11.Python_Functions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  What is Function ? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function is a block of code that only runs when it is called. Python functions return a value using a return statement, if one is specified. A function can be called anywhere after the function has been declared.\n",
    "\n",
    "By itself, a function does nothing. But, when you need to use a function, you can call it, and the code within the function will be executed.\n",
    "\n",
    "As our program grows larger and larger, functions make it more organized and manageable.Furthermore, it avoids repetition and makes the code reusable.\n",
    "\n",
    "##### **Inshort**,\n",
    "Python function is a sequence of statements that execute in a certain order, we associate a name with it. This lets us reuse code.\n",
    "\n",
    "We define a function using the ‘def’ keyword. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to Define a Python Function? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Syntax of Function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def function_name(parameters):\n",
    "    \"\"\"docstring\"\"\"\n",
    "    statement(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above shown is a function definition that consists of the following components.\n",
    "\n",
    "1. Keyword `def` that marks the start of the function header.\n",
    "2. A function name to uniquely identify the function. Function naming follows the same rules of writing identifiers in Python.\n",
    "3. Parameters (arguments) through which we pass values to a function. They are optional.\n",
    "4. A colon `(:)` to mark the end of the function header.\n",
    "5. Optional documentation string (docstring) to describe what the function does.\n",
    "6. One or more valid python statements that make up the function body. Statements must have the same indentation level (usually 4 spaces).\n",
    "7. An optional `return` statement to return a value from the function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inshort**, A function is denoted by the def keyword, followed by a function name, and a set of parenthesis.\n",
    "\n",
    "For this example, we’re going to create a simple function that prints out the statement It’s Monday! to the console. To do so, we can use this code: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_monday():\n",
    "    print(\"It's Monday!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we run our code, nothing happens. This is because, in order for our function to run, we need to call it. To do so, we can reference our function name like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It's Monday!\n"
     ]
    }
   ],
   "source": [
    "def print_monday():\n",
    "    print(\"It's Monday!\")\n",
    "\n",
    "print_monday()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s break down the main components of our function:\n",
    "\n",
    "* the def keyword is used to indicate that we want to create a function.\n",
    "* print_monday is the name of our function. This must be unique.\n",
    "* () is where our parameters will be stored. We’ll talk about this later.\n",
    "* `:` marks the end of the header of our function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "image/png": {
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:\\\\Users\\\\deepali\\\\OneDrive\\\\Desktop\\\\funct.png\",width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Docstrings \n",
    "\n",
    "The first string after the function header is called the docstring and is short for documentation string. It is briefly used to explain what a function does.\n",
    "\n",
    "Although optional, documentation is a good programming practice. Unless you can remember what you had for dinner last week, always document your code.\n",
    "\n",
    "In the above image, we have a docstring immediately below the function header. We generally use triple quotes so that docstring can extend up to multiple lines. This string is available to us as the __doc__ attribute of the function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For example:\n",
    "\n",
    "Try running the following into the Python shell to see the output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Paul. Good morning!\n"
     ]
    }
   ],
   "source": [
    "def greet(name):\n",
    "    \"\"\"\n",
    "    This function greets to\n",
    "    the person passed in as\n",
    "    a parameter\n",
    "    \"\"\"\n",
    "    print(\"Hello, \" + name + \". Good morning!\")\n",
    "\n",
    "greet('Paul')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    This function greets to\n",
      "    the person passed in as\n",
      "    a parameter\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(greet.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The return statement \n",
    "In a function, we use the return statement at the end of the function and it helps us to return the result of the function. This statement terminates the function execution and transfers the result where the function is called.\n",
    "\n",
    "Note that we cannot use the return statement outside of the function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Syntax:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "return [expression_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can contain the expression which gets evaluated and the value is returned to the caller function. If the return statement has no expression or does not exist itself in the function then it returns the **None** object.\n",
    "\n",
    "Consider the following example:\n",
    "\n",
    "Example 1: Creating Function with Return Statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sum is given by: 60\n"
     ]
    }
   ],
   "source": [
    "# defining Function\n",
    "\n",
    "def sum():\n",
    "    a = 20\n",
    "    b = 40\n",
    "    c = a+b\n",
    "    return c\n",
    "# calling sum() function in print statement\n",
    "print(\"The sum is given by:\",sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code, we have defined the function named sum, and it has a statement c = a+b, which computes the given values, and the result is returned by the return statement to the caller function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 2: Creating function without return statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "def sum():\n",
    "    a = 20\n",
    "    b = 40\n",
    "    c = a+b\n",
    "# calling sum() function in print statment \n",
    "print(sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code, we have defined the same function but this time we use it without the return statement and we have observed that the sum() function returned the None object to the caller function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example 3:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "def absolute_value(num):\n",
    "    \"\"\"This function returns the absolute\n",
    "    value of the entered number\"\"\"\n",
    "\n",
    "    if num >= 0:\n",
    "        return num\n",
    "    else:\n",
    "        return -num\n",
    "\n",
    "\n",
    "print(absolute_value(2))\n",
    "\n",
    "print(absolute_value(-4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  How does function works?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "image/jpeg": {
       "width": 300
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:\\\\Users\\\\deepali\\\\OneDrive\\\\Desktop\\\\funct_works.jpg\",width=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types of Functions \n",
    "Basically, we can divide functions into the following two types:\n",
    "\n",
    "1. Built-in functions - Functions that are built into Python.\n",
    "2. User-defined functions - Functions defined by the users themselves."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Built-in functions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python has several functions that are readily available for use. These functions are called built-in functions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Function | Description|\n",
    "|:---------|:----------|\n",
    "|abs()\t|Returns the absolute value of a number\n",
    "|all()\t|Returns True if all items in an iterable object are true\n",
    "any()\t|Returns True if any item in an iterable object is true\n",
    "ascii()\t|Returns a readable version of an object. Replaces none-ascii characters with escape character\n",
    "bin()\t|Returns the binary version of a number\n",
    "bool()\t|Returns the boolean value of the specified object\n",
    "bytearray()|\tReturns an array of bytes\n",
    "bytes()\t|Returns a bytes object\n",
    "callable()\t|Returns True if the specified object is callable, otherwise False\n",
    "chr()\t|Returns a character from the specified Unicode code.\n",
    "classmethod()|\tConverts a method into a class method\n",
    "compile()\t|Returns the specified source as an object, ready to be executed\n",
    "complex()\t|Returns a complex number\n",
    "delattr()\t|Deletes the specified attribute (property or method) from the specified object\n",
    "dict()\t|Returns a dictionary (Array)\n",
    "dir()\t|Returns a list of the specified object's properties and methods\n",
    "divmod()\t|Returns the quotient and the remainder when argument1 is divided by argument2\n",
    "enumerate()|\tTakes a collection (e.g. a tuple) and returns it as an enumerate object\n",
    "exec()\t|Executes the specified code (or object)\n",
    "filter()|\tUse a filter function to exclude items in an iterable object\n",
    "float()\t|Returns a floating point number\n",
    "format()|\tFormats a specified value\n",
    "frozenset()|\tReturns a frozenset object\n",
    "globals()\t|Returns the current global symbol table as a dictionary\n",
    "hasattr()\t|Returns True if the specified object has the specified attribute (property/method)\n",
    "hash()\t|Returns the hash value of a specified object\n",
    "help()\t|Executes the built-in help system\n",
    "hex()\t|Converts a number into a hexadecimal value\n",
    "id()\t|Returns the id of an object\n",
    "input()\t|Allowing user input\n",
    "int()\t|Returns an integer number\n",
    "isinstance()|\tReturns True if a specified object is an instance of a specified object\n",
    "iter()\t|Returns an iterator object\n",
    "len()\t|Returns the length of an object\n",
    "list()\t|Returns a list\n",
    "locals()|\tReturns an updated dictionary of the current local symbol table\n",
    "map()\t|Returns the specified iterator with the specified function applied to each item\n",
    "max()\t|Returns the largest item in an iterable\n",
    "memoryview()|\tReturns a memory view object\n",
    "min()\t|Returns the smallest item in an iterable\n",
    "open()\t|Opens a file and returns a file object\n",
    "pow()\t|Returns the value of x to the power of y\n",
    "print()\t|Prints to the standard output device\n",
    "range()\t|Returns a sequence of numbers, starting from 0 and increments by 1 (by default)\n",
    "repr()\t|Returns a readable version of an object\n",
    "reversed()|\tReturns a reversed iterator\n",
    "round()\t|Rounds a numbers\n",
    "set()\t|Returns a new set object\n",
    "setattr()|\tSets an attribute (property/method) of an object\n",
    "slice()\t|Returns a slice object\n",
    "sorted()\t|Returns a sorted list\n",
