz2YLt7W1Go1P6/T6aprGzc+4zsMz5fEYYhti2vbSseuLFIaVkMlssaTwqSZKhqjp5mZKXkqpebrWkpPVJ9lx31Vrrdrt4trM0qWmtrEp+8O57vHD1Gp9+cgfT8BoJV8NibXNj6TVXkRcZ5aIkmMfYjoZrDvmFL/8inY5Hx/X453/4f+N1HJyuj+5pGJbB6eSEvQ9H1FInDVJ6To+NwTqfvn+Lg3v3UCV4ro0ahSBL5nmjOfy0eCaSt5KSXqfLyeERRRljaBqTyT472wOKeYijCmLliFprCoayyqnrZjKWJzGZEWPnc259+H2EonP743eI0gW7F3aIooAwSMjzklooCKVisZgtEVU+rbHe6emYl19+CerGBLrtuep6YxWl6zoHBwdLCrjgxrUXOT05JInmLGYjVENHUU1cr4Ntuzx6NGI8aag+W1tbSEo0peGTlVmK67srbENrH9Xq8LaP+/fvr1BgTXGprAiRZ4WyV1O05etZygpFawo/BYFlmCv6/tqgt9rHb29vUxQZD+7/GeNpQJGX/Ou/+jcYnZ5i6IfM53PKukHnKbqCaeocHk5YWx8yHPbxvR7z6ZgXzr/CR+/d4qVXb/Dtt/6ID+/c4cVXb5CUC+xaQ8/Aty3eeP1L/PEf/ynf+No3MITBu9/5PpQFL730EqqAk9NjdEWlrCuSJEIzfljX9Uk8E8mrCIEgZ2enRy0kpSwZn54gc4+e0WUSTRv0Vhyh1I17UFEVqGpDTfE8j7KYogiFSiqcO+eT5BpCi7h0ccit4FMUReJ1N9B1Fd/3KAsYDAbLW/MdXNfn6Oh4RbBsad1npY1WwiIy49zOBo/vHyNkQbfboShKKqFgOz6yFhwcH3Ht2husr28yWwTNKFZpxtH6kt/V3u7bRGzBNMBK/K9eCgSapkkQLNjd3V2NtKsz07MkSZoCTUo0Q18NMmTZFKDuspfcboM+vvURg16f6XTKweFDXnvlS+ycu8j16y9x/8Ef4PtdpsEpmga6oTKbjxuU2lIveHR4hBCCS5cuMRlHdG2VyWTGlRevMy0XPBod4vUMXM3C1BXWOj3ufDjh1/6132B+MObO3X1217e4cHGH/dFjZospumtRxDFxHKIoUP0Y6vszsedVFNFgbTWFskhRtUYtXcqSski5d+/OqttwliRY1xVllaMqCo5toiqSKAqZjE8QSkMD6vWb2zWi5ObNm/T7/WbfuuRN1XVjQt3Sglqh6nZvfdart13h2q9to/8s87ZNGiFEY/xXVfi+vwK5tHRywzBWlPZWZbEtSNsuQhAEq0HCYrFYdRtazEGrYNkmcHv8CUbjia1B+8FrTccVpfG8uH37NltbG7zy6ktcv36dB/cfQS3odvusra3R63dYX19b4TVa11HTNNnY2CSKYhqFzJqTkxOSJOFLX/kyv/yNb+D7/opK//jxYzY2tojDmJOTMZZhMOj1V1KpLaWqxW201/m0eCZWXillc4sqS6RUmI5PsTWHbJGjVA67zsuM9m+hLH0pBAJNtQCHCo2oSpHFkE8+/YTLO2v8yl97BX+wxTSX/NPvfot7p/c4f3kbodcIXcFyXTTdQZJRKymGDd/69ve4+cab2K6HqgrW1zfRdZWPP/4IyzFZhDOiLERE8EtffZ0imzGfnaCIlMlkiqrb5HWBqqlcujDkzTduYhgaVa1QSg3fd6hkxvbuOv2ex2KecfHieR49ekSn00ETQFWiCQXTsYnnEVmWrkxVkiReKUS2gJq8CEBrVu9cRuRFw7Ju+sEWmcgIiwLddRh0PDq2SZYuePvtDxkMBtx/9Amvf+lVuu6ASnZ49HjEZH5CmB8xnU04d+E8/X6fNE3Zf7xPkmSsrW0s244wD2domsI8OkWtm0HS7rltDm4f0et3+O2v/jYffvgee48eMhhcZP/efaJFwKVzlzAUlUyGTEYBs9mEShaUlYlSSioEUZpSq091g3hGkreSnIxOsUwTr+NhGBaO5eJqHvFeSpw2zuS5zBu9LFVFWa4iOzs7tPpc5y5cwNKhEgpRlvIH3/kudw7u8au/9Wv4HYueatPrONRIBoN1Dg/3SZKUl19+hePD46XxdU1dVSRJSBDm6Ibgzu2PKKoSx9aoygTfM5FFgaZDFCRs7Zwjy2F96xLnLt7g1ns/YDIJSYqaWuj0ehskWY5j62zvbJLGEX63w3vvvcd4PObF6zcaNnOW4pgN7uLDDz9E1UuuXj9PkiRsiO7yjlMiZY7fHVIrJrquoes2ui7Y3Fyn2+1yeHhMHCX4fodzrsPFi+cZHx8znZxyYXcHQ1WYzBe4rs/J0YjjasSdu81UTdOh6/lsra83WxndpGO7qGVjW7u+sYVlWSyicOmPkWLaBsNBtyk6t7aW/ngGwSxkNo7ZGlzk4cOH5GHM9StX2Vnf5vGDh2RKxWg0WrJnZGOKk6cUVU6t1FTVT9nn/ZcSopnX26ZDvzcgW2qJ6RjMjwPu3LrHjeGQWqlBWfpSFAX+0s6pgdr1iMOIvKzY6Az44O6nvPvpe/ytv/t3OJw8RpUC3S4pqpCe37Bh+711FEXj049vcenyeT799FOEkuP7AAUaCAAAFfhJREFUPqfjI7Is4c7d29y8+Tovv/wiWZ6QhBHB7IBgMUUWDQ4gPp1x/uJV/N4GqBZ5UaAaKoNBH9f1CMICQ4ckCVGVZu+5f3iK5/sM19a4/MILq96tphvUSs0imoPI2NruMx43Q4qtrU0ODw+biZSmsbV9DcMwGAwGPHz4CN/3WR+s49rmSoLVdy36noGtDAmCgIcPH7K9vc2rOxdQNJ1KQpEvPe2qhp1y/vwuvuvwwQcfkASNYLQuBIPhAIUaTRV0PIe1jQ1M26DT9QmCadMfr3JMU8fUTSxVx7c6TI8nZPOSV6/doNPpkAYRjmUznpyAkHQ6PlmRk6YxlbIUEVcUfNt7ato8E8krEGiaQRKlJE5KEAS4nodjOvSMAW+8fJPb3/3eCssqVHDMprjRtEZzIU1jiqJguDbk8GTCn77zff72v/e3CWXACy+/wHhygqLkROEUzwyZpzlJlCJETafj8/DRXUajA/pdj9t3bq322P2+D6KgyEM8y8TULCZxRZbMOR2PoFborG1SVjW6YVMrGkmeEUYxe/sP8NwOrttBNwx63UZpZxEkvP/B++iawVe+8hUqCb1+D00Vy3ZYRikbVZpW/K+1kWr3hI7jkGU5qqpRleC5PYokZ+/xAY7tsbe3R6/TRVVywmBMEqXMFyH9wWAlyNL1O82WLZQYpgNILl88T7fbYTaZYps2hmFx69YtPMugzEvOXbhIFMcUVUlxeNjQt8YnpEnB9evXORkd45oGssxJopjx8QGT8ZirFy/RsWzyMGYRBoRxxHh6yiJaYNoNQk63dDQFhNoIi5/FIP+weCaSFyHwPI9Kr5hNp2ysrTXIpqqms97hdP+UatkaU42mgs7yZCVlb1kWjm0ilJo8L/n2d77La2+8gelrZFJhNp80244kQdU84mRGOJeEQYzfcUnzOVsbfbJkh6OjR8gyRbVMzp8/j2noDPtddFEii4rZ+GSpatPgDvKysUzd2NhiMp9hZDWmbTGeTjjYf9QgunSTC5cu0+u4IGreeecdLly4suozu67biPNpCnme8Nb3/gRZ59hWB4EBtU63s9aMibMaRZjEUTP/L4qKWmoYik0hJa5tM5mcIMsnjInpdMzx0ZirV69jmS4HBwd0/R5xGNDr9UjjBYtZzs7uFg8fPmB7exsFhSwrSNOcX/r6r1CWMR+8/yEnJydcvHIVyzHJioKsSJkvZrx846tsDAc8vPMAe6CQxDEP7n6MlBlXXtjC0CqCIGjsr6KQOE2QgOu75EWMQJCEMUmZ0e/2qPKCQb//1LR5JroNqqIQhYsV8Pn4+HCpNVawCBf43QYRViv1avVtIYEttUUIiWNZPHz4kDhO+drXvsZiMaPX6yBkTbQIUJWlc6Uimc5GPHj0CYdHjwgXE45Hh5RVTp6nK1RXS/1Rl4yIIk+RVUGcJiR5thIJ8TwP23VW/de2d7u7u81g0ENRBUWWMp2NCWbTld5CY7jXKJS3xtb37t5mfDJCUNO45irUtaAsJYZhYRjNUEJKmuNFteqECKE+8ZLwO3Q8h62tHUajUy5deYF5EBLEjVSVqgnyLGE2HaNrGhvrQ9YGw1VHZ7aYc/HSJXr9IZ1eF9txEarGdLbAcRxcx6coKny/iyJULMNCExq6prG3t8dHH32IlCW2o2NoatP6Wk7/kiwlK9Jlv/6J+mcL77Tt5rXc3X06DPyZWHnLqsTzXNIgxnVtOoMNjseHWLqFpgrKNKdYYlRLmQNNs1431KVRoCAKZximwsP7D/jrv/E3OD4+JolCzEDFVDRsr4dSGY0vcBEhCbCciuOjTxvcbFgQxxmb631KVLZ3d6mFgqYqyKqgyDPSYEK4WJCXIBSDvEzwfZ9z586hahaaqSOUHKWzzU1d4cWrV5hOp8wnU86fu8zewTHf//73uXTlKqejY25cu87u9g7BPGR9fcjo6JBb739Ap+tC3QhlAyseWVEUn8F4pGmIqukoqkDTFcoSgsWcXsfh1Vcuc3x0wAcf3ebC1Rc5PBxx9ep1Dvf22d3ZQtdVhKmxmI1Jioqv//LXCYIAyzIIk5jN3R3qWiB1nWkcs72+wW/+23+T/+ef/nPmi5A4L/C8Dlma4PtdxqN9pqd7JNGMx4/vcO3KFfI0xDHMxiJMtzidnhIlKZquI+qCssqgyqmrHCFqNFVB1026ns/VS5eZnD4d2/BMJK+isAI2a0pjrqxpCkkaYdo602hGp9NhvNQzyNIURT4Bg5dlSa/v860/+BZBMKdIM4b+BmtqH81UWRtuIguYfDLGdZoxr+frDAeb3L0bomsGluWwLkxUMiqhIBEUy2FAXdcYuso4iogWC0opUXUD1+vg+X6DQFMMsjBlfWNAnqcYpsZbb/0J1164wqWL55kvZgzXBmxsrrG3t4eqWBwdHKIKHWqF+39yl9t3btHpeqRpgqELdKMRmvY8B9s2lyo22hI5V9A4FNfMFyfYuovj6ly+/CLj0wP++I//gKoqyESPOCnor20SRgluOyIWMBkf03UtNrd3KPIU17Ppmz1G4wnzMCBJS2zPbywMsoyj08e8cPXaSt+slXkd9td4fPcjxqcjgnDKL/6rX+Xxg4f4rkcc56jC4eQkIKsKXN/heHxKRY1EIsucMk+glui6yvpgnddeeZ3Dx3urIvZHxTORvLKSBPEM22n8C6IoQeQqVq2hlQ5lEFJmGpq0kWTYuiAM5mSJTsfrYmgmSRDy5mtv8NKlV/BtG5EKwsMMNTC4//57qELjpUtXURWDKEzwvA6InI3tLSYnp1SyoqgqLMfBNCwqWaOoJUklWR8MmU+PiOIF4/kJosrRdJdCmGz3L5IrfXrdNY6mjxCZSpUYCCk52B8xHK6DquE4fUy7Q3dtne76EF01UAwYzSb83u/9Hlke8pWff528Tilkhq566ErjyFPRypY2lHlJgWFLLm32EEKlRrC395C79x8jFA2306G7eYmTkxM2+1uoiornuPT6faJwAZpCb2ON09mIXNWYL044+LMH/MLXvkIpSkzbII8llubwyYcfcenSJTY3N0ijlMOjfXZ3LqHWFj2vT5GkLKYjDk/voaPx0pUXCI/nDMwuWZQQzyeopkqYjcgLiLKUssqWMlQ5eRqvRteDwYArWxeIg5Cgzkj4IrTKgKqs0RSFIiuwTAPVFqRBysfvf8T2xnkm1YxCVkSLBSpPNGjzPCdJGzMPwzFYTMa8/fbbuBtdQiUiGiVcu3YDy2r2aZPJAmRNmmT0Bx6TyR02NrZ4eP+Izc31JoEth8lkSpqmyDKnriuiKGKxWNDvdAkWE/KqZLixzfXrLxJGKa5TrPbIvu9TSZ0LFy5w48YNHNtjEaQcHR2xtblJVZeYqo5uuERhQZml5GmMrAq6/QFhLel4DjoKWCaoGqaqYSgC17XZO9jj0aOHFEFEp9PBNGw03efGi6+RZA0XLopS6lpQS3B9j1o22sWmaeO6NgIFgcr29jaL2Zy4SHjre+9w5cYN1jcusphnLIJwhYOIokaruAXN10gOjw4Ioxl7e3co0oI3f/7nOHiwR1JEOI7DYjajkhXhvNFH09QKTTNIs5owCalkge06KHXDpH7ttdcwpU2OJEsFiv0FUMyRUqIIwXw6ZzhYp0wTbn/4CaZi8fqNN/jWP/sONy+/SBjMGkxAmiCE/hlQupSSMFiwv/+YCxd32bq0i73lcjA5pNsfUpc1UZggC4kQDWvYsk1EpZKlFf3+kCTOkXWDYhsM+jy6/wDPNRt8bJlTVQWLRQIoGKbNxuYOWVYQxwkHBwf4nUaayXEtHjx8zOuvv94wN+ZzBI2/3P7xEUka4Rog6xLKDF1t9AV6nkPPc1jvdDkdnfDKzZss5gH7+4fc//QO06WS0PkLu1y7dI2iEphmDxSBLEqKvCbLazTdoogSDNNdcvga00QpoVzy00RVsrNznulkhuP5DAyN/rBHlBT0K1BUA02X6KbNPAjQTRtUBUXXODh8xPVrLzIajbl99wPKcsG5nW0eP9zDsxxymbCYByyimHkwoT/ssZguyJKGuSKFxDAViqIp1Da3Gkjoez+4xZXzLyMVQVWYaIb91Lx5JroNALPpgjwrKbISy7AZ9tc4t3MOUcJ8umA47IMiEMsXEFixXRvS4gJVVTl3bofhcLiCNBZFgb/0FVaFRlXVS2M8myp/IpU0mcwajVihIYsSx7QwdJ2u75NES50HGrp4hUBVnxhmQ4MTyNKUJA5XtqaO43B8fIwmtKWyooXWCoYUGZoisEwdlRohwTJ1yjyl47u88vLLPHr0iPv3768MDXd2dtjZ2cE0bBRUXK9LVddkaUlR1dS1WLq4P8FelKVc0Zpa/TZZ1cznQWPZmqRomo5QNSzLAaFSVTVJ3KDqHM9FVfWVhUFRZPi+RxQHTKYn1LJoxEiMJ3JT7fO1+OvWNVNQUsmsmWTqGrppYJgmw+GQfr8RVtFNG8PysC2fMn16zjwTyVuVFaZuUWQlxwfHpEHGRmcNrdL4k2//KdcvXiOvcmy7eYFajdwWwNGQN5SVKMZ4fIKqQZJELOaNGcne48dQC5Iko8hrfMdHVTW63QFS1sRhQrKUrp/PZgSLBa6tc/nCLqOjPeIwXArQCYpS4ngdFNUkijOkhCCICII58/kUqFfK67KQTCYzZCEpspx+p8tiMubunY/Zf3SfMk8wNcHuZo+LuzuYmsr4ZMTb33uLew/vEWUxumWiGCaaZSN0k1rVkapBLQQo2krkef9wj7yIUXVBJVNqCloPZVXVybNGAHp7exfbcomihPk8IEwKUHQe7x/juD1OxzNOxhMUpelBV3WDWpvNJuxur2NaKu/94B0EJTdffxnPMVFQqMtGfnU6nSKpmc3nS5vcptAWikTVoJSN6lGn1+Hx48cMBmvIQvLKS69Ryor5fE5V1aji6QXbM5G8hmFQpRUyr5idTDA0g67fNKqD+YLtzU1qpSRKwkZaUzcwdAtFablOObpmIlR91TeUUqKJZko2Ojphc22TIGgwEipiBU2s65rFLFg5vPuuj22YxMGCfsdnNhmTZwnJUoDEdV1Mx2X3wmWm8wjX8VGEiqbqJGFEnsZMJhM21zc42m8sZU3dIIlDfNdifHrAndu3KIqCmzdvoigKX//613nxxRe5ffsuht7oSXS7XUpVRbEtME0qVSA1FakpxGWOYpnUlBRlTJKGDQg9TzAtjTia4ns2+lIPzXVdyjJfwTyDIKAsK+paYJkOimZiWz66YRMGMbJS8NzGjDtPE2bTCYpa0/EsxpMR33vrO7z2ynVevn6VxWzK5tqQKssoi5wiy0CBIArQDBXTNqipCBcT8jJjPBtT1pJSVqiKxptvfonpaEKVFmi1imY2xi+qIpHF05feZ2LPiwTf8Sly2LyyRVEUHE1mfPjBh3i+w0effsSNl86jGipVvgRg5xmCJXkzTgjjGIFGnj+RuC/yAt/xscwOaZySBws8r0OtN6tRWSQUWb56gzudToNrGB1jGho9f5PRwR5pGFIVOUKRVGWOP9ymlDW243EynqEoGgostRaeSE01K0hFkkZ4dofjo8fcuf0+a0OXy5de4Jt/9B2yBD64dYskCfnyl2+iWy5mURNHU+oaXK9DXlRYlkFd5yCa3naVJFi6WEEchVAZ9LvIsiDNEkzToBI1rtMhzxsp1aIqOHfuXINTFk/8k1vY5WQy4/qNbXTN4ehgxGxyQhxOGQ771EXKd/7o+9imymsvv0K0mHMwn7M+9MnCgDhYNF7Ldb0iT+ZFjKwLamjuCIbJRqfLIo44f+4CYRhxfvccaqFiqibT6QSpJli6RllWeJb+1LR5JlZeEBRJyfpwA1FDUeTcu3eXk/GI7lqHN958BV1XGwWbpYCIpjUSm5pQGvGOqpk4HR+fkKY5ogZD1wnmIUiIwoya6jMFnqYpqGpj6tdK3M/nc3zX4/rVK9y7c5fx6aiRlNKb5zUMg25/SJrmzOcBqvrEMbOW5VJR0eP09JTJZIKpG8wmYyqZ89FH7/L48W2mk0Pe/sEHXL12nZdee41SKizCHMftEMYZSVFhuR18y0OTCmot0NGwNQullBiKhrm8Bl3R0RUdKlBREVJF5lCkJUrd3KrLqmA+nyJETZYlhOECqFksZo39lKYxGKyhKSqL2YLD/SNEDR3XYX2th2ko7D26jyoqLl3cIY4WBNNTyjQhjzIszaQqc5I0oKxS4ixmEc+YRzMKmSEUSbfXmISnpeTl12+iGQbnz18kT3JUBOFsjkxydMWhzGvSOON0dPLUrHkmkldZOpVLKVdg7fuP7hMnEecv7uJ3GwxAa6PUaoq1BVNepGRZQRBEuJ7P/fv3eeedPyOJs8bFstNfyeJXVYHjWui6uuKHDQYDqBrZVNO0uXDhQkPJqSVJ3IBhwkWwUp/xPI8wivA6vaUf8CnXrt5ozEhoyJstDte2bfb29rh16wMePLjHG6+9yle++nP80jd+BdfvkhUlCBXd1KlqgWk5+J0ecZrTdToUcQ5FhcwL4kVAleUkQUgRp1R5c+vPsoKyrChySV0JLMNBExZlVi8VghRMy1jhKFpH92IpvN0SPzc2NojjmOPjY1RVwbIbG7Hj0RG6rvLC5QtoqsBzGi83pYYiz5lNpg0Wu2xc3/MiXUn/tyqUURSxtr7JxuY2/cEaKBpJ3DgUxUGI77hkScLkZEqV1YwOR0xPp0/NG9FqtH6eIYQIgE8+7/P4GcUacPp5n8TPIP5lXdfFuq7Xf9gPno09L3xS1/WXPu+T+FmEEOL7fxWv7Vm4rmdi2/A8nsdPEs+T93l8YeNZSd6//3mfwM8w/qpe2+d+Xc9EwfY8nsdPEs/Kyvs8nsdfOj735BVC/JoQ4pOle9Dvft7n85cNIcT/JIQYCSE+OHNsIIT4Z0KI28uv/eXxv7RT0ucVQojzQohvCiE+WrpA/SfL48/OtbXAls/jAajAXeAFwAB+ALz8eZ7TT3ANvwi8CXxw5th/Dfzu8vvfBf7e8vvfAH6fxvrgq8Bbn/f5P+W6toE3l9/7wKfAy8/StX3eK++XgTt1Xd+r6zoH/hGNm9AXJuq6/jYw+XOHfxP4B8vv/wGNcnx7/C/llPR5Rd3YNryz/D4APqIxxnlmru3zTt6fyDnoCxCb9dLKYPl1Y3n8C3m9QohLNALjb/EMXdvnnbx/Ieegv0LxhbteIYQH/B7wn9Z1vXjar/6QYz/Ta/u8k/cv5Bz0BYzj9pa5/DpaHv9CXa8QQqdJ3H9Y1/X/vjz8zFzb5528/y9wTQhxWQhhAH+Lxk3oix7/GPid5fe/A/yfZ47/u8vK/Kv8BZySPq9Yujz9j8BHdV3/N2d+9Oxc2zNQ1f4GTSV7F/gvPu/z+QnO/38DDoGCZvX594Eh8C+A28uvg+XvCuC/X17r+8CXPu/zf8p1fY3mtv8e8O7y8RvP0rU9n7A9jy9sfN7bhufxPH7ieJ68z+MLG8+T93l8YeN58j6PL2w8T97n8YWN58n7PL6w8Tx5n8cXNp4n7/P4wsb/BzhycPVWZn7hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show original images - Matplotlib will be presented later\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qHLIrEGjrUELuFQZg7xysBBGGdDabGGNRxTJnbcEfSIkKpGfdVYDWlvWNJplxDHPNyUNLlKPAJ3uRFEK4PRCxV90FBKpYmYoimVowzlGrVSGwhOUKcbmMcQ7jHLmxRJWAWrVETYbkKI82nMWg0BZCJQhUlUMH53j5e44k0QwGIUZLJkZn6fczmq0OU9PT/Oovf5Hvfe9V8mxIIC2t5g5ZNsCanChUDIZDjPM0aagCJseqdLtt1jfXWVycYXZm2t8za3FCIhF+XSjOVSJ3myxCvJpz8th+3r10HaNT6vV9XmlwcH9lg1u37tMbakYnJpg/OE/POPIMajiEy9E6Z+AU272UZ599jss3rrOxtUwYGKTKEaT42msRBGTaMEwTMivY2ukipKLVaqHTjNFGjQP79xNFAdfv3ufSlZssLi4yNzvN5uYOJ44f48ypE4DlRz9+i8OHDoFzbGy1CcOA+flZjPUcTZKmnHvsNCMz8zQ7PVrdHlOjDaan51BBGeNAhQFJ1mMwyGm1B+SpJUmH5HmCznO09mha4PmxKAopl+IPTL+PRhFAkKV50RuCLkjA9xeAIJBIGUJB+gipkAiMLeQapQr5KNhrjrXW9PtD+sOU9bU2o6Nj7FvYx9tXbvJ7X/06n/7kM5w+fRiLIRYWIUE4x7mjCzx2+Fd45+INNldXWFhcIIrGGY0l3W6PTFu09oywNYYoDBBSYq3dQwO76gACnBRUy1Ws9bJeqCTCOazRxQon2Wi1SXJDWCrR3F4DGTA53gA8U470BU7he2MhfM/s9UhfkYQShEp4nkQIwlKJXpJCmjFbrnDs2DGOHVoiUCH1ap2RsRIRDuF8ASUXCCvxlzBFSEO3t8H9BxeZHB/h8NJBzp46w/zcImGseOn7r3F/ZYU//+4rfPrTj/PsM4/x5k/eIBAp442Q7s4QJQtZV4VUymUEkgEZUijieolMD9hpbaNtSiBDj2qsI9UGJYNCkuUvNeDSOkrOIaTi7OnjLG9scWd1k4XZKbJE8+c/eJ08SfniF56nUvO8wVqzxa0r73GoUUXrhCTPGEaKE6ce4+D+A3xp+Jt87esvsrp5GWOGfO/173LizBEeOf4oiIDVlW2a3T5JLljfbnLowCKPHj9EFEhMbhBOsLm1zfLyJl/+xS/SaDS4+2CdUq1OP7d877W3GBtp8KnnnuLW3XVW19YYpDmLczMEgWRlbZtbt5eZmR7nxPGjTG1Z/uiP/yl3VjZYnJ1lfGKOcnmU/nCTal0hwgBtII5idJqiTcYw66EzB86hnENKRRwElKKIRqX61yUe8JEpAg5jfOJa4Tx5J30rYKxBa10kuhdrhQMlJcIWJJuUXrYTAmstwySh2+3hqqCrllwb4jii1+9z7fpNxufP8Qtf+CwHDk6TZSlRFGDyxEM1KRCFT+DxU0f5s++8Rqsz4PDRYxDv6v0SKSSRAkeAK5R3bRyZ9v2YAe9lKFBKGIXsdgSuIA6UUuAcg2GClJLJiTpCSrabXUrl2LPiUiIkWKv39HDfJrufstT8le5ISgIEURSR5jmhtb7YBIq45LkSIQTqfdq8NkVxLSpYpBTttEOnu8NOcwsVCKYmJ5mfm2FstIYTks9/+uNcu3Wfn7z9Bm+9ewHlDJtbW1y2Q5bmp3DOYo3BWkscRwQqQAhFox5jLQilvMqhs12ZpSj6zhO8KvAyq4Ngt/dxFIK9lwWFhPnZKV556xJSRayvbRHHJabGxv1zIQVCgTWaLB1w59467W4bAsHk2BRb7Rb1zpBTpx+l2e3wT//gAtWKoDfs8dqbPyGMK0RBmdXVHcIo5uTpR2isjTI50SgIaxCBQgE72zs8efYMR44cptPrk1qPSl754Y/55CeeZmp6mjAKOXFkgTzPKMUxQRixurbDteu3GR0Z4dCBAz65Y8lgmNDtDXAI9u2bY3p6is2tTWq1Krk1dNot0jz3vFPBDNtCpfLcWEy5VKJaKTPS+IgXAescThWQX+dYq7G5BWMpBRGhVORZjrVeOsMJDJ4g2y0ASgaEKqAcRtTKJRSWLBmyvrXD2mabqakDHDx0hEdOnGDh+L6Cb9CUpEThcNb3w84JnAjIje9jz546wU/evsRObZWpQ3PMToww7A+oVcpgPQsu8A9lKVCIUO2ZkXaTfsOldHsDQgXCen+Bw9Du9cidIIhiypU62oLODIOdLY6eOoGSBorWRqqwMNqABjrdPvcfrHLsyEHCwGvlzvkHwArvM6g4qJQCur2c+dFRDoyNEZicbqaxRqBsjBOgpcEmhsxa0kBQUjEOwa1bG7z17nVWVjuce/IJTp06SWN6hG6Wce3aHWZnZzlweIF9SzOYNOfm1dvYbsC1Kz+iHEFqE0wUItQY/8av/g46h/cuXODWtVcQNKmXStQqVUTaob3doVodJYgjUBIpAxKnwWkkFiECJMoLr0qihfA8ioVQCmZHxvgXf/w1xkZH+bVf/yXWVjfJMFSExGrBTK1Kt1bnpR/+gKnpUYIooLPTYnF6jlgNGZmZ4YXPPsvX/+wbDIZt+jrj8ps/IsxXmZios3T6SxxaOkIgI0wekQwkow2LRSMCyz/7k2+ysdHlU898kld++C53799lbLzG2MQI/+nf+XdpVEKcBZNZmq0BRxbnebC6ybWrN9hptThz5hGW9s0yzDVOOEZqEXmasba2zdrGNkcXJ3n8iQN8/U8vUBZjSB3TXl+l3+2g04Ret+8RtPKyahCGlKtlKpUytVqV04+fhv/t+39t/n0kpgi9RuzDFASgdRaEh/Q690hAKq8ia2PAOaSAKJBEoSKKFFHRDngpMaRUriJlQK4tSwcOcP78Yxw8tI/cOHTBwioVIpQCIdHG7klW1jmUkkxNjnL86AHeeuddBknKMMmJ4giphDd77Ca7K1b4wh+gCilJ4HDOoQJfb10hfyZJijGOcqlMOY696ccZWs0Wh5b2MT8z9VOZ4f1FpSgsm9tNLl6+ilKyaEG8nj4YJkghUfh+MFSKQCkvl4aScink2z/4AWubm/76OofGkeocBMRRiBBel3+wssrG5jbOWPYvzNOoVxA4qlHI/MwEE40qE/UKk6MNFucm+OQnHuezn3qKLE+5duMm1WqdOK5Qq48wPllnbn6U6dlJnIyQYYU0MwyTjF5vwO2793iwuka/73txuScPSpQMQfyU2NIOMuPJwl3S9uade4yNjmCco9dPUEFArRoWRDM0W322d9oMBwM6nS7N7SZZljI1Mcr4SJ04lIRhyKlHTxDIgOZOl+2tPndvb9PrWIbDLhsbq4VkmxMpi8Ijl9xoOu0ejz16nKWlGZ+IynHg4H4mJ8eoVEKPaASsbOxw6dp1kjQnCgO63R6379yj1WyTpjnSwaDfB+EoFRxOZgzWwchIg6mpCaQU9AcDkmRInmdonXt/hFIopQiDgCgKiaKIMAppjDQYHx//wPz7aBQB5xPFM8GWIFAeBhdKgddnd2m33f4/3DMFBUqiJEgJSgrCMGKkMUatOkKqIS7VeOzMGar1UbT1D5CVnmMY5pbMOIIwRgUxQgTcufeAF1/6Nv/on/w+Os/YvzDF5sYGt++vAJZub0iWW7+KakOudXFksGv5AVDCOwKlNwZ45ltAmmUkaUqlXCYKwoLyAp3lXLp0lQOLc1RK3lPghEcW1jq0tuhCjtjeaaKCiDTTCOfIc0uWm0JPtkgc5Sj0kFNKjDE464jjiJ1mi83NLUyhrzsEcSmmVIoQEnKdY61hZ8ez+2EomZ+eZLRWQwlJpBQL01PUSyGRhFKAb2+kYmZqnFxn9Hp9giBGiJDBIOf7r73HneUdytUGQVxD24j+UNPtDGh3+iRJipKCIBDk2iMZXwAClFT+vISCAukMdU5qvbx5Z3mVVBuee+7jfPLZp7lw8QrLD1YRUpBrGCaawcD7Eqx1bK5vEgWKc6cfoVaOAcPq2hp3bt/m2WfO8+zTTzE7NUcylDx40ObO7SbJsEOnvQPWIGSOw+IQZJnh3QvXOHfmDI+fOc3qWosglHz6uY8xPztFGCisc2TastPq02p3KJdL9PpD2p0+aZrRbLX5yZvvcP/+Ks46SnGJLIc0zRkOU7LcO/6iMGR8YoRBltDptkmz1C+SOi9SQxBEkrAUEISSuBxTr9dZXJgnissfmH8fiXbAWUsYhoVHPPdw33guYLcvToZDv6JKRRhGaGMIvJ0E7SyJdIViEICIiaIGIihRKtfYP1rhrYvXyd1VZBBw/uNPE4chtbJgu9VldXWNB8vLZElCFIWcOnGUjz91jtHRUVSgsMbxH//7v8kP3rzA17/9CnMzU4yN1Dl2aIlSoACHkhLPJAPOW4lcoSAYk2OcNxmlucEZy+jIKJ7N876DbrvP7bsPCIRkYqS+Z6N11qMSJxQq9KaZVj9ncnKas2dOMhhkrK9vMzVWp14t44wlEA7nBEoIwqJI7hYoJSW/8qUvsrLWAQmpzum0t5gbn/NzDU4TBJKbt++wsrYODvbNzDI9PkYACOl5F2F9T+7vHwjp2Nhc4c7dqzx+5jgrqyt0eoa4MkqlPMYTTzzC9naf+1tbuKhGv9enFEb0BwZLn3lj6A169O4kyDBm/74FSnEI8JckWYf3Z7SHmu9843s8WF1n3+ICX37hMxgrKIVw4b0rXLlyndFqmdu377G6ts7E6CgKQz0KqVTrjNcbDPt91tfXMGYN52IOH97HxMg4z3/yPCsrv8Lv/if/OZvrA/qdbSrTP2FxfoFXX/kLTj96inr5IFcvr/DaG2+zf98i1XrMV7/2LX7h5z7P0UPPkOaaa7fu0KjXCYSgrzXNdhsrHE+eO4GxgtX1TU6fOsFXfuXn+Od//CL//I//jMmJMU4+eoITj58jSxP6/R7dThfrJtjY3uD+g/tsba7T3NpikA5Is5QkTYtEcgQKSqWQUqnE5OQEhw4eYnR0nMmp+Q/Mv49GEXCOXOtisMbtaZ27bcKuEcbrx4IkTREIjAAnPUxUShHHEeVymVKpghWKclylrkJOnz7D7P5F3n7vEls7O7z5xjtMT0/SbLYwOufw0iLPffxpvvmt73L14hXeu3SJyfFxDi7t52Pnz1KtVgiDgHOnHuUn77zH1WvX2bcwx9LCHKGMvHZd9OO7tl8h5N5wS5blNNtdaqUITMbMSA0Q5HmOCiMEgs3NJrfu3OXZp897T0EB77PcoMLCQ59bpBJsbm1RKpWxTlCvxVy5tsH89BhKSYSSOFKElERR4M1Rzg+UOGeREkZrDe7lO75HNbu2a4F2Bin8vdhpttB5zvjICI8cP4oScq89kXLXGl2E8IVufW2Vza0N6tUK87NzPHiwxdxiDRU51rc7LMyMMzf7OFMjJd544w2c7gGWMI4ZJgmvv/EmYRTz+c89zzBJAYWzHiXGkfRQHD/Mtbq2ye27y1QrFQ4fXCIIBBFgLYzUKywLuHHrLguz0zxz/iy1Wo2N9VW2ly9RKoWe0LOQDFMmxieYnd1HHJYKpAHzi9N84XOf40+/9hK5VizfXcFkhiTJuHRJ09zZZHWtS5pbDj93kD/52ktUqhUqlQilJBFRMfyTkWSaOApo1GtkaY4UgnY/xVjD6GgDIQWf+8yzaGN4sLLGTrPFO+9cZHy0TrUUobOEldUH3L9/l067yaDfwwlDkiRkeYbDIooBOj9c5dvPUqVaGJAsx48/8oH595EoAlLKPVhjTGFwkH71yrXZs8naQjTebRW8P0B5WamYHZBSEsclapUGyJj52TmmJqbZ2Nii3Wmzub3FXFii3WzRbXc4f/Yks9MTXLx8lfsPHrC2sUmr1QRneefCe6ytr3Ho4H6efvIcjVqJ0yeOcP3aNZo7OzxYWeGRI4f29H92TTxCFRKhX3+zPOPNt9+l/OTj7JudJJDKE4lx7O29xrGxsQlAtVL2xhrjkM4QxyFOCKxxqECQ5t4irRTEocBax8hIjTiOsNah5O6a75GIxXn50lnvtLRgpSUMQrKs8FFoDbi9CcdrN25x/eZtwiBkYnyCpX373mfrfb8D1e39myRD+sMeeZ6iAkkcxWxsrLH/QIknHz/LhUsXGSZLLM5OszA3w/r8InHoZVYhBd3BgINLi9TqDS5euox1ktMnT1KvVfxnCt9SDZMhF69e527DSNgAACAASURBVMLVuzQadU4/cpzx0VGMdvTTnEE/JQpCZqfG+fjHnmR2soZSkiS1lMsxU1OjnjeS3vxTr9UZHxunUq7gnD9DJT2Ye/75ZxkME37wF6+xtdYkIGB2fgwpNM2ddZaXt6g1xnnpW99lOByytLTA9s4OIhCU4zKtVpfDS3NU48CbjPKU1bUNJsfHuHbzLvV6jYmJUZSCifERnj5/lj/846/RqDd4/NQxdtaX6Xfb2HzIj398ha31FZqtbZJhnzRNME57tCk9n4EUlGKJCkJPciPJtCOKq8Tlxgfm30eiCDgcWZYVPgHhbcSF6cFaUwwSCT866jzDopQEESKi3f4nIi6XiUoVoqhKGFWJwgbf+vYP+cnb1/gPfvc3OHJsEYDQ+qy1ziEkZJlmpFr1KkSa0u91SbOMze1N7q/cp1Yp82cvLnL8+KMcO3qEFz77LJ12l+X79zlxaD9Iz1prawu50DMDQkoGSUar1eX5Z59h39wEodglYiT4iWLSVHPvwRrnHj9NVIqwwidzIFRhfFIgJYMsY3unzcz0KPValWGScufeCpPjY36cSuBX/UJuM86R5qknI81PpdZmq8Obb19grD6O1UP2L86hrPcuCATvXbhMu9lhcX6Bx06fZm5mzt+o97kh974qvrl3b4X19Q2SZEC73SFNHbXSCOtrTR6srPPM+Ue5cesG7777GhfeuMzy3XWSZODbJGfITIpUklqtxq9/5dc4dvQE3/3ea6ysbWCM4YlzjzI2WqNaLWGtQVrHE6dOMlKrkvcHRGKCSiWiFkXMPnWa9c153nn3XUY/8QRRXEY7TT8ZkOUD6vUJgjBGqojRkUmiuEqmbcFBeBQmAlg6tMDf/o/+Fp/45BP8t//N32e5v0G9AtWS4Pr162R5FYlkfmaG2elxDh9YYGF+CucgzS2hjJlsNAgdaAuRCtna2uLlHzTpJwkvPP8ZusOUG/e22b84xcT0JL/2lS8xMzXOD195lf7mCoGCzeXb3Ll9he3NdZJ+i2Q4QGsNwiKUAClRUeht2S4nkAHVSpVSucapk2fYt/8Qt1dbH5h/H4ki4CfvFLnO0LkGyZ7u3+0lOOcIRehnBRB7BhulAmQx1aXCkDCKCMIQJySluEySaD7+1FOcePQocRRhrMGJYnoPz7QbC3GoOHxgH8eOHGRza5NWe8fr/hqyLKOda+6xTH+Ys7W5xaee+xhTE+NMjB1G5xolZTFvLhDS33BwdPsp9+4/4OLla3z5hc/7qTsDtsh+obxbcPnBClNTk0xOjaOUfw9fSPz0l7aefFxbX+eNt9/jhZ97HmMdwzSnXIoZrfupwN3y49sJh3GWVOdkOifJc/KiCNRrNaYmJli+v8rMdA1hnT82B8MkRWvPrUxPTjE1OYUpUIg/q78cTjichXani9Z+5j9JUmqlBufPPUpYrtHtdvnv/8E/Zm3jAc3tbbqbffKhA6lI89Sfc2CJwoA817z0nZfJNHzxC19gOEy4e3+Vb738fcbGqpw4fpA4KnHq+DFOPnKIUhzS6Q5w2kCoiAJv0qrEJdY2NkgyTRQ7tNYM0wFxHGCsJRCCen2E8bFxgqhEpg1KCXKn2Z1gkEqitePxxx/h5LFTvHflLXZ2toGM+ZlJBmnMMJFsrG/wt37r1xmfGAUcxgnuLW9zaOkAI9Uqznq1CGu5d2+Z2dlZjh4+SK1eZXVtnQsXrzAz7a+ztg6lAj733DP8aWuTPBuisyFJr4vNE3SekeXp3uK4iza9c9VRKZcJoxKVcoWlxf3s37efkdEx3r589wPz7yNRBMARSc+3WjQIRRzHpGlKpVrZkwvBu+Vyo8mEIZSWUlgiCgJKVhIbRWQlAY56PebckycYmZrBiZ+aigSKzGmUDIoL7pWHPHO88IXniaIy6+s7SNejl/aIUGAt3Z0WnZ0u9+/c4dLF95icGOfIoYN8/jOfplwuMz4+TlyOESik84rAzsYGab9PkCfUlSDwz733EQD9xE/fXb1+k6/86pcol2KkgLcuXGOkXuWRpQVyo1Eq5N76Gu++d51Wa0gpDtHa0esNObh/HizIwl5rjSNUfrCoGgdMjkxjUkmapAwGPZy2VGXM6SMHuHrzNmfPHEIoRQbo1LK1NaAU13E24LFTJxkfbRTenEIALVQabTRB4L30SZrQzxLC2hgzo1McOfk0O60WX3vxW6yurdPt9NAFx2MLA5jv7Ly3wVlFSVXRiaOfpVy+cgfDq3zmC58hCxw2knzsY+fptbtcevcOlVqVxuQYx04cRjmoNqp0egPGojJWaIQLqdWrjNXnWF3pEyzGpHrAZnOFQZYQlmtUopDR0VEvDxeKkzEGJQp5t7B8xgX5+Ru//Wt846UxXvrGH/DgwSqzsxO88MILTEzP8/jZp0ldjURD3h9w5cpVpsdqHD40hnYCbRzWWR5srLPv0H4+9dyzCCVpDQakTvL4k2dxKibPU3Y6Q+ZmHOMlzVjFcnNzhfXtTdqDFv2kT2IE2oTkKURBgIokKMhFwshInVpjlnKpTqU8wljjKLevpyTZMhevr/BB8dEoAoV7LgwDwGv13vrrMMYUo5ue4NqdOxdCoY0hyf08gS00Y1fMeYtAIoKfSosW9r6Wu9z7+yjnOBI4C0ePHEQWexFEcUSeas9DeF8uzjl2dlp0Oj3WN7aI4hKHDh5gf5aD9HC20WjggIW5OSbGx6nGMUGo3o+ePcudZgghmJudoVL23m5jIUkSJsfH9tyFDk+OSiF44uxpcDAcZh6BeEfx3hurYqYeoFwuMz01TZ5756LOc7Iso1QqUa2WmZoYIw7V3gGlSUqv3ydQisX5ORr1GrvjvErJPSlXSkEQBHsDXkmaUatVGR0dIU1TfvDqa1y/eZu7d+5htF+xjLF+LmBPDnYIZ3zhwjEYDsm1JbcWUSrRHwy4e28ZKQPKpZgDs9PcvHmfhelp6o0y15dXePWH73D65BEmxxsYazDOJ7GUlgDJxPgYa+sbTIyX0S6l0+rQqFWJI98zSyFQQu7NYUghya3X3ANZkKoWhIThcEgchbTbA0bqMe12wt17q8wuHMQ5R6AcUkle/Yt3SId9jiwt+BbDe5t4+73rvPPeVQ4fO0Kr3WFiYoxep0eepsxMztLvdhmplXG5Zn11Eyu2WVldo9VuMxwOybKUXGeYQr5VSiCV2BtMC8KAIAhxKqA+NsqJ4yeZnp2m081JbcbCwuwHpt9HowhQ9PoUN4PdoUJRaPDOP9zCr0j+5vkLkFqDcgoZKJ/0SkKgyLQm0xoj3jfQ44xvPZxFiOJBRKKNY31jy29AUq8zPzdDb9AjUJJhv4vAb+YQhBEgiKIQ6yy9Xp8fvf4GN27e4Ze++PPUahW6vQGdbo96vUYUxVQqZQ4fOlBMc3k5zTrQ1mCdZWx0hNHRkb3kUgJmZyaZnBzFT9wFaAeBCpifm+HUI4c9yWQ0pVLJ18qCC7DW73vgimI32hjj4NISzkJSKCza+NbCy6+WQZJQrZTIjaXTH9Dt9wmigOnZSYI49F4CJbF7U0y7W2h5G7MToIIAay137t7n5q07/OAvXmMwHNLv9rDaJ//uvRXCn4sIBFgDeAlY5z/dmUHnGcPBAGcNiwtz1GtVmpttkmTIkWP7mJmpMz07ye//0Tdp7TQ5/8RJ5ubG6fW61GtlhPQTlYeW9vH6G2/RbHUoVTK0zlDFXgM6zxAOpCgkB7wZzeZ+uEqEni8y1m90sryywtbODo8cP8nq6jKtVsrtO2tElcuMjM+wb/9ROv0BvX6Pjz/1BOPjIwjh96MwzrL8YJ00zTh68CBhKaZcUmysa/Ik5erlGywtzBMIRywd1ZLi1pXbbGxt0uv3ybKUJBmSphnWWsJQYbQhz3OQ/nrGhQV5Zt8+Pnb+PGcefYxSeQIhJNrA7fu9D8y9j0YREJBkGUZr3y+L3Q0lLK6Aj846gjAgDENPEu5KVcJ7twkUppANq/Uq5VqFIIoYpqmfrAsUoiDclCtkFCmLh1KwsrpKpVxmfm6GX/j5z8M3HXfv3CUIQ/I8Iwgj8tz4/Q21LFZHRbPdodPt8T/8o/+FsbFRjh05xLnHz7C1tYU2XporRRH1aoW4VPJabhQVY7UADp1rbt68Q61axQFjjQZOW3p5HyUVSZYz2qgxNTlGtztA5/59jdGsDvoYrQmlwBrtr430+y+A48jBQ+xbXCQulQgCD3mHwyG1aszkRIOtrU1agaTb6yMcjE+MMDE5Sr1WI81yX3ydZTAcIIrVs9PrYYyfE7h1+y5Xrt3gx2++Rb8/YDhMGA6GhEFAoBQyEBh/KHuFyonC7xFFe8Nf5ZIgzTKGaYYUUC2FjDWqhEqwubHJg/urPHP+MWrlEGcFY/UK//ZvfJmfvHmRV1/9MTMzNWZnJ+gPujxz/iniIGZqrMxYPebm9avURxzpoI2yCWUqGJ2xuxuhzv0mDU4IwriEsZbUeAgwyDPu3l3m+6++TK875MihM5x59GNcvnKdS5dvcW+ly8WrN3nmuSdZXFjkl3/xMzjrzU0rG12arR2SNOULn/sE6xtN/uhPvkYYBoyOjDBWrzI3PcnBwwcZrVfIk4z+9jKvX3uL5vp