From 783a4944d0ba4f07e08ade2c28dc2e7c8cdff01f Mon Sep 17 00:00:00 2001
From: Louis Oliphant <ltoliphant@wisc.edu>
Date: Thu, 30 Jan 2025 17:14:34 -0600
Subject: [PATCH] added louis lec 5 functions

---
 .../Lec_05_Using_Functions.ipynb              | 549 +++++++++++
 .../Lec_05_Using_Functions_Solution.ipynb     | 921 ++++++++++++++++++
 2 files changed, 1470 insertions(+)
 create mode 100644 s25/Louis_Lecture_Notes/05_Using_Functions/Lec_05_Using_Functions.ipynb
 create mode 100644 s25/Louis_Lecture_Notes/05_Using_Functions/Lec_05_Using_Functions_Solution.ipynb

diff --git a/s25/Louis_Lecture_Notes/05_Using_Functions/Lec_05_Using_Functions.ipynb b/s25/Louis_Lecture_Notes/05_Using_Functions/Lec_05_Using_Functions.ipynb
new file mode 100644
index 0000000..2207e29
--- /dev/null
+++ b/s25/Louis_Lecture_Notes/05_Using_Functions/Lec_05_Using_Functions.ipynb
@@ -0,0 +1,549 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Warmup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warmup 1 \n",
+    "# Create 3 variables.\n",
+    "# Assign each one a data value of a different type.\n",
+    "# Open the debugger to view these variables.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warmup 2\n",
+    "# Write expressions that meet the following criteria:\n",
+    "\n",
+    "# exp 1 -- use the \"or\" operator that prevents division by zero\n",
+    "# using short-circuiting\n",
+    "\n",
+    "# exp 2 -- use the \"and\" operator that prevents division by zero\n",
+    "# using short-circuiting\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warmup 3\n",
+    "# Decide if the following variables are valid in Python.\n",
+    "# Write VALID or INVALID next to them.\n",
+    "\n",
+    "# my.name = \"Meena\"\n",
+    "# 1st_place = \"Andy\"\n",
+    "# stop&go = False             \n",
+    "# False = 100                  \n",
+    "# end_game = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warmup 4\n",
+    "print(True and not False) # Write your prediction here!\n",
+    "print(type((5 // 1.2))) # Write your prediction here!\n",
+    "print(1 <= 2 and \"a\" < \"A\") # Write your prediction here!\n",
+    "print(1 <= 2 or \"a\" < \"A\") # Write your prediction here!\n",
+    "print(len(\"2\") + 1) # Write your prediction here!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warmup 5\n",
+    "\n",
+    "# Calculate how old a dog is in dog years given the year they were born\n",
+    "# 1 year is equal to 7 dog years!\n",
+    "# e.g. a dog born in 2019 is 28 years old (7 * 4)\n",
+    "\n",
+    "year_born = 2017\n",
+    "dog_years = ???\n",
+    "print(\"Your dog is \" + str(dog_years) + \" years old!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warmup 6\n",
+    "\n",
+    "# Given the number of seconds, compute the number of hours, minutes, and seconds.\n",
+    "#  e.g. if seconds is 65, the correct output (with \\n in-between) is 0 1 5\n",
+    "#  e.g. if seconds is 192, the correct output (with \\n in-between) is 0 3 12\n",
+    "#  e.g. if seconds is 3739, the correct output (with \\n in-between) is 1 2 19\n",
+    "\n",
+    "seconds = 3739\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reminder\n",
+    "\n",
+    "* a **literal value** is when you type the value yourself (e.g. `\"hello\"`, `3.14`, `True`)\n",
+    "* a **variable** is a named memory location that stores a value that can change as the program runs -- know the rules for variable names (also use meaninful names)\n",
+    "* An **assignment statement** is of the form `variable = expression`\n",
+    "* **Errors** can be divided into three categories:\n",
+    "    * Syntax errors\n",
+    "    * Runtime errors\n",
+    "    * Semantic errors\n",
+    "* **Jupyterlab features**: use debugger to view kernel state, ways of running Python code, shortcuts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "### Python Variable Names\n",
+    "*Python* requires that variables...\n",
+    " - Only use letters a-z (upper and lower), numbers, and underscores.\n",
+    " - Don’t start with a number\n",
+    " - Don’t use Python keywords (e.g., `and`, `False`, etc)\n",
+    " \n",
+    "In addition, *CS220 Course Staff* ask that variables...\n",
+    " - Only use the English alphabet and numbers\n",
+    " - Use snake_case for variable names (as opposed to camelCase or PascalCase)\n",
+    " - Make variable names *meaningful*\n",
+    " \n",
+    "Python won't run for the first set of rules; CS220 course staff will take off points for the second set of rules."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Using Functions\n",
+    "\n",
+    "## Readings\n",
+    "\n",
+    "- [Downey Ch 3 (\"Function Calls\" to \"Composition\")](https://greenteapress.com/thinkpython2/html/thinkpython2004.html)\n",
+    "- [Python for Everybody, 5.1 - 5.4](https://runestone.academy/ns/books/published//py4e-int/functions/toctree.html)\n",
+    "\n",
+    "## Learning Objectives\n",
+    "\n",
+    "- Call `input()` to accept and process string input\n",
+    "- Call type cast functions to convert to appropriate type: `int()`, `bool()`, `float()`, `str()`\n",
+    "    - Concatenate values onto strings within a print function by converting to `str()`\n",
+    "- Using correct vocabulary, explain a function call\n",
+    "    - call/invoke, parameter, argument, keyword (named) arguments, return value\n",
+    "- Use `sep`, `end` keyword arguments with the print() function\n",
+    "- Use some common built-in functions such as round(), abs()\n",
+    "- import functions from a built-in module using `import`, `from`\n",
+    "- Call functions in modules using the attribute operator `.`\n",
+    "- JupyterLab Features: Notebook cell Types (Code, Markdown, Raw) and Properties (Editable vs. Read-only)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Vocabulary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "| :- | :- |\n",
+    "| function definition | a grouping of lines of code; a way for us to tell our program to run that entire group of code |\n",
+    "| call / invoke | a statement that instructs the program to run all the lines of code in a function definition, and then come back afterward |\n",
+    "| parameter | variable that receives input to function |\n",
+    "| argument | value sent to a function (lines up with parameter) |\n",
+    "| keyword (named) argument | argument explicitly tied to a parameter |\n",
+    "| return value | function output sent back to calling code |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Previously Used Functions: `print()`, `type()`, and `len()`\n",
+    "\n",
+    "We have used functions before! Namely `print`, `type`, and `len`.\n",
+    "\n",
+    "What parameters did they have; what arguments did we send them?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"hello world\")\n",
+    "print(len(\"apples\"))\n",
+    "print(type(1 < 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a closer look at the `print` function.\n",
+    " 1. Use the [official Python reference](https://docs.python.org/3/library/functions.html#print)\n",
+    " 2. Use the [w3schools reference](https://www.w3schools.com/python/ref_func_print.asp)\n",
+    " 3. Use the `help` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "help(print)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"1\", \"two\", \"3!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Can you add a keyword argument that will connect the names with the word 'and'?\n",
+    "print('Bob', 'Alice', 'Dylan', 'Gretchen')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Can you add a keyword argument that will make the output appear on a single line?\n",
+    "print('Good')\n",
+    "print('Morning!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Guess what will get printed\n",
+    "print(\"hello\", \"world\", sep = \"|\", end = \";\\n\")\n",
+    "print(\"its\", \"so\", \"cold\", sep = \"^\", end = \"...\\n\")\n",
+    "\n",
+    "print(\"*\" * 4, \"#\" * 6, sep = \"\\t\", end = \"<END>\")\n",
+    "print(\"*\" * 6, \"#\" * 8, sep = \"||\", end = \"<END>\")\n",
+    "\n",
+    "print(\"\\n\", end = \"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## New Functions For Type Conversion\n",
+    "\n",
+    "| Function | Purpose |\n",
+    "| :- | :- |\n",
+    "| `int(value)` | Turns `value` into an integer |\n",
+    "| `float(value)` | Turns `value` into a float |\n",
+    "| `str(value)` | Turns `value` into a string |\n",
+    "| `bool(value)` | Turns `value` into a boolean |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This code works!\n",
+    "name = input(\"Enter your name: \")\n",
+    "print(type(name))\n",
+    "print(\"Hello \" + name + \"!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This code doesn't work! Try to fix it.\n",
+    "age = input(\"Enter your age: \")\n",
+    "print(type(age))\n",
+    "age = age + 10 # we can shorten this to age += 10\n",
+    "print(\"In 10 years, you will be \" + str(age))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This code doesn't work! Try to fix it.\n",
+    "temp = input(\"Enter your temperature: \")\n",
+    "print(type(temp))\n",
+    "temp_ok = temp < 101.1\n",
+    "print(\"You can enter lab: \" + str(temp_ok))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other Built-In Functions\n",
+    "We can refer to [this resource](https://www.w3schools.com/python/python_ref_functions.asp) to see other built-in functions in Python. Let's practice using `round` and `abs`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Print the help documentation for the round function\n",
+    "\n",
+    "# Print the help documentation for the abs function\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Round the number 84.7\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Round the number 19.74812 to the second decimal place\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the absolute value of -12\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get the square root of 81\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing From Other Modules\n",
+    "We'll look at two other common modules (collections of functions): `math` and `random`.\n",
+    "\n",
+    "There are a few ways we can do this...\n",
+    " - We can write `import <module_name>` and use a function by `<module_name>.<function_name>`.\n",
+    " - We can write `from <module_name> import <function_name>` and use a function(s) directly by its name.\n",
+    " - We can write `from <module_name> import *` and directly use all functions from the module by name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normally we like to have our imports at the top of our notebook\n",
+    "import math\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use dir() to see a directory list of the things (functions) in a module\n",
+    "dir(math)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Then use help() on the name of the function you want to use\n",
+    "# notice that to access the functions from the module you must use dot-notation since we imported\n",
+    "# the module\n",
+    "help(math.sqrt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use the . (attribute accessor)\n",
+    "result = math.sqrt(17)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the function directly\n",
+    "# then ONLY the sqrt function is imported and you do NOT use dot-notation\n",
+    "from math import sqrt\n",
+    "print(sqrt(17))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from random import randint\n",
+    "print(randint(1,10))\n",
+    "\n",