    "@staticmethod()|\tConverts a method into a static method\n",
    "str()\t|Returns a string object\n",
    "sum()\t|Sums the items of an iterator\n",
    "super()\t|Returns an object that represents the parent class\n",
    "tuple()\t|Returns a tuple\n",
    "type()|\tReturns the type of an object\n",
    "vars()\t|Returns the __dict__ property of an object\n",
    "zip()\t|Returns an iterator, from two or more iterators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. User defined Functions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function that you define yourself in a program is known as user defined function. You can give any name to a user defined function, however you cannot use the Python keywords as function name.\n",
    "\n",
    "In python, we define the user-defined function using `def` keyword, followed by the function name.\n",
    "\n",
    "Function name is followed by the parameters in parenthesis, followed by the colon\n",
    "\n",
    "For example:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def function_name(parameter_1, parameter_2, ...) :\n",
    "      statements\n",
    "      ...."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calling a user-defined function\n",
    "You can call a user defined function by using function name followed by the arguments in the parenthesis.\n",
    "\n",
    "For example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example of a user-defined function\n",
    "In the following example we have defined a user-defined function sum. This function can accept two arguments as we have defined this function with two parameters. Inside the print() function we are calling the function sum and passing the numbers x & y as arguments.\n",
    "\n",
    "In the sum() function we have a return statement that returns the sum of two parameters, that are passed to the function as arguments.\n",
    "\n",
    "As you can see, we have used a print() function in the following example without even defining that function, this is because print() is a built-in function, which is already available to use and we can just call it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sum of 10 and 20 is: 30\n"
     ]
    }
   ],
   "source": [
    "# Program to demonstrate the\n",
    "# use of user defined functions\n",
    "\n",
    "def sum(a,b):\n",
    "    total = a + b\n",
    "    return total\n",
    "\n",
    "x = 10\n",
    "y = 20\n",
    "\n",
    "print(\"The sum of\",x,\"and\",y,\"is:\",sum(x, y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Function Arguments \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python, you can define a function that takes variable number of arguments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Types of Python Function Arguments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Default Argument in Python\n",
    "Python Program arguments can have default values. We assign a default value to an argument using the assignment operator in python(=).\n",
    "\n",
    "When we call a function without a value for an argument, its default value (as mentioned) is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Deepali\n"
     ]
    }
   ],
   "source": [
    "def greeting(name='User'):\n",
    "    print(f\"Hello, {name}\")\n",
    "greeting('Deepali')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, User\n"
     ]
    }
   ],
   "source": [
    "greeting()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, when we call greeting() without an argument, the name takes on its default value- ‘User’.\n",
    "\n",
    "Any number of arguments can have a default value. But you must make sure to not have a non-default argument after a default argument.\n",
    "\n",
    "\n",
    "In other words, if you provide a default argument, all others succeeding it must have default values as well.\n",
    "\n",
    "The reason is simple. Imagine you have a function with two parameters.\n",
    "\n",
    "The first argument has a default value, but the second doesn’t. Now when you call it(if it was allowed), you provide only one argument.\n",
    "\n",
    "The interpreter takes it to be the first argument. What happens to the second argument, then? It has no clue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "non-default argument follows default argument (<ipython-input-16-394c235213d9>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-16-394c235213d9>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    def sum(a=1,b):\u001b[0m\n\u001b[1;37m            ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m non-default argument follows default argument\n"
     ]
    }
   ],
   "source": [
    "def sum(a=1,b):\n",
    "    return a+b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was all about the default arguments in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Python Keyword Arguments\n",
    "With keyword arguments in python, we can change the order of passing the arguments without any consequences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s take a function to divide two numbers, and return the quotient.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.5"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def divide(a,b):\n",
    "    return a/b\n",
    "divide(7,2)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can call this function with arguments in any order, as long as we specify which value goes into what."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "divide(a=1,b=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "divide(b=2,a=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, both give us the same thing. These are keyword python function arguments.\n",
    "\n",
    "But if you try to put a positional argument after a keyword argument, it will throw Python exception of SyntaxError."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "positional argument follows keyword argument (<ipython-input-20-c3d95257b15f>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-20-c3d95257b15f>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    divide(b=2,1)\u001b[0m\n\u001b[1;37m               ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m positional argument follows keyword argument\n"
     ]
    }
   ],
   "source": [
    "divide(b=2,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Python Arbitrary Arguments \n",
    "You may not always know how many arguments you’ll get. In that case, you use an asterisk(*) before an argument name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sayhello(*names):\n",
    "    for name in names:\n",
    "        print(f\"Hello, {name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then when you call the function with a number of arguments, they get wrapped into a Python tuple.\n",
    "\n",
    "We iterate over them using the for loop in python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Deepali\n",
      "Hello, Neelam\n",
      "Hello, Harsh\n"
     ]
    }
   ],
   "source": [
    "sayhello('Deepali','Neelam','Harsh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was all about the Python Function Arguments.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, we conclude that Python Function Arguments and its three types of arguments to functions. These are- default, keyword, and arbitrary arguments.\n",
    "\n",
    "\n",
    "Where default arguments help deal with the absence of values, keyword arguments let us use any order.\n",
    "\n",
    "Finally, arbitrary arguments in python save us in situations where we’re not sure how many arguments we’ll get."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A Note: Parameters vs. Arguments\n",
    "The terms parameter and argument refer to the same thing: passing information to a function. But, there is a subtle difference between the two.\n",
    "\n",
    "A parameter is the variable inside the parenthesis in a function. An argument is the value that is passed to a function when it is called. So, in our last example(cell no.17), “a” and “b” are parameters, and 7 and 2 are arguments."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s see the given example, which contains a function that accepts a string as the argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Python Function to find the sum of two variables:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter value of num1 : 20\n",
      "Enter value of num2 : 10\n",
      "Sum =  30\n"
     ]
    }
   ],
   "source": [
    "# defining a function with sum of two variables\n",
    "\n",
    "def sum(num1,num2):\n",
    "    return num1 + num2\n",
    "\n",
    "# taking value from a user as an input\n",
    "\n",
    "num1 = int(input(\"Enter value of num1 : \"))\n",
    "num2 = int(input(\"Enter value of num2 : \"))\n",
    "\n",
    "# calculating and printing sum of num1 and num2\n",
    "\n",
    "print(\"Sum = \",sum(num1,num2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Write a Python program to find the maximum from the given three numbers.\n",
    "Program to implement the given functionality:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def max_of_two(x,y):\n",
    "    if x > y:\n",
    "        return x\n",
    "    return y\n",
    "def max_of_three(x,y,z):\n",
    "    return max_of_two(x,max_of_two(z,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "print(max_of_three(3, 6, -5))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanation of the Program: \n",
    "\n",
    "In this example first, we will make a user-defined function named max_of_two to find the maximum of two numbers and then utilizes that function to find the maximum from the three numbers given by using the function max_of_three. For finding the maximum from three numbers, we pick two numbers from those three and apply the max_of_two function to those two, and again apply the max_of_two  function to the third number and result of the maximum of the other two."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Write a Python program to calculate the sum of all the numbers present in a list.\n",
    "Program to implement the given functionality:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sum(numbers):\n",
    "    total = 0\n",
    "    for element in numbers:\n",
    "        total+=element\n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(sum((2,5,1,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanation of the Program:\n",
    "\n",
    "In this example, we define a function named sum() that takes a list of numbers as input and we initialized a variable total to zero. Then, with the help of a for loop, we traverse the complete list and then update the value of the total variable to its previous value plus the value traversed at that time. We do the updation of the total variable until we reach the last of the list and then finally we return the value of the total variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Write a Python program to calculate the multiplication of all the numbers present in a list.\n",
    "Program to implement the given functionality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def multipy(numbers):\n",
    "    total = 1\n",
    "    for element in numbers:\n",
    "        total*=element\n",
    "    return total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "360"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multipy((4,2,9,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Explanation of the Program:\n",
