19_Class.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fb6f4ecb-d963-498a-b26c-168cef16547c",
   "metadata": {},
   "source": [
    "# mixin"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1cac242c-cbea-4a5c-81ad-a9d0b828866d",
   "metadata": {},
   "source": [
    "یک ابزار جدید نیست بلکه یک الگو است\n",
    "\n",
    "در مبحث شی گرایی و وراثت چندگانه مطرح است\n",
    "\n",
    "کلاسی که شامل متد هاست که کلاس های دیگر این قابلیت را میدهد\n",
    "\n",
    "رابطه میکسین رابطه (است یک) نیست\n",
    "is a\n",
    "\n",
    "هدف ما توسعه عملکرد هاست\n",
    "\n",
    "مث کلاس های دیگر است و بهتره اخر آن کلمه میکسین را اضافه بکنیم و هر کلاسی خواست از آن ارثبری میکند\n",
    "\n",
    "از کلاس های میکسین شی ایجاد نمیکنیم\n",
    "\n",
    "در دو حالت از میکسین استفاده میکنیم\n",
    "۱-تعداد زیادی از قابلیت های اختیاری را برای تعدادی از کلاس ها فراهم بکنیم\n",
    "۲-یک قابلیت خاص وجود دارد که میخاهیم در چند کلاس استفاده بکنیم\n",
    "\n",
    "اگر یکسری قابلیت ها را در کلاس پدر اضافه بکنیم در برخی کلاس ها که نمیخاهیم هم ارثبری میشود برای همین از میکسین استفاده میکنیم\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "814e933d-548d-44d6-901d-2a4d6790668b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are conected to wifi\n",
      "playing music\n",
      "\n",
      "playing music\n"
     ]
    }
   ],
   "source": [
    "class WifiMixin:\n",
    "    def connect_to_wifi(self):\n",
    "        print(\"You are conected to wifi\")\n",
    "\n",
    "class MusicMixin:\n",
    "    def play_music(self):\n",
    "        print(\"playing music\")\n",
    "        \n",
    "class Vehicle:\n",
    "    def move(self):\n",
    "        pass\n",
    "    \n",
    "class Car(Vehicle, WifiMixin, MusicMixin):\n",
    "    pass\n",
    "\n",
    "class AirPlane(Vehicle):\n",
    "    pass\n",
    "\n",
    "class MotorCycle(Vehicle, MusicMixin):\n",
    "    pass\n",
    "\n",
    "car = Car()\n",
    "car.connect_to_wifi()\n",
    "car.play_music()\n",
    "\n",
    "print()\n",
    "\n",
    "motor = MotorCycle()\n",
    "motor.play_music()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e88833-7f75-49ee-bd36-2ef0ea061416",
   "metadata": {},
   "source": [
    "# duck typing"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8f64b67f-fe42-4f42-b711-36a434feeb15",
   "metadata": {},
   "source": [
    "ما یک آبجکت را براساس متدها و رفتار آن قضاوت میکنیم نه چیزی که هست\n",
    "\n",
    "\n",
    "\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "44452980-25a7-4f65-a94d-a68a5fce14eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'capitalize', 'casefold', 'center', 'count', 'encode', 'endswith', 'expandtabs', 'find', 'format', 'format_map', 'index', 'isalnum', 'isalpha', 'isascii', 'isdecimal', 'isdigit', 'isidentifier', 'islower', 'isnumeric', 'isprintable', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'maketrans', 'partition', 'removeprefix', 'removesuffix', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']\n",
      "2\n",
      "['__add__', '__class__', '__class_getitem__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'count', 'index']\n",
      "2\n",
      "['__add__', '__class__', '__class_getitem__', '__contains__', '__delattr__', '__delitem__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__imul__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__', '__sizeof__', '__str__', '__subclasshook__', 'append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']\n",
      "\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "object of type 'int' has no len()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 15\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mdir\u001b[39m(li))\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m()\n\u001b[0;32m---> 15\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mdir\u001b[39m(\u001b[38;5;241m5\u001b[39m))\n",
      "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()"
     ]
    }
   ],
   "source": [
    "# مثل تابع لن که براش مهم نیست نوع شی نیست تا وقتی که آبجکت متد لن داشته باشد کافی است\n",
    "\n",
    "s = \"reza\"\n",
    "t = (1, 2)\n",
    "li = [3, 4]\n",
    "print(len(s))\n",
    "print(dir(s))\n",
    "print(len(t))\n",
    "print(dir(t))\n",
    "print(len(li))\n",
    "print(dir(li))\n",
    "\n",
    "print()\n",
    "\n",
    "print(len(5))\n",
    "print(dir(5))\n",
    "\n",
    "# گاهی اوقات ممکن است آبجکت آن متد را نداشته باشد\n",
    "# مفاهیمی وجود دارند که با آنها میشود این مشکل را کنترل کرد\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "042a87c9-a074-47d2-bf6e-a60c3cdb4dfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorry...\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# LBYL -> look before you leap\n",
    "\n",
    "# بی گدار به آب نزدن \n",
    "# اول اندیشه بعد بررسی کن\n",
    "\n",
    "# اول متدی را بررسی بکن وجود داردبعد استفاده بکن\n",
    "\n",
    "def check_len(obj):\n",
    "    # if \"__len__\" in dir(obj):\n",
    "    if hasattr(obj, \"__len__\"):\n",
    "        print(len(obj))\n",
    "    else:\n",
    "        print(\"sorry...\")\n",
    "        \n",
    "check_len(5)\n",
    "check_len([1, 2])\n",
    "\n",
    "# این روش کمی کد را طولانی تر میکند\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3425fb86-b022-4bdb-a3af-4e3a73be2f8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sorry...\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# EAFP -> its easier to ask for forgiveness than permission\n",
    "\n",
    "# بخشش بهتر از طلب بخشش است\n",
    "\n",
    "# کارت رو انجام بده اگر درست کار نکرد معذرت خاهی بکن\n",
    "\n",
    "def check_len(obj):\n",
    "    try:\n",
    "        print(len(obj))\n",
    "    except TypeError:\n",
    "        print(\"sorry...\")\n",
    "        \n",
    "check_len(5)\n",
    "check_len([1, 2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dd7032bb-1c09-4f77-89c0-003a55ec897d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "2\n",
      "2\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "d = {\"a\":1, \"b\":2, \"c\":3}\n",
    "\n",
    "if \"b\" in d.keys():\n",
    "    print(d[\"b\"])\n",
    "else:\n",
    "    print(\"sorry...\")\n",
    "    \n",
    "try:\n",
    "    print(d[\"b\"])\n",
    "except:\n",
    "    print(\"sorry...\") \n",
    "    \n",
    "# در دیکشنری میتونیم از گت استفاده بکنیم اگر وجود نداشته باشد نان میدهد\n",
    "\n",
    "print(d.get(\"b\"))\n",
    "print(d.get(\"f\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "daf27ea5-c669-4c1c-9ff3-993d2184ee50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am swimming...\n",
      "I am walking...\n",
      "I am flying...\n"
     ]
    }
   ],
   "source": [
    "class Duck:\n",
    "    def move(self):\n",
    "        print(\"I am swimming...\")\n",
    "        \n",
    "class Person:\n",
    "    def move(self):\n",
    "        print(\"I am walking...\")\n",
    "        \n",
    "class Plane:\n",
    "    def move(self):\n",
    "        print(\"I am flying...\")\n",
    "        \n",
    "def func(obj):\n",
    "    obj.move()\n",
    "    \n",
    "#  تا زمانیکه رفتار ماشبهی انجام میدهند برای ما مهم نیست و رفتار آنها مهم است که همان متد موو که در همه وجود دارد است \n",
    "d = Duck()\n",
    "h = Person()\n",
    "p = Plane()\n",
    "\n",
    "func(d)\n",
    "func(h)\n",
    "func(p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7233df7d-dfc8-49dc-8df7-17a21be6ff95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'ali', 'age': 12}\n"
     ]
    }
   ],
   "source": [
    "# __dict__\n",
    "\n",
    "# پراپرتی های آبجکت را بصورت دیکشنری نمایش میدهد\n",
    "\n",
    "class Person:\n",
    "    def __init__(self, **kwargs):\n",
    "        for key , value in kwargs.items():\n",
    "            setattr(self, key , value)\n",
    "            \n",
    "p = Person(name=\"ali\", age=12)\n",
    "\n",
    "print(p.__dict__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49118d09-9f85-4a3c-a1fa-fe3f14343dae",
   "metadata": {},
   "source": [
    "# call"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ec129161-cd0c-4cfd-af69-9968ce7edd59",
   "metadata": {},
   "source": [
    "در واقع توابع یک نوع آبجکتی هستند که قابلیت فراخوانی دارند\n",
    "\n",
    "یکسری آبجکتها قابلیت فراخوانی ندارند ام یکسری دارند\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9bec77f0-47c5-4ab5-b179-0d58a7d3d7c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "def func():\n",
    "    pass\n",
    "\n",
    "class A:\n",
    "    pass\n",
    "\n",
    "x = 5\n",
    "\n",
    "# از کجا بفهمیم یک آبجکت قابلیت فراخوانی دارد یا ن\n",
    "\n",
    "print(callable(x))\n",
    "print(callable(func))\n",
    "print(callable(A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "db46a9bf-e250-496d-a9b7-c0296ff1d95a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "class A:\n",
    "    pass\n",
    "\n",
    "a = A()\n",
    "\n",
    "# درسته که کلاس قابل فراخوانی است اما آبجکت قابل فراخوانی نیست\n",
    "print(callable(A))\n",
    "print(callable(a))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "62884040-b422-4e02-b753-9b3614eb04da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "init\n",
      "call\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# یکسری آبجکت ها را میخاهیم بنویسیم که قابلیت فراخوانی دارند\n",
    "# __call__\n",
    "\n",
    "class A:\n",
    "    def __init__(self, x):\n",
    "        print(\"init\")\n",
    "        self.x = x\n",
    "        \n",
    "    def __call__(self, z):\n",
    "        print(\"call\")\n",
    "        self.z = z\n",
    "\n",
    "a = A(2)\n",
    "\n",
    "a(5)\n",
    "print(a.z)\n",
    "\n",
    "# وقتی کاربرد دارد که میخاهیم دوباره مقداردهی بکنیم مثل اینیت"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf05d74-c65f-4f11-8971-8babb5b561c2",
   "metadata": {},
   "source": [
    "# type of methods in class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "21740a99-ae27-41ab-957c-56ccd06743fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'name : reza , product : phone , description : good'\n",
      "\n",
      "website : www.amir.ir\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "from pprint import pprint\n",
    "\n",
    "class Product:\n",
    "    def __init__(self, product_name, price, off):\n",
    "        self.product_name = product_name\n",
    "        self.price = price\n",
    "        self.off = off\n",
    "        \n",
    "    def __str__(self):\n",
    "        return self.product_name\n",
    "        \n",
    "class Comment:\n",
    "    website_name = \"www.amir.ir\"\n",
    "    \n",
    "    def __init__(self, product, name, description):\n",
    "        self.product = product\n",
    "        self.name = name\n",
    "        self.description = description\n",
    "        self.data = datetime.now()\n",
    "    # instance method\n",
    "    def show(self):\n",
    "        pprint(f\"name : {self.name} , product : {self.product} , description : {self.description}\")\n",
    "        \n",
    "    # class method\n",
    "    @classmethod\n",
    "    def info(cls):\n",
    "        print(f\"website : {cls.website_name}\")\n",
    "\n",
    "\n",
    "# instance methodes\n",
    "# به اینها اینستنس متد میگویند یعنی متد آبجکت\n",
    "# رایج ترین متدها\n",
    "# لازم نیست برای تعریف آنها از دکرتور استفاده بکنیم\n",
    "# برای دستیابی و استفاده از اینستنس اتریبیوت ها استفده میشه\n",
    "# باید خود آبجکت (سلف) را بگیرند تا بتوانند روی آن تغییر دهند\n",
    "\n",
    "# class methodes\n",
    "# به آبجکت و اتریبیوت های آن دسترسی ندراند\n",
    "# به کلاس اتریبیوت ها دسترسی دارند\n",
    "# قبل آن از این دکرتور استفاده میکنیم\n",
    "# @classmethod\n",
    "# با سی ال اس ما به خود کلاس دسترسی داریم نه اتریبیوت ها آبجکت و به خود کلاس اشاره میکند\n",
    "# cls\n",
    "\n",
    "\n",
    "\n",
    "phone = Product(\"phone\", 0, 0)\n",
    "c1 = Comment(phone, \"reza\", \"good\")\n",
    "c1.show()\n",
    "\n",
    "print()\n",
    "\n",
    "Comment.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b546acad-4e50-47b0-bddd-54d0c4e4c15f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'name : reza , product : phone , description : good'\n",
      "----------------------------------------\n",
      "the comment was censored!!!\n",
      "'name : reza , product : phone , description : what ****'\n"
     ]
    }
   ],
   "source": [
    "# یک نوع دیگر از کلاس متد کارکرد متفاوت دارد و آبجکت ایجاد میکند\n",
    "# قبل آن میتونیم یکسری تغییرات در ورودی آنها بدهیم\n",
    "\n",
    "from datetime import datetime\n",
    "from pprint import pprint\n",
    "\n",
    "class Product:\n",
    "    def __init__(self, product_name, price, off):\n",
    "        self.product_name = product_name\n",
    "        self.price = price\n",
    "        self.off = off\n",
    "        \n",
    "    def __str__(self):\n",
    "        return self.product_name\n",
    "        \n",
    "class Comment:\n",
    "    website_name = \"www.amir.ir\"\n",
    "    \n",
    "    def __init__(self, product, name, description):\n",
    "        self.product = product\n",
    "        self.name = name\n",
    "        self.description = description\n",
    "        self.data = datetime.now()\n",
    "    \n",
    "    def show(self):\n",
    "        pprint(f\"name : {self.name} , product : {self.product} , description : {self.description}\")\n",
    "        \n",
    "    @classmethod\n",
    "    def info(cls):\n",
    "        print(f\"website : {cls.website_name}\")\n",
    "    \n",
    "    @classmethod\n",
    "    def cesorship(cls, product, name, description):\n",
    "        print(\"the comment was censored!!!\")\n",
    "        sc = description.replace(\"slutty\" , \"****\")\n",
    "        return cls(product, name, sc)\n",
    "    # زیرا کلاس گرفته میتونه از همان آبجکت ایجاد بکنه\n",
    "\n",
    "    \n",
    "phone = Product(\"phone\", 0, 0)\n",
    "c1 = Comment(phone, \"reza\", \"good\")\n",
    "c1.show()\n",
    "print(\"-\" * 40)\n",
    "c2 = Comment.cesorship(phone, \"reza\", \"what slutty\")\n",
    "c2.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "1ef7a669-be03-4465-9c7c-ccde693acc4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'name : reza , product : phone , description : good'\n",
      "----------------------------------------\n",
      "the comment was censored!!!\n",
      "'name : reza , product : phone , description : what ****'\n",
      "-55\n"
     ]
    }
   ],
   "source": [
    "# static methods\n",
    "# نه به آبجکت کار داره نه به کلاس اما نیاز به آن در کلاس داریم\n",
    "# اگر قراره چنتا کلاس مختلف از آن استفاده بکنند همین بیرن کلاس تعریف کینم\n",
    "# ورودی اجباری مثل متد های دیگر ندارد\n",
    "# هم میتونیم داخل کلاس بنویسیم و هم خارجش\n",
    "# وقتی داخل کلاس استفاده میکنیم باید از دکرتور استاتیک متد استفاده بکنیم و اینکه برای فراخوانی از اسم کلاس یا آبجکت استفاده بکنیم\n",
    "# @staticmethod\n",
    "\n",
    "from datetime import datetime\n",
    "from time import sleep\n",
    "\n",
    "class Product:\n",
    "    def __init__(self, product_name, price, off):\n",
    "        self.product_name = product_name\n",
    "        self.price = price\n",
    "        self.off = off\n",
    "        \n",
    "    def __str__(self):\n",
    "        return self.product_name\n",
    "        \n",
    "class Comment:\n",
    "    website_name = \"www.amir.ir\"\n",
    "    \n",
    "    def __init__(self, product, name, description):\n",
    "        self.product = product\n",
    "        self.name = name\n",
    "        self.description = description\n",
    "        self.data = datetime.now().second\n",
    "    \n",
    "    def show(self):\n",
    "        pprint(f\"name : {self.name} , product : {self.product} , description : {self.description}\")\n",
    "        \n",
    "    @classmethod\n",
    "    def info(cls):\n",
    "        print(f\"website : {cls.website_name}\")\n",
    "    \n",
    "    @classmethod\n",
    "    def cesorship(cls, product, name, description):\n",
    "        print(\"the comment was censored!!!\")\n",
    "        sc = description.replace(\"slutty\" , \"****\")\n",
    "        return cls(product, name, sc)\n",
    "    \n",
    "    # static method \n",
    "    @staticmethod\n",
    "    def elapsed_time(time):\n",
    "        sleep(5)\n",
    "        print(datetime.now().second - time)\n",
    "    \n",
    "\n",
    "    \n",
    "phone = Product(\"phone\", 0, 0)\n",
    "c1 = Comment(phone, \"reza\", \"good\")\n",
    "c1.show()\n",
    "print(\"-\" * 40)\n",
    "c2 = Comment.cesorship(phone, \"reza\", \"what slutty\")\n",
    "c2.show()\n",
    "\n",
    "c2.elapsed_time(c2.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "929b8f7a-76f8-42e7-b819-41ea2fed5464",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the comment was censored!!!\n",
      "'name : reza , product : phone , description : what ****'\n",
      "2 days, 2:00:00.000304\n"
     ]
    }
   ],
   "source": [
    "# timedelta\n",
    "# برای گذشتن زمان استفاده میکنیم\n",
    "\n",
    "from datetime import datetime ,timedelta\n",
    "\n",
    "class Product:\n",
    "    def __init__(self, product_name, price, off):\n",
    "        self.product_name = product_name\n",
    "        self.price = price\n",
    "        self.off = off\n",
    "        \n",
    "    def __str__(self):\n",
    "        return self.product_name\n",
    "        \n",
    "class Comment:\n",
    "    website_name = \"www.amir.ir\"\n",
    "    \n",
    "    def __init__(self, product, name, description):\n",
    "        self.product = product\n",
    "        self.name = name\n",
    "        self.description = description\n",
    "        self.data = datetime.now()\n",
    "    \n",
    "    def show(self):\n",
    "        pprint(f\"name : {self.name} , product : {self.product} , description : {self.description}\")\n",
    "        \n",
    "    @classmethod\n",
    "    def info(cls):\n",
    "        print(f\"website : {cls.website_name}\")\n",
    "    \n",
    "    @classmethod\n",
    "    def cesorship(cls, product, name, description):\n",
    "        print(\"the comment was censored!!!\")\n",
    "        sc = description.replace(\"slutty\" , \"****\")\n",
    "        return cls(product, name, sc)\n",
    "    \n",
    "    # static method \n",
    "    @staticmethod\n",
    "    def elapsed_time(time):\n",
    "        print(datetime.now() - time)\n",
    "    \n",
    "\n",
