customer_segments.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
<title>customer_segments</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>

<style type="text/css">
    /*!
*
* Twitter Bootstrap
*
*/
/*!
 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2016 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
 */
/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
  font-family: sans-serif;
  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
  display: block;
}
audio,
canvas,
progress,
video {
  display: inline-block;
  vertical-align: baseline;
}
audio:not([controls]) {
  display: none;
  height: 0;
}
[hidden],
template {
  display: none;
}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
}
b,
strong {
  font-weight: bold;
}
dfn {
  font-style: italic;
}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
  background: #ff0;
  color: #000;
}
small {
  font-size: 80%;
}
sub,
sup {
  font-size: 75%;
  line-height: 0;
  position: relative;
  vertical-align: baseline;
}
sup {
  top: -0.5em;
}
sub {
  bottom: -0.25em;
}
img {
  border: 0;
}
svg:not(:root) {
  overflow: hidden;
}
figure {
  margin: 1em 40px;
}
hr {
  box-sizing: content-box;
  height: 0;
}
pre {
  overflow: auto;
}
code,
kbd,
pre,
samp {
  font-family: monospace, monospace;
  font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
  overflow: visible;
}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
  border: 0;
  padding: 0;
}
input {
  line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
  box-sizing: border-box;
  padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: textfield;
  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
  -webkit-appearance: none;
}
fieldset {
  border: 1px solid #c0c0c0;
  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
}
legend {
  border: 0;
  padding: 0;
}
textarea {
  overflow: auto;
}
optgroup {
  font-weight: bold;
}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
td,
th {
  padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
  *,
  *:before,
  *:after {
    background: transparent !important;
    color: #000 !important;
    box-shadow: none !important;
    text-shadow: none !important;
  }
  a,
  a:visited {
    text-decoration: underline;
  }
  a[href]:after {
    content: " (" attr(href) ")";
  }
  abbr[title]:after {
    content: " (" attr(title) ")";
  }
  a[href^="#"]:after,
  a[href^="javascript:"]:after {
    content: "";
  }
  pre,
  blockquote {
    border: 1px solid #999;
    page-break-inside: avoid;
  }
  thead {
    display: table-header-group;
  }
  tr,
  img {
    page-break-inside: avoid;
  }
  img {
    max-width: 100% !important;
  }
  p,
  h2,
  h3 {
    orphans: 3;
    widows: 3;
  }
  h2,
  h3 {
    page-break-after: avoid;
  }
  .navbar {
    display: none;
  }
  .btn > .caret,
  .dropup > .btn > .caret {
    border-top-color: #000 !important;
  }
  .label {
    border: 1px solid #000;
  }
  .table {
    border-collapse: collapse !important;
  }
  .table td,
  .table th {
    background-color: #fff !important;
  }
  .table-bordered th,
  .table-bordered td {
    border: 1px solid #ddd !important;
  }
}
@font-face {
  font-family: 'Glyphicons Halflings';
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
  position: relative;
  top: 1px;
  display: inline-block;
  font-family: 'Glyphicons Halflings';
  font-style: normal;
  font-weight: normal;
  line-height: 1;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
  content: "\002a";
}
.glyphicon-plus:before {
  content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
  content: "\20ac";
}
.glyphicon-minus:before {
  content: "\2212";
}
.glyphicon-cloud:before {
  content: "\2601";
}
.glyphicon-envelope:before {
  content: "\2709";
}
.glyphicon-pencil:before {
  content: "\270f";
}
.glyphicon-glass:before {
  content: "\e001";
}
.glyphicon-music:before {
  content: "\e002";
}
.glyphicon-search:before {
  content: "\e003";
}
.glyphicon-heart:before {
  content: "\e005";
}
.glyphicon-star:before {
  content: "\e006";
}
.glyphicon-star-empty:before {
  content: "\e007";
}
.glyphicon-user:before {
  content: "\e008";
}
.glyphicon-film:before {
  content: "\e009";
}
.glyphicon-th-large:before {
  content: "\e010";
}
.glyphicon-th:before {
  content: "\e011";
}
.glyphicon-th-list:before {
  content: "\e012";
}
.glyphicon-ok:before {
  content: "\e013";
}
.glyphicon-remove:before {
  content: "\e014";
}
.glyphicon-zoom-in:before {
  content: "\e015";
}
.glyphicon-zoom-out:before {
  content: "\e016";
}
.glyphicon-off:before {
  content: "\e017";
}
.glyphicon-signal:before {
  content: "\e018";
}
.glyphicon-cog:before {
  content: "\e019";
}
.glyphicon-trash:before {
  content: "\e020";
}
.glyphicon-home:before {
  content: "\e021";
}
.glyphicon-file:before {
  content: "\e022";
}
.glyphicon-time:before {
  content: "\e023";
}
.glyphicon-road:before {
  content: "\e024";
}
.glyphicon-download-alt:before {
  content: "\e025";
}
.glyphicon-download:before {
  content: "\e026";
}
.glyphicon-upload:before {
  content: "\e027";
}
.glyphicon-inbox:before {
  content: "\e028";
}
.glyphicon-play-circle:before {
  content: "\e029";
}
.glyphicon-repeat:before {
  content: "\e030";
}
.glyphicon-refresh:before {
  content: "\e031";
}
.glyphicon-list-alt:before {
  content: "\e032";
}
.glyphicon-lock:before {
  content: "\e033";
}
.glyphicon-flag:before {
  content: "\e034";
}
.glyphicon-headphones:before {
  content: "\e035";
}
.glyphicon-volume-off:before {
  content: "\e036";
}
.glyphicon-volume-down:before {
  content: "\e037";
}
.glyphicon-volume-up:before {
  content: "\e038";
}
.glyphicon-qrcode:before {
  content: "\e039";
}
.glyphicon-barcode:before {
  content: "\e040";
}
.glyphicon-tag:before {
  content: "\e041";
}
.glyphicon-tags:before {
  content: "\e042";
}
.glyphicon-book:before {
  content: "\e043";
}
.glyphicon-bookmark:before {
  content: "\e044";
}
.glyphicon-print:before {
  content: "\e045";
}
.glyphicon-camera:before {
  content: "\e046";
}
.glyphicon-font:before {
  content: "\e047";
}
.glyphicon-bold:before {
  content: "\e048";
}
.glyphicon-italic:before {
  content: "\e049";
}
.glyphicon-text-height:before {
  content: "\e050";
}
.glyphicon-text-width:before {
  content: "\e051";
}
.glyphicon-align-left:before {
  content: "\e052";
}
.glyphicon-align-center:before {
  content: "\e053";
}
.glyphicon-align-right:before {
  content: "\e054";
}
.glyphicon-align-justify:before {
  content: "\e055";
}
.glyphicon-list:before {
  content: "\e056";
}
.glyphicon-indent-left:before {
  content: "\e057";
}
.glyphicon-indent-right:before {
  content: "\e058";
}
.glyphicon-facetime-video:before {
  content: "\e059";
}
.glyphicon-picture:before {
  content: "\e060";
}
.glyphicon-map-marker:before {
  content: "\e062";
}
.glyphicon-adjust:before {
  content: "\e063";
}
.glyphicon-tint:before {
  content: "\e064";
}
.glyphicon-edit:before {
  content: "\e065";
}
.glyphicon-share:before {
  content: "\e066";
}
.glyphicon-check:before {
  content: "\e067";
}
.glyphicon-move:before {
  content: "\e068";
}
.glyphicon-step-backward:before {
  content: "\e069";
}
.glyphicon-fast-backward:before {
  content: "\e070";
}
.glyphicon-backward:before {
  content: "\e071";
}
.glyphicon-play:before {
  content: "\e072";
}
.glyphicon-pause:before {
  content: "\e073";
}
.glyphicon-stop:before {
  content: "\e074";
}
.glyphicon-forward:before {
  content: "\e075";
}
.glyphicon-fast-forward:before {
  content: "\e076";
}
.glyphicon-step-forward:before {
  content: "\e077";
}
.glyphicon-eject:before {
  content: "\e078";
}
.glyphicon-chevron-left:before {
  content: "\e079";
}
.glyphicon-chevron-right:before {
  content: "\e080";
}
.glyphicon-plus-sign:before {
  content: "\e081";
}
.glyphicon-minus-sign:before {
  content: "\e082";
}
.glyphicon-remove-sign:before {
  content: "\e083";
}
.glyphicon-ok-sign:before {
  content: "\e084";
}
.glyphicon-question-sign:before {
  content: "\e085";
}
.glyphicon-info-sign:before {
  content: "\e086";
}
.glyphicon-screenshot:before {
  content: "\e087";
}
.glyphicon-remove-circle:before {
  content: "\e088";
}
.glyphicon-ok-circle:before {
  content: "\e089";
}
.glyphicon-ban-circle:before {
  content: "\e090";
}
.glyphicon-arrow-left:before {
  content: "\e091";
}
.glyphicon-arrow-right:before {
  content: "\e092";
}
.glyphicon-arrow-up:before {
  content: "\e093";
}
.glyphicon-arrow-down:before {
  content: "\e094";
}
.glyphicon-share-alt:before {
  content: "\e095";
}
.glyphicon-resize-full:before {
  content: "\e096";
}
.glyphicon-resize-small:before {
  content: "\e097";
}
.glyphicon-exclamation-sign:before {
  content: "\e101";
}
.glyphicon-gift:before {
  content: "\e102";
}
.glyphicon-leaf:before {
  content: "\e103";
}
.glyphicon-fire:before {
  content: "\e104";
}
.glyphicon-eye-open:before {
  content: "\e105";
}
.glyphicon-eye-close:before {
  content: "\e106";
}
.glyphicon-warning-sign:before {
  content: "\e107";
}
.glyphicon-plane:before {
  content: "\e108";
}
.glyphicon-calendar:before {
  content: "\e109";
}
.glyphicon-random:before {
  content: "\e110";
}
.glyphicon-comment:before {
  content: "\e111";
}
.glyphicon-magnet:before {
  content: "\e112";
}
.glyphicon-chevron-up:before {
  content: "\e113";
}
.glyphicon-chevron-down:before {
  content: "\e114";
}
.glyphicon-retweet:before {
  content: "\e115";
}
.glyphicon-shopping-cart:before {
  content: "\e116";
}
.glyphicon-folder-close:before {
  content: "\e117";
}
.glyphicon-folder-open:before {
  content: "\e118";
}
.glyphicon-resize-vertical:before {
  content: "\e119";
}
.glyphicon-resize-horizontal:before {
  content: "\e120";
}
.glyphicon-hdd:before {
  content: "\e121";
}
.glyphicon-bullhorn:before {
  content: "\e122";
}
.glyphicon-bell:before {
  content: "\e123";
}
.glyphicon-certificate:before {
  content: "\e124";
}
.glyphicon-thumbs-up:before {
  content: "\e125";
}
.glyphicon-thumbs-down:before {
  content: "\e126";
}
.glyphicon-hand-right:before {
  content: "\e127";
}
.glyphicon-hand-left:before {
  content: "\e128";
}
.glyphicon-hand-up:before {
  content: "\e129";
}
.glyphicon-hand-down:before {
  content: "\e130";
}
.glyphicon-circle-arrow-right:before {
  content: "\e131";
}
.glyphicon-circle-arrow-left:before {
  content: "\e132";
}
.glyphicon-circle-arrow-up:before {
  content: "\e133";
}
.glyphicon-circle-arrow-down:before {
  content: "\e134";
}
.glyphicon-globe:before {
  content: "\e135";
}
.glyphicon-wrench:before {
  content: "\e136";
}
.glyphicon-tasks:before {
  content: "\e137";
}
.glyphicon-filter:before {
  content: "\e138";
}
.glyphicon-briefcase:before {
  content: "\e139";
}
.glyphicon-fullscreen:before {
  content: "\e140";
}
.glyphicon-dashboard:before {
  content: "\e141";
}
.glyphicon-paperclip:before {
  content: "\e142";
}
.glyphicon-heart-empty:before {
  content: "\e143";
}
.glyphicon-link:before {
  content: "\e144";
}
.glyphicon-phone:before {
  content: "\e145";
}
.glyphicon-pushpin:before {
  content: "\e146";
}
.glyphicon-usd:before {
  content: "\e148";
}
.glyphicon-gbp:before {
  content: "\e149";
}
.glyphicon-sort:before {
  content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
  content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
  content: "\e152";
}
.glyphicon-sort-by-order:before {
  content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
  content: "\e154";
}
.glyphicon-sort-by-attributes:before {
  content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
  content: "\e156";
}
.glyphicon-unchecked:before {
  content: "\e157";
}
.glyphicon-expand:before {
  content: "\e158";
}
.glyphicon-collapse-down:before {
  content: "\e159";
}
.glyphicon-collapse-up:before {
  content: "\e160";
}
.glyphicon-log-in:before {
  content: "\e161";
}
.glyphicon-flash:before {
  content: "\e162";
}
.glyphicon-log-out:before {
  content: "\e163";
}
.glyphicon-new-window:before {
  content: "\e164";
}
.glyphicon-record:before {
  content: "\e165";
}
.glyphicon-save:before {
  content: "\e166";
}
.glyphicon-open:before {
  content: "\e167";
}
.glyphicon-saved:before {
  content: "\e168";
}
.glyphicon-import:before {
  content: "\e169";
}
.glyphicon-export:before {
  content: "\e170";
}
.glyphicon-send:before {
  content: "\e171";
}
.glyphicon-floppy-disk:before {
  content: "\e172";
}
.glyphicon-floppy-saved:before {
  content: "\e173";
}
.glyphicon-floppy-remove:before {
  content: "\e174";
}
.glyphicon-floppy-save:before {
  content: "\e175";
}
.glyphicon-floppy-open:before {
  content: "\e176";
}
.glyphicon-credit-card:before {
  content: "\e177";
}
.glyphicon-transfer:before {
  content: "\e178";
}
.glyphicon-cutlery:before {
  content: "\e179";
}
.glyphicon-header:before {
  content: "\e180";
}
.glyphicon-compressed:before {
  content: "\e181";
}
.glyphicon-earphone:before {
  content: "\e182";
}
.glyphicon-phone-alt:before {
  content: "\e183";
}
.glyphicon-tower:before {
  content: "\e184";
}
.glyphicon-stats:before {
  content: "\e185";
}
.glyphicon-sd-video:before {
  content: "\e186";
}
.glyphicon-hd-video:before {
  content: "\e187";
}
.glyphicon-subtitles:before {
  content: "\e188";
}
.glyphicon-sound-stereo:before {
  content: "\e189";
}
.glyphicon-sound-dolby:before {
  content: "\e190";
}
.glyphicon-sound-5-1:before {
  content: "\e191";
}
.glyphicon-sound-6-1:before {
  content: "\e192";
}
.glyphicon-sound-7-1:before {
  content: "\e193";
}
.glyphicon-copyright-mark:before {
  content: "\e194";
}
.glyphicon-registration-mark:before {
  content: "\e195";
}
.glyphicon-cloud-download:before {
  content: "\e197";
}
.glyphicon-cloud-upload:before {
  content: "\e198";
}
.glyphicon-tree-conifer:before {
  content: "\e199";
}
.glyphicon-tree-deciduous:before {
  content: "\e200";
}
.glyphicon-cd:before {
  content: "\e201";
}
.glyphicon-save-file:before {
  content: "\e202";
}
.glyphicon-open-file:before {
  content: "\e203";
}
.glyphicon-level-up:before {
  content: "\e204";
}
.glyphicon-copy:before {
  content: "\e205";
}
.glyphicon-paste:before {
  content: "\e206";
}
.glyphicon-alert:before {
  content: "\e209";
}
.glyphicon-equalizer:before {
  content: "\e210";
}
.glyphicon-king:before {
  content: "\e211";
}
.glyphicon-queen:before {
  content: "\e212";
}
.glyphicon-pawn:before {
  content: "\e213";
}
.glyphicon-bishop:before {
  content: "\e214";
}
.glyphicon-knight:before {
  content: "\e215";
}
.glyphicon-baby-formula:before {
  content: "\e216";
}
.glyphicon-tent:before {
  content: "\26fa";
}
.glyphicon-blackboard:before {
  content: "\e218";
}
.glyphicon-bed:before {
  content: "\e219";
}
.glyphicon-apple:before {
  content: "\f8ff";
}
.glyphicon-erase:before {
  content: "\e221";
}
.glyphicon-hourglass:before {
  content: "\231b";
}
.glyphicon-lamp:before {
  content: "\e223";
}
.glyphicon-duplicate:before {
  content: "\e224";
}
.glyphicon-piggy-bank:before {
  content: "\e225";
}
.glyphicon-scissors:before {
  content: "\e226";
}
.glyphicon-bitcoin:before {
  content: "\e227";
}
.glyphicon-btc:before {
  content: "\e227";
}
.glyphicon-xbt:before {
  content: "\e227";
}
.glyphicon-yen:before {
  content: "\00a5";
}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
  content: "\20bd";
}
.glyphicon-rub:before {
  content: "\20bd";
}
.glyphicon-scale:before {
  content: "\e230";
}
.glyphicon-ice-lolly:before {
  content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
  content: "\e232";
}
.glyphicon-education:before {
  content: "\e233";
}
.glyphicon-option-horizontal:before {
  content: "\e234";
}
.glyphicon-option-vertical:before {
  content: "\e235";
}
.glyphicon-menu-hamburger:before {
  content: "\e236";
}
.glyphicon-modal-window:before {
  content: "\e237";
}
.glyphicon-oil:before {
  content: "\e238";
}
.glyphicon-grain:before {
  content: "\e239";
}
.glyphicon-sunglasses:before {
  content: "\e240";
}
.glyphicon-text-size:before {
  content: "\e241";
}
.glyphicon-text-color:before {
  content: "\e242";
}
.glyphicon-text-background:before {
  content: "\e243";
}
.glyphicon-object-align-top:before {
  content: "\e244";
}
.glyphicon-object-align-bottom:before {
  content: "\e245";
}
.glyphicon-object-align-horizontal:before {
  content: "\e246";
}
.glyphicon-object-align-left:before {
  content: "\e247";
}
.glyphicon-object-align-vertical:before {
  content: "\e248";
}
.glyphicon-object-align-right:before {
  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.2.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.2.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.2.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.2.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.2.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.2.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=1);
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2);
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=3);
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1);
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1);
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
@media (max-width: 991px) {
  #ipython_notebook {
    margin-left: 10px;
  }
}
[dir="rtl"] #ipython_notebook {
  float: right !important;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#login_widget {
  float: right;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  text-align: center;
  vertical-align: middle;
  display: inline;
  opacity: 0;
  z-index: 2;
  width: 12ex;
  margin-right: -12ex;
}
.alternate_upload .btn-upload {
  height: 22px;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
[dir="rtl"] #tabs li {
  float: right;
}
ul#tabs {
  margin-bottom: 4px;
}
[dir="rtl"] ul#tabs {
  margin-right: 0px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons {
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-right {
  padding-top: 1px;
  float: left !important;
}
[dir="rtl"] .list_toolbar .pull-left {
  float: right !important;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: baseline;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
#tree-selector {
  padding-right: 0px;
}
[dir="rtl"] #tree-selector a {
  float: right;
}
#button-select-all {
  min-width: 50px;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
[dir="rtl"] #new-menu {
  text-align: right;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
[dir="rtl"] #running .col-sm-8 {
  float: right !important;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI colors. */
.ansibold {
  font-weight: bold;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  border-left-width: 1px;
  padding-left: 5px;
  background: linear-gradient(to right, transparent -40px, transparent 1px, transparent 1px, transparent 100%);
}
div.cell.jupyter-soft-selected {
  border-left-color: #90CAF9;
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected {
  border-color: #ababab;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 5px, transparent 5px, transparent 100%);
}
@media print {
  div.cell.selected {
    border-color: transparent;
  }
}
div.cell.selected.jupyter-soft-selected {
  border-left-width: 0;
  padding-left: 6px;
  background: linear-gradient(to right, #42A5F5 -40px, #42A5F5 7px, #E3F2FD 7px, #E3F2FD 100%);
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
  border-left-width: 0px;
  padding-left: 6px;
  background: linear-gradient(to right, #66BB6A -40px, #66BB6A 5px, transparent 5px, transparent 100%);
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  padding: 0.4em;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. We need the 0 value because of how we size */
  /* .CodeMirror-lines */
  padding: 0;
  border: 0;
  border-radius: 0;
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul {
  list-style: disc;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ul ul {
  list-style: square;
  margin: 0em 2em;
}
.rendered_html ul ul ul {
  list-style: circle;
  margin: 0em 2em;
}
.rendered_html ol {
  list-style: decimal;
  margin: 0em 2em;
  padding-left: 0px;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
  margin: 0em 2em;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
  margin: 0em 2em;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  background-color: #fff;
  color: #000;
  font-size: 100%;
  padding: 0px;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: 1px solid black;
  border-collapse: collapse;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  border: 1px solid black;
  border-collapse: collapse;
  margin: 1em 2em;
}
.rendered_html td,
.rendered_html th {
  text-align: left;
  vertical-align: middle;
  padding: 4px;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget {
  float: right !important;
  float: right;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  margin-top: 6px;
}
span.save_widget span.filename {
  height: 1em;
  line-height: 1em;
  padding: 3px;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  display: none;
}
.command-shortcut:before {
  content: "(command)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
    </style>
<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

    </style>


<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
body {
  overflow: visible;
  padding: 8px;
}

div#notebook {
  overflow: visible;
  border-top: none;
}@media print {
  div.cell {
    display: block;
    page-break-inside: avoid;
  } 
  div.output_wrapper { 
    display: block;
    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">

<!-- Loading mathjax macro -->
<!-- Load mathjax -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            processEscapes: true,
            processEnvironments: true
        },
        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
    });
    </script>
    <!-- End of mathjax configuration --></head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Machine-Learning-Engineer-Nanodegree">Machine Learning Engineer Nanodegree<a class="anchor-link" href="#Machine-Learning-Engineer-Nanodegree">&#182;</a></h1><h2 id="Unsupervised-Learning">Unsupervised Learning<a class="anchor-link" href="#Unsupervised-Learning">&#182;</a></h2><h2 id="Project:-Creating-Customer-Segments">Project: Creating Customer Segments<a class="anchor-link" href="#Project:-Creating-Customer-Segments">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to successfully complete this project. Sections that begin with <strong>'Implementation'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a <code>'TODO'</code> statement. Please be sure to read the instructions carefully!</p>
<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p>
<blockquote><p><strong>Note:</strong> Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.</p>
</blockquote>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Getting-Started">Getting Started<a class="anchor-link" href="#Getting-Started">&#182;</a></h2><p>In this project, you will analyze a dataset containing data on various customers' annual spending amounts (reported in <em>monetary units</em>) of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.</p>
<p>The dataset for this project can be found on the <a href="https://archive.ics.uci.edu/ml/datasets/Wholesale+customers">UCI Machine Learning Repository</a>. For the purposes of this project, the features <code>'Channel'</code> and <code>'Region'</code> will be excluded in the analysis — with focus instead on the six product categories recorded for customers.</p>
<p>Run the code block below to load the wholesale customers dataset, along with a few of the necessary Python libraries required for this project. You will know the dataset loaded successfully if the size of the dataset is reported.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[1]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Import libraries necessary for this project</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">display</span> <span class="c1"># Allows the use of display() for DataFrames</span>

<span class="c1"># Import supplementary visualizations code visuals.py</span>
<span class="kn">import</span> <span class="nn">visuals</span> <span class="kn">as</span> <span class="nn">vs</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="c1"># Pretty display for notebooks</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="c1"># Load the wholesale customers dataset</span>
<span class="k">try</span><span class="p">:</span>
    <span class="n">data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&quot;customers.csv&quot;</span><span class="p">)</span>
    <span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;Region&#39;</span><span class="p">,</span> <span class="s1">&#39;Channel&#39;</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
    <span class="k">print</span> <span class="s2">&quot;Wholesale customers dataset has {} samples with {} features each.&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="n">display</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
<span class="k">except</span><span class="p">:</span>
    <span class="k">print</span> <span class="s2">&quot;Dataset could not be loaded. Is the dataset missing?&quot;</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Wholesale customers dataset has 440 samples with 6 features each.
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>12669</td>
      <td>9656</td>
      <td>7561</td>
      <td>214</td>
      <td>2674</td>
      <td>1338</td>
    </tr>
    <tr>
      <th>1</th>
      <td>7057</td>
      <td>9810</td>
      <td>9568</td>
      <td>1762</td>
      <td>3293</td>
      <td>1776</td>
    </tr>
    <tr>
      <th>2</th>
      <td>6353</td>
      <td>8808</td>
      <td>7684</td>
      <td>2405</td>
      <td>3516</td>
      <td>7844</td>
    </tr>
    <tr>
      <th>3</th>
      <td>13265</td>
      <td>1196</td>
      <td>4221</td>
      <td>6404</td>
      <td>507</td>
      <td>1788</td>
    </tr>
    <tr>
      <th>4</th>
      <td>22615</td>
      <td>5410</td>
      <td>7198</td>
      <td>3915</td>
      <td>1777</td>
      <td>5185</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Data-Exploration">Data Exploration<a class="anchor-link" href="#Data-Exploration">&#182;</a></h2><p>In this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you will track through the course of this project.</p>
<p>Run the code block below to observe a statistical description of the dataset. Note that the dataset is composed of six important product categories: <strong>'Fresh'</strong>, <strong>'Milk'</strong>, <strong>'Grocery'</strong>, <strong>'Frozen'</strong>, <strong>'Detergents_Paper'</strong>, and <strong>'Delicatessen'</strong>. Consider what each category represents in terms of products you could purchase.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[2]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display a description of the dataset</span>
<span class="n">display</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">describe</span><span class="p">())</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>440.000000</td>
      <td>440.000000</td>
      <td>440.000000</td>
      <td>440.000000</td>
      <td>440.000000</td>
      <td>440.000000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>12000.297727</td>
      <td>5796.265909</td>
      <td>7951.277273</td>
      <td>3071.931818</td>
      <td>2881.493182</td>
      <td>1524.870455</td>
    </tr>
    <tr>
      <th>std</th>
      <td>12647.328865</td>
      <td>7380.377175</td>
      <td>9503.162829</td>
      <td>4854.673333</td>
      <td>4767.854448</td>
      <td>2820.105937</td>
    </tr>
    <tr>
      <th>min</th>
      <td>3.000000</td>
      <td>55.000000</td>
      <td>3.000000</td>
      <td>25.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>3127.750000</td>
      <td>1533.000000</td>
      <td>2153.000000</td>
      <td>742.250000</td>
      <td>256.750000</td>
      <td>408.250000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>8504.000000</td>
      <td>3627.000000</td>
      <td>4755.500000</td>
      <td>1526.000000</td>
      <td>816.500000</td>
      <td>965.500000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>16933.750000</td>
      <td>7190.250000</td>
      <td>10655.750000</td>
      <td>3554.250000</td>
      <td>3922.000000</td>
      <td>1820.250000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>112151.000000</td>
      <td>73498.000000</td>
      <td>92780.000000</td>
      <td>60869.000000</td>
      <td>40827.000000</td>
      <td>47943.000000</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Selecting-Samples">Implementation: Selecting Samples<a class="anchor-link" href="#Implementation:-Selecting-Samples">&#182;</a></h3><p>To get a better understanding of the customers and how their data will transform through the analysis, it would be best to select a few sample data points and explore them in more detail. In the code block below, add <strong>three</strong> indices of your choice to the <code>indices</code> list which will represent the customers to track. It is suggested to try different sets of samples until you obtain customers that vary significantly from one another.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Select three indices of your choice you wish to sample from the dataset</span>
<span class="n">indices</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">7</span><span class="p">]</span>

<span class="c1"># Create a DataFrame of the chosen samples</span>
<span class="n">samples</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">indices</span><span class="p">],</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
<span class="k">print</span> <span class="s2">&quot;Chosen samples of wholesale customers dataset:&quot;</span>
<span class="n">display</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="kn">as</span> <span class="nn">sns</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">()</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Heatmap vs 75% percentile&quot;</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">((</span><span class="n">samples</span><span class="o">-</span><span class="n">data</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="n">q</span><span class="o">=</span><span class="mf">0.75</span><span class="p">))</span><span class="o">/</span><span class="n">data</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">ddof</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Chosen samples of wholesale customers dataset:
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>6353</td>
      <td>8808</td>
      <td>7684</td>
      <td>2405</td>
      <td>3516</td>
      <td>7844</td>
    </tr>
    <tr>
      <th>1</th>
      <td>22615</td>
      <td>5410</td>
      <td>7198</td>
      <td>3915</td>
      <td>1777</td>
      <td>5185</td>
    </tr>
    <tr>
      <th>2</th>
      <td>7579</td>
      <td>4956</td>
      <td>9426</td>
      <td>1669</td>
      <td>3321</td>
      <td>2566</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area">

<div class="prompt output_prompt">Out[3]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>&lt;matplotlib.axes._subplots.AxesSubplot at 0xd7c9090&gt;</pre>
</div>

</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAWYAAAEiCAYAAADK73hsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-1">Question 1<a class="anchor-link" href="#Question-1">&#182;</a></h3><p>Consider the total purchase cost of each product category and the statistical description of the dataset above for your sample customers.</p>
<ul>
<li>What kind of establishment (customer) could each of the three samples you've chosen represent?</li>
</ul>
<p><strong>Hint:</strong> Examples of establishments include places like markets, cafes, delis, wholesale retailers, among many others. Avoid using names for establishments, such as saying <em>"McDonalds"</em> when describing a sample customer as a restaurant. You can use the mean values for reference to compare your samples with. The mean values are as follows:</p>
<ul>
<li>Fresh: 12000.2977</li>
<li>Milk: 5796.2</li>
<li>Grocery: 3071.9</li>
<li>Detergents_paper: 2881.4</li>
<li>Delicatessen: 1524.8</li>
</ul>
<p>Knowing this, how do your samples compare? Does that help in driving your insight into what kind of establishments they might be?</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong>
Based upon above heatmap</p>
<ul>
<li>Index 2: Cafe-It seems like a cafe due to extremely large amount of milk and meat used abovbe 75 percentile.</li>
<li>Index 4: Supermarket- This estabilishment connsumes great amouunt of meat and fresh above 75 percentile and also purchases good amount of frozen.</li>
<li>Index 7: Meat Shop- This estabilishment purchases large amount of meat and stergent_paper due to large value above 75 percentile. </li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Feature-Relevance">Implementation: Feature Relevance<a class="anchor-link" href="#Implementation:-Feature-Relevance">&#182;</a></h3><p>One interesting thought to consider is if one (or more) of the six product categories is actually relevant for understanding customer purchasing. That is to say, is it possible to determine whether customers purchasing some amount of one category of products will necessarily purchase some proportional amount of another category of products? We can make this determination quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Assign <code>new_data</code> a copy of the data by removing a feature of your choice using the <code>DataFrame.drop</code> function.</li>
<li>Use <code>sklearn.cross_validation.train_test_split</code> to split the dataset into training and testing sets.<ul>
<li>Use the removed feature as your target label. Set a <code>test_size</code> of <code>0.25</code> and set a <code>random_state</code>.</li>
</ul>
</li>
<li>Import a decision tree regressor, set a <code>random_state</code>, and fit the learner to the training data.</li>
<li>Report the prediction score of the testing set using the regressor's <code>score</code> function.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Make a copy of the DataFrame, using the &#39;drop&#39; function to drop the given feature</span>
<span class="n">new_data</span> <span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;Milk&#39;</span><span class="p">],</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span> <span class="o">=</span> <span class="bp">False</span><span class="p">)</span>
<span class="n">display</span><span class="p">(</span><span class="n">new_data</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">DecisionTreeRegressor</span>
<span class="n">regressor</span> <span class="o">=</span><span class="n">DecisionTreeRegressor</span><span class="p">(</span><span class="n">random_state</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>

<span class="c1"># TODO: Split the data into training and testing sets(0.25) using the given feature as the target</span>
<span class="c1"># Set a random state.</span>
<span class="kn">from</span> <span class="nn">sklearn.cross_validation</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="n">mi</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
    <span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">new_data</span><span class="p">,</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;Milk&#39;</span><span class="p">],</span><span class="n">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span><span class="n">random_state</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
    <span class="n">regressor</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span><span class="n">y_train</span><span class="p">)</span>
    <span class="n">score</span> <span class="o">=</span> <span class="n">regressor</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span><span class="n">y_test</span><span class="p">)</span>
    <span class="n">mi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">score</span><span class="p">)</span>
<span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">mi</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>12669</td>
      <td>7561</td>
      <td>214</td>
      <td>2674</td>
      <td>1338</td>
    </tr>
    <tr>
      <th>1</th>
      <td>7057</td>
      <td>9568</td>
      <td>1762</td>
      <td>3293</td>
      <td>1776</td>
    </tr>
    <tr>
      <th>2</th>
      <td>6353</td>
      <td>7684</td>
      <td>2405</td>
      <td>3516</td>
      <td>7844</td>
    </tr>
    <tr>
      <th>3</th>
      <td>13265</td>
      <td>4221</td>
      <td>6404</td>
      <td>507</td>
      <td>1788</td>
    </tr>
    <tr>
      <th>4</th>
      <td>22615</td>
      <td>7198</td>
      <td>3915</td>
      <td>1777</td>
      <td>5185</td>
    </tr>
    <tr>
      <th>5</th>
      <td>9413</td>
      <td>5126</td>
      <td>666</td>
      <td>1795</td>
      <td>1451</td>
    </tr>
    <tr>
      <th>6</th>
      <td>12126</td>
      <td>6975</td>
      <td>480</td>
      <td>3140</td>
      <td>545</td>
    </tr>
    <tr>
      <th>7</th>
      <td>7579</td>
      <td>9426</td>
      <td>1669</td>
      <td>3321</td>
      <td>2566</td>
    </tr>
    <tr>
      <th>8</th>
      <td>5963</td>
      <td>6192</td>
      <td>425</td>
      <td>1716</td>
      <td>750</td>
    </tr>
    <tr>
      <th>9</th>
      <td>6006</td>
      <td>18881</td>
      <td>1159</td>
      <td>7425</td>
      <td>2098</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stderr output_text">
<pre>c:\python27\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  &#34;This module will be removed in 0.20.&#34;, DeprecationWarning)
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt output_prompt">Out[4]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>0.14993023044466353</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-2">Question 2<a class="anchor-link" href="#Question-2">&#182;</a></h3><ul>
<li>Which feature did you attempt to predict? </li>
<li>What was the reported prediction score? </li>
<li>Is this feature necessary for identifying customers' spending habits?</li>
</ul>
<p><strong>Hint:</strong> The coefficient of determination, <code>R^2</code>, is scored between 0 and 1, with 1 being a perfect fit. A negative <code>R^2</code> implies the model fails to fit the data. If you get a low score for a particular feature, that lends us to beleive that that feature point is hard to predict using the other features, thereby making it an important feature to consider when considering relevance.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<ul>
<li>I attempted to predict 'Milk' feature.</li>
<li>The average predicted score is 0.14993023044466353</li>
<li>This suggests that this feature is very relevant as it cannot be easily predicted by other features.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Visualize-Feature-Distributions">Visualize Feature Distributions<a class="anchor-link" href="#Visualize-Feature-Distributions">&#182;</a></h3><p>To get a better understanding of the dataset, we can construct a scatter matrix of each of the six product features present in the data. If you found that the feature you attempted to predict above is relevant for identifying a specific customer, then the scatter matrix below may not show any correlation between that feature and the others. Conversely, if you believe that feature is not relevant for identifying a specific customer, the scatter matrix might show a correlation between that feature and another feature in the data. Run the code block below to produce a scatter matrix.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[5]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Produce a scatter matrix for each pair of features in the data</span>
<span class="n">pd</span><span class="o">.</span><span class="n">scatter_matrix</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">diagonal</span> <span class="o">=</span> <span class="s1">&#39;kde&#39;</span><span class="p">);</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA1IAAAH3CAYAAACxaG5zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-3">Question 3<a class="anchor-link" href="#Question-3">&#182;</a></h3><ul>
<li>Using the scatter matrix as a reference, discuss the distribution of the dataset, specifically talk about the normality, outliers, large number of data points near 0 among others. If you need to sepearate out some of the plots individually to further accentuate your point, you may do so as well.</li>
<li>Are there any pairs of features which exhibit some degree of correlation? </li>
<li>Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict? </li>
<li>How is the data for those features distributed?</li>
</ul>
<p><strong>Hint:</strong> Is the data normally distributed? Where do most of the data points lie? You can use <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html">corr()</a> to get the feature correlations and then visualize them using a <a href="http://seaborn.pydata.org/generated/seaborn.heatmap.html">heatmap</a>(the data that would be fed into the heatmap would be the correlation values, for eg: <code>data.corr()</code>) to gain further insight.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<ul>
<li>The following can be made from above visualization:<ol>
<li>Detergents_Paper and Grocery: We can see a linear correlation here that's apparently strongly correlated and hence we can get rid of either of them.</li>
<li>Grocery and Milk: There is mildly linear correlation between grocery and milk which again points to the fact that grocery is not a very relevant feature.</li>
<li>Detergents_Paper and Milk: There is mildly linear correlation between both of them.</li>
</ol>
</li>
<li>This confirms the relevance of the "Milk" feature.</li>
<li>The data is mostly concentrated near the origin.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Data-Preprocessing">Data Preprocessing<a class="anchor-link" href="#Data-Preprocessing">&#182;</a></h2><p>In this section, you will preprocess the data to create a better representation of customers by performing a scaling on the data and detecting (and optionally removing) outliers. Preprocessing data is often times a critical step in assuring that results you obtain from your analysis are significant and meaningful.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Feature-Scaling">Implementation: Feature Scaling<a class="anchor-link" href="#Implementation:-Feature-Scaling">&#182;</a></h3><p>If data is not normally distributed, especially if the mean and median vary significantly (indicating a large skew), it is most <a href="http://econbrowser.com/archives/2014/02/use-of-logarithms-in-economics">often appropriate</a> to apply a non-linear scaling — particularly for financial data. One way to achieve this scaling is by using a <a href="http://scipy.github.io/devdocs/generated/scipy.stats.boxcox.html">Box-Cox test</a>, which calculates the best power transformation of the data that reduces skewness. A simpler approach which can work in most cases would be applying the natural logarithm.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Assign a copy of the data to <code>log_data</code> after applying logarithmic scaling. Use the <code>np.log</code> function for this.</li>
<li>Assign a copy of the sample data to <code>log_samples</code> after applying logarithmic scaling. Again, use <code>np.log</code>.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="kn">as</span> <span class="nn">sns</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">corr</span><span class="p">(),</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="c1"># TODO: Scale the data using the natural logarithm</span>
<span class="n">log_data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>

<span class="c1"># TODO: Scale the sample data using the natural logarithm</span>
<span class="n">log_samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>

<span class="c1"># Produce a scatter matrix for each pair of newly-transformed features</span>
<span class="n">pd</span><span class="o">.</span><span class="n">scatter_matrix</span><span class="p">(</span><span class="n">log_data</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">14</span><span class="p">,</span><span class="mi">8</span><span class="p">),</span> <span class="n">diagonal</span> <span class="o">=</span> <span class="s1">&#39;kde&#39;</span><span class="p">);</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAa0AAAFMCAYAAAB4TuMQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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"
>
</div>

