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<label class="md-nav__title" for="__nav_4">
|
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<span class="md-nav__icon md-icon"></span>
|
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Reference
|
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</label>
|
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<ul class="md-nav__list" data-md-scrollfix>
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<li class="md-nav__item">
|
||
<a href="../../../reference/" class="md-nav__link">
|
||
GreatAI reference
|
||
</a>
|
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</li>
|
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|
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|
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|
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|
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|
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<li class="md-nav__item">
|
||
<a href="../../../reference/utilities/" class="md-nav__link">
|
||
Utilities
|
||
</a>
|
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</li>
|
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|
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<li class="md-nav__item">
|
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<a href="../../../reference/large-file/" class="md-nav__link">
|
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LargeFile
|
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</a>
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</li>
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|
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<li class="md-nav__item">
|
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<a href="../../../reference/views/" class="md-nav__link">
|
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View models
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</a>
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</ul>
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</nav>
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<li class="md-nav__item md-nav__item--nested">
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<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
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Examples
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<ul class="md-nav__list" data-md-scrollfix>
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<li class="md-nav__item md-nav__item--nested">
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<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5_1" type="checkbox" id="__nav_5_1" >
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<label class="md-nav__link" for="__nav_5_1">
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Explainable Naive Bayes
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</label>
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<nav class="md-nav" aria-label="Explainable Naive Bayes" data-md-level="2">
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<span class="md-nav__icon md-icon"></span>
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Explainable Naive Bayes
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
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<li class="md-nav__item">
|
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<a href="../data/" class="md-nav__link">
|
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Simple example: data engineering
|
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</a>
|
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</li>
|
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|
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<li class="md-nav__item">
|
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<a href="../train/" class="md-nav__link">
|
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Optimise and train a model
|
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</a>
|
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</li>
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|
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|
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<li class="md-nav__item">
|
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<a href="../deploy/" class="md-nav__link">
|
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Hardening and deployment
|
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</a>
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</ul>
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</nav>
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<li class="md-nav__item md-nav__item--nested">
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<input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5_2" type="checkbox" id="__nav_5_2" >
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<label class="md-nav__link" for="__nav_5_2">
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Explainable SciBERT
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</label>
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<nav class="md-nav" aria-label="Explainable SciBERT" data-md-level="2">
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<label class="md-nav__title" for="__nav_5_2">
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<span class="md-nav__icon md-icon"></span>
|
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Explainable SciBERT
|
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
|
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|
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|
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|
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|
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|
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<li class="md-nav__item">
|
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<a href="../../scibert/" class="md-nav__link">
|
||
Summarising scientific publications from a tech-transfer perspective
|
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</a>
|
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</li>
|
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|
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|
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|
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|
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|
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|
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|
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|
||
<li class="md-nav__item">
|
||
<a href="../../scibert/data/" class="md-nav__link">
|
||
Explore data and feasibility of approach
|
||
</a>
|
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</li>
|
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|
||
|
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|
||
|
||
|
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|
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|
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|
||
<li class="md-nav__item">
|
||
<a href="../../scibert/train/" class="md-nav__link">
|
||
Fine-tune SciBERT
|
||
</a>
|
||
</li>
|
||
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|
||
|
||
|
||
|
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|
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|
||
|
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|
||
<li class="md-nav__item">
|
||
<a href="../../scibert/deploy/" class="md-nav__link">
|
||
Create an inference function
|
||
</a>
|
||
</li>
|
||
|
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|
||
|
||
|
||
|
||
|
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|
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|
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|
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<li class="md-nav__item">
|
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<a href="../../scibert/additional-files/" class="md-nav__link">
|
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Additional files in the repository
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</a>
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|
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|
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|
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|
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|
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</ul>
|
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</nav>
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|
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</ul>
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</nav>
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</li>
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|
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|
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|
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|
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|
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|
||
<li class="md-nav__item">
|
||
<a href="../../../explanation/" class="md-nav__link">
|
||
Explanation
|
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</a>
|
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</li>
|
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|
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|
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|
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</ul>
|
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</nav>
|
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</div>
|
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</div>
|
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</div>
|
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|
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|
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|
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<div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
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<div class="md-sidebar__scrollwrap">
|
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<div class="md-sidebar__inner">
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<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
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</nav>
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</div>
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</div>
|
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</div>
|
||
|
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|
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<div class="md-content" data-md-component="content">
|
||
<article class="md-content__inner md-typeset">
|
||
|
||
|
||
|
||
<a href="stacked-bars.ipynb" title="Download Notebook" class="md-content__button md-icon">
|
||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M5 20h14v-2H5m14-9h-4V3H9v6H5l7 7 7-7Z"/></svg>
|
||
</a>
|
||
|
||
|
||
|
||
|
||
<a href="https://github.com/schmelczer/great-ai/edit/main/docs/examples/simple/stacked-bars.ipynb" title="Edit this page" class="md-content__button md-icon">
|
||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
|
||
</a>
|
||
|
||
|
||
|
||
<h1>Stacked bars</h1>
|
||
|
||
<script>
|
||
(function (global, factory) {
|
||
typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
|
||
typeof define === 'function' && define.amd ? define(factory) :
|
||
(global = global || self, global.ClipboardCopyElement = factory());
|
||
}(this, function () { 'use strict';
|
||
|
||
function createNode(text) {
|
||
const node = document.createElement('pre');
|
||
node.style.width = '1px';
|
||
node.style.height = '1px';
|
||
node.style.position = 'fixed';
|
||
node.style.top = '5px';
|
||
node.textContent = text;
|
||
return node;
|
||
}
|
||
|
||
function copyNode(node) {
|
||
if ('clipboard' in navigator) {
|
||
// eslint-disable-next-line flowtype/no-flow-fix-me-comments
|
||
// $FlowFixMe Clipboard is not defined in Flow yet.
|
||
return navigator.clipboard.writeText(node.textContent);
|
||
}
|
||
|
||
const selection = getSelection();
|
||
|
||
if (selection == null) {
|
||
return Promise.reject(new Error());
|
||
}
|
||
|
||
selection.removeAllRanges();
|
||
const range = document.createRange();
|
||
range.selectNodeContents(node);
|
||
selection.addRange(range);
|
||
document.execCommand('copy');
|
||
selection.removeAllRanges();
|
||
return Promise.resolve();
|
||
}
|
||
function copyText(text) {
|
||
if ('clipboard' in navigator) {
|
||
// eslint-disable-next-line flowtype/no-flow-fix-me-comments
|
||
// $FlowFixMe Clipboard is not defined in Flow yet.
|
||
return navigator.clipboard.writeText(text);
|
||
}
|
||
|
||
const body = document.body;
|
||
|
||
if (!body) {
|
||
return Promise.reject(new Error());
|
||
}
|
||
|
||
const node = createNode(text);
|
||
body.appendChild(node);
|
||
copyNode(node);
|
||
body.removeChild(node);
|
||
return Promise.resolve();
|
||
}
|
||
|
||
function copy(button) {
|
||
const id = button.getAttribute('for');
|
||
const text = button.getAttribute('value');
|
||
|
||
function trigger() {
|
||
button.dispatchEvent(new CustomEvent('clipboard-copy', {
|
||
bubbles: true
|
||
}));
|
||
}
|
||
|
||
if (text) {
|
||
copyText(text).then(trigger);
|
||
} else if (id) {
|
||
const root = 'getRootNode' in Element.prototype ? button.getRootNode() : button.ownerDocument;
|
||
if (!(root instanceof Document || 'ShadowRoot' in window && root instanceof ShadowRoot)) return;
|
||
const node = root.getElementById(id);
|
||
if (node) copyTarget(node).then(trigger);
|
||
}
|
||
}
|
||
|
||
function copyTarget(content) {
|
||
if (content instanceof HTMLInputElement || content instanceof HTMLTextAreaElement) {
|
||
return copyText(content.value);
|
||
} else if (content instanceof HTMLAnchorElement && content.hasAttribute('href')) {
|
||
return copyText(content.href);
|
||
} else {
|
||
return copyNode(content);
|
||
}
|
||
}
|
||
|
||
function clicked(event) {
|
||
const button = event.currentTarget;
|
||
|
||
if (button instanceof HTMLElement) {
|
||
copy(button);
|
||
}
|
||
}
|
||
|
||
function keydown(event) {
|
||
if (event.key === ' ' || event.key === 'Enter') {
|
||
const button = event.currentTarget;
|
||
|
||
if (button instanceof HTMLElement) {
|
||
event.preventDefault();
|
||
copy(button);
|
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}
|
||
}
|
||
}
|
||
|
||
function focused(event) {
|
||
event.currentTarget.addEventListener('keydown', keydown);
|
||
}
|
||
|
||
function blurred(event) {
|
||
event.currentTarget.removeEventListener('keydown', keydown);
|
||
}
|
||
|
||
class ClipboardCopyElement extends HTMLElement {
|
||
constructor() {
|
||
super();
|
||
this.addEventListener('click', clicked);
|
||
this.addEventListener('focus', focused);
|
||
this.addEventListener('blur', blurred);
|
||
}
|
||
|
||
connectedCallback() {
|
||
if (!this.hasAttribute('tabindex')) {
|
||
this.setAttribute('tabindex', '0');
|
||
}
|
||
|
||
if (!this.hasAttribute('role')) {
|
||
this.setAttribute('role', 'button');
|
||
}
|
||
}
|
||
|
||
get value() {
|
||
return this.getAttribute('value') || '';
|
||
}
|
||
|
||
set value(text) {
|
||
this.setAttribute('value', text);
|
||
}
|
||
|
||
}
|
||
|
||
if (!window.customElements.get('clipboard-copy')) {
|
||
window.ClipboardCopyElement = ClipboardCopyElement;
|
||
window.customElements.define('clipboard-copy', ClipboardCopyElement);
|
||
}
|
||
|
||
return ClipboardCopyElement;
|
||
|
||
}));
|
||
</script>
|
||
<script>
|
||
document.addEventListener('clipboard-copy', function(event) {
|
||
const notice = event.target.querySelector('.notice')
|
||
notice.hidden = false
|
||
setTimeout(function() {
|
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notice.hidden = true
|
||
}, 1000)
|
||
})
|
||
</script>
|
||
|
||
|
||
|
||
|
||
<style type="text/css">
|
||
|
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pre { line-height: 125%; }
|
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td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
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span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
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td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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|
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|
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<style type="text/css">
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/*
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This file is taken from the built JupyterLab theme.css
|
||
Found on share/nbconvert/templates/lab/static
|
||
Some changes have been made and marked with CHANGE
|
||
*/
|
||
|
||
.jupyter-wrapper {
|
||
/* Elevation
|
||
*
|
||
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
|
||
*
|
||
* https://github.com/material-components/material-components-web
|
||
* https://material-components-web.appspot.com/elevation.html
|
||
*/
|
||
|
||
--jp-shadow-base-lightness: 0;
|
||
--jp-shadow-umbra-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.2
|
||
);
|
||
--jp-shadow-penumbra-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.14
|
||
);
|
||
--jp-shadow-ambient-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.12
|
||
);
|
||
--jp-elevation-z0: none;
|
||
--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
|
||
0px 1px 1px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 3px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
|
||
0px 2px 2px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 5px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
|
||
0px 4px 5px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 10px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
|
||
0px 6px 10px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 18px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
|
||
0px 8px 10px 1px var(--jp-shadow-penumbra-color),
|
||
0px 3px 14px 2px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
|
||
0px 12px 17px 2px var(--jp-shadow-penumbra-color),
|
||
0px 5px 22px 4px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
|
||
0px 16px 24px 2px var(--jp-shadow-penumbra-color),
|
||
0px 6px 30px 5px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
|
||
0px 20px 31px 3px var(--jp-shadow-penumbra-color),
|
||
0px 8px 38px 7px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
|
||
0px 24px 38px 3px var(--jp-shadow-penumbra-color),
|
||
0px 9px 46px 8px var(--jp-shadow-ambient-color);
|
||
|
||
/* Borders
|
||
*
|
||
* The following variables, specify the visual styling of borders in JupyterLab.
|
||
*/
|
||
|
||
--jp-border-width: 1px;
|
||
--jp-border-color0: var(--md-grey-400);
|
||
--jp-border-color1: var(--md-grey-400);
|
||
--jp-border-color2: var(--md-grey-300);
|
||
--jp-border-color3: var(--md-grey-200);
|
||
--jp-border-radius: 2px;
|
||
|
||
/* UI Fonts
|
||
*
|
||
* The UI font CSS variables are used for the typography all of the JupyterLab
|
||
* user interface elements that are not directly user generated content.
|
||
*
|
||
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
|
||
* is applied to a parent element. When children elements, such as headings, are sized
|
||
* in em all things will be computed relative to that body size.
|
||
*/
|
||
|
||
--jp-ui-font-scale-factor: 1.2;
|
||
--jp-ui-font-size0: 0.83333em;
|
||
--jp-ui-font-size1: 13px; /* Base font size */
|
||
--jp-ui-font-size2: 1.2em;
|
||
--jp-ui-font-size3: 1.44em;
|
||
|
||
--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
|
||
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
|
||
"Segoe UI Symbol";
|
||
|
||
/*
|
||
* Use these font colors against the corresponding main layout colors.
|
||
* In a light theme, these go from dark to light.
|
||
*/
|
||
|
||
/* Defaults use Material Design specification */
|
||
--jp-ui-font-color0: rgba(0, 0, 0, 1);
|
||
--jp-ui-font-color1: rgba(0, 0, 0, 0.87);
|
||
--jp-ui-font-color2: rgba(0, 0, 0, 0.54);
|
||
--jp-ui-font-color3: rgba(0, 0, 0, 0.38);
|
||
|
||
/*
|
||
* Use these against the brand/accent/warn/error colors.
|
||
* These will typically go from light to darker, in both a dark and light theme.
|
||
*/
|
||
|
||
--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
|
||
--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
|
||
--jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
|
||
--jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
|
||
|
||
/* Content Fonts
|
||
*
|
||
* Content font variables are used for typography of user generated content.
|
||
*
|
||
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
|
||
* is applied to a parent element. When children elements, such as headings, are sized
|
||
* in em all things will be computed relative to that body size.
|
||
*/
|
||
|
||
--jp-content-line-height: 1.6;
|
||
--jp-content-font-scale-factor: 1.2;
|
||
--jp-content-font-size0: 0.83333em;
|
||
--jp-content-font-size1: 14px; /* Base font size */
|
||
--jp-content-font-size2: 1.2em;
|
||
--jp-content-font-size3: 1.44em;
|
||
--jp-content-font-size4: 1.728em;
|
||
--jp-content-font-size5: 2.0736em;
|
||
|
||
/* This gives a magnification of about 125% in presentation mode over normal. */
|
||
--jp-content-presentation-font-size1: 17px;
|
||
|
||
--jp-content-heading-line-height: 1;
|
||
--jp-content-heading-margin-top: 1.2em;
|
||
--jp-content-heading-margin-bottom: 0.8em;
|
||
--jp-content-heading-font-weight: 500;
|
||
|
||
/* Defaults use Material Design specification */
|
||
--jp-content-font-color0: rgba(0, 0, 0, 1);
|
||
--jp-content-font-color1: rgba(0, 0, 0, 0.87);
|
||
--jp-content-font-color2: rgba(0, 0, 0, 0.54);
|
||
--jp-content-font-color3: rgba(0, 0, 0, 0.38);
|
||
|
||
--jp-content-link-color: var(--md-blue-700);
|
||
|
||
--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
|
||
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
|
||
"Segoe UI Symbol";
|
||
|
||
/*
|
||
* Code Fonts
|
||
*
|
||
* Code font variables are used for typography of code and other monospaces content.
|
||
*/
|
||
|
||
--jp-code-font-size: 13px;
|
||
--jp-code-line-height: 1.3077; /* 17px for 13px base */
|
||
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
|
||
--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
|
||
monospace;
|
||
--jp-code-font-family: var(--jp-code-font-family-default);
|
||
|
||
/* This gives a magnification of about 125% in presentation mode over normal. */
|
||
--jp-code-presentation-font-size: 16px;
|
||
|
||
/* may need to tweak cursor width if you change font size */
|
||
--jp-code-cursor-width0: 1.4px;
|
||
--jp-code-cursor-width1: 2px;
|
||
--jp-code-cursor-width2: 4px;
|
||
|
||
/* Layout
|
||
*
|
||
* The following are the main layout colors use in JupyterLab. In a light
|
||
* theme these would go from light to dark.
|
||
*/
|
||
|
||
--jp-layout-color0: white;
|
||
--jp-layout-color1: white;
|
||
--jp-layout-color2: var(--md-grey-200);
|
||
--jp-layout-color3: var(--md-grey-400);
|
||
--jp-layout-color4: var(--md-grey-600);
|
||
|
||
/* Inverse Layout
|
||
*
|
||
* The following are the inverse layout colors use in JupyterLab. In a light
|
||
* theme these would go from dark to light.
|
||
*/
|
||
|
||
--jp-inverse-layout-color0: #111111;
|
||
--jp-inverse-layout-color1: var(--md-grey-900);
|
||
--jp-inverse-layout-color2: var(--md-grey-800);
|
||
--jp-inverse-layout-color3: var(--md-grey-700);
|
||
--jp-inverse-layout-color4: var(--md-grey-600);
|
||
|
||
/* Brand/accent */
|
||
|
||
--jp-brand-color0: var(--md-blue-900);
|
||
--jp-brand-color1: var(--md-blue-700);
|
||
--jp-brand-color2: var(--md-blue-300);
|
||
--jp-brand-color3: var(--md-blue-100);
|
||
--jp-brand-color4: var(--md-blue-50);
|
||
|
||
--jp-accent-color0: var(--md-green-900);
|
||
--jp-accent-color1: var(--md-green-700);
|
||
--jp-accent-color2: var(--md-green-300);
|
||
--jp-accent-color3: var(--md-green-100);
|
||
|
||
/* State colors (warn, error, success, info) */
|
||
|
||
--jp-warn-color0: var(--md-orange-900);
|
||
--jp-warn-color1: var(--md-orange-700);
|
||
--jp-warn-color2: var(--md-orange-300);
|
||
--jp-warn-color3: var(--md-orange-100);
|
||
|
||
--jp-error-color0: var(--md-red-900);
|
||
--jp-error-color1: var(--md-red-700);
|
||
--jp-error-color2: var(--md-red-300);
|
||
--jp-error-color3: var(--md-red-100);
|
||
|
||
--jp-success-color0: var(--md-green-900);
|
||
--jp-success-color1: var(--md-green-700);
|
||
--jp-success-color2: var(--md-green-300);
|
||
--jp-success-color3: var(--md-green-100);
|
||
|
||
--jp-info-color0: var(--md-cyan-900);
|
||
--jp-info-color1: var(--md-cyan-700);
|
||
--jp-info-color2: var(--md-cyan-300);
|
||
--jp-info-color3: var(--md-cyan-100);
|
||
|
||
/* Cell specific styles */
|
||
|
||
--jp-cell-padding: 5px;
|
||
|
||
--jp-cell-collapser-width: 8px;
|
||
--jp-cell-collapser-min-height: 20px;
|
||
--jp-cell-collapser-not-active-hover-opacity: 0.6;
|
||
|
||
--jp-cell-editor-background: var(--md-grey-100);
|
||
--jp-cell-editor-border-color: var(--md-grey-300);
|
||
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
|
||
--jp-cell-editor-active-background: var(--jp-layout-color0);
|
||
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
|
||
|
||
--jp-cell-prompt-width: 64px;
|
||
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
|
||
--jp-cell-prompt-letter-spacing: 0px;
|
||
--jp-cell-prompt-opacity: 1;
|
||
--jp-cell-prompt-not-active-opacity: 0.5;
|
||
--jp-cell-prompt-not-active-font-color: var(--md-grey-700);
|
||
/* A custom blend of MD grey and blue 600
|
||
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
|
||
--jp-cell-inprompt-font-color: #307fc1;
|
||
/* A custom blend of MD grey and orange 600
|
||
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
|
||
--jp-cell-outprompt-font-color: #bf5b3d;
|
||
|
||
/* Notebook specific styles */
|
||
|
||
--jp-notebook-padding: 10px;
|
||
--jp-notebook-select-background: var(--jp-layout-color1);
|
||
--jp-notebook-multiselected-color: var(--md-blue-50);
|
||
|
||
/* The scroll padding is calculated to fill enough space at the bottom of the
|
||
notebook to show one single-line cell (with appropriate padding) at the top
|
||
when the notebook is scrolled all the way to the bottom. We also subtract one
|
||
pixel so that no scrollbar appears if we have just one single-line cell in the
|
||
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
|
||
*/
|
||
--jp-notebook-scroll-padding: calc(
|
||
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
|
||
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
|
||
);
|
||
|
||
/* Rendermime styles */
|
||
|
||
--jp-rendermime-error-background: #fdd;
|
||
--jp-rendermime-table-row-background: var(--md-grey-100);
|
||
--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
|
||
|
||
/* Dialog specific styles */
|
||
|
||
--jp-dialog-background: rgba(0, 0, 0, 0.25);
|
||
|
||
/* Console specific styles */
|
||
|
||
--jp-console-padding: 10px;
|
||
|
||
/* Toolbar specific styles */
|
||
|
||
--jp-toolbar-border-color: var(--jp-border-color1);
|
||
--jp-toolbar-micro-height: 8px;
|
||
--jp-toolbar-background: var(--jp-layout-color1);
|
||
--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
|
||
--jp-toolbar-header-margin: 4px 4px 0px 4px;
|
||
--jp-toolbar-active-background: var(--md-grey-300);
|
||
|
||
/* Statusbar specific styles */
|
||
|
||
--jp-statusbar-height: 24px;
|
||
|
||
/* Input field styles */
|
||
|
||
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
|
||
--jp-input-active-background: var(--jp-layout-color1);
|
||
--jp-input-hover-background: var(--jp-layout-color1);
|
||
--jp-input-background: var(--md-grey-100);
|
||
--jp-input-border-color: var(--jp-border-color1);
|
||
--jp-input-active-border-color: var(--jp-brand-color1);
|
||
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
|
||
|
||
/* General editor styles */
|
||
|
||
--jp-editor-selected-background: #d9d9d9;
|
||
--jp-editor-selected-focused-background: #d7d4f0;
|
||
--jp-editor-cursor-color: var(--jp-ui-font-color0);
|
||
|
||
/* Code mirror specific styles */
|
||
|
||
--jp-mirror-editor-keyword-color: #008000;
|
||
--jp-mirror-editor-atom-color: #88f;
|
||
--jp-mirror-editor-number-color: #080;
|
||
--jp-mirror-editor-def-color: #00f;
|
||
--jp-mirror-editor-variable-color: var(--md-grey-900);
|
||
--jp-mirror-editor-variable-2-color: #05a;
|
||
--jp-mirror-editor-variable-3-color: #085;
|
||
--jp-mirror-editor-punctuation-color: #05a;
|
||
--jp-mirror-editor-property-color: #05a;
|
||
--jp-mirror-editor-operator-color: #aa22ff;
|
||
--jp-mirror-editor-comment-color: #408080;
|
||
--jp-mirror-editor-string-color: #ba2121;
|
||
--jp-mirror-editor-string-2-color: #708;
|
||
--jp-mirror-editor-meta-color: #aa22ff;
|
||
--jp-mirror-editor-qualifier-color: #555;
|
||
--jp-mirror-editor-builtin-color: #008000;
|
||
--jp-mirror-editor-bracket-color: #997;
|
||
--jp-mirror-editor-tag-color: #170;
|
||
--jp-mirror-editor-attribute-color: #00c;
|
||
--jp-mirror-editor-header-color: blue;
|
||
--jp-mirror-editor-quote-color: #090;
|
||
--jp-mirror-editor-link-color: #00c;
|
||
--jp-mirror-editor-error-color: #f00;
|
||
--jp-mirror-editor-hr-color: #999;
|
||
|
||
/* Vega extension styles */
|
||
|
||
--jp-vega-background: white;
|
||
|
||
/* Sidebar-related styles */
|
||
|
||
--jp-sidebar-min-width: 250px;
|
||
|
||
/* Search-related styles */
|
||
|
||
--jp-search-toggle-off-opacity: 0.5;
|
||
--jp-search-toggle-hover-opacity: 0.8;
|
||
--jp-search-toggle-on-opacity: 1;
|
||
--jp-search-selected-match-background-color: rgb(245, 200, 0);
|
||
--jp-search-selected-match-color: black;
|
||
--jp-search-unselected-match-background-color: var(
|
||
--jp-inverse-layout-color0
|
||
);
|
||
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
|
||
|
||
/* Icon colors that work well with light or dark backgrounds */
|
||
--jp-icon-contrast-color0: var(--md-purple-600);
|
||
--jp-icon-contrast-color1: var(--md-green-600);
|
||
--jp-icon-contrast-color2: var(--md-pink-600);
|
||
--jp-icon-contrast-color3: var(--md-blue-600);
|
||
}
|
||
|
||
[data-md-color-scheme="slate"] .jupyter-wrapper {
|
||
/* Elevation
|
||
*
|
||
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
|
||
*
|
||
* https://github.com/material-components/material-components-web
|
||
* https://material-components-web.appspot.com/elevation.html
|
||
*/
|
||
|
||
/* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
|
||
* colors used in the theme as well. */
|
||
--jp-shadow-base-lightness: 32;
|
||
--jp-shadow-umbra-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.2
|
||
);
|
||
--jp-shadow-penumbra-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.14
|
||
);
|
||
--jp-shadow-ambient-color: rgba(
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
var(--jp-shadow-base-lightness),
|
||
0.12
|
||
);
|
||
--jp-elevation-z0: none;
|
||
--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
|
||
0px 1px 1px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 3px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
|
||
0px 2px 2px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 5px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
|
||
0px 4px 5px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 10px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
|
||
0px 6px 10px 0px var(--jp-shadow-penumbra-color),
|
||
0px 1px 18px 0px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
|
||
0px 8px 10px 1px var(--jp-shadow-penumbra-color),
|
||
0px 3px 14px 2px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
|
||
0px 12px 17px 2px var(--jp-shadow-penumbra-color),
|
||
0px 5px 22px 4px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
|
||
0px 16px 24px 2px var(--jp-shadow-penumbra-color),
|
||
0px 6px 30px 5px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
|
||
0px 20px 31px 3px var(--jp-shadow-penumbra-color),
|
||
0px 8px 38px 7px var(--jp-shadow-ambient-color);
|
||
--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
|
||
0px 24px 38px 3px var(--jp-shadow-penumbra-color),
|
||
0px 9px 46px 8px var(--jp-shadow-ambient-color);
|
||
|
||
/* Borders
|
||
*
|
||
* The following variables, specify the visual styling of borders in JupyterLab.
|
||
*/
|
||
|
||
--jp-border-width: 1px;
|
||
--jp-border-color0: var(--md-grey-700);
|
||
--jp-border-color1: var(--md-grey-700);
|
||
--jp-border-color2: var(--md-grey-800);
|
||
--jp-border-color3: var(--md-grey-900);
|
||
--jp-border-radius: 2px;
|
||
|
||
/* UI Fonts
|
||
*
|
||
* The UI font CSS variables are used for the typography all of the JupyterLab
|
||
* user interface elements that are not directly user generated content.
|
||
*
|
||
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
|
||
* is applied to a parent element. When children elements, such as headings, are sized
|
||
* in em all things will be computed relative to that body size.
|
||
*/
|
||
|
||
--jp-ui-font-scale-factor: 1.2;
|
||
--jp-ui-font-size0: 0.83333em;
|
||
--jp-ui-font-size1: 13px; /* Base font size */
|
||
--jp-ui-font-size2: 1.2em;
|
||
--jp-ui-font-size3: 1.44em;
|
||
|
||
--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
|
||
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
|
||
"Segoe UI Symbol";
|
||
|
||
/*
|
||
* Use these font colors against the corresponding main layout colors.
|
||
* In a light theme, these go from dark to light.
|
||
*/
|
||
|
||
/* Defaults use Material Design specification */
|
||
--jp-ui-font-color0: rgba(255, 255, 255, 1);
|
||
--jp-ui-font-color1: rgba(255, 255, 255, 0.87);
|
||
--jp-ui-font-color2: rgba(255, 255, 255, 0.54);
|
||
--jp-ui-font-color3: rgba(255, 255, 255, 0.38);
|
||
|
||
/*
|
||
* Use these against the brand/accent/warn/error colors.
|
||
* These will typically go from light to darker, in both a dark and light theme.
|
||
*/
|
||
|
||
--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
|
||
--jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
|
||
--jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
|
||
--jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
|
||
|
||
/* Content Fonts
|
||
*
|
||
* Content font variables are used for typography of user generated content.
|
||
*
|
||
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
|
||
* is applied to a parent element. When children elements, such as headings, are sized
|
||
* in em all things will be computed relative to that body size.
|
||
*/
|
||
|
||
--jp-content-line-height: 1.6;
|
||
--jp-content-font-scale-factor: 1.2;
|
||
--jp-content-font-size0: 0.83333em;
|
||
--jp-content-font-size1: 14px; /* Base font size */
|
||
--jp-content-font-size2: 1.2em;
|
||
--jp-content-font-size3: 1.44em;
|
||
--jp-content-font-size4: 1.728em;
|
||
--jp-content-font-size5: 2.0736em;
|
||
|
||
/* This gives a magnification of about 125% in presentation mode over normal. */
|
||
--jp-content-presentation-font-size1: 17px;
|
||
|
||
--jp-content-heading-line-height: 1;
|
||
--jp-content-heading-margin-top: 1.2em;
|
||
--jp-content-heading-margin-bottom: 0.8em;
|
||
--jp-content-heading-font-weight: 500;
|
||
|
||
/* Defaults use Material Design specification */
|
||
--jp-content-font-color0: rgba(255, 255, 255, 1);
|
||
--jp-content-font-color1: rgba(255, 255, 255, 1);
|
||
--jp-content-font-color2: rgba(255, 255, 255, 0.7);
|
||
--jp-content-font-color3: rgba(255, 255, 255, 0.5);
|
||
|
||
--jp-content-link-color: var(--md-blue-300);
|
||
|
||
--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
|
||
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
|
||
"Segoe UI Symbol";
|
||
|
||
/*
|
||
* Code Fonts
|
||
*
|
||
* Code font variables are used for typography of code and other monospaces content.
|
||
*/
|
||
|
||
--jp-code-font-size: 13px;
|
||
--jp-code-line-height: 1.3077; /* 17px for 13px base */
|
||
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
|
||
--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
|
||
monospace;
|
||
--jp-code-font-family: var(--jp-code-font-family-default);
|
||
|
||
/* This gives a magnification of about 125% in presentation mode over normal. */
|
||
--jp-code-presentation-font-size: 16px;
|
||
|
||
/* may need to tweak cursor width if you change font size */
|
||
--jp-code-cursor-width0: 1.4px;
|
||
--jp-code-cursor-width1: 2px;
|
||
--jp-code-cursor-width2: 4px;
|
||
|
||
/* Layout
|
||
*
|
||
* The following are the main layout colors use in JupyterLab. In a light
|
||
* theme these would go from light to dark.
|
||
*/
|
||
|
||
--jp-layout-color0: #111111;
|
||
--jp-layout-color1: var(--md-grey-900);
|
||
--jp-layout-color2: var(--md-grey-800);
|
||
--jp-layout-color3: var(--md-grey-700);
|
||
--jp-layout-color4: var(--md-grey-600);
|
||
|
||
/* Inverse Layout
|
||
*
|
||
* The following are the inverse layout colors use in JupyterLab. In a light
|
||
* theme these would go from dark to light.
