Traffic_Sign_Classifier.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />

<title>Traffic_Sign_Classifier</title>

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html {
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body {
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article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
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audio,
canvas,
progress,
video {
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audio:not([controls]) {
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[hidden],
template {
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a {
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a:hover {
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b,
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dfn {
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h1 {
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small {
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sup {
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sup {
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sub {
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svg:not(:root) {
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figure {
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hr {
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input,
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input[type="submit"] {
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td,
th {
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  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 4.7.0 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=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#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;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.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;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-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 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-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 {
  -ms-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-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.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,
.fa-gratipay: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-pp: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-resistance: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-y-combinator-square:before,
.fa-yc-square:before,
.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";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  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;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* 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;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: 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 {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  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;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#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#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
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;
}
.modal-header {
  cursor: move;
}
@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;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* 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 {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
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 .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.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: text-bottom;
  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;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.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_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.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;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.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;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.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;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-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.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.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);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.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-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* 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;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  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,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@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. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.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. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

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;
}
div.output_area .mglyph > img {
  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: 1px 0 1px 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:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.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;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.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;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
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.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.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;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 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;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  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;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button: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);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.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 .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#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;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -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.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.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;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.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;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#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;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#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;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#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.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.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 {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  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;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#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;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.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;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
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;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#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;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#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 */
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<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">
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<h1 id="Self-Driving-Car-Engineer-Nanodegree">Self-Driving Car Engineer Nanodegree<a class="anchor-link" href="#Self-Driving-Car-Engineer-Nanodegree">&#182;</a></h1><h2 id="Deep-Learning">Deep Learning<a class="anchor-link" href="#Deep-Learning">&#182;</a></h2><h2 id="Project:-Build-a-Traffic-Sign-Recognition-Classifier">Project: Build a Traffic Sign Recognition Classifier<a class="anchor-link" href="#Project:-Build-a-Traffic-Sign-Recognition-Classifier">&#182;</a></h2><p>In this notebook, a template is provided for you to implement your functionality in stages, which is required to successfully complete this project. If additional code is required that cannot be included in the notebook, be sure that the Python code is successfully imported and included in your submission if necessary.</p>
<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to  \n",
    "<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
</blockquote>
<p>In addition to implementing code, there is a writeup to complete. The writeup should be completed in a separate file, which can be either a markdown file or a pdf document. There is a <a href="https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md">write up template</a> that can be used to guide the writing process. Completing the code template and writeup template will cover all of the <a href="https://review.udacity.com/#!/rubrics/481/view">rubric points</a> for this project.</p>
<p>The <a href="https://review.udacity.com/#!/rubrics/481/view">rubric</a> contains "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. The stand out suggestions are optional. If you decide to pursue the "stand out suggestions", you can include the code in this Ipython notebook and also discuss the results in the writeup file.</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>
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<h2 id="Step-0:-Load-The-Data">Step 0: Load The Data<a class="anchor-link" href="#Step-0:-Load-The-Data">&#182;</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load pickled data</span>
<span class="kn">import</span> <span class="nn">pickle</span>

<span class="c1"># TODO: Fill this in based on where you saved the training and testing data</span>

<span class="n">training_file</span> <span class="o">=</span> <span class="s1">&#39;../data/train.p&#39;</span>
<span class="n">validation_file</span> <span class="o">=</span> <span class="s1">&#39;../data/valid.p&#39;</span>
<span class="n">testing_file</span> <span class="o">=</span> <span class="s1">&#39;../data/test.p&#39;</span>

<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">training_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">train</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">validation_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">valid</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">testing_file</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
    <span class="n">test</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
    
<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">],</span> <span class="n">train</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span>
<span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">valid</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">],</span> <span class="n">valid</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span>
<span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">test</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">],</span> <span class="n">test</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span>
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<h2 id="Step-1:-Dataset-Summary-&amp;-Exploration">Step 1: Dataset Summary &amp; Exploration<a class="anchor-link" href="#Step-1:-Dataset-Summary-&amp;-Exploration">&#182;</a></h2><p>The pickled data is a dictionary with 4 key/value pairs:</p>
<ul>
<li><code>'features'</code> is a 4D array containing raw pixel data of the traffic sign images, (num examples, width, height, channels).</li>
<li><code>'labels'</code> is a 1D array containing the label/class id of the traffic sign. The file <code>signnames.csv</code> contains id -&gt; name mappings for each id.</li>
<li><code>'sizes'</code> is a list containing tuples, (width, height) representing the original width and height the image.</li>
<li><code>'coords'</code> is a list containing tuples, (x1, y1, x2, y2) representing coordinates of a bounding box around the sign in the image. <strong>THESE COORDINATES ASSUME THE ORIGINAL IMAGE. THE PICKLED DATA CONTAINS RESIZED VERSIONS (32 by 32) OF THESE IMAGES</strong></li>
</ul>
<p>Complete the basic data summary below. Use python, numpy and/or pandas methods to calculate the data summary rather than hard coding the results. For example, the <a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.shape.html">pandas shape method</a> might be useful for calculating some of the summary results.</p>

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<h3 id="Provide-a-Basic-Summary-of-the-Data-Set-Using-Python,-Numpy-and/or-Pandas">Provide a Basic Summary of the Data Set Using Python, Numpy and/or Pandas<a class="anchor-link" href="#Provide-a-Basic-Summary-of-the-Data-Set-Using-Python,-Numpy-and/or-Pandas">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Replace each question mark with the appropriate value. </span>
<span class="c1">### Use python, pandas or numpy methods rather than hard coding the results</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="c1"># TODO: Number of training examples</span>
<span class="n">n_train</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>

<span class="c1"># TODO: Number of validation examples</span>
<span class="n">n_validation</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_valid</span><span class="p">)</span>

<span class="c1"># TODO: Number of testing examples.</span>
<span class="n">n_test</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="c1"># TODO: What&#39;s the shape of an traffic sign image?</span>
<span class="n">image_shape</span> <span class="o">=</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span>

<span class="c1"># TODO: How many unique classes/labels there are in the dataset.</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_train</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of training examples =&quot;</span><span class="p">,</span> <span class="n">n_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of testing examples =&quot;</span><span class="p">,</span> <span class="n">n_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Image data shape =&quot;</span><span class="p">,</span> <span class="n">image_shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of classes =&quot;</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">)</span>
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<pre>Number of training examples = 34799
Number of testing examples = 12630
Image data shape = (34799, 32, 32, 3)
Number of classes = 43
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<h3 id="Include-an-exploratory-visualization-of-the-dataset">Include an exploratory visualization of the dataset<a class="anchor-link" href="#Include-an-exploratory-visualization-of-the-dataset">&#182;</a></h3>
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<p>Visualize the German Traffic Signs Dataset using the pickled file(s). This is open ended, suggestions include: plotting traffic sign images, plotting the count of each sign, etc.</p>
<p>The <a href="http://matplotlib.org/">Matplotlib</a> <a href="http://matplotlib.org/examples/index.html">examples</a> and <a href="http://matplotlib.org/gallery.html">gallery</a> pages are a great resource for doing visualizations in Python.</p>
<p><strong>NOTE:</strong> It's recommended you start with something simple first. If you wish to do more, come back to it after you've completed the rest of the sections. It can be interesting to look at the distribution of classes in the training, validation and test set. Is the distribution the same? Are there more examples of some classes than others?</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">csv</span>

<span class="n">sign_dict</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;signnames.csv&#39;</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">csvfile</span><span class="p">:</span>
    <span class="nb">next</span><span class="p">(</span><span class="n">csvfile</span><span class="p">)</span>
    <span class="n">rd</span> <span class="o">=</span> <span class="n">csv</span><span class="o">.</span><span class="n">reader</span><span class="p">(</span><span class="n">csvfile</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">rd</span><span class="p">:</span>
        <span class="n">sign_dict</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Data exploration visualization code goes here.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="c1"># Visualizations will be shown in the notebook.</span>
<span class="o">%</span><span class="k">matplotlib</span> inline

<span class="n">idx</span><span class="p">,</span> <span class="n">count_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">idx2</span><span class="p">,</span> <span class="n">count_valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">idx3</span><span class="p">,</span> <span class="n">count_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">signs</span> <span class="o">=</span> <span class="p">[</span><span class="n">sign_dict</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">idx</span><span class="p">]</span>

<span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">rects_train</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">count_train</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">rects_valid</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">count_valid</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;#1155dd&quot;</span><span class="p">)</span>
<span class="n">rects_test</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">count_test</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;#15cc55&quot;</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Frequency&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">ind</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">signs</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Frequency of classes in training, validation and test datasets&#39;</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="n">rects_train</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rects_valid</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rects_test</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="p">(</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Validation&#39;</span><span class="p">,</span> <span class="s1">&#39;Test&#39;</span><span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h2 id="Step-2:-Design-and-Test-a-Model-Architecture">Step 2: Design and Test a Model Architecture<a class="anchor-link" href="#Step-2:-Design-and-Test-a-Model-Architecture">&#182;</a></h2><p>Design and implement a deep learning model that learns to recognize traffic signs. Train and test your model on the <a href="http://benchmark.ini.rub.de/?section=gtsrb&amp;subsection=dataset">German Traffic Sign Dataset</a>.</p>
<p>The LeNet-5 implementation shown in the <a href="https://classroom.udacity.com/nanodegrees/nd013/parts/fbf77062-5703-404e-b60c-95b78b2f3f9e/modules/6df7ae49-c61c-4bb2-a23e-6527e69209ec/lessons/601ae704-1035-4287-8b11-e2c2716217ad/concepts/d4aca031-508f-4e0b-b493-e7b706120f81">classroom</a> at the end of the CNN lesson is a solid starting point. You'll have to change the number of classes and possibly the preprocessing, but aside from that it's plug and play!</p>
<p>With the LeNet-5 solution from the lecture, you should expect a validation set accuracy of about 0.89. To meet specifications, the validation set accuracy will need to be at least 0.93. It is possible to get an even higher accuracy, but 0.93 is the minimum for a successful project submission.</p>
<p>There are various aspects to consider when thinking about this problem:</p>
<ul>
<li>Neural network architecture (is the network over or underfitting?)</li>
<li>Play around preprocessing techniques (normalization, rgb to grayscale, etc)</li>
<li>Number of examples per label (some have more than others).</li>
<li>Generate fake data.</li>
</ul>
<p>Here is an example of a <a href="http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf">published baseline model on this problem</a>. It's not required to be familiar with the approach used in the paper but, it's good practice to try to read papers like these.</p>

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<h3 id="Pre-process-the-Data-Set-(normalization,-grayscale,-etc.)">Pre-process the Data Set (normalization, grayscale, etc.)<a class="anchor-link" href="#Pre-process-the-Data-Set-(normalization,-grayscale,-etc.)">&#182;</a></h3>
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<p>Minimally, the image data should be normalized so that the data has mean zero and equal variance. For image data, <code>(pixel - 128)/ 128</code> is a quick way to approximately normalize the data and can be used in this project.</p>
<p>Other pre-processing steps are optional. You can try different techniques to see if it improves performance.</p>
<p>Use the code cell (or multiple code cells, if necessary) to implement the first step of your project.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">cv2</span>

<span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">img</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">cv2</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;float&#39;</span><span class="p">),</span> <span class="kc">None</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">NORM_MINMAX</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Preprocess the data here. It is required to normalize the data. Other preprocessing steps could include </span>
<span class="c1">### converting to grayscale, etc.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="kn">from</span> <span class="nn">sklearn.utils</span> <span class="k">import</span> <span class="n">shuffle</span>

<span class="n">X_train</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">shuffle</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>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">more_X_train</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">more_y_train</span> <span class="o">=</span> <span class="p">[]</span>

<span class="n">more2_X_train</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">more2_y_train</span> <span class="o">=</span> <span class="p">[]</span>

<span class="n">new_counts_train</span> <span class="o">=</span> <span class="n">count_train</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="n">n_train</span><span class="p">):</span>
    <span class="k">if</span><span class="p">(</span><span class="n">new_counts_train</span><span class="p">[</span><span class="n">y_train</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">&lt;</span> <span class="mi">3000</span><span class="p">):</span>
        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
            <span class="n">dx</span><span class="p">,</span> <span class="n">dy</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mf">1.7</span><span class="p">,</span> <span class="mf">1.8</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
            <span class="n">M</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">dx</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dy</span><span class="p">]])</span>
            <span class="n">dst</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">warpAffine</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">M</span><span class="p">,</span> <span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
            <span class="n">dst</span> <span class="o">=</span> <span class="n">dst</span><span class="p">[:,:,</span><span class="kc">None</span><span class="p">]</span>
            <span class="n">more_X_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span>
            <span class="n">more_y_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y_train</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>

            <span class="n">random_higher_bound</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">27</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
            <span class="n">random_lower_bound</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
            <span class="n">points_one</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">32</span><span class="p">,</span><span class="mi">0</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="mi">32</span><span class="p">],[</span><span class="mi">32</span><span class="p">,</span><span class="mi">32</span><span class="p">]])</span>
            <span class="n">points_two</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="n">random_higher_bound</span><span class="p">,</span> <span class="n">random_lower_bound</span><span class="p">],</span>\
                                     <span class="p">[</span><span class="n">random_lower_bound</span><span class="p">,</span> <span class="mi">32</span><span class="p">],[</span><span class="mi">32</span><span class="p">,</span> <span class="n">random_higher_bound</span><span class="p">]])</span>
            <span class="n">M</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">getPerspectiveTransform</span><span class="p">(</span><span class="n">points_one</span><span class="p">,</span> <span class="n">points_two</span><span class="p">)</span>
            <span class="n">dst</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">warpPerspective</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">M</span><span class="p">,</span> <span class="p">(</span><span class="mi">32</span><span class="p">,</span><span class="mi">32</span><span class="p">))</span>
            <span class="n">more2_X_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span>
            <span class="n">more2_y_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y_train</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
            
            <span class="n">tilt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
            <span class="n">M</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">getRotationMatrix2D</span><span class="p">((</span><span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span> <span class="n">tilt</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
            <span class="n">dst</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">warpAffine</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">M</span><span class="p">,</span> <span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_train</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
            <span class="n">more2_X_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dst</span><span class="p">)</span>
            <span class="n">more2_y_train</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">y_train</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
            
            <span class="n">new_counts_train</span><span class="p">[</span><span class="n">y_train</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">+=</span> <span class="mi">2</span>
    
<span class="n">more_X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">more_X_train</span><span class="p">)</span>
<span class="n">more_y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">more_y_train</span><span class="p">)</span>
<span class="n">more_X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">more_X_train</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">more_X_train</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">X_train</span><span class="p">,</span> <span class="n">more_X_train</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">y_train</span><span class="p">,</span> <span class="n">more_y_train</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="n">more2_X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">more_X_train</span><span class="p">)</span>
<span class="n">more2_y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">more_y_train</span><span class="p">)</span>
<span class="n">more2_X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">more2_X_train</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">more2_X_train</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">X_train</span><span class="p">,</span> <span class="n">more2_X_train</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">y_train</span><span class="p">,</span> <span class="n">more2_y_train</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="n">X_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_valid</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">y_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="k">import</span> <span class="n">train_test_split</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_valid</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_valid</span> <span class="o">=</span> <span class="n">train_test_split</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">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>

<span class="n">idx</span><span class="p">,</span> <span class="n">count_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">idx2</span><span class="p">,</span> <span class="n">count_valid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_valid</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">idx3</span><span class="p">,</span> <span class="n">count_test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">return_counts</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span><span class="mi">5</span><span class="p">))</span>
<span class="n">rects_train</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">count_train</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
<span class="n">rects_valid</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">count_valid</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;#1155dd&quot;</span><span class="p">)</span>
<span class="n">rects_test</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">count_test</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;#15cc55&quot;</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Frequency&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">ind</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">signs</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Frequency of classes in training, validation and test datasets&#39;</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">((</span><span class="n">rects_train</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rects_valid</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rects_test</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="p">(</span><span class="s1">&#39;Train&#39;</span><span class="p">,</span> <span class="s1">&#39;Validation&#39;</span><span class="p">,</span> <span class="s1">&#39;Test&#39;</span><span class="p">))</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h3 id="Model-Architecture">Model Architecture<a class="anchor-link" href="#Model-Architecture">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Define your architecture here.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>

<span class="n">EPOCHS</span> <span class="o">=</span> <span class="mi">60</span>
<span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">100</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">tensorflow.contrib.layers</span> <span class="k">import</span> <span class="n">flatten</span>

<span class="k">def</span> <span class="nf">LeNet</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>    
    <span class="c1"># Hyperparameters</span>
    <span class="n">mu</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">sigma</span> <span class="o">=</span> <span class="mf">0.1</span>
    
    <span class="c1"># TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 14x14x6.</span>
    <span class="n">conv1_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">conv1_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">6</span><span class="p">))</span>
    <span class="n">conv1</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">conv1_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">conv1_b</span>
    <span class="n">conv1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv1</span><span class="p">)</span>
    <span class="n">conv1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">conv1</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span>
    
    <span class="c1"># TODO: Layer 2: Convolutional. Output = 5x5x16.</span>
    <span class="n">conv2_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">16</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">conv2_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">16</span><span class="p">))</span>
    <span class="n">conv2</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv1</span><span class="p">,</span> <span class="n">conv2_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">conv2_b</span>
    <span class="n">conv2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv2</span><span class="p">)</span>
    <span class="n">conv2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">max_pool</span><span class="p">(</span><span class="n">conv2</span><span class="p">,</span> <span class="n">ksize</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span>
    
    <span class="c1"># TODO: Layer 3: Convolutional. Output = 1x1x400.</span>
    <span class="n">conv3_W</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">400</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">conv3_b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">400</span><span class="p">))</span>
    <span class="n">conv3</span>   <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">conv2</span><span class="p">,</span> <span class="n">conv3_W</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;VALID&#39;</span><span class="p">)</span> <span class="o">+</span> <span class="n">conv3_b</span>
    <span class="n">conv3</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">conv3</span><span class="p">)</span>

    <span class="c1"># TODO: Flatten. Input = 5x5x16. Output = 400.</span>
    <span class="n">layer2flat</span> <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">conv2</span><span class="p">)</span>
    
    <span class="c1"># Flatten conv3. Input = 1x1x400. Output = 400.</span>
    <span class="n">fc0</span>   <span class="o">=</span> <span class="n">flatten</span><span class="p">(</span><span class="n">conv3</span><span class="p">)</span>
    
    <span class="c1"># Concat layer2flat and x. Input = 400 + 400. Output = 800</span>
    <span class="n">fc0</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">fc0</span><span class="p">,</span> <span class="n">layer2flat</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
    
    <span class="c1"># Dropout</span>
    <span class="n">fc0</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">fc0</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">)</span>
    
    <span class="c1"># TODO: Layer 4: Fully Connected. Input = 800. Output = 43.</span>
    <span class="n">fc1_W</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">truncated_normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">800</span><span class="p">,</span> <span class="n">n_classes</span><span class="p">),</span> <span class="n">mean</span> <span class="o">=</span> <span class="n">mu</span><span class="p">,</span> <span class="n">stddev</span> <span class="o">=</span> <span class="n">sigma</span><span class="p">))</span>
    <span class="n">fc1_b</span>  <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">n_classes</span><span class="p">))</span>
    <span class="n">logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">fc0</span><span class="p">,</span> <span class="n">fc1_W</span><span class="p">)</span> <span class="o">+</span> <span class="n">fc1_b</span>
    
    <span class="k">return</span> <span class="n">logits</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="p">(</span><span class="kc">None</span><span class="p">))</span>
<span class="n">one_hot_y</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">one_hot</span><span class="p">(</span><span class="n">y</span><span class="p">,</span>  <span class="n">n_classes</span><span class="p">)</span>
<span class="n">keep_prob</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
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<h3 id="Train,-Validate-and-Test-the-Model">Train, Validate and Test the Model<a class="anchor-link" href="#Train,-Validate-and-Test-the-Model">&#182;</a></h3>
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<p>A validation set can be used to assess how well the model is performing. A low accuracy on the training and validation
sets imply underfitting. A high accuracy on the training set but low accuracy on the validation set implies overfitting.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Train your model here.</span>
<span class="c1">### Calculate and report the accuracy on the training and validation set.</span>
<span class="c1">### Once a final model architecture is selected, </span>
<span class="c1">### the accuracy on the test set should be calculated and reported as well.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>

<span class="n">rate</span> <span class="o">=</span> <span class="mf">0.00097</span>

<span class="n">logits</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="n">cross_entropy</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax_cross_entropy_with_logits</span><span class="p">(</span><span class="n">logits</span><span class="o">=</span><span class="n">logits</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">one_hot_y</span><span class="p">)</span>
<span class="n">loss_operation</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">cross_entropy</span><span class="p">)</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">AdamOptimizer</span><span class="p">(</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="n">rate</span><span class="p">)</span>
<span class="n">training_operation</span> <span class="o">=</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">minimize</span><span class="p">(</span><span class="n">loss_operation</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">correct_prediction</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">equal</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">logits</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">one_hot_y</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
<span class="n">accuracy_operation</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_mean</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">correct_prediction</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
<span class="n">saver</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">Saver</span><span class="p">()</span>

<span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">X_data</span><span class="p">,</span> <span class="n">y_data</span><span class="p">):</span>
    <span class="n">num_examples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_data</span><span class="p">)</span>
    <span class="n">total_accuracy</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">sess</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">get_default_session</span><span class="p">()</span>
    <span class="k">for</span> <span class="n">offset</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="n">num_examples</span><span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">):</span>
        <span class="n">batch_x</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">X_data</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">offset</span><span class="o">+</span><span class="n">BATCH_SIZE</span><span class="p">],</span> <span class="n">y_data</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">offset</span><span class="o">+</span><span class="n">BATCH_SIZE</span><span class="p">]</span>
        <span class="n">accuracy</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">accuracy_operation</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">batch_x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">batch_y</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
        <span class="n">total_accuracy</span> <span class="o">+=</span> <span class="p">(</span><span class="n">accuracy</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">batch_x</span><span class="p">))</span>
    <span class="k">return</span> <span class="n">total_accuracy</span> <span class="o">/</span> <span class="n">num_examples</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
    <span class="n">num_examples</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training...&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">()</span>
    <span class="n">validation_accuracy_figure</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="n">train_accuracy_figure</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="n">EPOCHS</span><span class="p">):</span>
        <span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span> <span class="o">=</span> <span class="n">shuffle</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="k">for</span> <span class="n">offset</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="n">num_examples</span><span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">):</span>
            <span class="n">end</span> <span class="o">=</span> <span class="n">offset</span> <span class="o">+</span> <span class="n">BATCH_SIZE</span>
            <span class="n">batch_x</span><span class="p">,</span> <span class="n">batch_y</span> <span class="o">=</span> <span class="n">X_train</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">end</span><span class="p">],</span> <span class="n">y_train</span><span class="p">[</span><span class="n">offset</span><span class="p">:</span><span class="n">end</span><span class="p">]</span>
            <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">training_operation</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">batch_x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">batch_y</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">0.5</span><span class="p">})</span>
            
        <span class="n">validation_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">X_valid</span><span class="p">,</span> <span class="n">y_valid</span><span class="p">)</span>
        <span class="n">validation_accuracy_figure</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">validation_accuracy</span><span class="p">)</span>
        
        <span class="n">train_accuracy</span> <span class="o">=</span> <span class="n">evaluate</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">train_accuracy_figure</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">train_accuracy</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;EPOCH </span><span class="si">{}</span><span class="s2"> ...&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Train Accuracy = </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">train_accuracy</span><span class="p">))</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Validation Accuracy = </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">validation_accuracy</span><span class="p">))</span>
        <span class="nb">print</span><span class="p">()</span>
        
    <span class="n">saver</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="s1">&#39;./lenet&#39;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model saved&quot;</span><span class="p">)</span>
</pre></div>

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<pre>Training...

EPOCH 1 ...
Train Accuracy = 0.780
Validation Accuracy = 0.778

EPOCH 2 ...
Train Accuracy = 0.894
Validation Accuracy = 0.890

EPOCH 3 ...
Train Accuracy = 0.935
Validation Accuracy = 0.929

EPOCH 4 ...
Train Accuracy = 0.937
Validation Accuracy = 0.932

EPOCH 5 ...
Train Accuracy = 0.952
Validation Accuracy = 0.948

EPOCH 6 ...
Train Accuracy = 0.957
Validation Accuracy = 0.953

EPOCH 7 ...
Train Accuracy = 0.958
Validation Accuracy = 0.954

EPOCH 8 ...
Train Accuracy = 0.955
Validation Accuracy = 0.951

EPOCH 9 ...
Train Accuracy = 0.961
Validation Accuracy = 0.957

EPOCH 10 ...
Train Accuracy = 0.961
Validation Accuracy = 0.958

EPOCH 11 ...
Train Accuracy = 0.962
Validation Accuracy = 0.959

EPOCH 12 ...
Train Accuracy = 0.956
Validation Accuracy = 0.952

EPOCH 13 ...
Train Accuracy = 0.963
Validation Accuracy = 0.959

EPOCH 14 ...
Train Accuracy = 0.964
Validation Accuracy = 0.960

EPOCH 15 ...
Train Accuracy = 0.960
Validation Accuracy = 0.956

EPOCH 16 ...
Train Accuracy = 0.963
Validation Accuracy = 0.959

EPOCH 17 ...
Train Accuracy = 0.964
Validation Accuracy = 0.961

EPOCH 18 ...
Train Accuracy = 0.964
Validation Accuracy = 0.960

EPOCH 19 ...
Train Accuracy = 0.964
Validation Accuracy = 0.960

EPOCH 20 ...
Train Accuracy = 0.963
Validation Accuracy = 0.961

EPOCH 21 ...
Train Accuracy = 0.964
Validation Accuracy = 0.960

EPOCH 22 ...
Train Accuracy = 0.963
Validation Accuracy = 0.959

EPOCH 23 ...
Train Accuracy = 0.964
Validation Accuracy = 0.961

EPOCH 24 ...
Train Accuracy = 0.964
Validation Accuracy = 0.961

EPOCH 25 ...
Train Accuracy = 0.964
Validation Accuracy = 0.961

EPOCH 26 ...
Train Accuracy = 0.964
Validation Accuracy = 0.961

EPOCH 27 ...
Train Accuracy = 0.961
Validation Accuracy = 0.957

EPOCH 28 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 29 ...
Train Accuracy = 0.963
Validation Accuracy = 0.961

EPOCH 30 ...
Train Accuracy = 0.964
Validation Accuracy = 0.961

EPOCH 31 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 32 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 33 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 34 ...
Train Accuracy = 0.963
Validation Accuracy = 0.959

EPOCH 35 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 36 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 37 ...
Train Accuracy = 0.956
Validation Accuracy = 0.951

EPOCH 38 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 39 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 40 ...
Train Accuracy = 0.956
Validation Accuracy = 0.952

EPOCH 41 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 42 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 43 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 44 ...
Train Accuracy = 0.963
Validation Accuracy = 0.961

EPOCH 45 ...
Train Accuracy = 0.963
Validation Accuracy = 0.959

EPOCH 46 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 47 ...
Train Accuracy = 0.963
Validation Accuracy = 0.961

EPOCH 48 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 49 ...
Train Accuracy = 0.962
Validation Accuracy = 0.959

EPOCH 50 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 51 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 52 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 53 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 54 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 55 ...
Train Accuracy = 0.950
Validation Accuracy = 0.946

EPOCH 56 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 57 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 58 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 59 ...
Train Accuracy = 0.963
Validation Accuracy = 0.960

EPOCH 60 ...
Train Accuracy = 0.963
Validation Accuracy = 0.961

Model saved
</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">latest_checkpoint</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">))</span>
    <span class="n">test_accuracy</span> <span class="o">=</span> <span class="n">evaluate</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="nb">print</span><span class="p">(</span><span class="s2">&quot;Test Accuracy = </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">test_accuracy</span><span class="p">))</span>
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<pre>INFO:tensorflow:Restoring parameters from ./lenet
Test Accuracy = 0.956
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<h2 id="Step-3:-Test-a-Model-on-New-Images">Step 3: Test a Model on New Images<a class="anchor-link" href="#Step-3:-Test-a-Model-on-New-Images">&#182;</a></h2><p>To give yourself more insight into how your model is working, download at least five pictures of German traffic signs from the web and use your model to predict the traffic sign type.</p>
<p>You may find <code>signnames.csv</code> useful as it contains mappings from the class id (integer) to the actual sign name.</p>

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<h3 id="Load-and-Output-the-Images">Load and Output the Images<a class="anchor-link" href="#Load-and-Output-the-Images">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Load the images and plot them here.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">matplotlib.image</span> <span class="k">as</span> <span class="nn">mpimg</span>
<span class="kn">import</span> <span class="nn">cv2</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="n">my_images</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">img</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="s1">&#39;test_images/&#39;</span><span class="p">)):</span>
    <span class="n">image</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="s1">&#39;test_images/&#39;</span> <span class="o">+</span> <span class="n">img</span><span class="p">)</span>
    <span class="n">my_images</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2RGB</span><span class="p">))</span>


<span class="n">my_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">my_images</span><span class="p">)</span>

<span class="n">my_images</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">my_images</span><span class="p">)</span>
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<h3 id="Predict-the-Sign-Type-for-Each-Image">Predict the Sign Type for Each Image<a class="anchor-link" href="#Predict-the-Sign-Type-for-Each-Image">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Run the predictions here and use the model to output the prediction for each image.</span>
<span class="c1">### Make sure to pre-process the images with the same pre-processing pipeline used earlier.</span>
<span class="c1">### Feel free to use as many code cells as needed.</span>
<span class="n">my_labels</span> <span class="o">=</span> <span class="p">[</span><span class="mi">18</span><span class="p">,</span> <span class="mi">14</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">22</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="c1"># Check Test Accuracy</span>

<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">latest_checkpoint</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">))</span>
    <span class="n">my_images</span> <span class="o">=</span> <span class="n">normalize</span><span class="p">(</span><span class="n">my_images</span><span class="p">)</span>
    <span class="n">output_accuracy</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">my_images</span><span class="p">,</span> <span class="n">my_labels</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Test Accuracy = </span><span class="si">{:.3f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">output_accuracy</span><span class="p">))</span>
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<pre>INFO:tensorflow:Restoring parameters from ./lenet
Test Accuracy = 0.857
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<h3 id="Analyze-Performance">Analyze Performance<a class="anchor-link" href="#Analyze-Performance">&#182;</a></h3>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### Calculate the accuracy for these 5 new images. </span>
<span class="c1">### For example, if the model predicted 1 out of 5 signs correctly, it&#39;s 20% accurate on these new images.</span>
<span class="n">softmax_logits</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">logits</span><span class="p">)</span>
<span class="n">top_k</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">top_k</span><span class="p">(</span><span class="n">softmax_logits</span><span class="p">,</span> <span class="n">k</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>

<span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span> <span class="k">as</span> <span class="n">sess</span><span class="p">:</span>
    <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">global_variables_initializer</span><span class="p">())</span>
    <span class="n">saver</span><span class="o">.</span><span class="n">restore</span><span class="p">(</span><span class="n">sess</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">train</span><span class="o">.</span><span class="n">latest_checkpoint</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">))</span>
    <span class="n">my_softmax_logits</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">softmax_logits</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">my_images</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>
    <span class="n">my_top_k</span> <span class="o">=</span> <span class="n">sess</span><span class="o">.</span><span class="n">run</span><span class="p">(</span><span class="n">top_k</span><span class="p">,</span> <span class="n">feed_dict</span><span class="o">=</span><span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">my_images</span><span class="p">,</span> <span class="n">keep_prob</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">})</span>

    
    <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">my_images</span><span class="p">),</span><span class="mi">6</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">14</span><span class="p">))</span>
    <span class="n">fig</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="o">.</span><span class="mi">4</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">2</span><span class="p">)</span>
    <span class="n">axs</span> <span class="o">=</span> <span class="n">axs</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>