</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAz8AAAHsCAYAAAD8YBPrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
AAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmQpdd53/c773r37tvL9PTsM5gBBzPYCIAEBG6WBFGh
qMVUSS4rtuyqpEpyVXarkg+qSlWcyodUOYnKFccuS3ZsS3KoiLIYWZQtGiRFEiRAAgMQ6wCYpfe9
737ffTv5cO690z3TPT0LZgFx/1NT1d33vsu957znPMv/+T9CSskQQwwxxBBDDDHEEEMMMcSPO7R7
fQNDDDHEEEMMMcQQQwwxxBB3A0PnZ4ghhhhiiCGGGGKIIYb4SGDo/AwxxBBDDDHEEEMMMcQQHwkM
nZ8hhhhiiCGGGGKIIYYY4iOBofMzxBBDDDHEEEMMMcQQQ3wkMHR+hhhiiCGGGGKIIYYYYoiPBIbO
zxBDDDHEEEMMMcQQQwzxkcDQ+RliiCGGGGKIIYYYYoghPhIYOj9DDDHEEEMMMcQQQwwxxEcCQ+dn
iCGGGGKIIYYYYoghhvhIwLjXN3A9TExMyGPHjt3r2xjiHmNubo7hPPjwIc0kbT9GAgIYyZvomrit
cw7nwv2POzHuV2M4D+5v3I05AMN5cL/gbo33bhjOg/sXd3NuvPrqqzUp5eSNvPe+dn6OHTvGuXPn
7vVtDHGP8dRTTw3nwYcQszWX713aZLqSZ7Xj8+mTkxyfKN7WOYdz4f7HnRj3qzGcB/c37sYcgOE8
uF9wt8Z7Nwznwf2Luzk3hBDzN/re+9r5GeLeI8skf/jDef7gpXm6QcJPP7SP/+FnTzNSMO/1rQ1x
FdpeTMOLGCtYjBTMa36/2xgrWOhCsNrx0YVgrGDd9XsY4oPD9ebT1teG4/7jjRtZV8YKFmGc8dZy
m3LOGM6Be4zFusdcw+XYWJHD44UP/PzDZ/7DhZu1DW7Hlrhf58bQ+RliV6SZ5O//8ev82esrPHW0
yoP7y/zxuUVemqnzJ3/vWcaK98ckHkItTn/x1gqplOhC8JmTk7xwaXPw+xcfOXDXHaCRgskXHzlw
Tx2wIT4YXD2/ts6nnV4bjvuPJ643D66GAISQ3D3y0xA7YbHu8b8//x5JBoYGv/Uzpz9wB2i41n94
cDPP8K28/2rcr3NjKHgwxK74R9+8yJ+9vsJ//7Mf4yt/7yf4v/7TJ/iD//xplpo+/9WXX0NKea9v
8b5C24uZrbm0vfiuX7vhRThBgiYETpAw13BJpaRimyw0PF66XLsn9zVSMDk+UbxvFrwfV9zo3LvV
OdrwIpwwQUPghAnzdXdwnoYXkUrJdCVPKiUNLxqO+32Kmx3/q9+/daydIOHN5daO52p4EZapcXy8
RCdQ82WIW8Pt7itzDRc/zhjNm/hxxjur7R3Pd7vXGT7z9z/aXsybyy2cMNm2Xl8PW22LWjfkzeUW
i3XvpubK/Tg3hpmfIXbEawtN/vG3LvLLTxzkv/jJk4O/P3NinP/pF87y2199i6+cW+JvfOLwPbzL
+we3Gx25lettjaQYQnB+tU2SQZZlHJso0vZiXrxYY77h8c5Km0ubDr/+zLFtEfv7JRpzP93Lhw03
Ovf2el9/DAwhSKTcNhZ+mPDD2TqZVFTYth+zfyQ3yDJ+ELSG4Ry4s7iZedLwIvww4WtvrZJmknLO
4InDVRAQxhkzmw7nV9sgYLHhXXOuPu3tm7PrSKAyZ3B0/P4yfj4M+CD2lfGCxVrbZ6HhEacpr842
WGh4BHHKlx4/xOHxAm0v5k9eXWSzG6Jpgl/7xJE7Qo+7GQzXg2txO99Jfy45QaKeXaBkX6Gk7nZu
P0z44Zxa+xtOiJ+k/NtXFzkzPUIpZ+y5jux1r/dqnIfOzxDXQErJP/h377CvnOMf/OLZa17/m584
zJ+8usj/8fwFfvHxA+RM/R7c5d3BjT6YWyOiqx2f+YZL2TOvOW6v8+32+ta/A9dQ3OYaLscnSxRN
g+9e3OAv31olb+nkTZ2coRHEGa8tNPnsqUkeLYzedWfterif7uV+xfXmRT+Sd2KixGrHH2Rerj7u
6jn6znKbFMmxsSKVvKmMHydkoe7ysf0VhIAvPX4IgK+8ukQUS9pBRMHUeXe1w4P7ynTCmETK26Y1
fFTnwM1s/P26jfGCRd42MISg6UUg4OiYKiC+3rmuHv+d5okhBC9c2sQJE75/qcZmNyRnanhhyssz
dUaKFh+bKnNyXwkEO845UJHeTx4fwwljlf0J42ve8+OCWzXebqQO53pjdvU9zDdckFAtWNuCF3nb
4FMPTPD+hoPrx7y30aUdJLT9mKY3w3/27AmafsQrcw0aboQfp4DkNz978p4Fyu7H9eDqPfhuG+y3
+530MzjFnMGJiRIPTVd49OAoAC9eqvG9SzVG8yalnMFnTk6SSIkhBF97axXHT8iQlHMmOUMnyaCY
M7Zl+m/lXu/lOA+dnyGuwdfeXOWNpTb/8FcepZy7diJqmuC3Pv8x/tY//yF/fG6Rv/MTx+7+Td4F
3MyDubWoL4ozXp5tYJvatuNuJPLefz2MMz55fGxg1Gw97uEDI4MNcabm8OVXFsgyyULDYzRvcHnD
wRlJCOOMExMl4kxi6GAZGn0C/iCVralU9nzD5dHC6E19N7ey+O+0gXSD+IY2+I8qdps3NxLJ+5NX
F+mGMWXb5PNn9g/maMuL+cNL86RSkmQZP//IAV5faoGEuboHCDRN8OWXF0BIlls+7SAiTiR2QaPt
x/xoocmD+8vb5kCfQnGzght9I69im8zWnZuejx9G3Mz60q/b8KOMtY7PsycnmK95ZFJiGhoPTpUp
mDrWVWtO/zp9xyaMM87NNdA0gSHEtvuodUPm6x77KjaHqgXSVNWIxIkkSDIKtkElZ5JkGQdG8jTc
6LrZvqNjRfaVc3TC+IYzgrcaHLob2Onat2q83WgdzljBIooz3l5pYWgaXT+m7cXX7Bt/8uoiry+1
iJOMNJM8sK/ERMnmV588zFjBImcZ5E2d8aLFattnpRUQZykI+PIr8zwwWcaNEiRgaoKGGw+ewXth
oN7qenCn5sfW7yCKMyRcs7/fadyoI7wbtrJDDA2+9PF+YGuRFy+rQMdD0xUmijb/9DuXqORMumHM
hbUua52AOJEUbJ21jk+WZbhBQmkHMZO9AnIf5Ge6nfEeOj9DbEOWSX7n+Quc3l/ml584tOv7nn1g
nI8fGeVffn+OX3/mKEL8+JW13kzUreFFg2hJ1495Y7l1zXH9uomiZeCEyTXna3gRtW5IkKS8v9bF
CWP2lXMcGyuy3gk5MVFkvRuw0lYOVt+Indl0GC/ZPUNIqI3O0PHjjIOjOWxTo5QzOFwtDJwpQwhe
vLzJbN3H0iBOM6p565oNuO3FzNddnDChZCvqCrDNqP6VJw/fsGLMThtIGGcIuO/UYO4X7DYP+38/
MVkCGETy+mMx33B5Zb6BlOBHCYerBY6NF+n4MQVT5+3lNmGSUeuGfPO9dZJU0ZuiJGO55bOvbHNp
swsSCpZBwdLZDEPmai6GpnFhvcvfeOowHT/mpZkaFzccRgsmLS/mQO9eRwrmDRkIfSPvG7PrCKBk
Nzg69uGmSW2Nxm+lfPXXi72c/q0b+1zDJclgsmKz3PLxoxQ/SShZJpWcyWY3pGDqHB4v4ARX1pa2
F/OVVxfpBglZmrHaDbi03iVn6RQsnV967CBzDZfLGw7n5hv4cYq2Aj95eoq8pZFKdT/jJQsBdIKY
ExNFqgWLhw+ObPtsOwU2PnNykqYfwQ2Uh95McKj/+t3Cbvd2I9m0nQyz/ngeGy9yfrXNn7+5wi88
emDb+ttfe5teRN2JqDkhlqHx9kp7cP3Fuse33l/nnZU2AoEbJKx0fPKWzqX1LqYm+KnTUzx3eh9N
NyJvapRsg46f0A1jRvImMzWXTELO0InTDDfMaHsRL8+qZ3C+7g72n60ZvDuZCbmV9eBOOmlbx/nt
lRZSCo6Nj3ygwbq9DPmrA6w7OcLXO2ciJWcOjFC0DNwooelHvLPS5t2VDkmS4QQJ52YbiskjIGdq
tNyYME1J0gzbVA50rRtxbKzIMw+MD+yJN3uBs2rBUpnjXQJy1/tMN7v33+56cEedHyHEAeBrwBmg
JKVMhBC/AzwFvCal/G/u5PWHuHl85+ImMzWXf/Q3H79uIyohBL/+zFH+/h+/wYuX63zq5MRdvMu7
gxt5MHd6AMcKFm+vtK85zhCC8ytXIi9fODu97Vwb7YB///YqcZoRxClPHKlyca3Lq/NNnDDh/bUO
mhAgVfbtgYkSBcPg8qYDQJxlrLUjkjRlvu6h64JzCw0Ajo6X+IkT44OF8tx8g/fXukRpRprBOytt
vvr6El96/NCAMrHc9PjDH86z3PRp+hEHRvI8dWyMM/srvL7UopIzubzp8onjYzcUldu6ic46zrYN
5LGDo5Tz19IEh9h9Hm79eylnbHN8AJwgYbHu4YQJYZLxz793mZylEyUZ0+U8K02fIEkZK1ocrhbo
BjEb3RBbh82Oz8X1DkkqKeZMRgsmQoJlCNY7MQ9MlKi7Ic+fX+O1xRZOGBNEGT/3yAGeP79G3tTx
45S//thBar2C9+vNkZGCySeOjdEJkmuMrPsBN5uRaHsxf/CDOX600MDSdZ48NsavPqnqI7dmd7c6
/YYQzNbcbdRWJ0wI4pSnj41jaLDZCdE1yFs6htBo+TFxlnFyssTMpsNSy1dry8PTtL2Yb5xf5zsX
NqkWTc4vdwjiFC9KyZsa34jWaLoRlbzJt97fwAtTSrZOIWeQZpIH95dJM4km4OcfOUCSSRBqHfvy
Kwv4UYwmNH769BSHxwoDo6flx+QtndGCOfiMlrndaN8JewWbdnr9bo3vbve227O5lyF+bKxIlknO
zdWZb3hICRfWO/zGZx4YUBpfuLTJQt3jxZkaB0byrHUCBIJNJ+Q/nl9jrGDx+z+YZakZ0HBDpFTz
IsskXpjw7loHJ0j51nsbPHJohNGCyVvLLU5NlalgIITEDWOSJOOh/RXGCha2obHaCXhof4VOqJyv
V+YazNQcZmsOjx4aZaxgXck6hwlr7WDQvBIh+NLjB2+7XuhW1oPbzSJcD1vHuWybSD7YYN2NOG59
1bT5hsvLsw1+MFvn2xc2eO70FPmeg3G9YMFnTk72AlsdTE3jKy8v8NZKm7m6jx8nxInE1MC2dA5X
CwRxRhAnZEA3SPGijKYbIoGFhssnTyhmSj/rKIBD1QI5Q2O8bHNgpEC1YPHM8fFdx2GrEpwhxDbm
wF643fXgTmd+GsBPA18FEEI8gXKCPiOE+KdCiE9IKV+5w/cwxE3g91+cY7Js84WHp/d87889Ms3/
/LXz/D8/XPhQOD83myK9EYnGnR7A4xPFHY9LpOTM9AjFnIEbJCRScn65zSvzDQxN8N0LNYIopZgz
iJOM58+v48UpaSaZqqj+KYfGCpyYLDFTczg33yRnaGSZxI9Smk5I00uIEkmWSZJMstLMCJKMuhNj
Gxp/++mjLDV9fu+7M3SCmFQKNAGaEDSdiC+/ssBowWSt7fPeWpfNbogXJYwVc5iGRjdI6ITxQL5W
wA1HdbduoqemyuRNfbCB7FQMfaN9Ze4XA/lWsddn2W0e7jU/SzlFU3LDFCFgrRMSJRmagLeW2kxV
crhhykNTZTRNsNj0ma97tP0YL0oIEzWwWpRQsjRypk4lbyKbPsstDwR89fUVnCCmlDPxooRvvruu
zrl/hDeXW/zHd9eYLOe2FbzvlhE5Ol5kqmLfFE3qdr/bGz3HzWQkPnNykpfn6vzVextkmcQwUmpO
ONict64Xfae/b+wOqK0HR3DChEvrDiutgIvrDo8crDBeyvHQ/jKJlHzH2sTtUVeffWAC09BAQpCk
vDzX4EcLDd5YVEGYiaJFlKZkGURJSphkIARvLiljeDRnqk7sQYQTxLy13GIkb/GFh6fphDF52+B4
T0Tln333Mt9+f4NOkJBkGedXOxyfKDKat+iGMUstD4Hglx47yEzXRQjJw+OjexqkewWbrvf67dBw
+8bk9WhMu117t2dwL0O8kjd56ECFV+ca2LrGoWqe9U7IV15b5MRkiaYb9YJcEKcSpJo3byw2mK17
TJVzzNZdojjDi1PCWNHdCpZG0Tbw4xRTVzTnlbaPG6U8eqhC10+4sO7gBDG1bshkKUfbj/jRYpPD
1QI/dXqKr59fY6bmUs4ZINR1T+0rs9LyODNdYaRgMltzB/Pz1YUmcSIZKxpMjeQBuS2Ittt47FXf
Wi1YN7Ue3Ml+MlePM3ywma4bddxGCiZlzySTksWGz0Y34I2lNk8fHyNMMk7tK3F2eoTD4wXeWWnz
5lKb8ZJFmkrOmQ1emW3ghjFLLZ8kVXZDnGVkvT08Z+lEqSSIM5Isw4kykjRDExLb0PHClHo3BCH4
+jtrpFKy6QTYhk6YpGy0feYbPrqmgm+2uZ8XLm3yxfzuQY/+3282a3e7431HnR8pZQAEWyhRzwDP
937+BvATwND5uU8wV3P59oVN/uufOqXqQ/ZAztT5pccO8EevLA5oUfcrbjUlPlIw96Tr3OgDOFaw
KPWKBEs5g5kNh//1L9+l6UY4YYqmSWQGoit6UTTIGxrvrztqo6pYTFfyvLXcJk4zcoZGmCiKghcl
bDoR3SAhTjIKtk6WSuI0IckkAsk7Kx3+l39/nq4fs9j0iDPQhMQQGhLJfMNDaDBetJmru9QdtTl2
A0WRiJOMcs7g7PQIS02fbpBwfKI4oMJdD33p2+dOTzFbd/jcg5McHSte17m5mb4yH1YH6EY/y9Z5
eLXRsNtnVzUeKWvdgCTJEEI5q4au5s1GNyTJJK8tttjohjhhSjln0vIjouSKRxslkm6Y0nAjVlo+
iZSATl7X2ej4gCBMVITfCWPcKOGt5RZjBZOj4wVOTJS3SapujRQ+emiUX+3RJm8k2HAnvtu9zvHm
cgsnSDgxuTN/favhMrPp8OVX5nl7qcNy28fQNCo5A40ra8PW9aLv/M3W3G3GDxJaXsxbK206fsyb
SzGvL7bYP5Lj158+StOPWG0GnJgskvVaDsxuOnSChIWGy/fNGgt1pe6VSUmUJNi6TphmeFGGqYOQ
Opc2XdY7AS0/YrKUQyA4OlHA0HRafsRMzWWqYg/u/eXZOm8utnCCmDBJkZmg6UZommA2c6nkTCaL
OdpBPDCgb5TSutf47/b6rY5z/7iNbsDMpstPn57aNcOw07W3PodXd6zfa19oeBG2rlEtWFxIu7x4
qU4lbzBRsqnYJg034s3lNoYQ6MBYyaQbmmx2QoI4ZbxkM193aQcRfpiBAF1AGEuSNCEsJGQSLte6
VGyLpabHYt0lSpXTmzMFcSJ55OAonTCm7cccqqp7C+KUlh9i6oLNTsjz76zRdGOV9dM0DlcLGEKw
2vKZqXXxwphMwlIzZiRv4UcpX319iWrR2nU89qpj3BpI2MuJutH5c7u4eq39IM+/l2M/X3cHwiZj
BYsgTml6ETlTI4gygjjj62+v8r1ewOuvPTjJn766xEzdJYgTyrZJpWASphKnJ3aB8qkxNOgRSvCj
FNvUGSuaOKEKvDbcCMPQcUIVNAmciGJOsFD3eH2hxeVNl+W2j5QQxilCCFQcRlC0dhdF2Pr5brRG
aCtud7zvtrU6Csz0fm4D10iJCSF+A/gNgCNHjty9OxuC339pHl0I/tbTN/69//xjB/jXL83zzXfX
+aXHD97Bu7s93GxK/EYjiTtFhN5cbPHKXAPL1IjijE8cGxsYOX1lNkvT+Bffn6HmRoSxWlQkqvGW
0CQHRnJIJG+udEikqsmxXMHbqx0eOzyCrmm8ttDk0rpL0wsZL1k0nJA4lUQZ+HGmmngJyIAoyTA6
PjqCbhjhhhIJjBYMnj42xr6RPA/tL/P8u+tsdEMmijaNrjJWT0wW+WsPTvLwgVHOHhxhpGDyq08e
vqlFp7+4d3p1TH2u8K2M152kN9xt3Mq83MvQa3sx7yy3+dpbK3hxQpplaBokaW8uZBkATpgCsN4O
WO8EZFIZUIYu0IC0dz5TV7ztdpahCUESq2ih58YEiXKgEVC2dXKWzonJMnlL8NTRMf7q/Q1qTkzB
0vjC2WlV6xLGVHpCKt1ge+3bbs7crUT2P4hi2j61Z8Bf36HA1xCCphvjhylBoqKoUxUbJ0oAyen9
FX7tk0cG197JiF5t+ay2AvwwHdCNT+0r8Y3za3hRSpymtLyQlhvxP/7Zm4wVbRpexP5KjiNjRcZL
NgdG8nRDhyiGWtfDjRJiNdSEiSTUE4SmYRuQZtDwYqSUitKIRieIiRPJ+ZUOxZzJWN7i7HSFp0+M
A/BvfjDHP/7WRVp+QpJmGLrAMgw2uyFTFZsHpstIKZgeVRLo/XWvPxY3asDutd5e/fqtjnP/uOPj
JS5vutc4ete79l7P4V6G2VjBIkgy4kzy8SNVZmsOTpjwymydxabHg/tKHK0WyJBMli0KlsFGJ0Tr
UR/V+2OCUO0bmVR7RyeIMTS4XJNUbAMdQa0bEGdK2MQ2NPw4I8sMgjjl4kYHQ9N4+tg4nSDmW++v
8/ZKm7Jl8P21Gt98d42aE9FwQnKmzoszNSQqC9D1E1ZaAWEiMTWBRBImGRc2HB6cKlGIMmruzmI6
e9Ux9v+eSHmNY9n//nf6bveaP/cKu2W7+7ieY/+VVxc5N9cgTDPOTld49NAo1YLFvpJNztR5f6PL
hfU2TS+iZBu8tdJmZtNVtUFJRpJBlMS0gpg0vbKu95FkYBsChMQ2BCcmiwRxSs0JaHsxUkKaZSQp
6Jp6vxdILm50+OTxMQ6N5hkv2YwVTV6bb+JFKboQuHFKzQ05Yhd2fab2Eu3ZC7cz3nfb+WkDld7P
FaB19RuklL8L/C7AU089NeyieZfghglfObfIzz0yzb5K7oaPe/JIlf2VHH/+xup97fzcTIbmeqpr
11tw+8etd0Jmag7Pnhjn5aUWnSChnDM4M13hpUs1am7EfMNV9JMoxe8V/4MyPk1dI5ESXQNDBzK1
4Gy6Ma25BpqAo+MFak6IGyupYTdK0TSNnCYhzkiliuboAkxDwwkzun5CnKrFUKKyACXDwDA0yjmj
R8ursNoOaHkRRycKjBYs9o/kyNsGcw2XswdHbskQ3clJvJ7xcL3xupP0hruNm52Xe0XI+spP379U
Y7bm0PJjsgyQyvEBMARsSewQSwbUxVQqqXtDh6Kh6oNKto6pa7i9CDMSiNQWahggJD3ahGCl6VMw
dR4+OMlyM0AXGjlL4/hEaSCdGiWSmhNiGxrHJ4p7jt+tFrbe7jwZCEpMbBeUALbV5rxwaVNFYOOU
505P8dJMfSAYcWKytGvPlI6vDKLvXtjk7eU2bpgyNWIzkjP59vsb/HC2znLTw4tVoIIkw9IFMpFU
iyqqqgmNWjfk7eUWM5subT/CC2OCKB084/3hjVMQWUbO1EhQ9EfNMKg7IX6UIlG1PiXL4ONHqsgM
uoESs3hlrsF/eHOVjW6kjtOgWrCZqtgsND2WWgFF2+S//MmTO9Yf3EmD9FbHeWtA5vFDo9uCVDvh
erLxW5/DrRLW/fcC13wfX3r8IF9+ZYGFhstGN8LWBW6UYJkayy2d+ZqLFAIniDjee96rBRvD0Bgr
mry7mpL0zqehDFOEwNA1glgxAPK2hmFoWALcUEXjQdUGWYbgqaPjVHIGc3WXS5sOOV3j/bUu5ZwS
RUgzSZxKglgSZwmmofPmYoswyTB0jSjNqOQVZfJAMc8TR6sEUcr5lTYNN+6JFRjXiBXsVrx/q3W2
9zO2qvFtzXbDdntiN8d+sxvScCPiJOMv3lzhL99eI2/pjBUtHpgs8eihERbrHpWcSSdMiOKMkqVs
l7S/rgOy5/VoXNkLQK0RRdsgjGKiVHB5o0smBQIVSK3YKkuYAXLLgV6U8sKlTQ6OFjhzoIKla4wV
bQq2utCz0xV+9sz+6z5Te4n23EncbefnJeA3gT8GngP+1V2+/hC74E9/tEw3TPi7zx69qeM0TfDF
R6f5g5fm6QRXIrr3G/aKxO20sVVsk2/OruP0VM32krecr7ss1D2KtqrZeW+9gwBG8wYvXKrx8myN
H823KNomnTCiZJvousRIIGcJwkSiaxpFS0cXguNjRS6sdQcUJIFygt5aarHS8vGjhCSVyJ5cccnS
iTNJlEmyVCKEer+egakLKnkTN0zwohRD10jSjHaUsNz0WW+H/PVezyZLV309njg6St2N8ELV3dkJ
Et5ZaQ9qja7X4Ozq7/Tqxf1qms/VRvz1xutO0xvuJvqfpU9r6Pi78+D7EbLXF5s03IjJ0rVR6n5m
RddgvROSZlc2wD6u/v1qxBJECmGakjcEUapUBG1DIIToGdUSU9fIEARRgqGJXs2BwaFqgQenyrx0
qYaugR8pyt1GO+Avz6+SNwwemCzx+KFR9o/sHWi51cLW250n2wQlbGPg+GyTnT+oZOenyjlmai5J
JvmVJw/zieNjO0Z5F+seX319mSBKeWe1zVjRYqnh0QlipBTM1h2myjmmyjnqTowQGrqWkvYCFnlT
oxumbHR8JOBGCbqmkbd1kjRj04lISBFCYglBjCTOVBAErlBckII4g/1FAyEEoZ1iajrdUJ2v66us
1Z/+aIk0kyy1/F5tYG9NkQASy9A4Wi2wr5LjyHhhW21Q30G80e/9Vut2tmbUj92EQuDNzI+rDe7H
D40Osn1bs4FbJayzTPLQgQqjBZMozjgyVqATxEyP5Dl7YIRK3mS8aHFpo0MqM+JMG9RhLDZd3Cjl
QDWHG8a0PFUD1PQiLEPjwpqDJgSWrp5PXcBY0SJKJV6UkqSShJQkTUET2LqGqcFE2ablxRQtHUM3
+dSpCRZ7TbAvb3SxTJ2WpyjOJ/eVmN10kDLDNjUsQ8PQUPS3HnU2lXBkNK/2HCl5d7VDJiUVW2W2
qnkLJ0x4c7nFsbHigMIGqLo2P+H8Woc3llu8vdLmMycnefjAyIDitdOY3EnhizuB+YbLQtPFNnTs
Xt3sfMPl7eX2NQ7c1TVFhhDomsDv1f4GsaRgS3Km3lOLzDg7PUIQpUyWc6y0fSxdsNTwrlnn+35L
bwgRvZ8smT2hAAAgAElEQVRBzW8hwNJVjVmUSvqVD2547YYhQdkqtsHp/WU+e2qSct7kCw9PD9Qd
b6Sp8V6iPXcSd1rtzQT+A/AY8HXgt1E1QC8Ar0spX76T1x/ixiCl5PdfnOPhgxWeOFK96eP/k4f3
8y++N8v3Ltb4uUf2Fkq4V7gepeZqnrEuBDM1FwkcHy8xW3euK2/Z9mK+c3GT717aJElVH4xPnZjk
wkaXFy7VWGsHdPyIVq+YPEgkaSIxdIPpqkE5Z1DKGYzkTKYqNi9dbnC57pKzDHQtpR1cyQ45YYYk
JM1U754sgyBScpQjOZvpEZM4TinmTDQBcZLR9tWGJoRSb0rSDCkhyTLeW+2St3T+9UtzTJRt8qbO
TM1hwwkRQCYlcw2PvKGx3glY74ZUCyaTJZs3l1uDBWtrP5GmFw2ofzeb2dlrvPZ67cOIt1fail61
0t6xc3Z/w5+q5Mgkg8h+H/3v3g8TOn7CxXWHNM0wDY0wVdtef8MzNdiaxNkJ/b/7iSRIUrKmD0CY
ZmQ9tcKRvK7OkWmKGiFhaiRHx4/56o+WmNl0kAimR3KcnR7h9753mbV2SNHWefLIGO+td1h3gj1V
wG4ng3M782Qn4/hqp90JElZbAd/frGEa2kDY4dFD1yrbtb2YL7+ywGvzDTa6EU4YUc5ZbHY8okzR
5zKpnMqmp/pw+XFKKntOSwZOkJIBXizJ66AJjTjJeHu5RU7XOVTNM9+MVYRfqGCNmWZEqQqAWKaO
EGDrgrYf0w4SHpgoYOg2y62AvKFoa9WCycymq+aOlGp9yZSRjQZTlRyfe3ACiaDuKoN8oueM30oN
zu3UZ7W9eCAWsdjwrltcvdMY3wxFrl/X9Y331smZOkGc8oWT04NzbJWwfme1xWY35Ei1wL97b5k/
elk5NNWCxRcemeazD06SSomp6SSJRAqVjZMS0hRqTkDTDUmyjDBJKdg6UaLW+c1ugBdLLF3NjcPV
AlGacWaqyHJTSaF3wgSZZVimjqVpjJdspkdzrLYDBJKJco6SbfSM6oy2nxB1I3KWIM4y3DDhYLXA
XN2lYAFCMFa0KecM1joqG1HOGaBJDo/mWWr42IZGO4g5v9rGjVIupQ4bHaUG92/bi5yZHkHTBEGc
kmQZUSIZzZuD73VrrdBu1OgPS+a/T3X77oVNNtohy22ffSWV2XWCZHtT9LrL2yvtHdtAPH54lI4f
c2nTwY9TNjuqPrNk6Sw2PP7ND+e5uO7gxjGOl1AumGiaQE/lNRQ3UOt+wVa1QqliLKMLldVxsyup
naT3407nkKiab00TsKV2se3FN1yjBfc2kHmnBQ9iVIZnK354J685xM3jpct1Lm44/MNfefSW+vV8
/PAoI3mTb723cV87P7thJ55xPxpfmTPobMn8XK+A1QkSbENlbTIp2T+aY/9oDidQcrXzdY9Mgt+j
sQRJikxSIMPQBI8frrLU9PjRgors15ywp9oGFVuj2+d3o+o3DB0KltoQdU0gNIGmwb6STdE2ODJW
oGAbBJEyiGdrXdwoQwpYbfpEcUrai/JkUpIzBHUnYl8lR5JJ8qaKJkepRAP2VXIYvSLdjY7qN5Tr
LcCfOTm5Td+/mreYb3j8zJkpkh0KHn+csje3i/78K1rGrp2z+xv+bN3BMjQ+fqTKeicYRFT73/3r
iy02uwE1JyTOII7UDqaLHqWNK5taHzpqg+s/+VvrfaBXFKurPiCECYkOo3mTNJWkSHRdYGoCXWjM
1lxmaw5hnHJ4rEjO1DgxoYQtNKExUbJY74Zc2nQ4e6ByQz1uPoi5cjNZhZ0ylv1MhiHEwPAK42wQ
6Y7TlM89OLnjXO+fb7Xpc2Gtw0LDU5FWDTKpxCU0TVNBCQlISdNPqOZNLEOjEyTkTUGUgBMoDr4E
4gxErIhPUkKUJFzadJBSkZs0oZFlGZWCjRemjOQNWl6IG6vX+/NhvRtxYrKIoWsYpk7djbEMXak9
BTEZkmrBYv9InpWWWsMsQ2OkYPNrnziyLdILDKiZ/WzYXjLncHv1WXejBnCrwR0kGTlTH1BPE3kl
hHBsrIihwVzdJW8YTJZt3l3tUHcihCYoWAYZMLPh8MThqpIeT5SccyVvkTc1Gl5IN1BPZJxJBIJ6
N1aBrF5tj66rLGApp/GxqREaboTnJJxf6XBkvEAmwYgThKaTphLTUhS191a7hGnGvoqNlJLVluoV
1wlV5D9OUwg1KnkNJ4x58sjYwCE3DY28pVGxTaSEIEqYKFpU8hZBnOFEyqDPJFQLJuWcyVzDZdMJ
ubjhEEQZ2kFBzQl5f73LgZE8NSfkgZ6QSJCogFyfZXC9cdwrO3SvcbWYxuce3Mf76x3CJGO0aPLu
amebPdFX1Zuu5Dk3X6flJRys5nlnuY0TxlTyBo8fGiVJM75zocZEyWSlHfDGYptuEA/qOQFarsr8
92t+hQBNQiZUrZ/QlOiKrgsMVKZHyn5G+MpnGNQgc2U/EICtga4LcqZOmkmeO71vsEb+/g/m2OyG
TJZt/s4zx27YAboXY3j/ynMNcdfwr16co1ow+YXHbo07a+gan31wkm+/r6Rdtev0B7ofsVMkaaRg
8mhhlKPjxWtS0TtRkrp+jBuq/yXbxNQ1Vpo+Zw+oKP5SwyfJejx7XUmr6Jpa6A+MFijbBk03ou6E
iq4kM5wgRROqnkI3NQxdRWcSCUGSoWWQt1QqXP0DL0xxw5RK3mR6JMeTR6r8zvPv48UpTpAoBZ9u
L6ODcp7SIMEyBAXbpGBDydZZ70LLi0gyyXhJbW6aEEyWVTNVQxOMFs2BATDXUBHxYs7AjzKiJGDT
CXn+3XWefWDipjM7HyX0558TJhgaO3bOHtDjGi6Gtsm7ax0ubzjU3YiXtDqjeZNizqDuhiw1feIs
Q9fU3DF6Km9b+d9wZZ/r/65pUDBVfU/bi7c5QG6YIVE9oQRKiUwTSgBASjB0nf0Vm8WmT8k2WQ0S
un6MrlnkLYOz0xW+f2mTJMuQWcah0RyzNYe8qV/zWXfLAtzqXLmZrMJu9QQ7KVB1g5g3llo8tL/C
SstnvRuwr5zb9bOstgKiNKVgGepZNFV931jRIkPgRQl2T8ExzTKSTOJFCYdGc+wbUSICP5ip4/Yc
2lQqw8XShXJohSRKVXNCTRMkSUqQSKIkJJPgRclg4PtjLwA/Tllu+hQtnbaf4EUJQqh5aRsay62A
XE/K1tAVLdeJUk7tU3VcfQO0T+lDSt5b6/aaLmvbZM53w+1E8+9GJmCrA96XJd/peofHC/zWz5zm
5dk6CMGh0TzfubjBSN5kueURJqoYvGQZvLbY5LnTUwRxxozuYBoaH5sq03Qjzi00yVtajyGQIntp
W9Gv3UtVBnckbxOnktW2jxelZFIZvw8fHFHrgxfS8RPqbogmNPKWjqkLwihjOQmUcmiW4QYJ5bxJ
EKdoukacQd2N+d5MHQ0YL5psdBP2V/KstgJWmh7dMKXtx6x2Ao5NFHhgskTLjzk4mmOp6bPU9MgZ
Orah8/5aF8vQODfX4OBoHltXnCrb0Pj0AxNMV/P4YcLvvnCZixvOoFfV1bj6+dxLOOdeoeGphuYl
yyRKM1Y7AdWi3QsGqT3zsUOjlHPmYP68vdzmnVUlT91wIl5baGDoGp97cJJumDBfc/n+5To1J2C9
owKshq5tq+cERU21DQ2/l9mhl90xerRVU9fo9jLIg2Pgmt916FEcVZ2m0RNKMU2NvGVw5kCZRw+O
ku+p/L6z3Ob582vYus7racqTh6s8e+r+bYEydH4+4lhqenzj3XV+83MPqM6+t4ifOj3Jn7+xwtsr
7R0pH/cz9qov2aq8cjW21mKsd0LGizYg0YRgoekNJE0lMFG0aHqq307bT1BiW5K2F2MZGu+udfDC
hDBOe05EqrjjkoHB04fREzM4OJonSjLypoYfpz19fkk7iLmw1mWybPd6PgiSTFKyDXRdFUvHaYql
CUq9KOCZ6TJ1N8KNUiZKNpMli7avHKY0yyiaOs+eGKfuRXz6gQleX2oNDIBjY0XeX+tSc0KCJMU2
dNU4r2TyyeNjQyfnOtg6/75wdnpX2sBIweQoRfJmg6Wmz0LDQ9cEcZrxsakKLS9mrR3ghDFR0qMz
aMqZTSXoSUqQ7HwPOj0ls7yJFKr3U82NB5ti3tJVbUikFMPCOCMFOp4y2CfLNkfHS6x1wh69UlDK
GcpJzqso8G/9zGl+MFtnZtPhULXAiBvy0IFrC1w/6Ej+zZxvt3oCJ0go5pTT0legansxby+36YQx
jx4aHQij7PZZ/DClWrB5db6FlKBnGo8frRKlyojMpMSPElY7IUmS0vIihGAQHe+GCeMFkzhRWb1K
zgAtQ2YaXpz0HBqVG95XzhGlAiNUY+HHSV+jAiGgnNNJ0wzb0AFJKqEbptiGxpnpKt0gppw3Ob/S
ASQbXZ9xaVOwdPZVclTTjIsbDk6UDBzCr76+xIV1h2pBUXczJE8cHruhBpW3k927W1nkrXvBF/M7
X6/tqTYC33xvHU3T8OOERw+O8rkHJ5nr1YSFcYYXJby93Oazpyb5zc8+sE3KeLnpcWnTwSlYtNyY
BIhCZaz2o/F9tLwIQ9dww974y35mVyl3GZogb2kULQND00gz8OKESMswpGCp5eFHKpN1JGeiI1S2
P1M1YDldo2SbBEnKaM8ZfnfVpemphUSgmvOutQKOjxc5MTnKlx4/xErL5397/n3ixGezG1C2DU5P
jXB4PM9nT+3j1YUmm92QU1PlgYLobM3l+GRpoMazNaPWx4dF6XNrQ3MNePr4GIerhW1O89VrxWdO
TvJ/vziDJjQKts5owSJKMtY6IZWcQZio3n2WrmjGXT9F9lwWS4Mou5K996NsW/DK0BTttWgbqrF5
qubJ1cIHZm9yxb0MEUDeMijaUMmZJFnG0fECR8dKHBkvbAtcdcKYOJXYhgrKdEJlL92vPfmGzs9H
HH/4gwUA/vYzNyd0cDU+e2oSIeBb72186Jwf2DsLsVv0eEBZyhnYps4jh0aZrXUZzVucmCjx2nyT
N5dahEmKqako4an9JV6da5BkKB69pjFVytF0I1KZoekaJdsiZxgstzziXkpbADlDEKeSsaKJF2dE
vX4/g6h+JslkipEJkixjZlM1o/N7nH1X17AMvZe2tjg+XiBQXBs2nYhj4wVmay4jeZONbogBbHYj
jowVODffxItTpio5giTjudP7BspOoCLILS9mX9mmkjOZGskNlH6GuD5uNLMxX3fpBAnjJYtuELPc
9Eml5OnjY8zVPaZHc3SCBFNTIgN5S2einCNMMupOyPbt7gqEgDjOiE3JaMHADZXjYxpXjKl+LYLM
epx0HcIUtEwyUbQ4MJrjk8fGqLshEyWbvKVzadPhD34wzytzDf675x7k4Eiev3pvg8Wmj6HBLz9+
6JrP/UFE8rduuDdzPiVbHeFH6UByteOrGoakV+vUj0hvzcYhd6bgXF3Qe3yiSN7UqeQt3CjhsUOj
PHp4hMubXd5Y7LDUdBFCwwlU7xUhBJ0w5e3VDqM5A03XB3Kz3SChYGlk8kpNF0CaShpuSCVn4sUx
ac/IMTWwTR0NOFDJc2yiyFzdJUokOUvjxEQB29QpWyaLLbB0lUESmiBMJaudgJOTZaI04/h4EdvU
0FAZy7mGS87UqRYsml7EkWqB8ZJ1Uw1rb7c+624aVjtdr79HXFjrstj0efr4OItNj5YX0/BCDENj
n5Vjte3jxilelIJgwDLoo+EZfOrkBALBufkar843MRIlKJIzlBEbxBJDF5i6Rk5XgYa095zmTMGR
aoFzc03cKCGIUgqmweGxPHlD1ZbuH83x9bdWmW94VGyD0YLFkWoBgXKOZjddAhnjRSlTIznKwiBn
6ryz3KYTpL2mq+p6TpgwVrKIEslTR8ao5E0SKfnE0Sp/8ZavahSTjLobcnSiyOFqgfOrHQqmTsuN
eGelzdkDIxhCMLvpDJ4zYwcK/oel3ufqhubTo3kOjxf4Yv7KerHTMdOVPG6Y8u5qhAQePzzKQ/vL
vWbVCW8saTScDGQ2qNVJJOQsjVHDIMoysjTDDbNt2ZxUQpZI0izGNjUMXVEqQTlnli6wDKFo+b22
G1GmMkVuGDNaNBEaPHG4ypkDIzzTk7/vf462F+NHKWGcEsUpeVPnSLVwX/fkGzo/H2EEccofvbLA
58/s5+Bo/rbONV6yeezQKN+5sMl/+9yDH9Ad3j/YLeI0oCwFCVkmeWelTZJmNJwYgeDFmZoqEBcC
IWD/iK2KWhEkqWoIZpuCoq1zaDTPy7M+ui5ouiE/e3aKv3pPNTNLpMTSdabKNk0/Yv9onmrB4hce
PYiuwQ9mGySZ5JvvrUEGQkgOjRU5VC0MMkp5U6Nk6+hCkqGuudj0BwXxB0dzjBVt3lntEKcZfpxS
MAQNN6Ttx1i6hhMlHB4r4EcpQZzym599gJGCyYuXarw8W8cNE+JU8sljYzxzYvy+5WR/GNH2Yl6Z
azBTc2i5EZWcyb6KzVo74L3VLqsdn7Sn/FTOWRi6yuaMF21KOYNlS+fyhkO0g/+TSRgvWUgglRmy
Z4BICQVL4/RUhfVOgCBD0zRaXkrQCy2mmeTcQoOWH/Pxw1VqTgjAextdol6jmffXuvyT71xiomTT
DRM+eWwM29B2jO7erArX1T1z5hsuL882tikz3sj5+oXzOUMniDO+cHZyEOA4c2CEomXgRsm2e+74
Md9+f4Ocqe8o3HC1g3TZ1LFNpeplaDA1ompCXl9qKVVFTbB/JEeQN2j7Md1ANatcb/s4gdGTmhWU
8zpRkmAZisYkZUSQKFskBXRdyYjXvRDRM1QFgtGcwYFqgSPjeTp+TBSnSARpj+Z0YtxWxo+UHB0v
8s5qByHBMgRF2+SRQxWkhGMTBV663OCipihKn35ggpKtDOzJssWXHj9EJW/uauj9OKK/R5zeX1E9
2DYdRvMGv/rkIZaaPj+cadBwI8Ik7WXdNKr5a413QwhaXowfJSy3AgQaU2WTVhBRtky6YUzOVJRp
P86Ya3oIKQaZnqlKnpfnm9iWjqEJTE3j+HiRU/vK2IbGhY0u5dDg6EQR29BpuhGZlDx2eJSfObuf
r59fZaJoM1Nz8COJFyYUbINqwWCsZOInCW03RvTqRFIpEUKw3PL5+rurvLvW4fNn9vP+epe1ToCQ
qs9c20/IWzpNLyLLVG3cG4tNzs03+fSpCX72zP5tDsPtrg33EmOF7Q3NDSEGNYN9lber14v+MScn
S0wUbR4/MspCw2Oh4dHyY+JejS5ccWpsU0MkGQXLQEpJx0vQhFoD+tQ1TVNredxjkfhxhi6uCN5k
KFGj6dEiXpiQi1McPybKFHPAMnTG8ja6LqgWzIG98+pCkySVmD0pyVcXmhi6xnQlx9kDFfK2cV9n
6obOz0cY/9+Plml5MX/32WMfyPk+c2qCf/Lty4oycZ9KXt8q+k7OzKZDkGSDqNTWxfjhAyN85+Im
TTeiHcQstXwmSjaGJlhsSPKWxlQ5z8VNJVMqhCoiTxNJy49p+Ypqcmy8yFLLp+UnnNpfpuFEdPwE
Q1f38emTEzx7aoKz0yMcHlfRlZobcXnDYbxgM1FWG+ovPHqA15daJIlqPufHGWGaUcmZjORM/EgZ
OfvKNqttn7ob89Zyk8mSzen9Fb72xjJLTkySqiVSM5VTtt7xOVgtMrPpMF93qfoWX3trmZVmMKgR
Mg1tMAduVvJ2iJ3R8CIsU+O501O8u9YmTiRpL3oXJEpQQ6Cid/vKtqrREBqdIMKNEkxdY18lR5Co
yG2cpWSZxAtlrwmq5HA132vOqbJGlbzFRNFE11XlbJIKZJIOIo6gNlQvzOj4MZdrDuvdgGrBRMis
J7SgKBCXNl0urzukwI8Wm7vWgsGNRfJ3Uml84dLmoMj4p09PDShXxyf2dsK39pzYWsjef/Y3nYCy
fYWj3/Zivvr6co/qZXF4LL+j2MFWZyyMM77w8DR1N+TgqKqTmG+sU8mZVHImZdvg8SOjIOH/PTfP
D2cagz4rRVt9L+0gRiSKsnaomgMUpdXUU8I0o2ir/kwbToiQAk1TRT6lnM6p/WV+8bGDfPX1Jd5a
6uCGMYam8fiREaSULHc8RnMWq+2ADMnRsQKVvIGp6by33mG27tFwQtU7SEoeP1Qlk5K8bezYvHU3
Q+/HEf15EsuMz5/dz8l9pcEanbcNvvDINOutgAvrHR45VMU2BU0/4jBXekC1vZivn19jZtNRdGih
aK1hkmLrGgdGbRaaKQJBwTYYL1rUuiFjRYvllk+1aPHY4VHOr7QJ4oRAF+wbyXFyX4k0kyy3fFIJ
UZry8SNVmm7Eq/MNKnmLf/niLL/9hTMcqRZZanhYhs7JyQLtMObEeJG8bfCpk5O8MlPnDb+tqFSa
wDI0/DDlsu8wWc6x1m7gBAlvLLZUbykBppCcmiypWh+hMkELdZfNbkTBzPjG+TVOTpYIkgx2qHnc
ig9DrehONWKplDTdiJyhD9aYqxs8b5VsT6Rkrqb6L210Qyxd9daqFgw2uiqbmPbq6oI4I0oUNVJH
ZXKOVAs8fWKMME15eabZa2QtMYXANAVSKrEjUM6RG8a4kVL503sO00jOJIgT6r3nOk6V3Pqfv7HC
G0stzhwYoeGG7CvblG0TQ/Nxwhi4kpW7XzN1Q+fnI4osk/zeCzOcPVDhmRNjH8g5n31ggv/zW5f4
4UyD585MfSDnvF/QX5i++voSOVPnhUubA0nV/v+xgsW5hQbLrXSwGFzY6KAJjUPVPPsqNgsNT0Xn
RnJEcUrO1rE1jccOV1lre4RJRidUXbqfPFIlySRHxgvoQvDSTB0pJXlL3+b4zNddgjglZ2gqCiPp
GUZ5SjmDv3pvHdmTckkzSZoq8YKCJQgTDUMTVAsWv/jYQWpOSJhkuFGCpmvkTX3AwS7aFkfHCyw1
VQNH09BwgoRvX9ig7sSUcrqqjbAN5fQJcd+mvD+MGCtYRHHGrOMwWcrx+TP7aXoRf/mOirbuH8lT
LZg0HKWslsqMdSckThQN4eBonmrRpOUJZCYZMUxiKSnaGUmq5uzJqTKjeZOLGw4zNYcDo3lO7SvT
8iNAoGkCN4gJE2VA+bES8RBC0R7fXmojBBQtg4lSnpypkWYqetx2lRN2YCTPkbHCbdeCXR1V7Itu
HB8vcXnTZabmMlW5thfS9b7f3TZqZeyLbUmMhheRMzSqBZOmFzFZtgYR3q2NfLc5Y8T84mMHKeev
OFFl2+TyposAjk8U+YkTE3T8mFLORAgNIZSzmTN1yjmlILavbKNpgpG8Ut6yDWUMv7vapWjpTJQs
Tu2rsNJ0CWIVCR4tmOQMnUxeqV8cLajeMAINvWfstIL/n733jpLsvu47P+/Ve5Vz5zg9PYPBJAwG
GORAMICkIJKiIBPKWsmSTB1LsrQ+Wtva9a5lH8lJx1rZR7ZlUbK4NmlJh6AFSyIpUAQpkghEGmAw
wARM6Jy7qrpy1cv7x6uqqe6pzqm6+33OmTMDdHXVr37v/sL9/e79XpWw187R8LpFPnSsnUxZ460x
e5N+aTJLb9RPsqCSU/RaHy/dlDbzqe92sNKtRNzvpi3owSe5SJdUprMl3C6R14dTi27HU0WVXFkn
5JHQDJOconGo1Y9ftssgvD+bQxBESqrGQGuAj53o4H++PUmyoGEBJdXg+lyOsE/iJx68k5duJIj6
ZVyiwBvDKZIFlYDHRWcoygeOtfHC5RkCHsk+cFsocn5sgU+d6eZEV5iXbiQwLRNXWuBsf4ysonF3
T5T2kAcLAdUwmViw17OemI9Myb7VGkkUuDqdpaQa+GURzbA36DnNVgJ96nQXT5/t4dpsluFkAZ9H
xELgjZEUA62B2+TD9yrV8VAvj29HTJgN55ilku2PH20jXdK4Mp3FJ7sw3S4kl8X4gn2zfqglQNAj
0Rn2cjORJ5FVUHQVRDuq48EjMY53hckUNQplnWRRQVHtcEmP6EJ3GViWfeujmZArG8iVmxu/20Vn
1GvPIZkCugGhiiz65eksbpdIuqiSr0SEuESBG3M5UgWNoNtEFG/1QbPe1DnOzwHlb9+f4+Z8gf/w
o2c3JG/diHsPRfHKIi/fTOw75wfsmNxYwN1wMa+G3zx5vAMQakVAP3mmi2RRpcXv5pvvzzGbVciY
GqZpUtJ08qqOYMGboymeuKONT9/dy9VZWw63KrF9qivCSKpAxC+TyKncnCvwx68M8cy9fVyYSHN9
Nsd7k2mOdYRpDXnshEjg2fPjPHOuj56ojxtzeVvmWLdwB0TSBQWXS6An6uMjJzqZyZbIqzrjC0WO
tYeYyym0BtyoHpm8onO41Y9Plgh57SKoh1v9tAW9BH12LHh7yC5W2dIj88m7ejjVHTlwm5+doH4T
HvbJ9LX4kUQ7TEakjN8jcaIrzEymzPlRBd2wb3dkl0hOsYtmBt0uprIlNNOFZpoEPTJ9MT939Yb5
zL19vDKUpFcz8Lld3D8Q41h7mD96aYjhRIGCohNwi8iSgFuWENBxS7aMriRCzjDojvqRXCIPDob5
4LEOLk1l+M778yi6nYcU9cvEA+5N54ItdVYG4gHGK+PmbG+U+wfiK6qMNZK0brRQp4oqHlm8rcZX
NUylL+anLWTy4EC8djjiEuyxlVd0DrcEuTqT4+2xBVpDHqgoqVXf/zPn+jjRFSZb0uiP+2s3Ra1+
DzG/jGFJ5Mu2s6JbcLQjRMwn8/pwkmROwUKgL+6nLeTj+myeiE+mLeQj7JWIBDy0CCJzubIdCimJ
dEW83N0TZWLBDpP0yyI+WaSsm+TKtnM6kijw1liKkFfmh872cqjF7tt8Nc8Ia9U+3iv5GVvJcrcS
i071WwNcmsoy2Bq4TQwi7ncjuwQuT2dZKKr43RLtPjuk/L3JDKmCikcWKKkmmZLK+bE0p7vCjC4U
6Y5EifllWkNePnisjZM9Ee4biDOaLDCVKTGTKVFUdRTdZCZXqtSVAkUzeHcijSDatYWeuzDB02d7
bUdIEE0AACAASURBVCGGii1Wc7cOtQSI+d28PbZAtqzXCqZqukHE50YW7bnAsix0w8Qti0T8Mg8N
xjnSFqKsG4ykCpzpifKLTxzl35auYmDiEkQ6wt6G8uF7iUbJ/UvzCJ861dZQ1KZRyY3HjrZydSZL
S8CNopvcf7yDS9P2f78/k6Wgalyeztr5YG4XnaKHsF/m0aOt/NA9vYwtFBmZL5AqakS8Mqpk0B70
0R3z8srNJJppq8TKFYl9zTBJFXUKmogpwJG2IFGfm5vzBQqKQWfEi24YhCp5YpGAzLH2ECe6wpim
fSsU8khILqFm1816U+c4PweUP3xxiO6Id0vr8ngkF/cPxHnlRnLL3rOZWG4xXxp+8/TZntsmt+FE
AU9FEnxyoUhHyMt7U2luzBWwsE/GT3SFefBIC8e7wjx7fpxcpcjh31yewTAt3hpJUdZNOwxNgC++
NgKWwDuTacZTRYYTRQIembZK7sYIRf745WFU3d6YCoApCLhcLhaKZdyyyEiyxKvDSTrDHt6dyBLy
SSTyqn2qLbvwynBXb5iI383NuTzTOZ2TXWEercT4x3zu22L9+1puhXEctM3PdtJoEw5wYSLNQGuA
qN/NY0daOdUT4dJkhtlsGZcIE+kSC0WFTMk+oYsH3JQUA3dF/ak96OZoe4B4wAPAVLrE1ZkcCwWF
dFHj6nSOsFfiWEeQqXSZkmogWLYMc8Bjh8b53SLT6TJFxWQkUaAj4sUvxXh/NsfVmRwlzUAzLe5o
D3KqO8qTxztq7d9MkvtSZ2U5Fa6lrCSnvR4BhtM9EbAg5nfXQuD8bhfZssZ8PsBMpkSpzbDrZQhw
eSqDWxJ5b9KuZr9QkcS9PJ3FNC2evzRNZ8TLVLrMw4Mt3EzmuT6bR3aJtmPbGaKk2fOCR5ZwCaCZ
FtmiVrstMgG/R+KV4SS5kl4Z/xaGadIRsp9xe8TLI4MtvDm2QEvQw5WZLGe6o6imwaF4gAtjaUwL
5nIK335/jh8423NLkXCNVdyb+dR3p6k/1Vc0k7BXYjZXpqwZixL7I36ZDxxrY2KhyELRDh8PuV3M
ZxXGUiW7LpBqOwYel51T8uCRVnrTJSzLYjiRJ+p385WL06RLGn0xP+9NZRhfKPI3l2ZqaqAC8OW3
JugMe/m5x47wvaEEYZ+MZsC12TzPXZjkpx8e4ExvlEPxwG2HBJ99/AjPvzdNSdXJlHVGUyV6Yj5K
qoZRURvVTQiIIo8caUHV7RuDiYUiJdXg2kyOj53s5BeeOEK2rNEf8y9SD93oWrGbymLLyeS/eGMe
LJjOlHjm3r5F62M9tpOkUVKMWthf3O/mkSOt9m2gV+KDd7bjcgnMZhVcLpGAR2Yua4sYRL1ueuM+
Pnaqk6BH4ntDSXKKxo25PMPzBZIFFREIenSOdYR5bzKNYZpoBsiyQEExECpiKgG3hGGYvDaUxCu7
Kn0qM58DEJjNlnn4SAufvrunVt/r8nSWixNpyoa5KDy4WVmz8yMIwjHgHwGH6n/PsqwPb0O7HLaR
ixNpXh1K8U+//wSyS1z9F9bBI0da+bfPX60VutpPrHQyXF/9u3qyVT/5SoKwSDHqmXN9TKSLpIoL
eFwiLlGsJQaPJgvkynbhu6FEAd3UONwSRHKJKCUdxTDxSS5mcwqJvMpcViHilZFEuxBdXrUlJw3T
ZCxZpKwZBL0ynooqnE8WQbAnO8PQeeVmgq6wD1G06/jMF8rousX9h1soqjpn+6Jcmc7RErSfp2Ga
vDm6QCwgL6p70kgm3Nn8rJ/lFvBGm/BankrlxLQr5iPilznVE+HBwRZeG0phWbYkblvIy0y2RCJX
ttXCFLuey0xWoTsW4JUb81ycSFNUbenlgmIyn1eYSpeI+iSSBRXLsp+ropsUFA3dtMiVVHySHQ8u
S4It4ywIJEsKiWKZoFfmrp5IrWbFqe5IbSO42XDIpc7KSqeM9f26nlvJRna8dKNzujtSC4EbXygh
CthS7343sYAbwQJLgKl0mYBbIq/o/OkbY0wsFEkVVARB4IGBOLoJLQEvw4kiV2Zy9MV85Io6WUVD
1U3Ojy7Y+VyGid/tYj6n4BJBkUTCPhctQS+zWYVLE2ksQaC/xU8ip9AT9eF3y1yfzeMSRaYyJfri
AYaTRfqiPuZzCqmySsznxjStStFUg2Re4S/emUAxTJ4511eT+K4+v9VyeZr11HenqRbBDnglTNPi
dHeEN8cW8EqLQ6gBYj438YDHfiYFhbAngCSJhH12/kVe0fHKLhTDRDdNHhiwFdYuTqYRsA87rkxn
uTKb5URnmKhfRq3kclTD1dIljWuzObyyi66olwcHW0gVFG7MFfDJIlZdsd76Z1gNs35jJIViWMxk
FHJlDcMymcuW0XQTpaKp7vO4aAt77RuJsBtBhPFkkWReZSZbJllQ6Yra9aseHmytFUUd2KBIzm4r
i60kkz+fV1goarxwdZaemL+hUuCLN+bxyiJlzeCx3tbafPPMub7bDnhGkwU0w+TKTBbJZdf8iQVk
gh6J14aSGKbFTLbME8faKKoGJVXHJQq4KwqBV2bSqEalhqBo5+8JLlvhUVNN5nIqmUr9vxa/h4BX
Juhzo2kWYZ9MyWUXdK8efmSKGg8MxDnZGSbok/aE0NF6bn6eBf4L8IcsLgDusMf4wxeHCXkkfvSB
vi1/70eP2hKIr9xM8OmzPVv+/rvNSifDQ/N5LowvkCqqXJvJ8ZlzfbXX2rVBgjWJFd20cEsiIa+E
x2WHoiDAxfE0370+z1Aiz3Aiz7GOED5ZYjiZJ+CRePRIGy/dnMfrFjGROHcizpfOj5Et61imjiDC
ne0hptJlLI9AwO2ipBhYlSp5flmkqJiUNQPTBLlSODVbVpFEkZjfTZfs5eZ8nrfHFvDJLn7gTDcT
CyWGE3boXMQn45XERdfzh1sbhzA5m5/1sdICXlMOq9QEgeVvJaonyMmcgihArhpe43UjSwLzORXN
MPDILizT4vJUhoWCStAjEQ3IFDWDkqbjke0PigbctvOtm4S8EiOVOHZdMymadp0YC7NWcNMwLSaT
JQSXUAvnum8gzsNHWnclHLKROMJ6biVXy2dBAFG062cNtgaIB2yZ56D3VoHXkmbazmdeQRDsvEuw
w0MU1eB1C0JeiaJm187xyy4KZVvO3u+WmMmW8EguRFFkOllkoMXPQkGpKDjZS3LIWy0+7OfGXJFs
WcPvkYgG3IS8EmXVpDVoC5yUNDuxOVXUMAwTQ7MQ/QL39Md4ZTjJaLJA2OumOxogV9Zrz2mvhbM2
Q52RpYdfp3sixALybX1Y3QR7ZBHdtGxbCroRiiqpQhkLC5/s4tyhGC1BD5++244yyJY0Ij6Zsm4y
n1PwyS5aAh5M06KsGaRLKi5RQBAEqrVugx6Js31RfG4XA/EAubLG77xwjbJmMpzI3yY1XR1Ds1mF
oUSee3pjAGiWiWFY5HRbFU41TbCESs0Yi4As0RrykMgpSC4BryySzmqkCip3doTIKrYoSFUcYzxV
XOQMrpXdtsvl5uKybrJQ1Ij53bVblKXtqj/EGkrkeeHqXO1w8RN3ddcObOCWNHpJNXh7bIG4X64c
hJZZqOSMHekIMpUuMZosMtDq58Z8HtXSKWkm6aJKa9jN8c4gV2byhLwSftnFSDJPUb0lmy+J4Ha5
SJU0smWNmN+uEWhYFr3RANHKXADsydze9Tg/umVZv79tLXHYESYWinzt3Wl+7rHD26LIdqo7Qtgr
8fKN/en8NKK6Kf3eUIK3xhfIlXUm0yXuPxyv1W+wE6Fv1TC4/1CczoiPc4fizOUUDrUEuDydJVfW
GUrkeWSwhdlcmQ8ca+NQPMBoqkDQk0KWBR450srJrjCXp7N4ZJHHjrbyznga3YKxRIHJhRKSS+RQ
i49U0T6d74n4CPkkvJWK7Zphn9yblq3GFfd7iPpk7u2P0RXxMpUuIUsimm6HSTxzro8HBuIg2CeT
y1U4d9gca1nA35uqKGhNZlaUcY753IylS/YJa8RHb8zP3T1RXh5KYGHLNHtlF5YFyYKC22XH68su
kTPdEaazZUzTJJFXKesm/S0BArKL071RROBz373JdE5BEqA16MawbDsPeCW6oz4Kik7c72bGJfLo
0VburtT/2o1ckEbx9Ju5lVz6HWI+d63Ojld28eE7b9XAqpfKThQU7u2PMRAP8L/emeT99zNky7ot
NtEe4KMnOjGw8EkuOsJevnttHlG0HSW/7KKoGiTyZUqqjmFatjoWApJoEfG5efBwnMG2IOOpIu1h
H+mCymNH27g0nQXLYiiRx8KqFWX9vlNdfOnNcWSXSDwoM9gapD3i5dc/fpwvvjbKTLYMWITq1Lf2
Ui7Pbt8GVNEta5FcetArNezDqp22BjyEvDJHO0KYlsXRtiCmYUtK35jP0RHx0Rby8O5UBnMizeXp
DCe7I/jcLg63BphKl1B0g7aQh4+d7GR8oUiyoJAqamSLGg8PxumO+nl/NoduWLw/k+OBw3EeHIg3
lJrOFDUuTqbJVyISrs1kuTiZpi/uo8v0cn02i26AxyXSEnDTFvJiWhZH2oL8zCOHa7LnwUqh4HRR
Y6Go8s2rs9zREWJqoURe0Ws32BtxXHbbLpeqtVXb//TZnlouYLV22Eptt8WLXLW5ajRZqM359eF0
r43YOXl23S8LURQIeuXKQSh0x3yc7Y8S9sqkCipzOYWxZIHeuJ9sUefuvohdeyroZTJTQjf9jCZt
AQtRAJ/ssiM6fDIRn8Snz/ZyojPEC1dnF32X3XY6N8qqzo8gCFUpsL8SBOEXgecApfpzy7JS29Q2
h23g8y+PIAA/s0Xy1ktxiQIPH2nh5Ru2MtlWiSk0OxG/THfEh7sSRijAovoWty1+PomgR+Joe5De
mI/7DsW5mcjTFgwwXJELbg95a0nhIa/Mx092Lgovq4oKdIa8XJzIoGoGZd0kr9pX0h++s4P2sJeX
rt9S/EkWVGZyCtGgG48s4JVdSJJIQHYR8btJl1S6Il47sb76FeoK8VVPUc/2RkkW1Q2HKDg0ZrUF
vNFCs5yMs25Z3N0T4abHrltzZ2eIh460sFBSkVwCmYKKJImc6orwratzlDVb/UcQ4JN3d3NlJsvQ
XAG/x74xSOYVSh6JdFHlM+f66I76+DfPX2U+WyJR0DjaHuBkV4SHB1vIljW+cXkWt+zC73YxnSmD
kOb1kRQPHI4vGyq5k/26nlvJ1cQRUkUVw7QoqHotvOWnHz5ce/+gx87PaAt6ajK25/pjpHIKszkF
w7SIBTxE/TJjC0VyJY23xhYYSxUpqTqiKHKqO0pe0WyZfAR8bheqZiCKIgXLoqzpzOUUAl4JC/jQ
ne218JP7BuI12d36fh9OFLizM4Tf42KhqCFUxRhaA/yT7ztxq4BrXW7PXgpnbZaNWdzvrtlAtfDz
0lya6utcgl04VhKhUJF9rs71s1mFiN/Nuf4Yw8k8ubJFe9hTy9PA0nngcJywVybovRV+1NfiJ+qT
uTSdpT/mpz3iZXqhxJ+9OUbIKzOcyHOyK7yoNs3SnFY7Ny1DWbPr0/llkTnTQtMNQj43/sqG2CO7
uPdQjGxR42R3GLi1fhyKB7g4mSYedNMR8nJlJkNJNRhLFbk8nQFY1kFYjeWcj51iqVpb9faqr8XP
Tz98eMXxUj+mqtLY1bmKSrmApeF0XkmkI+xhfKGE1y3RGnAzn1eJ+mV6Yj7agh4eHmwF4OEjrVye
zpLI1bbunDvUwk8+GOLZ8+OIGYGwT+Zwa4DZvIJPEnG77NvHSEDGMC26Il5O9kToiflr7az+vVcO
Q+pZy83PeWpit4Cd91PFAga3ulEO20OmpPFnr4/xyTNddG+yqOlKPHq0la9fmmUsVawlwx0EDrUE
ONMbJVfWOdwaWPTdV1v8AEaSBbKKVjuVrTo+jU4u6zdjAY9EwCOhGiYeWSQWcOMSRLxuF48cba0t
nHG/m8mFItdms4S9LgzD4Ifv62M2p1DSdO7ti5NVNPKKTknVySkQ8Uq1Qny1RbCs104aNxqi4NCY
1TaW6zndjPvdtIY8eGUXZd3k6bM95Moa1+ZyWBYEPDJHO4LILpHOqH1SG/e5Od4dpjPi5epM1l50
I76actPDh1uZzZW5OJlmIB7g0aMtfPv9OfKKTmfYS8Qnc2Umi1d2IQq2PKpHEjFNC0kQeGMiTV7R
aA95d/QUfjMb9rWKIywX3lL/2SVFX6QI1xLy4JZduESBBwfifO7Fm+iVMNTWgIeT3RFU3WQqXSDs
kznZHebe/hhvjS0wsVBiOlrGsuw6YSBwfS5Hb9RXqbXBIodlOUW2esW6p8/2LPqd6s11o/7cC2N+
t28Dqixnf0v7sP51T53qWuSofsLXzaWpDJphMpu1a06VNIPxZBHTtEjkFYYTeRBsB6J+fGWKWi0B
PlvS+ExXmFxZq53PWdgFkVfKaR1sDQK2uIclwORCCUWzyyec6Y5S1Ax6oj6eOddLuqTxxVdHmM0p
vHh9nl/76HH6WuxclzM9UcZTRWZzZRTNJOqXGGyz3/tEV/i2fNm1spzzsVOs5GivZbzUv6ZeuAXg
vcnMbTbsqguzDcgubszn8UhwoivGx052LjqweOZcH98bSpApqfhlO7Q24LFvdrqiPgqqwZXpLGGf
hFcWyRR1VMOuG2YrEIoEPbfchVzJLrjtrhSS3unDrK1gVefHsqzDO9EQh+3nz14fo6Aa/Pzj2+uv
PnLEPm14+UbyQDk/Eb98W3Ji/c9WW/wa/by+RkD9qU91M6ZUYnjtBHUDETv/wifZRc7qyZY0kkWV
u3qjBGSJN8dSpEs6rUEPAh5ms2VmsmXeGc0gukRaPRLdMR8jKXvjVZ3cA17pliJMXWKsw9aw0kK5
nk380tcCPHdhEpcg4nO76I55EbATYE93Rihomh1OE/RwKB7g6bO9/OkbYwzN59EME71gcXU2y3S6
BBa8PZZGN02OtAUZThRI5DT87jJhn8yheICz/TH6434uTWS4NJPl8nTWDstpCd4m8bsTLE3cXqsj
VLX7sEdmKFFgNFm4zSmI+OVlw1uqnyUJAi9cnasVRW0NuVE0E59bJOSRWSip6CYMtAS4PmfilVzk
VTvBXRREVN3O6znVHeFUd4TRVIHemI/JhSIjqQIxn5uRZJE/e3OcI60Bgh551cTjvXSLsxGa6fut
1WFc6XUjyQJRv0xZM3jyeAffG0qimyaDbXYOxuG2YMPQsdFUgQsTacJeu6bU/ZXDtbO9UXKKxmBr
oGYry+a0JvKUNYNTXWGGE3muTGeRXYJd60sUONoe5Mfu7yfsk7k0PYNuwrG2ACPJwqI1JO53V2rm
TRLzuxlK5PG5XQS90oYdH1j/Ld9W54JtpaO99Dk0ElyxAI/sojXk4WRXGJck1ObWkG/x72dL9mHI
ic4wumkyliwxlixyYy5PUTVoCbi5qyfM6e4oz745xkJJtev3IOCTXZztj3GoJXBb3teTlULSK+X9
NivrUXt7BnjesqycIAj/N3Av8JuWZb29ba1z2DJU3eTzL4/wyJEWW551GznSFqAj7OHlmwl+/MH+
bf2sZmO1jetqG5H6n2eKGrmShrqkKFr9JP/upJ0zcKorTKKoki16ONNr13vweaTbbmsOtwbtEIfu
CPcfitfqdGRLGn/6xijX53Ik86pd3BRIT2axTHh/JsfHT3baIRnlSkiGqtc2ec2QVHxQWM+pe/1r
L06kKSo6Ub9MUTUoqwZdER8dYS/fvDpLd8SHgH2KV/297zvVydfenWahqJIuasxmFAZbgwy2BRma
z5MuWlyfy6NoBi6XgK57GEkUmEyXONtrx5uPLhRpCbiZySr0Rn21miG7dQq/3jyQuN92Ur45PGvX
VxqRGko814e3VENCsiVtUXV3WxbbLooqidAV8dWqvYc9MpJob3BdgsAHj7eDBX9zeYbpTJnxhRIl
1bCdr75oLYzo0lSGr1ycIllQGWjxky0ZnDsUxyOLa3Iw98otzkbZL99vqbJjsqjilkUOB4N88+os
umExky3ha5Bbki/rFBQdryTWQrIjfpnPLHNYV081nOy5C5N4JRcXJtLc0xfj6kwOr8vFtVIOMGkJ
3Crsm8gpTKaLFBSdiE+ixe++TR2xKvjglV2buvGpsh7nYztywbbT0V5qw0vLHgQ9Eu0hb8O5dTxZ
5He+cRXdBEXTOXeoBX+nREfYy/PvTeNzu0gUFO7rj5MoKBxpDzGXVzEtC0kUeXCwhR+4u6cWJmvb
oB2eP5zM0x7y7plQt3rWI3jw/1iW9awgCI8BHwf+Hbb624Pb0jKHLeUrF6eYyZb513/nrm3/LEEQ
ePRIK9++No9ZScRzWB/1k7MF3F2pt1CdAKuTfMhrFxS7Xqxo+HulSqE7zyJHqf62pjPsoz/u5+HB
1kWhDW5JpCPsQ9FM3JKLoNtOsM4pOlOZEg8MxBfV+tAtO5SpKn1avQLfK2ovB4WqPO13rs8zkrQL
ld7REeIz9/ZyYSLNUKKAhR1yUj3Fq3KoJYDfIzGcKOB12zHmVGwv6JV4qLWFG3NZZNHLSKrIVFah
O+IlFnBzf0UgwwLckouoX+Yjxzvoivl21Ule7wlxxC/zwOE4eUVb9daq+v+qY3ehoGFhJ7Bb2HNj
NcTsyePti2qbnOqJ8Gvx41yaznB9zs77WyhoBD0SIY9ky08bxq0A9AojyQIdYS+zOYUj7UGmMiXM
yqZuM5uSpQcazgHH7rK0WGa1qG/9+I0H3PTH/YQ9MqPJAoewT+OvTGdxu0SSBZV7K6f4sHbHUK9I
KVfHTEfEy739MV66nkB2iUguF4ZlMZKyN8eHWgJ0hr2EfTJH2oLolnWbOqKrbh7ZrONT/S5rdT62
KxdspxztpY7eoRY7zL7Rdx9JFdBN+9DlteEsXkmiqBvMZBUmMyW6wj4WCrYqpGlZtIe8HG0LMpYq
0hf3oejmbZ9bH54f8y1WotsrrMf5qcpbfwL4fcuy/kIQhH++9U1y2Gosy+IPXxzmjvYgHzzWtiOf
+cjRVv787UmuzuRqSY+bZb8tvit9n9FUgblc2d5sYRe7qw+VWxrOdGkqw0s35vFUbmyqp/dA7bZG
0Qy+c22egMe+Kq+npOjMZRUWigrxgJvBtiB3tAX5q4tTqJqxSPigPnSo0RW4EwbXmOWe93ba9Xiy
yHMXJimqOsMJu4aHoot4JRc9MT89Mb8taTwiNTw1jPhlnjzezsWJBUqqXajvs48fweeRkASB10dS
WNj1pdySSNgjUdQMWivlw2I+N8c6QsznFI51hDjVE9l121i6cbCVGAsr9n/M50YUBGZz5VUTsus3
Vqm8ysXJND5ZQhJZ1He6Zd0WKx/xy+iWxXxeIeyReWd0gZmsXcvH65a4pz9WywWs/6xTFaWvE13h
mqDCZuypkTT4VtVm2m2aYR2pHkggsKaaKLU6MJItXvLUqTb6Wvy1mi/V8SuKAjfm8rw/m0OA2gbV
LYs8dbqL4WSeDxxrW/f3vm2zHQ/wgWOQzCvMZMtkShplzag5ZMPJPGDfbBqmBRYomsm7kxlC3uUF
HzbLWp2P+u+jaCa5skamqO2IPazV/upfB6wouFL9efXGGW45IgPxAKZp8srNJNmiyrCrQFvIQ7as
0h70EPZJTGcgkVPob/Hz1KkuLk1leHs8zcnKgVij/MXq5zaDmuJGWI/zMykIwh8AHwX+rSAIHmBr
K2Q6bAsv30hyZTrLb/+dMzumvlZf72crnJ9mkSxdD40mufr4/+U2E5mixuvDKYbmC9ycL3C2N3rb
ZmvpJN8V9dl/KidZCyUVPWFvgKq1YWazZYaSeSI+GdO8lasznizyuRdvUlJNdMPik3d10hH28tbY
ArIkkiyqt226oD4MY+9fgW83y9nvdtp1pqjx3IUJLk1m7dO6kopquGwZWqzF4VMtgZqy11J8HokH
DrfUlAqrEs7Pnh/nzZEUybyC5pPpjfo43hWirNsFON+ZTJMpaiwUVPxuEX/FMd9t6hfwlcZhlUYb
z9XC5KobK0GAu3uitIQ8tnywaZEraXz3+jy6aVdCr68HVg11TRc1vnFphncm0hWnUuaXP9TPh090
LPrs+s8KehafoGeK2qpO3XIsPRmvnujvtmraZmmGdSRT1PjCqyO8NbaAWxK5/1Ccjy1R8lxKba6t
hEhWb2cjfrk2flNFlVxZ41tXZwlXyljkyjpUig5nK0IjMZ973XbR6FYlVnKzUFQxTSjpBic6w4R9
9uteG0rywqVZZiuO+0OHWxAAQbBqF5e7GY5Y/T7VqIV3JtK10gHLtWkrnOa12l/961TNrOT3LI6s
qP5pJETkEoRaSHvYJ/PwkVam02VkUSBZUOiL+bmjI4SimsiSyKGWQK2sRsQv1/Kz6g/E6r9/Nb+n
UU7yXpkX1uP8/DDwfcC/sywrLQhCF4uV3xyalM+9OERr0MOn7+nesc/sivgYbA3w8o3ElggsbPSa
erdO+RpNclAfDqPilVy1xaz++1TjeT9yvIOhRIH7B+Krtr1+E6RqJq8PpxZNliGfTEfEi2pYLBTt
ui3VCe3V4SSZsk5PxMdkpsTFiQweOcf12SyPHmkjq2g80eC0sNEV+F6o7LwbLGe/mw2/WMm+U0UV
y4J0SaWgGIR9Ej1hP8mCQkkzeGMktSh3pVpk8L2pxZuA6nOez9sKU9VwylxZpyXoweMSEV3w1Olu
7ugIkStpvDOZRhZEXroxjySKHG4NEg80jzhGdeOwlsV7uY3nSu/dyLkSRYE3RlLM5xVeuZngWEeY
XDlHT8xXk6Stzg/pgkpZM/C77artRUVjNqfc1rblQn02u8lfetJfPdHfbdW0zbLT0teNxudossBb
YwssFDR00yTmz1G4YCwqarncXLtc/9dvhEMeW9RAgJrqaNU5WouzvxxLnZVq+QYRgTfHUrw3lWU0
VeSBw3FMy6In7qct6GE+rzC2UKxIvXspqHpTzAMRv0yoKOOWxVXtYauc5rXY36KaSm1B3ptKimM9
OgAAIABJREFUY1kCbcFAQ8GVpaHtIgIXJtJkyzrhiuz95EKRombQF/NjLRToiHgIuiXu77flzZfm
MK71hqdZ1BQ3wpqdH8uyioIgzAGPAdcBvfK3QxPz/kyO716b5x99/E480s6evD5ytIXn3ppEqxRO
3AwbGWSbmbA26zQ1muRyJY3ZrMJga4CSalBeImSw9LtmFY2OsGdNinn1k1V181n/2VWp7b64j7aQ
m6crBWi/+u4U4wtFrs9kmUmXMbE4HPdXiqKpvHgzwWNHWm+79Vn6mfslFHG7WM5+N2rXa9nIxP1u
BEEg4nPTGXYx2B6gN+rj4mSa4x1h9DqlvtUWZQuwLKF2MRT3uwl5Ja7NZJnMlOiO+BhLFXnwcAtx
v5vXR1L87c05skUdn1tkLlemLeRuusVxLf2/3me0dO6oytbmyhrvTKTt/B8LcmWN6UyJC2NpUgWV
092RmqrcSLJAUdHJllRSeQW3JPLqUIJP3NVFX8tiFcdGJ+ib3eQ3Gtv18rt7dazv5GYtU9R49vw4
ubJOyCvxTPWGzy7dQqqoouoGN+bydIV9Kz6rRpvRRrc3Eb99k3j/4XjD+kxbeVJfXVPmcmXcLpGu
sJdXhhLkFQ1JtA/e0iUNnyzSH/Pz8o35WpHvp051baxTt5i12sNWOM3LCRgtfU19TSWgJmn+zau2
4Ip83b47qz7b6neoChElCgoC2BEZyTxl1aSgGrhEgbyi8bETnTx81K77czOZr+UMLaV+XlnObvby
HmA9am+/AdwH3Al8HpCBLwKPbk/THLaCP3xxCJ/s4id2QXXtsaOtfPHVMd4ZT3PfQHz1X1iBjQyy
zdwWbfaUp1FewRsjKYYSeYYTec70Rnn6bE/DUIeNTijVyWg8WWShoFFSjFqxukbvWZ3Q4n4PvdFA
TS61pJkUVTuMoZq4vlwbdjN0YS+x3DNd77Out82Vbg+r7207uVal7o7AVMZOon9lKLkonHKlTcBS
ZaFqWx8YiBPzyVybswskVuv/nOmxbwETeYXWoIdMUaM76uXps71NZytr6f/1PKOVagKNJ4tMp8uU
VLsmUtQvI3BLaKKaBH5lJst8XuFIWwhNB80yuX8gTkE1GEkVbnN+GrEVm/ylY3s/jPWd3KyNJgtc
nEjXiog+MBCvKfTd2RlG0U1CHomBtkBNRGSlZ7U0zGm59akaCteIrZZj/sRd3YymCgQ9KaazZSyo
iYL85EOHyJbtorwLJZXB1uCt8M9Vbk93irXaw2b7bTUBoyr1in5wq+7RaKqAbpp0hLy8MpREMyxC
XqkWbVEvRLRQUnl9OGVLXntkVF2lqBqc7okQD7j56OlOQl6Zm4n8bSU0luuHlb7/Xp0X1hP29jRw
D/AWgGVZU4IghLalVQ5bwmy2zF9cmOTHH+gnugsnrg8NtiAIds7RZp0fWP8g2+iEtRWnPEsn1VRF
lvSRwRauzOQ42RmubWIaxeZvdEKp5SfIImXN4KmjXcu+Z9xvS/ieH01R1HUWiiqD7UEeGWzhhauz
tXolB6lW03ay3DNdz7Out82Vbg+r1MsvV28eGoVTNjpZvjieBsFO9l/qyNfXmWoPeZjNlbk8lQEL
xlNFHj/aRn/cTzzgpqwZPH22d02b9mZlrc9oubkjU9T4+uUZhufzqIbBia4Ijwy2cnkmW4urryaB
f+9mgpFEgaBPprfFh25YFFQDSbSTl9fa3vrQu72oxrRd7NhmTbiVQlcVjKl+/o/d349XFmtzbL3w
BTS+1alStbGV6k4tx1Y7f1VH61A8cJtwSl/Mz9cvz3BxIo1qmIjAWTlWO5BrFpbaQ6Ooj83229J5
oV7AqJ7lcvgOEaA95GU2V0aARbds1YLR1TycPvyLxCSyJW1R/bFqFMfSOb3hLWVdH+3VG57lWI/z
o1qWZQmCYI9jQXB2RE3O518ewTAtfvax3alTG/W7Od0d4eWbCX71yTt2/PM3OmC36nRs6aSqaCZv
TKSxgCszWU5V6i1tZQJu/clRo/yEpRN7VcL3iWNtjKaK9MZ89MRubZjXshg77BxLF8enTq1eWbv+
xPi9ycyy4ZT1r3v2/DgXK7Z6Z0eIe/vtTUt1k1MN38yicXdPlKlMiVRepSPsrcll74XFcqsT4Jeb
O0ZTBcZSRQJembjkQXIJdMV8nOqJ3NZHDx9pZSJdYj6ncGdnmCfuaEM1TQbidvLyWsdi9ee7neB/
UGlURLRK/aHEenO1qodWq9WdWo6tcv6WqpGFfPIi4YbRZIGJVBGPJBLyyoS8Eie6N1/PZztZqf83
029r3VOsFCHw+NE2Lk1ncIli7Zato+IQjaYWO8D1bY345Ya2Vv85lyYzvHIzQTzgQdWN2i3l0rY1
63PbCOtxfr5UUXuLCoLw94CfBf5we5rlsFmyZY3/8eooT93Vtasn948cbeGPXxqmqOr43esxt61h
IwN2O0456h2N+lohwJYm4K40yTaa2A/F7ROlnKIzkylxTXZxbTbP02d7ONwa2PLNocPm2IxtrvV3
q2IGIa+Mopu8PZZCMyw6wh5iPvdt4ZuxSo7PSLLAtdkc9xyK1d6/2W1lqxPga6FAFRljuKXeOJct
M5Up1cRgluujiF/m4yc7K0UlRW4m8rcJpqx1LO50gr/DLSL+lYuINnr2a3ley60lO/lcV1MjA3hj
JMX4QpGJhRK9MR+Dra1N7fjA9tb/Weu8XX8IVT3oAGr5nT7Zxcm+MLJL4JWhJAIQ9KRWFBtabp6p
fs5LNxPM5xTyZZ1YwH1bLbH9yHoED/6dIAgfBbLYeT//zLKsb6z3AwVBGABeA65g3yZ9bL3v4bA6
X3x1lJyi8/efOLKr7Xj0SCt/8J0h3hhZ4IkdqjG0FWzH6VjV0VhaT2WrYrCrn7W0dkiVRhP74VY7
XvjiZJqyajCfV1ioSCRXT4vWuxg0Qx2N/cx2OxVVMYPhRJ6couN1uexbHkVjJFXALYs8ebyD4WSe
Bw7H0S0L07SI+GTm8mVKql0SbjxZZCRVYCAeaNqQt61OgK/Wb6kW/X1vMsPp7gieSp2VK9NZzvZH
FxUYXvr7VdGS+qKSo8kCmbJGXtFrt7prGYt7WY1pP7DZUO3lalAtt5bsFPXrQlWNbKAlwtB8nouT
aSI+GcO0aAl6UHSTmF/mRNfW1PvbTrZzvKzFFpYTs6mKoVTng66Yjw94JTTDqs3NKynVrbQep4oq
UZ/Mya4wc/kyR9qCDQWO1sNe2AOsyfkRBMEFfN2yrCeBdTs8DfiGZVk/uQXv49CAsmbwxy8N88Sx
Nk5XQqt2i/sH4rhdIi/fSOwp52craHRr0uj0ZytumdYaLrF0Yq9OUgPxAG+PpVkoasT8bryyq9am
9SpdOTdFzclan03EL/PMuT4eGIhXVIdu5aVUJY+rNUOqi2RZtxWFeqO2aMalqQxfenOspu5ULe7Z
bIvhVt7yLlf0typkkFU0+lv8yzo+dkFaOzbfJdjKetUijG+MpDBMq6YAtVqh1e34fg7bz9JcreXU
HHf7udavCyGPjAUMzedt+xRArCi9FVWD7qiPbFnjtWH7drh6M9SMNrmb/bpYzEbDK4u1g47qHFK/
Dsf9bjrCnhUd4LXuC4JeiSNtQXpifp4+23Pba9bjzOyVPcCanB/LsgxBEIqCIEQsy8pswed+SBCE
F4E/tyzrd7fg/RzqePb8BIm8yt//4O7e+gD43C7u6Y/y4vXEbjdlx1nupmUt4Q9b8VmNPmcl7f4H
B+IsFBSiAXdtc7XexcAJs2leVns2Sxe4asz3qe7FeSmNJI+fPtuzKKk2W9LQTRhoCXBtNsezb40z
2BZsysVwreNvLSeojYr+rqWavV2QdpJrs3lifjd9cR8PDbYQ8so1oYqusA+4pQC10zkeDjtD9Xmt
Jku9mee62ZP5RmvJxUlbIKW6YX/saCtvjqYoqgZlzajdUIymCrWaYttR2HmzjstujZdFYjaK3WdV
Z2e5OWS1tXkj+4JGjs96nJm9sgdYTxJGGXhXEIRvAIXq/7Qs61fW+ZnTwDFAAf5CEIRvWpZ1cZ3v
4bAMmmHyue/e5N7+KA8e3rzC2lbwoePt/Ju/vsp0Jd79oLCTISfrSaiEal6HVpukhubzvDaSpCvi
o6ybPH70VlHT9SwGTphN87LefLDlnn8je1iawJ0taTx/aZprsznyiopPCjT9YrgSa9kASIJQk5hv
VPR3tQ2DVxKJ+WUWiiptIXftd6tCFdPZEkGv1PR5Ew5bw3bNpVt1Mr90HjjTE11UCPdUd4RT3RFG
UwW+c22e4WSekEcGa2vzXLf6e+0Wi8RsvBJPHe26LXx9vQenq9lQvbNYVYtbynqdmb2yB1iP8/PV
yp9NYVmWgu34IAjCV4DTQM35EQThs8BnAfr7d742zV7ny+cnGE+V+OefOoUgNEfW2pMnbOfnm1fm
+MmHDu12c3aMnbxCj/htNZhqjsVyn1W/QCiaiYAdWlPWTbyya1mVuPW0wwmzaU5WejabOa1rtIBG
/DKfffwIz741jk8KMpUpEk3k1xyutdssPUFey61ZvcT8eqW9q6EnfTE/bSFzUeiJM6YOJtv13Ldq
rK/1BuEQAXxyilzZrnET26bNcbPfOKx2K7Udz3ul91yrs7heZ2avzFerOj+CIPRbljVmWdZ/24oP
FAQhZFlWrvKfjwK/V/9zy7I+B3wO4L777muOSlh7hLJm8B9euM69/VE+fLx9t5tT40hbkP64n29d
PVjOD+zcFXp182VYFuOpIp/wNZ7Ili4Qd/dECfnkWnz5Zoq41U92zTrhHXSWezYbPa1baQH1eSQG
24J0hX1E5+Vlw7W2Izl2M+/Z6Dut1j+rScyvxmobBmdMHUy247lvx1hfqb2p4uIiyWuVwV/vGG7m
G4f15Ftu9fNe7j2X7gVGkwVCRfm2/t6IM7MX5qu13Pz8L+BeAEEQ/qdlWX9nk5/5uCAIv4l9+/Oi
ZVmvbfL9HCp88dVRZrJlfvdHzjbNrQ+AIAh85EQ7f/LaGCXVwOd27XaT9h1rPfVaukDU14dolMux
FvZ6uIHDxk/rVrK7pWEcyzk+W207m33PlVQRl+ufrdh47YUNg8PeZzvG+ko0Ghur2fpGxnAz3zg0
461U/XOpiqq46+TKtyq/rFlZi/NTv4se3OwHWpb1NeBrm30fh8Uk8gq/960bPH5HKw8fadnt5tzG
R4538PmXR3j5RoInT3bsdnP2HevJ+VlugdjoBNeME7vD+tnI81/J7tayGdkO29nsey73nVbqn2be
eDk4LGWrx/pqn7XesbHRMdysm/RmvJWqfy71oioHZQ1fi/NjLfNvhybiX3/tKkVV5zc+dXK3m9KQ
Bw7HCXokXrgy6zg/28B6FpitXiCacWJ32Bk2G661Hbaz2ffcqCPTrBsvB4etYDMO/nrHxn5bU5r1
cKT6XOpFVfZDf6+FtTg/dwuCkMW+AfJV/k3lvy3Lspq/ctU+59vvz/E/35rgFz94hKPtod1uTkPc
kshHTrTz/KUZfvMHTyO7xN1u0r5jtzZfzTqxO+wMm7G7nU7yXc97OHbs4LCYnRoX+3FNaeY5ZT/2
92qs6vxYluUkaDQxs9kyv/aldzjeGeJXPnLHbjdnRX7g7m7+4sIUL11P8KEmEmRw2DzNPLE7NDc7
meTr4OCwN3DG8M5y0PrbOX7fw2TLGn/3829QVA3+44/fg1dubj/18TvaiPhk/vKdqd1uStORKWoM
JwpkitpuN8XBYcvYT3a9n76Lg8N244yXteP01c6znjo/Dk3EfE7h5//7m1ybzfFHP31f04a71eOW
RJ463clfvTPlqL7V4ailOexH9pNd76fv4uCw3TjjZe04fbU7ODc/e5C/vTrHD/zHl3h/Jst/+ol7
+eCdeyeE7Afu7qagGnz90sxuN6VpqFe2MSyLVFHd7SY5OGya/WTX++m7ODhsN854WTtOX+0Ozs3P
HsGyLL53M8nvf+cmL15PcEd7kM/91H3c1RvZ7aati4cGWzjU4ud/vDbKD97Ts9vNaQo2omyzHYUh
HRxWYz12t58Um6rfZWg+T1k3kZqojpqDw26w0lywn8b+drPWvnLW/K3FcX6aHN0w+fqlWT734hDv
jKdpC3n4P586zs88OoBH2nthY6Io8JMPHuJffu0KV2eyHO90xALXq7TiXJM77Abrtbv9pCAU8cs8
frSN5y5M4JVdvHhjnk/4nHHncDBZbS7YT2N/u1lLXzlr/tbjhL01KYZp8aU3x/nQ73ybX/qTt8gU
VX7rB0/z4j/+EL/wxJE96fhU+cy5XtySyBe+N7rbTWkaIn6Zw62BdReA2+w1uZNo6bBW1mJ3S+1p
PXbd7OiWRSzgZrA1uOvhKc64ddhNls4Fo6nCbfa4n8b+drNaX+210Li9MD85Nz9NyMs3EvzWV69w
ZTrL3b0R/un3n+SjJztwifsj1CIWcPP02R6+fH6CX/3IHbSHvbvdpD3FVoUUOKdJDuthNbvb7/bU
LKE8+72fHZqf+rGgaiavD6fwyKJjj9tEs8w9a2GvzE+O89NEjKeK/NZXL/P1S7P0RH383o/dwyfP
dCHsw/jyX/rQUb781gS//52b/ManTu12c/YUWxVSUH+aNJ0tkSqqTTlJOTQHq9ndfrenZgnl2e/9
7ND81I+FXEnjncm0Y4/bSLPMPWthr8xPjvPTBJRUg//ynZv8l+/cRBQE/tHH7+TnHjvc9HV7NkN/
i58fuqeHP3ltjJ9/fJCeqG+3m7Sn2IqCZHvpNMmhOVjJ7g6CPTVDIcCD0M8OzU91LGSKGu9NZRx7
3GaaYe5ZC3tlfnKcn10kr+j8j1dH+dx3h0gWVD51dzf/1/cfpytyMByBX33yDv7q4hT/4i8v8bn/
7b7dbk7TsFOqLnvpNMlh91nNLg+aPe2W+tJB62eH5mOp7R9Ue3QU2G5nr9iD4/zsMJZlcX50gS+9
Oc5XLk5TVA0ev6OV//3JOzh3KL7bzdtRemN+fuUjd/Dbz7/P196d5vvv6trtJu06Ox0vu1dOkxx2
l7Xa5UGxp92Oaz8o/ezQfCxn+wfNHnd7Dmhm9oI9OM7PDmCaFm+PL/DX787w1+/NMJkuEXC7+NSZ
bn7swX7O9kV3u4m7xs8/NsjX35vhn3z5Iie6whxuDex2k3aVvRIv63CwcOxyMU5/OBxUHNu3cfph
b+M4P9uEYVq8Ppzi+femef7SDLNZBbdL5LE7WvmHHz3GU6c7CXic7ndLIv/pJ+7lk7/3Ej/z+df5
s88+dGDC/hqxV+JlHQ4Wjl0uxukPh4OKY/s2Tj/sbZzd9xZS1gxeG07x/Hsz/M2lGZIFFa8s8sSx
Nr7/ri4+dLydsNc5GVhKb8zP53/mfn7qv77OZ37/e/zBT53jdE9kt5u1K+yVeFmHg4Vjl4tx+sPh
oOLYvo3TD3sbx/nZBIZpcX0uxxvDKb79/jyv3ExS0gwCbhcfPtHBU6c7+eCdbfjdTjevxj39Mf7k
7z3IL3zhPD/0n1/hsx8Y5Jc/fHRfK94tx16Il3U4eDh2uRinPxwOKo7t2zj9sHfZE7vyixNpbszl
cUsibpeIR3bhlUR8bhde2YVPtv92SyK6YaIaJqpu/1FqfwwUrfK3bqJoJuUl/0/VTRBAFARcgoAo
gCAIuET736IoUFYNJtIlxlNFLk1lKaoGAIda/Pzwfb188M52Hj7SciA37ZvlTG+Ur/7K4/zmVy7z
Vxen+OUPH93tJjk4ODg4ODg4OOwj9oTz85cXpvijl4a39TMEAdwuEQDLAsOyMC0Ly1r8OlGAzrCX
3pifZ871crY/yr39MQ61HOxE/a0iHnDzuz9yllxZayoHslklLZu1XQ77h6qNSYKAblmOrW2SgzBm
98N3bKbv0Extcdgb1NsM4NjPEvaE8/PLHz7KTz50qHajo+gGZc2kpBqUdaPyt4miGcgusXZD5Jbs
P17ZhUcSK39ceGX79qj6/7yyC0kUEAThts+2LAvTskPcTMtCEgWkipPksH2Emig3qlklLZu1XQ77
h6qN5RWdy1MZTnZFCHolx9Y2yEEYs/vhOzbTd2imtjjsDeptRtFMBMAti4791LEnnJ+o3010l5Q0
BEHAJYBLvN0xcjgYNKukZbO2y2H/ULWxgFtCNyHglTAsy7G1DXIQxux++I7N9B2aqS0Oe4N6m3l3
MoMgWJxuiTr2U8eecH4cHHaTZpW0bNZ2OewfqjaWV3QkEQplnaBXcmxtgxyEMbsfvmMzfYdmaovD
3qDeZkJeCQEc+1mCYC1NamkiWltbrYGBgd1uhsMaMUwL3bRDA7fypmxkZATHDhzAsYX9wFbMEwfN
DrZrbt3rHDQ7OAhsxNYdO1g7+3kuOX/+vGVZ1pryUpr65mdgYIA333xzt5vhsAa2My75vvvuc+zA
AXBsYa+zVfPEQbIDJ+djeQ6SHRwENmrrjh2sjf0+lwiC8NZaX+tk7jtsCfUxptWcAIfdZzpT4i8u
TPJfXxrmf709yVyuvNtNcjjAOPPE+nH6zOGg4Nj69uL07y2a+ubHYe+wWlyyI9W5s1ybzfHbz7/P
N6/OLpJrl0SBH3+wn19/6rhTfNdhx1lpnnDmiMY0S86H83wctpt6W1c0k1xZI1PUHHvbInZyLmn2
+cLZ/ThsiKWGHfHLfOKu7obGvt+vWpsJy7L4g+8O8Tt/8z4Bj8Q/+NBRPn66k56oj8l0iT95bYwv
vDrKO+Np/vvPPug8B4cdZbl5YrU5otkX0u2k2mejyQLsUoi+M4c77AT1tv7GSIp3JtK8N5lx5oMt
YqV92lpYa7/vhfnCcX4c1s1yhl39s/S1FyfT5BWdwdagI7W4jai6ya//+UX+/K1JnjrdyW/94Gla
gp7az6N+N//y6bt44lgbv/Qnb/ELX3yTL/7cg07dKodtYbmFstE8sZKcb6P55iDy3lQGw7Iabga3
m52SW3Y2tctzUPom4pcJFWXcsujMB9tAo/l3LazHodkL84Xj/Dism9U2KvVVhZ89P04ipzCWKgIQ
9DgyuduBbpj8gz99i69fmuUfPnmMX/nI0YZFewE+dqqTf/1DZ/g/nn2H3/vWDf7hR4/tcGsd9ju1
4qhlnbJu8vTZHvpa/Mu+vhqOMZTIU9YMpDrbbTTfHDS2cjOx2oah0c93IlxmL5wW7xbN3Dfb4ZQ5
80FzUP9s1zMHbWS+WK8dbdYJdpwfh3WznGEvNcaBlgAXJ9KEvDKmZdEf9/PwYGvTTNr7BdO0+Mdf
vsjXL83yzz55kp997PCqv/OZc728dH2e//ztG3zq7m6Otgd3oKUOB4VUUSVf1hlfKLJQ1HjuwgQ/
/fDhZcd+xC/z+NE2nrswiVdy8eKNeT7hszd4zZLzsptsVR+sJbxwuVv9zYTLrAWnmOfyNGvfbJdT
5swHu8/SZ/v40bY19/t654uN2NFmnWDH+TmgbOa0ZjnDXmqM2bJGNddelkS6o741e/QH4Xp/K7As
i9/4y0v8+duT/NpHj63J8anyTz9xkm9dneO3vnqZ/+/vPrCNrXQ4aEiVRXIup9Ae8uKVXYymCoSK
8rLjWrcsYgH5tg3eTmy8m52titXPlbQVN9ErbbLXGy6z3nnc2dQuT7P2Tb29DCXyXJxMc6YnuuGw
qnp7ceaDnaPRWF06F+iWta5+r58vVpsLNuLcb3ZMOM7PAWQ9XvbSMLalIgf1LDXGU10RJhdK5BSN
wdYAh+KBDbXNYXn+329c4wuvjvILHxjklz98dF2/2xby8IsfOsq/+eurnB9d4Nyh2Da10uEgkSlq
vHhjnqjPzXiqSGvInhdeH07hkUVUzeT+gTgxvxvdsmrzyUqL2Ubj1PcTWxGrr2omRc0gmVcIeeTb
1PZyJQ1VM7f9hqkRzqZ2eZq1b+rD0y5PZcCC8VRx3TdAVXuZzyukCyrPnOujJ+Z35oMdYLmxulWh
a2uZCzbyWZsdE9vq/AiC0A18BTgJBC3L0gVB+F3gPuAty7J+dTs/v9lolhuNtXrZmaLGl8+Pk1M0
JFHEK7vwyOKyBtzIGD9zrm9d39mJ5107//WlYX7vWzf40fv7+PWnji+b47MSP/XQIT733SH+/QvX
+MLPPbgNrXQ4aKSKKnlFx++W6Iv6ubMjRHfExzuTacIemReGZ5nLKcxmS5zsihD0SrX5pBk3eDvJ
RvJxVmPp6XxJNZBdInUK+Is2KBZwd2+UQ/HAhp/BRsO0nE3t8jRj31TH7MXJNFgw2NZY1Gip3S79
71RRZT6v8MqNJAVFZ2yhxD//5KkDPx/sBEvHav0NfX3/A2sOma0ecB1qCaxpLtjo3L+ZMbHdNz8p
4CPAcwCCINyL7QQ9LgjC7wuCcL9lWW9scxuagmZKWFzqZUuCwHCiQEnRSRZVBuIB+lr8jKYKXJhI
E/bKjKfydEcDPDzYQlbRll3Mlhrjeo2zWa/3m40/e32M3/zKZb7vlK3qthHHByDgkfj5xw/z28+/
z/szOe7sDG1xSx0OCpmixmiqwGymzBvDKWayZQzTQhAFPnNvLy5BYDiZR9NNREGgqJoEvFKt2N5y
ipEHhY3m49T/vNHmoX5OLWsGUZ/MYFtwUZjS0g1KyLt6yMpKjpgzj28fGz1E3a7D14hf5kxPlPFU
sWEe8GiqULv1reaOvHhjfpEdx/1u0gWVbEkj5JOwTIuRVIHH72g7sPPBTlE/VlXNXPSsPnFXN4db
7Yid4URhWScp4pcZTRa4NpNDFGAkVSRb1ukIe9acK7TTc/+2Oj+WZZWBct3G7CHgG5V/vwA8DOx5
52ctk8pOqvWsRjWZcCRVwC2KPHdhgpJm8MqNBJ1hHz63yK999DhYdlkJRTdJ5jUsSrxwdZazvdFt
W8yc09/V+aMXh/itr17hA8fa+Pc/enbTUtU/dn8//+GF6/y3743wr56+a2sa6XCgyBQ1vvDqCG+P
pVANW32wPeRhoagylCjwlXeneexoKz7Zxdtjaa7OZknlFRI5hYBH2vJihs1yy74eVltHNRc3AAAg
AElEQVQjqj8Pe2SGEgVGkwXO+KP2BrNSF8WwLMqawdNne2vqevVzqiQIvHhj/rYwpeU2KMs5XKs5
Ys48vj1s9BB1uw9fGz3v8WSR5y5MUFR1RhJF7huIY1acmtpN5PwtB/z7TnXxylCSXElH0Qzcoshw
ouDYzxay3Lx4uidCvqSTLWuMpYoN56CVnKTHj7bxN5dneP7SDKphIosCjx1pxbCsdecK7RQ7nfMT
BYYq/84Ap5a+QBCEzwKfBejv79+5lm2QtU4qmz0JqxptdfHazCRWjcnPKzqvDSeRBBHdtFANi7aw
h3RRYyRV4ExPlDO9UW7M5YkHPHz4zjbSJZ37B+LbasAH+fR3Jcqawb/4q0v86evjfP9dnfz7H7kH
t7T5Gj2xgJtPn+3mubcm+ScfP+70/T5jI47Aen9nNFXg/NgC+ZKBbll4JZGCqqMZ/z97bxpk2Xmf
9/3Ofs7d7+29e3p2YDZgAJIAQVKkFhKiRBI2LWtJlDiWy6k4laqUS2X7g6VypVxZynGVlcTlVLmi
pCRVIgWOJIaiQ0okCIrmBhIYLIMZzD7T+3b77tvZz3nz4dy+7O7p6ekBZiXn+YCaRvftc/os7/tf
nv/zCAqmxkylSyxirq93kWWJnKlyYizHsbEsy03nlmaG7/fvfVi67HeC2+0RpZSOF8R8a7aMAHJz
KsWUzveuVyi3Pa6U2+RMDduP+PLZZX7r4wd3FCv4gnUzTSkUYlAQO9inu+3m0bZXKsujcN0fJdwJ
ZX07rex2n7sbRdXN3cIvn13marmLKsNCw8aLIvKmxiePDrNYt5mpdLm42gIpScCfmszzhacmEBJ4
fszrczWKaf2ReocfZtxqnvpr51foeiEXV1ocGs4wW+0CkDG32pJsTnBXmw6vz9YZyaQpd1x+NFtj
peGgKRKqouL6IVfXu5zel7/ljPiDxv1OflpArv/vHNDc/gNCiN8Hfh/gueeeE9u//7Bhr4vRB6mE
bX5oG70AU5Nv2ox2W7hutRCmdXUwy9N2AmIhqLQ9LF0ebIC/dHKcem+epuPzzmKT0/sKHBjam3DB
hiP5B+GOP0aCc0tN/umXznNxtc1/9fNH+CefPYYi3z2799/6xEH+9M0l/vztJf7zO1CMe4yHG+8n
EbgVd3vXzwmQAS+KCCLBh6cLvHB4iLOLDaIYVloOQ2mTK3EXWYa2G1JKh+QsjUrPu6sSvo+Cwd5O
2NyRP7jDmplPaXz0UImuF3BoKEPbCwZV9MPDac4vN1nveEwXLUxV3nUv2k5T2lxUW6zbfIqEmtR1
wyRAZatH2+0StXvVeXsUO3p3E7td990KpBuJ8/nlFtltQe3mzqG+y0zvnaBu+5iqTDGlsdRwKKV0
PnF4BIHAMpIZvx/OVKnbHmNZk7YXgATDWSOJc0jinDt9h3/an49bYVDIcMMtc1kAXS/E8SNsP2Y4
a2DpCicmcjuq9m18/deXysxUu1xZayNLEsfHs6x1XIJIoMmQtTROTWYf6qT1fic/PwT+S+BPgReB
P7rPx7/ruJOOzvvNfjdv5o4X4QbRluNtFibIGonIwOYKzPbgZ+OcK10P148YSmkMpdK8eGKUMBI8
f7A0oEyEQjBRsJguprhcbnNyIsldd2tHt+yAP3trkXNLTQTw7L7ClnN6jL2j5QT8q29c4Y9fn2c4
Y/C//93n+MWTY3f9OKcm8zw9ledLbz1Ofn6S8H4Sgc30qldnywPu9q02spYd0HVDJEki1/f0euHw
EC8cGuLEeI4LKy00ReLyWptra20KKQ0vjDkxluXiahsJ7tpsSMsO6LgB3l1QLLvdcd5vd2m3GZpX
Lq7R8QKurnV2XDMPlNKMZk3KHRc3iPjIdJHFuk3bC/jw/iK2n8z0KLJExwlYrNlbFPU2sD3RCoXY
8pwMkqqRxP9rezC0WzHvXnXeHiuB3vq6365AWkrpSIAkCTaXzDY+V257zFS7vHh8bNeZXrZ9dreZ
L1mWSBsqh4fTFNM6AjEwLG07AeeWWlxcafPmXJ3j4zl+/onRm+iZd6o09ih2fO8mLi63uLDa5tRE
jpNTeWCT4bS3qZDRT4CXGzavz9aIBQMacsZQyZsabWfn+1u3fXRN5hOHh3hjrkbB0jk5mRxrKK2z
1vLIWype+HD3Lu612psG/BXwDPAN4HdJZoC+B5wVQrxxL49/P/B+OjofxP8gY6p87ujElg3t3FJz
IExwo9Lj+UMlTqcKwNbg5+JKi1curvGxQ0N86ugIL59ZQFUkZms9olgwX++hKzLzNZsXnYBTk0nL
0g9i3lhqIgFvLTS4uNreVfWtbvtUOx5CgKnKdPa4mD7GjyGE4CtnV/jvv3aJes/jtz5+kH/02SfJ
mffuGv7qh6f45//fRS6ttjkxkbv9Bx7jocf7odtufObSapueFzKRMwlEvGOXGRLaxHrHRZUlXjg0
xMWVFm8v1Hl9pkbbTaqKCw2batuh7YW4YUw+pVHKGBiazDNTBbLWrf1/9orNwY8EPDNVuH3H6n3i
/dKPdgvQNgvM3Kj0ODGRY6JgbbkuG0nLhvnj2aUmnzo6QigEqiRxaa3NzHqXjhPwo5kaF1dbnJzM
kzHUwc9t3LeNzs47C01ePD665Tk5WEoPOkMZU71lFXinv/ledd4eNSXQeykwsNOeu1uBdCNgfWqo
sCN98fBwmtlql9lal9Gsedt1YqPA2XFDsqbKr++QqLtBRNP2UeTkHN5bbVIwDV4+s4AXRMzWesys
93DDiKYdMp5f5j/72MHBgP0XrDuLq+5Xx/d+Yjerke24uNzid758jigGRYbf/dxJRvMmHTfx9hrL
mtRyfmI2f2QYgFcvl1ElGdNQmMxb9LyQatul64fMVro3qXECW2LCIIzxAo+ZSpfhrMFzB0q8Plfn
8HB6z0n0g8K9FjwISDo8m/H6vTzmg8CddHQ+iP/BBo0sZ2mDjXS22kuqrv2flYDNGqYbgczFlRbf
ulxmLGfxg+sVfuO5/USxQFVkWk5IywlQJI+UrnCj2mOu3uMTR4b5pZPjTBUtKh2Pg0NpLq61SWlJ
W3Tz0O1mbGzC89Uehqawfzj9WO3nDjBT6fLP/uI9XrtR45l9ef7w7z3P0/vy9/y4f/PZKf6Hv7zE
l95a4p+9dPKeH+8x7j3eT3FmI8B+uTGPrsq8NlPldF/kZHMV0Q0ijo/nuLbWQZIhiGJWmg7Vrku1
57NUt2k5AbIEQSwwFBlVUZAliVhAteOxfyh11xKUmxTLrHvHM9+cVHpBfJNgQ8sOuLDS4vvXqxQs
bRBA7Bqg9QVmAMIw5vvXq5iqTMPx+eWTE4zmk6B0u/njRkLzf/1ojlcuruEGEZos88tPTRDGkNZV
Kl2Pf/ud6+QsjeGMkdDn3JDFhk3DDnCDiE8eHSZjqIP7cafB52ba1b1QenuUFOTuRhfiTpKn2xVI
gR2v3cbn2l7A6X0FPnqotCea+oXlFt+5so6uyvih4OREjlOT+cH5ztd7vL3QYLnhUOl6fPdKBUNV
0HWZrKkiI+EGMT0/JGNoZEyNasfb8j7cKVPmUXo+9oLt9GMBGJqMF8Q73qcLq22iGA4MpZmpdPnT
Nxd59kABL4hp2j7fXl1HkpLE6NRknobt0/Mi8pbGasvl7EIDCfCjmFOTeUIhcMKIZj3gL95Z4omx
LNPFFKEQnJjI0XZDDg8nMz8bnWGAuXqPthc89PfgscnpfcadDh5ufEaVJN5baREJwXvLrS1ykX4Q
c6CYouOFPDmW5cBQestG9NRkntduVMmYGvtLKdbaLjcqHc4vN1ms2/T8kDCKCWOIO4KDQxmG0klL
9A9+MEMhrXOj0uXdpSYgMFSFa+sdhABVkbYEL4s1m6+dW2G5YWMZKkEYcWw0+9Bm/w8ThBD8uzOL
/PN/fwFdlfnvvniK/+SFA3d1tmc3lNI6v3BslL84u8I//dzxD6wi9xgPB/YaRGxedxqOj67K/NwT
I5Q7Lh89lIiczFZ7VDoeV9Y61Hse37xUZqXhEMUCS5PpFn0ur3bwo5ggSn6vDEgy9MIIQxOohsIn
j47w+dMTd3Ue8F4GP9sD0c0FqTNzdd5danJmtj4wb/3GxTVeu1Gl0vE4MZHj6Ehm8PlbneOBoTSn
9xXouCGmKrNYt7mw3MKLYv76YpnPn55kupTi2X0FGr0Ax4vwo3jAu79R6aBKEkVTo9z1ubrWwQkj
bqx3OL/cpu365Cyd4bTOyYkcbhjTsANSmsJMpYuuSmSN5F4cIP2Binqbu0x36/5+kLnZ+40P2oXY
LXnaKSnajQ63QVHf6fvbP7dx7pv/jp1ojd+6XOb6ehc3jDFUib88v8Kl1TZRLFhru7h+xNmFOj0v
IhIQx4K2HyH3BGHWIIwFThDhhxGOLBOEEcNZ4wO9s4/K87GXpHb7jM57K03c/kzOm3N1uv0Rh83z
mKcmcigy3Kh08cOIQkobKOqtNl1qXQ8viBACXn5jAVmGq+sdoiimaQdEMcQiJowFS3UHN4po9nzm
azYZU0WXZfaVTD7RV4bMmiptLyBjbO0Mb6YubjxLD+O9eJz83GeokjTYuLaracDWRa9pBzh+8hC7
QYSpKoNhtQurLdY7LmNZk+WOhx9FFNIaVl+8YEPN7eJKi8PDGc4vt2j0fF6frTGeM7iy1qHZC0jr
KkEoMBWFSMS4YUzX81lqOEhAxtTo+RFN26PS8UkZKmHkY6gyxybyXCt3mK/1OECa12dq/Mkb8zTt
xLDs2FgeR5aIebi5nw8Dwijmd/7f8/zZW0t86olhfu83nmE0a9738/jVj+zjlYtlvne9yi8cG73v
x3+MB4PtVUYniJip9LhR6fFs3/ASwPFCvn+9ynytSxQn1JZYCIJI0HZgvZ1sdtGm3x0DmoC0IfOx
I8NMFiz+9of3cXpf4eYT+QDYHPzcbuO9k6r6rQLRfEoja2vomowqSXz7epLspAyFRs9HIgn6VpoO
+4qpwbFuFaDlUxq//pFp5ms9vvLuMt+7VqHjhsgSaKrMhdUWpqbw6uUypqZQbrks1G3OL7VYbtho
isRc3UGVIZ/SKXcc9hfTnF9pEQuB7cfIcpjMXLgBLx4fBQQ9L8IJI8ayJq/1qYq7zXjthO3BfijE
gL50N/F+52bvNz5oIn6r5Gm3pGj7tdnpZ3e6Jxuf2/zzXhAjwUAAYXMyO1/rYfsROUsDNyBnqqx3
PNY7Hg074Hq5Q88P8TctAm0nRNMkDFnG9pMAPGuqpHSV/SWLl05P8eKJsQ98bx/252MvHcGNn6l0
PM4tt3CDCEWWWah3ubLeptb1+ejBEu8sNbe8qyen8vzu507y/7y1QMHUqPUSOlrTDqh2XSodj64X
osoy4zmDcttlOGWw2HAopjQWGja2F6IpMjIJw+jGehc3inD8iJSh4KyHnJosoKkSHz04dBMtdwMd
N7jJL+hhuy+Pk597hJ021w2JaVOTadoBT03eTGXaPGz87UvrCEmQN3RkWVBIGViGgh/EXFhqcXa5
Sa3rM5QxSBsKz+wrblEASusqYQxCAkNT+PSxUeZqPY6OZGm6PhlTJYgSE8J61yMWEqaqUEzrlDIa
nh9TSGmsNB2cIMbUFNZaLoLkxT08nEFI0HVD/uytRV69tMZ8zWYyb2GqCl4YMl1McWri3lO2HmX4
Ycx//X+/zSsXy/zDzzzBb3/mCeT71O3Zjp8/NkLWVPnqu6uPk5+fYOykANl1Q9KmSqXjYWgKnzk+
xky1N5C2X6zZ/PHr83TcEMdLkh43EFsSne1QSRIhU1ew9IRX/uRYdpBM3W1srLUfxCx0O+q2T9cL
8YOY5abNwaE0nziacOY3VLS+faNKpesxkjWQJZ2r5Q6z1R6xgLyl8uLx0S1B6sbv3fz1xr+zttYf
Wlew/Qj65+mFMeW2S85SOVBKs9y0CWPBVNFkttrDCyNSmkws4IVDJc4ttSh3POq9gPGcQRBFdF1A
CK6udaj3fH7l2X00bJ8zc3Xma/ZgxqvtBQP/lTudS33Y6S73Ax+0C3Gr63krr6edsFv3aaf4ZPPP
n19uIUmCp4YKXFhp8Qc/mGGiYKFIEnYQsdxy6HkBiiwRhIL3llpJ90AIgliwnTMgAyMpnWLWQMQC
VZZo2CF+FBHH8NG7ZJ/xsKu97aUjuLEWLzcTU+K1tstLpycxVBkJiTfn68zVbSRgImey2nYHz8Fo
3uRD+4tJx6fa5cRELpnfLLcpWDpRHGNqMiChKwoAfhhxZbWHH4aIGEpZnSCOqHZ9nCAiFhCImDCO
iYXCNy6s8cRohoyhcWoyv+Paut5xman0+MzxMcpt947WkvuFx8nPPcCtNteNB38sa3J+ucXrc/XE
xXhTVWVj0Tu72KDthmRNhTfmamRMlf2lNC8eHwMJ/t2ZBdK6Sjl2eWYqz0LD5tJam5SuDBSAul5i
FjZf7dF1A84vt1hru7TdEEOV+l2ixM1bzZq03YAginH8iJPjed5caBDH9DdVhfm2QyQEh4ZS2EGE
JEs8O5UnYyUBU0pXUWSZpuMzmbf42x/Zx88/OTpQjnuMmyGE4He/fJ5XLpb553/jJH/vZx6s0pqh
Knz25DivXFzDC5/CUJUHej6Pcfex0/qkShIXV1sJ9TWOOTmZp9x2UWSJYkpnsWbzr791lTfn67Sd
ADeMuV1+rpLQ3VKKxETBZCht8LHDQ4Nh23tlYLg5wJip/thEceN7HSe4I0qSKkmcma1zYSVRSqp2
PaaLKaaHUuRTifx0tesynDZoOgFhJNhfSpE1NIQEh4bSWEay1W6ZBUppKJK0hbqyWLP5/rUK7y42
sL0AhMDQFA4Np3liJIOhKVxYbvPeSpvjY1ksTaHS9gjiGFWWODScoWb7NO2AOBa0HJ+eH1LvSYzn
TY4MZ1nveoOiVSgEp6cLFFM6L59JZrz+w9V1ZEkCAVfWOnuaA3lUKEf3E7frQuwWqN/qem73etKu
JS/hTnNzG7HETCWhp6l9s/lbxSebfz6IYixd6b8/Seew3vMppTXcIGa95RJEoMqCWEoYI7EkkJFQ
EETbyB6GCmlTI6UrPD2ZRwi4sNJCV5XBfMkHxW6qt/cLt0u+9lIkKKV0mk7ApdU2lqYkRRKRSM13
vZAjIxme3Vfg/FKTVy6toSsyOTPx/NqsdLlBRwPYVzC5ttYmZWikDY0vPjPF969VODNfp+uFxCTz
324YU+64KBJYuoaqyCgbM5uqTNEyKKR0Dg2niWNxkwjOhrjCoaEMNyo9Lq22WWs7Ay+nh6kD9FOb
/NzLCsGtNt+NB3+21kWCZFis7fLls0tbzLye3VfgGxfW6Pkh620XQ1P42KEh6nZAhCBvaggSSpqp
KYRC8ORYdtBOfvVymRePj9F0Ar5zZZ31jksYRkRI6KqMG0QcHslxaDjNbLXHesvj/EqTnhcxmjUI
45jXZ6toisKNSgdFFrScGD8OCSNBteMxWUzx+acmeOHwEG0n4Ealy1LDRpMlMrrCkZEMs9Ue47k2
DccfVHofb45b8Qc/mOPP31riH37miQee+GzgpWcm+NLbS3z/WpXPnLj7stqP8WCx0clI68lmutGB
ODmZR0JiuWETxYJza02mSym+8u4yV8sd3p5r0nI9XD8mEhDscoyMLqOrMlEkkBWZtKEyVUixv5QU
Qu6lJO0giKt2ubjSAgFX1zqDgeGN4eHdvFI2r1GhEAxnDEoZg5ypEcTJUO/0UIqWHYCAkYyJpavU
lzzGcgZzNRsk0FWZkf4sw0Zw9tqNCusdnyPDGdwo6lPlVE5N5Hj5zDwN28f2I144PMxiw6FgqZwY
zyHJEhdX25SbDm4QUzINfvXD+6jbPl94eoIvvbNEFMO+gsUXn5ni9dkaV8odIiGIo5j1lkcQw3rL
BQFDGY3PPTUx+BsnChbHxnK8vVjH0lRypsbX3lvl+nqXqaLFzz05smsSdKtg/2Gvxu8VizV7IA3+
QQt6e5mR2ul6biTbXS8Y0BSDSOxIU9wQLvny2SUE8PKZhYGoxebkf77eI2trqH2Vv1cvl9FVGQnY
X0yxUOvx5lydJSExktHpeklXIhaCKIqRJIgFRAIsTSZv6YRRTM1OhJhiAEnCC2KODGcZz1scGkpz
cbWNokjM1Xqs9r2APsjzMV/vcWa+jibLBHG8RfX2fmB78vXZk+M7zr09NZnf1f8wn0qMYC+vtRlK
63hhTMZM1Br/8LVZbD/i3eXmoCuTszS6XsjLZxbQFBlVkfjotoLKQs3BUBVMLUk2LV2hFyR0Vy+I