|
||
*/
|
||
|
||
--jp-inverse-layout-color0: white;
|
||
--jp-inverse-layout-color1: white;
|
||
--jp-inverse-layout-color2: var(--md-grey-200);
|
||
--jp-inverse-layout-color3: var(--md-grey-400);
|
||
--jp-inverse-layout-color4: var(--md-grey-600);
|
||
|
||
/* Brand/accent */
|
||
|
||
--jp-brand-color0: var(--md-blue-700);
|
||
--jp-brand-color1: var(--md-blue-500);
|
||
--jp-brand-color2: var(--md-blue-300);
|
||
--jp-brand-color3: var(--md-blue-100);
|
||
--jp-brand-color4: var(--md-blue-50);
|
||
|
||
--jp-accent-color0: var(--md-green-700);
|
||
--jp-accent-color1: var(--md-green-500);
|
||
--jp-accent-color2: var(--md-green-300);
|
||
--jp-accent-color3: var(--md-green-100);
|
||
|
||
/* State colors (warn, error, success, info) */
|
||
|
||
--jp-warn-color0: var(--md-orange-700);
|
||
--jp-warn-color1: var(--md-orange-500);
|
||
--jp-warn-color2: var(--md-orange-300);
|
||
--jp-warn-color3: var(--md-orange-100);
|
||
|
||
--jp-error-color0: var(--md-red-700);
|
||
--jp-error-color1: var(--md-red-500);
|
||
--jp-error-color2: var(--md-red-300);
|
||
--jp-error-color3: var(--md-red-100);
|
||
|
||
--jp-success-color0: var(--md-green-700);
|
||
--jp-success-color1: var(--md-green-500);
|
||
--jp-success-color2: var(--md-green-300);
|
||
--jp-success-color3: var(--md-green-100);
|
||
|
||
--jp-info-color0: var(--md-cyan-700);
|
||
--jp-info-color1: var(--md-cyan-500);
|
||
--jp-info-color2: var(--md-cyan-300);
|
||
--jp-info-color3: var(--md-cyan-100);
|
||
|
||
/* Cell specific styles */
|
||
|
||
--jp-cell-padding: 5px;
|
||
|
||
--jp-cell-collapser-width: 8px;
|
||
--jp-cell-collapser-min-height: 20px;
|
||
--jp-cell-collapser-not-active-hover-opacity: 0.6;
|
||
|
||
--jp-cell-editor-background: var(--jp-layout-color1);
|
||
--jp-cell-editor-border-color: var(--md-grey-700);
|
||
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
|
||
--jp-cell-editor-active-background: var(--jp-layout-color0);
|
||
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
|
||
|
||
--jp-cell-prompt-width: 64px;
|
||
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
|
||
--jp-cell-prompt-letter-spacing: 0px;
|
||
--jp-cell-prompt-opacity: 1;
|
||
--jp-cell-prompt-not-active-opacity: 1;
|
||
--jp-cell-prompt-not-active-font-color: var(--md-grey-300);
|
||
|
||
/* A custom blend of MD grey and blue 600
|
||
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
|
||
--jp-cell-inprompt-font-color: #307fc1;
|
||
/* A custom blend of MD grey and orange 600
|
||
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
|
||
--jp-cell-outprompt-font-color: #bf5b3d;
|
||
|
||
/* Notebook specific styles */
|
||
|
||
--jp-notebook-padding: 10px;
|
||
--jp-notebook-select-background: var(--jp-layout-color1);
|
||
--jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
|
||
|
||
/* The scroll padding is calculated to fill enough space at the bottom of the
|
||
notebook to show one single-line cell (with appropriate padding) at the top
|
||
when the notebook is scrolled all the way to the bottom. We also subtract one
|
||
pixel so that no scrollbar appears if we have just one single-line cell in the
|
||
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
|
||
*/
|
||
--jp-notebook-scroll-padding: calc(
|
||
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
|
||
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
|
||
);
|
||
|
||
/* Rendermime styles */
|
||
|
||
--jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
|
||
--jp-rendermime-table-row-background: var(--md-grey-900);
|
||
--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
|
||
|
||
/* Dialog specific styles */
|
||
|
||
--jp-dialog-background: rgba(0, 0, 0, 0.6);
|
||
|
||
/* Console specific styles */
|
||
|
||
--jp-console-padding: 10px;
|
||
|
||
/* Toolbar specific styles */
|
||
|
||
--jp-toolbar-border-color: var(--jp-border-color2);
|
||
--jp-toolbar-micro-height: 8px;
|
||
--jp-toolbar-background: var(--jp-layout-color1);
|
||
--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
|
||
--jp-toolbar-header-margin: 4px 4px 0px 4px;
|
||
--jp-toolbar-active-background: var(--jp-layout-color0);
|
||
|
||
/* Statusbar specific styles */
|
||
|
||
--jp-statusbar-height: 24px;
|
||
|
||
/* Input field styles */
|
||
|
||
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
|
||
--jp-input-active-background: var(--jp-layout-color0);
|
||
--jp-input-hover-background: var(--jp-layout-color2);
|
||
--jp-input-background: var(--md-grey-800);
|
||
--jp-input-border-color: var(--jp-border-color1);
|
||
--jp-input-active-border-color: var(--jp-brand-color1);
|
||
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
|
||
|
||
/* General editor styles */
|
||
|
||
--jp-editor-selected-background: var(--jp-layout-color2);
|
||
--jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
|
||
--jp-editor-cursor-color: var(--jp-ui-font-color0);
|
||
|
||
/* Code mirror specific styles */
|
||
|
||
--jp-mirror-editor-keyword-color: var(--md-green-500);
|
||
--jp-mirror-editor-atom-color: var(--md-blue-300);
|
||
--jp-mirror-editor-number-color: var(--md-green-400);
|
||
--jp-mirror-editor-def-color: var(--md-blue-600);
|
||
--jp-mirror-editor-variable-color: var(--md-grey-300);
|
||
--jp-mirror-editor-variable-2-color: var(--md-blue-400);
|
||
--jp-mirror-editor-variable-3-color: var(--md-green-600);
|
||
--jp-mirror-editor-punctuation-color: var(--md-blue-400);
|
||
--jp-mirror-editor-property-color: var(--md-blue-400);
|
||
--jp-mirror-editor-operator-color: #aa22ff;
|
||
--jp-mirror-editor-comment-color: #408080;
|
||
--jp-mirror-editor-string-color: #ff7070;
|
||
--jp-mirror-editor-string-2-color: var(--md-purple-300);
|
||
--jp-mirror-editor-meta-color: #aa22ff;
|
||
--jp-mirror-editor-qualifier-color: #555;
|
||
--jp-mirror-editor-builtin-color: var(--md-green-600);
|
||
--jp-mirror-editor-bracket-color: #997;
|
||
--jp-mirror-editor-tag-color: var(--md-green-700);
|
||
--jp-mirror-editor-attribute-color: var(--md-blue-700);
|
||
--jp-mirror-editor-header-color: var(--md-blue-500);
|
||
--jp-mirror-editor-quote-color: var(--md-green-300);
|
||
--jp-mirror-editor-link-color: var(--md-blue-700);
|
||
--jp-mirror-editor-error-color: #f00;
|
||
--jp-mirror-editor-hr-color: #999;
|
||
|
||
/* Vega extension styles */
|
||
|
||
--jp-vega-background: var(--md-grey-400);
|
||
|
||
/* Sidebar-related styles */
|
||
|
||
--jp-sidebar-min-width: 250px;
|
||
|
||
/* Search-related styles */
|
||
|
||
--jp-search-toggle-off-opacity: 0.6;
|
||
--jp-search-toggle-hover-opacity: 0.8;
|
||
--jp-search-toggle-on-opacity: 1;
|
||
--jp-search-selected-match-background-color: rgb(255, 225, 0);
|
||
--jp-search-selected-match-color: black;
|
||
--jp-search-unselected-match-background-color: var(
|
||
--jp-inverse-layout-color0
|
||
);
|
||
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
|
||
|
||
/* scrollbar related styles. Supports every browser except Edge. */
|
||
|
||
/* colors based on JetBrain's Darcula theme */
|
||
|
||
--jp-scrollbar-background-color: #3f4244;
|
||
--jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
|
||
|
||
--jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
|
||
|
||
/* hacks for setting the thumb shape. These do nothing in Firefox */
|
||
|
||
--jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
|
||
--jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
|
||
|
||
/* Icon colors that work well with light or dark backgrounds */
|
||
--jp-icon-contrast-color0: var(--md-purple-600);
|
||
--jp-icon-contrast-color1: var(--md-green-600);
|
||
--jp-icon-contrast-color2: var(--md-pink-600);
|
||
--jp-icon-contrast-color3: var(--md-blue-600);
|
||
}
|
||
|
||
:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: 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Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
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Licensed under the Apache License, Version 2.0.
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*//*!
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Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
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Licensed under the Apache License, Version 2.0.
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<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">great_ai</span> <span class="kn">import</span> <span class="n">query_ground_truth</span>
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<span class="n">data</span> <span class="o">=</span> <span class="n">query_ground_truth</span><span class="p">(</span><span class="s2">"train"</span><span class="p">)</span>
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</pre></div>
|
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<div id="cell-1" class="clipboard-copy-txt">from great_ai import query_ground_truth
|
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data = query_ground_truth("train")</div>
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</div>
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</div>
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<div class="jp-OutputArea jp-Cell-outputArea">
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<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
|
||
<pre><span style="color: rgb(255,255,0)">Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️</span>
|
||
<span style="color: rgb(255,255,0)">Cannot find credentials files, defaulting to using ParallelTinyDbDriver</span>
|
||
<span style="color: rgb(255,255,0)">The selected tracing database (ParallelTinyDbDriver) is not recommended for production</span>
|
||
<span style="color: rgb(255,255,0)">Cannot find credentials files, defaulting to using LargeFileLocal</span>
|
||
<span style="color: rgb(0,175,255)">GreatAI (v0.1.6): configured ✅</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 tracing_database: ParallelTinyDbDriver</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 large_file_implementation: LargeFileLocal</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 is_production: False</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 should_log_exception_stack: True</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 prediction_cache_size: 512</span>
|
||
<span style="color: rgb(0,175,255)"> 🔩 dashboard_table_size: 50</span>
|
||
<span style="color: rgb(255,255,0)">You still need to check whether you follow all best practices before trusting your deployment.</span>
|
||
<span style="color: rgb(255,255,0)">> Find out more at https://se-ml.github.io/practices</span>
|
||
</pre>
|
||
</div>
|
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</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span>
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<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
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<span class="kn">from</span> <span class="nn">great_ai.utilities</span> <span class="kn">import</span> <span class="n">unique</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">matplotlib.ticker</span> <span class="k">as</span> <span class="nn">mtick</span>
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<span class="n">first</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">feedback</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">feedback</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">)</span>
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<span class="n">second</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">feedback</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">feedback</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">)</span>
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<span class="n">third</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">feedback</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">feedback</span><span class="p">)</span> <span class="o">></span> <span class="mi">2</span><span class="p">)</span>
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<span class="n">domains</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">unique</span><span class="p">(</span><span class="n">domain</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">data</span> <span class="k">for</span> <span class="n">domain</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">feedback</span><span class="p">))</span>
|
||
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||
<span class="n">first</span> <span class="o">=</span> <span class="p">[</span><span class="n">first</span><span class="p">[</span><span class="n">d</span><span class="p">]</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">domains</span><span class="p">]</span>
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||
<span class="n">second</span> <span class="o">=</span> <span class="p">[</span><span class="n">second</span><span class="p">[</span><span class="n">d</span><span class="p">]</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">domains</span><span class="p">]</span>
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<span class="n">third</span> <span class="o">=</span> <span class="p">[</span><span class="n">third</span><span class="p">[</span><span class="n">d</span><span class="p">]</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">domains</span><span class="p">]</span>
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||
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||
<span class="c1"># Configure matplotlib to have nice, high-resolution charts</span>
|
||
<span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"figure.facecolor"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"white"</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"font.size"</span><span class="p">]</span> <span class="o">=</span> <span class="mi">20</span>
|
||
<span class="n">plt</span><span class="o">.</span><span class="n">rcParams</span><span class="p">[</span><span class="s2">"figure.figsize"</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
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<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
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||
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||
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
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||
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<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">domains</span><span class="p">,</span> <span class="n">first</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Primary domain"</span><span class="p">)</span>
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||
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">domains</span><span class="p">,</span> <span class="n">second</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Secondary domain"</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="n">first</span><span class="p">)</span>
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||
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">domains</span><span class="p">,</span> <span class="n">third</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">"Tertiary domain"</span><span class="p">,</span> <span class="n">bottom</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">first</span><span class="p">,</span> <span class="n">second</span><span class="p">))</span>
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||
<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">mtick</span><span class="o">.</span><span class="n">PercentFormatter</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
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||
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
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<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">"Ratio of all publications"</span><span class="p">)</span>
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||
<span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
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||
<span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="s2">"ss-distribution.png"</span><span class="p">,</span> <span class="n">dpi</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