    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">image</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">my_images</span><span class="p">):</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;input&#39;</span><span class="p">)</span>
        <span class="n">guess1</span> <span class="o">=</span> <span class="n">my_top_k</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">index1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">guess1</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">index1</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;top guess: </span><span class="si">{}</span><span class="s1"> (</span><span class="si">{:.0f}</span><span class="s1">%)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">guess1</span><span class="p">,</span> <span class="mi">100</span><span class="o">*</span><span class="n">my_top_k</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
        <span class="n">guess2</span> <span class="o">=</span> <span class="n">my_top_k</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
        <span class="n">index2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">guess2</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">index2</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;2nd guess: </span><span class="si">{}</span><span class="s1"> (</span><span class="si">{:.0f}</span><span class="s1">%)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">guess1</span><span class="p">,</span> <span class="mi">100</span><span class="o">*</span><span class="n">my_top_k</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span>
        <span class="n">guess3</span> <span class="o">=</span> <span class="n">my_top_k</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
        <span class="n">index3</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">guess3</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">index3</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;3rd guess: </span><span class="si">{}</span><span class="s1"> (</span><span class="si">{:.0f}</span><span class="s1">%)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">guess1</span><span class="p">,</span> <span class="mi">100</span><span class="o">*</span><span class="n">my_top_k</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
        <span class="n">guess4</span> <span class="o">=</span> <span class="n">my_top_k</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>
        <span class="n">index4</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">guess4</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">index3</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">4</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;4rd guess: </span><span class="si">{}</span><span class="s1"> (</span><span class="si">{:.0f}</span><span class="s1">%)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">guess1</span><span class="p">,</span> <span class="mi">100</span><span class="o">*</span><span class="n">my_top_k</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">]))</span>
        <span class="n">guess5</span> <span class="o">=</span> <span class="n">my_top_k</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">4</span><span class="p">]</span>
        <span class="n">index5</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argwhere</span><span class="p">(</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">guess5</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">X_train</span><span class="p">[</span><span class="n">index5</span><span class="p">]</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
        <span class="n">axs</span><span class="p">[</span><span class="mi">6</span><span class="o">*</span><span class="n">i</span><span class="o">+</span><span class="mi">5</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;5rd guess: </span><span class="si">{}</span><span class="s1"> (</span><span class="si">{:.0f}</span><span class="s1">%)&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">guess1</span><span class="p">,</span> <span class="mi">100</span><span class="o">*</span><span class="n">my_top_k</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">4</span><span class="p">]))</span>
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<pre>INFO:tensorflow:Restoring parameters from ./lenet
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K/sPwL6wO5a4vi7QZO6gGtBLPTbPGm2bwVplIKp+j60WiOuAXDce98yyXTOXQnSyv8lyGx0O+/aBRo4f9iTBv9ytJqP0nASQAOd8lwKLfLkCeCI2n8mee5rk2ef9/5FAOC9f9h7/yMg0+HvA/hn51wPayp+23u/i5/7xSC/5O8VZ5JHO9rlGQgcnwH1jTe2Hd8iT+fcNpCW5aG2a6zmc98N4EoAd7P8bgVpMwK0PLMgl5GA9j58FI9NpsNtizl9bvt4l4F8TAGS6wvb5Fr03h8+neyW6r/fI4a57h/kick4yC/+RUscfzeAbW3Kj7R+eB/awBNd573/Iqgf3sYTltuwtNxuABHqHnXOHQO5rPywc04Tcb9XWa7nMTvgdLLU/fMUSEt3hZLjgPe+FwB4svdW7/02AC8B8Bbn3HN4343e++8DfTc8ll50PBZsA9D03n+Ux9tDIAvY6WSZ9E9PlsejOAtZghaeN3uy0ARZ1gDcA+qTAWcaUz1IAaN/a5gsHz+ytEkwgF9wzm3gVcw7QU7pAPuiOAr5UUSnqfE4SOiPF7TX91MAfsA59xzWuL0VZHr9tjpmqbZpx6cA/KJzbj2bdt/etv9OkP9ezpFPpPaR+jiAlzjnXuDIZ7PonLuB77vakS9nD9dtDqR9gnPulc65MFGbBHXk5tk0hHMuzzJ1AHJ8z9AXbgVpplc75yLn3I+DzC6PLHG5z4M0QwG3gvxAr+V7PQFkwgk+qB8F8NPOuV2OfKB+A+QPpuvnQOaeX2QN/T6QdvwvQIPGn3mORsH3/sLpnteTS8CnAbzLkQ/lLpAf51L4Z5BMnu7IZ+230Tro3wngRc65YR6UdYzkWwDMOOfe7pwrsUyvdBR1Ac65H3POjfFzhRV601Honat48jUDMuWdrTyzLM8MgPAOhQnPmeTRjhZ5ct/4Z9DH5ie43hr/wG31DH5P3w3g055Mkxp/CHJlWADJ8zpHESZuALDXUSi214C0w1905EP9IyDi5VL4FIDX8btUBlkxUuG9PwCauL2L3/+ngT6MAQ+BrGQ/wM/8G6DJfMCHAfyuc24zt8uYc+5lvJ0qu9P13zPBEYog7T1YpgV+lkDA/TmW/SDofU792PHH+GEQKTPgo6BJwXrWgr0Vnf2wCHLtCu5Q+wDcwH3iv4H8QAN0P/xL0OLlWv77MIj38AIsjU+B3GQ28LhwOk7DTaB2fBM//8vanm3Jbxe/v38F4I+cRKxZz+8bnHMvds5dwmPQDN+n6Zzb6Zx7NsugAuoPZyvLiOuRo5+u6IR78hCX/SgftwZkPVhq4nILgEGnwg2CZPkbzrkhR5aG16NTlqtABLXQFvtAkQN6QVa2vXxcARTR5cv8e5C/TUVu69eCCF1fOs0jmyy7UJZLwj8GRuDj5Q+t0SE0K3cLOqMWhAgIUyA/ybI6/tdBq62DIN9VzcrcAZoMTAH4zIV+5rNok5eByHFTAH6Fy17Ozz4N9rFra8Ml26bt2lmQ6W+cX8hfBn0IHe/fBtK+zYE+Bn/SJpfr+f4TIK3l50Cr57WQCBAhgsUuPuf9IM3cHIixrpneXwDwztO0xVdZlvrvBt5XBE1Aj4IGjv8C8P2nudYoyCexpMreBJo0z4I65FvbznkLaFEyA9JgFdr2/xRooqvb998gE/05/pvnNs/jzOz+MZC/1tlGh3gdvy8hOsRhAM9QbfRJvtbdLO/26BD/CHJFmgT5xIZ6fBwUvWIOtML/QS7/EZAf9jy3zZ9A+uk7QaSkper6rhR5vuts5ZFyvVsBXM/bz+TrLah2nwttwcf8KLfVPID/DY6aoPY/C+R/r8s+oNpmA8vn6yAf2lmQRuP16vgO+XL5O7idzyY6xHaQO8YsgP8Dmqz9TZvMj7J8fgWd0SHewjKaBfW53zud7HD6/ruJ23HTEnXdkiLT/Wr/tXy9SdAY/U8AVp1Gpr8A4M/VbwcaQyb47/3g8Uod824Av6p+D4DIuNMg3/IMl18H4I4zvJ/t0SHaGf/tY+iZIgo8GfT9meNn/zSA/6n2n+7bVQT5ju4F9eHd4AguoL68n2V5KFwTpPW+hWU/ARpL1vG+Z4BcSZZ6/htSZPlVtf/ZoD43DXqX/wpLfGv4+P8F4O3qdwEUtitEnHlLyjkfBfBK9Xsj6Js0CeAPVPkrQYvY8HuM6zYLeodvBvA8k+XjT5ZL/YVJyoqEo2DPP+O9X4q9uGLxvbSNc+6FAD7svd98zivWhXDO/R4oDNcHlvm+fwBgj/f+Q+f5Pr2gD8AO7/2+83mvboBz7vkAft57f9ZRTs7Rff8HgI3e+7ct0/0+CeAB7/2SGuSLBawVugMUCuroOb72v4AWE58/48HnCc6574DG3L+7UHVYLjhKUPQNAE/wYhE7V9f+DoCf9mRmvyAwWZ6za5+VLG0SbJPgVDyWtnHkM/oskJZkNZgR771f9lTChnMD59xLQNpCB4oBfT2AJ/qVPGA8zuHIJWUCpJ16PsjP+Wne+zsuaMUMjxnOuWeCtO+nQIS9DwPYdq4n+IbzD5PlhcXFnt3EsDxwIL/RT4L8iz6HzmxYhscXXgZi/TuQL+lrbAL8uMcakKl1BGQe/TmbAD9usRPke9oLck15hU2aHrcwWV5ArGhNsMFgMBgMBoNhZcKiQxgMBoPBYDAYVhxsEmwwGAwGg8FgWHFYVp/gVU+6PvG9WLeV4kHnekvJ/mK5CABo1CSj6VOfSHGP77jppqTsm1/h7YZKKNWk7KJ9RXHvyNZqAIDRssSXXjVI9z02KS432QFaC8SxNMfUpBAViwNDAIDCKklusneSkrrUmxIOtA8ZqsrkXFJW8HTtmpcwfMPrKSzepu2XJ2X7D9D1RtZIfPqjC8cBAEPrh5KyB//2P3W81gsG51zS0JdspyAQ2ay0X9ZRW2QiWWc1YkpmVsiIjMolOq6QleNyebqOy0uY0tk5kkdlUY6LOUZ5DEmSVsxR8wzwuwQAg309yXZ/njIznpwV+VYalKVxaFDyfcSctKZakWuXeuu0EUnZ1DTJOioMyLNn6L2MIO/27AzdY3bqUFJ28223X3BZajkavjt47y+4HAFg88YtiSyjpC9K1QbL1A+ycT0pC30yjiX8cY11I9lCPikrFClef0+/vOeZMr3fM7OSkKlZpYR0RfVpCUOyb0i/qTvVj7N8n5L02f4+ul9teiYpO3XiJNW5Jq/s4PBaAMArXvWqpOyqa68CAMxXZBz+x0/eCACYnpMs1OtXbwEA3PQ1CbO9+4E7TJYMk+W5warVa+Uhecvp/BquYyPZe6YHkOPkSBfybqiTfew7ysJxrvVA2Uwu01kviuQYrhPkr47j67iU+6UdFzc7P0Olsszv9u979LzJ0jTBBoPBYDAYDIYVB5sEGwwGg8FgMBhWHJbVHWJgqD/ZfmTPwwCAQllS2fcP0/6tm9clZV/5z68AAGbHTyVl69eS2frRRx5NyrIRq9O1KahKpqJKVswmDc4OWO4TF4OBkcFwlaRscfFgsu3ZzHTi2JGkrNxLxzZzYmqfPUwuFqVY1hYLNTK7F/rFlSLD5v7paTE9nTxJz/eSl780KXvwyEMAgIPjUpduRIbNHFFL+vNgS/GqhN0XlOWj0aDjykXlSpGjd6JS1xlqaX+k3thGo8n/pWyBzSq+UWk7k5AfJhNLvSn7F3kzX5lOyjZt2Q4AmJ8Wk0ylcj/VISdlvkl1rM2LqS8qkkkwp9w5BvrpHWtA7tsNGBsbTrZdIkfZH/GPFqeJYBJVgmxyv2vGWmau5boA4Ng9RpeFADWNhpwbNxt8PXEjSvPb0O9crkByySrXGh/qoM9J7qvuF4aPJa7d+kTA8RPjKbW5sMjm5b0UMUhb1Lkp9TOGbd22GTa/5wti0s7m2MztM1IWU9nYoLhw5fl2eWUiL/A7FEVyl7qT61SqVLHFOXEtKzTqfJyuK/3wUUaVsTlVybLJ706k6hBMur6m3qc63SObket1C0yWhItBli2DDw92WkZO/A46Tmk57nQX1+MpOgezlKEs+R5n1YAv2eaBWrPJVVbf8OTYFHmo2oqrBc5wXPt1BXHjrDI5f88wTbDBYDAYDAaDYcVhWTXBsasl2znW1mzZLJl1h0fJUX//Iw8mZb5GWtyoobS0U6RB7SmJpq1RJY2rUxyVBqt3orycO88awrGxrUnZAw9TJthVa4RAB6c0gDVa1ZYH5TquSPsPHTqZlGUzVB/fFLLCyBitrPUq+PgJ0voePCbapOueeB3tU9rmxUnSLtanz2k2wXOOLJMhMpnONVVTreojXgH2aBJchhqm2ZCyGmtLcyUpy5Zpu6oUqdEsvU+LShXMigNUYikbn5HtabYONNQ5kSMtSaYk1oESc9oymEjKjjCZY+qovMdZ0HswNrJKzu0jra9XWol8kSwBcZ9YBLoBmrgY1L165R80pForGsfhOKVJTRgfclzE145a2REt16Wy8F9pdpJray0F1P5wP62RCATXTk1DC7hQh0h3/EO/wUm1tJbCa91bl0Fp1SKWZdyUJ2IlHbKZFE25eleD9r+m+ohjMmiuIVa1uMGaJPUZCUaw+brIssJtm8nrPq60ixk6fyBSYwXLZt6rvs1t71X9m3zt48eE6HzJDhrbo4I8Ew8z8E0ZS5sNGtcbTXmmroHJEsDFIUuXpuJtO4KQQpbrNKy1IEo0qVKWnvqh01obZalNhwsy1xkcXZ1sP3iY5yLKqpoJN1I39MFUkaIx1lUJFrqs+uYE7b8+rsYa4ObyKIJNE2wwGAwGg8FgWHmwSbDBYDAYDAaDYcVhWd0hSgNCghtklXijJqr2B+6nGKr9KsZrUJ3v2rUrKevLkq367//ur5Oy/h4qqyiCUiZL7glxRkwAhV4yR1eqygxTp7Lxk+LGMDQm5Lzd+75N56o1w/xJjkE8IEQCLNL53skzBWtGQ5kyckWq1+iInDu2mghKcUXOvXLHpQCArQsb0c2os+tBJhKzSmIOaSp7DpvAhksSs3J0jOL2ZoryblT4nIoiWVVT4glG/JoUMnK9LG+Xe+TVnpmXNm2yP0VGmW6aWar/nCLGLR4jF5lcj7yLi1WqQ70idpomm/Mqyol/03oyKS04cdepVug4Fa6zK6BNdSG+aIvBP8W2FqeQO4J5LGrxfOg0iQXTWetVO90T5BrqXFUeXC0yqm8npsEWs2HnRRNPihYzpe8oS8h7rQ/aWckuQRPyDrpkrOp08MioeN5Vdg/KZxVBKbiTKKJiNWYXJfX+ZpnwND1f7zg3UvfNsmnfKa8uZdlPqKJlRYIssqtbQ7mWNTnWeqTGmXDGkWPHkrL5CsW37SuJ61HsmMBal0rcc9/tdI9Gd5FVAZNlwMUgS9fiXhbGGX0A/0sj4ra4OcSdx6XE+o1YRpognMtSO2ey6luZz/Nx8r64ohD9R4fJra++sJCU1ViusXKvqbGbi347o4RAqdzVuCxWpMrgNeNUWZwJ8f+XJ4S9aYINBoPBYDAYDCsOy6oJhtLaXH3tFQCAPQ/tScqOHKCQZ4emJHTY9l2UMebyyy9Lyn7nnb8FAMirhUKzSprZnCIFeF5dNNRxuSKFYdvzgKw2I5A2cnZOVqpja0UDuGEjZXibrRxPyuq8EIrqcvHRIdLsHjq4X45znMVHkfNG15OWeWRkNCm7/fZbAQBPu+7JSVmJV8nzFQk3042IeXVYUyrEKLAqNDEuR7Jp5JSmokTtki3IcY1ZksP4uGQJWmDta7WiVpGsO8gXROPqub0nZ0UjEGXUa87hfIZUFrlSD7XzbENWvHceINJioaA0EMjz8yrNMmdsmhoXAt2eB+8DAGSKIt+JE2ShqGCZvP3PEnFqSLOU45Q2Q4hzWgsbQqmpzhY0xq5Tu6pX30HDkVXHhZ7YbGGv6fsFTbAmAYX/nfVqqOcMl/SqzCUEjc66Ok0C6eL8eh6Zll8AoCNGxayxWVRZEB2/jzVlyQjam0ZdxR7kNujpEYtNkfuG6n5J2KWWLFNxqIsKyZWTPhnx+OErotlr8DvRbHaGXWwo4mNQOPb0SfjNEEdRKT8R+6CFGkzKevsp1ObM9L3oNpgswwUf/7JsNTh1hkML455XcShjHiF9SpA0rQkOZPSSsqQWijR3aapRtsEW1JqaDIVv6Rxk3jM1dyLZzmXpPvlCb1JWzgVcgbjgAAAgAElEQVRym/pe8zd3bk6s8Fm+X6ag32M6t+nkXVzVQy9cjyoDhwesLBMH2TTBBoPBYDAYDIYVB5sEGwwGg8FgMBhWHJbVHWJ+QczId95zBwBgZkLMyE976lMBAOOnxB3ish2XAAC+9OUvJWWNGTqnZ1hi2nmOeRg7Ue3XmGDVG4lrQ73GTvqKqZZj1X5clbLKglynXiVV/YTKWtfXw07j82JCP7lALhmVmsSRdWziaCjzwSS7e0wqt4+ZcTK/777/vqTsvrto+8RhiUWMP0TXocjuBg0vZrhGMxAflBmdXSQmF+W42YNzfJxaj3FTVZWMmPOhEwIiWJYazc776hi2mZx2oSDMVySeZIihOVMRmc8skgxjL8cVsvQeOVWHApt7SkUhHNQXqWx+TlxuCmVqo96SuGF0A1qyASWFOvtQGlzbf7UnNTCvICGGaBcJvo4mcjS570aqfpoElGGXh0xLtqPO+zXDu5FGOtF18CkkviQJZRfHBlZoKDJSjt1+IhWHNcPDvVN9LWTp0y4kIYa27lcRZ5Kan5exLdCgisrkGQguWZVFLBfcn1SGskpdjwuBgCX7q9w/6zqed/Kcqgp8TrlnQEoyZE6NVObOYHLOOTluZm6Qn02yJnYLTJZcchHIss0fomNvI/HP8h3H6aIw1hWL4rNSYPdODyEYzrCLTF25wIQxzCm9p2/7D0gsf11vPXKGIbinKPIdGyRXjKHV4jZxcoa+67XafFLGSXZRLsk4/qxLyWWwxsEOAGCe34m9C8vjBmqaYIPBYDAYDAbDisOyaoIvv/yqZPvee28BADzv+c9JynZupf17HjqYlBU51NXtd3xHLsQrj1xJVg+jfbSiePTww0lZyEqTzYqj/cQJcthvqlXS9h1EaDuwTzSzJ46IFu/Sqyir3eFDDyVli540wIWcrCMWefWeUXnfd151Oe2D3O8oa5RnZiQkF1iLeuCAPHs+Iu3i9JQ4nHcjsklOcJVtiJ3tdYKt4Ki/WFVkjmonySrAKy1HEmlNraSzrNHQGcICu6LlahnldM+hYiYXRevrmxU+RxE8WNPhlAalyaHg8nlZBfeU6N0q5IRAl2WCyuWXS1i/3GoiV+7fd6D9MbsGiWZAk+DO6gy1FUWd+zVXjn+oSHepK/Gg9coqSWZ0KJ+0k1MimiUZ71LuoUPw+JRwaKEd9OulSXLdhnJZrAx11r6lhrfS4Yi40XQfCts622MzJq3h3JyMkZVFtuLooHrcjnmltS+w3DRRp6IlwhaAomZ+ec7sWBVtZVDIa6pNiIvnWwrRUa+Ek+1lzN11OZGpbrppCt0Gk2UrHs+yPBOXVvLFtaTEBNBq6eph8luuLN+aoMyv1cRiGUSdauVT38+0EJA+5Tusx7+Q9G96Qb6LCzWaC42U5T0ZGBwBAIzPioV0vkIW/IE+KauO0Pdz31GxqK/hb0ip0VmX84HuHdENBoPBYDAYDIbzBJsEGwwGg8FgMBhWHJbVHWLL1m3J9te+8kUAwNGjR5KyNcObAADHVMzVa7aQSfmSa3YmZZMTRCJbnBIz994DbGbOaE97ciAf7l+blBzdR+4QOk7s9ktCZhTR+x8/KSS42jyVjw6tT8oWKuSiEClTUaPJmVMy4g5R5Ax1eeU24fmc4VFx4u/hVGKTJ+XZF2fIRBBHy2MW+G6RzXa+RhLfUMwmeY752GIyD+ZlHfO5GeJKqpi6vD+r2iLLsZejZifhQmfB8cqe49gOVywIWXJugU1JLcntOjP7JLwtdb2ZOZJRBmLqWz9KZqtRFYdzFceQzinCZbeihUMWPBpayjrJbT7JIpeSoU2dm8QTTsks59SB+VynTTQtcZs236U51iR11DcM8kuNQazLwj/XUdaNqKlsk3kmq+rqhixkBR1Hmc3WOkaz2FO1fEOmKDG7Vuv8zmv5BjcWJb5+Nu3XVfbIE+Nitq5xN+9XQWrzWSbWpuppdJbDzqxm4R3U7i7h1j4W17J77vkCAKDRGE+5x4WFyZLrehHI0qXEWM9okiPLTZMXM8ElQLl8Zjhe7/SCfO8aTEpsydSZEu/dJy6Lul7heH1c53bL9yCMu17qH7xcjtflGzjMriprV62S4+r0zc0WJIb07kOTAICmil98PMR2n12e7H+mCTYYDAaDwWAwrDgsqyb44IFDyfamLVsBtJKp7r5vNwBgclpWCnN1Wg1s3ima4MF+0tzu+69HkrLGLIXTOLDnwaRsZIg0y9UZWbXGHAbr8qu3J2X9/bQCu3SbZPgaPyWZ7GZOkLZv7ciWpOyRQ1TXtRtEyzwxSyvikVXrkrJ7d1N9yr2iFcwz2W/7JVKHU6dI87xu8+ak7MhB0pKv3rAB3YxeDtmi6GeYY8KbijqGBkK2MLX2Csv6Fi1s+CGFQQvbozUMgXyhctKXe0K4Fgmfd2JqUuo1QzLSyusia5QX1Ao7DuGG1Nq4wU8YCHkA4DlXfSOWc09O0fty/x4hac6FSF2xbqULjzSNbAsCscalaFxTcth736lLSDuuJasbaz2KkVhQwNp9nZmqUlfhD0NmKp3hLQlzpjUv4TF0driopS4ddQz1cp1EozRNd7cgrwi52UA50gkKE8KMFGVCvD9VxomikFVtHyfhCHV/CKokRVbNUR36+4S8k0/6sxw3vGok2V5gS0xeh9AKl9aaydbH4B9xxzOFjGIh2xidE86SsWf16o0A2gjKXQKTJRddDLJUVuBCidq0qtKrhm9NRllIe8qkAY4KQuqfZhZcQ2lNXYplTQjOaWar01vqUiK4tWbMTDY6w0Y2lXZ4cpbqWsxIeNdnP4HC3d45JSS4fQ/QPGqkR+SbG2IrbVastecTpgk2GAwGg8FgMKw42CTYYDAYDAaDwbDisKzuEFkV0/DS7RQ/d0qR4Man2Jl646akbL5BavWMyiaV5Th51z3z6UlZY5LOHRsRM/jJg6R2nzo8l5T1lYmAtmP7Grnv9N0AgNHRy5Ky3qLcb26O6jCySohxmzeQar8Ri/N279AQ3a8q91u3idwbHrrvnqSs0Etq/v5eybBy7OQJAMDLX/7KpGzrNrrHwUPiRtKVcGSny2Uk/l+mwea8hpiwQ/aivH7rEqtKZ0zDrDIP5Vn+pZy4Q3jOzJdXNpzhYXKVGV0r7xDyQoJcmCdXm1iZ67JMPIkUASRuBEKGApsPY5UyLpunemlz+yxnm3to396k7CS7ZGwYE6JAd0DqHcxnLmVvS1YhFksLXzM5IC0zm9qOwj1EtiE+c1a5Q+R9IOXoLFmqXnWqmbIMqiyBnVmPtPXO81O1mBB9IJh0ukjo0MDOp9gLuwRZ1TdCbFltQs9wC7Y8I7ev5qAWOR6p7qZzi/MdZTFfPKuyYY7k6dq9NXFpi0OmUJUFbaxPxr4a+ybNqcx8Ve5regwQG7qKHc3/C0q+Eb8bWdWhQx936h07fpxN53FKYNoLDJMlH3YRyLJHuUMUOZZutSEksia3VT4nzxMV6Js1V1Fx6nlTy7zEMevLJTl3tkLXq6nMtcn4p93aUhNhar1op1timt5UYhCrTIU8fh+fFvLliSOHAQCvfuFLk7IPHyd3idk5FYiA4/b39i3PWGuaYIPBYDAYDAbDisOyaoJvvfmmZHvXZVcDAFaNicau0EurueKgOERH7Nk/PiHa1RwvYW6//46kbOYIaVIvX3t1UnZ4lrKv1RdkJXvlNeRAf/jgfUnZl/7PvwEAnv0M0VpeedWOZPvb374fALA4pzKd5clx3UWyUlu1gbR8Bw4dT8q27bgUAFAuCzHu+KF9AIATxyQr3cZNWwAAUzMS9qXE4Wge3icEq25EyFWu36ZGM6zg1TorDmUqzBnH34mVZqGWbMpKMMfq44wiSDRiJmH0CnGjWqR35+FDjyZlPaoKfcN07KyyQOQc1T8uiBZhnjXFWqsS0te3rIv5mSLX2ZUaKovPseNHAQCLs8uTD/2s4Ts1n0jRkGriWBBpNiU8WfqWwLX9B4QQkleFeQ7jpAVQVlrhGmt8dPi7OIRp8y3q4Y77te0CoKL2pZD4ujgqWgs0aa/Z7KQeuZjGt0hnRmQLS1bLnPtiXYceDGGLFP01tF8WooUqs7hKNSGj+ipZ5EpOjsvNS19r9pD1rhGJ9rPaDGG31JibbHVmD5ubFE1SXCVNUtyQ70guaBKb0ifjBt/Di8asW2Cy5L0XgSwjNW41WPNdKkqbBQN5qSRzhPANrNVUm/mQwU+u199DcsjnxQqby1ObTs5JK1crIYOr/np1BpX02jLYsVesfy1Z5FiX6lpzdQIA6up23zlAMird+pWk7PLtFFjg0BGVwZXH/vKAEeMMBoPBYDAYDIbzApsEGwwGg8FgMBhWHJbVHeLUicPJ9sMRuROMjIqrwmyV1OXb+rYmZUeOkMuASkaCMtt7xtaMJWWjZcq+NnlAxRJlD4rVI5KZbcelRIh75BGJAxx4ToePStl/f8aVyXaxTCaHqUlxVVi3g8w+R+bmk7L+foozvGajNOstt98KALhsu2TL62HXiEC4AwCwCeDA/gNJUcRZs4YGBtHNqHCcw2ZdSII+iUEp5uyw4lL8uSTjUUWFzw2muVJO2rGfM+pFVWmzkIlodJ2QzeYz5EJy6OADSdnqPjGrDI1STEu/KHWtztGL0l8UAl2IoblQUTLiR6nHmlSX5/+KuMHVjhvq2fmU6QV5h7oBUdRJJNGm2IQEl9FuE/w/LYVbCvT1gstCXqWh6ikwMU7HUGZXl6xTZkNlqi1yvXV85tDzW+IEI2SyS6upjieckjHucYamMmXG3Nty2nnHpRFdODarMpOGEKax8hdp8najhRhIMsjkxR2pmaWT4x4Zh9evJ4Kv99IPDx4Ut6CJeR4DsooYySQhp0zoMb+MugYLTLw9NC/j8Eyd+t2AymoWCI0+lno1G2Tab8ZiVu8WmCwJF4Msx8bk+z29QPVsqI+gy9A3JsqKO0QlzA1ShqOGaotalcnm6luZ47F1qE/Gzgm+UHUxzV1E+zakjPOKkpzKf/ad8Z2RctwMf3If3C3uoldcTu6wuxdF5kV2Czl8dHlcB00TbDAYDAaDwWBYcVhWTXBerTiOHCCNZ70q8/ChjaSlqyzKas5FtGKqz6swLewMPtAr2VTyOQrT8tDXJBRZb4E0wCOSCA6Dg0G1JVq6EC1raFRWYohklblqDYU+27PvaFI2cYoc/6+45glJ2dVPou1b7xPC3qlJ0m4fOLA/KSuGEE1yN1QX6fn27hdC1zBXfLbbyFRtCNrSluAqSfwVlQ+dCQLzSrvaDKwAdXIgzvWovOkFDosTx0KQHBkmGWzYsSUp23+CV6V1pZlVmZHWr6Zsfjkvsj60n7L6NVXYtFKR3rFYEUoqvAJvqtgyjbC/Ja4YPYzWskauM8xRV0Av309DBHOaCRHyx6v9nXQKrZHV57JWKyvEmSILv9mQft8/wGGXlAlIZ0Aq8tBVUSH4qmxNaXqteQ4ZlVrYQillnRrjNFJgWlK9bkGxd2OyXZ3fx1simYhV97HqazkXNHKKoBSFsH9Ke5iohVR4QP56lHrlguWeAbpGVllneoj8UuwZSsp6s1PJdnWCwiRFVbGSRI7G/Vpdrh20o9mGCl/JfXJSadYWQNvzKlNkFIhTsWSr8r4RNtBtMFkSLgZZViJ5ngrXuVBU5lAey2oq41pTK/gZYQjWQ/H0Ao9/alzq5RCsBZUWdbiPvncTKiRdtcrjbecnAC07lMkvkCp1KzcT8nHK4Kiz//H/R2dlTM8eJRLkliHRltfYYrAII8YZDAaDwWAwGAznBTYJNhgMBoPBYDCsOCyrO8TWjWLimZohPXn/8IDavwUAcHJmPCkLzu99ZXHYf/D+OwEA11/7pKRs8gS5EwyUxXQzyGaBy3YK4WlkhEzsg0r9HrHJJdLkJuUZsWEbkeAePSIO3TPs5uAaQgA4vJ/2b916SVK26yrKjPfPH/toUnZg734AgPJlx1Ouvw4AMDsv5v4tG8l0f/vN4l7RlYhTTFBJQNhMR5m29NTYPyCvTEYlJj7294sQImbOFVX2nQ0byKl+gF0cACCaJXNcQd23R9l4hgfIhabUJ2TJhQrFDJ44JvLNMAkrm5V6ZTPk2R81pQ4Rmxs1yaTG7jw6s0+eXUEyy9rjzgyfxrzw2qWh9T8gJrOWiJOBHKFpZ4k7gRwX4kKr5JFosEvDUL+4N61eTa5AC5xFEgAmx8UtqMDt3qs6Ub1K71JFmfyCia4ldjA/TdRR0u4iEdxDOuMOdyMq89IWPhAGVUNn8ymxrBudrllZJlZlVFvkuA1yRSlbO0jjajYjLkNT49T/coOXJ2Ujg9fQPZz0pYm525PtaPoIAGBNr8o4xR3lmHi7JJ1Hx8bNN+ickYyYTu+/YzdtbBZ3qmIvk7wgbnXBlcl3oY+LyZJwMcgSWZFbI6S4bEjdyzy3mZhvSWvZ+h+AS0ZcFWWZx6iZBUUqjjjjWlnascjj5PCAtOPENF28VtUEfVXvpC1VhjomSEd5uXbIkNoSgzglyHDYbKqRd4LZcrPCU0ejQq4vQ73mDmEwGAwGg8FgMJwXLKteqlySHOObNm8AAKzbvjMpO8h5pDesFo1xgTVxjzwkGd7WrSbN7J133JuUFedIAzzStzkp2zZG2r7rny6awoXaQwCAjMo7Xue1QLFXNMZzC6KBGhimevepDCZz07TqObxPtIePzlD9V18moduueyplsNuxQ7TD449SJrsxpQXPcRiZDWvWJGUH9xIhwuv4YV2IPBPZtGYwaOCabaVA68ormw2Z4GTJWO7t5TLR5jomWowOiAZi/VYKpZfpF61usUzaQqdC9GSUBqVYpLsPDYqMavP0DtamJExLyLueLwqBa4bJFxW1co643i7WGogUVWmmMyRQN6BFcxt3ksOSBPMq818IWxarlX/QSGiNa3gHWjTiLNPIaZmQxr+nX6wz+SJr7FfJmFGtSvjAhVmymPSod6SSYTKjV9kH40BmVPUKMtNZ8AIpUD1naI9WjXG3SVCwUZJvYvfdnHWrKe+v96wFasqwX85RfyoqjXrWhQxQ6gUO2R5VUXmBrSALkn1xbJD64vbLZbwrj9E9fONkUta/Rdpx7yzJa2FSiE7ZcujTMubWOARjrEJjZVkeD9/+9aQsZOj6zleln8bg0FSK/Jpsus4wgRcaJkvCxSDLdRmVXY+z4U6q7LMh22ijoUnAaaEdO8etwBVuKFnOzHE2QVXWy3ObggpNOTRI79Ck8BpbM9SlPEsYC3vLaurI15ydFe12GrPPp2i3Axm9oTLBzlc4hF9DqYfPI0wTbDAYDAaDwWBYcbBJsMFgMBgMBoNhxWFZ3SG0ev5LX/w8AOBNb7s6KZsYJ7PPNVcK4W3tKLk57Ni2Pil75BFyg5g8IkSZ6UlSxe/cJsS4a67dBQBoNg4lZYsVNnlHev5PZqaQyQ0AnGqZPB+7abO4adz6bXLYz6kscmXO7Farionn4YcfBgAM9Ivrw1p25zh18lhStns3uXvsOyhxghemyOzTVxRzfzciYpJGaxIZMmlk1DorRqdpPcfmrr6SmIwGeogo4BfEvFJyZDbZsEVkMLyB3omFnMpOVCBZuoKUISNmpgyTFAYU6W7btisAAOOHxcR3/AC5zUTK5B8y2NWUGS5x41Amngy7eNSUSajRTGEKdAF8S0zdzjKXQjRpxp0ZguLElUK7HZBs8yomcDDZZrwyuyWuMCITH9NxTmWAGuoV14gmZ2qsq/YshnZXMmuyvTB2nc/UGv93aQIdlKtO3G1xnhWet0VchXpmaby542Exb0c56lfaLS0bCEqKsRlciTKqzZqcmapeF5P2cc66WCiKLDdsJne0KS997uG9ROwd6JfxbtOovBObrt4OAHjkDnF3qcVMal4t436zTO9TTQ3dQdKZlhi6IQ60POfMJNVxsE/uu/tBIlhPzSp7cJfAZMnPeRHI8uSszBFyRXJBGBuQMWWuTrKOmzJvSI3VLincBCkuBiFR6dSCXC8Gybdfkc2K7Bkx1C9lkzPighBcI1o0pXzvrPqG9zDBcnFR3rtGM2S8U+5lLNcWN7o4xI2XW/QWuD7N5RlsTRNsMBgMBoPBYFhxWFZN8LdvuTnZ3nLZlQCAm267NSmbZ0foQzMPJ2Xf2f012liQlfF/2/ksAMDdp5QTf45WD1tXy/1Wr6YV2KNH9ydlNc5FXs4rrS872veosC6uLqHKfJNW0xsHNyRle3vp3tNKE7ztKiK1bR5TmlsO6XWwLhqtvceJGIdYVt37Dp6gwxtCztu4mjLoHTki9e9KpCxQXVheuU4tWkNpAcNir7ck8uhlzXJlWlalg/3ULuu2CEkjyxrjjMrgVirR8jabFy1BJpL7hTz2OUWcW7WWtAy7rro2KVuYIPnOqnB9xSy9Y/WidJtavc7PJKvWoAVvWfFyM+Rcl607nSaChWcQtUIm6qxvovX1rbQNoEVpCsfhdApKKx8y/7manDsUrCSxaP4PHThM91gQa89gj8i0t0zb81Vp4zLXtaazyDVSCBohs1Zaxjivjw/ZtqQkLRpgt+DY14VQND/HYR+Vdj3iZ6zXpX0SGaqmCNYbpzKKIUt9KG7IuJjvJ1n2rRHrzEkOVbX33luSsgpbX664TDSBq0pCYO5fR/JfPSVhMA8//ADddv5EUjY2SufMqEySoWdri00YVLzSQg0UafyYHxci82APtdHUbPcRj02WjItAlrMqK14pyEjNBzzy7acownJnBk7nU/TEKUUNNVbNhCy8ajgPY2gxp0OIilZ4ijO7acu21Edk5JicnEbYg+8cf+sqg2vg4anIp4gyIQSeaYINBoPBYDAYDIbzApsEGwwGg8FgMBhWHJbVHWJg9dpk+ynXPwUAMDEn8QTXjpK7wT33SPzf1aPk39A3KDFEv/71rwAAHj34SFL2NCbTrV0nxz205x4AQL4oZtZKZYb/S0zYEOfWK1tnXnlqV5tklh9eJWaL0VVkmlo4JvGEH91HpLaBNWK2qmbo+SanhHTVO0KmIhUiEX6e7r15y9akbOoUu0hkuyzNWDvYxKNN4YGI1BKHNphSFGEsW6BnK+XFDaQ2QybwIqSBxtZTrOehdSqAZo5Mafm6mMB6eqjtcwUxs8GL6SkKcSR1FjMW6/qtW5KyE0codvDeu8UUmGU3jpoKRVnhLGUN9e40Gp1uBVnOgpfJdBcxTrtsJIQxtd+nZEhL9wjofAfK7AZRUHGaGzXqSwP9o0nZ4CqK2Tw/fjApq8ySqXNoRI7rG5XtxgLJdOGEuKuUuP6NvAhoge1tzRaXhjQSXCBtaAJdaButK+gu+WlMr5bY14ueXLLiipCCg8tOU5MXg4lVXSebZbKKE3PkAHu0uKz0tZk5GktLWSH99gyQa1HvtJiqG7M0BrqKuDKV8tuSbb9A8hrqFdey2d69tC+W70OlyvFh1UvWDDGrlSk29L+mijNaYZc31ytjaeVkeJbu83ExWfL9LgJZiqMHEHn62CwoS38w/7cgkJRTM+Cd6RnTyMz0f2ZefQvZ/a2syHIFNdcIhLlJ8UiDb2jXiPb7afeyzpqE779X7hw+xV2imKc2aqa4iZwPmCbYYDAYDAaDwbDiYJNgg8FgMBgMBsOKw7La2Z/+tBuS7YlTZC6ZUHH91m4kxunG1ZLmeO0q2i7FwjZ95BSZVzLKXF4ukmp/elZMRrPztF2oigkgZpN2f5+4LOQzZIbJR8I+d8pUWiySyr7aELPQrivJtWPfwT1J2dwpUvPvuVdi/VZykwCAdZeIKXe4n8xMV18m8ZCbi3Tu4X1iEi5yJINsl7tDNFKDp4YICSriBpcF1wAA6C2RySWn1mP1KrmqDI70J2UbOO10rn8oKaslKTJVNIPQZkWJSOBiMY+HNMcFFfUgeCj0DEosys2X7gAATBzen5SdOk4RC8oqssQsyyZWKS8D8sr1IbxjUap5q0vgQ/zfzli/2q7lkvbWri4h9q68q3keXrINeT9KHCdzZJWYe4vcF/tyUpaZp3HBK1Ndbkhk39dHdZifE7PrAqe9LkZyTg9Xp6nY2LWU5xRTnSrj7dYgEt1obiXsiaT91u+ieK2Hb5J4rjFHo9ERLhost2xWTI9RhsbBbCRlOX6/G7MSkWe0h/rn9o1iDp/g65WKp+QmHF1nqCguSlllnh/s5zjt/bL/RJbco06p6CyOI4VUVX+vcpzRpnKxak3VToiDy5s6NwQoyXZhql2TJd/uIpBlMS9jYl9EbdUzJN+aSQ7S4bSrHE9tWr8WZxdr/nR71VCcxBHWLTygYgYX+T0a7JOyOnuWpgQNSq2EHi5D2mddwRAtKZdV7pDNQXQceB5hmmCDwWAwGAwGw4rDsqoYb73ptmR7cJS0Ohu2Suzd++6+CwDQ1y8awO3rSAP4jS98MymbOk4r06IiwPSyonhq5khSNsKZbBbmRVsUEplkIVrfHGilk41VbFkv2r6YHdsbddFaD42QJnjNqGinDh1hx/9FWTkdPnQUALB2s2iyX/j85wIAjh8WUt1ddxIZcG5K7jEyQqvFuWk5rhsRfNubLYv2zjiHWV7B9yq5jXDmtowiTWQiEtLoWiFS9o9S/Eqtc07CYqrVZh8TGi/ZKpqUQkXWevkCa0RU3MmILQBZVa91W4iAN37pNUnZ9CRpTuKqkDT6OGawfvSQaaeYlesF7anvsjjBLcSERPOZEu+xhYwRSHDyLCEzW6Sy9xVZ+11UK/q+XpJ3b5+s/AucLbBZEW36GJNHXZ8QdYo9ol0K1e5RMbkXmCxXqygrDmvtF5uiCa7HndqlRMfiWlTeHWUpPI6uwZSXsSgzR+9qqaBjWtNTNpU8It7Oq3e1yLqRjOLAHJ+gd75cEAvapVdQ3yiUREb1/ZQpbE1DrlcvcnYz3XmrQkx++FSwnKlMkpsuBQBU7r5drnOMxtIRNS4US/StmFJZHOOkr8lz5jiWab0ulXDhPTqldywAACAASURBVMh0V58ETJYBF4MsB4dkrBtiC2VVzS9medxTYkMjkIl1TOAU4nKaxrXtcNqfUq8GX3t2Udo7q7Kr9vRShQrKcpvrLbTcF0Dy8XOapBm+DTo+dQpZLp9YUqU96jHHko+WR6vffW+MwWAwGAwGg8FwnmGTYIPBYDAYDAbDisOyukM0VB6/qUky8Z+alFSKl125CwBwYI+Qw07tI/eATEUc+ysz5EleUur3EmvpM5GYqufm6DgduzSfYbW7MgX94EtfAQAY6hV3iKlpceLPsTWjf0DqMDdL+3fuECLB+FEi7FXHxbywfngLAGD3nQ8mZceOkcuGr4p5t8Bm2//2gucnZf/0qY/T/XPLEy/vu0UwEbcQqhJvCBV7mQUxUBbXkEJEclicEvLFKm7nddskHWeezeL1pjaV8b1UXXrZhLJxnZjZaotijgK7PsQtpptg9xbTTbFM8th21aVJ2YnDlM77xN69ci4TTjK9co9ZjkGdUayVmMmDzbi70no6nTY5EMZaj+g8ByHOruzLcDrkkkqRnOfdhYz0tf4BclPp6Zf2cpzWuloVt58Mx+ZuiKUVuV5xfYjYdFoclLL+eTLlxafE/SnPZtLFptS1WqOypqp/8i6lkuBSiIJdiOe9+mXJ9pc/+hEAwKpRaZ9Dx2is1W5LmeACo/ppwXMq8AVJq4s8yWjzlU9IinqZwDxxbL9c7yT1kY05ec/rZZJ1HiKXPff/V7J9xx4yuy/0ynvy1Cup323auVrqfx+5jFWOSrUGN9JYsuDkvQtOU7GyL2d4XGi1sIY6dp+Pi8ky4PEvy0hNs+YXqJ6TNalnjd0hinlFDq/RQKQ551HnUCzjVYrrg3aRCPGiW87l/xl14UpdtR+nWi6XlJuo6yQlBnam/m4k1VK3DR4ryrsCNd4/MyHf/3qI+79MroPdO6IbDAaDwWAwGAznCcubMU6FtxqfoJXs9z3r+qRs4yYiye3cuDMpKzvS+HzhX/5DLsSkgPWb1yRFpR7ONhOLJjgsJJoqy4njLCSHj8oS9HOf/U8AwItf/MykbPMWyUxWqdOqd0FlmStxSJmRUVnx9pRpxTRxQlbJm9bQM7uMaLnmJ0m7rSOfBVLPZ//9M0lZs9nkOnf3WiXUTmcvCpm38mrVV+bVaJ8iODnW7OZVY6zdRpqDgY2iCY5Zg+9jcdwP3LaKWjEem6bQOzfdcnNS1liQti89hTIV9vePJGWZZGmq47nQdr8KZbP1iqsBALMTEloozxnQBhXR6yhfp6LCpvkG36O7FMGtGePSDvCdq/yYyzJKu9DH1openTWQ1RilYcni2MsZ4DJK6+G4UYol0QDNT1FZRmmUqir8WjZizXO/XLs+Rf1ubmIyKQtX7CsIWbXSoDg/FZVGLk5CvKl6uc5nT8tw1C34xdf8ULJ9xyc/AgAYGZD3/Mhxem8bStIh3J/+EORZ+5b1QlbtLZF8V6lMjHkm1KwblrYd3nQVACByMg5PsqZr7xHRRj64R6wp06eozzZPSX+ZL1HZ9g0SLrM5SprCqUkZzyuLvF2SOoShJJNVMmcNV5T1Hce10m27AyZLwsUgy8V5ab9TMzT2LNZknMmyxHIqlFqwOPmGDhfH+9SIFLS9eowKnLqcCrnWX8533CMogFu5wJ1zDRXlFJnkOFWYnKJDTrJVUYkjENgLRVHhh0yrNZURMMuE6lTV93lAd8+uDAaDwWAwGAyG8wCbBBsMBoPBYDAYVhyW1R1idkZMKE++jrKlaVPBHbeQg33WK8JYk8zM1Vk5LjhnX7JduyzQtbPZhiqjczIZlUGHzQz3PHB3UhYzMefRYweSsvUqfnHMpld9nUqdXCNcTsw02y8hMtadd0vGuNkJOq5WkvXG6GoyCR85JhmAjh4l9xBfFfNHLs/3a3afiUcjyyaWjDKtN3hbZ2brK9LzlAvSjvEctc9Qnzjfr19Lci31qBiwbBaKlQk7EzLQKXtNkc3ZZfVmLyoXCtTIHBXXpSxkX0KkCRl07YIiZGzcRCTIo+seTsqOP3gP3VdlqFs7RuSvfRMnO+/RbWhJ6ZMShzI1Q1qIJ6xigQY5V8W8WWTy28gqcYMqlFgwLeEv2ZUpUuTCmPpzAVLW9CKLMpPaSorgOjy2CgCwoEiWs+Nkii1lxK2lnKN3qe4Xk7LYp7jEhLiWZ4i32S1YrRp1IEvtV52XsSPiGKo1LzIKZOWCGsfygUylMm0O9lA7D0dyvQK7no1PyLjuMuSeMq9IoTNzTAZakD4wtFpc2YaHSW71cekv5Vk6duqQxE3PO6qja8q3oFYht4CKk29GLbyfGXFfi3js1kNpJh/eiVl0G0yWXHYRyPLklIwzIRS6V65dTR5HC2p0CSS5OeUOkeqJ5To2EtK35rjNL1I7D2Tk3Yhy4Zur/R3kpCgJQqwzg6YgiROskHJg8HLQcawrNXovvQqaEHwoI3OHMBgMBoPBYDAYzg+WVRO8a9flyXaDk1ifOHYoKZtiQsu60S1J2anjRGrSK4X1a9YDaA0dlo3YWT7SmiN+vIasKDJ5WmXuuPzKpOzISVqhbt25S+oyJ5rC/h4KzzWnVr8xr8QLOWnCkbW0ws4/IKS72XFagY1tF1LD3AztX5iXVbdnclBZZcurqwxa3QzPWt+MytaTY6ZCb15kNDxIGvC6ylATNWglOKYyf40MU3vH80LmqFZpNd1U2YnyrKLT+dB7YtJ8XLPtMimsbU02y0ywmD4lmopFDgWTzYg2N2LNbVatb12NVrBrh0SWs3y9oPEHgHyeSB/9BWmPyQpnHewyXWKLljM1BE+ndjhynZmpAlmjWJL+NzBAGuBSWYXYyXYGtotZk39C9a9DDXp/ohnRYK3rEUH3pGhAXA/de2DVqqSsMkX9r1YTjVmZ611pqIx3bGGIU8QTqbK4C0MwBWSVPF7wA08HAHz8H76SlPVzWs3snIR/XF2i8bBfMTbnJ0mT3lyU/tdgJd63bv5GUhZINLrJmlzWUFqcKhMkQyg8ACioNI9Ffp9E/wdUWXt4fFZIqDW2MixWZNzMc4arobyQrqZY1ONVGT+L/OyRtgomWUOX9TN4VjBZEi4GWc5XZMwIBL8oI/X0TXrIZlNrgjmznDIg1vhHayiytCxyruO4MJVoTiuLemI1V7rQSGuCqVyTp5ND9XeDh2WV6E9CpKk6lAr0TjfVGBrGZa2NDdbeuLk81lPTBBsMBoPBYDAYVhxsEmwwGAwGg8FgWHFY3oxxXlTxBx7eAwCYVSaS7ZsvAQDMnRITSYNN0AUnDt2D7J4wfkJM0Fl2nM/mxAgQsfN5i4nTkYq9kJeMYk964rPouIaY5Ct1aZpilu4XXBsAIJMnG8BUTRzxizkiCORUnMP5GVb3N4XAU2cLb1WRFXbu3AEAOLhHXCmCuVnH0OtGhJiAsTJ+FHl5NaAYamWOT1mfFNNckM2JU9KO3/z2LbQRaSIi28UUoS3DprmGEnCVzUw6k5KvSTs32F2iqbkATHwsqvrnmYWgsy+hGXEVpA5VtjPVNEtjllwterLiGjDjAnFDSBLdAK+eOXnUlriRnYU5dhvpzYn7SJajWBbzYgjt7SF3iIJyiQlGXq9TIfG1Y0VwjDkdVFab55QsosQsq+rPciyUR6UOfRQ7eHFcYgeHEMV5ZZKssOmtqccKvl+zhTzYXe4sGtmsyOOVr30zAODG/y0E4AGOh12sintKP7uM+SnpfxluBCHOACGJU6wCf/saFeYU+dVz3Gw96OaKIX6tmDcHlO20r0zjpbIaY6FK16mq45LY4xlxFYgW6T2oHRaScczEsIaSbz1LY0CkiD9yle6TqcmSj7sIZKljHCfkeqdM/RkmsqmPVtSkZ+wtitxm2C2hUdfBd/lfymP7lO2aHnbPmbvB0tnoSsolMJsnKS0q15ywN1IxjSN+J5rx8riemSbYYDAYDAaDwbDisKya4EpdVgDf94ynAQDuvvOOpKzKWdOyKlRVdZE0bVFTyh55iEKZRdBhrljjqlgs+TxrX5sy12+wxqqutKuOV2L33ikr0HWrRVOciYh8cOrEiaSs5klbncnK0srHpN2OfZ9cm1fnk0r7mR2ileHwoBCs+tjZP5eT8E61Gmm3o4yQeroRfbzaq7aoEJk42Cer4DqH4amr96DIWuTpk6L9D9stK1nWBGhH+yyvnLWjfYVPqjmlG1Ar8RprNyIVHijoKeUooMyvjO4gIVpNUxPCmJyZVeHCKk16vrgs8uWFPXymu7T6sdJyhpBnLiVMGNRKvcSkjaImibGGPqMyNjleyVdmRTPl5js1y2GrN5Z2XcsZ3opF0SwXFbktZIdTUdOE3KOVLHmWqtIoh/GlqEL1LfC7VFMsS1FMaxZI92maBDIeFkDv3uoeyag3s0gWinpWtEuLMbVBqSwNmQ3kYkV0XWT14aKSUf8QySYXqSxUIculypZY54ydsXo3FlSbVnnMjvV7xyTHjGZuMllVa8yCUkxGFGCcw0EtKM1jjTOm5fPSy1s0al0HkyVwccjSKRnBsxZbZ1fjdqmocGhh7OxResp+Di85o9q7yeNV6qiUVug6t3XotZbMc48xQpk+vsDmtmJR3qdauJG26PH/jB5i0Xnc+YRpgg0Gg8FgMBgMKw42CTYYDAaDwWAwrDgsqztEf5+QwzZspoxsjYoQhWrzZGr5r68LAaCvQGS1Woq5Q8evyzJJR8eqgydzSb2uHPf7KA5vyDAHAPOLFIu4v2csKRseFneI2WnaPz8v5thedl+ox1L/Kse8zeXlObMF0vPPzEs8x/XraH/Vi5nk0f1HAAAZRQBIsoxlutkECwyzKW1SWfqDBW12XrmsRLTdV1Lxnct0rlcxn8VqoogCiale3GIyTIzI6jCHbEKRqwHIqNjL1UD2mEnKgvk/nxWbTC/H3CznVMTLTGf8xdiTiXJqVupVz5EsT83JPdBgEucyxT48WzhlokyeyytyDDMuMoowk+H3MVYuRRHLan5uOimrc8bGSJliM25psoMmsIbevuAky1Sk6hoyP0bKJSO4Q2S0uXCB2r3RVJm12ASby0v/K3Bc64oyz3p+l1p6n3uMNsLlxHfEtaxw7RMAAC979g1J2T9+8h8AAFn1njfZtWVSmWIbTDrUbkZzPBY11Jib4/jacVVl3mPJlYtiqi6xrDRZVUdArwQCliKXJuZiNQZ4fkGayhQfqt1IMelmNOmKyawZJ8b24P4TN7owHrvJko7j/49rWaqsfmGMc4rJVue5TVO5YoVv0rwiggf3s+AWAQALVdquqTlOama5tOyXvm0n2gh2gXSHlG+EQvgOl7NSryx/gGtKHjFPzvQI6tv+6/1umcZa0wQbDAaDwWAwGFYcllUTfPfd9yXbw4OkQZubEm3ZkUdJg7Z164akrDZLq6Oe0d6krMnO+QVFbAmrVq8UxqvGKGTZ7KysGFeNUtimWlNWvEePHgQAjAyK9nfdegmzdPQwrZL7BjdLHVij5xQxLuRxX6zqDCvUxI2crGTn6kRqqC1I6K6Ijyur8GpZDqMyvdBdZKp2zDJ5odGQZ4xjWl/Nzknbl/vp2fqHh5KyaIDeg4oKJxbCjbVmjGHtndIYBxVwVmspeU3ZjFWGsIJo5nOsUXBYLedwCC9N5igwsS6f1VkJ+Xi1dGw2SMs8c1Tep2MHHwYAzM6qjIC8ynddlnFMZ/mLU5blPtnnO4+LRN4Zbq9YaYCqwcqjyXdplQgaV59y43TVhVzHdZ7TGkqNwwqpdveBwaGsS1k+LqvODZs6RJrvwhBMAR/5zm3J9uuufyoA4Ade84qk7F+/8BkAwExNQktOsaZcRyMK25EmHYbQTiq0XZ3DarlY+mngVdVqysJXC+Qd+dw0VFCr0KH0u9Hkgdy1aAWDJUZl20oOUBYBrkSs492FD4MiSDZYM9Uarq87YLLkul4EsszmlCaVq+wUkToObaomL47HU51FruJJq1pWxsk+Jp5VlRa2UmPCuFKpJ2N2S/jLTvgUwrIegoNFUCXKRYE/jOqRUGEScxxr7Tb/j/U9uGLq3QjXySgC+vmEaYINBoPBYDAYDCsONgk2GAwGg8FgMKw4LKs7RG9J4hzecetdAIDqnJiMN69bBwDoKwlhZXyWYvM2IObtJsiN4NQpialbKHDWL+XEf3KOskRVF8QkP+g3AgAmpuTck6cepWuUpC67H3kg2Z6ZJnJOQ4XrzbL5oaaYeMVeuvcp9UyZPB134ojEGG46upBviZdHJiqn7tFskpuBi4V01Y0Y7Cf3hpkZcdsIcR2z2g7Hj5tV8t2ygeQxum5LUlYYoAx92t0lEAkyKvNRxDLQhLZg6tZOBwVFqsgzuS1SbLqYzYNaHiF7XDYjbR/Mddrktlih65TvP5iU7R1/CAAwdVxkHl4eTWTpBuhn9glro/O4WJnW5jl2d0PZybK8ndeZf/hyOgZkyALUQnLjbZ35L9hxI+Xqos1yYTtKo1kou1wg5TWUzBpM/PMq7nA1uOCkuENoF4hu5sV96u79yXYwnLtIxsPiAJORT+1JymYWaWzLFsXdLMRm1q47wc0om1efDO5ri3PS72ucQTGr+lc4w6kY7prM2uTsYy2OQolpWM4JbimNpjbzcmZHJfMsjxW6rzm+oVe+AmGr2YVCNVnytS8CWWb0mB+HbGiqLXjsibwe68I5mhzO3zbl8RHyHeSUO0Qvx+j1efnu1UOM+1iP91wYt/g7qPvRdstrEnartg8ZbRsN7bLCLm4t8X/pR2uo4vC9Vt/jUMd4efIjmCbYYDAYDAaDwbDisKya4GuvekKyffutNwMAysrLe+8e0qCN9EnGta3rSVO4MC0EunyeViGXX3llUuZZO+xSnmjnJU9Ktq97wlMAAHfecntStvt+0jyu3yYh0mo1tdJZCKst0ULOLpC2N69IVw88QqvyiXnJPHf8FIVG23LptqRsaoYIDAszQoybnqDny6h1SXBCdw2VcaYLsTjDGdKanV73GbVCDY7uObW0HF1FIfC2bBMyZO8QZUjKKk1wI4SAcfp6HK5H1aXBS8+M0kDnlTYxLI69zmyWofbVWolAEIlUBqWgTfEQ7b8r0QXLYxKGbWiULBoTBw8kZTUOFxY3lsfZ/2zRmjGO/2tyRNCsq+MqLIuWdTofp4llgbCoCY5B86w1wYk2oIXjRj+cjsXUol/i/bokaB/UhVxCwNEa3hSNRNBMaQJgqKOqVxcqmhI8fNf+ZHv3PfT/8h0yPl39vB8GAHz54AeTslyBxrEeFYopctQfCqqfhixOTaUBCu9Bi4gcWU4ilT6q1uSsn+q4rNJSZUOmPxXbybFGKquk1Aja+pzSGoVzocEaRdXvg/oso+rV5Co0Wk/uCpgspRR4fMuyWVHaXASrl9LMh2+aMvgG65kmm4XhT4eBdUxK1GNnaGZtPy7kw3EqIyYLO1JzDqWMTrS4mnvc4LFcE/ZCHfIqlGsQjctoAiV/r/UHO9EsqzL+vi4XCdk0wQaDwWAwGAyGFQebBBsMBoPBYDAYVhyW1R3iyJEjyfbIGMXhrc1LRqiBAXLoj5Tn95HD+wEAl23fkZRNTlCc3WJRzOWJaScSI23w3S8rU9DcON1vfkIIAMWY7jszPqnOlWuXmWgwNS37g43AqzjBBc5aV6vItQeHe7jOQpIq9Y5wpaVep07S/pwi9jVrdI+e0gC6Gc3ErKLMYry8cpG0TybPJA2nsgllySUkkB0JHGPQy/USM7pyRQiEgsWmrOXm5um4Yl5MZf1lMQwFqboWwh6b3KCLOLZlttWNn+6r4lOyKStSbh/lPpJhT1lk6TlzTr0lpeGFR9Ri3w/tqF0H0kxS3A6aMJbEZxbZhm2fEt9Xk1rEZSGF+daSzUiTOjqvnVbn1GsjvEvtT5SeuUgj6mJ/iExdslLuvuvLAIBrr3peUva0H7oBALDz1N6k7IF//gQAYNWijFmFiOLCFmJpoSa7MkXKNtrkblVZlDE3ofMosk0txHUviutbK+GR3x3d+BxXW7u2RHxNbZ6PuH/qXtXwnaZfyVqm3F3CuJVb1s/gWcFkyadeBLLU06w4CrHtxcUxkP4yykUvw6T4rMovEEdLjz3p2kwll+DKp2QZs89FrAjCtQUV2z7u/C6G7ZZW7uTwJe4QkXKVGRomd9N6JHkCqsH/oqlyIXBRIS/fz/MJ0wQbDAaDwWAwGFYclnXZNDIqK4DvfIu8/UPGEwBYv4ZIUiN9kj2sPksksmpNVgo5DpNVVxnXsgVeRVaVFoiXI81FKZsbp3N6IgnX5ip0j8UJ0Upn1PqnPEjL5Gwsq1+faXId1OrN07MszEoWn8Feus+x8ZNJWV8fZSsbP3Vc1ZXqWGtIOLdSjjTQi4vdnTHum3c+cOaDlsJHv3DuKmJ4zNBa7aDQaclmxFpyrXBN00ckSiUdgieFHBmOayiNeBKZLerUBDtFsImV9jipT0q9WnLdp4Q5C9eOdZi2tv8aWvmr69Bt8A2xVO25i/pV7seem5TtqtLYt+XeO+Qc1grWFIumGdMY1FSWnSpntWqoUHmNGlk3isoalgkZvnS9mBhVVYyYbIslgN8xxbYK46G2LITwiE5p+3xMdWilm3IdWjKnMRlMHRWxhTDfhdpDk2VydarD41iWkc6A54JlSpfRE2cUK62xSBrZnBNtaKGH5gPeSQu5FIJwml0rGS9TEnDGOTXGNlQoyYXZtpMhg3XUqR/WFrhApG42FBmyQNfLDsk80LNttndY5mN9A+QlUCoJ2fx8wjTBBoPBYDAYDIYVB5sEGwwGg8FgMBhWHFw68cVgMBgMBoPBYLh4YZpgg8FgMBgMBsOKg02CDQaDwWAwGAwrDjYJNhgMBoPBYDCsONgk2GAwGAwGg8Gw4mCTYIPBYDAYDAbDioNNgg0Gg8FgMBgMKw42CTYYDAaDwWAwrDjYJNhgMBgMBoPBsOJgk2CDwWAwGAwGw4qDTYINBoPBYDAYDCsONgk2GAwGg8FgMKw42CTYYDAYDAaDwbDiYJNgg8FgMBgMBsOKg02CDQaDwWAwGAwrDjYJNhgMBoPBYDCsONgk2GAwGAwGg8Gw4nBRToKdc/c552640PUwXLxwzn3LOfeEC10PDefcm51z7/suz32dc+6b57pOFwOcc+91zv3SebjuS51znzjH1/x/7L13nCVXcS/+PX3zvZN3ZzYHbVJeZWQhgSREEBkcEMkPHjYYHLDxA5tnY4MxD4ffMzbY2Bgn/CMZTDTJZCGJoADKu6tdaXOY2dnJd+7c2Of9UdWn6s7tmZ2F3dmrnfP9fKTprdvh9KnTp/tU1bfKGmO2nM5zngswxlxkjLnvDJw3Y4zZZYwZOM3n3ci6TJ7O854LMMY82xjzhTNw3hXGmJ3GmMxpPq/X5RxoB12ekx/B1tqLrbW3n8lrGGM+Yox5z5m8xs8KY8x+Y8wzz3Y72gHGmA8bYx4zxoTGmNfOs993TjZhGWNeCGDKWns///sSY8zXjTEnjDE2Zv8+Y8znjTHTxpgDxphXzvr9lSyfNsZ8wRjTp377G2PMmDHmh8aYNUr+Gl7sHTDGTBlj7gewH8CrT/cL+ckGY8xvGmPuM8ZUjDEfmWe/d7Ku53xGjDH9AP4HgH9Uslv4w6dkjPmuMWaD+u1tPA4eMcZcouTXz57srbX/BeASY8x23udjxphjxphJY8xuY8yv/jT3fy7gZM+U2m+rMaZsjPnYSU75pwD+rzpuzmfSGHMZP1snjDFvUfKUMeZuY8y6SGatrQD4VwC//zO275yFMeZmfk4mjDH759nvRn4eT/ZefS8At9jnj8zv8vO4Sz/P/Kzu4+fqNiXvMcb8xBjTGcmstUMAvgvgDbPadTvrsMj/Pbbgmz/H8GTXZRzOyY9gD48YPAjg1wH8ZK4djDGvArCQ1fobAXxU/bsG4NMAfmWO/T8IoApgBYBXAfgHY8zFfM2LQR9Yv8y/lwD8Pf/2FABXAVgJ4C4A/5vl3QB+F8B/AbgRQDeAPwLwcQB3gj7aljKOAngP6OMkFsaYzQB+EcCxk5zrtQC+aq2d4eOWA/gcqL/7ANwH4FP82yrQGNgE4EPgyd3QguqvAMRZkz8Jmaj/DMBGa20XgBcBeI8x5qo52n+uW5VO9kxF+CCAe+fbgfVyMwC9CJnzmQTp4a0ALgPwDmPMSpb/LoDPWmsPzbrEJwC8xsRbnRbSvnNdl9OgZ/Ftc+1gjEkBeD+Au+c7kTHmGgDd1tofKfEnAdwPYBmAPwTwGV68AsDfAHghgFtBOk6w/M8A/Lm1dmrWJT4O4NdiLv2b1toO/u/8edrnddn+umyGtfac+w9kEXsmgHeBJtL/H8AUgEcBXD1rv/8NYAeAMQD/BiDLv70WwF2zzmsBbAG9tGqgSbQI4Etn+55j+uCjAEIAM9zG32P5i7gfxgHcDuDChfRHzPkToBf7CQD7APwm909S60Dt/y4AH1P//jkAP+B2PAjgJvXbawHsZZ3tA/Aqlm8B8D0AE3zdT/0U/XIXgNfGyLsB7OZ2ufuI2S/Nfbo25rct9Eg1yQo8TrbN0s2f8/Z7AXxC/baZ9+8EcBuAP2P5raCPMQD4OwCvjLn+Q6CJ4rsAbgJwGMD/AnAc9LH3P9W+y0Af0ZMA7gFZyu6Ku2fe/38AOABgBPQB6PQL4CMA3qP2vQnAYfXv1QA+C2CY9flm9dtTQB+SkwCGALyP5VkAH+PrjYM+JFacoq7fA+Ajc/z2NQDPmz1OY/b7DoBXq3+/AcAPZul3BsAFAK4F8EmWXwBgB2+/FcAfzHH+6wHsi5Gfzzp7me5TkMVxEMBHWf423u8ogNeB56g5rnUegDtAz9W3QB9oH4vT2exnGGQweTuAJ1gnnwbQdzJdYY5n+RR02PJMqd9ezu14F9TcMsfY/dYpPJM7AWR4+0c8RteDnpPUHNfYA+DGhbSP++T7AP4awCiP0wTIUn2C++s3MP88dCXoY2EKfBT0rAAAIABJREFUwH+CFmLvUeePfXfxdoavdRD0zH0IQI5/Ww7gy6zHUdCiOuDffh/AEb7mYwBuOUVdPhPA/jl+ezuAv8SsuSRmvz8G8M/q39sAVAB0KtmdAN7I23uVfBDAAOvzv+c4fxJkiNigZLcD+NU59ve6fBLpMu6/pWAJfhGA/wDQA3rp/92s318F4Dmgj49tAN5xshNaaz8MWmX8paWV4QtPa4tPA6y1vwx6MF7IbfxLY8w20ErrdwD0A/gqgC8ZY9Lq0IX2x+sBPBfA5aCH+CULbRu79b8CmjD6QB8JnzXG9BtjCgA+AOC51tpOAE8F8AAf+qcAvgGgF8BaAH+rzvllY8zbF9qGGLwXwD+AHq75sBVAaK09vMDzbgPQsNbuVrIHAURWp4v53wAAa+0T4Bc0aLHyNGNMDsAtAB41xlwN4Hxr7Sf0RYwxK/iYO0AWLIAsyN0A1oAsah80xvTybx8EUAawCvTx9Lq5bsAYcxHIOv0q3j8650lhjAkAfInvcQ3fx+8YY57Du7wfwPstWT83gz4aAOA1fJ11oA/2N4I+NmGMebsx5ssLuf4cbfolAFVr7VcXsPuloJdEhNn6mgZ9GF4M4HEAlxpjekAviUfZdf5yKFf8LOwEsNEY08Vt+3tjTAnALtDHrW7jStDzsgHAG4wxt4KenWeBxuXJQp8+AfqQWwb6MPvlk+yv8WbQM34jaFEzBhpDwBy6mu9ZNsasN8aMG2PWn0IbHLi/3g1a5J0Ms3V4smfyEQDPNsasBbARpN8PgAwJtTmusRPy3C2kfdeCPpAGAPwf0Hz6AgBXALga5KWIBc/Xnwd9ZPSB5vSXzrV/DP4C1AeXgxYZa0AfJOD2Hga9H1YA+AMA1hhzPsjQcQ3r8jmgRRKMMTcYY8ZP4fqz72cDaP559wJ2j3se99pmK6DW5XEOb7kMZBQaAxkK3hx3cmttHfQcXzbrpz/j8Jjvm1a+kdel3M+TQZdNWAofwXdZa79qrW2AVvuzO+TvrLWHrLWjoAH8ikVv4eLhNgBfsdZ+kyfz/wsgB3o5RVhof7wM9PFy2Fo7BhXXswC8GmTV/Kq1NrTWfhNkDXwe/x6CYiVz1tpj1tpHWV4DfQCsttaWrbWOyGWtfYG19qclhV0Nssj97cn2BS2mZrtd5kMHyHKtMQGy9M77u7X2EZAF9UcgS9RfgD4a32yIBHeHMebj7KL/OIB/B/Aw6IMEoP56t7W2xh98RQDnsxvpFwD8sbV2mq/z7/Pcwy+CvB13WWuroEl2zjjNWbgGQL+19t3W2qq1di+AfwJ9GEZt3GKMWW6tLVpxjdVAH1RbrLUNa+2PrbWTAGCt/XNr7QsWeP0mGGM6QAuehRLdZut7Pn2NgJ6Z7wB4PugD9f0gq8tLjTHfM8Z8kT+uIkTn7gEAa+2vg8bG00BhFxW1bwjgndbaiqXwjJcB+Ddr7SP8Mf6uee57PUgXf8x6uAtkFFgofg3AH/LzXuFr/SK7f+fUFeZ4lq21B621Pdbag6fQBo0/BfAvtjU0IQ4L1iFvvxXAm0D98xbQ3DAFYC/r73u8kNKY4usstH1HrbV/a62tK13+jZp7/2ye+/k5kJXrA/xsfw60uDkpjDEG9JH2FmvtKH9wvBfNz+MqkPWsZq2905JZrQGyOl5kjElZa/fzgh08L/S0Xm3B+ACAP7LWFhew76nq8o2gZ/DDoEXfmwB8G0DWUMz5d40xN846frYufx8U4rSGz/MlDqeK4HUpaHddtmApfARry14J1GE6bkdPUgdAVo5zFatB9wgAsNaGoPvXVr2F9sfqWfsu5GUUYQOAX2JL0DivPG8AsIpf5reBBvwxY8xXjDEX8HG/B8AAuMcQcWVO6+VCwZbKvwfw27xyPBnGIA/lQlAE0DVL1gV5+Of93Vr719bay6y1t4H65U7Qc/sGkFV1F8j6WwWt7jshE8nIrHsqgSaaftDEO1vXc6FJ19baEsj1vRBsALB6lq7/AGSZAMhCvQ3ALmPMvcaY6OP2owC+DuA/jDFHjTF/ybFmPyv+BBRKsG+B+8/W98n09Ulr7ZXW2ucCuAT0EXs/aMH5QpC7U1uFo3M76wt/SN4F8na8Se07bK0tq3/PfgZPpsNR1l2EU31mP690uBP0Ml2BOXR1kmf5p4Yx5nKQ1fuvF3jIqerwgLX2edbaKwF8EWTVeitIb58CeRffZxSBlc8/fgrtm933p6rLI/xBM9f55kI/gDyAHytd/jfLAeD/A1nPvmGM2Rt516y1j4MWju8CWeT+wxjzM78rDZGMO621n1rgIaeqywestTdZa68Fhfm9DvSh+M+gueB/Avgof1BGcLrkc9xtrZ3ixee/g8Ifnqf297rEk0OXcVgKH8Enwzq1vR4UWwdQAHg++sEIOSLCQi1hZxOz23gU9DID4FaS60CxQRHm6o/ZOAZ6SccdB8zqP5ArN8Ih0IdIj/qvEFlyrbVft9Y+C7SK3QWyHMJaO2itfb21djXIMvX35mdPB9UFcll9yhgzCCGxHDbGPC1m/z2grltQOAAozjhpjNmqZJeBQh3Af7UbdRNola5dtVG4w6+BXsiXgOJ/66DFwwoAv2DJun8hlLt+DgzzsbN1PReadG0oPGOZ+v1kut43S9ed1trnAYC1do+19hUgV+JfgIgQBbZc/Im19iKQp+IFOD2Ev1tAlvRB1vc6AJ82xsSy+0H9vE39e7a+CqAwjkf1QdxH7wW5JLcCOMTW0XsBbFe7XgiKrZtEK5J87gizn+djODUd9hljtJ70sbPnuwTkZQqQHp87S49Za+2R+XQ117P8M+ImUJjCQdbhWwH8gjFmLtLrbB2e7JnUiOIWh0Du2/ustRMgN7Oee/Rzt5D2/ay6XDPrZT+fLvXzeAIUVnSx0mO3tbYDAPhj739ZazeBFm2/a4y5hX/7hLX2BtA7xIKe158VtwC4Wj2Pt4HCpb44x/5xz+MmozIDYG5d/jWAd7C1NtLlfgAp8FhnA9kWzD+HWpAxRv9bw+vyyaNL/xEM4DeMMWt5Vf8HYKY3OBbFGHO5MSaLVlfjEMhF0s6Y3cZPA3i+oVQjKdALugIiqEWYqz9m49MAftsYs8ZQDOTsj4gHALzcUFqh2XFRHwPwQmPMc4wxCWNM1hhzE193haH8qQVuWxFkcYIx5peMuJLHQA9vYyEdYYxJsx4NgBRfMwBZTVeDYqouh6zwr0IMu5U/NL8Fio2Mzm343Gn+d9YwU5ytYZ8D8G5jTMEYcz2AF0OyS3yc++JpfM/vBvA528p0fR/IFV4CEYyuAX1QXALgMzwZgNv1tfn6wlJo0OcAvMsYkzcU8/uaeQ75DLfxqYZi2P4EzS+BBwA8z1DaqZVoDjW4B8CkMeb3jTE51vclhpjBMMa82hjTz16JaMXeMJSK51L+GJsEufYWqusk6yMBIBpfkffnFlCfRfo+ClpcfDD2ZBSTq11snwe593+Br/HHAB6y1u6addw7QKS8o6DY/PN5IXMzKH4wwo0AvmaMGTDGvNwY08F99BxQKNJ35rnVTwN4raEcuHkA75xrR2vtAVDI0bv4WbgO9GKMsBvkJXs+zw3vAC3GInwIwP8xnA7OUPz+i3k7VlfzPcsnw3zPFMgduhmiww+BOAbPiTsXgG8CuJLPt5BnMmrDRaAP2n9g0T4Az2A9bgXpNeI49IHCln6a9gGkyzfzHNgLIhfNhR+C+vE3eay/GEQQijDnu4ufs38C8NeGUynyHP4c3n6BMWYLf5RN8nUaxpjzjTHPYB2UQR9fC9VlwO1I0T9N1ggP5Y8gMa2Xg0JQ/glk1YtD0/NoKa77AQDv5PO+FLTI/OysNjwLRPKOuASRLi8GjfPIs/UU0KL0AB/Xw++pLPf1qwA8HeT5mAtel22oyzlhT4ER+GT5D83ZITQrdyNaMxhE2RDGQXGRebX/H4JWW4dAcayalbmVFTYO4Atn+57n6IcXgybqcQBvZdlL+X4nQJkWLp7Vb3P2x6xzJ0GrsREehG8BvfwM/74J9BFZBL0APjBLF9fy9UdBlsmvgFbMqyAZIKIMFhfxMX8JsloXQWSVN6jzfQ1zMPD599tZf/q/m2L2axojc5zr+QC+FnOM/m+/+r0PlJ5pmvXxylnneyXLp0Hu175Zv98MiuXWsn/h64R8XJH/GwFZhm/C/Gz/fhBzeKHZIV7LbYyyQxwB8DT+LQtaLE2CVvdvQWt2iE+CQpPGQB8LUTs+BspeUQSt+F/C8leASBPToMXcByDP7R/o/o9p67ti9PGu+eaKec61HGT1yynZM0FWzRkeVxtnHXM+yOKbVLK3geaSHQAuVfKHQdaOftC4H+d+fBjA69V+Lfpk+du5XxeSHWIzKJxmChRL92FQ3KrW8THWx1vRmh3id1knU6Dn773z6QrzP8vrWefr52jrxhgd7p9j33dhnuwQvM9/Arhtoc8k7/NdANeqf1/G+jsB4Hdn6fZ9JxmPs7NDzGb8z55PT5ZR4GrQ+6fI9/Y5UCxm9Pt8764syEuxl8faTnDGFtCzu5/75XB0TtDHyD2s+1HQ3LGaf3sagOI8939TjC5vn2Pfj2CejAK8z72z9LKRx9YMj8Nnzto/w321Qclu4fs8BuDlSv5BNGev6efrTYHG8I8APMvr8smny7n+iz5YliQMJXv+VWvtt852W9oBP0t/GGOeC+BD1toNp71hbQhD1dV+y3LBjHaAMea3AKyz1v7eGb5OB+iFsNUuPLb2SQtjzHsBHLfW/s1pPu8LAfyytfZlp/O8p3D9TwHYZa1959m4/mKCrbr/DuAp9jS+9Nia9iCAp1trj5+u8/4U7bgbNP/+29lqw2LBGPNsAL9urV1wRqIFnncAtGi7wjbH3i8qvC5Py3kXrEv/Eew/gh1OpT8MxTzeDEpZtgKcxcBae9rLy3qcffAH27dBYRB/BbLkX3k6Pyg8ziw4BGUUZJ16NsgSel07LeQ8FgZDLPjHQBbCV4FCLjZZa09W/MWjzeB1eXZxrlc38ThzMKDY0E+BXBdfgeQn9Dj3EMVMGlBs6cv9B/CTDitBrtZlIPfom/wH8JMW54NiTztAoSm/6D+anrTwujyLWNKWYA8PDw8PDw8Pj6UJnx3Cw8PDw8PDw8NjyWFRwyHe/UcfdmbnYpoyOi3bKPnGRw5RJov9Dz/hZI/toxS2naskl/P27RfRxpRkkSpkCwCA3rUqtWVIeeFXZELZr1YFADy0W64xFVCGj86McLoayYTbXrmZ0vbtOySZjf7rC1Td9ZKLJff7lddcCwDY+cR+J+vrXA4A2LZ6o5Ot66GCXilIBc7JmVEAwOCocCvyhSwAYOM6STP4whe8RKemOmu47vobnC5nipTZanRi2v1e44xUy5dLWsMLt1BfHN0p2aR2HyUdmrSkBizVub5DWYplWRtz25EXoynFIm8rB4dF2HJIc5bHEHPBBLJOzHeQ3pYtlzG7LEdtPD6438mq/DdXkzoVw+Ok64q6cHF6/KzrcmDFKumpqDt155mWDcR1YRxkP9nTRLpSB9vQtsii/UzzjrLpTtPaLih9Uxa8We3n85iY68XtFzZavWW5vNTt2L/v4FnXIwCsW3+ea2jA922aOxUAkEjI3GaizEihHga0HfdUJNTzIH0rewb8gIVha8YlO5fNJYh+V+Mk2laNiI4PknKebJbmmWQgr7JkQPenH+sGt0ffUxjVkVHqffjRh70uo+t6XZ4WBEEyZkLVrY+ZUYMcAGDg5252ooueRYVd00X5Rnj8njsAAF31nJNdev5zAQA3PPUaJ3vsAKWo/uH9dzjZzoOP03kvu8rJerukuNqKBvWzPbjHyb758EMAgMpyyby6YvsV1NaVHU42uZ+O2XHn95ysNBgVkdNjp3V+jnuzhGH5jOnSW4I9PDw8PDw8PDyWHPxHsIeHh4eHh4eHx5LDooZDdHWIe3hklIp6fOQfP+Nk1SkKX9h+satKipf8wosAAFO1GSdrVMiFvnK1mO4bZTLdTw0PO9nYxBgAoHOTVCFM5cmVPTgj4RB966gI2YqejdLYlLiA/u1fPgQAyHZIAaVf/41fBwDcf59Uwpw6QWEBF22Q8/Sz6zyoiGs/nyXL/vfuuMfJVm0m98LmS65U5xsEAFRLRbQbgtwqt72+m8I1EuFjTnaI9VsqyhAbHqH7nqnJ2qvO4SkmK7rceOEAAKB8VM43Nkr9V621uvriXPVzVbU2cU4V56IX12IyTbJkoFyBNRpPUxNVJ+pNckXLUO4zyFAYiwnHWtradjTUprCQ1jYaiTtoOaRpv/lObrR++CjlDYzTSRSSkQx0KIX0cTVy1Slir3H7Kjcvt8Gq1kqoBU6y3+zzCsL6ggosLSqa7idylyclbCPyaisv+HyRQIjTakPtH3nitf6i0BHNt25E+jBy4SBotb9oWcAucb1btN00Xvg6ddWwMKQdk0n1euODAivvoKitiRj9nm14XXIbzgFdNvd91AlxbwK1X1cvAGDNRfItlOHPjxMqnHDyyBAAYFy9ZzuS9G2zabO8ow8doVDTUPVZPkVKL40dcbLhA4fc9qCh8MU1nd1OVgcdMzUox6Q4vLOz40Iny62lb4I1l0hl8UPF3QCAclG+heSe48Ih5h3Qpw3eEuzh4eHh4eHh4bHksKiW4P/43L+47YkyWdNuuuVWJxtYTuS3ifFJJ6tWqNhHVllm88soANs0hFgWMsnMVmSl099NK5QNG7fK+Wq032VXXe9kU1WSffErUqJ6alwsyrfeeiO3WSyye/ZQ4HcKYj28eANZc3s6JUi9GlL7DxyRFdbOE7R6W7VeLNQ2ReS8Jw5LesANK2k1mKgI4axd0JuXeyxOkMU6VOXHC7yYK5fHnaxsaXWY7xeSY3aI+kLz3lYuJ3JjtqvgZIcPEBlgeFRWoBUeG4oH4iwkDSVsImbxsi+hF54JupdEdrkT9Q/Qsnvs6ENONjVZ4XuScbB7kvSvuVOpyEqoCCUxBtW2gIkz8c7ag3ASbkfMsZFRxmhrVawpnC07yiwUMDG1LyPWr57lK9z2Y0eO0kZdigElTKuVyoZhy4Ujy65uSpIJOkllrgr5BvV+VdZto/0MwU0II6u+sqoFkbVbPxvR/SodOdtMjK6sMjc2uBMCZX1z12hujfvVSUI1eIKI+KXa4C6uzh1D3pp9BQCo1+lfDYjHJjpLQrVM+qPldG0Fr8snuy51D9Zjfuc7ygo5vOuSiwEAfeuFhF2aIWL+2NBuJytPEqHeNLqcbHSC3qm7n3jAyQ4ffZR/G3SybC4aVzKH1mYm3PauQSLO7VKdGvB2oyZJCU4coPZ0rZb5ednW8wAAK7eJJbs8RN78I7ulyGjovte0PTbO13jm4C3BHh4eHh4eHh4eSw7+I9jDw8PDw8PDw2PJYVHDIZ5/20vddpW9G4cOStjBFIdBLOsUklQyRd/pvSsl3+xMhdzRxSkhHi1ftQwAkAokPKEjRbfXkxJSXVcXhVKUj4vZ/6uf+RQA4CmXXOpkqzZe4bbvfXQnAKCk3KybtmwGAGzYdJ6T1YpEBjs+KoHfMw0y9/d0L3Oy4RG6z+PHJe/wBj5ftSJun/0HqY3ZoP2qWxtISEDNUDhJV5+4czIh9cG+cQmHKJbJhbK6R/IJFtLk7h6riC4HB4kg2d+ddbIgQefLFYScmIo2rXJ/s8swDMXt1OSCjzzmmuzB26l8r5OtWkGhKqYm4Sm5GrmeRkriPqrWWt2IVf59VOfr5LETtFlAhFGdE7ldm1rouG2t7W4Ocwhb94vJ9Rvw85lU+UFTTPhJJNNOlkyneT+VBzUr4THL+2iOqJVKTlaN8ofWRfdVJoI0ZaZkXWnyThQiEQbKxRpGv6k+SkR5V9uO4tgE1+NWxW2YVpdxFC6i82FbDvGK03nQ5GpncmJTXtroR3U+vqBVbnN9bhNGYSfyez0ai7oNfPJAk7KiMJaEJk2yy7audcRt1cTHKJStLclUAq/LJ7suT0KC4zoF6WVrnGjVFgrhTBekf0aP0Dt3fELevckMHRsYCRur8rt57yEJm6jVaZ5MKnJlnkn7iUDeqYW8jLHEGpJXk/J7NJNa9QK1zNgzWXlfN6K6DeskFLV30wkAwIkT8q6fGaJ36iJx4GLhLcEeHh4eHh4eHh5LDotqYpypqxQvnPJk80VScQ1FsoIWAtkvGa0ujKwyalWy7K7sGXCy81YQCe7ii6S6mi3TiuPIIxIg/sjX7wUAPOXqq53sF//8twEA2R6x1j42JBbMSy+l1cyBYQkGn5yk1VZHTlZJR04QWaeQElnIKb0aldZ6Vms3ysqvUqK2ruyXNgysIWvkmLI8tgsGh2Q1GuRJD10dspyrFKmvgoZY3E8M0qov2ylB/EGCiUZl2W94kFK82JJY/urTrA9FfExGacearL5sDVGWiqaKcZF1T91LIrK014Q0MHSEdly5UQiU+SylmfnhYw86WS1K8aYW+9H5rLIgRheOJ4adRWjDbQx7LxlZnIwev2wNiEmSpq09iQTtl8vmnSzDz3NDrb/rbIqvKmtPpUzbRVVVcVxVSkol6TrpjHgV8qmI3KasUIbGRrEoZNtklE4pI1bmyB7QMDKWBtjrUFAysOei3IYEnCYrnqEGNqWvYt2EuvJe9LzE5LtrspQ7vcqxUTqqMGyd2/QgMqbVGtlEg3EcKW2FrPNfQS2MxpgS1qPzqXRZUTUybRWMqXoWNaLdvDOA16W04cmvy3iouSdB8+Py9fIttOo8Si0WZNU8mSTL7drtz3CyPpfaThEVk0T0rqcUqThJ50upCoNl9sqlErJf7zbl0WSL/Exafs/y8Wn1bqvyu7aakO+eSpbaUFDtOm8TteHYzp1y7Ch9RzQqWqc+RZqHh4eHh4eHh4fHGYX/CPbw8PDw8PDw8FhyWNRwiPq0mOK7VpFpv6iqoa1gYlV1QvLi1tm3c/So5Nld20Pug+dulwolOEEks/v+48NONDhIsoG8XLe/Rtc7dOcBJ3v8OxRuUAzETV/t6Xfb66+4AQDwkquf5mSPPn4YALDjqBCn+jZQKMbkiLheV3TSPXVlxCXc2UsErFpGXA9rV1B/1KelPxKcGzGfb0Pfa0PuJ5+jsAXtretZcREAoFKXexwaIx1ODks+3nItIkopl4slfdQUqa4ySeEijaq4TZzTpCnEoDXeINQ5YmMIIi6LZVrGXYldckOHhXiFKXLN55ad70T1MSIfNGpCaLRMYGkijLBbKAzbOh6i5deI1NIUx2FaK0lF7tRsVlximSw9Txai28ky9WutplyeEaFHrcntrL8AEMSFXyhJVFSqkFXPO88VvSskbGKYQ5mqVdF3Bx+bz8mUePM2GqfVpOTEnuZHcW9JQqPaBda29o/O4erc5foY/qtzNEd6aBq/aB0HjhyYUOSmiKSqj42JAUoYzeji82lvf8xzEnn+rXKN20ZEVFQhKzwRWUWYde3WFQgjImUbEo+9LqNGPPl12fyZFfWFzImJLvrWWLn1EifLd9Dck0gKyX7tRgqXSK3f5GQpS2T8YlHmssMT1FeZNRIammUdreqVd6/lcIekCicsKFLxBJMSbVraXx6kd3hGJSBAmdo4eWLEiYoleh8W5bWI4pGDdE9Wvh3CRpwdNhEjO3PwlmAPDw8PDw8PD48lh0VdNvV3SAqq8jRZ+zpURagxtvylVUB3aMlq09shBJlX3ELVVI59/YtO9ti3vgwA6FRptdZ1klUq05DzpZK0uukSEbIdtDIppsRadHRaVjV7b6dKcuMP/djJrr3lRXSeS7Y52U9GqP2lmqx8Vw9Q/e6BvJy7yGnTRhqyTCpXmTxQk/tMcZW8Vcsk9Vi7oDsn66cGV8UrF6WdJkUW1LxkvULAVWbGDqvqP4b6vqNLVofJBh1b4Wo4AFCvcL/o0nIxHAgbY6kwMdZhqw52VseyrKbLY0zCUlUJgzRZNjMqZUydSV8zOn0R61cXUooYIzGG6LOKk9mlpXaPTm/ExDJliSlwP6TyMgamWc3VqlgznAVIW+fdRozFXnMLYxSujUycDQ0TJdFFqUpjaVleprpuJsCOTMngnC7TWOvuFFllGel73zHxBqxky1Su3maKBBD3QOjHQbZjnpGmvm+1zMed0LK5zwTSZ0GU7ikj83CaU1VmM6KDQFU/q3K+zHJVUllW6xN8OU2wIgtR82PF96yE9ejcTd0RpfBrJZclwjb0tHldAjhXdBn3maU8n5xObEqlDuvcT5VRbVI8TjN1roqbkXvsGaC5LpGSa2zaSt9H2TVrnSzyjmUUeS3BhDhNJE6r8dTDOsqEqqJcL38rHROS8vBR8obuUQkIRp6gJAHVCaUP9v4FymuaSNL5grRYfxM87pqIoGcQ3hLs4eHh4eHh4eGx5OA/gj08PDw8PDw8PJYcFjUcolwRd3MN5CK1FTGXB2zSr9fElXLeMsr/++xrhAR3x8f/gjb2Pexkl62nUIusEbf64SkiqJVCcQHMcLWZpPr+7ynQMYdPSAW3lctWuO1lEdmnIUStr//rBwAA659+i5Nd91TK3/fjhrh/j09RlZSpCTm2dxm5YxV/B6UaHRNkpF2Ru6I8KmS5dkFDuZdzqzcCAPIz4jY5zNVqijoynnM9m0CIRh05ChNJ1oVMOMNhEI2KhCK4HLbKXeM4WzGEqVOB63HtruOxqtIXI9tN+kipKj5JDo1IpuXeDd9yuSrtbyJ7tBE00SVyVyYSegzSIK03VCWhKCQgJ3pMcL7eiZLcZ53DgoKmCJbW/KBRmEOTFm20v96vdbspvCSKoFAkmqgA45BywfVZUuqqAckzXqnR2ExmZO7ZeZjJmCp/8RBfsD7Vfrm7NSEqrtqXE8W4lq1tckzTL8rN7bgqOod7msZ+X5eQiC/lueDCFeKK3dRHc/NAp0x4OZWbtMQ5oQfHjzjZE0cpF/eO/Xuc7PAUja0T6lEqRwwsTbDi3zXnJvL4Wn1P/HuSyguwAAAgAElEQVSjNaLgrMPrknAu6BJGxQRGClO6nC5yPYPHJJxgaP9DAICGkW+JkENH+y4SYnbf+ssBAKvWrHKyLv52qSdV5T0mr5Um5TukzDKrQgybQl8KNKdnCjLPdw9QZdtlfRudbN1KqmeQDuT7bjeHd06ob5xuPk8hLfeeZ9JdJi19lGaSvM5ffCbhLcEeHh4eHh4eHh5LDotqCS5BVjWFFK0+ilNi5Sx09QAAulSQ9/WryVoz8r1vOFmdA7FtRlWTYqJWz7QsLTuiylDKCjfKFajS+T4nq7JRJ5+WlWVWHdPL1srhE2Kt3EAGauz/xqecrMcQgebyi69xsi/d/zidr1NWanaaVt25pLoGp3IaURammSrdUwFSOa1dMDYhqeGwciMAIJ+S9Cur118GAKiKsQ1Tx8k6cHRYLO6oUp/OTJxwogavIjUJLn6BHyeNdBhjIpxTYlukzkJaFVJUeXKYflPVcjIc0F9TFQErjai2vbrCPESWs4l0StbBmRytvCtVeQ7COm0nFEmhkKcVfZCRlIITzIKr1zUpJ87qy39tnMU+hsA4fwa3JnKMdHErOaahrMNjU9TWbGLYyZ5xBVUzemBc9L1vF1U2WlaQ+SjVy5aSpFhM2gWxPRrTF0FC7seY1ldAMsGWeUX2tGxVyhZk3uzhdEsXr13tZE/rI9n5kGPPmxkCAHRNipU9oax4NUvPUFVVx7p+C6WB2tEjZOo7D5IV60cjMlccmSCCjq42aNjUmdA9wuPXKNZVxKFqquzYJvC65Hs6B3QZdHe77WRUpU2lBA3YCp8I5fsIkTdRqXTlJqpce+H1N4vsPOrbtEoDW6kQqX90j6SBHdxBVviZYSG0TY2RR0wb1FOKwJ9aRuNj+VapZLd6C1mhewdknGRWnUftuuVWJ+vpI+/BwUfFI7BpgCzGG7tlHPTw+zOZkvYH3EdxROgzAW8J9vDw8PDw8PDwWHLwH8EeHh4eHh4eHh5LDosaDtGzTNwv6wcoAH9kTPLxfuELlI93lcpb96qNtwEA7v/hj5xsxcqVAICZVXK+mTyZ2Cfvf8zJVrOJfVlWwiYqIbk7i9OKsFUm1+a0lXCHQloIdpMlOl4Hb6c4huIC8XRg9O5vAwA2r1jpZDc/5QoAwEPHJCA9zVWpiiNy72GS2tO1TIgJ08fJ5ZRLt2M4hPTV8hr11boNUsnmxDjnBK6KS6Zap/tNVOXY0gTJXAgE0OwDZ7TS4uKlzs3eVCHpZGeax+2ik3MyWW5GVIlcF43BQk4Ggm3wPakQiaiEUrvlCS6ocIgskx0rdXk2Guxn1Lm7gwyNx2JZ3KQRb04TenLs6srn5NipMp0vyicKqOgFTQaKTfep1+zhrL+zf+dTug05d51DI4YmFIH1KLntbnvui5zsQ0MULjFVFJdttcH5zTvbz+3aNLbimhcz9qIwl2bOFbugFbEmIsmsXr7GybavIpfn9l5xZW4uk7t1QDFKs2Xqs4SqRmXqihDNekuoylSFXnqetndJPFWwmgg/3RnJRf1AkubzHSfkoZzkOaBudOWp6Ob1K4/aELZhblmvS8K5oMtn3Hyj207yfaRUGEvABLCGqpRWZnL9cVVVdsMNNwEAVpy32ckSPC+PH33CyY48+k0AwO7v3ulkUwdoDmsUJdyywTrUxHKTVP2cpe+d/IrznGjNlU8HAGzcfp2Trb+UKt31LZfkBdueSnpNZb8lbWW1dg3I91F/gdofqDAWy2PILpIuvSXYw8PDw8PDw8NjyWFRLcEptUT91H+S1ff+hx9yss2baDX6R297i5P98OP/AEBWoAAwWSJCznNf8dtOdmiYrKY/2vUvTpao0OooX5SVZaaHVhxlRXgq1cnCl1R1x6fGlbWSrYGpgliHBzppdbtmQFZJO3dRbew7P/OfTnbV66mNvQmdOoqul85K6raQ04dl80I2SvexhXpY5elqE2wdkHYefvTrAIDioR862fQMER7LyqqICUp9Vp6Qyji2yiZERVyKKrw1GUCYFdVkIJHcQU4WxLGnmhBz7lm/zSUz3MZQpforsWpy3eKV6OigFHghVMW7kMhkvfllJ2nf4iJQ6dDqIfVjTuXuixbouZyM/UhlVWW9j1LYpZNyvi5e5ae1B4XJp2NF0VmlzJWQmlb+rWRFq6y+rb9KZqWmKnKISDRat3Semrrc3Qdojsjd+10nu3AzkVkPHxVPUkQwyne3HzFOdb0ju8SllWuq3BVGz5UmHtF2KiM672OPx4Ze8XhcxdPX+YrUuoIr7/WqNJGpkPrMqKKKRnl7mCONQKUUrI/Szp1W+vmKLiLjrE3IM9QNsiiOlmUsVibpXRFqLw6/e7Qs5HHQjpYgr0vCuaDLlz//uW5b0nrKnGjY7TUTSnW4MSYH7k3KexabNgAAUgmZuI7vJQ/54z/6jpPtu4MswLWjokvLZGeddCzJ70pNjNPcylxAek8MSmq7ke/Te90qWfHEtQCAi65/lpN1rqRvubWX3uRkgw/uAwDsGRJS/VQPeRVzumIcj+4g3h142tGOY8bDw8PDw8PDw8PjjMJ/BHt4eHh4eHh4eCw5LGo4xN//7fvcdmcfBUe/9lfe4GSZCpnau424UqZGyITeId4cXHXDDQAAWxaXQhDQDi9+/e852d3/9WEAwP69tztZD+fiSzQkz2F3hruhKs4Co0IfRmdo35GiqtwW+Q3kNMh1UDD4QF3aNbbjEQDAeRde6WTHObKjplxUGd4O5NYxPk5hAwf2ievhmXgh2gHTdUWoqpLbZfqEhJj0dFPO59GihARMTdL9hDXxpRkO7E+mVFUddoPUq+KGs0woCNR+gTgF5XwhudIayh1nVZhLwPkZjc6bGREElB/dRvup/LhRu0xD3HW2RmOiMikuwWwvufhySr/RMAmNhNm0A/r7e9z2RIn0V08oV12CwlmCpNxLOaoCGBM9Ule+tWqF+imt8n6nmHjRqypOjfKJKjMqdMZBxzYosmPEf1RkkpjoGKczG9NWvR97XfHYziEnu/jC9QCAnTMS/pLlsJAjx8R12S4wgQpj4d4IQxnn0fC2WhZ1ZKgIMeyLz+bEFbuml0hNW/IyR66u0vPcPyPhTV116siMepbEPa9CnmJ4TkHTs0ZtDIty7kKK+j7fIxWsRkDvkV0TKkSpRPPRYUXeqseQbROuPe1HpvK6JJwLuixnVC5nDqmr1aXv61yTYP/IoJMdnKY+77pwm5N18DdJ7bBUyj1w31do/5/82Mn6s6TrS7dJqImp0Nw+NCrv48cj8rpiYab6JE/wrTfTN8umDgn1a/DcPzgh+X/37LqdZDkh9Xdz9dxUWsJF0yvoPkemdjlZFCKTU9ULuUgpQrs4NlpvCfbw8PDw8PDw8FhyWFRL8M3PeYbb3nLhVQCAo/uOOtn2fgqSnti/28lW99NKISzLKum+O4mItc1IepU1l1EVlXFlZbz65a8DAOzdKaup+7/xaQBAV1X2S7P1cEDVtH58WNKXPfPVvwIAuHeHpF8bOXQIADA5JiurrSsp2D8/LhatPXd+HwBw6xU3ONk0B/ZXVG3sApOHGlNiYTK8qC0Gi6qmBWHVRRLsn5m4DwCw/wkJeO/soNXo+IisGA1bCYOkrP6zXEVIp/CJTHq1svRFg/Wa6hDLZZKtitoa0mBvQtkKkTLXLStil5lGZz4rknW2NiPHZPmYZFqv4kNui+xXnqRxYhvK8hzdZyBEkEwi2lZVgdoA5UCsvmVOH5jJKqs8TxFVtSpvxBhbImORJqVNcGpBbXHt6KB+yCh993WShWM0VGS5CveT5sM0GYD4B0WEjNKzaaNvw8aQhWadAhD70cEpeXaTx8gKtbFXxlyVrTYzaD9inGMGAgh4O1TsPyGcKkJRZCJvYhiSrnN5eU43FIg5tRbSPwMhPQd9SgepyCwYtJIYm3J3NV2PRer36FkLQrlefZpS1qUK8jyv76N3wDXKozE0RhbH40WxHoaujXLhkEmhNm5An214XQI4N3Q5XpH3RdT0eijzX5Xf7wcmVRrKVfQt0bNaLPjlKnnFj+y4Xc69i965122XKnI3XnIpAGCzEeK9LVHfP/DQt52s+AMi0GUVOfHSZ9zitq++ajsAYK36/lhboHF06OB9TtZ4gCy7gzvFwnt8BVWHO+9K+fZK9dPNTzyyz8kePkTJCwqhvHMadXqfl1Xqvbe/7pU4U/CWYA8PDw8PDw8PjyUH/xHs4eHh4eHh4eGx5LCofnabElfL43sfBwBsWLbOya6+aCMAYPShzztZeYbCIPok8gGpPJnJd3zzs0527Akyq1/xgpc62USVwiv6Nv2ckz3/l8k8/+1/e7+THRoi83w2IQH5jUCIQPc9TC79p73ubU62+7tfAwDc/fVPOtnh0cMAgLU9UhGlq5+q8gzukYouuS3krhgryfVmRsmtXpsQpt3jnOevUVBl6doFisFX6KK+yuWE9LX3CIeT6DyW/DeRFtec5Sj46clhJ0tyGEE2K+6h6RkKjShNyPnyPeQ2C8ty3Zki7ZfMiBspSEq4xPTEcf5dhVXkqD3VirjrQiamROEVAFAt0+/pTnVsio7V6ZCjpWUyIS7zlSvPBwD0ptts3ZmU/qzXI1+djMF8nh680WnlZmzlI8K4gALlomQCy2RJVQMMuOJaXvomy8S5vm4ZF6OsZ62T5iJ/0XVUhboEbQdpOfcUk+2achDHJBmONhvKLjDKbLkp8WaiXib3bG9HG4ZD6PSw/DeRUKwlYVPJIZzw1apXQSJJrsmObnGn9nHO8nRV5qxahS44kZT9aq76VVzO59imKhe6VgjnClVhAchQiEwqlOe9YxmFU124stfJHjhK8/AulVu+zq5VXZUryileX6R8pKcEr0sA54Yuw5r0WVSBs6zm2Okaz1sqvLOzQO+YrHrPDg9T9dWdP77LybZk6VvjFRulWtvRYXrHfU6FGETfJGuuu1qOPb4fAGDtBid71hXXu+39Q0TI/++dEp7a0Unv1euulGqD13H1vy/f96iT7dtMhLjuS2S/Qi+NuzV9MsYeuPsBAMAjQ0Kqr1VoHDcai1OVs83eyB4eHh4eHh4eHh5nHotqCV7VJyk0Nm6ketMbB2Sl0Fsgq83xmlR4G52h7Y6CWPYyXKkqpdK5jO28HQDwtf33O9mzXsMV5Xql1nYjuxEA8Pw3/amT/eB7XwAA7L/jo05WU8QXM8yr0HGxaG17CtXQvvs+qdSCBrW1Mi1kuWOHySI6rfhQ2y+/AgDw8BGpBDdyhCyh2YasS5IBWcbCRvsR41KKaDHQTavH4UHRR2NoJ22odDY2jpwEtl4oQkNEiGuUpS/CKEVOoNME8fnqShYF0+c0kUvO3eDqZEatsCPym4lpnlXB+Q0mUzZCbaZhQoayloQhVzRKihWzXCerft2IFbkdsFqRM1M9NObHFAkl4FSAdWXqjqpUNRlmWbfaAuTUo4wzk0Xqd8W1QUcHeWwyqm59L6dOGpOpoLlCXcy9RBrvyKvnhc85NaVyGcaQZ2yMdbvBY66uiCHTZbY41ZV5uA0RxlhRxBInz4YkllJWNSauZrtWOFn/xosAAOuZvAwAy9nQlJ4Sb0nQS+mUwlBb/2Mb6DbdmFGpB8HegUCRUAMuR5aYFgtmhgmbq6tykVVMtOwYFStjmXVYVqpvOHJl3GhqH3hdPrl1+e1v3em269wvYULmo1KD2ty1QdKo9vL3ThCI7qeG6Lti7LBUgpvJkFX14ft/5GT3HCbL7f1F0cv1NxORfc3ly52so4e2y1PyDZZLiTV6emoHAODHD9zjZNUsvb9WbBZi/Ev6aWx1ViRxwI7HKGXbthvE8rxsBelyVf9qJ3vE0vfa1IjcUzVKbxqTCu9MwFuCPTw8PDw8PDw8lhz8R7CHh4eHh4eHh8eSw6L62a/bfoXbHh0nU/3wcanQtHYjuW5qKRXszwSUqYqKJ+Bqb2sGJIB+8AgRz7oS4nK585//FgCw7rpXONnqayhX8VSnVEHZ8rxfAgD0bxIX/+c/9mW3PT7IpvoJVTFugFxJiR7JsXdgF5H9MooAsKqXXAU7pg47WWmcAtcPHpIcuomA3B+ZlBDycqydvi5xW7ULpkrinhpOUF8WVm1ysuyB/QCAckm5oaM8u1XRUapB91vollCZOodDVKclT7C1MWwm96NsxjtQ5nF/nwwJVaUsRWvGQF2wEVUkU7KoWlI6L6EPxQkKdxmZFpJBO2B4SvSYytL47++Wey7WyE8aqjzIcX1spISbICbEIIpcGS/J+ULQeOhSZLMse3R7u0Q2NinjJgqNaFrF87WTqlpVIUvCmRmZ6uqNqOKd7BeFs4Rha1iLKniHjgy3pyGuxnZBoHojRDQu6607qsFvONwlkdDVN6lqVC4v1Z7WbnkqAODCa1Y5WWc0QZXVM97NJF4VoqSfIQcdkhLNl7pPo9AYVTXSubon1Ty8n/Kkdk5IuE5nltqV0jE3YRTC0xo+gKQuedYe8Lrkdp0Durz77nvlHybSpfSzZXL19jWXOVmDQ8RCyDxZOUHhkzMj0mePTFMIwuO7parsdEjHdF0kCQH6UvR+7VD5m2caNJ9ONkSn06r8X71M18kUJWyzZCn8YmZG9JvqohCKjrSa6E9Q/YewKOEuwSpqQ7Yg314ZrgBrVahMyAT0xSr+5y3BHh4eHh4eHh4eSw6Lagmeqotlb7rBqawqsqIYOUpWlv6CpE07fJRWBWuXS1qNTJEsqYGyDudAq6l0VgK7K1OUXu3gN98nssN3AAAuu+11TnYoIIty8TypSnLlG69z25/42L8CAB4YlxXR5b2UcmR756VOtq/0IACgIysEMcP3OTYobU2VmQhUEwtvmKN+GJs56GSrl9E1grD9VrfH9kjFmFr2IQBANVQsJq4I1mRw5dV/oyar4Bm2iidzUrM8kyd9BColUGlcyIYRohWctsKalg3A6LUer4SbUvi4JWcz1QsA0nkZT6mM5fYri6SrBiTjMzpdqCze4IpM1Up7Eaqm6tI3uciqVJOxapGefYgiOOr0d1yZLY7MECOqq66enOHrKTV15Kk/syk5uK9brMLjXNmtWhFLibRHeZI41V0cYQ8x6ZRqioQU8fBSql1Bgi3GaD9LcNz9NNs5uM2a/Gda94qqfSWsvB6SvJ1qerD4RIF2xYQxspgBoC2KkfVQWaGQjHk1RSZ5VcURrN/EbhmnrtCZutE6kxwbSSGCRv0RtKMtyOuy6TRPZl2WR0+of8XMGzm+SWU9j5xU2jM1U+R3R03usVYka636jMLqbRcAAK665plOdsEaIkN2GCGvZdhiXFPvqZKa/xIZ8tJmdWY+Jq3VVdrL1DLaL6Xm6sY0eRitIjM3QtJXUpHvskzsCxS5ElWa280i6bL9RoyHh4eHh4eHh4fHGYb/CPbw8PDw8PDw8FhyWNRwiO6EuIy7+8kkvn/P4072xTu+CQB452t/3skGHyCyVb0uLoVpdskMTUp4RaGLXOiFDhViUKSwhK5l4mrfx278x/7mgJM9m0Mjsuslp11Hv5jsf+e33ggAOHhM3P2TCeq67s2Sg3jsW+QqWF1QweclCqHIFYSEkOfqPZlO8TNMzlAA+RWXSHhFhn3GfR2S47FdMHXkuNuup8mVEQaKuFHnewuVyzxytytXWVTJq1ocU/vR32xB3GKJBPWpztHr3NqmNRziZBkGdeCDeB5Nyw61GSEhlKdpO6yp+2QXllGeuYDJGXlFjEOFXEaTdRUy0hZQOYEt9XdJeaYi938TomJVsTk5T8Y4bD0m4tVMqmTakXssr8hyGeVWjQhzYzIFwNZ1aMTs67XqtpnDx2Qbndc6xiWdTVMfNWLCRM42QkVQCvnuQhUakmB9Kd6g20+HkNgGVW+anhDi7v6HKA/pwLQQd/tzpK90Sao91bmKo1XPvUml+LrxNpfoeTFKIwGThQLtPs4zSVORhXKc2xo1mY8sP5ONJvZrRHyU8R6GrSEF7QKvy6j9T35doqpIxdxtNtQxVkyWU8UxXa55RWTLcohYSoWnBDERK+ODpOt9D0l+4u0DpMvJaZlD82n6dsmp6rh19W5Lp0meTioCcRj1veg3ybmoo+qpAJDhqrBGh8rwZtM8zXmTjSJeg8NdrFmcMFBvCfbw8PDw8PDw8Fhy8B/BHh4eHh4eHh4eSw6LGg6x58c73PZDO3cBAGrKdfO6214GAAhzYp4fmiT3TEq5RYtlYjNeft1TnWz3Lsq/enToqFywSlkIdAXHNLvuk1VxS9/+z5Q9ov9Zr3Gyp954g9sucy677GrJWrGP2ZErN0hu3BpnEihWJItEzzIKz6hNSEaEMEOuJ5MT99BTLqOSiT3K9dQRkPtjucpp3C6YHBf32nQyYn1KKEohYv82LbM4f26u20kSnAeyXGzN/qARl/UhJieEZC6wcftByhw3ufOi7VbXTeSaAYAwykbS5JqLCwngzBgqPGRi6hCfLibX51lEVrGzO7kkdaFXwofGOBqkya3FnqvmO58nj7PCfL/qdKRRHmEdkNCtcgZnk/RM9nSKLEo6MoentqURWo3OfasamDJRnmAZ19VGD1p2bBfo8A12JWqRdd7F1vyqRuegrVNHlosSgjZ2ZC8AYEiVr01WyW3dq1jtNZ6/woRcI8FZXowq0W2UkqKxFSRVflveN5GWY6opOk9Shaplezlv6Yi40MvcnorKAw1ug3Ybu65pRxe61yW1+1zQpS47zQ01+tOrRsLKhOTUtWUOCeiS+053c9hBQYUJcJ/qTB9hhSbt3fd/z8k+w5m5rn/mLU62ZjOFXoZVCfmrH5LsVN0c5tDRPyDXG+dS8nl5R9jofWck3DXo42PyEiJh+MVRK8u4q3LZbKsn/0XWobcEe3h4eHh4eHh4LDksqiX43u/f7bbP20Z56zpXLneyg+O0Itm6WVYeG694CgBgz+1fcrKbr70cANDXIauRqRFa6S7Lygq0u0AWnOFpWWGNcEq8SlJWZ9k0WXf2PfSQkyV6hNS05kKy0hYbspINeXVbS6pgcK4el1ArnfEJsmSvXCMVe0oVWpWtWCX3Xq3QfjYlq6lCjqxc9VBFzLcJGsqiGRHeQjFPIMmW4EDlOTS8xNOkjxRXyMv3aaIRE0FqorfoGKNIcGy4RBizltPkNWNUDuJuGlsJRbKyFV6NKit8EFOhTrLjassz/6Ytz0waqCiG2XSJBl49jCGanUX09IqVszdLY7piZZxPcfUeXYipHplgdE5gJuo02UdjLK6zdqffY9pV53NPzYgekwl5Dgod1KBMUk6U6sg0XReAsrzoylo8Xow2rbUemuYxEtalP2ohyaL8w20FRaIJuIPDJrN4q45MVI2sIX0b1mkuqs6oKk6gPrCQ+86UuTKVqgQWsEWn2dHC3heldG09FF6qnNs9OYpMHVXWygfK+s/VxQZPSPtHo3Sq+vUW5cs1rWO2HY2HXpeEc0KXoZC+JKe9ainnnS+PCTkcRdKbWSHvrq5V9E3Su1pkJqSEAJds3uJk1Zl9AID9+6VK7fIsnW8gJ8THLq4/MDop+yXHRZdbtq4BAGy/8BIn62ML9fnKOnxwnM45npR2rdlEuYo7OkUW1rhmwrBYm6fGySPfaDSPZAAwnhjn4eHh4eHh4eHhcWbgP4I9PDw8PDw8PDyWHBY1HOKW5z3XbS9bTiWBbVbCCXrWkGl/BOIOecrzXwwAePh7X3Oyvbuo9N+Jg/ucrMAuh6AmLs6KjdyZ4mp/3s+/AgCQ33ilk5keyuE7rkrfZjJCzhufofbklM87zS7hlYqwd8G2bQCA4Xv3OlmpQu25bPtVTnZslFwApUlZg/SsJFdHpwrDCJkJUWm0V6ldQOX8BRByGEHSCOGtg8tXV2eEEFjl221UJVykMkHHphUZ0nKJ4VppQskif7Uiqs2U+Hwq9IH7zFYkgWx1UrnMs3QdO6N+L3F7GuJ+Ccvcbp0T2EYuyFanmw1UiERIYT1TY9L+CocVtFk0BAI1BUwz2XOsKo2scruzaVWqs8ruWZ1POK5a8twpeptCJJLJ1rV4dEhCnbisnm1wqeV8Tp7ZhHOJasaMbbquPrfyODu9qOgKN14nVdnTWjQeTHvbD6LnM4EYN2NTilIuBa7IVPU6PX8TU0IefmKCXLX9KtxsbbqLjq2osrCWzpNo6NyifC1Nc1QudCl/rgYUhxTVU/JM5ji8LaGmw+Pc1h2j4ubdN0ljtqzKzduI0CWHIsEuWJtsSye6g9fluaNLabXuH7rv4QN7nOjIg/cDAPrXCUE/M7ARALD58uucbDSkb42BtVJL4PLzKWFAUJHQln6e2ApW3sfff/xhAMADT0gp5URG+n719qcBAF789FudLM8J8afUu/me4xTecKyj38l6e+n7rjcn30cTxUHa76DUaJgc43CIeuuL0cbF0Z0BtPdM7uHh4eHh4eHh4XEGsKiW4CPjx+QfGbr0iQNiFcwM0uqwWh52sk09tBp4xi+9wsl2f/MLAIDkjKxaCjn6ng+srLACXjHaiqwy7r6TyHlPW3m5k/V20SpKr5yMOk9nns5TUYS3scMUTH5sUu6pMU0r7JIK9q/kaIW9crNc796H6NjVWy5zsg1rNgIAwooiMHBqlWQbrm5DvQ5vRIS3ivqdLHRGEQeTBbIU16ZkFdko03ZZ9W3IK0DTxDaLWFbSP+UpTjXXlCyNLX/KTFlXq9bIuqyPkRWnSg+kUgq5/RzLSt07Vw5M5UXnAadGi6ww+p4sYkymZxEz09LGE5NkJZipyto4yVNESqVSi4gwVq3eo55r7lf+q64XcepSKuVaF1eP0teIDMBN3Jc4AqRWhdtPCd0hql2OQKTOzT9nsmJxqrNlrar0mOQ5Jdb0fZbRTLqMSVnHRJMmugnfh1HWPrBeiyWZh/eO0PheXpCUkBd0riaZeibTU+QFCWqiy4QjTTaxR6VZzoIvCklyii3tIQrZO1dSc+TBMUrVeO90IF4AACAASURBVP+gkHsOFtmT1HTvTKbSbWC2p9UDoU3gdcmyc0CXs7TE0NUt6T4mj4uF9NhR2t44Lh7rfP9GAMDaS250svIoeTR3nJCUpWGGSPhXbL3QyY6O0/kGDz7qZD985EEAwOjUiJOVHhBy3h2cwnV4pYyTgKvYjk5Lpd+dJXpv9Fz0Qidbe8XPAQAKyvo/fpQswXt2iOX5xAS1vxHnIo2p2Hkm4C3BHh4eHh4eHh4eSw7+I9jDw8PDw8PDw2PJYVHDIf7zix912+MjFMpQyAiZyoT0TZ7NiBm8j3lif/TW1ztZZd1mAMDIITHJb+wi98rMuFSbSaTILZ1S56uwW+Db//RXTrblYspFPDQjeWmnFJGgwa76rqy4ZKZGybRvVQ7fSlTdRlVO23QlBZcfnZb1xqWXXUvt6pZg9qlJunYikLYG7FLKZNovJ6lRQeuRy7palRCCwSFyc2Szkicwm6XQkJTKL1ub5n5WrnX3a0wUiJ3nX/ro5mpgMUS2mHpzNiZEwih/vPs1IY9NmkM8Umlx9ZU45KJckbEhoQHtFdoyPC5ECebAwRq5vwa7HjOq3RFJrqh0Fuu5iinzF/J5NMdtmnOYdidUSInLl63jHfSzEYXHxOhHw+UJVojZMYpySKuEyGXOmWrr6oA0V8Rqw3CI5vzIcQ8PzSdWsamcF1KN84j0pCs7DY1S2NeOnBB3ew3lCq12i2yjoUHUOS6hUTnmaaWNdlVLGxrc8IZynSa7aa4wGZlLp1hxj43L3HzXof0AgAeHZO6ZjMnlHG2FOr6Gz9cUPtAu8LoEcI7osqnuZdTm1nZaFZbwxE+oZsH6q252si09FJawfPN2JxsdpfCFXff8wMm++MPPAgC+/A0JF03yC2imKNcIy/y7mkM76qLr+77/fQDAPen7nSzbQe+7dJ+Q4FZeTaEPm556rezXT+/98tigk+39/k8AAAd2S2hOiSuDNs/J0T98OISHh4eHh4eHh4fHGcGiWoJ/581vctvpPK0okkZWh2mOke/tlnriARsSS1bITTe97rcAAN/5x/c72aM7abWydYVYlssVWmVm0rLMWFXgFdGYBN/PPEwr44QiABRUlZd0geTruqTC23SBVkyjZVkRP86LqNqylU52/s2U4i1M9TpZNkkrY1VQDKmoHrpa3GajSkEqzVi7IFBpcdIpTrWjqsiVqrTKnK4J8aGeI93kc6LfFDOS6kWxVERWYRuzOmwiPc1rVbWxe8XZ71ptwvFXMEmu016QMZZigsfMjIzP6RkilNTrug3tZQGOMF2W1XYiSZbYQFm6bYOeg0ZDW4K5spzKHlflfzT3dVwVudbKTpEFujEhYyXhKjo25YCSTZaHmlAR7apTsvEzpoampEhTbcixt6WhrA/Vam12CxA6Eqg6YZsg0FbB+awoqs8sP8etybeaq0KOTtJcGiYl/WNYp3EeBlI9qquPyDjZlKpyOU1eIduQeUz7thqGyLNBtyIUc0XAUl2O2VscAgDcdfiQk91zhCxhh8syourcDZocGxlTda/IXNt+tiCvS8K5oMs5EjTG7KeqsHIascdvv8vJVgzQezO3YZmTrbmMPM1hIFUtD+XuBAAcfUhIcMNHyfoa1NR3jaW+SiZUnyVEmw1+DyQ6hNyeX0PpZM+7QizUF9z4LABA/xqxDoOTBBy470dO9MSPHwEAlAaLsl8tpvroIr8r23HEeHh4eHh4eHh4eJxR+I9gDw8PDw8PDw+PJYdFDYe49KJL3XaV8xxW69KEZI1M4hmdgpY97WFCTPLHOZj6lW9+l5P98JMfAQDs/ME3nGx5J7kPCqG4YUZGyS0w0C0u7UqJSGk9GXHTFxvibJmqkKt+dFTWDJk8hTQMnZDA/rBrLQDg1b/5DiebSBLRoFxTFW+46lRTuLyJGDzahU4uoOSiamlh0NVcotyRQSqlZPRXh02EnG91piIKLmSoH9NqPVYpzkOWa2pE05/m/Zp98LMPiUUsCS4p95TJ0/hIZsTVVyxx9b8ZcfE0nOcuzjHZXohCIAAgkeCHTVXlQ4KJbOp5CDgfdEdWqrVNslu2XtPJd/nPSQiO0XZVc21OW7jB3NXochmVDzlNz+eMyj0e/RqonMZR7vHYvJZnGYEav4ZdmSft+5gUtBESescqucSLKh/pvog8PCV9Zs6n5/mC5WucrLNA83V3QubhggqvCTjmrSHDCUcmqArV44PiLt81RC7iXccl9OhEhcZs1cokGXKlM50ytuGc9qrSJfvQk8n2eza9Lgnngi6btRTGyCKoynxlCic4+tC3neyB5dS3lz3jWU42sIFCNM+/+ulOtnL1Rjp280NOtuvHdwAAisclcYBlQnKgiNAp9Q00sJxCH9ZsvVjOvZlCZNZuu8DJOgdov0pRxs7RRyn0Ycfd33Gyw4/tBADUSkK+EzRlk4+RnTl4S7CHh4eHh4eHh8eSw6LaGOt1sYZGKZBSKZVeha2hM8pGmuIVccEoK6OlFeOuYVmNXn7bGwAAA5dLmo7//sQ/AQBqR6Qm99aVtGopTqn0VUmyCudUepXpumoXWyt3HJlysu5+sqBtffrLnGz7M18CADjeEKt1kc9ZVRaygNPLRNYnAAhccLqyQFe5Wo6yMq5HeyCjLBVRrL0JZOVm2MLYv3aDk60oUL8MH9nvZGMlusd8TgiSGbaAV4rS31F1sqZ0Z6bV9BGRJsycVtgo9VnrPTWR16IqRx2SMijJY3a6JFV1Smw5bGgrpju5sua4n9qLIGeSymRjaYUeaFIh30tZW+XZClpQa+iuHE0lk+qeG3VO4xR34bmz2zVt69RrTbaCUzT46P0znOItm5XprxpdSHk4ortThmCpGrhIde1PBQnt8YjGYAzfJNT6ZcKRHr4J9tIZRc5Kt05PmGHC7s6jUulqzwlKb9Wdl9SIa5b1AQA2DwixeIVRBMQSzW/DU6NOduAEEacGZ4QwO1mnObsWqhR+fINN1pygpanq2dZp/SJypdel1+WZxMmo2TH7sbetNiT62HcPEd6MIgluv/EaAEDPxtVOltp4CQCgf9UqJ8tdSpbb8pjoJbIEG/UZmFFp7Lp6Scc9y+U8hS4i5WXUd1t9jLzrRx7a6WSP3Usp2x794U+cbHIiep/Lt1xsNcRFhrcEe3h4eHh4eHh4LDn4j2APDw8PDw8PD48lh0UNhzApCRNocHC+VQSTJBOrgpQ0K+A8pYmKuAAiA/qIctE+/gQF3ff2SBD/i9/25wCAwoS4ACIz/Z5dO1TD6DzToVSM6yxIXt90ioLFt16xzsm2XX4l7bdKZENhVC1HXMzVGbrPunJSpRKtpv+A3Th15eKpReEhqZbdzzqMqqxVq5IrzTa0C49+t6oKWB+TEVNFCb4fHie9Jftkv85Oyjc4qlwkFc4jbFXcgbUxVXek/JtubeymOyYKkYjyHQNIdZIrKJsX91BlmsZRuSxhOJYvaJS/3VWba8pVPF9+yLMIK3kjXSiJCtmocRhPFNoAyH1N1ySkKMvjNgqLAIBShbarNZX/Mo5P5q6rRHbWj5hFDHIe4vlJjwmuMpdX7FKOdEG1Lu0POY9qXLbK2CyfcfE0ZxlG5/vkjjaK9FLn+TKh3NfR8xJnDbFKWY0wZvxG1wglv3PIuU6nAtH54BSdvVKVsXY4FHJMeZrytA9Pibt8qkLnljMDDbDiEpo4xRsqFAvhfLYd+U1Iu16XXpdnH83zH/eBIurOPE6hBY+NH3WyySGqd3DBDTc42bJNFBqR7e9yshWrKBwivVauEQW5pDXzXukjqlOQVaTomQqFNAztE5Lj7nvuBgA8cf8DTnZoxy4AQGlYab3hav2JbN734uLo0luCPTw8PDw8PDw8lhwWlxin8pyk+SM/qSuUsKyiqpqAVyFptQqOLMVjVVltdvRQwH5FWZYPlGg11ZHpc7K+G14EALj16T/vZAd37QYA7JmRKnIXbNzmttf30/IpnRYL5uPHKL1MQ1k6Z0Ar4qQRC1OaDYmBSvviluyKfBfdp07HlMqQ5byRFQtlu6CuVmmRBuvazMeB/ePD0qf7KkS0sCXRW1Txq16XfuxYvoJ+U5b+Sba0VtXYsDGpcpz1bg5+hIkLxGcTYzIr+i0MbAEArFwhVXBq+x4EAOyalLQ+dVfJrukiLdeQ9W57WSqSKWVJjbL06WpVnLfMqmfXsJVfV5ErWxrzeXH2oJOJZxVlaShX6SINpdswTiUxbbVx/al2TLA1SDmSkElyFS31qJXZihWG2rrNf0N9jdbSVNF5EkldJ6s9EKiGhkFM+7izmjicrspYKytR97eNeaCc86PpWrRdV/04zduFvHjISsq6NMK8X/HXARXu54ayBDpSYkPmgLjnKYgx7ZhorghaB1nY1CPtAa9Lbs45oMuTpkgzrTpyE5vWVZmsqvVjktru8BSlIBvZu8vJ+racR3+3bXGy1VtJtmy5kBxzXAk3rbx3VUVaK42Rx3No7+NOdvzEIADgwQelGt2RJ6jy4OTgsJPVmfAeX+ww7uUcxvzuU6R5eHh4eHh4eHh4nBH4j2APDw8PDw8PD48lh0UNhwiSYt7OpMnFn0+Iq7/CPwd1CcQ3tjXgvc6/r+0VV/XEBFW8qSl3eaqLTP/HyhKcncjQ9Y7PiKzIHpRx5J3MKmJckUMZKpNSHW75OiLgjVckh28yyy4qK+eOAvZNqNxMXAZPVwVKR35W5bct8+9JqPCQNkGtqnVEimtys7GbqzwuLpJD0xTykNRkjuh8Kn6+VqX+zhfEdVPinMmhCj8JI7eYIpFIjlvt6pa+d16mmHvKFqSK4NYLLgMArF250slOzFA4x57DB6X9YdQPC3TdtBmhKqErLIVRNTS5lwYTEQNFQhRSmKoixzIVYYAaVwhMqXCIDk5SalWO7FrkGVXXdYRZ5Ypt0jM/J2k1gznXqQqJqnIb6nWln2i8NuX/ZYKjiJx7NlS2ApeHNGy/Z7Ip5oMRaPIQ68soH7ONiUWJ7repL2JCfJw7Wg8hG/WZKCaRpXk1VZD5NVVTFSej+BXtIeY2JJXO1WhT+0V/1ZwSVzotJtVu69naCF6XdJpzQZdNiHH1R0TqWGJwzB2papp2kt6vJZWXuXRoPwDgyANCVNuzjioMnH/tJU62bht9w3TnVbXWGZnXxo5S2OLgIw872fceppDAo6MS0minmRip59ho3tZs5lhWdByTfb6qeqcf7T9mPDw8PDw8PDw8PE4zFtUS/M2vf9NtF8eJXDSjLLJVNglNTUpakIlJsrTWKzrNEq1WgqoQ0JIVOs/yPrHgvvzXfhUA0LFOqqlMssnRKKt0dt0AbewQq+XgsSG3nRgg89byAbEKTnNasExGCAJ1JoOlk7K2GB6mFVoqJam2chmyggVGkxB4NaisqSlmCoZh+5FwdIqoIMHB9KHoKIzIElYTPOhvQ5Eho5Qs1bpUYTtynPo2qay5ESGu3lQRiH9XBC2xG+jKXzGEg1ZeHBqhWPp3PfBdAMA+ZWlplMb5PpRlmdMINRcqiqtKx+SH2NXw2UOjrG0y1Ma6sqQmIkKqStMXcTebjFV8iHLiOAt8U9+YltMhk472E31H40dbv3RGPBtjaKizhUQT9qI2pBNqPETHqnRAdb5OU+W/mMpaYH3bRbJSnArixqD2iAjpttUCqK3wkZUxdqSq5yERHRujX6uekZA9ASYjXpyEkYESBPy7apfzLBht/YwIRPpyra4dR+Jsev4aLGr1JrQjvC558xzQZTMiTcSk97RNNMdZf+eAq3Spji1RGjOrvLWVGl2vtEkquIZ99J0V5tV4UWkvuxv0HjbjQgQvHqV3oFXEZnFU61E233uulbjZjLhUamcOT5aR4+Hh4eHh4eHh4XHa4D+CPTw8PDw8PDw8lhwWNRyiPCFB1yfGKSyhBjHZF/JEhOrPSqWTlauoid3dkuu3s6cHADCwfLmcvE7n7l8u4RAdHXQ+W5TrZmeoKtyJorjf127dBADYtEZcBZmsdE3vqlUAgPGSVAqbKZP7oTgkrgLLZLAOlUMxKsCj+FxIcrhEkFShAgnlR2YkOLg8axdVTQtCo6GqbXG4RlNOR9PqwkNEGAzlflJcXS+VlXCRpGM7Sf+kk9SBzRmTucqeql7UYLdYRoWkhMrHXWc/elpVhwti3C4hE/GmVfhCPXIvBeLMN5wTOlBhLHKEyjnrYiPazY2uKjaxApOBjN8kE+cSSo+JBvVNMqXcpMHcbrv4lbYmxFCPWUWCC9ndGyrdVktCQo2Ic3GOtaanpZXD58IhAkXO6+0jkm0tkPmjEsVfNBRrk0WZtHqg2wXaJczjsXlkt449GxPz0UpbBYSNFJPLtckV3ZqrNpGk5yWVlGcuqeYARHmntf5nX1dtx4Xh6HZFeaxN05iMyhLqlkdjqA1tQV6XvHkO6DJ2loqrQxnX9pj7Pil4PxWbZrnKaa0ohLYomcDDR0ecbEwRflfyHFepyLveRGGOdR26MU/YgibLx+U+dvd0sns/c2jHEePh4eHh4eHh4eFxRrGoJsbn3ypV2oamiTBWrCtyG68okjWV3orZVDOqKs04VyM5NC0EOptny0xerMjj47TqGajK+QbYyrj5ou1O9ugY1Ts3ZVWNLCfWsCkmZdVSIhs+Tu3PGkkVs6ybKo4l1Gqqp4urWCV0mq4o9Yy0qxEwEUiZU6NMYolqe5GpAEV8A1xDtSUvWrmbprwv0X6N2aIm8lrCmQdUlSO2DDaVlWdyYrUiteuRoJJlFbUKrtfk90YQWYJFl0GilXgYLVYDVeUowWvG5vX4qa1WTZulSNNWcOMqF2kZ9U1CsdLqM2SRTRmxhmY4nZ01mjQYU00vxhISlwUpkoUpZdVS1oc6kz+arAoRIShotbzo9H0hbzfqKu1ihs6X7BVLsGW/Q0dfj5N1dpP3KZeTeaZtoEmXcVa+IEpj1zqfJFT6qoY7jz5HoP4/67xNZvZZf1VTgpQiMinHl2VPjSYbhs7YpysVRlvy2prfNqbIWUEr2abO3kOzSAScU4LX5Sw8iXV5UktwhDgCfNx+WhajJAdlra3Tu7JRllRqE8PUVxPHJQnASCD6yAxQ5daG1XN1RMSLSxGpPyej+9QW4/lSwWkskBR4muAtwR4eHh4eHh4eHksO/iPYw8PDw8PDw8NjycHYeHu0h4eHh4eHh4eHxzkLbwn28PDw8PDw8PBYcvAfwR4eHh7/j703j7MsqerEv3Hv23Ovyqystau6u7qK3uiFpYfVFmhaEFB/qKDo2OPPdXQYdUQdFEF0QP2hoOPCD5wRRnZlEWiUvWka6J3e96rK2rKysnLPt797b8wf58Q952W+rMqCrqxXlfH9fKrzdry7xI0TETfinPM9x8PDw8Nj3cEvgj08PDw8PDw8PNYd/CLYw8PDw8PDw8Nj3cEvgj08PDw8PDw8PNYd/CLYw8PDw8PDw8Nj3cEvgj08PDw8PDw8PNYd/CLYw8PDw8PDw8Nj3cEvgj08PDw8PDw8PNYd/CLYw8PDw8PDw8Nj3cEvgj08PDw8PDw8PNYd/CLYw8PDw8PDw8Nj3cEvgj08PDw8PDw8PNYd/CLYw8PDw8PDw8Nj3cEvgj08PDw8PDw8PNYd/CLYw8PDw8PDw8Nj3eG8XAQbYx42xlx/tuvhcf7CGPMtY8w1Z7seGsaYNxpj/vR7vPYmY8xtT3edzgcYY95pjPmNM3Df1xhjPvY039MaY3Y/nfc8H2CMucwYc/cZuG/eGPOYMWbT03zfXSzLzNN53/MBxpiXG2M+cwbuO2qMedQYk3+a7+tluQK6QZbn5SLYWnu5tfaWM/kMY8wHjDF/ciaf8f3CGDNmjHnZ2a5HN8AY8z5jzOPGmMQYc9OS324yxsTGmLL6d/1J7vVqAIvW2u/y/19hjPmiMWbKGGM7nL/BGPNpY0zFGHPQGPPTS37/aS6vGGM+Y4zZoH57jzFm1hjzHWPMNlX+c7zZO2iMWTTGfBfAGICfebo/yOcajDG/boy52xjTMMZ8YMlv7oOkZf2Wk9xrBMB/BPD/q7KX8sKnaoz5ujFmp/rtTdwPHjLGXKHKX7B0srfWfhbAFcaYZ/I5HzLGHDPGLBhjnjDG/ML32xbnKlYxpm4xxtSVDB8/xS3/GMC71PUrjkljzFU8tqaMMb+pyrPGmDuMMTtcmbW2AeB/A/jdFd7jEq7nh1b/9ucXjDE/yONk3hgz1uH3MWNMTcnyS6e45TsApJt9HtNf5/H4mP7m8Vg9wOPqdap80BhzrzGmz5VZa48D+DqAX1pSv9Pta+ctznVZdoS11v/7Hv4B+ACAPznb9ThFHccAvOxs16Mb/gH4NQAvBXA3gJuW/HYTgNtO4143A3iD+v+9AP5fAD9CQ2rZ+R8F8HEAvQBeCGAewOX82+UAFgG8mH//CICP8W/PBfBNAHkA/x+Av+HyAQD3A3gngF2gzeyr+D4fBfDb30P7nFYbdPM/AP8PgB8F8PcAPrDkt10ALIDMKu/1JgDvV/8/zPL7CQAFlsvt/NsWAI8B6Afw6wA+z+UZALcD2NXh/r+v5Ho5gDwfPwPABIBnrVCvjvXnd9t9tmXwNMjwVGPqFgC/sMp7bQEwA6Cgyk42Jr8A4BUAtgGYBrCZy38XwO90uP92AFNOdkt++xKP4Q+dpH7LZHm6/bSb//E89rOgBclYh9/HsMrvFIDnAHhySdl3APwlgCKA1wKYAzDCvz0I4AoAV3EfCLn87wH8ZIf7vwDAQ99HX/Oy7GJZdqzH2W7UMySoMQAvA/A2AJ8A8H9AC4SHATx7yXn/HcAjAGYB/KObKNFhUcAdeTd3gBaAJoAygM+d7Xfu0Ab/BCABUOM6/g6Xv4bbYY4H96WraY8O9w8B/AVo8j8A+uinA33pYGBZfEj9/38A8G2ux/0Arle/3QRgP8vsAHjByW3/DdAHawrAx7+HdrkN38ciGECO23R7h992Y8kHG0AP95M9S2Tzp3z8DgAfUb9dzOf3AXgdgHdy+Q8B+AIf/w2An+7w/AcAvAe0A74ewBEA/w3AJIBjAP6TOncjgM8CWABwJ0hTtmIbgLShB0GLgrdo+WLJhtA9W/3/VgCfBHCC5flG9dtzQRuTBQDHAfwllxcAfIifNwfgLgCjpynrP8H3vwj+GoCfUf//SwC+vUS+NdCi9ToAH+XyZwB4hI9/G8CbV7j/CwAc6FC+l2X2k7pNQQuxCQD/xOVv4vPGAfw8TrIIBnAhgFtB4+orAP4WPCaXymzpGAZttH4PwD6WyScAbDiVrLDCWD4NGS4bU1x+C1a/MPmPAL5yGmPyUchm5HbuoxeAxkl2hWc8CeAHlpS9ntvpbWif+24C8C0A7wZ9zP8ENJ++CzSv7Qdt2lfspwCuBfBdbtd/Bi3o/0Tdv+O3i4/z/KxDoDH3XgBF/m0YwOdZjjOgBXzAv/0ugKP8zMcBvPQ0ZfkyfP8Lpz8E8A/q//cAaADoU2XfBPArfLxflU8A2MTy/PcV7p8BUAWwczV9zcvy3JJlp3/npTvEErwGwMcADII++n+z5Pc3ALgRtPjYA+APTnVDa+37AHwYwJ9ba3utta9+Wmv8NMBa+7OggfFqruOfG2P2gDQgvwFgBKTx+JwxJqcuXW17/CJIW3I1aBD/6Grrxmb9m0ETxgbQIuGTxpgRY0wPgL8G8AprbR+A5wO4jy/9Y5BmZQikffmf6p6fN8b83mrr0AHXsPnzCWPMW8zK/luXAEistUdWed89AGJr7ROq7H6Q1g/89373g7V2H/gDDdqsvMgYUwRpsR82xjwbwF5r7Uf0Q4wxo3zNraCdMgBsBmmNt4G0an9rjBni3/4WQB2kJft5/tcRxpjLAPwdqG9sUfc8JYwxAYDP8Ttu4/f4DWPMjXzKXwH4K2ttP6jPfYLLf46fswO0YP8V0GITxpjfM8Z8fjXPPwkOGmOOGGP+0RgzfJLzrgR9JByWyqsCWhheDuApAFcaYwZBH4mH2XT+eihT/BI8CmCXMaYfAIwxf2eMqYI0ysdAY9RhM2i87ATwS8aYHwKNnRtA/fJUrk8fAS3kNoIWZj97ivM13gga4z8A2tTMgvoQsIKsTjaWjTEXGGPmjDEXnEYdluKdPGa/ZU7OAVkqw1ONyYcAvNwYsx20adrH7/E71trWCs94FDLuwPJ8O2gT2gnXgRZImwD8D9B8+ioA1wB4NoAfX+lleL7+NGjzuQE0p//YSud3wJ+B2uBq0CZjG2hBAq7vEdD3YRTAmwFYY8xekKLjOSzLG0ELHhhjXmiMmTuN53fCh40xJ4wxXzLGXHWS8zqNx/3W2kVVpmU5ye4tV4GUQrMgRcEbO93cWhuBxvHSOpysr3lZtqPbZdmG9bAIvs1a+wVrbQza7S9tkL+x1h621s6AOvBPrXkN1w6vA3CztfbLPJm/C2R2eL46Z7Xt8ZOgxcsRa+0slF/PKvAzIK3mF6y1ibX2yyBt4Cv59wTkK1m01h6z1j7M5S3QAmCrtbZurU2JXNbaV1lrvydSGGjheAVoEnst6J3ftMK5g6Dd82rRC9Jca8yDNL0n/d1a+xBIg3o7SBP1Z6BF4xsNkeBuNcZ8mBdxHwbwQZDJaIDv0wLwdmtty1r7BZBFYK8xJuT3/ENrbYWf88GTvMOPg6wdt1lrm6BJ1q7y/Z8DMme93VrbtNbuB/B+0MLQ1XG3MWbYWlu21t6uyjeCNB6xtfYea+0CAFhr/9Ra+6pVPn8pprhOOwE8CySHD5/k/KXyPpm8pkFj5msAfhi0QP0rkNblx4wx3zDG/CsvrhzcvQcBwFr7n7lOLwLwKZBmxCEB8FZrbcNaWwONwX+01j7Ei/G3rfQSvNh8DkjmTR47nz3Jey/FLwP4fR7vDX7Wj/NmcUVZYYWxbK09ZK0dtNYeOo06aPwugItAH/33gTbzF69w7qplyMe/DeBXQe3zmyBt/SKA/Sy/bxhjfmLJ9Yv8HIc/BvC/rLWHtOcPLwAAIABJREFUV6jTuLX2f1prIyXL96i5950rXAeQFS0D4K95bH8KtLk5JYwxBrRI+01r7QwvON6B9vG4BaQ9a1lrv2lJrRaDtI6XGWOy1tox3rCD54XB5U9bNd4A2mzsBFmxvsgbyU44XVn+CmgMvg+06ftVAF8FUDDkc/51Y8wPLLl+qSxP1de8LAXdLstlWA+L4Al1XAU1mNby6UnqIEjLcb5iK+gdAQDW2gT0/lqrt9r22Lrk3JUm+07YCeAnWBM0xzvPFwLYwh/z14E6/DFjzM3GmGfwdb8DwAC40xBxZUXt5enAWrvfWnuAF+QPgjQ4K+3eZyGDcjUog3xENfohg/+kv1tr322tvcpa+zpQu3wTNG5/CaRVfQy0iG+Cdvd9kIlkmnfDDlXQRDMCmniXynoltMnaWlsFmb5Xg50Ati6R9ZtBmgmANNR7ADxmjLnLGOMWt/8E4IsAPmaMGTfG/LkxJrvKZ64IXmjfzR+s46A2e7nTxHbAUnmfSl4ftdZea619BWhj1QCZOt8F4NUgc6fWCrt7p9oXXkjeBrJ2/Ko694S1tq7+f+kYPJUMZ1h2Dqc7Zj+tZPgo6GM6ihVkdYqx/H3BWnuHtXaRNwQfBJmkX7nC6acrw4PW2ldaa68F8K+g+eC3QXL7OMi6+JdGEVj5/nMAYIy5GqSVf/dJXmFp25+uLI/ygmal+62EEQAlAPcoWf47lwPk4/4UgC8ZY/Y765q19imQBfFtII3cx4wxT8u30lr7LWttzVpbtda+E9SOL1rh9NOV5X3W2uuttdeB3Px+HrRQ/AcAfwTgPwH4J15QOqSy5Hucqq95WTK6XZadsB4WwafCDnV8Aci3DgAqoA4GADDGbF5y3Wo1YWcTS+s4DvqYAUh3kjtAvkEOK7XHUhwDfaQ7XQcsaT+QKdfhMMincVD967GsybXWftFaewOEaPR+Lp+w1v6itXYrSDP1d+bMhIOyoMV2JzwJarpVuQMAeAJAxhhziSq7CuTqAP6rzagXgXbp2lTr3B1+GfRBvgLk/xuBNg+jAF5rSbt/KZS5fgWc4GuXynoltMnakHvGRvX7qWR9YIms+6y1rwQAa+2T1tqfAmnh/wzAvxhjelhz8UfW2stAlopXgXw7n264MbKSvB8ALdIdlsqrB+TG8bC+iNvoHSCT5CUADrN29C4Az1SnXgryrVvAcmT43kvr6nAMpyfDDcYYLSd97dL5LoR8TAGS4yuWyLFgrT16MlmtNJbPAE42ZpfK8FRjUsP5LR4HmW/vttbOg8zMeu7R4+56kDbskDFmArSAfq0x5t4l9dU4XVluW/KxP5ks9XicArkVXa7kOGCt7QUAXuz9N2vtRaBN228ZY17Kv33EWvtC0DfEgsbrmcDpyPJhABcZFRkAK8vy3QD+gLW1TpZjALLgvs4Kst04+Ry6tH5eliuj22XpF8EAfs0Ys5139W8G7fQB9kUxxlxtjClguanxOMhE0s1YWsdPAPhhQ6FGsqAPdANEUHNYqT2W4hMA/qsxZhubO5aGCLoPwOsNhRVa6hf1IQCvNsbcaIwJjTEFY8z1/NxRQ/FTe7huZZDGCcaYn1Cm5FnQAItX0xDGmBzL0QDI8jMD/u0VvMgEa6reAtIALQMvNL8C8o109zZ87xz/f8FwfELWhn0KwNuNMT3GmBeAGO//xJd/mNviRfzObwfwKdvuFwUQY/atrMk7ADJtvx+0IP4XngzA9fq3k7WFJdegTwF4mzGmZMjn9+dOcsm/cB2fb8iH7Y/QPrHdB+CVhsJObQZpGRzuBLBgjPldY0yR5X2FMeY53FY/Y4wZYauE27HHhkLxXMmLsQWQaW+1ss6wPEIArn9l+LfrjDF7jTGBMWYjyNfzFl7YdMIXoGQN8t+7whjzWn7GHwJ4wFr72JLr/gBEyhsH+ebv5T72gyD/QYcfAPBvxphNxpjXG2N6uY1uBLnlfO0kr/oJADcZioFbAvDWlU601h4EuRy9jcfC80AfRocnQFayH+a54Q9AmzGH9wL4H4bDwRny3/8RPu4oq5ON5VPhZGPKUEikG51cjTFvAEVX+eIKt/sygGv5fqsZk64Ol4EWtH/PRQcAvITleAlIro7jsAHktgSQufZikJ/m1dx2N4N8L1fCJ0BuTtsN+e2fjN/wHVA7/jq//4+ACEIOK367eJy9H8C7DYdS5Dn8Rj5+lTFmNy/KFvg5MY+Zl7AM6qDF12plGXA9svS/psDziPMNf4Gbn40xbwIRur61wu3axqMlv+77ALyVr/8x0Cbzk0vqcAOI5O24BE6Wl4P6ubNsPRe0KT3I151uXwO8LLtSlivCngYj8Fz5h/boEJqVuwvLIxi4aAhzIL/Ikjr/90G7rcMgP1bNyryEBTYH4DNn+51XaIcfAU3Uc+CwWSCn+0dAJvNvgMMCraY9ltw7A9qNTXMn/E3Qx8/w7xcBuAP04bsZtNjQsriOnz8D0kzeDNoxb4FEgHARLC7ja/4cpLUug8gqv6Tu929YgYHPv9/C8tP/ruff3gXaMFRAC5S3YwUWOJ//wwD+rUO/0v/G1O8bAHyG738ISyI7APhpLq+AFt8blvz+gyBfbl32v/g5CV9X5n/TIM3w9Tg5238ExBxebXSIm7iOLjrEUQAv4t8KoM3SAmh3/5tYHh3ioyDXpFnQYsHV40Og6BVl0I7/R7n8p0CkiQrL5q8h4/bNuv071PVtHeTxNnXfA3zfY6DIMZtPcq9hkNavqMpeBtJq1rhf7VpyzV6Qxjejyt4EmkseAXClKn8QpO0YAfX7OW7HBwH8ojpvmTy5/Pe4XVcTHeJikDvNIsiX7n0gv1Ut42Msj9/G8ugQv8UyWQSNv3ecTFY4+Vi+gGV+wQp13dVBhmOq797F9Zjj/nTDKebCfwbwutWOST7n6wCuU/9/FctvCsBvLZHtX56iPy6NDrGU8b90Pj1VRIFng74/ZX63TwF4i/r9ZN+uAshKsZ/72qPgiC2gsTvG7XLE3RO0GLmT23wGNHds5d9eBKB8kve/voMsb+HfLgfNGRV+969CRXBa4X53LZHLLu5bNVA/fNmS8/PcVjtV2Uv5PY8BeL0q/1u0R685aV/zsjx3ZLnSP7dgWZcwFOz5F6y1XznbdekGfD/tYYx5BYD3Wmt3Pu0V60IYyq72XywnzOgGGGP+C4Ad1trfOcPP6QV9EC6x1h44k8/qBhhj3gFg0lr7nqf5vq8G8LPW2p98Ou97Gs//OIDHrLVvPRvPX0uwVveDAJ5rn8aPHmvT7gfwYmvt5NN13++hHneA5t9/PFt1WCsYY14O4D9ba1cdkWiV990E2rRdY9t979cUXpZPy31XLUu/CPaL4BSn0x6GfB5/EBSybBQcxcBa+7Snl/U4++AF21dBbhB/AdLkX/t0Lig8ziwMuaDMgLRTLwdpQp/XTRs5j9XBEAv+cZCG8A0gl4uLrLXHzmrFPE4bXpZnFz6Xtcf3CgPyDf04yHRxMyQ+ocf5B+czaUC+pa/3C+BzDptBptaNIPPor/oF8DmLvSDf016Qa8qP+0XTOQsvy7OIda0J9vDw8PDw8PDwWJ/w0SE8PDw8PDw8PDzWHdbUHeJdf/h7qdq52WwCAC6/9NL096uuuQYA0IolWkeLAzElmTAtm5ydAQDMzksUqalJsh5s27wlLTuyfwwA8MD9Eiau2aAETNe/7KVp2e7dFO7x6IxESZoty73veYiy9k7PTKVlAwUK3XfZRRKB7IpnUBz4HTskzF+5QT7Z5XotLYub9H6PPyIZA598jMLCbr9IwoK6VFGmUEjL/vsv/PJKMffWFMYYb0L4PmGtPeuyfN5LXpHKsbdAoY/78jLWqvMUt32uciItqzUpclaSkS5QQAUAcOyoxHkv16ifF/qH0rKNvT0AgJmJ43K/FmWijcOetGzbRTQ+dz9Tciv0FSVjbbZM1+/77jfTsicnaPwO7X5eWrbn0r1Uh0jipU88+QAAYP9+GX+LdRpttiD5D66+7hUAgM2bpSxr6byP/oOE1ewGOQJAouKVroV2I2klAIBmvZmWjU9Q9Lf9j0poz8cepz5x8IBkGj8yIeHHKzXqO9Oz0sdcBL5GReQ2Xaa5NFaJi5tNkkdsEyl0rdAWydUsL+ITtTV0avq4lyXDy/LpwfOetTutVC2hObGtYpaWYdWGtH0cUY4jq6QfZJYv14KA5upSUaIZBi26TzOWdoxiul/U0rmTuFqqMlEUqF9dOy8PiRy1RHCRe45qe44+ikSHUGa5Kekik6P8R4G6NuZOoZ0UJo5NnjFZek2wh4eHh4eHh4fHuoNfBHt4eHh4eHh4eKw7rKk7RKsl6v56lVLYh6GYXvNZUo03lKodIa3Tm+rakY2UsbW3tzctu4rNnpPjQqo8xC4XW0c2pWX7DpCJp7dQTMt6iyV+lLhA3Hnn7elxz4ZBAMD27ZIlOB9S0112+WVp2fZRymioTQUhZ0MMjew3FqqUITWXy6Vlz3rucwAAt33nDnn3PP2+QdW/WxApW0V4kvM6ws6mhxNffS8A4N1v+0ha9sWj5DpSyQ+nZbteQWEE3/tffzotu+Wv/xQA8Pdf+HJaNhVfCADoe8Mb07K/ePOr0uOX56jeB/eLie+zH/jfAICokRahFtNbHclem5Zd9pM3AAB+7hqpl87vuBoY0xUWuhRzs9X0OBoiQ1WhX7LlZgrkehTVJLP2QkR9v7ckad578jSGNm6URownKXFPqyUGsExALZbLiOtRnU1/QSJ9qrJAv8+y6xMAZI2Ml8jSeMoOyhxgjpOLxImpsbRsuEIuHqOBmAEjdk3KJjL9FWjqQSOS81p1Ml1GKnFaGHSvF9BaazSCDD0xk5d2TAzJw2Skb+SKVJbpk3GD2Yrcp0nHW7cMpGXNFt17zoo8Kmyqteqr1WIzL6LlcmknfVMfTBJtsl3hxboAXpbtOJdlGSnPwTCg9zZqPdBo0O/6fWI+tonMnYbbKpuT+agQ8IsrV4qqWytl5BnOLSFfkDm0yHVoJiKXuYbM31GL66Aal6vf9h0LeY1mVf0Trrd2mrR2udtEs0ZrpUCtA3PO9TVZm7nWa4I9PDw8PDw8PDzWHdZUE6xX3E77mihHbZNqTWWX4fYCuVCqmu6OlNZmbB9peBenRXN0+R4i1dxXuTctC50vuCLflWdJM9mXl11Sf0lIOhPHKRFQkpF6XbKTEqMdOiIEgQ09tEvOKgf2gLdOfUXRPI8fpmv2H5BkW1dfdTUA4IrLr0jLnty/DwBQygsxrlvwdO2ecnna1WbD5V3RqnTmcYtJE9pJP0/qO60FMKxtiGrltCyJtSs+9zH1AoY3npqQ4XbONlH3iURrer6grogwqJDmtqI04pmESS11IYc5K06hV3bv9ZjJcoHSGuXJshLXZZw6fmiQU326Qdojm4gFxTYnAADlCXlG1opFpFii/lLYIKS7/hLV9fjCRFpWnaF3inpF4OUqyzQ3mJb1FVgrMivknXKFKjvfkHr1nLbZY+2g9SarVYy5Hq/PX7VSjU/MZGXs9rD2qWdQ5s8rrrgEAFAqitY+Z6STPbKf5t+K4hcWsjQXF4oy5nJVTvyk5tdci87TmsKENYlu7gWAxGm72vi8dsnf7oGXJeF8kGU9lrr38Lqi0RSifD2httCaVPcpirW2m8sKSqoRr4HqingfRfTdzCCblpWK9NzeXpl3E7ZYRzW5tq8odY1Cuk9LaaPBmmf9rWzx89panj/K7Rp6w/VS2m3WRgd5qath8qB71pmG1wR7eHh4eHh4eHisO/hFsIeHh4eHh4eHx7rDmrpDZDKi8naEOO1g7dT4uix1kVDmkGqTTSnKzD07STF8L91zSVr2uU9+CgDw4H0SJ3jDCDn018ti5rbDVNZXFLPPVcotoWeCYiPe/+gjadnYoUP0TsqtIrzscipTdoHAxcuzUv9n7OHYpTl53iLXZ/fFEie4UCTThTHnb3brLLugFLR7gjOsaDIEu83EieobfG3YFouQTHNJXYhXiERGrssHiuDk+AM2kv6U/mqknyCqcf3OH1QWJfZ1xK4kJwJph94CuRtsHLxQXUXjYGpSyHLhMBHQ8r2ltGwooJjd2RlxUVqcOwgAaMbihpG6N2kTKtehhIW0rDwnBJwI9LwNPZvTsk19dDy3KDFKWzN0/Wxd3mlmkWSaLe6S5zkvjtlpeV6ZTLs9VSEKtg3uLkNLx+nkv4Hqrc662OYc1CEOa8QmWKPMkWb5aem1etzELLepsrRjnuOibyzJ/H9ExaLuyZOrzfSRJ9KyhOOHIhAXtYTfr9aqp2VZnl9b2oWOb63d6tw8HKlvhjRX98nUy5Lrcx7IEor0FbOp38VEBoDErXsUxVxi8ypCb5OOK8otz7mGxCrgcuo6oj57xR5aS/T0iTtEYGnS23mBuJkVlLvXBLt6Ts3JN6LBJOdaQ+RmnYuEXArTQb/qXChC/RuLS7vlxS260xp5Q3hNsIeHh4eHh4eHx/rDmqoYdagNt5PIloQwZjucF/MWr6XCjuU5vFlsZPdwzbWUbS6pyw7rIs7mtjAnIblmmQSnV/8l1rg2IrlfMZQ6XP/iFwMA+oaESHOUd0kjI6IlGh+nzDnP2CVaMxcqpKbqn+N3zuZkN/3QI6Rdu/QZ8tyeHtIU63fvGnwvzI30fKXlYC1CTt3Dsn7DJrK3TDh0y/Hj4sS/WGny4+W8fED9Kr84mZZVT0ifqDOzKaNC7mUcCU73CpabtSrTX0M0kecLooaEBbSsVZhuKCLbALXtrlEJDzjcS+OgXpb2mJkh7evIqIROKvUQWc4oAmu1RuMvVtp5m2Yukk4wUybtQ3aDhAMa7BXSXW2enmdzMv4Geil0Yt6I7BemaZzGSnQN1mYNbpT7lQo0xkIjdY2bC1x/6T+5oHuZcYkaiG6I6fxQTg+n89u5Jm+jGHGhLnMjw4VNAoCY+0lVZQebWSAte8GIRWD6BFkMWrG03dFFpUnK0vxbKIis6zHNkf2DEoRwQ5XG4pHjkm0wfRVV2YTnDWu0ntSdJicG6Xt2n0y9LAnngywLocx1Tm6RXoCw5lbzt+Nw5WxtVqfZsy4UmQpZxpbjQl4szfksERlLyvpcCFguBZFLQxHKC7zO6u+XdU+LtdGDimC3uEjfkMW61Cvjwr+qeiVORup7EEV0TVZZIALHWl+jRJxeE+zh4eHh4eHh4bHu4BfBHh4eHh4eHh4e6w5r6g5RV7HlQo4PaFXAVhdOr6XOi9gEEGTFzNFkt4WsKovYRFCvit3zxhtfTuerGHqfu/lmroA899hxiiuaUQS0zcNiZv3Ot78DADi+KDFENzHBbvNmIeZsHiJzrCb2uZiHA71icjgxT6StUknMTBluj6kZISEMDFDWncFBMUd0C6zygj993p4y3eTI5JIJwmW/ZiFmtvl9DwIA/uGv35mWLY5RmfJiQRYk/+r9X0zLPvUeOWHumc8FAOzuEeLcbI2IBg0rdXBd0KgYmJbN4ufTzjG0OkMatUOsYne3OJ7lgYa0V2+BTGqFgpjW5tmMOj8l5JfsEJHqCr0Sy3eISR3Zmo5ryRmg1HNrLZJjvSl9JRdKFqp6g8yo5VpB/e46oswflUUa2w0V4rnFPazUL7FO81ky6QXKtSa0fFFdXEaSbPeNRQeVmK89hCrDza+ab5os+QsAzjKpY2mnLavG/fQsuYscGxeC5MGjRBg+qjJ3VmZo3uzl+REA+gZkPqxw+w6ObEnLJsdpzNpExVh1pnb12WqmQ1a5WHXiRqW+Apq13KFBugReloTzQZZpBjQAWZ57NMnR8O9G+4EwOcwoUp3hPAWFWK6ts/tHqSCuXVt7aF0xMiDzJXsdwM7Jt7DJxN+p6HBapmM0mxy16aDqPGGB5syW+jZbdi0M1Tfcda2GJlqyu52WUJ7dZrKQedym3nFr49pyPn3PPTw8PDw8PDw8PFaFNdUEaw0vskQKq6hc1THv8CLlD+0iYulMNtV50hI1K6L17WEn7i0jskOdnCbt1OVXXpmWfermzwEAaooYtYl3TFsHRGM1dlx2v1XW3L7gOc9Oy6686ioAwLHDkjGuXKZdcHVBNEdbR2lHPD0tGt6+PtImFftkD3LJJRTa7cMf+0Radv1LXsIv330h0uJYkRIyp+vALmTIepXCs9QT0cplMqRtyEaiLWyOPQoAuGf/g/JcVkcnevdvaYcaKAf/ez79ofT48S99CQDQOyhtn+2j3ei2ka1p2TCTsJKcSp+mCFLnC/oLIou5Cmk+Wy1FWmOC4KzK5tZkC0ZWhVLLWBpPjUUZD7Osk9rQJ9rTYi9ZWIKcaBJqnD3KZuQZUUJ9oKLGUmOjCqHGmo9WJHPA3Pwxvlbu0+JMg00j8g5YM50pybgyTmOhVE+18jz/lWeUekS70m1oKaKTe9tAWaWay7lFaPJ7NxVZxREkjdIppjxhpdmp1Eg287NiJVhcYEtMReQ7Nkbaxfn9Empp5yaxoJV4zLZUdqyoRtfMRMo6w5n+0oxhQKo20lOQ4flSW+TimLVQaq5wx7YLtYdeloTzQZYjW3amxzVeS8xX1Xs7ba8K2wm2mAVKb9rDWtMetRba2kNzaykja5dchUJSNifG0rKFOQ4dqpdgbHnT+lbdrRLWABul4c2w9S+jiI9DBapDv3xKsNgg+UZlCXFZZ3V0EMi1hq13kbICgufq/Bqte7wm2MPDw8PDw8PDY93BL4I9PDw8PDw8PDzWHdbUzq78uVMXg6b2zmcH8aSuTEE5qmJduU0UCuREfeCJJ9OyVp3MMD1XXZ2W9eTpvIrKznL1tdcCAHbslsxsm7dR9qnGgo6RJ47aG9ld4tCT+9Ky2RNkDhrbvz8te/Y1dO9iTq4Fmy56+sSM2mRXjJ6SmIknOF7i1m0Sj/XxfU/R+d2YBSc5/Rh+lk1klXGJRXnHbXcCAO6ckVjOdZZlpLPSODuNMoslHcgQ7ijQrjfK9aXcGAMALM4pZ38maUyPK+LjdpJDcYvEcr6Y++D5lDEul5G+WspQOy02xZ0gidlVoSHjtMbuBpEipoYB9XPtSlGdJzNoozKRlqXmr0ibezkrZIe4n3FN3IjGp2SsjW4hN6NATR9JSraVMsN2Y6syTmZL9M4tFQO6WSWznY49Wm/R79VITMT9sZCAug2K45jGlG2LD8tNEDXkve+9n7JpPnLf3WmZI8w01JzrMlxpknGV3WdqCypeNMcMrZVl7LrY7LMqK9T+vJoj2fSrTfxVfk5L229dbGmVpdPapO0vABjOTJoviIuVy0IWKzO9ZbNr0IWqIC9Lwvkgy+l5cQlosWuVdllxlS70yju6j4xV38Aif6dGNgppv5+boDIu5LY6u2XquPY5NylqAjq7NISKuKcz88XsvqBjusdVunfSlL4TtpjcXpT6FwOeY3M6JjCtr3SWwCqT0iOdCZbr2MyvTX6ELuwyHh4eHh4eHh4eHmcWa6oJnjoxI8eTpB3q7RPNyjVXU9Y37QTv0qg067J7iHn3GDVlN7rnkt0AgP5+lVWKd7eZnLzmc5//fADAyBYJ4TLNO6fhXglZtqkku9tdFfp9o9qBlTkU2y7WIgPA0DCFTdv3lGisjkwQWWfTiJAHJqdIuzX5pORXH+J7v+B6ud+XbrkFAPDY2Bi6DbHayGZXPq0NlcOkGfz8p/8lLfv0Zz8MABg7IRq/KHLyUrtD1yfUVj9gQkGgSE9OU6y1CbGurMtf3paNjmS5MC19rMna0J6WvN3cVIXvp16q+xIUnRbqKr6d4Sw/WaWxaXHbJSoOXSvNpqfCoWVJq1BUYdNgSAPSgtJC8Xg2gVybYw2HZm3kWcXbbElss0ZFskuVF8myklNEtjike+YUaaPFGuxAaUD6snReRmlKKkzUaWOOhHScCaT/ZKyKtdZl0BmnXKYwHWrLqRTnpsbSok998i8AALf9+31pWZhmflJty/exSvsWp2NMyhL+PelAWtIWmxOay5pmbNRlJ4G+lsmxul5u2ogVzSdxGceMrpcjWJ3sYWcHXpaM80CWE8fG0+MME8oT9eKlHg4Xpz5jAVui44x8fwY5U2ev+pyVj3IGPxW+NeFQdKGyNPcPjQIANmzZlJZlN9DvRj3DqO9ig9c9dZVxt84hXCuzspZr8NyptdtOqx3mlHab5dpoiWUtdt8S9U213EFrbR/aMwevCfbw8PDw8PDw8Fh38ItgDw8PDw8PDw+PdYe1Jca1EW7oby4jpsu4Rb/r2IF5/n1IZVxr8XnPe+5z0rIjB8YAAPUBMVcW2LxbqUjZtp0XAADKTTF9F/rp3ouKQJdVpvjdFxOJ7qlHH0/LXOzhSWUq+NrXvwEACHNiXrAhHQ9vFsLb1DyZLu79rsS8fcYz6X7fvkdIDYObyHRxYk5MD92CKDn1OQBQm5U4r1+/+ZMAgE988r1p2eMH6N2SSHVFy22v4iHmc5xZRhEknJtL0MaGWE6aiJRc4yaZwKO6lCXs+K9dJJqLJNf64afSssfuIjeXh6+Xd3rmhVSfoq4CP7ulyCgNRULpJjSUS4BzIcnlxaUhYFKhPi9hk57O1JeaSVUbOg+EppXGcZkR8zlxh0gdFbSrS4Zck3SMypriSdTnuQP2KIKjyXL9xa2pFjDJ0mo6I9Whocx3Lk5lkmiyH90nEyhSjiIEdRtU6O40jrfVNE4n35yYSXcO7wUA3Bt8Ny0rs5tZojI2Bc5lJaNk5O4d6Lal37Nav8LxT9sJpToYbLCsyHY4z/CxVfHDqws8f6g7hxyDvtlSWQnZ7Jp0obm8E7wsCeeDLEMVAzfh99ZeV3X+NthIJrg4pO/dYGk4LetjGdaPi1tYk105rXIvK7Jb5s7LrknLhjZTrOLSoMxlMc+t7U4HqmKchTepS8CA2hS5X8wePpSWTU9QWWVevhFRhe6dK0nw4D7ODlvLK9e6mImU6rnOMydYIx2t1wR7eHh4eHh4eHisO6ypJnh2RsiVfebqAAAgAElEQVRPlQUizVy044K0LGENrw7TAdbWBCp7SIF3MOU5cbB2u6h77rorLSv1kIb3squvSsvKvLuJFKNplp28c3nR4OaV5mi4SBqhnTsl88ttt94KANi4VYhscwtUn42jQrpzTudzi6I93H+Qwpn0DkrYtDvvvQcAUGvJbrDhtFNdGPelqUNT8d+2WsakBT360L+lRR/99PsAAPtUtqFUA6zIbRkOuVPoFy1HpnCSrqquFXqHyC+riJHZhO5tlZa2VubsNo3l5EssihZ+/x3/CgD4+Cdld17/ASJa9gfSFysLpOmfU2FrZseELNlNsIqBUyhtACDtDwC2Rhpxm4g1pd6gfql5C5ZJKprU6sZxJpRxlWONRah6SxxxGDalDTAhaQ2CrGgSekJF2KsRsTaJVIi3XidnkXfAIdmsIkdWI5p78opg0uBQU5pYEzrtWSR1NUn3MiGbijlleP6yijxk+LiY35CW3fCCHwIAPHnXt9OyO++h7Ix63sn1U58Pc2K5c1pBq+drHne6yFkJkjYWlLqkQ7jFNFSdDpfFx60F0Qq6jGhBRjRhKZFMhetLIyxiudWoG+Flibb6nMuyLBblfepOK9w2jzgyt5QVcrTmGMjLtZij72ZzQUhwhonNvZsuTMt2PJM0wP1bJQNqzFbVBaMtXayV1tkEA5k7cxw6LcxLP8kXqF75ogQgyBTJcjipvnFlDjebNHSWT7q2vyhW/Sb/XFVBDhqN5RkBzyS6b3Xl4eHh4eHh4eHhcYbhF8EeHh4eHh4eHh7rDmvqDrH7wt3pcbVK5tXvfvfetGwfZ4B7w8/+VFrmSHBWeZKX2Xx95x13pGUjwxsBADu2i3vFhk0Ue7esSElNJq1VFKvnGJsXcspssWODxASe5ZjAgyqLnMtYtXm7EN4uXCCXh+kFMY3fw1l8nnOtkPiedd11AIDD48fSstsfeggAMLxV4gnvuZBMHA88+gi6DWXF3Ohjc06gzCqzRykG8t2f/kxaduApcqCPIh1ZmLOF5cVhv8DZ9XJFOS/pcOSuTTpEogzaypSpjF1pTEHIUyU2ETVV5pwqx0jMxMo9ZfIAAODbn/toWlYfoyyCJZW6LJcl8/7GrNRhoNGdxLiWIqH2M+mwT5EZIkPjJI6l/klCriStWJMZYv5N7heCyYyhTDMZJuMkkXI94VjABnKtc9MwoYy5ULlEZfncqCFuRtWE+ktgpN+kJt+akDuadRrPxiqSJddLu9GETLArqPjhttZ9JFWHSM2RgTOhd6AlRcrlKugls+Yllz0rLXvkMXLjmVlURBcmEmcK0jfA8mgzRCv5O7Sb2Pm52oXJxX3V4WFd/FB1adKiftdQsrQcpdxkpZ+0OKNh0oGI1RaDPg15230MKy9LwnkhSytrDSfVRBF+my7vgXqdfs7kmVXZ1WKXeU6l3i0OU/zfzRdflJbNRyTrBx+4PS2rsItBRs3FeY7pPrhhY1q2dUTiCA9m6DnlisyxTV63Jcp9IRyiNUtPXfpiK6JvZasmfSjHc7qL0w4ATUvzc0v51kXs9havEfPRa4I9PDw8PDw8PDzWHdZUE3xkXDKnXLRrFwBgeIM49l97zdUAgKkpIU71cuaRNtIaO2pfc821adnBgwcBAAMb5X4JO4MHyrl8jneeB6eFpGeKtOsqq8xQh6ZPpMdXbidCXFQRMtXmHaQBfuTxx9KyiQki60zOSNg0wzuvIyprzAtf+GIAwH1PSMa4rRy6TYceW5inHdjijGiWuwULSgs4wiTDrNKa3vONLwAA/vVW0dbX6iSHRO+9eNef7xOSYIY1wKaTNleH0eK/OtyMYAVNcFqkyrg/FTJSlnAGomZFaXCZfLl4SOT2CIfwuXrPFWnZBb0c1ieR/jIRKq1LF8FYkVnUorHRUIS30BEhIJaRxJB2olUXDUGDiZAuix8AhJzBzf0FkKYGShLRAJmE7tcm7zQDlCLEBEKoCJjAkVHa9tiFPFMa4xzLp1pVIfPqnAkuFg1vJsOaZ0i9MhwirVWXuWLySHcSHAFgZl7miR4mNwaKCFPlrJuNeSHWHOA5aGZGNIW9faRRnC0LGbJZpWsyKiNgJiUw6QHIo1KF2nLjNFCaHa3Z68hp4uuNunXEoZoiFd4yyJb4r8oSWKO+2EZyZM1boOoVO2uD6T5dkJcl4XyQZUGF+swxAbGp1OLlBpWFWkvLx62KrCWc1j9TkHlw404Ki1cYke/n+GGao44fEmJ2xlAf6lUk8bhA/SSn7heocLKHpmjNct/jD6dlrSa18/DAUFrmMuWW8oosN7iBz5e1XIvDbWZyoukvcgjcjNWWRveN8JpgDw8PDw8PDw8PjzMCvwj28PDw8PDw8PBYd1hTd4iKyrZ0FxPifuTVr0nLptm0MwBRq28cJcfvclXMlFk2X1dVJrCd7BieKYmJs8Vkl8PKtWGiRiaAuiJBNV38QpXtqqWy2x2cmgQADCnCzSCTiC7ec2laVmcTx6yKCVxjh/TRUSG83XY7OayPjR9Ny8AEnlpZru1lO/8l7DrSTagov43Y7aWqC2nZxENECByfFrNeZKm76exFhSKZcbJ53RWZSNH2REcOkWtdSMogVKa5NBalZmEsv49GwK4RJiNmmkKJ5Bs1lLsA94lQkQLmpyl7z2gg2Xmu2E59NrtnV1o2up2O3/Lu9yx7/tlEaMT02GiQKSwoyN44iuk4lxHTaaGHZNWnXJROMLnUKNeHrHNLMDKu4pjGX6KIqRmWdKBcVNLYlSo+caSCU4eZfv4r/SZks6MmC7XYhSVUbhpJi55Xr8tckQ+dOVX1OibvzU1Jv15orjJV4lnAfmW2dP2toNri4YcfAAAsKFewA0/QOH3yKXHrqrJbSVYRgZs8d7tMigAQcgx0o7L/OWKh0fFiA/nVYXlesiXD1F2hCJStOvcFFU81mz5bETLTfiR3zLAbVax8p1LTefdxqbwsXb3OA1nqHAcB+4REKg50yMuwYlFc5nJMpktq4triCJI9Q0JeG9pMBP2wpObBgO4zOronLdu+jVw6hwekHwQ8X+bVnD03I5ngjk2MAQAGN0i8/v4CuTkESpYVXptle+QbUexl94w5mTvLNXJ5yCXqO8vukFkVSz40tK5LzNrMtV4T7OHh4eHh4eHhse6wpprgWDmy920kos2dKkTaD91wAwBg0xbJdOLCm4Uqi8wRzlUdKIfzniHarVQVOavSpB1lRZU1eVcWKc1jhUPB6BwuYVb+b5EziW1QebdrfI1RRK0hdgYf6JWd0/T0GADgG7d+My278FJyZp8ryy7PacFzatdYyZBW+Ide8lJ0G8oqfE7M2+/GpJCGpu6n42pTZQbjnazRRIES7wDbQu+4rETLw6HpMFou5o4mxgXhcl2EUjynBIv2OzstsyBkTX++R3a8LqNhrCwGKFN9hvfcmBa94KZn07v1ipbVdGSMnH1oMlq5Rv1Nh4/LZ0irEKv4PZbD2TQjkYUjs2RzsqN3Q0OHQwOHQwt1G6by1kVcL6UNCCOrfuZQaxCtAhwRT/WvPGtFrCby1OnZM/OiRcuzEqNl5Z0yFfq9UZey7KCz6DyFbsO+Q2PpcZ1D8i2MH0zLHn/ouwCA6RNCVjnOxxX1jhFbRPK90o5RTG3RVCHpgjKfNyghllwrm466wM5jwPDMG+iMaNzH6jV5XszvFOYUyTSke9bqymIQO1KlnNZKMwYun2e6EV6Wrq7nviyNmsMaPGcm6oUKOZrLSmpNkmWiX9xQ3zHOtlkaEBmV+knj2gylzcBa5HpLyGZHpo4AAGYqQoLrHyRy24gocLFYkTWJYQLb6CZZj124iTLk6myoh2bJohAb9V3nLLu2IOs2pzHW4W4t62EzSh0bSLw7rAW8JtjDw8PDw8PDw2PdwS+CPTw8PDw8PDw81h3W1B2id0jcBLZvJhV7vS6Et4v3kptAqEyqzol/4vhkWlZlB+uLLr44LQvZ9moSWdcfm6S4vYtqqV9jC0pNOfsnThffFPV7Q6nsq4m7n9Thoi0UJ9gqE+1Wzh7XVKSxUi+ZH77+LXGHGORsc3uesTcte/AxIjNUFsSRPOQ9yj133pmW/dTLXo5uQE8s5hdbJRPYY/ffk5Y9MUEmt1ai91ls+siKCSzj3E7aeGwBn91hj6bM2i6DUpurgbOir1Bv55Gj79wxHCGb5nRfDFw/UTKP2VXm0UnJ/neCSZdb27IcdSfyRZkCags01hZnVRanHjJnZUoqex83Q0u5GTk3iDCUlk3Y9SFuKHIHk+RMJ/OlXS7btjRTKmYwHMHO6Dbmd1EuRTad4pRPDMdvjhtCmG05YpAy/dYb1A6BymY4wq5OcmX3YGpSiE7xAo2/YweEJGV53lxUWe8W69QWmYwi5TiCUiTyNTzeFxTRpV6m+4QqDnum6MytSr6uSdWga+O8BMvLoojq2qqJSdeRYgPVx2pMjo5VBkvXj7Q11cWRDZX7mjbKdhu8LN2J574s8+odLc9NhVBc5XJMDgsDmbcME7KF6C1xy8OCBA5I2AUsUgTiJsdsn52RWcqcIFeUYkGeO8OuL9URca9I6sr9L+A5Pa9cVth1I8nJfGrZ3zBQ8X/DbMDXqhjxDkq+IX+Q9bs7uWbW6KvpNcEeHh4eHh4eHh7rDmuqCc4qotAjvKvtyUnZ1277BgBgWIXkeMF11wEAJg6KI/YLnvNcAECidhQnqrTTeXRatLVzRdqhlLOy1jd8nFEh0AqsAR5sKvKPCqkU8U5uPFLZq3h3fmlRMrVs4zAfg8Oys1qs0W47Gpbd24PTFIakpyIaphxr3OpNEcnY4TEqG5D26BY89eXPpMeTGWrL++7+Tlr2IJMzGnoLz2FsTEZk3klHetKkP0rD4K5crmsWh/ul1ziFoLXLyR7tBDv6m1FaxQwTrppKq5JhbeiBpx5Nyw4dpYyAWy4eXfE1ugVhVvpgJiRNcByLJrhR43BEaqeewOWh1yQ4/l0RyywTNCykvYTwhmVlbToc/r0t719bLCbuSypsoeE6aNKJy/xnIGPXumx06j3jGt3HKsJewvUuFKSN8kH36g2aCzL3TU3SfDh+TMoCbpdqXXd0+hNFDVXGWhwdyrCX5q96TTRF9TJp9uoLkrWsh7VaRo0byTimNP1ak+hC5ClNf1Sl+dVZWuiepJHSGcqanIWq3YrQ4XkuU+EakW2+X3hZuqecB7JU36KY36daV+EeW3Tc19sh1Gfb55M1rhmZ89wSLpMTMvPo1l0AgJEtl8hZCbHfmhXRDh/m0K/VObEmAE11zH1HrYUsf0wjNU86Gel6pRw51e/cke6LyDhNsGbG0cV6zj6T6N4Z3cPDw8PDw8PDw+MMwS+CPTw8PDw8PDw81h3W1B1C25sbDXbenpY4h9Nf4ricKiPXrbfeCgB47Q+/Ki3LsrN1RWWRy7ANPa9MNzE7eecLEhuvWqnxM8Q0UwzpmgVF4tIq+zDHzuxK3T/PsfGg3CHKnO1tYGhDWjawQKaGbaNiGh9n80OhpMwCHFdxYlbMURl2v8h2Ia1q+ojESa0sUla4uaNCDmu5jDjatGWWm02cU70mJOl4zQ6dIlWmcWhVWaeWajOpd8hG1+neCZMvMqFy2E/rqs37JKPDdZHbIb7mWep+nd6pG5CoPu9Iiral4oxGNMaSmqKeuORMOpGUIdOpnlDCkMpsVpn5mCRjlDtKpxjKlvuN0VnktBnNkDnVKJcMx8ZJtDk4pmuyyqQXhvR+LeXe5DLLtTFwspyhaVDG8/DItmV17RbMTB5IjxdmaC5aXJQ5slqnd8woUk4Y0HFLxXJu8ryZU/GiS3k6Lqn5rsXZMKOa9P1mlWRe6BeXsI7oMOiilqoDm+rb+hh3Lv19cFkGrSahpjKUhyTpuNeZzsyysm6BlyXhfJBlWYVJD3iaqVblG+IydBbV+iidgVWbGXbj0tk23Zesr3ckLentIffJRN0vy4ez40Kgm53n+P6J1CVWbiwmdIQ47X7BsfkjLXS6Jmxbzrj4zsqlgb81RhMA2dVUf//dNW3x+M8gvCbYw8PDw8PDw8Nj3cEvgj08PDw8PDw8PNYd1tQd4rK9l6bHPWyeOXLkSFrm3A7mj0gkiKcOjAEAHnjk4bQszyzJSy68MC1rMWt1y0Yx3cycIPN8tS7m0Z4CmXgSFYLAsOl7ISOmlMEeMR+1ONVhY0HSPl7IKQeNCjJb5JjAs2U5r8bmqIVZift47ABFh8BGiZt49ZVXAgBGCpLDcIRZvPfdfge6DfM1sdNkQe89XBLzRR+bpsvKnhM7E7dKuWysS5vcRjHWfwAAAZvPNZu4kxuD7VDaFqr4NK1lOlax4eO2gBdcnbp6p1oHl4tuRUOZLS2bpgo5iWRi2W2p1hAzacTt4OJI0on8N6fijBpOY5xTcXt5nMbKHclFZGjvAs68KTa2BGL6DTI8PgP1O5sJdYzSwf5NAIDekkjj0BFnXhYzYNqv2tyg6BmZXmkPlFSO0S5DeVrmndk5Mp3nCyIj59oSKTeQfJ5jeyq3k4j7slVpsfv6hgEAA8MSCzS21H7zyqWtznHOczmJqBEUVGprhgl0zGcSWKMi92mxmTzIyjzs5uwoUfZlF3FGp+l1kUU6hhvR8U3dUYRug5dl+6ucy7IMYqmnSwmcDaSeMcuw3tKuIRyZIaN8DBp0XqsucdcjjsHeqMi1MzPsVhrIXDxYoHVRTUWlqDc5co6O/qHbmefjrJqY4xY1dKTyLDjPtlBH5eFIIC0VgcRFZApUJAi+XZvUjIs7bNbmC+o1wR4eHh4eHh4eHusOa6oJrldE81ldJAft6RnRMPVtIEd9vds8fHQcADCjMqnNztM101MS867UT7vVoaJcu2OIdj8HF+UZiSNsKefsQpF2TA0Vl66piDu2QnXdrDRC24fZEX1RdjqLdXq/R54S0ti/33YLACAqyK6sxLGRhwcl/m/C2uptnEkPAK657DIAwIFHJVNQt+A1r/u59DioU1s2npDd4VMPjQEAJhZECx+kKjodY5CgtYBOuR5qIsWyK4DUSV/HDg6Wnxd0ost1TBOnwRrLWBMylpO13K2HstKfRuzSOncvGrX59NhlH8oXxZpiQpJfMxbSY8wxtnW7N1OVjYoTnOFMSErTEAQ0To3KOJQ0aXwltoMWJ1Da36xoYR2JJolFA9Ks0XEYiuYKWbLYZAZEGrkpniMqqh/yoVGa5QHWIg+URIMVKdJRt+HYsaPpcZXJjUlZaeaD9r8A0GrQnNVU7xXF1Bh5yLUxaxRLRZHHMM+BtYoQtppV0kxVFyT2aE9mEz9YZ+2Tw5jr0CjL9yE0LrukVLbBFyU6ayRri4K2MWn4jyZOYVmZKK66b6R6WTLOA1nqT02TNagZTQ5njWxLEZITnnPCrMgoadD8VpsXebjjsuJqHxkn63pZxZC2oLVVuSrXlvl5F49K4IAspO+UOVtvXT1virP+HZ+T70aVPwSjisjWWKDf61XpBwkz5/Tc7ywQUVuQfsd49xnjPDw8PDw8PDw8PM4I/CLYw8PDw8PDw8Nj3WFN3SEef+TJ9PjYcTKvTs+KWn1ykVweLrl0T1qWzZM54PCx8bRsbpyOX/mSl6Zl1z7rGgBAqyHm0Q1MvptRJp4Kq93jWKdHJeSUmt5UxSywtY/cFi7oG0rLAjYVFPNiZnrqCXKDuP/JJ9KymQqZlGLlXhGwWWhqTtw0JifItWNwQEiBVW6P3qHuS5tcLU+nx9s2kPl5sX9nWjbIriPhCWl7Z/LQMZrzYPO4Noc4AhqWFXVEsDzcclusyeV3BgLla6HTby89MbZS1zhudjiPrt27RVxltrClq1tjA2tkArGj1SOXilVkm+8hEk2htD0tywXjfJ7EnLQxNVhLtXvAprUkkZbIZin2pHZtcPGXm00xnTmjZ5gtpiWBcmGybNJtKpJIg90hTE46SzmmOmQTeV6pn95pYV7ePWLZGk3E4zTqjUVxu2qLEd1lmKsodxLul1Ei7kiFXJbLpJ0tj7tIjcnYpUstyrWL0xN0MCiuMkVOfx7kxV0EHG+2VRf3tUadZJgryRgJjLRji020tiVlhomPkRqaLk1uEGhzanqFnMi+LbYt1S6n5m6ztHdI190l8LJ0L3Xuy7JZ1/MMx8rv4KKnCb11DsYeKqKx5fm0ob69iyco9XH/ts1p2Ugfp8Uuy1rixDzN2YlKM3/xhXsBALuUC2ZUk7mueYiuP3zg0bRsgd02A5Wm+eJtOwAARRW/eGGW6livKbcYl34+kHePIvqG6NjWKVHaE+M8PDw8PDw8PDw8zgzWVBM8NydhX4Y2Uga161/5yrTsjvvuAQDsGxNi2c7tlKGponYUoyPkxD+uyAP9j9IOdc8zr0zLajXSQl40uiUte/TQGAAgl1U7LN6i9ihizkBGCHZXbSEtWPm4hH3pKZJm6an9+9OyL3/rmwCAr951V1q265qrAADHTsgOa9MmIhcUVDi08hzttuca8p5HOFxNeV40xt2Ce7751fT4Md7YzR45lJaNc7gXvVtPd/UqS1DS4LApBb0fc+HQgqVF7du2jn7zwbLf2nZ6dvmNgmA5ScOFsolaOpRNtOzahLPqDI/uTss2bdywcvW6DEMqG9rkNFllWlWxzmQy1Ec39klGokwPtcPEpFhnatw2WmYxa3kSTS5MSPZZI+PPZZbL5NV0lDhNkQ7fI/dpcv+KNLGGH92MFAG3TnNOP0RztbFI77JgJtOyRioskVouR/XKKxILWt2rCdZMxZAHTEuV1ascLkvry5zlRJ3XYi37QlW07I7LWo9kDsxmOFySZrWyhtLWRQZ1Jibr7GYJZA5olGmeNip0kktS1WpjvbJ1QJ2XxE5TqMHh9XS2SveebfEN0b3wsnQnUtk5LMtmS947brlwkCpsGk9xtbLIqJJhkrIixuVKdGKrIvPz/ARpa3v6lCVylNZMm0d3pGUNN++qzIF5Pg7VHNtsyLycy9O3bUCFzSuzdWDjkGTAHeIMnfNH90n9p+gaq74HebYOx6GUVcpUr4ayzLtQqmGwNrZUrwn28PDw8PDw8PBYd/CLYA8PDw8PDw8Pj3WHNXWHWFwUktQz2G1hx04hU+0bJ3P6o08JsWx2lhztr9m7Ny37rV/7NQBASxHLZo6Tu8G+J8WVYmTXBQAkTiEAXLKVTARHjknc0xzHS9RZdS7YoGL4ztJzdmwYTssee4LquE+5ANz75OMAgKgo5tP7Hyen8iHlfD7J9S7khaxg2UziYggDwMg2uqa3p/uyVFWaQpaoW44TXJB4g0XO3Jc7IZnyms7koTLj1Nl81ptRWbm4VwaK0pZmfdMxJFNzmCpL2v7wjRQp0YUg7OBzr40vxpn3G8r8zbeJVX/KlCj+4t6tl6Rlw0yoQl7HPuxGygaQz0u7ZwJyHUiUC0htgcxai8pIWeKYyNm89MuGJYJDrGJFRjGdlwk1oY36fNRQsmWTX0ZlOAoClzFO3a8l48XF1DTKlOce01LuNvVqmc/XxFTqp4GO1dmg+reRMbhDmLz068wamei+FzRU+5gO48W5+8R6cKSmZWWy5ROqFR3rlQk9seob6fhTMVxdFsGMki+3bbMsZlUdKzxmd7QwJy5ozo3Kque5x2lSc/rctleyy8pMWsekrXRJ9bsGXpauNue+LJU4EHKsXO0tEkf0jWmqdpzn+LqFvoG0LFciYllWzW/lGSI5Hj+k5jIOHj24TdZW/UyCNCqwtOsnsXIryRZlrhtmd4nBYXF94LC+aM2Le+fMgaNch7G0zGUiLQ6IG12xn741JyoSd3hygebnmmJNuqOMWRsdrdcEe3h4eHh4eHh4rDusqSY4LIiW88g4aWLv+j8PpmV9g7Tr2bXjgrTM8o5iblp2D04LpPeBI6O0W7nv4YfSsv4K7X4yPeIMXuJt6caiIqUxaeBSpZVOKhL+aaCHdjBPqUxwt97+HQDAbQ/en5bFrFHeeoFofQdHqF533/9AWrZ7D2kNs1nZLS8ukhZuZlLCnxw+Splf9j/yOLoNtYoKacK7eZ0nPMOh43pLEuKqWqddX6wIElGDyqKmcsgPqF30RjBAB60vH+uSuF0HzNeqG6XbcjnPhaYJlRqhzvJv1mXX7TarWoPY20d9a+64WC/u+BL9HbxAztu2TXb03YRqTawzWX6vTEbGqSOgLcxPpGV11vgXC6JFzrM1oN4QcofT0gZawwsXxkk0U45wWFBjMsPXau1RU2uC+ZpQEVxzTORJVCipRoU0FrOzMgcMDZEsbEZrvVwvUpregK5JlNomCDtktesSRJHSqnGVQ0VCcYSppJP2UBU6wlGkLGMxt09gl48vrTw3LjtYKH0/4dBJTZW5U8eDSsmPalzFSUtXDwDQ4nkm0dk83T3axrhtew86b/n80cVcKi9LeYG296Dzzi1Z5nOyzHJyyAXL37ul3rtWp2/pgloj5Dkbbkllrg0XiSS3cPSAPKNB819DEe2GOIxZzwa51mRZRipkme4RWa5ZoMLOzowfBwBMHZZ11jRrgKs1sZoWec00uEGI1w1uhok5sSIscljLQIWCy6fhS5f3zzMBrwn28PDw8PDw8PBYd/CLYA8PDw8PDw8Pj3WHNXWHmFfq+UuGyVXhhTe8JC2bmCJV+8MP3idlB8mcGfaKOdM50xfYURwApo6TuTaXF1PufibJ7b3iirSsj800uQGVhY1jpWYUeSaj4ukdO071uvcRMQF89fZvU136xYSbHyAz8eSMuDS4jD3P3CNZ8OarZGqvQcy2NY6DnM2JGWlunkwdmWz3kXH6SqrrsOluPpY91YZNlMHmgqq4lSyys3+lpogWbBavKfOaBckmWxQZOOtRAm16o+eZtjJ3Y6meMdr05FLBKXcINsnVK9I/q2Ui/kVNZf5m82BJxWR89Yv+AwDgxVdIv+sfJPlXIPWfWew+GQLtZtdsgcZkX5+QI4KI3CUmp4VImrpQhGKq6+mhMaSz6mm5Sz0AACAASURBVLVa7PKQSBtmM9RvtBm0wea7WLEV82yqM4nKRqXcIVLxKTnGTNjLq6xXEbuzRA2pQ8SxncOCctPgV0q00ZYJQZEap/WWuPx0GxJFSnRsJB2PVDIjKjOyO9QuHywba1VWzcTdD+o8+h8ty/RnZboPC9RPbE3mApNIHTLsJtfUhC4XH1bpaUxKHFrOftLxyF03avMUWM77kt/t8vudbXhZcl3PC1mqdQUT00oF5SLR5PlPkcMcWa4WyTepFtC8VeqXTH85XsK1FsRdtHKcYgdX56Rs9ijlM+gfEaJaoY/m7LasnFBkyUX6jlVnjqdlC5MUW71WlZwPrq/mVRbBng3k0hqptcvYcQoiMDkn79Ti+Tuvp12ex2HXxvXMa4I9PDw8PDw8PDzWHdZUEzy6Q/Jb11mr8/mbP5uWPe/5pFXrKYiGqcBO+aGVHUXE4cTKsWho+odYE6V2xuPjlNHq0D7J6rabNbJZ5bjf6pAFampWQnvd9+jDAIAPf+bTaVlu4xDVJStNWGLHdZRll9RcIG3uoWNCLOrdQDu5iVnZqW3kMCQLc5IN5oFxumbbyKZl9Tvb0Nnu4gbJRvnwI8NkgNEtIvOJOWrTxoRoyp1yr1WT3aEjzhWUZjB9XluP5R220ZqP5XXVygF3plXayXplkf9KHaKm6xNKLRGS5rDUJxaI/gLdfHFGsuU88BDtxG99XDIaHq90J3VDRyjKc3icQmlIygKnJZD2OjZJxIaG6ud5Q4LR5MJm7MKmiZY8DOkZJiOCDFi7qrXI1oUS0oQeTRYyPB8o4TrlWRjI2M4YkqNJpI/kMlSHnArjlOH6WKMz1FG9DOS8pAOZqFuQqLYQaokiFHWIC5iWdOiebaGqHEEpVvdwFhZ134TLIj1suE2Nyn6lw3hZbntN3jKpWlNu5Cw/idI8dlAKdiRJudsZfWb3KQ1TeFmu+CrnnCzbzZLUPrGaZyIs13hmguWtUY85lFpJvr39m0bdBWlZsEBWVZ1ZrnqU5uoGZ5ijE2me1MRlPc+3mATpyJAAELusr8o6UBqmQAB9/RJCFnkS0uPH5XlHWYvcUH3DKcl1WabF2QTXaHXqNcEeHh4eHh4eHh7rDn4R7OHh4eHh4eHhse6wpu4Qx6cly0jvIDlRP/GkxMAd6Ccz5eF9T6ZlmweJJFVQceTKnGWsR2VSs+yW0DMkhLer2Al8kt0iAGCK3RK2btmSlrmMcnPzYt595DGp1z984IP0XEUumDtwEACQH5E4eIbrszAjrhQ5NuNPHxPn8jLHoO0Z3aTOI/PS1ddck5ZV2TVi/KBkpesWaLeTiGO1Zq2YTZxZLdx6UVp0VYkIS+Hdt6Zl48eYJKguddm76spWX8+Tq0KPivmcdeQpZVoP2UyuCXS2Ke4uUcQxFKsSp7bOcaLRIXtRoO6d6yEZVVpy7Ye/9hUAwNB35JocP/vIhJD9Jhe7k1DVJkdQu5cbsjdu8RRR7N2elg2x5erEtLi1LCzQGAt1oFH2T7BG2t+ZWHsV+a5YCJfVJZ8hN4zWos4y1RbElMqUuTAlEyk7WjZL50VVcT2q1vhYqQCyGepXJpR6hRz7OKumyQDL+0i3oD2WKsEoU7Uzp5s203Gn+NsnIV2pZ8SdSEguC1Wszeok34xqxzCj4j+zG0zSFvQ25Odqc3maOm35czvUvjOWX9uNjkpelu2174xzRJbqOOIcB5VYk+Xo+5lTmflc20fKVbNapvXA8UjK5pnA36Pm0zyvJQrKXc1yUIK4Id8hF09Ya0KVdwoS/iVRbmMBE/c3bRaXuWyJ3DuPlaVeR8bJPXC6Lb8DlsO55ihXN+YJIhOvjY+L1wR7eHh4eHh4eHisO6ypJnj/Psm41s/hxF70ohemZScmSEsbtUQtOH2CtMcLJ0SLfPuddwMArrz6yrQsy0SanoKE+0BAu4sTxyflfpN0n51bJKtbhbOzfOeb307LPv3Ff0+PY96VtZSm0BFpsspBvDdPO6a+7TvSskLEu7tIdnkLrFGuq/cc7aV6zywsSP1d6BGl3e4WDGUlHMpsQBrEiy6SNi2xdthmJFNanrOJXbdRyj75xVsAAA8ekx1jlLZZQ5VRW1XqQl5zmYwyihSQkuSU1iSKW8uP1S5ToqopTQVrE3v7ZYfdz1rFGZUVbXaedvaTyrHf7W5basfeUdPSBWhGEuooFxDhL6PInhkOy1RTmfOyBbJ+9KskeIsLRJazyXJNaaJIiFFEWnRrRKPf208WEU2qcyGCGkoz0U5wdJmkdHYs1lypE0NHgG1JHcou/J0iAbmMcdm80nAFnMkuI6TcUij17ma4JtBtZjvq1WzbHw3T1vYdiFhcZIJO+jelyXRaLRXySGf/c6H2rJF2dlnPjF2uwTy5nlOXrm7MdefIFHhZttfhZOhGWcaR1opzFjaVMS5xpF3F6nZi0N+kyGXPVdrcgLXDBbXuKRVoHt8yJKT04kaal+OGWDHdNzXWpDT9KWWme75XtL4xLxnnm2LtnjxElvsTZbl3xYW9jJfLPOiQJdBoy0fa4ddGr+81wR4eHh4eHh4eHusOfhHs4eHh4eHh4eGx7rCm7hAXXCDm8iOHDwAA7rn3zrTs4l27AADbNglhLGKH7qQh5th9B+jaQJHlRkcpXt7eC3emZRs5K9wdipR29513AAA2DUtMO0fwueub30rLFueE1JTnbHXXXHpxWjZ2nGPAqpjGzn3hxAlxvzj6GLmAXH6VZK2rszVgeLu0x+NPkUnhgs1SVmSn94qK09ctCEqSeeZyrvL2bUKe6h0gW3k2KzIq1cm8cRjiYtB7F7VPb1VlqqmQWUVn0Ml02K9ZJik1dfY3Fzq4jYShzOPsQmFCZWrhcwNlwstxNsIgI9fW6i6zjzyvxS4tbXVID3SGpG401AEFldGnyWa2el1cJMAuPm0kMo4dPKDiQrpMcItlGTdxQmPXKHcIy7G9F1Qc7maO7pdXSfWaDSK/lqviepIo9wXL8SxD3S1c1qtEEdmCzLI6LEyR21UQivmuGdPvSaRMjaDjtiieoY6p2V1o62EdyE8pCaVjV9T916x4XtKhsI2c5bI4qvGXYWKNVcyYRkO5lrH8QyVMy3LQ8XADvmesU511jNu8urHWKX5tt8DLMi3sULYc3SzLnPrWyPsud/1qGj2/OeKjmhR5jorUtS7TWt1l8QTQ4rJIfZOKOZJRT0liqOey9I1rqu9ZEKjvMH8PGvMS63dxkb4Ni2X5RjRdVs62idKo//I7pX1Wl9KzdX8J2bU1DL07hIeHh4eHh4eHh8cZwZpqgm942UvS44hJZkeOSfiyJzksWV05fo8MkVP2w/fdn5Y961nPomvHj6Vlt912GwDgl2+6KS3rz1GokCcefSwtOzFJWuEPfvADaVmDd0w3vuhladnG40JuO8qZVw4c1DsiItfMK82Ry5veU5TQbaM7ttH7QhDxjrdcE01UkR3bszkRSb5Au7bx4xPoNnzi5k+lxyXePeaUVtwwuUprFnKWtGgZK9q046zkLg4qJ/5eul9bmCC3e1TahJg1wVp/4BR+eleqQ88E6ca6Q6YipfEwLqyP1iLzzrk3FM2HnSdC2EJVyCG2U+ieLiXG9RSE3TbPmfMWF6S9qpxVqKTC5IQBZ1LLSea8DcM0XgZ65X6zU0cAADOLovV1JNOkJRreGls6bEae0WzS2IhiPXJU1je2MGQUaS0lkejwTE7zYpSGo0bv2ZaZijXGSSJybNRJu7JYFa2HzXWv3kBr7Ay/d5uinNunXaPuiDr6zFT12OEpHcLUaQ2eddmeJKOYy/aYhDJXNhQp2FmLFB8TLdbqa1JrzAQiPb5sh7B4Ki9kh/ovtwB11kCeXXhZOpz7skwzyQJo8msU8qKRTfhjVa1Km9ZYOxwqqWe4XTJKvu57ZrTVke+30BSSfYPNbNm81MUy0W5+UUKpVdU1dZ6DCzmdgZProEJFNvnZYUZpvFm7rEPlOUuAlqRN5+fl38woWhtZdu+M7uHh4eHh4eHh4XGG4BfBHh4eHh4eHh4e6w5r6g6RUbbxMjty9/ZK3M2b2JXhu7ffkZaNH6BsaSObR9OyHbuI/NZQzvkH9xNZ7vDhI2nZzk1kYr/8ssvSshMnyB2ipeKGvuI1NwIA+iCmgj3XXp0ev/+fPwYAmFKEN7DbQk5lFHNmoTgUE22dTRPHJiXO8eZLKIvatCIHDfZT3N2gKfWanKZ36S12Hxnn3vvvXlbWMcJkIPLt2UCuLUXl8pFlMpqOxepIVkaZfRwxrqXMf6GL7dpm1mPTjNFZvqRNXR0Ttf9LLUltJhl27DfaxEP9rcyxGQGgv5fIl836VFpWSeMDL3e56DYUeyTec4XdBHT86oEB+r2g3CEqTJwzgSKWNema3oL01cwwybupXAzKTHpsaSIhy0q7o8B1Bx3LV42rnn4yyxplMWtx1qFY3Ttht6tEmUndedpDJevcd6QIxpIcK2UZu3Gx+8ytDrr5XBzlJNLm8uXnpW+sOVdmeQzmjkZ1bsA2Emqy3Aya1knFoDXKiSnD8VFDdYkjmrabU2kcx8pFxrlJadJVpzi4rubt5v7u1QF5Wbqyc1+WUO4EgYuZnFHfdOfFpV4hx/HNs+q76E6M2txduB3VuiFJaN6KVTsWCkRkb8+IWec6CZlZ+23mAheHX+oasDz6iuIiY3gSrjWXu67FLZ3lsP09ACB076Lk7LLkZcK1kWkX9xwPDw8PDw8PDw+PM4M11QSPje1Pjxusie0ZECLN3XeTdvGhBx9MywbZEf/CHUJUC3mHsvviC9Oy279zOwDgS1/+Slq2wJnijh2Q587NkPb1hle+Ii3bwOHSRguSma20SUKA3XDDDQCAxw5IxrtD40To690sYaKqTHTbu2dvWvbYw48AADZtEeKXc0Svq3zZGQ7nNj8j2uF776H2KPWLtq5r0CkzWNv/8a5e/87ko0oimkanOcwqjeQAEwzbFIMtet5URbSPpofIhAUV2iywLtyZ0iAqdaHLWd9GioIj4rXUeRzCR2cp48cUI7Wz57BpVmk53A5chyDqmIipCxCoEHa9PTTWogXRdC+y1jcKFDmGs+jlctLuIWsBFptC7jCs0R9QxJC4sTzMnCP0WBUSxwR0TUZpf20o7W7yVJ+C0oqEFXp2LRbNs9MAa42Sy1yllUxJsrwsYOFHKhNSApUdq8ug3zHh0dhGPHJZwdqvwvJSF95I60hW1oDrEISuDpHK9lirUn8KdQgoK/NHxFkg9ROilss4pTMCdtBqmk56nA4p1tLz5T2DDlqoboGXpdSIT1z2y7kiSyhrseX5tFIRAlqT10KBmmOLRZrr8hmVbbPBmWvVtya1dKm1hMtsq79dTdauujCfABBy9s6kJXN2U2UGBWujo7rcqMSEvmxenmda9E5ZFU70/7L35nGWXVW9+Hefc+5U99bQVV09ptOdOSSdkDAI+BjCEAZBUBlEceA9AfWJA4jKw6eiOCIqg/L46VNBkPkpoBAgIjOEIWQgIQlJOp2kOz3WXHXrDuee/ftjrX3WunVvdXdid/VN1/p+Pv2p2/tM++y19z57r7W+axWZJJcqmTaarLXup7XX2n8XCJ4WIs1gMBgMBoPBYDglsEWwwWAwGAwGg2HdwfV3PDcYDAaDwWAwGM5cmCbYYDAYDAaDwbDuYItgg8FgMBgMBsO6gy2CDQaDwWAwGAzrDrYINhgMBoPBYDCsO9gi2GAwGAwGg8Gw7mCLYIPBYDAYDAbDuoMtgg0Gg8FgMBgM6w62CDYYDAaDwWAwrDvYIthgMBgMBoPBsO5gi2CDwWAwGAwGw7qDLYINBoPBYDAYDOsOtgg2GAwGg8FgMKw72CLYYDAYDAaDwbDuYItgg8FgMBgMBsO6gy2CDQaDwWAwGAzrDrYINhgMBoPBYDCsO5yRi2Dn3K3OuatOdz0MZy6ccx9wzv3I6a6HhnPu+c65Dz7Ea1/unPvKya7TmQDn3M875956Cu57uXPuayf5nt45d/7JvOeZAOfcpHPuDudc+RTc+5vOuUtP8j13sSyTk3nfMwHOuUucc98+BfctOedud85tOsn3NVmugkGQ5Rm5CPbeX+q9/8KpfIZz7t3OuT88lc/4r8I5t9c594zTXY/TDefchc65jzvnjjjnpp1zn3HOXaSO/6xz7nrn3Lxzbp9z7s3HmrCcc5cDeCSAj/P/tzrnPuGce4Anu10rzi855/6B73/QOffaFcefzgO27pz7vHNupzr2G865o865W5xzu1X5Vc65e/nfgnPuBgBtALu5fusS3NZ/r9vFOfccdfzxzrlruR8ccc59xDm39Rj3KwL43wD+XJVdwf2lzn+vUMd+0jl3wDl3j96IO+fOc859zTkXhzLv/c0AZp1zP8znvI+vnXfOfd8594qT1S4PNxxrTB5Pxqvg9QD+0XvfUPfoOyadczucc9dxH/mLFfX6tHPuMSvu/RYAf7DKe1zgnGs45973YNvgTAHPYbewrO5xzv2GOrbJkULhAefcnHPuq865xx3nlm8CtXm4x7hz7l+dc0vcJ35SHXukI6XYUefca1R5wTn3DefcjlDmvW8C+AcAv7Wi/l9gGS7yvzseems8vPFwl2VfeO/t30P4B+DdAP7wdNfjOHXcC+AZp7sep/sfgB8A8HMAxgEUeODdro7/IoAnASgC2A7gegCvP8b9/gbAb6v/bwbwPwE8AYAHsGvF+X8C4MsANgB4BICDAJ7NxzYCmAPwYgBl0GLrOj62FcDtAEYAvBrAv3N5AuCbAN4KYBdoM/s8AAt8/V8/hDZ6OYCvnG5ZnQRZVwG8sU+77OLjz+G2HgEwxBPlp49xvxcDuFb9vwjgXgCvAVAC8Cv8/yLL5T6W2/MA3KKu+ySAx/e5/8uUXC8FUOLfF3M/efQq9UpWKfcAzj/dcjgJclx1TB5Pxn3uVQJwFMBZquxYY/Kd/PxRAHcDeAyX/ziAd/a5fxnANICtfY59lp/zvmO8a48s+d38anJ+OP0D8JsAHsXj4yIeLy/lY+cCeC2PmRjAq1hWtVXutZXbuqzKPgDgQwBqAJ4Imk8v5WOf4jG/HcAUgC1c/lsAfrPP/c/i55dU2RcAvOIE39VkOcCy7FuP092op0hQewE8AzRRfhjAP4EmyVvDhKbO+18AvgdgBsA/BoGgz6KAO/L5LNw2gBaARQD/drrfuU8bvBdABmCZ6/ibXP58bodZHtyPOJH26HP/GMBfcCe7B7RIywc6VizAWRbvU/9/PICvcT1uAnCVOvZyAHtYZvcAeBmXnw/gizwwjgL40ENsm3Gu68Qqx197LJly3Z7YpzxB/0XwfgDPVP9/E4AP8u9XAfiaOlZlmV0M4HEAPsDlFwP4Hv9+HYA39Hn+zQDewG12FYB9AH4dwGEABwD8d3XuBIBPAJgHLajftLK/r7j3z4AmvCkAv6PlixUbwvBs9f9tAP4fgCNct19Rx34AwLe5HocA/CWXlwG8j583C+BbADY/RHnfDOCFqxx7FICFY1z7DwD+t/r/M1meTpXdB+DZoM3Q11X96/z7RQD+dpX7b2d5l1aUX8Qye4luU9CEfxDAe7n8N/i8BwD8DxxjEQzgHABfAo2r/wBt5t7XT2YrxzBosfl60KJwCjSvjh9PVlhlLD8EGR5vTB5Lxk8GcNeDGJPXALiIf38QwEtAm6YbAIyt8oxrAfzsirKXcju9Ed1z38sBfBXAX4EWAX8Imk/fAprX9gD4JRxj4cT99gZu14+AFg5/qO7f99vFv0v8rPtAY+5dACp8bCOAf2c5ToMW8BEf+y1utwUAdwB4+kOU5dsBvOMYx+ex+ubvZwD8h/p/FfQdvlCVvRfAn/Lv2yAby+tA883ZoDmvsMoz7gTwFPX/L2CVRbDJ8uEly37/zkh3iBV4PmgiGwN99P96xfGXAXgWgPMAXAgyfR4T3vu/BfDPAN7sva9573/4pNb4JMB7/9OggfHDXMc3O+cuBO20fg3AJGhn9W9s8g040fZ4JWhXdgVoEJ+wf6xzbjtIM/aHoAXp6wD8P0d+e1XQwHqO934YwA8CuJEvfRNIs7IBtMt7h7rnvzvnXn+CVXgygIPe+6ljHL91lbpXQYuJEzKJOec2gBaBN6nim0BaP/Df/Jj3fgm00LgUwF0ALnPOjYE2dbeyyeelUCYkfs5mkLw+B9I8DAHYAtJmbQdpwv+G6wPQAqgB2o3/D/632jtcAtKOvYzPD/c8kfePAPwbv+N2AE8H8GvOuWfxKW8D8Dbv/Qioz32Yy3+Wn7MDtGD/BdBiEc651zvn/v0Enx/apa88cQxZMy5Dt6wvBXCz5xmWcTOXHwEw4Zw7C8DVIHnVQGPof/W7ufd+P2hDfRHX953OuTrIAnAANEYDtoDGy04Ar3LOPRs0dq4GcAGojxwL7wd9MCZAC7OfPs75Gr8CGuNPAfXnGVAfAlaR1bHGsnPubOfcrHPu7BN8/rHG5PFk3CXDExiTtwC4msfdY0BKgTcBeKv3fnaVZ9wGcpEKzxgBuUj8+irnPw60QNoE4I9A8+nzAFzJz3zRKtcFF51/BW0+x0Fz+o+udn4f/Bmova4AKRa2A/hdPvbroM3WJGhT9wYA3pH72KsBPJZl+SzQJgnOuSc651Zrl5V1dyAN/2qyvAKk/b9rlVusHI8XAuh477+vylbK8pk8JneB5ta3g5RC7VWe0SVLxp+wGf6rrpdvZLLsf3xQZdmF9bAI/or3/lPe+w5oV7GyQf7ae3+/934a1IF/Ys1ruHb4cQCf9N5fy53mLQAqoI9TwIm2x0tAi5d93vsZAH/6IOrxUwA+xXLJvPfXgrSBP8THM5Bva8V7f8B7HwZZG7QA2Oa9b3jvcyKX9/553vvj1oEH0N+ANEv9jv930MT1ln7HQZspgHbQJ4Ia/51TZXMAhtXxOXRjDsAwL9L/CMB/AnguaMHzNtAu/kedc1905Ou8C7Qpew+A76j7tgH8gfe+7b3/FMgicJEjv9QXAvhd7/2S9/4WvnY1vAikhfuK974FmmT9Mc7XeCyASe/9H3jvW977PQD+DrSQB9fxfOfcRu/9ovf+OlU+AdJ4dLz313vv5wHAe/+n3vvnHe/BzrlCaBfv/e19jl/O7/IbK48pjKFb1seSVwYyo38UJKtXghZC7wBtZj7vyB9994rrF/g58N7/T1DfeBKAfwHQVOdlAH7Pe9/03i+DxuA/eu9v4c3TG1d7CV5sPhYk8xaPnU8c471X4udBLkD7PPnbvRHAixz56a4qK6wylr3393nvx7z39x3vwccak8eTMaOfDIHVx+SfgNr/i6C5ogDgcpDC4P3OuS8551694hm5DBlvAvD33vv7V6nTA977d3jvUyXLt6q5909WuQ4gK1oC4O08tv8FtLk5Lnjh8koAr/HeT3vvFwD8MbrH41YAO/neX+YNXwekdbzEOVfw3u/13t8NADwvjPU+rS/eCFp3/GOfuo2AvtG/771fOcYCTng88u/XgcbkJ0AuTP+Nr9/Dc+cXnXMvXnH9Sln+FsjUvx3A34L6wXnquMmyt26DKsserIdF8EH1uw6g7LpJT3qSuhekIThTsQ30jgAA/mjfj26t3om2x7YV56422ffDTgAvZk3QLO88nwjyqVsCLdZ/AcAB59wnnXMX83W/CcAB+CY7yK+qvewH59wkSJP8Tu/9B/oc/xHQYv453vujq9wm7JKHVzm+Eov8d0SVjUAG/+KKY13Hvfcf8N4/ynv/HAC7QYuiG0ALgh8GLbi+CDIjvVrVaxHAlPc+VfetgyaaSdDEu1LWq6FL1t77Osj0fSLYCWDbClm/AaSZAEhDfSGA251z33LOhcXtewF8BsAHmWjxZl7wnBBYA/1eSLusPH4+yOz9q977Lx/jVjPolvXx5PU57/3jvfdPAS0AHwPS8rwXZNp8E4D/u+L6YUi/Ai8kvwKydvyiOu+IZ2IXY+UYPJ4Mp1l2AQ92zP6rkuFtoI/pZqwiq+OM5RPCscbk8WSs0E+GwCpjkhcUP+69fyRo0/kOAL8Mcge5BaRx/wW2kATkMmQN2DNAJvLVsLLtH6ws9/OCZrX7rYZJkJXoeiXLT3M5QJyCuwB81jm3J1jXvPd3gSyIbwRw2Dn3Qefcg/pW8sbhZwA8lzdS+lgFZDG6znt/rEXjgx2P93rvf8h7/ygQkfkPQIupt4DcDp4P4C+dc+Pq+pXj8Rve+wXefL4H5P7wQ+p8k2X3sYGVZT+sh0Xw8bBD/T4b5FsHAEugDgYAcM5tWXHdiWrCTidW1vEB0McMQL6T3AHyDQpYrT1W4gDoI93vOmBF+4FMuQH3g3wax9S/atDkeu8/472/GkIM+zsuP+i9f6X3fhtIM/VOd4LhoNgE+lkAn/De/1Gf48/m5/yw9/67q93Hi7vChSfyXE9a8gPotkA8EmJCuhXdZtQqyC2gy8TEE8sfg0xcF4DacAFkot4I8odsg0g+e0EL3tVwBECKXlmvhi5Zc10m1PHjyfqeFbIe9t7/EAB47+/03v8EyJT4ZwA+6pyrsubi9733l4AsFc8DTbrHBffrvwct0EK76OM7QT6xb/Lev/c4t7sZ3bK+FcDl/IyAy9ErLwdyvfoVkHxi7/29IH/Zy9V520Amw37uNQmoLwSsHM8H8OBkOO6c03LS166c72LIxxQgOT5nhRzL3vv9x5LVamP5RHCsMXk8Ga9AlwxPYExqvAr0Mb8FZL79tidryHdBm9KAR0DcK64CmWvvc84dBH2oX+ic+446/78qy+0r+uCxZKnH41GQW9GlSo6j3vsaAPBi79e99+eCNtmvdc49nY+933v/RNA3xIPG6wmBFRavB/me7ltxrATgY6Dv0M8f51Yrx+P3ASTOuQtU2Wqy/F0A/9d7fwgiyzmQy4D+jmhZ9oMHKWP0/zVMlg8fWa4LYpwmJOxCL3nru6AP/DjIcfyP+diFIK3bFSDiOk05TAAAIABJREFUx7vQ7ZD+pwDef7rf9TjtcB2AV6n/XwQaVE8HmfheB/JlKh6vPfrc+xe5c24HmRuuXdG2/wzyQSyAtGFHISScHSAN/bNAJIIy6MNxFuij9nyQk3wE4PcBfIGvezGY4Q3yE1oGcM4JtMMIyMTUN2oCgKeBNJtPPsF2fTtWENP4HarcBhehm/H6pyBt7QYQwe0AhIk+CTL5vJDv8Wfg6BAr7v9HILMXQAuKKZALwx4A16jz3gDy370KxyY6fQjkKz8E4BLQ5NGXGMdtvQBa4BQBvBlkagv3eiVogTMOWgBfF57N8r0eZFKs8P93g/zRAHKNmeTfzwD5KZcBPBU0ucV835sAvPwE5fMurkMPKxnUX+8G8BsneK8fA/BZ9f8QHeJXQSbFV/P/iyuueyXIXQigxewMt/Oz0R014idBfr+bQGbMGr/zs0Bj9QV8Xj95Pgc0ji5hOb4PxybGXceyK4IimcxBxuQoaOP0XNCY/T3QRinI+DUggtBO1W9D3frKCscYyyfQ7scck8eScZ9zi6CN3/YTGZPqnE2g+bDG/38nyLRdAxFuQtSIEoh4tI3/H/zxw7+3gCw2oZ+/HL1kp18E+R6fxXX6HFYhU/H73AfSTicAXgDShgcy1fG+XW8D+d5vUmPiWfz7eaBFRFCQHAD1vYtYJiV+/j8AePcJyvJloH76iD7HCiCt4cf6vWuf8zdzv9Dz6wdBvrRVkIk8jyigzrmE+0vM//8UyEKxGfRt2qLaYgpCwBoDjcUyt/XLQOMyECdNlg8TWa5ajxN58YfbPzy4RXCIhjALWlQMqfN/mxv1ftDHWne+C0Akj1kAHzvd77xKO7yAB9gsgNdx2Y/y+86BPgKXqvOP2R4r7p2AzH1TINb3a0ALI8fHzwXwDZCJ45OghaOWxeP4+dOgD9QnQTvmrZAIECGCxSV8zZtBO8xF0EJGL/CvQZ+ICXzsZ1l2S3xt+Hc2H/886IOvj11zjHbdDdoA6AgBfuU/dawEmmhCBITXrrjfM0CLyGV+310rjl8E0iAmquyP+DkZaPES6n0faPd8FY69CJ4EMYdPNDrEy/neITrEfgBP4mNl0KJ6HrS7fw16o0N8ADR5zoAmsFCP94GiVyxym/4Il/8ESDu6xG32dsi4fcNq8oFoNhor5BkijPweH9fHFo/x3gV+722q7ErQwn4Z5IN95YprNoLM5iOqLHw89gJ4qir/JGihOAnq97Pcjt8F8Ep1Xo88ufz1fN8TiQ5xHmhjuwD6MP8tyG9Vy/gAy+N16I0O8VqWyQJo/AWFQV9Z4dhj+WyoMdinrquOyePJeJX7/TmA3zrRMcnn/BOAF6v/7wDNaTMA/kKVvxjAvxzj2W9Eb3SIlQunlfPp8SIKPAb0/VkERRT4FwC/o44f69tVBlmV9vD73waO2AIau3tZlvvCPUHWi2+y7KdBc0dY9D8Jxx5D94C+DVpW7+JjT+G61Vccf9Ix7vcRAD+u/j8OWngtgcbqT67Snx6n/v9I0HfuqJY9iB/wl+r/k6C5dwHUh68DcLXJ8uEny9X+hQXLuoRzbi8o9Ml/nO66DAL+K+3hKFj9u7z3O096xQYQzrn3A/iw9/5jp7suAY6SLvy09/4lp/g5NdAH4QLv/T2n8lmDAOfcq0CLt187yfe9DBQ67Qkn874P4vkfAsXL/r3T8fy1BPMBvgzasCyf5Ht/A8DPeXKZOC3gOrzLe/+Pp6sOawX2xX4PgB/wJ3EBw6b8m0DWh8Mn674PoR4my//6fU9YlrYItkVwjgfTHuwX+lSQn+1mUBzY6072QsEwGOAF9udAprW/AGnyH3UyJy7DqYVz7rEgzc89oHjHHwPwBO/9Dae1YoYHDefcU0Da96MgK8O7AJzrvT9wWitmeNAwWZ5eWC5rw0OFA/n4fQhkFv4kJD6h4czDC0BMfAcKZ/dSWwA/7LAFZGqdAJlHf9EWwA9bXATyBa2BXFNeZIumhy1MlqcR61oTbDAYDAaDwWBYn7AQaQaDwWAwGAyGdQdbBBsMBoPBYDAY1h3W1Cf4v122Lfe9mNhGScrisc358aMLFO98vi5xzxcXiMi74+xz8rJXvOoVAIB3/J+352Wt9hIA4I4bb5QHLlFioPGKJCXZPE7Pq7ckkdZZO+ne733f2/KyLRNb5T4JZdl887vflxf9/b99EgBQHKrJae0O/fCyt1jmmNqLzaW8bDim4439kkgm5sRew5Mb8rLRrRQz+4UvfVle9svPfLIOqn3asHPH1lyWEe+lWq1WfryT8Y+CdLFaHAMA2m05r5nyieqtHLvoZFmWl2UZlblIkoYNjVDbV2ryjCil8+ZnJPtiqy39yXt6kNet6Kj+kevjGuT1aXReXFCJy7iuxXI5L6oN0fG5GckImfJ7HjhyRF3qT7ssnev30oYHg0GQIwD855e+ncvy9o9/HADwVx/6ZH78qmfTvJlNVPOy645SMqWnnCeJ3NqVIgBg82QxL7tgjPr0WEHmzaUGza/LTSmrNGgOvON7kvvjCzd/CwAwf0iCMtTnOvnvfQ98FQCQJJJ4yjVozC6lMgfIFacOWZaZLBkmy5ODnTt2ybcyCd8qqdrYEMkwyeQ7lWYkB/0NbPF3NimJLEtlyqNRHRnNy+KhCgBgfkESpXWalDuprJZ8Za6VT0XmbSdrlyzh51Tk2zYyTM9rzc3nZUcP0zctbcmnZGyc1k8veokEKrrsissASF8DgA986P0AgLlF6S/bN+8CAHz9i9fkZbfdfsMpk6Vpgg0Gg8FgMBgM6w62CDYYDAaDwWAwrDusqTvEfEseVwWp2F0mhpGkRur35oK4DmSsnh+qitnn4x+j/ARORbZoLZK6f6gkpurJMXK5GCtX8rK0TdfUhkp5WafVoPrNNfKyLZvlmrBV2HP3nryoVqtx/eR5RTbVdzryTjHXcbggLhnt6Sn6q8wQ5SK1Ta0q7hWL82RyuPbTn87LfvmZT8YgoNVScnNkstGmm2AhHhqfyMsqKZk8OotybSHh9lNtloZ2UenUXeS4SJ6RtskkVyqLKai5SMejKJZrnTK+8T29l/uAf3tloorZ9cFDvRO/X6clZisXXDwaYh48sjDP18oeM44GMxrh5OR4/jukr4+U4SkK7a6dJoIZLZPCTmgb1QeCyc91yTHqKQvDOFXm0qyT8v1Edv38NiJ1nwKP/SSRdg8y1ba0cFT3gfAquvb63t1vBBw6PNWnNqcXmXR5TF5CJvHn/JDMaTvOmwQAzCZy4qM30tg5f1Lm15km9d8JZQYt8jVedWPPZtxKWcmgRi144RMelZdNXHYeACCqy5w7dYdEgHrvJ+4DAGw/57y8rHzrdwEAX31AcrHU8V+HmhZQKNC7u7EdJ+HOJxcmy+Pj4SLLpChrBJkeZY4KXpR67gm/9ZwXsytFsSSyTArssuClMZKMyibHJvOyIj+uqNwdSjy3R5E8pe3kPo0mVWx5Udz6Smmbz9N15W+qEkjGb5CpObbDc3qk6uDCtWo94dl9MYmVgE8hTBNsMBgMBoPBYFh3WFP1VD0TjV1aJM1oR+11Wp52AMupaNV8h9fpbdGa7r3j+wCAnedsy8v2L9NuZe/R6bzsCMhhe1HtlmPe/k5ulR3jMO+Sz9p2kVS2I/uDlqddyvyyOHQHTWgrlfrHTI7yTmuY6J1SdV6Ht37lRHZ0HrTLz1KlZUxol3TeubKrHhTEatfHyj1EmofAWrROUxzexzaOAQBqo2N5Wcqynj4scvMZ31BrGsMz1O4wEPIWp0Sv4JmRF3adQLeGusM7T737C7GyvVKBJgV6Ttbq1UTG6uKYL9Ha/6DZLBTEmpAkQXNyCIOEoPEGkKt7dezwIAKtFQ0kxS5NamgddV4URT3XhuOZVnGEw063YbbiYNetpY5dWoWoq34rr1lZqEOkB6uS7hd5tbRqXFsQBgwdpcUZ3rULAPDYYZlzI08aqWEZGtjIWqVNRXnHDQldU1Aa9QJri7xq70qJ+rdXYy1iDZAryXkbeCy1FFnq/N2ipXppmcgzN94m8/4tzbvomr5v+lBA9dkwLp+8Jz/npwAAW3Y99aQ95WTBZHksPLxkqVXWgXydqfUFK1yRxH0sWPp7x/NQS1mQXUS/C6nII+O1RqKWd+GTutSWObbBc15cVGsdNQOWYrp+NJI5LyiNl7zUoRMsqar+Hb73oYNiJTj/AgpAEJXUeoy7qu9If+mktJZLO/JOpxKmCTYYDAaDwWAwrDvYIthgMBgMBoPBsO6wpu4Q5aKYwX2HTDetlpDg4gLpxkcUkS1hlXzZixp/rEymoE2KLNepUPy6/fwXAKoFWuOXC6J+L/O9F5fkuUHtf+1nv5mXPfVpj89/L2ZELmio+LYRXzNWFWIRAplHuf3XSlTHGFKHQ0fI9N9YFhPA5Aaqt7ZOl4tkQr/pO9dLIX4OgwBtIQ6uB5pYFkzTG2pCjBuf3AQA2L9fCBJJkeRRG5H4yDUmSE1NS0zdiGPzDg+PqAdze7fEbJKyW4I2wXcTnHrJWuK8rwgCTH7T1m+Xn6/fM8QdFsHV2GxZjCWeY0OZsAYJDtrNgckM+oQ+adWz4D7SdaPg+qCKgtuBvl2W9ZSFlu2Xwb3LBUKVB1eLONZy7r2mH50u96ToDk7dU5b3ke4X7a3kgEBxY5DyHFmZVCZ0juPtF+QdRodpHi4rckyZ21S7HoXA2pl6/yjE7tZESp4XnRe3rnIYp4p09cABmSPTFpOG3TfysiM4SM+L5RPlgtm1y5cm/FaErjKNu0c+6rK8bOfW3XSWivH9hCc/DQCw/SyJQT8oMFkSzgRZdlRUZJfrHXsdr+JE2qfZpDYvKlfO3M1LEYibGblRFuRTg4RdKueW2j3XRuq5CbtpOFmGaC9QBBrmkCInlyu0Jkk7cu8Or80iFcM/XPHAwYN52VKD+smwytuQsetoqy2V+O6ttN5JUyGCnkqYJthgMBgMBoPBsO6wpprgc3cKGW3DZtrVHpoTx+n6Eu0Ukrpo9mqccawaiSZtYYkyoXz/+m/nZWE1v32TZKAL/ICxMQk7lvErX7p1V1722B+gsGMvfckr8rJffZ1oXH/tt18NAJiaFvJWEtM9Wx3ZlcW8o6vWRFvZiWjH3lySXQ3z3bBl61ZVRu98zlln5WV7DhymHwXRbg8KdKKxPJxY1hvkZX5qf17yvXneFardf5lF024q2gSL2qP3GTWlaixwWQMSrifj7H5pU3aWs4ui9c81kUrtKPoHpQX0vZrBoCnVO/ugFB6pisZ7qEx9rNUSmcfZ2uxqHyyyviHN+pyn28v3amyCtl2HLQwqV+96tavdxESqQ6LOC3qGThd7TT8vaIL7EDSj3nqlXeH7+K8qc+gNkxfq6pSWv5+2elDQVuMl4XmzHGvVXpCRIoAG0lOXOqQ35FGDCTVtRdwNPJg4lufmHBpFdGk3KXujV9aw2phojabrNF+O7HxEXnbOY+j4ZFHm7iOHaS659/bv5WVLc0fpuYloSTde9gQAwDN/7Pl52ZM5WxWUdSZM3ctu8Kw0JkvCmSBL3Vbha6MV8xmTzJYbivCGQLyXb02wfqUqSECwwFWrskYol0jTWiqp09D7PQtaeD2HJirDa8QaYK/Cf6Y+EM97raup+q4H5XFVW245TKhSZOdW1SwSL4HaCK2B5uduwVrANMEGg8FgMBgMhnUHWwQbDAaDwWAwGNYd1tQd4vbvfSv//dsv/l0AwIc+8pG8bDQhEllSEX15iW0yhxSZaoj1/CMbhZS2e/flAIDHPe5xedkEx6Wdm5vNy2YWyew+wtnkAOCnf+YX6Pxxcdf4zGe/kv+++kefzb+kuQoc4zdVmcBCxqosFtN+mWM3ljPZb0zuIuf97cMSJ3jzJJkNLrlyd172V+/8O6rrqLh4DAoSxeBLO+x0H2lzCMmtqch/7RaZXQrK2b+xzG4CKj5lOxDdlJmmypn0ysqEV2RTUaUm5rOMXRFmlON+QcUvDBb1tnJjifqYuIOZUbtkCNGr1zWgqcgjI0kw+Ys9qlQIJE5xDxkEaLeQ3MClTKz9rf9uxV91pG9gXkFwq+jKIsf30ZneOiyfSNVPE0diNgfHkXZf6H1eHr+5X/Y3XYdc3oLw6G6XkcGFNrq6nKioZFmi38WCHg/0bmlHu4bE4UeOMGaTLhcl8DOkLOVsT8sLklGvsczuSBMyj1UqQmqe5HE+sV1ivF5xxbMAAONVGUPThykb2Sc+Lt+Mb3/tOgDA0DYxvz/zGS8AAFy5W74Fw+PcHsotoL1E83S0Si8/nTBZEs4EWaaKWFbg+M+R+ibFvK5w+puahsynTpXRNzJVMekjR9cuLekozPRNLat4vI4/comKP10IAQO8dpVR37acTCfHmw36Nutst6HWadc0SdcMVeXbHMe0PorUWii4wBWcnDe/OMbvpoIOnEKYJthgMBgMBoPBsO6wpprgtCBawX967z8AAPbcsy8vq43QbqDZ0DE7aOc5VJZdZNASPfpKyRh3GWuAf+fP/yQv2zBG96uNbczLzj3vkQCAz1zzwbzsyCLtWoaVNvLgASHBve2tfwsAGN0gmXHmeTdT2iC7lRDOJXKiFRwdpnq3p0UbvWkTlW0allBwL3jeMwAAy225tjZEzu4L6tpBgdrcIorofYoFlf2PiWla4+p51+p13vQGydep8DmBsBEVhfgwylltCvNCMPMh+01R+kZUpjZz/WKbQcJoxUqrEl5Fa0XDiZHWirre83LCQSbku/lF6kdxJPVvdgaTGKcR3qofafDYV6hfOsZfKNVcOf5PRzd1nzsHTUmihBcrC0Je2ifFm65znvGuzzMyXf8+4dBCO+goTpokN2jQ3bfA9fSKxBmykMXCY8qzS6VdMqffBUXECiTjTGmFQlhArWNvcZan+cWFvMxVaB4e2iDWN2SiuRpq0zWFVMZxmcMoxYncPcpI43jBRUIeHtlMmcIuvfLKvGz3BfScsVGV1arTq00tVaghkmjwZGqyJJwJshwaEk15mzWpfUOVaUsqS0Jnvwy/O+rb1uG2X1yUNUKDM9tGOtglt1VRWdNKPJ92VF0aeqZky1y5K7weZ8BVRPZgKNPWi/D99F2F6KlX3i39XF52ySOoT3z962uz7hm8HmMwGAwGg8FgMJxi2CLYYDAYDAaDwbDusLYZ48bE/P+il78MAPCFz30tL7tn7176oSzHBc7wNjkurgj376V4s99/QDKKvfvDHwYALHRETZ826UZHDhzNy7KYft946568bLhKmcyShjy41RLzy733ULzeizdK1pqULd3eab8AUvNXVCa74SEidC1Oi7l86yZyz7jwLHmnc3btBAAst8QdYuMGij37wOHBc4fwKkZjgYP9JiqrX7CRdLwSpiOTVUHFIkzZTcArZ/8QG7aiMgcW2EUiVg75uTm7Lua6qMgkA6dN3fI7N8PrmLMIGdCU6d2FrEq9xLHujGpcP+XOEX7mJBIA7ZYmLgw2ujhkwaOhq6yX3ObzLHJ9MrR1uaP0xiLO4/GqE4uFXjtav8RtWma90YtVHfUDg4mxbwxiXRb+uJ6yQUSmfJTSFvXSkjIPh36u2zlms3pDvVeb71NUL1tiu2Wm3EFaIcZnJnPWQp3cyGZaMh4mt20BAPiSzBk6g2ZpmLNqSnh4lNj9SXFoUK7QfHDZ5U/Iy544TrHWN09InNEOPzpW7lQ5f8wrQk/CRJ0BFKrJknAmyLKl1hXFkIVPHQ9x50s6vjm7IOjY6Xn6Uu2ix9/cLJUGb4bMtl3Z/9i9TE2rI+ymoV0WD0/JWqPFXXBEBRwuJpwdrq/+VMfU781QF74N+psaHu05Ky8AfPe71wAA0lQImacSpgk2GAwGg8FgMKw7rKkmeNPOXfnvcy66EADw0Y99Oi9LWZO6pEhGZ02Qk3SxKrtDxKT523uvZJv71o2UbzpJZHeYclaTR+9+cl52/VdvBACUOrIl2jZG4cniTFgG+w4KOW/qIO1S2nXZM5THmJigwr50ODzX8LDKflKm3dbB+dvzsq2bKARMpSLPG+Kd9d17783Lzt1JodT2HZDMeIOCtiJ6hRAv3sn7FEtU1mgrAgCXbdl+dl5WnyMt+9SUaPUT3q4Ol1T4uTo7+ytFXcwJ07NMZO5Z+1pVeeUXVSidjDMKaS1mvhvtUk8GTYsiJuQ/dXgvfo+6aHoTJh/4TIdpk3B4g4R+GtkuBDKG1vr2Oz+Ej+tKqdarMQ7ndWV1Yw1XWeWeDyHzdDajhiKNtkI2o64sf/1C2IXX0GGjoq669NQx1CtYAxQ5pZ+me1AQKZZUh9tHh2fKFWjqFQIBR2d7Wg7h6VT2sCQQRVU7BWJVs7GYlx2dmwEAFDdKNsxomCxFnbZoq5wKh1es0fFGKuSYhqdzi5nMAa5IpNcdu87Py4aHSKNYUfebY01YUxmmiqwJi7XlIGg/lRVqUGCyJJwJsiwWRZZJ0Jrraobvj/62uVAoZYFXmMT6e0Z/U8U0TsNFygKa8PdwZHhY6pWHfpTzxjdJ5tM6Z+4t6nBo4dZay9z9GvyfkJlVFQUNtgorm+VXicw3b6ZQtfPz0odOJUwTbDAYDAaDwWBYd7BFsMFgMBgMBoNh3WFN3SFKRVHF//3fvRsA0G4r9wU2ay8sijvExCSRyDpNqWqrSWbRobKYmEcqZF5JW/W8bDihmIbbhyUW4f03kGvBliFxtL/ifHrGURVr7779Er94fppMRNP7JHbwhWeRGWchEbNQge/plPd5JaF6Lc8JeeusTUSIy1ozEJB9ZKdyFXjW1c8EANz0ve9j0OCUuS5ic0lcFBl1mFioYxqGZlluS1ssckzDWJHlKmwqG9UWajaFR1XpQ+Nnkdlk8eD9eVl9keRfVvUbU479R9lc1s9qlmnbje91hwgZcbQpP5jKdQzPEDu4EOk4nIOadUzFcWZzletzVDsBBH5OpE/s4yqSl2hviCg8Q2W64mkoUe4QRR+IHDqzkqpXm7NjdYmsX23ZRUI1v+e36uKcsJyjPi4SOjSw84PrDqFdOoJzjjYZB3l1vSP331hlxSsHs7oizMxzm5ZUm6XcqPV5mTfLCc2BtfENeZnnWKaNw4fyskJtJP9dYwKwK8q80Fpc5HcScmyT+0xtSMazC4Qf1RmrbC52yi3AMcunowKXBr5XPIAyNVkSzgRZJgV5xxAnWLtDBGJhlyy5LfR3qswx8PWibZHd/3RZxjdPIpH5BAeHrrXEzTOrs0ujcrOZZHcXAGixW9+i6k9Ndrvoisec+0Mokib/Lal5N+I5O1HfxSQQBdXcf+gQu0FkfYIMnwKYJthgMBgMBoPBsO6wpprgA/fJ7jFl/+v2kmhSpx6g46MjsrPkBCVYmheH/Q0TpOE9un9/XtZcJPJauiS7mp9/xc8BAD76z9fkZSU+vPs8CU9WLRM5q+mkOUaHRVO8ME87koP3PpCXXfSocwEAxarsIwqc4a1cFA110ArWCir7WY3IclkqZKoQSmR6RrTNt99OZLpiaU3FdEKojEgWvt3nsaZ9UULR3X4P7/7VDr5dp13rEbUbDU7+w8Mi80qJiIXJ3MG8LGgOa1skU9H4eUQcRCzWhPbdewF0Zx2sVkQedVZHLykyXZo7+0s7B3KbJoCE7Xsn0yQrQqa0EjlHQWUgLFdE/gMF36v5RB8NqdZMBSWAzubWL8dcP52MW/EXAGK+YVEVFkO4PaUKGVJa4RZrCbzOqBTCtPXT6PepS1eCwHDrPiS+wQu61B+atBeaJVNal5T7b6xkHrQySUmF3+Lu3VZjJO/ykbJ4NMiSNbco83B54jwAwJAacwtH76G/e8WiVdogFq+hKmkaCxXRFLaW0xUPFk2ZVkIFYk1DaZxCWKl2KnWtt1lLqvtxyJLmBk97aLLkdzrDZBmy3WkLnGMCdaSWYxGTAxM9F7PGtq2+K6EdUyhyOD8ugSLec/NVlPXZN8lqWlGE9sKS+o5VN9O9I9FkNzvBUiDP0/TKgJAJbnFGhaflkLVZKv0pt5Z25DubpfwMvzZhRU0TbDAYDAaDwWBYd7BFsMFgMBgMBoNh3WFtM8YVJZNafb7BZaJqnxgjE3tcEpV8BDIjzyyIKwXAPhLKDF5fovv90stfm5d9/Us3AQCadVHTX3wxme53nSvm90Z2HwCgVJQYeVu3CBmguUim9ZlDotpfOExuC5u2C+muHswQiTTr/FFS7W/bLO4XgSBWqY6qdyL7waFDEi/3S1/5CgBgYmICg4Zg2gCA6mZyS8gUSTDEAtTW5Q4T6AollVFvhNq5VlHEuOAm0pL7RUyCHNm+LS8rjpAT/8TZO/Ky9izJJT0sbiXapD7CWehaHSFfBsJbRzGgYo7t2FEuK/2yp+V5xpRtPfzOumLPro2T/4NFFPXWS5vvchJcrN0m+G+/FG590G3apd9FRR6tsrtPoky2yEj2idNzgZjtylzvVF0Togh3xQnOswH2q6mWWZ+McQ8zKM8QFMMcpEzQkiFMM/0C+U/a2XFLqtCyKAVTu5dYzTOcXSotyDw2snEcANBuCcFq+QDHPp+X+bOt4rQ3N9GYLk3KfeIyPcc3ZOyWEvpWaDeclPtTWw3KKr9n2/dKvaxeqsxBT+No8BxeTJaEM0GWHeUmkLG8Cjqqbk6a1uRpjrOrZM6JA7sI3CG7atrVPpzDQAUi6CR0cVYVmW/fTuR+78U94f77hdA4vURt2UgUYZnjDTvlDpHxR0LXoM4ZXvctyXd2vk3yH1UkzUA01jH1Oym5aXQy7Yp46mCaYIPBYDAYDAbDusOaaoITRRhrLNFqXzvdlzm7WqJIRAlrDdOO7BSKZdrp6B3WxDjtQOfmZbd55x4Kc1apyWtObCcN8MFZCau1YZJDfHUkvNrmjaKtfOAeIt3Vl2X388DddO8djzovL5vnsGClcdk5NT1pkYsF2aEuzVIIkLMuOicvCzv/b35bssPd9F3KbjcyKVrkQUFS1X7VAAAgAElEQVRaFwf7b3/pMwCAiiJaJEyMqKrwK0vLtLMrqzBnm7dTGwxlQnxcvpfIFzpTV3WS5DukwvWArQTlqrTPBN+vvSQa3KUlTZIjuTZKUq9Wh453vCKKcF76UkGRUVijrOsVtC96d+55p1tU1zZba7OrfdDwve/ST5fiut6ZL1XHVaA19cv3lIV4Y4VELEBl3ot3UmmjkVEO1dMWOXqlISrz1NVIZV5o+hA6SWue+Z264qFlfcp6Ncb9SIH9kuoNCpyyZERMOC3Gul9Sn26q7FLtJGTrk/s0OdSRj1XGRn7xpVkZ9wdbJJva1l3qufRXhy1szZB1yylZZUtiqakfJQJsaVwybRZ5/khbopmK8tB2qv6sXUq7CGI0/2aqJyc8FgvKyljhMq0lHRSYLAlngizLNbFUNpfu4V+aJMiWQyW3ggvaVUU2Y41rlyY4N8upUKQs6kpNbjjEVuc42ZSXLVUpE2C5Kt/UWiJa/+Y0yTpqzqu60je3pTLBhnVYkkrZIn8DZ2JZy9X5e637XRRIcJmsvTx/e7FG4e5ME2wwGAwGg8FgWHewRbDBYDAYDAaDYd1hTd0hWorEUq6SarztxVSdgsyhOl7e/AKpy8scgxcAPJti5ufEJLP7/CsBAF/6/HV5WYjhe/4lW/Oy0hhd4xTJpt3gbFEdIXuNbxhXv8llI1WxZw/eS/EU6zNiwi2fRWZ+TSKamaUYxE84V2IphviGGyfENOGZALSwKG4B4+OcyW5BzBGDAqfcHJosI1cRN4cxJkZMK8f4jJ38l5fkfebnyTQSKVeUrE7t7FSmnTEmxBWLKt4um/Badbk2KpC7Q02Z43TM4HaLZFxTWeTq7KoQxUKW3LmTnrd05D65Tzu8s86yFtgKmrTCJkhlqiwVBnO/6fsRwbRrx4q/gLgEaBpHaAevaWe5O4GclzAhLlI3THk8b1DxwTdvpr5fZ9chAJiZEnNqiU2DNZVpsN0k+TRU5iIXMsZ1mdZ4vPeUrHSRCO4hvXGHBxEFFUc5ZzQqc3MhmCadjN12cG3RMmLTeaJN8i0axw01ditVmpMrw+I6ljZIXvXDB/KyjMendqnxLZlrW9NEeu7MC8m4OE7uMK2CzIdtHseZknmYU7KOvECTjydqzHECQmQqdaBn027fMXCaYbIknAmybCzJO/qwBlITYFLsXYalzNbW3lcJt4+Oj1wI2UnLUrZ1jN1KVeCA2SlybSiMPSIvmxh7JD3DyTd1evH6/Hc0R3kRttQUeZH700FZegFcptdtxZSumYjF3fV7N9xGP3ZKDOlyjWUOtQ5kVx+/Rr5ng/llNhgMBoPBYDAYTiHWVBNcVMS4jDV/QfsLAEmBdgCVigpVFbROKsxV1CJt0tYRyR42f4B2Es1F0Qpu30Xa3EB8A4DAyWrPiVP9ECgEWasgDv5e7aI2bqSLZg6JVnNxhrRS9zH5DgAuvuBxAICluux4p5lIsPGKS/Oy5jLtnF0s2ujZOdqV33ufaB6LZRLPpppojAcFBZXqJ+X96nJLZFTukFavUJzKyxyTIdO2CoXVoXbMFudUGd2nPC6Et2EO15Mk+rnhYWov1yC5ju3YmRfVF0Xj0Z6m3wUV9iWETTvalL5472HaBWfzon0MWga9QY1Dvnv0EuicaqNiaTAzxnVpbrNeclggkenUTiFsWaa1375X4xq0sJFqsCKzbSKl1SqXSQtVHVFkmjL1n8omyWXfbN6b/64v0BirqnBQDR5PmSI4triOmiznol6yXJSHl1Iap6yfxnjwNE0BsQon18p6yTaBbKWi0yFIW7dP0MAUVYiiuVmyfE0viwqodjaRUIuxtPfc4bsBAO1pCfUYs0VEE0rjSPWdRTp3+chhOT4SSNKiNUqbHMarLXWIY7LolJWm0CUhBaQiSHI/aLfkvGXOuhZ5bdMYDJgs+T3PAFnuECMwbruZQ8t1xBLpPbdLR5ZjQ5xhtqw05UGb77QNjtsiUUVDdTovrct6ZnKMvp/nPeJ8OW+SteepyHdkl7TzngVq0/qMrKmSoWAhl7Vcq82Z4FSYs4T74p3Xfykvi/hb+Y0vCNk5A68JUpXBNfx0XZ37lME0wQaDwWAwGAyGdQdbBBsMBoPBYDAY1h3W1B2iXhcH+nI5qN1FDR5F9HtpWUzj23aQy8PYsKjxZw4wWW5BiFhHmTRTTURNf+F5TKaqSVy6OpsIKpkQ7aIGXeOGVXaWlpgAJieJpHNwWEzjh6bINeLm79wiz7v6BwAAXpluWg26T0E5v2/YwHH5lAnjyBEySex/4IG8zHF8yHYqJqpBQUORvhwHJvSKWLgwS+/h2yrrG5uaY+3vziS4bFEc42N2tB/ZLoTGeIhMRorfgbhI/4kKIsu4xPLvSL8amZc4jY36nfS3IXWtDZHJvaFiFTZYbl6Zx1tsctNm9CQJbgCKPMDySlUbLSxpJsHgwHfF1O0tc33ICR02wWmOWJa7Umi3A2q7oooJHMx8sU/VeeEZivya0XlOmXE31MQ1orNI46+t3DnK3A9byk2jw3LJXO87dcf/XZ1AB5WFKhu8oZhDZ5fqBJcPdbzNbiJF3X/5b6pdW1iW7SVp+5lpcj9xG7bkZVGNxmRTxVxfPkC/k7ZyXwvPi3rbG5A5oj4lBKwix0Yvj6usV0ysarVkLA0XqA6RcpMKxGSvJgvH76fHZItjmZbi3j5+umGy5MecAbK8epe4wlUXiDB+w53iqhAVqF2GKjK/BUJjpOI7O3bhi9Vc1mnSPNpuy7rnELtblsoyx561k9wDZ724Ity55wYAwOjIwbzs7I0yV599OeVAuOsGcUNrZfScizeLK2pniGSjvFPypU3cFQ85xIaW95yfoTqODctzb7uD8iPMLkjM4lMJ0wQbDAaDwWAwGNYd1lQTXCnLDqbgaXdUaKtQVUuksR3eKBqh3RfvBgDs2y9a2IX9tGPcd/9NeVl7kUKzPOrRkoVtbIR2mY26ItpF5Nitd8utMu2c0pbsUKKqaCYLRdoRj2+SsgZzrVr3CwGgcRftmMrb5T5DRSL7bJoUolacsqY7kV3wRz/5CXq3jtS1ykSfoUg0lIOClnJkd6yVSJR2oLHAmYW0xoB/V8pCkKh0aGfqFEGisIE0B8ObRFMRF0K2HFWJ0FSdXiJI0E4DwOhZEq5neYaIeukDIrd2i+Q6VhZNxWJKfWIh01qQXpJVCIPWT3uqySOt9uARNgB0qdZ9/q6a9NK7T861vl1a00CCUyWslSmVhIRYYs2Ga8m1G0ZG+cYyvvZxCEJfl3E/VpW5ojZEv5ea0q5DXNeWziKX9rZ70Cr5fhnjuog1vX2uq/8NGDpKVgVhFOdljt9RZ9+KOFRTqYt4RGP76IKQWttFGhu1UZWxkTOA1Q+J9jBtUNuP1Ebzshprn+ZUKMOWClEZLF5ZXWWr4pBOlTEJm1fkbI9tZaXrcIiwQkG0bT4Pgac0itwxWypzY4dDcZXiwSOtmiwJZ4IsD35JyGFLi1S/SFm9IpZlW30D87lVk7C5DZzKDoeE5JulQsYvjtAcO7xFLKBH2DK655Zv5mUNJh1eerFodTdVZJ0yso3kvnlWvov777ydHrsk38/JjXTNvCY+hndT66xAePdeZDnKWYKXpg7lZWMcPnd2QRHoTyFME2wwGAwGg8FgWHewRbDBYDAYDAaDYd1hTd0hfEPMF41lUt/HZXHodjGp55dVXNerrnoqAODuuyWW3bUfezMAYHpWVOgX7iTy2uhGMYfMNdkElIkzeJFNN7WymCPSZSLZFIZEnb/UVBnM2CF9+04xq+/bs4eu7YgJ466bvwcA2D16eV4Wczadowf352XnX04kv5mj4hz/teu+BQBIFIkoZE4pqZi2gwNlMmezckGREoLZOCpKm5YKZCobrUpZxLJ2Trri8GZygyiNiFtJv0RduaVFp0gK9nhlEoyrYs4ZP2cXAKChMvO1Z+l3QfWTUkw3l7PEzSFWNv9AEusmWfWJ57hG2W8eLHSs35wwpo77Pg3f3yOAzZbq4iF2gyip7Egpj4fRkY152dgmcn9ZmhJTbGOBxvaGCTlveKP8TutMwDksZt4K1z8tSnzJeovGUKfLpaEfCY4zFyl/BzE7al3BYMoRAIpq/IU+2iWr/HW0CZ3bKpW+32ZfrzkVZzQe3wUASNR83ThCc+CyyihWiWleHZ+YyMsKbTK1t1TTFQqSmWzzKI3P+TmZ92eniKzT3iLk2GKN7t1allEZYpQWlYtVkGGk3rMYAurGQoh1/M4uHjxdkMmSX/MMkOXcZol3v+zJHTNrSH6BlN0JO5pUHGLSq/uEGPkFlSVwlJcGLhHXgXn+plYScWOpjtK3tDYna6Z0gbP/NSToQKV4bv7b16mdN9RElgs16idek8h5rZTq72KIJa++e4Ew3klFbg2OE+xq8v1vHAnvsja+Z4PXYwwGg8FgMBgMhlMMWwQbDAaDwWAwGNYd1tQdYv6IxOttLRMT/OCysL/bbI5+0tOflpddeP4lAIBrP/VPedk9d+0FAEyOiEvDBRfvAgCkXtwmikVSpxdicTFIOPdkS8VNrHJ8Qp32N1axZ/OQs8rUMjRMpqR0WcruuuEOAMDWbWI+2jlBDNyFaYn/u3HLEwEA7/nAB/OyxSbXtSYmKr9MpoIMgxdZoKjS1QbPg0zHymX7c1G5ORRK1KY1FRs6bpHM4yFxfRjZSmzVWKfP7GeFzjP6upVFXf4TmTItlibI1WJ4s/STBscobi5LdIIyu8aUFUu7wWarrI+LgC6L+sShHeCgAgIf4v/2xvrVdjmXv41+v/DOIu8iTy+JinMdIoNMbBITYXmY+sVwQcriJWKXezUeChuEzT48THVYUq5T9VlyaypHck2Vq9NR8apbfd4zsNC7ynxvmux+7iGDgkS5AEVBRjqmKP/W3kNxeMeO9P0jR2muaiWSxjrENu+0hfW/cGgv/VCRYiYnaexObBAXpPoCPSPEgQUAVxET+hiPtdai3Dtd4vS7MzJOC7VdVGfFQm8t0vwRIrwAQMLuYx5qPue5u1wU17IQY7W5LH1jUGCyJJwJsrw7knlt+yUUe3f/1yU2b4jgoSPPpDyfJom4d0a8jkkiKSuwG0i6IO4uG6sUheO8HeLaMI0QmemoPKRD7bdBuZ8kytVibISeVx2R44cTkvXReXFDcxzBp6m+ck1eZ3U6Kt51n3VMlrv6qEhKPtTF0iYbDAaDwWAwGAynBGuqCR4pi6P2dCMQZCR2oGcN4b57JQbde/4/0pa+9Y/fkZeNleiai3ftyss45B1KSqPYcaRJDTEEAaDT4HiCEG1RlvIOqyi7SK39aWdEfosKUrjjAtolf/Ord8iJvHOZuVvq/7jnXAkAKOhdEDv2332vkOVKHKNWx6DtsMaq2Ro8TXASyy4t5fh/qYrJGuJYpopgWKpRO7ulJXUetWltUmIClzZwn3C9ezStEO7HgYi4CrrFnL5PgXa/1W0SG3GJYwdnR0R70eGYjbWiWBEaHOu3oXa3WSAwaK0vb+l1iMTMD54MgRVxcX2fd8l/9mZSi1S7hsxsUUG0M2XuI2UltWEmxNSGRXtUqtA1nYYQeiYnaDy4YZkzyopQGapdnRzPy+pMlms1pK5l1iQtd2Rst7NeWeS6bdel8u4pG1AxAtAaepUBT8eBDnOLmmMCqXV5XghKc6xVK2+XMRlaqK6IU0WOxzw8sSkvm9i4ma51Qn6ZaZJmb0plhSx6+fTUhqhPxErWQ57mjfa8WA/bdbp3QWkeG/WgPRT5los0t0dqggixWEOGRwBgBVwXaXJQYLIknAmynPViwYoXOWNtSdqs1aZKd3TMZ/6dkwABlFlnGavko4emSTZDJbFcX3jpIwEApYrIoL2XchhsSeV+bV5zDOksmE35Nt959L68NgGFsy8EADRuvl7uc5D60cRWIT6WK/QNn1WWhfCt1PHZC2xRbqushCEjYN8P/CmAaYINBoPBYDAYDOsOtgg2GAwGg8FgMKw7rKk7hI6DV6uR24KHmEC3baGUx0cOiqnlI+/+FwCAzhy8YysRz87eKqbQYkwnLMyJSfuLX/s8PWtYXC6e9INPBwAkkZQdOUQmiq9e93G5X03cKn7wqqcAALJE6lobp/3D2EZxq6hPUb333XZfXnbJL/0EHcvEET5g+9niuN757p30DGWqLbIpt1VvY9DglcN+ynH/EmW+iF3u3Z6XlQO5oancTkrUfiM7JAZzXKb31q4Pfc1c/chyIf2taseOSjca0gAPjUnfmdhJcRLb9dvysiU292n3mlqJTDutuvTPnBCm7eSBGKdIdcmAEqpcl4mVCWPdZ/RegxBnV8Uy5XTIFZUiuciHS7GY6kZGiSRSHREzqIuY1NKck/t1OJW5WOdQqInMIja3lZUcR5ZofGZHhSxX5H6wrFyigntRR9U/SK8/Ca4PUXAAoQmiIYW09t7Ik2KrsZEyCWlKuaIUh2l+rSr3k+VFItS4JTG1by+RDOcVSbae0tguOJmzqkxgqis7bi1R5MVAdBpVrnFM/lnoiDnVL5A5PR4SV6YwV7SWpf7BHcnruOX8s6PYR0H8Phk8mZosw/s9/GV59Y+/IP997T+9GwCwaaPMW/sOkvuk8rITkqP6bpQ8tXlaV9Hri9SmO3dfmRfVNm8DAEwf3Cv3O0Lrix0FkUF7iGRehMyXd3/vO/nvG+4mF4p6Tebvx+8md4izL9os9b/1FgBAQ7xrMLaDXC3qTuc4YGK5cvGIub9FXRy4UMe18T0bvB5jMBgMBoPBYDCcYqypJrjREgf7QpkeXY6kCtu2kma0Pi2EsX17OHPUkOw2z99Fu6hyUcKr3X3PzQCAu+4Tolpcop3E1JKED/nyd74AABiryk7stlvomo0SCQYzM7I7+vx/XgMAuPTRu/OyjZt3AQAmNqtQMU3azqTLonn82peuAwBUNkvZo2ZpF/zxT3wiL7vzjrsAAOOT4kQfiAJjFQlvMyhoKy1aFLRjmtTHf8dUCJwikyG9UutWNhARozYhTvwJZ+pqK43GoRlWCapd5Ogo7WTLiozVrpNFYM/eO/OyBaVJ37pjJwBg4wbRVAwxKW9kSsLHtJcpe1mrISaIWplIci0V8mupzeFtlLY5HM66sshhINGVMa7fCUGzrq/hslhpXYYLpO2pqZuENqmMS/+tcQa4uKjC3/HOv1wROS7NUlmstBBNFX4t4XmjMiL3bs/SmF2cFgJOuONwSWXHSklj1lD9MMtDvOmwfL3v7geYGVdSv0NLdbpC9/G7qb7aXCQL1UJbEVgnaR52XsZN4yhphZJlmRfbrJJrqwxlU03SUqUVmUwrjuaA2qho/8dHRW6dlObxtqoDOHSi01moFpnAOiohKIs8vzRaKlQek3uqscwpjvuqj6WPJfwNag+gSE2WhDNBlr/60h/Lf9/woXcDACbUez/AluhUzcBDbAXWC7Qif1UTr75JFZp3N5XUd3aZ5s5t4zLnjZ99GQAg0iRH/r7ueUA0y3fcvSf/PXeUZNk5Kn1iqUJl5521LS/r8MJpdkb1sWX+XZE6JPwysbIcBLJ5lPie8wDN2Dt1ME2wwWAwGAwGg2HdwRbBBoPBYDAYDIZ1hzV1hygVRQ0+NEQGn+mjYrosl8lF4dBBcWlosmvBWVvEfWFyK5lGjh6V8/bu2wsA8Cp+bYhLV66KcWmGXSPmVaapybPJRItFMQts3Srm8mDWdcrU0umQun98cmNetv8uMqcvLwib54tfvgEA8JJXPDsvm2Kz7dOedlVedtONN1IVjshD2mwq2FiT2I2DgmJR5NEBvbfviDmkzGaQMRVnNwnxKwtCqhths0qhLH0j5aw/d37vlrzstnsp81FxSMzj5114KQBg06iUPbCH3CDuuOuuvKyViZlpZo7qsEPFmN40Saap4bOFnNeYp37SmZJ+4jrUt2oleacWkzOaXcQ3kluXe0E2mMQ47cKSE/pWOy6FfLqKHxlcYpqqDzD5bWKTuPiUKjzl6HC8HF87i8TEupCRnEqQso6XsT3E7jgVFR9zfJLGSX1W3FoWpsh8V4mF4DhUYHcbLwRHyXao3jck6nI9RQOJgpJVk+NuahN0kSewRkPmuekZMnUWhoXo4oZofC4evT8vWzpArJexgvT9jF1afEsIja2UyhpqHm60uF+VZU4Veg6QcXappiIvtjm+aFQUU2yLTeiNWXFvq3L826gsc0qd5/FKSWQeh+xT2l2nn8wHBCZLwpkgy82qoqMJvU9zSUz9IQZyS7mspCm9R6kg38ViIMYpYvlYlea/8UjuV8rI5WFqWvqGi8llZUl9h+YXSS4zdZHghs0ST3p8nObT9pRk+htaoHNn90kAgqKjOrqOuFq0GiTXhlMZAcN3I5b1URSTrDvK8yEuBlmLu+uphGmCDQaDwWAwGAzrDmsbIk0RncY3U2gU35Cdzo3fJC3e9BHZUWzcQFlILr7sEXnZbJ1CilTHz87LnvRU0uI5RZ4pMhmmoZz0o2LCdVEZSsCZrSLRKiUduU/KWXJaRalXm3e6I4qYU+ZwJfWGnHfvPbSz3rHjsrysxES3YlF2vENM+Eq8zpFOzvHnnCPvOSjoNGV3mAYyRSxtVq6Rs3w5k7aIOAZMaaOQAmqsSV9alN3hnttuBwDctfeevKzepGc0lkSWty5+CwBwpyJoNZgYp/gi0FFzGtOkgbhf1atSpQw71RHRWI6eRW3eWPy+1KFB8i2qXOtl9uJPleYmaCU6inSWDWyINPntXb/jvdrhyPVmMwoZrsoqA9ToKLVnZUg0TnGSByPLyzLW1B9WGol9KbVrNC9aj21VEWo1v1yRD6v07NFNYjlpzJKcWy3RsoSx1khVxjsmyfVT2EeqLFujsD0PBW2l06gHeai6J6xJmp6RsXuEQx2WhmUe8y0ai/Ujoj3Mljnrl9LGe9bYVGqTedlQ0MQp8ugUj+32nGgZ68vye3SMLHuNhsi/lXFmR6VmXF4iq+FSLLGYwlySDEm/S+ssc5X9KjDNdB8PvMioPyX0tMJkyY8+A2SZqHnyWc/9QQDA+/7583nZCH8rE9aOA8BmJgmOKD370gxZuDrL8g1MWSH71eu+nJc57jt6KutwWaqsCc1g4Y7kGSUnV5V5npevHdBkTfChBdHgt9j6t6ysEsWYvtcbikKgm+UpeKop68CwTggaYQCIclro2ixPTRNsMBgMBoPBYFh3sEWwwWAwGAwGg2HdYU3dIaoqy9hYlRzrm0uyDr99D5Gf0kzMB/N1Mod+4RvXy40iMo1kmZg4o5RMLrFXMWMbdDyJRcUf4iBWSxL7sLHMJs6CcoFYEjNsMRB3ivK85YjuE8dyn2yR3C+qRXHiP3SIzD333ydknc3nk4vH3XffLfVqk9mnpBzJg7niO9/5JgYOmZhQXB8TcikYY+oqOxy7d4xsE+JGoUptNnNIzHpznMloVLknTHCM3ihRsSHZFaFQkP5SZHeXgnI1KRdFrgW+pqgIexXODhgrA1KR4zgGcw0ANJbZHNUUV4qQIamlMhqFTGpO3a89oO4QHrre/EOT1tBbWGA51lR85oRjOpaL0n9rVZJfSckiSMprfxW+d6YIjBmnEEoicT+IdJzUrJcs5Dl2cGlIyKq1YSI2Lk8JATeEKC4q950GZ7PqykzIz+t0kQcHU44AutIuFbg/lpUvh18k2+myMqcmo0yEkeCcaC+Q+1dHEV0TzpzYhDxj1nN7F1WA9YzdlupiLm226T5eteNSXRFOuR+VVSznCpMuFxaEgLPMhKDOvMyl9XkyxQ9tlTkl5pjyDfWejQ71wUTFpfds7o0GMQugyRLAmSHLRH2zXvyyXwEAvP/jN+dlo5xVtdyUb9IIy8PPCjks5skpUoRGDlOPrKjmwRYVFlRb+JCjQfl7FcohFrF8y0dT+T0c2l55gNX529dU52V8z6IiUEYc0KC1XzLlZkzyS9W8206oH0SxPEQ56WAtMHg9xmAwGAwGg8FgOMVYU03w/KLsBO/fT1lrpqZkhze/RMeHxoQ4tdQgDU5zQTmDZ7R7KKssKVGH1vPFVO2gWcOUtkQbWeBdyMKslMWsfZ1uya6rVhTn/BAKqtGW3XQIPZOq7C2lKh1vLct9yqw9/sxnr8nLPvSpfwYA3He/hHirsPbYKZGUWTM9MytarEFB5hWpiMlCVRXmrMJahEi1WYG1q6ObZKcfOAPjG4XMceXwowEAXhHLAgkiUzvQlLXRXiVd93zcK+f7TGluOwtU3mrLfRpMmuqobEktJnG25qTPhjgucVeSd3rnotp1t1hbqLXWsQp/M0jQhL3Qz12fMGFQmu5KkbU9miTWZsuIyvLjWH6NBRkPbqlXsxx+1ZQFaCtrksqahKjIbSE7nOL2CCFEEXDiEJZRaZSTKGirVCgmlqnOBpj1Id/1Dxk3GCgXpTEqXOeCl75/kDOK1QsS3qpYpXGXKi1jYYiOT5xzeV7mOkHlpMme9DfSDCXW0JdSISeWmOzbUW3byXQ70/XLTgmTM/O1Erl3PMxzsiIaZY7uEynNaVylubTREqJRo0WVHVZZCXNS6ACmczRZ8nlngCwB+a6UQN/AzVX53s0vkxW0nYiGdzmjuakyJG2RhBCSsXxrlvk7tqzmzpENNGcWlKY84m+XU9+4dkrzaabm7Lqa65rcjzL9PWDycazbmb/DbfVd5Kh+UHkDMbVM/beuvuEtXj8VVfjctK2thKcepgk2GAwGg8FgMKw72CLYYDAYDAaDwbDusKbuEI1M4sjNL5Oz/dCEOOKfUyVTQRSLWaBYoeP1trgvlDlLStpSxCPOwLKhLLF+XUqq9iEVKHZpkYgCk5t25mWzc2QWONARJ+5yImbYEpu8m8qE3u7QfaollfmlQc/btXlHXtZiN41aTcwV//lFim9bVISt0SLVO1buHMscf29sWAhiAwMvZpUCZ4AbKYvcChyv13eZ2+nv9H0SsxL3slxbyn1cvjwAACAASURBVH2BHftbymzi2XSTKjNcysfbqTLrBdNcpuO5qt++54eYuPuQonT9A4mso05MuV46FmTGz86UyShR5L1BglN1lKZRhAp27YiVu0fMRNMsVTGg2eS5tCgxQwPZM1Lmu9itHmdXx+gNUq47cUeJVF0DASZSsg3uELEy6XXqRKJNFTEoZbNdQZEjS0wmamjXmtAHdCUH0txKKCiSYInbR8+R9Yznm6oQdwO5OGprvxI6L66JyTZjVxnNOwrWbafSPUXBBKutpXkyLzWWlLAd952uscbtLLO5IswocqJjM2qsyDYRu8kVKzKHhyxbsYp6qjMeDhpMllyHM0CW+MYN+c/SFVcCAF7wtKvysg98iNwjE+Uu0mGXsxn1bUv529ZRc94iuyKk6ntX4IyrWVNlxOQZdUi7LHIH0N598hUGGoFMp/tEmA29midZ/h3lVhGqnfb59MaaQMff/diJ40SQZZbq2pw6DHDPMRgMBoPBYDAYTg3WVBM8skXyUi9wTvBzJrbnZS3OtPb9O27LyyY3U7ijmTkJw3L/LZQxrlCQnWCHiVDDajd14S563nxbNFYTmyiDyXs+9n/ystf/zh8DAG789+vysg0q607WoJ2LVyGcAukn7Ugmm+Wp/QCAl//Mc/Oy2VnaqX3uGzfmZSMjpN0uFsWxf+4wabxqiSLk8RZ8QWnXBgVOafdqrJkfUowkz2RErSVY5ow39RkJi5PzGZS+LSgR+ivdVBit8LdLm8nhtrRmQJM0mOCltYrhXWL1TkGrqOvQ4XtrLQdYY9Oo6113b/0HVVOh3zkXVR9eXNaVEY9/aAJLCCOotAbNBreJtgb0q0QfrbtEZutPShPOWu813aHUeJwq7YkPWjZFwEj4vMT39sNOlzVgcIlx3dn/Qng3KRwbIotStizv3Wgvc5kau/y3o9uWZe2d1vDxc1UdMh43kdLm+b5dX43jMDaU9jNYKLzqY57vGSVa08nWmbZojYJVoqiyh3ZSDsnVkfccYiKSG8CwWiZLvs0ZIMt3f+Pb+e+XP+7xAIDnvvRFedm/XvMxAMB8S9YSs/UQBlbuE35HuslCpjVFTmyzJdWpMKZBqd9qyXeq1aJrvFoGptDfykCUFnSYGOm6NLyu5z4+P0FZ6rgSmY5DGTLkKuJysPB2h9E8dRi8HmMwGAwGg8FgMJxi2CLYYDAYDAaDwbDusKbuEMVhif97zlYipm0YljiHd912KwCgpMzXCWv0K15ieg6BnLsXpyX+aGWIzCHjw+J+v4FJA82GvOb2bbsAAHMLYhb4yreJqFatSDzEljLJeDbxLs3LNWNDVS4TE0atNMLXihp/mVX683UVg5izkO3dc1deNn+EYgGPj4gbRqtDbhyXXHkZBg3azcHnsSPleBrsecoJ3gXTl3YNYLNZrM1n4bA2t8d9iGVsKuuo/pKyXa+l6qeqhRYbCDWhKmS8SdU1waqm42G2W4Gcp2IVM6tAE/aCSS7uMiQN5n7T9yO49PFZyBTDYWmZxkaqiX/8u6jiCYdwpaoIHbbpdZHc+Ldu/2D70xmgut1L+Nquyob6yzWBlJcq01rKNl2v4g43eYxnfdwhtAvEAPPiVFxjgTZlD3MGxnhIz0/UzrFyYwlmyJayxQaXkEQTYkL8V3Vt2gmmWJk/gzlcj+COknU7C8+Q4z4nbymSZhhXBfkWBFG3lNxa4dqWyn7F2aoSNad0uF6lATShmyz59xkgyw/fvDf/HZwgXCQEtfLoWQCA9KhkkJ1fJvfIpCzExxAz3alvV8hymiiSffimLi+KG2iIe5+oIAHhCqfiSqvD6PCXs4vKHNwX1JwY+lOqGHYZ9yftBpiw+40mADp+oM4JEH511miyHbweYzAYDAaDwWAwnGKsbca4JQmDMTtFZK97GnvysiFWGRVVtRYOEyGu2VbO9w36XVNZV9Cp8x+VCY4JbeV0NC+bHCIi3p7vP5CX7d9PGVuqpY3yDIjWt71Edd28YTwvmzlAId5GKlKH3ZdcBAD4+Ke+mpdd+fj/BgA4eFQ0wc0QWkqpTssF1qZ2hARXbxJ5cHhUdssDA7Vbb3KYk7QqpL4C71q1FjZjckOrK8QYyStVpLqwiywkZXUxHe8oLWyDd7epUjsEbW077e9UHzR9eo8ZfmvtS5yTAnqJV1rzkWdaUgSzXKmtQ35pYsoAIesTws5Ba+Vdz3kNDhXYlQOPz9PEsoS1Mpnq50Hz3EVMDGTGrqZmOen4PV06iV455iHNdMimnLShNbyu51oXtBmaABjqqOo1yJrgWGldQltkSs/R5PHXUJaTjLVApYKcV2arS1dbBIJSnwh3bU1+4XEadWQeDtqgYiLzmNb2tUOIJTWOAwm1o7NQcVmpIITihJ+9qOeFDvdFNX1UOQOhU6quoGlaPWjf6YPJknAmyPLOm/bmv2/7Lv19xAVisb786hcCAK69/6/zskKJvv1VTRJ0ZO0uKa2v5zfuKE1qmJ+7pk5H7Rwpi2urw1ld1XmJylSYhAycymIQSOSJmj3TYEVT/S701e6vHmuHtbaeLRXaEtzhKqRr9Mk0TbDBYDAYDAaDYd3BFsEGg8FgMBgMhnWHNXWHaDbE1D85Rq4HC0fERSLlWL/LS5JZrsIZYzoq5p1PyBDbicTxG56vcTqzDJVFqdhStkyQS8PC0pTUq0VxaydGJI5xRxGBoiF69vRhcd14xpOeAQC47dY787LrvkXEvn0zR/KyhQI5tk+pdxqrUn2ylpiZxmpsrkjEjF8aI6LdPfcJgW5QoB3ePZu+5rS7C5syNMGww4Qk7Zyf8XGd8SYwJOJE5BvMJZEiQwSrd5KISQ25G4Q2weua95LgwglOke8C4SDWRK9g1mtqJ49e03oee1e7Q/g1su08SERd9v0gF+060K/evZnzgttIRxHQwm/fJ76vJkKIy0If5pt+RheJr/fe/erc9945OXLlG3Wb7/p5PkQD7A8RqTiyDXYLaqh5bIHH6ZJyFQpWSG2iDDHQtQtJGAeRMpem4d5eyspsqk2XxVkmxAXVsbs1qTLjcRepYRy4OjqmaJBrsSSZ/sqcpcrrmM++Xyzw0A8EuRsUBg8mS8KZIMu4LWuN2266FgBwxWVX52VP+LGrAAAXHZX1xe0f/SAAYNOyfANLEbloljLlBsJk80i523W47RtKbnn7KJ+/Fmc7LZVlzdRNRA7xqdXLcH/TLmdRFr7N6lvJ5Df9pUy5b2Ve9dk8A51y12E/jqiwNstT0wQbDAaDwWAwGNYd1lQTHBdFK7hYJ+2ri2WvEPPvjZMSFiQQcxanRcsYlWkHUxAeVk6cGqrIXrBc5cwjS6Jx3baN7j0zK+FIxjkyWqQ0y1FB6hpzqLKrn/LkvGzLCGmyr/nUp/KyNmetKXEINACIqrTL2rhla162dPQAACBRHumFmHZC7Ux2bxHvUUZHJIzcoCBRmX5i/p22NbmN/moSWdD2xsqxP2gtvPLizzMMqbJWm7W1qs0iJlUUE7lfzFrmtiJSauVdwmGGnCJfhDp2hQsLGggVlihoIruyv4VLlAqizZl4XFcYMAwkNFklKAH0+wUSjVa49nuVXBGhQ+eFKHlJ0nOeDjOXR2bTjcRt5xQpI1Pa47w+feqliX39wpyFe+tMg9GKvxq6/+g6DBpaqk2nwlhU8gj9vEsnHt4n0/0g5vOVVapIc1tFZxjMyUiKFMravLbW7PB9NDe0qKwuYcbryvzI5xZUbUNILB3GKQvjVGmwYvRqCsPRYjeTkuqlrUIDApMl4UyQpU9n8t9333QNAKDwU8/Iyy5p0vpk1y03yDX8TW0pjXonY/K/ClDX5G9NqqwE6f/P3pvHW3pVZcLPOvNw56pbVbfmIVUZSCpkYAiEIQFEREA/2w/9sFs+bcH+iThAy6C00cZuRRG1HWhtFZVWoQVRQUWmEIZAIGNlHqoqNd6quvM98znvu/uPtfa71rl1bg0hdXNSdz+/X1Ln7nfa715773fvtdazlhDVC8aqnPbZ2my9RP5NExYv02Whk7nffHN9ODXbn9JZydZnNLdOwup1BzaVOnRlwRNinzkrJeuDXNAEBwQEBAQEBAQEBFwYhEVwQEBAQEBAQEDAqgP1Jr4EBAQEBAQEBAQEXLwImuCAgICAgICAgIBVh7AIDggICAgICAgIWHUIi+CAgICAgICAgIBVh7AIDggICAgICAgIWHUIi+CAgICAgICAgIBVh7AIDggICAgICAgIWHUIi+CAgICAgICAgIBVh7AIDggICAgICAgIWHUIi+CAgICAgICAgIBVh7AIDggICAgICAgIWHUIi+CAgICAgICAgIBVh7AIDggICAgICAgIWHUIi+CAgICAgICAgIBVh7AIDggICAgICAgIWHUIi+CAgICAgICAgIBVh7AIDggICAgICAgIWHW4KBfBRPQAEb38ma5HwMUJInorEf3OM/DctxPRr6/Qs95MRF9diWf1G4joCiL69gW69x1E9JwLce9lnueI6JKVel4/gYi+RkTXXID7/jYR/eTTfd+zPHO7yDKzks/tBxBRnogeJKINF+DenySi736673uWZwZZ9pEsL8pFsHPuOc65Wy/kM4joI0T0/gv5jAsJIjpIRK98puvRD5AJqUpEFfnvf53h3ByAXwLwm6bsdUR0v1z7dSK6whzLE9GHiOgYEc0S0R8SUdYc/x0pv52INpnyNxHR7y55/B8D+BEiWtejXnki+lMiepKIFonobiJ6zVNrkYsLRHQrETWMfB85yyX/FcBvmevfRkTfJqImEX1kyb23ENE3iGiGiD645Ni/EtH1S+79WwB+9Rzr/VEiOk5EC0T0KBH9x3O57mKEbMoiI8PKmRQdRPQ6AIvOubtN2c8R0SQRzRPRnxFRXsozRPS3RDRHRP9CRIPmml8kop9bcvvfBPCLMhec73vslr740fO99mIBEd1CRO0lstx5hkveAuA259ykXE9E9BtENC3/fYCISI4NE9FnRZb/m4jS5rl/QkTfv+Tevw7g186z/uc7n1y0eLbLErhIF8EBAU8BVzvnBuS/My023gDgYefcUYA/agD+N4CfBDAC4J8A/CPpLv/dAK4HcCWAPQCuBS+iQUTPB3AdgA0AvgrgPVI+DOCdAP6LfbBzrgHgXwD8hx71ygA4DOBlAIYBvA/Ax4lo+zm3wMWNtxn5XrrcSUQ0AeAmAJ8yxccAvB/An/W45D0A/gLADgDf5xe9RPRGAPudc0s1yv8I4CZ5ztnw3wFsd84NAXg9gPcT0XXL1Hs1aJVuNzIcOIui4ycB/JX/g4heDR6LrwCwHcBOAL8ih/8fAA7AWgALAN4q1+wA8DoA/8Pe2Dl3HMDDYJmcL/4AwLfOdMIqkeXHlshy/xnOfSuMLMELqe8DcDWAvQC+V87x594NYD1Yzt8PAER0A4AJ59zf2xs75+4AMNRjs3o2nOt8EmTZjb6T5UW5CCbRcsou5eNE9JeiHXvANpCc9x5i9fwsEf05ERXk2GnmYBLTIhG9BcCbAPyC7Hz+aWXf8DsDEf0VgK0A/knq/wtS/nppoznZ7V5urlm2rXrcP01EHySiKSI6QKxJS8w/tEQLLXL6qPn7hcQa1TkiupeMxkfksl/keYCI3iTllxDRl4m1PFNE9LGnudk8XgPgy+bvVwP4inPuq865DoDfALAJvBgF+CP6e865GefcKQC/B+DH5NgOAF91zjUBfAH8YQZ4N/ubzrn5Hs+/FcBrlxY656rOuVuccwedc7Fz7tMADoAX2SCilxPRESJ6BxGdJNYw/v/+eiJaQ0T/SKx1vAPArjM1AhH9B2Kt8zQRvc/KlJZYSfyzzd8biegTRHRKZPh2c+z5xFrXBSI6QUS/LeUFYs3otPSLbxHR+jPV8SniVQDukg0HAMA590nn3KcATPc4fweAL4qsvgVgJxENgRdc7116stz3TgDfdbaKOOcekL4B8CLNQeRi5PkuIpoE8OdS/p9FtseI6Md635lBRDuI6DYZS58noj/w43CpzKTMyjhFRO8moidEJh8nojE5tqyslhu/TyeINbQ3o3uc/iiAP5U2nQVr+98sx3YAuFXG75eg4/D3ALxTypfiVvQYh2ep1w8BmAOPdVv+ZmLXjQ8R0QyAW2QO/S2Zy/af7VlEdC2x9WeRiP4PEX3Mj0E6w7dMfuflWYdkzH2YiIpybC0RfVrkOENEXyGilBx7FxEdlWc+QkSvOJ/2OBcQ0VZwn/+mKf5RAB90zh0RZcQH0S3LL8m4+Qp4PKYBfAjAzyzzmFtxnrI8Q32DLJd/r76U5UW5CF6C1wP4W7CW7h8B/P6S428CL2R2gTV1v3S2Gzrn/his/fuA7Hxe97TW+ALDOffvARwC8Dqp/weIaA+AvwHwswDGAfwzeJFsTX7n2lY/AV4sPhes+fy+c60bsUvAZ8CatzGwRvQTRDRORGXwh+k1zrlBAC8CcI9c+l8B/BuAUQCbYbQ3MvDffZZH30ZsKv0knVl7ehUAa/4i+W/p31ee4fhmYm3vAwBeIpPUKwD4Tdqlzrm/Xub5D4F3zWeELDr2yDM8NoC1xJsA/DiAPyCiUTn2BwAaACbAi/RlF1DE7h5/CO4PE+aeZ4VMuv8E4F655hUAfpZYUwcAvwvgd0X7uQvAx6X8R+U5WwCsAWv66nLPdxPRp8/y6P8uH6Gv0Zn5AkvlezbcD+BVRDQC1vg/CO6Lv+Ocm1vmmnOSIQAQu8/UwJrH4+Bx6bEBPEa2AXgLsT/cO8EL+d0Azubu9NcA7gC35y0A/v251EnwdvC4fhmAjQBmwX0IWEZWZxq/RLRVPs5bz/DMa0SGjxJvvJbTsu0GEDvn7CL+OeA+53EvgPVEtAYsw5tlrrsJPA6/H8CUc245v/hzliEAEG+MfhXAO5Y55QUA9gNYB94E/wRYK3YNuF/9uzPcOwfg7wF8BNwf/gaiNTtH/AZ4rngugEvA49Jbod4B4Aj4m7AevLFzRHQpgLcBeJ7I8tUADkp9biSi5fq+x+tkIfYAEf2nM5x3FdiiYjcivWTp/ezvB/BKmVNfAp7/3g7gX5xzTyzzjPOSpeBM80mQZW/0pyydcxfdf2ABvhI8sX/elF8BoL7kvJ80f38PgCfk95vBWjp7XwfgEvn9EQDvf6bf9TttI/P3+wB83PydAnAUwMvP1lY97v1FAG81f79S2i6zzLNvAfBR+f0uAH+15H6fBX9Yy2BNyg8AKC455y/BPrObn0JbvBRADrxR+n3w4Mssc+5jAL7b/H0ZgCqAl8s93gcgBvAeOf5+AF8DTzwbwLtgBzbnAMDPgQf+x8Dm2K8BuBw82G8Db7ZGzPN2A4jO8j5ZAJ8H8D9N2cvBi8aMKTsJ4IUA0gDaAC4zx/7b0v5vjv0XAH9j/i4BaHmZLh0b8uwj8vsFAA4tud97APy5/L4NbKZeu+ScHwPwdQB7n4J8XwBgEEBe+tEigF3LnPsnAH59mWPvB/CRJWVjIrt7RZbXgLWJY+BF5m1g06m95tcA/Nl51D8N4EbwpjNr2rQFoGDO+zNbd/DHMJmzltxzK4AOgJIp+yh0HCYyM8cPGhk/BOAV5tiE9KHMcrLCGcbvObTBTrBmKAX+mD4IGWM9zn0xgMklZU+ge9xmpW22gzemvw7gPvAcsga8OPeLmNvAm76cuf5V4A/6udb/dwG8S37f4ttZ/n5zjzHxRXTPt98FM4cuOfel4LmaTNlXIWMQZ/iWybtXYcYDgBsAHJDfvwrgH5b2Ibn2JHhuz56nLK8Ab5zS4I3QcQA/vMy5bwLwjSVlEbrnqt3yPgSgIDK8T2S6GcBd4E3ZH4ks37/kfj8BtuZ8x/NJkOWzS5bOuVWhCZ40v2sACks0CIfN7yfBAl2N2Ah+fwCAcy4Gt43V8J1rW21ccu7hZc7rhW0AflC0QnOyC70RvGisAngjWLN0nIg+Q0SXyXW/AB44d8iO9IymYAvn3G3OuZZjzd3PgD+2ly9z+ix4AvTXPgyeCH8fPAGsBX+gvRbq18B+TfeAFwafAi8WTsr1H3LOXe2ce6O821fAH/q3gLWkD4FN6x6DAHq5SQBINK1/BV4gvW3J4WnXvQuvARgAL9C9T7HHk1geXfJ1ztXQ21WgF7YB2LhEvu8FayYA1lDvAfAwsRn9e6X8r8Cbob8lNvV/gAzB8Exwzn3TObfonGs65/4CvNH4nmVO75LvOdx7xjn3Rufc1eCFzv8A8NNgmd0P/rD8JBmypNz/bNoV+4zIsUZyMwCraTnljNsGTh93Z5PhjMjO43zH6d8bGT4E/qCtxzKyOsv4PSOcc/udcwccu/rsA3/Ql9Oo9ZJhBcCQ+dv/XnSMdzvn9jrn3gKW3YfBWrvrwdruHLqtI+csQyJ6LrgffOgMpy1t+/OV5VEnq4Bl7rccxsGb2DuNLP9VygEmAT4O4N+I3VjeDQDOucfBVsNbAJwkJhae07fTOfegc+6Y9Ouvg8fNdyrLisix4Zx7i8jy3eA2fy94AZYGy/IF1B1F4HzH49nmkyDL3ug7WQKrwx3ibNhifm8Fk2AA3lGV/AE6PaSHw7MbS+t/DPxhA8CsTXDbHDXnLNdWS3Ec/MHudR2wpG3BGlKPw2BN8Ij5r+yc+3UAcM591jn3KrDm6WGw5g7OuUnn3E845zaCHer/kJ56aCi/E+2F+8CLND3Zub9zzl3pnFsD4JfB7fgtOVZ3zr3NObfJObcTvFi80zkX2XuI+8JbwR/3KwHc55xry332mlMvR7f5yN6DAPwpeCHyA3L9ueAUWCu4VL7LoUu+Yq5aY46fTb4Hlsh30Dn3PQDgnHvMOffDYC3cbwD4OyIqO+fazrlfcc5dAdY4fC96EwTPBecl3/PAW8CajvvB2spvO+daAPZB3WOAM8jwLMig21d76Rg+jvOT4RgRWTnZa5fOf2noxxRgOb5miRwLzrmjZ5LVcuP3KeBMMnyMq0x2A/8Aus2kVwM44Zzr2rwR0ZVS5z8Gy/BOWZCc8zjsgZeDNc6HiP233wngB4joriXvY3G+stwk49/jTLK043EKbCF6jpHjsHNuAABksfcOmbteB+Dnvb+oc+6vnXM3guc7Bx6vTwVnG487lyiuesnyASyBLI7IOfev0PHoAHwbT12WvbC0/kGWvdGXsgyLYOCniGgzManjvWDTJiC+KUT0XGIC2C1LrjsBJVA8G7G0/h8H8FoieoVo2N4BoAnWXnos11ZL8XEAP0NEm8RX8l1Ljt8D4IeIKCs+sHbn+FGwj9GriQkFBWKSzmYiWk9M3itL3Spg7ROI6AeJyC/MZsGDsWuh2QtE5GWcJqIBsGP+UbBmqxf+GUp68/e4Tq4fB/A/AfyTYw0xpA02EuOFYHeJX+5x398G8MuimTsA4HlSn5eD/cs8XgaOENELfwSeBF7nnKuf7d09ZEH+STCJoyRayx89wyV/B5bRi8SH7VfQPfHdA+B7iGhMJumfNcfuALBATMQoSrtdSUTPAwAi+hEiGndsifA7+oiIbiKiq2QxtgDWpp+LfEekLxWIQ2G9CWxy/Owyl3wOwLVkSJ9yXQGsffB9sssflThs3U9B54kD4CgQA2Bt4n45Lw8mK37uLPVeR0Q/REQD0kavBvDDYNPqcvg4gDcTxzkuoXc/AwA4554Ef0BuIaIcMePachseBVvNXivzwS+Bzb8eHwbwa0S0Teo7TkRvkN89ZXWm8Xs2ENFrSMl1l4HH0T8s825tsDuQHad/CeDHpW1G5X0+suQZBPZr/hnpfwcA3Ch9/GU493G4FH8M3rw8V/77MJj38OozXPNxAG+XeW8U3dagpbgd3I5vk776BgDPN8eX/ZbJe/4JgA9JH/Zz1qvl9/cSk44JLMsILMtLiehm6c8N8OLrXGX5BiIalTnx+WDXr+VkeQS8qbHv85fgBdwmYo3lO3C6LAtgE7oPb3cAwMtFli/GU5TlU5hPgCBLX7++kqWt2EX3H7p9gq3v1Xac7pv6HrD5eg4c6sj6yP0ieHd1GMCPoNsneDf4Yz8H4FPP9Ds/hTZ6A5gcNwdmQAPsgP8g2Nz+ZfCO0rbpsm215N4ZsOliWjrsz4E/hCTHd4J9Yyvgj8HvLZHTC+T5M2At5WfAu+cJKZ+XOtwK4Aq55gPgxWsF7P/3FnO/fwHw3mXqejOYCFUFuyh8CsDuM7RbVtptoyn7KtgvbAa8CC6bYy+VtqvJc97U4543AfjMkrLfAS/mvwHxcwb7SB0BsL7HPfwOviFt4P97kxx/Oc7s4zkO4NPgyfEOMLmrp0+wnP9maYdp8ILkKICXmHp+TO51n8j/iLl2I5jwMWne0dfjoyKHClgj8H1S/sNGTiekz/hx/F4wWaJXPcfBWrxF6TPfAPCqs4yN/wPgjebvW6DRGfx/tyy55i8B/KD5ewu4j8+C2c++/AcBfPIcxuc4uK/PSTvuA/AT5vhp8pTyd0u7HgOb75M5q8e5u8DuN4vgiAV/DI6gYGV8XOTxziX9JQXg50Umi+Ax99/OJCucefxuFZlvXaauvyX3qoI/er+KM/gvghni/7Kk7OflHgvgaBr5Jcd/DMAfLJnH/lbq+1kAg1I+AR6HueWefxbZ3oLTfYKX+nkunUN/Csv4kcr514O/RxVw//0kgPeZ42f6lhXAHID90jYPAXi7HPs5kXtV3vl9Ur4XPE/4ee/TkDkRTGKqnOH9/0beqwK2Brz9LO31UwD+yPxN4Pl+Rv77AIwPrZzzqwD+s/l7GEycngf76qel/HkA7j4P2Z1xPgmyfPbI0v/nFyWrEkR0EMB/dM59/pmuS7/jO2kr4qQNH3bObXvaK/YMgDhE3hXOuZ8968lP73N/GsAW59wvrORzzwbRds6BNw8Hnun6fKcQTfhfAHi+e5onSCL6JoAfd+wy0VcgDiv4sHNuWQ3yswnEoaR+2pmEGU/TfT8IJgX/4dN536cT0s8+7Jz782e6Lt8pREN5N5iITxcVegAAIABJREFUefxpvvcnwBu/fz7ryc8QgizP+d5PSZZhERwWweeE82krYh/Rm8C7tfUAPgH2lVzRRWPAhQNxRq4vgHfyHwRr7699uheNARcOxC4oM2Dt1HeBrSA3PN2LxoALDyJ6GVj7PgUmDn0YwM6ne6ERcOERZLmyWA3ZTAJWHgT2E/0Y2L/oM1iS/SzgWY83gKMAENi39IfCAvhZhw1gU+sasHn0P4UF8LMWl4J9TwfArin/LiyanrUIslxBrGpNcEBAQEBAQEBAwOpEiA4REBAQEBAQEBCw6rCi7hC/+yvvTNTOQyNlAMDwkMZOjtsclaNd1+gc+TQfL+TLSVkk0TtOzqqFoNpaBACMbVqv1w4MAwBOzCwmZXNzTQBAJl9MytodrhblNO6862jTUMy/XRwnZdm0RISKNTIUOYndb9IROJJ3SbeSsnRWmiFOax1a/Iw4Us18LDdKZfWGv/Sudy8Xg29FsWPHlqSimTS/RyGvbZZJcZmDtpm/IGX2XikpbEUq83SGXzFtrBStDh9Pk17bEXk0TXt7sdgoi06rkMR9SZvj/tyOCe+Y7sTyDC3z16ZMvWJ/c7L7SbmhtbLI4UJOz3vosUPPuCxvuO6SpJL1mN+wq1KOZVprav+NOtzgNtdOKnP6VJKSPlAqamStVJvv04pUKJ2I79dp2zweUi1TmU4nZY6SHD49PGenreGRO/45RhYksoptOE9HUqbI5Hg8W3lHUXvp7TB5/OQzLkcASKUypla+SvaN3JJjAFI8D6574U1J0RWvehEAIFc5mZQ9fsdtAIChjs6bV136GgDAjS96XlL2yJMc+vb2u29Lyh469Djf9+rrkrLRoZHk93oZ++7QY0nZ5/bdBwBortUojuv3XsN13TCQlC0c5Gse/MqXk7LaZMW/nL5n8ntpBvNuxHEjyFIQZPn04Df+9IsqS5k4YrNuSKVOn3siOW9qrpqU3X0nt8uTd34pKWtNcxh/19H5GfHpc57Opz0+fK53iGMnv3s1Yjqt8siXOJrk2g269rpi7/UAgG1XvESvGVwvjzXfCnl3R2Z+jrsOAQDe+9ZXXjBZBk1wQEBAQEBAQEDAqkNYBAcEBAQEBAQEBKw6rKg7RNOYO1tNNivGTsvypRwAIJfP6kXeRSJSs0BLyiypL5Xha7xLBQBEYidv15tJWSx1iFP63HRKXBGsC4TT/UGW2KwbkTUviLnc1D9LYiY2poJsVupljAqRmNBdbC1efDyVVhcJX4WYrKGkP1Cw5m9537QxtWSk7u2O1j3xJjFbr5Yct+b0tLRVHGnbdsRlJSYjX3/D2JoE+ebOmNu7U9+I+cX4Q/gWz6e07bN5/u37KQA0epiZUnK/yPWwXvYy0fXZvrNj+nRa2o6Ma0ez6c13xiVAflv3IG/hyubU9aHg7VnGlaIm7hBJB4G6JeQLuaSsKHVoxdoH5po6jjttqQNZs6LUxZR5s50da7HU2w5n506XbavOsrdjMpeR33bs9g1sf/Pt26ue5ryhUQDApis0c2leRDj10MNJ2cLREwCAuYqO04HMEwCAnbsmkrLDR58E0D2vl7LcZrVZzcB+6snDye9JWst1GBxOyjoyKhcn9ZrsCB8fHLg8KStu5gy0m67UDOmHK48CABoV7S/6zr1M6P03vwZZSh0uAlnaWuq0Yf31Ti9KSeHaoSRxJa679gYAQDmvZfvv+QoAoHriYFIW1dmFxEXWvWzpw8xUZ/wFu75Yfk2S0rk6neM+UxpQ99T1E9xndl1+fVK2eTe7y6TLmmld3efM85Jvt3XDWHrWhUV/fZEDAgICAgICAgICVgArGyc41jV3o8W7lFqjlpR5wlg2q5oXkNe86B6lKFrDSs1oVxustSFzXlY0OK6j2rxsyh8z6395Rq1RSYpSZLTR8ryc1R7KjTJmu5KSnZMzdejEovlKqT7Sk98is/uJemiiItmBR844vfcJygXtOk3R0lrNvP9VyKl2LytatE5H26LSYTJibIhxsagV20bxIY9INK8AAC8jI0pPluvafRuNZSwaxrzRFvqjVpPdcVyfVhcJTt7N7Jxj10srIYQC49nvtY6dTn9pKhqRNl65xLv7Zquux2OWj9Wk+tePTJk3VhRMO3SEQNdo1E0Zt2sGOr5KRX7uwIBqOGIht3Xqeu1g0ZAi02IhirsGINfLyKctz+vSoYmcqVvtIfUyWgrpKyljmSIhD3axNvoG9i17aYGkzgUlIw9d+RwAwNjWsaSsVt8PAJg98WhS1liY4TtEQ0nZzDxrFB994p6k7MixB+TYZFJWKMr8ECnxuF2fT34/PMlkq4eN3FLyO2orqXnqSa7P0EYl4KzZvQMAsGGPaj8bJ7jPHH1UkxfGCXu2B4G1p4b1mUaQJXBxyDI26x5PNuuyTibf/l5X6zwzJkEErrteyWYbNrL2/OBDGt77+IF9AID67Amtg6yzYkOgS77XXYZUXXuls/ztLpZ1Xh4ZZU3/lu2qrd+66yoAwOA6JT5SjgmP5tWTKZO6tNGeGGe+lXLcrZAuOGiCAwICAgICAgICVh3CIjggICAgICAgIGDVYUXdIQYG1PwyMFgCAIwax+/yIJsdrVnaiR283VQVerPJanKrLB8ZZhPQ6OgaPU9MqiljuiyU+JW9qRYAshIPtFTS5oja1pQt5hxjsvcBbiND3PHkqDg6nahlyToQk4MlIPlYfVGsrhsuLb+pm9rVD+h025f5HyOQjLz38IASpbxp2roEeJ/7lontmrQVqWmmUGQZZbIqo0KO+1DKmMRb4vqSNganyMg69g801zQaLXmn0837tsz7Q3TJDaeX+V9p4z6TyXgTfJ+Z6wzpKxJTf6tliKQdHztYz9PYvIa4KO5N1Y66N8UyNnxsXcCQLEyX9ua28qDOBSnHc8W2reuSsoLxkpo8cgQAMDU3lZQ12yy0etPE+/YuEnopqMfe37tQpO0xEZXvHwAQCfG2L70hzkacSgnxeM2mpGjikt1cVtb3njnKbmFz8+oelsnztdZNrAU+vv+wmtrbIv9Mxri7rOW5OZ3SuaBcMnHBN3F5K6PH/Siy8UOdsLyooP0kKrArzeiW3UnZ6E7uE1NTs0lZ/QS7APQhb2oZBFkCF4csbZx67w6BlCFuy7c/ZT81Pehh/lNkvUW3b9kGANi0fkNSNrX3WgA6RwLA1NFHAABzhkDXEAIdergiAsC69eyqsmPHrqRsw5ZLAQADa5RAmc4PSk2N21gSx93I3IclNv3YuxZ2xXv3MdvdykyyQRMcEBAQEBAQEBCw6rCimuDaou5G86LRq1d0W9Pwocxi3ZlQQjbT+/jwSPNzc0nZbIWd7rMmdEdadrzplNmhSFYdm+GrUeWb10wIpgxpNp2ChH1KWQ21J60ZFVNOMqblS/q8pmjVnCXBeW2vJd95BZm5YT/vUJomTZvnR9lQUiTa11JsNY1CdjIhd/zhbEbbe1gy/Q0VtKwoTZ+3GhLR/HUFrotZ5s6EdYnMbtr/bhiy5Jyc2zCEvVj6YMaG1hLNp800lk40pKcT49Jmy17M+fv2l/qikNZ39hTGju140jYm4hyi9PLZ2pzR+vpBS2ZHnxZNg80Amc8yiaKU07KCjNMhQ/xp1nX+KEjfGDLZqtqijR4xBLvFRZ4XFhtar4wna5p6xX6e6diwfHxN1louPKF2hbQU3zmM2ijNlpO1Wy9LiiZ2MMElVTCkwwxr+zbvvTkpG5PbWItHLGO2k9V5bCTD98uauaAhlq9sWs8b3aPt10rLtyCnxwtyfc6ox1oydlpp1TI2pR+UTb127OQ6HH/oIb12hvtO1Oy2Ccib4NmBIEvg2ShLnXuSz4m1Fkudu6Munk5a8zFTU8Yy3BGSfdNM0G2xVnVaxion65CMCZuW83O11QRbGc2dAgAcP6rHW0Kk3myeN7SOtdFmmZVkhXNd4jjdupGQ4ExIWm2IlbGa9vM6KyAgICAgICAgIOCCICyCAwICAgICAgICVh1W1B3CkklyYnbJZUp63JOMjFrdW13ahjCGDP/OGSJNTswr+YJmqokcX1wxpICTp9iFYnpKTQUzMwt8vjGHe/IdAGSIzadlY2Zdu46fMzCslR0aZbNPvmhMzCU298QwsX79c8wWJCOisJnlKPKZ7NB3aBuzsXfYTxnzRcZx3WutjrmG24CyJu7gIJuzx4rq+lAWM3RjQeNKxlWWUbNlTEtyaxtj0DcfmXiHKWOayxVYHgXraiHuKzXT0ItCrrK7xJbcs5dLg01al/bxhO21YkaK+8yKnmRAA5CVxrOxmEmOU5cbipDlrPuLEP8KxszXEPNtqaCE2I1lHu/jwyajlIjUzekYaVWYCDPV0WxUNg415bg9R0y873SB3SraMMQ4yVCXNv3Bj86mcXPwBFcrs7z0lSy0vyahNQ1ps39gp3P/Jtr300OcvWnD7iuTstIAx/1MZ9QVbPN2NrFnt2rcz6ybBgBUKpq588i8xNzetDUpKwijdELiiQKAExN5xoyvspk/5sW06nJa/8Yky91mcUSD67gwNZ0UVWos34qZXitHD/E7Of22xFEvfU8/ytAjyBK4OGRps4mmkzI97slhljjoT7AcbP/daRiXhhMnOBbw4cf2JWUzB+8DALRmjuu1dZZ13Lbuaqdn3rUzYHWe5/LqjMYbnjzAZLvDD92elK3fwln/dl1+TVI2vIHJjWTca5wEDe52opMXTMVdpXxeIMYFBAQEBAQEBAQEXBCsqCb4OVepE38hzzs7m13Na2NSabNzykmII+MM7n2yR8Y1PEw6xQSaQ4c1lMrXb+cd0eOPn0zKpk/yjmhuVnfLTjTGzqQ3iTtaLx/WicyOKVc6BgAYHNUm3L6Twzlt2KxknXUbuV75gaQoCdGUMe/p752OM6bMq8H7MGOc0SA2ZceW1eRwcLLLNNGlEInWf8uwhr1aI970zVnVCNQl7FXUMCpwcZa3mnK/TbYcpUSCTne8HZOxrC1EzHRGK5sps1Z4uKjahoJkMZsx955vsIbRKD7QFjWm3dn7rpM2fdvv4uMVcvY/V4xPbEt+1+c589N8zbyg1/ba+D0SOslq/suiNS2bEHYbyzwOSpnRpCxX5fBGrcmDSdnCHI9Fq2DPtrkOVr9jdQWxaICtxj8jIZYyBSXbjBa4DkOq+Mdiky1DncpCUuaJkikT+olE69Vpm/bwGQczK5ts89zQq06GoCTts2jCTQ0ePAoAcBm1utSlLVxeW3xkHY/ntJHvzt2coaywaXNSVpY4dnlDeEqLOS9jtD0503dGRMr52GQhG+WxWDmuc/epYxy+67H7NavZ9BM8D7fmTe8QeaXMvJmWuSeVMxmxJMxYKtWPuqAgS774YpClzQIr3zHz0SKZ2axClsReZefESoPLnjz4WFJ2aN/X+dgRJQ5GFcngZ8jfKbf8d6fLymd+x/771TBhzhrc3+armiWwscC/Z08dSsou2/sCAMDErufpc0oD8rweIdLiXpr8QIwLCAgICAgICAgIuCAIi+CAgICAgICAgIBVhxW16R08YkguompvtYy9XBzxU3kT51BcJBomhm+nw6rzofJGvfd+NrPe/vWHk7JTJ9kk06yb+0USO5jU9F2rsdmgOKAq+bYhzSQRCA3px1e7sqD1qsxOcl0e0yxW1zxvDwBg3YQxtY8IucC4eFDau4IYQp6YCJp9yIwrFIxpTkzDKWMy9zGXI8Ny3DjKGWjGzX3qx7lPtBeUpOEkbmEXQUtcFbJljSWbE/NKJm+c74U04BpqjmuZ+LKtqrg0tJUY2ZrnsnRT+2JuiMlc4yUldeXEPD5tzeitHqYsqbYNBZkRM/qQabd+wPS8vktbiDIt6+8h5sXCQJc/DwDAmcxsRSE7jq9R6Q5Jm1SP6bhvSDzvqKnyzjnvcmGIduLSkDZuNzaTZOTdUAyZNarxveOWur+k2+wHkSlq/Ysprms7Z2MCcx+pt/WdanXuIx3j1uLdL1p5QzDpF5DxR0qyLurcVxWy4dFH1AR9Qkg0EZksgTKexq64NCkb2/pcAMDEJs0UNbSGx3PHuIa0hPBUW9AY7g0pc8YEnMsbUnOZZZMvm1jh6zhL1Zqx7UnZlg1b+PyU9p1HhXg7b6ypw3Kfck7fvSRErXxO2ygnmdBszNu+QZAlX3sxyNKQ+uNkyWVcDJK4uPrtjyVu7kJdrz24n10eDt3z5aSsflTWO3VtR59B1ZKwPcE5Y30WvawtydfkaIjFFSW27phy3NkgAnM878421R3ifvm+RmY+3XTZy/hHQb/hyZRuPqDOt4NbGR1t0AQHBAQEBAQEBASsOqyoJvjBJw4mv/1q3/qxZ0UD3DYZTFoNPq9QWJOUZRzvJO765iNJ2UP7OHRHQxV8AEl4ssho+HK84xga093rlhLfe3DcOIW3dXdUlZgt9YruyuZnJYRWpDvPjjiQn5xTTdS36vsBALsv19zez7lus1RPd+yU4fs5E0rN7+Qo039ap5ZJb+PDUNnMbF6TMTaiJLhxkXXtsGoGW1URmNGkpkTrW16nbTYmOdIH1mq4noKE27KdKBJH+9iQ4RrzSh6pnOJwL5VJDR9TmeXjzYZqjDNiCciNKMlxRDQerazubn2YrU7URdsC0B0KJicEszPwE54RTB4/lvzORJ58qJUslfmdTRIqpApcFmVMdqkBbqcBo0SuHGWiTntRNUmxkE/TJW3XIbEQjE1oX8mO8XEyzyCjpWhWWfvQmFPZNmaYXFmdndHzaixTq932Wu10zmi3pT8320r4iESj4kxiKidyrttUkX2ClAk7l/GZvUz7pSTzZTrWecdPNw3zJdiwk8MbXf7im7RsB4fYypUMIbbJ7T3z2JNJ2eSDTNqpn1IS1OIst6kdItmStn12DWc1W7tbidMbL2HN5eg6tfblJ3ZwvV7x3UnZyBjPpYceOJqU7VzHWsbtw0rIHMl5TZgJnSht5FYoFNP5IMiScTHIEoZw75X5ris9XGz+z6iKFvbQwceTskP33AYAqB9Wa3csFrWUSc3mQ4KWBlUuuWGeT1MFLUtlTteax1YjK9reZlW1zI05nls7VSVfQqyvUV2/uZUTnG3usX13JGUZsRZu2HODXitZQp15+6QKbmUs4EETHBAQEBAQEBAQsOoQFsEBAQEBAQEBAQGrDivqDrF2867kd05cH3IFY5IRQkuno2vzTMxq/OaiVvXO29lM8+gDJrZsle/TidR1YGCY1fmXXrk9Kdt5KZtXMlkTi1hu3ckqoY1sdh6wKabT0LpOTbKZdeq4mgoee4hdMsiY5+sVVuk/8sCRpMz7pl/9Is3OE6e53s2O+nPkxawxW1XyUr+gYwLjxt6UYTKIjY4yQWqipITApmT/aVXVbOIJFmlj7tqw+woAwLod2l+yoxxvOWWIUiQxhq1hKYmHaGzYOSF9AEB5A5vkhoyrxbSYnOaOqYwa4qbRnDNZ8EbYRDlkHPubcrhiyQNiKo+NzbDVkrjJzf5ybUmbGLg+BrONTdkQtxJnYk5GEjd0pKSuKYNCrmic0OxCLSHBuZSSMYrizrLtCs0uNLqBXV1KI2qqi8S82e10YCom4zw2Liz1KTajzh5Wgsb0JJdV53WcdmSuyJWUvDOY49/1vMqxHXEfaFlTnfxM9aH+4OabXpb8zog8smmdN1NCGopM9OVGxOb0k3k1I2+78eUAgPVm/KUl89PcsSeSsqMPfA4A8OiXvpKULT4pMb4r6jIUdXw2PhMX3YxjFLh/lNbvSIo2XftSAMD2vWo63XoVZ0cbW3t5UrbnRTwvZAuf17qK940d4+Nlrr8l7/psVa5HBshnGkGWUteLQJZdpv7Yu8ppm/mjbePuNTXFbX/8sTuTssZxkVdT1wgpuTpr5rLiGp5j88PqQupT7zqbtjeplrajndVimYNLOXUdzQ/yN7A6o2uvxhTP+c6Q4Do1/l2Z0jXVQSFxDhgXyaGJy6ReJkeDj6W8Qi5n/TeTBwQEBAQEBAQEBFxgrKgmmAwZpiEZZZqGtNaS0GflnIZZiqusHfr6bfclZQ/cxZqeuK07mKFh3glt2KK7jMuu5Ixyo2uNAzixVrXRVsduklBlpYIJ+5IzObQlA0vKHN+8m3dEE1tVG1YSzfP9dym5wJPpOlXdTe3fz6HUxjar5mvDdq4/ZVRz2olYPM12/zn7d0yIFCeaikLZZMob411oZkZJFbUF1to5EzssPcTXTFx+XVK24RLWIpDRaFTF6b5jiA9ZIdCVsprRKGEemO2dM2HaqMxtWtqsO+e0EL0yeR0OUwe4jzUquuvuLPKzC5YsV+RrO8aJvyEh7SzBLNndxv3FjCsWVUvb8Frhruw9/M7OaVkhx+0+nDfhdiTLX8uEUyLi9hxYp1qhLXtZAzy0UUkykWSuWjChetKisYiNFiWdUvnkRPuUzqvs8wWuV76oYe0ykvnv5MH9SVlF+mHc1L5UkGuHioNJWUsO10wYx2ZTNGH9xnAE8EOvfU3yOzGIQGVEosauxzr3zXb4xP0ZbTPsZM181oRrPLn/GwCAx7/xxaTswG2sNWwfU22Pk5CBlnKTIX6GJVOR+fIUU6zBTE9qJqzpr8lcYcoqU5yF6ooXvyopG9zAZKrNV708KZu89wAA4LETSn5dHOF+ULRZxmR8WlJRvyDIknExyNJmE03CSzprgWMsVpXkeEyywlUOa5tBQn3aDG9eA1waV2tnfpjXJHGP7Hl2JaERyJbJy5l8q4zWX+bq8piue1Lyza2dUpI12vwuCfEdwKzIcPLAvUlZeZQ1/KmiflNTYh1eqVVP0AQHBAQEBAQEBASsOoRFcEBAQEBAQEBAwKrDirpDOFLTZSQuCF0mzqLE+murS8A9d7E55N671Ik/Jxmfhsf12m272Ix55dWaBSeVZTKMc2oyKvqApyaLS0lMCpW6mj3n5zTW6Pg6Nu23DOluVrLDlQfULHD51azajw0pa9+3hWwV6X5jdppN+/sfU1eBobVstioOqdtETYiCcWTNFf0BZ7J3uTS/W3HAxH5N8/HanJrX4GO15tQVYWw7x7Fct317UrZQ5divD+97KCmbnpO4zMZcvX4TEwu3bt6SlI0O8L2zRgbVBY0le2KOzfUtY/UZyPI1xc2XJGUjTT5h5knj2iIuGSnjflEa4msLGS1rSOajTmTiHHo3Deov15aOM3Gp/b8mc1tLyAkdU+2hDPfRrCFCRD7znHF1Ka5lE92GXTuTsvkOj8l9930jKauKi0HGEH/yQj4cGVNyx8ZxdXUayfBzKiZeZavGprfYuC+kxdxWbujYbXd4TmnXVT45se8OmoxKLccytXHLO2JWjvrMrQUAGsadh8TW2e6YOObg9z04PZmUHapK3PTL9yRlAxJ/u31kX1L25Lc/w+ffpUSd8QKb3a/aozIiIe2cmNH583GfUcr0/eyYuoJ9903XAgB2DowlZZHEZp+c15ixjz18K5cV1V1u+EU38/1y6nKTW8/vOb2o8VQHHffZYsoQnqWbxyuUmep8EGTJuBhk2ZVN1JcZAl9H5pLpaf1OzR7m9U5nQb+fsaw/MhkTOKDoY+Vrf6lLXN8ukqC4idjPT0ayciKjaw57QuoMDglk6lAcZVJ73NY5tiH9MjbfwEaF3TlOHlPi8vqtBwEAgxuvMDeXOXiFPpX912MCAgICAgICAgICLjBWVBMc2ZAXfsdhcpBnUryrOXZYw29865v75DRDWivxzmlim4aq2n0Va2TvuPffkrK77vo6AKCYNsQpyfBVyqu2OS8hY25+9WuTso2bNiW/jx3iHeyDDz2YlB09yU7g6zYowWfvVezsv2OHXjtzTHbsj5owZ5JlrjKn7VGv8O98yYQKkZApHacat35B04TWyguZb01Zd5QpCevWqalWzms08iO6+1+7YzsAoBUpoeq+B78FADg0pU7144NyjQmJdfgJ1g50zE77ih2sHZ6bVSf9e/fdrdecYk1G2ux+h0dZa7F1k8py7Ua+T8mEp2sc5VAwbZOnvVDi+wzkVCvRaLFaIo7N8PKaYPSXBrGQNZYYCTnUMmOyIjHg0lZLK7/bVdVcdEQLkCmopn7NNs4UVRjX7FfHDjNB7cQhzRqYId+G+oyowG2cM/dLGZLFoSmW7z2PPJCUtUW9v9aE2xuVkGylvCHLjYzJ+aplaUuIu4wJB1QU7X7GaUg/Pyb7jeAIAHNNnSeEv4SO6YMt0RY9uWAyj01wnx/ZqO3TaLGm/OiDt+q9H2ZSyw17NfPYy668CgCwy1j4nIz3e+77QlJW+TqTrgpp1TJedfMrkt/XX7cXALDZaLM2l9nCcvjQt5Oy6B4e75MPqVbw5Hq2Au24VrWfWcn8OX//gaRsn2jWyrFq+iMh9zY6qjF794/9f+gHBFkyLgZZutiG+hLCr5k+mjJ3zk0p+a8xI98vG3pTOkLKzMWNef6e1Wb0++lVzzb7W6KOzujcPrCO58bCWl2vwJDIfcjMVNc3qwdZTkjK+SG1BLclmEBU1++1t9DV5/WbujjFlvLyuFoLKedDn66MKjhoggMCAgICAgICAlYdwiI4ICAgICAgICBg1WFF3SHSdLqjdtTUsqjFZoEH71MSXFOc6jMm69TwMP++/GpV42dLrH4/OvloUjY6zGah5+19QVLWkPvFJs5tIcvq/JFBVecfOaKEg7/7xCcAAKUBJQCUBtgV44F9as6pVdlF4NU3vzEp27yFYx8eeuyRpIwirn9zzsQflXplN6qLh48zS6n+c4eIjKklk+H3yWXVfNGaZZNH3Fb5UppNzeUNKreiZAmbOXUwKZtd5LbYsuM5Sdk1uyTj0YJmJLv7oQfkWpXV/Cib8yafUNeVE7Nqktm8mc0uo8ZD5tT0KQDAEZPtLLuZSV2DGzT+ojc5NWome5qQ4EqG0JjPSaaxjrqMdKS/x30WXzaVMVmoZHx2jMzSMkUUi0pmzAmZLjZuIT62Y3lUyWujG5ikmi7pM9IY6G9zAAAgAElEQVQpvs/69Wry3LyJSaFrh407jbhp5NM67udmlFBxfPIgAGBkzJAxC+zmkOroeKnWWPbZso6r4oC4Z8ypWa4ipMdcrHUoZIUAmDYEHOL4lzH1XzxSGzc9ElJMo6OuHFWJN54idTEZlNjehZT26VOnmAz60J1fTcouKTDB8Ie3a4avY6eY2PtJY5bePMLnbbrher325EEAgHPbkrJXXfPi5PfBExwL9V8f0rl7YJDn7huu1bniho08Fj/9bXWBObCLSVTDV+p55VGW26YxHeT3fJOzVd1/Ql1q2k3+BkRRf41JIMjS42KQJZnYu+0e7hCVOs8pNRNTH1UhIzqVpSfE5cq6DmlUeA6OW9o3ErgebgyGOOiSYM/x0rOk2Ltj6lop7uGikBZXVSqYONYlrqNrqIx8BriGcWmcl3de29S5OJvluTqmlZFl0AQHBAQEBAQEBASsOqyoJjgTq4YmLbuPFKmG6aSQlo49og7i+VhCvBgH8T27WSs4PKzXztWPynm6c7rm2ucDAK4zOcubda7DQNmEFGmxFnnRkLg+96XPJr8nNvPO9LWvU+JcUULPfPErn0vK7rv/fgDAkcnHk7KRse38vEHdJdUXuI6LC9oeDcnPTh0l7MVNeZfmyuTQPh84s38ir000O8aoIe9jw7RISJbiiIbPScvuMW2y2xRyfF42raG6OvI7nzPEDdnpNo3Mm03uQ/OLqqUcMo7/e57DoVjGM1rXg4/w9Y9P6g61HrEWc62pa0G0/42qhgyK5T2zA9oXC9IeVWh/iiRUTNRnmmAyGs2maK6ttrqQY7mUTMimbHS6FYfEUlMy+epLQ6xxbaVVGwDRIjfaqrk4IuSImapqtYZGmNw2rgpcLFZVpiQEtvXrlMy4Yx3Lee6Yku4OzTJ5LzJprXISVsgZzYXXGNuwQr6Pm2hAShLpMzkCwBc+/5Xktw/PF6e1nWsSvm5o27VJ2WiZtXSplL7P4gnu37NHlDhYl8x8++7W0HZ3HGFt390VHUsvvokznW16roaOHBjh341FYwHKqqyri2y1ufOeO5KyVoG1mut3aea075OsWINNtao9+AiH+dpzo2or16xn+U6Ma9+43zE5dnFa36kllkdLzu4XBFkyLgZZ2pkiLfNGZL6VddGWNhcNuU3CT9rscJ60my7otwa1HlZidzp5zSNl5y3R9JqqdH1LGw2eb1Px6VYvm8mzkPeWYNVQ5yTEZccQJCGa4FZLLaTVCn+vo4bO7dmy8w9ZEQRNcEBAQEBAQEBAwKpDWAQHBAQEBAQEBASsOqyoO0RB+SVoiok/bawX05McH7hZNybJDq/TR01s2TVr2eRaq6lZemaWTQkdk7HqyJNMmDr8+KeSspxkfNpzhZpzdl7KBIBaRZ+7UNcYqFfsZQKBsaiiEbH6Pm2SrTgxV5VHTNYsMUlYC0aryu+eNfH+nGTNihuGECFt1Gn0IQnHWlXkX2diNPrYiNYgk5J4zN4cDQAZidc7VNbYroNiXjl+ZH9S1pK4gynjQH9UXB42rNNMbyVxm7ChGVNZfV5aXDIyOa1rzpv6nSFpOr5PxmTBy4nLQ/qkvpVrM6kBhgSX8zGtjVkyIx3ddfrLjJ5PG6KEuHEUTFztnJDDbGZHEjed2LSXj/GdLmiM0lhIbZ1Y3SFaEbfX7MwpvZ+4QRUL+tyZERnj4+pe4V1PACCWbFHpvBlY4roR59SNxhH/Tpn4v+lsSq5Vd4gEhliTTpH8a4l94iKxUumMzgPf/Oa39A9xc3FQE7qTzIh7N12dlEXi4hPDuBRN8VirT6t70P1VNls//uhjSVlVsm4OXfHCpGwsy/P0AGm/qkcstwUzN1fJuDoJUSYvYxwAapKtr17XgZwdYrP7QM6MIYkXHVcMSXOC61AoqytTXjIBOpPlMG6Iu1L/Ta9BloKLQZbOuCD0mjW8q2enYchtyQfWZHCT7yd1ZXjLnHae+SL3eJrJ9Cqnmc82Gi1t05TUwZKnPSITWKBeleAFQ9rvkmtSlojn/zXZ8sSNJeoY10HfXiuUXTVoggMCAgICAgICAlYdVlQTvNBQVXBTdiv1tmqJHp1hR/eZSHcUxQxr2LZP6G5zZIS1gYWS7m6PHWCt8NwR1eBm6+yoPbJWtVMn5/i8o7dpHvOR0dcDAGpT6mhfNpm0JiY47FPstA7pFh8/+YQ6s28a44w3awyZanpacp8bx+90lrWLkQllQ3kJ8ZLR8xZlZ7gI1TL2D2y4O8lQYzIC+l2mDamS8btCk7XGZ97qGNVtS0hTlUWVr5MMdZHZqTpRw48OKDEjl5K2MpvgVMrITbQW1EtlYDfOfhdqtJOZPD+PrPY09v/q/Xq9E0k79Jv+sGX2wZHUrtbQeqfb/HtwQBsn0QCb9iJp41TGmHtkesnkVBO/fuN2AMD4xG49K2YNVquq2uHDUxw6pzan1h4YoqHfv6cNedKJTG27+6xDtl6qPDH9wv9rynw/taRNiFaY0H9k1cbMlPmrR/2K8m52nMqrxab/1oWki7a+d7vCY9FydDfuuQwAcN3zXpmUXbaJiacDpISnvGgZ22bs1qzGXbJ3FkzTx6Ih6tR07suu4fOyJhRjJBkdnSHbRDHLOmMIWwUhg6WsiUgyWFEf6oKCLKXsIpBlV3Y1r+U0ptRYvm1xR9cDiQyNtS35eJi27fWM0y/Q4/ar5wnQ9WYzKTtxUsO0daqLUoUeodZM+DKfdTSb2pCUZVKirabT62A14x3pO5El3yUq4x6vdAHQjz0mICAgICAgICAg4IIiLIIDAgICAgICAgJWHVbUHSKKjVlf1OlxQ00AizPi5mCyRBUzbPoYGx3W+0gWlWpTzeUDY+z68Prvf31SNi4xRIdH9Np9D3Iswq/d9gV9boVNT/XYuCfktA65Aqv2nQmo1xYiVLOj7gvr1wm5K6v2Ch92r6MWB7QkHvHIsAZBzcp7pjP63EaLL0rlV1RM5wYTjzESk1zbmE2ymfTS0xITT2TML5GQGyZPqin8hGTe23bJlUnZZUKQqM2quebRIxxPenZRTYdrJLNZlwHRuEOQz25jTUU+9Ksx3Xjzvo1VTGnvSqHw5i1rhvNEqpR5+VZ0OtGhH1AxYSa9d07NZMRLFfi9isZclYxi8yrkvEnPuizwOw8OKKl1QLJaWZNtVn7OHlPXqNl5iQFtxqSNrUlpT4iz7heSjckyPaQnGDEmdbXjGTG/J1kCoLi9WNcZf00U9587BFpm/pLu6GLryiEEKyOiWI7HhvxUKEmcbkPslEst1xNzk+xSduA+jWm7dx3Ld6Gq7VjKcVz3YkrdYjptQyQVd5lcxroZSYbF2LhTZVjmmaySIfM5/k2WYS0/nTEvQ2LtknG182RWRz3ty88sgiwZF4EsbazfKJlLDOHNu1uljJtgrxt5QplxRXDexcC6XPTwI3C9jvkspoaoFhlZtmX90UXs899P+11P+qeNse7dOWxdk0ok8J9m+1X0cbHbDfstuXAImuCAgICAgICAgIBVh7AIDggICAgICAgIWHVYUTt7U1LkAUC2yGb/tmGMUoeV4hmna/OMqP4zaWNScGzDLZrUx9vXbAcAuEjZ/JIhGXWTojUjZhpKq+p+cIivue8RjZs4MKQuFCPDHO0hNrER54TJ3jTxhNetZ8Z7p6WmUh/6L+6omSaXZhNu3thoRwclJW9N3StOeReBrL5Tv8AESEhMMW3r++DZ+MZu4iM8tKpq9m63uIHqdSOjLLf9xo3bkrLxTZyuMx7UGInT0+wacXJeIwjUhziurDUdkjEPqinJRjsQ1xxzXsrHlzXsXCduHzatsHeh6IpA4feWXSzYSO7bX/vOVKT19imBsynDzpZ4kA2Ttty/QzpjTI9CNW+bKCidJv9uVvXamRmJBZ5St5+RAsusbqJSNIQhTjZGZVfQaf4na9ozarMsOsbs6sWXNrKImzwxtOsm0oicmDKRIOR2XbFZyPcLOt3k+IzDupt5y6md4ttc2JxXGbmGmJGH9L1zko4+UzbyzfF4zhrDZSzuaI/e/eWk7O86PMe/+JWvSMo27bqKz2+p+1rn8KHk97CYxgfG1+nz5rhenZKmYnXyLo50PkyNyTUlNauTxMltNzSmeEtSbjsT33Sl2OdPCUGW8s7Pfll2rCzlG2mjJpHEYk9l9dvmvydd8fij06NEUZGjZmRinWPTcm1knSpkniRTl0yR5ZExcdUHClqHOMPuMCnjoulrHZnvYkrcUqwrp3dlcOZ5/tqUWROkJQJPbFxgK9KPa/M2QsqFQ399kQMCAgICAgICAgJWACtLjGsrISotzu8+DikA5ESDQzarltfYGi1jXpzva02N0XvnHbcDALZuvjQpm1i3FQBQKmqMwWPHOIvcQF41vUNF3vHMzamGcu2Y7mS9BjhntD+1OdZotU2Wl5EBJsZlSHe8jz3IMRazKSXweJ7bls1ar/E1vPudaShBrCgZYlJ5JSH0C3I2dqqQFhqGvDAoxEIbxzXusOatMafa83aDSVPFvLaPj/U7Y2LErhlkjQEZa8Jcldu+Y2L5ZmUXbJLxIWXIVc5nqMlov+s0/W7VEvtEO2ziYXrNYWRZC/Icy1lpCUnDEkH8TjzuM42FrU9L6psxMXV9zM62IUzEJckOZywUcZN3/nWrlZffFcNfOXLsCJc1LImCx2LFZICsyPN2rdcxkoXKoiKahoZ53pRYE07MmWxVQtZYb4hszQU+3qjp2I096dFoRTwBt2NjWPp+b2XbL7D9HKdbPCAk1Masjj9UeM6j9TpnDU3wfDi6Ucso5rntyl2anbFVPwAAOHjwSFK2tsD3W1fUuXQo5jadWdDzMnM6YC7Zzdk7916uRNgx0QZdajSKh2R+nstovTbt5Pi2A4NaFrd5jpg9pRrKxTn+VkSRlZsn+fQfmSrIknExyNKG+vXqUEtQK0o2y3xZ57q6t4AZblhHWPaxWUd5rXnbGfn6jHrmm5SQ35o6h9aFQOwqqtXP2uZL+TjvqqX1s2jakI9jsa532oY8LXOxM9prrwBOG+tersDfkob5ELUl22Crrv3uQiJoggMCAgICAgICAlYdwiI4ICAgICAgICBg1WFF3SEm1owmvzs+u21R9f0DYmadgjrBe617paImgFbEZoNqXVXthw4cAwCcPKrXXrOX1/hzM2oy3XfXQwCAG667PikbLk4AABrGRDttUig3KuyIHxty3gP79vG1A2riGRvgGIonD2pdT0l9CjlN3VzK8v0u2a0uGYMDQpwqauzgqTk2Nx8+prFx+wU2LqF3c2k01NTSEfeGjHFz6Eh85NqUvk9ths1nI2Prk7IN89xmRw8+kJSdOMSkxUZN5VuT7nv57h1J2ego96HRwRNJ2fEF/X30IJueqqTm8cMnWdadjMpjwDvsL6i5sTEv7jfWh0BMOymTCroq79myZvTYx3PsL7RMnO6OmBdTPWIZk3mVhtjZ0sZM5sRVoVmZTsoWT7GchzZpOs3xQR4HjcrhpOzUPI/dmLSv7NrBbk3bN2zU+tXVVah1iK8/fOChpGyhIXG1TZrmXZs4lXnRxC9emOU6NgwZM10Qc6KJ1dnpsDmu3TEEOnGroH4kxnUhobBoUczvcepJJQAfvfduAMD4lhuTsvy67QCAXc+9ISmbifcDANZt1nH63EtfBABImbjf4zIOyk7b9muP81x5zxOafjedV1Pnxr0vAQC84aXfnZSVpC8s1tS15Y6TbBI/buJOj45y3xotKqFnvsIub8cPPZmULcyKCb1zegRWGwe1PxFk+WyWZfdU4ePKa91LeZ5Py8MjSdl8XtYBps1icR3tGGJ5YYzn00JhrXmg6Da7Mhbz87qcReQ8Z1wbB+0lSV1Ph3XxiOW8jlmjRU3pMzbGsBCp0wWdn7MFrn+lqm6OTgIZtKtK+ryQCJrggICAgICAgICAVYcV1QTvu+Nbye+OLL9bbSXXlMu8+2k6GzaNdz+Hj6tGbvsia19H1m5Oyq658nkAgM9/7rNJ2RMPPgoAKJiwHy94HmuAX/wC3S3PzLFj+HOve05SduuXPp/8/vOP/i8A3Rl2BvK8a3v1d/2/SVkccX1u/fzXkjLXYQ2Ty6g2aesu3v1MztyflO3/Mmc/G9+ku/PqAu+sRgfU6b1f0DbhaVIx17Nidm6VLL/jSNmQ+kRT1zKhT+YPc6aiicv2JGV7LrkCAFA4rrv/kyKjwoC2z5WbtgMAtm3ekpSVhfC2cbP2jerjB5LfBx7lNm+YjEwoMLlgx47tSdHaLO9/F44dS8paPr2aIV9kS9xn22a77MOJWe5gTgh7nT7TBbcMmSESTQOZcG8+gVq9orKtSiicvCHG5Up8Yruqmov5SdbWlgfVCjKxnjX/G9arzJoS3idrNAR5+Z02GdxaTZPFMc8yG57WvlQRguOaUe0jo5Kqcf7oE1p/sfI4owPID