8cMIL/yxYJauy3hhIoYQx6I/ehEjYpirdgmiiKlCis89NbEjXbLsu0wXUxwZSVPK6Df5Uj4M+KlM
fu6lHnzLDug4AX4Qb9l8N7LeLzw9yXy9R8ao0/aSCqqpKVuqkDXbJ2tqPDma5fxyiygW1O0AVYaD
pTQ5S+PZfQU6XsDBoRQ/+8QIXTfkO9cqLDccWm5Aw/aZyJrcqHRRJZlaz8PSVQxVJqUnikufPjbG
H9ZmuF7tYqgKjh8xVbTwI4GpKUgCGk6AFAuWmg6SJCEhaLkhI2HEqxfLXFxpIclSonRkatTtgCCG
q5UO6y2P715d5+hYlqcm85ia8lBzQO83rpU7/MuvX+bFE2P89meeeNCnM8DPHBkmb2l89dzq4+Tn
EcRuPjJ128fpzwImXR7BUxN5pkspvDDmjZkaq22Xpp1QWhbrNjISS02brhsQRBAK0BSIb8F3k4Aw
FpiSxPRIGj8M8PyYthvw+9+7kShN3kNJ2o2q+bnlJggGA8NCSBwcyrPadnhmX4GsuVVee7eK+HBa
Jwgjmrbg4FCKg6X0lp8XJEmH40UcG89RSOnsL6WYLFqDIGe22qPjBZRSBm03kSLWVYWG7TNT7fHt
S2XcKCZvqggE6x0XPwgJ+xX4WEhcWmnjhRF+JPjrKyu8u9TgU08Ok7N0jo5mCCLBSNbghcND5C2N
mUqXrhvQ8yNsP6IXREhAFEccGikSCrHFG6lNwP5iGieI+Np7q9xY79ByfN5ZaLDctHliJHvLoG4n
/KR4ryzWbH7vm5cJY1Bl+Me/eHzPCdDtKGYzlS4vn1kYCE7crmOx2etJgmR+o+MyX7+ZAheKZC+/
Xu5yaa3NlbUWH9pfwtQUVtsOLTvg6+fXMDWZmWqXgqXz9mKDqbxFw0mMSs/MN6j3ki7kvqJB1rRY
bTn0vIggSsRlY+g/VzE/++QYZ2ab1J0Q0VcwPDqaxQ4i8imVWAi+f6NC1tRQ5MQYeYMF80Gej64b
stJwMDQFL4jouuH7+j3vF5ul6i+vdqj1vAFV8AtPT9J2Ar58dglTUxJVxV2ov6cm83ziyPBASvzA
UJoLKy3enK+jyBLNmYCnp3LkTY2mHaDKEvVeMgLRcQN+7omRQUfmR7M1FFliupRisWHz7lKTobTG
pZU2a02Xrhti6ApCEkgIvFAQhgJVlShYyf0qt1yQoeNEyIDtR8Sx4MJKi8mitWU9PzKU4c2FBgVL
o2EHDy0d9qcy+blXevDbN8P9pdRg8904zqHhNKdTBYqWzly9x0emdc4uNbc8HKokocqw2nYZzxv8
5vMH8OOYob7MKcCvfWR6i0LLn7+1yNX1Dmstl5SmgIC5qk3PDYiRcUKBIgf0PMiZaUrp5HN+mGT9
hqqQNTXyKZ20rvLNCyt4ocAOErWRDYpL3Fd4a7ohXzu/AlISQIlYoGsKYSzQFRlTl0GS0BQFTZap
dF0KljEIPh6mCsCDQBwL/smfvUvGUPkff/XpBzbjsxN0VeaXTo3xl+cT2VxTe0x9e1Rwq2Bzs0T1
atNhMm9Ryhj88EaVr19cYyRr0Oz5+InkD5oEsgTrbRcniOl6IVH8YxX9YJfEJ6XJiKTeixtE+BFo
ihgoTbade78h5lMap6cKA6+arKFt6fbsVHXdbV8opHU+fmQYP4z5Oy8cYHooxWy1t6XDf3GlzWwt
sQl4/kCJjx8ZHlz7c4uJclMYCeq2R85Mgh9JgnLHQ+lL0MoxdL2IybxFSleJBVwtd4iFQJIkZAkk
SSKKBV0fur5D7Z1lnhjLIgR8eH+RWAguLLf449fnqHR81tselqESxQJJxKiKTLnj4wUJFWonb6T5
eo+lhkPPDXDCiLrt0+wG/KBd5Ualy8Gh9E3+HzvhJ8V7Za7eI4zh4FCauf493kvyczuK2Wrbodln
TgxnjJt8+rb/ro09f6OIqsgVvnO1gh/FqLJ803NdSum4QUSl6yVBt66x1LB5/mCJibzF969XWWjY
iRqbG/Q9XCLeXW4NvOayukpPDWj0Qq6tdzlQSpPWVTpOuGXeTwB+KPjmxXViKVkL5P46Uusm1MyL
y21OTeUoWjphJFhsOMgSd8UXJmOqTBYsNFUm6HdL7is2SdX7UUws2NJd+w9XKlwtdymmdKZL1q5/
64b4yeakue0EBJHA8SO6fsi7S22mhyyiOObnnhzmR7P1jdMAaZNwQtej1vOQgLWmgx8J/u13ZzDV
RHwiFAI5BBHFKEpCd3VDgQgFl91eMkeIoKBr2FJEKMAJYq6td/nu1XUOj2ZxgohqxydrqmRMdYsU
/93ycbvb+KlMfu7VcOb2hX4yb1Hv+TcdZ0P4IBKCxbp9U8s7n9L4x794fNBiz1ka8/Uer83UdtR5
n6320DWZz54Y4yvvLqPKCnlLo+n4aKpKGEf9aqxELAQdL2Su0uVPXp/j/HK7n5x4TOVNTFUmrSuA
TM5KWp8yEooEXiSIBcQxdN0AQcKH9+Kk1elFMSktSaKiOEbXlL7HR0zG0AiimJlKd0eVu582fPmd
Zd5davE//0fPMJwxHvTp3ISXTk/yp28u8d2rFT57avxBn85j7BG3MoI8t9yk0vWodnzWOy5uEJE1
NeZrPTKGylLD5kApxcGhNNWuR9dPKCtuGCMDIr79sXUVohAEAkvT+I3nprH9iNGsydcvrHK90qVg
qZyazJOztLtChbqTuYmN63OnfPwNmmAxrYNgML+zJYC1A5abDkMpnZrtc2IiN6ihNdgAACAASURB
VKCc/NFrs5xbahLEgpSu8ORoFlmWePHEGAVL40tvL3FhtYUdxIxkDEayBqamsNJyWCu7eFGEoSiM
5Qy6/o8r2lL/P0Gf0tbxApZbNmMZE0uVWWw46JqEIkukVAXRL5xN5iyGsgm1pmH7lNseEzmTqvtj
AYYDpHliNEOl6+K3XAqmBpLE9XLS0W/aCetg+yDz9vvxkyKEcLCURpVhrtYbMDD2glslf5ufzdWG
w1LDBpJ72nXCm+bhdkqiDpTSTOa6vHa9St7SuLDcGgy+b74Pv/LsPlp2wLtLTc6ttDCURHBpsmAS
CxCx4OJqmziOieKYGAlJwP5SGlVNWB1uECMrSTETSZC1NIJYsNx0t/y9oQDbC0kZKqoChqIwnDVI
6wrTxTRuFPGh6eIgGcv254Duhi/MgVKa5w+WBrSzeyWqcsvjb5Kqn8ibWP3umtKfnzNVmWJKo2H7
jGT12/6tG8/JYs3m3HKTYkpnNGuw0nQYzRpkDQ1DlVEllZlKj5FMIiYxXUxRtHQurLS4Um5zoJi8
y+8tt7B0FVOTsb0QVU4y1IyhIEkShirTdEMESQKlyRDEyfoSxeCqMcGm0SwhBKauEsdJQqarEk4Q
9WfN4x8Xmm7j4/agaLE/lcnPvRrO3L7QHxhKc2AovWvL+1ayoNNDqQGnfLN6xicOD/PaXJXFRo/9
xTS/9pHpwXFDIfjk0RHqPZ/lpoPjRwyldUxd4epae9CRatg+37y8jiJJSYUiFsgS9PyI1ZbLQs3G
jyKcIEIiSZgKKYMIQRQJjo6kaTghTbuL3Sf+KyQvgyPFZJEYy5v88qlxDg1nSOsql/rHb/a7X/P1
3sBL4qcNbhDxe69c4empPF98ZupBn86O+PiRIYqphPr2OPl5dLB9DVIliT9/a5FKx+PSWotYSEmA
5QWsNB3aTshi3R5w7w8NZXjh0BCz1cSkMIqTgGYPuQ+yJCGpkNYVDo2keen0JGeXmkRC8NlT4xwd
zXBqIj+omH/QDXEvlKrtcxPvZ2jf8UK+eWENL4oxFJlf7FNBN//8tbUOl9baZCSJtJFUP1t2wMtn
FnjlUpn1jocmS5iawoemCyw0HF6frZM1VYQAS1MZz1qMF0yenS6wULMpt13COEaSJPwwouOGlNI6
B0upxPcjEElQpSnkUmqiDJrSyZjq4D4LJLKWxrP7kwSl6waEseDQcIbpYopXLq5xpdzm6++tkjVU
ojgeBCsfPzzEpbUWVp+at9Z2cIKIjudQ7rpcXmvj+BFX1jr8+kemgZtnueD2Mw4PA273vE0PpbYU
JPdKedst+dt4NkspndNrSdA8nje5uNbmRq17k1jSxpxepePxw5kqSw2HmUqHmUqXoYxBFMestV0O
bJvBODmew48iWk5isSEZSSem1vMxVYWlRo+uFzKcNlhre2R1hVacdPsOlFI8f6LEq5fKVLseta5H
zwsTQQTt5vDRVCTCWNDzQsIYFCnpwIxkDIoZnbYbMJY3+eihoT0XJfZ6r/IpbQsjZi+UzLsZA27v
1gBb/v3eSovpYoqRbMyvPDt10zF3Op/tdMu/+7GDifmxJnNtvYMiJWMMV9e7TBcsyl2X0/sKfOXd
ZX54o8pC3cYNIiw9ebf9KGa94xH4EZqcJGVCSBiKhB3FxJu7+/1FX1UkdFXi8HCa5aZDvesjJJCQ
aNsBhZROIaUxljX51uUyURyTNbRBF/l26/yDosX+VCY/cG/04G+1ed5KhWW3atit1DPeXWqy3HJI
mxpnl5pJi3xfYctx52s9vnV5nfGcwauXypTbLsW0QdP2kiBGQEZXCEUSsLQcH1WRqHQ8ZDlx59YU
iSCKyZoqPS/Ci2IsTUFGkLMMCimDlaZN6CXDw7oqkTM1YmAsl/BOZ6o9FFnm+YMldE0mZ2i8vdDg
8lqHtKFybCzLzz4xctcc3h8V/PGP5llpufzebzz7UNHdNkNTEof4r5xdfkx9e4SwfQ3azEMPQsFS
00aWJKpdl6lCClOT6XgB1W5APqUxX7P5B586zOszdYJQJMPM0A+kbw0ZKJg6YRxzcCTFyb6C0/sp
Mu11Q7wXlKqd9oWFhk0EFFLJsPeltQ6WoW6ho5xdaiIDjZ7Lh/YnXfm67eP4AY4XEkcxkZARimCu
3kORZA4Pp5mp9gaiCOsdj1KokTM1vCim64bISGgSSKpMz0+kyGPgi8/uQ5GgmDZ4eqrAt6+U+ebF
MtWuC8LgidEMzx8s0XACTk3k+PzTExQsjf/129dxgpiZSo/Fho2uyRwZzvD2fANZlvjutQqfODLM
qck8Xz2/wpW1LpamoMgSo1kDP4xp9gKiOPGCWm271OzEdyRraVsH6Wu9gTH3BtXwYcRen7eNguSd
YC+F1nxK45dOjifPBRI3at2bnmlVkji70KBu+1S7PuVWkbqdJLKCRIGwmNI5u9hkPGcO5LC/NVtO
1AWvVgiJ6ToRlS4sN1wypsynj43Rdn0sXU3mb0KYKKY4NJwhFIKxnImlKxTTGrIEbv/ZrLRdJCIy
howsQRRJ+FGEqiSxQ85KaKaWpnJ0JMt43hwE0BtJ8F6LEndyr/Ya192roPtWf1fLDnhqKg9iZ2W+
W53Pdrqlrsn89otPDmY3v3p+lfNLLZqOT8ZQUCSZ4bTBW/N1rpW7GJpCp08zlmQJRZbJGAq+LBFG
SfEkjGMioaArMooSowhBEIMqJYWvIBCoimC15eD6EaoCsiSTtzSQ4MXjo/xwpsZbCw28MGasP4+G
dPv7+iBpsXtOfiRJ+m+FEP/Npq8V4P8UQvyn9+TMHlHs5eXbaUHcnPUDN6lntAl4dl+BQkqnYScm
o34YDSKSjWPO15MqTs5U6XghRUtPxA40mbYsY8gyEgIhBFG/mqApMlMFi2ovMQe0DBVNkViqJwO5
hZTGWE6nmDbpegH7iiavXCpj9xOfjfA9lgQihlrPo+0ErHc8en5EKa0zs97DC0Kats9YxkRTFd5Z
qN9SqeYnFX4Y8398b5aPHx7i40eGHvTp7IqXTk/w8hsLfPvyOp97euJBn85j9HG7iuWWNagGYRjT
cQP8KKKY1ohiqHYSfwdTV/GDCEVKvo4i+Oq5VRw/HBRKIOnsRmxNgFQJhEg4/aqSqLv5XoQkkuHb
716t8LNP3nnFf68b4v2gVLXsgJ4bJr4lIknyLqy0sINwoNL15bNLLNRthjMGKV3h6GhmcH6WrlFI
JYpNiiIzVbR48fg4tZ5PueNS67q0nJC2E+BHMVfWOhiaQssOODScIowjrld6FC2dpuMTx4KGHRAL
+PxTk2T6w9BtN+DN+QaGpqDJ8IMb1SSZrfc4UCowV0toVLIscWoyMae+Vu4QxoJaz0eWJDRZoueF
rDZdJgoWjh8RxTEdT1CwNKaLKdwgYr3jJQFTIPo/a4J08/1A4pGY93m/AdheOwe3iwk20+A39vvt
z3TD8fHCmEbPp2UHzNZ6hJEgFoJSWsf1Y4YyOrYXcqPSTfbbMKTjhQylNJwgIoyToDaZxYnpeYJL
a20EMJwxCKIYTZGpd1wMXeXAkIWuyGT7So2K5FC3A3w7JmdqaKpMyxUUTR1VlTBUhRNjWZZbNusd
H8cP0RSJYkrjpacnsAx1V/PhvVzPuxUs38+ge7sK3IGhm4sA289nQ4FvKKWjynBtvUPYn/veTIcb
yxmUswYZQ8ELYmQZen6YzAfKyYxgGJN0DQ0FS5UYzZhcq3QG5tSWrJC3ktmwTCTwwhiIyVkqXTci
bSjYQdI5tHQVSQFNlhnOGfS8kKaTjEAoJOamr14uk9ZVMkb9tmv/g6TF3knnZ1qSpN8RQvwLSZIM
4E+Bd+7ReT1y2O3F3el7mxfE7Vn/U5P5LS/CM1OFQem12E9+Om7IoeE0xZSeDNN6IW8tNLiw3MKP
Yo4MZdA0GV1TqHQ8TFUFAVlTQZZkCimVJ0dzdP2Qa+UOSAy04ldaDrWuixcI3DAxM/z0sREmC8nA
8mQxRcHUWJMSyVRBMsCf0zVEXxlEVSWaPZ8wivmLd5aRFYmlus1IxuBapUtKU8intIHc98NognUv
8O/fXWGt7fIvfvXpB30qt8ULh0oMZ3S+en71cfLzkOBOK5bFlI4kSXTcEIHEzHo7MSaNoeNGHB01
SRkm55badJyQjKn2q8velt+zXTdJkxMOO5LE/iGLobSBE0QUQx1ZkfjQdIHXZ+usdzz2l1L8+h14
bux1Q7xX9GVIrvN8rceZuTqREEwWLIYyBilNYTxvDtbmuXoPU1MopnTWOy7Vns9E3Wa5scjzB0u8
1H9v1lsO8w2H5w8UqfV89pdSfPfqOmfmGvT8kCgWHB7OUO/5/OB6jaFMMngeRokhpBvE/VkfCQiZ
rXT5N9++Rt7SODqadG6adoAfeqQNhfOLLX722AhRDOW2S9P2+PiRkSSQKndYazs03YC0rvLCoRJv
z9dZbTkossxMrcuJiSxLDYd6LyAWgrGswWdPjrO/lCJnJsF0peOhKnLiP9KfQ9p8P9pOwGrDZbnh
JFLhD+m8z/sJwN5P5+BWMcJ25bftKoEtO6Go+pFAkWX8KGKp4SBLEpauEMUiSWK7Hj/s+bx2o0rO
Umn0AqaKFsutpIgpRxFBJBACvAhkkZiRPneoxKnJPO/044fljkc+pROGMVHc4RsX11D7wXRKk7F0
JUlsI8jqCpNFkzhO/Kimh1KM5kyeGMvQdQPmajZjeZOzS00+dXRkkORtv2Z7vZ53K1i+n0H3Rvfd
UBVqvRYnJnIDk+TN5+MFMeeXW6iKxBuz9YEy7m8+f4CvX1ijkErYPlPFFG0n4Pe+eZmWE3Kt3ObQ
cBZdk/kvPnmY0bzJM1MFVlsJRS1naaR1hZ4f0gpCen4P20+SGi+KaUchXbcLgKnJZHSVoYxOLpUY
3PfcEG8w7JMYKauSDEJCAG03WSNCkSiA1jo+P/PMMJom3zapvJdr+O1wJ8nP3wf+RJKk3wF+AfhL
IcT/cm9O69HCbi/uXl7q7Vk/EltezGJK37Jo/FJfalSVJL7y7jJvLzQIoxjHD7H9GF1VeMeus7+U
ppDSyRgaKU1mvZNo8ectlXxKZ3o4heNHTORNQOK1G1X8KMIPI7KWjiSFhFGMF0a8t9Lm0lqHatdn
OKMhy8nC2/OiRNFFTrJ+J0gG5nRFQVdlTE1NeKG6hirLHB/Pca3SZSSj4wYx87UeM9XuQ2mCdbch
hOD3v3uDY2NZfv7JkQd9OreFqsj88lPjfOmtZWw/JKX/1LJkHxrcacXyrfk6th8yXbQ4u+Thhkni
IwG2HzBT6SJE0gGOFYkDpRSVjs/tPAd1VeLgcJqWE6IrClfW2kjIjOUN0rrK1XKXlZZDxtQ4t9Tk
owf37rlxJxvivaAvL9Zsvnx2GdsLWW45vHh8jOcPlTgxkeNgKc33rlcGa/PBUprFus10yUKVE2rc
WNbk1ctl2m7IWM7gN5/fz1y9x6WVNjlT45VLa1xfN7m81qZhJwUiN0zWwqyh0nMDxrIGrgBZUnhi
LMtKwyFvaaQMlWbP58JaG1WSmcibNHoeLSeglNFZb7noikzb8/mr8yustlzelRJ/t5Sh8Q8+dYQL
q21WWg6nJvLMVLuEseC5A0UWGk7izRQLarbPkZHMgJY7kTd5fa6GqSnEAnRFZiRn4PgRDdvn5TML
PLuvwK99ZJpDw4kM+DcurjFT7eJFMRnj9mvHgxp8fj8B2J2+hy074M/eWhxIF2/MSNXthG2xYS56
cbUFUvL/i1ZCYf/OtQpdNyQIImpdDxkJWUoCz3rPwwsEliazr5ih50XcqHQwtaRaf3QkixBi0NEL
IwGSQFMUBNDyIy6ttXl6ssBczWa56dD2wiS5JaGvtZyAjKliyTJeGKNpCghBNqOx1nJYqCbKd0dH
M3hhTCGdJMfPTBdBlrYUCm51zfZ6Pe9WsHxfg24BQRhTbrk4QcT3r1U5NZm/6ZgSIEkCx09mCzeu
hR/HHB5ND5LjH85UKfdVOKcKFqstl6OjGSbyJqN5k1JK54c3qhwfy3I2aJLxFYIopuMlBYpYSmZ7
3CAior/2kwhZISfy6E4QUVI0ZElCUaQB7dkOYoSI2T9s4oYhk3mLUxN5zi01mal0ccIQPxL84EaF
TxwZ2VNSeS/W8L3gtiuSJEkf3vTlvwb+N+AHwHclSfqwEOLte3VyG3jYTdJ2e3H38lLfJJRQSg84
46WUfkuBhHOLTd5eaNBxQ7wgotaXqjVUBU2RaDotdEXGCyKato+qysSxQJIl1pouF5fbKLLEyckc
6x0PXZU5NZmn6YRU2i49L0KSEtrMWn/41vYjbD/k6akCLdvH8SOEBH4ImBAjUbC0ZMbHSuhyF1aa
zNYTyogbhBRSGj/35CjljksxlQgyPIwmWHcbP7he42q5y7/69WeQpIdz1mc7Xjo9yR//aIFvXVrn
bzwz+aBP56cee61YtuyAv75c5l/+1SXsIOKtuTrDWQMp/rF4gRsKVDXZCDVFJowE1ytdvDBE3GbI
R0ZiNGfiRTaGqpBP6cSxIGtqHBvPcWQkQ71Pz/XC6Mfc2D3iQW2ILTvx4rha7pLWFYIwZrbWZTRr
DjrTX7C2Bk0bX6uSxPeuV7aYWLe9gFAITk8VuLLW4ZVLa6y1XKJYUO24g45J1lQopjQMTSF2A5p9
apAbRaxWfdqOD0j0/JBYwJCh0wsilho2iiwTRknRy9QURrIJPXm15WIHEZaWVOrnqj1CIfjsyXG+
dn5l4EN3eDjDSstlrZ84marM3zw9yXDWYKXlJHYIUYQADg9naNg+miQxkjNZbjiDoKrjBQO6TscN
6LghQ30lyzCOd13bH7Qf0J0+b3faOZiv9Ti31CRrasxWu5ycyPH2QoNK12UkY/LFZ6aYq/dASq7x
hZUWf/DaDAh4a6HBVM5iqemAEJiajACcIMT1IzRFIYgFLccniiCIBH6YFCXbTkjd9ui5iSi1oasY
isRI1sQJIgxVxlJVzszX8YMwCYgjgU+MpkhoqoylyfS8kK4LmiJRSut0vIC1toumJMlyxtDQZBlN
kQd7+fYi7kahYKdrdifX826tDfdrjSmmdEppna4bcXA4TSGl3fQu1G0fXZN5aqjATLVLsxdwfrlF
1lQH122m0uXsYpMfzdZw/JBmLyCIYgw1oRZuqOjWbZ/Fus1bC0k3uO0mcZwqS3iBwImT9ViTZUIp
Tvze+ufhhzG2H2P7AW3Xx1RVimk9oU2HyZbgRRBFMRMFixdPjpGzNFJ6IqUfhIJj48mM1/MHSw91
LLeXUu7vbfu6AZzs/38BfPpun9Rm3I9F8YMmV7u9uHt5qfcilLDj7+hr6btBhO2FTOQNCpZOvRcg
yzBVsPD6FcUgFmiKjBuG/QfX5LmDJQSCjx0aouuGVDsedTtgLGdgaTJXVzvEQC+MUFQJ20vUQiDk
armDqSVylhsVA0vXCEVSVTJUmdGsSdpQeO5AiXZfqejYeI6OF9L2AjKGyscODW2ppD6s1Ii7gZfP
LFBIabx0+tGhkD1/sMRI1uBr51YfJz8PATavFbfiz2+smd++XMYOIg6UEpUeGWlLUhOLxLROUxPa
ahjFBHFMSlfJ5RIPiCiK6PqJB0zU97HQFShYGoosMZIxqPZ8um6YGDOPZfjN5/eTszSatk/HCwZD
zo8C6rY/oLE1bJ/DIxk+fXxsCwVpNwrzF6ytJtYba1o+pfHRQyVmKh3KLYfFuo2qKjw9ZbHScpkq
pjg5niNGkDZU3p6v4/oR1Y6HHyXGg7oChqaQUhVMQ0VREvU4VZFRZYk4TlT1rq13KHfcZO6GxHC2
6wbUej5vzCY8/M0msGM5kzPzDfKWxmjO4OBQhoW6zcmJHMNpnTfm6hRMjdlKFzeIeO16lfGcxfVK
lyPDGTp924OJvDmg63hBjKpIg+8dHk7vurY/an5Ad9w5kH5cSxDAjUqHVy6uYSgKXtTkw/uLA1+q
mUqX88tNFDmhPXlBxEwtufaaIjGcMYEkHrLdmFhObDKeGM3y3nKbYkrDDQXPHSyx0Ohyfb1Hxw2Q
JYlCSmO8YPHLJydYaPSodn3GcibDWR0/iFnvJOwNQ1UYzuikTY1SWiOMBAeG01xZ7WD7IRldpWMH
GKqMpio4QYQsS2RN9ZZF3J0KB+/7ej4i2JjnGsuZlDse+wrWjlYfm+NEWZJIGUqi9AjkrOTavHqp
TBhHVLsehqKQMVWemszzM0eGBx2fDQGWN+carLQcLFVFU8HxYjRZBjXCVBV6fkQQJ9YlMn3BKl3F
MlQaXQ9JBjsQuEFAIaVhqApumBicSoCQJJ6eynNqMj94V//Ws1O8cmmN8ZzJ/lJqx9mmhwm3TX6E
EL9wP07kVrjXi+LdSK52e3H3+lLvVoW41e8oWjqGKpM3NbKGyocPFAdBh4LEfMPGD2OKfZWiZIAV
njtQZLXlstRM5LI31EcKKY0z83UurLT49nqXQCRDzbJI/HwkkgdfUxT8KCZvaZiaiiQlNIjhjM6z
+3KkjCQIKrc9gjAmBgxVxvET1ThTU3hmX2EQUNxqQfxJQq3r8cqFNf7Oxw48UsppiizxhacT4YOu
F+6JvvIY9xYb78it1q0NWdz9pRQ/mq0ncqdhzHBaQ1MkBIIwBk1J/HssTSGIBJGAMBLIXsjBUg5T
T5zI31loYKgKlbaLqkgISaIXJMPXx8azPDmapZQx6Pkhn39qYqCIdSeysw8LSimdjKEyXbIYySYe
KRt/z16ltU+nCjcFfZCs1+WOTywkUprCUEbn8EiaI6MZfuHYGFMFi9//3g3m6z16fsRHD5SoX1nH
EhALgSZL5FM6T03mGMsZjGZNrpQ7fPdqhSgWpA2NlhswnDaQJYnhnIFAYGoKw2mdv/XMFG034Ic3
qkwWLQ6W0lxZ6/DOQgNdkZgYzdBygoE/iO1HuEFEMa2jyBKTeQs/FAylDZ4YyzJX63FqX55fPDU+
mEl9d7k5MH19YiTDR6aLZCz1oR58fr+4k87BgVKaZ/cVBsWAlK4hBKQMhV4nmbs9NZkfJKVOmJhG
rrddUrqKE0SkDY0oTpgXqiKTt5K9FxKK8tmFJnUn8V/SZIWxrMlstUfW1AjimK4b0XaSJOhD+wt8
4fQEf/jaDLYfo0gSf/+ThxjNmzh+iKWrvPT0BGEsWGu5/MXZZdwgJmslMu5pQ2G56eIGMboKB4ZS
g6LHbkXc28U5j8o6sVdsxK+nJvNYusKJidyOs835lManjo5wYbVFzwup9QTHh3ODmFeVJL51qcyN
ik2167KvkKxJHTfgO9cqfPLIMKWUTstO3l9VSWwOGraPpcno/e6Q5wuKGY16L8ANI3w1IKVq6FpC
gzQ1uV9Ul9AVKFk6uipzfCLHu0stNDnZP44Mp/nN5w8M/g5FkghEzCeODG/xoHyYsRfa2z/a7ftC
iP/p7p3OzbjXi+LdSq7u9Uu90+8IheDZ6SJpU6XW8XhyPMtk3hpk3PO1Hl035NXLZWIh8KKYWsfn
2nqXrhuyr5gaVKNadsAPZ2pcWGnxxkydIEqoDIFIBhvrvQBTV5GFwAtCwgjqXQ+pzwk9OJTi2HiW
Lz4zxfmVFu8sNhHAk2NZPnNijNWmy9X1DmNZk7YXkDW1LYvjw/6ifFB8+Z1lgkjwHz+//0Gfyh3j
pdMT/NFrc/z/7L13dGTXfef5ealyAgo5NNDoJtmZzdAMEinZIqlEywqWvNI4aByOzoSd8exoxrM+
41mH8Y683mN7duzx7owtyWlNr2WPbFnJNiVLJiVSzGyyA5vdQDdyKFSuV/Xi3T8eqlhAF9BI3SgA
9T2Hp8FCoeq+e3/3d+8vfX9Pnp/jQ3c1Z1+i/Ya19JYqSZyfzmG7MJIMcXWxDK7LaErHrktls5ba
swdUBdM0UWTPC9iXCHHvcJJwQGG4PYzjChRJptwRxnJc5goGliOo2B47VFvYTyKskYz4lnn7duO+
XstZtZGzYjV9fWd/HL8iky/bDLYHkSSZWEDl1cks07kyvYkgx0MJXriWRlFkbu+MkNUtJAkSAZXu
eHCp34rFU6UUPfEAkgRBn4rrurw6nuX9J3u5rcvrr5YIagy1h0gVTd6YyzOZKfPSeAZNlRlqD6Gb
XgqMX/UYP8M+BcPyajxnc2VsV2DarsfqJ0sMt4eYzpUReLWjB9pCNdpv8HqZVNPpEBAJqOtyKO5V
z38V8dDyHjT5ssU3LsyRr1jIwFze4A+eucqHT/dzqj/BpdkCZb9Df1uQRFDjG2/MY1g2FdMlGlQw
HS/F1LJd5KUG5H7Vi7hNZ8sMtoeomDaFskW6ZCIrEjG/wh09EdylVLq5fIXXpvIoSOTLFu+6o4v3
nuipEStVG69X+0SFfSqpkoFheg0sexNBjvbEWCyaPHy4c139u/Yb6u+vEb+6KqlTtUbu7GQWc2lN
ASJLe+vsVBZZlnj7oSTfuZzyGpoCY6kSkxmd1yeznBpMeDV4ZZNc2WK4I8Ll+SKSJChUbK9Rc8hH
RzSALEvkyjbZsmAwGUKSZAzTZjpXQZIlr7ePolB2HFxd8ME7vUa5iiQTD6v8q0duX7beu3HvrseN
G73po1gDN3tid6PHqYr2kNfQrlixGU0VCfgU0iWzFsmpdnp+cSKDT5FJFQ2CPhmfqtAZV7mtO4qL
qPUTev5amqsLJRZKBsIVKHgsT9pS4zLLdvFrMsWy46XBAH4FFElQrDhICL59aYHeRIBk2Ed/WwjX
FUT8KtmyyXSuzEyuzKmBxK6a561CCMETz41z14EEd/Ts6HbaFO4+0EZPLMCXz860jJ8mwVp6yxaC
Y71xZFkiUzJx3CK28NLWwDNwqjneEpAumjWjSJY9IoTFksHz14p0RvwkIz7m8gaaKjOV0XFdCRmB
4wqCPoUPn+6vXZR2y8G3FlYz2rZ6VrSHfIT8KobtYAuHiu2SKpoEVJnnr6bpjQUomjZvO9zBoc4I
Dx3q4F13dPFnL0zQFtYoVmw0WWYsVaIj6mcqo1MwLAplG79P4FckFksGl9tkaAAAIABJREFUX31t
mjt6YxzpidGZ9THUHubJC3NMZnQKhs1gWwgEfG80hSzLDCXD3NEb4+4DbSRDPv6fb19hIqN7za9l
mYDmdaZvC/kYX9RRZRlJwDtv7+K7o4s1VqrHT/YtS6cb6dxYHeduNJZXw2rpkfBW48ufe99Rnh1b
ZGyhRMGwGM/ofPGVST58esCrrTAdUqWleh1XoJsujgDdcFBVxStGlyVkWca1LIoVC1WWiAc1+hJB
5osmdw4kuLJQZCgZ5tJsgcWSxXyhgu0KJjNlJNkzdDRN4gsvTjDSFcG0vBYW1fTFA+2hWg1aZ8TP
w4c7uTibZ7FkULEdOqIax/viOzTTzY313l/TSwy+0YD3+6hfXRYlGm4Po8qQ1i1GOsOcGU7yjQuz
jC6WcBzwaQpPX05RMm2CqkKhbKKbtmcwOy6O693jsrrF5bk8jiswHUFQVUDAmeEEX3p1Gt306oOE
ImG5LpKQWKyYPDu6yKPHujnRH1/WqBqavyZ/Nawn7e2XbsVA1sLNVIq7wWpd2QNoWQ5t9bBZKpSs
HjbV9xUqXs7mmYNJvvXGvJfCIEnYwiVVMpDwPMUIKBkO2bKF44hlHmJj6dZkuy4BNCScWsTIcASG
A5XFEnOFCp3RAJ0RH8WK50VQJIlk2IcjBI8e6WZssch9B5u7EG678eK1DFcWSvzaD53a6aFsCrIs
8fipXv7omWvkypbX3KyFbcNmDo/V9FZOt5Z6+rhcmi54RfXmcuYCt+5nwVu1PIrs9ezJGw4vX0sz
la0Q8imEAxrDyRC3dXn0xyFFJhpQONob5xNnhjbc+HEn0GiONzrvWz0r4iGNY70xvnMlxUBbmPl8
BYFAN8CwXcylHPyXr2V54FCSp6+kWChUeG3aq/+QgIOdXmStVHFwhZeCrKkOtuMVXsZDCpmyxehC
ifm8gSsEL45lyBs2R3qiTGUrzCoVbMdhsWQR8WtcmMnTEfGRDPn48tkZZnIVZCR8mkw0oDGcjPDw
bR38yXPXuDBTQALm8mUSYQ3bFTxypJu84c3lwY5wrXZlNzoUtwON0iOBZdTpFcvh0SPd3NETZSxV
IqNbHvmPpnA1XUI3bF6eyLJQqJAtW5RN9y2iEgf8krtUqyFTqHgp7RKg+Dya+ZLpUjIdIgENSZbw
qV5riZJpE/B5NTrgsbIWKjYhn00i7KM3FuT16SxCSHRGwnztzRm+eyWFT5EZ6ZR43/FeYkGNhaLB
PQfayeoWH7tnYFfogGZGe8hHNKAylirWauTqo0SDyRCffuwI52ZyXJ4volu217Ik4KNg2uR00+u9
JMtkSiYFw8Z1vRpB7/4mcByBA+QNi1z5rVNgoWBQqDiAhCx7hBmuYaPIMgFNQVO8RtgHOyI8ONLR
sL50p4hKtoL1pL39rBDi1yRJ+i0a8P8IIf7lTRnZFrGRg62ZPU71wlVtgOar87TFQ9p1h40qScv+
pmI5nJvOYTmul6/ZFeGdt3XxvatpAqrM356fpT3swyd7UQq/JqO5nrfAXfIYy0sFm4osUGTvdbfu
0qTInpfIdV3Cfi9HOeLzDuLXp3NMZHRODiToigZ2TfHzduGJ5yaI+FUe30VEByvx+KlePvv0GE+e
n+OH7hnY6eHsGWzl8Kj3JFfxldemKRq21xdkqTFdfaRnNaiyt79VRcEVgoJhwRLVrSRJlAyLizN5
JOD9J3oxHJdHjnTtikvPapfRzcx7/VmxGaM14leJ+lX8qkzY59X9XJotYrmCiUyZjogPRZG4unQh
Hl/0GMBkSSagSfhkj5EzGtCYy/swbLx6nXiQrG4iSzJqwPP+FysOAZ9MtmyRr1hYtktfPMCJ/jij
CyWyZQdVkQj7NW7vivKFFye4MJOnZNr0xAP4NZmRZIShzjBjizply7tgy7JE2fJqNwuGzWiqRHfM
jypJjKW8ZqrN7lC8mWjUsPL1qRxzeYOLs3niAa8264VrGY71xrAdl1hAJRJQqFgOPlnm3HSeKwtF
FMVj8guoULa9GlxJlrhvqJ1YSEOR4KWJLCXDxqdKmK7L18/NEg1odEZ8uI5LZ8SPKwTZioVtuwRV
FdsRKLLEgfYgIPHTD40wV6gwky8T9WsIYDRVwrRd2iN+/KpXN2ILL1OkWLHpiPoJagrBfVwHeiMd
sJruaRQV/Ng9g9w33A5S48bQg8kQthAsFA1ifo3XJnOIIgRVT2d7ZBMGLoLOSICS6bWlbg9q5Mom
luvVeFaWnGHVc8EWXmP69ogPITQWSwbhgIpesbFcmYjPI1947uoiWd3ko3U923YbUUk91iO1F5b+
feFmDmQ7sZut0ZWoF67XpnJIkuBEMrFM0FZ6JK+lS8wXKhxMRshjMZAIYjmC77u9i5l8hUeOdBEN
arSFNWJ+jS+9OsVEpozretaOLC11cxd4LDVLJm8iqHFbV4SSYVMyHVxgLqNTdsBxXbQlI+jNuWKt
u7RP9nLaFUkmq5t85PTArl2LzSBX9mTxw3cNEN7Fh8Rdgwn6E0G+fHa6ZfxsI7ZyeFzXHLk/TtGw
mUiXGU0VOTeVx3bdtRirkYA7esKENJWFgkE8rJEumliOC5JLumQy0BbkJ942wrNjKfw+mYtzeU4N
JJqezaeKRnMMbOnQvlFvt9UuREPJMKcGErUm1UPtIa6ldXqX2KAqlgNCkK/YyIDpOkhCIuiTONab
4LHjPSSCGou6STLkI1u2ePpyikRIw7Rd0iXDy9sXIHC5mtJJLa3nixMZemIBXpnIohs2A4kgZdPl
QFuQ566mmciUmc5WMByHqF/lgZEkjxzp5spikZhf4zmfhqZ6EShVkfGrMoc6E5wZbr+uF93jJ/s4
2LE75GO7sTI9EuHJ2khHmNemskznKsiSYDZvENQUZnJljvfFuDRX5I6uCE88f63WOFySXCwHXNlz
UAQ0mWTYj0/zKKYzFS96l1VNkMGqCAzFxa5GknwyIX+QqZzOQDzIQsGgOxZAkWXedaST/vYQw+1h
BpOh6zJMri2WUBWJN+cKGLZTY+zLly3Oz3g1haoM7zuxe516W8FmejheWyzx+nSu4d9USxUafU91
XaqylTcshpJhgpqCX5OZylRwhSCgyGh+hY6In8WlBqexoMql2SLjaR3wsnVWdjLIlrxsgY5IgJDf
ZaQjwmLRpKfNR1vQx2hKx3UEr0xmOXPwrZ5tu7lsZD1pb3+99O8f3PzhbA+2yxq92bmM6/n8euGK
BlQkaChoVSMop1s8N5ZmdKHElYUSpwcSHGgPcWm+yHyhguV4ncLblj73wkyeVMlAkyVMIaGqMiFF
oi8WZCpbpmw5OK7w2IZUGduFeNjHh+/u4tkraeZzZVTXS5PTVMiXTXyKQkc4QHciQF63yVUsBhIh
emNB7Bt1T9xj+NKr01Qsl0/cN7jTQ9kSJEniB5aiP1ndJLGLlFwzYyuHx3XNkYVHez+Xr5DRLRRZ
wnElZMnr6u7iKXyB59xg6efR+RJ+n4KERMlyCPkUOkIBjvbEKRgWH71ngJODCXKGxf1+bUOpqxOL
OlfTpdoFayew2hxvdN7r9fVqZ0z1QlQ0vJ4p9Wxx8JaHt95RpSpeepkiSwQ0mQdHOnhhPI1tu+im
BggkSaa/Lcj5peibT5NrTaGrdLO1S2u6xFyuwovXFpnNVVAUL0pfsRzenCt6jKBlk0OdUXyKxGLJ
Y+/MlU0CPpmBSJD2sI9jfXESIY3MuEXZcLh3qA1HuDiORMAncaI/wX3D7QwmQ4ylVm9gud+w0hkJ
HhFE3rA41hvjtakchu1SqliUTa+haFBVCWoOfp9C2XQZSARoj/goGg5BzSWgqfg0iSM9Md51pJvR
+SKSJHlrpnp1vIWKBXgplLIkcF2XeMiHYbkUyxZji2VCPoW5qSxHeuM8O7bIpw97zbafenOB4fbw
MoP1VMhzcFxbij5WoxFp3eRYX7xGgHA1XSIWvD57ZrfWgqwXm+nhiNTY6bLaXFUbLgdUuUYe8vhJ
j05/PlupMXnO5ysYloOqKHRGfDx2tIfueIBCxeal8TRnrZzXjF4C24VoQAYhYdkOtguy6jVCbo+o
COGlyHXF/XzsrsElFtBZfJqC4bg1q6k65ocPd26p5nOn5GQ9aW9fWuv3Qogf3L7hbA+2wxq92dGj
jXz+ib54TfnA9SHTeqR1E78m88iRbi7M5EmEfHx3dBGB4KVraQRwcTbP3QfaeNcdXUxmdEI+lUtp
L5c7GtQIStAd9zr4FgyZZNgrdD3QFkIIrx/I05dTTGTKGHX1QRULZMklFvRhCi/R5oFD7bjCixo1
4rff6/jT58Y51hvjZP/uLwj9wJ19/Ld/GOWvz87wYw8M7ehY9srBupU6kuuaIyfDPHqkm6uLJWJ+
lZTsNbazJAgFZAzba0InQe0Ak/AOQ2wXBZAkBSFgKqNzLVUiGtT4+zcWONQZJVPymhqvN3V1YlHn
1//uYs1D/OnHjuyIAbTaHG9k3lfq64cPdzY8Y6o04xPpMhnd5IuvTPHJB4dXjQoNEeaeA228PJ4m
FlTxqwqxkMZDhzuZzpYJ+svYjosqywy0hZgvGMui/9cWvTWqfl5OtyiWbf7shQkms2UyukWubC9F
a7z0Ob8qo0oyruOQrQhSRZOIX0WR8HL8ZYnxtM5XX5vmGxcUhjpCZEoOdw4ksFxBOKBybirHeFrH
sB0eD/btuAe4mfVBvfwVKhaxoEbFcnnKmadQ9rIjLi8UQJLIlSxm82VM18WnyMQDEjlcXASukD0m
MOD5q2kM10U3bN57vBfLcYkEVAplC92ycV3BQsHk2SuL3DXYRsinIaHjU2TKeKmSZdNLZXrqzYVV
9+jKaEROtyiUvf5VJdNmbKFIUFVqhni9nO+V7JvVsJkejgCvT+WW/c1qc1XfcDnkU2rOklMDCaK6
hiUEfYkgOd1iYYnEwhEOmbJnVF+cLaAbNqmi15KgcNVECK/Fwcn+BOemcth4ZQ3CEeiGzWLBoi8R
JBnWKFkO33xjngPtIfoTQTRF9qLVyfC2re9Oysl68nAeBCaAJ4DvseFe3bce20FicLNzGdfz+SsF
o+p5WWsc1Q05l68wmy+DJJjOVjjZn8B2IV+xCGgKL41nONwVQQCaJNMZDZArmyRDGjO5Mhdn8rSH
NEzb4/kXgOm45Cs2vYkAqaJBzCcj1323LHmh+qAmc0dPnHcf6+b+kWTteZvxYLqZeG0yx7npPL/8
weNIUtNvmxvieF+Mo70x/uz5iR01fvbawbrRmsP6i169nsuXLZ68OEciqPGm5dIdDXq9HCwXJIho
Xr64C0gCFLwIkANItvdC2fL6iPhVBWToWuoE//Vzs/TGA1Qsl/cd77yu6LXR/r6aLmG7MJwMc3Wx
xNV0aceiP43meCPzvlJf20LUPLD1+SPtIR8VyyGjm7SFNAKqfF1UaKFokC2ZfOyeQY71x/mxB4Z5
x+2d11EMX5zJ85+/eQnbEczmda7MF+hrC9Wi/1nd4uvnZkmENCJ+lYcPd/LU5QUuzhYYSxWJBjw2
UNNyCPgUHFcgkLwLkCqjKApGxcR0XBaLFZIRPz3xANGAhqrIXnTCsJeaYztcSU0R8imYtncxH+kI
LyM62Kk6n2bTB6uNJx7SmFjUyeoWVxY8dlYEPDiS5IXxDAfaQ1Rsh+N9MQKqymy2Qm8iwKXZPCFN
JRHxkSnZ/MVLk5Rth5P9cc7P5JnJl+mM+gFQkPCpEmGfRtG0mF6iK0+ENMJ+Ddv1aoDH0zohn4Ju
OpQtry5ooWhwbia3qhe//rkEcKA9tCqr326uBVkv1nvPXMnyt/JvVouaVhsuh3wKF2bydEb9dIz5
aQv6uDxX4OxklsWiQcmwMS0vgiMQyKbNS0utRhRJomw69LUFeO/JPqKawqvTOSbS+lJNkGf0Vmv/
euIBhpJhXOEivIY/nJ/Jc3t3hJBP4T3HetYc80axk3KyHuOnB3gM+ATwj4CvAE8IIc7dzIFtFVsl
MbjZnqzq54+mvM7N6tLluHqRUCWJq2mvT89GKEOrG/KZKykWSyZD7SHGUjpXFooIXAzbi8jEAiqL
RYOvvz6LbtrISLRHfDgCMroN2Ji2SzSgYjkujiu4Ml/EWPq5aNgkQz40DYTlWcRBn0p/IsidgwkO
JMOkSkZtTPBWYfZeU4Kr4YnnxwloMh88vTfooSVJ4ofvHeCX/vo856fzHOuL7cg49sPBuhoapUEc
7AgveQmneH0qz2LRoGjaFCs2qizRFvahGzYOXupbtVlxOKhQKHsHoADCfoWAqpLRDRzhUdsvFCpE
AmESIa2mh+pTV9e6eFbpWa8ullBl7/+bERtNP64/D16f8vL3X5/O1Z790SPdnvET9C2Ldqd1k4mM
zncvL2LaLuOZMr/4A8cZTIauy/XP6Rbfu7qIIkuM50r4NYULc3nec7yXoE+haNg8fTnFa1NZbMfl
aG+M9rCP8bSOJksUKl66setCwK/QFQlQtrwmtDP5CrIEZ6fylC271ijzYGcY4cJUtsxcvoIrIORT
CPpUYkEVBNx7oJ1UqYIsyeQNq0awUyU62Ik6nxvpg1sdFaofz+hCkbNTWU71e+v71OUFXFdQNh1O
9Xs01PMFg7Bf5Y7uKN++tECubFAsewx+sgRIEkgSb84VKVUsNFVGALIkUbYcVElhoM3H20Y6CPoV
Xp3M8MzlNIWKg1+VyZct7uiOcqIvzlyhQlCT6YoGQYLuWIDZXJmpTBmE4LWpHAtFo6ERuXKe++JB
0iWz4R1ppyOBW8F2k2U10pH1+2S1uao2XG4P++iM+nn30R7yhsXvf3eMczMegQYC/KqKK0yPjARP
LhbyZUqmS8V2USSPPS4aUnEQHOmJ8qrj4rqCuYKBkDwSk0hAJatbXF0sIoHnKHO9BrZHe+LkDaum
+9tDPkzL5fXpLFG/tun13Uk5WU/NjwN8Hfi6JEl+PCPoW5Ik/ZIQ4rdv9gB3CtsRPbrR5z98uHPp
IqPw1OUFHsbz3BUNm/PTOQ52RBhLFQE2nDI2lS0znfPSIhxXkAhqRHvinJvNIyMhS/DyeBZNkfAr
CrplsVg0sYWLLHm55wXDpmR5zU6dJfY3JI/m1Ov9o5IMBXCFoCPi53BXlHcf72GuULmuuLiZPHO3
AiXD5kuvTPP+k717ihr6Q6f7+cxXL/JnL0zwiz94fEfGsJsP1q2gPg2iLaTRGfHXLlZp3QQhcIRg
oWBQthyE8FJfFgoGfk1GVSRc1+vlA56XWGKJ6U2AX1HoS3hNMw3bxZRlEkEfx3pjhP1qw/le6+JZ
pWfd6ZqftbDeqEGj86CR9xPglcksvfEgFcvh4cNvRclUSeKV8Syz+QpBTaFUsXl2bLFhvUTV66tI
Eqbt0hMLElBVvn5+hpHOCJmSRU43aqQWl+dLLJZMLs7ksV2IBTTuG0liWA5Fw2YuVyEeClCxXYaS
YaayZcwlR1jU7zXLnEpX0BQ40Z+gKxYAIBny4dcU2kIao6kiLoID7eFanr8qSdcRHWyFDW8zWEsf
7ERUqObYXChyfiYHEkykdU70xXGEYDgZ5rtXUpybyaPJEoe6IiTLFrN5A9Nx0U0X3XIwHcHFmcLS
OtkgHGRFxhVe+upsroJPk3lpfBG/pnI5WuSeoXZ6o0FiQZWMbnopjorMg4c6uHuo7br16okFeNeR
bpAgWzTRFGlVI7JRmu1QMtxwjW/2/elm4WbIy42M80apcfWsidfSJToifizhMper8OpkFt10WCx4
TeZVRUJTZSzHa1jsuC5vzBc9lkBNJRrwIsPnZvO4AroifqJ+lelMBU2R8KkqIc0jMtFUmd54gPm8
6RFmVby+kVVHR/3eEoAQ0pqEOjfCTsrJuuinloyex/EMn2HgvwBfvHnDag5sNXpUj0YHgS0EbWGt
timupr3DNOxTsV08KkmfsqzZ1XqQ1k18msyjR7p5aSJNUFO5d6id16e9i9JgMsTEopev3RbykXJM
fK5KMqyxUDQxHQuB5/mVFRkhXKwlSlW/ImM6noGUK1sc748z0hHm/pEkD450AJ6hU38Q7UdP/Vde
m6Fo2HzivgM7PZRtRVvYx2PHu/nLV6b4ufcf8dKjbjF268G6VVQvxG0hH/P5CtO5MgGfwhuzBQ60
h7g4W8C0vcuuACzHQVM8+up4yIcigW1XkGUZ03XojAXIVYqYSzzYLoJkNICmKszkK8QDKrd1xwj7
Ne472E40oF033zcyRAeToaY0eqrYiG5aeR40evZlTJt1nlKoNp+NMZ7WKZkWYymL0VSRr7w2fd0l
q/rZQghkoGDYRPwqbUGvF0vZcFgoWCgKxII+LNvl0kx+qYmqS9yvEQ2oZEomqix5zU2TsRqJzXzB
IBbQ8KsymizRFQ1w78E2JAEgYTo6AjjUFeHdx3pqhs7KlKjV0l9updGxlj7YibOnOp6V/feKFZtM
ySJTMogHfZzojXF5ochiySDq1zh6IMZCocJ83mAwGSJTstANC0mSKJs2tguS8JpTgpdiHlAViqaN
5bjEgiovXk1z+kAbjx3t5rPfGcO0RY3ZrxpteDy4/KLdGfXX1kk3HUYXig2dravN83r3y27AzZCX
9dYGVdMiv/jKJAFN8dpjnOzzmDXbPeKJizM5MiWvrtARAk2WkCSv+WwKA9v1KMztpcamumkjyxLP
ji4S9quE/Aq5ikVHxI/d4aXJZXWTaFClLx7EEXg0/H6FZMRfu0f2tgWXrXm1tnw4Gd/yPO2UnKyH
8OAPgRPAV4FfEkK8ftNHtcew2kGwclMMt4eZSOsUDRtVhsWCAUuvb6YQOm9YHGgLI6DG3x/xgysE
nVE/Ai/H/PJ8kVcnMqRKXuh0OBmmLaSR1i0KFS/yU6pYxMM+QEI3LNrDfgoVC91wCPpUjvfGVy0k
zpetWrF0xL8/SA/+9LlxDnWGuXeobaeHsu34+JlBvnJ2hi+/OrNjtNe78WDdKqppEIPtQVRZIhHW
6I4G+MbFOSYyJVwhONHnpdc4rmAyXaawVNNh2g6DbSFKhoXpgLzEYR/UZMq2iyrhXaqE4IfvHeS1
qdxSdMAhGlAb9p2A3W+IbiWK2Mhju5Jpc2UqUDzk4/aeKMWyjeu6JEPexbORN/hoT4xraZ37D3Yw
V6hw10CCiWy5djn9gVO9vDqZ8RpgCkE0qCGQsCXB0b4Yt3VFuZoq1S5Bhu3iU2W+744unh1NMZMr
Y7suiYCfMyPtdMcDNTKHjG6u2m9kPfN3q42O1fTBTkWJ46Hl/fcMy+WliQyZosEb83mCPpXzs3l8
qlwzlHsTQX70/iF+vXAJVZJoC0GhbGLaLn5N4aHDHUxkdKYzHoNfrmwhCYj4VPyavGS4gmW7VFxB
bzxIbzwAEphu405fVRmuNmD1qTIVy+F9h3tX3e+7bY9vBGvJy2aZK9erI6upy15k38dge3BZOxPS
sFjymoxXLIe2oMaRnhjj2TK3d0WYy3sNcefzBrbj6XbLFfRE/d59zbQIuAq2EPzwPYP89dkpyqZH
lHGiP048oFGxXR490sUzo4sUDIuRjjDH++PXjXkvZF+sJ/Lzo0AJ+BngX9YVbkuAEELsTOL/LsJq
B0GjTVH1yjx0qIMnL84R0GSeurzA48GNec6Gk2HyFYvjvXFiQW3ZAb2Syz8e0sjoJrrlMJ83MGxB
OKCRq9gENRnbFXTGAtw1mECSPE/DTK7CQFsQw3ZwhbtsjPUKMqdbPHV5gYCqNCyW3ou4NFfgpfEs
//79R/cE0cFKPHS4g9u6Inz26TE+cnf/nnzGncRq6ULVVNlzMzmG28OMZ3RGUyUE0BUJcHG2QJtu
kAz76IsH6YsHMW2Xim0jIXPPgTaeupwiVzEpVWy6Yn50y8EtVLBcz+PnNdvU+Ym3HSRTNkF4vWnW
2rO7+UK0VeOt/tnHUqUa0+ZoqsSZ4fZlejCtmzx6pIuK5fDGbJ6FgsEL42nODLVfd3nI6RbnZ/PM
5ytMZHQOdYR5c76IX/Oalp7oixMJqnzkngHKpoPlOAhXwq/JBDWVT9x3gEzZ5FuXZAKKTCLsoy2k
cVtXlPsPJpGAs5NZ4sEgAAc7ItzWFamt9Xovd6vNX7NcjnbSOK//7plsmc8+PUrJcEjrFh841UnJ
sJFllqUUxTvC/OIPHOfcdI6SaXNuKse1jI4rYKQryifuG+J3vnUZw7Hpc4N84FQ/t3dH+Zvzs1yY
ziMQHmlCb4znr6WXKO+99EVY3REb1TV8mryM0GMjaGa2vY1gNXnZKnPlenRkWvdoy9uW7mOdUV9t
3+R0i+ms185gIBlCliUOdoRJhv0c6AhzuDPMty+lsByXQ51h5vMG0YDCXNHEdARX0yV6Yn7ODLfj
IhjpiixLSa6/I8ZDGv1toTXXc7c7vWB9NT/yjd7TwtpY6yBYuSmq/z+WKtEW9m3Yc5bTLb7w4gRn
Jz22j6lMmY/eM1griq4X1pxu8fp0jpzuRWYsV6DK0JMIcNdgGz1RPwXDRlMV0kWDWFAl6NO450Ab
f/L8OIbpUjIdhtrDtc7PjXLXHSGWFUvvFUW5Gv7wmav4VJmP3L03iA5WQpIkfvrhg/y7v3iNZ64s
8rbDHTs9pD2DRmkP9Rfovz0/yyuTWSTgtu4o9x9sp2RYfPGlKRy81KSffPsI52ZyJCN+ri2WSIT8
6KbDbKGCLIO7lFJ7uDPK4a4opu3y0niWeFClI+ynZHhsZacGr2+4txexXcZbfcS9O+avNYFdeeF8
6HAHmiLTGwswk68sM5KqSOsmrhDEAhrT2QqvTuRoC/sI+RTSJRPXFXRE/XSE/TghwfnpHCMdEZAk
Pny6n8FkiJiucXogQcGwONAR5p23d9Yoyp8ZXSRfsbFssFyXN+fzGLazoca19Z7wlUQHt/pytNaZ
spPGefW7L88VuDiTB0mmaHjG0ImBeMMeKbGgxmS2TKpoMJs3uL0rSsXyPPL9bSHuPdjOQsGgM+rn
B0/3Ew9pOK7gzbkCJcPh7GQOIeAdt3XWGOWCSw2207pJsWITDqiL3p2YAAAgAElEQVQUK3btzN6K
sdpsbHtbRSN52Qxz5UbvOe0hjyBlsC1EZ9Tlw0trW987zHEFmaKXttYd8+qt/ZLMn3xvnJLlkMpX
6I4FOdwd4YGRJBPpMvmKhSJBumQykS1xoC1cG9NKWvNGc7Cy+W39M+3mdd69Led3ETZzEGxWGXm9
BGyiAe87CktUpHA96UDVMDnWFyejm2TLJif7EnznSorvXFlAlWWifpU7BzwvUrpk4xStJaaaOCGf
yovX0swVKnRFA9cVmlZZ6+qfQ5WkPaUoVyKrm/zFi1N86HQfyYh/p4dz0/DB0/382tff4HefGm0Z
P9uEtdIeYGlvGxZ+VcGwHYoVrw4kq1vkDJOo30dGt5jM6LSFfQy1a5yfyZPWTYaTYXriAR4YSfLM
lcUaPbIA/JpMbyJIpmR6NSG2w/NX0zeM+LSwHKvp+ZWR/0hApTvmFTDXG0n1qFJm65ZDMqqRr0go
Ery2xC6XDPtoD/t4YCRJrmItoxyueu3jIY2P1jVUrY7nu5dTXJorEFAV8hWT7liwxua0Xifbejzh
t+pytBsu344QhP1ejVVAk7mjN7qsn0s9E+q1xRJnJ7NEAxqG7ZArWwx3hHllMlujrT7aE1vmFI0E
VI++3JJRFS+KG/QptIV9y+4PqiRxfiZXW7f3neitfe9mjdX9UNO7UebKzcjkjfRHdzRAMuoDIfHg
SJLRVAlVEkiShOG4RPwqZshPMubjSG+Mtx3u4NuXFpgZLyMBfs1j89xITK/+OQzLrTVYbtZ9thG0
jJ9bhI0eBJtVRu0hH9GAyliqiMCjOFyNdKDewOpvCzHQFsIWAtsVqLJCR9hHxXFIlQxs16VsQXfM
TyKkIUkSAZ/MvcPt3HewfVlueKOGgFXv1l5XlH/6/ARly+En3n5wp4dyUxHQFD75tmF+4+8ucXYy
y6mB/REluJlYK+0BvL2tyjKX5vIIAY7jUc5H/CohTcN2BI7i0psIkioazOUrBFQZEEveP4n7Dya5
/2CyoSfvWrrENy/O1WoQ9trevBVopOevY8lqDzPU3pglq/5zPnx6gC++MgVCMJoqEtQUJAQxv8rl
hSK9iWDNcKrWltwosyCnWzz9Zopc2UJVZHpiAY72xRqyOa2FZurhtBvOlANtIXyKRMV2CKoyZ4ba
l3n16y/JSG+1jhICYkG1RpqAREOn6FAyzF0H2nj5WgafKtPfFuI9S0QVK0mWjvXFCftUSqa9LL1t
s8Zqs6Q43kxslLlyszK5mv4wLZcnx+Y8EirJ6+MYDahIQNGwPSIq2wHhEvGpdEa8fkBBTeH2rij5
ikV3PMDx3o0RFNQ/x2tTuWUNlptxn20ELeOnibEZZRQPaXzsnkHuG26/rmB1pYJaaWABnJ3KUjEd
FooGGd0reHvv8V5UuY8nL87X+os0CtdXsXLj20IsS4vYq4rSclz+4LtXeduhJEd7934p3E+8fZjP
f2eM//Nv3uCPfur+nR7OrsdqaQ9VxEMa77itk6Jh0xH24wpBxK8y0B7icFeYomlzZqid+w96jYWr
bFPd0cB1NSgrPxdgiDBd0cCGL8ItrI3VHFk30u2DyRCffHC4FkE/N50jq1uEAxrpksFDhzpqn7Fe
R1laN73IQW+MhYLB0d4YnzhzYFVdvhqaqYfTbrh8B/0q7z7eg5C8BsP1aWgrL8lD7eFaumJPPEBI
U25oOMdDGj/+wDDvXGqWu1rUtkqa4izpju2Yq71Q/7EebIS5cjtlMh7SODPcTr5iM9IRZq5QqTEA
gydDjx3tZjyjI0uS16h0SUb8muegHl0oUrGdDY+n/jmqxlYz77ONYFcYP3u9RmS7EQ9p1zXNa2To
rGxMl9Mt4gGNsF8loCm1C1h1w/e3hWodzRv1pqjiRjVOe1VRfvnsNDO5Cv/xgyd2eii3BNGAxj/7
vsP871+9wDNXFnnwUHKnh7SrcaO9kdMtwKM19WkypuUxOL3nWE9DZ8dwe5iXx7PM5Surpldt9Pv3
4r69FdisV73+72JBjTcXiiwUDIaSbRzvj6/6+fVpx/WGTdXAPtwZYaAttEy/bwQ38oTfSlm5WWfK
Zp4hp1tcWyxdtxfbQz466iilq41hV6aFV7+rPl0RWJfh3Ojcb/SemzFXu73+Y7ux2XleTeaGkmG6
Y37yhkXEry4zfKpEGcfq9AFQY9lNF00kSeLRI90Elwze9Y6n0b1xr5wBTW/87IZ83t2CqoJqGGrn
rZogATxwKNmQ5rRRR/NG37PWxt+LitJ2XH7rG5c52hvjXUe6dno4tww/9uAQn/vOGL/85fP89f/8
dlSlxY+yFay2N+r3rAAOdUQ4P5Pn1alsQ71YY1nU1qau3cz3t/TwziG41OcpqK3eX6tWIF2xOT+T
41hffBl5xnZdflfzhO+ErGz3mbKZZ1hJNnR6IMFH7xmsja067ysbjTbKolj5PNv5bHvx/G1GbHSe
15K5RkbIWvJZ1f8ILwPg1ECCVyazm9qLN1MWdxJNf1OpDwtX+yG0sDU0mtP61/yaTDTQuOt4sWIj
S1KNKWY1xEMaBzuW1wGNpUo17/Vew5denWY0VeJnHrkNWd4/1M8BTeEXPnCMCzN5Pv+dqzs9nD2L
avPMmF/Dr8k4QnhEBavoxRqrk18loCobpq5diZYe3jlUdee1RY9K+2R/HL8mk9bNhnq1ulbhgNcs
O+xTl63ZSt283dgLsrKRZ6hfn0LFq78AWCgYy/6uOu+2EMs+u5oWvlculfsB23GfWfkZN5K5+n17
o/dWf5+M+glqHovnbt2LNwtNH/nZDfm824VblSqw2pzeaJ7rmWJc1+VEf3xdY93rXmPDdvi/vvEm
x3pjvOd4904P55bjPcd7eORIF7/xd5d49/HuDdHltnBj5HRrWfPM27ujKB0ShuU23K853WImW+aV
iQyyLC9jddos9pMebgbUp61VowSm5dYaVq/FnFldq2LFa5adKhlIePr7VmAvyMp6n6H+bDMtF9dx
eWOugAAcx6Vs2LX08voUuO2Yn1Ya6s5gs/eZlZTRKz9jLblYudY3kqGVOqBk2vumwfx60fTGz16u
EanHrTQQVpvTG81zlSlGRuKF8TTfG0tzdbF0w7HuBjaereD3nhrj2qLOH/7kffuy4ackSfzyh07w
vv/8D/yLJ17mz//J2/CpTR9U3jWoFq4+cqSbCzN5yqbDlVQRCbizP7GsuLmqR+byBi5w90ACF7Hl
yM9+0cPNgPqzIFMyCahKjcb6zoEE0YC2JnNm/Vo9dLijRlSzmWbZm8FekJX1PsPKNTg+EMcSwuuX
Zdo8eXGuRjddPSe3Y372ukOxmbGZ+8zK9TrRF7/uMw52hBvKxWprfaPSgurv33eid8OEJvsBu+KG
crPD9M2AW50q0GhObzTPqiRRsVxSRQOfIjPSEV7XWPeCJ3A1zOTK/PY3L/PuY9284/bOnR7OjqE/
EeTXPnqKs5M5PvO1Czs9nF2HtdIo6ptnhvwqiZBGbyyIT5OJriAeqeqRkY4wPkWmaFrbyuq01/Vw
M6D+LAhoChXbXcb2VV2D1fRqvZc46FdpC2uMdEZuadrLXpCVtZ6hul9XEhYc741zoD2E3yd77SA0
peGZvtX52QuphbsVm7nPrFyv1SjLG8nFamt9Ixmq/n4wGdpXJQjrRdNHfvYLmt1AWF5ADbd3R9dN
ibsXPIGN4LqCf/uFswD8hx84tsOj2Xm890Qv//htw3z+O1c51BnhRx8Y2ukh7QrcyIvbqFj6RukO
ecPi1EDiuh5cLTQ/6s+CiF/lfccbtxVopFcb9Vhr5nNlN2KtPnbxkMbjwfXt1a2g2e8Lexmbuc9s
ptfXan+7lbVuRQzfwi03fiRJGga+B1wATCHEu2/1GJoRzW4gvOVRvj79YissUrsZv/f0KE9fTvGZ
j5xksH1nGvw1G37+8aOMp3X+t796nZ5YgEeP7b8aqI1iPWkU9funerm6UbpDM+qRFm6MjazhSr3a
qMdaSx62FzfqY7fevboVtPb5zmKj95nV1mu9d6ftWuu9XoKwEexU5OfvhBA/ukPf3bRoZgOhkeei
Wcd6K/C352b51a9d5H0nevj4mcGdHk7TQFVkfusTd/GJ332Wf/b/vsR//ZG7eaxlAK2JjXr2bqQn
mlmPtLA+bHYNG8lSSx62FxvZrzdz7lvruruwlfXarrVuRQzfwk4ZP98vSdJTwP8QQvzmDo2hhQ3g
Rt6H/cQ8882Lc/yLJ17m5ECC3/jh0/uS5GAthP0qf/ST9/Pjn3+Of/rHL/LrP3wnHzzdv9PDamqc
6Itf1xixhRY2ivV4ifeTrr4RNjMXrahLC7sBjWS7JbtvYSeMnxngdsAA/kqSpG8IIc5WfylJ0qeA
TwEcOHBgB4bXwmpYzfuwX/JIXVfwue+M8ZmvXeRYb4zPffJegr7Vmw3uZ8RDGn/8U/fxU7//Aj/z
p6/w5lyRf/3Y7fuqB9J6sHLvDLW3aMJb2BrW8hLvF129HmxlLlpRlxaaGTdqmNqS3R1gexNCGEKI
khDCBr4MnFjx+/8uhLhXCHFvZ+f+Zc/aTYwc+4F55tx0jh/5ve/xK1+5wLuOdPHEpx4gGfHv9LCa
GtGAxh/99H38T/cO8tt/f5lPfv45ZnLlnR5WU2Gre2c36YkWNoftXOP9oKvXi1sxF6392cJGsR0y
09rnN8ZOEB5EhRCFpf99O/Bbt3oMzY7d5p3by3mkZyez/O5TY/z1q9MkQhqf+chJPn5msJXqtk74
VYVf/aGTnBqM8ytfvsC7f/Mf+HfvPcLHzwyiKruCaf+mYit7Z7fpiRY2ju1e472sqzeKmz0Xrf3Z
wkaxXTLT2uc3xk6kvT0sSdJ/xEt7e0oI8b0dGENTY7cxcuy1PFLLcfmbc7N87ukxXhrPEvGr/NPv
O8Q/eech4sHd/Ww7AUmS+JH7h3jocAc/++dn+fm/fJ3Pf2eMf/3YHbznePe+NoK2snd2m55oYePY
7jXea7p6K7jZc9Hany1sFNslM619fmPccuNHCPFV4Ku3+nt3E3aj1b4X8kivLBT5ixcn+R8vTTGb
rzCUDPELHzjGR+8ZIBrY3c/WDBhKhvnTTz3A356f4//4+kX++Z+8RH8iyCffNsSHTvfTFQvs9BB3
BNvJ7NXC3sLNWOO9oKu3CzdzLlr7s4WNYjtlprXP10aryWkTomW13xoYtsP56TzfvrTA31+c59XJ
HLIE33dHF7/yoRN8/5EulFaB/rZCkiTec7yHR4928+SFOT739Bj/6asX+czXLvLgSJL3n+zlnbd3
tvomrQMtPbH30Vrj3YvW2rWwUbRk5tahZfw0KVpW+9bguILFksF83mCh4P03X6gwv/TzWKrE5fki
tiuQJLhzIMH/+r4jfOSu/RuBuJVQZM8Ies/xHq4sFPnSK9N86dVpfv4vXwdgOBniods6ODPczvcf
6SLWirw1REtP7H201nj3orV2LWwULZm5NWgZPy3sGrw2meMLL05QsRwqluv9a3v/GpZDeel13XRI
lwxccf1nxAIqXbEAA21B3nWki+N9cR48lKQ93EpJ2Ckc6ozwvzx2O//q0dsYS5X4h0sLPPVmii++
NMUfPzvONz79zpbx00ILLbTQQgstbAtaxk+To9WQ7i1MZXW+9Oo0AVUhoMkENAW/phBQZRIhH72a
93rQp9AR8dMV9dMZDdAZrf7sJ6C1+vI0KyRJYqQzwkhnhH/89oM4ruDibJ6Rjr3R+6a1l5sTrXXZ
X2itdwv1aMnD/kTL+GlitKgyl+O9J3p574nenR5GC7cIiixxvC++08PYFrT2cnOitS77C631bqEe
LXnYv9i/HLO7AK1GVS20sDfQ2svNida67C+01ruFerTkYf9CEqJBYUSToKOjQwwPD+/0MFpoAMcV
2K5AlaWbzoh29epVWnKw97AZGWrJQguwP+XgVurc3YL9KActXL8XWnJwa9DsOujFF18UQoh1BXWa
Ou1teHiYF154YaeH0cIK3OpQ8b333tuSgz2GzcpQSxZagP0nB630nMbYb3LQQuO98Mg7HmzJwU3G
btBBkiS9tN73ttLeWlgVOd1iLFUip1vLXm+FincXVlvHnURLhlrYTdjpPdTaLy204OFausR8oULM
r7X2wjZgvbptr+mgpo78tLBzWMvK30gX4haTys6i2bw1VXlQJanV/XyH8MZsgU9/4RUmM2V+6O4B
/u177mixIK6BZthD29n5vaWTW9gtWCmrOd3iubE0owslriyUOD2QaJ0dW8CNdFv9/G+nDmoGtIyf
Fhqi3sqfyZdJ62ZtU6y3C3H9xjIsl/sOtjPUHm4duLcQa63jrcZKRfvw4U5sIWpKdCxVal3IbjIK
FYsf/9z3cAW8/XAHn316jEtzBX7vk/fiV1sGUCM0wx7ars7vWzHkWkZTC7cSE4s6X3xlioAqEwmo
Nfn3azKPHOlmNFXizHB7Sxa3gLV0WyNdsREd1Oz6omX8bAOafZE3gxtZ+evpQlzdWDG/xjfG5iga
Fl3RwI5HH/YTbpW3Zj17YKWitYXgYEe4KTzr+wW/860rzOUN/vKfv53TgwneedsEP/sXZ/n0n73K
f/n4XchNWMS6GWynTm4Wj+d2dH7frCHX2qMt3ErkdIsnnr/G+ZkCXVE/hzsjtf2sSBJ5w6I75mco
uTd6wO0U6nWbYbkUKhY53SIe0hrqioMd63Ne7wZ90TJ+tojdsMibuQg08jRu9HOqG2s0VUIAB5MR
8oa1o9GH/Ybt8hivRL0sAA33wEp5We0S2Qye9f2AiuXwJ98b5/0nezg9mADgh88MktFNPvO1i3RE
/PzCB44hSbvbANpunbzaHtqNTq+1DLm1nqe1R1vYbjSSt+prM9kyo6kSumFzoWjQEfHX3nczzrO9
jtX2dnU+ry2WeP5qmlcns7w+lePxk30NdcV6dd5u0Bct42eLaPZF3shFYKVgV9+X1k3yZYunLi+s
mRt6bbEEErXUtvqNFbuqkjesPZErutuwHo/xRi5yK2XqRH+8FuEbWyxyLV2irey7LmVhtYNrtQvZ
brxcNjP+5twsubLFj9w/tOz1T71jhPmCwWefHiPkU/g3776jYQTIdQWSRNMbR/U6eTRV5OxUllP9
iS0bQCv13a10em3XXoiHNB4+3MnVdInhuhTktZ4np1sUyham5e549KuFvYGcbvGFFycoVGyiAZWP
3TMIwB8+e5WpTBm9YmPZLkPJEIslk4cOdyxLu2+dB+vHWunm1bmM6ho+Tb4uylN/XkNjJ2f1O9K6
SdmwWdRNkk0SLV8LN9X4kSSpD/gycAyICCFsSZJ+E7gXeEkI8TM38/tvBZolJWK1w3E146y+8NwW
grJh8+TF+WWXVXhL2GeyZcqmQ39biJJhL7tQVBXZ2cksAjg9kOCj9wzWNtapUIKhZLh1kW1SbKTo
EeDJ87O8MJbmYFcEvyKDgKxu8fcX5vGpMqoso5s24+kyIZ9Ce9jHtcUSp0KJhgfXalHGZo+o7jb8
7fk5OqN+HhxJLntdkiT+/fuPops2v/OtKzx9OcUP3tlHyKcykdG5PF/k8nyRa4slJEniSE+Uj58Z
5BP3HUBVmo8w9K2Ic5Hz0zkQMJHWt1WGtuL0WsuQaVTgfS1d4rmxNH5Nru2F6hhWfkajv6/X86ok
1ZxYE2mdx4N9q6a4rNyHArhzINGq29yl2GlnUv33n5vO8d0rKQKKzHzBIKDKhP0qf/XyFIbtYjku
B9qDHO6KckdPjON98Q1/x36U0UbPv8wZtFDki69M0hb2YVgux3pjABQNm5lMhbLhEAmoDaM8Y6nS
mjpioWDwzYtz9MSDBDWZTz18iKBfbdq1uNmRnzTwCPBFAEmS7sYzgh6WJOn/liTpjBDi+Zs8hpuK
G4Vht7oZ13NQ1h9opuVyZridoWT4ulSjak7nxKLO35yfZSqjM7pQ4lh/jDfnCiiSTFfMz2BbqEZj
WPXof2t2nsmMjiLLyIDEWxeKtG4ueXC88RUapLa1vDXNh2q0bjpXpmjYjHRErlNq56ZzPH05hV+T
yekmhYrDy+MZFooVgqOLnOyPc+dAgplsGdMWdEY1ihUbJAhpChdm8nRFfTwf8ddkshFWykezR1R3
GyzH5R/eWOD9J3sbRnVkWeI/ffgkZ4bb+e2/v8yvfOUCAJoiMZwMc7Q3yvtO9OAKeGZ0kf/wV+f4
2/Nz/Lcfu4eQr7kSCKo6+exUFgSMdEa2XYY26/Ray6j3CrwnCWgKEb/K6YEET16cRzdsri6WuGeo
HYHg3FSOF8YzBFQZRZZq+h64zsP71OUFihWb8zM5jvXFqVgOAVVhpDPCaKrIM6Mp+hJB2oLrS0mN
Blp6fDdip51JE4s6v//dMTK6RcinULFtRhdKLJYMHFcw/a0yiZBGpmwgIxNQZQYSIR66rWPdUdud
fsbqGNZDBHUzDLTVnr895Bk6L1xNk69YdMcDxPwaX3tzhn94Y4HFkoEAeuMB7uiJ8dDhjuscLg8f
7lw1+lvVEUhg2gK/JlM2XRZ1k4f712e07gRu6qklhKgAlbo0iQeAv1v6+UngQaBpjZ/1Cmn9xa1R
LUSxYlOxXT58up/BZGjd37/yMFwZZvzKa9OexZ6tkAhrDLeHeXJsjnzFpjvmX5ZqdG2xxLffXOAb
F+YpViyuLpZYKBosFAwqpk3FcWkL+RhdKOG4nocwFtRQJImxxSIC7xKR1k0QEPKrzOUNzk3niARU
NEWiULEwbZd4UENdWvP97olZD6reXQRrGgjr+Zz1zHXV6Pn2mwu8OVegZNhYrkvZcuiM+CkbNl9/
bYbXp3NcmMlxbVHHcgQV08EWAgTEAj4CPpmoX+Xr52ZIFU3Kts1UpkLEr9KXCCIEdET9PHq0G1sI
ri2WiOraumrImiWiulfwwtUMBcPm+490rfoeSZL4yN0DfOTuAdIlk4rl0BX1XxfdEULw/z0/wc99
8TX+zRde5b/+o7ubLhUuHtI41Z9gIq1vmwytlNfN1ESuFWH54itTXJor0hby0RHx8YUXJ1gomqiy
xFiqREY3CWgKl2YLFAybeFAjX7Zq+j6oKXzr4jxdUT/RkMY335hjJlemIxLAdiG8ZKRWLJfRVJFX
xrO8dC2Dpsrc3h3lngNtRPwqqixxdirLcHv4lu/D1nmxOrYyNzfLmVQ9S4qGTcSv0hby1SKMF2bz
nJ/KEvJpvDyR5tnRNI4QaLJMUJOxhEup4izdHWwE0Bbykyub+FWZka7IhtJVd9phth7ja7X3rLa2
G1nztG5SNGzCPpWiYdeeP1+2GEsVeWOmQMivkNVNdMPBcFw0VcJyXVRZRpNl5nJl/vjZa8RCGtPZ
Mo8c6WYuX+GJ58dxhcCyXe4fSdITC9S+t6ojciWLdMng4oxLUFNIbsAhVB+d3oh8b2VP3GqXXQIY
Xfo5Bxy/xd+/bmzGi9CoFqJYsZnI6GR0iyeeH+e9J3rWlTYwsajzue+OMp2tEA9qXupQ2ksdgrcE
fSJdZj5fYTxTomzaSMBIR5i5fMXz6sWDNa/gm3MFogGN8bRO2bRRJQkZiTdTJWRJMJkuE/QpCCF4
4vlxPnHmAA8f7uTcTI5S2eG7oykqtoMiSTxzJYWmyLw5l+f0gTaCmsIHTvXx8kSGREjjqcsLPEzn
mnVCa83jfjkAc7rFn784wSuTWSTg1ECCjy2lDG70c6rGcMVy+PDpgYaGdvV9c3mD1yaz+FWZ+YKJ
X4VrKZ0TvXH++1NXSBUtrqaKxAIqM9kyhi1wBKhL9+CK5dDfHiIR9taoZDhMZnQ0WUKSIBHyMdgW
IhpQmStUUGSZ56+m8dV5ktaSjVZh6/bi2dFFZAkeuq1jXe9vD69+cEmSxMfvO0C2bPGrX7vIX70y
zYfu6t+uoW4btlOGVnNE1TujVtYwNPq+tUg/AqpM2KcwmS3huC7xkOd8mkjrlE2X7qjM+KJOquh5
y/2qTCzoY6QjzBuzeb7y2gxz+QpCCKIBjdt7ohQNm86IH78qkyoZSMCjR7pZ1E3SRZOCYWNaXjTX
WboEnZ/OIcsyqgyffuzIpudwo3q8GTz3zYqtzs3NMGKrMv/C1TTTuTJtQe8zj/XFODuZ5fXpPLph
gwAhAAkkCfAJrLKLKwlcwBUCWZLwKTJHuqMENIV3H+vh/pHkjj/jRrAe46vRe+D6WhqgRkLgq0t3
XWs+VEni/HQO2/XO6YcOdXB2IstfvjLJs1fSlEyLRFBj4EAbdw0m0BTp/2fvzYPluu47v8+5e+/9
djw87ARBElwEiRIlSqIky7JnFDlx5LHjuMqOKp6KJ+VK/k1l8m+mKpX/klSWiafiuMZToyp7JrI9
mtiKZFnWQkmkFpAEAZAgHra3v967737vOfnjdDceHh6A90BspPirIgtodPe9fe+555zf7/dd+Pb5
DfphhlSKJJMa3qrgyFQR0xScvtamMYhpDhJSqfDjjHc2B3zqsWnOrHTH5/TS8RkurA94fK6Ca5kc
nirQDJKxctztkrtRg2DUnd5e6L9V7PRM7CUedPLTBarDP1eBzvY3CCH+APgDgEOHDt2/E7nDxHw3
VYTtn0FBlEnd6rVNFjcHfPv8+h3lnnUVcImVdkRjEHGtFbCv5jFddseJkyUEq52QjX7EbNWl4trM
1VzKrs16L+L0tQ4/vdrGtQyeO1BnquSw3AmoFVwEiv21Ap0wIc0l3dDg4GSBM6tdhIDlToQhusTp
JZQCxzbYGMTkSrFQL5DkipmKx/HZMqevdSg5Fn6c0QlT6gWHuYrHubUu335LEmfyJjjVne7LuxnQ
77VoBQn9OKU6ggxG2V1VrLYmw+0g0Yn20/vGnaTReO+HKYPhBi1Oc95c7RImGa5lUbBt/uL0Epu9
hFaYsNaNWOmARE8UQuhJdarssTBR4Nee3c/nnpjle+9sEiYZU2WHJ2YrvLzY5OKmT8E2OTZTwjQM
wiTHta4TKi+3dsYPb40PoJL3Lt5c6fLYTJmye++m/P/ipUo8fs8AACAASURBVGP8zZk1/tm/P8c/
eHofBefh+wTtJNqy0xjaq8DH1q7MwcnCTdzJ1U7I60sdKp7NpcaAF45M8lyxvuP57CQ4YAlBJ0xp
DGKEEpRskysNn36csdIN8UyThq+TlSDJ8OOMkmdRsEzOrfW4tOkT55KKZxNnOQLwbIOFWgXTNDg2
VeLtjQGOZfD1N1b5tWfnKXkWb2/0STNJ0bU4OlXm51fbBKnkmf0VLjd9Lrd8Xnp8Zs9FK7g1OfpW
8bAr949abL2eW6v6m4N4zLmFnblf2+N+FJNGcHfbMlBKcLXtYyAIkpxzq10GibzxA0r/Z2USEMOE
B8quxUK9yFTZYWGiyEzF3XPic79+415ie/JlCXGTd91OCdr2cX+l6XNmpct6L2axMeALT87tSiU3
U4pj02WUgDjJx5DZn1/pMIhT+lFGP8woOBa/+4kj7K8X8OMMMUT3rHdiOkGMn0oGUcps1aM9SIhy
yXI7ZKbs4FgGcZoTZjlEjM8pU4r5moefZCy3Q86t9XBM3aX+1ZP7blnoHP32kmeNu9O5Une13x4l
kruNB538/BD4J8CfAV8A/mT7G5RSfwT8EcBHP/pRdT9OYjdVlLupImz/zOGpEl8+5fC100v4sR4w
cxWPtSFcbL5euKHyN3pIWkN4w2zVw48zbEvyq0/tI1VyrLw2qkJmUlFxbVa6AfWS/g2uZTBXdUly
ffkWN/v8+9f6XG0FJPmAimfxD5+ep1KwKDgWQTrg/HqPLJMEIscwJJ5jcHa1R9OP8WyT5XaIAAZR
znzNpeiYSKmwDFhuB7y52mOh7rHZj5EKNgexrgQJxnCq3VzDdzug32sxWXSouDYXN30EcHS6dFcV
q8miQ5TmLHUC8lzzAmxTj4OXjs/wF68ts9mPcUzBlWZAP0q53AgIYg3JDJOEyy0ftyvY6Mf0ovyG
75eAAVQ8m7JrUvYsneADX3p2Pz++1OTcap9Xr7Ro9GOe3V9jYxDRizI+8/gMi5sDojQfPxtHJkv3
FJL0Qdw+3lju8snHdtf12W2YhuCffvFJfvuPfsRXX7nK73/66D39/t3E3Wy491pFH3VlJoo27SBh
puKMCcFbBWH8OEMpSKVkEGW8vtS5SahAz91aBfHttT4fOzLJRNHhe+9sIpXeHf7KyX2s9WJqaTbm
SHiWiWUKPMvAMCBIBK5psN6PWWoFbPZD0lTiJxm2qSHLAkE/zsil5EpzwOKmjyGg5Fps9CL21TxO
zFZIc4nnmCw2fCquRdE2eHu9j1TyttCV2137kQLkXhKZ3ay5vyiogO1j9NSBOmdXuoSpZK2rC6tv
rfURsOvOwO2OdTfXdLLoUPEsmv2IK60+SgosQ7DWCfCzm99vAJYlODBRxE8yolQicsFc1eM/+dhB
wjTfU6F0p3iYBbOtyddWHvZ2hbWdErSt4x7B8NnxeGO5w7nVHoemijc9D9vh8tZQ5CWTEKUZJ2ar
tIOEXqzpCEXHRAFhIvk3P1viNz9yQP97lHFmqUc/SIgk2AKqRZt+mLIUpRRskyDJ2OgpJob8odPX
OhQsk2f215gs6vnQNISG4QYp7Sih7josd0MWJgq3nAtGz/wgyrAM8BMNn7yb/fZe9xD3W+3NBv4a
+BDwDeC/Q3OAvgecVkq9cj+Pf6vYTYVpr1WE0QSyk4zgV148OuZY/HCxSZJLDRc7OIFhiJsmMEsI
olQyXXaoeDVMBKu9CNvU3Z7vX2hwcXOA5xicmC1zdKaE6xgIBG8sdzk6hL1JoBekrHZDgjgnTHMy
qciDhPPrPcI4xzBgruqipKRgmghDYAhFraAXetc28eOcTEqmii6dMCVMLUwEU2WHF45M8Z23N1jt
RvTDFImiXnSoFxw2BzFVz6Ljp/zGqQO7EoN42K3r+x07VYJ/8/mDfOzo5F1xfrZ+35HJEn/2k2sI
tCzxx49Osd6L+c5bG3zz7BpKwWYvouza5EoyiBOSXLe5pYLVdohC/3lrGEDBNdhXKfArJ2fZ6MdI
CVdbIV87vcyXTy1wfq2HaQjiTFf7fn6tzeHJEjMVl9VeSNmz+OLx+RuejS8VPoC1PYjY6Ees92Ke
3l+985v3GB8/NsULRyf5F99b5D978fADVX+7leT6reb1rd3PO83/W5+ryaJD2bOYKbtYpuALT85R
K15XP6q6NotJnzDNyRXYhuDlxQYrnZCrLZ8vPbufUCp+uNjg9aXOWAVxxNcxDfAsk6f2VbnS8Dm/
3iOKJT9fatMPM/wko+ZqGFvZtVhs+Lr6mgqEMGj5CUkOzx6oIYAn9lX4yOFJyq5FL0o5fa2juX1J
Rp5LelGGZXbZ6Mf8o48cYL0X0QlThFDUSw5fefwof3N2lYmCw+mlDgsTxZuu43ZIzjP7azehHvY6
j99pzf1FQgVs36M0g4ST8zVdcVcwXXbZ6McIoXhmqn7HhOFWsM07ySDfSn1wvRux1o24tNlncXNA
kkiSYb1M7ngGgABLKMquqbk/pknZNfjMiVk+dLDOmeXue37dH63pW5XRFhsDXfCwjTEk/eh06YbP
bJeUfuVSi1eXWjjDYsdLx2cAuNTwCeOMq+2AN5a7XG0FY7j8yfkqEwWHgmuhlKITJmwMIp6cq7Ls
BrT8hMYgoR3E/OCdBjXPJohzlFKYAjAEptLw9ihOkbmiH+X4ca6h7AUHyzSwLYOOn+Abgr85u8a5
tZ7ewxyZZKkVcKXp0wtTXlvu8tR8hapn0/KTHe/t1t/+xWfm98T5ebedvvsteJCiOzxb48f385i7
id1usHdbRbhTJbFW1HLPCMilpOzYnF7qUPIsNnp6AjtaLnOpOeDN5S6XW/74Qfm1Z/fz7be00lpz
kDCIMi5s9OkEKXEm8eOMz52Y5e/Ob9D0YwZRxudOzAx/k+JPXr7MIErxY4kSWr3JNA0urPXJFHiW
QZpLolRiGpqrcWSqxOXGgDCRwwpNTporNv0YmSumyy4vLzY4vdRhvReTyhwpFd0ADAEr7ZBUKqRU
ONNl6sO26G6u2cNuXd/PuN1vHnG5dvs926tL3SDl79/aIM10N04I+PZb60xXXN5IJevdiCDOibKc
xiBBqjEKYRzbk57x60CSSuoFE9sw6IQpm/2Yk/NVPMsYjleTimdhGQbPHqgzXXb49ecWeHqhdst7
+QGs7cHEmys9AJ65T8o7//jTR/knf/pTvvPWJl84OXdfjrFT7AQzHs3rSSrph9fdyrc+e0kqUXBL
o8+dpKUfmy7zk8st5mreOCGYLDokqeRbl9bx44yCbfLisWmWOwE/vdJiuR3SCVNag8scmipxvuSw
2o2Yq3qsdEIyKZmveqz3Iy43fW0nkOasdmJ+dqVFkstxB7YX5SQy56XHterSIEwBAVIRJBkl1ySX
UCtaWKbBej/iaktL2ZZci95QqUkBMpPEaU5zEHNutUfRtagXbY5NawW4q+2A+WpBK8JtDm6yNRhx
BrdCchA3JjuHp0p3ZW9wuznhFwkVsH2PMuqUE+mO69VWQMWzKNjWHfcxI9jmmeWehiLPlsaJ0m1l
kPdVObfWI1eKTpDy+EyZN1a6/Pxah7dXeyS5JL1lpnNzlB2TSsHm80/tI0wy1noxh6c0zO3wZInD
k+/eDuNR6QxuvX9RmqMUY352O1jk9z957AZO7vZx/8LRSRqDmOmSi59mvLnSZbkT0vBj/u78BvWC
TStIeWquQrlgs9mP+dvOOj+92kYBUyWHmufQD3NyV1Ev2vSjjDTXMuKbfsS/fuUKfqK71ShFLhWm
IcilIlUQD0Uo8uFGYX2g51jDECipME2D6YpLO0j42NGh4qQBCMFM2SWXioJjcnCiyNP7788+4N18
9tHSKH1AsVOmvR2buZfYLVb58GSJ2YrH5iAmSjMa/ZiKZxGlOd86v45ASwWOFqLVXsjVdsBb630A
1noRzx2soxQUHJMDEwX2TxQ4v94jznLqRYdBlHGlGXBoqshGN9IqKkqQo8clKEqOiSEE1pCTFKc5
loAwlcT9SHeNcoVtG5hCK3aBJibGSvN4ojSnH2e0g4RcStJc4ZoGwhBYpgAUFc8iVZL1fkgYZ+Nr
DPD6codBlO0oQ/t+3RTfC0z7qIIXpjnr3Yh91QIfOTzBxfUBCoFj66pMxbMQCAwleHutx0Y/QaHH
wB7WKwBMoOZZTFU8DNPgV56a45tn15kYVsRHC/Ox6TJ+nPHsgRozZZenF2rv23v5Xoo3l7sA96Xz
A/D5J2eZqbh89ZWrDzT52QlmfHiqNE5eXlvujEm525+9Dx2oU/HsG+b80cZ+ox+xuOnzy8ON/Zsr
Xf6vH1xivRtxpRkQHci4slDjuQN1PnZkkl6Usa/q8vJik36cUbAt0lyCEEyXXWxT0B12eZbbAc1B
TNkxmSi7fOftDXKpyJWiF6R0wxTXNogyiW1eV9ATQGOQ8oN3GiRSkeSSKNBcnX6UcXyuys+uNNno
G1xYH/CffuwgP7nc4lrbxzYMZiseE0UX24TNQUq96DBX8zh1qM7T8zW+987m2Bfp6HSZSw0NUz2/
1qfpJ7y11ue3nj/IlabPei9mvupxqTHgUnPAbMW77eZ1lKS823ngYaMCHuTGeqci4JcKWrU1ySWZ
1Ipav3pyH+0wubGKtS2uNH3WOgEr7YBcwSDRSAzY4om1OeBy0weliFPJG0sdzq702BhE1FybC5sD
/vL0MlJKGoNkT0kP6DWkWrA5PFXEtQymykV+54XDN1X638113a3oyP2MrWNkKwTua6eX2OhFdMIE
BHzt9BJfefHojp3pyaKDJQQXNvq8rfo0BzEdP+Fiw2ezG7Hej3Etk1xKLrd85msFagUbIQye2Feh
F2WsdUNWuyEl16YfZswMebrNIMU0dKEzyiUlxyZIMqoFi06YESYSCeQ3ot4R6GQhyEHlCksAQrHZ
j5kqu6D0vfutjxzkWisgyiSNfszBiSLfe2eTLz27/4Zu162u2YO8X++J5OduLs6dPjPalN0LhZm9
dJJeOj7D104va7KiEPyDk/toBwlprsYqbVu5EVXP1rA4y0QI8KOMDx+e0ARy2+DS5oAoztkcxByd
LDFVdnh8rswXntrHX51ewhDX+RqzZQchDGYrNq5lstwJCeKUJGc4mEEo3b3JBCS5wlAKA4NBFCHQ
VYCNfoxlGqx1/esqYEOcqi0MPNuk4yckWUqUSC67Nv/r313g1KEJkkwSbDl30JWEfnS9Svsoxr14
QHc7Tm4l/Tiq4J2+1mFx08c2Dd5c7dGNUtY6AWvdkCjLkUpyZLpI20/phgnNIB2vjXdaswx26AgB
QZpr/pmpz+lTx6dv8JMaQdh+48MH9ixX+UHc3ziz3OPodGnsw3WvwzYNfuv5A/zzv7+oBVgq3p0/
dA/iVl3iSmDjbnMrvylR2kFxc5QgzVU8Xr/WHWPte1GKUlpdbakTYluChUutMafRHvLoZsouR6eK
5ErRjzMGUQMhDCaKFgqNa09yRS/KkFLxyyfnubjRByG0WISCq+2AkjSJ0xy5ZQMygqPGmcIwBAXX
JMsVB+oFEIKrzT7tIKPq2XSCiO9e2KTpJ4RJxmtLmv8XxJkuggm9gf3U8Wmenq+RKcWpA3VevaJ/
05GpEgXHRMqhZ9sgZrUbcnK+yrnVHouNAZcaA07MVfjMiRkmCs74Go82ONc9wjapF51dKzjdzf2+
X/FuBRzebWwvHNWKNpVAvzYa2+dWe/zwUoOJgsN0xb3hvEbwxG+cXeOVy206QcJkyaFkW3z7rXU+
/8QcB6eKvHR8hq++epVLjQEXNgbkw0VishiSK8XFVK/ZCkW+y26PCYyGrwDqRYtTh+r84WeP3zfj
yytNf0fRkfsZdxojo+fhC0/OcaURkEjJbMXDs80bip9b96FxKmn5MZYwyFBMlV2KjsW55S79OCOT
iqutgKpnc3CyyFzF49eeneflxSYrnZA004lxnEo2+/F47upFGTXXwrENZsoOb63pAkcmJSiTmYrD
cjtC7nB/FdzA48oVGFLpPaIQTAx//8JEkd//1DEurPfphClP76/d1D3efv0elsLjI5/83E02v5cL
ei+q8XuZlDOlmChdn7wypTg8VWKu6tKL0zE3YlTNmSg6PHegTmMQU3Un+cyJGQ5OFGkHCSvdEKGg
6Ggc+NogYqLg0A5SAJ4/PMkT+ypsdCOSPNf8DnKEcEilouo5dIKUXEmk1IurISDNdUIDCkNAO0yw
TA2PA0UvTEDpgZ+r6xtlpRRxllF0dI/Jtkxcx8A0DIJUEqeSH1zcxBImR2dKHJ0pc2iqyHI75LWl
DmeWuw908O827tUDupuO41bJ6rMrXU7O1zAMwQtHJxlEmd7A5JI4y5mruCRZzjffXEUAzUGKY0GQ
wY8X2ziWAUoS7UA+vVUYgGODbVlDLLCgYJscn6twcr7KZ07M3FQxH/22R+2+fRA63lju8uFD93cT
8OunFvjfv3ORb5xZ4/dePHJfj7U1dhp3OxUZdjNHTw7JvK8udbAtA8MYmvtFKc1BzCDS+PiXjs8i
pdIcCsvktWsdojQHAa8sNlmYKGIa8F9+9jieYzJRcPjG2TVeXWzhWIJ91QKNfsQ3z60xVXYIk+vd
oppnY1sGcxUXyzTxbMHlpk+WQblgMVWyCVKJLQUJUm92cjkmMiul8GyTQ5NFcql4Y7lLL8xwLbBN
EyUV8xOloVeXPe4iv3atQ5pJ/CRntRfxzEKNlh9reHUqqXgWry916IQJnzw2xXo/4jMnZjg8WdqR
h/PnP73GyxcbrHUjDk8VeXK+ek/U2x7UPLNXPtndHmOvnGJLiPHY7gQp33pznaafUHJNPvXY9LjL
dqXl8923N2n0Y95a7+u1AMFSO2Ctp1/78WKTf/rFk2RKQ9S7YUYuIUz0grGaZEyUHLpRQpio2zWX
xlErGMyUPGaqLpc3B3QiDQd9Yr7KH372OCfvp+ml2LIXGf79fsZNY2T/zmOkG6ScXupwaLpIdylh
uuzcROpvBZrSUPIsGoMYgNmqx3ovIgdafkLRNYlziSElSkkmixa1gsNyJ+RqK+CTx6Y4UC9gCMF3
39ngr36+TDaEsn3q+DTvrPcITUHZtVjuREwUHRp+jIlWV0sk48R3a9iGVnmNs+vFUwG4tsmnjk+P
aQ0j245+nGIZBiXXYnFzwNnVLgi41gpu2js9TIXHXSc/QogppVTzfp7MTnE32fxeLui9aqXvdlIe
t5kbAzpBymonvEkBBLhBKeSTx6b41vl1JksO51Z7nF3t4doG3SDl/Fp/7Nh+bKrEsZkKUineXOny
kyttnl2o80rSwrFNulFKmklyBRNFi5ILG34EiXaANxUcmS6x1g0IEonUpr1IpZgsuVxtBhgmBHGu
ia5DeFuqER4UXBvbgLmKS9G2aAcpQZITpRmWMnj54ia9MKNgKzb6ETMVh/21ApuD+JGWN71XD+iO
BrhxRidI+fTx6TEuNleKkqOlH7thyk+vtri42acbpEh0cuqYJoaAtX6Mn2QYwiADsmGikyuQUjJs
6N1x4TIA1xI4psmxmSIKOD5bHpss2qbeDE4UHA5OFekG6buCin4QDybafsJyJ+T3Xjx8X4/zxL4K
j8+W+frrqw80+dkpbpXo3GmOrhVtXWSIU45OlenFKZlSFFyLzz81R5RI3lrXXIdcCTzbpORaZFIL
BQzijFhKZqounSBlquJyZLLE104v41oGR2ZKGAasdAPiTJJ2JPWCjSFMJooOv3pyHz++1GSx4XNk
usSVhk+lYDEdS1KZk0vdNTo0WeTIdJlelHBhTfM/M6UwDUiynP31Ap95fJbV7lXyXGIKiHOGhS+D
OJWkec6PFpsoARtdDXV2bIPpkku9aDNf8xjEGY/NlNnsx4RpzssXtYjDdMXlo4e1QMuVbZL1V1o+
3TBlqRUgFPTCjHc2BiS5HEOtHnTcTdf+dnyy3e4Tbnfc2xXUdhIZGG0qK67Nr57cR6YU37+wySDW
MMmWn/DOxoCfXW5rgYRBzMsXGxyb1vcvHip6RimYWUY3TGn5WuHzuYU6gzhloxeRphlJpnAtQa4U
QZqTZuqGNeRW0GnPEkOuWIX/6peO0wlTLm70Kbo2LxyZ3JPB+93E4ckSpw7U6ccpx6Y1DPN+xk1j
ROw8Rkbve3q+RsE2eWq+elMXxBKCs6vao0dKxcn9VSZLDjMVhy88Oaev5Waflp9iCmOo/mgQZ5KV
bsh3LzRY70c8t1BjpuJyoF5ipuoxXXK5sDHgr8+skeU5qVT0Q4sglbiWQZpJfUx04iMY03Y0B3h4
4+MczGE7TwGVgslk0cVPUg5MFLCE4IeLDV690mKq5NKLUn7nY4fIld5E3krF72FCWffS+fmREOI0
8H8Df62U2k0h4N3HXWTze7mgD7qVPoK+ffUV3WZebAw4Nl3idz52eNwi3aoUMlJ6mSg5zFcLnFnp
oJTgyFSN5iDm4GSRQ5NFltsBb2/06cdafGCtG7HRj8mlJExywjQjziRSKmarLiXH4peemOVf/vAy
K50QKRWZ0sT2INWdoBH8aRBl+FGmJzwJUujEKM8UOdcrA4aAMNVExk6YgBI4lsa9FxyTlW7IE3MV
giRnf93jy6cOUC3YnFl5tFVe7sUDutXMqxOmPDZT5lor4J2NAYsNnzPLbT57Yo4Xj03R9pPhdU/5
y9eWCeKM1U7ETNnlhWNTnFvr8anHJolyScGx+NFik3g7SBc9YcGI67VzFEwoehZKgWUO/Xgck08f
n+EjBydAwCDOOL2Dee0g0jLZXz61cN8Xtg/i7mIsdrD/PlZch/Gl5+b5n//2Ahu9iNnqg4G+3Sr2
0iHYutkc8TJ7cXrDs152LJRK+cihifFzcW61R6MfD8VeEpJcYiHY7MWYBkRJzldfvcI7Gz7dMKFe
cJgquzQGMa5n0/RjXlvqcHCiiBBQ9iz+0UcO8Oc/uwZKF52OTpcwRZeyZ7HRDWn5KUeny6RS4hra
V6mfZGS5noMzpR0l/+3PlnBNA0MY2JaCTFG0LSoFndis92J6QUI/yfBjqVX6FMOCleZnrnRCklxy
eKoACs6s9BjEmheQZE1WOxFF22CuXgD02vHKpRZBnPGTyy3CTENq9tdLHBqiFR70PLHbrv32hGMn
PtlE0bnJn+luj7uTt0slsG8QsUlSyceOTLLWi3j54iaTRZd+0mdhosChiSLfOrfOlVZAlOTUCw7n
1nqs9/W6X3EtGn4C+EwUbZJM0h7ECCSpgs1+jG0KLjd8klSy2dNy7pZhI2WCaxtkUq/pcGOy45iC
JFfj13RPCRzL4OBkiSf2VSi4FicXanzy+L2V179d1IpaOfVBiSbsBKXdife29X1l19oR/pUpxdHp
8jjZ+MzjM1QKNyIsBnHG//add4iSHFAIAc1+RDZUD275CUGS8eEDdR6fq2CZBr04w7EEVg6247Da
j+mGGQIN100yPR5GodBwRUcMjWkVWJZ+0TIEYrhPVFKRSsmJ2QqnDtT52ull2n7MSjvEsQz8WFdh
n1uo39bS4mEKXO0l+TmBVm77feB/EUL8GfAnSqm378uZDeNusvm9XtD70Uq/3QOUKYVtGZQ9SxNo
U8nXTi/zlReP7DjxbvVEqbj2WKmo4tqUXZAoPMfi2YU60xWXa80AhNZkP7fSox9rr4g4V3iOgR/l
nDo4wUcPT/LzKy2uNgOG8vJ0owTbEGSCccVHyuv43VxBlIFlSYShHxLbFLiWRZBkRKnCjxMkUPNM
0jzncsPnmYUavTBjf03w2KxO9kYL4aOu7rbb8XS7ez5qa7+zOeDcao9XLzVZ7oQMhpg0zzK4uNln
qR1SL9rEmcQ0BP0wI8lyBlFGL8qwTYNa0ebjx6a53PRJ0gGTJZt2kJLlcseK3IjzBXoxG/EHFJBI
/b+pokW96GIIwW9/9BBPzFX41vkNPEuTr+tFZ1y9udzyGUQZ19oB7aEh73by5gfxaMSZFS128MzC
/RE72Bpfenae/+lbF/jrM2t85ZNH7vvx7kXstEnd/qx3h5y5KJWkWcZPh4WAIM2JM8nT+6u8udLj
qX1Vyo7FkZkSq92IMys9Fhs+phD4cc6+mknFsBCG0NVT18I0IJOKTpDw/76xgmOZoBSGMHh6fw0p
FQrFSjvgctNHKsFfvrbEXMUbFx+MofmwZxmYhkmSS1qDhMmyQ72sRXBqdZvjs2X8JGeq5ODYxtDC
QCGVZLbs0PJTDGCzpyXwn91f59xqlyNTZa62fNZ6EWmuMMh4Z72vuztC8ZkTc/zeicOg4LXlDgXH
ZWGiyEK9wLm1HlIqGn7Cq5dbe5byf7exm679rRKVW6ExrrUCvlS4szfU7Y67dY2PUzmWDV9tR2Pe
2Q/eabDUClhs+mz2Y9pBRpzqjt3XBysstUOmSg6recSByQJNPyEaKnuuKoltGGxKbTehBEil1wcB
mAL6Ucq//ek1VrsRRcein+SoXGr4U6Jl27NcjDkgAj3Oyp7FbMXDMgTX2gEIxmbqI27yo1jE3E3s
BeJ+uw7z6Lu2ix/cav+gDVG1R49lwG98+MBNhYKPH53i9DXt+1Mr2Gz0Ii5s9Gn7GQo5NAdNWWlH
fDpK+dSxKTb6CfZcmZcvNtkcxLqDaWjFwGh4j3cKJQRC6EQnzIYeTcNOUMmGVCqUhP/vzXUubg7Y
6CcUHd3B3uhFOhlf7fH0/todf/vDgszvOvkZdnq+CXxTCPFLwL8C/lAI8Rrw3yqlfng/TvBus/mH
yUG40wM0WRyag/kJYZpzZLqEZxnjCXLUHRpVmQ5OFW/wROmF6fjfqgV7jAX+xtk1NnoxlimQSmAb
km6oTUo3ejEFx2S67PLUfIXPPq71/A3DYP9kAST4cQoYZK6k4cdgXMf5bsWCCqAfXZ9EBTBRNAnT
HLkFYJXmOUIIhNBme2kmSbKcomNRLby3+CJ3Osdb+SWMRAssIYgyycaw4taLMvpxTpTmGIZgqaWT
4JmKw6HJEiXHZL0fkytFOjSTnSpZeI7BdNnh51fbOvxp0AAAIABJREFUvL3eZ6rsICUYhoFtSZLs
Roiba0Kag2vp9yilXZSDNMM2DHIlcWwThcFkycFzLC5saOLr1VbARNFmpuzeZFD686sd2kHKRNG5
ibz5QTw68cZylwMTBeoPYDPy+FyFx2ZKfPPs+iOX/NyqMLHTJvXodOmm90ip8OOMpU6AaAp+/UML
NAcxtmkwW3G52g45Pqchx7NVjyDNqboaqj1ZckhkzkKtwOWmz8GJImu9iMOTZSRaUGa24nJx02ej
H1FyLKYrLv/hQo0fvNMkTHM6YYohDPZVNcZ/tuoRpz5hpijYJmEicSyTgmNpv608pxdlnJyvajiT
VPhxzpNzFZ6cr3B2tcfbGwMGccpcxaUXZXiOSdHVG5hr7YAzS10yKVls+Dy9v8pCzaMTZrQGsYb+
SV3Eu9Tojz3Kzqx0GUQZBcdgX93Dc0ykkjy1r7Yrl/p7Hbvp2t8qUdk6529HY9zpd9zuuNs9AftR
yo8Wm0SJ5O31HnEm+U5/HdCJcZrlTJcculGKYxp0fA0rXO2ElD2TJJO0fA1bc0wDQ4AhDBzbxLNN
qp7FmZUeuby+NiQS0lAXJhWCqqdbPIlS2KbAMgwM0wClKLiCOMkouhbTZYff+8QRljoRV1sBx2bK
Gg5dtJmremMkwMNYC/bKzd1pTrhd0rrT+2+1L9gurb9VHGinyJTi5P4aJcfCT7KbrEFGcepgnY6f
4NgGS+0A1zKZLBtaNGuoENmPUs6vDnhspoRjAULwiWNTvLLY1IgcdOeu4uqCdZLfDGO0hEIO1Y9M
IbAMBcJAIIly3V3Wvn45QSKZKDq0g4SFiQJVz+ap+er4ed8+nz4qsSfOD/C7wO8B68B/DfwVcAr4
c+C+2Xu/FzbHW+NOVZ9a0ea3nj/Iyfkq33+nQb1gU/auE+C6QbpjlWlUhdz6byPzK9BJSJxlXG2G
PLmvwrVmwlTFZbLg8rOszUTRZrLsMFFytSY7YBsGq52QTCpUrs1Nk1zhGILDM2WudQLi5MZHY/Q3
BWQKskxpHpBlEiS6EimV9gyar7qYpslqL6IfZxiGdg+/0vTvuxLLg4wb/BIaA7766lVyqe/Rk/sq
OukxFJ5l4CPoRSlIiWUIFIpEStZ7IYM4pR0kHJkq4UcZBcsgTHOSDK42Neyl4ScoqWgMEjphOmyV
S4S6npCqUWdH87ARwqDmWZw6OEGmtESlH2es9SJsQ2BbBlIo5qrusNrMeEKbqXh8+dTCDSpuXz61
cINp3nu10vd+jzeXuw8E8jaKLzw1xx//4BL9KL1v6nJ7jdttinazOZ4sOkSZpB2kzJQ8ulHKYsOn
6ukuvJ8M3cmjjLJnMVV0+PnVDmGc89yBOi8cnWSioCFTrqM5N++s9/jokSmOTJc4fbWDVBqqnGS6
uxTEGd94c403lroaumJoInpjoFU1B7H2XhPorpJnm1q6+mANIQR+mLPcDegMZbSjNCfLc86uaZnk
QZLRHMT4YU4r1zBbUwhafkLHT5FKMUhyap6NaxrkUlF0LZJckRUdDKFoDhJcS2Cbpu4uFW82LBzB
uLbDCB9U7FboYjdjYC/Q51sdd6ci2SDKePVSi16U0o8yXnxsil6offxWuyHtIOVA3aM5SLEMWOlq
w/OCo72dbCFoDiKU0p5PlqU5IaCIM8lPrnYI4kzD2LiOBFBAP9JG591Ij0ulRhxehWNoUQzTEMzM
lTk6XeZ3P36Ykwu1m0xTt5ujPozYCzf3VnPCre7zXhOr0blUXZtvXVqnF2XMVd1bfm5yqIiYK3XT
ejpS7fvuhU3OrHR1F7nq4loGfqJVWA0FBoo4kRgmKJXzo8UGSZaP5at1l1fgWoI4V3SjbLxfKDqC
JB3xBvX4KLk2pglhmpPlgizPcUyDghA4lonnGLiWtlzx4ww/NvjlJ+dY70cP7XnfS+wF9vZD4E+B
/1gptbTl9Z8IIf75vT2t93bsZqKsFW0+uYXkvtvqw/ZN9tdOLzNRsmn7KZ5tcHCixKVGQCdMWO9F
bPox72z4hHGCEODZJh8/MkmtaHOtGdAOYzzTpJ+lpEPpwk6oSe6LTZ96wSbNUuxckY9IcDtEJ0iZ
q7ukmcCxLA5OFvjN5w9wqRFwqeGTBgm2aZCkObZt3HcllgcdW+95J0hZ3BzoqmhT4/3fWOogFUMz
UBs/HnKwlCYXGihiqUhzrdTXLSYoFAXXpDrcvEil+VfLrYCio3X+tQpVSpQrhNIT2XTZwTRMhCEZ
xJJ8KK9ZcixePD7N5YZPlGYUHHOoDFVgrlbAtUzqBb3YaWicYqbi7MjpOThV5CsvHn2k4Yq/6NGL
Ui43A37z+QdHNP/8k7P8n99d5HsXGvwHz84/sOPeLm41n26vwN8OlrE12TeFGFdyR9//xadv3Oxv
dXPf+uz8y5cv8+ZKV5setkM+9+QscabNTqerDq1Ae3i4lsFSO6QXpfrZtwwmSy7HZ8u0h4pe1YKD
k2X0woyiYzJdcXn+8CRnV3q83uySK8WVho9U2rTQMQWrHa3g1ou0DL4pDMI0o+jYGCbYJpRcg4V6
iXNrWkY/k5K5motpGEyVHZqDmOmyi2dZVAsWBcfk+xcbHJwscnCqeMO1rRYevmn1boQudgPN2evv
2Om4W8fi2ZUuf/yDRaIs51LDZ3/do5Xl/OxyG3+ouFZxLaKhUmrbj8fwS1OA5wiCZKjMmkhsAzzH
QKEoehYdP9E8sEyT27NEjpfd0TJum4JKQSt7VjwDx7Lx45ypssM//tRRjkxpHkrZtW7oXDyK8/9e
EtTbdft2us97FT0ancul5oA0k5Rdi0Gc3fJzt0uW//yn11hqBbw17AqahsG1dsCx6Qrz9SLnVjok
qaQTJXSjHA/BpUZAkmVjJEimwBaQKsi38LVgmARIgWPrqqlhKgqOTcG1ODRZxDUF7TCl0U9wLEGt
YHPqUJ1emHLq4ATztQL/+tUrWIbBv3t9mT946bH7Jmd+L2NXyY8QwgS+rpT673f6d6XU/3hPz+o9
HnuZKHeaILcaj0WZxBJ6yuoGKf2hU/fIOdizTOarBdqDhNVOhECxuNnn7TX9oBycKDGI+timxSDK
2BQRf/rjywD8+HKL1W6soWpCkOY5/TglzxUlx8APtB/ECBeqqws7K71kQBjn1AouRdfk8GSJuWqB
VpDSC1NWuhG51A/o83NaKehR9vXZa2y956vtkGvtAENpZbxBkKIQFGyDQZoTZzmWYTBd8cjynF6U
kgxlJONc0egnCCEwBfix5vyEW+SqO0GGH2ekuU6c8mHSM1rQiq5F0bUoORbdMOFy0+dax6fsmPzg
nQZ+kjFR9Gj5Mc8dqDFV9qh4lvac2iKxfqdK3nutI/uLFmeHYgdP30952W3x/OEJagWbvz238cgk
PzttivZayb3dZm/rn0fwqBE/LlOKa82Ayy3N/Zkqu9SKDlIqemHKjy82WO1FKAX7ax5Fx+KxmQpx
lnO56VN2LaSUmCYoJE0/5lrLxzB0cuXYJhXPZLaqORglx8K2DISAjU5EP0o1x0iBwkAgyKQcdoIU
Skhyif4uTOarWu2tGya4lkGeK5RUrHdj+nFGP9KboE6UMl1yefZAFUMYXG0FfPXVq3z6+DQoOLum
FUm3+508qrGbuexuRTS2bmRX2yGXN30W1we8udrDTzIGUUqQSpyBQSrBMGBf1ePiZkCWa5+lrGiT
5BLD0OuEUjCINSfXEnpzm0pII4lrQhjpKn0mFeHQmEehxYmk1HDoKIdcKpqDlIKjIdGOaTFXdfnU
Y9O8eHzmtvftUZv/97Lvul2idKs9WZxK3ljuUvHujHQYncubK13eXhvw2lIHy4AvPn3rOXGn447U
jl3LGHr2CKbKDgI4fa3NRi8amiRrbyVLaMXWZEjU2i5mILjO3x5FBgSppFbQnSeV5SR5jpUJhKFY
bATjYu1UxeXDhyd4Zn+Nv/j5Mt+70KAXp3i2yeOzFS43fZpBwksPcM2529hV8qOUyoUQH7rfJ/N+
iu0DebcKIqP3nTpQ51vn1/Fs8yaFrU6Q8unHpzn4uHbPPbvS5bXlDocni5xb7VMr2ASJpFLQmMws
19CHJJeaeLrYYrkV8InHplmYKLDS0dKYaqj2lipoBvoR2U6Is4buwErd/BD1ogxhQJznnL7WoenH
WvnFtpituByoF6gVbdSQGDtyX3+UJtB3E6N7Pll0OHGtwiuLTQwBkZTIPKeX5TphsQ2iNCPJMo34
H2JqR+jCHOiGCdMlDz/JdzSVG3GwjGHyk29JTFe7Wj79yeMV8kaGbZpkWY4pTM4sd7Esg26YUrJN
yq7Nsws1Vnsh7SDhzEr3oRiOfRD3Ps4sD8UOHiDszTINPvfEDH/31gb50GPiYcdOm6Ldcji2z9t3
eh4sIVjtRCxu9Ck4NhvdiK++emUsYTtVtmn7CX6ckUnJpp+Q5XoT68cZj81WuNL0MU3d5RUIbMtE
oqFuhpGSKXDRXWRDCKZKHjMVl48cmuDQZJFXL7foBhlxlo8rIkmmcG2YKNgUbZPEtUhySRBr2FOu
JFXXJcl1l3mm4jKIddk4zHMGcYaSkiiT2Lag4toIIWgMEoQBdc/hrbUeb611UQgc0+CLz8zviefz
sJze73XslFgD/IvvXeRHi006QUrBEqz0YtJcEac5JcfEj3OKtk6QT1/rEGeSVpCj5NBbDw1JghsV
cLejMYQwaAe6S6TXFr1eGIAUepPsWFqMqFKw6EUZJcfCMQVFz2T/RHHMI77db3wU79VuE7K76eRp
+LjaNWilVrSZrxc4dbBOybNo9mMut3yqhVuf4/brOogz/DhFYDNT8Zgpu5Q8k/OrfVp+TJBkmELf
Xz9XWKaBn2bk+fX9xCgydf13bA8JxGmOZWnOWJhIojTh/EqffNg1niy7LEwUyKXiexcaNP2E6YqD
NRRPuNz0sQw4cp8lxu9V7AX2dloI8Vdofo8/elEp9f/c87N6n8Ve5DZH71vtREilODxZohenvLna
5WozoB0k+EnOT660eHp/jZeOz/DHP7iEKQyagxTPMalbDleaPo4hWKgXKDi6anClGZBJ6KmUuCkp
uh2eO1Dn8bkyUZYziFPCSD8xt/KGkUpXjzJAbSPK5Uov4LkEWdDY5M+dmMWzLC41BliWJtXXC/Yj
7evzbmI0eT1/cIKldsAgyZko2gyKDu1BRC/KMATYhiBVaujDIYYiFWo8QcWpounHO5qObb0vW3mR
o7emUtEOE6ZLLrmEjV5KK9BqMIYw8CwtaV12LcqeNa5+Idizn9Gjugh+EDr52VfVG+MHGZ9/cpa/
PL3C6Wsdnj888UCPfavYvinaDUTmbgjU3zi7xltrPa61Qw5OFOgECUEiOTGnq6IL9SLPLdTpRToB
emutrwsYUicooNWYgjin7FkkWUzJ0UIGm/0IUwhsQxtOZ7miWrRxbYNfemKWE7MVvv76KtdaAUmW
U3FtNtOYdFj+TVLJ5LTD556Y5e/e2iCTEMRaNXSQZEyVHTphiilMmoOEXpjRCmJsQzAIUw5OFhG+
Rgb0Qq0gWi/YNP1Eyyorxb6qhxKC1iBmseEzV3V37YnzsJze73XsBJFabYf8zZk1upFOdvdVXbIh
wiIfJpV5kBDZBqC7hILrYyJJbu0sotk9OrQPnEQJcA0wDEE+5HCa2vMa09Sy1FGq1f5Mod8zP13i