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<span class="kc">None</span>
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</pre></div>
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<div id="cell-2" class="clipboard-copy-txt">from collections import Counter
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import matplotlib.pyplot as plt
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from great_ai.utilities import unique
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import numpy as np
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import matplotlib.ticker as mtick
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first = Counter(d.feedback[0] for d in data if len(d.feedback) > 0)
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second = Counter(d.feedback[1] for d in data if len(d.feedback) > 1)
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third = Counter(d.feedback[2] for d in data if len(d.feedback) > 2)
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||
domains = sorted(unique(domain for d in data for domain in d.feedback))
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first = [first[d] / len(data) for d in domains]
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||
second = [second[d] / len(data) for d in domains]
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||
third = [third[d] / len(data) for d in domains]
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||
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||
# Configure matplotlib to have nice, high-resolution charts
|
||
%matplotlib inline
|
||
plt.rcParams["figure.facecolor"] = "white"
|
||
plt.rcParams["font.size"] = 20
|
||
plt.rcParams["figure.figsize"] = (14, 10)
|
||
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fig, ax = plt.subplots()
|
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plt.xticks(rotation=90)
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ax.bar(domains, first, label="Primary domain")
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ax.bar(domains, second, label="Secondary domain", bottom=first)
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ax.bar(domains, third, label="Tertiary domain", bottom=np.add(first, second))
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ax.yaxis.set_major_formatter(mtick.PercentFormatter(1))
|
||
ax.legend()
|
||
ax.set_ylabel("Ratio of all publications")
|
||
fig.tight_layout()
|
||
fig.savefig("ss-distribution.png", dpi=500)
|
||
None</div>
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EEKDAxUQECAEhIS1KdPHxUtWtRkbKdOnSwO0pI0ZMgQY5CWnryDvGHDhrp//766du1qDNLSk7sTmjVrppiYGB07dsxknn8HaenJLuS9e/eWJG3cuDHZ83/22WdJgrQkBQUFyc7OTjNmzDBpP3HihMLCwuTv769SpUo99foWLVqkx48fq2fPnibPmFtZWWncuHFJNv6TpDlz5kiSxowZYwzS0pO9A8aOHStJmjVrVpLjHBwckszZokUL2djY6ObNm5o4caLJYxE1atSQu7u7Dh06ZDKPk5NTkiAtSYULF1ZgYKCOHz+u8+fNX8Ho1auXSZCWpI4dO0qSfvvtN7PnAQAAwMuNlWm8NIYNGybpyS3dzs7OqlGjhjp06JBkd3dJqlq16jOdI3HH93967bXXJP3vXeL/lBi8//zzT5P269eva9y4cfr5558VGRmZ5N3oKT2fm1LdefLkUdOmTbVgwQLt2rXLuAlf4qp0ly5dUrssowMHDkiSatasmaSvWLFicnNz07lz55IcY2Vllex7m2vWrClra2sdPHgwSV+pUqWUM2dOkzZra2vlz59f9+7dS/YVd4UKFdLevXuTtO/cuVMTJ07U7t279ffffysmJsak/+LFiypSpEjSC05Gcj9jNzc3SdLNmzfNmgMAAAAvP8I0XhqJz0ebo0CBAs90juSeGbaxsXlq3+PHj41tt27dUpUqVXT27FlVrVpVbdq0kYuLi2xsbHTr1i1NnDgxxY3CUqu7W7duWrBggWbMmCEfHx89evRI8+fPV758+dS4cWOzri/x2fH8+fOneP5/h+no6Gi5uLgoe/bsScbb2NjI1dVVf//9d5K+lJ6/trGxSbUvNjbWpG3VqlUKDAyUnZ2d6tSpo+LFi8vR0VFWVlYKDQ1VWFiY2RuvSU/uNkjuvJIUFxdn9jwAAAB4uRGm8UpKaZf2F2HWrFk6e/ashg4dmuR1Xrt379bEiRNTPDa1ur29veXp6WnciOyXX37R9evXNWDAAGXLls2s2hJD7JUrV5J9J/rly5eTPebGjRt6/PhxkvPExsbq2rVrypUrl1nnfxZDhgxR9uzZFRERYXKbvSR17txZYWFh6XZuAAAAvLp4Zhp4wU6fPi1JatKkSZK+5w1+3bp108OHD7VgwQLNnDlTBoPBrI3HElWqVCnFOiIjI3XhwoUk7Z6enoqPj1d4eHiSvvDwcMXFxRnnTQ+nT59W2bJlkwTp+Ph47dixI93OCwAAgFcbYRp4wRI39goNDTVpP3jwoEaPHv1cc7do0UJOTk766quvFBYWpjp16iT77HFKWrZsqWzZsunbb79VVFSUsT0+Pl79+/dP9p3R7du3lyR9/vnnun//vrH9/v37GjhwoCRZ/Do7S7i7u+vUqVMm78FOSEhQcHCwjh49mm7nBQAAwKuNMA28YInPSPfp00cffvihBgwYoA8//FDe3t567733nmtuBwcHtW3b1hgsO3fubNHx7u7uGjNmjG7evClPT0916dJFAwYMUKVKlbR//36VL18+yTEtWrRQ06ZNtW/fPpUrV059+/ZVv3799MYbb2jfvn1q1qyZWrZs+VzXlZq+ffvqzp078vT0VLdu3dS7d29VqVJFX3/9td5///10Oy8AAABebYRp4AV77bXX9Ouvv6p+/frasWOHJk+erHPnzmnq1KkaM2bMc8+fuFJcsGBBNWzY0OLj+/Xrp8WLF8vDw0Pz5s3TnDlz9MYbb2jXrl3KnTt3sscsWbJEU6ZMUZ48eTRjxgxNnz5duXPn1uTJk7V48eLnup6n6dy5s+bOnauCBQtq/vz5WrRokdzc3LR37950vb0cAAAArzZDgiVbIOOF8PLyUkRExFPHHTt2LMlzosC8efMUFBSkwYMHa8SIERldDl4Q/j0A8EoJTv6tDy9ccHRGVwAgnaWWzViZBl4isbGx+uabb2RjY2PxLd4AAAAAzMersYCXwI4dOxQWFqbQ0FD98ccf6tGjhwoXLpzRZQEAAAAvLcI08BLYsmWLhg0bJhcXF3Xs2FFfffVVRpcEAAAAvNQI08BLIDg4WMHBwRldBgAAAPDK4JlpAAAAAAAsRJgGAAAAAMBChGkAAAAAACxEmAYAAAAAwEKEaQAAAAAALESYBgAAAADAQoRpAAAAAAAsRJgGAAAAAMBChGkAAAAAACxEmAZeMn5+fjIYDBldRpoxGAzy8/PL6DKeS2hoqAwGg4KDgzO6FAAAAKQRm4wuAOko2CmjK0hdcHSaTBMXF6c5c+bo+++/1x9//KE7d+4od+7cKlCggKpWraqGDRuqYcOGaXIuAAAAAJAI08ji4uLi1KBBA23YsEHOzs6qX7++ChcurJiYGB05ckSLFy/W8ePHCdPIUFWrVtWxY8fk6uqa0aUAAAAgjRCmkaUtWbJEGzZsUIUKFRQWFiYnJ9PV+Pv372vv3r0ZVB3whIODg15//fWMLgMAAABpiGemkaXt2rVLktSuXbskQVp6EmL8/f2TPXbJkiXy9/eXs7Oz7OzsVKZMGY0cOVKPHj1Kdvzx48fVvn17ubu7y9bWVvny5VONGjU0bdq0JGO3bt2qgIAAubi4yNbWVqVKldLAgQMVHZ301vbEZ5xjY2M1atQolSxZUra2tnJzc9OAAQMUExOTbD1Lly5V5cqVZW9vr3z58ql169a6dOlSsmNjYmI0efJk1atXT0WLFpWtra1cXFz0zjvv6Jdffkn2GHd3d7m7u+v27dvq16+f3N3dlS1bNgUHB+vzzz+XwWDQ/Pnzkz12//79MhgMatCgQbL9ydU3YsQIFS9eXLa2tvLw8NDgwYNT/FlIUnR0tD7//HOVLl1adnZ2yp07t959911t2bIlydh/PrMcERGhgIAAOTk5KXfu3GrSpIkuXLggSYqMjFTz5s2VN29e2dvby9/fX4cPH04y38mTJzVw4EB5eXkpb968srW1VdGiRdWpUyf9+eefqZ7/n571Zw8AAICMR5hGlpYnTx5JT8KNJdq3b68WLVro9OnTatKkibp37y4XFxcNGTJEAQEBio2NNRm/fv16VapUSfPnz1e5cuXUr18/NWnSRHFxcfrqq69Mxs6YMUN16tTRzp071ahRI/Xt21cuLi4aO3asfHx8dOvWrWRratGihb799lvVqFFDXbt2lb29vb766it17tw5ydgJEyboo48+UmRkpNq0aaOgoCD98ccf8vHx0c2bN5OMv3Hjhnr37q07d+6oTp066tevnxo2bKiDBw+qXr16mjVrVrI1xcTEqFatWlq9erXq1q2r3r17y8PDQ507d5aVlZVmzpyZ7HEzZsyQJHXp0iXZ/n9KSEhQ06ZN9cUXX8hgMKhHjx5q0KCB5syZo6ZNmyZ7zK1bt+Tj46MxY8bIyclJffr0UZMmTbR7927VrVvXeP5/27dvn2rUqCFJ6tixo6pWraqVK1fqnXfe0fHjx1W1alX9+eefatOmjerXr6+wsDDVqVNHd+/eNZln5cqVmj59utzc3PTRRx+pZ8+eKlu2rGbNmqUqVaro4sWLT73uf7LkZw8AAIDMwZCQkJCQ0UXAlJeXlyIiIp467tixYypTpkzKA16BDcgOHjwob29vxcbGqmXLlmrcuLEqV66sokWLpnjMvHnzFBQUpMaNG2vRokWyt7f/X0nBwRo2bJhCQkLUu3dvSdK1a9dUvHhxPXjwQJs3b1bNmjVN5vvzzz9VuHBhSdK5c+dUqlQp2dra6rfffjO5tbdbt26aNm2aOnbsaBJC/fz8FBYWpkqVKmnz5s1ycXGRJN27d08VKlTQ2bNndfHiRRUoUECSFBUVpVKlSilHjhw6cOCA3N3dJUnx8fH6v//7P61cuVLSk5Ca6NGjR7p69aqxzkTR0dGqVq2aLl26pIsXL5p8F+7u7jp37pxq166tNWvWyNHR0eTYBg0aaP369frjjz/0xhtvGNvv3Lmj1157Tblz59bZs2dlbW2d4s9CkhYvXqyWLVvqrbfe0vbt22VnZyfpyS8AqlSposjISNWsWVOhoaHGYzp37qyZM2eqU6dOmj59unH38lOnTsnLy0sPHz7UiRMnjN9NaGio8Q6F77//Xi1btjTO1aFDB82ZM0e5c+fWJ598okGDBhn7RowYoS+++MLkfw+SdPHiRbm6usrW1tbkWjZt2qT33ntPnTp1MrljIfH8Q4cONVmdtvRnn5qn/nsAAC+TzPL/cdJoM1UAmVdq2YyVaWRpnp6e+v7775U/f359//33atKkidzd3ZUnTx41btxY69atS3LMxIkTZWNjozlz5piER0kaMmSI8uTJo0WLFhnb5s+fr9u3b6tr165JgrQkk4D6/fffKyYmRj169EjyjOyXX36pnDlzauHChcnevjx27FhjmJIkR0dHtWzZUvHx8Sb/AS9atEiPHz9Wz549jWFRkqysrDRu3DhZWSX9z9rW1jZJkJYkJycntW/fXjdv3tS+ffuS9EvS+PHjkwRpSerataskJVkFXrx4se7evauPP/74qUFakubOnStJGjVqlDFISzLeKfBvMTEx+v7775UjRw6NHj3a5DVgJUuWVK9evRQTE6MFCxYkObZ69eomQVqS2rZtK+nJdzFw4ECTvjZt2kiSDh06ZNJeqFChJEFakurWraty5cpp48aNqV1yEub+7AEAAJB5sAEZsrymTZuqcePG2r59u3bs2KGDBw9qx44dWr16tVavXq02bdpo3rx5MhgMun//vg4fPixXV1eFhIQkO5+tra2OHTtm/Lxnzx5J0nvvvffUWg4cOCBJqlWrVpK+3Llzy9PTU+Hh4Tp+/LgqVKhg0u/l5ZXkGDc3N0kyuXU78RzJBftixYrJzc1N586dS9J35MgRjRs3TuHh4frrr7/08OFDk/7kbk22s7NT+fLlk7RLT74PDw8PLVy4UGPHjpWDg4MkaebMmbKxsdHHH3+c7HH/duDAAVlZWal69epJ+pJ7v/SJEyd0//59VatWzSSAJqpVq5ZGjhypgwcPJulL7jt+7bXXJEkVK1ZMEv4LFSokSUmeg05ISNCiRYs0b948HT58WDdv3lRcXJyxP3v27MlcacrM/dkDAAAg8yBM46WQLVs21a1bV3Xr1pX05JVZK1asUPv27bVgwQI1btxYjRo10s2bN5WQkKCrV69q2LBhZs2d+IxzYrBKTeIGYwULFky2P7E9ueemnZ2dk7TZ2Dz5T/SfQS3xHPnz50/2HAUKFEgSpvfs2aNatWopNjZWtWvXVsOGDZUrVy5ZWVnp0KFDWrNmTbKr5fny5TNZ+f0nKysrde7cWQMHDtQPP/ygoKAg7d+/XwcOHFCjRo2MIfVpoqOj5eLiomzZsiV7LcmNl57tO05uk7rE7zi1vsePH5u09+vXTyEhISpYsKDeffddFSpUyHiXw7x585L9ZUZqzP3ZAwAAIPMgTOOlZG1traZNm+qPP/7QyJEjtW3bNjVq1MgYmDw9PY0rvE+TGHQuXryoN998M9WxifNfvnxZ5cqVS9L/119/mYx7FonHXrlyJdlzXL58OUnbyJEj9eDBA23fvj3Jau/o0aO1Zs2aZM+VUpBO1L59ew0dOlQzZsxQUFCQ8ZZvSzbOcnJy0o0bN/T48eMkgTq5a/nnd5yctPiOU/P3339r0qRJeuONN7Rr1y7lzJnTpH/JkiXpcl4AAABkLjwzjZdaYtBJ3IwrR44cKleunI4cOaIbN26YNcdbb70lSSm+QuqfPD09Jclks6xEt27d0qFDh4yv4XpWlSpVkiSFhYUl6YuMjDS+5umfTp8+LRcXl2Rvm05uHnPlzZtXgYGB2rt3r3bu3KklS5bIw8PDeIeAOSpVqqT4+Hjt2LEjSV9y32Pp0qXl4OCgw4cPJ7v6vH37duO86SEyMlLx8fGqW7dukiD9559/KjIyMl3OCwAAgMyFMI0sbcmSJdq8ebPi4+OT9F2+fFnfffedJMnX19fY3q9fP8XExKh9+/bJhrGbN2+arFq3bdtWuXLl0rRp0xQeHp5k/D+fp23VqpWyZcumb7/9VqdPnzYZN2TIEN2+fVutWrVKdvMqc7Vs2dJ4jqioKGN7fHy8+vfvn+x34e7urhs3buj33383aZ89e7bFm2X9W+JGZM2aNdPdu3fVsWPHZDdBS0lQUJAkadCgQSbPcd+4cUMjR45MMj579uxq2bKl7ty5k2SDsjNnzmjSpEnKli2bWrdu/SyX81SJm77t2LHD5BbsxGv/92vVAAAA8HLiNm9kaXv37tXEiRNVoEABVa9eXR4eHpKks2fPav369Xrw4IE++OADBQYGGo9p37699u/fr6lTp6p48eJ69913VaRIEd24cUNnz55VeHi4goKCNH36dEmSq6urFi9erMDAQPn7++u9995T+fLldfv2bf3++++6cOGCzp49K+lJ0AoJCVH37t1VqVIlNW3aVHnz5lVYWJh2796t119/XWPHjn2ua3Z3d9eYMWP0ySefyNPTU82aNZOTk5M2btyoW7duqXz58klCc58+fbRx40ZVr15dTZs2lZOTkyIiIrRjxw4FBgZq+fLlz1xPtWrVVKFCBR0+fFjZsmVT+/btLTr+o48+0g8//KC1a9fqjTfe0AcffKDHjx9r+fLlqlKlis6cOZPkmDFjxujXX3/V5MmTtW/fPvn7++vatWv68ccfdefOHU2ePNn4v4W0VqBAATVv3lxLly5VxYoVVbduXUVHR2vz5s2ys7NTxYoVk+z+DQAAgJcPK9PI0j755BNNnjxZb731ln7//XdNnz5dISEh2rFjh/z8/LRw4UKtXLkyybO/U6ZM0bp16/T2229ry5Yt+uabb7R27VpFR0erf//+6tOnj8n4+vXrKyIiQi1bttTBgwf19ddfa9myZTIYDPr8889Nxnbr1k0bN27UW2+9pRUrVuibb77R33//rf79+2v37t3J7kBtqX79+mnx4sXy8PDQvHnzNGfOHOMzvLlz504yPiAgQOvWrVPZsmX1ww8/aPbs2bK1tdX27dtVv379564ncXX5gw8+SHFjtJQYDAYtW7ZMw4YNU3x8vCZPnqy1a9cqKChIP/74Y7LHuLi4aPfu3frss890/fp1ffPNN1q2bJmqVq2qDRs2qFu3bs99TamZPXu2/vOf/+jBgweaMmWKNm7cqAYNGmjXrl3p9qw2AAAAMhdDQuLDpMg0Unsx+D8dO3bsuZ69BdJKu3btNH/+fG3ZskW1a9fO6HJeSfx7AOCVEpxJfnEZHJ3RFQBIZ6llM1amATyXCxcuaOnSpSpTpkyy79cGAAAAXkY8Mw3gmSxevFgnT57U0qVL9ejRI40YMeKpr9ICAAAAXhaEaQDPZObMmQoPD5ebm5smTJigJk2aZHRJAAAAwAtDmAbwTJJ7BzQAAADwquCZaQAAAAAALESYBgAAAADAQoRpAAAAAAAslCnC9IABA1S7dm25ubnJ3t5eLi4u8vT01LBhw3T9+vVkj9m1a5fq1asnFxcX2dvbq3z58goJCVFcXJzF5z969KiaNm2qfPnyyc7OTqVLl9bQoUP14MGDJGNjYmI0ePBgeXh4yMnJSf7+/jpw4ECy827ZskUGg0E//fSTxTUBAAAAADIvQ0JCQkJGF5E9e3ZVqlRJZcuWVb58+XTv3j3t2bNHEREReu2117Rnzx65ubkZx69Zs0ZNmjSRnZ2dmjVrJhcXF61bt04nTpxQYGCgli1bZva59+7dq1q1aunx48cKDAyUm5ubtm3bpoiICFWrVk1bt26Vra2tcXy/fv2MOxcXLlxYCxcuVGxsrI4fP66CBQsax929e1dvvvmmqlWrpu+//96i7yO1F4P/07Fjx1SmTBmL5gbwcuLfAwCvlGCnjK7gieDojK4AQDpLLZtlit28b9++LTs7uyTtgwYN0qhRozR69GhNnTrVOLZjx46ytrZWaGiovLy8JEkjRoxQrVq1tHz5ci1dulTNmzd/6nnj4uIUFBSk+/fva82aNWrYsKEkKT4+Xk2bNtWKFSs0YcIEDRw4UJKUkJCgGTNmKCgoSHPmzJEkNW7cWH5+flq4cKE+++wz49wDBw7UgwcPNHHixOf7cgAAAAAAmU6muM07uSAtSU2bNpUknTp1yti2fPlyXb16Vc2bNzcG6cQ5Ro4cKUmaNm2aWecNCwvTsWPH5OvrawzSkmRlZaWvvvpKkjR9+nQlLt5fvXpV9+/fV9WqVY1jE/9+7tw5Y9uvv/6qqVOnavLkycqTJ49ZtQAAAAAAso5MEaZTsm7dOklS+fLljW3btm2TJAUEBCQZ7+vrKwcHB+3atUuPHj166vypzVWsWDGVKlVK586dU2RkpCTJ1dVV9vb22r9/v3Fc4pJ/0aJFJUkPHjxQhw4d9OGHHyowMNCs6wQAAAAAZC2Z4jbvRF9//bXu3r2r6OhoRUREaMeOHSpfvrzxNmtJOnHihCSpVKlSSY63sbGRh4eHjhw5osjIyKc+P5jaXJJUsmRJnTx5UidPnlTx4sVlZWWlTp06adKkSYqOjlahQoW0cOFC5cqVSy1btpQkDRkyRNevX9eUKVOe6TsAAAAAAGR+mWpl+uuvv9awYcMUEhKiHTt2KCAgQJs2bVLevHmNY6Kjn2z04OSU/MYTie23bt166vmeZa6xY8dqwIAB+u233zR79myVK1dOW7ZsUaFChfTbb78pJCREEydOVN68eRUcHKyCBQvKxsZGlStX1s6dO1OsZebMmfLy8pKXl5euXr361NqRuQUHB8tgMCg0NDSjS0kTfn5+MhgMGV3GczMYDPLz88voMgAAAPASyFQr05cvX5YkXblyRbt27dLAgQPl6empn376SZUqVcrg6p6wtbXV6NGjNXr0aJP2mJgYBQUFKSAgQK1atVJISIiGDRumoUOHqlq1avryyy8VEBCg06dPK3/+/Enm7dSpkzp16iRJJs+CP48357+ZJvOklz/a/vFcx1sa7ubOnat27do91zkTzZs3T0FBQWk6JwAAAICsI1OF6UT58+dX48aNValSJZUqVUpt2rTRf//7X0n/Wy1OXFX+t8R2Z2fnp54nLecaPny4Ll68qE2bNkmSxo0bp9q1ays4OFiSVLp0abm7u2vKlCkaPnz4U+fD0w0dOjRJW0hIiKKjo9W7d+8kP7eKFSu+mMIk9ejRQ82bN1eRIkVe2DnxdMeOHZODg0NGlwEAAICXQKYM04mKFi2qsmXL6tChQ7p27ZpcXV1VunRpRURE6OTJk6pcubLJ+NjYWJ09e1Y2NjYqVqzYU+cvXbq0JOnkyZPJ9ifuIp7SM9WJDh06pLFjx2ratGkqVKiQbt++rUuXLhmfo5akIkWKyNXVVUeOHHlqXTBP4i8q/mnevHmKjo5Wnz595O7u/sJrSuTq6ipXV9cMOz+S9/rrr2d0CQAAAHhJZKpnppNz6dIlSZK1tbUkqVatWpKkDRs2JBkbHh6u+/fvy8fHR7a2tk+dO7W5IiMjdfLkSRUtWjTVYB4bG6ugoCD5+fnp448/Nun7947iDx8+fGpNSD979+5VYGCgChQooOzZs8vNzU2dO3c2/m/snxKfEY6JidHw4cNVunRp2draql27dvLz81NQUJAkKSgoSAaDwfgnKipKUsrPTK9evVqtWrVSqVKl5OjoKEdHR1WuXFmTJk1SfHx8kjratWsng8GgyMhIffvttypfvrzs7e3l5+enjRs3ymAwGGv5t0ePHhlDvTm720vS0qVLVblyZdnb2ytfvnxq3bp1st9Povj4eE2fPl1VqlRRjhw55OjoqCpVqmjatGnJXk/iM8tXrlxR+/btlT9/fjk6OsrHx0e//vqrJOnevXvq37+/ihYtKltbW5UrV07Lli1LMld0dLTGjRunWrVqqXDhwsqePbvy5s2rhg0bavfu3cnWm9wz0//8WS1fvlxVq1aVg4ODXFxc1Lx5c128eNGs7w4AAACvlgxfmT558qTy58+fZBOw+Ph4DRkyRH///bd8fHyUO3duSVJgYKAGDBigpUuXqmfPnsbnix8+fKjBgwdLkrp27Woy1/3793X+/Hk5ODiY3HZbs2ZNlSlTRuHh4Vq7dq3xXdPx8fEaMGCAJKlLly6pPps7ZswYnT59WqtWrTK25cqVS4UKFdKGDRsUGxsrGxsbhYWF6c6dOypXrtyzflV4DnPmzFGnTp1ka2urhg0bys3NTadOndKsWbO0bt067dmzJ9lbsps0aaJ9+/bpvffeU6NGjZQvXz75+fnJ2dlZa9as0QcffGBy+/jTHgkYOHCgrKys5O3trUKFCik6Olrbtm1T7969tW/fPi1cuDDZ43r37q1ff/1V9evXV7169WRtba26deuqePHi+vHHHxUSEpLkv6EVK1bo+vXr+uSTT8z65dKECRPUr18/OTs7q02bNnJ2dtbGjRvl4+OT4iZ9rVu31uLFi+Xm5qaPP/5YBoNBq1atUrdu3bRjxw4tWrQoyTG3bt1StWrVlDNnTn300Ue6ceOGli5dqnfffVe7d+9W586ddePGDTVo0ECPHz/WkiVL1KxZM7m5uemtt94yznPs2DENGjRIvr6+ql+/vnLnzq3z589r7dq1+uWXX7Ru3bpkX3uXkqlTpxr/HahZs6b27t2rH374QYcPH9ahQ4fM+g4BAADw6sjwMP3zzz/r888/V/Xq1eXh4aE8efLoypUrCgsLU2RkpAoUKKDvvvvOOD5Xrlz67rvvFBgYKD8/PzVv3lwuLi5au3atTpw4ocDAQDVr1szkHL/99pv8/f1Vs2ZNk5VCa2trzZ07V7Vq1VJgYKACAwNVpEgRbd26VREREapWrZr69u2bYu1Hjx7ViBEjNH78+CS3FH/22Wfq3bu3atSoIW9vby1atEg5cuRQ9+7d0+R7g/lOnjypLl26yN3dXWFhYSpUqJCxb+vWrapbt6569+5t8guRROfOndN///vfZG/ZXrNmjRo1amTRBmTr169X8eLFTdri4+MVFBSkBQsWqEePHvL29k5y3IEDB3Tw4EF5eHiYtHfp0kX9+/fXwoUL1aNHD5O+mTNnSpJxY7vUREVFacCAAcqdO7cOHDhg/N/z6NGj9X//939auXJlkmOWLFmixYsXy9PTU+Hh4cqRI4ckaeTIkapZs6YWL16s+vXrq0WLFibHHT58WJ07d9bUqVNlZfXk5pg6deqoTZs28vf3V7Vq1RQaGio7OztJTwK7r6+vxo4da/IzKlOmjC5dupTkZ/Pnn3+qatWq6tu3r0VhesOGDdq3b5/efPN/G/e1aNFCS5Ys0Zo1a9S0aVOz5wIAAMDLL8Nv837nnXfUoUMHXb16VStXrtS4ceO0YsUKubi4aOjQoTpy5IjKli1rckyjRo0UFhYmX19frVixQt9++62yZcumb775RkuXLrVol2dvb2/t27dPH3zwgTZt2qQJEyYoOjpaX3zxhTZv3pzialRcXJzat28vb2/vZANyz549NWLECF24cEHTpk2Th4eHNmzYkOxO3khf06ZN0+PHjzVx4kSTIC1JtWvXVsOGDbVu3TrduXMnybEjRoxI02ef/x2kJcnKykq9e/eWJG3cuDHZ4z777LMkQVp6cpu5nZ2dZsyYYdJ+4sQJhYWFyd/f/6nP/EvSokWL9PjxY/Xs2dPkF0NWVlYaN26cMfT+05w5cyQ9uTsjMUhLkqOjo8aOHStJmjVrVpLjHBwckszZokUL2djY6ObNm5o4caIxSEtSjRo15O7urkOHDpnM4+TklOzPpnDhwgoMDNTx48d1/vz5p157ol69epkEaUnq2LGjpCe/kAMAAAD+KcNXpt944w1NnjzZ4uOqVaumn3/+2ayxfn5+SkhISLG/bNmyyT6TmRpra2vt2bMnxX6DwaDBgwcbbz1Hxkl8fjYsLEz79u1L0v/3338rLi4u2U3tqlatmqa1XL9+XePGjdPPP/+syMhI3bt3z6Q/pedzU6ojT548atq0qRYsWKBdu3bJx8dH0v9Wpbt06WJWXQcOHJD05NGHfytWrJjc3Nx07ty5JMdYWVkl+97mmjVrytraWgcPHkzSV6pUKeXMmdOkzdraWvnz59e9e/eS3aOgUKFC2rt3b5L2nTt3auLEidq9e7f+/vtvxcTEmPRfvHjR7B3Vk3slnZubmyTp5s2bZs0BAACAV0eGh2kgvV2/fl3Sk9eVpebu3btJ2goUKJBmddy6dUtVqlTR2bNnVbVqVbVp00YuLi6ysbHRrVu3NHHixBQ3Ckutjm7dumnBggWaMWOGfHx89OjRI82fP1/58uVT48aNzaot8TVwKd05UaBAgSRhOjo6Wi4uLsqePXuS8TY2NnJ1ddXff/+dpC+l569tbGxS7YuNjTVpW7VqlQIDA2VnZ6c6deqoePHicnR0lJWVlUJDQxUWFmb2xmtS8s+729g8+ScyLi7O7HkAAADwaiBM46X3z/eJ58qVy6JjLXlk4GlmzZqls2fPaujQoUle67V7925NnDjxmerw9vaWp6encSOyX375RdevX9eAAQOULVs2s2pL/I6uXLmS7CZ5ly9fTvaYGzdu6PHjx0nOExsbq2vXrln8fVtiyJAhyp49uyIiIlSmTBmTvs6dOyssLCzdzg0AAABk+DPTQHpL3AE68dVLaSHxVW2WrFiePn1a0pMdwv/teYNft27d9PDhQy1YsEAzZ86UwWAwa+OxRJUqVUqxjsjISF24cCFJu6enp+Lj4xUeHp6kLzw8XHFxccZ508Pp06dVtmzZJEE6Pj5eO3bsSLfzAgAAABJhGq+AHj16KFu2bOrbt69OnjyZpD8mJsbioJ0nTx5JsmiDq8SNvf797umDBw9q9OjRFp3/31q0aCEnJyd99dVXCgsLU506dVJ9P/q/tWzZUtmyZdO3335rfFe29CSY9u/fP9l3Rrdv316S9Pnnn+v+/fvG9vv372vgwIGSpA4dOjzjFT2du7u7Tp06ZfIe7ISEBAUHB+vo0aPpdl4AAABA4jZvvAJef/11zZkzR+3bt1e5cuUUEBCgUqVK6fHjxzp//rx+/fVX5c2bV8ePHzd7zrffflsODg4KCQnR9evXjc809+zZM8Xnftu0aaNx48apT58+2r59u0qWLKlTp07pp59+0ocffqgffvjhma/RwcFBbdu21aRJkyQ9uc3ZEu7u7hozZow++eQTeXp6qlmzZnJyctLGjRt169YtlS9fXr///rvJMS1atNCaNWv0448/qly5cmrUqJEMBoNWr16ts2fPqlmzZmrZsuUzX9PT9O3bV126dJGnp6eaNGmibNmyaefOnTp69Kjef/99rVu3Lt3ODQAAALAyjVdCq1attH//frVs2VK///67Jk+erO+//16nT59WYGCgpk6datF8uXPn1ooVK1S2bFnNmzdPQ4YM0ZAhQ1Ld9fm1117Tr7/+qvr162vHjh2aPHmyzp07p6lTp2rMmDHPe4nGleKCBQuqYcOGFh/fr18/LV68WB4eHpo3b57mzJmjN954Q7t27VLu3LmTPWbJkiWaMmWK8uTJoxkzZmj69OnKnTu3Jk+erMWLFz/X9TxN586dNXfuXBUsWFDz58/XokWL5Obmpr1796br7eUAAACAJBkSUntnFDKEl5eXIiIinjru2LFjSZ4Xxatr3rx5CgoK0uDBgzVixIiMLgcvGP8eAHilBCd/F9gLFxyd0RUASGepZTNWpoGXQGxsrL755hvZ2NhYfIs3AAAAAMvxzDSQhe3YsUNhYWEKDQ3VH3/8oR49eqhw4cIZXRYAAADw0iNMA1nYli1bNGzYMLm4uKhjx4766quvMrokAAAA4JVAmAaysODgYAUHB2d0GQAAAMArh2emAQAAAACwEGEaAAAAAAALEaYBAAAAALAQYTqL4zXhAPh3AAAA4MUjTGdh1tbWevz4cUaXASCDPX78WNbW1hldBgAAwCuFMJ2F5cyZU7dv387oMgBksNu3bytnzpwZXQYAAMArhTCdhbm4uOjmzZu6du2aYmJiuNUTeIUkJCQoJiZG165d082bN+Xi4pLRJQEAALxSeM90FmZra6siRYroxo0bioqKUlxcXEaXBOAFsra2Vs6cOVWkSBHZ2tpmdDkAAACvFLPDdFxcnB49eiQHBweT9m3btmnNmjVycHBQp06d5OHhkeZFImW2trYqWLCgChYsmNGlAAAAAMArw+zbvD/99FO5uLgoOjra2LZ06VLVqVNH3377rcaOHauqVavqwoUL6VIoAAAAAACZhdlhOjw8XP7+/nJycjK2DRs2TM7OzlqwYIG++uor3bp1S9988026FAoAAAAAQGZhdpi+cOGCSpQoYfwcGRmpEydOqGfPnmrVqpU+/fRTvffee9qwYUO6FAoAAAAAQGZhdpi+ffu2cuXKZfy8c+dOGQwGBQQEGNvKlSunP//8M20rBAAAAAAgkzE7TBcsWFBnz541ft6yZYvs7e1VuXJlY9vdu3dlY8MG4QAAAACAl5vZyfett97S2rVr9dNPP8nOzk7Lly9X7dq1lS1bNuOYs2fPqlChQulSKAAAAAAAmYXZK9P/+c9/FB8frw8++EDvvvuuYmJiNGjQIGP/w4cP9euvv8rb2ztdCgUAAAAAILMwe2X6zTff1N69ezV//nxJUrNmzVSlShVj/8GDB1WrVi199NFHaV8lAAAAAACZiEUPOL/55pv6+uuvk+17++23tWrVqjQpCgAAAACAzMzs27wBAAAAAMATFm+9HRERod9++003b95UXFxckn6DwaAhQ4akSXEAAAAAAGRGZofp27dv68MPP9T27duVkJCQ4jjCNAAAAADgZWd2mO7fv7+2bdumGjVqKCgoSG5ubrxTGgAAAADwSjI7Da9Zs0aVKlXS9u3bZWXFo9YAAAAAgFeX2ak4Ojpa/v7+BGkAAAAAwCvP7GRcsmRJXblyJT1rAQAAAAAgSzA7THfv3l3r1q3TxYsX07MeAAAAAAAyPbOfmX7vvfe0bds2VatWTUOHDlXlypXl7Oyc7NgiRYqkVX0AAAAAAGQ6Zodpd3d3GQwGJSQk6OOPP05xnMFgUGxsbJoUBwAAAABAZmR2mG7Tpo0MBkN61gIAAAAAQJZgdpieN29eOpYBAAAAAEDWwXuuAAAAAACwkNkr0//0559/6uDBg7p165acnJxUqVIlFS5cOK1rAwAAAAAgU7IoTJ87d06dO3fW5s2bk/TVqVNH06dPl7u7e1rVBgAAAABApmR2mL58+bKqV6+uixcvyt3dXb6+vipYsKD++usv/frrr9q0aZOqV6+uiIgIFShQID1rBgAAAAAgQ5n9zPSIESN08eJFjR07VqdOndK8efM0evRozZs3TydPntRXX32lS5cuaeTIkRYVcP36dc2aNUuNGzdWiRIlZG9vLycnJ1WvXl2zZ89WfHy8yfioqCgZDIYU/zRv3tyi80vSrl27VK9ePbm4uMje3l7ly5dXSEiI4uLikoy9ffu2unXrpsKFCytPnjx6//33debMmWTnnTVrlrJly6aDBw9aXBMAAAAAIPMyJCQkJJgz0N3dXa+//ro2bNiQ4piAgAAdP35cUVFRZhcwffp0de3aVQULFpS/v7+KFCmiK1euaOXKlYqOjlaTJk20bNky42u5oqKi5OHhoQoVKqhRo0ZJ5nvjjTcUGBho9vnXrFmjJk2ayM7OTs2aNZOLi4vWrVunEydOKDAwUMuWLTMZ/+GHH2rt2rVq1aqVHBwcNG/ePOXLl09Hjx6Vg4ODcdzFixdVrlw59ejRw+JfMHh5eSkiIsKiYwAAAF4ZwU4ZXcETwdEZXQGAdJZaNrPoNu+WLVumOqZy5coKDQ21qLhSpUpp7dq1ql+/vqys/rdQPmrUKFWtWlUrVqzQypUr1aRJE5PjKlasqODgYIvO9W+3b99Wx44dZW1trdDQUHl5eUl6sgpfq1YtLV++XEuXLjWudl+5ckWrVq3SsGHD9MUXX0iSvL291a5dO/30009q2rSpce4uXbqoUKFCxnEAAAAAgJeH2bd5Ozk56dy5c6mOOX/+vJycLPtNYa1atfT++++bBGlJKlCggLp06SJJFgd0cy1fvlxXr15V8+bNjUFakuzs7IyrydOmTTO2J15/1apVjW2Jf//nd/P999/r559/1pw5c5Q9e/Z0qR0AAAAAkHHMDtPVq1fX8uXLtWvXrmT79+7dq2XLlql69eppVly2bNkkSTY2SRfQL126pBkzZmjUqFGaMWOGfv/9d4vn37Ztm6Qnt6f/m6+vrxwcHLRr1y49evRIklSkSBFJ0v79+43jEpf8ixYtKunJ6nWfPn3Ut29feXt7W1wTAAAAACDzM/s270GDBmn9+vWqWbOmmjdvLn9/fxUsWFCXL19WaGiolixZIisrK/3nP/9Jk8JiY2O1YMECScmH3c2bNyd5RZefn5/mz59vDL1Pc+LECUlPbjX/NxsbG3l4eOjIkSOKjIxUmTJlVKBAAX3wwQcaNmyYzpw5Izs7O+P56tevL0nq3r27XFxcNGLECIuuFwAAAACQdZi9Ml2pUiUtX75cuXLl0qJFi9SxY0c1aNBAH3/8sRYuXKhcuXLpxx9/VOXKldOksIEDB+q///2v6tWrp3fffdfY7uDgoCFDhmj//v26efOmbt68qbCwMPn7+ys0NFS1a9fWvXv3zDpHdPSTTSNSujU9sf3WrVvGtvnz5ysoKEgbNmzQ0qVL5efnpy1btsjR0VHLly/XypUrNXv2bFlZWalnz55ycXFR9uzZ5efnp6NHjz7jtwEAAAAAyEzM3s070b1797RmzRodOHBA0dHRcnJykqenpxo1aiRHR8c0KWrSpEnq3bu3Xn/9de3cuVMuLi5PPSY2NlbVq1fX3r17FRISot69ez/1mFKlSunUqVM6deqUSpQokaS/WrVq2rVrl3bt2qW333471blu3LihsmXLKjAwUJMnT1afPn00bdo0jRs3TqVKlVL//v0VHR2tkydPys7OLsnxM2fO1MyZMyVJV69eferz6QAAAK8sdvMG8IKkyW7eiRwdHdWiRQu1aNHiuQtLzuTJk9W7d2+VLVtWW7duNStIS09uy/7444+1d+9ehYeHmxWmE1eeE1eo/y2x3dnZ+alz9erVS/b29hozZozu3bunadOmqXXr1urVq5ekJ9+br6+vFi9erPbt2yc5vlOnTurUqZMkmWyGBgAAAADIfMy+zftFCAkJUc+ePfXGG29o+/btKlCggEXH582bV5LMvs27dOnSkqSTJ08m6YuNjdXZs2dlY2OjYsWKpTrP+vXrtWjRIn333XfKkSOHzpw5o5iYGFWqVMk4JvH29yNHjphVGwAAAAAg80pxZTpx86/GjRsrZ86cxs/maNOmjcWFjB07VgMHDlTFihW1efNmubq6WjzHnj17JOmp4TdRrVq1tGjRIm3YsEEfffSRSV94eLju378vX19f2drapjhHdHS0OnfurA4dOuidd94x6UvcBVySHj58aO5lAAAAAAAyuRTDdLt27WQwGPTWW28pZ86cxs+pSUhIkMFgsDhMjxgxQl988YUqV66sTZs2pXpr94EDB1SxYsUk76XeunWrJkyYIElq1aqVSV90dLT++usvOTk5qWDBgsb2wMBADRgwQEuXLlXPnj2Nt1c/fPhQgwcPliR17do11do/+eQTSdL48eONbcWLF1f27Nn1008/qW/fvpKkdevWSZLKlSuX6nwAAAAAgMwvxTA9Z84cGQwGY/icO3duuhQwf/58ffHFF7K2tlaNGjU0adKkJGPc3d3Vrl07SVK/fv106tQp+fj4qHDhwpKk33//3fjO6BEjRsjHx8fk+FWrVikoKEht27bVvHnzjO25cuXSd999p8DAQPn5+al58+ZycXHR2rVrdeLECQUGBqpZs2Yp1r5lyxbNnj1b69atM9kR3NHRUd27d9eECRMUEBCgEiVKaO7cuXJzc0u3Z80BAAAAAC9OqivT/9S2bdt0KeDs2bOSpLi4OIWEhCQ7pmbNmsZ6WrdurVWrVmnfvn365Zdf9PjxY+XPn19NmzZVjx49VKNGDYvO36hRI4WFhenLL7/UihUr9PDhQ5UoUULffPONevXqleJq/N27d9WxY0e1bNlSDRo0SNI/evRoxcfHa9GiRQoNDZWPj48mT56c7E7eAAAAAICsxexXY4WHh8vd3V1FihRJccyFCxd09uxZ+fr6plmBr6LUtl8HAAB45fFqLAAvSGrZzOzdvP39/U1ukU7OggUL5O/vb1FxAAAAAABkNWaHaXMWsBM3IAMAAAAA4GWWpu+ZPnfunHLmzJmWUwIAAAAAkOmkuAGZJA0fPtzkc2hoaLLj4uLidP78eS1dulTVq1dPs+IAAAAAAMiMUg3TwcHBxr8bDAaFhoamGKglqVChQhozZkxa1QYAAAAAQKaUapjevn27pCfPQteqVUvt2rVL9hVZ1tbWypMnj0qXLi0rqzS9cxwAAAAAgEwn1TBds2ZN49/btm2rRo0ambQBAAAAAPAqSjVM/9PcuXPTsw4AAAAAALIM7skGAAAAAMBCFoXpv/76S927d1eJEiVkb28va2vrJH9sbMxe7AYAAAAAIEsyO/levHhRVatW1ZUrV1SuXDk9evRIRYsWla2trSIjIxUbG6uKFSvKyckpPesFAAAAACDDmb0yPXz4cF2+fFkbNmzQ4cOHJUlBQUE6fvy4IiMj9e677+rBgwdauXJluhULAAAAAEBmYHaY3rhxowICAvTOO+8k6StcuLCWLVumBw8eaOjQoWlaIAAAAAAAmY3ZYfry5csqV66c8bO1tbUePHhg/JwjRw7VqVNHa9asSdsKAQAAAADIZMx+ZjpXrlyKiYkxfs6dO7cuXrxoMsbJyUlXr15Nu+oAAACAf3F/uDijS5AkRWV0AQAylNkr00WLFtWFCxeMnytUqKBt27bp/v37kqT4+Hht2rRJhQsXTvsqAQAAAADIRMwO07Vr19b27dv1+PFjSVLbtm116dIl+fj4qH///qpWrZqOHDmiZs2apVuxAAAAAABkBmbf5t2hQwflzp1b165dU8GCBdWqVSvt379f3377rX7//XdJUvPmzTVo0KB0KxYAAAAAgMzAkJCQkPA8E1y9elWRkZFyd3dX/vz506quV5qXl5ciIiIyugwAAIBMyX3g+owuQZIUNaZ+RpcAIJ2lls3MXplOSd68eZU3b97nnQYAAAAAgCzD7Gemr169qvDwcN25cyfZ/tu3bys8PFzXrl1Ls+IAAAAAAMiMzA7TI0eO1Pvvvy9ra+tk+62trfX+++9r9OjRaVYcAAAAAACZkdlhevPmzapTp44cHByS7Xd0dFTdunW1cePGNCsOAAAAAIDMyOwwfeHCBRUvXjzVMcWKFTN5FzUAAAAAAC8js8O0wWBQTExMqmNiYmIUFxf33EUBAAAAAJCZmR2mS5cuneot3AkJCdq4caNKlCiRJoUBAAAAAJBZmR2mAwMDdfz4cfXo0UMPHjww6Xvw4IF69OihEydOqFmzZmleJAAAAAAAmYnZ75nu1auXlixZomnTpmn16tXy9fVVoUKFdPHiRYWHh+vSpUuqUKGC+vTpk47lAgAAAACQ8cwO0/b29goNDVW3bt30448/aunSpcY+KysrtWjRQpMnT5a9vX26FAoAAAAAQGZhdpiWJGdnZy1evFgTJ07Uvn37dOvWLTk7O6tq1apydXVNrxoBAAAAAMhULArTifLmzat69eqldS0AAAAAAGQJZm9ABgAAAAAAnkhxZbp9+/YyGAwaNWqU8ufPr/bt25s1ocFg0OzZs9OsQAAAAAAAMpsUw/S8efNkMBg0YMAA5c+fX/PmzTNrQsI0AAAAAOBll2KYPnv2rCSpUKFCJp8BAAAAAHjVpRimixYtmupnAAAAAABeVWxABgAAAACAhVJcmT5//vwzT1qkSJFnPhYAAAAAgMwuxTDt7u4ug8Fg8YQGg0GxsbHPVRQAAAAAAJlZimG6TZs2zxSmAQAAAAB42aX6aiwAAAAAAJAUG5ABAAAAAGChFFemU3PhwgUdPHhQ0dHRcnJykqenp9zc3NK6NgAAAAAAMiWLwvSpU6fUrVs3bdu2LUlfrVq1NGXKFJUqVSrNigMAAAAAIDMyO0yfPn1aPj4+un79uooXL67q1aurQIECunz5snbs2KGtW7eqevXq2rVrl0qUKJGeNQMAAAAAkKHMDtOff/65rl+/rokTJ6p79+6ysvrf49bx8fH69ttv1bdvX/3nP//Rjz/+mC7FAgAAAACQGZi9AdnWrVtVr1499ezZ0yRIS5KVlZV69+6tgIAAbdmyxaICrl+/rlmzZqlx48YqUaKE7O3t5eTkpOrVq2v27NmKj49P9rhdu3apXr16cnFxkb29vcqXL6+QkBDFxcVZdH5JOnr0qJo2bap8+fLJzs5OpUuX1tChQ/XgwYMkY2NiYjR48GB5eHjIyclJ/v7+OnDgQLLzbtmyRQaDQT/99JPFNQEAAAAAMi+zw3RMTIwqVqyY6hhPT089fvzYogKWLVumjh07au/evfL29lafPn3UpEkT/fe//9XHH3+spk2bKiEhweSYNWvWyNfXV+Hh4WrcuLF69OihmJgY9e3bV82bN7fo/Hv37lWVKlW0evVqvfPOO+rdu7dy5cql4cOHq06dOnr06JHJ+IEDB+rLL79U5cqVFRQUpN9//13+/v7666+/TMbdvXtXHTt2VMuWLdWgQQOLagIAAAAAZG5m3+ZdoUIFnT59OtUxp0+fVvny5S0qoFSpUlq7dq3q169vsuI9atQoVa1aVStWrNDKlSvVpEkTSdLt27fVsWNHWVtbKzQ0VF5eXpKkESNGqFatWlq+fLmWLl1qVqiOi4tTUFCQ7t+/rzVr1qhhw4aSnty23rRpU61YsUITJkzQwIEDJUkJCQmaMWOGgoKCNGfOHElS48aN5efnp4ULF+qzzz4zzj1w4EA9ePBAEydOtOj7AAAAAABkfmavTP/nP//RypUr9csvvyTbv379eq1atUqDBg2yqIBatWrp/fffT3LreIECBdSlSxdJUmhoqLF9+fLlunr1qpo3b24M0pJkZ2enkSNHSpKmTZtm1rnDwsJ07Ngx+fr6GoO09OS29a+++kqSNH36dOPK+NWrV3X//n1VrVrVODbx7+fOnTO2/frrr5o6daomT56sPHnymFULAAAAACDrSHFlesGCBUna3nvvPTVo0EC1a9eWr6+v8ufPrytXrigsLEzbtm3T+++/r2vXrqVZcdmyZXtSpM3/ykx8LVdAQECS8b6+vnJwcNCuXbv06NEj2drapjp/anMVK1ZMpUqV0smTJxUZGanixYvL1dVV9vb22r9/v3FcRESEJKlo0aKSpAcPHqhDhw768MMPFRgYaMnlAgAAAACyiBTDdLt27WQwGEzaEldot2zZkuxGY2vXrtW6devUpk2b5y4sNjbWGOj/GXZPnDghScm+z9rGxkYeHh46cuSIIiMjVaZMmVTPkdpcklSyZEmdPHlSJ0+eVPHixWVlZaVOnTpp0qRJio6OVqFChbRw4ULlypVLLVu2lCQNGTJE169f15QpUyy/aAAAAABAlpBimJ47d+6LrCOJgQMH6r///a/q1aund99919geHR0tSXJyckr2uMT2W7duPfUczzLX2LFjZW9vryVLlujGjRvy9PTU119/rUKFCum3335TSEiI5s2bp7x58yo4OFgzZszQ1atXVaFCBU2aNEnVqlVL9lwzZ87UzJkzJT25nRwAAAAAkHmlGKbbtm37IuswMWnSJI0fP16vv/66Fi5cmGF1JMfW1lajR4/W6NGjTdpjYmIUFBSkgIAAtWrVSiEhIRo2bJiGDh2qatWq6csvv1RAQIBOnz6t/PnzJ5m3U6dO6tSpkySZPAsOAAAAAMh8zN7N+0WZPHmyevfurbJly2rr1q1ycXEx6U9cLU5cVf63xHZnZ+ennist5xo+fLguXryoTZs2SZLGjRun2rVrKzg4WJJUunRpubu7a8qUKRo+fPhT5wMAAAAAZF5m7+b9IoSEhKhnz5564403tH37dhUoUCDJmNKlS0uSTp48maQvNjZWZ8+elY2NjYoVK/bU86U2lySdOnVKUsrPVCc6dOiQxo4da7zd+/bt27p06ZIqVapkHFOkSBG5urrqyJEjT60LAAAAAJC5mR2mixUrZtaf4sWLP1MhY8eOVd++fVWxYkVt375d+fLlS3ZcrVq1JEkbNmxI0hceHq779+/Lx8fnqTt5P22uyMhInTx5UkWLFk01mMfGxiooKEh+fn76+OOPTfoePXpk8vnhw4dPrQkAAAAAkPmZHabj4+OVkJCQ5M/NmzcVFRWlqKgoxcTEKD4+3uIiRowYoYEDB6py5craunWrXF1dUxwbGBgoV1dXLV261PhaKulJUB08eLAkqWvXribH3L9/X8ePH9f58+dN2mvWrKkyZcooPDxca9euNbnWAQMGSJK6dOmSZFfzfxozZoxOnz6t7777ztiWK1cuFSpUSBs2bFBsbKykJ++0vnPnjsqVK/e0rwMAAAAAkMkZEhLfd/UcTp8+rV69eunevXvauHGj7OzszD52/vz5ateunaytrdWzZ89kd9Z2d3dXu3btjJ9Xr16twMBA2dnZqXnz5nJxcdHatWt14sQJBQYG6scffzQJwKGhofL391fNmjUVGhpqMvfevXtVq1YtPX78WIGBgSpSpIi2bt2qiIgIVatWTVu3bk1xlfvo0aPy9PTU+PHj1aNHD5O+SZMmqXfv3nrrrbfk7e2tRYsW6eHDhyluQPZPXl5eJr8oAAAAwP+4D1yf0SVIkqLG1M/oEgCks9SyWZpsQFaiRAmtXLlSb7zxhoYNG5Zkp+vUnD17VpIUFxenkJCQZMfUrFnTJEw3atRIYWFh+vLLL7VixQo9fPhQJUqU0DfffKNevXqlupL8b97e3tq3b5+GDh2qTZs26c6dOypatKi++OILDRw4MMUgHRcXp/bt28vb21vdu3dP0t+zZ0/dvn1b06dP14EDB1ShQgVNmDDhqUEaAAAAAJD5pcnKdKKuXbvql19+UVRUVFpN+UpiZRoAACBlrEwDeFFSy2Zpupu3jY2NLl++nJZTAgAAAACQ6aRZmL527ZpWrVolNze3tJoSAAAAAIBMyexnpocPH55se2xsrC5cuKA1a9YoOjraouelAQAAAADIiswO08HBwan258qVS4MHD9Znn332vDUBAAAAAJCpmR2mt2/fnmy7lZWVcufOrddff102NmmyOTgAAAAAAJma2em3Zs2a6VkHAAAAAABZRpru5g0AAAAAwKvA4vuyd+zYoblz5+rgwYOKjo6Wk5OTKlWqpHbt2ql69erpUSMAAAAAAJmKRWG6Z8+emjp1qhISEkzaDx06pLlz56p79+6aNGlSmhYIAAAAAEBmY/Zt3t9++62mTJkiDw8PzZ07V2fPntWDBw909uxZzZkzRx4eHpoyZYqmTJmSnvUCAAAAAJDhDAn/XmZOQbly5RQdHa3//ve/cnZ2TtJ/48YNvfnmm3J2dtaRI0fSus5XipeXlyIiIjK6DAAAgEzJfeD6jC5BkhQ1pn5GlwAgnaWWzcxemY6MjFSTJk2SDdKS5OLioiZNmigyMvKZigQAAAAAIKswO0znyZNH2bNnT3VM9uzZ5erq+txFAQAAAACQmZkdphs1aqS1a9fq8ePHyfbHxMRo7dq1atSoUVrVBgAAAABApmR2mB41apScnJz0zjvvaNeuXcYdvRMSErRz50698847yp07t0aNGpVuxQIAAAAAkBmY/WqsihUrKiYmRn/99Zdq1KghGxsbubq66tq1a4qNjZUkFSxYUBUqVDA5zmAw6MyZM2lbNQAAAAAAGcjsMB0fH69s2bKpSJEiJu2vvfaayed/bw5u5mbhAAAAAABkGWaH6aioqHQsAwAAAACArMPsZ6YBAAAAAMAThGkAAAAAACxEmAYAAAAAwEKEaQAAAAAALESYBgAAAADAQoRpAAAAAAAsRJgGAAAAAMBChGkAAAAAACxEmAYAAAAAwEI2KXVYWVnJYDBYPKHBYFBsbOxzFQUAAAAAQGaWYpj29fV9pjANAAAAAMDLLsUwHRoa+gLLAAAAAAAg6+CZaQAAAAAALESYBgAAAADAQine5j18+PBnmtBgMGjIkCHPXBAAAAAAAJldimE6ODj4mSYkTAMAAAAAXnYphunt27e/yDoAAAAAAMgyUgzTNWvWfJF1AAAAAACQZbABGQAAAAAAFiJMAwAAAABgIYvC9F9//aXu3burRIkSsre3l7W1dZI/NjYp3jkOAAAAAMBLwezke/HiRVWtWlVXrlxRuXLl9OjRIxUtWlS2traKjIxUbGysKlasKCcnp/SsFwAAAACADGf2yvTw4cN1+fJlbdiwQYcPH5YkBQUF6fjx44qMjNS7776rBw8eaOXKlelWLAAAAAAAmYHZYXrjxo0KCAjQO++8k6SvcOHCWrZsmR48eKChQ4emaYEAAAAAAGQ2Zofpy5cvq1y5csbP1tbWevDggfFzjhw5VKdOHa1ZsyZtKwQAAAAAIJMxO0znypVLMTExxs+5c+fWxYsXTcY4OTnp6tWraVcdAAAAAACZkNlhumjRorpw4YLxc4UKFbRt2zbdv39fkhQfH69NmzapcOHCaV8lAAAAAACZiNlhunbt2tq+fbseP34sSWrbtq0uXbokHx8f9e/fX9WqVdORI0fUrFmzdCsWAAAAAIDMwOww3aFDBw0YMEDXrl2TJLVq1Uq9e/fWf//7X40fP1579+5Vs2bNNGjQIIuLWL58uXr27KkaNWooV65cMhgMatWqVbJjo6KiZDAYUvzTvHlzi8+/a9cu1atXTy4uLrK3t1f58uUVEhKiuLi4JGNv376tbt26qXDhwsqTJ4/ef/99nTlzJtl5Z82apWzZsungwYMW1wQAAAAAyLzMfs90yZIlNWDAAJO2CRMm6D//+Y8iIyPl7u6u/PnzP1MRI0eO1OHDh5UjRw4VLlxYx48ff+oxFSpUUKNGjZK0v/HGGxade82aNWrSpIns7OzUrFkzubi4aN26derbt6927typZcuWmYxv166d1q5dq1atWsnBwUHz5s1T7dq1dfToUTk4OBjHXbx4UZ9++qkGDBggT09Pi2oCAAAAAGRuZofplOTNm1d58+Z9rjkmTJigwoULq0SJEgoLC5O/v/9Tj6lYsaKCg4Of67y3b99Wx44dZW1trdDQUHl5eUmSRowYoVq1amn58uVaunSpcbX7ypUrWrVqlYYNG6YvvvhCkuTt7a127drpp59+UtOmTY1zd+nSRYUKFTKOAwAAAAC8PMy+zTs9+fv7q2TJkjIYDC/0vMuXL9fVq1fVvHlzY5CWJDs7O40cOVKSNG3aNGP7uXPnJElVq1Y1tiX+PbFPkr7//nv9/PPPmjNnjrJnz56u1wAAAAAAePGee2U6o1y6dEkzZszQ9evXlSdPHr399tsqX768RXNs27ZNkhQQEJCkz9fXVw4ODtq1a5cePXokW1tbFSlSRJK0f/9+4zERERGSnux2Lj1Zve7Tp4/69u0rb2/vZ74+AAAAAEDmlWXD9ObNm7V582aTNj8/P82fP98Yep/mxIkTkqRSpUol6bOxsZGHh4eOHDmiyMhIlSlTRgUKFNAHH3ygYcOG6cyZM7KzszOer379+pKk7t27y8XFRSNGjHjOKwQAAAAAZFaZ4jZvSzg4OGjIkCHav3+/bt68qZs3bxqfsw4NDVXt2rV17949s+aKjo6WJDk5OSXbn9h+69YtY9v8+fMVFBSkDRs2aOnSpfLz89OWLVvk6Oio5cuXa+XKlZo9e7asrKzUs2dPubi4KHv27PLz89PRo0dTrGXmzJny8vKSl5eXrl69aua3AQAAAADICFkuTOfLl0/Dhw9XpUqV5OzsLGdnZ/n6+mrTpk3y9vbW6dOnNWvWrHQ7v5OTk2bMmKFLly7pxo0bWr9+vUqWLKkbN26oR48e6tatm2rUqKEBAwZo5syZCg4O1tq1a3X9+nUFBATo4cOHyc7bqVMnRUREKCIi4rk3dAMAAAAApK8Uw/SkSZP022+/vchanouNjY0+/vhjSVJ4eLhZxySuPCeuUP9bYruzs/NT5+rVq5fs7e01ZswY3bt3T9OmTVPr1q3Vq1cvBQQEaOrUqbpw4YIWL15sVm0AAAAAgMwrxTDdp08fbdiwwfjZ2to60z8HnLiia+5t3qVLl5YknTx5MklfbGyszp49KxsbGxUrVizVedavX69Fixbpu+++U44cOXTmzBnFxMSoUqVKxjGVK1eWJB05csSs2gAAAAAAmVeKYdrOzk6PHj0yfk5ISFBCQsILKepZ7dmzR5KeGn4T1apVS5JMfmmQKDw8XPfv35ePj49sbW1TnCM6OlqdO3dWhw4d9M4775j0/fP7S+n2bgAAAABA1pNimPbw8NDGjRt15coVY9uLfg90cg4cOKD4+Pgk7Vu3btWECRMkSa1atTLpi46O1vHjx/XXX3+ZtAcGBsrV1VVLly41vuJKehJ8Bw8eLEnq2rVrqvV88sknkqTx48cb24oXL67s2bPrp59+MratW7dOklSuXLmnXiMAAAAAIHNL8dVYnTt3Vp8+ffTaa68Z24KDgxUcHJzqhAaDQbGxsRYVsXr1aq1evVqSdPnyZUnS7t271a5dO0mSq6urvv76a0lSv379dOrUKfn4+Khw4cKSpN9//934zugRI0bIx8fHZP5Vq1YpKChIbdu21bx584ztuXLl0nfffafAwED5+fmpefPmcnFx0dq1a3XixAkFBgaqWbNmKda9ZcsWzZ49W+vWrTPZEdzR0VHdu3fXhAkTFBAQoBIlSmju3Llyc3NTixYtLPpuAAAAAACZT4phulevXsqXL5/Wr1+vS5cuafv27SpSpIjc3d3TvIhDhw5p/vz5Jm2RkZGKjIyUJBUtWtQYplu3bq1Vq1Zp3759+uWXX/T48WPlz59fTZs2VY8ePVSjRg2Lzt2oUSOFhYXpyy+/1IoVK/Tw4UOVKFFC33zzjXr16pXiavzdu3fVsWNHtWzZUg0aNEjSP3r0aMXHx2vRokUKDQ2Vj4+PJk+eLDs7O4vqAwAAAABkPoYEMx+EtrKyUnBwsL744ov0rumV5+XlZXLbOQAAAP7HfeD6jC5BkhQ1pn5GlwAgnaWWzcx+z/TQoUPl5+eXVjUBAAAAAJBlpXib978NHTo0PesAAAAAACDLMDtMJ9qzZ49mzZqlgwcP6tatW3JyclLlypUVFBSUZOMvAAAAAABeRhaF6cGDB2v06NFJ3jd96NAhzZkzRwMGDNCoUaPStEAAAAAAADIbs5+ZXrZsmUaNGqUiRYpo1qxZioyM1IMHDxQZGalZs2apSJEiGjt2rH788cf0rBcAAAAAgAxndpj+9ttvlT9/fu3bt0/t27eXu7u7bG1t5e7urvbt22vfvn3KmzevpkyZkp71AgAAAACQ4cwO04cPH1ZgYKBcXV2T7Xd1ddX//d//6dChQ2lVGwAAAAAAmZLZYTo2NlYODg6pjnFwcFBsbOxzFwUAAAAAQGZmdpguXry4fvrpJ8XHxyfbHx8fr59//lnFixdPs+IAAAAAAMiMzA7TLVq00LFjx/TBBx/o1KlTJn1nzpxRYGCgjh49qhYtWqR5kQAAAAAAZCZmvxqrX79+2rBhg9avX69ffvlFr732mgoWLKjLly/r4sWLio+PV/Xq1dWvX7/0rBcAAAAAgAxn9sp09uzZtXnzZn355Zfy8PDQn3/+qX379unChQvy8PDQl19+qa1btyp79uzpWS8AAAAAABnO7JVpScqWLZs+//xzff7557p7966io6Pl5OSkHDlypFd9AAAAAABkOhaF6X/KkSMHIRoAAAAA8Eoy+zZvAAAAAADwBGEaAAAAAAALEaYBAAAAALAQYRoAAAAAAAsRpgEAAAAAsBBhGgAAAAAACz3Tq7EeP36s48eP69atW3JyclKZMmWULVu2tK4NAAAAAIBMyaKV6du3b6tLly5ydnZWxYoV5efnJ09PTzk7O6tLly66detWOpUJAAAAAEDmYfbK9O3bt1WtWjUdOXJEOXPmVI0aNVSwYEH99ddfOnTokGbOnKkdO3Zo165dypUrV3rWDAAAAABAhjJ7ZXr06NE6cuSIunbtqnPnzik0NFRLlixRaGiozp07p+7du+vo0aMaPXp0etYLAAAAAECGMztMr1y5Um+99ZamTJkiZ2dnkz4nJyd9++23evvtt7VixYq0rhEAAAAAgEzF7DB97tw5+fn5pTqmZs2aunDhwvPWBAAAAABApmZ2mHZ0dNTff/+d6pirV6/KwcHhuYsCAAAAACAzMztMV6lSRcuWLdOpU6eS7T9z5ox+/PFHValSJc2KAwAAAAAgMzJ7N+/+/furbt26qlKlinr27Cl/f38VLFhQly9fVmhoqL799lvdvXtXn376aXrWCwAAAABAhjM7TNeuXVtTp05V7969NWrUKI0aNcrYl5CQoGzZsmny5Ml655130qVQADBLsFNGV/BEcHRGVwAAAIB0ZHaYlqTOnTvrvffe08KFC3Xw4EFFR0fLyclJnp6eatWqlYoWLZpedQIAAAAAkGlYFKYlqUiRIho0aFB61AIAAAAAQJZg9gZkAAAAAADgiRRXpsPDwyVJVatWlZ2dnfGzOXx9fZ+/MgAAAAAAMqkUw7Sfn58MBoOOHTumUqVKGT+bIy4uLs0KBAAAAAAgs0kxTH/xxRcyGAxydXU1+QwAAAAAwKsuxTAdHByc6mcAAAAAAF5VZm9Adv78ed2+fTvVMXfu3NH58+efuygAAAAAADIzs8O0h4eHJk6cmOqYSZMmycPD47mLAgAAAAAgMzP7PdMJCQlKSEhIz1qANPXm/DczugT90faPjC4BAAAAQDowO0yb4/Lly3J0dEzLKYFn9sdZHjkAAAAAkD5SDdMLFiww+Xzo0KEkbdKTV2GdP39e33//vd58M+NXAwEAAAAASE+phul27doZX4dlMBi0Zs0arVmzJsm4xNu/HRwcNHTo0HQoEwAAAACAzCPVMD137lxJT8Jy+/bt1ahRI33wwQdJxllbWytPnjx6++235ezsnC6FAgAAAACQWaQaptu2bWv8+/z589WoUSO1adMm3YsCAAAAACAzM/vVWNu3b0+XIL18+XL17NlTNWrUUK5cuWQwGNSqVatUj9m1a5fq1asnFxcX2dvbq3z58goJCVFcXJzF5z969KiaNm2qfPnyyc7OTqVLl9bQoUP14MGDJGNjYmI0ePBgeXh4yMnJSf7+/jpw4ECy827ZskUGg0E//fSTxTUBAAAAADK3NN3N+1mMHDlShw8fVo4cOVS4cGEdP3481fFr1qxRkyZNZGdnp2bNmsnFxUXr1q1T3759tXPnTi1btszsc+/du1e1atXS48ePFRgYKDc3N23btk3Dhw/X1q1btXXrVtna2hrHDxw4UBMmTFCTJk1UuHBhLVy4UP7+/jp+/LgKFixoHHf37l117NhRLVu2VIMGDSz/UoBMKDO8akzidWMAAADIHCwK0/fu3dPUqVO1ceNGXbx4UY8ePUoyxmAw6MyZM2bPOWHCBBUuXFglSpRQWFiY/P39Uxx7+/ZtdezYUdbW1goNDZWXl5ckacSIEapVq5aWL1+upUuXqnnz5k89b1xcnIKCgnT//n2tWbNGDRs2lCTFx8eradOmWrFihSZMmKCBAwdKevLc+IwZMxQUFKQ5c+ZIkho3biw/Pz8tXLhQn332mXHugQMH6sGDB5o4caLZ3wOQ2fGqMQAAAOB/zL7N+9atW/L29taAAQMUERGhEydO6ObNm7py5YqioqIUFRWlmJgYxcfHW1SAv7+/SpYsadw1PDXLly/X1atX1bx5c2OQliQ7OzuNHDlSkjRt2jSzzhsWFqZjx47J19fXGKQlycrKSl999ZUkafr06cadyq9evar79++ratWqxrGJfz937pyx7ddff9XUqVM1efJk5cmTx6xaAAAAAABZi9kr0yNHjtTRo0c1e