    "    \n",
    "phone = Product(\"phone\", 0, 0)\n",
    "c2 = Comment.cesorship(phone, \"reza\", \"what slutty\")\n",
    "c2.show()\n",
    "\n",
    "c2.elapsed_time(c2.data - timedelta(days=2 , hours=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fb357c35-d88f-41cc-9cd4-3ca44e6b4f8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', 'cesorship', 'elapsed_time', 'info', 'show', 'website_name']\n"
     ]
    }
   ],
   "source": [
    "# ارث بری متدها\n",
    "# همه نوع متدها ارثبری میشوند\n",
    "\n",
    "from datetime import datetime\n",
    "from time import sleep\n",
    "\n",
    "class Product:\n",
    "    def __init__(self, product_name, price, off):\n",
    "        self.product_name = product_name\n",
    "        self.price = price\n",
    "        self.off = off\n",
    "        \n",
    "    def __str__(self):\n",
    "        return self.product_name\n",
    "        \n",
    "class Comment:\n",
    "    website_name = \"www.amir.ir\"\n",
    "    \n",
    "    def __init__(self, product, name, description):\n",
    "        self.product = product\n",
    "        self.name = name\n",
    "        self.description = description\n",
    "        self.data = datetime.now().second\n",
    "    \n",
    "    def show(self):\n",
    "        pprint(f\"name : {self.name} , product : {self.product} , description : {self.description}\")\n",
    "        \n",
    "    @classmethod\n",
    "    def info(cls):\n",
    "        print(f\"website : {cls.website_name}\")\n",
    "    \n",
    "    @classmethod\n",
    "    def cesorship(cls, product, name, description):\n",
    "        print(\"the comment was censored!!!\")\n",
    "        sc = description.replace(\"slutty\" , \"****\")\n",
    "        return cls(product, name, sc)\n",
    "    \n",
    "    # static method \n",
    "    @staticmethod\n",
    "    def elapsed_time(time):\n",
    "        sleep(5)\n",
    "        print(datetime.now().second - time)\n",
    "        \n",
    "class A(Comment):\n",
    "    pass\n",
    "\n",
    "    \n",
    "print(dir(A))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a4d132-8026-40bd-80de-db62bfb82efd",
   "metadata": {},
   "source": [
    "# setter getter property"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "337e5444-3a37-4e90-86d4-61c4377a263d",
   "metadata": {},
   "source": [
    "نباید دسترسی کامل به یکسری از پراپرتی ها داشت براینکه:\n",
    "    امنیت به خطر میافته-\n",
    "    اعتبار سنجی بکنیم قبل از تغییر مقدار\n",
    "\n",
    "برای همین از ستتر و گتتر استفاده میکنیم\n",
    "\n",
    "از توابع برای تغییر و ملاحظه مقدار آنها استفاده میکنیم\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "dfb5428e-a107-4bad-ba57-f6f8763e2676",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "light blue\n",
      "blue\n",
      "red\n",
      "blue\n"
     ]
    }
   ],
   "source": [
    "class Color:\n",
    "    def __init__(self, rgb, name):\n",
    "        self._rgb = rgb\n",
    "        if name:\n",
    "            self._name = name\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid name {name!r}\")\n",
    "        \n",
    "    def set_name(self, name):\n",
    "        if name:\n",
    "            self._name = name\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid name {name!r}\")\n",
    "        \n",
    "    def get_name(self):\n",
    "        return self._name\n",
    "    \n",
    "    def set_rgb(self, rgb):\n",
    "        self._rgb = rgb\n",
    "        \n",
    "    def get_rgb(self):\n",
    "        return self._rgb\n",
    " \n",
    "c1 = Color(0x6783f5, \"light blue\")\n",
    "print(c1.get_name())\n",
    "c1.set_name(\"blue\")\n",
    "print(c1.get_name())\n",
    "\n",
    "c2 = Color(0x7963f5, \"red\")\n",
    "print(c2._name)\n",
    "c2._name = \"blue\"\n",
    "print(c2._name)\n",
    "\n",
    "# چنتا مشکل دارد اینکه خوانایی کاهش میابد\n",
    "# اول ماژول بصورت مستقیم به پراپرتی دسترسی داشته باشد اما بعدها از ستتر و گتتر استفاده بکنیم تغییر کد ها مشکل است\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "b2cb952e-b86b-409d-9cfa-0c3fe904033f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set name\n",
      "red\n",
      "set name\n",
      "blue\n"
     ]
    }
   ],
   "source": [
    "# property\n",
    "# ابتدا ستتر و گتتر ها را بصورت خصوصی در میاوریم\n",
    "# از نام کلیدی پراپرتی استفاده میکنیم و گتتر و ستتر ها را بعنوان ورودی به آنها میدهیم\n",
    "# در این روش نیازه به اعتبار سنجی درموقع اعتبار دهی اولیه هم نیست\n",
    "# باعث میشه توابعی را نوشت و مثل اتریبیوت از آن استفاده کر\n",
    "# مقدار دهی اولیه را به همان شکل ساده و ابتدایی مینویسیم\n",
    "# در مقدار دهی اولیه مستقیما تابع اینیت پراپرتی ها را ست نمیکند بلکه از ستتر استفاده میکند\n",
    "\n",
    "\n",
    "class Color:\n",
    "    def __init__(self, rgb, name):\n",
    "        self.rgb = rgb\n",
    "        self.name = name\n",
    "        \n",
    "    def _set_name(self, name):\n",
    "        print(\"set name\")\n",
    "        if name:\n",
    "            self._name = name\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid name {name!r}\")\n",
    "        \n",
    "    def _get_name(self):\n",
    "        # همینطور که میبینید در ستتر اندراسکور را ست کرد پس همین اندر اسکور نیم را برمیگرداند\n",
    "        return self._name\n",
    "    \n",
    "    def _set_rgb(self, rgb):\n",
    "        self._rgb = rgb\n",
    "        \n",
    "    def _get_rgb(self):\n",
    "        return self._rgb\n",
    "    \n",
    "    # این نیم یک واسطه است که باعث میشه اندراسکور نیم مقدار دهی شود\n",
    "    name = property(_get_name, _set_name)\n",
    "    \n",
    "    rgb = property(_get_rgb, _set_rgb)\n",
    "    \n",
    "\n",
    "\n",
    "c = Color(0x7963f5, \"red\")\n",
    "print(c.name)\n",
    "c.name = \"blue\"\n",
    "print(c.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb7fee62-7f86-48e6-b98b-dfb3d3216337",
   "metadata": {},
   "source": [
    "# property - decorator"
   ]
  },
  {
   "cell_type": "raw",
   "id": "1e77b999-862a-4028-a462-f7b0141a4818",
   "metadata": {},
   "source": [
    "پراپرتی در واع یک کلاس داخلی پایتون است\n",
    "\n",
    "ورودی های پراپرتی :(به ترتیب)\n",
    "getter\n",
    "setter\n",
    "\n",
    "class property(fget=None, fset=None, fdel=none, doc=None)\n",
    "\n",
    "fdel\n",
    "زمانیکه آبجکت میخاد حذف بشه\n",
    "\n",
    "doc\n",
    "داک استرینگ اگر براش داک تعیین نکنیم از داک متد گت استفاده میکند\n",
    "\n",
    "اگر مثلا گتتر را ندهیم یا ستتر را ندهیم آن قابلیت ازش برداشته میشود\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c2d6fedf-fb2d-429e-8241-2e2016e81d63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on Color in module __main__ object:\n",
      "\n",
      "class Color(builtins.object)\n",
      " |  Color(rgb, name)\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, rgb, name)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  name\n",
      " |      new doc\n",
      "\n",
      "5\n",
      "deleting...\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "class Color:\n",
    "    def __init__(self, rgb, name):\n",
    "        self.rgb = rgb\n",
    "        self.name = name\n",
    "        \n",
    "    def _set_name(self, name):\n",
    "        if name:\n",
    "            self._name = name\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid name {name!r}\")\n",
    "        \n",
    "    def _get_name(self):\n",
    "        return self._name\n",
    "    \n",
    "    def _del_name(self):\n",
    "        print(\"deleting...\")\n",
    "        del self._name\n",
    "    \n",
    "    name = property(_get_name, _set_name, _del_name, \"new doc\")\n",
    "    \n",
    "c = Color(0x254564, \"red\")\n",
    "c.name = 5\n",
    "help(c)\n",
    "print(c.name)\n",
    "del c.name\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "27dedf0b-63f2-4823-a1d4-5dcbfa587e3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on Color in module __main__ object:\n",
      "\n",
      "class Color(builtins.object)\n",
      " |  Color(rgb, name)\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, rgb, name)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  name\n",
      " |      !!!!!\n",
      " |      good job\n",
      " |      !!!!!\n",
      "\n",
      "5\n",
      "deleting...\n"
     ]
    }
   ],
   "source": [
    "# روش دیگر نوشتن\n",
    "\n",
    "class Color:\n",
    "    def __init__(self, rgb, name):\n",
    "        self.rgb = rgb\n",
    "        self.name = name\n",
    "        \n",
    "    def _set_name(self, name):\n",
    "        if name:\n",
    "            self._name = name\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid name {name!r}\")\n",
    "        \n",
    "    def _get_name(self):\n",
    "        # برای نوشتن داک هم میتونیم برای گتتر داک بنویسیم \n",
    "        \"\"\"\n",
    "        !!!!!\n",
    "        good job\n",
    "        !!!!!\n",
    "        \"\"\"\n",
    "        return self._name\n",
    "    \n",
    "    def _del_name(self):\n",
    "        print(\"deleting...\")\n",
    "        del self._name\n",
    "    \n",
    "    # نیم را ابتدا تبدیل به یک پراپرتی میکنیم و در چنین حالتی گتتر و ستتر خاهد داشت\n",
    "    name = property()\n",
    "    name = name.setter(_set_name)\n",
    "    name = name.getter(_get_name)\n",
    "    name = name.deleter(_del_name)\n",
    "    \n",
    "    \n",
    "c = Color(0x254564, \"red\")\n",
    "c.name = 5\n",
    "help(c)\n",
    "print(c.name)\n",
    "del c.name    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d4cd1dd5-a011-4ab6-860e-818ccbc25a37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on Color in module __main__ object:\n",
      "\n",
      "class Color(builtins.object)\n",
      " |  Color(rgb, name)\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, rgb, name)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  name\n",
      " |      !!!!!\n",
      " |      good job\n",
      " |      !!!!!\n",
      "\n",
      "5\n",
      "deleting...\n"
     ]
    }
   ],
   "source": [
    "# استفاده از دکورتور در تعریف پراپرتی\n",
    "# از دکورتور ها در توابع ستتر و گتتر دلیتر استفاده میکنیم\n",
    "# و توابع گتتر و ستتر و دلیتتر را همنام پراپرتی قرار میدهیم\n",
    "\n",
    "class Color:\n",
    "    def __init__(self, rgb, name):\n",
    "        self.rgb = rgb\n",
    "        self.name = name\n",
    "    \n",
    "    @property \n",
    "    def name(self):\n",
    "        # برای نوشتن داک هم میتونیم برای گتتر داک بنویسیم \n",
    "        \"\"\"\n",
    "        !!!!!\n",
    "        good job\n",
    "        !!!!!\n",
    "        \"\"\"\n",
    "        return self._name\n",
    "    \n",
    "    @name.setter\n",
    "    def name(self, name):\n",
    "        if name:\n",
    "            self._name = name\n",
    "        else:\n",
    "            raise ValueError(f\"Invalid name {name!r}\")\n",
    "    \n",
    "    @name.deleter\n",
    "    def name(self):\n",
    "        print(\"deleting...\")\n",
    "        del self._name\n",
    "    \n",
    "    \n",
    "c = Color(0x254564, \"red\")\n",
    "c.name = 5\n",
    "help(c)\n",
    "print(c.name)\n",
    "del c.name    \n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "68220ccd-06b9-4e1e-868e-a6303945e582",
   "metadata": {},
   "source": [
    "کی باید از پراپرتی استفاده بکنیم کی اتریبیوت معمولی بنویسیم؟\n",
    "\n",
    "تابع را زمانی استفده بکنید که قرار است یک عمل یک اقدام صورت بگیره\n",
    "\n",
    "زمانی بخاهیم یک صفت یک توصیف داشته باشیم از پراپرتی استفاده میکنیم که قرار است یک تغییراتی روی آن بدهیم یا اعتبار سنجی بکنیم\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fdd9569d-7122-44a3-8848-34cb35854abf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n",
      "2.0\n"
     ]
    }
   ],
   "source": [
    "# میخاهیم یک لیست با قابلیت محاسبه میانگین درست بکنیم\n",
    "\n",
    "class NewList(list):\n",
    "    @property\n",
    "    def ave(self):\n",
    "        return sum(self)/len(self)\n",
    "\n",
    "li = NewList((1, 2, 3))\n",
    "print(li)\n",
    "print(li.ave)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38fdde14-752e-4154-9f15-10f00e3d6d95",
   "metadata": {},
   "source": [
    "# implementation - composition - aggregation"
   ]
  },
  {
   "cell_type": "raw",
   "id": "cf49654c-8c06-45de-bd9a-7d0dcecfed16",
   "metadata": {},
   "source": [
    "روابط مربوط به ترکیب\n",
    "\n",
    "رابطه\n",
    "has a\n",
    "\n",
    "این یک مفهوم است و سینتکس خاصی ندارد\n",
    "\n",
    "دیدگاه ما ممکن است نسبت به نوع پیاده سازی متفاوت باشد\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f8b9591d-0770-4b32-aae7-e4dcc0c36844",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "what is your name?\n",
      "['reza', 'ali']\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'e' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 22\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# با حذف آبجکت سوال هم به تنهایی معنایی ندارد\u001b[39;00m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m e\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43me\u001b[49m\u001b[38;5;241m.\u001b[39mquestion)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'e' is not defined"
     ]
    }
   ],
   "source": [
    "# composition\n",
    "# به یکدیگر وابستگی شدید دارند\n",
    "# برای اینکه نشان دهیم وابستگی شدید دارند در خود کلاس آبجکت را ایجاد میکنیم\n",
    "\n",
    "class Question:\n",
    "    def __init__(self, q:str, a:list):\n",
    "        self.q = q\n",
    "        self.a = a\n",
    "        \n",
    "class ExamPaper:\n",
    "    def __init__(self):\n",
    "        self.question = Question(\"what is your name?\", [\"reza\", \"ali\"])\n",
    "        \n",
    "    def __str__(self):\n",
    "        return f\"{self.question.q}\\n{self.question.a}\"\n",
    "  \n",
    "e = ExamPaper()\n",
    "print(e)\n",
    "\n",
    "# با حذف آبجکت سوال هم به تنهایی معنایی ندارد\n",
    "del e\n",
    "print(e.question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "721d3f93-e666-4ba6-b983-ab21b6e28881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "naser: 14\n",
      "mahsa: 13\n"
     ]
    }
   ],
   "source": [
    "# aggregation\n",
    "# وابستگی زیادی بهم ندارند و بدون هم نیز میتواندد وجود داشته باشند\n",
    "# همانطور که میبینید داخل کلاس بزرگتر ایجاد نکردیم زیرا بدون دانشگاه نیز معنا دارد\n",
    "\n",
    "\n",
    "class student:\n",
    "    def __init__(self, name, number):\n",
    "        self.name = name\n",
    "        self.number = number \n",
    "        \n",
    "    def __str__(self):\n",
    "        return f\"{self.name}: {self.number}\"\n",
    "        \n",
    "class University:\n",
    "    def __init__(self, students:list[student]):\n",
    "        self.students = students\n",
    "        \n",
    "st = [student(\"naser\", \"14\"), student(\"mahsa\", \"13\")]\n",
    "university = University(st)\n",
    "\n",
    "for s in university.students:\n",
    "    print(s)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59e0fba0-f9db-4089-816d-134e49c26b5d",
   "metadata": {},
   "source": [
    "# meta class"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2ad75453-7ade-43c1-b8da-8aaf2543965a",
   "metadata": {},
   "source": [
    "در موقع تعریف کلاس به دو نکته باید توجه کرد:\n",
    "همه چیز در پایتون شی هستند حتی کلاسی که تعریف میکنیم-همه کلاس ها یک نمونه از کلاس تایپ هستند"
   ]
  },
  {
   "attachments": {
    "Screenshot%20from%202023-08-02%2011-44-48.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "e35b31ca",
   "metadata": {},
   "source": [
    "![Screenshot%20from%202023-08-02%2011-44-48.png](attachment:Screenshot%20from%202023-08-02%2011-44-48.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e57abfcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.Human'>\n",
      "True\n",
      "----------------------------------------\n",
      "<class 'object'>\n",
      "----------------------------------------\n",
      "<class 'type'>\n",
      "True\n",
      "----------------------------------------\n",
      "<class 'type'>\n",
      "----------------------------------------\n",
      "<class 'int'>\n",
      "True\n",
      "<class 'type'>\n",
      "True\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "class Human:\n",
    "    pass\n",
    "\n",
    "human_obj = Human()\n",
    "\n",
    "# ساخت آبجکت باعث میشه من یک نمونه از آن داشته باشم\n",
    "# هیومن آبجکت یک نمونه از هیومن است\n",
    "print(type(human_obj))\n",
    "print(isinstance(human_obj, Human))\n",
    "print(\"-\" * 40)\n",
    "\n",
    "# همه کلاس هایی که میسازیم از کلاس آبجکت ارثبری میکنند\n",
    "print(Human.__base__)\n",
    "print(\"-\" * 40)\n",
    "\n",
    "# همه کلاس ها یک نمونه از کلاس تایپ هستند\n",
    "print(type(Human))\n",
    "print(isinstance(Human, type))\n",
    "print(\"-\" * 40)\n",
    "\n",
    "# خود تایپ یک نمونه از کلاس تایپ است\n",
    "print(type(type))\n",
    "print(\"-\" * 40)\n",
    "\n",
    "# همه انواع داده یک نمونه از متاکلاس تایپ هستند\n",
    "n = 5\n",
    "print(type(n))\n",
    "print(isinstance(n, int))\n",
    "print(type(int))\n",
    "print(isinstance(int, type))\n",
    "print(\"-\" * 40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2790558f-03cc-4dea-a5d5-deb9d0492fe6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.Human'>\n",
      "<class '__main__.HumanMeta'>\n",
      "<class 'type'>\n"
     ]
    }
   ],
   "source": [
    "# meta class\n",
    "# به کلاس هایی گفته میشه که وقتی یک شی ایجاد میشه یک کلاس است\n",
    "# example -> type\n",
    "# از کلاس تایپ باید ارثبری بکند\n",
    "\n",
    "class HumanMeta(type):\n",
    "    pass\n",
    "\n",
    "class Human(metaclass=HumanMeta):\n",
    "    pass\n",
    "\n",
    "h = Human()\n",
    "print(type(h))\n",
    "print(type(Human))\n",
    "print(type(HumanMeta))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5dc4d28-112e-4a24-9859-612f6a4bc0d8",
   "metadata": {},
   "source": [
    "# abstract class"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8fc8a484-3987-4868-b939-e56bfbe85cb5",
   "metadata": {},
   "source": [
    "کلاس های انتزاعی\n",
    "\n",
    "کلاسی داریم که کلیات درون ان بیان میشود و جزییات در کلاس کلاس های فرزند اعمال میشود\n",
    "\n",
    "فقط در کلاس بالایی متد ها تعریف میشوند و در کلاس های پایینی به جزییات آنها پرداخته میشود\n",
    "\n",
    "در پایتون اگر بخاهیم از ابسترکشن استفاده بکنیم باید از ماژول ای بی سی استفاده میکنیم\n",
    "\n",