Y4cnWZgs4Ee3Jt2/X+7VXjpXrUCLDDwzVd/TvmWy6FD2NOpmsTHAc8wdzT5hZz7Y2dUeIDi/1uPg
hIaVtgcJphAESU6SqSGcXivuTRZtBklOkN/obG4bjKXLDW7sCI0iyBRGlmMajDtJ/aHCrMLAH0Q4
loEp4LHpMiXXotHXqpK//dED5Armq94NYlaPcuwl+ZkEmsDnt7ymgA+SnzvEbvGiW0lyP9hskOSS
Sw2fhXqB9W7E2+t9NgcxT+2rjhW2AOZrHpmUrPdiDAFHp0p0woT5qsf+eoFDkyXOrPRYGQobZFKR
RVoYIUhyvvTsPIubA8I0x7YVMlU7QtuEcb1taqIToK2hANs0MA1IM0k3SHlro89vnDrAhw/VyZXi
0ESR00udR9rXZ2vsZDi3E5xhVCkbiVF0A71JiFKdZFQ9gSk8epGvRSEMKNiCWAJC4dk2ZVexOch0
NQ9uKV2t9EeoFy3iVGJKOV7UwkwTYDMJUaphKmGm+UIF22Rf1aVUsKi6Fsdmy3zmxAwVzx7fhzPL
u/dcer8sgu/XOLPSeyAS19vjcydmMQ3B355bf2SSn+2xm8rvXnH+rSChH2lVrJpnU3ItXNNAKqll
qqViEGc6uUlz3CH/L84yokThWYKio717okzSD1M6QUIuFZIYxzQYRBkVz8IUBpmUfOLYFG+t9/nx
YpOvv7bMcidiEGckQ+WnrV1jqWClG/LK5SaNQUKWy6Hlgo1hCOoFe2zcutwOcWyDgm3h2RpGd3K+
phXezAJJpnhspkSmYL5WoOSaCCHYHMQI4MOHJ/js4zO7lrZ+mE7v9zosIWj7CWGSYwpBP0w5s9yl
FehrHqU5G31QaEiaKfSaWvEsqkUbcu3h0o+y25pTj0QLPFsreo7utYFOpmMJpiFxLAPLFLiWgVCC
mZrLTNmjEyTMlF0OTRYZJDmff2KWhq8VRsu3gXa9n+7VbuNuvf5G88zry52bzD6BG+afkR1GaZgs
XW75uLbBJ45N8/dvb3BirkouNX+r6FnYhkGloOcXFCSZZL0fIwQ3dX1GY2OE5LENbkKUOMPXxuNI
gVKKgmNwdKpEnEtO7q9SLzoUbItPHndY74ZMFV02BjFnV7qcnK9xueW/J/YCe5G6/s/v54m8n2O3
D85WkpxjGbxwZIrvv7NJkOQ0BjGfPj7D9y9uMlm2b1AEKXsWByeKzFQ8vvDkLFfbAYvNAQXH4p3N
AftrBUquQdG1sC1FEGVkSuv8t4OYn1Q9nt5f49WrbSYKNpuDBJlLouzGJEhIkIbu+LjWdcjVKAyg
XnA0MT/JmKm4nF3pUXHWaIcJJ/fXaPl3Jhg/KrFTJWaU3GyFM4ze0/ZTTTa1Ld5Y7rK/5jFbdjk2
U8ISgn/3xgrJ8IIpCWGsyADHhJmyw0zFI0ybDOX4d+z6WEInRtMVl48dnmClG3F+rYufK9JhNmrZ
BhXb5LHZCq6tpW/TXDJT8fj8k7OcX+uNVXoObzPs2wsU4BdxEXyvRJBkXNwc8KWHwLupFW0+eniC
b5/f4L/5h08+8OPvNu5U+d3rhmdkYZBmkjjPSTPJwmSR3/roQZpBgongYnPAgXqR15Y6PL2/ylov
4u21AcvdgE8cnSJXisdmynz/wiY/udLWGHz03DpbcUmlVuS0DIGfZFxp+XSDhGZgc3a1R5Bc79wn
23DJI1/yt1b7DOIMyxJYQnB0qsRjsyWUguVOSJpLPFtQcl2KjkkQ62rwcjvENA2e2lelESTsq3lk
Umk1sskCXz51YMwZ3Kufz8N0er+XMVJj9SyTTqC7Yz+61ORb59dJMi044JgC2zSYq7k0+zFlxyHO
NPfTEGi54KLJZl8XM3eCPQM4FkP5ao0aMIavpZnmDZkCXMfENQUHJ0uYCI5Ol5ipeViGTogEkOaK
ffUCHz82BXDH+f/9cq/2EncDk9v62a1mn0kqWe2EfPvcOs4WXpwlBKevdQizDFMYHJkuEaeSYFjE
vLg5IFeKLJckqRz66ljkGPhxStE2WWwMtFcYGmIPNyN4NPdLYKKIh/Ln1jABN9BjJx1KAhoC9lVd
PNck6Oe0Bilhko9FDfphymvLHQx0wbXkWUO/oUd/L7AXqesTwP8BzCmlnhFCPAf8R0qpf3bfzu59
Ert9cEbvu9LyKbstenFC2bP40IEaLy826UYpn3xsmheOTt6wad3+3ZlUOKa2tUwyiWMafPr4LBc2
fPyhBKEfZyTD3fVKO+S5gzUOTRRZagV4loFXsNjoxqTqOgTOsQVSaS+AeLTRFpp0LyVMlGwOz5Qo
2RbdKKHuOSx3Qwquyaav/WVypYZuxo82LrQbpLy+3GEQZzeYe27f7MN1qFhrkPD6spatbQ4SPndi
hvl6gafmq5gI/v7CJhv9RGOv1VBeModq0SKTijjPyaVm7gh0GzvOJGKLdLVp6IrMvqpLpuDQVIml
TkjRHSZVCh6fK/P8wQk+e2KWb5xdY6UbYlsGj8+W+fjRKZ7aV72lU/leoAC/iIvgeyXOrvRQCp59
SMTTzz85y//w1+dZ6YTsrxceyjm829jrhqdW1BYGLxyZZBBnNylkdYOUs2s9Xl1qoYA4k/z2Rw9x
rRXwr358haVOyFo3pGib9OMM0xDjAohlCAq2iSW1XH4qFS8em+bARIEfXmyy1ouIUokaqUcO5+WM
6xj/o1NFUqX93BR6zp4qOczXPT7/xP/P3ptHx3Xdd56f9+q9erVv2AmAABeJFElRtERKkS3ZkS3b
cZTFbi9pZ5vYiZ0+nT7pTKc9PaeTTKfb6SXuzqSTTE+O7Z4sk4ntxE4kL3HsWN4iW7JELaS4bwBI
7ECh9u3VW+788VDFAggQO1AA3uccSigUUO/i7r97f7/vr4MbMwWO98S4OJ6jqPudecPjyB0/uDdO
STcxbBuPR+Jo1DkBtm1BW9iuK0L2EligZta/rpuV2oFQR8THaLaEZQtaQ5rjVhbxIdUECyxoC2no
VZt4UGVPzIm9fbg/wT9enaZQtZABTfVgVa074no0DyRCPvJlg6hfJVV08rxUbVA84PcqVE0b0xS0
BDUOtIWI+JxcPLIs1eXX/+HiBKZt1PvIcoUfdkJbrZS1CDw07u1eGkzx4mCKgWSBJw931OPi8hUD
Swg02cOtTIlXb6VoC/k41B5Gr9poXpnJrM7gTB7FI6MoMi0hP7IENyoGt9JlqhZYljUrQ+0cspoI
Kg1+bpoCrSEfyaKO3yNQPTLvONLB+bE8ybxOvmqgeCS6IhqSLPH4PR0UqiYHWuFgR9hJwaEp7GsN
ki0ZTm4vfa7c/3bYC6zE7e3TwMeATwIIIV6XJOkzgGv8LIOVBOIdD8ToSwTrRpApBCd6Yosmypr/
2X0tQY73xMhXTLqiPvyqB0PYPNwXJ1022Nca4ktnRshUzPpCatvw2IFWvlYep6TIzBSr9RODmshB
a8iHzyuTKxnkdQu/6uQT6GsJUDIs3tAbJxb08uThdp4fmHFOrmQJn+JxBkbV2RA42YyLTTtx1m58
ChWTi+NO0HhIU+hPBOunN42b/Zoy32S2QtyvoSkSZd3iZqrI3kSQ/kSQC+NZon4vYZ+ObloIGzRV
wcZC83jYEwvQG3fcG4tVR77W45HojQTY3xakatq0hLwEvQpVy+L+nhi6aRPWFC6MZUmXHHnyezvC
fPTxAxztjs7ZjCFB32wg4koyld+N3boIbgfqYgdbbPx8+8oUP/NI35aUYT1Y6YanNn8v9t7D+xIU
dIN9LSFyuoEpBF1xJyi6PCtQoHkdd7gDrUFnrhCOS9rR7ghdET+Fqolu2Pz0w3vpjgfIVUwys5vg
suGIJIQ0haDmYSxTwRKC9rCPw3siZEsm6VKVqlFCUyWOd0fpbwkS8il4JImcbhDUPAS9HvpbgwxM
5xnNVLiZKmHbgn1tQRRZJuxTeOP+FmZK1Xoi7s2u62YkEfBSNWyeHZzENG1Mu8L1qTxlw8IjS7RH
fNzbEXLib4XN/rYghzrDeGSZ4z0xemJ+bkwX8Xs9sweKCslcFQtBuugctsWDXjwSdEd9pBQPuum4
UHpkCVmG1qCGX5XRLUFX1EdH2EdA8xDyqexvCzEwXSBbNkCwqjgW2BlttZnUXOMRoKkybaEgg8kC
gzMF2sM+EgEv+bKBpjiqe55ZIROvKhPxq4xlnXjtdFEnXTAcsZG8DjiiFh7ZkaBuD2sUdZP7uiL0
xP3ops1gssBQskS2bKB6JAKawoGOEB0VH21hJ4fZ2450cqgrymiqxHi2QqqkE/SppAo6mirTEgo4
Oc0q5hwF4sY9QE3uf7vsBVZi/ASEEC9JcxVA5od9uKwjYZ/KO490rrhD1Ta9tU0pOJKJ2ZJBRndc
Jd5xpJPhTJmrk3ksIXj5ZorolOrk+jHsWVlWL1P5KraAoFfmVH+Cdx7r5B8uTPDarQxRv0I86OX9
J3s52hWdU87uBsWYdLnKyUqCkE8h7vfe4Tq2kQNlNQH5tdO7/W0hAO7rinC8O+YMdP+dm/3HD7bx
2dO3GMmUuDpVQBIQ8auOX3xriKfPjCKEIBZQOdQepqAbTOSciUv1SHRGnEkuFvDiVT3IkkwioHGo
K8RPPNDDPR3hOfFEjS54phD8+AN7iPpVAl6Fh/sTczYi8zdjK81UvhTuIticnB/L0Rry0hHZXLGD
GgfbQ3TH/Hz78vY2ftabvkSQ9rDvjiSAIZ8CFfB7ZRDgV2W690RIFquz8tUS739oL3ti/nq+oTMj
GcI+lRO9Mc4Mp0mENAzLmWNawxrTBZ2OiJ9i1eTJ+zrRVJlMyaBcNfGpMh5ZdkRQZIl4wOuoAkpO
zMqnnruBaTsJDo/sidATC3ArVUL1SBzbE2VgusCzlyeJB71rPkTZSUQDKqf6E+QqJvtbg5y5lSZd
NnjTgVamCjonemMc7YoynC4xninTFfPTGw/U185c2eBLZ8e4MV0g5PPQ3xKiXM1g2uBTfNzXFSFd
qs6e0Fsc644iS46HR75qcGW8AJJExO/laFeUyUKFimXTFwriUz0MTBecAz3J2WALcG/uN5hG9/mq
YSOAHEY9AXLNi6cP59B6NF0iVzEoGSZBTSOkKRzZEyXoVbg2mccjFanaNrdSgnLVREZGkm4nLY0H
Vd59opsnDncAzt7vxlSBz56+iW7aBDWFn32kjy+/PoppQ8Qnc7QryqP7W+t7m1zZYChVpCXgxT97
YD2cLvG969PEAs4erjbmt+seYCXGT1KSpAPMuhBKkvQ+YHxDSrXLWY9A8vkdMlxyXj95uIPBmQJv
PdzBWKbM504Pszfu5/s3nPwvvXE/HllmNFsRhadhAAAgAElEQVTGp8h4ZIngbNbyvtZA3W3qs6dv
YQubtpCPR/e3LnobVfOBrv0tx7qjmxYnstp6bHTnCvmUuuGzmCFlCufqOBrwEg94sWybfa1BvB6Z
r10YZ7pQJeD10BPz8yPHOglqCi8NzHAzXaZqWBxsD/H+h3oxhcAwbc6NZQj6FN7Qm+CRfS233Rtn
DS9FkjCFWFXslOuqtjs4P5rl6J4o8w6rNg1Jknjr4Xa+8MqIk49M9WxJOZqNxW5L66enx24nSx1K
FfEpHkYzZcqGzYtDM5zcm8AWgo6wj8lchafPjGALQdSn8uj+Voq6yY/e30U84OXpMyMIYHC6gKbI
hDSFdx3tYihVpCvmpyPsY3CmwJGuyB1zdG2zNZouUa7aFHUnhlPC2SxXTBuf6rnrPL4blSCzJSdJ
dcSnMJmrMJl3BATOjDjKqke7ok7KCt28HSA+cztAPBpQ+dlH+vjaxQnu6wxzZSJPW8TPvpYgU3md
HzrQQqpoEPUpPHcjSUfUUXMsGxaj6TJVU/DGA61oisyxPVFyFYOIX+XorNz9/MD7B3pidcGb3dJG
m8382NjF6jwacA67P3v6FvtbnHifxw+2EfGr9RQUPYkArWGNkXSJyWzFyftkQ9TvuNhO5XVaQ15u
pUv1zzweiBH2q0wXdYJehWRBx6vKfPTxA3fc3NbKEw2o9e/V9lGTOZ3hdJlDHZEVSdg3Kysxfn4F
+BRwWJKkUWAQ+NkNKdUupbZY5MvGmgyEhRad2qY3pxu0h330JYLE/V5i/nGG02UUWUb1eBjNVDBs
iwd6YhimIKQpaIqHimlxuNNRjuptCfDLbz5Q34jXYl+Wo5jE7JXuZmy+VxuQHw2oPH6wbU5czN0M
KUVyBAVKuukoM9mO3KgsS8SCXopVi0vjOdrCGt2JAA/3tzhqSooHWZb44Km99LYEGEwW2dsW4FhP
lIFkkVP9iTsmR2BJg+5umw7XVW3nUzEsrk0VeNt97VtajrcebucvfnCTFwdTvOXeti0tSzOx0Enp
/O9lS44bG0hYAmIBJz/QV8+PM5Ytc3k8z/62ELGASkfYx43pIqYt2NsSqLtG15Ky1g5LauM94lcZ
TpXqa0FIU+oqowPJIm2hChXDoly1GMuW2dfmpEJ4z4keIn51jrLlYvP4blCCXEgJtPY3C2BvS4Cy
Yc1KAusc6Yo4KStSJVqC2qIB4ke7HcUsUwhawxpT+QqZskHAK3OqL8GZkQyWEDx2oJVT/QniASd2
JxZQyJW9aIqMLEtcmsjhVWVSpSpH90TvCLz3SNIdgjfrVRcut5mftD7u9y7qJpouVxlOl4j4VJLp
Eulyld6WAI8fbOPCeJaIphILqFyayDOerWBZYGNztDPKjZkCfq+HvS3Buhpw4/4vpDkqcoNJ5+dC
mlIXbbpbGELdE6b1Tle97cxK1N4GgCclSQoCshAiv3HF2n0sdDW6GgNhsUWnMeCuFszT2xLg199+
mAtjWU4PpRiYKVKoGKSKNlVLUKia7GsNsicW4OWbKS6M5biVKtWvahMB75IL3B0DP+DdtM33am85
Gm+rrk7kOdWfAJgjQ1mbWGo/G/Or9LcESQS9WJYgFvTyY/d3cWYkQ7nqJA187EAbOd2Z3ATMSt0q
dV38RgO1I6LR13KnKMRSBt1yNh3b9ZraZXlcnshj2WLLxA5qPHqgBU2R+fblKdf4WQGNYzigeWgP
a4xly4xnyqSKVQ60hyjoJm/ojXFlMs+rwyn6EgHedri9bvis5AAkVzYYz1T4/nQSJLg2meNwZ4RM
ucq+thBHu5wkyKYQc+aOhVyAa+x0Jcj563VtjagbkXknV89gsjCb4Nbm+RsS16cKjGXKtEU0NI9n
wQDxhdqndhAX8atkyga5isHRrmj90MyrypzsayERLHBfV4SoX+XsSOaO+t+Iw6/dYOjOZyXGXu0w
teau2ugydgfitkCJNPs6WzL4h4sTnBnJYJg2siSxNxHAr3o4eU+Ckm5yb2eYh/cneGFgBtXjJE6v
9alaWWsHuo03fzdTRc6P3j2xeeO+ZL6r3nZmJWpv/wn4hBAiM/s6Dvy6EOI3N6pwu4nlXo2u9HPm
Lzq1jn5+LMtT9+9xFHpaAuxNBPi9Z68gSxKVqukozXicPA/FqhOI1xXx8fxAksLsieFyXNgWHPj3
79kUtbfVTvSN+ZaeHZwkVzFRPBKXxnLIspMx+13Huub87P62EKlilZlila6on2RBx7RF3eAMagqv
jaTrietifpX7u6MrXpiWMuh2+qbDZWnOjWQA6q4uW4VP9fDGAy186/IU/+7Hj2yZC952o3EMgxM/
dWk8j0eW+MbFCQQQ1BQEjqpf2bDwq1XiC9xALHUAUju8sYVN1bI50Rvn6mSelrCGT3Vu/Beba+52
iLLT3WsXWiMiPoWyYXF60FHzMyybfW0hWoMaw+kSuYpJS0gjrCkENYV3HOmkK+5f1ECd74KULRl8
/pVhXh/JIHCkx9/3UO9cN23NcdOGxXO2rffh125bc1Zj7JlCEA96l6yjRrGqfa1B+lqCjjeQbhDx
qeQqBoWKSevsgUgyrzORK+PzehyDJ+jFtO26AbVQuo7Gmz/E0onNd6q3yErc3t4lhPi3tRdCiLQk
ST8KuMbPOjB/sVitZX23Reduk5RfU3hkXwsSEj8YSBIPeOlJBHjnkU7S5SohLcV4roKAulLRcl3Y
ljvwN4LVTPSN+ZYkYH9rkIFkkb0tfnrjQYrV29mvG+tblp38DVALjGsQHBBgWs7V8WTekaVdzcK0
1ES00zcdLktzdiRLIuilJ771EtNvPdzOt69cYCBZ5MCsgIjL3Zk/ho92OfnRCroTk9kd9dMW1rBt
R6zg6B4nbmQoVaS3JbCizWjtZ+/rjDKaqVCcJ1n7roOrU3DaqRumGgutETndoDvux7Rt9rWEmMxV
qJgWNoK2kEbJsLg6mUcCDiUCdUXO5VJLohv2Ob+Tn4272NcavGscWbN6WGxXVmPsLbeOooG5YlW1
zw1rKjemiximjV/1YNvCUQeM+7mV8rK/NcS50SySJDi257Z6H8w1bkwh5vQLgPNjSyc234neIisx
fjySJGlCCB1AkiQ/sDVSQjuQ9Vos6u5tM8Xb96ez3G0A1nxCLeEETDbKavcScKS3Z4pEhpS6UlFf
y+2TiZ2UFK3RRdDJt2Q47mko2Ig5CWYb202RJJ45O8p0XufejnBdWhqcE52OiEZON2YDj1ef6PVu
E9FO33S4LM25kSzHe7ZO7KCRJw63wxcv8O3LU7va+Fmpm8z8MVxzMWuUk82VDb52YZyhmSKKDP2z
881K5ttGl5ZaOoV4wLsukrU7ccNUY6E1otFQzenGHcZjrmw4cRs+tR6DsxJqSXQHkwUEjsHVuA5t
lXvzbltzVrOfWUkdzW+3aEDlfQ/1cmpfAgRzxmeubHB1ssDAdIGwT6kLksxPxdH4vfmfv5varpGV
GD9/CXxTkqQ/nX39IeDP179Iu5f1nKxq6iDnR7N3xP0s1NGXGpzRgCPFWMt42phvaDWLebNTu7Hp
SwTnnJIsVnfRgMrwTIly1SLglQnMU7fazDrYyZsOl7tTqppcm8rzzmOdW10UAHriAe7tCPGty1P8
0uP7t7o4W8Jq3GQW2gA1xvLA7ZjNWjxIo2LTSjZa221uXoitCLhfaI1oNFQby9IYR5oqVlflklq7
FWjM29Ys7bWb1pzVjpm11FHdg6SBWp/yqTIVY64gyUpuAJcq104Vs1iJ4MHvSpJ0Fnhy9lsfF0J8
fWOK5dLISjvf3a5ll7o1WOy9mr+xc+2uLBiMfzeafXJcrI4X2oQs9vtPnxnlVqpEPOAlEZyr4LPQ
Z62mPC4ud+P8aA5bwAM9Wxvv08gTh9r5f743SL5i1F12dhNriYlonAfgTqXHWszmYr+znOc0+9y8
FFsRcD+/jpea5xfqA7Xvr3QDvVgSXZfNoxnGzG0VthADyQJDqSLHu2N3xFPPL+tK5oedLGaxkpsf
gNcAFSek4bX1L47LfFbT+TbCzezmTJHXZ5PqDSYLPNyfmDMJb4fN+mJlXI8BnipV8Sky8YBKulSl
Lexddb3v5AnHZWN5fVbs4HhP82yQnjjczif/cYDvX0/yI7NCIbuJtahONs4DyxGYWc7csR3m6pWw
XsblSn5nrWuyIknuHO+yJupKuskCF8eyIHASDs/2pYX69kr77k4Ws1iJ2tsHgP8KfAfH+emPJEn6
mBDiCyt5oCRJ/cCLwCWgKoR4x0p+f7exms63Ia4MUl0hux7MX2M7bNbvVsb1GOCJgJeQT6E3HqAt
bPOeE92rroOdPOG4bCxnhjPsmU182Cw81Bcn7FP41uWpXWn8rHY+Xk2OtPWQwt9urJdxudy6WI81
2Z3jXdZKrU+9PpoBAfvbQnNuFRfq2yvtd9stXnslrOTm5zeAU0KIKQBJktqAZ4EVGT+zfEMI4SZI
XQar7XzrfS3blwhyoidGXjfY3xqcE8y/HSbyu5VxPQb4ehqcO3nCcdk4hBC8NJji0QMtW12UOage
mTff28a3r0zXFcp2G6uZj+9QAF2GwMxulMJfL+NyuXWxXmuyO8e7rJVo4M7EtXczrlfad3dKTOBC
rMT4kWuGzywzgLzK5z4hSdJzwN8KIX5/lZ+xazjWHb1DZGCziQYcxZGFBsFmbdbX4q5xtzKu1wBf
L4NzJ084LhvH0EyJqbzOw/sSW12UO3jiUDt/9/o4F8Zy3N9E8UjQvG5gi80DaxEw2KkHK6uJp1Qk
adVGzFrnZ3eObx6adfwvl8X60kJ9ezX9rhnimzaClRg/X5Mk6evAZ2df/xTw1VU8cxy4F9CBL0qS
9E0hxOu1NyVJ+ijwUYC9e/eu4uN3DvOv5VcqMrDeLDYINmMiX6u7xlJlbLYB3mzlcWl+XhyYAeCR
fc118wPww4fakCT49pWppjJ+mt0NbDXzwFKiNrt5071Q0sfV5jFaa925c/zW0+zjf7ksJLpxN2Xf
7fg3rjfLvrkRQnwM+CRwfPbfp4QQ/2alDxRC6EKIohDCBL4CHJv3/qeEECeFECfb2tpW+vE7isar
S0uIui9nMxINqOxr3bibqfWoi40uo4vLVvLiYIrWkJcDbVt7SLIQrSGN4z0xvnV5aukf3kS20xy7
XuzmeXB+e5tC7Nq6cNnZ4383j/PlsCzjR5IkjyRJzwoh/lYI8a9m/z29mgdKkhRuePkm4MZqPmc3
sFNdFFaDWxcuLotj24LnriX5of0tTZHcdCHeeqidsyMZZgr6Vheljjuv7C7c9nZpxO0Pu5dlub0J
ISxJkkqSJEWFENk1PvNxSZI+juP29pwQ4sU1ft6OZbe7KDTi1oWLy+K8PpolWdB5233tW12URXni
cBu//+xVvnt1mn/yYM9WFwdw55XdhtveLo24/WH3spKYnwpwTpKkbwDF2jeFEL+6kgcKIb7K6mKF
diWuf+Zt3LpwcVmYb12aRJbgh+9tXuPn2J4orSGNb12eahrjB9x5ZbfhtrdLI25/2J2sxPj5u9l/
cDvlS3P6V7i4uLjsEoQQfO3CBA/1xYkHm9dtQ5YlnjjUxtcvTGBaNopntWKhLi4uLi4uq2dJ40eS
pJ8EeoQQ/2P29UtAG44BtGLBAxcXFxeX9eP8aI6rkwV+593Hlv7hLeath9v5/CsjvHIzzSP7m0+V
zsXFxcVl57Oco7f/DfhSw2sv8BDww8A/24Ayubi4uLgsk795dQSvIvPjx/dsdVGW5E33tKLIEt++
Mr3VRXFxcXFx2aUsx/jxCiGGG15/TwiREkLcAppPU9XFxcVll5AtG3zhlRF+5GjntvBbj/hUTvUn
+HaTSV67uLi4uOwelmP8xBtfCCH+RcPL3Z2Ix8XFxWUL+f9+cJOCbvLRN+/f6qIsm7cebufKZJ7R
THmri+Li4uLisgtZjvHzoiRJH5n/TUmSfhl4af2L5OLi4uKyFDMFnU9+9wY/fKiNY93RrS7OsqnJ
cf/d62NbXBIXFxcXl93IctTe/lfgGUmSfhp4dfZ7DwEa8O6NKpiLi4uLy+J84mtXKFUtfvOp+7a6
KCtif1uIB/fG+KvTw3zk8f1Nm5TVxcXFxWVnsuTNjxBiSgjxRuDjwNDsv/8ghHhUCDG5scVzcXFx
cZnPP1yY4K9eHuYXH9/HwfbwVhdnxfzUqV5uTBd59VZ6q4vi4uLi4rLLWHaiBSHEt4QQfzT771sb
WSgXFxcXl4UZzZT52Bde51h3hH/19nu3ujir4seO7yHo9fCXL97a6qK4uLi4uOwy3CxzLi4uLtuE
gm7yi392GtsW/NEHH0RTPFtdpFUR1BTe91APXzoz5gofuLi4uLhsKq7x4+Li4rINsGzBr33uNa5N
Ffi/fuZB9rVu70wDH33LAQA++d0bW1wSFxcXF5fdhGv8uLi4uDQ5Qgj+499d4tlLU/z2jx/hLfdu
/ywD3TE/7z/Zw2devMW1yfxWF8fFxcXFZZfgGj8uLi4uTc4ffPMaf/L9QT70pn5+7tH+rS7OuvGv
33GIoKbwG8+cx7LFVhfHxcXFxWUX4Bo/Li4uLk2KbQv+69cv89+fvcYHTvbwW08d2eoirSstIY3f
+rEjvDSY4hNfv7zVxXFxcXFx2QUsJ8+Pi4uLi8smM5op81vPnOdbl6f4p6d6+Y/vuR9Z3nk5cd73
UA+v3Urzye8O4FM8/NqT97i5f1xcXFxcNgzX+HFxcXFpAkpVkwtjOV4fyfLyUIpvXJxE8Uj8ux8/
wi+8sX9HGwT//ieOUjVt/uCb1zgznOHjP3mMvS2BrS6Wi4uLi8sOxDV+XFxcXLaAom5yeijFCwMz
vHBjhvOjWWphL50RHz//aD8ffqyfnvjONwIUj8wn3nec+3ui/KevXuKtv/cdfvJEN//04V5O9sV3
tOHn4uLi4rK5uMaPi4uLyyZQ0E1evZnmpcEUPxiY4cxwBtMWqB6JN/TG+ZUnDvJAT4zjPVHaI76t
Lu6mI0kSP/9oP+882skff+cGn395mL95dYS+lgA/crST95/s5WB7aKuL6eLi4uKyzXGNHxcXl21F
sqDzO1+5iN/rwa8qBDUPIU0h4lcJ+xTCPuf/EZ+KpsjIsoQESBJISLP/x/mPAAHYQiBmvxa1rwUI
BLaY/V7jezjv3/6927+jmxbJQpVkQWciW+H6VIGrU3mGkkVsAR5Z4tieCB95834e3d/Cyf44Aa87
FdfoiPj47Z84ysfeeYivnhvny6+P8yffH+REb8w1flxcXFxc1oy74rqsmWzJIFWqkgh4iQbUrS7O
tsKtu5VT1E1eG85QqlqUqxbFqoloUpVkjyzR1xLgnvYQP3Z/F6f2JXhwb5yg5k69SxHUFN5/spf3
n+wlWzbQFFecdKPY7vPQdi+/i8tm4o4X1/hxWSPZksHfnRvDEgKPJPHU/Xt27WBaKW7drY6+liDf
/dgT9ddCCIpVi3zFIFc2yVcM8hWTXMVAN+zbtzIw95YG5/JHlm7fBkmS437lfC0hS/NujKTbt0hy
w9eNv+NVZFpDXlpDGomgF9XjbtrXStTvjouNYrvPQ9u9/C4um4k7Xhxc48dlTaRKVSwh6Ir4Gc+V
SZWqu3IgrQa37tYHSZIIaQohTaErutWlcXHZXmz3eWi7l9/FZTNxx4uDeyTpsiYSAS8eSWI8V8Yj
SSQC3q0u0rbBrTsXF5etZrvPQ9u9/C4um4k7Xhwk0azO8kBra6vo7+/f6mK4rBLLFpi2QJElPGtI
zjg0NITbD5qL9WrbleL2heZlM/uE2w9cwO0H68FWzeXridsPmoet7E+vvPKKEEIs61Knqd3e+vv7
efnll7e6GC6rYD39Sk+ePOn2gyZiK32G3b7QnGx2n3D7gQu4/WCt7JT4D7cfNAdb3Z8kSXp1uT/r
ur25bAiNfqWWEKRK1a0ukss64baty3zcPrF53Jgu8BcvDPE/vn2dz788zGCyuNVFctmmuOPWZT3Z
Tv2pqW9+XLYvrl/pzsVtW5f5uH1i48mWDH7jmXN85fXxO967ryvCB0728P6TvYRcGXWXZeKOW5f1
ZDv1J3eWdNkQogGVp+7fw82Z4mxGSZedQq1t15onwM01sHNYqE+47bt+pItVPvjpH3B9qsCvvvUg
HzjVS2tIYyRd4rlrSZ5+bZR//+WL/P43rvILb+znQ2/aRzzYvBsPl5WzEeNpveZyFxfY/P60ljHh
Gj8uy2K1nez8WBZLCM6PZretP3GzsxWbzGhAXdOztto3eLeykX2lsU+47bt+WLbgVz7zKgPJIn/2
oYd57J7W+nsH28McbA/zoTft4+xwhv/7O9f5w29d589fuMm/fse9/PQjfds2iH2nsZaxt5Hjaa1z
ucvuYLn9d7P600JjYiW4xo/Lkqx24nX15Dee7brJdPvG5rOZfcVt3/Xjfz43wPM3Zvjd994/x/CZ
zwO9MT75cye5PJHjP3z5Ir/1xQt86ewYf/TBB+mM+jaxxC7zWevYc8eTy1bSjPuMhcbESthQwQNJ
kvZIkvSqJEkVSZKU2e/9viRJz0mS9Acb+WyX9WO1QWzbyf9zu7KdAgwbcfvG5rOZfcVt3/VhMlfh
vz97jXcc6eADJ3uX9TuHOyP85S89wu+9/wEujOV46g+f4/WRzAaX1OVurHXsuePJZStpxn3GWsfE
Rt/8pIC3AU8DSJL0IBASQjwuSdIfS5J0SghxeoPL4LJGVtvJXH/ijWe7Lopu39h8NrOvuO27Pvy3
r1/BsgW/+dQRJGn57muSJPHeh3p4oDfKL/zpaX760y/y5x8+xUN9iQ0srctirHXsuePJZStpxn3G
WsfEhho/QogKUGmYtH8I+Mbs188CjwKu8dPkrKWTuf7EG8t2XhTdvrG5bHZfcdt3bQwli/zNqyP8
4mP72NsSWNVnHGwP8/l/9ig//ekX+fCfvczf/vM3cqAttM4ldVmK9Rh77nhy2SqadZ+xljGx2Xl+
YkBu9uvs7GuXbUA0oLKvNbjsjpYtGQwmi2RLxorec1k5tbYBVlSvbjvsPpY7jpfqG27f2Xg+/dwA
iizzkTfvX9PndEX9/L8ffhhFlvjQn54m0wQuK7uRla6h68lyxqs7pl0WoyZ2oEgSqVJ1R/SRzRY8
yAKR2a8jwB2OyJIkfRT4KMDevXs3r2S7mLupeKxGoeZuwXGLKXTsdIncjf6bsiWDL7wyTF43CGsq
73uod06dz3/2ZgUw7sS2bBY2qm4XG6M12fq438tz16fv2nfcdl8b03mdz78ywnsf6qY9vHaxgt5E
gE/9/El+6pMv8L//zTn++GcfXJEbncv2o3HD+vWLE+QrJopH4i33tNHXMtcIW4/1wB3z68Nm1ONK
nlHbW0wXdG7NFDnRGyfkU1bUR5qxb2y28fMC8MvAXwNPAn82/weEEJ8CPgVw8uRJAc1ZcTuF5Roq
VcPmVH/ijklzIVKlKgXdJOhVKOjmHGWa+QodN1NFzo9m689//GDbHRurZqCZZEoXKsvNVJEzIxki
PpUb00VO7UtwPBBb9NlrVQ9aTn00o0LMTmEjNyuNfWMgWeCFgSTXpgpcHM1StSz6WkJ0Rn3sbw0t
2Hfcdl87f/niTaqmzS89vrZbn0Ye6ovzsXce4j///WU+89ItfuaRvnX77J3IZu071us5jZ8D1Mfg
eLrCQLJAyKdydTJHsWKytyUwZ1yux3rgjvm1s571uFi/mv+Mxw+2YQqx4M+lSlXGM2XOjGRAwHC6
zIN9ibrowXLK1qx9Y0ONH0mSVODvgQeArwP/FicG6DngjBDipaU+o1krbqdwt0nvZqrIVL5CR9jH
SyMZchWTjoi2ZBuUdZPvXZ/GtiHiU3jX0a76e/MD5xDMef5Qqrgm+cKNoJlkShvLohs2D+9L0JcI
gridS1YCEHc+eyBZ4PXRDMe7Y8sOYFzLrZErz7pxrLZuG0+DF7u9qfWNC+NZzo1kGE+XuZEskq8Y
KLLMtck8PlVetO/U5o19LSFyuuG2+wqxbMFfnx7m8Xta1z0+5yOP7+d715P8zlcu8eZ72uhNrC6W
aKezGfuObMngZqrIS4MpNFVe8PBxJafzjeU91h2tzw+j6TK6ZaOaFkJAa1i7Y/OqSBLpYpVy1SKk
KSsOaHfn+vVhverxbv13/p7g6TOjxIPqHEOo8bZwplAhU6ySCHqRgGReZ29LYNl9pFn7xkYLHhg4
NzyNvLiSz2jWitspLLQJrk3K/3h1moHpIudGMqgeD/tbg3M2M/NPmmqbqq+cGyOZq+LxyGiKTLpc
pRdnkZ0fOAdOItTa8/sTQYZTpaZSFVlrH2ys46phky8bZEvGgp+x1IJXK0tEU/nm4CQF3aA97OPx
g23c0xFmOq9zT0eYvpbgnGcPJAtcHMs6pzepEk/dv2fJAMa13ho1o0LMTmE1ddvYnuOZCuWqSXcs
gD1vIxQNqDx+sI0/eX4AjyRTrFpUTZtcxSSoeYj6FR472EZXzL/gaeFLgykGpovcmC5yoifmtvsK
ee7aNGPZCr/x1JF1/2xZlvjd9x7n7f/nd/mNZ87z5x865bq/LcBG7ztqY3EqX2FgusjbDnfcsbbe
zfiqrdGFiknIp9xxiIigPj+0hjRCmkJBN7AsgW2LOXNGtmTw3PVpfIqHimHzrqNtK/5b3bl+fViv
erxb/218RqZkYNuCvkSAyXylbgg13hZeGssS8aukSwbHe6K8+d42ju6JAk6M8VLGebP2jaZPctqs
FbdTWMgYaZyU37i/laFUEVmSyOnGHAPp868Mk6+YqB4Jv+rBq8qMZypMZCr4vDIeWXYuIMSdz2wc
LPM34U/5m0tVZL1kSmunfGdHM5wfyy64oN3NBbFmXDrGTBEB9dP1dKmKX/XgVz2Uqxa5slGv56fu
38Pro8619f62265KSwXfLjaBLrc+mlUhZiewmrpNlaoUKiayLHF+NMNETkdT0nRGfBzrjs75HHO2
3U1LkC4Z3NMWRJaczbNX8dAbD9C7gLMU5kIAACAASURBVAJZqlRFU2XedriDgWSRU/0Jt91XyOde
GiYR9PLkkfYN+fw9MT8fe+chfvvLF/nimTHe/YbuDXnOdmaj9x21uXVfS4gb00UujecIaArKrCF6
t81rLQbj+RtJRtIlehIB3tAbx6d66uXtawnS1xJc8HByvotT7Vm1tcEUYuFC3wV3rl8f1qse79Z/
G/cjmdI0VyfzjGXLRDUFU0B7SMNG1G8LZVniwb1xLk3kCPlUhmaK9MYDS8Z9rvfftN40vfHTrBW3
nVjqNqG2Sc6WDF4fzVComHSEfbw+nGVopsjelsAdfqGvD2d4fSSDV/Ewki5yX0eEezvDvHozhW7Y
5HWDWMDL0c5I/RZisfLMN4bWIl+4ESy3Dw7PlBhKFelPBO/YGEYDKuGSiqbKd7j01T53sQVveKbE
02dG8SkyIZ/C4wfbSJeqRIaUukGKBLYQlKoW6VKVp8+M8p4T3aTLVQplEw8SsryyxXyxCfRu9bFU
27qsH411uxzRkrJucnE8S7ZiMJHTOdASRABFw+SloRmGZor1vjWRq3BrpkTJMOkIa7ztvg7Oj2UJ
ehWKVXPBDVK2ZJCvGOiGTQ6Djoh2x9h3uTvTeZ1nL03yoTf1oymeDXvOzz3azzNnxvj3X77AW+5t
Ix50DxUb2eh9R21uzekG93aEKVctfKrMc9enecq/Z9YNzaCsO5vPfMVgeKaEKQT5isF0XmcqX6Vs
2EzlnBjbN9/bRtinzjF2Fornq32vtl61LGHoLdf9zp3r57LaWK71qMfa7X1tP7KgZ0fZQFNknjzc
wZnhNFcmc+QrFhfGsrzlnjaOdEVIFnQ6Ij4sIQhqCn2JABM5nQtjWQoVk6BPIZnX6+708z2CGr0J
mq1vNL3xA81ZcduF5dwmNN74FHSTM8MZhBCoioxh2bSGNIbTJUKzJ1Op2c1RpqQzU3A+Yzqn8/LN
FJN5nUTAi2UJ9ib8xIJ3TqSrEVHYapbqg8MzJX7vG5cxbVBk+PW3H77DAJpvTCiSNKdtTvTESBer
pApVpNn3L45m+Z/fG2Aqr9MS9LK/LYQpBMd7YyiyxIXxHEe7InTHA3znyhTpUpV4QAUh+NPnB7g+
lWemYNAa0mgLazx2sJX97SFy5aUn5rttABaqDzc+b2u425hqvKEtVAziAZUDbSEq1SQ3U0VMWxD1
q3SEfUwVdP74O9eZylcYzpRJF6sEvR66on7ecaSTkKZgCYEsOZuxRtdNx0Afwad68EgSD3THtsW4
bjb+5tURTFvwU6d6N/Q5Hlniv7z3fp76w+/xia9f4T//k/s39HnbkY3cdzTOrfmKwdmRzB0iQD5V
JlMyCHg9/GBghotjWY50RZFliaplY1k2CMiWqlydyHOoI0xHxMd4pszF8RyaKqMbNke6IkzlKrw0
lKIj4iOoKeyNB3jmzCiyLGHbgnef6KYz6tsQFbjdyGYIFyz23vxYsisT+XpscK5s8NLgjNM/FA8X
xrLsbwuimzYhzcuRrgDXpwv0twXJlBw3yaBX4XBHmELF4KvnxwlqCpZtMzBdxLIFE7kyQnLc6RcS
rGrW/rItjB+X1Z8iLHabMH/D1B3zOzc+ER9Rv4JA4oGeKN+9Ns1f/OAm2XKVtpCGpng41BXmynie
yZzOwHQRSYKMJOGRIezzktNN/KrMfV0xvKpcv+G4mSoylilT0J2bpWcHJ5ctotDsDKWKmDb0twQZ
mikylCouePvTaEzcnCkymdPZ3xpkMlfh2cuTIHBOUXpiPHN2lBduJBnLVMiWDeIBL1MFnbff18Hw
TIlPPXcD04bvX5/m199+mCcPdzjGj99LxbS5NlUgUzRJl6okCzoDSQ9nhzM8fk8bN9MlDrWH8Xk9
vOdE94IuTLUyL7dd1uIn7yo6rp7GOLD5Y+rmTJHXRzJoisy5kSwhTSWolZjK6qTKBrphMl1Q+NvX
hkHIeGQoGRa6bmHagkrVZjKn8/lXhnn0QAsRn8qtVImzIxnOj2braoxPnxnl6mSBeMBLb8JP2L/y
jeNu7wNCCP7q9DCn+uMcbA9v+PMOd0b4hTf28yffH+SnTvVyotdNu7eZNHpcnB5McX4sQ1hT6/E7
+1tDnBvNMlOoIJDIzrqs5ismj+5voWpavHIzzURO59xIhgtjWeIBL/GgiuLx8NZDbbw8lOKblyYZ
mC4gSRIdEY3WkMZ5n8pwusQbemO8NpzhezeS9M2KX/Rx2wDaypjr7TwfLFRvte+vJW1IowcOcMeh
VzzgpCFoDFs4PZKioDtiNWeHMwwki2RLVdrCGrYQVCyLY3uiKLLErVQJYQtmClXGs2WKVYts2eDG
VIGqZTNTrHKkS8O0Bb2JAAHNgwBag46IRqNgVaPAUjO2n2v8bAPWcoqwmOvS/A3TVF5nYLpAxbDx
KhKyBKeHLJL5ClG/Fw8SBd1EtywQkNMNJnM6lu08x6vJyLJEUPMgkOiMaAhE/YbjC68Mc2Ykg2Ha
yJLE3pYAEtwhorBd6U8EUWQYmimiyM7rhWhc8P7x2jTnR9NcncixrzVELKgS1BT8qkJQVXhtOM1Y
ugIIDMvGtCz8qodnL09xb0eIUtWmPawxlXeuoVOlKl1RPxXD4g29MQamCxQrJmXDomoI4kGZTNng
3FiGQsVp286oHxD8L4/uW3P9N7pqhHzLVwxyTxfXRm2MD84UMEx7NrjZrEvO53WTgg65isGeuJ+Z
YhVZBq9HQjdlJOBWqszeeBDDEpR0k4xuUKqYFCUJgeDscIZksYpHljjeE+VoS3TOou5TZOIBlXSp
SlvYe9e238q8U83MS4MpBpNFfuWJg5v2zF978h6+dHaM/+OL53n6n78Jj+yKH6wH8/v4Uht5AQgh
IYB4w5pt2janB9NYAjKlKpYliAZUFI8Te6cqjqiQIsvololhCbJlE9M2+PqFCVTP7Tz2Xo8z//tV
D2860MpAssCVyTySBP2JAK8toOi62THXy1Gj3A4s5eWx3L/nbsps/S3BO9R4PbKET5XrsWSXJ3L1
2OAXBpOMZcqYlo1u2oyky3TF/PTGg4R9Co+c6OY7V6cYTZd55uwoVcNCVTz0twSxhOMhYNmCTNlg
T8xHIqRh24KAV6ZYNQlpCi0BL6/dSpMqVhmcLswRWGq29nONn21AbROzUN6cpciVDRJBLxGfytE9
0TsUPwZnCkhAf0uAC+NZDMsm7NMYz5YZSZcxLeH48uuWY9gIuO7LM5WtIEvgV2XKho2ETUc4wKn+
OLGAxlvuaaNq2/QngphCMF3QqRo2kgQxv0JX1EdIU+aIKGxnelsC/PrbDy8a8zOfm6kiVybzhLwq
k/kK72gPUTYtChUT27Z5+VaKbMlANy3iAS/eikl72Dm1Sxd0bikyY+kSY5kyHtkJRLeEoCPs49JE
llzF4Fh3lLGAihAwlikT9qsYBR1hC1RFQjcFpi0YSpZ49uIETx7pXPVpfW2x8qkyFcPiXQe7NuXG
aDfTuKF66v49XBjLcnWiwNmRDIoMjx1o5eJ4DhmYzFcIeBU0j4xt2WTLBpmKgWlCwOshpKm0hTUq
poXqkYn6VWxbkNNNVI+MLWBv3M/VyTzXJvP4Z93b8rM3kiGfQm88QFvY5j0nuhdtv43KO7UT+Nzp
YcKawlP3dy39w+tE2Kfym0/dx7/83Bk+d9rN/bMeLHRaf7eNfE0kpH/2QMEUou4hcGU8x/XJAm0R
jesTBVpCKg/3t3BpIo8H6IkFGEtX0E0TWQLDtvHYEl1RjbCm4vN6ODeSpVS1qFo2XTEf93SEmCpU
uL87yuHOCBO5CjfTRQzTpiviYzxX4eZMkeOB2KKuzxtxK9NYb+migU+VF80ldrfPaIbbovn1ttr5
rdGIqhgWPsVTN4S+eWmSwZkippUm4FXqHiQVwyKnG5zoiXFfV4RL4zkm8xVm8lXKhsVM0dkDKIpM
pWoxma3Q1xJAkqBsWCiyhM8jE9EUTEvQEdYwLZuSbhL2KfS1BDnREyekKYR8Co8dbGWmVKUl4OXM
SAaf4ghv7GsNzRFYutuasBVt5ho/2wBFkrg4lq3HkzTmzbkbjXEotm3z7jf0EPI6HbavJVjfMFVN
wc1UEc0j41c8XBzPka8YBLwKEZ9KLKDQ65GpWDZjmTLnx7LEg17SJYNESEU3LN52XwfvPtFNe9Q3
59RmOFXiRE+MG1N5zo3mMC2bRNBLR9RPSFN2VGxAb8vCClgLMZGpMJ4uOy5Gps33byT58Jv24dcU
jnVHeXEwRVfEx9+fH0eW4J6OIF5FYThVIlWs4ldlVI/MG/pixPxeOiM+hlMlvnptnPFsmaFkid6Y
n6FUiaJhgQSaR+KdRzoJ+VWEEJwdyXAzWaRUNbkykWNgpsg7j3Quuz3utlitRDHIVXRcGdmSwc2Z
IqeHUngb8oOEfAp7EwFaw86J3K10iWReJ+xT0U1BHp3zYzlsW1CsWkR9XiqGxf17IigeGb/qYThd
ojB7WyiEYG88QH9bkOm8zumbaXLlKl5FZiJTIRHycnY0U9/gpctVEBDxLy6AsVYFwZ1KtmTw1XPj
vO+hHvzejRM6WIifeGAPn3nxFp/42hXedayLhCt+sCbm9/GlXIEW6vs1DwFFkvjS2TGGpkvIHkgE
NZ4fmKGkO2NUlSX2tfop6hY9iSABVWYsW2IqVyXtMTjeE+PBvTFaQ14GkkVMU3B1Io+mKKiKhKZ4
CHg9+FUFy67wnatTqIpMZEiprwPzXZ8XyjUX93sXTJS52nor6xYVw1q1lH8z3BY11luubKwqj1Kj
EVXbVw0kCwwli4ykS6gemVzZYG8iQE43CPkU3nWwa05bHN0Trau9vqE3zl+8OIQQzqGXpsi0Rbzo
hsWfPT/IrVSZUtVxkWsJaXTHfDzQG8MjS9iz8Z5+r4cvnxtDAu7pCONXPWiqzGu3Ms4eYDY3WcVc
uv0aY1LDPoX3P9S7aW22bONHkqQ24CNAf+PvCSE+vP7FcmnEFIIjXVGCPoViZWGlpYWoxaF0Rnw8
fyPJp797A9222RP1c7I/wTuPdDI0U0RTZIZTFUxbYFkCIWzagho53WA8W0aSfOQrToK0imnRGtLo
iQfY1xpiMluhYjpxJefGsrzRp3BhPMd0XufonigXxrN85+o0EZ9KS9BLxbCxBAS8Cl5VXlVswHZn
eKbEM2dGSZaqTOcqRHwKF8ZyfOXcOB88tZeQT0H1SAzNOPFUFdOikrexbB3dtBxXporAtGzCfpWH
92kA3NcZ4WaqhF+RsQSM5yrOYhL1EdQ89CWCvO9UL30JRwL1YHuYv375Fh2SRqlq8cpQGkWW624P
cHcf5caYpYUWq+W6fmy0stJOorZYjKRKTOQrvOtoFzn9doDrWLbMaLbMoY4w16YKXBzLMZErc097
mEy5ioXAr8qYBYGiSAQkD3sTAbweD+fGsmRLVWwhkQhqTOfLhHwKhzojvP1IgL8/P4ZtQcivUjRM
zLygN+4suulylfOjWSwh6jLuwB0LW+NGTzfsOcIJu7kPPHNmFN20+eDDezf92ZIk8fF3H+Ndf/Ac
n/jaZf7Le49vehm2I4vNZ/ONmVruuoVyrdU2yIv1/YhfZU/Mx2g2RdTvxAOFNQ+WJSjqJroQnNrX
ynPXptEUmZlSlYCmoikQD6mEfAq6YWHZAt20sIUgWdA51h2tK3sFvQo/eaIb3bDIlKs83N+COS/3
VyM1TxQJiVduphhNlbiVKfNAd5TW8OrjdxMBL7phc240S9in8J4TPSsyqJr19niteZQajajHaePp
M6MgBFcnC6izHh8CFj1IjgZUjnfHuDKRZypTZl9LkKlClZmCjkeC/VWbC2NZrkwWCPsULAHdEY2H
+lrweWVeH8kyXdCJB1QUGUYzJWQcV83RdJmOiI/+luicPUDIp/BYj3MjtJjaHFCPSQ37VK5M5OiJ
+3l0f+umtNtKbn6+CDwHPAtYG1Mcl4VIzLqVWEKsKJaiJeClYphcmshh2TYBzUuhYFGqWiTzOkOp
ItN5nYFkgZm8ozD2yP4E2rBErmyQ06v4FJmSbiKEoFy1EQhmChViAZWwplLUDXTT5pZRIuT18MJA
0nGzSpeYypU5P5Yj4le5NVNC8chEAwoVwyZZ0OlbQZbgnUK2ZPCDwRksITjaGeG5go4sS/i9MqWq
yWdP38K2BTem87SEfFRMJ8GkYQlKVZP2iI9qtowqy7SGffhUmXSxytnRDLpho3okLk7k8cgS7WEN
RZIYSZexLUf1J+6/Xd/3dYZpCTpKfo7UqoeuiA9D2NycKXJ+LDvndK+vYRLLlgxOD6UYSBYYTBY4
3hObs1gBK3L9WKmyUrO4N2w2dQEDj8xYusyZ4TTxoJdC2ZyTX+dgW4hbqRItIS8T+TKjmTIVw0QW
Eqmy0+ecwGrIV0ymiyW8ioymeEgWdVIlHY8kkSoZFKsmAKbl3CZFyypexYMiSzx7eZITPTEKFbNu
CNdi+MbTZZ6/kSQR1KiaFg/3JzjeG6sLMZweSs0RTtitqp5CCD770i2OdUc41h3dkjLc2xHmw2/q
59PPDfKBU708uDe+JeVYLZs9H9ztlmEhY+Yp/5251m6mioRL6pybnvlcGMtyfjSLLCQqVZuyYXJt
qkimVEVTZMI+lYlsCUkCGZjKOQdkVdMmkldoC2r8yNEukgWdwZkSQa+HnF1lIlvB45E51BGmYliO
a1ROxxaC5wdm5iQnnq8KO54pc+ZWmrLheILkKlXSJZOQ5sGnerg54/xdC+UUWgoJkCSBhGP4raQt
m/X2eD3yKNUwhSAeVPGrYfyDKYQQxH0aYZ8KEvUYzPn1lisbpIpVJAAJKlUDAEmCy5M5VI+Ebjgx
xVXDEbl5/sY0rSGNQx1h8hWDKxO5WZdohVupElG/l+6Yn564v27w1G6d5nv/POVfxCB2liGqhsVY
1lnPUsXqptzarcT4CQgh/s2GlcRlUVZ6KtroFnO8O8ZErkJP1McLgylmCjrJgo6myDxxqJ1Xb6YZ
y5Zn/f4VBpIF9reEmMhXKBk2yXyFvG4hSxDxK3THA8T8CsPpMtmSweBMEQmnA5cqJp1RPx4ZDBuu
TxWJ+JzgPK8q41NkHuiO0RLWePM9bTvG3a2RxhiY2iTQaBB8YfbU/tZMkYppY9qCfMVEQmciW6Gk
WwQ0DxM5nft74oxmHDe3kKbgUzzsifqoVC0QgqolKFctbs4U6Yz6KRkm5apFe8RL2KvS3xYkXzYp
VS1SxSo3pop87uVbxANetFl3qX/xxEEuTeR59VaKmWKV5weSHO+JgURdEOObg5MkCzoBr4f3nOih
tyVAqlTFqzo5AgZnCjy8LzHH5W8wWVzU9WOtp3LN5t6wmRQqJoWKiRb00hbW0E0bn+Lh4kQOCer5
dY7uiXJ1qoBhC070xDFtwVimhBSEkYxAlZ0+ZNo2lhAMTOXJ6yZej0zUpyLLEvGA1znYmCnx2s00
l8ZzTtxA1M8P39vGoc4IA8li3a+80RBWJInv3UgyndfJFKsEZkUY4HbOK++8nFe7pQ3nc240y+WJ
PB9/97EtLce/fPLeuvjBF3/lsW0jfrAV88FStwzzjZna6ftwqsR4rkzVsOtSxIuVOVsy+N61JNmK
SWk25ragm5imo8SYnXVpjQU0TvVrpIo68aCXjrBGQTcZz1S4MV3guevTvPtEDyOpMlXbRpE1jvfE
mc5X2N8aRJYlwj4FSXLc6sbSJbrj/jvqtmrYCJzDElvAka4It1IlJrI6toBcxSQzeyhmCVGX5g75
lPrfdzcjtbamHGuJrWpOaNbb4/U0ymqiQpliFRmwJAnTtjEtm6+dn0BTZCRJqiu4ZksGF0azfOXc
GBO5CmGfQkRTKVZNbEuiaFhEfCrvuK+Tr54fJ18x0BTPbEJrmapp8/LNNNenCxiGjabKPNAbozcW
5MieCF5V5sG9cbpi/jliWull3sL1JYKc6IlxM1WiK+rnvs7opglgrcT4+YokST8qhPjqhpXGZVGW
eypam6wmc86NzpOHO/B5PVydKGBboCkyEb9KT9zH2ZE0U7lyfeKaKRhYtmAyV0GRJTwSWMImqKpE
/Aq6ZdMe8oIkoZs2iixhmI6IgYRE2TAZminU3doUj0TFtChXbRIBLw/tTfDowZamlT5cCzWD87vX
pilUTIZTJQ51hhlMFuoLQGtQ4ztXpgh6FXyKjCEEe2J+pnI6toVzoq96CGsKhmkzlMyzNxFgbyJA
WFOxLJuiYfFD+1vIlqtoiofeeIBnzo7y/I0ksgyH2sOYdoDpvE62ZBDxq3REfKSKBkXd5JWbKU70
xDnZn2A8V8avKTzYF6dkmEQ0tW7I9CWCnB/NMpAsUrVsUsUqA9MG6eIgH37TvjlJ+trDPvoSwTkL
22KuH+uxADSre8NGky0ZXJrIoc66t/S3BOmM+OiI+BhIFnlkX2LOInSyL06mVEXYAsO2EUJwfbqE
YdnICEIhjVzZkVjXDRvNI4MCYb8H3YRsuYrf67jFXZ/KY1iOz3dhdpOT0x1DK+RT6obwpYksPXE/
6XKVmF/lYFuIC+NZvKqH711P0ht34uKa9ZR2K/jc6WF8qsxPPLBnS8sR0hR+46kj/OpnX+MzL93i
535oe4gfbMV8sNr+e6w76pwUAmdHMwuWuTaPjmfK2EJwqCNMMl+lM6qB5Lgzm5bABh7cG8cWgpF0
Ca/iwSNBQTfJlQ2G00VmStXZW50KEb/KVK5CLODlcFeY3oSfezvCjGbKZMoGz99I0hLSmCnoxEJe
UsUqx/ZE63V7fiyDEBL7W4MMJgvkdZP+lgBej0zFtOlPBHjsnlZuJAvISJg2BGc9Vmo3EvONVGDR
NWM1c0Iz3h6vl1FWc58TCC5N5tgT9xPy/v/svXmwnNd5n/mcb/9679t3v1gudgIESUgURWq3ZO3y
powUOxmPnSlXMks54xqnZiaVZGqcZJYau2amkpRdiTPxeNe45FjeZC0jW5RIiStIkAQIEOvdt+7b
e3/97Wf+OH2bAAgQuCRBXlJ6q7iBF43T/Z0+y/v+3udnUMqYNL2YpYYyHh0vOoDk43dN8JcvrPLC
UpOVVl8lzQwNU9fJ2RZhHON7Sj55fqPLQ/sreEHCWsfnwroCYRm6Rr0bIATYpoYmBJvdkLxjMF/v
kbUNXlxtc/e0qlj/8clFOgOstmPqt3yWxYzJF+7fPUzWv5kArO1cfn4J+CdCiBAIURVKKaUs3JGR
/TBeUwxLrIMF6spmlzCW+FGCFBKJoOOF1HsR3z1fo9oNECgMZsY2cCydfqi0wdOlDEsNjxQ5mOgm
J+cb7Blx6YcJujZYxwd/8+OUSs4hjBL2VTL4UcqDsyOc3+ixu+yya8RldtBvAq8szd7puFPSiC2D
x0Y35ORig6miy0qzz2wlO9wAun7MM/MNLlW76JqGlJKxnKVgBEgyrs5mJ0ATgmaiGhdXmmqjOj5T
5N27yzx6qcZarcd6x2dvOUNp0Jc1U3S5f+8IXhjjxymdfoQmJCNZCyFhve3TC2OKrkHGMogSpas2
dEXrMrSXEdVbPhPAUJ4UJQkXN3q0+qps/pVTy/z8+2avWdDhlTK3rY1+q8L3OXea+Xpv+PqvNd5p
B+fbnZdbmdHPHp9Sl9TZCi+utvnrc+tIoOAYw03oqy+sUOsEnFlqESSK4NbsR2ga5EyDjY5P0lHI
a0PTkDoYhiCKJbrQ+PSxMf7ihVWylsGTVzZZb/t0g0g1vJoa4wWLA5Ucd88Uh828K80+Z1c6BGFK
JW/jmjoTBYdaL2A0a7NQ9/jKqWU+f2KGWMprPCvuJEVqJ0cviPnzUyt89p4p1dPxFseP3zvFl55Y
4Ne+fo7PHp+kkrPf6iHdMl7PerCd+Xb9z25XjXH9+nij3jcYmI37ymw8HUikDk/m2FfJ8viVTXQh
QFMZ/8XNPqeW6yBVlW5vJctEwWEsb3NurUOSKnT9S+sdDCEwTI3LtS5fe2GViYLDA7MjVHsBo6bN
ZNFluuhi6trQtwXBcJx520SibC7u3VXi2GRhIKlXe/+DsyM0vJCWF5FIST+K2ewEZGyDjh/R6UfX
XFK3zFyvvgx96ODYkJh6O4jw1/M838y4nUvZ4qZ3Q1rs1nva+vxGszYF28SxNJJU9exVO30WGx4d
P0YC/YmE339ijjPLHepeSC+I6YURutDIWJrycIslmqYqRRlb57P3THNyocFqyydnG2RtjYJr0PUN
DC8kiFIylk7W0pgo2rT6CZ/YP0ok06FZ71PzdUxNI0pTfu6h2WEy7tXeezFjcm9G9Su9mc/uti8/
Uso777r2w3jdcXVGfmuBemaxwWYvIIgSKgO5zETe5azWZiznUPNCyjmLvGNS64RkLI1yxqbrRyRp
Sq0T0I8lSRLS0zTCVA6qPhJTEwgkXgyECX4QMZq3ubDRxdI1/uSZJfaP55mv9/jkscm3jN1/p6QR
LS8aGjymqSRO1GalsJExhgabnYBGP8Q0NEZzNtVegKPruLbO/rE8cVLH82O6QYwUKUgFgkgl2LpG
rROw2vJpegEXN7pEacpyo88///HjuLbCFNumRsa2ma1ksQzBvkqO9Y7PWsvHMTXytkHW1jk2VcA2
dXpBxHOLHV5abVP3Au7bXcaPVMXu8cubPHy+yudPzFDOWEwVXS6t98haBuMFG8dQxrX7Rl+WLV4t
c7tc7fKVU0uUs2ou7q287Hl0fVP8a3kGO1Xe8FpiO/Py+mrb3dOqotgNIvZVckO5AECtE/D0fINL
tS66rmEKQSJTvDAmY+lkLIPJvEO16+MFMUmqfEaKGeU1tdENcUydyaLDC8styq458OgCP0x5eqHJ
6ZU2f/+DB3hqvs5czePcWpu2H7Hc7DOWt/jC/Xs4OlVAG5jnlTMmSHnN3Lj6/f4gyhm/+sIq3SDm
Zx5480EHNwohBP/iJ+/mM//qEf73r5/jV79w31s9pFvGa10PtjPfbvazt/tnXe/XMlfvKUKiF17T
+7ZVack6BpomeNeuMrVuQC+IiDLnAQAAIABJREFU+avTqyzWe/hRQjljM5IzqXsB/TAlSZUX3L6x
LLOjWUhBIAewgxQ/TGn7IRlTHflcS1eXG6kuN92BMbny6gqGvi17R7JDQM7VsqYtOffukQwNL+RS
tctvfOcijqmTppLxgs14zuHiRhfLUHtYzjGuqQZsmbkOL0NX9Zku1j0+xKv3ib6e57nT4moyr6HB
P/rEXUPZ2vWyw1RKXEtjuujSj1Lu3VVkvb1M0bGIEgW0afUjLq53ubjRoT/4fbYBQZySpCmGpqEh
cQwdL4zpBaqNv9ELcWyNo1MFGl5Iux8TJpKpostozuKBvRVeXGvjmCYLdSVn3zOiKvmrzT4rjT66
ruEFSmmyb/TGfoc3ije7arcd2psA/lNgn5TyXwohdgNTUson79jo3uFxJ7IU128EdS/EWm0xkrHI
WCZ52+ADB0cZzVr8x2dVJt8UsKecIUmhnYYkKczXPVabfVp+ypZNmhdJdC2hnLWUR09fmR5aho4f
x2RtnTBR7sAIgakJ2okkZxsYusZCw3vL5ErblUZsJxu/ZfC40Q4Yy9nsG81wz0yRDx8aox8mfP3M
Ko6ps97ysQdN5ccmC+wZzbB/NMfsaJYvPz2PZWiEEWgiIWcpD6TWeptd5QzLzT6NXkCQpOwquRQy
JmGactdIASSstX06fsRG2yeMJesdxfu3dI39Y3l2lTJkbYPj00WqvYB+mKiNJlUbpEBwaCJPo6ey
S62+kvHVugH1XkgUK0T5aM4euIy/TOmC6/wI4nQAT1AXoS206xspT9mJ8obXEtv5TG50yNtLlvG8
c41cYLnh8diVTVabvjLHTVMCBEfGc6y0fLwwJmvqWAPUbdFV4JIoVY3v2YFnxHOLDZ5dbOEFEbOj
WRIp0IWg1vNZafQxdMFvfe8Ki00PUsncpqJLCiSrTY99o3mOzxT4sXum+Na5DfwwYX7TY7xgc2zq
le/3B03OKKXkdx+b4+B4jgdmdw5g4NBEnl/44D7+3Xcv89MP7OH+vTtnbDeL17IebGe+vd65OZKx
CKOUp+c3WdjsD4lvx2eKWKZGwTZVFSVJmdv0SKVal5cbHustn0u1Lr0wJk4kYZLimBqGrpGxdRxz
qwIAY1mFr79rosCJPWVafkTPj/nQoVG+c76KoQmMSKOSsZCCYT9O3Qv5zN2qWb0fxEPflq215vpD
7JdPLlLrBkPZ+1PzddZbAQfHckRpStOLSGTKfN1DIJguupRck599314OTeSHF6kn5+pDutv10JTt
9om+ndePLTLvbCXL3GaPuXpv2Fu71Xt7pask9GkqGcvZXFjvMlV02OyF7BvNsdToU3QMhFAXo785
u04Qp2x17lmGTpQk2KaBZUDbi2nGMUh48kqNpbrqQz4wmsOLEiSS8YJDMPB+u3emzNGpAs8tNwGY
Lrqc2FXifQcUnS3nKAneWjtASMGziw3eu68CvDo19q2K7cjefgNIgY8B/xLoAr8OPHAHxvWOj9vJ
UrzWy9H1G0HTi1hoeORcnShNcQ2dP39+GVNXfT2VvMP59a4iPGkapiaYKLh4gwblLZWSqSkNaD9S
SMM4TemFKV6YkqJoUFEao+smQRTT9JTvh6JPZbl7qsCppeZbIlfajjTi1Z7N9c9ki8SnDB4dPn7X
OO5VHP/feWyOajckY+kUM+ZQC+snCfO1HkGY8K1zG7QDBT+QqUTXBQuNHofGcoznHcaKNperPU7s
LrPpbWDqgqJjYGkav/PYHC0v5LvnN5AI+mHMwfE8B8dzPLivwksbHZZbfQRwZNA/9OjFKo9dqils
eqqe7pmVFlGc4FgG620fgQAh6QUJe0ayhEnK3kqWIwON+I0oXa/wI6h2eXG1BUJt9FdLPd4JcrU3
IrYr2blRI/X18sO/fGEVmaiMrkQSx1LpxNfa9EKVzNC0WPUI6MrYrt2PQQg2uxGZEQPX0illTLJx
ymIYI1OVNfTDkFo3ZrMXkbX0IZ624BikCGSaInSNFIljaHR9dZB6cHaEf//oJVIJF6sdHFNnLG9f
837faXLGW8XT8w1OL7f5n3/qOCq3uHPiH/7oIf7s1Ar/45+e5i/+4dsHfrCd2M58e73SuvnNHl6U
qL1SSgqOyWrbZ7YSE0QpX7+wynzDIx0oB8pZkzBKmN/sst4JCeOUNE3RNQ1DV5UbQxf0o0TZRuiC
8Xwex1JgnPMbHQ6O55GpZKnZpxcm7K1kuGuywJNX6iSp5K7pwjVePltj/eqlGt0g5tRCkz2VDKM5
my/evxtg2It0cq5OrRdS76mxuZZOP4pZavYZy9nousAPUkxNU3jtnrLEOLXY5MF9laGkbYvu1o8S
nllsXANN2W6f6E5dP27nHDc7ksXQ4MJGhzhNsTSNK7UehhCEUcq3rqwTxymnFpr4UUKUSkquyceP
TigZ/EiW+nRIpx+TSMl3LlSRqhkbQxckiaryGZr6vC3NwNAT4kj1jjW8hIbXBaDaCXj/gQqHRktc
2uwSJZKD41lafsSXnpqnF8T0g4T7Z8vDiw9A2bUQQiClJO+a2APy31Y1b6dV47Zz+XlQSvluIcSz
AFLKhhBiZ8yut2HcKktxuyXcW32xihmTE7tKfO9ilXo3JAX+6OQi9Z4PaHSjBMeMMXWhGp4F+FHC
XK2rskmov67e++JUQRCCKAXUjRjAC1IcS2BoglzWwQ9jPnrXBIYm+OTdkxybKTJTzrwlWYBbSSOu
/hxv9my2enscU5Hxtp7JzV73Sq2HY2hkLJ2zq22ytk7JtfjcPdM8t9QgiFXvTasf4ho6iaaBLik4
Fn6cUPfU5rLetVhrqoz9/lGl6f7J+2Z4Ym6TMyttLm90qfUibEMghMALVQOjqWtomuDH751msuBQ
zlj82XPLPH65TsOL2brWZm2DnKWzayTL8eki/8/3r9ALYkxdI0ola60+WdtgpuxScE3OrbfZl8u9
gspy9SY6RLsKrjE+fafI1d6o2Jo/85s9uMX58tV8krb++/nFJi+ttWkHIfVeQJpCkkrCBNLBN9Uy
BH4sh2Z2fpQihMDQNdp+RCVXZK7WoxskCIFy9R7NEsQpvSDCNiCVgihJ6YUxliYwNI2Sa9CPEkUc
QuBHKacWm9S9gLYfoQmNuybzXNjoMF1y+eSxyVe9yL3T58dvf2+OgmPwt94981YP5RWRsw3+2Y8d
5Rf/8Fn+4Il5fu59s2/1kN7w2M58u1GS4Uqtd8u9BLgGQPT+/RU2Oj7fPLtG1lZ+blNFl2LWZDcZ
Vpp9QElQr9T7SAlBFGPoGnECSZxScCyq3ZBRLD52ZJwX11os1338OOX5pSYHxrKcXulQyVoEiQIR
aLrGWqvPMwsNOn6Ea2ncKLb2PoFgseFh6IKVZp+9IxnOrrVxTJ2mF9ELE5AKoBTGCb0gwjIUrOe/
/MgBTi00eOzKJjlbAQ80AXdPFSllzKE09/nlJomUHJ8u8cJyCyHkK+ihn3Nvfz3YievH7Z7jdlcy
/IMPHeDLJ5dwTY0vPTXPsekimlDUzZmiS9Y2+NNnl+lHCZoQ1LsBX0skGUunHzXpBREdP2GiYGOb
GgfHsvhxgkglkUjRdY2irVN0TCYLNk/OBcOz21aYGiSJamuYLDm8d/8Izy42CaKEv3x+hVimRDEc
GMuSXte7G0vJ/XvKlDKm2lNgSI3didW47Vx+IiGEzuDENDA9vf6z+2HcZtwqS3E7Jdzr3XE/dWzy
hg3EahHTCOKUyaJDP0xIEkk5ayClpOAYrLR9Ov0YNIGlCzK2QNcNhJCkqfqCNb2YKElJfGWGmqTy
mt51oYFjGpi6RsE2mCjYjOdtRvM2u8uZ4WaxHR3oGxk3k0a8WjPq1YadW7095YzF7hF3+Exu9rpb
VaGRjOqzun/PCI9e3OBvXlonjFNafsRLqx1SmZIkkpmyi2Xo9AK12U3kHVpehB+m2IaGFyZ89t1T
dIKYhYaHlKAJQcsPSZKEbpQi0VhF4Fg6BdvAi1X17r/48AHqXsjlWhcpIe+aOKZOGCdMFlwqeYv9
Y+pCU8padH118C06Jp84OsHMSIaJgsMzCw0uV3tcqvZe4QWxdYDf8gO6Gu16vXP5D+Pa2MqOXV1N
uzpudxPtBjGrLZ8gUvhTlR1WC7WG+mc/Vt9a1zLobFX/hEAIGM1ZHJ0ssNToc3Asx1ythxcmzNe6
hElKwTFp+xECiaFp3LurRME1WW8F3DNT4Nx6h8mCw1TJZTLv8Oenlql7ofKQsDQurHeIpeTuqcJN
kzU/CPNjpdnn62fW+IUP7iNjbWcbfvPic/dM8aWDC/zaN17iM8enGMvvfPjBduO1zLd2P7ppL8r1
39PZSpaNjs9kweFKrcv6AC+dprC3kuH7lzfp+jHtwd6aDk6UfpwoGAkanRR0oXo5Egm2qSNQXjhf
f3ENS9foRQmTJZe1Vp+T8w2iVFXtqz0fIQWfODbBiyst2v0ITVNrcZymrzhXbJ1LaluEL12jE8R8
48watV6o9vOsRcbUqXcDSq6SOqWJMlW9a7LAWN6mlLV4z+wIm92ASsZk04uo5NRrG0Lw1RdWqHYC
ZbXR6DOat3FN4xp66Gt5Pjtt/diOFM+1DfaPZ9GEYKHRRyB4eq5OKWPS7kdMFBwGrAs0TVB0DWZK
LtMll2++uEbeNqh2PFxTJS0rWQWlqXVUv7AXxugxJEnKetsnHSS3NZRxpwDSFHRLo5K1OL/RwdI1
PnhwlMcv13AsnU5f4kcR1W6A5GUj3JYXsdrs40dKNscAt11wTU4vt3ZcNQ62d/n518BXgHEhxP8C
fAH4Z3dkVD8Acassxe2UcK92xz293ORytctsJUvOMYbNlCutPomUfOzIOL9VvcJyU2Gsi66l3HxT
yWYvREOQyxgDF+KEpqe0oJqmmqCFUOXSJAWkktHYpo4fpsSD8WgCspbBvbuKjOZsioMxn9hVestA
B7cT1y9QN6pQbFVxVENoyFjeuuEzuRERaL7eI3ve4JmFOnObHoYmcAyNvGuhaVC0bUxd8LMPzXL/
njK//8Q8620fgIYX0gsTLEPDilOemqvT8WN2lV3W2wHljEE5Y1EZs1is97ENnfGCxaWqx/PLLaaK
Di+ttXnsco2ya3F2pc16xydNJbOjGd6zZww0jbG8zU/eN8PZ1TZ/fmqFME2JA6lMT1PJU3N1Gj3V
y/WxI2M0+zEPzI4MF74vn1zk+aUmEvW8v3D/7h2ZiduJcTsb5O1UireoPgJ1yfHjFBkrI1N4Wb46
kTcJExjNq/4tXQia/RAhYc9IlmY/YrnVp94JWO/4mLpGN4jJmDq5nE2zHxFECamEJy/X+dDhMe6a
zLN/LMdkyWXPSIaCY/Knzyxxbq1NKCWWpvGhQ2OUMgYTRZdTS01mypkf2Dnxu4/NI6Xk5963c3HS
Qgj++U8c5zP/6rv86tfP8Wtf3PnwgzsVV19qGr0Ix9SGFe2b9a1drnZ59GKNpYbHpWqPwxN5PnJ4
jLJr8cjFKusdHwEcnSowkrXYU8lQsJVZ5XrL5zcfuYQXJeQTg7xtUuv6CCFo9iP2jWY5PJHn9EqL
6ZLL4pVNnp7bRBMqcz+Wt2j2Y8ZzDq1+xNm1DtagT2ij4/PiWocDE3kMIa6pYG2t2WdWWkRJoqo5
rsn8Zo+2F7K46ZG1NDqhapAvZkx++j17eHaxSSljqguyAGsA2fn6mTUqGaUSeWB2BIna07pBzHKj
T7UbECQJH8yP8ckbJG/f7rFdaWUYpVQ7AWmastzwWGn1ydsGKcocN0pSLlV7mLrGPbuKlDMWy3WP
bhDT9SN0XZ3tpssOSEGrH6o5iSBNU+IEpIzRNV35+Qz6x45OFWj1I+q9AEPT+JNnljEMjYfPbXB8
psh0yaHnx/SDiDiBIEo4tdjg7z6wl5YX8XuPz3FyoYEGHJks8Hce2DMk1u3UM8B2aG9/IIQ4Cfwo
6pL4U1LKs3dsZD8A8WpZilc7OG4ddLoDrGEYJSw2+kPTzKyp8+Jym5V2nySRSCEZyzm4poYUYOo6
984UmR3N8sj5Kpu9CMuAJE3ohTGdfjIs6TkCXEOn5YfYpk6SJjimhpRgaALX0jAMjTiR5ByDE7tL
fOTwGJu9cLg5LDQ8Fuoeo1mb9KpswU6JGy1QW8+m5UVD7e3LvT0pnz8xc1vZeYC8Y3L/njLPzNUx
Blm3ph8Txil528Q2dPaOZviRw+PsrmT4Hz59lPl6jwtrHb790gaXqz1iKWl5IZc2urT9GNcySKXk
/r0VdE3w6MVNhICmH9ILI/w4IYjgyHiOjU7AqUHpuuhadPyYThDRD1JiKXlwT5l2EBFLiWvrzJQz
9KOErh/TixT23NY1ekFMN0x45GKN9x8YHVLc6l5ItRMMiDI6navkcDstE7cT43Y2SGVuFw5duo2r
ekSUHHMZx1A469GczUJDXbI1IdBEipRQdE2SVBGaOn7C7Eh2UNUMqfWU8fGVWpecrVFwDJ5fUI70
AsFmL8LUY1Za6lIuNBhxbfw4oeAY6Jrgcq3LeksRBi1D4/xGFwnoCKSEdj9kTyXD3VPFHSeBeDOj
5UX8/uPzfOaeKXaVM7f+DW9hHBzP8ffeP8v//egV/t4HZoco9bdTvBFgoasvNf1A9bxufV+vvkBc
D34pZUyOTExwudbjI4fGuHdXiZYXcXymSLefJWebtANlbfC+/aPDPSeRko8eGceLEhbqHlnL4Nsv
rZOzDfwooetHfO9Sjc1uSNuPcQyDsbxJmEg6QYJpJDiGxkzJ5dBEnmOTBR69VCVNOxRdg10ll/v3
lPnmi2t0goi8bfLJY5M0+iHdfszJxQaWrqMJwUP7K0M5XpSkrHdDTKFjmRotL+abZ9c4PJ6HgWoC
YK7a4xunV2n0I+IoxXV0Sjllrr0lra92A7K2wcSAOtnoh8pg+20QN5tT1//6dhOAElXZOzZdZKrg
st72sUwdMajo76lkKbgWHzwwOrQZ+M3vXCSKJF6k2hdqvXAgaxSsNn10AbpQEkVDF2RMg2Zf9Vvl
HIOMpZMkKSsND11X4q4oSTENHS9M2GgHJGnKSM5SMjYvQkqod5WqKNc3eHahTrev1CJJKonly5qg
nXoG2A7t7T8A/0ZK+etX/dqvSCl/5U4M7E7HTuXBXx03mjTXow8PT+SpdgJ2j7jkbZOzq210TVDv
RbimMiQcyzs0em2ENjCsIuKMgI/dNc6pxSarbZ+uH2HoGmEsr9EyhrGkOdDr24YgTSFMUkxNkLWU
tC1FZY8PT+TYU8my2Q1Vozuq6frMUotHzleJUsnekQyfuXvqTfwUbx03W6BuJId7tczU1WSWy7Ue
Z1ZazG0qYs18rceZFYUCrnYC9o9lMXXB3tEMQsB//ZGDw0zJFsVrqzE1TFI22j5RmtIbQCguVjvs
LmeYKjqcnAcNQTFr0295dHxF5AsGTuDTZZejkwXm6j2CKKEfJkSxZL3r8+1zVZYafd67r8JIxsIQ
giRN6foxQkAUS6JEsumFRKnk4FiOgmty7CrZkiEEi3WPxYaHEDA7MLH8Ydxe3E4/2iMXq0gJzy+3
uG+myCMXq3zOVZfrr5xa4sxKG8fUmClmmCm7BEmi8OlRjClUssIxNMIkJUphqujwoUOjbHR8/r8z
GwhUxni50Wex7qFpYmiKZyQpugBN0+mGEXGSEiUpXhSTpJLFRp/pgmS17TO/2aPlxxwcyyGQaMop
GQT0woQXV1u4ls5Yzv6BnSO//f05ukHML3704Fs9lNuKX/zoIb58con/7a/O8Xu/8N4dB2d4tXij
8MdXX2pyjsFnDioy2hbc5erXvx78smUGvLeSfcV4rpeqb/3/WifgpY0OhyfyFByDjGngmjpxIhFC
SaBdyyDvKGQ9AlIkURjTDSLqXsBIzmK84PD5d02yu5KhlDF5af08lm7gDfp6v356jYKrjoHLTVWJ
aXohG+1gKE//4IFR9o/lCOKUSs5WBqd+TKMdYmgKk00KY3mbJ+fqNLyQphfhRSmOodPyIzRd4IcJ
DULKrsXnT+zCD1NeWm9zZbPHdOzy3fNV4GXZ9E6Nm82pV8Oh3877qXshtqkxWylyudYl6+gcmykS
J5KiY1LKmMOE8lTZBeDMaou5Rp+RnEnQiumFCcGA1BunKbqmoDe60BCaJEwkGVP1GM2UXExN4+m5
Ouf9Dl6cktU0wkSSShQBNIFL1S5LTYGp62RMnb6eMF1yledTPyJnG1i6DiKhHyXomnhFRXEnxnZk
b58C3iOE+D+klL87+LWfAH7lDR/VHY6dwIN/rZev6+UvD+wbAQlPzdWpdgM2eyG6gKW6R62TDORq
gtGcRTmjs9mT5B11g5/b7GIbGhMFZ9BYZ7DaCgiidHgB0oRCsoZJShBJpISya+JYhjLKiiSGBvfs
KVDO2Ni6xv6xHKDK+UXH5K9eWMU2VYk1iBIaXniNiddOiBstUNd/1o1+SN65OXSiH8SsNn2+V61h
GhpRkg4XrGfm62Qsg6mSw0YnYCRjk7HVhrZ/PItr69csFlsL4aePT5E8v0LHDyEW9MME19QxNcGB
sRw526DomkRpykLdI4gTDF0dToqOyYndJWIpubLZJW+b/Ph90/zqN87hD/o+oijANnUKbptzq23G
iw4fPTzOSrNPJWPRCWOOTOb57D1TfOvs2hCVvOXqXBzIAPdUMhydKtALY969u/yWGdm+XeP6SuPV
68LWPBzNKePQjGWw3g44s9wiQZGSmp7yVegGCf/gg/t5Ym6TY5MFHr9cJ5IpeUtl+II4ZbLk0vJC
/vDJBVZbfVp+NGhy1ZQk09TQNZ1+GJOzTXK2pohAvYg4Tsk5OkidOFEVpZPzdc47qhLZ9GI0AS+u
trANZZYogKytM5pTxonNXsTfOrHrB3JudIOY3/reFT5+dIKjU28Pf/BixuS/+dgh/sVfvsh3zlf5
kSPjb/WQXhE320/fKPzxzRIUV/ubbRl35h3zZdmzey0k4fnlJt0gvgYCs280O/zed/oRtU7ApVoP
KZWn1t95YC/nNzqcWmrQ8WNcUyeVEsvQ6AUQpSnv21fhnt0lHn5pg8vVLimSimthGWKYhXdtgwdn
R8g6Bsv1Pl8+ucjlag9dE5QyFkXXoOCYeH6sLDKaysvvybkah8fVnr6VQFlt+gNSqDIz/d7lTUoZ
k0u1DhnLZFfJRRNgWzpWqvET907Ri9Te9cjFKh86OMan75nkwHiO8+sd9lWyfP9yjThNGc87O04a
f3XcbE693rmmqvsR9W7IlVoXJLimznsPj1B2Lb754hqnV5rkbRNDCP745CLPLjS4Uu2Qsy11aUlT
hKHjRRE6goxlIYBd5Qx+HLNQ83BtAyElD+2rcHqlhUSSJKBrKsEtEPgaDHhW6Bq4ppobtqkhhIap
a4xkLe6eLlJwTe6fHRmiz0/sLvGNF9ewTW1HtjlsxXYuPxvAR4HfF0I8CPwSt+QT7cx4s3nw1y/M
r+fydXUGKohUhj5nG8OStYbg7HobSxeEBsSxKoeuthLGCy6uYZC1DXpBzEK9j5SS49MFnp5vcH6t
SyJVv4ClKSpUnEiEhP4AZy2AoBOyd0Sn4ammyDSVTJcdXEPnpZbylxnN29w7o0rY3SCi0QvIOyau
bbxtZs3Vn3UYpTx5pf6KL/TWs+z6MS+utihnLMIk5b2ziurT9CJW230qWZsgTtjoJKSpZG/FRdM0
Gl5EMHjtVEr8KOHzJ3ZdY2g5VrCxDZ1eEJKkipyTsQ1+7J4p8o7JcqNPGMXEaYJA4hgGeduglDUR
uuClpTaVnI1eVnKmUsZisxsQpRBJWNj0SBLJhY3TfPaeqWGj7kqrjxCCK7UuP3J4nJ86sYu/Prcx
9GHYuuA8NVdnualw2ocm8ry41ubSZve2jANfLQHwdqjOvpFxs3Vhay50g5g0lZycr5MCF9Y73DWZ
5/x6h4ylM1mw2T+WY7zo8PPv28d8vUeUpnzthTXqBORsi72jLrahU20HbHT7RDEEqcTWwDEF43mH
Vj9krdUnSiFOffKZPCVLAyHIRinjBYuMaXBmtU040O7rusZUXhkjj+UdekFMq68qRxoCXRcsNfsU
HAPX1Jir9yi4ty+HeKfMhd9/fJ5WP+IXP/b2qPpsxc8+tJffeWyO//WvzvLBg6MY+o1pYW9FvNp+
+kbij2+UILudPeJG+wQoVUSnH7G46Q2rRy0v4uRCg9WWj0D1XH7nwgYvrbVJUig4JiM5Cw3BTDnD
XK3L4ck8pazFrpLL0ak8Fze6xInkSr3HocmXPXUMIfDjBALoxwp1X8nZ9KNkUFmwqXYDepE6U7iG
zmqjx58+u0LBNfnw4THeMztCs6+8gzZ7AX6UIFAIbl2o9vn1ls9mN1Dk0SCmlLF4ZrGBbRrUnRA/
SodGx2GUMpa3WW37SFDm3G1/6A33WveOOxFbf6YhxA3n1OvFoT9ysYpjaqw2++wfzbF/TF2Q845J
wTWRKAqgBBYbHt+/VOPCeodWPyKMUlxTI00SWr7q1zZ1SZymGJpgvtYjIVUGurbOWsvn6bkGDS8g
YxkIkZDT1GtHqSRU7CtSoBOkdINg0GdUQg5gN6M5i4YXUnBNvnj/buY3ezw1V+fF1RaXqz1+9K6J
VxBhd1Js5/IjpJQt4MeFEL8CPAy8/QTAvLk8+BstzLdz+Vrc9Jir95gdyVJwzWu+6B86OMaZlRan
V1p86akFBHDvrhKfOjbJBwdSlulihqavej80IZjI28psVBOkUpJKiSYEc3WPhYZHvRsiNCjbBk0v
puAYIASRLsmYOp0gQaBcgqUEL4yGniEAF9a7nFvrsKvs0vJDfvnjRyhmFB56rR2gCUEvjDk0nhuS
XN6MeD2L5NZnPVfvoQvBpVr3ptmerGMQp1DO2lyqdvn2+Q1KGZPDE3kOjOYYy9qkqcSPU8J4YDSm
a+Qdkw8eGOX0aovFep+GF/KVU8t8/sQMx2eKrLV8nl9sYZs6jmUwnteZHctxeCJP04v4wyfneWq+
TqMfkSSQSojTCCHgoQMhvFNTAAAgAElEQVQjaAguV7v4cUrTC/nU3ZPkHYONdjB8nwLQNEWOQ0Ix
qypE37u4SduPuLDR5f/665f46fv3MFGwrzHUrHshlqkNEaWHxnNUu8EtEwu3SgDshOrsmxVbc7Tj
RzdcF67OOh+fLvLd81U0TXBpo0vGMthTzhBEiuTIoAehmDFhU1VrTF1DIoniFNfUma91qXYD2v1E
9e7pgrGCklCWXIulRo9GLyKj6/SjiHo3wC66yFRyYneJj901zu8+rqRbUawu48gU29QZydrsKmdY
aXp02z5pBGEcU3BziiqUpJxZbVPKWizWvdt6rjfrpXu7RduP+HffucSHD49xYvfbo7dhKyxD4x9/
+i7+qz94hv/4zBI//cCet3pIw3i1/fROQ1eufv1OP+K55eZQ9jy/2WMv2eH/S6RkouBQ95SB6JXN
Hn/8zJLCChcd9o/mWK73cQyNTj8iTiXfOV/lwf0VRjI2bV9h5HeXM/zIkfEhtGb/aI7vX6zxT/70
BZwBnORjR8aJUsmHD40NL1/feHENL0zww5RPH5tivaWSc1JK3rO3zM+9fx9n19p888w6QggW6+rA
LEFJn7vqc7ZNjU4QgQBT0zF01eu52Qto9kMcXcc2dWYrLn4kKWZMoiSl2vJZaqSsNHwemC0TWCm1
TsCHD42RcwwKc8pj7mpvuNslX76W2M7Z4HYk8Nudazey19g/mqM/UMdcrnaHtNj5zR4dX1ldrHd8
Lqx3BglMyUjWwdSV9D1nGzS8CMfQcEyDvKOz1g7ohiESEFIyv+lh6gqVfW69RZyqqk8hb2Mbqr8z
4WVQjqmBqWtMFmyklNR6ISXX5GK1i35ug4mCrZ6BgE4QMZF3uFTtcbnWY6Kwc+XN27n8/PnWv0gp
f2UAP/hv3/gh3fl4MylUN1qYt5qX+2FC7ipDzKvlU7/5yCXiFLp+RMExqeQsZsoZPnVsUpFi2gFn
VtuMZiwsU6fWCfjKqSUksNzyME2NcdNmYdOjOzAcreRsJgs2M6UMj1yo8viVOr0gZnbEpTvwEVD+
LxBLmMjZdIIYQ9MoZw3qvRg/VoflWjcmZYDQFdAcHIKrHVUNOLvWxrUNXlpV3gAfu2uCi9UuxwdS
qTcjXs8BuuVFzNd7w0xeGKlN4GbZnq6vsvKnFht4YYxAcHjfCDKVPD1fxzHVIpQOJAi1bpeRnEXZ
tQZc/IT1to+hwVrT40tPLlDKmDx8XgEPNKU/pJJzlC+PbfBXp1f49rkqPT8GxMCfAUxNwxDagPLT
ptoJ6AWJctE+GPPg7AjLDQ8vUmORqCZFHdWI2uhFHJ7Ns9xYUKjjKKGSdah1A/7xp44OTVy3Psut
CtV43uHuKdWPcqvEwq0SAG9nt+7txNVzNBh4I9zos9u6BBlCsFD3aPsRm92AOE3J2AZ7RzLDButH
LlY50MzxZ88tc2GjQ3WQhXV0gRckrLdDQKGtLV1TDbXAUqPPlWpPeTlIiY7E0nUMTZnnIpSuv+PH
NLwIUxMEKH14HKuegcPjWWbKDvWewlsbukRD4lqCXphiSOUhMZF3bjsreKO58HaMf/vwJRpexH//
qSNv9VBeU3z6+CT37S7xr//6Ip9/1y4sY2dUf26VzLzTDddXV3eenKvz11fWkYBxQcCFKnEiMXRB
GCZ8e20Dy9DY7AY8u9Ck4Br0goR7Zor4YcJCvcdmT81319QxDcFCvUclpxDT0yWXctZirtaj2Y/Q
gb85u8bD56tDs1HL0OkGMYcn8xia4JELVfwwGZJhN7sB9+4q8vPv28efPbeMZWhMllQPybm1Nn6c
UHYtokJCsx+z0fbxwoj5ukkYpZxf75CiKkmaIfGChCBWEjjHMAjTlL6XUOsFlByTcs5ElwrfrWsC
geTUUpPOoK806xj83EOz7K1kX+ENd7vky+3Gds8GNyLC3siy49Xm2tWVo0Y/vKZKuGWvcbnW5Uq1
y3TRZbXl88WDuwD47oUqJ+frPHGlhm3ozJQydPyYKFFKEttQZ8S1VkDONtEGZqZBlNCPYiSCMFYy
ZU1L0IXB88tN4lSd34oZA9PQqHUDar3omnHHqaoilbM2DS/EC2JWmh4TBXeoAjmz3OLRSzUuV7tc
qvY4MpHnw4fGhia6OzG2Q3v7n4QQE8ADg196Qkr5sTszrDsfbxaB4vqFeasJUiGlUz5z98uZma0v
49mVNrVuxEzJ4fHLm6RSkrdNDo1nKbvW0DfgpTWNuheQtU2Kg7Lo5Y0enX7C7pEMrqloHZ0gohck
TBYc6r0AL1KYypJj4Icxl6o9kjTFNkAbNMalKTT7IbvLLkGScmC8xPOLrUFPiaYc4QdhaDBddAhT
SbXjEyWSr51eY7O35UytmqGLrvGm0oJe6wF661lsdPyXy7dE3LerdI2eG669SM9Wsvy/Ty0wlnN5
frkxWKgU+nv/aE599qEq83/thVXqXogc+DZ85NAYJ+cbLHYCklSyp+xy3+4y9W5InKQULBPX1PjR
o+O8/+AoXT/m3z68iUQ1MeoCsrZGEKVIJEGcqAqbFCw4HnEKm72QPzm5yFy9T5xILF0x/veP5Tg8
WeAjh8e4sNGlF8R86YkFLmz08MKYIJbk7JhaJ2Sh4fHp4y8DK26USLgdY7pbHVh2qlv3Gx3Xz9H7
ZkrkXfOmn13DC0mlpJxVPh1jeYf37a9wZbOLlIL9oznOrLT4i+dWqHVDVprKDwokjmUQxgmpVJQ/
KZVhcV43qPV8TKHRCxNyjkElY5OxdMoZk1ovJJVQylhIJI9erNH2o2uIPlsRJOpCNVGwWGqYuIZG
mKakUmldP3l0inPr7W1lBd8Jc2G11ec/PHqFnzoxzfGZt6VgAiEEv/yJw/z8bz3JHz29yH/20M7A
dL+ZycxbjeO9+0boDrLfzy+1WGp4AwhQQM42SCU4hs6FjS4tPyKVEi9URM2GF3FkqsB79hr83hPz
aAhGczbv3lPivt0lposuCHj80iaLDY+NTkCnH9HpK+m0H6cEcUo5K/jAwVFmSu4wiVrv+kgEQsJK
q8+ppSYZy2B2NDu8aMzVezimTjljsdH2KWYsDk/keX6pRcYycEydRy/WODJeQENw2m/R8xXMJE2V
UXI3UJCdsmsiE5WYOTSW58BYlt9+bJ6spdP0Yw6P5ajklJw7ThQBdt9o9obecNfHG7EebPds8Hr/
zOtlj5NFl5VmfygN27LXeH65iR8qEl7Di/jWuXXes2eE0wOPpo2Oj23oOKaOZep8av8ULU9J449M
FPjq6RVcQ6fuBYznHBr9kCv1Psng4mMbkLVM2v2QeNDikEqodwLlCzdws9dQSVEJGAIMTWPfaIYr
NY+9IxnsjsbhceULGEQpj16qsVD3KLom5YzFhw+Nce8Or25vh/b2t4FfQ8ndBPBvhBD/nZTyj+/Q
2N4xcXy6CALKrsVcvUfXj4d6zq0DxNWksPW2z1rL40qtQ5KkFDMWaQqtfsSZ1Ra1bsClao+7Z4rc
v6dMzjbohwl/9PTC4DKkk6YSx1S6TE1AP0zww4RyxuLu6QIP+zWCJGWi6JCkitseRgmdIFZlUEOh
ab2sTcbSMDWNctaiH8V0/EgZbQk1EUYyNuWcRSIhiuHB2TLtICZrGbiW5Gcf2ksiJbMj2TcVdPBa
F6ytZ7GvkrumfHszCs3WRbrTV8hSKaHgWtwzXcYxtWE2fyznIIFaR6EjbdPA1DTSVBKmKUenCrhm
jyhJ6UUx3z1fZaMbEEQq6373dJHP3TNNwTV57LKCKkwWHAxNw7F0ZkoOL6120HSFM1+o93EsjWLG
IklTglii6RqOqZFKgzBS/kGqkqSyhZerXfK2wRPzmwSRkkmFSYymiSE6eQv9fXXZ/7VICl/twLJT
DjR3Oq6fo7fMlAkwDY2Co6QkI1mL9gBVu1WZbHoRjqVjaWK4DggBcSoJ45ReGJGmAtdSjux3TRU4
s9JGSkkiJZausX8syyePTQ5IhR6bvYC2H3N2tYOhaRQdk74fXzO0MErJOzpBkqBpGrtHMqoyZWq8
d1+F55ZaVDs+u8sZHpwd4e6Z26sCvxPmwv/5zfNICf/ok2/Pqs9WfPjQKO/ZW+bX/+YiX7x/F46p
v9VDAnYOTnfviMJXP3Z5k6YXsemFTBRVAnHKMXAtncVGH8fUmS66NPsRWVvn+HSRjU6gzIBNnQ8c
qNCPUvZUMozl7Gsw2F8/vcZio49raGiaYLyopHRSKJz9Rw+Pc3Aiz1JDJb1GMsoH0NJ1On5MJauM
jNfb/jXI7tmRLOfXOhgCKjmLn7xvhkRKlps+G+0AP06ZHVH7tx+n6AhMDZJUkCAwDaHQ3X6IFyUU
HJNDkzlyjoltGkwWLDKmSVmmFLMmKy2fKE7JOy/3Jd3Od/3VfuZ2pWzbPRvc7hp0K/DGljx+NGuz
3OxfkwQqZpQx+LMLTRpeRDlj4Zg67UHyutWPkChqZpykmLog7xrsH8sN1n6fUsbi/fsrfO30KmfX
O2jAaNbCNpTnTyeQdMNYnQOEJEhUEqyYsTANZUlgaUoGHycqsT1ZzNAJI86udjB1jUrO5shUgffv
r7DphehCcHqlRcdXPUAzZeU9+E6ivf1T4AEp5QaAEGIM+Bbww8vPTeJGkpZEymHD45aeE17+Ml6u
9TB0jSMTBZaafZpaNJQXHJ7MM1lwuG9X6WXfgN0lFjc9fut752j5MRudgA/sr+BYOu/aXebceoeu
H+P5Ef0kZa3ls9z0+ZEjY/TDhJJrMbfZo+Gpw83uUYeVRkAQp/QD1Vin+k10XENDFyZJItFQuNuJ
osO+0Sw/88Ae2n7E106v0fZj1tp9ar2AsZw9pIK92fFaD01XwwZO7CrxwOzIbZVv91ay3LurNLjc
SMpZk5xtXKMPBmVOq+uCsyttLENjNG8zO5LlObs5kJnFuKaBqWvMlF38IKactfn4XYqy9NUXVugG
qpn8vl0lNE0dZOu9gIsbXQQQxSmLjR6HxnM8tL+CH8bMbfYA1ddTcg2cos1EzsGPEp5ZaHKp2qHV
T9gzkiFjGaSpJEklJdfkXbtHmB3NsFD3mKv1eHG1xbHpIjnbeFWH81eTE9zqwLJTDjR3MrY7R/eO
ZDmxq0QniNg/mr3GGBDUJnt4LM9vPHyBXpSgaxqWoeg800WH9x8co3la4XCTRGXzUyDvKAiKLhRR
MEkla22fOE1Za/vcNZmn4amkx6nFJr0wwbZ1gjQmTV9+L0eninzq2CR/9twyXqgab3VNcKnWG/gI
9Xj33jJzdZW82c7n9HadC88sNPjyySX+/of2sXtkZ1EutxtCCH75k4f5u//+Cb705AL/+Qf2vdVD
2lFxffXnu+erZG2N2UoZx1RJybxjKm+VVLLe8snYOs8uNRHA3nKGphexZyRLkKRMF12mCg7tvpJh
d/14mHgMU5WcMjTB/vEcfpRyz3SBUtakP/guB1HC9y61qXdDsraBbepkLJ2zq21G8zafP7HrmvWj
2Qs5u9Ym6xicXmnxwOwId08VaXhVoiRloeHxt+/fgxfFtPshYStBJJJdBYc4hYKjc3Qyz3rHJ2Ma
g8Z7wUqrT8YyCZKUu6cKOKZO3jHRULjvq7/bt/Ndv9HPbHfv2e7Z4Fbjuh3wRq0T0PZDlpreDaVh
xYzJ50/M8JVTSzimTs42uHuqyOHxHBvtPsWMSRCriv+JPWXu310m5xqUXYvFhkfHjzi/1iFOUioZ
E00T1HshpYyprAv0FHNAdCu6JtVeQMbQyToGYZzS8WMMXVBwTI5M5ZmreURJShAm6n0hObGnNJS3
b51tdSHYPeIylrf4+F3jO9rUfiu2c/nRti4+g9gEdobod4fG1aXVF5ZbCCE5Pq1KgUenCtfQTLa+
jPObvYGLb5fZ0QyjWZs9IxkOTeTZXc68wjcAFOu95UccHM0PjKtMLENjrePjmjpHJvJcrnZ5ar7O
VMGh7gX86F0T3D1dpO6FbLR8fuPhi5QzOq6p8d79Cj+ZsY1h4+Wz8w1KWXuYHY7TlEQqhO279pR5
774KdS9ksxeStQxq3YB37ykPiW9vVRbgTlQlXu33ffH+3UNd7/X+DVuvd+/uEnsrWebrPZAMF7/D
43mCOFXZ+UAZwJUyJp0gQRLw8Pkqp5ZaFByDE7vLnNhTHuLEv/3SBuudAITAC2OiVNLxY6rdkP/k
3bs4t9ZhcpBpnCm7JCmstfukCJXF0wSG0Cm4Cnt6ZCLHZMGh2Y/53D1T7B/PDRt6NSGIU8haBom8
1rT2B6VX542M7czRYsbkC4M5drO5+dilGvvHcoSx5JNHJ3hmsclkwSKVgoWGR60TKOrT4GByYleJ
+6aLPHKxxu6SixelZGyds6stJosZgkRVCN+3v8KppSauaVDJWiQpxHGPIE4puiZ7Kxn2jWZ5YaVF
wTExNY1PHJtgftPDjxJ2lTOcWmoOkdc/CHMjTlL+6VdOM1lw+KWPH36rh/OGxPsPjPLQ/hF+/duX
+JkH9uBaO6P6s1Ni70iW8bxDLCX3z47w3n0jQ8jP1djr+U11mVlt+xhah6NTBS7XeuhCgRG+dnqV
ZxcaWLpGkqboukYvSIjjlPv3lolSyUcOj5GzDbpBTNePObWojIl/85FLHJsusmdw2TY0QT9KyVg6
qVQJjZxjXENcfH6xyZm1NomETj+m2g1AgGMpKpyGonlOlhxGshaTRZeZUmb4/Dc6/lAu9/Fjk+pg
7ii663NLTT5zfIoXV9uM5WyCOOU9e0euUb+83tju3vNGJ1Ru9efPVrJc3Ohgapoyfc/ZN0yq7v7/
2Xvz8LjO+773c2bOmX3DDEAABAgCXCRuomRRpERZsm9sybGsNLEcu4nj2EmbVnGT5qaN27TNzdM2
yU1ye+910yZdbKd2khs3TuzEchLbqW3JixbLIiWRkrgvWAmAwOzbmTNzzpz3/nEww8FwsAwIEDPg
fJ+Hz0MMMOe857y/9/e+v+37i3j4meMji3T8zz48glOx5s7nVHjH3h66PA5euBIlVzRILTimZlIF
0qqOIUw03aRgmJgCHHYZxW5H1U3cslXf8/iBXmK5Eg5ZomQIMlqJ0ahqpUI7FR7b18dkXOX89SwX
5zLkNKuvW8ClYCycAxula7fLGaAZ4+d/SZL0DeALCz//BPD19R/SzWhXmtPa0KrfJVfTn3xOuSGN
Y9CjcNgTQrZJfPLZS2Q1q2/Hew70sS3oIuC++VCeVi02rniuxFw6hg3Ihj3E8yXeva+XiVyeL746
iVYyGYvlODISxuu0IhKVxX8tqeJ3KwxHvIzH8xzf1c02v5OTYwli2SJ2u8UQNxByMxbPkVZ17HYJ
h2yjP+DhHXf1VJ+looglCYYXFH47snatVTHWfq8it5mCfpMnBLipfshEIMsSfpuCZlgpTQMhNw67
RLfPzVxWYzymYrdLzKQ1jg2HGQ57OT+b4cUrMaKZIvFsEVOYlE0w/RYBQlYzMIXJSMRHOZYn4Jbp
8bv43kUD2SYxm1ZJ5nWybp3hiJcfPbzdSlmqY7NJqzpnZtJEs0U03ahG9mpTBrZCfUarYznZrGw8
d/cGmEioZIoG/SEXj+3r5cJchu9ejFLUy1YRqw2KhuA7F+c5vjtCUS+jYhlE2/yuKmXtZEwlrRoE
3Qn6gy6Gu91ICcFMSqM34AIBXpeCxykznSowHstzcHsQp2xjLqvR43cisCjxTdNkKqneMU1O/+Tl
Cc7PZvhvH7kfn7OZ7ba18Yn33M2HPvUyf/qDcZ5+x+7NHk5LYTnnWa0uPTGe4M1rKYqG5TkPex3V
c8JYPEepbBLxOtAMk2jSoiSezxWIZUrM54rc3esn5FZIqTovXo1hCsFMqsDhgVDVOdUbcC0cmgXX
kipeh42MZjWjPDOTZiKR57BnoTZjgQBFAgp6GZtkGXKP7ZN481oSm2TRMMuSxAM7w2gli2BFM0zU
ko5LsdMbcONx2HnnXT3sDHsXUUPPZTXmMgU8DrvVxwYr+2W9GmJu9N6z0jl0qftXIkJzmSJjcbVK
UqWXl3YA1ToTwTKIfv4duxfdfyyWJ1c0mEoUGItnSecNtgWchDwOLlxPI4SELEuEXAqmsBxaKdWi
Mu9xOJHtNu7qs1hjpYW+j9czBfxOBU0v8spoHNUoc++OIHG1SMjrwGm3VTOWlkvXboczQDOEB/9S
kqQPAI8sfPQZIcQzGzOsG2hnytt6JQg0XDwVZrFKFMDtlLlroflXSS/zhy9e5dhIpJpiVMsyMpGw
yAp++EA/b0wl6fa7uG9HF89emGM0liejlbBJNnb1WOwhZllw31BXNWoElpEi26y+IYYQKAtsTA8M
h4nnSlbqi2ZwcS6DaZogWUwvQgi2BRxVr1bQY1FDP3N6GpdsNTM7NBBsCy/AeqNWbpN5HYGg22v1
UXh5NMaV+ZyVwmCT+PDRIXZEPBzsDzIc9jKZyBNwyTyyp5vDAyGrz8NshoyqE/Q4GOpy43TYCXkc
fOWNaU6MxYlmNcpCoMgSsiQjFnqrDITczKQLjEbzXI3m2Rn2YApBLFvE47DRH3JzLalSKJZxynbs
NgmfS25Ym1U7v1ZEz2Kpqd/c270+o50R9jgo6iYnryVwyDY8DjvdPseC06KEx2GlsJa0MroJkgRF
3aBYMjk2Esbnkjm0PcRgl5vfe/YSUwmVWL6IiWA+o1E0TIYjHvb0+BZ0g8VktWebj16/E7dTtprt
lsu4ZZljw5EqyclEIk+pbKKXBevj621tTCVU/uM3L/LOu3p44lDfZg9nXXF0OMyje7v51PdG+akH
d24pw249sJLzbCKRZzKh4pCt9C+/y87+7YFqpsREIo9ss3FpLotaNMiXDOK5EtGshtthpbqF3A6+
+tYsF2YzRHNFdnf7yBcNrkZzmKYgtsDy+CP39PPeQ33kNOt3//MHE8ykCpimILdQt5dWLYavPdt8
zKQK2CSJH1lw0sXVEvcMhOj2O4lnizx7YY4urwOP085Q2MNQ2MPLo3GmU1bq3r4+P11uRzU9O6Xq
3LcjZJ2BhEWw43bY2d8fYDjsXbcUqY3ce1ZzDl3q/jdorL1cvJ6pklSNdHuXNAyWul/F4T0WyzOf
1qpznyuUmU0XmEypBJ0KQbds1QgJGxIWqYZdkgh4HDy8pxu/U0a2WwQ5b01bRFY+p4LdLmG3STgV
O4WySTRbxCinGQy52b3Ni9d545mXM/Db4QywKo0lSZId+IYQ4jHgyxs7pMVolxDaUqhXgvVRAVmS
+Oa565xeyPk9PBji4V0RLs3nmMsUsdkgYnc2TDFKqzonxhJcmM0yky7Q7XOSUkvMZTTu6vWzd5uP
LreDP3l5nJMTSZAg5FFuyrGtRJS+c3EOSYL/+xvn8bssNqlun4OUpuN22JEkiR6flQo1FLbC3e+r
UwKGsGpdKvOFaA8vwHqh2rOlcKNnSzJX4q3pFDbJxlymwFy6yOmpJC7Zhr7QJfvn37GHHREP//ZH
DnJ2Nk3AqbAj7OEb566TyJVQZBsRnwO/S6FgmJTKgsvzWc5OZ0hpOkJImAuHymLZtBqhOmQeHIkw
l9M4vivCyfEkE4k83R4nIPH0o7uZTKqcmkyhFAw03UQ3xLJNaOvnt1HKQjvXZ7QCbrU3VaXmYCTi
Yy6rkcrrCJtExOPETg4hwLqstJCPr/HVt2aIeBy893A/sbzFTGW3SQTcCuPxHIl8iXLZZDDs5sR4
kpJhUigZDARdbO9y88iebr53aZ60ZvX/uH9HF07FTn/IXX0Gv2rJRbvq8mZQNgWf+OIb2CSJ337q
EJLUJp2dm8An3nM37/+vL/HHL43xT9+1d7OH0/Ko3fNPjCWYz2jMpAv0B93s6g4vygY57AmxM+xl
Ip5nJl3g4vUs89kCf/fWHLJdIqvplMplTGEn4nWQKxrEciXsdquvTsTnoLhAfX/6WurGYX2hBq8K
sZiJ7OL1LJpexuOU+fbF+Wqd0lgsZ6W3SRIuxYYsSbw2mSSrGSTUEsd3RRjochNwKhwcCC4Yd3mu
pzWuRvNcvJ7mbUNhXIp9UfbLep/vNmrvWe04G92/tob4geEwB/oD+JzysnXES92vMlfRbJFvX5jD
o9iZShUIOBUM0yKrKZbLJPLWGaGgmzhlG2rJpFAqIxDMZYoMj1gO6zMzKWS7jViuxFSigF9RUOxW
M3TZJnGgP2Cxty2kV54cT/DGtRRnptM3OeJXeg+thlUZP0KIsiRJqiRJwYVGp7cNWzGNpj4qoJas
Xj5gpSjF1RL3DgS56rST0QzsEuRLxqKO0IYQZAs6TsXGA8NhXroa5fiubgSCobCH6WSBaK5IIl/i
8f29fPG1Sbb5XKQ1g6mEuqjIsRKSzRbL+Jx2ygJ6g07mMkUcig2fUBB+yBQNXE6ZbbLltdm9zXcT
dXWVuGGhsWaXx9EWXoD1QO281vYFQpK4ZyAEC+x4IY+MYZropoRbsVtUkwvKbUfEU426jMWsxmZh
n5Owz4nfJVebI07GVXoDLs7OZCjqZcI+B3YJDnoDFEomDhn29gboC7q4NJ/lpcsxJhJ5CrrJNr+T
gS437z3Ux/aQm5BHoaSXyZUM9vQ2bkK7UnfrDtYH6xHprtQcZIo6ml4m5FGq7JJv39tDQtPp8TjQ
9DKlssDnlEmpJTxOhW6vVY+TKd7o9SBJEh6HHbVk9egAQdjrYFQtMZ4ooOplegNpRnqsRqYnxhLk
i2Uid3BK5B++MMqJ8QSf/NC9DHa1N8nBUrhvR4jH9vfymedH+ejxYYLuravb14JaJwZQs+eXcMl2
njjUz/nZDPcNhapsbrWopMHvjHhJ5Euk1BJhr4PtISvV9D0H+5hKqEynNMJeB2Gvg96g1WvtzEyq
Sn0/mykwkcjjVxWQYDDswSHbKBkmPrdcx0QmCHkdBFwK0ZxGyO3kngVikqGwh4BL4fXJJM+OXieW
LdHjtSjxK9GgRL7EjrCH5y9FOT2VIpopEfAohD1ODNPk2EjPTene7aATbkV3rSUSstT9KnOFBCVD
4JKxWproVjPrkDpNh/8AACAASURBVNtKQ/Q6ZdSiQaaoE8+V8DsVkARe2U6Pz8n9Q12cm82Q1azU
eGnhcOJ1yXR5HfzkAzuYTFoNd30uuVof7lBsi5r4VlMm2xDNxKo14C1Jkr4F5CsfCiH+93UfVQ3a
JYS2WqRVnTenU1W660KxjKbbyGgaEjDS7WU47GUqoeJS7GiGyWP7tmEIwfOXonz9rVkmEyr3DYWw
S1I1lz7oUhBYB5ntQTfRXLHqNXA77fT4XSiynbl0gWcvzDHS48UuSRzaHqyGZK08XAm7BHPpInYJ
dkW8XEtbxZE9hsndfX7CPifvbNDAqqLs7xsM8dW3Zimbgm+cu86HjuxY0kOwlXBTz5aFvkCV3k65
ooHbYcMl29kZ9qIZZbxOecn6h7DHygEfi+UQwK5uL8d3dVv3ys+QKeo8OBImkSshENgki/VtIqEi
Ad0+J10eB8l8iWupAumCTtEwMcomhinIFQwODgQ5ujNMNFfEJsE7927jzelUlZY8reqcnUnz4pUY
IbeCzyU37G7dwfpgPTyhtTqzInuzmQLX0xqnJpPYkUioJY6NhJlNF5nLaNjtNsJehXzJwOeUGery
YJMkSrrVbNHnlPE5Wegmbmc2XUC2WX1I+oIurkZzhNwKmaKBY4GC905NiXxtIsEnv3mR9x7s4wP3
D2z2cDYUv/L4Xbzv91/gsy+M8ittTuO9nqh3YhwaCJJbaP8gsKiiM0WdoYinoeFTi0q68bVkgZ0R
D0LA23Z28eBIhAdHIhwdCYOgWgA/u1C3UXG+lXSz2lCzqJvcMxCsFrVXHF2VRt1uxY5umGQ0nbt7
/dVIjc0mWQ5VpYhaKjMU9hJwOZhJa6i6wd5t/uqh+MR4gktzWXr9LuYyRYQQZItG9X7tqBNudZzN
RkIqcz6eyDNc884qRhHCqhFOawZhr5M9PR4uzedwyHYUG6glk6RqGdku2YZmmOSLBkJAVrMcW07F
Ro/Py6nJJKbJgv7WcdptHNkZ5shOGE/kiSyQGMiSRFrV+c6FeRwL0aFWbmK6Epoxfr628O+2ox1C
aKtBNbxcNBbRXT9xqJ9koVRVYIYQNx0w35xKcWkuixAW/fTbhrpwKLbqAbv+GrVeg4P9QS7P5zg1
kQTJop28dzBkeXclqiHZw4Mhjo2Eef+9A0wmVYIuhbmcxpHhCKOxPA+OhOkPuRsu/nPTab702hQh
r1VzMBbNEfY5mU0XODYcbmsPwWpxUxFgjdJ60r2diUSeQ/1BfC6Zp942SFItkStah81M4eZUpwp7
3LHhMCwUn1avV6OIMwV9oc7KOnQ+vLu7GlZPqCVkuw2/WyaVtzj5nbKNHr8Tn0uusodNxPNcz2j8
yctj2Gw2ZBs8/ehuvj8a5/tXY0SzRfb3B9jT41uyu3UHt471io7U6syK7J2fmSSp6nidMg6bjbfv
7mEo7LFYIINu9vcFqjpnIpFnKOJhf3+A71+RMBFsD7rZFnBxZGg72aLB65MJxqJ5Tk0lcdgtJqKR
bi+7eqxC7DsxJXI+o/Hxz7/O9pCb//Djh7dkulstDmwP8OQ9/Xz2xTF+9u0jhL2t6bm/3ah3YuQK
Budm0hgmVd3qdsqrPkgbQtAfclnMrbE8R4a6brCHDt7YW2sbTFfGUWHprIzlQH+g2nevkkpV6UX4
xKF+kmqput9UrjGbLPDKeIJdPi8Fj8UUOampzGc1bJKTM9NpLsxmkGUbWc1qbu53Wn2NdnV76Au6
eU9dun0F7aITbuc40+oNkqSphMqT7hs1PxWj6ND2IC9eiRLyOujxOXnXvj4uzmU5MtRFuqDzuZdG
6fVbPaDcip2y8KHYbQxHvPhcMkXd5LmxOfJFg4ym43baGexys2ubj2ShxJnpdLUp64HtQWySRZtt
CqtUomy2N2PnisaPJElDQohJIcSf3I4BtTuWytevRnyKBru6fcBiuusdeJZPebGMfVyy1bAwntcY
cnqrB+LaxWKXLPrauFqqevDfubeHvGZgt0m8OZ3m/GyGoYiHnWFvlZWldswPEqmOp0KtvVS/nqm4
yiefvcRcWsOp2Oj3u8iXdMJYLE/L1ZBsJTTyDtWmi52ZTi+a24BbWdT1uVHfnErqQ6N71RZU1tbh
+Jwy/oUUFFmyChh7vA4KRYuqMuJ1MBj0VLs5A5yZSXPpepapZIEHRyJMJlS+eylKRtOtnPKFHlKD
XZ6WTU3YCtgIT2jQo+BXFUJuB3nNIJbVCLkddHkcnL6WwmaTiOWKVdrbSi3hRCxPqVzm4EAAIWA8
nie90Mj3Yw8N89j+Xj774ihzmSJ9QSeX5nIYpiCl6miGiSxJvHkttYjOfSujUCrz8c+/Rk4z+PzP
Pbjln7eCf/74Xv7uzCyf/t5V/s379m/2cNaE9WaUrXdi+FwyB/qDeF0yec3A7ZRX5UCqTzfOFHX8
LpnzsxmuxnI3nRMa1RhXWDpnMwWKusn52QwOxcZUQuVRehb1azk20rin3bfPzzEayzEWy3F4MMQj
e7vJ6zpdXidO2YZpmjgUG/fvCDOX1XApFuvrji4P77yrl/FEnh+MxQGqGQWtHu3ZDFRrhjV9yZqf
2vl6/EAfCMiVDP7q9WvYbBKX5jI8/ehuHj/QR7aos8dmtUC5OJe1MkL8zqphO520+vi4FJmyWWZX
j48enxOE1ZPSZpNIazoSElnNQAiToEup6vh2PgusJvLzFeB+AEmS/koI8eMbO6T2xVLGS21BYTXi
04DuermUl9rmhkPd3iqVZD2jSH/AzWgsx7MX5unyKlWvQZfHwWRCZSqpUjYFpXL5prSUCq1irSJd
7jBf8RKPJ/LIkkTIo3A1mkNCostjMdjs6vY2rCHZqqjdfOpru1yKrZqDXXnXtV2fG5FarAa1G21R
Nzk5nqBsClIFHY/DTsitcHB7iI88NExeM/jK6WkyRZ3PvHCVTzy+r8rZv68vwMnxBKcmExR1kz09
Xq7OW7VbfrfM3b1+nrpvoLNZbTA2wsMY9jhAwkq7dDjpCzhJFhrrm4RaspoxuhWiWROHbOe+wRBj