+    "# wrong way to call randint, since only the function was imported, not the module\n",
+    "#print(random.randint(1,10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import all functions directly\n",
+    "# Now all functions from that module can be used directly, no need to use dot-notation\n",
+    "from math import *"
+   ]
+  },
+  {
+   "attachments": {
+    "26314fdb-40a3-4999-af02-6f6686f3211f.png": {
+     "image/png": "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"
+    },
+    "9279cd39-a0ab-4c23-bf4f-a00fe8fdd4fd.png": {
+     "image/png": "iVBORw0KGgoAAAANSUhEUgAABeoAAACkCAYAAADokR4QAAAMP2lDQ1BJQ0MgUHJvZmlsZQAASImVVwdYU8kWnltSSWgBBKSE3gQRKQGkhNACSC+CjZAECCXGQFCxo4sKrl1EwIauiih2QOyInUWx98WCgrIuFuzKmxTQdV/53nzf3PnvP2f+c+bcmXvvAKBxgieR5KCaAOSK86WxIQHMMckpTFIXIANTQAPGYDiPnydhR0dHAFgG2r+XdzcAIm+vOsq1/tn/X4uWQJjHBwCJhjhNkMfPhfgAAHgVXyLNB4Ao5y2m5EvkGFagI4UBQrxQjjOUuEqO05R4j8ImPpYDcQsAZBqPJ80AQP0y5JkF/Ayood4LsbNYIBIDoMGE2Dc3d5IA4lSIbaGNBGK5PivtB52Mv2mmDWryeBmDWDkXRSEHivIkObxp/2c6/nfJzZEN+LCGlZYpDY2Vzxnm7Vb2pHA5pkHcI06LjIJYG+IPIoHCHmKUmikLTVDao0b8PA7MGdCD2FnACwyH2AjiYHFOZISKT0sXBXMhhisEnSrK58ZDrA/xQmFeUJzKZqN0UqzKF1qfLuWwVfw5nlThV+7rgSw7ga3Sf50p5Kr0MfXCzPgkiKkQWxaIEiMhVofYKS87LlxlM6owkxM5YCOVxcrjt4Q4VigOCVDqYwXp0uBYlX1Jbt7AfLGNmSJupArvy8+MD1XmB2vh8xTxw7lgl4VidsKAjjBvTMTAXATCwCDl3LEuoTghTqXzQZIfEKsci1MlOdEqe9xcmBMi580hds0riFONxRPz4YJU6uPpkvzoeGWceGEWLyxaGQ++DEQADggETCCDNQ1MAllA1NbT0APvlD3BgAekIAMIgaOKGRiRpOgRw2scKAR/QiQEeYPjAhS9QlAA+a+DrPLqCNIVvQWKEdngKcS5IBzkwHuZYpR40FsieAIZ0T+882Dlw3hzYJX3/3t+gP3OsCEToWJkAx6ZGgOWxCBiIDGUGEy0ww1xX9wbj4BXf1hdcBbuOTCP7/aEp4R2wiPCdUIH4fZEUZH0pyhHgw6oH6zKRdqPucCtoaYbHoD7QHWojOvhhsARd4V+2Lgf9OwGWY4qbnlWmD9p/20GPzwNlR3FmYJShlD8KbY/j1S3V3cbVJHn+sf8KGNNG8w3Z7DnZ/+cH7IvgG34z5bYQmw/dhY7iZ3HjmANgIkdxxqxVuyoHA+urieK1TXgLVYRTzbUEf3D38CTlWcyz7nWudv5i7IvXzhV/o4GnEmSaVJRRmY+kw2/CEImV8x3GsZ0cXZxBUD+fVG+vt7EKL4biF7rd27eHwD4HO/v7z/8nQs7DsBeD7j9D33nbFnw06EGwLlDfJm0QMnh8gsBviU04E4zACbAAtjC+bgAd+AN/EEQCANRIB4kgwkw+ky4zqVgCpgB5oJiUAqWgdWgAmwAm8F2sAvsAw3gCDgJzoCL4DK4Du7C1dMJXoBe8A58RhCEhNARBmKAmCJWiAPigrAQXyQIiUBikWQkFclAxIgMmYHMQ0qRFUgFsgmpQfYih5CTyHmkHbmNPES6kdfIJxRDaagOaoxao8NRFspGw9F4dDyagU5GC9H56BK0HK1Gd6L16En0Inod7UBfoH0YwNQwPcwMc8RYGAeLwlKwdEyKzcJKsDKsGqvDmuBzvop1YD3YR5yIM3Am7ghXcCiegPPxyfgsfDFegW/H6/EW/Cr+EO/FvxHoBCOCA8GLwCWMIWQQphCKCWWErYSDhNNwL3US3hGJRD2iDdED7sVkYhZxOnExcR1xN/EEsZ34mNhHIpEMSA4kH1IUiUfKJxWT1pJ2ko6TrpA6SR/IamRTsgs5mJxCFpOLyGXkHeRj5CvkZ+TPFE2KFcWLEkURUKZRllK2UJoolyidlM9ULaoN1YcaT82izqWWU+uop6n3qG/U1NTM1TzVYtREanPUytX2qJ1Te6j2kaZNs6dxaONoMtoS2jbaCdpt2hs6nW5N96en0PPpS+g19FP0B/QP6gx1J3WuukB9tnqler36FfWXGhQNKw22xgSNQo0yjf0alzR6NCma1pocTZ7mLM1KzUOaNzX7tBhaI7SitHK1Fmvt0Dqv1aVN0rbWDtIWaM/X3qx9SvsxA2NYMDgMPmMeYwvjNKNTh6hjo8PVydIp1dml06bTq6ut66qbqDtVt1L3qG6HHqZnrcfVy9FbqrdP74bepyHGQ9hDhEMWDakbcmXIe/2h+v76Qv0S/d361/U/GTANggyyDZYbNBjcN8QN7Q1jDKcYrjc8bdgzVGeo91D+0JKh+4beMUKN7I1ijaYbbTZqNeozNjEOMZYYrzU+Zdxjomfib5JlssrkmEm3KcPU11Rkusr0uOlzpi6TzcxhljNbmL1mRmahZjKzTWZtZp/NbcwTzIvMd5vft6BasCzSLVZZNFv0WppajracYVlreceKYsWyyrRaY3XW6r21jXWS9QLrBusuG30brk2hTa3NPVu6rZ/tZNtq22t2RDuWXbbdOrvL9qi9m32mfaX9JQfUwd1B5LDOoX0YYZjnMPGw6mE3HWmObMcCx1rHh056ThFORU4NTi+HWw5PGb58+Nnh35zdnHOctzjfHaE9ImxE0YimEa9d7F34LpUu10bSRwaPnD2yceQrVwdXoet611tuDLfRbgvcmt2+unu4S93r3Ls9LD1SPao8brJ0WNGsxaxzngTPAM/Znkc8P3q5e+V77fP6y9vRO9t7h3fXKJtRwlFbRj32Mffh+Wzy6fBl+qb6bvTt8DPz4/lV+z3yt/AX+G/1f8a2Y2exd7JfBjgHSAMOBrzneHFmck4EYoEhgSWBbUHaQQlBFUEPgs2DM4Jrg3tD3EKmh5wIJYSGhy4Pvck15vK5NdzeMI+wmWEt4bTwuPCK8EcR9hHSiKbR6Oiw0StH34u0ihRHNkSBKG7Uyqj70TbRk6MPxxBjomMqY57GjoidEXs2jhE3MW5H3Lv4gPil8XcTbBNkCc2JGonjEmsS3ycFJq1I6hgzfMzMMReTDZNFyY0ppJTElK0pfWODxq4e2znObVzxuBvjbcZPHX9+guGEnAlHJ2pM5E3cn0pITUrdkfqFF8Wr5vWlcdOq0nr5HP4a/guBv2CVoFvoI1whfJbuk74ivSvDJ2NlRnemX2ZZZo+II6oQvcoKzdqQ9T47Kntbdn9OUs7uXHJuau4hsbY4W9wyyWTS1EntEgdJsaRjstfk1ZN7peHSrXlI3vi8xnwd+CPfKrOV/SJ7WOBbUFnwYUrilP1TtaaKp7ZOs5+2aNqzwuDC36bj0/nTm2eYzZg74+FM9sxNs5BZabOaZ1vMnj+7c07InO1zqXOz5/5e5Fy0oujtvKR5TfON58+Z//iXkF9qi9WLpcU3F3gv2LAQXyha2LZo5KK1i76VCEoulDqXlpV+WcxffOHXEb+W/9q/JH1J21L3peuXEZeJl91Y7rd8+wqtFYUrHq8cvbJ+FXNVyaq3qyeuPl/mWrZhDXWNbE1HeUR541rLtcvWfqnIrLheGVC5u8qoalHV+3WCdVfW+6+v22C8oXTDp42ijbc2hWyqr7auLttM3Fyw+emWxC1nf2P9VrPVcGvp1q/bxNs6tsdub6nxqKnZYbRjaS1aK6vt3jlu5+Vdgbsa6xzrNu3W2126B+yR7Xm+N3XvjX3h+5r3s/bXHbA6UHWQcbCkHqmfVt/bkNnQ0Zjc2H4o7FBzk3fTwcNOh7cdMTtSeVT36NJj1GPzj/UfLzzed0JyoudkxsnHzROb754ac+paS0xL2+nw0+fOBJ85dZZ99vg5n3NHznudP3SBdaHhovvF+la31oO/u/1+sM29rf6Sx6XGy56Xm9pHtR+74nfl5NXAq2euca9dvB55vf1Gwo1bN8fd7LgluNV1O+f2qzsFdz7fnXOPcK/kvub9sgdGD6r/sPtjd4d7x9GHgQ9bH8U9uvuY//jFk7wnXzrnP6U/LXtm+qymy6XrSHdw9+XnY593vpC8+NxT/KfWn1UvbV8e+Mv/r9beMb2dr6Sv+l8vfmPwZttb17fNfdF9D97lvvv8vuSDwYftH1kfz35K+vTs85QvpC/lX+2+Nn0L/3avP7e/X8KT8hS/AhisaHo6AK+3AUBPBoABz2fUscrzn6IgyjOrAoH/hJVnREVxB6AO/r/H9MC/m5sA7NkCj19QX2McANF0AOI9ATpy5GAdOKspzpXyQoTngI0JX9Ny08C/Kcoz5w9x/9wCuaor+Ln9F074fJCQmvv1AAAAimVYSWZNTQAqAAAACAAEARoABQAAAAEAAAA+ARsABQAAAAEAAABGASgAAwAAAAEAAgAAh2kABAAAAAEAAABOAAAAAAAAAJAAAAABAAAAkAAAAAEAA5KGAAcAAAASAAAAeKACAAQAAAABAAAF6qADAAQAAAABAAAApAAAAABBU0NJSQAAAFNjcmVlbnNob3SeRvRvAAAACXBIWXMAABYlAAAWJQFJUiTwAAAB12lUWHRYTUw6Y29tLmFkb2JlLnhtcAAAAAAAPHg6eG1wbWV0YSB4bWxuczp4PSJhZG9iZTpuczptZXRhLyIgeDp4bXB0az0iWE1QIENvcmUgNi4wLjAiPgogICA8cmRmOlJERiB4bWxuczpyZGY9Imh0dHA6Ly93d3cudzMub3JnLzE5OTkvMDIvMjItcmRmLXN5bnRheC1ucyMiPgogICAgICA8cmRmOkRlc2NyaXB0aW9uIHJkZjphYm91dD0iIgogICAgICAgICAgICB4bWxuczpleGlmPSJodHRwOi8vbnMuYWRvYmUuY29tL2V4aWYvMS4wLyI+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj4xNjQ8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpQaXhlbFhEaW1lbnNpb24+MTUxNDwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlVzZXJDb21tZW50PlNjcmVlbnNob3Q8L2V4aWY6VXNlckNvbW1lbnQ+CiAgICAgIDwvcmRmOkRlc2NyaXB0aW9uPgogICA8L3JkZjpSREY+CjwveDp4bXBtZXRhPgo0HBZGAAAAHGlET1QAAAACAAAAAAAAAFIAAAAoAAAAUgAAAFIAAEye8CqQSgAAQABJREFUeAHsnQfcHUXVhye0AEKkdyQI0pEEI4pIJIAICqKA0oWgqKiACoKgUgXpERALLRSVph8KSCLFUCIIKF1AEBN6lRIQCPXLM3Au553s3rt3b3lv+Z/fL7n33Z2ZnXl27+7sf86cGXLllVe+FWQiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAKDQmCIhPpB4a6DioAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEAkUFOoHzlypFCJgAiIgAiIQMcTuOWWW2IdfzBhWMfXVRUUAREQAREQgcM2mRYhdPv7lj1/dUZFQAREQAREQAREQAQaIyChvjF+yi0CIiACItAhBEwokFDfISdE1RABERABEahKQEJ9VTzaKQIiIAIiIAIiIAJ9R0BCfd+dcjVYBERABHqTgIT63jyvapUIiIAI9CoBCfW9embVLhEQAREQAREQAREoR0BCfTluyiUCIiACItBhBCTUd9gJUXVEQAREQASqEpBQXxWPdoqACIiACIiACIhA3xGQUN93p1wNFgEREIHeJGBC/frrr9+bDVSrREAEREAEeorAX/7yl9gexajvqdOqxoiACIiACIiACIhAaQIS6kujU0YREAEREIFOIiChvpPOhuoiAiIgAiJQi4CE+lqEtF8EREAEREAEREAE+ouAhPr+Ot9qrQiIgAj0LAEJ9T17atUwERABEehJAhLqe/K0qlEiIAIiIAIiIAIiUJqAhPrS6JRRBERABESgkwhIqO+ks6G6iIAIiIAI1CIgob4WIe0XAREQAREQAREQgf4iIKG+v863WisCIiACPUtAQn3Pnlo1TAREQAR6koCE+p48rWqUCIiACIiACIiACJQmIKG+NDplFAEREAER6CQCEuo76WyoLiIgAiIgArUISKivRUj7RUAEREAEREAERKC/CEio76/zrdaKgAiIQM8SkFDfs6dWDRMBERCBniQgob4nT6saJQIiIAIiIAIiIAKlCUioL41OGUVABERABDqJgIT6TjobqosIiIAIiEAtAhLqaxHSfhEQAREQAREQARHoLwIS6vvrfKu1IiACItCzBCTU9+ypVcNEQAREoCcJSKjvydOqRomACIiACIiACIhAaQIS6kujU0YREAEREIFOItBsoX7apNPD9Km3tqSJQ4ePCMPG7NKSslWoCIiACIhAdxCQUN8d50m1FAEREAEREAEREIF2ERgUof7+++8P//73v6u28VOf+lTcXyvt8ssvH5ZbbrmqZWmnCIiACIhA7xNotlD/1Pg9wvQpLRLqlx0RFh57Qu+fFLVQBERABEQgl0A7hHrepbBWvi/Z8ze3odohAiIgAiIgAiIgAiJQiEDbhfq99tqrUMWOPfbYmO7nP/95sA5mVsaNNtoomKiftV/byhO48cYbw6OPPhoWXHDBsO6665YvSDlFQAR6ggD34rIv+o3kLQrPhIL111+/aJaq6UyoHzZm5zB0+MiqaYvunD71ljBt0hlhqIT6osjCq6++GuaYY47C6fst4V//+tcwceLEsOmmm4aPfOQj/db8vmjv888/H84888ww66yzhi996Uth3nnn7cl2t+tafuutt8KUKVPCc889FznON998Ydlllw1DhgxpO9dWC/U8e3mXwr7xjW+UfobXAmPP31rptF8EREAEREAEREAERKA6gbYK9X/+85/DZZddFmtUS+yhM4mRxwv1/jv7ywr1vPAgQg8fPjxsu+22FCVLCHznO98JV111VRgxYkQYP358sld/ioAI9BMBu3+Xuec2krcexiYUNFuoX3js8TOE9SYJ9VNuCU+N37OUUH/yySeHhx9+OAotO+20Uz1oui7tTTfdFE466aTwj3/8I/zvf/+LA8aI0LvvvntYccUVc9tz/fXXhwkTJuTu9zt+8IMfhKFDh/pNpb7/4he/CI899lhYYYUVwg477FCzDPpBkydPDrPMMks48MADGxIHEXBHjnz72nzPe94TGGCfa665atah0xPYtV6knh/96EfDpz/96SJJuzbNcccdF372s5/F+u+zzz7h61//ete2Ja/i7biW//Of/4Qrr7wycH95/fXXB1RlttlmCx/+8IfDBhtsEN7//vcP2NfKPyTUt5KuyhYBERABERABERCB7iMwKEI9Ir0J8fUg814hlq+MaEReXqb/+c9/hlGjRoVTTjnFitOnIyCh3sHQVxHoYwLpvbee+66J9IavHR59vSrUf+5znwu33357QJj87W9/a0h77vP4448P/MuzY445JmyxxRaZu0844YTw05/+NHNfuvG2225rimcys/ruu+++MHr06HDGGWekh5np7yOPPDL86le/itsJA4hgX9baIW6WrVsj+exaL1IGg1YMeHSzXXzxxdHDmxmM22+//UxNGTduXDjxxBPj9n333Td87WtfmylNt29o9bX8u9/9Llx66aWFMDHws9VWWxVK22giCfWNElR+ERABERABERABEegtAl0j1KdCEWI/2+oRjPypk1DvaWR/l1CfzUVbRaAfCaSCe5F7b5k8jbBth0f9wweMLlXFpQ65Juab3oBHvYmXvSzUX3jhhcFC5OEhvscee8SQFHfeeWdAhDc755xzMsO8/OhHPwq/+c1vogc+s8HyDHGcwYA555wzL0nh7YMp1FNJwoVcfvnl0at8rbXWKlzvTk5o1zrXQK02bbjhhl0/M/LLX/5ymDRpUrzW8fhODRGbmaCEgKL/Os8886RJeuLvVl3LOOQw26YeW3vttcOuu+5aT5ZSaSXUl8KmTCIgAiIgAiIgAiLQswS6QqhPRXo8MhGAJNS39rqUUN9avipdBLqNQD3Cez1pm8VBQn2zSA5OOa+88kpcD+W///1vrADX0Ac+8IFKZa699tpgIX8++MEPBkT9NKb0brvtFvsHu+yyS/jhD39YydvKL4Mt1LeybYNVtgn1vTwo5dnWEup9Wn2vj0A9nvRpye3wrJdQn1LX3yIgAiIgAiIgAiLQ3wQ6XqjPEunxprdFZot4dWad4mZ51L/00kvhkUceCQsttFCYf/75sw7Vsm3PPvtsQNBYaqmlCnsFEpPzqaeeCi+++GJYeumlq+bLEurffPPN8OCDD0ZvLtpc1PAGe/LJJ2Ps3EUWWaTuhQGffvrpwD/qjIedTAREYHAIFBHgi6RpRe0l1Nemyj2cZwD3U8JsLLzwwnGByto5Q3j55ZdjLHYWYmR9Fxa2bKb94Q9/CN/97ndjkSyYedBBB81UPAI8a6dg559/fgxfF/94578tt9wycB20MzxIq4T6N954I4ZDIeb8EkssMdOghG93Pd+feeaZyvO0aDx7+gzE4Sem/5JLLtn0c5/Wv5OE+jK8+J3RP+N3tvjiiwcWK61mzRLq/XHpKy266KJ1r8PAdUcsdxasJX86GJbVDu4JrPvEYNsyyywTiPfeauOYjz/+eOSbdyza8eMf/zhvd6HtDPi1Mma9hPpCp0GJREAEREAEREAERKBvCHS0UJ8n0nN2BlOo5yXmrLPOCr///e+jSG9XywILLBCYKstCX8OGDbPN8fO8884LRxxxRPx+ww03ZArVv/71r8Oxxx4b07CAnsWtveaaa8Kee+4ZtxPHFK9CynvggQfiNv5jiv/+++8/wPuwsnPGF4TyX/7yl+Hcc8/1m2P6T37ykwHxIxVdvFBPyIGf/OQn4eqrrw4MTmC8wJH329/+9kx57SBM5WahPeL3ekNMYWYEzLxRz/XWWy9uOvTQQ+ML4umnnx5fGtlIHTbeeOO4X/+JgAgMDoFqQny1fa2urYT6fMI8t0477bS4Jot5rJMaMY+FKfmXPgOstHvuuSeGieHcemPhRTzcm7WQp4/dznOSQYTUqIsdj2dEGs/7Yx/7WBTveJZ+/vOfT7O35O9mC/U8/+kv8OmNRS6Jxc7gvLftttsu/O1vf4tcbMFR9iMAfuUrX4lJGdzgeUzfZcqUKZXsH/rQh6KQmbdAL7yPOuqoyuCIZeS5/9WvfjXy53cHa+u/WJpGPssK9WeffXYlXj11J1RMavQpTLylb2LXfTN48TujfBbD9b+z973vfXFtie9///sDRHvOqT8fvq4rrbRSJaY655fzjBFnnX3eOC7hXfiN++OShnPD+cLRIbXPfvazgbBSX/ziF2OYKa6vNPQOfVquoyzxHaH8kEMOif1SFn02W2211QKLNbMAdD2Wdy3bvWGxxRaLx6K/zFoVHBMxPs/KhLxJy2p1CBwJ9Slx/S0CIiACIiACIiAC/U2gY4X6aiI9pwzBgDR41/OSXK+V9aifPn16wPOJhWjzDM8bxGk8x81Y+O/oo4+Of/LChVdaasQftUXwvFDPy7V5GVJvXlDy7KKLLprpZQwPJ16o77jjjrxsYZ111gmHH374gAEGE+p5IcSz6qabbsrMv/nmm4cDDjigMrBgiXy9bZv/XH311ePLrI8RzCwBWwhyk002CRMmTPBZJNQPoKE/RGDwCGQJ8tTmsssuq1Sq7IynSgF1fpFQnw8MYRIBMc/yQsVMnTo1bLbZZlEQy8vLoCvPKBtczktXazvxoBEJEfl4luUZzw4EurTOeNjSJ8AQbHmuPffcc3EWGM9cwug0WsesOjVTqGcwnUH3PGNghQVrEdjNvvCFL0RRn4FzW6SWffwWGYDBYFXt/PO8xhPa28MPPxx4DnsB1u+n3TgMIIh/5jOfqSx26tOU/V5WqB8/fnxgAAe76667MmcNIqKb44QX6hvlxTFr/c4Q7BGPLaSTDSyRN7Vll122IpoTu33HHXeMSXDYWHXVVQckZ/YJgzB5xnEJAZPOhLRrFwcJZoemThVWHkK+MbNt3G+Z+ZJ3fZCOepGmqOVdy8aVWUBck74fnCfUcz9g0V1mkjZiDFDwuyoys6DMcZoh1PM+lGcsWG3PZZ7Jyy+/fF7Syv0zN0GVHfb8rZJEu0RABERABERABERABAoQ6EihvpZIX6BdNZOUFep50TnssMNi+bzYbLPNNvHl9l//+lccPDBheezYsdE7ySrSLKGe8hApGCxYZZVVoghx6qmnVjrheGfhZWSGl9Xee+9d8Yajk443IoMJt912W/jTn/4UPfFIz0ugDQjwtwn1fMfwnEREZxo3L+ZweOKJJ+I+vPW95xQvyObpiOc9YgFe/0wDnzhxYpyNQEbqg5e8iSdeqI8Fz/iPmMNrrLFGDLfD9P92hxiyeuhTBERgIIFUrPd72y3Sc2wTCmywz9enzPenxu8Rpk+5NSw89vgwdNmRsYhuXEyWxVVZZBUj5jf38pVXXjnexxF9GTzGDj744IoYyN+EyGEgFq9ZjPs4zxgGbidPnhwXbTVvYMRH9jViPFMJrcYzynuGp2WaF/DHP/7xAeIkM7JGjnz7PDHwzaw3L/gjclNHns88U5plJnaOHj06iui1yjXvYNIhotnzD0cAzguGsHrcccfFevLcZGYd4XywtN154qYXnskHGwZVGAjhWXvSSSfFPgD7mKnGTEWzF154IWy11VYV4XbbbbeNaRDz+Z3Rp/GD970m1MOhHl6k5zdAfwaD8c477xwHVOjTXnHFFZUZjd5Tnj7Ua6+9FhdQhice44R0wpgNYA4f1YR6HEPMEYQBHPpea665ZnjooYficfktYKzrwHmbe+6549/8Z9eubcB7ft11141hsagPg0YmxNNftOuT9Az+XPVOGKpx48bFGZb0OW+++ebY78Szn98cs2P8Me1YWZ9517IJ9ZaHgQ6OT7+TMFxZ1oywN1ZuK8PfNCrUp+9MVucyn9wfbLCz3vz2/K03n9KLgAiIgAiIgAiIgAgMJNBxQn3a4Wyk0ziwqQP/KivU4/HOizUhW5jW6z3j8drhpYd4png7eY+fZgn1COx4jPnQOsQk5eWGlxJehnihM/Ne7YgIvEyZKECaV199NfACbnkvv/zyyguVF+rxdORceLv77rsrU7EJzcNLqRneV3hmUR844BnmDUGIeMQYQpGJJqlQj1ckL7wyERCBziSQJdYPhkgPHRMKJNS/e61wTzXva+6leGx70QwRjucHg68Yg86zzz57/O7Fsf322y/wHPCGN/WYMWPiplQ89umKfPciNV6wJkpn5f3Wt74Vw3/gXevFYp5jG264YcyCQGgCY1YZPH8QLpthJnY2KtT7xXLx+ra2WB0JecfvDUNkt/jyeeKmF+oRNhGA3/ve91pxgb4D3sk8q+HlZ92dc845sY9DYoRf81K3zOl10yqhnnpzHeYZDHwfoVke9fXyQpQmFBSG2I6onToVMFjGoBmG0wdiulmtGPV5Qr0/LoM7f/zjHwecY8rHqYKQOFjq4W7XLvvwmKfv5o3rDWcJjBA39J2xadOmRecLvlN3+sPeEOu5RvFqpy+Jg0kRy7uW/b2Ia5V64bhRzahDtQG/annTfdxz/PlK9zfyt4T6RugprwiIgAiIgAiIgAj0HoGOEurrEekHM0Z9tcvg+OOPr3jU+fA1zRLqES/w4k+NKe0nnnhi3Iw4bwuX8ZJiL2jEl/cCv5UBd/ZhhDiwuMBeqPdlWj4+8cLDI4wp98SxxVhwkOncGB76Nl07bnjnP17yEXYwYrZuvfXW8bsX6nmBTKdax0T6TwREoGMISKgvdiqWOuSamHD6lFvCU+P3nOGhP2KGp/4JxTK/k6pMOBAv8F144YWVQVF/YC8Qe69ZOx6exQiLWaEf8KhF5CfOdyrk+2PU+k6IHRtgIU42Xv955kU7H97k73//+wChkWclzx8EPZ5zDERbaBjEPkTNZiwSaWJno0K9P1c8OxEHi1ieuOmF+lSgtXK9NzZ9FhOX8aRmUAdOCJ42eGP5+CQEIH0GrFVCfSy8yn+I4tddd10lRbOE+np5+d8QHux4pafGgrzmwMHMFmYZmpUV6v1x6WdSbmo4kXBtMjMmXUvArl3OM7MsvSMH5eAhz6AF5kNN0Ycz8Z2ZMPyusq6RmLGO//KuZf+bp59t1121ovtFqIcB97c8U+ibPDLaLgIiIAIiIAIiIAKdSaBjhPp6RHpQdoJQjwfgo48+Gnj54h8vLkx7xqMeQzSwRcqaJdR77/N4kHf+Q1xhai7Gd/M0IjY9HodMtcZDrh4zoX7JJZcMl1xySWZWKx9PMjwAsdtvv70isiDc+JA4vhBb5I4wBnjYY16ox4Mz9e7y+fVdBERgcAlkifRWo8Hwqh9sj3rEd0LlZNlgCfUIaIRawRjQNS9sX8fHHnusEvaMAVdCnjDbyhasJOQNITFaad7zn4Uv99hjj9zDURcGDjD6DjaA4L1/8xaThYF5aNcjhudWZsYOEzsbFeq9lzLHYxFLhFWer3hLWzvTuuSJm16oTz24rQxmFljIO8Lr2EK11qZqMyW8iDtYQj1cLPwKbWqWUF8vL/qkFnbw1ltvzXSKMOZZn2WFesIOmpMEfa955pknq/g4I5KwgykvO8/Vrl2Lo5/GqbfrjgMyaxKHC9ISHsf6vpmVqbLRykzXW/BCPeebdtSyfgl9U4uDf79q1Sxl6mDP31r10X4REAEREAEREAEREIHqBDpCqPedSKpbpCM5mEI9Xm94RVlc3zzErRDq8QDMekHBU9CEFBPqmdZuIQ/wjMRLsR4zoR6vL15+swyPP3h4of6CCy6IC9Nmpc/ahkcjsYQxL9Tj0UZ8ZJkIiEDnEUhFeoR5DHHQrN1ivQkF5plt9Sj7WTRGvfeOnzbp9DBt0hkzHXKwhHoLEzNThXI2mNes95YmVjrPkFabLRLLQIGJj1nHRBBkAJrQNRZCjXQMlhOXG0F7xRVXzMoatxGnntj6COEWiiQ3cYEdRcROX0xejHrSeK96n4cwP7R7iy22mGkWQJ646YV6wmtkxfK+9NJLK577JtR7AZ6FQHkW5xnrCbRyMVn6MHn9D+qEIOwHn5ol1NfDi3oQrol+WOrhz74iVlaoJywN9+Jaxz3hhBMCoRsxP5Bg127qae/rbGtHpEI9jirbbbddXFfCp+c71yTXap6jRpre/s67lr1Q7xcAtnxZn/20mGxW+22bf8cq8n5l+er9tOdvvfmUXgREQAREQAREQAREYCCBjhDqveBTtBM5WEK9DzFjKFlcjeniLLDHNGSzwRbqeUnBG+6ll14K3mvd6lfrs6xQj+e+iSx441sonfR4vCwizLA4rXl8SqhPKelvEeg8Av6eTe28IF9tX6tbYkJBO4X6YWN2DsPG7DKgaVlifSrUZ+UbUEjGHxaKhvAWzNIqYiYgktYGbrPyEfaE/ZxLZkIhviLCYngKI7q12kyAr9U+BoaJzV1L0M+rL8LzWWedFXf7mPx56WttN7E6HTjIy0e4N1sw1C8ma+mZlceAO4MQdk3bPj7p/xB2zixP3Cwr1FOuibN80u/JMi/ot8qjvta1kNZrsIR6GxBL101I65f3dzcK9bTllVdeCQxqEFbryiuvnKl5zJxkdmTebJA0Q961XEaop2wW+L3++uvTw9T1NwN6jYT1qnWwRmPU1ypfQn0tQtovAiIgAiIgAiIgAp1FoCOEepAg7iy//PJhueWWK0RoMIR6H3t90UUXjeLFKqusMiCm56mnnhoX0KIReUL95MmTY9zXtKG0iZcKzMe39wvC1uNRTzkMfPCSYgujsa2olRXqiQvKSyfGCz7xjYuahPqipJROBAaHQBEhvkiaVtTeRM12CfULjz1+Rqz57PubxaK3dg6WUM+CjoSBwRDf55hjDqtS1U/iWq+wwgoxDffzdLHIqplL7rTFNhE7mbGWFT7jhRdeqMTZZxYZYXnMnnvuuehVTxvzBohJ60U/Zg54j2wrq55PFlLHGx278847ByzWm1WOieBFRF1mCTB7gIXV6QuYca3bwrB54mYjQv33vve9ONON2OUMqmedCzyb8cjGOlGozwsFwwwRW2TUe2c3wsuHmPKx/u181fosK9T7NQby2suxbSAhL/RNGY/6tE3cM/g9ETbIz1ShH8g1X8TyrmX/my0a+objNSP8DWElm7GWRV77JdTnkdF2ERABERABERABEehPAh0j1NeLfzCEegRvhG+MjvuWW245U7WZhmwhcbxQf9FFF1VCz+B5lDUFfa+99oqeSRTaLKGe+KW2eF7egrCPPPJIuOGGGwIe+HhTMjMAKyvUE6/fFlLjZZ+p0UVNQn1RUkonAu0nUI8AX0/aZrWkXUI9oW6GrTc2V6T37bHwOYMl1CNqEc4G4zm02mqr+epV/W6iGXnw7k4XmiQzA7P33ntvGDp0aIynXrXAGjsR9BDkMIuVn2ZhIUn+YYiUJhTz9wEHHBDD0iEu80ybe+652TyTmQc86e64446Z9te74ZBDDqksIo9H9yc+8YncIggXYgup1+st7sOXePHTzlMa17sR4ZmBAQuXl7VGAWv07LDDDrGvQmM7RahHJLYwgFdccUWmwGrhYqh3s4R6FrSFB3baaaeFMWPGxO/+P7zPcbYgLCGzQnBOMTOhPhXSbb8PiXTxxReHVVddNe5q1mKyzRDqra580l/efvvt46as68en9d/zrmUv1BddTNbK5ZogxFMZ417BzJ1WmoT6VtJV2SIgAiIgAiIgAiLQfQQk1I8aVfFir3X6eFHCKwlj8bUdd9xxQBY86fw2L9Tjybjtttvm5mVqqn8ZaJZQj5ffnnvuGY/L9F28yLzYwoKBO+20U/S0RNTgxR7xAisr1JOXEA0PPPBAFEqI589CY96efvrpwIKBzFJAaGFRWkxCvaek7yLQOQTKCO9l8jTS4nYI9YS1SUPd1Kqzz2Oe9u0KffPkk08GBGGM0CyEzEkFbEQ1i0NOmArzfj3iiCMqi4TvvffelYFqa++DDz5YSduMeO++PLzNeX55b/cnnngixpXn+DynqLdfPBNx3p6ztCMrXAUipz0TSXvYYYdZc0p/Mlhhz2/qxZorNhvBF0pIG8Rc+gMYfP2C6cQQp59BGcyCgIE3X3fWgrFQRnniZiNCPc9oQuY9/vjjsQqIrQzk42TA74znuvfw7xSh3q+twCwQm91nHP0sALY1S6in72Lng/M2YcKEsNBCC9lh4+fBBx8czjzzzPj9vPPOi2K9JfCDB8ygSM99nlDPeVprrbViMYj8DMYNGzbMio2fXOMMHmD8zll3wKxsjHquLWaA0p/EIWPUjL60N64bFpbF2E/7zAhbhTgNr9RTPe9a9kI9vw9bD8DKrPVZJgROq0PeWJ0l1BsJfYqACIiACIiACIiACECg74V64svvvvvuVa8GXn4IHUOs93XWWSemRejYf//940JZeJDzYnX44YcPKMcL9XifmVhCIl7YeAkgxivTyo8++ujAS7xZs4R6PLeIh8tLDYbH3aabbhpFhLvuuivw4o/HJTZ27Niwxx57xO/814hQf+ONN8bF1SiHMEEIJoTAmXPOOePUaDyi8OTHzjjjjEooAwn1EYn+E4GOIuBj3FIxH5O+VkVTsb7oOiS1ys3a3w6hPuu49WxrhlDPwCfiVzVDUF1ppZVikhNPPDGMGzcufseTlwHlNddcMw6UIrbaswsBjBlhfGI8k4hNj4COIdZuuOGGUUTkmYdXt4nO3sM7Ji75H8/Vc889N+bGmxVhEcGTOtBmjoulYW/YxvOU5yxCIEZ8/m222SYuwM4is3hYm8c+bWR2m/dqjplK/ufjzlM2gwE88+g7cOzbbrstiqUsYotRT8RTPxBxySWXVJ7BnCfay2wGZrtxXmgzbUPE9aGB8sTNRoR66kj8/k022YSvmYbQSmgeroFOEerpa6288sqV+jIzgxl+9IXokzH7wa4PEjVLqKcsRHj6dhjnjZBIiOic/6uvvroy6JW1lgG/QUInYptttlkccFpqqaUC/7A8oZ59zDBlHQmMc4IQP2LEiPDwww/HuPH8NjHqxG/LD9SVFer9zBCudwaZODYzMrke+N3Sj8X8zAZ+o5wPGwBiRgBrGZnlXcteqCctvyv6lfxO6McXsXo869vhSW91llBvJPQpAiIgAiIgAiIgAiIAgbYK9V7sKRqLPu80URZWj2Dky8KrDc+rIsaL/r777huTMvWcqe15hhBuorgX6klP6AB7icvKjxejiebNEuo5DtOt8WZiQCDP8IbiRc9i3pKuEaGe/CyGR4igapbOTJBQX42W9onA4BEwwb3MPbeRvPW0uF+E+iJMmClloUsQevEuNgE8L3/q5Us6ZkYxuIsgm2eE1kFg97O18tLW2s7AN+HSmKGWZxtssEEgrFtW3HTEZQadTQjMKgNhkUXPES6bZQjBiK0milYrl2c9awd4kZ70zHBjEMUGI/LKSEP+5ImbjQr1HN/E7fR8cH0xK45zz++uU4R66sxsA+uz8Xdqvp/WTKGe43jBPT0ufy+22GLxGrFBNEvjZ2XYNgbkbIHWakI96RHGbYFky+8/cThBqE69/MsK9ZTNIAi/w2qWhr157LHHKg4v5Ct6LZtQzyAVa5Bwjs2IQ1/USAtTfmPE1Pc222yzReGf+0vq6e/TNft7q4V66mvvTI2+e1Vruz1/q6XRPhEQAREQAREQAREQgdoE2irUUx3isDfTbJG8esvE46hobFpEfV/v888/P75oMQ3fDO8lvN14qWWhMsyL7ZaOlwvEEv9igTcQIgcCQ1ZePLF4IcbwgDcPKyuTT148CE2AMeWal0Fvzz//fHyhSoUajo3wRugZXlK8UR7l4hWYJz7gNUh4ArwDeeFKbeLEiXF6tG8vaSiTFzibsm35qCciBnbooYdGgSj+of9EQAQGnQAv+2Vf9BvJW7ThJhQ0ezFZwtQMHZ69aGzRulm66VNvCdMmnTEjfM7OdYfQYV0Ua6OVl/eJgOoHSvFkxfMWMc97FJOf+NTf/OY3c8UpezbZILQdE6Gb0CKbb765bWrKJ578iIA8a1PjucEsuFTk9ukI94OHuw18+33Eh0fEtRjffl8zvvOs5FmYMqZsBgi23nrr2FfIW9SX80QZLMZpMxksL89LBlxSEZEyER433njjeI6tHTy/LfwPLBBrU+MZbWvvIAYvvvjiaZL4Nx7UDH6wFgECsnllW7x/wuTgVd0ss2ud81VNgM47HvzIhxBvRr3pVzCb0GaReKG+Gbzs/J188skDrgEEZmZkso7CAgssYFUa8InnOXWePHly3I7XOIOcmI/5Trz1VOjnuByTaye99ghFSB8y6/zb+SN0E7+5LEO4ZiYIMefh543rinw2s8b20cfjd2r9OdvOp82aYWYBA2b+t5x3LXuhnvUA4MT1xgBi2r/0x8r7zuAlbWIBamy++eaL1/WQIUPysrRsezuE+pZV3hVc9NnksuirCIiACIiACIiACIhABoG2C/XUwV48MupTeBPT1csKRoUPUiMhYgL/Fl544QGe6DWyxd3Tpk0LCP28vOW9tBUpp940r732WjwuseGXWGKJSoiDesupNz0vQ7DC4xIhgJd9mQiIgAg0k4AJBc0W6ptZRyurjFBveRv5RNB76qmnokBFmAoGdbM807OOQfg3PGIRs5Zeeukw++yzD0hG2fwrY9QhrQfHYqFanh+LLLJIDNnGM7Oo4aHOIMPUqVNjfRHn0zpTFunKGuWl4h4M7r777ihsM0OAkG/Uf5VVVonfix4L3oSIIw4/5yk9TtFyyqQj9rnNSmBgwIR5Xxb9CBvwYGDE1pppNk9/zHq/M/hPO/Akr+faqfc4aXquARgyS5Dj0k8sapx3vL0RsLOu12rl+OOSn+vOC+HV8jayj/oSaodrFGeS9Leclk0bs66pNJ397YV6m3HCLBYGkLKcVyxfN3xKqO+Gs6Q6ioAIiIAIiIAIiED7CAyKUN++5ulIIiACIiAC/UKg2UI9i8C2yvDQH7psc7z0W1XHesv1cdrrzZsVt7veMsqkR1wcPXp0mawxDx7MWV7DpQvskIyIoXg3Y7SPxTi9+IoYz4wNwqlgzPZDtBfPiEP/NZlAllDf5EMMWnES6gcNvQ4sAiIgAiIgAiIgAh1JQEJ9R54WVUoEREAERKBeAs0W6us9fr+nJ6SMjx1dDw/C6Fx00UX1ZGlKWsLLIESXNRaEHTNmTNnsHZ2PkCm33357rCMe/WuvvXZcxJPQKqxBYx73LCLK+gbMmBPPjj6lXVs5CfWdf+rs+dv5NVUNRUAEREAEREAERKCzCUio7+zzo9qJgAiIgAgUJGBCQbNC3xQ8rJK9Q4A41YSrKWMsZL7mmmuWydpQnunTpwdiXpe1NdZYo63h68rWs0w+zuV+++0X16HJy09sc+KWWzg78cwjpe2NEJBQ3wi99uS15297jqajiIAIiIAIiIAIiEDvEpBQ37vnVi0TAREQgb4iYEKBhPq+Ou1qbAsJsOgmoTnuvPPOuDAr6xsMHz48hrkh5v6oUaNaeHQVLQJvE5g0aVJcMJl1NXbbbbeewqLQNz11OtUYERABERABERABEWiYgIT6hhGqABEQAREQgU4gIKG+E86C6iACIiACIlCUgIT6oqSUTgREQAREQAREQAT6g4CE+v44z2qlCIiACPQ8AQn1PX+K1UAREAER6CkCEup76nSqMSIgAiIgAiIgAiLQMAEJ9Q0jVAEiIAIiIAKdQEBCfSecBdVBBERABESgKAEJ9UVJKZ0IiIAIiIAIiIAI9AcBCfX9cZ7VShEQARHoeQIS6nv+FKuBIiACItBTBCTU99TpVGNEQAREQAREQAREoGECEuobRqgCREAEREAEOoGAhPpOOAuqgwiIgAiIQFECEuqLklI6ERABERABERABEegPAhLq++M8q5UiIAIi0PMEJNT3/ClWA0VABESgpwhIqO+p06nGiIAIiIAIiIAIiEDDBCTUN4xQBYiACIiACHQCAQn1nXAWVAcREAEREIGiBCTUFyWldCIgAiIgAiIgAiLQHwQk1PfHeVYrRUAERKDnCUio7/lTrAaKgAiIQE8RkFDfU6dTjREBERABERABERCBhgnUFOobPoIKEAEREAEREIE2Elh//fXbeDQdSgREQAREQATKETChvlxu5RIBERABERABERABEeg1AhLqe+2Mqj0iIAIi0OcEJNT3+QWg5ouACIhAlxCQUN8lJ0rVFAEREAEREAEREIE2Eagp1EvwaNOZ0GFEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAS6kkCjjhgS6rvytKvSIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACnUJAQn2nnAnVQwREQAREQAREQAREQAREQAREQAREQAREQAREQAREoC8JSKjvy9OuRouACIiACIiACIiACIiACIiACIiACIiACIiACIiACHQKAQn1nXImVA8REAEREAEREIGmE/jK0XeFyXc81/RyVaAIdBuBj68+Xzj1e6t0W7VVXxEQAREQAREQAREQARHoGwIS6vvmVKuhIiACIiACItBfBA464/5w7l+e6K9Gq7UiUIXANusvGg7aebkqKbRLBERABERABERABERABERgsAhIqB8s8jquCIiACIiACIhAywjc+u8XwjaH3DGg/JWWnjPssvFCYa0V5wlDZx8yYJ/+EIFeIjD9tbfCjf96MZw+8elwz0OvDGjauQesHkYsP++AbfpDBERABERABERABERABERg8AlIqB/8c6AaiIAIiIAIiIAINJlA6k2PSP/LPYdLoG8yZxXX2QQQ7L9+/NQBYr286jv7nKl2IiACIiACIiACIiAC/UtAQn3/nnu1XAREQAREQAR6lsDG+9wSpj7+cqV9R+26VFh3NXkRV4DoS98QuPbOF8I+pzxcae/wxeYKE48aWflbX0RABERABERABERABERABDqDgIT6zjgPqoUIiIAIiIAIiEATCaw29vrw+htvVUq86piV5E1foaEv/UQAr/r19r6n0uTZZh0S7hy/duVvfREBERABERABERABERABEegMAhLqO+M8qBYiIAIiIAIiIAJNJLDSl64bUNr1x6884G/9IQL9RGDtPe8e0Nx7zvrYgL/1hwiIgAiIgAiIgAiIgAiIwOATkFA/+OdANRABERABERABEWgyAQn1TQaq4rqagIT6rj59qrwIiIAIiIAIiIAIiECfEJBQ3ycnWs0UgW4jcM8994SLLrooVpvvm2++efzXbe1QfUVABAaHgIT6weGuo3YmgWYJ9f99YXo45Lxbw7V3PRHeeuvd0FKtbPWQIUPCuqssGg7YekRYcN6hrTyUyhYBERABERABERABERCBQSUgoX5Q8evgIvAuAcToRmyllVZqJHvH5T3qqKNCyoQ27rPPPh1XV1VIBESg8whIqO+8c6IaDR6BZgn13znthnDVnY8PSkPWW22xMO7LHxmUY+ugIiACIiACIiACIiAC/UHg4YcfDlOnTg1PPvlkeOmll2Kj55577rDIIouE4cOHh6WWWqqlICTUtxSvCm8GARNre02I9myyRGm/v8h3BOxuY2TnNm3fv/71r/DHP/4xbqZNPh2e9SuuuGKapZI2c4c2ioAI9B0BCfV9d8rV4CoEmiXUr7X3xeG1N96scqTW7Zp91lnCjcds1roDqGQREAERKEhgl112KZTy9NNPL5ROiURABERABAaXwLPPPhsmTZoUbrzxxijQV6sNgv1aa60VxowZE+aff/5qSUvtk1BfCpsytYNAlnjdi+FPEKRNlG6EazcJ9QjvhLXxAnxe262Dm3U9ZOXpJg5Z9dc2ERCB5hCQUN8cjiqlNwg0S6gftddF4Y032xPyJiU/6yxDwt+P/Wy6WX+LgAiIQNsJSKhvO3IdUAREQARaRuDCCy8MF198cWb5s802W9z++uuvZ+7fbLPNwuc///nMfWU3drVQ348CZ9kT3W35qomyZcKfIAhTprdOEXR9R4+BiLLWSN6yxyybr+hv159rz6nacX2eaum0TwREoLcJSKjv7fOr1tVHQEJ9fbyUWgREoLcJ1OM0BAlzHKpFJX2HLZqvVrmt2m/OYrXeI4uma1U9Va4IiIAItIIAIW7Gjx8fpkyZUil+6aWXDqNGjYrRKpZccslAyBuMEDiPPPJIdDb9+9//Hh566KFKnmWXXTaMHTu2aSFxulaoLyr0VcjV+NIpom2NavbF7vTcIrwS6sQ6CECoR4zNEukpo1M6TiZA19Mm6t/N5tv82c++7R1XzcMeNlne92zPyt8p57abz5Hq3lwCdv1ynVvoJj65hvOMPNX25+XT9rcJSKjXlSAC7xKQUP8uC30TAREQgVRQr0WkyLuFlenfW4rkq3XsVu3379zVZq0XTdeqeqpcERABEWgFgXvvvTeceOKJ4X//+18sHoEebelDH/pQocPdfPPNUaM0wf4973lP2H333cMKK6xQKH+1RF0r1JvQR+PKCBkmmng4vSLWW9vKcPE8Buu7P7dp56bavqz6wiL1pCddJ51raxPni3r1g2W12Tq39bTfM/P50+umnjLbmfaf//xnWHXVVdt5SB2rzQT8y03eobNejsjHWg39ck/IY9PIdgn12fS4rvACwbbZZpsw66yzZicssXXixInhv//9b1hmmWXCxz/+8RIlKEurCLRTqP/WxsuFNZedPzzw1P/CqX+ZGh555uWmNEuhb5qCUYX0IYHnn38+XHbZZbHln/jEJ+JieN2C4Q9/+EN47bXXwiqrrNLUPrO9izTrncHeQ+zdpNnlt+J8pe/J9EfNmcSO59cNY1snvUNbHbv5s1XXdzczqafuCKX3339/eP/7318RV7nfnXnmmbF/+6UvfSnMO++89RTZ9LTXXXdd9MJeaKGFYjzzph+gywrMuubvu+++cOutt4Z2MsKT/sgjj6yI9BtuuGHYbrvtStH87W9/G6644oqYF7F+3333bdizvuuFensY1kvUHp5pvm5/+PgHbje2xdc/S7yyThDnrVb7fFn+PNfK59O247tdi+m1bEId7ShrWQzLltXMfFlt9ue26LE8M5+/WZ3uovUomw6x7MorrwxbbbVVWG655coWo3wdSIDfbbVZIllVtt+rifv++s5Kr23VCbRaqMcDY+rUqWGWWWYJX//61+v6DU+YMCHQAXvzzTfDpz71qbDRRhtVb0wT95588snh+9//fiyR6ZtzzTVX00pnQaXbbrstfO5zn+uYWWtNa1yXF9QqoX6O2WYJn1lzsQqd9VddJKy69LDK309Nmx7Ove7dqcGVHQW+/Onmx8Orr7+7cK2E+gLQlKQuAogDZ599dsyDFx1T3WvZEUccEae/f/SjHw2f/vSnayXviP04hhBDF6O966yzTkfUq0glEOCwPfbYI3z7298ukqVQGnsXacY7g72D+H5bM8sv1KCSifLel7OK67R36Kw6ptvo8/CbxXbYYYcBuxHURowYEUaOHDlog1etur4HNHSQ/njyySfD5ZdfHu64445wyy23BOJ746m82mqrBUTRJZZYoqGaIcjjFII39GmnnVYRwY877rjws5/9LJbNNUsffTDta1/7WuRA2y+44ILBrEpHHDvrmud9av3114/1u/TSS0s5YtfbuEMPPbQS7ob48vaMrLccS098e+LcY4TB+dGPfmS7Sn1KqM/A1o0PIWuGf9h2Yzt8/X1nx9pnAhZ/