    "abc -> Abstract Base Class\n",
    "\n",
    "به دو روش میشه از کلاس های ابسترکت استفاده کرد یکی اینکه از کلاس ای بی سی ارثبری کرد-ارثبری از ای بی سی متا\n",
    "\n",
    "کلاس های انتزاعی مشکلی ندارند که متدهای عادی داشته باشند\n",
    "\n",
    "از آنها شی نمیشه ایجاد کرد\n",
    "\n",
    "متدهای ابسترکت میتونن یک پیاده سازی از قبل داشته باشند\n",
    "\n",
    "میشه از آنها ارث بری کرد و اگر کلاسی همه متدهای آن ابسترکت آنرا پیاده سازی نکند خودش یک کلاس انتزاعی است\n",
    "\n",
    "بنابراین کلاسی که از کلاس انتزاعی ارثبری میکند باید تمام متد های آنرا پیاده سازی بکند وگرن نمیتواند شی بسازد\n",
    "\n",
    "اگر در کلاس ابسترکت برای مت ابسترکت یک پیاده سازی دیفالت داشته باشیم دلیل بر این نمیشود آن را در کلاس های زیرین تعریف نیکنیم\n",
    "\n",
    "برای استفاده از مقدار دیفالت در کلاس های زیرین فقط کافی است سوپر را صدا بزنیم\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b4f4265f-71d3-49dd-ac88-14aae6380431",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.Car'>\n",
      "Default\n",
      "under repair\n",
      "------------------------------\n",
      "<class 'abc.ABCMeta'>\n"
     ]
    }
   ],
   "source": [
    "# ABC\n",
    "# abstractmethod\n",
    "# برای ساخت متد های انتزاعی \n",
    "\n",
    "from abc import ABC, abstractmethod\n",
    "\n",
    "# Abstract Base Class\n",
    "class Vehicle(ABC):\n",
    "    \n",
    "    @abstractmethod\n",
    "    def move(self):\n",
    "        \"\"\"this method should be impolemented\"\"\"\n",
    "        print(\"Default\")\n",
    "        \n",
    "    @abstractmethod\n",
    "    def repair(self):\n",
    "        \"\"\"this method should be impolemented\"\"\"\n",
    "    \n",
    "    # هم میشه نوع را تعیین کرد هم ابسترک متود بودنش را\n",
    "    # @classmethod\n",
    "    # @abstractmethod\n",
    "    # def func(cls):\n",
    "    #     \"\"\"this method should be impolemented\"\"\"\n",
    "\n",
    "    def class_name(self):\n",
    "        print(self.__class__)\n",
    "\n",
    "        \n",
    "# concrete class        \n",
    "class Car(Vehicle):        \n",
    "    def move(self):\n",
    "        # از دیفالت کلاس انتزاعی استفاده میکند\n",
    "        super(Car, self).move()\n",
    "            \n",
    "    def repair(self):\n",
    "        print(\"under repair\")\n",
    "\n",
    "class Airplane(Vehicle):\n",
    "    def move(self):\n",
    "        print(\"Flying...\")\n",
    "    \n",
    "    def repair(self):\n",
    "        print(\"under repair...\")\n",
    "\n",
    "car = Car()\n",
    "car.class_name()\n",
    "car.move()\n",
    "car.repair()\n",
    "\n",
    "\n",
    "print(\"-\" * 30)\n",
    "# برای فهمیدن متای ای بی سی\n",
    "print(type(ABC))        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d21744af-a10c-4f76-8d7f-1bae78a3ba11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ABCMeta\n",
    "# متل قبلی فقط بجای ارثبری از ای بی سی متاکلاس آنرا مشخص میکنیم\n",
    "\n",
    "from abc import ABCMeta, abstractmethod\n",
    "\n",
    "# Abstract Base Class\n",
    "class Vehicle(metaclass=ABCMeta):\n",
    "    \n",
    "    @abstractmethod\n",
    "    def move(self):\n",
    "        \"\"\"this method should be impolemented\"\"\"\n",
    "        \n",
    "    @abstractmethod\n",
    "    def move(self):\n",
    "        \"\"\"this method should be impolemented\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "13070819-b524-4672-aa37-003e03c217e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class '__main__.Airplane'>\n",
      "Flying...\n",
      "under repair...\n",
      "ejecting...\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Can't instantiate abstract class AirVehicle with abstract methods eject, move, repair",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[65], line 59\u001b[0m\n\u001b[1;32m     56\u001b[0m a\u001b[38;5;241m.\u001b[39meject()\n\u001b[1;32m     58\u001b[0m \u001b[38;5;66;03m# از کلاس ابسترکت نمیتونیم نمونه یا شی بسازیم\u001b[39;00m\n\u001b[0;32m---> 59\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[43mAirVehicle\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: Can't instantiate abstract class AirVehicle with abstract methods eject, move, repair"
     ]
    }
   ],
   "source": [
    "\n",
    "from abc import ABC, abstractmethod\n",
    "\n",
    "# Abstract Base Class\n",
    "class Vehicle(ABC):\n",
    "    \n",
    "    @abstractmethod\n",
    "    def move(self):\n",
    "        \"\"\"this method should be impolemented\"\"\"\n",
    "        print(\"Default\")\n",
    "        \n",
    "    @abstractmethod\n",
    "    def repair(self):\n",
    "        \"\"\"this method should be impolemented\"\"\"\n",
    "\n",
    "    def class_name(self):\n",
    "        print(self.__class__)\n",
    "\n",
    "# Abstract Class\n",
    "class LandVehicale(Vehicle):\n",
    "    @abstractmethod\n",
    "    def brake(self):\n",
    "        \"\"\"this method should be impolemented\"\"\"\n",
    "\n",
    "# Abstract Class\n",
    "class AirVehicle(Vehicle):\n",
    "    @abstractmethod\n",
    "    def eject(self):\n",
    "        \"\"\"this method should be impolemented\"\"\"      \n",
    "        \n",
    "# concrete class        \n",
    "class Car(LandVehicale):        \n",
    "    def move(self):\n",
    "        print(\"Drive...\")\n",
    "            \n",
    "    def repair(self):\n",
    "        print(\"under repair...\")\n",
    "        \n",
    "    def brake(self):\n",
    "        print(\"braking...\")\n",
    "\n",
    "class Airplane(AirVehicle):\n",
    "    def move(self):\n",
    "        print(\"Flying...\")\n",
    "    \n",
    "    def repair(self):\n",
    "        print(\"under repair...\")\n",
    "\n",
    "    def eject(self):\n",
    "        print(\"ejecting...\")\n",
    "        \n",
    "        \n",
    "a = Airplane()\n",
    "a.class_name()\n",
    "a.move()\n",
    "a.repair()\n",
    "a.eject()\n",
    "\n",
    "# از کلاس ابسترکت نمیتونیم نمونه یا شی بسازیم\n",
    "a = AirVehicle()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8763ba89-a1ae-40f9-8518-061fe995dc75",
   "metadata": {},
   "source": [
    "# operator overloading"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6bec0fcc-e9ba-4a03-9e04-233f3bc386d8",
   "metadata": {},
   "source": [
    "سربارگذاری عملگرها\n",
    "\n",
    "یک عملگر با توجه به چه شی ایی  داره کار میکنه رفتار متفاوت میتونه نشون بده\n",
    "\n",
    "مثل رفتار جمع\n",
    "\n",
    "پشت صحنه متدهایی است که برای عملکردشون ایجاد شده\n",
    "\n",
    "یکسری متد ها که رفتار عملگر را نمایش میدهد\n",
    "\n",
    "میشه برای عملگر های دیگر مثل ضرب و کوچتر بزرگتر و ... تعریف کرد که متد مخصوص به خود را دارند\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "05df6628-3c6a-4821-93d2-b80a830c9ba0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# بطور مثال میتونیم تعریف کنیم عمگر جمع در اشیا حاصل از این کلاس چکاری انجام دهد\n",
    "\n",
    "class Person:\n",
    "    def __init__(self, name, age, national_code):\n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        self.national_code = national_code\n",
    "        \n",
    "    # خودش شی و آبجکت دوم را بعنوان ورودی میگرد\n",
    "    def __add__(self, other):\n",
    "        return self.age + other.age\n",
    "     \n",
    "    def __lt__(self, other):\n",
    "        return self.age < other.age\n",
    "    \n",
    "    def __str__(self):\n",
    "        return f\"{self.name}\"\n",
    "    \n",
    "p1 = Person(\"ali\", 15, \"12345\")\n",
    "p2 = Person(\"amir\", 22, \"22325\")\n",
    "print(p1 + p2)\n",
    "print(p1 < p2)\n",
    "\n"
   ]
  },
  {
   "attachments": {
    "Screenshot%20from%202023-08-02%2018-29-10.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "b8b08220",
   "metadata": {},
   "source": [
    "![Screenshot%20from%202023-08-02%2018-29-10.png](attachment:Screenshot%20from%202023-08-02%2018-29-10.png)"
   ]
  },
  {
   "attachments": {
    "Screenshot%20from%202023-08-02%2018-28-26.png": {
     "image/png": 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z7IB0dz+zp9fAduFgnBsM5ccdQx9LkEb4xul8ufvJxSo5x5oPlu6j8fkrpFrVxtneBDHnKn+u+Zt7MpB6kuU/HafJvPfosciD5pcucC1Zha2jE1UsIGxRBAPXRj/M77H8Nf964xvXh+6V+vLjGmvOROni3LYZVWS5YIgveEJzi5PHLjLOxQWnT7Zwomcc2VxkcZ8pLF/qTbNZLekwfzfOAy4QZ1wDl6oWqLIiWPvLHm5JgPiwbPm1al8u9O98skKW8lHz3yA7u5A1lR/5T/HaRcOF7b9y/MP5tKs9jLUeLQiNzKa8cy0MMpPRYP5IPe7XJP/9Ja7+vpAtnVcwtGY3Fh5owuUrcejaO1LF7DZr+vXkl/NF2Hr5if9BTjnN3K7N+V7KJlsqIimAFMv+n1fQxWUqjYeu5lC7i1y4mYGBfS2c7E3I8foKv3F7SZDzuHDsBLcGDaJyk+nsOTmMxDwJn9nd+flp68v3/1vwPzmB6/n5r3f58YOm/G+HB93C76Cu7ExNGz1yo3ayaMtFNECm90bWBbbji4aPprMz0OTn+Xo7SpG8hG8jk+D9M4M/msDPO09zMTqF7LxcMpPucMFrJz+N682Qn7xJKOLd8oKW8+lXB7hjVgNHa4G4iKOsnDSWhX7pBSkkbu2fxoAxi9hz7g6qSm40ql8do/gA9vwyiWlbo9CQx7WQEBLSowkJj35kQJFTTvLtmNls943GwLU93d5x4Oa6iXyx7QqpiRFE3LyfWsOVDV8wfd0pLsfnYWBphnTnMjfS8ojaMZnBU1ZxKCwBoxruONvkcs1rG18PHc3S4IyCx28SFhxLelwooVFaOVVSDGRSr1zgRmoCF/99dCDVPCJ00Ny4wIXEFK5cuE42xW8XKeYgM0bOYMOpK6QYVsapuh6Xt03n842RSMhkZhQsuGpuEhIUS1psCCEF7y+n+rFw+Ai+2e7NlXRTqtWuiklSMLsWzmf74/tmBUix/xJxN427F65wr7Cwc7PJLjyFS7/KhaupJF24yP1PmHNpE2MHTGXFoVASDKvToLEbldW3OfP7AsZ+feBB/8sOXMbEWX/gez0J2dgSo6xrXL6TQ+qlUC6npHH9/BUeOGtyKpfDIklJiSTscmrBQnIsR2YNZdyivfjd1aGKuxsOqjuc2/kjo4fM42RiwcN5kWyaOIb5uwK4mW1O9drlyfRdwvgRP3AiNo07oeeJfr2ruE8gPC1IhL+fr5ZuRRWw7LWCY3Nbwpk5vP/JTuLf/CD32ihVt8vqV6ZZqwrc9T3HtdSCnqlbhYErtvO/pmp8v+nC6D9iX/POg8LjHDzoof2rpLVFGdJ2qUav2RgWLeqCUXYckRcjiUlTUc7RlVo2emhu/MG6Q/cUoZdiXoPYZdIunycy1Q3CLvM2rW8pPEq21yImzEtgQOfmOFdxoZGZHnlJN/Hft4/VSzbhnaJ8vNLMa7HsuaHL6N102esoSuFVyIvF//cf8f/9TVdEoSR4S07tKigolDSK2BUUygiK2BUUygiK2BUUygiK2BUUygiK2BUUygiK2BUUygjPPC6roKDwdqL147Kl5kz3W0ypOhuvUCo4eNDjpZ5T3HgFhTKCInYFhTKCInYFhTKCInYFhTKCInYFhTKCInYFhTKCInYFhTKCInaF0ofKAEtbK4xLU7TttwBF7KUV4+bMPODDqdX9qfomOr1oR7dvN7BqtDv6z01cfNSu49jhc5od4115/Jp30aIeg+dv5fg5X04dO8LqoZVfQ9RcHkbt/WcBnW1KUjIi1fv9zKZF/an5wnfcFyd3hVKJyroObg7GWNZ1fxCFFQDDJkzf582p1f1KcBAQMG0zlsnd6lPX0UaLghMwqemKo6kZju61CoWjBlSV6fvzCqZ1c0bv6lG2/LqUNcejXyryzNNQO41m25nT7Bjn8ujR0vtRe23ccXMoWbFbVHGhbrsJTOr6nCg8L5W7QqnnEU3YOlOvigmW7nUfHQS0iWhDp/7tsRbSOXfSjwwtZv204Cmq6p3o1cAYOekwX4/4gu+XrufYtZynpH65kk1q16OOuRmOdWs/OtAUSvOccIGvSB7hp3xIkI1pMaAnNbT8/f4bYhdNqNaqB90aWP1HXuhVKAinLJRcqGDR9l06NtSH3HDOnEt+LVeFqypWwk4F0u1LXEkvmRILC/n1RmwS0dHJ77lZId4EpIPasQMdampX7aVbG/oVadznC1bsP8LelXOZMbBhsaN3/ucQ7ejz62mCPCbhogbBsDOLg8IID/Nj13hn7brajZrhqiMg3Qgh7EHEDwGTOj2YvnwH//j6ExLkw/Fdy/i//g2xVT181rB6Bz5buI3DZ/wI9DvO3pVf0NXR8OnFqZwYt/McAcu6YiKA2mUSB0JDCT4+hzbGT3/sUfSp2v5Tft52iDMBgQSfO8aeVf9Hb2czBEAwb8cCz0BOzW2JLqDbYjanwsMIDz7Ml831Hq2OVWOGL9jCEV9/gs4eYdvcnjgaFE6hi33rkczf5IGXnz8BXh6sm/0xroVCfatdx7HD+xRbRtTFufccth4/S4DnAjqYCpARTvClPFBVpmnjiloV6Gu5SlrbqMs506HvEIb0fR8nCxXpN7z5/dtNbNrr8yCacplDTiHS9wQn1K40blwdEzmGcK8IYvMyiYyI06L1VVHDqSa6gkzOtUjuR78Sbbvx3eo5tDVOIOLUYc5mmlClXhP6Ty9Psn8fVlyS0K05iOUbp9JIP46QY3vxTLOlcceBzF/ngNhnEvuii6ilHEfEqRN45jWkjYsNQuoVzvrfICUmiNvZxamvmip9F7FpZkssSON2RAjXDKrh0rI/Xzesh92ooSyOuIn/ieOYujagRZ1yCAkX8Q6+Q3bOXULuFAoKrqpAz2+XoKunJk8DanUF3Ht+yffR4fRZeQkNInbdf2DzN+9SLuUiJw7u5J6JO+17zWBNdQMGDt/A5byCdQkzC1T9v2OxtR22ealEh10lNkcG6R6RkclIDcpRw6UmutxEW0HBS5XY9eya03fUSAZ2bUhFvWzuBnqw6OvN/Ol5hZTXFXLtbUVOJWD9LII9R7Bt7ySccwJYM3k6/2hzWguAPhXtrRCRSLgbw329GTV6j2bmcO/PGQya45P/u2hEhUrGJEVJINjwweSxNDJO4eTXg/hs1x00CBgf+ZJdq3oxZqArB38MfbI4KRbPJdPxvvATPos6oL7uwbxJ67hezO8tmLRl3PjmWHKPIzMG8LlHNBrBlEZfbGD14NoMmdyL3UM38+eczzl2P0zZhT+Y8VmhMGWFVCJF/sHYiUs4HWtCqy/Xs6xPFaq3bI7dr5e4Ydia8VPewTonmEXDRrH2cjagx55Pt7Jt9FCGtfyTGZ4ZD6YIKhsDIn8ZSr/Ngdx78J003I2ORcIKvYoO2IhwQ0theEqPGy860HfBUr7o7Y7k+xvT+3akw+DZrD2mCP21IppiZiIiAOmp6Q88hux7MSRLAuVaDmDUh22o72iLsZBO9PUYMmXAqAGtGxpBThBHTyahZ2iIoaEBUoQvQWkCFes3wL4EFhTVLs1pYi6iuXuErYcKVu/lFAK27SEsT0DXpRkNzYo5Q5fTOLF6OV7R2ciaOHyOnCNBAtGyHBYi6Li2prmlSF74UY7dUWFoaIihoYobvme5I5tTr2GNR6yr5voelmwoLHQAmbSUFCRANLN4ykLhy1F6LLucwvVr8Uj1ylOhQTs6dY7hXqIH525nKbHkXisq1GoAGUnzcJTN8VvFl6sdmD24FWPmtWEsMprUG/hsX8BXy7y4Z2KBuY6AoPMO3548y7eP5aoxMsYISNF2bc3NMRFBjoulcPh6KT6eBAkElSnmJgIkFqMXSakkJD5UppybS64MiCIioDK3wFQEnUbTOHhu2uMPE29i9MjCn5QQV2S0Y01egSlXiajKptiTOflVNzof7MaAIYPoOWgWbQZ8ysV//mTTpt/5O+QeWvdY32oKYqSLKkSx6N9LZJtIziYzSwZE9AwKHaeR4vFeOoYOq8xwqFkHJ9emdB82mNYj5zP1fCem+SaRlCeDcJ51k37kRHLhPCUyYy5zUQPm9zu/SqWVRUVNcjKpEliUs8JSgOsFv4vWNliJIOclkZBcEG3+FcvWJCeSKoH61m6+/GoftwqHnZZyiL8aQe5zcxHQ09fLHxQyM/K9Ii1Retx4ADmLW747WDC2O+/3+Iyf90Zi3nYU3209xN9b5zG0UblS9kIvj5SSQooEgk5N6jk/3IOQkhJI1MgIOk40a2Cq/S0kOZl797KRETC3NH/Q3irzqrhUt0AnN5mb5305/PsyVh6+jSQaYWVtiJjhz+mADGRVDZo1UHEtOJDAwEACgyKIxppKNgYIyGSmpJAjg7paXdzMn1N7oRyNh85g7tSuOD7lGF9e2BnOJkmoyndgQMcK+SIWzGg0oAcuapnsMB8CUmVAJiMhgSwZ1I6NaWT94j0pN8wL70QJ0a4hjc1uExpY8I7hN8ixdKCCSXGGkIftqrkXQ5wWw+aWHsv+CBKpkZ6sn+3J5iXOdOg7mCEfd2JM/zPs8jtUJlbk5SR/fCKyaV63KoPWHqXDHR+WD/+CXbG+HDuXTqtWDvRZdYT3Y9OR72xl+LB1RGplbSOX65E30OCMcaXKWIunuSOpqDNsMdtG2JF0OZCAi9Gk61amSdvKiBlBnPSNQ5Ik9v+8gi4uU2k8dDWH2l3kws0MDOxr4WRvQo7XV/iN20tiiDf+aZ1oa9OFhYebEh22mU9Hr+P6/cmaLD+Yton2HzBxUj/qqrthFH6MqYefPN4jp55g+VJvms1qSYf5u3EecIE44xq4VLVAlRXB2l/2cKtAUDmBx/CM+4Bu1u34/pAnU5Il0v6aTK9fHoYdlwtbWvkxs5t6kuU/HafJvPfosciD5pcucC1Zha2jE1UsIGxRBAPX3nj42OPPA6CLXaXyiGiIjryGNgPnllKxPyQv/jwHl0/n4K+GGKoytXqaq2RQYWBujqFaeNLqyhLZaYmk5upiam6MrvikZZPlHNITU8jSXGfbrLlUmjuezq42mKjSSc6SQbrL7i8nU27GZPq0rIm1RTZXPK+RiB6mls/OM8/AAjMDVRHegIyUlUJCWi4gcTMklDiNMzaOztTShTtZGiI2zGSuzgh6tq1Ly06N0M1L5mboXn5Ytpgt1/LVlHNpE2MH3GHE+MF0aVqHBo0F0mOvcub3faxbfSB//nrvAPNm1EDv8540sDdGNyOJdBk0kSEEJ7bENvwicQWDlnQvkJMh8Tg3MEIlPk0VGqJ2TGZw4gjGDe1M05ru2EpJXPPaxtblK9gV/rDHyCkn+O6zBUjTBvOec3ks9G4SeCUWSZNDWGgcGbrnibj10NRKt84TGpdBvbDzBQOGxK390xgQN5BxI7rT2s2NRjpZxF8PYM/Gzfy2NQoNkPpvCJdS6qIbepHExy23qipONfUR5HRCgy6Th/Z45lXSyq2or87jt8uq636Ox6YhVCrSo5PJPPU1I/17sX6yG7pFebFyLqGLejJw7fUXOBcuUnXEZnY/K8+l33F98Jd0My/afZWS9vPpezPxzAL0WzDn0Ap6l4tm09APWBCozS75YqhqfsIf21tzqPcg1l7Tos/7hhDtBrLh4DTqZx1jescpHEx6Up4HD3po/yppBe2TF7aWT4d5YaVblGWXSb8VxvnkAIaFl8egSCucTUzEjRf8CyASUTtmPCfPi2Scus4+86Ite17ydULvn+7I8mP3gRt8ONyBFi0dUQVe0OpfSCk+ejQf1AejA3PYdb30Cx1ErFu1wVktE3d0N55FCP1VUMT+utEkciXgLFeemSiK4LNRWi1WSi1Gnil+xBQrtxzCtq7nzEezaVKnJibCBbTcL4tJNj4/9KR3RjIldFz+NaOmtpsTutmhbFp3hnSt566g8BJId/cyc3gM1dKCSH6DQstLTdbqvPbNkoPP0nGM+/0uviUwJVHErvCSSCREnCHhTVfjP0ZOdAhe0SWTd1nZllZQKPMoYldQKCMoYldQKCMoYldQKCMoYldQKCMoYldQKCM887isgoLC24nWj8sqZ+NfncfPxisovCoHD3q81HOKG6+gUEZQxK6gUEZQxK6gUEZQxK6gUEZQxK6gUEZQxK6gUEZQxK6gUEYAiWQHAAAgAElEQVRQxK6gUCwEdE1tsLXQe80RXrWHInaFUoVRgzGs2jibzuW12XVFqvb/lVO+HsxsYfyYmPWo3H4KKz288PM+xj8HZtFa7ynZvOUoYlfQAiKV+izH09eDmc2LHUf5xVFVo+/nI2hZ14VKJtrMWE0VNzcsTexxd7Z9RBTGzT/nt5+G0qpiGn671rF80S7CSmnoIeVaKgUtoKZ6vfpYmejj7mKDyjutRG6b1av/MX2c9ZGue+F1tWRuk300ZJYRzXt+gJ1Kw4UVE/hk1ZU3dIuudig9ll2wwL3Lh7xT01wrMcAUtEyBSIQSCTAHoEe9ju9TUaUh9qw3l16H6lTWONjrI8hZRP77otd3v32UHrHruvDR/75m6a7DHFw9gwHNK2FYWldKSg062LUZzy9//sO5gHN4eaxiepea1B2xidO+u5hUTxfdxtM5HOTP0q7GCKhx+mwvoedDCT31I50stfiBVI40b1IOUc4mPOjiwwCJ6vK0GPUtGw6cxC8okIDTB9j0/Vg6OBaae6usaTTwK1bvO87ZQD98Dm/m+xFNsH6q1RCw7PQDp4L2M8VVDYIxXZcFEB7qw+Zh1UutsSk9bny2F/P6jCB44GAG9+rD//3Wh3GXT/Dnxo1s+yuY2FI6j3p7ETBpPo3Viz+msloi9dZ5IlPs6PbNb3RKN8fCRIOzoyka31COH7Ojbr1WuJYXSbvqh/+1dHIT/bmRob07pgXz2tS2E0G6Q+S1zIJf9aj72SqWD69Kzo1zeB6MQbR1onHHsXyjE4nX1CNkCJa0/Wo9i3o5kHPdmyM7b6JT+x06TF5OLbNRDFgYVETIMJmMKH+OH7ekQdNGVDOWuBt6mvOxqUT8m0BpDUdResQOZEX7s/NHf3avrEzLnoMYMqAbI+a9x+DPQji0bQ2/bjnJjazn56NQDMSKdP/kQyrpSNze9zkDZ/1DrEbEstUstizvDWgQAOnGIRZ8fox3vj/J0q763DzwHRN/jdS6yytWsKeiSgBNLNGx9+OX16DtO1XQyQvipxGfsCU6/3ddy8rYyLfIAHTchzPtw0qIkRsZ2/9nAtMB1RbC1+xket+RdNg0gT1xT5aXFbGDr6cGMXbHDibUyuDsr1OZcbJ0W5TS48YXQkqL4tSmeYzo3IGPpi7nZHotuk/6hV+G1yy1LtZbh6E7jZx1EfKusGfDcWI1ABIJ3hvYdf71h2UQTc0wFUCW0ki9H/5FTiAmLhdZVYtuY/rToZkzlSz1yE2I4laiBlBRo1UL7FUS1z3/4bJsiKGhIYZ6CZzzvoxkWJdGzjqv/V3eFKXKsj+KHrbubejUqT2NK+sjSPeIjCy9LtbbhmhsjplaACmWu7GF3HFNHHfvaeB1Hy1RqwsGcgnp/keWotkz/ztc50+iS6/pLOydH7cu/vzfLP/6O/68kIO5pTkCKmqM3ILvyMfylLMwNtYFSrfFLi6lT+xqS5zb9WHwkI9p71IOdeZtzu5YwIbNezkTlc5/IuTXW4CUnkyqRga1FbZWAg+CuamsqWCrgseG1fuhxkWxZJxFOTOTLBnMBT30C+kz6/JuZny0l3k21ahdx5l67/dlZI9uzJxzg8B+a0lKSELGkmTPn5i09nyhUFEycl4SURfSAN0HAdhVJVT/t4HS82aCGW79ZrPh78P88dM42tne5tCSqfRu9wEj523ltCJ07ZIeiv+FXGR1TXoMboWlCCBi2XIIveo8PlmSSEpIREJFpYaNsS8Bz1iOiyFOBkQLylnc77Z6VKxTh4qGkBF7hcCT+1j74zYCckC0sqacqCHytDd3NCImLo2olHCewMBAAgMDCYlMxdjODjN1fv1TU1KQUFHR3R2b58wF9at1ZvLcWYxoZlWKBFSaLLuuOx+P74HjvROsWbaJ7X8FE1M2vK83g3SLPav20m/5Rzj0/AWPeqFcTDKnlrMx2fES2FBocM3jwrET3Bo0iMpNprPn5DAS8yR8ZnfjyxPaWTHVRF/leoaMi7E9VRxUcFMCk3bM2DKf1prbhPmFcT0+D3PXVrTSk4j1OkVEHuQEr+X7Pa1Z1Osdvt59mEERl4mTy1HdqQbWOjfZMLg7PwbnccH7HAn9e2Dd8ks8PEdxdc9M+i9OKsKA6NB4+P8x/ENzZJc4vD5a+Xr2/LVA6RF79inmtG/B1xkZD/dYFYpG1MPU3BjdImOx55CemEK22hgLE32eTCIj52WQlJRJyukFjJyUwOdje9C0eh1qEsSB2cu49N465r4vkJme+UAM2YHLmDhLl/+N6kDdSpYY3fXj8p1c1EYWmBkUHfNdykohIU3CwNwcQ3VR8eolstMSSc2WIec8QRE5dGlqTh0nO1Te19GkHmXBlAokDepIs/rv4GokkhF7Bc9161m0wotUGSABz7lDGH5xDJ989C51nRtRRZPEzfOH2LV9NetD8h379FMLmbbcjJkDmlHV2ICs5FRkKYbzwbdJt7lNWNT9CYCGf8+c5GanbtgLqlJl2Z95lbRyK+qr8/pvlxWpOmIzuye7oVvUGpqcS+iiYexssJo5bQyKXmbT3GTD4G78cteNNjXTCTjzL4kF1ktl25kf/viOjhaXWNr7Y1ZdfsaSqH4b5h9bQnfzoiUhJe1n0uREPl8zhEpFus4ymae+ov24vSTIIvaD1+MxrR6S9xzaf7KL+Dc1bxPMaP/Dfr7MnU3HGZ5F7NOXLAcPemj/KmmF0ohE1I4ZDAsvj0GRlj2bmIhwYg6P4OYmI1RFWfbceC6Fg8uUb1k4xA5NQhQXL98mRTajqksdKhrJxB5ez57I5+x9ZPmyeORI9psXbdnzkq8T+m8OUcO8sNItyrLLpN8KI1HOf6/bh/bgPa4ebdxb0dR0NwffUGB40b47g5peZc3Q069d6K+CIvb/IFJqFMFno56dKCWMc7eenSRp9efMyh5E99ZuVK3TACdDSLkTxt+bN7B0zTFinrvPmU3MBT9inpPqSsBZrjwvK0C+d4g1u4bTapAjdaqqOBj8+vf7AaRbO5jQ5Q+SU95M+S+LInaFpyIlnmff4v+xb/Gbrsl9sglaNoHh3kZcC3uDQpOzSE55c8W/LIrYFUoXmTfwP/OmK1E6KU2LiQoKCq+AInYFhTKCInYFhTKCInYFhTKCInYFhTKCInYFhTKCInYFhTKCInYFhTKCInYFhTKCInYFhTKCInaF14doTecfj3L2nwV0tinZrqdfdxSrNn5DVzuli99HaQkF7WPYhOn7vDm1uh9VC/89ddEeVzdrjG3ccXMo2a4nWtXAvUE3poxvjYkSTARQxK5QAqhsnalXxQRL97pUK/JSCoESixJVQKa/F34ZAtYdBtBFqxFfSy9KKyiUAAUXUQiv+8JpER2d/C4tp/jhE54LevXp9F55paOjiF3hhRAwqdOd6ct38I+vPyFB3vyz4xcmd6qeH3dPtKPPr6cJ8piEixoEw84sDgojPMyPXeOdHwngobJqzPAFWzji60/Q2SNsm9sTR4NHyzKs3oHPFm7j8Bk/Av2Os3flF3R1NHyQQu06jh3ep9gyoi7Oveew9fhZAjwX0MFUACmO0JCbSIIOLs0aKq48yt9nV3gB9J1H8+v68bgbSiRHhROUbE4tl3cZ+UND3KyHMnpzDJG+JzihdqVx4+qYyDGEe0UQm5dJZEQcMhXzM1JVoOe3S9DVU5OnAbW6Au49v+T76HD6rLyEBtCtOYjlG6fSSD+OkGN78UyzpXHHgcxf54DYZxL7omVMarriaGaBqv93LLa2wzYvleiwq8TmyICGqMvXyJGro1vTCUfVfvxL18UyWkex7ArFQyxPt4nDcTOUiNo5gQ+6DmRo/250m7Kf27IZjcZNoINFGgHrZzFlngc3JCA7gDWTP+OzidNZfCLmkbASUuQfjG3XhHr132fcjutoBB2qt2yOnQgINnwweSyNjFM4OW8Qgz//lu++nsigKTu5Y9aaMQNdUfNwiqCyMSBy8VDebdqC9kNWElRwe3XW3WgSJFBZ2WNnSJlHEbtC8TCsR/P6BgiaS+zb7E2CBCBx78R2/orSIBg3ooWbbvHyktM4sXo5XtHZyJo4fI6cI0EC0bIcFiJg1IDWDY0gJ4ijJ5PQMzTE0NAAKcKXoDSBivUbYF9oTqC5voclGwK5l/N4MSn510kLZpibKl1dceMVisXD2G9xxMQVutVVTiQuQYJq+pia6QHZz89MSiUh8aEy5dxccmVAFBEB0cQCcx0BQecdvj15lm8fe1xjZIwRcP8aOCkhjoSiLprVaPKjyQoiKiXipyJ2heIhpSWTnCeDTjlsyglw/xpnoRw2ViJIqSQl3o/+IucHjxBVL7XFJqUmkZQng3CedZN+5ERyoT+UJTJjLnNRAxbPy0hPHz0BkDLJyFSCgym+jULxyAjgTGBmfnjkAc3y3W1EbN4bQGcHFVKqH2fC8mP1SEkJJGpkBB0nmjUwffHttwx/TgdkIKtq0KyBimvB+fHZAoMiiMaaSjZPCW7xGKK5JRYCyFlxxL6hO+bfJhTLrlA8pBg8Fq+lh/sE6n68DI8mYVxOKUcdFwdMhGTOrlrB0fxoDsjJvhw7l06rVg70WXWE92PTke9sZfiooAfhouTC2pMfE6IUy/6fV9DFZSqNh67mULuLXLiZgYF9LZzsTcjx+gq/cfsePvb48wAImDvYYyKCJuoy18r4Sjwoll3hBcg6v5oxQ2ex/vglMi3rUK+2OakXj7Fm2hAmbLryMAafdJfdX05m6eEIYrL0MLdQE3/pGom5twgLjSMj/jwRtx6uzUu3zhMal0Fc2Hnu/5xzaRNjB0xlxaFQEgyr06CxG5XVtznz+wLGfn2ABFkm9d8QLqWkcS30IolPBKzQwdGpBmo03AsJ4VYpCb5Ykiix3kqY1x/rTQEAlRMT92xjdNV4dnzSmTnexVg4LCW8bKw3xbIr/CdRVW9N68oqNFEH2OP33xH6q6CIXeE/iIBxHVeqisl4rdlKuBLjG1AW6BT+k8ikHP2BT+IMifSJ5bnxJ8sIitgV/pPIGVH4KTHhHkFx4xUUygiK2BUUygiK2BUUygiK2BUUygiK2BUUygiK2BUUygjPPC6roKDwdvIyx2Wfuc+unOl+dZSz8Qra5uBBj5d6TnHjFRTKCIrYFRTKCIrYFRTKCIrYFRTKCIrYFRTKCIrYFRTKCIrYFRTKCIrYFUonghpjK1ssDZQuXFyUliqFqF3HscPnNDvGu6LzRmuiQ82BP7NpYV+qazPiinFzZh7w4dTq/lR9PF+VLS3G/MyuU2fx9TzKkZ+7YvlaIrSKVO3/K6d8PZjZ3LhES9KvO4pVG7+hq5125amIvdQh5EcvNTXD0b0Wpm8wFLFo+wGTJrSjrns1rQpOZV0HNwdjLOu6U+0RsevgPHYZSye0o0beefZvXMUvW86i1fgPhk2Yvs+bU6v7PTbQqKni5oaliT3uLraUZDQp0aoG7g26MWV8a62GmlbEXgp5O0KNi1T/sB8tjGXiT3sSVkJBGB55V7U7PT50RE+6ze/TRjHjxxVs8b6LNq+EV9k6U6+KCZbudR8baArVqYQ/QKa/F34ZAtYdBtClvPYkWurELhjY06TbhzS3V67Pe6OoatKhQ03UpON/OoSs5z/xyggmDjhYqkBznX+v5Dz/gZcrJX+AEV7/oKrSUSMCcoofPuG5oFefTu+V15pIS43Ydaxd6fbZz+w86sHa+TMY1dqm9FT+taGLfeuRzN/kgZefPwFeHqyb/TGuZtrvtmKFxjSrqoK8S4SEZz783bI+A7/+jb0nfAgK8cfnyFYWTemBi8XDOghmzvScvoxdx70JCPTh2I6FfPquPU8P+KxHs5l/E+g1h1a6gG5LvjkdQljAPqbULe6gL2BSpzvTl+/gH19/QoK8+WfHL0zuVB1DARDt6PPraYI8JuGiBsGwM4uDwggP82PXeOdH3XYdO9759Bf+PO5DUOAZ/l4/jfb2Oo+UZVi9A58t3MbhM34E+h1n78ov6OpYKEi8cQtmHvDBc2kvnBoM4aedJ/EPOMSMxjogxREachNJ0MGlWUOtufJvuXkUMXVsy0eDhzCgcz1s9bKJDtjHwo2b+dPzDhI6NPqfB6sH2qFTVIPIEukBP9Fn+Gau/+fD/4jYdf+Bzd+8S7mUi5w4uJN7Ju607zWDNdUNGDh8A5e16Grr1nKmuhqk+KtExhVc1qyqzpAlK5laVyQ66BR/eWso59iANsNnYRbtw8jtMUhG9Zm4ZhUj6wjc9jvK7n/yqNyyA6MXOWE9sR+zPROLKC2XO8EnOO7QiMYtamEpxxNxJpQ7adcIu1e8i6L1nUfz6/rxuBtKJEeFE5RsTi2Xdxn5Q0PcrIcyenMMkb4nOKF2pXHj6pjIMYR7RRCbl0lkRBwPlwVU1B61mIUqEfIkBLUah8YDmfd1FOGj/+COBLo1B7F841Qa6ccRcmwvnmm2NO44kPnrHBD7TGJftITKujZuDsZYlR/Dkno2lDfNIelmONeTJEBD1OVr5MjV0a3phKNqP/5a+HZvqdhVWNXrychPBvNhi8oYaRI4f2QVP238nSPnE3j43hpunN7BNlV5dIsUu0zGv8HEl4WLw41bM37KO1jnBLNo2CjWXs4G9Njz6Va2jR7KsJZ/MsMzXUuFiVjZVUBfACkmmtiC9hUrNOddF0OkyF+ZOHQZERoAFab2dujei0FCpGrfqQypo8vd/ZPoO9OTRBmEcr4s3Psd3T75kPVe67jxRHkSUQcX8LlfH9YcnkVTItj6v4nsLe7KnFiebhOH42YoEbVzAgPnniZBErF+fy5bF3an0bgJdDgwmQPrZxHsOYJteyfhnBPAmsnT+efBbOG+3yFAWgDLP53O2qBUynf9js3ftse6Xisamuxgf4o1H0weSyPjFE5+PYjPdt1Bg4DxkS/ZtaoXYwa6cvDHkAeDh6BvjcbzG/rM20NE0kOLlHU3mgQJ7KzssTME/xRembdS7IJFZ77+bRbv6MXht2UOyzYewP9uUSF8JGJOr+OH06+9im8dOq6taW4pkhdwlGN3VBga5ruMN3zPcmfUAOo1rIHaMwTtGHcBU3NTREBKTyOtoOfKybHcy5IR7dsxbOg19vlHcPFSFHG3CuQr2tC8VR105US8DweRbWCIIUCmP2fC82jXpCF1TdYXIfZXxLAezesbIGgusm+zNwkSgMS9E9v5K+oDRlVtRAs3XQ54FidMlIa7B1exLjCePODW0aOEzG5HO7UFVuYCaBrQuqER5Hhy9GQSegXfQYrwJSitN53qN8BeFcLN+9nlhbB10W4ikh61SHJaCqkyIJhhbipCyqtbrLdS7HLmLa7HaJCrlMPl3Y50jr5H/O7TXEt7/IVFbFsOZUibZ1n2A6zeFZbfcP9hVOYWmIqg02gaB89Ne+xPJeJNjLS64KRW589iZY0G6b7YU/9h4axtWP9fLzpN/p7OAsh5SVw+vp55c9YTkGqBpbkIohW9Vpym1+OZ5hphbFQC6wvG5pipBZDiiIkr1BHkROISJKimj6mZHlAcscskxSc+jDKjySVXAhARRRBNLDDXERB03uHbk2f59rGnNUbGGBX+QUrgXkIRQtZo8ncZBBGVlvb53kqxkxXETz078E+nvgwZ0pte05bRe9w1vHZvYdNWD87dzipwg1RUatmH/v2eNWe/xe49YaT+h+bsD8KRq1QPFo40yYmkSqC+tZsvv9r3SIhiWcoh/moE2gt5JpOVmf8NBH099ATI/yAabh39nkH/LMKySi2cnNxp1XsY/dp9xncxwXT+IZqEJAmQ8F8xnsU+2Y/kKaXd5uJtCarK+dmJKkQtrMJKackk58mgUw6bcgIPNuaFcthYiSClkpR4fz/hYdkvs8UmpSaRlCeDcJ51k37kRHKhP5QlMmMuc1FTjJVxPf38dpUyycjUjqV6exe0c2IJ3reEyb060GX0d2wPUdFo0CzW/nWEP38eSxs7FZCL3/cdqeviirNzEf+4uNN4yH9tcU4mMyWFHBnU1eriZp7fI3PDvPBOlBDtGtLY7DahgYEEBgYSGH6DHEsHKpho8xiITFxsHBIgmlvxYKFdx5qazpUwEbNJuBbK6YObWbTOiyRZxNLGCrUUg++Zf8mVVdRq6ETWv0H5dQwM5lKcLhUcyqErgJSSQooEgk5N6jkbPasigIqKbcfw9ZwJvP+07diMAM4EZiKratFtQDMsRAARm/cG0NlBhZTqx5mw/KFQSkogUSMj6DjRrIHpi3tDGf6cDshAVtWgWQMV14ILvkNQBNFYU8nGoFh5iuaWWAggZ8URq6VTQ2+nZS+MnMkt7+185/0Hy2u05aMhQ+jfeThDA/fhtfVOmQzalx3ijX9aJ9radGHh4aZEh23m09EbWP7TcZrMe48eizxofukC15JV2Do6UcUCwhZFMHBtlJYOoMikXLtGrKYlduUr4WAAYWmg02QCa1d+iOG9CPyCIonNMqJWi9ZYksRxz2BykLi6/Uc2tF/JyMYT2X60BxH/3iXHuDJ1apfHINWDCWdmcjLJH5+IbJrXrcqgtUfpcMeH5cO/YLemwIEotDaOyom+X4zhoyoCrXPP4Tnv3JPrElIMHovX0sN9AnU/XoZHkzAup5SjjosDJkIyZ1et4GhiQc7Jvhw7l06rVg70WXWE92PTke9sZfiwLYXe/hlIsez/eQVdXKbSeOhqDrW7yIWbGRjY18LJ3oQcr6/wG7eXwgb/yQwFzB3sMRFBE3WZa1raRXn7xf4AiZQrx1k76zgbvjVCJye99Ald1MPU3Bhd8cmxXZZzSE9MIVttjIWJPk8mkZHzMkhKykRz7wDzZtRA7/OeNLA3RjcjiXRZ4vb+aQyIG8i4Ed1p7eZGI50s4q8HsGfjZn7bGoUGFQbm5hiqhSetiyyRnZZImmz0/PKBvEvBhGcMxN6wNi6Oav4KyiPXdzlTf8xl2ActcWvbGRNVNvHXzrJ92RKWeBREU00LZPHwIVwdPYYB7RtSq4EDcko0l45tYueatXhlAVxn26y5VJo7ns6uNpio0knOkpEz/iUkMgVnKZwr6QUKkaLwPXmFjys5IopFvFcBWedXM2ZoDJ+M7U+HhnWoVzGHexeP8cf6Zfx6KPLhFEe6y+4vJ1NuxmT6tKyJtUU2VzyvkSjloQkNJfFdByIuFooMq7lGaFgSbcqHcaFgGzDn0ibGDrjDiPGD6dK0Dg0aC6THXuXM7/tYt/oACTIQG0HwzTTsEkKJfGJ+pYOjUw3UaIgJCXlkSvYqPPMqaeVW1Ffn4e2yIlVHbGb3ZLenLCbmErpoGDsbrGZOm6e4epqbbBjcjR+DX26oV9f9HI9NQ6hUpEcvk3lqAb8wkf+1Lkb5ggUfLP6L79/TJ+KXXvRbfVWrx1ZfCP1GzNi3BPtVHzB+T/yzLW9pQOXExD3bGF01nh2fdGaO96MLhwcPemj/KmkFbSIRtWMGw8LLY1CkZc8mJiKcmMMjuLnJCFVRljU3nkvhL+/T5YWt5dNhXljpFmXZZdJvhRNJKJc2FqN8ORHPXf8Q9053HFs2o/zaq9x+I66WgHn7QXRK+51P//4PCB1QVW9N68oqNFEH2ONXnB2C4qGI/TUipUYRfDbq2YlSwjh3q4QqoEnkSsBZrjwzUfHLTzu9gc3hHZlU04kaarhdUsfVn4lM0t9f8sHRVJIzn5/67UfAuI4rVcVkvNZsJVx7WyiK2BVeAU0kmyaP4JJtLGffiNALyEl5dMGrVCOTcvQHPokzJNInVqvrUorYFV6J3LuheN1907X4byFnROF3Rvv5vr377AoKClpFEbuCQhlBEbuCQhlBEbuCQhlBEbuCQhlBEbuCQhnhmcdlFRQU3k60flxWORv/6jw8G6+goB0OHvR4qecUN15BoYygiF1BoYygiF1BoYygiF1BoYygiF1BoYygiF1BoYygiF1BoYygiF1B4QVQ6Ztja2OKzvOTvnUoYlcodQiWbZm+bg1TW5ppMcqNSNX+v3LK14OZLYwfy1fAqFYPvlx/CO9zpzj+z3Ymupe+e19KX40Vyjh6uA+fyoDGFTh3WI8HwWheGTVV3NywNNHH3dkW8Uzag9tyBauOzF09h44WGUSe2sHvIYH8faX0RR5RxK5QqhBMWjOgRyXEvGBOno4rkdgBj4Z9ErF5ryfvWApk+Sxk1Kd/ElPqAhbk81a68YJRLd7v3R4369I4M1IoSYxbdqSVuYgm0hvfu69DdSIVKtuhFiTiLl8p1eG/30qxi9U7M/mrn9j29yE2fzOc9xxN0WakMoVioLKi0fD5bD18hoCA0/y9YTYfu9eh97LjnDs8mzamD82fYOZMz+nL2HXcm4BAH47tWMin79o/iGiuPfSo27whRoJEUlhooRh++lRt/xkLtx3iTEAgwX4n2L9mNiNaO6D34Fld7FuPZP4mD7z8/Anw8mDd7I9xNXv6rF906McaX3+2DHFAhQr7IZsICQ/m9JLu2L6Vynk2b6UbrwldyuABl+k/ZDD/396dx0VV738cf50zwzaAbIILqBkugQJuUJpiaWpZLlnXcjdTcsv1/uyW1rW0xRa9dq+m1zVN7VquaJYriiCKgCyigAuYGzvMsAnMOb8/wJ0MDRSc7/MfH8E5Z74znfd8v99zDt/PwD5TWNh/DCkhW1m7+ge2Hb3EzaKWZvj+I5BlQ+9VxfVrBo563Io7VjcLWo9bzJKxHlioBVw9dYp0u+eYsaQ716ztsVZb4eEicVCvgnU7Ji9fwmgPiUvhe9i8t5QmnXsRsMAT58mD+GdQdtUVbtC44dGyDjJGzicll8+pJep0/YBlX/fHJe8MIXt2kmPphvczrzJ5dgmxPT/jWKmMa78vWTunG0760xzY+TPptj70fO0DlrtbMXTUapIqqL2h5iZxZP9BLNp3oq2bBUUXIglLyiL72DkMtbCHr5Fhh1IyY3fw77/vYFlDP/oOHcawAYOYtfxNJhENjbsAACAASURBVJzex8YVS1n9axIG1ciFwxtZr7lXffYTtXro9ShI9t0ZPfQpLNVsgj4ZypSfLlAi2dBm0gpWjHEAI5RdrpZp+uZ0RniYc3X7FN6cGUS2CpJTGPO3fk7fd15lVfBKzlfVF63cALeGMqiFpF3NKp+va/Hq9hz15EL2zR3OlF/yyt6DpQtNHAu5UArY+DNh2vM4F59gwVtjWJF0DbBgy7vrWB8wkrc6/8QHQXdXY1D1x1nxwQliZu5ixWBn0vctYMrX0XcXjqwlamjYbyq6fIyNXx7j58VN6fLaKCaO7cvYeW2wy3yRucdKST28ki8PP+pWPl60nr60s5Ywpv3K6q0XyooeqnlEr9nA0eGf0PX6WSO70KmLB+ZqNqG/RXHNSocOoPA4IXGl9Hi6A21sV3E+p4r6drM62OlkUPMx5F3/BlfISM9AldxpP3AcbxQHEX0qgbOX0ki+XL6blz+dHGVKI/aw77IGnU4HwIWwo1weM4S2HZqhDTpVNW2swWp82AHQOuLRpRe9X+pMM1sZJecMSVcVQKZe55GM6Hqvnn0HyzbFYngcioA9JBo7e2xkUNOukn7LqEjNSyO9QIU65T+QHHC0l0Guy2uLD/PanQcqscbGWoKqCrtGW3bCqgqKcv2YRhK/n838ZnMY220YH/kOB1Wh8GokW76ZzVe7UpDtHagjg5nvDHYem3HHQRUyba2r8H59zVWjw66xdafr60MZNvgVfBtaUJx6gp3/+pQ1P+0nMVcBzGjceSCDB91rzn6RzVtiMYg5e6UZDXryFbCrWw9nmRvXOyTb+tS3vuWDVnPIylEAheOLJ7DwyK1FCFWUvEucrspqjyWFFCoqSBZYWNxsh2qIYfWUfqy1bUCzpzzx8n2JYaN6MmjO+yREjmdbbjYGBbQXNzPro223lUBWlWIyz8VTgvbGDXuNXAuvvlVCzQy7VXP6TJrG2AGdaGKtkJOwn//++3s27Ioh/bapVQnhX7xImy8eVUMfT6XxxzlR8Crd6vdi6MvfE7X1MqWSDT4j3sTXXOLG0yZKKmEhCZR08KRlB0+KVq8kPl8FZGwa+9KluRPnktIoqqpRVWkG6ZkKuOpwcrQESgAJm8aeuBadITHtCgnhV0iISMD62W7M8HKmnp1ESWwwodmv0ce1A352i/llb2rZvNu8Lq06+9IgPYELegWDXo+CNQ19fHDRJHHlHh2EZNua18f/DfcLm1j8Ywz6WjByrJFh17bow7jBbVCPrGfuXVfgheqmZu1m2Q/D6PSOB90/2UTg3+JI1TXDqz7kFatYaCjvBRXObfiK1T2/Y7TfZDbs6U98wlWKbZrg8VR9rAyBTAyZycGiKmqYMYWzKaWoblrcnnBDwymMckMGfLaWGd4FJEdGEH8xF+q1pau3lpLzwQQnG6H4IIu+3s/Tc7vTf0EgnRJPcT5XQ73mnjzhALEL4hm6IoVTocfIGtwf586zCAwaw7ktMxk8/8Stn0z5vxK2z4/l/WFdMS9xJXl/AD/WgidtamTYS6O/5bVOi7iWf61anpB6nEnmNjjYWnJ3CXgVtbSAnJxCJGsH7Kw0FcxTVZQiPVl514hZPIHx+VOZ9IY/Tz3lgRS3l6/mhPLU/K/4m30h+eXlkdW8SBaOGsG5gLEM6dmBlu0boeqvkLhvDT8vX0FwUWXaVIy5vT06bUV14xWu5WVjuKaCmkNMVDLGZ1vg5vkUdtIpspTLbP54JvYBb9LD148e7axQDJc5/esilsxfQUwxgMLF7TMYkjGU8W/3w9/bG1+zIjKTI9jy/Vr+uy4FI5B/aD4zFtkxc0hHmtpYUZRrQEXhQnQ0Gf18iD15uXxQo5Ife4gjqc/Q1Umu4H3VTPdcSlqsivrXPdTVZSVHXl30K3O6WlV8wcn4O6tHf43DggX0s694XqrkbOfd7h8S06wrbaSTHI5No6was4x9pw9Yv+QN3NLW8/ZLnxNemdrhlWjTmjlBPPfhMBpX+OSUSuGhj+g5fitZKmhaTWbrhtE8kbeDKS+8z76CSrShWsg0eHMpW4ef5p2+33DiId6P27kzsOqXkhZqGTWb3z57m0trrNFU1IuWZJJ44nfMRo9mu33FPXtpbjIxJY0Z9uF8prYC/aUETqdkU2rtRqvWTbCT9BxdtYHIygS9sm2KzWTTuYPUNa+oZ1fJvxhLdnmXZDy9ja1xw5nq5UeXNhbsC7125x4Ph8aTQYOfJGzJR8TUkhvvIuyPFZWCi7Ecu/gnm50KJ/WeG0hsmDkD7fABdOvQnBZtW2AjF5CaEMSWHxbxXWAylb+5Ubk2ZUUc5UxlDmdM4adluxiy8GVaetRHE5pyH22pQsZ4lg7rx7XcvFoz1RRhFyqgkn9mD0s+2sOSR92Uu6jog+YRELAD4i88mqADoJCfm/fIXv1BiLALtY9q4EyYKE92vx7PpwcEQbiLCLsgmAgRdkEwESLsgmAiRNgFwUSIsAuCiRBhFwQTIcIuCCZChF0QTIQIuyCYCBF24eGRnen91R6O7p1Hb5fqPfUs24xhyfdz6OMqTvHrxCchVD3d07y3LZRDywbR9Na/UZfd8PJ2xsbFB+9G1XvqyXWb4dO+L9Mm+GNbSxaXqG4i7EKV09RrRdsnbHH0acOTFS5IId1RT63qFR4PJrxAwrnXEF6uL05zEGEXqkX5IhQSD3mJZhkzs7JTWtWHcySuBCza8VL3+uJER4RduC8Sth79eG/RRvaGHSc6KpS9G//F1Jfc0UmA7MrApYeJCpxCay1Iut4sjIolLjacTRNa3VavT1PXj1HzfmB32HGiju5m/ScDaG51+2vp3Hsxaf56fgsJJzJ8P1u/+z/6NNfd2ELrNZ6NoYf44e02tHr9Y9btP0pE0Dx61ZFAySAm+ncUyYzWHTuIoTzi79mF+2DZKoClqybgo1PITYkjKteelq27MfrLDng7jyRgbSpnww5wQOuFn587tmoqccHxpJUWcjY+A5WGZQfSNGDAp99ibqGl1AhabQN8BsziiytxDPwuESNg3mIYi76fjq9lBtH7thKUVw+/F4fy2cpGyAOnsO2Kim0LL5rbOaAZ/DkLnV2pV2rgSuw50opVwEhK0nmKVXfMW3jSXLOd47Vk+ajqInp2oXLk+vSdPApvnULKzxN5pc9QRg7uS99p27mk2uE7fiK9HPKIWPUh0+YGckEBrkWwfOokJk1+j4UHUm9bvkk5+z/G9Xiatu1eYPzGZIySGe6dO+EqA5ILr0wdh6+NnoNzhzH875/y+ezJDJv2M5ft/Bk71AstN6cIGhcrzi4cSbdnnqXniO+IKl+6uujqFbIU0NR1w1WHyRNhFypH15ZO7ayQjIlsWxtKlgKgkH5gA7+kGJFsfHnWu5JFmtU8DixbRPCVa6jGDI7sPkaWArKjEw4yYN0e/w7WUBzFnoM5WOh06HRWKPFhROVJNGzXHrdb5gTG5C18uzqS9OI7X0ZfVvZLssO+jjjVxTBeqBTZxh47bdlcODXjltXH1WwyshR40pI6dhZAJVZ7VQxkZd9MplpSQokKyDIyINs6YG8mIZk9z6cHj/LpHbsbrW2wBvTXD5eVQVZFC6IbjWVr1EkymgrvCpgWEXahUpS8XHJLVTBzwsVJgtzydElOuNSVQTGQk3299ItaVjtF1jzQLTbFkENOqQrSSVZO+YoDubf8UlUoTE3itBEc/uxAFpZYSIBSSIEoKSSG8UIlFUQQElmIqmlJ3yEdy4bbyLh0H0LvRhoUQzghsWWLySs5WWQbVSQzTzq2r3P/t98KjnM4ogBV04yO7TWcPxFJZGQkkVHxXMGZxi5/UHDiDrK9Iw4SqEUZpOWKsIueXagcJZXAhSvo7zORNm/8h8CnY0nSO+HRuhG2Ui5HlyxmT3klBzU3jH3H8unSpREDl+zmhbR81MvrGDUm6ka1NPXW7Kl3BFFJY/s3i3m59XT8Ri5jV4/TnPq9ACu3lni62VIc/BHh47fd3O3O/QGQsG/khq0MxpQkzpv4lXgQPbtwH4pOLmPsyA9ZtT+RQkcP2j5lj+H0PpbPGMHENWe4USRGucrmWVP592/xpBZZYO+gJTPxPNklF4mNyaAg8yTxF29em1cuniQmo4CM2JNc/3Fx4hrGDZnO4l0xZOncae/nTRPtJUJ+nMe42TvIUlUMCdEk6vM4H3Oa7LsqNZjR3LMZWoykR0ffVqbZVIlab9XsodZ6E27SeDJ5y3oCmmay8Z3efPyoykRVgwet9SZ6duGxpHH3x7+JBmPKDraEPz5B/ytE2IXHkISNhxdN5VyCl68jrrJFKB9z4gKd8BhS0e/5kncydJw9klZrCi9WNxF24bGkFqQQHvKoW1GziGG8IJgIEXZBMBEi7IJgIkTYBcFEiLALgokQYRcEE3HPx2UFQaiZHuRx2XveZxfPdP914tl4oart3Bn4QPuJYbwgmAgRdkEwESLsgmAiRNgFwUSIsAuCiRBhFwQTIcIuCCZChF0Q7oPG0p56LnUwe9QNeQAi7EKtIzk+x3srlzO9s10VloSWaTp4KYfCApn5rM0dx5WwbtmfWat2EXrsEPv3bmCyT+1b96X2tVgwcRb4jJrOEL8GHPvNAgmomvIPWp7w9sbR1hKfVvWQQ/K4vvq0VPdFPln2MS86FHD20EZ+jI7k1zO1b21qEXahVpFs/RnSvzFy6QkOHs6olvXlbi9ZJePSfQDPO0oUHZnPmHd/IrWWLmonhvFCrWLT+UW62MsYz4YSdvVhpE6mQRNXtJJCRtIZMmtp0EGEXfhDEjr3Xkyav57fQsKJDN/P1u/+jz7Ndbdv0+IVZizZwsFj4YTt/5FvAjrRosfH/HZsP/8e0LCKTzAL2nTqgLWkkBMbQ/KNkbQlTXtOYv76XYRERHIi/ADbl/+Tt/0bYXFjX3Pc/Efz2ZpAgsOPExEcyMp/voGX3R/P+uVGg1gedpwfRjRCgwa3EWuIjjvB4W/7Ua8WJqdGDuPlhn9jyeZZdLKVK7wAo5am8fOEl5l92IjvPwJZNtQVswo3VMiP+JqBo9becmIIlWHeYhiLvp+Or2UG0fu2EpRXD78Xh/LZykbIA6ew7YqCXP8VPl8+lxecJIozz3LygorX2wv5QdKgs9Zg0boRms2Xq26orXHDo2UdZIycT0oun1NL1On6Acu+7o9L3hlC9uwkx9IN72deZfLsEmJ7fsaxUhnXfl+ydk43nPSnObDzZ9Jtfej52gcsd7di6KjVJFVQC07NTeLI/oNYtO9EWzcLii5EEpaURfaxcxhqYQ9fI8OuZMey88cNpNj8QdhLMgg5X1bf+8LhjazX1Me8wg1VChJO1Oqh1yMhufDK1HH42ug5OHsYkzZdxoiEze5ZbFryGmOHerHzq5N4DgngeSeJopPLGTXq30TnqZg1ep1/rf+Q56yBKrxWDoDcALeGMqiFpF3NKv8S0eLV7TnqyYXsmzucKb/klb0FSxeaOBZyoRSw8WfCtOdxLj7BgrfGsCLpGmDBlnfXsT5gJG91/okPgu6uJKHqj7PigxPEzNzFisHOpO9bwJSvo6mtNSJrZNgpPM22f33Btkpsmnp4JV8ervYWmRbr9vh3sIbiIPYczMFCVzZ0V+LDiMp7nZfatcfNLJO27VzRUMLRn9YRk1d2Tbzk90DW75+I/+t/Wj39/pnVwU4ng5qPIe/6N7hCRnoGquRO+4HjeKM4iOhTCZy9lEby5fLdvPzp5ChTGrGHfZc16Mrfz4Wwo1weM4S2HZqhDTpV9e2tYWpm2K086PdOP1rfq2f/YTlBl6Be55GM6Hqvnn0HyzbFYhDluStNtnXA3kxCMnueTw8e5dM7fm+0tsFasqWOTVnw0tMMt9z+KiU7MxuVagi7Rlt2wqoKinL9FY0kfj+b+c3mMLbbMD7yHQ6qQuHVSLZ8M5uvdqUg2ztQRwYz3xnsPDbjjoMqZNpaV/UYpEaqkWGXHVrz8puD7jlnNwtZQ9AlI407D2TwoHvN2S+yeUssBjFnrzTFkENOqQrSSVZO+YoDubf8UlUoTE3idKk97fUKYEldZ1skMssDr6Wui1P1hKekkEJFBckCC4ubr6AaYlg9pR9rbRvQ7ClPvHxfYtiongya8z4JkePZlpuNQQHtxc3M+mjbbeWbVaWYzHPxlKC9ccNeI9fCq2+VUCPDrlz+iYBnfqrUtuFfvEibL6q5Qaam4DiHIwp44dlmdGyvYeX88LL655IlDdp0wdvlIgmXrxIZdQljuya0HzgIr12LiMlXMWvSj8HP21XPbZ7SDNIzFXDV4eRoCZQAEjaNPXEtOkNi2hUSwq+QEJGA9bPdmOHlTD07iZLYYEKzX6OPawf87Bbzy97Usnm3eV1adfalQXoCF/QKBr0eBWsa+vjgokniyj06CMm2Na+P/xvuFzax+McY9LVg5Fgjwy48Ykoa279ZzMutp+M3chm7epzm1O8FWLm1xNPNluLgjwgfv5X49f/lQP+5vNAqgNU7nycuuYQGnu44alTU6ujbjSmcTSlFddPi9oQbGk5hlBsy4LO1zPAuIDkygviLuVCvLV29tZScDyY42QjFB1n09X6entud/gsC6ZR4ivO5Guo19+QJB4hdEM/QFSmcCj1G1uD+OHeeRWDQGM5tmcng+SduacD1REvYPj+W94d1xbzEleT9AfxYC560EWF/zEjmNjjYWiLflTUVtbSAnJxCJGsH7Kw0FcRRRSnSk5VXQnHiGsYNuczbE4bz8jMetPeTyE87R8iP21i5bAdZKnB1Bx+Mkbk6/S16t21My8bnCFkxjZ22M1jwVuP7aFMx5vb26LTS3W1SFa7lZWO4poKaQ0xUMsZnW+Dm+RR20imylMts/ngm9gFv0sPXjx7trFAMlzn96yKWzF9BTDGAwsXtMxiSMZTxb/fD39sbX7MiMpMj2PL9Wv67LgUjkH9oPjMW2TFzSEea2lhRlGtAReFCdDQZ/XyIPXm5/HafSn7sIY6kPkNXJ7mC91Uz3XMpabEq6l/3UFeXlRx5ddGvzOlqVXG/avyd1aO/xmHBAvrZVzzQVnK28273mQQVPWgjNLSeupX1oxuT9b936DEnkVf+pE1r5gTx3IfDaKypaAOVwkMf0XP8VrJU0LSazNYNo3kibwdTXniffQUP2s6/SqbBm0vZOvw07/T9hhMP8X7czp2BVb+UtFDLqNn89tnbXFpjjaaiXrQkk8QTv2M2ejTb7Svu2Utzk4l54KDfPM6NfyvTpthMNp07SF3zinp2lfyLsWSXH9J4ehtb44Yz1cuPLm0s2Bd67a829sFoPBk0+EnClnxETC258S7C/lhRKbgYy7GLf7LZqXBSq60NCldOx5Oeb83JuN8xolJSiTZlRRzlTGUOb0zhp2W7GLLwZVp61EcTmsIjudFijGfpsH5cy82rlj/GqQ4i7EIVU8nc9Q+676q+4+uD5hEQsAPiLzyaoAOgkJ+b98he/UGIsAu1j2rgTJgoT3a/Hs+nBwRBuIsIuyCYCBF2QTARIuyCYCJE2AXBRIiwC4KJEGEXBBMhwi4IJkKEXRBMhAi7IJgIEXZBMBEi7MLDIzvT+6s9HN07j94u1XvqWbYZw5Lv59DHVZzi14lPQqh6uqd5b1soh5YNoumtC1LIbnh5O2Pj4oN3o+o99eS6zfBp35dpE/yxrSUryVQ3EXahymnqtaLtE7Y4+rThyQpXn5HuKJ5Y9QqPBxNeIOHcawgv1xenOYiwC9WifMUZqcprwvwJGTOzslNa1YdzJK4ELNrxUvf64kRHhF24LxK2Hv14b9FG9oYdJzoqlL0b/8XUl9zRSYDsysClh4kKnEJrLUi63iyMiiUuNpxNE1pxayevqevHqHk/sDvsOFFHd7P+kwE0t7r9tf6ssKTWazwbQw/xw9ttaPX6x6zbf5SIoHn0qiOBkkFM9O8okhmtO3YQQ3nE4hXCfbBsFcDSVRPw0SnkpsQRlWtPy9bdGP1lB7ydRxKwNpWzYQc4oPXCz88dWzWVuOB40koLORufgUrDsgNpGjDg028xt9BSagSttgE+A2bxxZU4Bn6XiJHKFJZUsW3hRXM7BzSDP2ehsyv1Sg1ciT1HWrEKGElJOk+x6o55C0+aa7ZzvJasFVddRM8uVI5cn76TR+GtU0j5eSKv9BnKyMF96TttO5dUO3zHT6SXQx4Rqz5k2txALijAtQiWT53EpMnvsfBA6m1rtSln/8e4Hk/Ttt0LjN+YjFEyw71zJ1xlbi8sOXcYw//+KZ/PnsywaT9z2c6fsUO90HJziqBxseLswpF0e+ZZeo74jqjyBTOLrl4hSwFNXTdcdZg8EXahcnRt6dTOCsmYyLa1oWQpAArpBzbwS4oRycaXZ73NK3csNY8DyxYRfOUaqjGDI7uPkaWA7OiEg8wthSWjbhSW1OmsygtLSjRs1x63W+YExuQtfLs6kvTiO19GX1bjT7LDvo441cUwXqgU2cYeO23ZXDg145ZSA2o2GVkKPGlJHTsLoBJLOysGsrJvJlMtKaFEBWQZmUoWlgT01w+XlVFWtOJORmPZgpSSjKbCuwKmRYRdqBQlL5fcUhXMnHBxkiC3PF2SEy51ZVAM5GRfX3BeLVs5XtY80C22ShWWNPLndWItLLGQAKWQgsJaUIytmomxjVA5BRGERBaialrSd0jHsuE2Mi7dh9C7kQbFEE5IbAkASk4W2UYVycyTju3r3P/tt/LCkqqmrLDk+RORREZGEhkVzxWcaezyB9Vl7iDbO+IggVqUQVquCLvo2YXKUVIJXLiC/j4TafPGfwh8OpYkvRMerRthK+VydMli9pSXbVFzw9h3LJ8uXRoxcMluXkjLR728jlFjom7Wirk1e+odQaxUYcltN3e7c38AJOwbuWErgzElifMmfiUeRM8u3Ieik8sYO/JDVu1PpNDRg7ZP2WM4vY/lM0Ywcc0ZSq5vqFxl86yp/Pu3eFKLLLB30JKZeJ7skovExmRQkHmS+Is3r80rF08Sk1FARuxJrv+4rLDkdBbviiFL5057P2+aaC8R8uM8xs3eQZaqYkiIJlGfx/mY02UlpW9jRnPPZmgxkh4dfVtNdlMlCjtWs4da2FG4SePJ5C3rCWiaycZ3evPxo6oJVw0etLCj6NmFx5LG3R//JhqMKTvYEv74BP2vEGEXHkMSNh5eNJVzCV6+jriSP9/DFIgLdMJjSEW/50veydBx9kharamyWt1E2IXHklqQQnjIo25FzSKG8YJgIkTYBcFEiLALgokQYRcEEyHCLggmQoRdEEzEPR+XFQShZnqQx2XveZ9dPNP914ln44WqtnNn4APtJ4bxgmAiRNgFwUSIsAuCiRBhFwQTIcIuCCZChF0QTIQIuyCYCBF2QbgPGkt76rnUwexRN+QB1Pywm7dizJogju7+hn6uNb+5QvWTHJ/jvZXLmd7ZrgpLQss0HbyUQ2GBzHzW5o7jSli37M+sVbsIPXaI/Xs3MNmn9q37UuPTIzt40dHbCZuGHXm6ee37gIWqZoHPqOkM8WuDh6tFFYZdyxPe3jjauuHTqt5twZDqvsgnyz5mUAd7UoM3svw/i9h5pvatTV3j06Pk6clTVFDyyMsXq4mZOsnWnyH9GyOXnuDg4YxqWV/u9pJVMi7dB/C8o0TRkfmMefcnUmvpaVjje3aK9eQWqICenNxa+ikLVcam84t0sZcxng0l7OrDOB9kGjRxRSspZCSdIbMWn4I1P+yqnhy9CoqeHP2fby5UDa3XeDaGHmLNKB88+/2D/24/yPET4QQHLuLdTnVvH+batWLAe/9h0/5QIiKPsG/jfN7t5kYlCzjfBwvadOqAtaSQExtD8o2RtCVNe05i/vpdhEREciL8ANuX/5O3/RthcWNfc9z8R/PZmkCCw48TERzIyn++gZfdH08E5EaDWB52nB9GNEKDBrcRa4iOO8Hhb/tRr+Yn5y41fhh/PexqfQO5BgUww/cfgSwb6opZRf+fVIX8iK8ZOGrtLSeDcH8kbFt40dzOAc2ohSzVyeSeTiYly4OWT/oTMG8GCX3eY3eOCtbtmLx8CaM9JC6F72Hz3lKadO5FwAJPnCcP4p9B2VRZSUWNGx4t6yBj5HxSclk5ZiTqdP2AZV/3xyXvDCF7dpJj6Yb3M68yeXYJsT0/41ipjGu/L1k7pxtO+tMc2Pkz6bY+9HztA5a7WzF01GqSKqgFp+YmcWT/QSzad6KtmwVFFyIJS8oi+9g5DLWwh6/5YVf05OoV1IJccosBjFw4vJH1mvqYVxh2lYKEE7V6uFUTXP9oZZtMtr09km8iDFDneT7d9i/61e1IV28zdh8qpemb0xnhYc7V7VN4c2YQ2SpITmHM3/o5fd95lVXBKzlfVV+6cgPcGsqgFpJ2Nat8vq7Fq9tz1JML2Td3OFN+yStrv6ULTRwLuVAK2PgzYdrzOBefYMFbY1iRdA2wYMu761gfMJK3Ov/EB0F3V5JQ9cdZ8cEJYmbuYsVgZ9L3LWDK19HU1hqRNT/saj45uaWo+hwMKoBC6uGVfHn4UTfMNJSeDGRjlKGsdzacJOZcKf1cdDg4WiHJVnTq4oG5mk3ob1Fcs9KhAyg8TkhcKT2e7kAb21Wcz6mivt2sDnY6GdR8DHnXv80VMtIzUCV32g8cxxvFQUSfSuDspTSSL5fv5uVPJ0eZ0og97LusQafTAXAh7CiXxwyhbYdmaINOVU0ba7CaH3aMZKRmUOKSSroCIFOv80hGdL1Xz76DZZtiy78chL9Czc8j/8bnaMRY3ktrNDJIDjjayyDX5bXFh3ntzp1LrLGxlqCqwq7Rlp2wqoKiXD+mkcTvZzO/2RzGdhvGR77DQVUovBrJlm9m89WuFGR7B+rIYOY7g53HZtxxUIVMW+sqvIVXc9WCsJcSvWgkr1jlcEUB0NC480AGD7rXnP0im7fEYhBz9uql5pCVowAKxxdPYOGRWwsoqih5lzh9qQrnl7/FQwAACdVJREFUUyWFFCoqSBZYWNz8n68aYlg9pR9rbRvQ7ClPvHxfYtiongya8z4JkePZlpuNQQHtxc3M+mjbbeWbVaWYzHPxlKDl+sUFjVwLr75VQs0Pu+RIh4Fj6Fs/kR8XbeJkXgnhX7xImy8edcMElFTCQhIo6eBJyw6eFK1eSXy+CsjYNPalS3MnziWlUVRVI6zSDNIzFXDV4eRoCZQAEjaNPXEtOkNi2hUSwq+QEJGA9bPdmOHlTD07iZLYYEKzX6OPawf87Bbzy97Usnm3eV1adfalQXoCF/QKBr0eBWsa+vjgokniyj06C8m2Na+P/xvuFzax+McY9LVgFFnjwy679WHK1EG00RZhF/crk3bmPeommQT1D05e9bbfK5zb8BWre37HaL/JbNjTn/iEqxTbNMHjqfpYGQKZGDKTg0VV1ChjCmdTSlHdtLg94YaGUxjlhgz4bC0zvAtIjowg/mIu1GtLV28tJeeDCU42QvFBFn29n6fndqf/gkA6JZ7ifK6Ges09ecIBYhfEM3RFCqdCj5E1uD/OnWcRGDSGc1tmMnj+iQrevYTt82N5f1hXzEtcSd4fwI+14EmbGh92JT2S4LgsmjeMIzS+4FE3p8aTzG1wsLVEvmuKo6KWFpCTU4hk7YCdlaaCeaqKUqQnK68EQ2IMSXofNLFJN3stVU9S7Fn0XhCbVH7RLi+ShaNGcC5gLEN6dqBl+0ao+isk7lvDz8tXEFxUmTYVY25vj04r3d0mVeFaXjaGayqoOcREJWN8tgVunk9hJ50iS7nM5o9nYh/wJj18/ejRzgrFcJnTvy5iyfwVxBQDKFzcPoMhGUMZ/3Y//L298TUrIjM5gi3fr+W/61IwAvmH5jNjkR0zh3SkqY0VRbkGVBQuREeT0c+H2JOXy2/3qeTHHuJI6jN0dZIreF810z2Xkharov51D3V1WcmRVxf9ypyuVhVfcDL+zurRX+OwYAH97Cuelyo523m3+0yCqqo3rkSb1swJ4rkPh9FYU9EGKoWHPqLn+K1kqaBpNZmtG0bzRN4OprzwPvse2fe/TIM3l7J1+Gne6fsNJx7i/bidOwOrfilpoZZRs/nts7e5tMYaTUW9aEkmiSd+x2z0aLbbV9yzl+YmE1NVQa9sm2Iz2XTuIHXNK+rZVfIvxpJd3iUZT29ja9xwpnr50aWNBftCr925x8Oh8WTQ4CcJW/IRMbXkxrsI+2NFpeBiLMcu/slmp8JJfSjtgcq2KSviKGcqczhjCj8t28WQhS/T0qM+mtAUHslNF2M8S4f141puXrX8MU51EGEXahkVfdA8AgJ2QPyFRxN0ABTyc2vXxWIRdqH2UQ2cCRPlye7X4/n0gCAIdxFhFwQTIcIuCCZChF0QTIQIuyCYCBF2QTARIuyCYCJE2AXBRIiwC4KJEGEXBBMhwi48PLIzvb/aw9G98+jtUr2nnmWbMSz5fg59RH3AG8QnIVQ93dO8ty2UQ8sG0fTWv1GX3fDydsbGxQfvRtV76sl1m+HTvi/TJvhjW0sWl6huIuxCldPUa0XbJ2xx9GnDkxUuSCHdUU+t6hUeDya8QMK51xBeri9OcxBhF6pF+SIUEg95iWYZM7OyU1rVh3MkrgQs2vFS9/riREeEXbgvErYe/Xhv0Ub2hh0nOiqUvRv/xdSX3NFJgOzKwKWHiQqcQmstSLreLIyKJS42nE0TWnFrJ6+p68eoeT+wO+w4UUd3s/6TATS3uv21dO69mDR/Pb+FhBMZvp+t3/0ffZrrbmxxvR7dD2+3odXrH7Nu/1EigubRq44ESgYx0b+jSGa07thBDOURf88u3AfLVgEsXTUBH51CbkocUbn2tGzdjdFfdsDbeSQBa1M5G3aAA1ov/PzcsVVTiQuOJ620kLPxGag0LDuQpgEDPv0WcwstpUbQahvgM2AWX1yJY+B3iRgB8xbDWPT9dHwtM4jet5WgvHr4vTiUz1Y2Qh44hW1X1Jv16AZ/zkJnV+qVGrgSe460YhUwkpJ0nmLVHfMWnjTXbOd4LVk+qrqInl2oHLk+fSePwlunkPLzRF7pM5SRg/vSd9p2Lql2+I6fSC+HPCJWfci0uYFcUIBrESyfOolJk99j4YHU25ZvUs7+j3E9nqZtuxcYvzEZo2SGe+dOuMqA5MIrU8fha6Pn4NxhDP/7p3w+ezLDpv3MZTt/xg71QsvNKYLGxYqzC0fS7Zln6TniO6LK19ArunqFLAU0dd1w1WHyRNiFytG1pVM7KyRjItvWhpKlACikH9jALylGJBtfnvWuZJFmNY8DyxYRfOUaqjGDI7uPkaWA7OiEgwxYt8e/gzUUR7HnYA4WOh06nRVKfBhReRIN27XH7ZY5gTF5C9+ujiS9+M6X0ZeVAJPssK8jTnUxjBcqRbaxx05bNhdOzbhl9XE1m4wsBZ60pI6dBVCJ1V4VA1nZN5OplpRQogKyjAzItg7Ym0lIZs/z6cGjfHrH7kZrG6wB/fXDZWWQVdGC6EZj2Rp1koymwrsCpkWEXagUJS+X3FIVzJxwcZIgtzxdkhMudWVQDORkX1+DWi0rICFrHugWm2LIIadUBekkK6d8xYHcW36pKhSmJnHaCA5/diALSywkQCmkoLAW1GeqZmJsI1ROQQQhkYWompb0HdKxbLiNjEv3IfRupEExhBMSW1bjXMnJItuoIpl50rF9nfu//VZwnMMRBaiaZnRsr+H8iUgiIyOJjIrnCs40dvmDghN3kO0dcZBALcogLVeEXfTsQuUoqQQuXEF/n4m0eeM/BD4dS5LeCY/WjbCVcjm6ZDF7yis5qLlh7DuWT5cujRi4ZDcvpOWjXl7HqDFRd9SKq+g/ACWN7d8s5uXW0/EbuYxdPU5z6vcCrNxa4ulmS3HwR4SP33ZztwoL00nYN3LDVgZjShLnTfxKPIieXbgPRSeXMXbkh6zan0ihowdtn7LHcHofy2eMYOKaM5Rc31C5yuZZU/n3b/GkFllg76AlM/E82SUXiY3JoCDzJPEXb16bVy6eJCajgIzYk1z/cXHiGsYNmc7iXTFk6dxp7+dNE+0lQn6cx7jZO8hSVQwJ0STq8zgfc5rsuyo1mNHcsxlajKRHR99WptlUiVpv1eyh1noTbtJ4MnnLegKaZrLxnd58/KjKRFWDB631Jnp24bGkcffHv4kGY8oOtoQ/PkH/K0TYhceQhI2HF03lXIKXryOu5M/3MAXiAp3wGFLR7/mSdzJ0nD2SVmsKL1Y3EXbhsaQWpBAe8qhbUbOIYbwgmAgRdkEwESLsgmAiRNgFwUSIsAuCiRBhFwQTcc/HZQVBqJke5HHZPwy7IAiPFzGMFwQTIcIuCCZChF0QTIQIuyCYCBF2QTAR/w//A+ds3B28LwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "id": "75f0c03e",