12p0mg3RIfzggTTIEjlotwhpDmmqCOEKFIUEcMzo9xcLCPJ/6/JdYmF9AlusQRjhABRAEgw9Mv49EEXCFc01KPyCU5TlZXngFjPcOCOVNRVr71U4p5TcZRSCswGmHlQa0JVIBtVKJShwRxTG721RZawrSjMJ45DcoUUpy9swjbGw2CQLFsaOHMOYz/LM/+D/Z3NxEqYDcQhTJAtoWZiDn0P0BSkrqdUmz2eT1N95GCMnRwweIohhjDN00Rec5jhbOOirVKmmW0Rv4xMqLVbrRaBCGIaMjDZRS2Dwh0xrnoFyukGQZlXKZJMlAwGAwQCJI0oRQeIOVUpJKteZJz2J4p9VuMzo2ilSSMAjQJscYTZpnDIeeeE3S1COJwO/KVO/16Q8GBGHgN3+1hizNik1fDK1Wm1d/9Dr3H6ywubXNxvpm4UWAcikmiiJMrosNNQXDJPFWbyn3rM651gVP4z3Wea7RWcboSI2JsQZbmxvcvXefXq/P8899kmo5BCsIlMMKqESCZ544wYGFaf7wq/8Xt+/d5sDSPHlmUM4QSMnc9BRvvXsfYxKUsASBJAgk1hV7FRRQSuzu9FQMZOVGY41ja2eHi1evkWYJiwvz/Ft/8zeplEPeePMS//Xf/+/o9RJWVzbAZszOjIKwKBnQ7RvefPcqi/NjnDxxEKkCwjBAOIiDiDffepfPfuIpxkdqjFTLSKPJ+h16rS12ttbp9TpkOiVJEwaDAUmaoqQjUgHaZDgMQeQt7zIIWDpwiMceO8P2Tka7c4/jRw4zO+lbzCzLufjeWx+Yfx+JIgDF7kFSkGYFzLHFtGCgcM7vMGSNRTqJUKKYMRcETuC093GXgtAPHAmJtJZ8mFCpNrxs5yAQCrA4IciNKTRhv9+flAEqUGhtGBupcfKR45w7d5a7d++TZlkxpMTeuLOUfpJQW4pVcZso8kaR7/3gFa7fuMEXf+55pJRYa9je8QUmDEOGSYK2lv4g8duoGU0c+4KhjaHf62NxGJ0Rhd41p1SHIAzp9weIIuGTYUKW55g8RwlBEHgbdZLqYr7CW5SzLCPLNUGoUErS7nb5aZPkGCbD4j0deWbolP3k2XoYUCrFRGFUFCqfuBcuXeLajVtcvXaDQZKQpxnO+P0dojDwhSbzbZyQEikcQaH2AMWuxYI4riDw8wrlKCqm4DTlOGRytEGr1aTZ6tBoNKjVSqjiPgoconCPWhmyb2GGU4+e4K1336bfH7KxuUU5qjAxMsmJo0tcvXaB4WAHJSEMAoZJSqNRY3pqEiUVUkmywpZoCy4qz3M2dja5eeset+7cZd/+RZ46d57puQbOwPnzTzAxPsn61jJCZty4eoPpiXHmpw+w2Wxy884mnV6P8bGDRGGIxc9yHDq4RLvV9t4Sq70N2Vnu3L5Fa2ebbNhFD3sY44nUJEnp9QZkOqUch+TSz9WUyhFOCKJKiUpthKmZeU6ePMPmVol+f8DK8hYT9RpZmrK2usb2xtoHZt9HpAjAYOD3Zst1VkBZwDl/4wsZCvy8vyi0PWsF5UqNOAyplirUymVq5TLkhiuXr6NkzMj4dGE59ZN+AE744R8JhbQmsAHUaiXyXOOEYGSkzm/++t/gpW99l976Jt0kAbwcGMexh934Lbn9bscKozPyzDLaqJGlCS9+4xs0GnUmJydpNEYIAy9zdjq9AonsbkXqTT9h6OVOk3lkkOQpzjkqlTICP0fRarepV6r0Bx56e5OP9Ew9FESq96uX4hhrLWmWFUx8QfIF3nVpjNkrTH5oSJFmOZsb60RxTLVSLrZvl9y9v8za+jrrm1s8eLDq31P4rd4DqYjjEtZYcp2TJqlf7Z3fb8EBcRT5IRfpjUQIGAxzgkAxGGZ02x12N5mtVWKmJ0ax2ZCpsTpHDx/C6gxUWGyoapHCkBuFlKA1fO5Tz/L0U+d479Jl/uyb3+PJx84yeWYSrR3/N3XvGavpmd73/e7ytLefPmeGM8MpbEvuclmW2qJt0u5KssraRuQAQYIYSmAYTvIpSOwvQYAgAZyCAPnm2EGqISOOHceOXFS8WkvalVZL7rLMssyQ0+fMmVPf+tS75MP9vC83sBk5kQ0wZ0DMkJx5z5nzPvd1X9f/+pcrly5y7d09lILGBaOV9Y1NVJxiXQskixarseF7f3R0xP/8q3+DfFGwtbnOL/+Zf51Rf8jCw42be1SF4Vd+5V/j7/6ff4+33voBN659wORwirAp5y9e4flnLrC21ufe3gOqusQ5ePPaO3zv1R/y1S99nn/jX/3THOw94M7t2zx8cIeLZ9aYT4+5du11jKl4dLrP7bt7IBSdTpdu1iNIvQwIi5SKzmDA2uYOW2cu8MorX2KaS65c3MVayBJ4//p9vv/aD7l37w5Slh959j4WRcB7T1GWQbjibYui+xUavfwhQpFu2zaQWuFamqeOIlQc4aWgNA3bO7uM1kfht7egmweCu69sD+CSAxda1jiOmUxnrA0GK2biM089wXg8DSYiQiKlINI6dAYtOutbeV+321llJ7gsZX1tiywLFuV5keOcwxhHUZTB+2DlX69RSmNt8NUPbb1ajR1VVdM0TThIxjKdzSiKABQtAdWwWQm3rLXBTWltOERrhXNm1UFoLTGlwRhLURZEOqKsKs7sbBPpsGp0xlGYnCyKqZ0nz3Pu3LnL4eEx48mEpm4QzredhWgJXu0ew3+oCl2KloQUwe2mNV2p6gbvHWVpcD50Y9KZNkfBM+r3WV8bMJtN6YnWSwHXFomwkXAYmkaiI43UgsrCsNflpU9/it/67T/g1dff4sXnnsG2+hMhwuYk1hFRkqDjONClW7MW1xLVYh2R53Pu3L1NkeeYpmZzbcjacB1jPd/63T9k0Btw6cI5Lp3vU+Vf5u6tG4yP5wivaCrD9tYaKtI8efks8+0h19+/xcHBCW+9/Q7nz+3y/HPPMOh1OTl4iLUVW5s73Lt/j/fev0FVl0xmY/KiIIkTdJSQJhl1VeF8gxIOKcMmotvv0un2ybIuD/ZPWN/YBuGIIkdt4GQ643gy5ngy5bEzax95/j4WRUAI0a66ROsmHB6aJaoMofWXIhzeqFUW6iTBaEEUSaJehteK2jt0lnD1mScYrq8F9pwMDkSy3QlK33L78S26LvA+OAQpKfnhW2/z1BNXkULyJ3/hT7C5scG3vv17mKYIPIXGUNU1zlr6vQ5ZJ2PQ76F1sHGSKtCa66bh5OSU0ljSLAvUXmsp8oo4iun1ulhrGfT7ZJleUXMX83BgC9u03oqGNImpipKqqqmqAtki/dYuD14wv3TeY31QNc6LeSBf4UmShLKqmI4L5vM8/B3a4pImCcdH4+B6pFXYOjjH8dEJZVkyX8xblDoUHe9sIKtYVqYhpnGrm15KiTUGrcMWYGn04gkeiKEzAevC79VKYOuSTGuiSPKJp68yGZ8SdzNcU3NycszTl69ACywKEUbDum5ASHSkiCOJ8cFp59/783+W8cmc//S//Ct87pVPMuhp6iY4V8XdDC81SI2XoZMoSsedvX2SWPPdP/gOew8fUNclVbFgY33EpYtn+Z3vX2NnZ4svfunlQNMFtLV84XMvszno8Z//F/81x4+m/Po/+seMZ1Ne/omX2d7ZpZMmfOrZZ/hrr/4qW5tr/MLPfp39gwOuvX3E5154jm4asbd3n+/+4fc5PT1iPBtTlQXWWIbDAdaJAFwKRawEUBNH4dldjjb3Hh7yxHMJtZEUbk6sY4TWNL5ie3eH7TNnuPDY7keev49FEQhuuwqLR0tF3RSIVpQvhaBuQvKNkBIjwNaWKBLIpqEXRWRS4Y3FeoMSMaPBGmnaRYhoBfS41n9g2cbjbbj/xYd8f+dhbX2DG7fu44As0Tz99FUs8Ovf+s4qLMI0Vbvu+pDunCQJSulwCJsAnk0ms3Ar+7AN88aTZSlFMQEPaboeth6tdDpLE6IoaguMQ1iLcB7hHVVZrbYixnrqusIYS6/bodPp0LQPeVkWbG6sY4wljiNIYZHnOAtKaJTUwXYqlu0hCmvY6WyGNQ1KwGDQx/kwj+I91jn6/R5pmgYNR2tjvVxZBU8713onitVostTbL8HUZYfSsprRMhi71FYS6S61gF63i5YwO33Epd4IVS6oTipc/SwqHmKEYFFppnM4OHrE1maPYT+ml6ZoAcYLnFOc2d3m6U9c5A9/8IfsbHQYZtBJ4kD+sSCExotgVy61REvJg/v3+dmvfonXfvgmP3zzR+hkA5VucuPenD/5zYtsjEbgPRFBqDRdGI7nhnjzLI+//Dl++MYb3D46pffOdc5srXFpewuQOCGYn0yZFzXXrt2gWMx5+upFYm9470fv8M47P8JWC5pyQe0aGu/wtUAJg7cG5Q06CdiVExGNzygLhfXbCNulzOHC2jnOnFmjJxRYaGxDdXoCxYT19QHaTz7y/H08igAeqQIZR7YCmGX7VtcG0TK6lgEKkY5au6qAMpu2bU7ihKzTCWq68MJhLvW+XU8BIujel1jAkpDjXKB7LnfpVd0QxzFxHPH4hbOc2dnm4OFtmqYJPoaEHXOapiRJQGGLMuAaSoU9blmFYhFF4RYuypK0SEiSmE6nQ13VxHGM8575PNy0aZKEG95ZtAyEqKWpKi0wplrBiNZhZWpaPwMIkVtFWWLaQhRuZYuMwq0b6QDcVVW1MjxdHuIoilASJtPpykYtbj9PkZchMCUK24OmfX3TtJFoUrQRZeHrUEpRVVX4+lvAczUytGPYchRyXuC8p5ulbG6tUzc1SRIznc3p9roUZcnh8SHb2x2M1VgrmUxyfu0f/iZXHt/lxeef4sqlx4KQ2wdpd1XXfP2rX2DQiTg63MOLPETTKRHGOGcxJqg0x5M5dVMzHPS5/2CP0/EpeVmytjFia2uDT3/qk9ReMisbBmmMJTxTjw5PmE7nXL78OF/63MsU8wl7925w985tXntV8+Kzz5D1NsLh9ZaizHm4v8dXvvATnNvZZP/BLW7fvoNpDHmeU1WBuxCcgYLJjfWujbVrLeGURkcR3azP5UsX+cQzn6Qxkt/57u/zhc++yFOXz2KMYTyZ8N7ND3DO0viG16699ZHn72NRBJbCl0DgccRRRN2E4ASldKDhtg45AYGOA5sLh4pU+/AF+WSSZqjWYUgqSbuiZmljLcSPEZPa1dSSByMIWQG9bod+r0tdW2oPVW356a/8JK+9qrh79y7LNyRt/fqMMUxn85bB1zoJtQxIay0eSZykYe3nPb1ej36vi9aaxSInamXU84Vvk4WaQMCxlsGgT5om1EVYCy59F6QQpFnKIi9W3y8pJUVRYoxtNQJd0ra7mM7mVFUb+GGCNbVqv5+5KVosI5BojAntflnVq05gecPHcUykI7QOxSGsDW3rFRgKQ/g8DVGkQ+cgRMAzWmDStcxBvKRpwnscJxHPf/JZfu4bX+LWB9fw2uEyTdME7OLd997h+GTCxcev0MlGbG1lbG6uM50vOJ0uEDJBAY0DJxTOeNKsyyuvvMT1d1Nu3niLum6wjQMWvPve2/zab32XQX+db3ztp3n8/A5FWfIbv/VbvH/rDv1hn2/+0i9ybneHV994jz948xYvPPcE04Xh5vu3qYqcx87sAIKbt27zc1/9Ai89c4m/8Bf+HQ6mx/zg5Ii/mUZsPHaRyWzOY49t8Cd/6WtcvXSFe/ceIIXhgw9uMJ2cMB2fMp9MyOcLikWF847aNAgZCEtSSGprSdKES5cfJ+v0ebB/zM9+4yucO3cRL2KeeDTld777fQ6P32cw6PPujZsczo8oq4Yf3bnJN7/5Cx95/j4WRcA5vwpsRASUfHlTq1bss5w1lwyvJbquVUQSx3SyTnsrx0GiKz7MAZDh+m+JYQ7hlnRcvyoCyyxBay2bG+soFQCuqqqxzvGNn/oya/2Y3/vuH3D77n28dygdURRl+3XLVScTtA7BQtqGcS7cyiLceN55iqJsrcYlcnnw28IhWxJT7YMJp6j8ynm5bupVobE+gF5LwZL3jqqpqIrgoFNV1ep7UlXBhUa22w2lFHleUFbVqivAO7wXxHHSrqeaID+2PpixWk9dGZwO3y8tI2Qa1o55kePyMBJkWRre03a1GrekIO9cYCy2/AHfYgJSKp568gq//Kd/kc+89Emq6pSDRw+oaoMUgqg2lPmc2fSExXydURrwl5//xpeZzWYMBz2cbws8tNyIOTvrfbY215lub/HwbkqaROjIcuPWLayPefyxy8Ga7NZN7t6TzOZT3rl+ne2tbV5+8QV2d7apneTq1av0Jgua2nD9xk2EsyRRxObGkE4aswSWz+7u8snnnuPmjfeYzhd8cOsuM+8YDdf4+ld+ks3NbZR0PHZ2g717aaMohwAAIABJREFU95mMT7BNTbGYU5VlW4ADTR0FSml8yIUijmN6vR5Kx8zznOFg0K5cQx7m9vaIL3/xFf7aX/tv2NhYZ+fMDkVRcNg6UKXJx95yPBB35NIXwIebQsjQ7i87gPA7PyQWLX9/oMsGlpZ1LuzOXetTiGodfkLnsLT7Ee2YgHOt3kC0nPyCThZm324nYjabkxcl1nq6nQ79fg+tFfN5ydK1t26adtvQrsCiNBilNM2KpSdFaK1FS8ypm4Ykjqhaq/Ulr962YGikw46+rmuqqlqNHHVdt9yDILceDvpUVb3iOISHqM1faDcFIa7MttsGEQqJ88xmcxZ5HrjvWmMbhzU2jACthduSnxFUlRbLUr8R3hfddl3dNoG3rCpm8wVL3YdrZzClAssymMkGjkaW9FpcAr721S/xmc+8wMbagCevPsHe3h6LvEYrTV0b6rqgMTknx/usbW6jVczu9hqjQW91UXiCxZxuv+5+L8FbG8hc3R7CNxwc7HPz9l0uXrzCFz77Eg8OTnj3+k2uvf0OeEtZFXzli5/niScu4REsFiVvv/c+RycT1oYDPvXUZQadlDRRbcr0h+Or8IIvfvEr3Lx5l7wuuP/wEJdpBoMeO1sjpPJoIVCx4vqN9zg+PKRYzCiKBXmRU5UlbhkJJzwqkoj2Yul2U7q9bmBv5gXdbsTJZMxotEHWUZycTnn9rWvUVc37799iPJ7yxOMXefzCRWpj2d7Y+MjT9zEpAmBdg7Fh/aNbyqmS4R/rLLY1zFg+3N55ZKSZ5zkTMwMU53bPsqVjoigJHQOKJjz3aBk6DvCodm6VodcKtwes5uOtjQ2KxuId7Oysc0asc3A4Zf/RAdPprGX39dubVZDEoTXWWmOtoygKsixlY32NJI6pG8Ph8WlwSEJwOh5jrSWOYwb9fqjSWoduoO1MyqqiqktU66ZsbbE6VHESrwrjeDIlSRKOHh0SxzFaKwa97gqQ00qTFwWn4wlVXeOdW32dSRIjpKAoiha7iEjiaJWDF8cRaRIi3eaLnDzPaYxBo1b+BLJpWCxyEL4dF6I2VsxzfHJKXTdUdc1sPse1hKKN9RFRFBGJ4KMYxTHnHjtD4wyVM7z08svs7Ozwd/73v83xyYKTkzlpHFPkcw73H7CoStL+WYa9AYPeCKWSld5EAJEWxDH87ne/w/ntde7cusm777xNXZWUZsbLL7xIZSQ3797n/IXHefqpJ0kSzfdf+wHndndQSvJo/4AoLRkMRnzm088y0qIFZpfB5+G5WqYmz8rQ6V24+iz//l/8j/j13/gt/vG3/gEH431ufXCLx87s8uKnX6BY5Lz66usc7O9zcvyI+WwSLMQW09AlORPi99q0S62Dcej25jZbW5t8cPs+w7U1QPM//i+/Sm08IOn0hkHGfTxDSMn4cM7tWz/k3LlzfP6zr/DMxcc/8ux9LIpAuPkDAQJk66Eeblmhwg3rbIOz4RDHcRRWIC1Ip3VIBKobQ1HWQUwkP4yyWlJXQwvAimcQPvmyiof/rLWmriu6WYpxfqmZCWSZw2PysiaOk3Ar2oY0TVp+vafb7RHHgbefZWmrQvN0uglDY1ks8kCHzgMG0O1GKzLOMoNg1fl4T90URHEoiFVdh3xGuXQ5DpyD2WxOp5OhlCCKFJ0sDTd4m2aznNPn8wVlVbXKP0vdNGyurxNFmoXz9HppSPOVwdyzrCtcY4mTOIBROqLb6QbNQ0u1bRqzAltXUm0hV3yHXrfLxM5CcZNBRel8WO0pqch6KWUdCGI60uRlwYghUkVsbG63Gg1NHGmm4zmDUQfhDCdHjxALQ+f8JWwnQ3uFQK1Gr7xYkCaS8ekR92+/x/z0FC1g97Fdkt4lSgsbm1u88vLL6CimqGpOJ1PyvODM01eIZNCZ7myskWZpAKnbh0U40z404FHthkmRxgprw/u3s73Npz75HN/61j+iKRoqXfDGD6+xNdpgsciZTcbkixn5Yk6+WFC0JiEWj8EivSeKJEKGrtg6x/bWJlUdlKRN1VCUOW/fuM3a2ga7u2dwTUlelJzZ2mqt5yBOetgG9vePMdVSv/pPf/yxioAQ4jYwYxVw618WQqwD/yvwOHAb+DPe+9M/4nVWCjNapaCQ4WZyrWpQRlGbPmxb2qlF64AHaKkY9AckrV1X0xggyIjrJhBjRBa3WXs/JiFeUoV+7PuzyAvWN9Y4nc5Jk4zKGh4dHtMYw872FsfHxzR1E6zCsxATnsQJ3W7W3noN1ljyomA0HBJFEXletLctdNKUqqpoGkOWpvR63XasCYeoaRpoRx+tFNYY6vpDjn+IajerEUEpSVXX9Hu99tcNRRk2KkVRkre3/Hyx+LExSrK1sdGChpq1UVAsNlVNUzesr61RNzV5XrQgYk2WJjgXB6ygqgLI6VwYCbQiL+YBjJVy9bmWnUEnyzitJoEs5H1bBCSbm2ss8pzR+hpJGnE6nRAlMdsbW6Rpnz//b/85vv3b3+bm+9dxxnNycMJwmBK3TEbhS7IIskRRWY8CJtOS7/7+d7h3/w7z00c898TjXNi8QNICnQ/zihu39uDOEc+/9JNsbKwzWxTcu7/HpQuPcWZrk26WcmZrg0EnDfHtAhpoMaoIvAnZGM4ilEIAWoT0J8op33/9VU4Oj3nxqSu8+96bjA8n3Lx+m3+waFhfH1IVBacnJ5ycnFCUC5qmovZNIC5FChUpIhyx1sRRRK/XY//hIXv7j3j2mU8glWa+KPn6l79AVTecTKa8d/1drl6+xNXnnuLo6ARjPP/hv/vnmc1LPrh5n//sv/rvPvL8/YvoBL7qvT/6sX//S8A/9t7/ZSHEX2r//S/+P7+Eb7cDIQFoRUBp6cPOuTZddWn+YKgqx6Io6Xe7pEnSFgZJmoQV3PJ1pVi2caJlnC3ju/iwG1h1DAHAOzg+pZNlWGu5d3+P927cxFrP4eHRinm31HH3+71VArJ1FWmSkLsC4SRxHFHXzergVVWNFW61nVjeysb4FcK+BCK98zS2wVqHMQ1FUa6yEIbRYAXsJXHcFsywiSjLkiTO2u1JwCWWhSPSQXIaRZosTVcjRZal3Ll7jziKSOOY/YODFT16aftuWqxASLEaO+q6Xr0nzofRaMkXSNOUuqpDnHvT0O93QwJyVaO1pt/vsTYc0Ol0GIwGKCnpdLIVIzOWgqjb41PPPotrKh48eI9eJ6IsS+R0gmgc+50R/e6QOMqQXoEKmM47710n0vCJJ6/QzYIKdHx0SFkbmnSAVBFV44JPv4A7dx7Q7/X5zEvP0ZQ5SihwHmscKpJYA07T9pIi8D6cDzb3fAjO4T3S1RSzMdLWfO2Ln2MxO+LW3fvEOuAseDg9HQdQtixpmmZlAotQKK1CRoUNRK5+r0e30+GNt4L7UJqmbG9tUdWGH12/CULQ73awxiAl3H/0EITk6pVLdPp9sm6Pbm/A2+/e/cjT9y9jHPgm8JX21/8T8G3+iCLgHWBapFhJhPIgPFLaEOtlbPDmFx+e3bpuyIuGYs3Q6XQp8gV5FLHW7xN5g6ZBek2s4hBu6VqikAAjl1Hg4cWsD7NdYRwPJzlraLLOgNoY9h8+RNiaW9evM5k3oDLiROOMRSpPJ8oQHqbjaRDYNDVpnKJSRV1bnANj5zg7R6IoC0tTBEJLrNKww1eOsp4GT0UL1bRCqYjhIObg6AjvwZga01jSNOPk+DQ4K6cZVVWhlObw4IRuJ0Mi20g3T1U1jCdzjHNIHYFSLKoabSwyjki7KeA5nY6xwlI3lnKxAAix564h1tBJEnwnARk099NZThxHeBmKl/GW9dHaikk5GsiVIcfaIMZ5x+npBONqtIrodbr0On1y25BkCcPRkIgItzDoVJKKQMqKUsnFq+c4nR/y/v13sHUY6uzpnM6iwq7v8uDRIwo0PupxeDLh1s2bfP3zn8GWc44e3uHg8CGnp2O2djZZP3uO7fMvc+6xR7z2+uuMj/dJZMO9O9e5ev4cVy9c4dadBzgynE+CqtV6IgVTH7wenHcoD1poNAp8oJ87H+jrsj9kqiSum/DJz/0kDUP+yn//PzBaGxElnuPxLYr6lOPpKaWraYAGiVMxwjukq/BNgZWCbton6wxRqkdhYWYNVab4YDLh6HjBaOtpvvLZz3Lp3Bn+/q/97QB8NgvW19a59Pj5dkYTOAXPvfzUR56/P24R8MBviHDF/rfe+78K7HjvHwJ47x8KIbb/yFcRAussjRFgCGsxgNYlV0rZzuwBxQ6mHp6Ok8zHM9Z7PdbW1uh1A3MuSmKE1HgvWvJR8NuToV6jfAjp8NZyMl1wcHRCaRyDfp/N0ZBRv8sH77/PzVu3KBcL6qpESchSgXUCRZAne+tAGhrTkPUjyqqmrivyxYLGGcq6QUpBGovgDNN4vFfM8hndToeyWlBbR14uSBJFUZSUVc362hbeGW7deRhYeT5QRLXWlFWOQJGlKQeHbS6dMcRxxCIP+37BHCkFdVXRyRTTaU5T18SdDr1eh8FggFaKo4MDrDVkWcL6oEtRNJS2ac08HbW1gfobRZg6UJht3az4EYNeDyEgisKoBQFsXYK3xpoVJbyqKsqyWn2tvW6H8xfOEScx/f4AL4M8OKQmg9ICYwQySnj5M5/h9v27vPHWW6yPekzmOSa2vPb6W5zMX6U7WOPZTz5PWVbk8zm3H87pZXFr+7bG+naJ8Z71nXNcvXKJq1fPk3US/qdf/d+49+A+WZbxH//F/4AzO2d48OiIujFULQOzMSFoZOEhkpJ+FMLQlkxTWvswEQnqyvBPfv/7lJXhp778ec49dpatnxlx+fI23/net5nNTxiPJ5ychvQjSzCWkQKiOAoy5JacFcddrlx4AmfgvRsfIIylqzXXfvgGveE6X/jcl/mFn/llJuMFd27fZW1rh5s33+fu3i3Sq5L56RHVfMzDRydY6/nCp//lFYEveO/32oP+m0KId/95/6AQ4s8Bfw4gjdWKubeU9CzXdyEu/EMFIAQnlVhrYhHeEFM1mMbQVA2VCom3P84AkkJQmyBQCR4CIdEFIVkb9lFK8u6N23xwcECv22E+OeXk+ASspSpzmqoiz4uABlv7YXx6u5x23lK1JBzjTPBANA1FGf5MUQbab4hG7yKkx2GJUtW2+wbn6rY9NJT5AmNDMKuQAuFgvlisvP431tap6qpdQ5qWDDTFNLbd8dcs15VVWSCEw5oaKbLA028q8nmNaUJoRVmUlEVJFKWkWYqOYqIWIG2MoarDqjNr9/1lGTgHK5mzlAwH/VZFGHb7SRJ+b+D6h/d0vljgnGM46BNFEVm3Q5xEJFlwbFIqALrOe6QX7RbD4hC88MKL3L7/kMl0ShZJCmM5PD1ga2eH7c0RD+/eCoG1znH1wlm0FExnMwwSi2Jtc4vN7V1UHLHIFxR1zfX3bxInOoxYg/5K1TmeTknShLW1AVGkUVLQ8T6EuQiC9iQsJAPWLATGweF4xvF4wssvfJJzZ89ivaWqF5yOj7G2pq5L5vP5amyyNhjLiJYXEjZUEq0j0mzA1StPcu/OHvNJSSwVWMPumR2eeeYTPHHxMd5//yZvvHGdvYeP2FxPSbtDzm5vUeYFr732OrtbjzGezOl0ukSr2fef/vhjFQHv/V7784EQ4u8ArwCPhBC7bRewCxx8xJ/9q8BfBRj0Yr+csUU7v4dFTOtHZ23IowNsY5hUU/KiAAOjbpd8scCOhi24FvTh4d2R2JaGWdU1RWlJk4RMONIkDsm4UjDqd/nEU5e5/2CfsiyZjCcU+QJrG2bTKXVVhT2+8CFNyLo2cMTjvG7degLTqzGG2WJO0874zjvqpmq7EE01m1GbGl9aHh203PmmotdJiaOUWMN8EebougohIgJWvPylKYg34e9l2537MgG5MRWLYhG+j+1+3liDjgLxpG5qqrrG2mCxHQQ9hizNcLQW5Tg8gR7rW5qlsQ7RmDD/t2vGEBcfBEBVXbfZETZsTKIQH9a0nI1OlqKisPIdDHoIBEk3JopCWk4YNYK1euNCV6ClwCGQQnN29zyf/+zn+d73X2N6us98vmBtOER5j7AVT114jEWe000TIhE8DZ2QLPKGygruH5yyuXsBrYI/4/7hYXCkUpJevx8Uqe3fNy9LyqoKWygBVRNwDBUe3NUWZ4klCQmLRcODvUPOnDnDc594ml6vw+nJEfu3b7L/aA8IjsdVVTNfzFp9xZIR2FKCnCOKM7q9AbtnLrI+2uL96/cwxjHodMm05omL50m1Jp/PGc8M/UGfz164wPde/V6LS0E3TgHJ//Frv85zzzzDp57bXW25/oUWASFEF5De+1n7628A/wnw94B/E/jL7c9/95/j1VCRRrQ+gtb4D5lTLVgX9qUR1lq63YwkjoiE5szaOlIGiuzaaMhgtM5oNFoJh0JkeLi7kzgmidTKQVZJEfgHgLSWTEvyuuLOrdss5nPqpuLo6BjREmWMdVRVw2w2C8GlDhZFgTGG+WLehqfWCBEOftDSe6SMGQyGxHHZkn0EhYfJeIxzkCQxRwfzFWBXlFUA7SJLmeerjMZlzNpSYr0E/eIoyGKXsW2NgsZaXOsjiPf0spTJYs54Pkd4qBvbWn1JmsagoohOoki0DKm4gyG93gAdBepunpcoqUiTmG4bm+6dQyJWWaFxHJMkgrwomOzNSNOETpbRybK28FhELAL/QwUBU2MMUinquqRpgoPRdLag3+shY0CGQl40guHaGXZ2L/HB7TsMOiHhZ9TN6EWe4/sfMJ/n5J0O585fJE46lHPD+YtXuHj5Ku/f2eONH91i7+GUW7dvMhr1yNIU45pgLe4CT/+Jq5coioJ5npOXFVIF0xMlWjLYapXkA65EqAtvvXeDN669w5/6E1+jk8RgDb/97W9RTPYpipKHD+8zm8+oawsoyrJBSlo+YPDGiKKEJ68+xUuffp4vfe5n+Ft/6+9z/94jTG05c36T7e0QNdeYinev3+Cpp36SJBtSG8cnPvkpfvO3f5tur0vuY958+zb/yi/+Ei+8/CLrayNK4/iojz9OJ7AD/J221dPAr3rv/5EQ4vvA3xRC/FvAXeCX/6gX8t5/qDf3gTAU6KZBUae0ar0EQkuGC05EidQrBZ6UMtyabXipVBpLCP8oq5p+NyPWKrRx7b5aCIkQ4YDPZzNmsxknJycsFnOKssA0YTHkPCwWBVVtqBvDbBHa9saYlv5rAusrD0y5QTchVqL9+zisbTg+OgrKSO84s7OJ1hGzWYFSUXA9khFaRYAijhKkhKougA8zGJZFbTabr3j8QgTrrrpuVnLsJtIr5aQ1BqylbltlY0wQphjbRnsHr/ss62ArQ+5rPJK6KmhMQ5qERKQ0y1aU7UVegIdelqAihW4L0/IjS1PSdrPgnCMvCrI0RUZyxXnQWrG2NkQqidI6GGeahnyR4xFkWQZG4pxgNp9z69Ydzp3Z5Ge+/lN4V7C/9wGJDG6+VVkSS+h2EqI4QkYxQsecOTNk9+w5kiTlySuXSJOM3/yt3+GLX3iJy5ce45/83u+yKBYM+z20lGgh6fe69HpdHu4fcHJyyrndbZZSs/APLW29pSi34PKtW7expmbQTfGm5o3X32YxH5PPTsmLktlsxiIvWtA2ZGYYV60yNOI4YdAfsru7i/OSvYcPeHTwiLIswjrSezq9HkXdMMkbsu4mUmvWN4YIobn74B7dTpfBMOWVl17kp38q4/r1O+wdTlBx4JF81Mf/5yLgvb8JPP/P+O/HwE//v3kt5x3T+QKtVQBFIoWzwRFHK4VErOKXIx3inKNIE/f6GG/JouC1ZhE4EQ6+kwVeG9I4pZcFRpl3NuAMOmI5zxVFxWQ64+bNuxyfnDCdL7Am6OPrxqF1jDGG2jist+hIMBh2MI3BWMNwkOKd5e7dKU01JUsinr16kdGgw2I6pq4rZrnjzt4jVCSIY8Xk9DbeC0aj7aAEVILGBvLTeDKhNmUoViJw/aN29sYH8xDvHXVtmS+CeWQcRSuTk0hH9DoJpmnQQiFFTFEsODm4T1OVOGeQQpKkCUmUhAIQRWyMBghjENYjlOLw+CHvvP02SE3W6dHvjVqsIKLf7ZMkGjMYUCY1URSxtrlGr98jTZLAP9CaoizDSKJVmy3pP9RotJLibpKho4hiUTL2Y0xtSdOUvMg5vH9KHEcMBwOefPIqs+mM6Tznlc+8zHvvRty7eZ1AWrKgIUkznIrJnWK9v8ZTV55BSI1tLD968w3euPYOo16Ptf4AYT0boxFVWXDuzBlSHaGEwDhHv9NlnCQoQqCr865dBbZclnZHtczQfLB/wJ07t3nmicv0Y1iUBcd7dzl4cIfx9CFFXjMeLzDG09TLgFmPijQIT7fXZTDa5Ny5c1x7+yY/+tFtHty6z91b73Hw6D5JonnquSeZ5lMSITl75Qovvvg5hoPLWKtQSHa2N/nRO55v/uLPs725Qb/bZT4vefX11/nBG5KXX3j2I8/fx4IxiG8fCiuQwmGlCKBJu0fHurAHNyGPQKtgaeXaNyFr50mhZHCxsR4vQnsqW1NJCSvO/Y+LTWazBePxhEVRUJQfimmEDMo3ISSNsQipSKIAUOZFjXE1zofUHAFo5egkiihyVMWYcTOlqRZIBU1tKfMJ3V5CJ+2RZQl1YyiLMZ0O6DRuuU2KvkzJC4MndCpLoZSHFfCmZHDUWc7nQgrSJEGtrMJsYJw5G4Q3x0cIUzJIVZBbpwEAnM4XzPKKbqR5/qkLjHoZWSyZzSveeu8DTidzUBGj0YDBcC14OhBYkE1tqFvGplKKeZ6H9yNteREiFCTnw3w+nwdWXBRFDAYBjLXG0dQGpTTDfn+leSiKIGxaXx+RxMGkpakbBv0unSxlOnVsbW5y+mgP6ZqWCh0hVfDdd0LjhSaOkzDTW8u1N6/RVBV/4uvfIC9nvP7mNRbzORLB+nC4YjqmkWLU73GSppRlUGQqvQxj/b8/tgKwxvDo0QGXLz7G01cvYeqSRw8fMD09YTo+IS9yyrIO3Z7S+EjirMRLB7JGaoGONM88/QRxnHF4tCBLO9T5lNlsTFUXpGnKvb195nXO7pNX6A4G6Djh4eEx3azP+qDLdDIBZ+l3U0a94Frd6yQM+12SSNNpE57/WR8fjyLQsnlDaKaB1g9PymDB5YwJYppWXjzodkMrLENIqVaKOAkgU9Sy1jpZhpcK2xaAJbbgnaMRnliF1dU8L5jNF9RmSdpoTTBMmOfrpqGs6wBYNRXgsS7M2lJ64jggylubI4SvKBYzynIBkSASrQpOeQa9mP4gpdeJQCqcjzkZ1yAsSgWEOIolg7jDeOppTN2mBtvWAyBe5QUsGZZJJ0GrYJCaZWnw8xfh8+Id45MjpqfHlIsZ53fW6XZCwpG1DUo6TF1Q5TMEDadHj6gXMVmkcSrC1BVpEqOTjLXRgOFoiFJBwt3ULtjDu1Yk1ZJg8qJYEYjqNCWKwri2/LqscygXCqfWGu8ci8WCxjSrwBbZyrFtq7OwziKVZH3YC6NOOyomScza+jrFfIytHXGSgFJUxqCimKTTBQFlbTg5HtPpZHz9q1/k8YvbTKc95vMZ48mU9fURo+EALQXehwsjSeLANq0bpJA4a5FCtTJ0fqwYBNen4+Njdne22Fgb4J3jzWtvs7//kKoqqeuKqqoxTdh8hKh5QrpRFtPpJmSdjJc+/Un29k/Z2i4ZdHskZow1NVoKOp2MwWiEqDXGOaaLght377G99iTeC4qyoaoqZrM5J8fHnN3extaGuqpItER4x/j06J86dsuPj0UREEhsE/z/daSIEHjhwLqQExBHKGfptAc8VhJFeKAi7xHGkEpBR0sSreglP7YQ8WA8Icbch07gZDpt31xHXpTM8wKBQ2kRrJtcg5QOpRVl1aCkRQmDVFHAKwSgo7YdVERacvHxJ9ndOcd0fMzBw9uUwrG71SfSkq1hzlo/QyqNVGBdsIKaIagXlkIYzpzbCCONc8SdTgj3KC3GGpTWrK+NEEJQVTVlXbXEpCaQV7Smk2UrCXLViobuj28zmeQIa+hkjp11ybCj8E1JWcxYiyz5KCHNUjrmhGZeU0rH6VhS5YKnLj7P1vZZZJRyODkGGQpfFBuiRCC9BqmorSOxFVmaIoSnqhZEGrzX1PWSmm3J0hgpl5oDT2mDGaqwntl8TpYlJIlGuoYiz0nlFsN+p9VmhLczkpIsTuhmQ4wRdDsjfJQiXI0Wkqyb0tUwSCSLvOC3f+97NBZ++pe+iQcOjUSnGecev8TGaANhLL5uPQxV2EZ0Bh3Wt0bcvHWTKBNcfOwcEgdO4EW0wgEmsxnvXr/J8fGYV55/hk4s+Ot/469z+OgRs+mYaV5RmTSsKaMS7ytqVyK1IIpiesMtRoMRo9GIW7f2OTw5Zne7R16U7J9aHk3mxKlmba3Lo3v3scAPjq+R9YZEyXX+1J8eMTqjUQgePjpCq4xr79+hMPDY7hm+8sWfwNSGO3fu8d3vfO8jz9/HoggAWGcReKwUrWNQ+BFCYsVKYqsJe+kQYhpAwBC/HUaCpYFoSDL2YUXU1PR7HbQQiCU3QMLh0YQ8L/DOk+cFi0XOZDJhMp0GUpHWK/1+miaYCpyr23Zc4qynKGqck4wnhjRWrG9sESnPZHLCwUlAyHudlCjO0CpCSMV0VjCb1+ztT+n3RySZJI47wVqsaUgjgfOGuKOom3Dgs6xNG0LQ63XbtWfR+gwswcNwUwlXUecTbF1i6gJXWabTKaOuYtTtkXYitE6RaUXS1Tg8Mi5RIqaoBJN5zmCwy9rGFsY78tmYxpbgLMZCmoZirGWMM60R6mlBJ8vI0kDbttaGiGwpV5LmEAATZmmp5IoyG2THjsZYGmNJ22diPJ0RxwFjWFmWiSCnXuRzIPgNKq3wLihChZIr8db3X3+Do+MTPvvKS3RiweHpnNI6hlnMaDSk282oipyqrle+Ep6AsexsbfD2229zejym6gYYAAAgAElEQVTh3M4OSZatxGZLQHA2W/DgwQOcbUhixYO9PQ4fHbBYzGhaD0oBCO+RIrBgIh1McKIoIokimqbh8PAYKTWB5ZlzcHhMMQ/dcK/bYXNjDaEl87zieLagsgIVG3709nsURcPTT1ylaQzr62tsr414bHuTcxvrRC1lPhaSjUHvI8/ex6YIyKWAiMAaEyLQfK1zKB9Q9qWjz5J7L6VciVaKskLqHB1neClWARdFWba79mArFWvNrGrY2z/BNoayxQGWyjrT+ucBq7AS2aoZnWmVJGF7Hb6W9qGommAJ5mLN5tZZ1te32Lt/j/l8Rl4Z4kgRxx6P5fi0ojaewWCDJOkRxz3iKA2sMSlJUoXzDu0FiW1tw6um9UdoRwetw2HzH44wy5/HRxOqRUk36eA7Qwo3YXxak8h5G6XdQcYxqU6IM8jLitoYJgUUJuPchScRJFTG0liD8Y71jSFFlWNMRRRp6sayvT7CO0lRFDhRs0oY8iE7MaRDB62CdXbFHjTWEvsgkPK07sTtey+loNvpgZfs7x8GrUMcoWhVlh7yfM58NsbbmtIYpLdkiUYqjZAyJE/VNW9e+xFf/fKXOLu7jdaKbjdhUQefw6Jq2i2HbcdRgfft8yZDcnMSJZyOJ0xOp6Rp2I64tlI45zg8PGA+nzLqdzg6OOC9d99FCkeVL6iqEmtqKmMxtgFhEMKvLOGSNGUynTOdzKlqQ7fTYTDocvjoCKUkURR8BLJOSqffCUrTKGHezBmur7G9c47nn3+O05MZr/7wLfb291kfDFDAqNdbpSDHWjPopkRSfeTZ+9gUgURrpJYh/80HdZqKI7ypqa1BCxH2qs5Q1SVp6/3f63YYDYd0OlmIKFNLcCwYPCZpQprG6HZFcnhyyrd+71V+4jMvEilNURYUrcnHSlzoP2QvJnEcAkEWIf5aCtobLSTZSBVckJYBnc5YjucNSkWcv/o8cfsAO0JGgbUNd+/vsVjkzIsGKTRZt8d0PkcpSbeXoVRoeyOZtbTn4IbsW5KKc8HkQ0iBsEFFuFiUGBvIOvOpIdEjRsMeo+4FnHV0I40SjkeTgr1Wc97p9+gPukRRRH80xFeexAiMk+RFSZJAbQ1OWipj6Q8y4qhHsSjp6ZRHB0dIEYpRt58GRacOHYmSMngp1A3WhpCVwlp6vbCfl0qyvj4Kq0priZOITtYhSzrsntlGSsHf/rv/gMn4lCuXL3Ll8kWUkigpKIsptlkghUFrgZKaqjI0riHzmt9/7Q32jyf8yp/9lZWXo6krtoZ9dGUZHx/SiQN5qqlrXnvjLV544dNsbW0jRchLEEJx/tw5/vD7r3FyOObnv/mzZGmwMJOAsA033n2bjX7Cp569yne/87ucHh9zfLhPVeYB3CznGGhNYINPQJJ0SdMOnU6X4+MZs3lBPi852D8iEoKnLz+OUpLf+Cd/QLcXsb4xZH1tRF41pL0E0dlgvCjpDUfUxjJdLHjnnRvsntniE09e4emnnqVswExL+kmMlg6hYo5Oxx959j4eRcAvDShDv6WUQkdBompQREqhREiQ1e3DpVtfwcYaqrpCqtb0M02wNhhvNo0lihRahzXSYrbgxvu3ee4TT5MmCXVVBWVb2w1MZjPqqmpbT4FQgqYy1FVLdCFYPomQWtJ+zghjXeCPC0GUxGRp4N4XVZA+LzMBamOwzhKnaTDFlDVJkiKFItKKTjdlY3OIVC6k5ah+60wUQj200m06cOg6irygdJ5Ix5RlWEciBDqWIf5bBX2bjhPWN7cY9rpkaQQi8OFRodA11nLvwT6NtWFn7xxRFFMbg4514BIYSz5f4NMErSM6SUaWBLFSXdccHByu1IHDwQDvPEnq6HQyvPc8OjhESsnxySnWedbXRszmc3y7qplMZ+DDlqNpws14Zmeb45NjDg6PeeqJyxhTg1ZUZRFu/ywmUsG2LIpSZouSyaLg4aMJ5y8+TpaG1KJOmtCYAJBhHdPxhIPFDO8D5tLrdXn/9h0skq3NLfABwMvSDutrG+R5wcnpKbs722glsdYwmZyAb8jijNPjI06Pjzk9PaYsS6wx5PkijJKt/0NeGWIVIXVEmmacPbPL+bOP8+3f/QNmvuT46IR+NyUfdvng9m3Gk1O2NzfY2lwnSWN0kmK9ogRk6TgZz3iwdwBecOH8OX7+a1/lBz98g06vR5ootPCMj2fgHdPJtLUo+Wd/fDyKAMFYJERnCTrdPl448qpA65apJUS7ErQoBDJZcs2DCUljwt7eucDj9AQnMe+hqBr+4a9/i92dLZ66/Dj90YDT0zlxHJNlKfPFnMUipywrZrMZkQ4CHaUVeVHgvCdLU6xxbQcQKJ/B3y8g7XGkKYqKujQ4FSFlsDBvjMXlFUIJtA48ga0zG4HqW1vK0mBMsPQeDQfMF3PW13v0uhlxMlwZeMrWC8A5R6wCFnLgjjDGgQQlAkYhBKi4wruGpJORJh20iqncnGNToItgs+acC1Rn65EyojsYkViLsyZILiQIGdiGtfPUlcNJiW0apDCUyoTuS+twGJMAXCopqevgotw0wV/BNA1lGYrroN8niWMWi5xONwtJwNZRNTWzPCdKU2y7Gfri53+C1998i9t37/LOe+9y+fHzREpw//5dbDUhjWJM3SCkoraeovG8c+MuX/jyV/n0Cy+s2JUKSJTC2IaNbsI+nhu375DP56RJwtXLF3ni8iXuPTpktLYOQiEVXLz0OOcee4zf+87v8/1XX+Xc2TN89oVnuXPrDteuXSMRDfV8zBu3rnNy/Ii8WLDIF1R1ReUCGcs7Q2MtWSdDKsWTTz7Dud2zzOYl81nFxuYZZvOaB/tHPDo4ZP/hHrPZlPFiQieL2dt/SF4UDDe2ybojfu7nvsb5i5c4nZaBYNTrgXV0k4ifePnT3D855dzuJgLB+tag1TlUdDr/PxgH4EP7L+SSWhpaS+csyLBmkq0WYJnHJwh71qUXQJg9A9CoVcTthwfsPzpgvliws/UM/X6PSCn6vR5ZouhkGZEOhzYYlyq8CwxG144EouXP9wdDnHXMW+DH2kAe0loxm01XHn4qiRA4FvOwZ9ZRxM7OFp1O3NqRBTvuDz64S783IE0ykjhFyQgpNLHOwAesY5mMI1r3Ia013Swl0orT8WTFrhwMhmgdUTcNWSRb4ZRG6yDOmS9yqtqglCSJFWa1EhV46yhzH1yMIklVlzRV8ECMo4h+t4voBvpwVYeg2KLIMc7Q73dD51O6VgoemJ7GGsqqYonjdLudYFPe2pwLIcMIZsL/z6Jk5WS8FBwBPHH1MnVTcefuPYaDLi0sRxprtJR4J/FCUlUN+4+OOX/+AlGScefuQz71bB/Tmrk2dU0SRVS1I4ujQATSmjgOR2AwHLDt4Wg6o9ftkcUa68KzeP7CBV774X1Ojo54/c032d97yPj0hI4WGGcwdYlt6mDK0jIyvZCYtpjpSBMlCWnWYXd3l25/yOHJnHeu36LIK/4v5t4s1tLsPM971vrnf49nrHNOzdXV3exuNptkcyZFSRYgWIIDBUYcwAhiJ7DhmwxAruK73PoyARwEMBAj9k2iQHESQTYdi05EySYpimST7KGqq2vomk7Vmc+e/nmtlYtv7d2MrTYDyQl6A9VVtWv32efsvdf6v/V97/u8W5vbPHn8EKscx8dn1HUJ2vHSi9cZjUaAfK0kTdja3ETrgK7rWB+PaZuWqmoYZBFJEtHLMrq6Jc0iAmepijmHB/vU1Sf8OODXmOizA1n0SomcNAyhrZ2/ygQETmCSywQLYQRI+du2QsFp2444F6fZO+/d4u13b/Prf+GXWFsb+7O7PN+iEGNIliYYa1YmmDxLiX12Xq+Xk1kRu5RFTWc66qZbNSebtkFsDgac6PFns4k0yKwmikLyLAVrMW0Hgebg7JRFUZImKXGUEEUxzgY0jSVN+kCEtUIJApH/Wq8PCINArpTWEXnp8PK2xJTNJ1LOj8drfiRnULYBK7qH+XSKwhEGsRibHBhtaNsGpzoPHJFOtlKS/JREvvz3GQhVU9IZiY4zhST4SkybJs/W5LhSCesgjmPxbSTOB6yI3DkItfAIGkOgQ+g5L4CSPIAkDhiPhuxsb3Hv/l3uPfiQtmskfFUFYlTSIWXdMZnOyfKcr3/1y8S9dR7tH/DTd+9y5dIuwzwlixOs7UjjkNlkxmQyoSoqolBLv0fDeDziuz98m5dfuEHkU5wVmiRLCAPFw0cP0abi7PiErqkI4pSyLLwWoGJRLFiUJcY5wigCpbGuI9Ahg8GA9fUNkiQjSTNu3nyRW7cfU1ZTUo9wC7SlqCriOCSNNP2eTFtQAUalGGOZzObouEcQxhin6ZwlTiKslUZrGkJZzOnFQ3RgmM1OOT7epyo+Hu71idgEpHR3hFFImsYoZwi1fOA70xFp6eqHKgAnx4A4yVABLKo5aRbRmYowzokTR2sXVG3Ed3/0DvOq5G/+jb9GnuWYzvDsZEZxfsLGxjrFYkESh2glzrAoCnHeK++cqA4Xi0ISebVGqZIk0cxmc6azuWfpRVjjWBSVb3hJLoIxjtFwSL+fMxj0GAz7WGOp6oo4zoCAOJSk4LYrmRctloxFUXA6Cen5UnuJGl/iyLNUBmjOOYqyomkrOU+vXkPNcCyg0TC2KKBuS3q9mKaBouhI4z7Wetmxk9fd2pJQyzEoDlMa1zKfNx5JVtPrGVKv3qtqCbd0RqF1RKRDgpiVgWkynQpQxQg5N4ljRqOhZzjgN6OGWEeCJw/tyuxkXMvR2QmTRUyWJMRhQC/PKBdn3HnvkEu7W8ShwWmYzKeUVcuzg1OCuM/Xv/JFRr2cre01Ll9YZ7qoefT4MftKceXKJfJejrXwyuuvc/XGdQ6PDrn/8CFPnx5wdnhG1VkW0zm/8799i/X1db7y+c8xmUx5sv+UX/6V3yLPAn7/n/0uJlDkw4wPHtxndj4lDiIWhaOsFHWrQFmsbhjkCYnuQxiwe/EyWxd2WN/ZY9wf8cHtu6TU/NLnbtLLQv6gfsbR+QwbxuTDIVuDHB0YOubk/T7TusEGPc6nLUlq6NoO18zJo4BBlhN6HcW4n+O6hLAztPMZD967w6M79wjM/zcGon9rN4Un/7LMjDdCFgZR7hmLUVau/hZPCjJoF5DEMdYaCQG1IlGdTKa89c5d/uQnt7j58mcIdMDDDx8zmUyxxvDGp64z6PcY9HI4dCuWoeC0AC9XruolN1+MTF3raNuGsqp8+q+irGrapkXr0GcGSCMtjiMubG+SZalXoLGy2q5+aiWwjelsvvIHzOcLTs/OxTDiBUAfCWxkajIcDcnShDSR8BHkW/5/IL8Af14XifDZ2bnAPbOU6XQmvZUgWKHXtIambqTZWFc+XSnx6cI+bu3nQkj0CrEOYaiJk2gVPy54824l+Q0DSTwSQtJypOnQSJMzjiN6/Ry81XkZYHJ8eCxwHAx5ljE9X1CUJflGD2tboijibCIA1Zeuvcjmxpb4G2LRYwx1zKsv3+TZ0TG33v+Ay5cusnVhG5RibTzk0qWLPHryRHQFgcbULf1ej+GgT1GUHB4dMR4OeOPTr0pzb1FwdHzMxjiFTkZ+WiuquqKqKpq2AcTUpBTSqwkD8n6fMIixVvH+nbvkScq//Jff4/qFbfr9Ps50OBsRhzm1c5RFRba5Jqj4zgqctBQ0XL+X08tzusmURVGi0hibJMI6dBCgIAoJreXp8TGz2Uzs6k3zsevvE7EJAOAsprNUtgMl8+Cgk/x25xzKKZwWiIh1lqZusEpzfHrMeCTMvbw/IMlbfv///CN+dusBBAmjtR3+j2/9M37tm1/n5at7xGEoEdWtIU0ihoM+e7s7jMcjqkpgnrPZlKqq/QKvQfnE2iiiaRqqSnQFOghXnP9+L6frZGQ1HA4999D45lhNUQq0U2tF6zHciechdp3hw0ePuXP3nnTRlWDWxqPRKsdvCfZcLBaebCyjvTgKGQ4GfOb1V8XNZw2LhZixJMatXcmo66aBRjwVKHHsLTURbdthuhZrDZ1tyLKMLI/RgcS2xXG4at46E6CtZjqfIWnOEWvjEVmWCSdQCWx0sSiFO5CF9Hq5lw9DWdU4C0kUM5vNcUqy88pGos7yLEdr2cAmkwmHB89RStMbDOms4vRsSppoTs8Ljo4nDIYjdnZ2OTw9Z3v7Ap0TZlwQKWwXsruzw+6FLSazgtu3PwTbcGFzzM0bV/nBD3/E46f7vPf+XV544Qa/+o0vYqzl3dv32RiPubC5QRQpzo/PuHf/fbRraCrLfHrG0ydPUCgWi4LpoqCzhjgWTUkQBSShQgeKKE35wme+ynA85nd+73/l7oP3eeH6RXYubuDaiOOjBRf3Povdf8r05CFKKY6en7C+kRNnEY2z7Fx6mSDs01QtGo1WYvvOkpRAB1JhIYxarRzOdbz33nucnBwRRwFKf8LDR+R8GEgKsVJ+E3B0rUh0nQMViI04CMQObEyH7gKMdh5ZFYuU1mvakzjmjTc+x1e+9g021sYM0oRQa7QnuGitfApyQJIkhFFMuyiwDuqmoyhld5/PZwLOTBIKLSaQNE2pqnoVDCIhn9K5T5KYfq+HAs7Oz1ZpwOfTKVorhgOh6ljPA2jbljiOuLi7w3yxAAdP9p+tSuvANwe1b6RNJlMG/R5R1LC3c4GtzQ2fyxj411ITxbJZLUnDyw0s+LkoN91Jqe6s9SNA2aDi0MNbTMd8PvfHkEgwWw7vmZAGaudHnsYa1hjJ8yMViZCf5fuO49DrOrT/2l5B6EenDglqXV8b0x/2RTvgOvI4ppemrI9HnJ8eMJ0tqJuA7fUeRVFz5kU86xsbdBYSFdAfjOS9AJrO+ixHR57GjEcD+oMBf/Kjn3B4+JzDZ08A2QB3d7bZ3t4CoKo7kjjm1q33WfvS5wlUwpMnD9jff0QaB0zOz+n8hjWfL2ibls7Ucqx1GoxFBRDGOXmW0R8MmUwq9g8ecnh0zGCQsLnZI8kTVNcjPNNcvXwFYxP2T58KsFTL0SlQAWkYCYFbiUcjCqUCjmNp5gIY64gCsTw7a6iriqIqpAHctUTRJ3w6sFIHembAUkZquo40jkASslCBaAVkE2gJHURhTFkWGGNp6o7FomJne4/XXvssL730KdY3tnAowqXyT4nYI/TEnjiOWF8bMx6NODuf0HUWYx3WirNxGbJRlRUWR56lK2Kv8mPLVWCqUh7NLfLX2sdwtV3rS0eZMc/mM6qqZntr04eGSpNs6NN0prOpBIV4B15nDL08J45TtHLs7lyQhtmF7dWCK8oCnAhT2q71r6ti4YMur16+ROQ/XJI72HivgfLRYQNpcFWF18YGGNthTEfXtTSNUIiWaLYkkWZfoDVay+YbW+PDYQVwolAiaNIiLXY4Ah2sjhPLjMPluDWOQkKlqMpCVIdJitYh62sbPMtzTk6PmS9mJBGkkXT0x+MNhuMNhsMRw/EGcZLSyamRQCvOJguKouTi3gXp1AeKrfUxp67jnaMTjGcslFVF07YUVUNZ1QwGOUdHh9y58wFZkvDTn/6Irm1wXUVVLLwgqPL9EedHqmCVcJlAkWQp4/Ux62ubVFXHo0fPqKuGNz//KcLY0HSWw2fH3Htwxl/+jW+Q5j3euv19mqagWFj6vT5pnlB1LW1jCELHZDrzrMYWpTzB2Mvqw0BjTYszLUUx+6iyMy1J+gl3ESqlJHnHy0iNMdhWxn9tY4j82E669y1Zmsiiw6FRjIYjkjTHEpIkPd5447P89L17fOd7P+I3/+KvE4X+A6edf76QwLtDJfDEsLW9zen5lLJqaI0RW7IS/HnXtn5sE9HULWFYkucZa+Mxn3ntog8WXVA34uY6Oj72LIQQazvxy5eimpMoc+ECBIFmPp9jrCVNE5I4opdvEAav8uzggMdP9nHKx7M3DcNBn0sv7nHz+jW0VkxmM+qmYTqd0R/0JfXXbxwOaUyCTFXuPXhIvy/n3fm88McEUduB9GLyNCXPYmk6+Wak9XFutrUr27IEnwooZBmPdj6ZyNk4z9lYXxPw6UI2o67rOC1OVzmEXSv9gjTNieOYPEsI9Mjbx8WZV1UNx62l18vJ85TPvvEG165e4d6H9zk72SfIQy5fuUYQpcRpj8vXrhNFGU3nmC0a4jgkjzUqDOn1e5JU7Y8+L964Qrm7hWlLnh085/jsTLruSjHsZwz6OWVR8ubrn+IP/8W/AAcZZyhrOXx+5MNdpW/Sy3OqakEXSmWVpRlRnJLnPS5ducKLL97k61/8Gu+/94z9Z/u8cOUyaZRx7cZF3vrpQ+Jog7/87/0qv/lrX+Wtn/T51nf6nJxOqSrNo8fP2L24TX8sk4WLF29y4/o19p8f0ZqO4WiHurMkofhnWgP37t3m2bNnnB4f03UVUSSk7da0H7v+PhGbgPbNJmMszoFVFrw2f2kfW5aiyn1kDupaR56mpHGMUkLkieKUCxd2+ebGLn/4/be48+ARL9+8ThoGwiro5MOvfNJRHIaA4tKlPc4mUybzuczslaZtaoLAl75Ko7QkzljrsMat8vq6ruPo+JTpbCa5hV1HZ8xHMlqtRXfgsWCSXqR8dqBcPcUkIxLc9XUZTz0/OJSEYOfI84zRaMhoOCBJRWzTNLIhdn6TcFaORrP5nDAImExm0nTr5Vy9conIS5sHg55wGP0oS3vFntYQR6KXWBSlKPq8TkFIQTLSaxqJMj8+OfdS3mAFhCnykiQRZoHWS9qz6Da0UsRRSOS5B0uG5Ec3CWvt2pambgTnrWQSkmcjBoMh6+vrnJ7sU9UVTWeIQymFb39wn/5wjfWNC+RZhA4UHQ7jHIMsXfVn0jQlDDRpErOzvcXWxjqn51MOj04kWTqKccYS6oArl3Z5/ZWXeffWbQLX4YyhqUoqT3UOg5CmMx/ZixVEccywP2AwGnNp7zI3X3iZ0do679z+5xwePeell/bY2l7j4oUr3F+reOH6q3z5S1/g9v1H/Oz2u1y8tMls/gxVK2azglFV0WNIlqZkvYwojpkuCmaLOa+/fl1IW0phnOHho6fcunWLtqmxXct8NiHwycat7fi42ydiE1gqzaw/ny7PsYHW0pH2HyTtnYOxDtCBIkkEUhmEkZTxxmGMI04yiAO++Utf4598+4/orOPVG5dIQ5l9L+GFyjkP85BAjcGgz2g05PgopfYUoyAIIbSQxHRONirlz7ZlVXN0fIKzlrKqqGuh/I7Hw5W3oNfLJMZr54LvwssRAgeLoiCKIyLEojybz5nNJdEoCkM2NzaYzmYYY9i5sMVoOCKOY2bzhRiI4pjOSCJQnmVEcUQcxxRFQRiGnJ6d45yTHMJaNBBFURKGAf1+jzzLAFlkGxtrhIEmjgLyPOfs/Jyu67xNOZWjxaKgqmvKqkJr6VEsN8KmbbxnQzGfL1aTjGWITNcJFyHw2Xp5nuOsxJZFUbhCqi8xWEotR45iNa8boSytr22wc2GHWDWMRyNUkHA+q/juT/6YvDfkxo0XeOXVV0izGNvJsdIix78oTb04XRFHMRf3LvDSiy/w/PiUDx8+ZvvCBb6yvk5nBck26me88drL3L93l/J0TtsY2qqhroT03JmGsimxzoCWCLs8y4hT0X/EcYZxAR/cfcCPf/oWyhqs3eXoeMLhyS32dq/z+qdfYzhO+aff/hFlO2Fzs08vj2lbw7ysmC1KBnUDgShQO+tQYcDGxjqBClBY5kXB3XuPmc9Lruzt8OjxY6bz0gfQGMASx5/w48AyanuVFGSVf/OFLyCpui0qkHEZWqYHbdNSzEtCFVMGDXHmqFvDBw+eEGcDwjTj82++zls/e48sUVzc2aSfxiQeFBEEIa2V55/NF2xtb9J2Hc+eHyBOwQFNXVIViiiAeeVlw5G82YN+j63NjZUizjmo6pIn+/sUReWv/rI5hVEkxN0g4PRsgrWWQb+3Sh6KfBLwkrfYdp1fvPVqITeNjMXyLMNYQ9O0WCP9h63NdV8tOensZyl5lvnzbs2g32MZPybNU8vxyakkHhnDbL5AIRHk169dIc8y9nZ3ab3k99GT/ZWC8+d7AsY3FeM4RCkZrU5nM5qmIUniFckpz7IVDUmqiYbRQLIQkyRehbeWRUVVloCmrmuU6gh0wKIUCflgMOTKpcuErvAYOQijmL/51/8DwjihbBxhpCjKhj/87p/wqZtX0Rq0ssRhhOkcsYep9vKc3/pLfxGCgD/8/o958PARN65fYzzsk2cJHz78kN/7x99iMZth58csFhWLoqNYlKImDMT+HETSjO31csajIYPhGjeu3eTVV1/n8fNT3nvvHdIMvvHlL/HVL36FD+7v87Nbj/j6V75J3s+5/eAhk/KApjvDUXH92h4P3z+mKBc8fLJP2XW88Nqb3hcSEsUxs8WC/+v7f0wQwO72Fi++eIMnT57zzvd+KFhz5ajKApRFhzDvPuHHAacUJg4lAtuKYMR6Sm6oQnQEgUxAcTiMlQTcIIzAKiIdoExLWy5QwyHf+ePvEPVHmCCm1SHn5xMOvn/AG596kYsXNhn2N2lxBImFUGOV42wxZRinbA0HZGnI8aRm3tWc1zV0Djsz1K7B4kh0gnGSyDMvCsqqYv/Zc4y/Kh8dn1DXDVEYEcWR5yNYFs4IYsp1ftODthTzUxwH0gPpDItCZt9tLSo/nKOYlzgjjclAiwbA+cbdeNinbQST7awhz3uYTrgHQRiQxAGL+YwsywjDgDxLiKOIYa+3Ciw9Oj6h9UelqqolQTgMKKuKyWQmZF5fUkuIqqgVl5BXUb2Ju7Jt5QqeZRlxnIgvzGmU00RBTBQqj2w3OBf4yUZIVVtm85qy9E1LfxEwtqFclKJbIGDQ36CpI85Pjmi7miBKCAPQOPqppmpFGuzqkq3xkNFgQF2WnEwLzouaJM4ItGZjfYgCstEuYSSJS1t9TRa1aNdy7/a7zE5P0EoxrzPmTc3R7AkEEhJSlRrnNAEFb6MAACAASURBVKEKiWJQoaI3GrCxeZHXP/NV6kXL/r33uPXOT3jt1c/wqU9/gTpd58HkIe1gxMS03Lt3jzt3bvP5V17h6aP7xPNzeibieR+mx5YgijFVyPzklG58SmRndMUJH7z3Ab/yja9x7eIFerHinZ99l/n0nKg6Y6AddevQQSYQFB1x/fpNJBDsX799IjYBYHU2XIaPiFxV/m0ZyR0FAdqPupQCHWhG4z5hHNDr90jyjF4vpWgUd+/dRSU5O5evsDEe4RZzvvOdP4Ku4/XXPkeQhhSmxQQQBwFp07K/qBj1e7z28gusbY54cnBIDDTzglljSfMBTddycnrGdLpgMp1x/eplojDi0t4uRVlRFAW9PFt13bvOEIbSJW/bVvISFOhAzuGiE+g4P5+uSrbpbE5VSZio2Ia9dNgKHi2MtA8d+QjbhXO+nE7811GcT6csswrarqN4us+iKH3YiV3JeZf5dmtrI/I8ZXN9HYfj/Tt3qera9ytyH/1WUpaV8BirygNOA9osJc9SsixjNByunJiFf77RcEzXGWrVkKWSnBxFEf1exmDQYzabMV+IBbdp5aqVJgmpSeXnxopHv98jW+uTZ302NxwHR2coHdJ2hl4kx7Su6zg5nXgxV8O4b+n3ewz6PS4oaDvLdFbwgz/+iYw5m4qNQc6td97hBzsjXnvxBg8+/JAP798jDhxlUXA+OWZRzLE2Egm4c6CN4OW0YTjYYLw25guf/Qrf/Po3CcKM//q//fu89dO3ODo+4bOfyVjMKv7pt3+XH799m6vXr3Pn3Vv0ehlf/dwbPHt4h6psaV3IdNFw8/IO5ydPsHXF5KTi+NkDppND3v/gLkkyZHM0ZJRYnjy4w+T0mGJyyNnpGWfFEVXTEqcZbZgyGGVcvXadr3/16x+79j4xmwCeIKQDjXMBWAn4CLQmUtIbWI4OQSy9Dse8LEBBhqGzLZ1p2N7aJR2NeX4+x7YdQRiRZCnXL1/kYH+fn739Nlu7W4w218TKi8O2DbPphNAZxptjtjfWaE3L6fNDWq+eOykWWOfY292RJlhR8PjJPmvjEaPRcDVDl2Zft9I9iPtREoaqSnIGAx+sakywmtN3/qpc1w3OKXp5Li+Nc8Jf9GnHQSR26uUxSlKBROi01P2HoUhy/ZZKlqUM+j3KsmI2lyyDRVGshE8ngWY2m4od26PMRStRo5ViUUhnv21bUMJGaNoWU9eCcDPdShEZR5EXQgncReuYIJQ6bhmiIg3LzG9YS81EQ91IFRJ6EIkoIGWaIq9BJ7HbUUwUxsJzEM4UICi5XhbjRn0mMzFN1cYRx3igicNZGA16vHLzBo8fPSbop7xjJYfx/r17nB895+joiK6UBlu5mNN0Bca2GLPkDTiiGNJMzuq9fEiejvnCZ79IkqQ8ffZcJhnTktFogzRNOT094/T4iHEvw1QLBnHAZz51k6ou+F++9wOKxTmJarl48SLNoiPW0JkG0xomp0eM6BgOxmAW9JIBd+7cBmMIrGMxLXGtIc56BCnUnSGIEgajMTs7uxjfx/rTbp+ITcBaS1VW4uILQyHNWLHFOi/NDKJI3IRuWTU4UI66qcmylKquCCJBbfeikNhZdrY2ePD0gLau2c4z8iRmZ2uTH/z4FpPFjCv2EtvRluTRm5ZBHoPtmM9m6Chg3S9u1QlheCONabqW6VSCRpa6++lUMguyLBW8VhxiioaiLEQv7zqsExFTFPpjjTGYrqPyTcLO6xHatiMKQsIklB6JL4esMascg7KUfkOaJh6zppa9Tqy1nJ1PVmYikTKLPHgpIxa5sGZtPFptIoGPGI+jiMNjiV9fCpbmflIAeNmwRGanXqkJUq3FUUQUhV5YtOwVxKuUaB3KpiA3qZQajxhr2nbVnwiCAK1k6rDqkbTS3Q6CQCYwJiSMEvIsp+ksgQ5Zykx829cLtRqKsiFPIox1JKGi7hwYx+Y4p5wN2doYc//OLkfPHnN08JzpSQDWoK2lmE9ZLOa0bSVTACvKSR04dGBQ2hGogDBMuXzxOmujTbqu4+333qUxJeubm2yujfnKl97k/OycPA7ZuHSBNM+JlSEPoZ/kDPop48EmwywkDBTJKCHLE05OJzgH0+k5eT8jSzWjtQF5HoLpyJOUSAWcHxzQ1JaydVgUnQuJw5Q0HqJURlV//Pr7RGwCAHXTkHpH3NIuHMcR2opYRhaPB4k0DVGgMdqhAiirktF4jTDWPguwlbMoAVcvX6RrO57cvc8gTeglMV/63GsczybcuXOH2pRsjkdsxTHDXkpVVhyfHOHiiLzf59OvvcL0eMIH796hMg2Vh47UdcNsvmAynWKdpd/LqaqSSRB4F2JEFA1RStGZhrarMZ2h8TN4if+qMMYSR7FfTI7hoMdgMEArRVGI371uaon69vFhQRBQljVVUaKAyh8bAMowxCmD0openmFsgDWOU0/vBdlwwiBgc3OTXpavuvpdK/bfw+MTIRQtFjIhCGSa0LWSnTceyc8laknZbKquk8ZiGLG5tibVSd2sYDCDPF+FpYDDGuEMtE27Uk8u3Z3GiJ4i9cRihxOEmZHL+amXae/u7RGlPcqqwbQdOo4wrUGHAV1n+cqX3qTX6/HoyT6oPYb9lKapyZMEa8QAdWV3m9n0nMs7m8xfuMrpyTOSVDbqZ08PBAUWO3S9PEL1qbuaKA2J0pA0T+n3x/yHf/WvcePaS0wmJb/3+/+YD598wOZWn7XNG7z28os83n/M9OyML332Zaq65r3bd/jgfcf9+++QpBGz+QGz2TnOFGAdISOiXk7ajSiLGQencwbrHVkvp25rpkfP+Xd/87dIw5SHD57yoz95m7ZpOZ+cUlQ14/V1bt74FNujDbSBXH/CpwNK+YwXhxe8SMRV10qzxxpDu9zflSIMpSSLw2BVKtd1Q2osme8+EyJ+ex0TqoAkSymqEtt1XN5bQychNoCz2YQs1OxubtI0NWVZgLVEYegx4ClrmyFrG5ucz868wkyuXDhHr5etQKVS+mrathHUeBz5xS36B+WpSMur/rIHEIaRpxZpTyGyKB2QZTHWduR5IlqKPFmV70kSEkV6JcCRfHuFdbKoNNrrKTQ6UGRh4hODxRqs/Pc5t4Y4ir0AqPLho+HqaNE0zWokGAQB4WqUa1ecBWstSRqJAzERq3aaxJJo7CuHpm3pOrM6CihE1Wf8dCSOY39Esj9XmWgvqgo8M9J5GbCQnKbTGWGckKYpR8cnVE2fxMNgAu2oqkqyCvKUolywPs6IVOzzKUVnb7WY0UbDPuPxCGcWONuKFsJZitnMY941SlnyPCBoI1QASZKwtrbB1Ss32Nm5SJRkvP/Ttzk8PsTajmtXL/OZN77Im69/mm///rfprOHR06fsXLhA3Rka07KzNaY1DXkWEeqcppL3T7mENM8JiwJ0DYQURUdZtlg6HJq2azl8fsLd+w9EmRloRkPNxuaINEu5eWMLHWrOzp8yOIs/ZvV9QjYBlL9G+TNuEIgoxytFxNVnrffAByu0tv8ntJI5cl23JE3L4mxCzYJFa0l6AS5QrG9uMD8/o5zNsdYyHo3QaUjztOH5wQFRUXFxawtnHToQtx1RyKKoSXTE1tYWxjU0TSPZBllGHEaU5YLGtjKH9tDTtmtofSMPlqlB3jmnFbYzYB39JRdRS6NP1pQS+KUDrd0qERhliaKIIEgpimqVRxBaDVgp+T0fr26XicVynl5mEyy9A8In8NVI3TBppysbNA4Oj47p9/LV6HNjfY0n+89kouE3kvW1MfNFwaIoCIOAjbUhcRyRJjEosTnLUUSvJMQOt6oE2ralm89X0JTxeEQYas9hNFINtEuCsigvVacIukB+ljimsxZlDE4pDo+P2T88Is1SLmxvkeY5dVOxf3DAfFGQJJvUrSGMfZ6wEuxXkEX0+znN2pgsSznsGs7PT6mqglkx5/joBNAkuRyVLBVOdSRxymi4xtbmZb78ha8zHI5ZFHNu332fpvW+gtmcx0+eEqKYTufMFxW9wZiqcezsXuQLX3iDy5d2uffgPo8e3UUrh1KGUEFnO3p5yjyJmc81XauYTkrmk4Jev0fbNdy/d5+To1MOD4+Jk4AwgCCKQWni1HHrg7eFedBYfvze2x+7/D4Rm0CgRX6pkEUgoy+DNY5AiUhI4asE7OpqHNqIOIwJg4jUm4B0EDEajymcZpBkVNYvzqZhPYnQ25vce/8hBIqdKzvcuH6F+XRE+fyE5weHjIcDOquYFzW9OKM37BOriJ21Xa5e2eP5wTPefvcWs/mc+aKgM41P6A28sAmCQIJS5ou5XNXqiDzrSVqRWoJDReudJFItLBAxTF1XwhG0iqopAMWilDl/07ZY4zibnOKcY200Js1ioljw3Stkb+lWvoemaeiCDlvalRFJez3C0tWHgs60VEVBXXcSZBKGNE3rgy0a1tfGq2lC4PMEjenI04wsE4trEEoak7Vm1VxcVgLCRvxIR6DQ6FCvXrvpdEoYitFoKUXGscqoXJpqluj12Vxk2nkvp9/LQTmquqSsCp4dHtIZw/l0zulkyvXrV7l6ZY+T6ZSmFxOhSaMIpwLiQJH1cm597y7PDg+ZVyWzSmzEJycnWOukd2JCOluwNg7Jw5A4Svjim1/jK1/8Bhf3rlFWNR/cu8f+wYfCeDibYLqQR/f30a3m+tWXAcXB0TmfeullHj97zv7RjP3j+0zOj2kaCAgZZzlVWWDNgpev73Jh1OfddzvOjuckNuL4ySHh3hYbm2Pu335bLlqmxdUVSRiRr+8RRjGddbx39ylnswKCiGsv3PzY9feJ2ASWJaLzl/afD1A1VvBQSvvjAh8dH/CVgLWWLM1IU1G2OSBNU1ot4pS2beWY0DYkWrO7c4Gnh89XmO7hYMB8/wjTdExRRMOB+MuxOCNXsbiX4NqYtfEae7sXOD5ZzsUdRVGIscYI+2CJ36qqZSCHWo3SBEEmV+cw1CvCjjAMxHsgXEVpHkrzrENZ6Zg3dUs/T+UIYDts6+fpatlwc6vSWSHTlsBPLIzp6BxkQb7i+Pt3gKqpSeKUPO1xNpmAc6ytrQGOqqqFERhYX6lpGQemgkNXII1cK2wGjZKQ0EjCRAIQ2KU1qzxCcW6mXjrMKsC0MwbqWmzQWqOk/+qNVnIUSbOc0At0lo3JJI5obSfBsXVFVTfsPz8giiPu3n/AvCjZ2lznxpVd5tMF66Mxl7a36axwIJ8dHNIaw/7zA+aLKSenZ5R1LTi6OELVAUqHODrCQJPlMbs7O+ztXEIR8fDRPe7cvcv55Iy2q4njmK996UuMxxc5Oj7hxRducn4+I+9tMBqtc+fBU1qjePb8EFxNFMYEGFRXyns86PHSjcsU2w3VvOSHh+9SzBYUswXabtFLUs6rM5paekOh08ymM7r8AuiG2aKibAKy3ga9wYiN9Usfu/5+4SaglPr7wF8CDp1zn/b3rQO/DVwDPgT+fefcmZLL3H8D/CZQAP+Rc+7Hv+g5wlATJc6fbcUCpiwrEk2HQ3s2vQ4UViuMhlgHpGhCQpIoIk8TwjShqlowDZ12ECXkcS7uqjCidIZ8d8h6ann/g7u8ceMarmvpBSEPDveJ4oi9OCaJE4qDE3q9MXF/ICOmNCWoKrLegK0wYjAacXJyxGw+4/jkBGtj5sUc13RCOY4yma1XLVVTe7LuR8eDNE28BsLgnJh+TGc4m8w/uop6uWhjWuJIUNjzqTAF0sYRBC39fm91jg+DkMhoFAbrLM4qOguBjgiDGM9MoWk8vNSX31J2W5xtWR8PqOuag4NnAlwNAiHYBmBNi+lY9Qfc0h7sNG3dUVQy49dak+cBdftRLoLSMrrs5Zl3Ecp7bbuOrlmI9Vhr1sdj4ihZEaV1oBn0+n7jDPDxCyhjMU1D6yCLY5JWNuDFwTPKqmQnVfSHKUrBg9t/zN265ta166RKE6L4ja9/lSQIuHf3Hs+ePuDk9MTTp1viIGK8ntPWFe1sjs4dYZjQtGvs7l3n6tXr3PnwIaPNtynqmj/6/g8pyoo3v/jLfPHzb7Kxts7Pbt3hrffvoJRmrmPKOEdHit56j4uXdnj4wTvsDjQHB8eUk2Osa7mws8YXX3mN9d4GYRAz6Bk+9/mY7/3oHayp2T94QpRYUBVWyUjWOEsbROhhRNvMSNKczfWceNBjVrboOMQEfz6y0P8A/F3gH/7cfX8b+OfOub+jlPrb/u//JfAbwIv+15eB/87//m+8LTvMS+oMHkC+xE0qLefJZft4Sa+p6hrb73vOmohedBzThSE6TUmTjFkr+e9t1wmQ1CoCDXmWsr6+RlVVhH6uVNc1YSSgkOlsTpL1yPOM9fGIzsiMfrEQC2nbiTU2TQQvXnjWXNRE2Dj2GgCLTgLqRqS7wGoubmzHojBi93TWVzdLUZBdPVZpBd1H0mqHXVGNjJW8xKIsV2m/y8mB9aShwIdOOOdzCpRATYwxxEmMctKis0tWgE8TzrNUqigjcNK6btCZQCyWQqjI48lNZ2jqwoNDAqpaEO4nJ2ekWUKeZmxsrBHHkkw0LwqSOGYwGEqalBJR0fLn6rxzc3mkWH4+rLVUdSc8iFVvw2C0oNLTNJOND810XrB/8AyUI8tiqs5Pc4qaV2/eIA0jbv/0Z+RJxNnZGbOzE5pyQd0WVE2JUx0G0JGic4bQ5xTubG6ytbHGKy/e4Edvv88Pf/hjjk7PmcwLwjjhL/zqrzLsj5nOS04nJVsb22xubfLurQ/Y29vm9GxGHGqKYsZkckIaOQLlaKqSzY0Rn375ZTY3N3A2Zj6rSPs9di8lxGmG6TpaY5kVBaeTCYNBJk1aY1FOXIwboaFrpjTlBFxCP0jIkpzYfvyM8BduAs65P1RKXftX7v4t4Ff8n/8B8AfIJvBbwD90spK/r5QaK6V2nXPP/k3PsUzUWWbUaa29k9CX/n43cKv/yhZhrAVPuG2NpbOGSEGcJpRdhwpEWOIMoruOQnAa09UMej0G168xOTjAdnIeD8KQNEslKGOxIM37qyvsYlGsPOf25yYES4ddlmbeOuwItXRum0Y6+dK1l2NNmiSy0bVQVeUqYHQVP266lc8/8Gd3fJiHsXKlWxqRTCdTkLqul2+W30RA+g4Oo8zKnr3EljvkrK388wjeTVGWFfN5web6OouiEJVdMqAsS08MEtVjv98DoCyFNbhEsM3m8xUluihKHG5lCFsSjgIt8/+uk4UeeNhImiY4F680CM5BVVUyNVh+n1rJTD6Q6HpjpMGqtWgIoiSiFwwYDIdYIMpiJrMZnTUkYYRzmlQpjh4/5fLOBcgSDk+OWCzmONPQtiXT2RltV6G1YlE3AqLRijTqkacZ6+tDBr2cJAzZ2Vxn/+CIcjFHqYAXr98gT4ccnc349h98l9mi4s3PvspgOCCczNE64M6dO/zkrR9BV5LYBVE/IQwcL75wjd0LW1zY2ESpgGmnmNeG1BlUEDFe3+D05ITZoiA+D0iziCyLVtOSQIfS7CwmlGVNWXeofEw2WKMfOrqq+Nj192ftCVxYLmzn3DOl1La//yLw+Oce98Tf969tAkqpvwX8LYDRMFvFiRnTsMR8d21HHAZYa1bkHGNk58sy4bh3ONI0YtFUhHXFqD8kCKVTvqgqwt5AqoAwoGlb6Qg7cMZiO8NkOiOPQja3Npks5iitiZOYpiux1jDo9dBKUZUVs9mcoihpWqH2TGdzmko+7MbHpydxgvGz9TRJJAataXwpHPgN5SPiz/KqOp3N5LztOYBBIPSYKIpwfgSKwtN5Gv9aGEK1NNrIh1XMSMkKHLLsRYShfEiU9pi0MKAsRGWYLhtxKJQLOJ/MieOIUEcUi4qus4yGA4qyZD5fcHYmcuc8k/fNGqir0gNUOmn8qSUFSiLJprMZZVURepLTcjxaVTUORxRGXlsRrfoYxqsaS6VXCVNRGIJztE3N2WlDGMr/Y4yj3+uRZRlrG5tEWY6apmSjNQF+hAGmbQi6AltVdHScTM6YTibMZxOqtqJqKkwnzENrLVkk6cQaRa83YG08Jk0ydra3efjoMe+++z7HpxOCKOK/+M//Mza3d/i7//0/4OR0weXLV/kr/86vk8UhVWPY2Vynl6f0kzf5x9/6FovylGGuaKuStWHKKy9cpW0NP/7hezw7OWUe9Pjspz9NWbZUZcWnP/06P/jhnzBfnKImjjDUjMd9z4LM6PeHnmBV03QWqyNsB1VRE8YV0SD/t74JfNxN/Sn3uT/lPpxzfw/4ewAXd9ccfjwWRxFlWRNqDR55nacZTVWLDHfpNFRi3XQ4qrqhr8RrY71uIIpitOlk/u6xYqgAZ6XTVNc1dJaTk1PaPBXVoA7QYcBoNERHMXXT0NQNOhWII74ptjzThmFAXSZUdcmiECluEAgR2Tm8zNZ6uKhwBKTU1/4+jVIiGzad81fsjqZdoJUiy+LVeE9rDVYek8TJyotgvBtSa+UbnVIZxFHsF7cs/CVoUmtNU7cscepKKWo/9lw2XtNEsgqKslzBSM8nU7JUFm9Rimfg6PhEYrMHA6nG2hYdBIxHI+qm9spGUTpurK/5cl6qFgGYylFvySnse0PTkyf7K8ZCmiQEQcBkOpXpQJrSH/QIwoDxcLTSPcRR/FFoaSicfyPCUxSaXpoS9DKcCTnaL2haSy9PWbQNtbVUnvsotOhQxpRdRxqnJGHIeDRke2uLK5cvMpnO2H92xPPDE+aLAh2G7D/b59GTfd5/911efvlT/PKXP8P2MAfniHHMmwrXQhpYLm2N+OD8gCzrEaoA07YcHJ5wdjblweOnKB0wuriLc25VbSol+YiLuZjTzs4nZElGnCToIGTzwjZl2TCtO5STcF5DTDIckQzGtOrjl/qfdRM4WJb5Sqld4NDf/wS4/HOPuwTs/6IvthwRzmYLyroijmJsZ9BKe2ebf1ygV2iqpTw0CMOVzNQ5OfvTtKhEPhTWL1zlhEJkYBWScXBwzJUrlzB1TZ5nlHWFCgIGgz4GxXxW+lQkh8I/oRLC0XyxoCgKlBOGgZzZZZrRtC2mk8acVDXSfLNOzs9t17IK7mwE32X9AmfVE1A0bbGSUi+74Ao5D2ulVtONJXjDGNHWC97LETZSkURhSNdar7Vg9fVEhW3pWksc6pU2v6rqlYovCiMxOXkHYZLEOGs5Oj4hTVNm8wXT2ZyN9XUiP+Lr93LSLiGJY6q68cIvqcZCPwJUSjFfLFbHqWSUeNu1Pz5Zt+q/hGFIv5eTJFIZZWkmeRBp6kU8+OOXNFG11milGA17lHVIWXtdRRxjw5jR5jpdVXF4esqiKinKglmxkL6NFrCJVopUJ0Q6Ig4TRsMR49GIoqgpqo6T8xk6lFixtm347d/5R2RpSqobbuyOyFUN9QyFRbUN06Mj9suCZ4fPaauCva01rl/cZD6dcHZ6yoePnzCbF9ID8tLyrc11qkXBydEJeR5zae8Cz/c/9BoSzelkxngtYJj1UTqStOmrL9N0HVXTMjmZgosIlHyfH3f7s24Cvwv8deDv+N//95+7/z9VSv1PSENw8ov6AYB/88Vf75yjLpvV/HqZXCsct49AmXJl7Qi9Lh7AGktbNwSpoe4K4nxA1xqJuuo6gkgTByEn+4ccHR9zeXeHvlYUWvH8+JgsTXG+e7++NmY8XmdrfR1nHEdljUMaeMt5d9t1aM/1m80Kz+QzMgMPwDTCEug6aWgZP/MWgtIyXFShkDfIdgbjNweQks8aRWMFDAGew0rnfQRu9Ws5elzqELReNln5aANZKvECh1ItTWNWfYemtatY96OTM+lzZCko5eEh0u+Yzwv29naIwpA7d+8jbMCP+gHWCW5dFm+woi4bawiQ9yoMAyETZ/mqCVrXtVQSWrO5sb7yIuR5RpII+lygpRFZlqE8nMV20g+I48ZDYDSRhkg5XNuiXSf4rbamNjV57Jien9KWFQePHmK6hsnkjK6eAw2dsUCACiNcoEl7KZf3dtjd2uR8MuPx833SbEDVKr76jV9ChE8V/+h3fpteHPDVV19En37A058ccnY7pUWs8tPK0jnFcGOTvat7NFXFw8cPOT4+pm5q+oMeVlfEeU4YR/RDS9AtGKYBepgR722zt9Hnw3u3ODw+omkqfnbrHjdv3iQbbXNaWKaLgjDOSbMcEs3j+8/oTmfEz0+o6j8HT0Ap9T8iTcBNpdQT4L9CFv//rJT6G8Aj4K/4h/8TZDx4FxkR/se/6OsD1E2LUiLD1POFRIgpwVw5RCa8VJYBK9PLEpftrMN5FoF1ljQMaTrRD2gVYI0hiUJs12KdYTafs7G+RpYkhIhCsd/LCYKQum2oGnHxhWEi1UXb+Yagk6u4+Yi4WywWKymvUlrGaNpgO4sOAtIw9JOHBmMFdb6MK8Pr5BWKKJAIstD3BZYCnqWMerlYQK7e+NLd+Rp+6aL7SH6tVhVFGPhqQmufqiNNQ2sAvwE39YI4ErJPL89IvI+jbVtPItaeRxDSNi1xJFTl2gNLk1hSogMPb1zGxltveip8E1FrLdODpsEaryvQyv9dxqRlVYNz9Pu9FZhENjuhGzu/meVpLs+n8AlSFZ1pZQRrDca0aOdEGKQdXVszPzigOj2lXiyoJmdUVUFVzAlUR6QsOpKfIQhhNB6wuTEmH2UcHZ0wmZd0JuR8XnD50mUWdctsNuHpk0cM+zlbg5SXr11gnCbksaGuT1E6wjqIXESe9dFtw8lhwdnZOfv7z+V71IrRaEzVGRZ1Q5TExCHMzk8xnVzYlDMEzrE2HHB4eChTsiDgZDIjevqcmy+uE8U5YaiYTs+FzFyXRHFCnidcu37tY9ff/5vpwF/9mH/6tT/lsQ74T37R1/xXb03dkmcj2qYlCkpC1dK1DQqxhVpjJWREOTocUajplEiIO+sItKNrGugsEQG2tUSh6AWCRBMqQ2LmhK5hPj1jrw+9HoTMJOkoh8fnCw6Ozzk5m9O8/SGbYVlpiAAAIABJREFUW9t89vXXKRvh+C+KGca0lIuSqixxxhAGEr7ZdR2Jdzkaq1Y+/2IypalrWmtQOiBN9IpJuFzYwis0fpMQu/RSFBOG0i8AiGNpnKI+QrF1PotxOetfjRajGGN9MxGxvVrXYTqHbRxd1/qKQOy5gQ7Y3Fwnz4SDcHR8QtnI679czEmWEidiNDo6O+Pw5ITRaMja+piiKInCkLpuOD09Zz4viKOQixf36Pdy4ihivihWSPb5XDZO+Tl8FaM+EoFlqRiwpA1i6LrGH8ukSgu1ZDN2SvQJYRST5Qlon/qbp1ilyYsZgXbUdcH+o0fkSURQTiinh8wWC6ZtQWFKam1pOkOgA9IooZdlpHHMpZ09NtfG1HXD/bMFeZbxlTffIAwiJrMZ+0/vysSkXJBlQ9Yv7HGmNiiJqM5Lhr0RoXLMFgvQisXkiGfHRzR1Q922vHzzOkVR8vTpcy5UNZu9Pmtxhw4Cnp9MOD06xlkDyrA2FBv49t4W52XBh4/3sSczDCFRmtDdfYuiqnjp5husb2ywsX2BC1dvon3jtP3zVAL/f9yk2SWUW1EGOn+1sjRNu7IPO+3QgfLNN2EEFkVBHEXEm5FElWmNcoZYa9rOoI0hihSu62iamraqGY83aOqKeVXzs5++TVVWnE9LOqsYDNd4+ZVXuHjxMhf39mjKclWqzhdzKj/v176htfxzFIk2oG1bqroDJEAyjiLqtmFRljQe5rGsaMz/zdyb/Fq25fldn7XW7vc+ze1vdK/NpjKdVeVU2YULSwxghGQJMWOOmDKHEX8AEhPGCCEGIAYIgUBIGCQQsmSrGspOZ/vyxesi4van2e3aq2Gw1jlRadWzLRDo3Vm+vBFX98Rea/+a7/fzjRw+H2cUh3nAAcWtDp+FEMdcBed9lE+DiIf+EFBy+N6DQcnErUhYCx4uHUeSJDG4JCjuQtJQSGA2s6HrA768WdRBjh3lwH20MB/WmYdtQ55nbDZb9BTKaaUUIkvjAFZHSbNnGEc2210wWMkgPT6wFJR8rwAM9KiwBZomjYk2YhkrGWMMTgTAqEMikxkvEvKioK4rchvgqXY2TGZCiDDX0dNEJQU65kr0fY82OsS4e4eM/grvBWVZU9QNv/niTfCLnL1icbLmn372DWWR8/zqAu9FUBpOI01VslrUNFXIt1hfnAbg7GwY5zB0DJhwgTaGrh/4/Iuvj1oQYwxlnh9Jz/3Qk2YhPDZNUgRhJawSxWq5II16lmkcGbqen/70x6xWC7xYkWQ5o7bIqJgViN+pJP/Zr+/GJeDdsSw8hmBKgXPiKCARHrAepIomExcsx8YiZEImJUWaUGcZGnCzBh0evkSmeD0jjKXKct7cPfHmm2/QWvP667coKfnJj/+AplmwXJ1ycnpF1SyDock5UpWQJWEekSSK1KWRFhwwWs77OH0PA6U0STAIpJgD5DKu6HwUOykZor3SJHz8h3ZDxAFfoBGp46GWUsaotBDTdXjDO++Ofb+MOQoH09WB2aikCherex/qmmZpDAjNjuvY/T7wApSSZGmKjWKgNE1RUh4pR2H1mTFNOsiZhUBKdXyzF0X4e1XMUUQEbPu+beMwNLgThRQBwZZG30f8dw+XUhEQZjG23Dl3dBNKESjNwjuqVUWalSRphiVkHxy4i2Y2tF3L0LfkmeL68pxZT7R3bxkiLNRZgwRSKUlECB+9PL8gSXPOzs745nbDZq+pqor65DkmUez3jzy1O7b7ge7pCeECmWpVF5SpIhOecewRLsM5uH144uFpw2JZs1itEFmCnoN47Gm3o64qijRltpZxu+PduxvOTk95fn2FnnqcmzkwJPQ8h+1I3ZBlGaOe44tnQgJGa5y0jFPPoGcWpxdkcZYzdt23nr/vxCWQZxmLpopDGsWBkaqkIFEpAk9VF8c3mZ01SZqSeE8qHFkimPWItwaJ5f7Nl/zi86/YDxOzDwfz/vYGa2byTHH2yY/55MNXnC4X/PSnf4uubamKCmM8XT8xO1g0DYlS7MaRcejJ05CuOxvLNO2Z9PQ7O+5EhWAR6xzEcl/PM1YbJjMzxfYGiBkE9tgzWxdosFmWBadaLPVDX+uOh10Q2ILpIib3RHcl/v3KzVobVngyxK4fUosnrWNQiDyKnmz8PKWUrJYLDlmOq1V+1CzgfVzXFcc/733wZgzDwDiONHXFdUzvSbOUIg+48c1me0St11XFclHHlobwmUVMeZalR6JRnmXBezGO3D88hsteCFarJVVVhjtOxqBVYzBMJM4jVYZ0nkkCrmS9rHhxdUbXpXTtjs3jHQ/3D+w3T+zbLsBbAOvjADXauPOsYn12gXaSy49e8cPTM5wX3PYJ3s786AfXCDuxe7jlRFjKVPDJq+c0Zc4wDmy2j2z3HfcPj+TVkvOr51y/WpHlCuct036HAYq6pI4w2CxJaBZNqDjyHOs9ZSIwowmpz86ybJqgiFU5y9UpzWLJ//Z//AOmvmf7CG++fI33ju99748CIVt4snkkJbRRK/kdR44bY9nudsc0njRJsCbuwGVyfCAPcmEzm+NDNDuDcgaVp3gFk9Vs2w1ffv0lkzY4FfrzvCj54PoDPv3oA04+/CFVUeDtzLjb4oTCIkmylGk7HGWp+hh1PrPdbNmPI8MU3sTTNDGOY+hz0xCKekgrCoO/wPzDeXIpjq4+7xyzDjn2NrHH1sDYsEablIqKOY/WEyCOnAEf13aHSuCQHWBtiD0PFKIA8XDCoYQ8iqwGE/BrcBisSrx1SAF5koQ/L0TwA