3AdIxP7r1rhejUj7euRaDbSqf4j4CzR8whMP5e+t3BSx9XxY/x7+9y1SVlpfDsAYPOVL0vKGjOsqXlw6mhSFufZAnPN7suTsmNzfL/JQ2rFuf3+ewEAM4tqJajdo/P5bUVu31MbdiZlKSFHz1QfT8oeEiLjyBWvS8o2X/NCAEDZMHrmhPzsSckAMDXP9Y/i07WH3cylfkGQJXBxyNJmIvVZ1ax21WdXLRtraG6YNeSdBZ3fYtGQdqpqDR0Y4nkrNahWdufnJqsV98+1iVJ7JDG1X6e41wnJfcx3oynE+IrO/VFLtNVGDe4Je6mcfjcyUlYz13rrZKtm0pleQARNcEBAQEBAQEBAwKpDWAQHBAQEBAQEBASsOqyoO8TU0YPJbxJSjSsoGWnNOnZzKI+ourw6x6r4qVk1xx4+xOaXNWNKuLlkG5vQL33L9qRsYZFNCcMj6k5QLLIZdXFB71ceZpPCcyfUJL92g5pmJ48zISdj3Mb37GT3i0y0KSn75lf5/arzeu2AZO8ZXpMUYeM2NqEfPqqknkZN3mmDOrgPSQzA0bUm+06foCtCo/zRNsS4hQa7oJSNiTsj8Sajion3evwgAGB2SNt27U6OX3nZZWrW2yGmQJtBLJfhZ2RzSqjymZbWm2xz+YLKf1riarZMjMmBQRbO2rJmKmodY8LJlHGHaPqY1lk9Ly2uNosm/mJDCBvWZO5jzXb6LFCwtR76mM7WjSOS2M8tQ8qZF1NmYVDHbk6yQWVbSkCrzLAp88QhE3dYYkqObFL5DI1w+1NGH+wzuEXGpJc18b7XSr8aWasmRAnri/a8EuhmDhyVOhxMypoSY7U4rKScopgVT1WVRHtygV1w6iZuuf+V6bPMf4weAUl76DmcMWU/cdd9AICt192UlF0ywqbstbv2JmUzMzxuHr7j60nZP9z+CQDAp/9NiToZkVfdECTjhhw3sd4HOkqi+fbX2H3sjtzdSVlhgPtWbkxltOF6NpfvfNEL9Lxx7neN2cmkbP/X7gIAPPmo9oNaTTpHD5JSd7v1C4IsgYtDlnHXnO8J0iZrmvwcGdVv/7S4izWnlUDcWeB5q9XUObY2xaTvgvkupoRsZl0RPHkt1UWWW/LvaRX3c75NDyvfsaa65jQl7n+nYmJW+2vNxyQt3+l0Tr+fDT+/m4y6jQoTqpt1jV98IdGPM3lAQEBAQEBAQEDABcWKaoL3XrY9+d2R7U87q5rCSpN3D+PrNUxYpcJO4ATVDj/8IIdXGSzqGn77Nt79mKhNWLeWQ5+lMrpzanRYi0UF1U4tSo70uKU72o2bta7j6yQLXawhTOZOcdN947Z7krKZI7xTy2b13ilih+/nXK4aq2yOy3J5JQBm00Kgi3Rbls7yrqvV0p14v4B6aMIik+VoUbLQ5IeMpr8s79jS927XONzN1AElPngiwdrt25Oy0ijLl3L6XEq8/E/fyqYLuttcM6EkudF13Ce6IunU2SqweGh/UjT5OJMqa8Zi4EPKZEraZ52EEZpb0F2rJiezxATX/W+fIJ/TKaAjcQtzKZvNjdE2++W6aPwXTK77vIQ1Kg0oCS69yP184agSEzsSgqlpiHajEsasPKbXkh9DRnVhdTxZr9kwWpGZY6wVmTqshNNp0QDX6to3i2UhbY6NJWVNaYbJOSWiLNaF4GgmlbxX2/Shxql7HNCSfy10/DkJPfX4rV9NytavY417cZuarzZdzaGvYpP58nDxKwCAY/cpcerUMdbYpdra3jkJqZcxpEOYTJKRpHdMD+iYLW3icbrjGtVqXvayVwEAxjepRhFV1j49+e1vJEVP3Mnyr00aTVJb2sb1UoX1I4IsgYtDlrEh8CWfTfMB8tPtQFHXOGu2sFW6ckrJi1FD5syWauuT8K2TmsGvOM4W8oyxnHmNbFcW057foh5h50y2zajpMxVqtt7GgljPTMZYbx5OmWyukMy3lNf+UlnktVfOWGEXJTMrGoEYFxAQEBAQEBAQEHBBEBbBAQEBAQEBAQEBqw4r6g4xc+pQ8tv7QVcMQakFNk9OTGgM0WqVXSNmT6qqvV5h9fzd3zZm1iqbvPfsUbeDtGR6cmk1H8Riwq0Z1wdKsbmz3FA3jKiq6vl6lY8fPqommfv2HQQALE5pNp2i3Gd4QE0KN76Q61VvP5yUTUq2KzJmkmKe4/wNGKfxlpP6R5pNpV/guuIx8l7Kvk9LCBRzhhyWHWDzzKCJe0wL/G7tOc0iN/2YZFwz8RBHNzORamCdmvVyZZZlJm1N5lKHuJfpEKCIzYfRvMYlnDnIMWSnDmj2peqMzw6n90kVud65Ia3/lDjvz9XULN8RU1fKmLxicXOJ+8xsZ011GSGmlQrGRULGSWTIYZ4sV++oS0M9xf22NGTkI9NLe0HJZtUT3Pdrc1o2e5TdUIbG1TRaGOS5IG1IiClj+q0LIag2o9kAF05yH6rXdLzEYsrLl9TVojy2ld/NxCM9eILnppNz+k7tiNsmbxMEZqVtTCzz/kFXlM8eZR6GgNhgE/Sx+76QlN2zlvv31Te/Kilbt41JO5de/9KkbMPG7XztrvuSsofvvA0AUDmp49mJeTNF2q+yeTXVere1Tbs1TvuGXRyvdvOey5KyQXFlalZ0Pj/2AJvLH/zmF5OyI48w4bhd0zleYdsj1aOsXxBkCVwcsoxMxsmUuIukutwJuc554/q1bh2vYxZ3ajtGdf5mNUxsaCfZWpuLOm+1mjyXFUx89twAu3Km8urClsS7t7w3GxM9if+r9/YxijtN4ybor7FBiIUElzKug9ki/+6YNV9V7t2O9XvQkLVDYYVkGTTBAQEBAQEBAQEBqw4rqgmePK7hPijPj26azDgdx7uC8ojuVnZuZe3QgxXdjTYrki1nUbXD993HYVWOHdYdyrr1vBMaHFUCQGmUtb2ZgpLcmkIIihZ053HilGp4Hz/I2dxOzKom2GV4d14q6M5p0zhrA6+6RO8d1znXenVWiV+U4vNip4SbwZGNAIC2CTGVzvGOqVFdmVAh5wNns/X4HWBXYhlul4qpu89EljJh00oDojWtGQ3cIu8K5/arlqB+iuVbHldN48Aa7huFQdXgpyT7jnX6bzdVi1CfZbLH/ORxLZuelvN0xx4L6SJd1HsXRlhbX41VC3hKiJutjpZF/tm9Qvj0VlA/g7By5DEZGS1PB6drPDNJ6kR9wUYkodRK2l5Dos2ACX2WWmANu80sVzvKmtvmpM4PENJOKmWYrqYu7Y6MoY6Ol8hxn3OGtFNay+NqcEjDDyHPQnjkhD7vqGi7mkaOvovbsoykBkyv6Mx5ruhFpjrLeRGPjfYJ1S4duINJUmQIMXtfxiEhR7ZvTMqy268EAIxPTCRlxatY29eYVc2O1x6S+dzk86afSGiokbV6n7JYFPJZlWVHxu7R+zS05CPf4jBfD9x+V1K2MO8tASZ0YvLOfTcAl0GQJXBxyLJjrM6ZvLSBUT8m1jhDLC/mmVA2vlFJ3VGNw8BORyY82TSH8HRmHnSSObNiCPXpOZ5vyWT5TPtvpc1oF+n3IJasqh1LePP9yDa9kO4sCS5VZuuAJZF7AmXDrHFSki3UWlwzIt/2ChnbgiY4ICAgICAgICBg1SEsggMCAgICAgICAlYdVtSoNzyipuyJzazmz+ZVhV5psKq9GZmsYBI7L5UaTcoOHGCT6olJNV83RYt/8JCSYiaPsxrfx9sFgFTWZ/0yan/ispxxh6hUTZaoDKv0W8Z0XCrxNTt3q+vD9glxX5h7PClrzTJ5bzBvSGMSazGd0nfPSOaxjCHGVdtsam/U+y8maVecYG/+N479WTFJtyM1fSwserKZyfwlGfwGTcYbqrAMXdvEd55md4iGIdDNCpEtlTOxCBMi3ulmHQCIvGmqo+ZBnwUnNtltMhLnsjCksWRrxOcdm9PMOA0hvJG1XvqQwMYlw1uc0qn+2nfaanfE/lSNLFmO3RFyGdMHfdYgEz+0VmFz2wljOpsXt5fyoMo7LyazQkXHqRNyRGTcUXw8YdtaXSEu5UicU9eplJAx1m3QuSJb4jnneEXrdeQYEyGnp9XMa17FvKdkdzJ9xXefTNR/BJxzheWvOE/erBvXo8fZHP3InGZLXDjBcUgvu/HGpGzNTjanF8aVgLN+gk3oObXiwtNIc2aMW3+SglSoYPpdvcn948QBdVl59I5vAgCeuFtjsx9+kAnHtVMmpmh0+hxw5li7zw6zei8EWS5F/8myad0hMjz/xaae3nUwaut7ewJuybiSrdvE+QrabZ2PKhme82yWvVji3qeMy2Lkr2kbkpvvPD0z79nfpk19dTLqnpKWb3i2pPN8WVwVG+Yz691COsYdIi99x/CuE5fGWqPXpPz0o7++yAEBAQEBAQEBAQErgBXVBM9XdM1dnONdQRzp7qfhM4RklQxTafMONsrqDnXP3k0AgMKQ7momj/Lut7poNLgx71Y6C7qjSKd5a5IxmllH4gBuwnmhZAh7xDuXNet1p7PlUnH4Js3osv84h3oaMFpbxCAAACAASURBVKGjcuLcnTJa35rs0BYM4SYv+5GFuiGItfm8memVyZxyPuhSaLoexDjRFqbMzjISze7CopKi2qKlbZqwY8Mjkv2voTtGyG9niUuiLXSG+NadCk6ea8tEWx1bWUt/K5RNhp0B1jDOtLTtT4n2cqGupMlU6vQMSp6gZcvS6R6ZlvoAUcdqWISkaDLGxbEncpgMRyJnq83oiBZjzmhzU6IdLpjsfaUCy3lidENSVlwjmpCmtmtHiB6RJaWZpiMJdZgfUK1vJNPZfEs19ScPsbXgVEXvXZX+EplQPV4pkjKh/xI9iOk/ibbN9Z/G6axhteQlnR2oiTbInCfzcOe4zm1HFjls1fR+DfU4dskO/nfPJUnZxt1ctmatjudimeWSK+rnpmWITrVZls2J/WpBOznFmq1779UMZkef4Pl1YfJUUtapNbpftwtny4h1OsGzfxBk2Y1nryxrNf3epSREZ9YQrv03pG00wT58Waej37aUrFPGDDk8k9oNAKiYb1d7Xki+i2oRoBZblSNDquvZVNbC66f+rLVY87cyUzbh1+R3wbyTt6pS3RD95cORKWn92xLOMmU0xl6u6bQlRV849NcXOSAgICAgICAgIGAFEBbBAQEBAQEBAQEBqw4r6g6xZffe5HdTzP7lolG1ZzimrHc/AIDcMJsFFo3ZtjjMxy+7RrPDrd/Cuv2TxwwpYI6vcTbZjJgxW5G6HQwPs7mnU1TTa7mg6v6hMSa/FUdVPd9JMakmjtWsPjLCDIIR06wDEZuKIhP0jkr8zq2C3q8tDuKnZtR00upwXedq/WfiSRlzcCKZrtjBQhizzA2xv9isNDXpBy0T53BRCFXD1ow+wmVZ84x063QXiSRrnT0vozIiT+goqIkvluMLJrPP3DzLt2rqFUsfTFMvNwcTa9ET48yr+2vSfUbcyJl6R8l7nZ4drmWYfw6eDGhcSsTM1zHX+kxrjbrGim5LWccQZ4o57g/lkppVc1mWT8uM+1TKZIwTt4vmvJJtFsX8t2iyULXEjabTFXOSzP/lnTwJrsvNwZvlrLzT8m9/yfF09DAPOz8mTytCT31IpI3mFthsXVtUMmHt8EEAwNF7lNz02BbOxnfpC65MyrbsYfe14ZI+o1lXs+zsMTbVTt6/Lyn78r57AQDHZjRrpKt6Uqt5Jz/ILDPV9bKn+5c+10xs/YQgy248u2Q5P60uH60su2oNr9EYzRkh3teMK2RG5r0FQyCuz7NLi22eorhtdiIl6GeyPI8WR3Q+bTUOyb/qFoaO92uzLmD624eLzxg3h6FhXuOUR9WlIc7JGm1K5duS7HZWHC0hGA/kTExj8XHrtExW3MS70hDeLyCCJjggICAgICAgIGDVYUU1wf/4Rc0JnpVMcdTWnceAhE/qxKqRqzveuVBZdwUvedV3AQDGN6g2b/1GPn7zzVuTssYi7zYfuPfBpGzqJO/KymUNfTUyylrf4qbhpCxvnMFJdqgurRrMtuOdVyZndmDyKgPQsE3pBhcWslrWEE13I6PbpCdPcgYzm/2sIJl4hk12u35Bl4K3R4g0ZU9pm/m3TRlWXSwawY4JY7bQ4R1ltaEavXyOteZFExKrJHnQSwPajdOiDrEEgKhLgcLPazR0110R8lvD1MGPDEvG8q9EXSQ46j4IICvawrYJkZaQ83oQ955JlIrani2pWiGvGoRYVKi1mhmToh1Omz10RiwZGSPbVJKQSMtI7rfQUq1BM88nZk1eeyckkflF1RDU/i97bx5nx1Hdi3+r735nH41mtEteZNmSd2MMxjYGY5udJOwhC8nvJS95SXhZgPBLQiCEQJJfXlZIePB4IWHnJez7ZmxsvFu2JUuWZe3bSJp97n77dv3+OKf6nDtzZzTiWaNrTX0/H3nadburq+tUVXedc77nqGsqNVoXsmlpa5JVF0mokGZOA59UGm/WskRaA8KyagoQ5NptZmt9w7Ale6eN4No3W89hrWahzBduSl/E9elrmdRia6JlrNbpfqXz10tL+mkeR3lFrlThD3savMZPiBWscJTCKVrdz/H01H0/nxzMHMcOrcJvtSO8LJ/NsixNKPJfmtcjRfpy1uRSVc5L5+ibZKoofVsvkBZZvVaQcuubjljK4dUCtZ6WIyIQJ5ODcVmFQ5ZNjglpslaaiI8HmKy+arVk/8sOEFlyrCDZ6OqTdI2p6wAE3BZtMQ5dJjshLscMexWGMuC12izSq9Jrgj08PDw8PDw8PJYc/Eewh4eHh4eHh4fHksOiukMEvcpNgAlj1SlhrYUJIkKFkXybZ5ig1j8oavzeLnIq71QO24Zjm65dK2YGWyO1+vAhMX3v27OTGyCuD/mQspuEY2IK6BkSx/UcZ3OLNEknRfduKHt5uUzPUlIZXfJsWk52SbtyCSorq3i5F563ntssNoCccxmptZ+Jp6FNxGz60CYZ4/pFN92RiexsYpltyq7G5ykyR7VMxxUVE7gCkmFKmfQnpojsMa7IUZpbEVO/dDxY6+4r56WC2e0PmcQRaZcGvqjWZKGj/wmUi4RzDbCNNpOlcidwGYZMUsVndF4tSrZpzhaUUoRDd2LYFECaZNZQJtYoIpNZQ8k7m6X5ZxIS6zJhK9wmmZNQ5LZ0wPNKtTVguXQpsq3hLH/lmmbGURsbyhVL0UGkDe5ZlLxdlrx2i/dMaGUyti3KWrV9NoH11HAsR0W6YuJNvSCuK5NsLt12VEyo45GYTlewi1O1KqZf4+aJzuw4n6m7xZxsdj2KnbFaXNyOJEcvS7r42S/LyXHJ1ucyxlXU+pfg92GosrmlSnScTAg5PAStdVFViMblhotJL+t4WOdsmwm95rGMtH8ff6eE6n0cKt/BkNtYU++s0sgBAEChLG5qYYnak1Qun46cp2WUZFK6e5cDQCri46Q8Z43dYQNFSj+TaMeV3MPDw8PDw8PDw+OMYlE1wTriRa1Ku5BUp2hzw5CJLSo0lvtODyuyy+jrIkdtozSFaNBupF4R7eo0O6SPjD4Vl2XztEs6/3zRLFc5fFle7aZSytm/O01tTCTVTidPYU2qEG1YF8eEiqAcxPk4mVIEMcv3U1l3LJON0oFottK8K8um2m+vklAaP8O7equyijkNnc4W5rzldYg0p0Z2obgAxCFbNHEp3jirPPWDAyTDdcslVN5JkNzKishVb5HJzqr4ZS7zWUJ54jvik23aJzqilCpxykK1g641x+Oi8zicmGkZ8ucsIqnGIBMRi0UV6obnhs54mMvRPMkk5TnDKj1XXcuM5RwpDVCdyzRZscba1XJFtPyJkLRQkcqiVNMZBFkbHVakImd1SWXkfqZOz5RSJNQ0Ey9CRdirVFlrbVvMNaW1scZl1Ws/jdOptYcOiRZlrc7TZcGMvxpqPrPcGhUJvzV5ksbE5InjcdloIOMuM0jzt9GUmc+Rt9S6EEO/ttxz6jVlvvBhGgskkp0VeFnqdj+bZRlnwgUAy2FgFfk/6TLkqgdLcMa4ICvyrTIZGDXpn5C/L0KV2TS0tA6mVRo2d+TWVbodlTZZcNWaWGVN8cSUWOMiHjNV9UyGZVRTxHKXKTaZUd8zKfpmilLyTLZO32tJFf4SlrXligh9JtF+X1ceHh4eHh4eHh4eZxj+I9jDw8PDw8PDw2PJwdg2i1vq4eHh4eHh4eHhcabhNcEeHh4eHh4eHh5LDv4j2MPDw8PDw8PDY8nBfwR7eHh4eHh4eHgsOfiPYA8PDw8PDw8PjyUH/xHs4eHh4eHh4eGx5OA/gj08PDw8PDw8PJYc/Eewh4eHh4eHh4fHkoP/CPbw8PDw8PDw8Fhy8B/BHh4eHh4eHh4eSw7+I9jDw8PDw8PDw2PJwX8Ee3h4eHh4eHh4LDn4j2APDw8PDw8PD48lB/8R7OHh4eHh4eHhseTgP4I9PDw8PDw8PDyWHPxHsIeHh4eHh4eHx5KD/wj28PDw8PDw8PBYcjgnP4KNMU8YY24+2+3wOHdhjLnHGHPV2W6HhjHmbcaYv/wpr32rMebuZ7pN5wKMMR80xvzuGaj31caYzz3DdVpjzIXPZJ3nAowxm40xD52BejPGmCeNMYPPcL0bWJbJZ7LecwHGmNuMMV8+A/UOGWN2GmMyz3C9XpZzoB1keU5+BFtrt1hrf3Qm72GM+YQx5v1n8h7/tzDG7DfGvORst+NswxhzkTHmK8aYk8aYMWPMd4wxm9TvbzXGNIwxBfXv5nnqexWAaWvtVv7/S7nOEWOMbXF+vzHmS8aYojHmgDHm52f8/vNcXjTGfNkY069++3tjzLgx5l5jzGpV/su82TtgjJk2xmwFsB/ALzzTL+RnE/ij5OO6X4wxL1O/uxeSlvW756lvOYBfAvA/Vdkt/OFTMsbcYYxZr357B4+D7caYS1X5C2Yu9tbarwK41BhzOZ/zKWPMMWPMlDHmKWPMf3lGOuVZCB7fD3NfHDbG/LX+iDDG/MgYU1Ey3HWKKv8cwN+o6+eck8aYK3hujRhjfk+Vp4wx9xtj1roya20VwP8G8IdzPMdGbuenTr8Xzg3wnNjO83GfMeYdM37fb4wpK1l+9xRVfgBAvNnnOX0Hz8cn9TuP5+o+nldvVOW9xphHjDFdrsxaexzAHQB+fUb7TnesnbN4tsuyJay1/t9P8Q/AJwC8/2y34xRt3A/gJWe7HWf7H4DnAvh/APQDSIFeiE+q398K4O7TqO8bAN6i/n8T1/8amlKzzv8sgM8D6ARwA4BJAFv4ty0ApgHcxL9/BsDnVLt/DCAD4P8D8CEu7wHwGIAPAtgA2sy+kuv5LIC3/xR9dFp90K7/AHQAeG+LftnAv28AYAEkF1jfOwB8TP3/AMvv9QCyLJf7+LeVAJ4E0A3gtwF8ncuTAO5zbZhR/x8ruW4BkOHjiwEMA7hmjna1bD8/24VnWw7PgBx/E8CNANIAVgN4GMC71O8/AvBfFljXSgBjALKqbL45+U0AL+P7jgJYweV/COCdLepfA2DEyW7Gb9/lOfypedo3S5anO07b+R+AdwK4mufBJgAHALxJ/b4fC3xPAbgWwO4ZZfcC+FsAOQCvBTABYDn/tg3ApQCu4DGQ4PJ/AfCGFvW/AMD2GWWnM9a8LNtYli3bcbY79QwJaj+Al4Behl8A8O+gF+ETAJ4z47z/F8AOAOMA/tUtlGjxUcAD+ULQ7qIOoAagAOBrZ/uZW/TBJwFEAMrcxndy+au5HyZ4cl+ykP5oUX8CwP8ALf77QC/9eKLPnAwsi0+p/38egJ9wOx4DcLP67a0A9rLM9oE/OLnv7wS9sEYAfP6n7Jt+buuyuWQ9z7Vp7tM1LX67EDM+gkEfZTUAF82QzV/y8QcAfEb9dgGf3wXgjQA+yOUvBfBNPv4QgJ9vcf/HAfw9aAd8M4DDAP4AwAkAxwD8ijp3GYCvApgC8ABoYzBnH4C0oQdAHwXv1vLFjA2hu7f6/1UA/hPASZbn29RvzwXwELfjOIC/5fIsgE/x/SYAPAhg6KeU9+MAXsvHG3B6H8E/BPAL6v9/HcBPZsi3DPpovQ7AZ7n8YgA7+PjtAP5ojvpfAGBfi/JNLLM36D4FfYgNA/gkl7+DzzsK4Fcxz0cwgPMA3AWaV98H8GHwnJwps5lzGLSheBeAPSyTLwDoP5WsMMdc/ilk+PtQ6yxO78PklwB8/zTm5E7IZuQ+HqPrQPMkNcc9dgN44YyyN3E/vRfNa99bAdwD4O9AL/P3g9bTvwGta3sB/NZ84xT0IbKV+/X/gD7o36/qb/nu4uMM3+sgaM59BECOfxsA8HWW4xjoAz7g3/4QwBG+5y4At/yUsvxHAP/Uapwt4No/BfC/1P9fBKAKoEuV/RjAb/DxXlU+DGCQ5fntOepPAigBWL+QseZl+eySZat/56Q7xAy8GsDnAPSCXvofmvH7WwDcDvr4uAjAn5yqQmvtRwF8GsBfW2s7rbWvekZb/AzAWvuLoInxKm7jXxtjLgJpQH4XwHKQxuNrxpi0unSh/fFrIG3JlaBJ/DMLbRub9b8BWjD6QR8J/2mMWW6M6QBNrJdZa7sAXA/gUb70z0GalT6Q9uWfVJ1fN8a8a4FNuAnAsLV2VJVdxebPp4wx7zZz+29tBBBZaw8v8F4XAWhYa59SZY+BtH7gv4+5H6y1e8AvaNBm5UZjTA7ALQCeMMY8B8Ama+1n9E2MMUN8zV2gnTIArABpjVeDNNUfNsb08W8fBlABacl+lf+1hDFmM4B/Bo2NlarOU8IYEwD4Gj/jan6O3zXG3M6n/AOAf7DWdoPG3Be4/Jf5PmtBH+y/AfrYhDHmXcaYry/w/q5fnpjx0wE2s/+rMWZgniouA70kHGbKqwj6MNwC4GkAlxljekGb8CfYdP4mKFP8DOwEsMEY083t/WdjTAmkUT4GmqMOK0DzZT2AXzfGvBQ0d24FjctTuT59BvQhtwz0YfaLpzhf422gOf5C0KZmHDSGgDlkNd9cNsasM8ZMGGPWLfD+N2G2DD/Ic/YeMz8HZKYMTzUntwO4zRizBrRp2sPP8U5rbX2Oe+yEzDuwPN8H2oS2wnWgD6RBAH8BWk9fCeAqAM8B8Lq5HobX6y+BNp/9oDX9Z+c6vwX+CtQHV4I27qtBHyTg9h4GvR+GAPwRAGvIfey3AVzLsrwd9MEDY8wNxpiJhdzYGGNAGv6Zsvy0IXe17xpjrmhxqUOr+bjXWjutyrQsT7B7yxUgpdA4SFHwtlaVW2tD0Dye2Yb5xpqXZTPaXZZNWAofwXdba79prW2AdvszO+RD1tpD1tox0AB+86K3cPHwRgDfsNZ+jxfzvwGZHa5X5yy0P94A+ng5bK0dh/LrWQB+AaTV/Ka1NrLWfg+kDXw5/x6BfCVz1tpj1lo3yeqgD4BV1tqKtTYmcllrX2mtPWUb+MX2YZBmyeEukJllEGSCeTNIw9YKvaDd80LRCdJca0yCNL3z/m6t3Q7SoN4H0kT9Feij8W2GSHB3GWM+zR9xnwbwbyCTUQ/XUwfwPmtt3Vr7TZBFYJMxJsHP+afW2iLf59/meYbXgbRwd1tra6BF1i7w+a8FmbPeZ62tWWv3AvgY6MPQtfFCY8yAtbZgrb1PlS8DaTwa1tqHrbVTAGCt/Utr7StPdWNjTMr1i7X2SS4e4TatB3ANSA6fnqeamfKeT16joDnzQwCvAH2g/gNI6/Kzxpg7Dfmmr1HXurp7+dn+G7fpRgBfBGlGHCIA77HWVq21ZdAc/Fdr7Xb+GH/vPH2xjp/7T1kOd4OUAgvFfwXwxzzfq3yv1/FmcU5ZYY65bK09aK3ttdYePNWNjTG/AvqY0BuJPwRwPuil/1HQZv6COapYsAz5+O0gd4yvAvg9kLZ+GsBelt+dxpjXz7h+mu/j8OcAPm6tPTRHm45aa//JWhsqWf69Wns/OMd1AFnRkgD+kef2F0Gbm1OCP1x+DcDvWWvH+IPjA2iejytB2rO6tfbHltRqDZDWcbMxJmWt3c8bdvC60Dv7bi3xXtB3x7+qsreANhvrQVas7/BGshVOV5a/AZqDHwVt+n4TwA8AZA3xOO4wxrxwxvUzZXmqseZlKWh3Wc7CUvgIHlbHJVCHaS2fXqQOgLQc5ypWgZ4RAGCtjUDPr7V6C+2PVTPOnWuxb4X1AF7PmqAJ3nneAGAlv8zfCBrwx4wx3zDGXMzXvROAAfCAIeLKnNrLVjBEcvougH+21n7WlVtr91pr9/EH+TaQBmeu3fs4ZFIuBAWQj6hGN2Tyz/u7tfbvrLVXWGvfCOqXH4Pm7a+DtKpPgj7ia6DdfRdkIRnl3bBDCbTQLActvDNlPReaZG2tLYFM3wvBegCrZsj6j0CaCYA01BcBeNIY86Axxn3cfhLAdwB8zhhz1BAxKrXAezoN9Cch/eLaXrDWPsQvrOP8221OE9sCM+V9Knl91lp7tbX2ZaCNVRVk6vwbAK8CmTv1x5yrO9a+8Ifk3SBrx2+qc09aayvq/2fOwVPJcIxl53C6c/ZLSoY7QS/TIcwhq1PM5QXBGPMzoA32y6y1I67cWnu/tXaaNwT/BjJJv3yOak5XhgestS+31l4N4Cug9eDtILl9HmRd/FujCKxc/wS3+UqQVv7v5nm0mX1/urI8wh80c9U3F5YDyAN4WMny21wOkI/70wC+a4zZ66xr1tqnQRbE94I0cp8zxpzWu9IY89sg15RX8EYKXPc91tqytbZkrf0gqB9vnKOa05Xlo9bam62114Hc/H4V9KH4vwD8GYBfAfBJ/qB0iGXJdZxqrHlZMtpdlq2wFD6CT4W16ngdyLcOAIqgAQYAMMasmHHdQjVhZxMz23gU9DIDEO8k14J8gxzm6o+ZOAZ6Sbe6DpjRfyBTrsMhkE9jr/rXYVmTa639jrX2VgjR6GNcPmyt/TVr7SqQZuqfzQLDQRlyA/gugK9aa//iFKdb0Md2K+ym6syC3AEAPAUgaYzZqMqugJiQnkCzGfV80C5dm2qdWf+/gl7Il4L8XEPQ5mEI5PNaB3AJlLl+Dpzka2fKei40ydqQe8Yy9fupZL1vhqy7rLUvBwBr7W5r7ZtBWvi/AvAfxpgO1lz8mbV2M8hS8UrQontK8Lj+OJr7ZS64OTKXvB8HfaQ7zJRXB8iNo8kkyH30AZBJciOAQ6wdfRDA5erUSwDsV5pTjSTXPbOtDsdwejLsN8ZoOelrZ653CcjLFCA5vmyGHLPW2iPzyWquubwQGHL3+BjIpWvbKU6fb87OlOGp5qSG81s8DjLfPmStnQSZmfXao+fdzSBt2EFjzDDoA/q1xphHZrRX43RluXrGy34+Wer5OAJyK9qi5Nhjre0EAP7Y+wNr7fmgTdvvG2Nu4d8+Y629AfQOsaD5uiCwwuJdIN/TU7mSnY4snwBwvlGRATC3LP8OwJ+wttbJcj+ILL2c25kEyXW+NXRm+7ws50a7y9J/BAP4LWPMGt7V/xFopw+wL4ox5kpjTBazTY3HQSaSdsbMNn4BwCsMhRpJgV7QVRBBzWGu/piJLwD478aY1WzumBki6FEAbzIUVmimX9SnALzKGHO7MSZhjMkaY27m+w4Zip/awW0rgDROMMa83ogpeRw0wRqn6gTW8n0HwD3W2ll+w8aYl/FHJlhT9W6QBmgW+IPq+yDfSHe94TGS5v/PGo5PyNqwLwJ4nzGmwxjzAlAUiU/y5Z/mvriRn/l9AL5om/2iAGLMvoc1eftApu2PgT6I/4MXA3C7vjVff1hyDfoigPcaY/KGfH5/eZ5L/oPbeL0hH7Y/Q/PC9iiAlxsKO7UCpGVweADAlDHmD40xOZb3pcaYa7mvfsEYs9ySVcLt2BvGmBcZYy7jj7EpkGnvlLJm/Avoo+RVql/A97vOGLPJGBMYY5aBfD1/xB82rfBNKFmD/PcuNca8lmX+pwAet+Ju4fAnAD5hrT0K8s3fxGPsRSD/QYcXAviWMWbQGPMmY0wn99HtILecH87znF8A8FZDMXDzAN4z14nW2gMgl6P3GmPSxpjng16MDk+BrGSv4LXhT0CbMYePAPgLw+HgDPnvv4aPW8pqvrl8KhhjXgyaG6+11j4w47deXjuyxpikMeYtIJ/h78xR3fcAXM3yWsicdPfZDPqg/Rcu2gfgxSzHjSC5Oo5DP8htCSBz7QUgP80rue++AfK9nAtfALk5reEN+3z8hntB/fjb/PyvARGEHOZ8d/E8+xiAvzMcSpHX8Nv5+JXGmAv5o2yK79PgOfNiXtcqoI+vhcryLaAN4a2W3KH0b+sMhQ9MszzfASJ03TNHdU3z0ZJf96MA3sPX/yxok/mfM+5zK4jk7bgETpZbQOPcWbaeC9qUHuDrTnesAV6WbSnLOWF/CkZgu/9Dc3QIzcrdgNkRDFw0hAmQX2Renf/HoN3WIZAfq2ZlbmSBTQD48tl+5jn64TWghXoCHDYL5HS/A2QyvxMcFmgh/TGj7iRoNzbKg/D3QC8/w7+fD+B+0IvvG6CPDS2L6/j+YyDN5DdAO+aVkAgQLoLFZr7mr0Fa6wKIrPLrqr5vYW4G/i+z7Ip8rfu3jn//G9CGoQj6QHkf5mCB8/mvAPCtFuNK/9uvfu8H8GWu/yBmRHYA8PNcXgR9fPfP+P1FIF9uXfZxvk8047lGQRrQmzE/2385iDm80OgQb+U2uugQRwDcyL9lQZulKdDu/vcwOzrEZ0GuSeOgjwXXjk+BolcUQDv+n+HyN4NIE0WWzT9C5u0f6f6f0U6n2ajMkPVbVL37uN5joMgxK+Z57gGQ1i+nyl4C0mqWQeNzw4xrNoE0vklV9g7QWrIDwGWqfBtI27EcNO4nuB+3Afg1dd4seXL5u7hfFxId4gKQO800yJfuoyC/VS3jYyyPt2N2dIjfZ5lMg+bfB+aTFeafy+ug5mCLtt4BslZoGX5Ljd0HuR0TPJ5uPcVa+H8AvHGhc1K14Tr1/1ew/EYA/P4M2f7tPPd+L2ZHh5jJ+J+5np4qosBzQO+fAj/bFwG8W/0+37srC/qQ2ctjbSc4Ygto7u7nfjns6gR9jDzAfT4GWjtW8W83AijM8/z7QO8GLcuP8G9bQGtGkZ/9B1ARnOao78EZctnAY6vM4/AlM87PcF+tV2W38HMeQ3OIrw+jOXrNvGPNy/LZI8u5/rkPliUJY8x+UOiT75/ttrQD/m/6w1BCgo9Ya9c/4w1rQxjKrvY7lhNmtAOMMb8DYK219p1n+D6doBfCRmvtvjN5r3aAMeYDAE5Ya//+Ga73VQB+0Vr7hmey3tO4/+dB8bLn1CCfK2Ct7r8BeK59Bl96rE17DMBN1toTz1S9P0U77getv/96ttqwWDDG3Abgv1lrFxyRaIH1DoI2bVfZZt/7RYWX5TNS74Jl6T+C/UdwjNPpD0M+jy8C+dkOgaMYWGuf8fSyHmcf/MH2A5AbxP8AafKvfiY/KDzOLNgFZQykzbkNpAl9fjtt5DwWBkMs+F0gDeFbQC4X51trj53VhnmcL4RJRAAAIABJREFUNrwszy58LmuPnxYG5Bv6eZDp4huQ+IQe5x6cz6QB+Za+yX8AP+uwAmRqXQYyj/6m/wB+1mITyPe0E+Sa8jr/0fSshZflWcSS1gR7eHh4eHh4eHgsTfjoEB4eHh4eHh4eHksOi+oO8amPfypWO192OSUpq5RFE10tUxz3Rx67My4bKR4HABTU53rRUuSe88/bEpcd2UX8nJOHJc702jUrAQDDE3GMdQRZul9vf0dcFkYFAEBtfy4uO1EQjsOxAoXRPV46HpedHKU6ezslXnpQp0Z2ZyTMXa1UBABMTEl917zgGgBAIpuIyx5+5HEAQEeqJy5LcnjAgT4JDXjvNz8zV8y9RcX6tRtiwQVJN4ykab156t9kJCFaw4jyNkRRFJfVeB+WzEjm5kyWnrujW/oikSfZTE1L3OtGlcZLVg1jFi9sKDki6kYGT5Tk++SycVl3F92vNimhWkdOnKQ212R89vbTeHrdG4THdNmVlwEAipVCXPbZz1NG48mCJPtaPbQBAHDvnRK9bOeTW9tBlrNMQbWKyOzoQYouMz1RjMtc0MrH9klUsK9++8sAgLFDEjby/JWUDyPTIfOqEtHtiqFE5BmdIJnq/qqHJKdsR19clsxJ4p90hsZXpSLXFIoUDS1UsndPFwSqqw0VZlIyLlJJOg5MpMpoXOVzMjYHuulZhnpl/XjPn7+3HeSI2259cSzLZILWlihS4rXUzEhfxGVN2pAcSXjwAgnheVWOrlqxe0dcli0R3yS7RvJfXHgDresjT8saPrlzP90qkLYMXnZtfHzoyDgA4EM/uiMu21Oh8WGtdK0zWja1PxawzOdw3ZUAgOe/TLLPbu4huYXDEm3tiW9T9MevPyDj2OobnkV4WRLOBVn29PTKu5JbpC3wLkKwVe9P696R6ryWVvs4vLC6lvux9cObWYfBHD8jfm/ODolsVFhjd2zM/N1tZh0AJr7H7GfT1Z04MXLGZOk1wR4eHh4eHh4eHksO/iPYw8PDw8PDw8NjyWFR3SE+8omPx8epFJkvNl50WVzWmSczyJGTB+OyscIYAODktCTQclbT79V/JNdm6NpMSrTmuw7vAgAUGzpMHKnd81kxcaYsmR5yoWQJrUMSTdks1Tm0XDLlFtlkXFdm3Q6us1IvxWWRJdNsX/9gXPbY1u0AAJMUE0BUp3rSWaX1r9M9Dh/Yg3ZDMp2Kj8W7QfZU/DhN5i53rA0fCTY5pzNiAkumWDZW3EWSEZUt7xUZpfl2aeXukGF7U6DMdXUj9VSq1LByQcZTJmRZqq6P2GZjg4QqoyeIrDxVo0HyDVQbjLu2JmPDsiydabOdkUpLG1etJVecQp/MoUP7iLi844ldcdmJI8MAgBXLJJty/8AAAKBYEVeKiUlyfRgtSX1VHixWmXuDBPVnKiXzNNshLghd3eSGlEiI0BLOzqbkU63UAABRQ1wkSlW6d0m1oVzksoqU1et0TSIpsq0OdAIA0o2FJq9bPARqnDszqlV9YbnJev7FFlbtMlSm5HmHD4p8V1x5IwAguXZMyvbtBwBUjosL2oHvfo3qrY3GZVFI7UoukwzQE4Eko/v2AcqKOhKp1xHPY4OmSUk/qSeITb9qXjmTs56TiSStV1FS7pHo6ES7wsuy6dJntSybXBr4r9Y+xsue6p6E6wN1rXMjtE31zXaBUY4Ws8uaXBHMrDI9oFq7N8wui9ujXSRaV8h/VGv52qai+O/ieLN4TbCHh4eHh4eHh8eSw6JqgkdK4/FxuUTapBe//MVxmeEd3pPDkoRqpECaGZPIx2VXX0ZO/tNKO/z0vqfoICOPNFqknW4yKVrLRoV2U0FNdpuhpWsmy7LjDbKi6Yldtxtqz9Cga1auEs3kBBPwKmXRJoUV0iZ1GNmpWt5NZ7PSrp4Oqq9babuiErU1Lbyi9kGgd+u0d4tU/7DCFcmElLmdXdNOn7f6NUVmMgEdp0IhPUUh3SOphmzEVRfrIqsK7ywTablvTe31Mgm6vicQrYpTGhettKHBWher2t/guo8PSwjHCzeeR3Vk5JmcctI2xJrQCGmshg15pnaFCRRhjAloqYr0zdM7HgUAbH/0obiskwmHK5YJka0ekRZ2VM3TSSaKVmtSX0eW5ka+Xwil2Ww3AKABsRBMR6IZqNRJPh05uSbVkeUyuSZg7UmkNLfVGs3PWlXakORxGIVCCiwWiOw4MSWEyT5WeqXU3G0XRJF6xsZsYk28kimNotPORYoQCK6nPi4a/EefJIJk91WXx2VDLANz6EBcVpmm8Z0IZJ6GbCFbfdFz4rITpaPx8a4TtG5WjXodOQ1gYraeRhPErFOJau1nC41oImB5JURuQRtHyfey5N/PAVk2k+BaEBoZgVp3Az7PqnXLaVBbKWi1dbI1f85pYZtKW5yo293iLFdPKyJbU5lpOn+uds+8lz5vsYL3ek2wh4eHh4eHh4fHkoP/CPbw8PDw8PDw8FhyWFQjwkRB3CE2X0zxCF906/Vx2YNbtwEArCKMlZmAllHWx19585sAAMPHxSz9oY9+GADQyIjOPVmlx9NWmN5eijU6fkhMnM+59HkAgIIyhXYNCCGnUCMyz3hBYtQOLCOi22RJyAUdXWQ6DlU9ARMErOrqTIbMv72d4ueQY/N9pSBmq54cnbfx0s1oNzSgzDTxXkrvqUgOCUVeqFapX9JJRTZz9q5QkZkiMlcrThSS3KeTxfqsawN13yS7aRjxRID2YnGOKnllbszmyMYdNqTuBpvmgkC50vDfo8PDcVmxQiTILmWWd+bIWl0ase2Jh+keoSZpPgvAtqmGckV4momaFRUb+aL1RBod6u2Oy0YLRMqplKQfSjVykUjmxO2nZ4DiL3erPqxX6bzipNyjNj0ZH08kaP4eTQopJ8XxiLOKtAk2MaaUvTSVTvJ5MsDSzo1KPWeRzdDluozNPA/d0LYyaJ5d1NUccu4fLU2Uaj7EpkdldnVTMjDiujM+TDLfvufCuKxrJa1Ly8Oa1NegNSuZkv7OLacys0pcwiZ2CHm4L0ckx6mqciMD39u0ekWJG0sUr6+ChotHrkzoluWv16OsjifdZvCyJJwLsmwF00QdY3dC7RrCDhOaLOwEHDT5FdimP3TawnSbQQu3A9tijDW7Mcx2Uog9Vuz87hBx3U0uPO7HVlS6xXGI8JpgDw8PDw8PDw+PJYdF1QRrbcyG9WsBAOWikGb27d0NABgZkexqUxOkPdYapve/510AgD4Vjims0W6zq3cgLsvkSCOUUzuZ6jidt3yD7ILf9lt/AAB4cPvOuOzY6NPx8R33UKgYqKg1KSZYhTXRHk7zszSUljGdpx1zcVJ22KsHSYs8NSWZ7CLWRK0ZHJK2lkmDdujQXrQbrO4M3rHp6F8uNFxZEaoM61JrKqycIwOEdbWT551sR4eQIbOchS+TUafFmXF0ph3XFhVeTWkyApaNrYh20mn1Go2mdDn0m9qJO7F2dMlYdIyMUHNRXDanQDKcdXavAQBMTW5H20NtwMfGSPv6gx/8IC7bvpPmyWC/PN/KATcXpSMK07OJZSXO6gaVHa5RoLkxrfbkrofDaZFTXWXvq6WJ/FbQCtm0I8bJuMmkacDU1Xh1Y7ehntPJuabGYSme2zJekxwibXlOMkW2C5oIOC2yRoUue6PSdlsOxRWo/nFat3JKJtvgClqX1g2Klj2xjohVwQU3x2WTEyTLqaL0Y5Cgeo5VRNNvL9gQH1+8jMJkpvZsjcsaR4kcPXZE1siTbFmpVNWcjDNYyXMGPAYDTT+KyVmi/e9c3NffacHLku93Dsiy6YFiYpnAvbMi29Cl9F+1RiV4fWzKg2edNXTmlTPAat+EypwZW03VTUKVzXVeW1eLcGi21c86nJvTZOvsnXbmBXKNJ8Z5eHh4eHh4eHh4nCH4j2APDw8PDw8PD48lh0W1IWSzYkJZu4Yyzux6Skz9T+/eDwCYHhcCTD5NavLxMSHBZdj0um+/ZFLLM7lm5KSQ74rTJwEAtUkhryU4PnAmKW4Yh46Q+8VjD0tWnQNHtsXHtQKb0KFIOkVqY7ZLTK/dQ6uoDeNCoHP8gP5BefYQZMpKpGQPks8TqWdkUq61nOWqq6P9AgWHyuUj5cxwioGY4KGlY86G7DOgzSEhExpCReYImEBRLIoLiaO0ZVU8XsMBfpMqu1Iq5TLtqCxxdWWSicl08nu1Qqb5uiJXuFaHTTYhuibf0SMlHKsyiBQ5j007KSPnTRV6+dnaz4zuYOPMVNJfO5+gmMBf++oX4rLCFMXTPu+C8+KyHDNXp8syRzi0M/q6ZOwPMjG1ATHFVtlFIpqQsQ8mxCZUBreOtI5NTTJLZ5XsM3RNT7eMr/5emp9DvdLvy7uZ1NMQ4ZaZiFdSMb4LPP40CW5ZH8m0t0+eqV1glAHTxi4cihDI8dJ1lr04I6IiGC5fTmvzmi0Sw72/n9bc+oSYtH/0E4rN/vRxGS8NEOGxXhc3MckKJbKqqzje2QFqz4Wrb4jLrt74RgBAOPrjuOyxXfcAAMb2qzjyJXrOKBDTeI3jjAeRtMGRdwPVH8mE8q1qM3hZEs4FWbaK29v0e3yejic8d0zgZrjzlFuJpA6My5J8mFXv4ywT1DXRrq5I/SVeH2c7acxov53txiKkuhaBh1sRPFt0UlOGwTMIrwn28PDw8PDw8PBYclhUTXCQFI3m6vUbAQAH1U4wlaLf+3pEg3ZwP5Hl/uy9747L9uyjrDbfvuPuuKzuMqwk1a6myiS4fpXFqkC/X37FjXHZ/Y/tAABsvX+Hap9ojnqSpF0+OnIkLuvopZ14vSz7JKfNzKVESxQEVBbWJYyMC3+SzcmON+TtSEplt6syaWC83H5htfJ5CXFVZ01qy1BlgdqhuoxGmmzGxw2lbWtwprGCCklXYQ1jM0GC6kurUDkZ3t02VFsqeq+XpOOsZvFZ2v2GVdE8O36Apv85coFtKsSsdsXKGSsWjc2XEDHu3nsn0K5wu/ayCtO3c8fjAIDRExIWbsMgZUnszosVxJEdx6ZlnE+WaVykcjLvXb+XFGESVdIEpyvS/3aayHRGyS6j5zZrKeopEUYiRb/nVDa3fJ7GRm+fjNeVAwNc3+wc9s2MFcwqCznM3/GJMbQbEiqcn8umFWkNCzNqlGEEhsPFnXfRC+KytWspbGUmLZn37r6H1txDE5JVs1Lhtc3KOLBMGM6qV0sionFQVcRTFY0QhRLJffvwybjsyU464aKNG+KyNZeSVvOy5T+Jy36y9dsAgPGarKUVd5sWzB6tmUqk2pdM5WXJ7ToHZNkU9Mux2pQ11D1HgNllrTTBzSHQZhPo5EQpTLC1NKVOTLM1N61uom2vboUut9Rkt9DcNhEAZ5/VSq8bL7uLo/RtCa8J9vDw8PDw8PDwWHLwH8EeHh4eHh4eHh5LDotqQxhcsyE+7u6lWLknxoWAVqmSer4wLaS1VSsoE9W1z5XMcmVL5pJkp1x7kp38p8bFDJNOkEL/yBExQT//2tsBAEOrJE7wo4+TS8all1wRl01PS6zi89dsAQBMjKh4vTVqo86Cl+smcl6+U0yvxSn6PUirzDicNSyZkfNGJsj8GyjH9XSSTFiaXNYuqCnCUprNZtqi4TLKZZTZJ2BTeLLJWZ7tXDpjDMdLjEKJJVuts6FGx5BkIp5KQIdudtOoK9LTiVGRf429V7pVwOF0krPDtdwTyg1bZaiLs/0ow4+7tY0kPu62bd8CAIShmCDbDTWOi/vd73w/LvvWd74LAOhXsXd7ODNiqSbymSzS8clxcRMoM6FmsEtci3qdWbYq/WA4Nq9R2aoa3JZEWuRk9TzgzHORspNO8ZgrK5JlhcdXJisuGcu6KQpxOiEmYjeEoqYBNjvrUa1BrhujkzLv2wXaYpziGKqmofrU/c1Jn5635WYAwGUDKnPnETrzqUMS03pykrMqQhECWR6NjMhl1cW0Xv/c5nVx2fBjlC3x3qdlHo5V5dVTi6hu1VTURml93TYlsjywjOLbbt54W1x22wtJlg888r24rFDjbFuK0ROxOdgoN6hEon1N6F6WhHNBlrbJjcW5L7RyfZjtE2B1ZsqYLNfKZ0udhtnxeF1PpdQammGfP+V40/T9UeP3a1XF9a9HLdzGGFGk3SGCWeclnMtqU2Nnl0lcbJ8xzsPDw8PDw8PDw+OMYFG3T1deemV8nM9RmKIpFQ5tZJjIN6NHhIRz/fW0qw2MaG0efuQJAECpJNmkpidJA9yoSX11dt5ff96WuGzTxdcBAB68V8KhJSwR8aYgWqxUIJqjwS7SWt/0/JfGZXc8+Dm+VjS8kydIG11XPLZlQ7S7jQIhDE1zhqxIXVsqERnJKLXmSIW1wxCiQLsgnZb9Y9LtM3UsFacN1RlvjCuUMsd1SqpdvdtQhiqlV+guUs7+yRS1oVuF4ErHGW/kvP5BySxYKnFoLR0OrYVzvtsdNvExuG79TFFMdJCpJNpJ2WMODVGGxKkpGZ/thlG2otz34F1xmS0RIbGfyXCAaOXHSqLpdlrkQkVIdS683FixqM5jAo4i36VY05BUBEe7jMisJivzPlK/V6dIE1WpiRwT/HukiKQnj9MzactABxP18qvXxGWdTLDTY8CF75suyzozwVnwpquiBW8bqLYLiUrNK54vG85/Xly2YZDW14f3yfq07RCtv4WaDjvlslWp8IZMRFx+qfTjb735GgDAizaI3BrPpfte9J9PxGVfffx4fLxvivq5ohingcu6WJGysWGyzj1SG4zLkpvpWS69VNo/wRkNNVnVaccSKrRXYFowXNsFXpYAzg1Zak1wqyNnDW1SGMfXqorMbHagGKtmZ3DTFtcMs7WzSjucsRxqrknbLIdp1uamA/k9jLPb6Ta00kbPqg4tmcatg67xaYujo/WaYA8PDw8PDw8PjyUH/xHs4eHh4eHh4eGx5LC47hCbL46Pdz9BpLbShJiH06x1zykn91tuugUAcOygZLc5dpCyx00fF/JaolbiOrSzPLlc3Hzjz8Vl27ceAgAsy0js4Axn+zphFNml1hsfPr2L7nf5tRvjsqGBDQCA/cM747KODjI5FUtC7GucJNPsho2r47Lz15FpfO9TEiN5aCWZm48dOxqXBZw5J6wujoP46SCZEvOaixOs3SESmJ2NxrCjveItIZslwpUeiIVycVZZxJUnlWlmGWcT7KyJuToqsSlcZbRb3tUZH9fYZF6IpJ4qu10kNfEq9oeYTS7IKOtRUI/4Whl3SUcUVLE+jx/ncR61l9nu5AkhqP3gez8EADy5S8Z0nsf0tIpzPT1NfayJcS7+b01llypy3GUXOxQAKkzyyRdFZhke3hmV1S3HmdmCtIoZWpB5VWFiZkURNBvOfUG5uoTsMhWp/f7RPnKRWN4l8chTvXSswg7j5Cj1zWP7hRB7fJJcQBo17fvTfmjE41/GYNBBpu6Vq18Sl9XHafzuOiwZOUtstrZGZqAx1Kd15bbUvYbk9YaXXRqXXX8BuZoMCY8SqYuJMPwzbxKzemeXxGT/+kMHAQBPjKhsfbwmW2U6Ddi9rTQh5OetT1O7etdKhrKhlfSu6FVRT53bWqLJRNxec3EueFk+u2VpmuIEk4wiq7Krstlfk+AsWvjoMaJotvuCjh2c4PdYSvcP/96p1tMuzsCZUK4mJeXmFXLKz5omy7HvYL3pk2S284Np4SIRtGDTufOa3DlM/FCzzj8T8JpgDw8PDw8PDw+PJYdF1QSvW7kiPj68j7QrGeX5XRgjTWxW5Q5/zpXXAgAefFwc8etl0krlE7KD6WaNYmFStEAvffkvAQCe2ikEnqkRzkWeEM3ytVduAgAMq6xS2+8WjWzEWuEntx2Kyy6+kNp1ZORAXHZihHbgPX3i7F9hks6T20S7NrCMtL4ZFaart5/u3VBhoibHXdgpIRG1C6wOCdaYTSMznOtd53cPOEOXSvwFy1qOekMR1XgHGELK3DBJKm1InhUkuZpo8G2VxkbOyHmpomgJGh0UmicMpO+rDRfuR+6nAqPFRy4TXGFcxk5UJflGoWhGUk4r3JBddRTyPazOyXP28ZGP/lN8fMcPKTTR9LRYZ7J5eq6Elpk7UNkAK0wMrCstbI0JdHml9QBnpkqrHPUuxF52mRAYO9dRWCajtCPR0cPxcU8PEU5r6vdKOLtvHSmlWxHsMvwExWIhLpvKsIY6JQ96YozkfOyoZIo8NkHXROo52wV1pYV3WcagsnReeQmFo+rMyHn3PbwHADBRVqQr/msjpVHn6vo3iFXl1tvJsvczV4hGfS1rDXXYJcfeWXGhaPp/7k3PjY9Xr6YMft+6+5G47Me7SYM/XJGxExNOlRpq6iSRmR/qEHLsVeddAgAY7JD13Gm7jCKXpVIyJtoNXpaEc0GWTWhh1HUhUBNNoUM5lFrUQjvcVJ1pOh8QBapaypBlK2xXh4yhgV6Sf0adN6EyYYaT9C6tKMVz2pHWFTMuprs1RUjjZ1JEPBeerZXWtxVVLlokA7jXBHt4eHh4eHh4eCw5+I9gDw8PDw8PDw+PJYdFdYfo7FIZ0kbIIX74hMQEnhgns/YLb3phXLZsiFwLDg1/Oy4bH+drlEl1cICIZ7c8X5zqT46TieTYIXG+78yQjef8tfLoV15J93gqIebRo7vkmspJ2itMjIm5dcPm9QCA9esvi8tq4Ax2yk0jw4SEKBSF/5ED5GqRVDGBDx0kc1UmK64gxQkq686I2apd0FBuAo50lNJRdeOYhjq2IcdDVCYeTgikYmECDT4Om4IkshN/WkxljSSTDDpkHKxeTZkArRXz2KFDQqgaK5IcKkkx8qU55qZR7hBR4MgcghKPt8Mq7u1Uncz7PSoOrYlNWdKuRkimpUbUXvFlH/rJnfFxeZrchjTFJMHEwG41LqvsdjBVlmexAZU1h5ykPsyovklxdriU6th0J83J/HKJRZxfRQQc1IRgUymIm0ZimmSgY1wGHDM4k5K53dFJ42WgR4iuzux48LisPSVma67olvFVZlemQkXk6Ih44WLZ6k4LSqfBzQs75XnWriJC7u5HxL3j4LRzPUrMvBSRIttkB0hG114v5Oafex7JaH1e+uI4m7c7U3JtD7vAjE0KGbLSK2bZ599Gc3bjOnk/dH/pHgDAV7bKXDtZd21UptgaHR88JmPx0otuojqGlHsMuwMkEuIGlUi2swndyxI4V2QpkPi/rUhiijjIf6PZHhIzr6Lz1bWOEKfEhhzH9e/sFJZjbz8FB8irr0ADWetKvNZV1fs65G+WukrhF7bwh3BHWssaZ1dVD+KysOqMsa6TZkdFPjPwmmAPDw8PDw8PD48lh0XVBO85JMSW6QrtJF04LAAoc+ao61/84rhsx15y9t/1tGR4S2Zo1zdxWK69/torAACJtJDvntqxGwAQqgxuy1fS7ueq50hmnGpAGqHOpGi7Lt+yMj6+87tUjwlkJ75vD5FmNm28Tp6pSpq0Yyf3x2UB0wqmC7Jzti6bjs544w4i2Zd0884/CdlVtwuynWvj42rRhXpTWjkOfaIeBykXCkaTzVjj2qQJjkOkyHkuWViuUyrMdxCJI5EUImKxg+SW7ZAQeJ1JyXNfHSMNf1AVsmRgSO61utTtNN3JUMoKrNEcT8g4KXE2v+KEkPMCR4KLJKyYddkBW2/nzxqmytLGDiaPVZWFJWQtgI7SV4ucpl5ZAzg9nI7e46wuoicCuli2OgyiC42miXFJJsI0ktL/RpFjUh1Ud2derCRVlk8uJ3csceik/cOy9hwbJQ1Sd4dcG3A4u6gmGqeTBbIMVVQoOBcCrrFIee1PB9oS0+Dnzq0R7fp0RJr0qVGxjNQb9NxaERMHPFIa9UsuvwAA8KoXrovLLuuhe2y7V0jEX2G5vvq5sg5fy+v6fXc+HZf9OCuy/NVbKfTkBrXmvqVwOQDg4N7H4rI7RlymTWmsk0JQEivOk0cptN3QClmvN3B/VFSosCDQWdTaC16WhHNBlk1J39zfJsWnmXWms5ZGTQS0YNZ57lhrkR3xPKvCxuXZktfVJZrg7m5a7/NaZRxJPxc4JGWpJt9ZVb5PVa3zZV53teU24rCjdaXKNjExTrV+Hu12KyLgmYDXBHt4eHh4eHh4eCw5+I9gDw8PDw8PDw+PJYdFdYeYmBDi2egomYwnpsQcm8qTGfP8CyUz2/GT5HZQKYhPw9gwmZ7XrNkSl52/iWIV/uDbD0h9AZkuV61S8RBvopjAuU4h2Uw3SO2fDyRG4oaVYuI5cTGZa596SkxPw0fIvaFvtZiU1q+l9nQvk/v19VHcxF07JetUyObmyWmJyTddoP5I1MXO0M/m2nJJ+qhdUCnK0IljUKq4scn07KEVhi6+oyDJZh8dIzHFZpBUVspW9pI8kgkxlU2MkmtDqveSuGxZL7nFGCMuC2OFh+PjYJLMfSs6pe6QzX7DKjQnuEzHOU4zuXFZQkgYO7Zy/Of1YoLPdrIpC+ICE3IcZLtIWXAWikJBTF1JlkxdxRRtsD9LRbkJNPh3nbnIsOUtlZJ+72TCYa4uJIoc3yOj3BgyfeS6UlYmtpNHyEWpoGL5NtRa0cGuS1nlIpEp0bnFknKxqjGRQ43NcsaNV5UNMCbqSltHJ2mdmS7J2uPcdtoxP1WQVObhDK07/auujst2TlD/HVJrTI0zGNrZPCwgL0952w2rAADXLhP5HryfSFn/8ytPxmW7N5Op/ZYrVdxSlnltRMbQXffLerifPYl++aYNcdllXbTmDiiXMec6ZbTPjTMXN+R+B/idUUoKGbKL3Wpqanwi3b5kKi9Lwrkgy2YwaRq6T5v/6v9pdghg1wdVGphW8XjpOK9cYLr526qnW9bdjhy5kKRVQOEeiBvhQNG5rCpicImOK+o9VnXei6qxTpRGPZXzbmyKJ2xaPv08Zc88vCbYw8PDw8PDw8NjyWFRNcHHD5+Ij4vTtMsoKU3P+eedBwBYvWIToohbAAAgAElEQVR1XHbwacrIFk6JVm15J2X9euntb4jLfnTnQwCAqUkhPA110k7nxqvEsb8nRY72qaTsQKdYW5QtCAEmpULFXLiRnMWPHhPtcYXJNbt3CJHgwiupXbsPbovLjh3hrFPDo3FZyCSpICVtyHfTTtfUZPdTKJIGKpVYVDEtCGtFAY6dj3MGtYaQE6xlzWhD2p7nnXtW7VCTvJM1OiAKaweUiJAv0XlhSbTny5lQdcElF8p5y+keNpQQd90bRJZ7p0nTVxoXrWIy77IfiTahVudMcCrMWZJ34LsfvisuCzgTz/0/Es1IBKo7VFnF4kPTXjpEnfmsHjhNsNLw8ja50RSwhkmPOpsRV9OhZJvl2Dl5Ua4ixRrcTK9oHGpM4DikMsLtOUzzylkPAGCwrzs+XsYN61HaoI6cyxopa0CGVRIV1f46hz7LKGINQlJhVaui9XUaZS3HbJozL+Xbj4gTJKVNmQxp+wbyV8Vle/cTybigok3F0Y1UPS6cllWkHMNMmJ2PCQH0jh+QFeTB/ZJB0Q4Seauh+symSb7pnLQvOSFWtQe+Ruvl9CFZ429bSVa5/ZHIqMGWlUgRMuN2K4JVeZzmbKKmNI+sCWsKq5VpX+2hlyXhXJClUeHpYs63klLLjGtmduiz2bpjIMXvn5wiEHdyWUdG1sa+TrKYdedUaFBm0CUTcm1KZdYcYAvdxIRY1io1knVdhb10o2O6riyILSyeLntnK2acaaEGXyybqdcEe3h4eHh4eHh4LDn4j2APDw8PDw8PD48lh0W1s588qtwhxsm1IKFMqtdeRY7/UV1M0E9vIzONKYpN9dW3vhoA8NAju+OykZPkVtGVF3X+C5+3GQBwyToxm3QzaelESUymX/7yNwAAq6zE0Lv9tbfFxwMDdH3/gMTQO3aMzT0lUdqXilT38uXifvH4tsf5Woll29FD9zk4LLEWJ6bIzLCqX1xB0p0knskxMe23C27doMzQ02Tu2rpbXBWCFPVVPieO+ElHNlPuHYYztyWUV32jSmaVel1kedyZsLNiclmznrL2TVixCe7euxUA0NMt2cDWDYj813GMzKe3HojLahHd5+Ih6ftGnskXOoQi/000xUNmsp+R55wapzb2dsl9d+56lNo6LTGL2wE1nfmMY4CGyvCWdD4pyhvCWTAbisCSZjlm1L46w/VllXkzwy4LiS6ZS5OcfWhSxVoeOUJEnXKo2qJkkeXsk1lFIMowSS6rMvqVXIxfda1bSeppTQCk44IioTpXjEDTU7g96bCZstIOCHVfMTE0GwhxsMFLluYiSRZHZbLlw6gi9W17mNanHdMyb7ZuPwgAmK7IupmdovFttQk9T2Mj2yOm2B4VA3x4mub2Yz8RUtYxjmtqtTwiR6xVbjgupqh6qARnOmvUlHtTivohUrHgCz39aFd4WRLOBVlqmNj9T8G6TGoq7jr3gdWBdNlnoIlEzi5saVWW5zWxW7k25Hjd1bHdxwpTfF+5hSY22wy9vzp7xQ2ts0zv2rJKvlBnwmM1km+0SuQIgLOzyKGFK4gOGLzY3HGvCfbw8PDw8PDw8FhyWFRN8NTE8fjYsDt1cVKIcZvXUhayZQnR7E0VSCO05bnXxmX79tGOMjEmu9blVdqFXHXdUFyWG6Sd7Fd+JCGykkyw2r1XtMhgJd55N90QF1Ui2elkkqRZunqzaPaGjxKB4MS0lD21h3ZWF158eVzWmyWCT31aiHETZbq2p0u0hyHv3k5UhGSQjXjXnRStWbtg+C4hhxULtHsMIPIIeGdXV1p9cYyXehJwWWSUSiPJIbhCGRvpbuqfrhWSqe4khx3bu13C4lWSdI8tF4tWdzC3Pj7uXkVa66EJ6dMju0lrkSyKpWL5AF0zpbKPOX1IoNUvjQY/m+wneziDUnFUxntvB/XRxLRoVdoB9abQZ6z5DORZwvi55JnThsZ8RpHg8lnqp7zSNOS4b9KKtJJlQlx+QDQ3RQ4Z2KT1YM1sWJX+ipTmOWDNRjIlhBlHXHRtAYBijeruVPv9gJtdq+uYeKy5UES8ND9zX4dcO9hFz9KbbT9iXENl+qtUWWNTF/laQ78HWvvWKqyW09JVhCh8912U7SuqyZyshe5a0QCZIq1flZLSCnF0q2yXyKpbFIlIsgUt1ZD7jVdojW9SdDqLglWvLWeJgWoDj6NQjZcoSfc2aiwuu1iyfbYbvCy56ByQpQ5pZq2zHCbU7+5A6UodMS6YrRYNFNEuyedlVHa4rjQJJJ+Vvg1BY2fvUSE+nhgja/xkSWSVzMo7fNUAheYbUutfdxe9x0pluabOYfoqSWlDjUlyihMt0GTIwGWR1c/Jz97q2jMArwn28PDw8PDw8PBYcvAfwR4eHh4eHh4eHksOi+oOkVDq8mKJ1OU9PeK+cMW11wMAHtm1Jy7rW3kxAGB8TEwf+/eQa0GjII72V1xFWea2XLI8Lvv8f/5vAMCmDRfEZVsfp3jChw4ejMtue+WtAIAL1st5gLglhEzKGuyRutevIkX/iR1CWoumSY1fOSkm3KvYTHP3j78alyWYdDVdFXNUNk9miIYmEfEeJRG2lwkdACaHpC/KlkzbUUXivIZMdmo0RTd0GW8ESWY7pYwYThznwiTluafYiT+XlKx+HT3kTtI5KW4H4TSNCVOR2MG59PnxsS3RGOzrFGLkdCdlPLKRjKdKlWP9KnNUg01YkTJbhUz+aiiiQIXjBJtOmV4yJtqLUKWz/LiYzU0EDeceoJ45Yk+AXKe4HfSzaTJflPigOc6rllbZnEIma4zXRLb7jpHL0J6DR+KyqQLHEVVmw1C5oUQBDZJIETmiBF2TVYSQfJnLlFtLI0dyGVVEnckyHaeUl8MydmFZ1SVyHGCST3LRolguHJEVE3rUIPPnZF3GZZozMKZbRBy1zcZq/k10JIXpQnym/B4HPY2R5CZMl+W8UnyoY4bruK6tjKYcK1SNz/hQFzo3FjVOLBN1XBxUAOjieRxmlMtWm8Xs1vCy5JJzQZaaecbPG+mONi4urnabcGQ5fZqL66tiJvOanVHfVjmOBZxMS9lUmWR+dETee5MVzleQkLUxo9ZO5+7W0y1lHdzGqsrgW+dg1VWVR7PKcYTLzQ/Q9GyAuLs0wZ03+5czAq8J9vDw8PDw8PDwWHLwH8EeHh4eHh4eHh5LDovqDjE6LszECptNL7l4c1y2fBUx+rcfOBSXFRsUo+6JJ8VUGrAldXClmEKvvIZSS+57+sG4bNUAmc4nJoT1PzpFpvMVg8JOT3AaSRuIC0SjKmYdy8zZciS/b9xIzMn9im1ZqJLZYPhpiQSxeTPFB75o3SVx2YnCMTp/QsX/ZRN0b4fEgux2aWBPtl+c4D2BuEOs3kxuJEfuldi8EbP7m8LQsgkoqeI7Bpz6MhlIWSok80o4LXGHBzpoHFywVlwbxri+XFZkgAaZgvpUhICkcrXo7ab7dXTL7yeS1OcjUyI3UyJZV5VRpsrxNxsqZWSjhekxCpw5R66tWdeW9jLbdagoB42Yia3SPfOzBioeb5Kv6e7tjcv6WBaZsjDYMy4tZ7dE4ihzfuWxE8fishOHab5XJkTeDY5aUVfde/iAuDAVCxSxZbBXIqwsYxecvkD6uKeTfg+TQmGPMnRcVmk+C+wOoWM+9GepnhUd8uy5BD1TI2o/d4hQjUvUKDLOWGF/XLS8h+RbC8TUPsGhBKxyw5I0r8r9hPu2OY2rM3NLP6YrJLepglzrQqkHgYyNBOSagF9DRsUZja/W92tlOo3NyqoNHZx2vS7jZXw7xeneeVzeBWNFcUdrN3hZchvOAVm28pyyaOEGovvEOnmomPT8XkkpF5Isl+VVpJ6ODvoOyWRkzXPrm0mKC8naNbR+bxgSF8NuFQmiM0f1dKj1tJzm6BCT4vZWrFB894oasxV+X+joUPGIaBEIuFXc5MXyOPOaYA8PDw8PDw8PjyWHRdUEl8YlW1YatEO48QaJ/2s4vt3wmMSge3wbaYkUTwAdXfQ/N91ykdSXJy3Ssm7ZPjz2EO0et++S7DUdrBl60YskJvDYfibaQTJWWdU1hrWU6YzsUHuytK9Zv0F2W489zBrJilw7dozatXq1xKo9+CgR/9auWBmXOa3U2KjsbqM01xPKrrtdMGH74uNEgZ4xl5HnrrlMP2o7F/BxWjnxZx35T8n3+Bg53ecV8eGiLVcAADI52bXW91PGoxWh1FfPcqY6zc+oSgax3SNOoyD7v9Q6GkeVxyWedH2YNJXLVoqMsjnSRk8oomIUkzl0Fh9qT72u4l06UleivfadqZQK8snPFaqMUyHrcZJKSxE4rXZNhBZwHM+U0gakOkkWHStXxWVD59E8WJ+WsdI/RNaS3bslg+JTfDzKmSUBQIUERheT8taftyEuG2AtLSZknalzJsZSScYAGynQlxfrQwKk9UinRLbdHGczqWTmFGs2aj+yqm5nFNKcnDh6X1z2/MtvBgAEObGcHCo6AqvIw0na6niejqwS6PHriC4yzgtF6pcD4zJexvmSC9aKReAKtU4/xIaukiLWtGqD3FXFjHV6RqU1GsxRPWNP/SAu+z7P7W1HZY2vhC2jmLYFvCwJ54Ism7OhtVIL29m/GUcilzJnRUypOMEd/C7ty4tls6eb3lOppPS35fjNmayseSv6SYbn9aksgfqLkMdgUr0jUmzV618mWeSmOUDBdF20w3m4jKsyDkouC15TH7DGWxW57rKLpApurzeyh4eHh4eHh4eHxyLAfwR7eHh4eHh4eHgsOSyqO0R/txBpDuzdBQC45hohxj2ydScA4JtfuVcuKpOqPqiJWfS2V14DAOjuExOnNeQYn1dx7k4cIZO2JgDEVhplWnUxGUOV6tFYOSFid4RCWWLsJfJEodlyyYq47NgBMr1Ojkm3FqbJRLDygsG4bFkfHRdDiVOaSdA1/YpsZCwTxMLZZqSzjVvf+Jr4+Hv//gkAwKBKhXt4mNw6NL8j4cw+yjyUcX1fUsSGNJnh1l96VVzUOUQm9bHh/VLfSUp9vVaZsOt5IrmlIbLas+OR+HjrHnKhKHWKCeh5l5I7xLpNErP68BPbAQAV4W+hl02AJaNcCNjdP1IuHgk2AQVNHDjXxvaSZaWiSHAuVbGOS80EF6v2y2kmhQWFsirjvyqGZa6PxkNuUPq1k48byuya6yCTbiItprokd15KpUYdWrMmPj5vI8UFX3fheXFZF8uicFDme4mJGTXlEoMGudt0dAoNritP9ymrWKwRmyRLahC3nxOEIKX6vsFp38ORvarspQAAs1zWmGSFTO2mMdtE2dIaGc12DYFR6VlrJMPhaTnP0Ym7u6W/hzqlrUHcq4rQJYFk47I4dqqKRRvHtFbuNRtXbwAAFI58Ly576gCtFVMV5WaA9oWXJeFckKVOQ4+WhECCFlF8mnJ9SHE9aeUqk2NXhU713dPd6XIOyFqWYB+2QBEWEwG7uiWlvoTRPcnuC+qawMUO7hO3xGWT9B0zUVCplGt0TU3F2a8x2a+u+iB2lWkxQBeLeuw1wR4eHh4eHh4eHksOi6oJvuraq+PjE+MUTiu04tD97x+nrGrRtNII1Wh38YpbrozLBjpphxJFQhirR/Qod9xxf1w2OLAOAPCc64SUdtf99/H5shs5USBvflMVZ++wIr+HvEPtyMvup8pZfGxNdlubziNt5b3HZMceNWjHfGi/OPGvW3cZnffAD+OyIEn36+4VwkEQ0DNlO0Rr2S7472/6ufh46+c/AQBY1rMsLjt6nLQSodrP5TlMlR50adYEJK1oxTtzJP/BjIyNdJk0DKv6Zcfbz/0YGJHBOIf12XtUNMu79og8JkdIl9EYkbFTzFHZBWuEwNUYIDlMjAv5q1Lm45y0wUUOSySlrFJ3YcXsrPNaZ1RqLzTzVzgjkdLS5liO2YbSEPBhplfmUH45hRHsUBaCJIftqVcUUYc1HCnFfHNZ3zrV+OnplLkRn6vU7ckcWwF6ZJ7WmBiXLYvmKuQxkqqq0E5ZDi+ltPeOwDFV0yGg+F5oQyitS8A6llRBNOD7jpFZY9UWIXtunCIL2/YxkUc1fkqlKYw1fLPJVDq3k1ManZRokijyz4EKW9iR09m+JrkWrSVzdc/WTMGqdjEZMt0jgluZojCY+/eJGadYC5qrBRCYNtYBeVkCOEdkqRFbpWeHp9PsMPc4miyXZJV6VmmCO5nc290loVW7+Liu1q0OziJXVRx747S+Ogybqtuyprihf+e2duRF5o4kNzIh1tcSa4erakFNxyE4Z2uCW2l9g0VSBT9LRo6Hh4eHh4eHh4fHMwf/Eezh4eHh4eHh4bHksKhWva27dsbHz73hdgDAl74qsQ9PHOX4sEZMy8+/ZgMAYNM6KUunyNQ9Gco3/OM7qO7tT+6Iy97+O78LAKiWxCzwwL2PAQBWrRLT9+g4xY595CcST/iK51weH2cyTJCpiom9znF9s2lp16oVZCJYu07MsfuO0DVjI8ptYg0906ohIfUcPkom+1pZnMsdoyBqQzrOkDZTJ+kZq0VlHmezSk0FeA5Dep5MSsnSEeOq8ty97P7RH0h9GSaHjI6JDEyCSCFF5doyVaC+Glcy7xsS8mJ/P5ES66OShS8/TedOHFZxrHkMmobIrcbEk4oRU2CNDTomIabKIEEmKuUtgETaZTZTtsU2QCWU54utXmprnGHiRbcyf3WyWSuvYnNmU+RukOkTl5jMcurrZJdkdWs2kxISTIRZuXp1XJZngmOlIuNnGbtXAECK410mFOkkkeJ43l3ikpHisRSVpP0JlmlSET6yTEw1CUVqZfeXQhN50LW5vTL/AUCgshGaBDU0ARmXe3d9AwDQ1fOrcdlFFxHB8PijT8Vlw7XZWfFMC3M5WowX8Jp8clzmM09JRMplKNPRqS6a4pp1zNhWWbRs028AEHC86OddLDIPRr4AAChOSAbLBBMss7r9rWK2tgm8LLnsHJClRtxi1ReRI4xrMqFx2eGkLG1cdjgZG72d1Bd9vcodIktrtk2JXOpDfG1VXBu6mNyvVzLtCid8YPUii8ntcmKe3QN7e+R+EwVaO8sq+6hz46ira6tNrnczMDeH8BmF1wR7eHh4eHh4eHgsOSyqJnhw5dr4uF4nbenuJw7HZT2ckWvLBbKrue45vMvIjMZlkyVyuq6URIv1vR9+DQDwK7/2xrhsaJCu/eHX7o7LNq4kstxgr5B1XnjTzQCAkT3iNX7n/d+Vdi0nLdL3fyBZa6ZGSSN50w03xmU3vfA2uselkk3txDRp/qYrsqPbt+cIAOD89RfGZaPHKTNetSAEsSBFe5RGG2qCkyok2O2vuB4A8KlP3xGXdTOJKVkQuQ3laKffrZ6nOE7hsRplee6QFbL33PfjuMzwfk1vDhtcFioP+ipr90wg98joXOu8mxZdLlBlTfDx6bG4rFblnWxFNM/pBI2PvrRYESZYUTmqGAdZfnanEQaAAI6Y1V6UqoTaBzeYKKH7OMOMvrzSUnQwsaFDh+9hjWtuQLS1uT7S1BtFXrOh02rJXfqX0Xzp65V5E/E9SmUJw6a1tAnWKiSSafU7h1XTBLouWmfqRdGiGdZ+R1UhcjSYJNdQ4YLqHCrOjQUAcFw6W29vgmPg5osmtRRorT16UMJNDZ33IgDAxnViLakdJDLSmFqzIpdRTGurWt2YLTtTEzJvwupyOl+TvRTZ1ym7muozs9VA1l0vIselF5P2aX0k5NcdO0gTelzruJh0FUSzST7tDi/Lc0eWritsi3hoWlHuMgZmlCY450KkqecGk9/KRXl/jsdrq1qj2CzZWVdZ5Eq0/h09KaT9SDUi5NCwVoWIjPi4rjS8NW5DsSjvQMuW8pRqqxN1Rr0PQn5fNyuEDbcFiwKvCfbw8PDw8PDw8Fhy8B/BHh4eHh4eHh4eSw6LmzFu+QXx8fe+RgS1RChuCUO91JxrrhG3iUSW4gPWAp21hEzL27Y9HJdddhnFjF2xRq6dYvX8jh1PxGU3PP8WOmiIHebkJJkFduwXYtyPHxYXikqDXBqSSYk1apOksr/jLnEBmK7Qedfd+PK4bNlKMrwXD4qpoDBFJt7UKnEkv4gzX+3bJ23IcAzAE2PtRaYCgGRSsqa9/i1vAwB85iuPx2U93D/ZqvRZtyX52gl5ngSbvZ1MAaDuyBdpFUuRTS4pRYSKYzQru0kq62IRi7mmR2VAc5nBVJhalKpUT1WdF3GdOgNaUOYsOEeEpBExyS9Upvp6kogkQUJuohwC0E5oytTD7g2aoJHj5+tQfZzjfjKhjOkam+8m9x+Ky8pTZEYNFKkucIS2tIyfBMcENinlPsLHuYSc12SCdeWqLGqw24KSRaabXKzq05Jxsl4h02G9JOOwNk3PUlPJACvs8lBVGRudC47OGdg2UGbLusv2pMZbgs2jx/cJGdlkad5dtOb5cVkEIjTuPizjfLTEpJam+KZsxlVzyXL89Nqo9Pf0QTrvSELm1/GaittsZ7NjLJtRI2VKD7J0vHmLZCC8OtgPANjzqLgFnJisc/vUGOOMWUatH41oPlbOWYaXJYBzRJZNa2yrX6kw0RQTeDYxLuni7CoXsePH6F0zdlJc+RJuTWzKTmeb/gCiAW32zJhN2NO1uNeA7m0X9lffzo2OhlKzJvj9klTP5LL5Ri1IgT5jnIeHh4eHh4eHh8cZwqJqgn981674OASRZioqg8l5F2wCAIyMyc6zWCYdWpAQKtPoKO1+Vm0QUtoq3mUOH5XzDKsUX/X6347L6hW69uiIfP8PF6ktAys2x2Wve/3F8XGpQZqjlCLhpC3tTBtVIdzUnSbKioZ39VoKGXXwgIStSRuqZ+yoEHM2bboEAHBkv5xXZQJgR1bTuNoFIrgM6BmHOiSP/VSZQpDVlfa8HLF2L6+yfAVMglSZasost3Ik53X3UR+kAhmyATv7a41kPSQZ6Jz0JbWnrAazQ/Ogg9qgd+KIqA11rZFpuPoEo2XSIpeUFrnG2e/SKnxe2KZEqlJN+s5lXcrlVKY+R9BQ/ZDg/i5NyviNxojNOH3oiJRxfdbM3uVHSovjCG0mqZYj1gQnlYXAZKU/E6xRTqvsfQFrwpJZIdYmWGdRnZLwdyiSBrhekzFQYYJGXYWSqkfURq0JjjUcaD+EKuRb4Ah+Koyd05YFar4Mb6OslYlLpGzVOiK6vnjFpXHZYzso9OReFS6r2nAhj5R8mXQY7NsXl332Q7SefzMSDdboCSHjjHIbNQE4k6WyoHt5XHb15bSuXhMKcereB75PzzEqmk5HY1T8mzjkWKB0WMa2rw7Iy5JwLsgyUNYq2zIcGv/V1zitqSKgu2q00rvOF+t1K8E16Sx6TuOaSDal2aM/6s7N+nRngZhtMWjoa/givSaGTmutLAMNVhlH0alWT16XfcY4Dw8PDw8PDw8PjzMD/xHs4eHh4eHh4eGx5LCo7hAFxe+K+Ps7mxeH9/seJmJVrSDmkGw3mT2nahKrMwjIBJpSrhRZNrvnVMzCyGUriySecKFAppuSMh8U2Ow5qEz8UaCoL2yLyYmVFZOjFKexp1PMsfkMtfXRx8R8VGUCXqEg+40kd/t4VUxKI33Uxk0XXxGX7TtK9SSC9stOhfu3xoeZK68CALzmxTfHZZ/9/KcBAEllfmkwGWpcm5cjZz6TsgK7IoTK7pPiTHqR6jPn8pBXZnIXS1FZ75VUgYoj06l0bkFMGlAxFCNn9pHzXLNDTR7hvwlNoGMXg4QRxwnDpqko1K05+7DKNOVcRBp1mWtldh8qKCJkkvs7o7O11agea3VsSjaJqg6LWLaRch+xNTa7qf6PeXiaqNEqrqlOQuUKVbuCYPY+P8lmwGSnrBVgEmqUUDY4jvNslWm3ym4ttbD9iDiNJmMmkxz147v5pE4LaiTfY0/eGZdVJyh298oLXxmXXXkjxZ5d9bQyvx+j8f1ERdbXsETuYVFRyg4+RbFqtXk2UuPEpmg9TPbKGLtuIxEaN3ZLzNtG6UEAwAP3f03aMEL3q7QKKqqyrrlHNkrvkzDtJ0MHL8sZeBbLsiUXWr9reDVrqOepsXzL2s0hzWtUSvo2CJh0qKjXWf4OyaSU6yAf6l5qkTew+diGXKbaxfHwpyviSlNk0npVxxN2GQFVhY5A11BruivTGf9si7IzCa8J9vDw8PDw8PDwWHJYVE1wZ0bH56CdREqFkbIlOk71Skau8ZDOm7CiVcuyJjijthn1CRfOSLRY4B3RxIRcm05RRqvxspDvGhnaRRWUdiqs6ixk9HtWM6IiUgtPluS8cW5Dpa5C2WScZlKHd+J7WNm9HTtGGupV50vIuHpImuDj44rU0yb4xP0Pxcdvve55AIBXvOl1cdmXvvVlAMBUTYiDE06zoLaj7lgru43LtKa0lHXebZpIZOR2t7WaaIdrTiOphnbYlG3IhQETNFiTYZo0vC6rktRj4xMUIYMbETW0SpLbHSiNN48te0pSwOJChxlyR3WlCR7njqqoa0YybHXp7Y7L8qx16FahzzqynMFNXRsxccZpfwGgyjIL6yJbuLmo5mQQtvhdtTUuU1r+eIApwk/oDtX9TIPD96mYPokUt189k+F2l6vtJUcAQEJnYqJ2Jlss8aZJ38PjXD3PyKGdAIBDJ/bHZZs3rwcArF/24rjs/E0U8rK3IgTJ0ggd26Joigz3aT4lhMtcRto10EfjyOYVmfX4Pfj/2XvzODmu6l78e6url+nu2TUzkma0b7ZsyTvYxsY2ttnXEAKBJPDLQpaX5AVIgBfyEgeSkJeE7BB+eUkgwSHEIezGgAEbb8i7ZWuz9n1mpNm7p/eu+/44p+qcWSSNQBq3Nff7+egzpdO13Lrn1q2655zvOQCwZ8tdkWygn9JljitrZTBLoidNRFI7Tt9obDhdArhAdKktmrOkHZOfdHoyurd8WeaoQrnMp1PWYf5+8pV1uIdJw91tQlTv7iAiYlNce3Vfv9UAACAASURBVJXttL9CoAPknabbVSrQu/bYkFSCPTbMpGhdWTMICej63sHtV00wM9/HkSEY8wNnCXZwcHBwcHBwcFhwcB/BDg4ODg4ODg4OCw7zGg7xofe9I9retZPcNPv3CYls40Zyqw/lhEFXSpJRfKwkIQG2QMHZbarqWzOb1YdHhyLZzr2Us3RxR28ku+qal9M5YkIK2HN8GwAgNnk4ksXjEpaQnyQDfb4o7fJ8CslIJaQNoVc+kVSEGybaFIoSFuAxEa9eFVdQU4rcGivXSChIupNcGEPDUg2mUXDXswej7TAIwnhCUEu19gEAakP7ItlEkXTopySPcopzIBvl2oqxQ8RPqOHJbrFiXvqswkmmfV/WcuERxhMXjvo5yl85xZEWETt0wD7nOZwS7E/bRpFCfM5PrQmAhi9oVdxHuFWfp2D/OUMTa/yZBMwyk/ysylFa5XGbV+FBSb7/SlpV/quSrCmuCR+0PVmUvh4rcRhRIM9SurkVAJBRpMes2vbr9DzF6uKqjUJNlK8xEepKRS+EpBOtR8M7BJDzha7GjCLlVGs0wlQQRsOgqVXmjnA4JlRucy8IqzMJgmicy3xYZ/JmSuWQPrR1LwDg+eBQJKuwPlIdnZGst4NywWab5LrLV1A/JwoqtGhQ3O4H9hERemDoRCTL5cndWq0polYY2TIlx2qoS3VP4Q3q6DvWocFs7uDGg9Ml39MFoMsztm1mVIIQs3UZNjtLPAGHeWmSci0KuVCzVFjDwNNhXCGpTodhyK8xTg6QVO+FGIe+pNS7OcxlPLWynDdDNv02AAnn0bcUhntYOz86dZZgBwcHBwcHBweHBYd5tQQv7xVLzpaH9wAAjrIVFgB+47ffDQA4qSyuAa9c6oFYaPL9lKbl2K5dkWzk2EEAQGFELMseW18vuWhFJFvUROc+PiRW31VttEraeO2ySNbWLtbjiUlqQ//Jk5FsNEdWTasYXbZG29lmsSInufpWXRG6kpxuKqmC2YtMECgr63DFI9NyPDWvapoT9mw9GG3vfI7+XrxOLOCbb38rAODeI38fyeLJPAAgU9VVyshymFQrS8srVF0PvsQkJp36DIb6T5MnKmwFUZw0+AlVoY7XfTodl2GynK+sEjUmVwXKihkSN6auT9l6oVNxsUUyptpVj8iQaChYtfSPaClTKhzNtDiF1htNyonzHikjz2nakgXJUwQ0W+PKVDpTXHRyuW4620znS6mKcUbOUyqRGdoWxEMUYyu/pyrP8eVQqilrdJ1TpCnykR8NGNVWHn+62l+d+2uWzGsvOBYtWRdt+4bmkZqy4AecZrKiOj9g4vFUUg6PfTVngS2Pfk1IqGHOy2BC5sWjXPFSOekwPEDzwqqWRSLcfSTa3D9K84ImX1q2zBv1ipKikjMJUWaKPSesdKXT64XbvjqmcYlVTpdTf38x61Lr43TWzdl+M+odWJ+FVBemqrOKDBy+Y8pqxwlOaVYsyDgIdaA4+9D87tlSXIYE73xJzhNe2lptzmULr53F8zmL1RezEOisS5Hm4ODg4ODg4ODgcH7gPoIdHBwcHBwcHBwWHObVz35sVFwtx8cpWL5lqQTi/9f3KLfs4UGpGNeeJYJMdUjcnmu66JiL1yyRky+hsIPXvvzqSDQ+TCb7ipV8ps/segYA0H9wbyRrZTLPocl8JDvgPSVt6KHrtDJZBwDGJomAN56XY7Jt9Pv2Z6WaWhS4npZQkGSK1h7FkrijmluIhFAqiushx/kZTw5LTr5GQawqbdq59V4AwOWbbo9k1/3EzQCADUP7I9muL34BANBdFGdZkkM+koGsx+ocaO8ZRVTjyJFSUYL9Q2dJoPw5Fc4TnUxJHktdNcyYkHCgbibMXatDA/icOtTCY4ZKTR/KuZ511SSpQKdceBzH4cUbK7SlvVlIignOIVlSVe3qTD4MdH5jzpudUDknm3lMZ6XbERaDrCndpth9VzUqZIFVWrUSHhTnXMSptJRp9FWMS5nDICarqqogu3zjqt9Dd5vvyfXS3O6YriXIaq6r8KZKlV2NavxkmKirKwQ2Co4ckPCwpMedqsJ+6kyOqukKX9z59Sn65YqNuiqeF5I91W6hi10n/g7JSso9O3qAiFN5T0jLQVmFOgUzXdnBLF7SqCmzuMv1bh7rsKbuKRZWXfP1no2nwxBOl2FTX/y61Kyv2R38ZsZ+s3DlToGZuXxr/B4r1OV8GZ534zGZBwMOd9DvMyNxKohxWFldJdAP8/7W9DvCztSbNNzOEOkwOmtn2mHDOdvlCXZwcHBwcHBwcHA4T5hXs9Tzxwaj7ad3UeosPysW0u1jVPN8w8ZNkawloKonlVEh3Gy+9joAQGurrFB8Q9bhHY+JBXf/biLJvf7tb49ki1YQae366y6JZMc4lVpnRay6QUKlhIrTmkRbqtb3rAYAHImJ1XrtxSRb1SekusETtGLOF6RC3ViO+sEq+kCpyCmmykIESiaob5Ytk5rrjQJbG4229229BwAQ/5nbItlGrm6zcptYxS1beCuqhFg9IIJTXa3kowpiOiCfLR8pX1kEwmptul28ki0r64WvrBMhgcIqq2KYTk1bUGJx0oNRllsbUBum2hy4DVOsKkzsU3t5TPxLNJgluKQqLJY4jY4m4IQ68DRpMLQ4KV0EbGGIqfuLMalQV+KrMUNQZbBDUJ+Z8igkGvoqPU9CWeXDdFF5Tw0mM5OQGGdrRiYu7J62DJF7UqqtebYoj5Y0Y48tyzFpbIqJnM0NVvkPAMaGDkbb2cRM+0ZIBIwpi1M9rLI3xWvBnhFtKuQye1Z5XQI+tqarOPIzmYwpNhUThitqv5o2Vs5yL2FzYsoyFaY6DKakjZp5n2Eb/FmJNWrMNrD10OlyahtezLrUVlpvNivnbOSwyDg8s9rclHNHlU21tZn6Malyg8a57xPKq5Xg1Heau2ZnseRrVJjkXCjJ95ifDxur9wzbNUM0ZUdJU6n3tDNPdx7hLMEODg4ODg4ODg4LDu4j2MHBwcHBwcHBYcHBzFdVDgcHBwcHBwcHB4dGgbMEOzg4ODg4ODg4LDi4j2AHBwcHBwcHB4cFB/cR7ODg4ODg4ODgsODgPoIdHBwcHBwcHBwWHNxHsIODg4ODg4ODw4KD+wh2cHBwcHBwcHBYcHAfwQ4ODg4ODg4ODgsO7iPYwcHBwcHBwcFhwcF9BDs4ODg4ODg4OCw4uI9gBwcHBwcHBweHBQf3Eezg4ODg4ODg4LDg4D6CHRwcHBwcHBwcFhzcR7CDg4ODg4ODg8OCg/sIdnBwcHBwcHBwWHBwH8EODg4ODg4ODg4LDu4j2MHBwcHBwcHBYcHhgvwINsZsN8bc/EK3w+HCgDHmlcaYr7zQ7dAwxiSNMbuMMd3n6fzvMcY8dD7O3egwxnQZY543xqTOw7kfM8Zccq7Pe5rrWWPM2vm6XiPBGPMfxpg3n4fz/qYx5k/P9XnPcM2VrEt/Pq/bKDDGPGyMueI8nPcvjTG/cq7Pe4ZrOl02kC4vyI9ga+0l1tr7z+c1jDGfNcb80fm8xrmEMeagMea2F7odjQBjzD/yR05gjHnPHA75EwDRS88Y8zFjzHPGmJox5o5Zzv9OY8whY8ykMeYrxpgO9VuHMebL/NshY8w71W+X8QJuyBjzPiWPG2MeNcYsC2XW2jKAfwHwId4naYz5Zz5nzhjztDHmNWfXMxcmjDG/box5whhTNsZ8dg6HfBjAZ6y1JT4+aYz5F2PMhDFmwBjzfnXuZcaYLcaYEWPMJ6Zd91vGmKunnfsvAHx0ju2+0xjTz9fdbYz5xbkcdyHCGHOpMebb/GzYOey/GcBlAL6qZKd7Lv/aGDNqjPmhMaZXyd9ljPmbaaf/RwA/86MsQI0x64wxJWPMnWd77IUCY8wtxpj7jDHjxpiDc9j/DQBy1tqnlex9/CyO87OZZLlvjPmCMWbMGHOPMaZZHfMRPa8y/hzAR4wxibNo//2swzz/e36ux15oeLHrErhAP4IdHM6ArQB+DcBTZ9rRGHMNgFZr7RYl3gvggwDunmX/SwD8/wB+FkAPgAKAT6ldPgmgwr+9C8A/GLEMfhzAb4Ne3r9njFnM8vcD+G9r7ZFpl/s8gHfzpOEDOALgJgCtAP43gLuMMSvPdI8LAMcB/BFo0XBacF++G4D+SLkDwDoAKwDcAuCDxphX82//C8C/AlgF4M3hR68x5u0A9ltrn5h2ia8BuMUYs2QO7f44gJXW2hYAbwTwR8aYq07R7gvdqlQFcBeAX5jj/r8M4N+ttRY4/XNpjHkJgKsALAbwEEinMMa0gp7H39cn5sXRPQB+7ke4j08CePx0OywAXU6CnsXfmeP+vwLgc+F/jDGvAi1UbwWwEsBqAH/IP/8EAAtgEYAJ0DiAMWYVgDcA+Dt9YmttP4BdoOfrbPDr1tos/9twqp2cLmeg8XRprb3g/gE4COA20MvrLgD/BiAHYDuAq6ft978A7AAwCuAzAFL823sAPDTtvBbAWgDvBU3KFQB5AF9/oe/5DP3xOQABgCK394MsfyP3yRiA+wFcPJe+meX8MQCfADAE4ACAX+e+8rU+1P53ALhT/f9aAI9wO7YCuFn99h4A+1l/BwC8i+VrAfwAwDhf9z9/hH55CMB7zrDP7wP4p1P8dieAO6bJ/gTA59X/1/A4aQaQ4e3103Tzp7y9E0CSt7cAeAmA5QAeAxA/RRv2ALjpFL89C+CtvH0zgKMAPgDgBIB+AP+f2rcT9IE2wdf72PTxP+3cPwfgEIBh0Ad3pGMAnwXwR2rfmwEcVf9fCuC/AZxknf6m+u0lAJ7gdgwC+EuWp7i/h3mcPA6g5yz1/UcAPnuGfV4OYO802TEAr1T//xiAL/D2PQA28PYXAPwUgBYATwNoO8U17gXw7rNs+wbW2U9N0+eHAAwA+BzLf4f3Ow7g58Fz1inOuQrAA6Bn67ugD7Q7Z9PZ9OcYZED5MIB9rJO7AHScSVc4xfN8Fv2wFoCdw377Adwwx+fy7QA+zvJXA/gmb/89gHee4vzvAnDfWbb9HdxPd2Dq/PceAA8D+CsAIzxOYyCvwRDfy/+AmlNnOfeVPOZyAP4LwH+Cn0Gc5l3G20m+1mHQM/dpAE382yIA32A9jgB4EIDHv30I9GzkADwP4Naz7I/bABw8wz4J0HurT8k+D+BP1P9vBTCg2vTLvP0rAD7F21/X42HaNT4C8vzMtd33A/jFU/zmdPki0qW1dkFYgt8Iejm1gV7yfz/t93cBeBVoUlwP4PfOdEJr7T8C+HcAf2ZpJfiGc9ricwxr7c+CHoo3cHv/zBizHsB/APgtAF0Avgng69NcCXPtm18C8BoAl4Me4DnH4bHr8W7QZNEBsrz8t6G4zAyAvwXwGmttM4DrATzDh34MwHcAtAPog1oVGmO+YYz58FzbcAZsAk0Kc8UloA95AIC1dh/4w5f/1a21u9X+W/kYANgG4JXGmD7Qqngf6P4/aK2tnuJ6O0GW4ykwxvTw9bYr8WKQlbgXZFH7pDGmnX/7JIASgCWgj6efP9UNGmM2gqxo7+L9w3OeEcYYDzSJbeVjbgXwW2wRAIC/AfA3lqyfa0AfDQBZZ1sBLAN9sP8KaEKFMebDxphvzOX6c8AUfXP/LIXSKWbq7HZjTBuAq0GLxo8B+Gtr7dgprjGrzmaDMeZTxpgCyMLRD3pOQywGPTMrALyXrdO/DeB2kOX6TOFPnwcteDpBH2Y/O5c2MX4T9JzfBOqfUdAYAk6hq9M9z8aY5ez2XH4WbZgVfJ1VmPrcnu653A7gRmNME2g8bmeL/gZr7edPcZk565Db1AIKg/nAKXZ5KegDqRvAH4Pm1NcDuAI0rn7yNOdOAPgyaPHZAZrX3zLXtgH4P6B+uBy0yOiFWL8/AFpsdYEs6L8LwBpjNoCMHdewLl8FWiTBGHODMeZUY/9ssQ5AYK09qmRTdMnbPcaYTtDz+Aruk1tAunwLgCFr7ak4DmelS8bHOTTnYTOTf+R0OTsaUpcL4SP4IWvtN621dZDVbXoH/b219oi1dgQ0YH963lv4wuDtAO621t7LH1h/AaAJ9GIKMde++SnQh8tRa+0oVPzsHPAzIMvLN621gbX2XpAl8LX8ewDgUmNMk7W231obftRVQS//pdbakn4orLWvt9aeK+JKG2h1PFdkQdZpjXGQxel0vwH0AfOroMXa+wC8jK+93xjzVWPMD4wxb5t2fI7bGMEYEwct0v7VWrtL/VQF8FFrbdVa+02QV2CDMSYG4K0Aft9aO2mt3QZy8Z8KPwnyfjxkra2AJtkzxmkyrgHQZa39qLW2Yq3dD+D/gqxkYRvXGmMWWWvzVsJQqqAPqrXW2rq19klr7QQAWGv/1Fr7+jle/0yYru8s/9V60zr7OIAbQV6JTwKIA9gMWlB+3hjzgDHm16ddY4bOTgVr7a/xtW4E8CUAZfVzAOAPrLVla20R9Bx+xlq7zVo7CfqwnRX8sXkNSOcVfn6+Npc2MX4ZwEf4mS/ztX6S3b+n1BVO8Txbaw9ba9ustYfPog2nQti30/U467PH4/2/Qd6X5aAPib8B8JuGSHAPGGP+nRc6IXKgD/254mMA/tnODGkKcdxa+3fW2prS5V+r+ffjpzn3taBwqL/lZ/tLoMXNGWGMMaCPtPdZa0estTmQ1Vw/j0sArOBzP2jJ5FYHWR03GmPi1tqDvLAAzwtzGt9zwGzz73RdhtvNoEXiAdA7ZBxkAPsDAB8yxvwx6/JT04w9c34eGR8Cue17QfHhXzfGrFG/O13OjkbU5YL4CB5Q2wUAqWlxOnpSOgSyaiwELAXdLwDAWhuA+kJb9ObaN0un7XuqiX42rADwNrYCjfGq8wYAS/hF/naQJanfGHO3MeYiPu6DAAyAxwyRyU5pufwxMQr54JkL8iB3uEYL6OE83W+w1h6y1r7WWnsliNDzUdCH8V+AXGJvBPCXRhF6uG3RSp0trZ8DWbmmf3wNW2tr6v8F0CTUBYkpDnEIp8YUfVtrCyDX91ywAsDSafr+XZBlAiAL9XoAu4wxjxtjwo/bzwH4NoAvGGOOG2P+jD/2zzWm6zvPf7XetM5GrLVvt9ZeBvpw+jsAvwEKFdgGssb+ClvPQ0zR2ZnAH5IPgTwev6p+OmmZvMeY/hyeSYcjrLsQZ/vcflnpcCfoZdqDU+jqDM/zuUTYt9P1eLpn76+stZdZa9/ObXwQ9H58L8g6vBOk0xDNmPlRPSuMMZeDxsFfnWa36X1/tro8xh80pzrfqdAFIA3gSaXLb7EcILLRXgDfMcbsDz1s1tq9IC/iHQBOGCIwnY9352zz73Rdhts5S/iwtXaztfa9IJ19GmSBvRrkuUhgqqfrbJ/HR621OV58/iso/OG1aheny9nRcLoEFsZH8JmwTG0vB8XSARTwnQ5/MEJSCjFXy1ejYHp7j4NeZACiVeQyUFxQiFP1zXT0g17Qsx0HTOtLkBs3xBFQPGOb+pcJLbnW2m9ba28HrWB3gayGsNYOWGt/yVq7FGSV+pQ5P6mgngV9lM0V26G8DcaY1aBV9m7+5xtj1qn9L8PUkIUQYSzyIMhF/4S1dhzkztL3eTHYncQ6/GfQh8hb7alDKKbjJIAaZur7VJiib3Yjd6rfz6TvA9P03WytfS0AWGv3WGt/GuRK/D8AvmiMybDl4g+ttRtB3orX40cjJp0JU/RtybPRj6kepFPp7L0AtrBlMdRZBcBzAC5V+0U6O0v4oBCRqHnTfu/H2emwwxij9aSPnT7/xSAvU4D0+JppekxZa4+dTlenep7PJfhjex+mPreney6h5D2g+eSjIJ09y8/R4yALf4iz0eHNoPCmw8aYAdDC9q3GGE3M/XF12cvPf4jT6VI/j0OgsKJLlB5brbVZAOCPvQ9Ya1eDyEjvN8bcyr993lp7A+g9YkHP67nGHmqy0caZKbrk7UFr7ZSFuDHmUtD4+0fQ8/gkf1z+OLqcDRZkkNH/13C6JDSkLt1HMPA/jDF9bF37XZDFDeC4P2PM5Ybyhd4x7bhBkEvkxYLp7b0LwOuMMbeyRe0DIFfrI2qfU/XNdNwF4H8aY3rZZfihab8/A+AdhlJ9TY+JuhPAG4wxrzLGxIwxKWPMzXzdHmPMGw3F+JVBq8Y6ABhj3mYodhagFWbo1jkjjDEJ1qkBEOdrnupZ+CZoxamPj/PxHuijNsUfCQCFIbzBGHMjt/ujAL7EE9AkyKX9UWNMxhjzMgBvgmLL8vk3gl6c/8CiA6DYqB5QXNVh3q8XFDcWhgz8A2gSeAO74eYES6FCXwJwhzEmzdd/92kO+SLf4/XsivpDTH0JPAPgtYbSwS0GWRlCPAZgwhjzIWNME+v8UkNZOGCM+RljTBd7JsIVfd1QKp5N3M8TINfeXPXts75iAMIxdirW9mMA2qZN1P8GytbRzpbLXwLF7OlrdIMIL3ew6AAoC0QWZLXYz/slQZkI7j1Dm7uNMe8wxmS5j14FCkf6/mkOuwvAe4wxG/nj9g9OtaO19hDIzXgHPw/XgV6MIXaDvGav4/nh90AfjSE+DeCPjTEruL1dxpg38fasujrd83wmGEIKZPkB6zB5mkOmP7enfC6nHfeXoBCTAkiH17AObwbrkHETiBQ5F/wjaPFyOf/7NIgH8arTHHMXKByjz1Bc+uk4Dj8E9eOv81h/E4hgGuKU7zJ+zv4vgL/iMQyex1/F2683xqzlj7IJvk7dGLPBGPMK1kEJ9PE1V1163I44/dekzCnSWvEC5LuYqst/A/ALPM7bQWPzs9OuYUDhSf+T7/EAgBv4OjfhR9SlMaaN31Up7ut3gci03z7NYU6XaDxd6oZdcP8wNTuEZuGuxMysBWEGhDFQHGRa7f8R0OrqCCh2VbMw14Fe9mMAvvJC3/Mc+uRNoI+nMQC/zbK38L2Pg2IaL5nWh6fsm2nn9kGuvmEeoO8DvfgM/74awKOgl97dIHKM1stL+fojIKvk3aDV8hJIBogwg8VGPubPQFbrPMjq8151vnsA/O5p+uJ+1qX+d/Np9n8cwEvV/z87y/HvUb+/k/t6EhTW0KF+6wDwFf7tMGZhnwO4b9r1LmM9DAF4v5L/DiR7QriCL3GfhP/CbBo34/Rs/y4Qc3iu2SHew+0Ps0McA3Aj/5YCLZgmQJbV92Fmdoj/AIUqjYI+4sN23AnKXpEHWQnezPKfBhGdJkELur+FPMe/C+Ce07T1jln0dcdp9v9zAB9S/0+C0gCFGSveP8sx/wbgber/y0BjfhTAJ5T8baCPrzM9r12gsT/G130OwC+p32fok+Uf5n6dS3aINSC3fw7A90Afa/88Tcf9rI/fxszsEO9nneRAz+CfnE5XOP3zvJx1vvwUbV05iw4Pnqb/LuXxY+byXPLvt4B4Elr215Ax2qfG91GcZXaSaeNxenaI6Yz/6XPqmTIKXA16H+VBGQW+BOB/q99P9y5LgWJH9/NY2wnO2AJ6dg9ynx0Nzwmyvj3Guh8BzR1L+bcbAeRPc/83z6LL+0+z/+sw7fnmsTfI7f0MOKuO+v3nAXxyWn9+gcfet0Gx4OAxeRRAYo666wK9D3I8hrcAuN3p8sWny/Bf+JGyIGEoufMvWmu/+0K3pdHw4/SNoSINn7bWrjjnDXsBYIx5JYBfs9ae8+pTPyp41b4VwMuttSde4LZkQS+EddbaAy9kW84FjDFdoI/DK+xZWNTneO5HAfyCpZCJhoIx5j8B7LLW/sEL3ZZzAWPM5wHcZa09p9UejTG/AWCZtfaD5/K85xI8zj5trf3MC92WcwFD1St/w6oiC+fovJ8AsM9a+6kz7vwCwelyzuf9kXTpPoLdR/CsOJu+MRQTegsoZVkPmGltrf2t0x7o8KKFoco/3wOFQXwCZM2/0i7kCeVFBg5BGQFZp14J8lBcd65fTg7nH8aYm0DW9yFQ6sJPA1htqYCAw4sITpfziwu9monD/MCA4kL/ExRPdDemVVlyuOAQxjIbUGzpO9wH8IsOi0Gu1k6QG/FX3QfwixYbQLGnWVBoyk+6j6YXLZwu5xEL2hLs4ODg4ODg4OCwMOGyQzg4ODg4ODg4OCw4uI9gBwcHBwcHBweHBYd5jQl+7c03S+xFnS6dm5iMRNmWJmpUKiYHxanA1WRJChv5Pn27x3xJR9faSpXyFrVJPvd0gmRJrymSJWN0rPXkujB07oLmgZsg2ixVKiRSmVBLJWpXvV6JZAFou1xVbeXrBTU5X6VK3eCp9seTKQBATZU3GOe+aWmROgR3fv5zOh/rCwbP81UcTdikQO1hp/0GgPXQfe0tkWjj7VSlOZGXBAd7H3sAANBSE71t2vAaAMAN118TyZ4/RLnmf/j0A5Fs5+G9dN7Lropk7S1SRbGnTukP7eE9keze554FAJQXSRrlns1XUFsXZyPZxEE6ZseDP4hkhYGwoJheT4bbWlUz1RYEpRdcl8YYFw/1Y8Ja+4LrEQA+8i9fj3SZL1CK5XSb1MJo9imtbiw3EsnKJ2gee+zIUCTbV6J5rOrtjGQdXDG4GTJnjY5RMc6d35MUqdUEVRLOXn2zXGMRFYEyNVXx+XmpBFvevg8AsPJKqXwdNNPcNzQitXs6s6vonnqkdkCiMkr77ZCaFxMVereY2GAkW9tJNQXSvVKzpKODntNkQu7prk/+udMlw+ny3OCydasiXXoebdpAvxf5Hj357vEtvafSSbmf8QK9a6wnU7bvGT5W3j+1YOb7J84i30j63xp/kxSK8tHh+ZJ6O5MlXdcDea/n8xN8rNJ/LPwek8/JbJzanVGvxcCjaw/lpchliS+tvxw8vieo6x44OnDedOkswQ4ODg4ODg4ODgsO7iPYwcHBwcHBwcFhwWFewyG8hJjx87lxAECqWVw85YBdNyVlss+Ti6eGWiSbKJE53XpiLj96/DAAoJCTUISgyt/4dRVewSb2VFK+//04uyhUW+Mp6RrLFQRb21ojWaVC4VvV2wAAIABJREFU50ko90EqSe57vbKIx+k8fkz280Dt6V3RE8lODlOp7Lhy55TKFJ/Rv3sHGg/aOxHe8WyedbVfSzsAoHejlApPcrcM7dwVySaOketrLK/cKz652VavWRLJjhw7BAAIrIyNdJz6tjAqrreTh45E2wNmEbWhWXRZY33kBuSYOOu6OXtxJGvqo5LvvZeulTbkyXVXyiv3UHTPs4VDaMdPY+F1b6RaIIFyQ6VTcQBAR0pG9bJW0mNPIhPJungqacmqcd67FABwzeukxki2h/SXV9FIk6y+B7dI/YhtB0hn9aT068o+eTYuX94NAFjbsyaSNfnNAICxgYFIlttBoS79WyRk5tlddJ2SF49kpRYaF8eVS3KgRK6/QkkaW6mS/y7QcUsNgu5Fyl1epmdxUUb00R6j/ivFpU8nArq3+DbJjDYySLruXi/z05Jm0nm7J8/k0iaeN1e1R7Jtu44DACpHDkayWPoiukZpVK67Z2+0vX4dPVfZ5dL3hYBc3iualkayZHs736dczxunY+LrIxGy7A6OeX2RrLe1AwBgllwUyZZlSL9F9c5oFDhdEi4EXdbVt0vAMZU19apMehzaoisV8+vCUzoKQz0qKgTTj8Ic5NBMgoS1Sk3tR+epBKKXUo1DOtWcp99P+TyF4ehwiCDgNurYUM4wpt92ZT53PCVjtsqyQH8hcdipfueEoaNmlhDC8wFnCXZwcHBwcHBwcFhwmFdLcCwtX/bD/RS8f+P1L49kQYVWFOkmWQXHE2StGRsbk/2Yy6MMOfB8Wj3UlFUwZmjlVJiUlVONCW3JhFyjMJnjc8iaoA5ZYecrZLVOJKS7xkdp5dkUF+JUleO9y0VZ1VT5NJMF1X62Jg3zOQCgUKSg9zG2kAPA4qW0ek+lGiK+fxq01bc2y+/c5lRzJGm59BIAQMfyjkhWKO4HAIwOChmiNEFkD1NviWQj42Qd3r3vmUh29Ph2/k0sf6nQolGX4PtqUfp01wBZLXZZ0ZHH2/VqLpINHaL2tCwVC0rnOiJzLF4vluzSIFnrj+2WasFBOewPvcYMddi4PLREnB4ovSqPx6jdHcozsqqFSJzJojBJRwZpfNtuqZR98bWbAACZLunD8MxxGRY4tJXG/vHD0v8tlizxi7Jyje64WPRjlq6Xq4lF3/NpO92xOJI1rSBLxMTBQ5Es3U9eh3pexm21StaQVDwVyTKG5o2aJ/MHmA8aa8AyQ6v6FkXbbXGyuPs1uccTPIcWrbIGVal/21JCQl3WQvNTNiXPX2uW5suxnIzfTC9Z4Tff/pZI1j/0BbrW3q2RrAl07nJV9AdlSF+5/lYAQEevmhdiNEBsTJ6h0Ul6poNRIX7lxtmD1irtv7yXyFba25PgYXQgLve0oZdI1MePPY9Gg9Ml4ULQpVEE+Bh/Y+hvlzhbgBPqmEqVdF1Q7y6P5+JMXPb0Y6T/pDIFN7H3Odkik1QpIF2PTMh70caYJK5eSbW6/CdgEjnUuxJmptc8PL5UqswQFkoyf4fG47qVcRDw9tR6FZxMIJgfr6mzBDs4ODg4ODg4OCw4uI9gBwcHBwcHBweHBYf5DYdokstVOM9criAhAXEmj8VryoxfpP1iRmSJOAeXKzd8GDReLuQjWYXJK00ZyRNr0nQNq67hJ8l9WtdpW30x7bctYjKMJ66EuE8u++asnLtaJPeBSsWHbJpcT8m4uK3Gx8mVlUpL0Hjk1kiJq+OhRx4EALS2iyuocXAGEpxH95Ho7I1ES9auI5lKHjhyjMNAxkVvPudG9Iz4jCqg3/cfkbCJao1IEL4v+6UXkRsu5ql8h2lRSKyX5BVFaAzXglalzLXM2DMpcY/XU0QEa1+2LpK1ryZ33tCQEEWKg5y7s3E5cLMiCMkMcXk2Ykyo6GwS92wrPwaloZORrJ3H+ZXXXR/Jll7NuZpVmBGnyEauLJ0zkSPSY3eH6GnjCsrZ3Kzyee87eDza3jNG46EmqUIR7+Jcocrt2rKKcmy3HRK2TfMhCsFJ+hKiNMm6LwUyDidBN1pShJUyzyl1r/HCWnR6dTCZ78AJySObZzJvTqJOMMmy7pUypte003OV6hQSak8TH1uUPvOTRGrqWix9e93VrwAA/OA7X4lkY7seChsVybKrhei0w9AcnzkuucK96nMAgAkVRtbVdTUAYEWftGvFRTROAnXzLZ1EwPJzklt2cD/1x/6jw5HsxJP3AQBG9u2PZB//g99DI8DpknAh6LJUlG+JFM+taRVe1uQzIVC9PxP8fg0CiTUx/A3hmZmfbSFhFxDinK6jEBLxAl/6Nmnp3Wb0u1x9vFQ5hKKuXmQ+Hx+oQyLinJJ5/D1jFIGuzqGqdfVeDKMgjLLHWs6RHDP6ITh/cJZgBwcHBwcHBweHBYd5tQQXSkI2C9g6VFHB/pkmCqA3RlmOyrSS1RVRwpVQoAK2i3musKIsVhVehIyNykosN06ryJHhojqW21BQbB1fVrrtPbSi6u4VMp2fJLJPrSrtMj61YQqRLU6ygiJdJcPMUkbuvcZLq6Ag99TaTFbmhNeALJxZoVZuMeqrRcsljc2SVZRazFPptmo+WW77Nr8iknXwafQ4CHwiS9TiYvVt8+l88Zgih3C6l3hM9mtfL/qoMKOpmJDfU3x8Qln3Kry6rajUdmUmnGRUu1atpjb075SKTJURsibWy8ol8CJIkVZjIkSTetbCyj+tEAJoLEcWnaaKPFddKzh9XK8Q4+KW+kvxIGDCx6ooVpxUcBAAsH6pVEZc2k7HNmdElm4Wwkz/AFngq8KJBNgKle0QC4hXozYuVWTG4iDNKXu2fCeSTU6QNXpxqzzj2Rba3jsu4ycXljiap/Q9Z4Oa8qYMVKid8WbR20q2xB0alrktzamqvMWit06u3HV0VKWGYwvQig5Ji9faRIotjgsptD19EACQ8hRZNiSpKqtQcUismqVH76ENZUnq5sxZyZaVck8jlO4u9/y+SLYtHaZ+kndLKU1tHD30RCRLVOk5XrZS5qP9/WShRFlTkhoDTpd8vgtAl0mdTozfK9qR1MTvIqNMpHFmztVVZbkKfyNU1XdPuFlV6dDKCC24+v3DyQSUxAu9tcprWvfkeoEtcrvk3B6oXVbdk+W50NO3aUOCurShHn7jQIPToSliXGiZNp6zBDs4ODg4ODg4ODicF7iPYAcHBwcHBwcHhwWHefWz20DM7gl2byfj4pIJY8BLqiJKGAZRrSm3T6aF9xfXh61TkHdQEaLawBFyuR4+LLkI63VqQ60qpvZKicMrCtIdqbTkIDy0j9w9g4PiomrrpGsv6pb9eni7XBeyn5fkXHwxuacquwj8QEhXKXa7GyttyI/S9Zb2Sd7TxoEeOqErQ0IMYi2Ut3HxOmEupbMUqhLzxd3Vxy6t+PLVkSxuSW95VVbs6DiTnnqXR7IUV5tZ0i4hMJbDHfxAhdmokJtxdjlZlfO5NEA5L5Pa/cKhOxND4rbPF0iHeZUOMX+MKhXGrLjRg/psa8v5ce38OKjX6QGMxcWF1cbPaasiwjSVKJwgrnLqtmcprMUqZ5eN0bioTUiH2SptV0bkmcyyKyyjiK62QL/ni6Kn6qQ8VynO7Y1JIecND5Iex6y0dXScztPUJOTSkcN76D4LotvEBG0HNRW21E05SifSEiY1MEkuwrLXeOEQlaqEeOWK1JcdSeXqZKKplxD9TvKcXFBhXTXO61quS/WtAhOZjz9+dyTbv5Vc54UTEpOyskDP7OWqJFY4SqbkAlU50mu8rR4rjAxQ/x70Rb9jTfSuaOvaLO1K07hrS8nzlx+kZ3L8pFyjYwWFCKzYfEkka19J8/XocakU2ShwuiRcCLpsVWS0SQ4JyKt3UopDGeLqnRWFkKmE5DaYWV2Ni6uhrEhkdSaWWRVWAj63pxIUt2Spz1o7pZJfSpHwR05SbvXR45ITusI58HWuXzNL7uDoP2qaDL/lFP8cQcSw0+Q8Lzwx5gPOEuzg4ODg4ODg4LDgMK+W4IRVhJXQCqpXrRwEnkooohOnh0kkpanh2req4vU9tsT1H5I0LPv208qzBr1qob8FVe0qmSRrV7xZLEg1FQwehKsoyAp1bJhkpUlJjVXhVCir1ki1nHKBUrs0NYvVrMR5bYyyJjF/BwlVDSbFK+JyWa3oGgazDR1FNuN0YjmVOqz5IK3SrS/WtiKnnLJJWd22dXOKFJWqa/U6WvWneiUdT4ZT6SQVeS3GhDjfk/Ml1CqzjS2yyUBVlGunfs73S1qfk8cpFduebVKhbngfpeiqjKvQfh6EXlVsHzGfzuepcRxjEoLnNe66M+DSTy0ZGed9i2g7VRWrfJ2rGOmUPnacLE25Pdsj2cRhIr2MFuXYcpn6PZeTcVFgop2tSb96PL4CRazQz0G1Uua/qlxVjS0gNdlvrETXqydkjJhJ8rCkymJdCrMJadtDwNUlg6T0h4nRjn7jGYKRUSkXYzzOqyo9U5Gfk4KqlnlsjJ5FWxMvV5K9JWP7hST11JPfBgBM7NgVyTawVXyTasNG/tunZOHVNE1HPwVhC4tKNsgWoscq8pzuYJ0fKD4aySaS5HHqWrYqkoWWqbZVQoas+KTLZ/c8G8lG9z0OAKj5UtGwUeB0SbgQdNmkuHolZuubuOi3wpZ2G+gPGv6jSPFhKjJPWYcDZtjp6SjL77audrH+9y3tBgC0tW0Q2SpKYdm5SlLqtS7ujrYH9tP4OLrtqUh2/CjN6bsPHI5kgzn2miqPQWjh9ZU1N8at1D5RnrKnEADDtGpTq8idPzTuG9nBwcHBwcHBwcHhPMF9BDs4ODg4ODg4OCw4zGs4RKsimJQm2CWZkDCBMJwg1iSugnSG3OqTihSTaCKXdzwhpLoDe4i81n9MVYxjT2nNisuUOXVYv7JHycjJk8iIE6ei/DmFHOVnHDg2HsmGT5A7t71NiHEDR8ZYJvfZ2kFEvWpR3L8xzqFr6io8xLC7HDoUhNwaLe26ulmDwCgfT+jyUPlzJ/N0v8eel3CCwYPkvqoblS86Sbrs2Chumo7llwMAlvRKNaGWTs7L7Kuqg0xeK0xICEyJZdaq6jtJGWOJDOkymZGcs63dawAAnR0rI9myxUSKSqiKZbuZwDCu/DmtfJ5MQu49zaS7sIohACS4qp3OX9xo6Oim/m5LSx8vaiaiRPnwoUhm8vQcxKzcy9geqtRUGpCQkkkOFSlMyRVJ29WqyCwzYuua8MGkR02O0HkjDbsJE8o1GLoOK2Vxu7ZmSAfjKiTGttMYKEHGRZLjG+oFGZt5Ds+Y8GT+qHIbYw0YDtHSpMY5hxmNFMQl2j9M20Wrxn6KJrrYmMxPux/8OgDg3nsfiWRdk/QcvF25KK/hvytUX0QaOoMnU/8camunkoXO+7ySJfmoDRW5p8O7ibRzsCg6X76CXOfFhNxngUO14qNC/PJydL5N177y9I19AeB0SbgQdGlUSIMXo/kjrqqcgklydWWT9MMJZkpSXa5sanV9AfqbaRLy/DUd1Fevvk3ySq+9YiUAIDsm79T2FJHMY4rgDBX2VuXQiNqy2yLZQOwGAMCObVKZ75FtRD5+8LiE3Bw8SHmbAxV2Guf51td1DzjRcV2NxXArONPAO0dwlmAHBwcHBwcHB4cFh3m1BK9fJZVstj9NFsKKWt3GuRqYzowxyVadurKQWpBldGhIVhknTpA1cLKoiDI+rSQ6O2TVtf5iCvPPNMv5yrz6sSmpT97eLWm3giJZnLtUOrT9z9Px4yOycgr5OEcPiDUslaTVVFNGrNY1TsOkF3kB/y+IKanPVcts4xHjvFZJpeKHVdrU6tZjIlJMp2lh1kRJjbrFqyko/+KX3SKyVZQuLZFWOipTCquRPWKRHNhBqa6KJ6W/c6NkpdT1yeNpWRHHO4m0uGidVBtaupas0O3dkiomuYTIGRff+upI1tZBY+fwdknDs5rTaK1sbZf9mBDnx6X9HveRbcBKYyFWrSLy4SK1KveTTNpolqpQltkMlbrouxyl+JP785i0E1MW8RQz0FKKIOizByGhyIwe17A3ur8UmaSaJG9L4AsxqMCW+tHc0Ug2auhZG54U69IkG6FLgbIss0nFqtRFBbZkj5WFyFllBus8Ze85K8RU5accW9In1NxRZb0l4tL4FS1kGXr24Ycj2X3foe3FkzK3vZP/Xq+uF15tTBlsQlvQEbVfOOtfrmTPq+3v899HlOwQZiIcbW9XshuqdJ9fOyIp98bjOwAAtSWSQstv5ZRco6oS2ySNxfygzPuNAqdLwoWgS+0RTDH5Labe/h5Xz/U8sciG80xdsf8lEamMg/Vt5CG9dOnNkezVl5P1/KqXyDw4Mk7ktmNP7o5kJ4d53i2plLTK+lri1G61LnnXt11K1uNXrNgYydZ1vAUAcFlBqv/d/dAnAAAPP/WcnK9A/ZBRtteAPYNQXsCwGt382IGdJdjBwcHBwcHBwWEBwn0EOzg4ODg4ODg4LDjMazjExKi4ORCQCT6bERdAmeMJaoGEOdS4epwfV6S6MpnpjxwSc3+BSWR+Wr7rW9to+6JLJRg8nqT9Yr6ETaQ8uq7fIsdW8kKCC/lemay41S+6hFznu7ZLgPjoEIV2TE7KeU4MUJjGilUSDmG52oqNqVzEhnOSah+Ax78bnZWxMfCKW26Ktn2uVhPXJCUmgOkwllKd+vlEUrnwbrgZANCzak0ki3FYzNhxca8c234vAGD3fQ9GstwhGk/1vLi66+zOtsqZYlTFHqTIJZPukVyUvVe+HACwcvN1kWz5Jqp017Ho4ki2/noag/HUd6WtzMlr6RZiQleGqzl5OtifdR5MYTo0FNYsp9CUZjUG/RIRUpNZcYnlTlL+7aG8uOXCvKVl3e+cBzsRF1JLB1ega4+pqnQ89o1iowZFcnWamiLQKddgnZ8dzc+sMiGxoIgeA1z16khJnvdJzlda1TEzIQ+vLu0PyXuTej++v5o3X866uaNQknbm+R7HCnLfJknhXEtUxcajT1Ke1i/d/Y1I1sau83epc4dPhs7xuZf/fl/Jwsyt/Ur2Bh4Hm5XsiJroHuK/hzET2kpz5bS2AEA4a2QV0fLfOZ/3CZkWEE+TG7c0Lu+gEhNmt+19YJYrv7BwuiRcCLrMqtz/4DzBnmbWcjhETYVIBByqVdfvfkPz26KsvGvesJTeTzf2CtF/SZw6q/TI05GsMEJhJU1Gsj7H+iiUL7lG3r0643DyBIWW1CYk7KT4w4N0GykZiz1N1MbXLZPQtFW3vRUAkIt3RbItW7ZSu9RcHHDoXUzFl4W94MIhHBwcHBwcHBwcHM4T5tUSXK/Lcq5UIqtpVVWRqTNzKq6C/YMyE8aMkHAmxzn1klod+pxqC3GxJm28Yi1dqywpYyzXvm5WQejNzbSqHp4Qkp4qKIa2VrJeTYwLuaBjUScAYMMlsrLa/jRRCcpFWb2d7CdT4eIlYklDnGtoG1kRxdiypVeDYDJV3TYeC+cdr3tNtB3yqCxkxWs49UkxEFLRKFeU2e8LwRCriWoRV4TAE/u3AAD2bhG7xIEHyAJcPS6rf1vhdDOqXT5XGNLGO6NGeRNb/WMDeyLZ8MO00rVKlh96KQBg48tuj2TNi0nXfZtujmQDW0nnewbFXpJrI6t/k64Yx+tazzauJbhrEZFBU3oJXqQUf+WKjMGBHOnvaG44kk2UaSxPViWVYYq9PWkj99zJZNVsTfTYXKfteE4sDjV+uD1oC4Gs2etsPYmnxfqQSNFzOik8DxQ80kVLtlPOU6br1UpiRauElYvU8xfJVH94UZqixrMEJ5VJo4mtc16g0hZWaS7KWkkpeGDLfwEA7NDxSPYT/Pdade7wLE8q2Zf57zYlCzW4WslWhSpU1p5Vqv9W8Ka2Hs4245Wm/QUQzTia5DXBJMe7jgiZs7CELFKtquJkG885J9CMRoPTJeFC0KVnlM09xp4kVZEWhiYspV7E2FOWUWkh2wOay67rvjGS3bqGeqs7JtocP0yeyoJKWbroZe8GACQ3qZHA3oRYl1hrpzB+J3guV8S5/GNkHZ58YkskK/Z/CwCQGpZKuX2XvhYA8LarhWw+eYjekfsOCFUysHR/mlAdbc0T+9hZgh0cHBwcHBwcHBYc3Eewg4ODg4ODg4PDgsO8hkM0pYUclkqRGzPQbsUwX57yHsST9J9AuWNPnmQXeyDNr9YplGHdBsnvmy+S2/OxR8StnhsmJ09Lqi2SvfTKlwAAulaILJGQc3//O/cBAHbu2RHJmrkS3Fve9FORrHcZBafv2ym5Covc1MkxcX+0dVE/1OtCvotxCIj2locVsmxdUxgaA6Wk9I9hol+1JoEJNdD9HhyWqj6HJ0nXLRevj2TZDI2D6lHJJ3joibtp/6fExdOVItfNpvXi1jZl0vngiLjK9o5Rh48qV0q8QwiNr76FKBmrs+K6qefJ3TMwLvl/9+y6n2RN4ipqvf4VdL6EkOoSPXSfw7ldkazZUqhNkyf9EWMVBrZx151hc4OayrXNBMfxmrR7qE7jd7Aq4UMTBc5/WZf9QqrGopKESMTZfZvyJbSoXKGwiriqCtW2fCW1KSO601WFqkUmoSqybXGY8kUnVSXGDOheevokj/Ngis65Xz1rxSKFRlQDRcTjW/GUW1F+bjyyajYtIV4dVXquPF21kJ/J538gxM77nqBxu0YREF8S7S8I6z7eqWThE7tUycLaUtd58vxt4m3NBVqm3J99HG+iZ7lwJtZBJ6GrXrchnIUuU7KQbPWUldC4HRlyvHepXPV+geYmW1QVsxoETpd8bf77YtZlRc0VBQ7XK6o5NsZk7oSv3g1Mim8yMk9elaY56m3C1UbPSvo9/5xka57gkIvuV7w5kqVvovAEtMr7c/ZAFYVs+wxRpp2CY/wW6efgIWpDauBkJMvvJBLc7ZuWR7IjSyhBwfAxCR0crYYV42a2ysxTyFnjvpEdHBwcHBwcHBwczhPm1RJ8cliINIkErSRivqwo6jFa6ZQUWc7EaA2rLWjFPK2SPCNVdZq41nrXYrHwPbKFUsVUq7Lquu2mVwEAdjwllVMO7KaVyYq1iuS2Q0hS5QK165ZX3BzJDh6jpDJbHvtBJLvu6tcDAAYOi4WsMEptHRmQe+rgqmXVQParB5w6Rd2nF1q6g8azBI+VlY54wVZTlvkK1wc/NKGqyC0hO0PbUiHGlSpELDu243459y7Sx3WbpYrcTZduAgCsUTq3BbLgPvPs9yJZ/hEi0KVisuLd9Ipbo+2rr6LkPn2qfnlfhiyQRw4/Ecnqz5BVZWCnWHhP9FBKmVVXiiU73kU3P75N6qY/d4RSu2UCsb/U2TpTqon58cM//040EvIjBwEAMSOEsbEh3s70RrJyivo9FwhhosxjtMMIAbSjRn28tCIV/UpV0u3iZWJN9zNk+1l5sVQh6t1AFf1MmxBdqioVU3GMPDr9e4WCk9/NiZ5OyLNrB/g5PyHPWiJLNurEFBsAWaitSkkU8lm0RSKwXMkuaDxi3JAisIwyM7QpKXPH4hJZbL79hHi06jmysL0EglCDRSXbyn81cWot/32Tkl3Pl1usjEyGJ4hAeWealHVxM/flVtWloY9MZmQgTIj5nJJt578XKVnou7lB6e3gIbIUHvYlvZStkNcoNy6pGBsFTpeEC0GXk8rqG3qaqspaH70RAvEWx+K03dYlx97+E+TFXHqtvFfyD/4rAGBc9Xf3re8BAKRvFlI3mskeXysoojFfOC7OtjPWMzXtNChSL780ki3y6N0w8p0vRrLcSXonr1Am3te9jFKRfnOv1CAcKpL12Jsyx9K2ccQ4BwcHBwcHBwcHh/MD9xHs4ODg4ODg4OCw4DCv4RBHTwjxqMxm8nJNTN4JrvpS1ZVT+G9dR05zbrlaUVxGbc1EHqhVxJXb1UmB3ZvWSyT58p6VAIDuZnHvfvXLX6PrqrCJgX4hdLW0kithw4YNcr0eaut3vn+ftJXdyJmshHgUJrjaz5g4pCKimyJOeUwl8GPi8vJiTBT0Gi8cIqiKPursVynV5B4nqxzeYcSd3Zyhfkx50s8nT1LOwJ1PPhTJ1qbIzfXTK0Vvx0+SS/1LKsSgr432673uajn2xEEAgLVCmrj9ipdF2wcHyVX+rZ0SDpPlPNHXXSlj4rql5DL/xhPbI9mBNeTCb71U9su0k657OyRM45lHiXqybVBc8NUyub+mjOMGQ/4ItbukkmTvO0Kktrbl4v4qWwrniakldDOHl3SqdXVTjVy2Tep86Q66/6Z2GRdLNhH9pVM9X7FuukYiK8+IVVy0Ypb7Mym6aPKJeJFqlTbUUjRujh8Qcl69QuOwqVWe0wxXvENcV2iiMRzonJ7ssvT9eZ0654TjAyqXahPNGT1Vyb18fD+F+xx8SkJ8lvNw3Kw8j2ExvGdFhIcwE+HTeZM6tiN0qCr3puV+DF3pAOCrY65hd3pNPRvP8TG6ltVTfE5d1SysH6m4QghngyuV7AdlCnd5cKeqKMbzqtfRjUaD0yXhQtBlribfJHWeXnxFlgunl2psZo0APyWV4LKdFC429LwQyw7uptCCzTf9YiRL3EKUxv5j8u7qf4TGi6lJGFp9DYUGpnol1/qiNnk3Lw4vnZf57+h2mkebVdhb6+U073bURHP9X7uHrnF0byS7+Oc/AAC4rSQD5rP/9E8AADsu9QTC8RY4YpyDg4ODg4ODg4PD+cG8mjNMQr6540xkqysimOGqcHFfVgAVXjpVSiqQvMxVV5SBJpWi86UzYt3ZtJmsV1VVQqrKq7KhEUljlsxESTlkv7K0IcUkvkRMUjiVqmTdam4VWd0jS2imTaxXJwdphVNVQe81S6uteEylGfNoFaiNvpbt4PFE461VvvfdB6PtsPpNEBNLcKFOfdmyQtbw7RmyuHqe9G1ukCweo0dVqqskWeWee1qq0jx2lCy3T+dlHLzsFqpa13u5pMXLttF2KacshHGxOk7miEjy5DM3qy//AAAgAElEQVSPRbIKp8vrWSNV8N7cRcvg5rKkntnxPKVsW3+DWJ47e2gVvaRLkgtts1SzPTcs91Sp8Aq7Aav/hdi0ivrhPmX9Hp2g8WsmJZ1fJUlMCl3lL8sVjnriyjvDqc8yMdF3ejET3rqEuDgGIkfs2S0Wjq4Jaktft1h2RlUluGeG6Pk7NKAsUrvIe3OZStC0so+szPWhpyNZji1J9bikRKyzB8bEJY1jwFatSknRing+SsQa75k0VqwpnZymL60IoLtHiIa0dUKsjKF1zlMklApbYIS+AuznvxuU7KrouiJjfi9iKq2ckZKSItMWxwT95+VqPr+EH/OU2rGTz3NUGcxCO9NRdb5wxsmq6yX54n5G5uueJZS+KbVM10RrDDhdEi4EXUIR4xI+vdvKyuFUi/IuzvSKNzetjWSLs1TF9Mg37opkNk7zaWLNVZEMWfKWtnfLRTK3kJW2UJSKccf4EygoibJSCWlrMfcUACBmJNlA2yL6ptr/Q6E09i6iubPzts1ynp30/h1QnooVR2h+v0iRivt4bB2FwJj5nVsbbyZ3cHBwcHBwcHBwOM9wH8EODg4ODg4ODg4LDvMaDlEKVCWqBIUMqLSpyOfp90SzCMOqaUZ9r9eYlJWKCxmprZVM9qWCuIe8BLlME4rEsmULufEH+yUc4rLLL6HzJcQVaqZ0DbW1VBKCT5zdGrW6BL0bn8McmqRdNUvkIF+5suocGqEKAEUyX7lZ63Xy/86zd2BOePTRx+U/hvRlVTZKGydX1eZeqf9Tz1L/BpBxUB4iN3txWEgf2yYpBGHvbsn3Osljp2WjuHM64pRFMqs6qFgnHU3UxbU0acQ9XivRdZJ5ce8XLOmrWBT3UbyFQiiyCeWHGzpO7c9LFR9vCbUhlRGXUTJOirVVGS9BmPdTjfdGQ0sfhTkkdwtRonyIQhVKKi90LE03kVZhOp0cEtMSjEWyUo5yJwcZIVF0b6JckTvr8pzmTpC+86PiFKsOUh+mVqyMZDVfzhMbpjZkx5SMw5b2jUgnX7T+CtrYL+e2AxTu0e3JAxjnZ99X8Uh5hGFXKg6Dx1J9nnJYng2aW2QMtjbR8xevyXP13B4iah5V4zyMPPuaIqGs5L9CJRToqSgMMMmprnhteF7lVw97NFDuXquuV2f3qM43uwxhPlo59yb+z0rlTg1d6FV17jqXM9O5nD3eTmZlvFx2PRFmuzslfK1R4HTJ57sQdJmUEE3LIZCTkzKfctE0xFSfJXk73Sr3k1lOIWTdudFIVl/CYX+qsmZYHzDVti6SpNpIc9lA+qyWpPN4noyE1jYZd4NHKXwxGZP2t/XR9sXXCsEu1sHnzEk++Ax/j7X3SMXV+PNEvH5zj1SiuzdJbR1QYyOMGJyvVOwN+Hnl4ODg4ODg4ODgcH4xr5bgyZJYWUoVWslmU7KSTbJ1JWll9VOq0Xd60Vdp05ppRVwqikUxnqbVSMrKLbUnifiSywlBKc3G3nwga+PBGm0v9aT2dTkpq63mNFkKfUXoauaK6PGCkGuSFV4R1cVy5POtWMjKL8WrQasC0pMpukZQl/7w2IJdUqnbGgWlkSH1v/rMHTitD9T9hBzIIBBdFvPcL1VZj1XzpF9NHli6nggAV11zWyS7qJdSxmSNkNeSbDGuqqqDBZWuJ5YkK2dKERADJq3VCqKPeCftF48rq8QkjRNbkf3qASnYV+S7FBP7vEDdQIWt+g287tzHJM4gIc9fU5osDDEjsjiTx+JpsaS2Vsmy3lSQsVpgHSxaJ6lzei4lYsWOvFiMF+eoEl9qQCz/o8eIwLjjYUllV6iKhyXdQSnwbrrypZHMzxCJ7hv3fCWSpbg6YcdKqVflcxW52EnxBrR2koVjTA2MYR4XWo0IvUoN6J4pKPLweJW8Fe118c4Ms6FGE4rDWVBookCYeErtFm3relxhwsu0sopHT5q27PDjPrXLZlrSdRWyyBpUl7kiwcfEzUwrZEWdO+CcXZ7SW5r380tiTfW4ytiJgcZLd+d0ycdcALrM69fAJLW5VtPebmpzYEVWY50nffE6WtA7t16SbxOPiWpYKwS60BIMs0RE3GfVcfk2iY/TuVPKem6MWGkTSTq3n5R53rBVe8TKWMxYeh9kO4Wg3raE5tuxLeIxtkPkGcxuuiSS1cNz6zEWDTJXMc7BwcHBwcHBwcHhvMB9BDs4ODg4ODg4OCw4zKvvIBWXvH5lUDhETFdI4786gL7GLhSj3LFRBRsjzR8bo/N1tAipJ5+jPKV1dcKrr74eANCzdH0ku/vb3wUAXLHxikiWUTlEY2GVMkW2ivgzijRQiIhzsrYIXTxhXmR9f3Hd+1H+WOmPSoUJSA3oekVF3N5h82yg2sl5j63iFAX8e6DuJ8WhJnHPTj8USoSxAXLYHXhW8hNv7iYdTUxKR6YTlC2zyZNxUKuKMzCRIHnCVzri8IxAjRPfp7HqqzGbTNC2iakBGhZVUrkgwxJApq5k7Le0iqTXaDh6gsIDKsrHmg5JOVba3Rb2YVaIpK1FIoj6UiQPAQ/wVI+qesTV+TYvWhbJiuyiqzQvjmQD3IT2ZRICkcqLngtcdailQ4gXXKQQfkLaP845gduWi2vQbqXQlUD7kmM0v6hIHdSr2onM52bXpZ2nakZng56MjOl8hXywLXEhzDQlmmYcs5RdjsuU7Lt8b4UZe0NRX4GL+O9L1GPvm7BSpHqemeSj+2w2T+eUFNrRnCLCINpPVw/l8ylSkRcPhbJbvB4SrGRCyvdTxtwDJ/RdNQacLvnQC0CX+arEQySYvGtUFTmfiWk6RKJcom39CjFM0NffTEH4zXFSQjmxOBsdEaFE4SJjByX3fn2C5vvWZdfJfip8sSnF11P6CCfI9mUyymJhiFhBiHFpTg6QXipz+ihrvUmFKtbMLKEPPHiCecqp34BfVw4ODg4ODg4ODg7nF+4j2MHBwcHBwcHBYcFhXsMh4soNHjLsjWJERtkDVJhDmCEhrhiKoeuyVhJ3SKlIbpNyRVwFdXYvtLSJ27YwSe6SzlZxj6Yt5xhW+UXjdXGnZ31ynyYTEtJQqDG7NS73lOCQh+oJMfdbvhfPVy4en1wOnsoJbNh1YRSTNOnR+XzbgC50VQY69G1Nya3MyQ/L44rdytkwbIvqMy477WfUPXJ2grhy5wRlYtXufvoHkeyLNXLxvOy2WyNZ75pNtH9FmMO1I4ej7VYOc8h2STlejFG7amlxN1q+F2vEHe918DFpcUUaznlcLUm2kcok3bOtKWU2nvd8BvpHSC9eTXJJZjmdiudLOFJ1gsoTp1UmllbOye15Mvbr/OzG2iX3ZCJL4RBZlfpjkt1tTw1KqdixgPp6ZaswjkuB5BYe44wvwzlx9HYsorCLuho3ec5aYSqyX8mSvmPq+fM4xCVQqSDCPN1Wc+vZfduAaYLRmm6Jtjtb6PkbHx2OZIFHfaDSgqKdx+WN6n4mWfZDde6w99RTg+u4Ey5SnZHggV7VGTWi8C+VCUDnfeVNXz0kYclqXx2T5J8zNdkvHKlZFTsVZ13qYJZJflXUMzKOK0UKlSmMzpLd5gWG0yVf5wLQZUzZGisc2qIjB01YI0DnXuawyEkVTlgPSJZPyvsn8Pm92SLZiWYFd3jPehlX8Hg7rY+Vb6BUJJ4Z/ndk+85I1Nm7FADQtli+s44NUE79poP7I1nHTbcDAGJFeUdk+BtNf+FElbldnmAHBwcHBwcHBweH84N5tQQ3K0vqMH9+65Vsja3CMc2MY+uxF9OJXcMlgshyE7ya8oXQ9sDD3wQAtAt3BjfdQBWrnn1ccpLGOOC8TZHqMilpa//xIwCA5Wtk7XxogFY4RVUVLIwpn1DLN8NWa+PLCjU0LlplTTWGLd01ufdshtsTNF6eYN0mG6361dKN+6U0KjkNkScbhOkRi2vLEtJX+1KRmYByFV66RnIfVoqUY/DgQan8tShF5+tuEitfC6+WRyZkP39MBtnadVRhZ/PFl0ayDrZQb1DW4cNMtBzzpV29q4k+km0WWVAl6+XoSbE258bIKlGva3MhW/obmBg3PkptrJdFj7Vw24il2+TIupptVtWMmnjbl7Gf4gcif+y4HDtCfdMkZgYc3EEV3I7uPhLJmlvIqptpXhnJNPGyzONqr8r1m8pT35Z05aWQJFcVj4TP7fIVM7XGc0pdERwtExw9I89pyNXw/MbLR+qr+dDnedPqXNtLyKreqvJA+2W63zXq0f1p/qvtQw/x36uV7GVhvlZ1jTzLVGruKNe2VZ5A31M5UXnniiIlxvg8+mkJnW5vVhzpsD2rUmo/tjhWFNGoymfyMvKMWyZixttkbDQKnC55vwtAl3VNwOV+KysvYZznHm0JrvMcls/LfFor0X7ZVfJeHDpKhPHyI09GsuRNrwIgnnUAKDJvrlqSSm/pJfSejUMnHRCCXb1G87LnyTeVrW8AAHQmxFvYFFXulQueHCBPXV9N5knTS/mBB7dL7nfDxL66ZiSHv82T+9RZgh0cHBwcHBwcHBYc3Eewg4ODg4ODg4PDgsO8+vSMcgv4HIAdU/laq2HQuPK/1MMqpSqoPpMlf0khJ7n2wjzBo2MSnrDpsmsAAN9/6POR7PCxvQCA0qic79qrqBRvMi0m+UsvXxltf/Ur2wAA9/3g23K9EpF0Lr7s2kgWZwLd8aMHI1mMA9ezreKQioVph8uqhCGvR6xalwTsMvH9xgv2n4qwL1U7A9LDyUMSdnJs69MAgK5lN0SyZPdKAMCayyVX4UhAoSbdfZJf9vINlN/ZK4vOu3hMZFQJx4f3PgcAeGaflFKOJSVcYunmGwEAb3r5qyNZmnNQ5wriWn/sBIU39Gcllqa9nVxu7Srn83ieSGL9hw9FsolRDoeozXTxNGJ+2RCd7URwMJqgEUZDKMKMx+VbO+OqbDkTzyqKrJrl4VAbOBbJgiEKU3nm4IFI9twzz/BJ5BlpaiEXW0HlnixByI6VJPXt7iFV/LVO4RTtvcsjUTpFBNgTx7ZL+8sUwlJTIT3VKoUe6dzUnoliHyJZmMPUjzWe/WBwSEKP2tqJpJJKiyvzqk2vBAA82PdoJCvsI9dkUYWQrOG/P6Pm4VfyX3GCAmFQ0IQiRoX0Zd094bYum1tQ8/muCh2/W52nh8fgKhVRlOKfl6hS5st9EnrKoxsO31HlhS4bau2y5TLPtHXT3Dw0KnNFo8DpknEB6LKg3vMhAbeu8vGGXZlQsaFheNZgv8ydTz9EpPCLeuW9GOyneTS3Tea35EX03RO0CQlu5AS1YWxMrrusmTTcKtxvwEhb/RiXtlev9YnjJBs4ImETSzrpPMkhmZ8LI/ReHOuQNnT0UPjKf335G5HseSbJ1VQ4RDRU56k+QuPN5A4ODg4ODg4ODg7nGfNqCa5WJGjdY0twYJS1jFdCdZWmw/IyRMX/o3sxBXQPHhcijeWqbkeODEWyjVf2AQCuv0FSaI1P0DEZVX3nkrVU/SRfH4xksaRY+259DVkuj58YiGTpLKXi6lsuBKunHycLZjYjKaGKJVoddS6SVXwdtNqKKUJBWInFVwTACld5SWeFsNc4mI3gpQl8tJybOCEW0v7jtL1y7MpIlu5aCQDou/SmSFYaofveMaQsiEkK6L9i3cWR7PgYnW/gsKyCf7htKwBgJCfphArPiFXlgSYaTycXr45kHqfpGZncG8l2Fsja2bbxDZGs7wqy+mficu9jx2lM7NkhFoihcWr/bMH+sLPIGgSrVnPf6iJOETlGTDG1GqeAy4s14OQ49WtSsVA9JnUM9ctzunXL/QCAbJc8fyu76NzHcvLslibpWRodkrRpmXZJDbRhDVtDVJq2dn6mu5vEUjK2i0h5h/YJOS/gQ2Ld8kyOsdWhVJIxHAvnI2XVCkm5QQNa9OMpmSQnytSngap657eRJ6NrhaSHvJ+th99S5/k5tr4tVm+HbiYrj6v7/hZviv0HCP0rncpzEBZYrKtjHyrJ9lf5+ZNRAng8CPXMl+BH5x1K9spEWNVMrlfk892t0m89305j47rezkjWFiMr1HCbpOFrFDhdEi4EXdbUnB++a3RJvXB6qaj9Ypwr9cSEEK4fPUiUxmXXvzKStfaQ98ybeCKSlY+QRy/ZIfv1raO+6tPT1qyfFfLtAo+pilU5qMpV+pZvlH5uCug9PXz3f0SybGUXAGDpq2+PZBMBeYUfPro1kh2v0vnqU7yPsxDtzyOcJdjBwcHBwcHBwWHBwX0EOzg4ODg4ODg4LDjMb8U4RYIL82zWMTPq3qjA/jonR6xZVRmK3aJtLeIyynMM98iokJsO7if30UWXXSHnCyhEIqlCLsJ49DCnLwDUy9KGeIbcLytXiiu+rZVcrtt39EeysZPkkimrPKtdi8ld0dysKt6BGuvFZT9bDqtYSRtiMe6j2dzqLzh0m8K+mrmmsiosYd9TzwIAll91SyRb20ZhCYvWbI5kIyMUvrDrsUci2Vd/+N8AgG98R8aBz26TYl5VUirx78rdlK0Jme6Jhx8GADyWeDqSpbJEEUl0iCt/8dUU+rD6+pfKfuzCL41KWMz+h58CABzaLaEBhQK71Kd4c8L/NKIuCc08zgOVkLTOLrqqCocIuBrgsErJOVEht1ZLUsbAih4KN4gdGYtk258il96Gay+LZNdcQvSddQlxbyJLIUq+quKnCiUhmeSqgoGELbVX6Nq55yWs5ZntVCsrGZOpLtGxAgAwEJdjBzn8ZVLl74xyAStCT6VG96ld042CqspZnuOwjnJB5XCt0nOwcZPkGX3kkS0AgEMleUZGOYmr9paGXfC0Isl8nf+Oq/228463qRCSy6sz88Qm1GMQBiadUL8fwkyE0+UJnY6c26PzzQ/z47c3kLnU9NJ4au1UOaS5QmhTWrmAGwROl4QLQZeemkDCWzO6ei5v6rCJIMyxrpIJPFWgCXdtVr4lrrvlKgDAyQeEbJZ75qsAgBVtqiZg8zr62yYhYFOzR4dIz9w2EiLWuZy3h3dFspPfpsQDo08/HMk2XEahFOnFyyLZXf91JwBg/36Zn4tVIn3WdW2IeYazBDs4ODg4ODg4OCw4zG/ZIyOruRovf6q6tAyvWv2ErDM9S6taoyxoTSlaoaxc3h7JdnGqKnhiORo4TqSaME0ZACzt45QdcZ2Sg1ZbBUWWiykLU8KnbVuR7trxHJHojh4SK1dTkttjZCXet5ysW35cUo/EmAwYVxbvGmamQUs20SqpVCrN+O2FhzZzmml/NVS1LU4jtvf+hyJZTzetRptWiBWw9zJKYxaoXDlHmh4EABx/VkhwJ4+T9dWryko1weNqSgorRTass0UwlhWzYrqXyCWrrhAL9UVc57yrV5UbnCQL9aEntkSifU9S+rzCgKKUVGeSH+YryP/HQRPrqqBSh5XqtF1Vz1+N9yupMXuizBX2SpKuriVG97xkkdSUr4yQjWj/1scj2cWcL2nNBkltluwgjw2axXLhJRQxyDBhryDPaekwzQGDe2WM5MdJPzr9Wt4SqWNQpVgcDkm7ygzlM+luSjoj3gwa0DszWpa+D/J0P+Wc6jOf7vfi3o2R7KUbyCr+wLNSxek/2dL/c2p+audn+yI1pK/jMf09NbR38PZ+1a70LBketSjUgq71dbq6ip6yooXNGZPpFXeyxfS51r5I9pLNRPNavUTeGeM+jZeeoPGqODpdEi4EXVpNguO/3mzvT0UIDPg+xgvynvrOo/y+65b3zyU/Se+s7pdLqtZjPySS3P4vfiaSxTvJi928WrzirVfRMaZL+lGjPkjjrX5MyHnFAnk+x7bfE8mqh8ir2rf6JZGs4lEygqe++Ewku+deOs9wTr6jqqebRufplekswQ4ODg4ODg4ODgsO7iPYwcHBwcHBwcFhwWF+wyFiUprEj5GZv6Ls4dUauVR9XbUmRr9nm8SlaqvkKurqlPONLabfj/ZLXtFahdzpB3ZJyMLkEDlvVv6/9s7st477uuNnZu7OfZMo0rQYmdbitQkcu0WNJF0QFGiRIAiK1g9Ft7f+BQX6f7QvAQIUKVqgT22BJEXqplbtupIgL5IjWZYpKSJFiRTJe3n3ZZY+nPObc2jeSPaD6Fve7+eFgx9n7sz8tvn9zrqkau7pGTaDCAJVR7SbqpOpyfHuA3XA2nnIzxB3VR0biTLI2p6PjrNKIZtTkwZfHI/i0DgKilrIM6Yb7Q6ronPm3QcHq6uI+5Q5jANAm80JNq68mZZ9MMt1//JvazzBYydZXX3mlW+kZfMLy3ztM1fSso8vnyciovqWumEkkoHG97RrZ/OqCj82y6YPi88+r7/9DKuKnjp9Ni0bO8bnderqiLfxC1atX7vwn2nZ+o3rRETUa6pqXbH14fcpGywqa5x9ajfSceAcVyoNVc9OT7EjYWQc6LqSaq3c0vGXERMJr6SOHNPzbPYSmrF0911WscV3Vd0bzHObBovqWBEbc4hsh9t5Mq/OH509Hi+NprZZL8v3rhqLoh3J4FTramFPTDwCY4rVk9RFna7eN4pl7A5gvOfFgtZzR94jt6COLqN5Lquayvhe4Q+JiOiXf/eDtOydj9nUa8a84u+L+YkxDqI/kW7ykin7mfSXj02Z6zm/qsZcjRu3yNRlp2zKnMFRxgyhiljuvGNimb6X8Lzf8HXc79xjVfIt0r7REDOzdtxfHfxlgrZkjkJbRnbK7zP9u1wInvUIlKWZH2ub+5Lt7dKP/zst+2HCNfnG9383LTvx+m8SEdHGm8aQZY2/m5k763qHe5LNdU7jqvu+Wc9sSKfYUnOI8i6bMrZ9jeH/1Nd/jYiISsnJtOzttzmT3Y8ua++5WpFY7LGaeLiQwIEx13HZSQ8rFDskwQAAAAAAYOg4VElw3UiTQjEGL42qM1qS4Z2O75vMYyHv8Ew0MfLEKcXLa+HZ51hy1yPd6ezI1jNs6O6mvM3Xbq3pLmlWJMFV3yTRDq1kj58nE+r+tyDxmnqJOkTNnWAJ2fJpm3VFJF6x7tgTyRpjs8M5R8HIhGjqyf1KGX3+waGfY9xjzou4TnubGjTn9kXe1XqxvvdL3+Tc55PLC2lZdpkz882d0AxJxRdZctsu7+rdRBLsma6dz6skfXyKpcyTs/o7I+Mst8hnTe72Mksb7l25npbduMQh234hkksiouqek3zahPdp8nP6/8TCGOd52rqvffrWOmdxa/T0XTL+GSIiypdU3hP73Jebxk1mI+TxPmFCkRVHWU0ybsKh7azzQG1cV4ee7ipLH6IZHUs9X3876HJfOjY9n5aNz3CbtgK9X0syDdZjlVA3OqwZ6sTqvhNJhibf02tjkXdFkQmH5jJmDZ4gmJZWNFxWr8PBrioV1YLdES1ZYWIiLXv5ZdbAfOfbKqf7wTZnfvrJQ73WpbX6HdOlj8nxq8ah53k5XDNiHBdE0rr32qxgTnE2Z+aKdanffzHXuP/mjKbwP2Sa/qmn0qXmHDuLLS1pVsjCJI/PyTGVf3blo9KsmvccENCWzFFoy8S8o8s06Zk6Sx3irCO1aJoSU495j+fYTkW1BD/+KX+fwkjXK2/8AfeDhd/6I712nee6bOtCWra2ymHVfvmufVpdj40EfJ/jOZ0TRxdYM3fitd9Ly8oef3/f/tfz+lyXuadc2a7qeS7cm2cCH4gGMTG6BScVjvo4YT4JIAkGAAAAAABDBxbBAAAAAABg6DjkOMEq7ndhXE2CtDRWbmLilHrOcDrRE31xsIt76ryWyfK1K2dVzep9yqqRdeOsk3RZ1ZLNqOmD83uptvT3CsYyIuNE9b7NEsUKoSUTq/jE06x2L42osqjZZeP9wMRD9H1RZ0Sq1khE9h+YjFvJAGcX+7xYrU/i1GotdWhofcqmBTcqG2lZdZNNWs6+/npaNnOKTSMKc+Np2fETbA6R0xCSqTInlzFd22QLK8gDFYw+u9Xh/rF5ey0t++Qiq41W39c4h2vX2Mi/+dAoAyOn2umXQa+fOcTgmki8sMx1WzUxcC9/yuY8SVdVWLH06aRgYvi6OjaOZW3iQXQ/p/XvlHbTU2rmkCVW6WZ21VkuL6YPnca2XmueK5EBulvX/4+Kai03qqYu9RKrTGtGpdtu8bwQJjrPuH8nNmtTej+TXVL+Bv7gyQ8qO+og6rIoVluqJi2M8lxVMCrWXInnrD///l+mZUHI7fXDf//ntOyfbnEc1jXj5fNt+TtvuvRxaf5zRrV7WqrKmpDYUZBPE/NpnT7s8n1GjCp5Vv5eMG15NWCHqdzT6kT70lc5Fmp2XPudX+Tf3jMOyi7++wLpnDIooC2Zo9CWj8Nlj0tMXSSy8NkXY1jMA5JE1w2VXa6Df/uJZkC9VuX/v3JO59hv+exg9/rXNJbvzKTEFq6ouUNCuvDJZ7iRx7Y1j+CtTTZvuFnWDnD+LTZpvHle4xdv7/HzV0Lz3ZCscP2j53sHyrxD+lQO3kwOAAAAAADAE+ZQJcGxkaRG4mwWk0kPI5nbgn25tnlXE/WM01LI1wZZm3GNd0S5opYtP8u7wmJRDe137/N5jYo6/7Rlp+oZxzjfJJ7xPL5mdFx3TEsLLAVbPKnG+a2Inam6oToA+qlkyYi8XR70xO5BRApuHIvCmHf+UdIv/NaXzWNCpLkwJ9TPAcCcJ6FtwvsacmW9xiHIdm5peJXpFXZwmj6tDiMLz3LZzKwa8xdHuEvnitq1u6bum2Vum02Tv3xrmyUjH36omcburbLjZPXBw7QsbLb3v+4++sVzifv8f3BDpK3eYelTFGo4oskRDkG2V1NpgEQypMy4kRqI81uhaBzLcjxOK6Ye2iJC2umpdmZxkttvImfuK2N2rKfjoWXCKTbyfO+2USW15RlantZxUzRETVPvHdHERJ5KVOKI56Fe12ihfCcethogJjgsMcUXoNlQJ5RWi5+91tP6ceEmsyZ8oBuf0aS+z/fe+GMiIlo4q5L+v//HHxER0bsX7qRl/yuOu6+Yuu74VyIAAApDSURBVP2WhNdTdy2icfl3zox76/MSiYSoaRyP3alPm/MuiSNxZ0rH+8wih2d67tfVUSeb5w66NKF9cVxCjlVaWkd1CVH50b1VGjTQlsxRaEv7vUt1hGb+cBLgJDn4vdgnCZafiWy2Spn/Kntaj//11h0iIvrw/X9Iy67leL48f9E4Mx/n4yTQeZestk2CEiRldTZcldCW17u6bnsg326/rtd60g9C+07Jwe9/Kv22GuP09MOZYyEJBgAAAAAAQwcWwQAAAAAAYOg4XHMIoxaNnYrfqBp9kfcHxpEpkBw0kVGvxHLcM/E7owIfd0M1ls8XWcy/tKxZV2ZmWK1SymomqmqZVb01Y3JRKukzjI+yKqGQ0ft5HqtNI1J1eSYrKgKztfCcEXtk1FYhi/n9jJ6YkeOEjOpVzEeC7KA7yPVR9afxEA8UUd+9l2nLpMp12qxp/N/m2h0iIrr3gTqq3VxiBduZ115Iy5ZOLxIR0URJ79FpqYq7vMEqtAcfXU3L3rr6IRERbeyqei1pSP8MzTuJioc8+5792sa99OfNqjcYXLrLJgrbxkE0lnxPE2OqjixJHN5RYz40OyJ92lO1a1Zi7lprAjeeM/v6ALdPaFSeO2IqlM3qPcJYx1C1J+O9pz9elDGZz6mCti1Z68hktwvEicbPqDlEN+R3jns9cx63lYtlSWRdHgfPHCI08VULktVvJasq9J7ry7510uX3qIXa92NxPFp58etp2V9JPNo3j/88Lfv5ZQ4w+j8beu0liQU6ZlSeTg0+ZyaDhvn/rpijbZn/d3LcNhNz2p8mTn2NiIheXVbFek2uzYypyVi3xlnSek01VdtN+5G2eVYyBs7lTFz6AQFtyRyFtrRmDql5oDVpSM0EzTWf+ReRZpWlfb/HZX6i9psjDW63sHk/LXtf+smtB2p2+JUljrH+yksv6k2MOdgFcQpfNSaBdVmjtQ5+6ik287PrlX4f04e4rzmE9uNY6shmz32SQBIMAAAAAACGjkOVBOc83ck6YVpi0oK4Y5ugyQnfrCOKL3nT25GR2ojz04hxzOnKTjYhdYKbnGKpcGIcc2bn+Nop42RTyGvV5GSHHZpdUuzzbjWTM9lgRNpXr+tONhYJ8EhgYq6JK0HXPr/Lkmd2dJlA7hsOomOcxe1MD+6pksS6TTwqdJi9SH7PXtvk9kq62jk6IrlvntKc5fE0S/7ikraLb0LpTUTsGOdV1NGrvsGG/4nRNlDaNFbS+yiJvPcrjh39QqkNFnsB12PPzAp+jvveqAkn5oc8TpK2lo0VeNwFI5rlzzlWxBnTFhKWKYm0TXoSEnHTSKB9j9u5VFQtjm8cOPaaPKYbdXXaKPh8PJLVEHy+x9f7vtZ7Kc/P3y2oJKkjQywK9RlcKwbZg9NkEAyeJLiUVemhl+Ox4XXMXCTSp8Q4UwVjHFYr72tWRaeputs0EqAiZwn87p+qBm3lVQ5f9c5FdShtbrPjTClRKdQH790hIqLyuAmpVzSS+RIHzJoqanirpZOcKezs8vG0bGZqmYiIsnVto6sbfJ+9h9rmaw0+7o3ofP7MBN9jLKf1sb3KzrH13uCNSbQlcxTa0opzY+fdZp3l+jiAubBp+zSp8u3wfJtx7eC1OSdHNtLarlSLleBOjHEbvbCi/cCGL71+jef0tvkMd9KokcYJzs2UfZz9LCoxPuhAb7MOer7TAtosrE8OSIIBAAAAAMDQgUUwAAAAAAAYOg7VHCIxGWqcNtQzonYvFjG/MfZ3YvUuaezdjMfnBVk9ryHG4LvrmkHKl7ebn9fYeKGYQazdVgPx06dYXdNta8aqB5smjuk8G5BvbWlGsek5Vs1+fPtWWlZvi+q4pEb8U+OsAsqrVjc1cyjkVdyflzh+tbrJbid2Ib32IJpD9FP/J33K+u2z+ueMeTRyXmgc6NrcJ3p1debY22OV+NUNbcuyyUA4n2MVT6dj1N4u/VFoTTceoVZLDqpz9pWl7/S4dx8smm1WcXaMc5hzRksSdTitiblPrabjoScqLjPEqUcHHSF8VzexiQ8upg+VmjFtkIxwJ0+qWnV8TGNhxmKe0TbtOCrOecemNbJpJs/X313T8d5u8zUmzDhlJYWltcAJRO1YzKspU6HAY9Y/JFXdF6ET6NjIicPgjnH2rImpVZLXesxnuC5mstpGWTHdWjAqb6/J5kORcVScXGCnplNnVD379De4vn9jtpGW/fXf/C0REQVFve9GVc2Rvvoa5ytbWphNyyYmOA1k1tNxWJDYspkJHVdBRY739PkXFzmW+KnTp9Oyr4xwn7hb30zLTp1js4DMyOCNSbQlcxTaMuqj/rffC2fass8kwNmLxgfNJnxjiuXLWikyZqXuGmtmoYaIWt/FPJuDzc/p4iQq69xflGewfuBqztHvleyz+vIoxonPWU0E5rso1wT7HDyDg+/0BIEkGAAAAAAADB2HKgnOZ9Rpze03ExOCyoUP8fctzWUn4enOOBTxccbXHczOQ94l75VVYnzv/h0iIpr8poaHufrRe0REND2uO9VLFy4QEdGZc8tp2Vtv/iw9/os/41zs9YpKqiameBddr6nkdnnlHD9/rI4JGeOk4MjLDuypJX2GfI4lSzc+uZGWuU3XSMGIkQeGx0mCHf3CnPQ7z5b5n/lrMbtDycwXtTWU2t5D7i97Wyol2PG1m+ePsWQ+2hemxTni9QuvY4eIe0+7Q31UKDjL53QK/BK5KVn0fOMgWm2yFKjdUGlQIBqKgmmeQkYy9RmHnjjDvxOZysmJk0+3o5LlussaaKqm22Npc72hWpCRURt7kPuVy0dPRORLWxVz2mbTcyzNWt+4q9c6zYDxwM2K5CIyfc45aOSM1LcgDoB+fvAkwZvlcno8WWBp2a42G1Wlribz2kHreyzFyxa0HrPEc1q9rM5UEyJRvHl/Iy179xPuL5tbqn1rZ3hOO3PsVFoWFXkOvHj7tpZ1jbbvyjW+tqtS/9njXM/PnTmbls1IurKCGZLPi4ZiraLzzNTxaXkYlZx+tM6aofue9qfJFT7P9w4nFNMXAW3JHIW2jPZpwrhtgj6h7WwZidQ8NvNRLJJRlzFXTiSiz349JcSY/RD1caDLS1jWki7LqB0YZ32npt/3rZS79nGCswLv2H0Xzf3ctUHGflPFwdNIjENxUD+sYKKQBAMAAAAAgKEDi2AAAAAAADB0eP3iuQEAAAAAAHCUgSQYAAAAAAAMHVgEAwAAAACAoQOLYAAAAAAAMHRgEQwAAAAAAIYOLIIBAAAAAMDQgUUwAAAAAAAYOrAIBgAAAAAAQwcWwQAAAAAAYOjAIhgAAAAAAAwdWAQDAAAAAIChA4tgAAAAAAAwdGARDAAAAAAAhg4sggEAAAAAwNCBRTAAAAAAABg6sAgGAAAAAABDBxbBAAAAAABg6MAiGAAAAAAADB1YBAMAAAAAgKEDi2AAAAAAADB0YBEMAAAAAACGDiyCAQAAAADA0IFFMAAAAAAAGDqwCAYAAAAAAEPH/wHw1ukx2GLy1wAAAABJRU5ErkJggg==
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<h3 id="Project-Writeup">Project Writeup<a class="anchor-link" href="#Project-Writeup">&#182;</a></h3><p>Once you have completed the code implementation, document your results in a project writeup using this <a href="https://github.com/udacity/CarND-Traffic-Sign-Classifier-Project/blob/master/writeup_template.md">template</a> as a guide. The writeup can be in a markdown or pdf file.</p>