   "metadata": {},
   "source": [
    "![Screenshot%20from%202023-08-02%2018-28-26.png](attachment:Screenshot%20from%202023-08-02%2018-28-26.png)"
   ]
  },
  {
   "attachments": {
    "Screenshot%20from%202023-08-02%2018-28-47.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "93bb777f",
   "metadata": {},
   "source": [
    "![Screenshot%20from%202023-08-02%2018-28-47.png](attachment:Screenshot%20from%202023-08-02%2018-28-47.png)"
   ]
  },
  {
   "attachments": {
    "Screenshot%20from%202023-08-02%2018-28-09.png": {
     "image/png": 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OscPnGOfmhsvoXzjSK5E8zvFj309YtsSflp+3ofN3u3AdEEOicR3cnCxQ5UazbvFurkuAeDdt+RXRlrvl0BC3eQTtfxfJz707ifx3da0hZtsqfN/9jjfrD2Wdd2siLuZRxbUeBjlpaDAvm4OiNOTitKQ7eP+4mh5eE2nU4XN2HOhP7E2o4lwL65zdjHntK06WeyBFfiC/ueFLeK/VasjLKzMtl+8tmHSHPxcs5223KTQbsoZ9b54j5lo2Bg71cHEwId/vC4LH/UHyQ21nEkcetidxTx1LXNy6kG1dlzHIdRQbfToSdbmAqq4NqGogc+fQj6wJKpo6R29dzZGe3/BG2XAutTEXNMioSmqsUvMY0ZNJ9l/A4PcmsGDHCc7dSievsICc1JvE+O1g/rg+fDjfn+RyylkYtoyPvtjLTbM6ONsIJEYfZMWksSwMzioOIXH9z6kMGLOI3aduonL0oGmj2hglhbB78SSmbrmChkLiwsNJzrpFeNStewRLTj/Gt2O+ZFvgLQzcO9G9Y3WurZ/IZ1svkJESTfS1ktAaLmz8jGnrj3M+qRADSzOkm+e5mlnIle2TGfzJSvZFJmNUxxNX2wLi/Lby1ZBRLDlTvIWguUbkmTtkJUYQceWRy4iVnEKuxMSQlvYP566WXX2SyMstvGeN49a5GBIzbxB9vmjxr6J1LcX7MH3EdDYev0C6YQ1cautxfus0Pv35IhIyOdlFi/Sa+GjOXM0k+UwYF4urNC9mA2MHz2C9bwypeo40cLYi99zfLP9iPacfIiQZF2K4mpHMuX/uHcw09wgJaK7GEJOSzoWYy6WLufmxmxg7YArL90WQbFibxs08qKG+wclf5zD2q73FNv0w25HIiI3gfHoml89eoNSBlTM4H3mR9PSLRJ4vWvuQM4KYP3QU3247yRWpKm5etTFIDOevpR8zaOpeSg4AS7f3Mn3kF/xy8hJZRo7UcxQ5u2Eyg2bs5UZmIhERVx+9iF0JELT7dXoBy97LOTy7DZycxRujd5BU+QVVa/znv06vX4OWbatyO/AUcRnFvUS3JgOXb+N/LdQEfv02o36789LsTihUHB8f74pPRyvKf0g7FO5Dr+UYFi16G6O8RC6eu0h8pgorZ3fq2eqhufob6/clKELyCqNlMZHJPH+WixkeEHmeV3z9UuE+8vwWMeGbZAZ0bYVrTTeamulRmHqN03v2sOanTfinKwbxKqN1z6QgYil9WizVdrQKLwOFdzj96zxO//qiM6LwInhZdp0UFBQqOYqYKCgoaAVFTBQUFLSCIiYKCgpaQRETBQUFraCIiYKCglZQxERBQUErKGKioKCgFUrfzVFQUFB4GkpPwP6nX1DTEv/5F/0U/rP4+Hgr0xwFBQXtoIiJgoKCVlDEREFBQSsoYqKgoKAVFDFRUFDQCoqYKCgoaAVFTBQUFLSCIiYKCuWg0jfHztYUnRedkZcIRUwU7sOApp/tJtBvA0OcH2UeApZvfM2BoMMs7ln1mRiSYNmBaevXMqWNmVZvtVO7j2N7wAm2j3e/TywEjOr1ZOaGffifOo7voW1M9NT6l03Lx7gVM/YGcHxNP5wqeM/xv0KsQtdZG1kxtlGFr0CtcNRajk/hZUe0pIFnDUws3fGqc//FmvcEpJq7J1WMrfH0cHz0jX3/Cj08h01hQDMvGtjraVFMBEzquuNsaoazZz1My0QsWL/F7DWz6NfEnHi/7axdugyfC9q8rUbEse8yjgZ6M6OV8T2/Udk0wKO6MZZeXtR6lmIiGFPdxZO2I6fwQS3tdv+XREzU6Os/pxHiP4/wBB1XeOInKhyzSTsG9HRELDzLsROJWr0io/zciti+3ouOlgK5gQsZ+dE3LFr9FzFZ2vyivpraDRthbeKAp5vtQwX4md4trInjxImrSHqufNCvMXpajPolEBMBy+4LOea3hN42L8sVzgpPi3Gbt2hrLqK56E/gv7zI/MkQqVrDHrUgkXj+AknPKsliExaE52zLohodFYCG2IBTJGpUVHmjC421qCYvhZgYmJmhp2uKqYEiJhVDF4dWA/hs3ga8jwVyJiKEwMPbWfLJm9TQuy/caxNY/PshAkMCOfbHMqZ0roneA9UsYFi3G1NX7ubYqWACfbezeGxbqjwzZ1EPr1ZNMBIkUiMjuFw609DHqdPHLNy6j5MhoZwJPsKfa79keLvqZUZYXRzajeC7Td74BZ8mxM+b9V++j7vZw21HrN6PtYGn+eXD6qhQ4fDhJsKjznDipx7YVbiH6OPU6SMWlOTt1GF2r/w/+rgWrffoNpvG/rDTLHnHGAE1Lh//QcTZCCKOz6OLZdm8iZi69+WrDXs5ERLC6aPbWfCh5z3TMVQ2NB34BWv2+BIUGkzA/s38MLw5Nqq7cTj1X83xwN1MaeXMW9PWsj/wNP6rP6CaCAUxZ4jKlxGtmtKirvbmVMrc4VVEtyXjFkyju1EWty/EEnIeqrh60HHYXGoyhD4Lw8lDwLz9/7FuUW8cVBJpV6KIy65Bn+8XoSupgfzS6MQq3fh+7Te8YSWQn3SRs1c1uAyZTzsd9bMZjVQONKhnioiGuPOXi+/YFTBtP50183tim3mBkwd9SNV3wKPFu0z8qoDITt9xqlDEvsdcNn/9Glbp5zjis4MEE0869Z7O2toGDBy2kfOFDyYnp50nwPcYeo1b0dBBj9yroQSeTybl1CUyKuShqKn5wSI2zWiDBZnciA4nzqAWbm3681WThtiPHMLS2xH4HrbHq2Fb3KuIZF4K5nRcFgUpp7maLYNZUUyCfhs+W9YJE0MJDSrUNg14a8ockqN78G1wHgiWdPhiA4t6Vyf/sj8HdlxDp35HOk9eRj2zkQxYGEY2amp6uGNpYkD32SsYWMUWISuB2Au3yJGB7EucvyHxep1qNKhvjhCZpJWbOCunmAjmNO4/kq61DRAFAUPn6oiiyJsffY5jpows5XLl4Do2ByYp102WR/4pVk0YzZbroUTH5yEDum4T+X3rCJw6d8Llp3DCZEd6je6OvUriyo4JDJx9gmRJxOaN2WxZ2AP70sjUuA0YRUcrgdyzaxk2bAnhmTI61fuweOvndNDmpLsEsSoO1USQc7hzO7m4jdW4v9YBOzGHw98MZtJfmQAI+rbUsMzhaiFg3I7xn3TEJv8Mi4aOZN35PECP3R9tYeuoIQxt8zvTj2Y/kJycfpp1088QMWMf6/rbkHB4EZPmh1OO7pSLYNKBceNbYUkCB6YP4FPvW2gEU5p+tpE1g+vz4eTe7BqymTmfHqbjD8dY8o4+1/Z+z8RVF0svIy/1DwQ9kg79j37f7eea2oMJa9cx0rUKrdrWRRUcieg5jKnvOiJe/Jmx/RcQmgWofiFq7Q6mfTCCzpsmsDuR0lgtjW+zYexglp+4SW6JYkjx3IrXQB0V1apXQyRJK5eiV85pjmhHkx59eb/ve/Tt24duDW1QiVZ4du1D377v8f77fenVvNqzXah6qcnhSkgAZ4uFRKXWQ7xzg1saEK1tsRYBQw8a19NB0MSyZ7M/yRKARMKRbfx1pYxpiVVo2MgeFQWE/r6FiMwiiyy45s1W35RnI+Y6ppgZiiBnkZFZkoJEYkIisqBP475jef+NptS3N0Wde4fLNzOQAB33drSyFCmMOsjhmyoMDQ0xNFRxNTCIm7I5DZvUeSajp9qtFc3NRTS3D7Bl362ijimnE7J1N5GFArpuLWnyiGnWPRSEsOnHv7mcLaFJj2S/31UkRCytLVChok7b1jioJC4fPcR52bCojHrJnPI/j2ToRVPXspvdEsn7V7DMr4yQAMhZpGVIyAiYWZhpTQQqp2ei+YdVfZuyCgAR+0Eb8PlUzY/vDGLDVcUXeSyCBZ59RzOiR2s86thjaaiDWGLLeSICIBqbYqwSQEokPrGMpckpJCZLUKskLhNMjYs6dsKdjDLucCEpSSnIWGg//yp1kWHKEpJUkqKG2J+/YmGdrxnz2iC+aDoYZImc26HsXvAV8/ZdQTS3wFQEnaZT8Tk19b5IJZJMjJ7JAKQyN8dEBDnxDmWrUkpKIlkCQWWKuYkAKY+PSy5MJjmtJBKZgvwCZEAURUDE3NIcARV1RvxC4Ij7H87F2FiXu1NUmdTE5HI8LBlJIwECKpWotTqpnGKi8BQImHWazvKZnTG5fZw1c37kzI1Ucgzb8dmiobgVh5Iy00jXyKC2wtZKgBIDFqywtRahxOeQ00lLlwB9rG1MECiZX6uxtrV6Nt5hQQ45kgyCHnplVoPljAg2TurBZpOq1KnvgnvTLgwa1ol+X/8f/4SOY09aChkSqK/vYuYXe7hexsGSpXySLkVTAMglfVWl0sr5GE1aGhkSWFhZYynA5eKfizZFXqBcmFoqECVpF4nDkyKRmpyKjCVpR+czad3ZMkIhIxemciUmE3jU+SAANXp6KgRkcrJzteZdVs5pjsJTIGDrVBNjUSYt+HfW7DyEX2Ao14xrYF+2tbMiCDlXgKyqR/cBLbEQoeisxQC6Vi/TxaTbhIbdQCPo0LhvP9yNijq3To0e9O+oPRf5HgoTSUiSQDDEylK/tFzGjq7Us9VDyrjFP8GH2bFyMbv+KUTQtcHOTKAg0g//FAnRvgnNzG4QERpKaGgooVFXybesTlUTFSCTk55OvgzqWl54mD9aDgUTN96bNov/9fO4d0elbHYjTxKUKqGq0pkBb1UtEijBjKYDeuKmlsmLDCAkQ6ZIDFKQUOHYpBkOT3xWX8PFE/7c1IiYuDXFMflsUflCQwm/mIGxvT1mFXEPRHOsLIoGjMT4BK0svsJL4ZnIpN+4RlKyDjfTtXmA6FVF4mpQINfG1KPm21+yQf8Y/2hq0bqZEypALhkapWvsXvUn/Zb0xun9pXg3j+R8uhX1G1gi5ktgUBKfhuitqznS8xvecB3FRp+ORF0uoKpLbcwFDTIqZK2ZY0mSV7h4pRDZQY1DTQdUxKARq9Hru81M9cjmcmgI0dfTwK4h7T3UFMT54XdZA/nHWDbfl+bfvE7PRd60io0hLk2FnbMLNS0gclE0A9ddIS/cn9OZXehg+zYL97fgVuRmPhq1qUwGSsojYNJxDP83qD26BfZc9h3Fr/EPjuNyxhGWLfGn5edt6PzdLlwHxJBoXAc3JwtUudGsW7yb6xJAITGHj3B90CBqNJ/G7mNDSSmUCPiyO19eflhlyPfkKP/MOn7Y3Y5FvTvy1a79DIo+T6JsRW2XOtjoXGPj4B7MO/NgSe5BtMfRvmjqevHCLa0svsJLIiYZvjN53fdF5+N5oMLA3BxDdTlnSmWJvMwUMgp0MTU3Rld8cJiU5XyyUtLJDVvC2E8l/jf+HZq99g6O14PYNnMq2SPmMlwVXXxuQybt+A+MmpLKp2O706xmPWprQtj5f5/g7/I9C3ulc+ZC0dxbur2X6SNFbk8ZSteGjtRzjCNgw2RWXuzEj1+35lzEVTSAoGuMhYk+D2ZNRi7MJjU1B8HIAjMDVTnTIxkpN53kzAKQU4kIu4ymdV0cXOpjJsSQLN1k16wZmI/6gDebNuPNRgZIGTc59/cyVi5cR0Q+gMT1P6cyIHEg44b3oJ2HB011ckm6HMLunzezesuVoo6TsJdvptdB79NeNHYwRjc7lSxZIi08nMQenkSevVncwWSyIo8TEN+C9lZiOeUqQcOV7ZMZnDKccUO60qKuJ3ZSKnF+W9mybDk7o+7uIOWFLmXi57r8b2RnvBwtMbodzPmbBWgSYoi4lol9QhRxpb1b5k5MJDeyqnIzsniLXE7m6OwPGXZuDKPfew0v16bU1KRy7ew+dm5bw4bwQkAkLjyc5NdqEB5164FpjGhXj3pWKuT8aM6czXtYoZ6Y0qsulK+qPz1P+3V6tdeneG/6EMdyJ/IyOce/YsTp3myY7IFueYYtFxCxqBcD113W2mhTYQRL3l32N1+3Nyh/HUVzjY0j5mOxaBE9zMufHEmpf/LR6zM4mgsq14n8sW0ENTP3MumN/+Pwgzu6zwmRqh+s4o/B5xjdfQFnKrpfXGkRMO22mMM/dEQVMofuQ7cUe01Ph4+P98vgmfx3KIxcx0dD/bDWLc8zkcm6HsnZtBCGRlXBoFzPJI/46KvPX0gA5BT2fzecG5uMUJXnmRQkEXvmGjojRvBf2rP2AAAgAElEQVSnefmeSWHaZSJyi/6lObeHP6IGM9m9GW299Djsr70R9IlQudCvfy0CV35BxEsvJABGtOjQBH2yOL7rb25ocXNUEZPKhCaFCyFBXHhkoCucCbrynDL0JMhkX4/k1PXHBIsJJr4i0Wmu8PuafQz48W3qNaiCyv/KixFJTTSrBvUgLy3z1Tggqa6Fu4sRctzPrPlbOydfS6PWYlwKClpEJv3oHEaN2gsvytsCQCIrLfOFpa51CqPY+Ol4jmaGcEbLzp4iJgqVFzmDC4HK1bXaRSIp+iRJzyBm5ZyJgoKCVlDEREFBQSsoYqKgoKAVFDFRUFDQCoqYKCgoaAVFTBQUFLSCIiYKCgpaofTdHAUFBYWnofTQmvKi39PztC/6KSi8rPj4eCvTHAUFBe2giImCgoJWUMREQUFBKyhioqCgoBUUMVFQUNAKipgoKChoBUVMFBQUtIIiJgoK96MywNLOGmNt3ND1H0IRE4XHIOLUfxXHA72Z0cr4OSZrT/dvN7JylCf6jw38BBi3YsbeAI6v6Y/TfWIhWjRk8Hdb8D0VyPHDB1gzpIZWbvx7LKINXecdJOjQHLraPssuKVK73wI2LepP3Se+AKwisSsoPBI1NT08sDRxwNPN7vl0LgRM249lcvdGeDnbajVNlU0DPKobY+nlSa2yEatq8MGC5Uzt7orepYP8smoJa321d0EVgNplFFtPnmD7OLd7v5cqOuDuYYOxrSce1Z+tmFjUdMPrzQlMesdW651fEROFCiM8k4uFy0G0pUv/TtgIWZw6FsyzujKnbHFUtbvQu7Excup+vhr+GT8s2cDhuPyHPvtvUjOp35AG5mY4e9V/yFWjwjOu40KijgeQLBvTekAv6mh5ZFDERKHSIdq9xltN9KEgipOn0rR9+Wi5qKo5Yq8C6UYsF7KeTYplheJ56XIRIjo6RV09N9yfkCxQO3emc13tqokiJq8sAiYNejJt2XYOBZ4mPCwA351L+b/+TbBTUeTWrzrKqf2zeK3M5d06HuP5PeAkOz7y4IFptW51Xp/0E7uPBhISdJhdSyfT2UlP+/lu2hJ3HQHpajiRSWXu/X1UeYrDGNbuzMcLt7L/ZDChwb78seIz3nE2fHhyKhfG7ThFyNJ3MBFA7TaJvRERnPGdRfsKLxEJmDToUSZv/hzavpjJXWpjKIBg/iZzjoZyfHYbdAHd1l9yPCqSqDP7mdnq3vpTWTdj2JxfOBB4mrCgA2yd3Qtng7IhdHFoN4LvNnnjF3yaED9v1n/5Pu5md9tQ7T6O7f7H+WW4F659ZrHFN4iQo3PobCpAdhRnYgtBVYMWzappVQCUqy5eUUS77ny/ZhYdjJOJPr6foBwTajZsTv9pVUg73ZflcdVxc7PAyMSNBnYivqkaQMDY2Y06pqbgXhdTIYLSvoyKeiMWMS/nNjHnYrlR2426HYcyr4ENmvdmcChZW6O5ijouddEVZPLjLnJFU8HyxEro1h3Esp+n0FQ/kfDDf3A0045mbw3ku/XVEftOYs+tcq7RkhOJPn6Eo4VNaO9mi5BxgaDTV0mPD+NGBe+V0XcdxaoN4/E0lEi7EkVYmjn13F5jxNwmeNgMYdRv1zh9xBdT98a0bmCFkHwO/zM3ycu/TfjNMtcEqqrS69uf0NVTU6gBtboqnr1m8sOtKPquiEWDiH2PuWz++jWs0s9xxGcHCSaedOo9nbW1DRg4bCPnCwVM6rrjbGaBqv/3/Ghjj11hBrciL3EnXwYpgYsX05AaW1HHrS66XCP36RsNUMTklcWo6eu0NIeE36czaFYAeQCiEVUdjUm9IoG6ZCS79yrSh7vfAiQfZnq/afjc1iCYNWPqppUMqt2FUe+t48iqi1parNSnmoM1IhLJt+Mp6c+PLY9gS7fJY2lqnM6xrwbx8c6baBAwPjCTnSt7M2agOz7zwh+cMkl3OPrTNPxj5hOwqDPqy958M2l98eXuFUCsQveJw/AwlLiyYwIDZ58gWRKxeWM2Wxb2oOm4CXTeO5nfZ33K4d7LOTy7DcT8xvSPd9wV6jK9ULr4G2Mn/sSJOya0nbmBpX1rUrtNK+xXxXLVsB3jP+mITf4ZFg0dybrzeYAeuz/awtZRQxja5nemH80ubUOVrQEXFw+h3+ZQEkqXfzTcvnUHCWv0qlXHVoSrWrqqsJJOcwR0TSyxsrbGupw/VlbmGCpnAB5JXkI8aZKAVZsBjHy3PY2c7TAWsrh1OZ6cf+VEaIg/sI39t4t6mZx2mq27oykUVDg39HjIguK/QDTFzEREALIysko7/2PLY9SYdk2MID+Mg8dS0TM0xNDQACk6kLBMgWqNGuPwLGzGsCGtGhkgaGLZs9mfZAlAIuHINv66okEwbkprD92KxSVncmTNMvxu5SFrEgk4cIpkCURLKyxE0HFvRytLkcKogxy+qcLQ0BBDQxVXA4O4KZvTsEmde7wDzeXd/LSxrJAAyGSmpyMBopmF9tqNSuqZCNa9WLr/S1rrP6ykGm5sGkbXOaG8EndJPwPyg1cyc011vhzcljHftGcsMpqMqwRsm8MXS/0qdt/vPcgk3Sl7N61ESmIyEgKiiSnGAqRoZaajQq0uSk/S3HUPHleeBBMLzHUEBJ2OfHssiG/vi1VjZIyRNrJ3H6KxOWZqAaRE4hPLVICcQmKyBLX0MTXTAyqwMyRlkJxyN5xcUECBDIgiIqAyt8BUBJ2mU/E5NfX+h0kyMbrHs5SSEylv9qkpLHZFVGI5l8z/eyqlmMhJ+5g15BrVjcVy3W5ZLiApNlwRkkchJeG/ZAydV5pRvW4DXNxb0GPoYNqN+I4pZ7sw9ZgGjQQgIJT6pwKGxkUG+aANCljZWiFwufjfItZ21qiQ0aSnkaGtJRM5j5xcGRDRMyhzXO1x5QlMJbVQBuEs6yfN40ha2TglcuLPc04DInJR2UQVohb8cikzjbRCGXSssLUSIK24IgQrbK1FkDJITSlalZBL6kil+ldnZzRpKWRIoL6+i5lf7OF6mamYLOWTdCmagsfGIqCnr1fUr3Ky/6WXWj6Vc5ojZ3Mj8hSBAYEElPMnMDCE88kv7irrlwGVuRNutS3QKUjj2tlA9v+6lBX7byCJRljbGCJKKSQmySBWxd296GCYaNGEAT3dHjLCqLDr1J8uVYu6gWDejAHvNkAlF3IhNEKLYpJGQkIeMgLmlualBvrY8mSf5kRINrKqDi0bq4g7E0poaCihYdHcwgZHWwMEQEpPJ10CQacuDV0f46sIVjQbMp3ZU97B+WHHcLNDOBmag6yqR/cBLbEQAURsXx9A1+oqpIxgTkYWADLZycnkyqB2bkZTmyfvegWRfvinSIj2TWhmdoOI0OIyRl0l37I6VU0qIlF361WTEE+iltZLoJJ6JgpPi4oGQ39k63B7Us+HEnLuFlm6NWjeoQZidhjHAhORNCn4nbzOyDo1aDVzB3/1u4OqWg2MCnOQH9wUBmQKjNowe7c3/c8lYVzbFSdLFdLtvazeGafFk6IFXL54FQ2uGDvWwEY8wU2pAuWRJP5csJy33abQbMga9r15jphr2Rg41MPFwYR8vy8IHvcHyamnCYjOo5WXE4PWHaTzzQCWDfsM7xJfTJZLvTLRoRsTJ/XDS90do6jDTNlfzvE5KR7vH9fR03MCXu8vxbt5JOfTrWjgVh0TIY2glcs5WDz/yw89zNHEbnS3eZMf9h3lkzSJzL8m03vxXU9QLivK8n0KnXGMZfN9af7N6/Rc5E2r2Bji0lTYObtQ0wIiF0UzcN3Vu4/d/zwAutg7VkFEw62LcaRr0TNRxKSyIepham6MrvjgBE+W88lKSSdPbYyFiT4PBpGRC7NJTc0heuMMZusMp1cHL9p0aYpuYRrXIv5g7tIf+SVOAiTCl3/GdxYzGfF6A2yrGhJ9eB7zdurzyU+jMDl7odjbKCQuIoKUDnbs/WwaIY0nMP7dplTTS+X80b2sXrC8aFu4AvkuNLDAzEBVztRVRspNJzmzAJC4Fh5BosYVW2dX6unCzVxNBcoD+bGbGDvgJsPHD+btFg1o3Ewg684lTv66h/Vr9hatH2gus/Xz2TjOHk9Xd1tMVFmk5cpoLoZzJqUNdlHnSCxWRikhlGPhSbg2NkIlPrzX5Z5dw5gh8Ywe25/OTRrQsFo+CecO89uGpazad7F06iGnH+H7j+cgTR3M665VsNC7RuiFO0iafCIjEsnWPUv09buugnT9LBGJ2TSMPMv14oXd639OZUDiQMYN70E7Dw+a6uSSdDmE3T9vZvWWK2iAjH/CiU33QjfiHCn3ex4qJ1zq6iPIWUSEndfqUkHpVRfKV9Wfnqf/Or2I0/DN7JrsgW65i0UFRCwayo7Ga5jV3qD8bVzNNTYO7s68M89zRakC+V7yPZcHz6S7efnuvZT6Jx+9PoOjuYB+a2btW04fq1tsGtKNOaEvbnVMVXc0v21rx74+g1gXp8U5wQtCtB/IRp+pNMo9zLS3PsEnVTuuiY+Pt+KZVC4krmyfztCoKhiUO8LnER8dRfz+4VzbZFTOSryMXJBEbNTz7nwVyfc5so9fZo95+Z5JYdplIkpOT+UGs2vvVd4dVp3WbZxRhcZo9YW7iqNHq0F9Mdo7i52XX34hARGbtu1xVcskHtzFUS0JSQmKmFQypIwrnAm68uhA6ZGcuv588lNRKpbv4ApuSecTuWUDJ9/7kuYN6mIixKBlu68geQTM7UWf7DSe0es6zxk19T1c0M2LYNP6k2RpPXYFhUqIdPsPZgyLp1ZmWOlu64ugMCPtFTqCkE/AknGM+/U2gc9gyqaIiUIlRSI5+iTJLzobrxj5t8Lxu/Vs4q6c50wUFBReOhQxUVBQ0AqKmCgoKGgFRUwUFBS0giImCgoKWkEREwUFBa1QepxeQUFB4WkoPWeivJvz9Dz9uzkKCi8nPj7eyjRHQUFBOyhioqCgoBUUMVFQUNAKipgoKChoBUVMFBQUtIIiJgoKClpBERMFBQWtoIiJgoKCVlDEREGhPAQ1xtZ2WBooXaSiVPKaUtNg8HzmD3ZBX8eZfnMWMcJT70VnSgFQu49je8AJto93L/eWHe2gQ92BC9i08ANqa/OeYONWzNgbwPE1/XG6P16VHa3HLGDn8SACjx7kwIJ3sNTiFZoPRbSh67yDBB2aQ1fbZ9st9b1GsvLnr3nHXrvpVHIxkdEYOvLauLH07Tua8Z2dMJFeha+Ev+wImNR1x9nUDGfPelq9/Losol03Jk14Ey/PWlrt0CqbBnhUN8bSy5Na94iJDq5jl7JkwpvUKTzLnz+vZPEvQdr9Bq1hc6bt8ef4mn73CpnogLuHDca2nnhUf7bdUrSug2fj7nwyvh0mWqzXSi0mKn1DEvZu4GB2W6ZNfxP56AZ2X9dH/1ncZq/wRDz7wVqk9rv9aG0sk3TiKJHP6KvO95RD7UnPd53Rk27w69SRTJ+3nF/8b2v1mg2VnSsNa5pg6el1n5DdzZHwjCs357QfwdkCNp0H8HYV7UlA5RUTtQcTdxzF7++5dLNTASKWb36Dj99x/pzaFN0XnT+FZ4uqLp0710VNFqdPhJP7+CeeGsGkOtUtVaC5zD8X8p9VKkUCJjwPQS6LiI5OUXeX04MJiCoAvUZ0eb2K1kSg8opJYQQLuzWmUa9FhGUkEht7h8yIpbzXpCGdvg/mWTX1q4Cq/gi2nPTjt7FNcX33czb+fZKQ0JP4rPqI1jY62LUayYLthwgKDeaE9xLGNLMoNWzDltPYG3ScTcOcKR04RVu6zT/EKd/5dLN7PiYjVm1GSycVFMYSHpVz9+eWjRj41Wr+OBJAWPhpAg5sYdEnPXGzuNs1BTNXek1byk5ff0JCAzi8fSEfvebwiAFIj5Yz/ibUbxZtdQHdNnx9IpzIkD184lXRCxwETBr0YNqy7RwKPE14mD+Hti9mcpfaGAqAaE/fVScI856EmxoEw678GBZJVGQwO8e7UtZJUVk3Y9icXzgQeJqwoANsnd0LZ4N70zKs3ZmPF25l/8lgQoN9+WPFZ7zjbFgaQu0+ju3+x/lluBeufWaxxTeIkKNz6GwqgJRIRPg1JEEHt5ZNtDbVqbxiAoA+7UaOpMGFjUz5ZCP/1B3KyI7GLzpTlR79Oh40MDfHZciPrJveHuPEayQWGlOjzQjmLlvP2p+G4cZNrqXImNfqwLgfJtLaEEDEqp4bDsYWuLk73f0+hWiPq7s1RtbuuDk8H5PRredKbTVIaZe4mFi8TqaqzYc/reB/fRpidN2fv/b6Ep5ahfbDPueTt2yLjNmoERPXbmD2oOYYXznKrh0HuWjcmlGL1jKzg8VDvIECbp45gu+JWJIlQJNE9PGj+PoeJTKhYmt0+q6jWPXz1wxuXxfDpHOExSRjVP81Rsz9mWWD6qAjp3Mx8AhHgi6SLoGsiSfy6BF8fX05Hp1YenE5qqr0+vYnPnnbDVsDNTrGVfHsNZMfhtQtFRzduoNY9stcRnW0IzHgD373iUJuOJDv1v9Aj6oipWtaZhZ49P+eH794Fw/zfG5fuMSdfBnQcOV8HPmygE5dF5y1tGxQye/NyeXEouEM1JznUrzMpwNC0Lmc+aIz9dIg6t/k14GDWByZg8lr3+LzY3esXOpyYX4/Rm68RIFFVxb99T1vWregdX01J0Il7jrfQrkd71nP54tzjrV9VfQFkOJvcae4P4tVW/GamyHSxVVMHLKUaA2AClMHe3QT4pEQcfpgCh820OX2n5P4YMZRUmQQrAJZ+Mf3dB/9Lhv81nP1gfQkrvjM4dPgvqzd/zktiGbL/ybyR0VXXsUqdJ84DA9DiSs7JjBw9gmSJRGbN2azZWEPmo6bQOe9k9m74XPOHB3O1j8m4ZofwtrJ0zhU4mKrq93NzcXfGDvxJ07cMaHtzA0s7VuT2m1aYb8qlquyLd0mj6WpcTrHvhrExztvokHA+MBMdq7szZiB7vjMiyhtO5WtARcXD6Hf5lASyrjzubdvkSyBvbUD9oZwOv1J26icanj6KJ4tuTejiYkvAAq5cy6aG89j8vyKUPjPAbyji6YIWbHnuKoBCiPZ91cc+YCc/g8x1yQQLbC2rEymIGBqbooIyFmZZBb3aTntDgm5MqLDmwwd8jZtPJ2wNpBJv36VxDxAtKVV2wboyin47w8jz8AQQ0NDDHJOczKqEHW9Jnhpc/uiBMOGtGpkgKCJZc9m/yLvBomEI9v464oGwbgprT0quMonZ3JkzTL8buUhaxIJOHCKZAlESyssRMCoMe2aGEF+GAePpaJnaIihoQFSdCBhmQLVGjXGoYynobm8m5823iskRcmkkyEDghnmptpp+0rumSg8DXJOFtklg2thIQUAUjZZOaU/pFADIFLOfeMvFLW6qEfIGg1SiZhkHGLh51ux+b/edJn8A10FkAtTOe+7gW9mbSAkwwJLcxFEa3ovP0Hv+yMtMMLYSPsFFY3NMVMXrUXEJ5bxZuQUEpMlqKWPqZkekPf4yKQMklPu9ny5oIACGRBFREA0scBcR0DQ6ci3x4L49r7HNUbGGAEljoaUnEhyeQ6WRlO0SyWIqP4b0xyF542kKd4IFcS70xzRCGPDezuhXGKgKhXa36mXyc3JRQYEfT30BChaVNBw/eAPDDq0CMua9XBx8aRtn6H0e/Njvo8/Q9e5t0hOlQCJ08vH82NA3j1xSpk3OHdDAie5KDpRhaiFQVnKTCOtUAYdK2ytBEoPpghW2FqLIGWQmlLiUt9N+99MGaWMVFILZRDOsn7SPI6klfmlLJETf55zGrB4XER6+kX1KuWQnaOdgzSVybdVeOFIpCUmUSCDuq4nboYAejj16Et787KmIpOTnk6+DOpaXniYa3u0l0m8k4gEiObWlG7U6NhQ19UREzGP5LgITvhsZtF6P1JlEUtba9RSPIEn/6FAVlGviQu5/4QRGhpKaOgZYhN1qVrdCl0BpPR00iUQdOrS0NXo0VkRrGg2ZDqzp7yDs/5DwmSHcDI0B1lVj+4DWhZNRxCxfX0AXaurkDKCORlZAICUmkyKRkbQcaFlY9Mn3x7OPs2JkGxkVR1aNlYRdya0qIxh0dzCBkdbgwrFKZpbYiGAnJvIHS2dylM8k1eRErdBfoiRyA/+teT/2SF+BGe+Qfsa/Vn5V0uuZpnjaG+CWiiKryTKvHB/Tmd2oYPt2yzc34JbkZv5aNR6YrVywksmPS6OO5o22FdxpLoBRGaCTvMJrFvxLoYJ0QSHXeROrhH1WrfDklR8j54hH4lL2+axsdMKRjSbyLaDPYn+5zb5xjVoUL8KBhneTDg5g2OppwmIzqOVlxOD1h2k880Alg37jF2aknq4W0GiQzcmTuqHl7o7RlGHmbI/+8HsSvF4/7iOnp4T8Hp/Kd7NIzmfbkUDt+qYCGkErVzOwZTimNMCOXwqi7Ztq9N35QHeuJOFfHMLw0aG3W2Lss12fxtKd/hzwXLedptCsyFr2PfmOWKuZWPgUA8XBxPy/b4geNyeu4+VawMC5tUdMBFBc+U8cVo6EKiISWVC1MPU3BjdchYwZDmfrJR0Cg0sMDNQlTP6yEi56SRnFpB7MYKY1GYYR8WSUrwTIqX+Q2RcJvVyI7lQcmxDk8i5qBtkOcQTcanIoqQ7e5g9zZEvp/SmmWM1rNOD+eXTpUS1XcA37WKIvl4UoZywl2+m10Hv0140djBGNzuVLEEPU8tH5z9PbYyFiX45azQycmE2qak5aIDC2DNEZQ/EwbA+bs5q/gorpCBwGVPmFTC0Wxs8OnTFRJVHUlwQ25b+xE/ed5AAMkP5cdiHXBo1hgGdmlCvcXXk9FvEHt7EjrXr8MsFuMzWz2fjOHs8Xd1tMVFlkZYrI2f/Q/jFdFylKC5kFXVCKSGUY+FJuDY2QiU+fATPPbuGMUPiGT22P52bNKBhtXwSzh3mtw1LWbXvYtF6FYB0m10zJ2M1fTJ929TFxiKPC0fjSCm4TmREItm6Z0vrGEC6fpaIxGwaRp6l5Mf5sZsYO+Amw8cP5u0WDWjcTCDrziVO/rqH9Wv2kizLqP8JJzbdC92Ic6U2cBcdnF3qoEZDfHg417V0xLf03hzlioan5+muuhBxGr6ZXZM90C3PT5ULiFjyPZcHz6S7efmzUyn1Tz56fQZHX8iOVwXyv2goOxqvYVb7h7jimmtsHNydeWcKQbCg249/8cPr+kQv7k2/NZe0eqz9SVDVHc1v29qxr88g1sW9Au+GqVyYuHsro5yS2D66K7P8K7Aw/Bh8fLwVz6TyIHFl+3SGRlXBoNyRPY/46HNkH7/MHvPyPZPCtMtEvLCt84rkP4r4/cO5tskIVXmeSUESsVHFPrecwtGdh0js2APnNi2psu4SN15IP9aj1aC+GO2dxc7Lr4CQAKra7WhXQ4Xmyl52Bz+9kJSgiEklQsq4wpmgK48OlB5M/PPJzhNTsfxHcup6xeLLPLGRzVFvMamuC3XUcOOFvEORR8DcXvTJTiNLm28PvzAEjBu44ySm4bd2C1EFj3+ioihiolB50Vxk0+ThxNrdIegFvoxVmJHGM3pp+QUgk35wLqMTDbkYULzOpCUUMVGo1BTcjsDv9ovOxauFnH2F4JPaj1c5Z6KgoKAVFDFRUFDQCoqYKCgoaAVFTBQUFLSCIiYKCgpaQRETBQUFrVB6nF5BQUHhaSg9Z6K8m/P0PN27OQoKLy8+Pt7KNEdBQUE7KGKioKCgFRQxUVBQ0AqKmCgoKGgFRUwUFBS0giImCgoKWkEREwUFBa2giImCgoJWeInERMC0w5f4+K7gg+ovUbYV/iMI6JraYmeh9+R34bwivES90oiW73TG0UyFlPdKfIzzJUfA8o2vORB0mMU9qxYZkq4bY7Yc57TfJka56ryQXBk1HsPKn7+kaxVtmraIU/9VHA/0ZkZr4/vEQo8anT5hhbcfwf6HObT3c9rpaTHpR6B2H8f2gBNsH+/Os6xtwawNU9avYVp7y0cK5csjJgZNaN/MCE20H/6J/yExESzp9N3fBB6eQxebyjTmiVRz96SKsTWeHo5FV4TqVad+HXMMLJxp4PicelRZVLX44NPhtPFyw9FEmxGrqenhgaWJA56udvd0GuNWn7J6/hDaVsskeOd6li3aSaQ2v1f70PYXMKnrjrOpGc6e9TB9lqahW4X6Xs0Z8NlQPB/RrC/NN2D1GrajpblE7PGT3Ho1bhyoGCpHvBpXwcQGPB1V7EuoTJ82For/W2zJGQeYPSSLfbZ3CD6R+dxzo9foffq66iNd9sPv0rMxknvvBzaiVa9u2Ks0xCyfwOiVF7R/t88j2v95DS1yUgB+Zwtp6dWTgR3WEL4/nfKG85fEM9HBrV0rrOTL+J248sIuY3oxCHe77OOsR1Sjo/1bxJ8ADckxx9l/7BzJz72R9Gj41v+zd97xVRRfH35296b3SkgBpAdS6L2IIk0EFEWKFAFREAVE4f2BBRWxgCAqilIFbBQpISIthDQSAukFCIGEnpDey7277x9JIECQKBcIuM/ng8LdubuzszPfOefM3D19cZZ0pIeF6ClN6R2QHHBzNUZQSkg+ee4e9ct/8Pz1fWWNQUVaWPkKoaFnkUUreg7sgcVt6vFwiInUgl49nOBiEIFJ/xUpkWj52m+ER/7EOFcJJGde+imS+LgoQteMooEk8tjoHwkI3c7sbs0YMHc1e0OPEfLjSJw1DRn5gz9H937IE9WSiht4vc6WI8FsfcOrmo8tYNqkP28u/YW9weFEhPux4/t3eKaZabW6CJg2H8yclds5fDScUL/NfDW1J04327WCHQM+20vY/s8YYC+ARQ/e23OEwytH0LC6yAm29P3Yl9C9H9DbXF/N1Yxune0QlVLiIk9cT8epcaL7K5+wfvdhwiMjOB60mw2fTaV/s2qxD8mBji+9z6qdfoRFhHNk70Y+m9QZh9sKs4DtwC8IiNzFW54aEMx55tvjxMUcYePLTbpAQ1UAACAASURBVKi1nmvq0/2VT/jJ9zDHoiI4FriLtR+NpaODxJ2ff/XqmNB08Dus3OlPeGQ4gT4reKOb/Q2DW7BqzXNzv2WbXwjHI45wcPNS3njCFcOqAqIDgxbvJ2zvRzzVvAfTV+wk+Hgov09tiYSOlMgYcmQB0/Zd8DakRh4KN0dq0pOeDeDq5kAS6pKVf09RyEwIxO9QKd7d2uJmXMqFyCOcytaRH32aXFlDOy9PbC1MGPLR97zk5IhQeJVTpy9TLLrh4WGDmYUH7vVE/HJ0gIB5Mw+aWlqCZ3MshRgyFTBsPpYVP82mo3EG0Qd34F9Qj04DXmLRWjfEETPZeVlGdBrMp6sX0tdOoCwzmfhzOlpNWEIvA82Ns5HkhlfbepjXa0ObhhJ/JUoYm5ti320cIzy3sziqvLLYM7w82A2zvAj0lbhFsG5JSxcR5Eskn61KpmxEmzdXsmLiY5SdO4q/bxpivVZ0GjCVjw2SCZy9jyLBlsffX8ey4W6UpYSwb+t5DFr2of+sFbSweoUxSyO5NVW5QlHqMfz8bGnfpSONzWWuxAQRn55Pwsms2t2S6MiARev5fJAzUmkaJ6Njwa0VnYa/Q9sOjZn50kIS/vb5c020DNq9wXddjDGUtSiSBtPGvZjy+RxOPjOXfTkKmLVjxuqVTHYXuBi+nz8OaGnYoz9TlrXCYcYoPvDPRhFd8fRywNy5D3O/749TPSOKs86SdL7CpdGePc1ZHdhZtqSVq0hg8q13+RCIiYhbzx40EXLYdTiGB5iL6T4jczVgBXNDQpjru45x9bPwX/4Wnx6rUtOq6UHC1vwK66aO47ugS5QogEHPymMCN4bsbkJwZPCsqXQ0z+PwgrG8ue0SOgTM973LtpXDee0lT3wXx9NqzBT62AmUxK9m4sRviC5QMHB7nq9+eY/HbwjI3WSSF4Wy3fcSz4x1ZeDQDnwTdYQSJJoNGYqnocwF3z84cutI/VeI9V1xlgTQpXM5vbKjS015vE8jDLSRLJn0Kpsqg22Gtg1xVC5QBBh4T2TOsw0Qk39i6ugviSgEpE3Erd7K3JGT6b9hOtszbr1eScJmFsyOZOrmzUxvUUTYD7OZd7j2vdOw3URmDXBGLAjlizHT2ZBcCsYtmbxqPTPbPcvb47Yy7Ku/e/4CtlV/M8gj6JPxvLflBOWNx/LNxrfpat2V3l4G7AvQ8tjI2Yx3N+TKrpmMnO9PtgKCXShLd3zKkFefZV3gWs5ea0grbPO38vbkpew7W3BdGLMucaVUAVNnGrhIUIOY1H03R3SiR6+WiPlhHI54YIl06zAyWXu/Z0VgpZD8E8za06uDGZRFsv9wDkamppiamiAnhBJZIODcrj2uBk60beeCRDkRW34mpqDiIuXnffjFL/sOs3Apkdt2cEor4tB3KD0sAKM2DHumMWJ5Itu2ROttchAtrbAUQJELyK/K46lkkZZRjiK1YMhro+nftTUNbI0oz0rlQrYOkGjaszuukkyK/wGSFFNMTU0xNcriaEgSsmkbOt6TJW6JJl07U19SKAj8ja3Jlfl+S07w++ajlCgSDbp0xq2W/pL21Ba+3ZJInk6hOHkffolaEE2xsTVBEB3p1tMdQyWbkL2RlJpU3KNJ8TGC47RoWnSgTfUgiFJC0Opl/FVdSAB0BeQXyiAaY2Vd85JOnbdMBLtu9PIwoDgogHA9zWKPFgo5GVn/Kn2laGGDtYGAYNCHTw6H8clNx3Vm5pgJFliai6AUcjU9v1oUX0t2ZjYKNn97DV3yLrYem8y7nXsz9HFbQvOGMdBZpChoC7v0mQhco6k0+2XkqtPKl9m+6FM8F83k6eFzWfp8RQL1zPi/WLHgU7YklmFta42ARNPJmwidfNM5lRLMzQ1B7/awiLWNFQIyOWkZN5y9JDOTQgVsLK2xrOXZ5KxMsqruWSmnvLziKUmSCIINttYiiPYM/y6I4Td/udwMczMBqhbflEKysmqatHVotQACoighwC0rOnVcTASsu/airVEpEQGh5P2HtpdcR0FRACSkWkfzdehkAAHhmu0pYGpudkMnkPNzyNEqIMSzduZiDuVWv6xMcVoSJ7TWtM+TAWPsHSwQyKz8vgZ7R7s7L0/KV9iz5TBvdOpHt2df4MWCJ7Eji12b95Gux+epFBdTooC1YIRxtfFfkvQH817YwULHxrR0b03bviOZPGwI8z88R8SoNeRk5aBgS67/Emauia8mygqKNofUxALA8FqjSaI+jHmZ3OxcFOyxdrDDACqvK2Dm4IC5AHJ2FjkKIPyb518NJYesHBmQOfbd6yw/Ulr9IHLBRU5clO+sBIIRxkYCKDIlRSUP49KwGZ16d8CkPJbDIdk13sAjj5JNZo4Coi3eXZpjUpvvyNlkZCog1sfT0xEJEG06MGaYx419pugYQceLUKSmdG0vcTYqgoiICCIiE7iMAw0cTRDkK0REXkQnGNB+xCg8zSp6tEHDoYzuY1WLDqSQ67+Fv9JkjDtP560nLZDP72ZzsH73oSgZaWQogGiDnU1VrYxwdnfH2RSK0k8TcXgnaxb/wvEyEO0dsBN1JAeFcEknYuHRkQZZ8RX3HxFBdHI+5i4uWGkAZPLz8pCRcPb2xvEO7odx40HM+ug9JnW1v0376Dh9JJTLOgHzXqMY3rjSbTBxZ+SIThihIyU0jIsy/+75V0dOIzT4JOWKRIsOrSg5GVl5j1GcyjCkvpsdhrURKUMbbM0FkDNJy6h5RbVuWyZVu15PBBKc/l/aqVYN3QUOHzzBNA8PWr26iUPPZVDKCZaPeIfsyiK3iKzuNIHBF3ilaUO6vbuVP0elIzk3xExbjFJ947Wczq4vv+Npj9l0mrCKPU+dIPF8ESauLWjlakFZ4PuET9tBwi8/cmjYQvq2nsJ63z7EpZRTv1UTrAUdChLKtRpU/a1qNq2k5Bhbd57lhdeaIinlJPyh512igO7yGVKKFDzMXWnkJsF5GSyeYt6mRfTSXSQ2PJaUTC3Wnj3paSSTHhhAghbKotbw2fZeLBvehwV/7GVsQhIZih1NWjXFweA868cNZXGUlsSQo2SNHoZDj3fx8X+FM9vnM3p5Tg0TnAGdJv6Pic9ao3hkEPjC9zXueSmLWMeXfz7B4sFd+L/NPgyJu4SmYWuaOxpRnrqVZZtOVOxbue3zn8mh28yu156GAiBz5tfFrO/3PZM7zeDX/cNIOHmFMvOGuLd0wiTfh+nB8zmsvfl7NyI5u+GiEVC0ZzmdUrOY1GnLxLBNT7pay5wOCOLCQ7i9RGNmg529Pfa3/LHD1twAkDCxtqu5jJ0tFkYCoOP0+neYuzaApEwtJrZWyJeSOFdQztnoaLIKLxMdd/mmQGg50d+9w6JdMVwpNsGxvilpBxcz9c3VROYUkBJ/mvzKDlN2agNTx8zmuz0xZJk2oX0nLxpqLhL82+dMXbCbLAXkK7uZ98r7bAo+Q6FZA1o0EIlfN4ux83dzsSCDmJjKDVu680RHplOQHk10avUHpuPkbyvYcbaAvKQtfLct5doGrzu2kWiEpa1dDcftsbOzxLiqB5fFE5lQhiJa497KpSJ+kr+fz9/6hl0xRTi168Mzw/rT3uIS/mv/j8mfBla0gZKF/0fjmfjJrwSlaKnXuiMd3G0piN/Dyrkz+D66wgEpDFjKnBWHOJ1djqG5CSW5+ShyGvFRFynMjiM2VXv9XoMPc75EAUG6/QCT09n33gSmLdtB+BUDGnl74SZd4ujWxUwZv5DD2cq189X8/GXyT8WQlHfj80TJJyk2mby8ZGKTKmJcSkEEyyeOZ95aP07k29CifUc86utIPriBBVOWEFhS8exio9IpzIghJvXWwWbavBUNJdCdiSImt2YVu5Y3py6maDDpNo+ti5qzZfJE1p+u+5bJDakujHuz6ODXDLWuuTvJObuYOSubt1ePv3ED0jUUigPep9+0HWQ9qv7dHdvIh4UbGvJ/b3jVbIor5cQse46X1qSgQ8R13Dp85rRFDvmQfq9uI/NBtZtgRb8vdvFu+QcMmOdfwz6Vhw0jun34Fz88b8OZH0bx3NeJt+z29fX1qdtuTnHIIp5+/EHX4l9SEsryyZPZZS3VEKRU0OamEHOyjNSXA7E3FG4toygUXogl+1EVEqhVG8WdN+ZklBMmYg0llFLSEqq2sctc3LOdkGlt6e3dky6Wf+B7mxn0XiO6DmVslzOsnhD0CAgJYOhNn+42CKVx7Nh18rY/G6jTYvJwU0paYjhpdyh1+ngYp+9LfeoitWujqLDUWp1NubqH1dsm0nNsM9wfk/CNejDbpeULm5n+9O/k5j0a27Wl+q1p7QDpu1axNfX2HoIqJiqPEKVEfjudiSFmnI19gANZKSE378FdXt/ozm3j/dcSUOKOXo/N1IAqJiqPFsXnOBb8oCvxiKHkcfpI2B2L1enVHBUVlYcHVUxUVFT0giomKioqekEVExUVFb2giomKiopeUMVERUVFL1zbTq+ioqJyN1zbZ1IXf5vzsHHDb3NUVP5D+Pr6qG6OioqKflDFREVFRS+oYqKioqIXVDFRUVHRC6qYqKio6AVVTFRUVPSCKiYqKip6oe6LidSU8esOsvv/OmNc+ZHoNJSv9v/F5wNs75y3ReWfIZlgW88e81pn31Z5sAgYWjpSz8bogY+FOi8mUpOnGNreDiNBW/nuSRGHPs/Qy8kCUVv638ylcw8QbdoybtHP+B0NJeDgPlZNaIiR5zQ2Hwli8+ue3Iskmf8OAdu+H7Mv7CBfDatf0YENPXjt5wCOBW5gyj1J53lnzNq/xsqfPmCQkz6HlMhjo38gINSH+d3NbxILIxr2e4vvfQIJDznIgd3v0avmrJ33jTr+prWqpOXZ7AiIoxxAsKZbLy8Mi48QcOyReF3vg0dqyMgvv2NOJxNyE/ezKSCecL8rmLbzpJmlFXi3wFKIfXBve78BEWdPb5zM7THwaoC04zKykRstm1pjYtYM9wZGEF9+f6skNWbk25Po0SqFKAvgir5OrKGRlxe2FsZ4t66HGFxw7WXO5t3e5sclL+JSfonQbds4Fuuv91xE/5S6LSZVScvz9hEQWZHWULDoQu92xpRGBXDkAb19/FFDajKQ4e3NUXL+ZMGk/2N/ngII2LZ70DW7HULlfyvn6vx9fDShkD2O6YQH6TdTYG0wavciI1obI6cEEnjm3qRkEW4wS8zo9txgXCQdid9N59WVp2/7xvj7SZ0Wk6qk5UWBAYQXV3xm2qk3nUzLiT185NHNJ3OfkZwb4CKBfPEUpwsffKNKBhqUci21H5Y6shID2Jt4Dyt1W4xoO6AvzpKOtLCQGrP36R3JATdXYwSlhOST5+qEkECdjplUJS0vIeJwWOVbsY1o16szFvIJAoOv/IPOplIjUiumbT3K8W+fwUIAjcdMdsfEEOX3Ib3N/+6LxjzW7w2+/GUPwccjiDp6kO0r/8fzra1u8utrUc68O/N3H8H/m+G0aj+eJVsPc+z4HuZ1MgAETJsPZs7K7Rw+Gk6o32a+mtoTp5unQMGOAZ/tJWz/ZwywF8CiB+/tOcLhlSNoWD2QLNjS92NfQvd+cIf7+wdIzejW2Q5RKSUu8gTXHCyNE91f+YT1uw8THhnB8aDdbPhsKv2bVYt9SA50fOl9Vu30IywinCN7N/LZpM443Db4LWA78AsCInfxlqcGBHOe+fY4cTFH2PhyEx50zLwOWyYVScuNy2IICKnM6WroTe/u1ijJvxN4XpWSu0bJICHgEP7aDvT2cETIP03YsXPkpUVysfR2X9LQaOQyNszvgQ0FXEyI5qxJYzx6jGZBh7a4vDKB5ZFFtS4nObTEy80ce6fX+LqtI06WZeScjyMlR0Z0GsynqxfS106gLDOZ+HM6Wk1YQi8DzY2zoOSGV9t6mNdrQ5uGEn8lShibm2LfbRwjPLezOKq8stgzvDzYDbO8CPQ1EwnWLWnpIoJ8ieSzleYzRrR5cyUrJj5G2bmj+PumIdZrRacBU/nYIJnA2fsoEmx5/P11LBvuRllKCPu2nsegZR/6z1pBC6tXGLM0soYEXgpFqcfw87OlfZeONDaXuRITRHx6Pgknsx745Fp3xaQqaXliIEFXK5pJ06oX3R3gnG8QyXXFtnuYkdPx/3ouIYlLOLKsP5oUHxbOXEtFXmoB2xq+Ilg8zrTXu2HLVfbNG8PbPpfRCZZ0fGc9q8a1ZPys4fwxYSMXzGpX7lLVeY0d0Pl/zIiF20nI0QEavGZPoY+dQEn8aiZO/IboAgUDt+f56pf3ePyGlQvhehRFAIpC2e57iWfGujJwaAe+iTpCCRLNhgzF01Dmgu8fHNFT7F6s74qzJIAuncvplcNZasrjfRphoI1kyaRX2XS54nND24Y4KhcoAgy8JzLn2QaIyT8xdfSXRBQC0ibiVm9l7sjJ9N8wne0Zt16vJGEzC2ZHMnXzZqa3KCLsh9nMO/yAI6+V1Fk3x6htL7payyQFBHNJBpBo3rM7LlwhOOBEnfET/2toPLrR2VpEd2UfP++5XPEclDyO/7KdWK2AoUdXOlgJtS53DW00Py/7o1JIANGJtu1ckCgnYsvPxBRUxHLKz/vwi1/2HWbhUiK37eCUVsSh71B6WABGbRj2TGPE8kS2bYlGX8NPtLTCUgBFLiC/Kt6kZJGWUY4itWDIa6Pp37U1DWyNKM9K5UK2DpBo2rM7rpJMiv8BkhRTTE1NMTXK4mhIErJpGzo+oCXuu6GOWiYGePTqhp2Syo6gsxUdUWpEj56NELJ2cDj2Pi/9qVxDsrbGQgQlI52MarFaOTOTLBkEyRJrC6HW5a4fyOJqVjWJECywNBdBKeRqen61/URasjOzUbD523rqknex9dhk3u3cm6GP2xKaN4yBziJFQVvYlaJHh0CjqYxVyMhVp5Uvs33Rp3gumsnTw+ey9PmKvMiZ8X+xYsGnbEksw9rWGgGJppM3ETr5pnMqJZibG4LeJO/+UDfFRGpBrx5OcPlnAk5WblVz7k7v5iK5ewKIuq0/r6JPlKoRLEnXgnu63FzyZbCxs8dWgJTKz0UHR+xFULQ5ZOUqtS5Xoy8FoOSRmycDxtg7WCCQWSkoGuwd7e6821O+wp4th3mjUz+6PfsCLxY8iR1Z7Nq8j3Q9LlgpxcWUKGAtGGFcbfyXJP3BvBd2sNCxMS3dW9O270gmDxvC/A/PETFqDTlZOSjYkuu/hJlr4rmezFRB0eaQmlgAGFKlopJYZ52Ia9TJGkpNetKzgUBGcCAJWgARhx69cNcUER5wjOI7nUBFDygU5+VRpoCmcRu8rCuGrzY2mLAcGcmpP2MG1K8QGcGKjmOG4aFRKI09wvF8pdblbot8hYjIi+gEA9qPGIWnWcX1DRoOZXQfq1p0XIVc/y38lSZj3Hk6bz1pgXx+N5uD9bsPRclIq7C8RBvsbKpqZYSzuzvOplCUfpqIwztZs/gXjpeBaO+AnagjOSiESzoRC4+ONMiKJyIigoiICKKT8zF3ccFKAyCTn5eHjISztzeOd1iuMW48iFkfvcekrvYPZGDXQcukatdrLr6HoymF67teS8I5HFagbqHXO1W+vnJD25ZGh3CsYCCPOz7N0r1duBy7kTem/MSKb0Lo+l4P+i/6g9ZjEskwb4rHYzZIJQms+Wo7F2Qg/1Ctykk1VKMCHQm//MihYQvp23oK6337EJdSTv1WTbAWdChIXK9t1d+U69YUQMkxtu48ywuvNUVSykn4Y5ved4nqLp8hpUjBw9yVRm4SnJfB4inmbVpEL91FYsNjScnUYu3Zk55GMumBASRooSxqDZ9t78Wy4X1Y8MdexiYkkaHY0aRVUxwMzrN+3FAWR2lJDDlK1uhhOPR4Fx//VzizfT6jl+fUMAYM6DTxf0x81hrFI4PAF76/P3teqlH3xER0onvPlkiFfhyOqLBBBPPO9G5nTFl0AEdyVCmpQjA0x8bCGPEWm19B0RaRk1OMYGaDlYlUg1ugIJfkkVVQji45mqjsHtSLO0FGtQ6oXN3NwnlNMXr7Odq7mmNYlEOhouPi5lmMy57EtAmD6NLcm3pyDmcDf+HnFd+xLa5qmURHai3K6dITiDpfgEtWDMk3hcLkK7uZ94rIldkvM6htA1o0OMuRdbNYmdyP5R9350RM5YYt3XmiI9MpkKKJTq0+gnSc/G0FO/p/zFPlu/huW8q1wL3mju0iY2JtjalGuLWMIlNakE1+qQJl8UQmlPF0F2vcW7kghaSgy9/P52/VJ2fsALq264OnmUhR+mn8165j2XeBlXumsvD/aDwTT7zGqy88QZvWHWmky+F8/B62/bqKddEVjk9hwFLmrLBi/piuPGZuQkluPoqcRnzURQodLxKbWuUg6TgZfJjzA4fgKkgPxDK5luqizrxVXWrIqO/XMu7yx7y4wJ88BQS7gXzx+1sYfjuWWTvq7ma1+/p2esGWZ1f8xce9TWqOH+jOs37yEmyWLWOodc1dS87ZxRtPzse/5J7WtO5h3JtFB7/+23aZOSubt1ePp0GNroVCccD79Ju2gyxFxHXcOnzmtEUO+ZB+r257cL9hEqzo98Uu3i3/gAHz/GvYp3Lv8PX1qYOWiS6VX6c8ya/VPlIy9/BO3z0PrEp1EiWbvYsmcXGDGVJNlkl5JqeizmMweTK7rGuegbW5KcT814QEoCSU5Xdql5NlpL4ciL1hTZaJQuGFWLIVAJmLe7YTMq0tvb170sXyD3wf0G/GRNehjO1yhtUTgu6rkFRR98REpZYoFF2I5eiFOxRLDCftvtTnYaKUtFq0y+njYZyuxdmUq3tYvW0iPcc2w/0xCd8o7Z2/dA+QL2xm+tO/k5v3YK6viomKyl1TSuS305kYYsbZ2AczkAFQSsjNe3CXV8VERUUfFJ/jWPCDrsSDpU7uM1FRUXn4UMVERUVFL6hioqKiohdUMVFRUdELqpioqKjoBVVMVFRU9IIqJioqKnpBFRMVFRW9oIqJioqKXlDFREVFRS+oYqKioqIXVDFReXgQHRi0eD9hBz5nkOO97LoiTUZ9yYZlo2n+8L0k/oGhiolKnUPTagq/BAexeZrHjb9EFV3x9HLA3NEbL7d7KyY2jTxo89R0Zj7jqA6SWqK2k0odQ8CiZVvcra1o1qYlljW+Rk64KZG3vtESF3CELMWc7mOeo+mDzrv5kKCKiUqdo7pQ3FPNuAURA4OKIVESHcLxQtA060//5qqa1AZVTFTuIwIW7kOZu2IzB0KPER0ZwoHNXzFrYBNMBRCsn+Jz/wgCPuqBIWDY/QMC4mKJi9rLu91uyAeKZN+JiZ9vYl/oMSLD9vHLR8/RzKR6CUNce01m0QYfAsOPcTzQh7UfvIhntSyCGs9pbA4JYNOkNrR+/kN+9gvjuP/n9LcUoCiOqFNakBrSpZOzOlBqgfpyJJX7hnHrKfyw7nW8TWVyU+OIzLWmhccTTP6iA14OE5jy+3mOHfLD0rM93d3tELJOEBJ1idKyK0RfqvYGM6k+z33yNYZGGrQ60Gjq4/3cu3x2OY4R359Ch4jL0C/Y+PET2OWd4JDvVq5aeNNv+DxWNzHhpYnrSdIKWDT3pJmVDdLoT1nu4EI9bT6XY8+QXqaAfJXk5Fzk9nY09WiOIef5L74u95+gCq7K/UF0YsiMiXiZyqRunc7gZ15iwughDHlrFxcVKzpOm05/s5Ns+fBt5v+aiA7QJv7OvDdn8ObsT/FJuTEJjJz8O1Of6kzbdn2ZtjkFnWBAkx7dcBEB8168/lYfHMqiWP7yWGYs+IyFsycwdVUSxm0n8HIPM+C6CyU5mpC8fAJPdOlOv/HfE1kCoOPK5XRkBIyc3bini0ePCGoTqdwfTNvSrZ0Jgu4UOzeGUJFWWObqoV/5M1WHYN6R7l6GtTuXUsChVSsIvFyKosvgyL6jZMkg2tphI4KBZy+62Ypo4/Zz8JJUkRTcVOJcaBiXFGvadmh6g0muS9nO1+sjuHpDgi6Fgrw8ZEC0srlNIFilOqqbo3JfEM2tsdIIIGeQVj2TuZJNRpYMjY2xtDKiVsm65Xyysq+XU8rLKVcAUUQEJGsbLEUw6DgH36Nzbv4ymRZmNwR25awMsmrITqHTVmZoksQa0omo3IwqJir3Bbkgl1ytAgZ2ONoJUJVbRrDD0V4EOZ+c7IqoRE0J0/8Jutxs8mXQXPiDd9/fyYXqWQrlMjLPJFB++69XImBkbFQhOsVFFKuJJO+I6uao3B+KjhMcUYwitWDImK5U5PgWcXxyDIPcJOT8cIJjywGFoqwsShTQNOtER4d/3kXLYwMJyZYRXTrQyeoiMZVJwSPizlFm60Z9i9pIlIC1rTUioLuaRkZdTSNZh1AtE5X7g5yGz/I1DPOeTpsXv8WncyxJeXa4e7hhIeQStvI79lekyKMs4iD+GYMZ4vAUn+3x561cmYI/ZzH8q+u5zW9IUK7cZDbkH2bFEj86L3ySYct86HYqkbO5EvWataKRDcQuS+ClNeeuf+3m7wNgiEsDJ0R0XE4+S55qmdwR1TJRuW+UxK/itQnvsc7vFMW27rRtaU3+iYOsnjOe6RtOX3M9lLxDfPrm5+yIvEShaImNUT6nTqcj6y4QG5NBUWY8CReumwryhXhiMorIiI3nQmVg98KuOYx5bRnbj15CauBFx3ZNMMs8zvavZjLn51R0KOSfjOZUXgFnY06QfbPlIT1Gq+bGCEohMZFJPMDUWg8NdS9x+UPMfU1crnJPEV1eYr3vHNqVHGTugLfwzVFNk7/D19dHtUxUVG5FxKFnb1prFDL2/4G/KiS1QhUTFZVb0NDSqxWGpTFsWBtM4YOuzkOCGoBVUbmFMo58M41pv10h9Ky6jFNbVDFRUamBssvRBF5+0LV4uFDdHBUVFb2giomKiopeUMVERUVFL6hioqKiohdUMVFRUdELqpioqKjohWvb6VVUVFTuhmv7TNTflNw96m9zVP6rqL/NUVFRRlNX2wAAIABJREFU0RuqmKioqOgFVUxUVFT0giomKioqekEVExUVFb2giomKiopeUMVERUVFL6hioqKiohceSjERBDW9msr9QsDQ0pF6Nkaove7vqVtiYuDKU+/8yO7gcCJC/+Kn95+hiXH1AgKWPd7DN2gd4x6rW1X/b2NCx3e2Exq4jgnN7s1zMWv/Git/+oBBTno8v+jAoMX7CTvwBYNuyUxuRMN+b/G9TyDhIQc5sPs9ehnp79J/UykeG/0DAaE+zO9mfk+vJFj1YPbaVcztbasXoaxDI1JD80lf8vm4thC9ix3Hy/EY8RHLpnlyLZ21oTvjZz+Lmy6NK+obw+sOoi3u3g2xsPWkTdNaJh//J0iNGfn2JHq08aCBhR7PK7ri6eWAuaMXXm43DgXzbm/z45IJ9HQuIHzbWlYs20ZsLdIg1xrBln6L/iL04OcMdKg+lDU08vLC1sIVb496/yo9aq0xdKJlm86MeedlvPUglHVHTKSWPD24BZrL21gw82M+mvE/Np4VaDToabw0ACL1h7zBmKY6Ytav5GC2KiZ1B+GeugBG7V5kRGtj5NRAAs/cixc8C9zoOZvR7bnBuEg6EldN59UPlrFya2SNyc3/NVID2rR3wsLBG+8GNUvGvfbmlcwjBMZrERsO46XHLe/6GdYhMamHcz0R+UIK57WANpUz57RIts44GQPmXZjyaldML23n69/OorvT+VQeEYxoO6AvzpKO9LAQTt2PBy854OZqjKCUkHzy3D3qa1UCfLOQ3XskA03FwJevEBp6Flm0oufAHljcZT3qjpgoJRSVKAimppgKgGiCmYmIoiujTKuh2eg3GOpUSOD3qwkvetCVrYuIuD7/Lf6hu/hfb3f6vf09PoHhRIb9xU/z+uFqaE2bMR+zcW8wxyNC+Gvt2zxRv/qMKGDhPpS5KzZzIPQY0ZEhHNj8FbMGNql4HtcwxPWJ6Xy15QChx0M5vGMFs/s3wuheDQipGd062yEqpcRFnriWQhSNE91f+YT1uw8THhnB8aDdbPhsKv2bmV+fYSUHOr70Pqt2+hEWEc6RvRv5bFJnHG7rOwjYDvyCgMhdvOWpAcGcZ749TlzMETa+3KTWLodk144xH/zADv9QIqPDCf5rI1+81gtXQwCJlq/9RnjkT4xzlUBy5qWfIomPiyJ0zShuMFIMXOjzxlds8TtCZEQwf62bQz9XgxtrbNWa5+Z+yza/EI5HHOHg5qW88YTr9dCAeXfm7z6C/zfDadV+PEu2HubY8T3M62QA6EiJjCFHFjBt3wXvu/RQ606qC+1JouNKGN65H0O8trOu6Gn6eWgoPxHNCasBzB7fCuHkSlYndeeDTa/S392WsnPBbFz0EavDs1GdHomGnp7YWdjw4tINDC+4wMnzVyjxaEiH0Qv5tnUK9VrYcenEOTKsW+HaeSyf/O8kg2f4kKmAcesp/LDudbxNZXJT44jMtaaFxxNM/qIDXg4TmLLhNOUIWPf+H2uWDcdVqih3tqghz3+6DENZA+gzqFCBYN2Sli4iyJdIPltc+akRbd5cyYqJj1F27ij+vmmI9VrRacBUPjZIJnD2PooEWx5/fx3LhrtRlhLCvq3nMWjZh/6zVtDC6hXGLI3k1jlJoSj1GH5+trTv0pHG5jJXYoKIT88n4WQWtXGwBMsuzFn3LWOaGFKemUx8ZAmOrbwYNP1rOrZ8nzFv7SYzIRC/Q6V4d2uLm3EpFyKPcCpbR370aXKvXUSi5SvLWSqJoJURNBrcOr3EwgWpxE35nUsyYNaOGatXMtld4GL4fv44oKVhj/5MWdYKhxmj+MA/G9GhJV5u5tg7vcbXbR1xsiwj53wcKTkVF9KePc1ZHdhZtqSVq0hg8r93I+uOmCgZ/LliLc+3m8aUTQFMAZSS02z8bi/1Jqylj8VVdi08ycAlS3nOJon9OyNx6juQN5cpXBo2G98MVU5AAATEqz7MePFjgnMNaTtnKz+Nb0TT1hJrJwxhaVQhpt3eZ9fKF3Dq1IM2Rj4cLHNiyIyJeJnKpG6dzksfBZElizj0/Yiflw6l47Tp9N89i925DXju1SG4SDWXc7kHdyTWd8VZEkCXzuX0yo4uNeXxPo0w0EayZNKrbLpc8bmhbUMclQsUAQbeE5nzbAPE5J+YOvpLIgoBaRNxq7cyd+Rk+m+YzvacW69XkrCZBbMjmbp5M9NbFBH2w2zmHa6tSIo0Hf0WLzY2pDR+NRMnfkN0gYKB2/N89ct7PP7kDF7tup8PAlYwNySEub7rGFc/C//lb/HpsarU6FXmgQAFx1nxxlzWRObj9MynbPykHw5te9LBYjO7cgUeGzmb8e6GXNk1k5Hz/clWQLALZemOTxny6rOsC1zLuaqzGTug8/+YEQu3k5Bz3XGTsy5xpVQBU2cauEhwF2JSd9wcoCTuByYOn8L8xd/w9RfvMuXZMSy51J83XnCjJHwN60ufYJCrQuKaubzz0TxeXxJAsXUvhvaxU/cAXENHmv9uwnIVoJSkxLNoAfnMAXbHViS6LD6ZwBkdCEbW2JgJYNqWbu1MEHSn2LkxhCwZQObqoV/5M1WHYN6R7l6GYOpF+xYGty13LxAtrbAUQJELyC+snDCULNIyylGkFgx5bTT9u7amga0R5VmpXMjWARJNe3bHVZJJ8T9AkmKKqakppkZZHA1JQjZtQ8fWBn973X9X2Xp06tIUA6GciC0/E1NQUd/y8z784peNLNrRuVuLWs7gOq74rmRtRCZapYwL+/cTXaYgaGywtxZAdKRbT3cMlWxC9kZSalJxjybFxwiO06Jp0YE21YMg2mh+XvbHDUJScZkC8gtlEI2xsr67JZ26Y5lUUnIhjB3rwyr+Idjw1OcTaWuYwrqvd1DiPQBzoYjTpy6gQyEv6RSX5V64NXJBIgPt35/6P0NRYfE1t0+rrWgVuaiQomsfllcGFUUkCURza6w0AsgZpFW38JRsMrJkaGyMpZURorkl5tLflbsHN6PRVMYqZOSqSVO+zPZFn+K5aCZPD5/L0udBUUrJjP+LFQs+ZUtiGda21ghINJ28idDJN51TKcHc/B4sYQtW2FqLoBRyNT2/muutJTszGwVbLK1ru2qikJOZfd210pVTLgOIiCIg2FRcS7Rn+HdBDL/56+VmmJtVu5KcxdWsmqwOHRVdREAUJQT41yGDOicm1THyGMfr/W3J3vsp62NKMfECUFCqblcQKg171S65G+SCXHK1ChjY4WgnQG5V+9rhaC+CnE9OdglyQS55OgU0tylXq6jCP0MpLqZEAWvBCGNDroVlSpL+YN4LO1jo2JiW7q1p23ckk4cNYf6H54gYtYacrBwUbMn1X8LMNfHVJhoFRZtDamJBxd8BkJD0YaMruWTnyoAx9g4WCGRWnl+DvaMdAjLZ1wRCQVEqr/1vuq+SQ1aODMgc++51lh8prX4QueAiJy7K8NgdziMYYWwkgCJTUlRyV7HHOuXm3IDozLA3R9JEG8O67w6QpchkpJ4jXzGjSVMXJAQsmzbDSdRyIfWSulR8NxQdJziiuMJtGNMVGxFAxPHJMQxyk5DzwwmOLYfCGI6fKL9tuXuBkpFGhgKINtjZVHVXI5zd3XE2haL000Qc3smaxb9wvAxEewfsRB3JQSFc0olYeHSkQVY8ERERREREEJ2cj7mLC1YaQMknN08B0YZW3o24k61i3HgQsz56j0ld7WseOHIaoSFJlGNA+xGj8Ky0DAwaDmV0HytE3VXCjiRV9FUlm8wcBURbvLs0x+SfNoycRmjwScoViRYdWlFyMrLyHqM4lWFIfTc7DGsjUoY22JoLIGeSlnF3o6iOWiYCFj1eZUpnEy79/g2/V2aiLw3fxZ6LzzBqylK+dI7H9slemOT6s9PvqrqaA1QZqDW3hVLzvxRATsNn+RqGeU+nzYvf4tM5lqQ8O9w93LAQcglb+R37sxXgPNt/2MWob4bzWLVyLd1tEctk/vmIuDO6y2dIKVLwMHelkZsE52WweIp5mxbRS3eR2PBYUjK1WHv2pKeRTHpgAAlaKItaw2fbe7FseB8W/LGXsQlJZCh2NGnVFAeD86wfN5TFUec4cuQCr7s3wuvN3zg08iIHPh3DB4eUGtrQgE4T/8fEZ61RPDIIfOH7Gva8yCT/spRfB61gbOsprPftQ1xKOfVbu1PfRCH9wHJWhZVU3tgFDh88wTQPD1q9uolDz2VQygmWj3iH7JufUY3InPl1Mev7fc/kTjP4df8wEk5eocy8Ie4tnTDJ92F68HyCqn+lhhNKzm64aAQU7VlOp9ydmNRRy8SQ9gOfwiE/gO9WH7u+hFcczlczF7LrjCXdnu1Pwyw/ls/88KFfydGY2WBnb4/9LX/ssDU3ACRMrO1qLmNni4WRAOhIjY0hq/AKcYlXrjkc5ckxxGUXcD72FJmVHyqFycQl5ZF/JoaTla5KSfwqXpvwHuv8TlFs607bltbknzjI6jnjmb7hdOX+DoXcgM+YMnsNfiezMKjfgiZmqWz73wRm/ZJMfnYcUafLaldf0QhLW7sa7tkeOztLjKt6Zlk8kQllKKI17q1cKuIn+fv5/K1v2BVThFO7PjwzrD/tLS7hv/b/mPxpIPkKoGTh/9F4Jn7yK0EpWuq17kgHd1sK4vewcu4Mvo/WAuXE/vh/fP5nAldLJczMdOTmloOcRnzURQqz44hNrXKQdJwMPsz5EgUE6bYDR8kPY8nLU/jk12BS5fp4tGmCSUY0f377JmPn7Oby9SAIp9e/w9y1ASRlajGxtUK+lMS5gnLOxsSQXXiZhBPp1WImZ4mJzaHwYiyJVysdpYIIlk8cz7y1fpzIt6FF+4541NeRfHADC6YsIbAEdOkJRJ0vIDsuhuTyW+tr2rwVDSXQnYkiJvfuxtG1vDl1LUWDxsEdT4uLRJ3Je2isjn+V6sK4N4sOfs1Q65q7p5yzi5mzsnl79Xhq3nWtUBzwPv2m7dDvdu+7QNPmbXw2/F19FzD52HDWzfKq2RRXyolZ9hwvrUlBh4jruHX4zGmLHPIh/V7dRuaDuk/Bin5f7OLd8g8YMM+/hn0qDxtGdPvwL3543oYzP4ziua8T/3W4wNfXp666OaC9mkjk1Qddi/tASSjLJ09ml7VUQxhZQZubQszJMlJfDsTesIZQs6JQeCGWuvRTJW3sGt64Q33jc4/zcpwTJmINd62UkpZQtY1d5uKe7YRMa0tv7550sfwD37ucQf8toutQxnY5w+oJQY+AkACG3vTpboNQGseOXSfvOu5YZ8Xkv0MpaYnhpN2h1OnjYZy+L/XRA7rsWtQ3laiw1FqdTrm6h9XbJtJzbDPcH5PwjXowmwDkC5uZ/vTv5OY9GpsQpPqtae0A6btWsTX17lfiVDFReQgoJfLb6UwMMeNs7AMcyEoJuXkP7vL6RnduG++/loASd7QiznSXqGKi8nBQfI5jwQ+6Eo8YSh6nj4Tp7XR1dDVHRUXlYUMVExUVFb2giomKiopeUMVERUVFL6hioqKiohdUMVFRUdEL17bTq6ioqNwN1/aZ1LXf5jyM/Kvf5qioPAL4+vqobo6Kiop+UMVERUVFL6hioqKiohdUMVFRUdELqpioqKjoBVVMVFRU9IIqJioqKnpBFRMVlb9BMramnqMl9yD/3yNHHRMTDe7jlrBkXCuMDZox6vNlTPY2+gfHVR5FBNvHmbt2NbN7WOkx3ZrIY6N/ICDUh/ndzW86r4BZi2G8u24PIUcD8DvwKzO87897xDSe09h8JIjNr3veWwETnRj04Xq+n9oOM32dUk/n0RMKOtMGPDFtKiNGvMrr/R/DQpb/wXGVRw8jvCfOZkynNri7GOlRTDQ08vLC1sIV79b1bhgIgv0APlr1IaM6