    "\n",
    "In this example, we define a function named multiply() that takes a list of numbers as input and we initialized a variable total to one. Then, with the help of a for loop, we traverse the complete list and then update the value of the total variable to its previous value multiply by the value traversed at that time. We do the updation of the total variable until we reach the last of the list and then finally we return the value of the total variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Write a Python program that takes a string as an input and calculates the number of upper case and lower case letters present in the string.\n",
    "Program to implement the given functionality:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def string_test(string):\n",
    "    d={\"UPPER_CASE\" : 0,\"LOWER_CASE\":0}\n",
    "    for character in string:\n",
    "        if character.isupper():\n",
    "            d[\"UPPER_CASE\"]+=1\n",
    "        elif character.islower():\n",
    "            d[\"LOWER_CASE\"]+=1\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "    print(\"Original string : \",string)\n",
    "    print(\" No of Upper_case string : \", d[\"UPPER_CASE\"])\n",
    "    print(\" No of lower_case string : \", d[\"LOWER_CASE\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original string :  Hi , My name is Deepali. I hope my Python notes are useful to all .\n",
      " No of Upper_case string :  5\n",
      " No of lower_case string :  44\n"
     ]
    }
   ],
   "source": [
    "string_test(\"Hi , My name is Deepali. I hope my Python notes are useful to all .\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explanation of the Program:\n",
    "\n",
    "In this example, we initialized a dictionary having two keys named UPPER_CASE and LOWER_CASE with values 0. Then, with the help of a for loop, we traverse the string and check whether each character is either lower case or upper case and whatever that character is we increment the value of that variable by one, and we do the same process until we reached upto the end of the string."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Recursive function [Very important concept for interview]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function is said to be a recursive if it calls itself. For example, lets say we have a function abc() and in the body of abc() there is a call to the abc().\n",
    "\n",
    "Python example of Recursion\n",
    "In this example we are defining a user-defined function factorial(). This function finds the factorial of a number by calling itself repeatedly until the base case(We will discuss more about base case later, after this example) is reached."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Write a Python program that takes a number(non-negative integer) as an argument and calculates its factorial.\n",
    "Program to implement the given functionality:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a number to compute factorial :5\n",
      "120\n"
     ]
    }
   ],
   "source": [
    "def factorial(number):\n",
    "    \"\"\"This function calls itself to find\n",
    "    the factorial of a number\"\"\"\n",
    "    if number ==1:\n",
    "        return 1\n",
    "    else:\n",
    "        return number*factorial(number-1)\n",
    "\n",
    "number = int(input(\"Enter a number to compute factorial :\"))\n",
    "print(factorial(number))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Explanation of the Program:\n",
    "\n",
    "To implement this functionality, we use the concept of recursion with the base condition. Here we make a function named factorial that takes a number as an input and then recursively calls the same function up to we reach the base condition included in that function.\n",
    "\n",
    "To understand the recursive definition of the program, let’s understand the below image:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "image/png": {
       "width": 300
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:\\\\Users\\\\deepali\\\\OneDrive\\\\Desktop\\\\factorial.png\",width=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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jtLqS1Dmb7fb58x4mIHl9McnNfqN+xp/wTYtf2TvH3iDxlqHjyPxta+IPD1x4fm02TQxp6eXcTwTO7Ob26DqVhKFCgyGznjB+MdH/AOCQnxx8cfCzSF+JnxZGmeINIsTBo/hr7M99pemKrO6QG5juUVC7NukeGBtrszfvTycf/gmF+0h8XfAX7Q99+x/8V9Ru9SsZpdW0+0tr2f7Q+kazo4lknjilclvIkS3mUxglfMCsgGX3AHO/sQ+I739kj/gof4y/ZtvJ5I/DXiXU73QYY5GJXzIS91o1w3fzHhbyh1/1/PTI/oh8ZeOPB3w78P3Pivx5rdh4f0e0GZr7UrhLaBSei75CAWbGFUZZjwATX80X7fcreAf+CnGk+MtLJguTqHg/Wd6dTLbfZ4s4+luAR3r+iX4mfAH4PfGTW/DniD4o+F7LxLc+FHuJNKTUA0ttE915fmGS3LeTP/ql2iVHCnJABOaAPlWX9uHxL8VLiTTP2QvhVrvxMjV2iPifUv8AinvDCMDgsl1dqJLgofvRqiMR90nIqVPhT/wUF+Iv+k+OfjN4Y+GlvLy2meCPD41Rwh6IbzVGEiOO7JkZ6cV9/wBtbW1lbRWdnEkEECLHFFEoRERRhVVQAAAOABwBU9AHwB/wxd8VZf393+078Umuuu6G6tYYM/8AXERFce2ary/BP9u34cD7d8M/jrpfj6GL5l0Tx9oMcKSY7HUdPP2klhwMgAHv1r9B6KAPg3wV+2udA8WWXwv/AGr/AAfdfB7xZfv5On393Mt34Y1eQcf6Jqifukduvly42ZCly5Ar7xBBAIOQehriviH8OPA3xY8JX3gX4jaLaa9oWopsuLO7TcpP8Loww0ciZykiFXQ8qQea/P3wh4o8Z/sI+PNH+EPxT1e68Q/A3xPdLYeDfFuoPvuvDN4+fK0nVJuAbZgMW85wEAxwgYRAH6cUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf//R/fyiiigAooooA+CP+Cn3/JjHxO/656L/AOnmwr5i/wCCJ3/JuvjX/sdZv/TfY1+uvijwp4X8b6FdeF/Gmj2Gv6Ne7BdadqlrFeWc/lOsieZDMrxvtkRXXcpwygjkCsvwT8Ofh78NNOn0f4ceF9G8K2FzObma10TT7fToJZyqoZHjt441Zyqqu4gnAAzgCgD+Tv4Wa78L/Cv7cniPVf25rCe+sIdX1n+1Vv7ae9hj1QyN5MlzbqHlmtgM7FCOCDGdpQV6v/wUY/aJ/Z0+LPh7wn4G/Ze8P21r4Z8PX093qeq6bon9jWEl3cR+XbW8cZhgcsI0mc741yMbM4bH9G/xF/Zy+A3xb1OPW/iX4B8P+ItSiVUW9vrCKS62J91Gm2iRkHZWYr7VcP7P/wACH8IW/wAP5fhz4Ul8MWl19vg0aXRLKTT47vayfaFt2hMQmKOymTbvIYjPJoA/KWD4Ga1+0f8A8Ei/BPhbwVEL3X9Gsv7b0u2A3Nc3Om3l3HLbx4/5ayRPKkY7uQpwCSPn/wD4J+f8FD/h1+zX8Lb74EfHqw1jS5dB1O8n065trMzFFuGLz2lxCSksUsdxvIOCDvKts2Zb+hXwr4Q8J+BNDt/DHgfRNO8PaNaGQwadpVpFZWkRlcyOUhgVI1LuxZsKMsSTya8z8ffs1/s/fFLU21z4h/Dvw3r2puUL395psD3kmwAKHn2CV1AAG1mIxxigD8cPgt+2N+1b+2B+2cmlfBrWdQ0H4R2mp217qFlJp9g32XQrTYJBcXDwTSLPfFCqqsrFWlOw7ULDE/4LhaHqg8Q/CnxL9nc6a9lq9ibgKSi3CyW8gRmxhWZGyoJywVsfdNfvV4K8AeBvhvoq+HPh74e0vw1pSuZBZ6TZxWUHmN95ykKopY45YjJ7mrfirwf4S8daNL4d8baJp3iDSpyDLY6paxXts5GcFopldCRk4JHFAH5cfs8fHn4aftz/ALL2u/sueFbTVNI8RaX8OrXR7+41O3i+wxXgtVtIpoXinkklRLiMSDMaEoOx4r84f2Ff2oh+wD8RfHnwf/aK0DVtMs9TuLczmG38y4sL+y8xA/lMUMttcRvkSRlshEZAysSP6OPAHwS+Dvwpury++GXgnQPCtzqCLHdTaPptvZSTRodyo7QopZQeQpOAe1L8RPgn8H/i4kS/E/wXoXil4IzFBLqunwXU8CMclYpZEMkYJ5+RhQB/OJ+3P+0+f+CgXxJ8B/CH9nbw/q2pWWlz3H2cz2+y4v76+MaNIYlL+TbW8ceTJIVwGdnCqoNfVv8AwU6+GqfB39gH4O/C1ZkuX8L+INF0ye4iUqk9xBo+oLNKqnkCSXc4B5Ga/Y/4d/BX4Q/CRJU+GHgvQvCzXCCOeXStPgtZp0U5AlljQSSAHkb2Nbvjb4d/D/4laZDonxG8M6P4q063nW6htNbsLfUbeO4VWQSrHcJIiyBHZQwGQGIzgmgD4s/4Jaf8mLfDf/f17/09X9fkf/wWt/5Ob8I/9iJZ/wDpy1Kv6VPC3hLwr4G0K28L+CdG0/w/o1n5n2bTtKtYrKzh812lk8uCFUjTfI7O2FGWYk8kmuQ8bfBL4MfErU4db+I3gHwx4q1G3gW1hu9b0az1G4jt1ZnESyXEMjrGHdmCg4BYnGSaAPw4/wCCz6eNzoPwbdftH/CHizvA/l7/ALN/axSDZ52Pk8zyA3k7vm2+bt43Vv8Awx/aq/4J2fCX9nrRoPht4StNS+Kf9hQ2sen/APCOSXGtz69JAIyZNRltyhVrlt2Yp2wpxGmcJX7peJ/BnhDxr4fm8J+MNE0/W9EuECS6dqFrHc2rqv3QYpFZPl7ccdq8x8Bfsx/s8/C/WV8RfD/4c+G9C1aMsY7+002FbuLeCGEcxUyRgg4IRgMcUAfz9f8ABFaaOH9qDxXbSnbJJ4EvgqngkpqWm5H1x29qy/gD/wApitU/7KP47/8AQNVr+jrwp8Dfgn4E15vFPgf4feFvD2tOkkbajpWi2VleFJjmRTPBCkhDkfMN2D3pdP8Agd8FdJ8Yt8RNK+H/AIWs/Fb3M942u2+i2UWqNc3W/wA+Y3awicyS+Y/mPv3PubcTk0Afzqf8FP7Gb4Yft9+HviXdofsl/a+HPECOo6/2bN9nkXP94fZAfowr9J/+Cout/tNeBvh/4b+Lf7PHiTVdL0fRWuovFEeksr/6Pc+SbW7ZCrExxMsiSMOgkUn5QSM3/grf+zfqvxg+CumfEvwdYSX/AIh+H1zJJLb20ZkuLnSb7YlwEVAWdoZEjlA7IJCOTz9WfsMfEvVPip+y/wCC9R8T2N5Y67o1inh/V4NQheGZrrTEWHzWWRVLfaIRHNnGMuR2NAH5yfCz/gs58O9H+DOm2fxN8N+IdU+IWlafHaTmzW2+w6rcwxhFuXuHmV4POK7pgIH2Mx2BxxXhf/BMX4TfET46/tW63+2B4u082ujWN/rOqtdNG6W95rWtCdGhtd3DpbrPI7kE+WRGp5YY/bzUv2P/ANlfV9bPiHUvhN4Pnv2dpXkbRrULLI5yzSRiMRyMTyS6kk16/q194b+GPga+1SGxSx0Pwzps1yLLTbcKI7aziL+Vb28QAztXCRoOTgAUAfzZfte2x+L3/BVzSvBWmfvFh8QeEtHkcfMAkUdrPctj0iEkm4f7Jr+nyv59v+Ca/wAF/H/xb/an8c/tf/FTRbvSY7S+1G40+HUIJIXk1fWi5cRCVVJjtLWRk6AAyR7funH9BNABRRRQAUUUUAFcH8Tvhr4P+MHgLW/hr48sV1DQ9etXtbqE4DAHlJI2wdksTgPG45V1BHSu8qte2kV/Zz2M5kWK4ieJzDK8MgVwVJSSNlkRsHhkYMp5BBwaAPyV+HX7a3hr9kHw54o/Z+/at1m7u/FHwwuIbHQLy1tnurvxPoVxGZNPlTB8tbiKECObzZEVcKGdn3Gv1zr+Yz9uf/gmJ4++Fl7q3xc+DcuoeNfCU0kl7qNpO73et6ZuJaSR2OXvIByTKMyoOZAwVpT/AE50AFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/S/fyiiigAooooAKKKKACiiigAor5e/bM+OOr/ALOn7OPi34teHEtZtZ0pLOLTor1Gkgkuby7htxvRHjZgqyM5AYcL1r8ZvCv/AAUW/wCClvjrQ7fxP4I+EsXiHRrsyC31HSvB2sXtpMYnaNwk0Nw8bbHVlbDHDAg8g0Af0dUV+ZX7C/7Q37Y3xk8a+JNI/aV+Hk/g3SLDS47nTrmXw7qOiie6aZUaMSXrskmIyTtXnv0r9NaACivzc/at/wCCgPwr+F+hfE/4ceDPFsOmfFjwjYxnT7e7s2lt5ruVIZtkMjI9vI6RyYaN2DbgQAdpq9+y9+378I/ilpnwx+GfiTxZFqvxV8V6QsupQWlo0dtDfx273EscsqolvHIwjYLGhY7sKQCRQB+itFFfDv7e37XM/wCyJ8JLXxPoOmwat4m8QX39m6PBdlvssTrG0ktxOEZXdIlAGxWUszryBk0AfcVFfkV+wB8dv26Pjv4vg8bfGbS7dPhVq+k3s2nX8FlaWiSXsM0ccYjCuboocSjLKVJH3ulfrrQAUUUUAFFFFABRX5VfEr/gpHqvgD9s21/ZQj8BW97b3PiTw9oP9ttqjRyKuuLZsZfs32ZgTD9qOF8z5tvUZ4/VWgAooooAKKKKACivyosviJ/wUJf9tBvC11oNyPgp/wAJLNAL7+y7QR/2QA2x/tOPOwTj5s7q/VegAoorgfitr+peFfhd4x8UaLIsWoaPoGqX9o7KHVZ7W1kljYq2QwDKCQeDQB31FfjZ/wAEwP2xfjv+054z8c6N8XtXtdStdD0yyurJbewt7QpLNM6OSYUUsCoHB6Vmf8FJP2wfi94M+Lfgb9mv9nLWJNH8VanLaz6ld26RSSPPqkn2awsh5qSBc5Msny5IeLB4IIB+01Fc54P0jVdA8J6Noeu6pNrmpWFhb295qdwFWW9uYo1WWd1QKqmVwW2qABnAGK/G/wDbt/4KjeIfgh8T1+E/wDt9G1a+0IOviS/1OGS6gjvGxts4BDND88Iz5zEth2EeAyPQB+2dFfnZ8SfiD8cviV/wT1svjh8Pddbwz8QB4VsvF8sulQoIZVgh8+9t1inE/wAjweYUXJbeqc9ar/8ABND9qnWv2mfgjdQeO737d428G3gsdXuWVEe8t7ndJZ3TIgVVLqrxNgDLQlv4qAP0bor5W+Ov7V3hn4NeJrL4baP4Y8SePfiBq1kL/T/Dfh3T5ZpGtXd4luJ7plEEMHmRsrPudkwSUxzXi6eC/wBvb49f6R468WaT8BPDM/P9i+FkXWfEjRH+CfUpcQQSekltzxgrQB94eJPF3hTwbYHVfF+tadodkM5udSuorSEY6/PMyr+teBXX7an7JFndfY5vi94PMmcZi1e3ljz/ANdI3ZP/AB6vN/DH/BOv9mLSr8a/420fUviT4gbHm6x431O41m5m7/PG7LbHJ5/1NfQlp+zv+z/YWn9n2Pwz8H29rjb5MWg2CR49NogAoA7Xwd4+8C/ETS/7b8AeItK8S6fkKbrSL2G+hDHnBeB3UH2JzXW1+fXxg/Yh8OaN5/xb/ZJii+F3xT0aNrmx/sQC00fWPK+f7Bf2C4tWhnxt3BF2sQzbgMV9G/s0fG6x/aG+DHh/4oW9qdOvL6OS21XTmzusNUs3MN3bkN8wCyoSm7DGMqSBmgD3iiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA//9P9/KKKKACiiigD5p/ad/ap+G/7J3hPSvGPxLtNXu7HWNR/sy3XR7eG4lE3lPNl1mngATbGeQxOccd6+J/+Hz37KH/QF8cf+C2y/wDlhX0t+3F8avgH8EPAGga/+0F4Bj+IOj3+sfY7Kyk0yw1QW935EsnneXfssafu1Zdy/N82OhNfmN/w8D/4Jof9G0W3/hI+G/8A47QB9q/Dv/grL+zP8TfH3hz4c+HtJ8YRap4o1Wz0iye60+zS3W4vplhjMrJfOyoGcbiFYgdAelfp5X4bfBz9t/8A4J8+MPi14M8J+Bv2fbfQ/EWs69p1hpOpr4X0C2NlfXNwkcE4mglMsZikZW3xguuMrziv3JoA/HL/AILT+NP7H/Z48KeCoZNk3iPxRHPIufv2unW0zOMe000J/Cvjf4PftvftX/CH9m/who/wM+CUt34B8I6Ywv8AxLquk6nfW11M00txezxy2j28EUCzO4LFpNuMuyk7R+m37d37V3wh/Z01fwjpXxg+FieP7fWra9uNMup4LK4jtpIHiS4jVbtHKsQ8TMVxkEZ6V9d/Dz4r/CfxL8FNJ+KnhzUNL03wM+jxXgk8yGCy061SIF4JdpEcJt+Y3Q42FSuBjFAHyb+wn+3/AOH/ANr2DUvC2t6SnhrxzoluLy4sYZfNtL2zLhDPas37weWzKssb5271IZgTt/Rav5bf+CaVrD4l/wCChl94i+HFu1t4UtR4n1ARLGVWLR7jzYrSMjACYeaDAIHTGK/qSoA/LD9vz9lvx/8AtN/ED4a+B/CfhLRbPwve3zT+KvHQhtn1mxhgVsWyl0E4iaIEptLrJMY1fy1XLyf8E9/2YviJ+zT4o+I3gTxz4R0OfRNN1ISeGPHMcFsmranbzqCYWKqZzCsZVjvYCKUyRqZF5T9S6KACv5h/+Cr/AMM/j74S8XW3jD4leOIvEPgvxH4g1WTwpoqTzyNpEIEblGSSJI0/dsq/I79K/p4r8Lv+C4X/ACIvwq/7C2rf+iLegDvP+CcHgD9pD4c/DKz+NHjvxs/ij4YTeA7ufQPB1jJdXN5azpNFPEI7fyAhk8qGaNVjd2LSAAHJx86eFrT/AIKaftufErXP7T8ReJfgZ4a09RcRQSW+o6BbRQzOVjtrfyo7ee+lCgs7SyYGCSUDKlfob+z/APE+L4Lf8E1fDHxWktPt58L+BX1GK1LFBPNEJPKjZgCVV5CqswB2gk44r83v2ZNR/bY/4KKav4r17UfjtqPw78P6BNbxTQeH0e2bfeCRkhggtJrVvLVIyDJNOzcj753EAHn2u/tA/tefsAftM6f8NviP8Trv4jaCrWF3fw6ldT6nFc6XeNtbZ9rZ7i1uI1VioSTAYKTvRsH9dP8AgoT+2TN+yP8ADLT5fCsFte+NvFc81ro0N2peC3ht1U3N5Iild4i8yNUQkBncE5VWFfzuftxfBTRvgF+0Mnw/0/xhqXjfUU07T73W9V1aQSXR1G6eRijYLMoEHksA7u3zZ3YIA/QL/guBoOtp4v8Ahb4nYSPpEum6pYIQpMcV3FNFK+W6BpI3XAPURnHQ0AY/w/8AgZ/wVP8Aj98O7X49WPxe1HSRqtqdR0fSJtfvNMnv7dwXR0tbSJbKJJ1wYRIVDKQTtUg17L+xH+2J+0F+0f4Y+IX7MXjbV/7P+JcXhvUZfC/iu4T7HPFcxBbYx3Qgj5mt5JFkWRE3lVfcGYAn9XP2dvH3gzxX+zp4F8b+H761XQl8MWDPKJFEVoLS2RJ4pWzhGtmRkkBPylTnpXz/APs3f8FDvhL+1J8Ub74W/Dfw94lS4sLK61GXUr+C1isfsttJHEHyl1JKPMeVAgMYPzcgYNAH85/xR+G/x20T9uC0+GXi3xpFqnxUk8T+G7KLxUs0zRrqN4lkbC481olmxbiSEE+XkbOAcDP9AE3xF+LP7B37IfiHxl+0r4rh+I/jOPVpY9ElSeeaO6nvY41tLVpJYoXVI2jllkwOIw20liBX5LftLXNvZ/8ABYCxu7uRYYIPiD4BklkchUREh0kszE8AADJPpX6N/wDBZ7w/rWq/svaFq+nCWSy0XxhZXGoIikokU1pd28czkdAssixjPGZQOuKAPir4S6N/wUy/bu03U/i74c+KUvgrw+L2WKyWPVbzQ7KaeHrFaQadFI7RQnCNJMTk5+aRw+PRv2Zv22/2jvgH+0nH+yt+2BfNrMF3qUOlDU7wie8sbq8VfsUqXKbftFncl4yTKC6K4fcu1kP3Z/wSr8Z+GPEv7GXhDQ9DuYWv/DE+p6fq1qjAyW9xLfXFyhdc5HmxTJICeDkgdDX5E/8ABQfU7H4uf8FFdI8LfDW4F5qdrN4d8OyT2X77GqLPufBXIL2/nKj/ANxo2VsFTQB9Mf8ABWf9o346fBv42eEdB+FvjbV/DGnXvhZLy4ttOuDFHJcG9uozIwAOW2Ioz6AVQ8Cax/wUj/a3+KvhP466J/bPhT4SL4i0+6tNNttXj0e2m0S3u4zLvgWaKe+3wqxeSVGjlJYR/JhB5J/wWy/5OD8D/wDYmx/+l95X9FPwv0yy0X4aeEtG02JYLSw0PTbaCJAFVIobaNEUAcABQBxQB+Dul/tLfHub/gqM/wAJJfHWst4NHju608aKbg/Y/siK5WLy8fcGBxX1B/wUP+PH7Ymk+NNK+DH7LfhXxJFaXNpBPqvijSdGuLvdcXbuiWkN15EkNukSBZJZVYOCwG5Ajbvzu0b/AJTFSf8AZR7z/wBAkr6d/bg/bP8A2idT/avtf2RvgFrsXg2NtR0bQn1NVRbi71HWVgZWe4kjd7eCL7Si4iUOSrNuYFVAB5z8fv2fv2+/2XPhJc/H3Wf2jdZ1eXR5rP8AtPTYtc1SRE+1zpbx+ULpzDchZJV3I8SDbkgHGK+3/wBln9pjxh+07+wb8SPEnxDaKfxL4d0rxFod9dwxCEXoi0sTxXDInyLI6zbXCBV3KSFUECviz9tH9iXxj8Lv2cdf+LPxx+P3ij4ia3psmnx6VpV/JKtg19d3UUToi3N1dM+yBpnGwRHCFsAAqe4/4Jjf8mE/Hz/rv4g/9MUNAHnn/BED/ko/xQ/7Amm/+lElcD8FLz/hfP8AwV8vfEupn7Raaf4r128hMnzH7P4ftp4LAgHoVMEBHpjjpXff8EQP+Sj/ABQ/7Amm/wDpRJXln/BPxX8O/wDBTTVtC1Pi7N/4x08iTh/Pg+0O/B/ixE2fxoA/XL/gof8AtjD9mn4dReEfAsv2j4m+NI2tdDt4h5stjC58t75oxkllY7LdSPnm5wyxuK/Db9qz9kK6/Zq/Zx+G/irx2ZJ/iP471m+vteeaQyPZx+QjxWZYk7pELs878lpWIyyopP1p+1d+zB+3NrP7a/iL4+fBvwlcalFY3tlN4b1W4uNJuooVt7OGNTFbajM6r5Uu8pui+WT94uGw1fG/7al1+33ceH/DA/bJWcaWt5c/2J50WjRj7T5a+dj+y1Dn5Nv+s49Oc0Af0j/saWVpqX7HXwp06/iWe2uvBemwTROMrJHJbKrKw7ggkGvw6/4JVa1e/Cr9t3xV8IZZXe21Sx1rRJEz8rXWjT+dHKR3KxwTKP8AfNfd3/BLG4/a/k8O20XxkEw+FKeFLH/hCS8emKmzenlbWtQLs/6Pn/j4J9/mr8/P2H4T4i/4KpatrmnAtawa/wCONQYx8oIJkvolJP8Ad3Tpj3xQB/UGEQOZAo3EBS2OSBnAz6DJ/OnUUUAFFFFABX5+/sTINB+I/wC0r4DtflsNN+KN5qtvGPuxHWbeKeRFH8Khk4A4FfoFX5/fsOn+3PGP7SHjxOYNU+Ler6bA/aSLR4YYA6+qkscH2oA/QGiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA/9T9/KKKKACiiigDm/E3g3wh40tIrDxjoem67bQSedFDqdpDeRxyYK71WZXCtgkZAzg4ri/+FC/Az/onXhP/AMEdj/8AGa9YooA8z0/4LfBzSL+31XSfAnhmyvbOVJ7e5t9Hs4poZYyGR43SIMrKQCGBBB5FemUUUAfP37SH7NPwx/ak8AN4A+JdtN5UUwutP1GzZY77T7kDHmQSOjr8yna6MrI46jIUj8eLn/giFr41FrGw+McS6FLKZGEmhyGdcfdzEt6IpGCnG7cvsBnA/Un4pfBv9rHxV471PxB8M/2iR4F8N3X2f7F4fPgjStX+x+XBHHL/AKZcSrNL5syvL8w+TfsHCivP/wDhn79vBen7WELf73w40YfymoA9U/ZT/ZB+Fv7JHhG50DwGs9/qurGJ9Y1u+2m6vpIQdihVAWKFCzeXEvTcSzO2WP1XXwB/woP9vRen7VNo31+Hekj+U1H/AAon9vhen7UOnt/vfD7TB/KagD7/AKK+AP8AhR37fi9P2mdKb6+AdOH8pqP+FKf8FAF6ftJaI3+94EsR/KWgD7/r4W/bh/Ys/wCGzNC8K6L/AMJj/wAIf/wjN3d3Xm/2X/af2j7Ukabdv2q12bdmc5bOegrJ/wCFNf8ABQRen7RXh9v97wRaD+UtH/Cn/wDgoQvT9oPw03+94Ltx/KWgD3r4dfs8+HvCX7Nmm/s1eLLv/hJtGt/D8nh6/ufJNkby3mV0kYRrLK0LFXOMSMVIBBzX5a6P/wAEiPij8OPFd/e/BP8AaB1bwfpGpAxSyWlvc2uom3BJWKZrO9t47jbk8nyxnkKK+0P+FS/8FDF6fHvwq3+94OhH8paP+FVf8FEF6fHTwe318IoP5SUAfHvjz/giv4c8UXWn6novxb1W01L7L/xOr7V9L/te41PUXkd3ut/222MIYMqiM+YQFyZGYkn9VPjX8AvAX7RPwvk+F3xdtjqdnMIZvtVr/otxb30KlVu7Y5k8qQFmwCXUqxRg6kg/Nv8AwrD/AIKKL0+Nngpv97wpj+UlH/Ctf+CjC9PjJ4Fb/e8LuP5SUAfCF3/wRg8aWD3WgeE/jlcWnhbUHLXNnNpcykj+ESQxXyw3BA6s3l/QV+mn7JP7Gnww/ZD8LXmleDXn1fXNYMbavrt8qLc3PlD5Yo0QYht0YsyxgscnLO5AI86/4V3/AMFHF6fF3wA318NTD+T0f8ID/wAFIF6fFX4dN/veHbgfyegDzD9sT/gl/wCFP2o/iRL8WtD8Yz+D/EN9b28GpI9gNRtLw2sYhikC+fbvFIIlRGIZlIQfKDkn3X9mz9jPRfgz+zrrn7O/xF1eP4g6T4jvb641B5rR7JHgvYYYfJC+fM4KeSGWRXVlYgrtKgnmf+EG/wCCki9Pib8NW/3tAux/J6P+EM/4KUL0+I3wwb/e0O+H8moA+LfE/wDwRov9H8RX2p/A/wCMOoeGNLv3KGxu7SRp4rZusbXVrcw+eoyQFaJeOCxOSfq39kL/AIJqfC79lzxIPiLqmsXHjjxtEJUs9TubcWdtYrMhSQ29qJJj5rqzK0ryudpwoTLbul/4RH/gpYvTx98K2/3tG1Efyaj/AIRf/gpgvTxv8JW/3tJ1MfyagDjf22f+Cdf/AA2L8QdD8d/8LB/4RH+xtGXSPsv9i/2l5u24mn83zPt1rt/1u3btPTOecD9HNC0z+xdE0/RvM877BawWvmbdu/yUCbtuTjOM4ycetfCv/COf8FNF6eL/AIQN9dM1Yfyaj+wv+CnC9PE/wbb66frI/kaAPOLP/gmx9k/bFb9rP/hYu/d4km8Qf2B/YeP9aGHk/bPtx6bvv+Rz/dqL9sr/AIJm6B+0z8Qh8YfBfiyTwX4vlit4793tjd2t61mgjgm+SWKSCdI1RN6llKovyhssfTP7I/4KeL08QfBZv96y1wfyNH9m/wDBUBemsfBBv96114fyNAHzFJ/wSd8WeP8Aw5fWvx9+Pfifxtq6WnlaC119purHSbg4HnNFd3sr3AC8BFe3HPJbt9Lfsv8A7C93+zf8FviL8GX8eL4jtvHiXIjvP7G+wtYSXdm1nIxj+2z+eMbGA3R/dIzzkT/Y/wDgqIvTUfgW3+9B4hH8jR5H/BUhelz8Bm/3o/Eg/lQBifsQf8E//wDhjXxJ4o8Qf8J5/wAJf/wkljbWfk/2P/Zn2f7PI0m7d9tut+7djGFx6mvy9/aU0p/2RP8AgqP4c+MFypt/DXifWrTxEblhtRLbVN1lq+W4UvGzzyEdldM9cn9ffCY/4KRDxVo3/Cct8EW8N/2ha/2x/ZQ8RDUf7O81ftP2Tzv3P2jyd3leZ8m/G7jNepftGfsq/B39qbRtI0b4tafcXK6FcyXNhcWVwbW5iMyBJUEgB/dyYQsuOWRT2oA+jFZWUMpBBGQRyCDXw3+3B+xf/wANl+HfC2g/8Jj/AMIh/wAI1e3V55v9mf2n9o+0xpHt2/arXZt25zls56CvsTwl4asvBvhbSPCOmzXNxaaLY2+n28t5L51w8NrGsaGWQgF32qNzHljyea6GgD5ezp/7Hf7IxTUtTTVovhf4PMSXkkP2UX09hb7IAIvMk2NcTBEVPMb5mA3HrX5Hf8EVPhPqOo+KfiB+0DrCSNFFbr4bsZpBkT3N1Il5esCf4o1jgGR/z1Ir9r/jt8DfBP7RPw/n+GXxDfUBoV1c291cxafdG1ec2zF40kcBiYw+19v95VPatb4PfCHwL8Cfh7pXww+HFibDQtIWTyUdzLK7zO0kkksh5eR3YksfYDAAAAPTaKKKACiiigD8zv8Agq5ovicfsuH4h+C9Tv8ARtY8Da7p+pi90y5ltblba5ZrGRRJEyuF33EbnB/gB7V80f8ABILw9+0xqGgal448UeJLm0+FU91ez2Ol3VrBLNrerXLYuLwXMkRuRDG4O5xJiWYbeQjiv2R+JPw88L/FjwJrfw48aW7XWh+ILR7O9iRzG7RPg5VxyrAgFWHIIBrodB0LRvC+iWHhvw7ZQabpel28VpZ2dsgjhgghUJHGijhVVQAAKANaiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA//1f38ooooAKKKKACiiigAooooAwfFHinw34J0C98VeL9TtdG0fTo/Nu7++lWC3gjyF3SSOQqjJAyT1NeCf8Nn/sl/9Ff8Gf8Ag6tf/jlfM3/BW3xp/wAIp+xprWkrJ5cnivWdJ0ZMHBIWY37gfVLNgfYmvzd/Z+/YK/Y18S/AbwL8Q/2iPiteeCfFPjC0mvhpz+INH0qFrd7qaO1aGG+tJJiHhRGLb2BYnGBxQB+/nw9+O/wX+LN/daX8MfG+heKbyxhFxcwaTfw3ckMRYKHdY2YqpYgZPGa9Yr4R/ZD/AGIPgX+zLqupfEL4N+Jdc8Rx+JtOSyM+oX1jfWbQJKJA8D2dpbgncuM72GMjGa+7qAOV8b+NvC3w48Kan448bahHpWhaPD9ovryVXZIYgQu4iNWYjJA4BNS+DvGHhvx/4W0vxr4Pvk1PRNZtkvLC8jVlSeCQZVwHVWAI9QDX4h/8FPtW/aZ+Gba/FF8RtJ1fwD8W5YtE0/wI1mzapD5UMSs1mojkdgJEDSSLMimSZR5RLKBe/wCCYet/tMfFmPw3qVx8StHsPh58LFbw9d+BoLQjUZR9meOJ7weXGw3MwkilM0i74mAjBVhQB+6dFFcl488c+Fvhn4N1jx942v49M0PQrSS9vrqU8RxRjoAOWdjhUQZZ2IVQSQKAMb4h/F74WfCS2srz4oeLNH8Kw6i7xWj6vexWYuHjALiPzWXeVBBbHTIz1FdZ4c8SaB4v0Kx8T+FtQttW0jUoVuLO9s5Vmt7iF/uvHIpKsp7EHFfx6ftg/F/4q/tTeILn9o/xDYXGneA21WTwz4WtpW/dQRwIZ2iQDh5tpWS5kHHmSKoO1VVf6df2Ff8Akz74Sf8AYsWX/oJoA+sK8x+LHxl+GXwN8MxeMfivr0Hh3Rp7yOwju7hJZEa5lR5EjxCkjZKxuc4xx1r06v5ef+CjmoftP+E9UPwC+JvxD0v4kaVrOo3PjWw0/TrJ11TR4LcXJHnKIt0VssMsxRDNMFjhLfIq8gH9QoIIyOhpa/NH/gn54h/aT+MGnTftDfFr4m6L4n8L+J9Oa107wzo1qYl0i7hnUsJT5cPlTRAPHIjCZnDK3mlQu79LSQoJPbmgDifiB8Svh/8ACnw9J4r+JPiHTvDekRusZu9SuEt42kb7qJuILuccKoLHniuP+EP7Q/wX+PY1ZvhB4qs/Ew0JrddR+yrKv2c3XmeTu82NMh/JkwRkfKa/mP8A+Ck/7XXg/wDar+IXhS6+GV1qT+FfD+jNH9n1CH7MV1O5uJDcOsYdwQ0CW43ZzwR25/ZH/gnFffsdaT4D8aa7+zmmq6ZDYQaUnjDU/EUjwpLJaQ3MiT/vZniiRQ8zOUCKARnpmgD9RqK/K7xx/wAFg/2SPCOszaRo6eJvFqQvsN7o2nQraMQcNsa+ubSRgOxEe1uxI5r6A/Zv/b4/Z1/ag1hvC3gHU7zTfEYiedNF1y3W0vJoowS7QlJJoZiigsyxys6rliNoJAB9o0V5Z8YPjV8MfgN4On8d/FXXrbQtKhOxGlJaa5mwSIbeFAZJpWAyFRSQAWOFBI/PK3/4LJfskza4NKlsvF8FoXVf7Tk0y3NqATgsUS8a42gc8QlvQeoB+sFZ8OraXcXH2S3vLeWcEjyklVnyvX5Qc8d6+c/HP7X3wM8DfA+1/aJfWn1zwPezw20F9o0RuXeWd2jCmJjGyMjqVdX2shGGUHiv5l/2V/2jPhz8IP21Lz48eL2vh4XnvfEVwptbfzbrZqaziDMW5ef3i7vm4560Af2CUV494f8Ajx8Mdd+DVh8fLjV49E8F3+nrqa3+rlbMRW7navmBmIV2b5VUElmIAySBXwNrH/BY39kbTNb/ALKs7fxZqtrvZDqVppcK2uAcBgtxdQ3BU9R+6zjtQB+rVFeK/A79ob4Q/tGeFj4t+EniCDWLaEql3b4MN7ZSNnCXNu+JIicHaSNr4JRmHNeM+KP28/gH4O+P4/Zs12fVYfF32+x05n+xr9gSbUIYp4ma4Mg2oEmUuxX5efSgD7Qr50+FP7Wv7Ovxv8UTeDPhV41s/EOtW9pJfSWkENzG620LpG8mZoY1wrSIOueelePfDr/gol+zx8W/j3Zfs/fDiXV9d1S/N6sOswWsaaMzWFvLcShZZJlndSsLBXSAoxwQxU7q+Sv+Cfs/7Acnx31Ff2ZLfxdF4w/4R+9+0NrhY2v9n/aLbztuWP7zzPKxx0zQB+0NFfIP7R37cn7PX7Lt5FonxH1m4ufEE8K3Eeh6Rb/bL/yWOFeQFo4YQ38PmyoWHKggGvmDwn/wWO/ZL8R6vFperWfi3w1DK+37dqem272qAnAL/Y7u6mHqcRkD1oA/Sf4jfEfwT8JfBuofEH4i6rFonh7SvI+2X0yu6Q/aJkt4srGrud0siKMKeTzxzWB8I/jd8K/jvoF14p+EniCDxHpVjeNp9xc28csax3SRpKYyJo42yEkQ5Axz1618hf8ABS3WtI8R/wDBPzx/4g8P3sGo6ZqVv4curO8tZFlguIJta0545I3UlWRlIIIOCDXgf/BFJlT9mrxkzEADxxdEk8AAabp9AH3Z8Zv2xfgR8BvHOifDXx/q90vibxBHFLYabp9jPfTutxMYIdwhVtpllBVAeWINe5+OPiB4G+GmgSeKfiHr+neG9IiZY3vdUuY7WDzH+6geRlBdsfKoyT2FfzqfshXjfthf8FOdd+M2tZu9K8Pyal4lsYp/mVLSxaOw0mMZ4DQ+bDKMYy8Zb1rs/wBqH4MftEftm/t52/gLxP4d8WaH8LdJ1A6PYa22j3kek22n2cLTXd1HcSRfZWlu5EdYpSSrkwp8yhQQD9mvAH7Xn7MvxR15PC/gP4kaBqmrzOY4bIXQhnncfwwpMIzMfQR7sjkcUv7QX7VHwf8A2YbbRb74vXt9p1rr8lxFZT2thPeRtJbBGdHaFW2NtcFQ2NwDYztOPwx/4KKf8E/fgt+y98J9D+KHws1nVra9bWbfSZrDVLqO5+1edFLKJoWSKJ0ljMOWAyhU5AUj5vpXxnbeKP2qP+CQlp428emW/wDFXhizn1qDULobpZx4evZ7aS4LY3M0mnrKrOeWfLEnOSAftloms6V4j0aw8Q6FdR3um6pbQ3lncwndHPb3CCSKRD3V0YEH0Nadfk9/wR/+M138RP2bbr4e6vM0+ofDrUjYRM7bm/sy+DXFrknn5HE8SjoEjUDpgfW3x01n9ra+8UWfgT9nnQfDmm6Xd2K3F9448RXZnisZHkdGt4NMiXzZLhUQSK77oTvCttwTQB9MazrejeHdNn1nxBf2umafarvnu7yZLeCJfV5JCqqPckV8YeKP+CiX7L2jaq/h3wnrmofEPW0z/wAS7wVplxrcjdvkmiUWz5PA2zGsPRP2APBniXUoPFf7T3i7xB8bPEMTeaq67cNaaFbSd/suk2rLDEpycozOh/uivtnwr4M8IeBdKj0HwToeneH9Nixss9LtIrO3XHHEcKov6UAfEg/bg8b3Q+16X+zR8YZLLrvudGhtbgr6+Q1wWz7Zr0L4U/tt/Bj4neL4vhtqcWufD/xxcY8jw14202TRdRuCe0G8tDKxIO1FkLt1C8HH19Xhf7Qn7P3gT9o34e3ngfxlbKlyFabR9XiUC+0jUFGYbq1lGHR0cKWAYB1BVsg0Ae6UV8i/sTfFnxX8VvgjHB8RX8zxv4H1fUPBnih85MmqaLII2lY8ZeWJo5HOAC7NgYxX11QAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAf/W/fyiiigAooooA+Nv20vhD+0X8Y/AuhaH+zd45TwJrdlq32u/vH1O90sT2fkSJ5XmWMMzv+8ZW2sAvGc5Ar83f+GGP+Cqf/RxcH/hX+If/kCv0i/bS+BHxo+PngTQvDnwS8eSeAdU07Vvtt3ex3t5ZG4tvIkj8nfZfO3zurYb5eM9cV+bv/Dt39v7/o5K5/8ACg16gD0P4R/scf8ABSXwp8U/CHifx/8AHiHWvDOk65p97rGmjxRrlybywt50e4gEM1kkUnmRhl2Oyq2cEgc1+1Ffiv8ACP8AYE/bb8E/FPwh4x8X/H641vQtD1zT9Q1LTW1vWphe2drOkk0Bjm/duJUUrtf5TnB4r9qKAPz0/wCChH7JHxI/a88I+EfCPgXXtI0S10PUbnUr7+1TOBNI0Qig8sQRSfdVpd27H3hjPOPlrR/+CLXwyuvh5HbeNfHviG68etZJGdStXhOlW80abYo47WWAzyW8QCpgzozKvy+UDtHff8FVPgL8cfHvhnw58VfgXdazNd+F47m01nR9FuLhLu5s52R4p4YYGBlNu4cOoDOVkDAYRq+KfB//AAWV+Ivg34SWvgHWvAEOp+ONHsV0uPXLrUJI4ZJbdPKSe6svIMjTLtHmoJ0Ej5OU+7QBV/4JTfFPx98Jv2odd/ZY8RXzz6RqTavaPYGV3gtNa0TzHkmtw3C+ZHDKsmAvmYQtygFf0pV+B/8AwSq/ZQ+KEXxH1T9rL4xWV3pjXtvdDRItQi8q71C51Q7rm/aNgGji2FlQlR5vmll+VQW/fCgDxjxp+z78JPiF8TPCXxf8W6BFf+K/BBc6PfO74iDbioePPlyeVIxliLKTHJhlIOaTwF+z38I/hl8QvF/xS8FaBDpviTxzMs2s3cbuRKy4LBIyfLiEkgMsmxQXkYsxPGPaKKACv50/+CnX7SEXxM/aC039kvXdfbwP8O/DV7Yz+KdXaCa6NxcXEEd0JPItUllljt4ZQsMQXDTsWfCqrr/RZXxX8U/+CfH7Kfxn8e6t8TPiH4TuNR8Q620L3tymrX9usjQQpAmI4bhI1xHGo+VRnGTyTQB+JX7f3xz/AGTfGXwC+F3wa/Ze1oX2n+Cb+bdaf2dfWbRwNb7fOkku7aBZZZZSXkYEszsWPWv1s/4JxftF/B74hfA/wV8GfCOvG+8YeDvC1q2tacbK7hFsI2WJv38sKQSYd1H7uRuuemTX5if8FRP2PfgB+zV4A8Fa78HfD82jXus6xc2l5JLqF3eCSGODeqhbmaQLhucqAa/TT/gm9+zf8Ifh38E/Bvxq8J6PJaeLvGfha2XWb1ry4mS4ErrK2IZJGijy6KfkRemOlAH6SV4lbfs6/B21+MWrfHmPw5bt401zSxpF9fSFpElttqof3LkxrI8aLG7qoZoxtJwTn22igDx34K/AP4Vfs9+Hb7wt8JtETRNO1LUZ9Uuo1kklMlxOf70jMQkaBY41GAqKB1yT7FRRQB/K/wD8FgfBPgzwH+0h4Y0nwNoGl+HbGbwRZXMlrpNnDYwvM2o6khkaOBEUuVRVLEZIUDOAK/eP4ufA74DSfs1eKfCGtvY/C3wfrOnWc/iLVvD9vZaTJ5Fq8cv7x/s7xtvK+WdyMzK5ReWr8Q/+C1n/ACdD4T/7EKx/9OeqV+qX/BS74dePviV+xhc6X8PbO51K60y60rVb6xtEMk9zYWqt5oSMfM/lsyTFVBOIzgGgD4K+Cv7Sf/BOH9lSz1Xwn8J/Dniz4s6vql48o1e80OxuLqRCixi2iluFs5UthtLhRASWdic8AfGH7PPiWTUP+Ck/hXxPo/hmTwAmreOGmi8P+WYG0621ASA25QpHtDQykEBFXDfKoXAr6L/YM/4KCfBX9mH4PTfDXXvh9rd/4zn1OeQXmhW1rK+qmdgIY7iSWaKdHj4jVFSUYAI+YkV8+/D/AMV+MtS/4KZ+GPGfxa0Sbwhr2uePtOv7rSLwGOWwXVmje0hcOFbIhmiGWVWOckKSQAD3T/gpx4h1741/t2eGvgDcag9vpGky6DoNpHj93Bc+IDbyz3IB4Z2WeJSemIlHY5/Zfxt/wT+/Zh8T/Bi9+D+ieCNE0Bm09rbTteg06KTVbO8WPbFeNdfJcTyK4VpA8v74AqxwePzj/wCCqv7K3xRX4maT+1p8H7G71T7Db2n9tpYx+dc6ddaUwa2vhGuXeIoFDkKfKMW5jtb5eK+In/BY3X/HvwZvfAXg7wNfaH8Rdesv7KbU7O93QWs9woiknskVPtHnNlvJQkNExU73K/MAfpH+yT+wXpH7Nvg7xT8PfGfiSz+J/hnxHe2epRaXq2gxRWtneWyukkoimubyN2lUxjO1Svljk54/FP8AYf8ABfg7xH/wUcv/AAn4h0LTNU0NdS8XIumXtnDcWQWBbrygIJEaMCPaNg2/LgYxiv2C/wCCZvwr/aD8GfDHWPHP7ReveI77WvFs1u2naR4j1C7u7nTLC1EgDPFdO3kS3LyFmTAYIke7Byo/EaPx140/YS/by8Q/ELxd4SuNROna74gaKyndrBdRsNTM6RXFvcGOVdjJKkqkKwP3TtOcAH21/wAFn/GU3hTS/hh8BvCEUei+Fxb3ms3Gm2EK21o7QukFoqpGqoFhzOQgAXLgkZCkfoh+zn+w5+zToH7PHhbw34h+H/h/xFfaxoVldazqmpWENze3N3dwLLKyXLqZoVV3IiEbr5agbTuyx+Qf24Pg74v/AG8f2Xvhp+0v8K/D86+JNOsZ71vDcMq3VxPpmoFPMWKTZEZp7d4FdEVQXV5AoLhVbxH4P/8ABX7UfhL8INM+GPxF+Hd7qni/wlYR6PBci8FnHcCzQQwm7jliaWKVEVVlwHLsC3yZwADyT9kqS9/Zf/4Kd33wd8LX07eG73X9X8LXEBO83FgySy2PmdQZIZVhYv14ccBmFeX/ALePhXUvHn/BSfxT4F0a4+y33iXXfDOjW83OI5NR07TrZSdvJAMnIHUcV9c/8E3/ANnP4vfFz9pG+/bV+MOm3Gk6c11qWs6c1xCYBq2qasJo2a3iky4tLdJnZX+6WEaoWCvt8Q/aL/5TE6X/ANlF8Af+i9IoA/cf4F/sH/sz/s96jo3ibwF4XY+KNFieOPX728uJ72V5oWt5pGUyCBTJG7AqkSoM/KoODX4b/wDBG3/k7nW/+xN1X/0tsK/qMr+XP/gjb/ydzrf/AGJuq/8ApbYUAfUn7SPh3/gnd8Hv2ptS+Lfxp8ZeJfG3jk6sdX1HwfBBaapp8btGBBbXMZtY4xHCnl7IZLnftRd4Kk7vjP8Ab/8A2mvhb+0f4T8K3fwn+EupeE9K0W+eP/hJtQ02Cw89ZYW8uxT7KZYipCGUKZiRs+VQNxrnV1mT9jz/AIKBa144/aO8HXfiKxg1/WtRUPBHI95HqLzPbalaCcrBOwMiyKC67GyMpInHqv8AwUL/AGwb39rv4ZeH9R+HPgXxBpHw18N65/pPiDWYI4ludXnglSC3j8l5ol8uFZWbErnLDcE+XeAfTeuX13qH/BDWO4vZWmkSzsoFZzkiK38XxRRr9EjRVHsBS/8ABMvV7nw//wAE+Pjtr1kzJcabeeJryJk+8JINAtZFI9wV4rFbULS//wCCGMyW0iu9p5VvOoOTHIvjKNtrehKMrY9GFekf8Ei/DMXjX9jT4peDZn8uPXvEer6Y79dq3mj2UJP4B80AeGf8EPbCCTx18VdUYL51vpOkW6H+LZPPcM2PbMS5/Cv320f4k/DvxDrMnh3QPFOi6nqsPmeZY2eo29xdJ5RxJuhjkZxsPDZHB61/PJ/wRl1qfwl+0X8QvhnriGzv73w/IXhl+Vxd6RexxvFj+8BPISPRD6Vzf7Rnw/8Ajb/wT/8A2zL/APac8E6JJq/g/VtYvtWtrwxOdPaLWWka70y6ePP2d0MjrCzfeCpIu4hlABw37aP7Gv7U3wX0fT/jJ8aPGcfxa8O6ffw20rX+q6leSWoncbY5kuWjlSCYqIy1vMGzjlPlav2I/Zy+NPg79pj9gjxLNoPh6z8LQ6T4c1vwnqOh2QIsbWW3048W+75vIkgmRxuJZSzKzOVLH8nf2oP+CiXxE/bc8FWX7Pvww+Gt1ph1u8tp762tLl9av797ZhLHBDHFbQ7I1lVZGbazNtH3AG3fo98Ifg5q37FX/BNv4hxeOTDB4m1LQtc13UYFIK21/qVktpa2jSAkOybYUcr8vmFgu4YZgD4v/wCCIOqXMXxL+J2jIW+z3WhafdSAfdMltcuiZ98Tvj8a/o4r8Cf+CH3gi5S1+KfxInRhBLJpeh2r4+VniE1zcjPqokgP/AvpX77UAFFFFABRRXmfxL+Mnwv+DlrpuofFLxJZeGLPV7o2NndaixhtnuAhk8tpiPLjJRWI3soIBx0oA+S/2OALX41/tQ6XbcWifEZLtVHTz7uzR5z9SwGa/QCvzw/4J8anp3jCz+N/xQ0+6gu4fF/xY8QXdlJDIsgfTIBDDaPlSeGCuVPQjkcV+h9ABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQB//1/38ooooAKKKKACiiigAooooAKyZNA0KbUk1mXTbR9QjGEu2gQzqPaQruH4GtaigAooooAKKKKACvkH4o/t6fsn/AAX8d6n8M/iX45/sbxJo32f7bZf2Tqtz5X2qCO5i/e21nLC26GVG+VzjODgggfX1IQD1GaAPgdf+CoH7C7dPian46HrQ/nYVKP8Agp1+w03T4nRfjo2sD+djX3c1vA33o0P1UVEbCxb71tEfqi/4UAfDa/8ABTT9hxunxPt/x0nVh/OyqUf8FLf2H26fFC0/HTNUH87OvtptJ0pvvWVufrEn+FRHQdDb72nWh+sCf/E0AfGC/wDBSf8AYibp8UbL8dP1IfztKlX/AIKQ/sSt0+KWn/jZagP52tfYjeGvDjfe0qyP1t4//iaibwn4Vb72jaefraxH/wBloA+RV/4KN/sUN0+Kemfja3w/nbVKP+Civ7FbdPippX4wXg/nb19YN4L8HN97QtMP1s4f/iKibwJ4Hb73h7Sj9bKA/wDslAHyFb/t2/sHW+oyaxa/ELw3Dfzf6y6SzmSd/wDek+zBj17mt1f+ChH7GDdPitov4i4H84a+mj8PfALfe8NaOfrYQf8AxFRN8Nfh033vC2in66dbn/2nQB83r/wUC/Yzbp8V9C/Fph/OKs+L9t79hqPUn1mH4j+FU1CUYe7ClZ2HoZPKDH8TX023wu+Gbfe8I6Efrptt/wDG6iPwn+Fjfe8G+Hz9dLtf/jdAHgi/t7fsct0+LPh38Z2H80pk/wC3L+xleoI7r4qeFpkzkLLcBhn6Mte8N8IPhK33vBPh0/XSbT/41UTfBj4PN97wL4aP10i0/wDjVAHjEf7c/wCx4FCR/FrwuqqMAfbVUAD8BVG4/bH/AGJ7++h1O7+Jngue8tv9TcS3UDTR5/uOw3L+BFe4N8Efgw33vAPhc/XRrP8A+NVE3wK+CLfe+HvhU/XRLI/+0aAPLl/bd/ZEbp8XfCX46nCP5mpR+2v+yO3T4veEPx1a3H82r0ZvgF8Cm+98OfCR+uh2P/xmoj+z38A2+98NfCB+ug2B/wDaFAHBj9tH9kpunxf8G/jrNqP5yVMv7Zf7JrdPjB4K/HXLQfzlrsT+zp+z433vhj4NP18P2H/xiom/Zt/Z1b73ws8FH6+HtP8A/jFAHCan+1T+xtr0SQa18Ufh9qEcbbkS71jTplVvUCSQgHjrWrB+1z+ybDEkFv8AF7wHFGgCqi+INPRVA6ADzgAK6Bv2Zf2bm+98KPBB+vhzTj/7b1E37L37NDfe+EngU/Xw3pv/AMj0AZq/tcfsqt0+MfgL8fEunD+dxWrpH7Tn7NviDVbPQ9B+K/gjUdS1G4itLOztfEWnTXFzcTsEiiiiS4LySSOwVUUFmYgAEmq7fsr/ALMLfe+EHgI/Xwzpn/yPVvSv2Zv2b9C1Wz13RPhT4I0/UtOuIruzvLXw5p0FxbXEDB4pYpUtw8ckbqGVlIZWAIIIoA/Jn4tfszfGn4Ef8FDtE/aW+CPg/UfFHhLXr/8AtXW4tMRXa1OoB7bV4mTcpZpFke5iJGC74/gzX7pMqupRwGVhggjIIPY06igDK0zQtE0Xzf7H0+1sfPYvL9mgSHex6ltgGT7mvzg/4Kj6J8b/AIhfA/TvhD8FfCGqeJn8TalHPrc1ggaO3sdOZZo4nJZfmmufLZcZ4ibPUV+mlFAHyx+xj+z6n7M/7PXhr4ZXJR9ZWN9S1yWPBV9UvcPMAw4ZYRtgRv4kjU96+p6KKACiiigAr86/+Cp/w7/4T/8AY18VXUEXm3nhO6sPENsMZI+zS+RO3tttbiY/hX6KVXu7O01C1lsr+CO5t51KSwzIJI3RuqsrAgg9wRQB+O//AAS6/YWuvg7okP7QHxUtJIPGevWhXR9MmBVtJ064XmSZDjF1cKeVIzFGdpw7Oq/shRRQAUUV5X8TPjj8Hvg1ZC/+KXjHRvDKMu+OPULuOO4mA/55QZM0p9kRjQB6pRX5+/8ADxX4X+InK/B3wH8SfijFkql54Y8L3D2Jx3aa6NuVX32USft2a7oi/bvHH7PPxc0TS15lv4tDjvo4E7vMsM+5EA6nBx6GgD9AqK8k+Dnx1+FHx+8MHxb8JvENtrtjE4iuUjDRXNpMRnyrm3lCTQvwcB1G4DK5HNet0AFFFFABRRRQAUUUUAFFFFAH/9D9/KKKKACiiigAor42/bS/a6t/2PPAuheNrjwtJ4rGt6t/ZQto74WBiPkSTeZvME+7/V427R1znsfzd/4fj6Z/0Rq5/wDCjX/5XUAfvXRX4r/CP/gsTp3xV+KfhD4ZR/Ci40xvFmuafoovW15ZxbG/nSDzTH9gTeE37tu5c4xkda/aigD43/b7+MniT4Efsr+MPH/gvUDpfiOM2FlpN0qRyNHcXd3DGzBZVdCVhMjAMpHFfjd8LvG3/BY740eBtN+JPw11e/1fw5q/n/Yr0y+GbTzvs0z28mIrnyZQFljdclADjIyMGvsb/gtFrmrN8EPBPgPR7W5uZNa8StqEy28TyHyNMtZEIbaDgGS6Q891FfJ3w48Qf8FV9D/Z78Jz/A7wifC/gLwxo8f2OCG10u41TU4lLzTXbWuoebeOZpGZ1SKFNyMuxX++wB+h/wCwvp//AAUHs/GviR/2xXuG0FtLjGkedcaLMPt3nLuwNLdpB+6zy/y/jX6a1+SX/BOX/goVr37TOp6j8Jfi7b2dv41060bULHULNPIi1S1jYLMrwcrHcQ7lb5DtkQkhV2Et+ttAH5Gftvf8FBdD+Gdl8TPgTZ2Hirwl4yt9OjTwz4litmhsry5dIZXaCYlJU2F2jEio8bFSdwBGdD9iv/goL4d+KkXw0+Bf9m+KvFPiyXRlj8TeJ5rYyWNpew2zykzygvK/mSJ5ZkZEQMwO4jOPZP2y/wBlr4lftU+MPhv4Sl12zsPhHpmoNqHizT4y8Wp3U0SsYvLcAq6MmYVAKGJpGkIkwoR/7FP7L/xQ/ZW1n4h+BrvxBZ6r8LLzVBfeELV976nbmZQZTM2FRF27Y2X5vMkjMqiMMQ4B9+UUV+WH/BSP9rzV/hZoFl+zz8GHmvfin8QVSziSwy91ptldt5KtGF+YXV0xMduB8yjdJlSI9wB8Xft//wDBTX4haP8AFZvhj+zJ4kGj6b4VeW21jWbaC3ujqOo52yQxGeOVRBbEFNygGSTdyUVSf2Y/ZK8Z+JviJ+zV8OfHHjO+bU9c1rQLW7v7x0SNp55AdzlY1RAT6KoHtX89v7Z37IekfsnfsnfDHTL9Ibnxx4h8QT3niXUEw374WZ8uzifvBbBioP8AG5d+AwVf3q/YV/5M++En/YsWX/oJoA+sK/n/AP24P+ClTa34Bv8A4d/Cw+Mfhl8QtC8WosjXET2D3mjwJdxGRJAwlQSt5LmKWNCOMFtpNf0AV+YH7Rf7CvjP9q79o5vEPxg8Rwp8KdF8OT2nhvT9KLxX8GqXabHknVgVYpLifeGKuI4oim3fkA9p/Zr/AG4fh9+1L4513wp8NvD3iKPSNEsRdL4h1GzMFhdyrKkbwxspcI+HV1WRkkZQx2AKTX2tXyJ+xN8JPjN8DfglbfC74y61putzaDe3NrocuniQtFo6Pi3SaRwu4/eZFA/dxsqEkrgfXdAH4lft7/t7/GHw58ZLb9lb9lmPb4reSztdQ1KK2S6vW1C+CvDZWaTBoV/dyIZZWViC20FCjMflbx78VP8Agqh+xf8A2T8SPi1rU2raBqN2IJIdSntNZ05rh1L/AGafyCJbdmVSVMTxg4IVjyK/cWy/Y7/Zz0/40N+0NbeEiPiA17NqB1d9T1GT/SZ4mhd/sz3TWo/duVC+VtXgqAQCP59v24P+CgPj39pbRbT4J6r4Mg+G+gpq8V1ez39xc3txMbdnjjkZltYWS3TeZHWOCSRiq7ScFWAP6NP2cfjdov7RfwW8MfGLQrV7CHX7d2ms5HEj2t1bSvb3EJYY3BJo22MQpZNrYGcD0rxj4iXwh4R1vxY1ldakNF0661A2dlE091c/ZYml8qGNAzPJJt2ooBJYgYr5p/YT8CeAvh1+y14I8O/DnxLa+MNKNtNePrdmGWG7u7uZ5bjajgPGIpGMQSRVkUIA6q+4V9dUAfz2/GP/AIKiaH4q+MPwY8Q+E9R8WeB9B8PapdH4heH7iMxPJCstqVjeOJ2W5XasyqDtYZO5VzX64/st/tSeHv2qfDmueLfC3hrXfD+l6ZqC2lnLrdsIf7Rt3jV1uIWQvERu3qyJI5TCkn5wB+fnxZ/4Jp/Ev9oPxn8Yvil8YfFdpN4s1Bo4PhydNaSKxtLa0xJAtzGw3IGUC3IBYqxlmy7FDX6m/A3Rvid4e+EfhbQvjLqVlq/jOw06K31a909WEE0seVVsuAWfYFEjhVDvuZVUEAAHq9fz8ft2/tnftJar+1VH+yt+zxrMnhtba90vRUmsvLjutS1bVI4XG+5dS0EMRuFjwm3BVnZiCoX9vvjF8Ppviv8AC7xP8N4NXm0GTxHps+nrqduhkmtDMMeaih4yWXsN6/Wv5H/F/wCzpeaB+3FB+zU/jC9u7iXxTpOif8JO8JF2rX625Fx5fnMd8Xm8Dzedo+YdgD+ob9kLwN8e/h58Il8O/tH+I08U+Lhqd1P9vS7kvR9jkWPyYzLLHExKkNxt4z1NfULSIrKrMAW4UE4Jx6etflp8Svgr+1T+z3+yFp/wN/Zc1LUvHni++1e5W+8SSS22nXtlplz5s0jQtd3nyTbzHFG6uzKpZl2MFYfFnhr/AIJP3et/Cyf4o/tSfFO+8JeM7yG4urpdRlt7q306RGfYby9luH+0FlVZHKSJtzjLEZoA/ogor+dP/gjz+0J8TNR+LWtfAzxLr17rfhibQbnU9PgvJmuBY3dnNbp+4aXLxwyRSNmMELuCnaDkmL9tD9o345/tP/tXD9jf4Ea1c6Holpq39gTrbSvam+1G03tfXF3NF+9+y2myQeUpKkRGQqzFQoB/RgGVsgEHBwcdjXC/FLxJqHg34ZeLvGGkrE19oeg6nqVssylojNaW0k0YdQVJUsoyAQSO4r+e/wCMX/BM39oT9l7w1ZfF/wDZ8+IGveL/ABNZ3cK31l4f0+6s9UHnHb59uttcXMlwisQJUZR8hLHK7gPqX4jfDD4zftl/sa6J49+NGoa/8LfGnw007xJLrOnzaTPZNrpt7aN45Xhlktikc8UK7mVHj8x5AqgLtoA7r/gm5+3B8Y/2s/FfjXRPifa6Hb2/h/TrK6tDpFpNbuZLiWRG3mW4mBGFGMAc1+tlfyFf8E//ANlG/wD2qvE3i/RrHx5qPgQ6BYWl00+nwNO10J5XTY4W4t8BduRy3XoK/R7/AIKg/tffFDwZ4s0L9lT4IajeaZql9Y2za3qFgfLvrl78iO1sreQHzISwG+RkIZvMRQwG8MAfutuGSuRkDJHfBpa/nV8Q/wDBIf43+F/h7dfE3Q/irJffEiysWv5tLtoLiLzpY0DvbQakLkzPKMMI3aFVkbAIQfNX1D/wSn/bN8b/AB40zXvg98Wb+XWvEvhi0j1LTtXn2me70vekDpcsMGSWCV0xKctIsnzncuWAP2Jryn45aR488Q/B/wAZaD8LL1tP8YXujXcOi3KT/ZngvpI2ELiX/lnh+jdsV/Kb4R+Kf7Rdh+1R4y8K/BPVNSvPFvinWNd8OaSjXbt9lN5eMHmi81/KjaOFGxIcCEZkBBUGv0e8I/sU/G39lT4G/tCfE/4m+L9O1vVvEnw+1CAzabd3k93FdpmYyPcXEULOcD72d2786APuf9g74aftd/Dqy8aR/tXeJJ/EM1/Lpp0Mzar/AGn5KRLc/aQD/wAs9xaL/ex7V+gZIAya/Cn/AIIo+JPEGt6H8YJdc1C91Vra48PmJbmd52XKagSE8xiAWwO4B4zXBXf7G37cX7Znxt1PUv2q77VPh/4IKT3lnbw31nf2duFdEgsbSytrx0jkMbEvO6ndsJcszAEA/oRR0kUPGwZTyCDkH8a/J7/grf8AFz4m/CD4QeC9Z+F/ifU/C99feJGtrm40y4a3klhFpM+xmUgldyg49RX5B/tF+EdV/wCCeHx90iy/Zz+K15qUn2KHU51hmj320wlkQ2uoQwsbedXVA2yRBlW5TG1m/RX/AILC+IF8Wfsv/CXxUsRgGs61bagIicmMXWmSy7c99u7FAH35+w38Rdb8TfsZ+BviT8S9cn1K+fTdSvNU1bUZTLK0dte3QMksjckJFGBnsq1+Y37DXxC+J/7ZX7cfi74z69r+tWngvwukmp2ugxX06aehmJtNLtZIEcRErErzudvzyxFiDuNeo6N4xuPA3/BFM6xaMVmuvDl5pAwcErrGty2EmP8AtncMT7Vf/wCCJfhiC0+Bfj7xggC3Gq+K101mx/yz06yglT6gNev+tAHjf7Sv/BQD9pT4xftAXP7On7FqtAlhfXGmJfafFbz32r3FsCLiUTXSmG1tImV9silcqvmNKFYKvjuo/tV/8FGP2HPH2i237RMtzr+jaqGlWw1mW2v7e+t43UT/AGbULUs8U8YYDBkIQspeJlK5/Vb9lr/gnv8AD39kf4ia18X7bxff69eXekXVjM+rQ28EVtHNNFcTXG9cbWxDgkkAKzZr8s/+Cnf7THhf9qv4heC/gX8AoZPFx0C+uVN9p8ZmXUdTvfLhS3ssDMscYQ7pV+SRmyuVQMwB7X/wUg+InjjQYvgn+2p8CfFOt6doXiS3smexF3PFZNcRqNQsftNqkvlM80RminjIIIhCnPNftX8HvidpPxl+E/hf4qeHVUWvibSrfUEhL58mWRP3sDMB96GUNGxA6qeK/L79sj4FXHw6/wCCVdh4A1xo5tW+Htl4buZ2jYyIL83cNvdeWx5KKbuUKePlHQdK63/gjh4xuvEX7J934du2yPCvinUbC2Gc4t7mK3vR9P31xLQB6hL4N/bq/aFnk/4TfxFY/s/+DpHZRpPhiVNW8VXMOekupkCC1LDBV7cBx0ZK9Y+GH7DX7NPwuvTr1t4Uj8T+JJGEs/iHxXIdc1Sab/nqZbrekch7tDHHX1zRQA1ESNFjjUKqgBVAwAB0AHpTqKKAPzS/ay8Laf8As0/ELw1+2x8PIF0hINXstE+Jdrar5Vtq+gapMsH2ueNcK11aTtGUkwGYsu5sLg/pYCCAQcg9DXx3/wAFA7S1vf2M/ivDdgFF0IzAH/npDNFJGfqHUY96+i/hddXN98M/CN9eEtcXGhabLKT1Mj20bNn8TQB3VFFFABRRRQAUUUUAFFFFAH//0f38ooooAKKKKAPjb9tL9qTxB+yn4E0Lxh4d8FyeN5tX1b+zXtI7mS2MCeRJN5u6OC4J5jC4Kgc9e1fm7/w+R+J//Rv9z/4Nbn/5W1+9dFAH4r/CP/gq38RPiX8U/CHw7v8A4IXGj23ibXNP0ibUG1K4kFpHezpC0xRtPQMIw+7BZQcdR1r9qKKKAPzW/b8/bZ+I37HV/wCEZ/Dvg3T/ABFovieG8R7y8mni8i8tGQmImMFfnjkDKDgna2MgHHrXgn9vf9mLxL8HbH4taz4+0HRs6fHc6lpM99ENTtLsR7prUWZb7TLIrhlQJG3mAbkyDmvob4s/CH4dfHHwVefD34o6LDruhXrJI9vKWR45Y87JYpY2WSKVMnDowOCR0JB/Lm6/4Ip/s5za19rtvGHjK301mZmsxPYvIATkKk7WfCjoNyMcdTnkgHwh/wAEyNI1X4qft7a58XfDdhPYeHtNPiDWrpVXENvFrJmitbV2GVDEz7lUHJ8liOFNf0/V4v8AAv8AZ9+FP7OXg1PA/wAJ9Fj0qyZllu52Yy3l9OF2+dczt80jkdBwqjhFVeK9ooAKKKKACvwi/al/4J1/tW/E79qzxT8f/g94n0Tw/HqU9lLpV5/a97p+qW3kafBaSEPbWrGMkxuBsk5Q89SK/d2igD+RT9tL9nf9r/4LeFvDmq/tJ/EKTxnpmo6hNb6bA/iDUdZFvcpFud9l7Gix5TjcvJ6dK/Vr/glX8Gv2jfB+iWfxK+IfjF9X+HXiTwpCvhrRG1i9u109nnikQiymQW9viJXX90xxnA4Jr7r/AGpv2Rfhv+114e0Tw38SNS1zTbbQLyW+tn0Oe2gkeSWPyyJDc21yCuOgAU5717J8J/hroXwd+G/h34XeGZ7u50rwzYRadaTX7pJdPFCMKZWijiQse5VFHtQB6FRRRQAUUUUAfg3Y/wDBQ340/CH9ubWvg7+0brNtZfDmy1/U9OEr6ZDBJb2M/mNpV28saCQxMjQM7ZI2MWOcV6J/wVU+PP7NPjP9mSTwro3inw94t8W32o2Fx4fXR7y21Kaz8uZHuLhngaTyYnthJFksu9nAGQDj7j/aZ/Yf+Av7Vn2fUPiLp11Y6/ZxLBb69o0yW2orApLCF2kjlhljBYkCSNiuTsK5OfkbwR/wRm/Zn8N67Fq/ijW/E/iq2t5fMXTbq4gtLWVR0Sc20CTsPUxyx5+nFAFv/gjVoPifSf2WNU1LW/OTTtY8V311pEcqsFNtHb2sEksZbgo88ci/LxuRj1Jr9a6yPD+gaJ4V0Ow8NeG7GDTNK0u3jtLKztkEcMEEKhUjRF4VVUAAVr0AFFFFABX8ufxS/wCUxVl/2Ufwx/6BY1/UZXwXr/8AwTu+CviL9pSL9qW91rxSniuLW7DXls4ruyGl/adPEQiUxNYtP5Z8ldw8/ccnDDjAB83f8Fd/2j/ip8FPBHgjwZ8MNVuvDzeNJtUbUdVsWaG7WDTltgsEM6kNF5jXO52TD4QAMAWB8R+CX/BP/wDZg8S/APw/+01+0f8AEfWfEg1PRotc1SaXV47fTrbzIhNLaO5V7p54TmKQCdXaRSAqniv1x/aO/Zl+Ff7UngdPA3xRs53itZjdafqFlIIb6wuCpQvBIyuuGU4ZHVkbAypKqR8GeBP+CNP7NfhjxBBrPijXPEviu0tZvNTS7ueC2s5gOizm3hSZx67JI89DxkEA/Nj/AII+yWz/ALY149qhigfwtq5iQnJVDPbFRnJyQPc0vh3WrT9k/wD4Ktal4i+K5fTtGPi7Xbh7+6Bwun+JI7oWt4XwcoBdI0jjoA4OCCB+0HwJ/wCCc3wP/Z2+MLfGf4dax4oTUmS+h/s26urKTTFgvs7oRHHYxzBIztMf77I2LuLDOfUf2lv2NPgX+1XY2y/E7S5odXsEEVnrulSLa6pBDuLeUJWjkSSLLMQksbqpYlQrEmgDg/2rv27/AIV/s3fDK38aaFfaR441rVLmCDStFsNXhDXMbndLcNJCLgpBHGCd/lspcomRuyKHw/8Ajz4k/ad/Yk8cfFrWvCX/AAiX9seH/E0GnWKXbXxntbe0liE+8wQH55lkUKFIwoIJzgfPvgr/AIIzfsz+HPEEWseJtc8T+KLO3l8xNMuriC1tpVHRJ2toI5nHr5ckWfpxX6vad4d0HSPD9v4U0vT7a10a0tEsILCKJVto7WNPLWFYwNojCDaFxjHFAH81v/BHP4n/AA5+HHxG+Ii/ELxPpHhldS0Wy+yS6xew2EU7W87mRUkndEZ1Dg7Qc4ycYBxS/wCClWm6l8If2/PDnxv1SzmutC1OTw54hs5BhorgaIYIriCJj8u5fs6sy9vNVjwwJ/QzxV/wRp/ZZ8Qa9c6xpOr+LfD1tcytL/Z1he2klrCGJOyE3NnNKqjoN8j4r78+Nf7Onwl/aD8Cp8PvipoqatYW65srkN5V7YzBdgmtp1w0b4AyOUfGHVl4oA5bxR+2B+zt4a+E1z8Yl8daHf6Klg17aJb30TXV5Js3R20dvu877Q7EKYygdCfnC4OPxI/4Ir+BNe1X44+NvimbaWPRtL8OyaS86DZC19qN1bzJEOzbYrd2KjO3KE4yufsO3/4Ip/s4R6ybqfxf4zm00YK2f2iwWXOejTiy5U9MBFPvX6h/Cb4QfDj4G+C7X4f/AAt0O30HRLVjJ5MGWeaZgA008rlpJpnCgM7szEADOAAAD+bf9ga3hm/4Kd3kkqhmg1XxlJGT/CxjvEyP+AsR+Nfvj+2x/wAmjfF7/sUNV/8ARDV5Z8IP+Cd3wV+Cvx1n/aD8La14puvEU8+p3DWuoXdlJp4bVRIJgI4rGKXC+Ydn73jjO7v9cfFH4eaL8Wvh14j+GXiOe6ttL8T6bcaXdzWLpHcpDcoUdomkSVA4B4LIwz1BoA/Bz/gj94ouPA/wj/aL8aWlt9sn0DT9N1SK2wT5z2Vpqkyx4BBO8pjgg814P+yf4O8U/wDBST4x+Kbf9oz4ra9HbafZrqMej2V4sT3ImlKtHZQSiS3ggtx98JA3316ZJr92v2W/2K/hR+yTp/inTPh7qGuaxb+LjaHUE8QT2t0ALNZ1RYxb2lsNrCdt4cPnjGOc/JHjn/gjX+zd4o8WXHiHw7rviTwtZXczTSaVYS20ttDuJJS2aeB5Ikz0DNIB0GBgAA/HT/goV8Gv2e/gF8TtC+GXwHvJtQex0kz+IJ574X8q3s8reVFIyBY45FhUMURFwHUnrX6Hf8FTyD+xb8BiOQZtKx/4Jmr6h1z/AII+fslax4d0PQbSXxRo8uji48/UbDULY3uqPceX8941xZzx5j8vEawRwou5sqSc19E/Gz9iP4XfHv4QeCfgx431vxJHpHgRLRbC8sbmziv7j7HafY0a5eSzliYtHy3lxR5bkYHFAH5y6vod1rv/AARDs47JGklsrGG+2r/zzt/EpaUn2WLcx+lerf8ABFbWbWX9mjxfpLyKJdP8a3VxICcbYbjT7DYx9i0T8+1ff/hr9mXwJ4S/Zqn/AGXNMub+78LyaJqWiLc6i8U175OpmZndmiigjLo05KFY1xhe4zX4r/8ABLK+1L4afG74v/sf/EWabRtR8QWNzaqImCTJqOkGaGf7Mzqyl3tp3mRirKVhDYI6gHi/7Zf7b3i/9rj4qt8FPAvia08H/CpdSOnreXV0bS01JInIbUNQm+8bbCmSKAA8bfkeUqB+if7Jn/Dtn9k/S473Qvil4X1/xpND5d94nv7hPtLbx88drH8y2sB/uoS7DAkd8DFv/hyp+yz/ANDT8QP/AAY6X/8AKmj/AIcqfss/9DT8QP8AwY6X/wDKmgD1n/gob8S/Bni//gnx448ZeDNXtdZ0fxAmlW+m3tq/mQ3JOsW0b+W3fb5cmfdTXj//AARY0K5sP2avFOuXCFF1XxldCAn+OK2srNNw9vMLr9VNfOX/AAU3tfC37O/7Nfwp/Yu+HN7f6jGdQn1Ui9eOfUHsoJZjCsvkRQo3nXV2/l7Y1z5GOoOf1z/Yz+DE3wC/Zo8DfDbUIfJ1a108XurIeWXUtQZrm5jJHB8qSQxA/wB1BQB9P0UUUAFFFFAH5cf8FFP2hPhVqn7P/wARPgX4d8Waa/j+8vNH0F9Dlm8i9ja7vrR3bypQjPGLcli6bkAIOcV+meiWVnpujWGnacwe0tLWGCBlIIMUaBUII4IKgdK/nf8A+Cjf7Jvjv4u/t3eG9E+Gmn+ddfEfQbS7ubtwRb2jaYxs7q4uHAO2KC3S3JPViyooLsob9xP2dfgJ4O/Zs+FGj/CnwV5kttp6mW7vJv8AXX17Lgz3EgyQpdh8qg4RAqjhaAPcKKKKACiiigAooooAKKKKAP/S/fyiiigAooooAKKKKACiiigAorhPiZ8SfB/wg8C6v8SfH96dO8P6HCs97ciJ5jGjusa4jjVnYl3UAKCcmviL/h61+xD/ANDre/8Agj1L/wCRqAP0Yor5e+A37Y/7P/7S2uan4d+D+vz6vf6RaLe3cc2n3dmEgZxGGDXEUat8xAwCTX1DQAUV578V/iZ4b+Dfw6174n+Lxcto3h21N5eC0jEs5jDKvyIWQMcsOCwqx8MviHoHxZ+H+gfErwqLhdI8SWMWoWQukEU4hmGV3oGYK2OoDH60Ad1RRRQAUV85fHv9rD4Efsz/ANkx/GLxGNIn1zzTZW0NtPezyJBjfIY7eORkjBYAMwAJ4GSDj134f+PPDPxP8FaN8QfBly15oev2kd7YXDxPC0kEnKsY5ArqT6MAaAOwoorwT9oz9o74efsveA7f4i/ExdQfSbrU4dKQabAtxN9onjllXKNJGNu2FsnPXHHNAHvdFICGAYd+aWgD4A/4YHcfc/aO+P34+N8/+2dH/DBd6PuftIfHkf73jFW/9tK+lPjd+0T8G/2dfD8XiT4v+JbbQre5YpaQEPPeXbjGRBbQq80gXI3Mq7UyCxANfLngH/gqR+xz4+8QxeG4/FVzoE9xJ5UFxrtlJZWcjds3GXiiB7GZox6kHFAF/wD4YQ1cfc/aR+OY+vixG/8AbSj/AIYV8QD7n7SfxuH+94njb/21r79R0kRZI2DKwDKynIIPQg9wadQB8Af8MM+Kh9z9pX40/j4hib/22o/4Ye8aD7n7S/xj/HXIW/8AbevoP4oftH/Dz4R/Ej4f/CvxWuoNrXxKvJbHRTa26y24lieGNvPcyKY13TrghW4z6V75QB8Af8MR+Ph9z9pn4vf8C1eBv/aFH/DFHxHH3P2mviz+Oo25/wDaFff9fOXx5/ay+Af7NUVoPi74ph0u91BWe006CKW8v5kXPziC3R3SMkECSTZGWBAbPFAHhv8AwxZ8UR9z9pz4qD63lq3/ALRo/wCGMfi2PuftPfE4f701o3/tKtH4Qf8ABSP9kv4zeJLbwfoPiqTR9YvX8q0ttetX09bmQnaqRztugMjkgIhkDuThQTxX3dQB8Af8MbfGQfc/ag+JI/3jZt/7To/4Y7+Nw+5+1D8RP+BR2Tf+06+/68DT9o/4eSftCyfsyquof8JhFo/9uM32dfsP2UgH/XeZu38jjZj3oA+fP+GP/jwPuftR+Px9bWxb/wBko/4ZE/aBH3P2pfHQ/wB6wsG/9lr7/ooA+AP+GSf2ix9z9qfxoPrpWnn+lH/DJv7So+5+1T4vH+9oumtW58dP+CiX7L/7P3i248B+Mddu9Q8QWW0Xtho1o149ozAEJNJlIVkwclN5dR1AyM/Vnw1+IHh/4reAdB+JHhQzHR/EdjDqFkbmPypvJmG5d6ZO1sdRk0AfG/8Awyl+08PuftVeKh/vaBpjf1o/4ZX/AGqB9z9qzxIPr4a0tv619/0UAfAH/DLn7WQ+5+1dr/4+FdKb/wBmo/4Zg/a7H3P2sNaH18IaSf8A2evv+igD4A/4Zl/bFH3P2sdV/wCBeCtIb/2pR/wzV+2ePuftZ3//AALwLo7f+1K+2PGPjTwj8PPDl34v8daxZaBolh5f2rUNRnS2tofOkWKPfI5CrvkdUXJ5ZgO9Znw/+J/w6+K+kT6/8M/Eul+KNNtrlrOa70m6ju4Y7hUSRomeNmAcJIjFTzhge9AHx1/wzf8Atsj7n7Wt0P8Ae+H+it/7Vo/4Zz/biH3P2t5f+BfDrRG/9rV9/wBFAHzB8FPhV+0l4G8VXWrfGH45n4m6LLp8lvBpB8I6doPkXjSxOl19ps5HkfZGkkflEbT5m4nKiu3vf2dvglqPxRi+Nd34P05vHMMkcqa6qul5vhiECMWVgGIiATkHK8HIr2iigAorxD9oL9oDwH+zT8PX+JnxHW/fR0vbewI06Bbifzbnds+RnjG35Tk7uKd+z/8AH7wJ+0p8PIvid8OVvk0eW7uLJRqMC28/m2xAfKK8gxyMHdzQBJ4q/Z1+CPjj4g6d8VfF/g/TtX8WaQbZrHVLtWlmtzZuZINgLbB5chLqNv3iT1r2miigAor43/aV/bm+Cf7KniTSfC3xRj1p73WrFtQtv7Ms0uY/JWRojvZposNuU8YPFfV2leINM1bw7Z+KopPI0+9sotQWS4xHsgljEoaTJwuEOW5wPWgDaor4a/Zp/bs8B/tUfEnxP4D+G/hvWhp/heKWefX7sQpYTR+f5NvsVZGlDXIDSRqyg7EbOCMV9e+NvGfhv4d+ENZ8d+MLxdP0TQLGfUL+5ZWfyre3Qu5CqCztgYVVBZjgAEkCgDoDZ2jXaX7QRm6jjaFZyg8xY3KsyBsZCsyqSM4JAPYVYrwr4DftI/CL9pXQdS8S/CDVpdX0/SbwWN1JLZ3FmUnMayBQtxHGzDawOQCK8NT9vH4eWX7Vb/smeKtA1fQPEL3X2Sz1O8MB067klhFxa+WyyFx9qQhY8r/rCEOGoA+56KKKACiiigAooooAKKKKAP/T/fyiiigAooooAKK+Lv22/wBlhP2r/AWg+DpPHH/CCjR9X/tP7YbP7b5/7iWHytn2q12/6zdnc3TGO4/ND/hzNb/9HEj/AMEA/wDlxQB/QBRX4ifB/wD4JOwfDP4r+DviKvx2GsnwxrunauNO/sUQ/bPsVwk3keZ/asuzzNm3dsfGc7T0r9u6APy2/wCCv/jT/hGf2P7nQEk2v4t8Q6VpRUHlo4GfUGP0BtFz7ketfn58B/Av/BLPwl8CfAc/7UNzZ3HxD1ywbUdSEF9r00sUd5cSvarPDpMrQ25W1MXyuqMRhyDuyf1Q/bs/Zp8DftQ6T4U8KeM/iZa+AYtCuLrUFhmW3ke8adEiR9s1xAQIgrgEBslz0xz5/wCGv+CTP7H9l8NI/Cmq6Te63rM9riTxV/aFzDetO4JE8MMcptEVS2UjMTqVA3+YcsQD2z9kj4J/sa+ELG7+Kf7JtnZPa65B/Z11qFjq1/qKskTiQwvHeXM3kyKwBKlEfBGeDz9oV/MT/wAEu/EXif4O/twa98CIL9r3SdVOt6LqIjBME1xoPnywXQAJCkeTIqnJG2UjnII/p2oA/n9/4Kd+BvFvhL4gmD4efFXxLqfiT433NtpknwzjUTWlxaokdrHKqrKkcSeZGkce+FpHcuRJ8shFv/gl94N8UeO/GcWqePfiz4ltfEXwXln0VfhpOv2e0s7QwyWe943kZJE3M8b7YEljlRdz8oT+5Or+AfBOv+J9F8a61oWn3uv+HDMdJ1Oe3R7uy+0I0cohlI3oHRiGAOD1xkA0mieAPBHhvxFrni/QNC0/T9b8SyRS6vqNvbpHdXzwIscZmlA3OERQACcA5OMkkgHX15Z8avjD4L+Avw01v4p+Prr7NpOiwGQopHnXM7fLDbwKSN0szkIo6DOWIUEj1Ov5mP8Agpl+0DB45/a4tPgn8VJdUs/hX8O7uxe+s9CWOW+v5bu0iupp1WaWCLzikwt4i74hTe4DFmRgD4x/aZvfjT8c9PP7Y/xST7HpPjTXJtD8O2jFiqWdjG77LYEAC2tz+734/ezeax+bcT/UL+wr/wAmffCT/sWLL/0E1/P7+3d+2d8CP2ivhT4A+GHwS8Ma74ZsfBN2xittTtbS2to7NbYQRRQ/Z7u5YlcD7wHHOSa/WD/gmR+1n8PPir8NvDv7Pfh/TNatvEPgDwrbvqV1eQ26WEwhkSA/Z3juJJWy0gI3xJxnvwQD9Uq/lo/bp+FnxD0b4r3/AOzr8MfiV4q+Lf2+S+8caz4WuY/tA0S4jjmujJvWQx+a0MsshjihhOGjGHaRAP6l65G28A+CbPxpd/Ee00Kwh8U39kmm3WsR26Lez2cbB1hkmA3sgYAgEnoPQYAPzL/4JjaPqXj7w/fftK+IPjJ4g+Iev+ILRtG1jQ9Swlrot1DKk3lCNpJTuQDMLx+TG0UpPlgnC/rFXI+DPAHgj4daZPo3gPQtP0Cxubue/mt9Ot0t45Lq5cvLKwQDc7E9T0ACjCgAddQB+Dnx9/Yn/aQ/ak/btXxV8UfD11B8HLe+gsIL+PVdPzFoljCXKRQJdG6T7ZcB8nyt6GbcQAvHD/8ABUb9l79kn4I/CLQ9Y+Gem2PhTxsdVgtbfTrO9kllv7Bo5DM81vNLI22IhD5+AdxCsTvXH7M+D/2qf2ffH3xHufhH4Q8Z2Wo+MLOW8gn0lIrhJ0lsCwuVJkiVMxFGyA3Y4zX4E/tn/wDBNC9/Zj8BXXx/8GeMl8S6fpWp20mo6fqemRI0Iu7hY4pBl54bhPPkRHikjClW53DK0AfsX/wTU1bxlrH7F3w8ufG3nvcxwXtvZy3LM00umw3kyWhO7kKsIVI+xiVCOCK+yfGPh5vF3hHW/Ci6hdaSdZ06608X9kVW6tftUTRedCXVlEke7chKkBgODXxN/wAE4P2lda/aW/Z7j1fxVaWtprvhS/bw9eGxgS1tbhLeCGSCaOCMCOHdFIEaNAqBkJRVUqo+/aAP5B/jxbfGDwn8VdTs/hd8SfE3xR8P/s/zJNH4qmhMn/CPTXc0ULxxySTT71jnjSNWDKhaNyiKilq/ob/YO+H1n4W+D3/Cbad8Vda+LMHxAli15tX1gkFJzGIZUSN5J5o5AYxHMkkzlXj24Ug5+nLP4UfDPTrXxRZWHhfSra38bSSy+IoorSNE1R54vJkNyoUCTfHkNnqSxPLMT0vhrwz4e8G6DY+F/Cem2ukaRpkK29nY2USwW8ES9FREAVR9ByeetAG5X8vHwD+A/ib9uT9uHxP4s/aD0fWoPDcx1DX7xLiC5sI7m1tpYray0+KcqrIipJEMIwk8qNsMG+Yf1D18Jftkft6/C/8AZJ0waRcr/wAJJ47voDLYeH7aQJ5aNws97Lg/Z4SfujDSSY+VcbnUA/If/gqr+yR8Cv2dbHwL4v8Ag5YHw1ca9dXlld6Sl1PcRTLapHItzF9okkeNkLBJAG2nehABBLfuV+xl4w8TePv2V/hj4s8YyTT6xfeH7b7VcXDb5rgw5iWd2PLNMiLISeSWyea/Ej4X/sw/tO/8FJ/iPZfHj9pC8m8OeACFFntjNs01iG3i20i1fcUhbPN1LndncDMwIH9HPh3w9ovhLQNN8LeG7OLT9J0i0hsbG0gG2KC2t0EcUaD+6qKAKAPKP2jvhrb/ABb+DniHwTe+L7/wJazxRXc+vadIkMtnHYSrdF2kcrtiBiBkIdDtBG4DNfyqp4s+Mo+KA+MTfFDxYngufVz8PP8Ahbn2KX7WbFcNuK+f9o3eRibb5/neXxuLArX9is8ENzDJbXMayxSqUkjcBldWGCrA8EEcEHrXmB+B3wdPgIfCw+DNE/4Q9br7YNC+xRf2eJ/tH2vf5G3Z/r/nxjHbG3igDE/Zz+GVv8Ifg74f8D2fi6/8dW1tHLdQa7qMiTTXaX0rXW9ZE3boiZSYyXc7SBuIxjnf2qP2irT9mD4Xf8LNvfDt54nj/tK2077DYyCKXNyJCJNzK42rs54719GwwxW8SW9uixRRKEREAVVVRgAAcAAcACpKAP4p/hR8WfAlp+0hf/F34w+B5viHpd/f6tqdxoEjK3nXV80jq0wdHWQRNIWKlcFgM+lf0x+NP2zfgL+zN+zf8O/H+r6PNoVn4r0Kxu/DfgzSoovtaRT20U5hRCYoo4rUTKskh2qCQFBYhT+NH/BM/wD5SG6r/wBcvFH/AKG1fpF/wUk8SfsNeGNY8Pah+0z4c1Txp4w/s100bRtI1G7tZYrEzMTNMIrq3hijeXcokYNI5QhQwQ7QDxlv+CzOvx2C+KZfgBq6eFGlWL+1zrMghyTjhzpfkFueE83k/wAVfp9+zB+1J8Nv2r/AEnjr4eG6tmsrj7Hqem36BLqxudocK+0sjo6kMjoxDDg4YMq/kn8Z/wBr/wDaG+MX7M/ibwX8N/2atT8KfDxvDE0UuvaxJK1pbaHawbmltlltrSEmOCP90UklwQNoZsCsH/gjJrt34e8E/tC63bI9w+k2GhX8Nuvzb5IYdXfCr6vsUe/FAH3d+1P/AMFPvgv+zb4qn+Hmm6dd+OPFVgSuo2dhMlraWEmARFPdOr/veRlI45NnIcq3y14j8Jv+CzPwv8U+Kbbwz8XPA+o/D+O7kSFdSS9XVLWBpCAGuQbe1liiwcl1STHUjGSPiX/gkR4L0T4rftO+LPiL8QlXXdZ0PSZtYtnvgJmbU7+6RZL1twO6VQz4Y9Gk3D5gCP2w/aq/Ys+B37VraHffFKbUNJvtC86O31HR57a0upop8EwTSXFvP5kaldyLgbSWIPzHIB8Jf8FZP2qtJ0r4d6z+zNa+HbvUD4w0XRNct/ElvOjafFEuqLOqEBTv3rZ/KwfB8xT9fl3/AIJZfthWHwjs7H9nifwhqOrz+OvHETJrNvMqWtl/aENnZjzEKMW8sxbzhhkEfWv0W/b58D6T8M/+CaPib4eaBf3mp6X4b0/wzpdldX8sc1y9taavp8UQkkijiRiqKFyqLwK82/4Io/8AJs3i/wD7Hu8/9Num0AfVf7Wn7efwb/ZJFtovicXWv+LL+Fbm20DTdomW3ZmUT3Ez/u4IyVYLnc7EfKhGWHwp4U/4Lb+CbvW4LXx38LdV0HSbiRdt9ZapHqMqwMdplMD21ruAIJISRuhAyRz8ZfDzR9P/AGhv+Ctep6X8VIP7RsB418R77C//AHqvD4eiuzZWro25WjjFpErIRtKKVxg1+23/AAUM+Gfgzx7+yJ8QD4hsLZ5vDOjT6zpFyY1Etnd2C+anktjKeYqmJgMbkcr3oA+g4PjT4I1v4LXnx28EXieI/DUOh3uuWstqTH9pisopJHjHmKGjk3RtGyuoZHBDAEEV+bvhD/gsV8Fdc8D+LPGXiXwvqmhy+H30+30/TFuYbu71e51AXBCQgLGsaQrblpZHO1QwAyxVW+Zf+CZHizW9S/Y0/aP8G3jzSabomlX15Zb2LRxvqOlXYmSPJ+UZtldgOMvnqTXzV/wSe/Z5+Hvxz+Nuvar8TdKt9e0nwdpMd5Dpl4gltJ725mEcTTxtlZY40WQ+W4KsxUsCBggH6Ga/+318J/j1+yR4l+Lnxh+Dw1Tw1ofjGy0IeHpNVF1580kAniujKbeAIU3kbNreu6vsL9iP4qfBfxT+zVJ8Q/hv4Ug+GXguwvdUlubCW4VoLYWgD3NzJLwqqVG5ieABzXzd/wAFWfBXg3wP+xhead4K0HTPD9pL4o0iSSDS7OGyidwsqBmSFEUkKAASM4AHauT/AOCfR+GI/wCCa3iEfGe5Fr4Ha919NekM81vmyYxrJGHt2WbMmdgWM73LBACWwQCPxf8A8FjPC9z4tuPC3wH+FWvfEVYGkVLr7Q1ibgRnBlhtobW9maJhypkETYPzIp4r0z9mz/gqp8Ofjd8SLL4R+OfCGo/D3xPqlybGxS4uBf2kt7nattJJ5NvLDM7fKoeHbu+UsCQD8k/AT9sL4O/C3UfEnhr9gf8AZn8XeKf7SkgbUro3t3LIwg3rA8oCam8UXzOVVpIwcnODnHwb8QvEnxV8T/8ABQvw94v+L/hmPwT4u1Pxj4Su7jRrcgfY499iLbLB2PmNAsbuWIbeSSqn5QAfV3/Bbj/ktPw9/wCxXm/9LJa/RH9uP4rXXwp/4J3pJpsvk6h4r0PRfC9s4OCF1K2X7SB3ybOOcDHQnPavzu/4Lcf8lp+Hv/Yrzf8ApZLXsP8AwVjvLpP2SPgZp6bvs89xZTSc/L5kOk7UyPXEj4/GgD6F/wCCP/w4j8H/ALKN54/ihDaj431m+vFkK8tbaaTZQR+4WaKdh7ua/MP9rP4mf8FKfih8Ob//AIXl4U1jwv4BsTFPqcFlpJ07T2IkVYmupCXmdRIy4RpDHv2tt3AEfud/wTpggt/2KvhXHbEFDpVxIcf35Ly4d/8Ax4mqf/BSX/kyP4pf9eNh/wCnG0oA+OP+CJH/ACRH4gf9jVH/AOkUNeI/8FoPBtz4Q+Jfwr+O/huRrPVJoLjTXuYRtaK50eeO7s5c93zcSYPXEYHQDHt3/BEj/kiPxA/7GqP/ANIoav8A/BbK2gb9nzwPdtjzovGUcaeuySwvC2PbKLmgD9TPgr8RIPi58IvBvxOt1SMeJ9DsNUkiQ5WGa5hV5Ys/9M5CyH3FenV8I/8ABMy5ubr9h34YS3YIdbfVYxn/AJ5xatepH/44or7uoAKKKKACiiigAooooA//1P38ooooAKKKKAPj/wDbJ/ZH0r9sHwPongnVvElx4aj0XVf7VW4t7VbtpW8iSHYVeSMAYkznJ6dK/On/AIceeC/+isap/wCCaH/5Jr91qKAPxp+E/wDwR58JfCv4oeEviZa/EzUdQm8Ka3Ya1HaSaVFElw1hOk4jZxcMVDlMEgHGelfstRRQB+WP/BTr9i3xd+094S0Dxp8LUjuvGPg4XMK6ZI8cP9p2N2UZkSWQqizQum6MMyqwdxndtB/PLwv+2L/wUu+Hnw6tPgNa/C3VZdX02yGladq9z4W1OfWYraFfKjKBf9HneJQoSVoXBABbeTuP9LdFAH4v/wDBMf8AYU8f/BzXtR/aC+OcD2PinVbOS10nSppRLdW0N2wkuLm8I3bbiXAVU3FkUv5gDMAv7QUUUAFFFFABXhvi39mX9nfx74hvPFvjb4a+Ftd1rUCjXWoahpNtcXM5jRY0MkkkZZtqIqjJ4AA7V7lRQB+E/wDwVE/ZA0O08AeCm/Zp+DqtqjaxcjU/+EM8PNLcfZvI+TzxYwMwj39N3Gfev0A/YV+BnhH4XfATwLrr+BrTwr46vvDdrb6/cSaYthq8smQ7x3haNJi29QWWTnIGa+2KKACiiigAooooA/nf/a7/AGQv2k/gJ+1Bc/ta/st6Zfa/bahqsuutFpkBv76w1G+3/boZbJFMk9pcF5DujVtqSMj7dqu3lPxj+NH/AAUQ/bn0Gz+Cknwou9G0uW8hk1BNO0W+062uLiA7kF5eajK8UEUbgOEMiDcASWIXH9O1FAHyD+w9+zK37KfwG0/4dajdx32u311LrOuTw/6j+0LqOONo4SQC0cMcUcYY8uVLYXdtH19RRQAUUUUAZ2r3z6XpN7qcdvLdtaW8s628CGSWUxIWCIigsztjCgAkk4Ffxz6t8Lf2z/EvxeuvjV4w+CvjLxHr19qzaxdQ6r4R