catR6UtXouzr9fP4wT7evrubb567zmxaQy+Xcct2ysLkweHwTUQuHzqyY8vKjl42+cU/e51TUyn+
20/dz919/s0e0m3Dnm1+3n/fAH/y8jg/98gI2wKuzR5SU9gIRtn6vRMsB1NZiEXNzZsZVyUbJKvp
vHEt1VQ/m8pY6r87nshTFoKAU+G5sTlyRZ1tfteid5BQS5RNwb0LDbwrfYvOz2Y4OZEgkTc52BfA
6bAzFs/hdyp8+OgQSbXEyfEE44k83z4/R1/AzUtXojz96G5OX0u1JYPvWrEaY692vou6WW2NUl/z
YzW6lXhtIkEsp3E9bRmbU0mVh0YizGY04mppUT844Ka+gl0FB5PJguW0lm3c1evj7l4/x3dbqfUn
xhO8Op4gnivx6kSCPdt8zGWsPaMsrHKMdp631Rg/tX77XRs1kPXGZngWljJeqlSHPTdHfGqxXMpL
JT1pqWeq/a6ml3HJ9uo4JhKWx7Y34MRul3DKdgILaSlpVWcinufkeAKHYlvSk5RWdd6cSlkHayE4
N5PmwEIK132DIdwOG/mihN+l8L5D/RimWaXubOcFciuolQertqtczcHOFvRqemJOM5BtVOstmu17
UL+5/eBqnKmkylSygE2CH7t3ALfTzt5e/009nc7OpNne5aaomxTMMttDbhyKhFYyGQp7GY3lGQi5
CXsdVUpuaN/eW3cqgh6FR3Z3c+F6hrDXSckoE3ApJPKlm/SNvKBH0qrOYJeHkEfBRFAuC96cTVE2
4fOvjDMQcnM1luNdd2/jSjRHj7+bkYiXWL5IUi2RLS4mcqlnqtwq8mOUTf7Fl97g2xfm+e2nDvHE
Pf2bPaTbjl9+bC9//cYM/+U7V/jNHzu02cNpChvFKFvvxGg2ols/rkq6cVrVOTOdbio1tnYfr/3u
cNjLxetZTs0lKZWtXnCZor7oHciSxLnZGyl7XW7rfvv7A0ynCjjsNpwLjU6FsOqQA26FgNsiWDg1
kaQv4GZvr9/ac2YzVYNrLJ5b3GuozbCcHqt1FK+GzvummuGaxqH1c5EuGMTzRXb1+EhrOgMhD5IE
V2JZgi6lmtJYq2/riSYMITg8EOSCXWIirnI9W2Q6VQAWs4a+864ermeK7O/zE3Qpi6KX7YzVjF4s
8f81QZKkYeAV4DxQEkK851avWY/N6g20lPFS+7nP1bjBKayc8rKcR3ipIudKsaMpRLX4EMDvtArx
K0xvo7Ecj+3rvUnxweImXaOxHPdWmqi5rEiF2ynzicf3cXY2zeX5HIoi4ZKWfs47BfXz/sQeqy7r
xFiCN6ZTi7x5TxzqxxBi1YqyHrWb23cvRkmqOtv8TjKazmgsT2/AIlWQJQnZZqUwmabgSjRHNF9E
AoYiHpCg1+/i2QtzXLieQZFtHN8VIVPUqzUc7dx7607GwYEgD+/uJqsZ+BcYfCosPrWR3ReuRAl5
HEwlVLp9jioBgqoblMomAZeCTbJVDzH37wwT8jpI5a3UXgELVNk2MppeJXKpb/q3FeRH08v80z87
xbPn5/jV997NRx7cudlD2hTsjHj5yaM7+J+vTPLRh3ayt7d9Il+3i4Ww2YjuUuO6ldTYRhEpCXAp
dmzAXFbD51wcmTKE4MD2IF6HTL5kkCyUeOFKlLlMkWiuyGP7ehmL5xBC4p6BoOVwjeerka5s0Uq3
H4/nkW1wsD/Ay6Nxnh2bQwJ8zsRNRAjtgOX02EpZH42edaXGoXBjLiQkfjAa463pJKmC1b/pHXt7
ODQQ5GB/cFGfvqXGGfY46PE7KeheSkaZx/b3YtRknlRYQw0hqmUPFQNttdHLVsZqjJ97JUnKYK0R
98L/WfhZCCECa7jvt4QQP72G760KzXpy1ssLWW+A1KaRrVZZ3UrKS32Rc32xI8BQl4ftXe5qnU8t
09tYPMc2v+smoa5v0nUlmsM0TfKaUV0EQY9F03x8C3l0bxWN5t2ICasuqs6bV8FYLL8q2V1KZoMe
hafuG+ALJycwhUVP+o4aZr6gR+ETj+9jPJHHLklcjeWq9wo4FTS9zFxW477BEPv7A5yfzZAp6g3p
Nu+Efi1bCUGPRaDRiFgDFjNRHuwPgoDtITcPjUQwhODozghqqUyhZGIKk4P9AU5fS1XrzA5tD3Ji
PF71Ht87GOKdd/XcVPOzVeQnnivyTz7/OicnEvzmjx3kY8eHN3tIm4pfefwu/uaNGX7zq+f4//7h
sbYhe9iIOrv1wHJp52GPY82kM7XrMFvQcSy0ywh7HQ2zUsIeywFSFhabbKUepC/g5K3pFOevp+nx
uarscnbJok2urHGAn35oZ5VkYUfEg2EKMprBrm5vQ4frWnE7I8rL6bGlsj6WM65XksO0qpMtWHux
Q7Fxd18AUwh2hj3MZTXeta93EfnFSuOs3G8inuekz4mxYBwtZ2RXzpX159t2xIrGjxDCvgH3/SFJ
kl4AviyE+L31vngznpyVrPdmF1Ll75bqzrsWNBpH/Wf1Py8Kcy8UO/qcMsd3d1c/zxZ0SrpJBovp
7UBfAJ9bvum+lWLLuayGbaG2Z7DLzUO7I23psbmdqJ/3pWSzmT46K3nOA26FsNdJVjNwKXa6PA7O
TqfJFHUO9gcJuBUGuzzIkuWNq6Q4PRedwzStSN6Hjw4RcCvWZlfHNHcn9WvZalhKD9UzUabUEpfm
syDBC1eiPLqnh4jPQbfXSdqmc1dPAL/LSp+cTRXoD7nZ0eVhPJ4nU7T0Si17ZS1WIz+tnhb3xlSK
f/L514jlS/znn3wbP3rv9s0e0qYj4nPyzx+7i9/86jmeOz/PYwd6N3tIq8ZG1NmtB+pTl5qNmNb3
GqpPw6rtR7dUVkq9UzdZKJFSdS7NZXHYbeiGYH9/gB1dnkX9A7959jqvTyQYCHn40cOLa0V3Rrz0
Bpw3OdZWg+VIpW5nRHk5PdYo62O59hDLGbX1pQkCuHcwxJEdEs9emCNbNNjmdy1ZW13tvRjLoell
5HqnhGSlMPpccvUatUQ1teNZ7nzbbtiMpL1Z4C6gCPy1JEnPCSHerPxSkqSngacBhoaG1nSDZjw5
S1nFt7KQ1tOz2WgcwE2FkEulSi3lPap8v7KQutyO6jXOTKdvumaFXhFBtWGifyGfv1KfUj+udl0U
G4mV5qM2FW4p2V1Oviree1MI7hkIMhrL8UcvjfHqZAIhoC/g4vCOECGPUr1XslDir09Nc2I8gVux
E/ZaKU+VYtgKbfdyz9BBe2Cpg8ON6K6Pgl5mMqZit9mIZS2aVENYB5yXrsbY2+tjKqXyx98fYzpV
qHapPzYc5j0H+qqpnT8YjTdkMVyNh7NV9YgQgj8/OcW/++uz9PidfPmfPMyhgeDKX7xD8NHjO/mz
E5P81tfO8fY93bgdG+E7vTOx2nNFo1qTSgG9Q7HdlIZVaZexnC6vP/QWSmUGgm6GI16+PxrjxHic
8Xi+ulbPTaf57oV5imWTy9dzvO9QP0FPcNH11rKHLKcbbndEeblnaOb5VpM+N5lQuTyX4bH9faDY
ADh9LYVLtqPpJk8c7Fl27h7d07PQDsPOC1eiPOm2zpF/+drUIlKaLrdjRaKarRK5t93uGwohikKI
vBDCAL4KHKr7/WeEEA8IIR7o6elZ832CHoWR7pWjEit10u0PuKsMXKvFenrGG42j/rPKIXWpsda/
i9rvOxUbfpdSZfxa6ppWcVwIn0uuPlelZujFK1G+9tYME/Hlx9GBheXmo/Kul5Pd5aJHX3trhvMz
Gc7NpKueHlUv47RbRk1G04lmi4vu5XcpKLINl2JHCCiWrQZ8zchUB62PinxU1mta1au/q5UpCdgZ
8bDN7ySpltD0spX64pKtHkGynWLZRC2VUew2nHY7DtlGdqEuzO9ScCq2hiyGFSwnP7eiezcSKbXE
P/2zU/ybL7/Fg7vCfPWXHukYPnVQ7DZ+80cPMhFX+b1nL232cLYUVhsxrazxZ05fI6cZ9AfcZDWD
bFGnP+C2ml7WpGHtDHtXpctr12XIo+BxysxmNAQwEvEtWqtnZzMgSdzVGwBJsn6uw1r2kOV0w2Zk
JCz3DKt9vuWeqcLulsiXmM+W+Ob5uWpUvUKi1eVt3FOtFoYQdHkVdvX4Fp0jK6Q0fpdCVjMYT+Rv
+qxe/26VzI/bHvmRJMkvhMgu/Ph24A9u9xhqsZSF3uwE13tU18szvtQ4aj8bDnuZSqirHmv9NWVJ
IqmWKOnmTSwwb02n8dfU9dQ+V70HoNIwtd0Xxe1Gs7JWPw9gRd9mkwXmsxojkRusgsNhL195Y5pz
s2m0fJm+gIsev3Px3BdK+Jwy3V4HpXKZtw11cbA/WCXN6Mzl1sByHrv61JYXrkRxKXZ6/GaV3nwn
Xu7u9RPNaRzsD+BS7JyaTJJQS9jtcHevf5F+qmcxXK0MteLm+v2rMT7xxTeIZov86yf28Y8f3YXd
1h41LbcbD+/p5sPHhvgfL4zyxKE+3jbUtdlD2hJYzbliUa1JqYy2sKf7XXKVOnk1aViNEPY4KOkm
Z2ZS+J1Wben56xmiWY3xRJ4en7O6Vg/2B7DbYCKex75AcrAeWIkRt5UzEpaKuq+UPqcZlqNpf18A
2QayXUK2rZweX4ul7uF3KlyN5qukNMNhL5euZxd9Vn/tVn/Pq8VmpL09KknSb2Glvb0ghHhlE8aw
CI3yfdcjbLkeQrHUOJYqRFuNMDY66NSmwFVSnCRAksQirvP651rETrJEw9QOlsdalEltTVelVuP0
pJXudjWa577BUDV/+2MPDXNkR9eimp/6uXcrdj56fLia99usTHXQ+ljJqKhd20vNvUuxE3I78btk
Ht4VIZEvoRbLyHbboutU5LnCYths3WSrbK4lw+Q/fusSn37+KiMRL8/8wtu5Z7AT7VkJv/a+fXzv
4jz/4ktv8Le/9AgeR3vT4rYKVjpXLKo1cco8cbBnUR3Ora4pwQ0666ym8/W3ZiiUTOayRT7x2F3V
6x4YCPK7Tx3m7GyGg/0BDqxThHQl3dCqtVvLpbatlD731H0DPHP6GoVSme9fjRFXdU5NJnn60d24
nXLTZ77av//gkR0cHQkvIqVp9Fmj67Xie24Gt10jCSG+Dnz9dt93LVjtBN9KDuRKhb3LsXot9/NK
qPx9PbuY33Xjc4di41AktOQzLbWgNmpRtHoR9Gqw2vlcLSqy53XI2GwS9+/oIqsZHB0OL5qPh/d2
L/peo7nvD7lvKm5s1/fciths+b1VoyKhlnAqNoYjFp1tXC0RcMvs2mZFGvXyDZrUW5WdVpC9K/M5
/tlfnOLMdIafenCIX39yf+cQv0r4XQr/z4fu5ac/+wq//pUzfPJD97YM+9tmr8P1QqPnWI1xsFbU
r/+zsxkMk2r/nnhdetSBgeC6GT21WG/dsBJ51HpgpTPics+0I+LhZ46P8M1z1xmN5dm77cb7frSJ
97uUk7++x1Kjz7YiOpp8HbDWNI2VCnvXs/C32ZDrap/pdh1SWrkIerVo5hlWq4Ar85QrWilGpmlx
8u+MrEyD2orpRVsVrSK/tRHDpRrpLtcXoj7ddqUUiXaEXjb5zPOj/OfnLuN12Pn0R4/wwwf7NntY
bYe37+nml9+9l//07GWODof58LG1ERitJ1plHd4qlnqOjTTs6tf/wf4AL12JVvv3DC/BNtbKaEQ2
tJY+eyvdo8Ksu9a9NuhReGgk0vbvu5XQMX7WAWv1qK7kDViJ1Wu191tLyLWVUk9gazCMNMPWs9oN
elGK0cHmUowazfFW8Yq2GlpJfleSr6XG2khe6lMkgCWNqnbAK6NxfuNvz3FuNsOT9/Tz73/0ID1+
52YPq23xS+/ay2sTSf7tX59hR5eHR+oi0LcbrbQObwWNngM2lm210fqv9Iyr9O+B9oqs1b/HWqKn
9ZCPRsy6a20PsiPiafi+1wPtNGfrhY7xs05YSwRkJc/7Sqxeq40gVBoXViiqVxtybYXUkwq2QpRi
tc/Q7AZ9K/NU+V5CLZEp6Ovu9erAQivJ70rytVJRcb3u2Im3reVHCMHrk0n+23eu8tyFefoCLj71
00d476FOtOdWYbdJ/NeP3M/f/9TLfPzzr/HnTz+0qQx5rbQObwWNnuN2GHb167/SMy7gXkzN3C46
oFE0uxnyqJVQPyeVsoK1YkfEs65GDzSOfjVbp9mO6Bg/m4jV5Oc2+n2zEYRK40IAn2v1rEut5A1o
tUjUWrDaZ6go5NFoDs0wb25Kto6oVXzJfAmXbF/SSO5g7Wgl+V3OqVIZ31rIXtpJfoQQjMXyfOvc
HH/zxgxnZzL4XTL/6r37+AdvH8aldPrTrBcCLoU//gfH+PH//n0+/Ic/4HM/e5Sjw+FNGUsrrMP1
2FeXeo7badg1MnTaLbLW6D2uJ9FPOxjbtXM2Gs3xzOlrdHkdty2dcrPQMX42GSt57Rv9vtkIwq7u
G9THjTo4N0KzHpzbsThaKRK1VqzmGYKeSlOya7iUG03JNuLZKzIScCpcSxTQSmvPS+5gebSK/C6V
7li/3us7jTfCUtS6K8nP7dxMhRDMpjXOzWR4azrNW9Np3ryWJpYrAnBoIMBvP3WI9983gNfZ2RI3
An1BF1/8+HE++tlX+On/8Qq/9f5DfOjI4KaQIGzmOlzPyEijKOztNOwaGTrNHPZb5UDd6D2u13ha
wdheCbVzphkmLsXedDplq8xlM+ho+nXA7Z74ZiMIFdrL5Qyf+mdoxoPTbqHudoDVlMyx4R60sMdB
UTd5bmwOgdWrZXe3D5+rOdXQjsrvTkb9Br8Wj21a1clqOsUFg6eeWne1+mK90yw0vcyr40leuBLl
zak052YzpAtWQ1ebBHu2+XjnXT3cNxTiXfu2MRBy3/I9O1gZAyE3X/r54/zSF07xq3/5Jt+9OM+v
vW8/g13rm8bTytjoyMjtNOwaGTr1ZxNoXAO4mjPDVtlTWsXptRQqztazs2l6fS4mk2pT6ZTtev5b
1QlHkiQ78H8JIf7lBo+n7bBZE7/aCMJKRlJa1ZlI5DkxlsCp2KrP0IwHp91C3e2A2xUuD3oUjo2E
yRV1RiI+5jIar04k6PI6ODOdXtWm1K7Kr4MbaFbeaudcAu4dCFV7QlTkAxpT664mzaIZCCG4cD3L
i5djPH85yomxBEXDRLFLHOgP8L57+jjQH2D/wr9OdGfzEPE5+dOfe5BPfe8qf/Dtyzx7fp4PvG2A
nzi6g3sHQ9i2eOPYdkiDWi2WOl9UziZTcZVnTk/jkm34XPKitb1VD9SbjbUYjGlV55vnrnP6WgoJ
i7r82HB4UY+f5WS2Xc9/q9oFhBBlSZKOSJIkCSHERg+qndDqE7+ckVRRMPNZjdFonnfv6yVTtBbP
SLd31eHaraTQWwW3M1y+M+xlm99Fpqg3DHuvtCm1+hroYGU0K283FfK6lVUbwiulWaxWdopGmX/z
V2/xwpUY0ayVwrZnm4+fenCId+zt4dhIuGPotCDsNolf/KE9PPW2Af7g25d55tQ0f35yiojXwYO7
wuzrC3BXr5/BLjfbQ266PErL9Ai6VbRDGlQzWOp8kVZ1njl9jUtzObo8Cju6PIvW9kpnhs6e0jzW
ajAm1BLZok7AZf2tURZVfQ4ry2y7nv+a2RlOAX8tSdKXgHzlQyHEl9d9VG2Edp14uKFgRiI+rkbz
jMby9Aac1WdYbbh2qyn0VsHtCpfXzp8sSbxwJdrUptTOa6CDG2hG3paa89UcWpqRt+XglO2Mx/Mc
3xXhkb3dPLq3m/5gJ4WtXbA95OZ3P3CYf/3Efp47P8fzl6K8Ppni629dX/R3TtlGf9BFX9DFNr+L
xw/08vfu3b5Jo751tHoa1HogoZZwKXa6PA6Saokev3kTa+RWPFBvJtZqMIY9DvxOZdl+bcvJbLue
/5oxfsJAHHhXzWcCuKONn3adeABZkkjmSxRKZe4bDHG0LtTZDO4Ehd7OWCkcXjt/y7HdrCbPuyMH
Wx9L5fbLf8ikVwAAIABJREFUktR0c+RbYVf68i+8/dYepINNR9Ct8IH7B/nA/YMA5IsGV6M5ZlIF
ZlIas+kCM2mN+YzG6akUu3t8mzziDlZCZZ/wOu34XW6eum+goRNkqx2oNxNrNRiDHqtf2/7+AJmC
zsHtwabfdzue/1Zt/Agh/sFGDqSd0Y4Tn1atnhwu2Y6mmzx138C688d30BpoNhy+lk2pHddAB7eG
ypzfKoFBR3Y6qIXXKXN4MMThwdBmD6WDW4DAis76XXK1D1Az6OiF5nCrBuN43GrwupHssq0E22r/
UJKkuyRJek6SpDMLPx+WJOnXN25oHWwkqjTYPT66vApGp5Rry6I2HF4WolqMvlYEPQoj3WuLEHaw
9VAvX4YQHfnooIM7GAm1hFOxcc9AEKdiu+U9p4PVYa1783qfEdoB0mr5CyRJ+h7wL4FPCyHetvDZ
GSHEoY0aXHd3txgeHt6oy3dwCyibAsMUyDYJ+wYz9IyPj9ORg62HtchQRxbuTNTLyp0oB7dT57YL
7kQ56OBmdOTg9qDVddBrr70mhBCrCuo0U/PjEUKcqGNdMZoaWZMYHh7m1Vdf3chbdLAMlqoTud00
lA888EBHDm4BrdgvYa0y1JGFOwO1Mgs3N9l79zuO31Y52Ow11KH+bYyOPugAOnJwK1itbmsHHSRJ
0uur/dtmjJ+YJEm7sVI5kSTpg8Bsk2ProE2wnKB3aCjbB62qsDoy1MFSqJfZQwPBm2RlM8ezGWuo
s146WApCiC1DBd7B7UUzum2r6aBmjJ9fBD4D7JMkaRoYAz6y3BckSdoOfBU4APiEEIYkSb8HPAC8
LoT45bUNu4ONxnKC3gyryGZ7TO90tJrCqsjDalnBOrizkFZ13pxOkdMMdvX4mM0UQCzfZG+j0Qpr
aD2pfzs6eWvg+UtR/st3rnB6MoUkwZGdXXzs+DA/fLC3Ywx1sCqsptlsRVdsNfrxZtjeRoHHJEny
AjYhRHYVX0sA7waeAZAk6X4sI+hRSZL+uyRJR4UQJ9cy8FbCVtxMlhP01bKK1HoVirrJsZEwO8Od
QujbidulsFazBpZjBQOLKnkrraEOVoeK7BSKBs9emEcIwVgsB4DPJbMz4mVnxNuUjl1PndwKm/56
Uf/eShRrK+5z7QghBL/37GV+/7nL7Ai7+ZmHd2KYgm9fmOfjn3+NR/d28/9+6F56A67NHmoHLY5a
3VbUTbKaTlrVl2xY3YwOanV9sWrjR5KkCPDvgEcAIUnSi8BvCiHiS31HCKEBWo0X4iHgWwv/fxY4
DrS18dMKKREbgZU220Y0lPXCXvEqBJwKz43NkSvqbPO7tsw7agfcjn4Jq10D9V6mCivYUt9vdeXZ
wdrQqJ4npxm8Mh7HLtnoDTgZ6faxf3uAwwOhRXTqq73+eurkVug5sl5rYa1RrK26z7Uj/vCFUX7/
uct86Mggv/X+Q7gUOwD/x/v284WTU/z2187xY//lJf7kHx7j7j7/Jo+2g1ZDvS558p7tTMTznBxP
8Ma1FGem01V9V68rVssk1w76opm0tz8Hngd+fOHnjwB/ATzWxDVCwOjC/9PAwSa+25JohZSIpbDc
htnod/Wf1db4VLDc9eqFveJVGI3lEcBIxEemqLfUO9pqaDSvG90vodbIHYvnmEjk2YmXiUSenGZY
3vuwd0kPeqM1BDcXuXdkpr1QL4tpVWcikecbZ6+TK+r4nQqHBoLkigZel4xss+GS7SRVnR6/a5Hh
0ww2QidvZs+RRroVltbFS12jkm5a0k3OzKTwO5VFUazlCG7enE6RKxrs6va13D53J+H0VIr/8L8u
8uQ9/fyHHz+MrYZxS7bb+OhDOzky1MXP/tEJPvip7/OFf/wQhwaCmzjiDtaCjXL8LWmUxCGjGezy
eatntEb79UrjmoqrjCfy2CWpZc/FFTRj/ISFEL9V8/P/KUnS+5u8XxoILPw/AKTq/0CSpKeBpwGG
hoaavPztRyukRNSidpN74Uq04eFxqc10uc9Kumk1LVNslHSTo8Nhdka8iwykRl6CilchMC6TKeot
8Y62Kpr1tqxVwdZ/L+xxUNJNnh2bQwJkWxSIcmY6zUyqwPYuN0d3hvngkR0NPeiN1lArOxU6WBn1
snjfYIhnL8wxGc/zwuUoEa+LtFYikSui6mVGun24FRsjPV4kaNgRfrVoNZ1cj2bXXf1amEjkOTOd
rurlel3c6H61eryglxFCQizxN/XR10pk7txsGgCfU265d3onQC+bfOKLp+n1O/ndH79nkeFTiwPb
A3z5Fx7mJz79A37mcyf44sePs7vHd5tHe2diPYyWjYyaLOVoPDmeYDSWYyyW4/BgqLq+D20PggQ7
w15geYfkVFzlk9+6gGGCaQoObLeO+q2og6E54+c7kiT9JPDFhZ8/CHytyfu9DPz8wjUeA/64/g+E
EJ/BIlbggQceaPnOm62QElFB7aJJ5nVcio1d3T5GoznenE5VPam1nvrzsxleHo0RcCnMZYrs6r5h
+QPVhXJmJoUQEj0+L8+OzZHRDHoDzuoCWOrAEfQoHPaEms7Z76B5NGMwrEXBVjz3z1+Kkisa2JD4
8LEhdkQ87O8PMJVQ2dcXYDajkSoUUWQbTsWOYrORXZCpRmHzpdZQKx9gO1getbI4Gsvx+VcmGI3m
mUrkyWoGUMQQJi5FZnghxe0Dbxus1oC1Q5raWg46a1l39boVQVV/1+tiWBwRqo/aVPT4PQNBRmM3
9oWldEdtM2yA/f2BNUfkOrg1/PmJSa5G8/yPjz1AwLX8+x/s8vCnP3eMv//pl/nYZ0/wt7/0CGFv
R4duJNbLaLkVx99y0duliIYm4nkymsHDuyLMZTUO9AeYSOQ5MZbAqdiwSxI7w94VxzWeyGOYMBzx
Mh7Ps6fHx939gZY98zVj/Pw88CvA5xd+tgF5SZJ+BRBCiED9FyRJUoC/A+4FvgH8GlYN0AvAaSHE
iVsZfKtguZSIja5bqL1+rXAmcyVmUxpaqcxoLAcSTCXUajpaSTf5u8uzzKQKXI5mKZdNirrg1ESC
o7si1cNmJUVCtll9o16bTKIb5iIjqfJctV6C1dQIdbC+aMbjXWsAj8bynJ1J0x9yV78zEc9X5zJT
0Dk7m+byfI54rsiLl6N4nDJCABJ8+OgQ52czxNUS3x+NMRT2IEs21KJBUS+jm+ZNKTb1qJePVnIq
dNAc0qrObKrAeDTP1bkciXyJ8XiOqWSBnGaABGlNxy3buRLNMtDlXvcDdUWe0qq+IUQaa01FW+4A
0WivqKSR3DcYwu2UkSWJpFqipJuM5XJIUNXFZ6fTvDqZwKXYsUsS+/sDnJ/NUDZFNWrjdyoIYDSW
49xMGoS1Lzy6p6eh7qjVKT6X3DF8Ngn5osF/evYyD46Eeff+bav6zq4eH5/72aN88FMv84v/83X+
9OeOIdtX1f+xgzVgvbIV1hq5Tqs6f/naFNmFlOIPHtlxE3FBUTc50BfA55bpcluGz/OXo9Woz95e
P69NJonlNObTRd551zZmMxpnZ9L4nDIl3WQ0lkPTy8g1jIJpVceOhGkKxuN5ZBsc3B5kR8TT9PPf
LjTD9tZ05ZwQQufmmqBXmr1Ou2KjmXUasWfZJYnRaI7RWI6RHh/JfIn+oBuvQyaaLVY9fUeHw0wl
VPwumUzBYDyp4pRt6KZJKn+jxkcAQtwQco/Dhk2SmMto+FxyNQ+0dhyVEGkHtxdBj8Kje3oYT+QZ
XoFVL+xxUFwwgNVimXMzaY6OhLFLEgW9zKW5LAIYCnsYi1qeodl0gd6Ak3TBQC2V6Q26KZuC8UQe
h2LjsX29nL+exhTQG3DhVOz8xNEQfUHXmlj+OgZzeyGt6tXN9Mx0mqvzOQp6GcMUpAslDENQBryK
HQmJff1+ZLuN/f2BTSXiWAtuSkWL5zkzk17xXkuxK8HNKSWZgs4nv3WBgm5imCZPP7Kbq7EcZSEQ
wLHhCD6nQqaoU9JNXrwaYzKh4lHspDWdqYRKXC3x2L5e4EbUBuDN6RQsrNPRWJ6kWmrobOg4IVoD
f3Fyini+xGfee3dTNNaHB0P8zlP38C++9Aa/8/UL/Nu/d2ADR3lnYzmjpRkn+FrX3EQiz+lrKQIu
havRPEdHwhz2WFHdnGZQNExOjCWIZYt0+51IWHU+o7FcNeqzPejiu5eiOGU7EwmVr52Zxeu0c3ku
w307uiiWTQqlMiG3wgtXojzpXlwesX97gL3bfBzsb23DB5qL/CBJUhewF6hyKAohnl/vQbUzlorE
VDZIv6o01Ul3OYroiXh+UaqaIQRP3rPd2tgk2NXt4+xsmjevpRmN5plOqqTUEpeuZzm+K4LHKXM9
o5HWSuiGiUex43M7UOw2EmqJbEEnqxns6vZybjaDZhjcvyNM2OtclP4wFst36jNaAGlVr9Z5TSVU
nnQ3PoBVvMkRr4NS2cTtsDOTLiAhkS3qpPI6TruNbNHg9KTVc0W2SyTyRdSSQcijYJRNfE47PX4n
EY+DU5MpCsUyHocMArwuS7Xs7fUz0t0xhrc60qrOl16b4up8jstzOTxO6wBeKJbRjDIAJuCwg0u2
4XPJDIY8aGUTn6upbWjV2Mi6sZtS0SSI5oqWt0hiyXstxa50aHtwUSR2Ip5nJlUgltUBQSxf4rMv
jnFoIMDOiJdziQyZkM4PH+jDEIJsQecHo3HiDjvj8TxOu419fQG+Pxrj/GwGj1Ne5BA5PBDi4vUs
z12YQwCBcYtSvNFa7TghNhdG2eRzL41xZGcXR3aGm/7+B48McmY6zedeGuPBXWF++GDfuo5vPquR
yJfY1e3DId+5kaWljJblWjwsta7WFLkWUDGLpYWfAWRJ4uR4gkvRHMVSmYjPARK4FBtBl0I0q3Fp
LsNglxeEhG6YBFwKEZ+DgZCbPT0+Tl9L4XXJJOIqml7m7l5/w/KIQrGM12np81ZvXdEM1fU/An4Z
GAROY9FWvwy8a2OG1n5YKhJT8fKdHE9QNgWaYfLUfQNVy3gtFNFpVW9YpBb0KBweCDGVUJnNFJCA
eweCaEaZyaRKLF9iNq0RzxcJeRxsD7np9jqxITGb1vC7ZUqGyZW5LBfnsly8nuH1yQSmCTabxFym
yNGd4UXpD61eYHynYDWHvdqixKymI9sl7DbIaQajsSzbQx7yxTKvTSbJFw0M06SoGxgmeBfC3sGg
A6/LzkeO7WR/f4AXrkRxKTY0vcyDwxG+cHKCS/M5ZBs8cah/k95GBxuN2jzys7NpvnN+ntl0gWSh
hF2SSOV1yrCosN5tszEY9nBkZxc+l4zfqWxIpDit6mQ1naJuboheqj/oTCdVvn1+jrIJdhs8NBwB
WDL3HsCh2KprFQmKuslzY5YxIl+WKOpl5nMamYKOQ7ZR0A1em0jy5rUU87kik/E811IFPnRkB2GP
gxPjCWK5IoYpcCkSGU3nrl4/asnApdj4yhvT7N3mY6jLg9spc6AvQK6od1g4WxzfODvHtWSBX39y
7VGbX3vffl6dSPCv/upN7h20ovG3iqmEyr/7m7N8+8I8ACGPwq+9bz9//4Edt3ztdkUjR8Gi+sdo
jmdOX6PL61gxGj0VV3nm9HTVWVT/t/Xnxp0RL4cHQ2Q1g5FuL10eB2OxPLOpAkXDwGO3UbYJYtkS
24NujLLgK29Mo5cFb1zLEM/pdPscFPQyDpvEgyMR3IqdshDINohni0wm8hQNk8mkyv1DXVWdWsk4
OjebpmCU+avXpjjQH2w47lZBMy63XwaOAj8QQvyQJEn7gN/YmGG1Bm6VlacSiUmoJbKazg+uxplK
qiRVnS+cnOS9B/vo8jh44YpVQK7pZZ66b/AmiuiAU+HiXIbhiJeH93RXi1jLQvDYvl7G4jmGwh7e
nE4xHPayI+Kp3rfC+nb5epaSYZLXDAp6mUS+RLfXyVxGQy+beBwyQbdMKq9zNZrjcjSHYZh0eR2o
xTIJtcSubi95rcz+/gCZgr7ofqtNt+qgMdajNuyG3Nyck1uJ9synNTIL9NMl3UTXBefjKpIk8fpE
kmReJ1UoUSiV0Y0ypbKgZEBZgFGGgNvOvj4fXpdCX9CFIQRlIej1uzh/PcOFuQwj3T66fU7yJQND
tDxnSQdrwFRc5QsnJ5nLFBiN5hAmnJ3JUBaWsaNIINkBK+iDBDgViT29Ph7cFeF9h/rxu1eOgq+E
pei0K8W6lvMntCwb2mqf9+xsmoBT4eBAcNEhJ6GWmEyo9AXc9ASczKQK/K+zs+za5lvExpYp6Dxz
+houxU7JMFGLZQrFcpUKHqgaI6OxPLJd4sfu3c5X35zG61AY7vZSNk1mUho9Pic+p0xWM6z6PKy6
kPlMkb6gE003iXid7O3z8ca1FLpu8sXXrXtrZZP37O8l4nNW0+bqGxx20Dr4o5fG2Bnx8PiB3jVf
wyHb+P2ffBs/8gcv8s//4jSf/0cPYl+CLW41eOtamp/9oxMUDZN/9theRrq9fOHEJL/6l2+SVnX+
8Tt2rfnaWwWNSAY0w8Sl2G9yUDbSY184Ocn562m2+Vzs7vHdVB/4tbdmFp0bA26FA30BMkWdoS6P
da7UDMbjeYwySDYJSQKP08aPHO63yK6uJOgLKZyaTHItoVIWgh1dbrI+J4cGgwyGPJRMk3sHQkwm
rajPbFpjJlMgsVAeUXEEvTwaI6EW8Siy5Sx1yZSFaFmnSjPGjyaE0CRJQpIkpxDigiRJd2/YyDYZ
68HKU9svJ63qfPdilKSq41HsXI3meO7CPHabhMCyxpNqiWdOT/Mzx4erqRHlcyZfeWMaISCaKRFy
K5y+liKWLfLGdIrDgyFkm42vnLqGzWZDtsEnHt/Hjoinet/hiJcTo3GKeplLcxnsNhtayeDZ83Mg
oKCXcTnsOGw2nIqNWA7sNolM0SCuFukPuplOFbiWVCmVBedm0nzx1UkME2QbPP3obk5fS62YbtVB
Y6xXbUKl5sfyFtmrObmZgs5vfPUsWU3HKJtcSxYwBaglA7fDTl4zMAUkVJhNaXS5HWTUInnjxrVd
doliuUzZlDk1lWJHxMPJ8QTvOdBnkWdcmWUmWaDH78Qp23Dv6KrWhHWwtVDZmL95dpaZlEZRNys2
ThUlAbUfCsBhl4h4nXT7nLdsjFTGUR9pf+FKlPmsxmg0z7v39ZJBx+9euSHzcpiKq/zu351nKqki
SfD4gT4+9tAwcCPXPa3q2G0SKVVHkiRCXsciNjbFLjGTKhDPlwi6FNKapZc1o8wTe/otzy1etvld
ZIo6fpdMSi3x1rUEfpdCoVTGXDB8xhdSTyJeByG3g6+cmuZqNMfZ2QxayeBaSmWb38nVWI5jI2Fr
fb51nblsEbtNomwKzsykeWRPDw/tjoCA712O8tz5efwumQ8tFEp3sPkYjeZ4dSLJv35i3y0ZK2AR
IPz7Hz3Ir/7lm3z6+av8wv+2Z03XmUqofOxzr+BxyHzp48erTIBP3tPPL//5aX7n785zcHuAh/d0
39J42xlLpbpVnNH1vXPq9/+JRJ6r0Rx5rcy5bIaIz7loL02oJXJFg6lEgaRa4gsnJ/E47NVa3R1d
HhAwnVZJ5HVmkgUKRhlNLxPNFvnamVlKJZPrGZWxuIla1DEFGALUUplYrsRYVKVoGDx6dw8TsTy9
fhcX57MkVR2/U2Y6ZdHuH/ZYdYTXkgVmUhpjMRW7JJFfcLK26hmgGePnmiRJIeArwLckSUoCMxsz
rM3HWvLFl8v5TKglHtu3DRCoJQPNKLOr28tcRrNSRdQSXR4Fl2yzaoPcVhjz0PYgZ2cy7OnxWawb
sxlymkE0V0SWbKTyOocGAly4nq1SDI4n8uyIeKrsH6cmk7x0NY5ilyiUygRcCom8Ttm0DiTuBXYg
SQKXbEfTTew2OLjgRYh4HLgVO/FcCSHBF1+bojfg4t7BLsbjeU5OJEiqJXr9Luay2qIFsRZsNENe
q2G9ahPSqs54Io9LtrGrx1etM/v+lRhnptP4nTKxfAmv045ZFpimScmw+n2UygIFCQmT67kCRWPx
tWW7hEux8/CeMLGczgNDYRyKDeP/Z+9Ngyw77/O+33v2c/d7e19mumfBYDYAg4UAAYISKUKUSVGk
IImJJMuipcgpVdlJHMupfEisOEnZ5SqXUlasim0tsbWUqIi2IJGiJFKkKBLggn0wg1kwW3dP7333
7exLPpzbF909PUA3MAsGnOfTdM/te889533/7399njh+kzxDU9BUmayhcGT8zeHq93rv7128NbZr
y21YPtWOh93T/3orCMBUBZ+8b5yffXTqhgQ+cO2+ma0ls4f7BjJcLne5UukyktOvOXx3m2yYrXWx
g4BSrwpebrubet1zukq14/LjD05gajIDKY2TCw3OLbfougF5Q+XZy2X8IMYPQ7pugKZIHB3L9Wc1
1+/xupO01nT4/e/NUusm91mWBd++XCNrKJTSKl1XYihrULc9ql2PlZaDKgtkQ8ELYkZyZjKb19uf
Z5daLDQsLC8giiNqHZeG7TNVSjNX7XJxtU3WUJmpdHh0uvSu7PfG+/z9ZMdvBv7zywvIkuAnHpy4
Ie/32Ycn+daFMv/XVy/w2L4BHp4q7urvu27AP/i9lwijmD/4pcc2zYgpssS//uz9nFtp8T//ySn+
+n/8QQxVviHXfadhuy6g9Xv1o+ZmH3F9ZnrjvB+ArkjsHUhT67o8eSAJJGcqXRQhaDs+Dcvv+41R
FFNpu2R7FOi253N+tUPbTlrXbS/AiWPcIGahZvHtS2WOjOZ5eLrEcsPmzFKLIIiAZMas3HHImSqr
LYfT8w2WWw5RDHEEaU1m70CajpMEVes02Loq8bHDI1ypdHlsusRY0dxUyXqv2YLdsL093fvnPxdC
fAPIA391U67qFuDtHsY7nWPZ2vO59aB9+sQkddvjmxfKzFQ7ZHWVzz68h6+dX8NQJCQpGU7TemKi
e0opTFVmueUk9IFjOb52fo21louhSRRSKmN5E0WC2WqXKErYOE7NJ9oO37lc4eJah25vvqM3j4ui
SGiBoOMEPfFSQUHTKWZUhtIGWTMZgPWCiLyhcqncoeV4iFjCD2IW6jamqqBIgnLb5dxyi7+orzCa
02nZAR0n4Nh4ftcL/XoUsncK3skmvxEzU+v3LWH0a+L4ISld4VsXy5xaaND1AnRFxg1CLNfH7WXl
nSBEl8FUJQQxThCjK4KNkxpS7xr3D2fYU0wThB0i4v61llIak6UUpxYaOGFCg74e+Ox20PMu3h63
8iDZSp/6+P4Bzq20ubTWwvWDtw18IFlJeVPl5z84zdEbqDa/dd9Ml9LM1yxars+JycJ1xT93m2yY
LqUxFYWZapcwisltyGZuFPfN9Gx5PqWSNVQ+X59DUySeu1wmDCOyuoIbCEbzBmMFsy/6bLsBv/va
Ur+3/8Rkgd987jJXqza1rksQxqyHmPWux3BWp+n4qJIgipP2tvlaFy+IyOkqWUNwbCKHLARt20eR
BCN5g5GMzuWyjyLLOEFM3y0Vb+72GOi4AacWEja4dxqo3kymve8XhFHMn7yyyA8eGmI49+5ndACE
EPzLn7iPUwtN/rs/fIW/+B8+TGGH500UxfyTPz7JhdU2/+kXHt2WHCOlKfyfnznO3/3t5/md52b4
hx99Z9WlOx3XO9O3s93rzKvr8365WYWPHx3tz+8cHs1SSKn87ndniIGZcoejY3lSmszeYopCSkWW
NrO07immeHCywGy1y9nlFm4UEYQxYRThBIKGFWC5AUsNmzCKmR5KM1vp4vgRAhBRQmTRdnxO9myB
rsrkDYXpwTRKr4J8tWZRs5b68+3rVWsEtG3/upWt7WzBrQ6Q3jb4EUIYwC8DB4HTwO/EcfzNm31h
NxM7Yd+4URSf22UAiqaG7YZExGR0mCim+Nzj0/3ZoNcWGptaJvYPpRnK6hweyWHqCo9Nlzi1UMf2
4I2VFh+YLvEzH5ji/GqL+ZrFl04tIYC8rnCl0sVyQ8IIYmIkGXKmSiGtcmwsy6vzTQqmQtMOefLg
AIsNh/GCScZQMFWZlaZNx/UppFQ6rkbL9knpMif2FJkomIzlDd5YaSMhCMOY5YbDYt3m3FKLB6eK
/MwH9u6K8vB6CsR3At7NgX98Ir8rZ2N9hme6lCZnqnz3SoWLK226fogsBCsthyNjWV6abWIqMilV
hjhGkQQCCTdMsjwx4EcwmdNoOQFBlMz5rDdYKBIMZDT2D2f5zIlxDgxn+IBT6s8prF/rZx/ew6PT
pU06TxtZAHcz6HkX18etcirXD6Llus3JhQYSMFe1eOblBawgpNbxcILr/70A0qqE7UeoimAoa/Sz
kjcK29norVnV7b7TdkJ/G1+zUeMqn1LZM5DiH330IH/w/BwxMVEc07J99gyk+MB0iXKPOjaK3uxv
r9seUZRoXSzXLRabDhEQxBEfvXeYw6O5fhvMMycXuLDaoZhSGczo/O2FMl4QE0UxfhDRdUOESOY2
IkJcX8UPY1KazHzNomCqTBRTPLAnaTn9zAMTmLrMCzM1vnelytnlJqWURsPxyZoKIDg0nEHqsXoW
TY09RZMohrG8wbcvVzi/3ESTZR6eLr2jNribybT3/YJnL5ZZaTn8bzeYnjpnqPzGzz7IT/677/BP
v/Aav/Xzj+yIPvvXv36Rr5xZ5X/90SP8wKGh677uQwcH+aHDw/zWs1f4+09M99m/vp+w1TYBnFpo
bBIOXbfd+ZTKo/tKfQrqMIoJ4pjPPrynb6+eObnIyfkG9a6HF0YcGM6iKxIHhzOMF8z+zOCZpSYt
J5n5ObnQIIqh7YQcGs5ydqlJ2w0YyZvkDZkrlS5d18cLE1sjAXEMsiyQRIwqJcGsEAJJQNsOSKsy
yw2bkZxBFMdIQtBxg/58+7rUwedfvIogoVp/dF/pLW3Bus1dT/rfKv9gJ6vydwEfeBb4BHCUhPzg
PY23iiJ3yr5xIyg+t2YA1g+7q3WLYkrtU2LvG0z3y4OvLzaZqSYCdmM5g+9cqRBEMa9erXN0LI8T
RNxrsvCPAAAgAElEQVQ3WSCtKrx0tca3LpZZadoUUxrnVlqMZAwyporlhURRMpAuBKRUGUOTMTWZ
w6M59g2mKXdcal2fjufx8lyDgayOKkt85cwKbdsHAfcMZZOsoqH0s7j1roeuCL5xfo2YGMsNURRB
te2iyBKLDRtZFhiqxOce33dLqyC3C+/kwL9GI2kgve3w48afNzK2RVHMeMHg/Eqb5aaDLAmmSilW
Ww5RHLPUsJkomAxndSQhsP2AasfffBExVLo+GUNGCEHT9jFVibQh4QUxbhBxfqUJJ2MemS6RT6nX
6DnlU+o1rTIbn+X1Bj3vYne4Gerf271ufU3OlrusNGxWWi71jkunlxm8XsVHBjKmwkhGI4xhpekw
mDXwo5gzy80brv2w1UZfz2bvJOG1Ttd9aqFBDJyYLPSFAk09yXjO12yu1myeObnI0ycm6DgBV2sW
Cw27z27YtHy+daHMdy5XiIFSSuXAUBZDk3ljpcXrSy3KHbfvHBmqTDGlsdpyWGo63DucYbFHjBNG
MbIEUQwQI8syGVPBCgLOrbRRZMGZpTYfuXeIiUKKV67WuLDW5oHJAroq4XoRlbaP5SWl3uGswVrH
pWYlGdr1GYSCqeEEERN5kz96+SpeEKFIIYt1q68Nt5v9eifb8fcKvvDyAoWUyg/tUNR0N7h/ssD/
8skj/PMvneU/fOsKv/yDB97y9V8+tcyvf/0iP/nQJP/Nk/ve9v3/4UcP8pP/7jv80YvzO3r9jcKt
rh681edtnPf+8umlzbOIW9gVi6bGSsveZEfW/36m0sXxAi6stFlru9Cjtj80moMYal2vfxbPVpOE
Y63rcaLHAJwxFDQlYdlsO8m4w6W1FvWuT0yMH0XEEaiKTFYInCCCmMRPEEm1UJclZF2AENS6Pqam
sNJ0eO5yhYKp8IljyfVmLZUgSqiyAdpO0gVU7/p9cpetyaYvn15iteVypdLhqW3uzc3CToKfo3Ec
3wcghPgd4IWbekU3AG+XHb0ZTtn1NsHWDMDGw65ueQxlo02LYf31c7UuGb3GcsshBgbTOgt1m7Sh
4LRdGpaHrYZossRgWmem3MUNHJp2QL3bYv9ghoyuIiRBRldwPZ9CT9dlJGeQT6ksNx2CMGaxYZHW
FJYaNpaXtDxYbgBxQm+dZBLSmJrChw4OUe16SCLmezM1yi2HQkpjIKMxmNYJwzghS3B8soaKocq7
uqc3quJ2O7DbA3+dta/jBuwffHNGZ6NY4voQ98a1PFvrEkQwPZDmpZkaF9faqLKERFJqXmsna+ZD
BwZZaticX2kTRhEtx0+C4S3XEQJRGNK0I1QhiGNQFYEuKxRTCm0nIelouz6Vtsvh0dyO9srGZ7nd
oOdd7B7vRv17pxWjjVT7Cw0bxw9pOX5yKHL9wAdAVZKEzVNHR8jpCn/80jzZlJq0i+m3by+/VQ/+
xte0nYCsofboXLv9+UVFCJYbNmtth+GsDnHMMycXCKNk7ufBySIxSca2Znn4Ycy9o1kcP0JTBOdW
W8hItFyP0ZxOEMf9CpMkBHtKSetyMaUxNZDmxdkaXTdACAnhJzN3UY9u3vVDNEmiGwUokkzL9fnu
TJVqewnHD3lprs6n7h+jkNJ49Wqdy+UWfgSeH6LKEtODKX7xiX08um+gf19Gcglb47cvV2jbAV03
xFAFV8pdxgpJR8FusrF3sh1/L6Bhefz1mVV+9rG96MrNmZv53BPTvDhX51/95XnG8gafObH9XNHJ
+Qa/8oWTPLS3wL/8ieM7qhI9PFXksX0lfvvZK3zu8SkU+ebr/9zqVsudfl5/j2UNXltocna5xdRA
apPtDuKYo+N50ppyDUtqKaUls5UxKJJAkSW6Xoihyv3Z3vWuobW2w76BDKtthz8/tYyqSKiy4PhY
nuPjeV65WmexbtF2Qtqej+VFqJJgspiiaXvYEQgRE0bgRT2WTjmGOKbheHR9H1NNfIKBjM6TBweJ
epWq9WvN6onIqiCpJJ9bbvWlMNbJXbbem/2DaWYqHWaqHYazxi3xD3YS/PTTxHEcB7tRF75deLvs
6I12yt5uE2zNRmZ0hT0lk6GsxtMnJrbNGNyfKvSHUXOzyiau9SuVDvsGM7hBxKGRLFGcLL7Yj9k3
kKZgKjw8VeLscgtTlREKGEqae0dztB2fe0ezCKBgqtw3UWC21iVnqMzbFrYncLwARRJ4UYwkQJNl
Hpkq8sJcjdfmm8i9GZFK28ULY1ZbDmM5nU+fmODwart3jV0Oj2XJ6El2cTdD7zei4nY7sJsDv09V
6QScXW4CybpAsGntrg9xb1zLA6mEi//sUpNyx6Fp+T0mtpjBjElGV+i6IfN1i8liCjeI6DgBKy0H
gUAWSXlbAAjQJJAVGeKI4axBRMxA2mC0oFHt+HhBiOWHjCgGg1l9V3tl47N8q5aku9gZ3qlTuZuK
0XqANVPt9ForspxZahO9zWdIwE8+OMmP3j/Bsd5sjxvFlNsuQ1m9/7vbgZ0EjaWURtZQeGOlxVLT
ZiJv8sJMjaKZyBEUUhpX6zZDGR1EUtUeyRrMVDpUOy4pXcHutYCossANIvwootUOMFQZU5HQVZ3V
tkNGVxPdtzimYfk8eWCQTxwb4ytnV3j1aj2hrR3LcXKhiaoI0prCWN7kg/sH+erZZfKmSsPx0VUZ
XUneO4xiCimNMIqTiltGx1Al7hnOUrd9iimVgYzOZx/es4mJa31uqesGCODQSJZKx6WQUrlnONtP
zOw2MXin2vH3Ar742hJeGPFTD0/etM8QQvBrn32AStvlV/74NfwwvubzXpyt8Yv/8UWGswb//u89
vKtA7Bc+tI9f/oOX+eaFMh878s5puneKW91qudPPK6U0PD/ihYUGhiz1k5pbE/IZPfHzMrpyTUL8
sw9PcnK+TtP2CMIYXYZ6N/EF5V5i5uWrda6Uu1wud8kbCnM1m9GcTscJsLwQTUmSGYeGs6hKndGc
gRdGGIrC3lIKPzQZSKt87dwqbSdMZn+AIIKhrEbWVHGDEF2RKZga94xk+i1869ebT6n81MN7+MC+
Ul/w+bWFxqak09Z7sz4rtN4iN3WL5FJ2Evw8IIRo9f4tALP3swDiOI5zN+3q3iF2ctDdSKdsN5tu
N85LPwgaSNg0PnFsLGHz0uT+gfTAZIGsofLAZIHfeu4yQRRj+SFjBZNq1+O+iTzltsv+wTSfeXCC
oqltolw0LImCqUEMqiIxUjBpOz5OkCxSWRZMlUxkWaJgqkCMJCRiwAkibDfolVE7PH+lyk8+NInZ
cwKqPUd9a+Xi/Xwg7vTA72c8ejShR8beZEd7fbHZX7sDKY3vXq6yUEtopBUhOLnQ4P7JPHOVLmk9
z8n5BngQhDFjeYPL5Q6qLHNmsclnTkyw2LApt110VSYIktFpRUp4+CcLJvVugBOG+CHUbA9dkbD8
gKmBEodHZaptFzeM+LnHppgopt7xXrnrDN0YvJP7uNuK0XQp0ZSZXbP4+vmVtw18VBkODmX5O/eP
88Q9bzrWP//B6fdEwLsTu5s4GXuYLJicXGhwpMd2uZ6AODaex9RkjozlmC6lefZSmZb7ppBoTMxv
PnuZo2N5TFXmpz+wl5bj88ZKm0onkTK4dzTDo1MDtFyfN1bbfYmDl9Q6e0qpxOEQYPsBfhBRSKkM
ZXT2lFIUTJVKx6WU1nloqsgrczVsLyJnKrh+hBuEtN0ATRaU2w4vzoYstxzCMJn1S2kKx8bzHBvP
b/rOH5gu0XICCqbCs5cqFFMq+4cyPHV4mJMLjevORr0Xnuv7FV94aYEjY7lkFvQmwlBlfutzj/DL
v/8y//QLr/HNC2X+7mN7MVWZvzi9zG8/N8Oeoskf/oMPMpzdHenCx44MM5jR+fwLV29J8HOrWy13
+nkb99j+wXSf4XHra97KPh2dyPMvfvw+/s3XLrLUsBktGBwazrK3lGKhbvP8TCJ4/8T+AeaqFpVO
0h3k+kmFKIxi9g1kObfcZqFhM55PZvxUWeD4IZfLbWRJYrbaZThjYgddRBgnEgWSIIqSuaAoho7j
Y/tJQueBiQLFXkfT+vfY2AK/PsZxvXt0OyvEbxv8xHG8o1BfCFGM47j+7i/p3WO3N/TdOmW73XS7
/byNr8+ZKvM1q/9ZG6PkR/cN9MumGUMhYygcHMowWUzx9ImJa/rtP8wQz5xc4MMHB1lsOJTSKqoi
U0gbBGGIrspkdJlPPTDBSNbglbk6bTcgb6poskoYRv0KghNGvL7UJJ9SefrEZF/359WrdQxlc3n2
7mG5ec1kDGVTT/3GquRXzq4wU+7ghSFZo0TdToKmYz0Ha7bSJWMoZA0FP4hwggDLDdlT0llo2Jxe
bnJ8Is9gRmeu0knamIKIjCrT9UMkSYAUk5JlTFXD9UM+dHAATZG4byLPsbH8tmQgd3FnYac2cX3u
5aXZGldrFl03wAkiFGAjv4EEmCrYPkhSwnQ2WTSZLm1uJXsvBbw7uZZ8SuXxA4PULK/PxrbOIrfc
ssnob+7V9aTZOkmNhODiaoe0oRDFMWMFk2OpPLWuh6nJDGU1njo8wsmFBh0naS+WhcRITsdQpH6Q
FUQgCZkgTOYsE90Nl5QmcWmtTcPyeWW2zv6hDD947zBtO+CluRo5Q6FuB2QNhZGcQRDF/dblH7hn
kAf2FLclVJkaSJM1Ev0uTZEwNbl/XmyX7LjL5HZzcX6lxenFJr/6qRtLdHA95AyV3/3FR/m3X7/I
f/jWFb70WqJgIgl4+sFJfvXHjpI3d/98VVniv3pkkn//zcssN23G8uaNvvRNuNWO9G4+b2ogzUhO
79uUt0vIb5dcODqR5x8/dQ+/9rULyEKw1LR48uAguuoylEnaxlbbDildYY8mI0mCtbbL/sEUQ9mk
4iwLQcFMbPVDe4ssNxz+5o1Vul6EIGa+5jKQ0RjO6NheiBtGZDQFXZV5fP8Ary816XphMp/YsECw
Kbm9dZZyp0mn22E/biQNx9eBh27g+70r3Mobeis33fU+a2vZdKqU7vOvr2+0ra1nQRxjKDJ+FKMq
EgdHsqQ1mbSu8PpCk6bt43ghpxaaaEqLpG03JohCrtY8FFnCCaJEMNNNMgFxTP8Qz+kqi3Ubx4/u
znr0sNGoXW/NbBx0bDsBpYyOGyTc+x07eDNo0hV+7rEpDFWmYfnMVdpcqVpYXshstUtaV5jImxTS
Gh/aP8hyy+ZrZ9f49qUyNT8Zpp4sJAGxFyTECStNB1WRqVseV6sWta5317l5n+B6NnHjmqxZHuXe
TOFayyGMo0QdvPdaAchS4hitixyndZmhjMYnj4/dcEKD24HtbOx23QHr93M9u9lxAxSJTeJ+W99r
rtrlatViMKtz73AO2w8ppFScIGQgpdGwfBbqFoMZDQFEcUzOVJCF4NRCEz+I6LgBbhCjyILXFhrY
Xsha26Hcdhkvmhwbz+P4Id94Y41G1yNnqMxWLT5y78g1z3/92R8dzdFxffYNZDZlprdbM3eZ3G4u
/vNLC6iy4MdvkLbPTqDKEv/k4/fySz+wn5dma3hBzAN78u86YPnpD+zl//nby/zJK4u3hPb6VjvS
O/28d9ISv11ywdQVHttXejPJ3bMNG9vG1tt0AdKawo8cHSWMY07NNxjJ6Rzp6YtldIWG7SXCpy0b
SZJAwGjOZLnlMJY3uVDuMJY3sIOEtRNiFus2URwRRTEfOTTyJnlYpcPnX7iKqkibxJLfSwmwjbiR
wc97fxjoJuJWB1vbzQldb3NtpBF0/ajfV1lKJQw/dSsZaF9pOvhhxGrLQZEFBVNDUyTOrrRoWx4Z
Q00UzAEhJXSxuiyQVUFWVwjjiJlKhx8+MsKFlXZf/+LQSJYDgxmIk2sp2tr3jd7LRscSuMaobaeV
sI6NMwhXaxYDGZ1XMnU+88BE//4BjBdMbC9gsZkERxHgBRETRQ29Fxg916hQSKkU0yqHRjOkdZXT
Cw1eW6hjqAqSADdM2iXvm8gn5Bq6QscNruvc3G19uTOx3Zost11WWw7Hx3OcX2lxbrlF0wlRpaR1
O6VJyXCsAmGUDOb7PbIYWU4Eki+WOzQt/32xFrba2Ley7xtt75MHBqlaHtMbKvIbg6RvXizznSsV
4jjR4vjc49M8P1vDUCS+/sYaLdsjjCJcP+TEZJ6uH7DUdKh0PFaaDkISdFyfiYJKWlf55htr6IrM
npLJ3mKKxw4M8Pj+QeaqXaodj3LboW75XKl0eebkwibmzfmqxTMnF5JnKASyJPW157bqkihC9G3O
bjod7tqI3cEPI/705CIfOzxCKX3rE4U5Q+WHDt+4FrW9AykemSryxZNL37eaP+vYbUv8dskF2w1Y
bjgUUmqfQnxrtaVp+UyX0lxe60Ac89vPXSGKY5ZbDvQYOB/uyVJoqsSne509la5H2/FoOB4DaZVS
SmeublHueBiKRK3jsdJykSVBVtPImSpt581kbMPymal0KGX0XYkl3y4bcSODn53o3d3FTcTWzbWV
RvCJ/QO8uNCg4/oMZw0+fHCIPUWTC6tJVScCRrIGhbTG1UqXSjtREJZsQIDsSciSIKVKzNRswjhK
qgsFE0OVuW+8iKFJmLqS6F90XAbTOpYX8NylCvN1iyCIEEJwYm+BjK68r6sKWzM4x8fz/YrYTLXD
XK3LFOlrdEXWsT6DUExp/OnJRUazOhdW29Qtj/v3JEZlptJFVyUmCim6bkCQdLogAZNFk+PjeZ67
VOFyuYMEKAoIBJIkKKY0TFUhayoossQjU0VMTWYsb/LsxTIX1zoJ7eaxsWu+20bH6f3+HN9P2Ei0
0bB8DgynubTa4fmZKtWOx3cuV8kZMqoiUERS4dEUMDUZr0eBCuAGIYokIckCTZY4OpZHV6R3RIt8
J2PjwV1KaXz5coUwjrmw0r5GZLVmeXScoJ9N3ztg4kURxbRKTlf5s5OLxCJmNG9SSmv88PFRiOHr
59dQJUHT8SmYKisth5ypcLHcpm376IqM5Yd89N5hHt8/SD6lMkWae4YzlNs2fhgxlNE3MW82LZ/P
v3iVcytNhtIGEyUT149QZal/kK8L3JY7LlerXU7sKZIxFD58cIjj4/ltbdbbJXve7bp4vwdT3zi/
RqXj8dlHbh7Rwa3Gp0+M86t/dobzKy0Oj77nRsTfc9hOHmWm0sV2A37z2cvYfsSVSof7JvJ870oV
xw95+kSyXk7NN3hxtka57fLibI3xgslcrctIziStKpQyGsN5vZ8Af3GmxkynQyGtMVIwmFnrMl+3
cbwI34j54L4BLDekbrlUbR+BIKXJeGFIxw1Yatn8eC8Zu9ywma9beH5I1/XpuG8hBNfD7Wyh/f5T
n3ofY2uWru34m2gEz620cYOItKZS7rj8x+/M8NJcDT+MKZoqB4bTLDQcZGBvKY0QFg1b0LQSkdMo
ShznuZqNLgliWWGyYDA9mKFp+1ypdHD8kCOjOSYLJitNm4W6je2HjGSSwzAGHD8krSUteu/nlomt
GRzEZkV4WSojKPdVmTfqiqwjn1IZyxvQu1eWG3Jxrd13qtYN5VLDwlBl/CAmJCZvqozmdBYbNo4X
slC3KLdcRM9hIY55cG+B+bpDw/ITBXmRMM5ldIWjY3nShkLXCa4ZzmxaPs+cXOwJM2rsKZnv6+d4
J+J6TuJctcvVmsVq0+HscpOvnYOG7eEFMQKBJguWWz4dNyKMIYogpQoG0xqSSHSlun6EQGIwq3N8
LIemyozkEtYzU5V3TYt8p+Ka5MbEm8mNdYHqkZzevxeKEMzXLJYaNkLAfRP5/jzRTLWDpkjkTZWu
FzJZlJkqpWnZPrIkCOOEyXPvQIpHhMCPIl6cqSKiGFNX0RWJA0Obdbd+6uE9HBnL8dylpOq7kUVq
rtrlSrlD1wmptFsosmB6IN2fzZyrdVlq2LwwW0OTJebrNg9Nleg4QZL0UGQats+TBwc5Np7vB1Tb
3Y8b1R63naP0fsMXXl5gKKvzg28hInqn4ZP3jfG/f+ksXzy5xOG/czf4eSus2+31as46MVUYx1xZ
62B7EfeMZDmz1GS20sX2w55PFVFKa7SdgCuVDntLKZwgxAuTZHPb9vHCiFykkNYSBt65ahfLD4lj
QUqTqHU9Llc6qHJCp50zFA6NZFltOSw0Ldwgoul4jOVM3FDi0w9MkkspfdmAUkrj3pEsz89UAXjl
ar1vG66H29lCe7ft7SbhRmSodvMe/YyuG3B2qcnRsTySJBDAqucwWUwxkTeYq3Z5aa5GEEXkdRVd
limmZMI4IoxgupTiSqVL2lDwgijRdxE+IhYUUwrHJ3I8d7mK3Hvao3mTHzk2yrnlFudX2qw0bf7f
b1/h6GiefUMZBtM6i3WLM0tNyh0PxwsZzGpUui5DGf19MwO03bNShNgk7pUEHfRZX84tt1htO+iy
hKbKlDvuNZnzpuXzytU6K02btY6HEPDFk0vULb/fU3tissBXXl8hb2oJDaYiGMqazFa7GKrMty6V
sf2IiJiCodJ0PISs4TST1rbRnM5EweTekSzjeZNiTxgtjONrRMkgMViGIlFMqT2tKu198xzvRGwn
irtdNq1p+bw4W+PsYouL5TZNy4c4EUGWJIkgjOg4PmlDRgFCOdGVGM2nODiaZbXpMFe1KKUU8qbK
SM5g72Cap09MMlvrYqrvb2KTrff5muRGTJ8iXECf2Wn9XgRxzOHRLNMDKRq2x8Ee0+Px8TwrTR0v
iDEUCYTg6Z7mylfPrmB5AZKAf/TRgwRRzFfPrvCNN8osNWziOGYgHbFvKEO14/Hl00ublOOPjefJ
GArEydA1JNXilZaDH4aM5Q3aTsBHDg1T6bost+yElnemxmLdYqbSZf9QGgFU2i6SJLC8pGp4udzh
/EqLJw4M9tXot7sfN2recztH6f2Ectvlb86v8UtP7rsluji3CoM9PZg/O7nE//Qj9+5IJ+id4mZW
Bm921XE7u71xzde6HlfrNrPVLlEcc3a5RdcLE30wUyOKYgazOpYbcGq+gSJLWG7AWM5g32Cajhvi
+iF1y+M3vnGJvaUUi02bJ/YPsNp2GM0aKJKg64ZIUkTeVDk+kWcgrTFb7eKFMUXH5+GpIpWuhxdG
19BcP7S3yCtX6wykNC6stpmrdt+y9e12iiHvOPgRQhwAFuI4doUQHwHuB34vjuNG7yUfuwnXd0fi
RpTydvoe6xtyXYAvrakEEX3GoQMDGZ67VKFhu3z3UgVZkkhpCvcO53CCkEuVDt12QFpTMFWZXErF
aCTDbpdW29h+SM5UsbyAiIi5moWhSKQ0mZYdMJzVeXW+TssKWGnZWF5ISo04t9xi31AaU5ExNIWj
E3nOLrWYKVvEscD1o2u47u9UXC8j+eyl8jXiXlMkrC+rLYertS5uGFFuuWR0BT+MkBD9zDnAqcUG
1U7idIRhhJAkKh2XxdqbyuvnVlrYQcjDU0Uul1uoQsYOQtaaHm2nSscJUIREFCUHrCrLGKrMw3tL
xCIxrJIkWKzblDvutqwtG1FKaUiSIK0rZPWE3e+tWGru4ubhegdmx0329MaZrZrl0XUDJAlato/t
RygSEIEiJ9W95P9C1n0vSSS6X62uj+OHDGV12nbAiT0FRvJmnxUsZ6q8sdLm9GKT7DYB88brvRPX
x3b3eevBPTWQZmogzZnFJl6Q6J9tTB4oQnCl0sHyIlZbNgVT46/OLLN/MJNotw1lcPyIJw8OkjNV
5mpdTi40yBkq1a7L1brFeMGk7fpossRQVu8LCY7mTbKGwkrLZa6atNOeWWpuqvoUUxpfPbtCue1y
udwBBNWux6GRLJNFk9G8AQI6TsC3LpapWz6CmOWmzWPTA/zAoSFeuVrn/EqLhbqFoShocmKP1p/p
1vtRTGnM1rqbZqDeKW6no3Qr8KevLhJG8fuq5W0dn35gnF/5wmu8crXOw1Olm/IZ60yViVDxmwP3
N+q9b3Z71nbB/cY1P5TR+ZWnDlG1PNaaLn/04ixtx8fyYy6stal2XcQauH7EvcNZPjma5dxKm5Qq
MzWQ4r+8skjdcklrCm4QUkyrlNsuX3xtCUWWCKOIhuXTdnwGMzoxMS/N1YmJ6Tg+lpdo/FS7Hscn
8jzca4Ndvz81y4M4EWLWVBknjN62JHKrGfo2YjeVn/8CPCKEOAj8DvBF4A+BTwLEcVy78Zd3Z+JG
lPKu9x7b9VR3nICT8w2iOOFll4XoMw51vYDTSw2WGw5LDTsJWlyfsbzBU0dGuG88z4tzNQxF5qW5
GqWMTrnpEEYRpiaz2LDpeCEyUMxoGKrMkbE8oz1F8JWWy3LTptqrSrhBRBjH1C2PMV+nYft86r4x
/vz0MktNGyFi3CCi1vWYr1n90u6dTIBwvYxk0nKY2STutb7ZTy02QCQzVt+6uMbp+SYh8NzlMk8y
xFy1ywuzNRZqFq9crdNyAmQhIQS0nZDXl1oU0hqvzTd4abbGfNXi8moHXRGMF9P4Ycxqx8apJu1L
WUNBiJhiWielJe1xAI/vG6TtBhDHVC1vkxjZW5ExCMBQJbK6Ss68S4F7u7Dd2lOE4OxSs8/Gtj6z
tdZ0+MvXl6lZHqosockRQU/Axwt6FMsks389UW88P8LVQl5baBCRrCNZSMxWLX7o8AhBHNO0fFq2
T63rEsWQu86xcievj7lat6+evl7N2TeYvubgblqJLlAhpfaTHkCPudHn6Hge2wsRAnRNJogg7jHn
pVSFU/MVlpsW37mcYt9ghiCIaLs+M+VER22imCKrq4TEOH5IzlBRZIly2+HzLzYZy5soskBcLPPq
1RprbY8jozkOjmQ4s9zk5EKSq1ys2zy4t8CVSpem5fMb37jEiT0FJCnR/Ti72GK+biEJIBKEJFXg
MIoZyxksNR3ars9iI0bqzSVsdWRadtIeSxzz6tXGtnILu8HtdJRuNuI45gsvz/Pg3gIHh7O3+3Ju
OD5+bAT9GYkvvbZ804KfuWqXUwsNsoa6q4H7nWCnPt27Se5sF9znUyofPjjUTyCs75/nL1eZrTZa
m1YAACAASURBVFq07CSZFYYRHdcna2rIksDUFdpuQEaX8f2IL55cotp1sf0QL0hGIp69UCUmIo5g
/3CG1aZLwVSS10QRpxeaHBzJYHsRa20PLwy5ZzhLSlPQZcHXzq2iKRJpQyGlymiqhOdHHBrJEkYR
+wcTxuG3w+1ig9tN8BPFcRwIIZ4G/k0cx/9WCPHqzbqwOxk3IkO13Xtcb4A+bShIkuDBySIdN+Cx
6RJjRRNFCD7/4lWW6g6rLSfRgRFgOT6+H3K50qHe9RjNGcQCBtI6D0wWqBc9YhGjCpk3VtrIAmIE
XdtnRQhSuk/T9nCDgMtrbVZaLmGUtGzkUyqmllBgS5KckBzEMU8eGOT0QpOm7VHpOMxWJP7g+TkO
j2a5UulwdDx/xw7OX+95X28N5FMq908UmK9ZrLYdlpo2XhTRsgNaNrw6X+fE3gIvzdZYayfPTpaS
eYCBjApCkDEUziw1WWk6xMBkweTCWgcNQRBGNC0/CXAERL1WO1UBxfE5Mpql44W4Ycgzry6w2LQZ
zuhoPfXu9dmA6xnymuWhqRLHBwqbDoK7FLi3HtutvZrlsX8wQyxAxAml/XzV4v/+mwvM15O2piCK
kaSeRo+qoGsSlbZHLyYmjJPAKRYQhMnPbhBie2H/7/71V8/zyeNjpHQlacmo2RRTGqX09rN8d+r6
aFo+37xQ5tRCk3PLbR6dLm1q9di6NzYmPeq21+/Zd/2kTcTUZMwejZ4iJc9IkeBKuc35lRYDaZ3v
Xq5x/2SehuXjhxGykOh6IVEU8/GjozxxYJDlhg1CUO26SELw7ctlHpkq0XEDVls2iiwngVHHZbJk
kjNUgiDCCyPqtsfrS00qbZeUJrPWdnlob5G2E+D6SZtuw/Zwg4iSGXFuqcXBoTR/fWaFSjf5/WTB
5ONHRxLx5C3U2Mlc4AJnllo0LC8R0ibexDb3TvBepc19t3htocmF1Q7/8un7bvel3BRkDZWP3jvM
l08v888+dRRZugmtb+JN1q249/ONwk58urdqN96pqP1Gjb+a5dGy/b79mK9Z/KiZvKcXRYwXTSRh
I4REFAvqVoBAwg1D7hnKMN+wMZRE7DwixlAElXbQS2qFaJLAjyNAYrlh03F9vDBEAHuLKeI4SdqU
Wx5eT+/g9cUmWUPllat1HD9kvGCQ0VWOjmd5ZGqA5ZbNB/aVyBrqez5BsZvgxxdC/AzwOeDHer97
736z24gbkaHa7j1mKt1rBuhlIZK2JgliYkZyOscm8v3XJ4JWBuW2g6ZKZAyVoaxBylCTPtKOx6nF
BooksdZyWG3ZyAicMOKl2QpdL0AgyBoyqiLRsHwsL6CjBjwwkefCSoe4V3GK4gjiGFWScXyXjhOg
KYlFOjaR5yP3DnFxrc1iw+bR6RJvrHb6Wc87mQBhu2fVtPxNjEgt2+fUYoOBlIapJwOHQxmd2UqX
vKEyE1j4YYgqy+wfTLHctOm6AX4QY6gyB4cz5E2VwYxBx/VYbjh860Il0VsJY4azOllTJWcoyDLs
G0oTxRFN28ePYgSgSgJDlTA0mX1DGcIopu0E6LKM2WPtOzKW4/6JJFt2vSz99Q6C93tbynsNW4dj
19feYj1piZSEREqTUITghdkaK00HSYAiC/woRpMEsYBQimhaSQlIAKqAIAZJTujsZTkmRpDW1YTl
TZbIGSpeGBMLaDsBURS/7QzYnbo+5mpdLqy2GUhpVLtJJeV6NmrrdyRmk81+YKIAAo6PJbM4P/Hg
JHXL45GpEqcWGuTNZq/vPmCm3CUiJmOoHBrJYnkhThBtYpBLWBeT6kreUIlJ9H9Wmg5LTYc4ijkw
kOapwyNkDRUhBA3Lx1BkxnIm5ZbLatvF8QIulztMFE38UGD7IcMZnbrlE8YR1a7Ld65U0TUZ3U2S
JHXLo9bxuGc0u+1cYAxJIsYOGM0bm9jm7mIzfu+7s2R0hU+feP+ROKzjxx4Y56/OrPD8TJUnDgze
8PefKqU5MVmg7fo7rjrsFDvx6a7XAbKbanc+pfYqpgmTquOH24rEa5JE1w2JEIg4Zv9QilrXI29q
CAFdL2C5YSMJgRtEZA2FpuVjqDKGKrHUCHt7GwppgROE7C2muG9PgVMLTUppna4XcHwozzcvrOEG
oCoyAsFITqdp+XhBiB/GxDFIQnqz3fUGtLjeCuwm+PkF4JeBfxHH8YwQYh/w+zfnsu583IgM1cb3
mK9avLHSomH5AP1Fti5k+uTBa/UlSqmkBBpDv2Vhsmjwwf2DFFIayy0bIQSHRrI4XsRa0+bcUhsv
jNBkiSCKKKWSTWCqMqaqUOlY2F6QMBBFMW6YtM7EQIRgMGvSdgPcIOL8SounjgzTcQPmql0+fnSU
D+wr8cJMjSiON2U9u16wiY3oTsPGZ7U1A6QIwW8+e5mmHbDctHli/yDzdYv5WpdaL7Ob1lSG8yZZ
Tebscgs3TNpdFElgqjKWFxJGMU3bp9xx6To+khBMFk3KHY9SVietyaw0k/mgAwMG+wbSvDRbZ76e
zBYJBKoi8cnjYxwezfGVsytcrXZxwxA/iBjM6H2yha2B9kanZbsM1dbfv9ezPnc63irL+Oenl3H8
iKwhM1Yw+dsLazzzyjwrTRvLT3KjyX6N8XyQESgyjBdSrLYcohiiIGmHUDWJ4axBFMFK20FTJGQh
SOkywgsRvZbKnKFQijSGshFPn5jY9tm/19bHTjKyTctnqWHjBxE5QyVjKP0+9+2w9TsCvL7U7DsG
xZTGV86u9OcSfuToKK8vJQKp5Y7LcFan7fjoqoShyqhyEnSO5A3SusxTh4f7+23dSYoBIeC//fAB
TF1huW5zZrHJnqLJbNUia8qcXGhwfDzPvaNZIKZhe3xvtortBDRsP6Gv7bUj3z+RiKSGUUTXCyEW
3DOcZTijM1fp0nUTm1VM67jh9rObihDMlDtJBj6OGM+bd7R9v5modlz+/LVlfvrRPWT09y8B7w8d
HialyXzpteWbEvysMxzeLPvydj7d9arwW+cvgete41Ym1cFsosV4pdzBCSIUkbDvPj9bZU8pxXgY
M5TRyZsqbhDRsn32llJ88bVFZisWjh+iqTLZ3kyxF0Y4fogiJZTVjh+yfyCDqcvsLaWodFzyKYWu
51Pv+pwLW9i9irMfRCiSQBIgZEHOUCmkNB6YzPPEgcE+qdPttus7xW522g/Hcfzfr//QC4Ccm3BN
d7EF81WLX/vr8wRRQjX7c49N9as761jXl9hYGs2nVI6M5vjbN9YYyup0nJDpgQz/9SN7yZlJm5Lt
BvzGNy5yqdyl0XUppXUMTaGUVil3XZwgIKVJ3DOcY7VlE0UxcSyQgDAEP4zRZYgE7B9MgYiTzyml
cPyI1ZbL731vFi+IeGhvkb/3wel+wPaJY2Pvi5mfjZivWnxvpsp83WKykKLjBpxZbtFyAqodl7rl
c261iefH+GHS661KEsQx06UUZxaTzO9c1aaYUklrClODKdKaQrnjMpTSSesqq02L5d68FcCPPzDB
2eUWy02HesflZTfk8QMD/OOPH+KrZ5aZrXRJ6SrHx3McHMmyZyDFZx/ew6PTSZtMRlc2ZZR3kqVv
235fPHejA36nP8M7AVuzjHO1LllLZblhc6XcwfLC3iyey/eCKrM1G02V8aOAOAI/BjdIgqCcqdJ0
fKweuYkXBICEH0ZEQCml03F9xvMGIDg6luOx/QMcG8thbnBod9ra8V5YHzuZP9rIoCmJRMh532C6
z5p2PWz9jhuDoa1zCZMFk44bMF+zqVseB4YyHB3LcW6lxeVKF4mEpe2RqSIx8OenlimkVGQpISu4
WrP6TlLV8pg2VFpukiBTe6wVgqT1balp4wYRkiQxkjVoWT5GRk+cGQTljsuFtTaz1S5Hx3LsLaWx
vYC65dNxfeIW3DuaZaXlkNUVBtIaoznjGip8gLrtMZozOT6RsE89OFX8vtJ/2g3+6MV5vDDi5x+f
ut2XclNhajJPHRnhL19f5v/4zLH++ryRuJn25e2SJdsld1q2v2n+8skDg31fbTu7sx2T6lOHh/na
+VUMVearZ1comBr1jsdQWu/bbEOV0RWZrhtwdrlF200YIlVFopRSkwSrntBcLzS6aEqSFB/I6EwN
phnJGZyYLPDKfJ2VhsPZlSZhFCNLgihO2hbbbsBg2mC8kOITe/KM5EwymsLZlRaXK51+Qv5OwW6C
n88Bv77ld39/m9/dxQ3GbK1LEMH0QJrZarc/cAtsO2ux7giVepTFqiKhyTIDGZnBjN4/rNq2z1LT
ZiRn0HVD/F5PfxBFqBJEYRLIdAk5vVTnwGCGfEpF9wMsL6JmuYg4QlEkvDBmppIcxE3LpesGCAni
OEKRJWIheO5SmYf2Fnni4OD78hBcD1JbTsCFlRb3jOTImwo/dv8ES3WLatfHDULaTkhKlYlIyB9y
psZ4PmmBc6OIjpdU0wqoNGwfIcFEwSSIIi6utZPydkrj4GCaWEBGU5hvWGQNGV2WqHWTcvZczSKj
K3zo4BBOELG3mGKylEIRglMLjU30t1tpY98qS79VPPepwyObKH3v4uajlNJw/YjTi00UWfDCTA1d
lZhZ69K0fcIwJoygaXm03ZCO6xOGSbCjSNCTdSIGKl0XTQERx4wVUqy0HCQpySJ6QbLmsqZCSlWS
NgxV4t6RLBPF1KbnfScx/u1k/mjj/A7Qbwnd7Xfa5JDVElIYt5PYSNsPWW44rLUchnM6IzmDh6ZL
PHV0lDNLTf7i9BJrbZff+tZlZFnQcUOOjOX6AWcxpbLaclhqWrhexH9am+XQcIYgimn22P2+fbmC
KglEDDJgKBK2F+BFMaHvY2hyUl12fMyeaPFS3QZJMJAxyJoapioxlNFZaNiokoSQBKYuwzaJkabl
88JMjaWmzWLT5sRk4W7gcx0EYcQfPn+VJw4MvC+JDrbixx4Y54uvLfHtSxU+cu/wbb2WdyIl8nbt
a1uDryCON2nmVd/C7jQtn3ZP12tPMdWvogdxTDGtkdNV/uL1ZSw3oOn4DGX0njZXmv/vxas0rABT
l8lqCpVuQNsJ8IKIKE5ISWQpJK3JyJJMKaVieSGTBZOrtS4jWYMLa21atkel66IrMmEYY/W6fBQ5
CZaKGY2OG1DtesiSxOi4ga5K5HSVs8stvnu5wuMH7gz/7m2Dn96cz88C+4QQX9zwX1ngLsPbu8RO
NuB0KY0ikfC7RzEX1zqbKInbto/nR5s0GqI4YQN66vAID+4t8upcHU1JhAkVIfjCy/OcWmjghRFt
2+dKuUsUJwxe04MZ2rZHSDJv4nghta5PKZWUVDO6wpnlFqokaDuJgySLZI5AEklWueF4FE2VpYaD
qctIkkRalXnuUvltha/eC3gnDtx6kDpeMFluOEwNpBjPm2iqxBMHBnn2YplKJ6Lctvng/kF++MgI
pxcblDI6Hcen3PYgFjheQhFZ7XqEYYQsCVZbDcZyOittF9sL8cIYXRYMZg32DiSU4gsNi7rt4QQB
ipwQH3z59CKnF1t4fshq0+XhqRJfPbvCyYUGAjg0ksXsMbVsNerXy6K96RQm4rkz1Q7DWeNuS8st
hgCEiLG9CF1ODqDlZiJq23Q8Jgsmp5aaWE7Y39teELE1Ue+F4IcQRQFe2O0N48dExMhAWpMpmhpe
jwFyoW7zvStVXl9qXuME3CmMbhsrm64f0XZ8mpa/6Vo3viajKzfEgV/X45irdZEQfPn0MveMZAji
iKGM3qfGzqdUOm7A967U6LgBHddnNGeQM1SW6k7SpphPUUprhFGMF0S8vtSk3HbRei0pmiIYz5tU
La/HMCe4WO7g+BHzDZvRnEHb9jk4nGHPQJoLKy1kSTBbSdjqVpoOLSfJIB8aySJLLi/P1bDcEEUW
DGZ0HpsucWaxScv1OTaWZ89AiprloasSHzs8wpVKlw9Ml96Ta+C9gK+dW2WxYfPPPnXkdl/KLcEP
HBokayh86bXl2xr8vJWd2u7sf6dkLesJ6HXNvHVR460dFRuvJwY+eGCg30I2X7VYbtic6baw3IDR
nMFI3kTpjTR86dQSV8pdJCGwfAkjLxjNmciSix9EZAyV/QNpVloJeZLrhyw2AqIInCBEkSQEAkUS
ZHQFy/FxvLA3b6jx6NQAQRxxZqkFgBdG7BvI9MmampbPty9WWGra/P/svXmYW9d93/05F/fiYt9m
38gZiqRISqIoiaJsa3ESy4viOI0Tu42dtEma2G3j5klTP+2bpO/Tvm3Tvm/Tpm2atk6cNEuTOE3s
RmmcOLGjeJMsWZtNUhJFSuQsnH3DDHZc3It73j8uAGGGmCEwnOEAGHyfxxZnBhc4OOd3fuf8tu/v
+mqGmfXcrtKM7xXqifw8C8wD3cAvVf0+BVzci0EdFGzVH2bzxhvp8vHJd59gMp7BheDaapqBkJfx
lTRPnp8l6teQwL3DEdJ5i6+9scxapkDWLAJOw7x3Hu8hnbdI5y2+fGWR2XiWoKfcjBMGIh5ifp03
FpK8OrNGyOsmlS1g47iJFSFI5S16gjpj3T5emoqzkLMqVLm6KrFtWEwZFO1Scb1bxaM5UQuAwaiX
SCkPtpk3xk4vcKMxP7YtmVzJ4hIQ9TuKz60oJHIWqkvBLEo0l8Jz11aJ+dwc6vLTE9SxLMlz11ad
AnMF7ujx41JcXF/LIkuNKKN+nfGVLEXbJm1LpO5CETh9NjKOxyaVK2IXIVcokjIscgXnsps3bcxi
gT9/ZZ7ekE6otPYrKYOIX6swt02tvhU1hNqpTOVLYdIwOT0c4dxYrBLunljJNLXHv9VRPphTebPC
uDe+4lxoX19IkilYdAd0MqbFYtpA2hJRCvHYto3qEmguhZRRxK56XwnkTBshwK0qDEa9JOdMbCQr
6QJjPX7Moo1P05gtpU9lSj1lqulka1FCN6MslCObU6sZXpyMc2FmnVdnEzcY/zupUdrOcWJJyR09
ATxuF6Zlky445AOnhyMc7wsyWNKV06tZ/vTiDGvZAkVbIoTAsKTjpFCgL+jFq7k42R9iei3HN68t
k7eKeFSF8SVJLOAUPquKU5NXLEq+fHmRbKFI1KdhWjZuTcFtKXjcKmuZgkORq7kYCHsYiHhJ5Jbx
ul10+XUOx3xMxbMYZhGXIlAUQW/QwxcvLfDSVBwpoT/k4cfeMcZIzFfRD30h/aZpggcVUko+9dVr
HO7y8fjJvv0ezm2Brrp47139fPHVBQzrbvQSw+jtxnatRGqd/Tsla6mlQ97vvVGnbB5P0KNVDJ8/
ePE6VxZSZI0itrRZTOVRXQqDYQ9Z08ld1lQnYiuRhLxucgUbn1vF7RHkrCKpgsVAxEveKlIoOsyy
NpDMF9FEkdfnnFYbw1EvWcPG53YxGvOhuRQePBIjlbeYWMmQK9isZw0uLyYdtkkJ2UKRiM9NUFeR
QnA9nmUq7vQaa+YMgJsaP1LKKWAKePveD+dgoVa62quziZqX7pEuHyNdvkofiflkrsIEEtI1xlMZ
0nmL1+eTvLGUYiVlcHIghEdVsKTkcMzPp5++xl++ukDRtpEIol6VroDOkS4/c+s5nh9fwbQlBVNS
KF3SB6M+1nMmCwnD8VgkcqRyJgqlwjec3iCKhGhAI5WzcGtOON8s2hztCfA3zgzx5lK60myv2SME
O/XyhLwapwZDLKfzBPUw33m8F1VxyA5W0wVm17IUbUnecogevn19jbVsgZGYH5ciGIp4Wc2aIJyL
KEKiuQRCQMDtYmY9R7ZgUZTOnEc8Gk/cPcjUWprVVMlbIyWqqiAAd6kb++x6DtMqEvXrRLwqioBk
3twQ+Sl7wMs1PAXTRgL6FhGhWux2jRqMrZAe1UxIZE0+9/I0KcN0mhWX1i2gqzxyR4Q/e2UeiUNM
cDjmZ3w5Q9F2qM6d/G9BriAxrGLN95fgNNoVEPFpxAI6RSlRFYV7BiOMr2Zwqy7stSwvTMaJ+DRC
k2/VipXTnS7Pp7gwk+DUQKhmRKVZEPZpBLMabk3Zcq/Xin5uJ7c32wcxnxuXS7CczCMErGVNXpxY
w+Vynu0Pe3hhIk48Y5DIWFhFiVW08Wgu3nYkxtuOdLOaNjjSE2B8Oc0LEyt87coypm1j2pKIrqK5
BDG/G8uWmMUifUEPmsuhxLZs6fRqcimspQu4VYXXZtcp2BKP6iqlxLkrFNWiFNEf7fYT8zuEDzmj
iO5ScKsOq5wLgdejcj2e4S8vLXCiP7htk+TtcJB0wrPXVrkwk+DffPBu1D2of2lWfODeQT738gxf
u7LMe+7q35cxbGXMbHX23wpZy2YdUkunbNXa5MnzM1yaT5LMFQCB2+U4oYciPmbWcqiKYCVTACkx
TQvFpYBwjJi5dRvDlniFi3jGiQjPr+cwSrWcaonVU+K0NJC2JFuwWcsamEUVsSbxay7+9MIsbpfT
3PRtY11cj7vQFCe61BfylL6B5NpyhvlEjsGwl6+/sYxXu7EeeLdxK/qi7pofIcT3A/8O6KWUcQFI
KWWooU/cAdpVId6MFnXzQbyZ2lYVgi9eWuCvLy+WLi5FIl437znZx1OvL+B3uyoMIVPxDF+/ssRC
Mo8qnEuOV/ORKxRxu110B72Mr+bo8qtMxXOlppqQNSwyeQsAj1uhaEuW0gaKArLC8ga2EIzGfFyP
ZxmOOrSL77yzh4DH6VTudbt425GulogQ7NTLM7WawSxK7h+JkTRMgl6NmbUslg39YS+vLyQRLjAM
C6MoeWU2CRJ8bpXuoIfesENBfqfmwipKEJLZ9RzxdAFb2kjDQuDk7fs8KgNRnaVUnqxR5NvX405v
n6JER2LhRIQSOZOhiJecYdEf8eJxqzxy1GHaKZMcAJVowoWZdQZCXl6dW0dKwWhXuK5LYaMGY6uk
RzUTpuIZzs+sE/JoJPMmHzl7iIGot1JIr7kUvvN4H1+9ssRy2iCRL2AVnSa3Asgakmqzp9zQtAxN
QLffzUDYS0/Aw+t2kkJREvCpHO72o6pO5DKkq7g1hZP9oQ3RnXi2gC2lk5q1nufV2QRhr3ZDRKWZ
0Ohev5ncbkV3W93488qCw6hpFWUpOuziymKqFJkPEPU7UZuY36kBCvtUjvUG+MFzhzkc8/O5l6d5
aTLOteUUy6kC8Wwe1eVCEQJpSzS34M2lFENhLxPxLKZpkzMtDnf5CekultIGh6NepBBEvBoTq1lM
w8LtUjBtyZGeAIWFFDG/G5/bRU/Qw9uPdPFHL03jdzs0uw+MRukNeHh1Psl8Mo9Pd+FVXZzsD2JJ
edMmyTuZ23bDp756jZ6gzg/cP7zfQ7mteMcdXUR9Gp+/OL9vxs9Wxsx2+mAvyRRqjWdiJYNHc9Eb
1Jlbz4KEQ10+Eut5MgWn7uah0Rir6QL4JYm8iZAwl8hjFm0kCkLaLJR6+EytOM7XnqDOatrAo7kw
ixKPW0FzKRSLElVxMgMKxSILCSe9PmFYBHSNgmXz+nwKlwsyhSJLKYdE5fRwhO+5Z5AXp+JMLmc4
cyjK+EoGyzZv6AW4m9gqc6peNEJ48IvAB6SUrzf0CbeIdlGItQy4zQIPG2lRqzfeVvNwbixG2jAr
OZh508YrJfcdipEtOMXJT19ddrzE606+Z16CWxWc6Auzks0zu5ZjNOblG1eLLKWcC3ZIV7GQTK85
hH4SyBZs1FIqhc+tkddtClYRv64R9Gh0BTwEPCpW0elePhT28fL0Gt0BnWTe5J3He4DGeO/3Azvx
8iSyDvPZ+EqaiZU0p4cjxHxucoZF3rRYTuXRFEFvQOd6PI2iCFyKIFOwmVvPYdmS950a5fxMwmFm
c7vImUVW0war6YLjwS99lgB8qsLxvhCFos3r80mW0wa6puKWkqGoD8uWnBkJc2UxTW/Ig6oIukqX
mTIzy+ZoTiJr8uqsI39B3UmldFKqiqhi+45xjV4iW7Xh5b5CvtW3T+DQ1491+yuyd3khiVm06Q3p
uDIwswZFu/Iom+M9ZXnSALP0cyJvMaYqnBmJcHk+BUjyps2VxSQ9AQ+PHYsR9bl5+uoyScO8oc9T
3iyynjPR3QpSyqbv39XoXr+Z3KpCsJZxamwCpX5e1fpOdyksJvPE/G5m1rK4VRdmqe7S7RK8vpDk
3GiMwYgXr9tFumBy71CE7qBecRzlzCKLqTxpw6I74GZyVUHaElvaeHQPBdth4by8mKJoSxQgkbV4
w0yhay6O9PgYjjq05qm8RcSroQA506HknV7LksoXKEqBR1U4cyiKX1expWQ46sOybc6OdvHiZJzh
qJOm99BoDFNKFlN5grpWt8OokbltJ1ycWeeZqyv83BMn8Gj7k/q1X9BcCk/cM8CT35olW7DwufeH
3ruWMXMrEZ7tUI8DvzoFDkr1QrrK0d5AhQJdSpullEHIozK3nuOZayukS738bFuykjXwmkUMs8g7
xrq4MLdO0jApFKHcAjZjmgS8GgG3i0TOxK+56C05XxeSeXKmRSpn4XG7UGwbzaWQLVgc6fHT5dfR
VcFyyuAdR7pYTOU5NRDi/Mw6huk0T15M5Ql6VATsaU+3rRxN9aIRqVvcDcNHCDEKPA+8DhSklO/Z
7vXtoBC3M+A2b8CtNt5W83A45qc36CFpmAR0lSfucqJC8+s5np+IO5SmhkkibxIL6rhKYdOAx0Ui
b7KaLGAUJOen19FdCpa00aXEllAsOnUB5QuXV4XekA+7ZDxF/W4SOScq5NNdDIY93DsS4YXJVRZT
Bk9fW2E1bTjN9QBk66xno16e8ve6dyjC7HqW4aiXZM7k/Mw6p4ciXF/LYNmSqZUMthTkTBsFhyCi
P+whpGu8Npfk29NrWEUn3c22JYmsSdF+q3O1puDUBPUFeP89g3z9zSUETk2AS4BHV3n8ZC/X4zly
po3bJbizN4DHrXL2ULRSL7ZVNKda/pw+IrN4VBdPX12uUKhvNV+NHBqt2vByP3G4y8/p4QipvLWB
cjmeLZAuWChAtmBimE76o10iICneyEQMOJEeAMUFmg1hr2Mc+90qz4/HWc0YgJM253M771hdsgAA
IABJREFU6WFBr8ZIl69m3nrYp/H4iT4uzCSQUrKaNljJGPQE9KZe30b2+nZym8g63dg9qou8aVd0
8cbG1E4aa9YooqsuRrt9XF1MgRCoikLU5+ZdJ/u4azBMPFvg3Sf6WM0W6PK5mYpneHMxzfnra6gu
hbWMiWVBX0jH61ZJ5EzyBYelb6zLR8GWLCfzLKeMiv6QOJFmp75L4HMrCMWJAuqazkDYiyIEhaJN
2OsmU7B44FAUgOWUga65MEzHjC4UnXrCsE/j3JEuh2I3b7GFuN3S3LYbfvmpNwl5VD760KH9Hsq+
4AOnB/nM89f58uUlvud0czV23a0IT9ngUYXg6avLN3X41ronbnaOT8UzxAIeMobJfCLPUiqPaUuH
eKrE8GnbNv0hD0UJvQEvs/EchWK5ehs8LhfDMR/rWROrKB3SA5cg4nOTKRRRhOByLomQEoFNMmvi
01VCHjchr9PL59nxVRaSBn0hDwHdcXAd6dnIign1tT/YKW5VXzRi/LwkhPhD4E8Ao/xLKeUfN/SJ
Dv5KSvnD9bywHRTidsV1tS4QtQRlq3nYqv7izy7O8crMOlcWkpwdjfGOI1185fIS8bSBz60w1uXn
zr4AedPxvCwmc4S8GrYUuFWBpoBpu7m6nEUIkBJ0t8pIl5eJ5TQZwyZTsNBcCh5NJeLRWMkWuDib
wJYQ87orVpPP7eJEf7ByWWv19awFVQguzSXIFmwWkzmiPjdvLKbxaAqnBsPYSJI5i/WsQ1O9njXw
qE6BYqZQxLJhYjXjKCOzyHLSRFFA4LC/lC8UXrdCd8DDT33ncQYjXq4tZVhMFUA6BbSHYn6+994h
gh6NyXiGLp+T+49wmKbK9WJbzX21/MWzBaJ+rW5DtZFDY688bO2MsE/jwzWa+JWpyxcTBi4FYgGN
U/0h53Czc5j52jU+CPDpKj5VIV2wyFtFDMtiPqHi0VxEfW7ypo3EoTztDrg36J1aa+bVVR4ajeH3
qKymDO471F79XbaT2woLYqkbe7nmpVrfnRuNMZ/IsZzO0xPwcLwvyFLCYDACebPISMxT8fLGfG7+
/NoKyymDl6+voSmCZM5kYjXDYNiLoggeOd7FYtIABBem1xw9IcCywTCLWFJilkJ8li3xCBiJ+nFr
Ao/qJ+bXGV9O4el2YdmStaxJSHcR8brpCujoecUZj3BYLDVVwbRs+kIeFCBlWPg0p15F15Qt02Rv
dW7bCS9Pxfnry0v8k/feWSEdOmg4NxajJ6jz+QtzTWf87AaqDZm1TAGP6qroha32Rq174lj3xqah
p30RDsf8XJx1Ii3MUWpPIlGsIpblGEBFW/I3z47w/OQqfrfCU5eXoFT7eXo4iuoSXJ5LkrMkGdPG
KKa4dyTCSirPes4k5NVwCYdNNqCr9Ic9xDMGqxmD2fU8d/YFeexYT+VOV85YCng2smLu5R6+VX3R
iPETArJAdaRGAjsxfr5TCPE08MdSyv+03QvbQSFuVcy2XfrXZsNou3nYfBGZWs3w5mKKWEAnnjE4
1R/i1FCYD5weImcWubMvyELSYHY9R9ooYpgOM0jQo5LMm05hvE/jnsEwn3563GEGUh1LZjGZd6IW
CvjdGiGvykjUT1/Yw73DYb72xjKZguXUmoS9PDQW47HjPRs6/7b6etZCmc8/ZxURQFdQJ5O3yJtF
5pM5VEUhY1ispAyKEmwpCXtduFXheIFdguWkwXq2gAQUxYmspXImuls4FOQxHw+OxXjfXQOcGgoz
sZLhjp4ALkWQyBVI5S1OD0c4P7POo0d7GI76bvA6VRciw/a1V3vteNjLHOp2Ra05s0oRx2t6hlTO
xLJtLi849Tohr4pt2+RNJwpkl0K5xZJFbVpFgiEPQ1EvGbPIeqZAxO9mbj2PYVromouRqI/HjvXU
RVFfTe3aHdTbyvApoxEHVS29/bffNrqh6WnEr9FXorEXCL58ZZGgrvHgWIx03mJ8Oc38eg63S8Gy
bWwbUnmHHMEwHTKC9azJmZEIc4k8x/sC6JrL0T9zSTRRwBagugSP3NHD33pwpELEMr3mNK4eivpw
uxR6gjaPn+jl2fHVGyKMD47GSBkmQV2jP+zhzEi00r8k4FF3RVe0u06QUvKLf3mF7oDOjz08ut/D
2Te4FMH77xngMy9cJ5U3284IrDZkcoUi+VIrku32Rr3nbdincXoowpWFFPOJPAXdZqzHz/npdbKG
Q4h0tDfIiYEQJwZCPDe+ghSC6dUsblVwvD+Irip85coy4KTFWmaRP7s4j1EsYhRshrt8BNwqd/aH
cCkws5YDAe852cdC0uCxYz2cHnmL5XO/7nS3oi/qNn6klD+2o0+4EfPAcZzo0f8RQvy1lLJCmS2E
+DjwcYBDh5yQcKsrxK2K2bZrdlXLMKp7HhzuBHRVwa+rBLzOMp8bi/H01SXiWRNb2oxEQ3jdKssp
g/sO+XnXib5KtOb1+SRuTeHDDwwzHc8xl8g5G82y8btVvG6FRN5iKVXA7XKhugRTq1k0VeG7jw/w
yuw63X6d77qzl1ND4Rvmo5XXczOqm5N5NRdet1K5EDxxdABLSlI5k3TewqO5SJZydEMejUTOCT0f
6wsyu57DrTlGUsaw6At66A+6cSkufLqLO/tDfPiBQ4S8jvyoQtAT1JlN5PC6VcJeN2cORVlM5SsU
6GsZE4+mcKR7oze6TPG7HRtLOzgeDgJiPjfdQd3pyWXZnOwP8o2rK/h1lWtLaUwTNNVhhkznTRRF
kC0UCXg03C7BkS4/iuLUmxRtSSJnUbQlXX4Pg1EPJwZCDES8da3/QZaZrb77Zn1X/fNh3kplDHo0
5pI5JIJryxlGYj7mkzlShkXIo7GedajHfW4XQggsmw17+5Gj3bw0FcejOeQHc2s5XhNJXKqCiiTs
0Xj/6QEOdzme4yPdgYqT5m1Hugh6tcq4h6K+G7IJHhyLbWiM/OpsgnTeIm/ZRL3uG1J0mpnUZr/w
9TdXeH4izr/83rv2rdalWfCBewf47Wcn+atLi3x/m5E+bO4R9sRdPaxlC2/VENRAI7qznAVwbjQG
won+/8IXLjGbyBP1aIxEvZWUu4szCbJGEZdLcLw/RMSn0eXT6Q24WUzmHTZQRZArWEQDOnmXhe5y
cWIgSNij4dEUgrqGT3dhSVmTvr4V73SNsL0dBz4F9Ekp7xZCnAa+V0r5C418oJTSoJQ2J4T4M+Bu
qvoFSSk/DXwa4OzZsztNH246bBaO7az8eLZAOm/h96ik89aG3iv1CNjhmJ8zwxFShsmRbj9Rr7ty
EJX7BXX53JyfWSfi0xiO+vjgmSFGunyV9yjnnH/v6SFem0vwqa9dRQjI5IvEAipSShYSeSzLJpE3
CRsqEa/j9Z2OZ/nW1BoDER/X17J85MHDFGyb0Zh/w2e0A25oTnakiyfuGriB5nV6NYtSYlLpCugU
bacWwKspuFUnnSjoUXlpcg2f5kJKePhoF+851c961uSZaytEfBpfurRQoaBOZE0GQh6+685ebAnz
iRxJw6xQoA+EvOSMYiX65BKiUoC9mDQYX0nz+Ik+kobJVLy2jLWiUms13Aqb5WYGyLdyw7MsJPMY
lo2NTUB3eruEvG4U4Xhe/W4XEb+baytpfG6n6PrMoahTBOvWGYn5yRacSKYqRN2X2YMsM41890TW
2Xen+kMEPCppw+J/vXgdANOyOT+9XjJYC4x2+xFCkMxaZE2LLr/OYNQDVWfIXYPhit5WhWB6LVtq
Qug4qEJeDYRDOJM2LMZX0ng0FwGPStTnxqrqflv9PTY748rU5o8e7eHJ8zN4tFJN4D2DjmMlnuGF
iXhNmvyDDNuW/PsvXmYo4uUHz43s93D2HfeNRBmKePmzi/NtZ/xsNmSASgbGduyXjaaOl3usXZxZ
586+AGGvu9Tg3ubNxRRfvbLEaqZAd8ApQ4j5HRKFc6UMki9fWSSVt9BcgqWkQbaQRddcnBnxInAM
IoTK95weqKTPV2fxtDIacT38OvBPgF8DkFJeFEJ8BmjI+BFCBKWUqdKPDwO/0sjz7YLtrHxVCM5P
r5OzLKQNC8k8/WEn97KegyTs0/hQqTagVrHdo8d6SGRN57Cr8uSVLzdA5dl41qknifrc9Ic8LCQN
Dse8rGULLKULaIqCVZQsJAzW8wX8usMWhHDYnlbSBf7zl9+gy6+jKvDJd59oaQNo80W1VnOy6u+X
yJqVKEvEpzEY8XLXQJigR+XZaysspgzyVhEUONkX4rnxVVyKQsG2WUrleeryImcPxxiIeDZQUAfc
Hr702gIhj0bKMPmuk3343Sr3DkeIeh02rnIObjn6FPO536pL6PYzsZJmYjVNUNc6l5V9wq2wWZaf
LXvfP3hmqPLshx8YIep1Y9vzpA2305S04DCxpfMWOatI0ZZ4NIf4pCugk7dsAm6V4aiPqN+N7lLI
W04aVD0Fu82MZmmXUF0E/aVLC5yfWUcAp4cjvPdUfyUKFPZq6JrCxHKG1bSJaWfo9usc6w1wZSnF
kV4/PQGdM8MRVrMFRjddSMoy1R3QORTzOWlFwkmXc/b/W8XJozH/tuu7Vc2qJR2q7s196pZSecaX
M7yr5FhpVlKb240vvDrPq7NJ/sOH79235p7NBEURfM/pAf7HMxPEMwVi/vao/y2j2pDZLtOnGlvp
qZv1FnthIs5qxqRgOe1OUkaB3/zGOPGMSVBX6Q7q3DMU5n1391dYI8N+jZMDQc5PJ5HSxudx0RPw
oGsubCm5MLOOV3MR9KgYppMpEPSoledbHY0YPz4p5QtiI+WttYPPfFQI8a9xoj9PSymf38F7tAW2
svLXcs5h43a5uLaawio61KUjMW/DB8naFnSA1ReuqM/Nly4tkDJMVEXBKBSZS+RZSOZ4aKwLn+4Q
Gswn8/SHPBzq8nPXYJiJFafjtxAwEvNxsj/ExGqa7oDOUspgJV1Ac0HUrzPa5WdyNcNkPNOyxk+t
i2rM58YwbV6ZTRD0qOQMi6ffXK4QDXz9zWWWUgaLyTzfcbyHV9IG19eyWLbNa3NJ0oZFyKviVVVe
m0uiqy6Ktk3A7WItY5LMF0lkTXRNIVcoViioLy8kkcBA1MvqTIG1TAGv6qoYX7XYuMood18/PRzh
XCmV5cLsetMz8LUjbtYXptY6TK9mmYxncAlBOm8xvZZlrdQQ70fePlZ5Zj6ZY3wlzXzCwK0qBHQX
0rbJWw7zj207/VgcBjAXyZxA11wMRry891T/DQZzq8pHM7RLKEd5yk6GtYxJtmASKtU6pPIWlpQV
QgtVCH7r2QnOT69RsCRKzkJ3KUT9Osd64M6+IHcNhCtGy3Q8W2FjLK9XSNeYT+SQQjCfyHO4y8d8
Io9XczG+nCZvOdH4zYx0mzMNtspS2KpP3VhXgGvLGcZXMvSF6mf6axYDdS+QN4v8f39xmRP9QT54
39B+D6dp8P33D/NrXx/nf788w8ceO7Lfw9kz1FPPs5We2uzkevxEL96qZvEXZ51an2O9QV6YjCOB
Z6/FURXIFWxCusPg+c5jPZXaqql4hiuLKYSi4NcVQl4P02tZFMXp9dYV0FjLFpDSGdcbxTT9QZ3V
bIFTAyHeUeoV2MpoxPhZEULcQYl4SgjxIZz6nYYgpfwC8IVGnztQkE69jgTcqouQV2UtW6An6K7r
IKneRAXTRuJchAzTJpU3K96/8mH32nyi0jzx2nKKpaRB3iySMRwmuEMxH9+4uoIlJbqqlKhVXXz3
PQMMhLwEPSrX17JOY09dI6BrRH1u1nIFHrmjh89fnGVyNYOqwGgLew1qXQJjPrfT8VdI1rMF/utX
3sSyYTGZ467BCK/OrTMW8zOfyHGh5OXtD+k89foCLgU0VeB3q+Qsi76wh7uLYeYSeTKGSapg0u33
MLee43CXn7zpePdDXo3X5hwP62raIJUvcHUpzcxattLAdCvDulbEMZE1t+wv1cHeYvOhuLkvzOaL
+vRqll/6q8tYJUrTgbCXtaxJ1OfGo7k2NBxNGxYCh6pRlGpF0oaFbTsGj0s4RvbhmI/eoI7uUnjb
kS6ShnlDk8pWZmjcD+Ot+iIPjrOpOiLipKMqJPN5BDDW7d9AbpPIms4bSUHOtDBtG1tKvjm+gk9X
6VnTGYx4a36vskyV+3/1+HVyBYszI1HCPo07ugOV2qCnry7z6NGeyvoapn1DLSDA3YPhG1JeaqX2
vDqXIGmYnBmO8OBorJIiV8987beBupf4zW9MMLOW4/d/4iFcyvY90w4S7uwP8sDhKH/wwnV+4tEx
NjnX2wb11PNspafKZRDTa1kWkwYXZtZ4aLQLRREIIG1YfPnyIkGPm2S+wJ29ATKGiRAC27YpSjd9
IZ3feW6CQ11OxPjkQKhUfuT8/7FeP6eHwpwaDHGy38lAWc2YmEWbgbCHhUSeqXiWnFnkmavLdZHf
NDsaMX4+gVOLc0IIMQtMAHXRVXdQH8oHZtTn5nhfkNm1LEMRLyf6gyDEhrSW7bB5E907HAEJL07G
uVBqRlXdgCqka5U6PKvUeVxzOfSlSykDq2iTNi26/XrJKwmjXX5Gj71Vw7P5sK/e5Mf7gkzGM/tS
87NTb2Kt52p5b+LZAm5N4e6uCM+8uUzWtBmOeJlbzwESKcEWMBj28uBojPlEntcXUoDgUJefRM5k
MOzhw/ePcL4UZu4O6hSsIuPLGVYzBfy6i5MDocqlNOzTeMfRbkaiPr45scpol4/VdIG8ZfPU5SWG
or6GvutBLlLfb2ye+5td1CfjGXIFm56QznLSYLTbjy0h4tMIlLyBiazj4Fgp9dkp4lCfrudMPKqC
LDXf7A55+NGHj3BuNMZarsALE/EbGpfWGmOrycftbpew+SJ/91C4ZkTkibsHWMsVKqnH1fM6tZpB
cykc7QtwdTFFT8jD4agXn0fjgUNRkoYJ8kajtLoG7AU1zqX5dQK6Q12eKVh0BZy6zOp0NUvKyvqm
8iYXZtY3RIFenUu8Ve8T277Qeady0urRxe2wlMrz379yjcdP9vFwG3jMdxsfPXeIT372At8cj/P2
O7r2ezj7hrKeKkdly43FYz43ectmLWvi1VwkDZucVSRXamLfE/TQF/JytCfAtZU0mYJFzK8zEPaQ
zFrcNRSiK6DztTeWyVk2k6sZTg6EONYX5NtTawxHfUS8Oh85d6hyPxuK+ioEJ1Gfmz948TqX5pOM
dvuJlM6pVt+fjbC9jQOPCyH8gFJVt9PBLqBWtKYv7GGoRDNbrwcNbjzsD8f8lUt6xSAailTYfQBO
Lzi55pEj3bw0EeeNpRRCgK4KHjnazRuLKVJGESSsZApMrmY2pFrUijSUU3hGunz7kuq2U2/idmx7
tQ738lx3B3WWUnmWUk6/lYjXzUjUx2DYS09Q59xoF1+6tIDPraCrLrr9On7dxYfvH+HUULjCsJTK
mVyYXeeeoQjnp9cwLJvFZJ6AR91wcRvp8hHyOqktS6kCvUEdj6psq5h2yiTYzikp+43Nc7/dRb3L
52YhmWN2PQdSspDMMxDxkDeLPHq0p9SYdgYJzK3nCOoabpfTxK436MGWMJdwaJMfOdrF4yf7CPs0
RvBV9ER1RHAz3X4r4nYbb9UX+fHlNHNrOQqmTZIbIyIj3KgXp1ez/OVr80zFMxSsIprL6bWTNYuM
9gQqBurhLoeGenOEqSglhmmTM4v43BrJgsVjx3p4z6n+DUxtmym5K1Hgqr8h2EC+s5u9vqrRDv38
tsJ//NIbGFaRf/b+k/s9lKbE+08P8C8//xqfeeF62xo/9dxFapKIlO5XHzwzxJPnZ8gVikxeS3Np
zmHmPBTzIXB6N0b8GucCMU4OhBiK+kjnLQpFm5jfzexa1iE+UV3YttP4653HerCKTv1v0jBZyxY2
EDWVyRQAPvLgocq4ArraFvuzEba3f7zpZ4AE8LKU8vwuj+vAofrALBe13zMUcYrovY0dKDe7pFcz
9pTx4QdGmIpnSOctFpMOrbXPrRD0aJwcDPPB+4eZW8tRlGDaElUILClrHobNksKwU2/ihsvLSpqL
s+ucHoqQzJmVCNZWqR/l13T53HhLHvbq2gm3pnD2cBded4L1jMlAxMv5mfVKtKY6DW0xmWctW2Cs
J0DeKvLE0YGaCvODZ4Z58vwsHlW5wUDajTlplvU8CNjuop7ImqxmC7zjaDce1cViMkcqb3G8N+gc
XrkCX72yzBuLaVwlRrfRHj8Fs8hw1Mfhbj85o8iLk3GO9foZCHtv+OytGL5afc1vp/FW7cG9NJ8A
AYoQ3DscuSlTUiJr8uT5Wa7Hc4Q8GhGvxlDES3/YCwLuPxSlKOUNOgg2FlW/OBknb1m883gPC0mD
d53orasvRy19dmk+gWWDqsATdw/syZy1enRxK7w2l+APX5rmxx8e25BG2sFb8Gguvv/+YX7/+SmW
UifpDXr2e0i7jnprOzeTiJTP55EuHz/y9jEuzq7jcbvwaSrPja+QLRTxaC4+/ugdlTqgsE9jJOrj
yfMzxDQ3BcumL+jhjt4gYZ/D+hkoERf0hXSShlkz3bV6D5Y/v532ZyNpb2dL//t86ef3Ay8Cf18I
8Vkp5S/u9uAOEqo9X+Wi9lvxgm0+7Os5XF6dTbCYNFhKOtSILpdTdxTQVT726B28Npfgt74xwfhK
ntfnk7z7VH/NsTVLCsNOvYmVy8tKmktzCYcQYHqdS3NJFEXcwFpXPddlRbUVyuMRwEDYU+nRUT1H
5bW6OLsOgg09emrBUUyjdSmmncxJs6znQUGti3p10ev8eo6xngDrWQtbmjx1eZEzpdRWj6oQ9WnM
rOdYTuYZjPhwe1T+7sNjDEV9XJxdJ+LXaspdNTprvnNstX+DnpsbYPFsobKGa1mT3qDOQiJPykhj
2zYuIQj7tA1R9zKq9dZ0PIuUknhmlTPDkYb6clT/LZ4tcGowjN+tkilYW+qg3UArRxdrQUrJv/r8
JSJejZ9617H9Hk5T40feMcrvPDfJ7zw7yT9574n9Hs6uo97azu3O57DPaW46Hc9yPZ5hJW3Q5XMz
vZa9oUazbESFdI2nLi8yFPES0F0MRbz0BPSKE2ardNda+r7d9mcjxk8XcL+UMg0ghPgXwGeBx4CX
gY7xcwuoVTy621b2dsJbTYH8xkISiVP3c/+haCVKVJQSXXPxtrEurq6kONYbqPl+tTbwfqRN7dSb
WH15yRWK+HWV8dl1cmaRuwbDO2atqx5PmYJ8KyOkWtHVY6jUq5h2MiftnJLSKpiKZ1hK5RnrciiK
o343XtVFX8jD+Eqmkkr16lyCkagP1SU40RdkOOojU7Dw6mpDMtVZ81tDo/u3jJjPqckZifroCdqc
PRzl1dkEfo/K9GoWs7i997hidEk2yMZOdW7M5/QFKUrZNukutwtPfnuW5yfi/NsP3kPY2z6Xxr3A
WLef993Vz+8+N8U/+I6jBPTmbgDb6H1m87m7lXPpZudz+e/Pja8wuZLFrbkwinaJhuwtlPX3xGoa
AZzsDxHzuTk5GOL0UGSDo7VWuutB2OeNSNghSs1JSzCBUSllTghhbPFMBw2gVrTmdqG8WZKGyQOj
MU4NhAjo6ob0uNGYH1WB+WSesEfjroFwTSVQy5DbrxSanXorwj6N0Zif//3yNG8upTHMIi5F3DJr
XfV4tqOjLr92J8bbzRRzo3PSrikprYJyH4fx5QzXljMc7wsyFPGSyJkkDbPScbuWcb350lrvWnbW
/NaxkzmspTsnV510tp6gviEjYCvvcdnoqpaNelCPLu/IQX1Yzxb4N3/+OvcdivCDD3YamtaDjz92
hL94dYH/9cJ1fuLR5qW93mlK8OZzd7sIz3bvF/ZpvP1INzNrTtrzWLe/ZmT3/fcMMhXPENAdIpuA
R91g+FR/nzJJyubm7O2MRoyfzwDPCyH+T+nnDwB/UCJAuLTrI+vgtqKeQy7k1fibZw+RzJncNRgm
5NW2VALVG7jeBl/7iVoHvyUlpwbC+D0qmbzF3YNhishdY62rxwhp1FDZq1qNdgt5Nxu2M1jj2QK6
pvCuE328Pp8kVyhybcXx6N07FNngoKjHuG4kSthZ81vDTuZw8zNbZQRsl5p491C4JovcVthOb3Tk
oHH8u7+8wnrO5He/7x6UDrV1XbjvUJSHxmL8xtMT/PDbDuPRmrMR7G6kBN+qUyHs0yo9wbZzcp72
RbYlsoH9c0zvNxphe/vXQoi/AB4u/ervSylfKv37h3Z9ZB3cdmx3yFVqDgyLfCn9qx4lUKbdNUy7
aUOqWx385RSUopQEPCp3DTU/t30jirnceLGRS1IHu4+bGazVUVmfruKpYm3cjgxlp5fWm0UOO8x/
txfbZQRsriO4OL2+oXB5s0d4q7Xr1HjtHl6cjPMHL1znxx8Z49RgaL+H01L46Xcd46O/8Ty/982p
HUV/bodu2q2U4Ft1KuzEiXUDDf9guKH7Qjvp/bqMHyGEC7ggpbwbeOlmr++g/RDPOk0Tp+M51rIF
njw/ywfPDG2rBKo3Wi0vdbNgp/m3zYh6FXMia/K5l6c5X2q8eno4wocfGGmJ79huuNnFs1Y62145
Em5miLUbC1wro5ZcLCYNxlfSPH6ij6RhbpCl7dauU+O1O8gYFp/8owuMxLz8zLuP7/dwWg7vONrN
Y8d7+K9fucqHz440VCt1u3RTK94Lyth81iDqa2Ddjnq/LuNHSlkUQlwQQhySUl7f60F10HyI+dzk
zSJr2QJRn4ZHVTY0x9sqXad6ozVK2X27cDOGlWYc81aoVzHHswVShknI4/w9VUcPjw72BvVcPBup
FbsV3MwQ60QImgtluSinFh/p9jOxkmZiNU1v0LNBlrZbu1a+0DUT/s0XXmd6LcsffvztTV+036z4
p++9k+/5lWf41Fev8bNP1M/8djt1U6vdC8qo1QNyc1pcLbSj3m9kdw4ArwkhXgAy5V9KKb9310e1
Ce0WbmtFhH21+8lspwRaxZtYz8HfSjJYj2KO+dwEdY1ryxkEDttOs65Pu6PRi+cJVvXIAAAgAElE
QVReHrzb7dlE1iSVMyk0cQrrQUV1auTp4QjnxmI39BS6mT7eTblqJX25W/jiawt85vnrfPyxI5wb
i+33cFoWdw+F+YH7h/kfz4zzA/cPcawvWNdzrXLfuF1ohMCknvtCu81tI8bPv9yzUWyDdgy3tSoa
6ScDreVNrKfeqZ1kMOzT+NADIzw4FuvU/DQBmsWTuNWerd4DEupq2NnB7UM9uvZ26eN21Jc3w9Wl
NJ/8owvcOxzmH3fS3W4ZP//dJ/jy5UV+7o9f4Y/+3tvrIo1opfvGXmO3CUzacW6Vel8opfwaMAlo
pX+/CHxrj8ZVQXW4rShlpbdBB/uDsE9jrHvrS08iazKxkiGRNet6fSuglWVw83pUI+zTOD0c4fTI
jfSXHRxclPcsUJGd6j2ga0pdDTsPErbbZ7cL9eja26GPW1lf7gRrmQIf/92X0FWFT/3wA03LUtZK