WJMWuJnV367A97Q+07yJNBixAv9QH+Z3M69+ZSyae9LM0opm3i2wvJeJKgVz3Fp50/OV2YxsrB8ZqFNiIhmbcnX3OvYX9WTuvKdQ/Nex/YIxxlLtjqs8eggWvRgzrAGiNp7DQRn3IAEpCDcMWhHHJ5+jj61ASehSXnljIct+/JPEQn2+EV9Dk7btsLdwxdvDkerd974lutWdJSjoHLJRa0aOao8+7Pu6IyYaL2Zs9Sfwry8YXE8CRGyfWohvYAC75nTE8E7HH3T9Ve4J5j0G0NNaRJccQuiV+2GFitRv6IJGkMlIOn0tcZneqVQNQbi/ebIFjQEaAUDHqSNHydBJOPUdSHs9qEndERNtDEsHt6fdc8uIzM/g1Kl0CmK+5YUOben3aThldzr+oOtfFxAdGbzkAGH7FjKgRWcmLf6FA6HhhAfuZNW8YbhbVO+4AhbuQ5m7YjMHQo8RHRnCgc1fMWtgE0yFiuPWfT9mX9hfLOpnV23G1NB80k8EB65mXJNq3Ud0YsjivwjaOg0PvVmKRrTp1gEzQSYnNobr2SuNeazfmyz9ZQ/BxyOICj/ErtUfMKmXW7UZ1hDXXpNZtMGHwPBjHA/0Ye0HL+JpdfvBK7qNYnXoMTaNd0NCwnX8BqLjogj6eij1ajtSNPXp/son/OR7mGNRERwL3MXaj8bS0aGiUQw7zWVv5DG+ecYcAQ2t3txBTHwMMQGLGWhbrW6CCU0Hv8PKnf6ER4YT6LOCN7rdlONYcqDjS++zaqcfYRHhHNm7kc8mdcahqv1FBwYt3k/Y3o94qnkPpq/YSfDxUH6f2hIJKE+MIq5MQbTrSJfmd//Q6o6YAGBMr1dewf30ema/tZ6TzV/mlT7m/+D4fxzRhdae9pg7D2bhpm94xUvmTGwSOSaP0W3Mh6z+YjiulU/cuPUUfvjpY8b1bo5p5gkiE7Mwa/kEk7/4iRVjm2IAKEYWWJk5M2BUP5yu5Qxvz4hR3ljbduDFoa2v/excajiI0U85Y2mk0V8GRskV9xaWiOg4m5RSmSRKwLL3PFYtmUzfRsXE7PdlT3AKksezzFgwloo4qYjL0C/Y+O2bDG5cTITvVrYfLaDp8Hms/mY8zW4TS1Vykzjid5jICyUoKBSfO84hPz8OHT1Dfm0sFNGRAYvW892MIbR3Kic1OpbU8vp0Gv4OP/70Lr1tBXRXYvA76E/sFS0KMvlnwjh08BAHDx3jXNH1ljNo9wbfLRpL90aWGBkYYdu4F1M+n0Nf6yqTxpbH31/Hj/83HC/pJPu2budwmhP9Z63gxxltMQUQXfH0csDcuQ9zv/+S13o3xCD3HEnnK7NkFp0h6aIMkjPuLa3v2sWqY6kuSghaNomXdEmcSVN4e8xxDFIK/sFxFQAEgbLjSxj5xmbOlYNxi0ms2jSDdt0n81LbnXwWaceQGRPxMpVJ3Tqdlz4KIksWcej7ET8vHUrHadPpv3sWvoG+BOb2YUCbATzl/DsbLsgYtx/AE/UqZjG3p/rh8XUsUVqRRk/1x91Ax+k/93BCX7FKsT6uziIoxaRfyaqMl2jwfOJx6onFHFw4jpl/Vjx/wdiRhrbFnNMC5r14/a0+OJRFsezlV1iTVAoYsf2Nn/llygRe7rGFef63Jt5V8o6xZl4UMfP3sGa0A1cPLmPmkmhqm6nHsN1EZg1wRiwI5Ysx09mQXArGLZm8aj0z2z3L2+O2EvTVHj5/+yB9PjvMN88Yc373p8z4IfmaUNpWnkswyCPok/G8t+UE5Y3H8s3Gt+lq3ZXeXgbsCyjDwHsic55tgJj8E1NHf0lEISBtIm71VuaOnEz/DdPZnlPVjlbY5m/l7clL2Xe24HrcSU7jcpoOmko4uzkjknlXWf3qmGUCJZcSSEwrB7Skn0jgYsk/O64CKAUEbvHhXOV4KTm1hS1hpShSPdq0cUEybUu3diYIulPs3BhClgwgc/XQr/yZqkMw70h3L0OUvGB2+WWhGHjSv68rIiZ0Gvg4Dsplks8WIrr0pb+XAUhN6Ne/OVJ5Arv/TL7rNJPXMLDEylQEpZD8gqohIJNxNQNFMKb9iKm82LcjLV0s0ZSkk3IpHxkw8OxFN1sRbdx+Dl6SMDU1xdRU4lxoGJcUa9p2aHoPZlGJJl07U19SKAj8ja3JpRUfl5zg981HKVEkGnTpjFstvQntqS18uyWRPJ1CcfI+/BK1IJpiY2uCgETTnt1xlWRS/A+QpJhW3KNRFkdDkpBN29CxdbWFZaWEoNXL+Ku6kAAoheTmyygIWNlY3bUY1DHLREUvyLlkZFabeZVisrKKUbDE3MoC0dwaK40AcgZpGdWcEiWbjCwZGhtjaWUE5BHms58rQ0fSekBfGm47w4DHbZETvmb+uuZ8v6Q/T/b34uuiLvRrJlF6bDd7zusxYilpKjqoIiPLVfXUceqnBSxt+jGvPTGW9zuOA0Wm+EoE279cwOI9qYjWNliKYNBxDr5H59zcOGRamN2DVRMRaxsrBGRy0jJuiOGVZGZSqICNpTWWtTybnJVZKfKAUk55ecX9S5JYcS1b6wpRmbyJ0Mk3fVkpwdy82pKEUkhWVk2zroKskwEBSRIfNTdHRS+I1jjaG0KVgS6Y4+hoioBCQW4eckEuuVoFDOxwtBOgKhG4YIejvQhyPjnZFZ2vNMKHPedGMMl9AM+OTqWXtY64NftJCEzCP3sAQ54czChdW5qIxQT57EevCy7lxRTLCghGGBld7+pKfgzrZw5lo0V9mrZshWfHgYyd2I9RH/+PkxHT2JmbTb4Mmgt/8O77O7lQzVRS5DIyzyRQjoaq4I4k6sNAl8nNzkXBHmsHOwyoan0BMwcHzAWQs7PIqbymUvl/8V9dWyYnKwcFW3L9lzBzTXw1V0xB0eaQmlgb91+DkZGEgEJxUcldL7vXOTdHRQ8IZvQcNZwmlUsbpq1HMaKjIYLuCpERF9EVHSc4ohhFasGQMV2xEaFif8UYBrlJyPnhBMdWWjbaeHb7nkarcWfSmwOwLo9mz/4L6IrC+PPgVaj/PLPHN0HMD8LHL1N/wVcAbQZXM2UQTLGzNa66OcwbtKaFoxFy/mVOhh9k68qv+OOkFsHQgXpWAuWxgYRky4guHehkdZGYiAgiIiKIiDtHma0b9S0kQCY/Lw8ZCWdvbxzv4H4IFh68MPdD/m+U1202k+k4fSSUyzoB816jGN64svFN3Bk5ohNG6EgJDeNipUuZk5WNjESDDp1w/cdbXXUkB4VwSSdi4dGRBlnxFfcXEUF0cj7mLi5Y1cZMEK2xsxEBmYy0q3f97FTL5JFEwMD7TX7zHUz8JQ2NPJrhYKSQE7iGn6O1IKfhs3wNw7yn0+bFb/HpHEtSnh3uHm5YCLmErfyO/dlVXUsm+c/dxE1pThtDKI38i/2XZaCUY38e4MrwMbhIMtn+PgTm6lVKQJdKcqoWxVWDayNXJBLRic48t2gjc7yKSIk4TsKFXKjXlt5eGsrPBhKYooOyw6xY4kfnhU8ybJkP3U4lcjZXol6zVjSygdhlCby0JpXEkKNkjR6GQ4938fF/hTPb5zN6aVS1ClTdj4BFn9f439jeGJa7kOI3hd/Sbp3HyyLW8eWfT7B4cBf+b7MPQ+IuoWnYmuaORpSnbmXZphOV8SQtiQcPcWHsWBp2nsv2wy+TrZX5f/bOO76H+4/jz7vL3kMSJLFXEkmsWDU7rLYordqbolpUS3+o0UFVUUWrRW1tUVvNRAhJiOxhJCFihuwl63v3+yNDEBV8Y7T3fDwQl7vP/rw+78+4e/vN6sHChxRh8eViiyYvZDXf7mjH4t4dmb39IIOioklUrKntXAcb3SusHdyDBREPPncPoj3V7AvXpGJjbjz1WpcqJi8EEoYWFhjpCA/OWxWZ3MwUMvL1MLMwQU98cFhUlDyyUtIpmRVrbrB9+hxuvj6efm1rYZJ1mVM71rHwh7+4WtQHciJXMmZoAh+M7U/nZk40rprH7XOe/LlmGb/sj6X0Xocmfjerdr7DvK4adq05wK2iMPKC/mC1z2tMdo1nzXpfig1rQc8ES1MDHkyqglKQTWpqHnqPym+uAkoqYcFxaF6ph4NzA8yFsyTL19k+ZzoWo/vyhkdz3mhiiJxxnXMHlrNi0WrC8gBkru6ewoDEgYwb0YN2bm546OaQFBfIjnUb+HXTZTRA1vFFTFluzvQBrahpYkhOWgYKMvGhoST2cCc88npRB1PICj+OX0JL2luLZeSrCPkWh74YSlb0OEa+0wYXdzeEjCuc3raNn5dtIiDlbo/ODVrGhC/0+HxUZxpVs8L4ZgDR1/PIuB1GdHojdCNjyChRggyiw2NJd4Xw6IxCgVCS8f5yCMPPjeGD916lkYsHNTSpXIncz1+/r2RNaAFwhfCQW2TpnSXs8oNSIdrVp761hJIXRUhk7kMyVX5KXF2oX1V/ep706/Q6jT5lz/ohVCvT1Fa4c3w2I8/0Zs0kN/TKashKPmGLezFwnSWf7lvD4CoJbBz+JvPOlHdTU4sIVryz/ABftTcse0FPc4X1X3nT4YtB/5DfmXQat5NkBSSXCez8fSQ1Mvcy8fX/4Zldscl/OCJV+v7CzsHn+KD7QkKeQ9FqFwGzt37A89uOSIHz6T5sU8lA8yTs27dHtUxeBArCV/PRMB8q6ZU1UitkXQ0nMi2QYRGVMSzTMsklISoeDZalH3s+KCkcnDuCa+uNkcqyTPKTuBCexF8Xj/1jfosHcc25XeyMGMwk1+a0baSPp+/Tj6BPhORMv/618F8xk7CXXkgAjGnZoRkGZHF8+4GitZynQxWTFwFNCjGBp4j5x5suE3Lq8j+HI14jMiKRbN0IIp9mmHkqFLKvhnP66j/flfzI/BahuczWlfsZsORN6jtVRvK9rL1zLI+DJopfBvUgNy2zQl42fObo1MLV2Rjl0jpWHtDOwrkqJv8m5Fvsnfw6e593OrSKQrr3fEaP3gtR8c9HSACQyUr7F522Lohg7acf4p0ZSIiWjD1VTFRefJQMYvxVF7baRSYp6iRJWgxRPWeioqKiFVQxUVFR0QqqmKioqGgFVUxUVFS0giomKioqWkEVExWSdhUSAAAgAElEQVQVFa2giomKiopWKHk3R0VFReVpKDm0pr7o9/Q86Yt+KiovO/v27VGnOSoqKtpBFRMVFRWtoIqJioqKVlDFREVFRSuoYqKioqIVVDFRUVHRCqqYqKioaAVVTFSeDkEHk0p2WBn+R5qSZIiVXSVMyunm87/Ef6QFqDyASWum7/Xj+Mp+1PyHjiHVfJ+fvE7ht/0zWpmU+vyzZMcrYxby1/FT+Hsf5tDCt7HSvs/NshHt6f7NWlaMdsfgkTc/BiVl0v+BMhEtGzN47ia8Tvtz3PMQK4dW55noiWhDtwWHOXVkPt1sK7K7itTut5D1i/tT77GdghWHoPKfRLJxws3RBKtGjaj1D71CrFwfZxsjTGs4UdO0WC10cRm7jKXj36BOQSS7163gh42nSDNswdRdvo8UqKdDwKz9WCZ1b0KjurZa7dB3y8T93jKRqtN34U9M6e6C/sXDbPxlKau8nt5pVWl0nEez+eQJtoxreO+3VEUHXN1sMLF1x82xYsXEskZDGr0xnolv2z6RMKjfgFX5R4fV+f6LGfGBH9UzQ/G5UfRddh13er5TF335GhunjGJeQOEXiaWa3WhcwxSrKo2oJf3OpYr4+rNoS9f+nbARsvA6FkBFudEpXSZS7a70bmqCkvo3s0d8zuF0bfsRETBt0BgnC3No1AAzIYLkB6IQECrU8isg4rgfyf178cqAXtTZtYILj1l/z90yEYzr8/q7nXCzeZRtJWLXvAc9WlfH5Lmn+kVFRFdXy4WjZBDrexivsFslXv4EU0ccrSTQxHE+Jq/UzUV+cIR/FqinQbR7lS7NDCA/gpOn07Tr2/ghSFWrYS+BfO0CMVkVE2NpoXhWs8VC7raZnFBfArNAp25nOtd7fJvvuXdLsXY3Js38ns0H9rPhq+G8VtesbNNVMKflkP/xza+7OLzjRyb3bkJl/Wed2hcLHddxbPE9zsYRjXB5dw6bvE4R6D2fzmYC6FTmlVHfsHbvMQKCgwg8sZf1346lc12T+xqriJlrH2av2cuJwEDOeG9h4RD3u865jVry+R4/jv3SlxqSPq2mHyDIZw5t9QC9Nnx1IpTwwH38uvkEwXsm0lAHBKNuLAkOJyI8gL8+dNHiVETA1KMVrroCcnwo4UmlfAE79WTq8i0c8T9DaLAfXn8t43/9m2En3X3WqHZnPl60mYMnAwgK8GLnz5/xdl2jh0cnOTNu22kCl72NqQA6DSeyNyyMEK85tDd5jDQ79SiVNl+ObPmBSV1rYySAYPEG872DOP5lG/QAvVdmcTwinIiQg8xofW8Dlyo1Z/j8jRzyP0PwqUNs/rIXdQ1L36GHQ7uRzF2/B5+AMwT67OG3We/jan63xv+xzWRHEHKhAKTqtGxe9bHF4blPczRhSxk8IJr+QwbT5+2JLOk5issnd7Jh7UZ2nbrGnRJ/qyns/ux9knsPZMjA7gyd04GBH4dz8Pf1rPvzCGdTnp9HleeDgGk9V+qaWyL1n8cSG3vsCjK4EX6RW3l6NPp4BcuH1yQv/jTe+xIQ7Zxp3mUsX+nG4jP5EMWuUgSDNny2vBOmRjIaJHRsnOgyeT7JUT34JiAXyc6ZRtVMsLZrTG1pCzEhR/Fy9KD5K/WxUpKIOhnG9cxrRF0z5c6dhjRvXhtTJYEInyhuFdwhNipRi9aDRB3neugJCnmXYil2nyvadWfeyjl0MEkm6vhBTt0xpUbjFvSfWpm0M3346YKMXr1BLF83GQ+DREI9d+KdaUfzLgOZ+5sjYp+J7LpRhmstJZGo40fxLmhG+4a2CBkxnDoTT3pCMNfK6WvGwGU0v6z5EHcjmbTLEQSnWVC/4auM/K4ZbjZDGf3nFc4c9cLMtSmvOFkjJJ/DN+Q6uXk3Cb1eynWgVIVe3/yInr4OBRrQ0amCe68ZfHsjgj4/X0CDiH2P79jw1atYp5/j6L5t3DZ1p1PvaayqbcjA4WuJLvinNqOAfJvY2DTkptbUaVgPPa7c9V9dDp67mEABSeF7WfrpXlZWbU73gYMY1KsfM1b15cNznmxZ/QtrD0SToYCSfRmfDd/gs3k59V97jyFD+tJl/ALeHHmd07vW88uvWzmdkP/oKP8lFI83kq0hsT8Mpd+GIG7nAZILEzvWQLcgmO9HfMDGoo6iZ1UdW+Uq2XDXWhD0STryOf3mHuSKjhvjV61mlEtlWrethxQQzr1TF5nL++bzaUAfVh38gpZEsenzCexMK5QLqeYINu+ciEteIKsmTeVI6RmQVjCgqkMlRGSSbyaUCKKxx2u0soDbW6cxaI5f4XXRmCrVTEi9LINgy1uTxuJhks6x2YP4+K/raBAwOTSDv1b0ZsxAV/YtCH1Q9ORbeP84Fd+z3+O3uDM6cXv4euJvxJV33BIr033CcNyMZC5vG8/AL0+QLIvYvP4lmxb1wGPceDrvncTWOZ/i2fsnPL9sA2f/ZNrH2ygxukr1UDn2T8ZO+JETt0xpO2MNy/rUoHab1tj/coF4o3Z8+ElHbPJCWDxsFKujcwF9dny0ic2jhzKszVameWc/vM0AoOHmjVvIVEK/qiO2IsQ/hvvC5z7NKU3O9dNs+e4jerz+Dh8u2MtN+zcYM/9nJnjcp3maVM4fWsm0AV3pMvAL1gRJeLz/OT/P7UXlFypHzwZN3A5+XFuqUSjJJCTmo0j16T6mP51buVDNSp/85Mtcvd+Cyw9k/ZIDxGXLaNLDOegTj4yIVSXLZ7P1+TiIZpibighAVkZWSefPvZ1Amixg3WYAo95pT5O6dpgIWdyISyi0bI2b0q6ZMeQFc/hYKvpGRhgZGSJH+ROcKVC1SVMcKiKzRo1p3cQQQXOBXRt8SZYBZG4f/Z2/L2sQTDx4xU2vfGEpmRxduRyfG7komkT8Dp0mWQbRyhpLEXRd29HaSqQg4jCe1yWMjIwwMpKI9z/FdcWCxs3q3GM5PNBmCiMhMz0dGRDNLe9OdcvJC2CZ3IeOFU5tO9OtaxvqmIrIqTFE3yxLHgWMHJvyWtcuvOZuiyjIJF6KI+Nf4Qj28ZCTE+9d/ZdvsGPuPFznTuTN3lNZ9G6hc/OkyAMsnz2PrWezSm5VCpJJTiuZS5Kfl48CiOKLqMoSOjoACrLmrijmBaxgxkpHZg1uy5iv2zMWBU1GPH6/z2fmMh9um1pioSsg6Hbkm2On+Oa+UDXGJhhXQGpFEwvMdQSQE0lILFVBSgqJyTLUMsDMXB8ohwknZ5Cccvc+JT+f/MKKQgQkC0vMRND1mMK+01Puf5gkU+N71soeaDNFaAqKOpAkluF4/p95YcREMq1N+3cHMqj/W3hU1ScvIYR9P3zD+q1eXEgrrRD62DXpRv/BA3n31bqYk8nF4+uZvW4zewNuPNYc799MTvR2pr23k69ta9HAyYXGr/dlZM/uTJ8TT1C/X7lUIbEqhdaCKFXMNqaSy50cBRDRNyx1XE1OwnfpGDqvMMexnhPOri3pMWww7UbOZXJkV6b4p5JaoIAQyW8TF3A0rXSYMncSojmnAbFU+rWhpXJmGmkFCuhaY2stQLFoC9bYVhJBziA1pbDFKsUdW5KeyCLUpKWQIYPO1e3MmLmLq6UMUEXOI+liFI9eABDQN9AvFJ072XfXK8vJ8x9+DOvy9tSf2X1kOz9O7knd9OP8+r+BdO48mBmrjpQSEhGblsP59o8DHFg/h+FtjDm/bQEf9uhEz/EL2aYKSSn0qerkRFUjyL4VQ9CxXaxesJnAPBAr2WBdQbUupyaTolEQdJ1p1dRM+1ucShq3b+eiIGBhZVHSeCWLmjSsbYlufhpXIv05+Mcyfj54DVk0ppKNEWL2GU4EZqNIdWjVVOJSSBBBQUEEBUdxAxuq2RoiAHJ6OukyCLr1aOzyCFtFsKb50Gl8Oflt6j7sGG52ICeD7hRONwe0wlIEELF9bQDdHCXkjABOhucDCtnJyeQooFO3OR42j19B+eE++KbIiPbNaG5+jbCgojxGxJNn5UgV0/JI1N1y1dxOIPExrfznbpno1Hubsf0bofht5uv7d3BKI5jTetAHdK4cw8Fl3/5Hd3DupWQ0U+4rMNM3mLZxLu001wgPCCcuqQAL17a01Ze55XOcqIIHgiodaqm/C39Sii4opW65/y4AJc0fz9NZtG3rSJ8Vh3j9VhbK9U0MH/YbsVqpqnziYuPR4IJJterYiCe4Lks4DVvC5hH2pEYHEXjuBll61WnRoTpidjDH/BORZZndC3/izYaTaT50JfvfOMfZK9kYOtTH2cGUPJ+ZBIzbSXLqGfyicmndqCaDVh+m83U/lg//jD3F+VSUkhyLDm8xYWI/Gul0xzjCk8kHyzg+JyewZ8lqerqPp9H7y9jTIpzodGucGjpiKqRxasVPHE4pDDEvyBPvxLfobvMG3+735pM0mcy/J9H7h7ulfE8131/nGcdY/r0XLb5+jZ6L99D6wlkupUnY1XWmhiWEL45i4Or4h7cZAPSwr1YZEQ03Yi/xuGfznruYFIT+SO/Wy8nNyuUfhVBJYfcnHTlUkM2dl11DRH3MLEzQEx8cuxUlj6yUdAoMLTE3lMoY3RXknHSSM/PJOB/KhfRG6IWdI6V04WUcZv4nVUgd1IVWTTriaiySfSsG79/WsPgnHzIU4PZZwq5kYn87otRJVYVbZ8O5llWF6+FxaABNQhQh8ZlUvRFcIghK2nlCY9NxkSPuPcQl32T7jElYT5tEnzb1sLHMJcb7EinoY2b1z/nN1THB0tSAB29RUAqySU29gwaZK6FhJGpcsK3rQn09uJ6jIWrtdL7UHUGvDo1o09UDvYI0roTt5LtlS9h4qbBg8i6sZ+yA64z4cDBvtnSiaXOBrFsXOfnHLn5bubdw/UATx+YvvqTalx/SzdUWUymLtBwFTWwoISltsIs4R2JRGci3gzgWmoRLU2Mk8eG9LidyJWOGJvDB2P50buZE46p53D7nyZ9rlvHL/tiSqYeSfpR5H89HnjKY11wqY6l/haCYW8iaPMLDEsnWiyTq6t1Klq9GEpaYTePwSK4WLexe3T2FAYkDGTeiB+3c3PDQzSEpLpAd6zbw66bLaODhbQZAqolzPQMEJYuw4Gj+ccwpgxJXF+pX1Z+e8n2dXqTmiA1sn+SGXlnzACWfsKXziBs8g+4WZZu7cupuPnptOt4vxbyuHPldPIxtTVcyp71h2VMjzRXWDu7OgpACMHiFOft/4l3rG6wf+hbzgx63yWsPqd4H/Pl7O/a/O4jVl17+lX/RfiBr902hSY4nU7t8wr7U8psm+/btef6WyX8PmctbpjEsojKGZY7UuSREnSP7eBy7LMq2TArS4gh7KYQEypffCBIOjuDKeuMydhAUlPwkLkQUiUZOANv3xvPOcEdeaVMXKeisVl+4Kz/6tB7UB+O9c/gr7uUXEhCxadseFx2FxMPb8X4MISlGFZPngJxxmZBTl//5pvQAEp5Nciqc8uU3nNNXyxNaHuGb1nDyvVm0cKqHqXCWJ2j3WiAXv+968W52GhX0us4zRocGbs7o5Yax/reTZD36gTJCUFF5yZBv7mT68ARqZQaX7LY+Dwoy0h57XeHFJQ+/peMY98dN/J9wyqaKicpLiExy1EmSn3cy/mXk3QjF58aTP//8z5moqKj8K1DFREVFRSuoYqKioqIVVDFRUVHRCqqYqKioaAVVTFRUVLSCKiYqKipaoeTdHBUVFZWnoeTQmvqi39NTvhf9VFT+fezbt0ed5qioqGgHVUxUVFS0giomKioqWkEVExUVFa2giomKiopWUMVERUVFK6hioqKiohVUMVF5KiQDC+xszdB93gl5JgjomdliZ6mvfZ9A/wJUMXmpEanZ/xeO++9h+ismj9HASz3X2uThtwkmtJ76F34BR1j+fvVSnuYEjOv3ZMaa/fiePo7Xkd+Z4P7sPtpn3HQMK9bNops2HUuLNnRbcJhTR76jm+394epTvdMn/LzHhwBfT47s/YJ2+tqL+h8SVb560gKCeRsm/7aSqe2tnlgoVTF5qdGhhpsbVqYOuLvYPUZllnquod3D3VEKZtRyqo6pkQ3O9SqXhC9U6sKXK+fQr5kFCT5bWLVsOftiFKr1WY53RTd8qRZ9Px1Bm0YNqWaqxXBFB1zdbDCxdcPN8d6SNGn9Kb9+P5S2VTMJ+Os3li/+i/ByuAcuN4IVneYewN9zPl1tSnflctaTNtCrTINGLRjw2TDcn1Ao1W/A/kt4Ut++//icfJ0tU4dz3d2YOL/TRQ6jRGxf60VHK4Ecv0WM+mgrCTKAHh0bN6GSqQHuDW2RfDMrxAWFfpP36eNigBzng8/FinAxIdxXJsa07vUW9pKGsz+N54MVMdrPl1SNRk0rY2oD7tUk9t9+8DPVFeK7uRRKkh8+kQW0atSTgR1WEnowncf9VvcLbZkIhg606P4OrR0epXk6WDm9Su+uLpipk1mtkpcQhtchPy5mlDjGpEp1e3QEmcToGJJK9+eishcqrOXr07jL61SVNNw65cuFZ+EwR7LB0cEAQckh9nx8BfnoEYqK7n4hq3gkXZ1CEZBv4u9/CVk0p23XNpg+QTpeSDHRtXGl+8cL2XZ4D6vnTmNUO9uyEyqaULPdIGb+tpdDW5cwe0ovnFVbCxAwderJ1OVbOOJ/htBgP7z+Wsb/+jfD7n5bWdeejh/9wFYvP4KDTnJgzRQ6ORQvp4pU7/szx/x38XlrY0THfqzyP8PGIY5ISDgMWU9oRAgnNvzKkeAzLH3bBAEdnD/eSVhkGGHHF9DVSou9Q6pL6xbWiEouEcHnSlxrolOZV0Z9w9q9xwgIDiLwxF7WfzuWznVLrSNJNngMnMnKXV6cCgrA7+AGvh3RApuHz/Gw6vodx4N384mrDggmvL0skIgwPzYMq13uKYdk3YQBs35hp7c/waEBnDywge/GtMNBD0CiwZg/CAhex2AHCaSqDFwXTGRECP6r+1GtdCT/WE9FKTZ3odfUZfzl5UtgkB+eWxbx0asO6BXfYPIK0/f64b20N85Nh/D9tmOcCdzPtOa6gIa44DBSZQGjpi1x1+OxeYG6nohZ3Q68N3gIA7o1xk4/lxuBu1i0bgNbva/f64dYvzJN3+rP0MG9aV/bFNJjOfrbF6zd9DfB+Q8L/7+DaNedeSvn0MEkmajjBzl1x5QajVvQf2pl0s704acLxXdKNBi1hEWSCAUygo4Ojs0H8vXsy0SM/pPrsg613BthbWpAk4Z2iBHR+HkdQ79paxo76JMTH4R/dDKp0efIScjBpXFbXCuLZF4M4MylLPJTzhCfrT3HNoJFAxrYiyBfJ/bSnaKr+jT6eAXLh9ckL/403vsSEO2cad5lLF/pxuIz+RDZghUdZq5hcW9H8uJ8ObTtCroNOtJ50nLqm49iwKJgHnQ7rpB9+QxeXlY0belBLROZm2EniLyVQdT55H/2i12cXrOWTFmzjAG19chPiiUyOAdbZze6jf8RjwYzGfDJXpKifPA6mot768Y4GuRyNdiPCykaMkJjSCuJ5FH1BBg3YcKqFYx0ErgWcJjtRwqo3qYzoxc7YzOhH7O8UxBtGuDmaEKlymP4sbEtlc3ySL0SQVxqYUQFl2K4pAFrswY4O4j4xD7eNPIFEBOJSo17MfKDwbzzSnWMNclEHlrB9+v+4FBk8j1OjgSjmrw2eDQj+3WiobUO2Vf8+HPeBjbs8NVqo33ZMfZ4jVYWcHvrNAbN8SMXQDSmSjUTUi/fNy/JDGT5R1NZHZxB5bfnseGbTtg0bksz0y3sTuOeqYuSfobV00IIm76f1f1tuO25mInfhxbVkR4dvz3G0rcNuLJ3HhN+idX6lECs4kBVSQDNLW7cKsqHVIcOHWugWxDM9yM+YOONwut6VtWxVa6SDei6D2fKO9UQY9cxtv9CgrIAaSMRq7Yxte9IOq8fz47UB+PLidrC7MnBjN2yhfH1szn1y2SmHSvvyqtInf6f8H4tPXIjVzF8+FJCMxV0Hd/lh81f0OG1CXzQ6jCzji9nqq8vU/etYXCVZLyXfMK8M8Wtvtg8eFQ9CdTsO5khTnrc3D2RvtO9SVFAsPZn0c55dP/gHdb4/EZ8cWgGNmi8v6LP1zuISr1bS3LydW7mKmBUlWr2EjymmDz3aY5g2Y3Zv37BoNbGnN04h6FdOvP+lJ/4+z4hAQmXMT+y6KM3qZXiyZKJvXn9rTF8s+mkKiT3kXs7gTRZwLrNAEa9054mde0wEbK4EZfAnXuKSsPNfSv4LSiJAiWPq4cPE5qnIOhYUsnixVt8Es3MMRNAkTPJKPbJqSSTkJiPItWn+5j+dG7lQjUrffKTL3M1RQNI1Gn7Cg6STJz3EaIVI4yMjDDST+a0bzSyUSM8XCrglIxoR/OWddAV8gnauomwzML05l/Zw2avFGTRmhat65dzNH9EPYm2tG7rhJ6Sgu/BYHINC/NoeOcMJyMK0KnfjEalF0EKQtm0ePs9QlIYTSYZWTKIBphbPP6WznO3TJQ7V4lL0KDUsKbhq13oduM2SdtPcCnzflVUSIy5SKpcA6uaLenc9QoJt//gQOhttLlL928gL2AFM1Y6MmtwW8Z83Z6xKGgy4vH7fT4zl/mQUNKGFFKTUu6a7Jp88mUAEfG5DzNloKNTtFYhIxcnWr7BjrnzcJ07kTd7T2XRu4XO0JMiD7B89jy2ns3DwsoCAYk6IzfiP/K+MJUcTEyeYIHgUQjmWFmIoGRx+1ZGqZ2RAlKSUlCwwszCrJxnOh5RT4JlYVxiJXr/dILe9z+eb4yJcamY5GRuJ5dldWgoKAAQEEUJAR5rR+e5iwk5wXzfqzNHuvZlyJB36T1lGe+Ou4TP9o2s37SH09dyijIkc3P3JN4Mb0uvwYMZ0H0U8zoPYWLoAf5Yv5GtR86R8ixW9180impbKt375SR8l46h8wpzHOs54ezakh7DBtNu5FwmR3ZlypGKkV+lKC1iBSmRcucOOQpYCPoY6EHxKJITvZ1p7+3ka9taNHByofHrfRnZszvT58QT1G81qcmpKFiR5v09E1dHlrJ4FZSCVC6fzSz8GQAJSRvJV9JISZMBAyrZmCKQVBS+DpVsrRGQSSkRCKWo7CSkJzEIlVSSU2VA5sxPH7LEL7f0L5Ezr3Humgw1HxGOoI+BvgCKTE52zku6NZx3i5BdPzKpd2feHD2P30MlPAZ9weq/D7F14Vja2xcva8tkXDrGujkj6PbG+3y6/Ai3HLry8cI/Ofz3Sma86/JEW1ovLzIZ6enISFR1d8e2qJgki5o0rG2Jbn4aVyL9OfjHMn4+eA1ZNKaSjVEFVbpManIKMhLVmjXHoQJmDkpiAokKIFpibVmcC32qOjlR1Qiyb8UQdGwXqxdsJjAPxEo2WIsaYk/4cl0jYtrQg2rJkQQFBREUFERobAYm9vaY6wBKBmnpCoiWOLvX4FG2ikGtbkz68gtGtKpUdnnKCfj7RpOPLk379MO1yDLQrd6D/h3NETW3OeUXXbiupKSQlKqAaIV7y3oYPm7ByAn4nzxPviJRv5kzOeeDi/IYwoVEPao4WqNXnn6hZ4mViQByEgmJjz8yP3/LpDTKHa76/s483z9ZXqcD7w0ZQv9uwxkatAufTffu6BQkn2X/z5+z/7cfaPZWf4YM7s1743txZFck/v+ZHZ0CzvqeJrl/T2zazGCP9ygu7viCb5QZbBhhT2p0EIHnbpClV50WHaojZgdzzD8RuVS1P3L0KV6aUMq6s/S1As56HuXqoEFUbzGVHceGkVIg4zerOzOO5jxlPgvR3LhIXLZCQxMHajhKcEUG0zeYtnEu7TTXCA8IJy6pAAvXtrTVl7nlc5yoAsgLWc23O9qxuHdHZm8/yKCoaBIVa2o718FG9wprB/dgQUg8fn5X+dCpBm4f/8HRvtc4Mm8As44qZZSRLs2H/4/h71igNEzE572fyzjzIhO7eRG/d1vOIJfRrN3XkYi4fKq4OFHFUOHWkSWsPFVULpqrHPM8x7iGDXH+YCNHeyWSyzmW9PmMlDJK+kFkLv6+gLWdfmZk8wn8frgnUedvkmdSHacGlTHM2MP4k9M5UfqRMgKUqjpiryOgFFwiJu5lF5MSZNJjvFj9hRdrvzFGNy/r4VtxuTc589cizmxfhpGhQvZLIiQ6xpaYG0plzJkV5Jx0kjNlDC0sMNIRHrxHkcnNTCEjVyHr+CKmLDdn+oBW1DQxJCctjajt0/lSdwS9OjSiTVcP9ArSuBK2k++WLWHjJRko4FJYGCmvOhJ17lapufglwsJTaV85nLO3C++7GBpCckd7giMSinZnZOJDQ0ns4U545PV7dmxyg5Yx4Qs9Ph/VmUbVrDC+GUD09fxH5zVbxMzCBD2xjDuUPLJS0smRgbxIgqPyeLOlBU7O9ki+cWgyDjP/kyqkDupCqyYdcTUWyb4Vg/dva1j8kw+FZ+2S8f5yCMPPjeGD916lkYsHNTSpXIncz1+/r2RNaOHEJ/zXz5lfeSYjX62HpbGGtLR8kBOIDLlGlu01wi8XT5A0nD95jCtdu+MgSA+19JSMU3w/bDTx40bR51V3GjbSJftGKH/vWsWPvx3nxt2CJ2btZ0w1/R/je3hQ08qcOxHRxGfmk1iuegIlM4glw4dwcfQYBnRqRv2mjijpN7jguZ5tq1bjkwPyrShCrmRinxxGbBn9xKieM9Ul0ESHEJb2+JsaJa4u1K+qPz3l/jq9QXvmev5ID4uym6GcupuJk1L4dNWQew8ulaBw5/hMOo3bSfKLvpH1yLzu4ev11fn8I7eyTXEln7DFvRi4Og4NIg6D17BnSmNk3zl0+uAvkp5X/gVzOn23mxn5s+gyzbuMcyovG/q0nnOAX9615OIv/ej149nH2trft2/Pi2qZ/MvJ8WfJyJHstih7tC5IiyPsfB6Xh/lQSa8sy0Qh62o4KS+6kEC58hpxxYDzIZUxLNMyySUhKr7EKrq2fwe+4xrT3r0tLc22s6N9+IMAACAASURBVO8JRlBtIDr0YFDLi6waeuJfICSAnjsdX7FEyI1g5+7zT3RGSBWT50IuCWcDSHjEXTGBp4h5JumpSMqX15BTl8sVmnJ7P6v+Gk7bQXVxqimxL+TBl+KeBfLVLYx/80/S0p9P/NpGquKCiw3c2r2SbZcf77BaMaqYqLxk5BK8bDzDfY25FP4cO7KSQ1r684te22ji/2LmmCiUiNNkPKGxp4qJysvHnXjOnHzeifiXoaQT43fqqYJ4Mc6ZqKiovPSoYqKioqIVVDFRUVHRCqqYqKioaAVVTFRUVLSCKiYqKipaoeQ4vYqKisrTUHLORH035+kp97s5Kir/Mvbt26NOc1RUVLSDKiYqKipaQRUTFRUVraCKiYqKilZQxURFRUUrqGKioqKiFVQxUVFR0QqqmKioqGgFVUxUVMpAMrDAztaMCnD/86/lBRYTEWuPoczdsI8TgUEEntzLmhk9qPvYHopUKgYBq9e/4tApT37oWaVCGpJg1YGpv61ichvzcrrRLA8iNfv/wnH/PUx/xeS+cAWM6/dkxpr9+J4+jteR35ng/mw+RqjjOo4tfifY8qFrxQqYWJluc9by89gmGGs7aC2HpzWkav34fvkndHcz4rqfJ/43zGjadw5LP27K47tUVtE+IlVd3alsUgl3t2qU6ZHjqdDHffhkBjRvhJO9vhbFRIcabm5YmTrg7mJ3TwcQKnXhy5Vz6NfMggSfLaxatpx9Mdr0OStSrc9yvP33ML21SemYMa3nSl0zc+q618esIr1SCiY4OrvTdtRk+tbSbvd/QcVExK7dGzQ2lrn2+6cMGv8ZHw6di2eWSJXmHlTXfstVeSKEor+13/oF03YM6FkNsSCSYycSH+6E7WniuCfZIrav9aKjlUCO/yJGffQ1i3/9m7NZ2nSloUPtxk2oZOqAe0PbewT4mXm11VzixIl4ZH0X+vbT7sD8gooJaDSFI4JQ5ARbqlQZWz3QpCbznFylqDxDTNp0oa2FiCbWF/+bFSEl9yNSpbo9OoJMYnQMSRUVZZFqCMKzdYot6OiiIwBouOB3mkSNROXXu9JUi2rygoqJzO2TRwnNEan6zqd8NmgU3/80HncpgQObD3PrWbStlxyjWq8yYvoPbNp/nMDQEAJ997N5wWha2xWPhyI1+//Kcb/tfNKqDq9P+JG/vPwJDjrJgTVT6HSP53EBo3pvMWXFDo6dDsDfaws/jG1L5QpbTtCnUetmGAsyqeFh3HV7a0DNTh+zaPN+TgYGERJwlN2rZjGinWOpEVYPh3Yjmbt+Dz4BZwj02cNvs97H1fzhnVd07Mcq/zNsHOKIhITDkPWERoRw4sce2JW3h+hU4ZVR37Bu3zHOhARxxmc3v305CA+bwvLWaz6Vg8FnWPq2CQI6OH+8k7DIMMKOL6CrVam0CYbUeeszVuzyJiA4AJ89y/mo9X3O0SUbPAbOZOUuL04FBeB3cAPfjmiBTUnV2tBtwWFOHfySN+q1YfzyXZwM9OfPsQ2QgPyzIUTkKYjWHrSspz0z/wUVE5Djt/L9xlhkI2f6ff4xb9glcOib8XztmYKO2zj+8NrGJ83U1ZMyESzo/Nl3TOrXnprCDSICAriQYYlr1/EsXTSIWhIUrh24YmVWm34/bGBB3zrkX7vErXwTHJoP5OvZvaha1DrEym8xb9U3DGlbG7Ocq8TEa3Ae+j3f9XOsmAYkOeBU3wwRDZei44q8ywmYtZ/Gyu9H8nqNO4Qd3sf+k3FIDd9hwuxBFK6Titj3+I4Nyz7mrVp3CNq3jR2nM6nTexqrlg6h7kPET0mLxs/rGMFXc1BQuBMfyFEvL46evkhGeQYu0ZYuc9fy04TuNK2cz+XQcC7nV6F578/4dd0M2lsJaG6G4eXpTfjNAhRkMi6e4qjnUTyPniE++66prdvkI36aO4hXapihr6uPVa12jJ4/hdctik0aKzrMXMOvn/fGTTrPoW07OJZQmc6TlvPrhMYYAYgOuLrZYFK1I1N/XsiY9tXRTYsn+kp6ob/y7ItEX5NBqopTAwutTbFeUL85ElVe/4w5fWuVzCvlpDAOeEeTjUjtlu1xsq1KnNlzTeSLi5LGkYUfcn1xDMEXksgDMG7DnD3LeLdhZ16ttp6Ll4pvFtFPPcLEAbPwSpQxaPwp29YNoUbjtjQz3cLuNImGA0bT0VogJ3IVw4cvJTRTQdfxXX7Y/AUdKkLPxSo4VBVBucOtm8lF6yU6uL7aATvxDp5fD2bi35kACAa2VLe6Q3wBYNKODz/piE1eCIuHjWJ1dC6gz46PNrF59FCGtdnKNO8HPXYr6WdYPS2EsOn7Wd3fhtuei5n4fSjldfGl12Q4k7pURcz057sB41kfmwsGDRi5ci0Tm7zDp4O3ceKH/cz/1JOO3x5j6dsGXNk7jwm/xJYIpVVRWIJuOie+GcIXW8+RX2sQSzd8SiuLVrR30+XQ8Tx03Ycz5Z1qiLHrGNt/IUFZgLSRiFXbmNp3JJ3Xj2dHanE5mmOVsY1PRy7i0KXMu+tOcgI3EjRQR6KqY1VEkp7IHej9vJCWiZ7TSJbM70M98QK/fzKMz3ddQa76Jt8s/ZhmZuY4u9ZAKogh6kIZrtxVAIWMmFOcKhISQdLFgFtcu6WAUAm7SqWrXcOVg1s4lljY1HLOhnI+X0HQsaSShQBiZRo3sUcin6CtmwjLLBxF86/sYbNXSoUsjKJrhrmRCEoWGZnFMcgk3k5EEQxo2mcs77/uQQN7M3RybhF3PQMZ0HVtR2srkYKIw3helzAyMsLISCLe/xTXFQsaN6tTAaOnRO1WLagiKWT6/MG22NzCyznn+HPLaXIUiWotW+BYztlEwYWtLNt6lnSNwp3YQ3idLQDRCEsrQwQk6rR9BQdJJs77CNGKUWEe9ZM57RuNbNQID5dS01MlhxOrFnOgtJAAKFmkZcgoCJhbmmtNBF5Ay0SflgP642Qgc+GnGcw7eB7N0Q8xNP+NGe2H8cO6VmTVMEQTfQLfG+riSdlI2LUcyLhh3WjpXJMqlgZIxQt+snjf4p9CdlYp19uyjCwDiIgiIJhiZlLYsW/fyuCuQV5ASlIKCpYVkHydwoapyMhycYwaLqybzaI6XzHm1UHM9BgMisydm0HsWDibBfsvI1pYYiaCrscU9p2ecl+gMkmmxhWwayJiYWmOgExqQmKhFVhETlISWQpYmllQXiNaTk4iubhZK/nk5xfmX5LEwrisLApFZeRG/Efe97CSg4mJXqn/Z5GcnFNGLAqyRgYEJEn8F09zRCscHUwRKeBq3NVC8yvvElunfIzVL7/wYWNnLJVMfP7cyUVtHgH4FyFWfY+5yybTQrzE/p++Zv7Zm6Tn2dPr61n0qPqYgSnppKXLgAGVbEwRSCoSFB0q2VpXzJZm/h3uyAoI+ujr341ByQhj7cQebDCtQp0Gzrh6dGXQ8E70++p/nA8ax660FDJk0Lm6nRkzd3G1VPtQ5DySLkaRjw7FiiiJ2hiTZdJS0lCohIWNNbpQND0SMLaxwUQAOSWZ1KI4laJ/xSeKWyY1ORUFK9K8v2fi6shSUzEFpSCVy2czyxGODvr6EgIKd7JztGZdvnjTHDmNmwnZyOjStEcv6hkJJddjohOK2oEhNRq73l29VrkH0b4WNfQE5JvH2bh2N14nTxOSaIp9pSeobvkmQcHX0Ai6NO3TD1fjwvrQrd6D/h21ZyLfQ0Eit5NkEIywtjIouihgUs2F+rb6yBk3OB/gybYVP7D9fAGCng125gL54T74psiI9s1obn6NsKAggoKCCIqIJ8/KkSqmEiCTkZ6OjERVd3dsH9GGBNOGvDd1Dp/3c3vIYTINMX7+3NAImLTrR+9aRYtIhk707dMcfTTE+Z/imgyFYpCCjES1Zs1xeOyjrhpiT/hyXSNi2tCDasmRhfkLCiI0NgMTe3vMy2MeiBZYW4qATGLCbbR10uLFs0zI5sS69YR3HI/7K5+x7dgwriXkYlK5CpaGInfigrlo2BCXHnNZenM0Q5aGcud5J/kFQ3PeF//EPvSo1pcFq2w4eVkPlw6tqKEoRUOjQuljUso9rUm5r3FpiNr8K0d7fs3rLqNZu68jEXH5VHGujYWgQUF64Imnz8BlYi8XoDjo4FDDAYmzaMSq9Jq7gSlu2cQFBRJ1NQ3sGtPeTYf8Sz74xGkg7xjLv/eixdev0XPxHlpfOMulNAm7us7UsITwxVEMXH2Zs76nSe7fE5s2M9jjPYqLO6bTf1HIPWVQiIBpxzH8b1B79PLtifMazR8JD47jeUFrWPj3qyx4qyWfb9lD94jr6FR3oZ6tPvmXt7F447miBc4Cznoe5eqgQVRvMZUdx4aRUiDjN6sHCx9ShMWXi+soL2Q13+5ox+LeHZm9/SCDoqJJVKyp7VwHG90rrB3cgwURDz53D6I91ewLp66xMTe0svgKL6JlAuRGreSDwTNYfSiMaznGVLa3RL4RyO4lH9O79xAGDp/D33EyVZzqYvlC5uBJkTC0sMa6UiUq3f/H2gpTfQFEfcysrB/8faVKWFubYSCCkn6Mb8bM4nf/Gxi6dqJ7R0eu/DaBzzbHkJESRdQVGSggLiyclKzrRJy7ddfU1cQRFpZK1rVwzt4uvCrf3Mu0UTPZePIiWcbVqF9NJHLNJAZN38u1zETCwuLRADrGlmWnvZI1Via65csfgJJKWHAcGkQcnBtgLgDydbbPmc6vB2NRajTnje7dec1ZIubAcj76YBlheQAyV3dPYcCYxew4fR2pmhseTWpjnBTIjh8mMmXTZTRA1vFFTFl+lJiUfPRMDMlJy0BBJj40lMSs24RHXi/qYApZ4cfxS8gFQUR82JxOvsWhL4YybvFOAm7qUsPdDUfpOqe3LWD0kK85lnK3R+cGLWPCF3/iH5eKYmKFcc4loq/nkXEhjOj0TOIiY8goUYIMosNjSU+PJTy6aL1KScb7yyEM/+Z3TsQVYOfiQTMnKzIj97Ni6gR+Di0AzRXCQ26RlRhG2OUHpUK0q099awklL4qQyNwnbq33U+I3R3XR8PQ8rasLnUafsmf9EKqVaXor3Dk+m5FnerNmkht6ZTVsJZ+wxb0YuDpOa6NNuTFoz1zPH+lhUba6y6m7mTgphU9X/VP+ZtJp3E6SFZBcJrDz95HUyNzLxNf/h2d2Wc88C0Sq9P2FnYPP8UH3hYSUd7/4hUXA7K0f8Py2I1LgfLoP28RVLSya7Nu350Wc5vx3KQhfzUfDfKikV8bbLopC1tVwItMCGRZRGcMyhklFySUhKv7ZCwlAjj9LRo5kt4VUxqKsQkFaHGHn87j8iPwVD+Kac7vYGTGYSa7NadtIH09f7Y2gj4XkTL/+tfBfMZOwl15IAIxp2aEZBmRxfPuBorUc7aCKyYuEJoWYwFPE/ONNlwk5dfkZJehxyCXhbAAJj7jr0fkrQnOZrSv3M2DJm9R3qozke/n5iKQmil8G9SA3LbNiztQ8a3Rq4epsjHJpHSsPJGl1tUsVE5UXFIV07/mMHr0Xnpe1BYBMVlp5tltfEgoiWPvph3hnBhKiZWNPFROVFxclgxh/1Q+2dpFJijpJUgWE/K/aC1FRUXl+qGKioqKiFVQxUVFR0QqqmKioqGgFVUxUVFS0giomKioqWqHkOL2KiorK01ByzkR9N+fpedp3c1RUXlb27dujTnNUVFS0gyomKioqWkEVExUVFa2giomKiopWUMVERUVFK6hioqKiohVUMVFRUdEKqpioqKhoBVVMVP6zSAYW2Nma8djua1TK5CUQExHHPss5dno/X71mWTEe5FSeG4JVB6b+torJbcy1Wrc6ruPY4neCLR+63icWAsb1ezJjzX58Tx/H68jvTHB/Rh8cNGnN9L1+HF/Zj5oV6UBOrEy3OWv5eWwTjCswmgeifYZxPSES1VwaYmVcBTfnyi9DglXKjT7uwyczoHkjnOz1tSgmAqb1XKlrZk5d9/r3eOITKnXhy5Vz6NfMggSfLaxatpx9Mdr8wqxItT7L8fbfw/TWJvf8RrJxws3RBKtGjahVkWIimODo7E7bUZPpW+vZ9ZiXpG+q9si/EcG0HQN6VkMsiOTYiUStfv297BYjYvtaLzpaCeT4L2LUR1+z+Ne/OZul3W+0127chEqmDrg3tOVhmlGhLVpziRMn4pH1Xejbryn6FRlXKV4SMVF5/ojo6mq3uZi06UJbCxFNrC/+N5+FIwmRKtXt0RFkEqNjSKqoKIuUQhCe8SAo6qArAWi44HeaRI1E5de70vQZqYkqJv9BJOsmDJj1Czu9/QkODeDkgQ18N6YdDnp379FxHccW3+NsHNEIl3fnsMnrFIHe8+lctvfuJ0CfRq2bYSzIpIaHEVcy0zCgZqePWbR5PycDgwgJOMruVbMY0c6x1Airh0O7kcxdvwefgDME+uzht1nv42r+8LSJjv1Y5X+GjUMckZBwGLKe0IgQTvzYA7ty9wIDanb6iIXFaTvtyY4V/+Ndl8L1Hr3mUzkYfIalb5sgoIPzxzsJiwwj7PgCulqVTpuImWsfZq/Zy4nAQM54b2HhEPd7HaNLNngMnMnKXV6cCgrA7+AGvh3RAhvpbhg1+//Kcf8dTG5dly5TV3HQ/wy+v/alqgj5Z0OIyFMQrT1oWa8i51R3UV1d/McQzFoyZc0yBtTWIz8plsjgHGyd3eg2/kc8GsxkwCe7uS4XrTmYWyL1n8cSG3vsCjK4EX6RW3lamhJIDjjVN0NEw6XoYnemAmbtp7Hy+57YZsZw8vA+Ug0ccGv5DhNm5xPeaS6nC0Tse3zHhq9exTr9HEf3beO2qTudek9jVW1DBg5fS3QZnveUtGj8vI6h37Q1jR30yYkPwj86mZTTF8kol4WiQ42+i1k/vQ2WZHItKpRLhrVo2KY/s5s1xn7UUJbdDMPL055GjdviWlkk82IAZy5lkZ9yhvhsBcyL6sCgDZ8t74SpkYwGCR0bJ7pMnk9yVA++CcgFwYoOM9ewuLcjeXG+HNp2Bd0GHek8aTn1zUcxYFEw2ehQw80VK1NDun/5MwMr2yJk3eZCzA3uKED2RaKvybxWpypODSwQwrXrcKvsElL5DyFSp/8nvF9Lj9zIVQwfvpTQTAVdx3f5YfMXdHhtAh+0Osyskzklc3rJ1pDYH4bSb0MQt/O0mZQqOFQVQbnDrZvJReslOri+2gE78Q6eXw9m4t+Fzq8EA1uqW90hvgAwaceHn3TEJi+ExcNGsTo6F9Bnx0eb2Dx6KMPabGWa94OOiZX0M6yeFkLY9P2s7m/Dbc/FTPw+lPJ6/BRMOzDuw9ZYcZtD0wbw6Z4baAQzPD5by8rBDRgyqTfbh25g/qeedPz2GEvfNuDK3nlM+CW2xIFYiX0g6JN05HP6zT3IFR03xq9azSiXyrRuWw8pIBzRfThT3qmGGLuOsf0XEpQFSBuJWLWNqX1H0nn9eHYkUhKqlclN1owdzE8nrpNTrBhyAjcSNFBHoqpjVUSSKtyRmTrN+S8h2tG8ZR10hXyCtm4iLLOw5eVf2cNmrxRk0ZoWrevfM8Jo4nbw41otCwmArhnmRiIoWWRkFpsGMom3E1EEA5r2Gcv7r3vQwN4MnZxbxF3PQAZ0XdvR2kqkIOIwntcljIyMMDKSiPc/xXXFgsbN6lTICKnTsDUtLEQ0Nw+xaf+Nwo6ppBO4eQfhBQJ6DVvR7B+mWfeQH8j6JQeIy5bRpIdz0CceGRGrSpZISNRp+woOkkyc9xGiFaPCPOonc9o3GtmoER4upTe7ZZIP/sxyn1JCAqBkkZYhoyBgbmn+TDq6apn8lxDMsbIo7MC3b2WUMnsLSElKQcEKMwuze3Ya5OREkivCPpZ0ChufIiPLxRFouLBuNovqfMWYVwcx02MwKDJ3bgaxY+FsFuy/jGhhiZkIuh5T2Hd6yn2ByiSZGlfITolkYYGpCEriLRJLlYeclESyDIJkhoWpACmPDkspSCY5rTgQhfy8fBRAFEVAxMLKAgGJOiM34j/y/odzMDHRA/JKnk9NTC7DwlKQNTIgIEniM9kPVcXkv4SSRkqaDBhQycYUgeJ5tA6VbK0RkElJSnk2Drrz73BHVkDQR1//blNXMsJYO7EHG0yrUKeBM64eXRk0vBP9vvof54PGsSsthQwZdK5uZ8bMXVwtZbsrch5JF6PIB5TivipJD92efRw0aWlkyGBpXQkrAeKKros2tlQSQSlILRGI4rgLxeFxkUlNTkXBijTv75m4OrKUUCgoBalcPpsJ6D08CAB00NeXEFC4k53zTOpUneb8l5AT8PeNJh9dmvbph6txYSfWrd6D/h3NETW3OeUX/WychBckcjtJBsEIayuDoosCJtVcqG+rj5xxg/MBnmxb8QPbzxcg6NlgZy6QH+6Db4qMaN+M5ubXCAsKIigoiKCIePKsHKliKgEKd9LTyVNAp1Yj3Cz+eVwWTBvy3tQ5fN7PjYdtVhWEn+RUqoxUuTMDulQpFCjBHI8BPWmoo5Ab7kdghkKhGKQgI1GtWXMcHvusvobYE75c14iYNvSgWnJkYf6CggiNzcDE3h7z8pgAogXWliIgk5hwu8IXX0G1TP5jyMRuXsTv3ZYzyGU0a/d1JCIunyouTlQxVLh1ZAkrT+UAwt2RXamgZqi5TOzlAhQHHRxqOCBxFo1YlV5zNzDFLZu4oECirqaBXWPau+mQf8kHnzgN5B1j+fdetPj6NXou3kPrC2e5lCZhV9eZGpYQvjiKgasvkxvqy5nMrnSwfZNFB1tyI3wDH41eXyoBxfkSMO04hv8Nao9evj1xXqP5I+HBcVzJOMrypb60+qINnedux2XAWRJN6tCwpiVSThSrf9jBVRmggLOeR7k6aBDVW0xlx7FhpBTI+M3qzqy4hxWGck+K8kJW8+2Odizu3ZHZ2w8yKCqaRMWa2s51sNG9wtrBPf7f3p3H13StDRz/7X0yzxIRJIaqmjMUMddUSl2XolVTKEJV24tWaamxrVvUUEOrYk7pW1RCmtIhRGJIkMgoSJCgIWQ+iYxn7/ePDI2K4dZJRLu+//D55CRr7X32etaz1t7nPCyPuPdI7iI70tCxZEl7KeFGtUwQIpg8NTSY2thgZiDdu/5VFQpyMtAWGWFlY4GRfO/0qqqF5GZkk68N5Yvxk7k6dRLDe7vSxs2QOzci+Wn/JtZsCeKGAqCivRDJxWw3jKLOk1FhbElGFtSyNOHeJlTU4jtkZhZi9LB+FqigZhJ1NhFd12Y4tWqBtRRHupLMvkVzsZk8gr7uHejb1hRFm8z5Q+vZsHIzUYUACtcPzGJ06himThxMdxcX3A3zSUsMw2e7Nxt3JpUMnNs/8umcphjPHEo7JwuM7mSSqypkRUaSOtiV6Njk0gGmkhsdxMmUTvSwkys5rjI6knbPYGzGRKa+MYBOzVxxUDK5EryLneu/4oeYP+4gFYSvY9o8Iz6c1A+3hraY3zxNfHIRuttxRF3LwfF2DFfKR7fKrbhofs+tR3J06S1yNZ3AxeOYcH4Kb77WG7fW7jTWZXIt9iA/fOfF1shiQOZKZCTpvRsRGXPjnmWM7NCc5nYa1MJzRMQW3O+g9Kq8bk7NLdFgSNdFAWx41YrLG0YydG1c9aThf0FVlrowcJuJ345xNKx0A0AlL2ghnmeGsXWGC0aVDQi1iKhVQxmzOfGvnz/JliHrD/FJD9PKN/R019jxSSA953k8oJ/zeWmqL+kqaFpPw/c7Txrn/Mj0Ph8RcO8d3WoiU2/EN/iOPc+bg1YQ8aj3i2ssCauBqwn4vBeasKUMGr+zNGuqOv7+fk9LZlIdK76arTh6M++OD6a2UWUzvkru9Whis8IYH1MX00ozkwJSzl19vECsZvDzkon8vsMcTWWZSVEaF6PT+OHy0Qf2M6P07dSd349vzFhmOHfgBTdjAk5Uzwx6D00rRo5qQsiG+UQ99YEEwJxOPdtjQi5B+w7xe7XsqD8VyxwdSTExZAx4lpi4m9Vzp6Em0mWQEBZKwgNflEREaFIVdkLlzvVoTl1/8KvSH9rPUrok9ngdZPSX/6J5y7poTiQ9maxTd45vPAZTkJXz97i+DJrg3Moc9cp2vA5V/ZOv5c1WUzuPQeH6nrfpvudJ90PQP5XswKVMnvwjPG7W9FgUcrNynljrelccw7aZbxOYE0ZENSZ7T0EwEf7WVC0JIaLWtX4ppJ07Tlo1tyqeMxEEQS9EMBEEQS9EMBEEQS9EMBEEQS9EMBEEQS9EMBEEQS/KH6cXBEF4HOXPmdTcz+Y8ParyszmCUJP5+/uJZY4gCPohgokgCHohgokgCHohgokgCHohgokgCHohgokgCHohgokgCHohgokgVKAxscGhjhX/c4UKQQQToeaRbHsye8sm3u9mrcdKdDLPjPqGoBA/5na1+NPflTBv/gofbz3IiVNBHP7tO6a5Vs/3hhk4T2X3yWPsftu5agOYXJcBi7bx9VttMa+qJqro7wrCX2SM64T3Gd3BjZaOxnoMJgY0dnHB1tIJ19YOd134Uu3+LPZaxMj2NqQE72bTuvX4J+jzSyRlGg5fT2CIH3O7WFRsGctmzjxnZc1zrs3vWwBMLyQLGrRy5YVJ7zOiSdUM+6cgmBjj9p4vEVF+zG5v9qQ7I1QxybI7o19piFwcy9FjqVXyBc/SXYNWps6LQ+llK5EfspJJ737Kqo0/EZerz69hNuDZ59tS29IJ1zZ17ipXWh01gAHQXeHYsasoxq0ZMbIdxlXQRI0PJpJVD8YMewZDTSOGjOuDbbWdfeHhZAwN9XsJWXTrzws2MrpLJwi5WT0Vcus1csRAUkiNTyCtqposvW4lqXovYMnAEAMJQMfFk6dI1Wmo2+dl2lVBNKnhwUSmTv9X6WkayRavEDRdhjHQsYZ3uSaRLOmx6BChAV/h0aLs6pGx7zmHvSeC2TKmUfksqbFry+gF3+AbGMLZyNMcP+TNsindcapQnjmvxwAADK9JREFUH9vAeSq7TwTx7UQ3Wr+6iJ2HQwkLXEo/veXnxrh1aY+5pJAZHUVi+UrDhGde+g8rdx3keFg4EaePcGDTAiZ2b1BhhjXCqbsnS3b4EXz6DGHBfmxZ8DrO1vfvm9xgJJtCzvDtuAZo0OA0bgeRMREcWzMYh0e9zAzq0XXSZ2z3P8qZiHDOBB9gy2IP3O1LzqxRh9n8fPYMa/9tgYQBrf7jS1RsFFFBy3m54swomdJ04Ads2B/I6bOnCfZbz7tdat89QDX2uI+Zj9f+w4SGn+bkz958PrEj9mVvomzPgOW/EvrzYvo268Y76/dzPCyE799qgQYoiosgplBFtnOnUzN9lHO/W80emZrGDBrWHjXUF+/tPhwrdGHIkGZ6qWr/j6AWkJqSjYFDN95fPp2OlhIGjYfz2ZLXaWlRyO0ULQogWXVi1taNfDS8M43kG8SeTSDX1oUB76zBe9kg6stQvr63roXLqP/y5fwhuNgUcjPhMrcK9bQk0DjRsrkVMjquxJdVHpSw6jEHry886dM4j6hf/Tl4PBFNmyFMW+hByT6pjOPgZXiv+w8Dm+QR7r8Xn1M5NB02h01rx/HcffZS1ax4Th4+ytnr+aio5F0N48jhwxw5dRnto2Qoch36L9nGV9MG0a5uEUmR0SQV1aPDsA/YuP1jethK6G5GcTggkOibxagoaC+HciTgCAFHznD1zh/nzbDtu3y1xIOuja0wNjTGtkl3Ji+dRZ+youuSLT3nb2Xjh8Nw0Vzgl70+HE2pS78Z69k47XnMAGQnnF3ssajfi9lfr2BKj0YYZl0l/lp2Se2cO5eJ/10BTX1atrDR+xKrRpe6MHQewist7hA0O4DbGUX4Hs5kzaChtPNawqn8J927p0EhsV4fsrqdN7M6juSzT/I4Wm8MXayKubzrY5b8lo6KzHOj3uP1JkYUxG5iwoS1ROaoGDZ4ldW75tHzxWm82flXFhzPL7/4NHVMubT6DUZ6h3O7UI/dlevhVF8GNY9bN9NL90sMcO7dEwc5j4BPxzL9p5L6NpJJHRrZ5nG1GLDoztvv9cK+MIJV4yexOb4AMMbn3Z3smvwG47vtYU5g0T3Nqdln2Dwngqi5B9k8yp7bAauY/kUkj1rUz6jtBGb0r4+cE8Ky0e+w41IBmLTA02sb09sOYebYvRxbfZClMwPo9flR1v7bhGs//pdp31wqD5S2pX9LMszm2GfjmLfnPEVNPFjrPZPONp3p4WLIL0GFGLpOYNaQhsiXtvPWqBWE5wKab4nZtJfZIzzpt+MdfDLLzqM1ttq9zPRcyS9XKhQWU1K4kaKDphrqN6iPTJpeaxXV4MzEjM7DBtAgIwDfoCxU7hDi+wspDv157QXL6tu4etoVXWbnnE84dAvq9p3EiDYm5J79ig9WnCRLBWQHOnRqiqFURPienUTllMyWRdf82HU4A0W2o2OX5nfNOrpEH9Zs03MgATC0wtpMBjUXbU7ZEFBIvZ2KKpnQbvhbvN7HnRaOVhjk3yIxuSSzMnTuThdbmeKYXwlI1mBmZoaZmYarIaEkqzY8375pFcyaGp7t3JF6GpWc4P9j76XSalf55/l+9ynyVQ0NO3WkwSOm0cUX97BuTxzZOpW8S79wOK4YZDNq2ZoioaHpC11x0igkBv5GvGpWcozG6Zw6EY9i5oZ76wo3ltV8jm1axaErf6pQqOaSpVVQkbCuZa33wV9jMxPJthfD+tpxw3c/oaUFrQvOHuCnpNcZ/epL2Af8wK2/RS3Hqqek/MYWn8v0m/IcGiWLIO/dXCir9CZZY2tTMoBv39JWKCVZTEZaBiq2WNlY3RW8lfRU0qui5qTGoOSCVBUUpawBHRe3L2Rl00+Y0tuD+e5jQVXIuxmOz4qFLD+YhGxTCysZDN1n4X9q1p+PnjRL8yqYfGRsalkjoZCZkkrFuJqflkauCrWsbLB6xL+mpKeRXnY9q0UUFZUcv0Yjl7Rla1MSVDy/JcTzT7+s5mNhUWFzS80lPb2y1F1F0SmAhEYj/1OWOTL1Xx5GN7MrbNsf+ccbVRzHAb/zvDF1KP9u5MPmKyKaPAqTNpNYMK5pyV6TbE2/mR/yc/g8fktVQM0iI0sBTKhtb4lEWW1aA2rXsUNCISMto3pq8BblkaeoIBljbPzHpa5qo9g2fTDelvVo2qIVzu4v4zHhJUZ+8hEXwqeyPysDrQIG1/fx8fz9XK+Qu6tKIWmXz1GEAWWRUiPrY05WyMrIQqU2NvZ2GELp8kjC3N4eCwmUjHQyS9tUS/+V/1LbCpnpmajYkhX4BdM3x1ZYiqmoxZkkxT1KeVMDjI01SKjk3cnX+3taQ5c5MvUb1EV7fCf7LlRc1Skk7vfmcFpdmjQyeWK9e5pItboxa9lk2pjkEbluGgt/TUVyHMiiJa/zjCGgpBByIp4iDGk3fCTO5iWD2LDRYEb1skbW3Sb0ZHz11AEuTuV2mgKSGXa2Ze+vhEXD1jSvY4yivcGF0wHs3bCafReKkYzscbCWKIoO5kSGguzYng7WvxMVHk54eDjhMVcptG1APUsNoKDNzkZBQ31XV+o8ZPlh0mQAMxbPY2Ln2vcZJDoSToZwQydh0X0kw5qU3lcybcmI4R0wRkdiSCi/K1ASDDJQ0NCwfQec/udHXXVcOnaCZJ2MZRt3GqbHlhxfeDiRl7RYODpi/ShpgWyDXS0ZUEhNua33guY1NDMp5vTnA+hZyU+UFH/ee9G/ujv0dJJq02/uYl5rJJN1bAUfbTzMVctCWjVby2td3mOpZwSjv47j0q6VfDdgPR6tJ7PNvxcxiUXUa92SeqYqt377Eq/QfEAqn13/+I+e6ZK4lFSM6mSAU2MnNMShk+szdIk3s1zukBgexrnrWeDwPD1cDCi6Ekxwog4Kj7L+i8N0/PRFXlnlR5eLcVzJ0uDwXCsa14LoVecYszmJuBOnSB/1CvbdPsYvcBKXfeYyamVEhQ6UHZchHSZ8xIQhNqhtUgl+7WsuVhJNC8O3suKn3iwf2IkPd/sxKCYZg0ataVbHmKKkvaz69nxpEC4mLuAI1z08aNRxNj5Hx5NRrHBywWBW3OdU/vlUF0Zs5nOf7qwa1ouF+37G41w8qaodz7Zqir3hNbaNHczymHt/7y6yIw0dS5a0lxJu6H2CqKHB5J9Og6mNDWYG0r3rWlWhICcDbZERVjYWGMn3rnxVtZDcjGzyJRvq1zVGG/c9cz7eS5IOyDzGsg++xGndFBra22EEFGlD+WL8ZK5OncTw3q60cTPkzo1Iftq/iTVbgrihAKhoL0RyMdsNo6jzZFTIkQ3Ma2FtqqlkDa6i5GeTnqM8/HgKVFAziTqbiK5rM5xatcBaiiNdSWbfornYTB5BX/cO9G1riqJN5vyh9WxYuZmoQgCF6wdmMTp1DFMnDqa7iwvuhvmkJYbhs92bjTuT0AG5QSuZtd6auaM784yFKflZWlQUrkZGkjrYlejY5NIBpuPC8aNce3kQTpLm/um7cotf5r1BbvxUPId0o7WrC5L2Gqf27uXrdTs5nfHHiC4IX8e0eUZ8OKkfbg1tMb95mvjkQrS3o4jPdsMwNgFteSTQEh99iWxniI4v3cdS0wlcPI4J56fw5mu9cWvtTmNdJtdiD/LDd15sjSwGrhEdcYtcoziiku4NFbJDc5rbaVALzxERW3DPzx9XeakL8a3qj09f305v4DYTvx3jaFhpKq6SF7QQzzPD2DrDBaPKdtHUIqJWDWXM5sSqX56Y9GBJwBoG21Q+5JTMA0yfkcHMTQ86nvm8NNWXdBU0rafh+50njXN+ZHqfjwi4U6W9vz/JmpeWHeDjogX0nxPIk+qG/khYDVxNwOe90IQtZdD4nVzX46aJv7+fyExqouLozbw7PpjaRpXN5Cq516OJzQpjfExdTCvNTApIOXe1evY58kP40tOTAzaVZybFWYlEXSgk6SHHUzaJ687vxzdmLDOcO/CCmzEBJ/Q/gz4K2WkwHp0us+mNY3+DQAJgTqee7TEhl6B9h0r3cvRLBJOaSJdBQlgoCQ98URIRoUnV1KEHKSAl7jQpD3nVw4+nlC6JPV4HGf3lv2jesi6aE0nVExT/RLm+m3f+9T1Z2Y/6CFsNZ9AE51bmqFe243UoTe+bryCCiVDjqGQHLmXy5B+hurKrSruRT1b2k2q8ChTHsG3m2wTmhBFRRcmeCCZCzaNqSQgRJWv1SyHt3HHSqrCFGvqciSAITxsRTARB0AsRTARB0AsRTARB0AsRTARB0AsRTARB0AsRTARB0Ivyz+YIgiA8DsmutkMVfZ5cEIR/ErHMEQRBL0QwEQRBL0QwEQRBL0QwEQRBL/4fVbK91qlOmu8AAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "id": "f4f83021",
   "metadata": {},
   "source": [
    "![Screenshot%20from%202023-08-02%2018-28-09.png](attachment:Screenshot%20from%202023-08-02%2018-28-09.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e7be60e-5b1d-4ed3-8f21-76d58895b795",
   "metadata": {},
   "source": [
    "# slots"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f0f62742-5f80-47be-a377-3f373d1227f2",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "68b7641e-d84d-4835-87c2-3d58dcdff27b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'a': 1, 'b': 2}\n",
      "{'a': 1, 'b': 2, 'c': 3}\n",
      "{'a': 1, 'b': 2, 'c': 4}\n"
     ]
    }
   ],
   "source": [
    "# با داندر دیکت میشه تمام اتریبیوت های یک شی را ببنیم\n",
    "# بعد ساخت کلاس میشه اتریبیون اضافه کرد\n",
    "# و میشه به این دیکشنری یک کلید مقدار جدید اضافه کرد\n",
    "\n",
    "\n",
    "class MyClass:\n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        \n",
    "obj = MyClass(1, 2)\n",
    "print(obj.__dict__)\n",
    "obj.c = 3\n",
    "print(obj.__dict__)\n",
    "obj.__dict__[\"c\"] = 4\n",
    "print(obj.__dict__)\n",
    "\n",
    "# گاهی شاید بخاهیم این اجازه را ندهیم که اتریبیوتی به شی اضافه شود\n",
    "# با استفاده از داندر اسلاتس این کار را میشه انجام داد\n",
    "\n",
    "# __slots__\n",
    "# باعث:\n",
    "    # صرفه جویی در حافظه\n",
    "    # دسترسی سریع به اتریبیوت\n",
    "\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "cfb2d1e2-d203-4b9a-9b6a-3290b583f4ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "class MyClass:\n",
    "    __slots__ = (\"a\", \"b\")\n",
    "    \n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        \n",
    "obj = MyClass(1, 2)\n",
    "print(obj.a)\n",
    "print(obj.b)\n",
    "obj.a = 3\n",
    "obj.b = 3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "fa575db8-7518-4e1d-9f23-8f38ae989934",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'MyClass' object has no attribute '__dict__'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[73], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mb \u001b[38;5;241m=\u001b[39m b\n\u001b[1;32m     10\u001b[0m obj \u001b[38;5;241m=\u001b[39m MyClass(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__dict__\u001b[39;49m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'MyClass' object has no attribute '__dict__'"
     ]
    }
   ],
   "source": [
    "# دیکشنری را غیرفعال میکند\n",
    "\n",
    "class MyClass:\n",
    "    __slots__ = (\"a\", \"b\")\n",
    "    \n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        \n",
    "obj = MyClass(1, 2)\n",
    "print(obj.__dict__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a11c2b5a-4d88-46bd-8449-73b366a61dc0",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'MyClass' object has no attribute 'c'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[76], line 12\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mb \u001b[38;5;241m=\u001b[39m b\n\u001b[1;32m     11\u001b[0m obj \u001b[38;5;241m=\u001b[39m MyClass(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m---> 12\u001b[0m obj\u001b[38;5;241m.\u001b[39mc \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m6\u001b[39m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'MyClass' object has no attribute 'c'"
     ]
    }
   ],
   "source": [
    "# کلید هایی که برای آن مشخص میکنیم اجازه میده فقط آنها قابل مقداردهی باشند\n",
    "# اجازه ایجاد اتریبیوت دیگر را نمیدهد\n",
    "\n",
    "class MyClass:\n",
    "    __slots__ = (\"a\", \"b\")\n",
    "    \n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        \n",
    "obj = MyClass(1, 2)\n",
    "obj.c = 6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9a67f988-751c-4992-bf29-52978fd476a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# میشه بگیم چه اتریبیوت هایی بعدا میتونیم اضافه بکنیم\n",
    "class MyClass:\n",
    "    __slots__ = (\"a\", \"b\", \"c\")\n",
    "    \n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        \n",
    "obj = MyClass(1, 2)\n",
    "obj.c = 6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d5c5f9b0-0fe7-4c40-bcfb-0960a8a329f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{}\n",
      "{'c': 6}\n"
     ]
    }
   ],
   "source": [
    "# با داندر دیکت در فرزند یک دیکشنری خالی نمایش میدهد\n",
    "# محدودیت ناشی از اسلاتس فقط برای والد ایجاد میشه و برای فرزند اعمال نمیشود\n",
    "\n",
    "class ParentClass:\n",
    "    __slots__ = (\"a\", \"b\")\n",
    "    \n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        \n",
    "class MyClass(ParentClass):\n",
    "    def __init__(self, a, b):\n",
    "        super().__init__(a, b)\n",
    "        \n",
    "        \n",
    "obj = MyClass(1, 2)\n",
    "print(obj.__dict__)\n",
    "obj.c = 6\n",
    "print(obj.__dict__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d2751dee-2ba6-4a3b-b56f-6fa0b7232dab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "# میتونیم برای فرزند نیز اسلاتس ایجاد بکنیم\n",
    "\n",
    "class ParentClass:\n",
    "    __slots__ = (\"a\", \"b\")\n",
    "    \n",
    "    def __init__(self, a, b):\n",
    "        self.a = a\n",
    "        self.b = b\n",
    "        \n",
    "class MyClass(ParentClass):\n",
    "    __slots__ = (\"c\")\n",
    "    \n",
    "    def __init__(self, a, b):\n",
    "        super().__init__(a, b)\n",
    "        \n",
    "        \n",
    "obj = MyClass(1, 2)\n",
    "\n",
    "obj.c = 6\n",
    "print(obj.c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21535768-08c2-4dc0-8ab7-1a8cd4559c6d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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