1W4sZ5y29Ukg8lQ0EfyqsROzYoQgplT/AGU18Af8PRv2E/8Aopn/AJQNc/8AlfQB+Ziftof8FZY0WOP4PawiIAqqvgDVAAB0AG3gCv2s/ZS8Z/Fb4g/AHwp4w+N+kTaF421BL46rp9xYS6ZLAYr24ih3WswEke6BI2G77wO4cEV4N/w9G/YT/wCimf8AlA1z/wCV9H/D0b9hP/opn/lA1z/5X0Aff9FfAH/D0b9hP/opn/lA1z/5X0f8PRv2E/8Aopn/AJQNc/8AlfQB9/0V8Af8PRv2E/8Aopn/AJQNc/8AlfR/w9G/YT/6KZ/5QNc/+V9AH5if8E9fgd8avBX7dmpeL/GXw/8AFOg6FJH4jCapqei3tnZMZ3JixcTQpEfM/h+b5u2a6v8A4Kw/sxfGvXvjTofx9+Hvhy+8XaGNJs7G6gsLZ7+WxurCaRwJbaMM5tpVkU7lBXcHD7cru/RD/h6N+wn/ANFM/wDKBrn/AMr6P+Ho37Cf/RTP/KBrn/yvoA+GviF+1J+2v+158GvEXwp8Afs+an4Ti1PRp11vXLx7ryZrSNC09vYx3FrbAzXIXykiV7iQhyoH8Y1/+COfwv8Aif8ADO6+LWkfFHwN4j8LJrkGgSWja7o95p8NytodQSZEe5ijR2AuEyoJODnGK+zv+Ho37Cf/AEUz/wAoGuf/ACvo/wCHo37Cf/RTP/KBrn/yvoA/KzxH+zX+1b/wTw/aKvPi3+zl4Wu/HHgu9+1W9tHZ20+qZ0q4ZZGstRgtx9ojaFkQrOvyMY1bdkvGMP4tD9t7/gpj408K+Hbv4aXfgfwtobyMs91a3dnplu10yLPd3FzdhPtEiIoCRQqXC7gqncxr9b/+Ho37Cf8A0Uz/AMoGuf8Ayvo/4ejfsJ/9FM/8oGuf/K+gC9+0T+y7f6p+wVqv7MXwqT7ZfaVoGlWmlrIUhe+m0a5trttxJCLLdNbseSF8x+SByPym/wCCfnjP9s/9m/xrpfwFuPhDq8PhHxX4us59av8AVvD2qK+nJcGG1uriK5Qx26xiCJW3uHQbd2cZB/Uv/h6N+wn/ANFM/wDKBrn/AMr6P+Ho37Cf/RTP/KBrn/yvoA/P/wDbU/Y1+P3wp/aSX9sH9lfTLnXXn1GPW7vT9PiN5f2WquSLki0AMlza3WSXEe5gZJFKqgVq84+Mv7S37f8A+2L4Jk+AujfBTUvDtvq80Fvrc9ppV/B53lyK4hlub0LDZQFwrPvfcQuC+wsrfqP/AMPRv2E/+imf+UDXP/lfR/w9G/YT/wCimf8AlA1z/wCV9AHKfs+/sh6l+zJ+xR48+Hsy/wBs+OPFOg63e6sunh7hZL6ewkht7S1UKHkEaBUGFy8rMRwygfIH/BHf4PfFv4Y+OPiRd/EnwR4j8JwX2labHay65pN3pqTvHPKWWNrmKMOyggkLkgHmvur/AIejfsJ/9FM/8oGuf/K+j/h6N+wn/wBFM/8AKBrn/wAr6AOe/wCCqPgbxt8Q/wBlSfw74A8P6r4m1U+IdLmFjo9lNf3Rij83e/lQI77VyMnGBnmvlr4HfszfF3xx/wAEsfFvwNudC1Lwz4y1DVLu+stL1u2m0uec2d7a3kcTJcpGyrcCAojMAm4gkgAkfY3/AA9G/YT/AOimf+UDXP8A5X0f8PRv2E/+imf+UDXP/lfQB+Tn7Gnx9/af/Y08OeIvgsf2dPEviu91LV5NUi2Wl9ZXUVw8MVuyuY7G5FxABCrIVKgEsQxDDHmXxJ+D/wC2d4j/AGw9D+OHxX+FfiKS91PXtA1+8Hh/SbvVbGwsraWBYrYz2sc8Ykt7eBVdd5cEZb5ia/bD/h6N+wn/ANFM/wDKBrn/AMr6P+Ho37Cf/RTP/KBrn/yvoA/Pn/gsD8GfjB8TPi54F1L4b+BfEviu0tPDksFxcaJpF3qMUMpu5GCSPbRSKrlSDtJBwc19Af8ABTP4X6p4l/YH8L6wLOaO/wDAT6BqN3CyFZYoHtfsE6uhww2PcIzgjK7DnABr6G/4ejfsJ/8ARTP/ACga5/8AK+vpbRfEHwm/as+CVzf+HLz/AISLwH460/UdM+0eRPaGe3LzWVxtjuY4po2SRHVWZAcqGXIwSAfIf/BJ7xtbeLf2MfDWlJKJLnwpqWq6NcgdVY3LXkYI9obqPHtXr/8AwUA8MeJfGf7H/wAR/DPg/Sb7XdYvrOyW10/TbaS7u52S/tnYRwwq8jkKpYhVOACegr5l/wCCd/7LH7Q37JXjbx94P8bJp1/8Ptcf7Rpl/bXivN9ss5THDKbfAaMXVs+ZASSrRoPU1+sFAH5F/wDBH/4a/EX4Z/B/xxpnxI8K634UvLvxKk9vb63p1xp000ItIV8xEuI42ZNwI3AEZBFeGf8ABb/xvbx6D8L/AIbwyq09xd6lrdxEPvRpBHFbwMfZzLMB/uGv3nr8cPi7+xD8aP2kv269P+LXxYttOtvhR4ektbext0vUmurnT9M3TRwtCoyovLt3eTJBSJyM7gMgH6B/sifD25+Ff7Mnw08C38LW19p/h2ykvYWGGivbtPtNyh91mlcfhX0ZRRQAUUUUAFFFFABRRRQB/9X9/KKKKACiiigAooooAKKKKACivgz/AIKWfE3V/hZ+yB4v1jw3qVzpOtalPpulafeWcz29xFJcXcTSmOSMq6t9njlwVINfj78Cf2SP+ChP7QXwr0P4veEfjTNp2j+IFuXtINX8Wa9DeBLa4ltizpDbzoAzRFkxIcqQTjOAAf060V+ZX7C/7Ln7VvwE8a+JNc/aC+IsHjTS9T0uO0sLaLXNU1UwXKzK5kKX9vCiZQEblJbnGMV+mtABRX4wf8FEP2sPi54D8OfEv4Q+I/hXrFh4N120h03w147tJyIGuZI4pGE2xWjUtKsioPOjl2LnyzzjS/4J9/tZ/Fz4j6H8M/hT4e+FGsXPgfQdL/srxD48vJyYTd2tu5Bg3qkb/vwiuBLJLtbOxSOQD9jaKKZLLHDG80zrHHGpZ3YgKqgZJJPAAHU0APor+WX9v7/goB47+LnxQl0L4G+JtX0HwD4Rmeyg1DRryeyOr3r5ElzJLAyFoT5ZW2RjyitJjLkL+/n7Fusav4g/ZS+Fut6/fXOpaje+G7OW5u7yZ57iaRgcvJJIWd2Pckk0AfT1FFfze/8ABRn9rn4yeJfAcnwY+Jfww1v4aanD4sOp6LrAuW+y6npdmtzCFDoAjSqtxCZDDNKm7qEyoIB/SFRXwp+yf+1V8VP2lvEWo6pqPwj1XwT8OTpaXeg6/qcuX1KYyIBhGWLKSxOXRolljGw5lOVFfddABRX8/wB+3p+1N8fPip+0xbfsZfs36peaEI7q00y9uNPuDZ3OoalcIs8m67jIlhtLWJgJApXJSQvuUKB88/Gz9nz9t79gPR9L+Num/FWbUbOW/jtb2XTNQu50iupwzILq1vU8q5hkKEbnVvmxuUZU0Af1D0V81/sifHiT9pP9nzwp8XL21isdS1SGe31K1gJMUV9ZTPbzFN2SEkMfmIpJKq4BJIyfcPGOp63ovhHW9Y8M6Y2t6xY6ddXOn6ajrG17dRRM0MAd2RFMrgJlmAGckigDpKK/mZ/aR/bq8e6j8fPgv4i+Knw21/wBr/ww1Ca+1zw+Z3VdVinltmia3EqREo5t3A3B15+V35r9w/2WfjN8YPjZ4a1rxL8WvhhffDAJfqui2eoSM09zYPEp3SLIsUyyJIG3FoY1Ksu0EhjQB9R0UV/NX8ef2lP2m/23f2obv9nf9m3X7vw94Z0++u7Kz+w3c2lx3MNiCl1qGo3MJ85oCys0ceCAhQCNpTlgD+lSiv5WPif4e/bi/wCCafi7w34nufHr6tpOtSSeSbe/utQ0e7eEq89rd2l4qbXZWyHCBtpJjkDBtv8ASr8Evijp3xr+EfhL4r6VAbSDxRpVvqBti4kNvLIv72EuANxikDJnAztzgdKAPUqK8b+Pvjb4j/Dz4Wav4t+E3hCTx14nsjbG00SOQRmdGmQTtnIY7IS7KEDMWAAUjNfz/wAP7e/ilf25pvi2Phl4nbXJPDg8Lj4fefJ9sGtDapTb5HmdVP8Ay7eb/sUAf0yUV438AvG3xI+Ifws0jxZ8WvCEngXxPetcm70SSQSGBFmcQMDuLDfCEZg4VgxIKgYr2SgAor8Nv2sP+CuHwovfhhrfgn9nZ9X1HxJr1nLYJrckD6bb6ZFcKUkniaQrcNcqp/dbUUKxD78rtbvf+COHj3xz49+FvxBvfHXiPVvEdxa6/axQS6tfT30kUZtQSqNO7lVJ5IGBmgD9jKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigD/9b9/KKKKACiiigD42/bS+M/7QHwT8C6Fr37PPgJ/iBrN/q32S+sk0u/1Q29p5EknneXYOkifvFVdzHbzjqRX5u/8N+f8FOP+jaZ/wDwj/En/wAfr9Iv20viZ+0r8L/Amhat+zF4Qj8Y67d6t9m1C1ksJ9QENl5Ej+ZsgmhZf3qou4kjnGK/N3/hrv8A4K3f9ENtv/Ca1H/5OoA9D+Ef7bX/AAUO8X/FPwh4U8dfs/TaH4c1jXNPsdW1M+FtfthZWNxOkc85mnmMUflRszb5AUXGW4r9qK/Ff4R/tQ/8FPPEfxT8IeH/AIifBy30rwtqWuafaa1fr4fv4Da6dNOiXMwle8dEMcRZtzKwGMkHpX7UUAfkv/wVx8CfF74p/CfwT4B+FPhPWPFPmeIJdV1AaTayXPkLZWzwxCXYDtDm6YqD1KH0r4/8Ofsif8FRNR+C/h280Dx7/wAIRaeHdJhi0fwVp+s3ejX4hgDMFnW1iW3N1Mx3kXE5O59rmPGxftT/AIKW/tJftMfsw23hDxl8HhZHwrqn2mx1aa709btbW/Qq9vucsCgnjLhR0zEeckZm8E/8FaP2Xbv4O2Pi/wAba3c2XjGDT4/7R8Nwafcvcy6ikf7xLaQRm2MUsgJjd5lVVI37TxQB4d/wTD/bl+KPxN8cal+zp8eLybVtatrW4u9H1S7j2X/mWbf6VZ3ZGN7KpLxsyh12Ors2UC/t/X8zn/BLPwT4s+Mn7Y/iT9pCbT2s9F0eTWdSupkJFuupa/5qR2iHA3lY55XIAwoRScFlz/THQB8WftM/sgQftOfEb4c634y8T3H/AAg3g27kvNT8IGEG11SfBMcjSqQwJIWKRXDDyS4jMbOxY/Za/ZCT9lrxj8RLjwn4qurjwN4t1CO+0fwq8Q8jSX2gyP5zMzs+SYl2hcwonmGRwCv2nRQAV+Nv/BST9pvxXrmuaf8AsP8A7P3mX3jjxs0Npr8lq21rSzu1DLZb/wDlm08R824ckLHa/e4kJX9kq/Gj9qD/AIJQ6r+0T8dvFPxlt/iVb6FH4kktJF099Fa6aD7NaQWuDKLyINuMO77gxnHbNAHx3/wUE/Zp8IfssfslfCb4deHmivNUk8Q3d5r2qKMPqGovZgSP6iJMbIUP3UAzlixP7U/sKOh/ZA+EqhgSPC9lkA8j5TX85P7av/BP3UP2OfCvhzxPeeN4fFS+IdQmsBDFpjWJhMUXm7yxuZ92emMD61+rX/BKz9kC8+EmhWX7Rs3imPU4fiH4UhRNJWxMD2XnTxT5M5ncS48vb/q1659qAP2Or4B+KX7Bfhf46ftGX3xh+M+v3Hinwt/wjr6NpHhWaPyYtLnmQxSTRyo3zABnlQlRIs7K24iNAPv6igD5Y/ZC/Z3179mH4Ut8LdX8ZXPjC0ttTvLjS2ngWBdPsZnzHbRjLMe8j5O0SOwQBev1PRRQB5la/BT4NWXjQ/Eiz8B+GYPFrTy3J16LR7NNVM8yskkhvBEJ97qzKzb8sCQTgmv5nf25/j7+2j8R7HSvBv7THhG58AeBhq4mi/s7Q54ILuWHcqyCW6uHFxNHEzssIuI1JOWAwrL7v4k/a0+PH7I/7fus6L8dPE3iTVfhsNZ1CaHTJbhriH+wdVMpsLm3jJ2yfZgyZC85ieMfMMD07/go7+3r+zR8X/2cbz4U/CrWj4u1rxBeafOssdldWsWmRWc6XDSu11FCfMcIYgihiA7FtuBkA/Sv9g9/guv7Lvg2x+A2o3Gp+GLKGaF576MQ3/8AaLSNLeC7iUsI5vOkY7AzKEK7GZNrH6/r8rP+CQXw08UeAf2WrjWvE9vPZDxl4guNb06Ccbd2nm2treKcKeQJmhZlJ+9HsYfKQT+qdAH5X+K/+CYXhT4lax8Y/F/xX8V3XirxV8QJc+G9YuYRHL4eiiCyQKqI2yTY6rCwUKDbRhVCF2x99/A/wD4h+Fvwl8L/AA88VeJbjxfqmgafHZT6zcxiKS58vIT5QWOI0xGpZmdlUF2ZiTXqtFABXw78Lv2R/wBl39jTWde+OejXNxoLnSriz1PVNd1QyWsVpPNDPISZdqq7SQoARyfujJbB+35JI4Y2mlYIiKWZmOAFHJJPoBX8hf7Wn7Xuq/thfGyLS/EniSfwv8JtP1RodMhS3lmSCyRip1Ca1j+a4vJYwSqsQE3CMFF3uQD3z9t79pPV/wDgoR8V/CnwK/Zt0K+1rR9Gup3s5zE0UupXcwWN7pkfH2ezgjB2vLsIDM0gXgD+hn9n34UxfA/4KeDPhNHcJdv4Z0mCynuI1KJPcgb7iVVPIV5mdgDzg881+VX7O37Zn/BM79mHwmvhj4XzavBPMijUNXudFnl1PUXX+K4n2AkZ5WNAsSZO1Rk5/W74Q/Fjwb8cfh1pHxS+H881zoGuLO1nLcQtbysLaeS2k3Rv8y4kiYDPUc96AO41e0vNQ0m9sNPvZNNurm3lhgvYkSSS2lkQqkypIGRmjYhgGBUkYIIr8i/+HTHh8fDGK3TxxdL8X4/FZ8Sn4hCKT7SSbgHy/K87fxEBMD5u/wC1ZbzNhIr9haKAKGlWt3Y6XZ2V/ePqFzbwRRTXcqJG9xIihWlZIwqKzkFiFAUE4AAq/RRQB+Qn7bf7Ev7K/wANf2V/iH458DfDzTtI13SNOimsr2GW5aSGRrmFCyh5mXlWI5B61wX/AARE/wCSSfEj/sY7T/0kFfcv/BRP/kyv4qf9gmD/ANLLevhr/giJ/wAkk+JH/Yx2n/pIKAP24ooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKAP/9f9/KKKKACiiigAooooAKKKKAMHxP4W8NeNdBvPC3jDSrPW9H1CPyruw1CBLm2nTIOHjkDK2CARkcEAjkV8G3X/AASu/Yjuta/tc+BriKMszvZRazqKWrMxz90XG9R6KjqoHAGK/RCigDj/AAH8PvBPwv8AC9n4L+HuiWXh/RLBdsFlYxCKJSerNjl3bq7sS7HliTzXYUUUAFFFFABRRRQB4h8b/wBnH4M/tHaTpuh/Gfw9/wAJDZaRcPd2UX228svKmkTYzbrOeBmyvGGJHtmvQ/Avgfwt8NPB+keAfBNl/Zug6FapZafaebLP5MEf3U8yZ5JXx6uzH3rrKKACiiigAooooA8V+M/7OvwV/aE0iDRvjD4UsvEUNqS1tNIZLe7t88kQ3Vu8VxGrH7yrIFbA3A4r5y8Ef8Ey/wBjHwJrsXiKy8Brql3bS+dAms311qFtGewNtNKYJQOwlR/zr73ooAihhhtoY7e3jWKKJQkaIAqoqjAVQOAAOAB0qWiigAooooApanp1prGm3ek6ghktb2CS3nRXaMtFKpRwHQqykqTypBHUEGvg3/h1z+wn/wBEz/8AK/rn/wAsK+/6KAPgD/h1z+wn/wBEz/8AK/rn/wAsK+wvhh8MfA/wa8DaX8Nfhtpv9j+HNGEy2Nl589z5QuJpLiT97cySzNulkdvmc4zgYAAHe0UAFFFFABRRXP8AizwtoXjnwrrPgrxTa/bdF8QafdaXqNt5jxefZ3sTQzx+ZEySJvjdl3IysM5BBwaAMr4i/Dvwd8WPBWq/Dv4gaf8A2r4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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "image/jpeg": {
       "width": 300
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:\\\\Users\\\\deepali\\\\OneDrive\\\\Desktop\\\\Recursion.jpg\",width=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: factorial(1) is a base case for which we already know the value of factorial. The base case is defined in the body of function with this code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if num == 1:\n",
    "    return 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is a base case in recursion ?\n",
    "When working with recursion, we should define a base case for which we already know the answer. In the above example we are finding factorial of an integer number and we already know that the factorial of 1 is 1 so this is our base case.\n",
    "\n",
    "Each successive recursive call to the function should bring it closer to the base case, which is exactly what we are doing in above example.\n",
    "\n",
    "We use base case in recursive function so that the function stops calling itself when the base case is reached. Without the base case, the function would keep calling itself indefinitely.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Why use recursion in programming?\n",
    "We use recursion to break a big problem in small problems and those small problems into further smaller problems and so on. At the end the solutions of all the smaller subproblems are collectively helps in finding the solution of the big main problem.\n",
    "\n",
    "Advantages of recursion\n",
    "Recursion makes our program:\n",
    "1. Easier to write.\n",
    "2. Readable – Code is easier to read and understand.\n",
    "3. Reduce the lines of code – It takes less lines of code to solve a problem using recursion.\n",
    "\n",
    "Disadvantages of recursion\n",
    "1. Not all problems can be solved using recursion.\n",
    "2. If you don’t define the base case then the code would run indefinitely.\n",
    "3. Debugging is difficult in recursive functions as the function is calling itself in a loop and it is hard to understand which call is causing the issue.\n",
    "4. Memory overhead – Call to the recursive function is not memory efficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python Anonymous/Lambda Function\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python, an anonymous function is a function that is defined without a name.\n",
    "\n",
    "While normal functions are defined using the def keyword in Python, anonymous functions are defined using the lambda keyword.\n",
    "\n",
    "Hence, anonymous functions are also called lambda functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "image/png": {
       "width": 600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"C:\\\\Users\\\\deepali\\\\OneDrive\\\\Desktop\\\\lambda.png\",width=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How to use lambda Functions in Python?\n",
    "A lambda function in python has the following syntax.\n",
    "\n",
    "**Syntax of Lambda Function in python** <br>\n",
    "lambda arguments: expression\n",
    "\n",
    "Lambda functions can have any number of arguments but only one expression. The expression is evaluated and returned. Lambda functions can be used wherever function objects are required."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example of Lambda Function in python\n",
    "Here is an example of lambda function that doubles the input value.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# program to show use of lambda function\n",