8TBZ56lge6Dx8yGWQdiUF1VLJuGm7t7BGBmy6JpwttbSN7d3kUBk4g04SBG0vMc6UYlh6BTpUKKsxTyyD7sh4FxmCLEI2DSzk5PQuUiBHNMZzr4HYhzIoQMEVx6QErD69evsWYkzxT39/fsdlumaQzyaBkYloGJAEKF0nm1WpKmIRjl6vlLBm1o255Ea9ZNRWJG/Dzy/HTB+atTilTineHduxvabqAdNQa4evGCfT/zsNvz/PoapEePmtV6RVOXeDz3d3c8PgUT0LM/uqTIMxbLBUoKxr5DTxN6njDOwXIZiRqSoig4PQkcyGnU4B26Hzg9XVNIR5KAso5h+8Akw1bFz9/xQFLvA/56vViQJymHxFsAH0vQWWuMDQjvosgwZsYIT5oXWDwiTyFNkIni93//9zi7vAgUY6H4+u0dZxeXeBE+QJlkaOtIhGScLVKlZFmOtR49G8q8OEptD31/mIKrY1z6HEETVVlGCtB8fKvPEaAR5JzyyOQzxqCn+f02JHICD+isLJbe1oS1Zp7n0f1n0boPuX/GHgm/wPF/CynjZRGGd8MwRlu2jz1+vJT8e+1WFr/XuXC55Fl+1P4755hG3rMegCRNSRN11F+cxqGZELBsGvZtoCT3fY+1lqoKYqTD4T7MKYZxIsvSIxJd60hsUuqoaDxeKvF3resqtiUjXddTVWVYR0ZW42a7panr4Eq0Jc4I/sv/+r/hBx+95AeffsDYtRg9IWWYfQiCcEkSnq+6yCmrmrPVGpKcYrEkSxSPT3tSJallTz7PvFiv8SZDYpn7PZu2ZdITj7sWIRMmJzAe3jzsuXvc4RC8e3jk049e0nV73r39OpTwekSPI2WRcbJac/9wT5mXXJyf0rYtw7APQNdphDhAFSrFesFu33F7/4iUKmpMLO1+z/c/foXt93STDsi1rEKlKcYanu5uvvX8fScugTRN+f4nH9H3He1+h7MhWccayzSNGKNxbo5CmUOopcUJkFWYFG/bHplXpJVj9/TAz3/1c6RKKRan9Nry0eWHeJWhbQBezHoiFYLHhw15ojg/OWG363GOMLGeHfNsmfSMmQP7fpx1NMeE0r4osij40XR9H01D70EiZVkgEeGtH9dcaZrSLGrw77MO84juPnxPngReXpoobGIjuz/OB+KQ8IAKP+C//upsYBwD0bcqy2hyyo9WbWOCvuFId456fCAc/iG4/VSMZ0tjG7RoGvIiBJ+2XX8Emx4Gj3cPj8fDvor4MeAYTrJcLhAc+vbw+WVZqBwQgixJ6Pr+PVviOAtJYlbDE13XE6zfDUmcW7Rth1Th82rbfWALeIvw8G//m/8GmYL9fkPfbkLWojVYwtzACUmS5qRZysnpOReXF1xdnLLrBuQ8kOgtP361ZLaeS5EwDCO73Ts2ux2b3Z7NfmC778jKkmcvP2SxXCHHmX0f8O8//eM/JpEi9uyWuimYxh1GTzib4kyOBCQzzkz0bqbvJqw1KBEuZiElKsvYDXPg7smcbvJs2olFs2KzMTjruL2547effc6f/MFPyWVCry37scXZhO1uz5//xf/1refvO3EJ4ME7G8vT+JbzPkRXxR4UOK6RpPAhow+PmwySlHmcGfqBohhAKKw2OCkY5UiSVXgSkDlKpZSJZ/SeWQ/MRrOoFnTdEN845e8eGG0Caso5pjG44wICK5iYjAnJwmVR4Lw9XgL48MAba46T2TCEO4h3/NEdKCAeoKBuzIvA6rfWBMmwl8GGDPBX3qwy5gJKKTHz+58VYtMlk55C9p+1R77AgdkQyungMpRSHM1AMhp8DhFixxVhTD52LgypnHekSRo1CmG4WBZFvOiSo5dBRD3EwQtwXIvGyuQAJ5XRRCakPG6DDj/LGMPD04ZxHMmzjOViQRCPSaR0xyg058P2yFlD1/ZUZUG7fcToiaHrGMaBGRcyDwlGsyxLybOc9ekpl5cXVHmIezfWsa4kyo94PfHV/S3DONEOml+//opumvnB7/2Y+jLFIuitQLcjFsHzF88Do7HvWdQhGcpoyzgOOGvDJSU8iRJ4Z8NANU2YphlndPRjGLwI5jqEoJ80CIcXAi8Uq/Up19cj0zTQtSFh6v7+kX0/IlTC/dOG3sDnX37D3f0jF+en33r8vhOXgEoUbdvhnSfLU1bLRcikMwWb7QbhotEmykqPazZtSZUjSQHtSJ0idYIvvv6Guzd3XF9dc799otVP/OPXfx+X1Mis5iS5ZbVsyJTA9AOPt3ecrU+ZxplXr16FIE8XXGxt1zENA23Xx6RgT1nkR1TW4aEOqT5hFqBiWYsJgzs9aRKpyOv6aIk1xmCkfS/9PaxBg/YYJRV10xzpvAG1HleAxqJnTdf19DHyK8BJQn+tCnXEsFGEVuRA+EmT9EgwPrQ7QgrswkbqkIwryHDppEno6YX32MgGKLPsSAE6BJTKJAwiJ63DhN45yrI6hpIcL6OoKCyL4njIN9uJx6ctdVVS1zV1VVJmJZ4gcc6LnPV6jY///qPWPG635OMUEqu9oKoqwGNmjR62GD3weH9Pu3/i8eGWvmuxzpAWKjgaVcJitUQlGU3T8OHHH+O94+e//BXrZc16WfPVr7/g6fGJJFWYdE2zXPKXX3yFdikXrz7ENhfkZU23b1ktl2FFLQIUVeBJoudiHAdeXJ0wDoa6zmm3fRBwSUldNZysVuDBWUFV5gzjhJ50CHdJU5xQkBaIpODNzYanxx2X5+f8yZ/8MYum5C//8i/YbHdI4fn551+x2Xd89faO61cfUa7P+eDk8qga/eu+vhOXQJpk+CTHWo2Tgs7qgOSeDc4JnBdUqgA8zhqMtigl2auEtKiosgzT77j7suXdl1/wMM0UqyV7pfizL1/TDZZUPLCs1qQyhWRDcn5KdXXBftcz6Im206xXy+DX1x1jP2KnFiFM2FWoBCEmTMx6P+zKAxdPBEGLcXEN5jAmDLg4IMcIircp9qV5npA4wTyHsj6UfoIyLzg/OyFNUiYzh2rCGNxfwaqZOfwc6z1pXK/VVQ0iSKeHMejtiyIPb11nAzJMhXDUQ/qREFnEfet4UYCPuXpKyoAfkwInLNnBAo1gWTYBeV4e2phw+c1mQilIM4FzEmsM7TxjrAnzCDiuGW3kMxwGqU1dRqxZTEJ2gaF4CE85Pz05rmLf3t0jk4yyrFDRM5JgKfOMukh4881r0gTG7pF5bvFMWBl8AZMNA9K6yMkl5Aou6hy7uWe3b1nUDVIonlrDxq/YVxUP2z2XyrIfdrTTzMsPP+Ts6hqymlnlZMsCk+SoVOHszEO7ZdYDH12vWWYpo7KUGKz0PO52JDJB4KmbFatFsKvvti3zPKOnOcx21iXaSG4fOx4eWn746U/o9zO7mw7lPYmxXK4WXJ6uWNYVu+3MOEl23lBdnPGsbHCU5IsznE9A1d96/r4Tl4CeNYumBl8w6xEpQKUJsw9cf2f9katPVIshBHkWHsy27RhdSO7pJs2r3/sRy2bF7b6P4pVgoNm3HUom5JXGE4ZmbXT/GdvF3nPDbt9GGXIQu7Rty27f4nxQ2tVVCULEMJExmm7el9MHmGaSKJQIwzgh0vc6/TgYPJqbxHspNEDfDyBG2qEPb9DYR1vr4kpQHFuHIppqIIRqHAZrhwolSRKsDeTlg5moKHJCOS2O1cdh/QocNQxpkr53bEbPhos7ayDYbKVAyoRxChUAQFkWZJli1vavrCDfzwkCS1GHy0fKo1Mw7MPD2rEsC14+fx7/ruzIcbDORnNT+Fx8NBCpROKtYRwNeZ6ip56u7+iHLs4gBMY6ZKooi5pEpUiVsl6fcLI+ZbvrorgmY549nR6YZcFqsWJ9ck7abvnm3Q3r1YrTkxVpovCpop8mICXJgpMRHE1VMAjLft9RLov4Fg5y7bquGYeBWWuqYo13lr7Xx8xJqSRCSdKywHXBiLbft7Rdy/39E84brs5P+PCD59ze3SGV5OrqknfvbsmzAjMNJFXJqs749Hs/5vziOePk+Ud/8etvPX/fiUtg0prffvY5q2WFxBOQezb0THFHbpyJtkGBTyTaWeZB83DX4q1B9x2r1YqPP/mEv/13/g5//svPuX+8QSQl1juMEExmxFvPBxcLXrx8wbvbO968vaOuK376B58GZZsjKLeM4enxCa11eBDTlGmeAkW27dBzDNiwIXE3y4K8mUj3FSIMBIUH5w3bbfDYH4JChQipxtaGfMKmro/uPq31MYgjIL8ndNToK6WCWy1GmR+stIcho7WOpq5DG6I14zTiYhlO1BUMw4iQPlimZXhjJzEQ9ZA2fKD5AEfdQjcMdF0XNihFzma7I0kUeRb4BYffPUlC+b/bdSSJYtk0LBZNvDDtMcqsKguWi4Y8z3l3c3tEm9dVhTGWru+D0xDB+fkpy8WCuqo4WzumeQ5msTTMy4ahx6WKrCrY77fsdk9sd1v0PAGO2YYIeJygKEuEyqibU7RV/Pk//g1NUfDRh69A5JR5yXJdYdOKThum2fDu9oG7hyd+8JMfUVclSZ4i84REKvb7EWxKvxuoiyRg7oQll479vg9S5+jdyJOUtIL6bI0Ujmkcgr06+h+eNnuch3w64atvbun6kYuzNT/6vQ9YND/i9v6O+4cHHjZv+dk//TnjOFEVJVmxYLvXPN18SY7m9PSCd69/xfV6wcuzc96eFd96/r4Tl0AaibMBty0wsw4ILGPfh1KkKvaeEodEO4OdZuZpJpGeuq65urzkxfNngdlf1Tx/8Yrl8wLjJPPscSbs05/XHpUmZHnG2fkpaZbFnlsch2wHH3ddVyFXIE6m0yRMy40N6zolAwFXyUD8dS4EeSqvgq3XOZwTR2luVZZRDRkGaAcfwOHgH23B0Up9mJAfJvxpkqBkesSd2cjyFzIMGwXiOLs4DMwOUeAHX/phsv/4+HQc/CXJHMAdSRLKUucjGPR9XPxB3DOO4a1/FQVCAWQSFX1xxTkbE1ag0RD0zZu3x01G4C34GCsfeAkX52foeQ6fg9bvtyxJSpIeUO7meDFZa8Ol7BRZIikSQSLBGo2eBlxc44bNimWc5mN+QZFnVPUClSaA5KOPP+Lu5g5jfWBOEDZE4zDS9SOTNtzdP0ZthQVrwVmc0UgnKFNJgsfhGNqWRAlSpcBayqJgtVqQJZ6hT9DaUKThWR/7ARMxdEki40XqI1zH46zn6vyC7336CePUs9k9cXP7lne3t9zc3vLphx+yWqzAS37xyy8RKqXd79iXBYtmwbMXz1HKofUQ8GPf8vWduAQOb9KqLHE2RFoL7xmMiYKYIE0NrEHBFHfN8yBp0oKqzPnBR8+5uLzg5OwEjyBNc5ZLhfQFQmUY44LPznsu8kCsWaxWLBZLENHEVJVhSAPY2QTRTh56+24M4SJCyojh6sJkXL7nHHjvcC5MxcP0PHy8SnBcpSkledpscdaxWNRoF9ZAuJjIbEMZOU4jaZFHPb6lKsNNbp2jH9oILDHHEtnM9ijOmeKfKYv8WDWkaSjvpRT0Q0+WJez2A3mWHy+6g3HJxupmnKYocApQ1HkOwSTXVxdHrFVQOkYi0IGStNuHdaMNFGGZCu4fHkmThNVqSV3XhOyCFX0/8PS0CWX26QnTNPHmbdhpB7u1R0l5xIxB+CzzPKfr2uDJUFCd1Hg/07Yj1mjwFmOC5Nb4ECCaZylaa7SZScyMShOkSJBpSl6WzHFgO1sLeqabPb2eQagATdGat+9uKMoSmSQs6pRhMgx6wupApb55+5bVqqbMU27efsOLy1POz0/o2h3vbm5589U3vHx+SZ5G74QNoFBQGOfIyxyVpOz2M//6v/Z3aeqG2Tg++/IL2q7lZ7/4OZvtlixL+fTTD0lVzsPDluvr53zz9oZh0mFt+fjE937YhE0OKdfPLr71/H0nLgEhBP2oOT1dIaSPPa8jkQonBQjFPFm0s5hI5Q0jqoRPPv2EukxZLjIQjklPLNKM8/NLskmwMynTbJnnEWdmUgV9t8f6APsIuG/wfgbn0VOM9FYh3edps2W/byPcJCQIN01NlmVM04Q2gUNXLhbH2cC+bUOl4ELlAZ40DZi0OZbaBzmulGFKHIxH4dCZiDnfbnccMgInPTOMQcQjeA/n9BCrEHNcF56erI+twuGCenh8CoInITk/PyVNFKcn6xhooth1e5622yDyKUvSNGFRByvvUYMQqTtldBvKWFlY56JAKByisiw4Wa/I0jDM1fp9zmA/jFxfXpIkCV99/YYkSQKgNFHHv2O9XgWEWLzoDpXGcrnAWBMuFqVQQNWUKBGs0M5oxqHl8eGGbgiZENrOzMaSpgVCpazWJecXV7x8+YqXrz5hGGbatuPm/olfffE15+smhJ/iEUnKV2/eYYzl009+SFkVPDzdsX16ZBg6No9PzLNDa8v9wy5IrhcNm/ueX97e8vR4z7B/TpFnzGPPbrvn93/0I5T02FkjRYL1HuFgdA6VZJyerkiShD/4Gx+w2bX89vMveHt7x2evv6Aoc/7uv/qvoGRoNeumIUkKjFd88NEH9NrQthvkYElazZv7JxYLOFcFv/+Hf/it5+87cQkUZQlSMUyaPBVR/ZbiTOxjjeOQAy+EZz+GgZm3hrTIOb84YRqeEFaQWk2SKJoqp581epyxzlPnGT4VCCzGV+z2e2YXWAHCe/IsJ8uK3xHeOBfgoGVdUosqEGx0WM3pOVCCszT4BkTk/x3KVR8dgqkKPAOlJCiPUz4O+sLD17YdWZYdUVoHLYSeZ6pFfewnZz0zzTPWGNIkP0p/D9ZjJcPG4sDvOxiWxnFkt9uz2weMeZamdG1PmoV8QWtsCFRtOxYxvjtN3nP+ILgH5/m9FTWsGpOj489ZF9eTwf9wenJClqXYObQUJiZJi8hgEPHvXa2WgRaUJGEY6zxZHvwTxoRcxAMDgeiig9D0eO/xItCXk0RgBk3XbsiUQM8aa+cjk1EpUFlGmuYoqbi+fMbzZ69YrM5IM41QBUV1xzBohFRcXlyQZQl/+fNfst0+cXV5iZBhjR0UoZpx7FEqpShKXl5d0u77qJ70vHnzlpvbGxZ1SVVVlGXJ2O5p6iASMzHsNUty0iQOPa2lrCqur59xsl7x5eff8I/+/J+w3XfMs2XZrFifrGjbkaoqKMoaZBqq3jzjB9//hKftjs+710zTzL4bmGdDXddcnJ+x+P+RMfj/6Kvte/7Jrz/n8nzFDz9+ibGeJAmXwDD0VEVNkZZ048Q4DAifsqwbPIq/+NnPeHg45XSZsl4taZYNTs/07US/N9T5MuCpTc80j/T9HpFVZEUREGBupixzlmVJmeesl4FrMA4jwocHHhFSdaYxhIoGIZFhHCbSPCFNClbLJS5KhgOiewpGFR3MMC4Knw5JwcZahn5g0TR4/FENd+jRF01NGnMJwhrNUQiBS9MjMGS5bMBzJP+kaYK1hrfvHmNWIkf24MX5WcRxQZZlpKmi6/vj4T49WR+9GYumCdWMCWrGuirJ8xVKJceL4b2KkaMG4aA5cM4Fc5U7iJmCOzDPg8PwMP2HsAkJmogKPEd0W3BFBkK0FAE5P2lNpcroMFR88PI5u80j0s+0Y8d+t8XZif1+g541xjucEBD19nXVsCjO+OH3fkpRNVhXUNUZRelJ5YL99ol52NHutkyJ49nFM8qiZhxHPvv8S6QEpSxmnshSRSIm9psn7t6+44ff/z1OTk745WevMdOIcI7TkxVn5yfUVUV2dcHU9+we7tHTGJyaNsjFV6sV1+s1908b/tv//n8NfgthOD+/5HR9wqgdb24eKYslVxcvMM6ilGA2kDc5q6JCyoG/9bf/gNe/+ocMugMLt2/e8uziEulmvnr92beev+/EJQBEJZxl23YgJM4GQ0tdlZys1gyTx3UjdnZYAft9xzQ7Vk3GZ6+/5OT3Pz6W4p7Qwyb7kN4jhaQsMiQZ8yRYrFZkWco0TDw9PJLnBcvlgvViwaIKOW9zNNQoJbHx0GVpionT+IO3fRgG9DRSxI2CMcE+m6Ypk9bsd7ugdMsz8mgpzuLbvamrAORIUxZNzTjp4958ng3Gh2HlgQVwEEopmQCeaZrDHCECRIQIuoOQiBSGUmE/76MF970v37mQJ6BUHsvtYBayzjKMQZ9fxnTloigoiyLQeybNITilijkCLg5M5+ifn+KlsGqWZHkW/l23+/cqzHmOOLnquEVH0brDAAAgAElEQVQJGZDBxizgiEM/KA2nccIm9kg8svH3LouCqZ/R08h2u0EK997EJUWsJIKEu6kbTuoz8qxCkpLmDVImzMZyeXFFnRfsHiXtruXxacvp6YKsH7l8eUG2Hem7Fu/DpqbdBYZAluRM88xvP3/NZdvxo+9/yvXFGT/75S+p6oqT9QpjNd6FJKfwO4VKKk9TFosFZ+dn/OLz19xvtnz4wYdcnK15uvuGNM1QScFikfPm3YZpMpysTximKUiRk1A1z8bwzdt3ZFlGU5W0/YDVmr7rsfOEmSd2+++4lRgBsxSQ5aRlg5d7nJghcZRVwXJ9ws3rL5iFwSaebpxI0pQ5K9kagZxGvv78LR+/fEZSBVfhsl7g8pqHOZiDSul4uJkp05KT808w80TTeB42j1TLnHEesL4CAUNnmbUC7+jbDj33eGaSpGKYRvqxCz2/nWm7jkQpnjYbfNzdh3VhKF2LvCBVKULIaBBS7PY9Bx2BkPIYvV4WGfMsGEaLlFCWdRiAak1Y4QeA5oFOVOShbdDDfJziW2tI04SmqRGC47pNKXVEi2kdfPx1XaH1zKwdWVWg4kr29v4h9MXOx60JEcMdLrF5nrm7e+D58+sQ6OIhT9MICA25gVma0g0dd4/3dF3P/cMjZVFwfn4KwtGPA0KG8JYk5hYkSjGNEzqSfvOiwNrAYJAqp6orZhsO6apKmbo91kzooaXrO1CCfdthcFgcWZFjHMhM0SwqyrqkWJ8xOFDWIvods7EhFVqALHNcWZOs1lR5jpWOk6trbu7uyH2PEyMohfMKkRTsdjMff/Scl8sFf/rnf8mbtze8ffuOj1694JPn12SJYCEMydRze38HeFQmAkQmV5xfnCGE5Iu7d9zvd2jvKNOE/aiR9SXaeKpyQZqXaL5EpDn9NLPZ7mi7Pc+eX4NP0NqS5wsWiyV59SGP7TcIoXFiw+P2N5w8aZp88a3H7ztxCUgpMdPIPI5YHbL2pt5htEVmgtmMzHM4DNI76iKJfaGhTBWLuuGDV8+4urpksT5BOyjzkmkYGQfDbrNnkTjefPkFdhpYX23AOdp5pEoC1+/8JCivZj3z+PSI1oFVF/TpCaMe8Zjj22zSE855zk5OAiNvmuj74VjKLpqG87PTYBvWM9vdjnEao/w5sAK6vj3q542xaD0fRTHeW24fvgp9rRARtnnY44esvqYqca5gsajiWilUEEG0Io6DurqqomnIHYdsIfEntBR5lkWVoaIoMl69eI6UMvb4wajztNmQxO3CdrsnTRPaCAo5EIZ85BqEFKSBPA/Y80QpLi/OgmjLGIaILM+zLOC5jCGN3oluGNBTyGn45vWXsWKq4yrVUBYFCMsoBqSYedzes99teHi4Zd/uELiQLZFnSJHSLCqyvOD89JKLs2vy+oq7+yfyvKCsKlS8oA+iqbPTU05WS/Q08PnrzyODokTPHlXmzMZS1Q2u6/ng+Sm9nukf93zwyffY7/ckecbN45Y0TXh5fY4xli+/ekPbhayGF8+uaJoK7z2//fwrvn77ji/fvOX58+es1ivSTHJ2tsTomUlbilyRZxKspu92LMqUIl3xZh6Yh55tpHNfxIHiJx++4vHpDm00t3ePVGVGqhK8/evSAMLXd+ISUFKCsehxpG9bCiWQMpiK7h8f2T49MU0DiQoIKT1btDE0qWTd5KzKnCzPQuC8SlBZgfWCum4oVoFg/PTNF7joCa9SSZFlbLYDdZ6Sp4q6LBFRlmxt8AF4HwhGkzbsdgPw3iN/8Ngba8AEuW4Sw0fuHx/Ztx2vImMvTRT7dh84f3M47D5OzfHEgxEQZs47EpUGM0wsia21PD5tkFK8X+lF5eHBlHMI+giCo+RI4znoFtIslNnWut/xDmg9H/ULzsdUJRkqAqKRqu8HNttIwSly+mGgqatg3Y1bDu98BIrkx2oBDmIvuH94QghB09SUZR4VfzNWmqMC0vsw+1BRc5BFd6X3jr7vSFOFkjDOHW6GzeMd8zzStXuMmcgzhRQK40J68yF/Mc8qPnz5ES9evOLmAbbbe2bjsE6gEoMcpoh2DwPlPC9pqgolBXqauLm956ubb3DesFyvo1wb8qIkzULAzNnpCWmast9tI+zFH1OdBAE/V+QFF+fneO/58uuvw/C3Kvnw1QuWqxUySRjHgZvbWxIlqMqGuspZNAvyPMBTH5+eGKcxrGaThEnrQLEC+mHCC0fTlPSDxlvY7To2mx3r5fJbz9934hJIlOL55Tl1mVNkKQkOQQBiEgdCUoC3M8ZZ6qrmolojlaSpMtZN4NALlTB7GPRMlhu0tkyj52S1pC4yikSSK8n1WYOZNWMHNktZlBWZSqKarWcYh2iTDdP3kDDkUSqIZjxhMNj1PZ5QCpd5iCM/lNzGmABKMYY8UxRFglI58xwDSAkWanwQ0+wi3itLM6wIgJF26o4U3yDtBWNtkCiL4NCz1oAQISkpZg9KEUi0B+09HOYA7phHWJblMW3Ye8/Z2QnCgx41RR48BwcycACE5uR56MebOky8p0kHx2V0OJrZMDh/ZObNcyQzR4DIwStwerLmEKaC81gfxFJ5kZMfQ1rdkWSUKIV3ls3TI1NfcLJMwRq07mjbPX2/Bx/ceZPWYabkBLlKkTKjrpdcX73g/OyKyY603RByL4M2B6MNMkvD9sGHLUTIrEwpioRmoVkNLeOkqeNGJUuzcGGnCYukJlGSpi7BaohOymnS2GnAecfzqysuL8/Z7lrevH17FDKF4XRsI/MU6zyLRUNZ5lgj4tMnePXBS/K8RCUpyhhmo3ncbKMATPP4tEeplNPVgvPTNb/49RvSLGEYQljM6Xr97efv/8Oz/S/9lUjJxWKFc4Zh37Lfb1gtauqmYjYjwzjS5GW01zoUDqsHhPIUzQLhJDJZ4QRMs2G73yImT5JVjKPn7fae/vGO85MFftb8T//Df8frL75i0Sz5kz/+Y1ISpsnSdl0AYdowoDNuOhpd8qJiGnumSNfJs1DqmkgLOgA8qzLALg8T/a7v6XpD2+3xPgShLpfNMYE5SVKapqEqS/phDL4CGRJ5lidNXPOFqOsD/nucHJPW5FlGFpFcHmJEWqgumqZmsw1y1CLPeHgKHomqDBHubdcFyk9RoBIVY9LVEWDpvefLr76JFUXYvBy0Bwevwm7fkqbhESqLgqfNFj3rMA3P0mOVIZVi0dSso0y5LEIkuR6nEKYZW4LuMXAMT89OqasyGKBMyGsQWBZ1iUCQSQtuYBz2jMOOaeoYhn0Aq6YJeV6RZhXN4oSr6xc8e/YSIRe0nefs9BxrJW3X07ZtkGMLhdEGIeB+2pAXoaIp8hohJR80a1ZNxTiNIELATT/0tPsduAk9TWzae8o8J3Mjq6Yhz1P6bkNe5Hz80Sd473nzNrAIyqKgrhs2ux0XFxc8f37FZr9nmCbMNKHnmc44zk4uGa1ge7dFu5Q3X9+xPrvk2bMPyTLFZ68/j59TiXbv0Nrw6rwhTy3bzS3b/T1Dp3m433J98R0XC3kXds3jNGGMPkI4sizBhbTM2B4EfLaUAiUEImZUWxtZ9TIjVxaMoW4EzaLg/KRgv++4/WLL7c0NfbvHJ4rL81NWqxOyLJR/QiiyrCTPNVLtSaQM5dbkIgsw5CIk1r43s8Ry2sWUosM677AKk1IGaIRMKIvsr6j73JEpIA+9dKIoi/xovQVIi1BWF0XGOIxMSmCMpO8HqrIIazZP+Gy8DQ5A75BSHVHoh0FdohR5VcY1oaCpqmMYanagB8tAP9q3LXmWH4VCSsoj2utQOciYdrxv2yPFqGnqAAmNEFMV15ZhQq+O7Y0nRKiLzNO1fRBJIUJr5BxtF6LXl02DxOOdQQ8deEddlSg1s9/u4yAzKAOlkigU3kOa5mR5wfr0nOvr55ycXYBMcSTM1nJysiYvcnxUaR7ISt69B7f66L48JOsVaYbwsOt29H3LOA4kMnAw53EmV5AKy+XlCUbPCGe4vjjh4voSISSfff6a/b6jrqqAAI8Rct1Tz8Pjhourc75685arqwuc96zWJ5yenZEmBfiERDXs2xE9WzziyJYMM5MiStYt+12PknB5fsJme4eeDEM/IcV33Eo865nH+yeGoUcm8MmnL5n0SDuNzN4xex8OfKwEvAvqviSVqKREqDAhTdOZLJ1Rc0dFhd5O/P3/88+4uX+iqmr+6Kd/yPNn10jheXvzwDx7PvroU9K0DOnF3Y7ZOcZ5wHuDd47NboudHXhJXoRIrmRSESFtKIoc4YnsOn8c0E1ToBClSpGliuWyJs9y9Dxzc/P2WBrXVYjXWi4WqKo4ag08nu1uzzTp8EaMB7kqi9DfiUOJ76MSMSgGiyIPa695ZrGoYxsRHuwirgwPCPNxHEmzsK7ct+3R5LJcLEiShKfNJlQ4ec5u1+IiAj6P3gkXYanr9eoYIx6GiwXjOJGmSfDuC8EiotQOrkIR5cvGRAWldSFJOE2CwUoKjJ0DDGToOF/WrJY1VZ5x//DEZt/y7u0N9sBNhBBRn2WkZclitebDTz7l6vIlSVqjXYK0CucCxqwoUpqmZLPZMk86AFMEdH3PvtsjpWK5XFGUOYlK0CJBpAlJasgKgUwyrBnwzpCmOcJrsjxjv285XS9DgOvY84tf/zy6LiXOwj/+2ec8Pm2ZRk0S5zvr0zUnxvPJRx9zeXlOkqUIkVLKhCLJSFWGXi44rSoK7zktc4R0DNt7Mj/QJJY/+N4zbu/u0buOpkr49MMX/OrXv0ZPhnY38PCw/dbz9924BEygCqkkwWERQiFVoNMgojqMqP5KBNKLo2nGOoGwYRATZKYTCkOKY7vdkArL9z58jlc5ZVWijeHx8QFQ5EWBVCnGelSSRvNIGESNemL+K4RgkMeYq2maGKeJruujRt4xRiS3nufA4J/DYSliXsAwjkcfvZSK1XJJHpN5pQqxZd4Huuw4TQH0EYlA0zQFs02M4hrHMe7R5RFKcmAOQqiWgiElrCltVCzOsw4kmyQEtIRZh2EYLFk0tcwRh10WIS8wkIHC4Q7BKh4V48WOGQZJQpqlTON0vJwgwDUCKFUdf/8Dbl0AZV5gtaGfJvKyxLuQ1OS8oylrUiWYExUrkaCv2O12PD4+0Y8TSIkeDbOZKIqQ5pyXJWVVBXz8coUFvLXkWYIFiNbzNMqxs5hMFYbBISsiiUlILqZPWeuwSRYguGUNaYqeBvb7CesgLUrcDE5IirKiWa5ASt7e3jHMXTCUyZRUJfz4977PbBzjMPHm5p5hHHn77g7jHE7AZ6+/JM8yXl1dcXZ6yaJZUeYV435LKeHm668QJqQLLTKJ1z2PtxMn6wXSDnj3Hh+XSIW3YZ71+PQdvwTSRLFarRjniSQLUMn97Z7ZWpI0Q84aYrkYHF4hmEKmKSZy6ZWUJFIzKxlKSDuTSsGPf/gJq/UZn30TPujJGJqmYZosX725JU2/pmlW1E0DQmK9o58mdtsdxmjKtEBKMLOL5XNyHFYdkoa885R5iLGeIoOQ2Oea2DoIsSRJQguxWi6oqhJrDYlSWGcYpzHYkiMKPJFBvnw4bCJGmR3CP2Qcvk3TFLkEQZ7snGecpsAYrMIFFJyYh8s0OBRtFD9pPbNvW8qiCHBUH7QFD08b5nmmKssj0vyQbZAoSVmEqibLsqMFOc+DGzOoJsOFl+f5e09BURwHZlrPrMoC4UEmSRiIeg9G0HZhfdlUgeCU56FHxzv2+x2H9OVhmpBKUCYFSIuLF85qtWCxWrBoGrSVeCFASPRsSBJ/dHfmWRr0FHg2my3OWFQSAleNs9hofZZSUFQZSgjyvMRHgY5xHuM8mZJYL8jygpfXlzw9bmjbFus9WRZCUssiOPtOVmdM2jD0I9t9z+lpcLEu1gscnvvHR9JE8dWXX/KrX3xOWdSslycs6xXrpuLdzZabb96wa7f89G9+nzxTKCn4/LefUZZFqPq8jxVoRj8OzMYccyX+uq/vxCVQLxZMScLN4wNNXfBKKqq6wu8tXT8gZou3M16GrHh82J1ndEinECrFyJTOWWZjMUnOZtCsT054uTjjn/7qNZkoOG3OWZ+cMizPqYuClz8a+Pqzz9Fm4nm9Yhw1UyLJZM6yOcV7R7vdM2uHNTPb7UieBSZenoZpsvAalCfPK0LcV0lepEzjyK7dh2ARbQL30IMTgnHWCK2oq4q26yIzoD/6AZx1dINGochkSpInISl4duCIqTkuxpeXQCA2Wy+ZnSFLMrIsx5igJEyS5GguUlLF6bths22RUlFXxXFCjQ30niLPOT89OSLB9m13TC3q+5GuH4K+IM4DpJQ0dR0txfJ3QCIH4vJB7dd2fVj9SUG9bCBN6LqYPlzVQSY8Gx5vNizqgouzJctKcHf7llSNaLOn0yNZERRzaSpZLtZkaYESBRenH1NVKzYPFpUlZLnAyja0fFqRiICC16PFuSBEkqrA2Ak9HvIZFD7231lRkKgJJSTCZhQyIatW5CLBOUueSaoMcDNffPkZ7X4TNhWmZ3Yj11dX/OznvyJJSz784FMuL6/RMuehm3H7iSRJ+H5Tc36+ZrWseHh8oqxLfvziJTc39/zyV7/l/ubPKLMUZSbWTcn1xQlzvycTFbtx4ur5Sx63W/TQhrWtn7m4XvPubkbrifvN7lvP379MIOl/Bvw94NZ7/5P43/4j4N8D7uK3/Yfe+/8x/n//AfDvAhb49733//O/8GdIwWrZYMzEoimPZWhe5CQHmagP2fFJkoILEM/Zw2Q9Ck8qg6tN4nF2JisLdrsdy9UlRVXzqzfveJq+4OJi5OyTnMf7e3KZsFrUYAx6nJiGAT0OKOnJVBAFGT3gZkMqJcaHFd1sdFgxxVXQ4U0nVaDwZHH1V5qZLLWkiSXPCxIVIsIOh2bftvEzPsBHw+8wHcCkcYgWDnkAgpjZ0DTV8ecrJWPPL4O2XUqMs8cWISDGbNjfpwpnw7TfE/rnuqpQiYox5DlpGoRDWZoGq7IPVVcVh4rBdRhakTRNcPZ95PkcrbsH7Pk4jmELQtimBNJRqKDKoiAvsoDN9p5xDL29wId0ZmtIEkmWKtIkiI+CE3EmUYq6ytnbNkamO0KAbEaWVSRJirXQjyOlDG1fnpc4Z+m74diWKCXAhMFqliuUytGRmeispR165BwGuEmhYkUU/ONSCcqqROIpi5She2S3eWLf7snSgEjrtj1pnjKNFmMcnplff/ZbfvPbL5i04Zs3NyilOFmvOD/5G/zg44+4ubnh5s0NSZWy32+Z7UyzbPAIyixn93DHxYsXPLu+oCozRq3ZDRPzMLHvOjJCeK9KS87Pr3h8atnuRrJvZ4r8S1UC/znwnwL/xT/z3/8T7/1//DuHWYgfA/8O8DeA58D/IoT4gff/HKIBwXF3frpm0RRcnJ3gnWa7ewpWW6WQaQoiPHhIhRMO4QVeghcKJ0Lpk0Y6zth3JEJwfn7Fw3bLz37zmp/89E8YreJpP/CLX/2GMi+4WK1opCCRgrrK8W5G5xlVnjIr0HjyJNBdU6nYTgF0kihxXI1ttluctSyXi8gTOBzqcAADIju465z32KiMc97FgI5wkOuq+h2iMUC1KJnnma7rSRLFatEcd/fx88bZEE12AKIclH4hcFTFRGQX05FMINp6F3fZ7zMEFk2D9wJrgzhIK31ElfmYN5jlGQLC5qDIKeKAUGsdHrz48w8Cp34YY3VDECM5h4mAqN1+DywiWTfEruEB75jnEBH24Ytz8kyBD6Gwk57ouz3aTozzGFe1Qe5srQCRUlUNUgWLrpsNyWzROpi9DpP/cQoCnsMwUylFVVXB9u2CX8OK99qKcRyRvSJP8/AMJgl5lpHmKXiDlJ67+wd2u8eYmhzAJGVZU1UN9/d78rRAJSmL5Zp5NoyTpltVwW0qHcN+T7/bMXY9WEu7Dc7P8/NL/ubv/4jXX93SdT3tfs+MQhUVvRnZdT39pKmairKq0IMljwCbxdLQLB952g8M/f+LBCLv/f8uhPjoX/R98evfAv4r7/0EfC6E+A3wx8A/+Of9Ia0nUinwSjL2HUJGBZyQzNbxtNkxjpqiKMnT/GhNrVXO+eI0sPyShCrPyVKF1ROqrsAbfvPFV7y5f+IPVxecnF5xJTPM3GK05v7mlnY2XK6XPOyHkBGAZJgN8xyAD2leYPTMdrclLRvSLEeK0Hd7Fxj4cFDf6WPIaBKHiEKEFeA0zRzyAWcT9v1VVRx7fT3PFFlGfVqQpAFcMs+assjDm9tHxLYIVtkkEaRJkPUmWkZ5sQykGu8x1rBp97RdYCfWdRVceUkQEy2XS/Zty2bbYp1DnSbxTZ3y7PqS3W4fwj2yDARsNjvqOmxD6qrCWsvt/cPx8A7TFKlL6fGtf+AGShGqnsOcwNmQTaC1QU8BvppIGbYPkw7ldVHx7PoUo0fa/ZbN7glnNbMzzHbEuD7Io4VEyozV6or1+oymXtP1BpkkrJdlWOvKhGm0AWGvHPt9i0DgqtBKzdG2jPNIwlbC2SCQ8hGtpkfNNGoqEzYsgxxompIyS9GzoaorjJ2Y55YkS8mzAikzus4hKagKRV5kVEXOJOHp8Z4qkywXS05WK7rdlj/9h3/KZrvn+vKMk1rRzw7XP7ExE3/0kx9x/7TldSp4+eySfuxx84hSsFyW/PaLL1CJ4oOPf4JzjoeHRy6un9H/38y9ya9m2Xrm9Vvd7r7mdHGiy4i8mbcztrEpWS5dGyRkgWBQMyZITFCpBsUAZvwBDEo1QiDVCMmICQMQDBEDYFAzwNwql6vsctm+vk22kdGd5ut2uxoG79o78pZv2lbBID5lKjNPnDzN/vZe613v+zy/ZwrsTj1v3rz5xufv/0tP4D9TSv3HwD8G/vOU0h3wAfB7X/ucL/LH/sJLKfV3gb8LEj/+ox//uczMiTx5/CBbVePiXJNmYGBSfmlynbqOvh9kLr/dEFPET5H1+RlVVfLli6/47ItbdvsDX758TXGawNWUpcIqzXp7Tp0iGIVKhnHs8BGUKST81AZsGYhJoV0lVUku0ZUWm2v4WrbAkDmAUSm6fhRZbKb0lGUpu2RUxCizbaVcHtdJDHcIAd/5pZSvy0p2SSO79ThNMtZz8rANw8gpx4LLZEQWmqqp6dtWACvrNUopmqrC5oZiU0uFUefd+X63E2FPXrissVRVlacKMl+u60qOYEefo8NkYbrf7Ukx8cHTx4vUd56Br9erhRH56vXbBTvupykr5lJWROYcBiLGKprKoVUk+oG+P9KPLeM0cDoeKQvH4KWKqYqKiMWairo+43z7gLaf6IaAix6PHNOsCfJexUjbyrFCa0Xh3VK5FLnHEyYJmVVaYdEEpTBIMIuzlhQVCmE3WFsQFSilsdZhjEUpS0qamDTGFMQwkJLGFSWrZoU2UFeGD58/y+Rowdid2pbT8cTDB9ecn28ZugMqyNh3ao/s796yrRs+/uChaGnGFhUkUVmIT4rzszXKwDSNNOuaN3e3uMJydX3Bq9f//ycQ/TfA38vv3N8D/ivg7zArK37+lX7Bx0gp/S7wuwDfev4o/Xv/zr/Ny9ev+NMf/YiUAs4ors63vHr5FZt1TUrCfUthorTSJ/BhJPkJWzpSlESiZAxtNxDijrJa0bZHCgfPHp1xfv2IZEuGEDAAIWCnER0j0+koQNIU8SnhU5Tw0VHgnaYo6IdBtAGlsP600pyyffh4OpGQnMT9MMior+8XSvDpJKOipq65vDinLMu8gNllvBejwDDnFJ51XS9g05kEbJ0RpoKSikK8DWRlnjD4T+0RZzWb66vlyND1A/vMCCicox8GmmZFWRRY47i73ws8NYM9ghfC0WyYmvmKxhq++uoVrnBcX12yasS89ObtDYfjEa01V5eXgjPLbkfvPRfnZ/k9C+hcgld1g7Piq/dDBykQxpHmqma7rnnz5gVd13I47Plnf/iHuMLw8bc+RGfidFU2rDdXFMWGD55+F1fUoHsO7R3RR/qsQ/DBc761OKtIGLpB4Sep7oDMRJSHsXJyb803ckyCkBf//sTt/S3WyNFzdapxhaEpHM3qnLKsKV3BOIr0fH86ElXEFoaqqmnWGy6vLnjz9paH51uOJ5E973Z7Li8vMHWJW1eYpmBbPeD+86843t8R0Ty+fkRKHro9oe/YWMV9nkC4ouT5s+cYZ7i8vqBtBeQ6+ZFje2IYPTo/M7/o9a+0CKSUlmVFKfXfAv9r/s8vgOdf+9RnwIu/xtej6zre3siu/Z2PP2C333E8HnhwecbheOR0OImzfvYQBE9VGIE8Gknr0c4Jh78Q6ERRWDSR7aoCPzC1e5Qtc4qLxgEuBaKfmLqTJBtPPSYFDJFEZEoBH8SvnpLCJ0/MBhcQheDc7CpLgYh2/cCQceQzk0AbQ4yJru/ZHw40XlJohkEWma7vl4AR6a5LkzMlkQPHFN+BQoc+P2BhMQ+NGS5qc5z4LPzxXoCbx9MpjxiDCJyUXoJQQoiLJdo6Kw2w/DuBjGS7XpKU/SDtna7v2O/3gBIrcI45bxpBkylkXDr/PGKXtkvc+HxMggwkiYKWd0XJ1fmWujQcdiO3t2/56quXWKe5OD8TLX0Sc04IidNp4HgEo+/YbNPiCgxR5L1aa2IKcv3IBiokNSmlRMrBMeM04Yyl8/1CiZqPYTGKkCmEuODe5uMqSbIHG13iSotf9SilGUehYWkdUFqx2qwoywofFNfXj7jfn2j7iHYN24uCu8Md1ioOfUs5FDg0FGuGo8dax3azYew6pqGncgaQ3oUylmQculphy4oXL95grRCpD4eevvecbc8pi9ff+Pz9Ky0CSqknKaWv8n/+B8A/z//+vwD/g1Lqv0Yag98DfvhXfb0pJ+o+ffKIuir49LMvWDWVGFaip+9awY8rRVIiFtAYSqshTsRRMxUlZVlSVSIDXq1WxJwTeLPf8Ud/+E958OgJ5xcPOHtwhQ+REBMpeFQIxPEE0z6gMT8AACAASURBVESaBuLUkbxHkyitFqWZUSRlZf6fy/Ku79ntZfSilRI3CrKLFNlbUJXFMpPvs9347m5HPwxcX10udN2+F4GQMRqVIEXF3f2OOY9gSWT2Ui3ELFNWSPKQ1nrpOcwy7DFLm2cS0rxg1HWFQqjA3TQuu3WVwaQhsw+LbPXla00yEIjqrGyc8wrm3V1nOEyMkd3hIA20rKeIGRZqcs8k+MjIhNFAEipwUzeUlaNtj7QnkeeOk7g3h2Hg9v6O84sm5yNuWa2vaOoLquoBrpQ8COMcYezxaaLSxSIvDjEtEJQ5QUpbjU2Rccx6iiCdXWvMYt8GOPUd8uCZpSodhpFp0oyjpzCaqrCsV2cZOa/F/u5P9MPEMI6Se5gMd/uOH//0c37p+98hEUkEDt0BrMJWBlsV1OUK2yW2lwWb9Yr9oaU7HeU4GkXMhLLYokSVFcVqQ1HVpCjYdmFbOD7+8CPOt1tefPkW+PwXPn9/nRHh/wj8DvBAKfUF8F8Av6OU+htIqf8J8J8ApJT+WCn1PwP/AvDAf/pXTQYA1nVBozxfvnnFTz/7ElsWJCXWyXGc0MjIDiPGsynLVYMruTm1rN3EeeFwxwgx4OqK0Y+47QUf/+Zv8psPn/Hs+a/SD5HDoef3fvj7DF3P+XrNr377Y5zSDB6GMRKwDEHT5x1PoYnaom2J0ol+HNnt9pIxkLvNIXgGPzHuRH66WgnP7XwtTUNlNPf7PSpzAY6nE0M/8urNG1EOlkUWekjC0KppJGasP1DkDvb9br8Iha4uz/O5XYREbQ4DmZt098cT++NJkGSlsAWMNhQrSSvyPkCSPL45/ejh9RVlWXFqBz759EsKV1DVJU1TslmtuNhuKHPTTylZXOfqI6HYnl9lMVdkfxDb8zANhNDj/Jw1uMG4GpREkaUg77FRCR+OXG0rPvxgi+/u2b294fVXd5yOI6VuqJqSlCs0rRts+ZDzhx8RJui9YVuL+7AfJuH+G804BnrrcSEStbyXWlumUcp/8uI49APHk5B3ikJEQVOWLasMhS2XdCRPQgORhAFl8CFx6DRTKgXSulpR6y3j2yO7w45xCvRhoA+WsB/5pe9/l2QsX758hVIRkmflLFonVtZwurnj0L3mex9/SFGKz+B+f0ddQcTRjRP9MHEIgdT2FEHx7fMzxsGzT/fs7k588ulXPHnwIduzSx5cP+QHP/g3+d/+4S8OJf3rTAf+o1/w4f/uL/n8vw/8/b/q6/5L/w+//8/+Oa9u7khKS2qsZFlk37lYb0U/QMaPy0RAePs5pzBGRu8FtaUdsR/p2pH9vqUfRspiTX295cHVb3Pa71ExUWjN1PeM03y+HhjGYUFxqSRJRM5ZQhSWfl1VTOO7XIJhGEAlpmFc1ISya8s4zvvAerVivV6L2UWpHF4Sl3FgWZa5RE70Q7801kKIX0tBlp3aGJuThbJENM4RZT6HdnQSAmo0Q84FtF9TNxZ51j+Xy/MCdGo7+k5y7I3RbDYrMTVVlXARnPRBvPdoLTQgqUhytHkpxwxtRIB0ZjeoHKpplHlnmlJa6Ea5uhjCyNlqxXc+foZRkZc3ryXCDTkyzdclEbPT0VHVNdM4MQ6Jwhl2+yMkRT9NtJ30UIpS3I468xPmDEgp95XExA8DXd/T9YN8XEHICDn3tcDUPtui5b6Qr5fyvTtPPaZxYrLiL6mqkiePH7FZSdNvd+w4nDoePX7Mj3/yibhEt2uc1QQ/ohlIwYsHJUaePrxm9J5u6OWe0ZphHNm30ltqu47DsUVpC9rw6vVbYoLPv/iK/eFEWRQ8fvSQ9tTz1avXkpr1Da/3QjF4OJ347IuvuHzwgOcfPueTL16QFMiZxwgkw7