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<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  \n",
    "<strong>File -&gt; Download as -&gt; HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p>
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<h1 id="Traffic-Sign-Recognition"><strong>Traffic Sign Recognition</strong><a class="anchor-link" href="#Traffic-Sign-Recognition">&#182;</a></h1><h2 id="Writeup">Writeup<a class="anchor-link" href="#Writeup">&#182;</a></h2><p><strong>Build a Traffic Sign Recognition Project</strong></p>
<p>The goals / steps of this project are the following:</p>
<ul>
<li>Load the data set (see below for links to the project data set)</li>
<li>Explore, summarize and visualize the data set</li>
<li>Design, train and test a model architecture</li>
<li>Use the model to make predictions on new images</li>
<li>Analyze the softmax probabilities of the new images</li>
<li>Summarize the results with a written report</li>
</ul>
<h2 id="Rubric-Points">Rubric Points<a class="anchor-link" href="#Rubric-Points">&#182;</a></h2><h3 id="Here-I-will-consider-the-rubric-points-individually-and-describe-how-I-addressed-each-point-in-my-implementation.">Here I will consider the <a href="https://review.udacity.com/#!/rubrics/481/view">rubric points</a> individually and describe how I addressed each point in my implementation.<a class="anchor-link" href="#Here-I-will-consider-the-rubric-points-individually-and-describe-how-I-addressed-each-point-in-my-implementation.">&#182;</a></h3><hr>
<h3 id="Writeup-/-README">Writeup / README<a class="anchor-link" href="#Writeup-/-README">&#182;</a></h3><h4 id="1.-Provide-a-Writeup-/-README-that-includes-all-the-rubric-points-and-how-you-addressed-each-one.-You-can-submit-your-writeup-as-markdown-or-pdf.-You-can-use-this-template-as-a-guide-for-writing-the-report.-The-submission-includes-the-project-code.">1. Provide a Writeup / README that includes all the rubric points and how you addressed each one. You can submit your writeup as markdown or pdf. You can use this template as a guide for writing the report. The submission includes the project code.<a class="anchor-link" href="#1.-Provide-a-Writeup-/-README-that-includes-all-the-rubric-points-and-how-you-addressed-each-one.-You-can-submit-your-writeup-as-markdown-or-pdf.-You-can-use-this-template-as-a-guide-for-writing-the-report.-The-submission-includes-the-project-code.">&#182;</a></h4><p>You're reading it! Project code is given above.</p>
<h3 id="Data-Set-Summary-&amp;-Exploration">Data Set Summary &amp; Exploration<a class="anchor-link" href="#Data-Set-Summary-&amp;-Exploration">&#182;</a></h3><h4 id="1.-Provide-a-basic-summary-of-the-data-set.-In-the-code,-the-analysis-should-be-done-using-python,-numpy-and/or-pandas-methods-rather-than-hardcoding-results-manually.">1. Provide a basic summary of the data set. In the code, the analysis should be done using python, numpy and/or pandas methods rather than hardcoding results manually.<a class="anchor-link" href="#1.-Provide-a-basic-summary-of-the-data-set.-In-the-code,-the-analysis-should-be-done-using-python,-numpy-and/or-pandas-methods-rather-than-hardcoding-results-manually.">&#182;</a></h4><p>I used the the inbuilt python functions and numpy library to calculate summary statistics of the traffic</p>
<p>signs data set:</p>
<ul>
<li>Number of training examples = 34799</li>
<li>Number of testing examples = 12630</li>
<li>Image data shape = (34799, 32, 32, 3)</li>
<li>Number of unique classes = 43</li>
</ul>
<h4 id="2.-Include-an-exploratory-visualization-of-the-dataset.">2. Include an exploratory visualization of the dataset.<a class="anchor-link" href="#2.-Include-an-exploratory-visualization-of-the-dataset.">&#182;</a></h4><p>The exploratory visualization is shown above.</p>
<h3 id="Design-and-Test-a-Model-Architecture">Design and Test a Model Architecture<a class="anchor-link" href="#Design-and-Test-a-Model-Architecture">&#182;</a></h3><h4 id="1.-Describe-how-you-preprocessed-the-image-data.-What-techniques-were-chosen-and-why-did-you-choose-these-techniques?-Consider-including-images-showing-the-output-of-each-preprocessing-technique.-Pre-processing-refers-to-techniques-such-as-converting-to-grayscale,-normalization,-etc.-(OPTIONAL:-As-described-in-the-&quot;Stand-Out-Suggestions&quot;-part-of-the-rubric,-if-you-generated-additional-data-for-training,-describe-why-you-decided-to-generate-additional-data,-how-you-generated-the-data,-and-provide-example-images-of-the-additional-data.-Then-describe-the-characteristics-of-the-augmented-training-set-like-number-of-images-in-the-set,-number-of-images-for-each-class,-etc.)">1. Describe how you preprocessed the image data. What techniques were chosen and why did you choose these techniques? Consider including images showing the output of each preprocessing technique. Pre-processing refers to techniques such as converting to grayscale, normalization, etc. (OPTIONAL: As described in the "Stand Out Suggestions" part of the rubric, if you generated additional data for training, describe why you decided to generate additional data, how you generated the data, and provide example images of the additional data. Then describe the characteristics of the augmented training set like number of images in the set, number of images for each class, etc.)<a class="anchor-link" href="#1.-Describe-how-you-preprocessed-the-image-data.-What-techniques-were-chosen-and-why-did-you-choose-these-techniques?-Consider-including-images-showing-the-output-of-each-preprocessing-technique.-Pre-processing-refers-to-techniques-such-as-converting-to-grayscale,-normalization,-etc.-(OPTIONAL:-As-described-in-the-&quot;Stand-Out-Suggestions&quot;-part-of-the-rubric,-if-you-generated-additional-data-for-training,-describe-why-you-decided-to-generate-additional-data,-how-you-generated-the-data,-and-provide-example-images-of-the-additional-data.-Then-describe-the-characteristics-of-the-augmented-training-set-like-number-of-images-in-the-set,-number-of-images-for-each-class,-etc.)">&#182;</a></h4><p>First, I decided to normalize the data for better performance of the model. Now in this particular the dataset, the main problem was the lack of generalized data. Mainly some of the classes were higher represented and many classes were less in number. This may cause problem when it comes to training the data. 
So I tried to augument the dataset for better training of the model. At first I thought of using a technique to check the samples of a class and apply augumentation to all the images in that class but it was too computationally expensive even for a GPU workspace. After few hours of struggling, I decided to sparsely augument the data. Now the thing is, I know this is not enough, maybe in near future I shall try to find out a way to make the above process faster.</p>
<p>The new augumented dataset is visualised above.</p>
<h4 id="2.-Describe-what-your-final-model-architecture-looks-like-including-model-type,-layers,-layer-sizes,-connectivity,-etc.)-Consider-including-a-diagram-and/or-table-describing-the-final-model.">2. Describe what your final model architecture looks like including model type, layers, layer sizes, connectivity, etc.) Consider including a diagram and/or table describing the final model.<a class="anchor-link" href="#2.-Describe-what-your-final-model-architecture-looks-like-including-model-type,-layers,-layer-sizes,-connectivity,-etc.)-Consider-including-a-diagram-and/or-table-describing-the-final-model.">&#182;</a></h4><p>My final model consisted of the following layers:</p>
<table>
<thead><tr>
<th style="text-align:center">Layer</th>
<th style="text-align:center">Description</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">Input</td>
<td style="text-align:center">32x32x3 RGB image</td>
</tr>
<tr>
<td style="text-align:center">Convolution 5x5</td>
<td style="text-align:center">1x1 stride, valid padding, outputs 28x28x6</td>
</tr>
<tr>
<td style="text-align:center">RELU</td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:center">Max pooling</td>
<td style="text-align:center">2x2 stride,  outputs 14x14x6</td>
</tr>
<tr>
<td style="text-align:center">Convolution 5x5</td>
<td style="text-align:center">1x1 stride, valid padding, outputs 10x10x16</td>
</tr>
<tr>
<td style="text-align:center">RELU</td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:center">Max pooling</td>
<td style="text-align:center">2x2 stride,  outputs 5x5x16</td>
</tr>
<tr>
<td style="text-align:center">Convolution 5x5</td>
<td style="text-align:center">1x1 stride, valid padding, outputs 1x1x400</td>
</tr>
<tr>
<td style="text-align:center">RELU</td>
<td style="text-align:center"></td>
</tr>
<tr>
<td style="text-align:center">Flatten Layer (a)</td>
<td style="text-align:center">Convolution (1x1x400 -&gt; 400)  </td>
</tr>
<tr>
<td style="text-align:center">Flatten Layer (b)</td>
<td style="text-align:center">Max pooling (5x5x16 -&gt; 400)</td>
</tr>
<tr>
<td style="text-align:center">Concatenate layer</td>
<td style="text-align:center">(a) &amp; (b) -&gt; 800</td>
</tr>
<tr>
<td style="text-align:center">Dropout</td>
</tr>
<tr>
<td style="text-align:center">Fully Connected Layer</td>
<td style="text-align:center">Outputs 43 classes</td>
</tr>
</tbody>
</table>
<h4 id="3.-Describe-how-you-trained-your-model.-The-discussion-can-include-the-type-of-optimizer,-the-batch-size,-number-of-epochs-and-any-hyperparameters-such-as-learning-rate.">3. Describe how you trained your model. The discussion can include the type of optimizer, the batch size, number of epochs and any hyperparameters such as learning rate.<a class="anchor-link" href="#3.-Describe-how-you-trained-your-model.-The-discussion-can-include-the-type-of-optimizer,-the-batch-size,-number-of-epochs-and-any-hyperparameters-such-as-learning-rate.">&#182;</a></h4><p>To train the model, I used the following parameters:</p>
<ul>
<li>Epochs: 60</li>
<li>Batch Size: 100</li>
<li>Optimizer: Adam</li>
<li>Learning rate: 0.00097</li>
</ul>
<h4 id="4.-Describe-the-approach-taken-for-finding-a-solution-and-getting-the-validation-set-accuracy-to-be-at-least-0.93.-Include-in-the-discussion-the-results-on-the-training,-validation-and-test-sets-and-where-in-the-code-these-were-calculated.-Your-approach-may-have-been-an-iterative-process,-in-which-case,-outline-the-steps-you-took-to-get-to-the-final-solution-and-why-you-chose-those-steps.-Perhaps-your-solution-involved-an-already-well-known-implementation-or-architecture.-In-this-case,-discuss-why-you-think-the-architecture-is-suitable-for-the-current-problem.">4. Describe the approach taken for finding a solution and getting the validation set accuracy to be at least 0.93. Include in the discussion the results on the training, validation and test sets and where in the code these were calculated. Your approach may have been an iterative process, in which case, outline the steps you took to get to the final solution and why you chose those steps. Perhaps your solution involved an already well known implementation or architecture. In this case, discuss why you think the architecture is suitable for the current problem.<a class="anchor-link" href="#4.-Describe-the-approach-taken-for-finding-a-solution-and-getting-the-validation-set-accuracy-to-be-at-least-0.93.-Include-in-the-discussion-the-results-on-the-training,-validation-and-test-sets-and-where-in-the-code-these-were-calculated.-Your-approach-may-have-been-an-iterative-process,-in-which-case,-outline-the-steps-you-took-to-get-to-the-final-solution-and-why-you-chose-those-steps.-Perhaps-your-solution-involved-an-already-well-known-implementation-or-architecture.-In-this-case,-discuss-why-you-think-the-architecture-is-suitable-for-the-current-problem.">&#182;</a></h4><p>My final model results were:</p>
<ul>
<li>training set accuracy of <strong>96.3%</strong></li>
<li>validation set accuracy of <strong>96.1%</strong></li>
<li>test set accuracy of <strong>95.3%</strong></li>
</ul>
<p>I tried using the original LeNet architecture but it turns out, the model performs poorly. Changing the hyperparameters didn't help either. 
Next, I went through <a href="http://yann.lecun.com/exdb/publis/pdf/sermanet-ijcnn-11.pdf">this paper</a> and found a modified LeNet architecture for better performance. Now the secret was to augument the data as much as possible but as stated above, it took hours to compute. So, I decided to got for the minimal augumentation and apply the modified LeNet architecture and see how it worked. Finally got an accuracy of 95%. Not the best, but does a decent job.</p>
<h3 id="Test-a-Model-on-New-Images">Test a Model on New Images<a class="anchor-link" href="#Test-a-Model-on-New-Images">&#182;</a></h3><h4 id="1.-Choose-five-German-traffic-signs-found-on-the-web-and-provide-them-in-the-report.-For-each-image,-discuss-what-quality-or-qualities-might-be-difficult-to-classify.">1. Choose five German traffic signs found on the web and provide them in the report. For each image, discuss what quality or qualities might be difficult to classify.<a class="anchor-link" href="#1.-Choose-five-German-traffic-signs-found-on-the-web-and-provide-them-in-the-report.-For-each-image,-discuss-what-quality-or-qualities-might-be-difficult-to-classify.">&#182;</a></h4><p>I used seven German traffic signs that I found on the web are shown above.
The images might be a bit difficult to understand due to the clarity and also since there wasn't enough training data for some classes so the model might have some difficulty.</p>
<h4 id="2.-Discuss-the-model's-predictions-on-these-new-traffic-signs-and-compare-the-results-to-predicting-on-the-test-set.-At-a-minimum,-discuss-what-the-predictions-were,-the-accuracy-on-these-new-predictions,-and-compare-the-accuracy-to-the-accuracy-on-the-test-set-(OPTIONAL:-Discuss-the-results-in-more-detail-as-described-in-the-&quot;Stand-Out-Suggestions&quot;-part-of-the-rubric).">2. Discuss the model's predictions on these new traffic signs and compare the results to predicting on the test set. At a minimum, discuss what the predictions were, the accuracy on these new predictions, and compare the accuracy to the accuracy on the test set (OPTIONAL: Discuss the results in more detail as described in the "Stand Out Suggestions" part of the rubric).<a class="anchor-link" href="#2.-Discuss-the-model's-predictions-on-these-new-traffic-signs-and-compare-the-results-to-predicting-on-the-test-set.-At-a-minimum,-discuss-what-the-predictions-were,-the-accuracy-on-these-new-predictions,-and-compare-the-accuracy-to-the-accuracy-on-the-test-set-(OPTIONAL:-Discuss-the-results-in-more-detail-as-described-in-the-&quot;Stand-Out-Suggestions&quot;-part-of-the-rubric).">&#182;</a></h4><p>Here are the results of the prediction:</p>
<table>
<thead><tr>
<th style="text-align:center">Image</th>
<th style="text-align:center">Prediction</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">Danger</td>
<td style="text-align:center">Danger</td>
</tr>
<tr>
<td style="text-align:center">Keep Right</td>
<td style="text-align:center">Stop</td>
</tr>
<tr>
<td style="text-align:center">Yield</td>
<td style="text-align:center">Yield</td>
</tr>
<tr>
<td style="text-align:center">Bumpy Road</td>
<td style="text-align:center">Bumpy Road</td>
</tr>
<tr>
<td style="text-align:center">50km/h</td>
<td style="text-align:center">80km/h</td>
</tr>
<tr>
<td style="text-align:center">Construction</td>
<td style="text-align:center">Construction</td>
</tr>
<tr>
<td style="text-align:center">30km/h</td>
<td style="text-align:center">30km/h  </td>
</tr>
</tbody>
</table>
<p>The model was able to correctly guess 5 of the  7 traffic signs, which gives an accuracy of 85.7%. This compares favorably to the accuracy on the test set of 95.67% with sparse data.</p>
<h4 id="3.-Describe-how-certain-the-model-is-when-predicting-on-each-of-the-five-new-images-by-looking-at-the-softmax-probabilities-for-each-prediction.-Provide-the-top-5-softmax-probabilities-for-each-image-along-with-the-sign-type-of-each-probability.-(OPTIONAL:-as-described-in-the-&quot;Stand-Out-Suggestions&quot;-part-of-the-rubric,-visualizations-can-also-be-provided-such-as-bar-charts)">3. Describe how certain the model is when predicting on each of the five new images by looking at the softmax probabilities for each prediction. Provide the top 5 softmax probabilities for each image along with the sign type of each probability. (OPTIONAL: as described in the "Stand Out Suggestions" part of the rubric, visualizations can also be provided such as bar charts)<a class="anchor-link" href="#3.-Describe-how-certain-the-model-is-when-predicting-on-each-of-the-five-new-images-by-looking-at-the-softmax-probabilities-for-each-prediction.-Provide-the-top-5-softmax-probabilities-for-each-image-along-with-the-sign-type-of-each-probability.-(OPTIONAL:-as-described-in-the-&quot;Stand-Out-Suggestions&quot;-part-of-the-rubric,-visualizations-can-also-be-provided-such-as-bar-charts)">&#182;</a></h4><p>For the first image, the model is relatively sure that this is a Danger sign (probability of 80%), and the image does contain a stop sign. The top five soft max probabilities were</p>
<table>
<thead><tr>
<th style="text-align:center">Probability</th>
<th style="text-align:center">Prediction</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:center">80%</td>
<td style="text-align:center">Danger</td>
</tr>
<tr>
<td style="text-align:center">20%</td>
<td style="text-align:center">Traffic</td>
</tr>
<tr>
<td style="text-align:center">0%</td>
<td style="text-align:center">Construction</td>
</tr>
<tr>
<td style="text-align:center">0%</td>
<td style="text-align:center">Construction</td>
</tr>
<tr>
<td style="text-align:center">0%</td>
<td style="text-align:center">Keep Right</td>
</tr>
</tbody>
</table>

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