6Aro/Px3n+SlqTV+8xsTdT/XDveNRlFL9+zFHmy3ua078iOE+BjwcSAG3AEMAb8KvGtvhuagHcNt
7Ygyc9gLE3H0EtNQs3n8dpqK0aoyeBA9sK2IZkwR2iw7jx7tack9cDvQavtsr+WtVfXlTpA2LH70
t15gZi3H//y75xiMePd7SG2DDz0wzJcuLfLv/vIyD47GuHckst9Dajpsx1RbMG1enVsnqGttvQd3
ikbS3j4BnAOeB5BSvimE6N2TUVWhHcNt7YbyBlxK5RlfzvCuGkxD+41buaC0qgy2Y5Fiu6FZL86b
Zedm5CYHGa20z26HvLWqvmwU8UyBH/+dF3l1Lsmv/vADvO1I134Pqa0ghODff+g07/8vz/CTv/8t
nvzEO+gNevZ7WE2F7XSPBKQUyP0dYtOi7rQ3wJBSVmJnQggVbs+8tlu4rRE0QzrFzVDegGNdASQw
vpLZMxrenc7FrYaBW1EGD5IHthVwu9ITdgO1ZKcV90A9uFUd20r77HbJW7vKShmvzyf5gU89y6W5
JP/to/fz7lN9+z2ktkTE5+a//9D9jqH52y+RMaxd/4xWuGNtha10T7kx9j1DYXRNaZpzpZnQSOTn
a0KInwe8Qoh3Az8JfH5vhtUBNK9XeDOqmYbODEd4cDS26wX0tzoXrXRB2S0cFA9sK2C79IRmlMuD
Iju7oWNbaa6aVd5aBXmzyG99Y5L/9FdvEPJq/P5PPMTZ0Q6z217i3pEI//Wj9/Gx//kSP/ZbL/Ib
P3q20qLhVtEqd6ytsJXu6ezzm6MR4+dngR8HXgH+HvAFKeWv78moOgBuLZ3idtYR3I7D/1ZTS5rh
grIftR3NwiJ20NFOjXTbCbuVsrZf+6xRndKRt50hbVg8+e1ZfvWr15hdz/GeU338v99/D10Bfb+H
diDwrpN9/PIP3sfP/OF5PvLpb/Lrf+fsrtRXtVLK6laopXsa3efNWHe612jE+PkpKeUvAxWDRwjx
06XfdbAH2Kn1vh/ejL0+/HfDk7GfhkCre5g6uDW0WiPdgyKvrewh3ekaNaO8NRuklEzHczw/scpX
ryzz1StLZApFTg+H+cUPnebho937PcQDhw/cO0hAV/mHn/kW7/8vT/Off/A+3nm855bes5X3/81Q
7z4/KLp+Mxoxfn4E2Gzo/GiN33WwS9ipl64dvBmb0eoey3Zckw7qR6vJ70GR11Zbl2oclDXaTUgp
SeYsVjMGq5kCq+kC8UyB1bTzczxTYCGR5/X5JKlSfUlvUOcD9w7ytx4c4cxIBCFu3nOmg73Bd57o
5U9/6hF+8ve+xX/56zd57Fj3La1HK+//3cJB1SM3NX6EEB8BPgqMCSH+tOpPQSC+VwPrwMFOvHTt
6s1oZY9lu65JB/WjleT3IMlrK61LNQ7SGt0Mq2mDxaTBctpgKZlnOW2wnDJYTRccQ6dk5MQzBSy7
Nk9T0KPS5XfTE9T5vvuGODUY4vRwmFMDoY7B00S4oyfAn3ziYVKGuSvr0qr7f7dwUPVIPZGfZ4F5
oBv4parfp4CLezGoDm4Ne+XN2Cov9CDmi9bCdvOw1Zp05m5naNd5a5bv1fGINic2y8durVGzyN1O
8fd/72VenFzb8Du/20V3UCfmdzMc9XHvcISugJuY301XwE2X3/lbd0An6tfQ1U5z0laB1+3C6+6s
107RiB5pdd2wFW5q/Egpp4Ap4O1CiMPAMSnlU0IIL+DFMYI6aDLstjdjq7zQg5ovuhn1zMPmNenM
3c7QrvPWbN/roHtEmw1bycetrlGzyd1O8InvPEquUKQnqNMT1OkO6Pj1RrL6O+jgYKARPdIOumEr
1N3nRwjxMeBzwK+VfjUM/MleDKqD5sNW/SGatU/J7cZO5qEzdztDu85bu36vDnYHeyUf7SB333Fn
L0/cM8DZUpuFjuHTQQe10ch+bwfdsBUaaXL6CeBhIAkgpXwT6N2LQXXQfNgqL/Sg5otuxk7moTN3
O0O7zlu7fq8Odgd7JR8dueugg4ODRvZ7O+sGIWXt4r8bXijE81LKh4QQ35ZS3ieEUIFvSSlP79Xg
uru75ejo6F69fQe3AUVbYtkSVRG4lJ0VJ05OTtKRg/bDTmSjIwutg93Y+1uhIwftiUZl5iDJwV7u
p1bHQZKDDrbGyy+/LKWUdQV1GokNf00I8fOAVwjxbuAngc/vZID1YnR0lJdeemkvP6KDPcRu5Yue
PXu2Iwdthp3KRkcWWgN7nSvekYP2w05k5qDIQTvXXuwGDoocdLA9hBDfqve1jaS9/SywDLwC/D3g
C8D/3djQOjhIaOd80Q5uDR3ZaG901reDRtGRma3RmZsO9gIXptf55afe5Ne+do3peHa/h3NbUXfk
R0ppCyH+BPgTKeXyHo6pgzZBO+eLdnBr6MhGe6Ozvh00io7MbI3O3HSwm8ibRf7F/3mNP3xpuvK7
//ClK/yrv3E3Hzl3aB9HdvtQT5NTAfwL4B9SihQJIYrAr0gp/9VNnh0E/gw4BQSklJYQ4j8BZ3Hq
hX76FsffQROj0yukg63QkY32Rmd9O2gUHZnZGp256WC3ULQlP/2/vs0XX1vkH3zHHfyD77iDdN7i
5/74FX7uj1/Br6t8772D+z3MPUc9aW8/g8Py9qCUMialjAEPAQ8LIX7mJs/GgXcB3wQQQtyPYwQ9
CriFEA/ufOgdtALCPo2xbn9HWXdwAzqy0d7orG8HjaIjM1ujMzcd7Ab+81Nv8MXXFvnn33OK/+t9
Jwh5NAYjXj79dx7gwdEoP/e/LzK3ntvvYe456jF+/jbwESnlhBDiHUKIjwKPAH8KbBu5kVLmpZTV
bZffBvxV6d9PAW/fwZg76KCDDjrooIMOOuiggzpxYXqd//aVq/zA/cP83UfGNvxNV138x795BlvC
L/z5pX0a4e1DPcaPJqVcEUL8LvAfcAyfB4ETQLDBz4tQ6hMEJEo/b4AQ4uNCiJeEEC8tL3dKi1oN
iazJxEqGRNbc76F00GToyMbBQ2fNWw+dNeugEXTkpTVQtCU/+8ev0Bv08M8/cKrma0ZiPj722BG+
8MoCr80lbvMIby/qITwo04qcBU7JqsZAQoiHG/y8BBAq/TsErG9+gZTy08CnAc6ePVtfE6IO9g2J
rFnJQwa2pOOsfl0nbL8Rez03zTD3HarW9kAjspTImnzu5WlShklQ1/jQAyOdNW9ybF6z95zqx5Ky
o7cPCBo9K6ZXszx5fgaP5iKgqx293sR48tuzvD6f5Fc+ch9h79Zr9OOPjPHb35jgv33lKv/9hx64
jSO8vajH+LlXCJEEvEBSCFE2SATgafDznsOhyf4j4HHgtxt8voMmwuYL7d1D4Qod53wyRzxbIOzT
OhffbbDXc9Msc19N1VotGx20DhqVpal4hvMz64Q8GteWMzw4FuO074ZgfwdNhOo1e30+xWqmwEDE
09HbBwCN7u9E1uTJ87O8sZgm6nMzEvN29HqTIm8W+Y9fusLp4TDvv2dg29eGvRo/eO4Q/+OZCRaT
efpCjV7zWwM3TXuTUrqklCHgGcDCMWC+CnwF+IvtnhVCaEKIp4B7gS8CGpAXQjwNFKWUL9za8DvY
T2zuPYCkJh1np0fB1tjruWmWue9QtbY+GpYl6XjIoPTfThy/+VG1ZmbRxrb3X3d0cHvQ6P6OZwt4
VIWoT2MtWyBvFjt6vUnxhy9OM5fI87PvO4GiiJu+/oceOkTRlnzm+eu3YXT7g7r7/AD/T6NvLqU0
cSI81Xi+0ffpoDmx+UJ7uMvP4S7/DWHzzsV3a+z13DTL3HeoWlsfjcrS4S4/p4cjpPIWY92Obuig
uVG9Zv1hD17Nte+6o4Pbg0b3d8znJuBRGYn66AnafPDMUEevNyGsos1vPDPOfYcivP2OrrqeOdzl
59Fj3Xzu5Rn+0ePHcDretBcaaXL6NSHEYeCYlPIpIYQPcO3d0Dpodmx1od2sABu9+DZDjcrtwl4b
BfttdGxey3Zfz3ZGI7JUXvf3dmpGWgphn8aHHxjZUMe5U91xkPR4O6DRs2Lz6wEmVjKd9W4yfPG1
RabjOf7Zd59syIj5vjNDfPKzF/jW9TUeOBzbwxHuD+o2foQQHwM+DsSAO4Ah4Fdx+vh00AbYyWFV
74W23tfVyjtud+y1UdDI3O/mZaVZ6o06uL3orHvzYDd0+k7WriMDrYmdnBVj3f7OejcppJR8+uvX
GO3y8e5T/Q09+567+tCfVPjT83MH2/gBPgGco5S2JqV8UwjRuyejug3YD69UM3vCdkt5bfUd6/3u
tQrjO9g7lNdFFYKnry7fYHTuRF7L75nKmXWTHDTz3ujAQbWOKJg2D47GONzlv2GfX5xdJ21Y9AU9
jK9kmFrN1EV00JGB+uegntftRKdv976NrE+H4GTvsN/7pJZcTcUzLKXyjHUFSBrmDeu9kzHv9/ds
Bzw/EefCTIJf+L67cdVR61ONoEfju0708uevzPPPP3BXw883OxoxfgwpZaEcNhNCqLRoCet+eCma
zTOyWbHc6mGVyJq8NpfgmTdXiPg0Ah6VR4/2YElZ82K91Xs3S41KO+Bmh0e1TK5lTDyawpHuAOMr
aZ4bX2FmLYeuKTXXbDPFeS2684JpI+Gma9lse6OD2rJT1hEhXeOpiUWSeYu+kF5Zr0uzCT778gxe
TWFyNYstJZqqEJpUifrc26a/dWSg/jlw6IVn8agKAc/W9MKbdfpUPEMwq+1oDTb/razbt3qvvdLj
B/1CvJv7pNrx1Uhq6ma5em0uwTNXVxhfTnNtOcOZ4ciG9U5kTT778jSpvEXQo/LhOijvO/pgd/Dp
r4/T5XfzoQeGd/T8++7u5y9eXeD89DoPHI7u8uj2F40YP18TQvw84BVCvBv4SeDzezOsvcV+eKWa
yRNWq5dDKmeSyJqspg2CunNAJrImU6sZEBD1bn15SWRN/uc3J/nGm8sk8hb3DIXp9rv5zWfHGQh5
yVt25WJ9s+++3zUqrY7p1SyT8QxdPjfPjq9WDpxatRfVMpkziuTNIuMraS7NJYinC8wlcrzrRN8N
nrzqg8kwbQTg1hQWE3kUwLQlJ/pDjKcyPDQaYyDq3XYtm2lvtCMavTBuXt9TAyECuoqqCNYyBWbX
cgjgSLe/IhvJnMkvPXWFubU8blVwrCeA36tyoi/E1GqWP3jxeoUy+cxwhNVsgdGYn5EuH9CRAdg6
6l29dg698EyJXlhjJOqrzFV575fnNeZzUzBtXp1bR1UUXpiIo2tKJWoX9blZyxZAwOGYf8s1qI7m
HekOML6c5snzM0T97i0vpnuhxw9iSvRmxLMF0oaF362SNqwNa9RIxLDskEwbFpfmEpwaCFcM6fLn
bGUUVRu2BdPmmasrXI9nCXs1oj43D47GNpwVz11b4eXJOLGAzsRKmnOjW1Pel2XYJcSB1we3ijcX
U3z58hI/8/hxPNrOyvO/43gvLkXw168vHmjj52eBHwdewenV8wXgN/ZiUHuN/Ygu3Opn7mbYuLqX
w+UFp5eDrip8a2qNIz1+ArpGMmfyxUsLXJxZp1C0UYAzI9Gansap1QwvTKySzFmkDZPrqxkmVjLo
LhcZo8hwxEveLNb93TuF8RtRb4rLa3MJfu+bkyiKQipnoroE/WEvVxaSxKv6dVRH5MoyqSiCs4di
JA0TJPSFPMwmcoyvZOgL6RvWrPqS9OJknLxl0Rvw8OS3Z/CoKtmCxbG+BGGfm5BH5a6h8Lbr2Yn2
7R1u5nWt5f2dWs2wmDQYCHn4xuQKz11bRnO5UITgzv4gihAc7wuSNMzKel2cXUdKKErJUqqA2+Xi
3JEYz42vks5baKrCnX1BJuMZfumpN1AVgWXbfPLxOzk1FO5ECrhxH6hC3LB28WwBj+aqGC49QRtV
CJ69ulLZ+6oCn3z3CUJeDQlIKcgaRdyqUonaLaUMpuNZpJSoqsKZ4QjvOdV/w+dfnF7nxck4RVty
ad7p+J63bKQERQjSeWvLi+lu6/GDnhKdyJrMr+c4f32tss5P3DVQMxIIN6Ysb4z0F/CoLvy6imWD
36NSlJKp1QyvziU2GEWKIjg3FuNwzF9Z07Jhm8qZfHNilVTekcehqFphdSx/3tRqltlEjoCuOqlC
4q3vUz3G6dUsv/RXl7FssG3JqcEQQOdM2CF+/elxPJrC33774R2/R9in8eBolL9+fYl/+r4Tuzi6
/UcjbG828OvArwshYsCwlLIl0972I7pwK59ZTwh4syLZ9pmqXg4Zo8hCIkfOLLKUMlBVQcTnZjKe
KR26jiGUMqyKgtzsbbq2nGZuPY9h2li2jc+tYdlOJOH1+STdfp2PnDvUYX2i9mXsZnn29az9n78y
xxsLKabXcjw01sV61iRfsAAoWDa2tBkIeRlfSTsHpaaQN4s8fqIPy5a8OBnn2moaw7RxKYKkYXJm
OHJDXUcia5LKmximzfhKunKBOj+9TtGGQ11eri1lEAgerxE1qoVOtG/vMLWa4eLMOkGPdoPX1UlT
m8bjdjG/nqtcdMoRwFdm1zEtm/6Qh7xlky6YdAd0bCR3dAcoSslozLnouHCeKxYlMb/j/b1nKIzm
EvQFPTw3vsrEappE1sSWEsOSrKQMPvvyDP8o6rttkYJmlq2wT+PRoz2V6M1atlBZuzcWkgxHvNw1
GCagq4zEvPz/7L1pjGVnet/3e89+91u39q7qvZvshdsMxdmpESRqGQ1gYSCPFcNI5ASODOtLjAhI
AjhAjMDJhyD+ZiSQDFtIYEsxxvHI0kiRxjOaGZJDDrdRD5vdbPZSXV171a2733v287758N57Wd2s
bnY32Zzh8gBNdlfde8+557zneZ/l//8/0yWHZ07N8NzV+k3P/lY3ZLk5YHEij2sbHJmssFTvEyYZ
1xt9BDBVdLm83cO1DFBQ70ekSo3vwagzsN2NWNrt88ypWQBOz5eZzDv8wXPXuLDRRSrJ44t6PT3o
Z/fjXCQZreXtboRU8OmDVfwo5cJGh9fXO6w0/XEncJTA3Nohe3Fpl5XmgNNzFYI4I0wkiBTLgEGY
UvQsELqAUXB0UmQYgtfX2vSjhJmSN36GRve5F+gCyMFajpJn8XOH3u4OjJLVM/NlbjQG5D2TUxNl
Dtf2F0hYbg5IJRyZLLDcGHBiusjD8+VP9oT7sJ1uyJ/8zQa/9dRBaoX39pw8c3qWf/bnb7La9DlY
y79PZ/jTt3tRe/s+8LeG7zkH1IUQP1BK/bcP6NweqP00ugv3e8w7QUJG0LRXlps4e/gZe/H5t5KO
J/IOi9U8QZJiClhpDmj7KXEm2WiHQIdfPj1LybO4vtvHT1LiNOPqVo/JkksvTLi43uE7l7ZpDxJe
vdFASkXBNcjZNp87WuP8ZocZy6QxiPjSyakxvGV0zh/HQPd2sI07BWh3AwcavebwZJ4Xlxq8sdHG
FIJDE3kcE45MFbAMgzc22sSppOPHLO0OsEyDMJEcny5Q70Wcni/TJeH4ZJEMdRMs6dbzF8BkweXQ
ZJ4jtQKvr7foBU0ubfU0E1DAdi+k6Fp3FaR80u17QCbeJmYqoB+mXN8dEEQp//w7l9nuhJiGoFZ0
KHgWq02fIEn51GKVH682GShFM4ixDQNLGFzd6SGEoO0nuKbB9y7tECcS2xQIoOxZeLZJ3jU5O1+h
OYjpRSmLE3k+c2SSas7mn3/nLba7EQXXpDrkG47u/4PuFLxfSdWDkO3v+MmYG7na9DkyWUABcZKx
3gn4m9U2TT++CTaYKkU/TCm6Ggb145UWc2V3nJS2BglBlFH0LL5yYp5WEGMadTZaPoMwZTNMsC2D
TEqCKKWJTjKafkw/TLEMfa/f3OpyqJbnsYUqTT/m6HSRpZ0+vUjyB88v8dkjtTvyj94P+zgXSd5e
yx7n19ust3xafswgzlhuDKh4ti5emrobdxPXqzHg2St1fnh1l3ov4spOny8cm+JrTyyQKsVXzs6P
kQAtPyZOJP04JUhSlnYGDKKE2ZJHL0x5fb3NYws6jhjtBX6ScaCSI0ol1xp9lpsDnj4xTS9IiBPJ
dhxybLrIl05McfaARgFc3x28A753pFbAMmC5McAy4OyByk37zyd29/aHLyyTSsk/eProe/6sXxom
P399aYff/sKR935yPyN2L7C3ilKqK4T4B8AfKqX+JyHE6w/qxD6xt+12Fa+91aBRdW5Uaa/lHaJE
8t3r2ygYk45bfswry02qBZv2boxpCBareRqDDjNFh4VanuNTRXKuhlmcmS/znTe3ubrT49UbTTIF
y/U+G90QP85YafikWYZpGBpWUbOIpGSm5LLZDjlQ8Tg4cXMAfa/kxw+LvVugczvYxp0CtDtVO0fH
W9rp8/KShh3OVjx6foznmKw2fQxD8MTiBEv1AQdredqDiL+6uEWSKgwDVncHWI6JKQTLjQFnFypc
TLq4tsFq0+erubchFHvV2zS5tcd6O2CjHfDYYpWnjkzy3OU6jyxUSaXkUC3PgUruvq/XJ3b3drtr
OZFzODiRR0rFXMXjtdUW6XKT9ZbPIEyo5m0a/Zgwzmj0IlYaPv0o4fuXdhiEGdWCTa3g8MypOdZa
Pm9udYliLWQxU3K5vNOjHaRU8zYCmC97GIbAj1PKOd3J+Oa5dao5m+XGgK8+eoDfe+ZhvvHaGp5t
IARYdzF74n7WyoPoFLybKMCtMJ67gSON7Fb/UHQtHp4tca3eY6bocnRYEV9rBcxXPVabPk8sVjm3
2uZ6Y0A/TPEsk4VKjl6Y8K3zGzqYTSW/enaecs6mnLMR1LnR8jFMmCjYfOHENEoqvnV+E9s0KHkW
Xzg2ybnVNqstHym1eMnTJ6ap5DUSoD2I6UYphtByureiAu7n3t3N636aRZL36q/uh3s36sL1Qs3J
vbzdw7EMojTj6FSRI5MFNjsBBdek6Ud4lsnz1+rkHR3amUNo4g+v7nKjOQAFjX5MNe9Qzr19Lfeu
1U6Y0BrEHJ7Ic60+QKH405+sI4AgzVht+jxyoEI/TDEMwYX1Do1eRMOPeebULNvdcMwJC5IMP85w
LYNXbzQ5OOzyWkJwcaNDKhnD9w5O5vmdp49zYbPL2fnyHROfT/aO21s/Svk3P7rBVx6Zf18GS+sB
1Xmeu7L7sU1+LCHEPPB3gH/ygM7nI2fv5SHd+979Kl6jzfLYVIHru32uN/rMlLzxaz5ztEY/Sjg6
WWS5OeBfv7CEa5mstwOeOTVLcxCz1Q2xLJO5sstCNc9DsyUMQztba0g6DJOUjXbARjskySSbnYA0
VaRSEaQSS0CmJHnbIIglW22fi5s9umGCZ5n8u1dX+JUzc0zkHS5sdHjx2i4F18KPUharOT5/fOpD
78DuBmJzu2DsVpz9aFAc6Hu8n7LS6pBEvtMN+OG1Bq5p0AlTHp4pEiQZkVQgwY9TDlRyNAYRR6fy
XNjoEqcKIaAfSQZRiGlAJe9wZr7MgXKOK/UeBcdiEGtYxZubXXpRgmUYeMOJ72EqqeYdHp4tc73R
H2PCgyQjUwqZCNZbAfV+xBsbnX3V4j5MkKSfVbtd53cES33uap1q3iZMMh6eKfGt8xu4lsG5Vd0F
9ByT+YrH7375BJd3eqSZIowz2n6quwGxQV3GvLrS4OJmDyWhFyWgBFd3ehgColQSJylSCeJEMlly
UGhu4UY7wI9SZooFNruh7kAfrPJfefY42Hruap2v5m5//++X6L63U2AJMS42vJd1didRgFvV0G4V
JrjRHPDGeucda37k54MoHXdqDEPQjzRsdaaUox/2+cHlHaJUjvlT3ShhpeVTyVlUcxZdPyGTkjc2
OzR/mHB+vTMuWOz0YlaaPp85WiPJFPPDSn1rEJJmiuXdARc3OhyazOOYhoYrll2yIbLdtgxSpej4
Cd++uEWUSFabPnMVj3Y/5upWj4Va/h0J5kgBsJq3mS65t33Of9b9wXs9v/1k4u+kgDh6fT9MubjZ
4cyBytiXn54vs90Lxx256aLLmQNlqnmH3X5My485VMvz6UOao7vVDtnqBHTDDJVlKOD8Wpsozfjq
owfoBgn/+odL3Gj4mIZgtxdRcG0mSzamKTCEYLUZYAjt01Hwk7UWP7y2SyYV3SDhsYUqK8PiiCEE
Uimmi4KdXoQpFL1Qd5W+eW6d3/78EVp+zFw5x1TJRUo1Xlvn1tpkSnFurc3CMFG6m3vxib1t/8/L
K/TClN/5+WPv22d+8cQUf3pugyST2Kbxvn3uT9PuJfn5n4G/Ap5XSr0ihDgGXHkwp/XRsPfiMPd7
79Gpm7P4UTDdjRIeW6zymaM1JnLOWH0JBZZh8OZWlzc3O0gFhhIoobje6FN0LQ5UPG40Aio5h5xj
sdsLkcCPlhpc3OhwoJLnh1cbbHUCokxzhQZhSiJh9AiMmF+eY7HRCeiECSAwgNRQ/OCtHTY6IWGc
Uc1bXNrqUiu6NPsRL11v0PTjn7nN7l7tdio8e+1W2MbofftJgo9U1DKpaAcJXzoxdVPH749fWeG5
K3V6YUo/TJms5VltBfx4tYWU4NoC17bIpOS7l7YxhWCnG+EM4UlpJgENhUfdM20AACAASURBVMok
tIdr5uJWhx9cqtOLEvKuxcX1Dt0o4VCtiELxd586xHw1Nz7X7hALPiLDjomwYcJP1tr7Bom3dpE+
UfN52+4VJnW7zm8lb4+FC0aqbKO10pKSth9zcCKHaZr8vc8epuRZ/LuXV9juh/hRhm0IolSy24vR
RWTFdjcEpddLzhbEUpEMfYLIFK6liNKMrp/y/OVdtloBa62AVhDjWiYHa3nKniZEp0oxUbBvuz5u
p0p4r0T30We8X4H17QoYI5hYwbPohynLzQEKMAXs9CKmS56+dvt8jz8/v0G9H3F+rc3JmRJhIjAE
/PvXVlhp+vz6Izq4q/ciCh5cXG/zrfPrzJY9fhJLdvoRW92IbpigUISpJGeb9MOEQZzq+4Nipelz
Zq6MbQo2OwGOaXBqvoIh4Nkr2/QjyWrLZ76SY67ssdb2WW9rWOSoMHNho8MLQwEM2zB4aLrEsjkg
A8Ik40ZjwGEKbxPYv/MW2x0NcfzC8anbPufvFaL4oDsB7/X89sLQR4IT293gJpW1W0WJVpo+QoAf
SwqORcuP2elFCKOLQHM5r+0OcE2DiYJDlGa0/Ji8Y+LHKd+5uE0pZ9PohyAESEmUKfwoZbUdMFF0
uNEc8P236iw3fa7V+wwivV4WJvI4tqAXasi7aYBpwMrugLXWgCDKaAcp8xUX0xD89VtbOKbJUr3P
XMml3o954dou1ZxNkEiKnsViNYdnGdxoDvjBlTqXt3ssNwb83JHaGGr5bmqHt7sXn5i2JJP8q+ev
87ljNR4/+O6z1e7Wnj4xxR+9tMLra+2PzMDTexE8+AbwjT3/XgJ+80Gc1EfF3ovDvBtHMAo2bzQG
bHVDrmz32OyEOKbBxc0OR6eLnF/rkGWSzVZIL06REjzb5FfPzBGkkqJrY1shJ2dK/Icfr6IAP844
PV/CjyQF10KhbuINxBJsIBn+LAMsAUpKwlRimhlhlCGBrK/I2RZhIpEKPnesRiXnkHdM0pzDQjV/
W7jEh8n2a+PfydZbPt+5tD0mnn79yUVyrjW+5+fXO0RJxnY35PW1Ni9c2+XJQxP8/S8cpRXENAcx
mVT6T5ax1vYBfW/DOCNOFZlK8UyTME6ZKefohjEK7SBjefP5mEIwCFMurHeIs4xekBAkKRttH1MY
1LsRp4aSx6ONar+O1GhddvyEN9Y7NwWJt1Y/72YG0MfJ7rVYcqfOb8dPeGW5yRvrbV5aavDogQqV
gk03jLmy1SPKFJvdiKKrJVAvbHZBCB45UOXHy038JEMpkEIRpbDRDpEZlPM2vSAhStVYNEUN/8Qp
2GZGV0GQZFyr96nmHBTaj/zckRqZUry+3uZIrfCOJOJ23/+9wtfuxH+8l3tzpy68JQQXN99+/o9P
Fzm/pkVATAOeOTXDwkSeNzY6LO1q8QFLCG40Btxo+Kw0fbY6EQXXYiLn8NZ2j/VWQDuI2elc5+Rc
iSvbXXb7CWEiWW0FAORdi5Jns1j1SBNJnElMA1p+Qt62kApSqbmcecfix6stwiRjpuRhGpCzTV5d
bjCIJI4phn5a8cShCVzbpOTZzFU8PMvk0laX/3hunfVWgGObJJmkHSQUXYvTcyVeWGrQj1LyjsUz
p2a4sNlFKZgquez2Itp+ctt7917u8QfRNXqva3D0/r2CE1d29H6d880xl2bkO5+9XOeFq7ukmSKV
2lkv7/YouDav3Wjy1OEaO71Q339XJ9wnZ0uEieRGY8CVQUw7SHh0oUp7EFHJ2SgJnSGiY6UxQKB4
6nANzzIoezaWIZjI2+RdC9vQcMaFqsdPVts0+hpK7doGKhasd3z8KKPe07PgHNNCoDBNkytbPb5w
coqtbkgQZ5imQRhnTBddisPiwJXtni6ADiIODeXa96qPjpLtB+EPPsr2Zz/ZYLMT8r9+7dH39XM/
f3wSIeC5K7sfv+RHCPG/Af8MCIC/BB4H/rFS6t88oHP70Nt7eUj3kz3dzxF0g4T/eG6dF5caRIkE
AV85O4cfS7bbIdd3+/TDlE6QoJRiIu8gDMWL1xs8NFPSUpeZ4vx6myhVxGlGN8p4camFLWCtNUAM
nZIYJkGCtxMf0B2gnG1gGoJSzgKlsC0TxxQkUsui7vQjbENwrd7nQDXHYjXHTi9CDr/Ph92BpUpx
Zr5CwbMYhCnpPkKIYyhDlPLS9SZRmrHWDPBsk5XWgN975uHxPZeZ5Ppun4ubXXpRSmMQ0+zHXK/7
TJYc1ts+q00f2xQ4lolnGfgiww8zDBPyjoVtGHiWYBClbHQCrd4DjPIeAXiWQKIoeRadMKE9nPcU
pAoj1bKAti3oRxmWadAPU77x2upth5+Ovud+ydH13cFNCf3ji1VK3u2HLn7c7F6LJft1fkcduBGh
uO3HtPyUZ6/UmS47LO/6DGJdiGgPIpR0+PaFLX7+5DRRknFtu0+UyWGXQnd1DcCzxZD7kVJwTHrD
4sZek0CQSGypZZCjTBIlGRgglU74t7sRKFht+u+6PvYKIbwXovt+/Me9CobvZvvxFG/twqdKceZA
hYJjsTuIeP5aHVMYFHJ6vlnOtcZqbhruZ/JXF7do+zHn1lrs9iJsU8/KsgytxBinEtcyaPoxb6x3
afsRQujnNkokiYQgiWkNEjrDbmuagWcbfPpQlYJnghQ0/Iipgsdnjta4uNnlzc0ulZzNbi8iU4rp
kksmNYxZKXBNg+1eSMGzCJuSlWZAlGQ8f7VOo58QxCluqjlkE3mbasFhuxeSppKtTshuP+a15QZn
D1Rp9CMmiy6zFZevP7l4Ry7P/d7jByVs8X6d397332gOKLpNmv2Iy1tdLTASp4AWuRgVM3d7EYvV
PK0gZq3lc2mzS2MQUc1LemHC0m6f1iDBMASebXCgmqNWdBBAN4jp+An1fsSFjQ4LEx4nZ0q8stwE
0JC0ksvJmRIoLV0+mXeYLLpYJqSpotGPMU2D9XbIXNkDIah4NrWiw09Wm7QHGj4XoruKQRSjhKDs
QStOeeV6k7W2TozytsUvPDzFpw5P8NhClRvNAVGqi1+plHz/8g7LjcFNg9Fv1wl6P/zBR9WUUvzB
s0s8PFviFx6efl8/u5p3eGyhwvNXdvnHzzz0vn72T8vuBfb2K0qp/04I8TVgDfg68D3gY5/83K7l
fqeHdO8A0cNDZZ79ujqjn92uE/TNc+u8sdGhG2rlNj/Wm5QWI4CdboSUCqkUaQb1fsy0cNnuBtxo
+PhxhmUKDg3V34JhS0AM/9MJUsxh8LK3++OYEGdvB9OWbTBZdPHjjCjLcC2BbQriSGIICBPJibkS
R6YL/OJDs5ycK93zZOmfZavlHYpD0m/R21/h7EZzwE4vpODYqKFKUxBn1AoOUaJYaWkC87NXdjm/
0SGIM/phgmUaRIlkdxDRDmJMw2Cu4jKRd1mseby12RvPavDjFNcyyTJJxdMBVzvQ1WKB7tKNTKGr
0mVHV/uafQ3dEwIMoV+gFERpRt4xWW34/Psfr+LHGV95ZP4miNWtw/P2ch9Ga/XWhH4UqH9i2u61
WHIn/1LLO7T9hG6YEcQpm37MSrNPlIIYPs9hBkaU8NpKS3dpCjY73Yi8Y+rgW+onXqIhNrYFtm2Q
pm/7gr0m0EWQOFP0o2TMD6oVHQq2qWXSo5SyZ5MoSaoUR6e07O313cE7Kr97v//tiO638723Dvzc
y3+8G/n1vXYnufCRWUJ3TVAaNjSRc0gzxU4voj3k87y+2majE8BQIODqdo+rO31c06A9SMaFqNly
DhPohglxKskUSBmTZG8XLrJhIcPSigNkUiEQCKGIUi1D/z/++lleWm4QZ5JXbjRo9CMc22CnG9IN
U7Y7AQiFZZjUChbdIMUxDRKlEAqyLNOznWaLXG8MWKoPMAyIM0mt4PIbjy+w0wspexbz5Ry9IOXl
5aaG1UpFrejwS6dmma/m+NzRyXdV7rpfMYN3e27eL0jcvZ7frcet5G0ey1c5XCvw7YtbnJwtU/Is
Lm32aPsJEp+Xrzd4canBhY0Oa60A0xC0/ZiZkkeSSoI4w0Cw1vZxTZPpgoNtmkyXHD1EvB1QHyRE
aYaUiijOEAq+eHyStZbPRM5ipRUQJxkXN7uYhsC2DG60Bjy2UOFqvY9nCpqDhCOTBdp+i9WWTzvQ
36XeC0EZCJFhCY0CSaUa8kgVSabXIQIsU0Ps4ijjb1Y6fPWRBUA/G6YQtAbJUG1SF06niy7LzcG4
A9YNtPBCEGfvUA39aQpf/KzaDy7XubTV43//+uOIuxCRuVf74okpfv/ZJXphQsn78F/7exI8GP7/
q8AfK6WaD+ICf9hsP6Lr3oB+v4d0VEl8fa2NAh6eLeHZ5juq6be+1xRiDJkIopTXWz4oxYFKjuu7
A6RUTJU8JgounXCgOy9ZRpZBsidzGYQxmy0BQhFnEsc0udEYMFv22OiEpIkkhTGvR2aj4+tqMEA6
/JlEv+ZAJcdM2WOjFdBqx5q0a4FnGlTzDolU1PsR0wOPlZbPZ49NfqSc17tVozp+wsvXmyzVBwyi
lM12gGkYRFlKvRtgmSZ/+cYWG22f1iCmEyY8dXiSlaaFAURIlFQkUhEJyUZLEw87oU64klSilK7c
hkPIUphk/ObpRf4q2+bKTm/fiNU0DJRAzwUaBbzDqr8tdHJkmSaGIWgMQi5t6SD33EqLiaKLJcRN
lfEkk1RzNsem958E/0nF7vZ2txXN/YKq/T7r187O8deXdugECalUWAyD52EEPQpSm/2IXhhT8mxM
IQiSjDi5ebEYArIMQpkxbAzdZALG/IR02C2q5nUwP5FzaAUJxZxFoxfxJ+fW+dLJqX2hbnv5b+8m
UHA7uNPeYYmjgZ+HawVmSt54MOteYZF3XYdDUQc1TEa4ZdsbCUsoFMuNAU8drrHWCbAMfS092+Rf
fO8qUimSTLLdCVmo5emHCY1+jG0ZCKGYLXtkUrLVDQgzScm16GQxmYJk6HhNIO8IFALL1J02IfSM
pXAIRbQMWKjkiaWkPUj49hub9GLJle0Bx6fymENFNz+2OT1XRimwTT3PJeeYIBV/dn6dnW5MEKdc
2enx2GIFzzZwbJM0UxydLrDS9PnhtTqz5Rx5x+AzR2pc2OxQzTmsNn2u7vQ5OVPkV87MPdBn/d2K
jA8KEnev89ng7QLn545O8r1LO+x0IjpBzMvLDTIFz17eIZMK09CiEwcqOdpDPqllCEqeNeRpKgZx
yu5WjGMbtPyYyrDDOV92x7DoQ5N5jk0V2elGNAcRMOzgpgorkVzY7PLpg1XNE7UMPMtEKkWQpLx6
o0XZs5gueby12SVRiiDOODKVJ9zWAYBMJTJTSPTaVEDJs8gyiR8pUgkxiuv1Hn/0yjKf2qrx1JEa
TxysEqQZry4rDIFWDe0EY5XSp09M89zVOp5lEiaSr5ydHl/jT5Te9rff/8ESc2WPv/X4gxGA+NLJ
Kf6P71/jpaUmz5yZfSDH+CDtXpKfbwkhLqFhb/9ICDGN7np+rG1vR2Y0QHKiYN/R0WpCeDrOnuv9
kGrO5chk5Y78nhFkAgX/4ntXmSm5rLcDFidyfP7YFCuNPtWiSz9McExBOedQ9hIMIdjqRePj92OF
aSRU8w473YhMJdgmHKwVqOUs6mmMpSCFm6AtxnA46miDlVK/RqFhLFMFWwPjlHaGjhBIpTkm82WP
JFOcXajg2saHnuOzn92pGtX0Y1zbGHZ26limIJMKA0G9HzFfyfO9N7eJpMSzLAZhwrOXt3USYplU
XJtUZXSDFNcQTJU9CpaB55gYBqSJDnziYVJqCOgNsdWplOQcUwdww2p+PExsk1QnUSNel5Rgm+AI
qOQcQGCZBqnSEa8pBJYBvShlfiLHc1fres7PsDLe6Ee6AjlUhPNs86Zu5dGp96fb81HdAN+tonm3
wVzHT2j5MadmS2RKUe8FBOnNr1Ho9RJnGRZ6BpAhDN2ZlDHZnjahHMHg0M8+6u1gx7Ygb9uUPS14
Yg99g1IKiUIYgnY/0p3gLCPJNCEb3glZGhWO7uY73g4Wc+uwxOXmgKdPTr9jgOfdBsTjSnU/wTTf
Kc09EjtZbwa8OYQyebbJVNEl75hMFlzOb7SxDRPHMqgUtLJiaxAjVQ/LMAjiDIkubswXXDY7Ib0o
JR4mmhlvJ5iGYVDNOUyXHJqDlF4YD7k9CUpp2NtG22elMeDFa3W6ewh+qy2f0/NlposOW52A11Zb
FByTZ07NcbXeRylFlEmsSM9uMl0bIeD4VJG5ksd6J2DbNlis5djqhEwWXR6aLXF5u8cbmx2qeYdB
lDFdcjXP5LZX9YOxBwWJe7fn8Nbj7lX6ixKplTWrHmmmx0JEmaQfJDT6GZYhKLg2bT8mznSy7Tkm
5ZzN8ekCJc/i9bUO9V6oBUcENAcx51c7PLZYZSJwOTpVYLnhk7NNbrR8zq22GUQpvSgjTjI2OoEW
NbAELy836QQJb23rYohrmXzq4ARbvYCHZkpc2uoSDiGYUZaxUC2wWCsgpeLCRpftboiV6d87BlTz
Nm0/QQxvvm3qYulaKySI6xys5TEMQbsf0+rHTJc9wjTlicWJ8d6x3NQQ2GPTxbFfuJvr/nG1n6y2
eXGpwT/59dM41oNRY3vy8ASebfD81d2PV/KjlPofhryfjlIqE0L4wG88uFP7cNjelnuYZHiW+a6O
tpZ3xgNE93Z+7sTvAVhuDvAsQ0tPtnwypVhvB0wVXRYmchybLjBVcllr+lzc7JJlMFl0EOiuy6hr
I4AgzkhkNFZqE2hpVQMDyzIwBfT3bJoGYBigJNiWlrKUe36XphlvbevhiYNYIoEoURQ9g6miy9GZ
Isv1AYMwZbLg7Duv5qcZyD7ocxhVub99cYtBmA3ha8OBdFJPZI5TiRIQk+FYAjGUDO0GKf0gZaqk
53SUPQvXMvGTjCiLCaMMU4ixLC3oYDVOFS9db4yHklY8G2GAkgo/SYlShWEaeoNFIeWocmyQcwxc
W3d8ACxlYgBBkjJdculHKbPDSno3SsZBjm0ZfOnk1E2KcPdLTL3dPfkoboB3u/5uKrbU+zcRpfd+
1jdeW+XV5SbLzQFhnGooyh1CUYkOolzLxBQgjD1tXm76K4aAgqOnp8aZIkkhEhlNX0MrbUMgTMGh
qQIFx2Kq6FJ0TdZaIZlUxGlEy4+50RxweB/hg7sNWG8Hd7p1WOJo4Ocosbwdt+h2lirFqbkS13b7
hInkO5e2b5LhreUdwiSjPgjJ2SYFxyKSGcdniry+1ma97dPuJ2QkWkmr6pFzTAzDpdgOSKSG//36
I/NcqfcJ4oRaxyGMUw2lQz/Pthj6c0NgmYKcY+Olkm6on7uKYROlGUIYLDd9/s/vX2UQZzd9lyjT
hYvDtTyvr3fY6UYkqaLtrzFTdOlFGvq20hwQJhKFwk8sdnohueEg1UxqTkg1Z7HZ9bm83UMqyVwp
x2ePTPHj1SaeZXFmvnzfAhP3YnfyBw+KHH+rkuKta+jW446U/squzXevb3N1p8/FjQ5HpgoEiSRK
tQ8H3c3rRwFpBmGiC6FJJnnkQIXZsh4ynmQKU0CM7raGseTiZgfLFByfLnJqvswvPDTLi0t1Lm31
2eqGtP1IQ9QNg4m8Q842KOdsposOD81q+ewzcxUubXfx4xTTFHzu2NS4W1n0bAwheHSxwrnVNkGS
kUqJzDT/LEu0gNLp+TJdP+HFoEEWSzIJrilIUslOL+alpQYTBYcMmKl4fP7YFH6ifdRmNyBOJKbQ
svn7qSp+ohL6Tvv9Z69R8iz+s88cfGDHcC2Tzxyd5Pmruw/sGB+k3YvgQR74XeAQ8DvAAeBh4FsP
5tQ+HLa35X63wV4lb/P1Jw/ymSO1fTk/tz7gF9Y7vLrSJEgyrmz3qOZs4jTTDtAQHJ8pIoeSyKvN
AYYw+N0vnyCWkt1exHfe3CLJYLXtEyaKcs5CSqW7AaakG6RINExqruzRDCL8WEc7wyLvuNtjWQaJ
VDimwDQgSBQZ4KcQdSNGRQcDrfJzqJbj0cUqjmkwXXD59OEJ5ire+Fr8LASyHxQ04sR0kR95Dger
Q9WlOMMdwkjiTCc+Ar3RTeQtwhTCRFfIJaCUYKbi4VoGWaaG8AiBY2uoQhTKcYhrAK5jUHAd6r2Q
NJMoBF95ZI4Xl5okSm+41ZzNTNmlHySEQ0z5TMVjsZJnsuji2QYXNjucmC5S78UUcyZPHa7xwlKD
pd0Bs2WXs/MVru70qfdDHp4tjad4A3w1d38wtzvdk4/aBngv628UVC3V+1zc7IBgTJTee316YYpr
aRhtnGm1tVQmpLeqFAxNAkkKQmTYpsXiRI6rO/54PQne7gIrBYsTBaaKDld3BgRpRs4yEEDZsynn
LCzT4LeePETDjxlEKUopDKET7Y12wPXdAS9fb3K4VtgXsnQ3Aevt4E4HJ/P83i+fuonzs981vNuA
uJZ3CFNJJ0iZKbl4tnnTmqvkbb72xCJhIlmq64JWztI+9rHFKhPDz68VXAZxypOHJvCGg4WlHLbT
FRyfKYKAv3xjC9cwCIaKiENaz5Bvo3CAnGvRHERUPAuzmsM0BELBWntAL9Id3pZMqORsuqRjrp9W
8YIXrze0Epdh0E8jmgOQOZtEKuJUUnQt8q5+jyUMrtUH+FFCvZ8QphkbrZDFyRyfWqwRpCm/dmae
a7t9LYs/oed9fffS/QlM3KvdyR88CHL8SElxabfP9d0+jy1W37GG9goc6JETgs12yMVBhziVnJzN
cWGjg1JweDJP3rHYbIc4gxApoRcm40RUCEHONqn3IpqDmJKnIfKVvEvYjcbPZidMefl6U3fwhoWO
CxtdlnYHtH3NwcvZAtc2sUyNyih7FtvdmPlqykzJ47PHJjk9X+bCZoei22e7F+LHkpOzZeJU8uSR
CQ7V8rx2o0UlZ9OPMkzLpISilNNDeQ0h2BkOUp2tmEx4FlNljyjVkGg/TrEtg9NzJdZaPo1ByKFa
gadPTNMKYl6+3uTasDD8+GL1Jm7oJ0pv77Tl3QH/3xtb/KMvH3/gXJynT0zxv/zFm2x2AubvMMD8
w2D3Anv7Q+A14AvDf6+jpa8fePLzs9AZuJPtharsF+ztd/4j8uOtnzOytp9wZbtPydNO8Vq9T9tP
sC3BtR2fsmfRC2KN3Y5S8q6FEHB5s49jGeQckycPTbDWDggTSZRJPEuQZYok05tekg2r/UOCez9M
uZH0AYFnC+JMjaEWKEgySKRESpCCMc53tLFmaLiLgW51z1ddvvzwDL/48AzfubSNwuQvzm9wZr7C
G+ud8ab00w5k7/Yc3g3jvfd3+w2fPD5VpD2IuFbvkWR6GGSSKpQSxGmKbRo0BhFRomj572SV96OE
sJFxeLKgZyY4BmEsKbqSXqhhjaO3mAbMVzw6vuYpeEOs0qvLTQSSJJFYw0GKp3IlQJAFCTnX4rNH
a3zx+DR/9MoKfpSyVB+w29ewvQPk6IUpD82WODlT5Ox8hXLOxrNNqjkdGO61+yWm3umefNQ2wHtd
f0+fmGa5qcVSjk0VWdq9uQNUyzvITPLCtV1afjJU+cvuCoKkMqk7eUpS9iykkqSZFjwYgb3yjsmR
Wp5y3ubqTp8ozugHKa4Fk0UPazh35K8ubGEZUPRsSq7F9bpWdCrnbJ4+MUM/SrjRHPDY4s2dq3sJ
WG+3vg5O5m9LsL+fgDjvmBhCixDsleYefcbByTz/8OePj0VsJnLOmLv0Jz9Z50bDZ7MTcnK6yPeH
vI6ia7HRDkmlIkoy6v2QF5eadIOYas7RcKjEH8vSp5nmXRZdDSuK0gypFI8uVjCEQcdPWGv5ZJlO
mlSmaAwico5BmEoKjpan7gUJTT8izRRJqsbiJpYh6AYpnmuOOScovUestQKiVHP6/FjiC8Fs6vHw
nB62OlPxODVfHl+PG80BqZT3JTBxr/Zu/uD9Jsc3fc2zeebU7HjA862d11Ex9I31Dv0o5dyKhp0N
4pSia6Gk4uBEjoWJHNNFl88fm+TNrR4vXtvl/HoHfzifSSpFwTGZKmqxDdMQtAYxlZyDQlGwBWGm
9J4sIROSJJW8dqONZ2shDSmHY34ECCFwTEHeNmkNYvqxRy9Kmcy7PHNmlm6Q8Mev3EAqKLoWhyby
oGC27PHmVpdHDlSYyDtsdQP8OMM0xLAblZJKGER6xpWJwW4/ZqbkkijF1588yPcvbfPmVo+Ca7Hb
j7iy0yNJJXGqePrENAcn86S7Ctc2xr6w5Nn37Rs+LvYvn1vCNgz+/hePPPBjfenkFADPX9nl6z/3
4LpMH4TdS/JzXCn1W0KIvwuglPLFB6B48LPQGbgXu9XR3s/5d4OENze6BGmKzODsAU02HUQpk5ZD
mGXMFz3WWwFPHq6CgDNzZV6+0WC+qid3/82NFo1exFYvxLNM4iTDjyWOoTsInm0wVdCOz7YT4kSR
pBkSQZQoLEMnNqZpUHBNlNLnNURhYBiaAH1rIdky9KyZ+arLr52d56uPHhgONHQwEFzZ7lMYqqKN
HNhPO5C9m3O4031cbfh889wanm1SdLVc57cvbnGj6bPdDfmFh6bZ6ka4lsnRqQJWSyeWtaJNLedy
bLpAYxDx7Fu740pwNuTXuIbe2ADCRKGSjAsbXdZaPr98ZhbHNPib1Ta9MEGyR6kPMA3BZNElzjT0
IEn0/B7bMlBKq+v0o5QbTR/LMjg2VWBxIsczp+d49UYTpRStQYRjGkgpOTxR5PBUnkOTedZbAfV+
xHNX6zxyQPO49nLW3uszeqd78lHbAO9n/T19YprVps/SrobPjOSjR+vyyHQBAzHG3e99TvcKl+y1
dDiwx1aAENSKOsg2DEmUvt1RNAyDIJVMWYKnjkyy1Q64sNWm6rmkmSSMM0Ir5fJ2D4Ugk5KfPznN
bMXjqSM1elHKpW09qLHoNvdV/XvQak738vmj9fwbjy+wtDvgqSN6zsV+/uDWgtbrq22ubPcoeRbr
7ZA463Jps4tt6ur7pw5O4McpjSjlteUWQZiilKDZ1wF2Je/Q6Md6nKHslwAAIABJREFUZhrQHCQk
SuGZunNkGAbnVjucnitxabuLYehCVpoBhhYyKOdszCSlbFv044w4kwSxxBIKqTTRPVUKhODodIFy
ziZK5bhj1fRjcpaGZS/XBwgDCsPuwaj7e6v4xmFuFph4kH79g/YHeyXmRwOeR7b3OW0NEjzboOBo
Jb1uGOFaFqlUPDRX4mufWqQVxGx1Qr51flN34Usenz9mUXAtXrhax7Us/CRlpRHS6Cfj57jZj5ku
u0yXPPqJHl4cJwolNESy48f0BPiJJMkkhhB4joFpiqEQh2SrG47V3q7t9vlMUOMPX7jO81d3Kbom
M2WPTx+aoOhZbHdDtjoBKwWHyzs9vnB8ChBc3u5yaCJPvR/jWoJ6P6bf8BlEIVmm8OOUharHlZ0+
Vxt9OsNBvEeLxTFfud6PaAUxB8nflS980L7hw2T1XsQ3XlvjN59cYKbkvfsb3qOdmisxVXR5/urH
K/mJhRA5hsVlIcRxILrzW967jcikBUfjjT9sEJf76WyMyH4LlTwbnYAgTTk+VaQfZcwWHVpBjG0Y
OJbBsekSrm1QzFmUXJtrQzUxxzI4NVfm2u6AZj8kzXSHRwkDzzaxDb15lTyLWkG/T6H5IIYA2zRI
MolnCkxDz4oZKlhqPs9Q5vpWJoFpQCVnM1V0MYYwwKdPTGMKMVSsgUGYjuWgP8iN634kyUc2kqm+
tZLZ8RO+eW6dy9t9JvIOB2s5Lmx2OLfWxjMNVps+f/HGJgaCv35zi26Y0osSLMNgoZoby1CXPIen
jk7w0pJksxthgJahNQ1sWxCmGVmqr+8IQ94ZpByYcMlbmnqeswVhonAsgSEEjy9UaQzFM5YafWwB
USYRpkAJNMfLEByezNMNU1KpmCjoQXQjqFOcqaH6j2Jpt8/ZhQoHKnrDGq1pxN3BlO7F3u2efNQ2
wEcOVMYQ2Lsh96dK8dVHD/D6ehsUY2LwaF3OV3K4jknWf+exRonP3q7teGDpkJtgmQbHp4pc3u4T
pSnSVWSZBGFQyzt89miNHy01STJJI4ixDAM/Scfv3d4OiRINt8ukouVrYrMOmuC7l3Zuy5f4WbO9
we5s2eXwZOHu/bqAQZyy043wo4xukJAOg9EsE3oOW5RQcG38KCNKJaapOVOPLVRo+hGNQQxDdJxj
C4qOSSp1EquUouMnbHTCIYxOv84wYKrk0A5SoiwjijMaiVab0/NVNF/ENnSBK29bfPmhGVZbPgcq
OfKuxdee0NLEei6RwXzXA6nohRmmCcenC5yeL3F2vrLvd3+3Nf1+2v36g3vp5u891u18096Buuut
gDDRT5Zp6mtuSYkQmrfbCmL+0xtbvLLSpBOkPLZQYaGaw3McpFIcnCzw2GKV/3Rx6x0k9gwtOf6p
g1Ve3+hg5bXi23TeRQndgUmlwjIFecckj0HRtXFMQZzBRN5mqytIleT0dJlqzubl601+fKNFL0jw
o4yCa1N0Lb766AFevLZLYxAzW/JYbgwI04xqzuHhuTJfOjHFwYk83zy3RtNPxvuFQA9VXW0FXNnu
0fFTZsoajRBmGZW8jWOb47li73ZtP7F32v/1wjJJJvmvnz72gRxPCMGXTmjej5RqzAn+MNq9JD//
FD3c9KAQ4t8CXwT+ywdxUnvNEoKLG29Pzv7K2fkHfcj31e6nszGZ10o8660A04C/95nDus1+epZU
Kp69UqcfDefvSDWemXK4VuCpozX6QcrFrS7dMMGzDIQwyJTSFXylYShHp/Icm8ojhMHRqTzfe2uH
11f1bJkok/jDFk8nzIaJk8Iek+Pftr2JjwXkbIsjk/o89iq1jBzaV87Ov2O2zwcRyL5bB+5O57BX
pvpafcATezDeTT/Gswwm8jYtX8MQJwsZSSopezaVnD28/xFbnQFxqpgsaDB9vReSKWj5MQvVHDPl
HJ85OslLy02qeQcpdcC52Y0YUn/IJEgUnm3QjWLqayFXtvsM4gxDQck1ma14TJc8pkoub27qdSAk
WK6BQlDL2/ydJw+ytNsn51hEqcQyDY5NF8cBz/UhdwGlODKZZ6LgMlt2+fShCba7IW9udmj1Y6ZK
7njtvd8b1kctwdnPbl2Xe6vIe20v1ydMJZYQutOwUOWtrR7n1zu6kDFcl2cPVPj5k9P86U/W6YbZ
vp+p9vm7ZRq4JsyVPSSKXz0zSyuM+dGVBvVBiCmgE8R8/61twlQxWXAoe/YYYmlZgqcO1fjh1V12
+ro2ZpsmCxM5jkwVx5Cw2bL7QLoCDwIifbuA7G78up73I7UYjqOHBA9iST/KMISiUrBo+wmHJvKY
gOcYBLHEsTQnY66SJ+f0CGM9LFoA5ZyDzDLaYcqVrR4KLURyYqbIVNFlo+3TjzLyjo0lDKJMDblc
BqlUCHSBS6A7Q55lMll0cC3jHcNyAX7780doDmcVbXYC8mGKUpJq3hl3f7+ae9uf3u2a/mnbnfaE
+90vbh2o+/Bsic8dneRLx6f4f3+8xuWdHkLAv33pBguVHK+tNLEtEz/KWG+FLEzkeebUDCstH9Mw
kFJR9mxWpX+TwrptCg5UPVqhnscTxRLPMsk5Jl1fc3gTKZHCpGQb1IoOeUfDWG3TQKCFiGoFV0vb
Z5K/WW1qmfVUYQnJ4Vqew5P63o2kqG80BhhCcHgqz+vrbR6eKfPqjRYHJ/J87YlFtHSS4kdLTYqe
xSDOeGi2xFNHalzZ6SGA2YrL155Y5LWVFkGUsjiRH3cZ73RtP7GbbRCl/N8vLvOrZ+Y4Nl38wI77
xRNT/Mm5DS5t9ThzoPyBHff9tntRe/u2EOI14HNov/nfKKUeuOxDqhRn5isUPItBmI4lDz8sdj+V
jJxr8YunZvV8iTjjtZUWjiUouTZ/+8mD/OfDyuPeIaGgA/HRpnV2ocKLS7tsdnwcU0upOpYejvkb
jy/w5OEaLT/m2St1zq20ee1GizDJSFPGG6NSussjlYJhFck2NdwNoaWvFeBZEKTg2AYF1+ZvP7mI
n2Q3BQY/bYf2XrhFI5nqXzo1O4a97OWfFD2LgxN5Sm6CIeD8eptukFAr2BRci7Yf0eiFhIkkSaHt
xxyqFVBKEifwxkYHqfR8hFLO5ssPTXF6rkI/Svn+pR16YarVdwS4ponrGDyxOEEoJSVX0Cknw/tr
cnK2wG9++iBn5yssNwcEccb1ep+LmSTOFCXPYrbsUcrZfPpQjYO1PL0wYb6SGwsVXN8djJ+59dkS
QZoxX85hGIIXru3y7OU6SaaYKQf89796+qYk9hO7N2v6Mf0wpeBZ9MPbd7Yr+ZHUvYZXjgJOYDjV
PaLeC1lv+XQDmwsbHU7Pl/jBZYduGIwDJ2vYOQwzNU54Rt1b24RqziLnWHz6UJWtbsihqRz/xUNH
KDhX+faFHY7P6EGKecdmYcLm8nafOJHkHZNPHZqgmre5stPHtg2OTReIkozHFquUPe2jfrLW3nce
2vthDxIifav/upNf7/jJmOiOgE8fqlHN92n0YhzDoJzTA4XTTHFiukSYSFbbAdWcw0TB4eikQzFn
U3JN1tsB00VP8zKBhWqOVpDQ8rXgjQRcS5BkkgMVbyiHnOfFpSa2KUgzwUNTeS5sZtiGliq2hIFr
6UJKybM5OJHnkYUyLT/mmdOznFmo7PvdX19rc6imFUV3exGmYezrT38WeJx3Y3c6z/v9DpW8/Y6B
uqWczdGpAqlS/MX5TW40Bqw2A+IsI0oVpqGLWdWiyWeP1Di31h4PwH74YImCY3Jlp4fnCITSe/7B
Wp6iZ3F6tkQ/zlhvBVTylu4QGop0OK/NFlApODxyoMogSQnjjHLOImcbPDRb4PGDE7y5pQsnjb6e
K6T5YRa//uj8eD8YcZxeWNpFALW8i2UMWO8EZFLxzXNr/Pbnj/Lbnz/Czx2aIEqllny3TDKl6IYJ
v3J2jhMzRQ5N5DUywjJ4a9vnscXqOxLoT+zd7Y9fXqEbpvzDL38wXZ+RPX1yGoDnr9Y/HsmPEOK7
SqlfAv58n589MBsFl5lSY6jUh832C/zvVKGs5R2mSy6ZUmwmWuxgquhyrT7gqaO1dxCE9+MDtPyY
Kzt9bjQD6r2IU/MlGv2YiZzG7D55WCvNvbHR4cp2Hz/K9OwOUysQpXtyzDDVgZKVSWxLD0fNeyaW
ELqaqBRRP8E0BK4lyLsWTx6uve/BzXux98It2g/2MrK9QdBmO+BfPb/ETi8iSiWTBZdHFyuUXYt/
+ew1DAS2pZPLnX5E14+Jpd6gGoOYuZJDInUStLTrc2aurF8X6qRKCMGhyTx5x6RScDg7kSOKM9bb
AUGckXdNHluc4Ox8hVQpjtQKFD2LrV5IzrbI2YrfeGKBJNMV+41OyJ+9voEAHluscnAiP06qR8/c
Yi0/DlR7YcJ/eG0N1zaZyJsIIcawuk/s/swSgoubezrbj9y+s50qhTeUUh5BgAH6ccrFzT6DKOWf
br8BQ4hpJiWuKXCMt6XpPUvgDyEpMISuDgVPbCEwDIEpFH/9Vp1emHBxo8dyw0cqkEpyZadHLe/w
yGKFTEoa/ZiSa9ONNOfssYUqpmHwCw9Nc2m7i2dZPHWkxvn1DqmUN8H2jk69vx2BDzro3uvX95Lc
v31xi3NrbQTw0GyJomtx9kCFrU7IhY0O1zb6hKn2t+dWmmQKiq6Gm8ZpRiIz4tRAuPoZrOVt+nHK
owtVBPDWdg8/ygjTbDx/SWaKy9t9DtbyTBRs5soeO71IQ1uFwanZMt0w5UA1RyolpjDIuya1vEM5
Z9EYJARJxncu7bAwkR9fz70CLi9fb3KjMeDKTpfT8xUtxrOPP/1Z4HHeje09zzjRQhAdPxmLhtzv
d7h1oO7ovRM53RWKEqln3fX1uIOCa2KbgpPTZV5aboCCej9ipxtxYb1N04/Z6YYYQhAmEtcxMAyD
07MVPMdgoxMSpxmOIVhphWRKD7zWkDeL/7+9M4+P6yzv/feZfdcu2bJsS46deIvjxLFJQgJJCIHc
QCGFFChQWtqyFNpLtwvtbaE73LaQAl0ghQJlK2UJUBpCSGiIszpx4jibk9iSvEuWNNpm3977xzkz
Ho1mpBlJs0h6v5+PPtIczTnnOec8512f9/e0+xxmpIDCZhEODo7jc9vIpI3yZGgyhsduqL41ew1b
L93Qwo7uphn3aTAYZmgiht0qjJtJsVHQGXDlFBD72r1ctaWdUDzF7ff3E/AYQkwb2jxcuak915lK
K0Wb34nbbqPd68ytAW6EtsJyIJnO8MUHBtjX18qlG1pqeu41TS42d/rY/9Io73nFBTU991Iyb+dH
RFyAB2gXkRbOh4cHgHVVtA1YmTGgpbKa58+Q5BrU41FOjUcA88ar2cc6fHqCUDyVU3765mMnGA8n
OD0R5WV9bRw8HsRlszIVSxJJpHjm7BSxRIYbtnURNhtJIkbiS9IKn91YBJlKw3TivEqUiBEW43UZ
61WaPQ4u7PDx0sg0/aNhdnY3c2xkmjufPsvJYIQ371nfMM9rMX6U3Tc3mlvk/00eO2cnohwfixBO
prCLkUVdgGRGsa27iYPHx3HYLETiKSwo7FZBYayRmI4miSXSpNIZXA4brV4Ho+E4l25o4fDJcULx
NBPROENTMdp9TqwWuGxDC+tbPFy5ud2Qngy4afbY+eaBE2Qw1KQiiTQdfjdOu4VkKs1zZydxWA1f
cNqEgMtOIpnm2LkQ3wyfYG2z63wHOmqsNQi47bkGULvfSfxEmlgyzfoWdy6HimZhpJRie3cTXoeN
cMKY2S41MFIYAnz1Be2kMorhyRjheAqv08bgaIh0RuF12XDZDMUul0NIpIx9nHYbyUwKUcYAh1Lg
d9tJpgzZ9Qu7/FiBw6cnSWYyBCMJHnpphHa/m4vXtXB6IsKbLuthX19brmF9bCREMp1haDLOfS+c
o7vFTUopNrR4UWCqNhnvQjUbxNVsdM+Vc+p40JDtdtotnJ2IEQzHcVktOOxWkmnFK7YYAglnJo2y
3OO04nEYIUoiQovbEJZIpIyEl2/Zu5E1AReheIqvPjxIZ8CFN5Hmms3tfPGBgVwny2EFr9MQPogm
jXCm6ViKJreDRCpDLJUhreDUeIRLNzSzy+diY5uHp09NcnRkCp/TEDYYHIsQDCdx2IQLOrw5lcrs
2sTL1rcwFU8Sjqfwu+2MTGfIKHjN9jVFB7iWS52dX64fGAjy1OkJnjkzmZsxXMg1ZP1kd4+x3rLX
jMTI5niDDGmluKjLx9BUnKsv6OBEMIzLbmVHdxP9oyHOTkYZjyRxOSyMhlOMhhIk0sbLmgJaHFaC
5qDYDdt7mIgkeO7MFNPxJPFkhr4OL6F4Cr/TWMeVzij2bGzh4f5RHj4W5FwoRiLjwG6xMBZO4LHb
8TiMcPWuJidbOmemK8jOOv/bQ/247Faa3HZavA62rvFz+NQEzV4HPufMgWmfy0YinWY6BpF4inBe
6G32PQ3FzDXApgJeo3aSG5H/euoMZydj/M0tF9fl/FdvbuebB04Y4bwFCq/LhXJmft4LfAgjr89B
znd+poB/rJJdM6h3yNRSU5io8I5Dp2jxOmZ1hPravbR6HOwaamY6lqKv3Ttj1iHbiQrFUkbODwyJ
7BeGphiPJBmajIJS7N3URneTmzOTUc5MRFEIzw9N0tfuNRfJChY5n8g0kVYkM2ksBYmClQKv08bV
F7SxtbuJ08EIW7ub2NvXxvcPnWYqliQUT5FIpnnw6Cjb1gS4ypRGbAQW60fZDN35FeRsFKm0Ik0a
l8PKDVu7uOfIOTa0eBgYCRNNGvc1mkwTSZqhRyqDzWbBbrOQzhgiE267lVgyQ7vXhs9lNFICLgfN
bjs9LW7OTsR4dCDI82en2Nvbyr7eNsajCb77xCkODAZx2a247FY2d3jpa/cwHknS5XfitFmYiCQ5
MRYlgzFCODRpdKjafE4u6jKka8ejCUOmNZbivhdHuGX3Ota3eXjnFb1ctqGFqViSHWubSsoJa8qj
1WM0HNLK6KwWS3Cc9bNsCLDFIpwaj/Cjp8+yttlFq9cYvT87GSMcT6IQJqNJvA4r69u97NnYwngk
xdh0gnAyBSiSGWMRNgipTIqMEjr8Tkam46AUkWSaZDKDxSKkFaQyGeIxI538z144x6i53mvPhhae
PzNFLJkhlTaED5q9Dl5xYces/GX5f1ejPF+KRnexTk6pcLrs9hNjEV48N801m9vpHwkxEUkQjCRZ
3+Kmr91Li8fB/qMjnAxGOHxygul4imQqQ6fVxQUdXjxOG/Fkhu4WF7vWNbOly09fu5eTYxGiyTTj
kQQtHgcepw2X3YLTZiGRUjjsVvravGzq9HPo5ARWixBNpmnx2PHa/YxHk7htDlx2Kxd2BYinMzzU
P2Z2Yhzs623l1ESE0ekEk9EkyZTipXPTXDji56FjI/icRgjjo/1jeJ02osk0TpuVnhYPzR77nLN3
y6XObvLY8UfsM+SVszMQlV5DYX28vbuJk8EI19DBs2cnOTAYxO+yk84otq0NsKlDYbcLPa2e3MCA
z2nj1svWc8+RYWIJQ9kznswYIecWI7zRUP9UJNJGnrZYKkMyk6HV42BoykgkvLHNw8ZWH88PTeGw
WvC6bIAYybDTMB1JkcpkeH5oirFQnIMnbNgtFl69cw2DY+HcrE/2fZiOJlkbcJNKK4an4tiswrGR
ECgYnozx6q1dAAyMhnPROt3NRh6YcDzFSyNTxJ9Oz+pY3rRz9hpgzdwopfj8z/u5qMvPtRd11MWG
a7a08+WHBjl4fJyXb26cNl4lzNv5UUp9Gvi0iPy2UuqzNbCp5tQ6j5BNDK3+aCJNLJUxpKEDbvpH
QzllnVgqk2tw3rpn/ZzKMtnFbtvWBrAiHDk7hVWEriY33a1uXmk2RJ47M0kwHMdptzIZS/HMWSPJ
mtNuqIOBwm9m8VbZlO+onCqU121lY5uHrmY3Dx0b5dR4lBPjEa68oJ0PXreZx44Hufd5GA0niCbT
PHBslB3rmmY0Ihp9NLAUc4XUZK8rFEvhsFtpt1hIZjJc0ddGSikyKkO735CcNjKuC3ar0Oq10uJ2
0OZzkEilSSsIJ9K0eh1c3tuCAPFkhhavg0Q6gz2ZBoFIIgUIqXSGxwaDnBqPcGI8yoZmN0dHQ2Z+
DhAUboeNZredDn+GG7Z28qPDZ3l8ZBy33YooxWg4SQYxO7yZnHRtKJbiRDBCMGw0jMbDcd798k2s
b/Nw1TIt7BqRwgb7vPmNLMJjg0EmIkm8TisXdfmhCd50WQ8/O3KOAYeVcxNR0pkMPpedaDxNymU3
fSzFeDSNVSykJUO7x0lSZWjyOAjHUrhsVkSMdSUZUzrbZrOYHW4PJ4JhAk4bJ0bDKGUoQv4sPcy5
qTgjkTitHgcXdPpp9thn5OconBWo9v1c6DnyG6/55W/hYFU2p1L2nQ9GEoxMx/nJs8N4XVbWBNzY
rBa6/K7c7EjaDFkMuO00ex1MR1Ns6fKzZ2MLmYzRkN3V00yHz5nLIXTfi8McN9dNnJuOMzwZI+C2
0+FzMh5NsmNtAL/LTsBpJKXtafbQ5LHztr0bmI4lOTYW5nQwwng0wQNHR9jc6Wddk5veNi8P9Y9y
asKQSk8mDclrY3DNw2MDQc5NG9cUT6XxOmz4nTY8DqPztb7VvaJG6pdqxjDrJ16XjVQGIzw1luKO
Q6eIxNMMjoWxihizoRNx3rZvw6w1u9m6cV2LISgA8NK5EC+dC+FzWpmMJshkjDDWc1MJfnT4LDfv
7ObQiQkmo0k8DisXdvl4+75efv7SOSORqdtOOJbCYTPCps+FojisFiwWO01uB1YEh91COp1hPJzI
2QLn5dwT5kBIu8/JmckYINz1zBCZtCJjpsXo7fCRzihiKaOu2dvbyvFgBAVsW9M0Q9lxuXSOG5H7
XhjhheFpPnnrJdQg20xRXrapDZtF2P/S6Mrt/GRRSn1WRHYC2wFX3vZ/r4ZhtaLWeYQmI0n2Hx3B
ZTNG9m/Y2smhUxOcnYrmYmhPjkcYjyS549ApbtndU3JkJL/Q9rls7Fpn5JjY3h3g8eNBAk47Wzr8
ORGEX72qD6fdSjASZyKc5KLOAEfOThNPZmj1GotYE2mjkLNirPURwGETHDYLl/Q043HYWBNwcaA/
iNUiDE3GOHRinO1rA7xxdw/DU3GeH5qkt81Ls8c+QxJ6OeVrKuyolaog86/r7ESMdq8Tq8WYSfM5
bRwYCPLC2WmOjRoqOW67gDLU99r9TtrN2ZYWr4OxkLFW6A2XrKOzycV0NMkjA2Mk0gq7xUKbz8He
3lYOHg/yxIkJfvr8EBYkF/rksAqGjo+hGrWvr403XLIul2wxpRS71zfz/PAUAYeNw2cmURkjqVw8
laHL7+JVWztp8Ti4+7khXhqe4sxkHI/dyPN0x6HTvOvK3oZ+bsuRwobAXPmNtq0NcN+LI3gdRqz+
82en2NDmYV9vGxORJNPRFJPhhJGfy2ElpRRbOv3YbRYu7m7iB0+dwWG1EAzF6evwEU6kyGQynI0a
ITNtPgdrmlzGTEDGGNnf0ukjnspgETgyPEUkniacNJJmNrvsiAU8diuJVBqPw7psG8bZzkxh+Tsd
M+5N/0jImGEXo2N4zeYOYqkMkUSabWsDeBzGPZiKpelr97K+xZMru61iqMhYLIIg9LS62b2+mVds
6cDvtnPLpT0zGsLfOXiSu54ZYnQ6TrPbmJkYC8WxiNAVMBLJ7uppxuO0EU2kuWyjMVhyy+4e1rd5
GBgNc2Gnn7FQnGQahqdihoyyx8nZqRgbWzyMRRK4bTZaPcaakw1tbtwOY8Y4lVGcmYzhcxmzCi8M
T9Pd4qavo4mda5vwuSsRim1slipMr1g4V3Zws8PnJJVWxDIZ2s11NYPBcC4xcdaOfJt2rWvmZDBC
s8fBlk4/F3R6OXouxLOnJ3nh3DTTsQRPnhxnbZOLyze08MK5aXqaPWzu8HNiPILTZmV9i4fxSIJ2
n9DmNdbYhOMpuptdjIYSZJQypKZRxsyxqTT46q1dBGHGQMwlPc1MRpO47VYmo0nOTkRJK4VFhMdP
jBNOprFZLUQSaUBxy+4exiMJHhsM1iTf02rhcz8/xtomF6+/pLtuNvicNi7b0MIDR0eArXWzYzFU
InjwMeBajM7PncBNwANARZ0fEekFHgWeBxJKqRsr2X+pqfUi2eNjYYan4rkcF25TRz8bx33HodOM
R5K0eBwoKDoTlKVYoT1p7ru7pwWLRbhx+5rc9eSykAfD3P3sEA8cG8HtsDAZMRIXrm120xlwojKK
kXCC4ckY3S3unLBBk8dBMmVIXYcTKaYiCcYiCcKJNF97ZJA/umk7b9u7YUbCz/xRreWgAASlO8TF
Ksj86wqGEmQHYpw2Kwg47Rb2bGxlOpbGZRdOT8Rw2gSHzWhkYCrm7d3YyqODY7jsVo6Nhti6NkCr
x8EzZyZZ3+qmw+/IdYQHxyJs725iOpZkNBQnlkzjddqIJTMogYt7mnE7bLzhEsNfJiNJvnPw5PkY
/g0tjEzHWd/iJpxIE4mn8TltXHuR0fHJ5pm6Ydsavv/UaewWQ8o7Ek9xfCw8K5GjZumYtyGmDKlp
p9WKArZ0+rjBfMffvGc961s8fPnhAcZCCZIpI1zm9GQUt93C63et465nh4gmMwTcdnrbPaSVYv9L
o/hcNpw2K61eJ5f3tnL9RV08cGyUZo+dWDJDi9eCy2bh1HgUj81ORhReh41oKs1ULIXHaWNds4u+
di/XbO7IlUXLaaa31eMglsrMKn9bvHZiyTR+l42+Dh+b2s9L+N+ye92M8m53TzP3HDkHSs2QJM8+
0yt627jr2SHcdoux3sfjmBU6OjAaZjqepN3rwGa1EDIjBM6FjKSUr9q2hkgixWUbW2hy23nq1ESu
XB2PJkiNKqLxlJncWBk5wqKKE8EIbT4nIoo0iq6AC6vFyA+zc10zr714DS1uI0TP7bDS0+rmhq1d
nBiPcOjkBNvWBBieivH4iSAtXgfPnJ4r/Hd5sRQzEcXCuWy5TlsxAAAgAElEQVRmrrvh6Ri9bR6c
NivRVJoXzk3T7LXPSEw81/Gy9Wg8dYZoIs3J8SjxlGJgNMwPDp2myWPnoq4Ak9EkT52eQAkMjIbo
a/fl6g4wOlyv3taF2wyxPTke4Z7nhxieihFOZri4p4nJSJKxSIJdLZ4ZAzHZMNaTwQgDo2GcdkPJ
TSmYjqc4EQwTTxkhfS671VgzihGRgjLWAWkWx5Mnxnl0IMif3LxtVu6nWnP1lnZuu+dFRkNx2n3O
utqyECrxxjcDlwBPKqV+TUS6gC8s8Lw/VUq9Y4H7Lim1VKaZjCR5bDBI/2iIgdEQu8x8MfkFb35l
aswEqRkjke+6sm/WCFFhKJzDbuHy3lb6R0MMBsO5BevZ72/Ei6GEqVgTcOGwWQg47XQFXLzpsh4e
7h/j2LkQjwyM0eyyYbFYEBTDUzGaTKnkDW0eRswZg0t6mkkrxWAwzDVbOnjXlX2zGj3LRQEISnfU
ilWQ+dclIly2sYV2r9NYxOmyGWuqlCLgttEVcLChzcsN24z46PtfHDGUfKbj3PXsWdY2uXMNq6xy
TrHOrd9UhRJga5ffCKfx2JkIJ3Dbraxr8eQWz4ORoPXQqQkCLjtTsSRv27sBn9PGY4NBQvEUw1Mx
Wr0OBkfD3Pn0GfrafbwwNMWGNi97N7YSNRXlIsk0jw0G2dhW/aSFq5m5GmI+t421TW4cNoshcpDX
oGjy2Nmyxs+1F3YST2U4MjRFk9vBjnUBwrEUyXTGCGkDookUPpeddq+TJ45PMB0zGstOuyW3lmvH
uqbcoMz+oyOmTK6dWCJNPJUmlTEW6Cczil09ftJpI0xz/9ERrqGDu58bYjqezEn0N7rPNHnss8pf
l1k2HhgIsq7JzdBUFHfewE6Txz6rvPO77OYxLDMkfJs8huRxd7O76P+ztHoc2CwWJmIJ2rx2WjxO
nA4Lfe1+puOGuE3AZaPJZafFPVOx7MBAkExG8dzZSVq9Dtx2Gxva7HQH3HQ1ObFZhZ3dzfSPhogl
MzMGVrKdsJvd3bNCsILhBFPx5Iww7fkGsZZb53cpKPbu3uw2RBV8TjsZpTg7EaXZ45hR1pe6P4XH
u/nibnrbJnlxeIrTEzHsFqHD7yKWTtPideB12mj22tmx1liz0+yys6XLD8D+oyOklZrR4UopRV+H
jy2dfk6OG8qwTW5bTshm57omUMwo8w0bvDkBj2RK0dvu4bqLOtl/dJRWc/3y/S+O8OLwtJG4V4Td
65vnXC+7Gv2lUj7/834CLhtv3beh3qbwqm2dfOqnL/Kz58/xS3vX19uciqmk8xNVSmVEJCUiAeAc
sFCB8etEZD/wPaXUbQs8xpJQK2WarCpbWilu2NrFwFiIfX2ts863vs3Du67s43gwTCia4oGjo7mR
yKyc5Fw25pIhjoZ47swkKHhhaHpG4rpsYs6Ay85oKInDZuHV29fkZqJu3L6GZ5snsVjh6HCYdCbD
ZCzFuhY3cXPmZ+/GVk6NR4ilMkSTaTwOS67ALFYB1EsBaCEF6lwdtcLj5V9XtpGYXby+sdVLi9vB
s2cmmYwZsy5+ly23mPTx4+OcmojS4nHQ7DVGnQvPmX8vs+d+zfY1bF8b4IGjIzR7HFhE2L42QCiW
4vuHTnNiPJpLCDwZSXJmPErKfG6CMWW9a31zLlP9tDlaaBEhlcFMhgexRJqeVg9XbQpw4PhYLm9F
I8/arXQ2tnrZ19vKSCjGibEoLw5N8+JwKDcr3OpxYLEIz56ZZCKaYDySYE3ASbvfScBtx2a14LBZ
DHETMRLmXtBhhKiC8I6XbczNFuaPOO/sbgKBl/W18YPDpxidSnByIsrOdc0E3BG2rWnGZpFcYuNn
z07mOtw5if5lMGOYLX/z3+eBsRCCMYLd6nWwbW1gVrhS/js6aKp3lWrcppSixeuYswPhslu5qCuA
wxZmc4ePwWCESCLF7p5m1rd6OHRygkf6x/C5bDllxjPjUU4EI3hdNqLJDGMhQ7Y5HEvR2+6l1ecg
lkzzzJkJ/E6jo1dKra3wc2EZN98g1nILc64W2fcoPxG0TYSfPDc0KzHxXPvn1zdrm9287IJ2Xjg7
zcBYmHgqzZ6NLbxiS0dOXKN/NMQLZ6dRSvH4CSMJabPbnns/sz6XXX/sslu5cfsaNnf42NHdRMBt
n5motiC9w1Wb22l223lsMEjAbWcsnCCZVmztCnD1lnZ8Thv3HjmH32VHxZKE4km8ZvqEYv6u/WV+
+kdC/OS5IX7r2gvwOes/i7Z9bYB1zW7ufm5oxXd+HheRZuBfMVTfQsCBBZzzLHAhEAd+YOYKOpz9
p4i8B3gPwIYNtendlhppXaqRiFmqbN3Q6XfNmfk6qyzmcVrZ0Gqo65QTS5+tqA6fngAFXQEX9x4Z
JhRP0ul3cfPF3djMzlEskcFqMZw4G5Obm6afinN2IkpPq4dQLEkwkjSUoIBDJyd4215jseate9bn
JD3zwzeK3btaL3JcaIFaqqNW6nj515U/agrk7uWJYIQbtnblOg997d4Zo8xWEbatDeBz2WZkVy+8
llA8RSyZ5vKNraxtdueEMh4/Pk7GzO+wu6eFjFKMRxLsPzpCKJ5CRIywnTzFwKzdk5Ekz5yZzMWq
j4VjOGwWLt1gSNz63LaieSs0tScb3nb49AQe+zgjofisWeHtawPc/+KIIXeczDA0FeOWS42wF6sI
oXgSt83G6y7uxu20cdOOmYpL+X6eSGZQGOGbVhF2rmuit83HmkCGUxNRjo6EaHLbuPbCjtzaRasY
EuoCxFMZwvEUoWiqrvetEgrfZ2PUPsjwVIxYKpOTLy6kmPpmsTJ7vlnwbFLlq7d00OJzMBFOsqun
CQGu3NTGPUeGORGMMO2x59Z0PHNmklDcOO+mdh+pTIapWIZk2hBMiSbTXLWpjXtfOEcwnMRqscyI
CKj0nsxXLy6nMOdqUay+6Gv3MhlJIoDI+Xxb5e6fXX/a4XPi3mClt93L7p5m1jS5crMzN7uN+j8Y
TjAdM967jMrMGlwrXH+cH1afzcVT6vlNRpIcOjWBxSo537rnyDDNXjuDY2Gu2dyBzSqcnYwC4LYZ
SepL5WrU/jI//7q/H7vVwq9e1VdvUwAj7+CNO7r4+qMncqkWlhOVCB78lvnn50TkLiCQ32mp4Dhx
jI4PIvIjYCdwOO//twO3A1x++eVFsqrUhqUciSimypY/cljq+2sDhlTkKy7swO+yl90Jy18s2T8a
RsGMUXtgRm6RKza15Y6fs7Xdy4tDU4yZOSvWBJyIGBmg55M5bZRRnMUUqMU6auUcL3+/bAWyqd3L
wGiIgbEQnX5XrvDPn+U7MBDk2GhoRmx14blD8RQng1HGIwliCWNxelYow2Wz0hXwcGwkzHQ8RVfA
CYJ5/rn9rjBWfTya4MDA+UWq+aOWOiSh/mTf7ydPTBSdFfY5bdisgtViwec0ZnizIZC71zfjdRkN
EbfTVvQdzvfzZ85MoJTQ29bE2akooWgql2toXbOb1+5Yy76+Vta3eYzwqLyO/5YuP0+aua2eG5qa
ofy4XGjy2NnlaabF7cgNVJTKRl9uOT/fLPiMUFpgbZMrN2I/FjFG6Vs8DsYjCTr8maLv+Q3buvj2
wVOcmYjSGXCytsnFifEILw5PE3DZeXF4muPBha3fK2cQazmFOVeLUvVFNjR9Z1vznPXSXOHXhTNx
w6HYjJCyXeuaeWFo2kxtAZvavdxYkJcpVz91nF/DlqWcDnq+bWORxIzZzPGoEYJ9Yacfi0V43cVr
ceeFihai/WVuzk3H+O7B07z58h46/I2zvubV27v40oOD7H9phNfOkaC7EalE8OAW4GdKqUml1KCI
NIvIG5VS36/khCLiV0pNmx9fDjSkfPZSjkQUU2UrJ3Qtf6FhpefOFpDHx8IEBm2zRu3zc4sUHt8q
wlQ8yZ7eVravDRiNKYtwz5FhFBBLprHNIbHYKKM4S1Gg5s9gVXq87Pen4kl29TTPCD3M0uQpnWei
8FixXM4PO80eO3t7W/G77bkKcCqeZHdPM3t7W3OzO8+cnszlj5jL7/IbNOvxFO3sLLeG60qmcH1K
/gzDxjYvl21o4dGBIKLAmzfa6jNDT0qNwMLM98bvtOcSlVpF8LlsbF/blOtAXdbbkhstLmwUv3JL
B6m0yom7LOfR3HJC1Sop5+fqQBQ2bu9+bigXqtbb6uVkMJK3VmcdAbe96Hv+bpc9J5jjc9lys3FQ
PGH2UlKvMOdGIFtn2ESK1hfl1iNzfS/rP6VmaJo8dm7ds559va0gFG1DzHf8cjvoVpGcX2Y/o4zZ
4st7Wzk7FS050FLu+VY7X35wkGQmw29es9CVJtVhX28rTW47P3l2eOV2foCPKaXuyH5QSk2YCnAV
dX6Aa0TkLzFmf/YrpR6tcP+asJQjEZW+2IWVX3a2ZiEdoF2e82s78s9dbCF99nMpW/15lWmp0U9o
nFGcxRaoxWawFvoc5/p+/v1KJDNMR5NMRpKzOkm37O6Z0ZiZsQi1RCjKQq8/+92F+p6m+uSvTyl8
vpdtaCEYTuCwCR4zA3e5/lj4PZiZqPSZM5PzdqDA6IR1BZy5cLG5BkwanXLKtMWUN8XWdoAhVhJN
plHKyA8TcBc/R7Fthn/0znh2u3qKJ8yuBrUOc24ECuuM/KTl+YNIC3kPy6lrbSK5RKPZ+r8U8x2/
3A56dt/CkO9nzkzOCrOb61pWo7+Uw3QsyVcfOc5rd6yZswNZD2xWCzdu7+KuZ4aMCBSzrlkOVNL5
KaarV3GQn1LqTgyp7Iam0opsKV7sYseoVvhYvj2lYpMLbTJGP+25UabjY2H8kdnheI00irPQAjUr
UBGKpWYsEu1rr2wWrpzz52bpzPC3p05PFFXFKWzMFN5zMKTUCZ4f6St2/vyRyVI5pBoldFEzm1IN
5ez//vvpMwxPxRkJxWesMwMq6rgX8y9g1sDMVDQ5I5dUvl3XbO6YN1ysEZlL2GS+Bmu515c9RzSe
4p4j53KDGjdf3M1U1FjHFUmkOTMR5VUF6wXLbagWbi+VMLvS+6EpTmHUw3gkgd89/3MpdX/n86fC
gdKs4E65ZXal9WOpsqeY/YUDKPlrVvPVBTVz87VHTjAdS/H+ay+otylFueXSdXz74Cl++txwXXMP
VUqlggefAv7J/PwBDOGDFUu5BcNSNBSLHWMpwscKF8oXFjqlGvnZ2aB8m3b3NDMeThBNpEmkMtz1
7BDNbnuuwq6k0G5k8u9ZbuHyPKPcCzlHYSWSH/7WP3o+k3yp+1qoyPXtgyc5fGoCBVzY5eeVF3bM
CncoXJS9vbsJn3P282uU0EXNTOYqa2a8ywXrzGwic45Il9u4ze84Z8U0njtjLLLvHw3N8qdywsXq
RalrLkfYpJzjzHfu7Ht4YHAMq8VCp9/J+hYPx8fC3PfiCC8Oh4zEqekM/aNhugLOssughTami+0P
1RuEW2nkz8TEkxkeGwziMMVC5pJ4nuv+ljuwOjAaJhRP4XXYCMVTM+rxpRRuyrcTyA3aOQuuM9/X
srZl16zqhNnlEUum+eID/VyzpZ1dPY2plnnFpja6m1x874lTK7bz89vAnwLfMj//FKMDtOpZioZi
sWMsRfhY4UL5/EJnrkZ+riEVT7Gp3cezZyb59hMnaXEb6kOxVIpz04mc4lAjNWoWy3nRh/IEKrJU
0oAspeJTKFNeKgle4TF2djcxHUvhd9lJJNM8eWKcdCaTU/jL7p+9Nq/LRioDgjA8FZ+VvLRRQhc1
MylV1hR7l/PXmeXv1z8ayiXvzHaEsiPG8WSm6No0mOlz4+EELpsVr9PwIyUU9adG9aO5GpyVlOcn
xyIz1l2V2zHIfw8tYiSQHY8kcwIGLpuRWHg8kmRTu5dXbe0sO7/WYgfjZpUt65pK+pyeDZpJ/kzM
dCw5IwFtJcIG2e2VzObYRHJiJPmpDpZauCk/8uOZM5Ocm47RPxKeMTtZ6B+Fa1ZdNsuKajNUi289
dpLRUIIPXre53qaUxGIR3nDpOm6/v5+R6XhDCTLMRSVqb2HgI1W0ZdmyFBV8sWMsRfjYXIVOqUY+
MGN2IJpI8/TpCawWC6m0wuu04bDNVBxqlEbNUjBj4fI8QgFZKqlk5lPxycqUF87EzXUMBPwuGwOj
IUKxFHabpWhenuy1hWIpMhnFweNB7DYLgcGZa4gaKXRRc55SZU05Hfbsfll1wKzvDAaNRdMBp517
B2bK4peaDYwm0sSSGRBDHl0UJf2pEf1org5OueX5ZCTJHYdO8+JwiBaPg/Wt7rIbdPnvocdhoa/D
h8AMAYP1LR46/DMliBd7bQvZH8Ws+6HDYkuTLcsnI8mcEEUlwgb5s7TZQYa56oIsKaVmiJGkzJw6
1RBusorkVAb72nwcGwnPmJ0s5h+Fa1ZXUpuhGiRSGT7/82NcvrGFfX2t9TZnTt502Tr+5b5jfO+J
U7z3lY0ZnlfIvJ0fEfkHpdSHROS/KKIPo5T6hapYtoxYigq+1DGWIiY3W+ig1IyFx6Ua+fkSmAAt
XgcIjE4bSRP9TjttPgetXpVTHFpJFd9Cnmcllcx8KjtZmfK5Ks3CY2TlqPf1thKKpXh+aKpoXp78
a+tt9/LgsRE2thhqXIXSt8s5dHGlUso35+uwF1sbUKjUVEwWP/8Y+TOTsWSaG7Z2zcgTdPlElAOD
s5Ph5s+oZG2pN4tRusoSjCTyZmgSdPgdZTfo8s9x087zeZayxy22UL4YxWZfFjsYN6tsafPOEs2Z
Lw+MZuHCBsUGGcp5lq0eR1E1x3gyU1ZC1UqvBww10UKV0VL+0dfuLbpmVc8gFuebB05wZjLG3/zi
xUiDi8Vs7vSzr6+Vrz5ynN+4ZhNWS2PbC+XN/HzV/P331TRkubMUDcXFHqPUaNz6Nk+eLO5MpbZ5
G1IuG1f0tbH/6Ahuu9Xs7PQQcNtXdIFV6bOopMExX6VYTqVZ6jvZzsuOdU1zHh/gRDDCuck4T56Y
oLvJjc9pX5Csuqa2FPPNcn0mu71QHfBmd2lZ/Pz9DQGD07hsVg6dmpgx4t/qcTA4Fp61fyPOEpTz
Ds5nY7axmT9DU8l1FZ6j0vs019qkxQzGzTUQl3/tjRjO2GiUW48Ufi9/IOOmHeV1hIs9t3ITqi70
ekr5WSn/WKzPrxamY0k+c+9LXLGplVde2FFvc8ri167q5f1ff4J7nh/mNTvW1NuceZm386OUOmj+
/nn1zVk6VuNowlyzD6UWHpfbkCompbxa7ms5VNrgmKtSLNd35zrGfJVuMGJkkd+zsZXYsVH2bGzF
addx2MuZSjrshd9t8pSWxc8yGUkyGAzjslmKhuGUegcaVTxjKQasdnY3lcyjUimV3qe5vr/Ya5tv
/8V2sDSlWcy9LXxuwUh5CVUrJb+OKia/XMnsaSOWDfXmX+/vZyyc4N9u2tbwsz5ZXr29i3XNbr70
4MDK6PyIyNMUT4cmgFJK7VpyqxbJah1NmGs0rtKRumKNo8XOSi3HirISu5eiMVUr3836Q0Ypmtw2
FEqP4DYwtXp/SvlwuQqIxfZfibMEhe/pxtbF59+o9D61ehxLFtK0EJaivFsJVOPdXKp7W413r9w6
qtzZ05VWNiyW42Nhbt/fz+t2reWS9Y2p8FYMm9XCr1y5kY//+AhPnhjn0g0t9TZpTsoJe3td1a1Y
YlbraMJcoy31HKlbrp3RethdK9/N94f8NQfL4bmsNhrh/VmoAiKszFmCarynC7lPSxnSpKmcRng3
56Ia795S+v5KLBsWg1KKP77jaewWC39y8/Z6m1Mxb79iI5+/v5/b7nmJf3/3vnqbMyflhL0dz/4t
IhuBLUqpe0TEXc7+9WCljCYsZERpMaFQ1eL4WJjhqTib2r1FF1I3Kosp5CcjSSPhaIUhMbX0XT1y
uzxY6ob2XPltSpU3C1FAzGel+dpi39OF5OEp3KdaIU2a8mmkgdZyfWqxM1VLXUettLJhMXzjwAke
PDrGX75xJ2uaXPU2p2J8ThvvfcUmPv7jIzw+GOTy3sZVqSu78yIivwm8B2gFLgB6gM8Br6qOaQtn
JYwmNPqIUrlMRpI8NhikfzTEwGiIXT3Ny6YzutBCfjKSnJFwdHdPM2/es37VjpJrFsdSNjZKlSvz
lTfaL2eymPuxkLK92D4rZZBvOdMoz6Bcn1qKdoUuC6rDM6cn+fP/eo5rtrTz9n0b6m3OgnnnlRv5
1/39fPzHR/jO+65s2DVLlczcfADYBzwKoJR6SUQ6q2LVErDcRxMaaURpMWRHJ2/Y2sXAWIh9fa3L
5joWWsgHI4lcwlGA6Qpnu5a772qWlqVsbJQqV8opb7RfzmSh92MhZXuxffravboRWmcapSNQrk8t
VbtClwVLy8lghN/4yuO0eR38w1t2Y1kGUtGl8Dhs/OFrLuLD332aO548zS9e1lNvk4piqeC7caVU
IvtBRGwUF0LQVMBkJMnAaJjJSHLG9kYZUVos2euYMhMnLsXC4FrS5LHT116ZklOrx4HfZWM6lmQq
lsTvtM+Q/S32vDWauViIHxajVLlSuN0msmr8tNbv5ELK9rmkg5fCLzQLp17PIN9vy/WpldKuWEkc
GZrirbc/QjSZ5t9+dS9tPme9TVo0t+5ZzyXrm/mbO48wFWvMOqSSmZ+fi8gfA24ReTXwW8B/Vces
1cFcU9CNMqK0WFbKdVRCk8fOrXvWs6+3dcaan5USyqhZvpR6H/O3Z5OgrgY/rcc7uZAycTWWo5rS
FPPbhSRU1X5UP85MRPn246f4l58fpclt5+u/8TK2rQ3U26wlwWIR/vINO7jlnx/iz37wLJ96y+56
mzSLSjo/HwF+HXgaeC9wJ/CFahi1WphvCnqlTC2vlOuohCaPPZdwNMtKCWXULG9KvY/Z7cWys69U
P63XO7mQMnE1lqOa4pQKgyy3I639qPacm47x8LExHukf4+FjYwyORQC4+eK1fOz12+kMLD+Bg7nY
1dPMB6/bzKfvfYlrt3byC5d019ukGZTd+VFKZUTk+8D3lVIjVbRp1VDOFPRyzY9TDZb7vdAhB6ub
5eK/q8lP650rR1N7lst7OBer6R1dzgyOhvnuE6e45/lzPH92CgC/08bLNrXyjis2cs2WDi5a46+z
ldXjt6/fzP6XRvjwdw7T1+bl4p6mepuUo5wkpwJ8DPgg5hohEUkDn1VK/UV1zVvZzDcFPZcy03Iv
vCtlJYSM1SvkYDX6S6PR6P5b6COrKTSm0XPl6Pd36Wj097BcCt9RgIHRsPaRBkApxYGBIF94YIB7
nh9GgMt7W/nwa7fy8s1t7OhuwrqMBQ0qwWa18Ll37uGWf3qId3/lMb71nivY1OGrt1lAeTM/vwu8
HNirlBoAEJFNwL+IyO8qpW6rpoErnbmmoItNbQMrovCulJUSMlbrkIOVUtkvdxrZf0v5SKPYV00a
PVeOfn+XlkZ+Dysl+45qH2kMkukMdz59li/sH+Dp05O0eOx88LrNvPOKjSsupK0SOv0uvvxre3nr
7Y/w5s89zL/+yh72bKx//p9y1N7eCbwt2/EBUEr1A+8AfqVahmmKT23nF95ppXIdopWOnuZfGKvV
XxqNRvbf1ewjjfxcYHU/m2rQ6M97IWgfqR/pjOLwqQk+/uPnecXf/g//+z8OEY6n+Ks37uShj7yK
37/xolXd8cmypcvPd95/FT6njVs/9zAfv/N5Jursp+XM/NiVUqOFG5VSIyKihxeqSKnwk5VWeJfD
agvFWSpWYmW/HGlk/13NPtLIzwVW97OpBo3+vBeC9pHzKKWqklQzHE8xPBXjeDDC8dEwg2MRjp4L
cejkBKF4CptFuHpLO3/5hp1cv7VzWefpqRZ97V5+9DtX81c/eo7P39/P1x45zmt2ruG1O9awr6+V
5hr7bTmdn7m6Z3qIocoUhp+sxMK7XFZLKM5Sspr9pdFoVP9d7T7SqM8F9LOpBo38vBeC9pHzfOLH
R/jiAwM4bBbcdisBt52Ay2b+thNw28zfdqwWIZpIE0saP9Fkmmgyk/sciqcYDcUZnU4QTaZnnMfr
sNLb7uWNl3Zz+cZWXnlhBy3e1dvpLJeAy87fvvkSfv3qTXxhfz8/eXaI7z1xGoALOrzs6mnmojV+
Luryc+EaP91Nrqp0ZqG8zs8lIjJVZLsAej6vDqy0wltTXbS/aOZD+0jjop+NZj60jxhceUEbVouQ
SGWIJNNMx1JMRY1k42cmokyZn+OpTG6fbEfJbbfisltw2a24HVa8DhsbNnho9zlp9znp9DvZ2OZh
Y5uXdp+jao3y1cBFa/z83a2X8Ne3XMwTJ8Y5eHycJ46P80j/GHc8eTr3PbfdSmfASYfPid9lw2a1
YLcKFvPeK8BptSwoj9C8nR+llLXio2pqrtBT6nz1Vgqq9/k1pck+G5sIKaVyv8t5Vvq5rjzKfaaF
flOJDyxnv6mF7dU+R6XHX87Pa6lZ7vci335gUdfSqPfi2os6ufaiznm/F0umSWcULrt11SivNSIO
m4UrNrVxxaa23LbJaJIXh6d5YWiagdEwI9Nxzk3HGA0lSKYzpDOKdEaRled02xfWRakkyammTGqt
vjKXJHY9VWDqfX5NabLPJhRP8dyZSTa1++gfDbG9uwmf0zbns9LPdeVR7jMt9Jvta5vwueb2l0rP
0YjUwvZqn6PS4y/n57XULPd7kW9/IpkxRsztlgVdy3K/FwCuBTaYNdWnyW1nb28re3urqwhXjtqb
pkJqrb5S6nz1VoGp9/k1pck+G6/DRioDSiCVAa/DNu+z0s915VHuMy30G69rfn+p9ByNSC1sr/Y5
Kj3+cn5eS81yvxf59k/Hk0zHUgu+luV+LzQaAFFK1duGkojICHC83nZUjFisFnegBUFUMu5TyfgJ
VCY9/46LPx8KlYlOjaMy6ZLbq0c7cF4ZcOnOfxnwRNFz1JdGsaVyO8xnIxaLVWxOt0olYmJzuFQq
HlWZTHrOZzX3c632Pcn3heVKo/jNecp9V2f5TRn+knZAxgUAABF+SURBVL3epS2PausHtSjTl+7+
FPevSo9fvfpjKf2/Gn4w275616WLJc9+wYjzUmTUHNdS+vy1uxeV3IOVUC8U0nj1RG1ZyPVvVEp1
lPPFhu78rARE5HGl1OX1tqMW1OJaG+l+NootjWIHNJYtjcpqu0cr7Xob/Xq0fYujEeyrtw31Pn+j
2FBP9PVX9/p12JtGo9FoNBqNRqNZFejOj0aj0Wg0Go1Go1kV6M5P9bm93gbUkFpcayPdz0axpVHs
gMaypVFZbfdopV1vo1+Ptm9xNIJ99bah3ueHxrChnujrryJ6zY9Go9FoNBqNRqNZFeiZH41Go9Fo
NBqNRrMq0J0fjUaj0Wg0Go1GsyrQnR+NRqPRaDQajUazKtCdnyohIjtF5K0isrfetqwkROQDdTrv
WvO3iMgbReSPzOdrq7EddhF5vYhcZX5+h4h8QESaa2mHZmHocmF5IiI+EekREV+9bdGsTLSPaUDX
EbVCCx4sISJyl1LqtSLyIeBVwH8DLwdOKaX+qL7WLT0isge4EmgGJoBHlFKPL+Hx9wNZBxXz9w7g
GaXUK5bqPGXa8jOl1PUi8mkgCvwM2A1crpT6pRracQfwGMY93wPciZEF+ZeVUq+plR159lTVB1YC
q61cgJXjFyJyPfCnwJT5EwD8wN8ope6pp20AIvIhpdQ/iMglwGcxyksb8BGl1P76Wmc05IC/Apow
ynAFTAIfVUodrqdt0Bj3rxF8rN73odH9pNqsxjqiGLWsN3TnZwnJayD/HLhOKZUxtz+glLq6zuYt
KSJyG+AE7sEopALADUBKKfW/l+gcvwtcAnxZKXWfue3HSqmbluL4Fdpyj1LqhuzvvO3/o5S6roZ2
5M4nIs8opXbWww7znFX3gZXAaioXYGX5hYg8ANyolIrkbfMCdyulXl4/y3K2ZH3rbuC3lFJHRaQd
+EGD2Lcf+CWl1Nm8bd3At5RS19TPspwtdb9/jeBj9b4Pje4n1Wa11RHFqHW9UdOQnVXAdhH5d+AC
jIcYNbe76mdS1dhTZPblDhG5f6lOoJS6TUQcwK+LyPuAbyzVsRfAV0TkC8BJEfka8HNgF1Dr0eyw
iPwJhn+dFZHfB4JAvMZ2QA18YIWwmsoFWFl+Ecd4zx/J23YxEKuPObNoNWcOWpVSRwGUUqMi0kij
mlLkc+G2etEI968RfKwR7kMj+0m1WW11RDFqWm/omZ8lREQ25n08o5RKmvG71yilflwvu6qBiHwK
8AI/5fxU/auAuFLqQ1U4nw14J3CRUuojS338Mm3oBl4DdGGMTDyklHqqxja4gdcCx4CXgHdhVBDf
UEpN1tiWmvrAcmU1lQuwsvzCXOv3EYzGqAXIAIeBv1NKna6nbQAi8rG8j59WSk2IiB/DvvfVy64s
IrID+EuMMJbsGuMx4M+UUk/XzTCTRrh/jeBj9b4Pje4n1Wa11RHFqHmbUnd+NAtFRC4FrsAosCaB
hwGbUuqxuhqmqRnaBzTF0H6h0Wg0mkqoZb2hOz+aBSEixZQCBbhLKfXqWtujqT3aBzTFWA1+ISKf
UUr9Tr3tKIWIfLqR11eJyB8ppT5ebztK0Qj3rxF8rN73odH9RLN01Lre0J0fzYIQkQgzY5TBcNRd
Sqm2OpikqTHaBzTFWKl+YSpS7QSONcoMloj8AnBP/mL55YCIdCmlhuttB+RCrtJKqSN5265QShX6
cC1sqYuPNaofNZKfaKpLresN3fnRLAgROQhcX7jORER+ulJGdzVzo31AU4yV5BdzSNCeVEr9cX2t
AxE5AxwHhoE7gB8qpcbra9VMailfWyki8kmMNZxJoB14t1JqJKu+VSMb6u5jjeBHjewnmupT63pD
d340C8JcpDmmlEoUbLcppVJ1MktTQ7QPaIqxkvyi0SVosxL3ItIH/CLwegz1sB8opf65vtY1vuy5
iNyfVZgSkV3AZ4A/AP62hp2fuvtYvf2o0f1EU31qXW9oqWvNgsjX4y/YvqwaN5qFo31AU4wV5hfL
QoJWKTUAfBL4pIh0AW+os0lZGl323CoiDqVUQil1WERuAb6GkUy7VjSMj9XRjxrdTzRVptb1hp75
0Wg0Go2mCI0uQSsir1FK/aTedpSi0WXPRWQfMKiUOpe3zQrcqpT6jxrZUHcfq7cfNbqfaFYeuvOj
0Wg0Go2mKmjZc005aD/R1BLd+akhIpIG8hN2vVEpNbjIYw4ClyulRhdzHE39MLNof10p9Q7zsw04
CzyqlHqdqcSzXSn1CRH5MyCklPp7EbkP+AO9KLTxMUNIbsOo3MeBBMa6gjvqaphGU0VWg+y5ZvGs
FD/Ja+PZgRTw78Bt2XVcJfbpBX6klNopIpcDv7IQiXNTMOP2RlPsa1T0mp/aElVK7S71z+W4IFiz
JISBnSLiVkpFgVcDuczeSqkfAj+sl3GaxSEiAnwf+IpS6pfNbRuBXyj4XlXef12uaOpIiBLytXWw
RdO4rBQ/ybXxRKQT+AZGCN/HytnZHMhc6GDmhzDWq+nOTxkU621raoiI/KqIfFtE/gu429z2hyLy
mIgcFpE/N7d5ReS/ReQpEXlGRN6Sd5jfFpEnRORpEdlaj+vQLJo7gZvNv98GfDP7D9NH/rHUjiJi
EZEvi8hfVdlGzcK4HkgopT6X3aCUOq6U+mzh+y8Gf2e+40/nv+ci8mFz21Mi8glz2wUicpeIHBSR
/dn33/SHT4nI/wB/JyIviUiH+T+LiBzNftZoqsjzwC1Kqevzfq4Dnqi3YZqGYsX5ibmO7D3AB81y
3WqW7dm23XsL9xGRa0XkR+bfPhH5klnmHxaRN5nb/0VEHheRZ/Pah78DdAP/Y5b5iMiNIvKw2Tb8
trmODBH5hIg8Zx7z781tt5p1zlNZkYlS9po23ici3xGRIyLydXOAb1mhZ35qi1tEDpl/DyilbjH/
vhIjkVNQRG4EtgD7MEY+figirwA6MBZD3gwgIk15xx1VSl0mIr+FIdP5G7W4GM2S8h/AR82Cbxfw
b8A1ZexnA74OPKOU+usq2qdZODuYuxLPf//fBOwGLsHIO/KYWRntxlBeeplSKiIirea+twPvU0q9
JCIvA/4Zo7MFcCFwg1IqLSITwNuBf8CQkH1KKTWytJep0czidZxXL8vnplobomloVqSfKKX6xRDQ
6MQovyeVUntFxAk8KCJ3A6XWnvyp+f2LAUSkxdz+f826wgrcKyK7lFKfEZHfw5BKHxWRduBPMMr/
sIh8GPg9Efkn4BZgq1JKiUizecyPAq9RSp3O2/brJewFuBSjXjsDPIiRl+qBxd+x2qE7P7WlVNjb
T5VSQfPvG82fJ83PPozO0H4M+cn/hxEfuj9v/++Zvw9iaPRrlhmmzGovxqzPnRXs+nngP3XHZ/lg
VkBXY6z7+Sdmvv9XA99USqWBYTFyf+wFXgl8KRvPbVZ+PuAq4Nt5A2/OvFN92zwOGJ3pH2B0ft4N
fKla16fRZFlhsueaKrFK/ORGYJeIvNn83ITRtnuxxPdvAN6a/aDOJ539JRF5D0b7fS2wHThcsO8V
5vYHzbrBgSEgMQnEgC+aA60/Mr//IPBlEflPzrcnS9mbAA4opU4BmAP6vejOj2YBhPP+FuDjSqnP
F35JRC4D/hfwcRG5Wyn1F+a/4ubvNPqZLmd+CPw9cC3QVuY+DwHXicgnlVKxahmmWRTPAm/KflBK
fcAcmcvGdoeL7jU/FmBijnWEueMqpU6KyLCIXI8xq/z2BZ5To9FoNGUgIpsw2mXnMNp2v10oKW4O
epZ7vD6M6J69SqlxEfkyxfNBCcag2tuKHGMfhoz4W4EPAtcrpd5nRg7cDBwSkd1z2Hst59ucsEzb
nXrNT+PxE+DdefGZ60SkU0S6gYhS6msYDeTL6mmkpir8G/DnSqmn5/3meb6IMVP0n2KoxGkaj58B
LhF5f942T4nv7gfeYsZbdwCvAA5g5L/4NRHxAIhIq1JqChgQkVvNbSIil8xhxxcwFsTmzwhpNBqN
Zokxy+/PAf+oDFnlnwDvFxG7+f8LRcQ7xyF+Cnwg73gtGOIJYWBSDAXR/LDAacBv/v0I8HIR2Wzu
6zXP5wOalFJ3YggkZMUZLlBKPaqU+igwCqxfgL3LCt1YajCUUneLyDbgYXO6MgS8A9iMsXA5AySB
95c+imY5Yk4jf2YB+33KXAP2VRF5+1yympraY8ZWvxG4TUT+DzCCUYF9GHAXfP0OjDVAT2HEgv8f
pdQQcJc5Gve4iCQwOrx/jDGD8y8i8icY8qr/Ye5bjB9ihLvpkLcGQKqQ+kCj0dSV7LrurNT1V4FP
mf/7AkZ42BOmQMAI8MY5jvVXwD+JyDMYsyt/rpT6nog8iRFN0I8Rrpbldox64oxS6joR+VXgm+Z6
HTDWAE0DPxARF8bMzu+a//s7EdlibrsXow45XKG9ywqd50ej0WhWAWLkkLhNKVWOkIamyohISCnl
m+P/WqK8DsjCc7VcpZT6Ri1sXGob8q7ZhqG89i6dL0azktFhbxqNRrPCEZGPAN8F/qjetmhKI2VK
n4vIX4jIIfPntIh8ydz+DhE5YG7/vKkIhYiEROSvxZCyfcQMmdEUJ6qU2q2U2oGRc+0m5s/T0gv8
ciUnqUKYcsU25JG95p0YC9rft2RWFaDDszWNgO78aDQazQpHKfUJpdRGpdSyUuRZ4bjzOjB35G2/
EmPk/XoM9c6s9PkNGOEpa5VSHzWFLq4FgsA/muHSbwFebv4vzXlhCy/wiFLqEuB+4DdrcH3Lngpy
tXwCuMZ8lr9b6nti5Ej5HxH5BqZCl4j8qRj5Un4qIt8UkT8wt8+Vw+szIvKQiPTLeTWuQht25HWE
D5thTeWwHyPMHhH5vnn+Z8VQGMPcHhKRT4qRQ+ZeOZ9DrJy8Y/9voc9Do1kqdA9co9FoNJraU07q
g1LS5z804/C/BnxKKXVQRD4I7MHIDQXGerJz5nESnJe1PYgxo6EpgzJztXwE+AOl1OsAzI5CqRwp
+4CdSqkBEdmLoQS5GyPM7gmM5wNz5/Bai+EbWzHW8n2niA2fBT6tlPq6iDgA63zXas7K3ATcZW56
tymr78bwq+8qpcYwOtNPKKV+X0Q+ijEz9sF5bM7lHZv/rms01UV3fjQajUajaRzKlT7/M+CUUior
YCHAV5RSxUIbk+r8At9lKU3bIMyV+6Tc7x1QSg2Y218O/MBMUxAzwx2R+XN4fd9cg/TcHCGMDwP/
V0R6gO8ppV6a47ryE7Dvx1ARBfgdEckmY19vXsMYkAG+ZW7/GvC9MmzWKpOahkEXgBqNRqPRNCb7
gfeKyFeAVgzp8z8UkddjhMFdl/fdezGUnG5TSp0TkVbAr5Q6XnOrVxBSXq6Wawt3m+N75XRu58vh
lZ9nRYp9QSn1DRF5FCN3y09E5DeUUj8rcbxZs5CmrTcAVyqlIiJyH8VzyoChTFl23jGNpt7oNT8a
jUaj0TQmd2CsDXkKI19UVvr894B1QHZNx18opZ7DkLO9W0QOY+QJWVsnu1cEUn6ulvwcK8zxvUIe
BF4vIi5z5uRmgAXk8KLQBrPT1q+U+gxGaNyuCi+/CRg3Oz5bgSvy/mcBsrNavww8sECbNZq6oGd+
NBqNRqOpMcVkrpVSXwa+nPdZAX9o/uR/7zqKoJT6FufDkYqeSyn1HYw1IpriLCRXy2EgLSJPYTy/
T5f43gyUUo+JyA8xOreDwOPApPnvSnJ4UcQGJ/BOEUkCQ8BfVHYbuAt4n9mRfgEjcWaWMLBDRA6a
9r5lgTZrNHVB5/nRaDQajUajqQMi4lNKhUTEg6HE9x6l1BP1tmsuZJ4cVRpNo6NnfjQajUaj0Wjq
w+0ish1jPc1XGr3jo9GsBPTMj0aj0Wg0Gs0KRkTaMEQxCnmVKV+t0awadOdHo9FoNBqNRqPRrAq0
2ptGo9FoNBqNRqNZFejOj0aj0Wg0Go1Go1kV6M6PRqPRaDQajUajWRXozo9Go9FoNBqNRqNZFejO
j0aj0Wg0Go1Go1kV/H99Ho9PXF5omgAAAABJRU5ErkJggg==
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>After applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. For any pairs of features you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before).</p>
<p>Run the code below to see how the sample data has changed after having the natural logarithm applied to it.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the log-transformed sample data</span>
<span class="n">display</span><span class="p">(</span><span class="n">log_samples</span><span class="p">)</span>
<span class="c1">#print np.max(log_data)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>8.756682</td>
      <td>9.083416</td>
      <td>8.946896</td>
      <td>7.785305</td>
      <td>8.165079</td>
      <td>8.967504</td>
    </tr>
    <tr>
      <th>1</th>
      <td>10.026369</td>
      <td>8.596004</td>
      <td>8.881558</td>
      <td>8.272571</td>
      <td>7.482682</td>
      <td>8.553525</td>
    </tr>
    <tr>
      <th>2</th>
      <td>8.933137</td>
      <td>8.508354</td>
      <td>9.151227</td>
      <td>7.419980</td>
      <td>8.108021</td>
      <td>7.850104</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Outlier-Detection">Implementation: Outlier Detection<a class="anchor-link" href="#Implementation:-Outlier-Detection">&#182;</a></h3><p>Detecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many "rules of thumb" for what constitutes an outlier in a dataset. Here, we will use <a href="http://datapigtechnologies.com/blog/index.php/highlighting-outliers-in-your-data-with-the-tukey-method/">Tukey's Method for identfying outliers</a>: An <em>outlier step</em> is calculated as 1.5 times the interquartile range (IQR). A data point with a feature that is beyond an outlier step outside of the IQR for that feature is considered abnormal.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Assign the value of the 25th percentile for the given feature to <code>Q1</code>. Use <code>np.percentile</code> for this.</li>
<li>Assign the value of the 75th percentile for the given feature to <code>Q3</code>. Again, use <code>np.percentile</code>.</li>
<li>Assign the calculation of an outlier step for the given feature to <code>step</code>.</li>
<li>Optionally remove data points from the dataset by adding indices to the <code>outliers</code> list.</li>
</ul>
<p><strong>NOTE:</strong> If you choose to remove any outliers, ensure that the sample data does not contain any of these points!<br>
Once you have performed this implementation, the dataset will be stored in the variable <code>good_data</code>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># For each feature find the data points with extreme high or low values</span>
<span class="n">outliers</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">log_data</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
    