V2ufL8fy1srj07Xzu3XkfHt9OxutSzVOjZZdNn9Wm62TW0+ZnpnP34xOdz31KJsWof7nP/95zP6Zz3wmbLnllmWL6uh8dr7LVrJbzqe1L/39cp3ioWReSvxNGsw+LS/7uH9h/vq2/Z3ySb1rhe7xda03vc9b9nurhfp11103IH5gu+22WzjssMMKVRVxHjGITimG1wX/2mUS6ttFurOO00qhfuL+rZk9sfHhkyXUd9Zl1HO1ueqqq4L1URZccMEopsw333xV27n66qtHYQhPzYMOOqhq2np2Imqde+65MQt9wmY6cEion/lM2HlvtI9p7x9pn61Z5c9c8+ZvsTZUKzltX7W0nbQPx4Hbb7+9ZpVGjx4d38ksFnXNDAUSIE5fe+21MeWuu+6a6RiRJVoWKLrjk+C08ZWvfCXOssyqLIMkiOtrrbVW1u5C23784x9Hp5D11ltvgHPIuHHjYjgTCqF/jVA+mCahfiD9vGue+9Avf/nLwCD4b37zmzBkyJCBGZv4l184thFP+rRK3rO+0QVmJdSndN/5u9GHdk6xLd/sBepOFGiLALCODWnT81Btn5XtGdg2PjuVh7XJd4AQtUzAw8u2jFl+X26ZclqRJ6vNJk7Wezzah/lOZnrd1Ftmu9J7oZ5jslgJ3vVrrLFGu6rQluPY+S57sG45n7TP/3b527zk+Z5naR5L14m/Xaub/d6KtI889aa34zTy2WqhfoMNNogeQlZH4hPy4lHLrrnmmuhxbukk1BsJfbaSQCuF+l/tumal6ovNN2cYOvsslb/58tDTL4c333prwLYif3ztlJsl1BcBpTSlCXihnkJ4sT7++OOrltcqoR4xEVERw4lj4403rlqPenZKqJ+ZlvVNG+ljWt8mq7/WjPJnrnXzt1gbipSc1c4i+QYzjRfqmcFohnf3f/7zn4pHLds//OEPx0UlmyXWI0SbE8cNN9wQFl54YTt85TNPtKwk6MIvhKPhfdaM2UrcNzHuc3gemxEG5YMf/KD9WfiTkEybbLJJTH/++ecPmA1loW/mmGOOOItinnnmKVxuKxJKqB9INe+aJ3Qmv0GMGRGtmrH27LPPhr322iseh5j0Bx98cPzerP8OPPDAyiKzxx57bJh//vlLFS2hPgdbIw/tnCLbstmL1J0qTNcCkXYYEIK4GadCblb7fPv9cbLS+v2D+d06cr7zYwz8tnrrmFVuvWW0Kn0r6mbMqHO3/H5Tod54Ez/0C1/4QmWFcdverZ92vsvWv1vOJ+3zbS0qYpPPX7/8jTXy+3+7hNb97+tbpJ31pm9Gzdst1BMKZ/vtt69Z9bFjx1YGYkksob4mMiVoAoFWCfVp1T70/vnDAVuuHOada7a46xeX/Sdc8LeH02Sl/lbom1LYlKkKgVSoJylCfbUp8BLqqwBtwa48UafRQ1l/rWwf0/o1eX21RstvtH1Z+dN36XpDNFIm7UV49ca2TjUT6vHSxds1NUIAHnDAAWHSpElxF17g+++/f5qs1N/9KtQjguKxjPMKszjXXnvtAfwuueSSGMqKjYSlQaep1wjhiEDPTCgGQQhD2akmoX7gmal2T+e+yXOZ8Ejcn1ph//d//xe4BrFvfetbYc0133U2acbx/vGPf4STTjopFrXpppuGLbbYolSxEupzsJV9aOcU17bNXqjuZHG6FhDr/NRK59vo2+7z+TR+e6d8t46c7+hZ+/22euubVW69ZbQqfSvqZsyoc7f8fvOEetowbNiwKNZ3UxxR6p1ldr6z9hXZ1i3n03vGFxGvre0+n23js5Hfvy+nFd/9783Kr9bmetNbmY18tluoZ2Fom+KcV+/HH388Lojn99cS6om9SZicRRddNC6y5PMW+Y7H2MsvvxxDFc0222zxpanRGPUvvPBCYBEmvM7wRrEXpKIx6qnPo48+Gl555ZWw2GKLhQUWWKClU1xrccKz7t///ne87y6++OKDWpdadS27v11CPfUjbv3KS84bHnv2lfDkjBj1zTIJ9c0iqXKMQJZQj7jEwqvcC7KsqFDPfeWJJ54Izz33XBST8Kattnh3vR71hFF76qmnwtNPP12z/DyPeu7lCJXvfe97Y4zuavXzLLiH0zbu4TybCBdUNExBvVyqiTq+TvV+t75pmT6m9Wmq9dMaKb/ethRNb/Uumr5IumoMiuRvdZpaQj3Hf+mll6L37oMPPhg+8IEPhD//+c9NqVZZof7VV1+Nnv5LLbVUoZmaVlk8yfld8jumb1VklqfltU8YcG+hb1f0fmB5+aTPat7z/AZ++MMf+t2V74SrKcv7jTfeiGsLcKyyQn+lIjO+cP6pC7yIm1+m3b689HszhHrqSL/5rRmzE6ljveeW/FOmTImzOhZZZJG0ipW/Yct7Awvwcm8vel/nOcc7Du8FXHvMZsizavd0Zlh897vfjVlZhJeymm28AxFqrhXe9FZX86qHta2RYfuKfna9UF+0ofWmK/PQrvcYrUjvxepOF6hrtT9PvErzWTt92y2N7bO/O/HTOnK+o2MdKb+t3rpnlVtvGa1K34q6GTPq3C2/32pCvbFn4Rm866s9VC1tp37a+S5bv245n76dRetc7T7XyO+/LOui+fzvzefJE+vrTe/LLPu93UI99WTRLH6zecYUSJsGbWmyhHqEDLwxzjzzzEose9Ij9LDY0k9+8pMojFgZfPJiue2228ZN3FvOOuuscM4550Qhh40s5kV4rWox6nkBoAN7yimnxHJYuA9PMzNmt9H5RMTytt9++0WvFKapVltM9s4774wdVhaK8kYsZhYK3HHHHSubqcsyyywTXnzxxfCNb3wjEIvUGwKRLUTGTAZmNHjjRXP48OExP2VbLGkbTGBROfoI/Js4caLPGtv8zW9+M8w+++wDtpf94/7776873nSZPNXq106hvlo9Gtknob4ResqbRcAL9TvvvHM444wzYjI8QLmHZgk2tYT6Z555Jpxwwgkxvz8mwgpx7fHg84t4e+9Sn96+c88mhq4ZQgoCIPdpQgWYUT6iFf/SeqdCPcfnWUQMbTM8U7nXMusrz+6+++7w05/+ND7rfBoWzvvqV78att56a795wPd6uVjmaqLOvffeG2eywYH606ey54Llz/u0PlvR/pqVY/2ZTu6jWV3TT6t7ur2RvzudQxGhnvZzXfO7xfCIJVwFC3/SR8OImZ16hscdM/5j0Uj2Y9bP+fWvfx3/zvrPymefv74JzUP/bvLkyZVseBbTN+JdMM/o/9Fn5NjeqC/9n4985CN+c/x+5JFHhl/96ldRCOU+yMKa9IX8PYUFNhHa6wndQVgRW1CT+9b73ve+mY7NBtoDhzIDIz60TtZ5+dvf/ha22267eFy/MClisnlP0x+HLfdBQlKacR9lVgX3aX8fJSSLhWY577zzLPmAT4R0FrfF9txzz/iP79WEegZLefbYvZh7O/1UM/Qt6nrllVfapvjJQAczF3ASSo0ZL1wLX/ziF2PoH0K7wBqjv8y5xnw6rhP69+lx6CPDA0efLON9gN8O9fTG+wiCO/fl1Pw1ny4QjoDO7BeM30K1Z0pabpG/cTKy95pmLCCbd0y/sOwhhxwSGHSr1yTU5xCr96GdU0zbN3uxuhtE6iKA7IdPR8C3z+e1tvr9ts2n68Tv1lH0HR3rSPlt9dY9q9x6y2hV+lbUzZhR5275/RYR6mkP3qp0aHxsRbZ3i9n5Llvfbjmf1s48sTptfzWRnrSN/P7TYzX7b/97yyo7ZVBv+qwy693WLqEe4WXChAlREM8SjK3eiO90qPGA5AWCFyM69alQjwDNQoLWabf8/pOFfJm66T0+//SnP1WEbjq26YtELaEeYZzp3ry4YbxY8AJj3vJ0nllAl/pnGfGUidOPIMSLcfq7ve+++wJx/RHe84zjsTCv2U477RRjmWbNVrj66qsDnWyM+KPMOrC6sg1ByWYk8SJnsUx5cWXAgRdI6sv3LEPIt5f2rP1Ft/ECzYvMRhttVPgebnkYwEA4a4Z1olC/6tLDwtofWDA8/9Jr4cIbHwmvv1k9jr2E+mZcCSrDE/BC/e9///s4uPm73/0uJkGgsue6z1NNqMdrkft7tcUr6csRf9dEIPoC3/nOd/whBnz/xS9+MeDeYQsoDkjk/qDOqRerF+oR8lmwL89+8IMfhC9/+csz7WbwkHs7Xqx5lsesDBc7Rp6owzOFkALUBzGI+/wKK6xg2Wp+2rlNn1W1MvKuSUgG3jO7zeydmnrTL0uNvhv9C288I7lGU/Ptp7/aqVZUqMexgWsfo381YsSI6HVroqEXOH1b6duNHDkyXoef/OQnYx+KvpQtDO3T2vcbb7yxMkPSrm9E07/+9a8DhHJLzyfny4stM5sAAAxeSURBVMd9t30I0YjK1Yx7mgnUls7uIwjT9E9MXLf99skgHAJ+sxwXKJdZQLaILCK1Cad2zFqfxx13XLyHks4Pelg+OJrjB4KpidkMFo4aNSomQ3zmnOfdz9L7qPEic56nN841Fu/cHzdPqKc/TD/X+vv0OQmVYvbAAw/Ev/PqyLljoBenFm88Y7g/0t9lMNMPvvjr2NIh+jOzijxZhuCf5RVO35bnSZ7xu2DAyg9Mk9aueQYHUqGe/TYowjuDOQ6xvRnGIJjd83EyYqCoFQZLBhowriUbwKnnWBLqc2jZCczZPaib/UM2rYh/mGY9bH36Tn6o+nqm370Y7/eZMG98uqV91lH0opyJWn6bb2uR71nlFsnXjjStqJsxo/6d/Pv1fIsK9ZaHzjOCvT3gbHunf9r5LlvPbjifXnRPReq8dtu9Km8/2zv1PuZ/b3n19xzqTZ9XZj3b2yXUI2jgqY7HBEYIFUK5pIaYTwcZw/OeznmWUI9Hp037xEN91113jd7YCO28SJmIlHq7e6GeY+BNTgeal02MDjMvWlke9Yj0CCwIQhieQLw42ZTX6dOnR4GGOKAYnXYEkuWXXz7cdNNNsUx70WB/KtQzHRZhnPZiDE7gMQQnXnjwhkcEwk499dRKPEc6+HTksZQrL0N+0UcEN78gmefIdF9CO2Am1Mc/ZvxHO5mlwGyF66+/Pr402GACXlZ4XJU12sSikGZFxHoT6S1Ps8T6Vgn1hLlZYJ45rLqFP9dcdr6w92bvimr3PfZiOPCCuwbkf+bFV7WY7AAi+qPZBFKhnnsa4gX3LIz7dipc5gn1CHYIMhbrmoFERBfK5P7NvZvjYQwE2jODMDIIKYjpNlDJ/c28KhdaaKEw55xzxnyI0XjvYgiIPEdWXnnl6MjEPQ9PUoz8JlTxtxfq+Rvj3sqaSIQq4D7MvcoEHQQnBmbNCKdBe4wLzwj2E/IG0RExhfsslsb4L8vFjm19Xi/qcG/dZpttYn0R6RmYtnSWr9an9U27oY9Zqy1l9lv7fV57l/bb8t67u4VbUaHePMxpO97ItqAsfTC8jBFFb7311soAmzHyorANqhGCBk9pYuLboBgDgbaYLH1G61+l1y3eznjAI25yv7Dfe1ov/vae4/wOdt9995iX0DnUi743Rt3pP3IvMvPCM9sYBOAffUd+y9xD7D0lvZ9YGWU+ETDpm3IM6oWgzTHrMa5d2BAWhXtXav6ceMHcC/WWh7IQhAnzQh+XPqjdy+BngyPcw3mvwfJ4bLnlllF0T2cJZAn1iO8MUpinO9cJfUQzvP8pz+rCbxOxl+sGsZnrFWPGAuFiuBebmQBvf+PdPnr06PiuQkgbE/bTdByD+zrXEn17BpxskIB3DJ41Zp4H54HBZmYU49QDcwZBMByPeCZ4Rxq75v093crlEwcVBoesHn5fo999fHoGzO133mi5aX4Gp20ArWycegn1KdV3/u7Uh0/ewzKnGVU3Zz2Mq2booJ2egwlZbOvGNllHyYvyJmr5bfXizyq33jJalb4VdTNm1LlTf78pz3qFesvPQ4+Hd7eYne+y9e2G8+mF+m6ob9lzYfn87822ZX1yD8NLqOiCZZaez0atnUL93nvvXenAHn744ZkeJnT26XStscYaUchBAM4S6pl6ijc4L3RMdzWBBh6vvfZaFI7xakd491NUvVCPsARzeyn0LLOEel4EbeGj1MOfvEz/5kUDY5FFBCF7yWQbU53p3JsQnwr1eKnZIMDRRx89k7cmXkPkRyD33vFsp50YnX5eKsxSwZ3QNibqkwZvKTrkpIONmc+H9xKCmTdeMBC/sKy6+rRFvqfCezWxPk1L+bywINY3aq0U6ifu//ZU70brmObf+PDJEupTKPq7qQQQe6yPgpDG/Ya+GYORGM8iRBAfbzdPqOf3a0I7/TTuL/4+iXiGYGKDml5A4li1YtRzn7XQajw/8Nj1IgOCCs4cJq7hvGVesKlQjxhoA8ccG/PhJPCi9c8XL+oxwJDeN5mdxMAydUB8IwSaCTONcklFHeInM2uMQQUEIjjkhdd4u2XZ/9t574c+WxYBa7/fl/Ue7d+5fdpu4VZEqKdPxYA91y+/Mfo8ZgiGNrMtK8yKhb3huufeMXToUMsaQ1QxUxBDBM7qk9n1TZp0gIxtcOb3h9k9Kv4x4z8vtNLnSx0LcAqxvhtprR9Gfv+b5p0S4dd+s+xnUO5jH/sYX+NvG1GzjHH/pA9GyC7EXRsM5B5DmV78LVq+hWxJ22T5iwr1DBhwzXtLvdgZWLDZTwj6COfpNUL+xx57rDKLM52VlAr1iLiEGEMMxzjHeLV7w4Od/jqWetqzzcdypw3es91fF1n3evJjPh3Hs+fe23vfDqlpz7T0vs/9nnsD1z11SfupttgvZaUzOuyazxPqaS/hdDDPP25o8D8cgHgXszW7GiyuanbCwTFQzb0lfWZWzfjOTgn1OZQ69eGT97DMaUbVzVkP46oZOmin52CCDtuwbmuXdZRohz0sTATz2+rFn1VuvWW0Kn0r6mbMqHOn/n5TnhLqUyLZf3fD+fRCfbfdg7KpV9/qf2/VUnIP6wehHkGXDhmdUTyD+G37Fx6EBZtuy4sJoRHyhPpqPNmHIE0nFmP6sB3HC/Xjxo2riM0xofsvFep5ObPyGCTAUzI1L+QTYobFrlPzoWhSoR5xGiapaO7L4AUYbzCMlxfr9DMFFu9JOvS0HWOKLmIZxssDXvF4GvGiivn49uTxAr4J9X5AIGZ65z861bYmCC8o9pLt09T7PRXgs8T6NA3HaJZIT1kS6qEgE4GBBLKEelIcc8wxldkwqZiTJ9T7cAwIUggYqfEssHjzPEfNW5N0tYR6Lz7hHcugb2osaG4DjTwTTATzQj33SuLvZxl1Mg9g71VsnqLcj9OwalaOj+eNwMCzEGuUixd1eLYwGIHYhzjPAK4PARcPWPA/exdpdh+zVeUWbFbVZEX7blULSXY28q6aFNWSP6sJ9Tg/0Dehn4AgiOGsYOI2fxOOkOseEd+HDWEf/QUG99hHGETK8VbPYrJwTNfvoSwcICxshhddOab1g5hh6fs5vg7EMed+kXone6E+HTS0/LQJ7+3UQ9z2F/mkf+wHCCwP70rMivGe4Lav1qeFRknPh+Xz90rfNu9Rz0ABoVtMhLe8fPqZS1dccUVltg4zhyycSRr+hnuqnX9mZ+Khb+aFegZjcCSx2U9Zgz/k4z6Htz0zq7iOssyu7XTAwgT4dNA0LaNWOgZXLDQM9zULqcZAgw0K4XVPe1Lz12c6A8Hf07NC3yCmWzikrNBG6bHq+VtCvaPFCEIrzB6Ctco2kcBikGbFWMsqo9kP7axjlN1mgnRW/n4OfeN5dJNQZtey7+hYR8pv8+0r8j2r3CL52pGmFXUzZtS/k3+/nm+9Qr1C33h6nfXdDx42ev+xezy//041/3vLq2M/hb7hRYSOOV6UGC9FhBQwo5PK1E/EYZ7TTGkuItQTcubBBx8M06ZNi/+YSv29732vEieeKaa2wJMX6hFrLCan1cE+vVDP4IJ57OTNBCAfgy28rNlsACvLf1I3m07rhXq8SPF6xNJwPT4/QhJe9dj48eMrU4xtkMAfm+nkTCmlj4kIZOF9bBCBMDkW/5QwFF7QMqGevBZGyNeD73Zu8ICxQYw0Tb1/p0K8F+vTfZTdTJGe8lop1Cv0DYRl3UggT6jnvoWIbosz4rVt95Q8od5ELUJw4WGYZQwish8Rg/uLhb8hbS2hnvVDLNwB/dw07i9l4NVpodN4TttAgBfqq4l6Puawee8iZlr4n3TQgmOa0XfByxJjQNq+N8rFRB3WQWEQ1zxyUzHM6lH0095Fmv3O0Kpyi7arWroifbdq+bP2NfKumlVes7eZmEm5fuYFv3EL5WTHRDhGwDYHCNuOaIjAh/Dpw994Qdh+L5aHz3qE+jzR2YuliO4sdI/5GTBcw6lHdkw047+zzz47LhLK3/4344V64phbX9Ly8Wle0XkhZnzavO+ExqKu3Pvos3JPtXAvDB5wX0vj5+eVxfY333yzEsKHfhIzWlPz5yVPqOfeyO8hy6gj/V7M38v8LINUfDZhnRAzzDr1ZkI9ojehzDgPGIObNkvKp2cAyNbbYCCI98osI9wMdUoHYUyA5x5M/fPM0mXV2fIwq4Jj+Dj1zAqzmf30xW09KMtjn8wgw1LWdk/P86jnnJnTELNCzHHHym3ks59C3/w/AAAA//8LBGAYAABAAElEQVTtnQW4XNXZhTcWNGgI7l7cizaEllK8SHGXYMWhxYJDcG2LFylFSnEKFPfirj9QJCUECUEDwf55d1jDvidnfO69M/eu73lmtts6+9ja3/n2OHfeeecPoYwMHDiwTGr9Sdttt13FwgcccECYf/75O+S7/vrrA79KcuGFF1bK0pLpL7/8cjjhhBNi3/LG35KdrtApxoRwLNPxVSgW2mX8msuMjz4jHEPGmsZVGm82Pa/ebJ7uCndG34QZY2qX8/fxxx8Pf/7znysehnHHHTdstNFG4de//nXFvK2YQce73r61w/FMr03rrrtu4FePpPeoVr6Gpedbdpxct9ZZZ50O999a82frrCc8/1YPdSj28OkLdAg3Glh11VXDU089Fbbffvtw4oknhh9++CEsv/zy4ZVXXonH/69//Wts4quvvgrzzjtv+Pzzz8Nuu+0WjjrqqBi/0EILhXfffTf84Q9/iL+0P4Vnq/CnP/0p3HPPPWn0WP73338/jD/++DH+5ptvDltuuWX0P/bYY2GuueYaKz8R5557bvjjH/84VtqQIUPCTjvtNFY845pmmmli/BZbbBHOOOOMsfIoQmNab731itfh559/Pqy88soxy9lnnx1+97vfKXsHF5xmnHHGGLfnnnuGww47LPrvvvvusMEGG0T/G2+8EaaccsrANeW6664L6vPSSy8dXn/99fD3v/89rL766uGyyy4Lv//978Nkk00W3nzzzcA1VLLccsvFY7TxxhuHv/zlL4ru4C6xxBKxXKXxdihUReC2224L//73v4s5V1tttehP44jYddddSx6/YuEaPcvt+VKHEi9fsnyHcLWBpfa9IXz3fdlH/2qrCgvOMnlYbp5pwidffhOuffR/4dsK9Y437jjh8ZPXqbp+ZzQClRDgGqtnlH/+859h8cUXLxb5v//7v+Jz1/TTTx84f/v27RsWXnjh8MUXX4StttoqHH744TE/18lFFlkkxnON49pUSrj2cI3mennDDTcUsz377LOBayfCsyHXslR233338K9//SuNKutnXIccckjM88ILL4S11147+vPqVkVcY3/5y1/G4LHHHhs22WST8NJLL4U111wzxp1yyinFPqqMXK7hP/vZz2Jw5513ju80zcBlzjnnVBMd3CeffDLeDzpE1hDQcW/2M2Zn1VvD0Epm5RmTZxRE79dpZp7f8qRc3vnmm6/uZ968tpodxznFuVVOJp100rD55pvHOZs+L6gMc23DDTeMQZ4veI5ADj300Pi8MeusswaeVcYZZ5wYr78LLrggHHPMMTH4yCOPhGmnnVZJRVfze4899gh77bVXMT71KM++++4bnyNJu/TSS4vPSffee2+YZZZZ0iJF/8MPPxzHRgRzfcCAATHt6KOPjmGe77ge5cngwYPD3/72t8D176GHHsrLUldc2ifq5hmI57Vq5LPPPguLLrpozMp7EtearDz44IPF5+Ebb7wxLLjggjHLiBEjwlJLLRX9Bx98cHx+z5Yl/OWXX8brM36ex/bbbz+8UbbZZptw3333hSWXXDL84x//iHHDhg0LK6ywQvSffvrpxWvtmBIhDBo0KNx+++0KFt1rrrkmLLbYYsWwPJyjv/nNbxSsyoVTmHrqqWNe+ALuX7/97W/DySefXLJ8NfmYL2+//XZ8dtd9jXOAuV+tzDPPPPH+qfyaz6XmPOcS71ZI9r6sOup1H3jggeL7yYEHHhjoW2cI+B933HGxau4JK664Ys3N3HXXXTWXSQuM06pEfTmiJCVC0sGk/mbftNO6O9OfkkWtTPJUg0E5Yictn5LbaTz+dsBAD3QpKa+xp3HZsVUK59VbqUxXpXdG34QZY2iX87caop4HAUj6/v37d9XhaXo7Ot71Vtwux1PjbMZ5C1atPO70fEuPa6mx15o/rbNef1cT9fTzoosuCvvss0/sMsTIDDPMEB/keUhHHn300TD33HNHv0jtLFF/2mmnhSOPPDLm0R+ke79+/cIUU0zRgehtJlFPW5BG2YfJ77//PrZNeiXiWmOqh6j/+uuvI160kxL1o0aNCjPNNBPR4aqrroovmTzks/DBiy8P2eAFbjvuuGM4/vjjwy677BKuvPLKsNlmm4WzzjorltVfdxL19AGSjEWFUtIZJD1ttSJRXwqDUvEm6ksh4/h6EShH1FNnSoaxYMiibHcR9SnZw7NhKXniiSciicRCINdEpB6iHoJx0003rZqo5xq+wAJjFsS7gqhnARgidLzxxisFRdl4PbM1+1mrs+otO5g6EtXPtGjee3PKLaR5m41bWncz/SlRr4U11T/JJJNE0pdnrDyCXvlYcFpllVUiYQmhj8LFt99+Gxf2WLTbf//943OH8svtKqIe4njmmWdWsx3clBSnP4wD6U6invZTsve8884LKL9UI999912RXEUhY++99x6rWDVEPYuYeecAlZUj6iH+eUZFWLxgoSG9T6CcwrxKJb12p/EskqBkk33HT6/X5C91vedazyIRC0DnnHNOlxH1F198cTjiiCPiUNR+Oi756R+L3yjhnHnmmYoOlYh6FqRZmEZYIG8mmT506NDAAhTCQoYWsGNEE/+YJ9dee22skXeUUudnuSZ7LFGfd6MREKVuOErHbZebT9pn/OnYymGQLddq4VKkTraf6RjTsaf50jxpfKv4dZNICS6NP42rtb959dZaR2fl74y+CTP63C7nbzmifvLJJ48EvVboO+tYdEW9Ot71ttUuxzNdBC63WFwKh0bLl6q3M+LT8031l7vW1ppfdTbidgdR/+mnn4bZZ589dvuggw6KWjiQJZzraKWgPSMRqZ0S9bwc6GGOB9tLLrkkatukL5Box0hDq1GingdnHoCluYlGEy84Wc0svpBAC4UHbrT98wQtp9lmmy0mpUT96NGj44sMCWiL6QE5WwcvJiuttFKM5muE9KsUNFTvuOOO+EKGlhGYgg8vRAh95mGbuOeeey5+zfHBBx+E888/P6y//voxj/66m6inH6XI+s4i6WmzK4n6CccfN8w/U98w7OOvwvuffk3zTRET9U2B0ZUkCFQi6lmo5BkGIgzh3IWUy2rUk4aGPddJNOv54idPUg1zFj7TRdlKGvV8ZSXtSN55+vTpk9dEblxK/HAdRpMxT9BslYaqNBm/+eabgNY0IgI+ryx9WmONNWISC6TyN4qLSB0q5is2njWknVquP3l9TOP0bNrsZ8zOqjftezP86mdaV95zXKn362bjlvajmX4R9T//+c/jV3f11s0XeCzUoX3/9NNPR0UBzmGklEZ7ZxL1kKAociEcC54x8wSNeD13QdpPN910MVtnEfWffPJJ4MfXnjyTlZJXX321+OzJvNN1p1T+NF6LpdlrqPJUQ9TzhQTvJnnCsyXPvUh6LSOckviQ1XzJynHgeJT6miol6iGeuZ/oOR4Snq9BJ5hgAqqPki56os3Ps2EtUo2mPPVVk495ldWo5wsMvgxDUKDRVwoxooo/XdNLadSDh74GQ8EJRaVmCl8V8/7Eu44WHJpZP3XxVfA777wTF2H0JUKtbfRYor7SzSPv5pSCV6l8mreV/OnNNO9m20p9LdWXlKwiD2Q1D4jEp5I3vnT8lfKm6d3p11xMSXmRWmlcrX3Mq7fWOjorf2f0TZjR53Y5f0sR9b/4xS/iTT+7It9Zx6Oz69XxrreddjmejC+dh7WQ9dnrXquPOR0n16msqZvssa41f7Z8PeHuIOrpJ8Q72kFouFxxxRVFLSFelkRckC+PqE/NvJx66qlh6623JmsHgXiGXEIaJer/97//hYknnjigHSOtJD4pRpuFeAmfuEISIW+99VY0/6A0ubx48EKMpEQ9YZkJgiRHeyhPZK6GtKzpHvAEVxYKWFTgc1JefPRZaboYwAM+mvQInw9nPzVvBaKevmXJ+s4k6Wmvq4j6JeecKgzeYIHQd+IxJpn+/O83wtX/GUoXGhYT9Q1D6AoyCFQi6sn+3nvvhV/96leRnE+Lp6ZviD/ppJPieY2fBUMIvaykpmW4L8qcBvlSoj7PfELaV75+4h5SraREPV9NsQicJ/QJE2VIOgaRnZgZ42ulPMEMBNdpBIJBi9aN4iJSR6Q8C+JoQv73v/+NbWGOba211or+Wv70bNrs563OqreWsZXKy7NmbzV90yhRD+nG+xnC8wrPSbjl6k2JeshjvrLMiuZ3KdKS/MqTmr5hsRDCGim3+EYZNHuzJm46i6jn+sF1BJHGeQxk/tLrGaRm3vNupkgxyLM0nA8muVJNbWWohqifY4454heqeV/kpNr+LApmTUlC9EJQQ7LTPmYvkcsvvzwsu+yy6kbRFVHPNZtrNwu2PHPL9FnegoOuudx70JavRaoh4Kmvmnx5RH1qfoj3g2233baW7hXnc6k5z/Mx9w0EEzJ5x6imBjOZUZq66aabYiwmSUt9sZApVnUQU1n6mpd7U1ZhqNqKeixRn0fiCpRSZK7ScZt9007r7kx/OrZyGHRmHxqtWw841JM9DuXS1G6KgeJwWxUPLUCwGAHhhYjUMlEf4ajqT5iROTtvqqqgGzJliXrMO/DSJtt73dClTmkyPW/raaBdjidjy15/qiHrsyR9NWXqwbGZZXS+VdvXWvM3o6/dRdSnmuFoE2GHHrKYeNmTZ3x5RD1a47LhzqfVPECmwsOf7AcT3yyinrp40RN5k7XfzguJtJ045pyTqS3WkSNHRpM5jBXJEvXYApU9eB6+s9cEyH+06TFnk2dXHmIBgh3hhQnTMfQpxYIXBc4lpXMdZeEjK61C1NMvkfWdTdLTVmcR9X0K2vPn7LgETUSZfsqJwoQTjKtgdN/5cFT4vvBiWqsMOu/JMPrb74vFTNQXobCnSQikZJE0yPOqvuWWW8a6HmeJejQlMbuFQCBBIKfXSRYUMSWDVjiS2k4mnNqC5xopjULSEK73kIIIWvssSmYVOtCYlXkP7O9KyzYl6ikvszb4JfRL+4FAYqVfT4nUIy/3Jsx/pAKJCXkGecgCxTPPPFM0JdIoLiIpU1Lntddei19WqQ+QpnqHUlwlV/ehZj9jdla9lcZTTbqexarJW22eRt5Vq22jkXwiPMsR6tXWjxLAf/7zn/ichtIB8x1M0wW3tK7UJAoEtvalSfPkze80Hb/ypEQ98SJa8ect3vFMqb2HyKvnMPLrnM4S+KRJ6rFRz/OczOtgzx0iN70OUjdf6fDco2sMi38sAlYrshOfvU6pfDVEPXnpQ2p/njj6D7nKsUXyiGK0vNm/AwFf9n7CBA5fVKVfwMYMhT8R9RDCsmuPZj7zhvdGBK1rPf8T5jquZ/K8xUhMAPHczpcJfInLopBE86KzbNTTDgo4LJZyvecLsuxiBl+1YnoN85XgiZkiieZzek1XGi4mRKmTulkwbrZ8/PHHgXMJ6QytemnTUz9fwU011VR4a5YeS9SXu2lkSZE81Jp9085rozPiUpKoVYnpcuNO+59HAKUPGOXGl9aTtleuTJqvu/0aZ7l5XKmPelBspI5KbdSb3hl9E2b0qV3O35So56VOL0j14tqq5XS86+1fuxxPjS/vHsP1DJGrBzMerOVXuvLEAi36xxjTxcVK3aw1f6X6qknvLqKevmGeBfvpEggX2a5XXB5Rz4uBzM5AWENqo8GF9goa69kXimYS9XxqC9EiEgkiR6QTabzsakyQTWiIYG+fFxY03rmeSbJEPRqpkOoi8tFGGjhwYHx4hVhCe1R22/PM1aB9xL2MB3+JvgZQGNJKNi2JS00KKQ9uKxH19IdxZ19wiG+2dCZRf+tBtW+SVc34Vj/2ARP11QDlPHUjUC1RTwOYvIHMl2SJemxWQ8hogZBnOp7tMC+GKQUWF5UmO9eqC5cFTzayRiDPIGEgr7gn6AsntDf52gohDbMLlIEMoW42gEUgOCAUcZEsUU8c9XN/gewnL2TQRx99RFL8ykqmyAgPHz48arFzLUcoSzoEBPcAND6l4Z79GqBRXEqROqmZHkgyiNNaCBE9mzb7GbOz6o3AN/iXvis1WFWxeCu+ZxY7V/A0k6hHM10En9pgUYpNpvMEIlr7RLC4hsID1wMIZhG6peZ3Wp/yZIn61P481wyITxYkWBSErJbJDa4D9F17JFF3ZxH11C1TMPgh7cFAdsZ55+Faow1sy32lQ/k84flUxHT65Y/yVkvUk59NS9FaxyQQz7jptYyvNmXiRXXjYhKNL5N0PSQOu/WyXU84lTyinnRMynCP0KJAulhM3Zh01DWZ53+eX/kqg2stGtscfwR+S8o0hLuCqOdrCZl+4voLEY8JHO5VfB3GtYbxIVdffXXx3kZY87kUUS8tfsbL1w2dIZwPLJYjvJ/oS9xG2+JdhAUyhOPHYkm90mOJegDJI3rzCJQ88Jp9085rozPiUoK6XUjpFIe0/3k3/vT4VRpfWlfaRqVyad7u8utBKg+DavukB8VG6qi2rVrzdUbfhBl9aZfzF2KLhzgeaHTTqhVL529NBNJrVbU9bIdrU7VjaYV83UnU8wDIw7/kxRdfLNppV1weUU+aNkZVvqzLsw3zC2kmUU99w4YNixqYIsRpR2QNRA2b9ymN/KmgMcPLA4RQlqgnH1pJaOCgNV9KeHFEwylPeEnRtZ2HakivVOj7ggsuWIxCAzbvE+RWI+qLHe5kT2cS9WsuMX2x9wMX6h8WnHnyYviDgo36Kx56pxiuxXPzk++ZqK8FMOetGYFaiHpMrmB6S+RMlqincbQkeeGHqCglkEJ8TZP3Ob80RdOyaMFCvCAsWvKFEmbVyklWQzUl6lmATTVrs/WgiS9yMU1jUZFru0ilNE1++pbe+xTfCC56Ps4jddhEHNIPgThjf5M8XNWP1NW7iO4raVoj/s6qt5E+lSpbz7NqHr9Sqv5WiG8mUc/zC4S7pJLGMrbamZfZcya1u11ufqsd5ckS9aRjwoNzo5ygxZ0179GZRD3kMsQtC4DlhGvhKaecUlxQLJc3TUsXKLIkMPmqIeoxtYPZx1KSd31P83Ld4fojgZzVcVKc3FJEPen3339/0ewPiy3p5rJoy7Pgm50/qheXewMLuOkXu11B1NM2Wu9ZJSTiU2G/rh122CGNKuKUd03nnMHMJVKrSaQOjVQR4OswLTBzLje6sWy6gSyLcXxN0oj0aKJewEBUIhC31Uqzb9rVttuMfBqnxt2MOruyDj3g0Gb2OJRLy+tju5L1Ip0bIdmFVSN15GHajLjO6Jswo3/ZedOMPndGHbw4pcRSZ7ThOrsXgWpegtrtpad7Ea2+9c4m6rVJLJ+9SmtJvUMDHe0hXurQPEdLPCsi6tEuZ5EmFTSF0JqUBjppkM5o96DFIpMIkOYiJW699daiRgibWvGQmCf0Re1Bbk844YQdsvFiJVv6aPWjLSYtRUwz8OAsbREVZAy8lEHWo5HP57x8CpwVyCuwoq+pYMsYDU1ejEoJLy9ojyJs6JZHBrGowHUVYWEh3ZwrRhb+lAcyTTYklSYXDS9IKczpaPNGpbWr21lEfR4eu68+V1hijqnCWx98Ec6/683wvxGj8rLVHGfTNzVD5gIVEEhJEhZYK5keTE0e8Cyra3HaDCQVBE5WExCtVq5hXCulIZ+Ww881/aKLLoomD0TOpEQ9eTB5ANGPWQRpWxKPQDZgMi1LGPE+pOs6/eK6j2kFbchNWfqHRijX1qypCtIRrq+MTSYrxsSGMOuss0aNTpmDUHzq1ouLxsI9IktIggX3YH2pkEdkpn1I/XoXafY7Q2fVm/a9mf5qnlPVXjs+r0J08lxSbm8Gja8al3ko2+IQvVJmKFWW5yYWkCCUJWiTQ8oi5ea38isPX/XoS0el4fJMBXEsDkhpPMtgC10msxSPy9c3PA/SD2m3p+n42WiTMXJ+s6hZi/AlDXsbYfoq2y8WOyCTOXf1DFtL3XwxIJ4LJY6sgkdK5KdmsUaMGFHc+BTegOdwcMBkTSrUx3FOye80HX+63wjEcvq1VTYv9XGMSn09gBb/8ccfH4tltcj5YoD3gSz+HBO+ImUhKttPrvVgzrM44ywl1eSTiZu8r8CoF3KaZ2mUcVJhrNxPZL8/TdN8zrump+aaIKq110lavln+oUOHRtx1r21Esz7VpOdeyle9mCRqRNqWqK/lplIrQO14E6p1jK2cPyVc6SfHA/u06UW+lmNEubyLVCtrrwqDRkh2PSg2UkdnzZO0b9pVvdG2UhMizX7obrRvLm8EuA5pAy9czXs9aBqh5iPQ2UR983s8do2QNh9++GHUxhdZPnauro/BRIOIcF4Wsi8JlXrEQzGLELzI9evXL/5KEUOV6nJ6dQh0JVFfXY9qz2WivnbMXKL7EIBMQvsebfLJJ588mlaolpSCgKYcGvSUzRPycI/geozpDcwPVFu/6tO1HGIBkwrVlqdvLPTSh6mnnjqSfdVewxvBRf1uhqt3rWbUla2jFd+9sn1Mw9XwKrW8e6d19zS/vnqB4Eaxodpzhnn/1VdfRQWCUgt1jWKF7W2eGRFMuZS6djTaTq3lMc2F8gPXCEz9TTTRRLVWMVZ+SGBI4lJ26scqUIjIEvXaW0DPpH369AnsLZWn5JGtjz2jVD5vz49s/kbD9JFrLsKxLWVuqdF26i2vucf5wJ57WSWgauvVQlgtx7XauvPy8dUCXySIrMfUHNc6maHLK5PGoRgFB8U+LQj3Uhbj55133jRbXf62JeoZbTU3lVpR8U2oVsQ6J3+5h6d6Hn7yyPp2IeqxA12PcH4g9eBVT3u1lCl3fGupJy+vz+E8VBxnBHofAj2BqO99R80j7iwETNR3FrKu1wgYgXZEgHfDVMmnWWPgvQtlDNx2E94dU+U4jaFdx9Ns/FOTKqXMPDW7TdeXjwBfAch+PF9cLrDAAvkZk9hSRH2SpSovC6hs/ApJCzGLbfvsxt5VVeRMHRCALF944YVjXN6m5R0yNzGAZj1fvcgMDlVD2GNzn2sgCw86vixSs0cW9w9MGIugpwyLC3yJ26gmPXUhbU3UjxmC/3sqAnkLMZws+mS/p46bceWNvd7xtiJxnbdwUu/40nLMDz9MpojYbwR6LwIm6nvvsffIx0bARP3YmDjGCBgBI2AEjEA5BLRRM2aisPWOCSeTs+UQ67o0aV9XMvGiHjVK1GOGBg6DPZBkiqbUnh5q0271CGD2k68TMB2KyaJqv1apvoXyOdMNZrM59eUwXwLnSaMbx+bVaaI+DxXHtRQCXBARreq3VOc6sTMyD9NIE62+sKFj28gY07K9bY6kY7ffCBiBjgiYqO+Ih0O9G4FmEfXL7Hdj+Oa777sFzAnGGzc8etLa3dK2GzUCRsAIGIHeh8Dbb78dBgwY0GHgEIqrrLJKhzgHuh4BTMGssMIKseFrrrkmLLbYYmU70ShRj7JoutcA8wL77NK2Ltu4E8sigMmmZZZZJubB3nvevgplK2hSIiZ82POE/Wjef//9srX2798/9plrQWeYJzVRXxZ+JxqB7kVA5mvq6QUmc0xc14OcyxgBI9ATEDBR3xOOosfQLASaRdTvfcEj4Z7n32tWt2qqZ8BC04dTt1+2pjLObASMgBEwAkagXgQg69iQFTIWTV9/uV0vkp1Tjs1qn3nmmUjSr7baamUb4esI7JEjbKTKZrK1CJu+3nfffdG0CRvisnl2V2t919LfdsrLhs+33357tL+/9dZbt0TXMYnz5ptvRsIekzcI1wEIeja5bZaJm1KDNVFfChnHGwEjYASMgBEwAm2LgIn6tj107ngnINAsov6jz74OR175dLj/xeFxo8tO6OpYVbIB3Uo/my4M3nixME3fCcdKd4QRMAJGwAgYASNgBIyAEegpCJio7ylH0uMwAkbACBgBI2AEigiYqC9CYY8RCM0i6g2lETACRsAIGAEjYASMgBEwAp2HgIn6zsPWNRsBI2AEjIARMALdhICJ+m4C3s22JAIm6lvysLhTRsAIGAEjYASMgBEwAkagAwIm6jvA4YARMAJGwAgYASPQExBYaNuHw7ff/VAcyj0nzR8mnGCcYtgeI9BbEPj6mx/CgP1eLg53/PHGCc//dbli2B4jYASMgBEwAkbACBgBI2AEWgMBE/WtcRzcCyNgBIyAETACRqCJCKx+wFPhzfdGFWs8YceZw0oL9S2G7TECvQWB+5//LBxw3tDicGeffuJw6wmLF8P2GAEjYASMgBEwAkbACBgBI9AaCJiob43j4F4YASNgBIyAETACTUTg8IteD1fcNbxY4/yzTBTO3nN2a9UXEbGnNyCANv3Op78ZXn7nq+JwNxk4XTh8m7mKYXuMgBEwAkbACBgBI2AEjIARaA0ETNS3xnFwL4yAETACRsAIGIEmIvD0a5+FTY58rkONkPXbrd4vLDPfZCbsOyDjQE9DAIL+0Vc+Dxfe+mEHkp5xXjF44bDY3P66pKcdc4/HCBgBI2AEjIARMAJGoP0RMFHf/sfQIzACRsAIGAEjYARyEMhq1edkcZQR6FUIWJu+Vx1uD9YIGAEjYASMgBEwAkagzRAwUd9mB8zdNQJGwAgYASNgBKpHYIcTXwwPPDey+gLOaQR6KAIrLjxlOH//n/XQ0XlYRsAIGAEjYASMgBEwAkag/REwUd/+x9AjMAJGwAgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBiBNkbARH0bHzx33QgYASNgBIyAETACRsAIGAEjYASMgBEwAkbACBgBI2AE2h8BE/Xtfww9AiNgBIyAETACRsAIGIEeisD3339fHNk444zTwU8acT/88EPAP95448Xwd999F/1p+rjjjhvzkZefyik+DdOI8hUbLHjcvvHXfGAupPOrHeaf+m7XCBgBI2AEjIARMAKtioCJ+lY9Mu6XETACRsAIGAEjYASMQK9BQOS5SHa5AEBaKimJDtEukl3kqch3kaciVFWHCHfFp+XVrlzKuH3jr7mj+aA50VPmXzo++42AETACRsAIGAEj0F0IdDpR310Dc7tGwAgYASNgBIyAETACRqAVEYDkzJLqiktJc/ouQjSNV97UVV7ypaJwXj0Q9SL1VVfajurETeOVN3WVV+0RRhR2+2PI/hRH49+a82/MzPW/ETACRsAIGAEjYATaD4Fx7rzzzo4qJu03BvfYCBgBI2AEjIARMAJGwAh0CQKQs4gIbPlTIps4worLkumkS5QPszfy41I/5dCMRwjzU/uqU/1QGcJpPZRVXuLxp6K8bt/4ay7gMo+6c/7Rdjrf8WfDmsfE02cEP6IxxID/jIARMAJGwAgYASPQJghUJOqXXHLJ8O2338aXBD28tcnY3M1ejMDQoUPj6KeZZppejIKHbgSMgBEwAkbACDQDAZF+qQa1yEHiRHJn2xKxmBLl5KEMaYoXIUqcftnn7mxdhMmTbZ94SbYM+RG3b/yZG+08/9T37PxP5z5+nbuKt2sEjIARMAJGwAj0bAQ++uijOMCZZ565SwbKMxU/3gfGH3/88MQTTzTUbkWifvnll+9A0usBv6FWXdgIdDIC77zzTmyhX79+ndySqzcCRsAIGAEjYAR6KgKQgBCCiGy+i/jLeyYmv+Ipx0M74ZQYpx6loQxDHh7qiccvV2V56MdPXvyqiz6pL/gRtU9+/dQ+bfJz+8a/neYf5wbC/Mev+a/zjHmO4Gr+E9YY0/mvvKRbjIARMAJGwAgYgZ6JwIcffhgHNssss3TJAPXMjcuz+kMPPdRQuxWJ+pVXXjm+BOhhqKHWXNgIdBECb7/9dmzJRH0XAe5mjIARMAJGwAj0QAREavMcDPGHq18aTh/QgYFy5FO8oIFI5AGeNER5RNiTX/WSnrYvkl7llE91qC3catunLoT8Kq96Fa+w2x+zSAIuYC5cjP9P81xzqNb5J/Kd8sI1O//SeDDXeZTFX8eHeIRyEtpJw4q3awSMgBEwAkbACPQcBETUzzrrrF06KD0H3XfffQ21W5GoHzBgQIcG9NDTIdIBI9BiCLz11luxR9NOO22L9czdMQJGwAgYASNgBFodgZTQ49lXz7+QfJCQKWnNQzlEO6IHdLkiwEVEkkf1KY9ctSnSPiUi1T5xafsqm9e++koetz/G1I3xb//5x1xOzz/muc4ZnQ9yOW/y5r/Jes4EixEwAkbACBiBnonABx98EAc222yzdckAedZI5Z577kmDNfsrEvUDBw4svpzUXLsLGIFuQsBEfTcB72aNgBEwAkbACLQ5ApB7Is0ZCuGU9Fac8iideOIIS5SHcJpP6YonHw/5IuKz5VJiUmVw9WKAyy9tX2GRkuXaJ438bn/MQojx/0nznLnR2fNPc7ma+cc8pU/pfGaOa75TF6IwdSJpfvykW4yAETACRsAIGIGeh0BXE/Upgjxf3HXXXWlUzf6qiPqaa3UBI9DNCJio7+YD4OaNgBEwAkbACLQhAtKAF7nNEETqQd6mYeLTNPyQgrjk5UE9S/Cn5UWqq4zKiXSMjf34J9JRceRRf5Sm9tJ68tqnDpVJ+5yWIz0V5Vec2zf+nT3/0jmYzj/8/JivSDoXsWGvecx5pXIqo/mLS/8pazECRsAIGAEjYAR6FgLdSdSDpIn6njWfPJomIWCifmwghw8fHt54441IIiyzzDJjZ3BMRICL+muvvWacPB+MgBEwAr0MAQg+CD1E/tSkBsReSviRhx9k3zfffFMkDAmTj7L4If9xlV+wEpa2r/KQD1FbKRmqtkinftVHHbSPK/KRetL2SVN+yiNu3/i3y/zT+aPzQ2HmMOQ8818bzZLGuDT/OSc0/zXvdf6QF7/FCBgBI2AEjIAR6DkImKjvOcfSI+lBCHQ2Uc+LwahRoyJiE044YfFFu5Uh3GabbcJjjz0WVlhhhXDuuee2clfr7tuXX37Z8DF5/vnnw8YbbxzrASfwshgBI2AEjEDPRiC1+849XkQgoyYMmSfSnDj8IvtIhwiUNr5c8qmc8iqc1o9fRCNl1T7lkbR+wuRP2yeO9hUnNy+v+uH2xyycCCPjP4bobqX5x1xF0vlPnOa/5jBxIuI1v3H1ozx+ieJVd5qmPHaNgBEwAkbACBiB9kXARH37Hjv3vAcj0NlE/VNPPRW22GKLiOCJJ54Y1lhjjYpo7rbbboFNJWaeeeZw2223VczfzAx8evP73/8+Vnn55ZeHRRZZpJnVt0Rd//3vf8Naa60V+3LIIYeETTfdtO5+gRWYzTXXXOHaa69ti4WYugfrgkbACBiBXo4ARJ7IPEg7Ed3yQwhCYBKP1i6auhLKkQ7RKwKQfAhpCPFqQ4S+0lSeMtRDXtqSn3iR8Gofl3Kql3Tapww/0lMhH0IZta90tz/m+IGZMDf+3T//mKfMW44LfuYpfmnNM385XsRLo15lNLfT84N8hLPnh+a/XJW1awSMgBEwAkbACLQvAibq2/fYuec9GIFWJOp33nnncP/994fpppuuYZtVtRw6CIU111wzDB06NKy88srhL3/5Sy3Fuy3vLbfcEl599dUw0UQThUGDBlXsR0rUH3zwwWGzzTarWKZUhpdeeilsuOGGMfmoo44K66+/fqmsjjcCRsAIGIEegAAkYEpoMySRdylJTz7CKeFNPn4IdXDfhTxUOdLIT1l+8pMf4hAROUxeEYzEk04aQjnSCaeEZUz88U/9Uhvkz2ufOMTtG3/mQavNP/qEaP5zTmj+cw4x/5nDOmdx+ZFf6bgI+ZRf50J2/pNX50Is5D8jYASMgBEwAkagbREwUd+2h84d78kImKj/6eim2vTnnHNOWHHFFX9KbGHfvvvuG2699dYw6aSThkcffbRiT5tJ1NMYX0zw5cRss80Wbr755uLLYMWOOIMRMAJGwAi0DQIQdBB8WdKPeBF9IgMJK5+ITVziIPkg6MmDPyUCVY9cpSuctk85kezULfKReNVJHGVF1quM+qL+chBUl8oojjKV2ifd7Y8hf41/8+afSPNK8w/MNf+YtwoTh1/EPWnUyfxHiCcPorYIaz6n5VSnXJWLhf1nBIyAETACRsAItCUCJurb8rC50z0dgVYk6iGSR44cGTXEF1hggS47BDvttFN48MEHI+H98MMPxxeYLmu8gYa6m6i/+uqrw2GHHRZHcPHFF4elllqqgdG4qBEwAkbACLQiAiLv1DcIO0TEHS4/SEAR7IrDnN0LL7zQQcNdaaqDsIhFuUqjvmz7pElEGpKHelKCkTyqm3QRksQhlCVefuVNXbdv/Ft5/jE/kXT+x4gf/5jLOieZ/8iCCy4YBgwYUCTqidN5hKtfWlbpaV78FiNgBIyAETACRqA9ETBR357Hzb3u4Qi0IlHfHZBj7ubXv/51bHrrrbcOBxxwQHd0o642u5uoZ1FFG8muvfbaYciQIXWNw4WMgBEwAkagdRGAgIcARyAEU+ISAk9mbGQDXqQhRN/ZZ58d3ntvWCwrAjwGCn9p+Cc/msA/mdih/rQ9yv6Ud0xNhEsT6mgNd1xYGFOqYz0/1en2jX/Pnn/9+08X2BOKOZ+e1xD5nEec75x3pKXnN+cNZTgfVU7nkl0jYASMgBEwAkagvRAwUd9ex8u97SUINIuoR1PuzjvvDE8//XQYPXp0WHrppcPAgQPji3ulzWTRoH/ggQfGQrxv375hvfXWGys+GwHJftNNN8VoyPZZZ5012rbHdjt1TzzxxGHuueeOttRLbQ7LRqhsrIr8+c9/Dr/4xS+iP++PF5Tzzz8/vsQsv/zyceNbNPHZ/BYb7ZDWV1xxRbjjjjvCV199