/ZstWvXTtbW1urbt6+GDBmivXv3qkePHnJ0dNTGjRvTrdht27ZJkgICApL0+fr6ysHBQbt27dKjR49Mbs+2dK5ixYqpVKlSOnnypCIjI1W8eHG5urrK3t5e+/fvN46LiIiQJBUtWlSS9ODBA3Xo0EEffvihAgMDn+0iATwX94eLM7oESVJURhcAAACAdGX2yvTatWvl6+uroKAgk1Vkg8Ggt956Sz///LOOHz+uL7/8Ml0KlaQTJ05IkkqVKpWkz8bGRh4eHoqNjVVkZORzzSVJJUuWlCSdPHlS0pMV606dOmn27Nlq2rSp+vbtq8aNGytXrlxq2bKlJGnIkCG6fv26pkyZYvnFAQAAAACyDLPD9IULF1S5cuX/HWhlZfLMdL58+fTee+9p6dKlaVvhP0RHR0uSnJycku1PbL9161a6zDV27FgNGDBAv/32m2bPnq1y5cppy5YtKlSokH777TeFhIRo4sSJyps3r4KDg1WwYEHZ2NiocuXK2rlzZ6r1zJw5U15eXvLy8tLVq1efWj8AAAAAIOOYfZu3g4ODrKz+l72dnJx0+fJlkzH58+fXxYsX0666TMbW1lajR4/W6NGjTdpjYmIUFBSkgIAAtWrVSiEhIRo2bJiGDh2qatWq6csvv1RAQIBOnz6t/PnzJzt3p06d1KlTJ0kyeR4czy4z3O4bldEFAAAAAEgXZodpNzc3Xbhwwfi5bNmyCg8PV3x8vDFk79ixQwUKFEj7Kv+/xNXixFXlf0tsd3Z2fqFzDR8+XBcvXtSmTZskSePGjVPt2rUVHBwsSSpdurTc3d01ZcoUDR8+/KnzAQAAAAAyN7PDdM2aNfXjjz8qISFBBoNBzZo1U69evVSvXj29//77Cg0N1Z49e9S1a9d0K7Z06dKKiIjQyZMnTW45l6TY2FidPXtWNjY2KlasmFlzSf97JvrfTp06JSnlZ6oTHTp0SGPHjtW0adNUqFAh3b59W5cuXTI+Ry1JRYoUkaurq44cOfLUuoDMKjOs9Eus9gMAACBzMPuZ6bZt26pRo0b6888/JUldunRRo0aNtGnTJvXs2VMrVqyQj4+P8RVV6aFWrVqSpA0bNiTpCw8P1/379+Xj4/PUnbyfNldkZKROnjypokWLphrMY2NjFRQUJD8/P3388ccmff9+B/fDhw+fWhMAAAAAIGswO0xXqlRJ06ZNk5ubm6Qnu2evXLlS+/bt05IlS7R7926FhYWZdVv0swoMDJSrq6uWLl1qfC2V9CSoDh48WJKSrIzfv39fx48f1/nz503aa9asqTJlyig8PFxr1641tsfHx2vAgAGSnvzCILX3X48ZM0anT5/Wd999Z2zLlSuXChUqpA0bNig2NlbSk3da37lzR+XKlXvGKwcAAAAAZCZm3+adksqVK5vccn316lXlzZvX7ONXr16t1atXS5JxQ7Pdu3erXbt2kiRXV1d9/fXXkp4E1e+++06BgYHy8/NT8+bN5eLiorVr1+rEiRMKDAxUs2bNTOb/7bff5O/vr5o1ayo0NNTYbm1trblz56pWrVoKDAxUYGCgihQpoq1btyoiIkLVqlVT3759U6z76NGjGjFihMaPHy93d3eTvs8++0y9e/dWjRo15O3trUWLFilHjhzq3r272d8LAAAAACDzeu4wnSg6Olpjx47V5MmTdfv2bbOPO3TokObPn2/SFhkZaXxXdNGiRY1hWpIaNWqksLAwffnll1qxYoUePnyoEiVK6JtvvlGvXr1SXUn+N29vb+3bt09Dhw7Vpk2bdOfOHRUtWlRffPGFBg4cmOLt4nFxcWrfvr28vb2TDcg9e/bU7du3NX36dB04cEAVKlTQhAkTUtzJGwAAAACQtRgSEhISnjbo3Llz2r9/v7Jly6aqVauahMKHDx9qwoQJ+vrrr3Xz5k05ODjo7t276Vr0y87Ly8vkNnY8G/eB6zO6BEWNqZ/RJaSZzPB9Sk//TrNKnQCAZ8e/9QBelNSy2VOfme7Vq5eKFy+u//u//1OjRo3k7u6uqVOnSpJCQ0NVunRpDR48WPfv31fv3r2NK8oAAAAAALysUr3Ne/78+Zo8ebKsrKxUpkwZSdLx48fVq1cvOTo6qnPnzoqLi1Pnzp01ePBgvfbaay+kaAAAAAAAMlKqYXrevHnKnj27tm/frrffflvSk1dQ1alTRx06dFDhwoW1bt06vfnmmy+kWAAAAAAAMoNUb/P+/fff1bhxY2OQliRfX181atRICQkJmjNnDkEaAAAAAPDKSTVMR0dHq0SJEknaS5YsKUkmIRsAAAAAgFdFqmE6Pj5e2bJlS9Ke2GZvb58+VQEAAAAAkIk9dTdvS97bDAAAAADAqyDVDcgkKTg4WMHBwcn2WVtbJ2kzGAyKjY197sIAAAAAAMisnhqmExISLJrQ0vEAAAAAAGQ1qYbp+Pj4F1UHAAAAAABZxlNXpgEAAPCcgp0yuoIngqMzugIAeGk8dQMyAAAAAABgijANAAAAAICFuM0bAAAgnbk/XJzRJUiSojK6AAB4ibAyDQAAAACAhQjTAAAAAABYiDANAAAAAICFCNMAAAAAAFiIMA0AAAAAgIUI0wAAAAAAWIgwDQAAAACAhQjTAAAAAABYiDANAAAAAICFCNMAAAAAAFjIJqMLAABkYsFOGV3BE8HRGV0BAACACVamAQAAAACwEGEaAAAAAAALEaYBAAAAALAQYRoAAAAAAAsRpgEAAAAAsBBhGgAAAAAAC/FqLABAitwfLs7oEiRJURldAAAAwL+wMg0AAAAAgIUI0wAAAAAAWIgwDQAAAACAhQjTAAAAAABYiDANAAAAAICFCNMAAAAAAFiIMA0AAAAAgIUI0wAAAAAAWIgwDQAAAACAhQjTAAAAAABYiDANAAAAAICFCNMAAAAAAFiIMA0AAAAAgIUI0wAAAAAAWCjLhml3d3cZDIZk/xQoUMCiuf7880+1b99er732mmxtbeXu7q4+ffro5s2bScbGx8crJCREpUuXVs6cOeXt7a0tW7YkO+/Ro0dla2uryZMnP9M1AgAAAAAyJ5uMLuB5ODk5qU+fPknac+TIYfYcZ86ckY+Pj/7++2998MEHev311/Xbb79p4sSJ2rBhg3bu3Kk8efIYx0+ePFl9+/ZVnTp11KBBAy1btkzvvfeeIiIiVKFCBeO4uLg4tW/fXt7e3urevftzXScAAAAAIHPJ0mHa2dlZwcHBzzVHt27d9Pfff2vSpEnq2bOnsb1fv36aMGGCBg0apOnTpxvbp02bJn9/f23atEmS1KNHD5UoUUIzZszQ1KlTjeMmTJig33//XYcPH5bBYHiuGgEAAID08ub8NzO6BEnSH23/yOgSAItk6TD9vM6cOaNNmzbJ3d09yerxsGHDNHPmTC1cuFDjx4+Xo6OjJOncuXP64IMPjOM8PDzk6uqqc+fOGdtOnTqlL774QsOHD1fJkiVfzMUAAAAAz+CPs+czugQgS8rSYfrRo0f6/vvvdf78eTk6Oqp8+fLy9fWVtbW1Wcdv375dklS3bl1ZWZk+Pp4zZ05Vq1ZNmzZt0p49e1S7dm1JUpEiRbR//37juHPnzunatWsqWrSoJCkhIUEdOnTQm2++qb59+6bFZQIAAAAAMpksHaYvX76s1q1bm7R5eHho7ty5qlmz5lOPP3HihCSpVKlSyfaXLFlSmzZt0smTJ41hukuXLurbt6/ee+89lStXTsuXL5fBYFCnTp0kSVOmTNHevXt18OBBs0M9AAAAACBrybK7eQcFBWnr1q26fPmy7t27pz/++EOdO3dWVFSU3nvvPR0+fPipc0RHR0t6spFZchLbb926ZWzr1auXxo0bp9OnT2v69OnKmzev1q9fr4oVKyoqKkqff/65hgwZorJly2ratGlyd3eXtbW1Xn/9da1ZsybFWmbOnCkvLy95eXnp6tWrFnwTAAAAAIAXLcuuTA8dOtTk8xtvvKHp06crR44cGj9+vIKDg7Vq1ao0P6+VlZU+/fRTffrpp0n6OnbsqBIlSmjgwIFavXq1unXrpq5du6pJkyaaPn26mjRpooMHD+rNN5Nu8tCpUyfj6raXl1ea1w0AAPA0bET1anJ/uDijS5AkRWV0AYCFsmyYTkmXLl00fvx4hYeHP3Vs4spz4gr1vyW2Ozs7P3WuWbNmKTQ0VL/99ptsbGw0btw4lShRQlOmTJHBYNBbb72lTZs2ady4cVqwYIH5FwQAAPCCsBEVAJjvpQvTefPmlSTdu3fvqWNLly4tSTp58mSy/adOnZKU8jPViS5evKhPP/1UAwYMkKenpyTp2LFjeuedd4yvxXJ0dFTp0qV15MgR8y4EAAAAQNYUnPxjpC+2huQXDJF2suwz0ynZs2ePJKlYsWJPHevv7y9J2rRpk+Lj40367ty5o507d8rBwUFvvfVWqvN06dJFhQoV0hdffGHS/ujRI5PPDx8+fGpNAAAAAIDML0uG6WPHjiW78hwVFaUePXpIklq1amVsf/z4sY4fP64zZ86YjC9evLjq1q2rqKgoTZkyxaRv6NChunfvnlq3bm18x3Ryvv/+e/3888+aM2eOsmfPbmwvW7aswsPDdfv2bUlSZGSkjh49qnLlyll+wQAAAACATCVL3ub9ww8/aPz48fL19VXRokWVM2dOnTlzRuvXr9fDhw9Vr149kw3CLl68qDJlyqho0aKKiooymWvq1Kny8fFRr169tHXrVpUpU0Z79+7V9u3bVapUKX355Zcp1nHlyhX16dNHffv2lbe3t0nfZ599pg8++EA+Pj6qW7euVq5cKUnq379/2n0RAAAAAIAMkSXDtL+/v06cOKGDBw9q586dunfvnpydnVW9enW1bt1arVu3Nj6r/DTFixdXRESEvvjiC23YsEE///yzChYsqN69e2vo0KHKnTt3isd2795dLi4uGjFiRJK+hg0basaMGRo7dqwmT56skiVLauXKlcnu5A0AAJAZsKszkDYyw39LURldwCsgS4bpmjVrqmbNmmaPd3d3V0JCQor9bm5umjt3rsV1LF++PNX+f77uCgAAAADw8siSz0wDAAAAAJCRCNMAAAAAAFiIMA0AAAAAgIUI0wAAAAAAWIgwDQAAAACAhQjTAAAAAABYiDANAAAAAICFCNMAAAAAAFiIMA0AAAAAgIUI0wAAAAAAWIgwDQAAAACAhQjTAAAAAABYiDANAAAAAICFCNMAAAAAAFiIMA0AAAAAgIUI0wAAAAAAWIgwDQAAAACAhQjTAAAAAABYyCajCwBeecFOGV3BE8HRGV0BAAAAkGUQpoEM5v5wcUaXIEmKyugCAAAAgCyEMA0AAAAg8+NuPmQyhGkAAAAAmR5386U994HrM7oERY2pn9ElPDM2IAMAAAAAwEKEaQAAAAAALESYBgAAAADAQjwzDQDAi5QZNtBh8xwAAJ4bYRoAMkBm2PBDytqbfmRVmWEDnaiMLgAAgJcAt3kDAAAAAGAhVqYBAFlfZrh1Wnqpbp/ODHdPcOcEACAzI0wDALK8zHDrtMTt0wAAvEq4zRsAAAAAAAsRpgEAAAAAsBBhGgAAAAAACxGmAQAAAACwEGEaAAAAAAALEaYBAAAAALAQYRoAAAAAAAsRpgEAAAAAsBBhGgAAAAAAC9lkdAHIetwHrs/oEhQ1pn5GlwAAyAyCnTK6Aik4OqMrAABkAMI0AADIstwfLs7oEhSV0QUAADIEt3kDAAAAAGAhwjQAAAAAABYiTAMAAAAAYCGemQYAAADSQWbYtFVi41YgvWTZlek///xT7du312uvvSZbW1u5u7urT58+unnzpkXz3LhxQ3369JG7u7tsbW312muvqX379vrzzz+THb9o0SK9+eabypEjh8qXL6+lS5cmO+7KlStydXXVp59+avG1AQAAAAAytywZps+cOaPKlStr7ty5qlq1qvr27atixYpp4sSJevvtt3X9+nWz5rl+/brefvttTZw4UcWLF1ffvn1VtWpVzZ07V5UrV1ZkZKTJ+LVr16pVq1ZydHRUly5dFBcXp48++kjr1yf9rWP37t3l4uKiESNGpMk1AwAAAAAyjywZprt166a///5bkyZN0urVqzVmzBht27ZNffv21YkTJzRo0CCz5vnPf/6jkydPql+/ftq6davGjBmj1atXa+LEifr777/VrVs3k/HTpk1TyZIltWPHDn399dfatWuXnJ2dNWXKFJNxy5cv18qVKzV79mzZ29un2XUDAAAAADKHLBemz5w5o02bNsnd3V3du3c36Rs2bJgcHR21cOFC3bt3L9V57t69q4ULF8rR0VHBwcEmfT169FDRokW1ceNGk9Xpc+fOqVKlSrKxefKouZOTk0qVKqVz584Zx9y4cUM9evRQt27dVKNGjee8WgAAAABAZpTlwvT27dslSXXr1pWVlWn5OXPmVLVq1XT//n3t2bMn1Xn27NmjBw8eqFq1asqZM6dJn5WVld59912T80lSkSJFdOjQIcXHx0uSbt++rZMnT6po0aLGMb169ZK9vb3GjBnz7BcJAAAAAMjUslyYPnHihCSpVKlSyfaXLFlSknTy5Mk0n6dLly46ceKEatSoof79+8vHx0e3bt1S165dJUnr16/XokWL9N133ylHjhwWXBUAAAAAICvJcmE6Ojpa0pNbrJOT2H7r1q00n6dRo0aaN2+ebt26palTp8pgMGjhwoV6//33FR0drc6dO6tDhw565513tGLFCr3++uuytraWu7u7Zs6cacllAgAAAAAyMd4zbaG2bduqbdu2Sdo/+eQTSdL48eN14MAB/d///Z+aNGmiKVOmaOXKlercubMKFSqk+vWTf8/fzJkzjYH7+PHj8vLySr+LeE6uaTDH1atXlTdv3mc+3str6FPHUKdlnlYrdVqGOv+HOk09b61ZpU7p1fk3NKvUKb08/y1Rp2Wo83+yyr+hWaVO6cX8G5qRoqKiUuzLcmE6ccU4cWX53xLbnZ2dX8g8krRlyxbNnj1b69atk5OTk8aPH6+cOXNq3rx5cnR0VK1atbRp0yaNHTs2xTDdqVMnderU6annell4eXkpIiIio8t4KupMW9SZtqgzbVFn2ssqtVJn2qLOtEWdaYs6015WqjWtZbnbvEuXLi0p5WeiT506JSnlZ6HTep67d++qY8eOatmypRo0aCBJOnbsmEqXLi1HR0dJksFgkKenp44cOZLqXAAAAACArCHLhWl/f39J0qZNm4y7aie6c+eOdu7cKQcHB7311lupzvPWW2/J3t5eO3fu1J07d0z64uPjtWnTJpPzpWTgwIF68OCBJk6caNL+6NEjk88PHz5MdR4AAAAAQNaR5cJ08eLFVbduXUVFRWnKlCkmfUOHDtW9e/fUunVr46qw9OQZ5OPHj5uMzZEjh1q3bq179+4lec/05MmTFRUVpXfffVfFihVLsZZff/1VU6dO1eTJk5UnTx5je9myZXXkyBHjO6qjo6P166+/qly5cs962S+drHJLO3WmLepMW9SZtqgz7WWVWqkzbVFn2qLOtEWdaS8r1ZrWDAkJCQkZXYSlzpw5Ix8fH/39/9g776gorvcPf2aWJkgRkCZIsSB2BBEFBSs2FDs2BBV7b7EC1kRjjF1jwRJ71IiIig2wF+w9VkQUK0VstPf3hz/m67pLiZG5uzrPOXvizNw9PNm5U2573+fP0bZtWzg5OeHMmTOIiYlBxYoVcfLkSbnGLcdxAIAv/1dfvXqFevXq4Z9//kGjRo3g5uaGmzdvIiIiAmZmZjh58iTKlSun1OH9+/eoUaMGqlevju3bt8sdu3TpElxcXGBjY4P27dvj4MGDuHbtGqKiotCyZctv/GtISEhISEhISEhISEhIiI1aNqYBIDExESEhIdi/fz9evXoFS0tLtGvXDqGhoShVqpRc2fwa0wDw+vVrTJ06Fbt27cLTp09hYmKCFi1aYNq0abC2ts73748ZMwZr1qzBjRs3YG5urnB8165dmDx5Mv755x/Y2NhgwoQJ6Nu373/8v5aQkJCQkJCQkJCQkJBQBdS2MS0hISEhISEhISEhISEhwQq1WzMtISEhISHxLUlJSUFiYiJrDQkJpUj1U0JCQkJ1kRrTEsXG+vXrceXKlQLLXLt2DevXrxfJSEIssrKyWCsUidzcXCxatAju7u4wNDSEhoaGcOzixYsYNGhQvunzJNSbjIwMjB49GhYWFjA1NYW9vb1w7MyZM2jZsiUuXLjA0FDiR0aqnxIS344rV65g/PjxaNu2LZo0aSLsf/jwIbZt24aUlBSGdhLqjtSYlig2AgMDsWvXrgLLREREICgoSByhQnj27Blrhf9EVlYWLl68iNu3b7NWQZkyZfDTTz/h7t27rFXyJTMzE02bNsWIESNw79496Ovry8VVsLe3R3h4ODZu3MjQsmi8evUKf//9N6Kjo5GTk8NaR+VJS0tD3bp18fvvv8PKygpOTk5y575atWo4duwYNm/ezNDyE+rSYGrTpg327dunNDaJqjFs2DDcvHmTtUa+qFP9LIhbt27h999/xx9//IG0tDTWOgJZWVnYv38/fv/9d0yfPl3Y/+HDBzx//lwh7SoL1OW6VwdCQkJQq1YtzJkzB5GRkYiJiRGO5ebmomvXrtiwYQNDw/8xb948vH79mrVGgezcuVN6z/gSkpAoJjiOo6lTpxZYZurUqSSTyUQyKhgtLS3q3LkzHT58mLVKgWzdupU6depEr169EvbdvXuXKlSoQDzPE8/z1K5dO8rKymLmaGJiQhzHEc/z1KRJE9q+fTtlZ2cz81HGjBkzhDqak5NDoaGhxPO8XJmmTZtSnTp1GBkqsnTpUnJzc5M79/Hx8WRqaiqcezc3N8rIyGBoSRQUFFTop0+fPjRy5EhasWIFPX36VFS/sWPHEsdxtG7dOiIiCgsLUzj3rVq1ImdnZ1G9lMFxHLm5udHq1avp7du3rHXyJe96t7W1pRkzZoh+Tv8Nea7169enDRs20MePH1kryaFO9ZPo03PcwsJC7r508OBB0tHREe5LDg4O9PLlS4aWn9i3bx9ZWVkRz/NCPcjj1KlTxPM8bdq0iaHhJ9Tlun/w4AFFRUXJPXOysrIoJCSEqlevTnXr1qWdO3cy89u8eTNxHEfNmzeny5cv08SJExWuJTc3N2rSpAkjQ3k4jqMSJUpQz5496fjx46x1lMJxHJUpU4amTJlCCQkJrHVUAqkxLVFsFKUx3b17dzI1NRXJqGCqVq0qPFwrVKhAc+fOVYmH/5f4+PhQtWrV5Pa1bduWOI6jxo0bU82aNYnneVqxYgUjQ6KPHz/Sxo0bycvLS/hNLSwsaOLEifTgwQNmXp/j5OREnp6ewrayF9Z+/fqRpaWl2Gr54uXlpdC4b9iwIclkMurTpw+1bt2aOI6juXPnMjL8RN45z3th/fLz5X4tLS2aM2eOaH7lypWj5s2bC9vKzv2gQYPIzMxMNKf8aN26NWloaBDP82RoaEhDhgyhK1eusNZS4Pz58xQcHEz6+vrCOe3QoQMdOHCAtZoC27dvp6ZNmwp11MTEhEaPHk23bt1irUZE6lU/iYjc3d3J29tbbp+rqyuVKFGCpk+fToMHDyaO42jKlCmMDD9x7tw50tbWpjJlytCCBQuoe/fuCr9ruXLlqFOnTowM/4e6XPeBgYFkbGws13kfGhoqd3/X0NCgU6dOMfGrW7cuVahQQegwU3Yt9erVi2xtbRnYKTJ37lyqWLGi8JysWrUqLVq0iFJTU1mrCQwZMoRKlSolnNvWrVtTZGQk5ebmslZjhtSYlvimfD7yxHEcOTs7Kx2VCggIIG9vb5LJZNSmTRvW2gInTpygXr16ka6uLnEcRzo6OtStWzeKi4tjrSZgY2NDvXv3FrbT0tJIU1OTunTpQkREmZmZVLlyZapbty4rRTlu375No0aNIlNTU+I4jmQyGbVo0YJ27dpFOTk5zLx0dHRozJgxwrayh+z48eNJW1tbbLV8sbKyouDgYGH7xYsXxPM89evXT9jn5uZGtWrVYqEncP/+fWrXrh2VLl2aZs6cSXFxcXTr1i2Ki4ujGTNmUOnSpal9+/Z09uxZWrFiBdnY2BDP87Rr1y5R/LS1tWncuHHCtrJzP27cOJU594mJiRQaGko2NjbCS1a9evVo3bp19OHDB9Z6crx584aWL19OtWrVElzLlStHs2fPpufPn7PWk+P+/fs0fvx4srS0FFwbNmxIW7dupczMTGZe6lY/LSwsaODAgcL248ePieM4Gj16tLCvcePGCp3AYtOmTRsqVaqUMGtC2e/q7+9PFStWZKGngDpc946OjnKdDzk5OVS6dGlycnKixMREOnfuHBkaGlLnzp2Z+JUsWZKGDBkibCs75xMmTCAdHR2x1QokJiaG/P39hdkdurq6FBQURKdPn2atRkRE79+/pzVr1pC7u7tQN21sbGjq1KmUlJTEWk90pMa0xDeloNEnZaNTdevWpXv37rHWViA1NZUWLlwoN1rt5ORE8+fPp9evXzN109HRoUmTJgnb+/fvJ47j6O+//xb2DR8+XGVGLfLIG6329vYWftMyZcpQaGgok5uvkZER9e3bV9hW9pDt1q0bmZubi62WL9ra2nLnfteuXcTzPO3bt0/YN3r0aDIxMWGhJzBv3jwyNTWlx48fKz3+6NEjMjExod9//52IiBISEkhPT0+0qXampqYUGBgobCs79506daIyZcqI4lNUcnJyaPfu3XKjVsbGxjRixAi6ceMGaz0F4uPjhdFqnudJW1ubunTpQjExMazV5MjKylIYrTYzM6OffvqJ7t69K7qPutXPL+9LW7duJZ7n5c7zuHHjyNDQUHy5zzAxMaGgoCBhW9nvOnbsWNLX1xdbrUBU+bo3MjKS65Q+f/48cRxHixcvFvYFBASQvb09Cz3S09OjYcOGCdvKznnv3r3JyMhIbLUi8fLlS4XR6ho1atCyZcvozZs3rPWIiOjq1atyo9Wamprk5+cn917yvSM1piW+KQ8fPqSHDx/SgwcPiOM4GjlypLDv809iYiLzdZ1F5cSJExQYGEi6urpCD2GvXr3o3LlzTHxKly4t19M6YcIE4nmeXrx4Iez76aefqESJEiz0CiQ1NZUWLFhAZcqUUZjmO3z4cFF72729vals2bL5Tv9KTU0lExMT8vX1Fc2pMCwtLal///7C9siRI0lDQ4PS0tKEfWPGjCE9PT0WegKVKlWSG6lSxoABA8jJyUnY7tKlCxkbGxe3GhERtWjRgkxNTSk9PZ2IFM/9kydPSE9PT5jtoYokJiZSWFgYWVhYCA1ALy8v+uuvv1irKXDixAmytraW60itWrWqXAegqnDmzBk5V5lMRq1ataL4+HjRHNStflpbW8s1/gcMGEDa2tr07t07Yd+IESOYN1K1tbVp7NixwrayhtXgwYOZ3z8LQtWuez09PbnfdOHChcTzvFwjf+LEicxGfmvWrEmurq7C9pfnPCcnhypWrEgeHh4s9P4VBw4cIGtra+G8lyxZkgYPHkyJiYms1Yjo02j12rVr5Rzt7Ozo119/VZv3/a9FiuYt8U2xtbWFra0t7OzsEBoaCj8/P2Hf5x9ra2vo6emx1i0SpqamKFWqFHR0dEBE+PjxI9avX486derAz89P9MiLFSpUwL59+/Dx40dkZmZi27ZtqF69OkxNTYUyCQkJMDMzE9WrIE6fPo2goCBYWVlh5MiRePv2LYYNG4ZLly4hPDwcjo6OWLRoEUaMGCGaU79+/ZCYmIju3bsjPT1d7lhqaioCAwORkpKCAQMGiOZUGE5OToiMjMSrV6+QmpqKLVu2oHbt2jAwMBDKPHz4EBYWFgwtPzkYGhoWWMbIyAgPHjwQtu3s7JCRkVHcagCA4cOH49WrV2jZsqVCVOebN2+iU6dO+PDhA4YNGyaKz9dw48YNXLlyBa9evQIRwcTEBMeOHUOXLl3g4uKChw8fMvX78OED1q1bh3r16qF+/fpISkpCjRo1MH/+fPTs2RN3795Fhw4dsGjRIqaeecTFxaFbt27w8vJCUlISSpcujREjRsDT0xN79+6Fu7s7tm7dKoqLutXPmjVrYvfu3bh27Rru3r2LrVu3wtPTEyVKlBDKPHz4EJaWlgwtP2WZuH79eoFlLl26BAcHB5GM/j2qdt1bW1vLpUDdu3cvTE1N4eTkJOx7/vy53DNKTDp37owLFy7gt99+U3p81qxZuHv3Lrp16yayWdF58OABJk6ciICAACQlJUFTUxNt27aFmZkZli5disqVK+PIkSNMHd++fYv169dj0aJFSEpKAhGhRo0aePXqFcaNG4dKlSrh0qVLTB2LFbZteYnvGTs7Oxo0aBBrja8iMzOTNm/eTN7e3sJ0dUdHR/r9998pJSWFYmJiqEWLFsRxHPn7+4vqtnbtWuI4jmxsbMjBwYF4nqcFCxbIlalQoQK1bNlSVK8vSU9PpyVLllD16tWF37BWrVq0atUquRELIqLs7Gxq0qSJ6MHo8tb2a2lpkbm5OfE8Ty4uLqSjo0Mcx8nNAFAFIiIihLX8JUuWJJ7n6c8//5QrY21tTe3bt2dk+D8HFxeXAsvUqlVLbppq//79qXTp0sWtJhAWFiaMkmprawuBqPLqqpgB0YrKs2fP6Oeffxaue57nqWnTpvT3339TTk4O3b9/nwYOHEg8z1OLFi2YOF6/fp2GDRtGpUqVIp7nSUdHh3r06EEnTpyQK/fo0SOqVKkS2dnZMfEkInr9+jXNmzePKlWqJJx3T09P2rhxo9ya6TNnzlCZMmWoUqVKormpU/08evQoyWQyoU7yPE979+4VjmdnZ1Pp0qWpW7duDC0/jTrLZDI6duwYESmOUu7du5c4jqMJEyawUlSKKl/3o0ePJp7nafTo0TRp0iSSyWRyy6eIiBo0aEC1a9cW1SuPd+/ekbOzs5Dpok6dOoKvm5ubsA6dZfYTZeTk5NDOnTvJx8eHZDIZcRxHtra2NHPmTHr27BkREeXm5tLWrVvJyMiIWZyUCxcuUP/+/cnAwIA4jiM9PT0KDg6mixcvEtGnGBqzZ88mHR0dql+/PhNHMZAa0xLFRsmSJVXuoVQYd+7cobFjx1Lp0qWJ53nS0NCg9u3b06FDh5SW79ChA5O1NhMmTCATExMyMTGhYcOGyUVRPHHiBHEcR7/++qvoXnn07t1baOyVKFGCevXqRWfOnCnwOzNmzFCYcicGa9asESKg503trFq1KoWHh4vuUhT++OMPcnFxIRcXF5o3b57csZiYGDIyMqI//viDkd0nhg8fThzHUffu3RVSZyQkJFC3bt2I53kaPny4sN/V1ZXq1asnqueRI0fIz8+PLCwsSFNTk0xNTal169Yqlx7v0KFD1KlTJ9LS0iKO48jY2JhGjRpFd+7cUVo+7/oTk/Xr15Onp6dwHTk4ONDs2bPllp98SWhoKJPUiEePHqUePXpQiRIliOM40tfXp4EDBxYYLXny5MmkqakpoqX61E+iTymn2rdvTx06dFBIhXT06FGqWbMm8yUIjx8/JhMTEypRogSNGzeOOnfuTDzP0549e2jcuHGkp6dHVlZWBdZZMVGH6/7Zs2dUrlw54dlpbW0tFwPl2bNnpKmpKReMTmxSU1OpV69epKGhIbe8TCaTUUBAgLCcQhVISEigyZMnC+nb8jpIdu/enW+0bLGDEb59+5ZWrVpFtWvXFu73lStXpoULF8otOfucIUOGqOTSw2+F1JiWKDbq1KnDLILj19CoUSPhxmBlZVWkwFizZs1i0gAsiI8fP1JqairTnlaO46h8+fI0d+5cudyjBXH8+HEKCwsrZrP8effuHSUlJX33a3vEIC0tTYjmrKGhQba2tuTm5ka2trbCC42zs7Pw4H3y5AnVrVuXli1bxthc9ShfvrxwX6pduzatWbOG3r9/X+B3fv75Z+I4TiTDT+S9nLZu3ZqioqKKlCZl06ZNCimVxODzTrMlS5YUKZDPsmXLmI6iS3wbzp8/L9f4+zxYavny5VUm/ZS6XPdEn56dkZGRFBkZqdAwvX79Os2fP59u3rwputeXvHr1ivbv308bN26kPXv2qFx2ASISzrmpqSmNHTuW7t+/X+h3xD7vhoaGxPM8aWpqUqdOnYoUUJJV3RQLjoiI9VRzie+TTZs2oW/fvjh9+jSqV6/OWqdQeJ5Hw4YNMWjQIPj5+UEmkxX6nWvXruH8+fPo1auXCIbqw4EDB9CsWTPWGhIM+fjxI+bMmYN169bh/v37wn4HBwcEBARg3Lhx0NHRYWioHujq6qJr164YNGgQXFxcivSdxMRE3L9/H15eXsVs9z8mTpyI/v37w9bWVrS/+bV069YNAwcORP369VmrSDAgJycHUVFROHXqFF69egVDQ0O4u7ujbdu20NDQYK0HQH2ue4lvS926dTFo0CB07twZ2trarHWUYmNjg379+iE4OLjI8VnS09ORkpKiFs+Hr4J1a17i+yUuLo58fX2pZMmSNHr0aNqyZQvFxsZSXFycwkcVuHXrFmuFIvHgwQOKioqSG0HNysqikJAQql69OtWtW1dhmp3YNGzYkKZMmcLUoTDi4+Np6tSplJycrPT406dPaerUqcLaH1Vg27Zt1LBhw3xnTDx+/JgaNWpEO3bsENmsYNLT0+nx48cqM53u0KFDFBQUlO/vmJSUREFBQSqRwiklJYW1wnfHunXraP/+/aw18kWd6ifRp1F7BweHAu9LDg4OtGrVKpHN1Bd1ue6fP39OcXFx+d7b09LSKC4ujtnUeVX3U0dycnJYK6gcUmNaotj4Mtf058FJvvxIFJ3AwEAyNjaWm8YdGhoqN21NQ0ODTp06xcyxRIkSKr9evmvXrmRjY5PvdNTc3FwqW7Ys9ezZU2Sz/GnWrBk5OzsXWMbFxYVZ8Cl1oW3btuTo6FhgmUqVKjEP5CZRPMhkMhoxYgRrjXxRt/pZv379QlMLNWjQgMmUfoniZdiwYWRgYEBv375VejwjI4MMDAxo1KhRIpt9QtX9JL4PVGM+i8R3SUhICDiOY61RZB49elRoGZ7nYWBgwCzNAwCcOnUKjRs3Fqaj5ebmYunSpahUqRIOHDiA5ORkNGnSBL///rtoqVy+pEKFCkhMTGTyt4vKqVOn0LBhw3zrKMdxaNSoEY4ePSqyWf5cvXoVrVu3LrBM7dq1ERkZKZKRenLhwgU0adKkwDKenp44cOCASEaF8/z5c8THxyMlJQU5OTlKywQEBIhs9T8aNWpUaJm8+6eTkxPatWsHV1dXEcwUsbCwQG5uLpO/XRTUrX7evn0bHTt2LLBM9erVsX37dpGMCiY+Ph5nz57N91riOA5TpkxhYKaIql/3Bw8eRNOmTaGrq6v0uJ6eHpo1a4bo6Oh801MVJ6ru9yXTpk0rtMzn91EvLy9oaWmJYPY/1q9fX2iZzx0rVKggghVbpMa0RLERFhZWaJnc3FyVefG3s7MrcuPfwsIC7du3R2hoqFx+ZzF49uyZ3LqTS5cu4eXLlwgNDYW1tTWsra3Rtm1bHDt2TFSvz+nbty9CQ0Px6NEjlC1blplHQSQnJ8Pa2rrAMlZWVnj69KlIRoXz+vXrQvOHm5iY4OXLlyIZ5U9cXBx+/fVX4aVVWeOF4zhkZ2eL7vb8+XNYWVkVWMbc3BzPnz8XySh/srKyMGDAAKxfvz7fBiARgeM4pi/VsbGxAD6dU1ISiuXz/bt27cIvv/yCAQMGYMmSJWJqAgCaN2+OmJgY5Obmgud50f9+YahT/QSAtLQ0GBkZFVjGwMAAKSkp4gjlQ3p6Otq3b4+YmBildTQPVWhMq8t1n5iYCF9f3wLLODg4MOv4UXW/LwkLC5N7D/28nn65n+M4mJiYYNGiRejSpYtojoGBgf9qoKxKlSpYsmTJdx2jQmpMSzAhISEBq1atwpo1a5CcnMzkhfpLAgICkJCQgLi4OBgZGaFmzZowNzfHs2fPcOnSJaSmpsLb2xt6enq4evUqlixZgj179uDs2bMoXbq0aJ5ZWVlyN7ITJ04Io6h5WFtbM20E+vr64uDBg/Dw8MBPP/2E2rVrw8LCQukNmFVjW1dXFy9evCiwzIsXL1QqCIipqSnu3LlTYJk7d+4U+mJb3ERFRcHPzw85OTkoW7YsHB0dVSawDwAYGhoWOnMiMTERenp6Ihnlz5QpU7BmzRqUK1cO3bt3h42NjUr9lnl8+PAB/v7+uHnzJqZMmQJPT0/h/nns2DHMmDEDlStXxqJFi3D9+nWMHz8ey5cvh6urK4KCgkR1nTlzJtzd3dGnTx/8+uuvoneIFoY61U8AsLS0xJUrVwosc+XKFVGfk8oYO3