    <span class="c1"># TODO: Calculate Q1 (25th percentile of the data) for the given feature</span>
    <span class="n">Q1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span><span class="mf">25.</span><span class="p">)</span>
    
    <span class="c1"># TODO: Calculate Q3 (75th percentile of the data) for the given feature</span>
    <span class="n">Q3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">],</span><span class="mf">75.</span><span class="p">)</span>
    
    <span class="c1"># TODO: Use the interquartile range to calculate an outlier step (1.5 times the interquartile range)</span>
    <span class="n">step</span> <span class="o">=</span> <span class="p">(</span><span class="n">Q3</span><span class="o">-</span><span class="n">Q1</span><span class="p">)</span><span class="o">*</span><span class="mf">1.5</span>
    <span class="k">print</span> <span class="n">step</span>
    <span class="c1"># Display the outliers</span>
    <span class="k">print</span> <span class="s2">&quot;Data points considered outliers for the feature &#39;{}&#39;:&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">feature</span><span class="p">)</span>
    <span class="n">outlier</span><span class="o">=</span><span class="n">log_data</span><span class="p">[</span><span class="o">~</span><span class="p">((</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">Q1</span> <span class="o">-</span> <span class="n">step</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">log_data</span><span class="p">[</span><span class="n">feature</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">Q3</span> <span class="o">+</span> <span class="n">step</span><span class="p">))]</span>
    <span class="n">display</span><span class="p">(</span><span class="n">outlier</span><span class="p">)</span>
<span class="c1"># OPTIONAL: Select the indices for data points you wish to remove</span>
    <span class="n">outliers</span> <span class="o">+=</span> <span class="n">outlier</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="k">print</span> <span class="s1">&#39;Number of outliers:{}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">outliers</span><span class="p">))</span>
<span class="c1"># Remove the outliers, if any were specified</span>
<span class="n">good_data</span> <span class="o">=</span> <span class="n">log_data</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">log_data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">outliers</span><span class="p">])</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span> <span class="o">=</span> <span class="bp">True</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>2.53350786861
Data points considered outliers for the feature &#39;Fresh&#39;:
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>65</th>
      <td>4.442651</td>
      <td>9.950323</td>
      <td>10.732651</td>
      <td>3.583519</td>
      <td>10.095388</td>
      <td>7.260523</td>
    </tr>
    <tr>
      <th>66</th>
      <td>2.197225</td>
      <td>7.335634</td>
      <td>8.911530</td>
      <td>5.164786</td>
      <td>8.151333</td>
      <td>3.295837</td>
    </tr>
    <tr>
      <th>81</th>
      <td>5.389072</td>
      <td>9.163249</td>
      <td>9.575192</td>
      <td>5.645447</td>
      <td>8.964184</td>
      <td>5.049856</td>
    </tr>
    <tr>
      <th>95</th>
      <td>1.098612</td>
      <td>7.979339</td>
      <td>8.740657</td>
      <td>6.086775</td>
      <td>5.407172</td>
      <td>6.563856</td>
    </tr>
    <tr>
      <th>96</th>
      <td>3.135494</td>
      <td>7.869402</td>
      <td>9.001839</td>
      <td>4.976734</td>
      <td>8.262043</td>
      <td>5.379897</td>
    </tr>
    <tr>
      <th>128</th>
      <td>4.941642</td>
      <td>9.087834</td>
      <td>8.248791</td>
      <td>4.955827</td>
      <td>6.967909</td>
      <td>1.098612</td>
    </tr>
    <tr>
      <th>171</th>
      <td>5.298317</td>
      <td>10.160530</td>
      <td>9.894245</td>
      <td>6.478510</td>
      <td>9.079434</td>
      <td>8.740337</td>
    </tr>
    <tr>
      <th>193</th>
      <td>5.192957</td>
      <td>8.156223</td>
      <td>9.917982</td>
      <td>6.865891</td>
      <td>8.633731</td>
      <td>6.501290</td>
    </tr>
    <tr>
      <th>218</th>
      <td>2.890372</td>
      <td>8.923191</td>
      <td>9.629380</td>
      <td>7.158514</td>
      <td>8.475746</td>
      <td>8.759669</td>
    </tr>
    <tr>
      <th>304</th>
      <td>5.081404</td>
      <td>8.917311</td>
      <td>10.117510</td>
      <td>6.424869</td>
      <td>9.374413</td>
      <td>7.787382</td>
    </tr>
    <tr>
      <th>305</th>
      <td>5.493061</td>
      <td>9.468001</td>
      <td>9.088399</td>
      <td>6.683361</td>
      <td>8.271037</td>
      <td>5.351858</td>
    </tr>
    <tr>
      <th>338</th>
      <td>1.098612</td>
      <td>5.808142</td>
      <td>8.856661</td>
      <td>9.655090</td>
      <td>2.708050</td>
      <td>6.309918</td>
    </tr>
    <tr>
      <th>353</th>
      <td>4.762174</td>
      <td>8.742574</td>
      <td>9.961898</td>
      <td>5.429346</td>
      <td>9.069007</td>
      <td>7.013016</td>
    </tr>
    <tr>
      <th>355</th>
      <td>5.247024</td>
      <td>6.588926</td>
      <td>7.606885</td>
      <td>5.501258</td>
      <td>5.214936</td>
      <td>4.844187</td>
    </tr>
    <tr>
      <th>357</th>
      <td>3.610918</td>
      <td>7.150701</td>
      <td>10.011086</td>
      <td>4.919981</td>
      <td>8.816853</td>
      <td>4.700480</td>
    </tr>
    <tr>
      <th>412</th>
      <td>4.574711</td>
      <td>8.190077</td>
      <td>9.425452</td>
      <td>4.584967</td>
      <td>7.996317</td>
      <td>4.127134</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>2.31824827282
Data points considered outliers for the feature &#39;Milk&#39;:
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>86</th>
      <td>10.039983</td>
      <td>11.205013</td>
      <td>10.377047</td>
      <td>6.894670</td>
      <td>9.906981</td>
      <td>6.805723</td>
    </tr>
    <tr>
      <th>98</th>
      <td>6.220590</td>
      <td>4.718499</td>
      <td>6.656727</td>
      <td>6.796824</td>
      <td>4.025352</td>
      <td>4.882802</td>
    </tr>
    <tr>
      <th>154</th>
      <td>6.432940</td>
      <td>4.007333</td>
      <td>4.919981</td>
      <td>4.317488</td>
      <td>1.945910</td>
      <td>2.079442</td>
    </tr>
    <tr>
      <th>356</th>
      <td>10.029503</td>
      <td>4.897840</td>
      <td>5.384495</td>
      <td>8.057377</td>
      <td>2.197225</td>
      <td>6.306275</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>2.3988562138
Data points considered outliers for the feature &#39;Grocery&#39;:
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>75</th>
      <td>9.923192</td>
      <td>7.036148</td>
      <td>1.098612</td>
      <td>8.390949</td>
      <td>1.098612</td>
      <td>6.882437</td>
    </tr>
    <tr>
      <th>154</th>
      <td>6.432940</td>
      <td>4.007333</td>
      <td>4.919981</td>
      <td>4.317488</td>
      <td>1.945910</td>
      <td>2.079442</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>2.34932750101
Data points considered outliers for the feature &#39;Frozen&#39;:
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>38</th>
      <td>8.431853</td>
      <td>9.663261</td>
      <td>9.723703</td>
      <td>3.496508</td>
      <td>8.847360</td>
      <td>6.070738</td>
    </tr>
    <tr>
      <th>57</th>
      <td>8.597297</td>
      <td>9.203618</td>
      <td>9.257892</td>
      <td>3.637586</td>
      <td>8.932213</td>
      <td>7.156177</td>
    </tr>
    <tr>
      <th>65</th>
      <td>4.442651</td>
      <td>9.950323</td>
      <td>10.732651</td>
      <td>3.583519</td>
      <td>10.095388</td>
      <td>7.260523</td>
    </tr>
    <tr>
      <th>145</th>
      <td>10.000569</td>
      <td>9.034080</td>
      <td>10.457143</td>
      <td>3.737670</td>
      <td>9.440738</td>
      <td>8.396155</td>
    </tr>
    <tr>
      <th>175</th>
      <td>7.759187</td>
      <td>8.967632</td>
      <td>9.382106</td>
      <td>3.951244</td>
      <td>8.341887</td>
      <td>7.436617</td>
    </tr>
    <tr>
      <th>264</th>
      <td>6.978214</td>
      <td>9.177714</td>
      <td>9.645041</td>
      <td>4.110874</td>
      <td>8.696176</td>
      <td>7.142827</td>
    </tr>
    <tr>
      <th>325</th>
      <td>10.395650</td>
      <td>9.728181</td>
      <td>9.519735</td>
      <td>11.016479</td>
      <td>7.148346</td>
      <td>8.632128</td>
    </tr>
    <tr>
      <th>420</th>
      <td>8.402007</td>
      <td>8.569026</td>
      <td>9.490015</td>
      <td>3.218876</td>
      <td>8.827321</td>
      <td>7.239215</td>
    </tr>
    <tr>
      <th>429</th>
      <td>9.060331</td>
      <td>7.467371</td>
      <td>8.183118</td>
      <td>3.850148</td>
      <td>4.430817</td>
      <td>7.824446</td>
    </tr>
    <tr>
      <th>439</th>
      <td>7.932721</td>
      <td>7.437206</td>
      <td>7.828038</td>
      <td>4.174387</td>
      <td>6.167516</td>
      <td>3.951244</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>4.08935876094
Data points considered outliers for the feature &#39;Detergents_Paper&#39;:
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>75</th>
      <td>9.923192</td>
      <td>7.036148</td>
      <td>1.098612</td>
      <td>8.390949</td>
      <td>1.098612</td>
      <td>6.882437</td>
    </tr>
    <tr>
      <th>161</th>
      <td>9.428190</td>
      <td>6.291569</td>
      <td>5.645447</td>
      <td>6.995766</td>
      <td>1.098612</td>
      <td>7.711101</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>2.24228065442
Data points considered outliers for the feature &#39;Delicatessen&#39;:
</pre>
</div>
</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_html rendered_html output_subarea ">
<div>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>Fresh</th>
      <th>Milk</th>
      <th>Grocery</th>
      <th>Frozen</th>
      <th>Detergents_Paper</th>
      <th>Delicatessen</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>66</th>
      <td>2.197225</td>
      <td>7.335634</td>
      <td>8.911530</td>
      <td>5.164786</td>
      <td>8.151333</td>
      <td>3.295837</td>
    </tr>
    <tr>
      <th>109</th>
      <td>7.248504</td>
      <td>9.724899</td>
      <td>10.274568</td>
      <td>6.511745</td>
      <td>6.728629</td>
      <td>1.098612</td>
    </tr>
    <tr>
      <th>128</th>
      <td>4.941642</td>
      <td>9.087834</td>
      <td>8.248791</td>
      <td>4.955827</td>
      <td>6.967909</td>
      <td>1.098612</td>
    </tr>
    <tr>
      <th>137</th>
      <td>8.034955</td>
      <td>8.997147</td>
      <td>9.021840</td>
      <td>6.493754</td>
      <td>6.580639</td>
      <td>3.583519</td>
    </tr>
    <tr>
      <th>142</th>
      <td>10.519646</td>
      <td>8.875147</td>
      <td>9.018332</td>
      <td>8.004700</td>
      <td>2.995732</td>
      <td>1.098612</td>
    </tr>
    <tr>
      <th>154</th>
      <td>6.432940</td>
      <td>4.007333</td>
      <td>4.919981</td>
      <td>4.317488</td>
      <td>1.945910</td>
      <td>2.079442</td>
    </tr>
    <tr>
      <th>183</th>
      <td>10.514529</td>
      <td>10.690808</td>
      <td>9.911952</td>
      <td>10.505999</td>
      <td>5.476464</td>
      <td>10.777768</td>
    </tr>
    <tr>
      <th>184</th>
      <td>5.789960</td>
      <td>6.822197</td>
      <td>8.457443</td>
      <td>4.304065</td>
      <td>5.811141</td>
      <td>2.397895</td>
    </tr>
    <tr>
      <th>187</th>
      <td>7.798933</td>
      <td>8.987447</td>
      <td>9.192075</td>
      <td>8.743372</td>
      <td>8.148735</td>
      <td>1.098612</td>
    </tr>
    <tr>
      <th>203</th>
      <td>6.368187</td>
      <td>6.529419</td>
      <td>7.703459</td>
      <td>6.150603</td>
      <td>6.860664</td>
      <td>2.890372</td>
    </tr>
    <tr>
      <th>233</th>
      <td>6.871091</td>
      <td>8.513988</td>
      <td>8.106515</td>
      <td>6.842683</td>
      <td>6.013715</td>
      <td>1.945910</td>
    </tr>
    <tr>
      <th>285</th>
      <td>10.602965</td>
      <td>6.461468</td>
      <td>8.188689</td>
      <td>6.948897</td>
      <td>6.077642</td>
      <td>2.890372</td>
    </tr>
    <tr>
      <th>289</th>
      <td>10.663966</td>
      <td>5.655992</td>
      <td>6.154858</td>
      <td>7.235619</td>
      <td>3.465736</td>
      <td>3.091042</td>
    </tr>
    <tr>
      <th>343</th>
      <td>7.431892</td>
      <td>8.848509</td>
      <td>10.177932</td>
      <td>7.283448</td>
      <td>9.646593</td>
      <td>3.610918</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