wLhYgxQVQS3OkKXCkd5pDkfNdPkahGqjJwOnbcnV7Qhy2b7RWresNqNeFDxJLts0WJK0oATBBxSopydu3aXtj/w0CfH+rSSalvrGEapwwTcXgrY7Yxd8dR5LGcCHjmh7Qsy+wkk0bjlKXCZVEI6tzLOMtlb4BSYg0ui4IquwBjipxOEtnl89hNMOwAOZLMqOWYEGOicHbx9s/Tir4fJNE3Sqf/dOq5urxks1mxXjdy3rc2cxAkAVhnTQJYiAHpsqrlOFI4R1VWlKWTuXkS1+AwjiQPdSWSaacjWsnPJoubwY8jXdezO+xJBFxhsIUmYghxpCg1q9Wa7eacth2ZxkRKkhOplCYpFrdmTMIhiFFQcCFE+qFj8uL8HAf5Xl0/LItDDC6rGSU5OcQAk4SgBOWXlCeYclKTmLp8ZmKO05RTmQouLy+oqwRvb3H1mrOzM66vH/LJZ1KW3+/uQUmsnY6am5s3jMOJqnCs1qIhiEmYlW0/0PYtXT/gyhJXFhgzUFQVzaph9BNtO3B7t6epV3z8rY949Oia9jRy8/ae733no298/t6LRcD7yA9+8Ft857vfpqxr7toeo2Eae0QMZzFObLOExBg8CYuKCJQiBk7jSGEMyhWsVhuCLWnqLR8/P2dIJb/0a79BMhX9GDl1L3FVwdQPHIaJOp+pBWYdUUvKTCT6CT/0tKcT5WqVI8kKQjZpyOhs4nA8EDIeTGK+FOumJvhA20tefUyJFMWJp7Vme7ZiGAaaulrO+8EHFLBuGrbNuxDJYRiyBHlcFoH5eGFDoG3fhXt6ObrDKEgUrXVOPtJ53Bjp2k4MNxuBY9zf7zBacXm55fLiQrIUSimxTzmRWSsyaEWWGokSl/yGNAZWiKpwu5HQi5QSwyhgzqqqZBKR1XQABo8xsNo0/Povf4fPPv+E/e6GN29fM4w9bXcANNZZitJh7Ibt2ZqrB48YvGbyCpfn8SnBMA5gFMPQow3EpJn8wDD0NJUoUZ0RyXDMlnBplk64rOmQCoSc2ViQos47vl7k18bkUJlMho55gqKyuSuEQO9GjI5gKpr1OUVMdINntz8RApSV4+rqgpubt9zf3+LHAypFfuX7H7Fuapx2vL15KzZrHxj8SEoKU5T0o2eYJjYXZ0LPqhsObcfrm7c8fPCIB1fXPH/6IQ+urgnnie3mjN//g198FID3ZBEwxnB5eUnXjww+Zq93YJpy+KhWmCQNGR0jEu5oCNPEkHockKqKpDUhJY5dh8fj6jOePvoWk2pEQOMD2jnWdiuk3HEgHY84EqMSya+8EmTUmFZQVwWbpmbKZWnI89uEqOumaco5CHmMZ03uUMsC0GXYx5w3UJRiumlP7RLG2U4ekx/WspTob6PkPDovNkURM613pB/CAv8IGSWmlGIkYZ2RYJSMryqcI4ZJcFvWYnSBtx4/Senvc6y6ImGUoNeCD/mGjkyz3ZeE93Jt3nXIFQnoho6ul9Hls6ePc4ioz6IhR9t2KMAlh9YxQ047NnXF5cWW/f6etj1xOB65u7sTwMzQyuzdVjTlCucKymbN6BOndsDoAtAURYlWhspo0LDbiy6gyRkGWotcG8AHaYKGOE9byMyHdw1LuRYw9gMpBEGiF8XSsxGJtshzZ2t08D6H2OTmsFIkI0fXs7MLXrx6TQLudgfu9ns2oebB1ZZh1VAVhqkHqxVNXTEMI/tuL6EtOZ7OFhalDO3pyN39nje3t/zgt34Lax1T8Pzss89QSvHwwWPKouJ4aknpFtDc3d0Lf+AbXu/FIrBqVrx4+YZj17M9O+P/+Ud/wPG45+JszbrUqOBJGcMUoyIlLWEacWR9fYYfR4ZpwBUGLGiV0DFw2u9ZXYp67vWb1wzJMGFQDBAjlYLrukCreSynUSQJ+uhHsd6iKIoKqwTw6UNgd78XG3HO9JNcAo/T8mD0/UDbtby9uRFNvhNst7OWsiyWXMAYpXJQ3tPUNUUhcl+SMAK6rs0CIbEtSySXlJ/WOdarFT54DocTh8NJbvy6Rhlx9M1uw5k1YL62281glDnjcL1qMMailSEFLxp96ygLS1kKVGWOFzfWUpUFp77HaAleCYgQR2vD7f0Ol3fIcRyF1TeNaG3oh5GisByOB379ux/w4HyNNYHPPvmS129eE8OIs5pxksXMWI0tHecX11w9eMTZ9oKXb0/EFNiut6waiRI3GXl+e3fLel3jMtdgXky7U5f7I3Jdgw8MwyCuzQyD0Uqh1dx7UksmorUWk+lOTVOLXiW+83TEbHWfJw5zapJkVFpiUHz/O98lkfjpp5+wXlU4ZzjfNhQ2sLu/Y335kLqw3N/fsN/v0dhFXBVjJKTIxfk5UWkOx56z7QVGG+52e97c3lGWJQ+uLlhtzolJceonUurQylDXNb/xN37tG5+/92IRCCHQdQMfffic88tzfvSTx+yPNVpF1NSLJj94/BSZfGKYoOtHLlYOLS1u5lRcGZclmqKgWTesq4JNtWFVXdFFzZgUzihSCNjoqf2I8iOD98R8LjRW5uXi5R/xef5eqoSJMmf308Q4jTmVJhCDz6M5adaREk1To5CIraIolhmzzSlHIS84OsuBYwzLaDEmUSAqxIU3z8dTShxPrQR/IJyCafJs1itp/uk5YFSMTDF9jTeQ3smbE7BaNYt+XucyaI5B3+0PrNard5HkUSTECUjjSCJxf7eXo0Yj4Suz/6FrhyW+fBxF7TmOMh611nC2XRNi4HxTs12VjOPINA74nBw0P0RF4bBFIU5JUzCMifvDSEoGbYQ9WdcR64pclke0VrTtCAjeXZR8jpCA5AnTxDSOy+IqatRcBWmDyelN4rjUJCI+5B4Pc16jWLHzRJjKOcbol8ahEKVkUqNQeQojvR1rNSZvAPv9PV17pG0PnDVbuu4EUcaNPsikIvY9UwicTj3HQ8eDR9c8e/qUw7Fl8oH94cjp1PLsuRCib+92WGN5+vgp69XZ0uso6/c8lbjve771/AMuLs4kLbhwPH30CK0CN69f4Ae56ClFNIp13VCXNbUJECaU0WiVEPQYQnsxik1d0B13xClBsUbrAmccOu9qJilMChDUYvqZG3FKKyIwjJ5gwJoSlEAoh/wQNXVNU8nI69SeIMqDaaxBocRtmENDZ/hHmPxy7kdJ+a3z101BjFGFsYQYcqqQo67FvTe79Q55nDX0MllYr1dcZduv954qR4XPaUkqQco+/2EQBHpVVWgUKVOSQ+YyxHmWnv//4EN2MuqsrZCI8rpqFvZhWbp8g1sKrXG2YLVqePnyLS4z/ScvZqrCSR+hKWuUjuz2t/T9yO3dLX7ytF0nBN04ct5sqVYNq/WGwlXsdh1l7UA5QJpwh+OJ7UbnUSqcn22JaZKjh3NY7Qg+MvWR6NPPUZ9mKEwIE1ZLM0+utc1HnXel/ZwtCeTR8BwDJ4i1opBmb1lIf2KZ/ERxUN7d3eP9xPFwkMzFOHB9UeOshhjYNCXHw0DnJ/w04ApxDlprub/fc3lxiS0cn376BcoYHj26pjCWdd1k4VTg9vaOX/2Vb3Nxfsk0Cgdh1TTCxfzm08D7sQg45/jkk8/44e//AVHB4ydXWKPY7+7YrCr6FImZ4CMlsRCKCyIWlcEXgb7vMEZRWGnoTFOiVQdSucEfBryrGLVGe4tJicooPlg31FbOhGEW0zQNCUU/TKzWG8qyYhw9Pqf8PHn0MCfK9Nzv90BivW4gxEVhllJi8uPyMwuzLi4eA/l3sRULHET89Epr2nzMMDnpOCXZFWYF9sz+64dxCQXt+n5xEM7hpimmhXXYtt3i9hNJrwBF27YT2s7phPD55Miy3a7QSjwL/TDgnMFHcXUaLTCSszOJeEMpvJcd3FrH1eUVfTfy4OoSkiQLbTcrXCFI7bOzNR88ecTu1Z/Rn/YcDid2uz3j6CmdRL+XWoJK69WazfaMH3/yEqVWVKWmbBqs82zWEv12c3uXpy4FbdsJyMRP9O1AWSi0crTdwKlt6frdornwOYthjn5TShDhIUoidVOWi1gqITTl06ld+H7zhEUanlJ13d7dLwIvqw02Mx/OL9aUTUU/VkxTh5/ELGd04OHVGafjAYKna49oJNrc+8ib12/47PMvefTgAV3bc315yTB5vnrxmubQ4lPk2LWsU+Li4oxh9Lx+c8PrN7c8/+AZ3ss9p36xeh94TxYBdOQHv/3L2Xzi+T9/75/gJ1C64P6oOB6h7SwhaFKMlDphDDgnXZ1u6GgoJGa6SBQmp8pWFoIiJs93H14RlCMpzW1ak6IoAtsw0E8TPsEpRPph5HC/l3TimCgKjVGBcRJ8mDSYlDTtlOQAhOjZ7XeoCIUzNE29lJvC8BMiT0wyY2/7aWnmjaNg1I2TbICyEkinlNA9Wsk5Ht4tLt7LwlDXpdiMc4NO4B1yrHDWSDpRLgcP/SBn/LIUSW1EXHXWYrWijCHHqxlpdnpBhLuyZLPZ5N6IWwJSrRX/hHMOjSL0KWcXeCI99cYSJp1BH4bkPSp5qhR43HhW8S2jLhliTYoDIY1UK4XWHhUV2tTUq0c0q3OUqjE2MMVEF1rCELHe4pxnvS7RRrBjsxZCK4WzDeMY8b7DmhEfekLoGLqOwhqCn+jak4ioCreM96Tsr4VA7ERPYI2hcZaUIvhJtAQxMnQBP+YQlcKRFDhnlo1gDJ7JWMI0cG42lM7iQqAIgUorYnsUmpaBzbZiCiNDDNiiYHCON7sdNwPozTWvW8Vmc8npdODQtez7jicfPxfYjNKMY0LHku36CqM1l+cPGIYT23WFItJ17Tc+fu/HIoDi7u4O5wSh/b3vfMTt3Y673R4fPF3fcXN7J0gxpbg8P0enxISnDRNaK8qsc1dKE5PEaJdFwf4Yubm/5TtKMGYRTa0dKWgUEa0CeIGZhrxLt11HDJEif81xDsk0Ui2EEFBapMLWGgjyAKgk9NkYRWA0S22DF36AlNyBGZHWdbNfQCYKIUgOYEwRow1lUeYGm9hfp+xw09r83O5erFZ5WoGMNWMSRRzQdp3k+2UPQ4xRkpy0lmuW5Ai1Wa+wRspskQTn75UnIut1swSLxJiYQsBZJ9ZkNFMno7F3CcJJlJNJE6ZAPwyMceT8TCjJL756SZgAJZOPui9JKUNMkiiwz88vqJsNh1aOLyZEVus1bTeKpiD3L4w20sfJi6HcB0BMTFOg7weOx1OuhGQcOozD4hYUjEHKC3Wi68T4lTJzENLCS7DWMmVj1VxFCKsxfO1ulsrSKkTUZgw+SC6m0pqqrnAGwiQ276QC++ORGH3WJkSG3PeRLAL49//d36EoHP/3P/ohu/09VVkujelVXbHdiIr05VdfUVUl203DNPYUVuGc5ub27Tc+fe/FIjAOAz/9yc/YrNest2s+fP6ccew4HiPrxhF9RRwbEe14j04eg3ScXSFn0LppKHMHPqFwtsiOvxp7Unz6s59yfvkAHxX7dMxyVahMwilwRlMXBT43mbRV1FVJ2wlKa579o8XHn3RagkfJ5ByT5/fjMCwNuaEfxFKc9fQxzqEqWaJq5cEc+3Fp4DltsKWo6+7u75kmj3MyDhuGHq0NxujF9dcPIySIuUNPtjoXRUmFKNZCEMDmHARqjOKw3xOip6kqqlJy7iYf8zk+I8Tzg28zljyEIIKlcSREz6ntMUpTGiuTjgDlKIq5m7dviUGYhk+vJZKtLi33ux1d13GXU5ViDNzd3wntqCw4215SNRuqas3+MHLqJowpKSqHQnNx1mCt5ubmTizRGXOutWKz3tB3HWR9/TD2eXoi2K1hEOaeVDNy+0vMW7EYpnzug8QcijJrNAQ+UmPdLJ82C4w0Qcaqm6yilAXCGQlZaU8telWz3mxFdj31dD5XlirSdifGaeDJkwc8e/aUZDYMQ+DTz76iPQ3s25b2zYk3b26E11hXVNYRY6RsSp4+uma72fLoWpDlh+OBn3zyik9ef8YwDKxW77liUM6yOejy1RvaVpRRw9Cjsq34bFMzFoZxmNBEYhixRokH3slNal2BcU7MLPl87FxDSIah73BasVmvqNRGkntSRIcJnSIpTRglApD1eiWdZa24vd9xOrU57Riss1RFPisiu4fSGmMtyQdRlGUQ6pQVgl/vB8gOIjvVEiUWQu5sy5hynCaJ8VK5ZEfi0Mbc1a4rMbUYbfBRdsN5h44x5pgyyXEYxongRUkJ0p12+Vr74GmqUhKFqpKYlIQC5Xn4jEGz1mAUmTcI3h8BhTZyHjZaY1XGmxkjbstJJNg6KWIIdN2Bjz98isaz371mGEfGTAeKUSoIay1FWVKv1jTNlre3e5QuAcOqrvBR7DbDMDCOZILyJKnTdYU2ihTfJRe3XUc/DHR9JyKglIjRL3kLZSmVFsy/myyey0QmuwyVnhWSGfYaAy46rHEkkmwK+XOTSlkMJepJa827Y5PSKG2wRYUyilN/wPtsZY6JpDRFVVE3a4KquNvdislNKf7pH/4xVVksiVAyeYBH11dcnJ+zXQuk5cc//rOlcr29u2d3OKC04uH1xTc+f+/NItA0suscjnth/FmonEIxEn2HVhGjI4VJi0tvVZeUpcystXU06xVNs6JZbZhSZKUNZV2xWm340Sev+Mmf/QlPnjymPvsAZ+QIYY0h+sSgoDCCoD4liCnSDiHPvjXrdcPkw5IiPMtzZxONMeJR90EEQV3fC9raSUbgHFYi6HQpuefxosnlfQhhIfvOCsJ5YViagoUYgkSmK9ehritub3e5ey20Zj953mbMdFVWBGTMWRSO0/GIUnC5mIIcKgdtFK78OQuxsZayqOiHAa1kRCl8/g5rJbptvW7wJBRSfdhkuL/dEcJIWVgKm/jux49JoWcce47HHSlGpqmHLIuV9rVjs73g2Qcfgyr485+94Xg6AIZ6LWPFsiiIeFQuwedwlOQjg+/pji0XF2coNIfdgZubW4kmi4lxGul6yVFUSi8odqNFGi2/txybbNZ0kO+FsigWB+nUi9S7cKXcD5PPzeSci5FHuXKsGolBUPhdZ1ivK2xZ4Sgph56IJk09NiXWq5LrB48Zx0TnB9brLQ8eaO7u9lxszilKy2ZV0tQFX3z5BcPYMfQVn31+4MMPnnFze8fpdJ/5CB6lEtuNMBer8j0fEY6jdJbrzIzvhw7jLDF7+lFR3FvRozSopETOO4nuuiyE3zZOkSLImdJoTVGIPt8Hz2ZV8/rtHXc3Nxh3Js4/Y1k1JcTE0LeE6Ber67xru8IRQ6Af1CI+KYtCuq0pocuKkNN/rLUk4qKfr+tKDrcxigovLwQgVYCfPBFy5aIgiE1UK5UpQHOWgFrsxPOiIAsPucM/5BFqykzBkcmLhl1rkd3akJOA5u9nNGXhctNSdmOlbRbMqCxmEpqT9+OSUuS9jPGMVnlUJgw+Y400Fr3PYNIVQx9J0WOcoSoU+0PHOLS5T+IXTX9KoI2lqhu2mzOSMvRDoGk2GCsaaFM46cUoCJNkKmqU5BhmRkNViYtvGqeMeJOm35SnIMMwEKL0CKRf45YgVekxCHBlzlFwRvoxLEFEabFRywQgoLU4IuUaWFarFWAWPcYc4+a0E+9CP2Kd8CKcK2WRiJH1quRs0xCmyOm4Z0gFJMPQj9RlxYOrS5wzXJ2teXvzivVmxWE/cbfbgYJje8QVlvZGbNcRuLp+mMVMcGi7b3z+3otFQBvD/tRiC2HmCWBUQi9QYJwhTNmQk/UAIUT6cSTu9uxPB4pdQd3UPHn0iPPLK6qqxBjN4dShTMHThw9w2nA8dVyuq2wyiaRsLPGjSGiLzMDvx34x2sQo30uacrMjTQCarnDooGg7YRKO47SMkUKIQj8KAaPeQSyMkS6+1gqLGJbEiy9lpLNSoYxt905nn5tec0UhmYDFsvMUzsmMPwaMtTLO+xr4s8gjMI2icLM7zpBCZAwDKiUwipj5BjaHm6LIzrm4wErKouQs5w7M0BLymjCME4SWzboRF541XJ6vSGnkeNrRta3MyYPwBFOSbnjTrCiqBudqvId+8LiiQumEDwmlBSqqTcJ0omGYiFQ5Lk3BInO+3G6ZYspkpIjRZBdowPdThq/INQ8xoLxC11XuE+TrHCJ9+BrQNQorsXBF9vJLbwig6zo5lsaYHZ7yteuqXrQbpIR1VqzwY5SEah9QGBSG5EXHMHUT0+hJ1onnwpVcXV7x4fNnvH71mj//5Me03QGBU8vxqC4ruqEX7cpmmyE9AwHDFAI+RNGxfMPrvVgEAHyAY9sz7feAJ6kCH+My3SwqUbb5MRB9RBuLNjIGM9HiCoXNK7fEXMPheKLvI01jOO7ueXp9wXRxxnDYcXUpZ6S3d/cMw8ibN2+lyVKUWcEnDTtrDZOXm8bmhpncA9ITELiosPhUlPkw825qDVoXRDVl67EsInOpOJf2s2fBZHUeKeUH0S6Vg5z5Zyus2Fy7rqfvh4UrIDQlR9d3HI4nvPcU2QewUIai52y9pq5KiJ5koLIlKiX6SZKUfF54ttttHmOOxJhou5PwAJP0A0g5mLWu6PyARhh8fgzsdgdKO7G9WrHdlHz6+SecjvtMT+5z6q/JTU7LdnvB0yfP2J5f8uJli+S3yMTDx5EUJrRJ9N3AOA7ESZKbYpTFROWGbVVYTm3LNHmOWdo9ThNhGol+Wq5xSmLKkvch5cXUL5oBY81y9p6nKmjRtMxAF7EfS6x6GCdMTKwaFqT5OI60fS96BERuLZVrQWEtm9UWP/Z0SeFMwCWL8QLQHZTj8nIj+REh8ad/8ue03Ykf/finaB05v1gzTEKc2h0PHPueGCOHXiLmttst0dXYwqAjPHr6nqcSpwSnrieRODtbc+p2y0gmpsQwTVhlsiRWMYYJRV6JlV3m18LTz00cpajKkhevX/HVyzdcXT3GGsv52TldVEzTyDBOdK0kDXddn/3xMaf8xqVBluKUz+cyA1YI2VdBTu0JtG1LzAIUrRQxq7hmqanM+WXHntVnzr7jC87nSOnyJ2a68PxnMxkohEhZOhKIwCemJXkmIfy+ECVoU3BagfFrqUVKv1PDzeYX5xzH/Z5uDATExDTm5plWIjcui0JSc30+9qSUEV1QFQXRJBn5oajWDSkGDUXW4AAAIABJREFULs/WnG0KtILJj4zTSN91aGVxVUnyibJyFEXJumlyQ9YSgaIo6fvIFCRrARWIQRNClnYblR9mGakhRRT9MFujY/ZqlNIzCNKJ73KY62wUSklLTJiRY2Cb4+SqlCnP+X1LKQoABEUk5tFubhaOY2YpIMnS00SRJwhNXTFNnrbvmcaRU7Z16xrGIeGsYbvZsKkNViU0Huschyjvw+HYst+f2Gw39IMsLHUjxw+fR6paKdq+5XjqeP7RL1OWJY8fPaKuatp24M3NHS/f3H7j8/deLAJaJy5XBX4KnG4PuKJGjQY1BUw0OBwWSCoxJY8lj2KIMnN1jqZpKKoqY6IVlS2IPvLs8SOUstztOl69es1hf2Tz4APG05GYEu00cuq63JiLy4NIkoVESQ8OozUhi3RmXv8sJ00hYo0TfYIW8qz3E6nvUcqIo7GqiJl3oBSUzqBzFbDcyIj4SStxQw7DsPgKfJ4gJAVTThAy1qKtYUqRIQZSgCnbq62pCEo8DUaBiVIaW61YlYZEYgwBlTSMgft+IkTpZRx76bgb49DG4grN8dTlMVzKi2Pk4vyMaQrs9x3r8y2db4GJMN2xagqePXzCdlXTtUeMtzBZSfBpclVXFOiqQruah0+/za6dmHYtMRj6cZBeRgHWJtrTxNRHnClIGqKSubvRihTBObFJ+8mjC5VHoAeZAhSavgsoFSiMYux7VBnZbjcLEk16TFmBmSIpeMbgM5tQUdRlhrQM1E2Nc3JEKJyjqQuGKSxWcZV7AodjS0KciU0lE5t1I2EwpVEYGwnR0w0dx3Zi1ZRoA2UqqKoz/vf/4x/Sdh3f/uhDVrWhO93iXAAi1laEMVJYK1MxW/P0yce4zWMxzN119ONuAfKk9IsYwPJ6LxaBcZiw1vLo+gpnC45dz3pVsz/s+fxLebCmUXzV3kdKW6KVhaW0DoQ4Ax8C4zRyOB7Zbs/5sx/9jNV6y9MnzwgxcXZ2TrQlXd9LAu3xJDNga3BZUWeMQeUd3XtP3/ei10+ashDM1ekoo72mqXE2N/GinOMOx+MC4JgJRdIIlNGfJpGMxmpIXhYYl8/0U/D4KFOIwrn8+0yM40RSSMR0Cku2gcruR+kPCIzE5qCWmMJil91uNzS5T9IPUnU1dQM501CoSp5hEHqSsYZPPvt8aWxpLTtW09SS6luW3N/vZB5uHTc3t1l7EcF39B382veec39/Q9sJibmqHZGJ/WGPNoqzywucK6nqhk8//5KvXt6RsGzPrrIVeQItFObl90V+3pgSTS0gkpirsblxejy1xCAeD5fdhcMoEm5X5FjwLiwZESYLy6w1GOfo+4G+7xeNBBlUMk0TwYtpS2jHpVCflJIHURtpY2X9SFmK8co5x24n8vJhUDhn+L9++I/5lV/6mPPzNV0/cH19TuEMh8M997sjpgz8xt/8Tdq2o+9apphoR5GDu6RZrSo22w1Ns2K92pBwlEVJVWmqcrMg4gXKqvnmJeA9WQRSStzd76jLhsuLmuurS7RW3NzdLB1h0dGDSUIbUknhQ0R5D7m7ndI7A0w3jmyV0G9fvX7L5eUjqqoWD4AX88jx1OaOcaQuxcpbOJdhE4Kd8t6jsj8hV/ccjidCFpxIk4+lySTjziY78bpcpmf0VyYAWSdNtxliOY8b5zScmBtPc56B2FYVKn0NkwUZNJq/Qj6mkFjoQTGGZVLh3LujhwBENV3fM2ay0fF4AmWoawkbCV56EfP5FqUYc/N0ZigesviorCqmMFGXTiTIdcV3PvoA70VQpHVimHoZr/lRJijGUVcNVb1iszljt/c8efyYYUwkJRFo1lnIR6NxHBgng8oleEKafdbYZSGYF0yQM/4sCJpyzoGczVVOl7bLWFYMUwFjLGUGkYL0YcQroZYHWxEZh4mQhVrGmuWIp1TMFSLZUGVQSiqNppERndUSTvrg6pKb23sePrxiu1mT0iD2eGU4v7hgUmsGH0laQDmHrmN/FLTYalVjnV20DVpryqqhrhpUTEx9R386cPP27fLnX1c0/suv92YR+PzLFxIR7hzPtht8TuxVWi7+EHJwhQ+oZAnBU1R2UXo1dU1VlRSFzHrF/AJn5xd88OxjjK0ztGQALQ686APOWIwWvNV61WCtNMLmIIowSZZACpE3b+6YvMfZdxFWIYgHgCRmHWOEdTATZiSuXEZ2Ik1NuWuvljl3SjmLcOn+x7zD2zwNMfgwZN9CojAF4zAxhYCxCW2kZJ3BGCkEUozCBywL0bYjP4e4FxUxCMtgnOSaPry+xuQjzf5wpKkr6qbmXTLyHHFuOByO3N/v2G43wuJzBVVp6foT0Y/8zd/813n66JI//ZM/4nDc0bYnhrEVsZOfcnT4CkzFxdVjnC34ySefYHSJcSWuMJTW0fYn6WMoRV1VxEi2kweMlfk+sHTgbZ71S+qxcAlmVWBT11DDFEU5GadJJkhaZM4+y4DDOOCsw1hDN41MSUp+m8eIc3DsPAVqmppYVrhCejlt11MWBaumZrPZYoykLs9VoTYFp7bjo299i9u7Gw6nHkl+UjSrM/aHHf2x56Q0TSmhsv0wEUPHal3zvHhMURqK0koVsD7H6IIf/+wlijuil75NCIHCZqKUinz14pvJ/+/FIoASddft3T3rZs33v/ddXr15w5i7230m1tRVRSwSY+fz6CsBAa3nsYyUxjrvAD4Eum5EqZaHjy+pVxum0fP2TtKC5QGdiDHBahZ6SJMwprhw8q01nE7tQvyBkNN2RDs/l+0pq9HmVVdm0VkKbOVzfJ4Lk3dYHwLOmTy7NvTTSFUIR1AZ2cUEtBkXhVwIMj0wJpequcyVxGEh5vgoAStKCU2nKQWJNqcDzV3td1kFBd7nB8yIkUjSluQ18xpmHb2QjorcPRd/fOUM5WrN+XbFly++5HDcM46DPFzZFxFiYnNW4EOiqdeQLDc3B6pyxbEbMHFi8ulrlZXASeZ057KsqK3DJy/JTXlKMYxpsYHP5CATJGJMIY3NyXvGoc/kKhkzRh2XKYXQiqWxWJaFRMzl3oxSUGRJ+ezyrOta/C6Z6ExMrJsmbwbCN8TM96nc6DFGlJaYubIo2G42nJ1tiFGUja9e33C3P9DpOk92RqxOnK8cZaGpG4nBS1EqnVPXMo0dZ2fnjGNAqYoxT6LmUbPVmifPP/zGx++9WAR0Vm+d2pb7vaCTBCjhhC+XCqIX2muMUirPZBetRWgzwzpMrh6UNigjMtSQ4O7+gCsKUCbP8L1kAk7vghoFHSbz5WnyyxlelH5pEd+Iss5RuLkakI553410vWC9QqYeey+5hirvaDZ3rAsrCcFFNrDMxpc53ksrw+l0YoaEpqxHV2ruM6j5L1CaaQwMUTQPRot11DnpgpPeGVyMnpkJWXWImGck/0B6Ayhh8LtCAl6N1kTNO/GRsRRlkb+WPKAojw8D15ePGIeetj3m45mEtyRAaUvpzBLRdXV5zeE08OZmxxQMxoge37lCPBKWJTLMqIxyj2CM0ILE4uyyVVZ+vqqquL/fy/jP6DyW1YuXY5YJYwxZ/7Rc23ky9K5Se2czntOY5+8F/JwdvMqN3xk7plLMKdriL5g1FqJwVJngXLJqqqwolX7P+cUF9WpNrLZMfsCPAzFMTP1e+iFjT1GKSM5Zh8VSlxVlfcmpHfjy1QtCfk9C6HNSN+//caBpaknRmSZudjvOt2ecna0J0fPH/+KPSNETVVp2JZRCGZV3wbC4ybQ2OCeEn3GaKKbAw4cPicnQe80UwPsph3365Y032mSQhP45rf+YjT/kct26Uug8URRpReFISSK+2u4OleGfdVXRtu2yixdlwThJY6ouZWykgSHKHLksCtbrFcMwYazJTbopewxkLJaS/HMpK7XJngARv/S9gEytMWzWhZCF8+Kq83neLKBRAWK4pEnkZGMv1U2Zm2GSqMyiPyi1VEViICppmpq+H5jR6kUR+P7Hz/jgyTW3d2+Zxp7gJ3w+Ox/2HevNGcpYPnjyMduzM16+OfDi5Q3HY0s3RIy2XF5cMEV5WMepEwl1jAsotSwKphAFYZ5n+SFGiqwofPP2ZqnufPIMg1rUgPJ+y86emOPgWezHIEpNu2wks2BN0bbyszgjITdFlhQnoG0zC1EbXLaNhxDpuk5SrJ0lZSGXJD9p1OQpSoceJ4pkUdoyhQltSlbrmiElKucoNyVGJ16/PKAJwstwCmPkHj20HS9fv+RPfvJDyqLht/+tX+fi/Fzwb60AWsZxwi7q07/4ei8WgcI5aTzl5s0f/vGf0DQV49hjrWHo83lazSM1KRNJ7xqCChahzhwwOZt7JKQwyfFgmBYMdkrCy/cqE3RsVpVlyWffya4u5bxl9CKiiSESc2k3N9ykSScy23mnLUsxsswP6owZg3dxWOumkZ5HzjEYpwmFnO0dRuyshWb0E2DfMeeyPDqmOUDEo41e5t2CWZPrKwEaYdnV5GMG8PgQSTphsTJ9mKupfK21lqnJTNiVY4FiGMZMINbUVcmThyuePLpGkTju94QwZnOUjD5XzYqqKFHGSbp00dD3d6yaFdaWhNsDXTfQjxOFeld1zIDTWf2otSJ5SRAiSC5F8D77+Fm0D0XhMFmcNTdcUQoTZgy7VEAzjbnIU6GZwzjHpMl1zkElQZGsmNa0FqgpWZjmQ0QnSMyCspSnCZ7CSzzeLDATA0lOisp/p/wejqMYx7p8vEpTYho7wjRSrwra9kjwIlj72ec/ousTkZIPnz3H2JK2PdK1J1kEswQ9xMj9/f4bn7/3YhGQxNcN/dBzPJ14/eYtjx5eoZSMV/q+E/GQelfeyRgrgRIr56ltiUneIJCgDuvyOVnuW5l9a800jItBp2+FyKMSWGXwyWOVIL68NYyZ+DsOAz5mTX1eQOZdX2UdfQwT/ZCWTrIPbvF8ywhKehfjNEFM1FXJNM2Y8k7UYV56C8ZaUiTTjjQqiErPDwNj9vrPNBwptVU+Dkk/pCpLysKJEkslxkGgIUv8lvraeDUE5oQ+ncdsMxa9cG6hJ4M8nOM4MUwioKprx3q94vnTJ6xXDafTgVN7Ivgx+wx6xmnk/OyKhJZ+BzIdORxPaO2w1nF+dk4I9wzjRFG6rHaUhCKt5ZqvqpIwybQkqkSax4JKL8e24KUparQWfwMsxy2tNQaVPR5zFRlBvYt6k4VHFgOXf3etNWNOxg4hovTsXXjHEohZxyH9A/m+RutlMTjPx4w5n1KUqe8Wm5ik19INokDFBKIuSCkQvReTnILD4cAUPDHdoN2WD55+wMOHzzHVlYipuJffOSXe3twvE4Rv5gq9J4sAruTsw28xvnrB8f6OL774FJfg+uoBm+IM1SROaiSGCZRh7AuG1lKUgfWqoHSO2jbUpmala0pdEoeItpoYpAwcfKLP2XtddxTW3DjiQ8TpgsNhzxQmeSPGnmMvrP35GFKtGpEsx0RMejlbCuBDUdUVwwg6BGxpiWNkDIGyriVrYBjyCEuxWQltuBtFXaaUol7lTjySdw/S7LNpHmPl4wmJpPONk3UMwg6QI8CqqVk1Jc5Js4vcO9msz3IVBf3Y4f1Al0d+trDErGMPXpHiRFk6nBN2o59GFA6lDEFJ36QfR+rGgpmwhacsPK9fv+CwP1CYirf3B7r2gNKB0kFSHVVzRr2qKatz/vhPX2C0ZAeqBCoFtuuKw+HI7rZjjj8v5izDMBHDSSovq5jGcYF6TlNO7kkpL7qi83dGjnlSycxAGINGGoi6KAgmT3CGCYWnKEy2/TpM/tnS17iLIpYCHRNFVWN0KRVUrlJs9oVAIiSxFkPk9v6eOf+hcA6lIUTPNEW0doQ44Fxks7Eo5dDFhrKE02nHadyRYs/NfcdoKqrVirre8P1/7d9AmwKfFGWzkXsz1FnunHh4/S1crurC5PkvJQT8L7zei0VAoXBlTVk1DO6ED4E3b99S2GJpvMwNF1HXeUzKqT5Jo2KgSImARItrrfFfc3vNU4J+DHT9uMiBhe0nvYFxGnExl++TZ8o+f2uljEwpEXRcsgXm3SPmc14MkTnXzxqLrQ1d1wvnfpqEKgNYK/pzpWCY8ixaCa4sxkTUaZHDamsFi+394omYAzVnys3MuTdqpgzbZVdzOWE45XPDzBsAcspRzFx+g7VglBMSkOh/CSESkN/PGSe8AGszxku09pUruLrYcjruOR4PeTzXCysgL6BGGwKa7dkZ19ePubnfM3jPqqkXW3bT1NSpYhyluQoBincIMx8Cvutwk6Wqy+UazTkQwzDmn3mebkiFNIeCVK5EG4OPcv+Q3l3n+f5SSqYJIcYlUcg6i8lycXjHWoB3GQPzP7USx+ZCbo6JiPSchkHup8mK9oEJytICVhKvSEuTNqRACie0Eg7GOIw4l3JzsmS93lIUDae2JzLho0J3cowlE6xSjJTGLROCv0wu9NcJJH0O/PfAY0Sh/bsppX+glLoE/ifgIySU9D9MKd0pqYX+AfC3gBb42ymlf/KXfQ/nSrZnj+nbAR0TZugZp4F+aGlWFcM0l+BBvAHGiP/cGUal6McRVwdUnEj9iX17ZLX+f5l7k1jdsjQ961nd7v7mNLeJPiOy0llVzrTLNgYBsmCChMTIIyQmCJCFJyCE5AHGI4YeICyYICwxwBISQgIJBhbIIGwJYTwo25RdZGVb0WbcuM055293txoG31r7PyEybKvKg/hjEBHn3tP9e++1vvV97/u8W6LKls8kCw0hEWcvYIYQc0ktWYZlZFggGpJ4Y5DTgGj326YmVlEotf2A98LyA0GQ1Y0sIq/f3ENKNE3NOCr85B9ZWwuNNmUhDsuoMoSCFQeSWkAYpZw1RlPrOvvWw1JOzvNMQC2UXAk0EcFQyA0xk+tBcZjJCLSpa7HWjgN1VTP7mXmS6K/Zy9lUoBgaZeSI5ZylbSvOZ3jyZMVbz2/ZrCq+/P3fYxrODH3Py1evRIFZa6q6xVjHu+9/xO3Tt5hmxe/+9MccTyPvvaXzmNXiU2ToBzbbDevtGqEATfTTyBSEpCOTH51hKaPo9ZGRYAkU9fNMnbURyqjsohN9hdICnw1eMF91bsJO0wQqSsPPucVkFGNkHEaZKjjxa5QJlOQPSphoUV2OKIZpzKxGMT+FvBnVtUxqQkzokLLzUZSdCZhnmTRIQzjx9vNrYhw5xglnhJRVV4auu6LtNlTNmqa7ImlHUoaUPSFaSRVklMBRpGRkqQj/QIsAki78F1JKf1cptQF+Wyn1N4B/G/jfU0p/WSn1F4G/CPxHwL+GRJJ/H/jngf8y//ubX0rTthu6bk2YThz295ikOZyOdF1NDOK9l9QYszjx5ujpTwPzOItwxRiU0ZImu8xmM/2HoraTnSlmxPY0yfm6BJ86J6W1/NlMP/REJEtvnvIOWfQFeX4dYm7WnaWiENad9DpC3oG1lnOwSoJFI0mM3xxEr+5chTVStUyjfB+FNOqUUpy8cBFSbn5efj5RNTauQmXugPRYctaBznkD03xhDpAWoU2MEWuEPDxPEZUKpaf8mYwId3tJYq6riugH+uHMr334lLZ2TOczw+nIOAycjgeskyjwqm4z7alG25a6WfPq7jUJ4UCeMv+wBLhO88xVSWfO5Tdcmnvl55UUoUBKQw5IlZ05ZJqTq2wWy1ix+Bq9fK7W+hFlKQNarTQKC6O/8BuMVsKDIC0bh8s0ZGlCS7/hwhdAKi8l3AGlNdZoUjJSHVpL24jXpcqLiuhNapSOxJDl3tFjNdztD+z3e7yfcNUqk5dajK1Yrzco2xDRaFehrdjKKyu8DaVEYWqNwRlDXeahv+L1T5JK/CXwZf7vg1LqR8B7wJ9FIssB/hvgbyKLwJ8F/lqSu+3/VkpdK6XeyV/nV38PFD4atKlo2pZjTgRK2RrWNg3HUWLDvA9gLTYZvEoYBVQXZPg0zSQuXnKt5ErF+dI0KnCIUk4qpdkfDhhnadtmmefL0aPCRwmvDF54AZISK1dcBDDytY0xWcdvF912kW1Wzsmun8R9Z6yAMo2WcpNcbTwuNQu554L6MnlnCXmnHPPDkCjAS+9nrJGAjLoWFHYI45JZoPOZ1TkLpjwQabmJZYGNKDR1I0GvspPV2ULrkU/xXG87wtRz/+Y15/MZHz3WKQYvWQZoja1qmnbN8+fP6YfAafC0XYt1NSlEkS7PsygU2ya7FA2Ns5zPfUajB3a7fc4UkCOR0XqBgxTL9Ol0XqTjiZThJzqPPWGaPf0YhAuZEsPYL++dViL/1tmr4L0iVlXWgziSVcvC//gYEFPEOLssACBVm8rVZYgy1rXGEjwM4yTHrBDp2iqPWT3WSqV3tbni1ZtX7Pc77u7uiEEqwc16IzJ5ZeiHmfHVPc/f3mY594xN+Vp6Fi2LVcI4NFovupo/0CLw+KWU+gj4U8DfAd4qD3ZK6Uul1PP8194DPnv0aZ/nj33zIpAUT56/BZzYxT2u0dRKyTlVW2BGqwpnNUYnxlEu6LE/Y50o207HIwRP27RE7+lPJ642V2zXK2IamP0Z7yMhiLRzyMk9wzijlOZ8HvAx8vTpLauuIxrpgg8506/vB0kXmuWhKTff7OcFPCrluCVGAUW0TcTZilgH5mmUikClPPu/UIaCD2KCyRcqZBCET3MuLRV15Zi8J/ooU4OcZ2CtQVF6AcUJKZXC+TzkBUHMNkrpRRhTOQeZcDxkdoFW8n43rRw52mwWCiFSb2tIMgKtlOef/a0/TmMSX71+zWF3x2mYQUWpfFQkmkS7XtN2V1xdPeXjz+74xcdf5rSgGlPDbrdDaUVrW97sdnRdi48BksYgqTk+eJIW9uFpGAQOki3BsxdjFSmxPxzk7GtUns97Nt0KrdVyHcUWTLZalP6IQqkceWZKt77wAobc4zEY65ZKy4eAyqNjo6VHUmhPkkIlFYz0gGzuNczMePRc5M0OoxUmmGw2qwA5Zlhj+fHPfsF22wqG3tQ419Cuat48DHivQHtOw2dyrUm5YgSDWhYpmyc6Muv8p7AIKKXWwP8A/IcppX0pz37VX/1Vz/mv+Hp/HvjzADdPnhKTqKj07TUPLxx4wYq5yjJ7C4iwJSWZ0/sIlXWs2kYkncbSuIq2qtBAZY3w4fOIRM62CWtztpwR5aDQc0T1N+VY7CZ70Es+PYhn3Ps5y3cL/EN+XZ9dajJfj0uIicuOtBAuSrzyUM1+xmelYYxSxkpKjHTfY0y4yi47T0rk8Z3JuYxhudjlWqQUH1UaoqMX4YoceVCXuXhKiXmamDOnwJgc/GrM0lRLpKzdkFGainKG+fC77/D86Q0vvvx9hvOJcejphxljFa6yNLYBpdheXbPdPMHajs9/+VoyCIxl9pG6cdkPkJN6kMZaTFKppSS4bxck0nzO2YlloVNaRE7lc8sEYJonXC7zx4xWl9g3yfNDmbyQy7W48Btj/vlE4Rd8yOSkmRA8nSlsgbjc4QqRXo/jtJi2iiR7QcFZvehMlDZoZRapeWk4Sz/KkhCX6nZzxem0YZx7GtugjCGiOJwGtptrbm+forQDqoW+BSIUK0cZBaiUJLpXXXwWv+r1T7QIKKUcsgD8tyml/zF/+KtS5iul3gFe5o9/Dnzw6NPfB/5/7oWU0l8FmVm8952PknGO5289w0THZz/9e8yTPJROFzS0JkV50xUarRMam1e7RGUMbS1wCmMUbduIvzwvAtJZl1LRWcNcHj4jDcJxHKV8dY7VqltY/YmUjxkSzV3V0n0vDbcqC4JWXbckA5djQEkNls5eYQ1YwV7P8xLkUXQK5IbhNHt0TMsZPqbHbLvI43IUBDVecOYp9yt0EGKuVhLYorTM0330l4lJPk6Um9ZaJ3FnPhABximXtBGrLU5rGmf53nc/5NyfuL+753Q6ZlWhjBSty5OJSgAubbvidA68fr2jqmVcWlUOPwl4o3AXnXNSvhstWQD53D5NU+7ViIQclSPIU8r9EBGLGX3RClTOLtcgxsCc0qNFIC1uSJUThLRWeB849z1d14lM2RpMjJknwGJkK96QcRTvhjWWEFlERkk/EhuR5bpKBFvmkUjJh0CdEimCj1JhKq0wlUBytldbvvzqlO/Hlt3hhFKW58820l+yjpCvsdKKEGeMlpG1yt/XTzKpUiox5mzIP9AikLv9/zXwo5TSf/boj/5n4N8C/nL+9//06OP/vlLqv0Magrt/VD8AAJ1IxvLp53s+/emPsfMtcbznfBrw44GKRK0MRtfMKTDGs7gItVQFxihW6yfUjcM4OcvWTYtHMYdICDD0M/McIWmi0qANUUmk9DhPjOPAOA7M08jVuqO5uaZpKpq5QWlp2HgvR4fT+SyJOW3Ds2dPmCex4iol5KNxEuxXP5xRRLwfl3N6SgbQUhYbLSUmSQ6j5cbNxJjjFHI3WpOU8C5jTMt4SyW9NM+ApbtMUlmIFOhaeS+GcciTDgh5IjF64SK2bUvXNajCzI8Kpw2nhx5rDKuuZts1bFYNH37wNk5PfPLFJ+wOD8w5EbrtFK7SQjGqWrZXt6S04eNP9jzsBjab5xxPJ+Z55KZWhNCDcrnRq2mbijf393RtDVEtVm3iTPKepl6jIpx2R07HnjkGTv0g4qLKUeXfretWMgEZZ04MOG3RWuWQWNhu1rnRKEnSwyhlfdvKjttnvYFSkuxsgBQjKQXO54mmrnG1IyWhU4UcThqCNGZjYlFtWiPHQpM0ZK2H6B8kZFVroRo1pkYrB1hitBhtWdsdK9PgTyNjUrTrNVXb8aOf/JwQNefzzLrbZsWo9BTOfc9PP/2Eyjmur7e8/fy5BNxm2M4feBEA/gzwbwL/QCn19/PH/lJ++P97pdSfAz4F/vX8Z38dGQ/+DBkR/jv/2O+gFG234ur6mqfPntKHPWFwxOSXnXCaJ3xIhAzqjEncWdZaSf3J3f4Qhbf/5u6OzRW4+oppFl97zAyCgv5WSlR42ueorySwyuPpTNd1NLkAXtsaAAAgAElEQVRJWDz6eJVLZ5MVi2JbTUkSiI3WzGFePtY0DfM40tS1NCk1S6hl2S0KZ6BUDdKUkt3NFRnz5Mn9TAroNPhADBem4YViTObbSSR6iSAX+bNa9PJLOEZBbWf5sby/2X7bNdROkOTGaJ49vWW7WfHJZ18wTiOznxmmAT97XO0YxzknP9V0bcf9w46H3ZHZa+paItXPKXDuB0KcUJrlHD7N0yJ/7ppmyVSUs3NmLaaLFiPkkZ/JC2mpksoEwBgjZXA5tubf7/5hx2azpiQYFZ2AvCfha7JhrZRIrvOERtSSU/aRuOxRESBNPwxZ3JTDYEPIakubJwDVci+lbC/fbjcUFkQIAR00uhKlY6waqqbmPPQwztw+W6Gco2s7dsd+aRIrJYlG7737FjF4/sj3v7scSeTfOrtN/xA6gZTS/8mvPucD/Cu/4u8n4N/7x33dx6/ZB756c8AExXp7y/n1Z+i6xoeRs5c3HSOjF5USTiuSBiZpVKWkOOeEIEl0sVxd1+IGnIT77r3Hz1KG+QIqMRekdwxpoea+fPWKaZq4vrmmahqMNbAEWOa0oexQK7kBY6bRhOglklypJSiz73sSkXXXMXuROJcAzZAnDS73HlKSBKXgA8dzv1B0FHop41O+0Yv0t8SnmXycUTnwo6qrpVdBPjo4lzKFyUtyT3YtohR1FtTEWd6f1arON+zAH/v1X+fZkyvGsed42HE47Xnx1VfStKwrVvWGkL/OO+98h6urG/7Ob/8UpSrquqbvz6zXK6pKE+JIomacAq6qcM7RNLI7juPA4XhcmmogY915nHFWHqRpmpjzgxaCVFqkiFHSkTdao42mqgxL+aO4AECypVp2yGpp8papSXFYhhBRUWC3KTdVlZKqYp4DdS28BpWFbCFHk9dVvYwSK+eo64qu65ZmpDEyOSnE6jLBUFovzMopKFAVp/5I/3BixrLeXvHhR9+j72esqUlKFm9jNVdXK1KKXDWdAHgQaYBSLIvVN72+FYpBYePXMuOcOxKGqmkhzpwyTVdphSrjGS3XVjQBERWl+3ru5QJVVXeJ/Uoxgz0vDLgYpGIQJ6G8UzbPbWfvl8ZcjJcHfhinpc+AkoeMfAG9F9DoOE2gEm62hBg5nU6E2TNMI65yTMs8+1FjLwtLFCznYz/7RbEm/QIBixSSUEkCToFl9GeWJheZjKMY+hFjS1Mq5xDmsZXWapE+J3IkWm4sRaTCikmyAZrKsOoqpqnnfD6y2+942N2z3qyWhdTHtHD3q6rl5asHYlKLgWUOM0qD1RblI3NI5QQkIy6jsoBLNAzzPGWmoVQ1ksokPgfxVQR8uKDbiucBBDIiD2/AqMJFSEvF4HK/p2gtBALSSc8AvqZPKGIAHwq5OTcVM4T03A+SUZApRGUCU6qveZ4XGnWxkWttvqYviHkRMEbjKvEINE4Uo0lbXt+9QVvH8TTw//7oZ7TtGq0t+XbCWMOre6nWVq6myj4ViUlPS9/nm17fikXAGMvV1Q1VGunThDaW2nXUFlCCiVZGkwL4bKiIIYh0+DHDL/93Ecys8wgNNS320Ji8EHSiZLanrCY0WuOVWjTrWulcNcjXLFFpdVUxjhPnaZIFxRiOpxOH42npbE/TlKXInpRLMmucUJONmFeKMnGeU9ZJhMxQFCNKmaeU+DLJAdBQJhshyKKSclmcS9giJAKZSbe6oWQPlCmE9566diKjzdVN5RzaOJlCoKgrS5xHNJHtZoNCZvX7447j6UBIHqdkglBVNRhDUoa22zLNiU8/e0nTrOn7Ce9lEVRaMQwT8zTgnCw4JaCjrm12d0K9BI2IGnIcp2ygEZx4yi7N4MMiKpKvl6cqtjQGEbGPErOPVH9muZ6FZu2Dxwe7TE7K+wVQIsydEeKUbARyDft+XI4fLicWNXWzvKfOXvIOD8eT9C4qyUiIj44hpXEtUu4BozVt02Bnz3ZzRUgJ19Qi+lOiOA1eSMxJQfIzd4c7MTt5uQfKs1Ds8tvt9hufv2/FIuB94HAYcKknzhFb11TG4VXk4e6Bbt1JqakVNhlwcq4igLMVqEtAh+DHjVBhSPT9mco5TucDh9PIHJKAGwtlZ/YUrLTw/A0Puz3H04nteMV2e0VVyVlsyDz/eZqlU52dZk/qiqaqJD9xkkRb5yx6lBuqcw60YjrNBD9iMhR0yMm3IQMxE0ITit4z57L/4gXI5WjUX3twiy266NtLz6LMv2XBKDBWmbi0TSOQ0aQwWm7ecZzx84h1jrayjH2Pn0+89eSaj957xsuXn3M6HYhRACLWKVymFnsCT65vefr0OZVr+fzLHbdP3+XLF/co7Wg6EWFN84yxYO2aGCObxnAeBPG93x8AGXN2TU3b1Nw/7FjoTFZzHk70/XDpgZRRnSqJSZf5vlaK1bqluAxNTkv23rPqutzshUrbXG1MFxS8s1I9KRlJqywBLqnHIUe2WWtISaoPET0JjKYsImWUWVcVVeWYppnD8UjlatCKygnPUaEYhgFtZawcdST1UFcd1U1N3XQcz0e22y11s8ZVNdo4jHPMMZBUxMcxo/LdMqFSWstY+JFn5Fe9vhWLAEhJmNKMCrIbDNOEn6a8+knyi8ROeY7HgRQihpIqm2fHSiTB1hqGYcBWAoA4HEeOpxNKOUxJNsqvsmMUIcOSGZgSwyDjqVKW2rxzluNJIdIC9EPeFbKRR2stnLnVCqUU9/v9IjSJKTIOMlvWKuWGn76M/mImzWQBCChSKqPHuKjcrLEo/whImooOwCw3cAyBUMACCZSSJpWzFh+8/LwZY2aMOO72xwPEwPObFU9urmgbx34XhHATZurGElJGmBmDNU50CVXN8Tgwz7CqG7rVOktjpZPenyWp53q7YRwnYvRyvYyYahaRVN7FJOgjonI5flF4lu5+Wj4Wc8WlteRNKKWWh/brnyfNvvJeTyUzQGuc1ijvl88v96U2sjMXCbYcty5KQ1c5SiiM1pJn+djMVTIxFBcLd4oiLtNZlq21yW5QIVfPU6CqZXpixoH7ux3jMNO2I9ZVNF3H5mpLTAGfAvvTnm7VULv1gr07Hk8CmrGGpv2WZxE6a7m9vaaiRU2W+65lPI8LsCOGQGUdKUQiER2zIyAnxtocLCrgiKznTwNV2wnXfRgYxxFXaXxIi8c/lYZbdtMbrenHnFuvzCL+gIz9yt77x8lApVFnjSFpTYh+abY1jXj6D6eePkePF5Cp9x6VOf8l9bYsQMWLoHOXvhx1ihagdL/LDV70AUDehXT+HeQ8rh8xBUu5G7PoqQhmBHYi/Q1j5Ex+fbXlartiGnsk9CNBClSVYX8cMj9PbNTbqyuMcUx+JCSY55Rn7hptFXcPbzBWjkAxRqqq5njKYa7WUCmHn8WV2Q8DKSYpnaMieDkSaCVQlZCi2HRLj4isoFRp6Y+YzIYsKr+C91JKLQKjOudD+uAxSOTaBQTjM8lYFlDFY9egqDK1zv0pJVRrIVtVi/OxqtyyGNkcYV9sySEJ/cqlhCpVR1SLPVqhSEGyOI0yrNuWeZ7ZjQ8o40h3D/DiBRjFeex5vXvNPE/85ke/KbjzpuZwOpKS0I1vrr/lx4FxHOnPZzAzTCPvvfMO/bFiOh+IU8/5JAiuqZ+Ic2TdbRj6QeAKMS3jnqL5Pvc9daOXTL0nt1es1y3DJCVk17b0/UA/jpyP4j4zuYSrq4quy7Pkx5SZeIGFjNOU4RDZzYditerY7fY0tbjxhLjTcH93z3kQNv25H8UPMPsFWFHOqGgFMScBa4OuNP0sohYZIV12M8GaSaPLOSvimkLJUZk4nN+XFFkEP7GUsdfXCLRUcOrDODJPoqOYfUSnmac3G25vtpzPJ07HHTEO7I87zv2JSKJqaqraSZR41/DRd77DLz7+knM/oZTDVjX4iLEKH+RMT+2w2uFnueZNU+N9wX8llBPDTvQz4zwtaUAKKf/nEDgeT1RNlRcgsltS2plWm8XzURbHKqsmp4zZKg8myC7ftI1kFIRsRc79hspVWX0o9GqpJNVChiKJoGgp+1VAa2gak92WQraKWRiEugBLdK46it8lSOaaVCN52uR0TWUdWiWRUDuHBoY58vrVa+q2ZX2z5bPPP2d32PGD3/o+bVvj72f2uyNaKQ7HAyi4vbnmg2dPvvH5+1YsAikFfvbzH6PDRIXnz/zgA+q3nxGHPX/z85+RoqeuN3iHZNTVG1J0oE6QvDTcrGYYZ1wEN0/UzQqnLSmAxhDmCEFhkub+tBdJaPBoK3pzV9mlVJvCiEGjsYxDT9vUkjCkA3OaOZ2OKKXpMpI7eM80Thgn6rzNeoUxhuPxLM6xWhH7gaaqswW2WpgCEpIxEpNmCjNNVxO1NBfVnFVoKkG45OQ56xYFock3T6EMS5NKHGU+TGgSOodXaiP5f8M0Y4LkNgak3D7PR9I8o+bAZtvxG997zps3X+BM4nQ8EKMALzWiSvPjJCM2W1G7NS/vI1+8lAZp2zS8+ewVxlg2mw2rrmUepYmnlfACVytJew5JM5wH2qbGpxnjamikYtrvT2S5HQnNeTyjjGGaA/iY+zhivIlRjj9CSc7TEiXpRAX4GaIkJz1uDs6zMBitlpi2YZhybJksANHLGC9mJ6Bz+ZrHAErnWDe7dOS1KrZ00bMUPUZpQsqER6Pyz1AWBWtk7u+9xzUWYxOnrKB0zqJszel4ph/PvLp/iTtamk5zvWqwRD750afM88ybg0TGr1ctH73/LjF47nf3/O3/629/4/P3rVgE6qbhN37zhzAPDIcH/tbf/vv0+9eEcYeaR/7Ih28LCtpZulXH3f4gYZBhxqqERCxJJHiMAdNXWHOmqk8ZIY0EikyBEHKDB0kFnpTKXX0gf0whZ/cwzpyHgdU8i/LLiLIspStAzqyznwlKXFp1XS2osYIFk/Olpx/GLBMuhF9xn4ldlctEYZIyNNnErBM6VyNCu1H5vA86KWIOTS1VShEDxUiuDFTBK6JT1rIr0bBPs2cYe7IWhvWmxdU1m6rij37/Iz774jPOpweur1ZyTOozFquuGb3HmApXdRjXorTj57//KevViqqq6M8jm82a06lnyEGZTdPk+XzgfO7zjS8Nu65tWa86+uGcwRsGlHTSpwx30TlwdJo8wzCKCzNcAkadtdJISWCcgDpKg64kScUg90jIfSdt5AFX5qINEL+F+toxonIOtFn+W4xjapEpn89nNpsNbZYhl7TmGGI+Xih82+TjYcU4es7HE1dXW5SSUfBmvc5HiEo+J8nGREr4GOhWHaiEPVnef19RNzXGObT3NG3DRx99iFKK/XnEWUtTO+ZxZLfbkUKgKRXMr3h9KxaBGBLTFKmUY7264vrJEyoTOO8Dw35Ht9lynl7KjqhEwqlzLHNMEauEhKvNZbxjcvdXK1EFWmMIRh6UqBQoT0zZVkoiqcyuSxCDkGdiVneFEEj2otlfAjmV7Kym0rm5k0ESYcbEwDiqZQpR4JcpGfqhF1NOKCYg8aur7ABrGjHaGBOWHqZ4laQhVnoBqItttUwQpIcCUFh5edQVRP8QUqRLSnZArQUhpuUsbRXcblc83L/htNsT4sTYG0CcfdZaogKR1NZY12BMRVLiuMwCaAql5/bmJjcsZZGb5wxcNXJD9lnPrnNDTMg4aZFP15VIcadZjF1foyx5aaxJupKTo07Si5Hn8YivHA+kIadyH0RMNilTiQs0tryf5MZjyCpUmxOLJJZuzElHUnF0GSE3ThPGSsUgKU8qG8lsVqOO+YhScXW1/doocpwm0iTHEeFLSMNZZQnyxqzRVqErw1tvP1uQZilJCOqXr15xPJ64vrqhP5+4ezMwDQKl6ZqaVdd+4/P3rVgEpnnmly9eYVLApRk/BAKGKSps1WCrRmAbXmismoi1ihBlPi4lk6GqpDSr65r1uss3lsRgicAmEnO4RkqGGE225LJkGpSwzfJQ7fYSDfXsiaNqL40i8f1nbUKCrm0gi1GGKKGRQFbqJYzVEhHl03JzDeMo0lgrtOHCD3CuEo+AQh58WDrLlzMoC0Og/LMsGDFXN2WhMgoyqzBGT/ByY6+6hqa2pOQhzpACyU+8fjgwjQPjeCSFGYhsNl0OVI0kpamaDq0rEpYQFNPkmSefr0VNVbllXCamqXl5yAX0mbC6WZqk+8OB7WZF3pA5no4yBTEaHfQCZC36gUUanPsqWsqjPJfP6Lc8MiVLz1NujCqrvqYHWNx/XIw+Ug36xVHYqsvfjekiTdZKyMtd1z4Cv4p7r0iGy5jT6uxHMCImK+yKYjKbY5AQWqWIKJSps/dBoulcVVElCeFdrzv2xxPaGLr1is3Vli++fEH0kRg8KQWutmuutxtWTcPdm295KrFKiW3bMvdn/BQwtuI8w7mfeXq1YX8eGCfJFpjmmXHoqauaqhE3GilSVa2AIUjM85hVcVmQ4yeGoWeeI0pZYj4/SypQzLuklIiP/x1C5HA4cXf/wO3NLTGE5UGMMWRMV8IZeXj7YeB0OnHuh8WdprWlaQxx6Nnv9zmVWGr0aZrzGXImhsQYpjx5yBHYMWWmvzQRSSxz7CL+iUTCst4k5kkMN2RdfUqJcc6x6ioyTkF6AsZQGcM8jlgryb3vP7nmtqt49eJzzucjh8OBV14WyaubFavNmvVmy5Pnb6FdzeBhd54AT12tuX/YMc8zdd1wtd0sIanDcKauayEzK7i737HqWsIc8kRH1ruH3T4fmQxt03B3t7uwA2NYxnCV1YL9NhfNf1PX1BmvdklZksVTaZkkBXXxBpS5ucvpUeM4EZ1lu90sI8JpnvOuLOM9nzeh0kBerbrlvL84Th/p9mfvl+NKUTUKwkwvJOyUsiwZ6Q9UOe5Mci88gUStEC2M0oyj2L+HLGKqqmrhHdxe3/Lj3/s9tusVf/IHv0GdfTV+nviHv/MPvvH5+1YsAtYYWpOoaoOuOvTs8ecHjs5xOt3zu69fsGk01soKXTvHNI1oyvgmAz9JEkphzCIcEQ+6ERVa9OK6imWnzztCLNoB2XmGYRATENDkoFLvPdZrQECTRcIaU4KY6Ps+nxXDcjQQZNdEyDtH27W0eYEZxzH72rNBSV9GfCkqCgNBOvqihdCPoBeP596SIDTnz5dd3yRJYUpZfTiM44JSKyYmrRWb9QatPL/20ds837b4446Hw4OwE1RhOwHaYF1F3bS4qqPpVqQhoHRgGGdAgljn2XM4nnJOX0ehKpVorvKe98OAJjc0EQdkXRfxk84afFHHyUOd/RI6+wGWVwkJyQ5L5O+FPDlaJiZWQ2B5KKFAQ2VRbho5z5NYxnSmyJC9z7i1lD9XYuCKBNk5y2rVLZVB6S+kGPICH5cIu5hEuq2KriPr+4vJp1xTYyzO5OBaLfL44AOn0xkfPNfpipdv3iysh5LGBInj6cSbu3uin+mamjBPkhb9Tc/fH/YB/qfxevXVl/xX/8VfwRmNItLYSFtr3n625bsfXDGZxDzuMBh0SszjKG4srRYX2jAMC8jDBfGGG1tJBFguDUuvwKV840CeyUuZmqKczcVa2lLK+9PpzCeffs53vvMu6/VKeANVRZ27/cF76fhnsEU/DJeGloJzf+Y89KIv14Xfb6lcI1bUIBr20vGfsyh8tWoW5ZpANdRSXgL5OHFRDxb1ma0tzljOU88wTcSUOJ4HUiLPvqGpHVfbFmsTBsNmVbE73jGe9rz9wVOSlxvYB8kkWK22rDZX3Nw8ods+4XCe2G5u6McZY0QOfbW5wodA14r676uXL1FKE5vAdruhMAC7thHXnZXdUB4yOfsbI0692csYNcSYKVDzohAseoltt8qLoMSGKwtOqSX/sIBfymiveAVCGJdjSnmJ0tSx6jpSJvYWAVaIkeQjTV0tI8YiLxdR2bA0gtumWe7DVdctHEMRlZUjRsLlI8LyPUKEfLyzPlAFhdUGTYKQqJ2jayoqazmeTwzjQFe1bJ9u2G43HA5Hzueepzdb9oc9b17f0VSa42HgdDgwnA/f+Px9KxaBFCPH3WucNVitaLYtfkqMvYO4oa4q+mNAxYRGGlMkFoxTSiKsKXw/2blzhzYmwXwXPLcScZDYjkPGcWURCKL3jpGlk+1DzIo2mSuXOf5MJu7MUm3YTKQhXmSsp/OZeZooargCjpCxopT1JpksPFKLjbjMrLU2X0scVlkDABdPgVKXMVNCzpbSniu6gLg4HhM5tlxJL2O337NdOd569ykhzDKqJPD06Q3jOHI+93mn0tTNis16Tdt1fPzpFyhT81ZzhXU1ddLsdwdubq4XUpJUABXH80m+dxK7dcx49wSQ+Xkpi2hSyo26eLFWF8KPXCP5zZzL/ovcBInIqHVOF0GPDxc0+0VUFZdRYRFmLYK0GBfTWdFvjDlwBGCzXi2/WwzSRC67funql4yDEsA6L81DvVR2zjXEXG2Ui1IWKaWFL6Dz/e1nOepao7FOE0Ni6AfOxzNv7u/54IP3aZoGcq+jaRr684BKcr83leV0ONCfz8T0Lc8iVCphibhc4Gsi0c9M00CT56+VecY09JxOJ5wxoDWBrB9XlxGZACMjlavzRTG4ytE0DYkZ7wvA02cV3IX4o2PEq0CcwqK4k8UDdvsDN7dbaQjmm7o4/WQXkYakUorT8czhdBRPvzFZa15snZFzxpU3dUPfD9kJeHHBaVWos3HpXygtvgZrHeM4Mub4dK3zVKR01JQAJrWWM+2Y3XhKa1wGZWgtTStUYrvZ8MH77/Liy88ImZrz5uGO6GN2tuXF1YowaBhFSj30Z8zdA23bUVc16/VKDFVJosxLU8xVjnme2O33EvZpNPPZZwFUJEZPihEfZpqmkhl7iLnCka75OE5LPkEy0Lp2kVmnVKTfF1BrU7u8mAuMdZomrjYbrDGc+57Vqlve77KgFMRagZuWJl+RgJP7tJv1OlcUk2wwzuCMpmubzBiIeC8LRNENhBA5nfucdLyR8n2elp5BmXDox9b2GBmGEVcJw9B4qXY//fRz3tw/UFWO+/uH5WjXdS1t3bLfSxK0957zeWIYB8Z5pHLf/Kh/KxaB1arh137zqRhz8s26aq7YrBre3O242nRcb645a4OPgcjMOPYQLU7VMlaJGoImzmBjTWM6rBJceYwi7xRMDIQxJ/Qai3Mi6vBByvcYIjF5lE4YdFZ7gQ8DX718g1ImHxXg6e0tIKu6dZYQZVHo1i3GGdbrFSUa6/7+gZsr2bkOR0Fy7Q9HSIr98YjR6hI5Jrg/rHE4K7NyP47MMTJP4oPQWlNXDYtVNI+SYkoMQ0FuOepKyDNdXeFcRe1qVNCsuoYf/pF3ub1pCcOReXjDcJ7wM8RJkGWzn5ijx1UN7773LlrX3N2faDc3tIjWIIQTh3Rk062YSpDrNNE0MiFwtiVUdVZSesL4KEzGSn6f0ZY0RnaZziS9Fs1uf8hhpZ6ERucdepqzZbug0FLK2g+yI1Dk0M4ZKltlHUY2WhmzTC0IUmUUSEzMlUc5Et7eXGc0eKKq2yXYtVR6de2YvWfMPYSYR55l7Bfz9yvNyWEYuL/fYa3LGhGp6rzWImVWarElK3uZLgi9SKO05t133+Xd994hpsTuuJdjaZOrEGe4DR3zbJj9yI9/9iM5DpP4te9/F/g/fuXz961YBEL03Nw2xFhhlKE/nRn8GfqZP/ZH/ySV0+z3d0wxEdCMcyIqWemJc64E6mXOPgwDxjps1VCSXUpnd5oDCrvM2EuZGPMuJi9x4CVSzpgDxcz+cKCuaz54/11WXUNVVYucdBwnplnOog+7fY7UlgezeBWqSizKo5Obp6kr6myxNaZAJpQYP3K5WkrVMlVQsHzdQg7S+eYJIebkJLGPhiga+xASRqVME9Y8udlyc7Vm1VYQPIMXkvIwDExjEgdjiigjlZVzFSkqHvZH7vdnQqpxVU3TtMQsdR6nKVOA9PL7woVzsNmsOfd91gsIWMXmnS8E8RIoJUeqaZoYxomH3Z5plLyE5ffPJbmrssBHRP3EbEMWH0WC/BCRF6ZhHESxaBSrrPSsqipPgcTUlWIJjRW2xOl8ZnYCRC0EJkHO+0whMhJIUzwdRReQI92LRgRYeANSpVRYJxBZFj+CGK0K3FZnjYrRGovCT7MsXlnYZrTCKQjziE+BdX1FVznoWvaHiVf3DwQvTWFtDc+ePP3G5+9bsQgkIuN0pHIVIXm6ruZ0PHHz5Iof/rFf53DYczzvGWbPFCKDD1SVwCliNln43JWPudMvXVnRYausGJvzxbMml22ZOPTYZrk8dLPQf+Ukityc8yx69vxwinstz4xJuGQX9VddS8z4PM256Ze4f9jR1HUub8V2Opd4MmPBwtCLsvDc94zjxRdRutiP59Ml/VgmCiJfDjGIxzxlx2HGyxilF3z5etXQOEPTOFIY6PsT2QmDUvJeVrUV6k/VcHV1w93uwH4/4qMRo44SNWTxwftZ+hlkK0RKicPhKIBO69Ba3iujDfVawjjL7F/Anz4fg9RCQhr6adkh4dJrkRJYICTOSkd8HCcJlbUycoyhwGHzuVtrdF5Yx1FEPauVNO7K5KEQlpq6pm5qUpQpwOl8Znt1tQBPm0aEPOdzzzz7/P8lxVln5F2JhTOLd6NEwz++duQphH2kcIwpEaaZuqqXYNUE9NPIL7/8pUShO0vXNVkbY1AhMA8jbUaqNXWVLfQCRP3dH/3sG5+/b8UiUBnHR299h1dv7vj40y/YdCusMdy/2fO//I2/RQwzq0YCI8LsWdUV4zgSQ5LkHVWY8TL2qitL3TQ4Z2V0GHw+B8ubfDr1i3tOxkli8BCmX1xIw95H/DBlO6w0bO7vH/jpLz7m7edPuL25oarkZxmGkZhk0rDb75ez6v39A9M4UVcVTV3j8/x4znFZWgu4tO8HJj8v8+jSH4Bic34kcMny5t3hINMAazide5YlK8unpznn6WlHVzfUlaWtLa2NfPc7T/HTgcPhHqeoxcIAACAASURBVGcSu4cdwzgxjp5hHkkatrdPefrsOev1FT/5+Qu0W7F2LXPQrLsVfd/TdS1d2+JnlsCVtm3k4VQsnL27h7ulqdk/PACK2c8ZhCnHq5StwfMUOJ0GhqwQ1FpfuutePPpFhemDX6zYNi8ixXA0TpOcAnPZrY1hu97S9wNGWYZ+EtCHq+h7mUDcXF9jbYXCSGUX5Vh2PImkebNe8+r1DpMVoDEljosEWHF9tUUpMW3VdbU0/WSRkakDKaCVWZKQYmmmLiNUhW5qwdppRSCSVMR1jg8+fBelFbvdjnHs6YcTu/09zsLVds3v/M6P6Yeeuq3Y3d3Tdh2Na3h6+5xven0rFgHQvPpqj/fwztN3GYaB26srSVRNiJ10nHBKsXKO83imQhGNzJON1qRFnCOS1rpyVLXglUpHPuUd3hqZFBSevcySFZ7LjVg60+LRl25zmMXi+ulnn6MVPHlye9nN5hnvc8qvtQzDyP39Awlp2vjZi44+JSkz55nNer2kEpd5tnSrxQzT9z0+iOJNpLF5l64cIUo0WlE4luASnRN0ij2ZBKuVNO1IsO4a1uuK2sGb3YFp7NkP57wjy0hwtepQxtK0K4YxkBip6jXzDMfzxGZzTT8M9EPPOA5Ya3n25K1FTt21zdLQK2q8uqpyJiCs1yupgBSLS7BUY9aKS3Oa5nxdL7w8nY9uEi9WyEwlj0DO4H4WXNhyLs+legGS+tnnvEE5fvX9iLWBruuIMT7ySFRL76fvB87nM0oJNzKEwJQeOTqtBMcaa5bqB2AcpyV8xOWMw8KUCL5IlWXMKHmI4l3wQRSoJrtFI4lTP7A7SOhrXTk2qxWbZk23aiVyzmi+fPEVh+Oe+4cHtpsVIQis9q1nT3lye/ONT9+3ZBFI/Naf+KMQ4fXdPT/52c/ZHfe8/97bvP38OafjAeaBOI/Mc6DRFh/hPM/4GMFqjKmyP16jDcx+4nw+UR2PtEnIMfvjkdN5gFgtQZil/C+pMEWbLnuqpgRPTtNE5BJEeXf/wC+//IoPP3gvqxLlGCO5fYe8e13LZCDB0A+LfkCoRwUrFXPFIt3gwrsrR5eyWPX9gLVG4sByNVF891rrxU7sfQSTM/m0pPfOY2DdVKzritYZ/uQPf42Hh9ek2DNNJ6ZxIGXmn3Wa1WZNUobt9RNQFZ//8g0+tiglSDKtLFElnt5cM4xn3tw98ObNQSobpRmngaZp5MZT0hTbbNai/89d9ZR7FUXkdH0lBKfgA4fDid3+AEiISumFLAwH7wkxEYJZBDzLqCw/SFprbJRo9mXWn9/LVc4WAJbpxzxNkh1oBbAy5x6SuAMNKqo8FbrcsyWTwpWHNcfHW2NzY/KiC5lnSUECqSx0/r3KxlUWrYR8j9M4s80CphAjr3d7Xr15zeGwp3KSFvWv/sv/Ih9//Ak///mnRO+prOWDd56xbh27w55163hyteb2ak3MvYlf9fpWLALWaa7fqgkB1GrLzVu/xS+//Ir97sCYbog6ZRuqxjZNTpr1AhYhp/Pk5prShTuYUEbO0nKu67OqL3C1vr6MY5JMDhKXbMJSNcSYNec8Murkcr0fhpw1kKuPqiLkCUMRnjR1LQtKbtg553DW0fe9dKUzBSlmxd84Cp2ozsIW58TLrrSAMJYEXchl5YWIJP+d/4k50NNPEimeBGRqjGHVtTw83EP0+HkkzBPzPKHJuCsrkJa6WRECnPozSlmqqkbrCoWlqhu0V7SNY7Nu2azXvHi1L1oeDqeZc18wazJDf/rkKhOA3bLrpzRliXRa+h7nvmd/OIihKj/8pRcQ8/zdZEv0hRsQKZbi5C7XyzorWoSUlhu99FjKg1cmBQUiW3wkBW8+jqOoDB9Fjkulpijx9qXkLwt4+ToF9CKIcnsBqRqHzgt6+ZrTNINSi/IwJkgeeUJj4rA/8eUvX3L/cMf3Pnyff+ZP/Ra//du/w+lwQKfEi8+/5K3nT/lsf7ccS6VKhvPxwMuX+29+/v4wD+8/rZe28GL3GavVGruymFbz3c27fPXiNb+8e8GTzRW2qcCH3OiacJUi+hEVyfFL2SkXI+M4sVoLSSWRz6VG6DmrlQArl8nAcoaMi8xYZKZmOcMphNem0YtGYJwm9oeDjAetlPAhyg0UQgF9jjLTH6fcw4jL2Mgho0KfQ1ILWtz7QAgDKBiHcSkh67rOXWkRAYVMTIZUHLTZiJKIWnTzMSR8jNRWU1U119stz5/estvdQ5o4HPeEKPHq52EAK1blumrYbrYM4yTjOWUY+okYAzFKNoOziujPdG1FVdW889bzbK5JdJ3oHwprP4Qg49ByvXPza7NeyTEh90gOxxP3Dzv6XtKGU4GE6stDH7PTU8Jm5Zxd8gaMNgt1WCzEQjUqu3EZEXo/L9qAotgrSUd15bL47KLM/JrE/FGvplyP8rWBjG6TanEcx0WibnN0eEpcfr68eYhf4EKEBli3GyyaMEp0/HweaW3Fsw8/5Dvvv8tXX7zgqy9ekEJg7HtabYnDzBRH2rYGlWh1zVcvX/Hjn/yc66vbb3z+vhWLgAJuNmtO5zNvHnY8e/6UefZcPak4956/9+O/y5/+9R9SVYYRhR4M0XuCijl62nE6B7RO+UKP1LUXEGaa6TrHe++8y2rfM80RPz9KDE4GH2DsC/RCyLQ6ajQRRUSliIpBJJxa4WNkniIvXrzk5cvXPH16m8MyPPM0E1NAaTkCzPMkCsNp5v5hh3OWYZyonOOYARw2i0XatllKZRDfQjlCaG2xTpPSTD9Mi7xYegFhuZkBlGsIIdDVGqsS03zP9brjvecV7zytefXVGw6nPdMY5XfxHhqLqyxVXfHs+bso3fDZx/ckHAkZb1W1JaaAUaJnMKrBT8LDU+4SqvLs6VOUQn7fXPZ+8tkXWSNvaNsGo0UeXOXMgZ/+4vf55YuvOB5PUt3kxVZrTTJ6oTxJTmJimqN0xjOuLARR5vkoNvPKCLptHCfO557tdpOv0SRpS+rCkVh0C3nSJHN3URaKvdxjk/Snxn4UtqIumHBRfNZNjY+Rl2/eiPAnezjiLJWDdZKyZCoLRlyZIUWUjSQdSMqgdMrhpkZs7rVjHEZ24xve+d6W9hD47LNP+IeffMnzp0/49PUvCH3gnSfP+Y3v/QbnY8+LuwNGaYbJ8/Enn0uDNkFXfcutxDEmxmleAhL2+yPOVex2D1xdbXn7+TOGcUC7mjkz+WMU0IT3MsorSsBy043TRFU3KDJ/32hkEY9LFZBK3z03eEpCbEoJ8nmuWHeLO81na6qcUb2M/ZpagkRyaWtsyYMbpMSdBs75TN8PI6uulXNs/WTRw5dKRGCb4mXwGZ4JcO775WcVeAqL9hwuOxFIBoPAMzzGwrpreev5Let1R9/3nM4nkQRnSXFE1EkJRQhA0qSkBGRqKg7HgbpthUuoNXUl6T4gKcQAaX5c+YysV6vFjWeM4b133uZ+t+N0OlPX0rBKucs/ThP9MCxd/hACSRVTV65ysuBLwdekwEUqMM9ztgzHzPNTi+5CzFzSm5GGoCQ6l8lFmSQsjTs/Z5bkZWxHNn5pLbt6MRHJe5+NSFovITLz7LFaJOxyXT3TrNiu6mUi1K1Wi2/AaDL/weR+kvhemsrx7PaGn3zye7x+84pVI9CR0+HA86e3PL9+wgdvv0uVKl69eQNG0U8j9/s9cwoQs4R97L/x+ftWLAIo8aNrY3n27Bm/+PRzhnGkcRXX6w3f+95HfPyjXzBVDZ2rpYmjxJ89ek/SUDLt6qoWKvDsF8HNNE2ya2jBTl+8/jnw4xHvX+SeBh01QV+CPuXGkU+66PfFcBRCXBgAlbMk4jL2q+tKsN6uRqlsl7U2Z91HccWRFrho+XnlBjfZNnwpXWNKi8PxMWjz8SKQsnNSK3Bas+4antxeQ4rs9kemaZTxl4p5kYmkpHHG0TRrqrrh1ZujNLBcxe1tS4gp92Gk278/7tk/7Gjqhs1mzdXN1TI/f9jt2R+ObDebhfDTtg3aaOpK2AlyZnfsd3ve3D9wf//AOM15miOTnpjn9kVfX+Ap8vtGYgSFWUaCANbqhcKUHu308zwyzUho7aoT6fM4UsULcDVGiVVHgbOixQfR8JcJU+UqyYWMYfl5hnHgfO5Rj44QQD4iiuK0qStCFkKtVh0gOZgkQderioW8XCqBpq0YhjP3d294+csXOAc31yv2ux1T3/MnfvDrXK+3BB/4f3/3J3z11WuevLWV42ry+OjpJzlaucZ94+P3rVgErLFs1luUkXixH/zw+4zjxOe//JKbp1uST/zpf+6P8/D6gf2bHaZSi75cfACRdbvCGUtTV7RtQ11JRJSrRAG2NhUP+xNquDSRCiBkzgKUctPIAyi7SLAmP6yKSlux1iaWGfwwDAzDwPX1FpXVcsPQo7Xi9uaGEATiMeMZxpGubYUyay1jdho+FgOVqkQrTYgs3XSpNAJD9tfHTD4ulcDj8611FfM0USlDter4/ve+w2rlOOwfGPsjcxxQJjDngFVldQZVPGWzfsYnn90xTJH90ePDnpAkou32ZsvVdo21mvbpFU9utuz3B16+fs3nL16wXq1Yr1e889bz5b04ns9M08z1doO1lqZpKHHen3zyGff3Uh2IkzDrNnIPpjJ6Aac+TnwuI8EC/RTvv2Q++FlztV7nBqS/6EbqShb6IEco+Xidqza1oL/L+1mcm8WbURb+YRyZfDaSZXahc+KriKTlCANgnKZpqpwfIROE87nndD7jrGWz2dK2lspVNHW7GJCM1jjj+eLTX/DmzWv6/sSvPb9ltaoAz0dPrjn1R756+RkvXkR8hIfhQGhGdC2/w+2za+Yk+hWjNW9/8IdQDCqlPgD+GvA2soX+1ZTSf66U+k+Afxd4lf/qX0op/fX8Of8x8OeQIN3/IKX0v/6jvkcIgfM4MIwTzaphe7tBW03VOHbHI++//RY/+X9+DHPEWUWIPtNfxGkX/Cwz/Vz+rdct0wyd6oTSqhSVtbR1zTh4+jADJQE46+wNwAUzLotAyr+yeN1QSlyBSniDLs+Hi2QWCiUm5vFRgKhROmWloiwoaZL/v7u/p65r5nle5tMqx4OJMq1bVIfnvs/lpl3IuWWEKO/hpbFZjgP1ZsPVdsUPf/A9fL9D6yQPSjZOxZTEceYM3WrFerUlRkM/RIbJc7W9YvIR6yyHwwHBJwbx3itN42pur6946/kzvvjyVQ6CvSfFxPNnT1ivVgvZ+bMvfikjzpw92A8Du+OR8zAwBVE6qlze13Utyj+gMAQk6OMyCbDW0HV1Vvtl/Hc+w4NIpGOSxRIlBitrDCov6nLWz8eCLORK2etvyoJQeixKczqJTmCaJpIqcugLaVhSouPXKjLRSQTWq45yDC1S5QUZnxWXKQaIhhDEBHWcdszTQOU0bbPm6W1HUxlevvqS18cH+nHgcNxJ9qSxBDXhGoUioIhcX3VcbT+S0eQw4pzim17/JJWAB/5CSunvKqU2wG8rpf5G/rO/klL6Tx//ZaXUD4B/A/gh8C7wvymlfj2lb/Yylq7r7dMrTGXo5x5tNW+//5zPPv6Mu4c7nmzXDPsBP3gwmnn00uH1kaZuJRSkrlGQ3VeR+7t7krJsAjx764auremHkWH0FHWdTA4M41Tm0I/P3HFRCiolmOyqdpAUIGWnD4H94SAMwwhGe5wrbLxRnI6J7KEfJTQl3wDbnI6ruo5V1wEsRwEfRHkIcgauXGHwZWjqOC12W0rpm+Wx1gacgR/+5of8S//Cn+JweKDfv2Yaz8xeIthDStimwdoaW1Xc3r7NMFWc+kTTbVhf1YChiVmXv24yws0w5vP7oCaMkffv7beeUVx3TSNy4lev31DCQD768IPM2Au8+OoVx9OJ16/vlp9fiElmiYtTStM2OQQkygixbUSE1G07jDN0TcP5fF7Ugm2eNPh5Ykii/bAFzKoVVVXw4WfKgi224Lhc66pSOaPy676Ntm4JUUjJyujs9zCLPFgbjc1pRiKMkoU2qcg5V4DWWtou5xJYtwBO6spR15a6tlgjUNf7h1dYndhsKipnuN/dcTodMS5hnWbz/zH3JrG2Zml61rOav9/d6W4bTWZkRlZlpbMqiyzbCEtYeAIzxJABDBiYAQwYM7U8xFMkI4YghAQDhBgihMBlIwubKsrZRWVGxI3bnXPuOWc3f7v+tRaDb+19o0qZhVW2pdhS6EbcuO3Z+1/ra973eYsFq7MVMc2/DmMvrZ2aqOuCPBcnaj8EUCIR/0sfAjHG18Dr9O97pdRPgOd/wU/5d4H/LsY4Ar9SSn0G/DXgNzKPtdasN0uCCiiryJSlHzpQUJQZL796w9UHG2xm6bsJFTXW5GjlIcxoZYjq2De+Hwb5eU7rJSn/bWZO+Cqx6nr63smEPcpt+vVsw/cCjmPvLWKUaZ6xVioMUadpZncERIrizQfJPKxKwzSOaZCp8UGdwJN93yflnNh+SavHo7rMfO32O2b2ncpNxWmwCZJBcDRFGRUxGr73yccMQ8/hsMcQTgMs74P4CUxOXtYYk+NmzW4/0A2K4DXn1YK6EmjHoW2J0WHsewinsZbc1gnModgfdqc48UPbUpYlV5cXXwtNkRI+RpkZ3N3fM81zAnNqDCaZd/LT4C9PqLVjRiBJm69QEjkeYxqmaUitndYSFitxY5UMj1PFJO3WUfAlITLE9wGk0g6+T3oGuRjc5NBJKIZNn40oswpJ+LFkyaJ9DB610SJwCUmEKguBxwrUxXyNhXhcGUbmecK7gdlN5Jlmsag4tHte377D+QFtUjJXP1OkS0EsyIZMC+JOVTCGiTFtkGTl7Bn7f0liIaXUt4DfB/4R8DeA/1Qp9R8C/xipFu6RA+Iffu2nfcVffGic7LNGKzDSw+m8ZhxHLh9doqJmvVoykDHtJ8qypm97ecNIu/E5MEeIwVO6nFmDzXKMFmKNn2dZ/6mv+bwTqQcSj06995Uf0d5TMiIdXyEe0WSchnnBe+q6Ep132t97n+TMqTQVe6qQjeT24ySkGacJG+SDKfHcgo2ej31+eO+VF5nt+11zel9OQiGAPLM8eXRGVeX0fcvsRt7d3eC9RH9pK+Ghiowyr6maJYd2xM2aLCvp5pGb23s2a38asgkdV2LNh34k6qOZSQ6suqpOcVvHtmRKE/eylJbHJo+/aBtimsuYP+PdN6e/l2gDjgdWZu3JfKOUzIR8gr3GtLU4sfysFR8DAWMEAx/TDCnLpKoag8jCjweum4VxUBTFyVcil4W0Cv3gTofsUQV5zDfsup5VnqG1OSkcqzwXeEuQNrAoJMhUKXPSMMSEQtNGoQhodJphlVxePuVnn33Gw25H27e820mC09n5irqqCEoxTjMuOKwK1EWNzhU0HmMsMQbeXr+TdskYebZ+w+uf+xBQSi2A/wH4z2KMO6XUfwn8HaSu/jvAfwH8R5yWXn/mFf/8dyil/jbwtwHWZxV1nbNrDxz6juXlCpMZzpoNcfL4yXH77p5KlyxWK+62t2hl8VG8+vI7phvKGEFEaVnVjeNEVBkQsFpR5Bl1Jb3dsQSFwDAKhLKuS1kFzk5cY1YTrYYxQp5x97ClWTRkWY5WJvXWwgYQHbcoBSenmcbhRMg99rJFUeAOrayktODHjzxEnW6oI4EHDdtdxzROSUjiT47E48NPOhDkry8fzu999yl/8KPfYbEoafcjWoHSEFxarZoMAaA2lNWSs80l7m7PWVWiTEUzOKZpZrlYiPotepSWNVbEYk1OjJqunwidI88sq2V5qlrKskw3rfy921a8Eq9ev+XN22vevL1Ga81ysUilvzq57wjvLcjGGlQazh21BRBTUvR7kMyRSrxoarz37A8HylLSo2cnrEIVwY0Tx2DXxaKh7brTKvDo5BtHSYmy6UABsJllU4j/Q2stA+yYhEHJ03GMYNfHLVKKes8LSYW2qUU8PglRQZnn5IWlzDOspM1zffOWu4c7vO/weHSuaYoGuyrwBA59S9u27Hd7zAxqjuTacLZYcXP7jmHsmb2nqitWl2tAeIVlWf+LHQJKqSwdAP9NjPF/BIgxvv3a//+vgP85/edXwIdf++kfAK/+/K8ZY/z7wN8HePbhWXzxxRu0VSxWNQ3iGAsO9m1PmTfs544YRvQc0R5yNJM1mEz2unlmCYj4RSlFZkuMzZhnQ6Ey+skRdADj6YkEa4jpRuG4kkqEYaM02hZojlRXz2Fw9H2HMhaiYXaeLBMFmDD8oTiuIaNHE9AaMnNUq82E6Oi7SbiEPiRsuUiWlRLnX0wmFqM1bg5kxhCsZXIz0+zZdT1eaZTNIHgIEvWlg4foyY3lb/74h1xdrNk+vGPqDnT7HcSZqAJBiUuwKGvqRU1ZNrRdwM81wWt8EOlybjR+dsToZW2l8lMZrVWKc6t1UurphGmTUnnRiDPPzTKU6oeRn/78M+Hg9UPS6yuq3B6lAHKARTn4tFKgAsPYcYwWH6deWhgteHliwM8TNoWFGC1Y8uPBOE0zdV1JNRGkYjB5LtsaL+/X2WaZ0oICSkURdhklMempSjgSi5TWRB0IxNS6SOKvZyYrcoyV4WNmJaDUGEWuNUVaI7qpB6Mx2qKVpTCWxpQoF+gPe+om0g572vYam83E2tOOPZ0bmOIMNUx+JH+k0EC5GLGDYdo54my5u58ZR0eWBVbLSiqc4CiyQuTL/fiXPwSUfFX/a+AnMca/97Xvf5rmBQD/HvD/pn//n4D/Vin195DB4KfA//UX/R4hBOboqbPyND0FlU7cQD+9FzooBUWZ08+9BIVlGSglBBmkV2v7niJAbSxFSn0x6QNkEnEmzB7npU14L8c9Qh89x6AOpZDbCiHSCisuez938MeSVIJQgxEdwdGRNk0T4zSccGAyTU7hF4kAK3ZgeSu89viUhqzTNDkiqcdzwlmP40iMUfL9QmCaRumrtaWpKzabJdM00HYtIXiqsmDfvjvx8gtrsTZnsVihtGW/74lRkpatMQxpLpKl3XyMgXGaiTE7lcne++TfFwjG0eAkqUFTagvk61lVJdMkBpojAPTI8VNi/5D3Tun0tZY32trjUFb9uXWosBPKsnzP+k/D1CPf8MgbOAqw3gNaJdxFKXCTwyTP/5F6bBNOziQEXWZtEiNJ9ZElB6IxRgxTaYtxbCGyLBNOoBbBlYsRvBfhjg8UpaHIDHlhyHKNGx06g1e3b1DWM6mJgEdFRWEtypRUJmIawxhGAhItty4bLstztjcH+oeRcQ7YmNHOE33nGPB86/kVXTcyDk7Urr/h9c9TCfwN4D8A/lgp9U/T9/3nwL+vlPpRegs/B/5jgBjjnyil/nvgnyGbhf/kL9oMQJKSNiIm2R526EkxBaHP9OOId56zZkkWDGgPtaEfPF3bo1LJ5rzAOYiKfdfhUeRVKZwBrcgLS1Sayc3ow4APM8hmlyOAI4TI7PxpYg2KaRxx84w2EaOELDNOI0bLg65z8bofNQcxDWOOcMwjgHLRiH++7wfJKwjHm1PES+PkTi3DMZxi6jt8+tBXVYlJB5agq4XzV+QZmdUsSsuHz5/wV3/8e0xTR98feNjds9s/iO02y5iDShCMNZcXj3nYjgzjiLUVwzikAZyiKuW9GJOJJ8vsydQUj8IdJTvwYxr0erU6cRW8CpgQePP2mhglSaiuytM2w7mZsiqEJXkcGoIEiSQIjJwD6rRJcW7Ce+nDyyKH5Nw7rkmP7YQ19oQyC0H67ONBIiu6giNAZHIOEl4sy+xpliAVQEhhpLBcLnAe/CzzIsGmZcKUTNqGI1LdGqFWhRiZfGD2gdFNZIWmKCzrswVVkeOnia/efs5uvyXqmUcfrzhMLV0c8H5m01uGfsBoKMucw30rKVnRJXFRFAFYqSFrqVcFg015mBEWdUN3HwjeUtuK73z8CfC//uUOgRjj/8Gv7/P/l7/g5/xd4O/+//3ax5dS4lZbrcRQcn94YPKiBDS5pa4rlrYhj4aQzby72xP1EZdlCPG9xzMSGcZegJdldZJsqiileW4MWaZRSkpKoxU+7aJBo/URQirzhn4YmI7wh6QuFBSWOtk8h3RQFFE4/ccesSpLUJyglMcPo9ZGdt9ezD/eO8ZhOrnbirw4oaqOg68YjkReGYSqQDoAFNFHzs9WfO+7H/PB00tefPkzuu7Att0SVUrgCeK6tHnFanlBkS942HlCMChlUSqk2UGUD23MKBP0ROAd8vUwVmYumX0P6Bw6cUUuEpH3l59/wWq55MmjK8kg6Dox0miFioqyKgTcQkgtmCfyftJ/PASMNsTkFc2yDGvi6QBKWGe00ieRzjGZWSl7ioo/riQinIaKpwMntTdSSUj8nMiG5f0+cicEnloxm/lUkfjgT3oFSNFmxog7VSnJFbAKZTU+Cvw0zzW5ARUd/bDHzR2bTclh2PP5q8+ZzYSqhS5c5xXz5DCZpSwqrq+vcWHGR2l5xqJkPg+ECZg1RVERnGbWA9ZmTP1ErjWrpmGzOuP+5uE3Pn/fCMWgm2fOLjagI+3Y8eTZY2bhqYi4Y/Y0lKgx0MWDEIEzyIuMvh3Th0aAk/JhUHTDTkwvVU2WZ4R5InjQ0VPnFnJLS2BILALvQ4KMzIzTfEKPCeXX4v2fHcgplXIIUzla5MUJqiGIadGHv28vRGcvEWf61Hoc5bCyN5a3I8RUJahIbjOyDPYH2c1XZclhvwcVsEYxDR3LpuCH3/8Wj85r9ttrghsxOgIzw9SRFzna5Bids1ye8ejqW9xv+3TTBfyM7OS1ODHFxafoh/eSWqUUZ2ebUwt0HIhlZUlVimNut91TVgUfPH92gq0c9fdHlx+QuAMi5T3dwlonlaTMIIpCtiYxxBMrweYJNDpNaHMEkqb1XsoUNKlVkdteUORZlp1cmG6eWK+WErI6jFhrTkyDY2jM6dukWSDK+q4qS5qmZppcEhlZqQSU+DSURpB0UkRCmq2sNmvyjs5T2wAAIABJREFUMpJnit3umnnsmcaBL778FQHPJ9/9iMZYJmZUZQl+5t3rG1aLNbkt0EHxqDrDWJMMVhE/Bq6/fMdhP+BnzeWjJUYXXG4k2ak9tDx/8gytNH038PbFy9/4/H0jDgEfpByu6oJHV1dkdcbgBpQVO2nMLMoplFcUVUFWF9B3+JS3F6P07yC0naIs0cZSNZJHaI3s6tM2XW7PGKnKnHmuGYbpxPvzPqSg05iKi6QWJLHfgmca3//4RbP4GktONgHDkFqI9MCoJOaRoZRjnsPpxwkQVBRzc1KLKdTXREpyK55AGbOo7pyb8K5n0VQ8e3LB82eXWB0Zp44ss+y7PcZo1usVzjvQliKvKcqGYYpYUzG79wwF50bU13bxR8vrsdc+ypYh7c5TMMkRm11VFZPbsd3uCAnYeeyvBfvtEh9BvPPWGuEzfq0FOMZwiwtP0n2Pg0KVmVMikEim5X05mntQKaXpaBZCtizeHw9c4TM4JxuDoI+6AoO1KYPSB/Kk6gw+vv+6G6lI3Dxh7RJrdUKii3jrfifBHk1ds2gaPJJVUVc5VmmsDXg/0s4T2+2NVF0E6kXJu/s7fvLzn/M7v/8pGYaYRQ79nkM/cH52KXOFyVNa2b48Pn9EPw50bc+XX7xitVizWiw5O1vTvrxm7MTC/ejygnlydG3HNEw8vviGk4VCDJyfn4lWIDOMzjHHiAryIQl4nJ8xKBnEGNCZxdiANsKY8/OMMhqbW+pFTZ+SY8dxou96FrXDZjlaxZO6rioKYlCMo6MfR5FvJvGID+G0pw8hoGI8ORSVUEgAqKqS1XKB+AhEFDR7z5yku86lYVZMQM40L5B0206qlBASrlwkvM7NIqHODcyOEEWXIKWyKNJimIWobBQ//P6nxOBou455Grm9u5NV5jKjqGqihhANRVmxXG5o24hzsO8mtAabKfw8iSMwiXG8n4XJn9Z+Yv116QMdgKNi8f3Nfuzf/+SnP8NozUcfPieEwOHQEZOC7yhgObo4j4PG2Xsp2U8Hz/xeU5EO7/eS3BRBn0I9pFp4H0M+hfn02Xo/N+D0e/lTFZYciWnYdzyYj5qFLJNosMk5lqsFWmmGcZCLJTPsDy3dMHBz+45HV5diTPIzPkoYTZlbVHAM08Bh2qIzaDYVRWFw08TgR2o/8Pb6htkFumFAzZroNCbLeNgd6NqBeQ784Le+T9cJcOX+bssXL16yXp/x4PZ03cyjq+d0fcdZk2EQ1Pq7mzu8Sxflv+Bg8F/5SynNHCNGaTJtIc7CoreKoi7p+xGMQCR0jGwPHQERhRAF7hCA3ArBZUh4qzwvqKqCmMRATVmIAWRo8T5SVhVHKMVy0TBOc0KHH3kD4XSLgEeSjGWFaLOMuqpomjqZYhIh54iwDpGu7whBVk9t14mOPTneZGKd1kqZoKiPh5aU2xJKIWk5Hj8fXYyK5aImz+B8dca//bf+DWDi1YvPGIeOoT+w24uuYJwc3e07yqZkszkjK2q6MfD67ZbgRTU5hwlwbFa53HaTF8efSvxGYyXH8Qju1PqE2S7yPE3xbfo6FCwXC/76j38fYw0/+dkvuLt/wDlHXdenAV5dV+lgPQqzNIb3E3bx8YvIxc9SIR3DZiNCFI6pJz/Sn/CyXp2mSYaoRnwd7jhITduaPBevv075Bj5Zw7MUI7deLdN7Ji2hSYduCNIabneCgx8nx2a94oPnj/jgg8eJbKwpCkNZNaAU77ZvceMeFx12YdCZwiwrTGnYbUe63FNebXi2XvCnL254/OgCPWsKVWJWGZqMLBi0kywJHTPGztPkC/71f+3H9KPDzYF9O/C//4M/ZL3Z8PTpt1jUDSrCPBmiV3jnefn2zW98/r4Rh0CWWRFT6BSWaTRRyW28Oxzwc8A6sFq81HlR0DvJ+RO9tDr1fSLbdYnUKrx3ay2TmwhRPrhn6xVt12O0YoST3n10c5LTanxIjrbUs0ujl9x6cT6tnMQEEk7T6WGcUlyZ3Pp5lqF0jtLSjJivpQsHH08DtyGtAKVSmNLeXGYJuJl5lmz740NgTc1f/4PfoywL2rZnnEbm2Yngqapx3mPsUX8g33ZdT9+PjGPJoW25ODuTDEOkxLYmw1o53GQVOaVpuAR4HIdsx5Wcm8VfD/J1lhbJnW7V3W5/AnQe5yXW2pNWP0sBHMctwNF9d1RovsfCe6oqS22LS++ROSHijhWA5ESkeUpa5x0Hm8eK5rj2izrZw78mD46AmZPXIEmZtTrGkXsO+wPb7Y4nj6+4vHxEVVdkmfz5pylCqjKlbbAsFhW9GWi3B+ZZgVPsHlqyIqfrBrIsY1EtWJQ1f/h//mOquub8bI33wpRc5Jq6adgsNrQPLX3bs2oWbNYrDl3LbCNuHqkSHn21XFCXNc55DrsDRlv2hwPb+x03t9/wwaAxOXPI8G5EaU+zsbgYCN6h8JS5wU2eoAwuzMwKTFGi04DIBAve4EdDPwbyPGf7cGC9ahgngWcs6mVi+MHb7YQ1GU25YuoGIpaudWhqjJ6oCo01lr7r0VahInRdB0ZaAptlyQpa0fctVSl8wWEcmZ3DaFHVlcUiqfhgmsZTBTCME0YrbKYwOkAWqcplgmLEdCDMKCNZB4sQObR7+r5jnh3BORZ1zofP1hwOO6wKScuumWdw4R6dWfA1mVmyqi8wYcM0zETniQwsV5rDeCNBF1qj5gKYMTqikiyjLEVocmgPNHWNQsi3R5VdliovrXXiKvg035nY7vb0w3Da8eukQTg+YOLPn+VhVBF1dG9qSZ8mRtquTcANjet7lAKbDn20JczvJdw6aS2UbOhE4zDPZEl3UFjDEDxjmPEedNSUeYFWItWLgDU6KSoVVkcI701ab253VGXNd779HYpygdaWabQkSDB52RCYicVIsAPbsaWqFV7vMYyUTYPOLOMcUdFwuTjn1ctr7t9u2TRLni437L6643cuPmRWM+rxxSkP8vOXr7i6uGCeYPHoDK8UxIK7+wf6YZDNUx4JcYQx0Lct+Jnd9o6H7QOjG1icf8NjyGY/8XC4FsVcnHl9P6AzRVHlXF6dU5Yl226L8zMaxWpTc//unmkeCWlPHGMQjHOMaXKumaeZ3faB5XKVoBIFRV5ydXlGVdYUeUVZOg6Hnm7oObQH0AbnFJnS6EVJ3/V4H1hvVtiU+JPngmxyk+fZk8dcXp6z3x/QWiccuMA7x1bAIbOfORwOiZIbTrht5xIxOMqtlSc/+QnXjWQlhCicw6osUSHn2ZPnPH9yyRcvviR4xzi0aC3rPTc7plnAmqvVks3mEZv1JdquMDYny3PafkApjQ+zrDtRTJOjbTsRGMVAkRendkRHfbrNj9WPMbIbP772h1Yy9azl5d09v/zVF6d5ypxyH2KI6BBORJ+yKNP0HbIk0z2W4RI6q6UaCIFZ+EcC3GAWsIcPSZefM44Dh75nkUCdoh+Rw9GA6E1CwCYCVAwepyScREJawXvRYxRFzuxm7u8PlEXB5eUFV4+fodPhrrTwAN1Rt2E0mVJkFoapZzq0OD8yTCRQTU5GzjwGbl5fY23Gk0dXfO+jjwjzzDiMbJ4/493NO/7hH//ffPeTb/PV568FS79csFwuuev2YBU/+fJzNssVbpxYLBdobXh42PHXfvhDLi8vuH19y/XNLW3XkuWGzWZFkV8QvjYn+fOvb8QhoDV4RgIBazVNWeOCZ/YK5xQxzOK+M5lQeqock2kwEtRJ8Cm4M8EctFh/81xK3XEY6doWgmYuZhZPL2nqBpRBHSYC4iEwRslwLKkErdYUZU7wUBbHvl+dPPGZtaxWK+H/x0j0Qqu5u9+itay+hmE4TdWzLKPQmslIwGbfD3+GCXBMPjrmIShlcbMS7X70aBUwFj54/phVU/L2zRfkmWIYOg6HB5k5RKEEaZ1T5BUgGv9p3mJtQZbn9NNEZiXlKLMZNrM8ffyEs82GaRp5e30jB9HkpFKwMpA8ltZH2tIxEyGkXt97GXq+fnPN7MXnIBuTWWCwWiSvR+utDwGLPpmGjjiwY4twNBIdh3shRhRiJdfW0I9DMmmlwWvwDMmKnWWWIssxBpQlcf4jsXdiprFavPvRo2ZFlukTMu3+/oF5Djx+8pSqrERBaCStOiqZC82TJEwXuQS7WiNS57E/MLgOY2AcAyFq1ssl12/uGcaRR5eXnG/WNE3N7GZUXqBC5J/9/GeslwtUpvjVqy/5rU++xbv7LZNzDH6kKCuxqPuZm+0D2+2OT559SN3UNGXNZrnkcL/n5v6OXXcgxkCdFRBFUfr1lK0///pGHAJKK5pVzdCPTJOn1BVNWWCznHbvWK8W+NnTBY9XgfXZOdtdx5xHYh4xOjAEdxJxMHrqKpcwkOhpqhKrNTE4rC7JdE5V1IyTx02BoXcYk5HngpOK0TPPMiEvijpJZzWz66nKkuVyyWq55OxsQ1kWkp03jGkg6KmqIjncZlarihgV3suDErxkFUp/bChVcTKfTOPEIW0pTLI758qKK8w7louCj58/xuB48/YGH3p2h5Fx6ogEAZGiKM2Ks/NLNusrxkkxjjBMjq4/MLqZkKqKEATUSlDcvWupqows15RlweXFeWLvy6C0H4aTd/7sbE1msxPtGGRPP4wjX718zedfvmBIO3hjkmtvcijlKGKObhoZiBqTREdRtAmpkhK/wYCPsq5FgY/HrUKQ0NlcaD7ee/Z9R5ZlKJvRjhNaRcZ5ZkxqwDIv8Gm4OLme5VLix5QWVWGeZ6yWNX038NXLr1ivz1gt18RgGMfIoqnpeqkI5zCelIF1U2BMxFjFOLa4uSeGmWWKB3vx1VuCz+l3D/zoB7/NatGw3+3Y7ra8evuSw7AnqkBe5tTrHGcmNh+uOBwO/PLFF/y1v/pjbu/veXN7R9aUDKMjXzbovMQWFVlWMA8jt69v6O93HHYH8mVJ0xRkmWG3FQy8NTrlOPz61zfiEAgh0nYD0yhwRu8iuTUMnePm3RYVLZnJOex3tK5jqTTGZnhj8Wkif9weGS1T7b6XEtrUtUh1i46ikIe86wYW9RI/ebqux7s59fJ/Lk/OGPp2BDTb7ZamFljEctFwdXUhA6FR9v5Gi24+xsB+vyeakH4tRQgKP88Mc8oVKCRu3c0zeSqBlfKQ5wypCtBas1jV5MZw6HYUVlOXORfnK7a7W9rDjnmeiHEmz2SQ6ZNnf7nesFptOHQT0xQxJrI/zChjWK1XScMfWS4XxAjTKA97iDM1ktrkZ896vaJpGs5zceT55GLcbvfkeUZdV6dhnjKKh4cd1ze3Sb8vvoe6KjFGn7gJMcE+izyHdMPPqYIQ/YMMZ4XrmPwYSRB0TDDyMaJ8JMtkIHyMmj9BTfIMpaXdMtbgCZRNTd/1VIWlqQqMgaKosVbaotevr+mGAWtyQoRuGCgKQ53l7LtOXJyIfPko8jqaxmym6cZUtXhF1zpevb5GY3l08YiLsw2LakXX9gzDRJ7lFEXBrt+JlHyesJWhXtZURUFRZrz57Jrt/kDTLDmboR9n+m4gekVhcx5fXLJ/98Cwb4kx8MWXX1HYjPNlzjD2HNoZqwShJ1XnNxw0qo1AQpyKKAzWiEFHRQE+KgWH/YGu7dABCmMhHoGOGmUsmU2BGyGmZNc0FQ4ePzu22x0qasIcqPKBeZoIPrB9eGCcZsrCMAwOaxSzj0zTmLbT4TTtn9yIMYqqKqgTHvyulQTiRVPL7eg9bdehFJydSS7d0DvcnFRpRssacpoShEIcdeMk/bJN2vSiyMTaOo0U1mL0xLIpcW5gHATyoVVajyWikBbDO0VeMU0BHwzD6NBGM/sUguIPNHUps4p5pu16goeqbIhxput6Ls7PGCfH4dCdAlBijGQ2o6ok0ddNcvjlmTAcD23LdrdjfzikHytmpuPBOM8+bTUsSkkJP45TgqIqtDJi2kpqP4XG+SCwrCgbDgksS3i5mAxHyYEkvH8xMx0Hl8dAEKM1dVWxqEqG/kCWidy3LAtub2+5e3igqioW9ZI3b2+JQVOWM103MfQDWZZTL5qTASmEmRDfpwfFGBn7CR8FRe9joLA1j68e8+zRh1RlzqKuWdY1dw93dN2BaR6pFw2D0wQVqFYlZVXQTz0hBD76+GNefPWGJ4+foILmsG3RyrAoalaLBfM4c/36lna7Y+p7dKrK2r47aSp80kKEICvy3/T6RhwCwQfKoiA3hnEYebh/Q1mVVFXF+dpS555ymfFoc0V0nvbdjrPNgm4/09+2oKCsyiTGcWm15plnOUSC9xz6HUqE/QzqJddvvyQvCvAjh+2Wn3/+OT44styy3iwSwy8wtBP9MOFcoB8zUBuyTKLL266l73sym2N0RowiRtntO9pE19msVwQvN+DkHH3fM04TCkGWZ1birYtCDDpNU/H6zTUQcZOIbKZ55Pd/+AmFjVxfv8KNWw6HHd6PaGvQ2kLCW5dVyXK5Ybfv2e56lM64vb3n8eOnlFUlQzCCJNsaJe7DImMeJ7LcoLQ5ORbrqiYECdwsioIYwYf43uWXHujZBz775ed8/sWX7PcH4fFruf1dgrkURZ5uQKk0uq4nJh1+hNPDf/y2T3Zr0mrPJ+FYkecU6aCvCokMP2K6ZIQQudxsBGriHJcXkgkRo2eaBrbbls+/+AznA9YWrFYbfvx7P6ZpFnz2qy+4u+sZJ888d+hcc7t9Rwie880l69VSDubBsT/MtO2Oui5ZLhsUooCch4zzzSU2s/jB4Q4TP/jOp/xv/+gPedhveXHzire313z7kw8hm4l48swQu45+aFEqUOYVWhfcv7vlzauf8vTqMb/77d/isG/55S9+xev9C0LwnK2XXDx7ijGKfmilpYpCv4pKPDQ+yMzJ/ma62DfjEEBBCNLLlYUmv2jS1NYTdcSHA23bU9oCi6EsDfttL3pt5BAZZynTZE/sUDqSY9AoVHKyVWUhDjPfoaIMd0JwZJnn6aMl3dAzB8c87zFaURUF7b5nnHq8i5T1OVkm0tayFBntOMp+va6rU6SY7OJFPCJ2YqHq2ETMPQ7BjsMuDafWIUsBlqKE86ADm82CsrC0+wdmN+HdRJEbQixE1BwV2lqyvCDPq8Tsi9w9bCmKmvV6ycXFmWgQZofSSZthbTp8JKXJWCE8hXSTdym27eiQU4n8cxT0gPyZUYrDoU2R61LeZ1Zs3M45FBLKGhG4p44pWyCIfFowXeGkA3A+nFaOMUmDj7RhQYLFE9rtmBVoE+9PKVguGtqu5+7ugfVyiUky7K7ruX73lmFqWTZrPv30t/nog4/YrM949fqWaQzUdSMDQ++I3uPcSMTj5on9YZ88FgofPfv2wDAO3D9ssVqTZ5aiKOh6R2gnmjJnGid+8dkvWa/XNOuG0cx0fqCdBqxRKB3IbZbYlcKhKLMcJsOTq8eUWYGOipefv2b3sGfaj5w3azbrBXWTM7mJfuwZpgllNXgBzc4J3nokZ8d/GWShf5UvrTVNVWEURD8zp9COqOYUgKmlZ2bGGCHLvnn7GnOIyZHnmQaHioA6lkAxqf6lZM4yyYZXQJErnItMruP+YcfdwwPaGurGslyuGV2PtZbzsw3Be5QK+Fnxg9/5HnXV0DSC0srznOUCsixn0Swl4Ti6hMWKtF1/Qmv5EE5GnCO7oGka3Cw/fpwmwWEnebGEjEoU+3c+fo6fHdM0UpU5Y0ya/MmngaMw7GwyMVmTcUwpruuKT771sawNM0nsGd0oaUdzwDlH241smrUcQEax2+9p6iptKkaOLLwiL6gqgbOGdEPHCPPsuH8QZaBNqC43z8yteAyOgiQ3CUdRFcdBqKx4v751mCbHkGjKR43WcT5zxLNrJUGleZ6TZzZZyMUGnaU/myQyi6T40Hbs9ntevXlJZM/3vvttnj55xm99+gOUytk+dGy3B0DT1NIWjfOINoE8V5RlTm5rJufYt4eTAKssSzEVlaUoCDdrpnEiBIU1Gc55OUhvHbMBlSu+9e2Pef7xU8YwcXP/hsNhxzTPZLbCaEu337OfDihf0BQNOijGfuTLX3xBlRV88OQJy6ZhvWp4c/uGfXegnwbG2UmeIgo3p7TtxMMIBLz7zW7+b8QhELzn/uEeo96HTKJEP7493EvpFyv8PDPOwN6wCk+Z1Y5t+xI3edkzHzXhaIwyeKeJZQnG4tB0YULHDD+UvHjxmsIovvvRY8qPr7BZTlUv8Mry2etrvnz7ln/0R79gN9xxcbXih7/1LWpqcltQNiVVU4G2ZLkgxZSKdGNHVIGnzy7QJnL/sEMhHnbRoEvq8EcfPEMp6Iee65tbZj8xzQPOOR62W7btAyjF9z+64tPvfpemLnh3+5KuvSOGGa0ck5vY7TvKshEGY7GmLBv6wXHx7Jzr6zt+/4c/oKgawNCNDqUN1uY0eUOWGaapw+YZj58K6GKeYZ5huajY7Q/cvnuX1qwVBLmlVPCoGLAols2CcRx5++otD9t74SB+DewxTS4NpiLj2DOMk6QwjT1NXTG5VsJJrGZKPgTHSD/18qD5yDSJZuDh3rNoFmRZxsO+BasZE3p+2dScLSvcNBI8fPnFz/HBs1qt+PLlL5ncTLNo+PS3vkNT/oiyFK/DP/6nP2G/35PlFh8c0QiaO8szznLZ/BS5tC9udLRdz8PDnqePHzH7wHK5lK0HUOdG4DdK7NGHYWK1WLIfFHFwlGVBFWRrtL3fU5QZf/DxjyB63t7c8O7mljF6VvVTtNHcv9vy+d1LqqLibLXmt7/3scw6CBzGLQ+vb7l/uBNku5tAg58cWYRpdjg/y8OPqB2D+oavCGOMtO2AinKrFYVkwUU4nbaZs5hgCX3k1d0NzJE8hlPsU5jjCU6hzVHie0yV1TSLJt3QHltZrq4uKTPD7CN9mFnkJYNzPLQ7/vhnP+X13TvaqecHv/cJjx6f89G3njLf5xgyytKKbZRIXdUYY7m7v2OahOH3/NlTjDb88Z/8hPVqTV5kSbOeYbTi0MoeV0rpwG635d3dnQyWnCPPZei5WtUUuWZ2A1qJCMoYGPsJNzvOzs4IwYC2XFxcpSom8MWLl9ze3eO8Zr3ZENHYvJH0HEKCfhaE5GpcLteMfYexFh8jn/3qC7TWbPd71iupcOqqTGYuTqItbTT9OHC3vSfLoaqFDGWMZkxGnKO3QAGlSixENVOUhtFpjBGdyKqu2O1EfblaNhAVdV3hnOgp7u93GKtoqoLNmTg3nz69wo0Ts3Ps91vqsuRsfUZhnzKME904yrA3Ici2DztaM+JT7sQRLGMmibOvqzL5DsTynNuMLMWM3x0GllWFiQJbWS6X5MZQLwu2bUtT1/jEPpyDY50txQugUsSdMTRVhVaKZ08fkVmDUpFpnNgsznj9+oamWTC0E/v9lrevX/HBs6c8efSIs9Wau3f3zONESFbq3W6b2q/kcUif7dm75F+ZiVpmA4GYfCu//vWNOQTqqkpmDWHWF0WW+prI7CLtbo8Jhqmb+dVXXxI9rI2hsYbCaLpDB1omxuMkDr6yKNFaDoPgPcHIxHixXElw6DzjkWz7233Ly7fXfP76FW/6HX/lR9/nw0+e89A/MIeJ6+6eq2xDnVuGaYs1iqZeEL0lRmEaFlnk7u6WbmiJcaYsLSGOTC7StiPaWvLMcvflW9zs0mBxYNFU/JXf+YTloubi/Ex6UOdQwz0P2zeE4Nlu79FxZhh6hqFHacs0w9WjR/TDTNGcUSzWZFHjX75mnCYGN2DaPefnF0RmnGtZrdYMo0cxk+eGplnhxhGbWW7ebXl7/YA2moftjt/5/vdYLBqqshQPRBoxO+coqoLr+1tevX7Drt2S5YH1umS9XGIzy93dw8n+vFwsePXmDZuzxUloZYzh6dPNaR36/OkTfvqLP6UqS64uLiRZuhLidNtJotPtu3sBsVYFVivWjUEvmzRLaLi7e+Dm5i2PHz3m0dUVeV5yv91SlhWgKMuKqip4e32dZk4SW2a0oqnLEzuh63oOhwNxjhwOYo+uqxI/QpMXLFYLlNI0TcUcAnWe0dQFi8UZeZFJ4KnWTNOEc2IXP+xb3DxxeXZGhkYrKLOSOAVM1NS6ZniYePv2hvWy4Xe/8ymPLi9QwLg/oH0g05rtbks/DMKpxJMXlqwwDEPPHIOQt3REGfncG2sorCUzFfDrTUTfiENAKU2W5RAiMUjy6xHXPQwjxhryMkd7jcXyw9/9bZqyZrx94Oar10KRNQq8fEiPDIE8z04GSmuE+DDPTtxlydNeljU+Rj7/6jVfvnnFfbvjb/07/yZnT854ODxw8WjDNI9s1kvsPRgd6doDBENTGcZhwpqCoe8hKR7LMiNGxzxPtN2e7KAxRgw543ykD0eurs5QKtI0pXgcdo71qqYuc6y23NxsMQb6vmMaO4ZRIs+U0qgYqPMS5zw2KwlAN4roqR1Gxnlit9/x8LAlAh88+0CIM2MnYNDZsV6vUErx8LBldCM3t++43+6JIfLk8SMWiwVFURIjlFVNSPJfpSQl6OWb12z3W6ISVHdmRTnZHjraruPq8gLvPYfD4eT1B0G1SyzYLBx+awlBsWyW5FlB305AZLdrxS+gLdvDjt1uR11V+HlCG3BO6EZ+Drx5e0NRlDx/9pRxdOwPB64ua843G8qipO16mjLn0LU0TZUwZRL2maU8iqYu2W73HPKcGCLb7Y5Fs+D6+g7LIBsKNA8PW842Z9zfbzGZ5ebmFm0FaFrVJQERfmlj6buBxaJBYSXjYhlTCM1MR8vD/QP391tu3r7B+8DlZsHFZs35siC6mWFyDJNsa4ZpZLffM83S7++7PTYT/0bAC+DGyJAVDxaTiEzmBMT5da9vxCFgjKjUvBPAYtM0jE5CM0Pa+R7/sipLu/CsIBsbrpGgsKOdd56EH4+KCROlqCtJKKpSGEWe29OEvh8mbt+9E5VbnPnOp59w8WjFvt8RVKDtW4wV77hxlgxDCIZx6pmmgFULWncQySsQyxpcAAAcsUlEQVQxRY6N1HXOtz58yus31wxjy+w9oxPVWUTRNBXLZU1V5WJzrnKqKkczU2QFu12H1pFhHCjLgsNONgfeWnwEbSTOfLlYMrjIoe2pY4bNJHJbYKee7f7A8Lk4DFerFYta9Oab1QprBMoaCVzf3DIM8nst6oYnj64kLj3hw402ZFYkvhLqAQ/bB+mlY6DIKrTKCF4YCjFoyqKGGBnGCa0cCotSliIvmZ1Ue2VZiSRZWWIU5n6777CJONR2berJZ6bRUeYFRmVYHSlyy27fstsf8N5zeXHOcrng9t0LoQ1X4qgbC5nFzNPEm9evBZtmNH5RcXt7zdPHjzg/29AeDmgtPIdpmmi7gcViwaff+TZT37LdH8gyyzLlKkalGWfH9z79Dt04UB05iiHSDwOLZk1mGoK3PH38nPZwEOelUvJwdwfads9hd0ddG6qiZlHX5JkScM080/W96FQUaXslZqyoNVmRJ37FjDERN/bMyp/ckzYNR2c3k6UW59e9vhGHQAiB9rCnLuVD46aRtu3wcWa1akCDGx2Dm9BBp6n6gLIBZST48eHuQejDxjL1A5kVNmFT10kGPOMmR24znOtYryvu3+34B//kj/E+8Lf+rb/Jo2eXDAy82L7k/PEGUxjutlv6fkRnOdZUGGOpm5wiKxj6ma+++oxpmnjx1Su53SbBn8+zOOpmP2BN5NC1WC024CdPnqBPDjxJSKqrnDIzeDeyve/pupZD17Pd7XDzxNgPSTMQMMZSVTUfffABzWKFC4rBabp+j29hfbamGzv6ruX3f/R9FlXNm+sb7t69Zez2XF5c4CbDy9f3tKnf1lbESRbDdz/5mLPNhtyKNgArhpq6LljUFV9+9YJ/8kd/xP6wk2QnazG6SP8YcQcGwzTKarapS4iWuixlfTmL+SfLFM4JKt25gELUolpZjLbSws2yRZinkbPVgrOzFWWZszvs+eLVNZvNhuXmHGU7htnz5eu3WJtxfnZGWUgIK/PAcIipPJ94+viczXpF01RstzvGaWS72yVO4hKVWZ4+f4aLirJZcH5+RpUbDvsWHwJ/+sVXxH7i448+YrVaCpy0lIvm8kIQbG07slmssauauioYhxYbFT//yc958/Y1wU88f3ZJbjRXl0v2uz25NSwbMThlOufNzQ3tMDAHqRwnPzHHBKxJWzIxVR4pSyLoym1GnmU0ZUVTVyxqOVzgZ7/2+ftGHALySYvE5J8vc2ELBCJZaTgMcisqq1AhSs8DCXklWXFZnqEiJ0z3cR13pNRUpjrt6DWBYRr5xZ9+hlaap8/EPHNoW8YoMVDRzzy82xGJLMoKq3PCAEGD1UoIRcFhrKeymouLhvaw59BuOdtskg9eTEPLpqEqJasQJUEaxzVWVliU4EmRQNTA2Ld0hwODm/FolLIYmzNNI1oZirxk0TScbZYoneGnmUVTUlaRYXA01RnaRKZxZOh6vvOtDwjB4SbHarng8vyCd/dbysTxe/X6DVdXV0RlKPIKqwXmYQrDNDoUivPzNTFG7u4e+H/++E/YPWzRBoITRaLOjAxkEwEpzy0m4eGkN9Xy36fYr5j+zgIgBRl6yj8ixhqGAYVnvar5wfOP8X7m7u4dn33xlQxDry5RxnA4dORVTbNcM/UjMXqKsiRGT1MXGKXp2wMQmIYRYjyBUc7Pz6S/jrJLX29WtAnR9ezD5wKHmRxnmyWTD1xeXDIFeHt9yzg57h62BGC9XmGNpmslKyHLMrquozAzhV3w7t1b9vstX371BctlxbPHH8nBcjjgnGe9XDM5R9s5lILWbemm8bQiHecJpQx4T1Ak8MoMUZD3WiflqLipyI20Oou6Ic8s2+3uNz5+34hDQKXb0DkHAbwyZNqgc0PnWtp2T5lLz2mVpbCW9iDDsWMbEDJBgEUv66mQNPlFHtK0N6QIMck3/OnPfkHbtazrM548uiJ4T1FIMORebVFEtIaLswuMsuweOoZuQuWGIjdoJSuYohCBypPHK16HjixfJTiHgZiJ5LQ09J1lTMYgn0I6iqLAjSJfBvE9WA29c4zDSNuPYr9NQFPjfUrQLSmLgrIsQVlCBJVbapuT5xPTPLHySw4Kvnrxgs9+Zfnw6WOslhXsMPVM08DZ5kIGpYua+4cHjC3QK8vDwxaiYNRCiBAUr19fs9/vub65ZvuwpSzFoCVgj5giwMKJL1iWEg1/XPlaq095DLMXWlJEEoNRiq4/4OZRYsRLiYm7vHxEUxYoHfnq1YsU092hjAaTM0wz1ijyokIrzTg6Nmdr2t0eiATv6doWTaTKLD5AmGdBliNOw4fdA1eXl4zzTBxGyayoKmYfmLynKipCDGwPHXXd8ObmlqurKwKKV6+vefL4EWWRk9vspE8pipzKGB7u71B+5u6d4sXLF3Rdy3c/ec7VxRmHg8jghSytcaNHxYxhdLhpYnQHTGbIlMZ5z/7QJ5KxbKUigTnMBD/L+lYLWKXIClbNkmdPHrOsa9zk6J2AY3/T6xtxCMQQ6KcDRmfYrMDZBNKKnuChtg3KRbQzaJ2xvRtxYyC0M3HOiC6n0NIDoz37cScx2lozZzm2tEQv/EKjLWFyfP+Tb3O52LBZnpNnBdbJgHKaHPvXI9s3I0VdcP/yNV3bixFksaDIS7xX9KNj33ZUldBlvB+Zw4UQcl1Ea8t+n0JETUHWLLEhkhcFbScW4mmYALCZYbNYMI0D+76j6w887O+x88i+7QRhbUqCMoRgeXz5EavNBTG/YNms0cPEbn9A6YwYFGa2nDcNNhZ8+HzmT3/5p1xdnlPkAh4p8oqzq4airJi15flHHxBVwCgj1lnteThseXnzji9efMWf/PRnDGNL0xQ8uTrnyaMzQrp5tLVEFLlWiZ0oB1UMMiRVCmLwZLkl/n/tnUuMZFt6lb9/7/OOV74qq7Kq6766W4AlS9BYyBLIQ8A9aZh5hAdITGwJBgwaeeIpSDCwZCGBsGQQwhNAeIIEQkiMMBjobre5dPd9+dY7qyozMiLO++y9Gfwn85aubl3oq66KlCqWFMrIqFDFyn0iduy9/3+tRUcwDrEdeRZxcpgjwY8R4h1kjqp8yunzC5q2AwxxmqobE1AUBRvXM5vMiK3mB8TjNqYfHAcH+3RdR1pkEBmOjg8ZjNeJI4qIAvypvT18qLn/eMm775ywKk+Z7yek+QxPghsMddljxDJLpnzw0UfcOblFWkxwg1cV6PKCuqp56/YdsjRnUkxJkpiqriniDD90VNWaR48/YVOe07Udf/7nfx4JEBpHd1GTBUsS5bRNSzc0lF2FGOGi3GguItEo6nJa0gw9fgj0vkOM6hbwutc3sXZNHuzv8fXjOxhriZKYxvesXUvVtfTmmvcJAHgXsFaIbTSGQ0I8OtJaY8Cp/13TbnCtsLfY4/njp2ogaQ1d0zD0PcEPJFE0BpyasSVZtwy2V+FOZC0S6+Fc27bUdasa7NhQu5bz1ZLW9xTTgsViznQyIYo1VDKOEqqqVHHRVailCmM2VYUVQ5Zm1LUagyzmc7IiV+PTrieJE9a+wjtPmqZ0TXP1De28uvfWdTM6JLsxq8/Sj7LfyWTCya1bIBF9r5bb6mjMVZ06TbVHv27XiMDhwR6TYnJl5NH3gusHNpsKH4LqG1DvxDROSZKC9aZm6BvKcoN3anyRxQY/DKSxBnys1xsIntjGREbwgp51GCFPExivqbERSZYwuJ6madhULUM08DSAG/rRag0GFxBjOb5xE2MtXa/j4ZxnU1XUTYcbLdsvbd6NNVehGN1o0LqY6T4chH5weBdIJyl5loIT4lQYvOPew1OOjo5HI1dLMUkpS31+NwwqBx8P1fveaZpU21FV9ZiFoCVQBMqqom0bmrakqjdU9ZqL1QVpEvPON+7ifaCtWkI3EEatTNc29F135dbkej27sqNjchypNLyXQFlvcMHRD/3oyhRG4xTtkrxxdMjdO7dJQ0YUxzR9h8MwOENZ9iTT7KWfvWsxCej+RptgmrJnOp2P0WEGRFV5y9Mlx/s3uLV/zKcfP+H7/+d/YzvPe7fv8OTJU7pOm4ScV8lpYmKMqBHppcGlMXpy3g89Xdvx6b17RCYjTVJuf+2EbJJhsz3e/dN3OFtf4MWz2NtHAmzWNV07gLMMrVPRTrBsyoYQBGsDrg0U05yyaqmqlsVsX5fVQcB7FtMZznvSKGJVVkzymCiLWEwn9H1LWW5o65L1ZkNTq74hYDBRwiQtmM4WHB0ekyY5TTdwdn7B8mKDd4HZfDa21BryXMtvbdeyN5/zjXfvkuUxBKFtB6ykHMxnmCih6wcEddGNjCOSANIzyyznzxoYWmwYSAwcLaacHC2Y5yoRPpjktG1HEsXcvXObbjRpfX62pO96nj06vTJPzdKELE+ZTgoOp3u6EuosxqSje298lc0IlqbtdLkfp9gYmnZNHAxJOlHTFLEaB4fQDwN5XkAQ4jihaQasRAztmmkxJV3s01QVy2XFelPy1tu3sXHBZJ7T9I5NWzNb3MJ51auoU1FLVa+IkpSLdYnYGOdUmrzcrLS35OwZcRRz88Yxm7JkeXHBpw8+hjCADCRxxNH+Pn0zcL5ZavORUWVnVV1Q1ZX2+fcdnevJ8oS26+naBjeodkKsgBGiKGC0/oSxhjhJsFim0ykHe/s0VcsHH97nvdvfIPZCnEx59uQJvYM8OSJ489LP37WYBLQmuwYMkY0xkuB7FVaslxvqsuburdvEJFSrmscPTlkvS946PmY2K3hyqvv9ABhr8aPN2OV+9DLe+9Ivv65rsjhlsZiTxTkEtfGKIsFY0YOioaN1A4vgGQZ1/EnjHBx0vZZ5hmEgS9XVuG/V2CJNEpyDctPR9T1FVpBECTUtgibj5mlCn6XkScx8ssCYQLle4QeNQZfLPIVRCGSAJM0oiok6JMOVs09VlWNGvVw5GA1dPzbA5GTpnDS13Lt3n+lkynw6R0xMZCJNXYogjSL63uCHlrIpmU736Zoai8f3HYkRWgKWgBUo0oQkMhws9sZvxoYPPvwIMWa0IxesjdlbzEeBkx6UXRmKAmItsU3x6GFi13vcEMZ49566VX8/P2oCmq4jEMazCKchndRXISga6BI08JMwCrk0l0GwhCAaNpJkuCA4hH7wPHl+xmKxR9V0hGBYrRsmhY5vFEdMphMIgWfPz5jPJleeikIgS2Om0ykBx/OzZ6w3a2aTlOl0Dx86ZBQDhQBRpMnIVVmxWWvfRNfpdhARyqqidz1i9UMugSt3akGu4s6wcpX/WFUdWZZxcLAPC/jkT+5h4wRjY3oXaFuHjWJiO0arvwTXZhJIIo27DkG0U6utaOqGk4NDbsz28Y3n/GzJ08dnPH54ys2jY/IsveqRNuMb8FLcMYzGD1HQ4IjLNWMYW5OjKGI2neJ6TQVyfsCHiMhY+qHDe0fT1KzXG4beIUFXFd0w0PdO48tcII0zTNALU+QToijGGEccxRixWqfutEGpbVuGYWBvNqXIEuZFwcFixmq9Yr1aEoKjqRvapiV4T4++GWM0ZiqJU0QsbTfQtBooov348fh/O0QC06zAWkPTerIsJYkN9x88IolTvvne1ynyCGf0DZIlMZM85/nzp2zWZ5SbCxZVRxynCJ40NkQmcDAvONyfc3x0wMnxDc6X5zx+9JhyU1E1LZ13atkGFMVEuwuNvVqJBaNxXRhLMILzDjsm9YgIkbGIUe3HarOmqusr1+ckjYljA8YjJox+iipCSkRj3Qhaboxjw3w2Jc9SLka5sIakONpuYL6vXouGiOVqA0ErIhcXJcZEbMqaOE6uQkeTJKZtNTtS4+YaiiJlMZuMgacd9x6eUlUlRZFwtL+PjeBitcS7Xh2Isoynp890peMcVd1Q5DndmNI0uJ6rSkkwmvEQBv17RZvmnFfL/SiKSbME13uKLOdgb58syXCD586tEwbvIThcEKaTKYMPuOEypPWLcS0mgTiOmcQFauFnWV+U4IN+kyUZi8mEs805Tx6dcv/eYw73Drl14whrB+quJE4t61Wn5SkzZsEH0XDReBSitA0SvMaH25hgVDsvaDNS3/XEQ4RvPSaCaVFQNTVPnzyjyCf0TaUHlkFIohhG803dNwpmVChu1iXrdUPbdBA0esz5CdO8YLlakUYRrmuZFxmzPGG1PKMqS8r1ir7vaNoaYxj30ROGwXFwcMitWyd0XWC5qrlxY4801bjyyMZsVmqUAapHOMeSZxn7izld1/PsdMl6tWYx05yDqioxApMioazWPHzwCR98+GOmRcrtm8fcuX2Xs/M1z89Lbt085mB/n3W5QsRzevqMSZaS5xmrrlQ7syKnampMkWGMoeo9WZ5CMKPNt0fGykDnHVmSEBjTpbzu+ft+rBQQ6PsG7zqyNMX5jqZpiOOgrb1G/wY/OCaF1ua992olZiyL+eyzttrBqaQbGHpPEmfk2QQXjIaVmITZvCAEi0iMEJGlYETFaH3fslmvqJqKWTEZdRw1aWRYLp/z4P4DjBje/tpd7n79Lm3XslqdEUWGvUnB6mLFerlizQWbteYVeOcJaBMaNqDGhWNZXKBuS4ahJ0kCddvg0TOvYA1plGmjlhjVWORzhnZgs9yQ2ZRUEmwaUVY158slPghRrPF2Mlxzo1ECxCam7jratmExneuhi6hB5PJixacP7vN8eY4xQlmtefBYODgomM9yklTjoINT6agaYKrTbPABExsiY/HO0Tbt2MGm7ap947Q77TIH0HkGBjrfk8Sp1lyjhNaN8lIxJIVaVQ9cLsG1vNl3w1XgSBRZhIhE7GiqoSXOpm2ZFXNmkwmnp08R72i7mr5tcV4PjYi0YmKNJS8SJpMJTedwTkizjKpuaVoN1ZAQiKylbWtEPHmeQQjYy/CRakNV16rQG3r6vhubgByPHj/k2dkzHp8+JonVCSlOE/7XD9+nrjtWq5blas3Z+Tl1XTGfFdrqbCKMVU8/Bl3+ayqQJ44SWt/hhkCSWG0BEQ0PDVoquEoMTiLBO41z8z5o3FeAJI7QZjfP0Ksu3ns9OPPiSMcEIOfUDlxGA1MfNLPh6HBPY+fM6N3gHASNco+imK5TT4XNptTglFszTRjGsFqVTIqMqiwZhob1asV0WmBFt2wffvwBYgJWhKODBUcHhxzsz3n27DnBa1+Cc46h7Ri6jn60crv0VfBBRT7OeQbXMTjdAiKCEBgGbTfvnCcvCjyBth9Y7O1pgAsauV7kOYd7+4gXYrEMTc/QO6Lis783H+30CJ4svuYdg4JQbmo1txi7nTTWSthUJbPJhE25YbVZMbSeWwc3efedrzGfp/RdzTBeVDRdDGMNfvjsMFDQcAwx6qTT9w5SSJOMi3YDCSBoe+Wldr3v6LueSTHXIFOx2tQ0fsC05q0n+oyCEB/0xL9tSoZBl2FFXhDZSA95VAjIjcN9rATCaAixXq+omxrvB5JU8/KstYgVZrMZWVGw2dQ4Z7BWE5WcdwzNwGq1YjIpdMnq9DBKMiFJU54+fcyzs2ekkdbwq6rmYnnBYrGHjQwPHz3gydMnrMsVSWJ49OQJnz44Jc9mHB3dYDYLDP4Bp2dLVptWnZjEMoSgtmUmIs8jMMKm61S7EbRqIkAkVtN/JdIEIdHT/9hGDGEYE4n9lfcBgaueCSN6DXWVHBi6gcEOGgEfhzEfIFEL97ZjGI1jikInQVVpao5A2zW0XU+WZbRtSzA6WYoIcZQwdI6WFu+Fpmno2xwIxNaSxBZroK5Lnj57ynp9QZpY9hYzDvbm2LEXIzZqQz9YSGNLQC3vNYPSj3qHsRmsb2m6jraruez0C2I0Ei4S3dMHT9MNYAxxmvH2W3fp3MB6VTKbztibzMkSNdnxvSP4nrpuyW7mtElPGnWY8cvp0ur+ZbgWkwBAnmT6BokTfJDPcu6ShCenT/jwTz7h5PgW7731DtN0yjSfYI2nYmAyV695dVRxo2BCS0fWWpq2JYksWZbgg6HvPatNTZ6klFXNxcWaj+/dI5sm7B0tOHn7JovZjDhOIcQkWcqnnzyhyDIiGzGfaRkuSWK6rkMI1E3FdJKP5piO87M1WZzz/PlzZrMZN24cMCly7p7cYr1aU23WPHjwENAkJUYZdLWpmM0nWGtZHB5S5BOG3uE95HmBQxi6ga5TP8A7J7ep6pKQ5ySxITLC8dGh5tjVjVqlDx2z6ZTVas3Dx484W56zXm9wvuHgYM5iccTxzTkmnhKnewgJIVje/9En1J1av9skIs4y4jQHm2DiBJtAXTX0Tc/BZK4r29oRaoexHo/68rdtR5LE9F5LmKSJztYWDIZ4XLW4oFqPMBgNBA0WCTERERiLCQk4CE4IVstlPgQwGkqSJDFZpnFqwzBQlyUep0ayXUuSRnR9j7XqYXG0f4CIoapr1uuaqmq5cXCABE8SGZq6Y38xZblacnG+JIstf+ab77CYTzS3oGmIc8NiUrBeloAwWKHeqE1YcJ5uPE8oq/LKnbkfMwC0M1JNddI8xgq0XUNwHrExN27sY6OINE+5eeuER6enLObaGUsItFVD3Q3EYunqFtd0fPST+2oFN1ieP1/Stu1Yeu5e+tmTz0IetwcReQqUwLNtc3kBR+z4fBmuGx+4fpyuG5+3Qwg3Pv/gtZgEAETkD0MIv7BtHpfY8flyXDc+cP04XTc+L8PL6wY77LDDG4HdJLDDDm84rtMk8E+2TeBz2PH5clw3PnD9OF03Pl+Ia3MmsMMOO2wH12klsMMOO2wBW58EROSvisiPROQDEfnuljh8IiJ/JCLfE5E/HB87EJH/KCI/GX/uv2IOvyMipyLywxce+0IOovitccx+ICLfek18flNEHozj9D0R+fYL//b3Rj4/EpG/8gr43BWR/ywi74vIH4vI3x4f38oYfQmfrY3RV0Z4QW33um+ABT4E3kP79r4P/NwWeHwCHH3usX8AfHe8/13g779iDr8EfAv44f+LA/Bt4N+j/bq/CPzBa+Lzm8Df/YLn/tx47VLg3fGa2p8xnxPgW+P9GfDj8XW3MkZfwmdrY/RVb9teCfwF4IMQwkchhA74PeA7W+Z0ie8Avzve/13gr73KFwsh/Bfg7P+Tw3eAfx4U/xXYE5GT18DnZfgO8HshhDaE8DHwAXptf5Z8HoUQ/ud4fw28D9xhS2P0JXxehlc+Rl8V254E7gD3Xvj9Pl8+kK8KAfgPIvI/RORvjY/dDCE8Ar3gwPEWeL2MwzbH7dfH5fXvvLBFeq18ROQd4M8Bf8A1GKPP8YFrMEY/DbY9CXyR++E2yhV/MYTwLeCXgV8TkV/aAoefBtsat38MfB34s8Aj4B++bj4iMgX+NfB3Qggvt9B9TZy+gM/Wx+inxbYngfvA3Rd+/xrw8HWTCCE8HH+eAv8WXaY9uVw+jj9PXzevL+GwlXELITwJIbgQggf+KZ8tZ18LHxGJ0Q/cvwwh/Jvx4a2N0Rfx2fYYfRVsexL478A3ReRdEUmAXwF+/3USEJGJiMwu7wN/GfjhyONXx6f9KvDvXievES/j8PvA3xhPwH8RuLhcEr9KfG5P/dfRcbrk8ysikorIu8A3gf/2M35tAf4Z8H4I4R+98E9bGaOX8dnmGH1lbPtkEj3F/TF6WvobW3j999BT2+8Df3zJATgE/hPwk/HnwSvm8a/Q5WOPfmv8zZdxQJeWvz2O2R8Bv/Ca+PyL8fV+gL6pT154/m+MfH4E/PIr4POX0OXzD4Dvjbdvb2uMvoTP1sboq952HYM77PCGY9vbgR122GHL2E0CO+zwhmM3CeywwxuO3SSwww5vOHaTwA47vOHYTQI77PCGYzcJ7LDDG47dJLDDDm84/i+S3MzRjS1P+AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show manipulated image\n",
    "plt.imshow(img_tinted)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"scipy-matlab\">MATLAB files</a>\n",