    "\n",
    "double = lambda x:x * 2\n",
    "print(double((5)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above program, `lambda x: x * 2` is the lambda function. Here `x` is the argument and `x * 2` is the expression that gets evaluated and returned.\n",
    "\n",
    "This function has no name. It returns a function object which is assigned to the identifier double. We can now call it as a normal function. The statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "double = lambda x: x * 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "is nearly the same as:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def double(x):\n",
    "    return x*2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The purpose of lambdas \n",
    "A lambda is much more readable than a full function since it can be written in-line. Hence, it is a good practice to use lambdas when the function expression is small.\n",
    "\n",
    "The beauty of lambda functions lies in the fact that they return function objects. This makes them helpful when used with functions like `map` or `filter` which require function objects as arguments.\n",
    "\n",
    "### Map with lambda "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `map()` function in Python takes in a function and a list.\n",
    "\n",
    "The function is called with all the items in the list and a new list is returned which contains items returned by that function for each item.\n",
    "\n",
    "Here is an example use of map() function to double all the items in a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 10, 8, 12, 16, 22, 6, 24]\n"
     ]
    }
   ],
   "source": [
    "# Program to double each item in a list using map()\n",
    "\n",
    "my_list = [1, 5, 4, 6, 8, 11, 3, 12]\n",
    "\n",
    "new_list = list(map(lambda x: x * 2 , my_list))\n",
    "\n",
    "print(new_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter with lambda "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `filter()` function in Python takes in a function and a list as arguments.\n",
    "\n",
    "The function is called with all the items in the list and a new list is returned which contains items for which the function evaluates to True.\n",
    "\n",
    "Here is an example use of filter() function to filter out only even numbers from a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 6, 8, 12]\n"
     ]
    }
   ],
   "source": [
    "# Program to filter out only the even items from a list\n",
    "my_list = [1, 5, 4, 6, 8, 11, 3, 12]\n",
    "\n",
    "new_list = list(filter(lambda x: (x%2 == 0) , my_list))\n",
    "\n",
    "print(new_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Global , Local and Nonlocal variables "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Python, a variable declared outside of the function or in global scope is known as a global variable. This means that a global variable can be accessed inside or outside of the function.\n",
    "\n",
    "Let's see an example of how a global variable is created in Python.\n",
    "\n",
    "### Example 1: Create a Global Variable\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x inside: global\n",
      "x outside: global\n"
     ]
    }
   ],
   "source": [
    "x = \"global\"\n",
    "\n",
    "def foo():\n",
    "    print(\"x inside:\", x)\n",
    "\n",
    "\n",
    "foo()\n",
    "print(\"x outside:\", x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code, we created x as a global variable and defined a foo() to print the global variable x. Finally, we call the foo() which will print the value of x.\n",
    "\n",
    "What if you want to change the value of x inside a function?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnboundLocalError",
     "evalue": "local variable 'x' referenced before assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-5bb9d7e58dc3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-44-5bb9d7e58dc3>\u001b[0m in \u001b[0;36mfoo\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnboundLocalError\u001b[0m: local variable 'x' referenced before assignment"
     ]
    }
   ],
   "source": [
    "x = \"global\"\n",
    "\n",
    "def foo():\n",
    "    x = x * 2\n",
    "    print(x)\n",
    "\n",
    "foo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output shows an error because Python treats x as a local variable and x is also not defined inside foo().\n",
    "\n",
    "To make this work, we use the global keyword"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the global keyword \n",
    "In Python, global keyword allows you to modify the variable outside of the current scope. It is used to create a global variable and make changes to the variable in a local context.\n",
    "\n",
    "**Rules of global Keyword**<br>\n",
    "The basic rules for global keyword in Python are:\n",
    "\n",
    "1. When we create a variable inside a function, it is local by default.\n",
    "2. When we define a variable outside of a function, it is `global` by default. You don't have to use `global keyword`.\n",
    "3. We use `global keyword` to read and write a `global variable` inside a function.\n",
    "4. Use of `global keyword` outside a function has no effect.\n",
    "\n",
    "#### Use of global Keyword\n",
    "Let's take an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example 1: Accessing global Variable From Inside a Function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "a = 1 # global variable\n",
    "\n",
    "def add():\n",
    "    print(a)\n",
    "    \n",
    "add()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we may have some scenarios where we need to modify the global variable from inside a function.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example 2: Modifying Global Variable From Inside the Function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "UnboundLocalError",
     "evalue": "local variable 'a' referenced before assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-cafc5b44c9e4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-46-cafc5b44c9e4>\u001b[0m in \u001b[0;36madd\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;31m# increment a by 2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnboundLocalError\u001b[0m: local variable 'a' referenced before assignment"
     ]
    }
   ],
   "source": [
    "a = 1\n",
    "\n",
    "def add():\n",
    "    a = a+2 # increment a by 2\n",
    "    print(a)\n",
    "\n",
    "add()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is because we can only access the global variable but cannot modify it from inside the function.\n",
    "\n",
    "The solution for this is to use the global keyword.\n",
    "\n",
    "##### Example 3: Changing Global Variable From Inside a Function using global\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inside add(): 2\n",
      "In main: 2\n"
     ]
    }
   ],
   "source": [
    "c = 0 # global variable\n",
    "\n",
    "def add():\n",
    "    global c\n",
    "    c = c + 2 # increment by 2\n",
    "    print(\"Inside add():\", c)\n",
    "\n",
    "add()\n",
    "print(\"In main:\", c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above program, we define c as a global keyword inside the add() function.\n",
    "\n",
    "Then, we increment the variable c by 1, i.e c = c + 2. After that, we call the add() function. Finally, we print the global variable c.\n",
    "\n",
    "As we can see, change also occurred on the global variable outside the function, c = 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Global Nested Function "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example 4: Using a Global Variable in Nested Function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before calling bar:  20\n",
      "Calling bar now\n",
      "After calling bar:  20\n",
      "x in main:  25\n"
     ]
    }
   ],
   "source": [
    "def foo():\n",
    "    x = 20\n",
    "\n",
    "    def bar():\n",
    "        global x\n",
    "        x = 25\n",
    "    \n",
    "    print(\"Before calling bar: \", x)\n",
    "    print(\"Calling bar now\")\n",
    "    bar()\n",
    "    print(\"After calling bar: \", x)\n",
    "\n",
    "foo()\n",
    "print(\"x in main: \", x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above program, we declared a global variable inside the nested function bar(). Inside foo() function, x has no effect of the global keyword.\n",
    "\n",
    "Before and after calling bar(), the variable x takes the value of local variable i.e x = 20. Outside of the foo() function, the variable x will take value defined in the bar() function i.e x = 25. This is because we have used global keyword in x to create global variable inside the bar() function (local scope).\n",
    "\n",
    "If we make any changes inside the bar() function, the changes appear outside the local scope, i.e. foo()."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Local Variables "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A variable declared inside the function's body or in the local scope is known as a local variable.\n",
    "\n",
    "Example : Accessing local variable outside the scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 's' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-06e1daa78987>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Local\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mfoo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 's' is not defined"
     ]
    }
   ],
   "source": [
    "def foo():\n",
    "    s = \"Local\"\n",
    "foo()\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output shows an error because we are trying to access a local variable y in a global scope whereas the local variable only works inside foo() or local scope.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Example : Create a Local Variable\n",
    "Normally, we declare a variable inside the function to create a local variable.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Local\n"
     ]
    }
   ],
   "source": [
    "def foo():\n",
    "    s = \"Local\"\n",
    "    print(s)\n",
    "foo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nonlocal Variables \n",
    "Nonlocal variables are used in nested functions whose local scope is not defined. This means that the variable can be neither in the local nor the global scope.\n",
    "\n",
    "Let's see an example of how a nonlocal variable is used in Python.\n",
    "\n",
    "We use nonlocal keywords to create nonlocal variables.\n",
    "\n",
    "Example \n",
    ": Create a nonlocal variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "inner: nonlocal\n",
      "outer: nonlocal\n"
     ]
    }
   ],
   "source": [
    "def outer():\n",
    "    x = \"local\"\n",
    "\n",
    "    def inner():\n",
    "        nonlocal x\n",
    "        x = \"nonlocal\"\n",
    "        print(\"inner:\", x)\n",
    "\n",
    "    inner()\n",
    "    print(\"outer:\", x)\n",
    "\n",
    "\n",
    "outer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code, there is a nested inner() function. We use nonlocal keywords to create a nonlocal variable. The inner() function is defined in the scope of another function outer().\n",
    "\n",
    "Note : If we change the value of a nonlocal variable, the changes appear in the local variable."
   ]
  }
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