<div class="output_area">

<div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Number of outliers:48
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-4">Question 4<a class="anchor-link" href="#Question-4">&#182;</a></h3><ul>
<li>Are there any data points considered outliers for more than one feature based on the definition above? </li>
<li>Should these data points be removed from the dataset? </li>
<li>If any data points were added to the <code>outliers</code> list to be removed, explain why.</li>
</ul>
<p><strong> Hint: </strong> If you have datapoints that are outliers in multiple categories think about why that may be and if they warrant removal. Also note how k-means is affected by outliers and whether or not this plays a factor in your analysis of whether or not to remove them.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<ul>
<li>Yes, there are many data points(like 65,66,75,128,154) considered outliers for more than one feature based on IQR.</li>
<li>Yes, these data points should be removed from the dataset as they do not affect the decision that the feature is relevant or not. </li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Feature-Transformation">Feature Transformation<a class="anchor-link" href="#Feature-Transformation">&#182;</a></h2><p>In this section you will use principal component analysis (PCA) to draw conclusions about the underlying structure of the wholesale customer data. Since using PCA on a dataset calculates the dimensions which best maximize variance, we will find which compound combinations of features best describe customers.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-PCA">Implementation: PCA<a class="anchor-link" href="#Implementation:-PCA">&#182;</a></h3><p>Now that the data has been scaled to a more normal distribution and has had any necessary outliers removed, we can now apply PCA to the <code>good_data</code> to discover which dimensions about the data best maximize the variance of features involved. In addition to finding these dimensions, PCA will also report the <em>explained variance ratio</em> of each dimension — how much variance within the data is explained by that dimension alone. Note that a component (dimension) from PCA can be considered a new "feature" of the space, however it is a composition of the original features present in the data.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Import <code>sklearn.decomposition.PCA</code> and assign the results of fitting PCA in six dimensions with <code>good_data</code> to <code>pca</code>.</li>
<li>Apply a PCA transformation of <code>log_samples</code> using <code>pca.transform</code>, and assign the results to <code>pca_samples</code>.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
<span class="c1"># TODO: Apply PCA by fitting the good data with the same number of dimensions as features</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
<span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">good_data</span><span class="p">)</span>

<span class="c1"># TODO: Transform log_samples using the PCA fit above</span>
<span class="n">pca_samples</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">log_samples</span><span class="p">)</span>

<span class="c1"># Generate PCA results plot</span>
<span class="n">pca_results</span> <span class="o">=</span> <span class="n">vs</span><span class="o">.</span><span class="n">pca_results</span><span class="p">(</span><span class="n">good_data</span><span class="p">,</span> <span class="n">pca</span><span class="p">)</span>
<span class="k">print</span> <span class="n">pca_results</span><span class="p">[</span><span class="s1">&#39;Explained Variance&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">cumsum</span><span class="p">()</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-5">Question 5<a class="anchor-link" href="#Question-5">&#182;</a></h3><ul>
<li>How much variance in the data is explained<em> <strong>in total</strong> </em>by the first and second principal component? </li>
<li>How much variance in the data is explained by the first four principal components? </li>
<li>Using the visualization provided above, talk about each dimension and the cumulative variance explained by each, stressing upon which features are well represented by each dimension(both in terms of positive and negative variance explained). Discuss what the first four dimensions best represent in terms of customer spending.</li>
</ul>
<p><strong>Hint:</strong> A positive increase in a specific dimension corresponds with an <em>increase</em> of the <em>positive-weighted</em> features and a <em>decrease</em> of the <em>negative-weighted</em> features. The rate of increase or decrease is based on the individual feature weights.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer</strong></p>
<ul>
<li>72.52% is total variance of first and second principal components.</li>
<li>92.29% is the total variance of first four principal components.</li>
<li>In the first component, the information gain given by milk,grocery and detergents_paper is quite large whereas it does not predict the fresh and frozen features well. The spending habit resembles that of a supermarket.
In the second component,the information gain of fresh,frozen and delicatessen is increased and hence reducing the dependence on the three features mentuioned above. The spending habit resembles that of a restaurant.
In the third component, the information loss of milk,frozen occurs but gain for delicatessen is higher. The spending habit resembles that of a meat shop.
In the fourth component, the gain for fresh recovers but remains the same for milk and delicatessen. The spending habit resembles that of a deli.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it in six dimensions. Observe the numerical value for the first four dimensions of the sample points. Consider if this is consistent with your initial interpretation of the sample points.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display sample log-data after having a PCA transformation applied</span>
<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">pca_samples</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">pca_results</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">values</span><span class="p">))</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Dimensionality-Reduction">Implementation: Dimensionality Reduction<a class="anchor-link" href="#Implementation:-Dimensionality-Reduction">&#182;</a></h3><p>When using principal component analysis, one of the main goals is to reduce the dimensionality of the data — in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being explained. Because of this, the <em>cumulative explained variance ratio</em> is extremely important for knowing how many dimensions are necessary for the problem. Additionally, if a signifiant amount of variance is explained by only two or three dimensions, the reduced data can be visualized afterwards.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Assign the results of fitting PCA in two dimensions with <code>good_data</code> to <code>pca</code>.</li>
<li>Apply a PCA transformation of <code>good_data</code> using <code>pca.transform</code>, and assign the results to <code>reduced_data</code>.</li>
<li>Apply a PCA transformation of <code>log_samples</code> using <code>pca.transform</code>, and assign the results to <code>pca_samples</code>.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Apply PCA by fitting the good data with only two dimensions</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">good_data</span><span class="p">)</span>
<span class="c1"># TODO: Transform the good data using the PCA fit above</span>
<span class="n">reduced_data</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">good_data</span><span class="p">)</span>

<span class="c1"># TODO: Transform log_samples using the PCA fit above</span>
<span class="n">pca_samples</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">log_samples</span><span class="p">)</span>

<span class="c1"># Create a DataFrame for the reduced data</span>
<span class="n">reduced_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Dimension 1&#39;</span><span class="p">,</span> <span class="s1">&#39;Dimension 2&#39;</span><span class="p">])</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>Run the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display sample log-data after applying PCA transformation in two dimensions</span>
<span class="n">display</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">pca_samples</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Dimension 1&#39;</span><span class="p">,</span> <span class="s1">&#39;Dimension 2&#39;</span><span class="p">]))</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Visualizing-a-Biplot">Visualizing a Biplot<a class="anchor-link" href="#Visualizing-a-Biplot">&#182;</a></h2><p>A biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case <code>Dimension 1</code> and <code>Dimension 2</code>). In addition, the biplot shows the projection of the original features along the components. A biplot can help us interpret the reduced dimensions of the data, and discover relationships between the principal components and original features.</p>
<p>Run the code cell below to produce a biplot of the reduced-dimension data.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Create a biplot</span>
<span class="n">vs</span><span class="o">.</span><span class="n">biplot</span><span class="p">(</span><span class="n">good_data</span><span class="p">,</span> <span class="n">reduced_data</span><span class="p">,</span> <span class="n">pca</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Observation">Observation<a class="anchor-link" href="#Observation">&#182;</a></h3><p>Once we have the original feature projections (in red), it is easier to interpret the relative position of each data point in the scatterplot. For instance, a point the lower right corner of the figure will likely correspond to a customer that spends a lot on <code>'Milk'</code>, <code>'Grocery'</code> and <code>'Detergents_Paper'</code>, but not so much on the other product categories.</p>
<p>From the biplot, which of the original features are most strongly correlated with the first component? What about those that are associated with the second component? Do these observations agree with the pca_results plot you obtained earlier?</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Clustering">Clustering<a class="anchor-link" href="#Clustering">&#182;</a></h2><p>In this section, you will choose to use either a K-Means clustering algorithm or a Gaussian Mixture Model clustering algorithm to identify the various customer segments hidden in the data. You will then recover specific data points from the clusters to understand their significance by transforming them back into their original dimension and scale.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-6">Question 6<a class="anchor-link" href="#Question-6">&#182;</a></h3><ul>
<li>What are the advantages to using a K-Means clustering algorithm? </li>
<li>What are the advantages to using a Gaussian Mixture Model clustering algorithm? </li>
<li>Given your observations about the wholesale customer data so far, which of the two algorithms will you use and why?</li>
</ul>
<p><strong> Hint: </strong> Think about the differences between hard clustering and soft clustering and which would be appropriate for our dataset.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<ul>
<li>Other clustering algorithms with better features tend to be more expensive. In this case, K-means becomes a great solution for pre-clustering, reducing the space into disjoint smaller sub-spaces where other clustering algorithms can be applied hence K-Means produce tighter clusters than hierarchical clustering.</li>
<li>Does not assume clusters to be of any geometry. Works well with non-linear geometric distributions as well. In GMM a point belongs to each cluster to a different degree. Depending on the task, mixed membership may be more appropriate (e.g. news articles can belong to multiple topic clusters) or not (e.g. organisms can belong to only one species).</li>
<li>I am inclined to use K-means algorithm because hard clustering works well for our dataset as the features are easily distinguishable.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Creating-Clusters">Implementation: Creating Clusters<a class="anchor-link" href="#Implementation:-Creating-Clusters">&#182;</a></h3><p>Depending on the problem, the number of clusters that you expect to be in the data may already be known. When the number of clusters is not known <em>a priori</em>, there is no guarantee that a given number of clusters best segments the data, since it is unclear what structure exists in the data — if any. However, we can quantify the "goodness" of a clustering by calculating each data point's <em>silhouette coefficient</em>. The <a href="http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html">silhouette coefficient</a> for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). Calculating the <em>mean</em> silhouette coefficient provides for a simple scoring method of a given clustering.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Fit a clustering algorithm to the <code>reduced_data</code> and assign it to <code>clusterer</code>.</li>
<li>Predict the cluster for each data point in <code>reduced_data</code> using <code>clusterer.predict</code> and assign them to <code>preds</code>.</li>
<li>Find the cluster centers using the algorithm's respective attribute and assign them to <code>centers</code>.</li>
<li>Predict the cluster for each sample data point in <code>pca_samples</code> and assign them <code>sample_preds</code>.</li>
<li>Import <code>sklearn.metrics.silhouette_score</code> and calculate the silhouette score of <code>reduced_data</code> against <code>preds</code>.<ul>
<li>Assign the silhouette score to <code>score</code> and print the result.</li>
</ul>
</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Apply your clustering algorithm of choice to the reduced data </span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">KMeans</span>
<span class="n">clusterer</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">clusterer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>

<span class="c1"># TODO: Predict the cluster for each data point</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">)</span>

<span class="c1"># TODO: Find the cluster centers</span>
<span class="n">centers</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">cluster_centers_</span>

<span class="c1"># TODO: Predict the cluster for each transformed sample data point</span>
<span class="n">sample_preds</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">pca_samples</span><span class="p">)</span>
<span class="k">print</span> <span class="n">sample_preds</span>
<span class="c1"># TODO: Calculate the mean silhouette coefficient for the number of clusters chosen</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">silhouette_score</span>
<span class="n">score</span> <span class="o">=</span> <span class="n">silhouette_score</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span><span class="n">preds</span><span class="p">)</span>
<span class="k">print</span> <span class="n">score</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-7">Question 7<a class="anchor-link" href="#Question-7">&#182;</a></h3><ul>
<li>Report the silhouette score for several cluster numbers you tried. </li>
<li>Of these, which number of clusters has the best silhouette score?</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<ul>
<li>The scores for various cluster numbers-<ol>
<li>Silhouette score for 2 cluster numbers:0.447157742293</li>
<li>Silhouette score for 3 cluster numbers:0.393473191482</li>
<li>Silhouette score for 4 cluster numbers:0.330208290907</li>
<li>Silhouette score for 5 cluster numbers:0.350807035253</li>
</ol>
</li>
<li>The score with 2 cluster numbers is the best.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Cluster-Visualization">Cluster Visualization<a class="anchor-link" href="#Cluster-Visualization">&#182;</a></h3><p>Once you've chosen the optimal number of clusters for your clustering algorithm using the scoring metric above, you can now visualize the results by executing the code block below. Note that, for experimentation purposes, you are welcome to adjust the number of clusters for your clustering algorithm to see various visualizations. The final visualization provided should, however, correspond with the optimal number of clusters.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the results of the clustering from implementation</span>
<span class="n">vs</span><span class="o">.</span><span class="n">cluster_results</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">preds</span><span class="p">,</span> <span class="n">centers</span><span class="p">,</span> <span class="n">pca_samples</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Implementation:-Data-Recovery">Implementation: Data Recovery<a class="anchor-link" href="#Implementation:-Data-Recovery">&#182;</a></h3><p>Each cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the <em>averages</em> of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's center point corresponds to <em>the average customer of that segment</em>. Since the data is currently reduced in dimension and scaled by a logarithm, we can recover the representative customer spending from these data points by applying the inverse transformations.</p>
<p>In the code block below, you will need to implement the following:</p>
<ul>
<li>Apply the inverse transform to <code>centers</code> using <code>pca.inverse_transform</code> and assign the new centers to <code>log_centers</code>.</li>
<li>Apply the inverse function of <code>np.log</code> to <code>log_centers</code> using <code>np.exp</code> and assign the true centers to <code>true_centers</code>.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># TODO: Inverse transform the centers</span>
<span class="n">log_centers</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">centers</span><span class="p">)</span>

<span class="c1"># TODO: Exponentiate the centers</span>
<span class="n">true_centers</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">log_centers</span><span class="p">)</span>

<span class="c1"># Display the true centers</span>
<span class="n">segments</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;Segment {}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">centers</span><span class="p">))]</span>
<span class="n">true_centers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">true_centers</span><span class="p">),</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="n">true_centers</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">segments</span>
<span class="n">display</span><span class="p">(</span><span class="n">true_centers</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">((</span><span class="n">true_centers</span><span class="o">-</span><span class="n">data</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span><span class="o">/</span><span class="n">data</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">ddof</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
            <span class="n">square</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-8">Question 8<a class="anchor-link" href="#Question-8">&#182;</a></h3><ul>
<li>Consider the total purchase cost of each product category for the representative data points above, and reference the statistical description of the dataset at the beginning of this project(specifically looking at the mean values for the various feature points). What set of establishments could each of the customer segments represent?</li>
</ul>
<p><strong>Hint:</strong> A customer who is assigned to <code>'Cluster X'</code> should best identify with the establishments represented by the feature set of <code>'Segment X'</code>. Think about what each segment represents in terms their values for the feature points chosen. Reference these values with the mean values to get some perspective into what kind of establishment they represent.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong>
Based upon above heatmap
It seems like these segments are divided between those who purchase a lot of fresh and frozen goods above overall mean, but little detergent paper or grocery(Segment 1) under overall mean, and those customers which purchase lots of grocery, milk, detergent paper, delicatessant above the mean and little fresh produce(Segment 0). Segment 1 represents establisments like grocery shops but segment 0 represents establishments like restaurants.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-9">Question 9<a class="anchor-link" href="#Question-9">&#182;</a></h3><ul>
<li>For each sample point, which customer segment from<em> <strong>Question 8</strong> </em>best represents it? </li>
<li>Are the predictions for each sample point consistent with this?*</li>
</ul>
<p>Run the code block below to find which cluster each sample point is predicted to be.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the predictions</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">pred</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sample_preds</span><span class="p">):</span>
    <span class="k">print</span> <span class="s2">&quot;Sample point&quot;</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="s2">&quot;predicted to be in Cluster&quot;</span><span class="p">,</span> <span class="n">pred</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<ul>
<li>Each of the sample point is best represented by Cluster 1 due to match in purchase pattern wtih those in Cluster 1.</li>
<li>Yes,predictions for each sample point is consistent with my observations.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Conclusion">Conclusion<a class="anchor-link" href="#Conclusion">&#182;</a></h2>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>In this final section, you will investigate ways that you can make use of the clustered data. First, you will consider how the different groups of customers, the <strong><em>customer segments</em></strong>, may be affected differently by a specific delivery scheme. Next, you will consider how giving a label to each customer (which <em>segment</em> that customer belongs to) can provide for additional features about the customer data. Finally, you will compare the <strong><em>customer segments</em></strong> to a hidden variable present in the data, to see whether the clustering identified certain relationships.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-10">Question 10<a class="anchor-link" href="#Question-10">&#182;</a></h3><p>Companies will often run <a href="https://en.wikipedia.org/wiki/A/B_testing">A/B tests</a> when making small changes to their products or services to determine whether making that change will affect its customers positively or negatively. The wholesale distributor is considering changing its delivery service from currently 5 days a week to 3 days a week. However, the distributor will only make this change in delivery service for customers that react positively.</p>
<ul>
<li>How can the wholesale distributor use the customer segments to determine which customers, if any, would react positively to the change in delivery service?*</li>
</ul>
<p><strong>Hint:</strong> Can we assume the change affects all customers equally? How can we determine which group of customers it affects the most?</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<ul>
<li>The wholesale distributer can use customer segments to determine that:<ol>
<li>The customers in Segment 0(Cafes,Restaurants etc.) buy rationed amount of products and need the purchases on a 5 days basis and hence would not react positively to the change in delivery service.</li>
<li>The customers in Segment 1(Supermarkets,Grocery stores, etc.) tend to buy large amount of products in most of the categories and might not need purchases on a 5 days basis and hence would react positively to the change in delivery service.</li>
</ol>
</li>
</ul>
<p>To run A/B tests I would select a few customers from each segment and divide them into two groups; one which gets three days delivery and one which gets five days delivery. The purchasing habits would be monitered and the effects would be verified and appropiate delivery plan would be implemented for each of the segments.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-11">Question 11<a class="anchor-link" href="#Question-11">&#182;</a></h3><p>Additional structure is derived from originally unlabeled data when using clustering techniques. Since each customer has a <strong><em>customer segment</em></strong> it best identifies with (depending on the clustering algorithm applied), we can consider <em>'customer segment'</em> as an <strong>engineered feature</strong> for the data. Assume the wholesale distributor recently acquired ten new customers and each provided estimates for anticipated annual spending of each product category. Knowing these estimates, the wholesale distributor wants to classify each new customer to a <strong><em>customer segment</em></strong> to determine the most appropriate delivery service.</p>
<ul>
<li>How can the wholesale distributor label the new customers using only their estimated product spending and the <strong>customer segment</strong> data?</li>
</ul>
<p><strong>Hint:</strong> A supervised learner could be used to train on the original customers. What would be the target variable?</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<p>The wholesale distributor can label the new customers by assigning them cluster group as a label and use Customer Segment as a target variable when applying a supervised learning algorithm.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Visualizing-Underlying-Distributions">Visualizing Underlying Distributions<a class="anchor-link" href="#Visualizing-Underlying-Distributions">&#182;</a></h3><p>At the beginning of this project, it was discussed that the <code>'Channel'</code> and <code>'Region'</code> features would be excluded from the dataset so that the customer product categories were emphasized in the analysis. By reintroducing the <code>'Channel'</code> feature to the dataset, an interesting structure emerges when considering the same PCA dimensionality reduction applied earlier to the original dataset.</p>
<p>Run the code block below to see how each data point is labeled either <code>'HoReCa'</code> (Hotel/Restaurant/Cafe) or <code>'Retail'</code> the reduced space. In addition, you will find the sample points are circled in the plot, which will identify their labeling.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[&nbsp;]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython2"><pre><span></span><span class="c1"># Display the clustering results based on &#39;Channel&#39; data</span>
<span class="n">vs</span><span class="o">.</span><span class="n">channel_results</span><span class="p">(</span><span class="n">reduced_data</span><span class="p">,</span> <span class="n">outliers</span><span class="p">,</span> <span class="n">pca_samples</span><span class="p">)</span>
</pre></div>

</div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Question-12">Question 12<a class="anchor-link" href="#Question-12">&#182;</a></h3><ul>
<li>How well does the clustering algorithm and number of clusters you've chosen compare to this underlying distribution of Hotel/Restaurant/Cafe customers to Retailer customers? </li>
<li>Are there customer segments that would be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' by this distribution? </li>
<li>Would you consider these classifications as consistent with your previous definition of the customer segments?</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Answer:</strong></p>
<ul>
<li>This distribution of original data matches the distribution of our reduced data and proves that clustering correlated the features very well though some anomalies do exist.</li>
<li>There are some overlapping customers which cannot be classified as purely 'Retailers' or 'Hotels/Restaurants/Cafes' due to less well-defined seperation.</li>
<li>Yes, these classifications sre consistent with my previous definition of customer segments.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div>
<div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations and successfully answered each question above, you may finalize your work by exporting the iPython Notebook as an HTML document. You can do this by using the menu above and navigating to<br>
<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
</blockquote>

</div>
</div>
</div>
    </div>
  </div>
</body>

 
</html>