Fe3db7/99mO90HzyySfhoYceCo899lh49913469fv35hvvnmC/POO29YffXVY9+zfXjmmWcCGv+SG264Ieg4aiNcpeHSp/79+xejsqZv1llnnXD33XfHfjz33HNhlllmCcsuu2zYZJNNxupzsZKMZ+ONNw7PP/98/BrhkUceKWplZbI5aASMgBEwAm2IAKRcSoKL0BZ5DklPugg95SUfhB/31Q8+eD8MGzaGrG9DCNxlI9BjEJhxxhlDv37Tht13371I1HPO6rzGL0nj5CeNc91iBIyAETACRsAItDcCJurb+/i59z0UARG80047bd0jhCg+8MADc8vvuOOO4bzzzotpJ554YlhjjTXGygfJ/oc//GGs+Go3k4Ukx2wNcsYZZ4THH388XHLJJWPVRwQk8mSTTTZW2kEHHRSuv/76GM8n+uXwgHQoRfhTAYsL1113XYc2dt1116i5pMh33nknEvEK57lzzTVXOP3008Mcc8zRIfmiiy4KYFmt/O1vfwuLL754MXtK1O+9997hvvvuC0888UQxXR4WHNhQtxqNqeOPP76I+Y033hjmnHNOVWPXCBgBI2AE2hyBlKjXUCDzuB9C3knIR5if/KRxL4GoZ1HaYgSMQPciAFE/9dTTxOdSzmN+nK9o00PAy08vRcgrXiQ+4ZS4794RuXUjYASMgBEwAkagHgRM1NeDmssYgU5GoFGins1Lt91222Iv11xzzQDBjHY1m7OmUoqof/bZZwN2ziVod48YMSLUQ9SvuuqqUbOfutBKn2eeeaK9ezT9v/jii5JEPZr4aOZPPfXU4f7771dXct0sUb/SSiuFTz/9NKDpLmFj19/85jfFcWXHkpLliy66aMAWP5rso0aNiprtLChI6A/9koA5GEluu+22MHz48BjcaqutFF10t9xyy8BLmSRtW3EQ+diW/+ijj8I111yj6HDmmWfGLyOKESU8//rXv8L+++8fU4899tiw7rrrlsjpaCNgBIyAEWgnBETEi5gTEa94iDs06hEIPkQEnszgnHXWWfH+MmyYifoIkP+MQDciMMMMMxQUUvqHnXfeORLxMm/DOa7zXEoahBHCnN9K1zku4r4bh+OmjYARMAJGwAgYgToRMFFfJ3AuZgQ6E4FGifrtttsukt/08ZRTTinaeSeMVjua1pJSRL3S5fLiADmdJbeVnnVTjXrSKHfyySd30CKHSEezHBM0E044YbaKuKkWkZDmf//738dKTyNSoh7zPpDZvLCwSCE8+UoATfj0awMWLugbwgXxggsuCBtttFFc2Ejrx//Xv/41nHTSSTF6r732CnyZUEoasVFPnYMGDQqYzNHLGF8UYLcU0fhioMxfumCDlv4OO+xQJreTjIARMAJGoF0Q4J4HGcc9QkQdfuIREXYyf0Me4uQSj0b9xx+PsEZ9uxx097NHI4DyxjTT9ItEPec256vIernEKQ2XeM55XIR0RM+OMeA/I2AEjIARMAJGoK0QMFHfVofLne0tCIhYLmfqpRQWr7/+esC+OYJNekyypMJDPHbW0VRHuoqov+qqq4rEe9qfUn407ZdZZpmYPGDAgPCnP/2pVNYYnxL1e+65Z9Hszn777Rewi4/wRQEvLy+++GIk44nDDj5a/tVI2sbKK68cSY5S5Roh6tHUZ2+BPn36dKheXxjwdQSLDZXklVdeibbwybfNNtsUtesrlXO6ETACRsAItD4C3M9FzEHAi7RTHPc7tOdxRdBzHyOMe+655xa+lPso/O9//2v9wbqHRqCHIyCiHiUQnv84jzmncbUoJ5I+JeaJ4/zGRA4uQpzFCBgBI2AEjIARaE8ETNS353Fzr3s4Ao0Q9WiIa/PSI444Im7WmoXr7LPPjhrnxHcFUV+J1M72j/D7778fVllllZiEuRppsuflJS4l0dmAdtNNN41ZDz/88PCPf/yjg/mc1MxM1la86udlZ+TIkdF8zeeff67osM8++0QTQAsttFC48sori/FZTyNEPZvADh48OFtl1KhHsx4TPmjLV5K33347mvohH18JgIXFCBgBI2AE2h8ByDt+kO4i3nER7l/4Iekh9AijQa8y5CHMs8DIkR9box5ALEagmxHA9A026tnfSXbpRbiLmCcs8p7ucp4T5hlYeXRd6ObhuHkjYASMgBEwAkagTgRM1NcJnIsZgc5EoBGiHhMxxxxzTOwe2nJsPpqVdKPYriDqd99997DLLrtku1E2/NVXX4Ull1wy5sHePIRCOUmJ+qOOOqqoSa4NVWebbbaAzXaEjfN+9atfRX8WIxYIIO8xgVNO0MJHG7+UNELU77HHHtH0Tbbugw8+uLgh7gsvvJBNHiucfjmAeSEWGSxGwAgYASPQ/ghwz4O0g6iDdIekg5DnlxLyhBHi5B89enQsK9M3w4YNa39APAIj0OYIaDNZiHrObc7ZCSaYII5KxH1Kwou0z5L0FCDNYgSMgBEwAkbACLQnAibq2/O4udc9HIFGiHps0otkhrTHvntWUvvxXUHUH3fccUVzPNm+lAsvuOCCMbmS9jqZUqI+3TiV8WH+JzUXwyav2HlHzjnnnLDiiitGP5r2aLNjdicVSH6Jjk1an9JStxGiHkJ+s802S6uL/lqJ+v/85z/R/j+F2VR2m4L5G4sRMAJGwAi0PwIQdoiIO+6BSKpVT5h0EfPkEZlPPoh6a9SDksUIdD8CEPV9+04ev57k/BTZDkkvYUGOn8574glzbpNfZZTfrhEwAkbACBgBI9B+CJiob79j5h73AgREBtdjox4N8dNPPz2iVMqsS2oepyuI+jPOOCOsuuqqNR+5NdZYI24Ei6mXRx55pEhA5FXUDKKezVYffvjhWD2EPZvyzjzzzB2aU5/agahH4x8zQEi1x7nDYB0wAkbACBiBlkMAsh1CDrIOP0RdqlWPX5IS8/hlsx6XherPPvu0IdM3aPxyn2RBe9xxxwvvvPNOwOza119/pS405GLKb7LJ+oa7774rsPjcE2SxxRYrmKVbo7CA8nVAuYLj2BMl79j1lrHXczwh6qeccqqAjXrO6ZR4h6xnnhAHiU86Qhxpug4Qp0U75SHOYgSMgBEwAkbACLQPAibq2+dYuae9CIFGiPobb7wx/PGPf4xolSLIsdl++I/2yqslcHfeeedw//33h+mmmy5A9FeSVGu/VD8q1YEJmyuuuCJmu+2228YizdPyjRL1H3/8cVGzHlM7aBpKM1HtpG20A1F/5JFHFu3oV8JPY7RrBIyAETACrY8AxByknVyId5H3kHf8SMNFIO+5hxGH4LKw/8knI+si6mlrgw02CL/85a9iu7HS5O/BBx8Il112WVwYSKJr9rKRfJ8+ExZM191c1txczRVXUQCiE7vhYFjrhrtsCj/JJJOETz/9NP7S5tgYfsMNN4pRO+88qEispnl6gj/v2NUz9nJY9gScNAbm2lRTTR2/hGTu8ZNmvcLMRfxyKcu5yLmNq+dWuarbrhEwAkbACBgBI9A+CJiob59j5Z72IgQaIerZZHTbbbeNaG1TMHWCyZOsHHDAAeHmm2+O0a1M1GNTXv1H64wXvFKSkuj1mL559dVXw29/+9tYPfb0sauflSeeeCJstdVWMboSUX/QQQeF66+/PuZ99tlni9pP2ToVTje4bZbpm3XWWSe8/vrrHTbSVXt2jYARMAJGoD0RENkOGYcf4k4kPGQdpLzSiIfUUxhCHyH+vPPOq4uop4299to7LLDAAmUBRLP++OOHRNM7ZTOWScwje8tkb2rSEkssUdhfZ9dYJ4oDb7/9VtX1H374EWGmmWYKzz77TDjzzDM7lKuHrO5QQZsE8o5dPWMvh2WbQFFVN2X6ZtCgQZF057xOteU5h4mTiLCXtr3Ie64JnKMWI2AEjIARMAJGoD0RMFHfnsfNve7hCDRC1LMJ68orrxztrGMy5u677w64EjZLXWWVVRSs2iRKd2jUf/TRR3EsdBbNPTTES0mjRP2IESMCmvRIHglP/SyAQNaXyhMTfvw766yzolY+QUzQsPlsOWk2Uc/mgL/85S9jk9i7h/y3GAEjYASMQPsjkBJx3JsQkfcQdPgh7WQOB3KeOMWTJtM3X3zxec0a9fPMM29gwR9BY/yyy/4WXn755QL5/33hXjdP2HzzLcI000wT0y+44PyGTNbkkb2x4i74W2qppQobu+8cWzrmmKPDm2++WXWrPK/MMMOM4bnnng18VZhKPWR1Wr5d/HnHrp6xl8OyXbCopp8Q9ZNPPkVgM1kEIp4fBD2/Pn36xHMYvzaZ5ZwmjOCmRH6M9J8RMAJGwAgYASPQdgiYqG+7Q+YO9wYEGiHqwQe7s3oxXHzxxQOaYLPOOmtAa5xNTlU/efM06tl4buTIkSQXZZ999glPPfVU1M7+5z//WYzH07dv3zDxxBN3iGuG6Rsq3G+//cItt9wS64Ykn2iiiTq0o0CjRD31QNRD2CO77rprwB49uPFyfswxx0Q7+TGx8JdH5isNlz7TdwSSfs8994zadbxoIXziLD/hZhP1F154YTj55JOpOlx99dUVNR9jRv8ZASNgBIxAWyAg0l2as5B0sk3P/TAl8BTPvR0hjbjzzz+/Lhv1G230u7DaaqvFuoYMOS5+uRUDP/7169evcM88Nmr15hHVZKPfc845Z+jfv394443/hvfeG5ZWUfTnkb3FxB89U0wxRZhvvvnCqFGj4nPO119/nc3SIUzb2NSHGEV5YYxN/Y5lZpxxprDEEouHddddL5a9+OKL4rMTCxOffPJJh/rSwGSTTVYwXzJV4Z6/V6BfPG9RlsURzOfgZsnq8cefoPBMMWckaenLsGHvxnxpvVl/rWPOlk/DPMPxnMIcor96OUzzpP5q2847dtmxa6EprV/+SljyPMhcQ4YOHToWZhxnvmpA3n//g+K+CdNPP0OB6B4/DB8+PC5Y8Zw3/fTThy+/HBVeeeXlil+AVDt3Y8M1/DEf2Y8BG/UQ8cwVnhNxIeBpF5EGvfwcN9J0zuMiJu0jDP4zAkbACBgBI9B2COhZjOfV7pBqTF2X69c4d955Z9kdmAYOHFiuvNOMQEsiICK9ns1kGRAvkltvvXV8Ya00wDyiPjU5U6k86ZiJwVxMKs0i6h977LGwTcGED1LO/E0ziPrbb7+98Dn/XrGtvL+FFlooLmDwQliJqKc/mNLB9EyeZDf6bTZRr01v6fOVV16Z1wXHGQEjYASMQBsiABEnYo7uQ+Txg7ATSUeY+5BIevIprDQWdEeO/LhADOeT5JTJEzR+l156mZi09957hc8//3ysbGijTz755FFbH217CQTklltuFRZffLHCwvtPC/zsE/Pvf98W7rjjDmWNbh7ZqwwLL7xIWH/99TvsX8MYX3vttcIixHljKRzQ9lZbbR0WXXTRDsoF4MFGtZdeekkkbiFtjzrqaDXTwX3ppZcKzyJjFsE7JPwYSLHJpmMGiL6lZPV1110XMFMnEpYy4HnqqafEBYRsHbWOOVs+DS+88MLxa8WZZpo5jY4LEZhFgrROpda2845dOvZK9vkrYTn33HMX+r9h7OLJJ58Uv+pI+7vgggsVn+n46uOee+6Jx/2MM8aYImLvHhZ4Zp999mKxb7/9Jjz//PNxESu74FPr3C1WWqUHon7SSSeLGvWQ8ZzPkO385FdV9EXXAeJI109h5bVrBIyAETACRsAItBcCJurb63i5t70EgUaJemD64osvorkTyOdUVl111cJn6ZuH7bbbLkbnkd+8vKBBX62gLa5PdVXm4YcfDjvssEMM8rI2YMAAJdXk8gK9ySabxBcnSGc2l+VlJCu8nC+yyCIxesiQIWHttdeOfsZ3wQUXdCDWU/M/aBQut9xyxerYjJcvEMAvFcwJnXDCCXFBgi8L0D7DpE05QbuPvQAgyl988cUOWS+//PJif0ngmEOuI4ccckjYdNNNoz/9Gzx4cNDXDC+88EKa1MGfYl/vRr4dKnTACBgBI2AEWgoBkXS43BO5V/IjnLp0GrJe8aQR5p4JGfv555/VbPoGbXq06pGXX34pbkr72WefxXClvx133Ckss8wYkj8vL3169NFHikl5ZC+Js846W/jDH/7Q4cu0YqGC5513sI9/fEGLeoymPBjttNOgwAJCKWFhHTIdjXiI+vSrN8pA4rLnDJvNl5ItttgirLDCCgWt5wmKWSiHHHfccZF8T8nqYqaMB7L+sMMGd9iIttYxZ6rsEJxjjjkKewAdUDSh0iGxEGC+/OlPZ8XxklZP23nHLh17JaK+EpbffvtdOOKII2LX7733noACRCooeaywwoox6o9//EPAnCIb/J5+ekdTRGkZ+Z988omxjnOtc1d1VetC1PftO3l8dhY5D2HPIg7nreajFnWkMY9Luq4DcpWv2vadzwgYASNgBIyAEWgNBEzUt8ZxcC+MQAcEmkHUq0IIZzTAsEcL0c0nzu0mEOO8sCFnn3120ZZ8Z40DO/98fo7m/HTTTRfmmWee4gtSZ7XZzHqxSf/MM8+EJZdcsvDJ/cW5CxvNbM91GQEjYASMQNchACmHQMhBuCPE4Rc5xz0fP3FKg3zlB1HPD436zz77tGaNekyHsHCMVi9CnTxnvPjiC/ErsjfeeCO2GROTP75y3XTTzWIMJuUuvfTSaPLmZz/7WcHcx07xPku/DjrowICGPZJH9kJeDhlyfDQtQ57LL/97NE0HCbv++hsUyXj26Pn73y8jSzTVo8UFviCg7bfeejOglb3hhhuFWWaZJeY77bRTgxbCu8JGPSQ+ygQsAMw119xxwX7++eePfTn77L8U98WpZ8yxkpw/jtsJJ5xYMLMyWUy94Ybrw/333x81t3/+8+XCeuuNMffzxhuvx8WFetvOO3a1EPXqejkb9YceOjiaKGShaL/99o1zkXKQ1yeffErco+nVV1+JZh6JzxL1mCNC257nbkwxQe5PM80YczqXXHJxxIVy9cxdytUiqUa9iHrOYfy4nO+4aRznHmHOcX4cK10XKGcxAkbACBgBI2AE2g8BE/Xtd8zc416AQDOJ+p4CFxr+aPrblEv5I5pq01911VVhwQUXLF/AqUbACBgBI9BWCIiUkyvyDpIbkk4kPWGIeuLwi6iXW69GPWDNN9/8YY899shdxGaPG0zJ3HnnHR3Mz5Af8ymY5zviiMM7aIsvvfTSUeOdulOCOo/snb1gquTggw8ha7jhlUR5pQAAFPBJREFUhhvCjTfeEP38QUIfeOBBkXh/9913o1Y68diM5/kBDfujjz6qsEDwHtFRIEgpM/7448Wv4G666aYY3xVEPYsMqR3QKaecskAqnxTbv/XWW4pf0dUz5jGjG/sfbfqDDhqzwfx9990bFy3SXHxxyfEYPfqbgtb9ftGWf614U1/esWs2Ub/qqr+MX13SXmr+hmefvfbam+i4R8ADDzwQ/SlRj1IGX1C+//7wmMYfXw4ceOCBkfB+9NFHC1+dnBvT6pm7xUqr9DAP0ajfdttti+cV81mkvYh6XASXH+Q81wCR9nKrbNbZjIARMAJGwAgYgRZDwER9ix0Qd8cIgICJ+rHnAV8G8NkyLywzzTRmc7CxcznGOHkOGAEjYAR6PgKQ9JB0kHIIroh7uUqT1j0EvuLwo8kNac7mpfUIGtnYqsd8HORvVj788MNInuIimByBKEVYJEilT58JCnvrbBOj2Iz9mmv+Gf15ZO+Agim9zTcf85Ud2uDDh7+fVlXQqv9tUSt6t912jZuDnnba6VG7+sknnyyYNPlzh/ylAl1B1B966KFjbaR7yimnxq8fH3zwgXDRRRfF7tUz5lLjSrXDDz/8sLjJbam8xNfbdt6xazZRz1eiJ510ciSqU/M37NO04oorxUWrffbZO0DKIylR/8QTj8evNGNC8odJpbnnnid+aTJ48KExpZ65m1RZlXeGGWaIm8liSpJzm+ddkfQQ8ZD2CH6lc66LpCdOP/LhtxgBI2AEjIARMALth4CJ+vY7Zu5xL0DARH0vOMgeohEwAkbACBiBOhGAoENwRb5D2EHKE+anPClBT/zo0aNjOkT9F198XrON+rwuTzzxxAXTIXOFxRZbNNoFF6nIxpynn35aSDXF88qncZhuO+usMRt+5pG9W221VcEE3sppkZL+o446smDe57No6oVMaMtff/11JfOnCV1B1OfZaT/22GPDtNP2DylRX+uYMd9XSkRik57XfrZcvW3nHbtmE/X0dffdfx83CJb5Gwhqmb1JteLJmxL1fInBFxlZ0Xg5f3bddZdoIkhfOWTzZsPp3M2mVQqjUc9msoMGDSoS7pzT/GTShrFxbtE34lMynjw65znPIfktRsAIGAEjYASMQPshYKK+/Y6Ze9wLEDBR3wsOsodoBIyAETACRqAOBCDjIOIg6fjhh7TDvA1COvEyfUO6yhCHHxeivp7NZCt1GcLx8MOPiH1Ak3mPPX4fyU40xSXYDc/KvPPOF94s2K5/+umnCyZoxpifySN7N9988zBgwCqx+Guv/V8cf1rXlFNOFfr37x9eeeWVgh3+C+LCxKmnnhazYELv6qv/kWYv6W8lor7WMY8YMaLkuNjHZpVVBsb03XffrbjhbqkC9badd+w6g6hfYoklwi677Bq7j/kbzoW9994nhk877bTCngPPF4eWEvXplxvFDAXPtttuF5Zffvl4joAPZeqZu2md1fg5byaZZNLCfg07RpId4l3ke0rUQ8CLiBcZz5h1HUjJ+2radR4jYASMgBEwAkagtRAwUd9ax8O9MQIRARP1nghGwAgYASNgBIxAHgIi5EgTOQ+Rh+Y8JB0uInJefjTpEcpQB+ZnatWop/4tt9wqoEEPASrb37Hi5G///Q8I8847b4zZa689C+18EbXap5pqqsIGqfkmR5LiRW8e2YtJE7TCkcMOG1zVFwFDhgyJ5nBefPHFcOqppxTrlwczPmD4ySefRNyIbyWivp4xa2xZFxIaMhoZMuS4uAFwmgeNbchp5suoUaOiCZla8aa+vGPXGUQ9xw3zN5NOOmm49957CkT9eIUvLlaKx/KAA/bvsJCTEvVs4HvmmWekQ4/+Qw45NMw222zhnXfeDmxki7D5bq1zNxas4U9E/fbbb18k6vVlCtUwTn6c1xD0/PiCBpKeePyK4zwl3mIEjIARMAJGwAi0HwIm6tvvmLnHvQABE/W94CB7iEbACBgBI2AEGkQAwh2BpEuFeH4i8nFF4pOX34UXXlizRj11DBlyfJh66qmjSZmDDz4okrlp2xNNNFFhk86jo7mb4cPfC4ccMmbj1912271gGmexqMENQTx06NBiMYjy9dZbL/YRcyVowyN5ZO8ss8wSBg8+LKY/9NBD4a9/vTD69ffzny8X5pln7miX/Oqrr47E5qBBO0finTynnXZqYZHhBWUvbIw7X9hvv/1jmE3Yb7/939GfEvUXXHB+3CC3WKiCB4J3hhlmDB988H5h49aDOuSuRFbnmb6pZ8wdGk0C2EI/8sijYgxfNpx88slFMpvjizb6AgssEBdX9ttv38I4ZqgZbyrPO3aVxp50s+gth6UySeufBSHGACGf9/VEStRzfpx00knh//7vVVUT5yfzFElND9Uzd4uVVumBqGcz2R122CGOAaJdhDx+Ee8i7xXG5XxWHsZvMQJGwAgYASNgBNoXARP17Xvs3PMejICJ+h58cD00I2AEjIARMAINIADBiFYtIlIOFzIesk4a9aSLlMelDD+ln3vuuTVr1FPnOuusE9Zeex284eOPP4423//73zcLZO93YeaZZy5s5rp+tLFO+h133BGuvPIKvAECHW1hZOTIkQEb4e+++260yw1JT1kkJcXzyF7IyyOOOCJMN930MT9k/eOPPx7Hsuiii4XVV1894jB8+PBw6KGHxDEvueSSBXvsu8T8kLk33nhjeOutN+Pm9GuttXZcVCDxqKOOCm+//VbMB1m9zz77Rj/PZTfddGN47bXXCosbn8e4cn/akJQ82MRHe1t24yuR1XlEfT1jLtU/5goLHcKbDXYhpZlXyyzD5sDLx6Kyt15v23nHrtLY8/pcDkvlZzPjgw46WMHo5m2UmxL1ZMKuPXPhvffeK8yFGcO6664bJppo4lj+rLPOCs8883T01zN3Y8Ea/lgQwUY9pm+kPU9x/EhKxHMMFcYlzPHjWHGuKz0W9J8RMAJGwAgYASPQVgiYqG+rw+XO9hYETNT3liPtcRoBI2AEjIARqB0BCHeRcyLgqUVEPWQdQpq06XEh80TeQ9SPGvVlVaZjYmU//k0++eQFu/N7RvMgaXzWP2zYu+GMM84IH374YTFpgw02jER6MSLjefHFF2IZ9T+P7KVIv379IjHbt2/fTA1jgowVsyaYupGstdZaBSJ2PQXHch9++KH4lYES0PI//vgTQp8+fRQVXnrppYK98pOL4VKevHEef/yQSPRXIqvziHraqWfMpfrH5r4Q25hzyZMvv/wyatpr0aKetvOOXaWx5/WlHJZp/qOPPrq4eJOarUnzZIn6NC3133rrreGf/7w6jQp5/UgzZOdumlaNf8YZZ4rme9Co5zyGdJeLH4G0FzGPi3Ad0PWAfPhF2scM/jMCRsAIGAEjYATaCgET9W11uNzZ3oKAifrecqQ9TiNgBIyAETAC9SEAGQcpB2Enck4ENzUqXXFo0hOnX70a9dQNYfi73/2uoH29XFEDmXhk9OivCxvCPhMuueSSgpmbr8ZE/vgPqbj66r8pmBhZNMw++xyx7ySh5X7XXXcGNviUxj/xbOIJGX/NNdcU0v5FVFEwB0NdmK6ZYoopivGYzbn22mvGsr1Ohl/9arXA5qNzzjlnse2PPvow3HPPPQFyNisLL7xIWHPNNcNcc80Vbba/8cYbBQL7pGy2scKY/1l//Q3CsssuG7GC7D/hhBOimZUBAwaEzTffIo6TzUo5HqkcdtjhUdsdPC6//PI0KdQz5g4VJAE0uH/zmzWimRuIewS79Izx0ksvCe+//36SO9Tcdt6xqzT2Dg3+GCiHZZp/gw02iPOBuKuuurJgwuj2NDn6U6L+uuuujV9+YLOfeYnwhceTTz4RbrjhhnhOxcgf/2qdu2nZavwyfcNXJyLoMXPDuQ0Bz48+kCaSnno5F8mD4CpPjPCfETACRsAIGAEj0HYImKhvu0PmDvcGBEzU94aj7DEaASNgBIyAEagdAYh3yDiRctSgOOIhfgmTjktY/tQ955xz6jJ9k+0x2tbTTz99oa3vw4gRH0WCl3YqyYQTTlTQIJ4k9g9TOI0IRDOEJZvBpkR/qTrV9tdffx0XCUrlUzz5f/jhp68RFF/JFcEKsfrVVx0XLSqVrZRe65jL1Udd9BFTRtUcu2a2Xa5faVolLPfdd98w//wLxPm0//77hU8//TQtHv0pUX/ZZX+LCzSQ4f379497LYwYMWKsMnkRmj+cW43OXdWvzWR32mmnIDv0jJnjwUIP5zbCccLPjzmPy3lOXhH4lFF+1W/XCBgBI2AEjIARaA8ETNS3x3FyL3sZAibqe9kB93CNgBEwAkbACFSJQErCQRRCyGHqBaIOV6Q9aQhhBAIbP+Xxs5ksNroxUWMxAu2KAF8GLLPMsgHTRsj9999f+Jrj4tzh5BH1uRm7ITJPo15EPOc25zkuQrxE5z3kvhblROYrj10jYASMgBEwAkagfRAwUd8+x8o97UUImKjvRQfbQzUCRsAIGAEjUAMCEO38EBH1KXkPWS/BLyJfhL3I/PPOO6+wMepnNduoV912jUB3I7D88iuEbbfdttgNFqAGDz60w74IxcSCp12Ieoh4yHZ+EPEi3kXGK8x5L/Ke85x4rgm4FiNgBIyAETACRqA9ETBR357Hzb3u4QiYqO/hB9jDMwJGwAgYASNQJwIi6XFFyImcJ44fZJ1IfMhLkXfkww455TB9Y6K+zoPgYi2BwMor/yJstNFGBQ3z8cLQof8r2Na/NGgD3LwOYkLm0EMHR430K6+8orCXwtN52bolDo36ySefImCjHkIe4l0/mcKhY9KyT/0618kvSf2Ks2sEjIARMAJGwAi0PgIm6lv/GLmHvRABE/W98KB7yEbACBgBI2AEakBAhDyEnAh6XEh4ac1TnfKhUS/TN6Rj+uaTT0Zao74GzJ21tRBg7qNRroWp1updbb2ZYYYZw2STTRYGDRoUz2HOYxYWkFRrPjV7Qxr5RMrj6nqgOPJYjIARMAJGwAgYgfZBwER9+xwr97QXIWCivhcdbA/VCBgBI2AEjEANCIiUh6CDeBcxRzwCaak8hCHl+Sk/ZchjjXrQsRiB1kBANurZTJZzNf1xjough7Tn/E7Je5HychlR6m+NEboXRsAIGAEjYASMQDUImKivBiXnMQJdjICJ+i4G3M0ZASNgBIyAEWgTBCDZIfEQCHgRd9IqFmGvdPJi/kZhEfnnn39+GDnyY2vUR2T8ZwS6FwGZvkGjnnNYdurpFaZvOI85dznf8ZPOeY0LKa/FOfz6de+I3LoRMAJGwAgYASNQDwIm6utBzWWMQCcjIKK+k5tx9UbACBgBI2AEjECbISBCjm5DyEHeiagjTunSoicMoQe5RxxhtOovueSSQvibMGzYMIpZjIAR6EYEIOrHG2/8sM022xSJei3Cce7i5zxHRMQTz/mcdZWvG4fjpo2AETACRsAIGIEGEZhtttkarKG+4nfddVd9BX8sNc6dd9455jvfEtUMHDiwRIqjjUDrImCivnWPjXtmBIyAETACRqA7EYCYEzGPKw15+pSS8eRDZJeefPgR0th4c/Tor61RHxHxnxHoPgSmnHLKMMkkk0Sifuutt+5Ayqca8/KLiBdBT1g/nffK032jcstGwAgYASNgBIxAIwiYqG8EPZc1AkbACBgBI2AEjIARMAJdgICIOJqCjIOAR3DlF2FPPFr0CGnkHz16dCT0Tz/99PDhhx8W4mJyJO/LkXu0WymdmrJ5OpbDRAf9wHTPDwU/+d0+CHTEaQwm6X816eQ3/j9OqB/B64hb686/qaaaOiy++OJhpZVWiuenSHlp0jMO4nSMOc7SuP9xqMU5xPlvMQJGwAgYASNgBIxAPQhYo74e1FzGCBgBI2AEjIARMAJGoNcigGY8ZBxknfyAkWrPk47JG8g8SPo0DfL+3nvvDc8++2wk9ygLEYiQj3pT0zmESddCAK7a1wIAeZROXtqlDpGLqp82VEZxlEVUB3Wn41K6yuGm7ad+6nH7xr8r5x9tae5q/jHvmf/Yl5doHjM/mbMIcfgXXnjhMGDAgBhHWfLgkq76VU7zm3KKIx9+RO3EgP+MgBEwAkbACBgBI1ADAibqawDLWY2AETACRsAIGAEjYASMAAhAyqXENWSeyHjSIeu0iSz5yJ+S8ORByKN0wpCL1EU8dRBGKE9Ydah9yELiIBVxEfIRT1mRicTRDnXjEq/60jJpXaqbfMqjsmn7xLl9499d84+5qPnHXOXHPNe5xNzVeQBxr/yKwyU/P9L69OkTXYVJ14904pH0fKJ9wgh5LUbACBgBI2AEjIARqAcBE/X1oOYyRsAIGAEjYASMgBEwAr0WAcg6fiIFIShFZousE0lIPvy4pJGPMH7iEMoTpzRc0viRBvGndPzEkUY+fspDXYonDvIQwa86IRnVNnHUR5if/LgS4imjNpRH7RBWGmUU7/aNf1fOP5HnzD/NSUh55rjmNvOTPmmOkk/hNI040iireJ0HtEN5hHSdG2n7Khcz+c8IGAEjYASMgBEwAjUgYKK+BrCc1QgYASNgBIyAETACRsAIiJiGkJMfIg8CTyQeKBEnV/FoyotIpCzxqgdXpH5aL3Uoj9oRkUh+SEK1FRv8Mb/qTuOoV2QjZahX5alT9eAqH3n4EUed/MjLz+0bf80fzTNc5ktXzz/aVNtZP2HNb4h49Y++E0bwIyqbxlOW8SC45OXH/CefzhvSyWsxAkbACBgBI2AEjEA9CJiorwc1lzECRsAIGAEjYASMgBHotQikhJ3Iasg9/UTa4ULMQ+RBkBPmRxmFKUMe6iSNMC6ivJCBSqNsNp26KEcaLnXJpSyaxZRRvyEY8fNTX2iPsNLUD8LUTxpxhNV+Wp40t2/8Ne/kduX8Y26qXeYi85O5K1M2mq8i4Jnz6XynrNIUn5Lw1EmYeiTEpeem4u0aASNgBIyAETACRqAeBEzU14OayxgBI2AEjIARMAJGwAj0agQg6yDoIPQgA0XsQbojIvDIJ/JQhLqId/KRRhgXwS+hLGHIQeWhTdWNn7S0feUjjwhF/EjaX/VL/ScPfVAf1R/CaoM4yqlut2/8NTc0/7t7/mn+qj/MWc1t5i79Zd4ixBMmnvyEdY6oDGEtdBGnvJTX/MclDdH5GgP+MwJGwAgYASNgBIxAjQg0StT/P0/XGRgP5yDCAAAAAElFTkSuQmCC"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## JupyterLab Features -- Cell Properties\n",
+    "\n",
+    "### Cell Type\n",
+    "The cells in a notebook have different properties.  One of the most important  is the cell's type.  On the notebook's menubar, you can see the cells type in the drop-down menu.  A cell can be `Raw`, `Markdown`, or `Code`.\n",
+    "\n",
+    "![Screenshot 2024-09-12 at 5.04.19 PM.png](attachment:9279cd39-a0ab-4c23-bf4f-a00fe8fdd4fd.png)\n",
+    "\n",
+    "`Code` cells are ones where you enter Python commands, `Markdown` cells hold formatted text, and `Raw` cells are ones that do not get converted when exporting from a notebook to some other file type.\n",
+    "\n",
+    "### Editable vs. Read-Only cells\n",
+    "\n",
+    "If you click the gear icon on the right sidebar and look under the `common tools` section you can see if a cell has been marked as read-only or editable.  This can be used to protect from accidentally changing the content of a cell.\n",
+    "\n",
+    "<img src=\"attachment:26314fdb-40a3-4999-af02-6f6686f3211f.png\" width=\"50%\" alt=\"Property Inspector\">\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Summary\n",
+    "\n",
+    "You learned how to call **functions** and pass **parameters** to the **arguments** of the function.  You can pass parameters by position or by keyword argument.  Some functions send a value back, called the **return value**.  You learned how to **import** functions from a **module** and call those functions.  We looked specifically at type conversion functions, the input() function, some other built-in functions, and functions from the math and random modules."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/s25/Louis_Lecture_Notes/05_Using_Functions/Lec_05_Using_Functions_Solution.ipynb b/s25/Louis_Lecture_Notes/05_Using_Functions/Lec_05_Using_Functions_Solution.ipynb
new file mode 100644
index 0000000..f2e13d9
--- /dev/null
+++ b/s25/Louis_Lecture_Notes/05_Using_Functions/Lec_05_Using_Functions_Solution.ipynb
@@ -0,0 +1,921 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Warmup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warmup 1 \n",
+    "# Create 3 variables.\n",
+    "# Assign each one a data value of a different type.\n",
+    "# Open the debugger to view these variables.\n",
+    "my_cool_name = \"Jon\"\n",
+    "my_fav_num = 8\n",
+    "pi = 3.14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "False\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Warmup 2\n",
+    "# Write expressions that meet the following criteria:\n",
+    "\n",
+    "# exp 1 -- use the \"or\" operator that prevents division by zero\n",
+    "# using short-circuiting\n",
+    "\n",
+    "print(True or 1/0)\n",
+    "\n",
+    "# exp 2 -- use the \"and\" operator that prevents division by zero\n",
+    "# using short-circuiting\n",
+    "\n",
+    "print(False and 1/0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Warmup 3\n",
+    "# Decide if the following variables are valid in Python.\n",
+    "# Write VALID or INVALID next to them.\n",
+    "\n",
+    "# my.name = \"Meena\"  #INVALID\n",
+    "# 1st_place = \"Andy\" #INVALID\n",
+    "# stop&go = False    #INVALID        \n",
+    "# False = 100        #INVALID  \n",
+    "# end_game = True    #VALID"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n",
+      "<class 'float'>\n",
+      "False\n",
+      "True\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Warmup 4\n",
+    "print(True and not False) # True\n",
+    "print(type((5 // 1.2))) # float\n",
+    "print(1 <= 2 and \"a\" < \"A\") # False\n",
+    "print(1 <= 2 or \"a\" < \"A\") # True\n",
+    "print(len(\"2\") + 1) # 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Your dog is 49 years old!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Warmup 5\n",
+    "\n",
+    "# Calculate how old a dog is in dog years given the year they were born\n",
+    "# 1 year is equal to 7 dog years!\n",
+    "# e.g. a dog born in 2019 is 28 years old (7 * 4)\n",
+    "\n",
+    "year_born = 2017\n",
+    "dog_years = (2024 - year_born) * 7\n",
+    "print(\"Your dog is \" + str(dog_years) + \" years old!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "2\n",
+      "19\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Warmup 6\n",
+    "\n",
+    "# Given the number of seconds, compute the number of hours, minutes, and seconds.\n",
+    "#  e.g. if seconds is 65, the correct output (with \\n in-between) is 0 1 5\n",
+    "#  e.g. if seconds is 192, the correct output (with \\n in-between) is 0 3 12\n",
+    "#  e.g. if seconds is 3739, the correct output (with \\n in-between) is 1 2 19\n",
+    "\n",
+    "seconds = 3739\n",
+    "\n",
+    "# First, we have to get the number of hours.\n",
+    "# There are 60 * 60 = 3600 seconds in an hour, so\n",
+    "# we use integer division to get th ewhole number of hours\n",
+    "\n",
+    "hours = seconds // 3600\n",
+    "\n",
+    "# What we have left over is the remainder of seconds.\n",
+    "# We use the modulus operator to get the remainder\n",
+    "\n",
+    "seconds = seconds % 3600\n",
+    "\n",
+    "# Same thing for minutes!\n",
+    "# We use integer division to get the whole number of minutes\n",
+    "\n",
+    "minutes = seconds // 60\n",
+    "\n",
+    "# What we have left over is the remainder of seconds.\n",
+    "\n",
+    "seconds = seconds % 60\n",
+    "\n",
+    "# Print out the results\n",
+    "print(hours)\n",
+    "print(minutes)\n",
+    "print(seconds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reminder\n",
+    "\n",
+    "* a **literal value** is when you type the value yourself (e.g. `\"hello\"`, `3.14`, `True`)\n",
+    "* a **variable** is a named memory location that stores a value that can change as the program runs -- know the rules for variable names (also use meaninful names)\n",
+    "* An **assignment statement** is of the form `variable = expression`\n",
+    "* **Errors** can be divided into three categories:\n",
+    "    * Syntax errors\n",
+    "    * Runtime errors\n",
+    "    * Semantic errors\n",
+    "* **Jupyterlab features**: use debugger to view kernel state, ways of running Python code, shortcuts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jp-MarkdownHeadingCollapsed": true
+   },
+   "source": [
+    "### Python Variable Names\n",
+    "*Python* requires that variables...\n",
+    " - Only use letters a-z (upper and lower), numbers, and underscores.\n",
+    " - Don’t start with a number\n",
+    " - Don’t use Python keywords (e.g., `and`, `False`, etc)\n",
+    " \n",
+    "In addition, *CS220 Course Staff* ask that variables...\n",
+    " - Only use the English alphabet and numbers\n",
+    " - Use snake_case for variable names (as opposed to camelCase or PascalCase)\n",
+    " - Make variable names *meaningful*\n",
+    " \n",
+    "Python won't run for the first set of rules; CS220 course staff will take off points for the second set of rules."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Using Functions\n",
+    "\n",
+    "## Readings\n",
+    "\n",
+    "- [Downey Ch 3 (\"Function Calls\" to \"Composition\")](https://greenteapress.com/thinkpython2/html/thinkpython2004.html)\n",
+    "- [Python for Everybody, 5.1 - 5.4](https://runestone.academy/ns/books/published//py4e-int/functions/toctree.html)\n",
+    "\n",
+    "## Learning Objectives\n",
+    "\n",
+    "- Call `input()` to accept and process string input\n",
+    "- Call type cast functions to convert to appropriate type: `int()`, `bool()`, `float()`, `str()`\n",
+    "    - Concatenate values onto strings within a print function by converting to `str()`\n",
+    "- Using correct vocabulary, explain a function call\n",
+    "    - call/invoke, parameter, argument, keyword (named) arguments, return value\n",
+    "- Use `sep`, `end` keyword arguments with the print() function\n",
+    "- Use some common built-in functions such as round(), abs()\n",
+    "- import functions from a built-in module using `import`, `from`\n",
+    "- Call functions in modules using the attribute operator `.`\n",
+    "- JupyterLab Features: Notebook cell Types (Code, Markdown, Raw) and Properties (Editable vs. Read-only)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Vocabulary\n",
+    "\n",
+    "| Term | Definition |\n",
+    "| :- | :- |\n",
+    "| function definition | a grouping of lines of code; a way for us to tell our program to run that entire group of code |\n",
+    "| call / invoke | a statement that instructs the program to run all the lines of code in a function definition, and then come back afterward |\n",
+    "| parameter | variable that receives input to function |\n",
+    "| argument | value sent to a function (lines up with parameter) |\n",
+    "| keyword (named) argument | argument explicitly tied to a parameter |\n",
+    "| return value | function output sent back to calling code |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Previously Used Functions: `print()`, `type()`, and `len()`\n",
+    "\n",
+    "We have used functions before! Namely `print`, `type`, and `len`.\n",
+    "\n",
+    "What parameters did they have; what arguments did we send them?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello world\n",
+      "6\n",
+      "<class 'bool'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"hello world\")\n",
+    "print(len(\"apples\"))\n",
+    "print(type(1 < 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's take a closer look at the `print` function.\n",
+    " 1. Use the [official Python reference](https://docs.python.org/3/library/functions.html#print)\n",
+    " 2. Use the [w3schools reference](https://www.w3schools.com/python/ref_func_print.asp)\n",
+    " 3. Use the `help` function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function print in module builtins:\n",
+      "\n",
+      "print(...)\n",
+      "    print(value, ..., sep=' ', end='\\n', file=sys.stdout, flush=False)\n",
+      "    \n",
+      "    Prints the values to a stream, or to sys.stdout by default.\n",
+      "    Optional keyword arguments:\n",
+      "    file:  a file-like object (stream); defaults to the current sys.stdout.\n",
+      "    sep:   string inserted between values, default a space.\n",
+      "    end:   string appended after the last value, default a newline.\n",
+      "    flush: whether to forcibly flush the stream.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(print)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 two 3!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"1\", \"two\", \"3!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Bob and Alice and Dylan and Gretchen\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Can you add a keyword argument that will connect the names with the word 'and'?\n",
+    "print('Bob', 'Alice', 'Dylan', 'Gretchen', sep=\" and \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Good Morning!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Can you add a keyword argument that will make the output appear on a single line?\n",
+    "print('Good',end=\" \")\n",
+    "print('Morning!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello|world;\n",
+      "its^so^cold...\n",
+      "****\t######<END>******||########<END>\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Guess what will get printed\n",
+    "print(\"hello\", \"world\", sep = \"|\", end = \";\\n\")\n",
+    "print(\"its\", \"so\", \"cold\", sep = \"^\", end = \"...\\n\")\n",
+    "\n",
+    "print(\"*\" * 4, \"#\" * 6, sep = \"\\t\", end = \"<END>\")\n",
+    "print(\"*\" * 6, \"#\" * 8, sep = \"||\", end = \"<END>\")\n",
+    "\n",
+    "print(\"\\n\", end = \"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## New Functions For Type Conversion\n",
+    "\n",
+    "| Function | Purpose |\n",
+    "| :- | :- |\n",
+    "| `int(value)` | Turns `value` into an integer |\n",
+    "| `float(value)` | Turns `value` into a float |\n",
+    "| `str(value)` | Turns `value` into a string |\n",
+    "| `bool(value)` | Turns `value` into a boolean |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your name:  Louis\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'str'>\n",
+      "Hello Louis!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This code works!\n",
+    "name = input(\"Enter your name: \")\n",
+    "print(type(name))\n",
+    "print(\"Hello \" + name + \"!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your age:  53\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'int'>\n",
+      "In 10 years, you will be 63\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This code doesn't work! Try to fix it.\n",
+    "age = int(input(\"Enter your age: \"))\n",
+    "print(type(age))\n",
+    "age = age + 10 # we can shorten this to age += 10\n",
+    "print(\"In 10 years, you will be \" + str(age))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your temperature:  91.4\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'float'>\n",
+      "You can enter lab: True\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This code doesn't work! Try to fix it.\n",
+    "temp = float(input(\"Enter your temperature: \"))\n",
+    "print(type(temp))\n",
+    "temp_ok = temp < 101.1\n",
+    "print(\"You can enter lab: \" + str(temp_ok))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Other Built-In Functions\n",
+    "We can refer to [this resource](https://www.w3schools.com/python/python_ref_functions.asp) to see other built-in functions in Python. Let's practice using `round` and `abs`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function round in module builtins:\n",
+      "\n",
+      "round(number, ndigits=None)\n",
+      "    Round a number to a given precision in decimal digits.\n",
+      "    \n",
+      "    The return value is an integer if ndigits is omitted or None.  Otherwise\n",
+      "    the return value has the same type as the number.  ndigits may be negative.\n",
+      "\n",
+      "Help on built-in function abs in module builtins:\n",
+      "\n",
+      "abs(x, /)\n",
+      "    Return the absolute value of the argument.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Print the help documentation for the round function\n",
+    "help(round)\n",
+    "# Print the help documentation for the abs function\n",
+    "help(abs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "85"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Round the number 84.7\n",
+    "round(84.7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "19.75\n",
+      "19.75\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Round the number 19.74812 to the second decimal place\n",
+    "print(round(19.74812,2))\n",
+    "print(round(19.74812,ndigits=2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the absolute value of -12\n",
+    "abs(-12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the square root of 81\n",
+    "pow(81,0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Importing From Other Modules\n",
+    "We'll look at two other common modules (collections of functions): `math` and `random`.\n",
+    "\n",
+    "There are a few ways we can do this...\n",
+    " - We can write `import <module_name>` and use a function by `<module_name>.<function_name>`.\n",
+    " - We can write `from <module_name> import <function_name>` and use a function(s) directly by its name.\n",
+    " - We can write `from <module_name> import *` and directly use all functions from the module by name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normally we like to have our imports at the top of our notebook\n",
+    "import math\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " 'acos',\n",
+       " 'acosh',\n",
+       " 'asin',\n",
+       " 'asinh',\n",
+       " 'atan',\n",
+       " 'atan2',\n",
+       " 'atanh',\n",
+       " 'ceil',\n",
+       " 'comb',\n",
+       " 'copysign',\n",
+       " 'cos',\n",
+       " 'cosh',\n",
+       " 'degrees',\n",
+       " 'dist',\n",
+       " 'e',\n",
+       " 'erf',\n",
+       " 'erfc',\n",
+       " 'exp',\n",
+       " 'expm1',\n",
+       " 'fabs',\n",
+       " 'factorial',\n",
+       " 'floor',\n",
+       " 'fmod',\n",
+       " 'frexp',\n",
+       " 'fsum',\n",
+       " 'gamma',\n",
+       " 'gcd',\n",
+       " 'hypot',\n",
+       " 'inf',\n",
+       " 'isclose',\n",
+       " 'isfinite',\n",
+       " 'isinf',\n",
+       " 'isnan',\n",
+       " 'isqrt',\n",
+       " 'lcm',\n",
+       " 'ldexp',\n",
+       " 'lgamma',\n",
+       " 'log',\n",
+       " 'log10',\n",
+       " 'log1p',\n",
+       " 'log2',\n",
+       " 'modf',\n",
+       " 'nan',\n",
+       " 'nextafter',\n",
+       " 'perm',\n",
+       " 'pi',\n",
+       " 'pow',\n",
+       " 'prod',\n",
+       " 'radians',\n",
+       " 'remainder',\n",
+       " 'sin',\n",
+       " 'sinh',\n",
+       " 'sqrt',\n",
+       " 'tan',\n",
+       " 'tanh',\n",
+       " 'tau',\n",
+       " 'trunc',\n",
+       " 'ulp']"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use dir() to see a directory list of the things (functions) in a module\n",
+    "dir(math)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function sqrt in module math:\n",
+      "\n",
+      "sqrt(x, /)\n",
+      "    Return the square root of x.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Then use help() on the name of the function you want to use\n",
+    "# notice that to access the functions from the module you must use dot-notation since we imported\n",
+    "# the module\n",
+    "help(math.sqrt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.123105625617661\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Use the . (attribute accessor)\n",
+    "result = math.sqrt(17)\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.123105625617661\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Import the function directly\n",
+    "# then ONLY the sqrt function is imported and you do NOT use dot-notation\n",
+    "from math import sqrt\n",
+    "print(sqrt(17))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from random import randint\n",
+    "print(randint(1,10))\n",
+    "\n",
+    "# wrong way to call randint, since only the function was imported, not the module\n",
+    "#print(random.randint(1,10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import all functions directly\n",
+    "# Now all functions from that module can be used directly, no need to use dot-notation\n",
+    "from math import *"
+   ]
+  },
+  {
+   "attachments": {
+    "26314fdb-40a3-4999-af02-6f6686f3211f.png": {
+     "image/png": "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"
+    },
+    "9279cd39-a0ab-4c23-bf4f-a00fe8fdd4fd.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## JupyterLab Features -- Cell Properties\n",
+    "\n",
+    "### Cell Type\n",
+    "The cells in a notebook have different properties.  One of the most important  is the cell's type.  On the notebook's menubar, you can see the cells type in the drop-down menu.  A cell can be `Raw`, `Markdown`, or `Code`.\n",
+    "\n",
+    "![Screenshot 2024-09-12 at 5.04.19 PM.png](attachment:9279cd39-a0ab-4c23-bf4f-a00fe8fdd4fd.png)\n",
+    "\n",
+    "`Code` cells are ones where you enter Python commands, `Markdown` cells hold formatted text, and `Raw` cells are ones that do not get converted when exporting from a notebook to some other file type.\n",
+    "\n",
+    "### Editable vs. Read-Only cells\n",
+    "\n",
+    "If you click the gear icon on the right sidebar and look under the `common tools` section you can see if a cell has been marked as read-only or editable.  This can be used to protect from accidentally changing the content of a cell.\n",
+    "\n",
+    "<img src=\"attachment:26314fdb-40a3-4999-af02-6f6686f3211f.png\" width=\"50%\" alt=\"Property Inspector\">\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Summary\n",
+    "\n",
+    "You learned how to call **functions** and pass **parameters** to the **arguments** of the function.  You can pass parameters by position or by keyword argument.  Some functions send a value back, called the **return value**.  You learned how to **import** functions from a **module** and call those functions.  We looked specifically at type conversion functions, the input() function, some other built-in functions, and functions from the math and random modules."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
-- 
GitLab