Ysjhw5gvr16yMoKEhlryVAfa57juOQmZlZYJnMzMx8R9WLG1X3+5KYmBgsWLAAe/fuRUBAgMJ99M8//0SrVq3QrVs3XLhwAYsWLUKPHj1gZWUlWmN1zZo12LVrFyIiItCkSRMFx8OHD8PPzw8eHh64cOECtm3bBh8fH5w6dQo1atQQxVF0GE4xl/jByM7Oph07dsgloed5npo1a8ZajYg+BSArVaoUTZgwQSE9UkZGBv30009kbGxMt2/fppycHAoLCyOO42jkyJGiejo6OpKPj4+w3bx5czIzM5Mr07dvX4V9YvL5evmC1sqzyDGbR6NGjcjMzCzftDhpaWlkZmamUuv8OnfuTDo6OvmmGblx4wZpa2tTx44dRTaTx9XVlUqUKEHR0dFMPfKjdevWZGBgQE+fPlV6PCkpiQwMDFRi7bmNjQ05OjrSu3fvWKsUyOTJk8nGxibfPKOpqalkbW0tBCZ8+fIlmZiYiJ5bnOhTgMQaNWoQz/Oko6NDlSpVIm9vb2rYsKHcp1GjRqK7EalX/SQiCgoKIg0NDTp27JjS40ePHiWZTEa9evUSV+wLzM3NycXFRS0CKKnLde/q6kpOTk75Hs/NzSUnJyeqUaOGeFKfoep+X7Ju3ToqWbJkvinaLl26RHp6evTnn38SEdGVK1dIU1OT2rRpI5pjVFQUaWtr5xvEcd++faStrU179+4lok8BFXmep65du4rmKDZSY1qi2Ll37x6NHz+eLCwshEaUmZkZTZkyhR4+fMhaT8DPz6/QhpO3tze1a9dO2HZ2dqby5csXt5oco0ePJp7nafTo0TRp0iSSyWTUt29fuTINGjSg2rVri+r1Ob169aLAwMAifVixZcsW4jiO3N3d6fLly3LHLl26RHXq1CGe52nTpk2MDBU5e/YsaWhokImJCS1YsIBu375NGRkZdPv2bZo/fz6ZmJiQhoYGnT59mqmnjo4O03NbGNHR0UJe2YiICPrw4QMREX348IF27dpF5cqVI57nad++fYxNibS1tUXvsPsa7O3tafjw4QWWGT58ONnb2wvbPXr0IENDw+IVU8LnwRoL+rAKjqlO9ZPoU0e0rq6uUFejo6Pp2rVrFB0dTSNGjCBtbW3S1dWlGzduMPXU0dGhMWPGMHUoKupy3f/yyy/EcRwNHjxYoeH/7t07GjRoEPE8T7NmzZL8ikDNmjWpd+/eBZYJDAyUC0Tq5+dHpUuXLm41gXr16hXaMO7atatcR6mPjw9ZWVkVtxozpMa0RLGQlZVF27ZtoyZNmgij0HkjZhzHUXBwMGtFBYyNjWnixIkFlpk4cSIZGxsL2wMHDiQdHZ3iVpPj2bNnVK5cOeGFz9raWi4lybNnz0hTU5NGjx4tqpc60qtXL+Gl2dLSklxdXcnS0lIYVWc9kqKMFStWkKamptKRfk1NTVq5ciVrRTI1NVX56KghISHCuZfJZGRqakoymUw49yEhIawViYioQoUK1KdPH9YahaKtrV1oQ2XMmDFy98vx48eTtrZ2caupJepSP/PYs2cPGRgYKMxG4jiODA0NKSoqirUiVatWTaWyMxSEulz37969E2Z5lClThrp27Upjxoyhrl27UpkyZYjjOKpZsyazEXZV9/uSEiVKFOk9VFdXV9geO3YsaWpqFreagJ6eHk2aNKnAMpMmTaKSJUsK26NGjSItLa3iVmOG1JiW+Kb8888/NHbsWDIzMxMepK6urrR48WJ6/fo1EZHKNqb19PQK7REMCgoiPT09YXvs2LFyNwyxePfuHUVGRlJkZKRC/sTr16/T/Pnz850KLCHPH3/8QVWrVpUbkapWrZpKNErz48aNGzRkyBCqXbs2VahQgWrXrk1Dhw5lPvKTR5cuXahu3bqsNQolOjqaWrduTaVLlyZNTU0qXbo0+fr60oEDB1irCcycOZOsrKwoNTWVtUqBODg4kKOjo1zKvs/JzMykihUrkoODg7CvT58+ZG5uLpai2qEO9fNzXr58SXPnzqVOnTpR06ZNqVOnTvTbb7/Ry5cvWasREdHy5cvJyMiIHj9+zFqlUNTluif6lBO7a9euwsBJ3kcmk1H37t2Z58xWdb/PMTMzo4YNGxZYxtvbW24Z36BBg+QGeYqbUqVKFTqt3NfXl0qVKiVsDxs2jIyMjIpbjRlSY1rim/L5KN+YMWPo2rVrSsuoYmO6Xr16pKenR1evXlV6/PLly6SnpyeXT7NTp05y0xYlPsHzPE2bNq3AMjNmzGC6ZvpL3r59S0lJSfnmo5QoOg8fPiQzMzOaPn16vnm8JYpGTk4Ode7cmVxdXenIkSP5rklmTV6ue29vbzp+/LiwLjUnJ4eOHTtGXl5exPM8hYaGCt9xcnJiti5Z4scjISGBOnfuTLa2thQeHk6XL1+mhIQEpR/WqMt1/znPnz+nqKgo2rhxI0VFRdGLFy9YK8mh6n5En6Zw8zyfb+ye8ePHE8/zcsuoGjRoQLVq1RLNsX379sTzPP3xxx9Kjy9btow4jqMOHToI+5o1a0ZVq1YVS1F0OKIC8gNISPxLeJ4Hz/Po1asXgoOD4e7urrRM3759sWLFCgaG+RMdHY2WLVtCS0sLPXr0gIeHhxCh8Pjx49i4cSOysrIQFRUFHx8fvH//HpaWlvD19cWff/7JxPnWrVu4efMmMjIy0LNnTyYOyuB5HmFhYQgJCcm3zMyZMxESEqIyUTQlvh29e/fGw4cPERcXB1tbW9SsWVNphHGO47B69WrxBVUYnueVRr2n/0+Fkh+s0ozlkZmZifbt22Pv3r3gOA48z8PY2BivX79Gbm4uiAjNmzfHrl27oKWlhadPn2LUqFHo0KFDoTmKi4stW7Zg1apVuHjxItLS0mBgYAAXFxf06dMH/v7+TJwkio+8a0sVryV1ve4lvi3Jyclwd3dHYmIiDA0NUb16deE99MqVK0hNTUXZsmVx6tQpWFpa4unTp3B1dcWAAQNES+d2584d1KlTB2lpaShfvjzq1q0rOJ46dQp3796FoaEhTp8+jYoVK+LZs2ewsbHBwIEDsWDBAlEcxUZqTEt8U2bOnInVq1fj4cOH4DgOjo6OCAwMRM+ePWFpaQlAdRvTwKeXq4EDByItLU0hp5+hoSGWLFmCbt26AfiUW/P06dNwdHSEnZ2dqJ6XLl1C3759cfHiRWFfXqM0Li4OLVq0wNatWwvNr1hcFKUxPWbMGCxbtgxv374V0Uy9ePToEQCgTJkykMlkwnZRYJnfu6i5ezmOY96Z8u7dO6SkpOTrIfbv6O3t/a9yeH5OTEzMN7b59/z5559Yt24dLl26JDRQnZ2dERAQwDQf7ucQEQICArBp0yYQEWQyGUxNTfHy5Uvk5OSA4zj4+/tj48aNrFVVrn4CwNGjRwEAbm5u0NHREbaLQoMGDYpLq1D+TX7cNWvWFLONPOp+3Ut8O168eIHx48djy5YteP/+vbC/RIkS6NKlC3755ReYmZkxNARu376NQYMGKa173t7eWLJkCZycnAB8ut9mZmZCU1OzyO8G6obUmJYoFqKjo7Fy5UpERkYiKysLMpkMzZo1Q69eveDv76+yjWkAyMjIQEREhNxohbOzM9q2bQt9fX3Wevjnn3/g5uaGnJwcBAcH459//sG+ffuEly0igo2NDRo3box169aJ5vX5C5W3tzcCAwMRGBioUC4nJwePHj3C+PHjYWlpiQsXLojm+CVxcXH49ddfcfbsWaSkpCA3N1ehDMue/7zRips3b6JixYr5jl58CevRioSEhCKXtbW1LUaT/Pnzzz8xe/Zs3Lx5M98yrH9HieJh+fLlGDRoEFxcXDB79mx4eXlBJpMhJycHcXFxGD9+PM6fP48lS5ZgwIABTBxVuX5+7X0JAPPOM4n/Ru/evcFxHGbNmgVzc3P07t27SN9jOQvp9evXCA8PF57zyuogx3E4fPgwA7v8ycrKwu3bt4X30EqVKkFTU5O1lhyPHz+W6zitWbMmbGxsWGuJjtSYlihWnj9/jvDwcKxatQr3798XHrguLi5YtmwZXFxcGBuqH927d8fff/+N+Ph4VK5cGVOnTsW0adPkHhCdOnXC9evXcePGDdG8/s0LFRGB53msX79eGOkXm6ioKPj5+SEnJwdly5aFjY0NNDQ0lJZl1fOfN5Lyyy+/wNzcXKVHVtSJtWvXonfv3pDJZPDw8Cjw3Eu/4/eHm5sbXr58ievXr6NEiRIKx9+/f4+qVavC2NgY586dE91P1etnWFgYOI7D0KFDYWxsLGwXhdDQ0GK2kyhOlHWkFAVWs5Bu3boFb29vvHjxAgU1d1RhlpSE+iI1piVE4/Dhw1ixYgUiIiKQmZkJjuNQvXp19O3bF4MHD2atp8CbN2+QmpoKQ0NDGBgYsNYRsLCwQJMmTbBhwwYAUNqYHjVqFMLDw5GamiqaV94LFRFh2rRp8Pb2hpeXl0I5mUwGExMTNGzYEJUqVRLN70tq166N69evY9euXWjWrBkzDwnxqVq1Kp4+fYrjx48LU9HUgaysLNy8eRNpaWkwNDSEk5OTyo1UPHr0COvXr8fFixeF+2etWrXQs2dPZrMQvqRkyZLo378/fvvtt3zLjB49Gn/88QcyMjJENPuEutZPieJBla77vFlHZcqUgYaGhsrPQmrdujX27t2L8ePHo1+/frCxsYFMJhPd42s4fvy4wn3U09OTtZYcjx8/VnC0trZmrSU6yrs6JSSKgcaNG6Nx48Z4+fIl1q5di1WrVuHy5csYNmyYyjSms7OzMXfuXKxatQoPHjwQ9tvb26Nv374YM2ZMviMEYpGSklLozSpvjYqYhIWFCf9et24d/Pz8MGzYMFEd/g3Xrl2Dv7+/1JD+Bqjb2u67d+8iMDBQbRoqr169wvjx47Fp0yZ8+PBB2K+jo4Nu3brh559/hqmpKUPDT6xcuRLDhg1DZmam3CjQrl27MGPGDCxYsAD9+/dnaPiJvE6/gmA5zqBu9fPRo0cwMjIqsNP5zZs3SElJEfV6V8cpyZ+jitf9lw1iVekgy49jx46hVatWmDVrFmuVInP+/Hn07NkTt2/fBiAfhM7R0RHr16+Hq6srS0UkJCSgf//+OHjwoMKxpk2bYvny5aLHEmKKSFHDJSSUEhMTQ927d2etQUREHz9+pIYNGxLP8ySTycjW1pbc3d3J1taWZDIZ8TxPXl5e9PHjR6aeNjY2cikHwsLCiOd5uTJNmzalihUriq2mVpiamtKoUaNYa3wX5KXEu337ttx2YR9WqdEsLS1p2LBhTP72vyU5OZnKlStHHMeRkZEReXt7k7+/P3l7e5ORkRFxHEflypWj5ORkpp6HDh0inufJ0NCQpkyZQjExMXTr1i2KiYmhKVOmkKGhIclkMjp06BBTTyKi2rVrk62tLb17907p8Xfv3pG9vT3Vrl1bZLNPqFP9JCp6KsQvn1PFjbL7UlE+YnsqQ12ue1VHX1+fxo0bx1qjyNy5c4cMDQ2J4ziqX78+hYaG0vLlyyk0NJTq168v1Id//vmHmePTp0/J2tqaOI4je3t76tWrF40fP5569epFDg4OxHEclSlThp4+fcrMUWykkWkJpnh7e8Pb25u1BgBg3rx5iI2NRevWrfHbb7+hQoUKwrF79+5h9OjRiIyMxLx58zB+/Hhmno0aNcLmzZtx+/ZtODo6Khw/d+4cDh8+zHS0PycnBx8/foSurq7c/iNHjiAiIgK6urro168f7O3tGRl+milx6tQpZn//a3n37h1Wr16NS5cu4fHjx8jKylIoI3YwlYCAAHAcB0NDQ7ltVaV169aIjY0tNO2MKjBx4kTcv38fI0aMQFhYmNzoX3p6OkJDQ7FgwQJMmjQJq1atYub566+/Ql9fH+fPn0e5cuWE/Y6OjvD29kavXr3g4uKCX3/9FY0bN2bmCXwasRw0aBAaNGiAX375BV5eXtDQ0EBOTg6OHj2KCRMmICEhAePGjWPip071E/g0ckYquGIwb3ZZmTJl5LbVAXW57i9evIhTp06he/fuwv3/7du3GDRokPCs/+mnnzB8+HAmfi4uLsIIrzowffp0vHnzBlu3bkWnTp3kjoWFhWH79u3w9/fHjBkzRA0w+6VjUlISZs+ejVGjRslNm8/JycHvv/+OcePGYcaMGVi8eDETR9Fh25aXkFAdqlWrRtWqVaOcnBylx3NycqhatWrME8/funWLdHV1yczMjJYuXUr9+vUjnufp2rVrtHTpUjI3NydDQ0NKSEhg5jhixAjS1tam1NRUYd/mzZuJ53mh99/U1JQePXrEzPHhw4dkZmZG06dPp9zcXGYe/4bLly+Tubm53O+oqiMrqszLly/J0dGR+vXrR2/evGGtUyBmZmbUoEGDAst4enqSmZmZSEbKKVWqFAUHBxdYpm/fvlSqVCmRjPInNzeXunXrJlwrGhoaZGZmRhoaGsK11aVLF2Z+6lQ/iT6N+E6dOrXAMkOGDCF9fX2RjNQfdbnuu3TpQlZWVnL7hg4dShzHkb6+PmlpaRHP8xQdHc3E78iRI6SpqUkxMTFM/v6/xcrKSm7moTLat2+v8JuLia2tLfn4+BRYxsfHh2xtbcURUgGkkWkJif/n7t27GDp0aL7RKXmeR4sWLbBo0SKRzeRxdHTEjh070LVrVwwZMgTAp5GB6tWrg4hgZGSEnTt3Ms0zfPToUTRs2FDoqQY+BUozMjLCggULkJycjAkTJmDevHn4/fffmThOnToVVapUQWhoKMLDw1GzZk0YGRkplFOV9XMAMGLECLx48QJTp05FQECAsEZZ1Vi/fj3Mzc3h4+PDWkUpnTp1gq6uLlatWoVNmzahQoUK+Z571ulS3rx5U2jQmfr16zNNMQd8ioBd2PrN0qVLy+VNZQXHcdi4cSNat26N8PBwXLx4Ea9fv4ahoSGcnZ3Ru3dvdO3alZmfOtTP9evXy21funRJYR/wv1SIGzZsQLVq1cTSU3vU5bqPj49Hw4YNhe2srCysW7cObm5uiI2NxevXr+Hs7IyFCxcyiU+SmJiItm3bolmzZujatStcXFyUXkvApxlVrHn58mWhgVkrVaqEPXv2iGSkSHJyMrp3715gGRcXF8TGxoojpAqwbs1LSKgKhoaGNGjQoALLDB48mAwMDEQyKpiUlBSaP38++fv7U9OmTaljx440d+5cevXqFWs1Kl26NA0ZMkTYvnfvHnEcR6GhocI+X19fqlSpEgO7T6jT+rk8SpQoQZ06dWKtUSgymYxGjBjBWiNf1OncOzs7U1BQUIFlAgMDydnZWSQj5Tg6OlKdOnUKLOPu7i7FcigC6lA/ixoXIc9VT0+P2ehkHtu2baOGDRtSUlKS0uOPHz+mRo0a0Y4dO0Q2U0Rdrnt9fX366aefhO2TJ08Sx3EUHh4u7AsODiYbGxsWekI9/fK6+bKOqsK9noioTJky1K5duwLLsB6ZNjMzKzTWUY8ePZjPmhATaWRaQuL/qV69OrZv346wsDCULl1a4fjLly+xfft21KhRg4GdIkZGRhg+fDiztUgFkZ6eLrfG68SJE+A4Ds2bNxf2ValShVn+ZkC91s/lUbJkSZWPngp8St+Wm5vLWiNfVNntS4YPH45BgwZhxIgRqF69usLxS5cuYdu2bVi2bBkDu//Rrl07zJkzB4MGDcKsWbPkRn/S09MxZcoUnD17ltk65M9Zv349atasqfT3zOPq1au4ePEik9EqdaifefmtiQi9e/eGn58f2rZtq1AuLxVi3bp18x0RFItVq1YhNTUVVlZWSo+XKVMGaWlpWLVqFdq3by+ynTzqct1zHIfs7Gxh+/jx4+A4Ti4tZunSpfHixQsWekzysP8XGjVqhE2bNmHLli3w9/dXOL5jxw5EREQUOjJcnHh6emL79u0YNGgQ6tWrp3D8zJkz+Ouvv9CqVSsGdmyQGtMSEv/PkCFD4O/vDzc3N0yePBkNGzaEpaUlkpOTERsbixkzZuDFixdYuHAha1WVx9LSUq6xeujQIZQoUQIuLi7CvoyMDKZpxtShUfoljRo1wpkzZ1hrFErz5s0RExOD3NzcfJdNSBQNe3t7NG3aFG5ubggICECDBg1gbm6OZ8+eIS4uDn/++SdatGgBOzs7HD16VO67DRo0EM1zwoQJ2L17N5YvX46NGzeiRo0awv3z8uXLSE9PR6VKlTBhwgTRnPIjMDAQYWFhBTamd+/ejZCQEJWY+qmK9OrVS/h3XipEVf+trl69itatWxdYpnbt2oiMjBTJKH/U5bovW7YsTp8+LWxHRETA2toaDg4Owr4nT56gVKlSojl9zuf1VB0ICQkRGstLlixReA89fvw49PX1MXnyZGaOkyZNQlRUFLy8vODv76/guHnzZvA8j4kTJzJzFB3WQ+MSEqrEhAkT8p2+xnGc3HQm1jx79oyioqJow4YNtG7dOqUfVvj7+5Oenh5FRkbSwYMHSVdXl3x9feXKtGrViqpUqcLIUD25d+8emZqa0s8//6zSQdOSk5PJzs6OAgMD6cWLF6x11Jovpykqm0Kb3xRbsUlNTaV+/fqRnp6e3LRKPT096tevH71+/Vp0J2UUJWDW1KlTmaVukygetLW1adKkSQWWmTRpEmlra4tklD/qct1PmzaNOI6jDh06UPfu3YnneYWUk3Xq1CFPT09RvdSZs2fPUqVKleTOc96/K1WqRGfOnGGtSJGRkWRsbKy0bpqYmFBERARrRVHhiFQwn4GEBENOnz6N1atX4+LFi0hLS5MLSlO3bl3WesjKysKAAQOwfv36fKcD0v+nVMnJyRHZ7hNXr15FnTp18PHjRwCfgrcdP34cderUAQB8+PAB5ubm6NixI/PgXpGRkdi4cSNu3ryJt2/f4u7duwCAmzdvIjIyEt27dxdSq6gCt2/fRr169WBkZISaNWvKBXnLg3XQtEaNGuH169e4evUqtLS0YGdnBwsLC4U0PywDKOXm5mLJkiVy5z5vuuLFixexcuVKjBgxAhUrVmTil0dYWNhXp0cKDQ39xjZFIysrC7dv3xbun46OjtDU1GTiogye5xEWFoaQkJB8y/To0QPR0dHMpqeqS/1UJ6ytreHh4YGtW7fmW6ZLly6Ii4tDcnKyiGaKqMt1n5GRAR8fHyHNZM2aNRETEyM8lx48eIDy5ctjwoQJmDFjhmheX/LixQvs2LFDuJby0om9ePECDx48QLVq1VCiRAlmfso4efIkLly4IPce6uHhwVpL4O3bt4iIiFBw9PPzg56eHms9UZEa0xISasb48eMxZ84clCtXDt27d4eNjU2+06VZTnG6evWqkAexS5cuqF27tnDs1KlTmDNnDgYPHowmTZow8SMiBAYGYsOGDQCAEiVK4P3790IHRHJyMqytrTFz5kz89NNPTBy/5PHjx2jatGmheTNZdqQAKPLUblaemZmZaNGiBWJjY2FsbAxtbW08ffpUcElNTYWFhQV++uknTJ06VXQ/iW9P7969hX+vXbsWNWvWRM2aNRXK5UWfPnbsGFq1aoWIiAgRLT+hjvXz7du3WLp0KaKjo5GUlCR0pH4Ox3G4d+8eA7tPdOnSBbt378bFixeVRky+efMmnJ2d4evri7/++ouBofpy7do1AEDlypXl7v8PHz7E5cuX4erqyqxTevXq1Rg2bBg+fPigMNBw7do11KhRAytWrECfPn2Y+El8BzAcFZeQkPgKbGxsyNHRkd69e8daRa1ZvHgxcRxHffr0odTUVAoNDVWYItegQQOqX78+I0NFOnToIDgfPXqU7t69Sw8fPlT6kcifGTNmCFN9c3JylJ77pk2bFhqdWkJ9+DKab2FRsuvWrUv37t1j4qpu9TMlJYWqVKlCHMeRoaEhcRxHRkZGpKurK/ymZcqUITs7O6aeZ8+eJQ0NDTIxMaEFCxbQ7du3KSMjg27fvk3z588nExMT0tDQoNOnTzP1lPh2HDhwgHiep5o1a1JERAQNGjRI4VqqVq0atW7dmpGhxPeAFIBM4ofl0aNHX/1dljmcnz9/jkGDBqnclCR1Y/Xq1ahRowZWrlwJjuOUTqmrUKECoqOjGdgp58iRI/Dx8RGmqEl8HRs3boSHh4cwzVfZube3t1eJQESqSqNGjb7qe6ym9ucFRCQiODg4YMSIEUozIchkMpQqVYrpNEV1q58zZszAjRs3sHr1agQGBkImk2HkyJGYMmUKzpw5gyFDhkBPT4/5vbR27dpYunQpBg8ejJEjR2LkyJFyx2UyGZYtWyYsR1IVjh8/josXLyI1NRWGhoaoVatWoTmoxaJGjRoYMGAAevToAX19fdY6CsyePRuWlpaIi4uDgYEBLl68qFCmevXqwjR1sZk2bdpXfY/jOEyZMuUb2yhHWf74oqLqQQm/FVJjWuKHxc7O7qvWJH2ZCkJsypYti/T0dGZ/Xxl5N9t27dpBX1//X918Wd1sb9++jf79+xdYB8zMzJitmVRGbm4uqlWrxlpD7Xnw4EGhaTuMjY3x+vVrkYwKhoiwffv2QqfQitlIjY2N/arvfe060P/K59H7Q0ND0bBhQ5WN6K9u9XP37t1o0KABgoKC5PZzHAd3d3fs3bsX1apVw8yZMzF9+nRGlp8IDg6Gp6cnli5dijNnziA1NRVGRkZwd3fHwIED4eTkxNTvc86fP4+ePXsKy3ro/6coA4CjoyPWr18PV1dXloq4ceMGhgwZgnHjxsHf3x/9+/dn7vQ58fHx8Pf3l0vV+SXW1tbM1siHhYV91ffEbEwHBgb+6/t2Xl2VGtMSEt85AQEBzF7s/guBgYFYsmSJEPBBFci72bq7u0NfX79IN1/WN1sNDQ18+PChwDJJSUkoWbKkSEaF4+7uLqxNU3VUOYCSjo4OUlNTCyzz6NEj5nlxAeDjx49o2bIlYmNjhWuGPgt1krct9r1MHXIh5werwGxFRZ3qJwAkJibC19dX2OZ5Xq7Dx8zMDC1atMCWLVuYN6YBwMnJCYsWLWKtUSB3795F48aNkZ6eDk9PTzRq1AiWlpZ4+vQpjhw5guPHj6Np06Y4e/YsKlSowMzz8ePHWL16NVatWoXVq1cjPDwczs7O6N+/P7p168Y8EFVmZmahDqmpqZDJZCIZyRMTE8Pk7/4b1C1XNwukxrTED8vatWtZK3wV48ePx+XLl9GkSRPMmTMHLi4uBfa6ikF4eDg4joOlpSUA9bj5Vq5cWa6B8iUfPnzAkSNH4OzszMBOOTNnzkT9+vWxZcsW+Pv7s9bJly8DKOnr6yMjI0M4bm9vj/DwcJQuXZpJAKWaNWviwIEDyMzMhJaWlsLxtLQ0REdHo169eqK7fcns2bMRExODKVOmYPjw4TA1NUVYWBj69euH2NhYjB8/HvXq1cOff/7JWlVtuHjxIk6dOoXu3bsLHZJv377FoEGDEBERAV1dXfz0009Kp4GLgTrVTwDQ1dWVCzplaGioMNJnbm6OpKQksdXUlunTp+PNmzfYunUrOnXqJHcsLCwM27dvh7+/P2bMmCEE+mSBubk5Jk6ciIkTJyI6OhorVqxAZGQkBgwYgDFjxqB79+7o16+f0mB/YmBnZ4fz588XWObMmTNwdHQUyUgeLy8vJn/336BuubqZwGy1toSExFfxeT6//PJM8jwv5UgthCVLlhDHcTR8+HDKycmhsLAwITBJdnY2DRw4kHiepw0bNjA2/R9Tp06lVq1aEc/z5OXlRaNGjaKpU6cqfKZNm8bUU9UDKG3atIk4jqOOHTtSWlqa3LlPSUkhPz8/4nmeoqKimPh9TtWqVcnFxUXY/jJH8r1790hfX5/mzp3LQk8t6dKlC1lZWcntGzp0KHEcR/r6+qSlpUU8z1N0dDQTP3Wqn0RENWrUoC5dugjbnp6eVL58ecrJyRH21atXj+zt7UX1SkhIoISEBMrOzpbbLsqHNVZWVtShQ4cCy7Rv316hHqsCycnJNHPmTHJwcBDeR+rUqUNr1qyh9+/fi+oyfvx44nmetm3bRkQkdy0REYWHhxPP8/Tzzz+L6iXxfSE1piUklJCYmEi7d++m9evXU0REBCUmJrJWEvDy8iJvb+8ifSTyJzs7m3x8fIjjOLKysiJHR0fieZ46dOhAZcqUIY7jyM/Pj7WmHAVFIP4yGjFLnJycyNPTU9j+8gWGiKhfv35kaWkptppAUFAQcRxHWlpaZG5uTjzPk4uLC+no6BDHcTRkyBBmbp9TokQJGjZsmLDN8zxNnjxZroy/vz9VrVpVbDWlbN68mRo3bkzGxsYkk8moVKlS1KRJE9q8eTNrNYFy5cpR9+7dhe3MzEwyMDCgOnXq0Pv37ykpKYnMzMyoVatWzBzVpX4SEQ0bNowsLCwoNzeXiIgWLVpEHMeRj48PLV68mDp27Eg8z9PgwYNF9cq7F96+fVtuu7CPKnREa2lp0aRJkwosM3HiRNLS0hLJ6N+Rm5tLu3btImtra7nnkqmpKf3++++iebx+/Zrs7OxIJpNR586dycfHh3iep0WLFlHnzp1JQ0ODHB0dKSMjQzSnopCQkEDTp0+n9u3bU6NGjahdu3Y0ffp0lcrU8fbtW/rzzz9p1KhR1Lt3bxo5ciT9+eefKvdbioE0zVtC4jMSEhLQv39/HDx4UOFY06ZNsXz5ctjZ2Ykv9hlfG/hHQh6ZTIY9e/ZgxowZWLx4MZ4+fQoA2LlzJ4yMjDBlyhTRAnwUFXVYXwWoRwCl8PBwNGjQAAsWLMCVK1dARLhw4QKqVKmCUaNGKQRTYoWmpiZ0dHSEbX19fYWgeLa2tti9e7fYanIQEQICArBp0yYQEWQyGUqXLo2XL1/i8OHDOHLkCCIjI7Fx40amnsCnjAjW1tbCdnx8PN68eYP+/ftDR0cHVlZWaNu2Lfbv38/MUV3qJ/BpGmhmZiYeP34MGxsbDBgwAEeOHMGuXbtw4MABAICHhwdmzJghqldeXJS8qfzqFCeldOnSuHHjRoFlbt26BVNTU5GMikZSUpKwfjopKQk8z6NNmzbo3bs3Lly4gOXLl2P06NF49eqVKOvnS5Uqhbi4OAQEBMjlDh82bBgAoH79+ti0aRPztd2fs3LlSgwbNgyZmZly8TF27dqFGTNmYMGCBejfvz9DQ2Dv3r3o1asXXr9+rRDDY+TIkVizZg1at27N0FBkWLbkJSRUiadPnwq9qPb29tSrVy8aP3489erVixwcHIRcmU+fPmWtqhacOXOG2rdvTw4ODsK0SVUcASD61It+69YtOnHiBF27dk2YFijxdRgZGVHfvn2FbWUj0926dSNzc3Ox1ZTy7t07SkpKUske9cqVK8uNotauXZuqV68uV6ZZs2ZkbW0ttpocy5YtI47jyNXVlQ4fPixcQ9nZ2XT48GGqXbs28TxPy5YtY+pJRGRgYECjR48WtufMmUM8z8vllZ44cSLp6Oiw0FNAletnQcTHx9OWLVvo9OnTclO+JQqnZ8+eJJPJ8p3RsX37dpLJZBQQECCymSK5ubkUFRVFbdq0IU1NTeI4jiwsLGjy5Mn06NEjubLp6elUu3ZtJrOSLl++TMuXL6eZM2fS4sWLKT4+XnSHwjh06BDxPE+GhoY0ZcoUiomJoVu3blFMTAxNmTKFDA0NSSaT0aFDh5g5nj9/nrS1tUlDQ4N69uxJa9asof3799OaNWuoZ8+epKGhQdra2ir5+xYXUmNaQuL/GTRoEHEcR3PmzFFoTGVnZ9Ovv/5KHMeJPlVNHfnrr79IJpMJHROenp7SVPQfCG9vbypbtix9/PiRiBQb06mpqWRiYkK+vr6sFNWG4OBgubWmM2fOJI7jqE+fPrRnzx4aM2YM8TxPPXr0YGj5qZFvb29P7969U3r83bt35ODgQK6uriKbKVK1alXy8PAQtj08PKhs2bJyZQIDA5kuQ5D4sblz5w4ZGBgQz/Pk6elJU6ZMoaVLl1JISAg1aNBAaHD9888/TD2nTZtGtra2QhwXLy8v2rp1K2VlZRX4HdZLkVQVHx8fMjQ0pLt37yo9fvfuXTI0NCQfHx+Rzf5H+/btSUdHh06dOqX0+OnTp0lHR4fat28vshk7OKLPxuclJH5g7OzsUKlSpQKn9jVv3hy3bt3Cw4cPRfOaNm0aOI7D4MGDYWxsjGnTphXpe2LmIfySqlWrIjExEVFRUfD09GTi8D3z6NEjrF+/HhcvXkRqaioMDQ1Rq1Yt9OzZUyVy527evBndu3dHhw4dsHr1avz++++YNm0acnJykJqaiqCgIOzevRuRkZFo2bIla12VJjY2FrNnz8ayZctgZ2eHd+/eoWHDhjh37pyQFqt8+fKIiYlBmTJlmHmWLFkS/fv3x2+//ZZvmdGjR+OPP/6Qi+zOgunTpyM0NBTt27eHjo4ONm/ejBEjRsi5u7u7Q1NTE8eOHWNoKvEjc+7cOQQEBAh5pj9Pi+fo6Ih169bBzc2NpSJ4noeBgQF69uyJgQMHonLlyoV+Z+/evfjrr7/UIuuH2BgbG6Njx45YsWJFvmWCg4OxY8cOZsukzMzM0Lx5c6xfvz7fMj179kR0dDSeP38uohk7pDXTEhL/T3JyMrp3715gGRcXF9HXLIeFhYHjOHTp0gXGxsYICwsr0vdYNqbv3r2LwMBAlWpIN2rUCBzHYd26dbC2tkajRo2K9D2O43D48OFitis66rCeqmvXrjh48CDWrl2L3bt3o1SpUgAAV1dXXL9+HR8/fsTgwYNFa0g7ODiA4zgcOnQI9vb2cHBwKNL3OI7DvXv3itmuYLy9veHt7S1s6+rq4sSJE4iIiMDdu3dhZ2cHX19f6OrqspMEFPJfK0NV+u5HjhyJ/fv3Y+fOnQA+paIKCQkRjj948ADnzp3DhAkTRPFR5/r5OZGRkbh06RIeP36MrKwsheMcx2H16tWi+fTu3furvie2Z37Url0bN2/exMmTJ3HhwgWkpaXB0NAQzs7O8PDwYK0HAFi+fDm6d+/+r9Yct2zZstju/eo4+PA579+/L3QdfOnSpfH+/XuRjBRJS0uDjY1NgWXKli2L9PR0kYzYI41MS0j8P+bm5mjatCk2bNiQb5mePXviwIEDePbsmWhecXFxAIA6depAR0dH2C4KrHIY5r3gL1q0iMnfVwbP8+A4Djdv3kTFihXl8qIWBMdxyMnJKWa7onH48GE0a9YM+vr6GDZsGBo1agRLS0s8ffoUR44cwcKFC5GRkYHo6Gg0btyYtS7Wrl0rF0AJAJMASnZ2duA4DkeOHIG9vb2wXRQePHhQzHYF8+jRI2hpacHCwoKpR2G4ubnh+fPnuHnzJkqUKKFw/P3796hSpQpMTU1x9uxZBoaKXLt2DcCnnPOf3w8ePnyIy5cvw9XVVZTRfnWun8CnwJ2+vr64fv16gR0mYt9L87vH59fxk7dfle75Ev8OdX/OV6pUCUZGRjh9+nS+ZerWrYvXr18LMxbExt7eHuXLl1caqDcPHx8f/PPPPypxfxIFJpPLJSRUkPbt25O2tjadOHFC6fHTp0+Ttrb2D7UO5GsZM2YMOTo6CmtmJb4N6rCeShnqGkBJFeB5noKCglhrFMrnAcgOHTokrJnMzs6mI0eOUJ06dVQmAJnEt6VNmzbCOv64uDi6e/cuPXz4UOlHTL782/fv36e2bduSsbExhYWFUWxsLN26dYtiY2MpNDSUjI2Nyc/Pj+7fvy+q57/h5cuXtHPnTtq/f78UKFMJsbGxFBsbK+SzztsuykcVyMuLPXDgQEpJSZE7lpaWRsOGDSOe52n8+PFsBOlTfKG83Nxf1sGcnByaO3cu8TxPgwYNYmQoPtLItITE/3PhwgXUq1cPOTk58Pf3R8OGDWFpaYnk5GTExsZi8+bN4HkeJ06cgIuLC2tdlebdu3do1KgRLCwssGDBApVYx/s9oA7rqSS+LSYmJujTpw/mzJnDWqVAiAg9evTA5s2bwXEceJ4X0p/l5uaCiNC5c2ds2bKFtarEN0ZfXx+enp7Yt28fa5UC+f333zFjxgxcuHBB6TPpwYMHcHFxQUhICEaMGCG+4GcsW7YMa9euxb59+2BsbAwAOH/+PJo3by7c211dXXHkyBHmaZ3i4uLw66+/4uzZs0hJSUFubq5CGY7jkJ2dzcBOvUhPT0fdunVx8+ZN6Ovro0aNGsJ76OXLl5Geno5KlSrh9OnTMDAwYOKYnJwMFxcXJCcno2zZsqhfv77gePz4cTx8+BAWFhaIj4+HpaUlE0exkdZMS0j8P7Vq1cL27dvRq1cvbNy4EZs2bRKOERGMjY0RHh7OvCH98OFD3LhxA15eXsJDNDs7G9OnT8euXbugp6eHsWPHol27dswcdXV1sWLFCjRs2BAODg4wMjIScn1+jqqt+VN11GE91ee8ePECO3bswM2bN/H27VusWrVK2P/gwQNUq1ZN6ZRgif/h7u6OixcvstYoFI7jsHHjRrRu3Rrh4eG4ePEiXr9+Lazx7N27N7p27crE7XuJl6CqaGpqolq1aqw1CmXFihXo3Llzvp279vb26NSpE1asWMG8Mb1161ZwHCc0pAFg7NixSElJQVBQEJ49e4aoqCghbzMroqKi4Ofnh5ycHJQtWxaOjo7Q0JCaFl+LgYEBTp48iXHjxmHjxo04fvy4cExXVxfBwcH45ZdfmDWkAcDCwgInTpxA//79cfDgQSQkJMgdb9q0KZYvX/7DNKQBSNO8JSS+JCMjgzZu3EijR4+mvn370ujRo2nDhg0qM0U1MDCQjI2N5VJPhIaGEsdxwkdDQyPftAVicOzYMdLT0yOO40hTU5Osra3Jzs5O6YcVy5YtIwcHB0pKSlJ6/PHjx+Tg4ECrVq0S2Sx/HB0dqU6dOgWWcXd3p4oVK4pklD+rVq0iXV1dIWXK56lQrl69SjzPM/ttp0+fThoaGgWee01NTfrll19ENlPk7NmzpK2tTStXrmStorbk1b/bt28L20X5sErfo071k4iodevW1KJFC9YahaKjo1Po9NiffvqJSpQoIZJR/lhZWVFwcLCw/eLFC+J5nvr16yfsc3Nzo1q1arHQE3B1daUSJUpQdHQ0U4/8OHToEAUFBeV7LSUlJVFQUBDFxMSIK1YEMjMz6erVq3T8+HG6evUqZWZmslZS4PHjxxQZGUkbNmygyMhIevz4MWslJkjTvCUk1IxKlSqhevXq2LZtGwAgNzcXFhYWMDU1xYEDB5CcnIwmTZrAx8cHW7duZeLo4eGB+Ph4rF69Gt26dStyEBAxadCgAXJzc+V6fr/Ey8sLPM8jJiZGRLP8mTBhAubMmYP+/ftj1qxZMDIyEo6lp6djypQpWLx4McaNG4eff/6ZmefBgwfRvHlzVK9eHVOnTkV0dDSWL18uF+ClevXqsLW1RWRkpOh+derUgYGBQaEBVNLT03Hq1CkRzRSZNm0aTp48iYMHD6JmzZpwc3ODhYWFQoAqVYlGK/HfUaf6CQAXL15E/fr1sWrVKvj7+7PWyRcbGxuULl0aFy5cUHqciFCrVi28fPkSiYmJItvJo6OjgzFjxmDGjBkAgIiICLRv3x5RUVFo3rw5AGDMmDFYu3YtXr58ycyzRIkS8Pf3V9k0V35+frh16xZu3bqVbxknJydUrlwZO3bsENFM4ntCmoshIaFmPHv2TG6a2qVLl/Dy5UuEhobC2toa1tbWaNu2LdP8qJcvX0bXrl3Ro0cPZg6Fcfv2bXTs2LHAMtWrV8f27dtFMiqcCRMmYPfu3Vi+fDk2btyY73oqsVL65Mfs2bNhaWmJuLg4GBgYKJ2mXL16dWYNgbt37xZaNytXrlxgZH+x+DwV3sWLF/Od8q0qjekrV65g06ZNwtT+Q4cOAfi0POXs2bNo2rSpkCpNQjnqVD8BwNnZGYcPH0arVq3wxx9/oFatWvku62FZRzt16oT58+ejc+fOmD17Nuzt7YVjDx48wE8//YQrV65g5MiRzBzzMDY2lmskx8XFged51KtXT9jHcRw+fPjAQk+gZMmSclPRVY0LFy6gSZMmBZbx9PTEgQMHRDKS+B6RGtMSPyx5CefbtWsHfX39AhPQf0lAQEBxaRVKVlaW3KjUiRMnwHGc3DpAa2trPH36lIUeANV/wAKfciV+PrKrDAMDA6SkpIgjVATUYT0VAMTHx8Pf379AD2trayQnJ4to9T/ev39faF5mHR0dvHnzRiSj/FGVWRFFISQkBLNmzRICEH1+n8rNzUXXrl0xf/58DB06lJWiWqBO9RP4dC+dOHEiXr9+jbi4uHzTN7JuTE+bNg3Hjx/H9u3b8ffff6NMmTIwNzfHs2fPkJSUhJycHNSuXVuuA4sVTk5OiIyMxMyZMyGTybBlyxbUrl1b7p6aF+iJJY0bN1aJ2RH58fz5c1hZWRVYxtzcHM+fPxfJqHDu3LmDBQsWCAHdlKXsEjPeTO/evcFxHGbNmgVzc/Mi529XlXztYiA1piV+WAIDA8FxHNzd3aGvry9sFwT9fw5Klo1pa2trXLlyRdjeu3cvTE1N4eTkJOx7/vw50wZVy5Yt/1U+bBZYWlrK/Y7KuHLlCkqXLi2SUdEwNDTEH3/8gcWLF+P27dtIS0uDoaEhHB0doampyVoPAJCZmVlohNnU1FTIZDKRjOSxtrYuMI8nAJw+fVqUHMOFwSpX/L9ly5YtmDFjBnx8fDB79mxs3boVv/zyi3DcwcEBrq6u2L17N5PG9KNHj77qe2XLlv3GJoWjTvUTAEaOHImYmBg0adIEPXv2hJWVlUoGoSpZsiSOHz+OuXPnYs2aNbh3755QL8qXL4+goCCMHj0aWlpajE2B4cOHw8/PD9bW1tDQ0MC7d+8UIvqfPn0abm5ujAw/MXv2bLi5uWHGjBmYNGlSkfOji4WhoWGhU/YTExOZR0TP49SpU2jSpAnev38PDQ0NmJubK72WxFyhu3btWnAch59++gnm5uZYu3Ztkb4nNaYlJH4AwsPDwXGcEHFQVdf8fEnr1q3x+++/Y8yYMdDR0cHBgwcRFBQkV+aff/5hmo7ql19+gYeHBwYPHow5c+aozIPqcxo2bIg///wTx48fh6enp8LxY8eOYd++fSo7VV1TUxNVq1ZlraEUOzs7nD9/vsAyZ86cgaOjo0hG8jRv3hxLlizB1q1b0aVLF4XjW7ZsQVxcHAYNGsTATj1ZuHAhypcvj4iICGhpaeHvv/9WKOPk5ITY2Fjx5fCpTv7bF31W6XzUrX7u2bMH9erVU4upslpaWpg4cSImTpyIjIwMoTOyZMmSrNXkaNOmDZYvXy6kQezevbvcsyg2NhYZGRnw8fER1UvZqGSVKlUQGhqK8PBw1KxZU+mML1YNKzc3N+zatQvJyclKR/GfPHmCXbt2wcPDQ3Q3ZUyYMAEfP37E8uXL0bt3b5XolHrw4AEACJ13edsS/0MKQCYhoWY8f/4c9erVw/379wF8usGdOXNGmMr0/PlzWFtbY9iwYZg7dy4Tx0aNGiE1NRWXL1+Grq4uKlasmO8aOlapZ27fvo1atWohJycHgwYNQvPmzVGmTBkkJSVh3759WLZsGWQyGeLj4+VG/SUKJy9Q2pYtW9CpUydMnToV06ZNE6arrVmzBn379sXMmTMxfvx40f2SkpJQvXp1pKamom3btgrnfvfu3ShVqhQuXboEa2tr0f3UkbzZPYsWLQIAhXMOABMnTsTvv//OJHWbssZ0amoq0tLSCux4ZPHiqG71U19fHwMHDlT5XOgS/52vDSbKcZzS6crFzYEDB9C8eXOUK1cOv/32G3x8fKCtrY2PHz9i//79GD16NB48eCAX2I0lenp68PX1xZYtW1irSPwLpMa0hIQa8v79e6ER6uXlBX19feHYjRs3cPDgQfj4+KBSpUpM/Ir6wGX1gM0jKioK3bp1w5s3b+RetIkIBgYG2LRpE1q2bMnMTxmqtp5KGSkpKahVqxYSExPRoUMHpKWl4eDBg1iwYAGOHTuGnTt3oly5cjh//jyzWQvx8fHo1KkTEhISFM69nZ0d/vrrL+Y55YFP11JhI6ocx8HAwABOTk5o3749hgwZAm1tbZEMP1GyZEn06dMHCxYsAKC8Md2nTx/s3LlTZeIQhIWFYfr06UzvQfmhLvUT+JQZwczMTKWCNRbEixcvsGPHDiFI3qpVq4T9Dx48QLVq1VCiRAnGlqrJlzmF/w2sZsuFhoZi+vTp4DgOHMehVKlSSElJARGBiDBlyhRMnTqViduXlCpVCsHBwVLHlJohNaYlJAogKSkJFy5cQG5uLurVq6dy62cl/juvXr3C2rVrcebMGaSmpsLIyAju7u7o1asXTExMWOvJUdT1VAD7qViPHj1CQEAAjh49qnCsfv362LRpE/M1n1lZWYiMjMTp06flzr2vr6/KrD/39vZGWloaLl++DJlMBhsbGyFoUmJiInJyclCjRg1kZ2fj3r17+PjxI5ydnREXFydqR4WzszM0NDRw7tw5AIqN6dzcXDg5OaF06dIFpqMTE2UNflVCHeon8CkVXuvWrXH48GGlS2ZUidWrV2PYsGH48OGDEAMl7/xfu3YNNWrUwIoVK9CnTx/Gpp84ffo0Vq1ahYsXLyI1NRWGhoZwcXFBUFCQXGRviYI5cOAAFi1apPCcHzp0KJo2bcpaT6BVq1bIzMwsMC0ea/766y8sW7YMGzZsUBrcLSkpCQEBARg8eDDat2/PwJABome2lpBQMS5fvkxBQUHUunVrmjp1KmVkZBAR0eTJk0lLS4t4niee50lbW5vmzZvH2FbiR8bLy4tkMhn98ccflJWVxVqnSFy+fJmWL19OM2fOpMWLF1N8fDxrJbXiyZMnZG9vT127dqWEhAS5YwkJCdS1a1dycHCgp0+fUnp6OvXt25c4jqOQkBBRPWfNmkU8z9PcuXOJiCgsLIx4nheOT58+nXiepyVLlojqVRBfOkp8HevWraP27duTpqYm9erVixYuXEjr1q1T+mHJgQMHiOd5qlmzJkVERNCgQYMUzn+1atWodevWjAzlmTRpEvE8TxzHKXx4nqcJEyYw9UtISKDt27fTjh076NGjR0xdvhcuXbpEurq6tH79etYq+dKsWTNydnYusIyLiwu1aNFCJCP2SCPTEj80t27dgpubG96+fSv0Uvv6+sLf3x/dunWDnp4eHB0dkZKSggcPHoDjOBw8eFAuDZXYSGkJflyk9VTFS2pqKohIpfIgBwQE4MaNG4iPj8+3jKurK6pUqYJ169YhJycHVapUgYaGBq5duyaa5/v37+Hh4YHLly/D1dUVHMfh3LlzGDlyJI4dO4b4+Hi4u7sjLi5OJYLqAKo/Mv0lqlg/gf8tRfj8dfLLpQn0xSgwC5o0aYJbt27hxo0bMDAwUHr+e/TogVOnTjFdJgN8Gv3r0qULbG1tMWXKFDRq1AiWlpZ4+vQpjhw5gunTp+PRo0fYvHkzOnfuLLrfmDFjMH/+fOGccxyHkSNH4tdffxXdRZ2ZNm2awr5z585h79698PT0hIuLS74B3VilmbOyskLr1q2F4HjKGDhwICIjI/H48WMRzRjCrh0vIcGeXr16EcdxNHToUIqMjKRhw4YRz/NUtWpVatSoEaWmpgpl//77b+J5ntq1a8fQmJT2Un/ZY533X5bk5OTQwoULqU6dOmRgYEAymUw4duHCBRo4cCDdvn1bVKd3797RvXv3KC0tTeHYw4cPqV27dmRoaEgGBgbk6+srul9hGBkZ0dixY1lrqCXPnz+nw4cP0+PHjxWOxcfHk7OzszALpVq1anTixAkGloqYmZnR+PHjCywzfvx4MjMzE7aDg4NJV1e3uNUUSE1NpV69epGGhobcPUkmk1FAQAClp6eL7lQQqjQyra71k4ho7dq1Rf6wxNDQkPr37y9sKzv/P/30E5Nr50vq169PFhYW9OLFC6XHX7x4Qebm5tSgQQORzYg2bdokvGNUrlyZnJychLq5adMm0X3+DZmZmbRgwQJq27YttWnThn777Tf68OEDM5/C3ucKes9jhba2Nk2aNKnAMpMmTSJtbW2RjNijGt3DEhKMiIuLg4eHBxYuXAjgU9qpCxcu4OTJk1izZo1cBGo/Pz+0aNECZ86cYaULIP+1sKmpqTh37hymT5+OevXqyeV4FZvMzEy0aNECsbGxMDY2hr6+PjIyMoTj9vb2CA8PR+nSpUUN/LF48WKMHz8eJ06cgLu7u7D/zZs38PLyQmJiotDTvmfPHsTHx+Pq1asqs3a6Xr16uHjxImuNIpGVlYWIiIhCA6WJNXti+fLlCAsLw+XLl+XWaj9//hw+Pj54/fo1tLS0oKWlhWvXrqFFixa4evUqkzzDn/PmzRukp6cXWCYtLQ1v3rwRto2NjYtbSymGhoZYu3Yt5s2bh3PnzuHVq1cwNDSEm5ubFG+iENS1fgJAr169WCsUiczMzELjCKSmpkImk4lklD+XL19GQEAATE1NlR43NTVFp06dsH79epHNgFWrVkFDQwPR0dFo2LAhAODQoUNo0aIFVq9eja5du4ru9Dnr16/H5MmTsWbNGjRu3FjYn5ubi9atW+PQoUNyz/kdO3YwmzETExMj+t/8r5iamuLOnTsFlrlz547SEfXvFtateQkJlmhra9OoUaPk9o0aNYp4nqd3794plB87dixpamqKpfdVPHr0iIyMjGjVqlXMHGbMmEEcx9HUqVMpJyeHQkNDFXpSmzZtSnXq1BHVy9fXl2xtbRX2z549mziOIw8PD7p37x49f/6chg0bxmTtaUGow3oqIqKkpCSqXLlyvuv9WPSuN2vWjBwdHRX2T548mTiOo7Zt29K7d+8oJyeH5s6dSxzHKdwbWODs7EwmJiaUlJSk9HhiYiIZGxtTrVq1hH3dunUjGxsbsRTVFlUamVbX+qlOODk5kZeXl7Ct7PzXrFmTXF1dRTZTRFdXl8aNG1dgmXHjxjEZRTc1NaWOHTsq7O/QoQOZmJiI7vMlPXr0ICMjI8rOzpbbv2HDBuI4jiwtLWn16tW0bds2qlu3LvE8T8uXL2dkq3507tyZdHR06ObNm0qP37hxg7S1tZXWke8VaWRa4ocmMzNTIf+xgYEBAChNjaGnp6fy6+tsbGzg6+uLBQsWMItIunHjRnh4eCAkJASA4vo54NPodGRkpKheN27cUBptdufOneA4DuHh4XBwcAAALFiwAFFRUdi7d6/KpM2IiIhAo0aNEBgYiFWrVqnkeioAGD16NG7evImuXbsiODgYNjY2zNfJ3rlzB97e3gr7IyMjwfM8li5dKlzzo0ePxvr165nlQP+c0aNHo2fPnqhVqxaGDh0KDw8PIZr38ePHsWjRIqSmpmLUqFEAgOzsbBw6dAj169cXxW/ZsmVIS0vDuHHjhJR4CxYsEFJkfY6XlxfWrFkjiteXFDTamN8xjuOQnZ1dXEpyqGv9/BxVTznVtm1bzJkzB3/99Rc6deqkcHzNmjW4cuUKZs6cycBOnnLlymHPnj34+eeflaaazM3Nxd69e1GuXDnR3VJSUpSm3axUqRJ27dolus+XXLhwAQ0aNFC4rjds2ACO47B+/Xo0adIEANCiRQuULVsW27ZtQ//+/Vnoqh1jxozBzp074enpiZCQEDRv3hxlypRBUlIS9u3bJ6QbHDNmDGtV8WDdmpeQYEne6OnnFDRaoUojGQUxZswY0tHRYfb3dXR0aMyYMcK2st9t/Pjxoq+pMTAwoIkTJ8rty8zMJC0tLXJyclIoHxwcTEZGRmLpFYo6rKciIjI2NpYbAVIF9PT0FNZ5vXv3jmQymdLIpAMHDiQDAwOx9Apk9uzZpKmpKaxLzPtwHEeampr0888/C2VfvHhBy5cvp4sXLxa71/nz54nneYXfNSwsLN96KYaXMr52baJYqHP9JCJatWoV6erqKo3ZcfXqVeJ5nulsKSKi169fk52dHclkMurcuTP5+PgQz/O0aNEi6ty5M2loaJCjo6OQ0YMlP//8M3EcR61bt6Z//vlH7tjdu3epXbt2xPO83LUvFsrem4hU5/3IxMRE6ai+gYEBWVpaKuzv0aOHXMwJlhw6dIiCgoLynYmUlJREQUFBFBMTI67YF6xYsULpM4nnedLU1KSVK1cy9RMbaWRa4odH2aipOpOTk4MjR44ojLiLiY6ODlJTUwss8+jRI9HX1Hz8+BHv37+X23f9+nVkZWXBzc1NobyZmRnevXsnll6hqMv6qg8fPqBOnTqsNeTIyclRWHt89epV5Obmonbt2grljY2N8eHDB7H0CmTcuHHo1KkTNm7ciEuXLiEtLQ0GBgZwdnZGt27dhNkUwKf1bGKNsGzevBlaWloYMWKEwjGO45CVlSWsTUxJSYGNjQ02bNiAmjVriuL3Obm5uaL/zX+DOtfPgwcPol+/fqhevTqmTp2K6OhoLF++XDhetWpVVKlSBbt27WKav7lUqVKIi4tDQEAA/vrrL2H/sGHDAAD169fHpk2bRM3Pnh+jRo3C/v37ERUVhX379sHKygqWlpZITk5GUlIScnNz4enpKcxIERtVfm9KT09X8Lt79y7evHmDZs2aKZS3trYu9H1FLBYtWoRbt24pzd8MfIqkferUKaSlpSmdySIWwcHB8PT0xNKlSxVydw8cOBBOTk7M3FggNaYlfnjCwsIQFhamsF8VgpAo4+jRo0r3Z2dnIzExEWvWrMGlS5fQt29fkc3+R82aNXHgwAFkZmZCS0tL4XhaWhqio6NRr149Ub0sLCwU0gWdPHkSHMfB1dVVofybN2+YBXNShpeXF2uFIlG1alUkJCSw1pDD2toaFy5ckNt37NixfM99SkqKSgXNsre3x+TJk1lryHHs2DHUrVs33yBJn09PNTU1RZMmTXDs2DGx9NQKda6fs2fPhqWlJeLi4mBgYKA0SGL16tVx6tQpBnbylC1bFrGxsbhy5QpOnTolBMlzd3eHi4sLaz0BLS0tHDx4EHPnzkV4eDju3bsnpBkqV64cevfujTFjxkBTU5OJX37vTYDydycxl0yUKlVKIVDruXPnAADOzs4K5bOzs1GyZElR3ArjwoULwhT0/PD09MSBAwdEMsofJycnLFq0iLWGSiA1piV+eOhfplpn3SPr7e1doAMRoUGDBkzzPfbr1w/du3dH9+7dFaI1p6amIigoCCkpKRgwYICoXh4eHti6dStiY2Ph7e2N9+/fY+XKlQCApk2bKpS/du2aXGRdiaIxduxYIT9y5cqVWesA+HTdhIeHY8OGDejRoweePXuGZcuWgeM4+Pj4KJS/dOkSbGxsGJiqD3fu3EHPnj0V9hOR0vuqnZ2dSjSoVBF1rp/x8fHw9/cX4o0ow9raGsnJySJaFUz16tVRvXp11hoFoqmpiQkTJmDChAnIyMhAWloaDA0NVaLh92/fm/5t+f+Cs7MzoqKi8PTpU1haWgIAtmzZAo7jlHZI37lzRyjHmufPn+c7Kp2Hubk5nj9/LpKRRFGQGtMSPzSqPvVPGSEhIUob0zzPo1SpUnBzc1M6ZVlMunbtioMHD2Lt2rXYvXs3SpUqBQBwdXXF9evX8fHjRwwePBgtW7YU1WvkyJHYunUrmjVrhqpVq+LJkyd48eIFvL294ejoKFc2PT0dJ06cYDrCnx+PHj3C+vXrcfHiRaSmpsLQ0BC1atVCz549YWtry1oPZmZm8PX1Rb169TB8+PB8A6UBQIMGDURxGjt2LDZu3IhevXph2LBhyMjIQHZ2Njp06KCQXuj58+c4e/as0unLrNiyZQtWrVqFixcvCtO8XVxc0KdPH/j7+zNxevPmDfT19RX2BwUFCSlzPsfIyEguhZfE/1Dn+qlOKafUlZIlS6pEIxpQ/femPn364MCBA6hbty7at2+PO3fuICoqCuXLl4eHh4dc2ezsbBw7dgzNmzdnZCuPoaEhEhMTCyyTmJioEksRTp8+LTyT8t5DXFxcEBQUJPqsQ+YwXK8tISHxnbNmzRqqWbOmXIqkqlWrUnh4ODOnP//8kwwMDAQfNzc3SkxMVCi3cOFC4jiOIiIiGFjmz4oVK0hHR0dp2iltbW2VSPGRF4Do88BT+X3E5PDhw1S+fHkhcFfHjh0pJSVFodz06dOJ4ziKjo4W1U8Zubm51KNHD+H31NDQIAsLC9LQ0BB+227dujFxMzY2pkGDBhW5/KBBg6hUqVLFaKTeqGP9JFLtlFPr1q37qo+EehMcHCz3bDQyMqLY2FiFcjt37iSO42jNmjXiSyqhdevWZGBgQE+fPlV6PCkpiQwMDKhFixYim8kzadKkfFNf8jxPEyZMYOonNhyRiHMvJCQkfkjev3+PlJQUGBoaqkSP6vv373Ht2jWYmJjIBW/6nIcPHyItLQ1OTk5K132z4PDhw2jWrBn09fUxbNgwNGrUCJaWlnj69CmOHDmChQsXIiMjA9HR0WjcuDEzz7CwsCIvhwgNDS1mG0VevHgBQ0PDfM/ru3fvkJWVBQMDA+bLOpYvX45BgwbBxcUFs2fPhpeXF2QyGXJychAXF4fx48fj/PnzWLJkiejLJlxcXMDzvLAesTBq166NnJwchbXBEvKoU/0EgAkTJmDOnDnYsmULOnXqhKlTp2LatGlCGsk1a9agb9++mDlzJsaPHy+qG8/z/+o3IiJwHCd6Csz8nkOFwXEc7t27941tvg9OnDiBkydPwsTEBM2bN1c6fTo6Ohq3bt1Cz549VSI+yoEDB9C8eXOUK1cOv/32G3x8fKCtrY2PHz9i//79GD16NB48eICoqChmo+l//fUXunTpAltbW0yZMkXhPWT69Ol49OgRNm/ejM6dOzNxFBupMS0h8R2QlJSECxcuIDc3F/Xq1VOZwDQS35bmzZvj9OnTOH/+vNL8ovfu3YOLiwvc3d2xf/9+BoYS3xo3Nze8fPkS169fV5qj9/3796hatSqMjY2L3Kj9VowaNQoLFizAiRMn4O7uXmDZU6dOwcPDAyNHjsRvv/0mkqGEGKSkpKBWrVpITExEhw4dkJaWhoMHD2LBggU4duwYdu7ciXLlyuH8+fOid6byPA9NTU34+vr+qwjD06dPL0YrRezs7L66Y+TLYFsS6k1oaCimT58OjuPAcRxKlSqFlJQUIRbFlClTMHXqVGZ+DRo0wJ07d3D16lWlwSdfvnyJqlWrwtHREXFxcQwMGcBwVFxCQuJfcPnyZQoKCqLWrVvT1KlThVyYkydPJi0tLWHarLa2Ns2bN4+xrURxUKpUKQoODi6wTN++faWptN8Renp6NGrUqALLjBo1ivT09EQy+h///PMPyWQysrOzo5s3b+Zb7tatW2RnZ0caGhoKOXMlvg8SEhLIy8tL6bTPBg0a0OPHj5l4eXt7C1NPPT09ad26dfT+/XsmLhISRSU6Oppat25NpUuXJk1NTSpdujT5+vrSgQMHWKuRgYEBDRkypMAyQ4YMIQMDA5GM2CMFIJOQUANu3boFT09PvH37FkSEvXv34sKFC/D398fMmTOhp6eHatWqISUlBQ8ePMCYMWNQo0YNNGrUiJlzXFwcfv31V5w9exYpKSlKg5aImS7je+D9+/f5piHKo3Tp0gq5tCXUF47jCo2EW9jx4qJChQrCKImzszM6deqEhg0bChHwnzx5gsOHD2P79u34+PEjwsLCUKFCBSauEsWLqqaciomJwd27d7Fy5UqsW7cOQUFBGD58OHr06IHg4GCVj+gt8WPSrFkzpTmxVYHs7Gzo6uoWWEZXV/eHereTpnlLSKgBgYGBWL9+PYYMGYJmzZrh4MGDWLx4MSpXrgwzMzPs3LkThoaGAIBdu3ahQ4cOaNu2LXbu3MnENyoqCn5+fsjJyUHZsmVhY2MDDQ3lfXcxMTEi26kvlSpVgpGREU6fPp1vmbp16+L169e4ffu2aF6NGjUCx3FYt24drK2ti9yJw3EcDh8+XMx26o2bmxueP3+Omzdv5jvNu0qVKjA1NcXZs2cZGAJTp07FzJkzkZ2drTBVlYigoaGByZMnIyQkhImfhATwqREQERGBlStX4tChQyAiuLi4oH///vD391eJeB4SPzbPnj2Dubk5a40CqV69OnJycnD16lXwPK9wPDc3FzVq1ADHcbhy5QoDQ/GRGtMSEmqAvb09rK2tcezYMWFf/fr1cfLkSZw5cwaurq5y5Vu3bo2LFy8iKSlJbFUAnwINXb9+Hbt27VLZ3lV1JC/QT//+/TFr1iy5dFPp6emYMmUKFi9ejHHjxuHnn38WzSsvyM/NmzdRsWJFpQ9YZbAI9KNufB6A7JdffoGXlxc0NDSQk5ODo0ePYsKECTh37hyTAGSf8+DBA4SHh+PkyZNCPmELCwt4eHggMDDwqwMsSUgUBwkJCVi1ahXWrl2LJ0+eoGTJkti/fz/q1q3LxGf9+vUAgHbt2kFfX1/YLgoBAQHFpSUhMtra2vDz80P//v2ZziwsiF9++QUTJ05Eq1atMG/ePLnZRvfu3cPYsWMRERHBJOAgK6TGtISEGqCjo4PBgwfLBe4ZPXo05s+fj4yMDIURq3HjxmH+/PnIzMwUWxUAUKJECfj7+2PNmjVM/v73Snp6OurWrYubN29CX18fNWrUgKWlJZKTk3H58mWkp6ejUqVKOH36NAwMDFjrSnwDiAg9evTA5s2bwXEceJ6HsbExXr9+jdzcXBAROnfujC1btrBWlfiBycrKQkREhLCsR1knGcdxWL16NQO7/Nm7dy8GDBiApKQk/P3332jTpg0TD2UdkoUFJCNGkcclio9q1arh+vXr4DgO5cqVQ//+/REYGAgTExPWagKZmZlo1qwZjh49Cp7nYWVlJbyHJCUlITc3F56enjh06JDKZEIpbqQ10xISakBmZqYwjTuPvMaSsqmfenp6TB+wJUuWVIk0E98bBgYGOHnyJMaNG4eNGzfi+PHjwjFdXV0EBwfjl19+kRrS3xEcx2Hjxo1o3bo1wsPDcfHiRbx+/RqGhoZwdnZG79690bVrV9aaEj8wT548QdOmTXHr1q0C1++rSmP6yZMnCA8PR3h4OBISEqCjo4MePXqgVq1azJzCw8PBcRwsLS0BQOqI/kG5evUqTp48iRUrVuCvv/7C2LFjMXnyZLRv3x79+/dHgwYNWCtCS0sLBw8exNy5cxEeHo579+7h8ePHAIBy5cqhd+/eGDNmDDQ1NRmbioc0Mi0hoQbwPI+wsDC5NYdf5vL8nIKOiYG/vz8ePXqEkydPMvn7RcHBwQEtWrTAkiVLWKt8FVlZWbh9+zbS0tJgaGgIR0fHH+rhJSHxI5KSkgItLS2VWt/btWtXbN26FV27dkVwcHCBMTJsbW1FtvtEbm4u9uzZg1WrVmH//v3Izs5GtWrVEBwcjJ49eyp0VktIsCYtLQ3r16/HihUrhNFqR0dH9O/fHwEBAShVqhRrRQBARkaG8B5SsmRJ1jpMkEamJSTUhK/NQcmC2bNnw83NDTNmzMCkSZNU0v3Fixdq/QKlqamJqlWrstZQytGjRwstw/M8DAwMUKFCBaWzK8T2yQ9VGAmQ+LE4fPgwoqOjMWHCBOGF+fnz5+jUqROOHz8ODQ0NDB48GPPmzWNs+okDBw6gQYMG2LhxI2sVBR48eIDVq1djzZo1ePr0KfT09NCrVy8EBwfDzc2NtZ6ERL4YGhpi6NChGDp0KE6ePImVK1di27ZtGDVqFCZOnIhOnTphyJAhCjFzxKZkyZI4cuQIjhw5AiKCl5cX2rdvz9RJbKSRaQkJNaAo66eUIdbIdO/evRX2PXz4EHFxcbC1tUXNmjXlgmXlwXLan7u7O2xtbbF161Ymf/9ruHfvHk6cOIFWrVopXUP18uVL7N27F56enkwDPv2b+iqTyeDj44O5c+fC0dGRuc+XqMp6xCtXrmDTpk24efMm3r59i0OHDgH4dJ2dPXsWTZs2VZmRCon/hp+fH65du4a7d+8K+wICArBhwwaUL18eGRkZePbsGTZv3ozOnTszNP2Enp4ehgwZgtmzZ7NWUUAmkwEAXF1dERwcjK5du6rUqL7Et+Vrn3scx+HevXvf2Obb8c8//2D58uVYt24dUlJSwPM8cnNzwXEcfH19ER4eXqxL6yIjI/Hrr79i+vTp8PLykjsWGBiIP//8U1jiwXEc/Pz8sGPHjmLzUTWkxrSEhBpQ1OjInyNmYJKv8QPYRnPetGkT+vbti9OnT6tNrtHg4GDs2rULT548UTqlOysrC2XKlEGHDh2wbNkyBoafCAsLw7lz57Bv3z5UrFgR9erVg7m5OZ49e4aTJ0/in3/+QcuWLWFvb48LFy7g1KlTMDY2xrlz52Bvb18sPl/bmA4NDf3GNv+ekJAQzJo1S8jV/vl1c//+fVSoUAHz58/H0KFDWWpKfCPs7e3h5eWFtWvXAviU/szExAT169dHdHQ03rx5g2rVqsHBwQFHjhxhKwugTp06sLe3V8kgeDzPQ1NT81+lG+I4DgkJCcVoVTTi4uLw66+/CkHd8q7/z+E47ofK51sYdnZ2X32vf/DgwTe2+W9kZWVhx44d+OOPP3D06FEQESpWrIgBAwYgMDAQly5dwpw5c7B//3506dIFmzdvLjaX4OBgbN68GS9fvoSOjo6wf8+ePWjTpg309PQwcuRI6OvrY8WKFbh//z42bNjww8TzkBrTEhIS/5n/8uLBag3d0aNHMXfuXMTExKB///6oXbs2LCwslD6IVWWqb4UKFeDq6lrgQ7N79+6Ij48XNc/0l5w+fRoNGzbEggULEBwcLPebEhH++OMPjBo1CjExMahTpw7Wrl2L3r17o0+fPli5ciUzb1Vky5Yt6NatG3x8fDB79mxs3boVv/zyi1wnVJ06dWBgYICDBw8yNJX4Vujp6WH48OGYNWsWACA2NhaNGjXCxo0bhZfTIUOG4O+//2aW/vBztm/fjoCAAMTHx6Ny5cqsdeT42o5eZQ1XMYmKioKfnx9ycnJQtmzZAtehx8TEiGwnUZzcvXsXK1aswNq1a/Hq1SvwPI82bdpg0KBBaNy4sUL5jh074vDhw0hJSSk2pxo1asDKygr79u2T29++fXtERERg69at6NixIwAgOTkZ5cqVQ8OGDbFnz55ic1IlpDXTEhIS/xlWDeL/gre3NziOAxFh3rx5BfZmq8pU36SkJOGBlR9ly5bF7t27RTJSzpQpU9CsWTP069dP4RjHcRgwYAD27t2LkJAQREdHIzAwEOHh4VJjUAkLFy5E+fLlERERAS0tLfz9998KZZycnBAbGyu+nESxoK2tjffv3wvbx44dA8dxcp16BgYGeP36NQs9hRgEZmZm8PX1Rb169TB8+HC4uLgoXdYDiN8xybpR/LWEhYVBU1MTUVFRaNasGWsdCZFo3LgxYmNjQUSwtLTElClT0K9fP1hZWeX7HRcXF6XPhW9JcnIymjZtqrD/6NGjMDIyQocOHYR9FhYWaNWqFU6cOFGsTqqE1JiWkJD4JmRmZsLT0xP6+vrYv39/vpGlMzMz0aJFC7x9+xbHjh1jFoE6JCREJQOjFYSWlhbS09MLLPPmzRvm/19nz54tdMpx9erVsWjRImHb2dkZZ8+eLW41tePq1asIDAwsMF+nlZUVnj17JqKVRHFib28vN317x44dqFChAsqUKSPsS0xMhKmpKQs9oSPyS4gI06dPV4uOSVXn2rVr8Pf3lxrSPxgxMTFo2LAhBg0aBD8/P2HNf0H4+voW2Nj+FuRlEficR48e4fXr1/D19VW45u3t7Zl36ouJ1JiWkJD4JmzYsAHnz59HZGRkgQ1kLS0tjB07Fi1btsTGjRsRGBgonuRnhIWFMfm7/4WqVasiKioK8+fPV/obZ2ZmYs+ePcynWhIR7t+/X2CZL4O9aGhoQFtbuzi15CAibN++HdHR0UhKSsLHjx8VynAch8OHD4vmpAwiKnSq6rNnz+TWsUmoN7169cKIESNQp04daGlp4erVqwpr969cuVJsAfsKQx07ItWNkiVLFmtAqR+Njx8/4ty5c/ne64FPQf5Yc/PmzX99XVetWrXYM3vo6+sLuaTzOH/+PIBPHeHK+JGeSVJjWkJC4puwc+dOODg4oGXLloWWbd68OSpUqIC//vqLWWNaHenRowcGDRqEzp07Y9myZbCwsBCOJScnY8CAAUhMTMS4ceMYWn6KlL5jxw4cOHBA6cjK/v37sWPHDjRs2FDYd/fuXbn/n+Lk48ePaNmypTCdLm+6fx5526rQYKhQoUKB+dpzc3Nx/PhxVKlSRUQrieJk4MCBOH36NLZu3Qoigq+vL3766Sfh+LVr13D16lVMmzaNiZ86dkSqG40bN8apU6dYa3wXhIeHY9y4cfmuKc6716tCY5pVB1lhVKtWDVFRUcjIyBBySf/999/gOA6enp4K5R88eABLS0uxNdlBEhISEt8AKysr6tu3b5HL9+3bl6ysrIrRqGhkZmbSvn37aN68eTRt2jRh//v37+nZs2eUk5PD0E6enJwcatq0KXEcR3p6elS3bl3q2LEj1a1bl/T09IjjOGratClz57Nnz5KOjg7xPE9NmjSh0NBQWrp0KYWGhlLjxo2J53kqUaIEnT17loiIUlNTqUSJEhQcHCyK39SpU4njOAoJCaFXr14Rx3E0depUevr0KW3evJlsbW2pa9eulJ2dLYpPQcyaNYt4nqe5c+cSEVFYWBjxPC8cnz59OvE8T0uWLGGlKFFMpKWlUXp6usL+Fy9e0KVLlyg1NZWBlSIJCQmUlpZWYJn09HRKSEgQyUj9efjwIZmZmdH06dMpNzeXtY7asm/fPuI4jqpWrUrz5s0jjuOoXbt29PPPP5OPjw9xHEedO3emtWvXslYlIqILFy7QkiVL5K7tjIwMCggIIENDQ7K0tKT58+eL7rVixQriOI5q1apFCxYsoMGDBxPP82RlZaXwnMzNzSVLS0tq37696J6skBrTEhIS3wQtLS2aNGlSkctPmjSJtLW1i9GocPbt20dWVlbE8zxxHCfXSDl16hTxPE+bNm1iaKhIZmYmTZgwgYyMjIjjOOFTqlQpmjRpEmVmZrJWJCKiuLg4Kl++vOCX9xtzHEfly5en2NhYoey7d+/o1q1blJKSIopb1apVycXFRdjOa0znce/ePdLX1xcasCx59+4dOTs7E8/z5ObmRnXq1CGe52n06NHk5uZGPM9TvXr1KCsri7WqxA8Kz/NyHZHKmDFjhtz9VUKeoKAghU/Dhg2J53myt7endu3aKS3Tu3dv1uoqTZMmTcjU1FTolPryXr9q1SrS0NCgY8eOsVKUo0uXLgqDDEOHDiWO40hfX5+0tLSI53mKjo4W1SsnJ4eaN28u9yzX0tKiv/76S6HswYMHieM4Wrp0qaiOLJGmeUtISHwTSpQogYyMjCKXz8jIYLqmJj4+Hn5+fjA1NcXvv/+Os2fPyqWccnd3h729Pf7++2+VypWoqamJWbNmYcaMGbh16xZSU1NhZGSESpUqfXUamOKgQYMG+Oeff3Dy5ElcvHgRaWlpMDAwgLOzMzw8POSmUJcoUULU6W337t1DcHCwsM1xHLKysoRtBwcHtGrVCmvXrsXo0aNF81JGiRIlEBMTg+HDh2Pjxo1CAKd58+aB53n06NEDixcvzjdtjoREcUOfBmZYa6g1eTnFlfHw4UM8fPhQ6TGO47B69erikfoOuHDhAtq2bQt9fX1h3+cR3vv06YM///wTM2fOVEj7xIL4+Hi55U9ZWVlYt24d3NzcEBsbi9evX8PZ2RkLFy4UNTgdz/OIiorC5s2bcfLkSZiYmKB9+/aoWbOmQtmXL19i+PDhaNOmjWh+rJGevhISEt8EGxsbxMfHF7l8fHw8ypYtW4xGBTN9+nTo6uoiPj4eFhYWmDp1qkKZ2rVr48KFCwzsCofneeaBxgqD4zh4eHjAw8ODtYocmpqach05+vr6ePHihVwZW1tblYlGamhoiLVr12LevHk4d+4cXr16BUNDQ7i5uaF06dKs9ST+Iw4ODl/1PY7jFAL5qSrJycnQ09NjraGyPHjwgLXCd8nbt2/l1u7q6OgoZMRwdXVFeHi42GpKef78OaytrYXt+Ph4vHnzBv3794eOjg6srKzQtm1b7N+/X3Q3nufRvXt3dO/evcBy/v7+8Pf3F8lKNZAa0xISEt8Eb29vLF26FPHx8XB1dS2w7Pnz53Hy5MlC0ycVJydOnICfn1+BQa9sbGwQFRUlopUiR48ehZ2dXZE7Hq5cuYJLly6pRDAVVcXa2hpJSUnCdsWKFRWC/Vy8eFHloukaGxvDx8eHtYbENyY3N/ergt2xHA1ev3693PalS5cU9gGfUmE9evQIGzZsQLVq1cTSUztsbW1ZK3yXWFhYyHWUWlpa4vbt23Jl0tLSVCZlG8dxyM7OFraPHz8OjuPg5eUl7CtdurRC568EW6TGtISExDdhyJAhWLZsGTp16oS9e/fCyclJablbt26hU6dOkMlkGDRokMiW/yMjI6PQPK3v3r2TmxLGgoYNGyI0NBQhISHCvtmzZ2POnDl49eqVQvm///4b06ZNY96YzsrKQkREBM6ePYuUlBSlLyuspih6eHjg0KFDwrafnx8mT56Mvn37ol27doiNjcWhQ4fQrVs30d3yWLZsGdLS0jBu3Dhh+v6CBQuwYMEChbJeXl5Ys2aN2IoS34j8pvCqMoGBgUIHAMdxiIiIQEREhEK5vAa/rq6uQnovCeU8evQI586dA8dxqF27NmxsbFgrqS1VqlSRazzXr18fW7ZswbFjx1C/fn1cu3YN27ZtU5lsCGXLlsXp06eF7YiICFhbW8vNXnny5AlKlSrFQk8iH6TGtISExDfB0dERISEhCAsLg7OzMzp27IhGjRoJU5aSkpJw+PBh7NixAx8/fsS0adOYpoEoU6YMrl+/XmCZS5cuffUUzG+FstGnDx8+IDU1VXyZIvLkyRM0bdoUt27dKnD0jFVjulu3bkhMTMTDhw9hZ2eHESNGICIiAuHh4VizZg2ICOXLl8cvv/wiuhvwaZ3fkCFDMGHCBLl18KmpqUobXgkJCRg+fLjS9WsSEsVBXucNEaF3797w8/ND27ZtFcrJZDKYmJigbt26MDIyEtlS/RgzZgzmz58v3Dc5jsPIkSPx66+/MjZTT1q0aIERI0bgyZMnsLKywrhx4/DXX3/B29sbxsbGeP36NYgIkydPZq0KAOjcuTNCQ0PRsWNH6Ojo4NSpUxgxYoRcmZs3b6JcuXJsBCWUwyrymYSExPfJzJkzSUtLS4j6+PknLwLkrFmzWGvS4MGDSSaTCVE8v0w5tHfvXuI4jiZMmMBKkYgUo48SKboW9ZhY+Pv7E8dx1K1bN4qJiaG7d+/Sw4cPlX5UhaysLNq+fTv98ssvtGXLFnr79i0zlzFjxpCOjg69ePFCbn/euc3JyaHs7GzKzs6mFy9ekI6ODo0ePZqRrcSPjre3N61bt461htqzadMm4blZuXJlcnJyEp6dqpZVQl3IzMyk5ORk+vjxo7Dv1KlT1KpVK6pUqRI1b96c9u/fz9BQnjdv3lC9evWEzBfOzs5yabLu379PPM//q8wpEsWPNDItISHxTZk4cSK6d++O8PBwnDhxAk+fPgXwaa2Sp6cngoKCVGJ92IQJE7BlyxY0a9YMQ4cOFUb8oqKicPToUSxZsgSWlpYYNWoUW1E15MCBA2jQoAE2btzIWqXIaGhooEOHDqw1AADHjh1D3bp1812G8PlotampKZo0aYJjx46JpSchEh8/fsS5c+eQlJSEjx8/Ki3DejkHAMTExLBW+C5YtWoVNDQ0EB0dLUR0PnToEFq0aIHVq1erVFYJdUFTUxPm5uZy+9zd3bFnzx5GRgVTsmRJnDhxAteuXQMAVK5cWe5+z3Ecdu7cWWhcGglxkRrTEhIS3xxbW1ul0bFViTJlyuDAgQPo3Lmz3BS6Nm3agIhQrlw57Ny5s9B11RKKfPjwAXXq1GGtkS+NGjVCYGBggQ2RDRs2IDw8HEeOHBHR7BN37txBz549FfZTPimI7OzsFAKoSag34eHhGDduHFJSUpQeJyJwHKcSjWmJb8OVK1fQtm1budRITZo0Qdu2bREbG8tOTEIUirJW3s7ODnZ2duLLSRSI1JiWkJD4YalVqxZu376NqKgonDp1Skg55O7ujrZt20q5e7+SqlWrIiEhgbVGvsTGxsLb27vAMgkJCYiLixNH6AvevHkjlxc1j6CgILkX7TyMjIzw5s0bMdQkRGD//v3o27cvqlSpgkmTJmH06NHw8/MTcs0eOHAAnTp1QsuWLVmrCrx9+xZLly5FdHR0viPp6pTKiwUpKSmoVKmSwv5KlSph165d4gt9Bxw+fBgbN27EjBkzYGVlpXD8yZMnmDx5MgICAgp9JhQn0lp59UZ6U5SQkPihkclkaNOmDdq0acNaJV++Jm0OS8aOHYuAgADcuHFD5XNh58f79++Zdabo6+vj9evXCvttbW2VLpF4/fq1lMP3O+K3336DiYkJTp48CX19fYwePRo1a9bE+PHjMX78eKxevRoDBgxgmlrwc1JTvVV8UQAAIrpJREFUU+Hp6YkbN27AwMAA6enpMDQ0RGZmJt6/fw8AsLKygqamJmNT1SY3N1fpb6Spqck0DZo6s2jRIty6dUtpQxr4VC9PnTqFtLQ0Zo3pzZs3Y968eeA4DpUqVQIR4fbt25g3bx5q1aolTe9XA/jCi0hISEh8fzRq1EhpXtTP2bBhAxo1aiSSUf6EhYVBJpMJn2nTpgGA3L4vj7HEzMwMvr6+qFevHkJDQ7F7924cPXpU6YcV+XVQEBESEhKwd+9eZilp7OzscPbs2SKXP3v2rDT17zviwoUL8PX1lZud8HmKvj59+sDDwwMzZ85koafAjBkzcOPGDaxevVqYlj5y5EhkZGTg5MmTqFWrFsqVK4ebN28yNlV91K3jVNW5cOEC6tWrV2AZT09PxMfHi2SkSN5a+UOHDuH69eu4ceMGoqOjwfM8k2wXEv8eaWRaQkLih0TVp/p+zr8dlWD9Qubt7Q2O40BEmD59eoE+yvJPFwc8z8t5hIWFISwsLN/yRISJEyeKYKaIl5cXFixYgNOnT8Pd3b3AsqdOncL58+cxcuRIkewkipu3b9/C0tJS2NbR0UF6erpcGVdXV4SHh4utppTdu3ejQYMGCAoKktvPcRzc3d2xd+9eVKtWDTNnzsT06dMZWaoHBd2XZDKZwj6O45CdnV3MVurL8+fP8x2VzsPc3BzPnz8XyUgRaa28+iM1piUkJCTygeVU3zw+H5FSF0JCQpg36L+kQYMGgtPRo0dRtmxZpaO5eXlxGzdujL59+4ps+YmBAwdi4cKF6Nq1K/bt26d0HSUA3L59G926dYNMJsOAAQNEtpQoLiwsLPDixQth29LSErdv35Yrk5aWJlpHVGEkJibC19dX2OZ5Xm7NtJmZGVq0aIEtW7ZIjelC+Lcdp9L074IxNDREYmJigWUSExOZLpOR1sqrP1JjWkJC4oeloKm+jx49YjrVV50paMSXFZ/38PM8j6CgIISEhLATKoAKFSpgypQpmDp1KpydndGpUyc0bNgQZcqUAfApaM7hw4exfft2fPz4EWFhYahQoQJja4lvRZUqVeQaz/Xr18eWLVtw7Ngx1K9fH9euXcO2bdtQpUoVhpb/Q1dXVy59j6GhIZKTk+XKmJubIykpSWw1tUIdO05VHTc3N+zatQvJycmwsLBQOP7kyRPs2rULHh4eDOw+Ia2VV3+kxrSEhMQPgzpN9ZUoPh48eAAjIyPWGgUSGhoKAJg5cyY2bNigkLObiKChoYGwsDCV7RSQ+DpatGiBESNG4MmTJ7CyssK4cePw119/wdvbG8bGxnj9+jWICJMnT2atCgCwsbGRG/2rXLkyjh49itzcXKGRffz4caWNGQmJ4mTo0KGIiopC/fr18dtvv8HHxwfa2tr4+PEj9u/fj9GjRyMjIwPDhg1j6qlqM7kk/h0cSd0eEhISPwh5a3mBfzfV9/NRFwnlHD16FHZ2dihbtmyRyl++fBmXL1+W8uQWwoMHDxAeHo6TJ08Ko30WFhbw8PBAYGAgHBwcGBtKfGuysrLw+vVrlCpVClpaWgCA06dPY8aMGbh37x7s7OwwYsQI+Pj4MDb9xPDhw7Ft2zY8efIEHMdh8eLFGDZsGJo1awZfX1/ExsZi586dGDhwIBYvXsxaV+IHIzQ0VIjdwXEcSpUqhZSUFBARiEiYBcSKLzv5i4K0Vl61kBrTEhISPyQ8z0ujet8QmUyG0NBQud9z9uzZmDNnDl69eqVQfurUqZg2bRqzdZ9ZWVmIiIjA2bNnkZKSotSD4zgpmqqERCFcuHABK1euxMSJE2FjY4Ps7Gx07txZbr2nh4cHIiMjVX5GiMT3yYEDB7Bo0SKcOXMGqampMDIygru7O4YOHYqmTZsydfvaznppWYDqIE3zlpCQ+CFRh6m+6oSyftkPHz4gNTVVfJlCePLkCZo2bYpbt24VuCZNakxLSBROrVq1sGzZMmFbQ0MDO3fuxPnz53H37l3Y2dmhdu3a0gwfCWY0a9YMzZo1Y62hFKlRrP5IjWkJCYkfEltbW9YKEowYPXo0bt68ia5duyI4OBg2NjbMo7ZLSHxvuLi4wMXFhbWGhISERLEivT1ISEj8kEybNq1I5TiOw5QpU4rZRkJMDhw4gAYNGigE9ZKQYAHP8+B5Hjdu3EDFihWLvIZSWjcpISEhwR6pMS0hIfFDUlAU77wXWSKSGtPfIR8+fECdOnVYa0hIAPhfDnRdXV25bVVm/fr1X/U9KeCgRHEidUxJsEBqTEtISPyQxMTEKN2fmpqKc+fOYeHChWjVqhUGDBggsplEcVO1alUkJCSw1pCQACCfA13ZtioSGBj4rxr8eR2TUmNaojhRx44pCfVHakxLSEj8kHh5eeV7rG3btujSpQvc3Nzg7+8vopV6oy4vLWPHjkVAQABu3LiBypUrs9aRkJDj6NGjMDAwQM2aNVmrFIiGhgZ8fX3h5OTEWkVCAoB6dkxJqD9SaiwJCQmJfPD398eDBw9w5swZ1ioqz9fkygTAJDXW0aNHsWTJEkRHR2P48OFwcXHJN7J7gwYNxJWT+OGRyWTo378/li5dylolXxo2bIi4uDhwHId69eohODgYnTt3ho6ODms1CQkJCVGRGtMSEhIS+TBu3DgsWbIEb9++Za2i8nxN2huO45g0pvMa/nmPv4I6AVjlwZb4cTE3N0ePHj3w22+/sVYpkLt372LlypVYt24dXrx4AQMDA/To0QPBwcGoXr06az0JCTg4OGDEiBEYNmxYvmWWLFmC3377Dffv3xfRTOJ7QprmLSEhIZEPZ86cQYkSJVhrqAXqlCszJCREbaakS/x4eHt74+TJk6w1CqV8+fKYPXs2Zs6ciYiICKxcuRLLli3D0qVL4eLigv79+8Pf3x96enqsVSV+UB4+fIjU1NQCy6SmpkoxNCT+E1JjWkJC4ofk0aNHSvdnZ2cjMTERK1euxPHjx9G5c2eRzSSKm4IiuUtIsGbGjBmoU6cOpkyZgpCQEGhqarJWKhANDQ106NABHTp0QEJCAlatWoW1a9eiX79+GDVqFPbv34+6deuy1pSQUMqbN2+gpaXFWkNCjZEa0xISEj8kdnZ2BY5OEhEqVKiAuXPnimglISHxo/Pzzz+jatWqmDVrFlavXo0aNWrAwsJC4X7FcRxWr17NyFI5tra2mD59OurWrYsBAwYgKSkJL168YK0l8QPxZUd5amqq0s7znJwcPHr0CDt27ICDg4NYehLfIdKaaQkJiR+S/FK78DyPUqVKwc3NDW3btoW2tjYDOwkxyMrKwuHDh3Hz5k1kZGQI+cQ/fPiA9PR0mJqaftVacAmJ/0JR6xyrmAP58eTJE4SHhyM8PBwJCQnQ0dFBx44dMXPmTFhbW7PWk/hB+DwYZl5KtoIgIsybNw8jRowQwU7ie0RqTEtISEhI/HDs378fffr0QXJysvDCldcwOX36NDw8PLBhwwZ07dqVsanEj8a/Wb9pa2tbjCaFk5ubiz179mDVqlXYv38/srOzUa1aNQQHB6Nnz54wNDRk6ifx45HXUU5EWL9+PWrUqKE0zZxMJoOJiQkaN26MZs2aiS8q8d0gNaYlJCQkJH4o4uPj4enpCVNTU4wbNw5nz57F5s2b5Ub5ypcvj1q1amHbtm0MTSUkVJMHDx5g9erVWLNmDZ4+fQo9PT34+/sjODgYbm5urPUkJAB8GqUOCwtDSEgIaxWJ7xhpzbSEhMQPz7Fjx3Dx4kWkpaXB0NAQzs7OqF+/PmstiWJi+vTp0NXVRXx8PCwsLDB16lSFMrVr18aFCxcY2ElIqD7ly5cHALi6umLq1Kno2rWrFLVbQuVQpywTEuqL1JiWkJD4YTlx4gR69+6Nu3fvApBfX1WhQgWEh4ejXr16LBUlioETJ07Az88PFhYW+ZaxsbFBVFSUiFYSEvJERkZi48aNuHnzJt6+fSvcp27evInIyEh0794dZcqUYeJGRNDU1MTTp08xbdo0TJs2rdDvcBwnpSCSkPi/9u41qKrr7uP4bx/AGI0cvEREBPGONjYVtEkM9RbNCIpiikZrQMIMMXVEq0ltTKs1UVPSTBpNq7VVUayOTo1RYlRAJYhovMQYrKNSNQqCkMYI3qIBDud50YanRiBi5Wxgfz8zzHDOXnv8vWA857/XWv+FRodiGoAlHTlyRMOGDdOtW7c0cOBADRo0SO3atVNRUZE++ugjZWZmatiwYdq7d6+CgoLMjov76Pr162rTpk2NY77++mtmNWAKp9OpmJgYrV27VpL04IMP6ubNm5XXW7ZsqVdffVVOp1O/+tWvzIqpsrIy5efnm/bvA98VGxsrwzD0xhtvyNvbW7GxsXd1X33sjI+Ggz3TACxp+PDhSk9P16ZNmxQeHn7H9eTkZEVGRmro0KHasWOHCQlRV7p06aLAwMDKmefXXntNr7/++m17pkNCQnT16lUdO3bMrJiwqCVLlig+Pl6xsbF6++239c4772j+/Pm3/X0OHDhQTqdTmZmZJiYF6pdvO3mfPHlS3bt3b7Cd8dGwMDMNwJL279+vZ555pspCWpJGjx6tMWPGKDU11cXJUNdCQ0O1bNkyZWVlKSQk5I7rO3bs0P79+/XKK6+YkA5W9+3Z0suXL5dhGFUe7dOtWzf+bwK+49y5c5JUuf3h29dAXaKYBmBJNputsolOdbp166a0tDQXJYKrzJ49Wxs2bNDTTz+t+Ph4nT9/XpK0bds2ZWZmasmSJfLx8dHMmTPNDQpLysnJ0eTJk2s8H7dt27b68ssvXZgKqP++e1Sc2UfHwRoopgFYUt++fZWdnV3jmOzsbI55aYR8fX2VlpamcePG6a233qp8f9SoUXI6nerSpYvef//9791XDdQFd3d33bp1q8YxBQUFeuihh1yUCABQHYppAJa0YMECDRo0SH/+85/185///I7rS5Ys0e7du5WRkeH6cKhzQUFBysnJ0bZt2/Txxx/rq6++kt1u1+OPP67Ro0fL3Z2PR5ijV69eysjIuO10gf9269Ytpaenq0+fPiakA+qvvLy8e77X39//PiaBlfBtAYAlpaWlaciQIZo6daoWLVqkn/zkJ/L29tYXX3yhrKwsnT59WsOHD1dqauptexMNw9CcOXNMTI77xc3NTaNGjdKoUaPMjgJUioqK0tSpUzVjxgz94Q9/uO2aw+HQzJkzdfHiRSUkJJiUEKifAgICatweUR3DMFReXl4HiWAFdPMGYEl32+Xzu+j6CaAuORwOjRgxQmlpafLx8VGLFi10+vRpjRkzRgcOHNDFixc1evRobd682eyoQL0SExNzT8W0JK1ateo+p4FVUEwDsKQ9e/bc870DBw68j0lglmPHjik7O1v5+fkqKyu74zqrEGCW8vJyLViwQH/60590+fLlyve9vLwUHx+vOXPmsBUBAOoBimkAgKVcvnxZUVFRSklJkSRV9zHIKgSYzel06p///Gflnv7AwEC5ubmZHQsA8B881gQAWMovfvEL7dixQ0OHDtVzzz0nX19fZvlQb+Tl5cnLy0uenp4yDEM9evS4Y8y1a9dUXFxM0yTgLuXn5+vo0aMqKSmR3W5XUFCQOnToYHYsNALMTAOwPKfTqaKioiqX+kp0+WxsWrVqpV69eikrK8vsKMAd3NzcNG/evBq3GCxcuFBz585l5QTwPXJzczV58mTt3LnzjmvDhg3TsmXLFBAQ4PpgaDR4FA/AsjZu3KiEhAQdP3682k6edPlsfBwOh/r37292DKBKTqez2q0HAO5eUVGRQkJCVFBQoICAAA0YMEA+Pj4qLCzU3r17lZaWppCQEH3yySdq166d2XHRQFFMA7CkJUuWaNq0aXJ3d9eTTz7JUl8LCQoK0ueff252DOCeFRUVqXnz5mbHAOq1+fPnq6CgQG+++aZmzpx5W78Bh8Ohd955R7Nmzaps9gfcC5Z5A7Ckrl276saNG9q/f786depkdhy4UHp6usLCwrRr1y6FhISYHQfQmjVrKn+PiYlRRESEIiIi7hjncDiUl5enRYsWqVevXtq3b58LUwINS0BAgAIDAyubTVZl+PDhOnXqlM6fP++6YGhUmIYBYEkFBQWKi4ujkLagIUOGaMOGDRozZoxGjhypoKAg2e32KsdGR0e7OB2s6L/PxzUMQ8nJyUpOTr5j3LfzH82aNdNvf/tbl2YEGpqioiJNnDixxjHBwcHKyMhwTSA0ShTTACzJz89P33zzjdkxYILS0lIlJyeruLhYSUlJSkpKqixkvuV0OmUYBsU0XGLVqlWS/v13Fxsbq4iICI0ePfqOcW5ubmrdurWeeOIJeXl5uTgl0LDY7Xbl5ubWOCYvL6/ah6nA3aCYBmBJkyZN0l/+8hddu3ZNLVq0MDsOXGj27NlKSkpSr1699Oyzz6p9+/bsl4epJk2aVPl7UlKSIiIieJAD/I9CQkL03nvvacqUKVU2nTx48KA2btyoESNGmJAOjQV7pgFYksPh0Lhx45Sfn6/f//73CgoKoqi2CF9fX7Vp00aHDx9WkyZNzI4DAKgDn376qfr37y+Hw6Hx48dr8ODB8vHxUVFRkTIyMrR+/XrZbDbt27dPwcHBZsdFA0UxDcCy0tPTNXbsWJWUlFQ7hqOxGp/mzZtrypQpeuutt8yOAgCoQx9++KEmTZqk4uLi27bzOJ1OtWrVSomJiRo1apSJCdHQsa4NgCUlJycrMjJSDodDnTp1YqmvhfTs2VOFhYVmxwCqdePGDS1dulSpqakqKCiosr+DYRg6e/asCemAhmPkyJHKy8vTli1bdPToUV25ckV2u119+vRRREQER8zhf8bMNABL6tOnjz7//HNt27aN45EsZv369YqLi9Onn36q7t27mx0HuE1JSYlCQkJ04sQJeXp66urVq7Lb7SotLdXNmzclSe3bt5eHh4fOnTtnclqgfsrLy9Phw4dlGIb69esnPz8/syOhkWIaBoAl5eTkKDo6mkLagnx9fTV8+HA99thjmj59uoKDg6vt5jpgwAAXp4PVLViwQCdOnNDKlSsVExMjNzc3zZgxQ3PmzNHBgwc1depUNW/eXKmpqWZHBeqll19+WYsWLao8Ss4wDM2YMYOtPagTzEwDsCR/f39FRETo3XffNTsKXMxms8kwjNu+aFXH4XC4KhYgSerevbvat29fefatzWbTvHnzNHfuXEnSv/71L/Xu3VsvvPCC5s+fb2JSoP5Zv369Jk6cKMMwFBgYKKfTqZycHEnS2rVrNWHCBJMTorFhZhqAJf30pz9VSkqKysrK5OHhYXYcuNDcuXNrLKABM124cEHh4eGVr2022217ptu2bavQ0FBt2LCBYhr4jhUrVsjd3V2pqakaPHiwJGnXrl0KDQ3VypUrKaZx31FMA7CkBQsW6ODBgxo7dqwWLVqkgIAAsyPBRebNm2d2BKBazZo1k81mq3xtt9tVVFR02xhvb28VFBS4OhpQ7x07dkyjR4+uLKQlaejQoRo9enTlag/gfqKYBmBJvXv3VllZmQ4ePKitW7fKy8uryn2zdMwF4Ep+fn66cOFC5etevXopMzNTFRUVlUV2VlaW2rVrZ1ZEoN4qLi5WYGDgHe8HBgZqy5Ytrg+ERs/2/UMAoPGpqKiQu7u7/P395e/vL09PTzmdzjt+KioqzI4KwEIGDhyoPXv2VO7pf/bZZ3X27FmFhYVpyZIlGjt2rA4cOKCwsDCTkwL1T0VFRZVbtzw8PESbKNQFZqYBWNL58+fNjgATnT59WosXL9ahQ4dUXFxcZaMxViXADJMmTVJpaany8/Pl5+enF198Uenp6dqyZYvS0tIkSU8++aQWLFhgclKgfqInBlyJbt4AAEv5+OOPNXToUN28eVPu7u7y9vaWu3vVz5Y5xxf1xZEjR3TmzBkFBASoX79+t+2rBvBv357WUBuGYai8vLyOEqGxo5gGAEnXrl1TSUmJ7Ha7PD09zY6DOjRo0CBlZWVp6dKlio2NrbaQBgA0LPf6kIktXbhXPNYEYFnl5eVKSEhQ165d5eXlpYCAALVs2VJdu3ZVQkICT6obqcOHDysyMlIvvPAChTQANCIVFRX39APcK75FALCk0tJSDR8+XHv27JFhGPLz85OPj48KCwt1/vx5/frXv1ZKSorS0tLUpEkTs+PiPmrSpIn8/f3NjgFUWrNmzT3dFx0dfZ+TAABqg2XeACwpISFBr776qkaOHKm3335b3bp1q7x29uxZvfTSS9q6dasWLlyoV155xcSkuN9GjBih0tJS7dy50+wogKTa7/N0Op0yDKPKxnkAANehmAZgST/84Q8lSZ999lmVe6wqKir0ox/9SE6nU//4xz9cHQ91KDs7W/3799eyZcsUFRVldhxANptNHh4eCg8PV8+ePe/6vvnz59dhKgDA92GZNwBLOnPmjOLj46ttVmKz2RQaGqo//vGPLk6GupacnKwhQ4YoJiZGK1asUHBwsLy8vO4YZxiG5syZ4/qAsJxvz5bevHmzvvjiC8XFxWncuHFq2rSp2dEAADVgZhqAJXl5eWnixIlasmRJtWOmTp2qv/3tb7py5YoLk6Gu3W23V5bRwpXOnDmj5cuXKykpSV9++aU8PT313HPPKS4urnIlDQCgfqGYBmBJAwYMUE5Ojo4fP66HH374juuXLl3SI488ou7duyszM9OEhKgre/bsueuxAwcOrMMkwJ3Ky8uVnJys5cuXa9euXXI6nQoODtbkyZM1fvx4NW/e3OyIAID/oJgGYEl///vfNX78eHXs2FG/+c1vNHjwYPn4+KioqEgZGRlasGCBzp8/r/Xr12vcuHFmxwVgQbm5uVqxYoVWr16tixcv6qGHHlJKSoqeeOIJs6MBAEQxDcDCXn31VSUkJFTZRdfpdGrWrFlKSEgwIRkA/L/t27frxRdfVEFBgTZv3qxRo0aZHQkAIIppABZ34MABrVy5UkePHtWVK1dkt9vVp08fxcbGMvvTyOXl5WnNmjU6evSoSkpKZLfbFRQUpKioKHXs2NHseLC4ixcvKjExUYmJicrNzVXTpk0VGRmphQsXqkOHDmbHAwCIYhoAYEHLly/XtGnTVFpaqu9+DDZp0kSLFy/W5MmTTUoHq6qoqNCHH36oFStWKCUlReXl5erdu7fi4uIUFRUlu91udkQAwH+hmAYAWMru3bv19NNPq0WLFpo2bZqGDBkiHx8fFRYWKj09Xe+++66uX7+u1NRUPfXUU2bHhQWcO3dOK1eu1KpVq1RYWKjmzZtr/PjxiouL049//GOz4wEAqkExDcAySktLFRISohYtWiglJUUeHh7VjgsNDdWNGze0d+/easehYRo+fLgOHDigI0eOqEuXLndcP3v2rIKDg/X4448rJSXFhISwGjc3N0lS3759FRcXpwkTJtC1GwAaAIppAJaRmJiouLg4bd26VWFhYTWOTUlJUVhYmBITExUTE+OagHCJVq1aKTIyUn/961+rHRMXF6dNmzbp8uXLLkwGq7LZbPLw8JC3t/dd32MYhnJzc+swFQDg+7ibHQAAXOX9999X586dv7eQlv49e9mtWzdt3LiRYrqRuXnzptq0aVPjmIcfflg3b950USJAKisrU35+vtkxAAC1YDM7AAC4ytGjRzVo0KC7Hj9gwAB99tlndZYH5ujYsaPS09NrHPPRRx/J39/fRYlgdRUVFff0AwAwF8U0AMu4dOlSrZZRent766uvvqrDRDDDmDFjdPjwYU2ZMkUlJSW3Xbt69aqmT5+uQ4cO6ZlnnjEnIAAAaBBY5g3AMh588EFdv379rsdfv35dTZs2rcNEMMPs2bP1wQcfaNmyZVq3bp0effRR+fj4qKioSNnZ2bp69aoCAwM1e/Zss6MCAIB6jAZkACyjd+/estvtysrKuqvxISEhunr1qo4dO1bHyeBqV65c0axZs7Ru3Tp9/fXXle83a9ZMEydOVEJCglq2bGliQgAAUN9RTAOwjPj4eC1dulQHDx5U3759axx75MgR9evXT/Hx8Vq8eLGLEsLVysrKlJOToytXrshut6tHjx4chQYAAO4KxTQAy8jJydEPfvAD+fn5afv27erZs2eV406dOqWwsDBduHBBx48fV48ePVycFHXJzc1N48eP17p168yOAgAAGjD2TAOwjB49emju3LmaN2+e+vTpo8jISA0ZMkQdOnSQJBUUFGj37t3atGmTvvnmG73++usU0o1QixYt6NQNAAD+Z8xMA7CcN954Q6+99prKyspkGMZt15xOpzw8PDRv3jwaUDVSgwcPlqenp5KTk82OAgAAGjCKaQCWlJubq8TERO3bt0+FhYWSJB8fH4WEhOj5559Xx44dTU6IupKSkqLw8HBt375dw4YNMzsOAABooCimAQCWsmbNGm3cuFE7duxQRESE+vXrp3bt2t2xSkGSoqOjTUgIAAAaAoppAICl2Gw2GYah7378/Xcx7XQ6ZRiGHA6Hq+MBAIAGggZkAABLWbVqldkRAABAI8DMNAAAAAAAtWQzOwAAAAAAAA0NxTQAAAAAALXEnmkAQKPWuXNnGYahXbt2qVOnTurcufNd3WcYhs6ePVvH6QAAQENFMQ0AaNQqKipu69T93dfVoaUIAACoCQ3IAAAAAACoJfZMAwAAAABQSxTTAABLWbp0qUpKSsyOAQAAGjiWeQMALMVms+mBBx5QeHi4Jk2apNDQUNlsPFsGAAC1w7cHAICl/O53v1OnTp303nvvadSoUfL19dXLL7+sY8eOmR0NAAA0IMxMAwAs6ZNPPtHq1au1YcMGXb58WYZh6NFHH1VMTIx+9rOfqU2bNmZHBAAA9RjFNADA0srKyrR161YlJSUpJSVFZWVl8vDwUGhoqLZs2WJ2PAAAUE9RTAMA8B+XLl3SsmXLNH/+fJWXl8vhcJgdCQAA1FPuZgcAAMBsTqdTO3fuVFJSkpKTk1VWViY3NzezYwEAgHqMYhoAYFknT55UUlKS1q5dq8LCQjmdTnXr1k3R0dGKjo42Ox4AAKjHWOYNALCUy5cva/369UpKStKRI0fkdDrl6empcePGKSYmRv379zc7IgAAaAAopgEAlvLAAw+ovLxchmHoqaeeUkxMjMaMGaOmTZuaHQ0AADQgLPMGAFhKp06dFBMTo6ioKPn6+podBwAANFDMTAMAAAAAUEs2swMAAAAAANDQsMwbAGA5ZWVlSk5O1qFDh1RcXFzledKGYWjlypUmpAMAAA0By7wBAJZy8eJFDRs2TKdOnVJNH4GGYVRZZAMAAEjMTAMALOall17SyZMnNWHCBMXFxcnPz0/u7nwcAgCA2mFmGgBgKa1bt1bv3r2VkZFhdhQAANCA0YAMAGApt27d0mOPPWZ2DAAA0MBRTAMALOWRRx5Rbm6u2TEAAEADRzENALCUX/7yl/rggw904sQJs6MAAIAGjI4rAABLadu2rcLDw9W/f39Nnz5dwcHB8vLyqnLsgAEDXBsOAAA0GDQgAwBYis1mk2EYlcdiGYZR7ViOxgIAANVhZhoAYClz586tsYAGAAC4G8xMAwAAAABQSzQgAwAAAACgliimAQCNXmZmpvLy8u56fHZ2ttasWVOHiQAAQENHMQ0AaPQGDx6s1atX3/bem2++qdatW1c5fsuWLXr++eddkAwAADRUFNMAgEavqvYgt27dUklJievDAACARoFiGgAAAACAWqKYBgAAAACgliimAQAAAACoJYppAAAAAABqiWIaAGAJhmGYHQEAADQihrOqFqcAADQiNpvtnopph8NRB2kAAEBj4G52AAAAXKG2z46ZyQYAADVhZhoAAAAAgFpizzQAAAAAALVEMQ0AAAAAQC1RTAMAAAAAUEsU0wAAAAAA1BLFNAAAAAAAtUQxDQAAAABALVFMAwAAAABQS/8HusxecQl/xL0AAAAASUVORK5CYII="
|
||
class="
|
||
"
|
||
>
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
|
||
</div>
|
||
</div>
|
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</div>
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<hr>
|
||
<div class="md-source-file">
|
||
<small>
|
||
|
||
Last update:
|
||
<span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">July 16, 2022</span>
|
||
|
||
|
||
</small>
|
||
</div>
|
||
|
||
|
||
|
||
</article>
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</div>
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</div>
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