    "The functions `scipy.io.loadmat` and `scipy.io.savemat` allow you to read and write MATLAB files. You can read about them [in the documentation](http://docs.scipy.org/doc/scipy/reference/io.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"scipy-distance\">Distance between points</a>\n",
    "SciPy defines some useful functions for computing distances between sets of points.\n",
    "\n",
    "The function `scipy.spatial.distance.pdist` computes the distance between all pairs of points in a given set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1]\n",
      " [1 0]\n",
      " [2 0]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "\n",
    "# Create the following array where each row is a point in 2D space:\n",
    "# [[0 1]\n",
    "#  [1 0]\n",
    "#  [2 0]]\n",
    "x = np.array([[0, 1], [1, 0], [2, 0]])\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.         1.41421356 2.23606798]\n",
      " [1.41421356 0.         1.        ]\n",
      " [2.23606798 1.         0.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Compute the Euclidean distance between all rows of x.\n",
    "# d[i, j] is the Euclidean distance between x[i, :] and x[j, :],\n",
    "# and d is the following array:\n",
    "# [[ 0.          1.41421356  2.23606798]\n",
    "#  [ 1.41421356  0.          1.        ]\n",
    "#  [ 2.23606798  1.          0.        ]]\n",
    "d = squareform(pdist(x, 'euclidean'))\n",
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read all the details about this function [in the documentation](http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.pdist.html).\n",
    "\n",
    "A similar function (`scipy.spatial.distance.cdist`) computes the distance between all pairs across two sets of points; you can read about it [in the documentation](http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id=\"mpl\">Matplotlib</a>\n",
    "[Matplotlib](http://matplotlib.org/) is a plotting library. In this section give a brief introduction to the `matplotlib.pyplot` module, which provides a plotting system similar to that of MATLAB."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"mpl-plot\">Plotting</a>\n",
    "The most important function in matplotlib is `plot`, which allows you to plot 2D data. Here is a simple example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute the x and y coordinates for points on a sine curve\n",
    "x = np.arange(0, 3 * np.pi, 0.1)\n",
    "y = np.sin(x)\n",
    "\n",
    "# Plot the points using matplotlib\n",
    "plt.plot(x, y)\n",
    "plt.show()  # You must call plt.show() to make graphics appear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With just a little bit of extra work we can easily plot multiple lines at once, and add a title, legend, and axis labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute the x and y coordinates for points on sine and cosine curves\n",
    "x = np.arange(0, 3 * np.pi, 0.1)\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "\n",
    "# Plot the points using matplotlib\n",
    "plt.plot(x, y_sin)\n",
    "plt.plot(x, y_cos)\n",
    "plt.xlabel('x axis label')\n",
    "plt.ylabel('y axis label')\n",
    "plt.title('Sine and Cosine')\n",
    "plt.legend(['Sine', 'Cosine'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read much more about the plot function [in the documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"mpl-subplot\">Subplots</a>\n",
    "You can plot different things in the same figure using the `subplot` function. Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jomnl42Z0HKsU5u/BayO6kXKwkFdX7TIkg00VeVVNHXcvTgJg5thYXBztd+XmhXI0OTBjTAxuTibuWrTVYveYEOfvi63ZLE3I5u7LOtC/o/Ve1tASDOraiol9w5i7PsuQPcxtqshfW7WLlIOFvD6iG22auxsdx+q19Hbl7VHR7Mk9wbNfpRodR5hRRu4JnlyeSs+2zeW0XDN5/JoIugb58NBnKU1+1pfNFPmPO48yZ30WE/qEMjCqldFxbEb/jgHcdWkHliZksyL5kNFxhBlUVNdy16Ik3JxNTB8dI4t+zMTF0cTMsbFoDfcsTqK6tunmy23iXzCnsJyHPk+hS2tvHpd33c1u6hXhxIc248nlqezLLzU6jrhAz6/cye6jJ3jrxu4E+rgaHcemhPi588oN3Ug+WMib3zfdfv9WX+S1dZqpS5Kprqnj3bGxdr2jYWNxNDkwbUwMJgfFPYuNe2deXLjvth/mk00HuL1/Oy7t1MLoODZpcLdWjOkZwvu/7OXXPXlN8phWX+Qzfkpn874CXhgWRVt/D6Pj2KwgXzdeG9GN7YeKDHtnXlyY7ONlPLpsG92DfXjwqk5Gx7FpzwzpTMeWnjywNKVJrixk1UW+OauA6WvSGR4TxPDYYKPj2LyruwQyoU8oc9dnsVb2Y7EqNbV1TF2STJ2GGWPkfPHG5uZsYsaYWE5UVPPA0sbfj8Vq/zULy6qYuiSJkObuPD8syug4duPxayKJCPTioaUp5J6oMDqOOEvT16STsP84L10fRYifnNHVFDoFevHMtZ1Zl57PnPWZjfpYVlnkWmseW7advJJKZoyJxdPF0ehIdsPVycSMMTGUVtXw4NIUi9j5Tfy9jZnHeHdtBiPjghkaLSudm9LYniEM7BLI66t3sz27qNEexyqLfPHmg6zacYRHro6ga7CP0XHsTnhLL54e0jQjDXFhCsuquP/TZEL9PHjuui5Gx7E7SileuaEr/p4u3LukfhO/xmCWIldKDVRK7VZKZSilHjPHfZ5J+tETPL9yBxeH+zOpX9vGfCjxN06ONF5btZtt2YVGxxGnobXm0WXbyC+pZProGDzkyNUQvu7OvD0qmn3HSnm2kTaiu+AiV0qZgJnAIKAzMEYp1SibnFRU13LP4iQ8nB1580bZX9xIJ0caAV4u3Ls4Sa73aYE+2XyA1TuO8vDVneTI1WC92/lx92Ud+Dwxm9WNsITfHCPynkCG1jpTa10FLAGGmuF+/+K1VbvZdeQEb4zsTgsvWchgtJMjjf0FZbLlrYVJP3qCF1bu5OJwf27r187oOAK4b0A4jw6M4OJwf7PftzmKPAg4eMrH2Q23/YFSarJSKkEplZCXd34nyV/TNZCHr+7EZRGykMFSnDrS+Colx+g4gv8/cnV3duTNkXLkaikcTQ5MubQ97s7mn+IyR5Gf7qfkL6cyaK1naa3jtdbxAQHnt9NafFhz7rqsw3l9r2g89w4IJybElye/2C6XiLMAr67a1XDk2o0W3nLkag/MUeTZQJtTPg4GZGhmR5xMDkwfHQPAfUuSqGnCzYLEH63dlcuHv+1jYt8wLo9oaXQc0UTMUeRbgHClVFullDMwGvjKDPcrrEib5u68eH0UWw8UMm2N8RejtUe5xRU89FkKEYFePDYowug4ogldcJFrrWuAu4HVQBqwVGst73zZoaHRQYyIC+bdtRls2HvM6Dh2pa5O88DSFEqrapgxJkY2j7MzZjmPXGv9rda6o9a6vdb6JXPcp7BO/76uC2F+Htz/aTLHS6uMjmM3Pvg1k/UZ+Tx7bRfC5WLjdscqV3YKy+Xh4siMMTEcK63kkWXb0FqW8De2pAPHefP73Qzu2orRPdr88zcImyNFLswuKsiHRwdG8MPOo3y0Yb/RcWxacUU19y5JoqW3Ky8P74pScqqhPZIiF41iUr+2XB7Rgpe+SSP1UONtFmTPtNY8/sV2cgormD4mGh83J6MjCYNIkYtGoZTijZHdaebhxD2yhL9RfLL5AN9sO8yDV3UkLrS50XGEgaTIRaNp7uHMtNEx7D9WytNfpsp8uRmlHS7m+a/rl+Df0b+90XGEwaTIRaPq3c6PeweEszzpEJ8lZBsdxyaUVdVw9ydb8XZz4u1R0bIEX0iRi8Z3z+XhXNTBj6dXpJJ2uNjoOFZNa82Ty1PJzC9l2qho/D1djI4kLIAUuWh0JgfFO6Ni8HFz4q5FW2W+/AIs3nyQ5UmHmDqgI307mH8XPWGdpMhFkwjwcmH6mBj2HSvl8S+2y3z5eUg9VMRzX+2gf8cA7rlcNo8T/0+KXDSZ3u38ePCqTnydksOC3/cZHceqFJVXM2VRIn6ezrwj8+LiT6TIRZOackl7rohswYvfpLFlX4HRcaxCXZ3mwaXJHC6s4N2xsTT3cDY6krAwUuSiSTk4KN68MZrgZm7cuWgrucUVRkeyeDN+yuDHtFyeGhxJXGgzo+MICyRFLpqcj5sT74+Po6Sihrs+2Uq17F9+RmvSjvL2j3sYHhvEhL5hRscRFkqKXBgiItCbV27oypZ9x3lh5U6j41ikrPxSpn6aTFSQNy9fL/uoiDMz/8XjhDhLQ6ODSD1UxOx1WXQK9OKmXqFGR7IYxRXVTP4oAUcHxfvj4mR/cfG3ZEQuDPXYoEgu7RTAsyt2yMUoGtTU1nHPJ0lk5Zfy3k1xBDdzNzqSsHBS5MJQJgfF9DExhPq5c+eiRA4ck4s3v/RtGr/syePFYVH0ae9ndBxhBaTIheG8XZ2YM6EHdRpuXbCForJqoyMZZtGm/Xz42z4m9WvL6J4hRscRVkKKXFiEtv4evD8ujgPHypi8MIHKmlqjIzW5tbtzeWbFDi7rFMAT10QaHUdYESlyYTH6tPfj9ZHd2JRVwEOfbaOuzn6W8accLOTOj7cSEejF9DExmGTlpjgHctaKsChDo4PIKazg1VW7aO3jyuN2MDLdl1/KrfO34O/lzIe39MDLVa70I86NFLmwOHdc0o6cwnI++DUTX3dnplxquxdOyD1Rwc3zNqOBBbf0pIWXq9GRhBWSIhcWRynFc9d1obiimldX7cLDxcTNfcKMjmV2x0oquWn2JvJLKll0Wy/aBXgaHUlYKSlyYZFMDvXX/CyrquWZFTtwd3ZkRFyw0bHMprCsinFzN3OgoIz5t/QkJkT2UBHnT97sFBbLyeTAjDEx9OvgzyOfp7A8yTYuFVdcUc3N8zazN7eE2TfHy7ni4oJJkQuL5upkYtbNcfRu58cDS1P4ZNMBoyNdkGMllYydvZG0w8X8d1ws/TsGGB1J2AApcmHx3J0dmTexB5d2DOCJ5duZuz7L6Ejn5XBROTd+sIH0oyV8MD6OAZEtjY4kbIQUubAKrk4mPhgfz6CoQF5YuZM3Vu+2qvPMs/JLGfHfDeQWV7JwUi8uj5ASF+YjRS6shrNj/Zz5qPg2vLs2g3uXJFFRbfkrQDfsPcbw936jvLqWxZN707Ntc6MjCRsjZ60Iq+JocuCVG7rSNsCDV77bxaHCcmbfHI+/p4vR0U5r0ab9PLtiB6F+7syd0IMwfw+jIwkbJCNyYXWUUtxxSXv+e1MsaYeLGTJ9PZsyLWsL3IrqWp7+MpUnl6fSL9yf5XddJCUuGo0UubBag7q24vM7+uLmbGLM7I3MWJNOrQXMm+8+coJhM39j4cb9TO7fjrkTeuAty+5FI5IiF1YtKsiHr+/px7XdW/PmD3u4ac5GMvNKDMlSV6eZ/1sW1767nvySSj6c2IMnromUDbBEo1NaN/0IJj4+XickJDT54wrbpbXms8RsXli5k8rqOqZc2p4pl7ZvskukJR8s5NmvdpBysJDLOgXw2ojuBHhZ5ry9sF5KqUStdfyfb5c3O4VNUEpxY3wbLu0UwIsr05i2Jp0VyYeYekVHhnRrhaOpcQ4+DxeV89b3e/gsMZsALxfeHNmd4bFBcqFk0aRkRC5s0rr0PF5cmcbuoydo6+/BnZe257ro1rg4mmeEvutIMbN+zeSr5ByUglsvass9A8LxdJGxkWg8ZxqRX1CRK6VGAs8BkUBPrfVZtbMUuWgKdXWa73ceZfqadHYeLsbb1ZHB3VozPDaIuJBmOJzj3PXR4gpWpR7h2+2H2ZRVgJuTiVE92jCpX1vaNJcLJIvG11hFHgnUAR8AD0mRC0uktWZdej7Lkw6xKvUI5dW1+Lg5ERPiS1xIMyJbeePv5YKfhzPerk6UVddQWllDYVk1u46cYOfhYlIPFbEtuwiA8BaeDI1uzU29Qmnm4WzwsxP2pFHmyLXWaQ13fiF3I0SjUkrRv2MA/TsG8OKwGn5MO8qGvcdI3H+cn3fn/eP3e7s60rm1Nw9c2ZFBUYGEt/RqgtRCnL0mm9BTSk0GJgOEhMjVwYUxPFwcGRodxNDoIACKyqrJOlZKQWkl+SVVnKiowd3ZhIeLI16ujnQI8CS4mZsMVoRF+8ciV0r9CASe5lNPaq1XnO0Daa1nAbOgfmrlrBMK0Yh83J2Idvc1OoYQF+Qfi1xrfUVTBBFCCHF+ZGWnEEJYuQs9a+V6YAYQABQCyVrrq8/i+/KA/ef5sP5A/nl+r62Q10BeA3t//mCfr0Go1vovl5UyZEHQhVBKJZzu9Bt7Iq+BvAb2/vxBXoNTydSKEEJYOSlyIYSwctZY5LOMDmAB5DWQ18Denz/Ia/A/VjdHLkRTUUrtAO7SWv9sdBYh/o41jsiFOC2l1FilVIJSqkQpdVgp9Z1Sqt/53p/WuouUuLAGUuTCJiilHgDeAV4GWgIhwHvAUCNzCdEUrKrIlVIDlVK7lVIZSqnHjM7TlJRSbZRSa5VSaUqpHUqp+4zOZBSllEkplaSUWtnwsQ/wPPXTIF9orUu11tVa66+11g8rpVyUUu8opXIa/ryjlHJp+F5/pdRKpVShUqpAKbVOKeXQ8Ll9SqkrGv7+nFJqqVLqI6XUiYZ/g/hTMrVWSi1TSuUppbKUUvc24vP3VUp9rpTa1fDz0KexHstSKaXub/g3SFVKLVZKuRqdyUhWU+RKKRMwExgEdAbGKKU6G5uqSdUAD2qtI4HewF129vxPdR+QdsrHfQBXYPkZvv5J6l+zaKA70BN4quFzDwLZ1C9qawk8AZzpjaPrgCWAL/AV8C5AQ/F/DaQAQcAAYKpS6h8Xx52nacAqrXUE9c8n7R++3qYopYKAe4F4rXUUYAJGG5vKWFZT5NT/8mVorTO11lXU/0LZzWGz1vqw1nprw99PUP/LG2RsqqanlAoGBgNzTrnZD8jXWtec4dtuAp7XWudqrfOAfwPjGz5XDbSifsVctdZ6nT7zGQDrtdbfaq1rgYXUlyhADyBAa/281rpKa50JzKYRykUp5Q30B+YCNDxeobkfxwo4Am5KKUfAHcgxOI+hrKnIg4CDp3ycjR0WGYBSKgyIATYZm8QQ7wCPUH9Bk5OOAf4Nv9Sn05o/bgmxv+E2gNeBDOB7pVTmP0zZHTnl72WAa8NjhgKtG6ZnCpVShdSP7Fue7ZM6B+2APODDhumlOUopj0Z4HIultT4EvAEcAA4DRVrr741NZSxrKvLTbQhtd+dOKqU8gWXAVK11sdF5mpJSagiQq7VO/NOnNgAVwLAzfGsO9WV7UkjDbWitT2itH9RatwOuBR5QSg04x2gHgSytte8pf7y01tec4/2cDUcgFviv1joGKAXs7f2iZtQfjbel/j9kD6XUOGNTGcuaijwbaHPKx8HY2eGUUsqJ+hJfpLX+wug8BrgIuE4ptY/6qbXLlVIfa62LgGeAmUqpYUopd6WUk1JqkFLqNWAx8JRSKkAp5d/wtR9D/X8OSqkOqv7KEcVAbcOfc7EZKFZKPaqUcmt4MzZKKdXDLM/6j7KBbK31yaOxz6kvdntyBfX/ceZprauBL4C+BmcylDUV+RYgXCnVVinlTP3841cGZ2oyDUUzF0jTWr9ldB4jaK0f11oHa63DqP/3/0lrPa7hc28BD1D/JmYe9aPku4EvgReBBGAbsB3Y2nAbQDjwI1BC/cj+vXM9d7xhzvxa6t9MzaJ+R745gM95PtW/e6wjwEGlVKeGmwYAO839OBbuANC74T9sRf1rYFdv+P6ZVa3sVEpdQ/0cqQmYp7V+yeBITaZhYcs66ovo5PzwE1rrb41LZXMA9c8AABw3SURBVByl1KXUX/B7iNFZmppSKpr6/yicgUzgFq31cWNTNS2l1L+BUdSfzZUE3Ka1rjQ2lXGsqsiFEEL8lTVNrQghhDgNKXIhhLByUuRCCGHlzrSAolH5+/vrsLAwIx5aCCGsVmJiYv7prtlpliJXSs0DTi7WiPqnrw8LCyMhIcEcDy2EEHZDKXXai9aba2plPjDQTPclhBDiHJhlRK61/rVh/49GlXa4mLwTlfi6O+Hj5kRzD2e8XJ0a+2GFsBrHS6vIK6mktLKG8qr6Bar+Xi4EeLrg4+aEg8PpdroQ1q7J5siVUpOByQAhISHndR8fb9zPok0H/nBbSHN3ugX7EN3Gl8siWtA+wPOCswphDbTWbD9UxI87j7LtUBFph4s5WnzmNTEujg50D/YlLqwZPcKa0be9P65OpiZMLBqL2RYENYzIV57NHHl8fLw+nznynMJycgrLKSyrpqi8miPFFWzPLmJbdiE5RRUAdGntzbXdW3N9TBAtve16r3lhozJyT7Bo0wFWpx4hp6gCk4MivIUnka28iWzlRSsfNzxdHHF3NlGnIb+kkvySSg4UlLF1/3F25BRTU6fxcnHkmq6tuD42iJ5hzWW0bgWUUola6/i/3G5NRf53cgrL+S71CF+n5JB8sBBnkwM3xAVxe//2hPnb1S6fwgZprdmUVcDsXzNZsysXZ0cH+ocHMDAqkAERLWjm4XzW91VeVcuWfQWsSM7hu9TDlFXV0rGlJ/cOCOeaqFZS6BbM5ov8VPvyS5mzPpOlCdnU1NYxLDqIxwZF0EJG6MIKpR0u5vmvd7Ih8xjNPZy5uU8o43uH4ufpcsH3XVZVw3fbj/DfX/aSkVtCeAtPHriyIwOjAqnfj0pYkkYtcqXUYuBSwB84CjyrtZ57pq9v7CI/Kbe4gjnrs5j/+z6cTQ7cf2VHJvQJxdEk66CE5TteWsVbP+xh0ab9eLs5MXVAOKN7hjTKvHZtnebb7YeZviad9NwSLukYwIvDomjT3N3sjyXOX6OPyM9FUxX5SfvyS3n2qx38siePiEAv3hkdTUSgd5M9vhDnau2uXB7+PIXjZdWM6xXC/Vd2xNf97KdPzldtneajDft4Y/VuarXmvgEdmdy/HSaZbrEIdl3kUD/HuHrHUZ5ekUpReTVPDY5kfO9QOXwUFqW8qpaXv01j4cb9RAR68faoaCJbNf2g43BROc+u2MH3O4/Su11zpo2OkZMHLIDdF/lJ+SWVPPRZCj/vzuOKyJa8MbJbk4x0hPgnmXklTF6YSEZuCbf1a8vDAzvh4mjc6YFaaz5PzOaZFTtwdzbx9qho+nf8y+pw0YTOVOR2N1ns7+nCvAk9eHpIZ37Zk8v17/1OZl6J0bGEnftlTx5DZ/5GQWkVH0/qxVNDOhta4gBKKUbGt+Gruy/Cz9OZCR9uZubaDOQaBpbH7oocwMFBMalfWz75V2+Kyqu5/r3f+T0j3+hYwg5prZmzLpNbPtxMkK8bK+66iH7h/kbH+oPwll6suKsf13Zrzeurd/Posm1U19b98zeKJmOXRX5Sj7DmrLjrIlp6u3DzvM0s3XLQ6EjCjtTVaf799U5e/CaNqzoHsmxKX4s9S8TN2cS00dHce3kHliZkM/HDzRSVVxsdSzSw6yIHaNPcnWVT+tKnvR+PLNvG3PVZRkcSdqC6to4HP0th/u/7mNSvLe/dFIuHiyG7Sp81pRQPXNWJN0Z2Z3NWAaNnbeRYid1eJtOi2H2RA3i5OjFnQjyDogJ5YeVOpq9Jl3lA0WgqqmuZ8nEiy5MO8dBVHXlqcKRVraYcERfMnAk9yMwrYdSsjeQWVxgdye5JkTdwcTQxY0wMN8QG89YPe3jlu11S5sLsKqpr+ddHCazZlcsLw6K4+/JwqzwF9pKOAcy/pSc5heXc+MEGDhWWGx3JrkmRn8LR5MDrI7oxvncoH/yayVs/7DE6krAhVTV13LloK+vS83n1hvqfM2vWp70fCyf14lhJFaNnbeBIkYzMjSJF/icODop/X9eFUfFtmPFTBjPXZhgdSdiA6to67v5kKz/tyuXl67tyY3wboyOZRVxoMxbe1ouCkirGzd0kc+YGkSI/DQcHxcvDuzI0uv50q3nyBqi4AHV1mgeXpvD9zqP8+7oujO11fvvxW6roNr7MndiDgwVl3DxvM8UVcjZLU5MiPwOTg+LNkd0Z2CWQ51fuZEXyIaMjCSukteaFb3byVUoOjw6MYELfMKMjNYre7fx4f3wce46eYNL8LVRU1xodya5Ikf8NR5MD08ZE07tdcx76LIXfZNGQOEezfs3kw9/2cetFbbnjknZGx2lUl3VqwTujYkjYf5ypS5KprZOTBZqKFPk/cHE08cH4eNr5e3LHwkTSDhcbHUlYieVJ2fznu10M6daKpwZHWuXZKedqcLdWPD24M6t2HOGlb9KMjmM3pMjPgo+bEx/e0gMPF0cmfriZHDnVSvyDjZnHePizbfRp58ebN3a3qvPEL9St/dpy60VtmfdbliywayJS5Gepta8b82/tQWllLbctSKCsqsboSMJC7csv5Y6PEwn1c+f98XGGb35lhCcHRzKwSyAvfrOT73ccMTqOzZMiPwcRgd5MHxNN2pFiHlyaQp3MAYo/KSqvZtKCLQDMndADHzcngxMZw+SgeGd0NN2CfZn6aTK7jsiUZGOSIj9Hl0e05IlBkXyXeoR3fpQFQ+L/1TScK36goIz3x8XZ/UW/XZ1MzBofh6eLI7ctSJBzzBuRFPl5uO3itoyMC2b6Txl8nZJjdBxhIV75bhfr0vN5cVgUvdv5GR3HIrT0dmXWzfHknqhkyqKtVNXI9reNQYr8PCileOn6rsSHNuORz7fJYaNgRfIh5qzPYkKfUEb1sK0FPxcquo0vr93Qjc1ZBbywcqfRcWySFPl5cnZ04L2bYvFydeT2hYmyN7Md25lTzKPLttEzrDlPDelsdByLNCwmiMn927Fw436WJWYbHcfmSJFfgBbervx3XCw5heXc/2myvPlphwrLqrj94wR83ZyZeVMsTib5lTqTR67uRJ92fjyxfDs7coqMjmNT5KfuAsWFNueZIZ35aVcu09akGx1HNKG6Os3UT5M5WlTJf8fFEuDlYnQki+ZocmDG2BiauTtzx8eJFJZVGR3JZkiRm8G43qEMjw1i+k/p/Lonz+g4oom893MGP+/O4+lrOxMT0szoOFbB39OF98bFcqSoQo5izUiK3AyUUrw0rCsdW3gx9dNkDhfJyk9b93tGPm/9sIeh0a0ZZ2O7GTa22JBmPHNtF9buzuP9X/caHccmSJGbiZuziZk3xVJZXcs9nyTJVcZt2NHiCu5dkkS7AE9evr6rXeyhYm7jeoUwpFsr3li9m02Zx4yOY/WkyM2oQwtPXh7elYT9x3l99W6j44hGUFNbxz2LkyitrOW/VnDBZEullOI/w7sS6ufBPYuTyJfFQhdEitzMhkYHMa53CLN+zeSnXUeNjiPMbPqadDZnFfDS9VGEt/QyOo5V83J1YubYWIrKq2W+/AJJkTeCpwZ3pnMrbx5cmiLz5Tbkt4x8ZqzNYGRcMMNjg42OYxM6t/bm39d1YV16Pv/9RebLz5cUeSNwdTLx7tgYKmvquG9xMjUyX2718k5Uct+SZNoHePLvoV2MjmNTRvVow7XdW/PWD3tI2FdgdByrJEXeSNoFePLisCg27ytgupxfbtXq6jQPLE3mREU1746Nwd1Z5sXNSSnFy9dHEeTrxr2Lk+T88vMgRd6IhscGMyIumBlrM/h9r1wmzlrNWpfJuvR8nr22CxGB3kbHsUlerk68OzaGvJJKHvl8G1rLfPm5kCJvZM8P7UJbfw/u/zSZglIZaVibpAPHeWP1bgZ3bcWYnm2MjmPTugX78ujACL7feZSFG/cbHceqSJE3MndnR6aPjuF4aTWPfJ4iIw0rUlxRzb1Lkmjp7crLw+V88aZw60VtubRTAC9+kya7ip4DKfImEBXkw2ODIvgxLZePNshIwxporXlqeSo5hRVMHxNtt1f6aWoODoo3RnbH29WJexcnUV5Va3QkqyBF3kRuuSiMyyNa8NK3aaQdlpGGpVu29RBfpeRw34Bw4kKbGx3Hrvh7uvDWjd3Zc7SEF7+R/cvPhlmKXCk1UCm1WymVoZR6zBz3aWuUUrw+ohs+bk7cIyMNi5aVX8ozK1Lp1bY5d13Wweg4dql/xwBu79+ORZsOsCpVLt78Ty64yJVSJmAmMAjoDIxRSsnu+qfh1zDSyMiVkYalqqqp474lSTiZHHh7VDQmB5kXN8qDV3WiW7APj32xTRbW/QNzjMh7Ahla60ytdRWwBBhqhvu1SReHy0jDkr35w262ZRfx6g3daO3rZnQcu+bs6MC00TFU1dQxdUkytbKE/4zMUeRBwMFTPs5uuO0PlFKTlVIJSqmEvDz73rP7was60TVIRhqWZn16Ph/8ksnYXiEMjAo0Oo4A2vp78O/rurApq4D3ZQn/GZmjyE937PmX/zq11rO01vFa6/iAgAAzPKz1cnZ0YPqY+pHG/Z/KSMMSHCup5P6lyXRo4cnTg2Vm0JKMiAv+3xL+rQeOGx3HIpmjyLOBU1dKBAM5Zrhfm3ZypLExU0YaRtNa8/Dn2ygqr2bGmBjcnE1GRxKnUErx4rAoAr1duW9JEsUVcqHzPzNHkW8BwpVSbZVSzsBo4Csz3K/Nk5GGZVjw+z5+2pXL44MiiGwlS/AtkY+bE9PHRJNTWMFTy1NlYd2fXHCRa61rgLuB1UAasFRrveNC79ceKKV46fooWvm4cu9iGWkYIe1wMS9/t4vLI1owsW+Y0XHE34gLbc7UAeF8lZLDsq2HjI5jUcxyHrnW+lutdUetdXut9UvmuE974e3qxLTRMRwukpFGUyurquGexUn4uDnx+ohusgTfCtx5WQd6tW3OMytSycwrMTqOxZCVnRYgLrTZ/0YanyVmGx3Hbjz/9U725pXw9o3R+Hm6GB1HnAWTg+LtUdE4mRy4d0kSVTWy1z9IkVuMOy/rQO92zXl2xQ4ycmWk0di+TslhyZaD3HFJe/qF+xsdR5yD1r5uvDaiG6mHinlt1S6j41gEKXILYXJQTBtdf8bE3Z9spaJalvA3loMFZTzxxXZiQnx54MqORscR5+HqLoHc3CeUOeuz5Nq4SJFblJberrwxshu7jpzgP9+mGR3HJlXX1nHP4iRQMH10DE4m+RWwVk9cE0lkw7VxjxRVGB3HUPJTbGEuj2jJpH5tWbBhP6tSDxsdx+a8vno3yQcLeWV4N9o0dzc6jrgAf7g27pIku15YJ0VugR4dGEH3YB8e/nwbBwvKjI5jM37adZRZv2YyrncIg7u1MjqOMIP2AZ48PzSKTVkFTLPja+NKkVsgZ0cH3h0bC8Ddn2yVd+bNIKewnAeWptC5lTdPyRJ8mzIiLpgbYoOZ8VM669Ltcx8nKXIL1aa5O6+P6E5KdhH/+U7myy9EdW0d9y5Oorqmjpk3xeLqJEvwbc0Lw7rQIcCTqUuSOVpsf/PlUuQWbGBUILdcFMaHv+3ju+0yX36+Xlu1i4T9x3l5eFfa+nsYHUc0AndnR967KZayqlruWZxETa19HcVKkVu4xwdF0r2NLw9/vk1Wsp2HVamHmb0ui/G9Qxka/ZfdlYUNCW/pxUvXR7E5q4A3vt9jdJwmJUVu4ZwdHXjvplicTIopH2+lrKrG6EhWIzOvhIc+20b3Nr48NSTS6DiiCQyPDWZMzxDe/2WvXV24RYrcCgT5ujF9TAx7ck/w+BfbZT+Ws1BeVcudi7biZFK8d1MsLo4yL24vnruuM92DfXjosxS7OYqVIrcSF4cH8OCVHVmRnMOC3/cZHceiaa15dNk2dh89wTujYwiSS7bZFRdHE++Ni8PJpLjj40RKK23/KFaK3IrceWkHrohswYvfpLFh7zGj41isWb9m8lVKDg9d1YlLOtr31ajsVZCvGzPGxJKRW8Ijy7bZ/FGsFLkVcXBQvDUqmlA/d+76ZKssFjqNX/bk8eqqXVzTNZA7L21vdBxhoH7h/jwyMIJvth1m5toMo+M0KilyK+Pt6sTsm+Oprq1j8sJEefPzFPvyS7nnk610bOnF6yO6y/7igtv7t2NYdGve+H4P3++w3Tc/pcitULsAT2aMiWH3kWIe/mwbdXa8x8RJReXVTFqwBQcHxazx8Xi4OBodSVgApRSv3NCNbsE+3P9pMruPnDA6UqOQIrdSl3ZqwWODIvhm+2He/GG30XEMVV1bx52LEjlQUMb74+II8ZPNsMT/c3UyMWt8PO4ujkxasIW8E5VGRzI7KXIr9q+L2zGmZwgz1+5l6ZaDRscxhNaap79M5beMY/xneDd6t/MzOpKwQIE+rsy5OZ78kkpu+yiB8irb2u9fityKKaV4fmgXLg7354nl21mfnm90pCb3wa+ZLNlykLsv68CIuGCj4wgL1r2NL9NHx7Atu5B7bWzbWylyK+dkql/52aGFJ1M+TmRnTrHRkZrMF1uzeeW7XQzu1kqu9CPOylVdAnl2SGd+2HmUF1butJnTEqXIbYCXqxPzJvbA09WRm+dtZl9+qdGRGt1Pu47y8Ofb6Nvej7du7I6Dg5yhIs7OxIvaclu/tsz/fR/v/bzX6DhmIUVuI1r7urFwUk9q6+oYN3eTTV/6KnF/AXcu2kpkKy8+GB8ny+/FOXvimkiujwni9dW7bWKltBS5DenQwosFt/bkeGkVN8/bxPHSKqMjmV3qoSJunZ9AKx835t/SEy9XJ6MjCSvk4KB4fUQ3ruzckme/2sGyxGyjI10QKXIb0y3Yl9kT4tl3rIyb5myiwIbKPPVQETfN2YSniyMf3doTf08XoyMJK+ZocmDGmBgu6uDHI8u28c02693zX4rcBvVt78/sm+PZm1fC2NkbOVZi/efN7sgpYtzc+hJfMrm3XDhZmMXJc8xjQ3y5Z/FWlidZ58hcitxGXdIxgLkTepCVX8qY2RutehHE9uz6kbi7k4nF/5ISF+bl4eLI/Ft60qutHw8sTbHKNRlS5DasX7g/H07swYGCMm78YAP7j1nf2Sy/7slj1KwNeDg7smRyH1m1KRqFh4sj8yb2oF8Hfx5Zto35v2UZHemcSJHbuL4d/Fl0Wy+Ol1Ux/L3fSTlYaHSks/Zl0iFunb+FUD8Plt/ZV0pcNCo3ZxOzb47nys4tee7rnby4cqfV7GMkRW4H4kKbs2xKX9ycTYyetZE1aUeNjvS3tNbMXJvB1E+TiQ9rxqe396aFt6vRsYQdcHUy8f64OCb2DWPO+izuXLTVKpbzS5HbifYBnnxxZ186tPDkto8SmPZjukWONkoqa5jy8VZeX72b67q3Zv4tPfGWUwxFEzI5KJ67rgtPD+nM6p1HGDVrg8Xv/S9FbkdaeLny6e29GRYdxNs/7uHWBVsoLLOc0xP35pUwbOZv/JB2lKcGRzJtdDSuTrLYRxhjUr+2fDAujqy8UgZPX2fR+5lLkdsZd2dH3rqxOy8Oi+L3jGMMnr6e3zKM3Wyrrk6z4Pd9DJm+noLSKhZO6sltF7eTC0MIw13VJZCV9/YjxM+dyQsTeWHlTiqqLW+qRRmxaUx8fLxOSEho8scVf5R8sJD7P02uP0WxZxsevyayyacxDhaU8eiybfy+9xj9Owbw6g1daeUjF0sWlqWyppaXvknjow37aevvwcvXd6VP+6bfMlkplai1jv/L7VLk9q2iupa3f9jD7HWZtPBy5dFBnbiuexCmRt6EqrSyhlm/ZjJ7XSYKeGpIZ0b3aCOjcGHR1qfn88Ty7fWn9MYH8/DVEQR4Nd0KYyly8bdSDhbyxPLt7MgpJiLQi4eu6sSAyBZmL9bKmlqWJR7i7R/3kHeiksFdW/HYoAhZ5COsRnlVLdPWpDN7XSZOJsWEPmFM7t8OvybYMqJRilwpNRJ4DogEemqtz6qdpcgtU12d5pvth3nrhz1k5ZcSEejF2F4hDI0OwsftwqZcDheV88mmAyzefID8kiriQ5vxxOBIYkOamSm9EE0rK7+UGWvS+TL5EK5OJkbEBTMyrg1RQd6NdmTZWEUeCdQBHwAPSZHbhuraOpZvPcSCDfvYkVOMq5MDV3YO5OIO/vTt4Edws38ePWutycgt4efdeazdncumrALqtGZARAtu7hPGxeH+Mo0ibEJGbgkz12bwzfbDVNXUERHoxZBurejVzo9uwT5m3Wa5UadWlFI/I0Vuk7ZnF/HJ5gP8sPMo+Q2bbwX5uhHq506bZu608nXFpBR1Gmrr6jhUWEFWfgmZ+aUUllUD0LGlJ1dEtmRMzxCZQhE2q6ismq+35fBZYvb/VlA7OzrQpbU3rX3dCPR2paW3C4OiWp3374HhRa6UmgxMBggJCYnbv3//BT+uaDpaa/YcLeG3jHySDhZysKCM7OPl/yv3kwK9XWnr70HbAA+6tPbm0k4tCPKVs1CEfSkorSJhXwGbswpIzSniaHElR4oqKK+u5eNJvegX7n9e93veRa6U+hEIPM2nntRar2j4mp+REbldqq6tA8CkFEoh0yVCnIHWmpLKGpwdHc57uuVMRe54Fg9+xXk9orALTiZZUybE2VBKNdoVreS3UAghrNwFFblS6nqlVDbQB/hGKbXaPLGEEEKcLUMWBCml8oDzfbfTHzB2cxDjyWsgr4G9P3+wz9cgVGsd8OcbDSnyC6GUSjjdZL89kddAXgN7f/4gr8GpZI5cCCGsnBS5EEJYOWss8llGB7AA8hrIa2Dvzx/kNfgfq5sjF0II8UfWOCIXQghxCilyIYSwclZV5EqpgUqp3UqpDKXUY0bnaUpKqTZKqbVKqTSl1A6l1H1GZzKKUsqklEpSSq00OosRlFK+SqnPlVK7Gn4e+hidqakppe5v+D1IVUotVkq5Gp3JSFZT5EopEzATGAR0BsYopTobm6pJ1QAPaq0jgd7AXXb2/E91H5BmdAgDTQNWaa0jgO7Y2WuhlAoC7gXitdZRgAkYbWwqY1lNkQM9gQytdabWugpYAgw1OFOT0Vof1lpvbfj7Cep/eYOMTdX0lFLBwGBgjtFZjKCU8gb6A3MBtNZVWutCY1MZwhFwU0o5Au5AjsF5DGVNRR4EHDzl42zssMgAlFJhQAywydgkhngHeIT6K1PZo3ZAHvBhw/TSHKWUh9GhmpLW+hDwBnAAOAwUaa2/NzaVsaypyE+30bXdnTuplPIElgFTtdbFRudpSkqpIUCu1jrR6CwGcgRigf9qrWOAUsDe3i9qRv3ReFugNeChlBpnbCpjWVORZwNtTvk4GDs7nFJKOVFf4ou01l8YnccAFwHXKaX2UT+1drlS6mNjIzW5bCBba33yaOxz6ovdnlwBZGmt87TW1cAXQF+DMxnKmop8CxCulGqrlHKm/s2NrwzO1GRU/aV35gJpWuu3jM5jBK3141rrYK11GPX//j9pre1qJKa1PgIcVEp1arhpALDTwEhGOAD0Vkq5N/xeDMDO3vD9s3+8QpCl0FrXKKXuBlZT/y71PK31DoNjNaWLgPHAdqVUcsNtT2itvzUwkzDGPcCihgFNJnCLwXmalNZ6k1Lqc2Ar9WdzJWHny/Vlib4QQlg5a5paEUIIcRpS5EIIYeWkyIUQwspJkQshhJWTIhdCCCsnRS6EEFZOilwIIazc/wHu8fP7vucweAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute the x and y coordinates for points on sine and cosine curves\n",
    "x = np.arange(0, 3 * np.pi, 0.1)\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "\n",
    "# Set up a subplot grid that has height 2 and width 1,\n",
    "# and set the first such subplot as active.\n",
    "plt.subplot(2, 1, 1)\n",
    "\n",
    "# Make the first plot\n",
    "plt.plot(x, y_sin)\n",
    "plt.title('Sine')\n",
    "\n",
    "# Set the second subplot as active, and make the second plot.\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, y_cos)\n",
    "plt.title('Cosine')\n",
    "\n",
    "# Show the figure.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can read much more about the `subplot` function [in the documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplot)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id=\"mpl-images\">Images</a>\n",
    "You can use the `imshow` function to show images. Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from imageio import imread\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = imread('../assets/cat.jpg')\n",
    "img_tinted = img * [1, 0.95, 0.9]\n",
    "\n",
    "# Show the original image\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.imshow(img)\n",
    "\n",
    "# Show the tinted image\n",
    "plt.subplot(1, 2, 2)\n",
    "\n",
    "# A slight gotcha with imshow is that it might give strange results\n",
    "# if presented with data that is not uint8. To work around this, we\n",
    "# explicitly cast the image to uint8 before displaying it.\n",
    "plt.imshow(np.uint8(img_tinted))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}